diff --git a/DESCRIPTION b/DESCRIPTION
index 3ce98cd..e2b61e7 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -27,7 +27,6 @@ Imports:
     kableExtra,
     lubridate,
     MatchIt,
-    patchwork,
     propensity (>= 0.0.0.9000),
     PSW,
     ragg,
diff --git a/R/ggdag-mask.R b/R/ggdag-mask.R
deleted file mode 100644
index dc9f1d0..0000000
--- a/R/ggdag-mask.R
+++ /dev/null
@@ -1,42 +0,0 @@
-# this is all a hack to make work with quick plotting
-# TODO: when `geom_dag_label_repel2` exists, add to namespace as 1 then delete this first bit
-# copied from source to avoid recursion issue in overriding in ggdag namsespace
-ggdag_geom_dag_label_repel <- function(
-    mapping = NULL, data = NULL, parse = FALSE, ..., 
-    box.padding = grid::unit(0.35,"lines"), label.padding = grid::unit(0.25, "lines"), 
-    point.padding = grid::unit(1.5, "lines"), label.r = grid::unit(0.15, "lines"), 
-    label.size = 0.25, segment.color = "grey50", segment.size = 0.5, arrow = NULL, 
-    force = 1, max.iter = 2000, nudge_x = 0, nudge_y = 0, na.rm = FALSE, 
-    show.legend = NA, inherit.aes = TRUE) {
-  ggplot2::layer(data = data, mapping = mapping, stat = ggdag:::StatNodesRepel, 
-                 geom = ggrepel::GeomLabelRepel, position = "identity", 
-                 show.legend = show.legend, inherit.aes = inherit.aes, 
-                 params = list(parse = parse, box.padding = box.padding, 
-                               label.padding = label.padding, point.padding = point.padding, 
-                               label.r = label.r, label.size = label.size, segment.colour = segment.color %||% 
-                                 segment.colour, segment.size = segment.size, 
-                               arrow = arrow, na.rm = na.rm, force = force, max.iter = max.iter, 
-                               nudge_x = nudge_x, nudge_y = nudge_y, segment.alpha = 1, ...))
-}
-
-geom_dag_label_repel_internal <- function(..., seed = 10) {
-  ggdag_geom_dag_label_repel(
-    mapping = aes(x, y, label = label),
-    # TODO: make sure this looks ok. slightly different from above
-    box.padding = 2,
-    max.overlaps = Inf,
-    inherit.aes = FALSE,
-    family = getOption("book.base_family"),
-    seed = seed,
-    label.size = NA,
-    label.padding = 0.1
-  )
-}
-
-# apply to quick functions as well
-assignInNamespace("geom_dag_label_repel", geom_dag_label_repel_internal, ns = "ggdag")
-
-# override some other clumsy internals in ggdag until addressed
-
-assignInNamespace("scale_color_hue", ggplot2::scale_color_discrete, ns = "ggplot2")
-assignInNamespace("scale_edge_colour_hue", \(...) ggraph::scale_edge_colour_manual(..., values = ggokabeito::palette_okabe_ito()), ns = "ggraph")
diff --git a/R/setup.R b/R/setup.R
index 04047dc..30f22cb 100644
--- a/R/setup.R
+++ b/R/setup.R
@@ -31,7 +31,7 @@ theme_dag <- function() {
 }
 
 geom_dag_label_repel <- function(..., seed = 10) {
-  ggdag_geom_dag_label_repel(
+  ggdag::geom_dag_label_repel(
     aes(x, y, label = label),
     box.padding = 3.5,
     inherit.aes = FALSE,
diff --git a/_freeze/chapters/chapter-01/execute-results/html.json b/_freeze/chapters/chapter-01/execute-results/html.json
index 0f4e26c..13c6dc9 100644
--- a/_freeze/chapters/chapter-01/execute-results/html.json
+++ b/_freeze/chapters/chapter-01/execute-results/html.json
@@ -1,7 +1,7 @@
 {
-  "hash": "ccfc706d9bab0bbf8fafa9b700653ee4",
+  "hash": "ff9e98faf79959ed0a580bcf113af565",
   "result": {
-    "markdown": "\\mainmatter\n\n# What is a causal question? {#sec-causal-question}\n\n\n\n\n\n## Schrödinger's Causality\n\nThe heart of causal analysis is the causal question; it dictates what data we analyze, how we analyze it, and to which populations our inferences apply.\nThis book, being applied in nature, deals primarily with the analysis stage of causal inference.\nRelative to the complexity of specifying a good causal question, the analysis stage is considerably more straightforward.\nIn the first six chapters of this book, we'll discuss what a causal question is, how to improve our questions, and consider some examples.\n\nCausal questions are part of a broader set of questions we can ask with statistical techniques related to the primary tasks of data science: description, prediction, and causal inference [@hernan2019].\nUnfortunately, these tasks are often muddled by the techniques we use (regression, for instance, is helpful for all three tasks) and how we talk about them.\nWhen researchers are interested in causal inference from non-randomized data, we often use euphemistic language like \"association\" instead of declaring our intent to estimate a causal effect [@Hernan2018].\n\nIn a recent study of the language of analyses in epidemiologic research, for instance, the most common root word describing the estimated effect was \"associate,\" but many researchers also felt that \"associate\" implied at least *some* causal effect (@fig-word-ranking) [@haber_causal_language].\nOnly around 1% of the studies analyzed used the root word \"cause\" at all.\nYet, a third of studies had action recommendations, and researchers rated 80% of these recommendations as having at least some causal implication.\nOften, these studies have stronger action recommendations (alluding to causal effects) than those implied by the description of the effect (root words like \"associate\" and \"compare\").\nDespite how many studies implied that the goal was causal inference, only about 4% used formal causal models like those discussed in this book.\nHowever, most discussed how such a cause might be justified by previous research or theory.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Rankings of causal strength of root words used by researchers. Root words with more Strong rankings have stronger causal implications than those with many None or Weak rankings. Data from Haber et al.](chapter-01_files/figure-html/fig-word-ranking-1.png){#fig-word-ranking width=672}\n:::\n:::\n\n\nInstead of clear questions with obvious assumptions and goals, we end up with \"Schrödinger's causal inference\":\n\n> Our results suggest that \"Schrödinger's causal inference,\" — where studies avoid stating (or even explicitly deny) an interest in estimating causal effects yet are otherwise embedded with causal intent, inference, implications, and recommendations — is common.\n>\n> --- @haber_causal_language\n\n## Description, prediction, and explanation\n\nAn excellent first step to address this problem is recognizing that questions about description, prediction, and explanation are fundamentally different.\nData science in industry isn't quite as burdened by Schrödinger's causal inference as the academic sciences, but being explicit in the differences in analytic intent is still helpful.\nFor instance, when a stakeholder asks for \"drivers\" of a particular event, what are they asking?\nFor a model to predict the event?\nFor a deeper understanding of what causes the event?\nIt's a vague request, but it smacks of causal interest to us.\nWhen we're clear about our goals, we can use all three approaches more effectively (and, as we'll see, both descriptive analysis and prediction models are still helpful when the goal is to make causal inferences).\nMoreover, all three approaches are useful decision-making tools.\n\n### Description\n\nDescriptive analysis aims to describe the distribution of variables, often stratified by key variables of interest.\nA closely related idea is exploratory data analysis (EDA), although descriptive studies often have more explicit goals than those in EDA.\n\nDescriptive analyses are usually based on statistical summaries such as measures of centrality (means, medians) and spread (minimums, maximums, quartiles), but they also occasionally use techniques like regression modeling.\nThe goal of applying more advanced techniques like regression is different in descriptive analyses than in either predictive or causal studies.\n\"Adjusting\" for a variable in descriptive analyses means that we are removing its associational effect (and thus changing our question), *not* that we are controlling for confounding.\n\nIn epidemiology, a valuable concept for descriptive analyses is \"person, place, and time\" -- who has what disease, where, and when.\nThis concept is also a good template for descriptive analyses in other fields.\nUsually, we want to be clear about what population we're trying to describe, so we need to be as specific as possible.\nFor human health, describing the people involved, the location, and the period are all critical.\nIn other words, focus on the first principles of generating understanding of your data and describe your data accordingly.\n\n#### Examples\n\nCounting things is one of the best things we can do with data.\nEDA benefits both predictive and causal analyses, but descriptive analyses are valuable independent of the other analysis tasks.\nAsk any data scientist who thought they'd be developing complex machine learning models and found themselves spending most of their time on dashboards.\nUnderstanding the distributions of the data, particularly for key analysis goals (say, KPIs in industry or disease incidence in epidemiology), is critical for many types of decision-making.\n\nOne of the best recent examples of descriptive analyses arose from the COVID-19 pandemic [@Fox2022].\nIn 2020, particularly in the early months of the pandemic, descriptive analyses were vital to understanding risk and allocating resources.\nSince the coronavirus is similar to other respiratory diseases, we had many public health tools to reduce risk (e.g., distancing and, later, face masks).\nDescriptive statistics of cases by region were vital for deciding local policies and the strength of those policies.\n\nA great example of a more complex descriptive analysis during the pandemic was an [ongoing report by the Financial Times of expected deaths vs. observed deaths](https://www.ft.com/content/a2901ce8-5eb7-4633-b89c-cbdf5b386938) in various countries and regions[^3].\nWhile the calculation of expected deaths is slightly more sophisticated than most descriptive statistics, it provided a tremendous amount of information about current deaths without needing to untangle causal effects (e.g., were they due to COVID-19 directly? Inaccessible healthcare? Cardiovascular events post-COVID?).\nIn this (simplified) recreation of their plot from July 2020, you can see the staggering effect of the pandemic's early months.\n\n[^3]: John Burn-Murdoch was responsible for many of these presentations and gave a [fascinating talk on the subject](https://cloud.rstudio.com/resources/rstudioglobal-2021/reporting-on-and-visualising-the-pandemic/).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![2020 excess deaths vs. historical expected deaths from any cause. Data from the Financial Times.](chapter-01_files/figure-html/fig-ft-chart-1.png){#fig-ft-chart width=960}\n:::\n:::\n\n\nHere are some other great examples of descriptive analyses.\n\n-   Deforestation around the world. Our World in Data [@owidforestsanddeforestation] is a data journalism organization that produces thoughtful, usually descriptive reports on various topics. In this report, they present data visualizations of both absolute change in forest coverage (forest transitions) and relative change (deforestation or reforestation), using basic statistics and forestry theory to present helpful information about the state of forests over time.\n-   The prevalence of chlamydial and gonococcal infections [@Miller2004]. Measuring the prevalence of disease (how many people currently have a disease, usually expressed as a rate per number of people) is helpful for basic public health (resources, prevention, education) and scientific understanding. In this study, the authors conducted a complex survey meant to be representative of all high schools in the United States (the target population); they used survey weights to address a variety of factors related to their question, then calculated prevalence rates and other statistics. As we'll see, weights are helpful in causal inference for the same reason: targeting a particular population. That said, not all weighting techniques are causal in nature, and they were not here.\n-   Estimating race and ethnicity-specific hysterectomy inequalities [@Gartner2020]. Descriptive techniques also help us understand disparities in areas like economics and epidemiology. In this study, the authors asked: Does the risk of hysterectomy differ by racial or ethnic background? Although the analysis is stratified by a key variable, it's still descriptive. Another interesting aspect of this paper is the authors' work ensuring the research answered questions about the right target population. Their analysis combined several data sources to better estimate the true population prevalence (instead of the prevalence among those in hospitals, as commonly presented). They also adjusted for the prevalence of hysterectomy, e.g., they calculated the incidence (new case) rate only among those who could actually have a hysterectomy (e.g., they hadn't had one yet).\n\n#### Validity\n\nThere are two critical validity issues in descriptive analyses: measurement and sampling errors.\n\nMeasurement error is when we have mismeasured one or more variables in some capacity.\nFor descriptive analyses, mismeasuring things means we may not get the answer to our question.\nHowever, the degree to which that is the case depends on both the severity of the measurement error and the question itself.\n\nSampling error is a more nuanced topic in descriptive analyses.\nIt's related to the population we're analyzing (who should the analysis produce descriptions about) and uncertainty (how certain are we that the descriptions of those we have data for represent the population we're trying to describe.).\n\nThe population from which our data come and the population we're trying to describe must be the same for us to provide valid descriptions.\nConsider a dataset generated by an online survey.\nWho are the people who are answering these questions, and how do they relate to the people we want to describe?\nFor many analyses, the people who take the time to answer surveys are different than the people we want to describe, e.g., the group of people who fill out surveys may have a different distribution of variables than those who don't.\nResults from data like these are not technically biased because, outside of sample size-related uncertainty and measurement error, the descriptions are accurate---they're just not for the right group of people!\nIn other words, *we've gotten an answer to the wrong question*.\n\nNotably, sometimes our data represent the entire population (or close enough) that sampling error is irrelevant.\nConsider a company with certain data on every customer using their service.\nFor many analyses, this represents the entire population (current customers) about whom we want information.\nSimilarly, in countries with population-wide health registries, the data available for specific practical purposes is close enough to the entire population that no sampling is needed (although researchers might use sampling for simpler computations).\nIn these cases, there's not really such a thing as uncertainty.\nAssuming everything is well-measured, the summary statistics we generate *are inherently unbiased and precise* because we have information from everyone in the population.\nOf course, in practice, we usually have some mixture of measurement error, missing data, and so on, even in the best of circumstances.\n\nOne crucial detail of descriptive analysis is that confounding bias, one of the chief concerns of this book, is undefined.\nThat's because confounding is a causal concern.\nDescriptive analyses cannot be confounded because they are a statistical description of relationships *as-is*, not the mechanisms behind those relationships.\n\n#### Relationship to causal inference\n\nHumans see patterns very well.\nPattern-finding is a helpful feature of our brains, but it can also lead down a slippery slope of inference when we're not working with data or methods that can allow us to do that validly.\nThe biggest thing to be cautious of when your goal is to describe is making the leap from description to causation (implicitly or explicitly).\n\nBut, of course, descriptive analysis is helpful when we *are* estimating causal effects.\nIt helps us understand the population we're working with, the distribution of the outcomes, exposures (variables we think might be causal), and confounders (variables we need to account for to get unbiased causal effects for the exposure).\nIt also helps us be sure that the data structure we're using matches the question we're trying to answer, as we'll see in [Chapter -@sec-data-causal].\nYou should always do descriptive analyses of your data when conducting causal research.\n\nFinally, as we'll see in [Chapter -@sec-trials-std], there are certain circumstances where we can make causal inferences with basic statistics.\nBe cautious about the distinction between the causal question and the descriptive component here, too: just because we're using the same calculation (e.g., a difference in means) doesn't mean that all descriptions you can generate are causal. \nWhether a descriptive analysis overlaps with a causal analysis is a function of the data and the question.\n\n### Prediction\n\nThe goal of prediction is to use data to make accurate predictions about variables, usually on new data.\nWhat this means depends on the question, domain, and so on.\nPrediction models are used in about every setting imaginable, from peer-reviewed clinical models to bespoke machine learning models embedded in consumer devices.\nEven large language models like the ones ChatGPT is based on are prediction models: they predict what a response to a prompt would look like.\n\nPredictive modeling generally uses a different workflow than the workflow for causal modeling we'll present in this book.\nSince the goal of prediction is usually related to making predictions on new data, the workflow of this type of modeling focuses on maximizing predictive accuracy while retaining generalization to new data, sometimes called the bias-variance trade-off.\nIn practice, this means splitting your data into training sets (the part of the data you build your model on) and test sets (the part you assess your model with, a proxy for how it would perform on new data).\nUsually, data scientists use cross-validation or other sampling techniques to reduce further the risk of overfitting your model to the training set.\n\nThere are many excellent texts on predictive modeling, and so we refer you to those for a deeper exploration of the goals and methods of these techniques [@kuhn2013a; @harrell2001; @Kuhn_Silge_2022; @James_Witten_Hastie_Tibshirani_2022].\n\n#### Examples\n\nPrediction is the most popular topic in data science, largely thanks to machine learning applications in industry.\nPrediction, of course, has a long history in statistics, and many models popular today have been used for decades in and outside academia.\n\nLet's look at an example of prediction about COVID-19 [^chapter-01-1].\nIn 2021, researchers published the ISARIC 4C Deterioration model, a clinical prognostic model for predicting severe adverse outcomes for acute COVID-19 [@Gupta2021].\nThe authors included a descriptive analysis to understand the population from which this model was developed, particularly the distribution of the outcome and candidate predictors.\nOne helpful aspect of this model is that it uses items commonly measured on day one of COVID-related hospitalization.\nThe authors built this model using cross-validation by region of the UK and then tested the model on data from a hold-out region.\nThe final model included eleven items and a description of their model attributes, relation with the outcome, and so on.\nNotably, the authors used clinical domain knowledge to select candidate variables but did not fall into the temptation of interpreting the model coefficients as causal.\nWithout question, some of the predictive value of this model stems from the causal structure of the variables as they relate to the outcome, but the researchers had a different goal entirely for this model and stuck to it.\n\n[^chapter-01-1]: A natural model here is predicting cases, but infectious disease modeling is complex and usually uses techniques outside the usual predictive modeling workflow.\n\nHere are other good examples from the predictive space:\n\n-   Some of the most exciting work in predictive modeling is in industry.\n    Netflix regularly shares details on their modeling success and novel strategies in their [research blog](https://research.netflix.com/).\n    They also recently published a paper reviewing their use of deep learning models for recommender systems (in this case, recommending shows and movies to users) [@steck2021].\n    The authors explain their experimentation with models, the details of those models, and many of the challenges they faced, resulting in a practical guide on using such models.\n\n-   In early 2020, researchers experienced with predictive and prognostic modeling in health research published a review of models for diagnosis and prognosis of COVID-19 [@Wynants2020].\n    This review is interesting not just for its breadth but also the astounding number of models that were rated as poor quality: \"\\[232\\] models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting.\" This research is also a [living review](https://www.covprecise.org/).\n\n#### Validity\n\nThe key measure of validity in prediction modeling is predictive accuracy, which can be measured in several ways, such as root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC), and many more.\nA convenient detail about predictive modeling is that we can often assess if we're right, which is not true of descriptive statistics for which we only have a subset of data or causal inference for which we don't know the true causal structure.\nWe aren't always able to assess against the truth, but it's almost always required for fitting the initial predictive model [^chapter-01-2].\n\n[^chapter-01-2]: We say model singular, but usually data scientists fit many models for experimentation, and often the best prediction models are some combination of predictions from several models, called a stacked model\n\nMeasurement error is also a concern for predictive modeling because we usually need accurate data for accurate predictions.\nInterestingly, measurement error and missingness can be informative in predictive settings.\nIn a causal setting, this might induce bias, but predictive models can consume that information with no issue.\nFor instance, in the famous Netflix Prize, winning models leveraged information about whether or not a customer rated a movie at all to improve recommendation systems.\n\nLike descriptive analysis, confounding is undefined for predictive modeling.\nA coefficient in a prediction model cannot be confounded; we only care about whether or not the variable provides predictive information, not if that information is because of a causal relationship or something else.\n\n#### Relationship to causal inference\n\nThe single biggest risk in prediction is to assume that a given coefficient in a model has a causal interpretation.\nThere is a good chance that this isn't so.\nA model may predict well but may also have completely biased coefficients from a causal point of view.\nWe'll see more about this in @sec-pred-or-explain and the rest of the book.\n\nOften, people mistakenly use methods for selecting features (variables) for prediction models to select confounders in causal models.\nAside from their risk of overfitting, these methods are appropriate for prediction models but not for causal models.\nPrediction metrics cannot determine the causal structure of your question, and predictive value for the outcome does not make a variable a confounder.\nIn general, background knowledge (not prediction or associational statistics) should help you select variables for causal models @robinsImpossible; we'll discuss this process in detail in [Chapter -@sec-dags] and [Chapter -@sec-building-models].\n\nPrediction is nevertheless crucial to causal inference.\nFrom a philosophical perspective, we're comparing predictions from different *what if* scenarios: What would the outcome had one thing happened vs. if another thing happened?\nWe'll spend much time on this subject, particularly in [Chapter -@sec-counterfactuals].\nWe'll also talk a lot about prediction from a practical perspective: just like in prediction and some description, we'll use modeling techniques to answer causal questions.\nTechniques like propensity score methods and g-computation use model predictions to answer causal questions, but the workflow for building and interpreting the models themselves are quite different.\n\n### Causal Inference\n\nThe goal of causal inference is to understand the impact that a variable, sometimes called an exposure, has on another variable, sometimes called an outcome.\n\"Exposure\" and \"outcome\" are the terms we'll use in this book to describe the causal relationship we're interested in.\nImportantly, our goal is to answer this question clearly and precisely.\nIn practice, this means using techniques like study design (e.g., a randomized trial) or statistical methods (like propensity scores) to calculate an unbiased effect of the exposure on the outcome.\n\nAs with prediction and description, it's better to start with a clear, precise question to get a clear, precise answer.\nIn statistics and data science, particularly as we swim through the ocean of data of the modern world, we often end up with an answer without a question (e.g., `42`).\nThis, of course, makes interpretation of the answer difficult.\nIn @sec-diag, we'll discuss the structure of causal questions.\nWe'll discuss philosophical and practical ways to sharpen our questions in [Chapter -@sec-counterfactuals] and [Chapter -@sec-trials-std].\n\n::: callout-note\n## Causal inference and explanation\n\nSome authors use the phrases \"causal inference\" and \"explanation\" interchangeably.\nWe're a little more cautious about that.\nCausal inference always has a relationship to explanation, but we can accurately estimate the effect of one thing on another without understanding how it happens.\n\nConsider John Snow, the so-called father of epidemiology.\nIn 1854, Snow famously investigated a cholera outbreak in London and identified that specific water sources were to blame for the disease.\nHe was right: contaminated water was a mechanism for cholera transmission.\nYet, he didn't have enough information to explain how: *Vibrio cholerae*, the bacteria responsible for cholera, wasn't identified until nearly thirty years later.\n:::\n\n#### Examples\n\nWe'll see many examples of causal inference in this book, but let's continue with an example related to COVID-19.\nAs the pandemic continued and tools like vaccines and anti-viral treatments became available, policies like universal masking also began to change.\nIn February 2022, the US state of Massachusetts rescinded a statewide policy that required universal masking in public schools [@Cowger2022].\nIn the greater Boston area, some school districts continued the policy while others discontinued it; those that discontinued it also did so at different times over the following weeks after the policy change.\nThis difference in policy allowed researchers to take advantage of the differences in district policies over this period to study the impact of universal masking on COVID-19 cases.\nThe researchers included a descriptive analysis of the school districts to understand the distribution of factors related to COVID-19 and other determinants of health.\nTo estimate the effect of universal masking on cases, the authors used a technique common in policy-related causal inference called difference-in-differences to estimate this effect.\nTheir design alleviates some problematic assumptions needed for causal inference, but they also wisely controlled for potential confounders despite that advantage.\nThe authors found that districts that continued masking saw a drastically lower caseload than those that didn't; their analysis concluded that almost 12,000 additional cases occurred due to the policy change, nearly 30% of the cases in the districts during the 15 weeks of the study.\n\n<!-- TODO: add example when code and data are available -->\n\nHere are a few other interesting examples:\n\n-   Netflix regularly uses causal inference in their work.\n    In 2022, they published a [blog post summarizing some causal tasks](https://netflixtechblog.com/a-survey-of-causal-inference-applications-at-netflix-b62d25175e6f) they have engaged with.\n    One interesting example is localization.\n    Netflix, being worldwide, localizes content through subtitles and dubbing.\n    Randomized experiments were a bad idea because they meant withholding content from users, so researchers at Netflix used several approaches to understand the value of localization while addressing potential confounding.\n    One example is studying the impact of pandemic-related delays in dubbing.\n    Researchers used synthetic controls to simulate the impact on viewership with and without these delays.\n    Presumably, the timing of the pandemic-related delays was unrelated to many factors that would typically be related to dubbing processes, thus reducing some of the potential confounding.\n\n-   The Tuskegee Study is one of modern history's most infamous examples of medical abuse.\n    It is commonly pointed to as a source of distrust in the medical community from Black Americans.\n    Health economics researchers used a variation of difference-in-difference techniques to assess the effect of the Tuskegee Study on distrust and life expectancy in older Black men [@Alsan2018].\n    The results are important and disturbing: \"We find that the disclosure of the study in 1972 is correlated with increases in medical mistrust and mortality and decreases in both outpatient and inpatient physician interactions for older black men. Our estimates imply life expectancy at age 45 for black men fell by up to 1.5 years in response to the disclosure, accounting for approximately 35% of the 1980 life expectancy gap between black and white men and 25% of the gap between black men and women.\"\n\n#### Validity\n\nMaking valid causal inferences requires several assumptions that we'll discuss in @sec-assump.\nUnlike prediction, we generally cannot confirm that our causal models are correct.\nIn other words, most assumptions we need to make are unverifiable.\nWe'll come back to this topic time and time again in the book---from the basics of these assumptions to practical decision-making to probing our models for problems.\n\n### Why isn't the right causal model just the best prediction model? {#sec-pred-or-explain}\n\nAt this point, you may wonder why the right causal model isn't just the best prediction model.\nIt makes sense that the two would be related: naturally, things that cause other things would be predictors.\nIt's causality all the way down, so any predictive information *is* related, in some capacity, to the causal structure of the thing we're predicting. \nDoesn't it stand to reason that a model that predicts well is causal, too?\nIt's true that *some* predictive models can be great causal models and vice versa.\nUnfortunately, this is not always the case; causal effects needn't predict particularly well, and good predictors needn't be causally unbiased [@shmueli2010a]. \nThere is no way to know using data alone.\n\nLet's look at the causal perspective first because it's a bit simpler. \nConsider a causally unbiased model for an exposure but only includes variables related to the outcome *and* the exposure. \nIn other words, this model provides us with the correct answer for the exposure of interest but doesn't include other predictors of the outcome (which can sometimes be a good idea, as discussed in @sec-data-causal).\nIf an outcome has many causes, a model that accurately describes the relationship with the exposure likely won't predict the outcome very well.\nLikewise, if a true causal effect of the exposure on the outcome is small, it will bring little predictive value.\nIn other words, the predictive ability of a model, whether high or small, can't help us distinguish if the model is giving us the correct answer.\nOf course, low predictive power might also indicate that a causal effect isn't much use from an applied perspective, although that depends on several statistical factors.\n\nThere are two more complex reasons that predictive models won't always be unbiased causal models.\nFor the first reason, let's consider an accurate model from a causal perspective: it estimates effects on an outcome, and all of these effects are unbiased.\nEven in this ideal setting, you might get better predictions using a different model.\nThe reason has to do with the bias-variance trade-off in predictive modeling.\nWhen effects are small, data are noisy, predictors are highly correlated, or there's not much data, using a biased model like penalized regression might make sense.\nThese models intentionally introduce bias in favor of improved variance in out-of-data predictions.\nSince the goals of prediction and causal inference are different (accurate predictions, usually for out-of-data observations vs. an unbiased effect), the best model for inference is not necessarily the best prediction model.\n\n<!-- TODO: maybe include a simulation to prove it? -->\n\nSecondly, variables that are biased from a causal perspective often bring along with them predictive power.\nWe'll discuss which variables to include and *not* include in your models in @sec-dags, but let's consider a simple example.\nOne of the famous examples of confounded relationships is ice cream sales and crime in the summer.\n[Descriptively, ice cream sales and crime are related](https://slate.com/news-and-politics/2013/07/warm-weather-homicide-rates-when-ice-cream-sales-rise-homicides-rise-coincidence.html), but this relationship is confounded by weather, e.g., both ice cream sales and crime increases when it's warmer.\n(This is simplistic, of course, as weather itself doesn't cause crime, but it's on the causal pathway.)\n\nConsider a thought experiment where you are in a dark room.\nYour goal is to predict crime, but you don't know the weather or time of year.\nYou do, however, have information on ice cream sales.\nA model with ice cream sales on crime would be biased from a causal perspective---ice cream sales do not cause crime, even though the model would show an effect---but would provide some predictive value to your crime model.\nThe reason for both of these conditions is the same: weather and ice cream sales are correlated, and so are weather and crime.\nIce cream sales can successfully, if imperfectly, serve as a proxy for weather.\nThat results in a biased effect estimate of the causal impact of ice cream sales on crime but a partially effective prediction of crime.\nOther variables, too, which are invalid from a causal perspective, either by being biased themselves or by introducing bias into the causal effect estimate, often bring good predictive value.\nThus, predictive accuracy is not a good measure of causality.\n\nA closely related idea is the *Table Two Fallacy*, so-called because, in health research papers, descriptive analyses are often presented in Table 1, and regression models are often presented in Table 2 [@Westreich2013].\nThe Table Two Fallacy is when a researcher presents confounders and other non-effect variables, particularly when they interpret those coefficients as if they, too, were causal.\nThe problem is that in some situations, the model to estimate an unbiased effect of one variable may not be the same model to estimate an unbiased effect of another variable.\nIn other words, we can't interpret the effects of confounders as causal because they might *themselves* be confounded by another variable unrelated to the original exposure.\n\nDescriptive, predictive, and causal analyses will always contain some aspect of each other.\nA predictive model gains some of its predictive power from the causal structure of the outcome, and a causal model has some predictive power because it contains information about the outcome.\nHowever, the same model in the same data with different goals will have different usefulness depending on those goals.\n<!-- TODO: uncomment this when section is written -- We'll dive more deeply into this topic in @sec-causal-pred-revisit. -->\n\n## Diagraming a causal claim {#sec-diag}\n\nEach analysis task, whether descriptive, predictive, or inferential, should start with a clear, precise question. \nLet's diagram them to understand better the structure of causal questions (to which we'll return our focus). \nDiagramming sentences is a grammatical method used to visually represent the structure of a sentence, occasionally taught in grammar school.\nIn this technique, sentences are deconstructed into their constituent parts, such as subjects, verbs, objects, and modifiers, and then displayed using a series of lines and symbols.\nThe arrangement of these elements on the diagram reflects their syntactic roles and how they interact within the sentence's overall structure.\nBy breaking down sentences into these visual representations, diagramming can help learners grasp the nuances of sentence construction, identify grammatical errors, and appreciate the intricate connections between words.\nWe can apply a similar idea to *causal claims*.\nHere is an example of how one might diagram a causal claim.\nWe've pulled out the *cause*, the *effect*, the *subject* (for whom?), and the *timing* (when?).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example of diagraming a causal claim.](../images/sentence-diagram-1.png){#fig-diagram-1 width=2219}\n:::\n:::\n\n\nLet's start with a basic causal question: **Does smoking cause lung cancer?**\n\nThe causal claim here could be that *smoking causes lung cancer*.\n@fig-diagram-2 shows a potential diagram of this causal claim.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Diagram of the causal claim \"smoking causes lung cancer\".](../images/sentence-diagram-2.png){#fig-diagram-2 width=2219}\n:::\n:::\n\n\nLet's get more specific.\nA study was published in *JAMA* (the Journal of the American Medical Association) in 2005 titled \"Effect of Smoking Reduction on Lung Cancer Risk.\"\nThis study concluded: \"Among individuals who smoke 15 or more cigarettes per day, smoking reduction by 50% significantly reduces the risk of lung cancer\".\n[@godtfredsen2005effect] The study describes the time frame studied as 5-10 years.\nLet's diagram this causal claim.\nHere, we assume that the eligibility criteria and the target population for the estimated causal effect are the same (individuals who smoke 15 or more cigarettes per day); this need not always be the case.\nIn @sec-estimands, we will discuss other potential target populations.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example diagram of a more specific causal claim based on results from @godtfredsen2005effect.](../images/sentence-diagram-3.png){#fig-diagram-3 width=2222}\n:::\n:::\n\n\nTranslating this idea into asking good causal questions, we can map the following terms that you will see throughout this book to these diagrams: *exposure* (the cause), *outcome* (the effect), *eligibility criteria* (for whom?), *time zero* (when did the participants begin to be followed?), *target population*, (who can we estimate an outcome effect for?) and *follow-up period* (when?).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example diagram mapped to causal analysis terminology](../images/sentence-diagram-4.png){#fig-diagram-4 width=2219}\n:::\n:::\n\n\nAsking good causal questions means we map the *question* to the observable *evidence*.\nLet's return to the smoking example.\nOur initial question was: *Does smoking cause lung cancer?*; The evidence in the study shows: *For people who smoke 15+ cigarettes a day, reducing smoking by 50% reduces the risk of lung cancer over 5-10 years*.\nDoes the answer match the question?\nNot quite.\nLet's update our question to match what the study actually showed: *For people who smoke 15+ cigarettes a day, does reducing smoking by 50% reduce the lung cancer risk over 5-10 years?*\nHoning this skill — asking answerable causal questions — is essential and one we will discuss throughout this book.\n",
+    "markdown": "\\mainmatter\n\n# What is a causal question? {#sec-causal-question}\n\n\n\n\n\n## Schrödinger's Causality\n\nThe heart of causal analysis is the causal question; it dictates what data we analyze, how we analyze it, and to which populations our inferences apply.\nThis book, being applied in nature, deals primarily with the analysis stage of causal inference.\nRelative to the complexity of specifying a good causal question, the analysis stage is considerably more straightforward.\nIn the first six chapters of this book, we'll discuss what a causal question is, how to improve our questions, and consider some examples.\n\nCausal questions are part of a broader set of questions we can ask with statistical techniques related to the primary tasks of data science: description, prediction, and causal inference [@hernan2019].\nUnfortunately, these tasks are often muddled by the techniques we use (regression, for instance, is helpful for all three tasks) and how we talk about them.\nWhen researchers are interested in causal inference from non-randomized data, we often use euphemistic language like \"association\" instead of declaring our intent to estimate a causal effect [@Hernan2018].\n\nIn a recent study of the language of analyses in epidemiologic research, for instance, the most common root word describing the estimated effect was \"associate,\" but many researchers also felt that \"associate\" implied at least *some* causal effect (@fig-word-ranking) [@haber_causal_language].\nOnly around 1% of the studies analyzed used the root word \"cause\" at all.\nYet, a third of studies had action recommendations, and researchers rated 80% of these recommendations as having at least some causal implication.\nOften, these studies have stronger action recommendations (alluding to causal effects) than those implied by the description of the effect (root words like \"associate\" and \"compare\").\nDespite how many studies implied that the goal was causal inference, only about 4% used formal causal models like those discussed in this book.\nHowever, most discussed how such a cause might be justified by previous research or theory.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Rankings of causal strength of root words used by researchers. Root words with more Strong rankings have stronger causal implications than those with many None or Weak rankings. Data from Haber et al.](chapter-01_files/figure-html/fig-word-ranking-1.png){#fig-word-ranking width=672}\n:::\n:::\n\n\nInstead of clear questions with obvious assumptions and goals, we end up with \"Schrödinger's causal inference\":\n\n> Our results suggest that \"Schrödinger's causal inference,\" — where studies avoid stating (or even explicitly deny) an interest in estimating causal effects yet are otherwise embedded with causal intent, inference, implications, and recommendations — is common.\n>\n> --- @haber_causal_language\n\n## Description, prediction, and explanation\n\nAn excellent first step to address this problem is recognizing that questions about description, prediction, and explanation are fundamentally different.\nData science in industry isn't quite as burdened by Schrödinger's causal inference as the academic sciences, but being explicit in the differences in analytic intent is still helpful.\nFor instance, when a stakeholder asks for \"drivers\" of a particular event, what are they asking?\nFor a model to predict the event?\nFor a deeper understanding of what causes the event?\nIt's a vague request, but it smacks of causal interest to us.\nWhen we're clear about our goals, we can use all three approaches more effectively (and, as we'll see, both descriptive analysis and prediction models are still helpful when the goal is to make causal inferences).\nMoreover, all three approaches are useful decision-making tools.\n\n### Description\n\nDescriptive analysis aims to describe the distribution of variables, often stratified by key variables of interest.\nA closely related idea is exploratory data analysis (EDA), although descriptive studies often have more explicit goals than those in EDA.\n\nDescriptive analyses are usually based on statistical summaries such as measures of centrality (means, medians) and spread (minimums, maximums, quartiles), but they also occasionally use techniques like regression modeling.\nThe goal of applying more advanced techniques like regression is different in descriptive analyses than in either predictive or causal studies.\n\"Adjusting\" for a variable in descriptive analyses means that we are removing its associational effect (and thus changing our question), *not* that we are controlling for confounding.\n\nIn epidemiology, a valuable concept for descriptive analyses is \"person, place, and time\" -- who has what disease, where, and when.\nThis concept is also a good template for descriptive analyses in other fields.\nUsually, we want to be clear about what population we're trying to describe, so we need to be as specific as possible.\nFor human health, describing the people involved, the location, and the period are all critical.\nIn other words, focus on the first principles of generating understanding of your data and describe your data accordingly.\n\n#### Examples\n\nCounting things is one of the best things we can do with data.\nEDA benefits both predictive and causal analyses, but descriptive analyses are valuable independent of the other analysis tasks.\nAsk any data scientist who thought they'd be developing complex machine learning models and found themselves spending most of their time on dashboards.\nUnderstanding the distributions of the data, particularly for key analysis goals (say, KPIs in industry or disease incidence in epidemiology), is critical for many types of decision-making.\n\nOne of the best recent examples of descriptive analyses arose from the COVID-19 pandemic [@Fox2022].\nIn 2020, particularly in the early months of the pandemic, descriptive analyses were vital to understanding risk and allocating resources.\nSince the coronavirus is similar to other respiratory diseases, we had many public health tools to reduce risk (e.g., distancing and, later, face masks).\nDescriptive statistics of cases by region were vital for deciding local policies and the strength of those policies.\n\nA great example of a more complex descriptive analysis during the pandemic was an [ongoing report by the Financial Times of expected deaths vs. observed deaths](https://www.ft.com/content/a2901ce8-5eb7-4633-b89c-cbdf5b386938) in various countries and regions[^3].\nWhile the calculation of expected deaths is slightly more sophisticated than most descriptive statistics, it provided a tremendous amount of information about current deaths without needing to untangle causal effects (e.g., were they due to COVID-19 directly? Inaccessible healthcare? Cardiovascular events post-COVID?).\nIn this (simplified) recreation of their plot from July 2020, you can see the staggering effect of the pandemic's early months.\n\n[^3]: John Burn-Murdoch was responsible for many of these presentations and gave a [fascinating talk on the subject](https://cloud.rstudio.com/resources/rstudioglobal-2021/reporting-on-and-visualising-the-pandemic/).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![2020 excess deaths vs. historical expected deaths from any cause. Data from the Financial Times.](chapter-01_files/figure-html/fig-ft-chart-1.png){#fig-ft-chart width=960}\n:::\n:::\n\n\nHere are some other great examples of descriptive analyses.\n\n-   Deforestation around the world. Our World in Data [@owidforestsanddeforestation] is a data journalism organization that produces thoughtful, usually descriptive reports on various topics. In this report, they present data visualizations of both absolute change in forest coverage (forest transitions) and relative change (deforestation or reforestation), using basic statistics and forestry theory to present helpful information about the state of forests over time.\n-   The prevalence of chlamydial and gonococcal infections [@Miller2004]. Measuring the prevalence of disease (how many people currently have a disease, usually expressed as a rate per number of people) is helpful for basic public health (resources, prevention, education) and scientific understanding. In this study, the authors conducted a complex survey meant to be representative of all high schools in the United States (the target population); they used survey weights to address a variety of factors related to their question, then calculated prevalence rates and other statistics. As we'll see, weights are helpful in causal inference for the same reason: targeting a particular population. That said, not all weighting techniques are causal in nature, and they were not here.\n-   Estimating race and ethnicity-specific hysterectomy inequalities [@Gartner2020]. Descriptive techniques also help us understand disparities in areas like economics and epidemiology. In this study, the authors asked: Does the risk of hysterectomy differ by racial or ethnic background? Although the analysis is stratified by a key variable, it's still descriptive. Another interesting aspect of this paper is the authors' work ensuring the research answered questions about the right target population. Their analysis combined several data sources to better estimate the true population prevalence (instead of the prevalence among those in hospitals, as commonly presented). They also adjusted for the prevalence of hysterectomy, e.g., they calculated the incidence (new case) rate only among those who could actually have a hysterectomy (e.g., they hadn't had one yet).\n\n#### Validity\n\nThere are two critical validity issues in descriptive analyses: measurement and sampling errors.\n\nMeasurement error is when we have mismeasured one or more variables in some capacity.\nFor descriptive analyses, mismeasuring things means we may not get the answer to our question.\nHowever, the degree to which that is the case depends on both the severity of the measurement error and the question itself.\n\nSampling error is a more nuanced topic in descriptive analyses.\nIt's related to the population we're analyzing (who should the analysis produce descriptions about) and uncertainty (how certain are we that the descriptions of those we have data for represent the population we're trying to describe.).\n\nThe population from which our data come and the population we're trying to describe must be the same for us to provide valid descriptions.\nConsider a dataset generated by an online survey.\nWho are the people who are answering these questions, and how do they relate to the people we want to describe?\nFor many analyses, the people who take the time to answer surveys are different than the people we want to describe, e.g., the group of people who fill out surveys may have a different distribution of variables than those who don't.\nResults from data like these are not technically biased because, outside of sample size-related uncertainty and measurement error, the descriptions are accurate---they're just not for the right group of people!\nIn other words, *we've gotten an answer to the wrong question*.\n\nNotably, sometimes our data represent the entire population (or close enough) that sampling error is irrelevant.\nConsider a company with certain data on every customer using their service.\nFor many analyses, this represents the entire population (current customers) about whom we want information.\nSimilarly, in countries with population-wide health registries, the data available for specific practical purposes is close enough to the entire population that no sampling is needed (although researchers might use sampling for simpler computations).\nIn these cases, there's not really such a thing as uncertainty.\nAssuming everything is well-measured, the summary statistics we generate *are inherently unbiased and precise* because we have information from everyone in the population.\nOf course, in practice, we usually have some mixture of measurement error, missing data, and so on, even in the best of circumstances.\n\nOne crucial detail of descriptive analysis is that confounding bias, one of the chief concerns of this book, is undefined.\nThat's because confounding is a causal concern.\nDescriptive analyses cannot be confounded because they are a statistical description of relationships *as-is*, not the mechanisms behind those relationships.\n\n#### Relationship to causal inference\n\nHumans see patterns very well.\nPattern-finding is a helpful feature of our brains, but it can also lead down a slippery slope of inference when we're not working with data or methods that can allow us to do that validly.\nThe biggest thing to be cautious of when your goal is to describe is making the leap from description to causation (implicitly or explicitly).\n\nBut, of course, descriptive analysis is helpful when we *are* estimating causal effects.\nIt helps us understand the population we're working with, the distribution of the outcomes, exposures (variables we think might be causal), and confounders (variables we need to account for to get unbiased causal effects for the exposure).\nIt also helps us be sure that the data structure we're using matches the question we're trying to answer, as we'll see in [Chapter -@sec-data-causal].\nYou should always do descriptive analyses of your data when conducting causal research.\n\nFinally, as we'll see in [Chapter -@sec-trials-std], there are certain circumstances where we can make causal inferences with basic statistics.\nBe cautious about the distinction between the causal question and the descriptive component here, too: just because we're using the same calculation (e.g., a difference in means) doesn't mean that all descriptions you can generate are causal. \nWhether a descriptive analysis overlaps with a causal analysis is a function of the data and the question.\n\n### Prediction\n\nThe goal of prediction is to use data to make accurate predictions about variables, usually on new data.\nWhat this means depends on the question, domain, and so on.\nPrediction models are used in about every setting imaginable, from peer-reviewed clinical models to bespoke machine learning models embedded in consumer devices.\nEven large language models like the ones ChatGPT is based on are prediction models: they predict what a response to a prompt would look like.\n\nPredictive modeling generally uses a different workflow than the workflow for causal modeling we'll present in this book.\nSince the goal of prediction is usually related to making predictions on new data, the workflow of this type of modeling focuses on maximizing predictive accuracy while retaining generalization to new data, sometimes called the bias-variance trade-off.\nIn practice, this means splitting your data into training sets (the part of the data you build your model on) and test sets (the part you assess your model with, a proxy for how it would perform on new data).\nUsually, data scientists use cross-validation or other sampling techniques to reduce further the risk of overfitting your model to the training set.\n\nThere are many excellent texts on predictive modeling, and so we refer you to those for a deeper exploration of the goals and methods of these techniques [@kuhn2013a; @harrell2001; @Kuhn_Silge_2022; @James_Witten_Hastie_Tibshirani_2022].\n\n#### Examples\n\nPrediction is the most popular topic in data science, largely thanks to machine learning applications in industry.\nPrediction, of course, has a long history in statistics, and many models popular today have been used for decades in and outside academia.\n\nLet's look at an example of prediction about COVID-19 [^chapter-01-1].\nIn 2021, researchers published the ISARIC 4C Deterioration model, a clinical prognostic model for predicting severe adverse outcomes for acute COVID-19 [@Gupta2021].\nThe authors included a descriptive analysis to understand the population from which this model was developed, particularly the distribution of the outcome and candidate predictors.\nOne helpful aspect of this model is that it uses items commonly measured on day one of COVID-related hospitalization.\nThe authors built this model using cross-validation by region of the UK and then tested the model on data from a hold-out region.\nThe final model included eleven items and a description of their model attributes, relation with the outcome, and so on.\nNotably, the authors used clinical domain knowledge to select candidate variables but did not fall into the temptation of interpreting the model coefficients as causal.\nWithout question, some of the predictive value of this model stems from the causal structure of the variables as they relate to the outcome, but the researchers had a different goal entirely for this model and stuck to it.\n\n[^chapter-01-1]: A natural model here is predicting cases, but infectious disease modeling is complex and usually uses techniques outside the usual predictive modeling workflow.\n\nHere are other good examples from the predictive space:\n\n-   Some of the most exciting work in predictive modeling is in industry.\n    Netflix regularly shares details on their modeling success and novel strategies in their [research blog](https://research.netflix.com/).\n    They also recently published a paper reviewing their use of deep learning models for recommender systems (in this case, recommending shows and movies to users) [@steck2021].\n    The authors explain their experimentation with models, the details of those models, and many of the challenges they faced, resulting in a practical guide on using such models.\n\n-   In early 2020, researchers experienced with predictive and prognostic modeling in health research published a review of models for diagnosis and prognosis of COVID-19 [@Wynants2020].\n    This review is interesting not just for its breadth but also the astounding number of models that were rated as poor quality: \"\\[232\\] models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting.\" This research is also a [living review](https://www.covprecise.org/).\n\n#### Validity\n\nThe key measure of validity in prediction modeling is predictive accuracy, which can be measured in several ways, such as root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC), and many more.\nA convenient detail about predictive modeling is that we can often assess if we're right, which is not true of descriptive statistics for which we only have a subset of data or causal inference for which we don't know the true causal structure.\nWe aren't always able to assess against the truth, but it's almost always required for fitting the initial predictive model [^chapter-01-2].\n\n[^chapter-01-2]: We say model singular, but usually data scientists fit many models for experimentation, and often the best prediction models are some combination of predictions from several models, called a stacked model\n\nMeasurement error is also a concern for predictive modeling because we usually need accurate data for accurate predictions.\nInterestingly, measurement error and missingness can be informative in predictive settings.\nIn a causal setting, this might induce bias, but predictive models can consume that information with no issue.\nFor instance, in the famous Netflix Prize, winning models leveraged information about whether or not a customer rated a movie at all to improve recommendation systems.\n\nLike descriptive analysis, confounding is undefined for predictive modeling.\nA coefficient in a prediction model cannot be confounded; we only care about whether or not the variable provides predictive information, not if that information is because of a causal relationship or something else.\n\n#### Relationship to causal inference\n\nThe single biggest risk in prediction is to assume that a given coefficient in a model has a causal interpretation.\nThere is a good chance that this isn't so.\nA model may predict well but may also have completely biased coefficients from a causal point of view.\nWe'll see more about this in @sec-pred-or-explain and the rest of the book.\n\nOften, people mistakenly use methods for selecting features (variables) for prediction models to select confounders in causal models.\nAside from their risk of overfitting, these methods are appropriate for prediction models but not for causal models.\nPrediction metrics cannot determine the causal structure of your question, and predictive value for the outcome does not make a variable a confounder.\nIn general, background knowledge (not prediction or associational statistics) should help you select variables for causal models @robinsImpossible; we'll discuss this process in detail in [Chapter -@sec-dags] and [Chapter -@sec-building-models].\n\nPrediction is nevertheless crucial to causal inference.\nFrom a philosophical perspective, we're comparing predictions from different *what if* scenarios: What would the outcome had one thing happened vs. if another thing happened?\nWe'll spend much time on this subject, particularly in [Chapter -@sec-counterfactuals].\nWe'll also talk a lot about prediction from a practical perspective: just like in prediction and some description, we'll use modeling techniques to answer causal questions.\nTechniques like propensity score methods and g-computation use model predictions to answer causal questions, but the workflow for building and interpreting the models themselves are quite different.\n\n### Causal Inference\n\nThe goal of causal inference is to understand the impact that a variable, sometimes called an exposure, has on another variable, sometimes called an outcome.\n\"Exposure\" and \"outcome\" are the terms we'll use in this book to describe the causal relationship we're interested in.\nImportantly, our goal is to answer this question clearly and precisely.\nIn practice, this means using techniques like study design (e.g., a randomized trial) or statistical methods (like propensity scores) to calculate an unbiased effect of the exposure on the outcome.\n\nAs with prediction and description, it's better to start with a clear, precise question to get a clear, precise answer.\nIn statistics and data science, particularly as we swim through the ocean of data of the modern world, we often end up with an answer without a question (e.g., `42`).\nThis, of course, makes interpretation of the answer difficult.\nIn @sec-diag, we'll discuss the structure of causal questions.\nWe'll discuss philosophical and practical ways to sharpen our questions in [Chapter -@sec-counterfactuals] and [Chapter -@sec-trials-std].\n\n::: callout-note\n## Causal inference and explanation\n\nSome authors use the phrases \"causal inference\" and \"explanation\" interchangeably.\nWe're a little more cautious about that.\nCausal inference always has a relationship to explanation, but we can accurately estimate the effect of one thing on another without understanding how it happens.\n\nConsider John Snow, the so-called father of epidemiology.\nIn 1854, Snow famously investigated a cholera outbreak in London and identified that specific water sources were to blame for the disease.\nHe was right: contaminated water was a mechanism for cholera transmission.\nYet, he didn't have enough information to explain how: *Vibrio cholerae*, the bacteria responsible for cholera, wasn't identified until nearly thirty years later.\n:::\n\n#### Examples\n\nWe'll see many examples of causal inference in this book, but let's continue with an example related to COVID-19.\nAs the pandemic continued and tools like vaccines and anti-viral treatments became available, policies like universal masking also began to change.\nIn February 2022, the US state of Massachusetts rescinded a statewide policy that required universal masking in public schools [@Cowger2022].\nIn the greater Boston area, some school districts continued the policy while others discontinued it; those that discontinued it also did so at different times over the following weeks after the policy change.\nThis difference in policy allowed researchers to take advantage of the differences in district policies over this period to study the impact of universal masking on COVID-19 cases.\nThe researchers included a descriptive analysis of the school districts to understand the distribution of factors related to COVID-19 and other determinants of health.\nTo estimate the effect of universal masking on cases, the authors used a technique common in policy-related causal inference called difference-in-differences to estimate this effect.\nTheir design alleviates some problematic assumptions needed for causal inference, but they also wisely controlled for potential confounders despite that advantage.\nThe authors found that districts that continued masking saw a drastically lower caseload than those that didn't; their analysis concluded that almost 12,000 additional cases occurred due to the policy change, nearly 30% of the cases in the districts during the 15 weeks of the study.\n\n<!-- TODO: add example when code and data are available -->\n\nHere are a few other interesting examples:\n\n-   Netflix regularly uses causal inference in their work.\n    In 2022, they published a [blog post summarizing some causal tasks](https://netflixtechblog.com/a-survey-of-causal-inference-applications-at-netflix-b62d25175e6f) they have engaged with.\n    One interesting example is localization.\n    Netflix, being worldwide, localizes content through subtitles and dubbing.\n    Randomized experiments were a bad idea because they meant withholding content from users, so researchers at Netflix used several approaches to understand the value of localization while addressing potential confounding.\n    One example is studying the impact of pandemic-related delays in dubbing.\n    Researchers used synthetic controls to simulate the impact on viewership with and without these delays.\n    Presumably, the timing of the pandemic-related delays was unrelated to many factors that would typically be related to dubbing processes, thus reducing some of the potential confounding.\n\n-   The Tuskegee Study is one of modern history's most infamous examples of medical abuse.\n    It is commonly pointed to as a source of distrust in the medical community from Black Americans.\n    Health economics researchers used a variation of difference-in-difference techniques to assess the effect of the Tuskegee Study on distrust and life expectancy in older Black men [@Alsan2018].\n    The results are important and disturbing: \"We find that the disclosure of the study in 1972 is correlated with increases in medical mistrust and mortality and decreases in both outpatient and inpatient physician interactions for older black men. Our estimates imply life expectancy at age 45 for black men fell by up to 1.5 years in response to the disclosure, accounting for approximately 35% of the 1980 life expectancy gap between black and white men and 25% of the gap between black men and women.\"\n\n#### Validity\n\nMaking valid causal inferences requires several assumptions that we'll discuss in @sec-assump.\nUnlike prediction, we generally cannot confirm that our causal models are correct.\nIn other words, most assumptions we need to make are unverifiable.\nWe'll come back to this topic time and time again in the book---from the basics of these assumptions to practical decision-making to probing our models for problems.\n\n### Why isn't the right causal model just the best prediction model? {#sec-pred-or-explain}\n\nAt this point, you may wonder why the right causal model isn't just the best prediction model.\nIt makes sense that the two would be related: naturally, things that cause other things would be predictors.\nIt's causality all the way down, so any predictive information *is* related, in some capacity, to the causal structure of the thing we're predicting. \nDoesn't it stand to reason that a model that predicts well is causal, too?\nIt's true that *some* predictive models can be great causal models and vice versa.\nUnfortunately, this is not always the case; causal effects needn't predict particularly well, and good predictors needn't be causally unbiased [@shmueli2010a]. \nThere is no way to know using data alone.\n\nLet's look at the causal perspective first because it's a bit simpler. \nConsider a causally unbiased model for an exposure but only includes variables related to the outcome *and* the exposure. \nIn other words, this model provides us with the correct answer for the exposure of interest but doesn't include other predictors of the outcome (which can sometimes be a good idea, as discussed in @sec-data-causal).\nIf an outcome has many causes, a model that accurately describes the relationship with the exposure likely won't predict the outcome very well.\nLikewise, if a true causal effect of the exposure on the outcome is small, it will bring little predictive value.\nIn other words, the predictive ability of a model, whether high or small, can't help us distinguish if the model is giving us the correct answer.\nOf course, low predictive power might also indicate that a causal effect isn't much use from an applied perspective, although that depends on several statistical factors.\n\nThere are two more complex reasons that predictive models won't always be unbiased causal models.\nFor the first reason, let's consider an accurate model from a causal perspective: it estimates effects on an outcome, and all of these effects are unbiased.\nEven in this ideal setting, you might get better predictions using a different model.\nThe reason has to do with the bias-variance trade-off in predictive modeling.\nWhen effects are small, data are noisy, predictors are highly correlated, or there's not much data, using a biased model like penalized regression might make sense.\nThese models intentionally introduce bias in favor of improved variance in out-of-data predictions.\nSince the goals of prediction and causal inference are different (accurate predictions, usually for out-of-data observations vs. an unbiased effect), the best model for inference is not necessarily the best prediction model.\n\n<!-- TODO: maybe include a simulation to prove it? -->\n\nSecondly, variables that are biased from a causal perspective often bring along with them predictive power.\nWe'll discuss which variables to include and *not* include in your models in @sec-dags, but let's consider a simple example.\nOne of the famous examples of confounded relationships is ice cream sales and crime in the summer.\n[Descriptively, ice cream sales and crime are related](https://slate.com/news-and-politics/2013/07/warm-weather-homicide-rates-when-ice-cream-sales-rise-homicides-rise-coincidence.html), but this relationship is confounded by weather, e.g., both ice cream sales and crime increases when it's warmer.\n(This is simplistic, of course, as weather itself doesn't cause crime, but it's on the causal pathway.)\n\nConsider a thought experiment where you are in a dark room.\nYour goal is to predict crime, but you don't know the weather or time of year.\nYou do, however, have information on ice cream sales.\nA model with ice cream sales on crime would be biased from a causal perspective---ice cream sales do not cause crime, even though the model would show an effect---but would provide some predictive value to your crime model.\nThe reason for both of these conditions is the same: weather and ice cream sales are correlated, and so are weather and crime.\nIce cream sales can successfully, if imperfectly, serve as a proxy for weather.\nThat results in a biased effect estimate of the causal impact of ice cream sales on crime but a partially effective prediction of crime.\nOther variables, too, which are invalid from a causal perspective, either by being biased themselves or by introducing bias into the causal effect estimate, often bring good predictive value.\nThus, predictive accuracy is not a good measure of causality.\n\nA closely related idea is the *Table Two Fallacy*, so-called because, in health research papers, descriptive analyses are often presented in Table 1, and regression models are often presented in Table 2 [@Westreich2013].\nThe Table Two Fallacy is when a researcher presents confounders and other non-effect variables, particularly when they interpret those coefficients as if they, too, were causal.\nThe problem is that in some situations, the model to estimate an unbiased effect of one variable may not be the same model to estimate an unbiased effect of another variable.\nIn other words, we can't interpret the effects of confounders as causal because they might *themselves* be confounded by another variable unrelated to the original exposure.\n\nDescriptive, predictive, and causal analyses will always contain some aspect of each other.\nA predictive model gains some of its predictive power from the causal structure of the outcome, and a causal model has some predictive power because it contains information about the outcome.\nHowever, the same model in the same data with different goals will have different usefulness depending on those goals.\n<!-- TODO: uncomment this when section is written -- We'll dive more deeply into this topic in @sec-causal-pred-revisit. -->\n\n## Diagraming a causal claim {#sec-diag}\n\nEach analysis task, whether descriptive, predictive, or inferential, should start with a clear, precise question. \nLet's diagram them to understand better the structure of causal questions (to which we'll return our focus). \nDiagramming sentences is a grammatical method used to visually represent the structure of a sentence, occasionally taught in grammar school.\nIn this technique, sentences are deconstructed into their constituent parts, such as subjects, verbs, objects, and modifiers, and then displayed using a series of lines and symbols.\nThe arrangement of these elements on the diagram reflects their syntactic roles and how they interact within the sentence's overall structure.\nBy breaking down sentences into these visual representations, diagramming can help learners grasp the nuances of sentence construction, identify grammatical errors, and appreciate the intricate connections between words.\nWe can apply a similar idea to *causal claims*.\nHere is an example of how one might diagram a causal claim.\nWe've pulled out the *cause*, the *effect*, the *subject* (for whom?), and the *timing* (when?).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example of diagraming a causal claim.](../images/sentence-diagram-1.png){#fig-diagram-1 width=2219}\n:::\n:::\n\n\nLet's start with a basic causal question: **Does smoking cause lung cancer?**\n\nThe causal claim here could be that *smoking causes lung cancer*.\n@fig-diagram-2 shows a potential diagram of this causal claim.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Diagram of the causal claim \"smoking causes lung cancer\".](../images/sentence-diagram-2.png){#fig-diagram-2 width=2219}\n:::\n:::\n\n\nLet's get more specific.\nA study was published in *JAMA* (the Journal of the American Medical Association) in 2005 titled \"Effect of Smoking Reduction on Lung Cancer Risk.\"\nThis study concluded: \"Among individuals who smoke 15 or more cigarettes per day, smoking reduction by 50% significantly reduces the risk of lung cancer\".\n[@godtfredsen2005effect] The study describes the time frame studied as 5-10 years.\nLet's diagram this causal claim.\nHere, we assume that the eligibility criteria and the target population for the estimated causal effect are the same (individuals who smoke 15 or more cigarettes per day); this need not always be the case.\nIn @sec-estimands, we will discuss other potential target populations.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example diagram of a more specific causal claim based on results from @godtfredsen2005effect.](../images/sentence-diagram-3.png){#fig-diagram-3 width=2222}\n:::\n:::\n\n\nTranslating this idea into asking good causal questions, we can map the following terms that you will see throughout this book to these diagrams: *exposure* (the cause), *outcome* (the effect), *eligibility criteria* (for whom?), *time zero* (when did the participants begin to be followed?), *target population*, (who can we estimate an outcome effect for?) and *follow-up period* (when?).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example diagram mapped to causal analysis terminology](../images/sentence-diagram-4.png){#fig-diagram-4 width=2219}\n:::\n:::\n\n\nAsking good causal questions means we map the *question* to the observable *evidence*.\nLet's return to the smoking example.\nOur initial question was: *Does smoking cause lung cancer?*; The evidence in the study shows: *For people who smoke 15+ cigarettes a day, reducing smoking by 50% reduces the risk of lung cancer over 5-10 years*.\nDoes the answer match the question?\nNot quite.\nLet's update our question to match what the study actually showed: *For people who smoke 15+ cigarettes a day, does reducing smoking by 50% reduce the lung cancer risk over 5-10 years?*\nHoning this skill — asking answerable causal questions — is essential and one we will discuss throughout this book.",
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-    "markdown": "# The whole game: mosquito nets and malaria {#sec-whole-game}\n\n\n\n\n\nIn this chapter, we'll analyze data using techniques we learn in this book.\nWe'll play the [whole game](https://www.gse.harvard.edu/news/uk/09/01/education-bat-seven-principles-educators) of causal analysis using a few key steps:\n\n1.  Specify a causal question\n2.  Draw our assumptions using a causal diagram\n3.  Model our assumptions\n4.  Diagnose our models\n5.  Estimate the causal effect\n6.  Conduct sensitivity analysis on the effect estimate\n\nWe'll focus on the broader ideas behind each step and what they look like all together; however, we don't expect you to fully digest each idea. We'll spend the rest of the book taking up each step in detail.\n\n## Specify a causal question\n\nIn this guided exercise, we'll attempt to answer a causal question: does using a bed net reduce the risk of malaria?\n\nMalaria remains a serious public health issue.\nWhile malaria incidence has decreased since 2000, 2020 and the COVID-19 pandemic saw an increase in cases and deaths due primarily to service interruption [@worldma].\nAbout 86% of malaria deaths occurred in 29 countries.\nNearly half of all malaria deaths occurred in just six of those countries: Nigeria (27%), the Democratic Republic of the Congo (12%), Uganda (5%), Mozambique (4%), Angola (3%), and Burkina Faso (3%).\nMost of these deaths occurred in children under 5 [@mosquito].\nMalaria also poses severe health risks to pregnant women and worsens birth outcomes, including early delivery and low birth weight.\n\nBed nets prevent morbidity and mortality due to malaria by providing a barrier against infective bites by the chief host of malaria parasites, the mosquito.\nHumans have used bed nets since ancient times.\nHerodotus, the 5th century BC Greek author of *The Histories*, observed Egyptians using their fishing nets as bed nets:\n\n> Against the gnats, which are very abundant, they have contrived as follows:---those who dwell above the fen-land are helped by the towers, to which they ascend when they go to rest; for the gnats by reason of the winds are not able to fly up high: but those who dwell in the fen-land have contrived another way instead of the towers, and this is it:---every man of them has got a casting net, with which by day he catches fish, but in the night he uses it for this purpose, that is to say he puts the casting-net round about the bed in which he sleeps, and then creeps in under it and goes to sleep: and the gnats, if he sleeps rolled up in a garment or a linen sheet, bite through these, but through the net they do not even attempt to bite [@thehist].\n\nMany modern nets are also treated with insecticide, dating back to Russian soldiers in World War II [@nevill1996], although some people still use them as fishing nets [@gettleman2015].\n\nIt's easy to imagine a randomized trial that deals with this question: participants in a study are randomly assigned to use a bed net, and we follow them over time to see if there is a difference in malaria risk between groups.\nRandomization is often the best way to estimate a causal effect of an intervention because it reduces the number of assumptions we need to make for that estimate to be valid (we will discuss these assumptions in @sec-assump).\nIn particular, randomization addresses confounding very well, accounting for confounders about which we may not even know.\n\nSeveral landmark trials have studied the effects of bed net use on malaria risk, with several essential studies in the 1990s.\nA 2004 meta-analysis found that insecticide-treated nets reduced childhood mortality by 17%, malarial parasite prevalence by 13%, and cases of uncomplicated and severe malaria by about 50% (compared to no nets) [@lengeler2004].\nSince the World Health Organization began recommending insecticide-treated nets, insecticide resistance has been a big concern.\nHowever, a follow-up analysis of trials found that it has yet to impact the public health benefits of bed nets [@pryce2018].\n\nTrials have also been influential in determining the economics of bed net programs.\nFor instance, one trial compared free net distribution versus a cost-share program (where participants pay a subsidized fee for nets).\nThe study's authors found that net uptake was similar between the groups and that free net distribution --- because it was easier to access --- saved more lives, and was cheaper per life saved than the cost-sharing program [@cohen2010].\n\nThere are several reasons we might not be able to conduct a randomized trial, including ethics, cost, and time.\nWe have substantial, robust evidence in favor of bed net use, but let's consider some conditions where observational causal inference could help.\n\n-   Imagine we are at a time before trials on this subject, and let's say people have started to use bed nets for this purpose on their own.\n    Our goal may still be to conduct a randomized trial, but we can answer questions more quickly with observed data.\n    In addition, this study's results might guide trials' design or intermediary policy suggestions.\n\n-   Sometimes, it is also not ethical to conduct a trial.\n    An example of this in malaria research is a question that arose in the study of bed net effectiveness: does malaria control in early childhood result in delayed immunity to the disease, resulting in severe malaria or death later in life?\n    Since we now know bed net use is very effective, *withholding* nets would be unethical.\n    A recent observational study found that the benefits of bed net use in childhood on all-cause mortality persist into adulthood [@mosquito].\n\n-   We may also want to estimate a different effect or the effect for another population than in previous trials.\n    For example, both randomized and observational studies helped us better understand that insecticide-based nets improve malaria resistance in the entire community, not just among those who use nets, so long as net usage is high enough [@howard2000; @hawley2003].\n\nAs we'll see in @sec-trials-std and @sec-g-comp, the causal inference techniques that we'll discuss in this book are often beneficial even when we're able to randomize.\n\nWhen we conduct an observational study, it's still helpful to think through the randomized trial we would run were it possible.\nThe trial we're trying to emulate in this causal analysis is the *target trial.* Considering the target trial helps us make our causal question more accurate.\nLet's consider the causal question posed earlier: does using a bed net (a mosquito net) reduce the risk of malaria?\nThis question is relatively straightforward, but it is still vague.\nAs we saw in @sec-causal-question, we need to clarify some key areas:\n\n-   What do we mean by \"bed net\"?\n    There are several types of nets: untreated bed nets, insecticide-treated bed nets, and newer long-lasting insecticide-treated bed nets.\n\n-   Risk compared to what?\n    Are we, for instance, comparing insecticide-treated bed nets to *no* net?\n    Untreated nets?\n    Or are we comparing a new type of net, like long-lasting insecticide-treated bed nets, to nets that are already in use?\n\n-   Risk as defined by what?\n    Whether or not a person contracted malaria?\n    Whether a person died of malaria?\n\n-   Risk among whom?\n    What is the population to which we're trying to apply this knowledge?\n    Who is it practical to include in our study?\n    Who might we need to exclude?\n\nWe will use simulated data to answer a more specific question: Does using insecticide-treated bed nets compared to no nets decrease the risk of contracting malaria after 1 year?\nIn this particular data, [simulated by Dr. Andrew Heiss](https://evalsp21.classes.andrewheiss.com/example/matching-ipw/#program-background):\n\n> researchers are interested in whether using mosquito nets decreases an individual's risk of contracting malaria.\n> They have collected data from 1,752 households in an unnamed country and have variables related to environmental factors, individual health, and household characteristics.\n> The data is **not experimental**---researchers have no control over who uses mosquito nets, and individual households make their own choices over whether to apply for free nets or buy their own nets, as well as whether they use the nets if they have them.\n\nBecause we're using simulated data, we'll have direct access to a variable that measures the likelihood of contracting malaria, something we wouldn't likely have in real life.\nWe'll stick with this measure because we know the actual effect size.\nWe can also safely assume that the population in our dataset represents the population we want to make inferences about (the unnamed country) because the data are simulated as such.\nWe can find the simulated data in `net_data` from the {[causalworkshop](https://github.com/r-causal/causalworkshop)} package, which includes ten variables:\n\n<!-- (TODO: move this to causaldata?) -->\n\n`id`\n\n:   an ID variable\n\n`net` and `net_num`\n\n:   a binary variable indicating if the participant used a net (1) or didn't use a net (0)\n\n`malaria_risk`\n\n:   risk of malaria scale ranging from 0-100\n\n`income`\n\n:   weekly income, measured in dollars\n\n`health`\n\n:   a health score scale ranging from 0--100\n\n`household`\n\n:   number of people living in the household\n\n`eligible`\n\n:   a binary variable indicating if the household is eligible for the free net program.\n\n`temperature`\n\n:   the average temperature at night, in Celsius\n\n`resistance`\n\n:   Insecticide resistance of local mosquitoes.\n    A scale of 0--100, with higher values indicating higher resistance.\n\nThe distribution of malaria risk appears to be quite different by net usage.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(causalworkshop)\nnet_data |>\n  ggplot(aes(malaria_risk, fill = net)) +\n  geom_density(color = NA, alpha = .8)\n```\n\n::: {.cell-output-display}\n![A density plot of malaria risk for those who did and did not use nets. The risk of malaria is lower for those who use nets.](chapter-02_files/figure-html/fig-malaria-risk-density-1.png){#fig-malaria-risk-density width=672}\n:::\n:::\n\n::: {.cell}\n\n:::\n\n\nIn @fig-malaria-risk-density, the density of those who used nets is to the left of those who did not use nets.\nThe mean difference in malaria risk is about 16.4, suggesting net use might be protective against malaria.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnet_data |>\n  group_by(net) |>\n  summarize(malaria_risk = mean(malaria_risk))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  net   malaria_risk\n  <lgl>        <dbl>\n1 FALSE         43.9\n2 TRUE          27.5\n```\n\n\n:::\n:::\n\n\nAnd that's what we see with simple linear regression, as well, as we would expect.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nnet_data |>\n  lm(malaria_risk ~ net, data = _) |>\n  tidy()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 5\n  term        estimate std.error statistic  p.value\n  <chr>          <dbl>     <dbl>     <dbl>    <dbl>\n1 (Intercept)     43.9     0.377     116.  0       \n2 netTRUE        -16.4     0.741     -22.1 1.10e-95\n```\n\n\n:::\n:::\n\n\n## Draw our assumptions using a causal diagram\n\nThe problem that we face is that other factors may be responsible for the effect we're seeing.\nIn this example, we'll focus on confounding: a common cause of net usage and malaria will bias the effect we see unless we account for it somehow.\nOne of the best ways to determine which variables we need to account for is to use a causal diagram.\nThese diagrams, also called causal directed acyclic graphs (DAGs), visualize the assumptions that we're making about the causal relationships between the exposure, outcome, and other variables we think might be related.\n\nHere's the DAG that we're proposing for this question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![A proposed causal diagram of the effect of bed net use on malaria. This directed acyclic graph (DAG) states our assumption that bed net use causes a reduction in malaria risk. It also says that we assume: malaria risk is impacted by net usage, income, health, temperature, and insecticide resistance; net usage is impacted by income, health, temperature, eligibility for the free net program, and the number of people in a household; eligibility for the free net programs is impacted by income and the number of people in a household; and health is impacted by income.](chapter-02_files/figure-html/fig-net-data-dag-1.png){#fig-net-data-dag width=672}\n:::\n:::\n\n\n(We'll explore how to create and analyze DAGs in R in @sec-dags).\n\nIn DAGs, each point represents a variable, and each arrow represents a cause.\nIn other words, this diagram declares what we think the causal relationships are between these variables.\nIn @fig-net-data-dag, we're saying that we believe:\n\n-   Malaria risk is causally impacted by net usage, income, health, temperature, and insecticide resistance.\n-   Net usage is causally impacted by income, health, temperature, eligibility for the free net program, and the number of people in a household.\n-   Eligibility for the free net programs is determined by income and the number of people in a household.\n-   Health is causally impacted by income.\n\nYou may agree or disagree with some of these assertions.\nThat's a good thing!\nLaying bare our assumptions allows us to consider the scientific credibility of our analysis.\nAnother benefit of using DAGs is that, thanks to their mathematics, we can determine precisely the subset of variables we need to account for if we assume this DAG is correct.\n\n::: callout-tip\n## Assembling DAGs\n\nIn this exercise, we're providing you with a reasonable DAG based on knowledge of how the data were generated.\nIn real life, setting up a DAG is a challenge requiring deep thought, domain expertise, and (often) collaboration between several experts.\n:::\n\nThe chief problem we're dealing with is that, when we analyze the data we're working with, we see the impact of net usage on malaria risk *and of all these other relationships*.\nIn DAG terminology, we have more than one open causal pathway.\nIf this DAG is correct, we have *eight* causal pathways: the path between net usage and malaria risk and seven other *confounding* pathways.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![In the proposed DAG, there are eight open pathways that contribute to the causal effect seen in the naive regression: the true effect (in green) of net usage on malaria risk and seven other confounding pathways (in orange). The naive estimate is wrong because it is a composite of all these effects.](chapter-02_files/figure-html/fig-net-data-confounding-1.png){#fig-net-data-confounding width=1344}\n:::\n:::\n\n\nWhen we calculate a naive linear regression that only includes net usage and malaria risk, the effect we see is incorrect because the seven other confounding pathways in @fig-net-data-confounding distort it.\nIn DAG terminology, we need to *block* these open pathways that distort the causal estimate we're after.\n(We can block paths through several techniques, including stratification, matching, weighting, and more. We'll see several methods throughout the book.) Luckily, by specifying a DAG, we can precisely determine the variables we need to control for.\nFor this DAG, we need to control for three variables: health, income, and temperature.\nThese three variables are a *minimal adjustment set*, the minimum set (or sets) of variables you need to block all confounding pathways.\nWe'll discuss adjustment sets further in @sec-dags.\n\n## Model our assumptions\n\nWe'll use a technique called Inverse Probability Weighting (IPW) to control for these variables, which we'll discuss in detail in @sec-using-ps.\nWe'll use logistic regression to predict the probability of treatment---the propensity score.\nThen, we'll calculate inverse probability weights to apply to the linear regression model we fit above.\nThe propensity score model includes the exposure---net use---as the dependent variable and the minimal adjustment set as the independent variables.\n\n::: callout-tip\n## Modeling the functional form\n\nGenerally speaking, we want to lean on domain expertise and good modeling practices to fit the propensity score model.\nFor instance, we may want to allow continuous confounders to be non-linear using splines, or we may want to add essential interactions between confounders.\nBecause these are simulated data, we know we don't need these extra parameters (so we'll skip them), but in practice, you often do.\nWe'll discuss this more in @sec-using-ps.\n:::\n\nThe propensity score model is a logistic regression model with the formula `net ~ income + health + temperature`, which predicts the probability of bed net usage based on the confounders income, health, and temperature.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npropensity_model <- glm(\n  net ~ income + health + temperature,\n  data = net_data,\n  family = binomial()\n)\n\n# the first six propensity scores\nhead(predict(propensity_model, type = \"response\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n     1      2      3      4      5      6 \n0.2464 0.2178 0.3230 0.2307 0.2789 0.3060 \n```\n\n\n:::\n:::\n\n\nWe can use propensity scores to control for confounding in various ways.\nIn this example, we'll focus on weighting.\nIn particular, we'll compute the inverse probability weight for the *average treatment effect* (ATE).\nThe ATE represents a particular causal question: what if *everyone* in the study used bed nets vs. what if *no one* in the study used bed nets?\n\nTo calculate the ATE, we'll use the broom and propensity packages.\nbroom's `augment()` function extracts prediction-related information from the model and joins it to the data.\npropensity's `wt_ate()` function calculates the inverse probability weight given the propensity score and exposure.\n\nFor inverse probability weighting, the ATE weight is the probability of receiving the treatment you actually received.\nIn other words, if you used a bed net, the ATE weight is the probability that you used a net, and if you did *not* use a net, it is the probability that you did *not* use a net.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(propensity)\nnet_data_wts <- propensity_model |>\n  augment(newdata = net_data, type.predict = \"response\") |>\n  # .fitted is the value predicted by the model\n  # for a given observation\n  mutate(wts = wt_ate(.fitted, net))\n\nnet_data_wts |>\n  select(net, .fitted, wts) |>\n  head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 3\n  net   .fitted   wts\n  <lgl>   <dbl> <dbl>\n1 FALSE   0.246  1.33\n2 FALSE   0.218  1.28\n3 FALSE   0.323  1.48\n4 FALSE   0.231  1.30\n5 FALSE   0.279  1.39\n6 FALSE   0.306  1.44\n```\n\n\n:::\n:::\n\n\n`wts` represents the amount each observation will be up-weighted or down-weighted in the outcome model we will soon fit.\nFor instance, the 16th household used a bed net and had a predicted probability of 0.41.\nThat's a pretty low probability considering they did, in fact, use a net, so their weight is higher at 2.42.\nIn other words, this household will be up-weighted compared to the naive linear model we fit above.\nThe first household did *not* use a bed net; they're predicted probability of net use was 0.25 (or put differently, a predicted probability of *not* using a net of 0.75).\nThat's more in line with their observed value of `net`, but there's still some predicted probability of using a net, so their weight is 1.28.\n\n## Diagnose our models\n\nThe goal of propensity score weighting is to weight the population of observations such that the distribution of confounders is balanced between the exposure groups.\nPut another way, we are, in principle, removing the arrows between the confounders and exposure in the DAG, so that the confounding paths no longer distort our estimates.\nHere's the distribution of the propensity score by group, created by `geom_mirror_histogram()` from the halfmoon package for assessing balance in propensity score models:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nggplot(net_data_wts, aes(.fitted)) +\n  geom_mirror_histogram(\n    aes(fill = net),\n    bins = 50\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(x = \"propensity score\")\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the propensity scores of those who used nets (top, blue) versus those who who did not use nets (bottom, orange). The range of propensity scores is similar between groups, with those who used nets slightly to the left of those who didn't, but the shapes of the distribution are different.](chapter-02_files/figure-html/fig-mirror-histogram-net-data-unweighted-1.png){#fig-mirror-histogram-net-data-unweighted width=672}\n:::\n:::\n\n\nThe weighted propensity score creates a pseudo-population where the distributions are much more similar:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(net_data_wts, aes(.fitted)) +\n  geom_mirror_histogram(\n    aes(group = net),\n    bins = 50\n  ) +\n  geom_mirror_histogram(\n    aes(fill = net, weight = wts),\n    bins = 50,\n    alpha = .5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(x = \"propensity score\")\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the propensity scores of those who used nets (top, blue) versus those who who did not use nets (bottom, orange). The shaded region represents the unweighted distribution, and the colored region represents the weighted distributions. The ATE weights up-weight the groups to be similar in range and shape of the distribution of propensity scores.](chapter-02_files/figure-html/fig-mirror-histogram-net-data-weighted-1.png){#fig-mirror-histogram-net-data-weighted width=672}\n:::\n:::\n\n\nIn this example, the unweighted distributions are not awful---the shapes are somewhat similar here, and the overlap quite a bit---but the weighted distributions in @fig-mirror-histogram-net-data-weighted are much more similar.\n\n::: callout-caution\n## Unmeasured confounding\n\nPropensity score weighting and most other causal inference techniques only help with *observed* confounders---ones that we model correctly, at that.\nUnfortunately, we still may have unmeasured confounding, which we'll discuss below.\n\nRandomization is one causal inference technique that *does* deal with unmeasured confounding, one of the reasons it is so powerful.\n:::\n\nWe might also want to know how well-balanced the groups are by each confounder.\nOne way to do this is to calculate the standardized mean differences (SMDs) for each confounder with and without weights.\nWe'll calculate the SMDs with `tidy_smd()` then plot them with `geom_love()`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_df <- tidy_smd(\n  net_data_wts,\n  c(income, health, temperature),\n  .group = net,\n  .wts = wts\n)\n\nggplot(\n  plot_df,\n  aes(\n    x = abs(smd),\n    y = variable,\n    group = method,\n    color = method\n  )\n) +\n  geom_love()\n```\n\n::: {.cell-output-display}\n![A love plot representing the standardized mean differences (SMD) between exposure groups of three confounders: temperature, income, and health. Before weighting, there are considerable differences in the groups. After weighting, the confounders are much more balanced between groups.](chapter-02_files/figure-html/fig-love-plot-net-data-1.png){#fig-love-plot-net-data width=672}\n:::\n:::\n\n\nA standard guideline is that balanced confounders should have an SMD of less than 0.1 on the absolute scale.\n0.1 is just a rule of thumb, but if we follow it, the variables in @fig-love-plot-net-data are well-balanced after weighting (and unbalanced before weighting).\n\nBefore we apply the weights to the outcome model, let's check their overall distribution for extreme weights.\nExtreme weights can destabilize the estimate and variance in the outcome model, so we want to be aware of it.\nWe'll also discuss several other types of weights that are less prone to this issue in @sec-estimands.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnet_data_wts |>\n  ggplot(aes(wts)) +\n  geom_density(fill = \"#CC79A7\", color = NA, alpha = 0.8)\n```\n\n::: {.cell-output-display}\n![A density plot of the average treatment effect (ATE) weights. The plot is skewed, with higher values towards 8. This may indicate a problem with the model, but the weights aren't so extreme to destabilize the variance of the estimate.](chapter-02_files/figure-html/fig-ate-density-net-data-1.png){#fig-ate-density-net-data width=672}\n:::\n:::\n\n\nThe weights in @fig-ate-density-net-data are skewed, but there are no outrageous values.\nIf we saw extreme weights, we might try trimming or stabilizing them, or consider calculating an effect for a different estimand, which we'll discuss in @sec-estimands.\nIt doesn't look like we need to do that here, however.\n\n## Estimate the causal effect\n\nWe're now ready to use the ATE weights to (attempt to) account for confounding in the naive linear regression model.\nFitting such a model is pleasantly simple in this case: we fit the same model as before but with `weights = wts`, which will incorporate the inverse probability weights.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnet_data_wts |>\n  lm(malaria_risk ~ net, data = _, weights = wts) |>\n  tidy(conf.int = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 7\n  term   estimate std.error statistic  p.value conf.low\n  <chr>     <dbl>     <dbl>     <dbl>    <dbl>    <dbl>\n1 (Inte…     42.7     0.442      96.7 0            41.9\n2 netTR…    -12.5     0.624     -20.1 5.50e-81    -13.8\n# ℹ 1 more variable: conf.high <dbl>\n```\n\n\n:::\n:::\n\n\n\n\nThe estimate for the average treatment effect is -12.5 (95% CI -13.8, -11.3).\nUnfortunately, the confidence intervals we're using are wrong because they don't account for the dependence within the weights!\nGenerally, confidence intervals for propensity score weighted models will be too narrow unless we correct for this dependence.\nThe nominal coverage of the confidence intervals will thus be wrong (they aren't 95% CIs because their coverage is much lower than 95%) and may lead to misinterpretation.\n\nWe've got several ways to address this problem, which we'll discuss in detail in @sec-outcome-model, including the bootstrap, robust standard errors, and manually accounting for the dependence with empirical sandwich estimators.\nFor this example, we'll use the bootstrap, a flexible tool that calculates distributions of parameters using re-sampling.\nWe'll use the rsample package from the tidymodels ecosystem to work with bootstrap samples.\n\n::: callout-note\n## The Bootstrap\n\nThe bootstrap is a simple but flexible algorithm for calculating statistics using re-sampling with replacement.\nIt's handy when a closed-form solution doesn't exist to calculate something, as is commonly the case in causal inference (particularly for standard errors), and when we suspect the parametric calculations are not valid for a given situation.\n\nBootstrapping in R has a long tradition of writing functions to calculate the statistic of interest, starting with the classic boot package.\nThroughout the book, we'll use rsample, a more modern alternative for re-sampling, but generally, we start with writing a function to calculate the estimate we're interested in.\n\nLet's say we want to calculate `some_statistic()` for `this_data`.\nTo bootstrap for *R* samples, we:\n\n1.  Re-sample `this_data` with replacement.\n    The same row may appear multiple (or no) times in a given bootstrap sample, simulating the sampling process in the underlying population.\n\n    ``` r\n    indices <- sample(\n      # create a vector of indices:\n      # 1 through the number of rows\n      seq_len(nrow(this_data)), \n      # sample a vector indices \n      # that's the same length as `this_data`\n      size = nrow(this_data), \n      replace = TRUE\n    )\n    bootstrap_sample <- this_data[indices, ]\n    ```\n\n2.  Fit `some_statistic()` on the `bootstrap_sample`\n\n    ``` r\n    estimate <- some_statistic(bootstrap_sample)\n    ```\n\n3.  Repeat *R* times\n\nWe then end up with a distribution of `estimate`s, with which we can calculate population statistics, such as point estimates, standard errors, and confidence intervals.\n:::\n\nBecause the bootstrap is so flexible, we need to think carefully about the sources of uncertainty in the statistic we're calculating.\nIt might be tempting to write a function like this to fit the statistic we're interested in (the point estimate for `netTRUE`):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rsample)\n\nfit_ipw_not_quite_rightly <- function(split, ...) {\n  # get bootstrapped data sample with `rsample::analysis()`\n  .df <- analysis(split)\n\n  # fit ipw model\n  lm(malaria_risk ~ net, data = .df, weights = wts) |>\n    tidy()\n}\n```\n:::\n\n\nHowever, this function won't give us the correct confidence intervals because it treats the inverse probability weights as fixed values.\nThey're not, of course; we just estimated them using logistic regression!\nWe need to account for this uncertainty by bootstrapping the *entire modeling process*.\nFor every bootstrap sample, we need to fit the propensity score model, calculate the inverse probability weights, then fit the weighted outcome model.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rsample)\n\nfit_ipw <- function(split, ...) {\n  # get bootstrapped data sample with `rsample::analysis()`\n  .df <- analysis(split)\n\n  # fit propensity score model\n  propensity_model <- glm(\n    net ~ income + health + temperature,\n    data = .df,\n    family = binomial()\n  )\n\n  # calculate inverse probability weights\n  .df <- propensity_model |>\n    augment(type.predict = \"response\", data = .df) |>\n    mutate(wts = wt_ate(.fitted, net))\n\n  # fit correctly bootstrapped ipw model\n  lm(malaria_risk ~ net, data = .df, weights = wts) |>\n    tidy()\n}\n```\n:::\n\n\nNow that we know precisely how to calculate the estimate for each iteration let's create the bootstrapped dataset with rsample's `bootstraps()` function.\nThe `times` argument determines how many bootstrapped datasets to create; we'll do 1,000.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbootstrapped_net_data <- bootstraps(\n  net_data,\n  times = 1000,\n  # required to calculate CIs later\n  apparent = TRUE\n)\n\nbootstrapped_net_data\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# Bootstrap sampling with apparent sample \n# A tibble: 1,001 × 2\n   splits             id           \n   <list>             <chr>        \n 1 <split [1752/635]> Bootstrap0001\n 2 <split [1752/638]> Bootstrap0002\n 3 <split [1752/665]> Bootstrap0003\n 4 <split [1752/628]> Bootstrap0004\n 5 <split [1752/651]> Bootstrap0005\n 6 <split [1752/644]> Bootstrap0006\n 7 <split [1752/657]> Bootstrap0007\n 8 <split [1752/639]> Bootstrap0008\n 9 <split [1752/644]> Bootstrap0009\n10 <split [1752/635]> Bootstrap0010\n# ℹ 991 more rows\n```\n\n\n:::\n:::\n\n\nThe result is a nested data frame: each `splits` object contains metadata that rsample uses to subset the bootstrap samples for each of the 1,000 samples.\nNext, we'll run `fit_ipw()` 1,000 times to create a distribution for `estimate`.\nAt its heart, the calculation we're doing is\n\n``` r\nfit_ipw(bootstrapped_net_data$splits[[n]])\n```\n\nWhere *n* is one of 1,000 indices.\nWe'll use purrr's `map()` function to iterate across each `split` object.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nipw_results <- bootstrapped_net_data |>\n  mutate(boot_fits = map(splits, fit_ipw))\n\nipw_results\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# Bootstrap sampling with apparent sample \n# A tibble: 1,001 × 3\n   splits             id            boot_fits       \n   <list>             <chr>         <list>          \n 1 <split [1752/635]> Bootstrap0001 <tibble [2 × 5]>\n 2 <split [1752/638]> Bootstrap0002 <tibble [2 × 5]>\n 3 <split [1752/665]> Bootstrap0003 <tibble [2 × 5]>\n 4 <split [1752/628]> Bootstrap0004 <tibble [2 × 5]>\n 5 <split [1752/651]> Bootstrap0005 <tibble [2 × 5]>\n 6 <split [1752/644]> Bootstrap0006 <tibble [2 × 5]>\n 7 <split [1752/657]> Bootstrap0007 <tibble [2 × 5]>\n 8 <split [1752/639]> Bootstrap0008 <tibble [2 × 5]>\n 9 <split [1752/644]> Bootstrap0009 <tibble [2 × 5]>\n10 <split [1752/635]> Bootstrap0010 <tibble [2 × 5]>\n# ℹ 991 more rows\n```\n\n\n:::\n:::\n\n\nThe result is another nested data frame with a new column, `boot_fits`.\nEach element of `boot_fits` is the result of the IPW for the bootstrapped dataset.\nFor example, in the first bootstrapped data set, the IPW results were:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nipw_results$boot_fits[[1]]\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 5\n  term        estimate std.error statistic  p.value\n  <chr>          <dbl>     <dbl>     <dbl>    <dbl>\n1 (Intercept)     42.6     0.446      95.4 0       \n2 netTRUE        -11.7     0.630     -18.5 6.95e-70\n```\n\n\n:::\n:::\n\n\nNow we have a distribution of estimates:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nipw_results |>\n  mutate(\n    estimate = map_dbl(\n      boot_fits,\n      # pull the `estimate` for `netTRUE` for each fit\n      \\(.fit) .fit |>\n        filter(term == \"netTRUE\") |>\n        pull(estimate)\n    )\n  ) |>\n  ggplot(aes(estimate)) +\n  geom_histogram(fill = \"#D55E00FF\", color = \"white\", alpha = 0.8)\n```\n\n::: {.cell-output-display}\n![\"A histogram of 1,000 bootstrapped estimates of the effect of net use on malaria risk. The spread of these estimates accounts for the dependency and uncertainty in the use of IPW weights.\"](chapter-02_files/figure-html/fig-bootstrap-estimates-net-data-1.png){#fig-bootstrap-estimates-net-data width=672}\n:::\n:::\n\n\n@fig-bootstrap-estimates-net-data gives a sense of the variation in `estimate`, but let's calculate 95% confidence intervals from the bootstrapped distribution using rsample's `int_t()` :\n\n\n::: {.cell}\n\n```{.r .cell-code}\nboot_estimate <- ipw_results |>\n  # calculate T-statistic-based CIs\n  int_t(boot_fits) |>\n  filter(term == \"netTRUE\")\n\nboot_estimate\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 6\n  term    .lower .estimate .upper .alpha .method  \n  <chr>    <dbl>     <dbl>  <dbl>  <dbl> <chr>    \n1 netTRUE  -13.4     -12.5  -11.7   0.05 student-t\n```\n\n\n:::\n:::\n\n\nNow we have a confounder-adjusted estimate with correct standard errors.\nThe estimate of the effect of *all* households using bed nets versus *no* households using bed nets on malaria risk is -12.5 (95% CI -13.4, -11.7).\nBed nets do indeed seem to reduce malaria risk in this study.\n\n## Conduct sensitivity analysis on the effect estimate\n\nWe've laid out a roadmap for taking observational data, thinking critically about the causal question we want to ask, identifying the assumptions we need to get there, then applying those assumptions to a statistical model.\nGetting the correct answer to the causal question relies on getting our assumptions more or less right.\nBut what if we're more on the less correct side?\n\nSpoiler alert: the answer we just calculated is **wrong**.\nAfter all that effort!\n\nWhen conducting a causal analysis, it's a good idea to use sensitivity analyses to test your assumptions.\nThere are many potential sources of bias in any study and many sensitivity analyses to go along with them; we'll focus on the assumption of no confounding.\n\nLet's start with a broad sensitivity analysis; then, we'll ask questions about specific unmeasured confounders.\nWhen we have less information about unmeasured confounders, we can use tipping point analysis to ask how much confounding it would take to tip my estimate to the null.\nIn other words, what would the strength of the unmeasured confounder have to be to explain our results away?\nThe tipr package is a toolkit for conducting sensitivity analyses.\nLet's examine the tipping point for an unknown, normally-distributed confounder.\nThe `tip_coef()` function takes an estimate (a beta coefficient from a regression model, or the upper or lower bound of the coefficient).\nIt further requires either the 1) scaled differences in means of the confounder between exposure groups or 2) effect of the confounder on the outcome.\nFor the estimate, we'll use `conf.high`, which is closer to 0 (the null), and ask: how much would the confounder have to affect malaria risk to have an unbiased upper confidence interval of 0?\nWe'll use tipr to calculate this answer for 5 scenarios, where the mean difference in the confounder between exposure groups is 1, 2, 3, 4, or 5.\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tipr)\ntipping_points <- tip_coef(boot_estimate$.upper, exposure_confounder_effect = 1:5)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nThe observed effect (-11.72) WOULD be tipped by 1 unmeasured confounder\nwith the following specifications:\n  * estimated difference in scaled means between the unmeasured confounder\n    in the exposed population and unexposed population: 1\n  * estimated relationship between the unmeasured confounder and the outcome: -11.72\nThe observed effect (-11.72) WOULD be tipped by 1 unmeasured confounder\nwith the following specifications:\n  * estimated difference in scaled means between the unmeasured confounder\n    in the exposed population and unexposed population: 2\n  * estimated relationship between the unmeasured confounder and the outcome: -5.86\nThe observed effect (-11.72) WOULD be tipped by 1 unmeasured confounder\nwith the following specifications:\n  * estimated difference in scaled means between the unmeasured confounder\n    in the exposed population and unexposed population: 3\n  * estimated relationship between the unmeasured confounder and the outcome: -3.91\nThe observed effect (-11.72) WOULD be tipped by 1 unmeasured confounder\nwith the following specifications:\n  * estimated difference in scaled means between the unmeasured confounder\n    in the exposed population and unexposed population: 4\n  * estimated relationship between the unmeasured confounder and the outcome: -2.93\nThe observed effect (-11.72) WOULD be tipped by 1 unmeasured confounder\nwith the following specifications:\n  * estimated difference in scaled means between the unmeasured confounder\n    in the exposed population and unexposed population: 5\n  * estimated relationship between the unmeasured confounder and the outcome: -2.34\n```\n\n\n:::\n\n```{.r .cell-code}\ntipping_points |>\n  ggplot(aes(confounder_outcome_effect, exposure_confounder_effect)) +\n  geom_line(color = \"#009E73\", linewidth = 1.1) +\n  geom_point(fill = \"#009E73\", color = \"white\", size = 2.5, shape = 21) +\n  labs(\n    x = \"Confounder-Outcome Effect\",\n    y = \"Scaled mean differences in\\n confounder between exposure groups\"\n  )\n```\n\n::: {.cell-output-display}\n![A tipping point analysis under several confounding scenarios where the unmeasured confounder is a normally-distributed continuous variable. The line represents the strength of confounding necessary to tip the upper confidence interval of the causal effect estimate to 0. The x-axis represents the coefficient of the confounder-outcome relationship adjusted for the exposure and the set of measured confounders. The y-axis represents the scaled mean difference of the confounder between exposure groups.](chapter-02_files/figure-html/fig-tip-coef-net-1.png){#fig-tip-coef-net width=672}\n:::\n:::\n\n\nIf we had an unmeasured confounder where the standardized mean difference between exposure groups was 1, the confounder would need to decrease malaria risk by about -11.7.\nThat's pretty strong relative to other effects, but it may be feasible if we have an idea of something we might have missed.\nConversely, suppose the relationship between net use and the unmeasured confounder is very strong, with a mean scaled difference of 5.\nIn that case, the confounder-malaria relationship only needs to be 5.\nNow we have to consider: which of these scenarios are plausible given our domain knowledge and the effects we see in this analysis?\n\nNow let's consider a much more specific sensitivity analysis.\nSome ethnic groups, such as the Fulani, have a genetic resistance to malaria [@arama2015].\nLet's say that in our simulated data, an unnamed ethnic group in the unnamed country shares this genetic resistance to malaria.\nFor historical reasons, bed net use in this group is also very high.\nWe don't have this variable in `net_data`, but let's say we know from the literature that in this sample, we can estimate at:\n\n1.  People with this genetic resistance have, on average, a lower malaria risk by about 10.\n2.  About 26% of people who use nets in our study have this genetic resistance.\n3.  About 5% of people who don't use nets have this genetic resistance.\n\nWith this amount of information, we can use tipr to adjust the estimates we calculated for the unmeasured confounder.\nWe'll use `adjust_coef_with_binary()` to calculate the adjusted estimates.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nadjusted_estimates <- boot_estimate |>\n  select(.estimate, .lower, .upper) |>\n  unlist() |>\n  adjust_coef_with_binary(\n    exposed_confounder_prev = 0.26,\n    unexposed_confounder_prev = 0.05,\n    confounder_outcome_effect = -10\n  )\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nThe observed effect (-12.55) is updated to -12.3 by a confounder with the following specifications:\n* estimated prevalence of the unmeasured confounder in the exposed population: 0.26\n* estimated prevalence of the unmeasured confounder in the unexposed population: 0.05\n* estimated relationship between the unmeasured confounder and the outcome: -10The observed effect (-13.37) is updated to -13.12 by a confounder with the following specifications:\n* estimated prevalence of the unmeasured confounder in the exposed population: 0.26\n* estimated prevalence of the unmeasured confounder in the unexposed population: 0.05\n* estimated relationship between the unmeasured confounder and the outcome: -10The observed effect (-11.72) is updated to -11.47 by a confounder with the following specifications:\n* estimated prevalence of the unmeasured confounder in the exposed population: 0.26\n* estimated prevalence of the unmeasured confounder in the unexposed population: 0.05\n* estimated relationship between the unmeasured confounder and the outcome: -10\n```\n\n\n:::\n\n```{.r .cell-code}\nadjusted_estimates\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 5\n  effect_adjusted effect_observed\n            <dbl>           <dbl>\n1           -12.3           -12.5\n2           -13.1           -13.4\n3           -11.5           -11.7\n# ℹ 3 more variables: exposed_confounder_prev <dbl>,\n#   unexposed_confounder_prev <dbl>,\n#   confounder_outcome_effect <dbl>\n```\n\n\n:::\n:::\n\n\nThe adjusted estimate for a situation where genetic resistance to malaria is a confounder is -12.3 (95% CI -13.1, -11.5).\n\nIn fact, these data were simulated with just such a confounder.\nThe true effect of net use on malaria is about -10, and the true DAG that generated these data is:\n\n\n::: {.cell}\n::: {.cell-output-display}\n![The true causal diagram for `net_data`. This DAG is identical to the one we proposed with one addition: genetic resistance to malaria causally reduces the risk of malaria and impacts net use. It's thus a confounder and a part of the minimal adjustment set required to get an unbiased effect estimate. In otherwords, by not including it, we've calculated the wrong effect.](chapter-02_files/figure-html/fig-net-data-true-dag-1.png){#fig-net-data-true-dag width=672}\n:::\n:::\n\n\n\n\nThe unmeasured confounder in @fig-net-data-true-dag is available in the dataset `net_data_full` as `genetic_resistance`.\nIf we recalculate the IPW estimate of the average treatment effect of nets on malaria risk, we get -10.2 (95% CI -11.2, -9.3), much closer to the actual answer of -10.\n\nWhat do you think?\nIs this estimate reliable?\nDid we do a good job addressing the assumptions we need to make for a causal effect, mainly that there is no confounding?\nHow might you criticize this model, and what would you do differently?\nOk, we know that -10 is the correct answer because the data are simulated, but in practice, we can never be sure, so we need to continue probing our assumptions until we're confident they are robust. We'll explore these techniques and others in @sec-sensitivity.\n<!-- TODO: Maybe use sickle cell as an example of a precision variable in the variable selection section later in the book. Interesting instance because sickle cell can't be downstream. Consider in the context of over adjustment. -->\n\nTo calculate this effect, we:\n\n1.  Specified a causal question (for the average treatment effect)\n2.  Drew our assumptions using a causal diagram (using DAGs)\n3.  Modeled our assumptions (using propensity score weighting)\n4.  Diagnosed our models (by checking confounder balance after weighting)\n5.  Estimated the causal effect (using inverse probability weighting)\n6.  Conducted sensitivity analysis on the effect estimate (using tipping point analysis)\n\nThroughout the rest of the book, we'll follow these broad steps in several examples from medicine, economics, and industry.\nWe'll dive more deeply into propensity score techniques, explore other methods for estimating causal effects, and, most importantly, make sure, over and over again, that the assumptions we're making are reasonable---even if we'll never know for sure.\n",
+    "markdown": "# The whole game: mosquito nets and malaria\n\n\n\n\n\nIn this chapter, we'll analyze data using techniques we learn in this book.\nWe'll play the [whole game](https://www.gse.harvard.edu/news/uk/09/01/education-bat-seven-principles-educators) of causal analysis using a few key steps:\n\n1.  Specify a causal question\n2.  Draw our assumptions using a causal diagram\n3.  Model our assumptions\n4.  Diagnose our models\n5.  Estimate the causal effect\n6.  Conduct sensitivity analysis on the effect estimate\n\nWe'll focus on the broader ideas behind each step and what they look like all together; however, we don't expect you to fully digest each idea. We'll spend the rest of the book taking up each step in detail.\n\n## Specify a causal question\n\nIn this guided exercise, we'll attempt to answer a causal question: does using a bed net reduce the risk of malaria?\n\nMalaria remains a serious public health issue.\nAdditionally, while malaria incidence has decreased since 2000, 2020 and the COVID-19 pandemic saw an increase in cases and deaths due primarily to service interruption [@worldma].\nAbout 86% of malaria deaths occurred in 29 countries.\nStill, nearly half of all malaria deaths occurred in just six countries: Nigeria (27%), the Democratic Republic of the Congo (12%), Uganda (5%), Mozambique (4%), Angola (3%), and Burkina Faso (3%).\nMost of these deaths occurred in children under 5 [@mosquito].\nMalaria also poses severe health risks to pregnant women and worsens birth outcomes, including early delivery and low birth weight.\n\nBed nets prevent morbidity and mortality due to malaria by providing a barrier against infective bites by the chief host of malaria parasites, the mosquito.\nHumans have used bed nets since ancient times.\nHerodotus, the 5th century BC Greek author of *The Histories*, observed Egyptians using their fishing nets as bed nets:\n\n> Against the gnats, which are very abundant, they have contrived as follows:---those who dwell above the fen-land are helped by the towers, to which they ascend when they go to rest; for the gnats by reason of the winds are not able to fly up high: but those who dwell in the fen-land have contrived another way instead of the towers, and this is it:---every man of them has got a casting net, with which by day he catches fish, but in the night he uses it for this purpose, that is to say he puts the casting-net round about the bed in which he sleeps, and then creeps in under it and goes to sleep: and the gnats, if he sleeps rolled up in a garment or a linen sheet, bite through these, but through the net they do not even attempt to bite [@thehist].\n\nMany modern nets are also treated with insecticide, dating back to Russian soldiers in World War II [@nevill1996], although some people still use them as fishing nets [@gettleman2015].\n\nIt's easy to imagine a randomized trial that deals with this question: participants in a study are randomly assigned to use a bed net, and we follow them over time to see if there is a difference in malaria risk between groups.\nRandomization is often the best way to estimate a causal effect of an intervention because it reduces the number of assumptions we need to make for that estimate to be valid (we will discuss these assumptions in @sec-assump).\nIn particular, randomization addresses confounding very well, accounting for confounders about which we may not even know.\n\nSeveral landmark trials have studied the effects of bed net use on malaria risk, with several essential studies in the 1990s.\nA 2004 meta-analysis found that insecticide-treated nets reduced childhood mortality by 17%, malarial parasite prevalence by 13%, and cases of uncomplicated and severe malaria by about 50% (compared to no nets) [@lengeler2004].\nSince the World Health Organization began recommending insecticide-treated nets, insecticide resistance has been a big concern.\nStill, a follow-up analysis of trials found that it has yet to impact the public health benefits of bed nets [@pryce2018].\n\nTrials have also been influential in determining the economics of bed net programs.\nFor instance, one trial compared free net distribution versus a cost-share program (where participants pay a subsidized fee for nets).\nThe study's authors found that net uptake was similar between the groups and that free net distribution --- because it was easier to access --- saved more lives, and was cheaper per life saved than the cost-sharing program [@cohen2010].\n\nThere are several reasons we might not be able to conduct a randomized trial, including ethics, cost, and time.\nWe have substantial, robust evidence in favor of bed net use.\nStill, let's consider some conditions where observational causal inference helps answer questions about bed nets and malaria prevention.\n\n-   Imagine we are at a time before trials on this subject, and let's say people have started to use bed nets for this purpose on their own.\n    Our goal may still be to conduct a randomized trial, but we can answer questions more quickly with observed data.\n    In addition, this study's results might guide trials' design or intermediary policy suggestions.\n\n-   Sometimes, it is also not ethical to conduct a trial.\n    An example of this in malaria research is a question that arose in the study of bed net effectiveness: does malaria control in early childhood result in delayed immunity to the disease, resulting in severe malaria or death later in life?\n    Since we now know bed net use is very effective, *withholding* nets would be unethical.\n    A recent observational study found that the benefits of bed net use in childhood on all-cause mortality persist into adulthood [@mosquito].\n\n-   We may also want to estimate a different effect or the effect for another population than in previous trials.\n    For example, both randomized and observational studies helped us better understand that insecticide-based nets improve malaria resistance in the entire community, not just among those who use nets, so long as net usage is high enough [@howard2000; @hawley2003].\n\nAs we saw in @sec-causal-question and we'll see in @sec-g-comp, the causal inference techniques that we'll discuss in this book are often beneficial even when we're able to randomize.\n\nWhen we conduct an observational study, it's still helpful to think through the randomized trial we would run were it possible.\nThe trial we're trying to emulate in this causal analysis is the *target trial.* Considering the target trial helps us make our causal question more accurate.\nLet's consider the causal question posed earlier: does using a bed net (a mosquito net) reduce the risk of malaria?\nThis question is relatively straightforward, but it is still vague.\nIn conducting an analysis, we'll need to address several key questions:\n\n-   What do we mean by \"bed net\"?\n    There are several types of nets: untreated bed nets, insecticide-treated bed nets, and newer long-lasting insecticide-treated bed nets.\n\n-   Risk compared to what?\n    Are we, for instance, comparing insecticide-treated bed nets to *no* net?\n    Untreated nets?\n    Or are we comparing a new type of net, like long-lasting insecticide-treated bed nets, to nets that are already in use?\n\n-   Risk as defined by what?\n    Whether or not a person contracted malaria?\n    Whether a person died of malaria?\n\n-   Risk among whom?\n    What is the population to which we're trying to apply this knowledge?\n    Who is it practical to include in our study?\n    Who might we need to exclude?\n\nWe will use simulated data to answer a more specific question: Does using insecticide-treated bed nets decrease the risk of contracting malaria?\nIn this particular data, [simulated by Dr. Andrew Heiss](https://evalsp21.classes.andrewheiss.com/example/matching-ipw/#program-background):\n\n> researchers are interested in whether using mosquito nets decreases an individual's risk of contracting malaria.\n> They have collected data from 1,752 households in an unnamed country and have variables related to environmental factors, individual health, and household characteristics.\n> The data is **not experimental**---researchers have no control over who uses mosquito nets, and individual households make their own choices over whether to apply for free nets or buy their own nets, as well as whether they use the nets if they have them.\n\nBecause we're using simulated data, we'll have direct access to a variable that measures the likelihood of contracting malaria, something we wouldn't likely have in real life.\nWe'll stick with this measure because we know the actual effect size.\nWe'll use simulated data, `net_data`, from the {[causalworkshop](https://github.com/r-causal/causalworkshop)} package, which includes ten variables:\n\n<!-- (TODO: move this to causaldata?) -->\n\n`id`\n\n:   an ID variable\n\n`net` and `net_num`\n\n:   a binary variable indicating if the participant used a net (1) or didn't use a net (0)\n\n`malaria_risk`\n\n:   risk of malaria scale ranging from 0-100\n\n`income`\n\n:   weekly income, measured in dollars\n\n`health`\n\n:   a health score scale ranging from 0--100\n\n`household`\n\n:   number of people living in the household\n\n`eligible`\n\n:   a binary variable indicating if the household is eligible for the free net program.\n\n`temperature`\n\n:   the average temperature at night, in Celsius\n\n`resistance`\n\n:   Insecticide resistance of local mosquitoes.\n    A scale of 0--100, with higher values indicating higher resistance.\n\nThe distribution of malaria risk appears to be quite different by net usage.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(causalworkshop)\nnet_data |>\n  ggplot(aes(malaria_risk, fill = net)) +\n  geom_density(color = NA, alpha = .8)\n```\n\n::: {.cell-output-display}\n![A density plot of malaria risk for those who did and did not use nets. The risk of malaria is lower for those who use nets.](chapter-02_files/figure-html/fig-malaria-risk-density-1.png){#fig-malaria-risk-density width=672}\n:::\n:::\n\n::: {.cell}\n\n:::\n\n\nIn @fig-malaria-risk-density, the density of those who used nets is to the left of those who did not use nets.\nThe mean difference in malaria risk is about 16.4, suggesting net use might be protective against malaria.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnet_data |>\n  group_by(net) |>\n  summarize(malaria_risk = mean(malaria_risk))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  net   malaria_risk\n  <lgl>        <dbl>\n1 FALSE         43.9\n2 TRUE          27.5\n```\n\n\n:::\n:::\n\n\nAnd that's what we see with simple linear regression, as well, as we would expect.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nnet_data |>\n  lm(malaria_risk ~ net, data = _) |>\n  tidy()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 5\n  term        estimate std.error statistic  p.value\n  <chr>          <dbl>     <dbl>     <dbl>    <dbl>\n1 (Intercept)     43.9     0.377     116.  0       \n2 netTRUE        -16.4     0.741     -22.1 1.10e-95\n```\n\n\n:::\n:::\n\n\n## Draw our assumptions using a causal diagram\n\nThe problem that we face is that other factors may be responsible for the effect we're seeing.\nIn this example, we'll focus on confounding: a common cause of net usage and malaria will bias the effect we see unless we account for it somehow.\nOne of the best ways to determine which variables we need to account for is to use a causal diagram.\nThese diagrams, also called causal directed acyclic graphs (DAGs), visualize the assumptions that we're making about the causal relationships between the exposure, outcome, and other variables we think might be related.\n\nHere's the DAG that we're proposing for this question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![A proposed causal diagram of the effect of bed net use on malaria. This directed acyclic graph (DAG) states our assumption that bed net use causes a reduction in malaria risk. It also says that we assume: malaria risk is impacted by net usage, income, health, temperature, and insecticide resistance; net usage is impacted by income, health, temperature, eligibility for the free net program, and the number of people in a household; eligibility for the free net programs is impacted by income and the number of people in a household; and health is impacted by income.](chapter-02_files/figure-html/fig-net-data-dag-1.png){#fig-net-data-dag width=672}\n:::\n:::\n\n\n(We'll explore how to create and analyze DAGs in R in @sec-dags).\n\nIn DAGs, each point represents a variable, and each arrow represents a cause.\nIn other words, this diagram declares what we think the causal relationships are between these variables.\nIn @fig-net-data-dag, we're saying that we believe:\n\n-   Malaria risk is causally impacted by net usage, income, health, temperature, and insecticide resistance.\n-   Net usage is causally impacted by income, health, temperature, eligibility for the free net program, and the number of people in a household.\n-   Eligibility for the free net programs is determined by income and the number of people in a household.\n-   Health is causally impacted by income.\n\nYou may agree or disagree with some of these assertions.\nThat's a good thing!\nLaying bare our assumptions allows us to consider the scientific credibility of our analysis.\nAnother benefit of using DAGs is that, thanks to their mathematics, we can determine precisely the subset of variables we need to account for if we assume this DAG is correct.\n\n::: callout-tip\n## Assembling DAGs\n\nIn this exercise, we're providing you with a reasonable DAG based on knowledge of how the data were generated.\nIn real life, setting up a DAG is a challenge requiring deep thought, domain expertise, and (often) collaboration between several experts.\n:::\n\nThe chief problem we're dealing with is that, when we analyze the data we're working with, we see the impact of net usage on malaria risk *and of all these other relationships*.\nIn DAG terminology, we have more than one open causal pathway.\nIf this DAG is correct, we have *eight* causal pathways: the path between net usage and malaria risk and seven other *confounding* pathways.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![In the proposed DAG, there are eight open pathways that contribute to the causal effect seen in the naive regression: the true effect (in green) of net usage on malaria risk and seven other confounding pathways (in orange). The naive estimate is wrong because it is a composite of all these effects.](chapter-02_files/figure-html/fig-net-data-confounding-1.png){#fig-net-data-confounding width=1344}\n:::\n:::\n\n\nWhen we calculate a naive linear regression that only includes net usage and malaria risk, the effect we see is incorrect because the seven other confounding pathways in @fig-net-data-confounding distort it.\nIn DAG terminology, we need to *block* these open pathways that distort the causal estimate we're after.\n(We can block paths through several techniques, including stratification, matching, weighting, and more. We'll see several methods throughout the book.) Luckily, by specifying a DAG, we can precisely determine the variables we need to control for.\nFor this DAG, we need to control for three variables: health, income, and temperature.\nThese three variables are a *minimal adjustment set*, the minimum set (or sets) of variables you need to block all confounding pathways.\nWe'll discuss adjustment sets further in @sec-dags.\n\n## Model our assumptions\n\nWe'll use a technique called Inverse Probability Weighting (IPW) to control for these variables, which we'll discuss in detail in @sec-using-ps.\nWe'll use logistic regression to predict the probability of treatment---the propensity score.\nThen, we'll calculate inverse probability weights to apply to the linear regression model we fit above.\nThe propensity score model includes the exposure---net use---as the dependent variable and the minimal adjustment set as the independent variables.\n\n::: callout-tip\n## Modeling the functional form\n\nGenerally speaking, we want to lean on domain expertise and good modeling practices to fit the propensity score model.\nFor instance, we may want to allow continuous confounders to be non-linear using splines, or we may want to add essential interactions between confounders.\nBecause these are simulated data, we know we don't need these extra parameters (so we'll skip them), but in practice, you often do.\nWe'll discuss this more in @sec-using-ps.\n:::\n\nThe propensity score model is a logistic regression model with the formula `net ~ income + health + temperature`, which predicts the probability of bed net usage based on the confounders income, health, and temperature.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npropensity_model <- glm(\n  net ~ income + health + temperature,\n  data = net_data,\n  family = binomial()\n)\n\n# the first six propensity scores\nhead(predict(propensity_model, type = \"response\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n     1      2      3      4      5      6 \n0.2464 0.2178 0.3230 0.2307 0.2789 0.3060 \n```\n\n\n:::\n:::\n\n\nWe can use propensity scores to control for confounding in various ways.\nIn this example, we'll focus on weighting.\nIn particular, we'll compute the inverse probability weight for the *average treatment effect* (ATE).\nThe ATE represents a particular causal question: what if *everyone* in the study used bed nets vs. what if *no one* in the study used bed nets?\n\nTo calculate the ATE, we'll use the broom and propensity packages.\nbroom's `augment()` function extracts prediction-related information from the model and joins it to the data.\npropensity's `wt_ate()` function calculates the inverse probability weight given the propensity score and exposure.\n\nFor inverse probability weighting, the ATE weight is the probability of receiving the treatment you actually received.\nIn other words, if you used a bed net, the ATE weight is the probability that you used a net, and if you did *not* use a net, it is the probability that you did not use a net.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(propensity)\nnet_data_wts <- propensity_model |>\n  augment(newdata = net_data, type.predict = \"response\") |>\n  # .fitted is the value predicted by the model\n  # for a given observation\n  mutate(wts = wt_ate(.fitted, net))\n\nnet_data_wts |>\n  select(net, .fitted, wts) |>\n  head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 3\n  net   .fitted   wts\n  <lgl>   <dbl> <dbl>\n1 FALSE   0.246  1.33\n2 FALSE   0.218  1.28\n3 FALSE   0.323  1.48\n4 FALSE   0.231  1.30\n5 FALSE   0.279  1.39\n6 FALSE   0.306  1.44\n```\n\n\n:::\n:::\n\n\n`wts` represents the amount each observation will be up-weighted or down-weighted in the outcome model we will soon fit.\nFor instance, the first household used a bed net and had a predicted probability of 0.25.\nThat's a pretty low probability considering they did, in fact, use a net, so their weight is higher at 1.33.\nIn other words, this household will be up-weighted almost three times compared to the naive linear model we fit above.\nThe second household did *not* use a bed net; they're predicted probability of net use was 0.22 (or put differently, a predicted probability of *not* using a net of 0.78).\nThat's more in line with their observed value of `net`, but there's still some predicted probability of using a net, so their weight is 1.28.\n\n## Diagnose our models\n\nThe goal of propensity score weighting is to weight the population of observations such that the distribution of confounders is balanced between the exposure groups.\nPut another way, we are, in principle, removing the associational arrows between confounders and exposure in the DAG, so that the confounding paths no longer affect our estimates.\nHere's the distribution of the propensity score by group, created by `geom_mirror_histogram()` from the halfmoon package for assessing balance in propensity score models (as well as visualizing the pseudo-population the weights simulate):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nggplot(net_data_wts, aes(.fitted)) +\n  geom_mirror_histogram(\n    aes(fill = net),\n    bins = 50\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(x = \"propensity score\")\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the propensity scores of those who used nets (top, blue) versus those who who did not use nets (bottom, orange). The range of propensity scores is similar between groups, with those who used nets slightly to the left of those who didn't, but the shapes of the distribution are different.](chapter-02_files/figure-html/fig-mirror-histogram-net-data-unweighted-1.png){#fig-mirror-histogram-net-data-unweighted width=672}\n:::\n:::\n\n\nThe weighted propensity score creates a pseudo-population where the distributions are much more similar:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(net_data_wts, aes(.fitted)) +\n  geom_mirror_histogram(\n    aes(group = net),\n    bins = 50\n  ) +\n  geom_mirror_histogram(\n    aes(fill = net, weight = wts),\n    bins = 50,\n    alpha = .5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(x = \"propensity score\")\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the propensity scores of those who used nets (top, blue) versus those who who did not use nets (bottom, orange). The shaded region represents the unweighted distribution, and the colored region represents the weighted distributions. The ATE weights up-weight the groups to be similar in range and shape of the distribution of propensity scores.](chapter-02_files/figure-html/fig-mirror-histogram-net-data-weighted-1.png){#fig-mirror-histogram-net-data-weighted width=672}\n:::\n:::\n\n\nIn this example, the unweighted distributions are not awful---the shapes are fairly similar here---but the weighted distributions in @fig-mirror-histogram-net-data-weighted are much more similar.\n\n::: callout-caution\n## Unmeasured confounding\n\nPropensity score weighting and most other causal inference techniques only help with *observed* confounders---ones that we model correctly, at that.\nUnfortunately, we still may have unmeasured confounding, which we'll discuss below.\n\nRandomization is one causal inference technique that *does* deal with unmeasured confounding, one of the reasons it is so powerful.\n:::\n\nWe might also want to know how well-balanced the groups are by each confounder.\nOne way to do this is to calculate the standardized mean differences (SMDs) for each confounder with and without weights.\nWe'll calculate the SMDs with `tidy_smd()` then plot them with `geom_love()`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_df <- tidy_smd(\n  net_data_wts,\n  c(income, health, temperature),\n  .group = net,\n  .wts = wts\n)\n\nggplot(\n  plot_df,\n  aes(\n    x = abs(smd),\n    y = variable,\n    group = method,\n    color = method\n  )\n) +\n  geom_love()\n```\n\n::: {.cell-output-display}\n![A love plot representing the standardized mean differences (SMD) between exposure groups of three confounders: temperature, income, and health. Before weighting, there are considerable differences in the groups. After weighting, the confounders are much more balanced between groups.](chapter-02_files/figure-html/fig-love-plot-net-data-1.png){#fig-love-plot-net-data width=672}\n:::\n:::\n\n\nA standard guideline is that balanced confounders should have an SMD of less than 0.1 on the absolute scale.\n0.1 is just a rule of thumb, but if we follow it, the variables in @fig-love-plot-net-data are well-balanced after weighting (and unbalanced before weighting).\n\nBefore we apply the weights to the outcome model, let's check their overall distribution for extreme weights.\nExtreme weights can destabilize the estimate and variance in the outcome model, so we want to be aware of it.\nWe'll also discuss several other types of weights that are less prone to this issue in @sec-estimands.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnet_data_wts |>\n  ggplot(aes(wts)) +\n  geom_density(fill = \"#CC79A7\", color = NA, alpha = 0.8)\n```\n\n::: {.cell-output-display}\n![A density plot of the average treatment effect (ATE) weights. The plot is skewed, with higher values towards 8. This may indicate a problem with the model, but the weights aren't so extreme to destabilize the variance of the estimate.](chapter-02_files/figure-html/fig-ate-density-net-data-1.png){#fig-ate-density-net-data width=672}\n:::\n:::\n\n\nThe weights in @fig-ate-density-net-data are skewed, but there are no outrageous values.\nIf we saw extreme weights, we might try trimming or stabilizing them, or consider calculating an effect for a different estimand, which we'll discuss in @sec-estimands.\nIt doesn't look like we need to do that here, however.\n\n## Estimate the causal effect\n\nWe're now ready to use the ATE weights to (attempt to) account for confounding in the naive linear regression model.\nFitting such a model is pleasantly simple in this case: we fit the same model as before but with `weights = wts`, which will incorporate the inverse probability weights.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnet_data_wts |>\n  lm(malaria_risk ~ net, data = _, weights = wts) |>\n  tidy(conf.int = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 7\n  term   estimate std.error statistic  p.value conf.low\n  <chr>     <dbl>     <dbl>     <dbl>    <dbl>    <dbl>\n1 (Inte…     42.7     0.442      96.7 0            41.9\n2 netTR…    -12.5     0.624     -20.1 5.50e-81    -13.8\n# ℹ 1 more variable: conf.high <dbl>\n```\n\n\n:::\n:::\n\n\n\n\nThe estimate for the average treatment effect is -12.5 (95% CI -13.8, -11.3).\nUnfortunately, the confidence intervals we're using are wrong because they don't account for the dependence within the weights!\nGenerally, confidence intervals for propensity score weighted models will be too narrow unless we correct for this dependence.\nThe nominal coverage of the confidence intervals will thus be wrong (they aren't 95% CIs because their coverage is much lower than 95%) and may lead to misinterpretation.\n\nWe've got several ways to address this problem, which we'll discuss in detail in @sec-outcome-model, including the bootstrap, robust standard errors, and manually accounting for the dependence with empirical sandwich estimators.\nFor this example, we'll use the bootstrap, a flexible tool that calculates distributions of parameters using re-sampling.\nWe'll use the rsample package from the tidymodels ecosystem to work with bootstrap samples.\n\n::: callout-note\n## The Bootstrap\n\nThe bootstrap is a simple but flexible algorithm for calculating statistics using re-sampling with replacement.\nIt's handy when a closed-form solution doesn't exist to calculate something, as is commonly the case in causal inference (particularly for standard errors), and when we suspect the parametric calculations are not valid for a given situation.\n\nBootstrapping in R has a long tradition of writing functions to calculate the statistic of interest, starting with the classic boot package.\nThroughout the book, we'll use rsample, a more modern alternative for re-sampling, but generally, we start with writing a function to calculate the estimate we're interested in.\n\nLet's say we want to calculate `some_statistic()` for `this_data`.\nTo bootstrap for *R* samples, we:\n\n1.  Re-sample `this_data` with replacement.\n    The same row may appear multiple (or no) times in a given bootstrap sample, simulating the sampling process in the underlying population.\n\n    ``` r\n    indices <- sample(\n      # create a vector of indices:\n      # 1 through the number of rows\n      seq_len(nrow(this_data)), \n      # sample a vector indices \n      # that's the same length as `this_data`\n      size = nrow(this_data), \n      replace = TRUE\n    )\n    bootstrap_sample <- this_data[indices, ]\n    ```\n\n2.  Fit `some_statistic()` on the `bootstrap_sample`\n\n    ``` r\n    estimate <- some_statistic(bootstrap_sample)\n    ```\n\n3.  Repeat *R* times\n\nWe then end up with a distribution of `estimate`s, with which we can calculate population statistics, such as point estimates, standard errors, and confidence intervals.\n:::\n\nBecause the bootstrap is so flexible, we need to think carefully about the sources of uncertainty in the statistic we're calculating.\nIt might be tempting to write a function like this to fit the statistic we're interested in (the point estimate for `netTRUE`):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rsample)\n\nfit_ipw_not_quite_rightly <- function(split, ...) {\n  # get bootstrapped data sample with `rsample::analysis()`\n  .df <- analysis(split)\n\n  # fit ipw model\n  lm(malaria_risk ~ net, data = .df, weights = wts) |>\n    tidy()\n}\n```\n:::\n\n\nHowever, this function won't give us the correct confidence intervals because it treats the inverse probability weights as fixed values.\nThey're not, of course; we just estimated them using logistic regression!\nWe need to account for this uncertainty by bootstrapping the *entire modeling process*.\nFor every bootstrap sample, we need to fit the propensity score model, calculate the inverse probability weights, then fit the weighted outcome model.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rsample)\n\nfit_ipw <- function(split, ...) {\n  # get bootstrapped data sample with `rsample::analysis()`\n  .df <- analysis(split)\n\n  # fit propensity score model\n  propensity_model <- glm(\n    net ~ income + health + temperature,\n    data = .df,\n    family = binomial()\n  )\n\n  # calculate inverse probability weights\n  .df <- propensity_model |>\n    augment(type.predict = \"response\", data = .df) |>\n    mutate(wts = wt_ate(.fitted, net))\n\n  # fit correctly bootstrapped ipw model\n  lm(malaria_risk ~ net, data = .df, weights = wts) |>\n    tidy()\n}\n```\n:::\n\n\nNow that we know precisely how to calculate the estimate for each iteration let's create the bootstrapped dataset with rsample's `bootstraps()` function.\nThe `times` argument determines how many bootstrapped datasets to create; we'll do 1,000.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbootstrapped_net_data <- bootstraps(\n  net_data,\n  times = 1000,\n  # required to calculate CIs later\n  apparent = TRUE\n)\n\nbootstrapped_net_data\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# Bootstrap sampling with apparent sample \n# A tibble: 1,001 × 2\n   splits             id           \n   <list>             <chr>        \n 1 <split [1752/657]> Bootstrap0001\n 2 <split [1752/639]> Bootstrap0002\n 3 <split [1752/631]> Bootstrap0003\n 4 <split [1752/660]> Bootstrap0004\n 5 <split [1752/660]> Bootstrap0005\n 6 <split [1752/636]> Bootstrap0006\n 7 <split [1752/670]> Bootstrap0007\n 8 <split [1752/626]> Bootstrap0008\n 9 <split [1752/652]> Bootstrap0009\n10 <split [1752/638]> Bootstrap0010\n# ℹ 991 more rows\n```\n\n\n:::\n:::\n\n\nThe result is a nested data frame: each `splits` object contains metadata that rsample uses to subset the bootstrap samples for each of the 1,000 samples.\nNext, we'll run `fit_ipw()` 1,000 times to create a distribution for `estimate`.\nAt its heart, the calculation we're doing is\n\n``` r\nfit_ipw(bootstrapped_net_data$splits[[n]])\n```\n\nWhere *n* is one of 1,000.\nWe'll use purrr's `map()` function to iterate across each `split` object.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nipw_results <- bootstrapped_net_data |>\n  mutate(boot_fits = map(splits, fit_ipw))\n\nipw_results\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# Bootstrap sampling with apparent sample \n# A tibble: 1,001 × 3\n   splits             id            boot_fits       \n   <list>             <chr>         <list>          \n 1 <split [1752/657]> Bootstrap0001 <tibble [2 × 5]>\n 2 <split [1752/639]> Bootstrap0002 <tibble [2 × 5]>\n 3 <split [1752/631]> Bootstrap0003 <tibble [2 × 5]>\n 4 <split [1752/660]> Bootstrap0004 <tibble [2 × 5]>\n 5 <split [1752/660]> Bootstrap0005 <tibble [2 × 5]>\n 6 <split [1752/636]> Bootstrap0006 <tibble [2 × 5]>\n 7 <split [1752/670]> Bootstrap0007 <tibble [2 × 5]>\n 8 <split [1752/626]> Bootstrap0008 <tibble [2 × 5]>\n 9 <split [1752/652]> Bootstrap0009 <tibble [2 × 5]>\n10 <split [1752/638]> Bootstrap0010 <tibble [2 × 5]>\n# ℹ 991 more rows\n```\n\n\n:::\n:::\n\n\nThe result is another nested data frame with a new column, `boot_fits`.\nEach element of `boot_fits` is the result of the IPW for the bootstrapped dataset.\nFor example, in the first bootstrapped data set, the IPW results were:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nipw_results$boot_fits[[1]]\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 5\n  term        estimate std.error statistic  p.value\n  <chr>          <dbl>     <dbl>     <dbl>    <dbl>\n1 (Intercept)     42.0     0.426      98.5 0       \n2 netTRUE        -12.5     0.601     -20.9 1.99e-86\n```\n\n\n:::\n:::\n\n\nNow we have a distribution of estimates:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nipw_results |>\n  mutate(\n    estimate = map_dbl(\n      boot_fits,\n      # pull the `estimate` for `netTRUE` for each fit\n      \\(.fit) .fit |>\n        filter(term == \"netTRUE\") |>\n        pull(estimate)\n    )\n  ) |>\n  ggplot(aes(estimate)) +\n  geom_histogram(fill = \"#D55E00FF\", color = \"white\", alpha = 0.8)\n```\n\n::: {.cell-output-display}\n![\"A histogram of 1,000 bootstrapped estimates of the effect of net use on malaria risk. The spread of these estimates accounts for the dependency and uncertainty in the use of IPW weights.\"](chapter-02_files/figure-html/fig-bootstrap-estimates-net-data-1.png){#fig-bootstrap-estimates-net-data width=672}\n:::\n:::\n\n\n@fig-bootstrap-estimates-net-data gives a sense of the variation in `estimate`, but let's calculate 95% confidence intervals from the bootstrapped distribution using rsample's `int_t()` :\n\n\n::: {.cell}\n\n```{.r .cell-code}\nboot_estimate <- ipw_results |>\n  # calculate T-statistic-based CIs\n  int_t(boot_fits) |>\n  filter(term == \"netTRUE\")\n\nboot_estimate\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 6\n  term    .lower .estimate .upper .alpha .method  \n  <chr>    <dbl>     <dbl>  <dbl>  <dbl> <chr>    \n1 netTRUE  -13.4     -12.5  -11.7   0.05 student-t\n```\n\n\n:::\n:::\n\n\nNow we have a confounder-adjusted estimate with correct standard errors.\nThe estimate of the effect of *all* households using bed nets versus *no* households using bed nets on malaria risk is -12.5 (95% CI -13.4, -11.7).\nBed nets do indeed seem to reduce malaria risk in this study.\n\n## Conduct sensitivity analysis on the effect estimate\n\nWe've laid out a roadmap for taking observational data, thinking critically about the causal question we want to ask, identifying the assumptions we need to get there, then applying those assumptions to a statistical model.\nGetting the correct answer to the causal question relies on getting our assumptions more or less right.\nBut what if we're more on the less correct side?\n\nSpoiler alert: the answer we just calculated is **wrong**.\nAfter all that effort!\n\nWhen conducting a causal analysis, it's a good idea to use sensitivity analyses to test your assumptions.\nThere are many potential sources of bias in any study and many sensitivity analyses to go along with them; we'll focus on the assumption of no confounding.\n\nLet's start with a broad sensitivity analysis; then, we'll ask questions about specific unmeasured confounders.\nWhen we have less information about unmeasured confounders, we can use tipping point analysis to ask how much confounding it would take to tip my estimate to the null.\nIn other words, what would the strength of the unmeasured confounder have to be to explain our results away?\nThe tipr package is a toolkit for conducting sensitivity analyses.\nLet's examine the tipping point for an unknown, normally-distributed confounder.\nThe `tip_coef()` function takes an estimate (a beta coefficient from a regression model, or the upper or lower bound of the coefficient).\nIt further requires either the 1) scaled differences in means of the confounder between exposure groups or 2) effect of the confounder on the outcome.\nFor the estimate, we'll use `conf.high`, which is closer to 0 (the null), and ask: how much would the confounder have to affect malaria risk to have an unbiased upper confidence interval of 0?\nWe'll use tipr to calculate this answer for 5 scenarios, where the mean difference in the confounder between exposure groups is 1, 2, 3, 4, or 5.\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tipr)\ntipping_points <- tip_coef(boot_estimate$.upper, exposure_confounder_effect = 1:5)\n\ntipping_points |>\n  ggplot(aes(confounder_outcome_effect, exposure_confounder_effect)) +\n  geom_line(color = \"#009E73\", linewidth = 1.1) +\n  geom_point(fill = \"#009E73\", color = \"white\", size = 2.5, shape = 21) +\n  labs(\n    x = \"Confounder-Outcome Effect\",\n    y = \"Scaled mean differences in\\n confounder between exposure groups\"\n  )\n```\n\n::: {.cell-output-display}\n![A tipping point analysis under several confounding scenarios where the unmeasured confounder is a normally-distributed continuous variable. The line represents the strength of confounding necessary to tip the upper confidence interval of the causal effect estimate to 0. The x-axis represents the coefficient of the confounder-outcome relationship adjusted for the exposure and the set of measured confounders. The y-axis represents the scaled mean difference of the confounder between exposure groups.](chapter-02_files/figure-html/fig-tip-coef-net-1.png){#fig-tip-coef-net width=672}\n:::\n:::\n\n\nIf we had an unmeasured confounder where the standardized mean difference between exposure groups was 1, the confounder would need to decrease malaria risk by about -11.7.\nThat's pretty strong relative to other effects, but it may be feasible if we have an idea of something we might have missed.\nConversely, suppose the relationship between net use and the unmeasured confounder is very strong, with a mean scaled difference of 5.\nIn that case, the confounder-malaria relationship only needs to be 5.\nNow we have to consider: which of these scenarios are plausible given our domain knowledge and the effects we see in this analysis?\n\nNow let's consider a much more specific sensitivity analysis.\nSome ethnic groups, such as the Fulani, have a genetic resistance to malaria [@arama2015].\nLet's say that in our simulated data, an unnamed ethnic group shares this genetic resistance to malaria.\nFor historical reasons, bed net use in this fictional group is also very high.\nWe don't have this variable in `net_data`, but let's say we know from the literature that in this sample, we can estimate at:\n\n1.  People with this genetic resistance have, on average, about 10 lower malaria risk.\n2.  About 26% of people who use nets in our study have this genetic resistance.\n3.  About 5% of people who don't use nets have this genetic resistance.\n\nWith this amount of information, we can use tipr to adjust the estimates we calculated for the unmeasured confounder.\nWe'll use `adjust_coef_with_binary()` to calculate the adjusted estimates.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nadjusted_estimates <- boot_estimate |>\n  select(.estimate, .lower, .upper) |>\n  unlist() |>\n  adjust_coef_with_binary(\n    exposed_confounder_prev = 0.26,\n    unexposed_confounder_prev = 0.05,\n    confounder_outcome_effect = -10\n  )\n\nadjusted_estimates\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 4\n  effect_adjusted effect_observed\n            <dbl>           <dbl>\n1          -10.4            -12.5\n2          -11.3            -13.4\n3           -9.59           -11.7\n# ℹ 2 more variables:\n#   exposure_confounder_effect <dbl>,\n#   confounder_outcome_effect <dbl>\n```\n\n\n:::\n:::\n\n\nThe adjusted estimate for a situation where genetic resistance to malaria is a confounder is -10.4 (95% CI -11.3, -9.6).\n\nIn fact, these data were simulated with just such a confounder.\nThe true effect of net use on malaria is about -10, and the true DAG that generated these data is:\n\n\n::: {.cell}\n::: {.cell-output-display}\n![The true causal diagram for `net_data`. This DAG is identical to the one we proposed with one addition: genetic resistance to malaria causally reduces the risk of malaria and impacts net use. It's thus a confounder and a part of the minimal adjustment set required to get an unbiased effect estimate. In otherwords, by not including it, we've calculated the wrong effect.](chapter-02_files/figure-html/fig-net-data-true-dag-1.png){#fig-net-data-true-dag width=672}\n:::\n:::\n\n\n\n\nThe unmeasured confounder in @fig-net-data-true-dag is available in the dataset `net_data_full` as `genetic_resistance`.\nIf we recalculate the IPW estimate of the average treatment effect of nets on malaria risk, we get -10.2 (95% CI -11.2, -9.4), much closer to the actual answer of -10.\n\nWhat do you think?\nIs this estimate reliable?\nDid we do a good job addressing the assumptions we need to make for a causal effect, mainly that there is no confounding?\nHow might you criticize this model, and what would you do differently?\nOk, we know that -10 is the correct answer because the data are simulated, but in practice, we can never be sure, so we need to continue probing our assumptions until we're confident they are robust.\n<!-- TODO: Maybe use sickle cell as an example of a precision variable in the variable selection section later in the book. Interesting instance because sickle cell can't be downstream. Consider in the context of over adjustment. -->\n\nTo calculate this effect, we:\n\n1.  Specified a causal question (for the average treatment effect)\n2.  Drew our assumptions using a causal diagram (using DAGs)\n3.  Modeled our assumptions (using propensity score weighting)\n4.  Diagnosed our models (by checking confounder balance after weighting)\n5.  Estimated the causal effect (using inverse probability weighting)\n6.  Conducted sensitivity analysis on the effect estimate (using tipping point analysis)\n\nThroughout the rest of the book, we'll follow these broad steps in several examples from medicine, economics, and industry.\nWe'll dive more deeply into propensity score techniques, explore alternative methods for calculating causal effects, and, most importantly, make sure, over and over again, that the assumptions we're making are reasonable---even if we'll never know for sure.\n",
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-    "markdown": "# Estimating counterfactuals {#sec-counterfactuals}\n\n\n\n\n\n## Potential Outcomes {#sec-potential}\n\nLet's begin by thinking about the philosophical concept of a *potential outcome.* Prior to some \"cause\" occurring, for example receiving some exposure, the *potential outcomes* are all of the potential things that could occur depending on what you are exposed to.\nFor simplicity, let's assume an exposure has two levels:\n\n-   $X=1$ if you are exposed\n\n-   $X=0$ if you are not exposed\n\nUnder this simple scenario, there are two potential outcomes:\n\n-   $Y(1)$ the potential outcome if you are exposed\n\n-   $Y(0)$ the potential outcome if you are not exposed\n\nOnly *one* of these potential outcomes will actually be realized, the one corresponding to the exposure that actually occurred, and therefore only one is observable.\nIt is important to remember that these exposures are defined at a particular instance in time, so only one can happen to any individual.\nIn the case of a binary exposure, this leaves one potential outcome as *observable* and one *missing.* In fact, early causal inference methods were often framed as missing data problems; we need to make certain assumptions about the *missing counterfactuals*, the value of the potential outcome corresponding to the exposure(s) that did not occur.\n\nOur causal effect of interest is often some difference in potential outcomes $Y(1) - Y(0)$, averaged over a particular population.\n\n## Counterfactuals\n\nConceptually, the missing counterfactual outcome is one that would have occurred under a different set of circumstances.\nIn causal inference, we *wish* we could observe the conterfactual outcome that would have occurred in an alternate universe where the exposure status for a given observation was flipped.\nTo do this, we attempt to control for all factors that are related to an exposure and outcome such that we can *construct* (or estimate) such a counterfactual outcome.\n\nLet's think about a specific example.\nIce-T, best known as an American rapper and Fin on Law and Order: SVU, co-authored a book titled \"Split Decision: Life Stories\", published in 2022.\nHere is the synopsis:\n\n> **Award-winning actor, rapper, and producer Ice-T unveils a compelling memoir of his early life robbing jewelry stores until he found fame and fortune---while a handful of bad choices sent his former crime partner down an incredibly different path.**\\\n> \\\n> Ice-T rose to fame in the late 1980s, earning acclaim for his music before going on to enthrall television audiences as Odafin \"Fin\" Tutuola in *Law & Order: Special Victims Unit*.\n> But it could have gone much differently.\\\n>\n> \\\n> In this \"poignant and powerful\" (*Library Journal*, starred review) memoir, Ice-T and Spike, his former crime partner---collaborating with *New York Times* bestselling author Douglas Century---relate the shocking stories of their shared pasts, and how just a handful of decisions led to their incredibly different lives.\n> Both grew up in violent, gang-controlled Los Angeles neighborhoods and worked together to orchestrate a series of jewelry heists.\\\n>\n> \\\n> But while Ice-T was discovered rapping in a club and got his first record deal, Spike was caught for a jewelry robbery and did three years in prison.\n> As his music career began to take off, Ice made the decision to abandon the criminal life; Spike continued to plan increasingly ingenious and risky jewel heists.\n> And in 1992, after one of Spike's robberies ended tragically, he was sentenced to thirty-five years to life.\n> While he sat behind bars, he watched his former partner rise to fame in music, movies, and television.\\\n>\n> \\\n> \"Propulsive\" (*Publishers Weekly*, starred review), timely, and thoughtful, two men with two very different lives reveal how their paths might have very well been reversed if they made different choices.\n> All it took was a *split decision*.\n> [@split]\n\nThis premise is compelling because it implies that we are observing a *counterfactual*.\nThe book begins by setting up all the ways Ice-T and his friend Spike were similar prior to some important moment (both grew up in Los Angeles neighborhoods, both were involved with gangs, both worked together to orchestrate a series of jewelry heists, etc).\nThen something happens -- Ice-T makes a decision to abandon criminal life and Spike makes the opposite decision.\nWhat happens next for Ice-T includes fame and fortune, while Spike ends up with 35 years to life in prison.\nThis book is attempting a small study, two people who prior to some event were the same and after were different -- Spike's outcomes serve as the counterfactual to Ice-T's.\n\n::: {#tbl-causal-map layout-ncol=\"1\"}\n\n```{mermaid}\n%%| echo: false\nflowchart LR\nA{Ice-T} --> |observed| B(Abandons criminal life)\nA -.-> |missing counterfactual| C(Does one more heist)\nC -.-> D[35 years in prison]\nB --> E[Fame & Fortune]\n\nclassDef grey fill:#ddd\nclass D,C grey\n```\n\n```{mermaid}\n%%| echo: false\nflowchart LR\nA{Spike} -.-> |missing counterfactual| B(Abandons criminal life)\nA --> |observed| C(Does one more heist)\nC --> D[35 years in prison]\nB -.-> E[Fame & Fortune]\nclassDef grey fill:#ddd\nclass E,B grey\n```\n\n\nIce-T and Spike Causal Map\n:::\n\nIn practice, this is what we attempt to do with causal inference techniques.\nEven randomized trials are limited to a single factual world, so we compare the average effects of groups with different exposures.\nNow, having this as a concrete example of an attempt to construct a counterfactual scenario in the \"real-world\" there are several issues that we can immediately see, highlighting the difficulty in drawing such inference.\nFirst, while the synopsis implies that the two individuals were similar prior to the precipitating event that dictated their future opposite directions, we can easily identify factors in which perhaps they differed.\nIce-T decided to leave his life of crime, but that wasn't the only factor in his success: he had enough musical talent to make a career of it.\nDid Spike have Ice-T's musical talent?\nCan we really conclude that his life would have turned out exactly like Ice-T's if he had made the exact same choices as Ice-T?\nIf we want to truly estimate the causal effect of the decision to leave criminal life on Ice-T's future outcomes, we would need to observe his ultimate course both under making the decision and not.\nOf course this is not possible, so what can we do?\nPerhaps we can find someone else who is exactly like Ice-T who did not make the same decision and see how they fare.\nOf course, Ice-T is unique, it would be challenging to find someone exactly like him.\nAgain, this is attempted with Spike, and even so presents challenges.\nOften, instead of relying on a single individual, we rely on many individuals.\nWe could conduct an experiment where we *randomize* many individuals to leave criminal life (or not) and see how this impacts their outcomes *on average* (this randomized trial seems to present some ethical issues, perhaps we need to look to *observational* studies to help answer this question).\nIn any case, we must rely on statistical techniques to help construct these unobservable counterfactuals.\n\n### Potential Outcomes Simulation {#sec-po-sim}\n\nLet's suppose some happiness index, from 1-10 exists.\nWe are interested in assessing whether eating chocolate ice cream versus vanilla will increase happiness.\nWe have 10 individuals with two potential outcomes for each, one is what their happiness would be if they ate chocolate ice cream, (defined as `y_chocolate` in the code below), and one is what their happiness would be if they ate vanilla ice cream (defined as `y_vanilla` in the code below). We can define the true causal effect of eating chocolate ice cream (versus vanilla) on happiness for each individual as the difference between the two (@tbl-po).\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  y_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n)\n\ndata <- data |>\n  mutate(causal_effect = y_chocolate - y_vanilla)\n\ndata\n```\n:::\n\n::: {#tbl-po .cell tbl-cap='Potential Outcomes Simulation: The causal effect of eating chocolate (versus vanilla) ice cream on happiness'}\n::: {.cell-output-display}\n`````{=html}\n<table>\n <thead>\n<tr>\n<th style=\"empty-cells: hide;border-bottom:hidden;\" colspan=\"1\"></th>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"2\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">Potential Outcomes</div></th>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"1\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">Causal Effect</div></th>\n</tr>\n  <tr>\n   <th style=\"text-align:right;\"> id </th>\n   <th style=\"text-align:right;\"> $$Y_i(\\textrm{chocolate})$$ </th>\n   <th style=\"text-align:right;\"> $$Y_i(\\textrm{vanilla})$$ </th>\n   <th style=\"text-align:right;\"> $$Y_i(\\textrm{chocolate}) - Y_i(\\textrm{vanilla})$$ </th>\n  </tr>\n </thead>\n<tbody>\n  <tr>\n   <td style=\"text-align:right;\"> 1 </td>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n   <td style=\"text-align:right;\"> 3 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2 </td>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:right;\"> 3 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 3 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:right;\"> 2 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 0 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> -1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 7 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 8 </td>\n   <td style=\"text-align:right;\"> -2 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 8 </td>\n   <td style=\"text-align:right;\"> 7 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 9 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 3 </td>\n   <td style=\"text-align:right;\"> 2 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 10 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n</tbody>\n</table>\n\n`````\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ndata |>\n  summarize(\n    avg_chocolate = mean(y_chocolate),\n    avg_vanilla = mean(y_vanilla),\n    avg_causal_effect = mean(causal_effect)\n  )\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n  avg_chocolate avg_vanilla avg_causal_effect\n1           5.4         4.6               0.8\n```\n\n\n:::\n:::\n\n\nFor example, examining @tbl-po, the causal effect of eating chocolate ice cream (versus vanilla) for individual `4` is 0, whereas the causal effect for individual `9` is 2. The *average* potential happiness after eating chocolate is 5.4 and the *average* potential happiness after eating vanilla is 4.6. The *average* treatment effect of eating chocolate (versus vanilla) ice cream among the ten individuals in this study is 0.8. \n\nIn reality, we cannot observe both potential outcomes, in any moment in time, each individual in our study can only eat *one* flavor of ice cream. Suppose we let our participants choose which ice cream they wanted to eat and each choose their favorite (i.e. they knew which would make them \"happier\" and picked that one. Now what we *observe* is shown in @tbl-obs.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata_observed <- data |>\n  mutate(\n    exposure = case_when(\n      # people who like chocolate more chose that\n      y_chocolate > y_vanilla ~ \"chocolate\",\n      # people who like vanilla more chose that\n      y_vanilla >= y_chocolate ~ \"vanilla\"\n    ),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" ~ y_chocolate,\n      exposure == \"vanilla\" ~ y_vanilla\n    )\n  ) |>\n  # we can only observe the exposure and one potential outcome\n  select(id, exposure, observed_outcome)\ndata_observed\n```\n:::\n\n::: {#tbl-obs .cell tbl-cap='Potential Outcomes Simulation: The observed exposure and outcome used to estimate the effect of eating chocolate (versus vanilla) ice cream on happiness'}\n::: {.cell-output-display}\n`````{=html}\n<table>\n <thead>\n<tr>\n<th style=\"empty-cells: hide;border-bottom:hidden;\" colspan=\"1\"></th>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"1\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">Exposure</div></th>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"1\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">Observed Outcome</div></th>\n</tr>\n  <tr>\n   <th style=\"text-align:right;\"> id </th>\n   <th style=\"text-align:left;\"> $$X_i$$ </th>\n   <th style=\"text-align:right;\"> $$Y_i$$ </th>\n  </tr>\n </thead>\n<tbody>\n  <tr>\n   <td style=\"text-align:right;\"> 1 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 4 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 4 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 3 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 6 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:left;\"> vanilla </td>\n   <td style=\"text-align:right;\"> 5 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 6 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:left;\"> vanilla </td>\n   <td style=\"text-align:right;\"> 6 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 7 </td>\n   <td style=\"text-align:left;\"> vanilla </td>\n   <td style=\"text-align:right;\"> 8 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 8 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 7 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 9 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 5 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 10 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 6 </td>\n  </tr>\n</tbody>\n</table>\n\n`````\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        5.43\n2 vanilla          6.33\n```\n\n\n:::\n:::\n\n\nNow, the *observed* average outcome among those who ate chocolate ice cream is 5.4 (the same as the true average potential outcome), while the *observed* average outcome among those who ate vanilla is 6.3 -- quite different from the *actual* average (4.6). The estimated causal effect here could be calculated as 5.4 - 6.3 = -0.9. \n\nIt turns out here, these 10 participants *chose* which ice cream they wanted to eat and they always chose to eat their favorite! This artificially made it look like eating vanilla ice cream would increase the happiness in this population when in fact we know the opposite is true. The next section will discuss which assumptions need to be true in order to allow us to *accurately* estimate causal effects using observed data. As a sneak peak, our issue here was that how the exposure was decided, if instead we *randomized* who ate chocolate versus vanilla ice cream we would (on average, with a large enough sample) recover the true causal effect.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## we are doing something *random* so let's set a seed so we always observe the\n## same result each time we run the code\nset.seed(11)\ndata_observed <- data |>\n  mutate(\n    # change the exposure to randomized, generate from a binomial distribution\n    # with a probability 0.5 for being in either group\n    exposure = case_when(\n      rbinom(10, 1, 0.5) == 1 ~ \"chocolate\",\n      TRUE ~ \"vanilla\"\n    ),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" ~ y_chocolate,\n      exposure == \"vanilla\" ~ y_vanilla\n    )\n  ) |>\n  # we can only observe the exposure and one potential outcome\n  select(id, exposure, observed_outcome)\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        5.33\n2 vanilla          4.71\n```\n\n\n:::\n:::\n\n\n## Causal Assumptions {#sec-assump}\n\nLike most statistical approaches, the validity of a causal analysis depends on how well certain assumptions are met.\nAs mentioned in @sec-potential, the potential outcomes framework envisions that each individual possesses a range of potential outcomes for every conceivable value of some input.\nFor instance, as in the scenario previously described with two exposure levels (exposed: 1 and unexposed: 0), we can define potential outcomes for exposure ($Y(1)$) and no exposure ($Y(0)$), and subsequently analyze the difference between these outcomes, i.e., $Y(1) - Y(0)$, to comprehend the impact of the input (the exposure) on the outcome, $Y$.\nAt any given time, only one of these *potential outcomes* is observable -- namely, the outcome tied to the actual exposure the individual underwent.\nUnder certain assumptions, we can leverage data from individuals exposed to different inputs to compare the average differences in their observed outcomes.\nThe most common assumptions across the approaches we describe in this book are:\n\n1.  **Consistency**: We assume that the causal question you claim you are answering is consistent with the one you are *actually* answering with your analysis. Mathematically, this means that $Y_{obs} = (X)Y(1) + (1 - X)Y(0)$, in other words, the outcome you observe is exactly equal to the potential outcome under the exposure you received. Two common ways to discuss this assumption are: \n    * **Well defined exposure**: We assume that for each value of the exposure, there is no difference between subjects in the delivery of that exposure.\nPut another way, multiple versions of the treatment do not exist. \n    * **No interference**: We assume that the outcome (technically all *potential* outcomes, regardless of whether they are observed) for any subject does not depend on another subject's exposure.\n    \n::: callout-tip\n## Jargon\n\nAssumption 1 is sometimes referred to as *stable-unit-treatment-value-assumption* or SUTVA [@imbens2015causal].\nLikewise, these assumptions are sometimes referred to as *identifiability conditions* since we need them to hold in order to identify causal estimates.\n:::\n\n2.  **Exchangeability**: We assume that within levels of relevant variables (confounders), exposed and unexposed subjects have an equal likelihood of experiencing any outcome prior to exposure; i.e. the exposed and unexposed subjects are exchangeable.\nThis assumption is sometimes referred to as **no unmeasured confounding**.\n\n3.  **Positivity**: We assume that within each level and combination of the study variables used to achieve exchangeability, there are exposed and unexposed subjects.\nSaid differently, each individual has some chance of experiencing every available exposure level.\nSometimes this is referred to as the **probabilistic** assumption.\n\n\n\n\n::: callout-note\n## Apples-to-apples\n\nPractically, most of the assumptions we need to make for causal inference are so we can make an *apples-to-apples* comparison: we want to make sure we're comparing individuals that are similar --- who would serve as good proxies for each other's counterfactuals. \n\nThe phrase *apples-to-apples* stems from the saying \"comparing apples to oranges\", e.g. comparing two things that are incomparable. \n\nThat's only one way to say it. [There are a lot of variations worldwide](https://en.wikipedia.org/wiki/Apples_and_oranges). Here are some other things people incorrectly compare:\n\n* Cheese and chalk (UK English)\n* Apples and pears (German)\n* Potatoes and sweet potatoes (Latin American Spanish)\n* Grandmothers and toads (Serbian)\n* Horses and donkeys (Hindi)\n:::\n\n### Causal Assumptions Simulation\n\nLet's bring back our simulation from @sec-po-sim. Recall that we have individuals who will either eat chocolate or vanilla ice cream and we are interested in assessing the causal effect of this exposure on their happiness. Let's see how violations of each assumption may impact the estimation of the causal effect.\n\n#### Consistency violation\n\nTwo ways the consistency assumption can be violated is (1) lack of a well defined exposure and (2) interference. Let's see how these impact our ability to accurately estimate a causal effect.\n\n##### Well defined exposure\n\nSuppose that there were in fact two containers of chocolate ice cream, one of which was spoiled. Therefore, despite the fact that having an exposure \"chocolate\" could mean different things depending on where the individual's scoop came from (regular chocolate ice cream, or spoiled chocolate ice cream), we are lumping them all together under a single umbrella (hence the violation, we have \"multiple versions of treatment\"). You can see how this falls under consistency because the issue here is that the potential outcome we think we are estimating is not the one we are actually observing.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  y_spoiledchocolate = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),\n  y_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n) |>\n  mutate(causal_effect = y_chocolate - y_vanilla)\n\nset.seed(11)\ndata_observed <- data |>\n  mutate(\n    exposure_unobserved = case_when(\n      rbinom(10, 1, 0.25) == 1 ~ \"chocolate (spoiled)\",\n      rbinom(10, 1, 0.25) == 1 ~ \"chocolate\",\n      TRUE ~ \"vanilla\"\n    ),\n    observed_outcome = case_when(\n      exposure_unobserved == \"chocolate (spoiled)\" ~ y_spoiledchocolate,\n      exposure_unobserved == \"chocolate\" ~ y_chocolate,\n      exposure_unobserved == \"vanilla\" ~ y_vanilla\n    ),\n    exposure = case_when(\n      exposure_unobserved %in% c(\"chocolate (spoiled)\", \"chocolate\") ~ \"chocolate\",\n      exposure_unobserved == \"vanilla\" ~ \"vanilla\"\n    )\n  ) |>\n  select(id, exposure, observed_outcome)\n\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        2.75\n2 vanilla          4.67\n```\n\n\n:::\n:::\n\n\nWe know the *true* average causal effect of (unspoiled) chocolate in the sample is 0.8, however our estimated causal effect (because our data are not consistent with the question we are asking) is -1.9. This demonstrates what can go wrong when *well defined exposure* is violated.\n\n##### Interference \n\nInterference would mean that an individual's exposure impacts another's potential outcome. For example, let's say each individual has a partner, and their potential outcome depends on both what flavor of ice cream they ate *and* what flavor their partner ate. For example, in the simulation below, having a partner that received a different flavor of ice cream increases the happiness by two units.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  partner_id = c(1, 1, 2, 2, 3, 3, 4, 4, 5, 5),\n  y_chocolate_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_chocolate_vanilla = c(6, 6, 8, 7, 8, 7, 8, 9, 7, 8),\n  y_vanilla_chocolate = c(3, 5, 6, 7, 7, 8, 10, 8, 5, 7),\n  y_vanilla_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n)\n\nset.seed(11)\ndata_observed <- data |>\n  mutate(\n    exposure = case_when(\n      rbinom(10, 1, 0.5) == 1 ~ \"chocolate\",\n      TRUE ~ \"vanilla\"\n    ),\n    exposure_partner =\n      c(\"vanilla\", \"vanilla\", \"vanilla\", \"chocolate\", \"chocolate\", \"vanilla\", \"vanilla\", \"vanilla\", \"vanilla\", \"chocolate\"),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" & exposure_partner == \"chocolate\" ~ y_chocolate_chocolate,\n      exposure == \"chocolate\" & exposure_partner == \"vanilla\" ~ y_chocolate_vanilla,\n      exposure == \"vanilla\" & exposure_partner == \"chocolate\" ~ y_vanilla_chocolate,\n      exposure == \"vanilla\" & exposure_partner == \"vanilla\" ~ y_vanilla_vanilla\n    )\n  ) |>\n  select(id, exposure, observed_outcome)\n\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        7.33\n2 vanilla          5.57\n```\n\n\n:::\n:::\n\nNow our estimated causal effect (because interference exists) is 1.8. This demonstrates what can go wrong when *interference* occurs. One of the main ways to combat interference is change the *unit* under consideration. Here, each individual, each unique *id*, is considered a unit, and there is interference between units (i.e. between partners). If instead we consider each *partner* as a unit and randomize the partners rather than the individuals, we solve the interference issue, as there is not interference *between* different partner sets. This is sometimes referred to as a *cluster randomized trial*. What we decide to do within each cluster may depend on the causal question at hand. For example, if we want to know what would happen if *everyone* at chocolate ice cream versus if *everyone* at vanilla, we would want to randomize both partners to either chocolate or vanilla, as seen below.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(11)\n\n## we are now randomizing the *partners* not the individuals\npartners <- data.frame(\n  partner_id = 1:5,\n  exposure = case_when(\n    rbinom(5, 1, 0.5) == 1 ~ \"chocolate\",\n    TRUE ~ \"vanilla\"\n  )\n)\ndata_observed <- data |>\n  left_join(partners, by = \"partner_id\") |>\n  mutate(\n    # all partners have the same exposure\n    exposure_partner = exposure,\n    observed_outcome = case_when(\n      exposure == \"chocolate\" & exposure_partner == \"chocolate\" ~ y_chocolate_chocolate,\n      exposure == \"vanilla\" & exposure_partner == \"vanilla\" ~ y_vanilla_vanilla\n    )\n  ) |>\n  select(id, exposure, observed_outcome)\n\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        5.5 \n2 vanilla          4.38\n```\n\n\n:::\n:::\n\n\n#### Exchangeability violation\n\nWe have actually already seen an example of an exchangeability violation in @sec-po-sim. In that example, participants were able to choose the ice cream that they wanted to eat, so people who were more likely to have a positive effect from eating chocolate chose that, and those more likely to have a positive effect from eating vanilla chose that. \n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  y_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n)\ndata_observed <- data |>\n  mutate(\n    exposure = case_when(\n      # people who like chocolate more chose that\n      y_chocolate > y_vanilla ~ \"chocolate\",\n      # people who like vanilla more chose that\n      y_vanilla >= y_chocolate ~ \"vanilla\"\n    ),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" ~ y_chocolate,\n      exposure == \"vanilla\" ~ y_vanilla\n    )\n  ) |>\n  select(id, exposure, observed_outcome)\n\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        5.43\n2 vanilla          6.33\n```\n\n\n:::\n:::\n\n\nHow could we correct this? If we had some people who preferred chocolate ice cream but ended up taking vanilla instead, we could *adjust* for the preference, and the effect conditioned on this would no longer have an exchangeability issue. It turns out that this example as we have constructed it doesn't lend itself to this solution because participants chose their preferred flavor 100% of the time making this *also* a positivity violation. \n\n#### Positivity violation\n\nAs stated above, the previous example violates both *exchangeability* and *positivity*. How could we fix it? As long as *some* people chose outside their preference with some probability (even if it is small!) we can remove this violation. Let's say instead of everyone picking their flavor of preference 100% of the time, they just had a 80% chance of picking that flavor.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  y_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n)\n\nset.seed(11)\ndata_observed <- data |>\n  mutate(\n    prefer_chocolate = y_chocolate > y_vanilla,\n    exposure = case_when(\n      # people who like chocolate more chose that 80% of the time\n      prefer_chocolate ~ ifelse(rbinom(10, 1, 0.8), \"chocolate\", \"vanilla\"),\n      # people who like vanilla more chose that 80% of the time\n      !prefer_chocolate ~ ifelse(rbinom(10, 1, 0.8), \"vanilla\", \"chocolate\")\n    ),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" ~ y_chocolate,\n      exposure == \"vanilla\" ~ y_vanilla\n    )\n  ) |>\n  select(id, prefer_chocolate, exposure, observed_outcome)\n\nlm(\n  observed_outcome ~ I(exposure == \"chocolate\") + prefer_chocolate,\n  data_observed\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\nCall:\nlm(formula = observed_outcome ~ I(exposure == \"chocolate\") + \n    prefer_chocolate, data = data_observed)\n\nCoefficients:\n                   (Intercept)  \n                         6.156  \nI(exposure == \"chocolate\")TRUE  \n                         0.531  \n          prefer_chocolateTRUE  \n                        -1.469  \n```\n\n\n:::\n:::\n\nAfter *adjusting* for this variable (chocolate preference), we recover the correct causal effect. This value is not exactly the same as the truth we obtain with the (unobservable) potential outcomes because we are dealing with a small sample -- as our sample size increases this will get closer to the truth.\n\nCausal assumptions can be difficult to verify and may not hold for many data collection strategies.\nWe cannot overstate the importance of checking these criteria to the extent possible!\nFollowing any of the recipes in this book are unlikely to give valid answers if the causal assumptions are badly violated.\n",
-    "supporting": [],
+    "markdown": "# Estimating counterfactuals {#sec-counterfactuals}\n\n\n\n\n\n## Potential Outcomes {#sec-potential}\n\nLet's begin by thinking about the philosophical concept of a *potential outcome.* Prior to some \"cause\" occurring, for example receiving some exposure, the *potential outcomes* are all of the potential things that could occur depending on what you are exposed to.\nFor simplicity, let's assume an exposure has two levels:\n\n-   $X=1$ if you are exposed\n\n-   $X=0$ if you are not exposed\n\nUnder this simple scenario, there are two potential outcomes:\n\n-   $Y(1)$ the potential outcome if you are exposed\n\n-   $Y(0)$ the potential outcome if you are not exposed\n\nOnly *one* of these potential outcomes will actually be realized, the one corresponding to the exposure that actually occurred, and therefore only one is observable.\nIt is important to remember that these exposures are defined at a particular instance in time, so only one can happen to any individual.\nIn the case of a binary exposure, this leaves one potential outcome as *observable* and one *missing.* In fact, early causal inference methods were often framed as missing data problems; we need to make certain assumptions about the *missing counterfactuals*, the value of the potential outcome corresponding to the exposure(s) that did not occur.\n\nOur causal effect of interest is often some difference in potential outcomes $Y(1) - Y(0)$, averaged over a particular population.\n\n## Counterfactuals\n\nConceptually, the missing counterfactual outcome is one that would have occurred under a different set of circumstances.\nIn causal inference, we *wish* we could observe the conterfactual outcome that would have occurred in an alternate universe where the exposure status for a given observation was flipped.\nTo do this, we attempt to control for all factors that are related to an exposure and outcome such that we can *construct* (or estimate) such a counterfactual outcome.\n\nLet's think about a specific example.\nIce-T, best known as an American rapper and Fin on Law and Order: SVU, co-authored a book titled \"Split Decision: Life Stories\", published in 2022.\nHere is the synopsis:\n\n> **Award-winning actor, rapper, and producer Ice-T unveils a compelling memoir of his early life robbing jewelry stores until he found fame and fortune---while a handful of bad choices sent his former crime partner down an incredibly different path.**\\\n> \\\n> Ice-T rose to fame in the late 1980s, earning acclaim for his music before going on to enthrall television audiences as Odafin \"Fin\" Tutuola in *Law & Order: Special Victims Unit*.\n> But it could have gone much differently.\\\n>\n> \\\n> In this \"poignant and powerful\" (*Library Journal*, starred review) memoir, Ice-T and Spike, his former crime partner---collaborating with *New York Times* bestselling author Douglas Century---relate the shocking stories of their shared pasts, and how just a handful of decisions led to their incredibly different lives.\n> Both grew up in violent, gang-controlled Los Angeles neighborhoods and worked together to orchestrate a series of jewelry heists.\\\n>\n> \\\n> But while Ice-T was discovered rapping in a club and got his first record deal, Spike was caught for a jewelry robbery and did three years in prison.\n> As his music career began to take off, Ice made the decision to abandon the criminal life; Spike continued to plan increasingly ingenious and risky jewel heists.\n> And in 1992, after one of Spike's robberies ended tragically, he was sentenced to thirty-five years to life.\n> While he sat behind bars, he watched his former partner rise to fame in music, movies, and television.\\\n>\n> \\\n> \"Propulsive\" (*Publishers Weekly*, starred review), timely, and thoughtful, two men with two very different lives reveal how their paths might have very well been reversed if they made different choices.\n> All it took was a *split decision*.\n> [@split]\n\nThis premise is compelling because it implies that we are observing a *counterfactual*.\nThe book begins by setting up all the ways Ice-T and his friend Spike were similar prior to some important moment (both grew up in Los Angeles neighborhoods, both were involved with gangs, both worked together to orchestrate a series of jewelry heists, etc).\nThen something happens -- Ice-T makes a decision to abandon criminal life and Spike makes the opposite decision.\nWhat happens next for Ice-T includes fame and fortune, while Spike ends up with 35 years to life in prison.\nThis book is attempting a small study, two people who prior to some event were the same and after were different -- Spike's outcomes serve as the counterfactual to Ice-T's.\n\n::: {#tbl-causal-map layout-ncol=\"1\"}\n\n```{mermaid}\n%%| echo: false\nflowchart LR\nA{Ice-T} --> |observed| B(Abandons criminal life)\nA -.-> |missing counterfactual| C(Does one more heist)\nC -.-> D[35 years in prison]\nB --> E[Fame & Fortune]\n\nclassDef grey fill:#ddd\nclass D,C grey\n```\n\n```{mermaid}\n%%| echo: false\nflowchart LR\nA{Spike} -.-> |missing counterfactual| B(Abandons criminal life)\nA --> |observed| C(Does one more heist)\nC --> D[35 years in prison]\nB -.-> E[Fame & Fortune]\nclassDef grey fill:#ddd\nclass E,B grey\n```\n\n\nIce-T and Spike Causal Map\n:::\n\nIn practice, this is what we attempt to do with causal inference techniques.\nEven randomized trials are limited to a single factual world, so we compare the average effects of groups with different exposures.\nNow, having this as a concrete example of an attempt to construct a counterfactual scenario in the \"real-world\" there are several issues that we can immediately see, highlighting the difficulty in drawing such inference.\nFirst, while the synopsis implies that the two individuals were similar prior to the precipitating event that dictated their future opposite directions, we can easily identify factors in which perhaps they differed.\nIce-T decided to leave his life of crime, but that wasn't the only factor in his success: he had enough musical talent to make a career of it.\nDid Spike have Ice-T's musical talent?\nCan we really conclude that his life would have turned out exactly like Ice-T's if he had made the exact same choices as Ice-T?\nIf we want to truly estimate the causal effect of the decision to leave criminal life on Ice-T's future outcomes, we would need to observe his ultimate course both under making the decision and not.\nOf course this is not possible, so what can we do?\nPerhaps we can find someone else who is exactly like Ice-T who did not make the same decision and see how they fare.\nOf course, Ice-T is unique, it would be challenging to find someone exactly like him.\nAgain, this is attempted with Spike, and even so presents challenges.\nOften, instead of relying on a single individual, we rely on many individuals.\nWe could conduct an experiment where we *randomize* many individuals to leave criminal life (or not) and see how this impacts their outcomes *on average* (this randomized trial seems to present some ethical issues, perhaps we need to look to *observational* studies to help answer this question).\nIn any case, we must rely on statistical techniques to help construct these unobservable counterfactuals.\n\n### Potential Outcomes Simulation {#sec-po-sim}\n\nLet's suppose some happiness index, from 1-10 exists.\nWe are interested in assessing whether eating chocolate ice cream versus vanilla will increase happiness.\nWe have 10 individuals with two potential outcomes for each, one is what their happiness would be if they ate chocolate ice cream, (defined as `y_chocolate` in the code below), and one is what their happiness would be if they ate vanilla ice cream (defined as `y_vanilla` in the code below). We can define the true causal effect of eating chocolate ice cream (versus vanilla) on happiness for each individual as the difference between the two (@tbl-po).\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  y_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n) \n\ndata <- data |>\n  mutate(causal_effect = y_chocolate - y_vanilla)\n\ndata\n```\n:::\n\n::: {#tbl-po .cell tbl-cap='Potential Outcomes Simulation: The causal effect of eating chocolate (versus vanilla) ice cream on happiness'}\n::: {.cell-output-display}\n`````{=html}\n<table>\n <thead>\n<tr>\n<th style=\"empty-cells: hide;border-bottom:hidden;\" colspan=\"1\"></th>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"2\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">Potential Outcomes</div></th>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"1\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">Causal Effect</div></th>\n</tr>\n  <tr>\n   <th style=\"text-align:right;\"> id </th>\n   <th style=\"text-align:right;\"> $$Y_i(\\textrm{chocolate})$$ </th>\n   <th style=\"text-align:right;\"> $$Y_i(\\textrm{vanilla})$$ </th>\n   <th style=\"text-align:right;\"> $$Y_i(\\textrm{chocolate}) - Y_i(\\textrm{vanilla})$$ </th>\n  </tr>\n </thead>\n<tbody>\n  <tr>\n   <td style=\"text-align:right;\"> 1 </td>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n   <td style=\"text-align:right;\"> 3 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2 </td>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:right;\"> 3 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 3 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:right;\"> 2 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 0 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> -1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 7 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 8 </td>\n   <td style=\"text-align:right;\"> -2 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 8 </td>\n   <td style=\"text-align:right;\"> 7 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 9 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 3 </td>\n   <td style=\"text-align:right;\"> 2 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 10 </td>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:right;\"> 1 </td>\n  </tr>\n</tbody>\n</table>\n\n`````\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ndata |>\n  summarize(\n    avg_chocolate = mean(y_chocolate),\n    avg_vanilla = mean(y_vanilla),\n    avg_causal_effect = mean(causal_effect)\n  )\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n  avg_chocolate avg_vanilla avg_causal_effect\n1           5.4         4.6               0.8\n```\n\n\n:::\n:::\n\n\nFor example, examining @tbl-po, the causal effect of eating chocolate ice cream (versus vanilla) for individual `4` is 0, whereas the causal effect for individual `9` is 2. The *average* potential happiness after eating chocolate is 5.4 and the *average* potential happiness after eating vanilla is 4.6. The *average* treatment effect of eating chocolate (versus vanilla) ice cream among the ten individuals in this study is 0.8. \n\nIn reality, we cannot observe both potential outcomes, in any moment in time, each individual in our study can only eat *one* flavor of ice cream. Suppose we let our participants choose which ice cream they wanted to eat and each choose their favorite (i.e. they knew which would make them \"happier\" and picked that one. Now what we *observe* is shown in @tbl-obs.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata_observed <- data |>\n  mutate(\n    exposure = case_when(\n      # people who like chocolate more chose that\n      y_chocolate > y_vanilla ~ \"chocolate\",\n      # people who like vanilla more chose that\n      y_vanilla >= y_chocolate ~ \"vanilla\"\n    ),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" ~ y_chocolate,\n      exposure == \"vanilla\" ~ y_vanilla\n    )\n  ) |>\n  # we can only observe the exposure and one potential outcome\n  select(id, exposure, observed_outcome)\ndata_observed\n```\n:::\n\n::: {#tbl-obs .cell tbl-cap='Potential Outcomes Simulation: The observed exposure and outcome used to estimate the effect of eating chocolate (versus vanilla) ice cream on happiness'}\n::: {.cell-output-display}\n`````{=html}\n<table>\n <thead>\n<tr>\n<th style=\"empty-cells: hide;border-bottom:hidden;\" colspan=\"1\"></th>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"1\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">Exposure</div></th>\n<th style=\"border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; \" colspan=\"1\"><div style=\"border-bottom: 1px solid #ddd; padding-bottom: 5px; \">Observed Outcome</div></th>\n</tr>\n  <tr>\n   <th style=\"text-align:right;\"> id </th>\n   <th style=\"text-align:left;\"> $$X_i$$ </th>\n   <th style=\"text-align:right;\"> $$Y_i$$ </th>\n  </tr>\n </thead>\n<tbody>\n  <tr>\n   <td style=\"text-align:right;\"> 1 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 4 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 4 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 3 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 6 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 4 </td>\n   <td style=\"text-align:left;\"> vanilla </td>\n   <td style=\"text-align:right;\"> 5 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 5 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 6 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 6 </td>\n   <td style=\"text-align:left;\"> vanilla </td>\n   <td style=\"text-align:right;\"> 6 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 7 </td>\n   <td style=\"text-align:left;\"> vanilla </td>\n   <td style=\"text-align:right;\"> 8 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 8 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 7 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 9 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 5 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 10 </td>\n   <td style=\"text-align:left;\"> chocolate </td>\n   <td style=\"text-align:right;\"> 6 </td>\n  </tr>\n</tbody>\n</table>\n\n`````\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        5.43\n2 vanilla          6.33\n```\n\n\n:::\n:::\n\n\nNow, the *observed* average outcome among those who ate chocolate ice cream is 5.4 (the same as the true average potential outcome), while the *observed* average outcome among those who ate vanilla is 6.3 -- quite different from the *actual* average (4.6). The estimated causal effect here could be calculated as 5.4 - 6.3 = -0.9. \n\nIt turns out here, these 10 participants *chose* which ice cream they wanted to eat and they always chose to eat their favorite! This artificially made it look like eating vanilla ice cream would increase the happiness in this population when in fact we know the opposite is true. The next section will discuss which assumptions need to be true in order to allow us to *accurately* estimate causal effects using observed data. As a sneak peak, our issue here was that how the exposure was decided, if instead we *randomized* who ate chocolate versus vanilla ice cream we would (on average, with a large enough sample) recover the true causal effect.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## we are doing something *random* so let's set a seed so we always observe the\n## same result each time we run the code\nset.seed(11)\ndata_observed <- data |>\n  mutate(\n    # change the exposure to randomized, generate from a binomial distribution\n    # with a probability 0.5 for being in either group \n    exposure = case_when(\n      rbinom(10, 1, 0.5) == 1 ~ \"chocolate\",\n      TRUE ~ \"vanilla\"\n    ),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" ~ y_chocolate,\n      exposure == \"vanilla\" ~ y_vanilla\n    )\n  ) |>\n  # we can only observe the exposure and one potential outcome\n  select(id, exposure, observed_outcome)\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        5.33\n2 vanilla          4.71\n```\n\n\n:::\n:::\n\n\n## Causal Assumptions {#sec-assump}\n\nLike most statistical approaches, the validity of a causal analysis depends on how well certain assumptions are met.\nAs mentioned in @sec-potential, the potential outcomes framework envisions that each individual possesses a range of potential outcomes for every conceivable value of some input.\nFor instance, as in the scenario previously described with two exposure levels (exposed: 1 and unexposed: 0), we can define potential outcomes for exposure ($Y(1)$) and no exposure ($Y(0)$), and subsequently analyze the difference between these outcomes, i.e., $Y(1) - Y(0)$, to comprehend the impact of the input (the exposure) on the outcome, $Y$.\nAt any given time, only one of these *potential outcomes* is observable -- namely, the outcome tied to the actual exposure the individual underwent.\nUnder certain assumptions, we can leverage data from individuals exposed to different inputs to compare the average differences in their observed outcomes.\nThe most common assumptions across the approaches we describe in this book are:\n\n1.  **Consistency**: We assume that the causal question you claim you are answering is consistent with the one you are *actually* answering with your analysis. Mathematically, this means that $Y_{obs} = (X)Y(1) + (1 - X)Y(0)$, in other words, the outcome you observe is exactly equal to the potential outcome under the exposure you received. Two common ways to discuss this assumption are: \n    * **Well defined exposure**: We assume that for each value of the exposure, there is no difference between subjects in the delivery of that exposure.\nPut another way, multiple versions of the treatment do not exist. \n    * **No interference**: We assume that the outcome (technically all *potential* outcomes, regardless of whether they are observed) for any subject does not depend on another subject's exposure.\n    \n::: callout-tip\n## Jargon\n\nAssumption 1 is sometimes referred to as *stable-unit-treatment-value-assumption* or SUTVA [@imbens2015causal].\nLikewise, these assumptions are sometimes referred to as *identifiability conditions* since we need them to hold in order to identify causal estimates.\n:::\n\n2.  **Exchangeability**: We assume that within levels of relevant variables (confounders), exposed and unexposed subjects have an equal likelihood of experiencing any outcome prior to exposure; i.e. the exposed and unexposed subjects are exchangeable.\nThis assumption is sometimes referred to as **no unmeasured confounding**.\n\n3.  **Positivity**: We assume that within each level and combination of the study variables used to achieve exchangeability, there are exposed and unexposed subjects.\nSaid differently, each individual has some chance of experiencing every available exposure level.\nSometimes this is referred to as the **probabilistic** assumption.\n\n\n\n\n::: callout-note\n## Apples-to-apples\n\nPractically, most of the assumptions we need to make for causal inference are so we can make an *apples-to-apples* comparison: we want to make sure we're comparing individuals that are similar --- who would serve as good proxies for each other's counterfactuals. \n\nThe phrase *apples-to-apples* stems from the saying \"comparing apples to oranges\", e.g. comparing two things that are incomparable. \n\nThat's only one way to say it. [There are a lot of variations worldwide](https://en.wikipedia.org/wiki/Apples_and_oranges). Here are some other things people incorrectly compare:\n\n* Cheese and chalk (UK English)\n* Apples and pears (German)\n* Potatoes and sweet potatoes (Latin American Spanish)\n* Grandmothers and toads (Serbian)\n* Horses and donkeys (Hindi)\n:::\n\n### Causal Assumptions Simulation\n\nLet's bring back our simulation from @sec-po-sim. Recall that we have individuals who will either eat chocolate or vanilla ice cream and we are interested in assessing the causal effect of this exposure on their happiness. Let's see how violations of each assumption may impact the estimation of the causal effect.\n\n#### Consistency violation\n\nTwo ways the consistency assumption can be violated is (1) lack of a well defined exposure and (2) interference. Let's see how these impact our ability to accurately estimate a causal effect.\n\n##### Well defined exposure\n\nSuppose that there were in fact two containers of chocolate ice cream, one of which was spoiled. Therefore, despite the fact that having an exposure \"chocolate\" could mean different things depending on where the individual's scoop came from (regular chocolate ice cream, or spoiled chocolate ice cream), we are lumping them all together under a single umbrella (hence the violation, we have \"multiple versions of treatment\"). You can see how this falls under consistency because the issue here is that the potential outcome we think we are estimating is not the one we are actually observing.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  y_spoiledchocolate = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),\n  y_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n) |>\n  mutate(causal_effect = y_chocolate - y_vanilla)\n\nset.seed(11)\ndata_observed <- data |>\n  mutate(\n    exposure_unobserved = case_when(\n      rbinom(10, 1, 0.25) == 1 ~ \"chocolate (spoiled)\",\n      rbinom(10, 1, 0.25) == 1 ~ \"chocolate\",\n      TRUE ~ \"vanilla\"\n    ),\n    observed_outcome = case_when(\n      exposure_unobserved == \"chocolate (spoiled)\" ~ y_spoiledchocolate,\n      exposure_unobserved == \"chocolate\" ~ y_chocolate,\n      exposure_unobserved == \"vanilla\" ~ y_vanilla\n    ),\n    exposure = case_when(\n      exposure_unobserved %in% c(\"chocolate (spoiled)\", \"chocolate\") ~ \"chocolate\",\n      exposure_unobserved == \"vanilla\" ~ \"vanilla\"\n    )\n  ) |>\n  select(id, exposure, observed_outcome)\n\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        2.75\n2 vanilla          4.67\n```\n\n\n:::\n:::\n\n\nWe know the *true* average causal effect of (unspoiled) chocolate in the sample is 0.8, however our estimated causal effect (because our data are not consistent with the question we are asking) is -1.9. This demonstrates what can go wrong when *well defined exposure* is violated.\n\n##### Interference \n\nInterference would mean that an individual's exposure does not impact another's potential outcome. For example, let's say each individual has a partner, and their potential outcome depends on both what flavor of ice cream they ate *and* what flavor their partner ate. For example, in the simulation below, having a partner that received a different flavor of ice cream increases the happiness by two units.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  partner_id = c(1, 1, 2, 2, 3, 3, 4, 4, 5, 5),\n  y_chocolate_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_chocolate_vanilla = c(6, 6, 8, 7, 8, 7, 8, 9, 7, 8),\n  y_vanilla_chocolate = c(3, 5, 6, 7, 7, 8, 10, 8, 5, 7),\n  y_vanilla_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n)\n\nset.seed(11)\ndata_observed <- data |>\n  mutate(\n    exposure = case_when(\n      rbinom(10, 1, 0.5) == 1 ~ \"chocolate\",\n      TRUE ~ \"vanilla\"\n    ),\n    exposure_partner =\n      c(\"vanilla\", \"vanilla\", \"vanilla\", \"chocolate\", \"chocolate\", \"vanilla\", \"vanilla\", \"vanilla\", \"vanilla\", \"chocolate\"),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" & exposure_partner == \"chocolate\" ~ y_chocolate_chocolate,\n      exposure == \"chocolate\" & exposure_partner == \"vanilla\" ~ y_chocolate_vanilla,\n      exposure == \"vanilla\" & exposure_partner == \"chocolate\" ~ y_vanilla_chocolate,\n      exposure == \"vanilla\" & exposure_partner == \"vanilla\" ~ y_vanilla_vanilla\n    )\n  ) |>\n  select(id, exposure, observed_outcome)\n\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        7.33\n2 vanilla          5.57\n```\n\n\n:::\n:::\n\nNow our estimated causal effect (because interference exists) is 1.8. This demonstrates what can go wrong when *interference* occurs. One of the main ways to combat interference is change the *unit* under consideration. Here, each individual, each unique *id*, is considered a unit, and there is interference between units (i.e. between partners). If instead we consider each *partner* as a unit and randomize the partners rather than the individuals, we solve the interference issue, as there is not interference *between* different partner sets. This is sometimes referred to as a *cluster randomized trial*. What we decide to do within each cluster may depend on the causal question at hand. For example, if we want to know what would happen if *everyone* at chocolate ice cream versus if *everyone* at vanilla, we would want to randomize both partners to either chocolate or vanilla, as seen below.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(11)\n\n## we are now randomizing the *partners* not the individuals\npartners <- data.frame(\n  partner_id = 1:5,\n  exposure = case_when(\n      rbinom(5, 1, 0.5) == 1 ~ \"chocolate\",\n      TRUE ~ \"vanilla\"\n    )\n)\ndata_observed <- data |>\n  left_join(partners, by = \"partner_id\") |>\n  mutate(\n    # all partners have the same exposure\n    exposure_partner = exposure,\n    observed_outcome = case_when(\n      exposure == \"chocolate\" & exposure_partner == \"chocolate\" ~ y_chocolate_chocolate,\n      exposure == \"vanilla\" & exposure_partner == \"vanilla\" ~ y_vanilla_vanilla\n    )\n  ) |>\n  select(id, exposure, observed_outcome)\n\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        5.5 \n2 vanilla          4.38\n```\n\n\n:::\n:::\n\n\n#### Exchangeability violation\n\nWe have actually already seen an example of an exchangeability violation in @sec-po-sim. In that example, participants were able to choose the ice cream that they wanted to eat, so people who were more likely to have a positive effect from eating chocolate chose that, and those more likely to have a positive effect from eating vanilla chose that. \n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  y_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n) \ndata_observed <- data |>\n  mutate(\n    exposure = case_when(\n      # people who like chocolate more chose that\n      y_chocolate > y_vanilla ~ \"chocolate\",\n      # people who like vanilla more chose that\n      y_vanilla >= y_chocolate ~ \"vanilla\"\n    ),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" ~ y_chocolate,\n      exposure == \"vanilla\" ~ y_vanilla\n    )\n  ) |>\n  select(id, exposure, observed_outcome)\n\ndata_observed |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(observed_outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure  avg_outcome\n  <chr>           <dbl>\n1 chocolate        5.43\n2 vanilla          6.33\n```\n\n\n:::\n:::\n\n\nHow could we correct this? If we had some people who preferred chocolate ice cream but ended up taking vanilla instead, we could *adjust* for the preference, and the effect conditioned on this would no longer have an exchangeability issue. It turns out that this example as we have constructed it doesn't lend itself to this solution because participants chose their preferred flavor 100% of the time making this *also* a positivity violation. \n\n#### Positivity violation\n\nAs stated above, the previous example violates both *exchangeability* and *positivity*. How could we fix it? As long as *some* people chose outside their preference with some probability (even if it is small!) we can remove this violation. Let's say instead of everyone picking their flavor of preference 100% of the time, they just had a 80% chance of picking that flavor.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndata <- data.frame(\n  id = 1:10,\n  y_chocolate = c(4, 4, 6, 5, 6, 5, 6, 7, 5, 6),\n  y_vanilla = c(1, 3, 4, 5, 5, 6, 8, 6, 3, 5)\n) \n\nset.seed(11)\ndata_observed <- data |>\n  mutate(\n    prefer_chocolate = y_chocolate > y_vanilla ,\n    exposure = case_when(\n      # people who like chocolate more chose that 80% of the time\n      prefer_chocolate ~ ifelse(rbinom(10, 1, 0.8), \"chocolate\", \"vanilla\"),\n      # people who like vanilla more chose that 80% of the time\n      !prefer_chocolate ~ ifelse(rbinom(10, 1, 0.8), \"vanilla\", \"chocolate\")\n    ),\n    observed_outcome = case_when(\n      exposure == \"chocolate\" ~ y_chocolate,\n      exposure == \"vanilla\" ~ y_vanilla\n    )\n  ) |>\n  select(id, prefer_chocolate, exposure, observed_outcome)\n\nlm(observed_outcome ~ I(exposure == \"chocolate\") + prefer_chocolate, \n   data_observed)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\nCall:\nlm(formula = observed_outcome ~ I(exposure == \"chocolate\") + \n    prefer_chocolate, data = data_observed)\n\nCoefficients:\n                   (Intercept)  \n                         6.156  \nI(exposure == \"chocolate\")TRUE  \n                         0.531  \n          prefer_chocolateTRUE  \n                        -1.469  \n```\n\n\n:::\n:::\n\nAfter *adjusting* for this variable (chocolate preference), we recover the correct causal effect. This value is not exactly the same as the truth we obtain with the (unobservable) potential outcomes because we are dealing with a small sample -- as our sample size increases this will get closer to the truth.\n\nCausal assumptions can be difficult to verify and may not hold for many data collection strategies.\nWe cannot overstate the importance of checking these criteria to the extent possible!\nFollowing any of the recipes in this book are unlikely to give valid answers if the causal assumptions are badly violated.\n",
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-    "markdown": "# Target Trials and Standard Methods {#sec-trials-std}\n\n\n\n\n\n\n## Randomized trials {#sec-rand-trials}\n\nA *randomized trial* is one where the exposure (cause) of interest is *randomly assigned*.\n\n::: callout-note\nIn this book, we refer to analyses where the exposure is randomly assigned as a *randomized trial*, sometimes this is called an A/B test.\n:::\n\nWhy does randomization help?\nLooking at our assumptions in @sec-assump, randomized trials solve the well defined exposure portion of consistency by default -- the exposure is exactly what is randomized.\nLikewise for positivity; if we have randomly assigned folks to either the exposed or unexposed groups, we know the probability of assignment (and we know it is not exactly 0 or 1).\nRandomization alone does not solve the interference portion of consistency (for example, if we randomize some people to receive a vaccine for a communicable disease, their receiving it could lower the chance of those around them contracting the infectious disease because it changes the probability of exposure).\n*Ideal* randomized trials resolves the issue of exchangeability because the exposed and unexposed populations (in the limit) are inherently the same since their exposure status was determined by a random process (not by any factors that might make them different from each other).\nGreat!\nIn reality, we often see this assumption violated by issues such as *drop out* or *non-adherence* in randomized trials.\nIf there is differential drop out between exposure groups (for example, if participants randomly assigned to the treatment are more likely to drop out of a study, and thus we don't observe their outcome), then the observed exposure groups are no longer *exchangeable*.\nTherefore, in @tbl-assump-solved we have two columns, one for the *ideal* randomized trial (where adherence is assumed to be perfect and no participants drop out) and one for *realistic* randomized trials where this may not be so.\n\n| Assumption            | Ideal Randomized Trial | Realistic Randomized Trial | Observational Study  |\n|-----------------|-----------------|---------------------|-------------------|\n| Consistency (Well defined exposure) | 😄   | 😄       | 🤷 |\n| Consistency (No interference)       | 🤷   | 🤷       | 🤷 |\n| Positivity            | 😄   | 😄       | 🤷 |\n| Exchangeability       | 😄   | 🤷       | 🤷 |\n\n: Assumptions solved by study design. 😄 indicates it is solved by default, 🤷 indicates that it is *solvable* but not solved by default. {#tbl-assump-solved}\n\nWhen designing a study, the first step is asking an appropriate *causal question*. We then can map this question to a *protocol*, consisting of the following seven elements, as defined by @hernan2016using:\n\n* Eligibility criteria\n* Exposure definition\n* Assignment procedures\n* Follow-up period\n* Outcome definition\n* Causal contrast of interest\n* Analysis plan\n\nIn @tbl-protocol we map each of these elements to the corresponding assumption that it can address. For example, exchangeability can be addressed by the eligibility criteria (we can restrict our study to only participants for whom exposure assignment is exchangeable), assignment procedure (we could use random exposure assignment to ensure exchangeability), follow-up period (we can be sure to choose an appropriate start time for our follow-up period to ensure that we are not inducing bias -- we'll think more about this in a future chapter), and/or the analysis plan (we can adjust for any factors that would cause those in different exposure groups to lack exchangeability). \n\n<!-- TODO: link to the chapter where we talk about time zero? immortal time bias etc? is that now? -->\n\nAssumption | Eligibility Criteria | Exposure Definition| Assignment Procedures | Follow-up Period | Outcome Definition | Causal contrast | Analysis Plan\n------------|----------------- | ------------------|---------|----------|----------|--------- | -------\nConsistency (Well defined exposure) | |✔️|| | |\nConsistency (No interference) | | ✔️|✔️ | | ✔️ | | ✔️\nPositivity |✔️||✔️|| | | ✔️\nExchangeability |✔️||✔️|✔️|| | ✔️\n: Mapping assumptions to elements of a study protocol {#tbl-protocol}\n\nRecall our diagrams from @sec-diag (@fig-diagram-4); several of these protocol elements can be mapped to these diagrams when we are attempting to define our causal question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example diagram mapped to causal analysis terminology](chapter-04_files/figure-html/fig-diagram-4-1.png){#fig-diagram-4 width=672}\n:::\n:::\n\n\n\n## Target Trials\n\nThere are many reasons why randomization may not be possible. For example, it might not be ethical to randomly assign people to a particular exposure, there may not be funding available to run a randomized trial, or there might not be enough time to conduct a full trial. In these situations, we rely on observational data to help us answer causal questions by implementing a *target trial*. A *target trial* answers: What experiment would you design if you could? \n\n## Causal inference with `group_by()` and `summarize()` {#sec-group-sum}\n\nLet's suppose we are trying to estimate a causal effect of an exposure on an outcome, but the exposure is not *randomized*, in fact, there is a common cause of the exposure and outcome, making the exposed and unexposed groups *not exchangeable* without adjustment (violating the fourth assumption in @sec-assump).\n\n::: callout-note\nA **confounder** is a common cause of exposure and outcome.\n:::\n\n### One binary confounder\n\nLet's suppose this confounder is binary, see the simulation below:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(1)\nn <- 10000\nsim <- tibble(\n  # generate the confounder from a binomial distribution\n  # with a probability 0.5 for being in either group\n  confounder = rbinom(n, 1, 0.5),\n  # make the probability of exposure dependent on the\n  # confounder value\n  p_exposure = case_when(\n    confounder == 1 ~ 0.75,\n    confounder == 0 ~ 0.25\n  ),\n  # generate the exposure from a binomial distribution\n  # with the probability of exposure dependent on the confounder\n  exposure = rbinom(n, 1, p_exposure),\n  # generate the \"true\" average treatment effect of 0\n  # to do this, we are going to generate the potential outcomes, first\n  # the potential outcome if exposure = 0\n  # (notice exposure is not in the equation below, only the confounder)\n  # we use rnorm(n) to add the random error term that is normally\n  # distributed with a mean of 0 and a standard deviation of 1\n  y0 = confounder + rnorm(n),\n  # because the true effect is 0, the potential outcome if exposure = 1\n  # is identical\n  y1 = y0,\n  # now, in practice we will only see one of these, outcome is what is\n  # observed\n  outcome = (1 - exposure) * y0 + exposure * y1,\n  observed_potential_outcome = case_when(\n    exposure == 0 ~ \"y0\",\n    exposure == 1 ~ \"y1\"\n  )\n)\n```\n:::\n\n\nHere we have one binary `confounder`, the probability that `confounder = 1` is `0.5`.\nThe probability of the being exposed is `0.75` for those for whom `confounder = 1` `0.25` for those for whom `confounder = 0`.\nThere is no effect of the `exposure` on the `outcome` (the true causal effect is 0); the `outcome` effect is fully dependent on the `confounder`. In this simulation we generate the potential outcomes to drive home our assumptions; many of our simulations in this book will skip this step.\nLet's look at this generated data frame.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim |>\n  select(confounder, exposure, outcome, observed_potential_outcome)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 10,000 × 4\n   confounder exposure outcome observed_potential_out…¹\n        <int>    <int>   <dbl> <chr>                   \n 1          0        0 -0.804  y0                      \n 2          0        0 -1.06   y0                      \n 3          1        1 -0.0354 y1                      \n 4          1        1 -0.186  y1                      \n 5          0        0 -0.500  y0                      \n 6          1        1  0.475  y1                      \n 7          1        0  0.698  y0                      \n 8          1        0  1.47   y0                      \n 9          1        0  0.752  y0                      \n10          0        0  1.26   y0                      \n# ℹ 9,990 more rows\n# ℹ abbreviated name: ¹​observed_potential_outcome\n```\n\n\n:::\n:::\n\n\nGreat! Let's begin by proving to ourselves that this violates the exchangeability assumption. Recall from @sec-assump:\n\n> **Exchangeability**: We assume that within levels of relevant variables (confounders), exposed and unexposed subjects have an equal likelihood of experiencing any outcome prior to exposure; i.e. the exposed and unexposed subjects are exchangeable. This assumption is sometimes referred to as **no unmeasured confounding**.\n\nNow, let's try to estimate the effect of the `exposure` on the `outcome` assuming the two exposed groups are exchangeable.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  exposure avg_outcome\n     <int>       <dbl>\n1        0       0.228\n2        1       0.756\n```\n\n\n:::\n:::\n\n\nThe average outcome among the exposed is `0.76` and among the unexposed `0.23`, yielding an average effect of the exposure of `0.76-0.23=0.53`.\nLet's do a little R work to get there.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_outcome,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 1\n  estimate\n     <dbl>\n1    0.528\n```\n\n\n:::\n:::\n\n\nOk, so assuming the exposure groups are exchangeable (and assuming the rest of the assumptions from @sec-assump hold), we estimate the effect of the exposure on the outcome to be 0.53.\nWe *know* the exchaneability assumption is violated based on how we simulated the data.\nHow can we estimate an unbiased effect?\nThe easiest way to do so is to estimate the effect within each confounder class.\nThis will work because folks with the same value of the confounder have an equal probability of exposure.\nInstead of just grouping by the exposure, let's group by the confounder as well:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim |>\n  group_by(confounder, exposure) |>\n  summarise(avg_outcome = mean(outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n# Groups:   confounder [2]\n  confounder exposure avg_outcome\n       <int>    <int>       <dbl>\n1          0        0    -0.0185 \n2          0        1     0.00954\n3          1        0     0.994  \n4          1        1     1.01   \n```\n\n\n:::\n:::\n\n\nWe can now calculate the average effect within each confounder class as well as the overall causal effect.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## Average effect within each confounder class\nsim |>\n  group_by(confounder, exposure) |>\n  summarise(avg_outcome = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_outcome,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n  confounder estimate\n       <int>    <dbl>\n1          0   0.0280\n2          1   0.0122\n```\n\n\n:::\n\n```{.r .cell-code}\n## Overall average effect\n\nsim |>\n  group_by(confounder, exposure) |>\n  summarise(avg_outcome = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_outcome,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0) |>\n  summarise(mean(estimate)) # note, we would need to weight this if the confounder groups were not equal sized\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 1\n  `mean(estimate)`\n             <dbl>\n1           0.0201\n```\n\n\n:::\n:::\n\n\nGreat!\nNow our estimate is much closer to the true value (0).\n\n::: callout\nThe method we are using to solve the fact that our two groups are not exchangeable is known as **stratification**. We are *stratifying* by the confounder(s) and estimating the causal effect within each stratum. To get an overall average causal effect we are averaging across the strata. This can be a great tool if there are very few confounders, however it can suffer from the curse of dimensionality as the number of confounders as well as the number of levels within each confounder increases.\n:::\n\n### Two binary confounders\n\nLet's extend this to two binary confounders.\nThe simulation below now has two binary confounders.\nThe true causal effect of the `exposure` on the `outcome` is still 0.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(1)\nn <- 10000\nsim2 <- tibble(\n  confounder_1 = rbinom(n, 1, 0.5),\n  confounder_2 = rbinom(n, 1, 0.5),\n  p_exposure = case_when(\n    confounder_1 == 1 & confounder_2 == 1 ~ 0.75,\n    confounder_1 == 0 & confounder_2 == 1 ~ 0.9,\n    confounder_1 == 1 & confounder_2 == 0 ~ 0.2,\n    confounder_1 == 0 & confounder_2 == 0 ~ 0.1,\n  ),\n  exposure = rbinom(n, 1, p_exposure),\n  outcome = confounder_1 + confounder_2 + rnorm(n)\n)\n```\n:::\n\n\nNow we are going to group by both confounders and estimate the causal effect within each stratum.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim2 |>\n  group_by(confounder_1, confounder_2, exposure) |>\n  summarise(avg_y = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_y,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n# Groups:   confounder_1 [2]\n  confounder_1 confounder_2 estimate\n         <int>        <int>    <dbl>\n1            0            0  -0.122 \n2            0            1   0.0493\n3            1            0   0.0263\n4            1            1   0.0226\n```\n\n\n:::\n:::\n\n\nWe can also estimate the overall causal effect:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim2 |>\n  group_by(confounder_1, confounder_2, exposure) |>\n  summarise(avg_outcome = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_outcome,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0, .groups = \"drop\") |>\n  summarise(mean(estimate))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 1\n  `mean(estimate)`\n             <dbl>\n1         -0.00594\n```\n\n\n:::\n:::\n\n\n### Continuous confounder\n\nSo far our strategy has been to estimate the causal effect within strata where the individuals are *exchangeable*.\nHow could we extend this to a continuous confounder?\nLet's create another simulation:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(1)\nn <- 10000\nsim3 <- tibble(\n  confounder = rnorm(n),\n  p_exposure = exp(confounder) / (1 + exp(confounder)),\n  exposure = rbinom(n, 1, p_exposure),\n  outcome = confounder + rnorm(n)\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsim3\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 10,000 × 4\n   confounder p_exposure exposure outcome\n        <dbl>      <dbl>    <int>   <dbl>\n 1     -0.626      0.348        0 -0.840 \n 2      0.184      0.546        1  0.0769\n 3     -0.836      0.302        0 -1.30  \n 4      1.60       0.831        1  0.911 \n 5      0.330      0.582        1 -0.461 \n 6     -0.820      0.306        0 -1.16  \n 7      0.487      0.620        1 -0.780 \n 8      0.738      0.677        1 -0.656 \n 9      0.576      0.640        1  0.995 \n10     -0.305      0.424        0  2.91  \n# ℹ 9,990 more rows\n```\n\n\n:::\n:::\n\n\nIf we want to still use `group_by` and `summarise`, we could bin the continuous confounder, for example using it's quintiles, and estimate the causal effect within each bin:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim3 |>\n  mutate(confounder_q = ntile(confounder, 5)) |>\n  group_by(confounder_q, exposure) |>\n  summarise(avg_y = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_y,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 5 × 2\n  confounder_q estimate\n         <int>    <dbl>\n1            1   0.237 \n2            2   0.0622\n3            3   0.0264\n4            4   0.133 \n5            5   0.178 \n```\n\n\n:::\n\n```{.r .cell-code}\nsim3 |>\n  mutate(confounder_q = ntile(confounder, 5)) |>\n  group_by(confounder_q, exposure) |>\n  summarise(avg_y = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_y,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0) |>\n  summarise(estimate = mean(estimate))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 1\n  estimate\n     <dbl>\n1    0.127\n```\n\n\n:::\n:::\n\n\nGreat!\nNow, in practice we have many more than one or two binary confounders, we often have many many confounders, both continuous and binary.\nAttempting to estimate the causal effect within each unique stratum would be very inefficient (and often not possible!).\nWhat if instead we could create a single summary score for all of the confounders?\nHold onto this thought, we'll bring it up again when we introduce *propensity scores*.\n\n## When do standard methods succeed and fail? {#sec-standard}\n\nWhen teaching these topics, we are often asked when \"standard methods\" will succeed, i.e.: when can we just fit a linear regression model to estimate a causal effect?\nLet's start with the easiest example: the exposure is *randomized*.\nAs discussed in @sec-rand-trials, randomization ensures comparability and can simplify the methods needed to estimate a causal effect.\nIn the presence of a randomized exposure (assuming perfect adherence to the exposure assigned, no one dropped out of the study, etc.), simple tools like regression can be used to estimate a causal effect.\n\n### When correlation is causation\n\nWhen you have no confounders and there is a linear relationship between the exposure and the outcome, that *correlation is a causal relationship*.\nEven in these cases, using the methods you will learn in this book can help.\n\n1.  Adjusting for baseline covariates can make an estimate *more efficient*\n2.  Propensity score weighting is *more efficient* that direct adjustment\n3.  Sometimes we are *more comfortable with the functional form of the propensity score* (predicting exposure) than the outcome model\n\nLet's look at an example.\nI am going to simulate 100 observations.\nHere the treatment is randomly assigned and there are two baseline covariates: `age` and `weight`.\nOn average, the treatment causes a one unit increase in the outcome (this is called the *average treatment effect*, we will talk more about this quantity in future chapters).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nset.seed(10)\nn <- 100\nd <- tibble(\n  age = rnorm(n, 55, 20),\n  weight = rnorm(n),\n  # generate the treatment from a binomial distribution\n  # with the probability of treatment = 0.5\n  treatment = rbinom(n, 1, 0.5),\n  # generate the average causal effect of treatment: 1\n  y = 1 * treatment + 0.2 * age + 0.2 * weight + rnorm(n)\n)\n```\n:::\n\n\nWe can draw a causal diagram of the relationship described in the code above (@fig-diag).\n@sec-dags contains more information on these causal diagrams, but briefly, the arrows denote causal relationships, so since we have established that the treatment causes an increase in the outcome (an average treatment effect of 1), we see an arrow from `trt` to `y` in this diagram.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Causal Diagram of Example Randomized Study](chapter-04_files/figure-html/fig-diag-1.png){#fig-diag width=672}\n:::\n:::\n\n\nLet's examine three models: (1) an unadjusted model (@tbl-panel-1), (2) a linear model that adjusts for the baseline covariates (@tbl-panel-2), and (3) a propensity score weighted model (@tbl-panel-3).\n\n::: {#tbl-panel layout-ncol=\"2\"}\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nlibrary(gtsummary)\nlm(y ~ treatment, d) |>\n  tbl_regression() |>\n  modify_column_unhide(column = std.error) |>\n  modify_caption(\"Unadjusted regression\")\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"dnecmmlmpz\" 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border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#dnecmmlmpz .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#dnecmmlmpz .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#dnecmmlmpz .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#dnecmmlmpz .gt_left {\n  text-align: left;\n}\n\n#dnecmmlmpz .gt_center {\n  text-align: center;\n}\n\n#dnecmmlmpz .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#dnecmmlmpz .gt_font_normal {\n  font-weight: normal;\n}\n\n#dnecmmlmpz .gt_font_bold {\n  font-weight: bold;\n}\n\n#dnecmmlmpz .gt_font_italic {\n  font-style: italic;\n}\n\n#dnecmmlmpz .gt_super {\n  font-size: 65%;\n}\n\n#dnecmmlmpz .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#dnecmmlmpz .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#dnecmmlmpz .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#dnecmmlmpz .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#dnecmmlmpz .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#dnecmmlmpz .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#dnecmmlmpz .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <caption>Unadjusted regression</caption>\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Beta&lt;/strong&gt;\"><strong>Beta</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;SE&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>SE</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>95% CI</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;p-value&lt;/strong&gt;\"><strong>p-value</strong></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">treatment</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.93</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.803</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">-0.66, 2.5</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\">0.2</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"5\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> SE = Standard Error, CI = Confidence Interval</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nlm(y ~ treatment + age + weight, d) |>\n  tbl_regression() |>\n  modify_column_unhide(column = std.error) |>\n  modify_caption(\"Adjusted regression\")\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"jiuuhdyjhp\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#jiuuhdyjhp table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#jiuuhdyjhp thead, #jiuuhdyjhp tbody, #jiuuhdyjhp tfoot, #jiuuhdyjhp tr, #jiuuhdyjhp td, #jiuuhdyjhp th {\n  border-style: none;\n}\n\n#jiuuhdyjhp p {\n  margin: 0;\n  padding: 0;\n}\n\n#jiuuhdyjhp .gt_table {\n  display: table;\n  border-collapse: collapse;\n  line-height: normal;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  font-style: normal;\n  background-color: #FFFFFF;\n  width: auto;\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #A8A8A8;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #A8A8A8;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_caption {\n  padding-top: 4px;\n  padding-bottom: 4px;\n}\n\n#jiuuhdyjhp .gt_title {\n  color: #333333;\n  font-size: 125%;\n  font-weight: initial;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-color: #FFFFFF;\n  border-bottom-width: 0;\n}\n\n#jiuuhdyjhp .gt_subtitle {\n  color: #333333;\n  font-size: 85%;\n  font-weight: initial;\n  padding-top: 3px;\n  padding-bottom: 5px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-color: #FFFFFF;\n  border-top-width: 0;\n}\n\n#jiuuhdyjhp .gt_heading {\n  background-color: #FFFFFF;\n  text-align: center;\n  border-bottom-color: #FFFFFF;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_bottom_border {\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_col_headings {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_col_heading {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: normal;\n  text-transform: inherit;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: bottom;\n  padding-top: 5px;\n  padding-bottom: 6px;\n  padding-left: 5px;\n  padding-right: 5px;\n  overflow-x: hidden;\n}\n\n#jiuuhdyjhp .gt_column_spanner_outer {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: normal;\n  text-transform: inherit;\n  padding-top: 0;\n  padding-bottom: 0;\n  padding-left: 4px;\n  padding-right: 4px;\n}\n\n#jiuuhdyjhp .gt_column_spanner_outer:first-child {\n  padding-left: 0;\n}\n\n#jiuuhdyjhp .gt_column_spanner_outer:last-child {\n  padding-right: 0;\n}\n\n#jiuuhdyjhp .gt_column_spanner {\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  vertical-align: bottom;\n  padding-top: 5px;\n  padding-bottom: 5px;\n  overflow-x: hidden;\n  display: inline-block;\n  width: 100%;\n}\n\n#jiuuhdyjhp .gt_spanner_row {\n  border-bottom-style: hidden;\n}\n\n#jiuuhdyjhp .gt_group_heading {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: middle;\n  text-align: left;\n}\n\n#jiuuhdyjhp .gt_empty_group_heading {\n  padding: 0.5px;\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  vertical-align: middle;\n}\n\n#jiuuhdyjhp .gt_from_md > :first-child {\n  margin-top: 0;\n}\n\n#jiuuhdyjhp .gt_from_md > :last-child {\n  margin-bottom: 0;\n}\n\n#jiuuhdyjhp .gt_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  margin: 10px;\n  border-top-style: solid;\n  border-top-width: 1px;\n  border-top-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: middle;\n  overflow-x: hidden;\n}\n\n#jiuuhdyjhp .gt_stub {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-right-style: solid;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#jiuuhdyjhp .gt_stub_row_group {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-right-style: solid;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  padding-left: 5px;\n  padding-right: 5px;\n  vertical-align: top;\n}\n\n#jiuuhdyjhp .gt_row_group_first td {\n  border-top-width: 2px;\n}\n\n#jiuuhdyjhp .gt_row_group_first th {\n  border-top-width: 2px;\n}\n\n#jiuuhdyjhp .gt_summary_row {\n  color: #333333;\n  background-color: #FFFFFF;\n  text-transform: inherit;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#jiuuhdyjhp .gt_first_summary_row {\n  border-top-style: solid;\n  border-top-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_first_summary_row.thick {\n  border-top-width: 2px;\n}\n\n#jiuuhdyjhp .gt_last_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_grand_summary_row {\n  color: #333333;\n  background-color: #FFFFFF;\n  text-transform: inherit;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#jiuuhdyjhp .gt_first_grand_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-style: double;\n  border-top-width: 6px;\n  border-top-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_last_grand_summary_row_top {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: double;\n  border-bottom-width: 6px;\n  border-bottom-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_striped {\n  background-color: rgba(128, 128, 128, 0.05);\n}\n\n#jiuuhdyjhp .gt_table_body {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_footnotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#jiuuhdyjhp .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#jiuuhdyjhp .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#jiuuhdyjhp .gt_left {\n  text-align: left;\n}\n\n#jiuuhdyjhp .gt_center {\n  text-align: center;\n}\n\n#jiuuhdyjhp .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#jiuuhdyjhp .gt_font_normal {\n  font-weight: normal;\n}\n\n#jiuuhdyjhp .gt_font_bold {\n  font-weight: bold;\n}\n\n#jiuuhdyjhp .gt_font_italic {\n  font-style: italic;\n}\n\n#jiuuhdyjhp .gt_super {\n  font-size: 65%;\n}\n\n#jiuuhdyjhp .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#jiuuhdyjhp .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#jiuuhdyjhp .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#jiuuhdyjhp .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#jiuuhdyjhp .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#jiuuhdyjhp .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#jiuuhdyjhp .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <caption>Adjusted regression</caption>\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Beta&lt;/strong&gt;\"><strong>Beta</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;SE&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>SE</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>95% CI</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;p-value&lt;/strong&gt;\"><strong>p-value</strong></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">treatment</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">1.0</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.204</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.59, 1.4</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">age</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.20</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.005</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.19, 0.22</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">weight</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.34</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.106</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.13, 0.55</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\">0.002</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"5\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> SE = Standard Error, CI = Confidence Interval</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nd |>\n  mutate(\n    p = glm(treatment ~ weight + age, data = d) |> predict(type = \"response\"),\n    ate = treatment / p + (1 - treatment) / (1 - p)\n  ) |>\n  as.data.frame() -> d\nlibrary(PSW)\ndf <- as.data.frame(d)\nx <- psw(df,\n  \"treatment ~ weight + age\",\n  weight = \"ATE\", wt = TRUE,\n  out.var = \"y\"\n)\ntibble(\n  Characteristic = \"treatment\",\n  Beta = round(x$est.wt, 1),\n  SE = round(x$std.wt, 3),\n  `95% CI` = glue::glue(\"{round(x$est.wt - 1.96 * x$std.wt, 1)}, {round(x$est.wt + 1.96 * x$std.wt, 1)}\"),\n  `p-value` = \"<0.001\"\n) |>\n  knitr::kable(caption = \"Propensity score weighted regression\")\n```\n\n::: {.cell-output-display}\n\n\nTable: Propensity score weighted regression\n\n|Characteristic | Beta|    SE|95% CI   |p-value |\n|:--------------|----:|-----:|:--------|:-------|\n|treatment      |    1| 0.202|0.6, 1.4 |<0.001  |\n\n\n:::\n:::\n\n\nThree ways to estimate the causal effect.\n:::\n\nLooking at the three outputs in @tbl-panel, we can first notice that all three are \"unbiased\" estimates of the causal effect (we know the true average treatment effect is 1, based on our simulation) -- the estimated causal effect in each table is in the `Beta` column.\nGreat, so all methods give us an unbiased estimate.\nNext, let's look at the `SE` (standard error) column along with the `95% CI` (confidence interval) column.\nNotice the unadjusted model has a *wider* confidence interval (in fact, in this case the confidence interval includes the null, 0) -- this means if we were to use this method, even though we were able to estimate an unbiased causal effect, we would often conclude that we *fail to reject the null* that relationship between the treatment and outcome is 0.\nIn statistical terms, we refer to this as a *lack of efficiency*.\nLooking at the adjusted analysis in @tbl-panel-2, we see that the standard error is quite a bit smaller (and likewise the confidence interval is tighter, no longer including the null).\nEven though our baseline covariates `age` and `weight` were not *confounders* adjusting from them *increased the precision* of our result (this is a good thing! We want estimates that are both unbiased *and* precise).\nFinally, looking at the propensity score weighted estimate we can see that our precision was slightly improved compared to the adjusted result (0.202 compared to 0.204).\nThe magnitude of this improvement will depend on several factors, but it has been shown mathematically that using propensity scores like this to adjust for baseline factors in a randomized trial will *always* improve precision [@williamson2014variance].\nWhat can we learn from this small demonstration?\nEven in the perfect scenario, where we can estimate unbiased results without using propensity scores, the methods we will show here can be useful.\nThe utility of these methods only increases when exploring more complex examples, such as situations where the effect is *not* randomized, the introduction of time-varying confounders, etc.\n\nWhat if we did not have a randomized exposure?\nThere are many cases where randomization to a treatment is not ethical or feasible.\nStandard methods can still estimate unbiased effects, but more care needs to be given to the previously mentioned assumptions (@tbl-assump-solved).\nFor example, we need the exposed an unexposed groups to be *exchangeable*; this means we must adjust for *all confounders* with their correct functional form.\nIf everything is simple and linear (and there is no effect heterogeneity, that is everyone's causal effect is the same regardless of their baseline factors), then a simple regression model that adjusts for the confounders can give you an unbiased result.\nLet's look at a simple example such as this.\nNotice in the simulation below, the main difference compared to the above simulation is that the probability of treatment assignment is no longer 0.5 as it was above, but now dependent on the participants `age` and `weight`. For example, maybe doctors tend to prescribe a certain treatment to patients who are older and who weigh more.\nThe true causal effect is still 1, but now we have two confounders, `age` and `weight` (@fig-diag-2).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(7)\nn <- 100000\nd <- tibble(\n  age = rnorm(n, 55, 20),\n  weight = rnorm(n),\n  # generate the treatment from a binomial distribution\n  # with the probability of treatment dependent on the age and weight\n  treatment = rbinom(n, 1, 1 / (1 + exp(-0.01 * age - weight))),\n  ## generate the true average causal effect of the treatment: 1\n  y = 1 * treatment + 0.2 * age + 0.2 * weight + rnorm(n)\n)\n```\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n![Causal Diagram of Example Observation Study](chapter-04_files/figure-html/fig-diag-2-1.png){#fig-diag-2 width=672}\n:::\n:::\n\n\n::: {#tbl-panel-2 layout-ncol=\"2\"}\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nlm(y ~ treatment, d) |>\n  tbl_regression() |>\n  modify_column_unhide(column = std.error) |>\n  modify_caption(\"Unadjusted regression\")\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"lfkdhkjrur\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#lfkdhkjrur table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#lfkdhkjrur thead, #lfkdhkjrur tbody, #lfkdhkjrur tfoot, #lfkdhkjrur tr, #lfkdhkjrur td, #lfkdhkjrur th {\n  border-style: none;\n}\n\n#lfkdhkjrur p {\n  margin: 0;\n  padding: 0;\n}\n\n#lfkdhkjrur .gt_table {\n  display: table;\n  border-collapse: collapse;\n  line-height: normal;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  font-style: normal;\n  background-color: #FFFFFF;\n  width: auto;\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #A8A8A8;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #A8A8A8;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_caption {\n  padding-top: 4px;\n  padding-bottom: 4px;\n}\n\n#lfkdhkjrur .gt_title {\n  color: #333333;\n  font-size: 125%;\n  font-weight: initial;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-color: #FFFFFF;\n  border-bottom-width: 0;\n}\n\n#lfkdhkjrur .gt_subtitle {\n  color: #333333;\n  font-size: 85%;\n  font-weight: initial;\n  padding-top: 3px;\n  padding-bottom: 5px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-color: #FFFFFF;\n  border-top-width: 0;\n}\n\n#lfkdhkjrur .gt_heading {\n  background-color: #FFFFFF;\n  text-align: center;\n  border-bottom-color: #FFFFFF;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_bottom_border {\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_col_headings {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_col_heading {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: normal;\n  text-transform: inherit;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: bottom;\n  padding-top: 5px;\n  padding-bottom: 6px;\n  padding-left: 5px;\n  padding-right: 5px;\n  overflow-x: hidden;\n}\n\n#lfkdhkjrur .gt_column_spanner_outer {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: normal;\n  text-transform: inherit;\n  padding-top: 0;\n  padding-bottom: 0;\n  padding-left: 4px;\n  padding-right: 4px;\n}\n\n#lfkdhkjrur .gt_column_spanner_outer:first-child {\n  padding-left: 0;\n}\n\n#lfkdhkjrur .gt_column_spanner_outer:last-child {\n  padding-right: 0;\n}\n\n#lfkdhkjrur .gt_column_spanner {\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  vertical-align: bottom;\n  padding-top: 5px;\n  padding-bottom: 5px;\n  overflow-x: hidden;\n  display: inline-block;\n  width: 100%;\n}\n\n#lfkdhkjrur .gt_spanner_row {\n  border-bottom-style: 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vertical-align: middle;\n}\n\n#lfkdhkjrur .gt_from_md > :first-child {\n  margin-top: 0;\n}\n\n#lfkdhkjrur .gt_from_md > :last-child {\n  margin-bottom: 0;\n}\n\n#lfkdhkjrur .gt_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  margin: 10px;\n  border-top-style: solid;\n  border-top-width: 1px;\n  border-top-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: middle;\n  overflow-x: hidden;\n}\n\n#lfkdhkjrur .gt_stub {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-right-style: solid;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_stub_row_group {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-right-style: solid;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  padding-left: 5px;\n  padding-right: 5px;\n  vertical-align: top;\n}\n\n#lfkdhkjrur .gt_row_group_first td {\n  border-top-width: 2px;\n}\n\n#lfkdhkjrur .gt_row_group_first th {\n  border-top-width: 2px;\n}\n\n#lfkdhkjrur .gt_summary_row {\n  color: #333333;\n  background-color: #FFFFFF;\n  text-transform: inherit;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_first_summary_row {\n  border-top-style: solid;\n  border-top-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_first_summary_row.thick {\n  border-top-width: 2px;\n}\n\n#lfkdhkjrur .gt_last_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_grand_summary_row {\n  color: #333333;\n  background-color: #FFFFFF;\n  text-transform: inherit;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_first_grand_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-style: double;\n  border-top-width: 6px;\n  border-top-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_last_grand_summary_row_top {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: double;\n  border-bottom-width: 6px;\n  border-bottom-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_striped {\n  background-color: rgba(128, 128, 128, 0.05);\n}\n\n#lfkdhkjrur .gt_table_body {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_footnotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_left {\n  text-align: left;\n}\n\n#lfkdhkjrur .gt_center {\n  text-align: center;\n}\n\n#lfkdhkjrur .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#lfkdhkjrur .gt_font_normal {\n  font-weight: normal;\n}\n\n#lfkdhkjrur .gt_font_bold {\n  font-weight: bold;\n}\n\n#lfkdhkjrur .gt_font_italic {\n  font-style: italic;\n}\n\n#lfkdhkjrur .gt_super {\n  font-size: 65%;\n}\n\n#lfkdhkjrur .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#lfkdhkjrur .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#lfkdhkjrur .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#lfkdhkjrur .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#lfkdhkjrur .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#lfkdhkjrur .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#lfkdhkjrur .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <caption>Unadjusted regression</caption>\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Beta&lt;/strong&gt;\"><strong>Beta</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;SE&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>SE</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>95% CI</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;p-value&lt;/strong&gt;\"><strong>p-value</strong></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">treatment</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">1.8</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.027</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">1.8, 1.9</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"5\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> SE = Standard Error, CI = Confidence Interval</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nlm(y ~ treatment + age + weight, d) |>\n  tbl_regression() |>\n  modify_column_unhide(column = std.error) |>\n  modify_caption(\"Adjusted regression\")\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"ilyrfrwadb\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#ilyrfrwadb table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#ilyrfrwadb thead, #ilyrfrwadb tbody, #ilyrfrwadb tfoot, 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.gt_striped {\n  background-color: rgba(128, 128, 128, 0.05);\n}\n\n#ilyrfrwadb .gt_table_body {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#ilyrfrwadb .gt_footnotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#ilyrfrwadb .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#ilyrfrwadb .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#ilyrfrwadb .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#ilyrfrwadb .gt_left {\n  text-align: left;\n}\n\n#ilyrfrwadb .gt_center {\n  text-align: center;\n}\n\n#ilyrfrwadb .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#ilyrfrwadb .gt_font_normal {\n  font-weight: normal;\n}\n\n#ilyrfrwadb .gt_font_bold {\n  font-weight: bold;\n}\n\n#ilyrfrwadb .gt_font_italic {\n  font-style: italic;\n}\n\n#ilyrfrwadb .gt_super {\n  font-size: 65%;\n}\n\n#ilyrfrwadb .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#ilyrfrwadb .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#ilyrfrwadb .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#ilyrfrwadb .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#ilyrfrwadb .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#ilyrfrwadb .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#ilyrfrwadb .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <caption>Adjusted regression</caption>\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Beta&lt;/strong&gt;\"><strong>Beta</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;SE&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>SE</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>95% CI</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;p-value&lt;/strong&gt;\"><strong>p-value</strong></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">treatment</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.99</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.007</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.98, 1.0</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">age</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.20</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.000</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.20, 0.20</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">weight</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.20</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.003</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.20, 0.21</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"5\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> SE = Standard Error, CI = Confidence Interval</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nd |>\n  mutate(\n    p = glm(treatment ~ weight + age, data = d, family = binomial) |> predict(type = \"response\"),\n    ate = treatment / p + (1 - treatment) / (1 - p)\n  ) |>\n  as.data.frame() -> d\nlibrary(PSW)\ndf <- as.data.frame(d)\nx <- psw(df,\n  \"treatment ~ weight + age\",\n  weight = \"ATE\", wt = TRUE,\n  out.var = \"y\"\n)\ntibble(\n  Characteristic = \"treatment\",\n  Beta = round(x$est.wt, 1),\n  SE = round(x$std.wt, 3),\n  `95% CI` = glue::glue(\"{round(x$est.wt - 1.96 * x$std.wt, 1)}, {round(x$est.wt + 1.96 * x$std.wt, 1)}\"),\n  `p-value` = \"<0.001\"\n) |>\n  knitr::kable(caption = \"Propensity score weighted regression\")\n```\n\n::: {.cell-output-display}\n\n\nTable: Propensity score weighted regression\n\n|Characteristic | Beta|    SE|95% CI |p-value |\n|:--------------|----:|-----:|:------|:-------|\n|treatment      |    1| 0.014|1, 1   |<0.001  |\n\n\n:::\n:::\n\n\nThree ways to estimate a causal effect in a non-randomized setting\n:::\n\nFirst, let's look at @tbl-panel-2-1.\nHere, we see that the unadjusted effect is *biased* (it differs from the true effect, 1, and the true effect is *not* contained in the reported 95% confidence interval).\nNow lets compare @tbl-panel-2-2 and @tbl-panel-2-3.\nTechnically, both are estimating unbiased causal effects.\nThe output in the `Beta` column of @tbl-panel-2-2 is technically a *conditional* effect (and often in causal inference we want marginal effects), but because there is no treatment heterogeneity in this simulation, the conditional and marginal effects are equal.\n@tbl-panel-2-3, using the propensity score, also estimates an unbiased effect, but it is no longer the most *efficient* (that was true when the baseline covariates were merely causal for `y`, now that they are `confounders` the efficiency gains for using propensity score weighting are not as clear cut).\nSo why would we ever use propensity scores in this case?\nSometimes we have a better understanding of the functional form of the propensity score model compared to the outcome model.\nAlternatively, sometimes the outcome model is difficult to fit (for example, if the outcome is rare).\n\n::: callout-tip\n## Marginal versus conditional effects\n\nIn causal inference, we are often interested in *marginal* effects, mathematically, this means that we want to *marginalize* the effect of interest across the distribution of factors in a particular population that we are trying to estimate a causal effect for.\nIn an adjusted regression model, the coefficients are *conditional*, in other words, when describing the estimated coefficient, we often say something like \"a one-unit change in the exposure results in a `coefficient` change in the outcome *holding all other variables in the model constant*. In the case where the outcome is continuous, the effect is linear, and there are no interactions between the exposure effect and other factors about the population, the distinction between an conditional and a marginal effect is largely semantic. If there *is* an interaction in the model, that is, if the exposure has a different impact on the outcome depending on some other factor, we no longer have a single coefficient to interpret. We would want to estimate a *marginal* effect, taking into account the distribution of that factor in the population of interest. Why? We are ultimately trying to determine whether we should suggest the exposure to the target population, so we want to know *on average* whether it will be beneficial. Let's look at quick example: suppose that you are designing an online shopping site. Currently, the\"Purchase\" button is grey. Changing the button to red increases revenue by \\$10 for people who are *not* colorblind and decreases revenue by \\$10 for those who *are* colorblind -- *the effect is heterogeneous*. Whether you change the color of the button will depend on the *distribution* of colorblind folks that visit your website. For example, if 50% of the visitors are colorblind, your average effect of changing the color would be \\$0. If instead, 100% are colorblind, the average effect of changing the color would be -\\$10. Likewise, if 0% are colorblind, the average effect of changing the color to red would be \\$10. Your decision, therefore, needs to be based on the *marginal* effect, the effect that takes into account the distribution of colorblind online customers.\n:::\n",
+    "markdown": "# Target Trials and Standard Methods {#sec-trials-std}\n\n\n\n\n\n\n## Randomized trials {#sec-rand-trials}\n\nA *randomized trial* is one where the exposure (cause) of interest is *randomly assigned*.\n\n::: callout-note\nIn this book, we refer to analyses where the exposure is randomly assigned as a *randomized trial*, sometimes this is called an A/B test.\n:::\n\nWhy does randomization help?\nLooking at our assumptions in @sec-assump, randomized trials solve the well defined exposure portion of consistency by default -- the exposure is exactly what is randomized.\nLikewise for positivity; if we have randomly assigned folks to either the exposed or unexposed groups, we know the probability of assignment (and we know it is not exactly 0 or 1).\nRandomization alone does not solve the interference portion of consistency (for example, if we randomize some people to receive a vaccine for a communicable disease, their receiving it could lower the chance of those around them contracting the infectious disease because it changes the probability of exposure).\n*Ideal* randomized trials resolves the issue of exchangeability because the exposed and unexposed populations (in the limit) are inherently the same since their exposure status was determined by a random process (not by any factors that might make them different from each other).\nGreat!\nIn reality, we often see this assumption violated by issues such as *drop out* or *non-adherence* in randomized trials.\nIf there is differential drop out between exposure groups (for example, if participants randomly assigned to the treatment are more likely to drop out of a study, and thus we don't observe their outcome), then the observed exposure groups are no longer *exchangeable*.\nTherefore, in @tbl-assump-solved we have two columns, one for the *ideal* randomized trial (where adherence is assumed to be perfect and no participants drop out) and one for *realistic* randomized trials where this may not be so.\n\n| Assumption            | Ideal Randomized Trial | Realistic Randomized Trial | Observational Study  |\n|-----------------|-----------------|---------------------|-------------------|\n| Consistency (Well defined exposure) | 😄   | 😄       | 🤷 |\n| Consistency (No interference)       | 🤷   | 🤷       | 🤷 |\n| Positivity            | 😄   | 😄       | 🤷 |\n| Exchangeability       | 😄   | 🤷       | 🤷 |\n\n: Assumptions solved by study design. 😄 indicates it is solved by default, 🤷 indicates that it is *solvable* but not solved by default. {#tbl-assump-solved}\n\nWhen designing a study, the first step is asking an appropriate *causal question*. We then can map this question to a *protocol*, consisting of the following seven elements, as defined by @hernan2016using:\n\n* Eligibility criteria\n* Exposure definition\n* Assignment procedures\n* Follow-up period\n* Outcome definition\n* Causal contrast of interest\n* Analysis plan\n\nIn @tbl-protocol we map each of these elements to the corresponding assumption that it can address. For example, exchangeability can be addressed by the eligibility criteria (we can restrict our study to only participants for whom exposure assignment is exchangeable), assignment procedure (we could use random exposure assignment to ensure exchangeability), follow-up period (we can be sure to choose an appropriate start time for our follow-up period to ensure that we are not inducing bias -- we'll think more about this in a future chapter), and/or the analysis plan (we can adjust for any factors that would cause those in different exposure groups to lack exchangeability). \n\n<!-- TODO: link to the chapter where we talk about time zero? immortal time bias etc? is that now? -->\n\nAssumption | Eligibility Criteria | Exposure Definition| Assignment Procedures | Follow-up Period | Outcome Definition | Causal contrast | Analysis Plan\n------------|----------------- | ------------------|---------|----------|----------|--------- | -------\nConsistency (Well defined exposure) | |✔️|| | |\nConsistency (No interference) | | ✔️|✔️ | | ✔️ | | ✔️\nPositivity |✔️||✔️|| | | ✔️\nExchangeability |✔️||✔️|✔️|| | ✔️\n: Mapping assumptions to elements of a study protocol {#tbl-protocol}\n\nRecall our diagrams from @sec-diag (@fig-diagram-4); several of these protocol elements can be mapped to these diagrams when we are attempting to define our causal question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example diagram mapped to causal analysis terminology](chapter-04_files/figure-html/fig-diagram-4-1.png){#fig-diagram-4 width=672}\n:::\n:::\n\n\n\n## Target Trials\n\nThere are many reasons why randomization may not be possible. For example, it might not be ethical to randomly assign people to a particular exposure, there may not be funding available to run a randomized trial, or there might not be enough time to conduct a full trial. In these situations, we rely on observational data to help us answer causal questions by implementing a *target trial*. A *target trial* answers: What experiment would you design if you could? \n\n## Causal inference with `group_by()` and `summarize()` {#sec-group-sum}\n\nLet's suppose we are trying to estimate a causal effect of an exposure on an outcome, but the exposure is not *randomized*, in fact, there is a common cause of the exposure and outcome, making the exposed and unexposed groups *not exchangeable* without adjustment (violating the fourth assumption in @sec-assump).\n\n::: callout-note\nA **confounder** is a common cause of exposure and outcome.\n:::\n\n### One binary confounder\n\nLet's suppose this confounder is binary, see the simulation below:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(1)\nn <- 10000 \nsim <- tibble(\n  # generate the confounder from a binomial distribution\n  # with a probability 0.5 for being in either group \n  confounder = rbinom(n, 1, 0.5),\n  # make the probability of exposure dependent on the \n  # confounder value\n  p_exposure = case_when(\n    confounder == 1 ~ 0.75,\n    confounder == 0 ~ 0.25\n  ),\n  # generate the exposure from a binomial distribution\n  # with the probability of exposure dependent on the confounder\n  exposure = rbinom(n, 1, p_exposure),\n  # generate the \"true\" average treatment effect of 0 \n  # to do this, we are going to generate the potential outcomes, first \n  # the potential outcome if exposure = 0\n  # (notice exposure is not in the equation below, only the confounder)\n  # we use rnorm(n) to add the random error term that is normally\n  # distributed with a mean of 0 and a standard deviation of 1\n  y0 = confounder + rnorm(n),\n  # because the true effect is 0, the potential outcome if exposure = 1\n  # is identical\n  y1 = y0,\n  # now, in practice we will only see one of these, outcome is what is \n  # observed\n  outcome = (1 - exposure) * y0 + exposure * y1,\n  observed_potential_outcome = case_when(\n    exposure == 0 ~ \"y0\",\n    exposure == 1 ~ \"y1\"\n  )\n)\n```\n:::\n\n\nHere we have one binary `confounder`, the probability that `confounder = 1` is `0.5`.\nThe probability of the being exposed is `0.75` for those for whom `confounder = 1` `0.25` for those for whom `confounder = 0`.\nThere is no effect of the `exposure` on the `outcome` (the true causal effect is 0); the `outcome` effect is fully dependent on the `confounder`. In this simulation we generate the potential outcomes to drive home our assumptions; many of our simulations in this book will skip this step.\nLet's look at this generated data frame.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim |>\n  select(confounder, exposure, outcome, observed_potential_outcome)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 10,000 × 4\n   confounder exposure outcome observed_potential_out…¹\n        <int>    <int>   <dbl> <chr>                   \n 1          0        0 -0.804  y0                      \n 2          0        0 -1.06   y0                      \n 3          1        1 -0.0354 y1                      \n 4          1        1 -0.186  y1                      \n 5          0        0 -0.500  y0                      \n 6          1        1  0.475  y1                      \n 7          1        0  0.698  y0                      \n 8          1        0  1.47   y0                      \n 9          1        0  0.752  y0                      \n10          0        0  1.26   y0                      \n# ℹ 9,990 more rows\n# ℹ abbreviated name: ¹​observed_potential_outcome\n```\n:::\n:::\n\n\nGreat! Let's begin by proving to ourselves that this violates the exchangeability assumption. Recall from @sec-assump:\n\n> **Exchangeability**: We assume that within levels of relevant variables (confounders), exposed and unexposed subjects have an equal likelihood of experiencing any outcome prior to exposure; i.e. the exposed and unexposed subjects are exchangeable. This assumption is sometimes referred to as **no unmeasured confounding**.\n\nNow, let's try to estimate the effect of the `exposure` on the `outcome` assuming the two exposed groups are exchangeable.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 × 2\n  exposure avg_outcome\n     <int>       <dbl>\n1        0       0.228\n2        1       0.756\n```\n:::\n:::\n\n\nThe average outcome among the exposed is `0.76` and among the unexposed `0.23`, yielding an average effect of the exposure of `0.76-0.23=0.53`.\nLet's do a little R work to get there.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim |>\n  group_by(exposure) |>\n  summarise(avg_outcome = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure, \n    values_from = avg_outcome,  \n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0) \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 × 1\n  estimate\n     <dbl>\n1    0.528\n```\n:::\n:::\n\n\nOk, so assuming the exposure groups are exchangeable (and assuming the rest of the assumptions from @sec-assump hold), we estimate the effect of the exposure on the outcome to be 0.53.\nWe *know* the exchaneability assumption is violated based on how we simulated the data.\nHow can we estimate an unbiased effect?\nThe easiest way to do so is to estimate the effect within each confounder class.\nThis will work because folks with the same value of the confounder have an equal probability of exposure.\nInstead of just grouping by the exposure, let's group by the confounder as well:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim |>\n  group_by(confounder, exposure) |>\n  summarise(avg_outcome = mean(outcome))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 × 3\n# Groups:   confounder [2]\n  confounder exposure avg_outcome\n       <int>    <int>       <dbl>\n1          0        0    -0.0185 \n2          0        1     0.00954\n3          1        0     0.994  \n4          1        1     1.01   \n```\n:::\n:::\n\n\nWe can now calculate the average effect within each confounder class as well as the overall causal effect.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## Average effect within each confounder class\nsim |>\n  group_by(confounder, exposure) |>\n  summarise(avg_outcome = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure, \n    values_from = avg_outcome,  \n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0) \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 × 2\n  confounder estimate\n       <int>    <dbl>\n1          0   0.0280\n2          1   0.0122\n```\n:::\n\n```{.r .cell-code}\n## Overall average effect\n\nsim |>\n  group_by(confounder, exposure) |>\n  summarise(avg_outcome = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure, \n    values_from = avg_outcome,  \n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0) |>\n  summarise(mean(estimate)) # note, we would need to weight this if the confounder groups were not equal sized\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 × 1\n  `mean(estimate)`\n             <dbl>\n1           0.0201\n```\n:::\n:::\n\n\nGreat!\nNow our estimate is much closer to the true value (0).\n\n::: callout\nThe method we are using to solve the fact that our two groups are not exchangeable is known as **stratification**. We are *stratifying* by the confounder(s) and estimating the causal effect within each stratum. To get an overall average causal effect we are averaging across the strata. This can be a great tool if there are very few confounders, however it can suffer from the curse of dimensionality as the number of confounders as well as the number of levels within each confounder increases.\n:::\n\n### Two binary confounders\n\nLet's extend this to two binary confounders.\nThe simulation below now has two binary confounders.\nThe true causal effect of the `exposure` on the `outcome` is still 0.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(1)\nn <- 10000\nsim2 <- tibble(\n  confounder_1 = rbinom(n, 1, 0.5),\n  confounder_2 = rbinom(n, 1, 0.5), \n  \n  p_exposure = case_when(\n    confounder_1 == 1 & confounder_2 == 1 ~ 0.75,\n    confounder_1 == 0 & confounder_2 == 1 ~ 0.9,\n    confounder_1 == 1 & confounder_2 == 0 ~ 0.2,\n    confounder_1 == 0 & confounder_2 == 0 ~ 0.1,\n  ),\n  exposure = rbinom(n, 1, p_exposure),\n  outcome = confounder_1 + confounder_2 + rnorm(n) \n)\n```\n:::\n\n\nNow we are going to group by both confounders and estimate the causal effect within each stratum.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim2 |>\n  group_by(confounder_1, confounder_2, exposure) |>\n  summarise(avg_y = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_y,  \n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0) \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 × 3\n# Groups:   confounder_1 [2]\n  confounder_1 confounder_2 estimate\n         <int>        <int>    <dbl>\n1            0            0  -0.122 \n2            0            1   0.0493\n3            1            0   0.0263\n4            1            1   0.0226\n```\n:::\n:::\n\n\nWe can also estimate the overall causal effect:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim2 |>\n  group_by(confounder_1, confounder_2, exposure) |>\n  summarise(avg_outcome = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure, \n    values_from = avg_outcome,  \n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0, .groups = \"drop\") |>\n  summarise(mean(estimate)) \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 × 1\n  `mean(estimate)`\n             <dbl>\n1         -0.00594\n```\n:::\n:::\n\n\n### Continuous confounder\n\nSo far our strategy has been to estimate the causal effect within strata where the individuals are *exchangeable*.\nHow could we extend this to a continuous confounder?\nLet's create another simulation:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(1)\nn <- 10000 \nsim3 <- tibble(\n  confounder = rnorm(n), \n  p_exposure = exp(confounder) / (1 + exp(confounder)),\n  exposure = rbinom(n, 1, p_exposure),\n  outcome = confounder + rnorm(n) \n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsim3\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 10,000 × 4\n   confounder p_exposure exposure outcome\n        <dbl>      <dbl>    <int>   <dbl>\n 1     -0.626      0.348        0 -0.840 \n 2      0.184      0.546        1  0.0769\n 3     -0.836      0.302        0 -1.30  \n 4      1.60       0.831        1  0.911 \n 5      0.330      0.582        1 -0.461 \n 6     -0.820      0.306        0 -1.16  \n 7      0.487      0.620        1 -0.780 \n 8      0.738      0.677        1 -0.656 \n 9      0.576      0.640        1  0.995 \n10     -0.305      0.424        0  2.91  \n# ℹ 9,990 more rows\n```\n:::\n:::\n\n\nIf we want to still use `group_by` and `summarise`, we could bin the continuous confounder, for example using it's quintiles, and estimate the causal effect within each bin:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsim3 |>\n  mutate(confounder_q = ntile(confounder, 5)) |>\n  group_by(confounder_q, exposure) |>\n  summarise(avg_y = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_y,  \n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0) \n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 × 2\n  confounder_q estimate\n         <int>    <dbl>\n1            1   0.237 \n2            2   0.0622\n3            3   0.0264\n4            4   0.133 \n5            5   0.178 \n```\n:::\n\n```{.r .cell-code}\nsim3 |>\n  mutate(confounder_q = ntile(confounder, 5)) |>\n  group_by(confounder_q, exposure) |>\n  summarise(avg_y = mean(outcome)) |>\n  pivot_wider(\n    names_from = exposure,\n    values_from = avg_y,  \n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0) |>\n  summarise(estimate = mean(estimate))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 × 1\n  estimate\n     <dbl>\n1    0.127\n```\n:::\n:::\n\n\nGreat!\nNow, in practice we have many more than one or two binary confounders, we often have many many confounders, both continuous and binary.\nAttempting to estimate the causal effect within each unique stratum would be very inefficient (and often not possible!).\nWhat if instead we could create a single summary score for all of the confounders?\nHold onto this thought, we'll bring it up again when we introduce *propensity scores*.\n\n## When do standard methods succeed and fail? {#sec-standard}\n\nWhen teaching these topics, we are often asked when \"standard methods\" will succeed, i.e.: when can we just fit a linear regression model to estimate a causal effect?\nLet's start with the easiest example: the exposure is *randomized*.\nAs discussed in @sec-rand-trials, randomization ensures comparability and can simplify the methods needed to estimate a causal effect.\nIn the presence of a randomized exposure (assuming perfect adherence to the exposure assigned, no one dropped out of the study, etc.), simple tools like regression can be used to estimate a causal effect.\n\n### When correlation is causation\n\nWhen you have no confounders and there is a linear relationship between the exposure and the outcome, that *correlation is a causal relationship*.\nEven in these cases, using the methods you will learn in this book can help.\n\n1.  Adjusting for baseline covariates can make an estimate *more efficient*\n2.  Propensity score weighting is *more efficient* that direct adjustment\n3.  Sometimes we are *more comfortable with the functional form of the propensity score* (predicting exposure) than the outcome model\n\nLet's look at an example.\nI am going to simulate 100 observations.\nHere the treatment is randomly assigned and there are two baseline covariates: `age` and `weight`.\nOn average, the treatment causes a one unit increase in the outcome (this is called the *average treatment effect*, we will talk more about this quantity in future chapters).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nset.seed(10)\nn <- 100\nd <- tibble(\n  age = rnorm(n, 55, 20),\n  weight = rnorm(n),\n  # generate the treatment from a binomial distribution\n  # with the probability of treatment = 0.5\n  treatment = rbinom(n, 1, 0.5),\n  # generate the average causal effect of treatment: 1\n  y = 1 * treatment + 0.2 * age + 0.2 * weight + rnorm(n)\n)\n```\n:::\n\n\nWe can draw a causal diagram of the relationship described in the code above (@fig-diag).\n@sec-dags contains more information on these causal diagrams, but briefly, the arrows denote causal relationships, so since we have established that the treatment causes an increase in the outcome (an average treatment effect of 1), we see an arrow from `trt` to `y` in this diagram.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Causal Diagram of Example Randomized Study](chapter-04_files/figure-html/fig-diag-1.png){#fig-diag width=672}\n:::\n:::\n\n\nLet's examine three models: (1) an unadjusted model (@tbl-panel-1), (2) a linear model that adjusts for the baseline covariates (@tbl-panel-2), and (3) a propensity score weighted model (@tbl-panel-3).\n\n::: {#tbl-panel layout-ncol=\"2\"}\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nlibrary(gtsummary)\nlm(y ~ treatment, d) |>\n  tbl_regression() |>\n  modify_column_unhide(column = std.error) |>\n  modify_caption(\"Unadjusted regression\")\n```\n\n::: {.cell-output-display}\n```{=html}\n<div id=\"dnecmmlmpz\" 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.gt_first_grand_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-style: double;\n  border-top-width: 6px;\n  border-top-color: #D3D3D3;\n}\n\n#dnecmmlmpz .gt_last_grand_summary_row_top {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: double;\n  border-bottom-width: 6px;\n  border-bottom-color: #D3D3D3;\n}\n\n#dnecmmlmpz .gt_striped {\n  background-color: rgba(128, 128, 128, 0.05);\n}\n\n#dnecmmlmpz .gt_table_body {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#dnecmmlmpz .gt_footnotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#dnecmmlmpz .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#dnecmmlmpz .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#dnecmmlmpz .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#dnecmmlmpz .gt_left {\n  text-align: left;\n}\n\n#dnecmmlmpz .gt_center {\n  text-align: center;\n}\n\n#dnecmmlmpz .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#dnecmmlmpz .gt_font_normal {\n  font-weight: normal;\n}\n\n#dnecmmlmpz .gt_font_bold {\n  font-weight: bold;\n}\n\n#dnecmmlmpz .gt_font_italic {\n  font-style: italic;\n}\n\n#dnecmmlmpz .gt_super {\n  font-size: 65%;\n}\n\n#dnecmmlmpz .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#dnecmmlmpz .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#dnecmmlmpz .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#dnecmmlmpz .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#dnecmmlmpz .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#dnecmmlmpz .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#dnecmmlmpz .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <caption>Unadjusted regression</caption>\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Beta&lt;/strong&gt;\"><strong>Beta</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;SE&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>SE</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>95% CI</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;p-value&lt;/strong&gt;\"><strong>p-value</strong></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">treatment</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.93</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.803</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">-0.66, 2.5</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\">0.2</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"5\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> SE = Standard Error, CI = Confidence Interval</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nlm(y ~ treatment + age + weight, d) |>\n  tbl_regression() |>\n  modify_column_unhide(column = std.error) |>\n  modify_caption(\"Adjusted regression\")\n```\n\n::: {.cell-output-display}\n```{=html}\n<div id=\"qepoxygcps\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#qepoxygcps table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#qepoxygcps thead, #qepoxygcps tbody, #qepoxygcps tfoot, #qepoxygcps tr, #qepoxygcps td, #qepoxygcps th {\n  border-style: none;\n}\n\n#qepoxygcps p {\n  margin: 0;\n  padding: 0;\n}\n\n#qepoxygcps .gt_table {\n  display: table;\n  border-collapse: collapse;\n  line-height: normal;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  font-style: normal;\n  background-color: #FFFFFF;\n  width: auto;\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #A8A8A8;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #A8A8A8;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_caption {\n  padding-top: 4px;\n  padding-bottom: 4px;\n}\n\n#qepoxygcps .gt_title {\n  color: #333333;\n  font-size: 125%;\n  font-weight: initial;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-color: #FFFFFF;\n  border-bottom-width: 0;\n}\n\n#qepoxygcps .gt_subtitle {\n  color: #333333;\n  font-size: 85%;\n  font-weight: initial;\n  padding-top: 3px;\n  padding-bottom: 5px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-color: #FFFFFF;\n  border-top-width: 0;\n}\n\n#qepoxygcps .gt_heading {\n  background-color: #FFFFFF;\n  text-align: center;\n  border-bottom-color: #FFFFFF;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_bottom_border {\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_col_headings {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_col_heading {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: normal;\n  text-transform: inherit;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: bottom;\n  padding-top: 5px;\n  padding-bottom: 6px;\n  padding-left: 5px;\n  padding-right: 5px;\n  overflow-x: hidden;\n}\n\n#qepoxygcps .gt_column_spanner_outer {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: normal;\n  text-transform: inherit;\n  padding-top: 0;\n  padding-bottom: 0;\n  padding-left: 4px;\n  padding-right: 4px;\n}\n\n#qepoxygcps .gt_column_spanner_outer:first-child {\n  padding-left: 0;\n}\n\n#qepoxygcps .gt_column_spanner_outer:last-child {\n  padding-right: 0;\n}\n\n#qepoxygcps .gt_column_spanner {\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  vertical-align: bottom;\n  padding-top: 5px;\n  padding-bottom: 5px;\n  overflow-x: hidden;\n  display: inline-block;\n  width: 100%;\n}\n\n#qepoxygcps .gt_spanner_row {\n  border-bottom-style: hidden;\n}\n\n#qepoxygcps .gt_group_heading {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: middle;\n  text-align: left;\n}\n\n#qepoxygcps .gt_empty_group_heading {\n  padding: 0.5px;\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  vertical-align: middle;\n}\n\n#qepoxygcps .gt_from_md > :first-child {\n  margin-top: 0;\n}\n\n#qepoxygcps .gt_from_md > :last-child {\n  margin-bottom: 0;\n}\n\n#qepoxygcps .gt_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  margin: 10px;\n  border-top-style: solid;\n  border-top-width: 1px;\n  border-top-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: middle;\n  overflow-x: hidden;\n}\n\n#qepoxygcps .gt_stub {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-right-style: solid;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#qepoxygcps .gt_stub_row_group {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-right-style: solid;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  padding-left: 5px;\n  padding-right: 5px;\n  vertical-align: top;\n}\n\n#qepoxygcps .gt_row_group_first td {\n  border-top-width: 2px;\n}\n\n#qepoxygcps .gt_row_group_first th {\n  border-top-width: 2px;\n}\n\n#qepoxygcps .gt_summary_row {\n  color: #333333;\n  background-color: #FFFFFF;\n  text-transform: inherit;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#qepoxygcps .gt_first_summary_row {\n  border-top-style: solid;\n  border-top-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_first_summary_row.thick {\n  border-top-width: 2px;\n}\n\n#qepoxygcps .gt_last_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_grand_summary_row {\n  color: #333333;\n  background-color: #FFFFFF;\n  text-transform: inherit;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#qepoxygcps .gt_first_grand_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-style: double;\n  border-top-width: 6px;\n  border-top-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_last_grand_summary_row_top {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: double;\n  border-bottom-width: 6px;\n  border-bottom-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_striped {\n  background-color: rgba(128, 128, 128, 0.05);\n}\n\n#qepoxygcps .gt_table_body {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_footnotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#qepoxygcps .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#qepoxygcps .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#qepoxygcps .gt_left {\n  text-align: left;\n}\n\n#qepoxygcps .gt_center {\n  text-align: center;\n}\n\n#qepoxygcps .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#qepoxygcps .gt_font_normal {\n  font-weight: normal;\n}\n\n#qepoxygcps .gt_font_bold {\n  font-weight: bold;\n}\n\n#qepoxygcps .gt_font_italic {\n  font-style: italic;\n}\n\n#qepoxygcps .gt_super {\n  font-size: 65%;\n}\n\n#qepoxygcps .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#qepoxygcps .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#qepoxygcps .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#qepoxygcps .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#qepoxygcps .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#qepoxygcps .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#qepoxygcps .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <caption>Adjusted regression</caption>\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Beta&lt;/strong&gt;\"><strong>Beta</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;SE&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>SE</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>95% CI</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;p-value&lt;/strong&gt;\"><strong>p-value</strong></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">treatment</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">1.0</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.204</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.59, 1.4</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">age</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.20</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.005</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.19, 0.22</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">weight</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.34</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.106</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.13, 0.55</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\">0.002</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"5\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> SE = Standard Error, CI = Confidence Interval</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nd |>\n  mutate(\n    p = glm(treatment ~ weight + age, data = d) |> predict(type = \"response\"),\n    ate = treatment / p + (1 - treatment) / (1 - p)\n  ) |>\n  as.data.frame() -> d\nlibrary(PSW)\ndf <- as.data.frame(d)\nx <- psw(df, \n         \"treatment ~ weight + age\", \n         weight = \"ATE\", wt = TRUE,\n         out.var = \"y\")\ntibble(\n  Characteristic = \"treatment\",\n  Beta = round(x$est.wt, 1),\n  SE = round(x$std.wt, 3),\n  `95% CI` = glue::glue(\"{round(x$est.wt - 1.96 * x$std.wt, 1)}, {round(x$est.wt + 1.96 * x$std.wt, 1)}\"),\n  `p-value` = \"<0.001\"\n) |>\n  knitr::kable(caption = \"Propensity score weighted regression\")\n```\n\n::: {.cell-output-display}\nTable: Propensity score weighted regression\n\n|Characteristic | Beta|    SE|95% CI   |p-value |\n|:--------------|----:|-----:|:--------|:-------|\n|treatment      |    1| 0.202|0.6, 1.4 |<0.001  |\n:::\n:::\n\n\nThree ways to estimate the causal effect.\n:::\n\nLooking at the three outputs in @tbl-panel, we can first notice that all three are \"unbiased\" estimates of the causal effect (we know the true average treatment effect is 1, based on our simulation) -- the estimated causal effect in each table is in the `Beta` column.\nGreat, so all methods give us an unbiased estimate.\nNext, let's look at the `SE` (standard error) column along with the `95% CI` (confidence interval) column.\nNotice the unadjusted model has a *wider* confidence interval (in fact, in this case the confidence interval includes the null, 0) -- this means if we were to use this method, even though we were able to estimate an unbiased causal effect, we would often conclude that we *fail to reject the null* that relationship between the treatment and outcome is 0.\nIn statistical terms, we refer to this as a *lack of efficiency*.\nLooking at the adjusted analysis in @tbl-panel-2, we see that the standard error is quite a bit smaller (and likewise the confidence interval is tighter, no longer including the null).\nEven though our baseline covariates `age` and `weight` were not *confounders* adjusting from them *increased the precision* of our result (this is a good thing! We want estimates that are both unbiased *and* precise).\nFinally, looking at the propensity score weighted estimate we can see that our precision was slightly improved compared to the adjusted result (0.202 compared to 0.204).\nThe magnitude of this improvement will depend on several factors, but it has been shown mathematically that using propensity scores like this to adjust for baseline factors in a randomized trial will *always* improve precision [@williamson2014variance].\nWhat can we learn from this small demonstration?\nEven in the perfect scenario, where we can estimate unbiased results without using propensity scores, the methods we will show here can be useful.\nThe utility of these methods only increases when exploring more complex examples, such as situations where the effect is *not* randomized, the introduction of time-varying confounders, etc.\n\nWhat if we did not have a randomized exposure?\nThere are many cases where randomization to a treatment is not ethical or feasible.\nStandard methods can still estimate unbiased effects, but more care needs to be given to the previously mentioned assumptions (@tbl-assump-solved).\nFor example, we need the exposed an unexposed groups to be *exchangeable*; this means we must adjust for *all confounders* with their correct functional form.\nIf everything is simple and linear (and there is no effect heterogeneity, that is everyone's causal effect is the same regardless of their baseline factors), then a simple regression model that adjusts for the confounders can give you an unbiased result.\nLet's look at a simple example such as this.\nNotice in the simulation below, the main difference compared to the above simulation is that the probability of treatment assignment is no longer 0.5 as it was above, but now dependent on the participants `age` and `weight`. For example, maybe doctors tend to prescribe a certain treatment to patients who are older and who weigh more.\nThe true causal effect is still 1, but now we have two confounders, `age` and `weight` (@fig-diag-2).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(7)\nn <- 100000\nd <- tibble(\n  age = rnorm(n, 55, 20),\n  weight = rnorm(n),\n  # generate the treatment from a binomial distribution\n  # with the probability of treatment dependent on the age and weight\n  treatment = rbinom(n, 1, 1 / (1 + exp(-0.01 * age - weight))),\n  ## generate the true average causal effect of the treatment: 1\n  y = 1 * treatment + 0.2 * age + 0.2 * weight + rnorm(n)\n)\n```\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n![Causal Diagram of Example Observation Study](chapter-04_files/figure-html/fig-diag-2-1.png){#fig-diag-2 width=672}\n:::\n:::\n\n\n::: {#tbl-panel-2 layout-ncol=\"2\"}\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nlm(y ~ treatment, d) |>\n  tbl_regression() |>\n  modify_column_unhide(column = std.error) |>\n  modify_caption(\"Unadjusted regression\")\n```\n\n::: {.cell-output-display}\n```{=html}\n<div id=\"lfkdhkjrur\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#lfkdhkjrur table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#lfkdhkjrur thead, #lfkdhkjrur tbody, #lfkdhkjrur tfoot, #lfkdhkjrur tr, #lfkdhkjrur td, #lfkdhkjrur th {\n  border-style: none;\n}\n\n#lfkdhkjrur p {\n  margin: 0;\n  padding: 0;\n}\n\n#lfkdhkjrur .gt_table {\n  display: table;\n  border-collapse: collapse;\n  line-height: normal;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  font-style: normal;\n  background-color: #FFFFFF;\n  width: auto;\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #A8A8A8;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #A8A8A8;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_caption {\n  padding-top: 4px;\n  padding-bottom: 4px;\n}\n\n#lfkdhkjrur .gt_title {\n  color: #333333;\n  font-size: 125%;\n  font-weight: initial;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-color: #FFFFFF;\n  border-bottom-width: 0;\n}\n\n#lfkdhkjrur .gt_subtitle {\n  color: #333333;\n  font-size: 85%;\n  font-weight: initial;\n  padding-top: 3px;\n  padding-bottom: 5px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-color: #FFFFFF;\n  border-top-width: 0;\n}\n\n#lfkdhkjrur .gt_heading {\n  background-color: #FFFFFF;\n  text-align: center;\n  border-bottom-color: #FFFFFF;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_bottom_border {\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_col_headings {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_col_heading {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: normal;\n  text-transform: inherit;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: bottom;\n  padding-top: 5px;\n  padding-bottom: 6px;\n  padding-left: 5px;\n  padding-right: 5px;\n  overflow-x: hidden;\n}\n\n#lfkdhkjrur .gt_column_spanner_outer {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: normal;\n  text-transform: inherit;\n  padding-top: 0;\n  padding-bottom: 0;\n  padding-left: 4px;\n  padding-right: 4px;\n}\n\n#lfkdhkjrur .gt_column_spanner_outer:first-child {\n  padding-left: 0;\n}\n\n#lfkdhkjrur .gt_column_spanner_outer:last-child {\n  padding-right: 0;\n}\n\n#lfkdhkjrur .gt_column_spanner {\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  vertical-align: bottom;\n  padding-top: 5px;\n  padding-bottom: 5px;\n  overflow-x: hidden;\n  display: inline-block;\n  width: 100%;\n}\n\n#lfkdhkjrur .gt_spanner_row {\n  border-bottom-style: 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vertical-align: middle;\n}\n\n#lfkdhkjrur .gt_from_md > :first-child {\n  margin-top: 0;\n}\n\n#lfkdhkjrur .gt_from_md > :last-child {\n  margin-bottom: 0;\n}\n\n#lfkdhkjrur .gt_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  margin: 10px;\n  border-top-style: solid;\n  border-top-width: 1px;\n  border-top-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 1px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 1px;\n  border-right-color: #D3D3D3;\n  vertical-align: middle;\n  overflow-x: hidden;\n}\n\n#lfkdhkjrur .gt_stub {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-right-style: solid;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_stub_row_group {\n  color: #333333;\n  background-color: #FFFFFF;\n  font-size: 100%;\n  font-weight: initial;\n  text-transform: inherit;\n  border-right-style: solid;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n  padding-left: 5px;\n  padding-right: 5px;\n  vertical-align: top;\n}\n\n#lfkdhkjrur .gt_row_group_first td {\n  border-top-width: 2px;\n}\n\n#lfkdhkjrur .gt_row_group_first th {\n  border-top-width: 2px;\n}\n\n#lfkdhkjrur .gt_summary_row {\n  color: #333333;\n  background-color: #FFFFFF;\n  text-transform: inherit;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_first_summary_row {\n  border-top-style: solid;\n  border-top-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_first_summary_row.thick {\n  border-top-width: 2px;\n}\n\n#lfkdhkjrur .gt_last_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_grand_summary_row {\n  color: #333333;\n  background-color: #FFFFFF;\n  text-transform: inherit;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_first_grand_summary_row {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-top-style: double;\n  border-top-width: 6px;\n  border-top-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_last_grand_summary_row_top {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  padding-left: 5px;\n  padding-right: 5px;\n  border-bottom-style: double;\n  border-bottom-width: 6px;\n  border-bottom-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_striped {\n  background-color: rgba(128, 128, 128, 0.05);\n}\n\n#lfkdhkjrur .gt_table_body {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_footnotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#lfkdhkjrur .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#lfkdhkjrur .gt_left {\n  text-align: left;\n}\n\n#lfkdhkjrur .gt_center {\n  text-align: center;\n}\n\n#lfkdhkjrur .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#lfkdhkjrur .gt_font_normal {\n  font-weight: normal;\n}\n\n#lfkdhkjrur .gt_font_bold {\n  font-weight: bold;\n}\n\n#lfkdhkjrur .gt_font_italic {\n  font-style: italic;\n}\n\n#lfkdhkjrur .gt_super {\n  font-size: 65%;\n}\n\n#lfkdhkjrur .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#lfkdhkjrur .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#lfkdhkjrur .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#lfkdhkjrur .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#lfkdhkjrur .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#lfkdhkjrur .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#lfkdhkjrur .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <caption>Unadjusted regression</caption>\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Beta&lt;/strong&gt;\"><strong>Beta</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;SE&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>SE</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>95% CI</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;p-value&lt;/strong&gt;\"><strong>p-value</strong></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">treatment</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">1.8</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.027</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">1.8, 1.9</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"5\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> SE = Standard Error, CI = Confidence Interval</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nlm(y ~ treatment + age + weight, d) |>\n  tbl_regression() |>\n  modify_column_unhide(column = std.error) |>\n  modify_caption(\"Adjusted regression\")\n```\n\n::: {.cell-output-display}\n```{=html}\n<div id=\"yiyqgqwbps\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#yiyqgqwbps table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#yiyqgqwbps thead, #yiyqgqwbps tbody, #yiyqgqwbps tfoot, #yiyqgqwbps 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.gt_striped {\n  background-color: rgba(128, 128, 128, 0.05);\n}\n\n#yiyqgqwbps .gt_table_body {\n  border-top-style: solid;\n  border-top-width: 2px;\n  border-top-color: #D3D3D3;\n  border-bottom-style: solid;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n}\n\n#yiyqgqwbps .gt_footnotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#yiyqgqwbps .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#yiyqgqwbps .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#yiyqgqwbps .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#yiyqgqwbps .gt_left {\n  text-align: left;\n}\n\n#yiyqgqwbps .gt_center {\n  text-align: center;\n}\n\n#yiyqgqwbps .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#yiyqgqwbps .gt_font_normal {\n  font-weight: normal;\n}\n\n#yiyqgqwbps .gt_font_bold {\n  font-weight: bold;\n}\n\n#yiyqgqwbps .gt_font_italic {\n  font-style: italic;\n}\n\n#yiyqgqwbps .gt_super {\n  font-size: 65%;\n}\n\n#yiyqgqwbps .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#yiyqgqwbps .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#yiyqgqwbps .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#yiyqgqwbps .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#yiyqgqwbps .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#yiyqgqwbps .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#yiyqgqwbps .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <caption>Adjusted regression</caption>\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Beta&lt;/strong&gt;\"><strong>Beta</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;SE&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>SE</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>95% CI</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;p-value&lt;/strong&gt;\"><strong>p-value</strong></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">treatment</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.99</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.007</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.98, 1.0</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">age</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.20</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.000</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.20, 0.20</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">weight</td>\n<td headers=\"estimate\" class=\"gt_row gt_center\">0.20</td>\n<td headers=\"std.error\" class=\"gt_row gt_center\">0.003</td>\n<td headers=\"ci\" class=\"gt_row gt_center\">0.20, 0.21</td>\n<td headers=\"p.value\" class=\"gt_row gt_center\"><0.001</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"5\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> SE = Standard Error, CI = Confidence Interval</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code  code-fold=\"true\"}\nd |>\n  mutate(\n    p = glm(treatment ~ weight + age, data = d, family = binomial) |> predict(type = \"response\"),\n    ate = treatment / p + (1 - treatment) / (1 - p)\n  ) |>\n  as.data.frame() -> d\nlibrary(PSW)\ndf <- as.data.frame(d)\nx <- psw(df, \n         \"treatment ~ weight + age\", \n         weight = \"ATE\", wt = TRUE,\n         out.var = \"y\")\ntibble(\n  Characteristic = \"treatment\",\n  Beta = round(x$est.wt, 1),\n  SE = round(x$std.wt, 3),\n  `95% CI` = glue::glue(\"{round(x$est.wt - 1.96 * x$std.wt, 1)}, {round(x$est.wt + 1.96 * x$std.wt, 1)}\"),\n  `p-value` = \"<0.001\"\n) |>\n  knitr::kable(caption = \"Propensity score weighted regression\")\n```\n\n::: {.cell-output-display}\nTable: Propensity score weighted regression\n\n|Characteristic | Beta|    SE|95% CI |p-value |\n|:--------------|----:|-----:|:------|:-------|\n|treatment      |    1| 0.014|1, 1   |<0.001  |\n:::\n:::\n\n\nThree ways to estimate a causal effect in a non-randomized setting\n:::\n\nFirst, let's look at @tbl-panel-2-1.\nHere, we see that the unadjusted effect is *biased* (it differs from the true effect, 1, and the true effect is *not* contained in the reported 95% confidence interval).\nNow lets compare @tbl-panel-2-2 and @tbl-panel-2-3.\nTechnically, both are estimating unbiased causal effects.\nThe output in the `Beta` column of @tbl-panel-2-2 is technically a *conditional* effect (and often in causal inference we want marginal effects), but because there is no treatment heterogeneity in this simulation, the conditional and marginal effects are equal.\n@tbl-panel-2-3, using the propensity score, also estimates an unbiased effect, but it is no longer the most *efficient* (that was true when the baseline covariates were merely causal for `y`, now that they are `confounders` the efficiency gains for using propensity score weighting are not as clear cut).\nSo why would we ever use propensity scores in this case?\nSometimes we have a better understanding of the functional form of the propensity score model compared to the outcome model.\nAlternatively, sometimes the outcome model is difficult to fit (for example, if the outcome is rare).\n\n::: callout-tip\n## Marginal versus conditional effects\n\nIn causal inference, we are often interested in *marginal* effects, mathematically, this means that we want to *marginalize* the effect of interest across the distribution of factors in a particular population that we are trying to estimate a causal effect for.\nIn an adjusted regression model, the coefficients are *conditional*, in other words, when describing the estimated coefficient, we often say something like \"a one-unit change in the exposure results in a `coefficient` change in the outcome *holding all other variables in the model constant*. In the case where the outcome is continuous, the effect is linear, and there are no interactions between the exposure effect and other factors about the population, the distinction between an conditional and a marginal effect is largely semantic. If there *is* an interaction in the model, that is, if the exposure has a different impact on the outcome depending on some other factor, we no longer have a single coefficient to interpret. We would want to estimate a *marginal* effect, taking into account the distribution of that factor in the population of interest. Why? We are ultimately trying to determine whether we should suggest the exposure to the target population, so we want to know *on average* whether it will be beneficial. Let's look at quick example: suppose that you are designing an online shopping site. Currently, the\"Purchase\" button is grey. Changing the button to red increases revenue by \\$10 for people who are *not* colorblind and decreases revenue by \\$10 for those who *are* colorblind -- *the effect is heterogeneous*. Whether you change the color of the button will depend on the *distribution* of colorblind folks that visit your website. For example, if 50% of the visitors are colorblind, your average effect of changing the color would be \\$0. If instead, 100% are colorblind, the average effect of changing the color would be -\\$10. Likewise, if 0% are colorblind, the average effect of changing the color to red would be \\$10. Your decision, therefore, needs to be based on the *marginal* effect, the effect that takes into account the distribution of colorblind online customers.\n:::\n",
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-    "markdown": "# Expressing causal questions as DAGs {#sec-dags}\n\n\n\n\n\n## Visualizing Causal Assumptions\n\n> Draw your assumptions before your conclusions --@hernan2021\n\nCausal diagrams are a tool to visualize your assumptions about the causal structure of the questions you're trying to answer.\nIn a randomized experiment, the causal structure is quite simple.\nWhile there may be many causes of an outcome, the only cause of the exposure is the randomization process itself (we hope!).\nIn many non-randomized settings, however, the structure of your question can be a complex web of causality.\nCausal diagrams help communicate what we think this structure looks like.\nIn addition to being open about what we think the causal structure is, causal diagrams have incredible mathematical properties that allow us to identify a way to estimate unbiased causal effects even with observational data.\n\nCausal diagrams are also increasingly common.\nData collected as a review of causal diagrams in applied health research papers show a drastic increase in use over time [@Tennant2021].\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Percentage of health research papers using causal diagrams over time.](chapter-05_files/figure-html/fig-dag-usage-1.png){#fig-dag-usage width=672}\n:::\n:::\n\n\nThe type of causal diagrams we use are also called directed acyclic graphs (DAGs)[^chapter-05-1].\nThese graphs are directed because they include arrows going in a specific direction.\nThey're acyclic because they don't go in circles; a variable can't cause itself, for instance.\nDAGs are used for various problems, but we're specifically concerned with *causal* DAGs.\nThis class of DAGs is sometimes called Structural Causal Models (SCMs) because they are a model of the causal structure of a question [@hernan2021; @Pearl_Glymour_Jewell_2021].\n\n[^chapter-05-1]: An essential but rarely observed detail of DAGs is that dag is also an [affectionate Australian insult](https://en.wikipedia.org/wiki/Dag_(slang)) referring to the dung-caked fur of a sheep, a *daglock*.\n\nDAGs depict causal relationships between variables.\nVisually, the way they depict variables is as *edges* and *nodes*.\nEdges are the arrows going from one variable to another, sometimes called arcs or just arrows.\nNodes are the variables themselves, sometimes called vertices, points, or just variables.\nIn @fig-dag-basic, there are two nodes, `x` and `y`, and one edge going from `x` to `y`.\nHere, we are saying that `x` causes `y`.\n`y` \"listens\" to `x` [@Pearl_Glymour_Jewell_2021].\n\n\n::: {.cell}\n::: {.cell-output-display}\n![A causal directed acyclic graph (DAG). DAGs depict causal relationships. In this DAG, the assumption is that `x` causes `y`.](chapter-05_files/figure-html/fig-dag-basic-1.png){#fig-dag-basic width=288}\n:::\n:::\n\n\nIf we're interested in the causal effect of `x` on `y`, we're trying to estimate a numeric representation of that arrow.\nUsually, though, there are many other variables and arrows in the causal structure of a given question.\nA series of arrows is called a *path*.\nThere are three types of paths you'll see in DAGs: forks, chains, and colliders (sometimes called inverse forks).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Three types of causal relationships: forks, chains, and colliders. The direction of the arrows and the relationships of interest dictate which type of path a series of variables is. Forks represent a mutual cause, chains represent direct causes, and colliders represent a mutual descendant.](chapter-05_files/figure-html/fig-dag-path-types-1.png){#fig-dag-path-types width=672}\n:::\n:::\n\n\nForks represent a common cause of two variables.\nHere, we're saying that `q` causes both `x` and `y`, the traditional definition of a confounder.\nThey're called forks because the arrows from `x` to `y` are in different directions.\nChains, on the other hand, represent a series of arrows going in the same direction.\nHere, `q` is called a *mediator*: it is along the causal path from `x` to `y`.\nIn this diagram, the only path from `x` to `y` is mediated through `q`.\nFinally, a collider is a path where two arrowheads meet at a variable.\nBecause causality always goes forward in time, this naturally means that the collider variable is caused by two other variables.\nHere, we're saying that `x` and `y` both cause `q`.\n\n::: callout-tip\n## Are DAGs SEMs?\n\nIf you're familiar with structural equation models (SEMs), a modeling technique commonly used in psychology and other social science settings, you may notice some similarities between SEMs and DAGs.\nDAGs are a form of *non-parametric* SEM.\nSEMs estimate entire graphs using parametric assumptions.\nCausal DAGs, on the other hand, don't estimate anything; an arrow going from one variable to another says nothing about the strength or functional form of that relationship, only that we think it exists.\n:::\n\nOne of the significant benefits of DAGs is that they help us identify sources of bias and, often, provide clues on how to address them.\nHowever, talking about an unbiased effect estimate only makes sense when we have a specific causal question in mind.\nSince each arrow represents a cause, it's causality all the way down; no individual arrow is inherently problematic.\nHere, we're interested in the effect of `x` on `y`.\nThis question defines which paths we're interested in and which we're not.\n\nThese three types of paths have different implications for the statistical relationship between `x` and `y`.\nIf we only look at the correlation between the two variables under these assumptions:\n\n1.  In the fork, `x` and `y` will be associated, despite there being no arrow from `x` to `y`.\n2.  In the chain, `x` and `y` are related only through `q`.\n3.  In the collider, `x` and `y` will *not* be related.\n\nPaths that transmit association are called *open paths*.\nPaths that do not transmit association are called *closed paths*.\nForks and chains are open, while colliders are closed.\n\nSo, should we adjust for `q`?\nThat depends on the nature of the path.\nForks are confounding paths.\nBecause `q` causes both `x` and `y`, `x` and `y` will have a spurious association.\nThey both contain information from `q`, their mutual cause.\nThat mutual causal relationship makes `x` and `y` associated statistically.\nAdjusting for `q` will *block* the bias from confounding and give us the true relationship between `x` and `y`.\n\n::: callout-tip\n## Adjustment\n\nWe can use a variety of techniques to account for a variable.\nWe use the term \"adjustment\" or \"controlling for\" to refer to any technique that removes the effect of variables we're not interested in.\n:::\n\n@fig-confounder-scatter depicts this effect visually.\nHere, `x` and `y` are continuous, and by definition of the DAG, they are unrelated.\n`q`, however, causes both.\nThe unadjusted effect is biased because it includes information about the open path from `x` to `y` via `q`.\nWithin levels of `q`, however, `x` and `y` are unrelated.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Two scatterplots of the relationship between `x` and `y`. With forks, the relationship is biased by `q`. When accounting for `q`, we see the true null relationship.](chapter-05_files/figure-html/fig-confounder-scatter-1.png){#fig-confounder-scatter width=672}\n:::\n:::\n\n\nFor chains, whether or not we adjust for mediators depends on the research question.\nHere, adjusting for `q` would result in a null estimate of the effect of `x` on `y`.\nBecause the only effect of `x` on `y` is via `q`, no other effect remains.\nThe effect of `x` on `y` mediated by `q` is called the *indirect* effect, while the effect of `x` on `y` directly is called the *direct* effect.\nIf we're only interested in the direct effect, controlling for `q` might be what we want.\nIf we want to know about both effects, we shouldn't try to adjust for `q`.\nWe'll learn more about estimating these and other mediation effects in @sec-mediation.\n\n@fig-mediator-scatter shows this effect visually.\nThe unadjusted effect of `x` on `y` represents the total effect.\nSince the total effect is due entirely to the path mediated by `q`, when we adjust for `q`, no relationship remains.\nThis null effect is the direct effect.\nNeither of these effects is due to bias, but each answers a different research question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Two scatterplots of the relationship between `x` and `y`. With chains, whether and how we should account for `q` depends on the research question. Without doing so, we see the impact of the total effect of `x` and `y`, including the indirect effect via `q`. When accounting for `q`, we see the direct (null) effect of `x` on `y`.](chapter-05_files/figure-html/fig-mediator-scatter-1.png){#fig-mediator-scatter width=672}\n:::\n:::\n\n\nColliders are different.\nIn the collider DAG of @fig-dag-path-types, `x` and `y` are *not* associated, but both cause `q`.\nAdjusting for `q` has the opposite effect than with confounding: it *opens* a biasing pathway.\nSometimes, people draw the path opened up by conditioning on a collider connecting `x` and `y`.\n\nVisually, we can see this happen when `x` and `y` are continuous and `q` is binary.\nIn @fig-collider-scatter, when we don't include `q`, we find no relationship between `x` and `y`.\nThat's the correct result.\nHowever, when we include `q`, we can detect information about both `x` and `y`, and they appear correlated: across levels of `x`, those with `q = 0` have lower levels of `y`.\nAssociation seemingly flows back in time.\nOf course, that can't happen from a causal perspective, so controlling for `q` is the wrong thing to do.\nWe end up with a biased effect of `x` on `y`.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Two scatterplots of the relationship between `x` and `y`. The unadjusted relationship between the two is unbiased. When accounting for `q`, we open a colliding backdoor path and bias the relationship between `x` and `y`.](chapter-05_files/figure-html/fig-collider-scatter-1.png){#fig-collider-scatter width=672}\n:::\n:::\n\n\nHow can this be?\nSince `x` and `y` happen before `q`, `q` can't impact them.\nLet's turn the DAG on its side and consider @fig-collider-time.\nIf we break down the two time points, at time point 1, `q` hasn't happened yet, and `x` and `y` are unrelated.\nAt time point 2, `q` happens due to `x` and `y`.\n*But causality only goes forward in time*.\n`q` happening later can't change the fact that `x` and `y` happened independently in the past.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![A collider relationship over two points in time. At time point one, there is no relationship between `x` and `y`. Both cause `q` by time point two, but this does not change what already happened at time point one.](chapter-05_files/figure-html/fig-collider-time-1.png){#fig-collider-time width=672}\n:::\n:::\n\n\nCausality only goes forward.\nAssociation, however, is time-agnostic.\nIt's just an observation about the numerical relationships between variables.\nWhen we control for the future, we risk introducing bias.\nIt takes time to develop an intuition for this.\nConsider a case where `x` and `y` are the only causes of `q`, and all three variables are binary.\nWhen *either* `x` or `y` equals 1, then `q` happens.\nIf we know `q = 1` and `x = 0` then logically it must be that `y = 1`.\nThus, knowing about `q` gives us information about `y` via `x`.\nThis example is extreme, but it shows how this type of bias, sometimes called *collider-stratification bias* or *selection bias*, occurs: conditioning on `q` provides statistical information about `x` and `y` and distorts their relationship [@Banack2023].\n\n::: callout-tip\n## Exchangeability revisited\n\nWe commonly refer to exchangability as the assumption of no confounding.\nActually, this isn't quite right.\nIt's the assumption of no *open, non-causal* paths [@hernan2021].\nMany times, these are confounding pathways.\nHowever, conditioning on a collider can also open paths.\nEven though these aren't confounders, doing so creates non-exchangeability between the two groups: they are different in a way that matters to the exposure and outcome.\n\nOpen, non-causal paths are also called *backdoor paths*.\nWe'll use this terminology often because it captures the idea well: these are any open paths biasing the effect we're interested in estimating.\n:::\n\nCorrectly identifying the causal structure between the exposure and outcome thus helps us 1) communicate the assumptions we're making about the relationships between variables and 2) identify sources of bias.\nImportantly, in doing 2), we are also often able to identify ways to prevent bias based on the assumptions in 1).\nIn the simple case of the three DAGs in @fig-dag-path-types, we know whether or not to control for `q` depending on the nature of the causal structure.\nThe set or sets of variables we need to adjust for is called the *adjustment set*.\nDAGs can help us identify adjustment sets even in complex settings [@vanderzander2019].\n\n::: callout-tip\n## What about interaction?\n\nDAGs don't make a statement about interaction or effect estimate modification, even though they are an important part of inference.\nTechnically, interaction is a matter of the functional form of the relationships in the DAG.\nMuch as we don't need to specify how we will model a variable in the DAG (e.g., with splines), we don't need to determine how variables statistically interact.\nThat's a matter for the modeling stage.\n\nThere are several ways we use interactions in causal inference.\nIn one extreme, they are simply a matter of functional form: interaction terms are included in models but marginalized to get an overall causal effect.\nConversely, we're interested in *joint causal effects*, where the two variables interacting are both causal.\nIn between, we can use interaction terms to identify *heterogeneous causal effects*, which vary by a second variable that is not assumed to be causal.\nAs with many tools in causal inference, we use the same statistical technique in many ways to answer different questions.\nWe'll revisit this topic in detail in [Chapter -@sec-interaction].\n\nMany people have tried expressing interaction in DAGs using different types of arcs, nodes, and other annotations, but no approach has taken off as the preferred way [@weinberg2007; @Nilsson2021].\n:::\n\nLet's take a look at an example in R.\nWe'll learn to build DAGs, visualize them, and identify important information like adjustment sets.\n\n## DAGs in R\n\nFirst, consider a research question: Does listening to a comedy podcast the morning before an exam improve graduate students' test scores?\nWe can diagram this using the method described in @sec-diag (@fig-diagram-podcast).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![A sentence diagram for the question: Does listening to a comedy podcast the morning before an exam improve graduate student test scores? The population is graduate students. The start time is morning, and the outcome time is after the exam.](../images/podcast-diagram.png){#fig-diagram-podcast width=2267}\n:::\n:::\n\n\nThe tool we'll use for making DAGs is ggdag.\nggdag is a package that connects ggplot2, the most powerful visualization tool in R, to dagitty, an R package with sophisticated algorithms for querying DAGs.\n\nTo create a DAG object, we'll use the `dagify()` function.`dagify()` returns a `dagitty` object that works with both the dagitty and ggdag packages.\nThe `dagify()` function takes formulas, separated by commas, that specify causes and effects, with the left element of the formula defining the effect and the right all of the factors that cause it.\nThis is just like the type of formula we specify for most regression models in R.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndagify(\n  effect1 ~ cause1 + cause2 + cause3,\n  effect2 ~ cause1 + cause4,\n  ...\n)\n```\n:::\n\n\nWhat are all of the factors that cause graduate students to listen to a podcast the morning before an exam?\nWhat are all of the factors that could cause a graduate student to do well on a test?\nLet's posit some here.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(ggdag)\ndagify(\n  podcast ~ mood + humor + prepared,\n  exam ~ mood + prepared\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\ndag {\nexam\nhumor\nmood\npodcast\nprepared\nhumor -> podcast\nmood -> exam\nmood -> podcast\nprepared -> exam\nprepared -> podcast\n}\n```\n\n\n:::\n:::\n\n\nIn the code above, we assume that:\n\n-   a graduate student's mood, sense of humor, and how prepared they feel for the exam could influence whether they listened to a podcast the morning of the test\n-   their mood and how prepared they are also influence their exam score\n\nNotice we *do not* see podcast in the exam equation; this means that we assume that there is **no** causal relationship between podcast and the exam score.\n\nThere are some other useful arguments you'll often find yourself supplying to `dagify()`:\n\n-   `exposure` and `outcome`: Telling ggdag the variables that are the exposure and outcome of your research question is required for many of the most valuable queries we can make of DAGs.\n-   `latent`: This argument lets us tell ggdag that some variables in the DAG are unmeasured. `latent` helps identify valid adjustment sets with the data we actually have.\n-   `coords`: Coordinates for the variables. You can choose between algorithmic or manual layouts, as discussed below. We'll use `time_ordered_coords()` here.\n-   `labels`: A character vector of labels for the variables.\n\nLet's create a DAG object, `podcast_dag`, with some of these attributes, then visualize the DAG with `ggdag()`.\n`ggdag()` returns a ggplot object, so we can add additional layers to the plot, like themes.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag <- dagify(\n  podcast ~ mood + humor + prepared,\n  exam ~ mood + prepared,\n  coords = time_ordered_coords(\n    list(\n      # time point 1\n      c(\"prepared\", \"humor\", \"mood\"), \n      # time point 2\n      \"podcast\",  \n      # time point 3\n      \"exam\"\n    )\n  ),\n  exposure = \"podcast\",\n  outcome = \"exam\",\n  labels = c(\n    podcast = \"podcast\",\n    exam = \"exam score\",\n    mood = \"mood\",\n    humor = \"humor\",\n    prepared = \"prepared\"\n  )\n)\nggdag(podcast_dag, use_labels = \"label\", text = FALSE) +\n  theme_dag()\n```\n\n::: {.cell-output-display}\n![Proposed DAG to answer the question: Does listening to a comedy podcast the morning before an exam improve graduate students' test scores?](chapter-05_files/figure-html/fig-dag-podcast-1.png){#fig-dag-podcast width=384}\n:::\n:::\n\n\n::: callout-note\nFor the rest of the chapter, we'll use `theme_dag()`, a ggplot theme from ggdag meant for DAGs.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntheme_set(\n  theme_dag() %+replace%\n    # also add some additional styling\n    theme(\n      legend.position = \"bottom\",\n      strip.text.x = element_text(margin = margin(2, 0, 2, 0, \"mm\"))\n    )\n)\n```\n:::\n\n:::\n\n::: callout-tip\n## DAG coordinates\n\nYou don't need to specify coordinates to ggdag.\nIf you don't, it uses algorithms designed for automatic layouts.\nThere are many such algorithms, and they focus on different aspects of the layout, e.g., the shape, the space between the nodes, minimizing how many edges cross, etc.\nThese layout algorithms usually have a component of randomness, so it's good to use a seed if you want to get the same result.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# no coordinates specified\nset.seed(123)\npod_dag <- dagify(\n  podcast ~ mood + humor + prepared,\n  exam ~ mood + prepared\n)\n\n# automatically determine layouts\npod_dag |> \n  ggdag(text_size = 2.8)\n```\n\n::: {.cell-output-display}\n![](chapter-05_files/figure-html/unnamed-chunk-14-1.png){fig-align='center' width=384}\n:::\n:::\n\n\nWe can also ask for a specific layout, e.g., the popular Sugiyama algorithm for DAGs [@sugiyama1981].\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\npod_dag |> \n  ggdag(layout = \"sugiyama\", text_size = 2.8)\n```\n\n::: {.cell-output-display}\n![](chapter-05_files/figure-html/unnamed-chunk-15-1.png){fig-align='center' width=384}\n:::\n:::\n\n\nFor causal DAGs, the time-ordered layout algorithm is often best, which we can specify with `time_ordered_coords()` or `layout = \"time_ordered\"`.\nWe'll discuss time ordering in greater detail in @sec-time-ordered.\nEarlier, we explicitly told ggdag which variables were at which time points, but we don't need to.\nNotice, though, that the time ordering algorithm puts `podcast` and `exam` at the same time point since one doesn't cause another (and thus predate it).\nWe know that's not the case: listening to the podcast happened before taking the exam.\n\n<!-- TODO: if implementing a better way to use this algorithm while specifying one or more time points, then update this -->\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\npod_dag |> \n  ggdag(layout = \"time_ordered\", text_size = 2.8)\n```\n\n::: {.cell-output-display}\n![](chapter-05_files/figure-html/unnamed-chunk-16-1.png){fig-align='center' width=384}\n:::\n:::\n\n\nYou can manually specify coordinates using a list or data frame and provide them to the `coords` argument of `dagify()`.\nAdditionally, because ggdag is based on dagitty, you can use `dagitty.net` to create and organize a DAG using a graphical interface, then export the result as dagitty code for ggdag to consume.\n\nAlgorithmic layouts are lovely for fast visualization of DAGs or particularly complex graphs.\nOnce you want to share your DAG, it's usually best to be more intentional about the layout, perhaps by specifying the coordinates manually.\n`time_ordered_coords()` is often the best of both worlds, and we'll use it for most DAGs in this book.\n:::\n\nWe've specified the DAG for this question and told ggdag what the exposure and outcome of interest are.\nAccording to the DAG, there is no direct causal relationship between listening to a podcast and exam scores.\nAre there any other open paths?\n`ggdag_paths()` takes a DAG and visualizes the open paths.\nIn @fig-paths-podcast, we see two open paths: `podcast <- mood -> exam\"` and `podcast <- prepared -> exam`. These are both forks---*confounding pathways*. Since there is no causal relationship between listening to a podcast and exam scores, the only open paths are *backdoor* paths, these two confounding pathways.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag |> \n  # show the whole dag as a light gray \"shadow\" \n  # rather than just the paths\n  ggdag_paths(shadow = TRUE, text = FALSE, use_labels = \"label\")\n```\n\n::: {.cell-output-display}\n![`ggdag_paths()` visualizes open paths in a DAG. There are two open paths in `podcast_dag`: the fork from `mood` and the fork from `prepared`.](chapter-05_files/figure-html/fig-paths-podcast-1.png){#fig-paths-podcast width=672}\n:::\n:::\n\n\n::: callout-tip\n`dagify()` returns a `dagitty()` object, but underneath the hood, ggdag converts `dagitty` objects to tidy DAGs, a structure that holds both the `dagitty` object and a `dataframe` about the DAG.\nThis is handy if you want to manipulate the DAG programmatically.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag_tidy <- podcast_dag |> \n  tidy_dagitty()\n\npodcast_dag_tidy\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A DAG with 5 nodes and 5 edges\n#\n# Exposure: podcast\n# Outcome: exam\n#\n# A tibble: 7 × 9\n  name         x     y direction to       xend  yend\n  <chr>    <int> <int> <fct>     <chr>   <int> <int>\n1 exam         3     0 <NA>      <NA>       NA    NA\n2 humor        1     0 ->        podcast     2     0\n3 mood         1     1 ->        exam        3     0\n4 mood         1     1 ->        podcast     2     0\n5 podcast      2     0 <NA>      <NA>       NA    NA\n6 prepared     1    -1 ->        exam        3     0\n7 prepared     1    -1 ->        podcast     2     0\n# ℹ 2 more variables: circular <lgl>, label <chr>\n```\n\n\n:::\n:::\n\n\nMost of the quick plotting functions transform the `dagitty` object to a tidy DAG if it's not already, then manipulate the data in some capacity.\nFor instance, `dag_paths()` underlies `ggdag_paths()`; it returns a tidy DAG with data about the paths.\nYou can use several dplyr functions on these objects directly.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag_tidy |> \n  dag_paths() |> \n  filter(set == 2, path == \"open path\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A DAG with 3 nodes and 2 edges\n#\n# Exposure: podcast\n# Outcome: exam\n#\n# A tibble: 4 × 11\n  set   name        x     y direction to     xend  yend\n  <chr> <chr>   <int> <int> <fct>     <chr> <int> <int>\n1 2     exam        3     0 <NA>      <NA>     NA    NA\n2 2     podcast     2     0 <NA>      <NA>     NA    NA\n3 2     prepar…     1    -1 ->        exam      3     0\n4 2     prepar…     1    -1 ->        podc…     2     0\n# ℹ 3 more variables: circular <lgl>, label <chr>,\n#   path <chr>\n```\n\n\n:::\n:::\n\n\nTidy DAGs are not pure data frames, but you can retrieve either the `dataframe` or `dagitty` object to work with them directly using `pull_dag_data()` or `pull_dag()`.\n`pull_dag()` can be useful when you want to work with dagitty functions:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dagitty)\npodcast_dag_tidy |> \n  pull_dag() |> \n  paths()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n$paths\n[1] \"podcast <- mood -> exam\"    \n[2] \"podcast <- prepared -> exam\"\n\n$open\n[1] TRUE TRUE\n```\n\n\n:::\n:::\n\n:::\n\nBackdoor paths pollute the statistical association between `podcast` and `exam`, so we must account for them.\n`ggdag_adjustment_set()` visualizes any valid adjustment sets implied by the DAG.\n@fig-podcast-adustment-set shows adjusted variables as squares.\nAny arrows coming out of adjusted variables are removed from the DAG because the path is longer open at that variable.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggdag_adjustment_set(\n  podcast_dag, \n  text = FALSE, \n  use_labels = \"label\"\n)\n```\n\n::: {.cell-output-display}\n![A visualization of the minimal adjustment set for the podcast-exam DAG. If this DAG is correct, two variables are required to block the backdoor paths: `mood` and `prepared`.](chapter-05_files/figure-html/fig-podcast-adustment-set-1.png){#fig-podcast-adustment-set fig-align='center' width=384}\n:::\n:::\n\n\n@fig-podcast-adustment-set shows the *minimal adjustment set*.\nBy default, ggdag returns the set(s) that can close all backdoor paths with the fewest number of variables possible.\nIn this DAG, that's just one set: `mood` and `prepared`.\nThis set makes sense because there are two backdoor paths, and the only other variables on them besides the exposure and outcome are these two variables.\nSo, at minimum, we must account for both to get a valid estimate.\n\n::: callout-tip\n`ggdag()` and friends usually use `tidy_dagitty()` and `dag_*()` or `node_*()` functions to change the underlying data frame.\nSimilarly, the quick plotting functions use ggdag's geoms to visualize the resulting DAG(s).\nIn other words, you can use the same data manipulation and visualization strategies that you use day-to-day directly with ggdag.\n\nHere's a condensed version of what `ggdag_adjustment_set()` is doing:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\npodcast_dag_tidy |> \n  # add adjustment sets to data\n  dag_adjustment_sets() |>\n  ggplot(aes(\n    x = x, y = y, xend = xend, yend = yend,\n    color = adjusted, shape = adjusted\n  )) + \n  # ggdag's custom geoms: add nodes, edges, and labels\n  geom_dag_point() + \n  # remove adjusted paths\n  geom_dag_edges_link(data = \\(.df) filter(.df, adjusted != \"adjusted\")) + \n  geom_dag_label_repel() + \n  # you can use any ggplot function, too\n  facet_wrap(~ set) +\n  scale_shape_manual(values = c(adjusted = 15, unadjusted = 19))\n```\n\n::: {.cell-output-display}\n![](chapter-05_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=432}\n:::\n:::\n\n:::\n\nMinimal adjustment sets are only one type of valid adjustment set [@vanderzander2019].\nSometimes, other combinations of variables can get us an unbiased effect estimate.\nTwo other options available in ggdag are full adjustment sets and canonical adjustment sets.\nFull adjustment sets are every combination of variables that result in a valid set.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggdag_adjustment_set(\n  podcast_dag, \n  text = FALSE, \n  use_labels = \"label\",\n  # get full adjustment sets\n  type = \"all\"\n)\n```\n\n::: {.cell-output-display}\n![All valid adjustment sets for `podcast_dag`.](chapter-05_files/figure-html/fig-adustment-set-all-1.png){#fig-adustment-set-all fig-align='center' width=624}\n:::\n:::\n\n\nIt turns out that we can also control for `humor`.\n\nCanonical adjustment sets are a bit more complex: they are all possible ancestors of the exposure and outcome minus any likely descendants.\nIn fully saturated DAGs (DAGs where every node causes anything that comes after it in time), the canonical adjustment set is the minimal adjustment set.\n\n::: callout-tip\nMost of the functions in ggdag use dagitty underneath the hood.\nIt's often helpful to call dagitty functions directly.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nadjustmentSets(podcast_dag, type = \"canonical\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n{ humor, mood, prepared }\n```\n\n\n:::\n:::\n\n:::\n\nUsing our proposed DAG, let's simulate some data to see how accounting for the minimal adjustment set might occur in practice.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(10)\nsim_data <- podcast_dag |>\n  simulate_data()\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsim_data\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 500 × 5\n     exam  humor   mood podcast prepared\n    <dbl>  <dbl>  <dbl>   <dbl>    <dbl>\n 1 -0.435  0.263 -0.100  -0.630   1.07  \n 2 -0.593  0.317  0.143  -1.55    0.0640\n 3  0.786  1.97  -0.591  -0.318  -0.439 \n 4 -0.103  2.86  -0.139   1.07    0.754 \n 5 -0.614 -2.39   0.702   0.464   0.356 \n 6  1.01   1.21   0.910   0.769   0.561 \n 7  0.167 -1.37  -0.559  -0.866   0.214 \n 8  1.16   0.164 -0.743   0.969  -1.67  \n 9  0.650  0.215 -0.248   0.691  -0.303 \n10  0.156  0.713  1.19   -1.02   -0.219 \n# ℹ 490 more rows\n```\n\n\n:::\n:::\n\n\nSince we have simulated this data, we know that this is a case where *standard methods will succeed* (see @sec-standard) and, therefore, can estimate the causal effect using a basic linear regression model.\n@fig-dag-sim shows a forest plot of the simulated data based on our DAG.\nNotice the model that only included the exposure resulted in a spurious effect (an estimate of -0.1 when we know the truth is 0).\nIn contrast, the model that adjusted for the two variables as suggested by `ggdag_adjustment_set()` is not spurious (much closer to 0).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## Model that does not close backdoor paths\nunadjusted_model <- lm(exam ~ podcast, sim_data) |>\n  broom::tidy(conf.int = TRUE) |>\n  dplyr::filter(term == \"podcast\") |>\n  mutate(formula = \"podcast\")\n\n## Model that closes backdoor paths\nadjusted_model <- lm(exam ~ podcast + mood + prepared, sim_data) |>\n  broom::tidy(conf.int = TRUE) |>\n  dplyr::filter(term == \"podcast\") |>\n  mutate(formula = \"podcast + mood + prepared\")\n\nbind_rows(\n  unadjusted_model,\n  adjusted_model\n) |>\n  ggplot(aes(x = estimate, y = formula, xmin = conf.low, xmax = conf.high)) +\n  geom_vline(xintercept = 0, linewidth = 1, color = \"grey80\") +\n  geom_pointrange(fatten = 3, size = 1) +\n  theme_minimal(18) +\n  labs(\n    y = NULL,\n    caption = \"correct effect size: 0\"\n  )\n```\n\n::: {.cell-output-display}\n![Forest plot of simulated data based on the DAG described in @fig-dag-podcast.](chapter-05_files/figure-html/fig-dag-sim-1.png){#fig-dag-sim width=672}\n:::\n:::\n\n\n## Structures of Causality\n\n### Advanced Confounding\n\nIn `podcast_dag`, `mood` and `prepared` were *direct* confounders: an arrow was going directly from them to `podcast` and `exam`.\nOften, backdoor paths are more complex.\nLet's consider such a case by adding two new variables: `alertness` and `skills_course`.\n`alertness` represents the feeling of alertness from a good mood, thus the arrow from `mood` to `alertness`.\n`skills_course` represents whether the student took a College Skills Course and learned time management techniques.\nNow, `skills_course` is what frees up the time to listen to a podcast as well as being prepared for the exam.\n`mood` and `prepared` are no longer direct confounders: they are two variables along a more complex backdoor path.\nAdditionally, we've added an arrow going from `humor` to `mood`.\nLet's take a look at @fig-podcast_dag2.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag2 <- dagify(\n    podcast ~ mood + humor + skills_course,\n    alertness ~ mood,\n    mood ~ humor,\n    prepared ~ skills_course,\n    exam ~ alertness + prepared,\n    coords = time_ordered_coords(),\n    exposure = \"podcast\",\n    outcome = \"exam\",\n    labels = c(\n        podcast = \"podcast\",\n        exam = \"exam score\",\n        mood = \"mood\",\n        alertness = \"alertness\",\n        skills_course = \"college\\nskills course\",\n        humor = \"humor\",\n        prepared = \"prepared\"\n    )\n)\n\nggdag(podcast_dag2, use_labels = \"label\", text = FALSE)\n```\n\n::: {.cell-output-display}\n![An expanded version of `podcast_dag` that includes two additional variables: `skills_course`, representing a College Skills Course, and `alertness`.](chapter-05_files/figure-html/fig-podcast_dag2-1.png){#fig-podcast_dag2 width=480}\n:::\n:::\n\n::: {.cell}\n\n:::\n\n\nNow there are *three* backdoor paths we need to close: `podcast <- humor -> mood -> alertness -> exam`, `podcast <- mood -> alertness -> exam`, and`podcast <- skills_course -> prepared -> exam`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggdag_paths(podcast_dag2, use_labels = \"label\", text = FALSE, shadow = TRUE)\n```\n\n::: {.cell-output-display}\n![Three open paths in `podcast_dag2`. Since there is no effect of `podcast` on `exam`, all three are backdoor paths that must be closed to get the correct effect.](chapter-05_files/figure-html/fig-podcast_dag2-paths-1.png){#fig-podcast_dag2-paths width=1056}\n:::\n:::\n\n\nThere are four minimal adjustment sets to close all three paths (and eighteen full adjustment sets!).\nThe minimal adjustment sets are `alertness + prepared`, `alertness + skills_course`, `mood + prepared`, `mood + skills_course`.\nWe can now block the open paths in several ways.\n`mood` and `prepared` still work, but we've got other options now.\nNotably, `prepared` and `alertness` could happen at the same time or even after `podcast`.\n`skills_course` and `mood` still happen before both `podcast` and `exam`, so the idea is still the same: the confounding pathway starts before the exposure and outcome.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggdag_adjustment_set(podcast_dag2, use_labels = \"label\", text = FALSE)\n```\n\n::: {.cell-output-display}\n![Valid minimal adjustment sets that will close the backdoor paths in @fig-podcast_dag2-paths.](chapter-05_files/figure-html/fig-podcast_dag2-set-1.png){#fig-podcast_dag2-set width=672}\n:::\n:::\n\n\nDeciding between these adjustment sets is a matter of judgment: if all data are perfectly measured, the DAG is correct, and we've modeled them correctly, then it doesn't matter which we use.\nEach adjustment set will result in an unbiased estimate.\nAll three of those assumptions are usually untrue to some degree.\nLet's consider the path via `skills_course` and `prepared`.\nIt may be that we are better able to assess whether or not someone took the College Skills Course than how prepared for the exam they are.\nIn that case, an adjustment set with `skills_course` is a better option.\nBut perhaps we better understand the relationship between preparedness and exam results.\nIf we have it measured, controlling for that might be better.\nWe could get the best of both worlds by including both variables: between the better measurement of `skills_course` and the better modeling of `prepared`, we might have a better chance of minimizing confounding from this path.\n\n### Selection Bias and Mediation\n\nSelection bias is another name for the type of bias that is induced by adjusting for a collider [@lu2022].\nIt's called \"selection bias\" because a common form of collider-induced bias is a variable inherently stratified upon by the design of the study---selection *into* the study.\nLet's consider a case based on the original `podcast_dag` but with one additional variable: whether or not the student showed up to the exam.\nNow, there is an indirect effect of `podcast` on `exam`: listening to a podcast influences whether or not the students attend the exam.\nThe true result of `exam` is missing for those who didn't show up; by studying the group of people who *did* show up, we are inherently stratifying on this variable.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag3 <- dagify(\n  podcast ~ mood + humor + prepared,\n  exam ~ mood + prepared + showed_up,\n  showed_up ~ podcast + mood + prepared,\n  coords = time_ordered_coords(\n    list(\n      # time point 1\n      c(\"prepared\", \"humor\", \"mood\"), \n      # time point 2\n      \"podcast\",  \n      \"showed_up\",  \n      # time point 3\n      \"exam\"\n    )\n  ),\n  exposure = \"podcast\",\n  outcome = \"exam\",\n  labels = c(\n    podcast = \"podcast\",\n    exam = \"exam score\",\n    mood = \"mood\",\n    humor = \"humor\",\n    prepared = \"prepared\",\n    showed_up = \"showed up\"\n  )\n)\nggdag(podcast_dag3, use_labels = \"label\", text = FALSE)\n```\n\n::: {.cell-output-display}\n![Another variant of `podcast_dag`, this time including the inherent stratification on those who appear for the exam. There is still no direct effect of `podcast` on `exam`, but there is an indirect effect via `showed_up`.](chapter-05_files/figure-html/fig-podcast_dag3-1.png){#fig-podcast_dag3 width=432}\n:::\n:::\n\n\nThe problem is that `showed_up` is both a collider and a mediator: stratifying on it induces a relationship between many of the variables in the DAG but blocks the indirect effect of `podcast` on `exam`.\nLuckily, the adjustment sets can handle the first problem; because `showed_up` happens *before* `exam`, we're less at risk of collider bias between the exposure and outcome.\nUnfortunately, we cannot calculate the total effect of `podcast` on `exam` because part of the effect is missing: the indirect effect is closed at `showed_up`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag3 |> \n  adjust_for(\"showed_up\") |> \n  ggdag_adjustment_set(text = FALSE, use_labels = \"label\")\n```\n\n::: {.cell-output-display}\n![The adjustment set for `podcast_dag3` given that the data are inherently conditioned on showing up to the exam. In this case, there is no way to recover an unbiased estimate of the total effect of `podcast` on `exam`.](chapter-05_files/figure-html/fig-podcast_dag3-as-1.png){#fig-podcast_dag3-as width=432}\n:::\n:::\n\n\nSometimes, you can still estimate effects in this situation by changing the estimate you wish to calculate.\nWe can't calculate the total effect because we are missing the indirect effect, but we can still calculate the direct effect of `podcast` on `exam`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag3 |> \n  adjust_for(\"showed_up\") |> \n  ggdag_adjustment_set(effect = \"direct\", text = FALSE, use_labels = \"label\")\n```\n\n::: {.cell-output-display}\n![The adjustment set for `podcast_dag3` when targeting a different effect. There is one minimal adjustment set that we can use to estimate the direct effect of `podcast` on `exam`.](chapter-05_files/figure-html/fig-podcast_dag3-direct-1.png){#fig-podcast_dag3-direct width=432}\n:::\n:::\n\n\n#### M-Bias and Butterfly Bias\n\nA particular case of selection bias that you'll often see people talk about is *M-bias*.\nIt's called M-bias because it looks like an M when arranged top to bottom.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nm_bias() |> \n  ggdag()\n```\n\n::: {.cell-output-display}\n![A DAG representing M-Bias, a situation where a collider predates the exposure and outcome.](chapter-05_files/figure-html/fig-m-bias-1.png){#fig-m-bias width=384}\n:::\n:::\n\n\n::: callout-tip\nggdag has several quick-DAGs for demonstrating basic causal structures, including `confounder_triangle()`, `collider_triangle()`, `m_bias()`, and `butterfly_bias()`.\n:::\n\nWhat's theoretically interesting about M-bias is that `m` is a collider but occurs before `x` and `y`.\nRemember that association is blocked at a collider, so there is no open path between `x` and `y`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npaths(m_bias())\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n$paths\n[1] \"x <- a -> m <- b -> y\"\n\n$open\n[1] FALSE\n```\n\n\n:::\n:::\n\n\nLet's focus on the `mood` path of the podcast-exam DAG.\nWhat if we were wrong about mood, and the actual relationship was M-shaped?\nLet's say that, rather than causing `podcast` and `exam`, `mood` was itself caused by two mutual causes of `podcast` and `exam`, `u1` and `u2`, as in @fig-podcast_dag4.\nWe don't know what `u1` and `u2` are, and we don't have them measured.\nAs above, there are no open paths in this subset of the DAG.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag4 <- dagify(\n  podcast ~ u1,\n  exam ~ u2,\n  mood ~ u1 + u2,\n  coords = time_ordered_coords(list(\n    c(\"u1\", \"u2\"),\n    \"mood\",\n    \"podcast\", \n    \"exam\"\n  )),\n  exposure = \"podcast\",\n  outcome = \"exam\",\n  labels = c(\n    podcast = \"podcast\",\n    exam = \"exam score\",\n    mood = \"mood\",\n    u1 = \"unmeasured\",\n    u2 = \"unmeasured\"\n  ),\n  # we don't have them measured\n  latent = c(\"u1\", \"u2\")\n)\n\nggdag(podcast_dag4, use_labels = \"label\", text = FALSE)\n```\n\n::: {.cell-output-display}\n![A reconfiguration of @fig-dag-podcast where `mood` is a collider on an M-shaped path.](chapter-05_files/figure-html/fig-podcast_dag4-1.png){#fig-podcast_dag4 width=528}\n:::\n:::\n\n\nThe problem arises when we think our original DAG is the right DAG: `mood` is in the adjustment set, so we control for it.\nBut this induces bias!\nIt opens up a path between `u1` and `u2`, thus creating a path from `podcast` to `exam`.\nIf we had either `u1` or `u2` measured, we could adjust for them to close this path, but we don't.\nThere is no way to close this open path.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag4 |> \n  adjust_for(\"mood\") |> \n  ggdag_adjustment_set(use_labels = \"label\", text = FALSE)\n```\n\n::: {.cell-output-display}\n![The adjustment set where `mood` is a collider. If we control for `mood` and don't know about or have the unmeasured causes of `mood`, we have no means of closing the backdoor path opened by adjusting for a collider.](chapter-05_files/figure-html/fig-podcast_dag4-as-1.png){#fig-podcast_dag4-as width=528}\n:::\n:::\n\n\nOf course, the best thing to do here is not control for `mood` at all.\nSometimes, though, that is not an option.\nImagine if, instead of `mood`, this turned out to be the real structure for `showed_up`: since we inherently control for `showed_up`, and we don't have the unmeasured variables, our study results will always be biased.\nIt's essential to understand if we're in that situation so we can address it with sensitivity analysis to understand just how biased the effect would be.\n\nLet's consider a variation on M-bias where `mood` causes `podcast` and `exam` and `u1` and `u2` are mutual causes of `mood` and the exposure and outcome.\nThis arrangement is sometimes called butterfly or bowtie bias, again because of its shape.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbutterfly_bias(x = \"podcast\", y = \"exam\", m = \"mood\", a = \"u1\", b = \"u2\") |> \n  ggdag(text = FALSE, use_labels = \"label\")\n```\n\n::: {.cell-output-display}\n![In butterfly bias, `mood` is both a collider and a confounder. Controlling for the bias induced by `mood` opens a new pathway because we've also conditioned on a collider. We can't properly close all backdoor paths without either `u1` or `u2`.](chapter-05_files/figure-html/fig-butterfly_bias-1.png){#fig-butterfly_bias width=480}\n:::\n:::\n\n\nNow, we're in a challenging position: we need to control for `mood` because it's a confounder, but controlling for `mood` opens up the pathway from `u1` to `u2`.\nBecause we don't have either variable measured, we can't then close the path opened from conditioning on `mood`.\nWhat should we do?\nIt turns out that, when in doubt, controlling for `mood` is the better of the two options: confounding bias tends to be worse than collider bias, and M-shaped structures of colliders are sensitive to slight deviations (e.g., if this is not the exact structure, often the bias isn't as bad) [@DingMiratrix2015].\n\nAnother common form of selection bias is from *loss to follow-up*: people drop out of a study in a way that is related to the exposure and outcome.\nWe'll come back to this topic in [Chapter -@sec-longitudinal].\n\n### Causes of the exposure, causes of the outcome\n\nLet's consider one other type of causal structure that's important: causes of the exposure and not the outcome, and their opposites, causes of the outcome and not the exposure.\nLet's add a variable, `grader_mood`, to the original DAG.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag5 <- dagify(\n  podcast ~ mood + humor + prepared,\n  exam ~ mood + prepared + grader_mood,\n  coords = time_ordered_coords(\n    list(\n      # time point 1\n      c(\"prepared\", \"humor\", \"mood\"), \n      # time point 2\n      c(\"podcast\", \"grader_mood\"),  \n      # time point 3\n      \"exam\"\n    )\n  ),\n  exposure = \"podcast\",\n  outcome = \"exam\",\n  labels = c(\n    podcast = \"podcast\",\n    exam = \"exam score\",\n    mood = \"student\\nmood\",\n    humor = \"humor\",\n    prepared = \"prepared\",\n    grader_mood = \"grader\\nmood\"\n  )\n)\nggdag(podcast_dag5, use_labels = \"label\", text = FALSE)\n```\n\n::: {.cell-output-display}\n![A DAG containing a cause of the exposure that is not the cause of the outcome (`humor`) and a cause of the outcome that is not a cause of the exposure (`grader_mood`).](chapter-05_files/figure-html/fig-podcast_dag5-1.png){#fig-podcast_dag5 width=480}\n:::\n:::\n\n\nThere are now two variables that aren't related to *both* the exposure and the outcome: `humor`, which causes `podcast` but not `exam`, and `grader_mood`, which causes `exam` but not `podcast`.\nLet's start with `humor`.\n\nVariables that cause the exposure but not the outcome are also called *instrumental variables* (IVs).\nIVs are an unusual circumstance where, under certain conditions, controlling for them can make other types of bias worse.\nWhat's unique about this is that IVs can *also* be used to conduct an entirely different approach to estimating an unbiased effect of the exposure on the outcome.\nIVs are commonly used this way in econometrics and are increasingly popular in other areas.\nIn short, IV analysis allows us to estimate the causal effect using a different set of assumptions than the approaches we've talked about thus far.\nSometimes, a problem intractable using propensity score methods can be addressed using IVs and vice versa.\nWe'll talk more about IVs in @sec-iv-friends.\n\nSo, if you're *not* using IV methods, should you include an IV in a model meant to address confounding?\nIf you're unsure if the variable is an IV or not, you should probably add it to your model: it's more likely to be a confounder than an IV, and, it turns out, the bias from adding an IV is usually small in practice.\nSo, like adjusting for a potential M-structure variable, the risk of bias is worse from confounding [@Myers2011].\n\nNow, let's talk about the opposite of an IV: a cause of the outcome that is not the cause of the exposure.\nThese variables are sometimes called *competing exposures* (because they also cause the outcome) or *precision variables* (because, as we'll see, they increase the precision of causal estimates).\nWe'll call them precision variables because we're concerned about the relationship to the research question at hand, not to another research question where they are exposures [@Brookhart2006].\n\nLike IVs, precision variables do not occur along paths from the exposure to the outcome.\nThus, including them is not necessary.\nUnlike IVs, including precision variables is beneficial.\nIncluding other causes of the outcomes helps a statistical model capture some of its variation.\nThis doesn't impact the point estimate of the effect, but it does reduce the variance, resulting in smaller standard errors and narrower confidence intervals.\nThus, we recommend including them when possible.\n\nSo, even though we don't need to control for `grader_mood`, if we have it in the data set, we should.\nSimilarly, `humor` is not a good addition to the model unless we think it really might be a confounder; if it is a valid instrument, we might want to consider using IV methods to estimate the effect instead.\n\n### Measurement Error and Missingness\n\nDAGs can also help us understand the bias arising from mismeasurements in the data, including the worst mismeasurement: not measuring it at all.\nWe'll cover these topics in [Chapter -@sec-missingness], but the basic idea is that by separating the actual value from the observed value, we can better understand how such biases may behave [@Hernán2009].\nHere's a basic example of a bias called *recall bias*.\nRecall bias is when the outcome influences a participant's memory of exposure, so it's a particular problem in retrospective studies where the earlier exposure is not recorded until after the outcome happens.\nAn example of when this can occur is a case-control study of cancer.\nSomeone *with* cancer may be more motivated to ruminate on their past exposures than someone *without* cancer.\nSo, their memory about a given exposure may be more refined than someone without.\nBy conditioning on the observed version of the exposure, we open up many collider paths.\nUnfortunately, there is no way to close them all.\nIf this is the case, we must investigate how severe the bias would be in practice.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nerror_dag <- dagify(\n  exposure_observed ~ exposure_real + exposure_error,\n  outcome_observed ~ outcome_real + outcome_error,\n  outcome_real ~ exposure_real,\n  exposure_error ~ outcome_real,\n  labels = c(\n    exposure_real = \"Exposure\\n(truth)\",\n    exposure_error = \"Measurement Error\\n(exposure)\",\n    exposure_observed = \"Exposure\\n(observed)\",\n    outcome_real = \"Outcome\\n(truth)\",\n    outcome_error = \"Measurement Error\\n(outcome)\",\n    outcome_observed = \"Outcome\\n(observed)\"\n  ),\n  exposure = \"exposure_real\",\n  outcome = \"outcome_real\",\n  coords = time_ordered_coords()\n)\n\nerror_dag |> \n  ggdag(text = FALSE, use_labels = \"label\")\n```\n\n::: {.cell-output-display}\n![A DAG representing measurement error in observing the exposure and outcome. In this case, the outcome impacts the participant's memory of the exposure, also known as recall bias.](chapter-05_files/figure-html/fig-error_dag-1.png){#fig-error_dag width=528}\n:::\n:::\n\n\n## Recommendations in building DAGs\n\nIn principle, using DAGs is easy: specify the causal relationships you think exist and then query the DAG for information like valid adjustment sets.\nIn practice, assembling DAGs takes considerable time and thought.\nNext to defining the research question itself, it's one of the most challenging steps in making causal inferences.\nVery little guidance exists on best practices in assembling DAGs.\n@Tennant2021 collected data on DAGs in applied health research to better understand how researchers used them.\n@tbl-dag-properties shows some information they collected: the median number of nodes and arcs in a DAG, their ratio, the saturation percent of the DAG, and how many were fully saturated.\nSaturating DAGs means adding all possible arrows going forward in time, e.g., in a fully saturated DAG, any given variable at time point 1 has arrows going to all variables in future time points, and so on.\nMost DAGs were only about half saturated, and very few were fully saturated.\n\nOnly about half of the papers using DAGs reported the adjustment set used.\nIn other words, researchers presented their assumptions about the research question but not the implications about how they should handle the modeling stage or if they did use a valid adjustment set.\nSimilarly, the majority of studies did not report the estimand of interest.\n\n::: callout-note\nThe estimand is the target of interest in terms of what we're trying to estimate, as discussed briefly in [Chapter -@sec-whole-game].\nWe'll discuss estimands in detail in [Chapter -@sec-estimands].\n:::\n\n\n::: {#tbl-dag-properties .cell tbl-cap='A table of DAG properties measured by @Tennant2021. Number of nodes and arcs are the median number of variables and arrows in the analyzed DAGs, while the Node to Arc ratio is their ratio. Saturation proportion is the proportion of all possible arrows going forward in time to other included variables. Fully saturated DAGs are those that include all such arrows. @Tennant2021 also analyzed whether studies reported their estimands and adjustment sets.'}\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"kwsxntypwf\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#kwsxntypwf table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#kwsxntypwf thead, #kwsxntypwf tbody, #kwsxntypwf tfoot, #kwsxntypwf tr, #kwsxntypwf td, #kwsxntypwf th {\n  border-style: none;\n}\n\n#kwsxntypwf p {\n  margin: 0;\n  padding: 0;\n}\n\n#kwsxntypwf .gt_table {\n  display: table;\n  border-collapse: collapse;\n  line-height: normal;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  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{\n  text-align: left;\n}\n\n#kwsxntypwf .gt_center {\n  text-align: center;\n}\n\n#kwsxntypwf .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#kwsxntypwf .gt_font_normal {\n  font-weight: normal;\n}\n\n#kwsxntypwf .gt_font_bold {\n  font-weight: bold;\n}\n\n#kwsxntypwf .gt_font_italic {\n  font-style: italic;\n}\n\n#kwsxntypwf .gt_super {\n  font-size: 65%;\n}\n\n#kwsxntypwf .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#kwsxntypwf .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#kwsxntypwf .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#kwsxntypwf .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#kwsxntypwf .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#kwsxntypwf .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#kwsxntypwf .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;N = 144&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>N = 144</strong><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr class=\"gt_group_heading_row\">\n      <th colspan=\"2\" class=\"gt_group_heading\" scope=\"colgroup\" id=\"&lt;strong&gt;DAG properties&lt;/strong&gt;\"><strong>DAG properties</strong></th>\n    </tr>\n    <tr class=\"gt_row_group_first\"><td headers=\"dag_prop  label\" class=\"gt_row gt_left\">Number of Nodes</td>\n<td headers=\"dag_prop  stat_0\" class=\"gt_row gt_center\">12 (9, 16)</td></tr>\n    <tr><td headers=\"dag_prop  label\" class=\"gt_row gt_left\">Number of Arcs</td>\n<td headers=\"dag_prop  stat_0\" class=\"gt_row gt_center\">29 (19, 41)</td></tr>\n    <tr><td headers=\"dag_prop  label\" class=\"gt_row gt_left\">Node to Arc Ratio</td>\n<td headers=\"dag_prop  stat_0\" class=\"gt_row gt_center\">2.30 (1.78, 3.00)</td></tr>\n    <tr><td headers=\"dag_prop  label\" class=\"gt_row gt_left\">Saturation Proportion</td>\n<td headers=\"dag_prop  stat_0\" class=\"gt_row gt_center\">0.46 (0.31, 0.67)</td></tr>\n    <tr><td headers=\"dag_prop  label\" class=\"gt_row gt_left\">Fully Saturated</td>\n<td headers=\"dag_prop  stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"dag_prop  label\" class=\"gt_row gt_left\">    Yes</td>\n<td headers=\"dag_prop  stat_0\" class=\"gt_row gt_center\">4 (3%)</td></tr>\n    <tr><td headers=\"dag_prop  label\" class=\"gt_row gt_left\">    No</td>\n<td headers=\"dag_prop  stat_0\" class=\"gt_row gt_center\">140 (97%)</td></tr>\n    <tr class=\"gt_group_heading_row\">\n      <th colspan=\"2\" class=\"gt_group_heading\" scope=\"colgroup\" id=\"&lt;strong&gt;Reporting&lt;/strong&gt;\"><strong>Reporting</strong></th>\n    </tr>\n    <tr class=\"gt_row_group_first\"><td headers=\"reporting  label\" class=\"gt_row gt_left\">Reported Estimand</td>\n<td headers=\"reporting  stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"reporting  label\" class=\"gt_row gt_left\">    Yes</td>\n<td headers=\"reporting  stat_0\" class=\"gt_row gt_center\">40 (28%)</td></tr>\n    <tr><td headers=\"reporting  label\" class=\"gt_row gt_left\">    No</td>\n<td headers=\"reporting  stat_0\" class=\"gt_row gt_center\">104 (72%)</td></tr>\n    <tr><td headers=\"reporting  label\" class=\"gt_row gt_left\">Reported Adjustment Set</td>\n<td headers=\"reporting  stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"reporting  label\" class=\"gt_row gt_left\">    Yes</td>\n<td headers=\"reporting  stat_0\" class=\"gt_row gt_center\">80 (56%)</td></tr>\n    <tr><td headers=\"reporting  label\" class=\"gt_row gt_left\">    No</td>\n<td headers=\"reporting  stat_0\" class=\"gt_row gt_center\">64 (44%)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"2\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> Median (IQR); n (%)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nIn this section, we'll offer some advice from @Tennant2021 and our own experience assembling DAGs.\n\n### Iterate early and often\n\nOne of the best things you can do for the quality of your results is to make the DAG before you conduct the study, ideally before you even collect the data.\nIf you're already working with your data, at minimum, build your DAG before doing data analysis.\nThis advice is similar in spirit to pre-registered analysis plans: declaring your assumptions ahead of time can help clarify what you need to do, reduce the risk of overfitting (e.g., determining confounders incorrectly from the data), and give you time to get feedback on your DAG.\n\nThis last benefit is significant: you should ideally democratize your DAG.\nShare it early and often with others who are experts on the data, domain, and models.\nIt's natural to create a DAG, present it to your colleagues, and realize you have missed something important.\nSometimes, you will only agree on some details of the structure.\nThat's a good thing: you know now where there is uncertainty in your DAG.\nYou can then examine the results from multiple plausible DAGs or address the uncertainty with sensitivity analyses.\n\nIf you have more than one candidate DAG, check their adjustment sets.\nIf two DAGs have overlapping adjustment sets, focus on those sets; then, you can move forward in a way that satisfies the plausible assumptions you have.\n\n### Consider your question\n\nAs we saw in @fig-podcast_dag3, some questions can be challenging to answer with certain data, while others are more approachable.\nYou should consider precisely what it is you want to estimate.\nDefining your target estimate is an important topic and the subject of [Chapter -@sec-estimands].\n\nAnother important detail about how your DAG relates to your question is the population and time.\nMany causal structures are not static over time and space.\nConsider lung cancer: the distribution of causes of lung cancer was considerably different before the spread of smoking.\nIn medieval Japan, before the spread of tobacco from the Americas centuries later, the causal structure for lung cancer would have been practically different from what it is in Japan today, both in terms of tobacco use and other factors (age of the population, etc.).\n\nThe same is true for confounders.\nEven if something *can* cause the exposure and outcome, if the prevalence of that thing is zero in the population you're analyzing, it's irrelevant to the causal question.\nIt may also be that, in some populations, it doesn't affect one of the two.\nThe reverse is also true: something might be unique to the target population.\nThe use of tobacco in North America several centuries ago was unique among the world population, even though ceremonial tobacco use was quite different from modern recreational use.\nMany changes won't happen as dramatically as across centuries, but sometimes, they do, e.g., if regulation in one country effectively eliminates the population's exposure to something.\n\n### Order nodes by time {#chapter-05-chapter-05-chapter-05-sec-time-ordered}\n\nAs discussed earlier, we recommend ordering your variables by time, either left-to-right or up-to-down.\nThere are two reasons for this.\nFirst, time ordering is an integral part of your assumptions.\nAfter all, something happening before another thing is a requirement for it to be a cause.\nThinking this through carefully will clarify your DAG and the variables you need to address.\n\nSecond, after a certain level of complexity, it's easier to read a DAG when arranged by time because you have to think less about that dimension; it's inherent to the layout.\nThe time ordering algorithm in ggdag automates much of this for you, although, as we saw earlier, it's sometimes helpful to give it more information about the order.\n\nA related topic is feedback loops [@murray2022].\nOften, we think about two things that mutually cause each other as happening in a circle, like global warming and A/C use (A/C use increases global warming, which makes it hotter, which increases A/C use, and so on).\nIt's tempting to visualize that relationship like this:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndagify(\n  ac_use ~ global_temp,\n  global_temp ~ ac_use,\n  labels = c(ac_use = \"A/C use\", global_temp = \"Global\\ntemperature\")\n) |> \n  ggdag(layout = \"circle\", edge_type = \"arc\", text = FALSE, use_labels = \"label\")\n```\n\n::: {.cell-output-display}\n![A DAG representing the reciprocal relationship between A/C use and global temperature because of global warming. Feedback loops are useful mental shorthands to describe variables that impact each other over time compactly, but they are not true causal diagrams.](chapter-05_files/figure-html/fig-feedback-loop-1.png){#fig-feedback-loop width=432}\n:::\n:::\n\n\nFrom a DAG perspective, this is a problem because of the *A* part of *DAG*: it's cyclic!\nImportantly, though, it's also not correct from a causal perspective.\nFeedback loops are a shorthand for what really happens, which is that the two variables mutually affect each other *over time*.\nCausality only goes forward in time, so it doesn't make sense to go back and forth like in @fig-feedback-loop.\n\nThe real DAG looks something like this:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndagify(\n  global_temp_2000 ~ ac_use_1990 + global_temp_1990,\n  ac_use_2000 ~ ac_use_1990 + global_temp_1990,\n  global_temp_2010 ~ ac_use_2000 + global_temp_2000,\n  ac_use_2010 ~ ac_use_2000 + global_temp_2000,\n  global_temp_2020 ~ ac_use_2010 + global_temp_2010,\n  ac_use_2020 ~ ac_use_2010 + global_temp_2010,\n  coords = time_ordered_coords(),\n  labels = c(\n    ac_use_1990 = \"A/C use\\n(1990)\", \n    global_temp_1990 = \"Global\\ntemperature\\n(1990)\",\n    ac_use_2000 = \"A/C use\\n(2000)\", \n    global_temp_2000 = \"Global\\ntemperature\\n(2000)\",\n    ac_use_2010 = \"A/C use\\n(2010)\", \n    global_temp_2010 = \"Global\\ntemperature\\n(2010)\",\n    ac_use_2020 = \"A/C use\\n(2020)\", \n    global_temp_2020 = \"Global\\ntemperature\\n(2020)\"\n  )\n) |> \n  ggdag(text = FALSE, use_labels = \"label\")\n```\n\n::: {.cell-output-display}\n![A DAG showing the relationship between A/C use and global temperature over time. The true causal relationship in a feedback loop goes *forward*.](chapter-05_files/figure-html/fig-feedforward-1.png){#fig-feedforward width=480}\n:::\n:::\n\n\nThe two variables, rather than being in a feed*back* loop, are actually in a feed*forward* loop: they co-evolve over time.\nHere, we only show four discrete moments in time (the decades from 1990 to 2020), but of course, we could get much finer depending on the question and data.\n\nAs with any DAG, the proper analysis approach depends on the question.\nThe effect of A/C use in 2000 on the global temperature in 2020 produces a different adjustment set than the global temperature in 2000 on A/C use in 2020.\nSimilarly, whether we also model this change over time or just those two time points depends on the question.\nOften, these feedforward relationships require you to address *time-varying* confounding, which we'll discuss in [Chapter -@sec-longitudinal].\n\n### Consider the whole data collection process\n\nAs @fig-podcast_dag3 showed us, it's essential to consider the *way* we collected data as much as the causal structure of the question.\nConsidering the whole data collection process is particularly true if you're working with \"found\" data---a data set not intentionally collected to answer the research question.\nWe are always inherently conditioning on the data we have vs. the data we don't have.\nIf other variables influenced the data collection process in the causal structure, you need to consider the impact.\nDo you need to control for additional variables?\nDo you need to change the effect you are trying to estimate?\nCan you answer the question at all?\n\n<!-- TODO: revisit this after I've thought it through a bit more -->\n\n<!-- Let's consider an example: workers at a factory are potentially exposed to something (e.g., a carcinogenic) that may increase their risk of dying. Some workers are exposed, and some are not. The problem is that exposure *prior* to the study resulted in only those healthy enough to work being present at the start of the study. If there is another factor, say some important aspect of the worker's health, that also contributes to whether or not they are at work, we have selection bias. We are inherently stratifying on whether or not someone is at work. -->\n\n<!-- ```{r} -->\n\n<!-- dagify( -->\n\n<!--     death ~ health, -->\n\n<!--     exposed ~ exposed_prior + at_work, -->\n\n<!--     at_work ~ exposed_prior + health, -->\n\n<!--     exposure = \"exposed\", -->\n\n<!--     outcome = \"death\", -->\n\n<!--     latent = c(\"health\", \"exposed_prior\"), -->\n\n<!--     coords = time_ordered_coords(list( -->\n\n<!--         c(\"health\", \"exposed_prior\"), -->\n\n<!--         \"at_work\", -->\n\n<!-- \"exposed\", -->\n\n<!--         \"death\" -->\n\n<!--     )), -->\n\n<!--     labels = c( -->\n\n<!--         exposed_prior = \"Exposed\\n(prior)\", -->\n\n<!--         exposed = \"Exposed\\n(start)\", -->\n\n<!--         at_work = \"Working\", -->\n\n<!--         health = \"Health\\nStatus\", -->\n\n<!--         death = \"Death\" -->\n\n<!--     ) -->\n\n<!-- ) |>  -->\n\n<!--   ggdag(text = FALSE, use_labels = \"label\") -->\n\n<!-- ``` -->\n\n<!-- `at_work` is a collider: both health status and prior exposure have arrows going into it. Controlling for it induces a ... -->\n\n<!-- -   race/shooting (show the `effect` argument of `adjustmentSets` to get direct effect) -->\n\n<!-- -   healthy worker bias -->\n\n::: callout-tip\n## What about case-control studies?\n\nA standard study design in epidemiology is the case-control study.\nCase-control studies are beneficial when the outcome under study is rare or takes a very long time to happen (like many types of cancer).\nParticipants are selected into the study based on their outcome: once a person has an event, they are entered as a case and matched with a control who hasn't had the event.\nOften, they are matched on other factors as well.\n\nMatched case-control studies are selection biased by design [@mansournia2013].\nIn @fig-case-control, when we condition on selection into the study, we lose the ability to close all backdoor paths, even if we control for `confounder`.\nFrom the DAG, it would appear that the entire design is invalid!\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndagify(\n  outcome ~ confounder + exposure,\n  selection ~ outcome + confounder,\n  exposure ~ confounder,\n  exposure = \"exposure\",\n  outcome = \"outcome\",\n  coords = time_ordered_coords()\n) |> \n  ggdag(edge_type = \"arc\", text_size = 2.2)\n```\n\n::: {.cell-output-display}\n![A DAG representing a matched case-control study. In such a study, selection is determined by outcome status and any matched confounders. Selection into the study is thus a collider. Since it is inherently stratified on who is actually in the study, such data are limited in the types of causal effects they can estimate.](chapter-05_files/figure-html/fig-case-control-1.png){#fig-case-control width=432}\n:::\n:::\n\n\nLuckily, this isn't wholly true.\nCase-control studies are limited in the type of causal effects they can estimate (causal odds ratios, which under some circumstances approximate causal risk ratios).\nWith careful study design and sampling, the math works out such that these estimates are still valid.\nExactly how and why case-control studies work is beyond the scope of this book, but they are a remarkably clever design.\n:::\n\n### Include variables you don't have\n\nIt's critical that you include *all* variables important to the causal structure, not just the variables you have measured in your data.\nggdag can mark variables as unmeasured (\"latent\"); it will then return only usable adjustment sets, e.g., those without the unmeasured variables.\nOf course, the best thing to do is to use DAGs to help you understand what to measure in the first place, but there are many reasons why your data might be different.\nEven data intentionally collected for the research question might not have a variable discovered to be a confounder after data collection.\n\nFor instance, if we have a DAG where `exposure` and `outcome` have a confounding pathway consisting of `confounder1` and `confounder2`, we can control for either to successfully debias the estimate:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndagify(\n  outcome ~ exposure + confounder1,\n  exposure ~ confounder2,\n  confounder2 ~ confounder1,\n  exposure = \"exposure\",\n  outcome = \"outcome\"\n) |> \n  adjustmentSets()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n{ confounder1 }\n{ confounder2 }\n```\n\n\n:::\n:::\n\n\nThus, if just one is missing (`latent`), then we are ok:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndagify(\n  outcome ~ exposure + confounder1,\n  exposure ~ confounder2,\n  confounder2 ~ confounder1,\n  exposure = \"exposure\",\n  outcome = \"outcome\",\n  latent = \"confounder1\"\n) |> \n  adjustmentSets()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n{ confounder2 }\n```\n\n\n:::\n:::\n\n\nBut if both are missing, there are no valid adjustment sets.\n\nWhen you don't have a variable measured, you still have a few options.\nAs mentioned above, you may be able to identify alternate adjustment sets.\nIf the missing variable is required to close all backdoor paths completely, you can and should conduct a sensitivity analysis to understand the impact of not having it.\nThis is the subject of [Chapter -@sec-sensitivity].\n\nUnder some lucky circumstances, you can also use a *proxy* confounder [@miao2018].\nA proxy confounder is a variable closely related to the confounder such that controlling for it controls for some of the effects of the missing variable.\nConsider an expansion of the fundamental confounding relationship where `q` has a cause, `p`, as in @fig-proxy-confounder.\nTechnically, if we don't have `q`, we can't close the backdoor path, and our effect will be biased.\nPractically, though, if `p` is highly correlated with `q`, it can serve as a method to reduce the confounding from `q`.\nYou can think of `p` as a mismeasured version of `q`; it will seldom wholly control for the bias via `q`, but it can help minimize it.\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndagify(\n  y ~ x + q,\n  x ~ q,\n  q ~ p,\n  coords = time_ordered_coords()\n) |> \n  ggdag(edge_type = \"arc\")\n```\n\n::: {.cell-output-display}\n![A DAG with a confounder, `q`, and a proxy confounder, `p`. The true adjustment set is `q`. Since `p` causes `q`, it contains information about `q` and can reduce the bias if we don't have `q` measured.](chapter-05_files/figure-html/fig-proxy-confounder-1.png){#fig-proxy-confounder width=432}\n:::\n:::\n\n\n### Saturate your DAG, then prune\n\nIn discussing @tbl-dag-properties, we mentioned *saturated* DAGs.\nThese are DAGs where all possible arrows are included based on the time ordering, e.g., every variable causes variables that come after it in time.\n\n*Not* including an arrow is a bigger assumption than including one.\nIn other words, your default should be to have an arrow from one variable to a future variable.\nThis default is counterintuitive to many people.\nHow can it be that we need to be so careful about assessing causal effects yet be so liberal in applying causal assumptions in the DAG?\nThe answer to this lies in the strength and prevalence of the cause.\nTechnically, an arrow present means that *for at least a single observation*, the prior node causes the following node.\nThe arrow similarly says nothing about the strength of the relationship.\nSo, a minuscule causal effect on a single individual justifies the presence of an arrow.\nIn practice, such a case is probably not relevant.\nThere is *effectively* no arrow.\n\nThe more significant point, though, is that you should feel confident to add an arrow.\nThe bar for justification is much lower than you think.\nInstead, it's helpful to 1) determine your time ordering, 2) saturate the DAG, and 3) prune out implausible arrows.\n\nLet's experiment by working through a saturated version of the podcast-exam DAG.\n\nFirst, the time-ordering.\nPresumably, the student's sense of humor far predates the day of the exam.\nMood in the morning, too, predates listening to the podcast or exam score, as does preparation.\nThe saturated DAG given this ordering is:\n\n\n::: {.cell}\n::: {.cell-output-display}\n![A saturated version of `podcast_dag`: variables have all possible arrows going forward to other variables over time.](chapter-05_files/figure-html/fig-podcast_dag_sat-1.png){#fig-podcast_dag_sat width=528}\n:::\n:::\n\n\nThere are a few new arrows here.\nHumor now causes the other two confounders, as well as exam score.\nSome of them make sense.\nSense of humor probably affects mood for some people.\nWhat about preparedness?\nThis relationship seems a little less plausible.\nSimilarly, we know that a sense of humor does not affect exam scores in this case because the grading is blinded.\nLet's prune those two.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![A pruned version of @fig-podcast_dag_sat: we've removed implausible arrows from the fully saturated DAGs.](chapter-05_files/figure-html/fig-podcast_dag_pruned-1.png){#fig-podcast_dag_pruned width=528}\n:::\n:::\n\n\nThis DAG seems more reasonable.\nSo, was our original DAG wrong?\nThat depends on several factors.\nNotably, both DAGs produce the same adjustment set: controlling for `mood` and `prepared` will give us an unbiased effect if either DAG is correct.\nEven if the new DAG were to produce a different adjustment set, whether the result is meaningfully different depends on the strength of the confounding.\n\n### Include instruments and precision variables\n\nTechnically, you do not need to include instrumental and precision variables in your DAG.\nThe adjustment sets will be the same with and without them.\nHowever, adding them is helpful for two reasons.\nFirstly, they demonstrate your assumptions about their relationships and the variables under study.\nAs discussed above, *not* including an arrow is a more significant assumption than including one, so it's valuable information about how you think the causal structure operates.\nSecondly, it impacts your modeling decision.\nYou should always include precision variables in your model to reduce variability in your estimate so it helps you identify those.\nInstruments are also helpful to see because they may guide alternative or complementary modeling strategies, as we'll discuss in @sec-evidence.\n\n### Focus on the causal structure, then consider measurement bias\n\nAs we saw above, missingness and measurement error can be a source of bias.\nAs we'll see in [Chapter -@sec-missingness], we have several strategies to approach such a situation.\nYet, almost everything we measure is inaccurate to some degree.\nThe true DAG for the data at hand inherently conditions on the measured version of variables.\nIn that sense, your data are always subtly-wrong, a sort of unreliable narrator.\nWhen should we include this information in the DAG?\nWe recommend first focusing on the causal structure of the DAG as if you had perfectly measured each variable [@hernan2021].\nThen, consider how mismeasurement and missingness might affect the realized data, particularly related to the exposure, outcome, and critical confounders.\nYou may prefer to present this as an alternative DAG to consider strategies for addressing the bias arising from those sources, e.g., imputation or sensitivity analyses.\nAfter all, the DAG in \\@ fig-error_dag makes you think the question is unanswerable because we have no method to close all backdoor paths.\nAs with all open paths, that depends on the severity of the bias and our ability to reckon with it.\n\n<!-- TODO: I don't quite remember what I wanted to cover here, so revisit later to add or delete -->\n\n<!-- ### Be accurate, but focus on clarity -->\n\n### Pick adjustment sets most likely to be successful\n\nOne area where measurement error is an important consideration is when picking an adjustment set.\nIn theory, if a DAG is correct, any adjustment set will work to create an unbiased result.\nIn practice, variables have different levels of quality.\nPick an adjustment set most likely to succeed because it contains accurate variables.\nSimilarly, non-minimal adjustment sets are helpful to consider because, together, several variables with measurement error along a backdoor path may be enough to minimize the practical bias resulting from that path.\n\nWhat if you don't have certain critical variables measured and thus do not have a valid adjustment set?\nIn that case, you should pick the adjustment set with the best chance of minimizing the bias from other backdoor paths.\nAll is not lost if you don't have every confounder measured: get the highest quality estimate you can, then conduct a sensitivity analysis about the unmeasured variables to understand the impact.\n\n### Use robustness checks\n\nFinally, we recommend checking your DAG for robustness.\nYou can never verify the correctness of your DAG under most conditions, but you can use the implications in your DAG to support it.\nThree types of robustness checks can be helpful depending on the circumstances.\n\n1.  **Negative controls** [@Lipsitch2010]. These come in two flavors: negative exposure controls and negative outcome controls. The idea is to find something associated with one but not the other, e.g., the outcome but not the exposure, so there should be no effect. Since there should be no effect, you now have a measurement for how well you control for *other* effects (e.g., the difference from null). Ideally, the confounders for negative controls are similar to the research question.\n2.  **DAG-data consistency** [@Textor2016]. Negative controls are an implication of your DAG. An extension of this idea is that there are *many* such implications. Because blocking a path removes statistical dependencies from that path, you can check those assumptions in several places in your DAG.\n3.  **Alternate adjustment sets**. Adjustment sets should give roughly the same answer because, outside of random and measurement errors, they are all sets that block backdoor paths. If more than one adjustment set seems reasonable, you can use that as a sensitivity analysis by checking multiple models.\n\nWe'll discuss these in detail in [Chapter -@sec-sensitivity].\nThe caveat here is that these should be complementary to your initial DAG, not a way of *replacing* it.\nIn fact, if you use more than one adjustment set during your analysis, you should report the results from all of them to avoid overfitting your results to your data.\n",
+    "markdown": "# Expressing causal questions as DAGs {#sec-dags}\n\n\n\n\n\n## Visualizing Causal Assumptions\n\nSo you think correlation isn't causation?\nYou better be ready to name some proposed confounders!\nIn @sec-assump we discuss several assumptions that allow us to estimate unbiased causal effects with our current statistical tools; one of the main assumptions is *exchangeability*, also known as \"no unmeasured confounders\".\nThere is no statistical test that can confirm whether this assumption is met.\nInstead, we often use domain knowledge to construct an assumed world view of how different measured (or unmeasured) factors interact, and then *assuming that world view is correct* determine whether the proposed analysis included any unmeasured confounders.\nOne way to communicate one's world view with respect to how different factors interact is via a directed acyclic graph (DAG).\n\n::: callout-tip\n## Jargon\n\nWhy is it called a *directed acyclic graph*?\nLet's start with the third word: **graph**.\nA graph, as defined here, is a collection of *nodes* (sometimes these are called *vertices*) and *edges* that connect the nodes.\nIn mathematics, there is a whole field called *graph theory* which studies these graphs.\nFor our use, each *node* is a variable or factor, for example the exposure would be a node and likewise the outcome would be a node.\nEdges between nodes are depicted with *arrows* that imply causality.\nSo if we think the exposure causes the outcome we would draw an arrow from the exposure to the outcome.\nThis one way connection between nodes (*from* causes *to* effects) makes this graph **directed**.\nFinally, **acyclic** refers to the fact that there are no cycles or loops in the graph.\nThis makes sense because when thinking about causes and effects, loops are not possible without breaking the space-time continuum.\nOften when a DAG erroneously includes a \"loop\" it is because the analyst did not appropriately consider the timing of the factors in question.\n:::\n\nFor example, @fig-dag-ex, adapted from @mcgowan2023causal shows a sample DAG that suggests that `cause` causes `effect` and `other cause` causes both cause and effect.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Example DAG adapted from @mcgowan2023causal. Here, there are three nodes representing three factors: `cause`, `other cause`, and `effect`. The arrows demonstrate the causal relationships between these factors such that `cause` causes `effect` and `other cause` causes both `cause` and `effect`.](chapter-05_files/figure-html/fig-dag-ex-1.png){#fig-dag-ex width=672}\n:::\n:::\n\n\nThinking about any three nodes in a DAG, there are three ways they can be connected, via a *fork*, a *chain*, or a *collider*.\nExamining @fig-dag-3, the fork describes the scenario where the `q` node has two arrows extending from it, one pointing to `x` and one pointing to `y`.\nThis implies that `q` causes both `x` and `y`.\nIf `x` were an exposure and `y` and outcome, `q` would be a classic *confounder*.\nIf we were trying to quantify the causal relationship between `x` and `y`, the forking caused by `q` provides a potential *backdoor path* from `x` to `y` that could lead to a spurious estimate of the relationship between `x` and `y` if not accounted for (sometimes we refer to this \"accounting\" as closing the backdoor path).\nAssociations can flow through forks.\nThe second panel of @fig-dag-3 in @fig-dag-3 displays a chain.\nHere, the `x` node has an arrow to the `q` node which in turn has an arrow to the `y` node.\nIf `x` were an exposure and `y` and outcome, `q` would be a classic *mediator*.\nIn the final panel of @fig-dag-3, we see the collider.\nHere `y` has an arrow to `q` and `x` has an arrow to `q`.\nColliders *block* backdoor paths.\nOpposite from confounders, adjusting for a collider can actually *open* a backdoor path.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Three types of paths connecting three nodes, `x`, `y`, and `q`: a fork, a chain, and a collider.](chapter-05_files/figure-html/fig-dag-3-1.png){#fig-dag-3 width=960}\n:::\n:::\n\n\nOften, the goal in causal inference is to quantify the relationship between some exposure and some outcome.\nOne way this estimate can be biased is if we are actually reporting the *correlation* between these two factors rather than the *causal relationship*.\nThinking about these DAGs, spurious correlations can often be attributed to *open backdoor paths*, i.e. the relationship between other factors and our exposure and outcome of interest.\nOne way to think about these paths is to conceptualize associations *flowing* along the paths.\nWithout adjustment, associations flow through forks and chains, and are *blocked* by colliders.\nWith adjustment, the opposite is true, associations are blocked by forks and chains if the node in question is adjusted for, however an association *will* flow through a collider if it is adjusted for.\n\nWhile not strictly necessary, we recommend that DAGs are *time-ordered* from left to right.\nThis helps the analyst ensure that they are not erroneously making assumptions that violate the space-time continuum (for example it is impossible for something in the future to cause something from the past).\n\nHow do we use these in practice?\nThe basic idea is:\n\n1.  Specify your causal question\n2.  Using domain knowledge:\n\n-   Write all relevant variables as *nodes*\n-   Draw causal pathways as arrows (*edges*)\n\n## DAGs in R\n\nLet's begin by specifying our causal question: Does listening to a comedy podcast the morning before an exam improve graduate students test scores?\nWe can diagram this using the method describe in @sec-diag (@fig-podcast).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Does listening to a comedy podcast the morning before an exam improve graduate students test scores?](chapter-05_files/figure-html/fig-podcast-1.png){#fig-podcast width=672}\n:::\n:::\n\n\nThe first step is to specify your DAG.\nIn R, we can use the {ggdag} package for this along with the `dagify()` function.\nThe `dagify()` function takes formulas, separated by commas, that specify cause and effect, with the left element of of the formula specifying the effect and the right all of the factors that cause it.\nWhat are all of the factors that \"cause\" graduate students to listen to a podcast the morning before an exam?\nWhat are all of the factors that could \"cause\" a graduate student to do well on a test?\nLet's posit some here.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(ggdag)\ndagify(\n  podcast ~ mood + humor + prepared,\n  exam ~ mood + prepared\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ndag {\nexam\nhumor\nmood\npodcast\nprepared\nhumor -> podcast\nmood -> exam\nmood -> podcast\nprepared -> exam\nprepared -> podcast\n}\n```\n:::\n:::\n\n\nIn the code chunk above, we have posited that a graduate students mood, sense of humor, and how prepared they feel for the exam could influence whether they listened to a podcast the morning of the text.\nLikewise, we posit that their mood and how prepared they are also influences their exam score.\nNotice we *do not* see `podcast` in the `exam` equation -- this means that we assume that there is no causal relationship between podcast and the exam score.\nWe can add additional arguments to `dagify()`, for example, we can time order the coordinates, tag the exposure and outcome, and add labels.\nWe can save this `dagify` object and use the `ggdag()` function to visualize this DAG.\nThis function is a wrapper for a `ggplot2` call, meaning we can add layers like we would to a ggplot object.\nFor example, we can update the theme by adding `+ theme_dag()`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\npodcast_dag <- dagify(\n  podcast ~ mood + humor + prepared,\n  exam ~ mood + prepared,\n  coords = time_ordered_coords(),\n  exposure = \"podcast\",\n  outcome = \"exam\",\n  labels = c(\n    podcast = \"podcast\",\n    exam = \"exam score\",\n    mood = \"mood\",\n    humor = \"humor\",\n    prepared = \"prepared\"\n  )\n)\nggdag(podcast_dag, use_labels = \"label\", text = FALSE) + \n  theme_dag()\n```\n\n::: {.cell-output-display}\n![Proposed DAG to answer the question: Does listening to a comedy podcast the morning before an exam improve graduate students test scores?](chapter-05_files/figure-html/fig-dag-podcast-1.png){#fig-dag-podcast width=672}\n:::\n:::\n\n\nIn the previous section, we discussed *backdoor paths*.\nThese are paths between factors that could potentially lead us to drawing spurious conclusions about the relationship between our exposure and outcome.\nThe `ggdag_paths()` function will help us identify potential backdoor paths.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggdag_paths(podcast_dag)\n```\n\n::: {.cell-output-display}\n![](chapter-05_files/figure-html/fig-paths-1.png){#fig-paths width=672}\n:::\n:::\n\n\nIn @fig-paths we see two open paths, one through `mood` and one through `prepared`.\nThis tells us we need to find a way to account for these open, non-causal paths.\nSome ways to do this include:\n\n-   Randomization\n-   Stratification, adjustment, weighting, matching, etc.\n\nIn this particular scenario, randomization is likely not possible.\nIt would be challenging to justify randomizing students to listening to a full podcast prior to taking an exam (and it would likely have lots of issues with adherence!).\nStratification is similar to what we demonstrated in @sec-group-sum.\nWe could stratify the students into all possible mood and prepared categories and analyze the causal effect within each stratum -- again, this might pose challenges depending on the sample size and the number of categories we believe exists in each of these factors.\nThe next section will dive into some of these other tools we could use to account for these potential backdoor paths.\n\nThe {ggdag} package can also help us identify adjustment sets.\nIn this particular example, this yields the same result as above, since we need to adjust for both `mood` and `prepared` in order to close the backdoor paths.\nIt is possible, however, to have different ways to close backdoor paths depending on the number of factors and complexity causal relationships assumed.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggdag_adjustment_set(\n  podcast_dag,\n  use_labels = \"label\",\n  text = FALSE\n)\n```\n\n::: {.cell-output-display}\n![](chapter-05_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\nUsing our proposed DAG, let's simulate some data to see how this might occur in practice!\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(10)\nsim_data <- podcast_dag |>\n  simulate_data()\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsim_data\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 500 × 5\n     exam  humor   mood podcast prepared\n    <dbl>  <dbl>  <dbl>   <dbl>    <dbl>\n 1 -0.435  0.263 -0.100  -0.630   1.07  \n 2 -0.593  0.317  0.143  -1.55    0.0640\n 3  0.786  1.97  -0.591  -0.318  -0.439 \n 4 -0.103  2.86  -0.139   1.07    0.754 \n 5 -0.614 -2.39   0.702   0.464   0.356 \n 6  1.01   1.21   0.910   0.769   0.561 \n 7  0.167 -1.37  -0.559  -0.866   0.214 \n 8  1.16   0.164 -0.743   0.969  -1.67  \n 9  0.650  0.215 -0.248   0.691  -0.303 \n10  0.156  0.713  1.19   -1.02   -0.219 \n# ℹ 490 more rows\n```\n:::\n:::\n\n\nSince we have simulated this data, we know that this is a case where *standard methods will succeed* (see @sec-standard), and therefore can estimate the causal effect using a basic linear regression model.\n@fig-dag-sim shows a forest plot of the simulated data based on our DAG.\nNotice the model that only included the exposure resulted in a spurious effect (an estimate of -0.1 when we know the truth is 0), whereas the model that adjusted for the two variables as suggested by `ggdag_adjustment_set()` is not spurious (0.0).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## Model that does not close backdoor paths\nunadjusted_model <- lm(exam ~ podcast, sim_data) |>\n  broom::tidy(conf.int = TRUE) |>\n  dplyr::filter(term == \"podcast\") |>\n  mutate(formula = \"podcast\")\n\n## Model that closes backdoor paths\nadjusted_model <- lm(exam ~ podcast + mood + prepared, sim_data) |>\n  broom::tidy(conf.int = TRUE) |>\n  dplyr::filter(term == \"podcast\") |>\n  mutate(formula = \"podcast + mood + prepared\")\n\nbind_rows(\n  unadjusted_model,\n  adjusted_model\n) |> \n  ggplot(aes(x = estimate, y = formula, xmin = conf.low, xmax = conf.high)) +\n  geom_vline(xintercept = 0, linewidth = 1, color = \"grey80\") + \n  geom_pointrange(fatten = 3, size = 1) +\n  theme_minimal(18) +\n  labs(\n    y = NULL,\n    caption = \"correct effect size: 0\"\n  )\n```\n\n::: {.cell-output-display}\n![Forest plot of simulated data based on the DAG described in @fig-dag-podcast](chapter-05_files/figure-html/fig-dag-sim-1.png){#fig-dag-sim width=672}\n:::\n:::\n\n\n<!-- ## Common Structures of Bias -->\n\n<!-- ## Causal Inference is not (just) a statistical problem {#sec-quartets} -->\n\n<!-- ## Causal and Predictive Models, Revisited {#sec-causal-pred-revisit} -->\n",
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-    "markdown": "# Causal Inference is not (just) a statistical problem {#sec-quartets}\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1]  1.0683 -1.2592  1.4183 -0.4592  0.4267\n```\n\n\n:::\n:::\n\n\n## Causal and Predictive Models, Revisited {#sec-causal-pred-revisit}\n\n<!-- TODO:  -->\n<!-- -   Probably too long, but if possible, condense to a popout -->\n<!-- -   DAGs showing examples where prediction can lean on measured confounders, colliders. It's the amount of information a variable brings, not whether the coeffecient is unbiased affect of variable on outcome. -->\n<!-- -   Not practical to fit a prediction model with future variable -->\n<!-- -   Table 2 Bias examples. Unmeasure confounding of Z-Y relationship. Mediation example. -->\n",
-    "supporting": [],
+    "markdown": "# Preparing data to answer causal questions {#sec-data-causal}\n\n\n\n\n\n## Introduction to the data {#sec-data}\n\nThroughout this book we will be using data obtained from [Touring Plans](https://touringplans.com).\nTouring Plans is a company that helps folks plan their trips to Disney and Universal theme parks.\nOne of their goals is to accurately predict attraction wait times at these theme parks by leveraging data and statistical modeling.\nThe `{touringplans}` R package includes several datasets containing information about Disney theme park attractions.\nA summary of the attractions included in the package can be found by running the following:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(touringplans)\nattractions_metadata\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 14 × 8\n   dataset_name name  short_name park  land  opened_on \n   <chr>        <chr> <chr>      <chr> <chr> <date>    \n 1 alien_sauce… Alie… Alien Sau… Disn… Toy … 2018-06-30\n 2 dinosaur     DINO… DINOSAUR   Disn… Dino… 1998-04-22\n 3 expedition_… Expe… Expeditio… Disn… Asia  2006-04-07\n 4 flight_of_p… Avat… Flight of… Disn… Pand… 2017-05-27\n 5 kilimanjaro… Kili… Kilimanja… Disn… Afri… 1998-04-22\n 6 navi_river   Na'v… Na'vi Riv… Disn… Pand… 2017-05-27\n 7 pirates_of_… Pira… Pirates o… Magi… Adve… 1973-12-17\n 8 rock_n_roll… Rock… Rock Coas… Disn… Suns… 1999-07-29\n 9 seven_dwarf… Seve… 7 Dwarfs … Magi… Fant… 2014-05-28\n10 slinky_dog   Slin… Slinky Dog Disn… Toy … 2018-06-30\n11 soarin       Soar… Soarin'    Epcot Worl… 2005-05-05\n12 spaceship_e… Spac… Spaceship… Epcot Worl… 1982-10-01\n13 splash_moun… Spla… Splash Mo… Magi… Fron… 1992-07-17\n14 toy_story_m… Toy … Toy Story… Disn… Toy … 2008-05-31\n# ℹ 2 more variables: duration <dbl>,\n#   average_wait_per_hundred <dbl>\n```\n:::\n:::\n\n\nAdditionally, this package contains a dataset with raw metadata about the parks, with observations recorded daily.\nThis metadata includes information like the Walt Disney World ticket season on the particular day (was it high season -- think Christmas -- or low season -- think right when school started), what the historic temperatures were in the park on that day, and whether there was a special event, such as \"extra magic hours\" in the park on that day (did the park open early to guests staying in the Walt Disney World resorts?).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nparks_metadata_raw\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2,079 × 181\n   date       wdw_ticket_season dayofweek dayofyear\n   <date>     <chr>                 <dbl>     <dbl>\n 1 2015-01-01 <NA>                      5         0\n 2 2015-01-02 <NA>                      6         1\n 3 2015-01-03 <NA>                      7         2\n 4 2015-01-04 <NA>                      1         3\n 5 2015-01-05 <NA>                      2         4\n 6 2015-01-06 <NA>                      3         5\n 7 2015-01-07 <NA>                      4         6\n 8 2015-01-08 <NA>                      5         7\n 9 2015-01-09 <NA>                      6         8\n10 2015-01-10 <NA>                      7         9\n# ℹ 2,069 more rows\n# ℹ 177 more variables: weekofyear <dbl>,\n#   monthofyear <dbl>, year <dbl>, season <chr>,\n#   holidaypx <dbl>, holidaym <dbl>, holidayn <chr>,\n#   holiday <dbl>, wdwticketseason <chr>,\n#   wdwracen <chr>, wdweventn <chr>, wdwevent <dbl>,\n#   wdwrace <dbl>, wdwseason <chr>, …\n```\n:::\n:::\n\n\nSuppose the causal question of interest is:\n\n**Is there a relationship between whether there were \"Extra Magic Hours\" in the morning at Magic Kingdom and the average wait time for an attraction called the \"Seven Dwarfs Mine Train\" the same day between 9am and 10am in 2018?**\n\nLet's begin by diagramming this causal question (@fig-seven-diag).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Diagram of the causal question \"Is there a relationship between whether there were \"Extra Magic Hours\" in the morning at Magic Kingdom and the average wait time for an attraction called the \"Seven Dwarfs Mine Train\" the same day between 9am and 10am in 2018?\"](chapter-06_files/figure-html/fig-seven-diag-1.png){#fig-seven-diag width=672}\n:::\n:::\n\n\nHistorically, guests who stayed in a Walt Disney World resort hotel could access the park during \"Extra Magic Hours,\" during which the park was closed to all other guests.\nThese extra hours could be in the morning or evening.\nThe Seven Dwarfs Mine Train is a ride at Walt Disney World's Magic Kingdom.\nMagic Kingdom may or may not be selected each day to have these \"Extra Magic Hours.\" We are interested in examining the relationship between whether there were \"Extra Magic Hours\" in the morning and the average wait time for the Seven Dwarfs Mine Train on the same day between 9 am and 10 am.\nBelow is a proposed DAG for this question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Proposed DAG for the relationship between Extra Magic Hours in the morning at a particular park and the average wait time between 9 am and 10 am. Here we are saying that we believe 1) Extra Magic Hours impacts average wait time and 2) both Extra Magic Hours and average wait time are determined by the time the park closes, historic high temperatures, and ticket season.](chapter-06_files/figure-html/fig-dag-magic-1.png){#fig-dag-magic width=672}\n:::\n:::\n\n\nSince we are not in charge of Walt Disney World's operations, we cannot randomize dates to have (or not) \"Extra Magic Hours\", therefore, we need to rely on previously collected observational data and do our best to emulate the *target trial* that we would have created, should it have been possible.\nHere, our observations are *days*.\nLooking at the diagram above, we can map each element of the causal question to elements of our target trial protocol:\n\n-   **Eligibility criteria**: Days must be from 2018\n-   **Exposure definition**: Magic kingdom had \"Extra Magic Hours\" in the morning\n-   **Assignment procedures**: Observed -- if the historic data suggests there were \"Extra Magic Hours\" in the morning on a particular day, that day is classified as \"exposed\" otherwise it is \"unexposed\"\n-   **Follow-up period**: From park open to 10am.\n-   **Outcome definition**: The average posted wait time between 9am and 10am\n-   **Causal contrast of interest**: Average treatment effect (we will discuss this in @sec-estimands)\n-   **Analysis plan**: We use inverse probability waiting after fitting a propensity score model to estimate the average treatment effect of the exposure on the outcome of interest. We will adjust for variables as determined by our DAG (@fig-dag-magic)\n\n## Data wrangling and recipes\n\nMost of our data manipulation tools come from the `{dplyr}` package (@tbl-dplyr).\nWe will also use `{lubridate}` to help us manipulate dates.\n\n| Target trial protocol element | {dplyr} functions                           |\n|-------------------------------|---------------------------------------------|\n| Eligibility criteria          | `filter()`                                  |\n| Exposure definition           | `mutate()`                                  |\n| Assignment procedures         | `mutate()`                                  |\n| Follow-up period              | `mutate()` `pivot_longer()` `pivot_wider()` |\n| Outcome definition            | `mutate()`                                  |\n| Analysis plan                 | `select()` `mutate()`                       |\n\n: Mapping target trial protocol elements to commonly used `{dplyr}` functions {#tbl-dplyr}\n\nTo answer this question, we are going to need to manipulate both the `seven_dwarfs_train` dataset as well as the `parks_metadata_raw` dataset.\nLet's start with the `seven_dwarfs_train` data set.\nThe Seven Dwarfs Mine Train ride is an attraction at Walt Disney World's Magic Kingdom.\nThe `seven_dwarfs_train` dataset in the {touringplans} package contains information about the date a particular wait time was recorded (`park_date`), the time of the wait time (`wait_datetime`), the actual wait time (`wait_minutes_actual`), and the posted wait time (`wait_minutes_posted`).\nLet's take a look at this dataset.\nThe {skimr} package is great for getting a quick glimpse at a new dataset.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(skimr)\nskim(seven_dwarfs_train)\n```\n\n::: {.cell-output-display}\nTable: Data summary\n\n|                         |                   |\n|:------------------------|:------------------|\n|Name                     |seven_dwarfs_train |\n|Number of rows           |321631             |\n|Number of columns        |4                  |\n|_______________________  |                   |\n|Column type frequency:   |                   |\n|Date                     |1                  |\n|numeric                  |2                  |\n|POSIXct                  |1                  |\n|________________________ |                   |\n|Group variables          |None               |\n\n\n**Variable type: Date**\n\n|skim_variable | n_missing| complete_rate|min        |max        |median     | n_unique|\n|:-------------|---------:|-------------:|:----------|:----------|:----------|--------:|\n|park_date     |         0|             1|2015-01-01 |2021-12-28 |2018-04-07 |     2334|\n\n\n**Variable type: numeric**\n\n|skim_variable       | n_missing| complete_rate|  mean|      sd|     p0| p25| p50| p75| p100|hist  |\n|:-------------------|---------:|-------------:|-----:|-------:|------:|---:|---:|---:|----:|:-----|\n|wait_minutes_actual |    313996|          0.02| 23.99| 1064.06| -92918|  21|  31|  46|  217|▁▁▁▁▇ |\n|wait_minutes_posted |     30697|          0.90| 76.96|   33.99|      0|  50|  70|  95|  300|▆▇▁▁▁ |\n\n\n**Variable type: POSIXct**\n\n|skim_variable | n_missing| complete_rate|min                 |max                 |median              | n_unique|\n|:-------------|---------:|-------------:|:-------------------|:-------------------|:-------------------|--------:|\n|wait_datetime |         0|             1|2015-01-01 07:51:12 |2021-12-28 22:57:34 |2018-04-07 23:14:06 |   321586|\n:::\n:::\n\n\nExamining the output above, we learn that this dataset contains four columns and 321,631 rows.\nWe also learn that the dates span from 2015 to 2021.\nWe can also examine the distribution of each of the variables to detect any potential anomalies.\nNotice anything strange?\nLook at the `p0` (that is the minimum value) for `wait_minutes_actual`.\nIt is `-92918`!\nWe are not using this variable for this analysis, but we will for future analyses, so this is good to keep in mind.\n\nWe need this dataset to calculate our *outcome*.\nRecall from above that our outcome is defined as the average posted wait time between 9am and 10am.\nAdditionally, recall our eligibility criteria states that we need to restrict our analysis to days in 2018.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\nlibrary(lubridate)\nseven_dwarfs_train_2018 <- seven_dwarfs_train |>\n  filter(year(park_date) == 2018) |> # eligibility criteria \n  mutate(hour = hour(wait_datetime)) |> # get hour from wait\n  group_by(park_date, hour) |> # group by date\n  summarise(\n    wait_minutes_posted_avg = mean(wait_minutes_posted, na.rm = TRUE), \n    .groups = \"drop\") |> # get average wait time\n  mutate(wait_minutes_posted_avg = \n           case_when(is.nan(wait_minutes_posted_avg) ~ NA,\n                     TRUE ~ wait_minutes_posted_avg)) |> # if it is NAN make it NA \n  filter(hour == 9) # only keep the average wait time between 9 and 10\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_train_2018\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 362 × 3\n   park_date   hour wait_minutes_posted_avg\n   <date>     <int>                   <dbl>\n 1 2018-01-01     9                    60  \n 2 2018-01-02     9                    60  \n 3 2018-01-03     9                    60  \n 4 2018-01-04     9                    68.9\n 5 2018-01-05     9                    70.6\n 6 2018-01-06     9                    33.3\n 7 2018-01-07     9                    46.4\n 8 2018-01-08     9                    69.5\n 9 2018-01-09     9                    64.3\n10 2018-01-10     9                    74.3\n# ℹ 352 more rows\n```\n:::\n:::\n\n\nNow that we have our outcome settled, we need to get our exposure variable, as well as any other park-specific variables about the day in question that may be used as variables that we adjust for.\nExamining @fig-dag-magic, we see that we need data for three proposed confounders: the ticket season, the time the park closed, and the historic high temperature.\nThese are in the `parks_metadata_raw` dataset.\nThis data will require extra cleaning, since the names are in the original format.\n\n::: callout-tip\nWe like to have our variable names follow a clean convention -- one way to do this is to follow Emily Riederer's \"Column Names as Contracts\" format [@Riederer_2020].\nThe basic idea is to predefine a set of words, phrases, or stubs with clear meanings to index information, and use these consistently when naming variables.\nFor example, in these data, variables that are specific to a particular wait time are prepended with the term `wait` (e.g. `wait_datetime` and `wait_minutes_actual`), variables that are specific to the park on a particular day, acquired from parks metadata, are prepended with the term `park` (e.g. `park_date` or `park_temperature_high`).\n:::\n\nLet's first decide what variables we will need.\nIn practice, this decision may involve an iterative process.\nFor example, after drawing our DAG or after conducting diagnostic, we may determine that we need more variables than what we originally cleaned.\nLet's start by skimming this dataframe.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nskim(parks_metadata_raw)\n```\n\n::: {.cell-output-display}\nTable: Data summary\n\n|                         |                   |\n|:------------------------|:------------------|\n|Name                     |parks_metadata_raw |\n|Number of rows           |2079               |\n|Number of columns        |181                |\n|_______________________  |                   |\n|Column type frequency:   |                   |\n|character                |42                 |\n|Date                     |1                  |\n|difftime                 |46                 |\n|logical                  |6                  |\n|numeric                  |86                 |\n|________________________ |                   |\n|Group variables          |None               |\n\n\n**Variable type: character**\n\n|skim_variable         | n_missing| complete_rate| min| max| empty| n_unique| whitespace|\n|:---------------------|---------:|-------------:|---:|---:|-----:|--------:|----------:|\n|wdw_ticket_season     |       861|          0.59|   4|   7|     0|        3|          0|\n|season                |       253|          0.88|   4|  29|     0|       17|          0|\n|holidayn              |      1865|          0.10|   3|   7|     0|       43|          0|\n|wdwticketseason       |       761|          0.63|   4|   7|     0|        3|          0|\n|wdwracen              |      1992|          0.04|   4|   6|     0|        5|          0|\n|wdweventn             |      1832|          0.12|   3|  12|     0|        8|          0|\n|wdwseason             |        87|          0.96|   4|  29|     0|       17|          0|\n|mkeventn              |      1546|          0.26|   3|  11|     0|       10|          0|\n|epeventn              |       868|          0.58|   4|   5|     0|        4|          0|\n|hseventn              |      1877|          0.10|   4|   7|     0|        5|          0|\n|akeventn              |      2010|          0.03|   4|   4|     0|        2|          0|\n|holidayj              |      2037|          0.02|   5|  15|     0|        8|          0|\n|insession             |       105|          0.95|   2|   3|     0|       95|          0|\n|insession_enrollment  |       105|          0.95|   2|   4|     0|      100|          0|\n|insession_wdw         |       105|          0.95|   2|   4|     0|       94|          0|\n|insession_dlr         |       105|          0.95|   2|   4|     0|       94|          0|\n|insession_sqrt_wdw    |       105|          0.95|   2|   4|     0|       97|          0|\n|insession_sqrt_dlr    |       105|          0.95|   2|   4|     0|       97|          0|\n|insession_california  |       105|          0.95|   2|   4|     0|       89|          0|\n|insession_dc          |       105|          0.95|   2|   4|     0|       86|          0|\n|insession_central_fl  |       105|          0.95|   2|   4|     0|       71|          0|\n|insession_drive1_fl   |       105|          0.95|   2|   4|     0|       85|          0|\n|insession_drive2_fl   |       105|          0.95|   2|   4|     0|       95|          0|\n|insession_drive_ca    |       105|          0.95|   2|   4|     0|       91|          0|\n|insession_florida     |       105|          0.95|   2|   4|     0|       86|          0|\n|insession_mardi_gras  |       105|          0.95|   2|   4|     0|       82|          0|\n|insession_midwest     |       105|          0.95|   2|   4|     0|       75|          0|\n|insession_ny_nj       |       105|          0.95|   2|   4|     0|        8|          0|\n|insession_ny_nj_pa    |       105|          0.95|   2|   4|     0|       19|          0|\n|insession_new_england |       105|          0.95|   2|   4|     0|       45|          0|\n|insession_new_jersey  |       105|          0.95|   2|   4|     0|        2|          0|\n|insession_nothwest    |       105|          0.95|   2|   4|     0|       17|          0|\n|insession_planes      |       105|          0.95|   2|   4|     0|       81|          0|\n|insession_socal       |       105|          0.95|   2|   4|     0|       80|          0|\n|insession_southwest   |       105|          0.95|   2|   4|     0|       86|          0|\n|mkprddn               |       183|          0.91|  33|  41|     0|        2|          0|\n|mkprdnn               |      1358|          0.35|  29|  38|     0|        2|          0|\n|mkfiren               |       134|          0.94|  18|  65|     0|        8|          0|\n|epfiren               |       126|          0.94|  13|  35|     0|        2|          0|\n|hsfiren               |       485|          0.77|  24|  66|     0|        6|          0|\n|hsshwnn               |       164|          0.92|  10|  28|     0|        2|          0|\n|akshwnn               |       883|          0.58|  15|  33|     0|        2|          0|\n\n\n**Variable type: Date**\n\n|skim_variable | n_missing| complete_rate|min        |max        |median     | n_unique|\n|:-------------|---------:|-------------:|:----------|:----------|:----------|--------:|\n|date          |         0|             1|2015-01-01 |2021-08-31 |2017-11-05 |     2079|\n\n\n**Variable type: difftime**\n\n|skim_variable | n_missing| complete_rate|min        |max         |median   | n_unique|\n|:-------------|---------:|-------------:|:----------|:-----------|:--------|--------:|\n|mkopen        |         0|          1.00|21600 secs |32400 secs  |09:00:00 |        4|\n|mkclose       |         0|          1.00|54000 secs |107940 secs |22:00:00 |       13|\n|mkemhopen     |         0|          1.00|21600 secs |32400 secs  |09:00:00 |        5|\n|mkemhclose    |         0|          1.00|54000 secs |107940 secs |23:00:00 |       14|\n|mkopenyest    |         0|          1.00|21600 secs |32400 secs  |09:00:00 |        4|\n|mkcloseyest   |         0|          1.00|54000 secs |107940 secs |22:00:00 |       13|\n|mkopentom     |         0|          1.00|21600 secs |32400 secs  |09:00:00 |        4|\n|mkclosetom    |         0|          1.00|54000 secs |107940 secs |22:00:00 |       13|\n|epopen        |         0|          1.00|25200 secs |43200 secs  |09:00:00 |        6|\n|epclose       |         0|          1.00|61200 secs |90000 secs  |21:00:00 |        9|\n|epemhopen     |         0|          1.00|25200 secs |43200 secs  |09:00:00 |        6|\n|epemhclose    |         0|          1.00|61200 secs |90000 secs  |21:00:00 |       12|\n|epopenyest    |         0|          1.00|25200 secs |43200 secs  |09:00:00 |        6|\n|epcloseyest   |         0|          1.00|61200 secs |90000 secs  |21:00:00 |        9|\n|epopentom     |         0|          1.00|25200 secs |43200 secs  |09:00:00 |        6|\n|epclosetom    |         0|          1.00|61200 secs |90000 secs  |21:00:00 |        9|\n|hsopen        |         0|          1.00|21600 secs |36000 secs  |09:00:00 |        6|\n|hsclose       |         0|          1.00|50400 secs |86400 secs  |21:00:00 |       14|\n|hsemhopen     |         0|          1.00|21600 secs |36000 secs  |09:00:00 |        7|\n|hsemhclose    |         0|          1.00|50400 secs |93600 secs  |21:00:00 |       18|\n|hsopenyest    |         0|          1.00|21600 secs |36000 secs  |09:00:00 |        6|\n|hscloseyest   |         0|          1.00|50400 secs |86400 secs  |21:00:00 |       14|\n|hsopentom     |         0|          1.00|21600 secs |36000 secs  |09:00:00 |        6|\n|hsclosetom    |         0|          1.00|50400 secs |86400 secs  |21:00:00 |       14|\n|akopen        |         0|          1.00|25200 secs |32400 secs  |09:00:00 |        3|\n|akclose       |         0|          1.00|50400 secs |86400 secs  |20:00:00 |       16|\n|akemhopen     |         0|          1.00|25200 secs |32400 secs  |09:00:00 |        3|\n|akemhclose    |         0|          1.00|50400 secs |90000 secs  |20:00:00 |       17|\n|akopenyest    |         0|          1.00|25200 secs |32400 secs  |09:00:00 |        3|\n|akcloseyest   |         0|          1.00|50400 secs |86400 secs  |20:00:00 |       16|\n|akopentom     |         0|          1.00|25200 secs |32400 secs  |09:00:00 |        3|\n|akclosetom    |         0|          1.00|50400 secs |86400 secs  |20:00:00 |       16|\n|mkprddt1      |       183|          0.91|39600 secs |61200 secs  |15:00:00 |        5|\n|mkprddt2      |      1851|          0.11|50400 secs |73800 secs  |15:30:00 |        5|\n|mkprdnt1      |      1358|          0.35|68400 secs |82800 secs  |21:00:00 |       11|\n|mkprdnt2      |      1480|          0.29|0 secs     |84600 secs  |23:00:00 |        8|\n|mkfiret1      |       134|          0.94|66600 secs |80100 secs  |21:15:00 |       12|\n|mkfiret2      |      2069|          0.00|85800 secs |85800 secs  |23:50:00 |        1|\n|epfiret1      |       126|          0.94|64800 secs |81000 secs  |21:00:00 |        6|\n|epfiret2      |      2074|          0.00|85200 secs |85200 secs  |23:40:00 |        1|\n|hsfiret1      |       485|          0.77|0 secs     |82800 secs  |21:00:00 |       17|\n|hsfiret2      |      2045|          0.02|0 secs     |81000 secs  |21:00:00 |        5|\n|hsshwnt1      |       164|          0.92|65100 secs |79200 secs  |20:30:00 |       10|\n|hsshwnt2      |      1369|          0.34|72000 secs |82800 secs  |21:30:00 |       11|\n|akshwnt1      |       883|          0.58|65700 secs |76500 secs  |20:30:00 |       13|\n|akshwnt2      |      1149|          0.45|70200 secs |81000 secs  |21:45:00 |       13|\n\n\n**Variable type: logical**\n\n|skim_variable | n_missing| complete_rate| mean|count |\n|:-------------|---------:|-------------:|----:|:-----|\n|hsprddt1      |      2079|             0|  NaN|:     |\n|hsprddn       |      2079|             0|  NaN|:     |\n|akprddt1      |      2079|             0|  NaN|:     |\n|akprddt2      |      2079|             0|  NaN|:     |\n|akprddn       |      2079|             0|  NaN|:     |\n|akfiren       |      2079|             0|  NaN|:     |\n\n\n**Variable type: numeric**\n\n|skim_variable      | n_missing| complete_rate|        mean|         sd|          p0|         p25|         p50|         p75|        p100|hist  |\n|:------------------|---------:|-------------:|-----------:|----------:|-----------:|-----------:|-----------:|-----------:|-----------:|:-----|\n|dayofweek          |         0|             1|        4.00|       2.00|        1.00|        2.00|        4.00|        6.00|        7.00|▇▃▃▃▇ |\n|dayofyear          |         0|             1|      181.84|     106.34|        0.00|       89.00|      184.00|      273.00|      365.00|▇▇▇▇▇ |\n|weekofyear         |         0|             1|       26.09|      15.20|        0.00|       13.00|       26.00|       39.00|       53.00|▇▇▇▇▇ |\n|monthofyear        |         0|             1|        6.51|       3.48|        1.00|        3.00|        7.00|       10.00|       12.00|▇▅▆▅▇ |\n|year               |         0|             1|     2017.41|       1.74|     2015.00|     2016.00|     2017.00|     2019.00|     2021.00|▇▃▃▃▃ |\n|holidaypx          |         0|             1|        7.85|       6.89|        0.00|        3.00|        6.00|       10.00|       33.00|▇▅▂▁▁ |\n|holidaym           |         0|             1|        0.54|       1.35|        0.00|        0.00|        0.00|        0.00|        5.00|▇▁▁▁▁ |\n|holiday            |         0|             1|        0.10|       0.30|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|wdwevent           |         0|             1|        0.12|       0.32|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|wdwrace            |         0|             1|        0.04|       0.20|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|wdwmaxtemp         |         5|             1|       82.80|       8.53|       51.11|       78.29|       84.50|       89.54|       97.72|▁▁▃▇▆ |\n|wdwmintemp         |         6|             1|       65.50|      10.18|       27.48|       59.03|       68.35|       74.10|       81.28|▁▂▃▆▇ |\n|wdwmeantemp        |         6|             1|       74.15|       9.06|       39.75|       68.76|       76.37|       81.61|       87.76|▁▂▃▆▇ |\n|mkevent            |         0|             1|        0.26|       0.44|        0.00|        0.00|        0.00|        1.00|        1.00|▇▁▁▁▃ |\n|epevent            |         0|             1|        0.58|       0.49|        0.00|        0.00|        1.00|        1.00|        1.00|▆▁▁▁▇ |\n|hsevent            |         0|             1|        0.10|       0.30|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|akevent            |         0|             1|        0.03|       0.18|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|mkemhmorn          |         0|             1|        0.19|       0.40|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|mkemhmyest         |         0|             1|        0.19|       0.40|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|mkemhmtom          |         0|             1|        0.19|       0.40|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|mkemheve           |         0|             1|        0.13|       0.33|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|mkhoursemh         |         0|             1|       13.64|       1.98|        7.50|       13.00|       14.00|       15.00|       23.98|▁▇▅▁▁ |\n|mkhoursemhyest     |         0|             1|       13.65|       1.98|        7.50|       13.00|       14.00|       15.00|       23.98|▁▇▅▁▁ |\n|mkhoursemhtom      |         0|             1|       13.64|       1.98|        7.50|       13.00|       14.00|       15.00|       23.98|▁▇▅▁▁ |\n|mkemheyest         |         0|             1|        0.13|       0.33|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|mkemhetom          |         0|             1|        0.13|       0.33|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemhmorn          |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemhmyest         |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemhmtom          |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemheve           |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemheyest         |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemhetom          |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|ephoursemh         |         0|             1|       12.41|       0.96|        9.00|       12.00|       12.00|       13.00|       17.00|▁▇▃▂▁ |\n|ephoursemhyest     |         0|             1|       12.41|       0.96|        9.00|       12.00|       12.00|       13.00|       17.00|▁▇▃▂▁ |\n|ephoursemhtom      |         0|             1|       12.41|       0.96|        9.00|       12.00|       12.00|       13.00|       17.00|▁▇▃▂▁ |\n|hsemhmorn          |         0|             1|        0.18|       0.38|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|hsemhmyest         |         0|             1|        0.18|       0.38|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|hsemhmtom          |         0|             1|        0.18|       0.38|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|hsemheve           |         0|             1|        0.06|       0.25|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|hsemheyest         |         0|             1|        0.06|       0.25|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|hsemhetom          |         0|             1|        0.06|       0.25|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|hshoursemh         |         0|             1|       12.32|       1.49|        8.00|       11.00|       12.00|       13.00|       18.00|▁▇▇▂▁ |\n|hshoursemhyest     |         0|             1|       12.32|       1.49|        8.00|       11.00|       12.00|       13.00|       18.00|▁▇▇▂▁ |\n|hshoursemhtom      |         0|             1|       12.32|       1.49|        8.00|       11.00|       12.00|       13.00|       18.00|▁▇▇▂▁ |\n|akemhmorn          |         0|             1|        0.30|       0.46|        0.00|        0.00|        0.00|        1.00|        1.00|▇▁▁▁▃ |\n|akemhmyest         |         0|             1|        0.30|       0.46|        0.00|        0.00|        0.00|        1.00|        1.00|▇▁▁▁▃ |\n|akemhmtom          |         0|             1|        0.30|       0.46|        0.00|        0.00|        0.00|        1.00|        1.00|▇▁▁▁▃ |\n|akemheve           |         0|             1|        0.04|       0.20|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|akemheyest         |         0|             1|        0.04|       0.20|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|akemhetom          |         0|             1|        0.04|       0.20|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|akhoursemh         |         0|             1|       11.77|       1.80|        7.00|       11.00|       12.00|       13.00|       17.00|▂▇▇▅▁ |\n|akhoursemhyest     |         0|             1|       11.77|       1.80|        7.00|       11.00|       12.00|       13.00|       17.00|▂▇▇▅▁ |\n|akhoursemhtom      |         0|             1|       11.76|       1.80|        7.00|       11.00|       12.00|       13.00|       17.00|▂▇▇▅▁ |\n|mkhours            |         0|             1|       13.26|       2.01|        7.00|       12.00|       13.00|       15.00|       23.98|▂▇▇▁▁ |\n|mkhoursyest        |         0|             1|       13.26|       2.01|        7.00|       12.00|       13.00|       15.00|       23.98|▂▇▇▁▁ |\n|mkhourstom         |         0|             1|       13.26|       2.00|        7.00|       12.00|       13.00|       15.00|       23.98|▂▇▇▁▁ |\n|ephours            |         0|             1|       12.02|       0.64|        8.00|       12.00|       12.00|       12.00|       17.00|▁▁▇▁▁ |\n|ephoursyest        |         0|             1|       12.03|       0.64|        8.00|       12.00|       12.00|       12.00|       17.00|▁▁▇▁▁ |\n|ephourstom         |         0|             1|       12.02|       0.64|        8.00|       12.00|       12.00|       12.00|       17.00|▁▁▇▁▁ |\n|hshours            |         0|             1|       11.92|       1.19|        5.00|       11.00|       12.00|       12.50|       18.00|▁▁▇▂▁ |\n|hshoursyest        |         0|             1|       11.93|       1.20|        5.00|       11.00|       12.00|       12.50|       18.00|▁▁▇▂▁ |\n|hshourstom         |         0|             1|       11.92|       1.19|        5.00|       11.00|       12.00|       12.50|       18.00|▁▁▇▂▁ |\n|akhours            |         0|             1|       11.46|       1.68|        6.00|       10.50|       11.00|       12.50|       17.00|▁▃▇▃▁ |\n|akhoursyest        |         0|             1|       11.47|       1.68|        6.00|       10.50|       11.00|       12.50|       17.00|▁▃▇▃▁ |\n|akhourstom         |         0|             1|       11.46|       1.68|        6.00|       10.50|       11.00|       12.50|       17.00|▁▃▇▃▁ |\n|weather_wdwhigh    |         0|             1|       82.35|       7.86|       70.20|       74.60|       82.80|       90.60|       92.30|▅▃▂▂▇ |\n|weather_wdwlow     |         0|             1|       64.10|       9.26|       49.20|       55.80|       63.60|       74.00|       76.10|▅▅▃▂▇ |\n|weather_wdwprecip  |         0|             1|        0.15|       0.08|        0.03|        0.08|        0.12|        0.23|        0.35|▇▆▃▅▁ |\n|capacitylost_mk    |         0|             1|   422110.61|   36458.81|   352865.00|   385812.00|   433857.00|   456055.00|   473553.00|▅▂▁▇▆ |\n|capacitylost_ep    |         0|             1|   366897.04|   24019.96|   325168.00|   338367.00|   380763.00|   381963.00|   394662.00|▅▁▁▂▇ |\n|capacitylost_hs    |         0|             1|   287485.76|   33198.89|   203780.00|   279573.00|   301871.00|   311870.00|   321869.00|▂▁▁▁▇ |\n|capacitylost_ak    |         0|             1|   228193.83|   14967.82|   210779.00|   220778.00|   223178.00|   232777.00|   273873.00|▇▅▁▁▂ |\n|capacitylostwgt_mk |         0|             1| 41374025.71| 3621097.96| 34661635.00| 37641738.00| 42585643.00| 44577245.00| 46545047.00|▅▂▂▇▆ |\n|capacitylostwgt_ep |         0|             1| 35344939.61| 2201138.89| 31692832.00| 32692333.00| 36666337.00| 36668737.00| 37678138.00|▅▁▁▁▇ |\n|capacitylostwgt_hs |         0|             1| 27528647.53| 3049291.37| 19812520.00| 26772627.00| 28771129.00| 29761030.00| 30750931.00|▂▁▁▁▇ |\n|capacitylostwgt_ak |         0|             1| 22386447.82| 1398263.45| 20790321.00| 21780222.00| 21799422.00| 22780123.00| 26739827.00|▇▅▁▁▂ |\n|mkprdday           |         0|             1|        1.07|       0.59|        0.00|        1.00|        1.00|        1.00|        3.00|▁▇▁▁▁ |\n|mkprdngt           |         0|             1|        0.64|       0.90|        0.00|        0.00|        0.00|        2.00|        3.00|▇▁▁▃▁ |\n|mkfirewk           |         0|             1|        0.94|       0.26|        0.00|        1.00|        1.00|        1.00|        2.00|▁▁▇▁▁ |\n|epfirewk           |         0|             1|        0.94|       0.25|        0.00|        1.00|        1.00|        1.00|        3.00|▁▇▁▁▁ |\n|hsprdday           |         0|             1|        0.00|       0.00|        0.00|        0.00|        0.00|        0.00|        0.00|▁▁▇▁▁ |\n|hsfirewk           |         0|             1|        0.78|       0.45|        0.00|        1.00|        1.00|        1.00|        2.00|▂▁▇▁▁ |\n|hsshwngt           |         0|             1|        1.28|       0.62|        0.00|        1.00|        1.00|        2.00|        3.00|▁▇▁▅▁ |\n|hsfirewks          |         0|             1|        1.00|       0.00|        1.00|        1.00|        1.00|        1.00|        1.00|▁▁▇▁▁ |\n|akprdday           |         0|             1|        0.00|       0.00|        0.00|        0.00|        0.00|        0.00|        0.00|▁▁▇▁▁ |\n|akshwngt           |         0|             1|        1.04|       0.97|        0.00|        0.00|        1.00|        2.00|        3.00|▇▂▁▇▁ |\n:::\n:::\n\n\nThis dataset contains many more variables than the one we worked with previously.\nFor this analysis, we are going to select `date` (the observation date), `wdw_ticket_season` (the ticket season for the observation), `wdwmaxtemp` (the maximum temperature), `mkclose` (the time Magic Kingdom closed), `mkemhmorn` (whether Magic Kingdom had an \"Extra Magic Hour\" in the morning).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nparks_metadata_clean <- parks_metadata_raw |>\n  ##  based on our analysis plan, we will select the following variables\n  select(date, wdw_ticket_season, wdwmaxtemp, mkclose, mkemhmorn) |>\n  ## based on eligibility criteria, limit to 2018\n  filter(year(date) == 2018) |>\n  ## rename variables\n  rename(\n    park_date = date,\n    park_ticket_season = wdw_ticket_season,\n    park_temperature_high = wdwmaxtemp,\n    park_close = mkclose,\n    park_extra_magic_morning = mkemhmorn\n  )\n```\n:::\n\n\n## Working with multiple data sources\n\nFrequently we find ourselves merging data from multiple sources when attempting to answer causal questions in order to ensure that all of the necessary factors are accounted for.\nThe way we can combine datasets is via *joins* -- joining two or more datasets based on a set or sets of common variables.\nWe can think of three main types of *joins*: left, right, and inner.\nA *left* join combines data from two datasets based on a common variable and includes all records from the *left* dataset along with matching records from the *right* dataset (in `{dplyr}`, `left_join()`), while a *right* join includes all records from the *right* dataset and their corresponding matches from the *left* dataset (in `{dplyr}` `right_join()`); an inner join, on the other hand, includes only the records with matching values in *both* datasets, excluding non-matching records (in `{dplyr}` `inner_join()`.\nFor this analysis, we need to use a left join to pull in the cleaned parks metadata.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>\n  left_join(parks_metadata_clean, by = \"park_date\")\n```\n:::\n\n\n## Recognizing missing data\n\nIt is important to recognize whether we have any missing data in our variables.\nThe `{visdat}` package is great for getting a quick sense of whether we have any missing data.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(visdat)\nvis_miss(seven_dwarfs_train_2018)\n```\n\n::: {.cell-output-display}\n![](chapter-06_files/figure-html/unnamed-chunk-12-1.png){width=672}\n:::\n:::\n\n\nIt looks like we only have a few observations (2%) missing our outcome of interest.\nThis is not too bad.\nFor this first analysis we will ignore the missing values.\nWe can explicitly drop them using the `drop_na()` function from `{dplyr}`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>\n  drop_na()\n```\n:::\n\n\n## Exploring and visualizing data and assumptions\n\nThe *positivity* assumption requires that within each level and combination of the study variables used to achieve exchangeability, there are exposed and unexposed subjects (@sec-assump).\nWe can explore this by visualizing the distribution of each of our proposed confounders stratified by the exposure.\n\n### Single variable checks for positivity violations\n\n@fig-close shows the distribution of Magic Kingdom park closing time by whether the date had extra magic hours in the morning.\nThere is not clear evidence of a lack of positivity here as both exposure levels span the majority of the covariate space.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_train_2018, aes(x = factor(park_close), \n                                    group = factor(park_extra_magic_morning),\n                                    fill = factor(park_extra_magic_morning))) +\n  geom_bar(position = position_dodge2(width = 0.9, preserve = \"single\")) + \n  labs(fill = \"Extra Magic Morning\",\n       x = \"Time of Park Close\")\n```\n\n::: {.cell-output-display}\n![Distribution of Magic Kingdom park closing time by whether the date had extra magic hours in the morning](chapter-06_files/figure-html/fig-close-1.png){#fig-close width=672}\n:::\n:::\n\n\nTo examine the distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning we can use a mirrored histogram.\nWe'll use the {halfmoon} package's `geom_mirror_histogram()` to create one.\nExamining @fig-temp, it does look like there are very few days in the exposed group with maximum temperatures less than 60 degrees, while not necessarily a positivity violation it is worth keeping an eye on, particularly because the dataset is not very large, so this could make it difficult to estimate an average exposure effect across this whole space.\nIf we found this to be particularly difficult, we could posit changing our causal question to instead restrict the analysis to warmer days.\nThis of course would also restrict which days we could draw conclusions about for the future.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nggplot(seven_dwarfs_train_2018, aes(x = park_temperature_high, \n                                    group = factor(park_extra_magic_morning),\n                                    fill = factor(park_extra_magic_morning))) +\n  geom_mirror_histogram(bins = 20) + \n  labs(fill = \"Extra Magic Morning\",\n       x = \"Historic maximum temperature (degrees F)\")\n```\n\n::: {.cell-output-display}\n![Distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning](chapter-06_files/figure-html/fig-temp-1.png){#fig-temp width=672}\n:::\n:::\n\n\nFinally, let's look at the distribution of ticket season by whether there were extra magic hours in the morning.\nExamining @fig-ticket, we do not see any positivity violations.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_train_2018, aes(x = park_ticket_season, \n                                    group = factor(park_extra_magic_morning),\n                                    fill = factor(park_extra_magic_morning))) +\n  geom_bar(position = \"dodge\") + \n  labs(fill = \"Extra Magic Morning\",\n       x = \"Magic Kingdom Ticket Season\")\n```\n\n::: {.cell-output-display}\n![Distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning](chapter-06_files/figure-html/fig-ticket-1.png){#fig-ticket width=672}\n:::\n:::\n\n\n### Multiple variable checks for positivity violations\n\nWe have confirmed that for each of the three confounders, we do not see strong evidence of positivity violations.\nBecause we have so few variables here, we can examine this a bit more closely.\nLet's start by discretizing the `park_temperature_high` variable a bit (we will cut it into tertiles).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_train_2018 |>\n  ## cut park_temperature_high into tertiles\n  mutate(park_temperature_high_bin = cut(park_temperature_high, breaks = 3)) |>\n  ## bin park close time\n  mutate(park_close_bin = case_when(\n    hour(park_close) < 19 & hour(park_close) > 12 ~ \"(1) early\",\n    hour(park_close) >= 19 & hour(park_close) < 24 ~ \"(2) standard\",\n    hour(park_close) >= 24 | hour(park_close) < 12 ~ \"(3) late\"\n  )) |>\n  group_by(park_close_bin, park_temperature_high_bin, park_ticket_season) |>\n  ## calculate the proportion exposed in each bin\n  summarise(prop_exposed = mean(park_extra_magic_morning), .groups = \"drop\") |>\nggplot(aes(x = park_close_bin, y = park_temperature_high_bin, fill = prop_exposed)) + \n  geom_tile() + \n  scale_fill_gradient2(midpoint = 0.5) +\n  facet_wrap(~park_ticket_season) + \n  labs(y = \"Historic Maximum Temperature (F)\",\n       x = \"Magic Kingdom Park Close Time\",\n       fill = \"Proportion of Days Exposed\")\n```\n\n::: {.cell-output-display}\n![Check for positivity violations across three confounders: historic high temperature, park close time, and ticket season.](chapter-06_files/figure-html/fig-positivity-1.png){#fig-positivity width=864}\n:::\n:::\n\n\nInteresting!\n@fig-positivity shows an interesting potential violation.\nIt looks like 100% of days with lower temperatures (historic highs between 51 and 65 degrees) that are in the peak ticket season have extra magic hours in the morning.\nThis actually makes sense if we think a bit about this data set.\nThe only days with cold temperatures in Florida that would also be considered a \"peak\" time to visit Walt Disney World would be over Christmas / New Years.\nDuring this time there historically were always extra magic hours.\n\nWe are going to proceed with the analysis, but we will keep these observations in mind.\n\n## Presenting descriptive statistics\n\nLet's examine a table of the variables of interest in this data frame.\nTo do so, we are going to use the `tbl_summary()` function from the `{gtsummary}` package.\n(We'll also use the `{labelled}` package to clean up the variable names for the table.)\n\n\n::: {#tbl-unweighted-gtsummary .cell tbl-cap='A descriptive table of Extra Magic Morning in the touringplans dataset. This table shows the distributions of these variables in the observed population.'}\n\n```{.r .cell-code}\nlibrary(gtsummary)\nlibrary(labelled)\nseven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>\n  set_variable_labels(\n    park_ticket_season = \"Ticket Season\",\n    park_close = \"Close Time\",\n    park_temperature_high = \"Historic High Temperature\"\n  )\n\ntbl_summary(\n  seven_dwarfs_train_2018,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  # add an overall column to the table\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n```{=html}\n<div id=\"ypghaicrvy\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#ypghaicrvy table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  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 border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#ypghaicrvy .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#ypghaicrvy .gt_left {\n  text-align: left;\n}\n\n#ypghaicrvy .gt_center {\n  text-align: center;\n}\n\n#ypghaicrvy .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#ypghaicrvy .gt_font_normal {\n  font-weight: normal;\n}\n\n#ypghaicrvy .gt_font_bold {\n  font-weight: bold;\n}\n\n#ypghaicrvy .gt_font_italic {\n  font-style: italic;\n}\n\n#ypghaicrvy .gt_super {\n  font-size: 65%;\n}\n\n#ypghaicrvy .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#ypghaicrvy .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#ypghaicrvy .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#ypghaicrvy .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#ypghaicrvy .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#ypghaicrvy .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#ypghaicrvy .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;0&lt;/strong&gt;, N = 294&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>0</strong>, N = 294<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;1&lt;/strong&gt;, N = 60&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>1</strong>, N = 60<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 354&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Overall</strong>, N = 354<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">60 (20%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">78 (22%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">158 (54%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">193 (55%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">76 (26%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">37 (13%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">55 (16%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (6.1%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">20 (5.6%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">28 (9.5%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (7.9%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">91 (31%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">102 (29%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">78 (27%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">89 (25%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">40 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">57 (16%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">2 (0.6%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">84 (78, 89)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (76, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">84 (78, 89)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n:::\n:::\n",
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-    "markdown": "# Preparing data to answer causal questions {#sec-data-causal}\n\n\n\n\n\n## Introduction to the data {#sec-data}\n\nThroughout this book we will be using data obtained from [Touring Plans](https://touringplans.com).\nTouring Plans is a company that helps folks plan their trips to Disney and Universal theme parks.\nOne of their goals is to accurately predict attraction wait times at these theme parks by leveraging data and statistical modeling.\nThe `{touringplans}` R package includes several datasets containing information about Disney theme park attractions.\nA summary of the attractions included in the package can be found by running the following:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(touringplans)\nattractions_metadata\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 14 × 8\n   dataset_name name  short_name park  land  opened_on \n   <chr>        <chr> <chr>      <chr> <chr> <date>    \n 1 alien_sauce… Alie… Alien Sau… Disn… Toy … 2018-06-30\n 2 dinosaur     DINO… DINOSAUR   Disn… Dino… 1998-04-22\n 3 expedition_… Expe… Expeditio… Disn… Asia  2006-04-07\n 4 flight_of_p… Avat… Flight of… Disn… Pand… 2017-05-27\n 5 kilimanjaro… Kili… Kilimanja… Disn… Afri… 1998-04-22\n 6 navi_river   Na'v… Na'vi Riv… Disn… Pand… 2017-05-27\n 7 pirates_of_… Pira… Pirates o… Magi… Adve… 1973-12-17\n 8 rock_n_roll… Rock… Rock Coas… Disn… Suns… 1999-07-29\n 9 seven_dwarf… Seve… 7 Dwarfs … Magi… Fant… 2014-05-28\n10 slinky_dog   Slin… Slinky Dog Disn… Toy … 2018-06-30\n11 soarin       Soar… Soarin'    Epcot Worl… 2005-05-05\n12 spaceship_e… Spac… Spaceship… Epcot Worl… 1982-10-01\n13 splash_moun… Spla… Splash Mo… Magi… Fron… 1992-07-17\n14 toy_story_m… Toy … Toy Story… Disn… Toy … 2008-05-31\n# ℹ 2 more variables: duration <dbl>,\n#   average_wait_per_hundred <dbl>\n```\n\n\n:::\n:::\n\n\nAdditionally, this package contains a dataset with raw metadata about the parks, with observations recorded daily.\nThis metadata includes information like the Walt Disney World ticket season on the particular day (was it high season -- think Christmas -- or low season -- think right when school started), what the historic temperatures were in the park on that day, and whether there was a special event, such as \"extra magic hours\" in the park on that day (did the park open early to guests staying in the Walt Disney World resorts?).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nparks_metadata_raw\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2,079 × 181\n   date       wdw_ticket_season dayofweek dayofyear\n   <date>     <chr>                 <dbl>     <dbl>\n 1 2015-01-01 <NA>                      5         0\n 2 2015-01-02 <NA>                      6         1\n 3 2015-01-03 <NA>                      7         2\n 4 2015-01-04 <NA>                      1         3\n 5 2015-01-05 <NA>                      2         4\n 6 2015-01-06 <NA>                      3         5\n 7 2015-01-07 <NA>                      4         6\n 8 2015-01-08 <NA>                      5         7\n 9 2015-01-09 <NA>                      6         8\n10 2015-01-10 <NA>                      7         9\n# ℹ 2,069 more rows\n# ℹ 177 more variables: weekofyear <dbl>,\n#   monthofyear <dbl>, year <dbl>, season <chr>,\n#   holidaypx <dbl>, holidaym <dbl>, holidayn <chr>,\n#   holiday <dbl>, wdwticketseason <chr>,\n#   wdwracen <chr>, wdweventn <chr>, wdwevent <dbl>,\n#   wdwrace <dbl>, wdwseason <chr>, …\n```\n\n\n:::\n:::\n\n\nSuppose the causal question of interest is:\n\n**Is there a relationship between whether there were \"Extra Magic Hours\" in the morning at Magic Kingdom and the average wait time for an attraction called the \"Seven Dwarfs Mine Train\" the same day between 9am and 10am in 2018?**\n\nLet's begin by diagramming this causal question (@fig-seven-diag).\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Diagram of the causal question \"Is there a relationship between whether there were \"Extra Magic Hours\" in the morning at Magic Kingdom and the average wait time for an attraction called the \"Seven Dwarfs Mine Train\" the same day between 9am and 10am in 2018?\"](chapter-07_files/figure-html/fig-seven-diag-1.png){#fig-seven-diag width=672}\n:::\n:::\n\n\nHistorically, guests who stayed in a Walt Disney World resort hotel could access the park during \"Extra Magic Hours,\" during which the park was closed to all other guests.\nThese extra hours could be in the morning or evening.\nThe Seven Dwarfs Mine Train is a ride at Walt Disney World's Magic Kingdom.\nMagic Kingdom may or may not be selected each day to have these \"Extra Magic Hours.\" We are interested in examining the relationship between whether there were \"Extra Magic Hours\" in the morning and the average wait time for the Seven Dwarfs Mine Train on the same day between 9 am and 10 am.\nBelow is a proposed DAG for this question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Proposed DAG for the relationship between Extra Magic Hours in the morning at a particular park and the average wait time between 9 am and 10 am. Here we are saying that we believe 1) Extra Magic Hours impacts average wait time and 2) both Extra Magic Hours and average wait time are determined by the time the park closes, historic high temperatures, and ticket season.](chapter-07_files/figure-html/fig-dag-magic-1.png){#fig-dag-magic width=672}\n:::\n:::\n\n\nSince we are not in charge of Walt Disney World's operations, we cannot randomize dates to have (or not) \"Extra Magic Hours\", therefore, we need to rely on previously collected observational data and do our best to emulate the *target trial* that we would have created, should it have been possible.\nHere, our observations are *days*.\nLooking at the diagram above, we can map each element of the causal question to elements of our target trial protocol:\n\n-   **Eligibility criteria**: Days must be from 2018\n-   **Exposure definition**: Magic kingdom had \"Extra Magic Hours\" in the morning\n-   **Assignment procedures**: Observed -- if the historic data suggests there were \"Extra Magic Hours\" in the morning on a particular day, that day is classified as \"exposed\" otherwise it is \"unexposed\"\n-   **Follow-up period**: From park open to 10am.\n-   **Outcome definition**: The average posted wait time between 9am and 10am\n-   **Causal contrast of interest**: Average treatment effect (we will discuss this in @sec-estimands)\n-   **Analysis plan**: We use inverse probability waiting after fitting a propensity score model to estimate the average treatment effect of the exposure on the outcome of interest. We will adjust for variables as determined by our DAG (@fig-dag-magic)\n\n## Data wrangling and recipes\n\nMost of our data manipulation tools come from the `{dplyr}` package (@tbl-dplyr).\nWe will also use `{lubridate}` to help us manipulate dates.\n\n| Target trial protocol element | {dplyr} functions                           |\n|-------------------------------|---------------------------------------------|\n| Eligibility criteria          | `filter()`                                  |\n| Exposure definition           | `mutate()`                                  |\n| Assignment procedures         | `mutate()`                                  |\n| Follow-up period              | `mutate()` `pivot_longer()` `pivot_wider()` |\n| Outcome definition            | `mutate()`                                  |\n| Analysis plan                 | `select()` `mutate()`                       |\n\n: Mapping target trial protocol elements to commonly used `{dplyr}` functions {#tbl-dplyr}\n\nTo answer this question, we are going to need to manipulate both the `seven_dwarfs_train` dataset as well as the `parks_metadata_raw` dataset.\nLet's start with the `seven_dwarfs_train` data set.\nThe Seven Dwarfs Mine Train ride is an attraction at Walt Disney World's Magic Kingdom.\nThe `seven_dwarfs_train` dataset in the {touringplans} package contains information about the date a particular wait time was recorded (`park_date`), the time of the wait time (`wait_datetime`), the actual wait time (`wait_minutes_actual`), and the posted wait time (`wait_minutes_posted`).\nLet's take a look at this dataset.\nThe {skimr} package is great for getting a quick glimpse at a new dataset.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(skimr)\nskim(seven_dwarfs_train)\n```\n\n::: {.cell-output-display}\n\nTable: Data summary\n\n|                         |                   |\n|:------------------------|:------------------|\n|Name                     |seven_dwarfs_train |\n|Number of rows           |321631             |\n|Number of columns        |4                  |\n|_______________________  |                   |\n|Column type frequency:   |                   |\n|Date                     |1                  |\n|numeric                  |2                  |\n|POSIXct                  |1                  |\n|________________________ |                   |\n|Group variables          |None               |\n\n\n**Variable type: Date**\n\n|skim_variable | n_missing| complete_rate|min        |max        |median     | n_unique|\n|:-------------|---------:|-------------:|:----------|:----------|:----------|--------:|\n|park_date     |         0|             1|2015-01-01 |2021-12-28 |2018-04-07 |     2334|\n\n\n**Variable type: numeric**\n\n|skim_variable       | n_missing| complete_rate|  mean|      sd|     p0| p25| p50| p75| p100|hist  |\n|:-------------------|---------:|-------------:|-----:|-------:|------:|---:|---:|---:|----:|:-----|\n|wait_minutes_actual |    313996|          0.02| 23.99| 1064.06| -92918|  21|  31|  46|  217|▁▁▁▁▇ |\n|wait_minutes_posted |     30697|          0.90| 76.96|   33.99|      0|  50|  70|  95|  300|▆▇▁▁▁ |\n\n\n**Variable type: POSIXct**\n\n|skim_variable | n_missing| complete_rate|min                 |max                 |median              | n_unique|\n|:-------------|---------:|-------------:|:-------------------|:-------------------|:-------------------|--------:|\n|wait_datetime |         0|             1|2015-01-01 07:51:12 |2021-12-28 22:57:34 |2018-04-07 23:14:06 |   321586|\n\n\n:::\n:::\n\n\nExamining the output above, we learn that this dataset contains four columns and 321,631 rows.\nWe also learn that the dates span from 2015 to 2021.\nWe can also examine the distribution of each of the variables to detect any potential anomalies.\nNotice anything strange?\nLook at the `p0` (that is the minimum value) for `wait_minutes_actual`.\nIt is `-92918`!\nWe are not using this variable for this analysis, but we will for future analyses, so this is good to keep in mind.\n\nWe need this dataset to calculate our *outcome*.\nRecall from above that our outcome is defined as the average posted wait time between 9am and 10am.\nAdditionally, recall our eligibility criteria states that we need to restrict our analysis to days in 2018.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\nlibrary(lubridate)\nseven_dwarfs_train_2018 <- seven_dwarfs_train |>\n  filter(year(park_date) == 2018) |> # eligibility criteria\n  mutate(hour = hour(wait_datetime)) |> # get hour from wait\n  group_by(park_date, hour) |> # group by date\n  summarise(\n    wait_minutes_posted_avg = mean(wait_minutes_posted, na.rm = TRUE),\n    .groups = \"drop\"\n  ) |> # get average wait time\n  mutate(\n    wait_minutes_posted_avg =\n      case_when(\n        is.nan(wait_minutes_posted_avg) ~ NA,\n        TRUE ~ wait_minutes_posted_avg\n      )\n  ) |> # if it is NAN make it NA\n  filter(hour == 9) # only keep the average wait time between 9 and 10\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_train_2018\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 362 × 3\n   park_date   hour wait_minutes_posted_avg\n   <date>     <int>                   <dbl>\n 1 2018-01-01     9                    60  \n 2 2018-01-02     9                    60  \n 3 2018-01-03     9                    60  \n 4 2018-01-04     9                    68.9\n 5 2018-01-05     9                    70.6\n 6 2018-01-06     9                    33.3\n 7 2018-01-07     9                    46.4\n 8 2018-01-08     9                    69.5\n 9 2018-01-09     9                    64.3\n10 2018-01-10     9                    74.3\n# ℹ 352 more rows\n```\n\n\n:::\n:::\n\n\nNow that we have our outcome settled, we need to get our exposure variable, as well as any other park-specific variables about the day in question that may be used as variables that we adjust for.\nExamining @fig-dag-magic, we see that we need data for three proposed confounders: the ticket season, the time the park closed, and the historic high temperature.\nThese are in the `parks_metadata_raw` dataset.\nThis data will require extra cleaning, since the names are in the original format.\n\n::: callout-tip\nWe like to have our variable names follow a clean convention -- one way to do this is to follow Emily Riederer's \"Column Names as Contracts\" format [@Riederer_2020].\nThe basic idea is to predefine a set of words, phrases, or stubs with clear meanings to index information, and use these consistently when naming variables.\nFor example, in these data, variables that are specific to a particular wait time are prepended with the term `wait` (e.g. `wait_datetime` and `wait_minutes_actual`), variables that are specific to the park on a particular day, acquired from parks metadata, are prepended with the term `park` (e.g. `park_date` or `park_temperature_high`).\n:::\n\nLet's first decide what variables we will need.\nIn practice, this decision may involve an iterative process.\nFor example, after drawing our DAG or after conducting diagnostic, we may determine that we need more variables than what we originally cleaned.\nLet's start by skimming this dataframe.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nskim(parks_metadata_raw)\n```\n\n::: {.cell-output-display}\n\nTable: Data summary\n\n|                         |                   |\n|:------------------------|:------------------|\n|Name                     |parks_metadata_raw |\n|Number of rows           |2079               |\n|Number of columns        |181                |\n|_______________________  |                   |\n|Column type frequency:   |                   |\n|character                |42                 |\n|Date                     |1                  |\n|difftime                 |46                 |\n|logical                  |6                  |\n|numeric                  |86                 |\n|________________________ |                   |\n|Group variables          |None               |\n\n\n**Variable type: character**\n\n|skim_variable         | n_missing| complete_rate| min| max| empty| n_unique| whitespace|\n|:---------------------|---------:|-------------:|---:|---:|-----:|--------:|----------:|\n|wdw_ticket_season     |       861|          0.59|   4|   7|     0|        3|          0|\n|season                |       253|          0.88|   4|  29|     0|       17|          0|\n|holidayn              |      1865|          0.10|   3|   7|     0|       43|          0|\n|wdwticketseason       |       761|          0.63|   4|   7|     0|        3|          0|\n|wdwracen              |      1992|          0.04|   4|   6|     0|        5|          0|\n|wdweventn             |      1832|          0.12|   3|  12|     0|        8|          0|\n|wdwseason             |        87|          0.96|   4|  29|     0|       17|          0|\n|mkeventn              |      1546|          0.26|   3|  11|     0|       10|          0|\n|epeventn              |       868|          0.58|   4|   5|     0|        4|          0|\n|hseventn              |      1877|          0.10|   4|   7|     0|        5|          0|\n|akeventn              |      2010|          0.03|   4|   4|     0|        2|          0|\n|holidayj              |      2037|          0.02|   5|  15|     0|        8|          0|\n|insession             |       105|          0.95|   2|   3|     0|       95|          0|\n|insession_enrollment  |       105|          0.95|   2|   4|     0|      100|          0|\n|insession_wdw         |       105|          0.95|   2|   4|     0|       94|          0|\n|insession_dlr         |       105|          0.95|   2|   4|     0|       94|          0|\n|insession_sqrt_wdw    |       105|          0.95|   2|   4|     0|       97|          0|\n|insession_sqrt_dlr    |       105|          0.95|   2|   4|     0|       97|          0|\n|insession_california  |       105|          0.95|   2|   4|     0|       89|          0|\n|insession_dc          |       105|          0.95|   2|   4|     0|       86|          0|\n|insession_central_fl  |       105|          0.95|   2|   4|     0|       71|          0|\n|insession_drive1_fl   |       105|          0.95|   2|   4|     0|       85|          0|\n|insession_drive2_fl   |       105|          0.95|   2|   4|     0|       95|          0|\n|insession_drive_ca    |       105|          0.95|   2|   4|     0|       91|          0|\n|insession_florida     |       105|          0.95|   2|   4|     0|       86|          0|\n|insession_mardi_gras  |       105|          0.95|   2|   4|     0|       82|          0|\n|insession_midwest     |       105|          0.95|   2|   4|     0|       75|          0|\n|insession_ny_nj       |       105|          0.95|   2|   4|     0|        8|          0|\n|insession_ny_nj_pa    |       105|          0.95|   2|   4|     0|       19|          0|\n|insession_new_england |       105|          0.95|   2|   4|     0|       45|          0|\n|insession_new_jersey  |       105|          0.95|   2|   4|     0|        2|          0|\n|insession_nothwest    |       105|          0.95|   2|   4|     0|       17|          0|\n|insession_planes      |       105|          0.95|   2|   4|     0|       81|          0|\n|insession_socal       |       105|          0.95|   2|   4|     0|       80|          0|\n|insession_southwest   |       105|          0.95|   2|   4|     0|       86|          0|\n|mkprddn               |       183|          0.91|  33|  41|     0|        2|          0|\n|mkprdnn               |      1358|          0.35|  29|  38|     0|        2|          0|\n|mkfiren               |       134|          0.94|  18|  65|     0|        8|          0|\n|epfiren               |       126|          0.94|  13|  35|     0|        2|          0|\n|hsfiren               |       485|          0.77|  24|  66|     0|        6|          0|\n|hsshwnn               |       164|          0.92|  10|  28|     0|        2|          0|\n|akshwnn               |       883|          0.58|  15|  33|     0|        2|          0|\n\n\n**Variable type: Date**\n\n|skim_variable | n_missing| complete_rate|min        |max        |median     | n_unique|\n|:-------------|---------:|-------------:|:----------|:----------|:----------|--------:|\n|date          |         0|             1|2015-01-01 |2021-08-31 |2017-11-05 |     2079|\n\n\n**Variable type: difftime**\n\n|skim_variable | n_missing| complete_rate|min        |max         |median   | n_unique|\n|:-------------|---------:|-------------:|:----------|:-----------|:--------|--------:|\n|mkopen        |         0|          1.00|21600 secs |32400 secs  |09:00:00 |        4|\n|mkclose       |         0|          1.00|54000 secs |107940 secs |22:00:00 |       13|\n|mkemhopen     |         0|          1.00|21600 secs |32400 secs  |09:00:00 |        5|\n|mkemhclose    |         0|          1.00|54000 secs |107940 secs |23:00:00 |       14|\n|mkopenyest    |         0|          1.00|21600 secs |32400 secs  |09:00:00 |        4|\n|mkcloseyest   |         0|          1.00|54000 secs |107940 secs |22:00:00 |       13|\n|mkopentom     |         0|          1.00|21600 secs |32400 secs  |09:00:00 |        4|\n|mkclosetom    |         0|          1.00|54000 secs |107940 secs |22:00:00 |       13|\n|epopen        |         0|          1.00|25200 secs |43200 secs  |09:00:00 |        6|\n|epclose       |         0|          1.00|61200 secs |90000 secs  |21:00:00 |        9|\n|epemhopen     |         0|          1.00|25200 secs |43200 secs  |09:00:00 |        6|\n|epemhclose    |         0|          1.00|61200 secs |90000 secs  |21:00:00 |       12|\n|epopenyest    |         0|          1.00|25200 secs |43200 secs  |09:00:00 |        6|\n|epcloseyest   |         0|          1.00|61200 secs |90000 secs  |21:00:00 |        9|\n|epopentom     |         0|          1.00|25200 secs |43200 secs  |09:00:00 |        6|\n|epclosetom    |         0|          1.00|61200 secs |90000 secs  |21:00:00 |        9|\n|hsopen        |         0|          1.00|21600 secs |36000 secs  |09:00:00 |        6|\n|hsclose       |         0|          1.00|50400 secs |86400 secs  |21:00:00 |       14|\n|hsemhopen     |         0|          1.00|21600 secs |36000 secs  |09:00:00 |        7|\n|hsemhclose    |         0|          1.00|50400 secs |93600 secs  |21:00:00 |       18|\n|hsopenyest    |         0|          1.00|21600 secs |36000 secs  |09:00:00 |        6|\n|hscloseyest   |         0|          1.00|50400 secs |86400 secs  |21:00:00 |       14|\n|hsopentom     |         0|          1.00|21600 secs |36000 secs  |09:00:00 |        6|\n|hsclosetom    |         0|          1.00|50400 secs |86400 secs  |21:00:00 |       14|\n|akopen        |         0|          1.00|25200 secs |32400 secs  |09:00:00 |        3|\n|akclose       |         0|          1.00|50400 secs |86400 secs  |20:00:00 |       16|\n|akemhopen     |         0|          1.00|25200 secs |32400 secs  |09:00:00 |        3|\n|akemhclose    |         0|          1.00|50400 secs |90000 secs  |20:00:00 |       17|\n|akopenyest    |         0|          1.00|25200 secs |32400 secs  |09:00:00 |        3|\n|akcloseyest   |         0|          1.00|50400 secs |86400 secs  |20:00:00 |       16|\n|akopentom     |         0|          1.00|25200 secs |32400 secs  |09:00:00 |        3|\n|akclosetom    |         0|          1.00|50400 secs |86400 secs  |20:00:00 |       16|\n|mkprddt1      |       183|          0.91|39600 secs |61200 secs  |15:00:00 |        5|\n|mkprddt2      |      1851|          0.11|50400 secs |73800 secs  |15:30:00 |        5|\n|mkprdnt1      |      1358|          0.35|68400 secs |82800 secs  |21:00:00 |       11|\n|mkprdnt2      |      1480|          0.29|0 secs     |84600 secs  |23:00:00 |        8|\n|mkfiret1      |       134|          0.94|66600 secs |80100 secs  |21:15:00 |       12|\n|mkfiret2      |      2069|          0.00|85800 secs |85800 secs  |23:50:00 |        1|\n|epfiret1      |       126|          0.94|64800 secs |81000 secs  |21:00:00 |        6|\n|epfiret2      |      2074|          0.00|85200 secs |85200 secs  |23:40:00 |        1|\n|hsfiret1      |       485|          0.77|0 secs     |82800 secs  |21:00:00 |       17|\n|hsfiret2      |      2045|          0.02|0 secs     |81000 secs  |21:00:00 |        5|\n|hsshwnt1      |       164|          0.92|65100 secs |79200 secs  |20:30:00 |       10|\n|hsshwnt2      |      1369|          0.34|72000 secs |82800 secs  |21:30:00 |       11|\n|akshwnt1      |       883|          0.58|65700 secs |76500 secs  |20:30:00 |       13|\n|akshwnt2      |      1149|          0.45|70200 secs |81000 secs  |21:45:00 |       13|\n\n\n**Variable type: logical**\n\n|skim_variable | n_missing| complete_rate| mean|count |\n|:-------------|---------:|-------------:|----:|:-----|\n|hsprddt1      |      2079|             0|  NaN|:     |\n|hsprddn       |      2079|             0|  NaN|:     |\n|akprddt1      |      2079|             0|  NaN|:     |\n|akprddt2      |      2079|             0|  NaN|:     |\n|akprddn       |      2079|             0|  NaN|:     |\n|akfiren       |      2079|             0|  NaN|:     |\n\n\n**Variable type: numeric**\n\n|skim_variable      | n_missing| complete_rate|        mean|         sd|          p0|         p25|         p50|         p75|        p100|hist  |\n|:------------------|---------:|-------------:|-----------:|----------:|-----------:|-----------:|-----------:|-----------:|-----------:|:-----|\n|dayofweek          |         0|             1|        4.00|       2.00|        1.00|        2.00|        4.00|        6.00|        7.00|▇▃▃▃▇ |\n|dayofyear          |         0|             1|      181.84|     106.34|        0.00|       89.00|      184.00|      273.00|      365.00|▇▇▇▇▇ |\n|weekofyear         |         0|             1|       26.09|      15.20|        0.00|       13.00|       26.00|       39.00|       53.00|▇▇▇▇▇ |\n|monthofyear        |         0|             1|        6.51|       3.48|        1.00|        3.00|        7.00|       10.00|       12.00|▇▅▆▅▇ |\n|year               |         0|             1|     2017.41|       1.74|     2015.00|     2016.00|     2017.00|     2019.00|     2021.00|▇▃▃▃▃ |\n|holidaypx          |         0|             1|        7.85|       6.89|        0.00|        3.00|        6.00|       10.00|       33.00|▇▅▂▁▁ |\n|holidaym           |         0|             1|        0.54|       1.35|        0.00|        0.00|        0.00|        0.00|        5.00|▇▁▁▁▁ |\n|holiday            |         0|             1|        0.10|       0.30|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|wdwevent           |         0|             1|        0.12|       0.32|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|wdwrace            |         0|             1|        0.04|       0.20|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|wdwmaxtemp         |         5|             1|       82.80|       8.53|       51.11|       78.29|       84.50|       89.54|       97.72|▁▁▃▇▆ |\n|wdwmintemp         |         6|             1|       65.50|      10.18|       27.48|       59.03|       68.35|       74.10|       81.28|▁▂▃▆▇ |\n|wdwmeantemp        |         6|             1|       74.15|       9.06|       39.75|       68.76|       76.37|       81.61|       87.76|▁▂▃▆▇ |\n|mkevent            |         0|             1|        0.26|       0.44|        0.00|        0.00|        0.00|        1.00|        1.00|▇▁▁▁▃ |\n|epevent            |         0|             1|        0.58|       0.49|        0.00|        0.00|        1.00|        1.00|        1.00|▆▁▁▁▇ |\n|hsevent            |         0|             1|        0.10|       0.30|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|akevent            |         0|             1|        0.03|       0.18|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|mkemhmorn          |         0|             1|        0.19|       0.40|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|mkemhmyest         |         0|             1|        0.19|       0.40|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|mkemhmtom          |         0|             1|        0.19|       0.40|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|mkemheve           |         0|             1|        0.13|       0.33|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|mkhoursemh         |         0|             1|       13.64|       1.98|        7.50|       13.00|       14.00|       15.00|       23.98|▁▇▅▁▁ |\n|mkhoursemhyest     |         0|             1|       13.65|       1.98|        7.50|       13.00|       14.00|       15.00|       23.98|▁▇▅▁▁ |\n|mkhoursemhtom      |         0|             1|       13.64|       1.98|        7.50|       13.00|       14.00|       15.00|       23.98|▁▇▅▁▁ |\n|mkemheyest         |         0|             1|        0.13|       0.33|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|mkemhetom          |         0|             1|        0.13|       0.33|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemhmorn          |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemhmyest         |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemhmtom          |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemheve           |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemheyest         |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|epemhetom          |         0|             1|        0.13|       0.34|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|ephoursemh         |         0|             1|       12.41|       0.96|        9.00|       12.00|       12.00|       13.00|       17.00|▁▇▃▂▁ |\n|ephoursemhyest     |         0|             1|       12.41|       0.96|        9.00|       12.00|       12.00|       13.00|       17.00|▁▇▃▂▁ |\n|ephoursemhtom      |         0|             1|       12.41|       0.96|        9.00|       12.00|       12.00|       13.00|       17.00|▁▇▃▂▁ |\n|hsemhmorn          |         0|             1|        0.18|       0.38|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|hsemhmyest         |         0|             1|        0.18|       0.38|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|hsemhmtom          |         0|             1|        0.18|       0.38|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▂ |\n|hsemheve           |         0|             1|        0.06|       0.25|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|hsemheyest         |         0|             1|        0.06|       0.25|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|hsemhetom          |         0|             1|        0.06|       0.25|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|hshoursemh         |         0|             1|       12.32|       1.49|        8.00|       11.00|       12.00|       13.00|       18.00|▁▇▇▂▁ |\n|hshoursemhyest     |         0|             1|       12.32|       1.49|        8.00|       11.00|       12.00|       13.00|       18.00|▁▇▇▂▁ |\n|hshoursemhtom      |         0|             1|       12.32|       1.49|        8.00|       11.00|       12.00|       13.00|       18.00|▁▇▇▂▁ |\n|akemhmorn          |         0|             1|        0.30|       0.46|        0.00|        0.00|        0.00|        1.00|        1.00|▇▁▁▁▃ |\n|akemhmyest         |         0|             1|        0.30|       0.46|        0.00|        0.00|        0.00|        1.00|        1.00|▇▁▁▁▃ |\n|akemhmtom          |         0|             1|        0.30|       0.46|        0.00|        0.00|        0.00|        1.00|        1.00|▇▁▁▁▃ |\n|akemheve           |         0|             1|        0.04|       0.20|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|akemheyest         |         0|             1|        0.04|       0.20|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|akemhetom          |         0|             1|        0.04|       0.20|        0.00|        0.00|        0.00|        0.00|        1.00|▇▁▁▁▁ |\n|akhoursemh         |         0|             1|       11.77|       1.80|        7.00|       11.00|       12.00|       13.00|       17.00|▂▇▇▅▁ |\n|akhoursemhyest     |         0|             1|       11.77|       1.80|        7.00|       11.00|       12.00|       13.00|       17.00|▂▇▇▅▁ |\n|akhoursemhtom      |         0|             1|       11.76|       1.80|        7.00|       11.00|       12.00|       13.00|       17.00|▂▇▇▅▁ |\n|mkhours            |         0|             1|       13.26|       2.01|        7.00|       12.00|       13.00|       15.00|       23.98|▂▇▇▁▁ |\n|mkhoursyest        |         0|             1|       13.26|       2.01|        7.00|       12.00|       13.00|       15.00|       23.98|▂▇▇▁▁ |\n|mkhourstom         |         0|             1|       13.26|       2.00|        7.00|       12.00|       13.00|       15.00|       23.98|▂▇▇▁▁ |\n|ephours            |         0|             1|       12.02|       0.64|        8.00|       12.00|       12.00|       12.00|       17.00|▁▁▇▁▁ |\n|ephoursyest        |         0|             1|       12.03|       0.64|        8.00|       12.00|       12.00|       12.00|       17.00|▁▁▇▁▁ |\n|ephourstom         |         0|             1|       12.02|       0.64|        8.00|       12.00|       12.00|       12.00|       17.00|▁▁▇▁▁ |\n|hshours            |         0|             1|       11.92|       1.19|        5.00|       11.00|       12.00|       12.50|       18.00|▁▁▇▂▁ |\n|hshoursyest        |         0|             1|       11.93|       1.20|        5.00|       11.00|       12.00|       12.50|       18.00|▁▁▇▂▁ |\n|hshourstom         |         0|             1|       11.92|       1.19|        5.00|       11.00|       12.00|       12.50|       18.00|▁▁▇▂▁ |\n|akhours            |         0|             1|       11.46|       1.68|        6.00|       10.50|       11.00|       12.50|       17.00|▁▃▇▃▁ |\n|akhoursyest        |         0|             1|       11.47|       1.68|        6.00|       10.50|       11.00|       12.50|       17.00|▁▃▇▃▁ |\n|akhourstom         |         0|             1|       11.46|       1.68|        6.00|       10.50|       11.00|       12.50|       17.00|▁▃▇▃▁ |\n|weather_wdwhigh    |         0|             1|       82.35|       7.86|       70.20|       74.60|       82.80|       90.60|       92.30|▅▃▂▂▇ |\n|weather_wdwlow     |         0|             1|       64.10|       9.26|       49.20|       55.80|       63.60|       74.00|       76.10|▅▅▃▂▇ |\n|weather_wdwprecip  |         0|             1|        0.15|       0.08|        0.03|        0.08|        0.12|        0.23|        0.35|▇▆▃▅▁ |\n|capacitylost_mk    |         0|             1|   422110.61|   36458.81|   352865.00|   385812.00|   433857.00|   456055.00|   473553.00|▅▂▁▇▆ |\n|capacitylost_ep    |         0|             1|   366897.04|   24019.96|   325168.00|   338367.00|   380763.00|   381963.00|   394662.00|▅▁▁▂▇ |\n|capacitylost_hs    |         0|             1|   287485.76|   33198.89|   203780.00|   279573.00|   301871.00|   311870.00|   321869.00|▂▁▁▁▇ |\n|capacitylost_ak    |         0|             1|   228193.83|   14967.82|   210779.00|   220778.00|   223178.00|   232777.00|   273873.00|▇▅▁▁▂ |\n|capacitylostwgt_mk |         0|             1| 41374025.71| 3621097.96| 34661635.00| 37641738.00| 42585643.00| 44577245.00| 46545047.00|▅▂▂▇▆ |\n|capacitylostwgt_ep |         0|             1| 35344939.61| 2201138.89| 31692832.00| 32692333.00| 36666337.00| 36668737.00| 37678138.00|▅▁▁▁▇ |\n|capacitylostwgt_hs |         0|             1| 27528647.53| 3049291.37| 19812520.00| 26772627.00| 28771129.00| 29761030.00| 30750931.00|▂▁▁▁▇ |\n|capacitylostwgt_ak |         0|             1| 22386447.82| 1398263.45| 20790321.00| 21780222.00| 21799422.00| 22780123.00| 26739827.00|▇▅▁▁▂ |\n|mkprdday           |         0|             1|        1.07|       0.59|        0.00|        1.00|        1.00|        1.00|        3.00|▁▇▁▁▁ |\n|mkprdngt           |         0|             1|        0.64|       0.90|        0.00|        0.00|        0.00|        2.00|        3.00|▇▁▁▃▁ |\n|mkfirewk           |         0|             1|        0.94|       0.26|        0.00|        1.00|        1.00|        1.00|        2.00|▁▁▇▁▁ |\n|epfirewk           |         0|             1|        0.94|       0.25|        0.00|        1.00|        1.00|        1.00|        3.00|▁▇▁▁▁ |\n|hsprdday           |         0|             1|        0.00|       0.00|        0.00|        0.00|        0.00|        0.00|        0.00|▁▁▇▁▁ |\n|hsfirewk           |         0|             1|        0.78|       0.45|        0.00|        1.00|        1.00|        1.00|        2.00|▂▁▇▁▁ |\n|hsshwngt           |         0|             1|        1.28|       0.62|        0.00|        1.00|        1.00|        2.00|        3.00|▁▇▁▅▁ |\n|hsfirewks          |         0|             1|        1.00|       0.00|        1.00|        1.00|        1.00|        1.00|        1.00|▁▁▇▁▁ |\n|akprdday           |         0|             1|        0.00|       0.00|        0.00|        0.00|        0.00|        0.00|        0.00|▁▁▇▁▁ |\n|akshwngt           |         0|             1|        1.04|       0.97|        0.00|        0.00|        1.00|        2.00|        3.00|▇▂▁▇▁ |\n\n\n:::\n:::\n\n\nThis dataset contains many more variables than the one we worked with previously.\nFor this analysis, we are going to select `date` (the observation date), `wdw_ticket_season` (the ticket season for the observation), `wdwmaxtemp` (the maximum temperature), `mkclose` (the time Magic Kingdom closed), `mkemhmorn` (whether Magic Kingdom had an \"Extra Magic Hour\" in the morning).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nparks_metadata_clean <- parks_metadata_raw |>\n  ##  based on our analysis plan, we will select the following variables\n  select(date, wdw_ticket_season, wdwmaxtemp, mkclose, mkemhmorn) |>\n  ## based on eligibility criteria, limit to 2018\n  filter(year(date) == 2018) |>\n  ## rename variables\n  rename(\n    park_date = date,\n    park_ticket_season = wdw_ticket_season,\n    park_temperature_high = wdwmaxtemp,\n    park_close = mkclose,\n    park_extra_magic_morning = mkemhmorn\n  )\n```\n:::\n\n\n## Working with multiple data sources\n\nFrequently we find ourselves merging data from multiple sources when attempting to answer causal questions in order to ensure that all of the necessary factors are accounted for.\nThe way we can combine datasets is via *joins* -- joining two or more datasets based on a set or sets of common variables.\nWe can think of three main types of *joins*: left, right, and inner.\nA *left* join combines data from two datasets based on a common variable and includes all records from the *left* dataset along with matching records from the *right* dataset (in `{dplyr}`, `left_join()`), while a *right* join includes all records from the *right* dataset and their corresponding matches from the *left* dataset (in `{dplyr}` `right_join()`); an inner join, on the other hand, includes only the records with matching values in *both* datasets, excluding non-matching records (in `{dplyr}` `inner_join()`.\nFor this analysis, we need to use a left join to pull in the cleaned parks metadata.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>\n  left_join(parks_metadata_clean, by = \"park_date\")\n```\n:::\n\n\n## Recognizing missing data\n\nIt is important to recognize whether we have any missing data in our variables.\nThe `{visdat}` package is great for getting a quick sense of whether we have any missing data.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(visdat)\nvis_miss(seven_dwarfs_train_2018)\n```\n\n::: {.cell-output-display}\n![](chapter-07_files/figure-html/unnamed-chunk-12-1.png){width=672}\n:::\n:::\n\n\nIt looks like we only have a few observations (2%) missing our outcome of interest.\nThis is not too bad.\nFor this first analysis we will ignore the missing values.\nWe can explicitly drop them using the `drop_na()` function from `{dplyr}`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>\n  drop_na()\n```\n:::\n\n\n## Exploring and visualizing data and assumptions\n\nThe *positivity* assumption requires that within each level and combination of the study variables used to achieve exchangeability, there are exposed and unexposed subjects (@sec-assump).\nWe can explore this by visualizing the distribution of each of our proposed confounders stratified by the exposure.\n\n### Single variable checks for positivity violations\n\n@fig-close shows the distribution of Magic Kingdom park closing time by whether the date had extra magic hours in the morning.\nThere is not clear evidence of a lack of positivity here as both exposure levels span the majority of the covariate space.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_train_2018, aes(\n  x = factor(park_close),\n  group = factor(park_extra_magic_morning),\n  fill = factor(park_extra_magic_morning)\n)) +\n  geom_bar(position = position_dodge2(width = 0.9, preserve = \"single\")) +\n  labs(\n    fill = \"Extra Magic Morning\",\n    x = \"Time of Park Close\"\n  )\n```\n\n::: {.cell-output-display}\n![Distribution of Magic Kingdom park closing time by whether the date had extra magic hours in the morning](chapter-07_files/figure-html/fig-close-1.png){#fig-close width=672}\n:::\n:::\n\n\nTo examine the distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning we can use a mirrored histogram.\nWe'll use the {halfmoon} package's `geom_mirror_histogram()` to create one.\nExamining @fig-temp, it does look like there are very few days in the exposed group with maximum temperatures less than 60 degrees, while not necessarily a positivity violation it is worth keeping an eye on, particularly because the dataset is not very large, so this could make it difficult to estimate an average exposure effect across this whole space.\nIf we found this to be particularly difficult, we could posit changing our causal question to instead restrict the analysis to warmer days.\nThis of course would also restrict which days we could draw conclusions about for the future.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nggplot(seven_dwarfs_train_2018, aes(\n  x = park_temperature_high,\n  group = factor(park_extra_magic_morning),\n  fill = factor(park_extra_magic_morning)\n)) +\n  geom_mirror_histogram(bins = 20) +\n  labs(\n    fill = \"Extra Magic Morning\",\n    x = \"Historic maximum temperature (degrees F)\"\n  )\n```\n\n::: {.cell-output-display}\n![Distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning](chapter-07_files/figure-html/fig-temp-1.png){#fig-temp width=672}\n:::\n:::\n\n\nFinally, let's look at the distribution of ticket season by whether there were extra magic hours in the morning.\nExamining @fig-ticket, we do not see any positivity violations.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_train_2018, aes(\n  x = park_ticket_season,\n  group = factor(park_extra_magic_morning),\n  fill = factor(park_extra_magic_morning)\n)) +\n  geom_bar(position = \"dodge\") +\n  labs(\n    fill = \"Extra Magic Morning\",\n    x = \"Magic Kingdom Ticket Season\"\n  )\n```\n\n::: {.cell-output-display}\n![Distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning](chapter-07_files/figure-html/fig-ticket-1.png){#fig-ticket width=672}\n:::\n:::\n\n\n### Multiple variable checks for positivity violations\n\nWe have confirmed that for each of the three confounders, we do not see strong evidence of positivity violations.\nBecause we have so few variables here, we can examine this a bit more closely.\nLet's start by discretizing the `park_temperature_high` variable a bit (we will cut it into tertiles).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_train_2018 |>\n  ## cut park_temperature_high into tertiles\n  mutate(park_temperature_high_bin = cut(park_temperature_high, breaks = 3)) |>\n  ## bin park close time\n  mutate(park_close_bin = case_when(\n    hour(park_close) < 19 & hour(park_close) > 12 ~ \"(1) early\",\n    hour(park_close) >= 19 & hour(park_close) < 24 ~ \"(2) standard\",\n    hour(park_close) >= 24 | hour(park_close) < 12 ~ \"(3) late\"\n  )) |>\n  group_by(park_close_bin, park_temperature_high_bin, park_ticket_season) |>\n  ## calculate the proportion exposed in each bin\n  summarise(prop_exposed = mean(park_extra_magic_morning), .groups = \"drop\") |>\n  ggplot(aes(x = park_close_bin, y = park_temperature_high_bin, fill = prop_exposed)) +\n  geom_tile() +\n  scale_fill_gradient2(midpoint = 0.5) +\n  facet_wrap(~park_ticket_season) +\n  labs(\n    y = \"Historic Maximum Temperature (F)\",\n    x = \"Magic Kingdom Park Close Time\",\n    fill = \"Proportion of Days Exposed\"\n  )\n```\n\n::: {.cell-output-display}\n![Check for positivity violations across three confounders: historic high temperature, park close time, and ticket season.](chapter-07_files/figure-html/fig-positivity-1.png){#fig-positivity width=864}\n:::\n:::\n\n\nInteresting!\n@fig-positivity shows an interesting potential violation.\nIt looks like 100% of days with lower temperatures (historic highs between 51 and 65 degrees) that are in the peak ticket season have extra magic hours in the morning.\nThis actually makes sense if we think a bit about this data set.\nThe only days with cold temperatures in Florida that would also be considered a \"peak\" time to visit Walt Disney World would be over Christmas / New Years.\nDuring this time there historically were always extra magic hours.\n\nWe are going to proceed with the analysis, but we will keep these observations in mind.\n\n## Presenting descriptive statistics\n\nLet's examine a table of the variables of interest in this data frame.\nTo do so, we are going to use the `tbl_summary()` function from the `{gtsummary}` package.\n(We'll also use the `{labelled}` package to clean up the variable names for the table.)\n\n\n::: {#tbl-unweighted-gtsummary .cell tbl-cap='A descriptive table of Extra Magic Morning in the touringplans dataset. This table shows the distributions of these variables in the observed population.'}\n\n```{.r .cell-code}\nlibrary(gtsummary)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\n#BlackLivesMatter\n```\n\n\n:::\n\n```{.r .cell-code}\nlibrary(labelled)\nseven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>\n  set_variable_labels(\n    park_ticket_season = \"Ticket Season\",\n    park_close = \"Close Time\",\n    park_temperature_high = \"Historic High Temperature\"\n  )\n\ntbl_summary(\n  seven_dwarfs_train_2018,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  # add an overall column to the table\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"yhkiqdsruy\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#yhkiqdsruy table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, 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 padding-left: 5px;\n  padding-right: 5px;\n}\n\n#yhkiqdsruy .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#yhkiqdsruy .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#yhkiqdsruy .gt_left {\n  text-align: left;\n}\n\n#yhkiqdsruy .gt_center {\n  text-align: center;\n}\n\n#yhkiqdsruy .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#yhkiqdsruy .gt_font_normal {\n  font-weight: normal;\n}\n\n#yhkiqdsruy .gt_font_bold {\n  font-weight: bold;\n}\n\n#yhkiqdsruy .gt_font_italic {\n  font-style: italic;\n}\n\n#yhkiqdsruy .gt_super {\n  font-size: 65%;\n}\n\n#yhkiqdsruy .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#yhkiqdsruy .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#yhkiqdsruy .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#yhkiqdsruy .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#yhkiqdsruy .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#yhkiqdsruy .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#yhkiqdsruy .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;0&lt;/strong&gt;, N = 294&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>0</strong>, N = 294<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;1&lt;/strong&gt;, N = 60&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>1</strong>, N = 60<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 354&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Overall</strong>, N = 354<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">60 (20%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">78 (22%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">158 (54%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">193 (55%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">76 (26%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">37 (13%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">55 (16%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (6.1%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">20 (5.6%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">28 (9.5%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (7.9%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">91 (31%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">102 (29%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">78 (27%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">89 (25%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">40 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">57 (16%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">2 (0.6%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">84 (78, 89)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (76, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">84 (78, 89)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n",
+    "markdown": "# Building propensity score models {#sec-building-models}\n\n\n\n\n\nOften we are interested in how some *exposure* (or treatment) impacts an outcome.\nFor example, we could assess how an ad campaign (exposure) impacts sales (outcome), whether a particular medication (exposure) improves patient survival (outcome), or whether opening a theme park early to some visitors (exposure) reduces wait times later in the day (outcome).\nAs defined in the @sec-counterfactuals, <!--TODO make sure that this is correct --> an exposure in the context of this book is often a modifiable event or condition that occurs before the outcome.\nIn an ideal world, we would simply estimate the correlation between the exposure and outcome as the causal effect of the exposure.\nRandomized trials are the best practical examples of this idealized scenario: participants are randomly assigned to exposure groups.\nIf all goes well, this allows for an unbiased estimate of the causal effect between the exposure and outcome.\nIn the \"real world,\" outside this randomized trial setting, we are often *exposed* to something based on other factors.\nFor example, when deciding what medication to give a diabetic patient, a doctor may consider the patient's medical history, their likelihood to adhere to certain medications, and the severity of their disease.\nThe treatment is no longer random; it is *conditional* on factors about that patient, also known as the patient's *covariates*.\nIf these covariates also affect the outcome, they are *confounders*.\n\n::: callout-note\nA **confounder** is a common cause of exposure and outcome.\n:::\n\nSuppose we could collect information about all of these factors.\nIn that case, we could determine each patient's probability of exposure and use this to inform an analysis assessing the relationship between that exposure and some outcome.\nThis probability is the propensity score!\nWhen used appropriately, modeling with a propensity score can simulate what the relationship between exposure and outcome would have looked like if we had run a randomized trial.\nThe correlation between exposure and outcome will estimate the causal effect after applying a propensity score.\nWhen fitting a *propensity score model* we want to condition on all known confounders.\n\n::: callout-note\nA **propensity score** is the probability of being in the exposure group, conditioned on observed covariates.\n:::\n\n@rosenbaum1983central showed in observational studies conditioning on propensity scores can lead to unbiased estimates of the exposure effect as long as certain assumptions hold:\n\n1.  There are no unmeasured confounders\n2.  Every subject has a nonzero probability of receiving either exposure\n\n## Logistic Regression\n\nThere are many ways to estimate the propensity score; typically, people use logistic regression for binary exposures.\nThe logistic regression model predicts the exposure using known confounders.\nEach individual's predicted value is the propensity score.\nThe `glm()` function will fit a logistic regression model in R.\nBelow is pseudo-code.\nThe first argument is the model, with the exposure on the left side and the confounders on the right.\nThe `data` argument takes the data frame, and the `family = binomial()` argument denotes the model should be fit using logistic regression (as opposed to a different generalized linear model).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglm(\n  exposure ~ confounder_1 + confounder_2,\n  data = df,\n  family = binomial()\n)\n```\n:::\n\n\nWe can extract the propensity scores by pulling out the predictions on the probability scale.\nUsing the `augment()` function from the [{`broom`}](https://broom.tidymodels.org/) package, we can extract these propensity scores and add them to our original data frame.\nThe argument `type.predict` is set to `\"response\"` to indicate that we want to extract the predicted values on the *probability* scale.\nBy default, these will be on the linear logit scale.\nThe `data` argument contains the original data frame.\nThis code will output a new data frame consisting of all components in `df` with six additional columns corresponding to the logistic regression model that was fit.\nThe `.fitted` column is the propensity score.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglm(\n  exposure ~ confounder_1 + confounder_2,\n  data = df,\n  family = binomial()\n) |>\n  augment(type.predict = \"response\", data = df)\n```\n:::\n\n\nLet's look at an example.\n\n### Extra Magic Hours at Magic Kingdom {#sec-prop-dag}\n\nRecall our causal question of interest from @sec-data: **Is there a relationship between whether there were \"Extra Magic Hours\" in the morning at Magic Kingdom and the average wait time for an attraction called the \"Seven Dwarfs Mine Train\" the same day between 9am and 10am in 2018?** Below is a proposed DAG for this question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Proposed DAG for the relationship between Extra Magic Hours in the morning at a particular park and the average wait time between 9 am and 10 am. Here we are saying that we believe 1) Extra Magic Hours impacts average wait time and 2) both Extra Magic Hours and average wait time are determined by the time the park closes, historic high temperatures, and ticket season.](chapter-07_files/figure-html/fig-dag-magic-hours-wait-1.png){#fig-dag-magic-hours-wait width=672}\n:::\n:::\n\n\nIn @fig-dag-magic-hours-wait, we propose three confounders: the historic high temperature on the day, the time the park closed, and the ticket season: value, regular, or peak.\nWe can build a propensity score model using the `seven_dwarfs_train_2018` data set from the `{touringplans}` package.\nEach row of this dataset contains information about the Seven Dwarfs Mine Train during a particular hour on a given day.\nFirst, we need to subset the data to only include average wait times between 9 and 10 am.\nThen we will use the `glm()` function to fit the propensity score model, predicting `park_extra_magic_morning` using the four confounders specified above.\nWe'll add the propensity scores to the data frame (in a column called `.fitted` as set by the `augment()` function in the `{broom}` package).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\n\nseven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)\n\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\n```\n:::\n\n\nLet's take a look at these propensity scores.\n@tbl-df-ps shows the propensity scores (in the `.fitted` column) for the first six days in the dataset, as well as the values of each day's exposure, outcome, and confounders.\nThe propensity score here is the probability that a given date will have Extra Magic Hours in the morning given the observed confounders, in this case, the historical high temperatures on a given date, the time the park closed, and Ticket Season.\nFor example, on January 1, 2018, there was a 30.2% chance that there would be Extra Magic Hours at the Magic Kingdom given the Ticket Season (peak in this case), time of park closure (11 pm), and the historic high temperature on this date (58.6 degrees).\nOn this particular day, there were *not* Extra Magic Hours in the morning (as indicated by the 0 in the first row of the `park_extra_magic_morning` column).\n\n\n::: {#tbl-df-ps .cell tbl-cap='The first six observations in the `seven_dwarfs_9_with_ps` dataset, including their propensity scores in the `.fitted` column.'}\n\n```{.r .cell-code}\nseven_dwarfs_9_with_ps |>\n  select(\n    .fitted,\n    park_date,\n    park_extra_magic_morning,\n    park_ticket_season,\n    park_close,\n    park_temperature_high\n  ) |>\n  head() |>\n  knitr::kable()\n```\n\n::: {.cell-output-display}\n\n\n| .fitted|park_date  | park_extra_magic_morning|park_ticket_season |park_close | park_temperature_high|\n|-------:|:----------|------------------------:|:------------------|:----------|---------------------:|\n|  0.3019|2018-01-01 |                        0|peak               |23:00:00   |                 58.63|\n|  0.2815|2018-01-02 |                        0|peak               |24:00:00   |                 53.65|\n|  0.2900|2018-01-03 |                        0|peak               |24:00:00   |                 51.11|\n|  0.1881|2018-01-04 |                        0|regular            |24:00:00   |                 52.66|\n|  0.1841|2018-01-05 |                        1|regular            |24:00:00   |                 54.29|\n|  0.2074|2018-01-06 |                        0|regular            |23:00:00   |                 56.25|\n\n\n:::\n:::\n\n\nWe can examine the distribution of propensity scores by exposure group.\nA nice way to visualize this is via mirrored histograms.\nWe'll use the {halfmoon} package's `geom_mirror_histogram()` to create one.\nThe code below creates two histograms of the propensity scores, one on the \"top\" for the exposed group (the dates with Extra Magic Hours in the morning) and one on the \"bottom\" for the unexposed group.\nWe'll also tweak the y-axis labels to use absolute values (rather than negative values for the bottom histogram) via `scale_y_continuous(labels = abs)`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nggplot(\n  seven_dwarfs_9_with_ps,\n  aes(.fitted, fill = factor(park_extra_magic_morning))\n) +\n  geom_mirror_histogram(bins = 50) +\n  scale_y_continuous(labels = abs) +\n  labs(x = \"propensity score\", fill = \"extra magic morning\")\n```\n\n::: {.cell-output-display}\n![Mirrored histograms of estimated propensity scores for Extra Magic Hour days (exposed group, top) and days without Extra Magic hours (unexposed group, bottom)](chapter-07_files/figure-html/fig-mirrored-ps-1.png){#fig-mirrored-ps width=672}\n:::\n:::\n\n\nHere are some questions to ask to gain diagnostic insights we gain from @fig-mirrored-ps.\n\n<!-- TODO: This section needs to be clarified. -->\n\n1.  Look for lack of overlap as a potential positivity problem.\n    But too much overlap may indicate a poor model \n    <!-- (TODO: not necessarily. depends on the relative covariate distributions in the two groups). -->\n\n2.  Avg treatment effect among treated is easier to estimate with precision (because of higher counts) than in the control group.\n\n3.  A single outlier in either group concerning range could be a problem and warrant data inspection\n<!-- (TODO: do this here?) *look at the model coefs here*. -->\n\n<!-- *TODO* This may be a \"nice\" example -- should we also show a bad model (maybe only with ticket season or possibly overspecifying it) -->\n\n## Choosing what variables to include {#sec-choosing-vars}\n\nThe best way to decide what variables to include in your propensity score model is to look at your DAG and have at least a minimal adjustment set of confounders.\nOf course, sometimes, essential variables are missing or measured with error.\nIn addition, there is often more than one theoretical adjustment set that debiases your estimate; it may be that one of the minimal adjustment sets is measured well in your data set and another is not.\nIf you have confounders on your DAG that you do not have access to, sensitivity analyses can help quantify the potential impact.\nSee Chapter 11 for an in-depth discussion of sensitivity analyses.\n\nAccurately specifying a DAG improves our ability to add the correct variables to our models.\nHowever, confounders are not the only necessary type of variable to consider.\nFor example, variables that are predictors of the *outcome* *but not the exposure* can improve the precision of propensity score models.\nConversely, including variables that are predictors of the *exposure but not the outcome* (instrumental variables) can bias the model.\nLuckily, this bias seems relatively negligible in practice, especially compared to the risk of confounding bias [@Myers2011].\n\n::: callout-note\nSome estimates, such as the odds and hazard ratios, suffer from an additional problem called *non-collapsibility*.\nFor these estimates, adding noise variables (variables unrelated to the exposure or outcome) doesn't reduce precision: they can bias the estimate as well---more the reason to avoid data-driven approaches to selecting variables for causal models.\n:::\n\nAnother variable to be wary of is a *collider*, a descendant of both the exposure and outcome.\nIf you specify your DAG correctly, you can avoid colliders by only using adjustment sets that completely close backdoor paths from the exposure to the outcome.\nHowever, some circumstances make this difficult: some colliders are inherently stratified by the study's design or the nature of follow-up.\nFor example, loss-to-follow-up is a common source of collider-stratification bias; in Chapter XX, we'll discuss this further.\n\nA variable can also be both a confounder and a collider, as in the case of so-called butterfly bias:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggdag(butterfly_bias()) +\n  theme_dag()\n```\n\n::: {.cell-output-display}\n![A causal diagram that displays a variable `m`, which is both a collider and a confounder for `x` and `y`. In situations where you don't have all measured variables in a DAG, you may have to make a tough choice about which type of bias is the least bad.\n](chapter-07_files/figure-html/fig-butterfly-dag-1.png){#fig-butterfly-dag width=480}\n:::\n:::\n\n\nConsider @fig-butterfly-dag.\nTo estimate the causal effect of `x` on `y`, we need to account for `m` because it's a counfounder.\nHowever, `m` is also a collider between `a` and `b`, so controlling for it will induce a relationship between those variables, creating a second set of confounders.\nIf we have all the variables measured well, we can avoid the bias from adjusting for `m` by adjusting for either `a` or `b` as well.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggdag_adjustment_set(butterfly_bias()) +\n  theme_dag() +\n  theme(legend.position = \"bottom\")\n```\n\n::: {.cell-output-display}\n![The adjustment sets in the case of butterfly bias: we can get an unbiased effect by accounting for both `m` and the collider bias induced by adjusting for it. But what if we don't have `a` or `b`?\n](chapter-07_files/figure-html/fig-butterfly-sets-1.png){#fig-butterfly-sets width=480}\n:::\n:::\n\n\nHowever, what should we do if we don't have those variables?\nAdjusting for `m` opens a biasing pathway that we cannot block through `a` and `b`(collider-stratification bias), but `m` is also a confounder for `x` and `y`.\nAs in the case above, it appears that confounding bias is often the worse of the two options, so we should adjust for `m` unless we have reason to believe it will cause more problems than it solves [@DingMiratrix2015].\n\n### Don't use prediction metrics for causal modeling\n\nBy and large, metrics commonly used for building prediction models are inappropriate for building causal models.\nResearchers and data scientists often make decisions about models using metrics like R^2^, AUC, accuracy, and (often inappropriately) p-values.\nHowever, a causal model's goal is not to predict as much about the outcome as possible [@hernan2021]; the goal is to estimate the relationship between the exposure and outcome accurately.\nA causal model needn't predict particularly well to be unbiased.\n\nThese metrics, however, may help identify a model's best *functional form*.\nGenerally, we'll use DAGs and our domain knowledge to build the model itself.\nHowever, we may be unsure of the mathematical relationship between a confounder and the outcome or exposure.\nFor instance, we may not know if the relationship is linear.\nMisspecifying this relationship can lead to residual confounding: we may only partially account for the confounder in question, leaving some bias in the estimate.\nTesting different functional forms using prediction-focused metrics can help improve the model's accuracy, potentially allowing for better control.\n\nAnother technique researchers sometimes use to determine confounders is to add a variable, then calculate the percent change in the coefficient between the outcome and exposure.\nFor instance, we first model `y ~ x` to estimate the relationship between `x` and `y`.\nThen, we model `y ~ x + z` and see how much the coefficient on `x` has changed.\nA common rule is to add a variable if it changes the coefficient of`x` by 10%.\n\nUnfortunately, this technique is unreliable.\nAs we've discussed, controlling for mediators, colliders, and instrumental variables all affect the estimate of the relationship between `x` and `y`, and usually, they result in bias.\nIn other words, there are many different types of variables besides confounders that can cause a change in the coefficient of the exposure.\nAs discussed above, confounding bias is often the most crucial factor, but systematically searching your variables for anything that changes the exposure coefficient can compound many types of bias.\n",
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-    "markdown": "# Building propensity score models {#sec-building-models}\n\n\n\n\n\nOften we are interested in how some *exposure* (or treatment) impacts an outcome.\nFor example, we could assess how an ad campaign (exposure) impacts sales (outcome), whether a particular medication (exposure) improves patient survival (outcome), or whether opening a theme park early to some visitors (exposure) reduces wait times later in the day (outcome).\nAs defined in the @sec-counterfactuals, <!--TODO make sure that this is correct --> an exposure in the context of this book is often a modifiable event or condition that occurs before the outcome.\nIn an ideal world, we would simply estimate the correlation between the exposure and outcome as the causal effect of the exposure.\nRandomized trials are the best practical examples of this idealized scenario: participants are randomly assigned to exposure groups.\nIf all goes well, this allows for an unbiased estimate of the causal effect between the exposure and outcome.\nIn the \"real world,\" outside this randomized trial setting, we are often *exposed* to something based on other factors.\nFor example, when deciding what medication to give a diabetic patient, a doctor may consider the patient's medical history, their likelihood to adhere to certain medications, and the severity of their disease.\nThe treatment is no longer random; it is *conditional* on factors about that patient, also known as the patient's *covariates*.\nIf these covariates also affect the outcome, they are *confounders*.\n\n::: callout-note\nA **confounder** is a common cause of exposure and outcome.\n:::\n\nSuppose we could collect information about all of these factors.\nIn that case, we could determine each patient's probability of exposure and use this to inform an analysis assessing the relationship between that exposure and some outcome.\nThis probability is the propensity score!\nWhen used appropriately, modeling with a propensity score can simulate what the relationship between exposure and outcome would have looked like if we had run a randomized trial.\nThe correlation between exposure and outcome will estimate the causal effect after applying a propensity score.\nWhen fitting a *propensity score model* we want to condition on all known confounders.\n\n::: callout-note\nA **propensity score** is the probability of being in the exposure group, conditioned on observed covariates.\n:::\n\n@rosenbaum1983central showed in observational studies conditioning on propensity scores can lead to unbiased estimates of the exposure effect as long as certain assumptions hold:\n\n1.  There are no unmeasured confounders\n2.  Every subject has a nonzero probability of receiving either exposure\n\n## Logistic Regression\n\nThere are many ways to estimate the propensity score; typically, people use logistic regression for binary exposures.\nThe logistic regression model predicts the exposure using known confounders.\nEach individual's predicted value is the propensity score.\nThe `glm()` function will fit a logistic regression model in R.\nBelow is pseudo-code.\nThe first argument is the model, with the exposure on the left side and the confounders on the right.\nThe `data` argument takes the data frame, and the `family = binomial()` argument denotes the model should be fit using logistic regression (as opposed to a different generalized linear model).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglm(\n  exposure ~ confounder_1 + confounder_2,\n  data = df,\n  family = binomial()\n)\n```\n:::\n\n\nWe can extract the propensity scores by pulling out the predictions on the probability scale.\nUsing the `augment()` function from the [{`broom`}](https://broom.tidymodels.org/) package, we can extract these propensity scores and add them to our original data frame.\nThe argument `type.predict` is set to `\"response\"` to indicate that we want to extract the predicted values on the *probability* scale.\nBy default, these will be on the linear logit scale.\nThe `data` argument contains the original data frame.\nThis code will output a new data frame consisting of all components in `df` with six additional columns corresponding to the logistic regression model that was fit.\nThe `.fitted` column is the propensity score.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nglm(\n  exposure ~ confounder_1 + confounder_2,\n  data = df,\n  family = binomial()\n) |>\n  augment(type.predict = \"response\", data = df)\n```\n:::\n\n\nLet's look at an example.\n\n### Extra Magic Hours at Magic Kingdom {#sec-prop-dag}\n\nRecall our causal question of interest from @sec-data: **Is there a relationship between whether there were \"Extra Magic Hours\" in the morning at Magic Kingdom and the average wait time for an attraction called the \"Seven Dwarfs Mine Train\" the same day between 9am and 10am in 2018?** Below is a proposed DAG for this question.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Proposed DAG for the relationship between Extra Magic Hours in the morning at a particular park and the average wait time between 9 am and 10 am. Here we are saying that we believe 1) Extra Magic Hours impacts average wait time and 2) both Extra Magic Hours and average wait time are determined by the time the park closes, historic high temperatures, and ticket season.](chapter-08_files/figure-html/fig-dag-magic-hours-wait-1.png){#fig-dag-magic-hours-wait width=672}\n:::\n:::\n\n\nIn @fig-dag-magic-hours-wait, we propose three confounders: the historic high temperature on the day, the time the park closed, and the ticket season: value, regular, or peak.\nWe can build a propensity score model using the `seven_dwarfs_train_2018` data set from the `{touringplans}` package.\nEach row of this dataset contains information about the Seven Dwarfs Mine Train during a particular hour on a given day.\nFirst, we need to subset the data to only include average wait times between 9 and 10 am.\nThen we will use the `glm()` function to fit the propensity score model, predicting `park_extra_magic_morning` using the four confounders specified above.\nWe'll add the propensity scores to the data frame (in a column called `.fitted` as set by the `augment()` function in the `{broom}` package).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\n\nseven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)\n\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\n```\n:::\n\n\nLet's take a look at these propensity scores.\n@tbl-df-ps shows the propensity scores (in the `.fitted` column) for the first six days in the dataset, as well as the values of each day's exposure, outcome, and confounders.\nThe propensity score here is the probability that a given date will have Extra Magic Hours in the morning given the observed confounders, in this case, the historical high temperatures on a given date, the time the park closed, and Ticket Season.\nFor example, on January 1, 2018, there was a 30.2% chance that there would be Extra Magic Hours at the Magic Kingdom given the Ticket Season (peak in this case), time of park closure (11 pm), and the historic high temperature on this date (58.6 degrees).\nOn this particular day, there were *not* Extra Magic Hours in the morning (as indicated by the 0 in the first row of the `park_extra_magic_morning` column).\n\n\n::: {#tbl-df-ps .cell tbl-cap='The first six observations in the `seven_dwarfs_9_with_ps` dataset, including their propensity scores in the `.fitted` column.'}\n\n```{.r .cell-code}\nseven_dwarfs_9_with_ps |>\n  select(\n    .fitted,\n    park_date,\n    park_extra_magic_morning,\n    park_ticket_season,\n    park_close,\n    park_temperature_high\n  ) |>\n  head() |>\n  knitr::kable()\n```\n\n::: {.cell-output-display}\n\n\n| .fitted|park_date  | park_extra_magic_morning|park_ticket_season |park_close | park_temperature_high|\n|-------:|:----------|------------------------:|:------------------|:----------|---------------------:|\n|  0.3019|2018-01-01 |                        0|peak               |23:00:00   |                 58.63|\n|  0.2815|2018-01-02 |                        0|peak               |24:00:00   |                 53.65|\n|  0.2900|2018-01-03 |                        0|peak               |24:00:00   |                 51.11|\n|  0.1881|2018-01-04 |                        0|regular            |24:00:00   |                 52.66|\n|  0.1841|2018-01-05 |                        1|regular            |24:00:00   |                 54.29|\n|  0.2074|2018-01-06 |                        0|regular            |23:00:00   |                 56.25|\n\n\n:::\n:::\n\n\nWe can examine the distribution of propensity scores by exposure group.\nA nice way to visualize this is via mirrored histograms.\nWe'll use the {halfmoon} package's `geom_mirror_histogram()` to create one.\nThe code below creates two histograms of the propensity scores, one on the \"top\" for the exposed group (the dates with Extra Magic Hours in the morning) and one on the \"bottom\" for the unexposed group.\nWe'll also tweak the y-axis labels to use absolute values (rather than negative values for the bottom histogram) via `scale_y_continuous(labels = abs)`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nggplot(\n  seven_dwarfs_9_with_ps,\n  aes(.fitted, fill = factor(park_extra_magic_morning))\n) +\n  geom_mirror_histogram(bins = 50) +\n  scale_y_continuous(labels = abs) +\n  labs(x = \"propensity score\", fill = \"extra magic morning\")\n```\n\n::: {.cell-output-display}\n![Mirrored histograms of estimated propensity scores for Extra Magic Hour days (exposed group, top) and days without Extra Magic hours (unexposed group, bottom)](chapter-08_files/figure-html/fig-mirrored-ps-1.png){#fig-mirrored-ps width=672}\n:::\n:::\n\n\nHere are some questions to ask to gain diagnostic insights we gain from @fig-mirrored-ps.\n\n<!-- TODO: This section needs to be clarified. -->\n\n1.  Look for lack of overlap as a potential positivity problem.\n    But too much overlap may indicate a poor model \n    <!-- (TODO: not necessarily. depends on the relative covariate distributions in the two groups). -->\n\n2.  Avg treatment effect among treated is easier to estimate with precision (because of higher counts) than in the control group.\n\n3.  A single outlier in either group concerning range could be a problem and warrant data inspection\n<!-- (TODO: do this here?) *look at the model coefs here*. -->\n\n<!-- *TODO* This may be a \"nice\" example -- should we also show a bad model (maybe only with ticket season or possibly overspecifying it) -->\n\n## Choosing what variables to include {#sec-choosing-vars}\n\nThe best way to decide what variables to include in your propensity score model is to look at your DAG and have at least a minimal adjustment set of confounders.\nOf course, sometimes, essential variables are missing or measured with error.\nIn addition, there is often more than one theoretical adjustment set that debiases your estimate; it may be that one of the minimal adjustment sets is measured well in your data set and another is not.\nIf you have confounders on your DAG that you do not have access to, sensitivity analyses can help quantify the potential impact.\nSee Chapter 11 for an in-depth discussion of sensitivity analyses.\n\nAccurately specifying a DAG improves our ability to add the correct variables to our models.\nHowever, confounders are not the only necessary type of variable to consider.\nFor example, variables that are predictors of the *outcome* *but not the exposure* can improve the precision of propensity score models.\nConversely, including variables that are predictors of the *exposure but not the outcome* (instrumental variables) can bias the model.\nLuckily, this bias seems relatively negligible in practice, especially compared to the risk of confounding bias [@Myers2011].\n\n::: callout-note\nSome estimates, such as the odds and hazard ratios, suffer from an additional problem called *non-collapsibility*.\nFor these estimates, adding noise variables (variables unrelated to the exposure or outcome) doesn't reduce precision: they can bias the estimate as well---more the reason to avoid data-driven approaches to selecting variables for causal models.\n:::\n\nAnother variable to be wary of is a *collider*, a descendant of both the exposure and outcome.\nIf you specify your DAG correctly, you can avoid colliders by only using adjustment sets that completely close backdoor paths from the exposure to the outcome.\nHowever, some circumstances make this difficult: some colliders are inherently stratified by the study's design or the nature of follow-up.\nFor example, loss-to-follow-up is a common source of collider-stratification bias; in Chapter XX, we'll discuss this further.\n\nA variable can also be both a confounder and a collider, as in the case of so-called butterfly bias:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggdag(butterfly_bias()) +\n  theme_dag()\n```\n\n::: {.cell-output-display}\n![A causal diagram that displays a variable `m`, which is both a collider and a confounder for `x` and `y`. In situations where you don't have all measured variables in a DAG, you may have to make a tough choice about which type of bias is the least bad.\n](chapter-08_files/figure-html/fig-butterfly-dag-1.png){#fig-butterfly-dag width=480}\n:::\n:::\n\n\nConsider @fig-butterfly-dag.\nTo estimate the causal effect of `x` on `y`, we need to account for `m` because it's a counfounder.\nHowever, `m` is also a collider between `a` and `b`, so controlling for it will induce a relationship between those variables, creating a second set of confounders.\nIf we have all the variables measured well, we can avoid the bias from adjusting for `m` by adjusting for either `a` or `b` as well.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggdag_adjustment_set(butterfly_bias()) +\n  theme_dag() +\n  theme(legend.position = \"bottom\")\n```\n\n::: {.cell-output-display}\n![The adjustment sets in the case of butterfly bias: we can get an unbiased effect by accounting for both `m` and the collider bias induced by adjusting for it. But what if we don't have `a` or `b`?\n](chapter-08_files/figure-html/fig-butterfly-sets-1.png){#fig-butterfly-sets width=480}\n:::\n:::\n\n\nHowever, what should we do if we don't have those variables?\nAdjusting for `m` opens a biasing pathway that we cannot block through `a` and `b`(collider-stratification bias), but `m` is also a confounder for `x` and `y`.\nAs in the case above, it appears that confounding bias is often the worse of the two options, so we should adjust for `m` unless we have reason to believe it will cause more problems than it solves [@DingMiratrix2015].\n\n### Don't use prediction metrics for causal modeling\n\nBy and large, metrics commonly used for building prediction models are inappropriate for building causal models.\nResearchers and data scientists often make decisions about models using metrics like R^2^, AUC, accuracy, and (often inappropriately) p-values.\nHowever, a causal model's goal is not to predict as much about the outcome as possible [@hernan2021]; the goal is to estimate the relationship between the exposure and outcome accurately.\nA causal model needn't predict particularly well to be unbiased.\n\nThese metrics, however, may help identify a model's best *functional form*.\nGenerally, we'll use DAGs and our domain knowledge to build the model itself.\nHowever, we may be unsure of the mathematical relationship between a confounder and the outcome or exposure.\nFor instance, we may not know if the relationship is linear.\nMisspecifying this relationship can lead to residual confounding: we may only partially account for the confounder in question, leaving some bias in the estimate.\nTesting different functional forms using prediction-focused metrics can help improve the model's accuracy, potentially allowing for better control.\n\nAnother technique researchers sometimes use to determine confounders is to add a variable, then calculate the percent change in the coefficient between the outcome and exposure.\nFor instance, we first model `y ~ x` to estimate the relationship between `x` and `y`.\nThen, we model `y ~ x + z` and see how much the coefficient on `x` has changed.\nA common rule is to add a variable if it changes the coefficient of`x` by 10%.\n\nUnfortunately, this technique is unreliable.\nAs we've discussed, controlling for mediators, colliders, and instrumental variables all affect the estimate of the relationship between `x` and `y`, and usually, they result in bias.\nIn other words, there are many different types of variables besides confounders that can cause a change in the coefficient of the exposure.\nAs discussed above, confounding bias is often the most crucial factor, but systematically searching your variables for anything that changes the exposure coefficient can compound many types of bias.\n",
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+    "markdown": "# Using the propensity score {#sec-using-ps}\n\n\n\n\n\nThe propensity score is a *balancing* tool -- we use it to help us make our exposure groups *exchangeable*. There are many ways to incorporate the propensity score into an analysis. Commonly used techniques include stratification (estimating the causal effect within propensity score stratum), matching, weighting, and direct covariate adjustment. In this section, we will focus on *matching* and *weighting*; other techniques will be discussed once we introduce the *outcome model*. Recall at this point in the book we are still in the *design* phase. We have not yet incorporated the outcome into our analysis at all. \n\n## Matching\n\nUltimately, we want the exposed and unexposed observations to be *exchangeable* with respect to the confounders we have proposed in our DAG (so we can use the observed effect for one to estimate the counterfactual for the other). One way to do this is to ensure that each observation in our analysis sample has at least one observation of the opposite exposure that has *match*ing values for each of these confounders. If we had a small number of binary confounders, for example, we might be able to construct an *exact match* for observations (and only include those for whom such a match exists), but as the number and continuity of confounders increases, exact matching becomes less feasible. This is where the propensity score, a summary measure of all of the confounders, comes in to play. \n\nLet's setup the data as we did in @sec-building-models.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\n\nseven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)\n```\n:::\n\n\nWe can re-fit the propensity score using the `{MatchIt}` package, as below. Notice here the `matchit` function fit a logistic regression model for our propensity score, as we had in @sec-building-models. There were 60 days in 2018 where the Magic Kingdom had extra magic morning hours. For each of these 60 exposed days, `matchit` found a comparable unexposed day, by implementing a nearest-neighbor match using the constructed propensity score. Examining the output, we also see that the target estimand is an \"ATT\" (do not worry about this yet, we will discuss this and several other estimands in @sec-estimands).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(MatchIt)\nm <- matchit(\n  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n  data = seven_dwarfs_9)\nm\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nA matchit object\n - method: 1:1 nearest neighbor matching without replacement\n - distance: Propensity score\n             - estimated with logistic regression\n - number of obs.: 354 (original), 120 (matched)\n - target estimand: ATT\n - covariates: park_ticket_season, park_close, park_temperature_high\n```\n\n\n:::\n:::\n\nWe can use the `get_matches` function to create a data frame with the original variables that only consists of those who were matched. Notice here our sample size has been reduced from the original 354 days to 120. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmatched_data <- get_matches(m)\nglimpse(matched_data)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 120\nColumns: 18\n$ id                       <chr> \"5\", \"340\", \"12\", \"1…\n$ subclass                 <fct> 1, 1, 2, 2, 3, 3, 4,…\n$ weights                  <dbl> 1, 1, 1, 1, 1, 1, 1,…\n$ park_date                <date> 2018-01-05, 2018-12…\n$ wait_hour                <int> 9, 9, 9, 9, 9, 9, 9,…\n$ attraction_name          <chr> \"Seven Dwarfs Mine T…\n$ wait_minutes_actual_avg  <dbl> 33.0, 8.0, 114.0, 32…\n$ wait_minutes_posted_avg  <dbl> 70.56, 80.62, 79.29,…\n$ attraction_park          <chr> \"Magic Kingdom\", \"Ma…\n$ attraction_land          <chr> \"Fantasyland\", \"Fant…\n$ park_open                <time> 09:00:00, 09:00:00,…\n$ park_close               <time> 24:00:00, 23:00:00,…\n$ park_extra_magic_morning <dbl> 1, 0, 1, 0, 1, 0, 1,…\n$ park_extra_magic_evening <dbl> 0, 0, 0, 0, 0, 0, 0,…\n$ park_ticket_season       <chr> \"regular\", \"regular\"…\n$ park_temperature_average <dbl> 43.56, 57.61, 70.91,…\n$ park_temperature_high    <dbl> 54.29, 65.44, 78.26,…\n$ distance                 <dbl> 0.18410, 0.18381, 0.…\n```\n\n\n:::\n:::\n\n\n## Weighting\n\nOne way to think about matching is as a crude \"weight\" where everyone who was matched gets a weight of 1 and everyone who was not matched gets a weight of 0 in the final sample. Another option is to allow this weight to be smooth, applying a weight to allow, on average, the covariates of interest to be balanced in the weighted population. To do this, we will construct a weight using the propensity score. There are many different weights that can be applied, depending on your target estimand of interest (see @sec-estimand for details). For this section, we will focus on the \"Average Treatment Effect\" weights, commonly referred to as an \"inverse probability weight\". The weight is constructed as follows, where each observation is weighted by the *inverse* of the probability of receiving the exposure they received. \n\n$$w_{ATE} = \\frac{X}{p} + \\frac{(1 - X)}{1 - p}$$\n\nFor example, if observation 1 had a very high likelihood of being exposed given their pre-exposure covariates ($p = 0.9$), but they in fact were *not* exposed, their weight would be 10 ($w_1 = 1 / (1 - 0.9)$). Likewise, if observation 2 had a very high likelihood of being exposed given their pre-exposure covariates ($p = 0.9$), and they *were* exposed, their weight would be 1.1 ($w_2 = 1 / 0.9$). Intuitively, we give *more* weight to observations who, based on their measured confounders, appear to have useful information for constructing a counterfactual -- we would have predicted that they were exposed and but by chance they were not, or vice-versa. The `{propensity}` package is useful for implementing propensity score weighting.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(propensity)\n\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\nseven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>\n  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning)) \n```\n:::\n\n\n@tbl-df-wt shows the weights in the first column.\n\n\n::: {#tbl-df-wt .cell tbl-cap='The first six observations in the `seven_dwarfs_9_with_wt` dataset, including their propensity scores in the `.fitted` column and weight in the `w_ate` column.'}\n\n```{.r .cell-code}\nseven_dwarfs_9_with_wt |>\n  select(\n    w_ate,\n    .fitted,\n    park_date,\n    park_extra_magic_morning,\n    park_ticket_season,\n    park_close,\n    park_temperature_high\n  ) |>\n  head() |>\n  knitr::kable()\n```\n\n::: {.cell-output-display}\n\n\n| w_ate| .fitted|park_date  | park_extra_magic_morning|park_ticket_season |park_close | park_temperature_high|\n|-----:|-------:|:----------|------------------------:|:------------------|:----------|---------------------:|\n| 1.433|  0.3019|2018-01-01 |                        0|peak               |23:00:00   |                 58.63|\n| 1.392|  0.2815|2018-01-02 |                        0|peak               |24:00:00   |                 53.65|\n| 1.409|  0.2900|2018-01-03 |                        0|peak               |24:00:00   |                 51.11|\n| 1.232|  0.1881|2018-01-04 |                        0|regular            |24:00:00   |                 52.66|\n| 5.432|  0.1841|2018-01-05 |                        1|regular            |24:00:00   |                 54.29|\n| 1.262|  0.2074|2018-01-06 |                        0|regular            |23:00:00   |                 56.25|\n\n\n:::\n:::\n",
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-    "markdown": "# Using the propensity score {#sec-using-ps}\n\n\n\n\n\nThe propensity score is a *balancing* tool -- we use it to help us make our exposure groups *exchangeable*. There are many ways to incorporate the propensity score into an analysis. Commonly used techniques include stratification (estimating the causal effect within propensity score stratum), matching, weighting, and direct covariate adjustment. In this section, we will focus on *matching* and *weighting*; other techniques will be discussed once we introduce the *outcome model*. Recall at this point in the book we are still in the *design* phase. We have not yet incorporated the outcome into our analysis at all. \n\n## Matching\n\nUltimately, we want the exposed and unexposed observations to be *exchangeable* with respect to the confounders we have proposed in our DAG (so we can use the observed effect for one to estimate the counterfactual for the other). One way to do this is to ensure that each observation in our analysis sample has at least one observation of the opposite exposure that has *match*ing values for each of these confounders. If we had a small number of binary confounders, for example, we might be able to construct an *exact match* for observations (and only include those for whom such a match exists), but as the number and continuity of confounders increases, exact matching becomes less feasible. This is where the propensity score, a summary measure of all of the confounders, comes in to play. \n\nLet's setup the data as we did in @sec-building-models.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\n\nseven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)\n```\n:::\n\n\nWe can re-fit the propensity score using the `{MatchIt}` package, as below. Notice here the `matchit` function fit a logistic regression model for our propensity score, as we had in @sec-building-models. There were 60 days in 2018 where the Magic Kingdom had extra magic morning hours. For each of these 60 exposed days, `matchit` found a comparable unexposed day, by implementing a nearest-neighbor match using the constructed propensity score. Examining the output, we also see that the target estimand is an \"ATT\" (do not worry about this yet, we will discuss this and several other estimands in @sec-estimands).\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(MatchIt)\nm <- matchit(\n  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n  data = seven_dwarfs_9\n)\nm\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nA matchit object\n - method: 1:1 nearest neighbor matching without replacement\n - distance: Propensity score\n             - estimated with logistic regression\n - number of obs.: 354 (original), 120 (matched)\n - target estimand: ATT\n - covariates: park_ticket_season, park_close, park_temperature_high\n```\n\n\n:::\n:::\n\nWe can use the `get_matches` function to create a data frame with the original variables that only consists of those who were matched. Notice here our sample size has been reduced from the original 354 days to 120. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmatched_data <- get_matches(m)\nglimpse(matched_data)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 120\nColumns: 18\n$ id                       <chr> \"5\", \"340\", \"12\", \"1…\n$ subclass                 <fct> 1, 1, 2, 2, 3, 3, 4,…\n$ weights                  <dbl> 1, 1, 1, 1, 1, 1, 1,…\n$ park_date                <date> 2018-01-05, 2018-12…\n$ wait_hour                <int> 9, 9, 9, 9, 9, 9, 9,…\n$ attraction_name          <chr> \"Seven Dwarfs Mine T…\n$ wait_minutes_actual_avg  <dbl> 33.0, 8.0, 114.0, 32…\n$ wait_minutes_posted_avg  <dbl> 70.56, 80.62, 79.29,…\n$ attraction_park          <chr> \"Magic Kingdom\", \"Ma…\n$ attraction_land          <chr> \"Fantasyland\", \"Fant…\n$ park_open                <time> 09:00:00, 09:00:00,…\n$ park_close               <time> 24:00:00, 23:00:00,…\n$ park_extra_magic_morning <dbl> 1, 0, 1, 0, 1, 0, 1,…\n$ park_extra_magic_evening <dbl> 0, 0, 0, 0, 0, 0, 0,…\n$ park_ticket_season       <chr> \"regular\", \"regular\"…\n$ park_temperature_average <dbl> 43.56, 57.61, 70.91,…\n$ park_temperature_high    <dbl> 54.29, 65.44, 78.26,…\n$ distance                 <dbl> 0.18410, 0.18381, 0.…\n```\n\n\n:::\n:::\n\n\n## Weighting\n\nOne way to think about matching is as a crude \"weight\" where everyone who was matched gets a weight of 1 and everyone who was not matched gets a weight of 0 in the final sample. Another option is to allow this weight to be smooth, applying a weight to allow, on average, the covariates of interest to be balanced in the weighted population. To do this, we will construct a weight using the propensity score. There are many different weights that can be applied, depending on your target estimand of interest (see @sec-estimands for details). For this section, we will focus on the \"Average Treatment Effect\" weights, commonly referred to as an \"inverse probability weight\". The weight is constructed as follows, where each observation is weighted by the *inverse* of the probability of receiving the exposure they received. \n\n$$w_{ATE} = \\frac{X}{p} + \\frac{(1 - X)}{1 - p}$$\n\nFor example, if observation 1 had a very high likelihood of being exposed given their pre-exposure covariates ($p = 0.9$), but they in fact were *not* exposed, their weight would be 10 ($w_1 = 1 / (1 - 0.9)$). Likewise, if observation 2 had a very high likelihood of being exposed given their pre-exposure covariates ($p = 0.9$), and they *were* exposed, their weight would be 1.1 ($w_2 = 1 / 0.9$). Intuitively, we give *more* weight to observations who, based on their measured confounders, appear to have useful information for constructing a counterfactual -- we would have predicted that they were exposed and but by chance they were not, or vice-versa. The `{propensity}` package is useful for implementing propensity score weighting.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(propensity)\n\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\nseven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>\n  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))\n```\n:::\n\n\n@tbl-df-wt shows the weights in the first column.\n\n\n::: {#tbl-df-wt .cell tbl-cap='The first six observations in the `seven_dwarfs_9_with_wt` dataset, including their propensity scores in the `.fitted` column and weight in the `w_ate` column.'}\n\n```{.r .cell-code}\nseven_dwarfs_9_with_wt |>\n  select(\n    w_ate,\n    .fitted,\n    park_date,\n    park_extra_magic_morning,\n    park_ticket_season,\n    park_close,\n    park_temperature_high\n  ) |>\n  head() |>\n  knitr::kable()\n```\n\n::: {.cell-output-display}\n\n\n| w_ate| .fitted|park_date  | park_extra_magic_morning|park_ticket_season |park_close | park_temperature_high|\n|-----:|-------:|:----------|------------------------:|:------------------|:----------|---------------------:|\n| 1.433|  0.3019|2018-01-01 |                        0|peak               |23:00:00   |                 58.63|\n| 1.392|  0.2815|2018-01-02 |                        0|peak               |24:00:00   |                 53.65|\n| 1.409|  0.2900|2018-01-03 |                        0|peak               |24:00:00   |                 51.11|\n| 1.232|  0.1881|2018-01-04 |                        0|regular            |24:00:00   |                 52.66|\n| 5.432|  0.1841|2018-01-05 |                        1|regular            |24:00:00   |                 54.29|\n| 1.262|  0.2074|2018-01-06 |                        0|regular            |23:00:00   |                 56.25|\n\n\n:::\n:::\n",
+    "markdown": "# Evaluating your propensity score model {#sec-eval-ps-model}\n\n\n\n\n\nPropensity scores are inherently *balancing* scores. The goal is to *balance* the exposure groups across confounders. \n\n## Calculating the standardized mean difference\n\nOne way to assess balance is the *standardized mean difference*. This measure helps you assess whether the average value for the confounder is balanced between exposure groups. For example, if you have some continuous confounder, $Z$, and $\\bar{z}_{exposed} = \\frac{\\sum Z_i(X_i)}{\\sum X_i}$ is the mean value of $Z$ among the exposed,  $\\bar{z}_{unexposed} = \\frac{\\sum Z_i(1-X_i)}{\\sum 1-X_i}$ is the mean value of $Z$ among the unexposed, $s_{exposed}$ is the sample standard deviation of $Z$ among the exposed and $s_{unexposed}$ is the sample standard deviation of $Z$ among the unexposed, then the standardized mean difference can be expressed as follows:\n\n$$\nd =\\frac{\\bar{z}_{exposed}-\\bar{z}_{unexposued}}{\\frac{\\sqrt{s^2_{exposed}+s^2_{unexposed}}}{2}}\n$$\nIn the case of a binary $Z$ (a confounder with just two levels), $\\bar{z}$ is replaced with the sample proportion in each group (e.g., $\\hat{p}_{exposed}$ or $\\hat{p}_{unexposed}$ ) and $s^2=\\hat{p}(1-\\hat{p})$. In the case where $Z$ is categorical with more than two categories, $\\bar{z}$ is the vector of proportions of each category level within a group and the denominator is the multinomial covariance matrix ($S$ below), as the above can be written more generally as:\n\n$$\nd = \\sqrt{(\\bar{z}_{exposed} - \\bar{z}_{unexposed})^TS^{-1}(\\bar{z}_{exposed} - \\bar{z}_{unexposed})}\n$$\n\nOften, we calculate the standardized mean difference for each confounder in the full, unadjusted, data set and then compare this to an *adjusted* standardized mean difference. If the propensity score is incorporated using *matching*, this adjusted standardized mean difference uses the exact equation as above, but restricts the sample considered to only those that were matched. If the propensity score is incorporated using *weighting*, this adjusted standardized mean difference *weights* each of the above components using the constructed propensity score weight. \n\nIn R, the `{halfmoon}` package has a function `tidy_smd` that will calculate this for a data set.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\n\nsmds <- tidy_smd(\n  df,\n  .vars = c(confounder_1, confounder_2, ...),\n  .group = exposure,\n  .wts = wts # weight is optional\n)\n```\n:::\n\n\nLet's look at an example using the same data as @sec-using-ps. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\nlibrary(propensity)\n\nseven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)\n\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\nseven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>\n  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))\n```\n:::\n\n\nNow, using the `tidy_smd` function, we can examine the standardized mean difference before and after weighting.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nsmds <- \n  seven_dwarfs_9_with_wt |>\n  mutate(park_close = as.numeric(park_close)) |>\n  tidy_smd(\n    .vars = c(park_ticket_season, park_close, park_temperature_high),\n    .group = park_extra_magic_morning,\n    .wts = w_ate\n  )\nsmds\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 4\n  variable        method park_extra_magic_mor…¹     smd\n  <chr>           <chr>  <chr>                    <dbl>\n1 park_ticket_se… obser… 1                       0.391 \n2 park_close      obser… 1                       0.126 \n3 park_temperatu… obser… 1                       0.157 \n4 park_ticket_se… w_ate  1                       0.0413\n5 park_close      w_ate  1                      -0.0602\n6 park_temperatu… w_ate  1                       0.0613\n# ℹ abbreviated name: ¹​park_extra_magic_morning\n```\n\n\n:::\n:::\n\n\nFor example, we see above that the *observed* standardized mean difference (prior to incorporating the propensity score) for ticket season is 0.39, however after incorporating the propensity score weight this is attenuated, now 0.04.\n\nOne downside of this metric is it only quantifying balance *on the mean*, which may not be sufficient for continuous confounders, as it is possible to be balanced on the mean but severely imbalanced in the tails. At the end of this chapter we will show you a few tools for examining balance across the full distribution of the confounder.\n\n## Visualizing balance\n\n### Love Plots\n\nLet's start by visualizing these standardized mean differences. To do so, we like to use a *Love Plot* (named for Thomas Love, as he was one of the first to popularize them). The `{halfmoon}` package has a function `geom_love` that simplifies this implementation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(\n  data = smds,\n  aes(\n    x = abs(smd), \n    y = variable, \n    group = method, \n    color = method\n  )\n) +  \n  geom_love()\n```\n\n::: {.cell-output-display}\n![](chapter-09_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n:::\n\n\n\n### Boxplots and eCDF plots\n\nAs mentioned above, one issue with the standardized mean differences is they only quantify balance on a single point for continuous confounders (the mean). It can be helpful to visualize the whole distribution to ensure that there is not residual imbalance in the tails. Let's first use a boxplot. As an example, let's use the `park_temperature_high` variable. When we make boxplots, we prefer to always jitter the points on top to make sure we aren't masking and data anomolies -- we use `geom_jitter` to accomplish this. First, we will make the unweighted boxplot.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(\n  seven_dwarfs_9_with_wt, \n  aes(\n    x = factor(park_extra_magic_morning), \n    y = park_temperature_high,\n    group = park_extra_magic_morning\n    )\n) + \n  geom_boxplot(outlier.color = NA) + \n  geom_jitter() + \n  labs(x = \"Extra magic morning\",\n       y = \"Temperature high\")\n```\n\n::: {.cell-output-display}\n![Unweighted boxplot showing the difference in historical high temperature between days that had extra magic hours and those that did not.](chapter-09_files/figure-html/fig-boxplot-1.png){#fig-boxplot width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(\n  seven_dwarfs_9_with_wt, \n  aes(\n    x = factor(park_extra_magic_morning), \n    y = park_temperature_high,\n    group = park_extra_magic_morning,\n    weight = w_ate\n    )\n) + \n  geom_boxplot(outlier.color = NA) + \n  geom_jitter() + \n  labs(x = \"Extra magic morning\",\n       y = \"Historic temperature high\")\n```\n\n::: {.cell-output-display}\n![Weighted boxplot showing the difference in historical high temperature between days that had extra magic hours and those that did not after incorporating the propensity score weight (ATE weight).](chapter-09_files/figure-html/fig-weighted-boxplot-1.png){#fig-weighted-boxplot width=672}\n:::\n:::\n\n\nSimilarly, we can also examine the empirical cumulative distribution function (eCDF) for the confounder stratified by each exposure group. The unweighted eCDF can be visualized using `geom_ecdf`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_9_with_wt, \n       aes(x = park_temperature_high,\n           color = factor(park_extra_magic_morning))) + \n  geom_ecdf() + \n  scale_color_manual(\n    \"Extra Magic Morning\",\n    values = c(\"#5154B8\", \"#5DB854\"),\n    labels = c(\"Yes\", \"No\")\n  ) + \n  labs(x = \"Historic temperature high\",\n       y = \"Proportion <= x\")\n```\n\n::: {.cell-output-display}\n![Unweighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green).](chapter-09_files/figure-html/fig-ecdf-1.png){#fig-ecdf width=672}\n:::\n:::\n\n\nThe `{halfmoon}` package allows for the additional `weight` argument to be passed to `geom_ecdf` to display a weighted eCDF plot.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_9_with_wt, \n       aes(x = park_temperature_high,\n           color = factor(park_extra_magic_morning))) + \n  geom_ecdf(aes(weights = w_ate)) + \n  scale_color_manual(\n    \"Extra Magic Morning\",\n    values = c(\"#5154B8\", \"#5DB854\"),\n    labels = c(\"Yes\", \"No\")\n  ) + \n  labs(x = \"Historic temperature high\",\n       y = \"Proportion <= x\")\n```\n\n::: {.cell-output-display}\n![Weighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green) after incorporating the propensity score weight (ATE).](chapter-09_files/figure-html/fig-weighted-ecdf-1.png){#fig-weighted-ecdf width=672}\n:::\n:::\n\n\nExamining @fig-weighted-ecdf, we can notice a few things. First, compared to @fig-ecdf there is improvement in the overlap between the two distributions. In @fig-ecdf, the green line is almost always noticeably above the purple, whereas in @fig-weighted-ecdf the two lines appear to mostly overlap until we reach slightly above 80 degrees. After 80 degrees, the lines appear to diverge in the weighted plot. This is why it can be useful to examine the full distribution rather than a single summary measure. If we had just used the standardized mean difference, for example, we would have likely said these two groups are balanced and moved on. Looking at @fig-weighted-ecdf suggests that perhaps there is a non-linear relationship between the probability of having an extra magic morning and the historic high temperature. Let's try refitting our propensity score model using a natural spline. We can use the function `splines::ns` for this.\n\n<!-- TODO put a callout box here explaining what a natural spline is. -->\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close +\n      splines::ns(park_temperature_high, df = 5), # refit model with a spline\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\nseven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>\n  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))\n```\n:::\n\n\nNow let's see how that impacts the weighted eCDF plot\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_9_with_wt, \n       aes(x = park_temperature_high,\n           color = factor(park_extra_magic_morning))) + \n  geom_ecdf(aes(weights = w_ate)) + \n  scale_color_manual(\n    \"Extra Magic Morning\",\n    values = c(\"#5154B8\", \"#5DB854\"),\n    labels = c(\"Yes\", \"No\")\n  ) + \n  labs(x = \"Historic temperature high\",\n       y = \"Proportion <= x\")\n```\n\n::: {.cell-output-display}\n![Weighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green) after incorporating the propensity score weight where historic high temperature was modeled flexibly with a spline.](chapter-09_files/figure-html/fig-weighted-ecdf-2-1.png){#fig-weighted-ecdf-2 width=672}\n:::\n:::\n\n\nNow in @fig-weighted-ecdf-2 the lines appear to overlap across the whole space.\n",
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-    "markdown": "# Evaluating your propensity score model {#sec-eval-ps-model}\n\n\n\n\n\nPropensity scores are inherently *balancing* scores. The goal is to *balance* the exposure groups across confounders. \n\n## Calculating the standardized mean difference\n\nOne way to assess balance is the *standardized mean difference*. This measure helps you assess whether the average value for the confounder is balanced between exposure groups. For example, if you have some continuous confounder, $Z$, and $\\bar{z}_{exposed} = \\frac{\\sum Z_i(X_i)}{\\sum X_i}$ is the mean value of $Z$ among the exposed,  $\\bar{z}_{unexposed} = \\frac{\\sum Z_i(1-X_i)}{\\sum 1-X_i}$ is the mean value of $Z$ among the unexposed, $s_{exposed}$ is the sample standard deviation of $Z$ among the exposed and $s_{unexposed}$ is the sample standard deviation of $Z$ among the unexposed, then the standardized mean difference can be expressed as follows:\n\n$$\nd =\\frac{\\bar{z}_{exposed}-\\bar{z}_{unexposued}}{\\frac{\\sqrt{s^2_{exposed}+s^2_{unexposed}}}{2}}\n$$\nIn the case of a binary $Z$ (a confounder with just two levels), $\\bar{z}$ is replaced with the sample proportion in each group (e.g., $\\hat{p}_{exposed}$ or $\\hat{p}_{unexposed}$ ) and $s^2=\\hat{p}(1-\\hat{p})$. In the case where $Z$ is categorical with more than two categories, $\\bar{z}$ is the vector of proportions of each category level within a group and the denominator is the multinomial covariance matrix ($S$ below), as the above can be written more generally as:\n\n$$\nd = \\sqrt{(\\bar{z}_{exposed} - \\bar{z}_{unexposed})^TS^{-1}(\\bar{z}_{exposed} - \\bar{z}_{unexposed})}\n$$\n\nOften, we calculate the standardized mean difference for each confounder in the full, unadjusted, data set and then compare this to an *adjusted* standardized mean difference. If the propensity score is incorporated using *matching*, this adjusted standardized mean difference uses the exact equation as above, but restricts the sample considered to only those that were matched. If the propensity score is incorporated using *weighting*, this adjusted standardized mean difference *weights* each of the above components using the constructed propensity score weight. \n\nIn R, the `{halfmoon}` package has a function `tidy_smd` that will calculate this for a data set.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\n\nsmds <- tidy_smd(\n  df,\n  .vars = c(confounder_1, confounder_2, ...),\n  .group = exposure,\n  .wts = wts # weight is optional\n)\n```\n:::\n\n\nLet's look at an example using the same data as @sec-using-ps. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\nlibrary(propensity)\n\nseven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)\n\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\nseven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>\n  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))\n```\n:::\n\n\nNow, using the `tidy_smd` function, we can examine the standardized mean difference before and after weighting.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nsmds <- \n  seven_dwarfs_9_with_wt |>\n  mutate(park_close = as.numeric(park_close)) |>\n  tidy_smd(\n    .vars = c(park_ticket_season, park_close, park_temperature_high),\n    .group = park_extra_magic_morning,\n    .wts = w_ate\n  )\nsmds\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 4\n  variable        method park_extra_magic_mor…¹     smd\n  <chr>           <chr>  <chr>                    <dbl>\n1 park_ticket_se… obser… 1                       0.391 \n2 park_close      obser… 1                       0.126 \n3 park_temperatu… obser… 1                       0.157 \n4 park_ticket_se… w_ate  1                       0.0413\n5 park_close      w_ate  1                      -0.0602\n6 park_temperatu… w_ate  1                       0.0613\n# ℹ abbreviated name: ¹​park_extra_magic_morning\n```\n\n\n:::\n:::\n\n\nFor example, we see above that the *observed* standardized mean difference (prior to incorporating the propensity score) for ticket season is 0.39, however after incorporating the propensity score weight this is attenuated, now 0.04.\n\nOne downside of this metric is it only quantifying balance *on the mean*, which may not be sufficient for continuous confounders, as it is possible to be balanced on the mean but severely imbalanced in the tails. At the end of this chapter we will show you a few tools for examining balance across the full distribution of the confounder.\n\n## Visualizing balance\n\n### Love Plots\n\nLet's start by visualizing these standardized mean differences. To do so, we like to use a *Love Plot* (named for Thomas Love, as he was one of the first to popularize them). The `{halfmoon}` package has a function `geom_love` that simplifies this implementation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(\n  data = smds,\n  aes(\n    x = abs(smd), \n    y = variable, \n    group = method, \n    color = method\n  )\n) +  \n  geom_love()\n```\n\n::: {.cell-output-display}\n![](chapter-10_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n:::\n\n\n\n### Boxplots and eCDF plots\n\nAs mentioned above, one issue with the standardized mean differences is they only quantify balance on a single point for continuous confounders (the mean). It can be helpful to visualize the whole distribution to ensure that there is not residual imbalance in the tails. Let's first use a boxplot. As an example, let's use the `park_temperature_high` variable. When we make boxplots, we prefer to always jitter the points on top to make sure we aren't masking and data anomolies -- we use `geom_jitter` to accomplish this. First, we will make the unweighted boxplot.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(\n  seven_dwarfs_9_with_wt, \n  aes(\n    x = factor(park_extra_magic_morning), \n    y = park_temperature_high,\n    group = park_extra_magic_morning\n    )\n) + \n  geom_boxplot(outlier.color = NA) + \n  geom_jitter() + \n  labs(x = \"Extra magic morning\",\n       y = \"Temperature high\")\n```\n\n::: {.cell-output-display}\n![Unweighted boxplot showing the difference in historical high temperature between days that had extra magic hours and those that did not.](chapter-10_files/figure-html/fig-boxplot-1.png){#fig-boxplot width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(\n  seven_dwarfs_9_with_wt, \n  aes(\n    x = factor(park_extra_magic_morning), \n    y = park_temperature_high,\n    group = park_extra_magic_morning,\n    weight = w_ate\n    )\n) + \n  geom_boxplot(outlier.color = NA) + \n  geom_jitter() + \n  labs(x = \"Extra magic morning\",\n       y = \"Historic temperature high\")\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n\nWarning in fit$coef: partial match of 'coef' to\n'coefficients'\n```\n\n\n:::\n\n::: {.cell-output-display}\n![Weighted boxplot showing the difference in historical high temperature between days that had extra magic hours and those that did not after incorporating the propensity score weight (ATE weight).](chapter-10_files/figure-html/fig-weighted-boxplot-1.png){#fig-weighted-boxplot width=672}\n:::\n:::\n\n\nSimilarly, we can also examine the empirical cumulative distribution function (eCDF) for the confounder stratified by each exposure group. The unweighted eCDF can be visualized using `geom_ecdf`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_9_with_wt, \n       aes(x = park_temperature_high,\n           color = factor(park_extra_magic_morning))) + \n  geom_ecdf() + \n  scale_color_manual(\n    \"Extra Magic Morning\",\n    values = c(\"#5154B8\", \"#5DB854\"),\n    labels = c(\"Yes\", \"No\")\n  ) + \n  labs(x = \"Historic temperature high\",\n       y = \"Proportion <= x\")\n```\n\n::: {.cell-output-display}\n![Unweighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green).](chapter-10_files/figure-html/fig-ecdf-1.png){#fig-ecdf width=672}\n:::\n:::\n\n\nThe `{halfmoon}` package allows for the additional `weight` argument to be passed to `geom_ecdf` to display a weighted eCDF plot.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_9_with_wt, \n       aes(x = park_temperature_high,\n           color = factor(park_extra_magic_morning))) + \n  geom_ecdf(aes(weights = w_ate)) + \n  scale_color_manual(\n    \"Extra Magic Morning\",\n    values = c(\"#5154B8\", \"#5DB854\"),\n    labels = c(\"Yes\", \"No\")\n  ) + \n  labs(x = \"Historic temperature high\",\n       y = \"Proportion <= x\")\n```\n\n::: {.cell-output-display}\n![Weighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green) after incorporating the propensity score weight (ATE).](chapter-10_files/figure-html/fig-weighted-ecdf-1.png){#fig-weighted-ecdf width=672}\n:::\n:::\n\n\nExamining @fig-weighted-ecdf, we can notice a few things. First, compared to @fig-ecdf there is improvement in the overlap between the two distributions. In @fig-ecdf, the green line is almost always noticeably above the purple, whereas in @fig-weighted-ecdf the two lines appear to mostly overlap until we reach slightly above 80 degrees. After 80 degrees, the lines appear to diverge in the weighted plot. This is why it can be useful to examine the full distribution rather than a single summary measure. If we had just used the standardized mean difference, for example, we would have likely said these two groups are balanced and moved on. Looking at @fig-weighted-ecdf suggests that perhaps there is a non-linear relationship between the probability of having an extra magic morning and the historic high temperature. Let's try refitting our propensity score model using a natural spline. We can use the function `splines::ns` for this.\n\n<!-- TODO put a callout box here explaining what a natural spline is. -->\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close +\n      splines::ns(park_temperature_high, df = 5), # refit model with a spline\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\nseven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>\n  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))\n```\n:::\n\n\nNow let's see how that impacts the weighted eCDF plot\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_9_with_wt, \n       aes(x = park_temperature_high,\n           color = factor(park_extra_magic_morning))) + \n  geom_ecdf(aes(weights = w_ate)) + \n  scale_color_manual(\n    \"Extra Magic Morning\",\n    values = c(\"#5154B8\", \"#5DB854\"),\n    labels = c(\"Yes\", \"No\")\n  ) + \n  labs(x = \"Historic temperature high\",\n       y = \"Proportion <= x\")\n```\n\n::: {.cell-output-display}\n![Weighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green) after incorporating the propensity score weight where historic high temperature was modeled flexibly with a spline.](chapter-10_files/figure-html/fig-weighted-ecdf-2-1.png){#fig-weighted-ecdf-2 width=672}\n:::\n:::\n\n\nNow in @fig-weighted-ecdf-2 the lines appear to overlap across the whole space.\n",
+    "markdown": "# Causal estimands {#sec-estimands}\n\n\n\n\n\n## Estimands, Estimators, Estimates\n\nWhen analyzing to make causal inferences, we need to keep the causal question that we're interested in close to our chest.\nThe causal question helps guide not just the assumptions we need to make but also how we will go about answering the question.\nThis chapter deals with three ideas closely related to answering causal questions: the estimand, the estimator, and the estimate.\nThe **estimand** is the *target of interest*, the **estimator** is the method by which we approximate this estimand using data, and the **estimate** is the value we get when we plug our data into the estimator.\nYou can think of the **estimand** as a glossy picture of a beautiful cake we are trying to bake, the **estimator** as the recipe, and the **estimate** as the cake we pull out of our oven.\n\nSo far, we have been estimating the average treatment effect, the effect of the exposure of interest averaged across the whole population.\nThe **estimand** here is the expected value of the difference in potential outcomes across all individuals:\n\n$$E[Y(1) - Y(0)]$$\n\nThe **estimator** we use depends on the method we've chosen.\nFor example, in an A/B test or randomized controlled trial, our estimator could just be the average outcome among those who received the exposure A minus the average outcome among those who receive exposure B.\n\n$$\\sum_{i=1}^{N}\\frac{Y_i\\times X_i}{N_{\\textrm{A}}} - \\frac{Y_i\\times (1-X_i)}{N_{\\textrm{B}}}$$\n\nWhere $X$ is just an indicator for exposure A ($X = 1$ for exposure A and $X = 0$ for exposure B), $N_A$ is the total number in group A and $N_B$ is the total number in group B such that $N_A + N_B = N$.\nLet's motivate this example a bit more.\nSuppose we have two different designs for a call-to-action (often abbreviated as CTA, a written directive for a marketing campaign) and want to assess whether they lead to a different average number of items a user purchases.\nWe can create an A/B test where we randomly assign a subset of users to see design A or design B.\nSuppose we have A/B testing data for 100 participants in a dataset called `ab`.\nHere is some code to simulate such a data set.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nset.seed(928)\nab <- tibble(\n  # generate the exposure, x, from a binomial distribution\n  # with the probability exposure A of 0.5\n  x = rbinom(100, 1, 0.5),\n  # generate the \"true\" average treatment effect of 1\n  # we use rnorm(100) to add the random error term that is normally\n  # distributed with a mean of 0 and a standard deviation of 1\n  y = x + rnorm(100)\n)\n```\n:::\n\n\nHere the exposure is `x`, and the outcome is `y`.\nThe true average treatment effect is 1, implying that on average seeing call-to-action A increases the number of items purchased by 1.\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nab\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 100 × 2\n       x      y\n   <int>  <dbl>\n 1     0 -0.280\n 2     1  1.87 \n 3     1  1.36 \n 4     0 -0.208\n 5     0  1.13 \n 6     1  0.352\n 7     1 -0.148\n 8     1  2.08 \n 9     0  2.09 \n10     0 -1.41 \n# ℹ 90 more rows\n```\n\n\n:::\n:::\n\n\nBelow are two ways to estimate this in R.\nUsing a formula approach, we calculate the difference in `y` between exposure values in the first example.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nab |>\n  summarise(\n    n_a = sum(x),\n    n_b = sum(1 - x),\n    estimate = sum(\n      (y * x) / n_a -\n        y * (1 - x) / n_b\n    )\n  )\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 3\n    n_a   n_b estimate\n  <int> <dbl>    <dbl>\n1    54    46     1.15\n```\n\n\n:::\n:::\n\n\nAlternatively, we can `group_by()` `x` and `summarize()` the means of `y` for each group, then pivot the results to take their difference.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nab |>\n  group_by(x) |>\n  summarise(avg_y = mean(y)) |>\n  pivot_wider(\n    names_from = x,\n    values_from = avg_y,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 1\n  estimate\n     <dbl>\n1     1.15\n```\n\n\n:::\n:::\n\n\nBecause $X$, the exposure, was randomly assigned, this estimator results in an unbiased estimate of our estimand of interest.\n\nWhat do we mean by unbiased, though?\nNotice that while the \"true\" causal effect is 1, this estimate is not precisely 1; it is 1.149.\nWhy the difference?\nThis A/B test included a sample of 100 participants, not the whole population.\nAs that sample increases, our estimate will get closer to the truth.\nLet's try that.\nLet's simulate the data again but increase the sample size from 100 to 100,000:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(928)\nab <- tibble(\n  x = rbinom(100000, 1, 0.5),\n  y = x + rnorm(100000)\n)\n\nab |>\n  summarise(\n    n_a = sum(x),\n    n_b = sum(1 - x),\n    estimate = sum(\n      (y * x) / n_a -\n        y * (1 - x) / n_b\n    )\n  )\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 3\n    n_a   n_b estimate\n  <int> <dbl>    <dbl>\n1 49918 50082     1.01\n```\n\n\n:::\n:::\n\n\nNotice how the estimate is 1.01, much closer to the actual average treatment effect, 1.\n\nSuppose $X$ had not been randomly assigned.\nIn that case, we could use the pre-exposure covariates to estimate the conditional probability of $X$ (the propensity score) and incorporate this probability in a *weight* $(w_i)$ to estimate this causal effect.\nFor example, we could use the following *estimator* to *estimate* our average treatment effect *estimand*:\n\n$$\\frac{\\sum_{i=1}^NY_i\\times X_i\\times w_i}{\\sum_{i=1}^NX_i\\times w_i}-\\frac{\\sum_{i=1}^NY_i\\times(1-X_i)\\times w_i}{\\sum_{i=1}^N(1-X_i)\\times w_i}$$\n\n## Estimating treatment effects with specific targets in mind\n\nDepending on the study's goal, or the causal question, we may want to estimate different *estimands*.\nHere, we will outline the most common causal estimands, their target populations, the causal questions they may help answer, and the methods used to estimate them [@greifer2021choosing].\n\n### Average treatment effect\n\nA common estimand is the average treatment effect (ATE).\nThe target population is the *total sample* or population of interest.\nThe **estimand** here is the expected value of the difference in potential outcomes across all individuals:\n\n$$E[Y(1) - Y(0)]$$\n\nAn example research question is \"Should a policy be applied to all eligible patients?\" [@greifer2021choosing].\n\nMost randomized controlled trials are designed with the ATE as the target estimand.\nIn a non-randomized setting, you can estimate the ATE using full matching.\nEach observation in the exposed group is matched to at least one observation in the control group (and vice versa) without replacement.\nAlternatively, the following inverse probability weight will allow you to estimate the ATE using propensity score weighting.\n\n$$w_{ATE} = \\frac{X}{p} + \\frac{(1 - X)}{1 - p}$$\n\nIn other words, the weight is one over the propensity score for those in the exposure group and one over one minus the propensity score for the control group.\nIntuitively, this weight equates to the inverse probability of being in the exposure group in which you actually were.\n\nLet's dig deeper into this causal estimand using the Touring Plans data.\nRecall that in @sec-using-ps, we examined the relationship between whether there were \"Extra Magic Hours\" in the morning and the average wait time for the Seven Dwarfs Mine Train the same day between 9 am and 10 am.\nLet's reconstruct our data set, `seven_dwarfs` and fit the same propensity score model specified previously.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\n\nseven_dwarfs <- seven_dwarfs_train_2018 |>\n  filter(wait_hour == 9) |>\n  mutate(park_extra_magic_morning = factor(\n    park_extra_magic_morning,\n    labels = c(\"No Magic Hours\", \"Extra Magic Hours\")\n  ))\n\nseven_dwarfs_with_ps <- glm(\n  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n  data = seven_dwarfs,\n  family = binomial()\n) |>\n  augment(type.predict = \"response\", data = seven_dwarfs)\n```\n:::\n\n\nLet's examine a table of the variables of interest in this data frame.\nTo do so, we are going to use the `tbl_summary()` function from the `{gtsummary}` package.\n(We'll also use the `{labelled}` package to clean up the variable names for the table.)\n\n\n::: {#tbl-unweighted-gtsummary .cell tbl-cap='A descriptive table of Extra Magic Morning in the touringplans dataset. This table shows the distributions of these variables in the observed population.'}\n\n```{.r .cell-code}\nlibrary(gtsummary)\nlibrary(labelled)\nseven_dwarfs_with_ps <- seven_dwarfs_with_ps |>\n  set_variable_labels(\n    park_ticket_season = \"Ticket Season\",\n    park_close = \"Close Time\",\n    park_temperature_high = \"Historic High Temperature\"\n  )\n\ntbl_summary(\n  seven_dwarfs_with_ps,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  # add an overall column to the table\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"eugzvcxdgr\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>html {\n  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;\n}\n\n#eugzvcxdgr .gt_table {\n 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border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#eugzvcxdgr .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-left: 4px;\n  padding-right: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#eugzvcxdgr .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#eugzvcxdgr .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#eugzvcxdgr .gt_left {\n  text-align: left;\n}\n\n#eugzvcxdgr .gt_center {\n  text-align: center;\n}\n\n#eugzvcxdgr .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#eugzvcxdgr .gt_font_normal {\n  font-weight: normal;\n}\n\n#eugzvcxdgr .gt_font_bold {\n  font-weight: bold;\n}\n\n#eugzvcxdgr .gt_font_italic {\n  font-style: italic;\n}\n\n#eugzvcxdgr .gt_super {\n  font-size: 65%;\n}\n\n#eugzvcxdgr .gt_footnote_marks {\n  font-style: italic;\n  font-weight: normal;\n  font-size: 75%;\n  vertical-align: 0.4em;\n}\n\n#eugzvcxdgr .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#eugzvcxdgr .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#eugzvcxdgr .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#eugzvcxdgr .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#eugzvcxdgr .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#eugzvcxdgr .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\">\n  \n  <thead class=\"gt_col_headings\">\n    <tr>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 294&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>No Magic Hours</strong>, N = 294<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 60&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Extra Magic Hours</strong>, N = 60<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 354&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Overall</strong>, N = 354<sup class=\"gt_footnote_marks\">1</sup></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">60 (20%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">78 (22%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">158 (54%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">193 (55%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">76 (26%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">37 (13%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">55 (16%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (6.1%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">20 (5.6%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">28 (9.5%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (7.9%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">91 (31%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">102 (29%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">78 (27%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">89 (25%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">40 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">57 (16%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">2 (0.6%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">84 (78, 89)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (76, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">84 (78, 89)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><sup class=\"gt_footnote_marks\">1</sup> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nFrom @tbl-unweighted-gtsummary, 294 days did not have extra magic hours in the morning, and 60 did.\nWe also see that 30% of the extra magic mornings were during peak season compared to 20% of the non-extra magic mornings.\nAdditionally, days with extra magic mornings were more likely to close at 6 pm (18:00:00) compared to non-magic hour mornings.\nThe median high temperature on days with extra magic hour mornings is slightly lower (83 degrees) compared to non-extra magic hour morning days.\n\nNow let's construct our propensity score weight to estimate the ATE.\nThe `{propensity}` package has helper functions to estimate weights.\nThe `wt_ate()` function will calculate ATE weights.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(propensity)\nseven_dwarfs_wts <- seven_dwarfs_with_ps |>\n  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))\n```\n:::\n\n\nLet's look at a distribution of these weights.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(x = w_ate)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect (ATE) weights for whether or not a day had extra magic hours. ATE weights can range from 1 to infinity, so paying attention to the actual range of weights is important.](chapter-10_files/figure-html/fig-sd-ate-hist-1.png){#fig-sd-ate-hist width=672}\n:::\n:::\n\n\nMany weights in @fig-sd-ate-hist are close to 1 (the smallest possible value for an ATE weight), with a long tail leading to the largest weight (around 24).\nThis distribution highlights a potential problem with ATE weights.\nThey range from 1 to infinity; if your weights are too large in small samples, this can lead to bias in your estimates (known as finite sample bias).\n\n::: {.callout-warning}\n## Finite sample bias\n\nWe know that not accounting for confounders can bias our causal estimates, but it turns out that even after accounting for all confounders, we may still get a biased estimate in finite samples.\nMany of the properties we tout in statistics rely on *large samples*---how \"large\" is defined can be opaque.\nLet's look at a quick simulation.\nHere, we have an exposure, $X$, an outcome, $Y$, and one confounder, $Z$.\nWe will simulate $Y$, which is only dependent on $Z$ (so the true treatment effect is 0), and $X$, which also depends on $Z$.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(928)\nn <- 100\nfinite_sample <- tibble(\n  # z is normally distributed with a mean: 0 and sd: 1\n  z = rnorm(n),\n  # x is defined from a probit selection model with normally distributed errors\n  x = case_when(\n    0.5 + z + rnorm(n) > 0 ~ 1,\n    TRUE ~ 0\n  ),\n  # y is continuous, dependent only on z with normally distrbuted errors\n  y = z + rnorm(n)\n)\n```\n:::\n\n\nIf we fit a propensity score model using the one confounder $Z$ and calculate the weighted estimator, we should get an unbiased result (which in this case would be 0).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## fit the propensity score model\nfinite_sample_wts <- glm(\n  x ~ z, data = finite_sample, family = binomial(\"probit\")) |>\n  augment(newdata = finite_sample, type.predict = \"response\") |>\n  mutate(wts = wt_ate(.fitted, x))\n\nfinite_sample_wts |>\n  summarize(\n    effect = sum(y * x * wts) / sum(x * wts) - \n      sum(y * (1 - x) * wts) / sum((1 - x) * wts)\n  )\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 1\n  effect\n   <dbl>\n1  0.197\n```\n\n\n:::\n:::\n\n\nOk, here, our effect of $X$ is pretty far from 0, although it's hard to know if this is really bias, or something we are just seeing by chance in this particular simulated sample.\nTo explore the potential for finite sample bias, we can rerun this simulation many times at different sample sizes.\nLet's do that.\n\n\n::: {.cell hash='chapter-10_cache/html/sim-finite-sample-bias_917f43210915e4732926d02a7bd88116'}\n\n```{.r .cell-code}\nsim <- function(n) {\n  ## create a simulated dataset\n  finite_sample <- tibble(\n    z = rnorm(n),\n    x = case_when(\n      0.5 + z + rnorm(n) > 0 ~ 1,\n      TRUE ~ 0\n    ),\n    y = z + rnorm(n)\n  )\n  finite_sample_wts <- glm(\n    x ~ z, data = finite_sample, family = binomial(\"probit\")) |>\n    augment(newdata = finite_sample, type.predict = \"response\") |>\n    mutate(wts = wt_ate(.fitted, x))\n  bias <- finite_sample_wts |>\n    summarize(\n      effect = sum(y * x * wts) / sum(x * wts) - \n        sum(y * (1 - x) * wts) / sum((1 - x) * wts)\n    ) |> \n    pull()\n  tibble(\n    n = n,\n    bias = bias\n  )\n}\n\n## Examine 5 different sample sizes, simulate each 1000 times\nset.seed(1)\nfinite_sample_sims <- map_df(rep(c(50, 100, 500, 1000, 5000, 10000),\n                                 each = 1000), sim)\n\nbias <- finite_sample_sims %>%\n  group_by(n) %>%\n  summarise(bias = mean(bias))\n```\n:::\n\n\nLet's plot our results:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(bias, aes(x = n, y = bias)) +\n  geom_point() + \n  geom_line() + \n  geom_hline(yintercept = 0, lty = 2)\n```\n\n::: {.cell-output-display}\n![Finite sample bias present with ATE weights created using correctly specified propensity score model, varying the sample size from n = 50 to n = 10,000](chapter-10_files/figure-html/fig-finite-sample-bias-1.png){#fig-finite-sample-bias width=672}\n:::\n:::\n\n\nThis is an example of finite sample bias.\nNotice here, even when the sample size is quite large (5,000) we still see some bias away from the \"true\" effect of 0.\nIt isn't until a sample size larger than 10,000 that we see this bias disappear.\n\nEstimands that utilize weights that are unbounded (i.e. that theoretically can be infinitely large) are more likely to suffer from finite sample bias.\nThe likelihood of falling into finite sample bias depends on:\\\n(1) the estimand you have chosen (i.e. are the weights bounded?)\\\n(2) the distribution of the covariates in the exposed and unexposed groups (i.e. is there good overlap? Potential positivity violations, when there is poor overlap, are the regions where weights can become too large)\\\n(3) the sample size.\n:::\n\nThe `{gtsummary}` package allows the creation of *weighted* tables, which can help us build an intuition for the psuedopopulation created by the weights.\nFirst, we must load the `{survey}` package and create a survey design object.\nThe `{survey}` package supports calculating statistics and models using weights.\nHistorically, many researchers applied techniques incorporating weights to survey analyses.\nEven though we are not doing a survey analysis, the same techniques are helpful for our propensity score weights.\n\n\n::: {#tbl-weighted-gtsummary .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nlibrary(survey)\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_ate\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"uaaskkmbew\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>html {\n  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;\n}\n\n#uaaskkmbew .gt_table {\n  display: table;\n  border-collapse: collapse;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  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padding-left: 5px;\n  padding-right: 5px;\n}\n\n#uaaskkmbew .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#uaaskkmbew .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#uaaskkmbew .gt_left {\n  text-align: left;\n}\n\n#uaaskkmbew .gt_center {\n  text-align: center;\n}\n\n#uaaskkmbew .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#uaaskkmbew .gt_font_normal {\n  font-weight: normal;\n}\n\n#uaaskkmbew .gt_font_bold {\n  font-weight: bold;\n}\n\n#uaaskkmbew .gt_font_italic {\n  font-style: italic;\n}\n\n#uaaskkmbew .gt_super {\n  font-size: 65%;\n}\n\n#uaaskkmbew .gt_footnote_marks {\n  font-style: italic;\n  font-weight: normal;\n  font-size: 75%;\n  vertical-align: 0.4em;\n}\n\n#uaaskkmbew .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#uaaskkmbew .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#uaaskkmbew .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#uaaskkmbew .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#uaaskkmbew .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#uaaskkmbew .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\">\n  \n  <thead class=\"gt_col_headings\">\n    <tr>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 354&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>No Magic Hours</strong>, N = 354<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 357&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Extra Magic Hours</strong>, N = 357<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 711&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Overall</strong>, N = 711<sup class=\"gt_footnote_marks\">1</sup></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">78 (22%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">81 (23%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">160 (22%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">193 (54%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">187 (52%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">380 (53%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">83 (23%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">89 (25%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">172 (24%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (0.4%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">2 (0.2%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">50 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">72 (20%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">122 (17%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">20 (5.6%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">19 (5.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">39 (5.5%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">31 (8.7%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">31 (4.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">108 (30%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">86 (24%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">193 (27%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">95 (27%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">81 (23%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">176 (25%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">48 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">94 (26%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">142 (20%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">6 (1.6%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">7 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">84 (78, 89)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (78, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">84 (78, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><sup class=\"gt_footnote_marks\">1</sup> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nNotice in @tbl-weighted-gtsummary that the variables are more **balanced** between the groups.\nFor example, looking at the variable `park_ticket_season`, in @tbl-unweighted-gtsummary we see that a higher percentage of the days with extra magic hours in the mornings were during the peak season (30%) compared to the percent of days without extra magic hours in the morning (23%).\nIn @tbl-weighted-gtsummary this is balanced, with a weighted percent of 22% peak days in the extra magic morning group and 22% in the no extra magic morning group.\nThe weights change the variables' distribution by weighting each observation so that the two groups appear balanced.\nAlso, notice that the distribution of variables in @tbl-weighted-gtsummary now matches the Overall column of @tbl-unweighted-gtsummary.\nThat is what ATE weights do: they make the distribution of variables included in the propensity score for each exposure group look like the overall distribution across the whole sample.\n\nLet's look at another visualization that can help us understand the weighted psuedopopulation created by the ATE weights: a mirrored histogram.\nWe will plot the distribution of the propensity score for the days in the exposure group (days with extra magic hours in the morning) on the top half of the plot; then, we will mirror the distribution of the propensity score for the days in the\"unexposed group below it. This visualization allows us to compare the two distributions quickly. Then we will overlay weighted histograms to demonstrate how these distributions differ after incorporating the weights. The {halfmoon} package includes a function `geom_mirror_histogram()` to assist with this visualization.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_ate),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect (ATE) weight.](chapter-10_files/figure-html/fig-sd-mirror-hist-ate-1.png){#fig-sd-mirror-hist-ate width=672}\n:::\n:::\n\n\nWe can learn a few things from @fig-sd-mirror-hist-ate.\nFirst, we can see that there are more \"unexposed\" days---days without extra magic hours in the morning---compared to \"exposed\" in the observed population.\nWe can see this by examining the darker histograms, which show the distributions in the observed sample.\nSimilarly, the exposed days are upweighted more substantially, as evidenced by the light blue we see on the top.\nWe can also see that after weighting, the two distributions look similar; the shape of the blue weighted distribution on top looks comparable to the orange weighted distribution below.\n\n### Average treatment effect among the treated\n\nThe target population to estimate the average treatment effect among the treated (ATT) is the *exposed* (treated) population.\nThis causal estimand conditions on those in the exposed group:\n\n$$E[Y(1) - Y(0) | X = 1]$$\n\nExample research questions where the ATT is of interest could include \"Should we stop our marketing campaign to those currently receiving it?\" or \"Should medical providers stop recommending treatment for those currently receiving it?\" [@greifer2021choosing]\n\nThe ATT is a common target estimand when using matching; here, all exposed observations are included and \"matched\" to control observations, some of which may be discarded.\nAlternatively, the ATT can be estimated via weighting.\nThe ATT weight is estimated by:\n\n$$w_{ATT} = X + \\frac{(1 - X)p}{1 - p}$$\n\nLet's add the ATT weights to the `seven_dwarfs_wts` data frame and look at their distribution.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_wts <- seven_dwarfs_wts |>\n  mutate(w_att = wt_att(.fitted, park_extra_magic_morning))\n\nggplot(seven_dwarfs_wts, aes(w_att)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect among the treated (ATT) weights. The range of the ATT weights in this example is more stable than the ATE weights: the range is much smaller.](chapter-10_files/figure-html/fig-sd-att-hist-1.png){#fig-sd-att-hist width=672}\n:::\n:::\n\n\nCompared to the ATE weights, the weights in @fig-sd-att-hist look more *stable*; the distribution of the weights is not heavily skewed, and the range is small, from ground zero to a bit over one, with many at precisely one.\nThese weights are exactly one for all days in the exposed group.\nTheoretically, for unexposed days these weights can range from zero to infinity.\nStill, because we have many more unexposed days compared to exposed in this particular sample, the range is much smaller, meaning the vast majority of unexposed days are \"down-weighted\" to match the variable distribution of the exposed days.\nLet's take a look at the weighted table to see this a bit more clearly.\n\n\n::: {#tbl-weighted-att .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Treated Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_att\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"kyuiqrqycx\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>html {\n  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;\n}\n\n#kyuiqrqycx .gt_table {\n  display: table;\n  border-collapse: collapse;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  font-style: normal;\n 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padding-left: 5px;\n  padding-right: 5px;\n}\n\n#kyuiqrqycx .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#kyuiqrqycx .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#kyuiqrqycx .gt_left {\n  text-align: left;\n}\n\n#kyuiqrqycx .gt_center {\n  text-align: center;\n}\n\n#kyuiqrqycx .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#kyuiqrqycx .gt_font_normal {\n  font-weight: normal;\n}\n\n#kyuiqrqycx .gt_font_bold {\n  font-weight: bold;\n}\n\n#kyuiqrqycx .gt_font_italic {\n  font-style: italic;\n}\n\n#kyuiqrqycx .gt_super {\n  font-size: 65%;\n}\n\n#kyuiqrqycx .gt_footnote_marks {\n  font-style: italic;\n  font-weight: normal;\n  font-size: 75%;\n  vertical-align: 0.4em;\n}\n\n#kyuiqrqycx .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#kyuiqrqycx .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#kyuiqrqycx .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#kyuiqrqycx .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#kyuiqrqycx .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#kyuiqrqycx .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\">\n  \n  <thead class=\"gt_col_headings\">\n    <tr>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 60&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>No Magic Hours</strong>, N = 60<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 60&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Extra Magic Hours</strong>, N = 60<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 120&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Overall</strong>, N = 120<sup class=\"gt_footnote_marks\">1</sup></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">36 (30%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">70 (58%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">14 (12%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.9%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.4%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">13 (21%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">31 (26%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">4 (3.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">3 (4.6%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">3 (2.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">8 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">25 (21%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">0 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">83 (74, 88)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (75, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (75, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><sup class=\"gt_footnote_marks\">1</sup> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nWe again achieve a balance between groups, but the target values in @tbl-weighted-att differ from the previous tables.\nRecall in our ATE weighted table (@tbl-weighted-gtsummary), when looking at the `wdw_ticket_season` variable, we saw the weighted percent in the peak season balanced close to 22%, the overall percent of peak days in the observed sample.\nWe again see balance, but the weighted percent of peak season days is 30% in the exposed and unexposed groups, reflecting the percent in the unweighted exposure group.\nIf you compare @tbl-weighted-att to our unweighted table (@tbl-unweighted-gtsummary), you might notice that the columns are most similar to the exposed column from the unweighted table.\nThis is because the \"target\" population is the exposed group, the days with extra magic hours in the morning, so we're trying to make the comparison group (no magic hours) as similar to that group as possible.\n\nWe can again create a mirrored histogram to observe the weighted psuedopopulation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_att),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the treated (ATT) weight.](chapter-10_files/figure-html/fig-sd-mirror-hist-att-1.png){#fig-sd-mirror-hist-att width=672}\n:::\n:::\n\n\nSince every day in the exposed group has a weight of 1, the distribution of the propensity scores (the dark bars above 0 on the graph) in @fig-sd-mirror-hist-att overlaps exactly with the weighted distribution overlaid in blue.\nIn the unexposed population, we see that almost all observations are *down weighted*; the dark orange distribution is smaller than the distribution of the propensity scores and more closely matches the exposed distribution above it.\nThe ATT is easier to estimate when there are many more observations in the unexposed group compared to the exposed one.\n\n### Average treatment effect among the unexposed\n\nThe target population to estimate the average treatment effect among the unexposed (control) (ATU / ATC) is the *unexposed* (control) population.\nThis causal estimand conditions on those in the unexposed group.\n\n$$E[Y(1) - Y(0) | X = 0]$$\n\nExample research questions where the ATU is of interest could include \"Should we send our marketing campaign to those not currently receiving it?\" or \"Should medical providers begin recommending treatment for those not currently receiving it?\" [@greifer2021choosing]\n\nThe ATU can be estimated via matching; here, all unexposed observations are included and \"matched\" to exposed observations, some of which may be discarded.\nAlternatively, the ATU can be estimated via weighting.\nThe ATU weight is estimated by:\n\n$$w_{ATU} = \\frac{X(1-p)}{p}+ (1-X)$$ Let's add the ATU weights to the `seven_dwarfs_wts` data frame and look at their distribution.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_wts <- seven_dwarfs_wts |>\n  mutate(w_atu = wt_atu(.fitted, park_extra_magic_morning))\n\nggplot(seven_dwarfs_wts, aes(w_atu)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect among the unexposed (ATU) weights. The range of the ATU weights in this example is very similar to the ATE weights.](chapter-10_files/figure-html/fig-sd-atu-hist-1.png){#fig-sd-atu-hist width=672}\n:::\n:::\n\n\nThe distribution of the weights in @fig-sd-atu-hist weights looks very similar to what we saw with the ATE weights---several around 1, with a long tail.\nThese weights will be 1 for all observations in the unexposed group; they can range from 0 to infinity for the exposed group.\nBecause we have more observations in our unexposed group, our exposed observations are upweighted to match their distribution.\n\nNow let's take a look at the weighted table.\n\n\n::: {#tbl-weighted-atu .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Unexposed Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_atu\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"kgmohzmjrw\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>html {\n  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;\n}\n\n#kgmohzmjrw .gt_table {\n  display: table;\n  border-collapse: collapse;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  font-style: normal;\n 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padding-left: 5px;\n  padding-right: 5px;\n}\n\n#kgmohzmjrw .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#kgmohzmjrw .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#kgmohzmjrw .gt_left {\n  text-align: left;\n}\n\n#kgmohzmjrw .gt_center {\n  text-align: center;\n}\n\n#kgmohzmjrw .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#kgmohzmjrw .gt_font_normal {\n  font-weight: normal;\n}\n\n#kgmohzmjrw .gt_font_bold {\n  font-weight: bold;\n}\n\n#kgmohzmjrw .gt_font_italic {\n  font-style: italic;\n}\n\n#kgmohzmjrw .gt_super {\n  font-size: 65%;\n}\n\n#kgmohzmjrw .gt_footnote_marks {\n  font-style: italic;\n  font-weight: normal;\n  font-size: 75%;\n  vertical-align: 0.4em;\n}\n\n#kgmohzmjrw .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#kgmohzmjrw .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#kgmohzmjrw .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#kgmohzmjrw .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#kgmohzmjrw .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#kgmohzmjrw .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\">\n  \n  <thead class=\"gt_col_headings\">\n    <tr>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 294&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>No Magic Hours</strong>, N = 294<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 297&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Extra Magic Hours</strong>, N = 297<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 591&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Overall</strong>, N = 591<sup class=\"gt_footnote_marks\">1</sup></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">60 (20%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">63 (21%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">123 (21%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">158 (54%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">152 (51%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">310 (52%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">76 (26%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">82 (27%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">158 (27%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.2%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">37 (13%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">54 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">91 (15%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (6.1%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (5.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">35 (5.9%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">28 (9.5%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (4.7%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">91 (31%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">75 (25%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">166 (28%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">78 (27%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">70 (23%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">148 (25%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">40 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">77 (26%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">117 (20%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">5 (1.6%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">6 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">84 (78, 89)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (78, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">84 (78, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><sup class=\"gt_footnote_marks\">1</sup> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nNow the exposed column of @tbl-weighted-atu is weighted to match the unexposed column of the unweighted table.\n\nWe can again create a mirrored histogram to observe the weighted psuedopopulation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_atu),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the unexposed (ATU) weight.](chapter-10_files/figure-html/fig-sd-mirror-hist-atu-1.png){#fig-sd-mirror-hist-atu width=672}\n:::\n:::\n\n\nThe weights for the unexposed days are all 1, so the distribution of the propensity scores for this group exactly overlaps the weighted pseudopopulation in orange.\nThe blue bars indicate that the exposed population is largely upweighted to match the unexposed distribution.\n\n### Average treatment effect among the evenly matchable\n\nThe target population to estimate the average treatment effect among the evenly matchable (ATM) is the evenly matchable.\nThis causal estimand conditions on those deemed \"evenly matchable\" by some distance metric.\n\n$$E[Y(1) - Y(0) | M_d = 1]$$\n\nHere $d$ denotes a distance measure, and $M_d=1$ indicates that a unit is evenly matchable under that distance measure.[@samuels2017aspects; @d2018improving] Example research questions where the ATM is of interest could include \"Should those at clinical equipoise receive the exposure?\" [@greifer2021choosing]\n\nThe ATM is a common target estimand when using matching.\nHere, some exposed observations are \"matched\" to control observations, however some observations in both groups may be discarded.\nThis is often done via a \"caliper\", where observations are only matched if they fall within a pre-specified caliper distance of each other.\nAlternatively, the ATM can be estimated via weighting.\nThe ATM weight is estimated by:\n\n$$w_{ATM} = \\frac{\\min \\{p, 1-p\\}}{Xp + (1-X)(1-p)}$$\n\nLet's add the ATM weights to the `seven_dwarfs_wts` data frame and look at their distribution.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_wts <- seven_dwarfs_wts |>\n  mutate(w_atm = wt_atm(.fitted, park_extra_magic_morning))\n\nggplot(seven_dwarfs_wts, aes(w_atm)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect among the evenly matchable (ATM) weights for whether or not a day had extra magic hours. ATM weights can range from 0 to 1, so they are always stable.](chapter-10_files/figure-html/fig-sd-atm-hist-1.png){#fig-sd-atm-hist width=672}\n:::\n:::\n\n\nThe distribution of the weights in @fig-sd-atm-hist looks very similar to what we saw with the ATT weights.\nThat is because, in this particular sample, there are many more unexposed observations compared to exposed ones.\nThese weights have the nice property that they range from 0 to 1, making them *always* stable, regardless of the imbalance between exposure groups.\n\nNow let's take a look at the weighted table.\n\n\n::: {#tbl-weighted-atm .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Evenly Matchable Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_atm\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"mduhivknhv\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>html {\n  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;\n}\n\n#mduhivknhv .gt_table {\n  display: table;\n  border-collapse: collapse;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  font-style: normal;\n 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padding-left: 5px;\n  padding-right: 5px;\n}\n\n#mduhivknhv .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#mduhivknhv .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#mduhivknhv .gt_left {\n  text-align: left;\n}\n\n#mduhivknhv .gt_center {\n  text-align: center;\n}\n\n#mduhivknhv .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#mduhivknhv .gt_font_normal {\n  font-weight: normal;\n}\n\n#mduhivknhv .gt_font_bold {\n  font-weight: bold;\n}\n\n#mduhivknhv .gt_font_italic {\n  font-style: italic;\n}\n\n#mduhivknhv .gt_super {\n  font-size: 65%;\n}\n\n#mduhivknhv .gt_footnote_marks {\n  font-style: italic;\n  font-weight: normal;\n  font-size: 75%;\n  vertical-align: 0.4em;\n}\n\n#mduhivknhv .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#mduhivknhv .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#mduhivknhv .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#mduhivknhv .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#mduhivknhv .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#mduhivknhv .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\">\n  \n  <thead class=\"gt_col_headings\">\n    <tr>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 60&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>No Magic Hours</strong>, N = 60<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 60&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Extra Magic Hours</strong>, N = 60<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 120&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Overall</strong>, N = 120<sup class=\"gt_footnote_marks\">1</sup></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">36 (30%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">70 (58%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">14 (12%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.9%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.4%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">13 (21%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">31 (26%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">4 (3.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">3 (4.6%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">3 (2.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">8 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">25 (21%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">0 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">83 (74, 88)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (75, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (75, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><sup class=\"gt_footnote_marks\">1</sup> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nIn this particular sample, the ATM weights resemble the ATT weights, so this table looks similar to the ATT weighted table.\nThis similarity is not guaranteed, and the ATM pseudopopulation can look different from the overall, exposed, and unexposed unweighted populations.\nIt's thus essential to examine the weighted table when using ATM weights to understand what population inference will ultimately be drawn on.\n\nWe can again create a mirrored histogram to observe the ATM weighted psuedopopulation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_atm),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the evenly matchable (ATM) weight.](chapter-10_files/figure-html/fig-sd-mirror-hist-atm-1.png){#fig-sd-mirror-hist-atm width=672}\n:::\n:::\n\n\nAgain, the ATM weights are bounded by 0 and 1, meaning that all observations will be *down weighted*.\nThis removes the potential for finite sample bias due to large weights in small samples and has improved variance properties.\n\n### Average treatment effect among the overlap population\n\nThe target population to estimate the average treatment effect among the overlap population (ATO) is the overlap -- this is very similar to the \"even matchable\" from above.\nExample research questions where the ATO is of interest are the same as those for the ATM, such as \"Should those at clinical equipoise receive the exposure?\" [@greifer2021choosing]\n\nThese weights, again, are pretty similar to the ATM weights but are slightly attenuated, yielding improved variance properties.\n\nThe ATO weight is estimated by:\n\n$$w_{ATO} = X(1-p) + (1-X)p$$\n\nLet's add the ATO weights to the `seven_dwarfs_wts` data frame and look at their distribution.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_wts <- seven_dwarfs_wts |>\n  mutate(w_ato = wt_ato(.fitted, park_extra_magic_morning))\n\nggplot(seven_dwarfs_wts, aes(w_ato)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect among the overlap population (ATO) weights for whether or not a day had extra magic hours. Like ATM weights, ATO weights can range from 0 to 1, so they are always stable. ATO weights also have improved variance properties compared to the ATM weights.](chapter-10_files/figure-html/fig-sd-ato-hist-1.png){#fig-sd-ato-hist width=672}\n:::\n:::\n\n\nLike the ATM weights, the ATO weights are bounded by 0 and 1, making them more stable than the ATE, ATT, and ATU, regardless of the exposed and unexposed imbalance.\n\n::: {.callout-warning}\n## Finite sample bias - revisited\n\nLet's revisit our finite sample bias simulation, adding in the ATO weights to examine whether they are impacted by this potential for bias.\n\n\n::: {.cell hash='chapter-10_cache/html/sim-finite-sample-bias-2_4e1800bf54b6b2874f4e046f933337ae'}\n\n```{.r .cell-code}\nsim <- function(n) {\n  ## create a simulated dataset\n  finite_sample <- tibble(\n    z = rnorm(n),\n    x = case_when(\n      0.5 + z + rnorm(n) > 0 ~ 1,\n      TRUE ~ 0\n    ),\n    y = z + rnorm(n)\n  )\n  finite_sample_wts <- glm(\n    x ~ z, data = finite_sample, family = binomial(\"probit\")) |>\n    augment(newdata = finite_sample, type.predict = \"response\") |>\n    mutate(wts_ate = wt_ate(.fitted, x),\n           wts_ato = wt_ato(.fitted, x))\n  bias <- finite_sample_wts |>\n    summarize(\n      effect_ate = sum(y * x * wts_ate) / sum(x * wts_ate) - \n        sum(y * (1 - x) * wts_ate) / sum((1 - x) * wts_ate),\n      effect_ato = sum(y * x * wts_ato) / sum(x * wts_ato) - \n        sum(y * (1 - x) * wts_ato) / sum((1 - x) * wts_ato)\n    )\n  tibble(\n    n = n,\n    bias = c(bias$effect_ate, bias$effect_ato),\n    weight = c(\"ATE\", \"ATO\")\n  )\n}\n\n## Examine 5 different sample sizes, simulate each 1000 times\nset.seed(1)\nfinite_sample_sims <- map_df(rep(c(50, 100, 500, 1000, 5000, 10000), \n                                 each = 1000), sim)\n\nbias <- finite_sample_sims %>%\n  group_by(n, weight) %>%\n  summarise(bias = mean(bias))\n```\n:::\n\n\nLet's plot our results:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(bias, aes(x = n, y = bias, color = weight)) +\n  geom_point() + \n  geom_line() + \n  geom_hline(yintercept = 0, lty = 2)\n```\n\n::: {.cell-output-display}\n![Finite sample bias with ATE weights (orange) and ATO weights (blue) created using correctly specified propensity score model, varying the sample size from n = 50 to n = 10,000](chapter-10_files/figure-html/fig-finite-sample-bias-2-1.png){#fig-finite-sample-bias-2 width=672}\n:::\n:::\n\n\nNotice here the estimate using the ATO weights is almost immediately unbiased, even in fairly small samples.\n:::\n\nNow let's take a look at the weighted table.\n\n\n::: {#tbl-weighted-ato .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Overlap Population Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_ato\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"uaaskkmbew\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>html {\n  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;\n}\n\n#uaaskkmbew .gt_table {\n  display: table;\n  border-collapse: collapse;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 16px;\n  font-weight: normal;\n  font-style: normal;\n 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padding-left: 5px;\n  padding-right: 5px;\n}\n\n#uaaskkmbew .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#uaaskkmbew .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#uaaskkmbew .gt_left {\n  text-align: left;\n}\n\n#uaaskkmbew .gt_center {\n  text-align: center;\n}\n\n#uaaskkmbew .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#uaaskkmbew .gt_font_normal {\n  font-weight: normal;\n}\n\n#uaaskkmbew .gt_font_bold {\n  font-weight: bold;\n}\n\n#uaaskkmbew .gt_font_italic {\n  font-style: italic;\n}\n\n#uaaskkmbew .gt_super {\n  font-size: 65%;\n}\n\n#uaaskkmbew .gt_footnote_marks {\n  font-style: italic;\n  font-weight: normal;\n  font-size: 75%;\n  vertical-align: 0.4em;\n}\n\n#uaaskkmbew .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#uaaskkmbew .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#uaaskkmbew .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#uaaskkmbew .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#uaaskkmbew .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#uaaskkmbew .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\">\n  \n  <thead class=\"gt_col_headings\">\n    <tr>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 48&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>No Magic Hours</strong>, N = 48<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 48&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Extra Magic Hours</strong>, N = 48<sup class=\"gt_footnote_marks\">1</sup></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 96&lt;sup class=&quot;gt_footnote_marks&quot;&gt;1&lt;/sup&gt;\"><strong>Overall</strong>, N = 96<sup class=\"gt_footnote_marks\">1</sup></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">14 (29%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">14 (29%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (29%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">28 (58%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">28 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">55 (58%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">6 (13%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">6 (13%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">13 (13%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">0 (0.7%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">0 (0.4%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">9 (19%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">13 (27%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">22 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (3.7%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">4 (3.7%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (5.1%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">2 (2.5%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">14 (29%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">9 (20%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">23 (24%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">14 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">9 (19%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">23 (24%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">7 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">14 (29%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">21 (22%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">0 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">83 (75, 88)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (76, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (75, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><sup class=\"gt_footnote_marks\">1</sup> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nLike the ATM weights, the ATO pseudopopulation may not resemble the overall, exposed, or unexposed unweighted populations, so it is essential to examine these weighted tables to understand the population on whom we will draw inferences.\n\nWe can again create a mirrored histogram to observe the ATO weighted psuedopopulation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_ato),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the overlap population (ATO) weight.](chapter-10_files/figure-html/fig-sd-mirror-hist-ato-1.png){#fig-sd-mirror-hist-ato width=672}\n:::\n:::\n\n\n@fig-sd-mirror-hist-ato looks similar to the ATM pseudopopulation, with slight attenuation, as evidenced by the increased down weighting in the exposed population.\nWe prefer using the ATO weights when the overlap (or evenly matchable) population is the target, as they have improved variance properties.\nAnother benefit is that any variables included in the propensity score model (if fit via logistic regression) will be perfectly balanced on the mean.\nWe will discuss this further in @sec-eval-ps-model.\n\n## Choosing estimands\n\nSeveral factors need to be considered when choosing an estimand for a given analysis.\nFirst an foremost, the research question at hand will drive this decision.\nFor example, returing to the Touring Plans data, suppose Disney was interested in assessing whether they should add additional extra magic hours on days that don't currently have them---here, the ATU will be the estimand of interest.\nOther considerations include the available sample size and the distribution of the exposed and unexposed observations (i.e. might finite sample bias be an issue?).\nBelow is a table summarizing the estimands and methods for estimating them (including R functions and example code), along with sample questions extended from Table 2 in @greifer2021choosing.\n\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| Estimand | Target population                | Example Research Question                                                                                                                                  | Matching Method                               | Weighting Method       |\n+==========+==================================+============================================================================================================================================================+===============================================+========================+\n| ATE      | Full population                  | Should we decide whether to have extra magic hours *all* mornings to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                       | Full matching                                 | ATE Weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should a specific policy be applied to all eligible observations?                                                                                          | Fine stratification                           | `propensity::wt_ate()` |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = \"ATE\")`       | |                      |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| ATT      | Exposed (treated) observations   | Should we stop extra magic hours to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                                                        | Pair matching without a caliper               | ATT weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should we stop our marketing campaign to those currently receiving it?                                                                                     | Full matching                                 | `propensity::wt_att()` |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should medical providers stop recommending treatment for those currently receiving it?                                                                     | Fine stratification                           |                        |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = \"ATT\")`       |                        |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| ATU      | Unexposed (control) observations | Should we add extra magic hours for all days to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                                            | Pair matching without a caliper               | ATU weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should we extend our marketing campaign to those not receiving it?                                                                                         | Full matching                                 | `propensity::wt_atu()` |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should medical providers extend treatment to those not currently receiving it?                                                                             | Fine stratification                           |                        |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = \"ATC\")`       |                        |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| ATM      | Evenly matchable                 | Are there some days we should change whether we are offering extra magic hours in order to change the wait time for Seven Dwarfs Mine Train between 5-6pm? | Caliper matching                              | ATM weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Is there an effect of the exposure for some observations?                                                                                                  | `MatchIt::matchit(…, caliper = 0.1)`          | `propensity::wt_atm()` |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should those at clinical equipoise receive treatment?                                                                                                      | Coarsened exact matching                      |                        |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | Cardinality matching                          |                        |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, method = \"cardinality\")` |                        |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| ATO      | Overlap population               | Same as ATM                                                                                                                                                |                                               | ATO weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            |                                               | `propensity::wt_ato()` |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n\n",
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-    "markdown": "# Causal estimands {#sec-estimands}\n\n\n\n\n\n## Estimands, Estimators, Estimates\n\nWhen analyzing to make causal inferences, we need to keep the causal question that we're interested in close to our chest.\nThe causal question helps guide not just the assumptions we need to make but also how we will go about answering the question.\nThis chapter deals with three ideas closely related to answering causal questions: the estimand, the estimator, and the estimate.\nThe **estimand** is the *target of interest*, the **estimator** is the method by which we approximate this estimand using data, and the **estimate** is the value we get when we plug our data into the estimator.\nYou can think of the **estimand** as a glossy picture of a beautiful cake we are trying to bake, the **estimator** as the recipe, and the **estimate** as the cake we pull out of our oven.\n\nSo far, we have been estimating the average treatment effect, the effect of the exposure of interest averaged across the whole population.\nThe **estimand** here is the expected value of the difference in potential outcomes across all individuals:\n\n$$E[Y(1) - Y(0)]$$\n\nThe **estimator** we use depends on the method we've chosen.\nFor example, in an A/B test or randomized controlled trial, our estimator could just be the average outcome among those who received the exposure A minus the average outcome among those who receive exposure B.\n\n$$\\sum_{i=1}^{N}\\frac{Y_i\\times X_i}{N_{\\textrm{A}}} - \\frac{Y_i\\times (1-X_i)}{N_{\\textrm{B}}}$$\n\nWhere $X$ is just an indicator for exposure A ($X = 1$ for exposure A and $X = 0$ for exposure B), $N_A$ is the total number in group A and $N_B$ is the total number in group B such that $N_A + N_B = N$.\nLet's motivate this example a bit more.\nSuppose we have two different designs for a call-to-action (often abbreviated as CTA, a written directive for a marketing campaign) and want to assess whether they lead to a different average number of items a user purchases.\nWe can create an A/B test where we randomly assign a subset of users to see design A or design B.\nSuppose we have A/B testing data for 100 participants in a dataset called `ab`.\nHere is some code to simulate such a data set.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nset.seed(928)\nab <- tibble(\n  # generate the exposure, x, from a binomial distribution\n  # with the probability exposure A of 0.5\n  x = rbinom(100, 1, 0.5),\n  # generate the \"true\" average treatment effect of 1\n  # we use rnorm(100) to add the random error term that is normally\n  # distributed with a mean of 0 and a standard deviation of 1\n  y = x + rnorm(100)\n)\n```\n:::\n\n\nHere the exposure is `x`, and the outcome is `y`.\nThe true average treatment effect is 1, implying that on average seeing call-to-action A increases the number of items purchased by 1.\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nab\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 100 × 2\n       x      y\n   <int>  <dbl>\n 1     0 -0.280\n 2     1  1.87 \n 3     1  1.36 \n 4     0 -0.208\n 5     0  1.13 \n 6     1  0.352\n 7     1 -0.148\n 8     1  2.08 \n 9     0  2.09 \n10     0 -1.41 \n# ℹ 90 more rows\n```\n\n\n:::\n:::\n\n\nBelow are two ways to estimate this in R.\nUsing a formula approach, we calculate the difference in `y` between exposure values in the first example.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nab |>\n  summarise(\n    n_a = sum(x),\n    n_b = sum(1 - x),\n    estimate = sum(\n      (y * x) / n_a -\n        y * (1 - x) / n_b\n    )\n  )\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 3\n    n_a   n_b estimate\n  <int> <dbl>    <dbl>\n1    54    46     1.15\n```\n\n\n:::\n:::\n\n\nAlternatively, we can `group_by()` `x` and `summarize()` the means of `y` for each group, then pivot the results to take their difference.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nab |>\n  group_by(x) |>\n  summarise(avg_y = mean(y)) |>\n  pivot_wider(\n    names_from = x,\n    values_from = avg_y,\n    names_prefix = \"x_\"\n  ) |>\n  summarise(estimate = x_1 - x_0)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 1\n  estimate\n     <dbl>\n1     1.15\n```\n\n\n:::\n:::\n\n\nBecause $X$, the exposure, was randomly assigned, this estimator results in an unbiased estimate of our estimand of interest.\n\nWhat do we mean by unbiased, though?\nNotice that while the \"true\" causal effect is 1, this estimate is not precisely 1; it is 1.149.\nWhy the difference?\nThis A/B test included a sample of 100 participants, not the whole population.\nAs that sample increases, our estimate will get closer to the truth.\nLet's try that.\nLet's simulate the data again but increase the sample size from 100 to 100,000:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(928)\nab <- tibble(\n  x = rbinom(100000, 1, 0.5),\n  y = x + rnorm(100000)\n)\n\nab |>\n  summarise(\n    n_a = sum(x),\n    n_b = sum(1 - x),\n    estimate = sum(\n      (y * x) / n_a -\n        y * (1 - x) / n_b\n    )\n  )\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 3\n    n_a   n_b estimate\n  <int> <dbl>    <dbl>\n1 49918 50082     1.01\n```\n\n\n:::\n:::\n\n\nNotice how the estimate is 1.01, much closer to the actual average treatment effect, 1.\n\nSuppose $X$ had not been randomly assigned.\nIn that case, we could use the pre-exposure covariates to estimate the conditional probability of $X$ (the propensity score) and incorporate this probability in a *weight* $(w_i)$ to estimate this causal effect.\nFor example, we could use the following *estimator* to *estimate* our average treatment effect *estimand*:\n\n$$\\frac{\\sum_{i=1}^NY_i\\times X_i\\times w_i}{\\sum_{i=1}^NX_i\\times w_i}-\\frac{\\sum_{i=1}^NY_i\\times(1-X_i)\\times w_i}{\\sum_{i=1}^N(1-X_i)\\times w_i}$$\n\n## Estimating treatment effects with specific targets in mind\n\nDepending on the study's goal, or the causal question, we may want to estimate different *estimands*.\nHere, we will outline the most common causal estimands, their target populations, the causal questions they may help answer, and the methods used to estimate them [@greifer2021choosing].\n\n### Average treatment effect\n\nA common estimand is the average treatment effect (ATE).\nThe target population is the *total sample* or population of interest.\nThe **estimand** here is the expected value of the difference in potential outcomes across all individuals:\n\n$$E[Y(1) - Y(0)]$$\n\nAn example research question is \"Should a policy be applied to all eligible patients?\" [@greifer2021choosing].\n\nMost randomized controlled trials are designed with the ATE as the target estimand.\nIn a non-randomized setting, you can estimate the ATE using full matching.\nEach observation in the exposed group is matched to at least one observation in the control group (and vice versa) without replacement.\nAlternatively, the following inverse probability weight will allow you to estimate the ATE using propensity score weighting.\n\n$$w_{ATE} = \\frac{X}{p} + \\frac{(1 - X)}{1 - p}$$\n\nIn other words, the weight is one over the propensity score for those in the exposure group and one over one minus the propensity score for the control group.\nIntuitively, this weight equates to the inverse probability of being in the exposure group in which you actually were.\n\nLet's dig deeper into this causal estimand using the Touring Plans data.\nRecall that in @sec-using-ps, we examined the relationship between whether there were \"Extra Magic Hours\" in the morning and the average wait time for the Seven Dwarfs Mine Train the same day between 9 am and 10 am.\nLet's reconstruct our data set, `seven_dwarfs` and fit the same propensity score model specified previously.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\n\nseven_dwarfs <- seven_dwarfs_train_2018 |>\n  filter(wait_hour == 9) |>\n  mutate(park_extra_magic_morning = factor(\n    park_extra_magic_morning,\n    labels = c(\"No Magic Hours\", \"Extra Magic Hours\")\n  ))\n\nseven_dwarfs_with_ps <- glm(\n  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n  data = seven_dwarfs,\n  family = binomial()\n) |>\n  augment(type.predict = \"response\", data = seven_dwarfs)\n```\n:::\n\n\nLet's examine a table of the variables of interest in this data frame.\nTo do so, we are going to use the `tbl_summary()` function from the `{gtsummary}` package.\n(We'll also use the `{labelled}` package to clean up the variable names for the table.)\n\n\n::: {#tbl-unweighted-gtsummary .cell tbl-cap='A descriptive table of Extra Magic Morning in the touringplans dataset. This table shows the distributions of these variables in the observed population.'}\n\n```{.r .cell-code}\nlibrary(gtsummary)\nlibrary(labelled)\nseven_dwarfs_with_ps <- seven_dwarfs_with_ps |>\n  set_variable_labels(\n    park_ticket_season = \"Ticket Season\",\n    park_close = \"Close Time\",\n    park_temperature_high = \"Historic High Temperature\"\n  )\n\ntbl_summary(\n  seven_dwarfs_with_ps,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  # add an overall column to the table\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"eugzvcxdgr\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#eugzvcxdgr table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  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 border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#eugzvcxdgr .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#eugzvcxdgr .gt_left {\n  text-align: left;\n}\n\n#eugzvcxdgr .gt_center {\n  text-align: center;\n}\n\n#eugzvcxdgr .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#eugzvcxdgr .gt_font_normal {\n  font-weight: normal;\n}\n\n#eugzvcxdgr .gt_font_bold {\n  font-weight: bold;\n}\n\n#eugzvcxdgr .gt_font_italic {\n  font-style: italic;\n}\n\n#eugzvcxdgr .gt_super {\n  font-size: 65%;\n}\n\n#eugzvcxdgr .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#eugzvcxdgr .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#eugzvcxdgr .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#eugzvcxdgr .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#eugzvcxdgr .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#eugzvcxdgr .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#eugzvcxdgr .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 294&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>No Magic Hours</strong>, N = 294<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 60&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Extra Magic Hours</strong>, N = 60<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 354&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Overall</strong>, N = 354<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">60 (20%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">78 (22%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">158 (54%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">193 (55%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">76 (26%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">37 (13%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">55 (16%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (6.1%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">20 (5.6%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">28 (9.5%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (7.9%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">91 (31%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">102 (29%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">78 (27%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">89 (25%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">40 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">57 (16%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">2 (0.6%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">84 (78, 89)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (76, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">84 (78, 89)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nFrom @tbl-unweighted-gtsummary, 294 days did not have extra magic hours in the morning, and 60 did.\nWe also see that 30% of the extra magic mornings were during peak season compared to 20% of the non-extra magic mornings.\nAdditionally, days with extra magic mornings were more likely to close at 6 pm (18:00:00) compared to non-magic hour mornings.\nThe median high temperature on days with extra magic hour mornings is slightly lower (83 degrees) compared to non-extra magic hour morning days.\n\nNow let's construct our propensity score weight to estimate the ATE.\nThe `{propensity}` package has helper functions to estimate weights.\nThe `wt_ate()` function will calculate ATE weights.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(propensity)\nseven_dwarfs_wts <- seven_dwarfs_with_ps |>\n  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))\n```\n:::\n\n\nLet's look at a distribution of these weights.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(x = w_ate)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect (ATE) weights for whether or not a day had extra magic hours. ATE weights can range from 1 to infinity, so paying attention to the actual range of weights is important.](chapter-11_files/figure-html/fig-sd-ate-hist-1.png){#fig-sd-ate-hist width=672}\n:::\n:::\n\n\nMany weights in @fig-sd-ate-hist are close to 1 (the smallest possible value for an ATE weight), with a long tail leading to the largest weight (around 24).\nThis distribution highlights a potential problem with ATE weights.\nThey range from 1 to infinity; if your weights are too large in small samples, this can lead to bias in your estimates (known as finite sample bias).\n\n::: {.callout-warning}\n## Finite sample bias\n\nWe know that not accounting for confounders can bias our causal estimates, but it turns out that even after accounting for all confounders, we may still get a biased estimate in finite samples.\nMany of the properties we tout in statistics rely on *large samples*---how \"large\" is defined can be opaque.\nLet's look at a quick simulation.\nHere, we have an exposure, $X$, an outcome, $Y$, and one confounder, $Z$.\nWe will simulate $Y$, which is only dependent on $Z$ (so the true treatment effect is 0), and $X$, which also depends on $Z$.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nset.seed(928)\nn <- 100\nfinite_sample <- tibble(\n  # z is normally distributed with a mean: 0 and sd: 1\n  z = rnorm(n),\n  # x is defined from a probit selection model with normally distributed errors\n  x = case_when(\n    0.5 + z + rnorm(n) > 0 ~ 1,\n    TRUE ~ 0\n  ),\n  # y is continuous, dependent only on z with normally distrbuted errors\n  y = z + rnorm(n)\n)\n```\n:::\n\n\nIf we fit a propensity score model using the one confounder $Z$ and calculate the weighted estimator, we should get an unbiased result (which in this case would be 0).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n## fit the propensity score model\nfinite_sample_wts <- glm(\n  x ~ z,\n  data = finite_sample, family = binomial(\"probit\")\n) |>\n  augment(newdata = finite_sample, type.predict = \"response\") |>\n  mutate(wts = wt_ate(.fitted, x))\n\nfinite_sample_wts |>\n  summarize(\n    effect = sum(y * x * wts) / sum(x * wts) -\n      sum(y * (1 - x) * wts) / sum((1 - x) * wts)\n  )\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 1\n  effect\n   <dbl>\n1  0.197\n```\n\n\n:::\n:::\n\n\nOk, here, our effect of $X$ is pretty far from 0, although it's hard to know if this is really bias, or something we are just seeing by chance in this particular simulated sample.\nTo explore the potential for finite sample bias, we can rerun this simulation many times at different sample sizes.\nLet's do that.\n\n\n::: {.cell hash='chapter-11_cache/html/sim-finite-sample-bias_b577deb94bbf5d3cb05b98c1452f7862'}\n\n```{.r .cell-code}\nsim <- function(n) {\n  ## create a simulated dataset\n  finite_sample <- tibble(\n    z = rnorm(n),\n    x = case_when(\n      0.5 + z + rnorm(n) > 0 ~ 1,\n      TRUE ~ 0\n    ),\n    y = z + rnorm(n)\n  )\n  finite_sample_wts <- glm(\n    x ~ z,\n    data = finite_sample, family = binomial(\"probit\")\n  ) |>\n    augment(newdata = finite_sample, type.predict = \"response\") |>\n    mutate(wts = wt_ate(.fitted, x))\n  bias <- finite_sample_wts |>\n    summarize(\n      effect = sum(y * x * wts) / sum(x * wts) -\n        sum(y * (1 - x) * wts) / sum((1 - x) * wts)\n    ) |>\n    pull()\n  tibble(\n    n = n,\n    bias = bias\n  )\n}\n\n## Examine 5 different sample sizes, simulate each 1000 times\nset.seed(1)\nfinite_sample_sims <- map_df(rep(c(50, 100, 500, 1000, 5000, 10000),\n  each = 1000\n), sim)\n\nbias <- finite_sample_sims %>%\n  group_by(n) %>%\n  summarise(bias = mean(bias))\n```\n:::\n\n\nLet's plot our results:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(bias, aes(x = n, y = bias)) +\n  geom_point() +\n  geom_line() +\n  geom_hline(yintercept = 0, lty = 2)\n```\n\n::: {.cell-output-display}\n![Finite sample bias present with ATE weights created using correctly specified propensity score model, varying the sample size from n = 50 to n = 10,000](chapter-11_files/figure-html/fig-finite-sample-bias-1.png){#fig-finite-sample-bias width=672}\n:::\n:::\n\n\nThis is an example of finite sample bias.\nNotice here, even when the sample size is quite large (5,000) we still see some bias away from the \"true\" effect of 0.\nIt isn't until a sample size larger than 10,000 that we see this bias disappear.\n\nEstimands that utilize weights that are unbounded (i.e. that theoretically can be infinitely large) are more likely to suffer from finite sample bias.\nThe likelihood of falling into finite sample bias depends on:\\\n(1) the estimand you have chosen (i.e. are the weights bounded?)\\\n(2) the distribution of the covariates in the exposed and unexposed groups (i.e. is there good overlap? Potential positivity violations, when there is poor overlap, are the regions where weights can become too large)\\\n(3) the sample size.\n:::\n\nThe `{gtsummary}` package allows the creation of *weighted* tables, which can help us build an intuition for the psuedopopulation created by the weights.\nFirst, we must load the `{survey}` package and create a survey design object.\nThe `{survey}` package supports calculating statistics and models using weights.\nHistorically, many researchers applied techniques incorporating weights to survey analyses.\nEven though we are not doing a survey analysis, the same techniques are helpful for our propensity score weights.\n\n\n::: {#tbl-weighted-gtsummary .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nlibrary(survey)\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_ate\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"uaaskkmbew\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#uaaskkmbew table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#uaaskkmbew thead, #uaaskkmbew tbody, #uaaskkmbew tfoot, #uaaskkmbew tr, #uaaskkmbew td, #uaaskkmbew th 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15px;\n}\n\n#uaaskkmbew .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#uaaskkmbew .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 354&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>No Magic Hours</strong>, N = 354<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 357&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Extra Magic Hours</strong>, N = 357<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 711&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Overall</strong>, N = 711<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">78 (22%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">81 (23%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">160 (22%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">193 (54%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">187 (52%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">380 (53%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">83 (23%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">89 (25%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">172 (24%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (0.4%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">2 (0.2%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">50 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">72 (20%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">122 (17%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">20 (5.6%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">19 (5.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">39 (5.5%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">31 (8.7%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">31 (4.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">108 (30%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">86 (24%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">193 (27%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">95 (27%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">81 (23%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">176 (25%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">48 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">94 (26%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">142 (20%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">6 (1.6%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">7 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">84 (78, 89)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (78, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">84 (78, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nNotice in @tbl-weighted-gtsummary that the variables are more **balanced** between the groups.\nFor example, looking at the variable `park_ticket_season`, in @tbl-unweighted-gtsummary we see that a higher percentage of the days with extra magic hours in the mornings were during the peak season (30%) compared to the percent of days without extra magic hours in the morning (23%).\nIn @tbl-weighted-gtsummary this is balanced, with a weighted percent of 22% peak days in the extra magic morning group and 22% in the no extra magic morning group.\nThe weights change the variables' distribution by weighting each observation so that the two groups appear balanced.\nAlso, notice that the distribution of variables in @tbl-weighted-gtsummary now matches the Overall column of @tbl-unweighted-gtsummary.\nThat is what ATE weights do: they make the distribution of variables included in the propensity score for each exposure group look like the overall distribution across the whole sample.\n\nLet's look at another visualization that can help us understand the weighted psuedopopulation created by the ATE weights: a mirrored histogram.\nWe will plot the distribution of the propensity score for the days in the exposure group (days with extra magic hours in the morning) on the top half of the plot; then, we will mirror the distribution of the propensity score for the days in the\"unexposed group below it. This visualization allows us to compare the two distributions quickly. Then we will overlay weighted histograms to demonstrate how these distributions differ after incorporating the weights. The {halfmoon} package includes a function `geom_mirror_histogram()` to assist with this visualization.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(halfmoon)\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_ate),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect (ATE) weight.](chapter-11_files/figure-html/fig-sd-mirror-hist-ate-1.png){#fig-sd-mirror-hist-ate width=672}\n:::\n:::\n\n\nWe can learn a few things from @fig-sd-mirror-hist-ate.\nFirst, we can see that there are more \"unexposed\" days---days without extra magic hours in the morning---compared to \"exposed\" in the observed population.\nWe can see this by examining the darker histograms, which show the distributions in the observed sample.\nSimilarly, the exposed days are upweighted more substantially, as evidenced by the light blue we see on the top.\nWe can also see that after weighting, the two distributions look similar; the shape of the blue weighted distribution on top looks comparable to the orange weighted distribution below.\n\n### Average treatment effect among the treated\n\nThe target population to estimate the average treatment effect among the treated (ATT) is the *exposed* (treated) population.\nThis causal estimand conditions on those in the exposed group:\n\n$$E[Y(1) - Y(0) | X = 1]$$\n\nExample research questions where the ATT is of interest could include \"Should we stop our marketing campaign to those currently receiving it?\" or \"Should medical providers stop recommending treatment for those currently receiving it?\" [@greifer2021choosing]\n\nThe ATT is a common target estimand when using matching; here, all exposed observations are included and \"matched\" to control observations, some of which may be discarded.\nAlternatively, the ATT can be estimated via weighting.\nThe ATT weight is estimated by:\n\n$$w_{ATT} = X + \\frac{(1 - X)p}{1 - p}$$\n\nLet's add the ATT weights to the `seven_dwarfs_wts` data frame and look at their distribution.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_wts <- seven_dwarfs_wts |>\n  mutate(w_att = wt_att(.fitted, park_extra_magic_morning))\n\nggplot(seven_dwarfs_wts, aes(w_att)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect among the treated (ATT) weights. The range of the ATT weights in this example is more stable than the ATE weights: the range is much smaller.](chapter-11_files/figure-html/fig-sd-att-hist-1.png){#fig-sd-att-hist width=672}\n:::\n:::\n\n\nCompared to the ATE weights, the weights in @fig-sd-att-hist look more *stable*; the distribution of the weights is not heavily skewed, and the range is small, from ground zero to a bit over one, with many at precisely one.\nThese weights are exactly one for all days in the exposed group.\nTheoretically, for unexposed days these weights can range from zero to infinity.\nStill, because we have many more unexposed days compared to exposed in this particular sample, the range is much smaller, meaning the vast majority of unexposed days are \"down-weighted\" to match the variable distribution of the exposed days.\nLet's take a look at the weighted table to see this a bit more clearly.\n\n\n::: {#tbl-weighted-att .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Treated Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_att\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning: There were 5 warnings in `mutate()`.\nThe first warning was:\nℹ In argument: `df_stats = pmap(...)`.\nCaused by warning in `svymean.survey.design2()`:\n! Sample size greater than population size: are weights correctly scaled?\nℹ Run `dplyr::last_dplyr_warnings()` to see the 4\n  remaining warnings.\nThere were 5 warnings in `mutate()`.\nThe first warning was:\nℹ In argument: `df_stats = pmap(...)`.\nCaused by warning in `svymean.survey.design2()`:\n! Sample size greater than population size: are weights correctly scaled?\nℹ Run `dplyr::last_dplyr_warnings()` to see the 4\n  remaining warnings.\n```\n\n\n:::\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"nhvedgwgja\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#nhvedgwgja table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#nhvedgwgja thead, #nhvedgwgja tbody, #nhvedgwgja tfoot, #nhvedgwgja tr, #nhvedgwgja td, #nhvedgwgja th {\n  border-style: none;\n}\n\n#nhvedgwgja p {\n  margin: 0;\n  padding: 0;\n}\n\n#nhvedgwgja .gt_table {\n  display: table;\n  border-collapse: collapse;\n  line-height: normal;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 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{\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#nhvedgwgja .gt_footnote {\n  margin: 0px;\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#nhvedgwgja .gt_sourcenotes {\n  color: #333333;\n  background-color: #FFFFFF;\n  border-bottom-style: none;\n  border-bottom-width: 2px;\n  border-bottom-color: #D3D3D3;\n  border-left-style: none;\n  border-left-width: 2px;\n  border-left-color: #D3D3D3;\n  border-right-style: none;\n  border-right-width: 2px;\n  border-right-color: #D3D3D3;\n}\n\n#nhvedgwgja .gt_sourcenote {\n  font-size: 90%;\n  padding-top: 4px;\n  padding-bottom: 4px;\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n#nhvedgwgja .gt_left {\n  text-align: left;\n}\n\n#nhvedgwgja .gt_center {\n  text-align: center;\n}\n\n#nhvedgwgja .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#nhvedgwgja .gt_font_normal {\n  font-weight: normal;\n}\n\n#nhvedgwgja .gt_font_bold {\n  font-weight: bold;\n}\n\n#nhvedgwgja .gt_font_italic {\n  font-style: italic;\n}\n\n#nhvedgwgja .gt_super {\n  font-size: 65%;\n}\n\n#nhvedgwgja .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#nhvedgwgja .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#nhvedgwgja .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#nhvedgwgja .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#nhvedgwgja .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#nhvedgwgja .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#nhvedgwgja .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 60&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>No Magic Hours</strong>, N = 60<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 60&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Extra Magic Hours</strong>, N = 60<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 120&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Overall</strong>, N = 120<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">36 (30%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">70 (58%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">14 (12%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.9%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.4%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">13 (21%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">31 (26%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">4 (3.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">3 (4.6%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">3 (2.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">8 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">25 (21%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">0 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">83 (74, 88)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (75, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (75, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nWe again achieve a balance between groups, but the target values in @tbl-weighted-att differ from the previous tables.\nRecall in our ATE weighted table (@tbl-weighted-gtsummary), when looking at the `wdw_ticket_season` variable, we saw the weighted percent in the peak season balanced close to 22%, the overall percent of peak days in the observed sample.\nWe again see balance, but the weighted percent of peak season days is 30% in the exposed and unexposed groups, reflecting the percent in the unweighted exposure group.\nIf you compare @tbl-weighted-att to our unweighted table (@tbl-unweighted-gtsummary), you might notice that the columns are most similar to the exposed column from the unweighted table.\nThis is because the \"target\" population is the exposed group, the days with extra magic hours in the morning, so we're trying to make the comparison group (no magic hours) as similar to that group as possible.\n\nWe can again create a mirrored histogram to observe the weighted psuedopopulation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_att),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the treated (ATT) weight.](chapter-11_files/figure-html/fig-sd-mirror-hist-att-1.png){#fig-sd-mirror-hist-att width=672}\n:::\n:::\n\n\nSince every day in the exposed group has a weight of 1, the distribution of the propensity scores (the dark bars above 0 on the graph) in @fig-sd-mirror-hist-att overlaps exactly with the weighted distribution overlaid in blue.\nIn the unexposed population, we see that almost all observations are *down weighted*; the dark orange distribution is smaller than the distribution of the propensity scores and more closely matches the exposed distribution above it.\nThe ATT is easier to estimate when there are many more observations in the unexposed group compared to the exposed one.\n\n### Average treatment effect among the unexposed\n\nThe target population to estimate the average treatment effect among the unexposed (control) (ATU / ATC) is the *unexposed* (control) population.\nThis causal estimand conditions on those in the unexposed group.\n\n$$E[Y(1) - Y(0) | X = 0]$$\n\nExample research questions where the ATU is of interest could include \"Should we send our marketing campaign to those not currently receiving it?\" or \"Should medical providers begin recommending treatment for those not currently receiving it?\" [@greifer2021choosing]\n\nThe ATU can be estimated via matching; here, all unexposed observations are included and \"matched\" to exposed observations, some of which may be discarded.\nAlternatively, the ATU can be estimated via weighting.\nThe ATU weight is estimated by:\n\n$$w_{ATU} = \\frac{X(1-p)}{p}+ (1-X)$$ Let's add the ATU weights to the `seven_dwarfs_wts` data frame and look at their distribution.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_wts <- seven_dwarfs_wts |>\n  mutate(w_atu = wt_atu(.fitted, park_extra_magic_morning))\n\nggplot(seven_dwarfs_wts, aes(w_atu)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect among the unexposed (ATU) weights. The range of the ATU weights in this example is very similar to the ATE weights.](chapter-11_files/figure-html/fig-sd-atu-hist-1.png){#fig-sd-atu-hist width=672}\n:::\n:::\n\n\nThe distribution of the weights in @fig-sd-atu-hist weights looks very similar to what we saw with the ATE weights---several around 1, with a long tail.\nThese weights will be 1 for all observations in the unexposed group; they can range from 0 to infinity for the exposed group.\nBecause we have more observations in our unexposed group, our exposed observations are upweighted to match their distribution.\n\nNow let's take a look at the weighted table.\n\n\n::: {#tbl-weighted-atu .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Unexposed Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_atu\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"ctpbxfhgco\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#ctpbxfhgco table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#ctpbxfhgco thead, #ctpbxfhgco tbody, #ctpbxfhgco tfoot, #ctpbxfhgco tr, #ctpbxfhgco td, #ctpbxfhgco th {\n  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15px;\n}\n\n#ctpbxfhgco .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#ctpbxfhgco .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 294&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>No Magic Hours</strong>, N = 294<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 297&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Extra Magic Hours</strong>, N = 297<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 591&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Overall</strong>, N = 591<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">60 (20%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">63 (21%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">123 (21%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">158 (54%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">152 (51%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">310 (52%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">76 (26%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">82 (27%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">158 (27%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.2%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">37 (13%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">54 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">91 (15%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (6.1%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (5.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">35 (5.9%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">28 (9.5%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (4.7%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">91 (31%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">75 (25%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">166 (28%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">78 (27%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">70 (23%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">148 (25%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">40 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">77 (26%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">117 (20%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">5 (1.6%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">6 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">84 (78, 89)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (78, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">84 (78, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nNow the exposed column of @tbl-weighted-atu is weighted to match the unexposed column of the unweighted table.\n\nWe can again create a mirrored histogram to observe the weighted psuedopopulation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_atu),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the unexposed (ATU) weight.](chapter-11_files/figure-html/fig-sd-mirror-hist-atu-1.png){#fig-sd-mirror-hist-atu width=672}\n:::\n:::\n\n\nThe weights for the unexposed days are all 1, so the distribution of the propensity scores for this group exactly overlaps the weighted pseudopopulation in orange.\nThe blue bars indicate that the exposed population is largely upweighted to match the unexposed distribution.\n\n### Average treatment effect among the evenly matchable\n\nThe target population to estimate the average treatment effect among the evenly matchable (ATM) is the evenly matchable.\nThis causal estimand conditions on those deemed \"evenly matchable\" by some distance metric.\n\n$$E[Y(1) - Y(0) | M_d = 1]$$\n\nHere $d$ denotes a distance measure, and $M_d=1$ indicates that a unit is evenly matchable under that distance measure.[@samuels2017aspects; @d2018improving] Example research questions where the ATM is of interest could include \"Should those at clinical equipoise receive the exposure?\" [@greifer2021choosing]\n\nThe ATM is a common target estimand when using matching.\nHere, some exposed observations are \"matched\" to control observations, however some observations in both groups may be discarded.\nThis is often done via a \"caliper\", where observations are only matched if they fall within a pre-specified caliper distance of each other.\nAlternatively, the ATM can be estimated via weighting.\nThe ATM weight is estimated by:\n\n$$w_{ATM} = \\frac{\\min \\{p, 1-p\\}}{Xp + (1-X)(1-p)}$$\n\nLet's add the ATM weights to the `seven_dwarfs_wts` data frame and look at their distribution.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_wts <- seven_dwarfs_wts |>\n  mutate(w_atm = wt_atm(.fitted, park_extra_magic_morning))\n\nggplot(seven_dwarfs_wts, aes(w_atm)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect among the evenly matchable (ATM) weights for whether or not a day had extra magic hours. ATM weights can range from 0 to 1, so they are always stable.](chapter-11_files/figure-html/fig-sd-atm-hist-1.png){#fig-sd-atm-hist width=672}\n:::\n:::\n\n\nThe distribution of the weights in @fig-sd-atm-hist looks very similar to what we saw with the ATT weights.\nThat is because, in this particular sample, there are many more unexposed observations compared to exposed ones.\nThese weights have the nice property that they range from 0 to 1, making them *always* stable, regardless of the imbalance between exposure groups.\n\nNow let's take a look at the weighted table.\n\n\n::: {#tbl-weighted-atm .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Evenly Matchable Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_atm\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning: There were 5 warnings in `mutate()`.\nThe first warning was:\nℹ In argument: `df_stats = pmap(...)`.\nCaused by warning in `svymean.survey.design2()`:\n! Sample size greater than population size: are weights correctly scaled?\nℹ Run `dplyr::last_dplyr_warnings()` to see the 4\n  remaining warnings.\nThere were 5 warnings in `mutate()`.\nThe first warning was:\nℹ In argument: `df_stats = pmap(...)`.\nCaused by warning in `svymean.survey.design2()`:\n! Sample size greater than population size: are weights correctly scaled?\nℹ Run `dplyr::last_dplyr_warnings()` to see the 4\n  remaining warnings.\n```\n\n\n:::\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"oerjgubvma\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#oerjgubvma table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#oerjgubvma thead, #oerjgubvma tbody, #oerjgubvma tfoot, #oerjgubvma tr, #oerjgubvma td, #oerjgubvma th {\n  border-style: none;\n}\n\n#oerjgubvma p {\n  margin: 0;\n  padding: 0;\n}\n\n#oerjgubvma .gt_table {\n  display: table;\n  border-collapse: collapse;\n  line-height: normal;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 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{\n  text-align: left;\n}\n\n#oerjgubvma .gt_center {\n  text-align: center;\n}\n\n#oerjgubvma .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#oerjgubvma .gt_font_normal {\n  font-weight: normal;\n}\n\n#oerjgubvma .gt_font_bold {\n  font-weight: bold;\n}\n\n#oerjgubvma .gt_font_italic {\n  font-style: italic;\n}\n\n#oerjgubvma .gt_super {\n  font-size: 65%;\n}\n\n#oerjgubvma .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#oerjgubvma .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#oerjgubvma .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#oerjgubvma .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#oerjgubvma .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#oerjgubvma .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#oerjgubvma .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 60&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>No Magic Hours</strong>, N = 60<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 60&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Extra Magic Hours</strong>, N = 60<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 120&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Overall</strong>, N = 120<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">36 (30%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">35 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">70 (58%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">7 (12%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">14 (12%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">1 (0.9%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (0.4%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">13 (21%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">18 (30%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">31 (26%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.3%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">4 (3.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">3 (4.6%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">3 (2.3%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">11 (18%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">8 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">17 (28%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">25 (21%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">0 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">83 (74, 88)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (75, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (75, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nIn this particular sample, the ATM weights resemble the ATT weights, so this table looks similar to the ATT weighted table.\nThis similarity is not guaranteed, and the ATM pseudopopulation can look different from the overall, exposed, and unexposed unweighted populations.\nIt's thus essential to examine the weighted table when using ATM weights to understand what population inference will ultimately be drawn on.\n\nWe can again create a mirrored histogram to observe the ATM weighted psuedopopulation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_atm),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the evenly matchable (ATM) weight.](chapter-11_files/figure-html/fig-sd-mirror-hist-atm-1.png){#fig-sd-mirror-hist-atm width=672}\n:::\n:::\n\n\nAgain, the ATM weights are bounded by 0 and 1, meaning that all observations will be *down weighted*.\nThis removes the potential for finite sample bias due to large weights in small samples and has improved variance properties.\n\n### Average treatment effect among the overlap population\n\nThe target population to estimate the average treatment effect among the overlap population (ATO) is the overlap -- this is very similar to the \"even matchable\" from above.\nExample research questions where the ATO is of interest are the same as those for the ATM, such as \"Should those at clinical equipoise receive the exposure?\" [@greifer2021choosing]\n\nThese weights, again, are pretty similar to the ATM weights but are slightly attenuated, yielding improved variance properties.\n\nThe ATO weight is estimated by:\n\n$$w_{ATO} = X(1-p) + (1-X)p$$\n\nLet's add the ATO weights to the `seven_dwarfs_wts` data frame and look at their distribution.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nseven_dwarfs_wts <- seven_dwarfs_wts |>\n  mutate(w_ato = wt_ato(.fitted, park_extra_magic_morning))\n\nggplot(seven_dwarfs_wts, aes(w_ato)) +\n  geom_histogram(bins = 50)\n```\n\n::: {.cell-output-display}\n![A histogram of the average treatment effect among the overlap population (ATO) weights for whether or not a day had extra magic hours. Like ATM weights, ATO weights can range from 0 to 1, so they are always stable. ATO weights also have improved variance properties compared to the ATM weights.](chapter-11_files/figure-html/fig-sd-ato-hist-1.png){#fig-sd-ato-hist width=672}\n:::\n:::\n\n\nLike the ATM weights, the ATO weights are bounded by 0 and 1, making them more stable than the ATE, ATT, and ATU, regardless of the exposed and unexposed imbalance.\n\n::: {.callout-warning}\n## Finite sample bias - revisited\n\nLet's revisit our finite sample bias simulation, adding in the ATO weights to examine whether they are impacted by this potential for bias.\n\n\n::: {.cell hash='chapter-11_cache/html/sim-finite-sample-bias-2_9686855e1e6892045126c02b0904e0c3'}\n\n```{.r .cell-code}\nsim <- function(n) {\n  ## create a simulated dataset\n  finite_sample <- tibble(\n    z = rnorm(n),\n    x = case_when(\n      0.5 + z + rnorm(n) > 0 ~ 1,\n      TRUE ~ 0\n    ),\n    y = z + rnorm(n)\n  )\n  finite_sample_wts <- glm(\n    x ~ z,\n    data = finite_sample, family = binomial(\"probit\")\n  ) |>\n    augment(newdata = finite_sample, type.predict = \"response\") |>\n    mutate(\n      wts_ate = wt_ate(.fitted, x),\n      wts_ato = wt_ato(.fitted, x)\n    )\n  bias <- finite_sample_wts |>\n    summarize(\n      effect_ate = sum(y * x * wts_ate) / sum(x * wts_ate) -\n        sum(y * (1 - x) * wts_ate) / sum((1 - x) * wts_ate),\n      effect_ato = sum(y * x * wts_ato) / sum(x * wts_ato) -\n        sum(y * (1 - x) * wts_ato) / sum((1 - x) * wts_ato)\n    )\n  tibble(\n    n = n,\n    bias = c(bias$effect_ate, bias$effect_ato),\n    weight = c(\"ATE\", \"ATO\")\n  )\n}\n\n## Examine 5 different sample sizes, simulate each 1000 times\nset.seed(1)\nfinite_sample_sims <- map_df(rep(c(50, 100, 500, 1000, 5000, 10000),\n  each = 1000\n), sim)\n\nbias <- finite_sample_sims %>%\n  group_by(n, weight) %>%\n  summarise(bias = mean(bias))\n```\n:::\n\n\nLet's plot our results:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(bias, aes(x = n, y = bias, color = weight)) +\n  geom_point() +\n  geom_line() +\n  geom_hline(yintercept = 0, lty = 2)\n```\n\n::: {.cell-output-display}\n![Finite sample bias with ATE weights (orange) and ATO weights (blue) created using correctly specified propensity score model, varying the sample size from n = 50 to n = 10,000](chapter-11_files/figure-html/fig-finite-sample-bias-2-1.png){#fig-finite-sample-bias-2 width=672}\n:::\n:::\n\n\nNotice here the estimate using the ATO weights is almost immediately unbiased, even in fairly small samples.\n:::\n\nNow let's take a look at the weighted table.\n\n\n::: {#tbl-weighted-ato .cell tbl-cap='A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Overlap Population Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.'}\n\n```{.r .cell-code}\nseven_dwarfs_svy <- svydesign(\n  ids = ~1,\n  data = seven_dwarfs_wts,\n  weights = ~w_ato\n)\ntbl_svysummary(\n  seven_dwarfs_svy,\n  by = park_extra_magic_morning,\n  include = c(park_ticket_season, park_close, park_temperature_high)\n) |>\n  add_overall(last = TRUE)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning: There were 10 warnings in `mutate()`.\nThe first warning was:\nℹ In argument: `df_stats = pmap(...)`.\nCaused by warning in `svymean.survey.design2()`:\n! Sample size greater than population size: are weights correctly scaled?\nℹ Run `dplyr::last_dplyr_warnings()` to see the 9\n  remaining warnings.\n```\n\n\n:::\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning: There were 5 warnings in `mutate()`.\nThe first warning was:\nℹ In argument: `df_stats = pmap(...)`.\nCaused by warning in `svymean.survey.design2()`:\n! Sample size greater than population size: are weights correctly scaled?\nℹ Run `dplyr::last_dplyr_warnings()` to see the 4\n  remaining warnings.\n```\n\n\n:::\n\n::: {.cell-output-display}\n\n```{=html}\n<div id=\"uaaskkmbew\" style=\"padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;\">\n<style>#uaaskkmbew table {\n  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n#uaaskkmbew thead, #uaaskkmbew tbody, #uaaskkmbew tfoot, #uaaskkmbew tr, #uaaskkmbew td, #uaaskkmbew th {\n  border-style: none;\n}\n\n#uaaskkmbew p {\n  margin: 0;\n  padding: 0;\n}\n\n#uaaskkmbew .gt_table {\n  display: table;\n  border-collapse: collapse;\n  line-height: normal;\n  margin-left: auto;\n  margin-right: auto;\n  color: #333333;\n  font-size: 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{\n  text-align: left;\n}\n\n#uaaskkmbew .gt_center {\n  text-align: center;\n}\n\n#uaaskkmbew .gt_right {\n  text-align: right;\n  font-variant-numeric: tabular-nums;\n}\n\n#uaaskkmbew .gt_font_normal {\n  font-weight: normal;\n}\n\n#uaaskkmbew .gt_font_bold {\n  font-weight: bold;\n}\n\n#uaaskkmbew .gt_font_italic {\n  font-style: italic;\n}\n\n#uaaskkmbew .gt_super {\n  font-size: 65%;\n}\n\n#uaaskkmbew .gt_footnote_marks {\n  font-size: 75%;\n  vertical-align: 0.4em;\n  position: initial;\n}\n\n#uaaskkmbew .gt_asterisk {\n  font-size: 100%;\n  vertical-align: 0;\n}\n\n#uaaskkmbew .gt_indent_1 {\n  text-indent: 5px;\n}\n\n#uaaskkmbew .gt_indent_2 {\n  text-indent: 10px;\n}\n\n#uaaskkmbew .gt_indent_3 {\n  text-indent: 15px;\n}\n\n#uaaskkmbew .gt_indent_4 {\n  text-indent: 20px;\n}\n\n#uaaskkmbew .gt_indent_5 {\n  text-indent: 25px;\n}\n</style>\n<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n  <thead>\n    \n    <tr class=\"gt_col_headings\">\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Characteristic&lt;/strong&gt;\"><strong>Characteristic</strong></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;No Magic Hours&lt;/strong&gt;, N = 48&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>No Magic Hours</strong>, N = 48<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Extra Magic Hours&lt;/strong&gt;, N = 48&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Extra Magic Hours</strong>, N = 48<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n      <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Overall&lt;/strong&gt;, N = 96&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;\"><strong>Overall</strong>, N = 96<span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span></th>\n    </tr>\n  </thead>\n  <tbody class=\"gt_table_body\">\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Ticket Season</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    peak</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">14 (29%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">14 (29%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">28 (29%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    regular</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">28 (58%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">28 (58%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">55 (58%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    value</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">6 (13%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">6 (13%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">13 (13%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Close Time</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\"><br /></td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\"><br /></td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    16:30:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">0 (0.7%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">0 (0.4%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    18:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">9 (19%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">13 (27%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">22 (23%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    20:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (3.7%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">2 (3.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">4 (3.7%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    21:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">2 (5.1%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">0 (0%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">2 (2.5%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    22:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">14 (29%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">9 (20%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">23 (24%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    23:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">14 (28%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">9 (19%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">23 (24%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    24:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">7 (14%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">14 (29%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">21 (22%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">    25:00:00</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">0 (0.3%)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">1 (1.7%)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">1 (1.0%)</td></tr>\n    <tr><td headers=\"label\" class=\"gt_row gt_left\">Historic High Temperature</td>\n<td headers=\"stat_1\" class=\"gt_row gt_center\">83 (75, 88)</td>\n<td headers=\"stat_2\" class=\"gt_row gt_center\">83 (76, 87)</td>\n<td headers=\"stat_0\" class=\"gt_row gt_center\">83 (75, 88)</td></tr>\n  </tbody>\n  \n  <tfoot class=\"gt_footnotes\">\n    <tr>\n      <td class=\"gt_footnote\" colspan=\"4\"><span class=\"gt_footnote_marks\" style=\"white-space:nowrap;font-style:italic;font-weight:normal;\"><sup>1</sup></span> n (%); Median (IQR)</td>\n    </tr>\n  </tfoot>\n</table>\n</div>\n```\n\n:::\n:::\n\n\nLike the ATM weights, the ATO pseudopopulation may not resemble the overall, exposed, or unexposed unweighted populations, so it is essential to examine these weighted tables to understand the population on whom we will draw inferences.\n\nWe can again create a mirrored histogram to observe the ATO weighted psuedopopulation.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +\n  geom_mirror_histogram(bins = 50) +\n  geom_mirror_histogram(\n    aes(fill = park_extra_magic_morning, weight = w_ato),\n    bins = 50,\n    alpha = 0.5\n  ) +\n  scale_y_continuous(labels = abs) +\n  labs(\n    x = \"propensity score\",\n    fill = \"Extra Magic Morning\"\n  )\n```\n\n::: {.cell-output-display}\n![A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the overlap population (ATO) weight.](chapter-11_files/figure-html/fig-sd-mirror-hist-ato-1.png){#fig-sd-mirror-hist-ato width=672}\n:::\n:::\n\n\n@fig-sd-mirror-hist-ato looks similar to the ATM pseudopopulation, with slight attenuation, as evidenced by the increased down weighting in the exposed population.\nWe prefer using the ATO weights when the overlap (or evenly matchable) population is the target, as they have improved variance properties.\nAnother benefit is that any variables included in the propensity score model (if fit via logistic regression) will be perfectly balanced on the mean.\nWe will discuss this further in @sec-eval-ps-model.\n\n## Choosing estimands\n\nSeveral factors need to be considered when choosing an estimand for a given analysis.\nFirst an foremost, the research question at hand will drive this decision.\nFor example, returing to the Touring Plans data, suppose Disney was interested in assessing whether they should add additional extra magic hours on days that don't currently have them---here, the ATU will be the estimand of interest.\nOther considerations include the available sample size and the distribution of the exposed and unexposed observations (i.e. might finite sample bias be an issue?).\nBelow is a table summarizing the estimands and methods for estimating them (including R functions and example code), along with sample questions extended from Table 2 in @greifer2021choosing.\n\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| Estimand | Target population                | Example Research Question                                                                                                                                  | Matching Method                               | Weighting Method       |\n+==========+==================================+============================================================================================================================================================+===============================================+========================+\n| ATE      | Full population                  | Should we decide whether to have extra magic hours *all* mornings to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                       | Full matching                                 | ATE Weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should a specific policy be applied to all eligible observations?                                                                                          | Fine stratification                           | `propensity::wt_ate()` |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = \"ATE\")`       | |                      |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| ATT      | Exposed (treated) observations   | Should we stop extra magic hours to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                                                        | Pair matching without a caliper               | ATT weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should we stop our marketing campaign to those currently receiving it?                                                                                     | Full matching                                 | `propensity::wt_att()` |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should medical providers stop recommending treatment for those currently receiving it?                                                                     | Fine stratification                           |                        |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = \"ATT\")`       |                        |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| ATU      | Unexposed (control) observations | Should we add extra magic hours for all days to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                                            | Pair matching without a caliper               | ATU weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should we extend our marketing campaign to those not receiving it?                                                                                         | Full matching                                 | `propensity::wt_atu()` |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should medical providers extend treatment to those not currently receiving it?                                                                             | Fine stratification                           |                        |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = \"ATC\")`       |                        |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| ATM      | Evenly matchable                 | Are there some days we should change whether we are offering extra magic hours in order to change the wait time for Seven Dwarfs Mine Train between 5-6pm? | Caliper matching                              | ATM weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Is there an effect of the exposure for some observations?                                                                                                  | `MatchIt::matchit(…, caliper = 0.1)`          | `propensity::wt_atm()` |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  | Should those at clinical equipoise receive treatment?                                                                                                      | Coarsened exact matching                      |                        |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | Cardinality matching                          |                        |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, method = \"cardinality\")` |                        |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n| ATO      | Overlap population               | Same as ATM                                                                                                                                                |                                               | ATO weights            |\n|          |                                  |                                                                                                                                                            |                                               |                        |\n|          |                                  |                                                                                                                                                            |                                               | `propensity::wt_ato()` |\n+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+\n\n",
-    "supporting": [],
+    "markdown": "# Fitting the outcome model {#sec-outcome-model}\n\n\n\n\n\n## Using matched data sets\n\nWhen fitting an outcome model on matched data sets, we can simply subset the original data to only those who were matched and then fit a model on these data as we would otherwise. For example, re-performing the matching as we did in @sec-using-ps, we can extract the matched observations in a dataset called `matched_data` as follows.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\nlibrary(MatchIt)\n\nseven_dwarfs_9 <- seven_dwarfs_train_2018 |> \n  filter(wait_hour == 9) \n\nm <- matchit(\n  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n  data = seven_dwarfs_9\n)\nmatched_data <- get_matches(m)\n```\n:::\n\n\nWe can then fit the outcome model on this data. For this analysis, we are interested in the impact of extra magic morning hours on the average posted wait time between 9 and 10am. The linear model below will estimate this in the matched cohort.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlm(wait_minutes_posted_avg ~ park_extra_magic_morning, data = matched_data) |>\n  tidy(conf.int = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 7\n  term   estimate std.error statistic  p.value conf.low\n  <chr>     <dbl>     <dbl>     <dbl>    <dbl>    <dbl>\n1 (Inte…    67.0       2.37     28.3  1.94e-54    62.3 \n2 park_…     7.87      3.35      2.35 2.04e- 2     1.24\n# ℹ 1 more variable: conf.high <dbl>\n```\n\n\n:::\n:::\n\n\nRecall that by default `{MatchIt}` estimates an average treatment effect among the treated. This means among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 7.9 minutes (95% CI: 1.2-14.5). \n\n## Using weights in outcome models\n\nNow let's use propensity score weights to estimate this same estimand. We will use the ATT weights so the analysis matches that for matching above.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(propensity)\n\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\nseven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>\n  mutate(w_att = wt_att(.fitted, park_extra_magic_morning))\n```\n:::\n\n\nWe can fit a *weighted* outcome model by using the `weights` argument. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nlm(wait_minutes_posted_avg ~ park_extra_magic_morning, \n   data = seven_dwarfs_9_with_wt,\n   weights = w_att) |>\n  tidy()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 5\n  term           estimate std.error statistic   p.value\n  <chr>             <dbl>     <dbl>     <dbl>     <dbl>\n1 (Intercept)       68.7       1.45     47.3  1.69e-154\n2 park_extra_ma…     6.23      2.05      3.03 2.62e-  3\n```\n\n\n:::\n:::\n\n\nUsing weighting, we estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 6.2 minutes. \nWhile this approach will get us the desired estimate for the point estimate, the default output using the `lm` function for the uncertainty (the standard errors and confidence intervals) are not correct.\n\n\n## Estimating uncertainty\n\nThere are three ways to estimate the uncertainty:\n\n1. A bootstrap\n2. A sandwich estimator that only takes into account the outcome model\n3. A sandwich estimator that takes into account the propensity score model\n\nThe first option can be computationally intensive, but should get you the correct estimates.\nThe second option is computationally the easiest, but tends to overestimate the variability. There are not many current solutions in R (aside from coding it up yourself) for the third; however, the `{PSW}` package will do this. \n\n### The bootstrap\n\n1. Create a function to run your analysis once on a sample of your data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfit_ipw <- function(split, ...) {\n  .df <- analysis(split)\n  \n  # fit propensity score model\n  propensity_model <- glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) \n  \n  # calculate inverse probability weights\n  .df <- propensity_model |>\n    augment(type.predict = \"response\", data = .df) |>\n    mutate(wts = wt_att(.fitted, \n                        park_extra_magic_morning,\n                        exposure_type = \"binary\"))\n  \n  # fit correctly bootstrapped ipw model\n  lm(wait_minutes_posted_avg ~ park_extra_magic_morning, \n     data = .df, weights = wts) |>\n    tidy()\n}\n```\n:::\n\n\n\n2. Use {rsample} to bootstrap our causal effect\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rsample)\n\n# fit ipw model to bootstrapped samples\nbootstrapped_seven_dwarfs <- bootstraps(\n  seven_dwarfs_9, \n  times = 1000, \n  apparent = TRUE\n)\n\nipw_results <- bootstrapped_seven_dwarfs |> \n  mutate(boot_fits = map(splits, fit_ipw)) \n\nipw_results\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# Bootstrap sampling with apparent sample \n# A tibble: 1,001 × 3\n   splits            id            boot_fits       \n   <list>            <chr>         <list>          \n 1 <split [354/124]> Bootstrap0001 <tibble [2 × 5]>\n 2 <split [354/134]> Bootstrap0002 <tibble [2 × 5]>\n 3 <split [354/126]> Bootstrap0003 <tibble [2 × 5]>\n 4 <split [354/127]> Bootstrap0004 <tibble [2 × 5]>\n 5 <split [354/125]> Bootstrap0005 <tibble [2 × 5]>\n 6 <split [354/125]> Bootstrap0006 <tibble [2 × 5]>\n 7 <split [354/143]> Bootstrap0007 <tibble [2 × 5]>\n 8 <split [354/123]> Bootstrap0008 <tibble [2 × 5]>\n 9 <split [354/134]> Bootstrap0009 <tibble [2 × 5]>\n10 <split [354/134]> Bootstrap0010 <tibble [2 × 5]>\n# ℹ 991 more rows\n```\n\n\n:::\n:::\n\n\n\nLet's look at the results.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nipw_results |>\n  mutate(\n    estimate = map_dbl(\n      boot_fits,\n      \\(.fit) .fit |>\n        filter(term == \"park_extra_magic_morning\") |>\n        pull(estimate)\n    )\n  ) |>\n  ggplot(aes(estimate)) +\n  geom_histogram(bins = 30, fill = \"#D55E00FF\", color = \"white\", alpha = 0.8) + \n  theme_minimal()\n```\n\n::: {.cell-output-display}\n![](chapter-11_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\n\n3. Pull out the causal effect\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get t-based CIs\nboot_estimate <- int_t(ipw_results, boot_fits) |> \n  filter(term == \"park_extra_magic_morning\")\nboot_estimate\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 6\n  term           .lower .estimate .upper .alpha .method\n  <chr>           <dbl>     <dbl>  <dbl>  <dbl> <chr>  \n1 park_extra_ma… -0.368      7.10   11.6   0.05 studen…\n```\n\n\n:::\n:::\n\n\nWe estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 7.1 minutes, 95% CI (-0.4, 11.6). \n\n### The outcome model sandwich\n\nThere are two ways to get the sandwich estimator. The first is to use the same weighted outcome model as above along with the `{sandwich}` package. Using the `sandwich` function, we can get the robust estimate for the variance for the parameter of interest, as shown below.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(sandwich)\nweighted_mod <- lm(wait_minutes_posted_avg ~ park_extra_magic_morning, \n   data = seven_dwarfs_9_with_wt,\n   weights = w_att) \n\nsandwich(weighted_mod)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n                         (Intercept)\n(Intercept)                    1.488\npark_extra_magic_morning      -1.488\n                         park_extra_magic_morning\n(Intercept)                                -1.488\npark_extra_magic_morning                    8.727\n```\n\n\n:::\n:::\n\n\nHere, our robust variance estimate is 8.727. We can then use this to construct a robust confidence interval.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrobust_var <- sandwich(weighted_mod)[2, 2]\npoint_est <- coef(weighted_mod)[2]\nlb <- point_est - 1.96 * sqrt(robust_var)\nub <- point_est + 1.96 * sqrt(robust_var)\nlb\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\npark_extra_magic_morning \n                  0.4383 \n```\n\n\n:::\n\n```{.r .cell-code}\nub\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\npark_extra_magic_morning \n                   12.02 \n```\n\n\n:::\n:::\n\nWe estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 6.2 minutes, 95% CI (0.4, 12). \n\nAlternatively, we could fit the model using the `{survey}` package. To do this, we need to create a design object, like we did when fitting weighted tables.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(survey)\n\ndes <- svydesign(\n  ids = ~1,\n  weights = ~w_att,\n  data = seven_dwarfs_9_with_wt\n)\n```\n:::\n\n\nThen we can use `svyglm` to fit the outcome model.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsvyglm(wait_minutes_posted_avg ~ park_extra_magic_morning, des) |>\n  tidy(conf.int = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 7\n  term  estimate std.error statistic   p.value conf.low\n  <chr>    <dbl>     <dbl>     <dbl>     <dbl>    <dbl>\n1 (Int…    68.7       1.22     56.2  6.14e-178   66.3  \n2 park…     6.23      2.96      2.11 3.60e-  2    0.410\n# ℹ 1 more variable: conf.high <dbl>\n```\n\n\n:::\n:::\n\n### Sandwich estimator that takes into account the propensity score model\n\nThe correct sandwich estimator will also take into account the propensity score model. The `{PSW}` will allow us to do this. This package has some quirks, for example it doesn't work well with categorical variables, so we need to create dummy variables for `park_ticket_season` to pass into the model. *Actually, the code below isn't working because it seems there is a bug in the package. Stay tuned!*\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(PSW)\n\nseven_dwarfs_9 <- seven_dwarfs_9 |>\n  mutate(park_ticket_season_regular = ifelse(park_ticket_season == \"regular\", 1, 0),\n         park_ticket_season_value = ifelse(park_ticket_season == \"value\", 1, 0)\n  )\npsw(data = seven_dwarfs_9, \n    form.ps = \"park_extra_magic_morning ~ park_ticket_season_regular + park_ticket_season_value + park_close + park_temperature_high\",\n    weight = \"ATT\",\n    wt = TRUE,\n    out.var = \"wait_minutes_posted_avg\")\n```\n:::\n",
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-    "markdown": "# Fitting the outcome model {#sec-outcome-model}\n\n\n\n\n\n## Using matched data sets\n\nWhen fitting an outcome model on matched data sets, we can simply subset the original data to only those who were matched and then fit a model on these data as we would otherwise. For example, re-performing the matching as we did in @sec-using-ps, we can extract the matched observations in a dataset called `matched_data` as follows.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\nlibrary(touringplans)\nlibrary(MatchIt)\n\nseven_dwarfs_9 <- seven_dwarfs_train_2018 |> \n  filter(wait_hour == 9) \n\nm <- matchit(\n  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n  data = seven_dwarfs_9\n)\nmatched_data <- get_matches(m)\n```\n:::\n\n\nWe can then fit the outcome model on this data. For this analysis, we are interested in the impact of extra magic morning hours on the average posted wait time between 9 and 10am. The linear model below will estimate this in the matched cohort.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlm(wait_minutes_posted_avg ~ park_extra_magic_morning, data = matched_data) |>\n  tidy(conf.int = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 7\n  term   estimate std.error statistic  p.value conf.low\n  <chr>     <dbl>     <dbl>     <dbl>    <dbl>    <dbl>\n1 (Inte…    67.0       2.37     28.3  1.94e-54    62.3 \n2 park_…     7.87      3.35      2.35 2.04e- 2     1.24\n# ℹ 1 more variable: conf.high <dbl>\n```\n\n\n:::\n:::\n\n\nRecall that by default `{MatchIt}` estimates an average treatment effect among the treated. This means among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 7.9 minutes (95% CI: 1.2-14.5). \n\n## Using weights in outcome models\n\nNow let's use propensity score weights to estimate this same estimand. We will use the ATT weights so the analysis matches that for matching above.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(propensity)\n\nseven_dwarfs_9_with_ps <-\n  glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) |>\n  augment(type.predict = \"response\", data = seven_dwarfs_9)\nseven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>\n  mutate(w_att = wt_att(.fitted, park_extra_magic_morning))\n```\n:::\n\n\nWe can fit a *weighted* outcome model by using the `weights` argument. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nlm(wait_minutes_posted_avg ~ park_extra_magic_morning, \n   data = seven_dwarfs_9_with_wt,\n   weights = w_att) |>\n  tidy()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 5\n  term           estimate std.error statistic   p.value\n  <chr>             <dbl>     <dbl>     <dbl>     <dbl>\n1 (Intercept)       68.7       1.45     47.3  1.69e-154\n2 park_extra_ma…     6.23      2.05      3.03 2.62e-  3\n```\n\n\n:::\n:::\n\n\nUsing weighting, we estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 6.2 minutes. \nWhile this approach will get us the desired estimate for the point estimate, the default output using the `lm` function for the uncertainty (the standard errors and confidence intervals) are not correct.\n\n\n## Estimating uncertainty\n\nThere are three ways to estimate the uncertainty:\n\n1. A bootstrap\n2. A sandwich estimator that only takes into account the outcome model\n3. A sandwich estimator that takes into account the propensity score model\n\nThe first option can be computationally intensive, but should get you the correct estimates.\nThe second option is computationally the easiest, but tends to overestimate the variability. There are not many current solutions in R (aside from coding it up yourself) for the third; however, the `{PSW}` package will do this. \n\n### The bootstrap\n\n1. Create a function to run your analysis once on a sample of your data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfit_ipw <- function(split, ...) {\n  .df <- analysis(split)\n  \n  # fit propensity score model\n  propensity_model <- glm(\n    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,\n    data = seven_dwarfs_9,\n    family = binomial()\n  ) \n  \n  # calculate inverse probability weights\n  .df <- propensity_model |>\n    augment(type.predict = \"response\", data = .df) |>\n    mutate(wts = wt_att(.fitted, \n                        park_extra_magic_morning,\n                        exposure_type = \"binary\"))\n  \n  # fit correctly bootstrapped ipw model\n  lm(wait_minutes_posted_avg ~ park_extra_magic_morning, \n     data = .df, weights = wts) |>\n    tidy()\n}\n```\n:::\n\n\n\n2. Use {rsample} to bootstrap our causal effect\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rsample)\n\n# fit ipw model to bootstrapped samples\nbootstrapped_seven_dwarfs <- bootstraps(\n  seven_dwarfs_9, \n  times = 1000, \n  apparent = TRUE\n)\n\nipw_results <- bootstrapped_seven_dwarfs |> \n  mutate(boot_fits = map(splits, fit_ipw)) \n\nipw_results\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# Bootstrap sampling with apparent sample \n# A tibble: 1,001 × 3\n   splits            id            boot_fits       \n   <list>            <chr>         <list>          \n 1 <split [354/140]> Bootstrap0001 <tibble [2 × 5]>\n 2 <split [354/145]> Bootstrap0002 <tibble [2 × 5]>\n 3 <split [354/124]> Bootstrap0003 <tibble [2 × 5]>\n 4 <split [354/131]> Bootstrap0004 <tibble [2 × 5]>\n 5 <split [354/123]> Bootstrap0005 <tibble [2 × 5]>\n 6 <split [354/133]> Bootstrap0006 <tibble [2 × 5]>\n 7 <split [354/143]> Bootstrap0007 <tibble [2 × 5]>\n 8 <split [354/133]> Bootstrap0008 <tibble [2 × 5]>\n 9 <split [354/132]> Bootstrap0009 <tibble [2 × 5]>\n10 <split [354/128]> Bootstrap0010 <tibble [2 × 5]>\n# ℹ 991 more rows\n```\n\n\n:::\n:::\n\n\n\nLet's look at the results.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nipw_results |>\n  mutate(\n    estimate = map_dbl(\n      boot_fits,\n      \\(.fit) .fit |>\n        filter(term == \"park_extra_magic_morning\") |>\n        pull(estimate)\n    )\n  ) |>\n  ggplot(aes(estimate)) +\n  geom_histogram(bins = 30, fill = \"#D55E00FF\", color = \"white\", alpha = 0.8) + \n  theme_minimal()\n```\n\n::: {.cell-output-display}\n![](chapter-12_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\n\n3. Pull out the causal effect\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get t-based CIs\nboot_estimate <- int_t(ipw_results, boot_fits) |> \n  filter(term == \"park_extra_magic_morning\")\nboot_estimate\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 6\n  term           .lower .estimate .upper .alpha .method\n  <chr>           <dbl>     <dbl>  <dbl>  <dbl> <chr>  \n1 park_extra_ma… -0.317      6.93   11.9   0.05 studen…\n```\n\n\n:::\n:::\n\n\nWe estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 6.9 minutes, 95% CI (-0.3, 11.9). \n\n### The outcome model sandwich\n\nThere are two ways to get the sandwich estimator. The first is to use the same weighted outcome model as above along with the `{sandwich}` package. Using the `sandwich` function, we can get the robust estimate for the variance for the parameter of interest, as shown below.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(sandwich)\nweighted_mod <- lm(wait_minutes_posted_avg ~ park_extra_magic_morning, \n   data = seven_dwarfs_9_with_wt,\n   weights = w_att) \n\nsandwich(weighted_mod)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n                         (Intercept)\n(Intercept)                    1.488\npark_extra_magic_morning      -1.488\n                         park_extra_magic_morning\n(Intercept)                                -1.488\npark_extra_magic_morning                    8.727\n```\n\n\n:::\n:::\n\n\nHere, our robust variance estimate is 8.727. We can then use this to construct a robust confidence interval.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrobust_var <- sandwich(weighted_mod)[2, 2]\npoint_est <- coef(weighted_mod)[2]\nlb <- point_est - 1.96 * sqrt(robust_var)\nub <- point_est + 1.96 * sqrt(robust_var)\nlb\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\npark_extra_magic_morning \n                  0.4383 \n```\n\n\n:::\n\n```{.r .cell-code}\nub\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\npark_extra_magic_morning \n                   12.02 \n```\n\n\n:::\n:::\n\nWe estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 6.2 minutes, 95% CI (0.4, 12). \n\nAlternatively, we could fit the model using the `{survey}` package. To do this, we need to create a design object, like we did when fitting weighted tables.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(survey)\n\ndes <- svydesign(\n  ids = ~1,\n  weights = ~w_att,\n  data = seven_dwarfs_9_with_wt\n)\n```\n:::\n\n\nThen we can use `svyglm` to fit the outcome model.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsvyglm(wait_minutes_posted_avg ~ park_extra_magic_morning, des) |>\n  tidy(conf.int = TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 7\n  term  estimate std.error statistic   p.value conf.low\n  <chr>    <dbl>     <dbl>     <dbl>     <dbl>    <dbl>\n1 (Int…    68.7       1.22     56.2  6.14e-178   66.3  \n2 park…     6.23      2.96      2.11 3.60e-  2    0.410\n# ℹ 1 more variable: conf.high <dbl>\n```\n\n\n:::\n:::\n\n### Sandwich estimator that takes into account the propensity score model\n\nThe correct sandwich estimator will also take into account the propensity score model. The `{PSW}` will allow us to do this. This package has some quirks, for example it doesn't work well with categorical variables, so we need to create dummy variables for `park_ticket_season` to pass into the model. *Actually, the code below isn't working because it seems there is a bug in the package. Stay tuned!*\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(PSW)\n\nseven_dwarfs_9 <- seven_dwarfs_9 |>\n  mutate(park_ticket_season_regular = ifelse(park_ticket_season == \"regular\", 1, 0),\n         park_ticket_season_value = ifelse(park_ticket_season == \"value\", 1, 0)\n  )\npsw(data = seven_dwarfs_9, \n    form.ps = \"park_extra_magic_morning ~ park_ticket_season_regular + park_ticket_season_value + park_close + park_temperature_high\",\n    weight = \"ATT\",\n    wt = TRUE,\n    out.var = \"wait_minutes_posted_avg\")\n```\n:::\n",
+    "markdown": "# Continuous exposures\n\n\n\n\n\n## Calculating propensity scores for continuous exposures\n\nPropensity scores generalize to many other types of exposures, including continuous exposures.\nAt its heart, the workflow is the same: we fit a model where the exposure is the outcome and then use that model to weight a second outcome model.\nFor continuous exposures, linear regression is the simplest way to create propensities.\nInstead of probabilities, we use the cumulative density function.\nThen, we use this density to weight the outcome model.\n\nLet's take a look at an example.\nIn the `touringplans` data set, we have information about the posted waiting times for rides.\nWe also have a limited amount of data on the observed actual times.\nThe question we will consider is this: Do posted wait times for the Seven Dwarfs Mine Train at 8 am affect actual wait times at 9 am?\nHere's our DAG:\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Proposed DAG for the relationship between posted wait in the morning at a particular park and the average wait time between 5 pm and 6 pm.](chapter-12_files/figure-html/fig-dag-avg-wait-1.png){#fig-dag-avg-wait width=672}\n:::\n:::\n\n\nIn @fig-dag-avg-wait, we're assuming that our primary confounders are when the park closes, historic high temperatures, whether or not the ride has extra magic morning hours, and the ticket season.\nThis is the only minimal adjustment set in the DAG, as well.\nThe confounders precede the exposure and outcome, and (by definition) the exposure precedes the outcome.\nThe average posted wait time is, in theory, a manipulable exposure because the park could post a time different from what they expect.\nThe adjustment set\n\nThe model is similar to the binary exposure case, but we need to use linear regression, as the posted time is a continuous variable.\nSince we're not using probabilities, we'll calculate denominators for our weights from a normal density.\nWe then calculate the denominator using the `dnorm()` function, which calculates the normal density for the `exposure`, using `.fitted` as the mean and `mean(.sigma)` as the SD.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlm(\n  exposure ~ confounder_1 + confounder_2,\n  data = df\n) |>\n  augment(data = df) |>\n  mutate(\n    denominator = dnorm(exposure, .fitted, mean(.sigma, na.rm = TRUE))\n  )\n```\n:::\n\n\n## Diagnostics and stabilization\n\nContinuous exposure weights, however, are very sensitive to modeling choices.\nOne problem, in particular, is the existence of extreme weights, an issue that can also affect other types of exposures.\nWhen some observations have extreme weights, the propensities are *destabilized,* which results in wider confidence intervals.\nWe can stabilize them using the marginal distribution of the exposure.\nA common way to calculate the marginal distribution for propensity scores is to use a regression model with no predictors.\n\n::: callout-caution\nExtreme weights destabilize estimates, resulting in wider confidence intervals.\nExtreme weights can be an issue for any time of weight (including those for binary and other types of exposures) that is not bounded.\nBounded weights like the ATO (which are bounded to 0 and 1) do not have this problem, however---one of their many benefits.\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# for continuous exposures\nlm(\n  exposure ~ 1,\n  data = df\n) |>\n  augment(data = df) |>\n  transmute(\n    numerator = dnorm(exposure, .fitted, mean(.sigma, na.rm = TRUE))\n  )\n\n# for binary exposures\nglm(\n  exposure ~ 1,\n  data = df,\n  family = binomial()\n) |>\n  augment(type.predict = \"response\", data = df) |>\n  select(numerator = .fitted)\n```\n:::\n\n\nThen, rather than inverting them, we calculate the weights as `numerator / denominator`.\nLet's try it out on our posted wait times example.\nFirst, let's wrangle our data to address our question: do posted wait times at 8 affect actual weight times at 9?\nWe'll join the baseline data (all covariates and posted wait time at 8) with the outcome (average actual time).\nWe also have a lot of missingness for `wait_minutes_actual_avg`, so we'll drop unobserved values for now.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(touringplans)\neight <- seven_dwarfs_train_2018 |>\n  filter(wait_hour == 8) |>\n  select(-wait_minutes_actual_avg)\n\nnine <- seven_dwarfs_train_2018 |>\n  filter(wait_hour == 9) |>\n  select(park_date, wait_minutes_actual_avg)\n\nwait_times <- eight |>\n  left_join(nine, by = \"park_date\") |>\n  drop_na(wait_minutes_actual_avg)\n```\n:::\n\n\nFirst, let's calculate our denominator model.\nWe'll fit a model using `lm()` for `wait_minutes_posted_avg` with our covariates, then use the fitted predictions of `wait_minutes_posted_avg` (`.fitted`) to calculate the density using `dnorm()`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\ndenominator_model <- lm(\n  wait_minutes_posted_avg ~\n    park_close + park_extra_magic_morning + park_temperature_high + park_ticket_season,\n  data = wait_times\n)\n\ndenominators <- denominator_model |>\n  augment(data = wait_times) |>\n  mutate(\n    denominator = dnorm(\n      wait_minutes_posted_avg,\n      .fitted,\n      mean(.sigma, na.rm = TRUE)\n    )\n  ) |>\n  select(park_date, denominator, .fitted)\n```\n:::\n\n\nWhen we only use the inverted values of `denominator`, we end up with several extreme weights:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndenominators |>\n  mutate(wts = 1 / denominator) |>\n  ggplot(aes(wts)) +\n  geom_histogram(fill = \"#E69F00\", color = \"white\", bins = 50) +\n  scale_x_log10(name = \"weights\") \n```\n\n::: {.cell-output-display}\n![A histogram of the inverse probability weights for posted waiting time. Weights for continuous exposures are prone to extreme values, which can unstabilize estimates and variance.](chapter-12_files/figure-html/fig-hist-sd-unstable-1.png){#fig-hist-sd-unstable width=672}\n:::\n:::\n\n\nIn @fig-hist-sd-unstable, we see several weights over 100 and one over 10,000; these extreme weights will put undue stress on specific points, complicating the results we will estimate.\n\nLet's now fit the marginal density to use for stabilized weights:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnumerator_model <- lm(\n  wait_minutes_posted_avg ~ 1,\n  data = wait_times\n)\n\nnumerators <- numerator_model |>\n  augment(data = wait_times) |>\n  mutate(\n    numerator = dnorm(\n      wait_minutes_posted_avg,\n      .fitted,\n      mean(.sigma, na.rm = TRUE)\n    )\n  ) |>\n  select(park_date, numerator)\n```\n:::\n\n\nWe also need to join the fitted values back to our original data set by date, then calculate the stabilized weights (`swts`) using `numerator / denominator`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwait_times_wts <- wait_times |>\n  left_join(numerators, by = \"park_date\") |>\n  left_join(denominators, by = \"park_date\") |>\n  mutate(swts = numerator / denominator)\n```\n:::\n\n\nThe stabilized weights are much less extreme.\nStabilized weights should have a mean close to 1 (in this example, it is `round(mean(wait_times_wts$swts), digits = 2)`); when that is the case, then the pseudo-population (that is, the equivalent number of observations after weighting) is equal to the original sample size.\nIf the mean is far from 1, we may have issues with model misspecification or positivity violations [@hernan2021].\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(wait_times_wts, aes(swts)) +\n  geom_histogram(fill = \"#E69F00\", color = \"white\", bins = 50) +\n  scale_x_log10(name = \"weights\") \n```\n\n::: {.cell-output-display}\n![A histogram of the stabilized inverse probability weights for posted waiting time. These weights are much more reasonable and will allow the outcome model to behave better.](chapter-12_files/figure-html/fig-hist-sd-stable-1.png){#fig-hist-sd-stable width=672}\n:::\n:::\n\n\nWhen we compare the exposure---average posted wait times---to the standardized weights, we still have one exceptionally high weight.\nIs this a problem, or is this a valid data point?\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(wait_times_wts, aes(wait_minutes_posted_avg, swts)) +\n  geom_point(size = 3, color = \"grey80\", alpha = 0.7) +\n  geom_point(\n    data = function(x) filter(x, swts > 10),\n    color = \"firebrick\",\n    size = 3\n  ) +\n  geom_text(\n    data = function(x) filter(x, swts > 10),\n    aes(label = park_date),\n    size = 5,\n    hjust = 0,\n    nudge_x = -15.5,\n    color = \"firebrick\"\n  ) +\n  scale_y_log10() +\n  labs(x = \"Average Posted Wait\", y = \"Stabilized Weights\")\n```\n\n::: {.cell-output-display}\n![A scatter of the stabilized inverse probability weights for posted waiting time vs. posted waiting times. Days with more values of `wait_minutes_posted_avg` farther from the mean appear to be downweighted, with a few exceptions. The most unusual weight is for June 23, 2018.](chapter-12_files/figure-html/fig-stabilized-wts-scatter-1.png){#fig-stabilized-wts-scatter width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nwait_times_wts |> \n  filter(swts > 10) |> \n  select(\n    park_date, wait_minutes_posted_avg, .fitted, \n    park_close, park_extra_magic_morning, \n    park_temperature_high, park_ticket_season\n  ) |>\n  knitr::kable()\n```\n\n::: {.cell-output-display}\n\n\n|park_date  | wait_minutes_posted_avg| .fitted|park_close | park_extra_magic_morning| park_temperature_high|park_ticket_season |\n|:----------|-----------------------:|-------:|:----------|------------------------:|---------------------:|:------------------|\n|2018-06-23 |                      81|    28.1|24:00:00   |                        0|                 91.36|regular            |\n\n\n:::\n:::\n\n\nOur model predicted a much lower posted wait time than observed, so this date was upweighted.\nWe don't know why the posted time was so high (the actual time was much lower), but we did find an artist rendering from that date of [Pluto digging for Seven Dwarfs Mine Train treasure](https://disneyparks.disney.go.com/blog/2018/06/disney-doodle-pluto-sniffs-out-fun-at-seven-dwarfs-mine-train/).\n\n## Fitting the outcome model for continuous exposures\n",
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-    "markdown": "# Continuous exposures\n\n\n\n\n\n## Calculating propensity scores for continuous exposures\n\nPropensity scores generalize to many other types of exposures, including continuous exposures.\nAt its heart, the workflow is the same: we fit a model where the exposure is the outcome and then use that model to weight a second outcome model.\nFor continuous exposures, linear regression is the simplest way to create propensities.\nInstead of probabilities, we use the cumulative density function.\nThen, we use this density to weight the outcome model.\n\nLet's take a look at an example.\nIn the `touringplans` data set, we have information about the posted waiting times for rides.\nWe also have a limited amount of data on the observed actual times.\nThe question we will consider is this: Do posted wait times for the Seven Dwarfs Mine Train at 8 am affect actual wait times at 9 am?\nHere's our DAG:\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Proposed DAG for the relationship between posted wait in the morning at a particular park and the average wait time between 5 pm and 6 pm.](chapter-13_files/figure-html/fig-dag-avg-wait-1.png){#fig-dag-avg-wait width=672}\n:::\n:::\n\n\nIn @fig-dag-avg-wait, we're assuming that our primary confounders are when the park closes, historic high temperatures, whether or not the ride has extra magic morning hours, and the ticket season.\nThis is the only minimal adjustment set in the DAG, as well.\nThe confounders precede the exposure and outcome, and (by definition) the exposure precedes the outcome.\nThe average posted wait time is, in theory, a manipulable exposure because the park could post a time different from what they expect.\nThe adjustment set\n\nThe model is similar to the binary exposure case, but we need to use linear regression, as the posted time is a continuous variable.\nSince we're not using probabilities, we'll calculate denominators for our weights from a normal density.\nWe then calculate the denominator using the `dnorm()` function, which calculates the normal density for the `exposure`, using `.fitted` as the mean and `mean(.sigma)` as the SD.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlm(\n  exposure ~ confounder_1 + confounder_2,\n  data = df\n) |>\n  augment(data = df) |>\n  mutate(\n    denominator = dnorm(exposure, .fitted, mean(.sigma, na.rm = TRUE))\n  )\n```\n:::\n\n\n## Diagnostics and stabilization\n\nContinuous exposure weights, however, are very sensitive to modeling choices.\nOne problem, in particular, is the existence of extreme weights, an issue that can also affect other types of exposures.\nWhen some observations have extreme weights, the propensities are *destabilized,* which results in wider confidence intervals.\nWe can stabilize them using the marginal distribution of the exposure.\nA common way to calculate the marginal distribution for propensity scores is to use a regression model with no predictors.\n\n::: callout-caution\nExtreme weights destabilize estimates, resulting in wider confidence intervals.\nExtreme weights can be an issue for any time of weight (including those for binary and other types of exposures) that is not bounded.\nBounded weights like the ATO (which are bounded to 0 and 1) do not have this problem, however---one of their many benefits.\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# for continuous exposures\nlm(\n  exposure ~ 1,\n  data = df\n) |>\n  augment(data = df) |>\n  transmute(\n    numerator = dnorm(exposure, .fitted, mean(.sigma, na.rm = TRUE))\n  )\n\n# for binary exposures\nglm(\n  exposure ~ 1,\n  data = df,\n  family = binomial()\n) |>\n  augment(type.predict = \"response\", data = df) |>\n  select(numerator = .fitted)\n```\n:::\n\n\nThen, rather than inverting them, we calculate the weights as `numerator / denominator`.\nLet's try it out on our posted wait times example.\nFirst, let's wrangle our data to address our question: do posted wait times at 8 affect actual weight times at 9?\nWe'll join the baseline data (all covariates and posted wait time at 8) with the outcome (average actual time).\nWe also have a lot of missingness for `wait_minutes_actual_avg`, so we'll drop unobserved values for now.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(touringplans)\neight <- seven_dwarfs_train_2018 |>\n  filter(wait_hour == 8) |>\n  select(-wait_minutes_actual_avg)\n\nnine <- seven_dwarfs_train_2018 |>\n  filter(wait_hour == 9) |>\n  select(park_date, wait_minutes_actual_avg)\n\nwait_times <- eight |>\n  left_join(nine, by = \"park_date\") |>\n  drop_na(wait_minutes_actual_avg)\n```\n:::\n\n\nFirst, let's calculate our denominator model.\nWe'll fit a model using `lm()` for `wait_minutes_posted_avg` with our covariates, then use the fitted predictions of `wait_minutes_posted_avg` (`.fitted`) to calculate the density using `dnorm()`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(broom)\ndenominator_model <- lm(\n  wait_minutes_posted_avg ~\n    park_close + park_extra_magic_morning + park_temperature_high + park_ticket_season,\n  data = wait_times\n)\n\ndenominators <- denominator_model |>\n  augment(data = wait_times) |>\n  mutate(\n    denominator = dnorm(\n      wait_minutes_posted_avg,\n      .fitted,\n      mean(.sigma, na.rm = TRUE)\n    )\n  ) |>\n  select(park_date, denominator, .fitted)\n```\n:::\n\n\nWhen we only use the inverted values of `denominator`, we end up with several extreme weights:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndenominators |>\n  mutate(wts = 1 / denominator) |>\n  ggplot(aes(wts)) +\n  geom_histogram(fill = \"#E69F00\", color = \"white\", bins = 50) +\n  scale_x_log10(name = \"weights\")\n```\n\n::: {.cell-output-display}\n![A histogram of the inverse probability weights for posted waiting time. Weights for continuous exposures are prone to extreme values, which can unstabilize estimates and variance.](chapter-13_files/figure-html/fig-hist-sd-unstable-1.png){#fig-hist-sd-unstable width=672}\n:::\n:::\n\n\nIn @fig-hist-sd-unstable, we see several weights over 100 and one over 10,000; these extreme weights will put undue stress on specific points, complicating the results we will estimate.\n\nLet's now fit the marginal density to use for stabilized weights:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnumerator_model <- lm(\n  wait_minutes_posted_avg ~ 1,\n  data = wait_times\n)\n\nnumerators <- numerator_model |>\n  augment(data = wait_times) |>\n  mutate(\n    numerator = dnorm(\n      wait_minutes_posted_avg,\n      .fitted,\n      mean(.sigma, na.rm = TRUE)\n    )\n  ) |>\n  select(park_date, numerator)\n```\n:::\n\n\nWe also need to join the fitted values back to our original data set by date, then calculate the stabilized weights (`swts`) using `numerator / denominator`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwait_times_wts <- wait_times |>\n  left_join(numerators, by = \"park_date\") |>\n  left_join(denominators, by = \"park_date\") |>\n  mutate(swts = numerator / denominator)\n```\n:::\n\n\nThe stabilized weights are much less extreme.\nStabilized weights should have a mean close to 1 (in this example, it is `round(mean(wait_times_wts$swts), digits = 2)`); when that is the case, then the pseudo-population (that is, the equivalent number of observations after weighting) is equal to the original sample size.\nIf the mean is far from 1, we may have issues with model misspecification or positivity violations [@hernan2021].\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(wait_times_wts, aes(swts)) +\n  geom_histogram(fill = \"#E69F00\", color = \"white\", bins = 50) +\n  scale_x_log10(name = \"weights\")\n```\n\n::: {.cell-output-display}\n![A histogram of the stabilized inverse probability weights for posted waiting time. These weights are much more reasonable and will allow the outcome model to behave better.](chapter-13_files/figure-html/fig-hist-sd-stable-1.png){#fig-hist-sd-stable width=672}\n:::\n:::\n\n\nWhen we compare the exposure---average posted wait times---to the standardized weights, we still have one exceptionally high weight.\nIs this a problem, or is this a valid data point?\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(wait_times_wts, aes(wait_minutes_posted_avg, swts)) +\n  geom_point(size = 3, color = \"grey80\", alpha = 0.7) +\n  geom_point(\n    data = function(x) filter(x, swts > 10),\n    color = \"firebrick\",\n    size = 3\n  ) +\n  geom_text(\n    data = function(x) filter(x, swts > 10),\n    aes(label = park_date),\n    size = 5,\n    hjust = 0,\n    nudge_x = -15.5,\n    color = \"firebrick\"\n  ) +\n  scale_y_log10() +\n  labs(x = \"Average Posted Wait\", y = \"Stabilized Weights\")\n```\n\n::: {.cell-output-display}\n![A scatter of the stabilized inverse probability weights for posted waiting time vs. posted waiting times. Days with more values of `wait_minutes_posted_avg` farther from the mean appear to be downweighted, with a few exceptions. The most unusual weight is for June 23, 2018.](chapter-13_files/figure-html/fig-stabilized-wts-scatter-1.png){#fig-stabilized-wts-scatter width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nwait_times_wts |>\n  filter(swts > 10) |>\n  select(\n    park_date, wait_minutes_posted_avg, .fitted,\n    park_close, park_extra_magic_morning,\n    park_temperature_high, park_ticket_season\n  ) |>\n  knitr::kable()\n```\n\n::: {.cell-output-display}\n\n\n|park_date  | wait_minutes_posted_avg| .fitted|park_close | park_extra_magic_morning| park_temperature_high|park_ticket_season |\n|:----------|-----------------------:|-------:|:----------|------------------------:|---------------------:|:------------------|\n|2018-06-23 |                      81|    28.1|24:00:00   |                        0|                 91.36|regular            |\n\n\n:::\n:::\n\n\nOur model predicted a much lower posted wait time than observed, so this date was upweighted.\nWe don't know why the posted time was so high (the actual time was much lower), but we did find an artist rendering from that date of [Pluto digging for Seven Dwarfs Mine Train treasure](https://disneyparks.disney.go.com/blog/2018/06/disney-doodle-pluto-sniffs-out-fun-at-seven-dwarfs-mine-train/).\n\n## Fitting the outcome model for continuous exposures\n",
-    "supporting": [
-      "chapter-13_files/figure-html"
-    ],
+    "markdown": "# Categorical exposures\n\n\n\n\n\n## Calculating propensity scores for categorical exposures\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -0.6711  0.9282  0.9044 -0.1768  1.4206\n```\n\n\n:::\n:::\n\n\n## Diagnostics with many categories\n\n## Fitting the outcome model again\n",
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     "filters": [
       "rmarkdown/pagebreak.lua"
     ],
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-    "markdown": "# Categorical exposures\n\n\n\n\n\n## Calculating propensity scores for categorical exposures\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1]  0.55045 -0.09999  1.27700 -0.90324 -0.65693\n```\n\n\n:::\n:::\n\n\n## Diagnostics with many categories\n\n## Fitting the outcome model again\n",
-    "supporting": [],
+    "markdown": "# Missingness\n\n\n\n\n\n## Missingness as structural bias\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -0.5199 -1.1160  1.4866  1.0149 -1.4796\n```\n\n\n:::\n:::\n\n\n## Multiple Imputation\n\n## Combining MICE and IPW\n",
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+    ],
     "filters": [
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diff --git a/_freeze/chapters/chapter-15/execute-results/html.json b/_freeze/chapters/chapter-15/execute-results/html.json
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@@ -1,8 +1,10 @@
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-    "markdown": "# G-Computation {#sec-g-comp}\n\n\n\n\n\n## The Parametric G-Formula\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -1.4226 -1.1423  1.3277  2.0461  0.3256\n```\n\n\n:::\n:::\n\n\n## Calculating estimates with G-Computation\n\n## The Natural Course\n",
-    "supporting": [],
+    "markdown": "# G-Computation {#sec-g-comp}\n\n\n\n\n\n## The Parametric G-Formula\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1]  0.5788 -1.2622 -0.9627 -0.9578  0.9935\n```\n\n\n:::\n:::\n\n\n## Calculating estimates with G-Computation\n\n## The Natural Course\n",
+    "supporting": [
+      "chapter-15_files"
+    ],
     "filters": [
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diff --git a/_freeze/chapters/chapter-16/execute-results/html.json b/_freeze/chapters/chapter-16/execute-results/html.json
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--- a/_freeze/chapters/chapter-16/execute-results/html.json
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-    "markdown": "# Interaction {#sec-interaction}\n\n\n\n\n\n## Functional form, hetereogenous effects, and joint causes\n\n## Fitting interaction terms in causal models\n\n<!-- Maybe also: -->\n<!-- ## type ii selection bias, induced by EM [@lu2022] -->",
-    "supporting": [],
+    "markdown": "# Causal survival analysis\n\n\n\n\n\n## Preparing data for survival analysis\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -0.1047 -0.7075 -0.4694  1.2174 -1.6054\n```\n\n\n:::\n:::\n\n\n## Pooled logistic regression\n\n## Confidence intervals for causal survival models\n",
+    "supporting": [
+      "chapter-16_files"
+    ],
     "filters": [
       "rmarkdown/pagebreak.lua"
     ],
diff --git a/_freeze/chapters/chapter-17/execute-results/html.json b/_freeze/chapters/chapter-17/execute-results/html.json
index bca9a7d..e3026b1 100644
--- a/_freeze/chapters/chapter-17/execute-results/html.json
+++ b/_freeze/chapters/chapter-17/execute-results/html.json
@@ -1,8 +1,10 @@
 {
-  "hash": "ddc7311c8eb85246136331989d1ca532",
+  "hash": "4295ad1482e0a56a886c07d3b85f2c3b",
   "result": {
-    "markdown": "# Missingness {#sec-missingness}\n\n\n\n\n\n## Missingness as structural bias\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1]  2.6365  1.8457 -0.4574  0.1535  1.1835\n```\n\n\n:::\n:::\n\n\n## Multiple Imputation\n\n## Combining MICE and IPW\n",
-    "supporting": [],
+    "markdown": "# Causal mediation analysis\n\n\n\n\n\n## Estimating direct effects\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 1.64179 1.65583 0.22422 0.02398 0.86649\n```\n\n\n:::\n:::\n\n\n## Estimating indirect effects\n\n## Allowing for interactions\n",
+    "supporting": [
+      "chapter-17_files"
+    ],
     "filters": [
       "rmarkdown/pagebreak.lua"
     ],
diff --git a/_freeze/chapters/chapter-18/execute-results/html.json b/_freeze/chapters/chapter-18/execute-results/html.json
index dee376e..9cf2374 100644
--- a/_freeze/chapters/chapter-18/execute-results/html.json
+++ b/_freeze/chapters/chapter-18/execute-results/html.json
@@ -1,8 +1,10 @@
 {
-  "hash": "b9dc2626f7c608a76f7c7caba0addd09",
+  "hash": "f1d3d81eeb5540167668bf71aebc6749",
   "result": {
-    "markdown": "# Causal inference across time {#sec-longitudinal}\n\n\n\n\n\n## Loss to follow-up\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -0.3767 -0.6036 -0.6856  0.2084 -0.2844\n```\n\n\n:::\n:::\n\n\n## Time-varying confounding and exposures\n\n",
-    "supporting": [],
+    "markdown": "# Sensitivity analysis\n\n## Quantitative bias analyses\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -0.6136 -0.3190  0.7672  0.3058  0.4258\n```\n\n\n:::\n:::\n\n\n## Tipping point analyses\n",
+    "supporting": [
+      "chapter-18_files"
+    ],
     "filters": [
       "rmarkdown/pagebreak.lua"
     ],
diff --git a/_freeze/chapters/chapter-19/execute-results/html.json b/_freeze/chapters/chapter-19/execute-results/html.json
index 51757db..bd67c52 100644
--- a/_freeze/chapters/chapter-19/execute-results/html.json
+++ b/_freeze/chapters/chapter-19/execute-results/html.json
@@ -1,8 +1,10 @@
 {
-  "hash": "b054d57b42451b94833be81a7144a2c7",
+  "hash": "e048fceeeb7a0c5e7ada95e14bb9e9e4",
   "result": {
-    "markdown": "# Causal survival analysis {#sec-survival}\n\n\n\n\n\n## Preparing data for survival analysis\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1]  0.5082 -0.3546 -0.2759  0.2368  0.1820\n```\n\n\n:::\n:::\n\n\n## Pooled logistic regression\n\n## Confidence intervals for causal survival models\n",
-    "supporting": [],
+    "markdown": "# Other approaches to causal inference\n\n\n\n\n\n## Targeted Learning\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1]  1.0362  2.1558 -0.2929  0.8677  0.2885\n```\n\n\n:::\n:::\n\n\n## Instrumental variable analysis\n\n## Regression discontinuity\n\n## Difference-in-Difference\n",
+    "supporting": [
+      "chapter-19_files"
+    ],
     "filters": [
       "rmarkdown/pagebreak.lua"
     ],
diff --git a/_freeze/chapters/chapter-20/execute-results/html.json b/_freeze/chapters/chapter-20/execute-results/html.json
index a673f5e..a85cc1d 100644
--- a/_freeze/chapters/chapter-20/execute-results/html.json
+++ b/_freeze/chapters/chapter-20/execute-results/html.json
@@ -1,7 +1,7 @@
 {
-  "hash": "35e28ba55bdcd7229ce521d2f04664ff",
+  "hash": "e048fceeeb7a0c5e7ada95e14bb9e9e4",
   "result": {
-    "markdown": "# Causal mediation analysis {#sec-mediation}\n\n\n\n\n\n## Estimating direct effects\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -0.65327  1.14621 -1.26733 -0.25579  0.04294\n```\n\n\n:::\n:::\n\n\n## Estimating indirect effects\n\n## Allowing for interactions\n",
+    "markdown": "# Other approaches to causal inference\n\n\n\n\n\n## Targeted Learning\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.9632 1.2420 0.6826 1.3024 0.6905\n```\n:::\n:::\n\n\n## Instrumental variable analysis\n\n## Regression discontinuity\n\n## Difference-in-Difference\n",
     "supporting": [],
     "filters": [
       "rmarkdown/pagebreak.lua"
diff --git a/_freeze/chapters/chapter-21/execute-results/html.json b/_freeze/chapters/chapter-21/execute-results/html.json
deleted file mode 100644
index 6be21ba..0000000
--- a/_freeze/chapters/chapter-21/execute-results/html.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
-  "hash": "36e848491cb5cef73186bb49bc886756",
-  "result": {
-    "markdown": "# Sensitivity analysis  {#sec-sensitivity}\n\n## Quantitative bias analyses\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1]  0.1561  0.1837  1.1757 -1.4884  1.2839\n```\n\n\n:::\n:::\n\n\n## Tipping point analyses\n",
-    "supporting": [],
-    "filters": [
-      "rmarkdown/pagebreak.lua"
-    ],
-    "includes": {},
-    "engineDependencies": {},
-    "preserve": {},
-    "postProcess": true
-  }
-}
\ No newline at end of file
diff --git a/_freeze/chapters/chapter-22/execute-results/html.json b/_freeze/chapters/chapter-22/execute-results/html.json
deleted file mode 100644
index 841272d..0000000
--- a/_freeze/chapters/chapter-22/execute-results/html.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
-  "hash": "694f0dd77bdbaa14770402cc10478f68",
-  "result": {
-    "markdown": "# Machine Learning and Causal Inference {#sec-causal-ml}\n\n\n\n\n\n## Prediction and causal inference, again\n\n## Augmented propensity scores\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1]  0.16530  0.13763  0.00311  0.86606 -0.01228\n```\n\n\n:::\n:::\n\n\n## Targeted Learning\n\n\n",
-    "supporting": [],
-    "filters": [
-      "rmarkdown/pagebreak.lua"
-    ],
-    "includes": {},
-    "engineDependencies": {},
-    "preserve": {},
-    "postProcess": true
-  }
-}
\ No newline at end of file
diff --git a/_freeze/chapters/chapter-23/execute-results/html.json b/_freeze/chapters/chapter-23/execute-results/html.json
deleted file mode 100644
index a6f3f4d..0000000
--- a/_freeze/chapters/chapter-23/execute-results/html.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
-  "hash": "0c7ac0b35b32d9ca07d6e09c8886a3c1",
-  "result": {
-    "markdown": "# Instrumental variables and friends {#sec-iv-friends}\n\n\n\n\n\n## Instrumental variable analysis\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -1.9767 -0.1417  1.6082  2.1477  1.0221\n```\n\n\n:::\n:::\n\n\n## Regression discontinuity\n\n## Difference-in-Difference\n",
-    "supporting": [],
-    "filters": [
-      "rmarkdown/pagebreak.lua"
-    ],
-    "includes": {},
-    "engineDependencies": {},
-    "preserve": {},
-    "postProcess": true
-  }
-}
\ No newline at end of file
diff --git a/_freeze/chapters/chapter-24/execute-results/html.json b/_freeze/chapters/chapter-24/execute-results/html.json
deleted file mode 100644
index 3991bf0..0000000
--- a/_freeze/chapters/chapter-24/execute-results/html.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
-  "hash": "715b7b713ca756bc9a8fd6f63b3a03e1",
-  "result": {
-    "markdown": "# Evidence {#sec-evidence}\n\n\n\n\n\n## Triangulation\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] -0.7151 -0.8504  0.7125 -0.6134 -0.3643\n```\n\n\n:::\n:::\n\n\n## Generalization and transportability\n\n## Ragged evidence\n",
-    "supporting": [],
-    "filters": [
-      "rmarkdown/pagebreak.lua"
-    ],
-    "includes": {},
-    "engineDependencies": {},
-    "preserve": {},
-    "postProcess": true
-  }
-}
\ No newline at end of file
diff --git a/_freeze/index/execute-results/html.json b/_freeze/index/execute-results/html.json
index a4695ac..2543401 100644
--- a/_freeze/index/execute-results/html.json
+++ b/_freeze/index/execute-results/html.json
@@ -1,8 +1,10 @@
 {
-  "hash": "ef3157b3e2d1d97cd016993a6b9fcb07",
+  "hash": "7931c030f9ba3140960cc93b05a2a00f",
   "result": {
-    "markdown": "# Preface {.unnumbered}\n\nWelcome to *Causal Inference in R*.\nAnswering causal questions is critical for scientific and business purposes, but techniques like randomized clinical trials and A/B testing are not always practical or successful.\nThe tools in this book will allow readers better make causal inferences with  observational data with the R programming language.\nBy its end, we hope to help you:\n\n1.  Ask better causal questions.\n2.  Understand the assumptions needed for causal inference\n3.  Identify the target population for which you want to make inferences\n4.  Fit causal models and check their problems\n5.  Conduct sensitivity analyses where the techniques we use might be imperfect\n\nThis book is for both academic researchers and data scientists.\nAlthough the questions may differ between these settings, many techniques are the same: causal inference is as helpful for asking questions about cancer as it is about clicks.\nWe use a mix of examples from medicine, economics, and tech to demonstrate that you need a clear causal question and a willingness to be transparent about your assumptions.\n\nYou'll learn a lot in this book, but ironically, you won't learn much about conducting randomized trials, one of the best tools for causal inferences.\nRandomized trials, and their cousins, A/B tests (standard in the tech world), are compelling because they alleviate many of the assumptions we need to make for valid inferences.\nThey are also sufficiently complex in design to merit their own learning resources.\nInstead, we'll focus on observational data where we don't usually benefit from randomization.\nIf you're interested in randomization techniques, don't put away this resource just yet: many causal inference techniques designed for observational data improve randomized analyses, too.\n\nWe're making a few assumptions about you as a reader:\n\n1.  You're familiar with the [tidyverse](https://www.tidyverse.org/) ecosystem of R packages and their general philosophy. For instance, we use a lot of dplyr and ggplot2 in this book, but we won't explain their basic grammar. To learn more about starting with the tidyverse, we recommend [R for Data Science](https://r4ds.hadley.nz/).\n2.  You're familiar with basic statistical modeling in R. For instance, we'll fit many models with `lm()` and `glm()`, but we won't discuss how they work. If you want to learn more about R's powerful modeling functions, we recommend reading [\"A Review of R Modeling Fundamentals\"](https://www.tmwr.org/base-r.html) in [Tidy Modeling with R](https://www.tmwr.org).\n3.  We also assume you have familiarity with other R basics, such as [writing functions](https://r4ds.hadley.nz/functions.html). [R for Data Science](https://r4ds.hadley.nz/) is also a good resource for these topics. (For a deeper dive into the R programming language, we recommend [Advanced R](https://adv-r.hadley.nz/index.html), although we don't assume you have mastered its material for this book).\n\nWe'll also use tools from the tidymodels ecosystem, a set of R packages for modeling related to the tidyverse.\nWe don't assume you have used them before.\ntidymodels also focuses on predictive modeling, so many of its tools aren't appropriate for this book.\nNevertheless, if you are interested in this topic, we recommend [Tidy Modeling with R](https://www.tmwr.org).\n\nThere are also several other excellent books on causal inference.\nThis book is different in its focus on R, but it's still helpful to see this area from other perspectives.\nA few books you might like:\n\n-   [*Causal Inference: What If?*](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)\n-   [*Causal Inference: The Mixtape*](https://mixtape.scunning.com/)\n-   [*The Effect*](https://theeffectbook.net/)\n\nThe first book is focused on epidemiology.\nThe latter two are focused on econometrics.\nWe also recommend *The Book of Why* @pearl2018why for more on causal diagrams.\n\n## Conventions\n\n### Modern R Features\n\nWe use two modern R features in R 4.1.0 and above in this book.\nThe first is the native pipe, `|>`.\nThis R feature is similar to the tidyverse's `%>%`, with which you may be more familiar.\nIn typical cases, the two work interchangeably.\nOne notable difference is that `|>` uses the `_` symbol to direct the pipe's results, e.g., `.df |> lm(y ~ x, data = _)`.\nSee [this Tidyverse Blog post](https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/) for more on this topic.\n\nAnother modern R feature we use is the native lambda, a way of writing short functions that looks like `\\(.x) do_something(.x)`.\nIt is similar to purrr's `~` lambda notation.\nIt's also helpful to realize the native lambda is identical to `function(.x) do_something(.x)`, where `\\` is shorthand for `function`.\nSee [R for Data Science's chapter on iteration](https://r4ds.hadley.nz/iteration.html) for more on this topic.\n\n## Theming\n\nThe plots in this book use a consistent theme that we don't include in every code chunk, meaning if you run the code for a visualization, you might get a slightly different-looking result.\nWe set the following defaults related to ggplot2:\n\n<!-- TODO: make sure these are up to date -->\n\n\n::: {.cell}\n\n```{.r .cell-code}\noptions(\n  # set default colors in ggplot2 to colorblind-friendly\n  # Okabe-Ito and Viridis palettes\n  ggplot2.discrete.colour = ggokabeito::palette_okabe_ito(),\n  ggplot2.discrete.fill = ggokabeito::palette_okabe_ito(),\n  ggplot2.continuous.colour = \"viridis\",\n  ggplot2.continuous.fill = \"viridis\",\n  # set theme font and size\n  book.base_family = \"sans\",\n  book.base_size = 14\n)\n\nlibrary(ggplot2)\n\n# set default theme\ntheme_set(\n  theme_minimal(\n    base_size = getOption(\"book.base_size\"),\n    base_family = getOption(\"book.base_family\")\n  ) %+replace%\n    theme(\n      panel.grid.minor = element_blank(),\n      legend.position = \"bottom\"\n    )\n)\n```\n:::\n\n\nWe also mask a few functions from ggdag that we like to customize:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntheme_dag <- function() {\n  ggdag::theme_dag(base_family = getOption(\"book.base_family\"))\n}\n\ngeom_dag_label_repel <- function(..., seed = 10) {\n  ggdag::geom_dag_label_repel(\n    aes(x, y, label = label),\n    box.padding = 3.5,\n    inherit.aes = FALSE,\n    max.overlaps = Inf,\n    family = getOption(\"book.base_family\"),\n    seed = seed,\n    label.size = NA,\n    label.padding = 0.1,\n    size = getOption(\"book.base_size\") / 3,\n    ...\n  )\n}\n```\n:::\n",
-    "supporting": [],
+    "markdown": "# Preface {.unnumbered}\n\nWelcome to *Causal Inference in R*.\nAnswering causal questions is critical for scientific and business purposes, but techniques like randomized clinical trials and A/B testing are not always practical or successful.\nThe tools in this book will allow readers better make causal inferences with  observational data with the R programming language.\nBy its end, we hope to help you:\n\n1.  Ask better causal questions.\n2.  Understand the assumptions needed for causal inference\n3.  Identify the target population for which you want to make inferences\n4.  Fit causal models and check their problems\n5.  Conduct sensitivity analyses where the techniques we use might be imperfect\n\nThis book is for both academic researchers and data scientists.\nAlthough the questions may differ between these settings, many techniques are the same: causal inference is as helpful for asking questions about cancer as it is about clicks.\nWe use a mix of examples from medicine, economics, and tech to demonstrate that you need a clear causal question and a willingness to be transparent about your assumptions.\n\nYou'll learn a lot in this book, but ironically, you won't learn much about conducting randomized trials, one of the best tools for causal inferences.\nRandomized trials, and their cousins, A/B tests (standard in the tech world), are compelling because they alleviate many of the assumptions we need to make for valid inferences.\nThey are also sufficiently complex in design to merit their own learning resources.\nInstead, we'll focus on observational data where we don't usually benefit from randomization.\nIf you're interested in randomization techniques, don't put away this resource just yet: many causal inference techniques designed for observational data improve randomized analyses, too.\n\nWe're making a few assumptions about you as a reader:\n\n1.  You're familiar with the [tidyverse](https://www.tidyverse.org/) ecosystem of R packages and their general philosophy. For instance, we use a lot of dplyr and ggplot2 in this book, but we won't explain their basic grammar. To learn more about starting with the tidyverse, we recommend [R for Data Science](https://r4ds.hadley.nz/).\n2.  You're familiar with basic statistical modeling in R. For instance, we'll fit many models with `lm()` and `glm()`, but we won't discuss how they work. If you want to learn more about R's powerful modeling functions, we recommend reading [\"A Review of R Modeling Fundamentals\"](https://www.tmwr.org/base-r.html) in [Tidy Modeling with R](https://www.tmwr.org).\n3.  We also assume you have familiarity with other R basics, such as [writing functions](https://r4ds.hadley.nz/functions.html). [R for Data Science](https://r4ds.hadley.nz/) is also a good resource for these topics. (For a deeper dive into the R programming language, we recommend [Advanced R](https://adv-r.hadley.nz/index.html), although we don't assume you have mastered its material for this book).\n\nWe'll also use tools from the tidymodels ecosystem, a set of R packages for modeling related to the tidyverse.\nWe don't assume you have used them before.\ntidymodels also focuses on predictive modeling, so many of its tools aren't appropriate for this book.\nNevertheless, if you are interested in this topic, we recommend [Tidy Modeling with R](https://www.tmwr.org).\n\nThere are also several other excellent books on causal inference.\nThis book is different in its focus on R, but it's still helpful to see this area from other perspectives.\nA few books you might like:\n\n-   [*Causal Inference: What If?*](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)\n-   [*Causal Inference: The Mixtape*](https://mixtape.scunning.com/)\n-   [*The Effect*](https://theeffectbook.net/)\n\nThe first book is focused on epidemiology.\nThe latter two are focused on econometrics.\nWe also recommend *The Book of Why* @pearl2018why for more on causal diagrams.\n\n## Conventions\n\n### Modern R Features\n\nWe use two modern R features in R 4.1.0 and above in this book.\nThe first is the native pipe, `|>`.\nThis R feature is similar to the tidyverse's `%>%`, with which you may be more familiar.\nIn typical cases, the two work interchangeably.\nOne notable difference is that `|>` uses the `_` symbol to direct the pipe's results, e.g., `.df |> lm(y ~ x, data = _)`.\nSee [this Tidyverse Blog post](https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/) for more on this topic.\n\nAnother modern R feature we use is the native lambda, a way of writing short functions that looks like `\\(.x) do_something(.x)`.\nIt is similar to purrr's `~` lambda notation.\nIt's also helpful to realize the native lambda is identical to `function(.x) do_something(.x)`, where `\\` is shorthand for `function`.\nSee [R for Data Science's chapter on iteration](https://r4ds.hadley.nz/iteration.html) for more on this topic.\n\n## Theming\n\nThe plots in this book use a consistent theme that we don't include in every code chunk, meaning if you run the code for a visualization, you might get a slightly different-looking result.\nWe set the following defaults related to ggplot2:\n\n<!-- TODO: make sure these are up to date -->\n\n\n::: {.cell}\n\n```{.r .cell-code}\noptions(\n  # set default colors in ggplot2 to colorblind-friendly \n  # Okabe-Ito and Viridis palettes\n  ggplot2.discrete.colour = ggokabeito::palette_okabe_ito(),\n  ggplot2.discrete.fill = ggokabeito::palette_okabe_ito(),\n  ggplot2.continuous.colour = \"viridis\",\n  ggplot2.continuous.fill = \"viridis\",\n  # set theme font and size\n  book.base_family = \"sans\",\n  book.base_size = 14\n)\n\nlibrary(ggplot2)\n\n# set default theme\ntheme_set(\n  theme_minimal(\n    base_size = getOption(\"book.base_size\"), \n    base_family = getOption(\"book.base_family\")\n  ) %+replace% \n    theme(\n      panel.grid.minor = element_blank(),\n      legend.position = \"bottom\"\n    )\n)\n```\n:::\n\n\nWe also mask a few functions from ggdag that we like to customize:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntheme_dag <- function() {\n  ggdag::theme_dag(base_family = getOption(\"book.base_family\"))\n}\n\ngeom_dag_label_repel <- function(..., seed = 10) {\n  ggdag::geom_dag_label_repel(\n    aes(x, y, label = label),\n    box.padding = 3.5, \n    inherit.aes = FALSE,\n    max.overlaps = Inf, \n    family = getOption(\"book.base_family\"),\n    seed = seed,\n    label.size = NA, \n    label.padding = 0.1,\n    size = getOption(\"book.base_size\") / 3,\n    ...\n  ) \n}\n```\n:::\n",
+    "supporting": [
+      "index_files"
+    ],
     "filters": [
       "rmarkdown/pagebreak.lua"
     ],
diff --git a/_freeze/site_libs/DiagrammeR-styles/styles.css b/_freeze/site_libs/DiagrammeR-styles/styles.css
new file mode 100644
index 0000000..d6446ab
--- /dev/null
+++ b/_freeze/site_libs/DiagrammeR-styles/styles.css
@@ -0,0 +1,14 @@
+.DiagrammeR,.grViz pre {
+  white-space: pre-wrap;       /* CSS 3 */
+  white-space: -moz-pre-wrap;  /* Mozilla, since 1999 */
+  white-space: -pre-wrap;      /* Opera 4-6 */
+  white-space: -o-pre-wrap;    /* Opera 7 */
+  word-wrap: break-word;       /* Internet Explorer 5.5+ */
+}
+
+.DiagrammeR g .label {
+  font-family: Helvetica;
+  font-size: 14px;
+  color: #333333;
+}
+
diff --git a/_freeze/site_libs/grViz-binding/grViz.js b/_freeze/site_libs/grViz-binding/grViz.js
new file mode 100644
index 0000000..f801317
--- /dev/null
+++ b/_freeze/site_libs/grViz-binding/grViz.js
@@ -0,0 +1,91 @@
+HTMLWidgets.widget({
+
+  name: 'grViz',
+
+  type: 'output',
+
+  initialize: function(el, width, height) {
+
+    return {
+      // TODO: add instance fields as required
+    };
+  },
+
+  renderValue: function(el, x, instance) {
+    //  Use this to sort of make our diagram responsive
+    //  or at a minimum fit within the bounds set by htmlwidgets
+    //  for the parent container
+    function makeResponsive(el){
+       var svg = el.getElementsByTagName("svg")[0];
+       if (svg) {
+        if (svg.width) {svg.removeAttribute("width")}
+        if (svg.height) {svg.removeAttribute("height")}
+        svg.style.width = "100%";
+        svg.style.height = "100%";
+       }
+    }
+
+    if (x.diagram !== "") {
+
+      if (typeof x.config === "undefined"){
+        x.config = {};
+        x.config.engine = "dot";
+        x.config.options = {};
+      }
+
+      try {
+
+        el.innerHTML = Viz(x.diagram, format="svg", engine=x.config.engine, options=x.config.options);
+
+        makeResponsive(el);
+
+        if (HTMLWidgets.shinyMode) {
+          // Get widget id
+          var id = el.id;
+
+          $("#" + id + " .node").click(function(e) {
+            // Get node id
+            var nodeid = e.currentTarget.id;
+            // Get node text object and make an array
+            var node_texts = $("#" + id + " #" + nodeid + " text");
+            //var node_path = $("#" + nodeid + " path")[0];
+            var text_array = node_texts.map(function() {return $(this).text(); }).toArray();
+            // Build return object *obj* with node-id, node text values and node fill
+            var obj = {
+              id: nodeid,
+              //fill: node_path.attributes.fill.nodeValue,
+              //outerHMTL: node_path.outerHTML,
+              nodeValues: text_array
+            };
+            // Send *obj* to Shiny's inputs (input$[id]+_click  e.g.: input$vtree_click))
+            Shiny.setInputValue(id + "_click", obj, {priority: "event"});
+          });
+        }
+
+        // set up a container for tasks to perform after completion
+        //  one example would be add callbacks for event handling
+        //  styling
+        if (typeof x.tasks !== "undefined") {
+          if ((typeof x.tasks.length === "undefined") ||
+           (typeof x.tasks === "function")) {
+             // handle a function not enclosed in array
+             // should be able to remove once using jsonlite
+             x.tasks = [x.tasks];
+          }
+          x.tasks.map(function(t){
+            // for each tasks add it to the mermaid.tasks with el
+            t.call(el);
+          });
+        }
+      } catch(e){
+        var p = document.createElement("pre");
+        p.innerText = e;
+        el.appendChild(p);
+      }
+    }
+
+  },
+
+  resize: function(el, width, height, instance) {
+  }
+});
diff --git a/_freeze/site_libs/htmlwidgets/htmlwidgets.js b/_freeze/site_libs/htmlwidgets/htmlwidgets.js
new file mode 100644
index 0000000..1067d02
--- /dev/null
+++ b/_freeze/site_libs/htmlwidgets/htmlwidgets.js
@@ -0,0 +1,901 @@
+(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      cel.style.position = "absolute";
+      var pad = unpackPadding(sizing.padding);
+      cel.style.top = pad.top + "px";
+      cel.style.right = pad.right + "px";
+      cel.style.bottom = pad.bottom + "px";
+      cel.style.left = pad.left + "px";
+      el.style.width = "100%";
+      el.style.height = "100%";
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var rect = el.getBoundingClientRect();
+            var result = bindingDef.initialize(el, rect.width, rect.height);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        var getSize = function(el) {
+          if (sizeObj) {
+            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
+          } else {
+            var rect = el.getBoundingClientRect();
+            return {w: rect.width, h: rect.height}
+          }
+        };
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          var size = getSize(el);
+          initResult = binding.initialize(el, size.w, size.h);
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = getSize(el);
+          var resizeHandler = function(e) {
+            var size = getSize(el);
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
diff --git a/_freeze/site_libs/viz/viz.js b/_freeze/site_libs/viz/viz.js
new file mode 100644
index 0000000..be9f332
--- /dev/null
+++ b/_freeze/site_libs/viz/viz.js
@@ -0,0 +1,206 @@
+/*
+Viz.js 1.8.2 (Graphviz 2.40.1, Expat 2.2.5, Emscripten 1.37.33)
+Copyright (c) 2014-2018 Michael Daines
+Licensed under MIT license
+
+This distribution contains other software in object code form:
+
+Graphviz
+Licensed under Eclipse Public License - v 1.0
+http://www.graphviz.org
+
+Expat
+Copyright (c) 1998, 1999, 2000 Thai Open Source Software Center Ltd and Clark Cooper
+Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006 Expat maintainers.
+Licensed under MIT license
+http://www.libexpat.org
+
+zlib
+Copyright (C) 1995-2013 Jean-loup Gailly and Mark Adler
+http://www.zlib.net/zlib_license.html
+*/
+(function(global) {
+var Module = function(Module) {
+  Module = Module || {};
+var Module=typeof Module!=="undefined"?Module:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}Module["arguments"]=[];Module["thisProgram"]="./this.program";Module["quit"]=(function(status,toThrow){throw toThrow});Module["preRun"]=[];Module["postRun"]=[];var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;if(Module["ENVIRONMENT"]){if(Module["ENVIRONMENT"]==="WEB"){ENVIRONMENT_IS_WEB=true}else if(Module["ENVIRONMENT"]==="WORKER"){ENVIRONMENT_IS_WORKER=true}else if(Module["ENVIRONMENT"]==="NODE"){ENVIRONMENT_IS_NODE=true}else if(Module["ENVIRONMENT"]==="SHELL"){ENVIRONMENT_IS_SHELL=true}else{throw new Error("Module['ENVIRONMENT'] value is not valid. must be one of: WEB|WORKER|NODE|SHELL.")}}else{ENVIRONMENT_IS_WEB=typeof window==="object";ENVIRONMENT_IS_WORKER=typeof importScripts==="function";ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof require==="function"&&!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_WORKER;ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER}if(ENVIRONMENT_IS_NODE){var nodeFS;var nodePath;Module["read"]=function shell_read(filename,binary){var ret;ret=tryParseAsDataURI(filename);if(!ret){if(!nodeFS)nodeFS=require("fs");if(!nodePath)nodePath=require("path");filename=nodePath["normalize"](filename);ret=nodeFS["readFileSync"](filename)}return binary?ret:ret.toString()};Module["readBinary"]=function readBinary(filename){var ret=Module["read"](filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process["argv"].length>1){Module["thisProgram"]=process["argv"][1].replace(/\\/g,"/")}Module["arguments"]=process["argv"].slice(2);if(typeof module!=="undefined"){module["exports"]=Module}process["on"]("uncaughtException",(function(ex){if(!(ex instanceof ExitStatus)){throw ex}}));process["on"]("unhandledRejection",(function(reason,p){process["exit"](1)}));Module["inspect"]=(function(){return"[Emscripten Module object]"})}else if(ENVIRONMENT_IS_SHELL){if(typeof read!="undefined"){Module["read"]=function shell_read(f){var data=tryParseAsDataURI(f);if(data){return intArrayToString(data)}return read(f)}}Module["readBinary"]=function readBinary(f){var data;data=tryParseAsDataURI(f);if(data){return data}if(typeof readbuffer==="function"){return new Uint8Array(readbuffer(f))}data=read(f,"binary");assert(typeof data==="object");return data};if(typeof scriptArgs!="undefined"){Module["arguments"]=scriptArgs}else if(typeof arguments!="undefined"){Module["arguments"]=arguments}if(typeof quit==="function"){Module["quit"]=(function(status,toThrow){quit(status)})}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){Module["read"]=function shell_read(url){try{var xhr=new XMLHttpRequest;xhr.open("GET",url,false);xhr.send(null);return xhr.responseText}catch(err){var data=tryParseAsDataURI(url);if(data){return intArrayToString(data)}throw err}};if(ENVIRONMENT_IS_WORKER){Module["readBinary"]=function readBinary(url){try{var xhr=new XMLHttpRequest;xhr.open("GET",url,false);xhr.responseType="arraybuffer";xhr.send(null);return new Uint8Array(xhr.response)}catch(err){var data=tryParseAsDataURI(url);if(data){return data}throw err}}}Module["readAsync"]=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,true);xhr.responseType="arraybuffer";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}var data=tryParseAsDataURI(url);if(data){onload(data.buffer);return}onerror()};xhr.onerror=onerror;xhr.send(null)};if(typeof arguments!="undefined"){Module["arguments"]=arguments}Module["setWindowTitle"]=(function(title){document.title=title})}Module["print"]=typeof console!=="undefined"?console.log:typeof print!=="undefined"?print:null;Module["printErr"]=typeof printErr!=="undefined"?printErr:typeof console!=="undefined"&&console.warn||Module["print"];Module.print=Module["print"];Module.printErr=Module["printErr"];for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=undefined;var STACK_ALIGN=16;function staticAlloc(size){assert(!staticSealed);var ret=STATICTOP;STATICTOP=STATICTOP+size+15&-16;return ret}function dynamicAlloc(size){assert(DYNAMICTOP_PTR);var ret=HEAP32[DYNAMICTOP_PTR>>2];var end=ret+size+15&-16;HEAP32[DYNAMICTOP_PTR>>2]=end;if(end>=TOTAL_MEMORY){var success=enlargeMemory();if(!success){HEAP32[DYNAMICTOP_PTR>>2]=ret;return 0}}return ret}function alignMemory(size,factor){if(!factor)factor=STACK_ALIGN;var ret=size=Math.ceil(size/factor)*factor;return ret}function getNativeTypeSize(type){switch(type){case"i1":case"i8":return 1;case"i16":return 2;case"i32":return 4;case"i64":return 8;case"float":return 4;case"double":return 8;default:{if(type[type.length-1]==="*"){return 4}else if(type[0]==="i"){var bits=parseInt(type.substr(1));assert(bits%8===0);return bits/8}else{return 0}}}}function warnOnce(text){if(!warnOnce.shown)warnOnce.shown={};if(!warnOnce.shown[text]){warnOnce.shown[text]=1;Module.printErr(text)}}var functionPointers=new Array(0);var funcWrappers={};function dynCall(sig,ptr,args){if(args&&args.length){return Module["dynCall_"+sig].apply(null,[ptr].concat(args))}else{return Module["dynCall_"+sig].call(null,ptr)}}var GLOBAL_BASE=8;var ABORT=0;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort("Assertion failed: "+text)}}function getCFunc(ident){var func=Module["_"+ident];assert(func,"Cannot call unknown function "+ident+", make sure it is exported");return func}var JSfuncs={"stackSave":(function(){stackSave()}),"stackRestore":(function(){stackRestore()}),"arrayToC":(function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}),"stringToC":(function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret})};var toC={"string":JSfuncs["stringToC"],"array":JSfuncs["arrayToC"]};function ccall(ident,returnType,argTypes,args,opts){var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);if(returnType==="string")ret=Pointer_stringify(ret);if(stack!==0){stackRestore(stack)}return ret}function setValue(ptr,value,type,noSafe){type=type||"i8";if(type.charAt(type.length-1)==="*")type="i32";switch(type){case"i1":HEAP8[ptr>>0]=value;break;case"i8":HEAP8[ptr>>0]=value;break;case"i16":HEAP16[ptr>>1]=value;break;case"i32":HEAP32[ptr>>2]=value;break;case"i64":tempI64=[value>>>0,(tempDouble=value,+Math_abs(tempDouble)>=+1?tempDouble>+0?(Math_min(+Math_floor(tempDouble/+4294967296),+4294967295)|0)>>>0:~~+Math_ceil((tempDouble- +(~~tempDouble>>>0))/+4294967296)>>>0:0)],HEAP32[ptr>>2]=tempI64[0],HEAP32[ptr+4>>2]=tempI64[1];break;case"float":HEAPF32[ptr>>2]=value;break;case"double":HEAPF64[ptr>>3]=value;break;default:abort("invalid type for setValue: "+type)}}var ALLOC_STATIC=2;var ALLOC_NONE=4;function allocate(slab,types,allocator,ptr){var zeroinit,size;if(typeof slab==="number"){zeroinit=true;size=slab}else{zeroinit=false;size=slab.length}var singleType=typeof types==="string"?types:null;var ret;if(allocator==ALLOC_NONE){ret=ptr}else{ret=[typeof _malloc==="function"?_malloc:staticAlloc,stackAlloc,staticAlloc,dynamicAlloc][allocator===undefined?ALLOC_STATIC:allocator](Math.max(size,singleType?1:types.length))}if(zeroinit){var stop;ptr=ret;assert((ret&3)==0);stop=ret+(size&~3);for(;ptr<stop;ptr+=4){HEAP32[ptr>>2]=0}stop=ret+size;while(ptr<stop){HEAP8[ptr++>>0]=0}return ret}if(singleType==="i8"){if(slab.subarray||slab.slice){HEAPU8.set(slab,ret)}else{HEAPU8.set(new Uint8Array(slab),ret)}return ret}var i=0,type,typeSize,previousType;while(i<size){var curr=slab[i];type=singleType||types[i];if(type===0){i++;continue}if(type=="i64")type="i32";setValue(ret+i,curr,type);if(previousType!==type){typeSize=getNativeTypeSize(type);previousType=type}i+=typeSize}return ret}function Pointer_stringify(ptr,length){if(length===0||!ptr)return"";var hasUtf=0;var t;var i=0;while(1){t=HEAPU8[ptr+i>>0];hasUtf|=t;if(t==0&&!length)break;i++;if(length&&i==length)break}if(!length)length=i;var ret="";if(hasUtf<128){var MAX_CHUNK=1024;var curr;while(length>0){curr=String.fromCharCode.apply(String,HEAPU8.subarray(ptr,ptr+Math.min(length,MAX_CHUNK)));ret=ret?ret+curr:curr;ptr+=MAX_CHUNK;length-=MAX_CHUNK}return ret}return UTF8ToString(ptr)}var UTF8Decoder=typeof TextDecoder!=="undefined"?new TextDecoder("utf8"):undefined;function UTF8ArrayToString(u8Array,idx){var endPtr=idx;while(u8Array[endPtr])++endPtr;if(endPtr-idx>16&&u8Array.subarray&&UTF8Decoder){return UTF8Decoder.decode(u8Array.subarray(idx,endPtr))}else{var u0,u1,u2,u3,u4,u5;var str="";while(1){u0=u8Array[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}u1=u8Array[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}u2=u8Array[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u3=u8Array[idx++]&63;if((u0&248)==240){u0=(u0&7)<<18|u1<<12|u2<<6|u3}else{u4=u8Array[idx++]&63;if((u0&252)==248){u0=(u0&3)<<24|u1<<18|u2<<12|u3<<6|u4}else{u5=u8Array[idx++]&63;u0=(u0&1)<<30|u1<<24|u2<<18|u3<<12|u4<<6|u5}}}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}}function UTF8ToString(ptr){return UTF8ArrayToString(HEAPU8,ptr)}function stringToUTF8Array(str,outU8Array,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127){if(outIdx>=endIdx)break;outU8Array[outIdx++]=u}else 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stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127){++len}else if(u<=2047){len+=2}else if(u<=65535){len+=3}else if(u<=2097151){len+=4}else if(u<=67108863){len+=5}else{len+=6}}return len}var UTF16Decoder=typeof TextDecoder!=="undefined"?new TextDecoder("utf-16le"):undefined;function allocateUTF8(str){var size=lengthBytesUTF8(str)+1;var ret=_malloc(size);if(ret)stringToUTF8Array(str,HEAP8,ret,size);return ret}function demangle(func){return func}function demangleAll(text){var regex=/__Z[\w\d_]+/g;return text.replace(regex,(function(x){var y=demangle(x);return x===y?x:x+" ["+y+"]"}))}function jsStackTrace(){var err=new Error;if(!err.stack){try{throw new Error(0)}catch(e){err=e}if(!err.stack){return"(no stack trace available)"}}return err.stack.toString()}function stackTrace(){var 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tempDoublePtr=STATICTOP;STATICTOP+=16;function ___assert_fail(condition,filename,line,func){abort("Assertion failed: "+Pointer_stringify(condition)+", at: "+[filename?Pointer_stringify(filename):"unknown filename",line,func?Pointer_stringify(func):"unknown function"])}function _emscripten_get_now(){abort()}function _emscripten_get_now_is_monotonic(){return ENVIRONMENT_IS_NODE||typeof dateNow!=="undefined"||(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&self["performance"]&&self["performance"]["now"]}var ERRNO_CODES={EPERM:1,ENOENT:2,ESRCH:3,EINTR:4,EIO:5,ENXIO:6,E2BIG:7,ENOEXEC:8,EBADF:9,ECHILD:10,EAGAIN:11,EWOULDBLOCK:11,ENOMEM:12,EACCES:13,EFAULT:14,ENOTBLK:15,EBUSY:16,EEXIST:17,EXDEV:18,ENODEV:19,ENOTDIR:20,EISDIR:21,EINVAL:22,ENFILE:23,EMFILE:24,ENOTTY:25,ETXTBSY:26,EFBIG:27,ENOSPC:28,ESPIPE:29,EROFS:30,EMLINK:31,EPIPE:32,EDOM:33,ERANGE:34,ENOMSG:42,EIDRM:43,ECHRNG:44,EL2NSYNC:45,EL3HLT:46,EL3RST:47,ELNRNG:48,EUNATCH:49,ENOCSI:50,EL2HLT:51,EDEADLK:35,ENOLCK:37,EBADE:52,EBADR:53,EXFULL:54,ENOANO:55,EBADRQC:56,EBADSLT:57,EDEADLOCK:35,EBFONT:59,ENOSTR:60,ENODATA:61,ETIME:62,ENOSR:63,ENONET:64,ENOPKG:65,EREMOTE:66,ENOLINK:67,EADV:68,ESRMNT:69,ECOMM:70,EPROTO:71,EMULTIHOP:72,EDOTDOT:73,EBADMSG:74,ENOTUNIQ:76,EBADFD:77,EREMCHG:78,ELIBACC:79,ELIBBAD:80,ELIBSCN:81,ELIBMAX:82,ELIBEXEC:83,ENOSYS:38,ENOTEMPTY:39,ENAMETOOLONG:36,ELOOP:40,EOPNOTSUPP:95,EPFNOSUPPORT:96,ECONNRESET:104,ENOBUFS:105,EAFNOSUPPORT:97,EPROTOTYPE:91,ENOTSOCK:88,ENOPROTOOPT:92,ESHUTDOWN:108,ECONNREFUSED:111,EADDRINUSE:98,ECONNABORTED:103,ENETUNREACH:101,ENETDOWN:100,ETIMEDOUT:110,EHOSTDOWN:112,EHOSTUNREACH:113,EINPROGRESS:115,EALREADY:114,EDESTADDRREQ:89,EMSGSIZE:90,EPROTONOSUPPORT:93,ESOCKTNOSUPPORT:94,EADDRNOTAVAIL:99,ENETRESET:102,EISCONN:106,ENOTCONN:107,ETOOMANYREFS:109,EUSERS:87,EDQUOT:122,ESTALE:116,ENOTSUP:95,ENOMEDIUM:123,EILSEQ:84,EOVERFLOW:75,ECANCELED:125,ENOTRECOVERABLE:131,EOWNERDEAD:130,ESTRPIPE:86};function ___setErrNo(value){if(Module["___errno_location"])HEAP32[Module["___errno_location"]()>>2]=value;return value}function _clock_gettime(clk_id,tp){var now;if(clk_id===0){now=Date.now()}else if(clk_id===1&&_emscripten_get_now_is_monotonic()){now=_emscripten_get_now()}else{___setErrNo(ERRNO_CODES.EINVAL);return-1}HEAP32[tp>>2]=now/1e3|0;HEAP32[tp+4>>2]=now%1e3*1e3*1e3|0;return 0}function ___clock_gettime(){return _clock_gettime.apply(null,arguments)}function ___lock(){}function ___map_file(pathname,size){___setErrNo(ERRNO_CODES.EPERM);return-1}var ERRNO_MESSAGES={0:"Success",1:"Not super-user",2:"No such file or directory",3:"No such process",4:"Interrupted system call",5:"I/O error",6:"No such device or address",7:"Arg list too long",8:"Exec format error",9:"Bad file number",10:"No children",11:"No more processes",12:"Not enough core",13:"Permission denied",14:"Bad address",15:"Block device required",16:"Mount device busy",17:"File exists",18:"Cross-device link",19:"No such device",20:"Not a directory",21:"Is a directory",22:"Invalid argument",23:"Too many open files in system",24:"Too many open files",25:"Not a typewriter",26:"Text file busy",27:"File too large",28:"No space left on device",29:"Illegal seek",30:"Read only file system",31:"Too many links",32:"Broken pipe",33:"Math arg out of domain of func",34:"Math result not representable",35:"File locking deadlock error",36:"File or path name too long",37:"No record locks available",38:"Function not implemented",39:"Directory not empty",40:"Too many symbolic links",42:"No message of desired type",43:"Identifier removed",44:"Channel number out of range",45:"Level 2 not synchronized",46:"Level 3 halted",47:"Level 3 reset",48:"Link number out of range",49:"Protocol driver not attached",50:"No CSI structure available",51:"Level 2 halted",52:"Invalid exchange",53:"Invalid request descriptor",54:"Exchange full",55:"No anode",56:"Invalid request code",57:"Invalid slot",59:"Bad font file fmt",60:"Device not a stream",61:"No data (for no delay io)",62:"Timer expired",63:"Out of streams resources",64:"Machine is not on the network",65:"Package not installed",66:"The object is remote",67:"The link has been severed",68:"Advertise error",69:"Srmount error",70:"Communication error on send",71:"Protocol error",72:"Multihop attempted",73:"Cross mount point (not really error)",74:"Trying to read unreadable message",75:"Value too large for defined data type",76:"Given log. name not unique",77:"f.d. invalid for this operation",78:"Remote address changed",79:"Can   access a needed shared lib",80:"Accessing a corrupted shared lib",81:".lib section in a.out corrupted",82:"Attempting to link in too many libs",83:"Attempting to exec a shared library",84:"Illegal byte sequence",86:"Streams pipe error",87:"Too many users",88:"Socket operation on non-socket",89:"Destination address required",90:"Message too long",91:"Protocol wrong type for socket",92:"Protocol not available",93:"Unknown protocol",94:"Socket type not supported",95:"Not supported",96:"Protocol family not supported",97:"Address family not supported by protocol family",98:"Address already in use",99:"Address not available",100:"Network interface is not configured",101:"Network is unreachable",102:"Connection reset by network",103:"Connection aborted",104:"Connection reset by peer",105:"No buffer space available",106:"Socket is already connected",107:"Socket is not connected",108:"Can't send after socket shutdown",109:"Too many references",110:"Connection timed out",111:"Connection refused",112:"Host is down",113:"Host is unreachable",114:"Socket already connected",115:"Connection already in progress",116:"Stale file handle",122:"Quota exceeded",123:"No medium (in tape drive)",125:"Operation canceled",130:"Previous owner died",131:"State not recoverable"};var PATH={splitPath:(function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)}),normalizeArray:(function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift("..")}}return parts}),normalize:(function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";path=PATH.normalizeArray(path.split("/").filter((function(p){return!!p})),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path}),dirname:(function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return"."}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir}),basename:(function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");if(lastSlash===-1)return path;return path.substr(lastSlash+1)}),extname:(function(path){return PATH.splitPath(path)[3]}),join:(function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))}),join2:(function(l,r){return PATH.normalize(l+"/"+r)}),resolve:(function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:FS.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){return""}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=PATH.normalizeArray(resolvedPath.split("/").filter((function(p){return!!p})),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."}),relative:(function(from,to){from=PATH.resolve(from).substr(1);to=PATH.resolve(to).substr(1);function trim(arr){var start=0;for(;start<arr.length;start++){if(arr[start]!=="")break}var end=arr.length-1;for(;end>=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i<length;i++){if(fromParts[i]!==toParts[i]){samePartsLength=i;break}}var outputParts=[];for(var i=samePartsLength;i<fromParts.length;i++){outputParts.push("..")}outputParts=outputParts.concat(toParts.slice(samePartsLength));return outputParts.join("/")})};var TTY={ttys:[],init:(function(){}),shutdown:(function(){}),register:(function(dev,ops){TTY.ttys[dev]={input:[],output:[],ops:ops};FS.registerDevice(dev,TTY.stream_ops)}),stream_ops:{open:(function(stream){var tty=TTY.ttys[stream.node.rdev];if(!tty){throw new FS.ErrnoError(ERRNO_CODES.ENODEV)}stream.tty=tty;stream.seekable=false}),close:(function(stream){stream.tty.ops.flush(stream.tty)}),flush:(function(stream){stream.tty.ops.flush(stream.tty)}),read:(function(stream,buffer,offset,length,pos){if(!stream.tty||!stream.tty.ops.get_char){throw new FS.ErrnoError(ERRNO_CODES.ENXIO)}var bytesRead=0;for(var i=0;i<length;i++){var result;try{result=stream.tty.ops.get_char(stream.tty)}catch(e){throw new FS.ErrnoError(ERRNO_CODES.EIO)}if(result===undefined&&bytesRead===0){throw new FS.ErrnoError(ERRNO_CODES.EAGAIN)}if(result===null||result===undefined)break;bytesRead++;buffer[offset+i]=result}if(bytesRead){stream.node.timestamp=Date.now()}return bytesRead}),write:(function(stream,buffer,offset,length,pos){if(!stream.tty||!stream.tty.ops.put_char){throw new FS.ErrnoError(ERRNO_CODES.ENXIO)}for(var i=0;i<length;i++){try{stream.tty.ops.put_char(stream.tty,buffer[offset+i])}catch(e){throw new FS.ErrnoError(ERRNO_CODES.EIO)}}if(length){stream.node.timestamp=Date.now()}return i})},default_tty_ops:{get_char:(function(tty){if(!tty.input.length){var result=null;if(ENVIRONMENT_IS_NODE){var BUFSIZE=256;var buf=new Buffer(BUFSIZE);var bytesRead=0;var isPosixPlatform=process.platform!="win32";var fd=process.stdin.fd;if(isPosixPlatform){var usingDevice=false;try{fd=fs.openSync("/dev/stdin","r");usingDevice=true}catch(e){}}try{bytesRead=fs.readSync(fd,buf,0,BUFSIZE,null)}catch(e){if(e.toString().indexOf("EOF")!=-1)bytesRead=0;else throw e}if(usingDevice){fs.closeSync(fd)}if(bytesRead>0){result=buf.slice(0,bytesRead).toString("utf-8")}else{result=null}}else if(typeof window!="undefined"&&typeof window.prompt=="function"){result=window.prompt("Input: ");if(result!==null){result+="\n"}}else if(typeof readline=="function"){result=readline();if(result!==null){result+="\n"}}if(!result){return null}tty.input=intArrayFromString(result,true)}return tty.input.shift()}),put_char:(function(tty,val){if(val===null||val===10){Module["print"](UTF8ArrayToString(tty.output,0));tty.output=[]}else{if(val!=0)tty.output.push(val)}}),flush:(function(tty){if(tty.output&&tty.output.length>0){Module["print"](UTF8ArrayToString(tty.output,0));tty.output=[]}})},default_tty1_ops:{put_char:(function(tty,val){if(val===null||val===10){Module["printErr"](UTF8ArrayToString(tty.output,0));tty.output=[]}else{if(val!=0)tty.output.push(val)}}),flush:(function(tty){if(tty.output&&tty.output.length>0){Module["printErr"](UTF8ArrayToString(tty.output,0));tty.output=[]}})}};var MEMFS={ops_table:null,mount:(function(mount){return MEMFS.createNode(null,"/",16384|511,0)}),createNode:(function(parent,name,mode,dev){if(FS.isBlkdev(mode)||FS.isFIFO(mode)){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}if(!MEMFS.ops_table){MEMFS.ops_table={dir:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr,lookup:MEMFS.node_ops.lookup,mknod:MEMFS.node_ops.mknod,rename:MEMFS.node_ops.rename,unlink:MEMFS.node_ops.unlink,rmdir:MEMFS.node_ops.rmdir,readdir:MEMFS.node_ops.readdir,symlink:MEMFS.node_ops.symlink},stream:{llseek:MEMFS.stream_ops.llseek}},file:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr},stream:{llseek:MEMFS.stream_ops.llseek,read:MEMFS.stream_ops.read,write:MEMFS.stream_ops.write,allocate:MEMFS.stream_ops.allocate,mmap:MEMFS.stream_ops.mmap,msync:MEMFS.stream_ops.msync}},link:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr,readlink:MEMFS.node_ops.readlink},stream:{}},chrdev:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr},stream:FS.chrdev_stream_ops}}}var node=FS.createNode(parent,name,mode,dev);if(FS.isDir(node.mode)){node.node_ops=MEMFS.ops_table.dir.node;node.stream_ops=MEMFS.ops_table.dir.stream;node.contents={}}else if(FS.isFile(node.mode)){node.node_ops=MEMFS.ops_table.file.node;node.stream_ops=MEMFS.ops_table.file.stream;node.usedBytes=0;node.contents=null}else if(FS.isLink(node.mode)){node.node_ops=MEMFS.ops_table.link.node;node.stream_ops=MEMFS.ops_table.link.stream}else if(FS.isChrdev(node.mode)){node.node_ops=MEMFS.ops_table.chrdev.node;node.stream_ops=MEMFS.ops_table.chrdev.stream}node.timestamp=Date.now();if(parent){parent.contents[name]=node}return node}),getFileDataAsRegularArray:(function(node){if(node.contents&&node.contents.subarray){var arr=[];for(var i=0;i<node.usedBytes;++i)arr.push(node.contents[i]);return arr}return node.contents}),getFileDataAsTypedArray:(function(node){if(!node.contents)return new Uint8Array;if(node.contents.subarray)return node.contents.subarray(0,node.usedBytes);return new Uint8Array(node.contents)}),expandFileStorage:(function(node,newCapacity){if(node.contents&&node.contents.subarray&&newCapacity>node.contents.length){node.contents=MEMFS.getFileDataAsRegularArray(node);node.usedBytes=node.contents.length}if(!node.contents||node.contents.subarray){var prevCapacity=node.contents?node.contents.length:0;if(prevCapacity>=newCapacity)return;var CAPACITY_DOUBLING_MAX=1024*1024;newCapacity=Math.max(newCapacity,prevCapacity*(prevCapacity<CAPACITY_DOUBLING_MAX?2:1.125)|0);if(prevCapacity!=0)newCapacity=Math.max(newCapacity,256);var oldContents=node.contents;node.contents=new Uint8Array(newCapacity);if(node.usedBytes>0)node.contents.set(oldContents.subarray(0,node.usedBytes),0);return}if(!node.contents&&newCapacity>0)node.contents=[];while(node.contents.length<newCapacity)node.contents.push(0)}),resizeFileStorage:(function(node,newSize){if(node.usedBytes==newSize)return;if(newSize==0){node.contents=null;node.usedBytes=0;return}if(!node.contents||node.contents.subarray){var oldContents=node.contents;node.contents=new Uint8Array(new ArrayBuffer(newSize));if(oldContents){node.contents.set(oldContents.subarray(0,Math.min(newSize,node.usedBytes)))}node.usedBytes=newSize;return}if(!node.contents)node.contents=[];if(node.contents.length>newSize)node.contents.length=newSize;else while(node.contents.length<newSize)node.contents.push(0);node.usedBytes=newSize}),node_ops:{getattr:(function(node){var attr={};attr.dev=FS.isChrdev(node.mode)?node.id:1;attr.ino=node.id;attr.mode=node.mode;attr.nlink=1;attr.uid=0;attr.gid=0;attr.rdev=node.rdev;if(FS.isDir(node.mode)){attr.size=4096}else if(FS.isFile(node.mode)){attr.size=node.usedBytes}else if(FS.isLink(node.mode)){attr.size=node.link.length}else{attr.size=0}attr.atime=new Date(node.timestamp);attr.mtime=new Date(node.timestamp);attr.ctime=new Date(node.timestamp);attr.blksize=4096;attr.blocks=Math.ceil(attr.size/attr.blksize);return attr}),setattr:(function(node,attr){if(attr.mode!==undefined){node.mode=attr.mode}if(attr.timestamp!==undefined){node.timestamp=attr.timestamp}if(attr.size!==undefined){MEMFS.resizeFileStorage(node,attr.size)}}),lookup:(function(parent,name){throw FS.genericErrors[ERRNO_CODES.ENOENT]}),mknod:(function(parent,name,mode,dev){return MEMFS.createNode(parent,name,mode,dev)}),rename:(function(old_node,new_dir,new_name){if(FS.isDir(old_node.mode)){var new_node;try{new_node=FS.lookupNode(new_dir,new_name)}catch(e){}if(new_node){for(var i in new_node.contents){throw new FS.ErrnoError(ERRNO_CODES.ENOTEMPTY)}}}delete old_node.parent.contents[old_node.name];old_node.name=new_name;new_dir.contents[new_name]=old_node;old_node.parent=new_dir}),unlink:(function(parent,name){delete parent.contents[name]}),rmdir:(function(parent,name){var node=FS.lookupNode(parent,name);for(var i in node.contents){throw new FS.ErrnoError(ERRNO_CODES.ENOTEMPTY)}delete parent.contents[name]}),readdir:(function(node){var entries=[".",".."];for(var key in node.contents){if(!node.contents.hasOwnProperty(key)){continue}entries.push(key)}return entries}),symlink:(function(parent,newname,oldpath){var node=MEMFS.createNode(parent,newname,511|40960,0);node.link=oldpath;return node}),readlink:(function(node){if(!FS.isLink(node.mode)){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}return node.link})},stream_ops:{read:(function(stream,buffer,offset,length,position){var contents=stream.node.contents;if(position>=stream.node.usedBytes)return 0;var size=Math.min(stream.node.usedBytes-position,length);assert(size>=0);if(size>8&&contents.subarray){buffer.set(contents.subarray(position,position+size),offset)}else{for(var i=0;i<size;i++)buffer[offset+i]=contents[position+i]}return size}),write:(function(stream,buffer,offset,length,position,canOwn){if(!length)return 0;var node=stream.node;node.timestamp=Date.now();if(buffer.subarray&&(!node.contents||node.contents.subarray)){if(canOwn){node.contents=buffer.subarray(offset,offset+length);node.usedBytes=length;return length}else if(node.usedBytes===0&&position===0){node.contents=new Uint8Array(buffer.subarray(offset,offset+length));node.usedBytes=length;return length}else if(position+length<=node.usedBytes){node.contents.set(buffer.subarray(offset,offset+length),position);return length}}MEMFS.expandFileStorage(node,position+length);if(node.contents.subarray&&buffer.subarray)node.contents.set(buffer.subarray(offset,offset+length),position);else{for(var i=0;i<length;i++){node.contents[position+i]=buffer[offset+i]}}node.usedBytes=Math.max(node.usedBytes,position+length);return length}),llseek:(function(stream,offset,whence){var position=offset;if(whence===1){position+=stream.position}else if(whence===2){if(FS.isFile(stream.node.mode)){position+=stream.node.usedBytes}}if(position<0){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}return position}),allocate:(function(stream,offset,length){MEMFS.expandFileStorage(stream.node,offset+length);stream.node.usedBytes=Math.max(stream.node.usedBytes,offset+length)}),mmap:(function(stream,buffer,offset,length,position,prot,flags){if(!FS.isFile(stream.node.mode)){throw new FS.ErrnoError(ERRNO_CODES.ENODEV)}var ptr;var allocated;var contents=stream.node.contents;if(!(flags&2)&&(contents.buffer===buffer||contents.buffer===buffer.buffer)){allocated=false;ptr=contents.byteOffset}else{if(position>0||position+length<stream.node.usedBytes){if(contents.subarray){contents=contents.subarray(position,position+length)}else{contents=Array.prototype.slice.call(contents,position,position+length)}}allocated=true;ptr=_malloc(length);if(!ptr){throw new FS.ErrnoError(ERRNO_CODES.ENOMEM)}buffer.set(contents,ptr)}return{ptr:ptr,allocated:allocated}}),msync:(function(stream,buffer,offset,length,mmapFlags){if(!FS.isFile(stream.node.mode)){throw new FS.ErrnoError(ERRNO_CODES.ENODEV)}if(mmapFlags&2){return 0}var bytesWritten=MEMFS.stream_ops.write(stream,buffer,0,length,offset,false);return 0})}};var IDBFS={dbs:{},indexedDB:(function(){if(typeof indexedDB!=="undefined")return indexedDB;var ret=null;if(typeof window==="object")ret=window.indexedDB||window.mozIndexedDB||window.webkitIndexedDB||window.msIndexedDB;assert(ret,"IDBFS used, but indexedDB not supported");return ret}),DB_VERSION:21,DB_STORE_NAME:"FILE_DATA",mount:(function(mount){return MEMFS.mount.apply(null,arguments)}),syncfs:(function(mount,populate,callback){IDBFS.getLocalSet(mount,(function(err,local){if(err)return callback(err);IDBFS.getRemoteSet(mount,(function(err,remote){if(err)return callback(err);var src=populate?remote:local;var dst=populate?local:remote;IDBFS.reconcile(src,dst,callback)}))}))}),getDB:(function(name,callback){var db=IDBFS.dbs[name];if(db){return callback(null,db)}var req;try{req=IDBFS.indexedDB().open(name,IDBFS.DB_VERSION)}catch(e){return callback(e)}if(!req){return callback("Unable to connect to IndexedDB")}req.onupgradeneeded=(function(e){var db=e.target.result;var transaction=e.target.transaction;var fileStore;if(db.objectStoreNames.contains(IDBFS.DB_STORE_NAME)){fileStore=transaction.objectStore(IDBFS.DB_STORE_NAME)}else{fileStore=db.createObjectStore(IDBFS.DB_STORE_NAME)}if(!fileStore.indexNames.contains("timestamp")){fileStore.createIndex("timestamp","timestamp",{unique:false})}});req.onsuccess=(function(){db=req.result;IDBFS.dbs[name]=db;callback(null,db)});req.onerror=(function(e){callback(this.error);e.preventDefault()})}),getLocalSet:(function(mount,callback){var entries={};function isRealDir(p){return p!=="."&&p!==".."}function toAbsolute(root){return(function(p){return PATH.join2(root,p)})}var check=FS.readdir(mount.mountpoint).filter(isRealDir).map(toAbsolute(mount.mountpoint));while(check.length){var path=check.pop();var stat;try{stat=FS.stat(path)}catch(e){return callback(e)}if(FS.isDir(stat.mode)){check.push.apply(check,FS.readdir(path).filter(isRealDir).map(toAbsolute(path)))}entries[path]={timestamp:stat.mtime}}return callback(null,{type:"local",entries:entries})}),getRemoteSet:(function(mount,callback){var entries={};IDBFS.getDB(mount.mountpoint,(function(err,db){if(err)return callback(err);try{var transaction=db.transaction([IDBFS.DB_STORE_NAME],"readonly");transaction.onerror=(function(e){callback(this.error);e.preventDefault()});var store=transaction.objectStore(IDBFS.DB_STORE_NAME);var index=store.index("timestamp");index.openKeyCursor().onsuccess=(function(event){var cursor=event.target.result;if(!cursor){return callback(null,{type:"remote",db:db,entries:entries})}entries[cursor.primaryKey]={timestamp:cursor.key};cursor.continue()})}catch(e){return callback(e)}}))}),loadLocalEntry:(function(path,callback){var stat,node;try{var lookup=FS.lookupPath(path);node=lookup.node;stat=FS.stat(path)}catch(e){return callback(e)}if(FS.isDir(stat.mode)){return callback(null,{timestamp:stat.mtime,mode:stat.mode})}else if(FS.isFile(stat.mode)){node.contents=MEMFS.getFileDataAsTypedArray(node);return callback(null,{timestamp:stat.mtime,mode:stat.mode,contents:node.contents})}else{return callback(new Error("node type not supported"))}}),storeLocalEntry:(function(path,entry,callback){try{if(FS.isDir(entry.mode)){FS.mkdir(path,entry.mode)}else if(FS.isFile(entry.mode)){FS.writeFile(path,entry.contents,{canOwn:true})}else{return callback(new Error("node type not supported"))}FS.chmod(path,entry.mode);FS.utime(path,entry.timestamp,entry.timestamp)}catch(e){return callback(e)}callback(null)}),removeLocalEntry:(function(path,callback){try{var lookup=FS.lookupPath(path);var stat=FS.stat(path);if(FS.isDir(stat.mode)){FS.rmdir(path)}else if(FS.isFile(stat.mode)){FS.unlink(path)}}catch(e){return callback(e)}callback(null)}),loadRemoteEntry:(function(store,path,callback){var req=store.get(path);req.onsuccess=(function(event){callback(null,event.target.result)});req.onerror=(function(e){callback(this.error);e.preventDefault()})}),storeRemoteEntry:(function(store,path,entry,callback){var req=store.put(entry,path);req.onsuccess=(function(){callback(null)});req.onerror=(function(e){callback(this.error);e.preventDefault()})}),removeRemoteEntry:(function(store,path,callback){var req=store.delete(path);req.onsuccess=(function(){callback(null)});req.onerror=(function(e){callback(this.error);e.preventDefault()})}),reconcile:(function(src,dst,callback){var total=0;var create=[];Object.keys(src.entries).forEach((function(key){var e=src.entries[key];var e2=dst.entries[key];if(!e2||e.timestamp>e2.timestamp){create.push(key);total++}}));var remove=[];Object.keys(dst.entries).forEach((function(key){var e=dst.entries[key];var e2=src.entries[key];if(!e2){remove.push(key);total++}}));if(!total){return callback(null)}var completed=0;var db=src.type==="remote"?src.db:dst.db;var transaction=db.transaction([IDBFS.DB_STORE_NAME],"readwrite");var store=transaction.objectStore(IDBFS.DB_STORE_NAME);function done(err){if(err){if(!done.errored){done.errored=true;return callback(err)}return}if(++completed>=total){return callback(null)}}transaction.onerror=(function(e){done(this.error);e.preventDefault()});create.sort().forEach((function(path){if(dst.type==="local"){IDBFS.loadRemoteEntry(store,path,(function(err,entry){if(err)return done(err);IDBFS.storeLocalEntry(path,entry,done)}))}else{IDBFS.loadLocalEntry(path,(function(err,entry){if(err)return done(err);IDBFS.storeRemoteEntry(store,path,entry,done)}))}}));remove.sort().reverse().forEach((function(path){if(dst.type==="local"){IDBFS.removeLocalEntry(path,done)}else{IDBFS.removeRemoteEntry(store,path,done)}}))})};var NODEFS={isWindows:false,staticInit:(function(){NODEFS.isWindows=!!process.platform.match(/^win/);var flags=process["binding"]("constants");if(flags["fs"]){flags=flags["fs"]}NODEFS.flagsForNodeMap={"1024":flags["O_APPEND"],"64":flags["O_CREAT"],"128":flags["O_EXCL"],"0":flags["O_RDONLY"],"2":flags["O_RDWR"],"4096":flags["O_SYNC"],"512":flags["O_TRUNC"],"1":flags["O_WRONLY"]}}),bufferFrom:(function(arrayBuffer){return Buffer.alloc?Buffer.from(arrayBuffer):new Buffer(arrayBuffer)}),mount:(function(mount){assert(ENVIRONMENT_IS_NODE);return NODEFS.createNode(null,"/",NODEFS.getMode(mount.opts.root),0)}),createNode:(function(parent,name,mode,dev){if(!FS.isDir(mode)&&!FS.isFile(mode)&&!FS.isLink(mode)){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}var node=FS.createNode(parent,name,mode);node.node_ops=NODEFS.node_ops;node.stream_ops=NODEFS.stream_ops;return node}),getMode:(function(path){var stat;try{stat=fs.lstatSync(path);if(NODEFS.isWindows){stat.mode=stat.mode|(stat.mode&292)>>2}}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}return stat.mode}),realPath:(function(node){var parts=[];while(node.parent!==node){parts.push(node.name);node=node.parent}parts.push(node.mount.opts.root);parts.reverse();return PATH.join.apply(null,parts)}),flagsForNode:(function(flags){flags&=~2097152;flags&=~2048;flags&=~32768;flags&=~524288;var newFlags=0;for(var k in NODEFS.flagsForNodeMap){if(flags&k){newFlags|=NODEFS.flagsForNodeMap[k];flags^=k}}if(!flags){return newFlags}else{throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}}),node_ops:{getattr:(function(node){var path=NODEFS.realPath(node);var stat;try{stat=fs.lstatSync(path)}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}if(NODEFS.isWindows&&!stat.blksize){stat.blksize=4096}if(NODEFS.isWindows&&!stat.blocks){stat.blocks=(stat.size+stat.blksize-1)/stat.blksize|0}return{dev:stat.dev,ino:stat.ino,mode:stat.mode,nlink:stat.nlink,uid:stat.uid,gid:stat.gid,rdev:stat.rdev,size:stat.size,atime:stat.atime,mtime:stat.mtime,ctime:stat.ctime,blksize:stat.blksize,blocks:stat.blocks}}),setattr:(function(node,attr){var path=NODEFS.realPath(node);try{if(attr.mode!==undefined){fs.chmodSync(path,attr.mode);node.mode=attr.mode}if(attr.timestamp!==undefined){var date=new Date(attr.timestamp);fs.utimesSync(path,date,date)}if(attr.size!==undefined){fs.truncateSync(path,attr.size)}}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),lookup:(function(parent,name){var path=PATH.join2(NODEFS.realPath(parent),name);var mode=NODEFS.getMode(path);return NODEFS.createNode(parent,name,mode)}),mknod:(function(parent,name,mode,dev){var node=NODEFS.createNode(parent,name,mode,dev);var path=NODEFS.realPath(node);try{if(FS.isDir(node.mode)){fs.mkdirSync(path,node.mode)}else{fs.writeFileSync(path,"",{mode:node.mode})}}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}return node}),rename:(function(oldNode,newDir,newName){var oldPath=NODEFS.realPath(oldNode);var newPath=PATH.join2(NODEFS.realPath(newDir),newName);try{fs.renameSync(oldPath,newPath)}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),unlink:(function(parent,name){var path=PATH.join2(NODEFS.realPath(parent),name);try{fs.unlinkSync(path)}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),rmdir:(function(parent,name){var path=PATH.join2(NODEFS.realPath(parent),name);try{fs.rmdirSync(path)}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),readdir:(function(node){var path=NODEFS.realPath(node);try{return fs.readdirSync(path)}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),symlink:(function(parent,newName,oldPath){var newPath=PATH.join2(NODEFS.realPath(parent),newName);try{fs.symlinkSync(oldPath,newPath)}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),readlink:(function(node){var path=NODEFS.realPath(node);try{path=fs.readlinkSync(path);path=NODEJS_PATH.relative(NODEJS_PATH.resolve(node.mount.opts.root),path);return path}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}})},stream_ops:{open:(function(stream){var path=NODEFS.realPath(stream.node);try{if(FS.isFile(stream.node.mode)){stream.nfd=fs.openSync(path,NODEFS.flagsForNode(stream.flags))}}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),close:(function(stream){try{if(FS.isFile(stream.node.mode)&&stream.nfd){fs.closeSync(stream.nfd)}}catch(e){if(!e.code)throw e;throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),read:(function(stream,buffer,offset,length,position){if(length===0)return 0;try{return fs.readSync(stream.nfd,NODEFS.bufferFrom(buffer.buffer),offset,length,position)}catch(e){throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),write:(function(stream,buffer,offset,length,position){try{return fs.writeSync(stream.nfd,NODEFS.bufferFrom(buffer.buffer),offset,length,position)}catch(e){throw new FS.ErrnoError(ERRNO_CODES[e.code])}}),llseek:(function(stream,offset,whence){var position=offset;if(whence===1){position+=stream.position}else if(whence===2){if(FS.isFile(stream.node.mode)){try{var stat=fs.fstatSync(stream.nfd);position+=stat.size}catch(e){throw new FS.ErrnoError(ERRNO_CODES[e.code])}}}if(position<0){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}return position})}};var WORKERFS={DIR_MODE:16895,FILE_MODE:33279,reader:null,mount:(function(mount){assert(ENVIRONMENT_IS_WORKER);if(!WORKERFS.reader)WORKERFS.reader=new FileReaderSync;var root=WORKERFS.createNode(null,"/",WORKERFS.DIR_MODE,0);var createdParents={};function ensureParent(path){var parts=path.split("/");var parent=root;for(var i=0;i<parts.length-1;i++){var curr=parts.slice(0,i+1).join("/");if(!createdParents[curr]){createdParents[curr]=WORKERFS.createNode(parent,parts[i],WORKERFS.DIR_MODE,0)}parent=createdParents[curr]}return parent}function base(path){var parts=path.split("/");return parts[parts.length-1]}Array.prototype.forEach.call(mount.opts["files"]||[],(function(file){WORKERFS.createNode(ensureParent(file.name),base(file.name),WORKERFS.FILE_MODE,0,file,file.lastModifiedDate)}));(mount.opts["blobs"]||[]).forEach((function(obj){WORKERFS.createNode(ensureParent(obj["name"]),base(obj["name"]),WORKERFS.FILE_MODE,0,obj["data"])}));(mount.opts["packages"]||[]).forEach((function(pack){pack["metadata"].files.forEach((function(file){var name=file.filename.substr(1);WORKERFS.createNode(ensureParent(name),base(name),WORKERFS.FILE_MODE,0,pack["blob"].slice(file.start,file.end))}))}));return root}),createNode:(function(parent,name,mode,dev,contents,mtime){var node=FS.createNode(parent,name,mode);node.mode=mode;node.node_ops=WORKERFS.node_ops;node.stream_ops=WORKERFS.stream_ops;node.timestamp=(mtime||new Date).getTime();assert(WORKERFS.FILE_MODE!==WORKERFS.DIR_MODE);if(mode===WORKERFS.FILE_MODE){node.size=contents.size;node.contents=contents}else{node.size=4096;node.contents={}}if(parent){parent.contents[name]=node}return node}),node_ops:{getattr:(function(node){return{dev:1,ino:undefined,mode:node.mode,nlink:1,uid:0,gid:0,rdev:undefined,size:node.size,atime:new Date(node.timestamp),mtime:new Date(node.timestamp),ctime:new Date(node.timestamp),blksize:4096,blocks:Math.ceil(node.size/4096)}}),setattr:(function(node,attr){if(attr.mode!==undefined){node.mode=attr.mode}if(attr.timestamp!==undefined){node.timestamp=attr.timestamp}}),lookup:(function(parent,name){throw new FS.ErrnoError(ERRNO_CODES.ENOENT)}),mknod:(function(parent,name,mode,dev){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}),rename:(function(oldNode,newDir,newName){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}),unlink:(function(parent,name){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}),rmdir:(function(parent,name){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}),readdir:(function(node){var entries=[".",".."];for(var key in node.contents){if(!node.contents.hasOwnProperty(key)){continue}entries.push(key)}return entries}),symlink:(function(parent,newName,oldPath){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}),readlink:(function(node){throw new FS.ErrnoError(ERRNO_CODES.EPERM)})},stream_ops:{read:(function(stream,buffer,offset,length,position){if(position>=stream.node.size)return 0;var chunk=stream.node.contents.slice(position,position+length);var ab=WORKERFS.reader.readAsArrayBuffer(chunk);buffer.set(new Uint8Array(ab),offset);return chunk.size}),write:(function(stream,buffer,offset,length,position){throw new FS.ErrnoError(ERRNO_CODES.EIO)}),llseek:(function(stream,offset,whence){var position=offset;if(whence===1){position+=stream.position}else if(whence===2){if(FS.isFile(stream.node.mode)){position+=stream.node.size}}if(position<0){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}return position})}};STATICTOP+=16;STATICTOP+=16;STATICTOP+=16;var FS={root:null,mounts:[],devices:{},streams:[],nextInode:1,nameTable:null,currentPath:"/",initialized:false,ignorePermissions:true,trackingDelegate:{},tracking:{openFlags:{READ:1,WRITE:2}},ErrnoError:null,genericErrors:{},filesystems:null,syncFSRequests:0,handleFSError:(function(e){if(!(e instanceof FS.ErrnoError))throw e+" : "+stackTrace();return ___setErrNo(e.errno)}),lookupPath:(function(path,opts){path=PATH.resolve(FS.cwd(),path);opts=opts||{};if(!path)return{path:"",node:null};var defaults={follow_mount:true,recurse_count:0};for(var key in defaults){if(opts[key]===undefined){opts[key]=defaults[key]}}if(opts.recurse_count>8){throw new FS.ErrnoError(ERRNO_CODES.ELOOP)}var parts=PATH.normalizeArray(path.split("/").filter((function(p){return!!p})),false);var current=FS.root;var current_path="/";for(var i=0;i<parts.length;i++){var islast=i===parts.length-1;if(islast&&opts.parent){break}current=FS.lookupNode(current,parts[i]);current_path=PATH.join2(current_path,parts[i]);if(FS.isMountpoint(current)){if(!islast||islast&&opts.follow_mount){current=current.mounted.root}}if(!islast||opts.follow){var count=0;while(FS.isLink(current.mode)){var link=FS.readlink(current_path);current_path=PATH.resolve(PATH.dirname(current_path),link);var lookup=FS.lookupPath(current_path,{recurse_count:opts.recurse_count});current=lookup.node;if(count++>40){throw new FS.ErrnoError(ERRNO_CODES.ELOOP)}}}}return{path:current_path,node:current}}),getPath:(function(node){var path;while(true){if(FS.isRoot(node)){var mount=node.mount.mountpoint;if(!path)return mount;return mount[mount.length-1]!=="/"?mount+"/"+path:mount+path}path=path?node.name+"/"+path:node.name;node=node.parent}}),hashName:(function(parentid,name){var hash=0;for(var i=0;i<name.length;i++){hash=(hash<<5)-hash+name.charCodeAt(i)|0}return(parentid+hash>>>0)%FS.nameTable.length}),hashAddNode:(function(node){var hash=FS.hashName(node.parent.id,node.name);node.name_next=FS.nameTable[hash];FS.nameTable[hash]=node}),hashRemoveNode:(function(node){var hash=FS.hashName(node.parent.id,node.name);if(FS.nameTable[hash]===node){FS.nameTable[hash]=node.name_next}else{var current=FS.nameTable[hash];while(current){if(current.name_next===node){current.name_next=node.name_next;break}current=current.name_next}}}),lookupNode:(function(parent,name){var err=FS.mayLookup(parent);if(err){throw new FS.ErrnoError(err,parent)}var hash=FS.hashName(parent.id,name);for(var node=FS.nameTable[hash];node;node=node.name_next){var nodeName=node.name;if(node.parent.id===parent.id&&nodeName===name){return node}}return FS.lookup(parent,name)}),createNode:(function(parent,name,mode,rdev){if(!FS.FSNode){FS.FSNode=(function(parent,name,mode,rdev){if(!parent){parent=this}this.parent=parent;this.mount=parent.mount;this.mounted=null;this.id=FS.nextInode++;this.name=name;this.mode=mode;this.node_ops={};this.stream_ops={};this.rdev=rdev});FS.FSNode.prototype={};var readMode=292|73;var writeMode=146;Object.defineProperties(FS.FSNode.prototype,{read:{get:(function(){return(this.mode&readMode)===readMode}),set:(function(val){val?this.mode|=readMode:this.mode&=~readMode})},write:{get:(function(){return(this.mode&writeMode)===writeMode}),set:(function(val){val?this.mode|=writeMode:this.mode&=~writeMode})},isFolder:{get:(function(){return FS.isDir(this.mode)})},isDevice:{get:(function(){return FS.isChrdev(this.mode)})}})}var node=new FS.FSNode(parent,name,mode,rdev);FS.hashAddNode(node);return node}),destroyNode:(function(node){FS.hashRemoveNode(node)}),isRoot:(function(node){return node===node.parent}),isMountpoint:(function(node){return!!node.mounted}),isFile:(function(mode){return(mode&61440)===32768}),isDir:(function(mode){return(mode&61440)===16384}),isLink:(function(mode){return(mode&61440)===40960}),isChrdev:(function(mode){return(mode&61440)===8192}),isBlkdev:(function(mode){return(mode&61440)===24576}),isFIFO:(function(mode){return(mode&61440)===4096}),isSocket:(function(mode){return(mode&49152)===49152}),flagModes:{"r":0,"rs":1052672,"r+":2,"w":577,"wx":705,"xw":705,"w+":578,"wx+":706,"xw+":706,"a":1089,"ax":1217,"xa":1217,"a+":1090,"ax+":1218,"xa+":1218},modeStringToFlags:(function(str){var flags=FS.flagModes[str];if(typeof flags==="undefined"){throw new Error("Unknown file open mode: "+str)}return flags}),flagsToPermissionString:(function(flag){var perms=["r","w","rw"][flag&3];if(flag&512){perms+="w"}return perms}),nodePermissions:(function(node,perms){if(FS.ignorePermissions){return 0}if(perms.indexOf("r")!==-1&&!(node.mode&292)){return ERRNO_CODES.EACCES}else if(perms.indexOf("w")!==-1&&!(node.mode&146)){return ERRNO_CODES.EACCES}else if(perms.indexOf("x")!==-1&&!(node.mode&73)){return ERRNO_CODES.EACCES}return 0}),mayLookup:(function(dir){var err=FS.nodePermissions(dir,"x");if(err)return err;if(!dir.node_ops.lookup)return ERRNO_CODES.EACCES;return 0}),mayCreate:(function(dir,name){try{var node=FS.lookupNode(dir,name);return ERRNO_CODES.EEXIST}catch(e){}return FS.nodePermissions(dir,"wx")}),mayDelete:(function(dir,name,isdir){var node;try{node=FS.lookupNode(dir,name)}catch(e){return e.errno}var err=FS.nodePermissions(dir,"wx");if(err){return err}if(isdir){if(!FS.isDir(node.mode)){return ERRNO_CODES.ENOTDIR}if(FS.isRoot(node)||FS.getPath(node)===FS.cwd()){return ERRNO_CODES.EBUSY}}else{if(FS.isDir(node.mode)){return ERRNO_CODES.EISDIR}}return 0}),mayOpen:(function(node,flags){if(!node){return ERRNO_CODES.ENOENT}if(FS.isLink(node.mode)){return ERRNO_CODES.ELOOP}else if(FS.isDir(node.mode)){if(FS.flagsToPermissionString(flags)!=="r"||flags&512){return ERRNO_CODES.EISDIR}}return FS.nodePermissions(node,FS.flagsToPermissionString(flags))}),MAX_OPEN_FDS:4096,nextfd:(function(fd_start,fd_end){fd_start=fd_start||0;fd_end=fd_end||FS.MAX_OPEN_FDS;for(var fd=fd_start;fd<=fd_end;fd++){if(!FS.streams[fd]){return fd}}throw new FS.ErrnoError(ERRNO_CODES.EMFILE)}),getStream:(function(fd){return FS.streams[fd]}),createStream:(function(stream,fd_start,fd_end){if(!FS.FSStream){FS.FSStream=(function(){});FS.FSStream.prototype={};Object.defineProperties(FS.FSStream.prototype,{object:{get:(function(){return this.node}),set:(function(val){this.node=val})},isRead:{get:(function(){return(this.flags&2097155)!==1})},isWrite:{get:(function(){return(this.flags&2097155)!==0})},isAppend:{get:(function(){return this.flags&1024})}})}var newStream=new FS.FSStream;for(var p in stream){newStream[p]=stream[p]}stream=newStream;var fd=FS.nextfd(fd_start,fd_end);stream.fd=fd;FS.streams[fd]=stream;return stream}),closeStream:(function(fd){FS.streams[fd]=null}),chrdev_stream_ops:{open:(function(stream){var device=FS.getDevice(stream.node.rdev);stream.stream_ops=device.stream_ops;if(stream.stream_ops.open){stream.stream_ops.open(stream)}}),llseek:(function(){throw new FS.ErrnoError(ERRNO_CODES.ESPIPE)})},major:(function(dev){return dev>>8}),minor:(function(dev){return dev&255}),makedev:(function(ma,mi){return ma<<8|mi}),registerDevice:(function(dev,ops){FS.devices[dev]={stream_ops:ops}}),getDevice:(function(dev){return FS.devices[dev]}),getMounts:(function(mount){var mounts=[];var check=[mount];while(check.length){var m=check.pop();mounts.push(m);check.push.apply(check,m.mounts)}return mounts}),syncfs:(function(populate,callback){if(typeof populate==="function"){callback=populate;populate=false}FS.syncFSRequests++;if(FS.syncFSRequests>1){console.log("warning: "+FS.syncFSRequests+" FS.syncfs operations in flight at once, probably just doing extra work")}var mounts=FS.getMounts(FS.root.mount);var completed=0;function doCallback(err){assert(FS.syncFSRequests>0);FS.syncFSRequests--;return callback(err)}function done(err){if(err){if(!done.errored){done.errored=true;return doCallback(err)}return}if(++completed>=mounts.length){doCallback(null)}}mounts.forEach((function(mount){if(!mount.type.syncfs){return done(null)}mount.type.syncfs(mount,populate,done)}))}),mount:(function(type,opts,mountpoint){var root=mountpoint==="/";var pseudo=!mountpoint;var node;if(root&&FS.root){throw new FS.ErrnoError(ERRNO_CODES.EBUSY)}else if(!root&&!pseudo){var lookup=FS.lookupPath(mountpoint,{follow_mount:false});mountpoint=lookup.path;node=lookup.node;if(FS.isMountpoint(node)){throw new FS.ErrnoError(ERRNO_CODES.EBUSY)}if(!FS.isDir(node.mode)){throw new FS.ErrnoError(ERRNO_CODES.ENOTDIR)}}var mount={type:type,opts:opts,mountpoint:mountpoint,mounts:[]};var mountRoot=type.mount(mount);mountRoot.mount=mount;mount.root=mountRoot;if(root){FS.root=mountRoot}else if(node){node.mounted=mount;if(node.mount){node.mount.mounts.push(mount)}}return mountRoot}),unmount:(function(mountpoint){var lookup=FS.lookupPath(mountpoint,{follow_mount:false});if(!FS.isMountpoint(lookup.node)){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}var node=lookup.node;var mount=node.mounted;var mounts=FS.getMounts(mount);Object.keys(FS.nameTable).forEach((function(hash){var current=FS.nameTable[hash];while(current){var next=current.name_next;if(mounts.indexOf(current.mount)!==-1){FS.destroyNode(current)}current=next}}));node.mounted=null;var idx=node.mount.mounts.indexOf(mount);assert(idx!==-1);node.mount.mounts.splice(idx,1)}),lookup:(function(parent,name){return parent.node_ops.lookup(parent,name)}),mknod:(function(path,mode,dev){var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);if(!name||name==="."||name===".."){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}var err=FS.mayCreate(parent,name);if(err){throw new FS.ErrnoError(err)}if(!parent.node_ops.mknod){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}return parent.node_ops.mknod(parent,name,mode,dev)}),create:(function(path,mode){mode=mode!==undefined?mode:438;mode&=4095;mode|=32768;return FS.mknod(path,mode,0)}),mkdir:(function(path,mode){mode=mode!==undefined?mode:511;mode&=511|512;mode|=16384;return FS.mknod(path,mode,0)}),mkdirTree:(function(path,mode){var dirs=path.split("/");var d="";for(var i=0;i<dirs.length;++i){if(!dirs[i])continue;d+="/"+dirs[i];try{FS.mkdir(d,mode)}catch(e){if(e.errno!=ERRNO_CODES.EEXIST)throw e}}}),mkdev:(function(path,mode,dev){if(typeof dev==="undefined"){dev=mode;mode=438}mode|=8192;return FS.mknod(path,mode,dev)}),symlink:(function(oldpath,newpath){if(!PATH.resolve(oldpath)){throw new FS.ErrnoError(ERRNO_CODES.ENOENT)}var lookup=FS.lookupPath(newpath,{parent:true});var parent=lookup.node;if(!parent){throw new FS.ErrnoError(ERRNO_CODES.ENOENT)}var newname=PATH.basename(newpath);var err=FS.mayCreate(parent,newname);if(err){throw new FS.ErrnoError(err)}if(!parent.node_ops.symlink){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}return parent.node_ops.symlink(parent,newname,oldpath)}),rename:(function(old_path,new_path){var old_dirname=PATH.dirname(old_path);var new_dirname=PATH.dirname(new_path);var old_name=PATH.basename(old_path);var new_name=PATH.basename(new_path);var lookup,old_dir,new_dir;try{lookup=FS.lookupPath(old_path,{parent:true});old_dir=lookup.node;lookup=FS.lookupPath(new_path,{parent:true});new_dir=lookup.node}catch(e){throw new FS.ErrnoError(ERRNO_CODES.EBUSY)}if(!old_dir||!new_dir)throw new FS.ErrnoError(ERRNO_CODES.ENOENT);if(old_dir.mount!==new_dir.mount){throw new FS.ErrnoError(ERRNO_CODES.EXDEV)}var old_node=FS.lookupNode(old_dir,old_name);var relative=PATH.relative(old_path,new_dirname);if(relative.charAt(0)!=="."){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}relative=PATH.relative(new_path,old_dirname);if(relative.charAt(0)!=="."){throw new FS.ErrnoError(ERRNO_CODES.ENOTEMPTY)}var new_node;try{new_node=FS.lookupNode(new_dir,new_name)}catch(e){}if(old_node===new_node){return}var isdir=FS.isDir(old_node.mode);var err=FS.mayDelete(old_dir,old_name,isdir);if(err){throw new FS.ErrnoError(err)}err=new_node?FS.mayDelete(new_dir,new_name,isdir):FS.mayCreate(new_dir,new_name);if(err){throw new FS.ErrnoError(err)}if(!old_dir.node_ops.rename){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}if(FS.isMountpoint(old_node)||new_node&&FS.isMountpoint(new_node)){throw new FS.ErrnoError(ERRNO_CODES.EBUSY)}if(new_dir!==old_dir){err=FS.nodePermissions(old_dir,"w");if(err){throw new FS.ErrnoError(err)}}try{if(FS.trackingDelegate["willMovePath"]){FS.trackingDelegate["willMovePath"](old_path,new_path)}}catch(e){console.log("FS.trackingDelegate['willMovePath']('"+old_path+"', '"+new_path+"') threw an exception: "+e.message)}FS.hashRemoveNode(old_node);try{old_dir.node_ops.rename(old_node,new_dir,new_name)}catch(e){throw e}finally{FS.hashAddNode(old_node)}try{if(FS.trackingDelegate["onMovePath"])FS.trackingDelegate["onMovePath"](old_path,new_path)}catch(e){console.log("FS.trackingDelegate['onMovePath']('"+old_path+"', '"+new_path+"') threw an exception: "+e.message)}}),rmdir:(function(path){var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);var node=FS.lookupNode(parent,name);var err=FS.mayDelete(parent,name,true);if(err){throw new FS.ErrnoError(err)}if(!parent.node_ops.rmdir){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}if(FS.isMountpoint(node)){throw new FS.ErrnoError(ERRNO_CODES.EBUSY)}try{if(FS.trackingDelegate["willDeletePath"]){FS.trackingDelegate["willDeletePath"](path)}}catch(e){console.log("FS.trackingDelegate['willDeletePath']('"+path+"') threw an exception: "+e.message)}parent.node_ops.rmdir(parent,name);FS.destroyNode(node);try{if(FS.trackingDelegate["onDeletePath"])FS.trackingDelegate["onDeletePath"](path)}catch(e){console.log("FS.trackingDelegate['onDeletePath']('"+path+"') threw an exception: "+e.message)}}),readdir:(function(path){var lookup=FS.lookupPath(path,{follow:true});var node=lookup.node;if(!node.node_ops.readdir){throw new FS.ErrnoError(ERRNO_CODES.ENOTDIR)}return node.node_ops.readdir(node)}),unlink:(function(path){var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);var node=FS.lookupNode(parent,name);var err=FS.mayDelete(parent,name,false);if(err){throw new FS.ErrnoError(err)}if(!parent.node_ops.unlink){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}if(FS.isMountpoint(node)){throw new FS.ErrnoError(ERRNO_CODES.EBUSY)}try{if(FS.trackingDelegate["willDeletePath"]){FS.trackingDelegate["willDeletePath"](path)}}catch(e){console.log("FS.trackingDelegate['willDeletePath']('"+path+"') threw an exception: "+e.message)}parent.node_ops.unlink(parent,name);FS.destroyNode(node);try{if(FS.trackingDelegate["onDeletePath"])FS.trackingDelegate["onDeletePath"](path)}catch(e){console.log("FS.trackingDelegate['onDeletePath']('"+path+"') threw an exception: "+e.message)}}),readlink:(function(path){var lookup=FS.lookupPath(path);var link=lookup.node;if(!link){throw new FS.ErrnoError(ERRNO_CODES.ENOENT)}if(!link.node_ops.readlink){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}return PATH.resolve(FS.getPath(link.parent),link.node_ops.readlink(link))}),stat:(function(path,dontFollow){var lookup=FS.lookupPath(path,{follow:!dontFollow});var node=lookup.node;if(!node){throw new FS.ErrnoError(ERRNO_CODES.ENOENT)}if(!node.node_ops.getattr){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}return node.node_ops.getattr(node)}),lstat:(function(path){return FS.stat(path,true)}),chmod:(function(path,mode,dontFollow){var node;if(typeof path==="string"){var lookup=FS.lookupPath(path,{follow:!dontFollow});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}node.node_ops.setattr(node,{mode:mode&4095|node.mode&~4095,timestamp:Date.now()})}),lchmod:(function(path,mode){FS.chmod(path,mode,true)}),fchmod:(function(fd,mode){var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(ERRNO_CODES.EBADF)}FS.chmod(stream.node,mode)}),chown:(function(path,uid,gid,dontFollow){var node;if(typeof path==="string"){var lookup=FS.lookupPath(path,{follow:!dontFollow});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}node.node_ops.setattr(node,{timestamp:Date.now()})}),lchown:(function(path,uid,gid){FS.chown(path,uid,gid,true)}),fchown:(function(fd,uid,gid){var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(ERRNO_CODES.EBADF)}FS.chown(stream.node,uid,gid)}),truncate:(function(path,len){if(len<0){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}var node;if(typeof path==="string"){var lookup=FS.lookupPath(path,{follow:true});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(ERRNO_CODES.EPERM)}if(FS.isDir(node.mode)){throw new FS.ErrnoError(ERRNO_CODES.EISDIR)}if(!FS.isFile(node.mode)){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}var err=FS.nodePermissions(node,"w");if(err){throw new FS.ErrnoError(err)}node.node_ops.setattr(node,{size:len,timestamp:Date.now()})}),ftruncate:(function(fd,len){var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(ERRNO_CODES.EBADF)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}FS.truncate(stream.node,len)}),utime:(function(path,atime,mtime){var lookup=FS.lookupPath(path,{follow:true});var node=lookup.node;node.node_ops.setattr(node,{timestamp:Math.max(atime,mtime)})}),open:(function(path,flags,mode,fd_start,fd_end){if(path===""){throw new FS.ErrnoError(ERRNO_CODES.ENOENT)}flags=typeof flags==="string"?FS.modeStringToFlags(flags):flags;mode=typeof mode==="undefined"?438:mode;if(flags&64){mode=mode&4095|32768}else{mode=0}var node;if(typeof path==="object"){node=path}else{path=PATH.normalize(path);try{var lookup=FS.lookupPath(path,{follow:!(flags&131072)});node=lookup.node}catch(e){}}var created=false;if(flags&64){if(node){if(flags&128){throw new FS.ErrnoError(ERRNO_CODES.EEXIST)}}else{node=FS.mknod(path,mode,0);created=true}}if(!node){throw new FS.ErrnoError(ERRNO_CODES.ENOENT)}if(FS.isChrdev(node.mode)){flags&=~512}if(flags&65536&&!FS.isDir(node.mode)){throw new FS.ErrnoError(ERRNO_CODES.ENOTDIR)}if(!created){var err=FS.mayOpen(node,flags);if(err){throw new FS.ErrnoError(err)}}if(flags&512){FS.truncate(node,0)}flags&=~(128|512);var stream=FS.createStream({node:node,path:FS.getPath(node),flags:flags,seekable:true,position:0,stream_ops:node.stream_ops,ungotten:[],error:false},fd_start,fd_end);if(stream.stream_ops.open){stream.stream_ops.open(stream)}if(Module["logReadFiles"]&&!(flags&1)){if(!FS.readFiles)FS.readFiles={};if(!(path in FS.readFiles)){FS.readFiles[path]=1;Module["printErr"]("read file: "+path)}}try{if(FS.trackingDelegate["onOpenFile"]){var trackingFlags=0;if((flags&2097155)!==1){trackingFlags|=FS.tracking.openFlags.READ}if((flags&2097155)!==0){trackingFlags|=FS.tracking.openFlags.WRITE}FS.trackingDelegate["onOpenFile"](path,trackingFlags)}}catch(e){console.log("FS.trackingDelegate['onOpenFile']('"+path+"', flags) threw an exception: "+e.message)}return stream}),close:(function(stream){if(stream.getdents)stream.getdents=null;try{if(stream.stream_ops.close){stream.stream_ops.close(stream)}}catch(e){throw e}finally{FS.closeStream(stream.fd)}}),llseek:(function(stream,offset,whence){if(!stream.seekable||!stream.stream_ops.llseek){throw new FS.ErrnoError(ERRNO_CODES.ESPIPE)}stream.position=stream.stream_ops.llseek(stream,offset,whence);stream.ungotten=[];return stream.position}),read:(function(stream,buffer,offset,length,position){if(length<0||position<0){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}if((stream.flags&2097155)===1){throw new FS.ErrnoError(ERRNO_CODES.EBADF)}if(FS.isDir(stream.node.mode)){throw new FS.ErrnoError(ERRNO_CODES.EISDIR)}if(!stream.stream_ops.read){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}var seeking=typeof position!=="undefined";if(!seeking){position=stream.position}else if(!stream.seekable){throw new FS.ErrnoError(ERRNO_CODES.ESPIPE)}var bytesRead=stream.stream_ops.read(stream,buffer,offset,length,position);if(!seeking)stream.position+=bytesRead;return bytesRead}),write:(function(stream,buffer,offset,length,position,canOwn){if(length<0||position<0){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(ERRNO_CODES.EBADF)}if(FS.isDir(stream.node.mode)){throw new FS.ErrnoError(ERRNO_CODES.EISDIR)}if(!stream.stream_ops.write){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}if(stream.flags&1024){FS.llseek(stream,0,2)}var seeking=typeof position!=="undefined";if(!seeking){position=stream.position}else if(!stream.seekable){throw new FS.ErrnoError(ERRNO_CODES.ESPIPE)}var bytesWritten=stream.stream_ops.write(stream,buffer,offset,length,position,canOwn);if(!seeking)stream.position+=bytesWritten;try{if(stream.path&&FS.trackingDelegate["onWriteToFile"])FS.trackingDelegate["onWriteToFile"](stream.path)}catch(e){console.log("FS.trackingDelegate['onWriteToFile']('"+path+"') threw an exception: "+e.message)}return bytesWritten}),allocate:(function(stream,offset,length){if(offset<0||length<=0){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(ERRNO_CODES.EBADF)}if(!FS.isFile(stream.node.mode)&&!FS.isDir(stream.node.mode)){throw new FS.ErrnoError(ERRNO_CODES.ENODEV)}if(!stream.stream_ops.allocate){throw new FS.ErrnoError(ERRNO_CODES.EOPNOTSUPP)}stream.stream_ops.allocate(stream,offset,length)}),mmap:(function(stream,buffer,offset,length,position,prot,flags){if((stream.flags&2097155)===1){throw new FS.ErrnoError(ERRNO_CODES.EACCES)}if(!stream.stream_ops.mmap){throw new FS.ErrnoError(ERRNO_CODES.ENODEV)}return stream.stream_ops.mmap(stream,buffer,offset,length,position,prot,flags)}),msync:(function(stream,buffer,offset,length,mmapFlags){if(!stream||!stream.stream_ops.msync){return 0}return stream.stream_ops.msync(stream,buffer,offset,length,mmapFlags)}),munmap:(function(stream){return 0}),ioctl:(function(stream,cmd,arg){if(!stream.stream_ops.ioctl){throw new FS.ErrnoError(ERRNO_CODES.ENOTTY)}return stream.stream_ops.ioctl(stream,cmd,arg)}),readFile:(function(path,opts){opts=opts||{};opts.flags=opts.flags||"r";opts.encoding=opts.encoding||"binary";if(opts.encoding!=="utf8"&&opts.encoding!=="binary"){throw new Error('Invalid encoding type "'+opts.encoding+'"')}var ret;var stream=FS.open(path,opts.flags);var stat=FS.stat(path);var length=stat.size;var buf=new Uint8Array(length);FS.read(stream,buf,0,length,0);if(opts.encoding==="utf8"){ret=UTF8ArrayToString(buf,0)}else if(opts.encoding==="binary"){ret=buf}FS.close(stream);return ret}),writeFile:(function(path,data,opts){opts=opts||{};opts.flags=opts.flags||"w";var stream=FS.open(path,opts.flags,opts.mode);if(typeof data==="string"){var buf=new Uint8Array(lengthBytesUTF8(data)+1);var actualNumBytes=stringToUTF8Array(data,buf,0,buf.length);FS.write(stream,buf,0,actualNumBytes,undefined,opts.canOwn)}else if(ArrayBuffer.isView(data)){FS.write(stream,data,0,data.byteLength,undefined,opts.canOwn)}else{throw new Error("Unsupported data type")}FS.close(stream)}),cwd:(function(){return FS.currentPath}),chdir:(function(path){var lookup=FS.lookupPath(path,{follow:true});if(lookup.node===null){throw new FS.ErrnoError(ERRNO_CODES.ENOENT)}if(!FS.isDir(lookup.node.mode)){throw new FS.ErrnoError(ERRNO_CODES.ENOTDIR)}var err=FS.nodePermissions(lookup.node,"x");if(err){throw new FS.ErrnoError(err)}FS.currentPath=lookup.path}),createDefaultDirectories:(function(){FS.mkdir("/tmp");FS.mkdir("/home");FS.mkdir("/home/web_user")}),createDefaultDevices:(function(){FS.mkdir("/dev");FS.registerDevice(FS.makedev(1,3),{read:(function(){return 0}),write:(function(stream,buffer,offset,length,pos){return length})});FS.mkdev("/dev/null",FS.makedev(1,3));TTY.register(FS.makedev(5,0),TTY.default_tty_ops);TTY.register(FS.makedev(6,0),TTY.default_tty1_ops);FS.mkdev("/dev/tty",FS.makedev(5,0));FS.mkdev("/dev/tty1",FS.makedev(6,0));var random_device;if(typeof crypto!=="undefined"){var randomBuffer=new Uint8Array(1);random_device=(function(){crypto.getRandomValues(randomBuffer);return randomBuffer[0]})}else if(ENVIRONMENT_IS_NODE){random_device=(function(){return require("crypto")["randomBytes"](1)[0]})}else{random_device=(function(){return Math.random()*256|0})}FS.createDevice("/dev","random",random_device);FS.createDevice("/dev","urandom",random_device);FS.mkdir("/dev/shm");FS.mkdir("/dev/shm/tmp")}),createSpecialDirectories:(function(){FS.mkdir("/proc");FS.mkdir("/proc/self");FS.mkdir("/proc/self/fd");FS.mount({mount:(function(){var node=FS.createNode("/proc/self","fd",16384|511,73);node.node_ops={lookup:(function(parent,name){var fd=+name;var stream=FS.getStream(fd);if(!stream)throw new FS.ErrnoError(ERRNO_CODES.EBADF);var ret={parent:null,mount:{mountpoint:"fake"},node_ops:{readlink:(function(){return stream.path})}};ret.parent=ret;return ret})};return node})},{},"/proc/self/fd")}),createStandardStreams:(function(){if(Module["stdin"]){FS.createDevice("/dev","stdin",Module["stdin"])}else{FS.symlink("/dev/tty","/dev/stdin")}if(Module["stdout"]){FS.createDevice("/dev","stdout",null,Module["stdout"])}else{FS.symlink("/dev/tty","/dev/stdout")}if(Module["stderr"]){FS.createDevice("/dev","stderr",null,Module["stderr"])}else{FS.symlink("/dev/tty1","/dev/stderr")}var stdin=FS.open("/dev/stdin","r");assert(stdin.fd===0,"invalid handle for stdin ("+stdin.fd+")");var stdout=FS.open("/dev/stdout","w");assert(stdout.fd===1,"invalid handle for stdout ("+stdout.fd+")");var stderr=FS.open("/dev/stderr","w");assert(stderr.fd===2,"invalid handle for stderr ("+stderr.fd+")")}),ensureErrnoError:(function(){if(FS.ErrnoError)return;FS.ErrnoError=function ErrnoError(errno,node){this.node=node;this.setErrno=(function(errno){this.errno=errno;for(var key in ERRNO_CODES){if(ERRNO_CODES[key]===errno){this.code=key;break}}});this.setErrno(errno);this.message=ERRNO_MESSAGES[errno];if(this.stack)Object.defineProperty(this,"stack",{value:(new Error).stack,writable:true})};FS.ErrnoError.prototype=new Error;FS.ErrnoError.prototype.constructor=FS.ErrnoError;[ERRNO_CODES.ENOENT].forEach((function(code){FS.genericErrors[code]=new FS.ErrnoError(code);FS.genericErrors[code].stack="<generic error, no stack>"}))}),staticInit:(function(){FS.ensureErrnoError();FS.nameTable=new Array(4096);FS.mount(MEMFS,{},"/");FS.createDefaultDirectories();FS.createDefaultDevices();FS.createSpecialDirectories();FS.filesystems={"MEMFS":MEMFS,"IDBFS":IDBFS,"NODEFS":NODEFS,"WORKERFS":WORKERFS}}),init:(function(input,output,error){assert(!FS.init.initialized,"FS.init was previously called. If you want to initialize later with custom parameters, remove any earlier calls (note that one is automatically added to the generated code)");FS.init.initialized=true;FS.ensureErrnoError();Module["stdin"]=input||Module["stdin"];Module["stdout"]=output||Module["stdout"];Module["stderr"]=error||Module["stderr"];FS.createStandardStreams()}),quit:(function(){FS.init.initialized=false;var fflush=Module["_fflush"];if(fflush)fflush(0);for(var i=0;i<FS.streams.length;i++){var stream=FS.streams[i];if(!stream){continue}FS.close(stream)}}),getMode:(function(canRead,canWrite){var mode=0;if(canRead)mode|=292|73;if(canWrite)mode|=146;return mode}),joinPath:(function(parts,forceRelative){var path=PATH.join.apply(null,parts);if(forceRelative&&path[0]=="/")path=path.substr(1);return path}),absolutePath:(function(relative,base){return PATH.resolve(base,relative)}),standardizePath:(function(path){return PATH.normalize(path)}),findObject:(function(path,dontResolveLastLink){var ret=FS.analyzePath(path,dontResolveLastLink);if(ret.exists){return ret.object}else{___setErrNo(ret.error);return null}}),analyzePath:(function(path,dontResolveLastLink){try{var lookup=FS.lookupPath(path,{follow:!dontResolveLastLink});path=lookup.path}catch(e){}var ret={isRoot:false,exists:false,error:0,name:null,path:null,object:null,parentExists:false,parentPath:null,parentObject:null};try{var lookup=FS.lookupPath(path,{parent:true});ret.parentExists=true;ret.parentPath=lookup.path;ret.parentObject=lookup.node;ret.name=PATH.basename(path);lookup=FS.lookupPath(path,{follow:!dontResolveLastLink});ret.exists=true;ret.path=lookup.path;ret.object=lookup.node;ret.name=lookup.node.name;ret.isRoot=lookup.path==="/"}catch(e){ret.error=e.errno}return ret}),createFolder:(function(parent,name,canRead,canWrite){var path=PATH.join2(typeof parent==="string"?parent:FS.getPath(parent),name);var mode=FS.getMode(canRead,canWrite);return FS.mkdir(path,mode)}),createPath:(function(parent,path,canRead,canWrite){parent=typeof parent==="string"?parent:FS.getPath(parent);var parts=path.split("/").reverse();while(parts.length){var part=parts.pop();if(!part)continue;var current=PATH.join2(parent,part);try{FS.mkdir(current)}catch(e){}parent=current}return current}),createFile:(function(parent,name,properties,canRead,canWrite){var path=PATH.join2(typeof parent==="string"?parent:FS.getPath(parent),name);var mode=FS.getMode(canRead,canWrite);return FS.create(path,mode)}),createDataFile:(function(parent,name,data,canRead,canWrite,canOwn){var path=name?PATH.join2(typeof parent==="string"?parent:FS.getPath(parent),name):parent;var mode=FS.getMode(canRead,canWrite);var node=FS.create(path,mode);if(data){if(typeof data==="string"){var arr=new Array(data.length);for(var i=0,len=data.length;i<len;++i)arr[i]=data.charCodeAt(i);data=arr}FS.chmod(node,mode|146);var stream=FS.open(node,"w");FS.write(stream,data,0,data.length,0,canOwn);FS.close(stream);FS.chmod(node,mode)}return node}),createDevice:(function(parent,name,input,output){var path=PATH.join2(typeof parent==="string"?parent:FS.getPath(parent),name);var mode=FS.getMode(!!input,!!output);if(!FS.createDevice.major)FS.createDevice.major=64;var dev=FS.makedev(FS.createDevice.major++,0);FS.registerDevice(dev,{open:(function(stream){stream.seekable=false}),close:(function(stream){if(output&&output.buffer&&output.buffer.length){output(10)}}),read:(function(stream,buffer,offset,length,pos){var bytesRead=0;for(var i=0;i<length;i++){var result;try{result=input()}catch(e){throw new FS.ErrnoError(ERRNO_CODES.EIO)}if(result===undefined&&bytesRead===0){throw new FS.ErrnoError(ERRNO_CODES.EAGAIN)}if(result===null||result===undefined)break;bytesRead++;buffer[offset+i]=result}if(bytesRead){stream.node.timestamp=Date.now()}return bytesRead}),write:(function(stream,buffer,offset,length,pos){for(var i=0;i<length;i++){try{output(buffer[offset+i])}catch(e){throw new FS.ErrnoError(ERRNO_CODES.EIO)}}if(length){stream.node.timestamp=Date.now()}return i})});return FS.mkdev(path,mode,dev)}),createLink:(function(parent,name,target,canRead,canWrite){var path=PATH.join2(typeof parent==="string"?parent:FS.getPath(parent),name);return FS.symlink(target,path)}),forceLoadFile:(function(obj){if(obj.isDevice||obj.isFolder||obj.link||obj.contents)return true;var success=true;if(typeof XMLHttpRequest!=="undefined"){throw new Error("Lazy loading should have been performed (contents set) in createLazyFile, but it was not. Lazy loading only works in web workers. Use --embed-file or --preload-file in emcc on the main thread.")}else if(Module["read"]){try{obj.contents=intArrayFromString(Module["read"](obj.url),true);obj.usedBytes=obj.contents.length}catch(e){success=false}}else{throw new Error("Cannot load without read() or XMLHttpRequest.")}if(!success)___setErrNo(ERRNO_CODES.EIO);return success}),createLazyFile:(function(parent,name,url,canRead,canWrite){function LazyUint8Array(){this.lengthKnown=false;this.chunks=[]}LazyUint8Array.prototype.get=function LazyUint8Array_get(idx){if(idx>this.length-1||idx<0){return undefined}var chunkOffset=idx%this.chunkSize;var chunkNum=idx/this.chunkSize|0;return this.getter(chunkNum)[chunkOffset]};LazyUint8Array.prototype.setDataGetter=function LazyUint8Array_setDataGetter(getter){this.getter=getter};LazyUint8Array.prototype.cacheLength=function LazyUint8Array_cacheLength(){var xhr=new XMLHttpRequest;xhr.open("HEAD",url,false);xhr.send(null);if(!(xhr.status>=200&&xhr.status<300||xhr.status===304))throw new Error("Couldn't load "+url+". Status: "+xhr.status);var datalength=Number(xhr.getResponseHeader("Content-length"));var header;var hasByteServing=(header=xhr.getResponseHeader("Accept-Ranges"))&&header==="bytes";var usesGzip=(header=xhr.getResponseHeader("Content-Encoding"))&&header==="gzip";var chunkSize=1024*1024;if(!hasByteServing)chunkSize=datalength;var doXHR=(function(from,to){if(from>to)throw new Error("invalid range ("+from+", "+to+") or no bytes requested!");if(to>datalength-1)throw new Error("only "+datalength+" bytes available! programmer error!");var xhr=new XMLHttpRequest;xhr.open("GET",url,false);if(datalength!==chunkSize)xhr.setRequestHeader("Range","bytes="+from+"-"+to);if(typeof Uint8Array!="undefined")xhr.responseType="arraybuffer";if(xhr.overrideMimeType){xhr.overrideMimeType("text/plain; charset=x-user-defined")}xhr.send(null);if(!(xhr.status>=200&&xhr.status<300||xhr.status===304))throw new Error("Couldn't load "+url+". Status: "+xhr.status);if(xhr.response!==undefined){return new Uint8Array(xhr.response||[])}else{return intArrayFromString(xhr.responseText||"",true)}});var lazyArray=this;lazyArray.setDataGetter((function(chunkNum){var start=chunkNum*chunkSize;var end=(chunkNum+1)*chunkSize-1;end=Math.min(end,datalength-1);if(typeof lazyArray.chunks[chunkNum]==="undefined"){lazyArray.chunks[chunkNum]=doXHR(start,end)}if(typeof lazyArray.chunks[chunkNum]==="undefined")throw new Error("doXHR failed!");return lazyArray.chunks[chunkNum]}));if(usesGzip||!datalength){chunkSize=datalength=1;datalength=this.getter(0).length;chunkSize=datalength;console.log("LazyFiles on gzip forces download of the whole file when length is accessed")}this._length=datalength;this._chunkSize=chunkSize;this.lengthKnown=true};if(typeof XMLHttpRequest!=="undefined"){if(!ENVIRONMENT_IS_WORKER)throw"Cannot do synchronous binary XHRs outside webworkers in modern browsers. Use --embed-file or --preload-file in emcc";var lazyArray=new LazyUint8Array;Object.defineProperties(lazyArray,{length:{get:(function(){if(!this.lengthKnown){this.cacheLength()}return this._length})},chunkSize:{get:(function(){if(!this.lengthKnown){this.cacheLength()}return this._chunkSize})}});var properties={isDevice:false,contents:lazyArray}}else{var properties={isDevice:false,url:url}}var node=FS.createFile(parent,name,properties,canRead,canWrite);if(properties.contents){node.contents=properties.contents}else if(properties.url){node.contents=null;node.url=properties.url}Object.defineProperties(node,{usedBytes:{get:(function(){return this.contents.length})}});var stream_ops={};var keys=Object.keys(node.stream_ops);keys.forEach((function(key){var fn=node.stream_ops[key];stream_ops[key]=function forceLoadLazyFile(){if(!FS.forceLoadFile(node)){throw new FS.ErrnoError(ERRNO_CODES.EIO)}return fn.apply(null,arguments)}}));stream_ops.read=function stream_ops_read(stream,buffer,offset,length,position){if(!FS.forceLoadFile(node)){throw new FS.ErrnoError(ERRNO_CODES.EIO)}var contents=stream.node.contents;if(position>=contents.length)return 0;var size=Math.min(contents.length-position,length);assert(size>=0);if(contents.slice){for(var i=0;i<size;i++){buffer[offset+i]=contents[position+i]}}else{for(var i=0;i<size;i++){buffer[offset+i]=contents.get(position+i)}}return size};node.stream_ops=stream_ops;return node}),createPreloadedFile:(function(parent,name,url,canRead,canWrite,onload,onerror,dontCreateFile,canOwn,preFinish){Browser.init();var fullname=name?PATH.resolve(PATH.join2(parent,name)):parent;var dep=getUniqueRunDependency("cp "+fullname);function processData(byteArray){function finish(byteArray){if(preFinish)preFinish();if(!dontCreateFile){FS.createDataFile(parent,name,byteArray,canRead,canWrite,canOwn)}if(onload)onload();removeRunDependency(dep)}var handled=false;Module["preloadPlugins"].forEach((function(plugin){if(handled)return;if(plugin["canHandle"](fullname)){plugin["handle"](byteArray,fullname,finish,(function(){if(onerror)onerror();removeRunDependency(dep)}));handled=true}}));if(!handled)finish(byteArray)}addRunDependency(dep);if(typeof url=="string"){Browser.asyncLoad(url,(function(byteArray){processData(byteArray)}),onerror)}else{processData(url)}}),indexedDB:(function(){return window.indexedDB||window.mozIndexedDB||window.webkitIndexedDB||window.msIndexedDB}),DB_NAME:(function(){return"EM_FS_"+window.location.pathname}),DB_VERSION:20,DB_STORE_NAME:"FILE_DATA",saveFilesToDB:(function(paths,onload,onerror){onload=onload||(function(){});onerror=onerror||(function(){});var indexedDB=FS.indexedDB();try{var openRequest=indexedDB.open(FS.DB_NAME(),FS.DB_VERSION)}catch(e){return onerror(e)}openRequest.onupgradeneeded=function openRequest_onupgradeneeded(){console.log("creating db");var db=openRequest.result;db.createObjectStore(FS.DB_STORE_NAME)};openRequest.onsuccess=function openRequest_onsuccess(){var db=openRequest.result;var transaction=db.transaction([FS.DB_STORE_NAME],"readwrite");var files=transaction.objectStore(FS.DB_STORE_NAME);var ok=0,fail=0,total=paths.length;function finish(){if(fail==0)onload();else onerror()}paths.forEach((function(path){var putRequest=files.put(FS.analyzePath(path).object.contents,path);putRequest.onsuccess=function putRequest_onsuccess(){ok++;if(ok+fail==total)finish()};putRequest.onerror=function putRequest_onerror(){fail++;if(ok+fail==total)finish()}}));transaction.onerror=onerror};openRequest.onerror=onerror}),loadFilesFromDB:(function(paths,onload,onerror){onload=onload||(function(){});onerror=onerror||(function(){});var indexedDB=FS.indexedDB();try{var openRequest=indexedDB.open(FS.DB_NAME(),FS.DB_VERSION)}catch(e){return onerror(e)}openRequest.onupgradeneeded=onerror;openRequest.onsuccess=function openRequest_onsuccess(){var db=openRequest.result;try{var transaction=db.transaction([FS.DB_STORE_NAME],"readonly")}catch(e){onerror(e);return}var files=transaction.objectStore(FS.DB_STORE_NAME);var ok=0,fail=0,total=paths.length;function finish(){if(fail==0)onload();else onerror()}paths.forEach((function(path){var getRequest=files.get(path);getRequest.onsuccess=function getRequest_onsuccess(){if(FS.analyzePath(path).exists){FS.unlink(path)}FS.createDataFile(PATH.dirname(path),PATH.basename(path),getRequest.result,true,true,true);ok++;if(ok+fail==total)finish()};getRequest.onerror=function getRequest_onerror(){fail++;if(ok+fail==total)finish()}}));transaction.onerror=onerror};openRequest.onerror=onerror})};var SYSCALLS={DEFAULT_POLLMASK:5,mappings:{},umask:511,calculateAt:(function(dirfd,path){if(path[0]!=="/"){var dir;if(dirfd===-100){dir=FS.cwd()}else{var dirstream=FS.getStream(dirfd);if(!dirstream)throw new FS.ErrnoError(ERRNO_CODES.EBADF);dir=dirstream.path}path=PATH.join2(dir,path)}return path}),doStat:(function(func,path,buf){try{var stat=func(path)}catch(e){if(e&&e.node&&PATH.normalize(path)!==PATH.normalize(FS.getPath(e.node))){return-ERRNO_CODES.ENOTDIR}throw e}HEAP32[buf>>2]=stat.dev;HEAP32[buf+4>>2]=0;HEAP32[buf+8>>2]=stat.ino;HEAP32[buf+12>>2]=stat.mode;HEAP32[buf+16>>2]=stat.nlink;HEAP32[buf+20>>2]=stat.uid;HEAP32[buf+24>>2]=stat.gid;HEAP32[buf+28>>2]=stat.rdev;HEAP32[buf+32>>2]=0;HEAP32[buf+36>>2]=stat.size;HEAP32[buf+40>>2]=4096;HEAP32[buf+44>>2]=stat.blocks;HEAP32[buf+48>>2]=stat.atime.getTime()/1e3|0;HEAP32[buf+52>>2]=0;HEAP32[buf+56>>2]=stat.mtime.getTime()/1e3|0;HEAP32[buf+60>>2]=0;HEAP32[buf+64>>2]=stat.ctime.getTime()/1e3|0;HEAP32[buf+68>>2]=0;HEAP32[buf+72>>2]=stat.ino;return 0}),doMsync:(function(addr,stream,len,flags){var buffer=new Uint8Array(HEAPU8.subarray(addr,addr+len));FS.msync(stream,buffer,0,len,flags)}),doMkdir:(function(path,mode){path=PATH.normalize(path);if(path[path.length-1]==="/")path=path.substr(0,path.length-1);FS.mkdir(path,mode,0);return 0}),doMknod:(function(path,mode,dev){switch(mode&61440){case 32768:case 8192:case 24576:case 4096:case 49152:break;default:return-ERRNO_CODES.EINVAL}FS.mknod(path,mode,dev);return 0}),doReadlink:(function(path,buf,bufsize){if(bufsize<=0)return-ERRNO_CODES.EINVAL;var ret=FS.readlink(path);var len=Math.min(bufsize,lengthBytesUTF8(ret));var endChar=HEAP8[buf+len];stringToUTF8(ret,buf,bufsize+1);HEAP8[buf+len]=endChar;return len}),doAccess:(function(path,amode){if(amode&~7){return-ERRNO_CODES.EINVAL}var node;var lookup=FS.lookupPath(path,{follow:true});node=lookup.node;var perms="";if(amode&4)perms+="r";if(amode&2)perms+="w";if(amode&1)perms+="x";if(perms&&FS.nodePermissions(node,perms)){return-ERRNO_CODES.EACCES}return 0}),doDup:(function(path,flags,suggestFD){var suggest=FS.getStream(suggestFD);if(suggest)FS.close(suggest);return FS.open(path,flags,0,suggestFD,suggestFD).fd}),doReadv:(function(stream,iov,iovcnt,offset){var ret=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];var curr=FS.read(stream,HEAP8,ptr,len,offset);if(curr<0)return-1;ret+=curr;if(curr<len)break}return ret}),doWritev:(function(stream,iov,iovcnt,offset){var ret=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];var curr=FS.write(stream,HEAP8,ptr,len,offset);if(curr<0)return-1;ret+=curr}return ret}),varargs:0,get:(function(varargs){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret}),getStr:(function(){var ret=Pointer_stringify(SYSCALLS.get());return ret}),getStreamFromFD:(function(){var stream=FS.getStream(SYSCALLS.get());if(!stream)throw new FS.ErrnoError(ERRNO_CODES.EBADF);return stream}),getSocketFromFD:(function(){var socket=SOCKFS.getSocket(SYSCALLS.get());if(!socket)throw new FS.ErrnoError(ERRNO_CODES.EBADF);return socket}),getSocketAddress:(function(allowNull){var addrp=SYSCALLS.get(),addrlen=SYSCALLS.get();if(allowNull&&addrp===0)return null;var info=__read_sockaddr(addrp,addrlen);if(info.errno)throw new FS.ErrnoError(info.errno);info.addr=DNS.lookup_addr(info.addr)||info.addr;return info}),get64:(function(){var low=SYSCALLS.get(),high=SYSCALLS.get();if(low>=0)assert(high===0);else assert(high===-1);return low}),getZero:(function(){assert(SYSCALLS.get()===0)})};function ___syscall10(which,varargs){SYSCALLS.varargs=varargs;try{var path=SYSCALLS.getStr();FS.unlink(path);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall140(which,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(),offset_high=SYSCALLS.get(),offset_low=SYSCALLS.get(),result=SYSCALLS.get(),whence=SYSCALLS.get();var offset=offset_low;FS.llseek(stream,offset,whence);HEAP32[result>>2]=stream.position;if(stream.getdents&&offset===0&&whence===0)stream.getdents=null;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall145(which,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(),iov=SYSCALLS.get(),iovcnt=SYSCALLS.get();return SYSCALLS.doReadv(stream,iov,iovcnt)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall146(which,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(),iov=SYSCALLS.get(),iovcnt=SYSCALLS.get();return SYSCALLS.doWritev(stream,iov,iovcnt)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall192(which,varargs){SYSCALLS.varargs=varargs;try{var addr=SYSCALLS.get(),len=SYSCALLS.get(),prot=SYSCALLS.get(),flags=SYSCALLS.get(),fd=SYSCALLS.get(),off=SYSCALLS.get();off<<=12;var ptr;var allocated=false;if(fd===-1){ptr=_memalign(PAGE_SIZE,len);if(!ptr)return-ERRNO_CODES.ENOMEM;_memset(ptr,0,len);allocated=true}else{var info=FS.getStream(fd);if(!info)return-ERRNO_CODES.EBADF;var res=FS.mmap(info,HEAPU8,addr,len,off,prot,flags);ptr=res.ptr;allocated=res.allocated}SYSCALLS.mappings[ptr]={malloc:ptr,len:len,allocated:allocated,fd:fd,flags:flags};return ptr}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall195(which,varargs){SYSCALLS.varargs=varargs;try{var path=SYSCALLS.getStr(),buf=SYSCALLS.get();return SYSCALLS.doStat(FS.stat,path,buf)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall197(which,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(),buf=SYSCALLS.get();return SYSCALLS.doStat(FS.stat,stream.path,buf)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}var PROCINFO={ppid:1,pid:42,sid:42,pgid:42};function ___syscall20(which,varargs){SYSCALLS.varargs=varargs;try{return PROCINFO.pid}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall221(which,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(),cmd=SYSCALLS.get();switch(cmd){case 0:{var arg=SYSCALLS.get();if(arg<0){return-ERRNO_CODES.EINVAL}var newStream;newStream=FS.open(stream.path,stream.flags,0,arg);return newStream.fd};case 1:case 2:return 0;case 3:return stream.flags;case 4:{var arg=SYSCALLS.get();stream.flags|=arg;return 0};case 12:case 12:{var arg=SYSCALLS.get();var offset=0;HEAP16[arg+offset>>1]=2;return 0};case 13:case 14:case 13:case 14:return 0;case 16:case 8:return-ERRNO_CODES.EINVAL;case 9:___setErrNo(ERRNO_CODES.EINVAL);return-1;default:{return-ERRNO_CODES.EINVAL}}}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall3(which,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(),buf=SYSCALLS.get(),count=SYSCALLS.get();return FS.read(stream,HEAP8,buf,count)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall33(which,varargs){SYSCALLS.varargs=varargs;try{var path=SYSCALLS.getStr(),amode=SYSCALLS.get();return SYSCALLS.doAccess(path,amode)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall5(which,varargs){SYSCALLS.varargs=varargs;try{var pathname=SYSCALLS.getStr(),flags=SYSCALLS.get(),mode=SYSCALLS.get();var stream=FS.open(pathname,flags,mode);return stream.fd}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall54(which,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(),op=SYSCALLS.get();switch(op){case 21509:case 21505:{if(!stream.tty)return-ERRNO_CODES.ENOTTY;return 0};case 21510:case 21511:case 21512:case 21506:case 21507:case 21508:{if(!stream.tty)return-ERRNO_CODES.ENOTTY;return 0};case 21519:{if(!stream.tty)return-ERRNO_CODES.ENOTTY;var argp=SYSCALLS.get();HEAP32[argp>>2]=0;return 0};case 21520:{if(!stream.tty)return-ERRNO_CODES.ENOTTY;return-ERRNO_CODES.EINVAL};case 21531:{var argp=SYSCALLS.get();return FS.ioctl(stream,op,argp)};case 21523:{if(!stream.tty)return-ERRNO_CODES.ENOTTY;return 0};default:abort("bad ioctl syscall "+op)}}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall6(which,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD();FS.close(stream);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___syscall91(which,varargs){SYSCALLS.varargs=varargs;try{var addr=SYSCALLS.get(),len=SYSCALLS.get();var info=SYSCALLS.mappings[addr];if(!info)return 0;if(len===info.len){var stream=FS.getStream(info.fd);SYSCALLS.doMsync(addr,stream,len,info.flags);FS.munmap(stream);SYSCALLS.mappings[addr]=null;if(info.allocated){_free(info.malloc)}}return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}var cttz_i8=allocate([8,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,4,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,5,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,4,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,6,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,4,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,5,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,4,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,7,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,4,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,5,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,4,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,6,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,4,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,5,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0,4,0,1,0,2,0,1,0,3,0,1,0,2,0,1,0],"i8",ALLOC_STATIC);function ___unlock(){}function __exit(status){Module["exit"](status)}function _exit(status){__exit(status)}var _environ=STATICTOP;STATICTOP+=16;function ___buildEnvironment(env){var MAX_ENV_VALUES=64;var TOTAL_ENV_SIZE=1024;var poolPtr;var envPtr;if(!___buildEnvironment.called){___buildEnvironment.called=true;ENV["USER"]=ENV["LOGNAME"]="web_user";ENV["PATH"]="/";ENV["PWD"]="/";ENV["HOME"]="/home/web_user";ENV["LANG"]="C.UTF-8";ENV["_"]=Module["thisProgram"];poolPtr=staticAlloc(TOTAL_ENV_SIZE);envPtr=staticAlloc(MAX_ENV_VALUES*4);HEAP32[envPtr>>2]=poolPtr;HEAP32[_environ>>2]=envPtr}else{envPtr=HEAP32[_environ>>2];poolPtr=HEAP32[envPtr>>2]}var strings=[];var totalSize=0;for(var key in env){if(typeof env[key]==="string"){var line=key+"="+env[key];strings.push(line);totalSize+=line.length}}if(totalSize>TOTAL_ENV_SIZE){throw new Error("Environment size exceeded TOTAL_ENV_SIZE!")}var ptrSize=4;for(var i=0;i<strings.length;i++){var line=strings[i];writeAsciiToMemory(line,poolPtr);HEAP32[envPtr+i*ptrSize>>2]=poolPtr;poolPtr+=line.length+1}HEAP32[envPtr+strings.length*ptrSize>>2]=0}var ENV={};function _getenv(name){if(name===0)return 0;name=Pointer_stringify(name);if(!ENV.hasOwnProperty(name))return 0;if(_getenv.ret)_free(_getenv.ret);_getenv.ret=allocateUTF8(ENV[name]);return _getenv.ret}function _gettimeofday(ptr){var now=Date.now();HEAP32[ptr>>2]=now/1e3|0;HEAP32[ptr+4>>2]=now%1e3*1e3|0;return 0}var _llvm_ceil_f64=Math_ceil;var _llvm_fabs_f32=Math_abs;var _llvm_fabs_f64=Math_abs;var _llvm_floor_f64=Math_floor;var _llvm_pow_f64=Math_pow;var _llvm_sqrt_f32=Math_sqrt;var _llvm_sqrt_f64=Math_sqrt;function _llvm_trap(){abort("trap!")}function _longjmp(env,value){Module["setThrew"](env,value||1);throw"longjmp"}function _emscripten_memcpy_big(dest,src,num){HEAPU8.set(HEAPU8.subarray(src,src+num),dest);return dest}function _setenv(envname,envval,overwrite){if(envname===0){___setErrNo(ERRNO_CODES.EINVAL);return-1}var name=Pointer_stringify(envname);var val=Pointer_stringify(envval);if(name===""||name.indexOf("=")!==-1){___setErrNo(ERRNO_CODES.EINVAL);return-1}if(ENV.hasOwnProperty(name)&&!overwrite)return 0;ENV[name]=val;___buildEnvironment(ENV);return 0}function _time(ptr){var ret=Date.now()/1e3|0;if(ptr){HEAP32[ptr>>2]=ret}return ret}function _times(buffer){if(buffer!==0){_memset(buffer,0,16)}return 0}if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function _emscripten_get_now_actual(){var t=process["hrtime"]();return t[0]*1e3+t[1]/1e6}}else if(typeof dateNow!=="undefined"){_emscripten_get_now=dateNow}else if(typeof self==="object"&&self["performance"]&&typeof self["performance"]["now"]==="function"){_emscripten_get_now=(function(){return self["performance"]["now"]()})}else if(typeof performance==="object"&&typeof performance["now"]==="function"){_emscripten_get_now=(function(){return performance["now"]()})}else{_emscripten_get_now=Date.now}FS.staticInit();__ATINIT__.unshift((function(){if(!Module["noFSInit"]&&!FS.init.initialized)FS.init()}));__ATMAIN__.push((function(){FS.ignorePermissions=false}));__ATEXIT__.push((function(){FS.quit()}));__ATINIT__.unshift((function(){TTY.init()}));__ATEXIT__.push((function(){TTY.shutdown()}));if(ENVIRONMENT_IS_NODE){var fs=require("fs");var NODEJS_PATH=require("path");NODEFS.staticInit()}___buildEnvironment(ENV);DYNAMICTOP_PTR=staticAlloc(4);STACK_BASE=STACKTOP=alignMemory(STATICTOP);STACK_MAX=STACK_BASE+TOTAL_STACK;DYNAMIC_BASE=alignMemory(STACK_MAX);HEAP32[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE;staticSealed=true;var ASSERTIONS=false;function intArrayFromString(stringy,dontAddNull,length){var len=length>0?length:lengthBytesUTF8(stringy)+1;var u8array=new Array(len);var numBytesWritten=stringToUTF8Array(stringy,u8array,0,u8array.length);if(dontAddNull)u8array.length=numBytesWritten;return u8array}function intArrayToString(array){var ret=[];for(var i=0;i<array.length;i++){var chr=array[i];if(chr>255){if(ASSERTIONS){assert(false,"Character code "+chr+" ("+String.fromCharCode(chr)+")  at offset "+i+" not in 0x00-0xFF.")}chr&=255}ret.push(String.fromCharCode(chr))}return ret.join("")}var decodeBase64=typeof atob==="function"?atob:(function(input){var keyStr="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=";var output="";var chr1,chr2,chr3;var enc1,enc2,enc3,enc4;var i=0;input=input.replace(/[^A-Za-z0-9\+\/\=]/g,"");do{enc1=keyStr.indexOf(input.charAt(i++));enc2=keyStr.indexOf(input.charAt(i++));enc3=keyStr.indexOf(input.charAt(i++));enc4=keyStr.indexOf(input.charAt(i++));chr1=enc1<<2|enc2>>4;chr2=(enc2&15)<<4|enc3>>2;chr3=(enc3&3)<<6|enc4;output=output+String.fromCharCode(chr1);if(enc3!==64){output=output+String.fromCharCode(chr2)}if(enc4!==64){output=output+String.fromCharCode(chr3)}}while(i<input.length);return output});function intArrayFromBase64(s){if(typeof ENVIRONMENT_IS_NODE==="boolean"&&ENVIRONMENT_IS_NODE){var buf;try{buf=Buffer.from(s,"base64")}catch(_){buf=new Buffer(s,"base64")}return new Uint8Array(buf.buffer,buf.byteOffset,buf.byteLength)}try{var decoded=decodeBase64(s);var bytes=new Uint8Array(decoded.length);for(var i=0;i<decoded.length;++i){bytes[i]=decoded.charCodeAt(i)}return bytes}catch(_){throw new Error("Converting base64 string to bytes failed.")}}function tryParseAsDataURI(filename){if(!isDataURI(filename)){return}return intArrayFromBase64(filename.slice(dataURIPrefix.length))}function invoke_d(index){try{return Module["dynCall_d"](index)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_dddd(index,a1,a2,a3){try{return Module["dynCall_dddd"](index,a1,a2,a3)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_di(index,a1){try{return Module["dynCall_di"](index,a1)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_i(index){try{return Module["dynCall_i"](index)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_ii(index,a1){try{return Module["dynCall_ii"](index,a1)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_iii(index,a1,a2){try{return Module["dynCall_iii"](index,a1,a2)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_iiidd(index,a1,a2,a3,a4){try{return Module["dynCall_iiidd"](index,a1,a2,a3,a4)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_iiii(index,a1,a2,a3){try{return Module["dynCall_iiii"](index,a1,a2,a3)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_iiiii(index,a1,a2,a3,a4){try{return Module["dynCall_iiiii"](index,a1,a2,a3,a4)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_iiiiii(index,a1,a2,a3,a4,a5){try{return Module["dynCall_iiiiii"](index,a1,a2,a3,a4,a5)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_iiiiiii(index,a1,a2,a3,a4,a5,a6){try{return Module["dynCall_iiiiiii"](index,a1,a2,a3,a4,a5,a6)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_iiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){try{return Module["dynCall_iiiiiiiiiii"](index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_v(index){try{Module["dynCall_v"](index)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_vi(index,a1){try{Module["dynCall_vi"](index,a1)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_vii(index,a1,a2){try{Module["dynCall_vii"](index,a1,a2)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_viii(index,a1,a2,a3){try{Module["dynCall_viii"](index,a1,a2,a3)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_viiiddi(index,a1,a2,a3,a4,a5,a6){try{Module["dynCall_viiiddi"](index,a1,a2,a3,a4,a5,a6)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_viiii(index,a1,a2,a3,a4){try{Module["dynCall_viiii"](index,a1,a2,a3,a4)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_viiiii(index,a1,a2,a3,a4,a5){try{Module["dynCall_viiiii"](index,a1,a2,a3,a4,a5)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_viiiiii(index,a1,a2,a3,a4,a5,a6){try{Module["dynCall_viiiiii"](index,a1,a2,a3,a4,a5,a6)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}function invoke_viiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){try{Module["dynCall_viiiiiiiii"](index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){if(typeof e!=="number"&&e!=="longjmp")throw e;Module["setThrew"](1,0)}}Module.asmGlobalArg={"Math":Math,"Int8Array":Int8Array,"Int16Array":Int16Array,"Int32Array":Int32Array,"Uint8Array":Uint8Array,"Uint16Array":Uint16Array,"Uint32Array":Uint32Array,"Float32Array":Float32Array,"Float64Array":Float64Array,"NaN":NaN,"Infinity":Infinity};Module.asmLibraryArg={"abort":abort,"assert":assert,"enlargeMemory":enlargeMemory,"getTotalMemory":getTotalMemory,"abortOnCannotGrowMemory":abortOnCannotGrowMemory,"invoke_d":invoke_d,"invoke_dddd":invoke_dddd,"invoke_di":invoke_di,"invoke_i":invoke_i,"invoke_ii":invoke_ii,"invoke_iii":invoke_iii,"invoke_iiidd":invoke_iiidd,"invoke_iiii":invoke_iiii,"invoke_iiiii":invoke_iiiii,"invoke_iiiiii":invoke_iiiiii,"invoke_iiiiiii":invoke_iiiiiii,"invoke_iiiiiiiiiii":invoke_iiiiiiiiiii,"invoke_v":invoke_v,"invoke_vi":invoke_vi,"invoke_vii":invoke_vii,"invoke_viii":invoke_viii,"invoke_viiiddi":invoke_viiiddi,"invoke_viiii":invoke_viiii,"invoke_viiiii":invoke_viiiii,"invoke_viiiiii":invoke_viiiiii,"invoke_viiiiiiiii":invoke_viiiiiiiii,"___assert_fail":___assert_fail,"___buildEnvironment":___buildEnvironment,"___clock_gettime":___clock_gettime,"___lock":___lock,"___map_file":___map_file,"___setErrNo":___setErrNo,"___syscall10":___syscall10,"___syscall140":___syscall140,"___syscall145":___syscall145,"___syscall146":___syscall146,"___syscall192":___syscall192,"___syscall195":___syscall195,"___syscall197":___syscall197,"___syscall20":___syscall20,"___syscall221":___syscall221,"___syscall3":___syscall3,"___syscall33":___syscall33,"___syscall5":___syscall5,"___syscall54":___syscall54,"___syscall6":___syscall6,"___syscall91":___syscall91,"___unlock":___unlock,"__exit":__exit,"_clock_gettime":_clock_gettime,"_emscripten_asm_const_iii":_emscripten_asm_const_iii,"_emscripten_get_now":_emscripten_get_now,"_emscripten_get_now_is_monotonic":_emscripten_get_now_is_monotonic,"_emscripten_memcpy_big":_emscripten_memcpy_big,"_exit":_exit,"_getenv":_getenv,"_gettimeofday":_gettimeofday,"_llvm_ceil_f64":_llvm_ceil_f64,"_llvm_fabs_f32":_llvm_fabs_f32,"_llvm_fabs_f64":_llvm_fabs_f64,"_llvm_floor_f64":_llvm_floor_f64,"_llvm_pow_f64":_llvm_pow_f64,"_llvm_sqrt_f32":_llvm_sqrt_f32,"_llvm_sqrt_f64":_llvm_sqrt_f64,"_llvm_trap":_llvm_trap,"_longjmp":_longjmp,"_setenv":_setenv,"_time":_time,"_times":_times,"DYNAMICTOP_PTR":DYNAMICTOP_PTR,"tempDoublePtr":tempDoublePtr,"ABORT":ABORT,"STACKTOP":STACKTOP,"STACK_MAX":STACK_MAX,"cttz_i8":cttz_i8};// EMSCRIPTEN_START_ASM
+var asm=(/** @suppress {uselessCode} */ function(global,env,buffer) {
+"use asm";var a=new global.Int8Array(buffer);var b=new global.Int16Array(buffer);var c=new global.Int32Array(buffer);var d=new global.Uint8Array(buffer);var e=new global.Uint16Array(buffer);var f=new global.Uint32Array(buffer);var g=new global.Float32Array(buffer);var h=new global.Float64Array(buffer);var i=env.DYNAMICTOP_PTR|0;var j=env.tempDoublePtr|0;var k=env.ABORT|0;var l=env.STACKTOP|0;var m=env.STACK_MAX|0;var n=env.cttz_i8|0;var o=0;var p=0;var q=0;var r=0;var s=global.NaN,t=global.Infinity;var u=0,v=0,w=0,x=0,y=0.0;var z=0;var A=global.Math.floor;var B=global.Math.abs;var C=global.Math.sqrt;var D=global.Math.pow;var E=global.Math.cos;var F=global.Math.sin;var G=global.Math.tan;var H=global.Math.acos;var I=global.Math.asin;var J=global.Math.atan;var K=global.Math.atan2;var L=global.Math.exp;var M=global.Math.log;var N=global.Math.ceil;var O=global.Math.imul;var P=global.Math.min;var Q=global.Math.max;var R=global.Math.clz32;var S=env.abort;var T=env.assert;var U=env.enlargeMemory;var V=env.getTotalMemory;var W=env.abortOnCannotGrowMemory;var X=env.invoke_d;var Y=env.invoke_dddd;var Z=env.invoke_di;var _=env.invoke_i;var $=env.invoke_ii;var aa=env.invoke_iii;var ba=env.invoke_iiidd;var ca=env.invoke_iiii;var da=env.invoke_iiiii;var ea=env.invoke_iiiiii;var fa=env.invoke_iiiiiii;var ga=env.invoke_iiiiiiiiiii;var ha=env.invoke_v;var ia=env.invoke_vi;var ja=env.invoke_vii;var ka=env.invoke_viii;var la=env.invoke_viiiddi;var ma=env.invoke_viiii;var na=env.invoke_viiiii;var oa=env.invoke_viiiiii;var pa=env.invoke_viiiiiiiii;var qa=env.___assert_fail;var ra=env.___buildEnvironment;var sa=env.___clock_gettime;var ta=env.___lock;var ua=env.___map_file;var va=env.___setErrNo;var wa=env.___syscall10;var xa=env.___syscall140;var ya=env.___syscall145;var za=env.___syscall146;var Aa=env.___syscall192;var Ba=env.___syscall195;var Ca=env.___syscall197;var Da=env.___syscall20;var Ea=env.___syscall221;var Fa=env.___syscall3;var Ga=env.___syscall33;var Ha=env.___syscall5;var Ia=env.___syscall54;var Ja=env.___syscall6;var Ka=env.___syscall91;var La=env.___unlock;var Ma=env.__exit;var Na=env._clock_gettime;var Oa=env._emscripten_asm_const_iii;var Pa=env._emscripten_get_now;var Qa=env._emscripten_get_now_is_monotonic;var Ra=env._emscripten_memcpy_big;var Sa=env._exit;var Ta=env._getenv;var Ua=env._gettimeofday;var Va=env._llvm_ceil_f64;var Wa=env._llvm_fabs_f32;var Xa=env._llvm_fabs_f64;var Ya=env._llvm_floor_f64;var Za=env._llvm_pow_f64;var _a=env._llvm_sqrt_f32;var $a=env._llvm_sqrt_f64;var ab=env._llvm_trap;var bb=env._longjmp;var cb=env._setenv;var db=env._time;var eb=env._times;var fb=0.0;
+// EMSCRIPTEN_START_FUNCS
+function rQ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,A=0;y=l;l=l+16|0;j=y+8|0;x=4;w=T1(40)|0;c[w>>2]=0;s=y;o=0;e=$(29,b|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,w|0,x|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;a:do if((g|0)!=1){o=0;v=$(23,e<<3|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){e=V6(c[f>>2]|0,w|0,x|0)|0;if(!e)bb(f|0,p|0);z=p}else e=-1;if((e|0)!=1){if(!(a[194954]|0))t=0;else{o=0;e=_(3)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,w|0,x|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){n=0;k=0;m=0;j=0;i=0;d=v;h=v;e=z;g=0;u=74;break}t=e}o=0;e=aa(97,b|0,110064)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,w|0,x|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)!=1){c[47323]=0;b:do if(e|0?(i=a[e>>0]|0,i<<24>>24):0){h=e;f=i;g=0;while(1){i=h+1|0;if(!(f<<24>>24))break b;switch(f<<24>>24|0){case 99:{e=8;u=16;break}case 105:{e=18;u=16;break}case 109:{e=1;u=16;break}case 114:{e=4;u=16;break}case 115:{e=2;u=16;break}default:e=g}if((u|0)==16){u=0;e=g|e;c[47323]=e}h=i;f=a[i>>0]|0;g=e}}while(0);if(d|0){o=0;ca(20,0,110068,j|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){n=0;k=t;m=0;j=0;i=0;d=v;h=v;e=z;g=0;u=74;break}}o=0;r=aa(98,b|0,0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)!=1){m=c[r+16>>2]|0;if(c[47323]&2|0){o=0;ja(26,c[15712]|0,m|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){n=0;k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break}}o=0;g=$(25,b|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)!=1){n=0;while(1){if(!g)break;o=0;e=aa(99,b|0,g|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){h=V6(c[f>>2]|0,w|0,x|0)|0;if(!h)bb(f|0,p|0);z=p}else h=-1;if((h|0)==1){k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break a}i=n;while(1){if(!e)break;if(!((c[47134]|0)==2?(c[(c[e+16>>2]|0)+8>>2]|0)!=0:0))u=32;c:do if((u|0)==32){u=0;do if(a[194954]|0){k=c[e>>2]&3;j=(c[c[((k|0)==3?e:e+48|0)+40>>2]>>2]|0)>>>4;k=(c[c[((k|0)==2?e:e+-48|0)+40>>2]>>2]|0)>>>4;if(j>>>0>k>>>0){o=0;f=ca(34,t|0,k|0,j|0)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){d=V6(c[h>>2]|0,w|0,x|0)|0;if(!d)bb(h|0,p|0);z=p}else d=-1;if((d|0)==1){k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break a}if(f|0)break c;o=0;ka(64,t|0,k|0,j|0);f=o;o=0;if((f|0)!=0&(p|0)!=0){h=V6(c[f>>2]|0,w|0,x|0)|0;if(!h)bb(f|0,p|0);z=p}else h=-1;if((h|0)==1){k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break a}break}else{o=0;f=ca(34,t|0,j|0,k|0)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){d=V6(c[h>>2]|0,w|0,x|0)|0;if(!d)bb(h|0,p|0);z=p}else d=-1;if((d|0)==1){k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break a}if(f|0)break c;o=0;ka(64,t|0,j|0,k|0);f=o;o=0;if((f|0)!=0&(p|0)!=0){h=V6(c[f>>2]|0,w|0,x|0)|0;if(!h)bb(f|0,p|0);z=p}else h=-1;if((h|0)==1){k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break a}break}}while(0);c[v+(i<<3)+4>>2]=e;o=0;d=$(30,e|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){h=V6(c[f>>2]|0,w|0,x|0)|0;if(!h)bb(f|0,p|0);z=p}else h=-1;if((h|0)==1){k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break a}c[v+(i<<3)>>2]=d;i=i+1|0}while(0);o=0;e=aa(100,b|0,e|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){h=V6(c[f>>2]|0,w|0,x|0)|0;if(!h)bb(f|0,p|0);z=p}else h=-1;if((h|0)==1){k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break a}}o=0;g=aa(86,b|0,g|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74;break a}n=i}o=0;j=$(31,n<<3|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)!=1){o=0;ma(28,v|0,n|0,8,101);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)!=1){g=c[m>>2]|0;o=0;ia(110,g+2|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)!=1){i=c[m+16>>2]|0;d=i+(g*40|0)|0;i=i+((g+1|0)*40|0)|0;q=c[15712]|0;h=0;while(1){if((h|0)>=(n|0))break;if((h|0)>0?c[47323]&16|0:0){o=0;ja(26,q|0,m|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74;break a}}e=c[v+(h<<3)+4>>2]|0;k=c[e>>2]&3;g=c[(c[(c[((k|0)==3?e:e+48|0)+40>>2]|0)+16>>2]|0)+112>>2]|0;e=c[(c[(c[((k|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0)+112>>2]|0;if((g|0)==(e|0)){o=0;ma(29,m|0,g|0,i|0,d|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74;break a}}else{o=0;ka(65,m|0,e|0,i|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74;break a}o=0;ka(65,m|0,g|0,d|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74;break a}}o=0;e=ca(35,m|0,i|0,d|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,w|0,x|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74;break a}if(e|0){k=t;m=j;i=r;d=v;h=v;break a}e=j+(h<<3)|0;o=0;ma(30,s|0,m|0,d|0,i|0);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,w|0,x|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74;break a}g=s;k=c[g+4>>2]|0;c[e>>2]=c[g>>2];c[e+4>>2]=k;o=0;ia(111,m|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74;break a}h=h+1|0}o=0;ha(8);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)!=1){o=0;e=$(32,r|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,w|0,x|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)!=1){c[r+20>>2]=e;o=0;e=$(33,r|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,w|0,x|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)!=1){c[r+24>>2]=e;o=0;ka(66,n|0,j|0,r|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)!=1){w=U6(189296,1,w|0,x|0)|0;x=z;o=0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)!=1){k=t;m=j;i=r;d=v;h=v;e=0;g=q;u=74}else{k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74}}else{k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74}}else{k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74}}else{k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74}}else{k=t;m=j;i=r;d=v;h=v;e=z;g=q;u=74}}else{k=t;m=j;i=r;d=v;h=v;e=z;g=0;u=74}}else{k=t;m=j;i=r;d=v;h=v;e=z;g=0;u=74}}else{k=t;m=0;i=r;d=v;h=v;e=z;g=0;u=74}}else{n=0;k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74}}else{n=0;k=t;m=0;j=0;i=r;d=v;h=v;e=z;g=0;u=74}}else{n=0;k=t;m=0;j=0;i=0;d=v;h=v;e=z;g=0;u=74}}else{n=0;k=0;m=0;j=0;i=0;d=0;h=v;e=z;g=0;u=74}}else{n=0;k=0;m=0;j=0;i=0;d=0;h=0;e=z;g=0;u=74}while(0);d:while(1){if((u|0)==74){u=0;if(e|0){q=g;continue}o=0;ia(112,i|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){A=g;f=h;q=d;r=i;s=j;t=m;v=k;u=n;e=z;g=A;h=f;d=q;i=r;j=s;m=t;k=v;n=u;u=74;continue}if(c[47323]&4|0){o=0;na(8,g|0,i|0,n|0,m|0,d|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){f=g;q=h;r=d;s=i;t=j;v=m;A=k;u=n;e=z;g=f;h=q;d=r;i=s;j=t;m=v;k=A;n=u;u=74;continue}}o=0;oa(10,b|0,i|0,n|0,m|0,d|0,0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){f=g;q=h;r=d;s=i;t=j;v=m;A=k;u=n;e=z;g=f;h=q;d=r;i=s;j=t;m=v;k=A;n=u;u=74;continue}q=g}if(!(a[194954]|0))e=0;else{o=0;ia(113,k|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;g=q;u=74;continue}e=0}while(1){if((e|0)>=(n|0))break;o=0;ia(105,c[m+(e<<3)+4>>2]|0);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,w|0,x|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;g=q;u=74;continue d}e=e+1|0}o=0;ia(105,j|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;g=q;u=74;continue}o=0;ia(114,i|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;g=q;u=74;continue}o=0;ia(105,h|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,x|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;g=q;u=74}else break}U1(w|0);l=y;return}function sQ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0;q=l;l=l+64|0;p=q+32|0;o=q+16|0;j=q;k=q+48|0;T4(110935,a)|0;T4(110946,a)|0;m=b+16|0;n=j+8|0;i=k+4|0;g=0;while(1){if((g|0)>=(c[b>>2]|0))break;d=c[m>>2]|0;e=c[d+(g*40|0)+20>>2]|0;f=c[d+(g*40|0)+24>>2]|0;if((e|0)==(f|0)){lR(j,e);d=~~+h[j>>3];e=~~+h[n>>3]}else{nR(k,(c[e>>2]&1|0)==0?e:f,d+(g*40|0)|0);d=c[k>>2]|0;e=c[i>>2]|0}c[o>>2]=g;c[o+4>>2]=d;c[o+8>>2]=e;w4(a,110966,o)|0;g=g+1|0}f=b+4|0;e=b+20|0;d=0;while(1){if((d|0)>=(c[f>>2]|0))break;o=c[e>>2]|0;b=c[o+(d*24|0)+16>>2]|0;r=+h[o+(d*24|0)>>3];c[p>>2]=c[o+(d*24|0)+12>>2];c[p+4>>2]=b;h[p+8>>3]=r;w4(a,110986,p)|0;d=d+1|0}T4(111008,a)|0;l=q;return}function tQ(a){a=a|0;var b=0.0,d=0.0,e=0,f=0;f=c[a>>2]&3;e=c[(c[((f|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0;a=c[(c[((f|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0;b=+h[e+16>>3]-+h[a+16>>3];d=+h[e+24>>3]-+h[a+24>>3];return ~~(d*d+b*b)|0}function uQ(a,b){a=a|0;b=b|0;return (c[a>>2]|0)-(c[b>>2]|0)|0}function vQ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+32|0;j=n;lR(n+16|0,d);k=d+32|0;m=d+36|0;g=0;while(1){if((g|0)>=(c[k>>2]|0))break;h=c[(c[m>>2]|0)+(g<<2)>>2]|0;do if(!(a[h+36>>0]|0)){i=c[h+20>>2]|0;if((i|0)==(d|0)){jR(j,h,c[h+24>>2]|0);ER(b,f,h,0.0)|0;break}else{jR(j,h,i);ER(b,e,h,0.0)|0;break}}while(0);g=g+1|0}c[b>>2]=(c[b>>2]|0)+2;l=n;return}function wQ(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+32|0;h=k;lR(k+16|0,b);i=b+32|0;j=b+36|0;f=0;while(1){if((f|0)>=(c[i>>2]|0))break;g=c[(c[j>>2]|0)+(f<<2)>>2]|0;e=c[g+20>>2]|0;if((e|0)==(b|0))e=c[g+24>>2]|0;jR(h,g,e);ER(a,d,g,0.0)|0;f=f+1|0}c[a>>2]=(c[a>>2]|0)+1;c[d+24>>2]=b;c[d+20>>2]=b;l=k;return}function xQ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0.0,m=0.0,n=0,o=0.0,p=0.0,q=0,r=0,s=0,t=0,u=0,v=0,w=0.0,x=0.0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0;H=l;l=l+128|0;C=H+64|0;n=H+48|0;D=H+32|0;E=H+16|0;F=H;g=0;i=e;while(1){if(!i)break;g=g+1|0;i=c[i+8>>2]|0}B=JK((g*56|0)+-112|0)|0;c[C+52>>2]=0;c[C+48>>2]=0;A=e+8|0;e=c[A>>2]|0;i=c[e+8>>2]|0;g=c[e+20>>2]|0;if(c[g>>2]&1)g=c[e+24>>2]|0;jR(n,e,g);j=e;q=e;p=0.0;o=+h[n>>3];z=0;m=0.0;x=+h[n+8>>3];while(1){y=i+8|0;if(!(c[y>>2]|0))break;v=kR(j,i)|0;cV(d,v,c[q+12>>2]|0);t=q+36|0;u=i+36|0;if((a[t>>0]|0)==(a[u>>0]|0))if((c[y>>2]|0)==(f|0)){jR(E,i,v);e=E;G=12}else{j=q;e=z;k=x}else{lR(D,v);e=D;G=12}if((G|0)==12){G=0;w=+h[e>>3];k=+h[e+8>>3];e=a[t>>0]|0;s=e<<24>>24==0;r=(q|0)==(c[A>>2]|0);if(s){if(!(a[u>>0]|0))e=0;else e=(c[i+20>>2]|0)==(v|0)?4:2;n=v+48|0;q=g+48|0;j=g+40|0;g=r?0:p>o?4:2}else{if(e<<24>>24==(a[u>>0]|0))e=0;else e=(c[i+20>>2]|0)==(v|0)?1:3;n=v+40|0;q=g+40|0;j=g+48|0;g=r?0:m>x?1:3}mR(C,s&1,+h[j>>3],+h[q>>3],+h[n>>3],g,e);q=z+1|0;g=B+(z*56|0)|0;e=C;j=g+56|0;do{c[g>>2]=c[e>>2];g=g+4|0;e=e+4|0}while((g|0)<(j|0));if((a[t>>0]|0)!=(a[u>>0]|0)?(c[y>>2]|0)==(f|0):0){jR(F,i,v);n=(a[u>>0]|0)==0;e=v+40|0;g=v+48|0;p=+h[(n?g:e)>>3];mR(C,n&1,+h[(n?e:g)>>3],p,p,n?(o>w?4:2):x>k?1:3,0);n=z+2|0;g=B+(q*56|0)|0;e=C;j=g+56|0;do{c[g>>2]=c[e>>2];g=g+4|0;e=e+4|0}while((g|0)<(j|0));g=v;j=i;p=o;o=w;e=n;m=x}else{g=v;j=i;p=o;o=w;e=q;m=x}}v=i;i=c[y>>2]|0;q=j;z=e;x=k;j=v}i=W1(B,z*56|0)|0;e=z+-1|0;g=0;while(1){if((g|0)>=(z|0))break;if((g|0)>0)c[i+(g*56|0)+48>>2]=i+((g+-1|0)*56|0);if((g|0)<(e|0)){G=g+1|0;c[i+(g*56|0)+52>>2]=i+(G*56|0);g=G;continue}else{g=g+1|0;continue}}c[b>>2]=z;c[b+4>>2]=i;l=H;return}function yQ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0;j=Vz(25692,c[4581]|0)|0;k=a+8|0;i=0;while(1){if((i|0)>=(c[a>>2]|0))break;b=(c[k>>2]|0)+(i*72|0)|0;if(!(c[b>>2]&4)){while(1){d=c[(c[b+36>>2]|0)+8>>2]|0;if(!d)break;d=c[d+20>>2]|0;if(!d)break;if(!(c[d>>2]&1))b=d;else break}g=JK(40)|0;c[g+36>>2]=b;h[g+8>>3]=+h[b+40>>3];f=b;d=c[b>>2]|0;while(1){c[f>>2]=d|4;d=c[c[f+36>>2]>>2]|0;if(!d)break;e=c[d+24>>2]|0;if(!e)break;d=c[e>>2]|0;if(d&1|0)break;else f=e}h[g+16>>3]=+h[f+56>>3];eR(j,g,+h[b+48>>3])}i=i+1|0}return j|0}function zQ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0;j=Vz(25692,c[4581]|0)|0;k=a+8|0;i=0;while(1){if((i|0)>=(c[a>>2]|0))break;b=(c[k>>2]|0)+(i*72|0)|0;if(!(c[b>>2]&2)){while(1){d=c[(c[b+36>>2]|0)+12>>2]|0;if(!d)break;d=c[d+20>>2]|0;if(!d)break;if(!(c[d>>2]&1))b=d;else break}g=JK(40)|0;c[g+36>>2]=b;h[g+8>>3]=+h[b+48>>3];f=b;d=c[b>>2]|0;while(1){c[f>>2]=d|2;d=c[(c[f+36>>2]|0)+4>>2]|0;if(!d)break;e=c[d+24>>2]|0;if(!e)break;d=c[e>>2]|0;if(d&1|0)break;else f=e}h[g+16>>3]=+h[f+64>>3];eR(j,g,+h[b+40>>3])}i=i+1|0}return j|0}function AQ(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;j=e+20|0;i=e+24|0;e=0;while(1){if((e|0)>=(b|0))break;g=c[d+(e<<3)>>2]|0;h=c[d+(e<<3)+4>>2]|0;f=0;while(1){if((f|0)>=(g|0))break;k=h+(f*56|0)|0;dR(JQ(c[((a[k>>0]|0)==0?j:i)>>2]|0,k)|0,k);f=f+1|0}e=e+1|0}return}function BQ(a){a=a|0;var b=0,d=0;d=a+20|0;LQ(c[d>>2]|0);b=a+24|0;LQ(c[b>>2]|0);MQ(c[d>>2]|0);MQ(c[b>>2]|0);NQ(c[d>>2]|0,a);NQ(c[b>>2]|0,a);OQ(c[d>>2]|0);OQ(c[b>>2]|0);return}function CQ(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0,B=0.0,C=0.0,D=0,E=0.0;z=l;l=l+224|0;n=z+176|0;m=z+208|0;x=z+160|0;y=z+128|0;k=z+96|0;t=z+64|0;u=z;o=z+32|0;v=u+8|0;h[v>>3]=1797693134862315708145274.0e284;h[u>>3]=1797693134862315708145274.0e284;w=u+24|0;h[w>>3]=-1797693134862315708145274.0e284;p=u+16|0;h[p>>3]=-1797693134862315708145274.0e284;T4(110183,a)|0;c[n>>2]=10;c[n+4>>2]=10;w4(a,110589,n)|0;T4(138265,a)|0;i=b+4|0;j=b+12|0;q=t+8|0;r=t+16|0;s=t+24|0;g=0;while(1){if((g|0)>=(c[i>>2]|0))break;D=(c[j>>2]|0)+(g*72|0)+40|0;c[t>>2]=c[D>>2];c[t+4>>2]=c[D+4>>2];c[t+8>>2]=c[D+8>>2];c[t+12>>2]=c[D+12>>2];c[t+16>>2]=c[D+16>>2];c[t+20>>2]=c[D+20>>2];c[t+24>>2]=c[D+24>>2];c[t+28>>2]=c[D+28>>2];C=+h[q>>3];B=+h[r>>3];A=+h[s>>3];h[k>>3]=+h[t>>3];h[k+8>>3]=C;h[k+16>>3]=B;h[k+24>>3]=A;w4(a,138284,k)|0;g=g+1|0}g=0;while(1){if((g|0)>=(d|0))break;D=c[f+(g<<3)+4>>2]|0;k=e+(g<<3)|0;c[m>>2]=c[k>>2];c[m+4>>2]=c[k+4>>2];c[n>>2]=c[u>>2];c[n+4>>2]=c[u+4>>2];c[n+8>>2]=c[u+8>>2];c[n+12>>2]=c[u+12>>2];c[n+16>>2]=c[u+16>>2];c[n+20>>2]=c[u+20>>2];c[n+24>>2]=c[u+24>>2];c[n+28>>2]=c[u+28>>2];KQ(o,a,D,m,b,n);c[u>>2]=c[o>>2];c[u+4>>2]=c[o+4>>2];c[u+8>>2]=c[o+8>>2];c[u+12>>2]=c[o+12>>2];c[u+16>>2]=c[o+16>>2];c[u+20>>2]=c[o+20>>2];c[u+24>>2]=c[o+24>>2];c[u+28>>2]=c[o+28>>2];g=g+1|0}T4(110606,a)|0;i=b+8|0;g=0;while(1){if((g|0)>=(c[b>>2]|0))break;D=(c[i>>2]|0)+(g*72|0)+40|0;c[t>>2]=c[D>>2];c[t+4>>2]=c[D+4>>2];c[t+8>>2]=c[D+8>>2];c[t+12>>2]=c[D+12>>2];c[t+16>>2]=c[D+16>>2];c[t+20>>2]=c[D+20>>2];c[t+24>>2]=c[D+24>>2];c[t+28>>2]=c[D+28>>2];E=+h[t>>3];A=+h[q>>3];B=+h[r>>3];C=+h[s>>3];h[y>>3]=E;h[y+8>>3]=A;h[y+16>>3]=B;h[y+24>>3]=C;w4(a,138321,y)|0;h[u>>3]=+h[(+h[u>>3]<E?u:t)>>3];h[v>>3]=+h[(+h[v>>3]<A?u:t)+8>>3];h[p>>3]=+h[(+h[p>>3]>B?u:t)+16>>3];h[w>>3]=+h[(+h[w>>3]>C?u:t)+24>>3];g=g+1|0}v=~~(+h[v>>3]+10.0);y=~~(+h[p>>3]+10.0);D=~~(+h[w>>3]+10.0);c[x>>2]=~~(+h[u>>3]+10.0);c[x+4>>2]=v;c[x+8>>2]=y;c[x+12>>2]=D;w4(a,110631,x)|0;l=z;return}function DQ(b,d,e,f,g,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0;N=l;l=l+112|0;F=N+88|0;E=N+72|0;M=N+32|0;I=N+16|0;J=N;K=N+56|0;L=N+40|0;G=c[15712]|0;w=(i|0)==0;x=J+8|0;y=I+8|0;v=0;m=0;n=0;o=0;p=0;i=0;while(1){if((v|0)>=(e|0))break;z=c[g+(v<<3)+4>>2]|0;A=z+48|0;u=(c[(c[((c[z>>2]&3|0)==3?z:A)+40>>2]|0)+16>>2]|0)+16|0;B=z+16|0;C=(c[B>>2]|0)+16|0;c[E>>2]=c[u>>2];c[E+4>>2]=c[u+4>>2];c[E+8>>2]=c[u+8>>2];c[E+12>>2]=c[u+12>>2];c[F>>2]=c[C>>2];c[F+4>>2]=c[C+4>>2];c[F+8>>2]=c[C+8>>2];c[F+12>>2]=c[C+12>>2];EQ(K,E,F);c[I>>2]=c[K>>2];c[I+4>>2]=c[K+4>>2];c[I+8>>2]=c[K+8>>2];c[I+12>>2]=c[K+12>>2];C=z+-48|0;u=(c[(c[((c[z>>2]&3|0)==2?z:C)+40>>2]|0)+16>>2]|0)+16|0;t=(c[B>>2]|0)+56|0;c[E>>2]=c[u>>2];c[E+4>>2]=c[u+4>>2];c[E+8>>2]=c[u+8>>2];c[E+12>>2]=c[u+12>>2];c[F>>2]=c[t>>2];c[F+4>>2]=c[t+4>>2];c[F+8>>2]=c[t+8>>2];c[F+12>>2]=c[t+12>>2];EQ(L,E,F);c[J>>2]=c[L>>2];c[J+4>>2]=c[L+4>>2];c[J+8>>2]=c[L+8>>2];c[J+12>>2]=c[L+12>>2];t=c[f+(v<<3)>>2]|0;u=c[f+(v<<3)+4>>2]|0;s=t*3|0;D=s+1|0;if((s|0)>=(n|0)){if(m|0)U1(i);p=KK(D<<4)|0;m=p;n=D;o=p;i=p}if(!(a[u>>0]|0)){k=+(GQ(u,d)|0);j=+h[I>>3]}else{j=+FQ(u,d);k=+h[y>>3]}r=m+16|0;h[m+16>>3]=j;h[m+24>>3]=k;c[p>>2]=c[r>>2];c[p+4>>2]=c[r+4>>2];c[p+8>>2]=c[r+8>>2];c[p+12>>2]=c[r+12>>2];r=1;q=u;s=2;while(1){if((r|0)>=(t|0))break;q=u+(r*56|0)|0;if(!(a[q>>0]|0))k=+(GQ(q,d)|0);else j=+FQ(q,d);O=m+(s+2<<4)|0;Q=m+(s+1<<4)|0;P=m+(s<<4)|0;h[P>>3]=j;h[m+(s<<4)+8>>3]=k;c[Q>>2]=c[P>>2];c[Q+4>>2]=c[P+4>>2];c[Q+8>>2]=c[P+8>>2];c[Q+12>>2]=c[P+12>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];r=r+1|0;s=s+3|0}if(!(a[q>>0]|0)){k=+(GQ(q,d)|0);j=+h[J>>3]}else{j=+FQ(q,d);k=+h[x>>3]}Q=m+(s<<4)|0;O=s+1|0;P=m+(O<<4)|0;h[P>>3]=j;h[m+(O<<4)+8>>3]=k;c[Q>>2]=c[P>>2];c[Q+4>>2]=c[P+4>>2];c[Q+8>>2]=c[P+8>>2];c[Q+12>>2]=c[P+12>>2];if(0>1){P=KB(c[((c[z>>2]&3|0)==3?z:A)+40>>2]|0)|0;Q=KB(c[((c[z>>2]&3|0)==2?z:C)+40>>2]|0)|0;c[M>>2]=P;c[M+4>>2]=Q;w4(G,110142,M)|0}dO(z,c[((c[z>>2]&3|0)==2?z:C)+40>>2]|0,m,D,25680);if((!w?(H=c[(c[B>>2]|0)+96>>2]|0,H|0):0)?(a[H+81>>0]|0)==0:0){c[E>>2]=c[I>>2];c[E+4>>2]=c[I+4>>2];c[E+8>>2]=c[I+8>>2];c[E+12>>2]=c[I+12>>2];c[F>>2]=c[J>>2];c[F+4>>2]=c[J+4>>2];c[F+8>>2]=c[J+8>>2];c[F+12>>2]=c[J+12>>2];yO(b,z,E,F)}v=v+1|0}U1(o);l=N;return}function EQ(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;h[b>>3]=+h[b>>3]+ +h[d>>3];e=b+8|0;h[e>>3]=+h[e>>3]+ +h[d+8>>3];c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];return}function FQ(a,b){a=a|0;b=b|0;var d=0.0,e=0;b=JQ(c[b+24>>2]|0,a)|0;e=c[b+36>>2]|0;d=+h[e+40>>3];return +((+h[e+56>>3]-d)*(+(c[a+44>>2]|0)/+((c[b+24>>2]|0)+1|0))+d)}function GQ(a,b){a=a|0;b=b|0;var d=0.0,e=0;e=JQ(c[b+20>>2]|0,a)|0;b=c[e+36>>2]|0;d=+h[b+48>>3];return ~~((1.0-+(c[a+44>>2]|0)/+((c[e+24>>2]|0)+1|0))*(+h[b+64>>3]-d)+d)|0}function HQ(a){a=a|0;return 0}function IQ(a){a=a|0;return 0}function JQ(a,b){a=a|0;b=b|0;a=nb[c[a>>2]&63](a,b+8|0,512)|0;if(!a)qa(110155,110161,423,110169);a=c[a+16>>2]|0;a=nb[c[a>>2]&63](a,b+16|0,512)|0;if(!a)qa(110180,110161,425,110169);else return a|0;return 0}function KQ(b,d,e,f,g,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0.0,m=0,n=0,o=0,p=0.0,q=0.0,r=0.0,s=0.0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0.0,C=0.0,D=0.0;z=l;l=l+32|0;y=z+16|0;u=z+8|0;m=z;j=c[(c[(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0)+16>>2]|0)+112>>2]|0;t=c[f+4>>2]|0;if(!(a[t>>0]|0)){k=+h[j+56>>3];s=+h[j+40>>3];j=GQ(t,g)|0;k=(s+k)*.5}else{k=+h[j+64>>3];s=+h[j+48>>3];j=~~((s+k)*.5);k=+FQ(t,g)}A=~~k;p=+h[i>>3];r=+(A|0);p=p<r?p:r;h[i>>3]=p;v=i+8|0;q=+h[v>>3];s=+(j|0);q=q<s?q:s;h[v>>3]=q;w=i+16|0;k=+h[w>>3];r=k>r?k:r;h[w>>3]=r;x=i+24|0;k=+h[x>>3];s=k>s?k:s;h[x>>3]=s;c[m>>2]=A;c[m+4>>2]=j;w4(d,110682,m)|0;o=c[f>>2]|0;m=t;n=1;f=A;while(1){if((n|0)>=(o|0))break;m=t+(n*56|0)|0;if(!(a[m>>0]|0))j=GQ(m,g)|0;else f=~~+FQ(m,g);B=+(f|0);D=p<B?p:B;h[i>>3]=D;k=+(j|0);C=q<k?q:k;h[v>>3]=C;B=r>B?r:B;h[w>>3]=B;k=s>k?s:k;h[x>>3]=k;c[u>>2]=f;c[u+4>>2]=j;w4(d,110704,u)|0;n=n+1|0;p=D;q=C;r=B;s=k}j=c[(c[(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0)+112>>2]|0;if(!(a[m>>0]|0)){k=+h[j+56>>3];D=+h[j+40>>3];j=GQ(m,g)|0;k=(D+k)*.5}else{k=+h[j+64>>3];D=+h[j+48>>3];j=~~((D+k)*.5);k=+FQ(m,g)}A=~~k;C=+(A|0);h[i>>3]=p<C?p:C;D=+(j|0);h[v>>3]=q<D?q:D;h[w>>3]=r>C?r:C;h[x>>3]=s>D?s:D;c[y>>2]=A;c[y+4>>2]=j;w4(d,110718,y)|0;c[b>>2]=c[i>>2];c[b+4>>2]=c[i+4>>2];c[b+8>>2]=c[i+8>>2];c[b+12>>2]=c[i+12>>2];c[b+16>>2]=c[i+16>>2];c[b+20>>2]=c[i+20>>2];c[b+24>>2]=c[i+24>>2];c[b+28>>2]=c[i+28>>2];l=z;return}function LQ(a){a=a|0;var b=0;a=Tz(a)|0;while(1){if(!a)break;b=Tz(c[a+16>>2]|0)|0;while(1){if(!b)break;c[b+32>>2]=oR(c[b+24>>2]|0)|0;b=c[b>>2]|0}a=c[a>>2]|0}return}function MQ(a){a=a|0;var b=0;a=Tz(a)|0;while(1){if(!a)break;b=Tz(c[a+16>>2]|0)|0;while(1){if(!b)break;if(c[b+24>>2]|0)cR(b);b=c[b>>2]|0}a=c[a>>2]|0}return}function NQ(a,b){a=a|0;b=b|0;var d=0;d=Tz(a)|0;while(1){if(!d)break;a=Tz(c[d+16>>2]|0)|0;while(1){if(!a)break;SQ(a,b);a=c[a>>2]|0}d=c[d>>2]|0}return}function OQ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0;e=Tz(a)|0;while(1){if(!e)break;i=e+8|0;f=Tz(c[e+16>>2]|0)|0;while(1){if(!f)break;a=f+24|0;a:do if(c[a>>2]|0){if(c[47323]&8|0)PQ(f,~~+h[i>>3]);g=f+32|0;tR(c[g>>2]|0);b=c[a>>2]|0;d=f+28|0;a=0;while(1){if((a|0)>=(b|0))break a;c[(c[(c[d>>2]|0)+(a<<2)>>2]|0)+44>>2]=(c[(c[(c[g>>2]|0)+4>>2]|0)+(a*12|0)+4>>2]|0)+1;a=a+1|0}}while(0);f=c[f>>2]|0}e=c[e>>2]|0}return}function PQ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0.0,m=0.0;j=l;l=l+32|0;d=j;g=a+24|0;a:do if((c[g>>2]|0)>=2){i=c[15712]|0;m=+h[a+8>>3];k=+h[a+16>>3];c[d>>2]=b;h[d+8>>3]=m;h[d+16>>3]=k;w4(i,110739,d)|0;f=a+32|0;a=a+28|0;b=0;while(1){if((b|0)>=(c[g>>2]|0))break a;e=c[(c[(c[f>>2]|0)+4>>2]|0)+(b*12|0)+8>>2]|0;b:do if(Xz(e)|0){QQ(i,c[(c[a>>2]|0)+(b<<2)>>2]|0);T4(110759,i)|0;d=nb[c[e>>2]&63](e,0,128)|0;while(1){if(!d)break b;T4(110764,i)|0;QQ(i,c[(c[a>>2]|0)+(c[d>>2]<<2)>>2]|0);T4(153599,i)|0;d=nb[c[e>>2]&63](e,d,8)|0}}while(0);b=b+1|0}}while(0);l=j;return}function QQ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0.0,k=0.0,m=0.0;g=l;l=l+80|0;f=g+40|0;e=g;if(!(a[d>>0]|0)){m=+h[d+16>>3];j=+h[d+8>>3];k=+h[d+24>>3];i=RQ(c[d+32>>2]|0)|0;e=RQ(c[d+36>>2]|0)|0;h[f>>3]=m;h[f+8>>3]=j;h[f+16>>3]=k;h[f+24>>3]=j;c[f+32>>2]=i;c[f+36>>2]=e;w4(b,110770,f)|0}else{k=+h[d+8>>3];j=+h[d+16>>3];m=+h[d+24>>3];f=RQ(c[d+32>>2]|0)|0;i=RQ(c[d+36>>2]|0)|0;h[e>>3]=k;h[e+8>>3]=j;h[e+16>>3]=k;h[e+24>>3]=m;c[e+32>>2]=f;c[e+36>>2]=i;w4(b,110770,e)|0}l=g;return}function RQ(a){a=a|0;switch(a|0){case 0:{a=110821;break}case 1:{a=110816;break}case 2:{a=110809;break}case 3:{a=110802;break}case 4:{a=110794;break}default:a=0}return a|0}function SQ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0;w=l;l=l+16|0;r=w+8|0;t=w;u=c[a+32>>2]|0;v=c[a+28>>2]|0;p=a+24|0;m=r+4|0;n=t+4|0;j=0;a=c[p>>2]|0;a:while(1){q=j+1|0;if((q|0)>=(a|0))break;o=v+(j<<2)|0;k=q;while(1){if((k|0)>=(a|0)){j=q;continue a}b:do if(((sR(u,j,k)|0)==0?(sR(u,k,j)|0)==0:0)?(s=v+(k<<2)|0,TQ(c[o>>2]|0,c[s>>2]|0)|0):0){f=c[o>>2]|0;a=c[f+48>>2]|0;i=c[s>>2]|0;d=c[i+48>>2]|0;e=(d|0)==0;if(a)if(e)a=1;else a=+h[a+8>>3]!=+h[d+8>>3];else a=e^1;g=a&1;UQ(r,f,i,0,g);a=c[r>>2]|0;i=c[m>>2]|0;f=g^1;UQ(t,c[o>>2]|0,c[s>>2]|0,1,f);d=c[t>>2]|0;e=c[n>>2]|0;switch(i|0){case -1:{VQ(c[s>>2]|0,c[o>>2]|0,g,0,a,b);VQ(c[s>>2]|0,c[o>>2]|0,f,1,d,b);if((e|0)!=1)break b;WQ(c[o>>2]|0,c[s>>2]|0,f,b);break b}case 0:switch(e|0){case -1:{VQ(c[s>>2]|0,c[o>>2]|0,g,0,a,b);VQ(c[s>>2]|0,c[o>>2]|0,f,1,d,b);break b}case 0:{VQ(c[o>>2]|0,c[s>>2]|0,0,g,a,b);VQ(c[o>>2]|0,c[s>>2]|0,1,f,d,b);break b}case 1:{VQ(c[o>>2]|0,c[s>>2]|0,0,g,a,b);VQ(c[o>>2]|0,c[s>>2]|0,1,f,d,b);break b}default:break b}case 1:{VQ(c[o>>2]|0,c[s>>2]|0,0,g,a,b);VQ(c[o>>2]|0,c[s>>2]|0,1,f,d,b);if((e|0)!=-1)break b;WQ(c[o>>2]|0,c[s>>2]|0,f,b);break b}default:break b}}while(0);k=k+1|0;a=c[p>>2]|0}}l=w;return}function TQ(a,b){a=a|0;b=b|0;if(!(+h[a+8>>3]==+h[b+8>>3]))qa(110892,110161,843,110923);if((+h[a+16>>3]==+h[b+16>>3]?+h[a+24>>3]==+h[b+24>>3]:0)?(c[a+32>>2]|0)==(c[b+32>>2]|0):0)a=(c[a+36>>2]|0)==(c[b+36>>2]|0)&1;else a=0;return a|0}function UQ(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;g=0;while(1){b=XQ(b,e)|0;if(!b){h=0;break}d=XQ(d,f)|0;if(!d){i=6;break}if(!(TQ(b,d)|0)){i=7;break}g=g+1|0}if((i|0)==6)qa(138394,110161,871,110841);else if((i|0)==7)h=ZQ(b,YQ(b,d)|0,g+1|0,1-e|0)|0;c[a>>2]=g;c[a+4>>2]=h;return}function VQ(b,d,e,f,g,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,l=0,m=0,n=0,o=0,p=0;o=i+20|0;n=i+24|0;i=JQ(c[((a[b>>0]|0)==0?o:n)>>2]|0,b)|0;qR(c[i+32>>2]|0,c[b+40>>2]|0,c[d+40>>2]|0);k=1;while(1){if((k|0)>(g|0))break;l=XQ(b,e)|0;m=XQ(d,f)|0;do if(!(a[b>>0]|0)){j=JQ(c[n>>2]|0,l)|0;if(+h[l+8>>3]==+h[b+16>>3]){p=(c[b+32>>2]|0)==1;i=(sR(c[i+32>>2]|0,c[b+40>>2]|0,c[d+40>>2]|0)|0)!=0;b=c[j+32>>2]|0;if(p)if(i){qR(b,c[m+40>>2]|0,c[l+40>>2]|0);i=j;break}else{qR(b,c[l+40>>2]|0,c[m+40>>2]|0);i=j;break}else if(i){qR(b,c[l+40>>2]|0,c[m+40>>2]|0);i=j;break}else{qR(b,c[m+40>>2]|0,c[l+40>>2]|0);i=j;break}}else{p=(c[b+36>>2]|0)==1;i=(sR(c[i+32>>2]|0,c[b+40>>2]|0,c[d+40>>2]|0)|0)!=0;b=c[j+32>>2]|0;if(p)if(i){qR(b,c[l+40>>2]|0,c[m+40>>2]|0);i=j;break}else{qR(b,c[m+40>>2]|0,c[l+40>>2]|0);i=j;break}else if(i){qR(b,c[m+40>>2]|0,c[l+40>>2]|0);i=j;break}else{qR(b,c[l+40>>2]|0,c[m+40>>2]|0);i=j;break}}}else{j=JQ(c[o>>2]|0,l)|0;if(+h[l+8>>3]==+h[b+16>>3]){p=(c[b+32>>2]|0)==2;i=(sR(c[i+32>>2]|0,c[b+40>>2]|0,c[d+40>>2]|0)|0)!=0;b=c[j+32>>2]|0;if(p)if(i){qR(b,c[l+40>>2]|0,c[m+40>>2]|0);i=j;break}else{qR(b,c[m+40>>2]|0,c[l+40>>2]|0);i=j;break}else if(i){qR(b,c[m+40>>2]|0,c[l+40>>2]|0);i=j;break}else{qR(b,c[l+40>>2]|0,c[m+40>>2]|0);i=j;break}}else{p=(c[b+36>>2]|0)==2;i=(sR(c[i+32>>2]|0,c[b+40>>2]|0,c[d+40>>2]|0)|0)!=0;b=c[j+32>>2]|0;if(p)if(i){qR(b,c[m+40>>2]|0,c[l+40>>2]|0);i=j;break}else{qR(b,c[l+40>>2]|0,c[m+40>>2]|0);i=j;break}else if(i){qR(b,c[l+40>>2]|0,c[m+40>>2]|0);i=j;break}else{qR(b,c[m+40>>2]|0,c[l+40>>2]|0);i=j;break}}}while(0);b=l;d=m;k=k+1|0}return}function WQ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0;while(1){if(!(TQ(b,d)|0))break;g=XQ(b,1)|0;d=XQ(d,e)|0;b=g}g=c[(JQ(c[((a[b>>0]|0)==0?f+20|0:f+24|0)>>2]|0,b)|0)+32>>2]|0;rR(g,c[b+40>>2]|0,c[d+40>>2]|0);return}function XQ(a,b){a=a|0;b=b|0;if(!a)qa(110828,110161,798,110832);else return c[((b|0)==0?a+48|0:a+52|0)>>2]|0;return 0}function YQ(b,c){b=b|0;c=c|0;var d=0,e=0,f=0;f=l;l=l+16|0;e=f;d=a[b>>0]|0;if(d<<24>>24==(a[c>>0]|0)?!(+h[b+8>>3]!=+h[c+8>>3]):0){if(!(d<<24>>24))b=_Q(b,c,3,1)|0;else b=_Q(b,c,4,2)|0;l=f;return b|0}gA(1,110854,e)|0;bb(189296,1);return 0}function ZQ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0;j=1;while(1){if((j|0)>(e|0))break;i=XQ(b,f)|0;g=+h[i+8>>3]==+h[b+16>>3];do if(!(a[b>>0]|0))if(g){g=(c[b+32>>2]|0)==1;break}else{g=(c[b+36>>2]|0)==3;break}else if(g){g=(c[b+32>>2]|0)==4;break}else{g=(c[b+36>>2]|0)==2;break}while(0);b=i;j=j+1|0;d=g?0-d|0:d}return d|0}function _Q(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0.0,k=0.0,l=0,m=0,n=0,o=0;j=+h[a+24>>3];k=+h[b+16>>3];do if(!(j<k)?(g=+h[a+16>>3],i=+h[b+24>>3],!(g>i)):0){if(k<j&g<k){b=$Q(a,b,d,e)|0;break}if(k<g&g<i){b=0-($Q(b,a,d,e)|0)|0;break}if(!(g==k))if(j==k){d=c[a+36>>2]|0;return ((d|0)==(c[b+32>>2]|0)?0:(d|0)==(e|0)?1:-1)|0}else{d=c[a+32>>2]|0;b=(d|0)==(c[b+36>>2]|0)?0:(d|0)==(e|0)?1:-1;break}if(!(j==i))if(j<i){o=(c[a+36>>2]|0)==(d|0);d=bR(c[b+32>>2]|0,c[a+32>>2]|0,d,e)|0;return (o?d:0-d|0)|0}else{o=(c[b+36>>2]|0)==(e|0);b=bR(c[a+32>>2]|0,c[b+32>>2]|0,d,e)|0;b=o?b:0-b|0;break}o=c[a+32>>2]|0;n=c[b+32>>2]|0;if((o|0)==(n|0)){b=c[b+36>>2]|0;if((c[a+36>>2]|0)==(b|0)){b=0;break}}else b=c[b+36>>2]|0;m=(n|0)==(d|0);if((n|0)==(b|0)){if(m){b=1;break}if((n|0)==(e|0)){b=-1;break}if((o|0)!=(d|0)?(c[a+36>>2]|0)!=(d|0):0){b=1;break}if((o|0)!=(e|0)?(c[a+36>>2]|0)!=(e|0):0){b=-1;break}b=0;break}f=(b|0)==(e|0);if(m&f){b=(c[a+36>>2]|0)==(e|0);if((o|0)==(d|0)){if(!b){b=-1;break}}else if(b){b=1;break}b=0;break}l=(b|0)==(d|0);if((n|0)==(e|0)&l){b=(o|0)==(e|0);if((c[a+36>>2]|0)==(d|0)){if(!b){b=-1;break}}else if(b){b=1;break}b=0;break}if(!n){if(l){b=aR(o,c[a+36>>2]|0,d)|0;break}if(f){b=0-(aR(o,c[a+36>>2]|0,e)|0)|0;break}}f=c[a+36>>2]|0;if(m&(b|0)==0){b=aR(f,o,d)|0;break}else{b=0-(aR(f,o,e)|0)|0;break}}else b=0;while(0);return b|0}function $Q(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0.0;i=+h[a+24>>3];j=+h[b+24>>3];do if(i<j){a=c[a+36>>2]|0;if((a|0)==(d|0)?(c[b+32>>2]|0)==(e|0):0){a=-1;break}if((a|0)==(e|0)?(c[b+32>>2]|0)==(d|0):0){a=1;break}a=0}else{f=c[b+32>>2]|0;g=(f|0)==(e|0);if(i==j)if(g){a=bR(c[a+36>>2]|0,c[b+36>>2]|0,d,e)|0;break}else{a=0-(bR(c[b+36>>2]|0,c[a+36>>2]|0,d,e)|0)|0;break}if(g?(c[b+36>>2]|0)==(e|0):0){a=-1;break}if((f|0)==(d|0)?(c[b+36>>2]|0)==(d|0):0){a=1;break}a=0}while(0);return a|0}function aR(a,b,c){a=a|0;b=b|0;c=c|0;return ((a|0)==(c|0)?((b|0)==(c|0))<<31>>31:1)|0}function bR(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;if((a|0)==(d|0)){b=(a|0)==0;if(b)return 0;else b=b&1}return ((a|0)!=0|(b|0)!=(c|0))<<31>>31|0}function cR(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;g=c[a+28>>2]|0;h=c[a+24>>2]|0;d=c[a+32>>2]|0;a=0;a:while(1){e=a+1|0;if((e|0)>=(h|0))break;f=g+(a<<2)|0;b=e;while(1){if((b|0)==(h|0)){a=e;continue a}switch(YQ(c[f>>2]|0,c[g+(b<<2)>>2]|0)|0){case 1:{qR(d,a,b);break}case -1:{qR(d,b,a);break}default:{}}b=b+1|0}}return}function dR(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=a+24|0;e=c[f>>2]|0;d=e+1|0;c[f>>2]=d;c[b+40>>2]=e;e=a+28|0;a=c[e>>2]|0;if(!a)a=KK(d<<2)|0;else a=MK(a,d<<2)|0;c[e>>2]=a;c[a+((c[f>>2]|0)+-1<<2)>>2]=b;return}function eR(a,b,d){a=a|0;b=b|0;d=+d;var e=0,f=0,g=0;g=l;l=l+16|0;f=g;h[f>>3]=d;e=nb[c[a>>2]&63](a,f,512)|0;if(!e){e=JK(24)|0;h[e+8>>3]=+h[f>>3];c[e+16>>2]=Vz(25728,c[4581]|0)|0;nb[c[a>>2]&63](a,e,1)|0}f=c[e+16>>2]|0;nb[c[f>>2]&63](f,b,1)|0;l=g;return}function fR(a,b,d){a=a|0;b=b|0;d=d|0;pR(c[b+32>>2]|0);U1(c[b+28>>2]|0);U1(b);return}function gR(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0.0,f=0.0;e=+h[b>>3];f=+h[c>>3];if(!(e>f))if(e<f)a=!(+h[b+8>>3]>=+h[c+8>>3])<<31>>31;else a=0;else a=!(+h[b+8>>3]<=+h[c+8>>3])&1;return a|0}function hR(a,b,d){a=a|0;b=b|0;d=d|0;Rz(c[b+16>>2]|0)|0;U1(b);return}function iR(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0.0,f=0.0;f=+h[b>>3];e=+h[c>>3];return (f>e?1:(f<e)<<31>>31)|0}function jR(b,d,e){b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0,j=0;i=(a[d+36>>0]|0)!=0;j=e+40|0;do if((c[d+24>>2]|0)==(e|0)){f=+h[j>>3];if(i){g=(+h[e+64>>3]+ +h[e+48>>3])*.5;break}else{g=+h[e+48>>3];f=(+h[e+56>>3]+f)*.5;break}}else if(i){g=(+h[e+64>>3]+ +h[e+48>>3])*.5;f=+h[e+56>>3];break}else{g=+h[e+64>>3];f=(+h[e+56>>3]+ +h[j>>3])*.5;break}while(0);h[b>>3]=f;h[b+8>>3]=g;return}function kR(a,b){a=a|0;b=b|0;var d=0;d=c[a+20>>2]|0;if((d|0)!=(c[b+20>>2]|0)?(d|0)!=(c[b+24>>2]|0):0)d=c[a+24>>2]|0;return d|0}function lR(a,b){a=a|0;b=b|0;var c=0.0;c=(+h[b+64>>3]+ +h[b+48>>3])*.5;h[a>>3]=(+h[b+56>>3]+ +h[b+40>>3])*.5;h[a+8>>3]=c;return}function mR(b,d,e,f,g,i,j){b=b|0;d=d|0;e=+e;f=+f;g=+g;i=i|0;j=j|0;var k=0,l=0;a[b>>0]=d;h[b+8>>3]=e;if(f<g){k=0;l=i;d=j;e=f;f=g}else{k=1;l=j;d=i;e=g}h[b+24>>3]=f;h[b+16>>3]=e;c[b+36>>2]=d;c[b+32>>2]=l;a[b+1>>0]=k;return}function nR(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=c[b+36>>2]|0;do if((c[e+4>>2]|0)!=(d|0)){if((c[e+12>>2]|0)==(d|0)){e=~~((+h[b+56>>3]+ +h[b+40>>3])*.5);d=~~+h[b+48>>3];break}if((c[e+8>>2]|0)==(d|0)){e=~~+h[b+40>>3];d=~~((+h[b+64>>3]+ +h[b+48>>3])*.5);break}if((c[e>>2]|0)==(d|0)){e=~~+h[b+56>>3];d=~~((+h[b+64>>3]+ +h[b+48>>3])*.5)}else{e=0;d=0}}else{e=~~((+h[b+56>>3]+ +h[b+40>>3])*.5);d=~~+h[b+64>>3]}while(0);c[a>>2]=e;c[a+4>>2]=d;return}function oR(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;d=JK(8)|0;c[d>>2]=a;e=d+4|0;c[e>>2]=JK(a*12|0)|0;b=0;while(1){if((b|0)>=(a|0))break;g=PU()|0;f=c[e>>2]|0;c[f+(b*12|0)+8>>2]=g;c[f+(b*12|0)>>2]=0;b=b+1|0}return d|0}function pR(a){a=a|0;var b=0,d=0,e=0;d=a+4|0;b=0;while(1){e=c[d>>2]|0;if((b|0)>=(c[a>>2]|0))break;Rz(c[e+(b*12|0)+8>>2]|0)|0;b=b+1|0}U1(e);U1(a);return}function qR(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=l;l=l+16|0;f=e;c[f>>2]=d;d=c[(c[a+4>>2]|0)+(b*12|0)+8>>2]|0;nb[c[d>>2]&63](d,f,1)|0;l=e;return}function rR(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;e=l;l=l+16|0;f=e;c[f>>2]=d;a=a+4|0;g=c[(c[a>>2]|0)+(b*12|0)+8>>2]|0;nb[c[g>>2]&63](g,f,2)|0;c[f>>2]=b;d=c[(c[a>>2]|0)+(d*12|0)+8>>2]|0;nb[c[d>>2]&63](d,f,2)|0;l=e;return}function sR(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=l;l=l+16|0;f=e;c[f>>2]=d;d=c[(c[a+4>>2]|0)+(b*12|0)+8>>2]|0;d=(nb[c[d>>2]&63](d,f,512)|0)!=0&1;l=e;return d|0}function tR(a){a=a|0;var b=0,d=0,e=0,f=0;b=c[a>>2]|0;switch(b|0){case 0:break;case 1:{c[(c[a+4>>2]|0)+4>>2]=0;break}default:{e=uR(b)|0;f=a+4|0;d=0;b=0;while(1){if((d|0)>=(c[a>>2]|0)){b=0;break}if(!(c[(c[f>>2]|0)+(d*12|0)>>2]|0))b=vR(a,d,b,e)|0;d=d+1|0}while(1){d=wR(e)|0;if((d|0)<=-1)break;c[(c[f>>2]|0)+(d*12|0)+4>>2]=b;b=b+1|0}xR(e)}}return}function uR(a){a=a|0;var b=0;b=JK(8)|0;c[b+4>>2]=JK(a<<2)|0;c[b>>2]=-1;return b|0}function vR(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;i=a+4|0;g=c[i>>2]|0;j=g+(b*12|0)|0;c[j>>2]=1;g=c[g+(b*12|0)+8>>2]|0;h=g+4|0;d=d+1|0;g=Tz(g)|0;while(1){if(!g)break;f=c[(c[h>>2]|0)+8>>2]|0;if((f|0)<0)f=c[g+8>>2]|0;else f=g+(0-f)|0;f=c[f>>2]|0;if(!(c[(c[i>>2]|0)+(f*12|0)>>2]|0))d=vR(a,f,d,e)|0;g=c[g>>2]|0}c[j>>2]=2;yR(e,b);return d+1|0}function wR(a){a=a|0;var b=0,d=0;b=c[a>>2]|0;if((b|0)==-1)a=-1;else{d=c[(c[a+4>>2]|0)+(b<<2)>>2]|0;c[a>>2]=b+-1;a=d}return a|0}function xR(a){a=a|0;U1(c[a+4>>2]|0);U1(a);return}function yR(a,b){a=a|0;b=b|0;var d=0;d=(c[a>>2]|0)+1|0;c[a>>2]=d;c[(c[a+4>>2]|0)+(d<<2)>>2]=b;return}function zR(a){a=a|0;var d=0,e=0,f=0;e=c[a>>2]|0;c[a+8>>2]=e;c[a+12>>2]=c[a+4>>2];d=a+16|0;a=0;while(1){if((a|0)>=(e|0))break;f=c[d>>2]|0;b[f+(a*40|0)+18>>1]=b[f+(a*40|0)+16>>1]|0;a=a+1|0}return}function AR(a){a=a|0;var d=0,e=0,f=0;d=c[a+8>>2]|0;c[a>>2]=d;c[a+4>>2]=c[a+12>>2];e=a+16|0;a=0;while(1){if((a|0)>=(d|0))break;f=c[e>>2]|0;b[f+(a*40|0)+16>>1]=b[f+(a*40|0)+18>>1]|0;a=a+1|0}d=d+2|0;while(1){if((a|0)>=(d|0))break;b[(c[e>>2]|0)+(a*40|0)+16>>1]=0;a=a+1|0}return}function BR(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=JK(((c[a>>2]|0)*6|0)+(b<<1)<<2)|0;c[a+20>>2]=JK((((c[a>>2]|0)*3|0)+b|0)*24|0)|0;e=c[a>>2]|0;f=a+16|0;a=0;while(1){if((a|0)>=(e|0))break;c[(c[f>>2]|0)+(a*40|0)+28>>2]=d;d=d+24|0;a=a+1|0}e=e+2|0;while(1){if((a|0)>=(e|0))break;c[(c[f>>2]|0)+(a*40|0)+28>>2]=d;d=d+(b<<2)|0;a=a+1|0}return}function CR(a){a=a|0;var b=0;b=JK(24)|0;c[b>>2]=0;c[b+16>>2]=JK(a*40|0)|0;return b|0}function DR(a){a=a|0;var b=0,d=0;d=c[a+16>>2]|0;b=c[a>>2]|0;c[d+(b*40|0)+32>>2]=b;c[a>>2]=b+1;return d+(b*40|0)|0}function ER(a,b,d,e){a=a|0;b=b|0;d=d|0;e=+e;var f=0,g=0;g=a+4|0;f=c[g>>2]|0;c[g>>2]=f+1;g=c[a+20>>2]|0;a=g+(f*24|0)|0;c[g+(f*24|0)+12>>2]=c[b+32>>2];c[g+(f*24|0)+16>>2]=c[d+32>>2];h[a>>3]=e;c[g+(f*24|0)+8>>2]=0;FR(b,f);FR(d,f);return a|0}function FR(a,d){a=a|0;d=d|0;var e=0,f=0;e=a+16|0;f=b[e>>1]|0;c[(c[a+28>>2]|0)+(f<<16>>16<<2)>>2]=d;b[e>>1]=f+1<<16>>16;return}function GR(a){a=a|0;var b=0;b=a+16|0;U1(c[(c[b>>2]|0)+28>>2]|0);U1(c[b>>2]|0);U1(c[a+20>>2]|0);U1(a);return}function HR(a,d,e){a=a|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0;g=c[a>>2]|0;i=a+16|0;f=0;while(1){if((f|0)>=(g|0))break;c[(c[i>>2]|0)+(f*40|0)>>2]=-2147483648;f=f+1|0}XU();a:do if(!(_U(d)|0)){c[d+8>>2]=0;c[d>>2]=0;d=a+20|0;b:while(1){g=aV()|0;if(!g){f=0;break a}c[g>>2]=0-(c[g>>2]|0);if((g|0)==(e|0)){f=0;break a}i=g+16|0;j=g+28|0;f=0;while(1){if((f|0)>=(b[i>>1]|0))continue b;k=(c[d>>2]|0)+((c[(c[j>>2]|0)+(f<<2)>>2]|0)*24|0)|0;l=IR(a,k,g)|0;m=c[l>>2]|0;do if((m|0)<0){n=~~-(+h[k>>3]+ +(c[g>>2]|0));if((m|0)==-2147483648){c[l>>2]=n;if(_U(l)|0){f=1;break a}c[l+8>>2]=g;c[l+12>>2]=k;break}else{if((m|0)>=(n|0))break;bV(l,n);c[l+8>>2]=g;c[l+12>>2]=k;break}}while(0);f=f+1|0}}}else f=1;while(0);return f|0}function IR(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=c[b+12>>2]|0;if((e|0)==(c[d+32>>2]|0))e=c[b+16>>2]|0;return (c[a+16>>2]|0)+(e*40|0)|0}function JR(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;g=c[a>>2]|0;LF(g,3,b)|0;b=c[g+92>>2]|0;do if(b){d=c[b+16>>2]|0;h=c[d+12>>2]|0;c[a+76>>2]=h;e=c[d+16>>2]|0;c[a+84>>2]=e;f=a+80|0;c[f>>2]=c[d>>2];c[a+88>>2]=c[b+4>>2];d=a+152|0;e=c[d>>2]|c[e>>2];c[d>>2]=e;b=c[g+80>>2]|0;if(!b){c[a+60>>2]=0;b=999;break}else{g=c[b+16>>2]|0;c[a+60>>2]=c[g+12>>2];i=c[g+16>>2]|0;c[a+68>>2]=i;c[a+72>>2]=c[b+4>>2];c[d>>2]=e|c[i>>2];c[a+64>>2]=c[((h|0)==0?f:g)>>2];b=300;break}}else b=999;while(0);return b|0}function KR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(!(kF(a)|0))if((b|0)!=0?(d=c[b>>2]|0,(d|0)!=0):0){tb[d&127](a);a=0}else a=0;else a=1;return a|0}function LR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+4>>2]|0,d|0):0)tb[d&127](a);c[(c[a>>2]|0)+24>>2]=0;sF(a);return}function MR(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0.0,i=0.0;e=+h[b+504>>3];f=+h[b+512>>3];i=+h[b+352>>3];if(!(c[b+360>>2]|0)){g=+h[d>>3]+e;d=d+8|0;e=f}else g=-(f+ +h[d+8>>3]);f=+h[b+528>>3]*i*(+h[d>>3]+e);h[a>>3]=+h[b+520>>3]*i*g;h[a+8>>3]=f;return}function NR(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0,k=0.0;f=+h[a+504>>3];g=+h[a+512>>3];i=+h[a+352>>3];j=+h[a+520>>3]*i;i=+h[a+528>>3]*i;a:do if(!(c[a+360>>2]|0)){a=0;while(1){if((a|0)>=(e|0))break a;h[d+(a<<4)>>3]=(+h[b+(a<<4)>>3]+f)*j;h[d+(a<<4)+8>>3]=(+h[b+(a<<4)+8>>3]+g)*i;a=a+1|0}}else{a=0;while(1){if((a|0)>=(e|0))break a;k=-(j*(+h[b+(a<<4)+8>>3]+g));h[d+(a<<4)+8>>3]=(+h[b+(a<<4)>>3]+f)*i;h[d+(a<<4)>>3]=k;a=a+1|0}}while(0);return d|0}function OR(a,b){a=a|0;b=b|0;var d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+8>>2]|0,d|0):0)tb[d&127](a);return}function PR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+12>>2]|0,d|0):0)tb[d&127](a);rF(a);return}function QR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+24>>2]|0,d|0):0)tb[d&127](a);return}function RR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+28>>2]|0,d|0):0)tb[d&127](a);return}function SR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+16>>2]|0,d|0):0){b=c[a+160>>2]|0;xb[d&31](a,c[(c[(c[a>>2]|0)+308>>2]|0)+(b<<2)>>2]|0,b,c[a+156>>2]|0)}return}function TR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+20>>2]|0,d|0):0)tb[d&127](a);return}function UR(a,b){a=a|0;b=b|0;var d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+32>>2]|0,d|0):0)tb[d&127](a);return}function VR(a,b){a=a|0;b=b|0;var d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+36>>2]|0,d|0):0)tb[d&127](a);return}function WR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+40>>2]|0,d|0):0)tb[d&127](a);return}function XR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+44>>2]|0,d|0):0)tb[d&127](a);return}function YR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+48>>2]|0,d|0):0)tb[d&127](a);return}function ZR(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+52>>2]|0,d|0):0)tb[d&127](a);return}function _R(a,b){a=a|0;b=b|0;var d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+56>>2]|0,d|0):0)tb[d&127](a);return}function $R(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+60>>2]|0,d|0):0)tb[d&127](a);return}function aS(a,b){a=a|0;b=b|0;var d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+64>>2]|0,d|0):0)tb[d&127](a);return}function bS(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+68>>2]|0,d|0):0)tb[d&127](a);return}function cS(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;g=c[a+60>>2]|0;if(g|0?(h=c[g+72>>2]|0,h|0):0)yb[h&15](a,b,d,e,f);return}function dS(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+76>>2]|0,d|0):0)tb[d&127](a);return}function eS(a,b){a=a|0;b=b|0;var d=0,e=0;d=c[a+60>>2]|0;if(d|0?(e=c[d+80>>2]|0,e|0):0)ub[e&63](a,b);return}function fS(a){a=a|0;var b=0,d=0;b=c[a+60>>2]|0;if(b|0?(d=c[b+84>>2]|0,d|0):0)tb[d&127](a);return}function gS(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0;k=l;l=l+48|0;f=k+32|0;h=k;i=k+16|0;j=c[b+60>>2]|0;m=c[e>>2]|0;do if(m|0?a[m>>0]|0:0){m=c[b+16>>2]|0;if(m|0?(c[m+144>>2]|0)==0:0)break;if(!(c[b+152>>2]&8192)){c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];MR(i,b,f);c[h>>2]=c[i>>2];c[h+4>>2]=c[i+4>>2];c[h+8>>2]=c[i+8>>2];c[h+12>>2]=c[i+12>>2]}else{c[h>>2]=c[d>>2];c[h+4>>2]=c[d+4>>2];c[h+8>>2]=c[d+8>>2];c[h+12>>2]=c[d+12>>2]}if(j|0?(g=c[j+88>>2]|0,g|0):0){c[f>>2]=c[h>>2];c[f+4>>2]=c[h+4>>2];c[f+8>>2]=c[h+8>>2];c[f+12>>2]=c[h+12>>2];vb[g&127](b,f,e)}}while(0);l=k;return}function hS(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;f=c[b+60>>2]|0;g=(c[b+16>>2]|0)+16|0;i=R2(d,58)|0;h=(i|0)!=0;if(h)a[i>>0]=0;if(f|0?(iS(c[b+68>>2]|0,d,g),e=c[f+92>>2]|0,e|0):0)ub[e&63](b,g);if(h)a[i>>0]=58;return}function iS(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+32|0;g=k+16|0;j=k+8|0;i=k;c[d>>2]=b;c[d+32>>2]=5;e=zT(b)|0;f=c[a+16>>2]|0;if(!((f|0)!=0?(u5(e,f,c[a+20>>2]|0,4,102)|0)!=0:0))h=3;a:do if((h|0)==3){switch(AT(b,d,c[a+24>>2]|0)|0){case 0:break a;case 1:break;default:{gA(1,111046,g)|0;break a}}a=KK((l2(b)|0)+16|0)|0;c[i>>2]=b;m4(a,111011,i)|0;if(PJ(a)|0){c[j>>2]=b;gA(0,111020,j)|0}U1(a)}while(0);l=k;return}function jS(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;f=c[b+60>>2]|0;g=(c[b+16>>2]|0)+56|0;i=R2(d,58)|0;h=(i|0)!=0;if(h)a[i>>0]=0;if(f|0?(iS(c[b+68>>2]|0,d,g),e=c[f+92>>2]|0,e|0):0)ub[e&63](b,g);if(h)a[i>>0]=58;return}function kS(a,b,d,e){a=a|0;b=b|0;d=d|0;e=+e;var f=0,h=0,i=0,j=0;h=c[a+60>>2]|0;i=a+16|0;j=(c[i>>2]|0)+96|0;if(h|0?(iS(c[a+68>>2]|0,b,j),f=c[h+92>>2]|0,f|0):0)ub[f&63](a,j);j=c[i>>2]|0;c[j+136>>2]=d;g[j+140>>2]=e;return}function lS(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0;m=l;l=l+16|0;k=m;j=c[b+60>>2]|0;b=c[b+16>>2]|0;c[b+160>>2]=d;a:do if((d|0)!=0&(j|0)!=0){j=b+144|0;i=b+152|0;g=b+148|0;b=d;b:while(1){f=b+4|0;b=c[b>>2]|0;if(!b)break a;c:do switch(a[b>>0]|0){case 115:{if(!(s2(b,111067)|0)){c[j>>2]=3;b=f;continue b}if(!(s2(b,111088)|0)){e=115;while(1){d=b+1|0;if(!(e<<24>>24))break;b=d;e=a[d>>0]|0}h[i>>3]=+r5(d);b=f;continue b}break}case 100:{if(!(s2(b,137327)|0)){c[j>>2]=1;b=f;continue b}if(!(s2(b,137320)|0)){c[j>>2]=2;b=f;continue b}break}case 105:{if(s2(b,134335)|0?s2(b,111073)|0:0)break c;c[j>>2]=0;b=f;continue b}case 98:{if(!(s2(b,111083)|0)){h[i>>3]=2.0;b=f;continue b}break}case 102:{if(!(s2(b,111101)|0)){c[g>>2]=1;b=f;continue b}break}case 117:{if(!(s2(b,111108)|0)){c[g>>2]=0;b=f;continue b}break}case 116:{if(!(s2(b,111117)|0)){b=f;continue b}break}default:{}}while(0);c[k>>2]=b;gA(0,111125,k)|0;b=f}}while(0);l=m;return}function mS(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0;j=l;l=l+32|0;g=j;d=c[a+60>>2]|0;if((d|0?(i=d+96|0,f=c[i>>2]|0,f|0):0)?c[(c[a+16>>2]|0)+144>>2]|0:0){d=b+16|0;h[g>>3]=(+h[d>>3]+ +h[b>>3])*.5;h[g+8>>3]=(+h[b+24>>3]+ +h[b+8>>3])*.5;b=g+16|0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];if(!(c[a+152>>2]&8192)){NR(a,g,g,2)|0;f=c[i>>2]|0}vb[f&127](a,g,e)}l=j;return}function nS(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+48|0;m=o;f=c[a+60>>2]|0;if((f|0?(n=f+100|0,c[n>>2]|0):0)?(k=a+16|0,j=c[k>>2]|0,c[j+144>>2]|0):0){if(!(e&4)){f=e;g=0}else{f=e&-5;e=j+16|0;g=m;h=e;i=g+40|0;do{c[g>>2]=c[h>>2];g=g+4|0;h=h+4|0}while((g|0)<(i|0));g=e;h=j+56|0;i=g+40|0;do{c[g>>2]=c[h>>2];g=g+4|0;h=h+4|0}while((g|0)<(i|0));g=1}if(!(c[a+152>>2]&8192)){e=d+10|0;if((c[47363]|0)<(d|0)){c[47363]=e;e=MK(c[47364]|0,e<<4)|0;c[47364]=e}else e=c[47364]|0;NR(a,b,e,d)|0;xb[c[n>>2]&31](a,e,d,f)}else xb[c[n>>2]&31](a,b,d,f);if(g|0){g=(c[k>>2]|0)+16|0;h=m;i=g+40|0;do{c[g>>2]=c[h>>2];g=g+4|0;h=h+4|0}while((g|0)<(i|0))}}l=o;return}function oS(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;e=l;l=l+64|0;f=e;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];g=f+32|0;b=b+16|0;c[g>>2]=c[b>>2];c[g+4>>2]=c[b+4>>2];c[g+8>>2]=c[b+8>>2];c[g+12>>2]=c[b+12>>2];h[f+16>>3]=+h[f>>3];h[f+24>>3]=+h[f+40>>3];h[f+48>>3]=+h[g>>3];h[f+56>>3]=+h[f+8>>3];nS(a,f,4,d);l=e;return}function pS(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0;i=c[a+60>>2]|0;do if((i|0?(j=i+104|0,h=c[j>>2]|0,h|0):0)?c[(c[a+16>>2]|0)+144>>2]|0:0){if(c[a+152>>2]&8192|0){zb[h&15](a,b,d,e,f,g&255);break}h=d+10|0;if((c[47363]|0)<(d|0)){c[47363]=h;h=MK(c[47364]|0,h<<4)|0;c[47364]=h}else h=c[47364]|0;NR(a,b,h,d)|0;zb[c[j>>2]&15](a,h,d,e,f,g&255)}while(0);return}function qS(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;e=c[a+60>>2]|0;do if((e|0?(g=e+108|0,f=c[g>>2]|0,f|0):0)?c[(c[a+16>>2]|0)+144>>2]|0:0){if(c[a+152>>2]&8192|0){vb[f&127](a,b,d);break}e=d+10|0;if((c[47363]|0)<(d|0)){c[47363]=e;e=MK(c[47364]|0,e<<4)|0;c[47364]=e}else e=c[47364]|0;NR(a,b,e,d)|0;vb[c[g>>2]&127](a,e,d)}while(0);return}function rS(b,d){b=b|0;d=d|0;var e=0,f=0;e=c[b+60>>2]|0;if((d|0?!((e|0)==0|(a[d>>0]|0)==0):0)?(f=c[e+112>>2]|0,f|0):0)ub[f&63](b,d);return}function sS(b,d,e,f,g,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0.0,k=0.0,m=0,n=0,o=0.0,p=0.0,q=0.0,r=0.0,s=0,t=0.0,u=0.0,v=0.0,w=0.0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0;F=l;l=l+112|0;B=F+64|0;E=F;n=F+96|0;x=F+48|0;y=F+32|0;C=c[b+60>>2]|0;if(!b)qa(111282,111178,691,111189);if(!d)qa(111208,111178,692,111189);if(!(a[d>>0]|0))qa(111213,111178,693,111189);D=NF(d)|0;if(!D){if((C|0)!=0&(iN(d)|0)!=0?(m=c[C+116>>2]|0,m|0):0)yb[m&15](b,d,e,f,g&255)}else{s=b+432|0;c[B>>2]=c[s>>2];c[B+4>>2]=c[s+4>>2];c[B+8>>2]=c[s+8>>2];c[B+12>>2]=c[s+12>>2];QF(n,D,B);s=c[n>>2]|0;m=c[n+4>>2]|0;if(!((s|0)<1&(m|0)<1)){n=E+16|0;c[n>>2]=c[e>>2];c[n+4>>2]=c[e+4>>2];c[n+8>>2]=c[e+8>>2];c[n+12>>2]=c[e+12>>2];c[E>>2]=c[e>>2];c[E+4>>2]=c[e+4>>2];c[E+8>>2]=c[e+8>>2];c[E+12>>2]=c[e+12>>2];z=E+8|0;A=E+24|0;d=1;while(1){if((d|0)>=(f|0))break;G=e+(d<<4)|0;v=+h[G>>3];h[E>>3]=+h[(+h[E>>3]<v?E:G)>>3];w=+h[e+(d<<4)+8>>3];h[z>>3]=+h[(+h[z>>3]<w?E:G)+8>>3];h[n>>3]=+h[(+h[n>>3]>v?n:G)>>3];h[A>>3]=+h[(+h[A>>3]>w?n:G)+8>>3];d=d+1|0}k=+h[n>>3];j=+h[E>>3];t=k-j;u=+h[A>>3];v=+h[z>>3];w=u-v;o=+(m|0);p=+(s|0);q=t/p;r=w/o;a:do switch(tS(i)|0){case 1:if(q<r){p=q*p;o=q*o;break a}else{p=r*p;o=r*o;break a}case 2:{p=q*p;break}case 3:{o=r*o;break}case 4:{p=q*p;o=r*o;break}default:{}}while(0);if(p<t){t=(t-p)*.5;j=j+t;h[E>>3]=j;k=k-t;h[n>>3]=k}if(o<w){w=(w-o)*.5;h[z>>3]=v+w;h[A>>3]=u-w}if(!(c[b+152>>2]&8192)){c[B>>2]=c[E>>2];c[B+4>>2]=c[E+4>>2];c[B+8>>2]=c[E+8>>2];c[B+12>>2]=c[E+12>>2];MR(x,b,B);c[E>>2]=c[x>>2];c[E+4>>2]=c[x+4>>2];c[E+8>>2]=c[x+8>>2];c[E+12>>2]=c[x+12>>2];c[B>>2]=c[n>>2];c[B+4>>2]=c[n+4>>2];c[B+8>>2]=c[n+8>>2];c[B+12>>2]=c[n+12>>2];MR(y,b,B);c[n>>2]=c[y>>2];c[n+4>>2]=c[y+4>>2];c[n+8>>2]=c[y+8>>2];c[n+12>>2]=c[y+12>>2];k=+h[n>>3];j=+h[E>>3]}if(j>k){h[E>>3]=k;h[n>>3]=j}j=+h[z>>3];k=+h[A>>3];if(j>k){h[z>>3]=k;h[A>>3]=j}if(C|0){G=c[b+72>>2]|0;c[B>>2]=c[E>>2];c[B+4>>2]=c[E+4>>2];c[B+8>>2]=c[E+8>>2];c[B+12>>2]=c[E+12>>2];c[B+16>>2]=c[E+16>>2];c[B+20>>2]=c[E+20>>2];c[B+24>>2]=c[E+24>>2];c[B+28>>2]=c[E+28>>2];xS(b,D,B,g,G)}}}l=F;return}function tS(b){b=b|0;if(a[b>>0]|0)if(c3(b,141116)|0)if(c3(b,141048)|0)if(!(c3(b,111221)|0))b=4;else b=(dP(b)|0)<<24>>24!=0&1;else b=3;else b=2;else b=0;return b|0}function uS(a,b){a=a|0;b=+b;if(c[a+60>>2]|0)h[(c[a+16>>2]|0)+152>>3]=b;return}function vS(a,b){a=a|0;b=b|0;b=LF(a,1,b)|0;if(!b)b=999;else{b=c[b+16>>2]|0;c[a+184>>2]=c[b+4>>2];c[a+172>>2]=c[b+12>>2];c[a+176>>2]=c[b>>2];c[a+180>>2]=c[b+16>>2];b=300}return b|0}function wS(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;f=i;TC(b,137483,280,1)|0;g=b+16|0;c[(c[g>>2]|0)+144>>2]=a;if((yC(b)|0)!=(b|0))c[(c[(yC(b)|0)+16>>2]|0)+144>>2]=a;e=QA(b,111226)|0;d=a+172|0;if((e|0)!=0?(c[d>>2]=0,(vS(a,e)|0)==999):0){d=MF(a,1,e)|0;c[f>>2]=e;c[f+4>>2]=d;gA(1,111233,f)|0;d=-1}else{e=c[d>>2]|0;if(!e)d=-1;else{RJ(1);qK(b,c[c[a+180>>2]>>2]&1);d=c[(c[g>>2]|0)+8>>2]|0;c[(c[(yC(b)|0)+16>>2]|0)+8>>2]=d;_N();d=c[e>>2]|0;if(d|0?(tb[d&127](b),h=c[e+4>>2]|0,h|0):0)c[(c[g>>2]|0)+148>>2]=h;RJ(0);d=0}}l=i;return d|0}function xS(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+176|0;k=n+8|0;j=n;h=n+40|0;if(!b)qa(111282,111286,52,111300);if(!d)qa(111312,111286,53,111300);i=c[d+8>>2]|0;if(!i)qa(111315,111286,54,111300);if(!(a[i>>0]|0))qa(111324,111286,55,111300);r3(h,c[d+28>>2]|0)|0;i=h+(l2(h)|0)|0;a[i>>0]=58;a[i+1>>0]=0;F4(h,g)|0;if((yS(b,h)|0)==999){c[j>>2]=h;gA(0,111336,j)|0}g=c[b+92>>2]|0;if(g|0?(m=c[g>>2]|0,m|0):0){c[k>>2]=c[e>>2];c[k+4>>2]=c[e+4>>2];c[k+8>>2]=c[e+8>>2];c[k+12>>2]=c[e+12>>2];c[k+16>>2]=c[e+16>>2];c[k+20>>2]=c[e+20>>2];c[k+24>>2]=c[e+24>>2];c[k+28>>2]=c[e+28>>2];xb[m&31](b,d,k,f)}l=n;return}function yS(a,b){a=a|0;b=b|0;b=LF(c[a>>2]|0,4,b)|0;if(!b)b=999;else{b=c[b+16>>2]|0;c[a+92>>2]=c[b+12>>2];c[a+96>>2]=c[b>>2];b=300}return b|0}function zS(a){a=a|0;return 1}function AS(b){b=b|0;var c=0;a[b+536>>0]=0;c=b+336|0;h[c>>3]=+h[c>>3]+10.0/+h[b+352>>3];a[b+537>>0]=1;return 0}function BS(b){b=b|0;var c=0;a[b+536>>0]=0;c=b+336|0;h[c>>3]=+h[c>>3]-10.0/+h[b+352>>3];a[b+537>>0]=1;return 0}function CS(b){b=b|0;var c=0;a[b+536>>0]=0;c=b+344|0;h[c>>3]=+h[c>>3]-10.0/+h[b+352>>3];a[b+537>>0]=1;return 0}function DS(b){b=b|0;var c=0;a[b+536>>0]=0;c=b+344|0;h[c>>3]=+h[c>>3]+10.0/+h[b+352>>3];a[b+537>>0]=1;return 0}function ES(b){b=b|0;var c=0;a[b+536>>0]=0;c=b+352|0;h[c>>3]=+h[c>>3]*1.1;a[b+537>>0]=1;return 0}function FS(b){b=b|0;var c=0;a[b+536>>0]=0;c=b+352|0;h[c>>3]=+h[c>>3]*.9090909090909091;a[b+537>>0]=1;return 0}function GS(b){b=b|0;var d=0,e=0,f=0,g=0.0,i=0.0;e=b+536|0;d=(a[e>>0]|0)==0;a[e>>0]=d&1;if(d){d=c[b+448>>2]|0;e=c[b+452>>2]|0;i=+(d>>>0);g=+(e>>>0);f=i/+(d|0)<g/+(e|0);h[b+352>>3]=(f?i:g)/+((f?d:e)|0);e=b+336|0;c[e>>2]=0;c[e+4>>2]=0;c[e+8>>2]=0;c[e+12>>2]=0;a[b+537>>0]=1}return 0}function HS(b){b=b|0;var d=0,e=0;d=c[(c[b>>2]|0)+168>>2]|0;e=b+580|0;if(!(c[e>>2]|0)){c[e>>2]=d;e=(c[d+16>>2]|0)+112|0;a[e>>0]=a[e>>0]|2;XS(b,d)}sI(b,d);a[b+540>>0]=1;return}function IS(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0,k=0;k=l;l=l+16|0;f=k;switch(d|0){case 1:{c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];c[f+8>>2]=c[e+8>>2];c[f+12>>2]=c[e+12>>2];QS(b,f);WS(b);a[b+538>>0]=1;a[b+541>>0]=1;j=13;break}case 2:{a[b+538>>0]=1;a[b+541>>0]=2;j=13;break}case 3:{c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];c[f+8>>2]=c[e+8>>2];c[f+12>>2]=c[e+12>>2];QS(b,f);a[b+538>>0]=1;a[b+541>>0]=3;j=13;break}case 4:{a[b+536>>0]=0;if(!(c[b+360>>2]|0)){f=b+352|0;i=+h[f>>3];d=b+336|0;h[d>>3]=(+h[e>>3]-+((c[b+448>>2]|0)>>>0)*.5)*.10000000000000009/(+h[b+520>>3]*i)+ +h[d>>3];g=+h[e+8>>3]-+((c[b+452>>2]|0)>>>0)*.5;d=b+528|0}else{f=b+352|0;i=+h[f>>3];d=b+336|0;h[d>>3]=+h[d>>3]-(+h[e+8>>3]-+((c[b+452>>2]|0)>>>0)*.5)*.10000000000000009/(+h[b+528>>3]*i);g=+h[e>>3]-+((c[b+448>>2]|0)>>>0)*.5;d=b+520|0}j=b+344|0;h[j>>3]=g*.10000000000000009/(+h[d>>3]*i)+ +h[j>>3];h[f>>3]=i*1.1;j=13;break}case 5:{a[b+536>>0]=0;j=b+352|0;i=+h[j>>3]*.9090909090909091;h[j>>3]=i;if(!(c[b+360>>2]|0)){f=b+336|0;h[f>>3]=+h[f>>3]-(+h[e>>3]-+((c[b+448>>2]|0)>>>0)*.5)*.10000000000000009/(+h[b+520>>3]*i);g=+h[e+8>>3]-+((c[b+452>>2]|0)>>>0)*.5;f=b+528|0}else{f=b+336|0;h[f>>3]=(+h[e+8>>3]-+((c[b+452>>2]|0)>>>0)*.5)*.10000000000000009/(+h[b+528>>3]*i)+ +h[f>>3];g=+h[e>>3]-+((c[b+448>>2]|0)>>>0)*.5;f=b+520|0}j=b+344|0;h[j>>3]=+h[j>>3]-g*.10000000000000009/(+h[f>>3]*i);j=13;break}default:{}}if((j|0)==13)a[b+537>>0]=1;b=b+560|0;c[b>>2]=c[e>>2];c[b+4>>2]=c[e+4>>2];c[b+8>>2]=c[e+8>>2];c[b+12>>2]=c[e+12>>2];l=k;return}function JS(b,c,d){b=b|0;c=c|0;d=d|0;a[b+538>>0]=0;a[b+541>>0]=0;return}function KS(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0,j=0.0,k=0;k=l;l=l+16|0;e=k;i=b+560|0;j=(+h[d>>3]-+h[i>>3])/+h[b+520>>3];g=(+h[d+8>>3]-+h[b+568>>3])/+h[b+528>>3];if(!(+B(+j)<.0001&+B(+g)<.0001)){switch(a[b+541>>0]|0){case 0:{c[e>>2]=c[d>>2];c[e+4>>2]=c[d+4>>2];c[e+8>>2]=c[d+8>>2];c[e+12>>2]=c[d+12>>2];QS(b,e);break}case 2:{f=+h[b+352>>3];if(!(c[b+360>>2]|0)){e=b+336|0;h[e>>3]=+h[e>>3]-j/f;e=b+344|0;h[e>>3]=+h[e>>3]-g/f}else{e=b+336|0;h[e>>3]=+h[e>>3]-g/f;e=b+344|0;h[e>>3]=+h[e>>3]+j/f}a[b+537>>0]=1;break}default:{}}c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2]}l=k;return}function LS(a,b,c){a=a|0;b=b|0;c=c|0;return}function MS(a){a=a|0;return}function NS(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;j=c[b>>2]|0;if(d){d=$4(d,138821)|0;if(d|0){i=jF(d,0)|0;I4(d)|0;f=5}}else{i=jF(c[15647]|0,0)|0;f=5}if((f|0)==5?i|0:0){g=j+168|0;d=c[g>>2]|0;if(d|0){f=c[j+172>>2]|0;if((f|0)!=0?(h=c[f+4>>2]|0,(h|0)!=0):0){tb[h&127](d);d=c[g>>2]|0}vK(d);sB(c[g>>2]|0)|0}YC(i,0,137483,280,1);YC(i,1,137447,304,1);YC(i,2,137460,176,1);c[g>>2]=i;c[(c[i+16>>2]|0)+144>>2]=j;if((nG(j,i,e)|0)!=-1){c[b+580>>2]=0;c[b+576>>2]=0;a[b+537>>0]=1}}return}function OS(a,b){a=a|0;b=b|0;a=c[a>>2]|0;nG(a,c[a+168>>2]|0,b)|0;return}function PS(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;g=c[a>>2]|0;f=g+160|0;e=c[f>>2]|0;if((e|0)!=0?(c[g+164>>2]|0)==0:0){i=g+192|0;h=c[i>>2]|0;c[f>>2]=0;c[i>>2]=0;f=h}else{f=0;e=0}pG(g,c[g+168>>2]|0,b,d)|0;if(e|0){i=c[a>>2]|0;c[i+160>>2]=e;c[i+192>>2]=f}return}function QS(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0.0,j=0.0,k=0.0,m=0;f=l;l=l+80|0;e=f+48|0;g=f;m=f+32|0;c[e>>2]=c[d>>2];c[e+4>>2]=c[d+4>>2];c[e+8>>2]=c[d+8>>2];c[e+12>>2]=c[d+12>>2];RS(m,b,e);k=+h[m>>3];j=+h[m+8>>3];i=1.0/+h[b+352>>3];h[g+16>>3]=i+k;h[g+24>>3]=i+j;h[g>>3]=k-i;h[g+8>>3]=j-i;d=c[(c[b>>2]|0)+168>>2]|0;c[e>>2]=c[g>>2];c[e+4>>2]=c[g+4>>2];c[e+8>>2]=c[g+8>>2];c[e+12>>2]=c[g+12>>2];c[e+16>>2]=c[g+16>>2];c[e+20>>2]=c[g+20>>2];c[e+24>>2]=c[g+24>>2];c[e+28>>2]=c[g+28>>2];d=SS(d,e)|0;e=b+576|0;if((d|0)!=(c[e>>2]|0)){TS(b);c[e>>2]=d;US(b);a[b+537>>0]=1}l=f;return}function RS(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0.0,i=0;if(!(c[b+360>>2]|0)){e=+h[b+352>>3];f=+h[d+8>>3];g=e;i=b+528|0;e=+h[d>>3]/(+h[b+520>>3]*e)-+h[b+504>>3]}else{e=+h[b+352>>3];f=-+h[d>>3];g=e;i=b+520|0;e=+h[d+8>>3]/(+h[b+528>>3]*e)-+h[b+504>>3]}g=f/(+h[i>>3]*g)-+h[b+512>>3];h[a>>3]=e;h[a+8>>3]=g;return}function SS(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;g=l;l=l+32|0;f=g;e=jC(a)|0;a:while(1){if(!e){e=9;break}d=XA(a,e)|0;while(1){if(!d)break;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];c[f+16>>2]=c[b+16>>2];c[f+20>>2]=c[b+20>>2];c[f+24>>2]=c[b+24>>2];c[f+28>>2]=c[b+28>>2];if((jQ(d,f)|0)<<24>>24){e=6;break a}d=ZA(a,d)|0}e=kC(a,e)|0}do if((e|0)!=6)if((e|0)==9){d=lC(a)|0;while(1){if(!d)break;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];c[f+16>>2]=c[b+16>>2];c[f+20>>2]=c[b+20>>2];c[f+24>>2]=c[b+24>>2];c[f+28>>2]=c[b+28>>2];if((gQ(d,f)|0)<<24>>24){e=12;break}d=mC(a,d)|0}if((e|0)==12)break;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];c[f+16>>2]=c[b+16>>2];c[f+20>>2]=c[b+20>>2];c[f+24>>2]=c[b+24>>2];c[f+28>>2]=c[b+28>>2];d=VS(a,f)|0;if(!d){d=a;break}else break}while(0);l=g;return d|0}function TS(b){b=b|0;var d=0;d=c[b+576>>2]|0;a:do if(d|0)switch(IC(d)|0){case 0:{d=(c[d+16>>2]|0)+112|0;a[d>>0]=a[d>>0]&-2;break a}case 1:{d=(c[d+16>>2]|0)+117|0;a[d>>0]=a[d>>0]&-2;break a}case 2:{d=(c[d+16>>2]|0)+115|0;a[d>>0]=a[d>>0]&-2;break a}default:break a}while(0);c[b+584>>2]=0;return}function US(b){b=b|0;var d=0,e=0;e=b+584|0;d=c[e>>2]|0;if(d|0){U1(d);c[e>>2]=0}d=c[b+576>>2]|0;a:do if(d|0)switch(IC(d)|0){case 0:{b=(c[d+16>>2]|0)+112|0;a[b>>0]=a[b>>0]|1;b=GA(d,0,141101,0)|0;if(!b)break a;c[e>>2]=EK(RA(d,b)|0,d)|0;break a}case 1:{b=(c[d+16>>2]|0)+117|0;a[b>>0]=a[b>>0]|1;b=GA(xC(d)|0,1,141101,0)|0;if(!b)break a;c[e>>2]=EK(RA(d,b)|0,d)|0;break a}case 2:{b=(c[d+16>>2]|0)+115|0;a[b>>0]=a[b>>0]|1;b=GA(xC(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)|0,2,141101,0)|0;if(!b)break a;c[e>>2]=EK(RA(d,b)|0,d)|0;break a}default:break a}while(0);return}function VS(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0.0,o=0.0;m=l;l=l+32|0;e=m;g=c[a+16>>2]|0;i=c[g+180>>2]|0;j=g+184|0;d=1;while(1){if((d|0)>(i|0)){k=4;break}f=c[(c[j>>2]|0)+(d<<2)>>2]|0;c[e>>2]=c[b>>2];c[e+4>>2]=c[b+4>>2];c[e+8>>2]=c[b+8>>2];c[e+12>>2]=c[b+12>>2];c[e+16>>2]=c[b+16>>2];c[e+20>>2]=c[b+20>>2];c[e+24>>2]=c[b+24>>2];c[e+28>>2]=c[b+28>>2];f=VS(f,e)|0;if(!f)d=d+1|0;else{a=f;break}}do if((k|0)==4){o=+h[g+24>>3];n=+h[g+40>>3];if(((+h[b+16>>3]>=+h[g+16>>3]?+h[g+32>>3]>=+h[b>>3]:0)?+h[b+24>>3]>=o:0)?n>=+h[b+8>>3]:0)break;a=0}while(0);l=m;return a|0}function WS(b){b=b|0;var d=0,e=0,f=0;f=b+580|0;d=c[f>>2]|0;a:do if(d|0)switch(IC(d)|0){case 0:{e=(c[d+16>>2]|0)+112|0;a[e>>0]=a[e>>0]&-7|4;break a}case 1:{e=(c[d+16>>2]|0)+117|0;a[e>>0]=a[e>>0]&-7|4;break a}case 2:{e=(c[d+16>>2]|0)+115|0;a[e>>0]=a[e>>0]&-7|4;break a}default:break a}while(0);d=b+588|0;e=c[d>>2]|0;if(e|0){U1(e);c[d>>2]=0}d=c[b+576>>2]|0;c[f>>2]=d;b:do if(d|0)switch(IC(d)|0){case 0:{f=(c[d+16>>2]|0)+112|0;a[f>>0]=a[f>>0]|2;XS(b,d);break b}case 1:{f=(c[d+16>>2]|0)+117|0;a[f>>0]=a[f>>0]|2;YS(b,d);break b}case 2:{f=(c[d+16>>2]|0)+115|0;a[f>>0]=a[f>>0]|2;ZS(b,d);break b}default:break b}while(0);return}function XS(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;d=a+592|0;do if((yC(b)|0)==(b|0))if(!(xB(b)|0)){GF(d,0,111494);break}else{GF(d,0,111486);break}else GF(d,0,111500);while(0);GF(d,1,KB(b)|0);c[a+596>>2]=2;f=a+604|0;d=0;e=2;while(1){d=LA(b,0,d)|0;if(!d)break;GF(f,e,c[d+8>>2]|0);GF(f,e+1|0,RA(b,d)|0);GF(f,e+2|0,0);e=e+3|0}c[a+608>>2]=e;d=GA(b,0,141055,0)|0;if(!d){d=GA(b,0,111477,0)|0;if(d|0)g=11}else g=11;if((g|0)==11)c[a+588>>2]=EK(RA(b,d)|0,b)|0;return}function YS(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;f=a+592|0;GF(f,0,111481);GF(f,1,KB(b)|0);c[a+596>>2]=2;f=a+604|0;g=yC(xC(b)|0)|0;e=2;d=0;while(1){d=LA(g,1,d)|0;if(!d)break;GF(f,e,c[d+8>>2]|0);GF(f,e|1,RA(b,d)|0);e=e+2|0}c[a+608>>2]=e;d=GA(xC(b)|0,1,141055,0)|0;if(!d){d=GA(xC(b)|0,1,111477,0)|0;if(d|0)h=6}else h=6;if((h|0)==6)c[a+588>>2]=EK(RA(b,d)|0,b)|0;return}function ZS(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;h=a+592|0;GF(h,0,111450);k=b+48|0;GF(h,1,KB(c[((c[b>>2]&3|0)==3?b:k)+40>>2]|0)|0);k=(xB(xC(c[((c[b>>2]&3|0)==3?b:k)+40>>2]|0)|0)|0)!=0;GF(h,3,k?137738:141747);k=b+-48|0;GF(h,4,KB(c[((c[b>>2]&3|0)==2?b:k)+40>>2]|0)|0);c[a+596>>2]=7;i=a+604|0;j=yC(xC(c[((c[b>>2]&3|0)==2?b:k)+40>>2]|0)|0)|0;g=7;d=0;a:while(1){while(1){d=LA(j,2,d)|0;if(!d)break a;e=d+8|0;f=c[e>>2]|0;if(!(s2(f,111455)|0)){l=5;break}if(!(s2(f,111464)|0)){l=7;break}if(s2(f,111473)|0)break;GF(h,6,RA(b,d)|0)}if((l|0)==5){l=0;GF(h,2,RA(b,d)|0)}else if((l|0)==7){l=0;GF(h,5,RA(b,d)|0)}GF(i,g,c[e>>2]|0);GF(i,g+1|0,RA(b,d)|0);g=g+2|0}c[a+608>>2]=g;d=GA(xC(c[((c[b>>2]&3|0)==2?b:k)+40>>2]|0)|0,2,141055,0)|0;if(!d){d=GA(xC(c[((c[b>>2]&3|0)==2?b:k)+40>>2]|0)|0,2,111477,0)|0;if(d|0)l=13}else l=13;if((l|0)==13)c[a+588>>2]=EK(RA(b,d)|0,b)|0;return}function _S(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;g=$S(a,b,c[d>>2]|0)|0;d=c[d+4>>2]|0;while(1){f=c[d+4>>2]|0;if(!f)break;b=0;while(1){e=c[f+(b*20|0)+4>>2]|0;if(!e)break;JF(a,c[d>>2]|0,e,c[f+(b*20|0)+8>>2]|0,g,f+(b*20|0)|0)|0;b=b+1|0}d=d+8|0}return}function $S(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=KK(12)|0;if(!b)b=0;else b=D4(b)|0;c[e+4>>2]=b;c[e+8>>2]=D4(d)|0;d=a+100|0;c[e>>2]=c[d>>2];c[d>>2]=e;return e|0}function aT(b,c){b=b|0;c=c|0;bT(b);a[b+44>>0]=0;cT(b)|0;PO(b)|0;return}function bT(b){b=b|0;var d=0,e=0;d=c[b+32>>2]|0;a:do if(d|0)while(1){e=c[d>>2]|0;if(!e)break a;if((a[e>>0]|0)==103?d4(e,111509)|0:0)_S(b,0,c[d+4>>2]|0);d=d+8|0}while(0);return}function cT(a){a=a|0;var b=0;b=LF(a,2,111522)|0;if(!b)a=999;else{c[a+148>>2]=c[(c[b+16>>2]|0)+12>>2];a=300}return a|0}function dT(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;a=c[a+148>>2]|0;if((a|0)!=0?(e=c[a>>2]|0,(e|0)!=0):0)a=lb[e&127](b,d)|0;else a=0;return a|0}function eT(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;return (c[b>>2]|0)-(c[d>>2]|0)|0}function fT(a){a=a|0;var b=0.0,d=0.0;a=c[a+16>>2]|0;d=+h[a+16>>3];b=+h[a+24>>3];return (gT(~~(d>b?d:b)>>>0)|0)+1|0}function gT(a){a=a|0;var b=0,c=0;if(!a)a=-1;else{c=a>>>0>65535;a=c?a>>>16:a;c=c?16:0;b=a>>>0>255;a=b?a>>>8:a;c=b?c|8:c;b=a>>>0>15;a=b?a>>>4:a;c=b?c|4:c;b=a>>>0>3;a=(b?c|2:c)+((b?a>>>2:a)>>>0>1&1)|0}return a|0}function hT(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;r=l;l=l+32|0;q=r;p=iT(b,d,e,f,g)|0;e=jT(p)|0;if((e|0)>=0){n=q+16|0;o=q+24|0;m=q+8|0;k=g+32|0;e=0;g=0;while(1){if((g|0)>=(d|0))break;f=b+(g*40|0)+32|0;do if(c[f>>2]|0){kT(q,p,b+(g*40|0)|0);i=+h[n>>3];j=+h[o>>3];if(!(c[q>>2]|0)){a[(c[f>>2]|0)+36>>0]=1;break}if(+h[m>>3]==0.0){f=c[f>>2]|0;h[f+16>>3]=i;h[f+24>>3]=j;a[f+36>>0]=1;break}if((a[k>>0]|0)==1){f=c[f>>2]|0;h[f+16>>3]=i;h[f+24>>3]=j;a[f+36>>0]=1}else e=1}while(0);g=g+1|0}lT(p)}l=r;return e|0}function iT(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;g=JK(28)|0;j=Vz(25912,c[4584]|0)|0;h=g+20|0;c[h>>2]=j;do if(j){j=TV()|0;c[g+24>>2]=j;if(!j){U4(111740,14,1,c[15712]|0)|0;i=6;break}else{c[g>>2]=a;c[g+4>>2]=b;c[g+8>>2]=d;c[g+12>>2]=e;c[g+16>>2]=f;break}}else{U4(111740,14,1,c[15712]|0)|0;i=6}while(0);if((i|0)==6){a=c[h>>2]|0;if(a|0)Rz(a)|0;a=c[g+24>>2]|0;if(a|0)VV(a)|0;U1(g);g=0}return g|0}function jT(a){a=a|0;var b=0;b=uT(a)|0;if((b|0)>=0){vT(a);wT(a);b=Rz(c[a+20>>2]|0)|0}return b|0}function kT(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0.0,j=0,k=0.0,m=0,n=0,o=0,p=0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0.0,L=0,M=0;M=l;l=l+464|0;J=M+416|0;L=M+400|0;G=M+384|0;m=M+352|0;n=M+320|0;o=M+288|0;p=M+256|0;r=M+224|0;s=M+192|0;t=M+160|0;u=M+128|0;v=M+96|0;x=M+64|0;y=M+32|0;H=M;z=c[d+32>>2]|0;e=+h[z>>3];C=d+16|0;A=(e*2.0+ +h[C>>3])*.125;D=z+8|0;I=d+24|0;i=+h[I>>3];K=(+h[D>>3]*2.0+i)*.5;if(!z)qa(111533,111543,445,111553);f=J;g=f+36|0;do{c[f>>2]=0;f=f+4|0}while((f|0)<(g|0));B=z+16|0;h[B>>3]=+h[d>>3]-e;E=d+8|0;F=z+24|0;h[F>>3]=i+ +h[E>>3];mT(m,b,d,J);j=c[m>>2]|0;f=c[m+4>>2]|0;e=+h[m+8>>3];m=m+16|0;c[L>>2]=c[m>>2];c[L+4>>2]=c[m+4>>2];c[L+8>>2]=c[m+8>>2];c[L+12>>2]=c[m+12>>2];a:do if(!j){c[a>>2]=0;c[a+4>>2]=f;h[a+8>>3]=e;a=a+16|0;c[a>>2]=c[L>>2];c[a+4>>2]=c[L+4>>2];c[a+8>>2]=c[L+8>>2];c[a+12>>2]=c[L+12>>2]}else{h[F>>3]=+h[E>>3];mT(n,b,d,J);g=c[n>>2]|0;m=c[n+4>>2]|0;i=+h[n+8>>3];n=n+16|0;c[G>>2]=c[n>>2];c[G+4>>2]=c[n+4>>2];c[G+8>>2]=c[n+8>>2];c[G+12>>2]=c[n+12>>2];if(!g){c[a>>2]=0;c[a+4>>2]=m;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break}if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];f=m;e=i}else g=j;h[F>>3]=+h[E>>3]-+h[D>>3];mT(o,b,d,J);m=c[o>>2]|0;j=c[o+4>>2]|0;i=+h[o+8>>3];o=o+16|0;c[G>>2]=c[o>>2];c[G+4>>2]=c[o+4>>2];c[G+8>>2]=c[o+8>>2];c[G+12>>2]=c[o+12>>2];if(!m){c[a>>2]=0;c[a+4>>2]=j;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break}if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];f=j;e=i}else m=g;h[B>>3]=+h[d>>3];h[F>>3]=+h[I>>3]+ +h[E>>3];mT(p,b,d,J);g=c[p>>2]|0;j=c[p+4>>2]|0;i=+h[p+8>>3];p=p+16|0;c[G>>2]=c[p>>2];c[G+4>>2]=c[p+4>>2];c[G+8>>2]=c[p+8>>2];c[G+12>>2]=c[p+12>>2];if(!g){c[a>>2]=0;c[a+4>>2]=j;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break}if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];m=g;f=j;e=i}h[F>>3]=+h[E>>3]-+h[D>>3];mT(r,b,d,J);g=c[r>>2]|0;j=c[r+4>>2]|0;i=+h[r+8>>3];r=r+16|0;c[G>>2]=c[r>>2];c[G+4>>2]=c[r+4>>2];c[G+8>>2]=c[r+8>>2];c[G+12>>2]=c[r+12>>2];if(!g){c[a>>2]=0;c[a+4>>2]=j;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break}if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];m=g;f=j;e=i}h[B>>3]=+h[C>>3]+ +h[d>>3];h[F>>3]=+h[I>>3]+ +h[E>>3];mT(s,b,d,J);g=c[s>>2]|0;j=c[s+4>>2]|0;i=+h[s+8>>3];s=s+16|0;c[G>>2]=c[s>>2];c[G+4>>2]=c[s+4>>2];c[G+8>>2]=c[s+8>>2];c[G+12>>2]=c[s+12>>2];if(!g){c[a>>2]=0;c[a+4>>2]=j;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break}if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];m=g;f=j;e=i}h[F>>3]=+h[E>>3];mT(t,b,d,J);g=c[t>>2]|0;j=c[t+4>>2]|0;i=+h[t+8>>3];t=t+16|0;c[G>>2]=c[t>>2];c[G+4>>2]=c[t+4>>2];c[G+8>>2]=c[t+8>>2];c[G+12>>2]=c[t+12>>2];if(!g){c[a>>2]=0;c[a+4>>2]=j;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break}if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];m=g;f=j;e=i}h[F>>3]=+h[E>>3]-+h[D>>3];mT(u,b,d,J);g=c[u>>2]|0;j=c[u+4>>2]|0;i=+h[u+8>>3];u=u+16|0;c[G>>2]=c[u>>2];c[G+4>>2]=c[u+4>>2];c[G+8>>2]=c[u+8>>2];c[G+12>>2]=c[u+12>>2];if(!g){c[a>>2]=0;c[a+4>>2]=j;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break}if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];f=j;e=i}else g=m;j=(c[J+28>>2]|0)==0;do if(!(c[J+24>>2]|0))if(j){if(c[J+32>>2]|0){w=38;break}if(!((c[J+12>>2]|0)==0&(c[J>>2]|0)==0))w=38}else w=46;else if(j)w=38;else w=46;while(0);b:do if((w|0)==38)if(!(c[J+32>>2]|0)){h[B>>3]=+h[d>>3]-+h[z>>3];p=v+4|0;r=v+8|0;s=v+16|0;m=F;j=E;i=+h[I>>3];c:while(1){while(1){h[m>>3]=+h[j>>3]+i;if(!(+h[B>>3]<=+h[C>>3]+ +h[d>>3])){w=46;break b}mT(v,b,d,J);n=c[v>>2]|0;o=c[p>>2]|0;k=+h[r>>3];c[G>>2]=c[s>>2];c[G+4>>2]=c[s+4>>2];c[G+8>>2]=c[s+8>>2];c[G+12>>2]=c[s+12>>2];if(!n)break c;if(k<e)break;else{m=B;j=B;i=A}}c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];m=B;j=B;i=A;g=n;f=o;e=k}c[a>>2]=0;c[a+4>>2]=o;h[a+8>>3]=k;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break a}else w=46;while(0);d:do if((w|0)==46)if((c[J+12>>2]|0)==0&(c[J>>2]|0)==0){h[B>>3]=+h[d>>3]-+h[z>>3];k=+h[E>>3];n=x+4|0;o=x+8|0;p=x+16|0;i=k;k=+h[I>>3]+k;while(1){h[F>>3]=k;if(!(k>=i-+h[D>>3]))break d;mT(x,b,d,J);j=c[x>>2]|0;m=c[n>>2]|0;i=+h[o>>3];c[G>>2]=c[p>>2];c[G+4>>2]=c[p+4>>2];c[G+8>>2]=c[p+8>>2];c[G+12>>2]=c[p+12>>2];if(!j)break;if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];g=j;f=m;e=i}i=+h[E>>3];k=+h[F>>3]-K}c[a>>2]=0;c[a+4>>2]=m;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break a}while(0);i=+h[d>>3];k=+h[C>>3]+i;h[B>>3]=k;q=+h[D>>3];h[F>>3]=+h[E>>3]-q;j=c[J+4>>2]|0;do if(!(c[J+8>>2]|0)){m=c[J>>2]|0;if(!((j|0)==0&(m|0)==0)){w=59;break}if(c[J+20>>2]|0){j=0;m=0;w=59;break}if(c[J+32>>2]|0){j=0;m=0;w=59}}else{m=c[J>>2]|0;w=59}while(0);e:do if((w|0)==59){f:do if((j|0)==0&(m|0)==0){h[B>>3]=k;p=y+4|0;r=y+8|0;s=y+16|0;m=F;j=E;i=q;g:while(1){while(1){h[m>>3]=+h[j>>3]-i;i=+h[d>>3];if(!(+h[B>>3]>=i-+h[z>>3]))break f;mT(y,b,d,J);n=c[y>>2]|0;o=c[p>>2]|0;k=+h[r>>3];c[G>>2]=c[s>>2];c[G+4>>2]=c[s+4>>2];c[G+8>>2]=c[s+8>>2];c[G+12>>2]=c[s+12>>2];if(!n)break g;if(k<e)break;else{m=B;j=B;i=A}}c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];m=B;j=B;i=A;g=n;f=o;e=k}c[a>>2]=0;c[a+4>>2]=o;h[a+8>>3]=k;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break a}while(0);if(c[J+20>>2]|0)break;if(c[J+32>>2]|0)break;h[B>>3]=+h[C>>3]+i;i=+h[E>>3];n=H+4|0;o=H+8|0;p=H+16|0;k=i;i=i-+h[D>>3];while(1){h[F>>3]=i;if(!(i<=+h[I>>3]+k))break e;mT(H,b,d,J);j=c[H>>2]|0;m=c[n>>2]|0;i=+h[o>>3];c[G>>2]=c[p>>2];c[G+4>>2]=c[p+4>>2];c[G+8>>2]=c[p+8>>2];c[G+12>>2]=c[p+12>>2];if(!j)break;if(i<e){c[L>>2]=c[G>>2];c[L+4>>2]=c[G+4>>2];c[L+8>>2]=c[G+8>>2];c[L+12>>2]=c[G+12>>2];g=j;f=m;e=i}k=+h[E>>3];i=+h[F>>3]+K}c[a>>2]=0;c[a+4>>2]=m;h[a+8>>3]=i;a=a+16|0;c[a>>2]=c[G>>2];c[a+4>>2]=c[G+4>>2];c[a+8>>2]=c[G+8>>2];c[a+12>>2]=c[G+12>>2];break a}while(0);c[a>>2]=g;c[a+4>>2]=f;h[a+8>>3]=e;a=a+16|0;c[a>>2]=c[L>>2];c[a+4>>2]=c[L+4>>2];c[a+8>>2]=c[L+8>>2];c[a+12>>2]=c[L+12>>2]}while(0);l=M;return}function lT(a){a=a|0;VV(c[a+24>>2]|0)|0;U1(a);return}function mT(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0.0,j=0,k=0,m=0,n=0.0,o=0,p=0,q=0,r=0.0,s=0,t=0;t=l;l=l+48|0;q=t+32|0;s=t+16|0;p=t;g=c[e+32>>2]|0;if(!g)qa(111533,111543,382,111562);o=g+16|0;c[p>>2]=c[o>>2];c[p+4>>2]=c[o+4>>2];c[p+8>>2]=c[o+8>>2];c[p+12>>2]=c[o+12>>2];o=d+4|0;m=0;g=0;while(1){if((m|0)>=(c[o>>2]|0))break;j=c[d>>2]|0;k=j+(m*40|0)|0;do if((k|0)!=(e|0)){if(+h[j+(m*40|0)+16>>3]>0.0?+h[j+(m*40|0)+24>>3]>0.0:0)break;g=g+((nT(e,k)|0)!=0&1)|0}while(0);m=m+1|0}oT(e,q);m=c[d+24>>2]|0;m=XV(m,c[m>>2]|0,q)|0;if(!m){c[b>>2]=g;h[b+8>>3]=0.0;s=b+16|0;c[s>>2]=c[p>>2];c[s+4>>2]=c[p+4>>2];c[s+8>>2]=c[p+8>>2];c[s+12>>2]=c[p+12>>2]}else{k=m;i=0.0;while(1){if(!k)break;j=c[(c[k+4>>2]|0)+16>>2]|0;if((j|0)!=(e|0)){pT(j,s);n=+qT(q,s);if(n>0.0){g=g+1|0;i=+rT(e,j,q,n,f)+i}d=c[j+32>>2]|0;if(((d|0)!=0?(a[d+36>>0]|0)!=0:0)?(oT(j,s),r=+qT(q,s),r>0.0):0){g=g+1|0;i=+sT(e,j,q,r,f)+i}}k=c[k>>2]|0}SV(m);c[b>>2]=g;h[b+8>>3]=i;s=b+16|0;c[s>>2]=c[p>>2];c[s+4>>2]=c[p+4>>2];c[s+8>>2]=c[p+8>>2];c[s+12>>2]=c[p+12>>2]}l=t;return}function nT(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0.0,g=0.0;a=c[a+32>>2]|0;if(+h[b+16>>3]==0.0?+h[b+24>>3]==0.0:0){if((((a|0)!=0?(d=+h[b>>3],e=+h[a+16>>3],d>e):0)?d<+h[a>>3]+e:0)?(f=+h[b+8>>3],g=+h[a+24>>3],f>g):0)a=f<+h[a+8>>3]+g&1;else a=0;return a|0}qa(111599,111543,219,111636);return 0}function oT(a,b){a=a|0;b=b|0;var d=0.0,e=0.0;a=c[a+32>>2]|0;e=+h[a+16>>3];c[b>>2]=~~e;d=+h[a+24>>3];c[b+4>>2]=~~d;c[b+8>>2]=~~(+h[a>>3]+e);c[b+12>>2]=~~(+h[a+8>>3]+d);return}function pT(a,b){a=a|0;b=b|0;var d=0.0,e=0.0;e=+h[a>>3];c[b>>2]=~~e;d=+h[a+8>>3];c[b+4>>2]=~~d;c[b+8>>2]=~~(+h[a+16>>3]+e);c[b+12>>2]=~~(+h[a+24>>3]+d);return}function qT(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;i=c[a+8>>2]|0;j=c[b>>2]|0;if((((i|0)>=(j|0)?(k=c[a>>2]|0,l=c[b+8>>2]|0,(k|0)<=(l|0)):0)?(e=c[a+12>>2]|0,f=c[b+4>>2]|0,(e|0)>=(f|0)):0)?(g=c[a+4>>2]|0,h=c[b+12>>2]|0,(g|0)<=(h|0)):0)d=(+(c[((e|0)<(h|0)?a:b)+12>>2]|0)-+(c[((g|0)>(f|0)?a:b)+4>>2]|0))*(+(c[((i|0)<(l|0)?a:b)+8>>2]|0)-+(c[((k|0)>(j|0)?a:b)>>2]|0));else d=0.0;return +d}function rT(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=+e;f=f|0;var g=0.0,h=0.0,i=0,j=0;j=l;l=l+16|0;i=j;a=tT(a,b)|0;f=f+(((a|0)<0?5:a)<<2)|0;a=c[f>>2]|0;if(!a){c[f>>2]=b;g=e}else{pT(a,i);g=+qT(d,i);g=g>e?g:0.0;a=c[f>>2]|0;if((c[a+32>>2]|0)!=0?(oT(a,i),h=+qT(d,i),h>e):0)g=h>g?h:g;if(!(g>0.0)){c[f>>2]=b;g=e}}l=j;return +g}function sT(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=+e;f=f|0;var g=0.0,h=0.0,i=0,j=0;j=l;l=l+16|0;i=j;a=tT(a,b)|0;f=f+(((a|0)<0?5:a)<<2)|0;a=c[f>>2]|0;if(!a){c[f>>2]=b;g=e}else{pT(a,i);g=+qT(d,i);g=g>e?g:0.0;a=c[f>>2]|0;if((c[a+32>>2]|0)!=0?(oT(a,i),h=+qT(d,i),h>e):0)g=h>g?h:g;if(!(g>0.0)){c[f>>2]=b;g=e}}l=j;return +g}function tT(b,d){b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0.0,k=0.0;e=c[b+32>>2]|0;f=c[d+32>>2]|0;if((e|0)==(f|0))qa(111578,111543,276,111588);do if((a[e+36>>0]|0)!=0?(a[f+36>>0]|0)!=0:0){j=+h[b>>3];if(j==0.0?+h[b+8>>3]==0.0:0){e=-1;break}k=+h[d>>3];i=+h[d+8>>3];if(!(k==0.0&i==0.0)){g=+h[b+8>>3];if(i<g){if(k<j){e=0;break}e=k>j?2:1;break}e=k<j;if(i>g){if(e){e=6;break}e=k>j?8:7;break}if(!e)if(k>j)e=5;else e=-1;else e=3}else e=-1}else e=-1;while(0);return e|0}function uT(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;k=l;l=l+32|0;d=k+24|0;e=k+16|0;f=k;g=fT(a)|0;h=a+4|0;i=e+4|0;j=a+20|0;b=0;while(1){if((b|0)>=(c[h>>2]|0)){b=0;break}m=JK(32)|0;o=(c[a>>2]|0)+(b*40|0)|0;n=m+12|0;c[m+28>>2]=o;xT(f,o);c[n>>2]=c[f>>2];c[n+4>>2]=c[f+4>>2];c[n+8>>2]=c[f+8>>2];c[n+12>>2]=c[f+12>>2];n=c[n>>2]|0;c[e>>2]=(((c[m+20>>2]|0)-n|0)/2|0)+n;n=c[m+16>>2]|0;c[i>>2]=(((c[m+24>>2]|0)-n|0)/2|0)+n;c[d>>2]=c[e>>2];c[d+4>>2]=c[e+4>>2];c[m+8>>2]=yT(d,g)|0;n=c[j>>2]|0;if(!(nb[c[n>>2]&63](n,m,1)|0)){b=-1;break}else b=b+1|0}l=k;return b|0}function vT(a){a=a|0;var b=0,d=0,e=0;d=a+20|0;e=c[d>>2]|0;b=a+24|0;a=nb[c[e>>2]&63](e,0,128)|0;while(1){if(!a)break;e=c[b>>2]|0;YV(e,a+12|0,c[a+28>>2]|0,e,0)|0;e=c[d>>2]|0;a=nb[c[e>>2]&63](e,a,8)|0}return}function wT(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;e=a+20|0;f=Xz(c[e>>2]|0)|0;d=0;while(1){if(!(Xz(c[e>>2]|0)|0))break;g=c[e>>2]|0;a=c[(c[g+8>>2]|0)+4>>2]|0;if(!a){h=8;break}b=c[(c[g+4>>2]|0)+8>>2]|0;if((b|0)<0)a=c[a+8>>2]|0;else a=a+(0-b)|0;if(!a){h=8;break}nb[c[g>>2]&63](g,a,4096)|0;U1(a);d=d+1|0}if((h|0)==8)qa(111649,111543,616,111652);if((f|0)==(d|0))return;else qa(111664,111543,623,111652)}function xT(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0.0,i=0,j=0,k=0,l=0.0;d=c[b+32>>2]|0;if(!d){e=0.0;f=0.0}else{e=+h[d>>3];f=+h[d+8>>3]}l=+h[b>>3];k=~~+A(+(l-e));g=+h[b+8>>3];j=~~+A(+(g-f));i=~~+N(+(l+e+ +h[b+16>>3]));if((i|0)==2147483647)qa(111676,111543,264,111703);d=~~+N(+(g+f+ +h[b+24>>3]));if((d|0)==2147483647)qa(111713,111543,266,111703);else{c[a>>2]=k;c[a+4>>2]=j;c[a+8>>2]=i;c[a+12>>2]=d;return}}function yT(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;f=0;e=c[a>>2]|0;d=c[a+4>>2]|0;while(1){a=b+-1|0;if((b|0)<=0)break;i=e>>>a&1;b=d>>>a&1;g=b+-1|0;h=g&(e^d);g=g&0-i;f=i<<1|f<<2|i^b;b=a;e=h^e^g;d=h^d^g}return f|0}function zT(b){b=b|0;var d=0,e=0,f=0,g=0;d=l2(b)|0;if(d>>>0>=(c[47365]|0)>>>0){d=d+11|0;c[47365]=d;d=MK(c[47366]|0,d)|0;c[47366]=d;if(!d)d=0;else e=4}else{d=c[47366]|0;e=4}if((e|0)==4){g=d;while(1){e=a[b>>0]|0;if(!(e<<24>>24))break;f=e&255;if(e3(f)|0)e=(d3(f)|0)&255;a[g>>0]=e;g=g+1|0;b=b+1|0}a[g>>0]=0}return d|0}function AT(b,e,f){b=b|0;e=e|0;f=f|0;var g=0.0,i=0,j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0.0;F=l;l=l+144|0;s=F+96|0;p=F+80|0;v=F+124|0;t=F+72|0;w=F+64|0;x=F+56|0;C=F+48|0;D=F+40|0;E=F+32|0;y=F+24|0;z=F+16|0;A=F+8|0;B=F;i=F+120|0;m=F+116|0;n=F+112|0;o=F+108|0;c[e+32>>2]=f;r=b;while(1)if((a[r>>0]|0)==32)r=r+1|0;else break;c[o>>2]=255;b=a[r>>0]|0;a:do if(b<<24>>24==35){c[p>>2]=i;c[p+4>>2]=m;c[p+8>>2]=n;c[p+12>>2]=o;if((b5(r,111755,p)|0)<=2){b=a[r>>0]|0;q=12;break}switch(f|0){case 0:{G=+((c[i>>2]|0)>>>0)*.00392156862745098;h[C>>3]=G;g=+((c[m>>2]|0)>>>0)*.00392156862745098;h[D>>3]=g;j=+((c[n>>2]|0)>>>0)*.00392156862745098;h[E>>3]=j;k=+((c[o>>2]|0)>>>0)*.00392156862745098;BT(G,g,j,t,w,x);h[e>>3]=+h[t>>3];h[e+8>>3]=+h[w>>3];h[e+16>>3]=+h[x>>3];h[e+24>>3]=k;b=0;break a}case 1:{a[e>>0]=c[i>>2];a[e+1>>0]=c[m>>2];a[e+2>>0]=c[n>>2];a[e+3>>0]=c[o>>2];b=0;break a}case 3:{j=+((c[i>>2]|0)>>>0)*.00392156862745098;h[C>>3]=j;k=+((c[m>>2]|0)>>>0)*.00392156862745098;h[D>>3]=k;G=+((c[n>>2]|0)>>>0)*.00392156862745098;h[E>>3]=G;CT(j,k,G,y,z,A,B);a[e>>0]=~~+h[y>>3]*255;a[e+1>>0]=~~+h[z>>3]*255;a[e+2>>0]=~~+h[A>>3]*255;a[e+3>>0]=~~+h[B>>3]*255;b=0;break a}case 2:{c[e>>2]=(((c[i>>2]|0)*65535|0)>>>0)/255|0;c[e+4>>2]=(((c[m>>2]|0)*65535|0)>>>0)/255|0;c[e+8>>2]=(((c[n>>2]|0)*65535|0)>>>0)/255|0;c[e+12>>2]=(((c[o>>2]|0)*65535|0)>>>0)/255|0;b=0;break a}case 4:{h[e>>3]=+((c[i>>2]|0)>>>0)*.00392156862745098;h[e+8>>3]=+((c[m>>2]|0)>>>0)*.00392156862745098;h[e+16>>3]=+((c[n>>2]|0)>>>0)*.00392156862745098;h[e+24>>3]=+((c[o>>2]|0)>>>0)*.00392156862745098;b=0;break a}default:{b=0;break a}}}else q=12;while(0);b:do if((q|0)==12){if(!(b<<24>>24!=46?((b&255)+-48|0)>>>0>=10:0)){b=l2(r)|0;if(b>>>0>=(c[47367]|0)>>>0){b=b+11|0;c[47367]=b;b=MK(c[47368]|0,b)|0;c[47368]=b;if(!b){b=-1;break}}else b=c[47368]|0;m=b;i=r;c:while(1){n=i+1|0;i=a[i>>0]|0;switch(i<<24>>24){case 0:break c;case 44:{i=32;break}default:{}}a[m>>0]=i;m=m+1|0;i=n}a[m>>0]=0;c[s>>2]=t;c[s+4>>2]=w;c[s+8>>2]=x;if((b5(b,111769,s)|0)==3){k=+h[t>>3];k=k<1.0?k:1.0;k=k>0.0?k:0.0;h[t>>3]=k;j=+h[w>>3];j=j<1.0?j:1.0;j=j>0.0?j:0.0;h[w>>3]=j;g=+h[x>>3];g=g<1.0?g:1.0;g=g>0.0?g:0.0;h[x>>3]=g;switch(f|0){case 0:{h[e>>3]=k;h[e+8>>3]=j;h[e+16>>3]=g;h[e+24>>3]=1.0;b=0;break b}case 1:{DT(k,j,g,C,D,E);a[e>>0]=~~(+h[C>>3]*255.0);a[e+1>>0]=~~(+h[D>>3]*255.0);a[e+2>>0]=~~(+h[E>>3]*255.0);a[e+3>>0]=-1;b=0;break b}case 3:{DT(k,j,g,C,D,E);CT(+h[C>>3],+h[D>>3],+h[E>>3],y,z,A,B);a[e>>0]=~~+h[y>>3]*255;a[e+1>>0]=~~+h[z>>3]*255;a[e+2>>0]=~~+h[A>>3]*255;a[e+3>>0]=~~+h[B>>3]*255;b=0;break b}case 2:{DT(k,j,g,C,D,E);c[e>>2]=~~(+h[C>>3]*65535.0);c[e+4>>2]=~~(+h[D>>3]*65535.0);c[e+8>>2]=~~(+h[E>>3]*65535.0);c[e+12>>2]=65535;b=0;break b}case 4:{DT(k,j,g,C,D,E);h[e>>3]=+h[C>>3];h[e+8>>3]=+h[D>>3];h[e+16>>3]=+h[E>>3];h[e+24>>3]=1.0;b=0;break b}default:{b=0;break b}}}}i=ET(r)|0;c[v>>2]=i;if(!i)b=-1;else{b=c[47369]|0;if(!(((b|0)!=0?(u=c[b>>2]|0,(a[u>>0]|0)==(a[i>>0]|0)):0)?(s2(u,i)|0)==0:0)){b=u5(v,25948,2491,12,103)|0;c[47369]=b;if(!b)switch(f|0){case 0:{c[e>>2]=0;c[e+4>>2]=0;c[e+8>>2]=0;c[e+12>>2]=0;c[e+16>>2]=0;c[e+20>>2]=0;h[e+24>>3]=1.0;b=1;break b}case 1:{a[e+2>>0]=0;a[e+1>>0]=0;a[e>>0]=0;a[e+3>>0]=-1;b=1;break b}case 3:{c[e>>2]=0;b=1;break b}case 2:{c[e+8>>2]=0;c[e+4>>2]=0;c[e>>2]=0;c[e+12>>2]=65535;b=1;break b}case 4:{c[e>>2]=0;c[e+4>>2]=0;c[e+8>>2]=0;c[e+12>>2]=0;c[e+16>>2]=0;c[e+20>>2]=0;h[e+24>>3]=1.0;b=1;break b}default:{b=1;break b}}}switch(f|0){case 0:{h[e>>3]=+(d[b+4>>0]|0)*.00392156862745098;h[e+8>>3]=+(d[b+5>>0]|0)*.00392156862745098;h[e+16>>3]=+(d[b+6>>0]|0)*.00392156862745098;h[e+24>>3]=+(d[b+10>>0]|0)*.00392156862745098;b=0;break b}case 1:{a[e>>0]=a[b+7>>0]|0;a[e+1>>0]=a[b+8>>0]|0;a[e+2>>0]=a[b+9>>0]|0;a[e+3>>0]=a[b+10>>0]|0;b=0;break b}case 3:{j=+(d[b+7>>0]|0)*.00392156862745098;h[C>>3]=j;k=+(d[b+8>>0]|0)*.00392156862745098;h[D>>3]=k;G=+(d[b+9>>0]|0)*.00392156862745098;h[E>>3]=G;CT(j,k,G,y,z,A,B);a[e>>0]=~~+h[y>>3]*255;a[e+1>>0]=~~+h[z>>3]*255;a[e+2>>0]=~~+h[A>>3]*255;a[e+3>>0]=~~+h[B>>3]*255;b=0;break b}case 2:{c[e>>2]=(d[b+7>>0]|0)*257;c[e+4>>2]=(d[b+8>>0]|0)*257;c[e+8>>2]=(d[b+9>>0]|0)*257;c[e+12>>2]=(d[b+10>>0]|0)*257;b=0;break b}case 4:{h[e>>3]=+(d[b+7>>0]|0)*.00392156862745098;h[e+8>>3]=+(d[b+8>>0]|0)*.00392156862745098;h[e+16>>3]=+(d[b+9>>0]|0)*.00392156862745098;h[e+24>>3]=+(d[b+10>>0]|0)*.00392156862745098;b=0;break b}default:{b=0;break b}}}}while(0);l=F;return b|0}function BT(a,b,c,d,e,f){a=+a;b=+b;c=+c;d=d|0;e=e|0;f=f|0;var g=0.0,i=0.0,j=0.0,k=0.0,l=0.0,m=0.0;i=b<c?b:c;m=b>c?b:c;m=m<a?a:m;i=m-(i>a?a:i);g=i/m;if(m>0.0)if(g>0.0){j=(m-a)/i;k=(m-b)/i;l=(m-c)/i;do if(!(m==a)){if(m==b){a=j+2.0-l;break}if(m==c)a=k+4.0-j;else a=0.0}else a=(b-c)/i;while(0);a=a*60.0;if(a<0.0)a=a+360.0}else a=0.0;else{g=0.0;a=0.0}h[d>>3]=a*.002777777777777778;h[f>>3]=m;h[e>>3]=g;return}function CT(a,b,c,d,e,f,g){a=+a;b=+b;c=+c;d=d|0;e=e|0;f=f|0;g=g|0;h[d>>3]=1.0-a;h[e>>3]=1.0-b;h[f>>3]=1.0-c;b=+h[d>>3];c=+h[e>>3];c=b<c?b:c;h[g>>3]=c;b=+h[f>>3];c=b<c?b:c;h[g>>3]=c;h[d>>3]=+h[d>>3]-c;h[e>>3]=+h[e>>3]-+h[g>>3];h[f>>3]=+h[f>>3]-+h[g>>3];return}function DT(a,b,c,d,e,f){a=+a;b=+b;c=+c;d=d|0;e=e|0;f=f|0;var g=0.0,i=0.0,j=0.0,k=0,l=0;a:do if(!(b<=0.0)){i=!(a>=1.0)?a*6.0:0.0;l=~~i;i=i-+(l|0);a=(1.0-b)*c;j=(1.0-i*b)*c;b=(1.0-(1.0-i)*b)*c;switch(l|0){case 0:{g=a;i=b;a=c;k=8;break a}case 1:{g=a;i=c;a=j;k=8;break a}case 2:{g=b;i=c;k=8;break a}case 3:{g=c;i=j;k=8;break a}case 4:{g=c;i=a;a=b;k=8;break a}case 5:{g=j;i=a;a=c;k=8;break a}default:break a}}else{g=c;i=c;a=c;k=8}while(0);if((k|0)==8){h[d>>3]=a;h[e>>3]=i;h[f>>3]=g}return}function ET(b){b=b|0;var d=0,e=0;d=a[b>>0]|0;if(((d<<24>>24!=98?(e=b+1|0,!(d<<24>>24==119|(Q2(e,137266,4)|0)==0)):0)?!(d<<24>>24==108|(Q2(e,137271,4)|0)==0):0)?(Q2(e,137276,8)|0)!=0:0){do if(d<<24>>24==47){d=R2(e,47)|0;if(!d)b=e;else{if((a[e>>0]|0)!=47){e=(j3(137285,e,4)|0)==0;b=e?d+1|0:b;break}d=c[47370]|0;if((d|0?a[d>>0]|0:0)?j3(137285,d,3)|0:0){b=GT(d,b+2|0)|0;break}b=b+2|0}}else{d=c[47370]|0;if(((d|0)!=0?(a[d>>0]|0)!=0:0)?(j3(137285,d,3)|0)!=0:0)b=GT(d,b)|0}while(0);b=zT(b)|0}return b|0}function FT(a,b){a=a|0;b=b|0;return c3(c[a>>2]|0,c[b>>2]|0)|0}function GT(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;f=l;l=l+16|0;e=f;g=l2(a)|0;g=(l2(b)|0)+g|0;d=g+13|0;if((g+3|0)>>>0<(c[47371]|0)>>>0)d=c[47372]|0;else{c[47371]=d;d=MK(c[47372]|0,d)|0;c[47372]=d}c[e>>2]=a;c[e+4>>2]=b;m4(d,137290,e)|0;l=f;return c[47372]|0}function HT(a){a=a|0;c[47370]=a;return}function IT(a,b,c,d,e){a=a|0;b=+b;c=+c;d=+d;e=+e;var f=0,g=0;f=l;l=l+208|0;g=f;JT(g,+h[a>>3],+h[a+8>>3],b,c,d,e);a=KT(g)|0;l=f;return a|0}function JT(a,b,c,d,e,f,g){a=a|0;b=+b;c=+c;d=+d;e=+e;f=+f;g=+g;var i=0,j=0;h[a>>3]=b;h[a+8>>3]=c;j=a+16|0;h[j>>3]=d;i=a+24|0;h[i>>3]=e;h[a+32>>3]=0.0;c=+K(+(+F(+f)/e),+(+E(+f)/d));h[a+56>>3]=c;e=+K(+(+F(+g)/e),+(+E(+g)/d));h[a+40>>3]=1.0;h[a+48>>3]=0.0;e=e-+A(+((e-c)*.15915494309189535))*6.283185307179586;h[a+64>>3]=g-f>3.141592653589793&e-c<3.141592653589793?e+6.283185307179586:e;QT(a);RT(a);ST(a);g=+h[j>>3];g=(g-+h[i>>3])/g;h[a+168>>3]=g;h[a+176>>3]=(2.0-g)*g;g=1.0-g;h[a+184>>3]=g;h[a+192>>3]=g*g;return}function KT(a){a=a|0;var b=0,c=0.0,d=0,e=0.0,f=0.0,g=0.0,i=0.0,j=0,k=0,l=0,m=0,n=0.0,o=0.0,p=0,q=0,r=0.0,s=0.0,t=0.0,u=0.0,v=0.0,w=0.0,x=0.0,y=0.0,z=0.0;q=JK(8)|0;f=+h[a+56>>3];e=+h[a+64>>3]-f;p=1;b=0;while(1){o=e/+(p|0);if(!(b<<24>>24==0&(p|0)<1024))break;a:do if(!(o<=1.5707963267948966))b=0;else{c=f;d=0;b=1;while(1){if(!(b<<24>>24!=0&(d|0)<(p|0)))break a;n=c+o;b=+LT(a,c,n)<=1.0e-05&1;c=n;d=d+1|0}}while(0);p=p<<1}i=+E(+f);u=+F(+f);d=a+16|0;s=+h[d>>3];v=s*i;j=a+24|0;c=+h[j>>3];g=c*u;i=c*i;c=+h[a>>3];k=a+40|0;t=+h[k>>3];l=a+48|0;r=+h[l>>3];e=t*v+c-r*g;m=a+8|0;n=+h[m>>3];g=r*v+t*g+n;s=-(u*s);MT(q,c,n);NT(q,e,g);n=+G(+(o*.5));n=+F(+o)*.3333333333333333*(+C(+(n*n*3.0+4.0))+-1.0);c=f;f=g;g=r*s+t*i;i=t*s-r*i;b=0;while(1){if((b|0)>=(p|0))break;r=c+o;u=+E(+r);v=+F(+r);x=+h[d>>3];z=x*u;w=+h[j>>3];t=w*v;u=w*u;w=+h[k>>3];y=+h[l>>3];s=w*z+ +h[a>>3]-y*t;t=y*z+w*t+ +h[m>>3];x=-(v*x);v=w*x-y*u;u=y*x+w*u;OT(q,e+i*n,f+g*n,s-v*n,t-u*n,s,t);c=r;e=s;f=t;g=u;i=v;b=b+1|0}PT(q);return q|0}function LT(a,b,c){a=a|0;b=+b;c=+c;var d=0.0,e=0.0,f=0.0,g=0.0,i=0.0;d=c+b;i=+h[a+16>>3];e=+h[a+24>>3]/i;g=+E(+d);f=+E(+(d*2.0));d=+E(+(d*3.0));a=e<.25?10392:10648;return +(((e*.001+4.98)*e+.207)/(e+.0067)*i*+L(+(((+h[a+32>>3]*e+ +h[a+40>>3])*e+ +h[a+48>>3])/(+h[a+56>>3]+e)*g+((+h[a>>3]*e+ +h[a+8>>3])*e+ +h[a+16>>3])/(+h[a+24>>3]+e)+((+h[a+64>>3]*e+ +h[a+72>>3])*e+ +h[a+80>>3])/(+h[a+88>>3]+e)*f+((+h[a+96>>3]*e+ +h[a+104>>3])*e+ +h[a+112>>3])/(+h[a+120>>3]+e)*d+(((+h[a+160>>3]*e+ +h[a+168>>3])*e+ +h[a+176>>3])/(+h[a+184>>3]+e)*g+((+h[a+128>>3]*e+ +h[a+136>>3])*e+ +h[a+144>>3])/(+h[a+152>>3]+e)+((+h[a+192>>3]*e+ +h[a+200>>3])*e+ +h[a+208>>3])/(+h[a+216>>3]+e)*f+((+h[a+224>>3]*e+ +h[a+232>>3])*e+ +h[a+240>>3])/(+h[a+248>>3]+e)*d)*(c-b))))}function MT(a,b,d){a=a|0;b=+b;d=+d;var e=0;c[47373]=100;e=JK(1600)|0;c[a>>2]=e;h[e>>3]=b;h[e+8>>3]=d;c[a+4>>2]=1;return}function NT(a,b,d){a=a|0;b=+b;d=+d;var e=0,f=0;f=c[a>>2]|0;e=(c[a+4>>2]|0)+-1|0;OT(a,+h[f+(e<<4)>>3],+h[f+(e<<4)+8>>3],b,d,b,d);return}function OT(a,b,d,e,f,g,i){a=a|0;b=+b;d=+d;e=+e;f=+f;g=+g;i=+i;var j=0,k=0,l=0,m=0;l=a+4|0;j=c[l>>2]|0;k=c[47373]|0;if((j+3|0)<(k|0))a=c[a>>2]|0;else{c[47373]=k<<1;j=W1(c[a>>2]|0,k<<5)|0;c[a>>2]=j;a=j;j=c[l>>2]|0}h[a+(j<<4)>>3]=b;m=j+1|0;h[a+(j<<4)+8>>3]=d;h[a+(m<<4)>>3]=e;k=j+2|0;h[a+(m<<4)+8>>3]=f;h[a+(k<<4)>>3]=g;c[l>>2]=j+3;h[a+(k<<4)+8>>3]=i;return}function PT(a){a=a|0;var b=0;b=c[a>>2]|0;NT(a,+h[b>>3],+h[b+8>>3]);c[a>>2]=W1(c[a>>2]|0,c[a+4>>2]<<4)|0;c[47373]=0;return}function QT(a){a=a|0;var b=0.0,c=0.0,d=0.0,e=0.0;d=+h[a+16>>3];b=+h[a+24>>3];b=+C(+(d*d-b*b));d=b*+h[a+40>>3];b=b*+h[a+48>>3];e=+h[a>>3];h[a+104>>3]=e-d;c=+h[a+8>>3];h[a+112>>3]=c-b;h[a+120>>3]=e+d;h[a+128>>3]=c+b;return}function RT(a){a=a|0;var b=0.0,c=0.0,d=0.0,e=0.0,f=0.0,g=0.0,i=0.0,j=0.0;d=+h[a+16>>3];i=+h[a+56>>3];j=+E(+i)*d;b=+h[a+24>>3];i=+F(+i)*b;g=+h[a+64>>3];d=+E(+g)*d;b=+F(+g)*b;g=+h[a>>3];c=+h[a+40>>3];e=+h[a+48>>3];h[a+72>>3]=c*j+g-e*i;f=+h[a+8>>3];h[a+80>>3]=e*j+f+c*i;h[a+88>>3]=g+c*d-e*b;h[a+96>>3]=f+e*d+c*b;return}function ST(a){a=a|0;var b=0.0,c=0.0,d=0.0,e=0.0,f=0.0,g=0.0,i=0.0,j=0.0,k=0.0,l=0.0,m=0.0,n=0;k=+h[a+24>>3];l=+h[a+16>>3];e=k/l;m=+h[a+48>>3];n=+B(+m)<.1;j=+h[a+40>>3];do if(n){b=m/j;c=+J(+(b*e));d=-c;if(j<0.0){b=+J(+(b/e));e=d;d=3.141592653589793-c;c=4.71238898038469-b;b=1.5707963267948966-b;break}else{b=+J(+(b/e));e=-3.141592653589793-c;c=1.5707963267948966-b;b=-1.5707963267948966-b;break}}else{b=j/m;c=+J(+(b/e));d=c+1.5707963267948966;if(m<0.0){b=+J(+(b*e));e=c+-1.5707963267948966;c=b+3.141592653589793;break}else{b=+J(+(b*e));e=d;d=c+4.71238898038469;c=b;b=b+-3.141592653589793;break}}while(0);g=+h[a+56>>3];e=e-+A(+((e-g)*.15915494309189535))*6.283185307179586;b=b-+A(+((b-g)*.15915494309189535))*6.283185307179586;f=d-+A(+((d-g)*.15915494309189535))*6.283185307179586;g=c-+A(+((c-g)*.15915494309189535))*6.283185307179586;i=+h[a+64>>3];if(!(e<=i)){e=+h[a+72>>3];c=+h[a+88>>3];c=e<c?e:c}else c=+E(+e)*l*j+ +h[a>>3]-k*+F(+e)*m;h[a+136>>3]=c;if(!(b<=i)){e=+h[a+80>>3];d=+h[a+96>>3];d=e<d?e:d}else d=+E(+b)*l*m+ +h[a+8>>3]+ +F(+b)*k*j;h[a+144>>3]=d;if(!(f<=i)){f=+h[a+72>>3];b=+h[a+88>>3];b=f>b?f:b}else b=+E(+f)*l*j+ +h[a>>3]-k*+F(+f)*m;h[a+152>>3]=b-c;if(!(g<=i)){m=+h[a+80>>3];b=+h[a+96>>3];b=m>b?m:b}else b=+E(+g)*l*m+ +h[a+8>>3]+ +F(+g)*k*j;h[a+160>>3]=b-d;return}function TT(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0,j=0,k=0.0,m=0,n=0;n=l;l=l+80|0;m=n;UT(b);k=+h[e+56>>3];g=+h[e+64>>3];switch(a[e+80>>0]|0){case 116:{g=(+h[e+48>>3]-+VT(d))*.5+g;f=4;break}case 98:{g=g-(+h[e+48>>3]-+VT(d))*.5;f=4;break}default:{}}if((f|0)==4)g=g+-1.0;h[m>>3]=k;h[m+8>>3]=g;c[m+20>>2]=c[e+8>>2];c[m+16>>2]=c[e+4>>2];h[m+32>>3]=+h[e+16>>3];j=b+16|0;f=QA(c[(c[j>>2]|0)+8>>2]|0,137297)|0;e=m+56|0;c[e>>2]=f;i=m+60|0;c[i>>2]=c[(c[j>>2]|0)+212>>2];j=m+64|0;a[j>>0]=0;if(!((f|0)!=0?(a[f>>0]|0)!=0:0))c[e>>2]=137308;if((a[d+4>>0]|0)==1){e=c[d>>2]|0;lS(b,c[(c[b>>2]|0)+336>>2]|0);f=c[e+24>>2]|0;if(!f)hS(b,137314);else hS(b,f);XT(b,e,m);if(a[j>>0]|0)U1(c[i>>2]|0)}else WT(b,c[d>>2]|0,m);YT(b);l=n;return}function UT(a){a=a|0;var d=0,e=0,f=0;d=aI(a)|0;e=c[d>>2]|0;a=c[e+4>>2]|0;c[d+4>>2]=a;c[d+12>>2]=c[e+12>>2];switch(a|0){case 2:{a=e+8|0;f=6;break}case 0:{a=e+8|0;f=6;break}case 1:{a=e+8|0;f=6;break}case 3:{a=e+8|0;f=6;break}default:{}}if((f|0)==6)c[d+8>>2]=c[a>>2];c[d+208>>2]=c[e+208>>2];c[d+228>>2]=c[e+228>>2];c[d+244>>2]=c[e+244>>2];f=d+260|0;b[f>>1]=b[f>>1]&-2|b[e+260>>1]&1;return}function VT(b){b=b|0;var d=0.0,e=0,f=0;switch(a[b+4>>0]|0){case 1:{e=c[b>>2]|0;b=e+72|0;e=e+56|0;f=5;break}case 3:{e=c[b>>2]|0;b=e+24|0;e=e+8|0;f=5;break}case 2:{e=c[b>>2]|0;b=e+32|0;e=e+16|0;f=5;break}default:d=0.0}if((f|0)==5)d=+h[b>>3]-+h[e>>3];return +d}function WT(d,e,f){d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0.0,p=0.0,q=0,r=0,s=0;n=l;l=l+96|0;j=n+64|0;i=n+32|0;g=n+16|0;k=n;m=b[e+4>>1]|0;if(m<<16>>16>=1){r=e+8|0;p=+h[e+24>>3];o=+h[r>>3];h[k>>3]=(o+p)*.5+ +h[f>>3];h[k+8>>3]=(+h[e+16>>3]+ +h[e+32>>3])*.5+ +h[f+8>>3];q=c[e>>2]|0;s=f+16|0;f=a[e+6>>0]|0;c[g>>2]=c[k>>2];c[g+4>>2]=c[k+4>>2];c[g+8>>2]=c[k+8>>2];c[g+12>>2]=c[k+12>>2];c[i>>2]=c[s>>2];c[i+4>>2]=c[s+4>>2];c[i+8>>2]=c[s+8>>2];c[i+12>>2]=c[s+12>>2];c[i+16>>2]=c[s+16>>2];c[i+20>>2]=c[s+20>>2];c[i+24>>2]=c[s+24>>2];c[i+28>>2]=c[s+28>>2];c[j>>2]=c[r>>2];c[j+4>>2]=c[r+4>>2];c[j+8>>2]=c[r+8>>2];c[j+12>>2]=c[r+12>>2];c[j+16>>2]=c[r+16>>2];c[j+20>>2]=c[r+20>>2];c[j+24>>2]=c[r+24>>2];c[j+28>>2]=c[r+28>>2];iU(d,m<<16>>16,q,g,(p-o)*.5,i,j,f)}l=n;return}function XT(e,f,g){e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0.0,n=0.0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;y=l;l=l+176|0;t=y+96|0;v=y+64|0;w=y+136|0;p=y;q=y+128|0;r=f+48|0;c[v>>2]=c[r>>2];c[v+4>>2]=c[r+4>>2];c[v+8>>2]=c[r+8>>2];c[v+12>>2]=c[r+12>>2];c[v+16>>2]=c[r+16>>2];c[v+20>>2]=c[r+20>>2];c[v+24>>2]=c[r+24>>2];c[v+28>>2]=c[r+28>>2];m=+h[g>>3];n=+h[g+8>>3];r=f+84|0;i=c[r>>2]|0;if(!(c[f>>2]|0))x=(c[f+8>>2]|0)!=0;else x=1;u=f+108|0;j=c[u>>2]|0;if(j|0)ZT(g,j,186544);h[v>>3]=+h[v>>3]+m;s=v+16|0;h[s>>3]=+h[s>>3]+m;s=v+8|0;h[s>>3]=+h[s>>3]+n;s=v+24|0;h[s>>3]=+h[s>>3]+n;if(x?(c[e+152>>2]&4|0)==0:0){c[t>>2]=c[v>>2];c[t+4>>2]=c[v+4>>2];c[t+8>>2]=c[v+8>>2];c[t+12>>2]=c[v+12>>2];c[t+16>>2]=c[v+16>>2];c[t+20>>2]=c[v+20>>2];c[t+24>>2]=c[v+24>>2];c[t+28>>2]=c[v+28>>2];s=_T(e,g,f,t,w)|0}else s=0;o=f+42|0;j=b[o>>1]|0;if(!(j&32)){k=c[f+20>>2]|0;if(k){j=$T(e,k,c[f+28>>2]|0,j&65535,q)|0;if(!(b[o>>1]&4)){c[t>>2]=c[v>>2];c[t+4>>2]=c[v+4>>2];c[t+8>>2]=c[v+8>>2];c[t+12>>2]=c[v+12>>2];c[t+16>>2]=c[v+16>>2];c[t+20>>2]=c[v+20>>2];c[t+24>>2]=c[v+24>>2];c[t+28>>2]=c[v+28>>2];oS(e,t,j)}else{o=d[f+33>>0]|0;c[t>>2]=c[v>>2];c[t+4>>2]=c[v+4>>2];c[t+8>>2]=c[v+8>>2];c[t+12>>2]=c[v+12>>2];c[t+16>>2]=c[v+16>>2];c[t+20>>2]=c[v+20>>2];c[t+24>>2]=c[v+24>>2];c[t+28>>2]=c[v+28>>2];TM(e,aU(p,t,o)|0,4,4,j)}U1(c[q>>2]|0)}while(1){j=c[i>>2]|0;if(!j)break;bU(e,j,g);i=i+4|0}i=c[r>>2]|0;uS(e,1.0);j=f+24|0;while(1){k=i+4|0;i=c[i>>2]|0;if(!i)break;if(!(a[i+100>>0]|0)){i=k;continue}cU(e,i,g,c[j>>2]|0,c[k>>2]|0);i=k}if(a[f+33>>0]|0){c[t>>2]=c[v>>2];c[t+4>>2]=c[v+4>>2];c[t+8>>2]=c[v+8>>2];c[t+12>>2]=c[v+12>>2];c[t+16>>2]=c[v+16>>2];c[t+20>>2]=c[v+20>>2];c[t+24>>2]=c[v+24>>2];c[t+28>>2]=c[v+28>>2];dU(e,f,t)}}if(s|0)eU(e,w);if((x?c[e+152>>2]&4|0:0)?(c[t>>2]=c[v>>2],c[t+4>>2]=c[v+4>>2],c[t+8>>2]=c[v+8>>2],c[t+12>>2]=c[v+12>>2],c[t+16>>2]=c[v+16>>2],c[t+20>>2]=c[v+20>>2],c[t+24>>2]=c[v+24>>2],c[t+28>>2]=c[v+28>>2],_T(e,g,f,t,w)|0):0)eU(e,w);if(c[u>>2]|0)fU(g,186544);l=y;return}function YT(a){a=a|0;var b=0;b=c[a+16>>2]|0;c[b+208>>2]=0;c[b+228>>2]=0;c[b+244>>2]=0;c[b+212>>2]=0;bI(a);return}function ZT(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0;i=a+16|0;f=c[i>>2]|0;if(f|0){if(!(c[b>>2]|0)){f=0;g=d}else{c[d>>2]=f;f=c[b>>2]|0;g=i}c[g>>2]=f}f=c[a+20>>2]|0;if(f|0){g=b+4|0;if(!(c[g>>2]|0)){f=0;g=d}else{c[d+4>>2]=f;f=c[g>>2]|0;g=i}c[g+4>>2]=f}e=+h[a+32>>3];if(e>=0.0){f=b+16|0;if(!(+h[f>>3]>=0.0))e=-1.0;else{h[d+16>>3]=e;e=+h[f>>3];d=i}h[d+16>>3]=e}return}function _T(d,e,f,g,h){d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+224|0;n=u+8|0;k=u;t=u+40|0;j=u+184|0;i=u+56|0;m=c[d+16>>2]|0;o=m+208|0;c[h>>2]=c[o>>2];p=m+228|0;c[h+4>>2]=c[p>>2];q=m+244|0;c[h+8>>2]=c[q>>2];r=m+212|0;c[h+12>>2]=c[r>>2];s=m+260|0;a[h+16>>0]=(b[s>>1]<<15&65535)<<16>>16>>15;h=c[f+16>>2]|0;if((h|0)!=0?(a[h>>0]|0)!=0:0)i=0;else{kA(t,128,i);i=e+60|0;h=c[i>>2]|0;if(!h){h=D4(dI(d,c[m+8>>2]|0,t)|0)|0;c[i>>2]=h;a[e+64>>0]=1}nA(t,h)|0;i=c[47374]|0;c[47374]=i+1;c[k>>2]=i;m4(j,137391,k)|0;nA(t,j)|0;i=t+4|0;h=c[i>>2]|0;if(h>>>0>=(c[t+8>>2]|0)>>>0){lA(t,1)|0;h=c[i>>2]|0}a[h>>0]=0;h=c[t>>2]|0;c[i>>2]=h;i=1}h=cI(d,0,c[f>>2]|0,c[f+12>>2]|0,c[f+8>>2]|0,h,c[m+8>>2]|0)|0;if(i)oA(t);do if(h|0){if((c[o>>2]|0)==0?(b[s>>1]&1)==0:0)break;c[n>>2]=c[g>>2];c[n+4>>2]=c[g+4>>2];c[n+8>>2]=c[g+8>>2];c[n+12>>2]=c[g+12>>2];c[n+16>>2]=c[g+16>>2];c[n+20>>2]=c[g+20>>2];c[n+24>>2]=c[g+24>>2];c[n+28>>2]=c[g+28>>2];kI(d,n);cS(d,c[o>>2]|0,c[p>>2]|0,c[q>>2]|0,c[r>>2]|0)}while(0);l=u;return h|0}function $T(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var h=0.0,i=0,j=0;j=l;l=l+16|0;i=j;if(!((JJ(b,f,i)|0)<<24>>24)){jS(a,b);b=1}else{jS(a,c[f>>2]|0);b=c[f+4>>2]|0;h=+g[i>>2];if(!b)kS(a,137314,d,h);else kS(a,b,d,h);b=e>>>1&1|2}hS(a,137379);l=j;return b|0}function aU(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0.0,j=0.0;c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];e=a+32|0;b=b+16|0;c[e>>2]=c[b>>2];c[e+4>>2]=c[b+4>>2];c[e+8>>2]=c[b+8>>2];c[e+12>>2]=c[b+12>>2];if((d|0)>1){j=+(d|0)*.5;i=+h[a>>3]+j;h[a>>3]=i;d=a+8|0;g=+h[d>>3]+j;h[d>>3]=g;f=+h[e>>3]-j;h[e>>3]=f;e=a+40|0;j=+h[e>>3]-j;h[e>>3]=j}else{f=+h[e>>3];g=+h[a+8>>3];i=+h[a>>3];j=+h[a+40>>3]}h[a+16>>3]=f;h[a+24>>3]=g;h[a+48>>3]=i;h[a+56>>3]=j;return a|0}function bU(e,f,g){e=e|0;f=f|0;g=g|0;var i=0,j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;v=l;l=l+176|0;r=v+96|0;s=v+136|0;t=v+64|0;p=v;q=v+128|0;u=f+48|0;c[t>>2]=c[u>>2];c[t+4>>2]=c[u+4>>2];c[t+8>>2]=c[u+8>>2];c[t+12>>2]=c[u+12>>2];c[t+16>>2]=c[u+16>>2];c[t+20>>2]=c[u+20>>2];c[t+24>>2]=c[u+24>>2];c[t+28>>2]=c[u+28>>2];j=+h[g>>3];k=+h[g+8>>3];if(!(c[f>>2]|0))u=(c[f+8>>2]|0)!=0;else u=1;h[t>>3]=+h[t>>3]+j;o=t+16|0;h[o>>3]=+h[o>>3]+j;o=t+8|0;h[o>>3]=+h[o>>3]+k;o=t+24|0;h[o>>3]=+h[o>>3]+k;if(u?(c[e+152>>2]&4|0)==0:0){c[r>>2]=c[t>>2];c[r+4>>2]=c[t+4>>2];c[r+8>>2]=c[t+8>>2];c[r+12>>2]=c[t+12>>2];c[r+16>>2]=c[t+16>>2];c[r+20>>2]=c[t+20>>2];c[r+24>>2]=c[t+24>>2];c[r+28>>2]=c[t+28>>2];o=_T(e,g,f,r,s)|0}else o=0;n=f+42|0;i=b[n>>1]|0;a:do if(!(i&32)){m=c[f+20>>2]|0;if(m|0){i=$T(e,m,c[f+28>>2]|0,i&65535,q)|0;if(!(b[n>>1]&4)){c[r>>2]=c[t>>2];c[r+4>>2]=c[t+4>>2];c[r+8>>2]=c[t+8>>2];c[r+12>>2]=c[t+12>>2];c[r+16>>2]=c[t+16>>2];c[r+20>>2]=c[t+20>>2];c[r+24>>2]=c[t+24>>2];c[r+28>>2]=c[t+28>>2];oS(e,r,i)}else{n=d[f+33>>0]|0;c[r>>2]=c[t>>2];c[r+4>>2]=c[t+4>>2];c[r+8>>2]=c[t+8>>2];c[r+12>>2]=c[t+12>>2];c[r+16>>2]=c[t+16>>2];c[r+20>>2]=c[t+20>>2];c[r+24>>2]=c[t+24>>2];c[r+28>>2]=c[t+28>>2];TM(e,aU(p,r,n)|0,4,4,i)}U1(c[q>>2]|0)}if(a[f+33>>0]|0){c[r>>2]=c[t>>2];c[r+4>>2]=c[t+4>>2];c[r+8>>2]=c[t+8>>2];c[r+12>>2]=c[t+12>>2];c[r+16>>2]=c[t+16>>2];c[r+20>>2]=c[t+20>>2];c[r+24>>2]=c[t+24>>2];c[r+28>>2]=c[t+28>>2];dU(e,f,r)}i=f+88|0;switch(a[f+92>>0]|0){case 1:{XT(e,c[i>>2]|0,g);break a}case 3:{hU(e,c[i>>2]|0,g);break a}default:{WT(e,c[i>>2]|0,g);break a}}}while(0);if(o|0)eU(e,s);if((u?c[e+152>>2]&4|0:0)?(c[r>>2]=c[t>>2],c[r+4>>2]=c[t+4>>2],c[r+8>>2]=c[t+8>>2],c[r+12>>2]=c[t+12>>2],c[r+16>>2]=c[t+16>>2],c[r+20>>2]=c[t+20>>2],c[r+24>>2]=c[t+24>>2],c[r+28>>2]=c[t+28>>2],_T(e,g,f,r,s)|0):0)eU(e,s);l=v;return}function cU(f,g,i,j,k){f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;var m=0.0,n=0,o=0.0,p=0.0,q=0,r=0.0,s=0.0,t=0.0,u=0.0,v=0,w=0,x=0.0,y=0,z=0,A=0,B=0,C=0;A=l;l=l+32|0;y=A+16|0;z=A;r=+h[i>>3];x=+h[i+8>>3];q=(j|0)==0?137314:j;jS(f,q);hS(f,q);t=+h[g+48>>3];o=+h[g+56>>3];u=+h[g+64>>3];p=+h[g+72>>3];s=t+r;r=u+r;x=o+x;q=g+100|0;i=a[q>>0]|0;if((i&1)!=0?(n=c[g+96>>2]|0,((e[g+80>>1]|0)+(e[g+84>>1]|0)|0)<(c[n+104>>2]|0)):0){i=b[g+86>>1]|0;do if(i<<16>>16)if(((e[g+82>>1]|0)+(i&65535)|0)==(c[n+100>>2]|0)){B=a[n+32>>0]|0;C=(B<<24>>24)/2|0;j=(C<<24>>24)+(d[n+33>>0]|0)|0;i=j;j=j&255;m=x-+(C<<24>>24);n=B;break}else{n=a[n+32>>0]|0;i=0;j=((n<<24>>24)/2|0)<<24>>24;m=x;break}else{C=a[n+32>>0]|0;j=((C<<24>>24)/2|0)<<24>>24;i=j+(d[n+33>>0]|0)|0;m=x;n=C}while(0);h[z+8>>3]=m-+(j|0);h[z>>3]=r+ +(((n<<24>>24)/2|0)<<24>>24);c[y>>2]=c[z>>2];c[y+4>>2]=c[z+4>>2];c[y+8>>2]=c[z+8>>2];c[y+12>>2]=c[z+12>>2];gU(f,y,0.0,p-o+ +(i&255|0)+ +(n<<24>>24));i=a[q>>0]|0}if(i&2?(v=b[g+86>>1]|0,w=c[g+96>>2]|0,((e[g+82>>1]|0)+(v&65535)|0)<(c[w+100>>2]|0)):0){i=b[g+84>>1]|0;do if(!(i<<16>>16)){i=a[w+32>>0]|0;C=(i<<24>>24)/2|0;n=(C<<24>>24)+(d[w+33>>0]|0)|0;j=n&255;n=n&255;m=+(C<<24>>24);h[z>>3]=s-m-+(n|0);if((c[w+104>>2]|0)==(e[g+80>>1]|0)){j=n<<1&255;break}if((k|0)!=0?(b[k+86>>1]|0)!=v<<16>>16:0)j=~~(+(j&255)-u+ +h[w+64>>3]-m)&255}else{if(((e[g+80>>1]|0)+(i&65535)|0)==(c[w+104>>2]|0)){i=a[w+32>>0]|0;C=(i<<24>>24)/2|0;j=(C&255)+(d[w+33>>0]|0)&255;h[z>>3]=s-+(C<<24>>24);break}i=a[w+32>>0]|0;m=+(((i<<24>>24)/2|0)<<24>>24);h[z>>3]=s-m;if((k|0)!=0?(b[k+86>>1]|0)!=v<<16>>16:0)j=~~(+h[w+64>>3]-u-m)&255;else j=0}while(0);h[z+8>>3]=x-+(((i<<24>>24)/2|0)<<24>>24);c[y>>2]=c[z>>2];c[y+4>>2]=c[z+4>>2];c[y+8>>2]=c[z+8>>2];c[y+12>>2]=c[z+12>>2];gU(f,y,u-t+ +(j&255)+ +(i<<24>>24),0.0)}l=A;return}function dU(e,f,g){e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0;p=l;l=l+160|0;o=p+112|0;n=p;j=p+144|0;m=c[f+24>>2]|0;hS(e,(m|0)==0?137314:m);m=f+42|0;i=b[m>>1]|0;if(!(i&384))lS(e,c[(c[e>>2]|0)+336>>2]|0);else{c[j+4>>2]=0;c[j>>2]=0;i=i&65535;if(!(i&256)){if(i&128|0){i=137320;k=4}}else{i=137327;k=4}if((k|0)==4)c[j>>2]=i;lS(e,j)}k=f+33|0;uS(e,+(d[k>>0]|0));a:do if(!(b[m>>1]&4)){i=b[f+36>>1]&15360;if(!(i<<16>>16)){i=a[k>>0]|0;if((i&255)>1){q=+(i&255)*.5;h[g>>3]=+h[g>>3]+q;n=g+8|0;h[n>>3]=+h[n>>3]+q;n=g+16|0;h[n>>3]=+h[n>>3]-q;n=g+24|0;h[n>>3]=+h[n>>3]-q};c[o>>2]=c[g>>2];c[o+4>>2]=c[g+4>>2];c[o+8>>2]=c[g+8>>2];c[o+12>>2]=c[g+12>>2];c[o+16>>2]=c[g+16>>2];c[o+20>>2]=c[g+20>>2];c[o+24>>2]=c[g+24>>2];c[o+28>>2]=c[g+28>>2];oS(e,o,0);break}j=n+16|0;m=d[k>>0]|0;c[o>>2]=c[g>>2];c[o+4>>2]=c[g+4>>2];c[o+8>>2]=c[g+8>>2];c[o+12>>2]=c[g+12>>2];c[o+16>>2]=c[g+16>>2];c[o+20>>2]=c[g+20>>2];c[o+24>>2]=c[g+24>>2];c[o+28>>2]=c[g+28>>2];aU(j,o,m)|0;do switch(((i&65535)+-1024|0)>>>10&4194303|0){case 7:{qS(e,j,2);break a}case 3:{qS(e,n+32|0,2);break a}case 1:{qS(e,n+48|0,2);break a}case 0:{g=n+64|0;c[n>>2]=c[g>>2];c[n+4>>2]=c[g+4>>2];c[n+8>>2]=c[g+8>>2];c[n+12>>2]=c[g+12>>2];qS(e,n,2);break a}case 11:{qS(e,j,3);break a}case 5:{qS(e,n+32|0,3);break a}case 2:{g=n+80|0;c[g>>2]=c[j>>2];c[g+4>>2]=c[j+4>>2];c[g+8>>2]=c[j+8>>2];c[g+12>>2]=c[j+12>>2];qS(e,n+48|0,3);break a}case 8:{g=n+64|0;c[n>>2]=c[g>>2];c[n+4>>2]=c[g+4>>2];c[n+8>>2]=c[g+8>>2];c[n+12>>2]=c[g+12>>2];qS(e,n,3);break a}case 13:{qS(e,j,4);break a}case 6:{g=n+80|0;c[g>>2]=c[j>>2];c[g+4>>2]=c[j+4>>2];c[g+8>>2]=c[j+8>>2];c[g+12>>2]=c[j+12>>2];qS(e,n+32|0,4);break a}case 10:{g=n+80|0;c[g>>2]=c[j>>2];c[g+4>>2]=c[j+4>>2];c[g+8>>2]=c[j+8>>2];c[g+12>>2]=c[j+12>>2];g=n+96|0;o=n+32|0;c[g>>2]=c[o>>2];c[g+4>>2]=c[o+4>>2];c[g+8>>2]=c[o+8>>2];c[g+12>>2]=c[o+12>>2];qS(e,n+48|0,4);break a}case 12:{g=n+64|0;c[n>>2]=c[g>>2];c[n+4>>2]=c[g+4>>2];c[n+8>>2]=c[g+8>>2];c[n+12>>2]=c[g+12>>2];qS(e,n,4);break a}case 9:{qS(e,j,2);qS(e,n+48|0,2);break a}case 4:{g=n+64|0;c[n>>2]=c[g>>2];c[n+4>>2]=c[g+4>>2];c[n+8>>2]=c[g+8>>2];c[n+12>>2]=c[g+12>>2];qS(e,n,2);qS(e,n+32|0,2);break a}default:break a}while(0)}else{m=d[k>>0]|0;c[o>>2]=c[g>>2];c[o+4>>2]=c[g+4>>2];c[o+8>>2]=c[g+8>>2];c[o+12>>2]=c[g+12>>2];c[o+16>>2]=c[g+16>>2];c[o+20>>2]=c[g+20>>2];c[o+24>>2]=c[g+24>>2];c[o+28>>2]=c[g+28>>2];TM(e,aU(n,o,m)|0,4,4,0)}while(0);l=p;return}function eU(d,e){d=d|0;e=e|0;var f=0,g=0,h=0;h=c[d+16>>2]|0;f=h+208|0;if((c[f>>2]|0)==0?(b[h+260>>1]&1)==0:0)d=0;else{dS(d);d=c[f>>2]|0}if((d|0)!=(c[e>>2]|0)){U1(d);c[f>>2]=c[e>>2]}d=h+228|0;f=c[d>>2]|0;g=e+4|0;if((f|0)!=(c[g>>2]|0)){U1(f);c[d>>2]=c[g>>2]}d=h+244|0;f=c[d>>2]|0;g=e+8|0;if((f|0)!=(c[g>>2]|0)){U1(f);c[d>>2]=c[g>>2]}d=h+212|0;f=c[d>>2]|0;g=e+12|0;if((f|0)!=(c[g>>2]|0)){U1(f);c[d>>2]=c[g>>2]}h=h+260|0;b[h>>1]=b[h>>1]&-2|a[e+16>>0]&1;return}function fU(a,b){a=a|0;b=b|0;var d=0.0,e=0;e=c[b>>2]|0;if(e|0)c[a+16>>2]=e;e=c[b+4>>2]|0;if(e|0)c[a+20>>2]=e;d=+h[b+16>>3];if(d>=0.0)h[a+32>>3]=d;return}function gU(a,b,d,e){a=a|0;b=b|0;d=+d;e=+e;var f=0,g=0,i=0;f=l;l=l+64|0;g=f+32|0;i=f;c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];h[i+16>>3]=+h[b>>3]+d;h[i+24>>3]=+h[b+8>>3]+e;c[g>>2]=c[i>>2];c[g+4>>2]=c[i+4>>2];c[g+8>>2]=c[i+8>>2];c[g+12>>2]=c[i+12>>2];c[g+16>>2]=c[i+16>>2];c[g+20>>2]=c[i+20>>2];c[g+24>>2]=c[i+24>>2];c[g+28>>2]=c[i+28>>2];oS(a,g,1);l=f;return}function hU(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0.0,k=0.0,m=0.0,n=0.0;i=l;l=l+64|0;g=i;k=+h[e>>3];n=k+ +h[d>>3];m=+h[e+8>>3];j=m+ +h[d+8>>3];k=k+ +h[d+16>>3];m=m+ +h[d+24>>3];h[g>>3]=k;h[g+8>>3]=m;h[g+32>>3]=n;h[g+40>>3]=j;h[g+16>>3]=n;h[g+24>>3]=m;h[g+48>>3]=k;h[g+56>>3]=j;f=c[d+36>>2]|0;if(!f)f=c[e+56>>2]|0;e=c[d+32>>2]|0;if(!e)qa(137334,137342,623,137354);if(!(a[e>>0]|0))qa(137368,137342,624,137354);else{sS(b,e,g,4,1,f);l=i;return}}function iU(d,e,f,g,i,j,k,m){d=d|0;e=e|0;f=f|0;g=g|0;i=+i;j=j|0;k=k|0;m=m|0;var n=0.0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0.0,J=0,K=0,L=0,M=0.0,N=0.0,O=0,P=0,Q=0,R=0;R=l;l=l+128|0;K=R+104|0;O=R+48|0;P=R+16|0;L=R;J=L;c[J>>2]=0;c[J+4>>2]=0;M=+h[g>>3];N=M-i;I=M+i;J=L+8|0;h[J>>3]=(+h[k+24>>3]-+h[k+8>>3])*.5+ +h[g+8>>3];eS(d,1);D=P+16|0;E=P+4|0;F=P+24|0;G=O+4|0;H=O+16|0;s=(m|0)==0;t=O+24|0;u=O+8|0;v=O+32|0;w=O+40|0;x=O+48|0;y=j+16|0;z=j+4|0;A=P+8|0;q=0;while(1){if((q|0)>=(e|0))break;switch(a[f+(q*24|0)+6>>0]|0){case 108:{i=N;break}case 114:{i=I-+h[f+(q*24|0)+8>>3];break}default:i=M-+h[f+(q*24|0)+8>>3]*.5}h[g>>3]=i;B=f+(q*24|0)+16|0;h[J>>3]=+h[J>>3]-+h[B>>3];C=f+(q*24|0)+4|0;p=c[f+(q*24|0)>>2]|0;r=0;while(1){if((r|0)>=(b[C>>1]|0))break;o=p+4|0;m=c[o>>2]|0;if(m){h[D>>3]=+h[(+h[m+16>>3]>0.0?m:j)+16>>3];c[P>>2]=c[((c[m>>2]|0)==0?j:m)>>2];k=c[((c[m+4>>2]|0)==0?j:m)+4>>2]|0;c[E>>2]=k;m=c[m+24>>2]&127;if(!m)Q=12;else m=c[F>>2]&-128|m}else{h[D>>3]=+h[y>>3];c[P>>2]=c[j>>2];k=c[z>>2]|0;c[E>>2]=k;Q=12}if((Q|0)==12){Q=0;m=c[F>>2]&-128}c[F>>2]=m;hS(d,k);c[O>>2]=c[p>>2];c[G>>2]=P;h[H>>3]=+h[p+16>>3];if(s)n=1.0;else n=+h[p+24>>3];h[t>>3]=n;c[A>>2]=c[(c[o>>2]|0)+8>>2];c[u>>2]=c[p+8>>2];o=p+32|0;h[v>>3]=+h[o>>3];h[w>>3]=+h[B>>3];a[x>>0]=108;h[L>>3]=i;c[K>>2]=c[L>>2];c[K+4>>2]=c[L+4>>2];c[K+8>>2]=c[L+8>>2];c[K+12>>2]=c[L+12>>2];gS(d,K,O);n=+h[o>>3]+i;h[g>>3]=n;p=p+56|0;r=r+1|0;i=n}q=q+1|0}fS(d);l=R;return}function jU(a){a=a|0;U1(c[a>>2]|0);U1(c[a+4>>2]|0);U1(c[a+8>>2]|0);U1(c[a+16>>2]|0);U1(c[a+12>>2]|0);U1(c[a+20>>2]|0);U1(c[a+24>>2]|0);return}function kU(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;if(a|0){j=a+4|0;e=0;g=c[a>>2]|0;while(1){if((e|0)>=(b[j>>1]|0))break;k=g+4|0;d=0;f=c[g>>2]|0;while(1){if((d|0)>=(b[k>>1]|0))break;U1(c[f>>2]|0);h=c[f+8>>2]|0;if(h|0?(i=c[f+12>>2]|0,i|0):0)tb[i&127](h);d=d+1|0;f=f+56|0}e=e+1|0;g=g+24|0}d=c[a>>2]|0;if(d|0)U1(d);U1(a)}return}function lU(a){a=a|0;U1(c[a+32>>2]|0);U1(a);return}function mU(b,d){b=b|0;d=d|0;switch(a[b+4>>0]|0){case 1:{nU(c[b>>2]|0);break}case 3:{lU(c[b>>2]|0);break}default:kU(c[b>>2]|0)}if(d|0)U1(b);return}function nU(a){a=a|0;var b=0,d=0,e=0;e=a+84|0;if((c[a+100>>2]|0)==-1)Rz(c[e>>2]|0)|0;else{b=c[e>>2]|0;U1(c[a+92>>2]|0);U1(c[a+96>>2]|0);while(1){d=c[b>>2]|0;if(!d)break;oU(d);b=b+4|0}U1(c[e>>2]|0)}jU(a);U1(a);return}function oU(a){a=a|0;mU(a+88|0,0);jU(a);U1(a);return}function pU(b,e,f){b=b|0;e=e|0;f=f|0;var g=0;b=c[(c[(c[b+16>>2]|0)+104>>2]|0)+72>>2]|0;if((a[b+4>>0]|0)!=2?(g=qU(c[b>>2]|0,e)|0,(g|0)!=0):0){c[f>>2]=d[g+35>>0];b=g+48|0}else b=0;return b|0}function qU(a,b){a=a|0;b=b|0;var d=0,e=0;d=c[a+4>>2]|0;if(!((d|0)!=0?(c3(d,b)|0)==0:0))e=3;a:do if((e|0)==3){d=c[a+84>>2]|0;while(1){a=c[d>>2]|0;if(!a){a=0;break a}a=rU(a,b)|0;if(!a)d=d+4|0;else break}}while(0);return a|0}function rU(b,d){b=b|0;d=d|0;var e=0;e=c[b+4>>2]|0;if(!((e|0)!=0?(c3(e,d)|0)==0:0))if((a[b+92>>0]|0)==1)b=qU(c[b+88>>2]|0,d)|0;else b=0;return b|0}function sU(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;return 0}function tU(d){d=d|0;var f=0.0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;p=d+92|0;c[p>>2]=JK((c[d+100>>2]<<2)+4|0)|0;n=JK((c[d+104>>2]<<2)+4|0)|0;c[d+96>>2]=n;o=d+32|0;m=c[d+84>>2]|0;while(1){l=c[m>>2]|0;if(!l)break;d=b[l+82>>1]|0;f=+h[l+72>>3];if(d<<16>>16==1)i=~~f;else{i=~~((f-+(O((a[o>>0]|0)+-1|0,(d&65535)+-1|0)|0))/+(d&65535));i=(i|0)>1?i:1}j=b[l+80>>1]|0;f=+h[l+64>>3];if(j<<16>>16==1)k=~~f;else{k=~~((f-+(O((a[o>>0]|0)+-1|0,(j&65535)+-1|0)|0))/+(j&65535));k=(k|0)>1?k:1}q=e[l+86>>1]|0;g=(d&65535)+q|0;d=q;while(1){if((d|0)>=(g|0))break;q=(c[p>>2]|0)+(d<<2)|0;r=c[q>>2]|0;c[q>>2]=(r|0)>(i|0)?r:i;d=d+1|0}d=e[l+84>>1]|0;g=(j&65535)+d|0;while(1){if((d|0)>=(g|0))break;r=n+(d<<2)|0;q=c[r>>2]|0;c[r>>2]=(q|0)>(k|0)?q:k;d=d+1|0}m=m+4|0}return}function uU(a){a=a|0;var b=0,d=0;d=l;l=l+16|0;b=d;if(a>>>0<21)a=c[55840+(a<<2)>>2]|0;else{c[b>>2]=a;m4(194959,137395,b)|0;a=194959}l=d;return a|0}function vU(a,b,d){a=a|0;b=b|0;d=d|0;var f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;k=a+104|0;l=a+100|0;j=d+16|0;f=0;g=0;while(1){if((f|0)>(c[k>>2]|0))break;i=tC(d,uU(f)|0,1)|0;TC(i,137447,304,1)|0;m=i+16|0;c[(c[m>>2]|0)+176>>2]=0;o=JK((c[l>>2]<<2)+4|0)|0;n=c[m>>2]|0;c[n+172>>2]=o;c[n+184>>2]=0;n=JK((c[l>>2]<<2)+4|0)|0;c[(c[m>>2]|0)+180>>2]=n;if(!g)c[(c[j>>2]|0)+192>>2]=i;else c[(c[g+16>>2]|0)+164>>2]=i;f=f+1|0;g=i}j=b+16|0;f=0;g=0;while(1){if((f|0)>(c[l>>2]|0))break;i=tC(b,uU(f)|0,1)|0;TC(i,137447,304,1)|0;o=i+16|0;c[(c[o>>2]|0)+176>>2]=0;m=JK((c[k>>2]<<2)+4|0)|0;n=c[o>>2]|0;c[n+172>>2]=m;c[n+184>>2]=0;n=JK((c[k>>2]<<2)+4|0)|0;c[(c[o>>2]|0)+180>>2]=n;if(!g)c[(c[j>>2]|0)+192>>2]=i;else c[(c[g+16>>2]|0)+164>>2]=i;f=f+1|0;g=i}f=c[a+84>>2]|0;while(1){g=c[f>>2]|0;if(!g)break;o=g+84|0;n=tC(d,uU(e[o>>1]|0)|0,0)|0;o=tC(d,uU((e[g+80>>1]|0)+(e[o>>1]|0)|0)|0,0)|0;wU(d,n,o,~~+h[g+64>>3]);o=g+86|0;n=tC(b,uU(e[o>>1]|0)|0,0)|0;o=tC(b,uU((e[g+82>>1]|0)+(e[o>>1]|0)|0)|0,0)|0;wU(b,n,o,~~+h[g+72>>3]);f=f+4|0}xU(d);xU(b);return}function wU(a,d,f,g){a=a|0;d=d|0;f=f|0;g=g|0;var h=0,i=0;h=iB(a,d,f,0,0)|0;if(!h){i=iB(a,d,f,0,1)|0;TC(i,137460,176,1)|0;b[(c[i+16>>2]|0)+170>>1]=g;d=d+16|0;a=c[d>>2]|0;h=c[a+180>>2]|0;if(!h)a=KK((c[a+184>>2]<<2)+8|0)|0;else a=MK(h,(c[a+184>>2]<<2)+8|0)|0;g=c[d>>2]|0;c[g+180>>2]=a;g=g+184|0;h=c[g>>2]|0;c[g>>2]=h+1;c[a+(h<<2)>>2]=i;d=c[d>>2]|0;c[(c[d+180>>2]|0)+(c[d+184>>2]<<2)>>2]=0;d=f+16|0;a=c[d>>2]|0;h=c[a+172>>2]|0;if(!h)a=KK((c[a+176>>2]<<2)+8|0)|0;else a=MK(h,(c[a+176>>2]<<2)+8|0)|0;g=c[d>>2]|0;c[g+172>>2]=a;g=g+176|0;f=c[g>>2]|0;c[g>>2]=f+1;c[a+(f<<2)>>2]=i;i=c[d>>2]|0;c[(c[i+172>>2]|0)+(c[i+176>>2]<<2)>>2]=0}else{i=(c[h+16>>2]|0)+170|0;f=e[i>>1]|0;b[i>>1]=(f|0)>(g|0)?f:g}return}function xU(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0;d=c[(c[a+16>>2]|0)+192>>2]|0;while(1){f=d+16|0;h=c[(c[f>>2]|0)+164>>2]|0;if(!h)break;if(iB(a,d,h,0,0)|0){d=h;continue}g=iB(a,d,h,0,1)|0;TC(g,137460,176,1)|0;b[(c[g+16>>2]|0)+170>>1]=0;d=c[f>>2]|0;e=c[d+180>>2]|0;if(!e)d=KK((c[d+184>>2]<<2)+8|0)|0;else d=MK(e,(c[d+184>>2]<<2)+8|0)|0;i=c[f>>2]|0;c[i+180>>2]=d;i=i+184|0;e=c[i>>2]|0;c[i>>2]=e+1;c[d+(e<<2)>>2]=g;f=c[f>>2]|0;c[(c[f+180>>2]|0)+(c[f+184>>2]<<2)>>2]=0;f=h+16|0;d=c[f>>2]|0;e=c[d+172>>2]|0;if(!e)d=KK((c[d+176>>2]<<2)+8|0)|0;else d=MK(e,(c[d+176>>2]<<2)+8|0)|0;e=c[f>>2]|0;c[e+172>>2]=d;e=e+176|0;i=c[e>>2]|0;c[e>>2]=i+1;c[d+(i<<2)>>2]=g;d=c[f>>2]|0;c[(c[d+172>>2]|0)+(c[d+176>>2]<<2)>>2]=0;d=h}return}function yU(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;g=a+92|0;e=0;f=0;b=c[(c[(c[b+16>>2]|0)+192>>2]|0)+16>>2]|0;while(1){b=c[b+164>>2]|0;if(!b)break;b=c[b+16>>2]|0;h=b+232|0;c[(c[g>>2]|0)+(f<<2)>>2]=(c[h>>2]|0)-e;e=c[h>>2]|0;f=f+1|0}a=a+96|0;e=0;f=0;b=c[(c[(c[d+16>>2]|0)+192>>2]|0)+16>>2]|0;while(1){b=c[b+164>>2]|0;if(!b)break;b=c[b+16>>2]|0;h=b+232|0;c[(c[a>>2]|0)+(f<<2)>>2]=(c[h>>2]|0)-e;e=c[h>>2]|0;f=f+1|0}return}function zU(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;g=h+4|0;d=h;c[d>>2]=c[4647];e=c[b+100>>2]|0;if((e|0)!=1?(f=b+104|0,(c[f>>2]|0)!=1):0){c[b+92>>2]=JK((e<<2)+4|0)|0;c[b+96>>2]=JK((c[f>>2]<<2)+4|0)|0;a[g>>0]=a[d>>0]|0;a[g+1>>0]=a[d+1>>0]|0;a[g+2>>0]=a[d+2>>0]|0;a[g+3>>0]=a[d+3>>0]|0;f=oB(137473,g,0)|0;a[g>>0]=a[d>>0]|0;a[g+1>>0]=a[d+1>>0]|0;a[g+2>>0]=a[d+2>>0]|0;a[g+3>>0]=a[d+3>>0]|0;g=oB(137478,g,0)|0;TC(f,137483,280,1)|0;TC(g,137483,280,1)|0;vU(b,f,g);uL(f,2,2147483647)|0;uL(g,2,2147483647)|0;yU(b,f,g);AU(f,g)}else tU(b);l=h;return}function AU(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=(c[b+16>>2]|0)+192|0;while(1){d=c[d>>2]|0;if(!d)break;f=d+16|0;d=c[f>>2]|0;e=c[d+172>>2]|0;if(e){U1(e);d=c[f>>2]|0}e=c[d+180>>2]|0;if(e){U1(e);d=c[f>>2]|0}d=d+164|0}sB(a)|0;sB(b)|0;return}function BU(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0.0;p=l;l=l+272|0;n=p+104|0;o=p+136|0;j=p+72|0;k=p;g=p+144|0;c[k+48>>2]=b;switch(IC(b)|0){case 0:{e=c[b+60>>2]|0;break}case 1:{e=xC(b)|0;break}case 2:{e=xC(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0;break}default:{}}i=k+52|0;c[i>>2]=e;m=c[e+60>>2]|0;h[k+32>>3]=+h[d+16>>3];c[k+16>>2]=c[d+4>>2];c[k+20>>2]=c[d+8>>2];c[k+40>>2]=0;f=LW(c[d>>2]|0,o,k)|0;if(!f){kA(n,128,g);a[d+82>>0]=0;e=D4(CU(b,n)|0)|0;c[d>>2]=e;if((c[d+12>>2]|0)==1)e=eQ(e)|0;else e=bQ(e,c[i>>2]|0)|0;U1(c[d>>2]|0);c[d>>2]=e;xK(c[(c[m+16>>2]|0)+144>>2]|0,d);oA(n);e=c[o>>2]|0}else{e=f+4|0;if((a[e>>0]|0)==1){if((c[(c[f>>2]|0)+24>>2]|0)==0?DU(b)|0:0){i=D4(DU(b)|0)|0;c[(c[f>>2]|0)+24>>2]=i}m=EU(m,c[f>>2]|0,0,k)|0;c[o>>2]=c[o>>2]|m;m=c[f>>2]|0;r=+h[m+64>>3]*.5;q=+h[m+72>>3]*.5;FU(j,-r,-q,r,q);m=c[f>>2]|0;c[n>>2]=c[j>>2];c[n+4>>2]=c[j+4>>2];c[n+8>>2]=c[j+8>>2];c[n+12>>2]=c[j+12>>2];c[n+16>>2]=c[j+16>>2];c[n+20>>2]=c[j+20>>2];c[n+24>>2]=c[j+24>>2];c[n+28>>2]=c[j+28>>2];GU(m,n,15)}else{HU(c[(c[m+16>>2]|0)+144>>2]|0,c[f>>2]|0,k);n=c[f>>2]|0;q=+h[n+24>>3]*.5;r=+h[n+32>>3]*.5;FU(j,-q,-r,q,r);n=(c[f>>2]|0)+8|0;c[n>>2]=c[j>>2];c[n+4>>2]=c[j+4>>2];c[n+8>>2]=c[j+8>>2];c[n+12>>2]=c[j+12>>2];c[n+16>>2]=c[j+16>>2];c[n+20>>2]=c[j+20>>2];c[n+24>>2]=c[j+24>>2];c[n+28>>2]=c[j+28>>2]}h[d+24>>3]=+h[j+16>>3]-+h[j>>3];h[d+32>>3]=+h[j+24>>3]-+h[j+8>>3];c[d+72>>2]=f;if((a[e>>0]|0)==1){U1(c[d>>2]|0);c[d>>2]=D4(141611)|0}e=c[o>>2]|0}l=p;return e|0}function CU(b,d){b=b|0;d=d|0;var e=0;a:do switch(IC(b)|0){case 0:{nA(d,KB(b)|0)|0;break}case 1:{nA(d,KB(b)|0)|0;break}case 2:{nA(d,KB(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0)|0;e=b+-48|0;nA(d,KB(c[((c[b>>2]&3|0)==2?b:e)+40>>2]|0)|0)|0;if(!(xB(xC(c[((c[b>>2]&3|0)==2?b:e)+40>>2]|0)|0)|0)){nA(d,141747)|0;break a}else{nA(d,137738)|0;break a}}default:{}}while(0);e=d+4|0;b=c[e>>2]|0;if(b>>>0>=(c[d+8>>2]|0)>>>0){lA(d,1)|0;b=c[e>>2]|0}a[b>>0]=0;d=c[d>>2]|0;c[e>>2]=d;return d|0}function DU(b){b=b|0;var c=0,d=0;c=QA(b,137729)|0;if(!((c|0)!=0?(a[c>>0]|0)!=0:0))d=3;do if((d|0)==3){c=QA(b,140823)|0;if(c|0?a[c>>0]|0:0)break;c=0}while(0);return c|0}function EU(f,g,i,j){f=f|0;g=g|0;i=i|0;j=j|0;var k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0;w=l;l=l+16|0;u=w+8|0;t=w;v=g+108|0;k=c[v>>2]|0;if(k|0)ZT(j,k,186576);c[g+80>>2]=i;k=LU(f,g,j)|0;r=g+36|0;f=b[r>>1]|0;if(!(f&128))a[g+32>>0]=2;i=g+33|0;if(!(f&32))a[i>>0]=1;zU(g);n=c[g+104>>2]|0;p=a[g+32>>0]|0;f=O(n+1|0,p)|0;o=d[i>>0]<<1;q=c[g+100>>2]|0;p=O(q+1|0,p)|0;m=g+96|0;f=o+f|0;i=0;while(1){if((i|0)>=(n|0))break;f=(c[(c[m>>2]|0)+(i<<2)>>2]|0)+f|0;i=i+1|0}n=g+92|0;i=p+o|0;m=0;while(1){if((m|0)>=(q|0))break;i=(c[(c[n>>2]|0)+(m<<2)>>2]|0)+i|0;m=m+1|0}n=g+38|0;do if(b[r>>1]&1){r=b[n>>1]|0;m=r&65535;if(r<<16>>16?(s=b[g+40>>1]|0,s<<16>>16):0){if(!((f|0)>(m|0)|(i|0)>(s&65535|0))){i=0;f=0;break}gA(0,137529,t)|0;k=1;i=0;f=0;break}gA(0,137563,u)|0;k=1}while(0);u=e[n>>1]|0;h[g+64>>3]=+(((f|0)>(u|0)?f:u)|0);u=e[g+40>>1]|0;h[g+72>>3]=+(((i|0)>(u|0)?i:u)|0);if(c[v>>2]|0)fU(j,186576);l=w;return k|0}function FU(a,b,c,d,e){a=a|0;b=+b;c=+c;d=+d;e=+e;h[a>>3]=b;h[a+8>>3]=c;h[a+16>>3]=d;h[a+24>>3]=e;return}function GU(e,f,g){e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0.0,n=0.0,o=0,p=0,q=0.0,r=0.0,s=0,t=0,u=0,v=0,w=0,x=0,y=0.0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0.0,K=0;I=l;l=l+64|0;G=I+32|0;H=I;E=c[e+84>>2]|0;k=c[e+80>>2]|0;if((k|0?(i=c[k+24>>2]|0,i|0):0)?(j=e+24|0,(c[j>>2]|0)==0):0)c[j>>2]=D4(i)|0;F=e+48|0;i=f+16|0;n=+h[i>>3];m=+h[f>>3];k=~~(n-m-+(~~+h[e+64>>3]|0));if((k|0)<=-1)qa(137496,137342,1761,137506);o=f+24|0;y=+h[o>>3];p=f+8|0;q=+h[p>>3];r=+(~~+h[e+72>>3]|0);s=~~(y-q-r);if((s|0)<=-1)qa(137519,137342,1764,137506);t=b[e+36>>1]|0;j=t&65535;a:do if(j&1){b:do if((k|0)>0)switch(j&6){case 4:{h[i>>3]=m+r;break b}case 2:{J=+(k|0);h[i>>3]=n+J;m=m+J;h[f>>3]=m;break b}default:{J=+(k>>>1|0);m=m+J;h[f>>3]=m;h[i>>3]=n-J;break b}}while(0);if((s|0)>0)switch(t&24){case 16:{y=q+r;h[o>>3]=y;s=0;i=0;break a}case 8:{J=+(s|0);y=y+J;h[o>>3]=y;h[p>>3]=q+J;s=0;i=0;break a}default:{J=+(s>>>1|0);h[p>>3]=q+J;y=y-J;h[o>>3]=y;s=0;i=0;break a}}else{s=0;i=0}}else i=k;while(0);n=+(d[e+33>>0]|0);C=e+32|0;u=a[C>>0]|0;q=+(u<<24>>24);D=e+104|0;k=c[D>>2]|0;t=(i|0)/(k|0)|0;o=i-(O(t,k)|0)|0;o=~~(((o|0)>-1?.5:-.5)+ +(o|0));B=e+96|0;p=u<<24>>24;i=~~(m+n+q);j=0;while(1){if((j|0)>(k|0))break;A=(c[B>>2]|0)+(j<<2)|0;k=c[A>>2]|0;c[A>>2]=i;i=i+t+((j|0)<(o|0)&1)+k+p|0;j=j+1|0;k=c[D>>2]|0}A=e+100|0;k=c[A>>2]|0;t=(s|0)/(k|0)|0;p=s-(O(t,k)|0)|0;p=~~(((p|0)>-1?.5:-.5)+ +(p|0));z=e+92|0;o=u<<24>>24;i=~~(y-n-q);j=0;while(1){if((j|0)>(k|0))break;x=(c[z>>2]|0)+(j<<2)|0;k=c[x>>2]|0;c[x>>2]=i;i=i-t+(((j|0)<(p|0))<<31>>31)-k-o|0;j=j+1|0;k=c[A>>2]|0}u=(g|0)==0;v=H+16|0;w=H+24|0;x=H+8|0;i=E;while(1){o=c[i>>2]|0;if(!o)break;k=b[o+84>>1]|0;if(u){j=0;p=b[o+80>>1]|0;s=b[o+86>>1]|0;t=b[o+82>>1]|0}else{j=k<<16>>16==0?8:0;s=b[o+86>>1]|0;j=s<<16>>16==0?j|4:j;p=b[o+80>>1]|0;t=b[o+82>>1]|0;j=(((p&65535)+(k&65535)|0)==(c[D>>2]|0)?j|2:j)|((t&65535)+(s&65535)|0)==(c[A>>2]|0)}K=c[B>>2]|0;k=k&65535;h[H>>3]=+(c[K+(k<<2)>>2]|0);E=a[C>>0]|0;h[v>>3]=+((c[K+((p&65535)+k<<2)>>2]|0)-E|0);p=c[z>>2]|0;h[w>>3]=+(c[p+((s&65535)<<2)>>2]|0);h[x>>3]=+((c[p+((t&65535)+(s&65535)<<2)>>2]|0)+E|0);c[G>>2]=c[H>>2];c[G+4>>2]=c[H+4>>2];c[G+8>>2]=c[H+8>>2];c[G+12>>2]=c[H+12>>2];c[G+16>>2]=c[H+16>>2];c[G+20>>2]=c[H+20>>2];c[G+24>>2]=c[H+24>>2];c[G+28>>2]=c[H+28>>2];IU(o,G,j&g);i=i+4|0}a[e+35>>0]=g;c[F>>2]=c[f>>2];c[F+4>>2]=c[f+4>>2];c[F+8>>2]=c[f+8>>2];c[F+12>>2]=c[f+12>>2];c[F+16>>2]=c[f+16>>2];c[F+20>>2]=c[f+20>>2];c[F+24>>2]=c[f+24>>2];c[F+28>>2]=c[f+28>>2];l=I;return}function HU(d,e,f){d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0.0,k=0.0,m=0,n=0,o=0.0,p=0.0,q=0.0,r=0,s=0.0,t=0.0,u=0.0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0.0,Q=0.0,R=0.0;O=l;l=l+112|0;H=O+48|0;I=O+16|0;J=O;c[I>>2]=0;c[I+4>>2]=0;c[I+8>>2]=0;c[I+12>>2]=0;c[I+16>>2]=0;c[I+20>>2]=0;c[I+24>>2]=0;c[I+28>>2]=0;K=e+4|0;L=f+16|0;M=f+32|0;N=I+16|0;r=b[K>>1]|0;n=r<<16>>16;g=0;j=-1.0;m=0;while(1){if((m|0)>=(n|0)){g=1;break}i=c[e>>2]|0;if((b[i+(m*24|0)+4>>1]|0)>1){g=0;break}i=c[(c[i+(m*24|0)>>2]|0)+4>>2]|0;if(!i){k=+h[M>>3];h[N>>3]=k;i=L}else{if(c[i+24>>2]&127|0){g=0;break}k=+h[(+h[i+16>>3]>0.0?i:L)+16>>3];h[N>>3]=k;i=(c[i>>2]|0)==0?L:i}i=c[i>>2]|0;c[I>>2]=i;if(!(j==-1.0)){if(k!=j){g=0;break}}else j=k;if(g){if(s2(i,g)|0){g=0;break}}else g=i;m=m+1|0}a[e+6>>0]=g;E=f+48|0;F=f+20|0;G=I+4|0;w=f+40|0;x=I+24|0;y=d+144|0;z=H+4|0;v=J+8|0;A=H+16|0;B=H+24|0;C=H+8|0;D=H+12|0;f=(g|0)==0;j=0.0;s=0.0;t=0.0;u=0.0;n=0;g=r;while(1){if((n|0)>=(g<<16>>16|0))break;k=0.0;q=0.0;m=0;p=0.0;j=0.0;i=c[e>>2]|0;while(1){if((m|0)>=(b[i+(n*24|0)+4>>1]|0))break;c[H>>2]=EK(c[(c[i+(n*24|0)>>2]|0)+(m*56|0)>>2]|0,c[E>>2]|0)|0;i=(c[e>>2]|0)+(n*24|0)|0;g=c[(c[i>>2]|0)+(m*56|0)+4>>2]|0;if(!g){h[N>>3]=+h[M>>3];c[I>>2]=c[L>>2];c[G>>2]=c[F>>2];c[x>>2]=c[x>>2]&-128|c[w>>2]&127}else{g=c[g+24>>2]&127;if(!g)g=c[w>>2]&127;c[x>>2]=c[x>>2]&-128|g;r=c[(c[i>>2]|0)+(m*56|0)+4>>2]|0;h[N>>3]=+h[(+h[r+16>>3]>0.0?r:L)+16>>3];c[I>>2]=c[((c[r>>2]|0)==0?L:r)>>2];c[G>>2]=c[((c[r+4>>2]|0)==0?L:r)+4>>2]}i=c[y>>2]|0;c[z>>2]=nb[c[i>>2]&63](i,I,1)|0;LO(J,d,H);Q=+h[J>>3];o=+h[v>>3];U1(c[(c[(c[e>>2]|0)+(n*24|0)>>2]|0)+(m*56|0)>>2]|0);i=c[e>>2]|0;r=c[i+(n*24|0)>>2]|0;c[r+(m*56|0)>>2]=c[H>>2];h[r+(m*56|0)+32>>3]=Q;h[r+(m*56|0)+16>>3]=+h[A>>3];h[r+(m*56|0)+24>>3]=+h[B>>3];c[r+(m*56|0)+4>>2]=c[z>>2];c[r+(m*56|0)+8>>2]=c[C>>2];c[r+(m*56|0)+12>>2]=c[D>>2];P=+h[N>>3];R=+h[B>>3];k=R>k?R:k;q=Q+q;m=m+1|0;p=P>p?P:p;j=o>j?o:j}h[i+(n*24|0)+8>>3]=q;g=(n|0)==0;do if(f)if(g){o=p;k=p-k;g=i;break}else{o=p;k=t-u+p-k;g=i+(n*24|0)|0;break}else{o=j;k=g?p:j;g=i+(n*24|0)|0}while(0);h[g+16>>3]=k;s=q>s?q:s;t=o+t;u=+h[i+(n*24|0)+16>>3]+u;n=n+1|0;g=b[K>>1]|0}h[e+24>>3]=s;h[e+32>>3]=g<<16>>16==1?j:t;l=O;return}function IU(e,f,g){e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0.0,A=0.0;y=l;l=l+64|0;v=y+32|0;x=y;j=e+24|0;if((c[j>>2]|0)==0?(i=c[(c[e+96>>2]|0)+24>>2]|0,i|0):0)c[j>>2]=D4(i)|0;t=e+36|0;w=b[t>>1]|0;i=w&65535;a:do if(i&1){k=+h[e+64>>3];q=+h[e+72>>3];j=f+16|0;m=+h[j>>3];o=+h[f>>3];n=m-o-k;b:do if(n>0.0)switch(i&6){case 4:{m=o+k;h[j>>3]=m;k=o;break b}case 2:{m=n+m;h[j>>3]=m;k=o+n;h[f>>3]=k;break b}default:{p=n*.5;k=p+o;h[f>>3]=k;m=m-p;h[j>>3]=m;break b}}else k=o;while(0);i=f+24|0;p=+h[i>>3];j=f+8|0;n=+h[j>>3];o=p-n-q;if(o>0.0)switch(w&24){case 16:{o=n+q;h[i>>3]=o;break a}case 8:{q=o+p;h[i>>3]=q;n=n+o;h[j>>3]=n;o=q;break a}default:{o=o*.5;n=o+n;h[j>>3]=n;o=p-o;h[i>>3]=o;break a}}else o=p}else{k=+h[f>>3];n=+h[f+8>>3];m=+h[f+16>>3];o=+h[f+24>>3]}while(0);s=e+48|0;c[s>>2]=c[f>>2];c[s+4>>2]=c[f+4>>2];c[s+8>>2]=c[f+8>>2];c[s+12>>2]=c[f+12>>2];c[s+16>>2]=c[f+16>>2];c[s+20>>2]=c[f+20>>2];c[s+24>>2]=c[f+24>>2];c[s+28>>2]=c[f+28>>2];a[e+35>>0]=g;A=+(d[e+33>>0]|0);z=+(d[e+34>>0]|0);p=k+A+z;h[x>>3]=p;q=n+A+z;s=x+8|0;h[s>>3]=q;n=m-A-z;f=x+16|0;h[f>>3]=n;o=o-A-z;r=x+24|0;h[r>>3]=o;j=e+88|0;switch(a[e+92>>0]|0){case 1:{w=c[j>>2]|0;c[v>>2]=c[x>>2];c[v+4>>2]=c[x+4>>2];c[v+8>>2]=c[x+8>>2];c[v+12>>2]=c[x+12>>2];c[v+16>>2]=c[x+16>>2];c[v+20>>2]=c[x+20>>2];c[v+24>>2]=c[x+24>>2];c[v+28>>2]=c[x+28>>2];GU(w,v,g);break}case 3:{i=c[j>>2]|0;k=+h[i+24>>3];m=n-p-+h[i+16>>3];c:do if(m>0.0)switch(w&6){case 4:{h[f>>3]=n-m;break c}case 2:{h[x>>3]=m+p;break c}default:break c}while(0);k=o-q-k;d:do if(k>0.0)switch(w&24){case 16:{h[r>>3]=o-k;break d}case 8:{h[s>>3]=k+q;break d}default:break d}while(0);c[v>>2]=c[x>>2];c[v+4>>2]=c[x+4>>2];c[v+8>>2]=c[x+8>>2];c[v+12>>2]=c[x+12>>2];c[v+16>>2]=c[x+16>>2];c[v+20>>2]=c[x+20>>2];c[v+24>>2]=c[x+24>>2];c[v+28>>2]=c[x+28>>2];JU(i,v);break}default:{i=c[j>>2]|0;k=+h[i+32>>3];m=n-p-+h[i+24>>3];e:do if(m>0.0?(u=w&6,u<<16>>16!=6):0)switch(u&7){case 4:{h[f>>3]=n-m;break e}case 2:{h[x>>3]=m+p;break e}default:{A=m*.5;h[x>>3]=A+p;h[f>>3]=n-A;break e}}while(0);k=o-q-k;f:do if(k>0.0)switch(w&24){case 16:{h[r>>3]=o-k;break f}case 8:{h[s>>3]=k+q;break f}default:{A=k*.5;h[s>>3]=A+q;h[r>>3]=o-A;break f}}while(0);w=i+8|0;c[w>>2]=c[x>>2];c[w+4>>2]=c[x+4>>2];c[w+8>>2]=c[x+8>>2];c[w+12>>2]=c[x+12>>2];c[w+16>>2]=c[x+16>>2];c[w+20>>2]=c[x+20>>2];c[w+24>>2]=c[x+24>>2];c[w+28>>2]=c[x+28>>2];x=b[t>>1]&768;KU(c[j>>2]|0,(x&1023)==512?108:(x&1023)==256?114:110)}}l=y;return}function JU(a,b){a=a|0;b=b|0;c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];c[a+16>>2]=c[b+16>>2];c[a+20>>2]=c[b+20>>2];c[a+24>>2]=c[b+24>>2];c[a+28>>2]=c[b+28>>2];return}function KU(d,e){d=d|0;e=e|0;var f=0,g=0,h=0;g=b[d+4>>1]|0;f=0;while(1){if((f|0)>=(g|0))break;h=(c[d>>2]|0)+(f*24|0)+6|0;if(!(a[h>>0]|0))a[h>>0]=e;f=f+1|0}return}function LU(d,f,g){d=d|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;l=f+84|0;t=c[l>>2]|0;u=KL()|0;v=PU()|0;i=0;h=0;k=Tz(t)|0;while(1){if(!k)break;j=Tz(c[k+8>>2]|0)|0;while(1){if(!j)break;j=c[j>>2]|0;i=i+1|0}h=h+1|0;if(a[k+12>>0]|0)TU(v,h);k=c[k>>2]|0}h=JK((i<<2)+4|0)|0;c[l>>2]=h;i=0;j=0;k=0;q=0;r=Tz(t)|0;while(1){if(!r)break;s=q&65535;p=0;o=Tz(c[r+8>>2]|0)|0;while(1){if(!o)break;n=c[o+8>>2]|0;c[h>>2]=n;k=MU(d,n,f,g)|0|k;l=NU(u,q,p,n)|0;b[n+86>>1]=s;b[n+84>>1]=l;l=l+(e[n+80>>1]|0)|0;m=q+(e[n+82>>1]|0)|0;if(UU(v,m)|0){p=n+100|0;a[p>>0]=a[p>>0]|2}p=l;h=h+4|0;o=c[o>>2]|0;i=(l|0)>(i|0)?l:i;j=(m|0)>(j|0)?m:j}q=q+1|0;r=c[r>>2]|0}c[f+100>>2]=j;c[f+104>>2]=i;Rz(t)|0;Rz(v)|0;NL(u);return k|0}function MU(f,g,i,j){f=f|0;g=g|0;i=i|0;j=j|0;var k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0.0;u=l;l=l+16|0;t=u+8|0;s=u;c[g+96>>2]=i;q=g+36|0;o=b[q>>1]|0;if(!(o&64)){if(!(b[i+36>>1]&64))n=2;else n=a[i+34>>0]|0;a[g+34>>0]=n}if(!(o&32)){n=a[i+88>>0]|0;if(n<<24>>24<=-1)if(!(b[i+36>>1]&32))n=1;else n=a[i+33>>0]|0;a[g+33>>0]=n}n=g+88|0;p=g+92|0;switch(a[p>>0]|0){case 1:{o=EU(f,c[n>>2]|0,g,j)|0;i=c[n>>2]|0;n=o;o=i+64|0;i=i+72|0;break}case 3:{o=OU(c[n>>2]|0,j)|0;i=c[n>>2]|0;n=o;o=i+16|0;i=i+24|0;break}default:{HU(c[(c[f+16>>2]|0)+144>>2]|0,c[n>>2]|0,j);i=c[n>>2]|0;n=0;o=i+24|0;i=i+32|0}}k=+((d[g+33>>0]|0)+(d[g+34>>0]|0)<<1|0);m=+h[o>>3]+k;k=+h[i>>3]+k;i=g+38|0;do if(b[q>>1]&1){o=b[i>>1]|0;if(o<<16>>16?(r=b[g+40>>1]|0,r<<16>>16):0){if(!(m>+(o&65535)|k>+(r&65535))){m=0.0;k=0.0;break}if((a[p>>0]|0)==3){m=0.0;k=0.0;break}gA(0,137614,s)|0;n=1;m=0.0;k=0.0;break}gA(0,137647,t)|0;n=1}while(0);v=+(e[i>>1]|0);h[g+64>>3]=m>v?m:v;m=+(e[g+40>>1]|0);h[g+72>>3]=k>m?k:m;l=u;return n|0}function NU(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var f=0,g=0,h=0,i=0,j=0;j=d+80|0;i=(e[j>>1]|0)+-1|0;h=0;a:while(1){f=c;while(1){if(h)break a;g=i+f|0;while(1){if((g|0)<(f|0)){c=f;h=1;continue a}if(SL(a,g,b)|0)break;g=g+-1|0}f=g+1|0}}h=d+82|0;f=c;while(1){if((f|0)<(c+(e[j>>1]|0)|0))g=b;else break;while(1){if((g|0)>=((e[h>>1]|0)+b|0))break;QL(a,f,g);g=g+1|0}f=f+1|0}return c|0}function OU(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0,j=0,k=0,m=0;m=l;l=l+16|0;j=m;i=m+8|0;d=d+52|0;g=b+32|0;RF(i,c[d>>2]|0,c[g>>2]|0);k=c[i>>2]|0;i=c[i+4>>2]|0;if((i&k|0)==-1){c[j>>2]=c[g>>2];gA(1,137697,j)|0;d=1;f=0.0;e=0.0}else{a[(c[(c[d>>2]|0)+16>>2]|0)+114>>0]=1;d=0;f=+(i|0);e=+(k|0)}c[b>>2]=0;c[b+4>>2]=0;c[b+8>>2]=0;c[b+12>>2]=0;h[b+16>>3]=e;h[b+24>>3]=f;l=m;return d|0}function PU(){return Vz(55924,c[4581]|0)|0}function QU(a,b,d){a=a|0;b=b|0;d=d|0;d=JK(12)|0;c[d>>2]=c[b>>2];return d|0}function RU(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function SU(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;b=c[b>>2]|0;e=c[d>>2]|0;return ((b|0)>(e|0)?1:((b|0)<(e|0))<<31>>31)|0}function TU(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=b;nb[c[a>>2]&63](a,e,1)|0;l=d;return}function UU(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=b;b=(nb[c[a>>2]&63](a,e,512)|0)!=0&1;l=d;return b|0}function VU(a){a=a|0;var b=0;if(!(c[47375]|0)){b=JK((a<<2)+4|0)|0;c[47375]=b;c[b>>2]=189504;c[47386]=a}c[47387]=0;return}function WU(){U1(c[47375]|0);c[47375]=0;c[47387]=0;return}function XU(){c[47387]=0;return}function YU(){var a=0,b=0,d=0;b=c[47387]|0;d=c[47375]|0;a=1;while(1){if((a|0)>(b|0)){a=5;break}if((c[(c[d+(a<<2)>>2]|0)+4>>2]|0)==(a|0))a=a+1|0;else{a=4;break}}if((a|0)==4)qa(138394,137741,59,137747);else if((a|0)==5)return}function ZU(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;f=c[47375]|0;g=c[f+(a<<2)>>2]|0;h=c[g>>2]|0;while(1){b=(a|0)/2|0;d=c[f+(b<<2)>>2]|0;e=f+(a<<2)|0;if((c[d>>2]|0)>=(h|0))break;c[e>>2]=d;c[d+4>>2]=a;a=b}c[e>>2]=g;c[g+4>>2]=a;return}function _U(a){a=a|0;var b=0,d=0;d=l;l=l+16|0;b=c[47387]|0;if((b|0)==(c[47386]|0)){gA(1,137755,d)|0;a=1}else{b=b+1|0;c[47387]=b;c[(c[47375]|0)+(b<<2)>>2]=a;ZU(b);YU();a=0}l=d;return a|0}function $U(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;e=c[47375]|0;f=c[e+(a<<2)>>2]|0;g=c[f>>2]|0;h=c[47387]|0;i=(h|0)/2|0;while(1){if((a|0)>(i|0))break;d=a<<1;b=c[e+(d<<2)>>2]|0;if((d|0)<(h|0)){l=d|1;j=c[e+(l<<2)>>2]|0;k=(c[b>>2]|0)<(c[j>>2]|0);d=k?l:d;b=k?j:b}if((g|0)>=(c[b>>2]|0))break;c[e+(a<<2)>>2]=b;c[b+4>>2]=a;a=d}c[e+(a<<2)>>2]=f;c[f+4>>2]=a;return}function aV(){var a=0,b=0,d=0,e=0;b=c[47387]|0;if(!b)a=0;else{e=c[47375]|0;d=e+4|0;a=c[d>>2]|0;c[d>>2]=c[e+(b<<2)>>2];b=b+-1|0;c[47387]=b;if(b|0)$U(1);YU()}return a|0}function bV(a,b){a=a|0;b=b|0;c[a>>2]=b;ZU(c[a+4>>2]|0);YU();return}function cV(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;m=b+16|0;j=c[m>>2]|0;j=(a[j+((c[e+12>>2]|0)*40|0)+36>>0]|0)!=(a[j+((c[e+16>>2]|0)*40|0)+36>>0]|0);k=~~((+h[d+64>>3]+-3.0-+h[d+48>>3])*.5);l=~~((+h[d+56>>3]+-3.0-+h[d+40>>3])*.5);g=(k|0)<(l|0)?k:l;n=d+4|0;f=0;while(1){b=c[n>>2]|0;if((f|0)>=(b|0)){g=f;break}i=c[d+8+(f<<2)>>2]|0;o=c[m>>2]|0;if((a[o+((c[i+12>>2]|0)*40|0)+36>>0]|0)==(a[o+((c[i+16>>2]|0)*40|0)+36>>0]|0)){g=f;break}dV(i,g);f=f+1|0}while(1){if((g|0)>=(b|0))break;f=c[d+8+(g<<2)>>2]|0;if(j|(f|0)==(e|0)){dV(f,a[(c[m>>2]|0)+((c[f+12>>2]|0)*40|0)+36>>0]|0?k:l);b=c[n>>2]|0}g=g+1|0}return}function dV(a,b){a=a|0;b=b|0;var d=0,e=0;d=a+8|0;e=c[d>>2]|0;c[d>>2]=e+1;if((e|0)>=(b|0)){c[d>>2]=0;h[a>>3]=+h[a>>3]+16384.0}return}function eV(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0.0,z=0.0,A=0.0,B=0.0,C=0,D=0,E=0.0;x=l;l=l+112|0;u=x+64|0;t=x+96|0;m=x+32|0;v=x;w=JK(28)|0;b=tB(a)|0;s=w+4|0;c[s>>2]=b;b=JK(b*72|0)|0;r=w+12|0;c[r>>2]=b;e=v+8|0;h[e>>3]=1797693134862315708145274.0e284;h[v>>3]=1797693134862315708145274.0e284;f=v+24|0;h[f>>3]=-1797693134862315708145274.0e284;g=v+16|0;h[g>>3]=-1797693134862315708145274.0e284;i=m+16|0;j=m+8|0;k=m+24|0;d=jC(a)|0;n=1797693134862315708145274.0e284;o=1797693134862315708145274.0e284;p=-1797693134862315708145274.0e284;q=-1797693134862315708145274.0e284;while(1){if(!d)break;C=d+16|0;D=c[C>>2]|0;z=(+h[D+96>>3]+ +h[D+88>>3])*.5;z=z<1.0?1.0:z;y=+h[D+80>>3]*.5;y=y<1.0?1.0:y;E=+h[D+16>>3];B=E-z;h[m>>3]=B;z=E+z;h[i>>3]=z;E=+h[D+24>>3];A=E-y;h[j>>3]=A;y=E+y;h[k>>3]=y;B=+h[(n<B?v:m)>>3];h[v>>3]=B;A=+h[(o<A?v:m)+8>>3];h[e>>3]=A;z=+h[(p>z?v:m)+16>>3];h[g>>3]=z;y=+h[(q>y?v:m)+24>>3];h[f>>3]=y;D=b+40|0;c[D>>2]=c[m>>2];c[D+4>>2]=c[m+4>>2];c[D+8>>2]=c[m+8>>2];c[D+12>>2]=c[m+12>>2];c[D+16>>2]=c[m+16>>2];c[D+20>>2]=c[m+20>>2];c[D+24>>2]=c[m+24>>2];c[D+28>>2]=c[m+28>>2];c[b>>2]=c[b>>2]|1;c[(c[C>>2]|0)+112>>2]=b;b=b+72|0;d=kC(a,d)|0;n=B;o=A;p=z;q=y}h[v>>3]=n+-36.0;h[e>>3]=o+-36.0;h[g>>3]=p+36.0;h[f>>3]=q+36.0;D=c[r>>2]|0;f=c[s>>2]|0;c[u>>2]=c[v>>2];c[u+4>>2]=c[v+4>>2];c[u+8>>2]=c[v+8>>2];c[u+12>>2]=c[v+12>>2];c[u+16>>2]=c[v+16>>2];c[u+20>>2]=c[v+20>>2];c[u+24>>2]=c[v+24>>2];c[u+28>>2]=c[v+28>>2];f=oV(D,f,t,u)|0;if(c[47323]&1|0){r=c[r>>2]|0;C=c[s>>2]|0;D=c[t>>2]|0;c[u>>2]=c[v>>2];c[u+4>>2]=c[v+4>>2];c[u+8>>2]=c[v+8>>2];c[u+12>>2]=c[v+12>>2];c[u+16>>2]=c[v+16>>2];c[u+20>>2]=c[v+20>>2];c[u+24>>2]=c[v+24>>2];c[u+28>>2]=c[v+28>>2];fV(r,C,u,f,D)}e=w+8|0;c[e>>2]=JK((c[t>>2]|0)*72|0)|0;c[w>>2]=c[t>>2];d=c[t>>2]|0;b=0;while(1){if((b|0)>=(d|0))break;D=(c[e>>2]|0)+(b*72|0)+40|0;C=f+(b<<5)|0;c[D>>2]=c[C>>2];c[D+4>>2]=c[C+4>>2];c[D+8>>2]=c[C+8>>2];c[D+12>>2]=c[C+12>>2];c[D+16>>2]=c[C+16>>2];c[D+20>>2]=c[C+20>>2];c[D+24>>2]=c[C+24>>2];c[D+28>>2]=c[C+28>>2];b=b+1|0}U1(f);c[u>>2]=c[v>>2];c[u+4>>2]=c[v+4>>2];c[u+8>>2]=c[v+8>>2];c[u+12>>2]=c[v+12>>2];c[u+16>>2]=c[v+16>>2];c[u+20>>2]=c[v+20>>2];c[u+24>>2]=c[v+24>>2];c[u+28>>2]=c[v+28>>2];c[w+16>>2]=gV(w,u)|0;l=x;return w|0}function fV(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0.0,i=0,j=0,k=0.0,m=0.0,n=0.0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0.0,v=0.0;q=l;l=l+128|0;p=q+96|0;o=q+64|0;j=q+32|0;r=q+16|0;s=q;m=+h[d+16>>3];n=+h[d>>3];k=+h[d+24>>3];g=+h[d+8>>3];i=c[15712]|0;T4(137770,i)|0;c[s>>2]=10;c[s+4>>2]=10;c[s+8>>2]=~~(m+10.0-n);c[s+12>>2]=~~(k+10.0-g);w4(i,138200,s)|0;h[r>>3]=10.0-n;h[r+8>>3]=10.0-g;w4(i,138248,r)|0;T4(138265,i)|0;d=0;while(1){if((d|0)>=(b|0))break;v=+h[a+(d*72|0)+48>>3];u=+h[a+(d*72|0)+56>>3];t=+h[a+(d*72|0)+64>>3];h[j>>3]=+h[a+(d*72|0)+40>>3];h[j+8>>3]=v;h[j+16>>3]=u;h[j+24>>3]=t;w4(i,138284,j)|0;d=d+1|0}T4(138302,i)|0;d=0;while(1){if((d|0)>=(f|0))break;t=+h[e+(d<<5)+8>>3];u=+h[e+(d<<5)+16>>3];v=+h[e+(d<<5)+24>>3];h[o>>3]=+h[e+(d<<5)>>3];h[o+8>>3]=t;h[o+16>>3]=u;h[o+24>>3]=v;w4(i,138321,o)|0;d=d+1|0}T4(138339,i)|0;h[p>>3]=n;h[p+8>>3]=g;h[p+16>>3]=m;h[p+24>>3]=k;w4(i,138321,p)|0;T4(138086,i)|0;l=q;return}function gV(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0;A=l;l=l+32|0;t=A+16|0;k=A;w=c[a>>2]|0;y=CR(w<<2|2)|0;z=Vz(55960,c[4581]|0)|0;v=Vz(55996,c[4581]|0)|0;w=JK(w<<7)|0;f=JK(c[a>>2]<<4)|0;x=a+8|0;g=b+16|0;i=k+8|0;j=b+24|0;m=b+8|0;d=0;while(1){if((d|0)>=(c[a>>2]|0))break;n=c[x>>2]|0;o=n+(d*72|0)|0;c[n+(d*72|0)+32>>2]=4;p=n+(d*72|0)+36|0;c[p>>2]=f+(d<<2<<2);q=n+(d*72|0)+40|0;e=+h[n+(d*72|0)+56>>3];if(e<+h[g>>3]){h[k>>3]=e;h[i>>3]=+h[n+(d*72|0)+48>>3];c[t>>2]=c[k>>2];c[t+4>>2]=c[k+4>>2];c[t+8>>2]=c[k+8>>2];c[t+12>>2]=c[k+12>>2];s=hV(y,z,t,w,1)|0;c[s+20>>2]=o;c[c[p>>2]>>2]=s}e=+h[n+(d*72|0)+64>>3];if(e<+h[j>>3]){h[k>>3]=+h[q>>3];h[i>>3]=e;c[t>>2]=c[k>>2];c[t+4>>2]=c[k+4>>2];c[t+8>>2]=c[k+8>>2];c[t+12>>2]=c[k+12>>2];s=hV(y,v,t,w,0)|0;c[s+20>>2]=o;c[(c[p>>2]|0)+4>>2]=s}if(+h[q>>3]>+h[b>>3]){c[t>>2]=c[q>>2];c[t+4>>2]=c[q+4>>2];c[t+8>>2]=c[q+8>>2];c[t+12>>2]=c[q+12>>2];s=hV(y,z,t,w,1)|0;c[s+24>>2]=o;c[(c[p>>2]|0)+8>>2]=s}if(+h[n+(d*72|0)+48>>3]>+h[m>>3]){c[t>>2]=c[q>>2];c[t+4>>2]=c[q+4>>2];c[t+8>>2]=c[q+8>>2];c[t+12>>2]=c[q+12>>2];s=hV(y,v,t,w,0)|0;c[s+24>>2]=o;c[(c[p>>2]|0)+12>>2]=s}d=d+1|0}o=JK(c[y>>2]<<2)|0;r=a+4|0;s=a+12|0;p=t+8|0;m=0;q=0;n=0;while(1){d=c[r>>2]|0;if((n|0)>=(d|0)){b=0;break}g=c[s>>2]|0;i=g+(n*72|0)|0;j=g+(n*72|0)+36|0;c[j>>2]=o+(m<<2);b=g+(n*72|0)+40|0;c[t>>2]=c[b>>2];c[t+4>>2]=c[b+4>>2];c[t+8>>2]=c[b+8>>2];c[t+12>>2]=c[b+12>>2];b=g+(n*72|0)+56|0;k=g+(n*72|0)+32|0;d=nb[c[v>>2]&63](v,t,512)|0;while(1){if(!d)break;if(!(+h[d+8>>3]<+h[b>>3]))break;C=c[d>>2]|0;B=c[j>>2]|0;f=c[k>>2]|0;c[k>>2]=f+1;c[B+(f<<2)>>2]=C;c[(c[d>>2]|0)+24>>2]=i;d=nb[c[v>>2]&63](v,d,8)|0}f=g+(n*72|0)+64|0;d=nb[c[z>>2]&63](z,t,512)|0;while(1){if(!d){u=20;break}e=+h[f>>3];if(!(+h[d+16>>3]<e))break;b=c[d>>2]|0;B=c[j>>2]|0;C=c[k>>2]|0;c[k>>2]=C+1;c[B+(C<<2)>>2]=b;c[(c[d>>2]|0)+24>>2]=i;d=nb[c[z>>2]&63](z,d,8)|0}if((u|0)==20){u=0;e=+h[f>>3]}h[p>>3]=e;b=g+(n*72|0)+56|0;d=nb[c[v>>2]&63](v,t,512)|0;while(1){if(!d){u=25;break}e=+h[b>>3];if(!(+h[d+8>>3]<e))break;D=c[d>>2]|0;B=c[j>>2]|0;C=c[k>>2]|0;c[k>>2]=C+1;c[B+(C<<2)>>2]=D;c[(c[d>>2]|0)+20>>2]=i;d=nb[c[v>>2]&63](v,d,8)|0}if((u|0)==25){u=0;e=+h[b>>3]}h[t>>3]=e;h[p>>3]=+h[g+(n*72|0)+48>>3];d=nb[c[z>>2]&63](z,t,512)|0;while(1){if(!d)break;if(!(+h[d+16>>3]<+h[f>>3]))break;B=c[d>>2]|0;C=c[j>>2]|0;D=c[k>>2]|0;c[k>>2]=D+1;c[C+(D<<2)>>2]=B;c[(c[d>>2]|0)+20>>2]=i;d=nb[c[z>>2]&63](z,d,8)|0}D=c[k>>2]|0;m=D+m|0;q=(D|0)>(q|0)?D:q;n=n+1|0}while(1){if((b|0)>=(d|0))break;iV((c[s>>2]|0)+(b*72|0)|0);b=b+1|0;d=c[r>>2]|0}d=c[y>>2]|0;D=c[y+16>>2]|0;c[D+(d*40|0)+32>>2]=d;d=d+1|0;c[D+(d*40|0)+32>>2]=d;BR(y,q);d=0;while(1){if((d|0)>=(c[a>>2]|0))break;jV((c[x>>2]|0)+(d*72|0)|0,y);d=d+1|0}Rz(z)|0;Rz(v)|0;U1(w);kV(y);zR(y);l=A;return y|0}function hV(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0;h=nb[c[d>>2]&63](d,e,512)|0;do if(!h){b=DR(b)|0;if(!f)qa(138183,138131,291,138190);else{h=c[b+32>>2]|0;i=f+(h<<5)|0;f=f+(h<<5)+8|0;c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];c[f+8>>2]=c[e+8>>2];c[f+12>>2]=c[e+12>>2];c[i>>2]=b;a[b+36>>0]=g;nb[c[d>>2]&63](d,i,1)|0;break}}else i=h;while(0);return c[i>>2]|0}function iV(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0;a:do if((+h[b+64>>3]+-3.0-+h[b+48>>3])*.5<2.0){i=c[b+32>>2]|0;j=b+36|0;g=0;while(1){if((g|0)>=(i|0))break a;d=c[(c[j>>2]|0)+(g<<2)>>2]|0;b:do if(a[d+36>>0]|0){f=c[d+20>>2]|0;if((f|0)==(b|0)){e=c[d+24>>2]|0;d=e;e=c[e>>2]|0;do{c[d>>2]=e|8;d=c[c[d+36>>2]>>2]|0;if(!d)break b;d=c[d+24>>2]|0;e=c[d>>2]|0}while(!(e&1|0))}else{e=f;d=c[f>>2]|0;do{c[e>>2]=d|8;d=c[(c[e+36>>2]|0)+8>>2]|0;if(!d)break b;e=c[d+20>>2]|0;d=c[e>>2]|0}while(!(d&1|0))}}while(0);g=g+1|0}}while(0);c:do if((+h[b+56>>3]+-3.0-+h[b+40>>3])*.5<2.0){i=c[b+32>>2]|0;j=b+36|0;g=0;while(1){if((g|0)>=(i|0))break c;d=c[(c[j>>2]|0)+(g<<2)>>2]|0;d:do if(!(a[d+36>>0]|0)){f=c[d+20>>2]|0;if((f|0)==(b|0)){e=c[d+24>>2]|0;d=e;e=c[e>>2]|0;do{c[d>>2]=e|16;d=c[(c[d+36>>2]|0)+4>>2]|0;if(!d)break d;d=c[d+24>>2]|0;e=c[d>>2]|0}while(!(e&1|0))}else{e=f;d=c[f>>2]|0;do{c[e>>2]=d|16;d=c[(c[e+36>>2]|0)+12>>2]|0;if(!d)break d;e=c[d+20>>2]|0;d=c[e>>2]|0}while(!(d&1|0))}}while(0);g=g+1|0}}while(0);return}function jV(a,b){a=a|0;b=b|0;var d=0,e=0,f=0.0,g=0.0,i=0.0,j=0,k=0,l=0,m=0,n=0,o=0,p=0.0,q=0;g=+h[a+56>>3]-+h[a+40>>3];i=+h[a+64>>3]-+h[a+48>>3];f=(i+g)*.5+500.0;if((i+-3.0)*.5<2.0){l=(c[a>>2]&8|0)==0;f=l?16384.0:f;p=l?16384.0:g}else p=g;if((g+-3.0)*.5<2.0){l=(c[a>>2]&16|0)==0;i=l?16384.0:i;f=l?16384.0:f}l=a+36|0;d=c[l>>2]|0;e=c[d+8>>2]|0;if((e|0)!=0?(j=c[d+4>>2]|0,(j|0)!=0):0){j=ER(b,e,j,f)|0;e=a+4|0;d=c[e>>2]|0;c[e>>2]=d+1;c[a+8+(d<<2)>>2]=j;d=c[l>>2]|0}e=c[d+4>>2]|0;if((e|0)!=0?(k=c[d>>2]|0,(k|0)!=0):0){k=ER(b,e,k,f)|0;j=a+4|0;d=c[j>>2]|0;c[j>>2]=d+1;c[a+8+(d<<2)>>2]=k;d=c[l>>2]|0}e=c[d+8>>2]|0;if((e|0)!=0?(m=c[d+12>>2]|0,(m|0)!=0):0){m=ER(b,e,m,f)|0;k=a+4|0;d=c[k>>2]|0;c[k>>2]=d+1;c[a+8+(d<<2)>>2]=m;d=c[l>>2]|0}e=c[d+12>>2]|0;if((e|0)!=0?(n=c[d>>2]|0,(n|0)!=0):0){n=ER(b,e,n,f)|0;m=a+4|0;d=c[m>>2]|0;c[m>>2]=d+1;c[a+8+(d<<2)>>2]=n;d=c[l>>2]|0}e=c[d+4>>2]|0;if((e|0)!=0?(o=c[d+12>>2]|0,(o|0)!=0):0){o=ER(b,e,o,i)|0;n=a+4|0;d=c[n>>2]|0;c[n>>2]=d+1;c[a+8+(d<<2)>>2]=o;d=c[l>>2]|0}e=c[d+8>>2]|0;if(e|0?(q=c[d>>2]|0,q|0):0){b=ER(b,e,q,p)|0;o=a+4|0;q=c[o>>2]|0;c[o>>2]=q+1;c[a+8+(q<<2)>>2]=b}return}function kV(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+16|0;i=j+8|0;h=j;f=a+16|0;g=c[15712]|0;e=0;while(1){if((e|0)>=(c[a>>2]|0)){b=10;break}b=c[f>>2]|0;d=b+(e*40|0)+20|0;if((c[d>>2]|0)==0?(c[h>>2]=e,w4(g,138096,h)|0,(c[d>>2]|0)==0):0){b=5;break}b=b+(e*40|0)+24|0;if((c[b>>2]|0)==0?(c[i>>2]=e,w4(g,138148,i)|0,(c[b>>2]|0)==0):0){b=8;break}e=e+1|0}if((b|0)==5)qa(138118,138131,311,138138);else if((b|0)==8)qa(138170,138131,313,138138);else if((b|0)==10){l=j;return}}function lV(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0.0,f=0.0;e=+h[b+8>>3];f=+h[c+8>>3];if(!(e>f))if(!(e<f)){f=+h[b>>3];e=+h[c>>3];if(f>e)a=1;else a=(f<e)<<31>>31}else a=-1;else a=1;return a|0}function mV(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0.0,f=0.0;e=+h[b>>3];f=+h[c>>3];if(!(e>f))if(!(e<f)){f=+h[b+8>>3];e=+h[c+8>>3];if(f>e)a=1;else a=(f<e)<<31>>31}else a=-1;else a=1;return a|0}function nV(a){a=a|0;var b=0,d=0;d=a+8|0;U1(c[(c[d>>2]|0)+36>>2]|0);b=a+12|0;U1(c[(c[b>>2]|0)+36>>2]|0);U1(c[d>>2]|0);U1(c[b>>2]|0);GR(c[a+16>>2]|0);Rz(c[a+20>>2]|0)|0;Rz(c[a+24>>2]|0)|0;U1(a);return}function oV(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;p=l;l=l+32|0;h=p;i=b<<2;f=i+4|0;i=i+5|0;o=KK(i*56|0)|0;i=JK(i<<2)|0;g=f*5|1;j=KK(g*72|0)|0;m=g<<5;k=JK(m)|0;m=JK(m)|0;c[h>>2]=c[e>>2];c[h+4>>2]=c[e+4>>2];c[h+8>>2]=c[e+8>>2];c[h+12>>2]=c[e+12>>2];c[h+16>>2]=c[e+16>>2];c[h+20>>2]=c[e+20>>2];c[h+24>>2]=c[e+24>>2];c[h+28>>2]=c[e+28>>2];pV(a,b,h,o,0);z5(173);qV(f,i);AV(f,o,i,g,j)|0;n=rV(f,o,j,0,k)|0;c[h>>2]=c[e>>2];c[h+4>>2]=c[e+4>>2];c[h+8>>2]=c[e+8>>2];c[h+12>>2]=c[e+12>>2];c[h+16>>2]=c[e+16>>2];c[h+20>>2]=c[e+20>>2];c[h+24>>2]=c[e+24>>2];c[h+28>>2]=c[e+28>>2];pV(a,b,h,o,1);qV(f,i);AV(f,o,i,g,j)|0;f=rV(f,o,j,1,m)|0;g=JK(O(n<<5,f)|0)|0;b=0;a=0;while(1){if((b|0)>=(f|0))break;h=m+(b<<5)|0;e=0;while(1){if((e|0)>=(n|0))break;q=a+((sV(g+(a<<5)|0,h,k+(e<<5)|0)|0)!=0&1)|0;e=e+1|0;a=q}b=b+1|0}q=MK(g,a<<5)|0;U1(o);U1(i);U1(j);U1(k);U1(m);c[d>>2]=a;l=p;return q|0}function pV(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0;j=l;l=l+96|0;h=j+64|0;i=j;c[h>>2]=c[d>>2];c[h+4>>2]=c[d+4>>2];c[h+8>>2]=c[d+8>>2];c[h+12>>2]=c[d+12>>2];c[h+16>>2]=c[d+16>>2];c[h+20>>2]=c[d+20>>2];c[h+24>>2]=c[d+24>>2];c[h+28>>2]=c[d+28>>2];yV(h,f,1,i);d=zV(e,1,i)|0;g=0;while(1){if((g|0)>=(b|0))break;k=a+(g*72|0)+40|0;c[h>>2]=c[k>>2];c[h+4>>2]=c[k+4>>2];c[h+8>>2]=c[k+8>>2];c[h+12>>2]=c[k+12>>2];c[h+16>>2]=c[k+16>>2];c[h+20>>2]=c[k+20>>2];c[h+24>>2]=c[k+24>>2];c[h+28>>2]=c[k+28>>2];yV(h,f,0,i);d=zV(e,d,i)|0;g=g+1|0}l=j;return}function qV(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;d=0;while(1){if((d|0)>(a|0))break;c[b+(d<<2)>>2]=d;d=d+1|0}g=a+1|0;d=1;while(1){if((d|0)>(a|0))break;h=~~(+B5()*+(g-d|0)+ +(d|0));e=b+(d<<2)|0;f=b+(h<<2)|0;if((d|0)!=(h|0)){h=c[e>>2]|0;c[e>>2]=c[f>>2];c[f>>2]=h}d=d+1|0}return}function rV(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;g=a*5|0;j=g+1|0;l=JK(j<<2)|0;c[47388]=JK(j<<4)|0;c[47389]=JK((a*56|0)+56|0)|0;j=JK(a<<2)|0;c[47390]=j;k=0;while(1){if((k|0)>(g|0))break;if(tV(d+(k*72|0)|0,b)|0)break;k=k+1|0}h=c[47388]|0;i=c[47389]|0;g=1;while(1){if((g|0)>(a|0))break;c[h+(g<<4)+8>>2]=c[b+(g*56|0)+48>>2];m=b+(g*56|0)+44|0;c[h+(g<<4)+4>>2]=c[m>>2];c[h+(g<<4)>>2]=g;n=i+(g*56|0)|0;o=b+(g*56|0)|0;c[n>>2]=c[o>>2];c[n+4>>2]=c[o+4>>2];c[n+8>>2]=c[o+8>>2];c[n+12>>2]=c[o+12>>2];c[i+(g*56|0)+16>>2]=c[m>>2];c[i+(g*56|0)+32>>2]=g;c[i+(g*56|0)+48>>2]=1;g=g+1|0}c[47391]=a;c[47392]=0;c[j>>2]=1;g=c[d+(k*72|0)+40>>2]|0;if((g|0)<=0){g=c[d+(k*72|0)+48>>2]|0;if((g|0)>0)g=uV(l,f,0,b,d,0,k,g,e,2)|0;else g=0}else g=uV(l,f,0,b,d,0,k,g,e,1)|0;U1(l);U1(c[47388]|0);U1(c[47389]|0);U1(c[47390]|0);return g|0}function sV(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0,g=0.0;g=+h[(+h[b>>3]>+h[c>>3]?b:c)>>3];f=+h[(+h[b+16>>3]<+h[c+16>>3]?b:c)+16>>3];h[a+16>>3]=f;h[a>>3]=g;e=+h[(+h[b+8>>3]>+h[c+8>>3]?b:c)+8>>3];d=+h[(+h[b+24>>3]<+h[c+24>>3]?b:c)+24>>3];h[a+24>>3]=d;h[a+8>>3]=e;return !(g>=f)&!(e>=d)&1|0}function tV(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0;f=c[a+4>>2]|0;do if((c[a+68>>2]|0)!=2?!((f|0)<1|(c[a>>2]|0)<1):0){if(!((c[a+40>>2]|0)<1?(c[a+44>>2]|0)<1:0)){if((c[a+48>>2]|0)>=1){a=0;break}if((c[a+52>>2]|0)>=1){a=0;break}}d=+h[b+(f*56|0)+24>>3];e=+h[b+(f*56|0)+8>>3];if(!(d>e+1.0e-07))if(d<e+-1.0e-07)a=0;else a=+h[b+(f*56|0)+16>>3]>+h[b+(f*56|0)>>3]&1;else a=1}else a=0;while(0);return a|0}function uV(a,b,d,e,f,g,i,j,k,l){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;l=l|0;var m=0,n=0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0.0,J=0,K=0.0,L=0,M=0,N=0,O=0,P=0;D=(k|0)==0;C=i;a:while(1){y=f+(C*72|0)|0;if((C|0)<1)break;i=a+(C<<2)|0;if(c[i>>2]|0)break;c[i>>2]=1;v=f+(C*72|0)+8|0;t=f+(C*72|0)+16|0;p=+h[t>>3];w=f+(C*72|0)+24|0;x=f+(C*72|0)+32|0;o=+h[x>>3];if((p>o?(A=c[y>>2]|0,I=+h[e+(A*56|0)>>3],I==+h[e+(A*56|0)+16>>3]):0)?(A=c[f+(C*72|0)+4>>2]|0,K=+h[e+(A*56|0)>>3],K==+h[e+(A*56|0)+16>>3]):0){i=b+(d<<5)|0;if(D){h[i>>3]=I;q=p;p=K}else{h[i>>3]=o;q=-I;o=-K}h[b+(d<<5)+8>>3]=o;h[b+(d<<5)+16>>3]=p;h[b+(d<<5)+24>>3]=q;d=d+1|0}i=f+(C*72|0)+40|0;z=c[i>>2]|0;do if((z|0)<1?(L=f+(C*72|0)+44|0,(c[L>>2]|0)<1):0){i=f+(C*72|0)+48|0;n=f+(C*72|0)+52|0;if((c[i>>2]|0)>0?(M=c[n>>2]|0,(M|0)>0):0){m=c[f+(M*72|0)>>2]|0;l=c[y>>2]|0;if((M|0)==(j|0)){l=vV(g,l,m)|0;d=uV(a,b,d,e,f,g,c[n>>2]|0,C,k,1)|0;g=l;l=1;break}else{l=vV(g,m,l)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,1)|0;g=l;i=n;l=1;break}}d=uV(a,b,d,e,f,g,z,C,k,2)|0;d=uV(a,b,d,e,f,g,c[L>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,1)|0;i=n;l=1}else P=19;while(0);do if((P|0)==19){P=0;A=f+(C*72|0)+48|0;r=c[A>>2]|0;if((r|0)<1?(N=f+(C*72|0)+52|0,(c[N>>2]|0)<1):0){n=f+(C*72|0)+44|0;if((z|0)>0?(O=c[n>>2]|0,(O|0)>0):0){m=c[f+(C*72|0)+4>>2]|0;l=c[f+(z*72|0)+4>>2]|0;if((O|0)==(j|0)){l=vV(g,l,m)|0;d=uV(a,b,d,e,f,g,c[n>>2]|0,C,k,2)|0;g=l;l=2;break}else{l=vV(g,m,l)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,2)|0;g=l;i=n;l=2;break}}d=uV(a,b,d,e,f,g,z,C,k,2)|0;d=uV(a,b,d,e,f,g,c[n>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;i=N;l=1;break}u=f+(C*72|0)+44|0;s=c[u>>2]|0;m=(s|0)>0;if((z|0)>0){if(m){if((r|0)>0?(E=f+(C*72|0)+52|0,F=c[E>>2]|0,(F|0)>0):0){n=c[f+(F*72|0)>>2]|0;m=c[f+(z*72|0)+4>>2]|0;if(!((l|0)==2&(F|0)==(j|0))?!((l|0)==1&(s|0)==(j|0)):0){l=vV(g,n,m)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,l,c[u>>2]|0,C,k,2)|0;g=l;i=E;l=1;break}l=vV(g,m,n)|0;d=uV(a,b,d,e,f,g,c[u>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[E>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,l,c[i>>2]|0,C,k,2)|0;g=l;i=A;l=1;break}m=c[y>>2]|0;if(+B(+(+h[x>>3]-+h[e+(m*56|0)+24>>3]))<=1.0e-07?+B(+(+h[w>>3]-+h[e+(m*56|0)+16>>3]))<=1.0e-07:0){n=c[f+(z*72|0)+4>>2]|0;m=c[e+(m*56|0)+44>>2]|0;if((l|0)==1&(z|0)==(j|0)){l=vV(g,m,n)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,l,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,l,c[u>>2]|0,C,k,2)|0;g=l;i=f+(C*72|0)+52|0;l=1;break}else{l=vV(g,n,m)|0;d=uV(a,b,d,e,f,g,c[u>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+52>>2]|0,C,k,1)|0;g=l;l=2;break}}n=c[f+(C*72|0)+4>>2]|0;m=c[f+(z*72|0)+4>>2]|0;if((l|0)==1&(s|0)==(j|0)){l=vV(g,m,n)|0;d=uV(a,b,d,e,f,g,c[u>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,l,c[f+(C*72|0)+52>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,l,c[A>>2]|0,C,k,1)|0;g=l;l=2;break}else{l=vV(g,n,m)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+52>>2]|0,C,k,1)|0;g=l;i=u;l=2;break}}}else if(!m)break a;if((r|0)>0?(G=f+(C*72|0)+52|0,H=c[G>>2]|0,(H|0)>0):0){n=c[y>>2]|0;if(+B(+(+h[t>>3]-+h[e+(n*56|0)+8>>3]))<=1.0e-07?+B(+(+h[v>>3]-+h[e+(n*56|0)>>3]))<=1.0e-07:0){m=c[f+(H*72|0)>>2]|0;if((l|0)==2&(r|0)==(j|0)){l=vV(g,m,n)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,l,c[i>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,l,c[f+(C*72|0)+44>>2]|0,C,k,2)|0;g=l;i=G;l=1;break}else{l=vV(g,n,m)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+44>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[G>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,2)|0;g=l;i=A;l=1;break}}m=c[f+(H*72|0)>>2]|0;n=c[e+((c[f+(C*72|0)+4>>2]|0)*56|0)+44>>2]|0;if((l|0)==2&(H|0)==(j|0)){l=vV(g,n,m)|0;d=uV(a,b,d,e,f,g,c[G>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,l,c[f+(C*72|0)+44>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,l,c[i>>2]|0,C,k,2)|0;g=l;i=A;l=1;break}else{l=vV(g,m,n)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+44>>2]|0,C,k,2)|0;g=l;i=G;l=1;break}}o=+h[t>>3];n=c[y>>2]|0;if(((+B(+(o-+h[e+(n*56|0)+8>>3]))<=1.0e-07?+B(+(+h[v>>3]-+h[e+(n*56|0)>>3]))<=1.0e-07:0)?(J=c[f+(C*72|0)+4>>2]|0,+B(+(+h[x>>3]-+h[e+(J*56|0)+8>>3]))<=1.0e-07):0)?+B(+(+h[w>>3]-+h[e+(J*56|0)>>3]))<=1.0e-07:0)if((l|0)==1){l=vV(g,n,J)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+44>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,l,c[f+(C*72|0)+52>>2]|0,C,k,1)|0;g=l;i=A;l=1;break}else{l=vV(g,J,n)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+52>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,l,c[i>>2]|0,C,k,2)|0;g=l;i=f+(C*72|0)+44|0;l=2;break}j=c[f+(C*72|0)+4>>2]|0;if(((+B(+(o-+h[e+(j*56|0)+24>>3]))<=1.0e-07?+B(+(+h[v>>3]-+h[e+(j*56|0)+16>>3]))<=1.0e-07:0)?+B(+(+h[x>>3]-+h[e+(n*56|0)+24>>3]))<=1.0e-07:0)?+B(+(+h[w>>3]-+h[e+(n*56|0)+16>>3]))<=1.0e-07:0){m=c[e+(j*56|0)+44>>2]|0;j=c[e+(n*56|0)+44>>2]|0;if((l|0)==1){l=vV(g,j,m)|0;d=uV(a,b,d,e,f,g,c[i>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+44>>2]|0,C,k,2)|0;d=uV(a,b,d,e,f,l,c[f+(C*72|0)+52>>2]|0,C,k,1)|0;g=l;i=A;l=1;break}else{l=vV(g,m,j)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+52>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,l,c[i>>2]|0,C,k,2)|0;g=l;i=f+(C*72|0)+44|0;l=2;break}}d=uV(a,b,d,e,f,g,z,C,k,2)|0;d=uV(a,b,d,e,f,g,c[A>>2]|0,C,k,1)|0;d=uV(a,b,d,e,f,g,c[f+(C*72|0)+44>>2]|0,C,k,2)|0;i=f+(C*72|0)+52|0;l=1}while(0);j=C;C=c[i>>2]|0}return d|0}function vV(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;f=l;l=l+16|0;r=f+4|0;i=f;e=(c[47392]|0)+1|0;c[47392]=e;m=c[47389]|0;wV(b,d,r,i);r=c[r>>2]|0;h=c[m+(b*56|0)+32+(r<<2)>>2]|0;i=c[m+(d*56|0)+32+(c[i>>2]<<2)>>2]|0;n=c[47391]|0;g=n+1|0;n=n+2|0;c[47391]=n;q=c[47388]|0;c[q+(g<<4)>>2]=b;c[q+(n<<4)>>2]=d;o=q+(h<<4)+4|0;p=q+(g<<4)+4|0;c[p>>2]=c[o>>2];c[q+(c[o>>2]<<4)+8>>2]=g;c[q+(g<<4)+8>>2]=n;c[q+(n<<4)+4>>2]=g;j=q+(i<<4)+8|0;c[q+(n<<4)+8>>2]=c[j>>2];c[q+(c[j>>2]<<4)+4>>2]=n;c[o>>2]=i;c[j>>2]=h;j=m+(b*56|0)+48|0;o=c[j>>2]|0;i=m+(d*56|0)+48|0;k=c[i>>2]|0;c[m+(b*56|0)+16+(r<<2)>>2]=d;c[m+(b*56|0)+32+(o<<2)>>2]=g;c[m+(b*56|0)+16+(o<<2)>>2]=c[q+(c[p>>2]<<4)>>2];c[m+(d*56|0)+32+(k<<2)>>2]=n;c[m+(d*56|0)+16+(k<<2)>>2]=b;c[j>>2]=(c[j>>2]|0)+1;c[i>>2]=(c[i>>2]|0)+1;d=c[47390]|0;c[d+(a<<2)>>2]=h;c[d+(e<<2)>>2]=g;l=f;return e|0}function wV(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0.0,h=0,i=0,j=0.0,k=0.0,l=0,m=0,n=0;l=c[47389]|0;m=l+(a*56|0)|0;n=l+(b*56|0)|0;f=0;g=-4.0;i=0;while(1){if((i|0)==4)break;h=c[l+(a*56|0)+16+(i<<2)>>2]|0;if((h|0)>=1?(j=+xV(m,l+(h*56|0)|0,n),j>g):0){f=i;g=j}i=i+1|0}c[d>>2]=f;f=0;i=0;g=-4.0;while(1){if((i|0)==4)break;h=c[l+(b*56|0)+16+(i<<2)>>2]|0;if((h|0)>=1?(k=+xV(n,l+(h*56|0)|0,m),k>g):0){f=i;g=k}i=i+1|0}c[e>>2]=f;return}function xV(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0,g=0.0,i=0.0;g=+h[a>>3];i=+h[b>>3]-g;d=+h[a+8>>3];f=+h[b+8>>3]-d;g=+h[c>>3]-g;d=+h[c+8>>3]-d;e=d*f+g*i;if(!(d*i-g*f>=0.0))d=-e/(+C(+(d*d+g*g))*+C(+(f*f+i*i)))+-2.0;else d=e/(+C(+(d*d+g*g))*+C(+(f*f+i*i)));return +d}function yV(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0,l=0.0;c[e>>2]=c[a>>2];c[e+4>>2]=c[a+4>>2];c[e+8>>2]=c[a+8>>2];c[e+12>>2]=c[a+12>>2];i=e+32|0;j=a+16|0;c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];if(!d){f=a+8|0;g=j;i=a+24|0;d=a}else{f=a+24|0;g=a;i=a+8|0;d=j}k=+h[g>>3];l=+h[i>>3];h[e+16>>3]=+h[d>>3];h[e+24>>3]=l;h[e+48>>3]=k;h[e+56>>3]=+h[f>>3];a:do if(b|0){d=0;while(1){if((d|0)==4)break a;j=e+(d<<4)+8|0;l=+h[j>>3];b=e+(d<<4)|0;h[j>>3]=+h[b>>3];h[b>>3]=-l;d=d+1|0}}while(0);return}function zV(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;h=b+3|0;i=b+1|0;j=b+2|0;k=a+(b*56|0)+44|0;l=a+(h*56|0)+44|0;e=0;f=b;while(1){if((f|0)>(h|0))break;do if((f|0)!=(b|0))if((f|0)==(h|0)){c[l>>2]=b;g=j;break}else{c[a+(f*56|0)+44>>2]=f+1;g=f+-1|0;break}else{c[k>>2]=i;g=h}while(0);c[a+(f*56|0)+48>>2]=g;c[a+(f*56|0)+32>>2]=0;n=a+(f*56|0)|0;m=d+(e<<4)|0;c[n>>2]=c[m>>2];c[n+4>>2]=c[m+4>>2];c[n+8>>2]=c[m+8>>2];c[n+12>>2]=c[m+12>>2];X6(a+(g*56|0)+16|0,m|0,16)|0;e=e+1|0;f=f+1|0}return b+4|0}function AV(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0;c[47393]=e<<1;c[47394]=e;k=JK(e*80|0)|0;c[47395]=1;c[47396]=1;Y6(f|0,0,e*72|0)|0;g=BV(c[d+4>>2]|0,b,f,k)|0;e=1;while(1){if((e|0)>(a|0))break;c[b+(e*56|0)+40>>2]=g;c[b+(e*56|0)+36>>2]=g;e=e+1|0}j=CV(a)|0;g=2;i=1;while(1){if((i|0)>(j|0))break;e=DV(a,i+-1|0)|0;h=DV(a,i)|0;while(1){if((e|0)>=(h|0)){e=1;break}EV(c[d+(g<<2)>>2]|0,b,f,k);e=e+1|0;g=g+1|0}while(1){if((e|0)>(a|0))break;FV(e,b,f,k);e=e+1|0}i=i+1|0}e=DV(a,j)|0;while(1){if((e|0)>=(a|0))break;EV(c[d+(g<<2)>>2]|0,b,f,k);g=g+1|0;e=e+1|0}U1(k);return c[47395]|0}function BV(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;f=KV()|0;c[e+(f*40|0)>>2]=2;s=e+(f*40|0)+8|0;k=b+(a*56|0)|0;l=b+(a*56|0)+16|0;OV(s,k,l);n=KV()|0;c[e+(f*40|0)+36>>2]=n;c[e+(n*40|0)>>2]=3;c[e+(n*40|0)+28>>2]=f;j=KV()|0;c[e+(f*40|0)+32>>2]=j;c[e+(j*40|0)>>2]=2;r=e+(j*40|0)+8|0;PV(r,k,l);c[e+(j*40|0)+28>>2]=f;l=KV()|0;c[e+(j*40|0)+32>>2]=l;c[e+(l*40|0)>>2]=3;c[e+(l*40|0)+28>>2]=j;k=KV()|0;c[e+(j*40|0)+36>>2]=k;c[e+(k*40|0)>>2]=1;c[e+(k*40|0)+4>>2]=a;c[e+(k*40|0)+28>>2]=j;j=KV()|0;c[e+(k*40|0)+32>>2]=j;c[e+(j*40|0)>>2]=3;c[e+(j*40|0)+28>>2]=k;g=KV()|0;c[e+(k*40|0)+36>>2]=g;c[e+(g*40|0)>>2]=3;c[e+(g*40|0)+28>>2]=k;k=JV(d)|0;i=JV(d)|0;m=JV(d)|0;o=JV(d)|0;p=d+(k*72|0)+8|0;q=d+(i*72|0)+8|0;t=d+(o*72|0)+24|0;c[t>>2]=c[s>>2];c[t+4>>2]=c[s+4>>2];c[t+8>>2]=c[s+8>>2];c[t+12>>2]=c[s+12>>2];X6(q|0,s|0,16)|0;c[p>>2]=c[q>>2];c[p+4>>2]=c[q+4>>2];c[p+8>>2]=c[q+8>>2];c[p+12>>2]=c[q+12>>2];p=d+(k*72|0)+24|0;q=d+(i*72|0)+24|0;s=d+(m*72|0)+8|0;c[s>>2]=c[r>>2];c[s+4>>2]=c[r+4>>2];c[s+8>>2]=c[r+8>>2];c[s+12>>2]=c[r+12>>2];X6(q|0,r|0,16)|0;c[p>>2]=c[q>>2];c[p+4>>2]=c[q+4>>2];c[p+8>>2]=c[q+8>>2];c[p+12>>2]=c[q+12>>2];h[d+(o*72|0)+16>>3]=1073741824.0;h[d+(o*72|0)+8>>3]=1073741824.0;h[d+(m*72|0)+32>>3]=-1073741824.0;h[d+(m*72|0)+24>>3]=-1073741824.0;c[d+(i*72|0)>>2]=a;c[d+(k*72|0)+4>>2]=a;c[d+(i*72|0)+40>>2]=o;c[d+(k*72|0)+40>>2]=o;c[d+(i*72|0)+48>>2]=m;c[d+(k*72|0)+48>>2]=m;c[d+(m*72|0)+40>>2]=k;c[d+(o*72|0)+48>>2]=k;c[d+(m*72|0)+44>>2]=i;c[d+(o*72|0)+52>>2]=i;c[d+(k*72|0)+56>>2]=j;c[d+(i*72|0)+56>>2]=g;c[d+(m*72|0)+56>>2]=l;c[d+(o*72|0)+56>>2]=n;c[d+(i*72|0)+68>>2]=1;c[d+(k*72|0)+68>>2]=1;c[d+(o*72|0)+68>>2]=1;c[d+(m*72|0)+68>>2]=1;c[e+(n*40|0)+24>>2]=o;c[e+(l*40|0)+24>>2]=m;c[e+(j*40|0)+24>>2]=k;c[e+(g*40|0)+24>>2]=i;c[b+(a*56|0)+32>>2]=1;return f|0}function CV(a){a=a|0;var b=0.0;b=+(a|0);a=0;while(1){if(!(b>=1.0))break;b=+x2(b);a=a+1|0}return a+-1|0}function DV(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;d=+(a|0);c=d;a=0;while(1){if((a|0)>=(b|0))break;c=+x2(c);a=a+1|0}return ~~+N(+(d/c))|0}function EV(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0,X=0;W=l;l=l+96|0;S=W+32|0;f=W+16|0;T=W;s=S;k=b+(a*56|0)|0;r=s+56|0;do{c[s>>2]=c[k>>2];s=s+4|0;k=k+4|0}while((s|0)<(r|0));U=S+16|0;V=S+24|0;i=+h[V>>3];H=S+8|0;j=+h[H>>3];do if(!(i>j+1.0e-07)){if(!(i<j+-1.0e-07)?+h[U>>3]>+h[S>>3]:0){G=4;break}G=(IV(a,b,1)|0)==0;f=c[S+36>>2]|0;if(G){n=0;G=7}else{f=GV(S,U,f,b,e)|0;G=20}}else G=4;while(0);if((G|0)==4){c[f>>2]=c[S>>2];c[f+4>>2]=c[S+4>>2];c[f+8>>2]=c[S+8>>2];c[f+12>>2]=c[S+12>>2];c[S>>2]=c[U>>2];c[S+4>>2]=c[U+4>>2];c[S+8>>2]=c[U+8>>2];c[S+12>>2]=c[U+12>>2];c[U>>2]=c[f>>2];c[U+4>>2]=c[f+4>>2];c[U+8>>2]=c[f+8>>2];c[U+12>>2]=c[f+12>>2];E=S+36|0;F=c[E>>2]|0;G=S+40|0;f=c[G>>2]|0;c[E>>2]=f;c[G>>2]=F;if(!(IV(a,b,2)|0)){n=1;G=7}else{f=GV(S,U,f,b,e)|0;G=19}}if((G|0)==7){m=GV(S,U,f,b,e)|0;f=JV(d)|0;c[d+(f*72|0)+68>>2]=1;s=d+(f*72|0)|0;k=d+(m*72|0)|0;r=s+72|0;do{c[s>>2]=c[k>>2];s=s+4|0;k=k+4|0}while((s|0)<(r|0));q=+h[H>>3];h[d+(f*72|0)+16>>3]=q;h[d+(m*72|0)+32>>3]=q;q=+h[S>>3];h[d+(f*72|0)+8>>3]=q;h[d+(m*72|0)+24>>3]=q;c[d+(m*72|0)+48>>2]=f;c[d+(m*72|0)+52>>2]=0;c[d+(f*72|0)+40>>2]=m;c[d+(f*72|0)+44>>2]=0;g=c[d+(f*72|0)+48>>2]|0;if((g|0)>0){k=d+(g*72|0)+40|0;if((c[k>>2]|0)==(m|0))c[k>>2]=f;g=d+(g*72|0)+44|0;if((c[g>>2]|0)==(m|0))c[g>>2]=f}g=c[d+(f*72|0)+52>>2]|0;if((g|0)>0){k=d+(g*72|0)+40|0;if((c[k>>2]|0)==(m|0))c[k>>2]=f;g=d+(g*72|0)+44|0;if((c[g>>2]|0)==(m|0))c[g>>2]=f}E=KV()|0;G=KV()|0;F=d+(m*72|0)+56|0;D=c[F>>2]|0;c[e+(D*40|0)>>2]=2;C=e+(D*40|0)+8|0;c[C>>2]=c[S>>2];c[C+4>>2]=c[S+4>>2];c[C+8>>2]=c[S+8>>2];c[C+12>>2]=c[S+12>>2];c[e+(D*40|0)+4>>2]=a;c[e+(D*40|0)+32>>2]=G;c[e+(D*40|0)+36>>2]=E;c[e+(E*40|0)>>2]=3;c[e+(E*40|0)+24>>2]=m;c[e+(E*40|0)+28>>2]=D;c[e+(G*40|0)>>2]=3;c[e+(G*40|0)+24>>2]=f;c[e+(G*40|0)+28>>2]=D;c[F>>2]=E;c[d+(f*72|0)+56>>2]=G;if(n)G=19;else G=20}if((G|0)==19)if(!(IV(a,b,1)|0)){g=1;G=21}else{g=1;G=32}else if((G|0)==20)if(!(IV(a,b,2)|0)){g=0;G=21}else{g=0;G=32}if((G|0)==21){o=GV(U,S,c[S+40>>2]|0,b,e)|0;n=JV(d)|0;c[d+(n*72|0)+68>>2]=1;s=d+(n*72|0)|0;k=d+(o*72|0)|0;r=s+72|0;do{c[s>>2]=c[k>>2];s=s+4|0;k=k+4|0}while((s|0)<(r|0));q=+h[V>>3];h[d+(n*72|0)+16>>3]=q;h[d+(o*72|0)+32>>3]=q;q=+h[U>>3];h[d+(n*72|0)+8>>3]=q;h[d+(o*72|0)+24>>3]=q;c[d+(o*72|0)+48>>2]=n;c[d+(o*72|0)+52>>2]=0;c[d+(n*72|0)+40>>2]=o;c[d+(n*72|0)+44>>2]=0;k=c[d+(n*72|0)+48>>2]|0;if((k|0)>0){m=d+(k*72|0)+40|0;if((c[m>>2]|0)==(o|0))c[m>>2]=n;k=d+(k*72|0)+44|0;if((c[k>>2]|0)==(o|0))c[k>>2]=n}k=c[d+(n*72|0)+52>>2]|0;if((k|0)>0){m=d+(k*72|0)+40|0;if((c[m>>2]|0)==(o|0))c[m>>2]=n;k=d+(k*72|0)+44|0;if((c[k>>2]|0)==(o|0))c[k>>2]=n}D=KV()|0;F=KV()|0;E=d+(o*72|0)+56|0;C=c[E>>2]|0;c[e+(C*40|0)>>2]=2;A=e+(C*40|0)+8|0;c[A>>2]=c[U>>2];c[A+4>>2]=c[U+4>>2];c[A+8>>2]=c[U+8>>2];c[A+12>>2]=c[U+12>>2];c[e+(C*40|0)+4>>2]=a;c[e+(C*40|0)+32>>2]=F;c[e+(C*40|0)+36>>2]=D;c[e+(D*40|0)>>2]=3;c[e+(D*40|0)+24>>2]=o;c[e+(D*40|0)+28>>2]=C;c[e+(F*40|0)>>2]=3;c[e+(F*40|0)+24>>2]=n;c[e+(F*40|0)+28>>2]=C;c[E>>2]=D;c[d+(n*72|0)+56>>2]=F;F=f;f=0}else if((G|0)==32){F=f;o=GV(U,S,c[S+40>>2]|0,b,e)|0;f=1}a:do if((F|0)>0){D=d+(o*72|0)+24|0;E=d+(o*72|0)+32|0;C=(f|0)!=0;y=g?b+(a*56|0)+48|0:b+(a*56|0)+44|0;z=T+8|0;g=0;f=0;x=F;b:while(1){k=d+(x*72|0)|0;v=d+(x*72|0)+24|0;if(!(LV(v,D)|0))break a;m=d+(x*72|0)+56|0;u=c[m>>2]|0;n=KV()|0;p=KV()|0;c[e+(u*40|0)>>2]=1;c[e+(u*40|0)+4>>2]=a;c[e+(u*40|0)+32>>2]=n;c[e+(u*40|0)+36>>2]=p;c[e+(n*40|0)>>2]=3;c[e+(n*40|0)+24>>2]=x;c[e+(n*40|0)+28>>2]=u;c[e+(p*40|0)>>2]=3;w=JV(d)|0;c[e+(p*40|0)+24>>2]=w;A=d+(w*72|0)|0;c[d+(w*72|0)+68>>2]=1;c[e+(p*40|0)+28>>2]=u;g=(x|0)==(F|0)?w:g;u=d+(x*72|0)+32|0;if(+B(+(+h[u>>3]-+h[E>>3]))<=1.0e-07?+B(+(+h[v>>3]-+h[D>>3]))<=1.0e-07:0)f=w;s=A;r=s+72|0;do{c[s>>2]=c[k>>2];s=s+4|0;k=k+4|0}while((s|0)<(r|0));c[m>>2]=n;c[d+(w*72|0)+56>>2]=p;t=d+(x*72|0)+48|0;m=d+(x*72|0)+52|0;k=(c[m>>2]|0)<1;do if((c[t>>2]|0)<1){if(k)break b;s=d+(x*72|0)+40|0;k=c[s>>2]|0;do if((k|0)>0?(M=d+(x*72|0)+44|0,N=c[M>>2]|0,(N|0)>0):0){r=d+(x*72|0)+60|0;k=c[r>>2]|0;if((k|0)<=0){c[d+(w*72|0)+40>>2]=N;c[d+(w*72|0)+44>>2]=-1;c[M>>2]=-1;c[d+(N*72|0)+48>>2]=w;break}if((c[d+(x*72|0)+64>>2]|0)==1){c[d+(w*72|0)+40>>2]=N;c[M>>2]=-1;c[d+(w*72|0)+44>>2]=k;c[d+((c[s>>2]|0)*72|0)+48>>2]=x;p=w;n=N}else{c[d+(w*72|0)+44>>2]=-1;t=d+(w*72|0)+40|0;c[t>>2]=c[M>>2];n=c[s>>2]|0;c[M>>2]=n;c[s>>2]=k;c[d+(k*72|0)+48>>2]=x;p=x;k=c[t>>2]|0}c[d+(n*72|0)+48>>2]=p;c[d+(k*72|0)+48>>2]=w;c[d+(w*72|0)+60>>2]=0;c[r>>2]=0}else G=77;while(0);do if((G|0)==77){G=0;n=d+(k*72|0)+48|0;p=c[n>>2]|0;k=d+(k*72|0)+52|0;if((p|0)>0?(c[k>>2]|0)>0:0){k=c[d+(p*72|0)+4>>2]|0;if((k|0)>0?(HV(k,b,U)|0)==0:0){c[d+(w*72|0)+44>>2]=-1;c[d+(x*72|0)+44>>2]=-1;c[s>>2]=-1;c[d+((c[d+(w*72|0)+40>>2]|0)*72|0)+52>>2]=w;break}c[d+(x*72|0)+44>>2]=-1;c[d+(w*72|0)+44>>2]=-1;c[d+(w*72|0)+40>>2]=-1;c[d+((c[s>>2]|0)*72|0)+48>>2]=x;break}c[n>>2]=x;c[k>>2]=w}while(0);if(+B(+(+h[u>>3]-+h[E>>3]))<=1.0e-07?C&+B(+(+h[v>>3]-+h[D>>3]))<=1.0e-07:0){k=c[y>>2]|0;if((k|0)>0?(HV(k,b,S)|0)!=0:0){c[d+((c[m>>2]|0)*72|0)+40>>2]=x;n=d+(w*72|0)+52|0;k=w}else{c[d+((c[d+(w*72|0)+52>>2]|0)*72|0)+44>>2]=w;n=m;k=x}c[n>>2]=-1;c[d+(k*72|0)+48>>2]=-1;k=c[m>>2]|0;break}k=c[m>>2]|0;m=d+(k*72|0)+40|0;n=c[m>>2]|0;p=d+(k*72|0)+44|0;if((n|0)>0?(O=c[p>>2]|0,P=(n|0)==(x|0),(O|0)>0):0){c[d+(k*72|0)+60>>2]=P?O:n;c[d+(k*72|0)+64>>2]=P?1:2}c[m>>2]=x;c[p>>2]=w}else{if(!k){i=+h[u>>3];j=+h[H>>3];q=i-j;if(!(+B(+q)<=1.0e-07)){h[z>>3]=i;i=+h[S>>3];h[T>>3]=(+h[U>>3]-i)*(q/(+h[V>>3]-j))+i;s=(MV(T,v)|0)!=0}else s=+h[v>>3]>+h[S>>3];r=d+(x*72|0)+40|0;k=c[r>>2]|0;do if((k|0)>0?(Q=d+(x*72|0)+44|0,R=c[Q>>2]|0,(R|0)>0):0){p=d+(x*72|0)+60|0;k=c[p>>2]|0;if((k|0)<=0){c[d+(w*72|0)+40>>2]=R;c[d+(w*72|0)+44>>2]=-1;c[Q>>2]=-1;c[d+(R*72|0)+48>>2]=w;break}if((c[d+(x*72|0)+64>>2]|0)==1){c[d+(w*72|0)+40>>2]=R;c[Q>>2]=-1;c[d+(w*72|0)+44>>2]=k;c[d+((c[r>>2]|0)*72|0)+48>>2]=x;n=w;m=R}else{c[d+(w*72|0)+44>>2]=-1;X=d+(w*72|0)+40|0;c[X>>2]=c[Q>>2];m=c[r>>2]|0;c[Q>>2]=m;c[r>>2]=k;c[d+(k*72|0)+48>>2]=x;n=x;k=c[X>>2]|0}c[d+(m*72|0)+48>>2]=n;c[d+(k*72|0)+48>>2]=w;c[d+(w*72|0)+60>>2]=0;c[p>>2]=0}else G=106;while(0);do if((G|0)==106){G=0;m=d+(k*72|0)+48|0;n=c[m>>2]|0;k=d+(k*72|0)+52|0;if((n|0)>0?(c[k>>2]|0)>0:0){k=c[d+(n*72|0)+4>>2]|0;if((k|0)>0?(HV(k,b,U)|0)==0:0){c[d+(w*72|0)+44>>2]=-1;c[d+(x*72|0)+44>>2]=-1;c[r>>2]=-1;c[d+((c[d+(w*72|0)+40>>2]|0)*72|0)+52>>2]=w;break}c[d+(x*72|0)+44>>2]=-1;c[d+(w*72|0)+44>>2]=-1;c[d+(w*72|0)+40>>2]=-1;c[d+((c[r>>2]|0)*72|0)+48>>2]=x;break}c[m>>2]=x;c[k>>2]=w}while(0);if(+B(+(+h[u>>3]-+h[E>>3]))<=1.0e-07?C&+B(+(+h[v>>3]-+h[D>>3]))<=1.0e-07:0){k=c[t>>2]|0;c[d+(k*72|0)+40>>2]=x;c[d+(k*72|0)+44>>2]=-1;k=d+(x*72|0)+52|0;X=c[k>>2]|0;c[d+(X*72|0)+40>>2]=w;c[d+(X*72|0)+44>>2]=-1;c[d+(w*72|0)+48>>2]=X;c[d+(w*72|0)+52>>2]=-1;c[k>>2]=-1;k=-1}else G=116;do if((G|0)==116){G=0;k=c[t>>2]|0;c[d+(k*72|0)+40>>2]=x;m=d+(k*72|0)+44|0;if(s){c[m>>2]=w;X=d+(x*72|0)+52|0;v=c[X>>2]|0;c[d+(v*72|0)+40>>2]=w;c[d+(v*72|0)+44>>2]=-1;c[X>>2]=-1;break}else{c[m>>2]=-1;k=d+(x*72|0)+52|0;X=c[k>>2]|0;c[d+(X*72|0)+40>>2]=x;c[d+(X*72|0)+44>>2]=w;c[d+(w*72|0)+48>>2]=X;c[d+(w*72|0)+52>>2]=-1;k=c[k>>2]|0;break}}while(0);break}s=d+(x*72|0)+40|0;k=c[s>>2]|0;do if((k|0)>0?(I=d+(x*72|0)+44|0,J=c[I>>2]|0,(J|0)>0):0){r=d+(x*72|0)+60|0;k=c[r>>2]|0;if((k|0)<=0){c[d+(w*72|0)+40>>2]=J;c[d+(w*72|0)+44>>2]=-1;c[I>>2]=-1;c[d+(J*72|0)+48>>2]=w;break}if((c[d+(x*72|0)+64>>2]|0)==1){c[d+(w*72|0)+40>>2]=J;c[I>>2]=-1;c[d+(w*72|0)+44>>2]=k;c[d+((c[s>>2]|0)*72|0)+48>>2]=x;p=w;n=J}else{c[d+(w*72|0)+44>>2]=-1;X=d+(w*72|0)+40|0;c[X>>2]=c[I>>2];n=c[s>>2]|0;c[I>>2]=n;c[s>>2]=k;c[d+(k*72|0)+48>>2]=x;p=x;k=c[X>>2]|0}c[d+(n*72|0)+48>>2]=p;c[d+(k*72|0)+48>>2]=w;c[d+(w*72|0)+60>>2]=0;c[r>>2]=0}else G=51;while(0);do if((G|0)==51){G=0;n=d+(k*72|0)+48|0;p=c[n>>2]|0;k=d+(k*72|0)+52|0;if((p|0)>0?(c[k>>2]|0)>0:0){k=c[d+(p*72|0)+4>>2]|0;if((k|0)>0?(HV(k,b,U)|0)==0:0){c[d+(w*72|0)+44>>2]=-1;c[d+(x*72|0)+44>>2]=-1;c[s>>2]=-1;c[d+((c[d+(w*72|0)+40>>2]|0)*72|0)+52>>2]=w;break}c[d+(x*72|0)+44>>2]=-1;c[d+(w*72|0)+44>>2]=-1;c[d+(w*72|0)+40>>2]=-1;c[d+((c[s>>2]|0)*72|0)+48>>2]=x;break}c[n>>2]=x;c[k>>2]=w}while(0);if(+B(+(+h[u>>3]-+h[E>>3]))<=1.0e-07?C&+B(+(+h[v>>3]-+h[D>>3]))<=1.0e-07:0){k=c[y>>2]|0;if((k|0)>0?(HV(k,b,S)|0)!=0:0){c[d+((c[t>>2]|0)*72|0)+40>>2]=x;m=d+(w*72|0)+52|0;k=w}else{c[d+((c[d+(w*72|0)+48>>2]|0)*72|0)+44>>2]=w;k=x}c[m>>2]=-1;c[d+(k*72|0)+48>>2]=-1;k=c[t>>2]|0;break}k=c[t>>2]|0;m=d+(k*72|0)+40|0;n=c[m>>2]|0;p=d+(k*72|0)+44|0;if((n|0)>0?(K=c[p>>2]|0,L=(n|0)==(x|0),(K|0)>0):0){c[d+(k*72|0)+60>>2]=L?K:n;c[d+(k*72|0)+64>>2]=L?1:2}c[m>>2]=x;c[p>>2]=w}while(0);c[A>>2]=a;c[d+(x*72|0)+4>>2]=a;if((k|0)>0)x=k;else break a}U4(138424,19,1,c[15712]|0)|0}else{g=0;f=0}while(0);NV(a,F,o,1,d,e);NV(a,g,f,2,d,e);c[b+(a*56|0)+32>>2]=1;l=W;return}function FV(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;if(!(c[b+(a*56|0)+32>>2]|0)){f=b+(a*56|0)|0;g=b+(a*56|0)+16|0;h=b+(a*56|0)+36|0;c[h>>2]=c[d+((GV(f,g,c[h>>2]|0,b,e)|0)*72|0)+56>>2];a=b+(a*56|0)+40|0;c[a>>2]=c[d+((GV(g,f,c[a>>2]|0,b,e)|0)*72|0)+56>>2]}return}function GV(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0.0,i=0,j=0.0,k=0.0,l=0.0,m=0.0,n=0.0,o=0,p=0.0,q=0,r=0;o=a+8|0;q=b+8|0;a:while(1){b:do switch(c[f+(d*40|0)>>2]|0){case 3:{r=3;break a}case 2:{g=+h[o>>3];i=f+(d*40|0)+8|0;j=+h[f+(d*40|0)+16>>3];k=j+1.0e-07;do if(!(g>k)){l=j+-1.0e-07;if(!(g<l)?+h[a>>3]>+h[i>>3]:0)break;if(+B(+(g-j))<=1.0e-07?(m=+h[i>>3],+B(+(+h[a>>3]-m))<=1.0e-07):0){g=+h[q>>3];do if(!(g>k)){if(!(g<l)?+h[b>>3]>m:0)break;d=f+(d*40|0)+32|0;break b}while(0);d=f+(d*40|0)+36|0;break b}d=f+(d*40|0)+32|0;break b}while(0);d=f+(d*40|0)+36|0;break}case 1:{g=+h[o>>3];i=c[f+(d*40|0)+4>>2]|0;if(+B(+(g-+h[e+(i*56|0)+8>>3]))<=1.0e-07?(n=+h[a>>3],+B(+(n-+h[e+(i*56|0)>>3]))<=1.0e-07):0)j=n;else r=19;do if((r|0)==19){r=0;if(+B(+(g-+h[e+(i*56|0)+24>>3]))<=1.0e-07?(p=+h[a>>3],+B(+(p-+h[e+(i*56|0)+16>>3]))<=1.0e-07):0){j=p;break}if(!(HV(i,e,a)|0)){d=f+(d*40|0)+36|0;break b}else{d=f+(d*40|0)+32|0;break b}}while(0);if(!(+B(+(g-+h[q>>3]))<=1.0e-07))if(!(HV(i,e,b)|0)){d=f+(d*40|0)+36|0;break b}else{d=f+(d*40|0)+32|0;break b}else if(+h[b>>3]<j){d=f+(d*40|0)+32|0;break b}else{d=f+(d*40|0)+36|0;break b}}default:{r=31;break a}}while(0);d=c[d>>2]|0}if((r|0)==3)return c[f+(d*40|0)+24>>2]|0;else if((r|0)==31){U4(138358,35,1,c[15712]|0)|0;qa(138394,138396,349,138408)}return 0}function HV(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0,g=0.0,i=0,j=0,k=0.0;f=b+(a*56|0)+16|0;g=+h[b+(a*56|0)+24>>3];i=b+(a*56|0)|0;e=+h[b+(a*56|0)+8>>3];do if(!(g>e+1.0e-07)){if(!(g<e+-1.0e-07)?+h[f>>3]>+h[i>>3]:0){j=4;break}d=+h[c+8>>3];if(+B(+(g-d))<=1.0e-07){d=+h[c>>3]<+h[f>>3]?1.0:-1.0;break}if(!(+B(+(e-d))<=1.0e-07)){k=+h[f>>3];d=(+h[i>>3]-k)*(d-g)-(+h[c>>3]-k)*(e-g);break}else{d=+h[c>>3]<+h[i>>3]?1.0:-1.0;break}}else j=4;while(0);do if((j|0)==4){d=+h[c+8>>3];if(+B(+(g-d))<=1.0e-07){d=+h[c>>3]<+h[f>>3]?1.0:-1.0;break}if(!(+B(+(e-d))<=1.0e-07)){k=+h[i>>3];d=(+h[f>>3]-k)*(d-e)-(+h[c>>3]-k)*(g-e);break}else{d=+h[c>>3]<+h[i>>3]?1.0:-1.0;break}}while(0);return d>0.0|0}function IV(a,b,d){a=a|0;b=b|0;d=d|0;return c[b+((c[((d|0)==1?b+(a*56|0)+48|0:b+(a*56|0)+44|0)>>2]|0)*56|0)+32>>2]|0}function JV(a){a=a|0;var b=0,d=0,e=0;e=l;l=l+16|0;d=e;b=c[47395]|0;if((b|0)<(c[47394]|0)){c[a+(b*72|0)>>2]=-1;c[a+(b*72|0)+4>>2]=-1;c[a+(b*72|0)+68>>2]=1;c[47395]=b+1;l=e;return b|0}else{e=c[15712]|0;c[d>>2]=b;w4(e,138483,d)|0;qa(138394,138396,88,138521)}return 0}function KV(){var a=0;a=c[47396]|0;if((a|0)<(c[47393]|0)){c[47396]=a+1;return a|0}else{U4(138444,30,1,c[15712]|0)|0;qa(138394,138396,72,138475)}return 0}function LV(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a+8>>3];d=+h[b+8>>3];if(!(c>d+1.0e-07))if(c<d+-1.0e-07)a=0;else a=+h[a>>3]>=+h[b>>3]&1;else a=1;return a|0}function MV(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a+8>>3];d=+h[b+8>>3];if(!(c<d+-1.0e-07))if(c>d+1.0e-07)a=0;else a=+h[a>>3]<+h[b>>3]&1;else a=1;return a|0}function NV(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;q=f+(d*72|0)+24|0;p=(e|0)==1;a:while(1){o=f+(b*72|0)+24|0;j=f+(b*72|0)+48|0;k=f+(b*72|0)+52|0;l=f+(b*72|0)|0;m=f+(b*72|0)+4|0;n=f+(b*72|0)+56|0;if((b|0)<=0)break;while(1){if(!(LV(o,q)|0))break a;d=c[j>>2]|0;e=(d|0)>0;do if(p){if(e?(c[f+(d*72|0)+4>>2]|0)==(a|0):0)break;d=c[k>>2]|0;if((d|0)<=0){b=d;continue a}if((c[f+(d*72|0)+4>>2]|0)!=(a|0)){b=d;continue a}}else{if(e?(c[f+(d*72|0)>>2]|0)==(a|0):0)break;d=c[k>>2]|0;if((d|0)<=0){b=d;continue a}if((c[f+(d*72|0)>>2]|0)!=(a|0)){b=d;continue a}}while(0);if((c[l>>2]|0)!=(c[f+(d*72|0)>>2]|0)){b=d;continue a}if((c[m>>2]|0)!=(c[f+(d*72|0)+4>>2]|0)){b=d;continue a}r=c[f+(d*72|0)+56>>2]|0;e=c[g+(r*40|0)+28>>2]|0;h=g+(e*40|0)+32|0;i=c[n>>2]|0;if((c[h>>2]|0)==(r|0))c[h>>2]=i;else c[g+(e*40|0)+36>>2]=i;e=c[f+(d*72|0)+48>>2]|0;c[j>>2]=e;do if((e|0)>0){h=f+(e*72|0)+40|0;if((c[h>>2]|0)==(d|0)){c[h>>2]=b;break}e=f+(e*72|0)+44|0;if((c[e>>2]|0)==(d|0))c[e>>2]=b}while(0);e=c[f+(d*72|0)+52>>2]|0;c[k>>2]=e;do if((e|0)>0){h=f+(e*72|0)+40|0;if((c[h>>2]|0)==(d|0)){c[h>>2]=b;break}e=f+(e*72|0)+44|0;if((c[e>>2]|0)==(d|0))c[e>>2]=b}while(0);r=f+(d*72|0)+24|0;c[o>>2]=c[r>>2];c[o+4>>2]=c[r+4>>2];c[o+8>>2]=c[r+8>>2];c[o+12>>2]=c[r+12>>2];c[f+(d*72|0)+68>>2]=2}}return}function OV(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0;e=+h[b+8>>3];f=+h[d+8>>3];do if(!(e>f+1.0e-07)){if(!(+B(+(e-f))<=1.0e-07)){c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];break}if(+h[b>>3]>+h[d>>3]+1.0e-07){c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];break}else{c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];break}}else{c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2]}while(0);return}function PV(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0;e=+h[b+8>>3];f=+h[d+8>>3];do if(!(e<f+-1.0e-07)){if(!(+B(+(e-f))<=1.0e-07)){c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];break}if(+h[b>>3]<+h[d>>3]){c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];break}else{c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];break}}else{c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2]}while(0);return}function QV(a){a=a|0;var b=0;b=JK(8)|0;if(b|0){c[b+4>>2]=a;c[b>>2]=0}return b|0}function RV(a,b){a=a|0;b=b|0;if(b){b=QV(b)|0;c[b>>2]=a;a=b}return a|0}function SV(a){a=a|0;var b=0;do{b=a;a=c[a>>2]|0;U1(b)}while((a|0)!=0);return}function TV(){var a=0;a=JK(1992)|0;if(a|0)c[a>>2]=UV(a)|0;return a|0}function UV(a){a=a|0;var b=0;b=_V(a)|0;c[b+4>>2]=0;a=a+1972|0;c[a>>2]=(c[a>>2]|0)+1;return b|0}function VV(a){a=a|0;WV(a,c[a>>2]|0);U1(c[a>>2]|0);U1(a);return 0}function WV(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;g=a+1980|0;h=a+1912|0;i=a+1936|0;a:do if((c[b+4>>2]|0)>0){d=0;while(1){if((d|0)==64)break a;e=b+8+(d*20|0)+16|0;f=c[e>>2]|0;if(f|0?(WV(a,f),U1(c[e>>2]|0),eW(b,d),c[g>>2]=(c[g>>2]|0)+-1,c[h>>2]|0):0)c[i>>2]=(c[i>>2]|0)+1;d=d+1|0}}else{d=0;while(1){if((d|0)==64)break a;if(c[b+8+(d*20|0)+16>>2]|0?(eW(b,d),c[g>>2]=(c[g>>2]|0)+-1,c[h>>2]|0):0)c[i>>2]=(c[i>>2]|0)+1;d=d+1|0}}while(0);return}function XV(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;if(!b)qa(138879,138529,194,138537);e=c[b+4>>2]|0;if((e|0)<=-1)qa(138549,138529,195,138537);if(!d)qa(138821,138529,196,138537);i=a+1952|0;c[i>>2]=(c[i>>2]|0)+1;a:do if((e|0)>0){i=0;e=0;while(1){if((i|0)==64)break a;f=b+8+(i*20|0)+16|0;if((c[f>>2]|0)!=0?(jW(d,b+8+(i*20|0)|0)|0)!=0:0){g=XV(a,c[f>>2]|0,d)|0;if(!e)e=g;else{f=e;while(1){h=c[f>>2]|0;if(!h)break;else f=h}c[f>>2]=g}}i=i+1|0}}else{g=0;e=0;while(1){if((g|0)==64)break a;f=b+8+(g*20|0)|0;if((c[b+8+(g*20|0)+16>>2]|0)!=0?(jW(d,f)|0)!=0:0)e=RV(e,f)|0;g=g+1|0}}while(0);return e|0}function YV(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;p=l;l=l+64|0;k=p+52|0;m=p+32|0;n=p+16|0;o=p;c[k>>2]=0;if(!((b|0)!=0&(e|0)!=0))qa(138735,138529,247,138563);if((f|0)>-1?(j=c[e>>2]|0,(c[j+4>>2]|0)>=(f|0)):0){g=0;while(1){if((g|0)>=2)break;if((c[b+(g<<2)>>2]|0)>(c[b+(g+2<<2)>>2]|0)){h=8;break}else g=g+1|0}if((h|0)==8)qa(138610,138529,250,138563);i=a+1912|0;g=a+1908|0;h=c[g>>2]|0;if(c[i>>2]|0){q=h|0?a+1924|0:a+1916|0;c[q>>2]=(c[q>>2]|0)+1}if(!h){q=a+1964|0;c[q>>2]=(c[q>>2]|0)+1}if(!(ZV(a,b,d,j,k,f)|0))g=0;else{if(c[i>>2]|0){q=c[g>>2]|0?a+1948|0:a+1944|0;c[q>>2]=(c[q>>2]|0)+1}g=_V(a)|0;q=a+1976|0;c[q>>2]=(c[q>>2]|0)+1;q=c[e>>2]|0;c[g+4>>2]=(c[q+4>>2]|0)+1;bW(n,q);c[m>>2]=c[n>>2];c[m+4>>2]=c[n+4>>2];c[m+8>>2]=c[n+8>>2];c[m+12>>2]=c[n+12>>2];q=m+16|0;c[q>>2]=c[e>>2];dW(a,m,g,0)|0;bW(o,c[k>>2]|0);c[m>>2]=c[o>>2];c[m+4>>2]=c[o+4>>2];c[m+8>>2]=c[o+8>>2];c[m+12>>2]=c[o+12>>2];c[q>>2]=c[k>>2];dW(a,m,g,0)|0;c[e>>2]=g;g=a+1980|0;c[g>>2]=(c[g>>2]|0)+2;g=1}l=p;return g|0}qa(138575,138529,248,138563);return 0}function ZV(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+80|0;m=q+52|0;n=q+48|0;o=q+32|0;p=q+16|0;k=q;c[n>>2]=0;if(!((b|0)!=0&(e|0)!=0&(f|0)!=0))qa(138653,138529,311,138667);if((g|0)>-1?(i=e+4|0,h=c[i>>2]|0,(h|0)>=(g|0)):0){if(c[a+1912>>2]|0){h=c[a+1908>>2]|0?a+1948|0:a+1944|0;c[h>>2]=(c[h>>2]|0)+1;h=c[i>>2]|0}do if((h|0)>(g|0)){i=cW(b,e)|0;h=e+8+(i*20|0)+16|0;i=e+8+(i*20|0)|0;if(!(ZV(a,b,d,c[h>>2]|0,n,g)|0)){iW(o,b,i);c[i>>2]=c[o>>2];c[i+4>>2]=c[o+4>>2];c[i+8>>2]=c[o+8>>2];c[i+12>>2]=c[o+12>>2];j=0;break}else{bW(p,c[h>>2]|0);c[i>>2]=c[p>>2];c[i+4>>2]=c[p+4>>2];c[i+8>>2]=c[p+8>>2];c[i+12>>2]=c[p+12>>2];j=c[n>>2]|0;c[m+16>>2]=j;bW(k,j);c[m>>2]=c[k>>2];c[m+4>>2]=c[k+4>>2];c[m+8>>2]=c[k+8>>2];c[m+12>>2]=c[k+12>>2];j=a+1980|0;c[j>>2]=(c[j>>2]|0)+1;j=dW(a,m,e,f)|0;break}}else if((h|0)==(g|0)){c[m>>2]=c[b>>2];c[m+4>>2]=c[b+4>>2];c[m+8>>2]=c[b+8>>2];c[m+12>>2]=c[b+12>>2];c[m+16>>2]=d;j=a+1980|0;c[j>>2]=(c[j>>2]|0)+1;j=dW(a,m,e,f)|0;break}else qa(138712,138529,341,138667);while(0);l=q;return j|0}qa(138680,138529,312,138667);return 0}function _V(a){a=a|0;a=a+1968|0;c[a>>2]=(c[a>>2]|0)+1;a=T1(1288)|0;$V(a);return a|0}function $V(a){a=a|0;var b=0;c[a>>2]=0;c[a+4>>2]=-1;b=0;while(1){if((b|0)==64)break;aW(a+8+(b*20|0)|0);b=b+1|0}return}function aW(a){a=a|0;fW(a);c[a+16>>2]=0;return}function bW(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+32|0;g=i+16|0;h=i;if(!b)qa(138879,138718,99,138725);fW(g);d=1;f=0;while(1){if((f|0)==64)break;do if(c[b+8+(f*20|0)+16>>2]|0){e=b+8+(f*20|0)|0;if(!d){iW(h,g,e);c[g>>2]=c[h>>2];c[g+4>>2]=c[h+4>>2];c[g+8>>2]=c[h+8>>2];c[g+12>>2]=c[h+12>>2];d=0;break}else{c[g>>2]=c[e>>2];c[g+4>>2]=c[e+4>>2];c[g+8>>2]=c[e+8>>2];c[g+12>>2]=c[e+12>>2];d=0;break}}while(0);f=f+1|0}c[a>>2]=c[g>>2];c[a+4>>2]=c[g+4>>2];c[a+8>>2]=c[g+8>>2];c[a+12>>2]=c[g+12>>2];l=i;return}function cW(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;k=l;l=l+32|0;i=k+16|0;j=k;if(!((a|0)!=0&(b|0)!=0))qa(138735,138718,125,138742);d=0;e=0;f=0;g=1;h=0;while(1){if((h|0)==64)break;if(c[b+8+(h*20|0)+16>>2]|0){n=b+8+(h*20|0)|0;p=hW(n)|0;iW(j,a,n);c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];n=(hW(i)|0)-p|0;m=(g|0)!=0|(n|0)<(f|0);o=m|(p|0)<(e|0)&(n|0)==(f|0);d=o?h:d;e=o?p:e;f=o?n:f;g=m?0:g}h=h+1|0}l=k;return d|0}function dW(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;if(!b)qa(138901,138718,167,138753);if(!d)qa(138879,138718,168,138753);do if((c[d>>2]|0)<64){a=0;while(1){if((a|0)>=64){e=10;break}if(!(c[d+8+(a*20|0)+16>>2]|0)){e=8;break}a=a+1|0}if((e|0)==8){f=d+8+(a*20|0)|0;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];c[f+16>>2]=c[b+16>>2];f=0;g=d;break}else if((e|0)==10)qa(138763,138718,178,138753)}else{if(c[a+1912>>2]|0){h=c[a+1908>>2]|0?a+1948|0:a+1944|0;c[h>>2]=(c[h>>2]|0)+1}if(!e)qa(138776,138718,187,138753);else{kW(a,d,b,e);f=1;g=(c[d+4>>2]|0)==0?a+1972|0:a+1976|0;break}}while(0);c[g>>2]=(c[g>>2]|0)+1;return f|0}function eW(a,b){a=a|0;b=b|0;if(!((a|0)!=0&b>>>0<64))qa(138780,138718,201,138808);if(!(c[a+8+(b*20|0)+16>>2]|0))qa(139302,138718,202,138808);else{aW(a+8+(b*20|0)|0);c[a>>2]=(c[a>>2]|0)+-1;return}}function fW(a){a=a|0;var b=0;b=0;while(1){if((b|0)==4)break;c[a+(b<<2)>>2]=0;b=b+1|0}return}function gW(a){a=a|0;var b=0,d=0,e=0;e=l;l=l+16|0;d=e;c[d>>2]=1;c[d+8>>2]=-1;b=1;while(1){if((b|0)==2)break;c[d+(b+2<<2)>>2]=0;c[d+(b<<2)>>2]=0;b=b+1|0}c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];l=e;return}function hW(a){a=a|0;var b=0,d=0,e=0;if(!a)qa(138821,138823,128,138835);a:do if((c[a>>2]|0)>(c[a+8>>2]|0))b=0;else{b=1;d=0;while(1){if((d|0)==2)break a;e=O(c[a+(d+2<<2)>>2]|0,b)|0;b=e-(c[a+(d<<2)>>2]|0)|0;d=d+1|0}}while(0);return b|0}function iW(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0;g=l;l=l+16|0;f=g;if(!((b|0)!=0&(d|0)!=0))qa(138844,138823,195,138852);do if((c[b>>2]|0)>(c[b+8>>2]|0)){c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2]}else{if((c[d>>2]|0)>(c[d+8>>2]|0)){c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];break}else e=0;while(1){if((e|0)==2)break;j=c[b+(e<<2)>>2]|0;h=c[d+(e<<2)>>2]|0;c[f+(e<<2)>>2]=(j|0)<(h|0)?j:h;h=e+2|0;j=c[b+(h<<2)>>2]|0;i=c[d+(h<<2)>>2]|0;c[f+(h<<2)>>2]=(j|0)>(i|0)?j:i;e=e+1|0}c[a>>2]=c[f>>2];c[a+4>>2]=c[f+4>>2];c[a+8>>2]=c[f+8>>2];c[a+12>>2]=c[f+12>>2]}while(0);l=g;return}function jW(a,b){a=a|0;b=b|0;var d=0,e=0;if((a|0)!=0&(b|0)!=0)d=0;else qa(138864,138823,216,138871);while(1){if((d|0)>=2){d=1;break}e=d+2|0;if((c[a+(d<<2)>>2]|0)>(c[b+(e<<2)>>2]|0)){d=0;break}if((c[b+(d<<2)>>2]|0)>(c[a+(e<<2)>>2]|0)){d=0;break}else d=d+1|0}return d|0}function kW(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,h=0,i=0,j=0;if(!b)qa(138879,138881,40,138891);if(!d)qa(138901,138881,41,138891);j=a+1912|0;if(c[j>>2]|0){i=c[a+1908>>2]|0?a+1932|0:a+1928|0;c[i>>2]=(c[i>>2]|0)+1}h=b+4|0;i=c[h>>2]|0;lW(a,b,d);d=a+1324|0;mW(a);f=hW(a+1852|0)|0;f=(hW(a+1868|0)|0)+f|0;if(c[j>>2]|0?(f|0)!=0&(c[a+1908>>2]|0)==0:0){j=a+1960|0;g[j>>2]=+((c[a+1320>>2]|0)>>>0)/+(f|0)+ +g[j>>2]}j=_V(a)|0;c[e>>2]=j;c[h>>2]=i;c[j+4>>2]=i;nW(a,b,j,d);if(((c[c[e>>2]>>2]|0)+(c[b>>2]|0)|0)==65)return;else qa(138903,138881,86,138891)}function lW(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0;h=l;l=l+16|0;g=h;if(!b)qa(138879,138881,106,139290);if(!d)qa(138901,138881,107,139290);else e=0;while(1){if((e|0)>=64)break;if(!(c[b+8+(e*20|0)+16>>2]|0)){f=7;break}j=b+8+(e*20|0)|0;i=a+4+(e*20|0)|0;c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];c[i+16>>2]=c[j+16>>2];e=e+1|0}if((f|0)==7)qa(139302,138881,111,139290);e=a+4|0;j=a+1284|0;c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];c[j+16>>2]=c[d+16>>2];d=a+1304|0;c[d>>2]=c[e>>2];c[d+4>>2]=c[e+4>>2];c[d+8>>2]=c[e+8>>2];c[d+12>>2]=c[e+12>>2];e=1;while(1){if((e|0)==65)break;iW(g,d,a+4+(e*20|0)|0);c[d>>2]=c[g>>2];c[d+4>>2]=c[g+4>>2];c[d+8>>2]=c[g+8>>2];c[d+12>>2]=c[g+12>>2];e=e+1|0}c[a+1320>>2]=hW(d)|0;$V(b);l=h;return}function mW(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;u=l;l=l+48|0;i=u+32|0;k=u+16|0;m=u;oW(a);pW(a);q=a+1844|0;s=a+1848|0;t=a+1892|0;n=a+1852|0;o=a+1884|0;p=a+1868|0;j=a+1888|0;f=0;e=0;while(1){d=c[q>>2]|0;b=c[s>>2]|0;if((b+d|0)>=65)break;h=65-(c[t>>2]|0)|0;if((d|0)<(h|0)&(b|0)<(h|0)){g=-1;h=0}else{r=12;break}while(1){if((h|0)==65)break;if(!(c[a+1584+(h<<2)>>2]|0)){v=a+4+(h*20|0)|0;iW(k,v,n);c[i>>2]=c[k>>2];c[i+4>>2]=c[k+4>>2];c[i+8>>2]=c[k+8>>2];c[i+12>>2]=c[k+12>>2];b=hW(i)|0;d=c[o>>2]|0;iW(m,v,p);c[i>>2]=c[m>>2];c[i+4>>2]=c[m+4>>2];c[i+8>>2]=c[m+8>>2];c[i+12>>2]=c[m+12>>2];v=hW(i)|0;b=v-(c[j>>2]|0)+(d-b)|0;d=(b|0)>-1;b=d?b:0-b|0;d=(d^1)&1;if((b|0)<=(g|0))if((b|0)==(g|0)){v=(c[a+1844+(d<<2)>>2]|0)<(c[a+1844+(f<<2)>>2]|0);b=g;d=v?d:f;e=v?h:e}else{b=g;d=f}else e=h}else{b=g;d=f}g=b;h=h+1|0;f=d}qW(a,e,f)}if((r|0)==12){b=(d|0)>=(65-(c[t>>2]|0)|0)&1;d=0;while(1){if((d|0)==65)break;if(!(c[a+1584+(d<<2)>>2]|0))qW(a,d,b);d=d+1|0}d=c[q>>2]|0;b=c[s>>2]|0}if((b+d|0)!=65)qa(139046,138881,210,139132);v=c[t>>2]|0;if((d|0)<(v|0)|(b|0)<(v|0))qa(139143,138881,212,139132);else{l=u;return}}function nW(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;if(!b)qa(138879,138881,297,138943);if(!d)qa(138953,138881,298,138943);if(!e)qa(138955,138881,299,138943);else f=0;while(1){if((f|0)>=65){e=14;break}e=c[a+1324+(f<<2)>>2]|0;if(e>>>0>=2){e=9;break}switch(e|0){case 0:{dW(a,a+4+(f*20|0)|0,b,0)|0;break}case 1:{dW(a,a+4+(f*20|0)|0,d,0)|0;break}default:{}}f=f+1|0}if((e|0)==9)qa(138957,138881,303,138943);else if((e|0)==14)return}function oW(a){a=a|0;var b=0,d=0,e=0,f=0;d=l;l=l+16|0;e=d;c[a+1848>>2]=0;c[a+1844>>2]=0;b=a+1852|0;f=a+1868|0;gW(e);c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];c[f+8>>2]=c[e+8>>2];c[f+12>>2]=c[e+12>>2];c[b>>2]=c[e>>2];c[b+4>>2]=c[e+4>>2];c[b+8>>2]=c[e+8>>2];c[b+12>>2]=c[e+12>>2];c[a+1888>>2]=0;c[a+1884>>2]=0;b=0;while(1){if((b|0)==65)break;c[a+1584+(b<<2)>>2]=0;c[a+1324+(b<<2)>>2]=-1;b=b+1|0}l=d;return}function pW(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;o=l;l=l+304|0;k=o+32|0;m=o+16|0;n=o;b=0;while(1){if((b|0)==65)break;c[k+(b<<2)>>2]=hW(a+4+(b*20|0)|0)|0;b=b+1|0}b=0;d=0;e=0;g=0;a:while(1){if((g|0)==64)break;i=g+1|0;j=a+4+(g*20|0)|0;h=k+(g<<2)|0;f=i;while(1){if((f|0)==65){g=i;continue a}iW(n,j,a+4+(f*20|0)|0);c[m>>2]=c[n>>2];c[m+4>>2]=c[n+4>>2];c[m+8>>2]=c[n+8>>2];c[m+12>>2]=c[n+12>>2];p=hW(m)|0;p=p-(c[h>>2]|0)-(c[k+(f<<2)>>2]|0)|0;q=p>>>0>e>>>0;r=q?f:b;f=f+1|0;b=r;d=q?g:d;e=q?p:e}}qW(a,d,0);qW(a,b,1);l=o;return}function qW(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;e=a+1584+(b<<2)|0;if(c[e>>2]|0)qa(139246,138881,257,139281);c[a+1324+(b<<2)>>2]=d;c[e>>2]=1;g=a+1844+(d<<2)|0;f=a+1852+(d<<4)|0;e=a+4+(b*20|0)|0;if(!(c[g>>2]|0)){c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];c[f+8>>2]=c[e+8>>2];c[f+12>>2]=c[e+12>>2]}else{iW(h,e,f);c[f>>2]=c[h>>2];c[f+4>>2]=c[h+4>>2];c[f+8>>2]=c[h+8>>2];c[f+12>>2]=c[h+12>>2]}c[a+1884+(d<<2)>>2]=hW(f)|0;c[g>>2]=(c[g>>2]|0)+1;l=i;return}function rW(){var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=l;l=l+1200|0;r=t+800|0;g=t;c[47397]=0;c[47398]=-2;f=r;i=200;n=g;p=0;q=0;o=r;a:while(1){b[o>>1]=p;if((f+(i<<1)+-2|0)>>>0>o>>>0)k=i;else{m=o-f>>1;e=m+1|0;if(i>>>0>9999){s=95;break}k=i<<1;k=k>>>0<1e4?k:1e4;h=T1(k*6|3)|0;if(h){W6(h|0,f|0,e<<1|0)|0;i=h+(k>>>1<<2)|0;W6(i|0,n|0,e<<2|0)|0;if((f|0)==(r|0)){e=0;f=h}else{U1(f);e=0;f=h}}else{e=4;i=n}j=(e|0)==0;h=f+(m<<1)|0;switch((j?((f+(k<<1)+-2|0)>>>0>h>>>0?0:9):e)&15){case 9:{e=1;s=96;break a}case 4:{s=95;break a}case 0:{g=j?i+(m<<2)|0:g;n=i;o=j?h:o;break}default:{e=0;break a}}}if((p|0)==31){e=0;s=96;break}e=b[65534+(p<<1)>>1]|0;j=e<<16>>16;do if(e<<16>>16!=-82){h=c[47398]|0;if((h|0)==-2){h=GX()|0;c[47398]=h}if((h|0)>=1)if(h>>>0<296)i=d[139321+h>>0]|0;else i=2;else{c[47398]=0;i=0}h=i+j|0;if(h>>>0<=271?(i|0)==(a[139617+h>>0]|0):0){p=a[139889+h>>0]|0;e=p<<24>>24;if(p<<24>>24<1){h=0-e|0;s=23;break}else{c[47398]=-2;g=g+4|0;c[g>>2]=c[47399];j=(q|0)==0?0:q+-1|0;h=o;break}}else s=22}else s=22;while(0);if((s|0)==22){s=0;p=a[140161+p>>0]|0;h=p&255;if(!(p<<24>>24)){h=c[47398]|0;b:do switch(q|0){case 0:{c[47397]=(c[47397]|0)+1;MW(143761);h=o;break}case 3:if((h|0)<1)if(!h){e=1;s=96;break a}else{h=o;break b}else{c[47398]=-2;h=o;break b}default:h=o}while(0);while(1){if(e<<16>>16==8)break;if((h|0)==(f|0)){e=1;s=96;break a}e=h+-2|0;g=g+-4|0;h=e;e=b[65534+(b[e>>1]<<1)>>1]|0}g=g+4|0;c[g>>2]=c[47399];e=1;j=3}else s=23}do if((s|0)==23){s=0;j=d[140277+h>>0]|0;e=c[g+(1-j<<2)>>2]|0;do switch(h|0){case 4:{s=26;break a}case 2:{c[47400]=sW(c[g+-4>>2]|0,2)|0;break}case 3:{c[47400]=sW(c[g+-4>>2]|0,1)|0;break}case 5:{e=uW()|0;break}case 8:{vW(c[47401]|0);break}case 9:{wW(c[g>>2]|0);break}case 18:{xW(c[g>>2]|0);break}case 19:{yW();break}case 20:{xW(c[g>>2]|0);break}case 21:{yW();break}case 22:{xW(c[g>>2]|0);break}case 23:{yW();break}case 24:{xW(c[g>>2]|0);break}case 25:{yW();break}case 26:{xW(c[g>>2]|0);break}case 27:{yW();break}case 28:{xW(c[g>>2]|0);break}case 29:{yW();break}case 30:{xW(c[g>>2]|0);break}case 31:{yW();break}case 32:{xW(c[g>>2]|0);break}case 33:{yW();break}case 34:{e=c[g+-4>>2]|0;break}case 35:{e=c[g>>2]|0;break}case 38:{e=c[47401]|0;i=c[e+4>>2]|0;if(i>>>0>=(c[e+8>>2]|0)>>>0){lA(e,1)|0;i=c[47401]|0;e=i;i=c[i+4>>2]|0}a[i>>0]=0;p=c[e>>2]|0;c[e+4>>2]=p;if(zW(p)|0){s=51;break a}c[(c[g>>2]|0)+80>>2]=c[47402];e=Vz(56032,c[4580]|0)|0;c[(c[g>>2]|0)+84>>2]=e;c[47402]=c[g>>2];c[(c[g>>2]|0)+108>>2]=c[c[47403]>>2];e=c[g>>2]|0;break}case 39:{e=c[47401]|0;i=c[e+4>>2]|0;if(i>>>0>=(c[e+8>>2]|0)>>>0){lA(e,1)|0;i=c[47401]|0;e=i;i=c[i+4>>2]|0}a[i>>0]=0;p=c[e>>2]|0;c[e+4>>2]=p;if(zW(p)|0){s=56;break a}e=c[47402]|0;c[47402]=c[e+80>>2];break}case 40:{e=c[g>>2]|0;break}case 41:{e=c[g+-4>>2]|0;break}case 42:{e=c[g+-4>>2]|0;break}case 43:{e=c[g+-4>>2]|0;break}case 44:{e=c[g+-4>>2]|0;break}case 45:{e=c[g+-4>>2]|0;break}case 48:{e=c[g>>2]|0;break}case 49:{e=c[g>>2]|0;break}case 50:{a[(c[g+-8>>2]|0)+12>>0]=1;e=c[g>>2]|0;break}case 51:{AW();break}case 52:{e=BW()|0;break}case 53:{e=c[g>>2]|0;break}case 54:{e=c[g>>2]|0;break}case 55:{e=(c[g+-8>>2]|0)+100|0;a[e>>0]=a[e>>0]|1;e=c[g>>2]|0;break}case 56:{CW(c[g+-4>>2]|0,c[g>>2]|0,1);break}case 57:{e=c[g+-12>>2]|0;break}case 58:{CW(c[g+-4>>2]|0,c[g>>2]|0,2);break}case 59:{e=c[g+-12>>2]|0;break}case 60:{CW(c[g+-4>>2]|0,c[g>>2]|0,3);break}case 61:{e=c[g+-12>>2]|0;break}case 62:{CW(c[g>>2]|0,uW()|0,2);break}case 63:{e=c[g+-8>>2]|0;break}case 64:{e=c[g+-4>>2]|0;break}case 65:{e=c[g>>2]|0;break}default:{}}while(0);i=0-j|0;p=g+(i<<2)|0;i=o+(i<<1)|0;g=p+4|0;c[p+4>>2]=e;e=(d[140449+h>>0]|0)+-41|0;p=b[i>>1]|0;h=p+(b[65766+(e<<1)>>1]|0)|0;if(h>>>0<272?(a[139617+h>>0]|0)==(p|0):0){e=a[139889+h>>0]|0;j=q;h=i;break}e=a[140519+e>>0]|0;j=q;h=i}while(0);i=k;p=e;q=j;o=h+2|0}if((s|0)==26){tW();e=1;s=96}else if((s|0)==51){MW(140347);tW();e=1;s=96}else if((s|0)==56){MW(140398);tW();e=1;s=96}else if((s|0)==95){MW(140558);e=2;s=96}if((s|0)==96)if((f|0)!=(r|0))U1(f);l=t;return e|0}function sW(b,d){b=b|0;d=d|0;var e=0;e=JK(8)|0;a[e+4>>0]=d;c[e>>2]=b;return e|0}function tW(){var a=0,b=0;a=c[47402]|0;b=c[47400]|0;if(b|0){mU(b,1);c[47400]=0}c[14021]=67;while(1){if(!a)break;b=c[a+80>>2]|0;GW(a);a=b}c[14021]=56;c[14030]=68;b=c[47405]|0;nb[c[b>>2]&63](b,0,64)|0;c[14030]=56;c[14039]=69;b=c[47406]|0;nb[c[b>>2]&63](b,0,64)|0;c[14039]=56;JW();return}function uW(){var a=0,d=0,e=0,f=0,g=0,h=0;e=c[47406]|0;f=JK(40)|0;if(Xz(c[47405]|0)|0)wW(0);a=Xz(e)|0;b[f+4>>1]=a;a:do if(!a)a=e;else{c[f>>2]=JK(a*24|0)|0;a=0;d=nb[c[e>>2]&63](e,0,128)|0;while(1){if(!d){a=e;break a}g=(c[f>>2]|0)+(a*24|0)|0;h=d+8|0;c[g>>2]=c[h>>2];c[g+4>>2]=c[h+4>>2];c[g+8>>2]=c[h+8>>2];c[g+12>>2]=c[h+12>>2];c[g+16>>2]=c[h+16>>2];c[g+20>>2]=c[h+20>>2];a=a+1|0;d=nb[c[e>>2]&63](e,d,8)|0}}while(0);nb[c[a>>2]&63](e,0,64)|0;return f|0}function vW(b){b=b|0;var d=0,e=0,f=0;e=JK(64)|0;f=b+4|0;d=c[f>>2]|0;if(d>>>0>=(c[b+8>>2]|0)>>>0){lA(b,1)|0;d=c[f>>2]|0}a[d>>0]=0;b=c[b>>2]|0;c[f>>2]=b;c[e+8>>2]=D4(b)|0;c[e+12>>2]=c[c[47403]>>2];f=c[47405]|0;nb[c[f>>2]&63](f,e,1)|0;return}function wW(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;i=JK(32)|0;j=c[47405]|0;e=Xz(j)|0;k=i+8|0;a[i+14>>0]=d;a:do if(!e){c[k>>2]=JK(56)|0;b[i+12>>1]=1;h=D4(195059)|0;k=c[k>>2]|0;c[k>>2]=h;c[k+4>>2]=c[c[47403]>>2]}else{b[i+12>>1]=e;c[k>>2]=JK(e*56|0)|0;d=0;e=Tz(j)|0;while(1){if(!e)break a;f=(c[k>>2]|0)+(d*56|0)|0;g=e+8|0;h=f+56|0;do{c[f>>2]=c[g>>2];f=f+4|0;g=g+4|0}while((f|0)<(h|0));d=d+1|0;e=c[e>>2]|0}}while(0);nb[c[j>>2]&63](j,0,64)|0;k=c[47406]|0;nb[c[k>>2]&63](k,i,1)|0;return}function xW(a){a=a|0;var b=0,d=0.0,e=0,f=0,g=0,i=0,j=0;j=l;l=l+32|0;g=j;i=JK(8)|0;f=c[c[47403]>>2]|0;c[g>>2]=c[a>>2];c[g+4>>2]=c[a+4>>2];c[g+8>>2]=c[a+8>>2];c[g+12>>2]=c[a+12>>2];c[g+16>>2]=c[a+16>>2];c[g+20>>2]=c[a+20>>2];c[g+24>>2]=c[a+24>>2];c[g+28>>2]=c[a+28>>2];if(f|0){a=g+4|0;if((c[a>>2]|0)==0?(b=c[f+4>>2]|0,b|0):0)c[a>>2]=b;a=g+16|0;if(+h[a>>3]<0.0?(d=+h[f+16>>3],d>=0.0):0)h[a>>3]=d;if((c[g>>2]|0)==0?(e=c[f>>2]|0,e|0):0)c[g>>2]=e;a=c[f+24>>2]&127;if(a|0){f=g+24|0;e=c[f>>2]|0;c[f>>2]=e&127|a|e&-128}}f=c[(c[47404]|0)+144>>2]|0;c[i>>2]=nb[c[f>>2]&63](f,g,1)|0;c[i+4>>2]=c[47403];c[47403]=i;l=j;return}function yW(){var a=0,b=0;b=c[47403]|0;a=c[b+4>>2]|0;U1(b);c[47403]=a;return}function zW(b){b=b|0;var c=0;while(1){c=a[b>>0]|0;if(!(c<<24>>24)){b=0;break}if(c<<24>>24==32)b=b+1|0;else{b=1;break}}return b|0}function AW(){var b=0,d=0,e=0;e=Vz(56068,c[4580]|0)|0;b=c[47402]|0;d=JK(16)|0;c[d+8>>2]=e;if(a[b+112>>0]&2)a[d+12>>0]=1;e=c[b+84>>2]|0;nb[c[e>>2]&63](e,d,1)|0;return}function BW(){var a=0;a=c[(c[47402]|0)+84>>2]|0;return nb[c[a>>2]&63](a,0,256)|0}function CW(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;g=JK(16)|0;f=c[47402]|0;h=c[f+84>>2]|0;h=c[(nb[c[h>>2]&63](h,0,256)|0)+8>>2]|0;c[g+8>>2]=b;nb[c[h>>2]&63](h,g,1)|0;a[b+92>>0]=e;if(a[f+112>>0]&1)a[b+100>>0]=1;c[b+88>>2]=d;return}function DW(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function EW(a,b,d){a=a|0;b=b|0;d=d|0;Rz(c[b+8>>2]|0)|0;U1(b);return}function FW(a,b,d){a=a|0;b=b|0;d=d|0;KW(c[b+8>>2]|0);U1(b);return}function GW(a){a=a|0;Rz(c[a+84>>2]|0)|0;jU(a);U1(a);return}function HW(a,b,d){a=a|0;b=b|0;d=d|0;U1(c[b+8>>2]|0);U1(b);return}function IW(a,d,e){a=a|0;d=d|0;e=e|0;var f=0,g=0,h=0;g=d+12|0;a=b[g>>1]|0;if(a<<16>>16){h=d+8|0;e=0;f=c[h>>2]|0;while(1){if((e|0)>=(a<<16>>16|0))break;U1(c[f>>2]|0);e=e+1|0;f=f+56|0;a=b[g>>1]|0}U1(c[h>>2]|0)}U1(d);return}function JW(){var a=0,b=0;a=c[47403]|0;while(1){b=c[a+4>>2]|0;if(!b)break;U1(a);a=b}return}function KW(b){b=b|0;var d=0;d=b+88|0;switch(a[b+92>>0]|0){case 1:{GW(c[d>>2]|0);break}case 2:{kU(c[d>>2]|0);break}default:{}}jU(b);U1(b);return}function LW(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;f=l;l=l+160|0;e=f+8|0;g=f;c[g>>2]=0;c[g+4>>2]=0;c[47403]=g;c[47402]=0;c[47400]=0;c[47404]=c[(c[(c[d+52>>2]|0)+16>>2]|0)+144>>2];c[47405]=Vz(56104,c[4580]|0)|0;c[47406]=Vz(56140,c[4580]|0)|0;kA(e,128,f+24|0);c[47401]=e;if(!(PW(a,e,d)|0)){rW()|0;c[b>>2]=FX()|0;a=c[47400]|0}else{c[b>>2]=2;a=0}Rz(c[47405]|0)|0;Rz(c[47406]|0)|0;c[47405]=0;c[47406]=0;c[47403]=0;oA(e);l=f;return a|0}function MW(b){b=b|0;var d=0,e=0,f=0;e=l;l=l+16|0;d=e;if(!(a[189661]|0)){a[189661]=1;f=NW()|0;c[d>>2]=b;c[d+4>>2]=f;gA(1,140575,d)|0;OW()}l=e;return}function NW(){return u_(c[47407]|0)|0}function OW(){var b=0,d=0,e=0,f=0;f=l;l=l+16|0;e=f;b=c[47410]|0;c[b+4>>2]=c[b>>2];d=c[47419]|0;if((d|0)>0){mA(b,c[47417]|0,d)|0;b=c[47410]|0}mA(b,c[47416]|0,c[47418]|0)|0;d=c[47410]|0;b=c[d+4>>2]|0;if(b>>>0>=(c[d+8>>2]|0)>>>0){lA(d,1)|0;b=c[47410]|0;d=b;b=c[b+4>>2]|0}c[d+4>>2]=b+1;a[b>>0]=0;b=c[47410]|0;d=c[b>>2]|0;c[b+4>>2]=d;c[e>>2]=d;gA(3,140591,e)|0;l=f;return}function PW(b,e,f){b=b|0;e=e|0;f=f|0;c[47410]=e;kA(189644,128,0);c[47408]=b;a[189663]=0;a[189660]=0;a[189661]=0;c[47418]=0;c[47419]=0;a[189662]=1;f=f+52|0;e=UY(wK(d[(c[(c[f>>2]|0)+16>>2]|0)+115>>0]|0)|0)|0;c[47407]=e;h_(e,c[(c[(c[f>>2]|0)+16>>2]|0)+144>>2]|0);i_(c[47407]|0,70,27);j_(c[47407]|0,71);return 0}function QW(b,d,e){b=b|0;d=d|0;e=e|0;do if(!(c3(d,140603)|0)){c[47399]=UW(e)|0;a[189662]=0;c[47409]=286}else{if(c3(d,140609)|0?c3(d,140612)|0:0){if(!(c3(d,140615)|0)){a[189662]=1;c[47399]=VW(e)|0;c[47409]=287;break}if(!(c3(d,140623)|0)){c[47399]=WW(b,e,0)|0;c[47409]=288;break}if(!(c3(d,140628)|0)){c[47399]=WW(b,0,1)|0;c[47409]=290;break}if(!(c3(d,140644)|0)){c[47399]=WW(b,0,32)|0;c[47409]=295;break}if(!(c3(d,140630)|0)){c[47399]=WW(b,0,4)|0;c[47409]=291;break}if(!(c3(d,140632)|0)){c[47399]=WW(b,0,64)|0;c[47409]=292;break}if(!(c3(d,140634)|0)){c[47399]=WW(b,0,2)|0;c[47409]=289;break}if(!(c3(d,140636)|0)){c[47399]=WW(b,0,8)|0;c[47409]=293;break}if(!(c3(d,140640)|0)){c[47399]=WW(b,0,16)|0;c[47409]=294;break}if(!(c3(d,140646)|0)){XW(e);c[47409]=283;break}if(!(c3(d,140649)|0)){c[47409]=277;break}if(!(c3(d,140652)|0)){c[47409]=280;break}if(!(c3(d,140655)|0)){c[47399]=YW(e)|0;c[47409]=285;break}if(!(c3(d,140618)|0)){c[47409]=262;break}else{TW(d);break}}a[189662]=0;c[47409]=260}while(0);return}function RW(b,d){b=b|0;d=d|0;do if(!(c3(d,140603)|0)){c[47409]=264;a[189662]=1}else{if(c3(d,140609)|0?c3(d,140612)|0:0){if(!(c3(d,140615)|0)){c[47409]=265;a[189662]=0;break}if(!(c3(d,140618)|0)){c[47409]=263;break}if(!(c3(d,140623)|0)){c[47409]=266;break}if(!(c3(d,140628)|0)){c[47409]=270;break}if(!(c3(d,140630)|0)){c[47409]=271;break}if(!(c3(d,140632)|0)){c[47409]=272;break}if(!(c3(d,140634)|0)){c[47409]=269;break}if(!(c3(d,140636)|0)){c[47409]=273;break}if(!(c3(d,140640)|0)){c[47409]=274;break}if(!(c3(d,140644)|0)){c[47409]=275;break}if(!(c3(d,140646)|0)){c[47409]=(c[47409]|0)==283?282:258;break}if(!(c3(d,140649)|0)){c[47409]=(c[47409]|0)==277?276:278;break}if(!(c3(d,140652)|0)){c[47409]=(c[47409]|0)==280?279:281;break}if(!(c3(d,140655)|0)){c[47409]=(c[47409]|0)==285?284:259;break}else{TW(d);break}}c[47409]=261}while(0);return}function SW(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;if(a[189662]|0){b=0;while(1){if(!e)break;h=d+1|0;g=a[d>>0]|0;if((g&255)>31){d=c[47410]|0;f=c[d+4>>2]|0;if(f>>>0>=(c[d+8>>2]|0)>>>0){lA(d,1)|0;f=c[47410]|0;d=f;f=c[f+4>>2]|0}c[d+4>>2]=f+1;a[f>>0]=g;b=b+1|0}d=h;e=e+-1|0}if(b|0)c[47409]=267}return}function TW(b){b=b|0;var d=0,e=0,f=0;d=l;l=l+16|0;e=d;c[47409]=268;a[189661]=1;f=NW()|0;c[e>>2]=b;c[e+4>>2]=f;gA(1,140659,e)|0;l=d;return}function UW(b){b=b|0;var d=0;d=JK(120)|0;c[d+100>>2]=-1;a[d+88>>0]=-1;ZW(d,56400,22,b,141611);return d|0}function VW(a){a=a|0;var c=0;c=JK(104)|0;b[c+80>>1]=1;b[c+82>>1]=1;ZW(c,56224,22,a,140965);return c|0}function WW(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;f=l;l=l+32|0;e=f;c[e>>2]=0;c[e+4>>2]=0;c[e+8>>2]=0;c[e+12>>2]=0;c[e+16>>2]=0;c[e+20>>2]=0;c[e+24>>2]=0;c[e+28>>2]=0;h[e+16>>3]=-1.0;c[e+24>>2]=d&127;if(b|0)ZW(e,56200,3,b,140816);d=c[a+144>>2]|0;e=nb[c[d>>2]&63](d,e,1)|0;l=f;return e|0}function XW(a){a=a|0;c[47399]=0;ZW(189596,56192,1,a,140752);return}function YW(a){a=a|0;var b=0;b=JK(40)|0;ZW(b,56176,2,a,140698);return b|0}function ZW(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;n=o;m=o+8|0;while(1){k=c[g>>2]|0;if(!k)break;i=c[g+4>>2]|0;c[m>>2]=k;j=u5(m,e,f,8,104)|0;if(!j){c[n>>2]=k;c[n+4>>2]=h;gA(0,140704,n)|0;i=1}else{i=lb[c[j+4>>2]&127](b,i)|0;i=(i|(d[189660]|0))&255}a[189660]=i;g=g+8|0}l=o;return}function _W(a,b){a=a|0;b=b|0;return c3(c[a>>2]|0,c[b>>2]|0)|0}function $W(a,b){a=a|0;b=b|0;c[a+36>>2]=D4(b)|0;return 0}function aX(a,b){a=a|0;b=b|0;c[a+32>>2]=D4(b)|0;return 0}function bX(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;g=i;f=(Y3(a[d>>0]|0)|0)<<24>>24;switch(f|0){case 82:{e=d+1|0;if(!(c3(e,140763)|0)){c[b>>2]=114;e=0}else h=5;break}case 76:{h=6;break}default:{e=d+1|0;h=5}}do if((h|0)==5)if(!(c3(e,140768)|0))h=6;else{if((f|0)!=67?(c3(e,140772)|0)==0:0){c[g>>2]=d;gA(0,140778,g)|0;e=1;break}c[b>>2]=110;e=0}while(0);if((h|0)==6){c[b>>2]=108;e=0}l=i;return e|0}function cX(a,b){a=a|0;b=b|0;c[a+4>>2]=b;return 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tX(a,b){a=a|0;b=b|0;c[a+4>>2]=D4(b)|0;return 0}function uX(a,d){a=a|0;d=d|0;var e=0,f=0,g=0;g=l;l=l+16|0;f=g;e=g+4|0;do if(!(fX(d,141325,0,65535,e)|0)){d=c[e>>2]|0;if(!d){gA(0,141333,f)|0;d=1;break}else{b[a+82>>1]=d;d=0;break}}else d=1;while(0);l=g;return d|0}function vX(d,e){d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;i=k;h=0;a:while(1){b:while(1){g=e+1|0;e=a[e>>0]|0;if(!(e<<24>>24))break a;e=e<<24>>24;f=(d3(e)|0)+-98|0;switch(f>>>1|f<<31|0){case 5:{j=5;break b}case 9:{f=2048;break b}case 8:{j=6;break b}case 0:{j=7;break b}default:{}}c[i>>2]=e;c[i+4>>2]=e;gA(0,141272,i)|0;e=g}if((j|0)==5){j=0;f=1024}else if((j|0)==6){j=0;f=4096}else if((j|0)==7){j=0;f=8192}e=g;h=h|f}if(h<<16>>16!=15360){j=d+36|0;b[j>>1]=b[j>>1]|h}l=k;return 0}function wX(d,e){d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;j=k+8|0;i=k;h=D4(e)|0;g=d+42|0;f=0;e=h;while(1){d=A4(e,141182)|0;if(!d)break;if(((Y3(a[d>>0]|0)|0)&255|0)==82){e=d+1|0;if(!(c3(e,141185)|0)){b[g>>1]=b[g>>1]|4;d=f;e=0;f=d;continue}if(!(c3(e,141192)|0)){b[g>>1]=b[g>>1]|2;d=f;e=0;f=d;continue}else{c[i>>2]=d;gA(0,141198,i)|0;f=1;e=0;continue}}if(!(c3(d,141236)|0)){b[g>>1]=b[g>>1]&-385;d=f;e=0;f=d;continue}if(c3(d,141242)|0?c3(d,141252)|0:0){if(!(c3(d,141258)|0)){b[g>>1]=b[g>>1]|128;d=f;e=0;f=d;continue}if(!(c3(d,141265)|0)){b[g>>1]=b[g>>1]|256;d=f;e=0;f=d;continue}else{c[j>>2]=d;gA(0,141198,j)|0;f=1;e=0;continue}}b[g>>1]=b[g>>1]|32;d=f;e=0;f=d}U1(h);l=k;return f|0}function xX(a,b){a=a|0;b=b|0;c[a+8>>2]=D4(b)|0;return 0}function yX(a,b){a=a|0;b=b|0;c[a+12>>2]=D4(b)|0;return 0}function zX(d,e){d=d|0;e=e|0;var f=0,g=0,h=0;h=l;l=l+16|0;f=h;switch((Y3(a[e>>0]|0)|0)<<24>>24|0){case 66:{if(!(c3(e+1|0,141128)|0)){d=d+36|0;b[d>>1]=b[d>>1]|16;d=0}else g=7;break}case 84:{if(!(c3(e+1|0,141134)|0)){d=d+36|0;b[d>>1]=b[d>>1]|8;d=0}else g=7;break}case 77:{if(!(c3(e+1|0,141137)|0))d=0;else g=7;break}default:g=7}if((g|0)==7){c[f>>2]=e;gA(0,141143,f)|0;d=1}l=h;return d|0}function AX(a,d){a=a|0;d=d|0;var e=0,f=0;f=l;l=l+16|0;e=f;if(!(fX(d,141122,0,65535,e)|0)){b[a+38>>1]=c[e>>2];a=0}else a=1;l=f;return a|0}function BX(d,e){d=d|0;e=e|0;var f=0,g=0,h=0;h=l;l=l+16|0;f=h;switch((Y3(a[e>>0]|0)|0)<<24>>24|0){case 76:{if(!(c3(e+1|0,140768)|0)){d=d+36|0;b[d>>1]=b[d>>1]|4;d=0}else g=7;break}case 82:{if(!(c3(e+1|0,140763)|0)){d=d+36|0;b[d>>1]=b[d>>1]|2;d=0}else g=7;break}case 67:{if(!(c3(e+1|0,140772)|0))d=0;else g=7;break}default:g=7}if((g|0)==7){c[f>>2]=e;gA(0,140778,f)|0;d=1}l=h;return d|0}function CX(b,d){b=b|0;d=d|0;var e=0,f=0;f=l;l=l+16|0;e=f;if(!(fX(d,141720,0,127,e)|0)){a[b+88>>0]=c[e>>2];b=0}else b=1;l=f;return b|0}function DX(b,d){b=b|0;d=d|0;var e=0,f=0;f=l;l=l+16|0;e=f;if((a[d>>0]|0)==42){b=b+112|0;a[b>>0]=a[b>>0]|1;b=0}else{c[e>>2]=d;gA(0,141680,e)|0;b=1}l=f;return b|0}function EX(b,d){b=b|0;d=d|0;var e=0,f=0;f=l;l=l+16|0;e=f;if((a[d>>0]|0)==42){b=b+112|0;a[b>>0]=a[b>>0]|2;b=0}else{c[e>>2]=d;gA(0,141643,e)|0;b=1}l=f;return b|0}function FX(){var b=0;b=(a[189661]|a[189660])<<24>>24;_Y(c[47407]|0);oA(189644);return b|0}function GX(){var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+16|0;i=j;c[47409]=0;d=0;a:do{b:do switch(a[189663]|0){case 2:{b=-1;break a}case 0:{a[189663]=1;h=6;b=141732;d=0;break}default:{b=c[47408]|0;if(!(a[b>>0]|0)){a[189663]=2;h=7;b=141739;break b}else{d=HX(b)|0;h=d-b|0;break b}}}while(0);c[47417]=c[47416];c[47419]=c[47418];c[47416]=b;c[47418]=h;e=c[47412]|0;f=e-(c[47411]|0)|0;g=c[47407]|0;if(!f)b=k_(g,b,h,(h|0)==0&1)|0;else{if(e>>>0<(c[47413]|0)>>>0)b=e;else{lA(189644,1)|0;b=c[47412]|0}a[b>>0]=0;b=c[47411]|0;c[47412]=b;b=k_(g,b,f,0)|0}if(!((b|0)!=0|(a[189661]|0)!=0)){g=v_(t_(c[47407]|0)|0)|0;h=NW()|0;c[i>>2]=g;c[i+4>>2]=h;gA(1,140575,i)|0;OW();a[189661]=1;c[47409]=268}if(d|0)c[47408]=d;b=c[47409]|0}while((b|0)==0);l=j;return b|0}function HX(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;f=h;d=b+1|0;e=a[b>>0]|0;a:do if(e<<24>>24==60){e=a[d>>0]|0;if(e<<24>>24==33)if(!(Q2(b+2|0,141747,2)|0)){e=IX(b+4|0)|0;d=e;e=a[e>>0]|0;g=8}else{e=d;g=5}else g=6;b:while(1)if((g|0)==5){d=e;e=a[e>>0]|0;g=6;continue}else if((g|0)==6){switch(e<<24>>24){case 62:case 0:{g=8;continue b}default:{}}e=d+1|0;g=5;continue}else if((g|0)==8)if(e<<24>>24==62){g=10;break}else{g=9;break}if((g|0)==9){gA(0,141750,f)|0;a[189660]=1;break}else if((g|0)==10){d=d+1|0;break}}else{d=b;b=e;while(1){switch(b<<24>>24){case 60:case 0:break a;case 38:{e=d+1|0;if((a[e>>0]|0)==35)g=15;else e=$P(e,189644)|0;break}default:g=15}if((g|0)==15){g=0;e=c[47412]|0;if(e>>>0>=(c[47413]|0)>>>0){lA(189644,1)|0;e=c[47412]|0}c[47412]=e+1;a[e>>0]=b;e=d+1|0}d=e;b=a[e>>0]|0}}while(0);l=h;return d|0}function IX(b){b=b|0;var c=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+16|0;i=j;f=1;c=b;a:while(1){e=(f|0)==0;d=c;b:while(1){if(e)break a;g=d+1|0;switch(a[d>>0]|0){case 0:{c=g;break a}case 60:{h=5;break b}case 62:{c=-1;break b}default:d=g}}if((h|0)==5){h=0;c=1}f=f+c|0;c=g}d=c+-1|0;do if(a[d>>0]|0){c=c+-3|0;if(c>>>0>=b>>>0?(Q2(c,141747,2)|0)==0:0)break;gA(0,141791,i)|0;a[189660]=1}while(0);l=j;return d|0}function JX(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;d=T1(28)|0;if(!d)d=0;else{e=0;f=0;while(1){if((e|0)>=(b|0))break;r=(c[(c[a+(e<<2)>>2]|0)+4>>2]|0)+f|0;e=e+1|0;f=r}o=d+8|0;c[o>>2]=KX(f<<4)|0;p=d+12|0;c[p>>2]=KX((b<<2)+4|0)|0;e=f<<2;q=d+16|0;c[q>>2]=KX(e)|0;e=KX(e)|0;r=d+20|0;c[r>>2]=e;c[d+4>>2]=f;c[d>>2]=b;f=0;h=0;while(1){c[(c[p>>2]|0)+(h<<2)>>2]=f;if((h|0)>=(b|0))break;j=a+(h<<2)|0;n=c[j>>2]|0;k=c[n+4>>2]|0;l=f+-1|0;g=0;i=f;m=k;while(1){if((g|0)>=(m|0))break;m=(c[o>>2]|0)+(i<<4)|0;e=(c[n>>2]|0)+(g<<4)|0;c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];m=i+1|0;c[(c[q>>2]|0)+(i<<2)>>2]=m;e=c[r>>2]|0;c[e+(i<<2)>>2]=i+-1;s=c[j>>2]|0;g=g+1|0;i=m;m=c[s+4>>2]|0;n=s}s=l+k|0;c[(c[q>>2]|0)+(s<<2)>>2]=f;c[e+(f<<2)>>2]=s;f=i;h=h+1|0}KY(d)}return d|0}function KX(a){a=a|0;if(!a)a=0;else a=T1(a)|0;return a|0}function LX(a){a=a|0;var b=0,d=0;U1(c[a+8>>2]|0);U1(c[a+12>>2]|0);U1(c[a+16>>2]|0);U1(c[a+20>>2]|0);d=a+24|0;b=c[d>>2]|0;if(b|0){U1(c[b>>2]|0);U1(c[d>>2]|0)}U1(a);return}function MX(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+32|0;m=p+16|0;j=p;c[m>>2]=c[b>>2];c[m+4>>2]=c[b+4>>2];c[m+8>>2]=c[b+8>>2];c[m+12>>2]=c[b+12>>2];n=RY(a,d,m)|0;c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];o=RY(a,f,m)|0;c[j>>2]=c[b>>2];c[j+4>>2]=c[b+4>>2];c[j+8>>2]=c[b+8>>2];c[j+12>>2]=c[b+12>>2];c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];m=AY(j,d,n,m,f,o,a)|0;d=c[a+4>>2]|0;j=d+1|0;f=1;h=d;while(1){h=c[m+(h<<2)>>2]|0;k=f+1|0;if((h|0)==(j|0))break;else f=k}i=T1(k<<4)|0;h=i+(f<<4)|0;c[h>>2]=c[e>>2];c[h+4>>2]=c[e+4>>2];c[h+8>>2]=c[e+8>>2];c[h+12>>2]=c[e+12>>2];a=a+8|0;while(1){d=c[m+(d<<2)>>2]|0;f=f+-1|0;h=i+(f<<4)|0;if((d|0)==(j|0))break;e=(c[a>>2]|0)+(d<<4)|0;c[h>>2]=c[e>>2];c[h+4>>2]=c[e+4>>2];c[h+8>>2]=c[e+8>>2];c[h+12>>2]=c[e+12>>2]}c[h>>2]=c[b>>2];c[h+4>>2]=c[b+4>>2];c[h+8>>2]=c[b+8>>2];c[h+12>>2]=c[b+12>>2];if(f|0)qa(141809,141816,148,141822);if(n|0)U1(n);if(o|0)U1(o);c[g+4>>2]=k;c[g>>2]=i;U1(m);l=p;return 1}function NX(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0;j=l;l=l+48|0;h=j+32|0;g=j+16|0;f=j;i=c[a>>2]|0;d=c[a+4>>2]|0;e=d+-1|0;a=0;while(1){if((a|0)>=(d|0)){a=1;break}m=i+(((e+a|0)%(d|0)|0)<<4)|0;k=i+(a<<4)|0;c[f>>2]=c[m>>2];c[f+4>>2]=c[m+4>>2];c[f+8>>2]=c[m+8>>2];c[f+12>>2]=c[m+12>>2];c[g>>2]=c[k>>2];c[g+4>>2]=c[k+4>>2];c[g+8>>2]=c[k+8>>2];c[g+12>>2]=c[k+12>>2];c[h>>2]=c[b>>2];c[h+4>>2]=c[b+4>>2];c[h+8>>2]=c[b+8>>2];c[h+12>>2]=c[b+12>>2];if((GY(f,g,h)|0)==1){a=0;break}else a=a+1|0}l=j;return a|0}function OX(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+64|0;m=u+48|0;k=u+32|0;s=4;j=T1(40)|0;c[j>>2]=0;n=u+16|0;q=u;r=c[d>>2]|0;i=c[d+4>>2]|0;s=U6(189680,1,j|0,s|0)|0;j=z;o=0;d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,s|0,j|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1)d=z;else d=0;while(1){if(d|0){d=-1;break}o=0;c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];ja(28,n|0,m|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,s|0,j|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){d=z;continue};c[e>>2]=c[n>>2];c[e+4>>2]=c[n+4>>2];c[e+8>>2]=c[n+8>>2];c[e+12>>2]=c[n+12>>2];h=e+16|0;o=0;c[m>>2]=c[h>>2];c[m+4>>2]=c[h+4>>2];c[m+8>>2]=c[h+8>>2];c[m+12>>2]=c[h+12>>2];ja(28,q|0,m|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,s|0,j|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){d=z;continue};c[h>>2]=c[q>>2];c[h+4>>2]=c[q+4>>2];c[h+8>>2]=c[q+8>>2];c[h+12>>2]=c[q+12>>2];c[47459]=0;o=0;ia(115,4);d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,s|0,j|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){d=z;continue}g=c[47460]|0;d=c[47459]|0;c[47459]=d+1;d=g+(d<<4)|0;c[d>>2]=c[r>>2];c[d+4>>2]=c[r+4>>2];c[d+8>>2]=c[r+8>>2];c[d+12>>2]=c[r+12>>2];o=0;c[k>>2]=c[e>>2];c[k+4>>2]=c[e+4>>2];c[k+8>>2]=c[e+8>>2];c[k+12>>2]=c[e+12>>2];c[m>>2]=c[h>>2];c[m+4>>2]=c[h+4>>2];c[m+8>>2]=c[h+8>>2];c[m+12>>2]=c[h+12>>2];d=fa(1,a|0,b|0,r|0,i|0,k|0,m|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,s|0,j|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1)d=z;else{t=8;break}}if((t|0)==8)if((d|0)==-1)d=-1;else{c[f+4>>2]=c[47459];c[f>>2]=c[47460];d=0}U1(s|0);l=u;return d|0}function PX(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0.0,g=0.0,i=0.0;d=+h[b>>3];e=b+8|0;f=+h[e>>3];i=f*f+d*d;g=+C(+i);if(i>1.0e-06){h[b>>3]=d/g;h[e>>3]=f/g};c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];return}function QX(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+32|0;e=f+16|0;d=f;if((c[47463]|0)<(a|0)){b=c[47460]|0;if(!b){e=T1(a<<4)|0;c[47460]=e;if(!e){e=c[15712]|0;c[d>>2]=141831;c[d+4>>2]=531;c[d+8>>2]=141966;w4(e,141839,d)|0;bb(189680,1)}}else{d=W1(b,a<<4)|0;c[47460]=d;if(!d){d=c[15712]|0;c[e>>2]=141831;c[e+4>>2]=537;c[e+8>>2]=141984;w4(d,141839,e)|0;bb(189680,1)}}c[47463]=a}l=f;return}function RX(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0.0,k=0,m=0.0,n=0,o=0.0,p=0.0,q=0,r=0.0,s=0.0,t=0.0,u=0.0,v=0.0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0.0,U=0.0,V=0.0,W=0.0;R=l;l=l+336|0;J=R+320|0;I=R+304|0;w=R+144|0;q=R+128|0;G=R+112|0;H=R+96|0;Q=R+80|0;A=R+64|0;C=R+48|0;K=R+32|0;L=R+16|0;M=R;x=R+288|0;y=R+272|0;z=R+256|0;B=R+240|0;D=R+224|0;E=R+208|0;N=R+192|0;O=R+176|0;P=R+160|0;i=c[47462]|0;do if((c[47461]|0)<(e|0)){if(!i){i=T1(e*40|0)|0;c[47462]=i;if(!i){i=-1;break}}else{i=W1(i,e*40|0)|0;c[47462]=i;if(!i){i=-1;break}}c[47461]=e;k=6}else k=6;while(0);if((k|0)==6){h[i>>3]=0.0;k=1;j=0.0;while(1){if((k|0)>=(e|0))break;n=d+(k<<4)|0;F=d+(k+-1<<4)|0;c[I>>2]=c[n>>2];c[I+4>>2]=c[n+4>>2];c[I+8>>2]=c[n+8>>2];c[I+12>>2]=c[n+12>>2];c[J>>2]=c[F>>2];c[J+4>>2]=c[F+4>>2];c[J+8>>2]=c[F+8>>2];c[J+12>>2]=c[F+12>>2];v=+SX(I,J)+j;h[i+(k*40|0)>>3]=v;k=k+1|0;j=v}F=e+-1|0;n=i+(F*40|0)|0;k=1;while(1){if((k|0)>=(e|0))break;S=i+(k*40|0)|0;h[S>>3]=+h[S>>3]/+h[n>>3];k=k+1|0}k=0;while(1){if((k|0)>=(e|0))break;S=i+(k*40|0)+8|0;n=i+(k*40|0)|0;v=+TX(+h[n>>3]);c[J>>2]=c[f>>2];c[J+4>>2]=c[f+4>>2];c[J+8>>2]=c[f+8>>2];c[J+12>>2]=c[f+12>>2];UX(x,J,v);c[S>>2]=c[x>>2];c[S+4>>2]=c[x+4>>2];c[S+8>>2]=c[x+8>>2];c[S+12>>2]=c[x+12>>2];S=i+(k*40|0)+24|0;v=+VX(+h[n>>3]);c[J>>2]=c[g>>2];c[J+4>>2]=c[g+4>>2];c[J+8>>2]=c[g+8>>2];c[J+12>>2]=c[g+12>>2];UX(y,J,v);c[S>>2]=c[y>>2];c[S+4>>2]=c[y+4>>2];c[S+8>>2]=c[y+8>>2];c[S+12>>2]=c[y+12>>2];k=k+1|0}c[I>>2]=c[f>>2];c[I+4>>2]=c[f+4>>2];c[I+8>>2]=c[f+8>>2];c[I+12>>2]=c[f+12>>2];c[J>>2]=c[g>>2];c[J+4>>2]=c[g+4>>2];c[J+8>>2]=c[g+8>>2];c[J+12>>2]=c[g+12>>2];WX(d,e,i,I,J,G,A,H,C);c[q>>2]=c[G>>2];c[q+4>>2]=c[G+4>>2];c[q+8>>2]=c[G+8>>2];c[q+12>>2]=c[G+12>>2];c[w>>2]=c[A>>2];c[w+4>>2]=c[A+4>>2];c[w+8>>2]=c[A+8>>2];c[w+12>>2]=c[A+12>>2];c[I>>2]=c[H>>2];c[I+4>>2]=c[H+4>>2];c[I+8>>2]=c[H+8>>2];c[I+12>>2]=c[H+12>>2];c[J>>2]=c[C>>2];c[J+4>>2]=c[C+4>>2];c[J+8>>2]=c[C+8>>2];c[J+12>>2]=c[C+12>>2];if(!(XX(a,b,q,w,I,J,d,e)|0)){c[J>>2]=c[A>>2];c[J+4>>2]=c[A+4>>2];c[J+8>>2]=c[A+8>>2];c[J+12>>2]=c[A+12>>2];UX(z,J,.3333333333333333);c[I>>2]=c[G>>2];c[I+4>>2]=c[G+4>>2];c[I+8>>2]=c[G+8>>2];c[I+12>>2]=c[G+12>>2];c[J>>2]=c[z>>2];c[J+4>>2]=c[z+4>>2];c[J+8>>2]=c[z+8>>2];c[J+12>>2]=c[z+12>>2];YX(B,I,J);v=+h[B>>3];u=+h[B+8>>3];c[J>>2]=c[C>>2];c[J+4>>2]=c[C+4>>2];c[J+8>>2]=c[C+8>>2];c[J+12>>2]=c[C+12>>2];UX(D,J,.3333333333333333);c[I>>2]=c[H>>2];c[I+4>>2]=c[H+4>>2];c[I+8>>2]=c[H+8>>2];c[I+12>>2]=c[H+12>>2];c[J>>2]=c[D>>2];c[J+4>>2]=c[D+4>>2];c[J+8>>2]=c[D+8>>2];c[J+12>>2]=c[D+12>>2];ZX(E,I,J);t=+h[E>>3];p=+h[E+8>>3];q=c[47462]|0;r=+h[G>>3];s=+h[H>>3];o=+h[G+8>>3];m=+h[H+8>>3];n=Q+8|0;i=-1;j=-1.0;k=1;while(1){if((k|0)>=(F|0))break;T=+h[q+(k*40|0)>>3];U=+_X(T);V=+TX(T);W=+VX(T);T=+$X(T);h[Q>>3]=V*v+r*U+W*t+s*T;h[n>>3]=W*p+V*u+o*U+m*T;S=d+(k<<4)|0;c[I>>2]=c[Q>>2];c[I+4>>2]=c[Q+4>>2];c[I+8>>2]=c[Q+8>>2];c[I+12>>2]=c[Q+12>>2];c[J>>2]=c[S>>2];c[J+4>>2]=c[S+4>>2];c[J+8>>2]=c[S+8>>2];c[J+12>>2]=c[S+12>>2];T=+SX(I,J);S=T>j;i=S?k:i;j=S?T:j;k=k+1|0}S=d+(i<<4)|0;Q=d+(i+-1<<4)|0;c[I>>2]=c[S>>2];c[I+4>>2]=c[S+4>>2];c[I+8>>2]=c[S+8>>2];c[I+12>>2]=c[S+12>>2];c[J>>2]=c[Q>>2];c[J+4>>2]=c[Q+4>>2];c[J+8>>2]=c[Q+8>>2];c[J+12>>2]=c[Q+12>>2];ZX(N,I,J);c[J>>2]=c[N>>2];c[J+4>>2]=c[N+4>>2];c[J+8>>2]=c[N+8>>2];c[J+12>>2]=c[N+12>>2];PX(L,J);Q=i+1|0;N=d+(Q<<4)|0;c[I>>2]=c[N>>2];c[I+4>>2]=c[N+4>>2];c[I+8>>2]=c[N+8>>2];c[I+12>>2]=c[N+12>>2];c[J>>2]=c[S>>2];c[J+4>>2]=c[S+4>>2];c[J+8>>2]=c[S+8>>2];c[J+12>>2]=c[S+12>>2];ZX(O,I,J);c[J>>2]=c[O>>2];c[J+4>>2]=c[O+4>>2];c[J+8>>2]=c[O+8>>2];c[J+12>>2]=c[O+12>>2];PX(M,J);c[I>>2]=c[L>>2];c[I+4>>2]=c[L+4>>2];c[I+8>>2]=c[L+8>>2];c[I+12>>2]=c[L+12>>2];c[J>>2]=c[M>>2];c[J+4>>2]=c[M+4>>2];c[J+8>>2]=c[M+8>>2];c[J+12>>2]=c[M+12>>2];YX(P,I,J);c[J>>2]=c[P>>2];c[J+4>>2]=c[P+4>>2];c[J+8>>2]=c[P+8>>2];c[J+12>>2]=c[P+12>>2];PX(K,J);c[I>>2]=c[f>>2];c[I+4>>2]=c[f+4>>2];c[I+8>>2]=c[f+8>>2];c[I+12>>2]=c[f+12>>2];c[J>>2]=c[K>>2];c[J+4>>2]=c[K+4>>2];c[J+8>>2]=c[K+8>>2];c[J+12>>2]=c[K+12>>2];RX(a,b,d,Q,I,J)|0;c[I>>2]=c[K>>2];c[I+4>>2]=c[K+4>>2];c[I+8>>2]=c[K+8>>2];c[I+12>>2]=c[K+12>>2];c[J>>2]=c[g>>2];c[J+4>>2]=c[g+4>>2];c[J+8>>2]=c[g+8>>2];c[J+12>>2]=c[g+12>>2];RX(a,b,S,e-i|0,I,J)|0;i=0}else i=0}l=R;return i|0}function SX(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[b>>3]-+h[a>>3];d=+h[b+8>>3]-+h[a+8>>3];return +(+C(+(d*d+c*c)))}function TX(a){a=+a;var b=0.0;b=1.0-a;return +(b*b*(a*3.0))}function UX(a,b,d){a=a|0;b=b|0;d=+d;var e=0;h[b>>3]=+h[b>>3]*d;e=b+8|0;h[e>>3]=+h[e>>3]*d;c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];return}function VX(a){a=+a;return +(a*a*3.0*(1.0-a))}function WX(a,b,d,e,f,g,i,j,k){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;var m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0,s=0,t=0,u=0,v=0,w=0.0,x=0.0,y=0.0,z=0.0,A=0.0,B=0,C=0,D=0,E=0,F=0,G=0,H=0.0,I=0.0,J=0,K=0,L=0,M=0;G=l;l=l+128|0;B=G+112|0;v=G+80|0;u=G;r=G+96|0;s=G+64|0;t=G+48|0;C=G+32|0;D=G+16|0;E=a+(b+-1<<4)|0;q=0;w=0.0;x=0.0;y=0.0;z=0.0;A=0.0;while(1){if((q|0)>=(b|0))break;K=d+(q*40|0)+8|0;c[v>>2]=c[K>>2];c[v+4>>2]=c[K+4>>2];c[v+8>>2]=c[K+8>>2];c[v+12>>2]=c[K+12>>2];c[B>>2]=c[K>>2];c[B+4>>2]=c[K+4>>2];c[B+8>>2]=c[K+8>>2];c[B+12>>2]=c[K+12>>2];H=+fY(v,B)+x;J=d+(q*40|0)+24|0;c[v>>2]=c[K>>2];c[v+4>>2]=c[K+4>>2];c[v+8>>2]=c[K+8>>2];c[v+12>>2]=c[K+12>>2];c[B>>2]=c[J>>2];c[B+4>>2]=c[J+4>>2];c[B+8>>2]=c[J+8>>2];c[B+12>>2]=c[J+12>>2];p=+fY(v,B)+A;c[v>>2]=c[J>>2];c[v+4>>2]=c[J+4>>2];c[v+8>>2]=c[J+8>>2];c[v+12>>2]=c[J+12>>2];c[B>>2]=c[J>>2];c[B+4>>2]=c[J+4>>2];c[B+8>>2]=c[J+8>>2];c[B+12>>2]=c[J+12>>2];o=+fY(v,B)+y;L=a+(q<<4)|0;M=d+(q*40|0)|0;I=+gY(+h[M>>3]);c[B>>2]=c[a>>2];c[B+4>>2]=c[a+4>>2];c[B+8>>2]=c[a+8>>2];c[B+12>>2]=c[a+12>>2];UX(s,B,I);I=+hY(+h[M>>3]);c[B>>2]=c[E>>2];c[B+4>>2]=c[E+4>>2];c[B+8>>2]=c[E+8>>2];c[B+12>>2]=c[E+12>>2];UX(t,B,I);c[v>>2]=c[s>>2];c[v+4>>2]=c[s+4>>2];c[v+8>>2]=c[s+8>>2];c[v+12>>2]=c[s+12>>2];c[B>>2]=c[t>>2];c[B+4>>2]=c[t+4>>2];c[B+8>>2]=c[t+8>>2];c[B+12>>2]=c[t+12>>2];YX(r,v,B);c[v>>2]=c[L>>2];c[v+4>>2]=c[L+4>>2];c[v+8>>2]=c[L+8>>2];c[v+12>>2]=c[L+12>>2];c[B>>2]=c[r>>2];c[B+4>>2]=c[r+4>>2];c[B+8>>2]=c[r+8>>2];c[B+12>>2]=c[r+12>>2];ZX(u,v,B);c[v>>2]=c[K>>2];c[v+4>>2]=c[K+4>>2];c[v+8>>2]=c[K+8>>2];c[v+12>>2]=c[K+12>>2];c[B>>2]=c[u>>2];c[B+4>>2]=c[u+4>>2];c[B+8>>2]=c[u+8>>2];c[B+12>>2]=c[u+12>>2];I=+fY(v,B)+w;c[v>>2]=c[J>>2];c[v+4>>2]=c[J+4>>2];c[v+8>>2]=c[J+8>>2];c[v+12>>2]=c[J+12>>2];c[B>>2]=c[u>>2];c[B+4>>2]=c[u+4>>2];c[B+8>>2]=c[u+8>>2];c[B+12>>2]=c[u+12>>2];q=q+1|0;w=I;x=H;y=o;z=+fY(v,B)+z;A=p}o=y*x-A*A;p=o>=0.0?o:-o;if(!(p>=1.0e-06?(m=(y*w-A*z)/o,n=(x*z-A*w)/o,!(p<1.0e-06|m<=0.0|n<=0.0)):0))F=6;if((F|0)==6){c[v>>2]=c[a>>2];c[v+4>>2]=c[a+4>>2];c[v+8>>2]=c[a+8>>2];c[v+12>>2]=c[a+12>>2];c[B>>2]=c[E>>2];c[B+4>>2]=c[E+4>>2];c[B+8>>2]=c[E+8>>2];c[B+12>>2]=c[E+12>>2];m=+SX(v,B)*.3333333333333333;n=m};c[g>>2]=c[a>>2];c[g+4>>2]=c[a+4>>2];c[g+8>>2]=c[a+8>>2];c[g+12>>2]=c[a+12>>2];c[B>>2]=c[e>>2];c[B+4>>2]=c[e+4>>2];c[B+8>>2]=c[e+8>>2];c[B+12>>2]=c[e+12>>2];UX(C,B,m);c[i>>2]=c[C>>2];c[i+4>>2]=c[C+4>>2];c[i+8>>2]=c[C+8>>2];c[i+12>>2]=c[C+12>>2];c[j>>2]=c[E>>2];c[j+4>>2]=c[E+4>>2];c[j+8>>2]=c[E+8>>2];c[j+12>>2]=c[E+12>>2];c[B>>2]=c[f>>2];c[B+4>>2]=c[f+4>>2];c[B+8>>2]=c[f+8>>2];c[B+12>>2]=c[f+12>>2];UX(D,B,n);c[k>>2]=c[D>>2];c[k+4>>2]=c[D+4>>2];c[k+8>>2]=c[D+8>>2];c[k+12>>2]=c[D+12>>2];l=G;return}function XX(a,b,d,e,f,g,i,j){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;var k=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0.0,s=0.0,t=0,u=0.0,v=0,w=0.0,x=0,y=0.0,z=0,A=0,B=0,C=0,D=0.0,E=0;C=l;l=l+64|0;B=C;z=(j|0)==2;y=+h[d>>3];u=+h[d+8>>3];v=B+8|0;w=+h[e>>3];x=B+16|0;p=+h[e+8>>3];q=B+24|0;r=+h[f>>3];s=+h[g>>3];t=B+32|0;o=+h[f+8>>3];n=+h[g+8>>3];e=B+40|0;f=B+48|0;g=B+56|0;d=1;k=4.0;m=4.0;while(1){h[B>>3]=y;h[v>>3]=u;D=m*.3333333333333333;h[x>>3]=D*w+y;h[q>>3]=D*p+u;D=k*.3333333333333333;h[t>>3]=r-D*s;h[e>>3]=o-D*n;h[f>>3]=r;h[g>>3]=o;if(d?(D=+aY(B,4),D<+aY(i,j)+-.001):0){d=0;break}if(bY(a,b,B)|0){A=5;break}if(m==0.0&k==0.0){A=9;break}E=m>.01;d=0;k=E?k*.5:0.0;m=E?m*.5:0.0}a:do if((A|0)==5){QX((c[47459]|0)+4|0);e=c[47460]|0;d=1;while(1){if((d|0)==4){d=1;break a}E=c[47459]|0;h[e+(E<<4)>>3]=+h[B+(d<<4)>>3];D=+h[B+(d<<4)+8>>3];c[47459]=E+1;h[e+(E<<4)+8>>3]=D;d=d+1|0}}else if((A|0)==9)if(z){QX((c[47459]|0)+4|0);e=c[47460]|0;d=1;while(1){if((d|0)==4){d=1;break a}E=c[47459]|0;h[e+(E<<4)>>3]=+h[B+(d<<4)>>3];D=+h[B+(d<<4)+8>>3];c[47459]=E+1;h[e+(E<<4)+8>>3]=D;d=d+1|0}}else d=0;while(0);l=C;return d|0}function YX(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;h[b>>3]=+h[b>>3]+ +h[d>>3];e=b+8|0;h[e>>3]=+h[e>>3]+ +h[d+8>>3];c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];return}function ZX(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;h[b>>3]=+h[b>>3]-+h[d>>3];e=b+8|0;h[e>>3]=+h[e>>3]-+h[d+8>>3];c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];return}function _X(a){a=+a;a=1.0-a;return +(a*a*a)}function $X(a){a=+a;return +(a*a*a)}function aY(a,b){a=a|0;b=b|0;var c=0.0,d=0,e=0.0,f=0.0,g=0;c=0.0;d=1;while(1){if((d|0)>=(b|0))break;g=d+-1|0;e=+h[a+(d<<4)>>3]-+h[a+(g<<4)>>3];f=+h[a+(d<<4)+8>>3]-+h[a+(g<<4)+8>>3];c=+C(+(f*f+e*e))+c;d=d+1|0}return +c}function bY(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0.0,p=0.0,q=0.0,r=0.0,s=0.0,t=0,u=0,v=0.0,w=0,x=0.0,y=0,z=0,A=0,B=0,C=0,D=0.0,E=0.0;C=l;l=l+64|0;t=C+32|0;u=C;w=u+16|0;y=d+16|0;z=d+32|0;A=d+48|0;B=d+8|0;g=d+24|0;i=d+40|0;j=d+56|0;k=u+8|0;m=u+24|0;f=0;a:while(1){if((f|0)>=(b|0)){e=1;break}n=a+(f<<5)|0;c[u>>2]=c[n>>2];c[u+4>>2]=c[n+4>>2];c[u+8>>2]=c[n+8>>2];c[u+12>>2]=c[n+12>>2];n=a+(f<<5)+16|0;c[w>>2]=c[n>>2];c[w+4>>2]=c[n+4>>2];c[w+8>>2]=c[n+8>>2];c[w+12>>2]=c[n+12>>2];n=cY(d,u,t)|0;b:do if((n|0)!=4){o=+h[u>>3];p=+h[k>>3];q=+h[w>>3];r=+h[m>>3];e=0;while(1){if((e|0)>=(n|0))break b;s=+h[t+(e<<3)>>3];if((!(s<1.0e-06|s>.999999)?(x=s*s*s,E=s*3.0,D=1.0-s,s=E*s*D,v=D*D,E=v*E,D=v*D,v=+h[y>>3]*E+ +h[d>>3]*D+ +h[z>>3]*s+ +h[A>>3]*x,x=+h[g>>3]*E+ +h[B>>3]*D+ +h[i>>3]*s+ +h[j>>3]*x,s=v-o,D=x-p,!(D*D+s*s<.001)):0)?(E=v-q,D=x-r,!(D*D+E*E<.001)):0){e=0;break a}e=e+1|0}}while(0);f=f+1|0}l=C;return e|0}function cY(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0,j=0.0,k=0,m=0,n=0,o=0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0;v=l;l=l+96|0;s=v+48|0;t=v+24|0;k=v;u=v+80|0;p=+h[b>>3];g=+h[b+16>>3]-p;q=+h[b+8>>3];j=+h[b+24>>3]-q;c[u>>2]=0;a:do if(!(g==0.0)){j=j/g;m=a+16|0;n=a+32|0;o=a+48|0;dY(+h[a+8>>3]-+h[a>>3]*j,+h[a+24>>3]-+h[m>>3]*j,+h[a+40>>3]-+h[n>>3]*j,+h[a+56>>3]-+h[o>>3]*j,s);h[s>>3]=j*p-q+ +h[s>>3];e=BY(s,t)|0;if((e|0)==4)b=4;else{f=s+8|0;i=s+16|0;k=s+24|0;g=1.0/g;b=0;while(1){if((b|0)>=(e|0))break;j=+h[t+(b<<3)>>3];if(j>=0.0&j<=1.0?(dY(+h[a>>3],+h[m>>3],+h[n>>3],+h[o>>3],s),q=(+h[s>>3]-p+((+h[k>>3]*j+ +h[i>>3])*j+ +h[f>>3])*j)*g,q>=0.0&q<=1.0):0)eY(j,d,u);b=b+1|0}b=c[u>>2]|0}}else{dY(+h[a>>3],+h[a+16>>3],+h[a+32>>3],+h[a+48>>3],s);h[s>>3]=+h[s>>3]-p;r=BY(s,t)|0;if(!(j==0.0)){if((r|0)==4){b=4;break}m=a+8|0;n=a+24|0;o=a+40|0;e=a+56|0;f=s+8|0;i=s+16|0;k=s+24|0;g=1.0/j;b=0;while(1){if((b|0)>=(r|0))break;j=+h[t+(b<<3)>>3];if(j>=0.0&j<=1.0?(dY(+h[m>>3],+h[n>>3],+h[o>>3],+h[e>>3],s),p=(+h[s>>3]-q+((+h[k>>3]*j+ +h[i>>3])*j+ +h[f>>3])*j)*g,p>=0.0&p<=1.0):0)eY(j,d,u);b=b+1|0}b=c[u>>2]|0;break}dY(+h[a+8>>3],+h[a+24>>3],+h[a+40>>3],+h[a+56>>3],s);h[s>>3]=+h[s>>3]-q;i=BY(s,k)|0;b=(i|0)==4;b:do if((r|0)==4){if(b){b=4;break a}else b=0;while(1){if((b|0)>=(i|0))break b;eY(+h[k+(b<<3)>>3],d,u);b=b+1|0}}else{if(b){b=0;while(1){if((b|0)>=(r|0))break b;eY(+h[t+(b<<3)>>3],d,u);b=b+1|0}}else e=0;while(1){if((e|0)>=(r|0))break b;f=t+(e<<3)|0;b=0;while(1){if((b|0)>=(i|0))break;g=+h[f>>3];if(g==+h[k+(b<<3)>>3])eY(g,d,u);b=b+1|0}e=e+1|0}}while(0);b=c[u>>2]|0}while(0);l=v;return b|0}function dY(a,b,c,d,e){a=+a;b=+b;c=+c;d=+d;e=e|0;h[e+24>>3]=d-a+(b-c)*3.0;h[e+16>>3]=(c+a)*3.0-b*6.0;h[e+8>>3]=(b-a)*3.0;h[e>>3]=a;return}function eY(a,b,d){a=+a;b=b|0;d=d|0;var e=0;if(a>=0.0&a<=1.0){e=c[d>>2]|0;h[b+(e<<3)>>3]=a;c[d>>2]=e+1}return}function fY(a,b){a=a|0;b=b|0;return +(+h[b+8>>3]*+h[a+8>>3]+ +h[b>>3]*+h[a>>3])}function gY(a){a=+a;var b=0.0;b=1.0-a;return +(b*b*(b+a*3.0))}function hY(a){a=+a;return +(a*a*((1.0-a)*3.0+a))}function iY(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0.0,j=0,k=0,m=0,n=0,q=0,r=0,s=0,u=0,v=0,w=0,x=0,y=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0.0;H=l;l=l+112|0;E=H+80|0;D=H+64|0;C=H+48|0;G=4;F=T1(40)|0;c[F>>2]=0;w=H+32|0;x=H+16|0;y=H;A=H+96|0;G=U6(189856,1,F|0,G|0)|0;F=z;o=0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1)e=z;else e=0;a:while(1){if(e|0){e=-2;break}g=a+4|0;o=0;ia(116,c[g>>2]|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue}c[47503]=0;c[47504]=0;o=0;ia(117,c[g>>2]<<1|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue}m=(c[47506]|0)/2|0;c[47507]=m;c[47508]=m+-1;m=c[g>>2]|0;g=c[a>>2]|0;e=0;f=-1;i=t;while(1){if((e|0)>=(m|0))break;I=+h[g+(e<<4)>>3];v=i>I;u=v?e:f;e=e+1|0;f=u;i=v?I:i}v=g+(f<<4)|0;c[x>>2]=c[v>>2];c[x+4>>2]=c[v+4>>2];c[x+8>>2]=c[v+8>>2];c[x+12>>2]=c[v+12>>2];v=g+(((f|0)==0?m:f)+-1<<4)|0;c[w>>2]=c[v>>2];c[w+4>>2]=c[v+4>>2];c[w+8>>2]=c[v+8>>2];c[w+12>>2]=c[v+12>>2];v=g+(((f|0)==(m+-1|0)?0:f+1|0)<<4)|0;c[y>>2]=c[v>>2];c[y+4>>2]=c[v+4>>2];c[y+8>>2]=c[v+8>>2];c[y+12>>2]=c[v+12>>2];I=+h[x>>3];if((+h[w>>3]==I?I==+h[y>>3]:0)?+h[y+8>>3]>+h[x+8>>3]:0)v=15;else v=12;b:do if((v|0)==12){v=0;o=0;e=ca(36,w|0,x|0,y|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,G|0,F|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}if((e|0)==1){g=c[47510]|0;e=c[47511]|0;f=0;while(1){if((f|0)>=(m|0))break b;j=c[a>>2]|0;if(!(((f|0)>0?(B=f+-1|0,+h[j+(f<<4)>>3]==+h[j+(B<<4)>>3]):0)?+h[j+(f<<4)+8>>3]==+h[j+(B<<4)+8>>3]:0)){u=c[47503]|0;c[g+(u<<3)>>2]=j+(f<<4);c[g+(u<<3)+4>>2]=g+(((u|0)%(m|0)|0)<<3);c[e+(u<<2)>>2]=g+(u<<3);c[47503]=u+1}f=f+1|0}}else v=15}while(0);c:do if((v|0)==15){v=0;j=c[47510]|0;e=c[47511]|0;g=m;while(1){f=g+-1|0;if((g|0)<=0)break c;k=c[a>>2]|0;if(((g|0)<(m|0)?+h[k+(f<<4)>>3]==+h[k+(g<<4)>>3]:0)?+h[k+(f<<4)+8>>3]==+h[k+(g<<4)+8>>3]:0){g=f;continue}g=c[47503]|0;c[j+(g<<3)>>2]=k+(f<<4);c[j+(g<<3)+4>>2]=j+(((g|0)%(m|0)|0)<<3);c[e+(g<<2)>>2]=j+(g<<3);c[47503]=g+1;g=f}}while(0);o=0;ja(29,e|0,c[47503]|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue}j=0;e=c[47504]|0;d:while(1){if((j|0)>=(e|0)){s=0;break}k=j+1|0;g=k;while(1){if((g|0)>=(e|0)){j=k;continue d}o=0;ja(30,j|0,g|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue a}g=g+1|0;e=c[47504]|0}}while(1){if((s|0)>=(e|0))break;o=0;f=aa(105,s|0,b|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){j=V6(c[g>>2]|0,G|0,F|0)|0;if(!j)bb(g|0,p|0);z=p}else j=-1;if((j|0)==1){e=z;continue a}if(f|0)break;s=s+1|0}if((s|0)==(e|0)){e=c[15712]|0;o=0;c[C>>2]=141858;c[C+4>>2]=192;c[C+8>>2]=141869;ca(32,e|0,141839,C|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue}else{v=41;break}}u=b+16|0;k=0;while(1){if((k|0)>=(e|0))break;o=0;f=aa(105,k|0,u|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){j=V6(c[g>>2]|0,G|0,F|0)|0;if(!j)bb(g|0,p|0);z=p}else j=-1;if((j|0)==1){e=z;continue a}if(f|0)break;k=k+1|0}if((k|0)==(e|0)){e=c[15712]|0;o=0;c[D>>2]=141858;c[D+4>>2]=200;c[D+8>>2]=141902;ca(32,e|0,141839,D|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue}else{v=48;break}}o=0;e=aa(106,s|0,k|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,G|0,F|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue}if(!e){e=c[15712]|0;o=0;c[E>>2]=141858;c[E+4>>2]=207;c[E+8>>2]=141940;ca(32,e|0,141839,E|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue}o=0;ia(118,2);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue}else{v=53;break}}if((s|0)==(k|0)){o=0;ia(118,2);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,G|0,F|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){e=z;continue}else{v=56;break}}c[A>>2]=b;c[A+4>>2]=0;f=A+8|0;c[f>>2]=u;c[A+12>>2]=0;o=0;ja(31,1,A|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue}c[47509]=c[47507];q=s;e:while(1){if((q|0)==-1){g=f;e=0;break}r=c[47513]|0;c[r+(q*52|0)>>2]=2;e=0;while(1){if((e|0)>=3){v=65;break}v=c[r+(q*52|0)+4+(e<<4)+12>>2]|0;if(v|0?(c[v>>2]|0)==1:0){v=68;break}e=e+1|0}if((v|0)==65){v=0;if((e|0)==3){e=c[47505]|0;k=c[e+(c[47508]<<2)>>2]|0;o=0;e=ca(36,u|0,c[c[e+(c[47507]<<2)>>2]>>2]|0,c[k>>2]|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){j=V6(c[g>>2]|0,G|0,F|0)|0;if(!j)bb(g|0,p|0);z=p}else j=-1;if((j|0)==1){e=z;continue a}j=(e|0)==1;n=j?f:k;j=j?k:f}else v=68}if((v|0)==68){v=0;k=c[r+(q*52|0)+4+(e<<4)>>2]|0;m=c[r+(q*52|0)+4+(e<<4)+4>>2]|0;o=0;e=ca(36,c[k>>2]|0,c[c[r+(q*52|0)+4+(((e+1|0)%3|0)<<4)+4>>2]>>2]|0,c[m>>2]|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){j=V6(c[g>>2]|0,G|0,F|0)|0;if(!j)bb(g|0,p|0);z=p}else j=-1;if((j|0)==1){e=z;continue a}j=(e|0)==1;n=j?k:m;j=j?m:k}do if((q|0)==(s|0)){o=0;ja(31,2,j|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}o=0;ja(31,1,n|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}e=0}else{m=c[47505]|0;if((c[m+(c[47507]<<2)>>2]|0)!=(n|0)?(c[m+(c[47508]<<2)>>2]|0)!=(n|0):0){o=0;j=$(34,n|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}o=0;ja(32,2,j|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}o=0;ja(31,1,n|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}if((j|0)<=(c[47509]|0)){e=0;break}c[47509]=j;e=0;break}o=0;k=$(34,j|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}o=0;ja(32,1,k|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}o=0;ja(31,2,j|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){g=V6(c[e>>2]|0,G|0,F|0)|0;if(!g)bb(e|0,p|0);z=p}else g=-1;if((g|0)==1){e=z;continue a}if((k|0)>=(c[47509]|0)){e=0;break}c[47509]=k;e=0}while(0);while(1){if((e|0)>=3){q=-1;continue e}g=c[r+(q*52|0)+4+(e<<4)+12>>2]|0;if(g|0?(c[g>>2]|0)==1:0)break;e=e+1|0}q=(g-(c[47513]|0)|0)/52|0}while(1){if(!g)break;g=c[g+4>>2]|0;e=e+1|0}o=0;ia(118,e|0);g=o;o=0;if((g|0)!=0&(p|0)!=0){j=V6(c[g>>2]|0,G|0,F|0)|0;if(!j)bb(g|0,p|0);z=p}else j=-1;if((j|0)==1)e=z;else{v=94;break}}if((v|0)==41)e=-1;else if((v|0)==48)e=-1;else if((v|0)==53){c[d+4>>2]=2;e=c[47512]|0;c[e>>2]=c[b>>2];c[e+4>>2]=c[b+4>>2];c[e+8>>2]=c[b+8>>2];c[e+12>>2]=c[b+12>>2];F=e+16|0;c[F>>2]=c[u>>2];c[F+4>>2]=c[u+4>>2];c[F+8>>2]=c[u+8>>2];c[F+12>>2]=c[u+12>>2];c[d>>2]=e;e=0}else if((v|0)==56){c[d+4>>2]=2;e=c[47512]|0;c[e>>2]=c[b>>2];c[e+4>>2]=c[b+4>>2];c[e+8>>2]=c[b+8>>2];c[e+12>>2]=c[b+12>>2];F=e+16|0;c[F>>2]=c[u>>2];c[F+4>>2]=c[u+4>>2];c[F+8>>2]=c[u+8>>2];c[F+12>>2]=c[u+12>>2];c[d>>2]=e;e=0}else if((v|0)==94){c[d+4>>2]=e;g=c[47512]|0;while(1){e=e+-1|0;if(!f)break;F=g+(e<<4)|0;E=c[f>>2]|0;c[F>>2]=c[E>>2];c[F+4>>2]=c[E+4>>2];c[F+8>>2]=c[E+8>>2];c[F+12>>2]=c[E+12>>2];f=c[f+4>>2]|0}c[d>>2]=g;e=0}U1(G|0);l=H;return e|0}function jY(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;h=l;l=l+64|0;e=h+48|0;g=h+32|0;f=h+16|0;d=h;if((c[47516]|0)<(a|0)){b=c[47510]|0;if(!b){g=T1(a<<3)|0;c[47510]=g;if(!g){g=c[15712]|0;c[d>>2]=141858;c[d+4>>2]=523;c[d+8>>2]=142108;w4(g,141839,d)|0;bb(189856,1)}g=T1(a<<2)|0;c[47511]=g;if(!g){g=c[15712]|0;c[f>>2]=141858;c[f+4>>2]=527;c[f+8>>2]=142127;w4(g,141839,f)|0;bb(189856,1)}}else{f=W1(b,a<<3)|0;c[47510]=f;if(!f){f=c[15712]|0;c[g>>2]=141858;c[g+4>>2]=533;c[g+8>>2]=142147;w4(f,141839,g)|0;bb(189856,1)}g=W1(c[47511]|0,a<<2)|0;c[47511]=g;if(!g){g=c[15712]|0;c[e>>2]=141858;c[e+4>>2]=539;c[e+8>>2]=142167;w4(g,141839,e)|0;bb(189856,1)}}c[47516]=a}l=h;return}function kY(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+32|0;e=f+16|0;d=f;if((c[47506]|0)<(a|0)){b=c[47505]|0;if(!b){e=T1(a<<2)|0;c[47505]=e;if(!e){e=c[15712]|0;c[d>>2]=141858;c[d+4>>2]=573;c[d+8>>2]=142063;w4(e,141839,d)|0;bb(189856,1)}}else{d=W1(b,a<<2)|0;c[47505]=d;if(!d){d=c[15712]|0;c[e>>2]=141858;c[e+4>>2]=580;c[e+8>>2]=142085;w4(d,141839,e)|0;bb(189856,1)}}c[47506]=a}l=f;return}function lY(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0;d=+h[b+8>>3];e=+h[b>>3];d=(+h[c>>3]-e)*(+h[a+8>>3]-d)-(+h[a>>3]-e)*(+h[c+8>>3]-d);return (d>0.0?1:d<0.0?2:3)|0}function mY(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;a:while(1){if((b|0)>3)e=0;else{b=9;break}while(1){if((e|0)>=(b|0)){b=8;break a}f=e+1|0;d=(f|0)%(b|0)|0;g=(e+2|0)%(b|0)|0;if(!(uY(e,g,a,b)|0))e=f;else break}vY(c[a+(e<<2)>>2]|0,c[a+(d<<2)>>2]|0,c[a+(g<<2)>>2]|0);b=b+-1|0;while(1){if((d|0)>=(b|0))continue a;g=d+1|0;c[a+(d<<2)>>2]=c[a+(g<<2)>>2];d=g}}if((b|0)==8){a=c[15712]|0;c[h>>2]=141858;c[h+4>>2]=324;c[h+8>>2]=142003;w4(a,141839,h)|0}else if((b|0)==9)vY(c[a>>2]|0,c[a+4>>2]|0,c[a+8>>2]|0);l=i;return}function nY(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;i=c[47513]|0;j=i+(a*52|0)|0;k=i+(b*52|0)|0;g=0;while(1){if((g|0)==3)break;l=i+(a*52|0)+4+(g<<4)|0;m=i+(a*52|0)+4+(g<<4)+4|0;n=i+(a*52|0)+4+(g<<4)+12|0;f=0;while(1){if((f|0)==3)break;o=c[c[l>>2]>>2]|0;h=c[c[i+(b*52|0)+4+(f<<4)>>2]>>2]|0;if((o|0)==(h|0)){d=c[c[i+(b*52|0)+4+(f<<4)+4>>2]>>2]|0;if((c[c[m>>2]>>2]|0)==(d|0))e=10;else e=8}else{d=c[c[i+(b*52|0)+4+(f<<4)+4>>2]>>2]|0;e=8}if(((e|0)==8?(e=0,(o|0)==(d|0)):0)?(c[c[m>>2]>>2]|0)==(h|0):0)e=10;if((e|0)==10){c[n>>2]=k;c[i+(b*52|0)+4+(f<<4)+12>>2]=j}f=f+1|0}g=g+1|0}return}function oY(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=c[47513]|0;d=0;e=0;while(1){if((e|0)==3)break;d=d+((lY(c[c[f+(a*52|0)+4+(e<<4)>>2]>>2]|0,c[c[f+(a*52|0)+4+(e<<4)+4>>2]>>2]|0,b)|0)!=2&1)|0;e=e+1|0}return ((d|0)==3|(d|0)==0)&1|0}function pY(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=c[47513]|0;e=d+(a*52|0)|0;a:do if(!(c[e>>2]|0)){c[e>>2]=1;if((a|0)==(b|0))d=1;else{f=0;while(1){if((f|0)>=3)break;e=c[d+(a*52|0)+4+(f<<4)+12>>2]|0;if(e){if(pY((e-d|0)/52|0,b)|0){d=1;break a}d=c[47513]|0}f=f+1|0}c[d+(a*52|0)>>2]=0;d=0}}else d=0;while(0);return d|0}function qY(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+32|0;e=f+16|0;d=f;if((c[47514]|0)<(a|0)){b=c[47512]|0;if(!b){e=T1(a<<4)|0;c[47512]=e;if(!e){e=c[15712]|0;c[d>>2]=141858;c[d+4>>2]=593;c[d+8>>2]=141966;w4(e,141839,d)|0;bb(189856,1)}}else{d=W1(b,a<<4)|0;c[47512]=d;if(!d){d=c[15712]|0;c[e>>2]=141858;c[e+4>>2]=599;c[e+8>>2]=141984;w4(d,141839,e)|0;bb(189856,1)}}c[47514]=a}l=f;return}function rY(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=c[47508]|0;e=c[47507]|0;f=(d|0)>=(e|0);if((a|0)==1)if(f){c[b+4>>2]=c[(c[47505]|0)+(e<<2)>>2];a=190028;d=-1}else{a=190028;d=-1}else if(f){c[b+4>>2]=c[(c[47505]|0)+(d<<2)>>2];a=190032;d=1}else{a=190032;d=1}f=(c[a>>2]|0)+d|0;c[a>>2]=f;c[(c[47505]|0)+(f<<2)>>2]=b;return}function sY(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;f=c[47509]|0;g=c[47505]|0;b=c[47507]|0;while(1){if((b|0)>=(f|0)){e=4;break}d=b+1|0;if((lY(c[c[g+(d<<2)>>2]>>2]|0,c[c[g+(b<<2)>>2]>>2]|0,c[a>>2]|0)|0)==1)break;else b=d}a:do if((e|0)==4){b=c[47508]|0;while(1){if((b|0)<=(f|0)){b=f;break a}d=b+-1|0;if((lY(c[c[g+(d<<2)>>2]>>2]|0,c[c[g+(b<<2)>>2]>>2]|0,c[a>>2]|0)|0)==2)break;else b=d}}while(0);return b|0}function tY(a,b){a=a|0;b=b|0;c[((a|0)==1?190032:190028)>>2]=b;return}function uY(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;f=c[c[d+(((a+-1+e|0)%(e|0)|0)<<2)>>2]>>2]|0;i=c[c[d+(a<<2)>>2]>>2]|0;g=c[c[d+(((a+1|0)%(e|0)|0)<<2)>>2]>>2]|0;k=(lY(f,i,g)|0)==1;j=c[c[d+(b<<2)>>2]>>2]|0;if(k)if((lY(i,j,f)|0)==1){f=lY(j,i,g)|0;g=1;h=5}else f=0;else{f=lY(i,j,g)|0;g=2;h=5}a:do if((h|0)==5)if((f|0)==(g|0)){f=0;while(1){if((f|0)>=(e|0)){f=1;break a}g=f;f=f+1|0;h=(f|0)%(e|0)|0;if((h|0)==(b|0)|((g|0)==(b|0)|((g|0)==(a|0)|(h|0)==(a|0))))continue;if(xY(i,j,c[c[d+(g<<2)>>2]>>2]|0,c[c[d+(h<<2)>>2]>>2]|0)|0){f=0;break}}}else f=0;while(0);return f|0}function vY(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;e=c[47504]|0;f=c[47515]|0;if((e|0)<(f|0))h=e;else{wY(f+20|0);h=c[47504]|0}g=c[47513]|0;c[47504]=h+1;f=g+(h*52|0)|0;c[f>>2]=0;c[g+(h*52|0)+4>>2]=a;c[g+(h*52|0)+8>>2]=b;c[g+(h*52|0)+16>>2]=0;c[g+(h*52|0)+20>>2]=b;c[g+(h*52|0)+24>>2]=d;c[g+(h*52|0)+32>>2]=0;c[g+(h*52|0)+36>>2]=d;c[g+(h*52|0)+40>>2]=a;c[g+(h*52|0)+48>>2]=0;e=0;while(1){if((e|0)==3)break;c[g+(h*52|0)+4+(e<<4)+8>>2]=f;e=e+1|0}return}function wY(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+32|0;e=f+16|0;d=f;if((c[47515]|0)<(a|0)){b=c[47513]|0;if(!b){e=T1(a*52|0)|0;c[47513]=e;if(!e){e=c[15712]|0;c[d>>2]=141858;c[d+4>>2]=552;c[d+8>>2]=142024;w4(e,141839,d)|0;bb(189856,1)}}else{d=W1(b,a*52|0)|0;c[47513]=d;if(!d){d=c[15712]|0;c[e>>2]=141858;c[e+4>>2]=558;c[e+8>>2]=142043;w4(d,141839,e)|0;bb(189856,1)}}c[47515]=a}l=f;return}function xY(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0,g=0,h=0;f=lY(a,b,c)|0;if((((f|0)!=3?(g=lY(a,b,d)|0,(g|0)!=3):0)?(h=lY(c,d,a)|0,(h|0)!=3):0)?(e=lY(c,d,b)|0,(e|0)!=3):0)return ((f|0)==1^(g|0)==1)&((h|0)==1^(e|0)==1)&1|0;if(((yY(a,b,c)|0)==0?(yY(a,b,d)|0)==0:0)?(yY(c,d,a)|0)==0:0)return (yY(c,d,b)|0)!=0|0;return 1}function yY(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0,g=0.0;e=+h[a>>3];g=+h[b>>3]-e;f=+h[a+8>>3];d=+h[b+8>>3]-f;e=+h[c>>3]-e;f=+h[c+8>>3]-f;if((lY(a,b,c)|0)==3?f*d+e*g>=0.0:0)a=f*f+e*e<=d*d+g*g&1;else a=0;return a|0}function zY(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0,j=0.0,k=0,l=0,m=0,n=0,o=0,p=0.0,q=0;m=T1(d<<2)|0;n=T1((d<<3)+8|0)|0;o=n+8|0;f=0;while(1){if((f|0)>=(d|0))break;c[m+(f<<2)>>2]=-1;h[o+(f<<3)>>3]=-2147483647.0;f=f+1|0}h[n>>3]=-2147483648.0;a:while(1){if((a|0)==(b|0))break;l=o+(a<<3)|0;j=+h[l>>3];h[l>>3]=j==-2147483647.0?0.0:-j;k=0;f=-1;while(1){if((k|0)>=(d|0)){a=f;continue a}i=o+(k<<3)|0;g=+h[i>>3];if(g<0.0){q=(a|0)<(k|0);p=+h[(c[e+((q?k:a)<<2)>>2]|0)+((q?a:k)<<3)>>3];j=-(p+ +h[l>>3]);if(p!=0.0&g<j){h[i>>3]=j;c[m+(k<<2)>>2]=a;g=j}f=g>+h[o+(f<<3)>>3]?k:f}k=k+1|0}}U1(n);return m|0}function AY(a,b,d,e,f,g,h){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0;j=l;l=l+32|0;k=j+16|0;m=j;i=c[h+4>>2]|0;c[m>>2]=c[a>>2];c[m+4>>2]=c[a+4>>2];c[m+8>>2]=c[a+8>>2];c[m+12>>2]=c[a+12>>2];c[k>>2]=c[e>>2];c[k+4>>2]=c[e+4>>2];c[k+8>>2]=c[e+8>>2];c[k+12>>2]=c[e+12>>2];if(!(TY(m,b,k,f,h)|0)){a=c[h+24>>2]|0;c[a+(i<<2)>>2]=g;m=i+1|0;c[a+(m<<2)>>2]=d;a=zY(m,i,i+2|0,a)|0}else{a=T1((i<<2)+8|0)|0;m=i+1|0;c[a+(i<<2)>>2]=m;c[a+(m<<2)>>2]=-1}l=j;return a|0}function BY(a,b){a=a|0;b=b|0;var c=0,d=0.0,e=0.0,f=0.0,g=0.0,i=0.0,j=0;d=+h[a+24>>3];a:do if(d<1.0e-07&d>-1.0e-07)a=CY(a,b)|0;else{g=+h[a+16>>3]/(d*3.0);f=+h[a+8>>3]/d;e=g*g;d=g*2.0*e-g*f+ +h[a>>3]/d;e=f*.3333333333333333-e;e=e*e*4.0*e;f=d*d+e;if(!(f<0.0)){i=(+C(+f)-d)*.5;e=+E2(i);d=+E2(-d-i)+e;h[b>>3]=d;if(f>0.0)a=1;else{d=d*-.5;h[b+16>>3]=d;a=1;c=7}}else{i=+K(+(+C(+-f)),+-d);d=+E2(+C(+-e)*.5)*2.0;h[b>>3]=d*+E(+(i*.3333333333333333));h[b+8>>3]=+E(+((i+6.283185307179586)*.3333333333333333))*d;d=+E(+((i+-6.283185307179586)*.3333333333333333))*d;a=2;c=7}if((c|0)==7){h[b+(a<<3)>>3]=d;a=3}c=0;while(1){if((c|0)>=(a|0))break a;j=b+(c<<3)|0;h[j>>3]=+h[j>>3]-g;c=c+1|0}}while(0);return a|0}function CY(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a+16>>3];do if(!(c<1.0e-07&c>-1.0e-07)){d=+h[a+8>>3]/(c*2.0);c=d*d-+h[a>>3]/c;if(!(c<0.0))if(c==0.0){h[b>>3]=-d;a=1;break}else{c=+C(+c)-d;h[b>>3]=c;h[b+8>>3]=d*-2.0-c;a=2;break}else a=0}else a=DY(a,b)|0;while(0);return a|0}function DY(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;d=+h[a+8>>3];c=+h[a>>3];if(d<1.0e-07&d>-1.0e-07)a=c<1.0e-07&c>-1.0e-07?4:0;else{h[b>>3]=-c/d;a=1}return a|0}function EY(a){a=a|0;U1(c[a>>2]|0);U1(a);return}function FY(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;e=c[a+4>>2]|0;i=(e*3|0)+-2|0;d=c[47518]|0;if((i|0)>(c[47517]|0)){if(!d)d=T1(i<<4)|0;else d=W1(d,i<<4)|0;c[47518]=d;c[47517]=i;h=d;g=d}else{h=d;g=d}f=c[a>>2]|0;c[h>>2]=c[f>>2];c[h+4>>2]=c[f+4>>2];c[h+8>>2]=c[f+8>>2];c[h+12>>2]=c[f+12>>2];X6(h+16|0,f|0,16)|0;e=e+-1|0;d=1;a=2;while(1){if((d|0)>=(e|0))break;j=h+(a+2<<4)|0;k=h+(a+1<<4)|0;m=h+(a<<4)|0;l=f+(d<<4)|0;c[m>>2]=c[l>>2];c[m+4>>2]=c[l+4>>2];c[m+8>>2]=c[l+8>>2];c[m+12>>2]=c[l+12>>2];X6(k|0,l|0,16)|0;c[j>>2]=c[k>>2];c[j+4>>2]=c[k+4>>2];c[j+8>>2]=c[k+8>>2];c[j+12>>2]=c[k+12>>2];d=d+1|0;a=a+3|0}l=h+(a<<4)|0;m=f+(d<<4)|0;c[l>>2]=c[m>>2];c[l+4>>2]=c[m+4>>2];c[l+8>>2]=c[m+8>>2];c[l+12>>2]=c[m+12>>2];X6(h+(a+1<<4)|0,m|0,16)|0;c[b+4>>2]=i;c[b>>2]=g;return}function GY(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0;d=+h[b+8>>3];e=+h[b>>3];d=(+h[c>>3]-e)*(+h[a+8>>3]-d)-(+h[a>>3]-e)*(+h[c+8>>3]-d);return (d>.0001?1:(d<-.0001)<<31>>31)|0}function HY(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0;e=+h[a>>3];f=+h[b>>3];if(e!=f){d=+h[c>>3];if(!(e<d&d<f))if(f<d)a=d<e;else a=0;else a=1}else{f=+h[a+8>>3];e=+h[c+8>>3];d=+h[b+8>>3];if(!(f<e&e<d))if(d<e)a=e<f;else a=0;else a=1}return a&1|0}function IY(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+48|0;i=m+32|0;h=m+16|0;g=m;c[g>>2]=c[a>>2];c[g+4>>2]=c[a+4>>2];c[g+8>>2]=c[a+8>>2];c[g+12>>2]=c[a+12>>2];c[h>>2]=c[b>>2];c[h+4>>2]=c[b+4>>2];c[h+8>>2]=c[b+8>>2];c[h+12>>2]=c[b+12>>2];c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];j=GY(g,h,i)|0;if((j|0)==0?(c[g>>2]=c[a>>2],c[g+4>>2]=c[a+4>>2],c[g+8>>2]=c[a+8>>2],c[g+12>>2]=c[a+12>>2],c[h>>2]=c[b>>2],c[h+4>>2]=c[b+4>>2],c[h+8>>2]=c[b+8>>2],c[h+12>>2]=c[b+12>>2],c[i>>2]=c[d>>2],c[i+4>>2]=c[d+4>>2],c[i+8>>2]=c[d+8>>2],c[i+12>>2]=c[d+12>>2],(HY(g,h,i)|0)!=0):0)f=1;else k=3;do if((k|0)==3){c[g>>2]=c[a>>2];c[g+4>>2]=c[a+4>>2];c[g+8>>2]=c[a+8>>2];c[g+12>>2]=c[a+12>>2];c[h>>2]=c[b>>2];c[h+4>>2]=c[b+4>>2];c[h+8>>2]=c[b+8>>2];c[h+12>>2]=c[b+12>>2];c[i>>2]=c[e>>2];c[i+4>>2]=c[e+4>>2];c[i+8>>2]=c[e+8>>2];c[i+12>>2]=c[e+12>>2];f=GY(g,h,i)|0;if((f|0)==0?(c[g>>2]=c[a>>2],c[g+4>>2]=c[a+4>>2],c[g+8>>2]=c[a+8>>2],c[g+12>>2]=c[a+12>>2],c[h>>2]=c[b>>2],c[h+4>>2]=c[b+4>>2],c[h+8>>2]=c[b+8>>2],c[h+12>>2]=c[b+12>>2],c[i>>2]=c[e>>2],c[i+4>>2]=c[e+4>>2],c[i+8>>2]=c[e+8>>2],c[i+12>>2]=c[e+12>>2],HY(g,h,i)|0):0){f=1;break};c[g>>2]=c[d>>2];c[g+4>>2]=c[d+4>>2];c[g+8>>2]=c[d+8>>2];c[g+12>>2]=c[d+12>>2];c[h>>2]=c[e>>2];c[h+4>>2]=c[e+4>>2];c[h+8>>2]=c[e+8>>2];c[h+12>>2]=c[e+12>>2];c[i>>2]=c[a>>2];c[i+4>>2]=c[a+4>>2];c[i+8>>2]=c[a+8>>2];c[i+12>>2]=c[a+12>>2];a=GY(g,h,i)|0;c[g>>2]=c[d>>2];c[g+4>>2]=c[d+4>>2];c[g+8>>2]=c[d+8>>2];c[g+12>>2]=c[d+12>>2];c[h>>2]=c[e>>2];c[h+4>>2]=c[e+4>>2];c[h+8>>2]=c[e+8>>2];c[h+12>>2]=c[e+12>>2];c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];k=(O(f,j)|0)<0;f=(O(GY(g,h,i)|0,a)|0)>>>31;f=k?f:0}while(0);l=m;return f|0}function JY(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a>>3]-+h[b>>3];d=+h[a+8>>3]-+h[b+8>>3];return +(d*d+c*c)}function KY(a){a=a|0;c[a+24>>2]=LY(c[a+4>>2]|0)|0;MY(a);return}function LY(a){a=a|0;var b=0,d=0,e=0,f=0;e=a+2|0;f=T1(e<<2)|0;b=V1(O(a,a)|0,8)|0;d=0;while(1){if((d|0)>=(a|0))break;c[f+(d<<2)>>2]=b;b=b+(a<<3)|0;d=d+1|0}while(1){if((a|0)>=(e|0))break;c[f+(a<<2)>>2]=0;a=a+1|0}return f|0}function MY(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0;p=l;l=l+32|0;i=p+16|0;g=p;j=c[a+4>>2]|0;m=c[a+8>>2]|0;n=c[a+16>>2]|0;o=c[a+20>>2]|0;d=c[a+24>>2]|0;b=0;while(1){if((b|0)>=(j|0))break;q=c[o+(b<<2)>>2]|0;e=m+(b<<4)|0;f=m+(q<<4)|0;c[g>>2]=c[e>>2];c[g+4>>2]=c[e+4>>2];c[g+8>>2]=c[e+8>>2];c[g+12>>2]=c[e+12>>2];c[i>>2]=c[f>>2];c[i+4>>2]=c[f+4>>2];c[i+8>>2]=c[f+8>>2];c[i+12>>2]=c[f+12>>2];r=+NY(g,i);f=c[d+(b<<2)>>2]|0;h[f+(q<<3)>>3]=r;h[(c[d+(q<<2)>>2]|0)+(b<<3)>>3]=r;a=b+-1|0;a=(q|0)==(a|0)?b+-2|0:a;while(1){if((a|0)<=-1)break;if((OY(b,a,m,n,o)|0?OY(a,b,m,n,o)|0:0)?(k=m+(a<<4)|0,c[g>>2]=c[e>>2],c[g+4>>2]=c[e+4>>2],c[g+8>>2]=c[e+8>>2],c[g+12>>2]=c[e+12>>2],c[i>>2]=c[k>>2],c[i+4>>2]=c[k+4>>2],c[i+8>>2]=c[k+8>>2],c[i+12>>2]=c[k+12>>2],PY(g,i,j,j,j,m,n)|0):0){c[g>>2]=c[e>>2];c[g+4>>2]=c[e+4>>2];c[g+8>>2]=c[e+8>>2];c[g+12>>2]=c[e+12>>2];c[i>>2]=c[k>>2];c[i+4>>2]=c[k+4>>2];c[i+8>>2]=c[k+8>>2];c[i+12>>2]=c[k+12>>2];r=+NY(g,i);h[f+(a<<3)>>3]=r;h[(c[d+(a<<2)>>2]|0)+(b<<3)>>3]=r}a=a+-1|0}b=b+1|0}l=p;return}function NY(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0,g=0;e=l;l=l+32|0;f=e+16|0;g=e;c[g>>2]=c[a>>2];c[g+4>>2]=c[a+4>>2];c[g+8>>2]=c[a+8>>2];c[g+12>>2]=c[a+12>>2];c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];d=+C(+(+JY(g,f)));l=e;return +d}function OY(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0;g=l;l=l+64|0;h=g+48|0;i=g+32|0;j=g+16|0;k=g;n=d+(c[f+(a<<2)>>2]<<4)|0;m=d+(a<<4)|0;e=d+(c[e+(a<<2)>>2]<<4)|0;f=d+(b<<4)|0;c[k>>2]=c[n>>2];c[k+4>>2]=c[n+4>>2];c[k+8>>2]=c[n+8>>2];c[k+12>>2]=c[n+12>>2];c[j>>2]=c[m>>2];c[j+4>>2]=c[m+4>>2];c[j+8>>2]=c[m+8>>2];c[j+12>>2]=c[m+12>>2];c[i>>2]=c[e>>2];c[i+4>>2]=c[e+4>>2];c[i+8>>2]=c[e+8>>2];c[i+12>>2]=c[e+12>>2];c[h>>2]=c[f>>2];c[h+4>>2]=c[f+4>>2];c[h+8>>2]=c[f+8>>2];c[h+12>>2]=c[f+12>>2];f=QY(k,j,i,h)|0;l=g;return f|0}function PY(a,b,d,e,f,g,h){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;p=l;l=l+64|0;n=p+48|0;m=p+32|0;k=p+16|0;j=p;i=0;while(1){if((i|0)>=(d|0)){o=4;break}r=g+(i<<4)|0;q=g+(c[h+(i<<2)>>2]<<4)|0;c[j>>2]=c[a>>2];c[j+4>>2]=c[a+4>>2];c[j+8>>2]=c[a+8>>2];c[j+12>>2]=c[a+12>>2];c[k>>2]=c[b>>2];c[k+4>>2]=c[b+4>>2];c[k+8>>2]=c[b+8>>2];c[k+12>>2]=c[b+12>>2];c[m>>2]=c[r>>2];c[m+4>>2]=c[r+4>>2];c[m+8>>2]=c[r+8>>2];c[m+12>>2]=c[r+12>>2];c[n>>2]=c[q>>2];c[n+4>>2]=c[q+4>>2];c[n+8>>2]=c[q+8>>2];c[n+12>>2]=c[q+12>>2];if(!(IY(j,k,m,n)|0))i=i+1|0;else{e=0;break}}a:do if((o|0)==4)while(1){if((e|0)>=(f|0)){e=1;break a}q=g+(e<<4)|0;r=g+(c[h+(e<<2)>>2]<<4)|0;c[j>>2]=c[a>>2];c[j+4>>2]=c[a+4>>2];c[j+8>>2]=c[a+8>>2];c[j+12>>2]=c[a+12>>2];c[k>>2]=c[b>>2];c[k+4>>2]=c[b+4>>2];c[k+8>>2]=c[b+8>>2];c[k+12>>2]=c[b+12>>2];c[m>>2]=c[q>>2];c[m+4>>2]=c[q+4>>2];c[m+8>>2]=c[q+8>>2];c[m+12>>2]=c[q+12>>2];c[n>>2]=c[r>>2];c[n+4>>2]=c[r+4>>2];c[n+8>>2]=c[r+8>>2];c[n+12>>2]=c[r+12>>2];if(!(IY(j,k,m,n)|0)){e=e+1|0;o=4}else{e=0;break}}while(0);l=p;return e|0}function QY(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;g=l;l=l+48|0;h=g+32|0;i=g+16|0;j=g;c[j>>2]=c[e>>2];c[j+4>>2]=c[e+4>>2];c[j+8>>2]=c[e+8>>2];c[j+12>>2]=c[e+12>>2];c[i>>2]=c[a>>2];c[i+4>>2]=c[a+4>>2];c[i+8>>2]=c[a+8>>2];c[i+12>>2]=c[a+12>>2];c[h>>2]=c[b>>2];c[h+4>>2]=c[b+4>>2];c[h+8>>2]=c[b+8>>2];c[h+12>>2]=c[b+12>>2];f=GY(j,i,h)|0;c[j>>2]=c[e>>2];c[j+4>>2]=c[e+4>>2];c[j+8>>2]=c[e+8>>2];c[j+12>>2]=c[e+12>>2];c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];c[h>>2]=c[d>>2];c[h+4>>2]=c[d+4>>2];c[h+8>>2]=c[d+8>>2];c[h+12>>2]=c[d+12>>2];e=GY(j,i,h)|0;c[j>>2]=c[a>>2];c[j+4>>2]=c[a+4>>2];c[j+8>>2]=c[a+8>>2];c[j+12>>2]=c[a+12>>2];c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];c[h>>2]=c[d>>2];c[h+4>>2]=c[d+4>>2];c[h+8>>2]=c[d+8>>2];c[h+12>>2]=c[d+12>>2];d=(GY(j,i,h)|0)>0;l=g;return (d?e|f:e&f)>>>31^1|0}function RY(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;s=l;l=l+80|0;j=s+64|0;i=s+48|0;g=s+32|0;k=s+16|0;o=s;p=c[a+4>>2]|0;q=c[a+8>>2]|0;r=c[a+16>>2]|0;m=c[a+20>>2]|0;n=T1((p<<3)+16|0)|0;if((b|0)==-2222){c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];b=SY(a,j)|0}if((b|0)>-1){a=c[a+12>>2]|0;f=c[a+(b+1<<2)>>2]|0;a=c[a+(b<<2)>>2]|0}else{f=p;a=p}b=0;while(1){if((b|0)>=(a|0)){b=a;break}u=q+(b<<4)|0;c[o>>2]=c[u>>2];c[o+4>>2]=c[u+4>>2];c[o+8>>2]=c[u+8>>2];c[o+12>>2]=c[u+12>>2];u=q+(c[m+(b<<2)>>2]<<4)|0;t=q+(c[r+(b<<2)>>2]<<4)|0;c[k>>2]=c[u>>2];c[k+4>>2]=c[u+4>>2];c[k+8>>2]=c[u+8>>2];c[k+12>>2]=c[u+12>>2];c[g>>2]=c[o>>2];c[g+4>>2]=c[o+4>>2];c[g+8>>2]=c[o+8>>2];c[g+12>>2]=c[o+12>>2];c[i>>2]=c[t>>2];c[i+4>>2]=c[t+4>>2];c[i+8>>2]=c[t+8>>2];c[i+12>>2]=c[t+12>>2];c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];if((QY(k,g,i,j)|0)!=0?(c[i>>2]=c[d>>2],c[i+4>>2]=c[d+4>>2],c[i+8>>2]=c[d+8>>2],c[i+12>>2]=c[d+12>>2],c[j>>2]=c[o>>2],c[j+4>>2]=c[o+4>>2],c[j+8>>2]=c[o+8>>2],c[j+12>>2]=c[o+12>>2],(PY(i,j,a,f,p,q,r)|0)!=0):0){c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];c[j>>2]=c[o>>2];c[j+4>>2]=c[o+4>>2];c[j+8>>2]=c[o+8>>2];c[j+12>>2]=c[o+12>>2];e=+NY(i,j)}else e=0.0;h[n+(b<<3)>>3]=e;b=b+1|0}while(1){if((b|0)>=(f|0)){b=f;break}h[n+(b<<3)>>3]=0.0;b=b+1|0}while(1){if((b|0)>=(p|0))break;t=q+(b<<4)|0;c[o>>2]=c[t>>2];c[o+4>>2]=c[t+4>>2];c[o+8>>2]=c[t+8>>2];c[o+12>>2]=c[t+12>>2];t=q+(c[m+(b<<2)>>2]<<4)|0;u=q+(c[r+(b<<2)>>2]<<4)|0;c[k>>2]=c[t>>2];c[k+4>>2]=c[t+4>>2];c[k+8>>2]=c[t+8>>2];c[k+12>>2]=c[t+12>>2];c[g>>2]=c[o>>2];c[g+4>>2]=c[o+4>>2];c[g+8>>2]=c[o+8>>2];c[g+12>>2]=c[o+12>>2];c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];if((QY(k,g,i,j)|0)!=0?(c[i>>2]=c[d>>2],c[i+4>>2]=c[d+4>>2],c[i+8>>2]=c[d+8>>2],c[i+12>>2]=c[d+12>>2],c[j>>2]=c[o>>2],c[j+4>>2]=c[o+4>>2],c[j+8>>2]=c[o+8>>2],c[j+12>>2]=c[o+12>>2],(PY(i,j,a,f,p,q,r)|0)!=0):0){c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];c[j>>2]=c[o>>2];c[j+4>>2]=c[o+4>>2];c[j+8>>2]=c[o+8>>2];c[j+12>>2]=c[o+12>>2];e=+NY(i,j)}else e=0.0;h[n+(b<<3)>>3]=e;b=b+1|0}h[n+(p<<3)>>3]=0.0;h[n+(p+1<<3)>>3]=0.0;l=s;return n|0}function SY(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;m=l;l=l+32|0;f=m+8|0;e=m;h=m+24|0;i=a+8|0;j=a+12|0;k=h+4|0;d=0;while(1){if((d|0)>=(c[a>>2]|0)){d=-1111;break}o=c[j>>2]|0;n=c[o+(d<<2)>>2]|0;c[h>>2]=(c[i>>2]|0)+(n<<4);g=d+1|0;c[k>>2]=(c[o+(g<<2)>>2]|0)-n;c[e>>2]=c[h>>2];c[e+4>>2]=c[h+4>>2];c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];if(!(NX(e,f)|0))d=g;else break}l=m;return d|0}function TY(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=l;l=l+64|0;n=t+48|0;m=t+32|0;p=t+16|0;o=t;r=c[f+4>>2]|0;s=c[f+8>>2]|0;q=c[f+16>>2]|0;g=(e|0)<0;do if((b|0)<0)if(g){g=0;i=0;f=0;h=0}else{b=c[f+12>>2]|0;f=0;h=0;i=e;j=b;b=b+(e<<2)|0;k=9}else{if(g){k=c[f+12>>2]|0;f=0;h=0;i=b;j=k;b=k+(b<<2)|0;k=9;break}g=c[f+12>>2]|0;if((b|0)>(e|0)){f=c[g+(e+1<<2)>>2]|0;h=c[g+(e<<2)>>2]|0;i=b;j=g;b=g+(b<<2)|0;k=9;break}else{f=c[g+(b+1<<2)>>2]|0;h=c[g+(b<<2)>>2]|0;i=e;j=g;b=g+(e<<2)|0;k=9;break}}while(0);if((k|0)==9){g=c[j+(i+1<<2)>>2]|0;i=c[b>>2]|0}b=0;while(1){if((b|0)>=(h|0)){k=13;break}j=s+(b<<4)|0;e=s+(c[q+(b<<2)>>2]<<4)|0;c[o>>2]=c[a>>2];c[o+4>>2]=c[a+4>>2];c[o+8>>2]=c[a+8>>2];c[o+12>>2]=c[a+12>>2];c[p>>2]=c[d>>2];c[p+4>>2]=c[d+4>>2];c[p+8>>2]=c[d+8>>2];c[p+12>>2]=c[d+12>>2];c[m>>2]=c[j>>2];c[m+4>>2]=c[j+4>>2];c[m+8>>2]=c[j+8>>2];c[m+12>>2]=c[j+12>>2];c[n>>2]=c[e>>2];c[n+4>>2]=c[e+4>>2];c[n+8>>2]=c[e+8>>2];c[n+12>>2]=c[e+12>>2];if(!(IY(o,p,m,n)|0))b=b+1|0;else{g=0;break}}a:do if((k|0)==13){while(1){if((f|0)>=(i|0))break;e=s+(f<<4)|0;k=s+(c[q+(f<<2)>>2]<<4)|0;c[o>>2]=c[a>>2];c[o+4>>2]=c[a+4>>2];c[o+8>>2]=c[a+8>>2];c[o+12>>2]=c[a+12>>2];c[p>>2]=c[d>>2];c[p+4>>2]=c[d+4>>2];c[p+8>>2]=c[d+8>>2];c[p+12>>2]=c[d+12>>2];c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];c[n>>2]=c[k>>2];c[n+4>>2]=c[k+4>>2];c[n+8>>2]=c[k+8>>2];c[n+12>>2]=c[k+12>>2];if(!(IY(o,p,m,n)|0)){f=f+1|0;k=13}else{g=0;break a}}while(1){if((g|0)>=(r|0)){g=1;break a}e=s+(g<<4)|0;k=s+(c[q+(g<<2)>>2]<<4)|0;c[o>>2]=c[a>>2];c[o+4>>2]=c[a+4>>2];c[o+8>>2]=c[a+8>>2];c[o+12>>2]=c[a+12>>2];c[p>>2]=c[d>>2];c[p+4>>2]=c[d+4>>2];c[p+8>>2]=c[d+8>>2];c[p+12>>2]=c[d+12>>2];c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];c[n>>2]=c[k>>2];c[n+4>>2]=c[k+4>>2];c[n+8>>2]=c[k+8>>2];c[n+12>>2]=c[k+12>>2];if(!(IY(o,p,m,n)|0))g=g+1|0;else{g=0;break}}}while(0);l=t;return g|0}function UY(a){a=a|0;return VY(a,0,0)|0}function VY(a,b,c){a=a|0;b=b|0;c=c|0;return WY(a,b,c,0)|0}function WY(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;if(!d){g=T1(496)|0;if(!g)g=0;else{c[g+12>>2]=35;c[g+16>>2]=107;c[g+20>>2]=105;d=35;h=6}}else{g=kb[c[d>>2]&63](496)|0;if(!g)g=0;else{h=c[d>>2]|0;c[g+12>>2]=h;c[g+16>>2]=c[d+4>>2];c[g+20>>2]=c[d+8>>2];d=h;h=6}}do if((h|0)==6){c[g+8>>2]=0;c[g+32>>2]=0;c[g+376>>2]=16;j=g+12|0;i=kb[d&63](256)|0;d=g+388|0;c[d>>2]=i;if(!i){tb[c[g+20>>2]&127](g);g=0;break}h=kb[c[j>>2]&63](1024)|0;i=g+44|0;c[i>>2]=h;if(!h){e=g+20|0;tb[c[e>>2]&127](c[d>>2]|0);tb[c[e>>2]&127](g);g=0;break}c[g+48>>2]=h+1024;if(!f){f=XY(j)|0;c[g+352>>2]=f;if(!f){e=g+20|0;tb[c[e>>2]&127](c[i>>2]|0);tb[c[e>>2]&127](c[d>>2]|0);tb[c[e>>2]&127](g);g=0;break}}else c[g+352>>2]=f;c[g+372>>2]=0;c[g+364>>2]=0;c[g+300>>2]=0;c[g+464>>2]=0;c[g+460>>2]=0;c[g+124>>2]=0;c[g+244>>2]=0;d=g+468|0;a[d>>0]=33;h=g+232|0;a[h>>0]=0;a[g+233>>0]=0;c[g+392>>2]=0;c[g+396>>2]=0;a[g+400>>0]=0;f=g+228|0;c[f>>2]=0;YY(g+412|0,j);YY(g+436|0,j);ZY(g,b);if(b|0?(c[f>>2]|0)==0:0){_Y(g);g=0;break}if(!e){c[g+224>>2]=i$()|0;break}else{a[h>>0]=1;c[g+224>>2]=O0()|0;a[d>>0]=a[e>>0]|0;break}}while(0);return g|0}function XY(b){b=b|0;var d=0;d=kb[c[b>>2]&63](188)|0;if(d|0){YY(d+80|0,b);YY(d+104|0,b);f_(d,b);f_(d+20|0,b);f_(d+40|0,b);f_(d+60|0,b);a[d+131>>0]=0;f_(d+132|0,b);c[d+152>>2]=0;c[d+156>>2]=0;a[d+160>>0]=0;b=d+164|0;c[b>>2]=0;c[b+4>>2]=0;c[b+8>>2]=0;c[b+12>>2]=0;c[b+16>>2]=0;c[b+20>>2]=0;a[d+128>>0]=1;a[d+129>>0]=0;a[d+130>>0]=0}return d|0}function YY(a,b){a=a|0;b=b|0;c[a>>2]=0;c[a+4>>2]=0;c[a+8>>2]=0;c[a+12>>2]=0;c[a+16>>2]=0;c[a+20>>2]=b;return}function ZY(d,e){d=d|0;e=e|0;var f=0,g=0,h=0;c[d+276>>2]=56;V0(d+252|0);if(e|0)c[d+228>>2]=gZ(e,d+12|0)|0;c[d+356>>2]=0;Y$(d+148|0,d+144|0,0)|0;c[d>>2]=0;c[d+4>>2]=0;e=d+116|0;g=d+52|0;h=g+64|0;do{c[g>>2]=0;g=g+4|0}while((g|0)<(h|0));c[e>>2]=d;c[d+120>>2]=0;e=d+128|0;c[e>>2]=0;c[e+4>>2]=0;c[e+8>>2]=0;c[e+12>>2]=0;e=c[d+8>>2]|0;c[d+24>>2]=e;c[d+28>>2]=e;c[d+36>>2]=0;c[d+40>>2]=0;e=d+312|0;g=d+404|0;c[g>>2]=0;c[g+4>>2]=0;g=d+280|0;f=d+304|0;c[g>>2]=0;c[g+4>>2]=0;c[g+8>>2]=0;c[g+12>>2]=0;c[g+16>>2]=0;g=e;h=g+36|0;do{c[g>>2]=0;g=g+4|0}while((g|0)<(h|0));b[e+36>>1]=0;a[f>>0]=1;c[d+308>>2]=0;c[d+360>>2]=0;c[d+368>>2]=0;c[d+380>>2]=0;c[d+236>>2]=0;c[d+248>>2]=0;c[d+240>>2]=0;c[d+472>>2]=0;c[d+476>>2]=0;a[d+484>>0]=0;a[d+485>>0]=0;c[d+488>>2]=0;c[d+492>>2]=0;return}function _Y(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;if(b|0){e=b+364|0;g=b+20|0;d=c[b+360>>2]|0;while(1){if(!d){d=c[e>>2]|0;if(!d)break;c[e>>2]=0}h=c[d>>2]|0;tb[c[g>>2]&127](c[d+36>>2]|0);$Y(c[d+44>>2]|0,b);tb[c[g>>2]&127](d);d=h}e=b+300|0;d=c[b+296>>2]|0;while(1){if(!d){d=c[e>>2]|0;if(!d)break;c[e>>2]=0}h=c[d+8>>2]|0;tb[c[g>>2]&127](d);d=h}$Y(c[b+372>>2]|0,b);$Y(c[b+368>>2]|0,b);aZ(b+412|0);aZ(b+436|0);d=b+12|0;tb[c[g>>2]&127](c[b+228>>2]|0);if((a[b+484>>0]|0)==0?(f=c[b+352>>2]|0,f|0):0)bZ(f,(c[b+472>>2]|0)==0&1,d);tb[c[g>>2]&127](c[b+388>>2]|0);tb[c[g>>2]&127](c[b+460>>2]|0);tb[c[g>>2]&127](c[b+8>>2]|0);tb[c[g>>2]&127](c[b+44>>2]|0);tb[c[g>>2]&127](c[b+392>>2]|0);tb[c[g>>2]&127](c[b+236>>2]|0);d=c[b+248>>2]|0;if(d|0)tb[d&127](c[b+240>>2]|0);tb[c[g>>2]&127](b)}return}function $Y(a,b){a=a|0;b=b|0;var d=0;b=b+20|0;while(1){if(!a)break;d=c[a+4>>2]|0;tb[c[b>>2]&127](c[a+16>>2]|0);tb[c[b>>2]&127](a);a=d}return}function aZ(a){a=a|0;var b=0,d=0,e=0;d=a+20|0;b=c[a>>2]|0;while(1){if(!b)break;e=c[b>>2]|0;tb[c[(c[d>>2]|0)+8>>2]&127](b);b=e}b=c[a+4>>2]|0;while(1){if(!b)break;e=c[b>>2]|0;tb[c[(c[d>>2]|0)+8>>2]&127](b);b=e}return}function bZ(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;h=l;l=l+16|0;f=h;g=a+20|0;cZ(f,g);d=d+8|0;while(1){e=dZ(f)|0;if(!e)break;if(!(c[e+16>>2]|0))continue;tb[c[d>>2]&127](c[e+20>>2]|0)}eZ(a);eZ(a+132|0);eZ(g);eZ(a+40|0);eZ(a+60|0);aZ(a+80|0);aZ(a+104|0);if(b<<24>>24){tb[c[d>>2]&127](c[a+184>>2]|0);tb[c[d>>2]&127](c[a+164>>2]|0)}tb[c[d>>2]&127](a);l=h;return}function cZ(a,b){a=a|0;b=b|0;var d=0;d=c[b>>2]|0;c[a>>2]=d;c[a+4>>2]=d+(c[b+8>>2]<<2);return}function dZ(a){a=a|0;var b=0,d=0,e=0;d=c[a+4>>2]|0;e=c[a>>2]|0;do{if((e|0)==(d|0)){b=0;break}b=e;e=e+4|0;c[a>>2]=e;b=c[b>>2]|0}while(!(b|0));return b|0}function eZ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;d=a+8|0;e=a+16|0;b=0;while(1){f=c[(c[e>>2]|0)+8>>2]|0;g=c[a>>2]|0;if(b>>>0>=(c[d>>2]|0)>>>0)break;tb[f&127](c[g+(b<<2)>>2]|0);b=b+1|0}tb[f&127](g);return}function fZ(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;f=hZ(a)|0;if(!f){c[a+276>>2]=57;f=iZ(a,b,d,e)|0}return f|0}function gZ(b,d){b=b|0;d=d|0;var e=0,f=0;e=0;do{f=e;e=e+1|0}while((a[b+f>>0]|0)!=0);d=kb[c[d>>2]&63](e)|0;if(!d)d=0;else W6(d|0,b|0,e|0)|0;return d|0}function hZ(b){b=b|0;var d=0;d=b+228|0;if(!(nb[(a[b+232>>0]<<24>>24?38:37)&63](b+148|0,b+144|0,c[d>>2]|0)|0))b=e_(b,c[d>>2]|0)|0;else b=0;return b|0}function iZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;g=l;l=l+16|0;h=g;c[h>>2]=d;j=b+144|0;i=c[j>>2]|0;i=ob[c[i>>2]&127](i,d,e,h)|0;f=jZ(b,c[j>>2]|0,d,e,i,c[h>>2]|0,f,(a[b+480>>0]|0)==0&1)|0;l=g;return f|0}function jZ(b,e,f,g,h,i,j,k){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;k=k|0;var m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0,X=0,Y=0,Z=0,_=0,$=0,aa=0,ba=0,ca=0,da=0,ea=0,fa=0,ga=0,ha=0,ia=0,ja=0,ka=0,la=0,ma=0,na=0,oa=0,pa=0,qa=0,ra=0,sa=0,ta=0,ua=0,va=0,wa=0,xa=0,ya=0,za=0,Aa=0,Ba=0,Ca=0,Da=0,Ea=0,Fa=0,Ga=0;Ga=l;l=l+16|0;Da=Ga;c[Da>>2]=i;r=c[b+352>>2]|0;s=b+144|0;if((c[s>>2]|0)==(e|0)){p=b+284|0;q=b+288|0;na=b+296|0}else{na=b+296|0;q=c[na>>2]|0;p=q;q=q+4|0}t=k<<24>>24!=0;pa=b+252|0;u=b+80|0;v=b+476|0;w=b+84|0;x=b+320|0;y=b+412|0;z=b+316|0;A=b+424|0;B=b+428|0;C=b+324|0;ra=b+4|0;D=b+485|0;E=r+132|0;F=b+312|0;ta=r+129|0;G=r+128|0;H=r+80|0;I=r+92|0;J=r+96|0;K=b+136|0;L=b+88|0;M=b+488|0;va=b+112|0;wa=r+131|0;xa=b+116|0;ya=r+130|0;za=b+108|0;Aa=b+356|0;N=b+340|0;O=b+132|0;P=b+344|0;Q=b+348|0;R=b+328|0;S=b+349|0;T=b+420|0;U=r+120|0;V=r+116|0;W=b+92|0;X=b+472|0;Y=b+336|0;Z=b+332|0;_=b+96|0;$=b+256|0;aa=b+464|0;ba=b+460|0;ca=r+160|0;da=r+184|0;ea=r+180|0;fa=r+164|0;ga=b+128|0;ha=b+12|0;ia=b+16|0;ja=b+268|0;ka=b+120|0;la=r+176|0;ma=r+168|0;o=e;n=f;e=h;a:while(1){c[p>>2]=n;f=c[Da>>2]|0;c[q>>2]=f;i=f;b:do if((e|0)<1){if(t&(e|0)!=0){m=7;break a}switch(e|0){case -1:{i=5;m=280;break a}case 0:{m=9;break a}case -2:{i=6;break a}case -4:{m=10;break a}case -15:{e=15;break b}default:{}}c[Da>>2]=g;e=0-e|0;i=g}while(0);h=pb[c[pa>>2]&127](pa,e,n,i,o)|0;c:do switch(h|0){case 2:{m=58;break a}case -1:{m=184;break a}case 1:{i=kZ(b,0,n,c[Da>>2]|0)|0;if(i|0){m=280;break a}i=c[s>>2]|0;break}case 4:{if(!(c[w>>2]|0))i=1;else{m=lZ(y,o,n,c[Da>>2]|0)|0;c[z>>2]=m;if(!m){i=1;m=280;break a}c[B>>2]=c[A>>2];c[C>>2]=0;i=0}c[x>>2]=0;m=274;break}case 7:{i=c[w>>2]|0;if(!i)m=275;else{yb[i&15](c[ra>>2]|0,c[z>>2]|0,c[x>>2]|0,c[C>>2]|0,1);c[z>>2]=0;mZ(y);i=o}break}case 57:{i=kZ(b,1,n,c[Da>>2]|0)|0;if(i|0){m=280;break a}i=c[s>>2]|0;break}case 6:{a[D>>0]=0;m=nZ(b,E,75395,36)|0;c[F>>2]=m;if(!m){i=1;m=280;break a}a[ta>>0]=1;if(!(c[w>>2]|0))m=33;else{if(!(ob[c[o+52>>2]&127](o,n,c[Da>>2]|0,p)|0)){i=32;m=280;break a}i=c[o+64>>2]|0;i=lZ(y,o,n+i|0,(c[Da>>2]|0)+(0-i)|0)|0;if(!i){i=1;m=280;break a}oZ(i);c[B>>2]=c[A>>2];c[C>>2]=i;i=0;m=34}break}case 14:{m=33;break}case 8:{i=c[z>>2]|0;if(!i)i=1;else{yb[c[w>>2]&15](c[ra>>2]|0,i,c[x>>2]|0,c[C>>2]|0,0);mZ(y);i=0}if(!((c[x>>2]|0)==0?!(a[D>>0]|0):0)){f=a[ta>>0]|0;a[ta>>0]=1;do if(c[M>>2]|0?c[va>>2]|0:0){h=nZ(b,E,75395,36)|0;if(!h){i=1;m=280;break a}if(!(a[D>>0]|0))e=h+20|0;else{e=h+20|0;c[e>>2]=c[Aa>>2]}a[wa>>0]=0;if(!(pb[c[va>>2]&127](c[xa>>2]|0,0,c[e>>2]|0,c[h+16>>2]|0,c[h+24>>2]|0)|0)){i=21;m=280;break a}if(!(a[wa>>0]|0)){if(c[x>>2]|0)break;a[ta>>0]=f;break}if(((a[ya>>0]|0)==0?(oa=c[za>>2]|0,oa|0):0)?(kb[oa&63](c[ra>>2]|0)|0)==0:0){i=22;m=280;break a}}while(0);a[D>>0]=0}e=c[L>>2]|0;if(!e)m=274;else{tb[e&127](c[ra>>2]|0);i=o}break}case 34:{m=qZ(b,o,n,c[Da>>2]|0)|0;c[N>>2]=m;if(!m){i=1;m=280;break a}else m=80;break}case 22:{m=rZ(b,o,n,c[Da>>2]|0)|0;c[P>>2]=m;if(!m){i=1;m=280;break a}a[Q>>0]=0;c[R>>2]=0;a[S>>0]=0;m=80;break}case 23:{a[Q>>0]=1;c[R>>2]=142188;m=80;break}case 24:{a[S>>0]=1;c[R>>2]=142194;m=80;break}case 25:{c[R>>2]=142197;m=80;break}case 26:{c[R>>2]=142203;m=80;break}case 27:{c[R>>2]=142210;m=80;break}case 28:{c[R>>2]=142217;m=80;break}case 29:{c[R>>2]=142226;m=80;break}case 30:{c[R>>2]=142234;m=80;break}case 32:case 31:{if((a[G>>0]|0)!=0?(c[O>>2]|0)!=0:0){if(!(sZ(y,(c[R>>2]|0)==0?((h|0)==32?142243:142253):142255)|0)){i=1;m=280;break a}if(!(tZ(y,o,n,c[Da>>2]|0)|0)){i=1;m=280;break a}c[R>>2]=c[B>>2];i=o}else m=275;break}case 36:case 35:{if(a[G>>0]|0){if(!(uZ(c[N>>2]|0,c[P>>2]|0,a[Q>>0]|0,a[S>>0]|0,0,b)|0)){i=1;m=280;break a}if((c[O>>2]|0)!=0?(qa=c[R>>2]|0,(qa|0)!=0):0){switch(a[qa>>0]|0){case 40:{m=93;break}case 78:{if((a[qa+1>>0]|0)==79)m=93;break}default:{}}if((m|0)==93){m=0;i=c[A>>2]|0;if((i|0)==(c[T>>2]|0)){if(!((vZ(y)|0)<<24>>24)){i=1;m=280;break a}i=c[A>>2]|0}c[A>>2]=i+1;a[i>>0]=41;i=c[A>>2]|0;if((i|0)==(c[T>>2]|0)){if(!((vZ(y)|0)<<24>>24)){i=1;m=280;break a}i=c[A>>2]|0}c[A>>2]=i+1;a[i>>0]=0;c[R>>2]=c[B>>2];c[B>>2]=c[A>>2]}c[q>>2]=n;zb[c[O>>2]&15](c[ra>>2]|0,c[c[N>>2]>>2]|0,c[c[P>>2]>>2]|0,c[R>>2]|0,0,(h|0)==36&1);mZ(y);i=o}else m=275}else m=275;break}case 38:case 37:{if(a[G>>0]|0){i=c[o+64>>2]|0;i=wZ(b,o,a[Q>>0]|0,n+i|0,(c[Da>>2]|0)+(0-i)|0,H)|0;if(i|0){m=280;break a}e=c[J>>2]|0;c[J>>2]=c[I>>2];if(!(uZ(c[N>>2]|0,c[P>>2]|0,a[Q>>0]|0,0,e,b)|0)){i=1;m=280;break a}if((c[O>>2]|0)!=0?(sa=c[R>>2]|0,(sa|0)!=0):0){switch(a[sa>>0]|0){case 40:{m=108;break}case 78:{if((a[sa+1>>0]|0)==79)m=108;break}default:{}}if((m|0)==108){m=0;i=c[A>>2]|0;if((i|0)==(c[T>>2]|0)){if(!((vZ(y)|0)<<24>>24)){i=1;m=280;break a}i=c[A>>2]|0}c[A>>2]=i+1;a[i>>0]=41;i=c[A>>2]|0;if((i|0)==(c[T>>2]|0)){if(!((vZ(y)|0)<<24>>24)){i=1;m=280;break a}i=c[A>>2]|0}c[A>>2]=i+1;a[i>>0]=0;c[R>>2]=c[B>>2];c[B>>2]=c[A>>2]}c[q>>2]=n;zb[c[O>>2]&15](c[ra>>2]|0,c[c[N>>2]>>2]|0,c[c[P>>2]>>2]|0,c[R>>2]|0,e,(h|0)==38&1);mZ(y);i=o}else m=275}else m=275;break}case 12:{if(a[G>>0]|0){i=c[o+64>>2]|0;i=xZ(b,o,n+i|0,(c[Da>>2]|0)+(0-i)|0)|0;e=c[F>>2]|0;f=c[U>>2]|0;if(e){c[e+4>>2]=f;c[(c[F>>2]|0)+8>>2]=(c[V>>2]|0)-(c[U>>2]|0);c[U>>2]=c[V>>2];if(!(c[K>>2]|0))e=1;else{c[q>>2]=n;e=c[F>>2]|0;Ab[c[K>>2]&0](c[ra>>2]|0,c[e>>2]|0,d[e+33>>0]|0,c[e+4>>2]|0,c[e+8>>2]|0,c[Aa>>2]|0,0,0,0);e=0}}else{c[V>>2]=f;e=1}if(!i){i=e;m=274}else{m=280;break a}}else m=275;break}case 5:{a[D>>0]=0;a[ta>>0]=1;if(!(c[w>>2]|0)){c[x>>2]=75395;i=1}else{m=c[o+64>>2]|0;m=lZ(y,o,n+m|0,(c[Da>>2]|0)+(0-m)|0)|0;c[x>>2]=m;if(!m){i=1;m=280;break a}c[B>>2]=c[A>>2];i=0}if((((a[ya>>0]|0)==0?(c[M>>2]|0)==0:0)?(ua=c[za>>2]|0,ua|0):0)?(kb[ua&63](c[ra>>2]|0)|0)==0:0){i=22;m=280;break a}if(!(c[F>>2]|0)){e=nZ(b,E,75395,36)|0;c[F>>2]=e;if(!e){i=1;m=280;break a}c[e+24>>2]=0;m=133}else m=133;break}case 13:{i=1;m=133;break}case 15:{if(((a[G>>0]|0)!=0?(c[F>>2]|0)!=0:0)?(c[K>>2]|0)!=0:0){c[q>>2]=n;i=c[F>>2]|0;Ab[c[K>>2]&0](c[ra>>2]|0,c[i>>2]|0,d[i+33>>0]|0,0,0,c[i+20>>2]|0,c[i+16>>2]|0,c[i+24>>2]|0,0);i=o}else m=275;break}case 16:{if((a[G>>0]|0)!=0?(c[F>>2]|0)!=0:0){h=lZ(H,o,n,c[Da>>2]|0)|0;c[(c[F>>2]|0)+28>>2]=h;if(!(c[(c[F>>2]|0)+28>>2]|0)){i=1;m=280;break a}c[J>>2]=c[I>>2];if(c[W>>2]|0){c[q>>2]=n;i=c[F>>2]|0;zb[c[W>>2]&15](c[ra>>2]|0,c[i>>2]|0,c[i+20>>2]|0,c[i+16>>2]|0,c[i+24>>2]|0,c[i+28>>2]|0);i=o;break c}if(c[K>>2]|0){c[q>>2]=n;i=c[F>>2]|0;Ab[c[K>>2]&0](c[ra>>2]|0,c[i>>2]|0,0,0,0,c[i+20>>2]|0,c[i+16>>2]|0,c[i+24>>2]|0,c[i+28>>2]|0);i=o}else m=275}else m=275;break}case 9:{if(nb[c[o+44>>2]&63](o,n,c[Da>>2]|0)|0){c[F>>2]=0;m=275;break c}if(!(a[G>>0]|0)){c[I>>2]=c[J>>2];c[F>>2]=0;m=275;break c}i=lZ(H,o,n,c[Da>>2]|0)|0;if(!i){i=1;m=280;break a}e=nZ(b,r,i,36)|0;c[F>>2]=e;if(!e){i=1;m=280;break a}if((c[e>>2]|0)!=(i|0)){c[I>>2]=c[J>>2];c[F>>2]=0;m=275;break c}c[J>>2]=c[I>>2];c[(c[F>>2]|0)+24>>2]=0;a[(c[F>>2]|0)+33>>0]=0;if(!(c[X>>2]|0))i=(c[na>>2]|0)!=0;else i=1;a[(c[F>>2]|0)+34>>0]=(i^1)&1;if(!(c[K>>2]|0))m=275;else i=o;break}case 10:{if(!(a[G>>0]|0)){c[I>>2]=c[J>>2];c[F>>2]=0;m=275;break c}i=lZ(H,o,n,c[Da>>2]|0)|0;if(!i){i=1;m=280;break a}e=nZ(b,E,i,36)|0;c[F>>2]=e;if(!e){i=1;m=280;break a}if((c[e>>2]|0)!=(i|0)){c[I>>2]=c[J>>2];c[F>>2]=0;m=275;break c}c[J>>2]=c[I>>2];c[(c[F>>2]|0)+24>>2]=0;a[(c[F>>2]|0)+33>>0]=1;if(!(c[X>>2]|0))i=(c[na>>2]|0)!=0;else i=1;a[(c[F>>2]|0)+34>>0]=(i^1)&1;if(!(c[K>>2]|0))m=275;else i=o;break}case 18:{c[Y>>2]=0;c[Z>>2]=0;if(!(c[_>>2]|0))m=275;else{n=lZ(y,o,n,c[Da>>2]|0)|0;c[Z>>2]=n;if(!n){i=1;m=280;break a}c[B>>2]=c[A>>2];i=o}break}case 21:{if(!(ob[c[o+52>>2]&127](o,n,c[Da>>2]|0,p)|0)){i=32;m=280;break a}if(!(c[Z>>2]|0))m=275;else{i=c[o+64>>2]|0;i=lZ(y,o,n+i|0,(c[Da>>2]|0)+(0-i)|0)|0;if(!i){i=1;m=280;break a}oZ(i);c[Y>>2]=i;c[B>>2]=c[A>>2];i=o}break}case 19:{if((c[Z>>2]|0)!=0?(c[_>>2]|0)!=0:0){i=c[o+64>>2]|0;i=lZ(y,o,n+i|0,(c[Da>>2]|0)+(0-i)|0)|0;if(!i){i=1;m=280;break a}c[q>>2]=n;yb[c[_>>2]&15](c[ra>>2]|0,c[Z>>2]|0,c[Aa>>2]|0,i,c[Y>>2]|0);i=0}else i=1;mZ(y);m=274;break}case 20:{if((c[Y>>2]|0)!=0?(c[_>>2]|0)!=0:0){c[q>>2]=n;yb[c[_>>2]&15](c[ra>>2]|0,c[Z>>2]|0,c[Aa>>2]|0,0,c[Y>>2]|0);i=0}else i=1;mZ(y);m=274;break}case 58:{if(c[u>>2]|0)yZ(b,o,n,c[Da>>2]|0);i=zZ(b,o,Da,g,j,k)|0;if(i|0){m=280;break a}if(!(c[Da>>2]|0)){m=189;break a}else i=o;break}case 44:{i=c[aa>>2]|0;do if((c[$>>2]|0)>>>0>=i>>>0){if(!i){h=c[ha>>2]|0;c[aa>>2]=32;h=kb[h&63](32)|0;c[ba>>2]=h;if(!h){m=198;break a}else break}f=c[ia>>2]|0;h=c[ba>>2]|0;i=i<<1;c[aa>>2]=i;i=lb[f&127](h,i)|0;if(!i){m=193;break a}c[ba>>2]=i;i=c[da>>2]|0;if(i|0){i=lb[c[ia>>2]&127](i,c[aa>>2]<<2)|0;if(!i){i=1;m=280;break a}c[da>>2]=i}}while(0);a[(c[ba>>2]|0)+(c[$>>2]|0)>>0]=0;if(a[ca>>0]|0){i=BZ(b)|0;if((i|0)<0){i=1;m=280;break a}c[(c[da>>2]|0)+(c[ea>>2]<<2)>>2]=i;c[ea>>2]=(c[ea>>2]|0)+1;c[(c[fa>>2]|0)+(i*28|0)>>2]=6;if(c[ga>>2]|0)i=o;else m=275}else m=275;break}case 50:{i=(c[ba>>2]|0)+(c[$>>2]|0)|0;if((a[i>>0]|0)==124){i=2;m=280;break a}a[i>>0]=44;if((a[ca>>0]|0)!=0?(c[ga>>2]|0)!=0:0)i=o;else m=275;break}case 49:{f=c[ba>>2]|0;e=c[$>>2]|0;i=a[f+e>>0]|0;if(i<<24>>24==44){i=2;m=280;break a}if(i<<24>>24==0&(a[ca>>0]|0)!=0?(Ba=(c[fa>>2]|0)+((c[(c[da>>2]|0)+((c[ea>>2]|0)+-1<<2)>>2]|0)*28|0)|0,(c[Ba>>2]|0)!=3):0){c[Ba>>2]=5;i=(c[ga>>2]|0)==0&1;f=c[ba>>2]|0;e=c[$>>2]|0}else i=1;a[f+e>>0]=124;m=274;break}case 59:case 60:{a[ta>>0]=1;if(c[M>>2]|0){e=c[o+64>>2]|0;e=lZ(H,o,n+e|0,(c[Da>>2]|0)+(0-e)|0)|0;if(!e){i=1;m=280;break a}f=nZ(b,E,e,0)|0;c[I>>2]=c[J>>2];do if(c[ja>>2]|0){if(!(a[ya>>0]|0)){if(a[ta>>0]|0){m=219;break}}else if(c[na>>2]|0){m=219;break}if(!f){i=11;m=280;break a}if(!(a[f+34>>0]|0)){i=24;m=280;break a}}else m=219;while(0);if((m|0)==219?(m=0,(f|0)==0):0){a[G>>0]=a[ya>>0]|0;if((h|0)!=60){m=275;break c}i=c[ka>>2]|0;if(!i){m=275;break c}vb[i&127](c[ra>>2]|0,e,1);i=o;break c}i=f+32|0;if(a[i>>0]|0){i=12;m=280;break a}if(c[f+4>>2]|0){i=CZ(b,f,(h|0)==60&1)|0;e=(i|0)==0;if(e){i=(e^1)&1;m=274;break c}else{m=280;break a}}if(!(c[va>>2]|0)){a[G>>0]=a[ya>>0]|0;m=275;break c}a[wa>>0]=0;a[i>>0]=1;h=(pb[c[va>>2]&127](c[xa>>2]|0,0,c[f+20>>2]|0,c[f+16>>2]|0,c[f+24>>2]|0)|0)==0;a[i>>0]=0;if(h){i=21;m=280;break a}e=a[ya>>0]|0;if(!(a[wa>>0]|0)){a[G>>0]=e;i=o;break c}else i=0}else{e=a[ya>>0]|0;a[G>>0]=e;i=1}if((e<<24>>24==0?(Ca=c[za>>2]|0,(Ca|0)!=0):0)?(kb[Ca&63](c[ra>>2]|0)|0)==0:0){i=22;m=280;break a}else m=274;break}case 40:{if(!(c[ga>>2]|0))m=275;else{n=qZ(b,o,n,c[Da>>2]|0)|0;c[N>>2]=n;if(!n){i=1;m=280;break a}c[ea>>2]=0;c[la>>2]=0;a[ca>>0]=1;i=o}break}case 42:case 41:{if(!(a[ca>>0]|0))m=275;else{if(!(c[ga>>2]|0))i=1;else{i=kb[c[ha>>2]&63](20)|0;if(!i){i=1;m=280;break a}m=i+4|0;c[m>>2]=0;c[m+4>>2]=0;c[m+8>>2]=0;c[m+12>>2]=0;c[i>>2]=(h|0)==41?2:1;c[q>>2]=n;vb[c[ga>>2]&127](c[ra>>2]|0,c[c[N>>2]>>2]|0,i);i=0}a[ca>>0]=0;m=274}break}case 43:{if((a[ca>>0]|0)!=0?(c[(c[fa>>2]|0)+((c[(c[da>>2]|0)+((c[ea>>2]|0)+-1<<2)>>2]|0)*28|0)>>2]=3,(c[ga>>2]|0)!=0):0)i=o;else m=275;break}case 51:{e=0;m=247;break}case 53:{e=1;m=247;break}case 52:{e=2;m=247;break}case 54:{e=3;m=247;break}case 45:{f=0;m=258;break}case 47:{f=1;m=258;break}case 46:{f=2;m=258;break}case 48:{f=3;m=258;break}case 55:{if(!(EZ(b,o,n,c[Da>>2]|0)|0)){i=1;m=280;break a}else i=o;break}case 56:{if(!(FZ(b,o,n,c[Da>>2]|0)|0)){i=1;m=280;break a}else i=o;break}case 0:{if((e|0)==14)i=o;else m=275;break}case 3:{if(!(c[w>>2]|0))m=275;else i=o;break}case 11:{if((a[G>>0]|0)!=0?(c[K>>2]|0)!=0:0)i=o;else m=275;break}case 17:{if(!(c[_>>2]|0))m=275;else i=o;break}case 33:{if((a[G>>0]|0)!=0?(c[O>>2]|0)!=0:0)i=o;else m=275;break}case 39:{if(!(c[ga>>2]|0))m=275;else i=o;break}default:m=275}while(0);if((m|0)==33)if(!(ob[c[o+52>>2]&127](o,n,c[Da>>2]|0,p)|0)){i=32;m=280;break}else{i=1;m=34}else if((m|0)==80){m=0;if((a[G>>0]|0)!=0?(c[O>>2]|0)!=0:0)i=o;else m=275}else if((m|0)==133){m=0;if((a[G>>0]|0)!=0?(c[F>>2]|0)!=0:0){e=c[o+64>>2]|0;e=lZ(H,o,n+e|0,(c[Da>>2]|0)+(0-e)|0)|0;c[(c[F>>2]|0)+16>>2]=e;e=c[F>>2]|0;if(!(c[e+16>>2]|0)){i=1;m=280;break}c[e+20>>2]=c[Aa>>2];c[J>>2]=c[I>>2];if((h|0)==13&(c[K>>2]|0)!=0)i=o;else m=274}else m=274}else if((m|0)==247){m=0;if(a[ca>>0]|0){i=c[Da>>2]|0;if(e)i=i+(0-(c[o+64>>2]|0))|0;f=BZ(b)|0;if((f|0)<0){i=1;m=280;break}c[(c[fa>>2]|0)+(f*28|0)>>2]=4;c[(c[fa>>2]|0)+(f*28|0)+4>>2]=e;i=qZ(b,o,n,i)|0;if(!i){i=1;m=280;break}e=c[i>>2]|0;c[(c[fa>>2]|0)+(f*28|0)+8>>2]=e;i=0;do{h=i;i=i+1|0}while((a[e+h>>0]|0)!=0);c[ma>>2]=(c[ma>>2]|0)+i;if(c[ga>>2]|0)i=o;else m=275}else m=275}else if((m|0)==258)if(a[ca>>0]|0){e=(c[ga>>2]|0)==0;i=e&1;m=(c[ea>>2]|0)+-1|0;c[ea>>2]=m;c[(c[fa>>2]|0)+((c[(c[da>>2]|0)+(m<<2)>>2]|0)*28|0)+4>>2]=f;if(!(c[ea>>2]|0)){if(!e){e=DZ(b)|0;if(!e){i=1;m=280;break}c[q>>2]=n;vb[c[ga>>2]&127](c[ra>>2]|0,c[c[N>>2]>>2]|0,e)}a[ca>>0]=0;c[ma>>2]=0;m=274}else m=274}else m=275;if((m|0)==34){m=0;if((a[G>>0]|0)!=0?(c[F>>2]|0)!=0:0){e=c[o+64>>2]|0;e=lZ(H,o,n+e|0,(c[Da>>2]|0)+(0-e)|0)|0;if(!e){i=1;m=280;break}oZ(e);c[(c[F>>2]|0)+24>>2]=e;c[J>>2]=c[I>>2];if((h|0)==14&(c[K>>2]|0)!=0)i=o;else m=274}else m=274}if((m|0)==274){m=0;if(!(i<<24>>24))i=o;else m=275}if((m|0)==275){m=0;if(!(c[u>>2]|0))i=o;else{yZ(b,o,n,c[Da>>2]|0);i=o}}switch(c[v>>2]|0){case 3:{m=278;break a}case 2:{i=35;m=280;break a}default:{}}e=c[Da>>2]|0;o=i;n=e;e=ob[c[i>>2]&127](i,e,g,Da)|0}d:switch(m|0){case 7:{c[j>>2]=n;i=0;break}case 9:{c[p>>2]=f;i=4;break}case 10:{i=(o|0)==(c[s>>2]|0);if(!i?(a[(c[na>>2]|0)+20>>0]|0)==0:0){c[j>>2]=n;i=0;break}if((a[b+484>>0]|0)!=0|i^1)if((pb[c[pa>>2]&127](pa,-4,g,g,o)|0)==-1)i=29;else{c[j>>2]=n;i=0}else i=3;break}case 58:{do if((a[D>>0]|0?(Ea=a[ta>>0]|0,a[ta>>0]=1,c[M>>2]|0):0)?c[va>>2]|0:0){i=nZ(b,E,75395,36)|0;if(!i){i=1;break d}Da=i+20|0;c[Da>>2]=c[Aa>>2];a[wa>>0]=0;if(!(pb[c[va>>2]&127](c[xa>>2]|0,0,c[Da>>2]|0,c[i+16>>2]|0,c[i+24>>2]|0)|0)){i=21;break d}if(!(a[wa>>0]|0)){a[ta>>0]=Ea;break}if(((a[ya>>0]|0)==0?(Fa=c[za>>2]|0,Fa|0):0)?(kb[Fa&63](c[ra>>2]|0)|0)==0:0){i=22;break d}}while(0);c[b+276>>2]=58;i=pZ(b,n,g,j)|0;break}case 184:{i=(e|0)==28?10:(e|0)==12?17:2;break}case 189:{c[b+276>>2]=59;i=0;break}case 193:{c[aa>>2]=(c[aa>>2]|0)>>>1;i=1;break}case 198:{c[aa>>2]=0;i=1;break}case 278:{c[j>>2]=c[Da>>2];i=0;break}case 280:break}l=Ga;return i|0}function kZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+32|0;p=q+16|0;m=q+12|0;i=q+8|0;j=q+4|0;k=q;c[p>>2]=0;c[m>>2]=0;c[i>>2]=0;c[k>>2]=-1;n=b+144|0;o=b+284|0;g=(d|0)!=0;a:do if(rb[(a[b+232>>0]<<24>>24?2:1)&3](d,c[n>>2]|0,e,f,o,i,j,p,m,k)|0){if((c[k>>2]|0)==1&(g^1)?(a[(c[b+352>>2]|0)+130>>0]=1,h=b+488|0,(c[h>>2]|0)==1):0)c[h>>2]=0;h=b+140|0;if(!(c[h>>2]|0))if(!(c[b+80>>2]|0)){e=0;d=0}else{yZ(b,c[n>>2]|0,e,f);e=0;d=0}else{d=c[p>>2]|0;if(!d)d=0;else{f=c[n>>2]|0;d=lZ(b+436|0,f,d,d+(lb[c[f+28>>2]&127](f,d)|0)|0)|0;if(!d){d=1;break}c[b+452>>2]=c[b+448>>2]}g=c[i>>2]|0;if(g){i=c[n>>2]|0;g=lZ(b+436|0,i,g,(c[j>>2]|0)+(0-(c[i+64>>2]|0))|0)|0;if(!g){d=1;break}}else g=0;xb[c[h>>2]&31](c[b+4>>2]|0,g,d,c[k>>2]|0);e=g}do if(!(c[b+228>>2]|0)){g=c[m>>2]|0;h=g;if(g|0){k=c[g+64>>2]|0;m=c[n>>2]|0;if((k|0)==(c[m+64>>2]|0)?(k|0)!=2|(g|0)==(m|0):0){c[n>>2]=h;break}c[o>>2]=c[p>>2];d=19;break a}g=c[p>>2]|0;if(g|0){h=b+436|0;if(!d){d=c[n>>2]|0;d=lZ(h,d,g,g+(lb[c[d+28>>2]&127](d,g)|0)|0)|0;if(!d){d=1;break a}}d=e_(b,d)|0;mZ(h);if((d|0)!=18)break a;c[o>>2]=c[p>>2];d=18;break a}}while(0);if((d|0)!=0|(e|0)!=0){mZ(b+436|0);d=0}else d=0}else d=g?31:30;while(0);l=q;return d|0}function lZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;do if(!(tZ(b,d,e,f)|0))d=0;else{e=b+12|0;d=c[e>>2]|0;if((d|0)==(c[b+8>>2]|0)){if(!((vZ(b)|0)<<24>>24)){d=0;break}d=c[e>>2]|0}c[e>>2]=d+1;a[d>>0]=0;d=c[b+16>>2]|0}while(0);return d|0}function mZ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;e=a+4|0;b=c[e>>2]|0;a:do if(!b){c[e>>2]=c[a>>2];b=a}else{d=c[a>>2]|0;while(1){if(!d){b=a;break a}g=c[d>>2]|0;c[d>>2]=b;c[e>>2]=d;f=d;d=g;b=f}}while(0);c[b>>2]=0;c[a+16>>2]=0;c[a+12>>2]=0;c[a+8>>2]=0;return}function nZ(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0;w=e+8|0;a:do if(!(c[w>>2]|0))if(g){a[e+4>>0]=6;c[w>>2]=64;h=e+16|0;i=kb[c[c[h>>2]>>2]&63](256)|0;c[e>>2]=i;if(!i){c[w>>2]=0;h=0;break}else{Y6(i|0,0,256)|0;j=c_(b,f)|0;j=(c[w>>2]|0)+-1&j;i=e;o=27;break}}else h=0;else{v=c_(b,f)|0;k=c[w>>2]|0;j=k+-1|0;l=c[e>>2]|0;m=v&0-k;u=e+4|0;n=j>>>2;i=0;j=j&v;while(1){h=c[l+(j<<2)>>2]|0;if(!h)break;if((d_(f,c[h>>2]|0)|0)<<24>>24)break a;if(!(i<<24>>24))h=(m>>>((d[u>>0]|0)+-1|0)&n|1)&255;else h=i;t=h&255;i=h;j=j-t+(j>>>0<t>>>0?k:0)|0}if(g){i=a[u>>0]|0;h=i&255;if(!((c[e+12>>2]|0)>>>(h+-1|0))){h=e+16|0;i=e;o=27;break}q=h+1&255;j=i+1&255;s=1<<j;r=s+-1|0;i=s<<2;h=e+16|0;t=kb[c[c[h>>2]>>2]&63](i)|0;if(t){Y6(t|0,0,i|0)|0;p=0-s|0;n=j+-1|0;o=r>>>2;m=0;while(1){if(m>>>0>=(c[w>>2]|0)>>>0)break;i=c[(c[e>>2]|0)+(m<<2)>>2]|0;if(i|0){i=c_(b,c[i>>2]|0)|0;k=((i&p)>>>n&o|1)&255;i=i&r;j=0;while(1){l=t+(i<<2)|0;if(!(c[l>>2]|0))break;l=j<<24>>24==0?k:j;x=l&255;i=i+(i>>>0<x>>>0?s:0)-x|0;j=l}c[l>>2]=c[(c[e>>2]|0)+(m<<2)>>2]}m=m+1|0}tb[c[(c[h>>2]|0)+8>>2]&127](c[e>>2]|0);c[e>>2]=t;a[u>>0]=q;c[w>>2]=s;k=((v&p)>>>n&o|1)&255;i=r&v;j=0;while(1){if(!(c[t+(i<<2)>>2]|0)){j=i;i=e;o=27;break a}x=j<<24>>24==0?k:j;w=x&255;i=i+(i>>>0<w>>>0?s:0)-w|0;j=x}}else h=0}else h=0}while(0);if((o|0)==27){h=kb[c[c[h>>2]>>2]&63](g)|0;c[(c[i>>2]|0)+(j<<2)>>2]=h;h=c[(c[i>>2]|0)+(j<<2)>>2]|0;if(!h)h=0;else{Y6(h|0,0,g|0)|0;c[c[(c[i>>2]|0)+(j<<2)>>2]>>2]=f;h=e+12|0;c[h>>2]=(c[h>>2]|0)+1;h=c[(c[i>>2]|0)+(j<<2)>>2]|0}}return h|0}function oZ(b){b=b|0;var c=0,d=0,e=0;e=b;c=b;while(1){d=a[e>>0]|0;if(!(d<<24>>24))break;switch(d<<24>>24|0){case 10:case 13:case 32:{if((c|0)!=(b|0)){if((a[c+-1>>0]|0)!=32){a[c>>0]=32;c=c+1|0}}else c=b;break}default:{a[c>>0]=d;c=c+1|0}}e=e+1|0}if((c|0)!=(b|0)){b=c+-1|0;b=(a[b>>0]|0)==32?b:c}a[b>>0]=0;return}function pZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;d=IZ(b,0,c[b+144>>2]|0,d,e,f,(a[b+480>>0]|0)==0&1)|0;if((d|0)==0?(RZ(b)|0)<<24>>24==0:0)d=1;return d|0}function qZ(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;g=c[a+352>>2]|0;b=lZ(g+80|0,b,d,e)|0;do if((b|0)!=0?(f=nZ(a,g+20|0,b,24)|0,(f|0)!=0):0)if((c[f>>2]|0)==(b|0)){c[g+96>>2]=c[g+92>>2];if(!(TZ(a,f)|0)){f=0;break}}else c[g+92>>2]=c[g+96>>2];else f=0;while(0);return f|0}function rZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;m=c[b+352>>2]|0;k=m+80|0;n=m+92|0;h=c[n>>2]|0;l=m+88|0;if((h|0)==(c[l>>2]|0))if(!((vZ(k)|0)<<24>>24))g=0;else{h=c[n>>2]|0;i=4}else i=4;a:do if((i|0)==4){c[n>>2]=h+1;a[h>>0]=0;d=lZ(k,d,e,f)|0;if((d|0)!=0?(j=d+1|0,g=nZ(b,m+40|0,j,12)|0,(g|0)!=0):0){if((c[g>>2]|0)!=(j|0)){c[n>>2]=c[m+96>>2];break}h=c[n>>2]|0;i=m+96|0;c[i>>2]=h;if(a[b+232>>0]|0){e=a[j>>0]|0;b:do if(e<<24>>24==120)if((((a[d+2>>0]|0)==109?(a[d+3>>0]|0)==108:0)?(a[d+4>>0]|0)==110:0)?(a[d+5>>0]|0)==115:0){switch(a[d+6>>0]|0){case 0:{h=m+152|0;break}case 58:{h=nZ(b,m+60|0,d+7|0,8)|0;break}default:{f=0;d=120;break b}}c[g+4>>2]=h;a[g+9>>0]=1;break a}else{f=0;d=120}else{f=0;d=e}while(0);c:while(1){switch(d<<24>>24){case 0:break a;case 58:{e=0;break c}default:{}}d=f+1|0;f=d;d=a[j+d>>0]|0}while(1){d=(h|0)==(c[l>>2]|0);if((e|0)>=(f|0))break;if(d){if(!((vZ(k)|0)<<24>>24)){g=0;break a}h=c[n>>2]|0}d=a[j+e>>0]|0;c[n>>2]=h+1;a[h>>0]=d;e=e+1|0;h=c[n>>2]|0}if(d){if(!((vZ(k)|0)<<24>>24)){g=0;break}h=c[n>>2]|0}c[n>>2]=h+1;a[h>>0]=0;h=nZ(b,m+60|0,c[i>>2]|0,8)|0;c[g+4>>2]=h;if(h){d=c[i>>2]|0;if((c[h>>2]|0)==(d|0)){c[i>>2]=c[n>>2];break}else{c[n>>2]=d;break}}else g=0}}else g=0}while(0);return g|0}function sZ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;g=b+12|0;h=b+8|0;while(1){e=a[d>>0]|0;if(!(e<<24>>24)){i=7;break}f=c[g>>2]|0;if((f|0)==(c[h>>2]|0)){if(!((vZ(b)|0)<<24>>24)){d=0;break}f=c[g>>2]|0;e=a[d>>0]|0}c[g>>2]=f+1;a[f>>0]=e;d=d+1|0}if((i|0)==7)d=c[b+16>>2]|0;return d|0}function tZ(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;c[h>>2]=d;g=a+12|0;if((c[g>>2]|0)==0?(vZ(a)|0)<<24>>24==0:0)d=0;else f=3;a:do if((f|0)==3){d=b+56|0;f=a+8|0;while(1){if((pb[c[d>>2]&127](b,h,e,g,c[f>>2]|0)|0)>>>0<2)break;if(!((vZ(a)|0)<<24>>24)){d=0;break a}}d=c[a+16>>2]|0}while(0);l=i;return d|0}function uZ(b,d,e,f,g,h){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0;i=f<<24>>24==0;n=b+12|0;m=c[n>>2]|0;a:do if(!(i&(g|0)==0)){j=b+20|0;f=0;while(1){if((f|0)>=(m|0))break;if((c[(c[j>>2]|0)+(f*12|0)>>2]|0)==(d|0)){f=1;break a}else f=f+1|0}if((!i?(k=b+8|0,(c[k>>2]|0)==0):0)?(a[d+9>>0]|0)==0:0){c[k>>2]=d;l=9}else l=9}else l=9;while(0);b:do if((l|0)==9){j=b+16|0;do if((m|0)==(c[j>>2]|0))if(!m){c[j>>2]=8;f=kb[c[h+12>>2]&63](96)|0;c[b+20>>2]=f;if(f|0)break;c[j>>2]=0;f=0;break b}else{i=b+20|0;f=lb[c[h+16>>2]&127](c[i>>2]|0,m*24|0)|0;if(!f){f=0;break b}c[j>>2]=m<<1;c[i>>2]=f;break}else f=c[b+20>>2]|0;while(0);h=c[n>>2]|0;c[f+(h*12|0)>>2]=d;c[f+(h*12|0)+8>>2]=g;a[f+(h*12|0)+4>>0]=e;if(!(e<<24>>24))a[d+8>>0]=1;c[n>>2]=(c[n>>2]|0)+1;f=1}while(0);return f|0}function vZ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;b=a+4|0;e=c[b>>2]|0;do if(e){f=a+16|0;g=c[f>>2]|0;if(!g){c[a>>2]=e;c[b>>2]=c[e>>2];c[e>>2]=0;l=c[a>>2]|0;b=l+8|0;c[f>>2]=b;c[a+8>>2]=(c[l+4>>2]|0)+(l+8);c[a+12>>2]=b;b=1;break}d=a+8|0;if(((c[d>>2]|0)-g|0)<(c[e+4>>2]|0)){k=c[e>>2]|0;c[e>>2]=c[a>>2];l=c[b>>2]|0;c[a>>2]=l;c[b>>2]=k;b=c[f>>2]|0;W6(l+8|0,b|0,(c[d>>2]|0)-b|0)|0;b=c[a>>2]|0;l=a+12|0;c[l>>2]=(c[l>>2]|0)-(c[f>>2]|0)+(b+8);c[f>>2]=b+8;c[d>>2]=(c[b+4>>2]|0)+(b+8);b=1}else h=6}else h=6;while(0);a:do if((h|0)==6){e=c[a>>2]|0;k=a+16|0;do if(!e){i=k;b=c[k>>2]|0}else{j=c[k>>2]|0;b=j;if((j|0)!=(e+8|0)){i=k;break}f=a+8|0;g=(c[f>>2]|0)-b<<1;h=a+12|0;d=(c[h>>2]|0)-b|0;if((g|0)<0){b=0;break a}b=b_(g)|0;if(!b){b=0;break a}b=lb[c[(c[a+20>>2]|0)+4>>2]&127](e,b)|0;if(!b){b=0;break a}c[a>>2]=b;c[b+4>>2]=g;b=c[a>>2]|0;c[h>>2]=b+8+d;c[k>>2]=b+8;c[f>>2]=b+8+g;b=1;break a}while(0);j=a+8|0;b=(c[j>>2]|0)-b|0;if((b|0)>=0){if((b|0)>=1024){b=b<<1;if((b|0)<0){b=0;break}}else b=1024;d=b_(b)|0;if((d|0)!=0?(l=kb[c[c[a+20>>2]>>2]&63](d)|0,(l|0)!=0):0){c[l+4>>2]=b;c[l>>2]=c[a>>2];c[a>>2]=l;h=a+12|0;a=c[h>>2]|0;e=c[k>>2]|0;d=a;f=e;if((a|0)==(e|0)){g=l+8|0;e=f}else{g=l+8|0;W6(g|0,e|0,d-f|0)|0;e=c[i>>2]|0;d=c[h>>2]|0}c[h>>2]=l+8+(d-e);c[k>>2]=g;c[j>>2]=l+8+b;b=1}else b=0}else b=0}while(0);return b|0}function wZ(b,d,e,f,g,h){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0;b=a_(b,d,e,f,g,h)|0;do if(!b){d=h+12|0;b=c[d>>2]|0;if((e<<24>>24==0?(c[h+16>>2]|0)!=(b|0):0)?(i=b+-1|0,(a[i>>0]|0)==32):0){c[d>>2]=i;b=i}if((b|0)==(c[h+8>>2]|0)){if(!((vZ(h)|0)<<24>>24)){b=1;break}b=c[d>>2]|0}c[d>>2]=b+1;a[b>>0]=0;b=0}while(0);return b|0}function xZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0;H=l;l=l+16|0;B=H;C=H+4|0;g=c[b+352>>2]|0;D=g+104|0;F=b+272|0;E=c[F>>2]|0;c[F>>2]=1;if((c[D>>2]|0)==0?(vZ(D)|0)<<24>>24==0:0)g=1;else{n=d+20|0;o=b+484|0;p=b+144|0;q=b+412|0;r=d+64|0;s=g+132|0;t=b+428|0;u=b+424|0;v=b+224|0;w=b+112|0;m=g+130|0;x=g+128|0;y=g+131|0;z=b+116|0;A=g+112|0;j=g+116|0;k=d+40|0;a:while(1){b:do switch(ob[c[n>>2]&127](d,e,f,B)|0){case -4:{g=0;break a}case -1:{G=37;break a}case 0:{G=39;break a}case 28:{if((a[o>>0]|0)==0?(c[p>>2]|0)==(d|0):0){G=19;break a}g=c[r>>2]|0;g=lZ(q,d,e+g|0,(c[B>>2]|0)+(0-g)|0)|0;if(!g){g=1;break a}h=nZ(b,s,g,0)|0;c[u>>2]=c[t>>2];if(!h){G=9;break a}i=h+32|0;if(a[i>>0]|0){G=11;break a}g=h+16|0;if(!(c[g>>2]|0)){a[i>>0]=1;g=c[h+4>>2]|0;g=xZ(b,c[v>>2]|0,g,g+(c[h+8>>2]|0)|0)|0;a[i>>0]=0;if(!g)break b;else break a}if(c[w>>2]|0){a[y>>0]=0;a[i>>0]=1;h=(pb[c[w>>2]&127](c[z>>2]|0,0,c[h+20>>2]|0,c[g>>2]|0,c[h+24>>2]|0)|0)==0;a[i>>0]=0;if(h){g=21;break a}if(a[y>>0]|0)break b}a[x>>0]=a[m>>0]|0;break}case 6:case 9:{if(!(tZ(D,d,e,c[B>>2]|0)|0)){g=1;break a}break}case -3:{c[B>>2]=e+(c[r>>2]|0);G=22;break}case 7:{G=22;break}case 10:{g=lb[c[k>>2]&127](d,e)|0;if((g|0)<0){G=27;break a}h=x_(g,C)|0;e=0;while(1){if((e|0)>=(h|0))break;g=c[j>>2]|0;if((c[A>>2]|0)==(g|0)){if(!((vZ(D)|0)<<24>>24)){g=1;G=35;break a}g=c[j>>2]|0}i=a[C+e>>0]|0;c[j>>2]=g+1;a[g>>0]=i;e=e+1|0}break}default:{G=41;break a}}while(0);if((G|0)==22){G=0;g=c[j>>2]|0;if((c[A>>2]|0)==(g|0)){if(!((vZ(D)|0)<<24>>24)){g=1;break}g=c[j>>2]|0}c[j>>2]=g+1;a[g>>0]=10}e=c[B>>2]|0}if((G|0)==9){a[x>>0]=a[m>>0]|0;g=0}else if((G|0)==11)if((c[p>>2]|0)==(d|0)){c[b+284>>2]=e;g=12}else g=12;else if((G|0)==19){c[b+284>>2]=e;g=10}else if((G|0)==27)if((c[p>>2]|0)==(d|0)){c[b+284>>2]=e;g=14;G=35}else{g=14;G=35}else if((G|0)==37)if((c[p>>2]|0)==(d|0)){c[b+284>>2]=e;g=4}else g=4;else if((G|0)==39)if((c[p>>2]|0)==(d|0)){c[b+284>>2]=c[B>>2];g=4}else g=4;else if((G|0)==41)if((c[p>>2]|0)==(d|0)){c[b+284>>2]=e;g=23}else g=23;c[F>>2]=E}l=H;return g|0}function yZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;p=l;l=l+16|0;n=p;o=p+4|0;c[n>>2]=e;if(!(a[d+68>>0]|0)){if((c[b+144>>2]|0)==(d|0)){g=b+288|0;h=b+284|0}else{h=c[b+296>>2]|0;g=h+4|0}i=b+44|0;j=d+56|0;k=b+48|0;m=b+80|0;e=b+4|0;do{c[o>>2]=c[i>>2];b=pb[c[j>>2]&127](d,n,f,o,c[k>>2]|0)|0;c[g>>2]=c[n>>2];q=c[i>>2]|0;vb[c[m>>2]&127](c[e>>2]|0,q,(c[o>>2]|0)-q|0);c[h>>2]=c[n>>2]}while(b>>>0>1)}else vb[c[b+80>>2]&127](c[b+4>>2]|0,e,f-e|0);l=p;return}function zZ(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0;m=l;l=l+16|0;j=m;k=c[d>>2]|0;if((c[a+144>>2]|0)==(b|0)){i=a+284|0;c[i>>2]=k;h=a+288|0}else{i=c[a+296>>2]|0;h=i+4|0}c[i>>2]=k;c[d>>2]=0;n=ob[c[b+12>>2]&127](b,k,e,j)|0;e=c[j>>2]|0;c[h>>2]=e;switch(n|0){case 42:{if(c[a+80>>2]|0){yZ(a,b,k,e);e=c[j>>2]|0}c[d>>2]=e;c[f>>2]=e;e=(c[a+476>>2]|0)==2?35:0;break}case 0:{c[i>>2]=e;e=4;break}case -2:{if(!(g<<24>>24))e=6;else{c[f>>2]=k;e=0}break}case -4:case -1:{if(!(g<<24>>24))e=2;else{c[f>>2]=k;e=0}break}default:{c[i>>2]=e;e=23}}l=m;return e|0}function AZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;h=l;l=l+16|0;g=h;c[g>>2]=d;d=zZ(b,c[b+144>>2]|0,g,e,f,(a[b+480>>0]|0)==0&1)|0;g=c[g>>2]|0;if(!((d|0)!=0|(g|0)==0)){c[b+276>>2]=57;d=iZ(b,g,e,f)|0}l=h;return d|0}function BZ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;i=c[a+352>>2]|0;j=i+184|0;if(!(c[j>>2]|0)){b=kb[c[a+12>>2]&63](c[a+464>>2]<<2)|0;c[j>>2]=b;if(!b)b=-1;else{c[b>>2]=0;d=4}}else d=4;do if((d|0)==4){h=i+176|0;b=c[h>>2]|0;g=i+172|0;d=c[g>>2]|0;f=i+164|0;e=c[f>>2]|0;if(b>>>0<d>>>0)g=e;else{if(!e){b=kb[c[a+12>>2]&63](896)|0;if(!b){b=-1;break}else{d=b;b=32}}else{b=lb[c[a+16>>2]&127](e,d*56|0)|0;if(!b){b=-1;break}d=b;b=c[g>>2]<<1}c[g>>2]=b;c[f>>2]=d;b=c[h>>2]|0;g=d}c[h>>2]=b+1;d=c[i+180>>2]|0;if(d|0){a=c[(c[j>>2]|0)+(d+-1<<2)>>2]|0;f=g+(a*28|0)+16|0;d=c[f>>2]|0;if(d|0)c[g+(d*28|0)+24>>2]=b;d=g+(a*28|0)+20|0;e=c[d>>2]|0;if(!e)c[g+(a*28|0)+12>>2]=b;c[f>>2]=b;c[d>>2]=e+1}j=g+(b*28|0)+12|0;c[j>>2]=0;c[j+4>>2]=0;c[j+8>>2]=0;c[j+12>>2]=0}while(0);return b|0}function CZ(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+16|0;n=p;o=b+300|0;f=c[o>>2]|0;if(!f){f=kb[c[b+12>>2]&63](24)|0;if(!f)f=1;else{m=f;g=4}}else{c[o>>2]=c[f+8>>2];m=f;g=4}do if((g|0)==4){h=d+32|0;a[h>>0]=1;i=d+12|0;c[i>>2]=0;j=b+296|0;k=m+8|0;c[k>>2]=c[j>>2];c[j>>2]=m;c[m+12>>2]=d;f=b+308|0;c[m+16>>2]=c[f>>2];a[m+20>>0]=e;c[m>>2]=0;c[m+4>>2]=0;g=c[d+4>>2]|0;e=g+(c[d+8>>2]|0)|0;c[n>>2]=g;if(!(a[d+33>>0]|0))f=IZ(b,c[f>>2]|0,c[b+224>>2]|0,g,e,n,0)|0;else{d=b+224|0;f=c[d>>2]|0;f=ob[c[f>>2]&127](f,g,e,n)|0;f=jZ(b,c[d>>2]|0,g,e,f,c[n>>2]|0,n,0)|0}if(!f){f=c[n>>2]|0;if((e|0)!=(f|0)?(c[b+476>>2]|0)==3:0){c[i>>2]=f-g;c[b+276>>2]=60;f=0;break}a[h>>0]=0;c[j>>2]=c[k>>2];c[k>>2]=c[o>>2];c[o>>2]=m;f=0}}while(0);l=p;return f|0}function DZ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;g=l;l=l+16|0;d=g+4|0;e=g;b=c[a+352>>2]|0;f=b+176|0;b=kb[c[a+12>>2]&63](((c[f>>2]|0)*20|0)+(c[b+168>>2]|0)|0)|0;if(!b)b=0;else{c[e>>2]=b+((c[f>>2]|0)*20|0);c[d>>2]=b+20;HZ(a,0,b,d,e)}l=g;return b|0}function EZ(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;j=a+64|0;if(!(c[j>>2]|0))if(!(c[a+80>>2]|0))d=1;else{yZ(a,b,d,e);d=1}else{h=b+64|0;g=d+(c[h>>2]<<1)|0;d=g+(lb[c[b+28>>2]&127](b,g)|0)|0;f=a+412|0;g=lZ(f,b,g,d)|0;if((g|0)!=0?(c[a+428>>2]=c[a+424>>2],i=lb[c[b+32>>2]&127](b,d)|0,i=lZ(f,b,i,e+(0-(c[h>>2]<<1))|0)|0,(i|0)!=0):0){GZ(i);vb[c[j>>2]&127](c[a+4>>2]|0,g,i);mZ(f);d=1}else d=0}return d|0}function FZ(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;g=a+68|0;if(!(c[g>>2]|0))if(!(c[a+80>>2]|0))b=1;else{yZ(a,b,d,e);b=1}else{f=a+412|0;h=c[b+64>>2]|0;b=lZ(f,b,d+(h<<2)|0,e+(O(h,-3)|0)|0)|0;if(!b)b=0;else{GZ(b);ub[c[g>>2]&63](c[a+4>>2]|0,b);mZ(f);b=1}}return b|0}function GZ(b){b=b|0;var c=0,d=0,e=0;a:while(1){switch(a[b>>0]|0){case 0:break a;case 13:{d=b;c=13;e=4;break a}default:{}}b=b+1|0}if((e|0)==4){while(1){if(c<<24>>24==13){a[d>>0]=10;e=b+1|0;b=(a[e>>0]|0)==10?b+2|0:e}else{a[d>>0]=c;b=b+1|0}d=d+1|0;c=a[b>>0]|0;if(!(c<<24>>24))break;else e=4}a[d>>0]=0}return}function HZ(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;k=(c[b+352>>2]|0)+164|0;j=c[(c[k>>2]|0)+(d*28|0)>>2]|0;c[e>>2]=j;c[e+4>>2]=c[(c[k>>2]|0)+(d*28|0)+4>>2];if((j|0)==4){c[e+8>>2]=c[g>>2];d=c[(c[k>>2]|0)+(d*28|0)+8>>2]|0;while(1){j=a[d>>0]|0;k=c[g>>2]|0;c[g>>2]=k+1;a[k>>0]=j;if(!(a[d>>0]|0))break;else d=d+1|0}c[e+12>>2]=0;c[e+16>>2]=0}else{h=c[(c[k>>2]|0)+(d*28|0)+20>>2]|0;i=e+12|0;c[i>>2]=h;j=e+16|0;c[j>>2]=c[f>>2];c[f>>2]=(c[f>>2]|0)+(h*20|0);d=(c[k>>2]|0)+(d*28|0)+12|0;h=0;while(1){d=c[d>>2]|0;if(h>>>0>=(c[i>>2]|0)>>>0)break;HZ(b,d,(c[j>>2]|0)+(h*20|0)|0,f,g);d=(c[k>>2]|0)+(d*28|0)+24|0;h=h+1|0}c[e+8>>2]=0}return}function IZ(b,d,e,f,g,h,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0,X=0,Y=0,Z=0,_=0,$=0,aa=0,ba=0,ca=0,da=0,ea=0,fa=0,ga=0,ha=0,ia=0,ja=0,ka=0,la=0,ma=0,na=0;ma=l;l=l+48|0;ja=ma+28|0;ea=ma+32|0;ka=ma+24|0;Z=ma;c[ja>>2]=f;_=c[b+352>>2]|0;if((c[b+144>>2]|0)==(e|0)){fa=b+284|0;Y=b+288|0}else{Y=c[b+296>>2]|0;fa=Y;Y=Y+4|0}c[fa>>2]=f;t=e+4|0;ga=b+80|0;u=b+476|0;v=e+44|0;w=e+64|0;x=_+80|0;y=_+96|0;z=_+92|0;A=_+129|0;B=b+112|0;C=b+116|0;D=b+428|0;E=b+424|0;F=b+304|0;G=b+120|0;ha=b+4|0;H=_+130|0;$=b+60|0;I=b+364|0;J=b+12|0;K=b+360|0;L=e+28|0;ia=b+308|0;aa=e+56|0;M=b+16|0;N=b+52|0;O=b+412|0;P=b+388|0;Q=b+56|0;R=b+232|0;S=b+233|0;T=b+468|0;U=b+104|0;V=b+372|0;W=e+40|0;X=b+72|0;ba=e+68|0;ca=b+44|0;da=b+48|0;a:while(1){s=c[ja>>2]|0;c[ea>>2]=s;s=ob[c[t>>2]&127](e,s,g,ea)|0;f=c[ea>>2]|0;c[Y>>2]=f;k=f;b:do switch(s|0){case -3:{la=6;break a}case -4:{la=15;break a}case 0:{la=20;break a}case -1:{la=21;break a}case -2:{la=23;break a}case 12:{f=17;break a}case -5:{la=134;break a}case 9:{s=c[w>>2]|0;s=(nb[c[v>>2]&63](e,(c[ja>>2]|0)+s|0,k+(0-s)|0)|0)&255;a[ka>>0]=s;do if(s<<24>>24){f=c[$>>2]|0;if(f|0){vb[f&127](c[ha>>2]|0,ka,1);break}if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,c[ea>>2]|0)}else{j=c[w>>2]|0;j=lZ(x,e,(c[ja>>2]|0)+j|0,(c[ea>>2]|0)+(0-j)|0)|0;if(!j){f=1;la=56;break a}k=nZ(b,_,j,0)|0;c[z>>2]=c[y>>2];if((a[A>>0]|0)!=0?(a[H>>0]|0)==0:0){if(!k){f=c[G>>2]|0;if(f|0){vb[f&127](c[ha>>2]|0,j,0);break}if(!(c[ga>>2]|0))break;yZ(b,e,c[ja>>2]|0,c[ea>>2]|0);break}}else{if(!k){f=11;la=56;break a}if(!(a[k+34>>0]|0)){f=24;la=56;break a}}f=k+32|0;if(a[f>>0]|0){f=12;la=56;break a}if(c[k+28>>2]|0){f=15;la=56;break a}if(!(c[k+4>>2]|0)){if(!(c[B>>2]|0)){if(!(c[ga>>2]|0))break;yZ(b,e,c[ja>>2]|0,c[ea>>2]|0);break}a[f>>0]=1;j=KZ(b)|0;a[f>>0]=0;if(!j){f=1;la=56;break a}if(!(pb[c[B>>2]&127](c[C>>2]|0,j,c[k+20>>2]|0,c[k+16>>2]|0,c[k+24>>2]|0)|0)){f=21;la=56;break a}c[E>>2]=c[D>>2];break}if(a[F>>0]|0){f=CZ(b,k,0)|0;if(!f)break;else{la=58;break a}}f=c[G>>2]|0;if(!f){if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,c[ea>>2]|0)}else vb[f&127](c[ha>>2]|0,c[k>>2]|0,0);break b}while(0);break}case 1:case 2:{f=c[I>>2]|0;if(!f){f=kb[c[J>>2]&63](48)|0;if(!f){f=1;la=75;break a}j=kb[c[J>>2]&63](32)|0;c[f+36>>2]=j;if(!j){la=63;break a}c[f+40>>2]=j+32}else c[I>>2]=c[f>>2];o=f+44|0;c[o>>2]=0;c[f>>2]=c[K>>2];c[K>>2]=f;s=f+12|0;c[f+16>>2]=0;c[f+20>>2]=0;r=(c[ja>>2]|0)+(c[w>>2]|0)|0;q=f+4|0;c[q>>2]=r;p=f+8|0;c[p>>2]=lb[c[L>>2]&127](e,r)|0;c[ia>>2]=(c[ia>>2]|0)+1;q=c[q>>2]|0;p=q+(c[p>>2]|0)|0;c[Z>>2]=q;q=f+36|0;c[ka>>2]=c[q>>2];r=f+40|0;j=c[r>>2]|0;while(1){na=pb[c[aa>>2]&127](e,Z,p,ka,j+-1|0)|0;j=c[ka>>2]|0;m=c[q>>2]|0;k=m;n=j-k|0;if((na|0)==1?1:(c[Z>>2]|0)>>>0>=p>>>0)break;k=(c[r>>2]|0)-k<<1;j=lb[c[M>>2]&127](m,k)|0;if(!j){la=69;break a}c[q>>2]=j;na=j+k|0;c[r>>2]=na;c[ka>>2]=j+n;j=na}c[f+24>>2]=n;c[s>>2]=k;a[j>>0]=0;f=LZ(b,e,c[ja>>2]|0,s,o)|0;if(f|0){la=75;break a}f=c[N>>2]|0;if(!f){if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,c[ea>>2]|0)}else vb[f&127](c[ha>>2]|0,c[s>>2]|0,c[P>>2]|0);mZ(O);break}case 3:case 4:{na=(c[ja>>2]|0)+(c[w>>2]|0)|0;c[ka>>2]=0;na=lZ(O,e,na,na+(lb[c[L>>2]&127](e,na)|0)|0)|0;c[Z>>2]=na;if(!na){f=1;la=89;break a}c[D>>2]=c[E>>2];f=LZ(b,e,c[ja>>2]|0,Z,ka)|0;if(f|0){la=79;break a}c[D>>2]=c[E>>2];f=c[N>>2]|0;if(!f)j=1;else{vb[f&127](c[ha>>2]|0,c[Z>>2]|0,c[P>>2]|0);j=0}f=c[Q>>2]|0;if(!f){if(j|0?c[ga>>2]|0:0)yZ(b,e,c[ja>>2]|0,c[ea>>2]|0)}else{if(c[N>>2]|0){c[fa>>2]=c[Y>>2];f=c[Q>>2]|0}ub[f&63](c[ha>>2]|0,c[Z>>2]|0)}mZ(O);MZ(b,c[ka>>2]|0);if((c[ia>>2]|0)==0?(c[u>>2]&-2|0)!=2:0){la=92;break a}break}case 5:{if((c[ia>>2]|0)==(d|0)){f=13;break a}n=c[K>>2]|0;c[K>>2]=c[n>>2];c[n>>2]=c[I>>2];c[I>>2]=n;f=(c[ja>>2]|0)+(c[w>>2]<<1)|0;j=lb[c[L>>2]&127](e,f)|0;if((j|0)!=(c[n+8>>2]|0)){la=96;break a}if($3(c[n+4>>2]|0,f,j)|0){la=96;break a}c[ia>>2]=(c[ia>>2]|0)+-1;f=c[Q>>2]|0;if(!f){if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,c[ea>>2]|0)}else{j=c[n+16>>2]|0;m=n+12|0;if((j|0)!=0&(a[R>>0]|0)!=0){f=(c[m>>2]|0)+(c[n+28>>2]|0)|0;while(1){k=a[j>>0]|0;if(!(k<<24>>24))break;a[f>>0]=k;f=f+1|0;j=j+1|0}j=c[n+20>>2]|0;c:do if((j|0)!=0&(a[S>>0]|0)!=0){a[f>>0]=a[T>>0]|0;while(1){f=f+1|0;k=a[j>>0]|0;if(!(k<<24>>24))break c;a[f>>0]=k;j=j+1|0}}while(0);a[f>>0]=0;f=c[Q>>2]|0}ub[f&63](c[ha>>2]|0,c[m>>2]|0)}k=n+44|0;while(1){m=c[k>>2]|0;if(!m)break;f=c[U>>2]|0;if(!f){f=m;j=m}else{ub[f&63](c[ha>>2]|0,c[c[m>>2]>>2]|0);f=m;j=c[k>>2]|0}c[k>>2]=c[j+4>>2];c[m+4>>2]=c[V>>2];c[V>>2]=m;c[(c[f>>2]|0)+4>>2]=c[m+8>>2]}if(!(c[ia>>2]|0)){la=117;break a}break}case 10:{f=lb[c[W>>2]&127](e,c[ja>>2]|0)|0;if((f|0)<0){f=14;break a}j=c[$>>2]|0;if(j|0){s=c[ha>>2]|0;na=x_(f,ka)|0;vb[j&127](s,ka,na);break b}if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,c[ea>>2]|0);break}case 7:{f=c[$>>2]|0;if(f|0){a[ka>>0]=10;vb[f&127](c[ha>>2]|0,ka,1);break b}if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,k);break}case 8:{f=c[X>>2]|0;if(!f){if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,k)}else tb[f&127](c[ha>>2]|0);f=OZ(b,e,ea,g,h,i)|0;if(f|0)break a;if(!(c[ea>>2]|0)){la=133;break a}break}case 6:{j=c[$>>2]|0;if(!j){if(!(c[ga>>2]|0))break b;yZ(b,e,c[ja>>2]|0,k);break b}if(!(a[ba>>0]|0))f=k;else{na=c[ja>>2]|0;vb[j&127](c[ha>>2]|0,na,f-na|0);break b}while(1){c[ka>>2]=c[ca>>2];na=pb[c[aa>>2]&127](e,ja,f,ka,c[da>>2]|0)|0;c[Y>>2]=c[ja>>2];s=c[ca>>2]|0;vb[j&127](c[ha>>2]|0,s,(c[ka>>2]|0)-s|0);if(na>>>0<2)break;c[fa>>2]=c[ja>>2];f=c[ea>>2]|0}break}case 11:{if(!(EZ(b,e,c[ja>>2]|0,k)|0)){f=1;break a}break}case 13:{if(!(FZ(b,e,c[ja>>2]|0,k)|0)){f=1;break a}break}default:if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,k)}while(0);na=c[ea>>2]|0;c[ja>>2]=na;c[fa>>2]=na;switch(c[u>>2]|0){case 2:{f=35;break a}case 3:{la=160;break a}default:{}}}switch(la|0){case 6:{if(i<<24>>24){c[h>>2]=c[ja>>2];f=0;break}c[Y>>2]=g;f=c[$>>2]|0;if(!f){if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,g)}else{a[ka>>0]=10;vb[f&127](c[ha>>2]|0,ka,1)}if(d)if((c[ia>>2]|0)==(d|0)){c[h>>2]=g;f=0}else f=13;else f=3;break}case 15:{if(i<<24>>24){c[h>>2]=c[ja>>2];f=0;break}if((d|0)>0)if((c[ia>>2]|0)==(d|0)){c[h>>2]=c[ja>>2];f=0}else f=13;else f=3;break}case 20:{c[fa>>2]=f;f=4;break}case 21:{if(!(i<<24>>24))f=5;else{c[h>>2]=c[ja>>2];f=0}break}case 23:{if(!(i<<24>>24))f=6;else{c[h>>2]=c[ja>>2];f=0}break}case 56:break;case 58:break;case 63:{tb[c[b+20>>2]&127](f);f=1;la=75;break}case 69:{f=1;la=75;break}case 79:{MZ(b,c[ka>>2]|0);la=89;break}case 92:{f=NZ(b,c[ea>>2]|0,g,h)|0;break}case 96:{c[fa>>2]=f;f=7;break}case 117:{f=NZ(b,c[ea>>2]|0,g,h)|0;break}case 133:{c[b+276>>2]=61;f=0;break}case 134:{if(i<<24>>24){c[h>>2]=c[ja>>2];f=0;break}f=c[$>>2]|0;do if(!f){if(c[ga>>2]|0)yZ(b,e,c[ja>>2]|0,g)}else if(!(a[ba>>0]|0)){c[ka>>2]=c[ca>>2];pb[c[aa>>2]&127](e,ja,g,ka,c[da>>2]|0)|0;na=c[ca>>2]|0;vb[c[$>>2]&127](c[ha>>2]|0,na,(c[ka>>2]|0)-na|0);break}else{na=c[ja>>2]|0;vb[f&127](c[ha>>2]|0,na,g-na|0);break}while(0);if(!d){c[fa>>2]=g;f=3;break}if((c[ia>>2]|0)==(d|0)){c[h>>2]=g;f=0;break}else{c[fa>>2]=g;f=13;break}}case 160:{c[h>>2]=c[ea>>2];f=0;break}}l=ma;return f|0}function JZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;q=l;l=l+16|0;p=q;k=b+296|0;m=c[k>>2]|0;do if(m){n=c[m+12>>2]|0;h=n+4|0;j=c[h>>2]|0;i=n+12|0;g=j+(c[i>>2]|0)|0;j=j+(c[n+8>>2]|0)|0;c[p>>2]=g;o=n+33|0;if(!(a[o>>0]|0))g=IZ(b,c[m+16>>2]|0,c[b+224>>2]|0,g,j,p,0)|0;else{s=b+224|0;r=c[s>>2]|0;r=ob[c[r>>2]&127](r,g,j,p)|0;g=jZ(b,c[s>>2]|0,g,j,r,c[p>>2]|0,p,0)|0}if(!g){g=c[p>>2]|0;if((j|0)!=(g|0)?(c[b+476>>2]|0)==3:0){c[i>>2]=g-(c[h>>2]|0);g=0;break}a[n+32>>0]=0;s=m+8|0;c[k>>2]=c[s>>2];g=b+300|0;c[s>>2]=c[g>>2];c[g>>2]=m;g=b+276|0;if(!(a[o>>0]|0)){c[g>>2]=58;g=IZ(b,(c[b+472>>2]|0)!=0&1,c[b+144>>2]|0,d,e,f,(a[b+480>>0]|0)==0&1)|0;break}else{c[g>>2]=57;s=b+144|0;g=c[s>>2]|0;g=ob[c[g>>2]&127](g,d,e,p)|0;g=jZ(b,c[s>>2]|0,d,e,g,c[p>>2]|0,f,(a[b+480>>0]|0)==0&1)|0;break}}}else g=23;while(0);l=q;return g|0}function KZ(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+16|0;n=q;m=c[b+352>>2]|0;h=m+156|0;o=b+412|0;p=b+424|0;a:do if(!(c[h>>2]|0)){d=0;i=b+420|0;k=b+468|0;j=12}else{d=c[p>>2]|0;i=b+420|0;if((d|0)==(c[i>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break}d=c[p>>2]|0}c[p>>2]=d+1;a[d>>0]=61;g=b+468|0;e=(c[(c[h>>2]|0)+20>>2]|0)+(((a[g>>0]|0)!=0)<<31>>31)|0;f=0;while(1){if((f|0)>=(e|0)){d=1;k=g;j=12;break a}d=c[p>>2]|0;if((d|0)==(c[i>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break a}d=c[p>>2]|0}k=a[(c[(c[h>>2]|0)+16>>2]|0)+f>>0]|0;c[p>>2]=d+1;a[d>>0]=k;f=f+1|0}}while(0);b:do if((j|0)==12){cZ(n,m+60|0);c:while(1){do{e=dZ(n)|0;if(!e)break c;h=e+4|0}while(!(c[h>>2]|0));if(d<<24>>24){d=c[p>>2]|0;if((d|0)==(c[i>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break b}d=c[p>>2]|0}c[p>>2]=d+1;a[d>>0]=12}g=c[e>>2]|0;while(1){e=a[g>>0]|0;d=c[p>>2]|0;f=(d|0)==(c[i>>2]|0);if(!(e<<24>>24))break;if(f){if(!((vZ(o)|0)<<24>>24)){d=0;break b}d=c[p>>2]|0;e=a[g>>0]|0}c[p>>2]=d+1;a[d>>0]=e;g=g+1|0}if(f){if(!((vZ(o)|0)<<24>>24)){d=0;break b}d=c[p>>2]|0}c[p>>2]=d+1;a[d>>0]=61;f=(c[(c[h>>2]|0)+20>>2]|0)+(((a[k>>0]|0)!=0)<<31>>31)|0;e=0;while(1){if((e|0)>=(f|0)){d=1;continue c}d=c[p>>2]|0;if((d|0)==(c[i>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break b}d=c[p>>2]|0}j=a[(c[(c[h>>2]|0)+16>>2]|0)+e>>0]|0;c[p>>2]=d+1;a[d>>0]=j;e=e+1|0}}cZ(n,m);d:while(1){do{e=dZ(n)|0;if(!e)break d}while(!(a[e+32>>0]|0));if(d<<24>>24){d=c[p>>2]|0;if((d|0)==(c[i>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break b}d=c[p>>2]|0}c[p>>2]=d+1;a[d>>0]=12}f=c[e>>2]|0;while(1){e=a[f>>0]|0;if(!(e<<24>>24)){d=1;continue d}d=c[p>>2]|0;if((d|0)==(c[i>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break b}d=c[p>>2]|0;e=a[f>>0]|0}c[p>>2]=d+1;a[d>>0]=e;f=f+1|0}}d=c[p>>2]|0;if((d|0)==(c[i>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break}d=c[p>>2]|0}c[p>>2]=d+1;a[d>>0]=0;d=c[b+428>>2]|0}while(0);l=q;return d|0}function LZ(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0;O=l;l=l+80|0;K=O+16|0;L=O;M=c[b+352>>2]|0;k=M+20|0;i=nZ(b,k,c[g>>2]|0,0)|0;if(!i){i=SZ(M+80|0,c[g>>2]|0)|0;if((i|0)!=0?(j=nZ(b,k,i,24)|0,(j|0)!=0):0)if((a[b+232>>0]|0)!=0?(TZ(b,j)|0)==0:0)i=1;else A=6;else i=1}else{j=i;A=6}a:do if((A|0)==6){x=c[j+12>>2]|0;k=e+36|0;m=b+376|0;t=b+388|0;u=ob[c[k>>2]&127](e,f,c[m>>2]|0,c[t>>2]|0)|0;i=u+x|0;n=c[m>>2]|0;if((i|0)>(n|0)){i=i+16|0;c[m>>2]=i;i=lb[c[b+16>>2]&127](c[t>>2]|0,i<<4)|0;if(!i){c[m>>2]=n;i=1;break}c[t>>2]=i;if((u|0)>(n|0))ob[c[k>>2]&127](e,f,u,i)|0}J=c[t>>2]|0;r=e+28|0;H=b+412|0;I=b+428|0;w=j+20|0;q=b+424|0;s=0;p=0;k=0;while(1){if((p|0)>=(u|0))break;o=c[(c[t>>2]|0)+(p<<4)>>2]|0;o=rZ(b,e,o,o+(lb[c[r>>2]&127](e,o)|0)|0)|0;if(!o){i=1;break a}i=(c[o>>2]|0)+-1|0;if(a[i>>0]|0){A=15;break}a[i>>0]=1;f=s+1|0;c[J+(s<<2)>>2]=c[o>>2];n=c[t>>2]|0;if(a[n+(p<<4)+12>>0]|0){G=lZ(H,e,c[n+(p<<4)+4>>2]|0,c[n+(p<<4)+8>>2]|0)|0;c[J+(f<<2)>>2]=G;if(!G){i=1;break a}}else{b:do if(!(a[o+8>>0]|0))i=1;else{i=0;while(1){if((i|0)>=(x|0)){i=1;break b}m=c[w>>2]|0;if((o|0)==(c[m+(i*12|0)>>2]|0))break;i=i+1|0}i=a[m+(i*12|0)+4>>0]|0}while(0);i=wZ(b,e,i,c[n+(p<<4)+4>>2]|0,c[n+(p<<4)+8>>2]|0,H)|0;if(i|0)break a;c[J+(f<<2)>>2]=c[I>>2]}c[I>>2]=c[q>>2];i=c[o+4>>2]|0;do if(i)if(a[o+9>>0]|0){i=UZ(b,i,o,c[J+(f<<2)>>2]|0,h)|0;if(!i){i=s;break}else break a}else{a[(c[o>>2]|0)+-1>>0]=2;k=k+1|0;i=s+2|0;break}else i=s+2|0;while(0);s=i;p=p+1|0}if((A|0)==15){if((c[b+144>>2]|0)!=(e|0)){i=8;break}c[b+284>>2]=c[(c[t>>2]|0)+(p<<4)>>2];i=8;break}c[b+380>>2]=s;i=c[j+8>>2]|0;c:do if((i|0)!=0?(v=c[i>>2]|0,(a[v+-1>>0]|0)!=0):0){i=0;while(1){if((i|0)>=(s|0)){f=0;p=k;G=s;break c}if((c[J+(i<<2)>>2]|0)==(v|0)){A=37;break c}i=i+2|0}}else{i=-1;A=37}while(0);if((A|0)==37){c[b+384>>2]=i;f=0;p=k;G=s}while(1){if((f|0)>=(x|0))break;i=c[w>>2]|0;n=i+(f*12|0)|0;k=c[n>>2]|0;m=(c[k>>2]|0)+-1|0;d:do if((a[m>>0]|0)==0?(y=i+(f*12|0)+8|0,z=c[y>>2]|0,(z|0)!=0):0){i=c[k+4>>2]|0;do if(i)if(a[k+9>>0]|0){i=UZ(b,i,k,z,h)|0;if(!i){i=p;k=G;break d}else break a}else{a[m>>0]=2;i=p+1|0;break}else{a[m>>0]=1;i=p}while(0);c[J+(G<<2)>>2]=c[c[n>>2]>>2];c[J+(G+1<<2)>>2]=c[y>>2];k=G+2|0}else{i=p;k=G}while(0);f=f+1|0;p=i;G=k}c[J+(G<<2)>>2]=0;e:do if(!p)i=0;else{o=b+396|0;n=c[o>>2]|0;F=b+400|0;f=a[F>>0]|0;i=f&255;do if(!(p<<1>>i)){i=1<<i;m=b+392|0;if(!n){k=i;A=55}else{k=i;i=n}}else{i=f;do{E=i;i=i+1<<24>>24}while((p>>(E&255)|0)!=0);i=(i&255)>3?i:3;a[F>>0]=i;i=i&255;m=b+392|0;k=lb[c[b+16>>2]&127](c[m>>2]|0,12<<i)|0;if(!k){a[F>>0]=f;i=1;break a}else{c[m>>2]=k;k=1<<i;A=55;break}}while(0);f:do if((A|0)==55){i=k;while(1){if(!i){i=-1;break f}E=i+-1|0;c[(c[m>>2]|0)+(E*12|0)>>2]=-1;i=E}}while(0);E=i+-1|0;c[o>>2]=E;w=M+40|0;x=b+424|0;y=b+420|0;z=k+-1|0;A=0-k|0;B=z>>>2;C=b+233|0;D=b+468|0;i=0;v=p;g:while(1){while(1){if((i|0)>=(G|0))break e;e=J+(i<<2)|0;n=c[e>>2]|0;f=n+-1|0;if((a[f>>0]|0)==2)break;a[f>>0]=0;i=i+2|0}VZ(b,L);WZ(K,L);a[f>>0]=0;f=nZ(b,w,n,0)|0;if(!f){i=1;break}f=c[f+4>>2]|0;if(!f){i=1;break}u=c[f+4>>2]|0;if(!u){i=27;break}q=u+20|0;r=u+16|0;p=0;while(1){f=c[q>>2]|0;o=c[r>>2]|0;if((p|0)>=(f|0))break;o=a[o+p>>0]|0;f=c[x>>2]|0;if((f|0)==(c[y>>2]|0)){if(!((vZ(H)|0)<<24>>24)){i=1;break g}f=c[x>>2]|0}c[x>>2]=f+1;a[f>>0]=o;p=p+1|0}XZ(K,o,f);do{t=n;n=n+1|0}while((a[t>>0]|0)!=58);XZ(K,n,YZ(n)|0);while(1){f=c[x>>2]|0;if((f|0)==(c[y>>2]|0)){if(!((vZ(H)|0)<<24>>24)){i=1;break g}f=c[x>>2]|0}t=a[n>>0]|0;c[x>>2]=f+1;a[f>>0]=t;if(!(a[n>>0]|0))break;else n=n+1|0}t=ZZ(K)|0;q=c[m>>2]|0;r=t&A;n=0;s=z&t;while(1){if((c[q+(s*12|0)>>2]|0)!=(E|0))break;if((c[q+(s*12|0)+4>>2]|0)==(t|0)){f=c[q+(s*12|0)+8>>2]|0;o=c[I>>2]|0;while(1){P=a[o>>0]|0;p=P<<24>>24==0;if(p|P<<24>>24!=(a[f>>0]|0))break;f=f+1|0;o=o+1|0}if(p){i=8;break g}}if(!(n<<24>>24))n=(r>>>((d[F>>0]|0)+-1|0)&B|1)&255;P=n&255;s=s+((s|0)<(P|0)?k:0)-P|0}if(a[C>>0]|0){a[(c[x>>2]|0)+-1>>0]=a[D>>0]|0;f=c[c[u>>2]>>2]|0;while(1){n=c[x>>2]|0;if((n|0)==(c[y>>2]|0)){if(!((vZ(H)|0)<<24>>24)){i=1;break g}n=c[x>>2]|0}P=a[f>>0]|0;c[x>>2]=n+1;a[n>>0]=P;if(!(a[f>>0]|0))break;else f=f+1|0}}P=c[I>>2]|0;c[I>>2]=c[x>>2];c[e>>2]=P;c[(c[m>>2]|0)+(s*12|0)>>2]=E;c[(c[m>>2]|0)+(s*12|0)+4>>2]=t;c[(c[m>>2]|0)+(s*12|0)+8>>2]=P;v=v+-1|0;i=i+2|0;if(!v)break e}break a}while(0);while(1){if((i|0)>=(G|0)){i=h;break}a[(c[J+(i<<2)>>2]|0)+-1>>0]=0;i=i+2|0}while(1){i=c[i>>2]|0;if(!i)break;a[(c[c[i+12>>2]>>2]|0)+-1>>0]=0;i=i+4|0}if(!(a[b+232>>0]|0))i=0;else{i=c[j+4>>2]|0;if(!i){i=c[M+156>>2]|0;if(!i){i=0;break}k=c[g>>2]|0}else{i=c[i+4>>2]|0;if(!i){i=27;break}j=c[g>>2]|0;while(1){k=j+1|0;if((a[j>>0]|0)==58)break;else j=k}}if((a[b+233>>0]|0)!=0?(N=c[c[i>>2]>>2]|0,(N|0)!=0):0){j=0;while(1){m=j+1|0;if(!(a[N+j>>0]|0))break;else j=m}}else m=0;c[g+4>>2]=k;p=i+20|0;c[g+16>>2]=c[p>>2];c[g+8>>2]=c[c[i>>2]>>2];c[g+20>>2]=m;q=0;while(1){r=q+1|0;if(!(a[k+q>>0]|0))break;else q=r}j=c[p>>2]|0;n=r+m+j|0;f=i+24|0;if((n|0)>(c[f>>2]|0)){j=n+24|0;o=kb[c[b+12>>2]&63](j)|0;if(!o){i=1;break}c[f>>2]=j;f=i+16|0;W6(o|0,c[f>>2]|0,c[p>>2]|0)|0;j=b+360|0;while(1){j=c[j>>2]|0;if(!j)break;n=j+12|0;if((c[n>>2]|0)==(c[f>>2]|0))c[n>>2]=o}tb[c[b+20>>2]&127](c[f>>2]|0);c[f>>2]=o;n=o;j=c[p>>2]|0}else{n=i+16|0;f=n;n=c[n>>2]|0}j=n+j|0;W6(j|0,k|0,r|0)|0;if(m|0){P=j+q|0;a[P>>0]=a[b+468>>0]|0;W6(P+1|0,c[c[i>>2]>>2]|0,m|0)|0}c[g>>2]=c[f>>2];i=0}}while(0);l=O;return i|0}function MZ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;e=a+104|0;f=a+372|0;d=a+4|0;while(1){if(!b)break;a=c[e>>2]|0;if(!a)a=b;else{ub[a&63](c[d>>2]|0,c[c[b>>2]>>2]|0);a=b}h=b+4|0;g=c[h>>2]|0;c[h>>2]=c[f>>2];c[f>>2]=b;c[(c[a>>2]|0)+4>>2]=c[b+8>>2];b=g}return}function NZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;q=l;l=l+16|0;j=q;c[b+276>>2]=62;k=b+284|0;c[k>>2]=d;m=b+144|0;n=b+288|0;o=b+80|0;i=b+476|0;g=d;a:while(1){c[j>>2]=0;r=c[m>>2]|0;r=ob[c[r>>2]&127](r,g,e,j)|0;d=c[j>>2]|0;c[n>>2]=d;h=d;switch(r|0){case -15:{p=3;break a}case -4:{p=7;break a}case 0:{p=12;break a}case -1:{p=13;break a}case -2:{p=15;break a}case 15:{if(c[o>>2]|0)yZ(b,c[m>>2]|0,g,h);break}case 11:{if(!(EZ(b,c[m>>2]|0,g,h)|0)){d=1;break a}break}case 13:{if(!(FZ(b,c[m>>2]|0,g,h)|0)){d=1;break a}break}default:{d=9;break a}}d=c[j>>2]|0;c[k>>2]=d;switch(c[i>>2]|0){case 2:{d=35;break a}case 3:{p=18;break a}default:{}}g=d}do if((p|0)==3){if(c[o>>2]|0){yZ(b,c[m>>2]|0,g,h);if((c[i>>2]|0)==2){d=35;break}d=c[j>>2]|0}c[f>>2]=d;d=0}else if((p|0)==7){c[f>>2]=g;d=0}else if((p|0)==12){c[k>>2]=d;d=4}else if((p|0)==13)if(!(a[b+480>>0]|0)){c[f>>2]=g;d=0}else d=5;else if((p|0)==15)if(!(a[b+480>>0]|0)){c[f>>2]=g;d=0}else d=6;else if((p|0)==18){c[f>>2]=d;d=0}while(0);l=q;return d|0}function OZ(b,d,e,f,g,h){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0;B=l;l=l+16|0;y=B+8|0;z=B+4|0;x=B;i=c[e>>2]|0;c[y>>2]=i;if((c[b+144>>2]|0)==(d|0)){s=b+284|0;c[s>>2]=i;t=b+288|0}else{t=c[b+296>>2]|0;s=t;t=t+4|0}c[s>>2]=i;c[e>>2]=0;k=d+8|0;m=b+60|0;u=b+80|0;v=b+4|0;w=b+476|0;n=d+68|0;o=b+44|0;p=d+56|0;q=b+48|0;a:while(1){j=ob[c[k>>2]&127](d,c[y>>2]|0,f,z)|0;i=c[z>>2]|0;c[t>>2]=i;r=i;b:do switch(j|0){case 40:{A=6;break a}case 0:{A=23;break a}case -2:{A=24;break a}case -4:case -1:{A=26;break a}case 7:{i=c[m>>2]|0;if(i|0){a[x>>0]=10;vb[i&127](c[v>>2]|0,x,1);break b}if(c[u>>2]|0)yZ(b,d,c[y>>2]|0,r);break}case 6:{j=c[m>>2]|0;if(!j){if(!(c[u>>2]|0))break b;yZ(b,d,c[y>>2]|0,r);break b}if(!(a[n>>0]|0))i=r;else{r=c[y>>2]|0;vb[j&127](c[v>>2]|0,r,i-r|0);break b}while(1){c[x>>2]=c[o>>2];r=pb[c[p>>2]&127](d,y,i,x,c[q>>2]|0)|0;c[t>>2]=c[z>>2];i=c[o>>2]|0;vb[j&127](c[v>>2]|0,i,(c[x>>2]|0)-i|0);if(r>>>0<2)break;c[s>>2]=c[y>>2];i=c[z>>2]|0}break}default:{A=28;break a}}while(0);r=c[z>>2]|0;c[y>>2]=r;c[s>>2]=r;switch(c[w>>2]|0){case 3:{A=30;break a}case 2:{i=35;break a}default:{}}}if((A|0)==6){i=c[b+76>>2]|0;if(!i){if(c[u>>2]|0)yZ(b,d,c[y>>2]|0,r)}else tb[i&127](c[v>>2]|0);i=c[z>>2]|0;c[e>>2]=i;c[g>>2]=i;i=(c[w>>2]|0)==2?35:0}else if((A|0)==23){c[s>>2]=i;i=4}else if((A|0)==24)if(!(h<<24>>24))i=6;else{c[g>>2]=c[y>>2];i=0}else if((A|0)==26)if(!(h<<24>>24))i=20;else{c[g>>2]=c[y>>2];i=0}else if((A|0)==28){c[s>>2]=i;i=23}else if((A|0)==30){c[g>>2]=c[z>>2];i=0}l=B;return i|0}function PZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;h=l;l=l+16|0;g=h;c[g>>2]=d;d=OZ(b,c[b+144>>2]|0,g,e,f,(a[b+480>>0]|0)==0&1)|0;g=c[g>>2]|0;do if(!((d|0)!=0|(g|0)==0)){d=b+276|0;if(!(c[b+472>>2]|0)){c[d>>2]=58;d=pZ(b,g,e,f)|0;break}else{c[d>>2]=63;d=QZ(b,g,e,f)|0;break}}while(0);l=h;return d|0}function QZ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;d=IZ(b,1,c[b+144>>2]|0,d,e,f,(a[b+480>>0]|0)==0&1)|0;if((d|0)==0?(RZ(b)|0)<<24>>24==0:0)d=1;return d|0}function RZ(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;m=a+16|0;a=a+360|0;while(1){a=c[a>>2]|0;if(!a){a=1;break}i=(c[a+24>>2]|0)+1|0;j=a+36|0;f=c[j>>2]|0;e=f+i|0;l=a+4|0;d=c[l>>2]|0;if((d|0)==(e|0)){a=1;break}k=a+8|0;b=c[k>>2]|0;g=b+i|0;h=a+40|0;if((g|0)>((c[h>>2]|0)-f|0)){e=lb[c[m>>2]&127](f,g)|0;if(!e){a=0;break}b=a+12|0;f=c[j>>2]|0;if((c[b>>2]|0)==(f|0))c[b>>2]=e;b=a+16|0;d=c[b>>2]|0;if(d|0)c[b>>2]=e+(d-f);c[j>>2]=e;c[h>>2]=e+g;e=e+i|0;d=c[l>>2]|0;b=c[k>>2]|0}W6(e|0,d|0,b|0)|0;c[l>>2]=e}return a|0}function SZ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;f=b+12|0;g=b+8|0;while(1){e=c[f>>2]|0;if((e|0)==(c[g>>2]|0)){if(!((vZ(b)|0)<<24>>24)){d=0;break}e=c[f>>2]|0}i=a[d>>0]|0;c[f>>2]=e+1;a[e>>0]=i;if(!(a[d>>0]|0)){h=6;break}else d=d+1|0}if((h|0)==6){i=b+16|0;d=c[i>>2]|0;c[i>>2]=c[f>>2]}return d|0}function TZ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;n=c[b+352>>2]|0;j=n+80|0;k=n+92|0;l=n+88|0;m=n+60|0;n=n+96|0;i=d+4|0;h=c[d>>2]|0;a:while(1){switch(a[h>>0]|0){case 0:{e=1;break a}case 58:{g=c[d>>2]|0;while(1){e=c[k>>2]|0;f=(e|0)==(c[l>>2]|0);if((g|0)==(h|0))break;if(f){if(!((vZ(j)|0)<<24>>24)){e=0;break a}e=c[k>>2]|0}f=a[g>>0]|0;c[k>>2]=e+1;a[e>>0]=f;g=g+1|0}if(f){if(!((vZ(j)|0)<<24>>24)){e=0;break a}e=c[k>>2]|0}c[k>>2]=e+1;a[e>>0]=0;e=nZ(b,m,c[n>>2]|0,8)|0;if(!e){e=0;break a}f=c[n>>2]|0;if((c[e>>2]|0)==(f|0))c[n>>2]=c[k>>2];else c[k>>2]=f;c[i>>2]=e;break}default:{}}h=h+1|0}return e|0}function UZ(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0;k=a[f>>0]|0;i=c[d>>2]|0;h=(i|0)==0;do if(!(k<<24>>24))if(h){m=0;j=11}else h=28;else if(((!h?(a[i>>0]|0)==120:0)?(a[i+1>>0]|0)==109:0)?(a[i+2>>0]|0)==108:0){h=a[i+3>>0]|0;if((h<<24>>24==110?(a[i+4>>0]|0)==115:0)?(a[i+5>>0]|0)==0:0){h=39;break}m=h<<24>>24==0&1;j=11}else{m=0;j=11}while(0);a:do if((j|0)==11){l=m<<24>>24!=0;h=1;i=1;n=0;while(1){j=i<<24>>24!=0;if(!(k<<24>>24))break;do if(j){if((n|0)<=36?k<<24>>24==(a[142257+n>>0]|0):0)break;i=0}else i=0;while(0);do if(!(l|h<<24>>24==0)){if((n|0)<=29?k<<24>>24==(a[142294+n>>0]|0):0)break;h=0}while(0);k=n+1|0;n=k;k=a[f+k>>0]|0}if((m&255|0)!=(j&(n|0)==36&1|0)){h=l?38:40;break}if(!(h<<24>>24!=0&(n|0)==29)){o=b+468|0;n=n+((a[o>>0]|0)!=0&1)|0;h=b+372|0;j=c[h>>2]|0;do if(!j){h=b+12|0;i=kb[c[h>>2]&63](28)|0;if(!i){h=1;break a}j=n+24|0;m=kb[c[h>>2]&63](j)|0;c[i+16>>2]=m;if(!m){tb[c[b+20>>2]&127](i);h=1;break a}else{c[i+24>>2]=j;j=i;break}}else{i=j+24|0;if((n|0)>(c[i>>2]|0)){k=j+16|0;l=n+24|0;m=lb[c[b+16>>2]&127](c[k>>2]|0,l)|0;if(!m){h=1;break a}c[k>>2]=m;c[i>>2]=l}c[h>>2]=c[j+4>>2]}while(0);c[j+20>>2]=n;i=j+16|0;W6(c[i>>2]|0,f|0,n|0)|0;h=a[o>>0]|0;if(h<<24>>24)a[(c[i>>2]|0)+(n+-1)>>0]=h;c[j>>2]=d;c[j+12>>2]=e;i=d+4|0;c[j+8>>2]=c[i>>2];if((a[f>>0]|0)==0?((c[b+352>>2]|0)+152|0)==(d|0):0)h=0;else h=j;c[i>>2]=h;c[j+4>>2]=c[g>>2];c[g>>2]=j;if((e|0)!=0?(p=c[b+100>>2]|0,(p|0)!=0):0){vb[p&127](c[b+4>>2]|0,c[d>>2]|0,c[i>>2]|0?f:0);h=0}else h=0}else h=40}while(0);return h|0}function VZ(a,b){a=a|0;b=b|0;var d=0;d=b;c[d>>2]=0;c[d+4>>2]=0;b=b+8|0;c[b>>2]=$Z(a)|0;c[b+4>>2]=0;return}function WZ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;d=b;g=c[d+4>>2]^1936682341;e=a;c[e>>2]=c[d>>2]^1886610805;c[e+4>>2]=g;e=b+8|0;g=e;d=c[g+4>>2]^1685025377;f=a+8|0;c[f>>2]=c[g>>2]^1852075885;c[f+4>>2]=d;f=b;b=c[f+4>>2]^1819895653;d=a+16|0;c[d>>2]=c[f>>2]^1852142177;c[d+4>>2]=b;d=c[e+4>>2]^1952801890;b=a+24|0;c[b>>2]=c[e>>2]^2037671283;c[b+4>>2]=d;c[a+40>>2]=a+32;b=a+48|0;c[b>>2]=0;c[b+4>>2]=0;return}function XZ(b,e,f){b=b|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,A=0,B=0;n=e+f|0;o=b+40|0;p=b+40|0;q=b+32|0;r=b+33|0;s=b+34|0;t=b+35|0;h=b+36|0;i=b+37|0;j=b+38|0;k=b+39|0;l=b+24|0;m=b+48|0;f=e;do{while(1){g=c[o>>2]|0;if(f>>>0>=n>>>0){e=0;break}if(g>>>0>=p>>>0){e=1;break}e=a[f>>0]|0;c[o>>2]=g+1;a[g>>0]=e;f=f+1|0}if(g>>>0<p>>>0)break;A=d[q>>0]|0;B=R6(d[r>>0]|0|0,0,8)|0;g=z;y=R6(d[s>>0]|0|0,0,16)|0;g=g|z;w=R6(d[t>>0]|0|0,0,24)|0;g=g|z|(d[h>>0]|0);u=R6(d[i>>0]|0|0,0,40)|0;g=g|z;x=R6(d[j>>0]|0|0,0,48)|0;g=g|z;v=R6(d[k>>0]|0|0,0,56)|0;v=B|A|y|w|u|x|v;g=g|z;x=l;u=g^c[x+4>>2];w=l;c[w>>2]=v^c[x>>2];c[w+4>>2]=u;_Z(b,2);w=b;g=c[w+4>>2]^g;u=b;c[u>>2]=c[w>>2]^v;c[u+4>>2]=g;c[o>>2]=q;u=m;u=J6(c[u>>2]|0,c[u+4>>2]|0,8,0)|0;g=m;c[g>>2]=u;c[g+4>>2]=z}while(e);return}function YZ(b){b=b|0;var c=0;c=0;while(1){if(!(a[b>>0]|0))break;c=c+1|0;b=b+1|0}return c|0}function ZZ(a){a=a|0;var b=0,e=0,f=0,g=0,h=0,i=0;f=a+32|0;h=(c[a+40>>2]|0)-f|0;e=a+48|0;e=J6(c[e>>2]|0,c[e+4>>2]|0,h|0,0)|0;e=R6(e|0,z|0,56)|0;b=z;switch(h<<24>>24|0){case 7:{e=R6(d[a+38>>0]|0|0,0,48)|0|e;b=z|b;g=3;break}case 6:{g=3;break}case 5:{g=4;break}case 4:{g=5;break}case 3:{g=6;break}case 2:{g=7;break}case 1:{g=8;break}default:{}}if((g|0)==3){h=R6(d[a+37>>0]|0|0,0,40)|0;e=h|e;b=z|b;g=4}if((g|0)==4){b=d[a+36>>0]|0|b;g=5}if((g|0)==5){h=R6(d[a+35>>0]|0|0,0,24)|0;e=h|e;b=z|b;g=6}if((g|0)==6){h=R6(d[a+34>>0]|0|0,0,16)|0;e=h|e;b=z|b;g=7}if((g|0)==7){h=R6(d[a+33>>0]|0|0,0,8)|0;b=z|b;e=h|e;g=8}if((g|0)==8)e=e|(d[f>>0]|0);h=a+24|0;i=h;g=c[i+4>>2]^b;f=h;c[f>>2]=c[i>>2]^e;c[f+4>>2]=g;_Z(a,2);f=a;b=c[f+4>>2]^b;g=a;c[g>>2]=c[f>>2]^e;c[g+4>>2]=b;g=a+16|0;b=g;e=c[b+4>>2]|0;f=g;c[f>>2]=c[b>>2]^255;c[f+4>>2]=e;_Z(a,4);f=a;a=a+8|0;z=c[a+4>>2]^c[f+4>>2]^c[g+4>>2]^c[h+4>>2];return c[a>>2]^c[f>>2]^c[g>>2]^c[h>>2]|0}function _Z(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;e=a+8|0;f=a+24|0;g=a+16|0;d=0;while(1){if((d|0)>=(b|0))break;h=e;n=c[h>>2]|0;h=c[h+4>>2]|0;o=a;o=J6(c[o>>2]|0,c[o+4>>2]|0,n|0,h|0)|0;k=z;m=R6(n|0,h|0,13)|0;l=z;h=Q6(n|0,h|0,51)|0;h=(m|h)^o;l=(l|z)^k;m=f;n=c[m>>2]|0;m=c[m+4>>2]|0;j=g;j=J6(c[j>>2]|0,c[j+4>>2]|0,n|0,m|0)|0;i=z;q=R6(n|0,m|0,16)|0;p=z;m=Q6(n|0,m|0,48)|0;m=(q|m)^j;p=(p|z)^i;o=J6(k|0,o|0,m|0,p|0)|0;k=z;q=a;c[q>>2]=o;c[q+4>>2]=k;q=R6(m|0,p|0,21)|0;n=z;p=Q6(m|0,p|0,43)|0;m=f;c[m>>2]=(q|p)^o;c[m+4>>2]=(n|z)^k;i=J6(j|0,i|0,h|0,l|0)|0;j=z;m=R6(h|0,l|0,17)|0;k=z;l=Q6(h|0,l|0,47)|0;h=e;c[h>>2]=(m|l)^i;c[h+4>>2]=(k|z)^j;h=g;c[h>>2]=j;c[h+4>>2]=i;d=d+1|0}return}function $Z(a){a=a|0;var b=0;while(1){b=c[a+472>>2]|0;if(!b)break;else a=b}return c[a+492>>2]|0}function a_(b,d,e,f,g,h){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0;L=l;l=l+16|0;I=L;J=L+4|0;K=c[b+352>>2]|0;o=d+16|0;p=d+40|0;q=b+144|0;r=b+284|0;s=e<<24>>24==0;t=h+12|0;v=h+16|0;w=h+8|0;x=d+44|0;y=d+64|0;z=b+436|0;A=b+452|0;B=b+448|0;C=(K+80|0)==(h|0);D=b+268|0;E=K+130|0;F=K+129|0;G=b+296|0;H=b+224|0;i=0;a:while(1){b:while(1){switch(ob[c[o>>2]&127](d,f,g,I)|0){case -4:{i=0;n=61;break a}case 0:{n=4;break a}case -1:{n=6;break a}case 6:{n=21;break b}case -3:{n=22;break b}case 7:case 39:{n=23;break b}case 10:{j=lb[c[p>>2]&127](d,f)|0;c:do if((j|0)<0)if((c[q>>2]|0)==(d|0)){c[r>>2]=f;j=1;i=14}else{j=1;i=14}else{if(s&(j|0)==32){k=c[t>>2]|0;if((c[v>>2]|0)==(k|0)){j=4;break}if((a[k+-1>>0]|0)==32){j=4;break}}m=x_(j,J)|0;k=0;while(1){if((k|0)>=(m|0)){j=0;break c}j=c[t>>2]|0;if((j|0)==(c[w>>2]|0)){if(!((vZ(h)|0)<<24>>24)){j=1;i=1;break c}j=c[t>>2]|0}M=a[J+k>>0]|0;c[t>>2]=j+1;a[j>>0]=M;k=k+1|0}}while(0);switch(j&7){case 4:case 0:break b;default:{}}break}case 9:{j=c[y>>2]|0;j=(nb[c[x>>2]&63](d,f+j|0,(c[I>>2]|0)+(0-j)|0)|0)&255;if(j<<24>>24){n=31;break b}j=c[y>>2]|0;j=lZ(z,d,f+j|0,(c[I>>2]|0)+(0-j)|0)|0;if(!j){i=1;n=61;break a}m=nZ(b,K,j,0)|0;c[B>>2]=c[A>>2];do if(C){if(!(c[D>>2]|0))if(!m)break b;else break;if(!(a[E>>0]|0)){j=(m|0)!=0;if(!(a[F>>0]|0)){n=45;break}else{u=j;n=47;break}}else{j=(m|0)!=0;if(!(c[G>>2]|0)){n=45;break}else{u=j;n=47;break}}}else if(!(a[F>>0]|0))if(!m){i=11;n=61;break a}else{n=46;break}else{j=(m|0)!=0;if(!(a[E>>0]|0)){u=j;n=47;break}else{n=45;break}}while(0);if((n|0)==45)if(j)n=46;else{i=11;n=61;break a}else if((n|0)==47?(n=0,!u):0)break b;if((n|0)==46?(n=0,(a[m+34>>0]|0)==0):0){i=24;n=61;break a}k=m+32|0;if(a[k>>0]|0){n=49;break a}if(c[m+28>>2]|0){n=52;break a}j=c[m+4>>2]|0;if(!j){n=55;break a}m=j+(c[m+8>>2]|0)|0;a[k>>0]=1;m=a_(b,c[H>>2]|0,e,j,m,h)|0;a[k>>0]=0;j=(m|0)==0;if(j)break b;else{j=(j^1)&1;i=m}break}default:{n=58;break a}}if(j)break a}if((n|0)==21){n=0;if(!(tZ(h,d,f,c[I>>2]|0)|0)){i=1;n=61;break}}else if((n|0)==22){c[I>>2]=f+(c[y>>2]|0);n=23}else if((n|0)==31){n=0;f=c[t>>2]|0;if((f|0)==(c[w>>2]|0)){if(!((vZ(h)|0)<<24>>24)){i=1;n=61;break}f=c[t>>2]|0}c[t>>2]=f+1;a[f>>0]=j}do if((n|0)==23){n=0;f=c[t>>2]|0;if(s){if((c[v>>2]|0)==(f|0))break;if((a[f+-1>>0]|0)==32)break}if((f|0)==(c[w>>2]|0)){if(!((vZ(h)|0)<<24>>24)){i=1;n=61;break a}f=c[t>>2]|0}c[t>>2]=f+1;a[f>>0]=32}while(0);f=c[I>>2]|0}if((n|0)==4)if((c[q>>2]|0)==(d|0)){c[r>>2]=c[I>>2];i=4;n=61}else{i=4;n=61}else if((n|0)==6)if((c[q>>2]|0)==(d|0)){c[r>>2]=f;i=4;n=61}else{i=4;n=61}else if((n|0)==49)if((c[q>>2]|0)==(d|0)){c[r>>2]=f;i=12;n=61}else{i=12;n=61}else if((n|0)==52)if((c[q>>2]|0)==(d|0)){c[r>>2]=f;i=15;n=61}else{i=15;n=61}else if((n|0)==55)if((c[q>>2]|0)==(d|0)){c[r>>2]=f;i=16;n=61}else{i=16;n=61}else if((n|0)==58)if((c[q>>2]|0)==(d|0)){c[r>>2]=f;i=23;n=61}else{i=23;n=61}l=L;return i|0}function b_(a){a=a|0;var b=0;b=a+8|0;return ((a|0)<1?0:(b|0)>0?b:0)|0}function c_(a,b){a=a|0;b=b|0;var c=0,d=0,e=0;c=l;l=l+80|0;d=c+16|0;e=c;VZ(a,e);WZ(d,e);XZ(d,b,YZ(b)|0);b=ZZ(d)|0;l=c;return b|0}function d_(b,c){b=b|0;c=c|0;var d=0;while(1){d=a[b>>0]|0;if(d<<24>>24!=(a[c>>0]|0)){c=0;break}if(!(d<<24>>24)){c=1;break}c=c+1|0;b=b+1|0}return c|0}function e_(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+1040|0;i=k;f=c[b+124>>2]|0;a:do if(!f)e=18;else{e=0;while(1){if((e|0)==256)break;c[i+(e<<2)>>2]=-1;e=e+1|0}g=i+1028|0;c[g>>2]=0;j=i+1024|0;c[j>>2]=0;h=i+1032|0;c[h>>2]=0;do if(nb[f&63](c[b+244>>2]|0,d,i)|0){f=c[b+12>>2]|0;e=y_()|0;e=kb[f&63](e)|0;c[b+236>>2]=e;if(!e){e=c[h>>2]|0;if(!e)e=1;else{tb[e&127](c[j>>2]|0);e=1}}else{e=ob[(a[b+232>>0]<<24>>24?65:64)&127](e,i,c[g>>2]|0,c[j>>2]|0)|0;if(!e)break;c[b+240>>2]=c[j>>2];c[b+248>>2]=c[h>>2];c[b+144>>2]=e;e=0}break a}while(0);e=c[h>>2]|0;if(e|0)tb[e&127](c[j>>2]|0);e=18}while(0);l=k;return e|0}function f_(b,d){b=b|0;d=d|0;a[b+4>>0]=0;c[b+8>>2]=0;c[b+12>>2]=0;c[b>>2]=0;c[b+16>>2]=d;return}function g_(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;l=c[b+352>>2]|0;m=b+424|0;n=b+420|0;o=b+412|0;p=b+428|0;q=l+152|0;r=b+368|0;j=l+60|0;k=l+80|0;a:while(1){h=d;b:while(1){if(!(a[d>>0]|0)){d=1;break a}f=a[h>>0]|0;switch(f<<24>>24){case 0:case 12:{s=5;break b}case 61:break b;default:{}}e=c[m>>2]|0;if((e|0)==(c[n>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break a}e=c[m>>2]|0;f=a[h>>0]|0}c[m>>2]=e+1;a[e>>0]=f;h=h+1|0}if((s|0)==5){s=0;d=c[m>>2]|0;if((d|0)==(c[n>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break}d=c[m>>2]|0}c[m>>2]=d+1;a[d>>0]=0;d=nZ(b,l,c[p>>2]|0,0)|0;if(d|0)a[d+32>>0]=1;d=(a[h>>0]|0)==0?h:h+1|0;c[m>>2]=c[p>>2];continue}d=c[m>>2]|0;if((c[p>>2]|0)==(d|0))f=q;else{if((d|0)==(c[n>>2]|0)){if(!((vZ(o)|0)<<24>>24)){d=0;break}d=c[m>>2]|0}c[m>>2]=d+1;a[d>>0]=0;f=nZ(b,j,c[p>>2]|0,8)|0;if(!f){d=0;break}e=c[f>>2]|0;i=c[p>>2]|0;d=i;if((e|0)==(i|0)){i=SZ(k,e)|0;c[f>>2]=i;if(!i){d=0;break}d=c[p>>2]|0}c[m>>2]=d}c:while(1){i=h+1|0;e=a[i>>0]|0;g=(d|0)==(c[n>>2]|0);switch(e<<24>>24){case 0:case 12:break c;default:{}}if(g){if(!((vZ(o)|0)<<24>>24)){d=0;break a}d=c[m>>2]|0;e=a[i>>0]|0}c[m>>2]=d+1;a[d>>0]=e;h=i;d=c[m>>2]|0}if(g){if(!((vZ(o)|0)<<24>>24)){d=0;break}d=c[m>>2]|0}c[m>>2]=d+1;a[d>>0]=0;if(UZ(b,f,0,c[p>>2]|0,r)|0){d=0;break}c[m>>2]=c[p>>2];d=(a[i>>0]|0)==0?i:h+2|0}return d|0}function h_(a,b){a=a|0;b=b|0;var d=0,e=0;if(a|0?(d=a+4|0,e=(c[d>>2]|0)==(c[a>>2]|0),c[a>>2]=b,e):0)c[d>>2]=b;return}function i_(a,b,d){a=a|0;b=b|0;d=d|0;if(a|0){c[a+52>>2]=b;c[a+56>>2]=d}return}function j_(a,b){a=a|0;b=b|0;if(a|0)c[a+60>>2]=b;return}function k_(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;g=(b|0)==0;a:do if(!(g|(e|0)<0)?!((d|0)==0&(e|0)!=0):0){h=b+476|0;switch(c[h>>2]|0){case 3:{c[b+280>>2]=33;g=0;break a}case 2:{c[b+280>>2]=36;g=0;break a}case 0:{if((c[b+472>>2]|0)==0?(l_(b)|0)<<24>>24==0:0){c[b+280>>2]=1;g=0;break a}break}default:{}}c[h>>2]=1;if(e|0){g=n_(b,e)|0;if(!g){g=0;break}W6(g|0,d|0,e|0)|0;g=o_(b,e,f)|0;break}a[b+480>>0]=f;if(!f)g=1;else{g=b+24|0;j=c[g>>2]|0;d=b+292|0;c[d>>2]=j;f=c[b+28>>2]|0;c[b+40>>2]=f;e=b+276|0;f=ob[c[e>>2]&127](b,j,f,g)|0;c[b+280>>2]=f;if(f|0){c[b+288>>2]=c[b+284>>2];c[e>>2]=66;g=0;break}switch(c[h>>2]|0){case 3:{j=c[b+144>>2]|0;xb[c[j+48>>2]&31](j,c[d>>2]|0,c[g>>2]|0,b+404|0);c[d>>2]=c[g>>2];g=2;break a}case 1:case 0:{c[h>>2]=2;g=1;break a}default:{g=1;break a}}}}else i=3;while(0);if((i|0)==3)if(g)g=0;else{c[b+280>>2]=41;g=0}return g|0}function l_(b){b=b|0;var d=0;d=b+492|0;if(!(c[d>>2]|0))c[d>>2]=p_()|0;if(!(a[b+232>>0]|0))b=1;else b=g_(b,142324)|0;return b|0}function m_(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;return c[a+280>>2]|0}function n_(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;a:do if(!a)d=0;else{if((b|0)<0){c[a+280>>2]=1;d=0;break}switch(c[a+476>>2]|0){case 3:{c[a+280>>2]=33;d=0;break a}case 2:{c[a+280>>2]=36;d=0;break a}default:{m=a+32|0;j=c[m>>2]|0;n=a+28|0;e=c[n>>2]|0;d=e;if((j-e|0)>=(b|0))break a;p=a+24|0;k=c[p>>2]|0;i=e-k|0;e=i+b|0;if((e|0)<0){c[a+280>>2]=1;d=0;break a}l=a+8|0;f=c[l>>2]|0;q=f;g=k-q|0;h=(g|0)>1024;b=h?1024:g;e=b+e|0;do if((e|0)>(j-q|0)){d=j-k|0;d=(d|0)==0?1024:d;do d=d<<1;while((d|0)<(e|0)&(d|0)>0);if((d|0)<1){c[a+280>>2]=1;d=0;break a}e=kb[c[a+12>>2]&63](d)|0;if(!e){c[a+280>>2]=1;d=0;break a}c[m>>2]=e+d;d=c[p>>2]|0;if(!d){d=e+(c[n>>2]|0)|0;c[n>>2]=d;c[l>>2]=e;o=22;break}else{q=d;o=q-(c[l>>2]|0)|0;o=(o|0)<1024?o:1024;W6(e|0,d+(0-o)|0,(c[n>>2]|0)-q+o|0)|0;tb[c[a+20>>2]&127](c[l>>2]|0);c[l>>2]=e;d=e+((c[n>>2]|0)-(c[p>>2]|0))+o|0;c[n>>2]=d;e=e+o|0;o=22;break}}else if(h){e=g-b|0;X6(f|0,f+e|0,b+i|0)|0;e=0-e|0;d=(c[n>>2]|0)+e|0;c[n>>2]=d;e=(c[p>>2]|0)+e|0;o=22}while(0);if((o|0)==22)c[p>>2]=e;c[a+288>>2]=0;c[a+284>>2]=0;c[a+292>>2]=0;break a}}}while(0);return d|0}function o_(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;a:do if(!b)d=0;else{f=b+476|0;switch(c[f>>2]|0){case 3:{c[b+280>>2]=33;d=0;break a}case 2:{c[b+280>>2]=36;d=0;break a}case 0:{if((c[b+472>>2]|0)==0?(l_(b)|0)<<24>>24==0:0){c[b+280>>2]=1;d=0;break a}break}default:{}}c[f>>2]=1;g=b+24|0;j=c[g>>2]|0;h=b+292|0;c[h>>2]=j;k=b+28|0;i=(c[k>>2]|0)+d|0;c[k>>2]=i;c[b+40>>2]=i;k=b+36|0;c[k>>2]=(c[k>>2]|0)+d;a[b+480>>0]=e;d=b+276|0;i=ob[c[d>>2]&127](b,j,i,g)|0;c[b+280>>2]=i;if(i|0){c[b+288>>2]=c[b+284>>2];c[d>>2]=66;d=0;break}switch(c[f>>2]|0){case 3:{d=2;break}case 1:case 0:{if(!e)d=1;else{c[f>>2]=2;d=1;break a}break}default:d=1}k=c[b+144>>2]|0;xb[c[k+48>>2]&31](k,c[h>>2]|0,c[g>>2]|0,b+404|0);c[h>>2]=c[g>>2]}while(0);return d|0}function p_(){var a=0,b=0,d=0;b=l;l=l+16|0;a=b;if(!(q_(a)|0)){d=s_()|0;d=(X4()|0)^d;c[a>>2]=d;a=r_(142378,O(d,2147483647)|0)|0}else a=r_(142365,c[a>>2]|0)|0;l=b;return a|0}function q_(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;f=l;l=l+16|0;e=m2(142365,0,f)|0;if((e|0)<0)b=0;else{b=0;while(1){g=W4(e,a+b|0,4-b|0)|0;d=g+b|0;if((g|0)>0)if(d>>>0<4)b=d;else{b=1;break}if((c[(d2()|0)>>2]|0)!=4){b=0;break}}l4(e)|0}l=f;return b|0}function r_(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=l;l=l+16|0;e=f;d=Ta(142443)|0;if(d|0?(s2(d,142463)|0)==0:0){d=c[15712]|0;c[e>>2]=a;c[e+4>>2]=8;c[e+8>>2]=b;c[e+12>>2]=4;w4(d,142465,e)|0}l=f;return b|0}function s_(){var a=0,b=0;b=l;l=l+16|0;a=b;if(!(Ua(a|0,0)|0)){l=b;return c[a+4>>2]|0}else qa(142390,142412,819,142423);return 0}function t_(a){a=a|0;if(!a)a=41;else a=c[a+280>>2]|0;return a|0}function u_(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;if(!a)a=0;else{b=a+284|0;d=c[b>>2]|0;if(d|0?(e=a+292|0,f=c[e>>2]|0,d>>>0>=f>>>0):0){g=c[a+144>>2]|0;xb[c[g+48>>2]&31](g,f,d,a+404|0);c[e>>2]=c[b>>2]}a=(c[a+404>>2]|0)+1|0}return a|0}function v_(a){a=a|0;do switch(a|0){case 41:{a=142502;break}case 1:{a=143774;break}case 2:{a=143761;break}case 3:{a=143744;break}case 4:{a=143712;break}case 5:{a=143697;break}case 6:{a=143679;break}case 7:{a=143664;break}case 8:{a=143644;break}case 9:{a=143616;break}case 10:{a=143581;break}case 11:{a=143564;break}case 12:{a=143537;break}case 13:{a=143517;break}case 14:{a=143479;break}case 15:{a=143452;break}case 16:{a=143410;break}case 17:{a=143363;break}case 18:{a=143346;break}case 19:{a=143295;break}case 20:{a=143272;break}case 21:{a=143226;break}case 22:{a=143199;break}case 23:{a=143148;break}case 24:{a=143112;break}case 25:{a=143060;break}case 26:{a=143015;break}case 27:{a=143e3;break}case 28:{a=142974;break}case 29:{a=142936;break}case 30:{a=142904;break}case 31:{a=142871;break}case 32:{a=142837;break}case 33:{a=142820;break}case 34:{a=142799;break}case 35:{a=142783;break}case 36:{a=142766;break}case 37:{a=142722;break}case 38:{a=142642;break}case 39:{a=142583;break}case 40:{a=142519;break}default:a=0}while(0);return a|0}function w_(a,b){a=a|0;b=b|0;var e=0,f=0,g=0,h=0,i=0;e=c[b>>2]|0;f=0;a:while(1){if(e>>>0<=a>>>0)break;h=e+-1|0;g=d[h>>0]|0;do if((g&248|0)==240)if((f+1|0)>>>0>3){i=5;break a}else f=0;else{if((g&240|0)==224)if((f+1|0)>>>0>2){i=8;break a}else{f=0;break}if((g&224|0)==192)if((f+1|0)>>>0>1){i=11;break a}else{f=0;break}else if(!(g&128))break a;else break}while(0);e=h;f=f+1|0}if((i|0)==5)e=e+3|0;else if((i|0)==8)e=e+2|0;else if((i|0)==11)e=e+1|0;c[b>>2]=e;return}function x_(b,c){b=b|0;c=c|0;do if((b|0)>=0){if((b|0)<128){a[c>>0]=b;b=1;break}if((b|0)<2048){a[c>>0]=b>>>6|192;a[c+1>>0]=b&63|128;b=2;break}if((b|0)<65536){a[c>>0]=b>>>12|224;a[c+1>>0]=b>>>6&63|128;a[c+2>>0]=b&63|128;b=3;break}if((b|0)<1114112){a[c>>0]=b>>>18|240;a[c+1>>0]=b>>>12&63|128;a[c+2>>0]=b>>>6&63|128;a[c+3>>0]=b&63|128;b=4}else b=0}else b=0;while(0);return b|0}function y_(){return 1908}function z_(e,f,g,h){e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;i=0;while(1){if((i|0)==364){i=0;break}a[e+i>>0]=a[56576+i>>0]|0;i=i+1|0}a:while(1){if((i|0)>=128){j=5;break}switch(a[56648+i>>0]|0){case 0:case 28:break;default:if((c[f+(i<<2)>>2]|0)!=(i|0)){e=0;break a}}i=i+1|0}b:do if((j|0)==5){q=e+72|0;m=e+372|0;n=e+884|0;o=(g|0)==0;l=0;while(1){if((l|0)>=256)break;p=c[f+(l<<2)>>2]|0;do if((p|0)==-1){a[q+l>>0]=1;b[m+(l<<1)>>1]=-1;a[n+(l<<2)>>0]=1;a[n+(l<<2)+1>>0]=0}else{if((p|0)<0){if(o|(p|0)<-4){e=0;break b}a[q+l>>0]=3-p;a[n+(l<<2)>>0]=0;b[m+(l<<1)>>1]=0;break}if((p|0)<128){i=a[56648+p>>0]|0;switch(i<<24>>24){case 0:case 28:break;default:if((p|0)!=(l|0)){e=0;break b}}a[q+l>>0]=i;a[n+(l<<2)>>0]=1;a[n+(l<<2)+1>>0]=p;b[m+(l<<1)>>1]=(p|0)==0?-1:p&65535;break}if((A_(p)|0)<0){a[q+l>>0]=0;b[m+(l<<1)>>1]=-1;a[n+(l<<2)>>0]=1;a[n+(l<<2)+1>>0]=0;break}if((p|0)>65535){e=0;break b}i=p>>>8;j=p>>>5&7;k=1<<(p&31);if(!(c[56940+((d[143788+i>>0]<<3|j)<<2)>>2]&k))a[q+l>>0]=c[56940+((d[144044+i>>0]<<3|j)<<2)>>2]&k|0?26:28;else a[q+l>>0]=22;a[n+(l<<2)>>0]=x_(p,n+(l<<2)+1|0)|0;b[m+(l<<1)>>1]=p}while(0);l=l+1|0}c[e+368>>2]=h;c[e+364>>2]=g;if(!o){c[e+328>>2]=108;c[e+332>>2]=108;c[e+336>>2]=108;c[e+340>>2]=109;c[e+344>>2]=109;c[e+348>>2]=109;c[e+352>>2]=110;c[e+356>>2]=110;c[e+360>>2]=110}c[e+56>>2]=13;c[e+60>>2]=14}while(0);return e|0}function A_(b){b=b|0;var c=0;switch(b>>8|0){case 223:case 222:case 221:case 220:case 219:case 218:case 217:case 216:{b=-1;break}case 0:{if(!(a[56648+b>>0]|0))b=-1;else c=4;break}case 255:{if((b|1|0)==65535)b=-1;else c=4;break}default:c=4}return b|0}function B_(a,b){a=a|0;b=b|0;a=lb[c[a+364>>2]&127](c[a+368>>2]|0,b)|0;if(a>>>0>65535)a=0;else a=c[56940+(((d[144044+(a>>>8)>>0]|0)<<3|a>>>5&7)<<2)>>2]&1<<(a&31);return a|0}function C_(a,b){a=a|0;b=b|0;a=lb[c[a+364>>2]&127](c[a+368>>2]|0,b)|0;if(a>>>0>65535)a=0;else a=c[56940+(((d[143788+(a>>>8)>>0]|0)<<3|a>>>5&7)<<2)>>2]&1<<(a&31);return a|0}function D_(a,b){a=a|0;b=b|0;a=lb[c[a+364>>2]&127](c[a+368>>2]|0,b)|0;if(a>>>0>65535)a=1;else a=(A_(a)|0)>>>31;return a|0}function E_(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;q=l;l=l+16|0;m=q;n=b+364|0;o=b+884|0;p=b+368|0;k=b+72|0;while(1){i=c[e>>2]|0;if((i|0)==(f|0)){h=0;break}r=d[i>>0]|0;j=o+(r<<2)+1|0;r=a[o+(r<<2)>>0]|0;b=r<<24>>24;if(!(r<<24>>24)){b=x_(lb[c[n>>2]&127](c[p>>2]|0,i)|0,m)|0;if((b|0)>(h-(c[g>>2]|0)|0)){h=2;break}i=c[e>>2]|0;j=m;i=i+((d[k+(d[i>>0]|0)>>0]|0)+-3)|0}else{if((h-(c[g>>2]|0)|0)<(b|0)){h=2;break}i=i+1|0}c[e>>2]=i;W6(c[g>>2]|0,j|0,b|0)|0;c[g>>2]=(c[g>>2]|0)+b}l=q;return h|0}function F_(a,e,f,g,h){a=a|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0;m=a+372|0;n=a+364|0;o=a+368|0;l=a+72|0;while(1){k=c[e>>2]|0;a=k>>>0<f>>>0;i=c[g>>2]|0;if(!(a&i>>>0<h>>>0))break;a=b[m+((d[k>>0]|0)<<1)>>1]|0;if(!(a<<16>>16)){a=(lb[c[n>>2]&127](c[o>>2]|0,k)|0)&65535;i=c[e>>2]|0;j=(d[l+(d[i>>0]|0)>>0]|0)+-3|0}else{j=1;i=k}c[e>>2]=i+j;k=c[g>>2]|0;c[g>>2]=k+2;b[k>>1]=a}return ((i|0)!=(h|0)|a^1?0:2)|0}function G_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0;a:do if(e>>>0<f>>>0){n=b+72|0;do switch(a[n+(d[e>>0]|0)>>0]|0){case 12:{h=f$(12,b,e+1|0,f,g)|0;break a}case 13:{h=f$(13,b,e+1|0,f,g)|0;break a}case 2:{h=e+1|0;if((f-h|0)<=0){h=-1;break a}switch(a[n+(d[h>>0]|0)>>0]|0){case 16:{h=g$(b,e+2|0,f,g)|0;break a}case 15:{h=b$(b,e+2|0,f,g)|0;break a}case 7:case 6:case 5:case 29:case 24:case 22:{c[g>>2]=e;h=29;break a}default:{c[g>>2]=h;h=0;break a}}}case 9:{if((e+1|0)==(f|0)){c[g>>2]=f;h=-15;break a}else k=3;break}case 10:case 21:{k=3;break}case 30:{h=X_(b,e+1|0,f,g)|0;break a}case 35:{c[g>>2]=e+1;h=38;break a}case 20:{c[g>>2]=e+1;h=25;break a}case 4:{i=e+1|0;h=f-i|0;if((h|0)<=0){h=-26;break a}if((a[i>>0]|0)==93){if((h|0)<=1){h=-1;break a}if((a[e+2>>0]|0)==62){c[g>>2]=e+3;h=34;break a}}c[g>>2]=i;h=26;break a}case 31:{c[g>>2]=e+1;h=23;break a}case 32:{h=e+1|0;if((f-h|0)<=0){h=-24;break a}switch(a[n+(d[h>>0]|0)>>0]|0){case 33:{c[g>>2]=e+2;h=36;break a}case 15:{c[g>>2]=e+2;h=35;break a}case 34:{c[g>>2]=e+2;h=37;break a}case 32:case 36:case 35:case 11:case 21:case 10:case 9:{c[g>>2]=h;h=24;break a}default:{c[g>>2]=h;h=0;break a}}}case 36:{c[g>>2]=e+1;h=21;break a}case 11:{c[g>>2]=e+1;h=17;break a}case 19:{h=h$(b,e+1|0,f,g)|0;break a}case 5:{if((f-e|0)<2){h=-2;break a}if(!(lb[c[b+340>>2]&127](b,e)|0))if(!(lb[c[b+328>>2]&127](b,e)|0)){c[g>>2]=e;h=0;break a}else{h=19;i=2}else{h=18;i=2}break}case 6:{if((f-e|0)<3){h=-2;break a}if(!(lb[c[b+344>>2]&127](b,e)|0))if(!(lb[c[b+332>>2]&127](b,e)|0)){c[g>>2]=e;h=0;break a}else{h=19;i=3}else{h=18;i=3}break}case 7:{if((f-e|0)<4){h=-2;break a}if(!(lb[c[b+348>>2]&127](b,e)|0))if(!(lb[c[b+336>>2]&127](b,e)|0)){c[g>>2]=e;h=0;break a}else{h=19;i=4}else{h=18;i=4}break}case 24:case 22:{h=18;i=1;break}case 23:case 27:case 26:case 25:{h=19;i=1;break}default:{c[g>>2]=e;h=0;break a}}while(0);if((k|0)==3){j=f;i=e;b:while(1){h=i;i=i+1|0;if((j-i|0)<=0){k=18;break}switch(a[n+(d[i>>0]|0)>>0]|0){case 10:case 21:continue b;case 9:break;default:{k=17;break b}}if((h+2|0)==(f|0)){k=17;break}}if((k|0)==17){c[g>>2]=i;h=15;break}else if((k|0)==18){c[g>>2]=i;h=15;break}}k=b+328|0;l=b+332|0;m=b+336|0;j=e+i|0;c:while(1){i=f-j|0;if((i|0)<=0){k=99;break}switch(a[n+(d[j>>0]|0)>>0]|0){case 29:{k=56;break c}case 10:case 9:case 21:case 30:case 20:case 36:case 35:case 32:case 11:{k=70;break c}case 34:{k=89;break c}case 33:{k=92;break c}case 15:{k=95;break c}case 27:case 26:case 25:case 24:case 22:{e=h;j=j+1|0;h=e;continue c}case 5:{if((i|0)<2){h=-2;break a}if(!(lb[c[k>>2]&127](b,j)|0)){k=60;break c}e=h;j=j+2|0;h=e;continue c}case 6:{if((i|0)<3){h=-2;break a}if(!(lb[c[l>>2]&127](b,j)|0)){k=64;break c}e=h;j=j+3|0;h=e;continue c}case 7:{if((i|0)<4){h=-2;break a}if(!(lb[c[m>>2]&127](b,j)|0)){k=68;break c}e=h;j=j+4|0;h=e;continue c}case 23:{i=j+1|0;switch(h&63){case 18:break;case 41:{h=19;j=i;continue c}default:{e=h;j=i;h=e;continue c}}h=f-i|0;if((h|0)<=0){h=-1;break a}switch(a[n+(d[i>>0]|0)>>0]|0){case 29:{k=74;break c}case 27:case 26:case 25:case 24:case 22:{h=41;j=j+2|0;continue c}case 5:{if((h|0)<2){h=-2;break a}if(!(lb[c[k>>2]&127](b,i)|0)){k=78;break c}h=41;j=j+3|0;continue c}case 6:{if((h|0)<3){h=-2;break a}if(!(lb[c[l>>2]&127](b,i)|0)){k=82;break c}h=41;j=j+4|0;continue c}case 7:{if((h|0)<4){h=-2;break a}if(!(lb[c[m>>2]&127](b,i)|0)){k=86;break c}h=41;j=j+5|0;continue c}default:{h=19;j=i;continue c}}}default:{k=98;break c}}}switch(k|0){case 56:{c[g>>2]=j;h=0;break a}case 60:{c[g>>2]=j;h=0;break a}case 64:{c[g>>2]=j;h=0;break a}case 68:{c[g>>2]=j;h=0;break a}case 70:{c[g>>2]=j;break a}case 74:{c[g>>2]=i;h=0;break a}case 78:{c[g>>2]=i;h=0;break a}case 82:{c[g>>2]=i;h=0;break a}case 86:{c[g>>2]=i;h=0;break a}case 89:if((h|0)==19){c[g>>2]=j;h=0;break a}else{c[g>>2]=j+1;h=32;break a}case 92:if((h|0)==19){c[g>>2]=j;h=0;break a}else{c[g>>2]=j+1;h=31;break a}case 95:if((h|0)==19){c[g>>2]=j;h=0;break a}else{c[g>>2]=j+1;h=30;break a}case 98:{c[g>>2]=j;h=0;break a}case 99:{h=0-h|0;break a}}}else h=-4;while(0);return h|0}function H_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0;a:do if(e>>>0<f>>>0){m=b+72|0;b:do switch(a[m+(d[e>>0]|0)>>0]|0){case 2:{h=__(b,e+1|0,f,g)|0;break a}case 3:{h=W_(b,e+1|0,f,g)|0;break a}case 9:{h=e+1|0;if((f-h|0)<=0){h=-3;break a}c[g>>2]=(a[m+(d[h>>0]|0)>>0]|0)==10?e+2|0:h;h=7;break a}case 10:{c[g>>2]=e+1;h=7;break a}case 4:{i=e+1|0;if((f-i|0)<=0){h=-5;break a}if((a[i>>0]|0)==93){h=e+2|0;if((f-h|0)<=0){h=-5;break a}if((a[h>>0]|0)==62){c[g>>2]=h;h=0;break a}else h=f}else h=f;break}case 5:{h=f;if((h-e|0)<2){h=-2;break a}if(!(lb[c[b+352>>2]&127](b,e)|0)){i=e+2|0;break b}else{c[g>>2]=e;h=0;break a}}case 6:{h=f;if((h-e|0)<3){h=-2;break a}if(!(lb[c[b+356>>2]&127](b,e)|0)){i=e+3|0;break b}else{c[g>>2]=e;h=0;break a}}case 7:{h=f;if((h-e|0)<4){h=-2;break a}if(!(lb[c[b+360>>2]&127](b,e)|0)){i=e+4|0;break b}else{c[g>>2]=e;h=0;break a}}case 8:case 1:case 0:{c[g>>2]=e;h=0;break a}default:{i=e+1|0;h=f}}while(0);j=b+352|0;k=b+356|0;l=b+360|0;c:while(1){f=h-i|0;if((f|0)<=0){f=49;break}switch(a[m+(d[i>>0]|0)>>0]|0){case 10:case 9:case 8:case 1:case 0:case 2:case 3:{f=47;break c}case 5:{if((f|0)<2){f=32;break c}if(lb[c[j>>2]&127](b,i)|0){f=32;break c}i=i+2|0;continue c}case 6:{if((f|0)<3){f=36;break c}if(lb[c[k>>2]&127](b,i)|0){f=36;break c}i=i+3|0;continue c}case 7:{if((f|0)<4){f=40;break c}if(lb[c[l>>2]&127](b,i)|0){f=40;break c}i=i+4|0;continue c}case 4:{if((f|0)<=1){f=47;break c}e=i+1|0;if((a[e>>0]|0)!=93){i=e;continue c}if((f|0)<=2){f=47;break c}i=i+2|0;if((a[i>>0]|0)==62){f=46;break c}else{i=e;continue c}}default:{i=i+1|0;continue c}}}if((f|0)==32){c[g>>2]=i;h=6;break}else if((f|0)==36){c[g>>2]=i;h=6;break}else if((f|0)==40){c[g>>2]=i;h=6;break}else if((f|0)==46){c[g>>2]=i;h=0;break}else if((f|0)==47){c[g>>2]=i;h=6;break}else if((f|0)==49){c[g>>2]=i;h=6;break}}else h=-4;while(0);return h|0}function I_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;a:do if(e>>>0<f>>>0){l=b+72|0;b:do switch(a[l+(d[e>>0]|0)>>0]|0){case 4:{i=e+1|0;h=f;if((h-i|0)<=0){h=-1;break a}if((a[i>>0]|0)==93){f=e+2|0;if((h-f|0)<=0){h=-1;break a}if((a[f>>0]|0)==62){c[g>>2]=e+3;h=40;break a}}break}case 9:{h=e+1|0;if((f-h|0)<=0){h=-1;break a}c[g>>2]=(a[l+(d[h>>0]|0)>>0]|0)==10?e+2|0:h;h=7;break a}case 10:{c[g>>2]=e+1;h=7;break a}case 5:{h=f;if((h-e|0)<2){h=-2;break a}if(!(lb[c[b+352>>2]&127](b,e)|0)){i=e+2|0;break b}else{c[g>>2]=e;h=0;break a}}case 6:{h=f;if((h-e|0)<3){h=-2;break a}if(!(lb[c[b+356>>2]&127](b,e)|0)){i=e+3|0;break b}else{c[g>>2]=e;h=0;break a}}case 7:{h=f;if((h-e|0)<4){h=-2;break a}if(!(lb[c[b+360>>2]&127](b,e)|0)){i=e+4|0;break b}else{c[g>>2]=e;h=0;break a}}case 8:case 1:case 0:{c[g>>2]=e;h=0;break a}default:{i=e+1|0;h=f}}while(0);e=b+352|0;j=b+356|0;k=b+360|0;c:while(1){f=h-i|0;if((f|0)<=0){f=39;break}switch(a[l+(d[i>>0]|0)>>0]|0){case 4:case 10:case 9:case 8:case 1:case 0:{f=37;break c}case 5:{if((f|0)<2){f=30;break c}if(!(lb[c[e>>2]&127](b,i)|0))f=2;else{f=30;break c}break}case 6:{if((f|0)<3){f=33;break c}if(!(lb[c[j>>2]&127](b,i)|0))f=3;else{f=33;break c}break}case 7:{if((f|0)<4){f=36;break c}if(!(lb[c[k>>2]&127](b,i)|0))f=4;else{f=36;break c}break}default:f=1}i=i+f|0}if((f|0)==30){c[g>>2]=i;h=6;break}else if((f|0)==33){c[g>>2]=i;h=6;break}else if((f|0)==36){c[g>>2]=i;h=6;break}else if((f|0)==37){c[g>>2]=i;h=6;break}else if((f|0)==39){c[g>>2]=i;h=6;break}}else h=-4;while(0);return h|0}
+function Ew(a,b,d,e,f,g,i,j,k){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;var l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;if(!((a|0)>-1&((b|0)>0&(d|0)>0)))qa(95162,94943,843,95188);j=zw(b,d,a,i,j,1)|0;if(!j)qa(95233,94943,847,95188);u=c[j+20>>2]|0;t=c[j+24>>2]|0;m=b+1|0;l=0;while(1){if((l|0)==(m|0))break;c[u+(l<<2)>>2]=0;l=l+1|0}a:do switch(i|0){case 1:{i=c[j+28>>2]|0;l=0;while(1){if((l|0)>=(a|0)){q=0;break}m=c[e+(l<<2)>>2]|0;if(!((m|0)>-1&(m|0)<(b|0))){s=13;break}r=c[f+(l<<2)>>2]|0;if(!((r|0)>-1&(r|0)<(d|0))){s=13;break}r=u+(m+1<<2)|0;c[r>>2]=(c[r>>2]|0)+1;l=l+1|0}if((s|0)==13)qa(138394,94943,862,95188);while(1){if((q|0)==(b|0)){l=0;break}s=q+1|0;d=u+(s<<2)|0;c[d>>2]=(c[d>>2]|0)+(c[u+(q<<2)>>2]|0);q=s}while(1){if((l|0)==(a|0))break;r=u+(c[e+(l<<2)>>2]<<2)|0;s=c[r>>2]|0;h[i+(s<<3)>>3]=+h[g+(l<<3)>>3];d=c[f+(l<<2)>>2]|0;c[r>>2]=s+1;c[t+(s<<2)>>2]=d;l=l+1|0}while(1){if((b|0)<=0)break a;f=b+-1|0;c[u+(b<<2)>>2]=c[u+(f<<2)>>2];b=f}}case 2:{i=c[j+28>>2]|0;l=0;while(1){if((l|0)>=(a|0)){r=0;break}m=c[e+(l<<2)>>2]|0;if(!((m|0)>-1&(m|0)<(b|0))){s=25;break}q=c[f+(l<<2)>>2]|0;if(!((q|0)>-1&(q|0)<(d|0))){s=25;break}q=u+(m+1<<2)|0;c[q>>2]=(c[q>>2]|0)+1;l=l+1|0}if((s|0)==25)qa(138394,94943,880,95188);while(1){if((r|0)==(b|0)){l=0;break}s=r+1|0;d=u+(s<<2)|0;c[d>>2]=(c[d>>2]|0)+(c[u+(r<<2)>>2]|0);r=s}while(1){if((l|0)==(a|0))break;r=u+(c[e+(l<<2)>>2]<<2)|0;s=c[r>>2]|0;d=s<<1;h[i+(d<<3)>>3]=+h[g>>3];h[i+((d|1)<<3)>>3]=+h[g+8>>3];d=c[f+(l<<2)>>2]|0;c[r>>2]=s+1;c[t+(s<<2)>>2]=d;g=g+16|0;l=l+1|0}while(1){if((b|0)<=0)break a;f=b+-1|0;c[u+(b<<2)>>2]=c[u+(f<<2)>>2];b=f}}case 4:{i=c[j+28>>2]|0;l=0;while(1){if((l|0)>=(a|0)){p=0;break}m=c[e+(l<<2)>>2]|0;if(!((m|0)>-1&(m|0)<(b|0))){s=37;break}r=c[f+(l<<2)>>2]|0;if(!((r|0)>-1&(r|0)<(d|0))){s=37;break}r=u+(m+1<<2)|0;c[r>>2]=(c[r>>2]|0)+1;l=l+1|0}if((s|0)==37)qa(138394,94943,899,95188);while(1){if((p|0)==(b|0)){l=0;break}s=p+1|0;d=u+(s<<2)|0;c[d>>2]=(c[d>>2]|0)+(c[u+(p<<2)>>2]|0);p=s}while(1){if((l|0)==(a|0))break;r=e+(l<<2)|0;c[i+(c[u+(c[r>>2]<<2)>>2]<<2)>>2]=c[g+(l<<2)>>2];d=c[f+(l<<2)>>2]|0;r=u+(c[r>>2]<<2)|0;s=c[r>>2]|0;c[r>>2]=s+1;c[t+(s<<2)>>2]=d;l=l+1|0}while(1){if((b|0)<=0)break a;f=b+-1|0;c[u+(b<<2)>>2]=c[u+(f<<2)>>2];b=f}}case 8:{g=0;while(1){if((g|0)>=(a|0)){n=0;break}l=c[e+(g<<2)>>2]|0;if(!((l|0)>-1&(l|0)<(b|0))){s=48;break}r=c[f+(g<<2)>>2]|0;if(!((r|0)>-1&(r|0)<(d|0))){s=48;break}r=u+(l+1<<2)|0;c[r>>2]=(c[r>>2]|0)+1;g=g+1|0}if((s|0)==48)qa(138394,94943,915,95188);while(1){if((n|0)==(b|0)){g=0;break}s=n+1|0;d=u+(s<<2)|0;c[d>>2]=(c[d>>2]|0)+(c[u+(n<<2)>>2]|0);n=s}while(1){if((g|0)==(a|0))break;d=c[f+(g<<2)>>2]|0;r=u+(c[e+(g<<2)>>2]<<2)|0;s=c[r>>2]|0;c[r>>2]=s+1;c[t+(s<<2)>>2]=d;g=g+1|0}while(1){if((b|0)<=0)break a;f=b+-1|0;c[u+(b<<2)>>2]=c[u+(f<<2)>>2];b=f}}case 16:{l=0;while(1){if((l|0)>=(a|0)){o=0;break}m=c[e+(l<<2)>>2]|0;if(!((m|0)>-1&(m|0)<(b|0))){s=59;break}r=c[f+(l<<2)>>2]|0;if(!((r|0)>-1&(r|0)<(d|0))){s=59;break}r=u+(m+1<<2)|0;c[r>>2]=(c[r>>2]|0)+1;l=l+1|0}if((s|0)==59)qa(138394,94943,930,95188);while(1){if((o|0)==(b|0))break;s=o+1|0;d=u+(s<<2)|0;c[d>>2]=(c[d>>2]|0)+(c[u+(o<<2)>>2]|0);o=s}W6(c[j+28>>2]|0,g|0,O(c[j+40>>2]|0,a)|0)|0;g=0;while(1){if((g|0)==(a|0))break;d=c[f+(g<<2)>>2]|0;r=u+(c[e+(g<<2)>>2]<<2)|0;s=c[r>>2]|0;c[r>>2]=s+1;c[t+(s<<2)>>2]=d;g=g+1|0}while(1){if((b|0)<=0)break a;f=b+-1|0;c[u+(b<<2)>>2]=c[u+(f<<2)>>2];b=f}}default:qa(138394,94943,944,95188)}while(0);c[u>>2]=0;c[j+8>>2]=a;if(k)j=Fw(j,k)|0;return j|0}function Fw(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0.0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0;t=c[a+20>>2]|0;u=c[a+24>>2]|0;e=c[a+16>>2]|0;r=c[a+4>>2]|0;a:do if(b){m=r<<2;n=KK(m)|0;d=0;while(1){if((d|0)>=(r|0))break;c[n+(d<<2)>>2]=-1;d=d+1|0}b:do switch(e|0){case 1:{k=c[a+28>>2]|0;f=0;e=c[t>>2]|0;d=0;c:while(1){if((d|0)>=(c[a>>2]|0)){v=f;w=n;break b}l=d+1|0;m=t+(l<<2)|0;i=t+(d<<2)|0;d=f;while(1){b=c[m>>2]|0;if((e|0)>=(b|0))break;b=u+(e<<2)|0;f=c[b>>2]|0;g=c[n+(f<<2)>>2]|0;if((g|0)<(c[i>>2]|0)){c[u+(d<<2)>>2]=f;h[k+(d<<3)>>3]=+h[k+(e<<3)>>3];c[n+(c[b>>2]<<2)>>2]=d;d=d+1|0}else{if((c[u+(g<<2)>>2]|0)!=(f|0))break c;s=k+(g<<3)|0;h[s>>3]=+h[s>>3]+ +h[k+(e<<3)>>3]}e=e+1|0}c[m>>2]=d;f=d;e=b;d=l}qa(95235,94943,1792,95260);break}case 2:{s=c[a+28>>2]|0;switch(b|0){case 1:{b=c[t>>2]|0;e=0;d=0;d:while(1){if((e|0)>=(c[a>>2]|0)){v=d;w=n;break b}k=e+1|0;l=t+(k<<2)|0;i=t+(e<<2)|0;while(1){e=c[l>>2]|0;if((b|0)>=(e|0))break;e=u+(b<<2)|0;f=c[e>>2]|0;g=c[n+(f<<2)>>2]|0;if((g|0)<(c[i>>2]|0)){c[u+(d<<2)>>2]=f;q=b<<1;r=d<<1;h[s+(r<<3)>>3]=+h[s+(q<<3)>>3];h[s+((r|1)<<3)>>3]=+h[s+((q|1)<<3)>>3];c[n+(c[e>>2]<<2)>>2]=d;d=d+1|0}else{if((c[u+(g<<2)>>2]|0)!=(f|0))break d;q=b<<1;r=g<<1;p=s+(r<<3)|0;h[p>>3]=+h[p>>3]+ +h[s+(q<<3)>>3];r=s+((r|1)<<3)|0;h[r>>3]=+h[r>>3]+ +h[s+((q|1)<<3)>>3]}b=b+1|0}c[l>>2]=d;b=e;e=k}qa(95235,94943,1815,95260);break}case 4:{b=c[t>>2]|0;e=0;d=0;e:while(1){if((e|0)>=(c[a>>2]|0)){v=d;w=n;break b}l=e+1|0;k=t+(l<<2)|0;i=t+(e<<2)|0;while(1){e=c[k>>2]|0;if((b|0)>=(e|0))break;e=u+(b<<2)|0;f=c[e>>2]|0;g=c[n+(f<<2)>>2]|0;if((g|0)<(c[i>>2]|0)){c[u+(d<<2)>>2]=f;q=b<<1;r=d<<1;h[s+(r<<3)>>3]=+h[s+(q<<3)>>3];h[s+((r|1)<<3)>>3]=+h[s+((q|1)<<3)>>3];c[n+(c[e>>2]<<2)>>2]=d;d=d+1|0}else{if((c[u+(g<<2)>>2]|0)!=(f|0))break e;q=b<<1;r=g<<1;p=s+(r<<3)|0;h[p>>3]=+h[p>>3]+ +h[s+(q<<3)>>3];h[s+((r|1)<<3)>>3]=+h[s+((q|1)<<3)>>3]}b=b+1|0}c[k>>2]=d;b=e;e=l}qa(95235,94943,1835,95260);break}case 2:{q=~~+h[s+8>>3];i=c[a>>2]|0;e=q;g=0;d=0;f:while(1){if((g|0)>=(i|0))break;k=g+1|0;l=c[t+(k<<2)>>2]|0;f=d;b=q;d=c[t+(g<<2)>>2]|0;while(1){if((d|0)>=(l|0)){q=b;g=k;d=f;continue f}q=~~+h[s+((f<<1|1)<<3)>>3];f=f+1|0;e=(e|0)>(q|0)?e:q;b=(b|0)<(q|0)?b:q;d=d+1|0}}U1(n);p=e-q+1|0;o=KK(O(p,m)|0)|0;p=O(p,r)|0;d=0;while(1){if((d|0)>=(p|0))break;c[o+(d<<2)>>2]=-1;d=d+1|0}d=0;b=c[t>>2]|0;e=0;g:while(1){if((e|0)>=(c[a>>2]|0)){v=d;w=o;break b}m=e+1|0;n=t+(m<<2)|0;l=t+(e<<2)|0;while(1){e=c[n>>2]|0;if((b|0)>=(e|0))break;e=c[u+(b<<2)>>2]|0;f=b<<1;j=+h[s+((f|1)<<3)>>3];g=(O(~~j-q|0,r)|0)+e|0;i=o+(g<<2)|0;k=c[i>>2]|0;if((k|0)<(c[l>>2]|0)){c[u+(d<<2)>>2]=e;k=d<<1;h[s+(k<<3)>>3]=+h[s+(f<<3)>>3];h[s+((k|1)<<3)>>3]=j;c[i>>2]=d;d=d+1|0}else{if((g|0)>=(p|0)){d=55;break g}if((c[u+(k<<2)>>2]|0)!=(e|0)){d=57;break g}k=k<<1;i=s+(k<<3)|0;h[i>>3]=+h[i>>3]+ +h[s+(f<<3)>>3];h[s+((k|1)<<3)>>3]=j}b=b+1|0}c[n>>2]=d;b=e;e=m}if((d|0)==55)qa(95292,94943,1869,95260);else if((d|0)==57)qa(95313,94943,1870,95260);break}case 3:{f=~~+h[s+8>>3];i=c[a>>2]|0;d=f;e=0;g=0;h:while(1){if((g|0)>=(i|0))break;k=g+1|0;l=c[t+(k<<2)>>2]|0;b=d;d=c[t+(g<<2)>>2]|0;while(1){if((d|0)>=(l|0)){d=b;g=k;continue h}q=~~+h[s+(e<<1<<3)>>3];b=(b|0)>(q|0)?b:q;f=(f|0)>(q|0)?f:q;e=e+1|0;d=d+1|0}}U1(n);e=d-f+1|0;o=KK(O(e,m)|0)|0;e=O(e,r)|0;d=0;while(1){if((d|0)>=(e|0))break;c[o+(d<<2)>>2]=-1;d=d+1|0}d=0;b=c[t>>2]|0;e=0;i:while(1){if((e|0)>=(c[a>>2]|0)){v=d;w=o;break b}m=e+1|0;n=t+(m<<2)|0;l=t+(e<<2)|0;while(1){e=c[n>>2]|0;if((b|0)>=(e|0))break;e=c[u+(b<<2)>>2]|0;g=b<<1;j=+h[s+(g<<3)>>3];i=o+((O(~~j-f|0,r)|0)+e<<2)|0;k=c[i>>2]|0;if((k|0)<(c[l>>2]|0)){c[u+(d<<2)>>2]=e;q=d<<1;h[s+(q<<3)>>3]=j;h[s+((q|1)<<3)>>3]=+h[s+((g|1)<<3)>>3];c[i>>2]=d;d=d+1|0}else{if((c[u+(k<<2)>>2]|0)!=(e|0))break i;q=k<<1;h[s+(q<<3)>>3]=j;q=s+((q|1)<<3)|0;h[q>>3]=+h[q>>3]+ +h[s+((g|1)<<3)>>3]}b=b+1|0}c[n>>2]=d;b=e;e=m}qa(95313,94943,1905,95260);break}default:{v=0;w=n;break b}}break}case 4:{k=c[a+28>>2]|0;e=0;d=0;b=c[t>>2]|0;j:while(1){if((e|0)>=(c[a>>2]|0)){v=d;w=n;break b}l=e+1|0;m=t+(l<<2)|0;i=t+(e<<2)|0;while(1){f=c[m>>2]|0;if((b|0)>=(f|0))break;e=u+(b<<2)|0;f=c[e>>2]|0;g=c[n+(f<<2)>>2]|0;if((g|0)<(c[i>>2]|0)){c[u+(d<<2)>>2]=f;c[k+(d<<2)>>2]=c[k+(b<<2)>>2];c[n+(c[e>>2]<<2)>>2]=d;d=d+1|0}else{if((c[u+(g<<2)>>2]|0)!=(f|0))break j;s=k+(g<<2)|0;c[s>>2]=(c[s>>2]|0)+(c[k+(b<<2)>>2]|0)}b=b+1|0}c[m>>2]=d;e=l;b=f}qa(95235,94943,1929,95260);break}case 8:{e=0;d=0;b=c[t>>2]|0;k:while(1){if((e|0)>=(c[a>>2]|0)){v=d;w=n;break b}k=e+1|0;l=t+(k<<2)|0;i=t+(e<<2)|0;while(1){f=c[l>>2]|0;if((b|0)>=(f|0))break;e=u+(b<<2)|0;f=c[e>>2]|0;g=c[n+(f<<2)>>2]|0;if((g|0)>=(c[i>>2]|0)){if((c[u+(g<<2)>>2]|0)!=(f|0))break k}else{c[u+(d<<2)>>2]=f;c[n+(c[e>>2]<<2)>>2]=d;d=d+1|0}b=b+1|0}c[l>>2]=d;e=k;b=f}qa(95235,94943,1948,95260);break}default:{a=0;break a}}while(0);c[a+8>>2]=v;U1(w)}while(0);return a|0}function Gw(a,b,c,d,e,f,g){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;if(!d)Hw(a,c,e,g,b,f);else Iw(a,c,e,g,b,f);return}function Hw(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0;if((c[a+32>>2]|0)!=1)qa(94919,94943,1121,95483);if((c[a+16>>2]|0)!=1)qa(95364,94943,1122,95483);p=c[a+28>>2]|0;q=c[a+20>>2]|0;r=c[a+24>>2]|0;s=c[a>>2]|0;o=c[a+4>>2]|0;a=c[d>>2]|0;i=(a|0)!=0;if(!f){if(!i)a=KK(O(e<<3,s)|0)|0;f=0;a:while(1){if((f|0)>=(s|0))break;n=O(f,e)|0;i=0;while(1){if((i|0)>=(e|0))break;h[a+(i+n<<3)>>3]=0.0;i=i+1|0}l=f+1|0;m=c[q+(l<<2)>>2]|0;i=c[q+(f<<2)>>2]|0;while(1){if((i|0)>=(m|0)){f=l;continue a}j=p+(i<<3)|0;k=r+(i<<2)|0;f=0;while(1){if((f|0)>=(e|0))break;t=+h[b+((O(c[k>>2]|0,e)|0)+f<<3)>>3]*+h[j>>3];o=a+(f+n<<3)|0;h[o>>3]=+h[o>>3]+t;f=f+1|0}i=i+1|0}}if(g)Kw(a,s,e)}else{if(!i)a=KK(O(e<<3,o)|0)|0;f=O(o,e)|0;i=0;while(1){if((i|0)>=(f|0)){i=0;break}h[a+(i<<3)>>3]=0.0;i=i+1|0}b:while(1){if((i|0)>=(s|0))break;l=i+1|0;m=c[q+(l<<2)>>2]|0;n=O(i,e)|0;i=c[q+(i<<2)>>2]|0;while(1){if((i|0)>=(m|0)){i=l;continue b}j=p+(i<<3)|0;k=r+(i<<2)|0;f=0;while(1){if((f|0)>=(e|0))break;u=a+((O(c[k>>2]|0,e)|0)+f<<3)|0;h[u>>3]=+h[u>>3]+ +h[b+(f+n<<3)>>3]*+h[j>>3];f=f+1|0}i=i+1|0}}if(g)Kw(a,o,e)}c[d>>2]=a;return}function Iw(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;m=n;if((c[a+32>>2]|0)!=1)qa(94919,94943,1163,95335);if((c[a+16>>2]|0)!=1)qa(95364,94943,1164,95335);h=c[d>>2]|0;j=c[a>>2]|0;k=c[a+4>>2]|0;i=(h|0)!=0;if(!f){if(!i)h=KK(O(e<<3,j)|0)|0;i=0;while(1){if((i|0)>=(e|0))break;c[m>>2]=h+((O(i,j)|0)<<3);Jw(a,b+((O(i,k)|0)<<3)|0,m,0);i=i+1|0}if(!g)Kw(h,e,j)}else{if(!i)h=KK(O(e<<3,k)|0)|0;i=0;while(1){if((i|0)>=(e|0))break;c[m>>2]=h+((O(i,k)|0)<<3);Jw(a,b+((O(i,j)|0)<<3)|0,m,f);i=i+1|0}if(!g)Kw(h,e,k)}c[d>>2]=h;l=n;return}function Jw(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0.0;if((c[a+32>>2]|0)!=1)qa(94919,94943,1222,95392);i=c[a+16>>2]|0;switch(i|0){case 4:case 1:break;default:qa(95421,94943,1223,95392)}n=c[a+20>>2]|0;m=c[a+24>>2]|0;o=c[a>>2]|0;k=c[a+4>>2]|0;j=c[d>>2]|0;a:do switch(i|0){case 1:{l=c[a+28>>2]|0;a=(e|0)!=0;f=(j|0)!=0;if(!b){if(!a){if(f)f=j;else f=KK(o<<3)|0;a=0;b:while(1){if((a|0)>=(o|0))break a;i=f+(a<<3)|0;h[i>>3]=0.0;j=a+1|0;e=c[n+(j<<2)>>2]|0;a=c[n+(a<<2)>>2]|0;g=0.0;while(1){if((a|0)>=(e|0)){a=j;continue b}p=g+ +h[l+(a<<3)>>3];h[i>>3]=p;a=a+1|0;g=p}}}if(!f)j=KK(k<<3)|0;a=0;while(1){if((a|0)>=(k|0)){a=0;break}h[j+(a<<3)>>3]=0.0;a=a+1|0}c:while(1){if((a|0)>=(o|0)){f=j;break a}f=a+1|0;i=c[n+(f<<2)>>2]|0;a=c[n+(a<<2)>>2]|0;while(1){if((a|0)>=(i|0)){a=f;continue c}b=j+(c[m+(a<<2)>>2]<<3)|0;h[b>>3]=+h[b>>3]+ +h[l+(a<<3)>>3];a=a+1|0}}}else{if(!a){if(f)f=j;else f=KK(o<<3)|0;a=0;d:while(1){if((a|0)>=(o|0))break a;i=f+(a<<3)|0;h[i>>3]=0.0;j=a+1|0;e=c[n+(j<<2)>>2]|0;a=c[n+(a<<2)>>2]|0;g=0.0;while(1){if((a|0)>=(e|0)){a=j;continue d}p=g+ +h[b+(c[m+(a<<2)>>2]<<3)>>3]*+h[l+(a<<3)>>3];h[i>>3]=p;a=a+1|0;g=p}}}if(f)f=j;else f=KK(k<<3)|0;a=0;while(1){if((a|0)>=(k|0)){a=0;break}h[f+(a<<3)>>3]=0.0;a=a+1|0}e:while(1){if((a|0)>=(o|0))break a;i=a+1|0;j=c[n+(i<<2)>>2]|0;e=b+(a<<3)|0;a=c[n+(a<<2)>>2]|0;while(1){if((a|0)>=(j|0)){a=i;continue e}k=f+(c[m+(a<<2)>>2]<<3)|0;h[k>>3]=+h[k>>3]+ +h[e>>3]*+h[l+(a<<3)>>3];a=a+1|0}}}}case 4:{l=c[a+28>>2]|0;a=(e|0)!=0;f=(j|0)!=0;if(!b){if(!a){if(f)f=j;else f=KK(o<<3)|0;a=0;f:while(1){if((a|0)>=(o|0))break a;i=f+(a<<3)|0;h[i>>3]=0.0;j=a+1|0;e=c[n+(j<<2)>>2]|0;a=c[n+(a<<2)>>2]|0;g=0.0;while(1){if((a|0)>=(e|0)){a=j;continue f}p=g+ +(c[l+(a<<2)>>2]|0);h[i>>3]=p;a=a+1|0;g=p}}}if(!f)j=KK(k<<3)|0;a=0;while(1){if((a|0)>=(k|0)){a=0;break}h[j+(a<<3)>>3]=0.0;a=a+1|0}g:while(1){if((a|0)>=(o|0)){f=j;break a}f=a+1|0;i=c[n+(f<<2)>>2]|0;a=c[n+(a<<2)>>2]|0;while(1){if((a|0)>=(i|0)){a=f;continue g}b=j+(c[m+(a<<2)>>2]<<3)|0;h[b>>3]=+h[b>>3]+ +(c[l+(a<<2)>>2]|0);a=a+1|0}}}else{if(!a){if(f)f=j;else f=KK(o<<3)|0;a=0;h:while(1){if((a|0)>=(o|0))break a;i=f+(a<<3)|0;h[i>>3]=0.0;j=a+1|0;e=c[n+(j<<2)>>2]|0;a=c[n+(a<<2)>>2]|0;g=0.0;while(1){if((a|0)>=(e|0)){a=j;continue h}p=g+ +h[b+(c[m+(a<<2)>>2]<<3)>>3]*+(c[l+(a<<2)>>2]|0);h[i>>3]=p;a=a+1|0;g=p}}}if(f)f=j;else f=KK(k<<3)|0;a=0;while(1){if((a|0)>=(k|0)){a=0;break}h[f+(a<<3)>>3]=0.0;a=a+1|0}i:while(1){if((a|0)>=(o|0))break a;i=a+1|0;j=c[n+(i<<2)>>2]|0;e=b+(a<<3)|0;a=c[n+(a<<2)>>2]|0;while(1){if((a|0)>=(j|0)){a=i;continue i}k=f+(c[m+(a<<2)>>2]<<3)|0;h[k>>3]=+h[k>>3]+ +h[e>>3]*+(c[l+(a<<2)>>2]|0);a=a+1|0}}}}default:qa(138394,94943,1315,95392)}while(0);c[d>>2]=f;return}function Kw(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0,f=0,g=0;d=O(b<<3,c)|0;f=KK(d)|0;W6(f|0,a|0,d|0)|0;d=0;while(1){if((d|0)>=(b|0))break;g=O(d,c)|0;e=0;while(1){if((e|0)>=(c|0))break;h[a+((O(e,b)|0)+d<<3)>>3]=+h[f+(e+g<<3)>>3];e=e+1|0}d=d+1|0}U1(f);return}function Lw(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0;x=c[a+20>>2]|0;y=c[a+24>>2]|0;z=c[b+20>>2]|0;A=c[b+24>>2]|0;w=c[a+32>>2]|0;if(!((w|0)==1?(w|0)==(c[b+32>>2]|0):0))qa(95005,94943,1408,95512);w=c[a>>2]|0;if(((c[a+4>>2]|0)==(c[b>>2]|0)?(n=c[a+16>>2]|0,(n|0)==(c[b+16>>2]|0)):0)?(m=b+4|0,B=KK(c[m>>2]<<2)|0,(B|0)!=0):0){d=0;while(1){if((d|0)>=(c[m>>2]|0)){e=0;d=0;break}c[B+(d<<2)>>2]=-1;d=d+1|0}a:while(1){if((d|0)>=(w|0))break;j=d+1|0;k=x+(j<<2)|0;l=-2-d|0;i=c[x+(d<<2)>>2]|0;while(1){if((i|0)>=(c[k>>2]|0)){d=j;continue a}f=c[y+(i<<2)>>2]|0;g=z+(f+1<<2)|0;f=c[z+(f<<2)>>2]|0;while(1){if((f|0)>=(c[g>>2]|0))break;d=B+(c[A+(f<<2)>>2]<<2)|0;if((c[d>>2]|0)!=(l|0)){c[d>>2]=l;e=e+1|0}f=f+1|0}i=i+1|0}}d=rw(w,c[m>>2]|0,e,n,1)|0;b:do if(!d)d=0;else{t=c[d+20>>2]|0;u=c[d+24>>2]|0;c:do switch(n|0){case 1:{r=c[a+28>>2]|0;n=c[b+28>>2]|0;b=c[d+28>>2]|0;c[t>>2]=0;e=0;f=0;d:while(1){if((e|0)>=(w|0)){v=f;break c}o=e+1|0;p=x+(o<<2)|0;q=t+(e<<2)|0;a=c[x+(e<<2)>>2]|0;while(1){if((a|0)>=(c[p>>2]|0))break;k=c[y+(a<<2)>>2]|0;l=z+(k+1<<2)|0;m=r+(a<<3)|0;k=c[z+(k<<2)>>2]|0;while(1){if((k|0)>=(c[l>>2]|0))break;e=A+(k<<2)|0;g=c[e>>2]|0;i=B+(g<<2)|0;j=c[i>>2]|0;if((j|0)<(c[q>>2]|0)){c[i>>2]=f;c[u+(f<<2)>>2]=c[e>>2];h[b+(f<<3)>>3]=+h[n+(k<<3)>>3]*+h[m>>3];f=f+1|0}else{if((c[u+(j<<2)>>2]|0)!=(g|0))break d;s=b+(j<<3)|0;h[s>>3]=+h[s>>3]+ +h[n+(k<<3)>>3]*+h[m>>3]}k=k+1|0}a=a+1|0}c[t+(o<<2)>>2]=f;e=o}qa(95534,94943,1468,95512);break}case 2:{s=c[a+28>>2]|0;b=c[b+28>>2]|0;o=c[d+28>>2]|0;c[t>>2]=0;e=0;f=0;e:while(1){if((e|0)>=(w|0)){v=f;break c}p=e+1|0;q=x+(p<<2)|0;r=t+(e<<2)|0;n=c[x+(e<<2)>>2]|0;while(1){if((n|0)>=(c[q>>2]|0))break;k=c[y+(n<<2)>>2]|0;l=z+(k+1<<2)|0;a=n<<1;m=s+(a<<3)|0;a=s+((a|1)<<3)|0;k=c[z+(k<<2)>>2]|0;while(1){if((k|0)>=(c[l>>2]|0))break;e=A+(k<<2)|0;g=c[e>>2]|0;i=B+(g<<2)|0;j=c[i>>2]|0;if((j|0)<(c[r>>2]|0)){c[i>>2]=f;c[u+(f<<2)>>2]=c[e>>2];i=k<<1;g=b+(i<<3)|0;i=b+((i|1)<<3)|0;j=f<<1;h[o+(j<<3)>>3]=+h[g>>3]*+h[m>>3]-+h[i>>3]*+h[a>>3];h[o+((j|1)<<3)>>3]=+h[g>>3]*+h[a>>3]+ +h[i>>3]*+h[m>>3];f=f+1|0}else{if((c[u+(j<<2)>>2]|0)!=(g|0))break e;i=k<<1;g=b+(i<<3)|0;i=b+((i|1)<<3)|0;j=j<<1;e=o+(j<<3)|0;h[e>>3]=+h[e>>3]+ +h[g>>3]*+h[m>>3]-+h[i>>3]*+h[a>>3];j=o+((j|1)<<3)|0;h[j>>3]=+h[g>>3]*+h[a>>3]+ +h[i>>3]*+h[m>>3]+ +h[j>>3]}k=k+1|0}n=n+1|0}c[t+(p<<2)>>2]=f;e=p}qa(95534,94943,1497,95512);break}case 4:{r=c[a+28>>2]|0;n=c[b+28>>2]|0;b=c[d+28>>2]|0;c[t>>2]=0;e=0;f=0;f:while(1){if((f|0)>=(w|0)){v=e;break c}o=f+1|0;p=x+(o<<2)|0;q=t+(f<<2)|0;l=c[x+(f<<2)>>2]|0;while(1){if((l|0)>=(c[p>>2]|0))break;k=c[y+(l<<2)>>2]|0;m=z+(k+1<<2)|0;a=r+(l<<2)|0;k=c[z+(k<<2)>>2]|0;while(1){if((k|0)>=(c[m>>2]|0))break;f=A+(k<<2)|0;g=c[f>>2]|0;i=B+(g<<2)|0;j=c[i>>2]|0;if((j|0)<(c[q>>2]|0)){c[i>>2]=e;c[u+(e<<2)>>2]=c[f>>2];c[b+(e<<2)>>2]=O(c[n+(k<<2)>>2]|0,c[a>>2]|0)|0;e=e+1|0}else{if((c[u+(j<<2)>>2]|0)!=(g|0))break f;i=O(c[n+(k<<2)>>2]|0,c[a>>2]|0)|0;s=b+(j<<2)|0;c[s>>2]=(c[s>>2]|0)+i}k=k+1|0}l=l+1|0}c[t+(o<<2)>>2]=e;f=o}qa(95534,94943,1523,95512);break}case 8:{c[t>>2]=0;e=0;f=0;g:while(1){if((f|0)>=(w|0)){v=e;break c}a=f+1|0;n=x+(a<<2)|0;b=t+(f<<2)|0;l=c[x+(f<<2)>>2]|0;while(1){if((l|0)>=(c[n>>2]|0))break;k=c[y+(l<<2)>>2]|0;m=z+(k+1<<2)|0;k=c[z+(k<<2)>>2]|0;while(1){if((k|0)>=(c[m>>2]|0))break;f=A+(k<<2)|0;g=c[f>>2]|0;i=B+(g<<2)|0;j=c[i>>2]|0;if((j|0)>=(c[b>>2]|0)){if((c[u+(j<<2)>>2]|0)!=(g|0))break g}else{c[i>>2]=e;c[u+(e<<2)>>2]=c[f>>2];e=e+1|0}k=k+1|0}l=l+1|0}c[t+(a<<2)>>2]=e;f=a}qa(95534,94943,1543,95512);break}default:{qw(d);d=0;break b}}while(0);c[d+8>>2]=v}while(0);U1(B)}else d=0;return d|0}function Mw(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0.0,K=0.0,L=0.0,M=0.0;F=c[a+20>>2]|0;G=c[a+24>>2]|0;H=c[b+20>>2]|0;B=c[b+24>>2]|0;C=c[d+20>>2]|0;D=c[d+24>>2]|0;E=c[a+32>>2]|0;if(!((E|0)==1?(E|0)==(c[b+32>>2]|0):0))qa(95005,94943,1574,95559);E=c[a>>2]|0;if(((((c[a+4>>2]|0)==(c[b>>2]|0)?(c[b+4>>2]|0)==(c[d>>2]|0):0)?(q=c[a+16>>2]|0,(q|0)==(c[b+16>>2]|0)):0)?(q|0)==(c[d+16>>2]|0):0)?(p=d+4|0,I=KK(c[p>>2]<<2)|0,(I|0)!=0):0){e=0;while(1){if((e|0)>=(c[p>>2]|0)){f=0;e=0;break}c[I+(e<<2)>>2]=-1;e=e+1|0}a:while(1){if((e|0)>=(E|0))break;m=e+1|0;n=F+(m<<2)|0;o=-2-e|0;l=c[F+(e<<2)>>2]|0;while(1){if((l|0)>=(c[n>>2]|0)){e=m;continue a}j=c[G+(l<<2)>>2]|0;k=H+(j+1<<2)|0;j=c[H+(j<<2)>>2]|0;while(1){if((j|0)>=(c[k>>2]|0))break;g=c[B+(j<<2)>>2]|0;i=C+(g+1<<2)|0;g=c[C+(g<<2)>>2]|0;while(1){if((g|0)>=(c[i>>2]|0))break;e=I+(c[D+(g<<2)>>2]<<2)|0;if((c[e>>2]|0)!=(o|0)){c[e>>2]=o;f=f+1|0}g=g+1|0}j=j+1|0}l=l+1|0}}e=rw(E,c[p>>2]|0,f,q,1)|0;b:do if(!e)e=0;else{y=c[e+20>>2]|0;z=c[e+24>>2]|0;c:do switch(q|0){case 1:{v=c[a+28>>2]|0;u=c[b+28>>2]|0;d=c[d+28>>2]|0;r=c[e+28>>2]|0;c[y>>2]=0;f=0;g=0;d:while(1){if((f|0)>=(E|0)){A=g;break c}s=f+1|0;t=F+(s<<2)|0;b=y+(f<<2)|0;q=c[F+(f<<2)>>2]|0;while(1){if((q|0)>=(c[t>>2]|0))break;o=c[G+(q<<2)>>2]|0;p=H+(o+1<<2)|0;a=v+(q<<3)|0;o=c[H+(o<<2)>>2]|0;while(1){if((o|0)>=(c[p>>2]|0))break;l=c[B+(o<<2)>>2]|0;m=C+(l+1<<2)|0;n=u+(o<<3)|0;l=c[C+(l<<2)>>2]|0;while(1){if((l|0)>=(c[m>>2]|0))break;f=D+(l<<2)|0;i=c[f>>2]|0;j=I+(i<<2)|0;k=c[j>>2]|0;if((k|0)<(c[b>>2]|0)){c[j>>2]=g;c[z+(g<<2)>>2]=c[f>>2];h[r+(g<<3)>>3]=+h[n>>3]*+h[a>>3]*+h[d+(l<<3)>>3];g=g+1|0}else{if((c[z+(k<<2)>>2]|0)!=(i|0))break d;x=r+(k<<3)|0;h[x>>3]=+h[x>>3]+ +h[n>>3]*+h[a>>3]*+h[d+(l<<3)>>3]}l=l+1|0}o=o+1|0}q=q+1|0}c[y+(s<<2)>>2]=g;f=s}qa(95582,94943,1642,95559);break}case 2:{x=c[a+28>>2]|0;w=c[b+28>>2]|0;r=c[d+28>>2]|0;s=c[e+28>>2]|0;c[y>>2]=0;f=0;g=0;e:while(1){if((g|0)>=(E|0)){A=f;break c}t=g+1|0;u=F+(t<<2)|0;v=y+(g<<2)|0;a=c[F+(g<<2)>>2]|0;while(1){if((a|0)>=(c[u>>2]|0))break;m=c[G+(a<<2)>>2]|0;q=H+(m+1<<2)|0;d=a<<1;b=x+(d<<3)|0;d=x+((d|1)<<3)|0;m=c[H+(m<<2)>>2]|0;while(1){if((m|0)>=(c[q>>2]|0))break;l=c[B+(m<<2)>>2]|0;n=C+(l+1<<2)|0;p=m<<1;o=w+(p<<3)|0;p=w+((p|1)<<3)|0;l=c[C+(l<<2)>>2]|0;while(1){if((l|0)>=(c[n>>2]|0))break;g=D+(l<<2)|0;i=c[g>>2]|0;j=I+(i<<2)|0;k=c[j>>2]|0;if((k|0)<(c[v>>2]|0)){c[j>>2]=f;c[z+(f<<2)>>2]=c[g>>2];K=+h[b>>3];L=+h[o>>3];J=+h[d>>3];M=+h[p>>3];j=l<<1;i=r+(j<<3)|0;j=r+((j|1)<<3)|0;k=f<<1;h[s+(k<<3)>>3]=(L*K-M*J)*+h[i>>3]-(M*K+J*L)*+h[j>>3];L=+h[b>>3];J=+h[p>>3];K=+h[d>>3];M=+h[o>>3];h[s+((k|1)<<3)>>3]=(M*K+J*L)*+h[i>>3]+(M*L-K*J)*+h[j>>3];f=f+1|0}else{if((c[z+(k<<2)>>2]|0)!=(i|0))break e;L=+h[b>>3];K=+h[o>>3];M=+h[d>>3];J=+h[p>>3];j=l<<1;i=r+(j<<3)|0;j=r+((j|1)<<3)|0;k=k<<1;g=s+(k<<3)|0;h[g>>3]=+h[g>>3]+(K*L-J*M)*+h[i>>3]-(J*L+M*K)*+h[j>>3];K=+h[b>>3];M=+h[p>>3];L=+h[d>>3];J=+h[o>>3];k=s+((k|1)<<3)|0;h[k>>3]=(J*L+M*K)*+h[i>>3]+(J*K-L*M)*+h[j>>3]+ +h[k>>3]}l=l+1|0}m=m+1|0}a=a+1|0}c[y+(t<<2)>>2]=f;g=t}qa(95582,94943,1674,95559);break}case 4:{v=c[a+28>>2]|0;u=c[b+28>>2]|0;b=c[d+28>>2]|0;d=c[e+28>>2]|0;c[y>>2]=0;f=0;g=0;f:while(1){if((g|0)>=(E|0)){A=f;break c}r=g+1|0;s=F+(r<<2)|0;t=y+(g<<2)|0;p=c[F+(g<<2)>>2]|0;while(1){if((p|0)>=(c[s>>2]|0))break;m=c[G+(p<<2)>>2]|0;a=H+(m+1<<2)|0;q=v+(p<<2)|0;m=c[H+(m<<2)>>2]|0;while(1){if((m|0)>=(c[a>>2]|0))break;l=c[B+(m<<2)>>2]|0;n=C+(l+1<<2)|0;o=u+(m<<2)|0;l=c[C+(l<<2)>>2]|0;while(1){if((l|0)>=(c[n>>2]|0))break;g=D+(l<<2)|0;i=c[g>>2]|0;j=I+(i<<2)|0;k=c[j>>2]|0;if((k|0)<(c[t>>2]|0)){c[j>>2]=f;c[z+(f<<2)>>2]=c[g>>2];w=O(c[o>>2]|0,c[q>>2]|0)|0;w=O(w,c[b+(l<<2)>>2]|0)|0;x=d+(f<<2)|0;c[x>>2]=(c[x>>2]|0)+w;f=f+1|0}else{if((c[z+(k<<2)>>2]|0)!=(i|0))break f;w=O(c[o>>2]|0,c[q>>2]|0)|0;w=O(w,c[b+(l<<2)>>2]|0)|0;x=d+(k<<2)|0;c[x>>2]=(c[x>>2]|0)+w}l=l+1|0}m=m+1|0}p=p+1|0}c[y+(r<<2)>>2]=f;g=r}qa(95582,94943,1706,95559);break}case 8:{c[y>>2]=0;f=0;g=0;g:while(1){if((g|0)>=(E|0)){A=f;break c}a=g+1|0;q=F+(a<<2)|0;b=y+(g<<2)|0;o=c[F+(g<<2)>>2]|0;while(1){if((o|0)>=(c[q>>2]|0))break;m=c[G+(o<<2)>>2]|0;p=H+(m+1<<2)|0;m=c[H+(m<<2)>>2]|0;while(1){if((m|0)>=(c[p>>2]|0))break;l=c[B+(m<<2)>>2]|0;n=C+(l+1<<2)|0;l=c[C+(l<<2)>>2]|0;while(1){if((l|0)>=(c[n>>2]|0))break;g=D+(l<<2)|0;i=c[g>>2]|0;j=I+(i<<2)|0;k=c[j>>2]|0;if((k|0)>=(c[b>>2]|0)){if((c[z+(k<<2)>>2]|0)!=(i|0))break g}else{c[j>>2]=f;c[z+(f<<2)>>2]=c[g>>2];f=f+1|0}l=l+1|0}m=m+1|0}o=o+1|0}c[y+(a<<2)>>2]=f;g=a}qa(95582,94943,1729,95559);break}default:{qw(e);e=0;break b}}while(0);c[e+8>>2]=A}while(0);U1(I)}else e=0;return e|0}function Nw(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;if((c[a+32>>2]|0)!=2)qa(95100,94943,1971,95607);if((b|0)>=1){h=c[a+8>>2]|0;g=h+b|0;if((g|0)>=(c[a+12>>2]|0))Ow(a,g+10|0)|0;i=h<<2;g=b<<2;W6((c[a+20>>2]|0)+i|0,d|0,g|0)|0;W6((c[a+24>>2]|0)+i|0,e|0,g|0)|0;g=c[a+40>>2]|0;if(g|0){i=O(g,b)|0;h=O(g,h)|0;W6((c[a+28>>2]|0)+h|0,f|0,i|0)|0}f=a+4|0;h=0;while(1){if((h|0)>=(b|0))break;g=c[d+(h<<2)>>2]|0;if((g|0)>=(c[a>>2]|0))c[a>>2]=g+1;g=c[e+(h<<2)>>2]|0;if((g|0)>=(c[f>>2]|0))c[f>>2]=g+1;h=h+1|0}i=a+8|0;c[i>>2]=(c[i>>2]|0)+b}return a|0}function Ow(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;if((c[a+32>>2]|0)==2){d=a+20|0;f=b<<2;c[d>>2]=MK(c[d>>2]|0,f)|0;d=a+24|0;c[d>>2]=MK(c[d>>2]|0,f)|0;d=c[a+40>>2]|0;if((d|0)>0){f=a+28|0;e=c[f>>2]|0;d=O(d,b)|0;if(!e)d=KK(d)|0;else d=MK(e,d)|0;c[f>>2]=d}}else{d=a+24|0;c[d>>2]=MK(c[d>>2]|0,b<<2)|0;d=c[a+40>>2]|0;if((d|0)>0){f=a+28|0;e=c[f>>2]|0;d=O(d,b)|0;if(!e)d=KK(d)|0;else d=MK(e,d)|0;c[f>>2]=d}}c[a+12>>2]=b;return a|0}function Pw(a){a=a|0;var b=0,d=0.0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0;a:do if(!a)a=0;else{l=c[a+20>>2]|0;k=c[a+24>>2]|0;b:do switch(c[a+16>>2]|0){case 1:{i=c[a+28>>2]|0;e=c[a>>2]|0;b=0;c:while(1){if((b|0)>=(e|0))break b;f=b+1|0;g=c[l+(f<<2)>>2]|0;b=c[l+(b<<2)>>2]|0;d=1.0/+(g-b|0);while(1){if((b|0)>=(g|0)){b=f;continue c}k=i+(b<<3)|0;h[k>>3]=+h[k>>3]*d;b=b+1|0}}}case 2:{f=c[a+28>>2]|0;g=c[a>>2]|0;b=0;d:while(1){if((b|0)>=(g|0))break b;i=b+1|0;j=c[l+(i<<2)>>2]|0;e=c[l+(b<<2)>>2]|0;d=1.0/+(j-e|0);while(1){if((e|0)>=(j|0)){b=i;continue d}if((c[k+(e<<2)>>2]|0)!=(b|0)){m=e<<1;n=f+(m<<3)|0;h[n>>3]=+h[n>>3]*d;m=f+((m|1)<<3)|0;h[m>>3]=+h[m>>3]*d}e=e+1|0}}}case 4:{qa(138394,94943,2192,95648);break}case 8:break;default:{a=0;break a}}while(0)}while(0);return a|0}function Qw(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;if((a|0)!=0?(b=a+8|0,d=c[b>>2]|0,e=c[a+4>>2]|0,(e|0)==(c[a>>2]|0)):0){g=c[a+24>>2]|0;a=c[a+20>>2]|0;f=rw(e,e,d,8,1)|0;W6(c[f+20>>2]|0,a|0,(e<<2)+4|0)|0;W6(c[f+24>>2]|0,g|0,d<<2|0)|0;c[f+8>>2]=c[b>>2];a=vw(f,1)|0;qw(f);a=Bw(a)|0;e=a+8|0;d=KK(c[e>>2]<<3)|0;c[a+28>>2]=d;e=c[e>>2]|0;b=0;while(1){if((b|0)>=(e|0))break;h[d+(b<<3)>>3]=1.0;b=b+1|0}c[a+16>>2]=1;c[a+40>>2]=8}else a=0;return a|0}function Rw(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;p=c[a+20>>2]|0;q=c[a+24>>2]|0;v=c[a+4>>2]|0;n=c[a>>2]|0;r=v<<2;s=KK(r)|0;t=KK(r+4|0)|0;u=KK(r)|0;r=KK(r)|0;o=t+4|0;a=0;while(1){if((a|0)>=(v|0))break;c[s+(a<<2)>>2]=0;a=a+1|0}c[o>>2]=v;a=0;while(1){if((a|0)>=(v|0)){h=1;k=0;break}c[u+(a<<2)>>2]=-1;a=a+1|0}a:while(1){if((k|0)>=(n|0))break;g=p+(k<<2)|0;l=k+1|0;m=p+(l<<2)|0;a=c[g>>2]|0;while(1){f=c[m>>2]|0;if((a|0)>=(f|0))break;j=o+(c[s+(c[q+(a<<2)>>2]<<2)>>2]<<2)|0;c[j>>2]=(c[j>>2]|0)+-1;a=a+1|0}a=h;j=c[g>>2]|0;while(1){if((j|0)>=(f|0)){h=a;k=l;continue a}h=q+(j<<2)|0;f=s+(c[h>>2]<<2)|0;i=c[f>>2]|0;g=u+(i<<2)|0;if((c[g>>2]|0)<(k|0)){c[g>>2]=k;f=o+(i<<2)|0;if(!(c[f>>2]|0)){c[f>>2]=1;g=a;a=i;f=r+(i<<2)|0}else{c[r+(i<<2)>>2]=a;c[o+(a<<2)>>2]=1;g=a+1|0;f=s+(c[h>>2]<<2)|0}c[f>>2]=a;a=g}else{i=r+(i<<2)|0;c[f>>2]=c[i>>2];i=o+(c[i>>2]<<2)|0;c[i>>2]=(c[i>>2]|0)+1}j=j+1|0;f=c[m>>2]|0}}c[t>>2]=0;a=0;f=0;while(1){if((a|0)>=(h|0))break;p=a+1|0;o=t+(p<<2)|0;q=(c[o>>2]|0)+f|0;c[o>>2]=q;a=p;f=q}c[d>>2]=r;a=0;while(1){if((a|0)>=(v|0)){a=h;break}q=t+(c[s+(a<<2)>>2]<<2)|0;d=c[q>>2]|0;c[q>>2]=d+1;c[r+(d<<2)>>2]=a;a=a+1|0}while(1){if((a|0)<=0)break;v=a+-1|0;c[t+(a<<2)>>2]=c[t+(v<<2)>>2];a=v}c[t>>2]=0;c[e>>2]=t;c[b>>2]=h;U1(u);U1(s);return}function Sw(){return +(+(x5()|0)*4.656612875245797e-10)}function Tw(a){a=a|0;if((a|0)>1)return (x5()|0)%(a|0)|0|0;else qa(95682,95688,58,95698);return 0}function Uw(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;a:do if((a|0)<1)b=0;else{b=KK(a<<2)|0;d=0;while(1){if((d|0)==(a|0))break;c[b+(d<<2)>>2]=d;d=d+1|0}while(1){if((a|0)<=1)break a;e=Tw(a)|0;d=a+-1|0;g=b+(d<<2)|0;f=c[g>>2]|0;e=b+(e<<2)|0;c[g>>2]=c[e>>2];c[e>>2]=f;a=d}}while(0);return b|0}function Vw(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0;d=0;while(1){if((d|0)>=(a|0))break;e=c+(d<<3)|0;h[e>>3]=+h[b+(d<<3)>>3]-+h[e>>3];d=d+1|0}return c|0}function Ww(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0.0,f=0.0;d=0;e=0.0;while(1){if((d|0)>=(a|0))break;f=+h[c+(d<<3)>>3]*+h[b+(d<<3)>>3]+e;d=d+1|0;e=f}return +e}function Xw(a,b,c,d){a=a|0;b=b|0;c=c|0;d=+d;var e=0,f=0;e=0;while(1){if((e|0)>=(a|0))break;f=c+(e<<3)|0;h[f>>3]=+h[f>>3]*d+ +h[b+(e<<3)>>3];e=e+1|0}return c|0}function Yw(a,b,c,d){a=a|0;b=b|0;c=c|0;d=+d;var e=0,f=0;e=0;while(1){if((e|0)>=(a|0))break;f=b+(e<<3)|0;h[f>>3]=+h[c+(e<<3)>>3]*d+ +h[f>>3];e=e+1|0}return b|0}function Zw(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0.0,f=0,g=0.0;f=O(c,b)|0;d=O(d,b)|0;e=0.0;c=0;while(1){if((c|0)>=(b|0))break;g=+h[a+(c+f<<3)>>3]-+h[a+(c+d<<3)>>3];e=g*g+e;c=c+1|0}g=+C(+e);return +(g>1.0e-15?g:1.0e-15)}function _w(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0.0,f=0,g=0.0;f=O(c,b)|0;d=O(d,b)|0;e=0.0;c=0;while(1){if((c|0)>=(b|0))break;g=+h[a+(c+f<<3)>>3]-+h[a+(c+d<<3)>>3];e=g*g+e;c=c+1|0}return +(+C(+e))}function $w(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0,f=0.0;d=0.0;e=0;while(1){if((e|0)>=(c|0))break;f=+h[a+(e<<3)>>3]-+h[b+(e<<3)>>3];d=f*f+d;e=e+1|0}return +(+C(+d))}function ax(a,b,c,d,e,f,g){a=+a;b=+b;c=+c;d=+d;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0,s=0.0;p=l;l=l+80|0;n=p+48|0;m=p+24|0;o=p;i=0;while(1){if((i|0)>=(f|0)){j=0;break}q=+h[g+(i<<3)>>3];h[n+(i<<3)>>3]=q;h[m+(i<<3)>>3]=q;i=i+1|0}while(1){if((j|0)>=(e|0))break;k=O(j,f)|0;i=0;while(1){if((i|0)>=(f|0))break;s=+h[g+(i+k<<3)>>3];r=n+(i<<3)|0;q=+h[r>>3];h[r>>3]=s<q?s:q;r=m+(i<<3)|0;q=+h[r>>3];h[r>>3]=s>q?s:q;i=i+1|0}j=j+1|0}q=+h[m>>3]-+h[n>>3];c=q!=0.0?(c-a)/q:1.0;q=+h[m+8>>3]-+h[n+8>>3];s=(d-b)/q;c=q!=0.0?(c<s?c:s):c;h[o>>3]=a;h[o+8>>3]=b;h[o+16>>3]=0.0;j=0;while(1){if((j|0)>=(e|0))break;k=O(j,f)|0;i=0;while(1){if((i|0)>=(f|0))break;r=g+(i+k<<3)|0;h[r>>3]=(+h[r>>3]-+h[n+(i<<3)>>3])*c+ +h[o+(i<<3)>>3];i=i+1|0}j=j+1|0}l=p;return}function bx(a,b){a=a|0;b=b|0;if((tB(a)|0)==1){a=c[(c[(jC(a)|0)+16>>2]|0)+132>>2]|0;c[a>>2]=0;c[a+4>>2]=0;c[a+8>>2]=0;c[a+12>>2]=0}else{cx(a);if(!((dP(QA(b,95704)|0)|0)<<24>>24))b=My(a,186208)|0;else b=dx(a)|0;Vy(a,b,186208);ex(b)}return}function cx(a){a=a|0;var b=0;b=xC(c[c[(c[(jC(a)|0)+16>>2]|0)+112>>2]>>2]|0)|0;a=c[46925]|0;if((b|0)!=(a|0)){c[46555]=0;c[46925]=b;c[46926]=GA(b,0,95713,0)|0;c[46927]=GA(c[46925]|0,1,95721,0)|0;c[46928]=GA(c[46925]|0,1,95738,0)|0;a=c[46925]|0}c[46929]=QA(a,95738)|0;Gy(186208);c[46554]=1;h[23280]=+_O(c[46925]|0,c[46926]|0,1.0,0.0);c[46556]=c[46927];c[46557]=c[46928];c[46558]=c[46929];return}function dx(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+144|0;e=f;d=f+8|0;b=c[46555]|0;c[46555]=b+1;c[e>>2]=b;m4(d,96234,e)|0;d=Hy(ND(a,d,1)|0)|0;e=d+8|0;b=jC(a)|0;while(1){if(!b)break;oC(c[e>>2]|0,b,1)|0;c[(c[(c[b+16>>2]|0)+112>>2]|0)+12>>2]=d;b=kC(a,b)|0}l=f;return d|0}function ex(a){a=a|0;Uy(a);return}function fx(a,b){a=a|0;b=b|0;var d=0;d=KK(32)|0;c[d>>2]=4;h[d+8>>3]=.75;c[d+16>>2]=1073741824;c[d+20>>2]=1;c[d+24>>2]=a;c[d+28>>2]=b;return d|0}function gx(a){a=a|0;U1(a);return}function hx(a){a=a|0;var b=0,d=0;if(!a)return;d=c[a+8>>2]|0;do if(d|0){if(c[a>>2]|0){qw(d);b=c[a+12>>2]|0;if(!b)break;qw(b);break}if(c[a+36>>2]|0?(qw(d),b=c[a+12>>2]|0,b|0):0)qw(b)}while(0);qw(c[a+16>>2]|0);qw(c[a+20>>2]|0);b=c[a+24>>2]|0;if(b|0?(c[a>>2]|0)>0:0)U1(b);hx(c[a+28>>2]|0);U1(a);return}function ix(a,b,d,e,f,g,i,j,k,m){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;m=m|0;var n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0.0,v=0;v=l;l=l+32|0;n=v+16|0;o=v+12|0;p=v+8|0;q=v+4|0;r=v;c[n>>2]=a;c[o>>2]=0;c[p>>2]=0;c[q>>2]=0;c[r>>2]=0;c[i>>2]=0;c[j>>2]=0;c[b>>2]=0;c[g>>2]=0;c[e>>2]=0;s=k+28|0;t=k+8|0;u=+(c[a+4>>2]|0);f=d;do{jx(a,n,f,o,0,r,p,q,k,m);f=c[n>>2]|0;if(!f)break;d=c[f+4>>2]|0;f=c[i>>2]|0;if(!f){c[i>>2]=c[p>>2];c[j>>2]=c[q>>2]}else{a=Lw(f,c[p>>2]|0)|0;qw(c[i>>2]|0);qw(c[p>>2]|0);c[i>>2]=a;a=Lw(c[q>>2]|0,c[j>>2]|0)|0;qw(c[j>>2]|0);qw(c[q>>2]|0);c[j>>2]=a}f=c[b>>2]|0;if(f|0)qw(f);c[b>>2]=c[n>>2];f=c[e>>2]|0;if(f|0)qw(f);f=c[o>>2]|0;c[e>>2]=f;a=c[g>>2]|0;if(a){U1(a);f=c[o>>2]|0}c[g>>2]=c[r>>2];a=c[n>>2]|0;c[r>>2]=0;if(!(+h[t>>3]*u<+(d|0)))break}while((c[s>>2]|0)==1);l=v;return}function jx(a,b,d,e,f,g,i,j,k,m){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;m=m|0;var n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0.0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0;H=l;l=l+32|0;D=H+28|0;q=H+24|0;B=H+20|0;E=H+16|0;o=H+12|0;F=H+8|0;G=H+4|0;v=H;c[D>>2]=0;c[q>>2]=0;c[E>>2]=0;c[F>>2]=0;c[G>>2]=0;c[b>>2]=0;c[e>>2]=0;c[i>>2]=0;c[j>>2]=0;C=c[a>>2]|0;p=k+24|0;c[m>>2]=c[p>>2];n=c[p>>2]|0;a:do switch(n|0){case 12:{c[p>>2]=3;c[m>>2]=3;jx(a,b,d,e,f,g,i,j,k,m);if((((c[b>>2]|0)==0?(c[p>>2]=4,c[m>>2]=4,jx(a,b,d,e,f,g,i,j,k,m),(c[b>>2]|0)==0):0)?(c[p>>2]=6,c[m>>2]=6,jx(a,b,d,e,f,g,i,j,k,m),(c[b>>2]|0)==0):0)?(c[p>>2]=9,c[m>>2]=9,jx(a,b,d,e,f,g,i,j,k,m),(c[b>>2]|0)==0):0){c[p>>2]=2;c[m>>2]=2;jx(a,b,d,e,f,g,i,j,k,m)}c[p>>2]=12;p=0;q=0;k=0;o=0;n=0;m=0;break}case 9:case 10:{d=c[k+20>>2]|0;if((n|0)==9)qx(a,d,E,o,B);else rx(a,d,E,o,B);u=c[a+20>>2]|0;v=c[a+24>>2]|0;t=c[o>>2]|0;if(!(c[k+28>>2]|0)){if((t|0)==(C|0)?1:+h[k+8>>3]*+(C|0)<+(t|0)){p=0;q=0;k=0;o=0;n=0;m=0;break a}}else if((t|0)==(C|0)){p=0;q=0;k=0;o=0;n=0;m=0;break a}if((t|0)>=(c[k>>2]|0)){z=c[B>>2]|0;y=z<<2;x=KK(y)|0;y=KK(y)|0;z=KK(z<<3)|0;c[B>>2]=0;s=c[E>>2]|0;r=0;o=0;b:while(1){if((r|0)>=(C|0))break;d=s+(r<<2)|0;if((c[d>>2]|0)!=-1){c[x+(o<<2)>>2]=r;c[y+(c[B>>2]<<2)>>2]=c[d>>2];q=c[B>>2]|0;k=q+1|0;c[B>>2]=k;h[z+(q<<3)>>3]=1.0;r=r+1|0;o=k;continue}n=c[u+(r<<2)>>2]|0;q=r+1|0;k=u+(q<<2)|0;p=c[k>>2]|0;d=0;m=n;while(1){if((m|0)>=(p|0))break;d=((c[s+(c[v+(m<<2)>>2]<<2)>>2]|0)>>>31^1)+d|0;m=m+1|0}w=1.0/+(d|0);d=p;while(1){if((n|0)>=(d|0)){r=q;continue b}m=v+(n<<2)|0;if((c[s+(c[m>>2]<<2)>>2]|0)>-1){c[x+(o<<2)>>2]=r;c[y+(c[B>>2]<<2)>>2]=c[s+(c[m>>2]<<2)>>2];d=c[B>>2]|0;m=d+1|0;c[B>>2]=m;h[z+(d<<3)>>3]=w;d=c[k>>2]|0}else m=o;n=n+1|0;o=m}}C=Dw(o,C,t,x,y,z,1,8)|0;c[i>>2]=C;C=pw(C)|0;c[j>>2]=C;i=Mw(C,a,c[i>>2]|0)|0;c[b>>2]=i;if(!i){p=z;q=y;k=x;o=x;n=y;m=z}else{Jw(c[j>>2]|0,f,g,0);p=c[b>>2]|0;q=p+36|0;c[q>>2]=c[q>>2]|3;c[b>>2]=Bw(p)|0;c[e>>2]=0;p=z;q=y;k=x;o=x;n=y;m=z}}else{p=0;q=0;k=0;o=0;n=0;m=0}break}case 5:{A=26;break}case 1:{m=k+20|0;nx(a,c[m>>2]|0,D,q);d=c[p>>2]|0;if((d|0)==2){d=m;A=24}else A=25;break}case 2:{d=k+20|0;A=24;break}case 3:{kx(a,c[k+20>>2]|0,F,G,v);A=12;break}case 4:{lx(a,c[k+20>>2]|0,F,G,v);A=12;break}case 6:{mx(a,F,G,v);A=12;break}default:{p=0;q=0;k=0;o=0;n=0;m=0}}while(0);do if((A|0)==12){r=c[v>>2]|0;if(!(c[k+28>>2]|0)){if((r|0)==(C|0)?1:+h[k+8>>3]*+(C|0)<+(r|0)){p=0;q=0;k=0;o=0;n=0;m=0;break}}else if((r|0)==(C|0)){p=0;q=0;k=0;o=0;n=0;m=0;break}if((r|0)>=(c[k>>2]|0)){t=C<<2;s=KK(t)|0;t=KK(t)|0;u=KK(C<<3)|0;c[B>>2]=0;o=c[G>>2]|0;p=c[F>>2]|0;n=0;d=0;c:while(1){if((n|0)>=(c[v>>2]|0))break;q=n+1|0;k=o+(q<<2)|0;m=c[o+(n<<2)>>2]|0;while(1){if((m|0)>=(c[k>>2]|0)){n=q;continue c}c[s+(d<<2)>>2]=c[p+(m<<2)>>2];c[t+(c[B>>2]<<2)>>2]=n;y=c[B>>2]|0;z=y+1|0;c[B>>2]=z;h[u+(y<<3)>>3]=1.0;m=m+1|0;d=z}}C=Dw(d,C,r,s,t,u,1,8)|0;c[i>>2]=C;c[j>>2]=pw(C)|0;c[e>>2]=0;i=Mw(c[j>>2]|0,a,c[i>>2]|0)|0;c[b>>2]=i;if(!i){p=u;q=t;k=s;o=s;n=t;m=u}else{Jw(c[j>>2]|0,f,g,0);c[j>>2]=Pw(c[j>>2]|0)|0;p=c[b>>2]|0;q=p+36|0;c[q>>2]=c[q>>2]|3;c[b>>2]=Bw(p)|0;p=u;q=t;k=s;o=s;n=t;m=u}}else{p=0;q=0;k=0;o=0;n=0;m=0}}else if((A|0)==24){ox(a,c[d>>2]|0,D,q);d=c[p>>2]|0;A=25}while(0);if((A|0)==25)if((d|0)==5)A=26;else A=27;if((A|0)==26){px(a,c[k+20>>2]|0,D,q);A=27}do if((A|0)==27){d=c[q>>2]|0;if(!(c[k+28>>2]|0)){if((d|0)==(C|0)?1:+h[k+8>>3]*+(C|0)<+(d|0)){p=0;q=0;k=0;o=0;n=0;m=0;break}}else if((d|0)==(C|0)){p=0;q=0;k=0;o=0;n=0;m=0;break}if((d|0)>=(c[k>>2]|0)){s=C<<2;r=KK(s)|0;s=KK(s)|0;t=KK(C<<3)|0;c[B>>2]=0;q=c[D>>2]|0;d=0;p=0;while(1){if((p|0)>=(C|0))break;m=q+(p<<2)|0;n=c[m>>2]|0;if((n|0)>-1){c[r+(c[B>>2]<<2)>>2]=p;c[s+(c[B>>2]<<2)>>2]=d;A=c[B>>2]|0;o=A+1|0;c[B>>2]=o;h[t+(A<<3)>>3]=1.0;if((n|0)!=(p|0)){c[r+(o<<2)>>2]=c[m>>2];c[s+(c[B>>2]<<2)>>2]=d;A=c[B>>2]|0;c[B>>2]=A+1;h[t+(A<<3)>>3]=1.0;c[q+(c[m>>2]<<2)>>2]=-1}c[m>>2]=-1;d=d+1|0}p=p+1|0}C=Dw(c[B>>2]|0,C,d,r,s,t,1,8)|0;c[i>>2]=C;C=pw(C)|0;c[j>>2]=C;i=Mw(C,a,c[i>>2]|0)|0;c[b>>2]=i;if(!i){p=t;q=s;k=r;o=r;n=s;m=t}else{Jw(c[j>>2]|0,f,g,0);c[j>>2]=Pw(c[j>>2]|0)|0;p=c[b>>2]|0;q=p+36|0;c[q>>2]=c[q>>2]|3;c[b>>2]=Bw(p)|0;c[e>>2]=0;p=t;q=s;k=r;o=r;n=s;m=t}}else{p=0;q=0;k=0;o=0;n=0;m=0}}while(0);d=c[D>>2]|0;if(d|0)U1(d);d=c[E>>2]|0;if(d|0)U1(d);if(k|0)U1(o);if(q|0)U1(n);if(p|0)U1(m);d=c[F>>2]|0;if(d|0)U1(d);d=c[G>>2]|0;if(d|0)U1(d);l=H;return}function kx(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0.0,k=0,l=0,m=0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0;t=c[a+20>>2]|0;u=c[a+24>>2]|0;w=c[a>>2]|0;v=w<<2;c[d>>2]=KK(v)|0;c[e>>2]=KK(v+4|0)|0;v=KK(v)|0;g=0;while(1){if((g|0)>=(w|0))break;c[v+(g<<2)>>2]=g;g=g+1|0}c[f>>2]=0;r=c[e>>2]|0;c[r>>2]=0;s=c[a+28>>2]|0;a:do if(!b){g=0;a=0;b:while(1){while(1){if((a|0)>=(w|0)){a=0;j=0.0;b=0;break b}b=a;a=a+1|0;if((c[v+(b<<2)>>2]|0)==-1)continue;b=c[t+(b<<2)>>2]|0;if(((c[t+(a<<2)>>2]|0)-b|0)==1)break}l=c[u+(b<<2)>>2]|0;c[v+(l<<2)>>2]=-1;m=c[d>>2]|0;c[m+(g<<2)>>2]=l;n=t+(l+1<<2)|0;k=c[t+(l<<2)>>2]|0;g=g+1|0;while(1){if((k|0)>=(c[n>>2]|0))break;b=u+(k<<2)|0;i=c[b>>2]|0;if((l|0)!=(i|0)?((c[t+(i+1<<2)>>2]|0)-(c[t+(i<<2)>>2]|0)|0)==1:0){c[v+(i<<2)>>2]=-1;c[m+(g<<2)>>2]=c[b>>2];g=g+1|0}k=k+1|0}b=c[f>>2]|0;i=c[r+(b<<2)>>2]|0;if((g-i|0)<5){e=b+1|0;c[f>>2]=e;c[r+(e<<2)>>2]=g;continue}i=i+1|0;b=b+1|0;c[f>>2]=b;c[r+(b<<2)>>2]=i;b=i;while(1){if(!((i|0)<(g|0)&(b|0)<(g|0)))continue b;e=i+3|0;e=(g|0)<(e|0)?g:e;q=(c[f>>2]|0)+1|0;c[f>>2]=q;c[r+(q<<2)>>2]=e;b=b+1|0;i=e}}c:while(1){do{n=b;while(1){if((n|0)>=(w|0)){a=0;break c}p=v+(n<<2)|0;i=c[p>>2]|0;if((i|0)!=-1)break;n=n+1|0}b=n+1|0;q=c[t+(b<<2)>>2]|0;l=1;m=c[t+(n<<2)>>2]|0;while(1){if((m|0)>=(q|0))break;k=c[u+(m<<2)>>2]|0;if((n|0)!=(k|0)?(c[v+(k<<2)>>2]|0)!=-1:0)if((i|0)!=-1){o=+h[s+(m<<3)>>3];if((l|0)==0?!(o>j):0)l=0;else{l=0;a=k;j=o}}else i=-1;m=m+1|0}}while((l|0)!=0);c[v+(a<<2)>>2]=-1;c[p>>2]=-1;q=c[d>>2]|0;c[q+(g<<2)>>2]=n;e=g+2|0;c[q+(g+1<<2)>>2]=a;q=(c[f>>2]|0)+1|0;c[f>>2]=q;c[r+(q<<2)>>2]=e;g=e}while(1){if((a|0)>=(w|0))break a;if((c[v+(a<<2)>>2]|0)==(a|0)){u=g+1|0;c[(c[d>>2]|0)+(g<<2)>>2]=a;g=(c[f>>2]|0)+1|0;c[f>>2]=g;c[r+(g<<2)>>2]=u;g=u}a=a+1|0}}else{r=Uw(w)|0;n=0;g=0;while(1){if((n|0)>=(w|0)){q=0;a=0;j=0.0;break}a=c[r+(n<<2)>>2]|0;d:do if((c[v+(a<<2)>>2]|0)!=-1?(p=c[t+(a<<2)>>2]|0,((c[t+(a+1<<2)>>2]|0)-p|0)==1):0){k=c[u+(p<<2)>>2]|0;c[v+(k<<2)>>2]=-1;l=c[d>>2]|0;c[l+(g<<2)>>2]=k;m=t+(k+1<<2)|0;i=c[t+(k<<2)>>2]|0;g=g+1|0;while(1){if((i|0)>=(c[m>>2]|0))break;a=u+(i<<2)|0;b=c[a>>2]|0;if((k|0)!=(b|0)?((c[t+(b+1<<2)>>2]|0)-(c[t+(b<<2)>>2]|0)|0)==1:0){c[v+(b<<2)>>2]=-1;c[l+(g<<2)>>2]=c[a>>2];g=g+1|0}i=i+1|0}i=c[e>>2]|0;a=c[f>>2]|0;b=c[i+(a<<2)>>2]|0;if((g-b|0)<5){q=a+1|0;c[f>>2]=q;c[i+(q<<2)>>2]=g;break}b=b+1|0;a=a+1|0;c[f>>2]=a;c[i+(a<<2)>>2]=b;a=b;while(1){if(!((b|0)<(g|0)&(a|0)<(g|0)))break d;q=b+3|0;q=(g|0)<(q|0)?g:q;m=(c[f>>2]|0)+1|0;c[f>>2]=m;c[i+(m<<2)>>2]=q;a=a+1|0;b=q}}while(0);n=n+1|0}while(1){if((q|0)>=(w|0)){a=0;break}m=c[r+(q<<2)>>2]|0;n=v+(m<<2)|0;b=c[n>>2]|0;if((b|0)!=-1){p=c[t+(m+1<<2)>>2]|0;k=1;l=c[t+(m<<2)>>2]|0;while(1){if((l|0)>=(p|0))break;i=c[u+(l<<2)>>2]|0;if((m|0)!=(i|0)?(c[v+(i<<2)>>2]|0)!=-1:0)if((b|0)!=-1){o=+h[s+(l<<3)>>3];if((k|0)==0?!(o>j):0)k=0;else{k=0;a=i;j=o}}else b=-1;l=l+1|0}if(!k){c[v+(a<<2)>>2]=-1;c[n>>2]=-1;n=c[d>>2]|0;c[n+(g<<2)>>2]=m;p=g+2|0;c[n+(g+1<<2)>>2]=a;n=c[e>>2]|0;g=(c[f>>2]|0)+1|0;c[f>>2]=g;c[n+(g<<2)>>2]=p;g=p}}q=q+1|0}while(1){if((a|0)>=(w|0))break;if((c[v+(a<<2)>>2]|0)==(a|0)){u=g+1|0;c[(c[d>>2]|0)+(g<<2)>>2]=a;t=c[e>>2]|0;g=(c[f>>2]|0)+1|0;c[f>>2]=g;c[t+(g<<2)>>2]=u;g=u}a=a+1|0}U1(r)}while(0);U1(v);return}function lx(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0.0,j=0,k=0,m=0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;A=l;l=l+16|0;q=A+8|0;y=A+4|0;z=A;c[y>>2]=0;c[z>>2]=0;v=c[a+20>>2]|0;u=c[a+24>>2]|0;w=c[a>>2]|0;x=w<<2;c[d>>2]=KK(x)|0;c[e>>2]=KK(x+4|0)|0;x=KK(x)|0;g=0;while(1){if((g|0)>=(w|0))break;c[x+(g<<2)>>2]=g;g=g+1|0}Rw(a,q,y,z);c[f>>2]=0;s=c[e>>2]|0;c[s>>2]=0;t=c[a+28>>2]|0;g=0;a=0;a:while(1){k=c[q>>2]|0;m=c[z>>2]|0;do{if((a|0)>=(k|0))break a;n=a;a=a+1|0;p=m+(a<<2)|0;j=c[p>>2]|0;n=c[m+(n<<2)>>2]|0}while((j-n|0)<2);m=c[y>>2]|0;k=c[s+(c[f>>2]<<2)>>2]|0;while(1){if((n|0)>=(j|0))break;r=m+(n<<2)|0;c[x+(c[r>>2]<<2)>>2]=-1;j=g+1|0;c[(c[d>>2]|0)+(g<<2)>>2]=c[r>>2];if((j-k|0)>3){g=(c[f>>2]|0)+1|0;c[f>>2]=g;c[s+(g<<2)>>2]=j;g=j}else g=k;k=g;n=n+1|0;g=j;j=c[p>>2]|0}if((g|0)<=(k|0))continue;r=(c[f>>2]|0)+1|0;c[f>>2]=r;c[s+(r<<2)>>2]=g}b:do if(!b){a=0;i=0.0;j=0;c:while(1){do{b=j;while(1){if((b|0)>=(w|0)){a=0;break c}q=x+(b<<2)|0;k=c[q>>2]|0;if((k|0)!=-1)break;b=b+1|0}j=b+1|0;r=c[v+(j<<2)>>2]|0;n=1;p=c[v+(b<<2)>>2]|0;while(1){if((p|0)>=(r|0))break;m=c[u+(p<<2)>>2]|0;if((b|0)!=(m|0)?(c[x+(m<<2)>>2]|0)!=-1:0)if((k|0)!=-1){o=+h[t+(p<<3)>>3];if((n|0)==0?!(o>i):0)n=0;else{n=0;a=m;i=o}}else k=-1;p=p+1|0}}while((n|0)!=0);c[x+(a<<2)>>2]=-1;c[q>>2]=-1;r=c[d>>2]|0;c[r+(g<<2)>>2]=b;e=g+2|0;c[r+(g+1<<2)>>2]=a;r=(c[f>>2]|0)+1|0;c[f>>2]=r;c[s+(r<<2)>>2]=e;g=e}while(1){if((a|0)>=(w|0))break b;if((c[x+(a<<2)>>2]|0)==(a|0)){v=g+1|0;c[(c[d>>2]|0)+(g<<2)>>2]=a;g=(c[f>>2]|0)+1|0;c[f>>2]=g;c[s+(g<<2)>>2]=v;g=v}a=a+1|0}}else{s=Uw(w)|0;r=0;a=0;i=0.0;while(1){if((r|0)>=(w|0)){a=0;break}p=c[s+(r<<2)>>2]|0;b=x+(p<<2)|0;j=c[b>>2]|0;if((j|0)!=-1){q=c[v+(p+1<<2)>>2]|0;m=1;n=c[v+(p<<2)>>2]|0;while(1){if((n|0)>=(q|0))break;k=c[u+(n<<2)>>2]|0;if((p|0)!=(k|0)?(c[x+(k<<2)>>2]|0)!=-1:0)if((j|0)!=-1){o=+h[t+(n<<3)>>3];if((m|0)==0?!(o>i):0)m=0;else{m=0;a=k;i=o}}else j=-1;n=n+1|0}if(!m){c[x+(a<<2)>>2]=-1;c[b>>2]=-1;b=c[d>>2]|0;c[b+(g<<2)>>2]=p;q=g+2|0;c[b+(g+1<<2)>>2]=a;b=c[e>>2]|0;g=(c[f>>2]|0)+1|0;c[f>>2]=g;c[b+(g<<2)>>2]=q;g=q}}r=r+1|0}while(1){if((a|0)>=(w|0))break;if((c[x+(a<<2)>>2]|0)==(a|0)){v=g+1|0;c[(c[d>>2]|0)+(g<<2)>>2]=a;u=c[e>>2]|0;g=(c[f>>2]|0)+1|0;c[f>>2]=g;c[u+(g<<2)>>2]=v;g=v}a=a+1|0}U1(s)}while(0);U1(c[y>>2]|0);U1(c[z>>2]|0);U1(x);l=A;return}function mx(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;r=c[a+20>>2]|0;s=c[a+24>>2]|0;v=c[a>>2]|0;u=v<<2;c[b>>2]=KK(u)|0;c[d>>2]=KK(u+4|0)|0;u=KK(u)|0;q=KK(v<<4)|0;f=0;while(1){if((f|0)>=(v|0))break;c[u+(f<<2)>>2]=f;f=f+1|0}c[e>>2]=0;c[c[d>>2]>>2]=0;p=c[a+28>>2]|0;t=Uw(v)|0;f=0;m=0;while(1){if((m|0)>=(v|0)){o=0;break}a=c[t+(m<<2)>>2]|0;a:do if((c[u+(a<<2)>>2]|0)!=-1?(n=c[r+(a<<2)>>2]|0,((c[r+(a+1<<2)>>2]|0)-n|0)==1):0){j=c[s+(n<<2)>>2]|0;c[u+(j<<2)>>2]=-1;k=c[b>>2]|0;c[k+(f<<2)>>2]=j;l=r+(j+1<<2)|0;i=c[r+(j<<2)>>2]|0;f=f+1|0;while(1){if((i|0)>=(c[l>>2]|0))break;a=s+(i<<2)|0;g=c[a>>2]|0;if((j|0)!=(g|0)?((c[r+(g+1<<2)>>2]|0)-(c[r+(g<<2)>>2]|0)|0)==1:0){c[u+(g<<2)>>2]=-1;c[k+(f<<2)>>2]=c[a>>2];f=f+1|0}i=i+1|0}i=c[d>>2]|0;a=c[e>>2]|0;g=c[i+(a<<2)>>2]|0;if((f-g|0)<5){o=a+1|0;c[e>>2]=o;c[i+(o<<2)>>2]=f;break}g=g+1|0;a=a+1|0;c[e>>2]=a;c[i+(a<<2)>>2]=g;a=g;while(1){if(!((g|0)<(f|0)&(a|0)<(f|0)))break a;o=g+3|0;o=(f|0)<(o|0)?f:o;l=(c[e>>2]|0)+1|0;c[e>>2]=l;c[i+(l<<2)>>2]=o;a=a+1|0;g=o}}while(0);m=m+1|0}while(1){if((o|0)>=(v|0)){a=0;break}m=c[t+(o<<2)>>2]|0;n=u+(m<<2)|0;a=c[n>>2]|0;if((a|0)!=-1){l=c[r+(m+1<<2)>>2]|0;g=0;k=c[r+(m<<2)>>2]|0;while(1){if((k|0)>=(l|0))break;j=c[s+(k<<2)>>2]|0;if((m|0)!=(j|0)?(c[u+(j<<2)>>2]|0)!=-1:0){i=g<<1;if((a|0)==-1)a=-1;else{h[q+(i<<3)>>3]=+(j|0);h[q+((i|1)<<3)>>3]=+h[p+(k<<3)>>3];g=g+1|0}}k=k+1|0}if((g|0)>0){o4(q,g,16,81);g=(g|0)<3?g:3;a=0;while(1){if((a|0)>=(g|0))break;l=~~+h[q+(a<<1<<3)>>3];c[u+(l<<2)>>2]=-1;c[(c[b>>2]|0)+(f<<2)>>2]=l;a=a+1|0;f=f+1|0}c[n>>2]=-1;n=f+1|0;c[(c[b>>2]|0)+(f<<2)>>2]=m;m=c[d>>2]|0;f=(c[e>>2]|0)+1|0;c[e>>2]=f;c[m+(f<<2)>>2]=n;f=n}}o=o+1|0}while(1){if((a|0)>=(v|0))break;if((c[u+(a<<2)>>2]|0)==(a|0)){s=f+1|0;c[(c[b>>2]|0)+(f<<2)>>2]=a;r=c[d>>2]|0;f=(c[e>>2]|0)+1|0;c[e>>2]=f;c[r+(f<<2)>>2]=s;f=s}a=a+1|0}U1(t);U1(u);return}function nx(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;p=c[a+20>>2]|0;q=c[a+24>>2]|0;r=c[a>>2]|0;f=c[a+4>>2]|0;k=KK(r<<2)|0;c[d>>2]=k;a=0;while(1){if((a|0)>=(r|0))break;c[k+(a<<2)>>2]=a;a=a+1|0}c[e>>2]=f;a:do if(!b){f=0;b:while(1){if((f|0)>=(r|0))break a;b=f+1|0;g=p+(b<<2)|0;h=k+(f<<2)|0;i=k+(f<<2)|0;a=c[p+(f<<2)>>2]|0;while(1){if((a|0)>=(c[g>>2]|0)){f=b;continue b}j=q+(a<<2)|0;d=c[j>>2]|0;if(((f|0)!=(d|0)?(o=k+(d<<2)|0,(c[o>>2]|0)==(d|0)):0)?(c[h>>2]|0)==(f|0):0){c[o>>2]=f;c[i>>2]=c[j>>2];c[e>>2]=(c[e>>2]|0)+-1}a=a+1|0}}}else{b=Uw(r)|0;a=0;while(1){if((a|0)>=(r|0))break;g=c[b+(a<<2)>>2]|0;h=p+(g+1<<2)|0;f=c[p+(g<<2)>>2]|0;while(1){if((f|0)>=(c[h>>2]|0))break;i=q+(f<<2)|0;j=c[i>>2]|0;if(((g|0)!=(j|0)?(l=c[d>>2]|0,m=l+(j<<2)|0,(c[m>>2]|0)==(j|0)):0)?(n=l+(g<<2)|0,(c[n>>2]|0)==(g|0)):0){c[m>>2]=g;c[n>>2]=c[i>>2];c[e>>2]=(c[e>>2]|0)+-1}f=f+1|0}a=a+1|0}U1(b)}while(0);return}function ox(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0.0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;r=c[a+20>>2]|0;s=c[a+24>>2]|0;t=c[a>>2]|0;g=c[a+4>>2]|0;o=KK(t<<2)|0;c[d>>2]=o;f=0;while(1){if((f|0)>=(t|0))break;c[o+(f<<2)>>2]=f;f=f+1|0}c[e>>2]=g;q=c[a+28>>2]|0;a:do if(!b){f=0;i=0.0;k=0;while(1){if((k|0)>=(t|0))break a;l=k+1|0;m=c[r+(l<<2)>>2]|0;n=o+(k<<2)|0;g=1;b=c[r+(k<<2)>>2]|0;while(1){if((b|0)>=(m|0))break;a=c[s+(b<<2)>>2]|0;if(((k|0)!=(a|0)?(c[o+(a<<2)>>2]|0)==(a|0):0)?(c[n>>2]|0)==(k|0):0){j=+h[q+(b<<3)>>3];if((g|0)==0?!(j>i):0)g=0;else{g=0;f=a;i=j}}b=b+1|0}if(g|0){k=l;continue}c[o+(f<<2)>>2]=k;c[o+(k<<2)>>2]=f;c[e>>2]=(c[e>>2]|0)+-1;k=l}}else{p=Uw(t)|0;o=0;f=0;i=0.0;while(1){if((o|0)>=(t|0))break;k=c[p+(o<<2)>>2]|0;l=c[d>>2]|0;m=l+(k<<2)|0;if((c[m>>2]|0)==(k|0)){n=c[r+(k+1<<2)>>2]|0;b=c[r+(k<<2)>>2]|0;g=1;while(1){if((b|0)>=(n|0))break;a=c[s+(b<<2)>>2]|0;if((k|0)!=(a|0)?(c[l+(a<<2)>>2]|0)==(a|0):0){j=+h[q+(b<<3)>>3];if((g|0)==0?!(j>i):0)g=0;else{g=0;f=a;i=j}}b=b+1|0}if(!g){c[l+(f<<2)>>2]=k;c[m>>2]=f;c[e>>2]=(c[e>>2]|0)+-1}}o=o+1|0}U1(p)}while(0);return}function px(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0.0,k=0,l=0,m=0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0;s=c[a+20>>2]|0;t=c[a+24>>2]|0;u=c[a>>2]|0;g=c[a+4>>2]|0;p=KK(u<<2)|0;c[d>>2]=p;f=0;while(1){if((f|0)>=(u|0))break;c[p+(f<<2)>>2]=f;f=f+1|0}c[e>>2]=g;r=c[a+28>>2]|0;a:do if(!b){f=0;i=0.0;k=0;while(1){if((k|0)>=(u|0))break a;b=c[s+(k<<2)>>2]|0;l=k+1|0;m=c[s+(l<<2)>>2]|0;n=p+(k<<2)|0;o=+(m-b|0);g=1;while(1){if((b|0)>=(m|0))break;a=c[t+(b<<2)>>2]|0;if(((k|0)!=(a|0)?(c[p+(a<<2)>>2]|0)==(a|0):0)?(c[n>>2]|0)==(k|0):0){j=+h[r+(b<<3)>>3]/(+((c[s+(a+1<<2)>>2]|0)-(c[s+(a<<2)>>2]|0)|0)*o);if((g|0)==0?!(j>i):0)g=0;else{g=0;f=a;i=j}}b=b+1|0}if(g|0){k=l;continue}c[p+(f<<2)>>2]=k;c[p+(k<<2)>>2]=f;c[e>>2]=(c[e>>2]|0)+-1;k=l}}else{q=Uw(u)|0;p=0;f=0;i=0.0;while(1){if((p|0)>=(u|0))break;k=c[q+(p<<2)>>2]|0;l=c[d>>2]|0;m=l+(k<<2)|0;if((c[m>>2]|0)==(k|0)){b=c[s+(k<<2)>>2]|0;n=c[s+(k+1<<2)>>2]|0;o=+(n-b|0);g=1;while(1){if((b|0)>=(n|0))break;a=c[t+(b<<2)>>2]|0;if((k|0)!=(a|0)?(c[l+(a<<2)>>2]|0)==(a|0):0){j=+h[r+(b<<3)>>3]/(+((c[s+(a+1<<2)>>2]|0)-(c[s+(a<<2)>>2]|0)|0)*o);if((g|0)==0?!(j>i):0)g=0;else{g=0;f=a;i=j}}b=b+1|0}if(!g){c[l+(f<<2)>>2]=k;c[m>>2]=f;c[e>>2]=(c[e>>2]|0)+-1}}p=p+1|0}U1(q)}while(0);return}function qx(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;m=c[a+20>>2]|0;n=c[a+24>>2]|0;l=c[a>>2]|0;j=KK(l<<2)|0;c[d>>2]=j;a=0;while(1){if((a|0)>=(l|0))break;c[j+(a<<2)>>2]=-100;a=a+1|0}c[e>>2]=0;c[f>>2]=0;a:do if(!b){b=0;b:while(1){if((b|0)>=(l|0))break a;if((c[j+(b<<2)>>2]|0)!=-100){b=b+1|0;continue}g=c[e>>2]|0;c[e>>2]=g+1;c[j+(b<<2)>>2]=g;g=b+1|0;h=m+(g<<2)|0;a=c[m+(b<<2)>>2]|0;while(1){if((a|0)>=(c[h>>2]|0)){b=g;continue b}i=c[n+(a<<2)>>2]|0;if((b|0)!=(i|0)){c[j+(i<<2)>>2]=-1;c[f>>2]=(c[f>>2]|0)+1}a=a+1|0}}}else{i=Uw(l)|0;h=0;while(1){if((h|0)>=(l|0))break;j=c[i+(h<<2)>>2]|0;k=c[d>>2]|0;a=k+(j<<2)|0;c:do if((c[a>>2]|0)==-100){b=c[e>>2]|0;c[e>>2]=b+1;c[a>>2]=b;b=m+(j+1<<2)|0;a=c[m+(j<<2)>>2]|0;while(1){if((a|0)>=(c[b>>2]|0))break c;g=c[n+(a<<2)>>2]|0;if((j|0)!=(g|0)){c[k+(g<<2)>>2]=-1;c[f>>2]=(c[f>>2]|0)+1}a=a+1|0}}while(0);h=h+1|0}U1(i)}while(0);c[f>>2]=(c[f>>2]|0)+(c[e>>2]|0);return}function rx(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+16|0;n=q+4|0;o=q;p=c[a+20>>2]|0;m=c[a+24>>2]|0;h=c[a>>2]|0;a=KK(h<<2)|0;c[d>>2]=a;g=0;while(1){c[n>>2]=g;if((g|0)>=(h|0))break;c[a+(g<<2)>>2]=-100;g=(c[n>>2]|0)+1|0}c[e>>2]=0;c[f>>2]=0;k=xx(h,(h<<1)+-2|0)|0;a:do if(!b){a=0;while(1){c[n>>2]=a;if((a|0)>=(h|0))break a;j=a+1|0;zx(k,a,(c[p+(j<<2)>>2]|0)-(c[p+(a<<2)>>2]|0)|0)|0;a=j}}else{g=Uw(h)|0;a=0;while(1){if((a|0)>=(h|0))break;j=c[g+(a<<2)>>2]|0;c[n>>2]=j;zx(k,j,(c[p+(j+1<<2)>>2]|0)-(c[p+(j<<2)>>2]|0)|0)|0;a=a+1|0}U1(g)}while(0);b:while(1){j=(Ax(k,n,o)|0)==0;a=c[e>>2]|0;if(j)break;c[e>>2]=a+1;c[(c[d>>2]|0)+(c[n>>2]<<2)>>2]=a;a=c[n>>2]|0;j=c[p+(a<<2)>>2]|0;while(1){if((j|0)>=(c[p+(a+1<<2)>>2]|0))continue b;i=c[m+(j<<2)>>2]|0;if((a|0)!=(i|0)){c:do if((c[(c[d>>2]|0)+(i<<2)>>2]|0)==-100){Cx(k,i)|0;c[(c[d>>2]|0)+(i<<2)>>2]=-1;g=p+(i+1<<2)|0;a=c[p+(i<<2)>>2]|0;while(1){if((a|0)>=(c[g>>2]|0))break c;b=m+(a<<2)|0;h=c[b>>2]|0;if((i|0)!=(h|0)?(c[(c[d>>2]|0)+(h<<2)>>2]|0)==-100:0){h=Bx(k,h)|0;c[o>>2]=h;zx(k,c[b>>2]|0,h+1|0)|0}a=a+1|0}}while(0);c[f>>2]=(c[f>>2]|0)+1;a=c[n>>2]|0}j=j+1|0}}c[f>>2]=(c[f>>2]|0)+a;yx(k);l=q;return}function sx(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;d=+h[a+8>>3];c=+h[b+8>>3];return (d>c?-1:d<c&1)|0}function tx(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;if((ww(a,0)|0)!=0?(c[a+16>>2]|0)==1:0)f=a;else f=Qw(a)|0;do if(!b)b=0;else{if(ww(b,0)|0?(c[b+16>>2]|0)==1:0)break;b=Aw(b,0)|0}while(0);d=ux(f,b,d)|0;b=vx(d,e)|0;if((f|0)!=(a|0))c[d+36>>2]=1;return b|0}function ux(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;if(!a)a=0;else{e=KK(44)|0;c[e>>2]=0;c[e+4>>2]=c[a+4>>2];c[e+8>>2]=a;c[e+12>>2]=b;c[e+16>>2]=0;c[e+20>>2]=0;c[e+24>>2]=d;c[e+28>>2]=0;c[e+32>>2]=0;c[e+36>>2]=0;a=e}return a|0}function vx(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+32|0;e=k+20|0;f=k+16|0;g=k+12|0;h=k+8|0;i=k+4|0;j=k;c[f>>2]=0;if((c[a>>2]|0)<((c[b+16>>2]|0)+-1|0)?(ix(c[a+8>>2]|0,i,c[a+12>>2]|0,j,0,f,g,h,b,e),d=c[i>>2]|0,d|0):0){i=c[j>>2]|0;j=ux(d,i,c[f>>2]|0)|0;c[a+28>>2]=j;c[j+40>>2]=c[e>>2];c[j>>2]=(c[a>>2]|0)+1;c[j+4>>2]=c[d>>2];c[j+8>>2]=d;c[j+12>>2]=i;c[j+16>>2]=c[g>>2];c[a+20>>2]=c[h>>2];c[j+32>>2]=a;vx(j,b)|0}l=k;return a|0}function wx(a){a=a|0;var b=0;while(1){b=c[a+28>>2]|0;if(!b)break;else a=b}return a|0}function xx(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=KK(28)|0;c[f>>2]=0;c[f+4>>2]=a;c[f+8>>2]=b;c[f+12>>2]=-1;e=f+16|0;c[e>>2]=KK((b<<2)+4|0)|0;d=0;while(1){if((d|0)>(b|0))break;c[(c[e>>2]|0)+(d<<2)>>2]=0;d=d+1|0}b=(a<<2)+4|0;e=f+20|0;c[e>>2]=KK(b)|0;d=0;while(1){if((d|0)>(a|0))break;c[(c[e>>2]|0)+(d<<2)>>2]=0;d=d+1|0}b=KK(b)|0;c[f+24>>2]=b;d=0;while(1){if((d|0)>(a|0))break;c[b+(d<<2)>>2]=-999;d=d+1|0}return f|0}function yx(a){a=a|0;var b=0,d=0,e=0,f=0;if(a|0){e=a+16|0;b=c[e>>2]|0;if(b|0){f=a+8|0;d=0;while(1){if((d|0)>(c[f>>2]|0))break;Cy(c[b+(d<<2)>>2]|0,105);d=d+1|0;b=c[e>>2]|0}U1(b)}b=c[a+20>>2]|0;if(b|0)U1(b);U1(c[a+24>>2]|0);U1(a)}return}function zx(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;g=a+8|0;j=a+20|0;i=a+24|0;k=a+16|0;if(!a)qa(138953,95743,69,95759);while(1){if((c[g>>2]|0)<(d|0)){h=4;break}e=(c[j>>2]|0)+(b<<2)|0;f=c[e>>2]|0;if(!f)break;l=c[(c[i>>2]|0)+(b<<2)>>2]|0;c[e>>2]=0;c[a>>2]=(c[a>>2]|0)+-1;Fy(f,105,(c[k>>2]|0)+(l<<2)|0)}if((h|0)==4)qa(95778,95743,70,95759);c[a>>2]=(c[a>>2]|0)+1;e=a+12|0;if((c[e>>2]|0)<(d|0))c[e>>2]=d;c[(c[i>>2]|0)+(b<<2)>>2]=d;e=KK(4)|0;c[e>>2]=b;f=c[(c[k>>2]|0)+(d<<2)>>2]|0;if(!f)e=By(e)|0;else e=Dy(f,e)|0;c[(c[j>>2]|0)+(b<<2)>>2]=e;c[(c[k>>2]|0)+(d<<2)>>2]=e;return a|0}function Ax(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;if((a|0)!=0?(c[a>>2]|0)>=1:0){f=a+12|0;e=c[f>>2]|0;c[d>>2]=e;c[a>>2]=(c[a>>2]|0)+-1;d=a+16|0;g=c[(c[d>>2]|0)+(e<<2)>>2]|0;c[b>>2]=c[(Ey(g)|0)>>2];Fy(g,105,(c[d>>2]|0)+(e<<2)|0);d=c[d>>2]|0;if(!(c[d+(e<<2)>>2]|0)){while(1){if((e|0)<=-1)break;if(c[d+(e<<2)>>2]|0)break;e=e+-1|0}c[f>>2]=e}e=c[b>>2]|0;c[(c[a+20>>2]|0)+(e<<2)>>2]=0;c[(c[a+24>>2]|0)+(e<<2)>>2]=-999;e=1}else e=0;return e|0}function Bx(a,b){a=a|0;b=b|0;return c[(c[a+24>>2]|0)+(b<<2)>>2]|0}function Cx(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;if((a|0)!=0?(e=c[a>>2]|0,(e|0)>=1):0){h=a+24|0;d=c[(c[h>>2]|0)+(b<<2)>>2]|0;c[a>>2]=e+-1;f=a+20|0;e=a+16|0;Fy(c[(c[f>>2]|0)+(b<<2)>>2]|0,105,(c[e>>2]|0)+(d<<2)|0);a=a+12|0;if((d|0)==(c[a>>2]|0)?(g=c[e>>2]|0,(c[g+(d<<2)>>2]|0)==0):0){while(1){if((d|0)<=-1)break;if(c[g+(d<<2)>>2]|0)break;d=d+-1|0}c[a>>2]=d}c[(c[f>>2]|0)+(b<<2)>>2]=0;c[(c[h>>2]|0)+(b<<2)>>2]=-999;d=1}else d=0;return d|0}function Dx(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0,j=0,k=0.0,l=0.0,m=0,n=0.0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;if(!(ww(a,0)|0))qa(95795,95831,260,95846);w=xw(a)|0;x=c[w+20>>2]|0;y=c[w+24>>2]|0;e=w+16|0;a=w+28|0;if((c[e>>2]|0)==1)v=c[a>>2]|0;else{U1(c[a>>2]|0);c[e>>2]=1;v=KK(c[w+8>>2]<<3)|0;c[a>>2]=v}r=KK(c[w>>2]<<2)|0;a=0;while(1){e=c[w>>2]|0;if((a|0)>=(e|0)){q=0;a=e;break}c[r+(a<<2)>>2]=-1;a=a+1|0}a:while(1){if((q|0)>=(a|0)){g=0.0;k=0.0;f=0;u=0;t=a;break}p=q+1|0;i=x+(p<<2)|0;m=x+(q<<2)|0;j=(c[i>>2]|0)-(c[m>>2]|0)|0;c[r+(q<<2)>>2]=q;e=c[m>>2]|0;while(1){o=c[i>>2]|0;if((e|0)>=(o|0))break;a=c[y+(e<<2)>>2]|0;if((q|0)!=(a|0))c[r+(a<<2)>>2]=q;e=e+1|0}n=+(j|0);e=c[m>>2]|0;while(1){if((e|0)>=(o|0))break;a=c[y+(e<<2)>>2]|0;if((q|0)!=(a|0)){i=c[x+(a+1<<2)>>2]|0;a=c[x+(a<<2)>>2]|0;l=+(i-a|0)+n;while(1){if((a|0)>=(i|0))break;l=(c[r+(c[y+(a<<2)>>2]<<2)>>2]|0)==(q|0)?l+-1.0:l;a=a+1|0}h[v+(e<<3)>>3]=l;if(!(l>0.0)){s=23;break a}}e=e+1|0}q=p;a=c[w>>2]|0}if((s|0)==23)qa(95868,95831,290,95846);while(1){if((u|0)>=(t|0))break;i=u+1|0;j=x+(i<<2)|0;e=c[x+(u<<2)>>2]|0;while(1){if((e|0)>=(c[j>>2]|0))break;a=c[y+(e<<2)>>2]|0;if((u|0)==(a|0))a=f;else{k=+_w(d,b,u,a)+k;g=+h[v+(e<<3)>>3]+g;a=f+1|0}f=a;e=e+1|0}u=i;t=c[w>>2]|0}n=+(f|0);g=k/(g/n*n);a=0;b:while(1){if((a|0)>=(t|0))break;i=a+1|0;j=c[x+(i<<2)>>2]|0;e=c[x+(a<<2)>>2]|0;while(1){if((e|0)>=(j|0)){a=i;continue b}f=v+(e<<3)|0;if((a|0)!=(c[y+(e<<2)>>2]|0))h[f>>3]=+h[f>>3]*g;e=e+1|0}}return w|0}function Ex(a,b,d,e,f){a=a|0;b=b|0;d=+d;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0.0,p=0,q=0.0,r=0.0,s=0.0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0.0,S=0.0;L=c[a>>2]|0;M=c[a+20>>2]|0;N=c[a+24>>2]|0;if(!(ww(a,0)|0))qa(95795,95831,333,95876);O=Dx(a,b,e)|0;P=KK(56)|0;Q=P+32|0;h[Q>>3]=1.0;c[P+20>>2]=0;c[P+24>>2]=0;h[P+40>>3]=.01;c[P+48>>2]=~~+C(+(+(c[a>>2]|0)));g=L<<3;I=KK(g)|0;c[P+12>>2]=I;a=0;while(1){if((a|0)>=(L|0))break;h[I+(a<<3)>>3]=d;a=a+1|0}J=KK(L<<2)|0;H=KK(g)|0;j=0;while(1){if((j|0)>=(L|0)){n=0;break}k=H+(j<<3)|0;h[k>>3]=0.0;l=j+1|0;m=M+(l<<2)|0;i=c[M+(j<<2)>>2]|0;a=0;d=0.0;while(1){if((i|0)>=(c[m>>2]|0))break;g=c[N+(i<<2)>>2]|0;if((j|0)!=(g|0)){d=+_w(e,b,j,g);d=+h[k>>3]+d;h[k>>3]=d;a=a+1|0}i=i+1|0}if((a|0)<=0){p=14;break}h[k>>3]=d/+(a|0);j=l}if((p|0)==14)qa(95908,95831,358,95876);while(1){if((n|0)>=(L|0)){a=0;l=0;break}c[J+(n<<2)>>2]=-1;n=n+1|0}a:while(1){if((l|0)>=(L|0))break;c[J+(l<<2)>>2]=l;j=M+(l<<2)|0;m=l+1|0;n=M+(m<<2)|0;i=c[j>>2]|0;while(1){g=c[n>>2]|0;if((i|0)>=(g|0))break;g=J+(c[N+(i<<2)>>2]<<2)|0;if((c[g>>2]|0)!=(l|0)){c[g>>2]=l;a=a+1|0}i=i+1|0}k=c[j>>2]|0;while(1){if((k|0)>=(g|0)){l=m;continue a}i=c[N+(k<<2)>>2]|0;j=M+(i+1<<2)|0;i=c[M+(i<<2)>>2]|0;while(1){if((i|0)>=(c[j>>2]|0))break;g=J+(c[N+(i<<2)>>2]<<2)|0;if((c[g>>2]|0)!=(l|0)){c[g>>2]=l;a=a+1|0}i=i+1|0}k=k+1|0;g=c[n>>2]|0}}g=a+L|0;F=P+4|0;c[F>>2]=rw(L,L,g,1,1)|0;g=rw(L,L,g,1,1)|0;G=P+8|0;c[G>>2]=g;a=c[F>>2]|0;do if(!((g|0)==0|(a|0)==0)){B=c[a+20>>2]|0;A=c[a+24>>2]|0;y=c[a+28>>2]|0;E=c[g+28>>2]|0;z=c[g+20>>2]|0;t=c[g+24>>2]|0;c[z>>2]=0;c[B>>2]=0;d=0.0;o=0.0;p=0;i=0;b:while(1){if((p|0)>=(L|0)){p=58;break}u=p+L|0;c[J+(p<<2)>>2]=u;k=M+(p<<2)|0;v=p+1|0;w=M+(v<<2)|0;x=H+(p<<3)|0;q=0.0;r=0.0;j=c[k>>2]|0;while(1){a=c[w>>2]|0;if((j|0)>=(a|0))break;g=c[N+(j<<2)>>2]|0;a=J+(g<<2)|0;if((c[a>>2]|0)!=(u|0)){c[a>>2]=u;c[A+(i<<2)>>2]=g;switch(f|0){case 0:{s=1.0;break}case 1:{s=(+h[H+(g<<3)>>3]+ +h[x>>3])*.5;break}case 2:{s=+D(+(+Zw(e,b,p,g)),.4);break}default:{p=42;break b}}S=-1.0/(s*s);h[y+(i<<3)>>3]=S;c[t+(i<<2)>>2]=g;R=S*s;n=E+(i<<3)|0;h[n>>3]=R;o=+_w(e,b,p,g)*R+o;R=+h[n>>3];i=i+1|0;q=S+q;r=R+r;d=R*s+d}j=j+1|0}n=c[k>>2]|0;while(1){if((n|0)>=(a|0))break;k=c[N+(n<<2)>>2]|0;l=M+(k+1<<2)|0;m=H+(k<<3)|0;j=c[M+(k<<2)>>2]|0;while(1){if((j|0)>=(c[l>>2]|0))break;g=N+(j<<2)|0;a=J+(c[g>>2]<<2)|0;if((c[a>>2]|0)!=(u|0)){c[a>>2]=u;switch(f|0){case 0:{s=2.0;break}case 1:{s=(+h[m>>3]*2.0+ +h[x>>3]+ +h[H+(c[g>>2]<<3)>>3])*.5;break}case 2:{s=+D(+(+Zw(e,b,p,c[g>>2]|0)),.4);break}default:{p=53;break b}}c[A+(i<<2)>>2]=c[g>>2];R=-1.0/(s*s);h[y+(i<<3)>>3]=R;c[t+(i<<2)>>2]=c[g>>2];S=R*s;a=E+(i<<3)|0;h[a>>3]=S;o=+_w(e,b,c[g>>2]|0,k)*S+o;S=+h[a>>3];i=i+1|0;q=R+q;r=S+r;d=S*s+d}j=j+1|0}n=n+1|0;a=c[w>>2]|0}c[A+(i<<2)>>2]=p;x=I+(p<<3)|0;S=-(q*+h[x>>3]);h[x>>3]=S;h[y+(i<<3)>>3]=S-q;c[t+(i<<2)>>2]=p;h[E+(i<<3)>>3]=-r;i=i+1|0;c[B+(v<<2)>>2]=i;c[z+(v<<2)>>2]=i;p=v}if((p|0)==42){U4(95915,29,1,c[15712]|0)|0;qa(138394,95831,418,95876)}else if((p|0)==53){U4(95915,29,1,c[15712]|0)|0;qa(138394,95831,460,95876)}else if((p|0)==58){d=o/d;a=0;while(1){if((a|0)>=(i|0))break;N=E+(a<<3)|0;h[N>>3]=+h[N>>3]*d;a=a+1|0}h[Q>>3]=d;c[(c[F>>2]|0)+8>>2]=i;c[(c[G>>2]|0)+8>>2]=i;U1(J);U1(H);qw(O);K=P;break}}else{Fx(P);K=0}while(0);return K|0}function Fx(a){a=a|0;var b=0;if(a|0){b=c[a+4>>2]|0;if(b|0)qw(b);b=c[a+8>>2]|0;if(b|0)qw(b);b=c[a+12>>2]|0;if(b|0)U1(b);b=c[a+20>>2]|0;if(b|0)tb[c[a+16>>2]&127](b);U1(a)}return}function Gx(a,b,d,e,f,g){a=a|0;b=b|0;d=+d;e=e|0;f=f|0;g=g|0;var i=0,j=0.0,k=0,l=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0.0,N=0;K=c[a>>2]|0;if(ww(a,0)|0?(c[a+16>>2]|0)==1:0){k=O(K,b)|0;j=0.0;i=0;while(1){if((i|0)>=(k|0))break;q=+h[e+(i<<3)>>3];j=q*q+j;i=i+1|0}a:do if(j==0.0){i=0;while(1){if((i|0)>=(k|0))break a;h[e+(i<<3)>>3]=+Sw()*72.0;i=i+1|0}}while(0);F=c[a+20>>2]|0;G=c[a+24>>2]|0;H=c[a+28>>2]|0;i=KK(56)|0;J=i+32|0;h[J>>3]=1.0;c[i+20>>2]=0;c[i+24>>2]=0;c[i>>2]=a;h[i+40>>3]=.01;c[i+48>>2]=~~+C(+(+(c[a>>2]|0)));I=KK(K<<3)|0;c[i+12>>2]=I;k=0;while(1){if((k|0)>=(K|0))break;h[I+(k<<3)>>3]=d;k=k+1|0}a=(c[a+8>>2]|0)+K|0;D=i+4|0;c[D>>2]=rw(K,K,a,1,1)|0;a=rw(K,K,a,1,1)|0;E=i+8|0;c[E>>2]=a;k=c[D>>2]|0;if(!((a|0)==0|(k|0)==0)){x=c[k+20>>2]|0;y=c[k+24>>2]|0;z=c[a+20>>2]|0;A=c[a+24>>2]|0;v=c[k+28>>2]|0;B=c[a+28>>2]|0;c[z>>2]=0;c[x>>2]=0;p=0.0;q=0.0;w=0;s=0;b:while(1){if((s|0)>=(K|0))break;t=s+1|0;u=F+(t<<2)|0;l=0.0;m=0.0;r=c[F+(s<<2)>>2]|0;o=p;d=q;a=w;while(1){if((r|0)>=(c[u>>2]|0))break;k=c[G+(r<<2)>>2]|0;if((k|0)==(s|0)){j=o;k=a}else{c[y+(a<<2)>>2]=k;n=+h[H+(r<<3)>>3];switch(f|0){case 2:{j=n*n;j=j==0.0?-1.0e5:-1.0/j;break}case 1:{j=n*n==0.0?-1.0e5:-1.0/n;break}case 0:{j=-1.0;break}default:{L=23;break b}}h[v+(a<<3)>>3]=j;c[A+(a<<2)>>2]=k;M=j*n;N=B+(a<<3)|0;h[N>>3]=M;d=+_w(e,b,s,k)*M+d;M=+h[N>>3];l=j+l;m=M+m;j=M*n+o;k=a+1|0}r=r+1|0;o=j;a=k}c[y+(a<<2)>>2]=s;w=I+(s<<3)|0;p=-(l*+h[w>>3]);h[w>>3]=p;h[v+(a<<3)>>3]=p-l;c[A+(a<<2)>>2]=s;h[B+(a<<3)>>3]=-m;w=a+1|0;c[x+(t<<2)>>2]=w;c[z+(t<<2)>>2]=w;p=o;q=d;s=t}if((L|0)==23)qa(138394,95831,591,96012);j=(g|0)==0?1.0:q/p;if(j==0.0)i=0;else{k=0;while(1){if((k|0)>=(w|0))break;N=B+(k<<3)|0;h[N>>3]=+h[N>>3]*j;k=k+1|0}h[J>>3]=j;c[(c[D>>2]|0)+8>>2]=w;c[(c[E>>2]|0)+8>>2]=w}}else{Fx(i);i=0}return i|0}qa(95945,95831,525,96012);return 0}function Hx(a){a=a|0;Fx(a);return}function Ix(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=+e;return +(+Jx(a,b,c,d,e))}function Jx(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;var g=0,i=0,j=0.0,k=0,m=0,n=0,o=0,p=0.0,q=0,r=0.0,s=0.0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0,B=0.0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,P=0,Q=0,R=0,S=0,T=0,U=0;T=l;l=l+16|0;Q=T+12|0;S=T+8|0;n=T+4|0;k=T;m=c[a+4>>2]|0;i=c[a+8>>2]|0;c[Q>>2]=0;c[S>>2]=0;c[n>>2]=0;K=c[a+12>>2]|0;c[k>>2]=0;L=xw(i)|0;M=c[m>>2]|0;N=O(M,b)|0;g=N<<3;R=KK(g)|0;if((R|0)!=0?(P=O(b<<3,M)|0,W6(R|0,d|0,P|0)|0,J=KK(g)|0,c[S>>2]=J,(J|0)!=0):0){I=c[i+20>>2]|0;J=c[i+24>>2]|0;E=c[i+28>>2]|0;F=c[L+28>>2]|0;G=c[m+28>>2]|0;H=a+24|0;switch(c[H>>2]|0){case 1:{Kx(c[a+20>>2]|0,M,b,d,k,n);g=c[k>>2]|0;if(!g){A=0.0;B=0.0;g=0}else{m=yw(m,g)|0;A=0.0;B=0.0}break}case 2:{g=c[a+20>>2]|0;A=+h[g+8>>3];B=+h[g>>3];g=0;break}default:{A=0.0;B=0.0;g=0}}y=(K|0)==0;z=a+40|0;x=a+48|0;i=c[n>>2]|0;j=1.0;k=0;a:while(1){w=k+1|0;if(!((k|0)<(e|0)&j>f))break;b:do if((c[H>>2]|0)==4){n=c[S>>2]|0;k=0;while(1){if((k|0)>=(M|0))break b;o=O(k,b)|0;a=0;while(1){if((a|0)>=(b|0))break;h[n+(a+o<<3)>>3]=0.0;a=a+1|0}k=k+1|0}}else{q=0;while(1){if((q|0)>=(M|0))break;u=q+1|0;v=I+(u<<2)|0;p=0.0;t=c[I+(q<<2)>>2]|0;k=-1;while(1){if((t|0)>=(c[v>>2]|0))break;n=J+(t<<2)|0;a=c[n>>2]|0;if((q|0)==(a|0))k=t;else{s=+_w(d,b,q,a);o=E+(t<<3)|0;r=+h[o>>3];if(r==0.0)r=0.0;else{if(s==0.0){r=r/+h[G+(t<<3)>>3]*.0001;a=0;while(1){if((a|0)>=(b|0))break;s=r*(+Sw()+.0001);U=d+((O(c[n>>2]|0,b)|0)+a<<3)|0;h[U>>3]=s+ +h[U>>3];a=a+1|0}s=+_w(d,b,q,c[n>>2]|0);r=+h[o>>3]}r=r/s}h[F+(t<<3)>>3]=r;p=r+p}t=t+1|0}if((k|0)<=-1){D=25;break a}h[F+(k<<3)>>3]=-p;q=u}Gw(L,0,d,0,S,0,b)}while(0);c:do if(!y){n=c[S>>2]|0;k=0;while(1){if((k|0)>=(M|0))break c;o=K+(k<<3)|0;q=O(k,b)|0;a=0;while(1){if((a|0)>=(b|0))break;v=a+q|0;U=n+(v<<3)|0;h[U>>3]=+h[U>>3]+ +h[R+(v<<3)>>3]*+h[o>>3];a=a+1|0}k=k+1|0}}while(0);d:do switch(c[H>>2]|0){case 1:{k=c[S>>2]|0;a=0;while(1){if((a|0)>=(M|0)){D=50;break d}o=O(a,b)|0;n=0;while(1){if((n|0)>=(b|0))break;D=n+o|0;U=k+(D<<3)|0;h[U>>3]=+h[U>>3]+ +h[i+(D<<3)>>3];n=n+1|0}a=a+1|0}}case 2:{Lx(M,b,d,c[S>>2]|0,B,A);k=c[S>>2]|0;p=+h[z>>3];a=c[x>>2]|0;if((c[H>>2]|0)==2)Mx(m,B,b,d,k,p,a,Q);else D=51;break}default:{k=c[S>>2]|0;D=50}}while(0);if((D|0)==50){p=+h[z>>3];a=c[x>>2]|0;D=51}if((D|0)==51){D=0;+fy(m,b,d,k,p,a,0,Q)}if(c[Q>>2]|0)break;j=+Nx(M,b,d,c[S>>2]|0);j=j/+C(+(+Ww(N,d,d)));W6(d|0,c[S>>2]|0,P|0)|0;k=w}if((D|0)==25)qa(96049,95831,895,96060);qw(L);if(g){qw(g);qw(m)}}else{qw(L);j=1.0;i=0}U1(R);g=c[S>>2]|0;if(g|0)U1(g);if(i|0)U1(i);l=T;return +j}function Kx(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0.0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0,u=0,v=0,w=0.0,x=0,y=0,z=0,A=0,B=0.0;p=c[a+8>>2]|0;A=c[a+12>>2]|0;x=c[a+16>>2]|0;z=c[a+32>>2]|0;y=c[z+20>>2]|0;z=c[z+24>>2]|0;l=a+20|0;r=c[l>>2]|0;m=a+24|0;q=c[m>>2]|0;n=a+28|0;o=c[n>>2]|0;w=+h[a>>3];a:do if((p|2|0)==3){if(!r){if((q|0)!=0|(o|0)!=0)qa(96094,95831,692,96111);else{i=0;j=0}while(1){if((i|0)>=(A|0))break;v=c[x+(i<<2)>>2]|0;s=+((c[y+(v+1<<2)>>2]|0)-(c[y+(v<<2)>>2]|0)|0)+1.0;i=i+1|0;j=j+~~(s*s)|0}q=j<<2;r=KK(q)|0;c[l>>2]=r;q=KK(q)|0;c[m>>2]=q;o=KK(j<<3)|0;c[n>>2]=o}u=0;a=0;while(1){if((u|0)>=(A|0))break;p=c[x+(u<<2)>>2]|0;t=y+(p<<2)|0;j=c[t>>2]|0;i=c[z+(j<<2)>>2]|0;j=c[z+(j+1<<2)>>2]|0;b:do if((i|0)!=(j|0)){B=+Zw(e,d,i,j);B=B*B;n=y+(p+1<<2)|0;s=+((c[n>>2]|0)-(c[t>>2]|0)|0);c[r+(a<<2)>>2]=p;c[q+(a<<2)>>2]=p;h[o+(a<<3)>>3]=w/B;k=s*s*B;s=-(w/(B*s));j=c[t>>2]|0;while(1){a=a+1|0;i=c[n>>2]|0;if((j|0)>=(i|0))break;c[r+(a<<2)>>2]=p;c[q+(a<<2)>>2]=c[z+(j<<2)>>2];h[o+(a<<3)>>3]=s;j=j+1|0}k=w/k;m=c[t>>2]|0;while(1){if((m|0)>=(i|0))break b;l=c[z+(m<<2)>>2]|0;c[r+(a<<2)>>2]=l;c[q+(a<<2)>>2]=p;h[o+(a<<3)>>3]=s;j=c[t>>2]|0;while(1){a=a+1|0;i=c[n>>2]|0;if((j|0)>=(i|0))break;v=c[z+(j<<2)>>2]|0;c[r+(a<<2)>>2]=l;c[q+(a<<2)>>2]=v;h[o+(a<<3)>>3]=k;j=j+1|0}m=m+1|0}}while(0);u=u+1|0}i=Dw(a,b,b,r,q,o,1,8)|0;a=0}else{switch(p|0){case 2:case 4:break;default:{i=0;a=0;break a}}do if(!r)if((q|0)!=0|(o|0)!=0)qa(96094,95831,734,96111);else{u=A<<2;v=KK(u)|0;c[l>>2]=v;u=KK(u)|0;c[m>>2]=u;t=KK(A<<3)|0;c[n>>2]=t;break}else{t=o;u=q;v=r}while(0);a=KK(O(b<<3,d)|0)|0;j=O(d,b)|0;i=0;while(1){if((i|0)>=(j|0)){o=0;break}h[a+(i<<3)>>3]=0.0;i=i+1|0}while(1){if((o|0)>=(A|0))break;p=c[x+(o<<2)>>2]|0;l=y+(p<<2)|0;m=c[l>>2]|0;k=+Zw(e,d,c[z+(m<<2)>>2]|0,c[z+(m+1<<2)>>2]|0);c[v+(o<<2)>>2]=p;c[u+(o<<2)>>2]=p;k=w/k;h[t+(o<<3)>>3]=k;l=c[l>>2]|0;m=c[y+(p+1<<2)>>2]|0;p=O(p,d)|0;j=l;while(1){if((j|0)>=(m|0))break;n=O(c[z+(j<<2)>>2]|0,d)|0;i=0;while(1){if((i|0)>=(d|0))break;r=a+(i+p<<3)|0;h[r>>3]=+h[r>>3]+ +h[e+(i+n<<3)>>3];i=i+1|0}j=j+1|0}k=k*(1.0/+(m-l|0));i=0;while(1){if((i|0)>=(d|0))break;r=a+(i+p<<3)|0;h[r>>3]=k*+h[r>>3];i=i+1|0}o=o+1|0}i=Dw(o,b,b,v,u,t,1,8)|0}while(0);c[f>>2]=i;c[g>>2]=a;return}function Lx(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=+e;f=+f;var g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0.0,p=0;f=f*e;g=0;a:while(1){if((g|0)>=(a|0))break;l=g+1|0;m=O(g,b)|0;i=l;while(1){if((i|0)==(a|0)){g=l;continue a}k=O(i,b)|0;e=1.0/+Zw(c,b,g,i);j=0;while(1){if((j|0)>=(b|0))break;p=j+m|0;n=j+k|0;o=f*((+h[c+(p<<3)>>3]-+h[c+(n<<3)>>3])*e);p=d+(p<<3)|0;h[p>>3]=o+ +h[p>>3];n=d+(n<<3)|0;h[n>>3]=+h[n>>3]-o;j=j+1|0}i=i+1|0}}return}function Mx(a,b,d,e,f,g,h,i){a=a|0;b=+b;d=d|0;e=e|0;f=f|0;g=+g;h=h|0;i=i|0;var j=0,k=0;k=Wx(a,b)|0;j=$x(a,b)|0;+cy(k,j,c[a>>2]|0,d,e,f,g,h,i);return}function Nx(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0,k=0.0,l=0;f=0;i=0.0;while(1){if((f|0)>=(a|0))break;j=O(f,b)|0;e=0;g=0.0;while(1){if((e|0)>=(b|0))break;l=e+j|0;k=+h[d+(l<<3)>>3]-+h[c+(l<<3)>>3];e=e+1|0;g=k*k+g}f=f+1|0;i=+C(+g)+i}return +i}function Ox(a,b,d,e,f){a=a|0;b=b|0;d=+d;e=e|0;f=f|0;var g=0,i=0,j=0,k=0.0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0.0,u=0.0,v=0.0,w=0.0,x=0,y=0,z=0,A=0,B=0,E=0.0,F=0.0;A=c[a>>2]|0;p=c[a+20>>2]|0;q=c[a+24>>2]|0;if(!(ww(a,0)|0))qa(95795,95831,1034,96133);r=A<<3;B=KK(r)|0;j=0;while(1){if((j|0)>=(A|0))break;m=B+(j<<3)|0;h[m>>3]=0.0;n=j+1|0;o=p+(n<<2)|0;l=c[p+(j<<2)>>2]|0;g=0;k=0.0;while(1){if((l|0)>=(c[o>>2]|0))break;i=c[q+(l<<2)>>2]|0;if((j|0)!=(i|0)){k=+_w(e,b,j,i);k=+h[m>>3]+k;h[m>>3]=k;g=g+1|0}l=l+1|0}if((g|0)<=0){z=11;break}h[m>>3]=k/+(g|0);j=n}if((z|0)==11)qa(95908,95831,1046,96133);g=KK(56)|0;y=g+32|0;h[y>>3]=1.0;c[g+20>>2]=0;c[g+24>>2]=0;h[g+40>>3]=.01;c[g+48>>2]=~~+C(+(+(c[a>>2]|0)));x=KK(r)|0;c[g+12>>2]=x;i=0;while(1){if((i|0)>=(A|0))break;h[x+(i<<3)>>3]=d;i=i+1|0}do if((A|0)>2)if(!f){i=hy(A,b,e)|0;break}else{i=gy(A,b,e)|0;break}else i=xw(a)|0;while(0);f=g+4|0;c[f>>2]=yw(a,i)|0;qw(i);j=xw(c[f>>2]|0)|0;c[g+8>>2]=j;i=c[f>>2]|0;if((j|0)==0|(i|0)==0){Px(g);g=0}else{r=c[i+20>>2]|0;q=c[i+24>>2]|0;p=c[i+28>>2]|0;f=c[j+28>>2]|0;v=0.0;w=0.0;m=0;while(1){if((m|0)>=(A|0))break;o=m+1|0;n=r+(o<<2)|0;u=0.0;d=0.0;j=-1;s=v;t=w;l=c[r+(m<<2)>>2]|0;while(1){if((l|0)>=(c[n>>2]|0))break;i=c[q+(l<<2)>>2]|0;if((i|0)==(m|0)){k=u;i=l}else{E=+D(+(+Zw(e,b,m,i)),.6);k=1.0/(E*E);h[p+(l<<3)>>3]=k;F=k*E;a=f+(l<<3)|0;h[a>>3]=F;t=F*+_w(e,b,m,i)+t;F=+h[a>>3];k=k+u;d=F+d;i=j;s=F*E+s}u=k;j=i;l=l+1|0}a=x+(m<<3)|0;k=-(u*+h[a>>3]);h[a>>3]=k;if((j|0)<=-1){z=31;break}h[p+(j<<3)>>3]=k-u;h[f+(j<<3)>>3]=-d;v=s;w=t;m=o}if((z|0)==31)qa(96154,95831,1112,96133);k=w/v;j=c[r+(A<<2)>>2]|0;i=0;while(1){if((i|0)>=(j|0))break;A=f+(i<<3)|0;h[A>>3]=+h[A>>3]*k;i=i+1|0}h[y>>3]=k;U1(B)}return g|0}function Px(a){a=a|0;Fx(a);return}function Qx(a,b,c){a=a|0;b=b|0;c=c|0;+Jx(a,b,c,50,.001);return}function Rx(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0;z=c[a>>2]|0;A=c[a+20>>2]|0;B=c[a+24>>2]|0;if(!(ww(a,0)|0))qa(95795,95831,1149,96165);v=Dx(a,b,e)|0;w=c[v+28>>2]|0;a=KK(8)|0;x=KK(z<<2)|0;y=KK(z<<3)|0;i=0;while(1){if((i|0)>=(z|0)){o=0;break}l=y+(i<<3)|0;h[l>>3]=0.0;m=i+1|0;n=A+(m<<2)|0;j=c[A+(i<<2)>>2]|0;f=0;k=0.0;while(1){if((j|0)>=(c[n>>2]|0))break;g=c[B+(j<<2)>>2]|0;if((i|0)!=(g|0)){k=+_w(e,b,i,g);k=+h[l>>3]+k;h[l>>3]=k;f=f+1|0}j=j+1|0}if((f|0)<=0){p=11;break}h[l>>3]=k/+(f|0);i=m}if((p|0)==11)qa(95908,95831,1167,96165);while(1){if((o|0)>=(z|0)){m=0;f=0;break}c[x+(o<<2)>>2]=-1;o=o+1|0}a:while(1){if((m|0)>=(z|0))break;c[x+(m<<2)>>2]=m;j=A+(m<<2)|0;n=m+1|0;o=A+(n<<2)|0;i=c[j>>2]|0;while(1){g=c[o>>2]|0;if((i|0)>=(g|0))break;g=x+(c[B+(i<<2)>>2]<<2)|0;if((c[g>>2]|0)!=(m|0)){c[g>>2]=m;f=f+1|0}i=i+1|0}l=c[j>>2]|0;while(1){if((l|0)>=(g|0)){m=n;continue a}i=c[B+(l<<2)>>2]|0;j=A+(i+1<<2)|0;i=c[A+(i<<2)>>2]|0;while(1){if((i|0)>=(c[j>>2]|0))break;g=x+(c[B+(i<<2)>>2]<<2)|0;if((c[g>>2]|0)!=(m|0)){c[g>>2]=m;f=f+1|0}i=i+1|0}l=l+1|0;g=c[o>>2]|0}}e=rw(z,z,f,1,1)|0;c[a>>2]=e;if(!e){Sx(a);a=0}else{p=c[e+20>>2]|0;q=c[e+24>>2]|0;r=c[e+28>>2]|0;c[p>>2]=0;g=0;f=0;while(1){if((g|0)>=(z|0))break;s=g+z|0;c[x+(g<<2)>>2]=s;l=A+(g<<2)|0;t=g+1|0;u=A+(t<<2)|0;b=y+(g<<3)|0;j=c[l>>2]|0;while(1){g=c[u>>2]|0;if((j|0)>=(g|0))break;g=c[B+(j<<2)>>2]|0;i=x+(g<<2)|0;if((c[i>>2]|0)!=(s|0)){c[i>>2]=s;c[q+(f<<2)>>2]=g;o=r+(f<<3)|0;h[o>>3]=(+h[y+(g<<3)>>3]+ +h[b>>3])*.5;h[o>>3]=+h[w+(j<<3)>>3];f=f+1|0}j=j+1|0}o=c[l>>2]|0;while(1){if((o|0)>=(g|0))break;j=c[B+(o<<2)>>2]|0;l=A+(j+1<<2)|0;m=y+(j<<3)|0;n=w+(o<<3)|0;j=c[A+(j<<2)>>2]|0;while(1){if((j|0)>=(c[l>>2]|0))break;g=B+(j<<2)|0;i=x+(c[g>>2]<<2)|0;if((c[i>>2]|0)!=(s|0)){c[i>>2]=s;c[q+(f<<2)>>2]=c[g>>2];i=r+(f<<3)|0;h[i>>3]=(+h[m>>3]*2.0+ +h[b>>3]+ +h[y+(c[g>>2]<<3)>>3])*.5;h[i>>3]=+h[w+(j<<3)>>3]+ +h[n>>3];f=f+1|0}j=j+1|0}o=o+1|0;g=c[u>>2]|0}c[p+(t<<2)>>2]=f;g=t}c[e+8>>2]=f;A=Yp()|0;B=a+4|0;c[B>>2]=A;W6(A|0,d|0,168)|0;B=c[B>>2]|0;c[B+16>>2]=0;c[B+40>>2]=1;A=B+96|0;h[A>>3]=+h[A>>3]*.5;c[B+80>>2]=20;U1(x);U1(y);qw(v)}return a|0}function Sx(a){a=a|0;var b=0;if(a|0){b=c[a>>2]|0;if(b|0)qw(b);a=c[a+4>>2]|0;if(a|0)Zp(a)}return}function Tx(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;g=l;l=l+16|0;h=g;c[h>>2]=0;sq(e,b,c[a>>2]|0,c[a+4>>2]|0,d,f,h);if(!(c[h>>2]|0)){l=g;return}else qa(96184,95831,1262,96190)}function Ux(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0;c[g>>2]=0;g=c[d+120>>2]|0;a:do switch(g|0){case 5:case 6:{if((c[b>>2]|0)>2){b=Ox(b,a,0.0,f,(g|0)!=6&1)|0;Qx(b,a,f);Px(b)}break}case 4:{g=0;while(1){if((g|0)==1)break a;i=Rx(b,a,d,f)|0;Tx(i,b,e,a,f);Sx(i);g=g+1|0}}case 1:{d=0;h=6;break}case 2:{d=1;h=6;break}case 3:{d=2;h=6;break}default:{}}while(0);b:do if((h|0)==6){g=0;while(1){if((g|0)==1)break b;i=Ex(b,a,.05,f,d)|0;+Jx(i,a,f,50,.001);Fx(i);g=g+1|0}}while(0);return}function Vx(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0.0,j=0,k=0;k=l;l=l+16|0;g=k;c[g>>2]=d;j=c[a>>2]|0;a=c[j+8>>2]|0;i=+h[j>>3];j=c[a>>2]|0;Jw(a,b,g,0);a=0;f=0.0;while(1){if((a|0)>=(j|0))break;e=+h[b+(a<<3)>>3]+f;a=a+1|0;f=e}e=+(j|0);d=c[g>>2]|0;a=0;while(1){if((a|0)>=(j|0))break;g=d+(a<<3)|0;h[g>>3]=(+h[b+(a<<3)>>3]*e-f)*i+ +h[g>>3];a=a+1|0}l=k;return d|0}function Wx(a,b){a=a|0;b=+b;var d=0,e=0;d=KK(8)|0;e=KK(16)|0;c[d>>2]=e;h[e>>3]=b;c[e+8>>2]=a;c[d+4>>2]=25;return d|0}function Xx(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;f=l;l=l+16|0;e=f;c[e>>2]=d;Jw(c[a>>2]|0,b,e,0);l=f;return c[e>>2]|0}function Yx(a){a=a|0;var b=0;b=KK(8)|0;c[b>>2]=a;c[b+4>>2]=26;return b|0}function Zx(a){a=a|0;if(a|0)U1(a);return}function _x(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;f=c[a>>2]|0;e=~~+h[f>>3];f=f+8|0;a=0;while(1){if((a|0)>=(e|0))break;h[d+(a<<3)>>3]=+h[f+(a<<3)>>3]*+h[b+(a<<3)>>3];a=a+1|0}return d|0}function $x(a,b){a=a|0;b=+b;var d=0,e=0,f=0,g=0,i=0,j=0.0,k=0,l=0,m=0,n=0.0,o=0,p=0,q=0.0;m=c[a>>2]|0;o=c[a+20>>2]|0;p=c[a+24>>2]|0;k=c[a+28>>2]|0;l=KK(8)|0;i=KK((m<<3)+8|0)|0;c[l>>2]=i;h[i>>3]=+(m|0);i=i+8|0;q=+(m+-1|0);j=1.0/q;b=q*b;a=0;a:while(1){if((a|0)>=(m|0))break;e=i+(a<<3)|0;h[e>>3]=j;f=a+1|0;g=c[o+(f<<2)>>2]|0;d=c[o+(a<<2)>>2]|0;while(1){if((d|0)>=(g|0)){a=f;continue a}if((a|0)==(c[p+(d<<2)>>2]|0)?(n=+h[k+(d<<3)>>3],(n>=0.0?n:-n)>0.0):0)h[e>>3]=1.0/(n+b);d=d+1|0}}c[l+4>>2]=27;return l|0}function ay(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0.0,k=0,l=0,m=0,n=0;i=c[a>>2]|0;k=c[a+20>>2]|0;l=c[a+24>>2]|0;m=c[a+28>>2]|0;n=KK(8)|0;d=KK((c[a>>2]<<3)+8|0)|0;c[n>>2]=d;h[d>>3]=+(i|0);d=d+8|0;a=0;a:while(1){if((a|0)>=(i|0))break;e=d+(a<<3)|0;h[e>>3]=1.0;f=a+1|0;g=c[k+(f<<2)>>2]|0;b=c[k+(a<<2)>>2]|0;while(1){if((b|0)>=(g|0)){a=f;continue a}if((a|0)==(c[l+(b<<2)>>2]|0)?(j=+h[m+(b<<3)>>3],(j>=0.0?j:-j)>0.0):0)h[e>>3]=1.0/j;b=b+1|0}}c[n+4>>2]=27;return n|0}function by(a){a=a|0;U1(c[a>>2]|0);if(a|0)U1(a);return}function cy(a,b,c,d,e,f,g,i,j){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=+g;i=i|0;j=j|0;var k=0.0,l=0,m=0.0,n=0,o=0,p=0;o=c<<3;n=KK(o)|0;o=KK(o)|0;l=0;m=0.0;while(1){if((l|0)<(d|0))j=0;else break;while(1){if((j|0)>=(c|0))break;p=(O(j,d)|0)+l|0;h[n+(j<<3)>>3]=+h[e+(p<<3)>>3];h[o+(j<<3)>>3]=+h[f+(p<<3)>>3];j=j+1|0}k=+dy(a,b,c,n,o,g,i);j=0;while(1){if((j|0)>=(c|0))break;h[f+((O(j,d)|0)+l<<3)>>3]=+h[n+(j<<3)>>3];j=j+1|0}l=l+1|0;m=k+m}U1(n);U1(o);return +m}function dy(a,b,d,e,f,g,h){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=+g;h=h|0;var i=0,j=0.0,k=0,l=0,m=0,n=0.0,o=0.0,p=0,q=0,r=0,s=0.0,t=0,u=0;r=c[a+4>>2]|0;p=c[b+4>>2]|0;q=d<<3;i=KK(q)|0;k=KK(q)|0;t=KK(q)|0;l=KK(q)|0;k=nb[r&63](a,e,k)|0;m=Vw(d,f,k)|0;o=+(d|0);s=+C(+(+Ww(d,m,m)))/o;n=s*g;o=1.0/o;m=0;f=t;j=1.0;g=s;while(1){if(!(g>n&(m|0)<(h|0)))break;i=nb[p&63](b,k,i)|0;g=+Ww(d,k,i);if((m|0)>0)Xw(d,i,f,g/j)|0;else W6(f|0,i|0,q|0)|0;u=nb[r&63](a,f,l)|0;j=g/+Ww(d,f,u);Yw(d,e,f,j)|0;t=Yw(d,k,u,-j)|0;m=m+1|0;j=g;l=u;g=+C(+(+Ww(d,t,t)))*o}U1(i);U1(k);U1(f);U1(l);return +g}function ey(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0;C=l;l=l+16|0;B=C;y=c[a+4>>2]|0;z=y<<3;A=KK(z)|0;u=KK(z)|0;v=KK(z)|0;w=c[a+20>>2]|0;x=c[a+24>>2]|0;s=c[a+28>>2]|0;t=c[15712]|0;r=0;while(1){if((r|0)<(b|0))g=0;else break;while(1){if((g|0)>=(y|0)){a=0;break}q=(O(g,b)|0)+r|0;h[A+(g<<3)>>3]=+h[d+(q<<3)>>3];h[v+(g<<3)>>3]=+h[e+(q<<3)>>3];g=g+1|0}while(1){if((a|0)<(f|0))n=0;else{g=0;break}while(1){if((n|0)>=(y|0))break;o=c[w+(n<<2)>>2]|0;p=n+1|0;q=c[w+(p<<2)>>2]|0;m=o;j=0.0;i=0.0;while(1){if((m|0)>=(q|0))break;g=c[x+(m<<2)>>2]|0;k=+h[s+(m<<3)>>3];if((g|0)==(n|0))i=k;else j=+h[A+(g<<3)>>3]*k+j;m=m+1|0}if(j==0.0){c[B>>2]=q-o;w4(t,96212,B)|0}h[u+(n<<3)>>3]=(+h[v+(n<<3)>>3]-j)/i;n=p}W6(A|0,u|0,z|0)|0;a=a+1|0}while(1){if((g|0)>=(y|0))break;h[e+((O(g,b)|0)+r<<3)>>3]=+h[A+(g<<3)>>3];g=g+1|0}r=r+1|0}U1(A);U1(u);U1(v);l=C;return e|0}function fy(a,b,d,e,f,g,h,i){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;g=g|0;h=h|0;i=i|0;var j=0;j=c[a>>2]|0;c[i>>2]=0;switch(h|0){case 0:{h=Yx(a)|0;i=ay(a)|0;f=+cy(h,i,j,b,d,e,f,g,0);Zx(h);by(i);break}case 1:{ey(a,b,d,e,g,0)|0;f=0.0;break}default:f=0.0}return +f}function gy(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;o=l;l=l+32|0;k=o;m=o+20|0;f=o+16|0;g=o+12|0;i=o+8|0;h[k>>3]=1.0;j=a<<3;n=KK(j)|0;j=KK(j)|0;b=0;while(1){if((b|0)>=(a|0))break;e=b<<1;h[n+(b<<3)>>3]=+h[d+(e<<3)>>3];h[j+(b<<3)>>3]=+h[d+((e|1)<<3)>>3];b=b+1|0}c[m>>2]=b;if((a|0)>2)d=Zr(n,j,a,i)|0;else{c[i>>2]=0;d=0}e=rw(a,a,1,1,2)|0;b=0;while(1){c[m>>2]=b;if((b|0)>=(c[i>>2]|0))break;p=b<<1;c[f>>2]=c[d+(p<<2)>>2];c[g>>2]=c[d+((p|1)<<2)>>2];Nw(e,1,f,g,k)|0;b=(c[m>>2]|0)+1|0}if((a|0)==2){c[f>>2]=0;c[g>>2]=1;Nw(e,1,f,g,k)|0;b=0}else b=0;while(1){c[m>>2]=b;if((b|0)>=(a|0))break;Nw(e,1,m,m,k)|0;b=(c[m>>2]|0)+1|0}a=Cw(e)|0;qw(e);p=vw(a,0)|0;qw(a);U1(d);U1(n);U1(j);l=o;return p|0}function hy(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0;m=l;l=l+16|0;g=m+8|0;i=m;h[i>>3]=1.0;k=a<<3;j=KK(k)|0;k=KK(k)|0;e=0;while(1){if((e|0)>=(a|0))break;f=O(e,b)|0;h[j+(e<<3)>>3]=+h[d+(f<<3)>>3];h[k+(e<<3)>>3]=+h[d+(f+1<<3)>>3];e=e+1|0}c[g>>2]=e;e=_r(j,k,a,0)|0;f=rw(a,a,1,1,2)|0;b=0;while(1){c[g>>2]=b;if((b|0)<(a|0))d=1;else{b=0;break}while(1){if((d|0)>=(c[e+(b*12|0)>>2]|0))break;Nw(f,1,g,(c[e+(b*12|0)+4>>2]|0)+(d<<2)|0,i)|0;d=d+1|0;b=c[g>>2]|0}b=b+1|0}while(1){c[g>>2]=b;if((b|0)>=(a|0))break;Nw(f,1,g,g,i)|0;b=(c[g>>2]|0)+1|0}i=vw(Cw(f)|0,0)|0;qw(f);U1(j);U1(k);$r(e);l=m;return i|0}function iy(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0;e=l;l=l+16|0;f=e;jy(f,c);ky(a,b,c,f);ly(b,f,d);my(f);l=e;return}function jy(a,b){a=a|0;b=b|0;c[a>>2]=KK(b<<4)|0;c[a+4>>2]=b;c[a+8>>2]=0;return}function ky(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0;D=l;l=l+64|0;r=D+32|0;u=D+48|0;v=D+16|0;w=D;t=b<<2;A=KK(t)|0;C=KK(t)|0;c[v>>2]=0;c[v+4>>2]=0;c[v+8>>2]=0;c[v+12>>2]=0;s=KK(t)|0;t=KK(t)|0;f=0;while(1){if((f|0)>=(b|0))break;c[s+(f<<2)>>2]=f;f=f+1|0}q=b+-1|0;it(a,s,0,q);f=0;while(1){if((f|0)>=(b|0))break;c[t+(c[s+(f<<2)>>2]<<2)>>2]=f;f=f+1|0}oy(u,a,s,b);f=1;while(1){if((f|0)>=(b|0)){f=0;break}c[A+(c[s+(f<<2)>>2]<<2)>>2]=c[s+(f+-1<<2)>>2];f=f+1|0}while(1){if((f|0)>=(q|0))break;p=f+1|0;c[C+(c[s+(f<<2)>>2]<<2)>>2]=c[s+(p<<2)>>2];f=p}k=e+8|0;m=e+4|0;n=v+4|0;o=w+4|0;p=w+8|0;j=0;while(1){if((j|0)>=(d|0))break;if(!((py(u,v)|0)<<24>>24))break;f=c[k>>2]|0;b=c[m>>2]|0;if((f|0)<(b|0))b=c[e>>2]|0;else{c[m>>2]=b<<1;b=W1(c[e>>2]|0,b<<5)|0;c[e>>2]=b;f=c[k>>2]|0}c[k>>2]=f+1;f=b+(f<<4)|0;c[f>>2]=c[v>>2];c[f+4>>2]=c[v+4>>2];c[f+8>>2]=c[v+8>>2];c[f+12>>2]=c[v+12>>2];f=c[v>>2]|0;b=c[t+(f<<2)>>2]|0;g=c[n>>2]|0;i=c[t+(g<<2)>>2]|0;if((b|0)>0?(x=c[s+(b+-1<<2)>>2]|0,y=C+(x<<2)|0,(c[t+(c[y>>2]<<2)>>2]|0)<(i|0)):0){c[w>>2]=x;c[o>>2]=g;h[p>>3]=+h[a+(g<<3)>>3]-+h[a+(x<<3)>>3];c[r>>2]=c[w>>2];c[r+4>>2]=c[w+4>>2];c[r+8>>2]=c[w+8>>2];c[r+12>>2]=c[w+12>>2];qy(u,r);c[y>>2]=g;c[A+(g<<2)>>2]=x}if((i|0)<(q|0)?(z=c[s+(i+1<<2)>>2]|0,B=A+(z<<2)|0,(c[t+(c[B>>2]<<2)>>2]|0)>(b|0)):0){c[w>>2]=f;c[o>>2]=z;h[p>>3]=+h[a+(z<<3)>>3]-+h[a+(f<<3)>>3];c[r>>2]=c[w>>2];c[r+4>>2]=c[w+4>>2];c[r+8>>2]=c[w+8>>2];c[r+12>>2]=c[w+12>>2];qy(u,r);c[B>>2]=f;c[C+(f<<2)>>2]=z}j=j+1|0}U1(A);U1(C);U1(s);U1(t);ry(u);l=D;return}function ly(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;k=KK(a<<2)|0;l=b+8|0;i=c[l>>2]|0;j=(i<<1)+a|0;h=j<<2;e=KK(h)|0;h=KK(h)|0;f=0;while(1){if((f|0)>=(a|0))break;c[k+(f<<2)>>2]=1;f=f+1|0}f=0;while(1){if((f|0)>=(i|0)){f=0;break}n=c[b>>2]|0;m=c[n+(f<<4)+4>>2]|0;n=k+(c[n+(f<<4)>>2]<<2)|0;c[n>>2]=(c[n>>2]|0)+1;m=k+(m<<2)|0;c[m>>2]=(c[m>>2]|0)+1;f=f+1|0}while(1){if((f|0)>=(j|0))break;g[h+(f<<2)>>2]=1.0;f=f+1|0}i=KK(a*20|0)|0;c[d>>2]=i;f=0;while(1){if((f|0)>=(a|0))break;c[i+(f*20|0)>>2]=1;c[i+(f*20|0)+8>>2]=h;c[i+(f*20|0)+4>>2]=e;c[e>>2]=f;g[h>>2]=0.0;n=c[k+(f<<2)>>2]|0;h=h+(n<<2)|0;e=e+(n<<2)|0;f=f+1|0}U1(k);while(1){e=c[l>>2]|0;if(!e)break;n=e+-1|0;c[l>>2]=n;m=c[b>>2]|0;ny(i,c[m+(n<<4)>>2]|0,c[m+(n<<4)+4>>2]|0)}return}function my(a){a=a|0;U1(c[a>>2]|0);return}function ny(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,h=0,i=0,j=0;f=a+(b*20|0)|0;h=c[f>>2]|0;i=c[a+(b*20|0)+4>>2]|0;e=0;while(1){if((e|0)>=(h|0)){j=4;break}if((c[i+(e<<2)>>2]|0)==(d|0))break;else e=e+1|0}if((j|0)==4?(c[f>>2]=h+1,c[i+(h<<2)>>2]=d,i=c[a+(d*20|0)+4>>2]|0,h=a+(d*20|0)|0,j=c[h>>2]|0,c[h>>2]=j+1,c[i+(j<<2)>>2]=b,c[a+8>>2]|0):0){j=c[a+(b*20|0)+8>>2]|0;g[j>>2]=+g[j>>2]+-1.0;j=c[a+(d*20|0)+8>>2]|0;g[j>>2]=+g[j>>2]+-1.0}return}function oy(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0.0,k=0,l=0;f=e+-1|0;c[a+4>>2]=f;c[a+8>>2]=f;c[a>>2]=KK(f<<4)|0;e=0;while(1){if((e|0)>=(f|0))break;l=c[d+(e<<2)>>2]|0;g=e+1|0;k=c[d+(g<<2)>>2]|0;j=+h[b+(k<<3)>>3]-+h[b+(l<<3)>>3];i=c[a>>2]|0;c[i+(e<<4)>>2]=l;c[i+(e<<4)+4>>2]=k;h[i+(e<<4)+8>>3]=j;e=g}e=(f|0)/2|0;while(1){if((e|0)<=-1)break;sy(a,e);e=e+-1|0}return}function py(a,b){a=a|0;b=b|0;var d=0,e=0;d=a+4|0;if(!(c[d>>2]|0))a=0;else{e=c[a>>2]|0;c[b>>2]=c[e>>2];c[b+4>>2]=c[e+4>>2];c[b+8>>2]=c[e+8>>2];c[b+12>>2]=c[e+12>>2];b=c[a>>2]|0;e=b+((c[d>>2]|0)+-1<<4)|0;c[b>>2]=c[e>>2];c[b+4>>2]=c[e+4>>2];c[b+8>>2]=c[e+8>>2];c[b+12>>2]=c[e+12>>2];c[d>>2]=(c[d>>2]|0)+-1;sy(a,0);a=1}return a|0}function qy(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;m=n;j=a+4|0;g=c[j>>2]|0;d=a+8|0;if((g|0)==(c[d>>2]|0)){c[d>>2]=g<<1;d=W1(c[a>>2]|0,g<<5)|0;c[a>>2]=d;k=a;i=c[j>>2]|0}else{k=a;i=g;d=c[a>>2]|0}c[j>>2]=i+1;j=d+(g<<4)|0;c[j>>2]=c[b>>2];c[j+4>>2]=c[b+4>>2];c[j+8>>2]=c[b+8>>2];c[j+12>>2]=c[b+12>>2];while(1){if((g|0)<=0)break;d=c[k>>2]|0;e=+h[d+(g<<4)+8>>3];a=g>>>1;f=+h[d+(a<<4)+8>>3];if(!(e<f)){if(!(e==f))break;if(!((x5()|0)&1))break;d=c[k>>2]|0}j=d+(g<<4)|0;c[m>>2]=c[j>>2];c[m+4>>2]=c[j+4>>2];c[m+8>>2]=c[j+8>>2];c[m+12>>2]=c[j+12>>2];b=d+(a<<4)|0;c[j>>2]=c[b>>2];c[j+4>>2]=c[b+4>>2];c[j+8>>2]=c[b+8>>2];c[j+12>>2]=c[b+12>>2];j=(c[k>>2]|0)+(a<<4)|0;c[j>>2]=c[m>>2];c[j+4>>2]=c[m+4>>2];c[j+8>>2]=c[m+8>>2];c[j+12>>2]=c[m+12>>2];g=a}l=n;return}function ry(a){a=a|0;U1(c[a>>2]|0);return}function sy(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0;m=l;l=l+16|0;i=m;j=a+4|0;while(1){d=b<<1;g=d|1;if((d|0)<(c[j>>2]|0)){n=c[a>>2]|0;e=+h[n+(d<<4)+8>>3];f=+h[n+(b<<4)+8>>3];if(!(e<f))if(!(e==f?((x5()|0)&1|0)!=0:0))k=6}else k=6;if((k|0)==6){k=0;d=b}do if((g|0)<(c[j>>2]|0)){n=c[a>>2]|0;e=+h[n+(g<<4)+8>>3];f=+h[n+(d<<4)+8>>3];if(!(e<f)){if(!(e==f))break;if(!((x5()|0)&1))break}d=g}while(0);if((d|0)==(b|0))break;g=c[a>>2]|0;n=g+(d<<4)|0;c[i>>2]=c[n>>2];c[i+4>>2]=c[n+4>>2];c[i+8>>2]=c[n+8>>2];c[i+12>>2]=c[n+12>>2];g=g+(b<<4)|0;c[n>>2]=c[g>>2];c[n+4>>2]=c[g+4>>2];c[n+8>>2]=c[g+8>>2];c[n+12>>2]=c[g+12>>2];n=(c[a>>2]|0)+(b<<4)|0;c[n>>2]=c[i>>2];c[n+4>>2]=c[i+4>>2];c[n+8>>2]=c[i+8>>2];c[n+12>>2]=c[i+12>>2];b=d}l=m;return}function ty(a,b,d,e,f,g,i,j){a=a|0;b=b|0;d=+d;e=+e;f=f|0;g=g|0;i=i|0;j=j|0;var k=0,l=0,m=0,n=0.0;l=(f|0)!=0;if(!l){f=KK(b<<3)|0;if(!(ju(a,b,f,b)|0))m=3;else{a=1;m=17}}else m=3;if((m|0)==3){k=JK(b<<2)|0;c[g>>2]=k;a=0;while(1){if((a|0)>=(b|0))break;c[k+(a<<2)>>2]=a;a=a+1|0}a=b+-1|0;it(f,k,0,a);n=+h[f+(c[k>>2]<<3)>>3];e=(+h[f+(c[k+(a<<2)>>2]<<3)>>3]-n)*e/+(a|0);e=e<d?d:e;a=0;g=1;d=n;while(1){if((g|0)>=(b|0))break;n=+h[f+(c[k+(g<<2)>>2]<<3)>>3];a=a+(n-d>e&1)|0;g=g+1|0;d=n}c[j>>2]=a;a:do if(!a){k=KK(4)|0;c[i>>2]=k;c[k>>2]=b}else{j=KK(a<<2)|0;c[i>>2]=j;a=0;g=1;while(1){if((g|0)>=(b|0))break a;if(+h[f+(c[k+(g<<2)>>2]<<3)>>3]-+h[f+(c[k+(g+-1<<2)>>2]<<3)>>3]>e){c[j+(a<<2)>>2]=g;a=a+1|0}g=g+1|0}}while(0);if(l)a=0;else{a=0;m=17}}if((m|0)==17)U1(f);return a|0}function uy(a,b){a=a|0;b=b|0;var d=0,e=0,f=0.0,g=0,i=0,j=0.0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;d=c[46930]|0;if(d|0)vn(d);c[46930]=un(b,b,0.0)|0;d=c[46931]|0;if(d|0)U1(d);c[46931]=JK(b<<2)|0;d=c[46932]|0;if(d|0)U1(d);n=JK(b<<3)|0;c[46932]=n;o=c[46930]|0;p=c[46931]|0;i=0;while(1){if((i|0)>=(b|0)){e=10;break}e=a+(i<<2)|0;g=o+(i<<2)|0;f=0.0;d=0;while(1){if((d|0)==(b|0))break;j=+h[(c[e>>2]|0)+(d<<3)>>3];h[(c[g>>2]|0)+(d<<3)>>3]=j;j=+B(+j);f=f<j?j:f;d=d+1|0}if(!(f!=0.0)){e=15;break}h[n+(i<<3)>>3]=1.0/f;c[p+(i<<2)>>2]=i;i=i+1|0}a:do if((e|0)==10){m=b+-1|0;d=0;l=0;b:while(1){if((l|0)<(m|0)){e=l;k=d;f=0.0}else break;while(1){if((e|0)>=(b|0))break;a=c[p+(e<<2)>>2]|0;j=+B(+(+h[(c[o+(a<<2)>>2]|0)+(l<<3)>>3]));j=+h[n+(a<<3)>>3]*j;a=f<j;i=a?e:k;e=e+1|0;k=i;f=a?j:f}if(f==0.0){d=0;break a}d=p+(l<<2)|0;if((k|0)!=(l|0)){i=c[d>>2]|0;a=p+(k<<2)|0;c[d>>2]=c[a>>2];c[a>>2]=i}g=c[o+(c[d>>2]<<2)>>2]|0;a=l+1|0;f=1.0/+h[g+(l<<3)>>3];e=a;while(1){if((e|0)>=(b|0)){d=k;l=a;continue b}i=c[o+(c[p+(e<<2)>>2]<<2)>>2]|0;d=i+(l<<3)|0;j=+h[d>>3]*f;h[d>>3]=j;c:do if(j!=0.0){d=a;while(1){if((d|0)>=(b|0))break c;q=i+(d<<3)|0;h[q>>3]=+h[q>>3]-+h[g+(d<<3)>>3]*j;d=d+1|0}}while(0);e=e+1|0}}d=+h[(c[o+(c[p+(m<<2)>>2]<<2)>>2]|0)+(m<<3)>>3]!=0.0&1}else if((e|0)==15){h[n+(i<<3)>>3]=0.0;d=0}while(0);return d|0}function vy(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0,k=0,l=0;k=c[46930]|0;l=c[46931]|0;j=0;while(1){if((j|0)>=(d|0)){e=d;break}g=c[l+(j<<2)>>2]|0;i=k+(g<<2)|0;f=0.0;e=0;while(1){if((e|0)==(j|0))break;f=+h[a+(e<<3)>>3]*+h[(c[i>>2]|0)+(e<<3)>>3]+f;e=e+1|0}h[a+(j<<3)>>3]=+h[b+(g<<3)>>3]-f;j=j+1|0}while(1){b=e+-1|0;if((e|0)<=0)break;g=l+(b<<2)|0;f=0.0;while(1){if((e|0)>=(d|0))break;f=+h[a+(e<<3)>>3]*+h[(c[k+(c[g>>2]<<2)>>2]|0)+(e<<3)>>3]+f;e=e+1|0}e=a+(b<<3)|0;h[e>>3]=(+h[e>>3]-f)/+h[(c[k+(c[g>>2]<<2)>>2]|0)+(b<<3)>>3];e=b}return}function wy(a){a=a|0;var b=0;b=KK(8)|0;c[b>>2]=a;c[b+4>>2]=0;return b|0}function xy(a,b){a=a|0;b=b|0;var d=0,e=0;if(a|0)do{d=a;a=c[a+4>>2]|0;e=c[d>>2]|0;if(e|0)tb[b&127](e);U1(d)}while((a|0)!=0);return}function yy(a,b){a=a|0;b=b|0;b=wy(b)|0;c[b+4>>2]=a;return b|0}function zy(a){a=a|0;return c[a>>2]|0}function Ay(a){a=a|0;return c[a+4>>2]|0}function By(a){a=a|0;var b=0;b=KK(12)|0;c[b>>2]=a;c[b+4>>2]=0;c[b+8>>2]=0;return b|0}function Cy(a,b){a=a|0;b=b|0;var d=0,e=0;if(a|0)do{d=a;a=c[a+4>>2]|0;e=c[d>>2]|0;if(e|0)tb[b&127](e);U1(d)}while((a|0)!=0);return}function Dy(a,b){a=a|0;b=b|0;b=By(b)|0;if(a|0){c[b+4>>2]=a;c[a+8>>2]=b}return b|0}function Ey(a){a=a|0;return c[a>>2]|0}function Fy(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;if(a|0){e=c[a+4>>2]|0;f=c[a+8>>2]|0;g=c[a>>2]|0;if(g|0)tb[b&127](g);U1(a);if(e|0)c[e+8>>2]=f;c[(f|0?f+4|0:d)>>2]=e}return}function Gy(a){a=a|0;c[a>>2]=0;c[a+4>>2]=0;return}function Hy(a){a=a|0;var b=0;b=JK(64)|0;Gy(b+36|0);c[b+8>>2]=a;return b|0}function Iy(a){a=a|0;if(a|0){cz(c[a+32>>2]|0);U1(a)}return}function Jy(a){a=a|0;return tB(c[a+8>>2]|0)|0}function Ky(a,b){a=a|0;b=b|0;var d=0,e=0;c[b+4>>2]=0;d=a+4|0;e=c[d>>2]|0;c[((e|0)==0?a:e+4|0)>>2]=b;c[d>>2]=b;return}function Ly(a,b){a=a|0;b=b|0;var d=0;d=c[a>>2]|0;if(!d){c[a>>2]=b;a=a+4|0}else c[b+4>>2]=d;c[a>>2]=b;return}function My(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;Ny(a,b);j=c[b>>2]|0;h=c[j+4>>2]|0;while(1){if(!h)break;k=c[h+8>>2]|0;i=jC(k)|0;d=c[(c[i+16>>2]|0)+112>>2]|0;a=c[d+16+4>>2]|0;d=c[d+8>>2]|0;f=i;i=kC(k,i)|0;while(1){if(!i)break;e=c[(c[i+16>>2]|0)+112>>2]|0;g=c[e+16+4>>2]|0;if((g|0)<(a|0)){f=i;d=c[e+8>>2]|0;a=g}i=kC(k,i)|0}i=c[(c[d+16>>2]|0)+112>>2]|0;k=i+4|0;c[k>>2]=c[k>>2]|8;c[h>>2]=f;k=c[h+4>>2]|0;Ky((c[i+12>>2]|0)+36|0,h);h=k}Gy(b);return j|0}function Ny(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;g=i+8|0;e=c[b+24>>2]|0;if(!((e|0)!=0?(d=tC(a,e,0)|0,(d|0)!=0):0))f=3;a:do if((f|0)==3){e=b+20|0;b:do if(c[e>>2]|0){d=jC(a)|0;while(1){if(!d)break b;if((cP(c[c[(c[d+16>>2]|0)+112>>2]>>2]|0,c[e>>2]|0,0)|0)<<24>>24)break a;d=kC(a,d)|0}}while(0);d=jC(a)|0}while(0);if(0){f=c[15712]|0;c[h>>2]=KB(d)|0;w4(f,96223,h)|0}c[g+4>>2]=0;c[g>>2]=0;Oy(a,d,b,1,g);l=i;return}function Oy(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0;l=d+8|0;k=c[l>>2]|0;c[l>>2]=k+1;l=b+16|0;c[(c[(c[l>>2]|0)+112>>2]|0)+16+4>>2]=k;c[(c[(c[l>>2]|0)+112>>2]|0)+24>>2]=k;k=(e|0)==0;j=aB(a,b)|0;while(1){if(!j)break;e=c[j>>2]&3;g=c[((e|0)==2?j:j+-48|0)+40>>2]|0;if((g|0)==(b|0)){e=c[((e|0)==3?j:j+48|0)+40>>2]|0;g=c[(c[j+16>>2]|0)+120>>2]|0;if(!(c[g>>2]|0))c[g>>2]=-1}else{e=c[(c[j+16>>2]|0)+120>>2]|0;if(!(c[e>>2]|0)){c[e>>2]=1;e=g}else e=g}g=e+16|0;h=c[(c[g>>2]|0)+112>>2]|0;i=c[h+16+4>>2]|0;do if(!i){c[h+8>>2]=b;Py(f,j);Oy(a,e,d,0,f);i=(c[(c[l>>2]|0)+112>>2]|0)+24|0;c[i>>2]=c[(c[(c[((c[i>>2]|0)<(c[(c[(c[g>>2]|0)+112>>2]|0)+24>>2]|0)?b:e)+16>>2]|0)+112>>2]|0)+24>>2];if((c[(c[(c[g>>2]|0)+112>>2]|0)+24>>2]|0)>=(c[(c[(c[l>>2]|0)+112>>2]|0)+16+4>>2]|0)){e=0;do{h=Qy(f)|0;g=(c[c[(c[h+16>>2]|0)+120>>2]>>2]|0)==1;g=c[((c[h>>2]&3|0)==((g?2:3)|0)?h:h+((g?-1:1)*48|0)|0)+40>>2]|0;if(!(c[(c[(c[g+16>>2]|0)+112>>2]|0)+12>>2]|0)){if(!e)e=Ry(a,d)|0;Sy(e,g)}}while((h|0)!=(j|0));if(e|0){if((c[(c[(c[l>>2]|0)+112>>2]|0)+12>>2]|0)==0?(Jy(e)|0)>1:0)Sy(e,b);if(!k?(c[(c[(c[l>>2]|0)+112>>2]|0)+12>>2]|0)==(e|0):0){Ly(d,e);break}Ky(d,e)}}}else{g=c[(c[l>>2]|0)+112>>2]|0;if((c[g+8>>2]|0)!=(e|0)){h=g+24|0;g=c[h>>2]|0;c[h>>2]=(g|0)<(i|0)?g:i}}while(0);j=bB(a,j,b)|0}if(!k?(c[(c[(c[l>>2]|0)+112>>2]|0)+12>>2]|0)==0:0){l=Ry(a,d)|0;Sy(l,b);Ly(d,l)}return}function Py(a,b){a=a|0;b=b|0;c[(c[(c[b+16>>2]|0)+120>>2]|0)+4>>2]=c[a>>2];c[a>>2]=b;b=a+4|0;c[b>>2]=(c[b>>2]|0)+1;return}function Qy(a){a=a|0;var b=0,d=0,e=0;b=c[a>>2]|0;if(!b)qa(138394,96254,65,96266);d=a+4|0;e=c[d>>2]|0;if((e|0)>0){c[a>>2]=c[(c[(c[b+16>>2]|0)+120>>2]|0)+4>>2];c[d>>2]=e+-1;return b|0}else qa(96244,96254,61,96266);return 0}function Ry(a,b){a=a|0;b=b|0;return Hy(Ty(a,b)|0)|0}function Sy(a,b){a=a|0;b=b|0;oC(c[a+8>>2]|0,b,1)|0;c[(c[(c[b+16>>2]|0)+112>>2]|0)+12>>2]=a;return}function Ty(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;d=l;l=l+144|0;f=d;e=d+8|0;g=b+12|0;b=c[g>>2]|0;c[g>>2]=b+1;c[f>>2]=b;m4(e,96234,f)|0;b=ND(a,e,1)|0;TC(b,137483,280,1)|0;l=d;return b|0}function Uy(a){a=a|0;var b=0,d=0;b=c[a+36>>2]|0;while(1){if(!b)break;d=c[b+4>>2]|0;Uy(b);b=d}Iy(a);return}function Vy(a,b,c){a=a|0;b=b|0;c=c|0;Wy(a,b,+h[c+32>>3]);return}function Wy(a,b,d){a=a|0;b=b|0;d=+d;var e=0,f=0,g=0,i=0;e=b+36|0;f=0;while(1){e=c[e>>2]|0;if(!e)break;Wy(a,e,d);e=e+4|0;f=f+1|0}e=mz(a,b,d)|0;c[b+32>>2]=e;a=jz(e)|0;if((f|0)>0)d=+Xy(f,a,e,b,d);else d=3.141592653589793;if((((a|0)==1?(g=c[b>>2]|0,g|0):0)?c[(c[(c[g+16>>2]|0)+112>>2]|0)+8>>2]|0:0)?(i=b+48|0,h[i>>3]=d,d<0.0):0)h[i>>3]=d+6.283185307179586;return}function Xy(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;var g=0.0,i=0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;v=l;l=l+48|0;t=v;u=JK(a*56|0)|0;c[t+40>>2]=c[e+36>>2];p=e+16|0;j=+h[p>>3];q=t+8|0;h[q>>3]=j;h[t>>3]=j;c[t+44>>2]=c[e>>2];j=6.283185307179586/+(b|0);h[t+16>>3]=j;r=t+24|0;h[r>>3]=-1.0;s=t+32|0;h[s>>3]=-1.0;k=0;o=0;g=0.0;while(1){i=c[d>>2]|0;if(!i)break;d=c[i>>2]|0;if(!(c[(c[(c[d+16>>2]|0)+112>>2]|0)+4>>2]&8))d=o;else{n=u+(o*56|0)|0;c[n>>2]=d;h[u+(o*56|0)+8>>3]=j*+(k|0);d=o+1|0;g=+Yy(n,t,f)}k=k+1|0;o=d;d=i+4|0}a:do switch(o|0){case 1:{h[u+40>>3]=1.0;d=0;break}case 2:{j=+h[u+64>>3]-+h[u+8>>3];Zy(u,u+56|0,j>3.141592653589793?6.283185307179586-j:j);d=0;break}default:{n=u+8|0;m=u;d=0;while(1){if((d|0)>=(o|0)){d=0;break a}d=d+1|0;if((d|0)==(o|0)){i=u;j=+h[n>>3]+6.283185307179586-+h[m+8>>3];k=m+56|0}else{k=m+56|0;i=k;j=+h[m+64>>3]-+h[m+8>>3]}Zy(m,i,j);m=k}}}while(0);while(1){if((d|0)>=(o|0))break;_y(u+(d*56|0)|0,t,b,f);d=d+1|0}U1(u);if((a|0)==1){f=g+f*.5;$y(e,-f,0.0,0.0);h[p>>3]=+h[p>>3]+f;u=e+56|0;c[u>>2]=c[u>>2]|1}else h[p>>3]=+h[q>>3];l=v;return +((+h[s>>3]+ +h[r>>3])*.5+-3.141592653589793)}function Yy(a,b,d){a=a|0;b=b|0;d=+d;var e=0,f=0.0,g=0.0,i=0,j=0,k=0.0;i=0;f=0.0;g=0.0;e=b+40|0;while(1){j=c[e>>2]|0;if(!j)break;e=c[j>>2]|0;if(!e)e=0;else e=c[(c[(c[e+16>>2]|0)+112>>2]|0)+8>>2]|0;if((e|0)==(c[a>>2]|0)){k=+h[j+16>>3];e=i+1|0;f=f+d+k*2.0;g=g<k?k:g}else e=i;i=e;e=j+4|0}h[a+32>>3]=f;c[a+48>>2]=i;h[a+16>>3]=g+d+ +h[b>>3];h[a+24>>3]=g;return +g}function Zy(a,b,c){a=a|0;b=b|0;c=+c;var d=0.0,e=0.0;e=+h[b+16>>3];d=+h[a+16>>3];c=(d*+h[b+32>>3]+e*+h[a+32>>3])/(c*2.0*e*d);c=c<1.0?1.0:c;a=a+40|0;if(c>+h[a>>3])h[a>>3]=c;a=b+40|0;if(c>+h[a>>3])h[a>>3]=c;return}function _y(a,b,d,e){a=a|0;b=b|0;d=d|0;e=+e;var f=0.0,g=0.0,i=0,j=0.0,k=0.0,l=0.0,m=0.0,n=0,o=0,p=0.0,q=0.0,r=0,s=0,t=0,u=0.0,v=0.0,w=0,x=0,y=0.0,z=0,A=0,B=0,C=0,D=0.0,G=0.0;z=b+8|0;y=+h[z>>3];A=b+24|0;k=+h[A>>3];B=b+32|0;j=+h[B>>3];f=+h[a+16>>3]*+h[a+40>>3];x=(d|0)==1;if(x){v=+h[a+32>>3];g=v*.15915494309189535;g=f>g?f:g;f=g*6.283185307179586-v;if(f>0.0){e=f/+(c[a+48>>2]|0)+e;l=0.0;f=g}else{l=0.0;f=g}}else l=+h[a+8>>3]-+h[a+32>>3]/(f*2.0);q=+h[a+24>>3]+f;r=q>y;s=a+48|0;t=((c[s>>2]|0)+1|0)/2|0;u=1.0/f;v=e/f*.5;w=a+8|0;n=b+40|0;o=0;p=0.0;g=l;while(1){n=c[n>>2]|0;if(!n)break;i=c[n>>2]|0;if(!i)i=0;else i=c[(c[(c[i+16>>2]|0)+112>>2]|0)+8>>2]|0;if((i|0)==(c[a>>2]|0)?(jz(c[n+32>>2]|0)|0)>=1:0){l=+h[n+16>>3];m=l*u;do if(!x)if((c[s>>2]|0)==1){g=+h[w>>3];break}else{g=g+v+m;break}else{if(g!=0.0)g=(c[s>>2]|0)==2?3.141592653589793:m+g;j=g;k=k<0.0?g:k}while(0);G=+E(+g)*f;D=+F(+g)*f;$y(n,G,D,+az(n,G,D,g));g=(x?(l+e)*u:m+v)+g;o=o+1|0;i=o;l=(o|0)==(t|0)?g:p}else{i=o;l=p}n=n+4|0;o=i;p=l}f=r?q:y;if((d|0)>1?(C=c[a>>2]|0,(C|0)==(c[b+44>>2]|0)):0)h[(c[(c[C+16>>2]|0)+112>>2]|0)+24>>3]=p;h[z>>3]=f;h[A>>3]=k;h[B>>3]=j;return}function $y(a,b,d,e){a=a|0;b=+b;d=+d;e=+e;var f=0,g=0.0,i=0.0,j=0.0,k=0,l=0,m=0,n=0,o=0.0,p=0.0;m=c[a+8>>2]|0;n=e!=0.0;f=jC(m)|0;while(1){if(!f)break;k=c[(c[f+16>>2]|0)+132>>2]|0;i=+h[k>>3];l=k+8|0;g=+h[l>>3];if(n){o=+E(+e);p=+F(+e);j=o*i-p*g;g=p*i+o*g}else j=i;h[k>>3]=j+b;h[l>>3]=g+d;f=kC(m,f)|0}f=a+36|0;while(1){f=c[f>>2]|0;if(!f)break;$y(f,b,d,e);f=f+4|0}return}function az(a,b,d,e){a=a|0;b=+b;d=+d;e=+e;var f=0.0,g=0,i=0,j=0.0,k=0.0,l=0,m=0,n=0,o=0;m=c[a+8>>2]|0;f=+h[a+48>>3];do if(!(f>=0.0)){if((jz(c[a+32>>2]|0)|0)==2){f=e+-1.5707963267948966;break}l=c[a>>2]|0;n=l+16|0;i=c[(c[n>>2]|0)+132>>2]|0;f=+h[i>>3]+b;k=+h[i+8>>3]+d;f=k*k+f*f;i=jC(m)|0;g=l;while(1){if(!i)break;if((i|0)!=(l|0)?(o=c[(c[i+16>>2]|0)+132>>2]|0,j=+h[o>>3]+b,k=+h[o+8>>3]+d,j=k*k+j*j,j<f):0){f=j;g=i}i=kC(m,i)|0}if((l|0)!=(g|0)){j=+h[a+24>>3];k=+h[a+16>>3]-j;g=c[n>>2]|0;i=c[g+132>>2]|0;f=+h[i>>3];if(f>-k?(c[a+56>>2]&1|0)!=0:0){d=+C(+(d*d+b*b));b=+K(+(+h[i+8>>3]),+(f+k));f=+E(+b);f=e+1.5707963267948966-b-+I(+((k-j/f)/d*f));break}else{f=+K(+(+h[i+8>>3]),+f);f=e+3.141592653589793-f-+h[(c[g+112>>2]|0)+24>>3];f=f>6.283185307179586?f+-6.283185307179586:f;break}}else f=0.0}else{f=3.141592653589793-f+e;f=f<0.0?f+6.283185307179586:f}while(0);return +f}function bz(){return JK(12)|0}function cz(a){a=a|0;var b=0,d=0;if(a|0){b=c[a>>2]|0;while(1){if(!b)break;d=c[b+4>>2]|0;U1(b);b=d}U1(a)}return}function dz(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;f=ez(d)|0;d=a+8|0;c[d>>2]=(c[d>>2]|0)+1;d=a+4|0;e=c[d>>2]|0;if((b|0)==0|(e|0)==(b|0)){c[(e|0?e+4|0:a)>>2]=f;c[f+8>>2]=e;e=0}else{e=b+4|0;d=c[e>>2]|0;c[e>>2]=f;c[f+8>>2]=b;e=d;d=d+8|0}c[d>>2]=f;c[f+4>>2]=e;return}function ez(a){a=a|0;var b=0;b=JK(12)|0;c[b>>2]=a;return b|0}function fz(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;b=c[a>>2]|0;d=b;while(1){if(!b)break;g=b+4|0;e=c[g>>2]|0;f=b+8|0;c[g>>2]=c[f>>2];c[f>>2]=e;b=e}f=a+4|0;g=c[f>>2]|0;c[f>>2]=d;c[a>>2]=g;return a|0}function gz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=c[a>>2]|0;if((d|0)!=(b|0)){f=b+8|0;e=c[f>>2]|0;c[a>>2]=b;c[f>>2]=0;b=a+4|0;a=c[b>>2]|0;c[a+4>>2]=d;c[d+8>>2]=a;c[b>>2]=e;c[e+4>>2]=0}return}function hz(a){a=a|0;var b=0,d=0,e=0;d=bz()|0;e=d+4|0;b=0;while(1){a=c[a>>2]|0;if(!a)break;dz(d,b,c[a>>2]|0);b=c[e>>2]|0;a=a+4|0}return d|0}function iz(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;f=a;while(1){j=c[f>>2]|0;if(!j){k=6;break}if((c[j>>2]|0)==(b|0))break;f=j+4|0}if((k|0)==6)qa(96270,96277,217,96288);i=j+8|0;f=c[i>>2]|0;g=j+4|0;b=c[g>>2]|0;c[(f|0?f+4|0:a)>>2]=b;h=a+4|0;c[(b|0?b+8|0:h)>>2]=f;b=0;f=a;while(1){f=c[f>>2]|0;if(!f)break;if((c[f>>2]|0)==(d|0)){k=9;break}b=f;f=f+4|0}if((k|0)==9){do if(!e)if((f|0)==(c[a>>2]|0)){c[a>>2]=j;c[g>>2]=f;c[i>>2]=0;f=f+8|0;break}else{c[b+4>>2]=j;c[i>>2]=b;c[g>>2]=f;f=f+8|0;break}else if((f|0)==(c[h>>2]|0)){c[h>>2]=j;c[g>>2]=0;c[i>>2]=f;f=f+4|0;break}else{c[i>>2]=f;f=f+4|0;c[g>>2]=c[f>>2];c[(c[f>>2]|0)+8>>2]=j;break}while(0);c[f>>2]=j}return}function jz(a){a=a|0;return c[a+8>>2]|0}function kz(a,b){a=a|0;b=b|0;lz(a,fz(b)|0);U1(b);return}function lz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=c[b>>2]|0;if(d|0){e=a+4|0;f=c[e>>2]|0;c[f+4>>2]=d;c[d+8>>2]=f;c[e>>2]=c[b+4>>2];d=a+8|0;c[d>>2]=(c[d>>2]|0)+(c[b+8>>2]|0)}return}function mz(a,b,d){a=a|0;b=b|0;d=+d;var e=0,f=0.0,g=0.0,i=0.0,j=0.0,k=0,l=0,m=0,n=0,o=0.0;m=c[b+8>>2]|0;nz(a,b);l=oz(m)|0;a=qz(pz(l)|0)|0;rz(m,a);m=sz(a,m)|0;a=jz(m)|0;j=+tz(m);k=(a|0)==1;f=+(a|0);i=f*.15915494309189535*(j+d);g=k?0.0:i;a=m;while(1){a=c[a>>2]|0;if(!a)break;if(c[(c[(c[(c[a>>2]|0)+16>>2]|0)+112>>2]|0)+4>>2]&8|0){e=4;break}a=a+4|0}if((e|0)==4)gz(m,a);d=1.0/f;e=0;a=m;while(1){a=c[a>>2]|0;if(!a)break;n=(c[a>>2]|0)+16|0;c[(c[(c[n>>2]|0)+112>>2]|0)+16>>2]=e;h[(c[(c[n>>2]|0)+112>>2]|0)+24>>3]=0.0;f=6.283185307179586*d*+(e|0);o=+E(+f)*g;n=c[(c[n>>2]|0)+132>>2]|0;h[n>>3]=o;h[n+8>>3]=+F(+f)*g;e=e+1|0;a=a+4|0}o=k?j*.5:i;h[b+16>>3]=o;h[b+24>>3]=o;h[b+48>>3]=-1.0;sB(l)|0;return m|0}function nz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=c[b+8>>2]|0;d=jC(f)|0;while(1){if(!d)break;e=XA(a,d)|0;while(1){if(!e)break;if((c[(c[(c[(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0)+112>>2]|0)+12>>2]|0)==(b|0))mB(f,e,1)|0;e=ZA(a,e)|0}d=kC(f,d)|0}return}function oz(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;g=k;f=zz(a,g)|0;g=c[g>>2]|0;i=tB(g)|0;h=Az(g)|0;i=i+-3|0;e=0;while(1){if((e|0)>=(i|0))break;j=Jz(h)|0;d=aB(g,j)|0;while(1){if(!d)break;b=c[d>>2]&3;a=c[((b|0)==2?d:d+-48|0)+40>>2]|0;if((j|0)==(a|0))a=c[((b|0)==3?d:d+48|0)+40>>2]|0;Iz(h,a);d=bB(g,d,j)|0}Bz(g,j,f);d=aB(g,j)|0;while(1){if(!d)break;b=c[d>>2]&3;a=c[((b|0)==2?d:d+-48|0)+40>>2]|0;if((j|0)==(a|0))a=c[((b|0)==3?d:d+48|0)+40>>2]|0;b=(c[a+16>>2]|0)+236|0;c[b>>2]=(c[b>>2]|0)+-1;Hz(h,a);d=bB(g,d,j)|0}wC(g,j)|0;e=e+1|0}sB(g)|0;Gz(h);l=k;return f|0}function pz(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+144|0;b=f;e=f+8|0;d=c[46933]|0;c[46933]=d+1;c[b>>2]=d;m4(e,96303,b)|0;e=ND(a,e,1)|0;TC(e,137483,280,1)|0;b=jC(a)|0;while(1){if(!b)break;oC(e,b,1)|0;d=b+16|0;c[(c[(c[d>>2]|0)+112>>2]|0)+16+12>>2]=0;c[(c[(c[d>>2]|0)+112>>2]|0)+32>>2]=0;d=(c[(c[d>>2]|0)+112>>2]|0)+4|0;c[d>>2]=c[d>>2]&-2;b=kC(a,b)|0}b=jC(a)|0;while(1){if(!b)break;d=c[(c[b+16>>2]|0)+112>>2]|0;if(!(c[d+4>>2]&1)){c[d+16>>2]=0;yz(a,b,e)}b=kC(a,b)|0}l=f;return e|0}function qz(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;if((tB(a)|0)!=1){d=jC(a)|0;while(1){if(!d)break;b=0;e=aB(a,d)|0;while(1){if(!e)break;b=b+1|0;e=bB(a,e,d)|0}if((b|0)==1)xz(d,d,0,0);d=kC(a,d)|0}b=0;f=0;d=jC(a)|0;while(1){if(!d)break;g=c[(c[d+16>>2]|0)+112>>2]|0;g=(c[g+32>>2]|0)+(c[g+16+12>>2]|0)|0;e=(g|0)>(b|0);b=e?g:b;f=e?d:f;d=kC(a,d)|0}b=bz()|0;e=f+16|0;d=(c[(c[e>>2]|0)+112>>2]|0)+16+4|0;while(1){d=c[d>>2]|0;if((d|0)==(f|0))break;dz(b,0,d);d=c[(c[d+16>>2]|0)+112>>2]|0;g=d+4|0;c[g>>2]=c[g>>2]|16;d=d+16|0}dz(b,0,f);g=c[(c[e>>2]|0)+112>>2]|0;a=g+4|0;c[a>>2]=c[a>>2]|16;if(c[g+32>>2]|0){a=bz()|0;d=(c[(c[e>>2]|0)+112>>2]|0)+24|0;while(1){d=c[d>>2]|0;if((d|0)==(f|0))break;dz(a,0,d);d=c[(c[d+16>>2]|0)+112>>2]|0;g=d+4|0;c[g>>2]=c[g>>2]|16;d=d+16|0}kz(b,a)}}else{b=bz()|0;g=jC(a)|0;dz(b,0,g);g=(c[(c[g+16>>2]|0)+112>>2]|0)+4|0;c[g>>2]=c[g>>2]|16}return b|0}function rz(a,b){a=a|0;b=b|0;var d=0;d=jC(a)|0;while(1){if(!d)break;if(!(c[(c[(c[d+16>>2]|0)+112>>2]|0)+4>>2]&16))wz(a,d,b);d=kC(a,d)|0}return}function sz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;g=l;l=l+16|0;f=g;d=uz(a,b)|0;c[f>>2]=d;a:do if(d){e=0;while(1){if((e|0)>=10)break a;a=vz(a,b,f)|0;h=d;d=c[f>>2]|0;if((h|0)==(d|0)|(d|0)==0)break;else e=e+1|0}}while(0);l=g;return a|0}function tz(a){a=a|0;var b=0.0,d=0.0,e=0.0,f=0;b=0.0;while(1){a=c[a>>2]|0;if(!a)break;f=c[(c[c[(c[(c[a>>2]|0)+16>>2]|0)+112>>2]>>2]|0)+16>>2]|0;d=+h[f+32>>3];d=d>b?d:b;e=+h[f+40>>3];b=e>d?e:d;a=a+4|0}return +b}function uz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;k=Kz()|0;e=jC(b)|0;while(1){if(!e)break;d=XA(b,e)|0;while(1){if(!d)break;c[c[(c[d+16>>2]|0)+120>>2]>>2]=0;d=ZA(b,d)|0}e=kC(b,e)|0}j=1;d=0;while(1){h=c[a>>2]|0;if(!h)break;i=c[h>>2]|0;g=aB(b,i)|0;while(1){if(!g)break;f=g+16|0;if((c[c[(c[f>>2]|0)+120>>2]>>2]|0)>0){a=nb[c[k>>2]&63](k,0,128)|0;while(1){if(!a)break;e=c[a+8>>2]|0;if((c[c[(c[e+16>>2]|0)+120>>2]>>2]|0)>(c[c[(c[f>>2]|0)+120>>2]>>2]|0)?(l=c[e>>2]&3,(c[((l|0)==2?e:e+-48|0)+40>>2]|0)!=(i|0)):0)d=d+((c[((l|0)==3?e:e+48|0)+40>>2]|0)!=(i|0)&1)|0;a=nb[c[k>>2]&63](k,a,8)|0}Qz(k,g)}g=bB(b,g,i)|0}e=aB(b,i)|0;while(1){if(!e)break;a=c[(c[e+16>>2]|0)+120>>2]|0;if(!(c[a>>2]|0)){c[a>>2]=j;Pz(k,e)}e=bB(b,e,i)|0}a=h+4|0;j=j+1|0}Oz(k);return d|0}function vz(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;e=c[d>>2]|0;k=jC(b)|0;a:while(1){if(!k)break;j=aB(b,k)|0;while(1){if(!j)break;g=c[j>>2]&3;f=c[((g|0)==3?j:j+48|0)+40>>2]|0;if((f|0)==(k|0))f=c[((g|0)==2?j:j+-48|0)+40>>2]|0;i=0;while(1){if((i|0)>=2)break;h=hz(a)|0;iz(a,k,f,i);g=uz(a,b)|0;if((g|0)<(e|0)){cz(h);if(!g){e=0;break a}else e=g}else{cz(a);a=h}i=i+1|0}j=bB(b,j,k)|0}k=kC(b,k)|0}c[d>>2]=e;return a|0}function wz(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;h=bz()|0;e=XA(a,b)|0;while(1){if(!e)break;f=e+-48|0;dz(h,0,c[((c[e>>2]&3|0)==2?e:f)+40>>2]|0);f=(c[(c[(c[((c[e>>2]&3|0)==2?e:f)+40>>2]|0)+16>>2]|0)+112>>2]|0)+4|0;c[f>>2]=c[f>>2]|32;e=ZA(a,e)|0}e=_A(a,b)|0;while(1){if(!e)break;f=e+48|0;dz(h,0,c[((c[e>>2]&3|0)==3?e:f)+40>>2]|0);f=(c[(c[(c[((c[e>>2]&3|0)==3?e:f)+40>>2]|0)+16>>2]|0)+112>>2]|0)+4|0;c[f>>2]=c[f>>2]|32;e=$A(a,e)|0}a:do if((jz(h)|0)>1){f=d+4|0;a=d;while(1){e=c[a>>2]|0;if(!e){g=13;break a}a=e+4|0;if(!(c[(c[(c[(c[e>>2]|0)+16>>2]|0)+112>>2]|0)+4>>2]&32))continue;if(c[(c[(c[(c[c[((e|0)==(c[f>>2]|0)?d:a)>>2]>>2]|0)+16>>2]|0)+112>>2]|0)+4>>2]&32|0)break}dz(d,e,b)}else g=13;while(0);b:do if((g|0)==13){c:do if((jz(h)|0)>0){e=d;while(1){e=c[e>>2]|0;if(!e)break c;if(c[(c[(c[(c[e>>2]|0)+16>>2]|0)+112>>2]|0)+4>>2]&32|0)break;e=e+4|0}dz(d,e,b);break b}while(0);dz(d,0,b)}while(0);e=h;while(1){e=c[e>>2]|0;if(!e)break;d=(c[(c[(c[e>>2]|0)+16>>2]|0)+112>>2]|0)+4|0;c[d>>2]=c[d>>2]&-33;e=e+4|0}cz(h);return}function xz(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;while(1){g=c[(c[(c[b+16>>2]|0)+112>>2]|0)+16>>2]|0;if(!g)break;h=d+1|0;i=g+16|0;j=c[(c[i>>2]|0)+112>>2]|0;b=j+16|0;f=c[b+12>>2]|0;if(f){if((f|0)>(d|0)){k=11;break}b=c[b+4>>2]|0;f=b;if((b|0)==(e|0))b=j;else{d=j+24|0;if(!((c[j+32>>2]|0)!=0?(c[d>>2]|0)==(e|0):0))e=b;c[d>>2]=f;b=c[(c[i>>2]|0)+112>>2]|0;c[b+32>>2]=c[b+16+12>>2];b=c[(c[i>>2]|0)+112>>2]|0}b=b+16|0}c[b+4>>2]=a;c[(c[(c[i>>2]|0)+112>>2]|0)+16+12>>2]=h;b=g;d=h}if((k|0)==11?(c[j+32>>2]|0)<=(d|0):0){c[j+24>>2]=a;c[(c[(c[i>>2]|0)+112>>2]|0)+32>>2]=h}return}function yz(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;g=(c[(c[b+16>>2]|0)+112>>2]|0)+4|0;c[g>>2]=c[g>>2]|1;g=aB(a,b)|0;while(1){if(!g)break;f=c[g>>2]&3;e=c[((f|0)==2?g:g+-48|0)+40>>2]|0;if((e|0)==(b|0))e=c[((f|0)==3?g:g+48|0)+40>>2]|0;f=e+16|0;if(!(c[(c[(c[f>>2]|0)+112>>2]|0)+4>>2]&1)){mB(d,g,1)|0;c[(c[(c[f>>2]|0)+112>>2]|0)+16>>2]=b;yz(a,e,d)}g=bB(a,g,b)|0}return}function zz(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;k=l;l=l+144|0;j=k+8|0;i=k;e=k+16|0;h=c[46934]|0;c[46934]=h+1;c[i>>2]=h;m4(e,96312,i)|0;i=ND(b,e,1)|0;TC(i,137483,280,1)|0;h=c[46934]|0;c[46934]=h+1;c[j>>2]=h;m4(e,96312,j)|0;h=b+24|0;a[j>>0]=a[h>>0]|0;a[j+1>>0]=a[h+1>>0]|0;a[j+2>>0]=a[h+2>>0]|0;a[j+3>>0]=a[h+3>>0]|0;j=oB(e,j,0)|0;e=jC(b)|0;while(1){if(!e)break;oC(i,e,1)|0;h=tC(j,KB(e)|0,1)|0;TC(h,137447,304,1)|0;c[(c[(c[e+16>>2]|0)+112>>2]|0)+16>>2]=h;e=kC(b,e)|0}f=jC(b)|0;while(1){if(!f)break;g=c[(c[(c[f+16>>2]|0)+112>>2]|0)+16>>2]|0;h=g+16|0;e=XA(b,f)|0;while(1){if(!e)break;mB(i,e,1)|0;m=c[(c[(c[(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0)+112>>2]|0)+16>>2]|0;n=iB(j,g,m,0,1)|0;TC(n,137460,176,1)|0;c[(c[n+16>>2]|0)+116>>2]=e;n=(c[h>>2]|0)+236|0;c[n>>2]=(c[n>>2]|0)+1;m=(c[m+16>>2]|0)+236|0;c[m>>2]=(c[m>>2]|0)+1;e=ZA(b,e)|0}f=kC(b,f)|0}c[d>>2]=j;l=k;return i|0}function Az(a){a=a|0;var b=0,c=0;c=Cz()|0;b=jC(a)|0;while(1){if(!b)break;Hz(c,b);b=kC(a,b)|0}return c|0}function Bz(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0;m=c[(c[b+16>>2]|0)+236>>2]|0;p=m<<2;o=KK(p)|0;p=KK(p)|0;i=0;l=aB(a,b)|0;n=0;f=0;while(1){if(!l)break;e=c[l>>2]&3;g=c[((e|0)==2?l:l+-48|0)+40>>2]|0;if((g|0)==(b|0))g=c[((e|0)==3?l:l+48|0)+40>>2]|0;h=0;k=aB(a,b)|0;j=i;while(1){if(!k)break;if((k|0)!=(l|0)){i=c[k>>2]&3;e=c[((i|0)==2?k:k+-48|0)+40>>2]|0;if((e|0)==(b|0))e=c[((i|0)==3?k:k+48|0)+40>>2]|0;i=iB(a,g,e,0,0)|0;if(i)if(g>>>0<e>>>0){e=j+1|0;h=i+16|0;i=c[(c[h>>2]|0)+116>>2]|0;if(!i)h=1;else{wC(d,i)|0;c[(c[h>>2]|0)+116>>2]=0;h=1}}else{h=1;e=j}else e=j}else e=j;k=bB(a,k,b)|0;j=e}if(!h){c[p+(n<<2)>>2]=g;e=n+1|0}else{c[o+(f<<2)>>2]=g;e=n;f=f+1|0}i=j;l=bB(a,l,b)|0;n=e}e=m+-1-i|0;a:do if((e|0)>0){if((e|0)<(n|0))g=0;else{if((e|0)!=(n|0))break;f=c[o>>2]|0;g=f+16|0;e=0;while(1){if((e|0)>=(n|0))break a;m=c[p+(e<<2)>>2]|0;TC(iB(a,f,m,0,1)|0,137460,176,1)|0;d=(c[g>>2]|0)+236|0;c[d>>2]=(c[d>>2]|0)+1;m=(c[m+16>>2]|0)+236|0;c[m>>2]=(c[m>>2]|0)+1;e=e+1|0}}while(1){if((g|0)>=(n|0)){f=2;break}f=g|1;if((f|0)>=(n|0)){f=2;break}d=c[p+(g<<2)>>2]|0;m=c[p+(f<<2)>>2]|0;TC(iB(a,d,m,0,1)|0,137460,176,1)|0;d=(c[d+16>>2]|0)+236|0;c[d>>2]=(c[d>>2]|0)+1;m=(c[m+16>>2]|0)+236|0;c[m>>2]=(c[m>>2]|0)+1;g=g+2|0;e=e+-1|0}while(1){if((e|0)<=0)break a;m=c[p>>2]|0;n=c[p+(f<<2)>>2]|0;TC(iB(a,m,n,0,1)|0,137460,176,1)|0;m=(c[m+16>>2]|0)+236|0;c[m>>2]=(c[m>>2]|0)+1;n=(c[n+16>>2]|0)+236|0;c[n>>2]=(c[n>>2]|0)+1;f=f+1|0;e=e+-1|0}}while(0);U1(p);U1(o);return}function Cz(){return Vz(18240,c[4581]|0)|0}function Dz(a,b,d){a=a|0;b=b|0;d=d|0;d=KK(16)|0;c[d+12>>2]=0;c[d+8>>2]=c[b+8>>2];return d|0}function Ez(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function Fz(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;b=c[b>>2]|0;e=c[d>>2]|0;return ((b|0)<(e|0)?-1:(b|0)>(e|0)&1)|0}function Gz(a){a=a|0;Rz(a)|0;return}function Hz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=l;l=l+16|0;f=d;e=b+16|0;c[f+8>>2]=c[(c[e>>2]|0)+236>>2];a=(nb[c[a>>2]&63](a,f,1)|0)+12|0;c[(c[e>>2]|0)+164>>2]=c[a>>2];c[a>>2]=b;l=d;return}function Iz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;g=h;f=b+16|0;c[g+8>>2]=c[(c[f>>2]|0)+236>>2];g=nb[c[a>>2]&63](a,g,4)|0;if(!g)qa(96322,96325,107,96335);e=g+12|0;d=c[e>>2]|0;if((d|0)==(b|0)){b=c[(c[f>>2]|0)+164>>2]|0;c[e>>2]=b;if(!b)nb[c[a>>2]&63](a,g,2)|0}else{do{a=(c[d+16>>2]|0)+164|0;d=c[a>>2]|0;e=(d|0)!=0}while(e&(d|0)!=(b|0));if(e)c[a>>2]=c[(c[d+16>>2]|0)+164>>2]}l=h;return}function Jz(a){a=a|0;var b=0,d=0,e=0,f=0;d=nb[c[a>>2]&63](a,0,128)|0;if(d){f=d+12|0;b=c[f>>2]|0;e=c[(c[b+16>>2]|0)+164>>2]|0;c[f>>2]=e;if(!e)nb[c[a>>2]&63](a,d,2)|0}else b=0;return b|0}function Kz(){return Vz(18276,c[4581]|0)|0}function Lz(a,b,d){a=a|0;b=b|0;d=d|0;d=KK(12)|0;c[d+8>>2]=c[b+8>>2];return d|0}function Mz(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function Nz(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;b=c[b>>2]|0;e=c[d>>2]|0;return (b>>>0>e>>>0?1:(b>>>0<e>>>0)<<31>>31)|0}function Oz(a){a=a|0;Rz(a)|0;return}function Pz(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e+8>>2]=b;nb[c[a>>2]&63](a,e,1)|0;l=d;return}function Qz(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e+8>>2]=b;nb[c[a>>2]&63](a,e,2)|0;l=d;return}function Rz(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;do if((a|0)!=0?(c[a+24>>2]|0)<=0:0){h=c[a+4>>2]|0;g=h+32|0;b=c[g>>2]|0;if(b){b=ob[b&127](a,2,0,h)|0;if((b|0)<0){b=-1;break}}else b=0;if(c[a+28>>2]|0)_z(a,0)|0;f=(b|0)==0;if(f){nb[c[c[a+16>>2]>>2]&63](a,0,64)|0;if((Xz(a)|0)>0){b=-1;break}d=a+8|0;b=c[d>>2]|0;if((c[b+12>>2]|0)>0){e=a+12|0;ob[c[e>>2]&127](a,c[b+8>>2]|0,0,h)|0;b=c[d>>2]|0}else e=a+12|0;ob[c[e>>2]&127](a,b,0,h)|0}b=c[a+20>>2]|0;if(b){if(f&(b|0)==1)ob[c[a+12>>2]&127](a,a,0,h)|0}else U1(a);b=c[g>>2]|0;if(!b)b=0;else{ob[b&127](a,6,0,h)|0;b=0}}else b=-1;while(0);return b|0}function Sz(a){a=a|0;var b=0,d=0,e=0;e=a+8|0;b=c[e>>2]|0;d=c[b>>2]|0;a:do if(!(d&12)){if(!(d&3)){d=b+8|0;a=c[d>>2]|0;c[d>>2]=0;break}a=Tz(a)|0;d=c[e>>2]|0;b=c[d+8>>2]|0;d=b+(c[d+12>>2]<<2)|0;while(1){if(b>>>0>=d>>>0)break a;c[b>>2]=0;b=b+4|0}}else a=c[b+4>>2]|0;while(0);e=c[e>>2]|0;c[e>>2]=c[e>>2]&-4097;c[e+16>>2]=0;c[e+4>>2]=0;return a|0}function Tz(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;g=a+8|0;a=c[g>>2]|0;b=c[a>>2]|0;if(!(b&4096)){a:do if(!(b&3)){if(b&112|0){a=c[a+8>>2]|0;break}a=c[a+4>>2]|0;if(!a)a=0;else{while(1){b=a+4|0;d=c[b>>2]|0;if(!d){e=a;break}c[b>>2]=c[d>>2];c[d>>2]=a;a=d}while(1){b=c[e>>2]|0;if(!b)break a;d=c[b+4>>2]|0;if(!d){e=b;continue}while(1){c[b+4>>2]=c[d>>2];c[d>>2]=b;b=c[d+4>>2]|0;if(!b)break;else{f=d;d=b;b=f}}c[e>>2]=d;e=d}}}else{e=c[a+8>>2]|0;f=e+(c[a+12>>2]<<2)|0;b=0;a=0;while(1){if(e>>>0>=f>>>0)break a;d=c[e>>2]|0;if(d){if(!b){b=d;a=d}else c[b>>2]=d;while(1){d=c[b>>2]|0;if(!d)break;else b=d}c[e>>2]=b}e=e+4|0}}while(0);g=c[g>>2]|0;c[g+4>>2]=a;c[g>>2]=c[g>>2]|4096}else a=c[a+4>>2]|0;return a|0}function Uz(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;s=a+8|0;if(c[c[s>>2]>>2]&4096|0)Wz(a,0)|0;r=c[a+4>>2]|0;m=c[r>>2]|0;n=c[r+4>>2]|0;q=r+8|0;u=c[q>>2]|0;o=c[r+20>>2]|0;p=a+20|0;f=c[p>>2]&-32769;c[p>>2]=f;a:do if(!b){if(d&384|0){f=c[s>>2]|0;e=c[f+8>>2]|0;if(!e){e=0;break}if(d&256){e=c[e+4>>2]|0;c[f+4>>2]=e;if(!e){e=0;break}}else c[f+4>>2]=e;if((u|0)<0){e=c[e+8>>2]|0;break}else{e=e+(0-u)|0;break}}if(d&4098|0){e=c[s>>2]|0;if(c[e>>2]&144|0){e=0;break}e=c[e+8>>2]|0;if(!e){e=0;break}else{h=e;t=84;break}}if(!(d&64))e=0;else{i=r+16|0;b:do if((c[i>>2]|0)!=0|(u|0)<0){j=a+12|0;k=(u|0)<0;f=0-u|0;e=c[(c[s>>2]|0)+8>>2]|0;while(1){if(!e)break b;g=c[e>>2]|0;h=c[i>>2]|0;if(h|0){if(k)b=c[e+8>>2]|0;else b=e+f|0;vb[h&127](a,b,r)}if((c[q>>2]|0)>=0){e=g;continue}ob[c[j>>2]&127](a,e,0,r)|0;e=g}}while(0);e=c[s>>2]|0;c[e+4>>2]=0;c[e+8>>2]=0;c[(c[s>>2]|0)+16>>2]=0;e=0}}else{if(d&2049|0){g=r+12|0;e=c[g>>2]|0;h=(d&1|0)==0;if(!(h|(e|0)==0)){e=nb[e&63](a,b,r)|0;if(!e){e=0;break}else f=e}else f=b;do if((u|0)>-1)h=f+u|0;else{e=ob[c[a+12>>2]&127](a,0,12,r)|0;if(e|0){c[e+8>>2]=f;h=e;break}if(!(c[g>>2]|0)){e=0;break a}e=c[r+16>>2]|0;if(h|(e|0)==0){e=0;break a}vb[e&127](a,f,r);e=0;break a}while(0);g=c[s>>2]|0;e=c[g>>2]|0;do if(!(e&128)){if(!(e&16))if(!(e&32)){t=51;break}else{t=48;break}f=c[g+4>>2]|0;e=(f|0)!=0;if(!(d&8192)){if(!e){t=48;break}if((f|0)==(c[g+8>>2]|0)){t=48;break}r=f+4|0;a=c[r>>2]|0;c[h+4>>2]=a;c[a>>2]=h;c[h>>2]=f;c[r>>2]=h;break}else{if(!e){t=51;break}e=c[f>>2]|0;if(!e){t=51;break}c[h>>2]=e;c[e+4>>2]=h;c[h+4>>2]=f;c[f>>2]=h;break}}else if(!(d&8192))t=48;else t=51;while(0);if((t|0)==48){e=c[g+8>>2]|0;c[h>>2]=e;if(!e)e=h;else c[h+4>>2]=c[e+4>>2];c[e+4>>2]=h;c[(c[s>>2]|0)+8>>2]=h}else if((t|0)==51){f=g+8|0;e=c[f>>2]|0;if(!e){c[f>>2]=h;e=h}else{r=e+4|0;c[c[r>>2]>>2]=h;c[h+4>>2]=c[r>>2]}c[e+4>>2]=h;c[h>>2]=0}e=c[s>>2]|0;f=e+16|0;g=c[f>>2]|0;if((g|0)>-1)c[f>>2]=g+1;c[e+4>>2]=h;if((u|0)<0){e=c[h+8>>2]|0;break}else{e=h+(0-u)|0;break}}h=c[s>>2]|0;do if(!(d&512)){e=c[h+4>>2]|0;if(e|0){if((u|0)<0)g=c[e+8>>2]|0;else g=e+(0-u)|0;if((g|0)==(b|0))break}e=b+m|0;if((n|0)<0){b=c[e>>2]|0;t=68}else{b=e;t=68}}else t=68;while(0);if((t|0)==68){g=(u|0)<0;i=(n|0)<0;j=(o|0)==0;k=(n|0)<1;l=0-u|0;e=h+8|0;do{e=c[e>>2]|0;if(!e){e=0;break a}if(g)f=c[e+8>>2]|0;else f=e+l|0;f=f+m|0;if(i)f=c[f>>2]|0;do if(j)if(k){f=s2(b,f)|0;break}else{f=$3(b,f,n)|0;break}else f=ob[o&127](a,b,f,r)|0;while(0)}while((f|0)!=0);f=c[p>>2]|0}c[p>>2]=f|32768;if(!(d&4098)){do if(!(d&8)){f=c[s>>2]|0;if(!(d&16)){c[f+4>>2]=e;break}if((e|0)==(c[f+8>>2]|0)){c[f+4>>2]=0;e=0;break a}else{e=e+4|0;t=108;break}}else{f=c[s>>2]|0;t=108}while(0);if((t|0)==108){e=c[e>>2]|0;c[f+4>>2]=e;if(!e){e=0;break}}if((u|0)<0){e=c[e+8>>2]|0;break}else{e=e+(0-u)|0;break}}else{h=e;t=84}}while(0);if((t|0)==84){e=c[h>>2]|0;if(e){c[e+4>>2]=c[h+4>>2];e=c[h>>2]|0}f=(c[s>>2]|0)+8|0;g=c[f>>2]|0;if((h|0)==(g|0)){c[f>>2]=e;e=c[(c[s>>2]|0)+8>>2]|0;if(e|0)c[e+4>>2]=c[h+4>>2]}else{f=h+4|0;c[c[f>>2]>>2]=e;e=g+4|0;if((h|0)==(c[e>>2]|0))c[e>>2]=c[f>>2]}e=c[s>>2]|0;f=e+4|0;if((h|0)==(c[f>>2]|0))g=c[h>>2]|0;else g=0;c[f>>2]=g;t=e+16|0;c[t>>2]=(c[t>>2]|0)+-1;if((u|0)<0)e=c[h+8>>2]|0;else e=h+(0-u)|0;f=c[r+16>>2]|0;if(!((d&2|0)==0|(f|0)==0))vb[f&127](a,e,r);if((c[q>>2]|0)<0)ob[c[a+12>>2]&127](a,h,0,r)|0}return e|0}function Vz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+16|0;g=j;a:do if((a|0)!=0&(b|0)!=0?(f=T1(40)|0,(f|0)!=0):0){c[f>>2]=0;c[f+16>>2]=0;c[f+4>>2]=0;bA(f,a,0)|0;d=f+20|0;i=a+32|0;c[d>>2]=0;c[d+4>>2]=0;c[d+8>>2]=0;c[d+12>>2]=0;c[d+16>>2]=0;d=c[i>>2]|0;do if(d){c[g>>2]=0;d=ob[d&127](f,1,g,a)|0;if((d|0)>=0)if(d){e=c[g>>2]|0;if(e|0)if(!(c[b+4>>2]&c[e>>2])){d=f;h=12;break}else{d=f;break}d=a+28|0;if(!(c[d>>2]|0)){d=f;h=12}else{U1(f);e=ob[c[d>>2]&127](0,0,40,a)|0;if(!e){d=0;break a}c[e>>2]=0;c[e+16>>2]=0;c[e+4>>2]=0;bA(e,a,0)|0;c[e+20>>2]=1;c[e+24>>2]=0;c[e+32>>2]=0;c[e+28>>2]=0;d=e;f=e;h=11}}else{d=f;h=11}else{d=f;h=12}}else{d=f;h=11}while(0);if((h|0)==11){e=ob[c[d+12>>2]&127](d,0,28,a)|0;c[g>>2]=e;if(!e){d=f;h=12}else{c[e>>2]=c[b+4>>2];g=e+4|0;c[g>>2]=0;c[g+4>>2]=0;c[g+8>>2]=0;c[g+12>>2]=0;c[g+16>>2]=0;c[g+20>>2]=0}}if((h|0)==12){U1(d);d=0;break}c[d+8>>2]=e;c[d>>2]=c[b>>2];c[d+16>>2]=b;e=c[i>>2]|0;if(e)ob[e&127](d,5,f,a)|0}else d=0;while(0);l=j;return d|0}function Wz(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;f=c[c[a+16>>2]>>2]|0;j=a+8|0;g=c[j>>2]|0;h=c[g>>2]|0;d=h&4096;if(!b)if(!d)b=-1;else{e=d;b=c[g+4>>2]|0;i=5}else if(!(c[g+16>>2]|0)){e=0;i=5}else b=-1;a:do if((i|0)==5){c[g>>2]=h&-4097;if(!(h&3)){d=g+4|0;if(!(h&12)){c[d>>2]=0;d=g+8|0}c[d>>2]=b;if(e|0){b=0;break}c[(c[j>>2]|0)+16>>2]=-1;b=0;break}c[g+4>>2]=0;if(!e){c[g+16>>2]=0;while(1){if(!b){b=0;break a}j=c[b>>2]|0;nb[f&63](a,b,32)|0;b=j}}e=c[g+8>>2]|0;a=e+(c[g+12>>2]<<2)|0;while(1){if(e>>>0>=a>>>0){b=0;break a}d=c[e>>2]|0;if(d){c[e>>2]=b;b=c[d>>2]|0;c[d>>2]=0}e=e+4|0}}while(0);return b|0}function Xz(a){a=a|0;var b=0,d=0,e=0;b=a+8|0;d=c[b>>2]|0;if(c[d>>2]&4096){Wz(a,0)|0;d=c[b>>2]|0}e=d+16|0;a=c[e>>2]|0;do if((a|0)<0){b=c[d>>2]|0;if(b&12|0){a=Yz(c[d+4>>2]|0)|0;c[e>>2]=a;break}if(b&112){a=0;b=d+8|0;while(1){b=c[b>>2]|0;if(!b)break;a=a+1|0}c[e>>2]=a}}while(0);return a|0}function Yz(a){a=a|0;var b=0;if(!a)return 0;else{b=Yz(c[a+4>>2]|0)|0;return b+1+(Yz(c[a>>2]|0)|0)|0}return 0}function Zz(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0;G=l;l=l+128|0;z=G+120|0;r=G;F=a+8|0;e=c[F>>2]|0;if(c[e>>2]&4096){Wz(a,0)|0;e=c[F>>2]|0}E=c[a+4>>2]|0;A=c[E>>2]|0;B=c[E+4>>2]|0;x=E+8|0;D=c[x>>2]|0;C=c[E+20>>2]|0;t=a+20|0;c[t>>2]=c[t>>2]&-32769;h=c[e+4>>2]|0;a:do if(!b)if((d&448|0)==0|(h|0)==0)e=0;else{if(!(d&64)){b:do if(!(d&256)){e=h;while(1){f=e+4|0;g=c[f>>2]|0;if(!g)break b;c[f>>2]=c[g>>2];c[g>>2]=e;e=g}}else{e=h;while(1){f=c[e>>2]|0;if(!f)break b;a=f+4|0;c[e>>2]=c[a>>2];c[a>>2]=e;e=f}}while(0);c[(c[F>>2]|0)+4>>2]=e;if((D|0)<0){e=c[e+8>>2]|0;break}else{e=e+(0-D)|0;break}}b=E+16|0;if((c[b>>2]|0)!=0|(D|0)<0){j=a+12|0;k=(D|0)<0;i=0-D|0;e=h;do{h=e;while(1){e=h+4|0;f=c[e>>2]|0;if(!f)break;c[e>>2]=c[f>>2];c[f>>2]=h;h=f}e=c[h>>2]|0;f=c[b>>2]|0;if(f|0){if(k)g=c[h+8>>2]|0;else g=h+i|0;vb[f&127](a,g,E)}if((c[x>>2]|0)<0)ob[c[j>>2]&127](a,h,0,E)|0}while((e|0)!=0);e=c[F>>2]|0}c[e+16>>2]=0;c[e+4>>2]=0;e=0}else{y=a+16|0;u=(d&4098|0)==0;c:do if(u|(c[(c[y>>2]|0)+4>>2]|0)!=8)v=45;else{k=(B|0)<0;e=b+A|0;if(k)e=c[e>>2]|0;i=(C|0)==0;j=(B|0)<1;g=nb[c[a>>2]&63](a,b,4)|0;while(1){if(!g){v=45;break c}f=g+A|0;if(k)f=c[f>>2]|0;do if(i)if(j){f=s2(e,f)|0;break}else{f=$3(e,f,B)|0;break}else f=ob[C&127](a,e,f,E)|0;while(0);if(f|0){v=45;break c}if((g|0)==(b|0))break;g=nb[c[a>>2]&63](a,g,8)|0}h=c[(c[F>>2]|0)+4>>2]|0;c[z>>2]=c[h+4>>2];c[z+4>>2]=c[h>>2];e=z;v=180}while(0);d:do if((v|0)==45){do if(!(d&2565)){if(d&32|0){if((D|0)<0)g=c[b+8>>2]|0;else g=b+(0-D)|0;e=g+A|0;if((B|0)<0)f=c[e>>2]|0;else f=e;if(!h){e=b;f=z;h=z;v=198;break}else{e=b;s=f;j=b;v=64;break}}if(h){if((D|0)<0)e=c[h+8>>2]|0;else e=h+(0-D)|0;if((e|0)!=(b|0)){f=b+A|0;if((B|0)<0){g=b;e=0;s=c[f>>2]|0;j=0;v=64}else{g=b;e=0;s=f;j=0;v=64}}else{e=0;g=b;f=z;k=z;j=0;v=141}}else{e=0;g=b;f=z;h=z;v=198}}else{if(!(d&512)){e=b+A|0;if((B|0)<0)f=c[e>>2]|0;else f=e}else f=b;if(!h){e=0;g=b;f=z;h=z;v=198}else{g=b;e=0;s=f;j=0;v=64}}while(0);e:do if((v|0)==64){f:do if((c[(c[y>>2]|0)+4>>2]|0)==4?(q=c[(c[F>>2]|0)+24>>2]|0,!((d&516|0)==0|(q|0)==0)):0){b=(D|0)<0;m=(B|0)<0;n=(C|0)==0;o=(B|0)<1;p=0-D|0;k=0;f=h;while(1){if((k|0)>=(q|0)){f=z;k=z;m=0;break}if(b)i=c[f+8>>2]|0;else i=f+p|0;i=i+A|0;if(m)i=c[i>>2]|0;do if(n)if(o){i=s2(s,i)|0;break}else{i=$3(s,i,B)|0;break}else i=ob[C&127](a,s,i,E)|0;while(0);if(!i){v=80;break}c[r+(k<<2)>>2]=i;f=c[((i|0)<0?f+4|0:f)>>2]|0;if(!f){e=0;break a}else k=k+1|0}if((v|0)==80)if(b){e=c[f+8>>2]|0;break a}else{e=f+p|0;break a}while(1){if((m|0)>=(q|0))break f;do if((c[r+(m<<2)>>2]|0)<0){i=h+4|0;b=c[i>>2]|0;if((c[r+((m|1)<<2)>>2]|0)<0){c[i>>2]=c[b>>2];c[b>>2]=h;c[f+4>>2]=b;f=b;h=k;i=b+4|0;break}else{c[k>>2]=b;c[f+4>>2]=h;f=h;h=b;i=b;break}}else{i=c[h>>2]|0;if((c[r+((m|1)<<2)>>2]|0)>0){p=i+4|0;c[h>>2]=c[p>>2];c[p>>2]=h;c[k>>2]=i;h=i;break}else{c[f+4>>2]=i;c[k>>2]=h;f=i;i=i+4|0;break}}while(0);k=h;m=m+2|0;h=c[i>>2]|0}}else{f=z;k=z}while(0);n=(D|0)<0;o=(B|0)<0;p=(C|0)!=0;q=(B|0)<1;r=0-D|0;i=h;g:while(1){h=i;while(1){if(n)i=c[h+8>>2]|0;else i=h+r|0;i=i+A|0;if(o)i=c[i>>2]|0;do if(!p)if(q){i=s2(s,i)|0;break}else{i=$3(s,i,B)|0;break}else i=ob[C&127](a,s,i,E)|0;while(0);if(!i){v=140;break g}if((i|0)>=0)break;b=h+4|0;m=c[b>>2]|0;if(!m){v=121;break g}if(n)i=c[m+8>>2]|0;else i=m+r|0;i=i+A|0;if(o)i=c[i>>2]|0;do if(!p)if(q){i=s2(s,i)|0;break}else{i=$3(s,i,B)|0;break}else i=ob[C&127](a,s,i,E)|0;while(0);if((i|0)>=0){v=118;break}c[b>>2]=c[m>>2];c[m>>2]=h;c[f+4>>2]=m;h=c[m+4>>2]|0;if(!h){f=m;h=k;v=198;break e}else f=m}if((v|0)==118){v=0;if(!i){v=119;break}c[k>>2]=m;c[f+4>>2]=h;i=c[m>>2]|0;if(!i){f=h;h=m;v=198;break e}else{f=h;k=m;continue}}b=c[h>>2]|0;if(!b){v=139;break}if(n)i=c[b+8>>2]|0;else i=b+r|0;i=i+A|0;if(o)i=c[i>>2]|0;do if(!p)if(q){i=s2(s,i)|0;break}else{i=$3(s,i,B)|0;break}else i=ob[C&127](a,s,i,E)|0;while(0);if((i|0)>0){i=b+4|0;c[h>>2]=c[i>>2];c[i>>2]=h;c[k>>2]=b;i=c[b>>2]|0;if(!i){h=b;v=198;break e}else{k=b;continue}}if(!i){v=137;break}c[f+4>>2]=b;c[k>>2]=h;i=c[b+4>>2]|0;if(!i){f=b;v=198;break e}else{f=b;k=h}}if((v|0)==119){c[f+4>>2]=h;f=h;h=m;v=141;break}else if((v|0)==121){c[f+4>>2]=h;f=h;h=k;v=198;break}else if((v|0)==137){c[k>>2]=h;k=h;h=b;v=141;break}else if((v|0)==139){c[k>>2]=h;v=198;break}else if((v|0)==140)if(!h){h=k;v=198;break}else{v=141;break}}while(0);do if((v|0)==141){c[t>>2]=c[t>>2]|32768;i=h+4|0;c[k>>2]=c[i>>2];c[f+4>>2]=c[h>>2];if(!(d&516)){if(d&8|0){c[i>>2]=c[z>>2];c[h>>2]=0;c[z>>2]=h;v=169;break}if(d&16|0){v=z+4|0;c[h>>2]=c[v>>2];c[i>>2]=0;c[v>>2]=h;v=175;break}if(!u){e=f;v=180;break d}if(d&2049|0){if(c[(c[y>>2]|0)+4>>2]&4|0){e=h;break}c[i>>2]=0;e=z+4|0;c[h>>2]=c[e>>2];c[e>>2]=h;e=h;v=205;break}if(!(d&32)){e=0;break a}if(!(c[(c[y>>2]|0)+4>>2]&4)){c[e+4>>2]=0;x=z+4|0;c[e>>2]=c[x>>2];c[x>>2]=j;e=(c[F>>2]|0)+16|0;c[e>>2]=(c[e>>2]|0)+1;e=h;break}e=c[E+16>>2]|0;if(e|0)vb[e&127](a,g,E);if((c[x>>2]|0)<0){ob[c[a+12>>2]&127](a,j,0,E)|0;e=h}else e=h}else e=h}else if((v|0)==198){c[f+4>>2]=0;c[h>>2]=0;if(!(d&8))if(!(d&16)){if(d&516|0){e=f;break d}if(!(d&2049)){if(!(d&32)){g=0;e=f;break d}x=(c[F>>2]|0)+16|0;c[x>>2]=(c[x>>2]|0)+1}else{e=0;v=205}}else v=175;else v=169}while(0);if((v|0)==169){h=z+4|0;e=c[h>>2]|0;if(!e){e=f;break}while(1){f=e+4|0;g=c[f>>2]|0;if(!g)break;c[f>>2]=c[g>>2];c[g>>2]=e;e=g}c[h>>2]=c[e>>2]}else if((v|0)==175){e=c[z>>2]|0;if(!e){e=f;break}while(1){f=c[e>>2]|0;if(!f)break;x=f+4|0;c[e>>2]=c[x>>2];c[x>>2]=e;e=f}c[z>>2]=c[e+4>>2]}else if((v|0)==205){i=E+12|0;h=c[i>>2]|0;j=(d&1|0)==0;if(!(j|(h|0)==0))g=nb[h&63](a,g,E)|0;do if(g){if((D|0)>-1){e=g+D|0;v=215;break}e=ob[c[a+12>>2]&127](a,0,12,E)|0;if(e|0){c[e+8>>2]=g;break}if((c[i>>2]|0)!=0?(w=c[E+16>>2]|0,!(j|(w|0)==0)):0){vb[w&127](a,g,E);v=215}else v=215}else v=215;while(0);if((v|0)==215)if(!e){e=f;break}f=(c[F>>2]|0)+16|0;g=c[f>>2]|0;if((g|0)>-1)c[f>>2]=g+1}c[e+4>>2]=c[z>>2];c[e>>2]=c[z+4>>2];h:do if(!((d&516|0)==0?1:(c[(c[y>>2]|0)+4>>2]&8|0)==0)){m=(D|0)<0;if(m)f=c[e+8>>2]|0;else f=e+(0-D)|0;n=(B|0)<0;f=f+A|0;if(n)f=c[f>>2]|0;j=(C|0)==0;k=(B|0)<1;b=0-D|0;while(1){i=e+4|0;g=c[i>>2]|0;if(!g)break h;while(1){h=c[g>>2]|0;if(!h)break;z=h+4|0;c[g>>2]=c[z>>2];c[z>>2]=g;g=h}c[i>>2]=g;if(m)h=c[g+8>>2]|0;else h=g+b|0;h=h+A|0;if(n)h=c[h>>2]|0;do if(j)if(k){h=s2(f,h)|0;break}else{h=$3(f,h,B)|0;break}else h=ob[C&127](a,f,h,E)|0;while(0);if(h|0)break h;c[i>>2]=c[g>>2];c[g>>2]=e;e=g}}while(0);c[(c[F>>2]|0)+4>>2]=e;if((D|0)<0){e=c[e+8>>2]|0;break a}else{e=e+(0-D)|0;break a}}while(0);if((v|0)==180){if((D|0)<0)g=c[h+8>>2]|0;else g=h+(0-D)|0;f=c[E+16>>2]|0;if(!((d&2|0)==0|(f|0)==0))vb[f&127](a,g,E);if((c[x>>2]|0)<0)ob[c[a+12>>2]&127](a,h,0,E)|0;a=(c[F>>2]|0)+16|0;E=c[a>>2]|0;c[a>>2]=(E|0)<1?-1:E+-1|0}do{f=e+4|0;e=c[f>>2]|0}while((e|0)!=0);c[f>>2]=c[z>>2];c[(c[F>>2]|0)+4>>2]=c[z+4>>2];e=d&2|0?g:0}while(0);l=G;return e|0}function _z(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;if(c[c[a+8>>2]>>2]&4096|0)Wz(a,0)|0;f=(b|0)!=0;if(f){if(c[c[b+8>>2]>>2]&4096|0)Wz(b,0)|0;if((c[b+16>>2]|0)==(c[a+16>>2]|0)){d=b;e=7}else b=0}else{d=0;e=7}a:do if((e|0)==7){while(1){if(!d)break;if((d|0)==(a|0)){b=0;break a}d=c[d+28>>2]|0;e=7}d=a+28|0;e=c[d>>2]|0;if(e|0){g=e+24|0;c[g>>2]=(c[g>>2]|0)+-1}c[a+32>>2]=0;c[d>>2]=0;if(f){c[d>>2]=b;c[a>>2]=28;g=b+24|0;c[g>>2]=(c[g>>2]|0)+1;break}else{c[a>>2]=c[c[a+16>>2]>>2];b=e;break}}while(0);return b|0}function $z(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0;a:do if(!(d&99)){if((d&516|0)==0?(e=c[(c[a+16>>2]|0)+4>>2]|0,!((d&384|0)!=0&(e&12|0)==0)):0){if(e&12|0){if(!(d&408)){e=0;break}o=(d&136|0)!=0;p=(d&272|0)!=0;l=0;f=0;e=0;n=a;while(1){if(!n)break;m=nb[c[c[n+16>>2]>>2]&63](n,b,d)|0;do if(!m)g=l;else{h=c[n+4>>2]|0;i=c[h+4>>2]|0;j=c[h+20>>2]|0;g=m+(c[h>>2]|0)|0;if((i|0)<0)k=c[g>>2]|0;else k=g;if(e|0){do if(!j)if((i|0)<1){g=s2(k,f)|0;break}else{g=$3(k,f,i)|0;break}else g=ob[j&127](n,k,f,h)|0;while(0);if(!(o&(g|0)<0|p&(g|0)>0)){g=l;break}}g=n;f=k;e=m}while(0);l=g;n=c[n+28>>2]|0}c[a+32>>2]=l;break}if(!(d&24)){e=0;break}j=a+32|0;f=c[j>>2]|0;if(f){e=c[(c[f+4>>2]|0)+8>>2]|0;g=c[(c[f+8>>2]|0)+4>>2]|0;if((e|0)<0)e=c[g+8>>2]|0;else e=g+(0-e)|0;if((e|0)==(b|0))e=b;else{f=a;h=32}}else{f=a;h=32}do if((h|0)==32){while(1){if(!f){h=33;break}e=nb[c[c[f+16>>2]>>2]&63](f,b,4)|0;if(e|0){h=36;break}f=c[f+28>>2]|0;h=32}if((h|0)==33){c[j>>2]=0;e=0;break a}else if((h|0)==36){c[j>>2]=f;break}}while(0);i=(d<<4&128^128)+128|0;e=nb[c[c[f+16>>2]>>2]&63](f,e,d)|0;while(1){h=f+16|0;while(1){if(!e)break;else g=a;while(1){if((g|0)==(f|0))break a;if(nb[c[c[g+16>>2]>>2]&63](g,e,4)|0)break;g=c[g+28>>2]|0}e=nb[c[c[h>>2]>>2]&63](f,e,d)|0}f=c[f+28>>2]|0;c[j>>2]=f;if(!f){e=0;break a}e=nb[c[c[f+16>>2]>>2]&63](f,0,i)|0}}else f=a;while(1){if(!f){f=0;e=0;break}e=nb[c[c[f+16>>2]>>2]&63](f,b,d)|0;if(e|0)break;f=c[f+28>>2]|0}c[a+32>>2]=f}else e=nb[c[c[a+16>>2]>>2]&63](a,b,d)|0;while(0);return e|0}function aA(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;g=a+32|0;f=nb[c[a>>2]&63](a,0,128)|0;do{if(!f){e=0;break}h=c[g>>2]|0;e=f;f=nb[c[a>>2]&63](a,f,8)|0;e=nb[b&63]((h|0)==0?a:h,e,d)|0}while((e|0)>=0);return e|0}function bA(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;f=a+4|0;n=c[f>>2]|0;a:do if(n)if(b){m=c[c[a+16>>2]>>2]|0;g=a+8|0;if(c[c[g>>2]>>2]&4096|0)Wz(a,0)|0;e=c[n+32>>2]|0;if(e|0?(ob[e&127](a,3,b,n)|0)<0:0){b=0;break}c[f>>2]=b;e=c[b+28>>2]|0;c[a+12>>2]=(e|0)==0?52:e;e=c[c[g>>2]>>2]|0;if(!(e&112)){do if(!(e&2))if(!(e&3))if(!(d&1))break;else{b=n;break a}else if((d&3|0)==3){b=n;break a}else break;else if(d&2|0){b=n;break a}while(0);h=Tz(a)|0;e=c[g>>2]|0;l=c[e>>2]|0;c[e>>2]=l&-4097;c[e+4>>2]=0;c[e+16>>2]=0;b:do if(l&3|0){l=c[e+8>>2]|0;f=l+(c[e+12>>2]<<2)|0;e=l;while(1){if(e>>>0>=f>>>0)break b;c[e>>2]=0;e=e+4|0}}while(0);i=(d&2|0)==0;j=b+8|0;k=b+4|0;l=b+24|0;while(1){if(!h){b=n;break a}d=c[h>>2]|0;if(i){e=c[j>>2]|0;if((e|0)<0)e=c[h+8>>2]|0;else e=h+(0-e)|0;g=c[k>>2]|0;e=e+(c[b>>2]|0)|0;if((g|0)<0)e=c[e>>2]|0;f=c[l>>2]|0;if(!f)e=dA(0,e,g)|0;else e=nb[f&63](a,e,b)|0;c[h+4>>2]=e}nb[m&63](a,h,32)|0;h=d}}else b=n}else b=n;else{c[f>>2]=b;n=c[b+28>>2]|0;c[a+12>>2]=(n|0)==0?52:n}while(0);return b|0}function 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f=0,g=0,h=0;h=l;l=l+32|0;f=h+16|0;g=h;if((zB(a)|0)!=0?(c[g>>2]=0,c[g+4>>2]=0,c[g+8>>2]=0,c[g+12>>2]=0,c[f>>2]=c[g>>2],c[f+4>>2]=c[g+4>>2],c[f+8>>2]=c[g+8>>2],c[f+12>>2]=c[g+12>>2],(gB(a,b,e,f)|0)!=0):0)f=0;else f=((d[a+24>>0]|0)>>>2&1^1)&255|(b|0)!=(e|0);l=h;return f|0}function dB(a,b){a=a|0;b=b|0;fB(a,b);return}function eB(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0;oC(b,d,1)|0;oC(b,e,1)|0;h=dC(b,96)|0;i=h+48|0;l=rB(b,2)|0;j=c[i>>2]|0;k=c[h>>2]|0;m=h+8|0;c[m>>2]=f;c[m+4>>2]=g;m=h+56|0;c[m>>2]=f;c[m+4>>2]=g;g=l<<4;c[h>>2]=k&12|g|2;c[i>>2]=j&12|g|3;c[h+88>>2]=d;c[h+40>>2]=e;fB(b,h);if(a[b+24>>0]&64){TC(h,96404,16,0)|0;vA(b,h)}zC(b,h);return h|0}function fB(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0;i=l;l=l+16|0;d=i;h=c[b>>2]&3;g=(h|0)==2?b:b+-48|0;h=(h|0)==3?b:b+48|0;e=c[h+40>>2]|0;f=c[g+40>>2]|0;while(1){if(!a)break;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];if(gB(a,e,f,d)|0)break;j=YA(a,e)|0;m=a+44|0;hB(c[m>>2]|0,j+32|0,g);k=a+48|0;hB(c[k>>2]|0,j+24|0,g);j=YA(a,f)|0;hB(c[m>>2]|0,j+28|0,h);hB(c[k>>2]|0,j+20|0,h);a=QD(a)|0}l=i;return}function gB(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;h=l;l=l+48|0;f=h;if(!((b|0)==0|(d|0)==0)?(c[f>>2]=c[e>>2],c[f+4>>2]=c[e+4>>2],c[f+8>>2]=c[e+8>>2],c[f+12>>2]=c[e+12>>2],c[f+40>>2]=b,g=YA(a,d)|0,(g|0)!=0):0){e=a+48|0;g=g+20|0;Wz(c[e>>2]|0,c[g>>2]|0)|0;a=c[e>>2]|0;a=nb[c[a>>2]&63](a,f,4)|0;c[g>>2]=Sz(c[e>>2]|0)|0}else a=0;l=h;return a|0}function hB(a,b,d){a=a|0;b=b|0;d=d|0;Wz(a,c[b>>2]|0)|0;nb[c[a>>2]&63](a,d,1)|0;c[b>>2]=Sz(a)|0;return}function iB(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;n=l;l=l+48|0;i=n+24|0;k=n+16|0;j=n;do if(!(HB(a,2,e,k,0)|0))if(!e){if(f|0?(zB(a)|0)==0:0){m=18;break};c[j>>2]=0;c[j+4>>2]=0;c[j+8>>2]=0;c[j+12>>2]=0;m=7}else m=17;else{o=j;c[o>>2]=0;c[o+4>>2]=0;o=k;g=c[o+4>>2]|0;m=j+8|0;c[m>>2]=c[o>>2];c[m+4>>2]=g;c[j>>2]=2;m=7}while(0);a:do if((m|0)==7){c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];g=gB(a,b,d,i)|0;b:do if(!g){if(yB(a)|0?(c[i>>2]=c[j>>2],c[i+4>>2]=c[j+4>>2],c[i+8>>2]=c[j+8>>2],c[i+12>>2]=c[j+12>>2],h=gB(a,d,b,i)|0,h|0):0){g=h;break}do if(f|0){g=yC(a)|0;c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];g=gB(g,b,d,i)|0;if(!g){if(!(yB(a)|0))break;g=yC(a)|0;c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];g=gB(g,d,b,i)|0;if(!g)break}dB(a,g);break b}while(0);m=17;break a}while(0)}while(0);if((m|0)==17)if(!f)g=0;else m=18;if((m|0)==18)if((cB(a,b,d)|0)!=0?(HB(a,2,e,k,1)|0)!=0:0){g=k;g=eB(a,b,d,c[g>>2]|0,c[g+4>>2]|0)|0;LB(a,2,g)}else g=0;l=n;return g|0}function jB(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;d=(c[b>>2]&3|0)==3;g=d?b+-48|0:b;d=d?b:b+48|0;b=c[g+40>>2]|0;h=YA(a,c[d+40>>2]|0)|0;f=a+44|0;kB(c[f>>2]|0,h+32|0,g);e=a+48|0;kB(c[e>>2]|0,h+24|0,g);b=YA(a,b)|0;kB(c[f>>2]|0,b+28|0,d);kB(c[e>>2]|0,b+20|0,d);return}function kB(a,b,d){a=a|0;b=b|0;d=d|0;Wz(a,c[b>>2]|0)|0;nb[c[a>>2]&63](a,d,2)|0;c[b>>2]=Sz(a)|0;return}function lB(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0;e=l;l=l+16|0;f=e;d=(c[d>>2]&3|0)==2?d:d+-48|0;g=c[d>>2]&3;h=c[((g|0)==3?d:d+48|0)+40>>2]|0;g=c[((g|0)==2?d:d+-48|0)+40>>2]|0;c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];if(gB(b,h,g,f)|0){if((yC(b)|0)==(b|0)){if(a[b+24>>0]&64)PA(d);DC(b,d);$C(d);h=d+8|0;JB(b,2,c[h>>2]|0,c[h+4>>2]|0)}if(!(BE(b,d,58,0,0)|0))if((yC(b)|0)==(b|0)){eC(b,d);d=0}else d=0;else d=-1}else d=-1;l=e;return d|0}function mB(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;h=l;l=l+16|0;e=h;f=oC(a,c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0,d)|0;g=oC(a,c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0,d)|0;do if((f|0)!=0&(g|0)!=0){c[e>>2]=c[b>>2];c[e+4>>2]=c[b+4>>2];c[e+8>>2]=c[b+8>>2];c[e+12>>2]=c[b+12>>2];e=gB(a,f,g,e)|0;f=(e|0)==0;if(!((d|0)!=0&f)){if(f)break}else{fB(a,b);e=b}d=c[e>>2]&3;l=h;return ((d|0)==(c[b>>2]&3|0)?e:e+(((d|0)==3?-1:1)*48|0)|0)|0}while(0);l=h;return 0}function nB(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;b=b+8|0;e=d+8|0;e=K6(c[b>>2]|0,c[b+4>>2]|0,c[e>>2]|0,c[e+4>>2]|0)|0;return ((e|0)==0?0:e>>31|1)|0}function oB(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;h=l;l=l+16|0;g=h;j=pB(e)|0;f=lb[c[(c[j>>2]|0)+4>>2]&127](c[j+12>>2]|0,72)|0;c[f>>2]=c[f>>2]&-4;i=f+64|0;c[i>>2]=j;k=f+24|0;d=c[d>>2]|0;c[k>>2]=d;a[k>>0]=d&255|8;c[f+60>>2]=f;e=lb[c[c[j+4>>2]>>2]&127](f,e)|0;c[(c[i>>2]|0)+16>>2]=e;if(HB(f,0,b,g,1)|0){i=g;j=c[i+4>>2]|0;k=f+8|0;c[k>>2]=c[i>>2];c[k+4>>2]=j}k=qB(f)|0;LB(k,0,f);l=h;return k|0}function pB(b){b=b|0;var d=0,e=0,f=0,g=0;d=(b|0)!=0;if(d){e=c[b>>2]|0;e=(e|0)==0?18736:e}else e=18736;g=kb[c[e>>2]&63](b)|0;f=lb[c[e+4>>2]&127](g,80)|0;c[f>>2]=e;c[f+12>>2]=g;if(d){g=c[b+4>>2]|0;c[f+4>>2]=(g|0)==0?18612:g;b=c[b+8>>2]|0;b=(b|0)==0?18712:b}else{c[f+4>>2]=18612;b=18712}c[f+8>>2]=b;a[f+52>>0]=1;return f|0}function qB(b){b=b|0;var d=0,e=0;c[b+36>>2]=UD(b,18792,c[4589]|0)|0;c[b+40>>2]=UD(b,18756,c[4589]|0)|0;d=(yC(b)|0)==(b|0);c[b+44>>2]=UD(b,d?18404:18440,c[4589]|0)|0;d=(yC(b)|0)==(b|0);c[b+48>>2]=UD(b,d?18476:18512,c[4589]|0)|0;c[b+52>>2]=UD(b,18548,c[4589]|0)|0;d=QD(b)|0;if(!((d|0)!=0?(e=rB(d,0)|0,c[b>>2]=c[b>>2]&15|e<<4,e=c[d+52>>2]|0,nb[c[e>>2]&63](e,b,1)|0,!(a[d+24>>0]&64)):0))tA(b);zC(b,b);return b|0}function rB(a,b){a=a|0;b=b|0;var d=0;d=(c[a+64>>2]|0)+24+(b<<3)|0;b=d;b=J6(c[b>>2]|0,c[b+4>>2]|0,1,0)|0;a=z;c[d>>2]=b;c[d+4>>2]=a;z=a;return b|0}function sB(b){b=b|0;var d=0,e=0,f=0,g=0;f=QD(b)|0;g=(f|0)==0;if(g?(d=b+64|0,(c[(c[c[d>>2]>>2]|0)+16>>2]|0)!=0):0){DC(b,b);g=b+8|0;JB(b,0,c[g>>2]|0,c[g+4>>2]|0);d=c[d>>2]|0;tb[c[(c[d>>2]|0)+16>>2]&127](c[d+12>>2]|0);d=0}else e=4;do if((e|0)==4){d=OD(b)|0;while(1){if(!d)break;e=PD(d)|0;sB(d)|0;d=e}d=jC(b)|0;while(1){if(!d)break;e=kC(b,d)|0;vC(b,d)|0;d=e}UB(b);DC(b,b);if(((((WD(b,c[b+40>>2]|0)|0)==0?(WD(b,c[b+36>>2]|0)|0)==0:0)?(WD(b,c[b+48>>2]|0)|0)==0:0)?(WD(b,c[b+44>>2]|0)|0)==0:0)?(WD(b,c[b+52>>2]|0)|0)==0:0){if(a[b+24>>0]&64?MA(b)|0:0){d=-1;break}$C(b);e=b+8|0;JB(b,0,c[e>>2]|0,c[e+4>>2]|0);if(!g){RD(f,b)|0;eC(f,b);d=0;break}d=b+64|0;while(1){e=c[d>>2]|0;f=c[e+48>>2]|0;if(!f)break;GC(b,c[f>>2]|0)|0}tb[c[(c[e+4>>2]|0)+20>>2]&127](c[e+16>>2]|0);if(!(aD(b)|0)){d=c[d>>2]|0;g=c[d+12>>2]|0;f=(c[d>>2]|0)+12|0;ub[c[f>>2]&63](g,b);ub[c[f>>2]&63](g,d);d=0}else d=-1}else d=-1}while(0);return d|0}function tB(a){a=a|0;return Xz(c[a+40>>2]|0)|0}function uB(a){a=a|0;var b=0,c=0,d=0;b=0;c=jC(a)|0;while(1){if(!c)break;d=(vB(a,c,0,1)|0)+b|0;b=d;c=kC(a,c)|0}return b|0}function vB(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;f=YA(a,b)|0;if(f){if(!e)b=0;else b=wB(c[a+44>>2]|0,f+32|0)|0;if(d)b=(wB(c[a+44>>2]|0,f+28|0)|0)+b|0}else b=0;return b|0}function wB(a,b){a=a|0;b=b|0;var d=0;Wz(a,c[b>>2]|0)|0;d=Xz(a)|0;c[b>>2]=Sz(a)|0;return d|0}function xB(b){b=b|0;return a[b+24>>0]&1|0}function yB(a){a=a|0;return (xB(a)|0)==0|0}function zB(a){a=a|0;return (d[a+24>>0]|0)>>>1&1|0}function AB(a,b){a=a|0;b=b|0;return a|0}function BB(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;if(!d){f=1392;d=c[f>>2]|0;f=c[f+4>>2]|0;c[e>>2]=d;c[e+4>>2]=f;f=J6(d|0,f|0,2,0)|0;e=1392;c[e>>2]=f;c[e+4>>2]=z}else{if(!f)a=cD(a,d)|0;else a=fD(a,d)|0;c[e>>2]=a;c[e+4>>2]=0}return 1}function CB(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return 0}function DB(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;if((c&1|0)==0&0==0)hD(a,c)|0;return}function EB(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return ((c&1|0)==0&0==0?c:0)|0}function 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vC(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;f=d+8|0;e=f;if(iC(b,c[e>>2]|0,c[e+4>>2]|0)|0){if((yC(b)|0)==(b|0)){e=aB(b,d)|0;while(1){if(!e)break;g=bB(b,e,d)|0;lB(b,e)|0;e=g}if(a[b+24>>0]&64)OA(d);DC(b,d);$C(d);g=f;JB(b,1,c[g>>2]|0,c[g+4>>2]|0)}if(!(BE(b,d,59,0,0)|0))if((yC(b)|0)==(b|0)){eC(b,d);e=0}else e=0;else e=-1}else e=-1;return e|0}function wC(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;e=h;d=c[b>>2]|0;do if(!(d&3))if((QD(b)|0)==(a|0)){d=c[b>>2]|0;g=5;break}else{gA(1,96488,e)|0;f=-1;break}else g=5;while(0);a:do if((g|0)==5)switch(d&3){case 1:{f=vC(a,b)|0;break a}case 2:case 3:{f=lB(a,b)|0;break a}case 0:{f=sB(b)|0;break a}default:{}}while(0);l=h;return f|0}function xC(a){a=a|0;var b=0;switch(c[a>>2]&3){case 2:case 3:{b=c[(c[a+40>>2]|0)+24>>2]|0;break}case 1:{b=c[a+24>>2]|0;break}case 0:{b=a;break}default:{}}return b|0}function yC(a){a=a|0;var b=0;switch(c[a>>2]&3){case 2:case 3:{b=(c[a+40>>2]|0)+24|0;break}case 1:{b=a+24|0;break}case 0:{b=a+60|0;break}default:{}}return c[b>>2]|0}function zC(b,d){b=b|0;d=d|0;var e=0;e=c[b+64>>2]|0;if(!(a[e+52>>0]|0))JC(b,d,100,0);else AC(b,d,c[e+48>>2]|0);return}function AC(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;a:do if(d|0){AC(a,b,c[d+8>>2]|0);switch(c[b>>2]&3){case 0:{e=c[d>>2]|0;break}case 1:{e=(c[d>>2]|0)+12|0;break}case 2:{e=(c[d>>2]|0)+24|0;break}default:break a}e=c[e>>2]|0;if(e|0)vb[e&127](a,b,c[d+4>>2]|0)}while(0);return}function BC(b,d,e){b=b|0;d=d|0;e=e|0;var f=0;f=c[b+64>>2]|0;if(!(a[f+52>>0]|0))JC(b,d,101,e);else CC(b,d,e,c[f+48>>2]|0);return}function CC(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;a:do if(e|0){CC(a,b,d,c[e+8>>2]|0);switch(c[b>>2]&3){case 0:{f=(c[e>>2]|0)+4|0;break}case 1:{f=(c[e>>2]|0)+16|0;break}case 2:{f=(c[e>>2]|0)+28|0;break}default:break a}f=c[f>>2]|0;if(f|0)xb[f&31](a,b,c[e+4>>2]|0,d)}while(0);return}function DC(b,d){b=b|0;d=d|0;var e=0;e=c[b+64>>2]|0;if(!(a[e+52>>0]|0))JC(b,d,102,0);else EC(b,d,c[e+48>>2]|0);return}function EC(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;a:do if(d|0){EC(a,b,c[d+8>>2]|0);switch(c[b>>2]&3){case 0:{e=(c[d>>2]|0)+8|0;break}case 1:{e=(c[d>>2]|0)+20|0;break}case 2:{e=(c[d>>2]|0)+32|0;break}default:break a}e=c[e>>2]|0;if(e|0)vb[e&127](a,b,c[d+4>>2]|0)}while(0);return}function FC(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=dC(a,12)|0;c[e>>2]=b;c[e+4>>2]=d;d=(c[a+64>>2]|0)+48|0;c[e+8>>2]=c[d>>2];c[d>>2]=e;return}function GC(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;e=(c[a+64>>2]|0)+48|0;d=c[e>>2]|0;a:do if(!d)d=-1;else{if((c[d>>2]|0)!=(b|0)){while(1){if(!d){d=-1;break a}e=d+8|0;f=c[e>>2]|0;if((c[f>>2]|0)==(b|0))break;else d=f}if(f)c[e>>2]=c[f+8>>2]}else c[e>>2]=c[d+8>>2];eC(a,d);d=0}while(0);return d|0}function HC(a,b){a=a|0;b=b|0;var d=0;d=yC(a)|0;a:do if((d|0)==(yC(b)|0))switch(c[b>>2]&3){case 0:{while(1){if((b|0)==(a|0)){b=1;break a}b=QD(b)|0;if(!b){b=0;break}}break}case 1:{b=b+8|0;b=(nC(a,c[b>>2]|0,c[b+4>>2]|0,0)|0)!=0;break a}default:{b=(mB(a,b,0)|0)!=0;break a}}else b=0;while(0);return b&1|0}function IC(a){a=a|0;return c[a>>2]&3|0}function JC(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0,g=0,h=0;h=l;l=l+16|0;g=h;f=TC(a,96512,44,0)|0;switch(c|0){case 100:{e=KC(f,b,100)|0;if(!(LC(e,b)|0))MC(e,a,b,d)|0;break}case 101:{if(((LC(KC(f,b,100)|0,b)|0)==0?(LC(KC(f,b,102)|0,b)|0)==0:0)?(e=KC(f,b,101)|0,(LC(e,b)|0)==0):0)MC(e,a,b,d)|0;break}case 102:{NC(KC(f,b,100)|0,b);NC(KC(f,b,101)|0,b);e=KC(f,b,102)|0;if(!(LC(e,b)|0))MC(e,a,b,d)|0;break}default:gA(1,96524,g)|0}l=h;return}function KC(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;g=l;l=l+16|0;e=g;a:do switch(c[b>>2]&3){case 0:switch(d|0){case 100:{d=a+8|0;break a}case 101:{d=a+20|0;break a}case 102:{d=a+32|0;break a}default:{f=14;break a}}case 1:switch(d|0){case 100:{d=a+12|0;break a}case 101:{d=a+24|0;break a}case 102:{d=a+36|0;break a}default:{f=14;break a}}case 2:switch(d|0){case 100:{d=a+16|0;break a}case 101:{d=a+28|0;break a}case 102:{d=a+40|0;break a}default:{f=14;break a}}default:f=14}while(0);if((f|0)==14){gA(1,96558,e)|0;d=0}a=c[d>>2]|0;if(!a){a=xC(b)|0;a=UD(a,18828,c[4589]|0)|0;c[d>>2]=a}l=g;return a|0}function LC(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=l;l=l+32|0;e=d;f=OC(b)|0;b=e+8|0;c[b>>2]=f;c[b+4>>2]=z;b=nb[c[a>>2]&63](a,e,4)|0;l=d;return b|0}function MC(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;f=dC(xC(d)|0,32)|0;c[f+20>>2]=d;g=OC(d)|0;d=f+8|0;c[d>>2]=g;c[d+4>>2]=z;c[f+16>>2]=b;if(e|0){g=dC(b,8)|0;c[f+24>>2]=g;c[g>>2]=e}nb[c[a>>2]&63](a,f,1)|0;return f|0}function NC(a,b){a=a|0;b=b|0;b=LC(a,b)|0;if(b|0)nb[c[a>>2]&63](a,b,2)|0;return}function OC(a){a=a|0;a=a+8|0;z=c[a+4>>2]|0;return c[a>>2]|0}function PC(a,b,d){a=a|0;b=b|0;d=d|0;QC(b);eC(c[b+16>>2]|0,b);return}function QC(a){a=a|0;var b=0,d=0;b=a+16|0;a=c[a+24>>2]|0;while(1){if(!a)break;d=c[a+4>>2]|0;eC(c[b>>2]|0,a);a=d}return}function RC(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;i=k;h=c[b+16>>2]|0;f=h;while(1){if(!f){f=0;break}g=c[f>>2]|0;if((g|0)==(d|0)){j=7;break}if((a[d>>0]|0)==(a[g>>0]|0)?(s2(d,g)|0)==0:0){j=7;break}f=c[f+4>>2]|0;if((f|0)==(h|0)){f=0;break}}do if((j|0)==7){if(!(c[b>>2]&4)){if((f|0)==(h|0)&(e|0)==0){f=h;break}SC(b,f,e);break}if(e)if((h|0)==(f|0))f=h;else gA(1,96583,i)|0}while(0);l=k;return f|0}function SC(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;c[a+16>>2]=b;e=c[a>>2]|0;d=d<<2&4;c[a>>2]=e&-5|d;if(e&2|0){e=(e&3|0)==3?-1:1;c[a+(e*48|0)+16>>2]=b;e=a+(e*48|0)|0;c[e>>2]=c[e>>2]&-5|d}return}function TC(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;h=xC(a)|0;f=RC(a,b,0)|0;a:do if((d|0)!=0&(f|0)==0){d=dC(h,d)|0;c[d>>2]=fD(h,b)|0;switch(c[a>>2]&3){case 0:{UC(a,d);g=d;break a}case 1:{UC(a,d);g=d;break a}case 2:case 3:{UC(a,d);g=d;break a}default:{}}}else g=f;while(0);if(e|0)RC(a,b,1)|0;return g|0}function UC(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=c[a+16>>2]|0;do if(d){e=d+4|0;f=c[e>>2]|0;if((f|0)==(d|0)){c[e>>2]=b;e=d;d=b;break}else{c[b+4>>2]=f;e=b;break}}else{e=b;d=b}while(0);c[d+4>>2]=e;if(!(c[a>>2]&4))SC(a,b,0);return}function VC(a,b){a=a|0;b=b|0;var d=0,e=0;e=xC(a)|0;d=RC(a,b,0)|0;if(!d)b=-1;else{WC(a,d);if(!(c[a>>2]&3)){XC(e,a,d);b=d}else{BE(yC(e)|0,a,60,d,0)|0;b=d}hD(e,c[d>>2]|0)|0;eC(e,b);b=0}return b|0}function WC(a,b){a=a|0;b=b|0;var d=0;a=c[a+16>>2]|0;do{d=a+4|0;a=c[d>>2]|0}while((a|0)!=(b|0));c[d>>2]=c[b+4>>2];return}function XC(a,b,d){a=a|0;b=b|0;d=d|0;if((c[b+16>>2]|0)==(d|0)){a=c[d+4>>2]|0;SC(b,(a|0)==(d|0)?0:a,0)}return}function YC(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;var f=0,g=0;f=(d|0)>-1;g=f?d:0-d|0;a:do switch(b|0){case 0:{TC(a,c,g,e)|0;if(!f){b=OD(a)|0;while(1){if(!b)break a;YC(b,0,c,d,e);b=PD(b)|0}}break}case 3:case 2:case 1:{d=(b|0)==1;b=jC(a)|0;while(1){if(!b)break a;b:do if(d)TC(b,c,g,e)|0;else{f=XA(a,b)|0;while(1){if(!f)break b;TC(f,c,g,e)|0;f=ZA(a,f)|0}}while(0);b=kC(a,b)|0}}default:{}}while(0);return}function ZC(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0;a:do switch(b|0){case 0:{BE(a,a,61,c,1)|0;break}case 3:case 2:case 1:{e=(b|0)==1;d=jC(a)|0;while(1){if(!d)break a;b:do if(e)VC(d,c)|0;else{b=XA(a,d)|0;while(1){if(!b)break b;VC(b,c)|0;b=ZA(a,b)|0}}while(0);d=kC(a,d)|0}}default:{}}while(0);return}function _C(a,b,c){a=a|0;b=b|0;c=c|0;VC(b,c)|0;return}function $C(a){a=a|0;var b=0,d=0,e=0;d=xC(a)|0;b=a+16|0;a=c[b>>2]|0;if(a|0)do{e=a;a=c[a+4>>2]|0;hD(d,c[e>>2]|0)|0;eC(d,e)}while((a|0)!=(c[b>>2]|0));c[b>>2]=0;return}function aD(a){a=a|0;return WD(a,bD(a)|0)|0}function bD(a){a=a|0;var b=0,d=0;if(!a)d=187844;else d=(c[a+64>>2]|0)+20|0;b=c[d>>2]|0;if(!b){b=UD(a,18864,c[4589]|0)|0;c[d>>2]=b;a=186312;c[a>>2]=-2147483648;c[a+4>>2]=0;a=186320;c[a>>2]=2147483647;c[a+4>>2]=-1}return b|0}function cD(a,b){a=a|0;b=b|0;return dD(bD(a)|0,b)|0}function dD(a,b){a=a|0;b=b|0;a=eD(a,b)|0;if(!a)a=0;else a=c[a+16>>2]|0;return a|0}function eD(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+32|0;e=d;c[e+16>>2]=b;b=nb[c[a>>2]&63](a,e,4)|0;l=d;return b|0}function fD(a,b){a=a|0;b=b|0;var d=0,e=0;if(!b)d=0;else{e=bD(a)|0;d=eD(e,b)|0;if(!d){d=(l2(b)|0)+24|0;if(!a)d=T1(d)|0;else d=dC(a,d)|0;a=d+8|0;c[a>>2]=1;c[a+4>>2]=0;a=d+20|0;r3(a,b)|0;c[d+16>>2]=a;nb[c[e>>2]&63](e,d,1)|0}else{e=d+8|0;b=e;b=J6(c[b>>2]|0,c[b+4>>2]|0,1,0)|0;c[e>>2]=b;c[e+4>>2]=z}d=c[d+16>>2]|0}return d|0}function gD(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;if(!b)d=0;else{e=bD(a)|0;d=eD(e,b)|0;if(!d){d=(l2(b)|0)+24|0;if(!a)d=T1(d)|0;else d=dC(a,d)|0;g=186312;f=c[g+4>>2]|0;a=d+8|0;c[a>>2]=c[g>>2]|1;c[a+4>>2]=f;a=d+20|0;r3(a,b)|0;c[d+16>>2]=a;nb[c[e>>2]&63](e,d,1)|0}else{g=d+8|0;f=g;f=J6(c[f>>2]|0,c[f+4>>2]|0,1,0)|0;c[g>>2]=f;c[g+4>>2]=z}d=c[d+16>>2]|0}return d|0}function hD(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;if((b|0)!=0?(d=bD(a)|0,e=eD(d,b)|0,(e|0)!=0):0)if((c[e+16>>2]|0)==(b|0)?(b=e+8|0,g=b,g=J6(c[g>>2]|0,c[g+4>>2]|0,-1,-1)|0,f=z,b,c[b>>2]=g,c[b+4>>2]=f,b=186320,(g|0)==0&(f|0)==0|(c[b>>2]|0)==0&(c[b+4>>2]|0)==0):0){VD(a,d,e)|0;a=0}else a=0;else a=-1;return a|0}function iD(a){a=a|0;var b=0;if(!a)a=0;else{a=a+-12|0;b=186312;a=c[b>>2]&c[a>>2]}return a|0}function jD(a){a=a|0;var b=0,d=0,e=0;if(a|0){d=186312;a=a+-12|0;e=a;b=c[e+4>>2]|c[d+4>>2];c[a>>2]=c[e>>2]|c[d>>2];c[a+4>>2]=b}return}function kD(a){a=a|0;c[46966]=a;c[4725]=1;return}function lD(a,b){a=a|0;b=b|0;c[46967]=a;c[46968]=b;c[46969]=0;return}function mD(){var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;if(!(c[46970]|0)){c[46970]=1;if(!(c[46971]|0))c[46971]=1;if(!(c[46962]|0))c[46962]=c[15647];if(!(c[46963]|0))c[46963]=c[15679];k=c[46972]|0;if(!((k|0)!=0?(c[k>>2]|0)!=0:0)){nD();k=oD(c[46962]|0,16384)|0;c[c[46972]>>2]=k}pD()}a:while(1){j=c[46973]|0;a[j>>0]=a[194805]|0;e=(c[(c[c[46972]>>2]|0)+28>>2]|0)+(c[46971]|0)|0;f=j;b:while(1){h=f;while(1){f=c[18904+(d[h>>0]<<2)>>2]|0;if(b[63856+(e<<1)>>1]|0){c[46974]=e;c[46975]=h}c:while(1){g=f&255;do{f=g+(b[64042+(e<<1)>>1]|0)|0;if((e|0)==(b[64252+(f<<1)>>1]|0))break c;k=b[64788+(e<<1)>>1]|0;e=k<<16>>16}while(k<<16>>16<=92);f=c[19928+(g<<2)>>2]|0}e=b[64998+(f<<1)>>1]|0;f=h+1|0;if((b[64042+(e<<1)>>1]|0)==219)break;else h=f}d:while(1){k=j;e:while(1){e=b[63856+(e<<1)>>1]|0;if(!(e<<16>>16)){e=b[63856+(c[46974]<<1)>>1]|0;f=c[46975]|0}else e=e<<16>>16;c[46964]=j;i=f;c[46965]=i-k;a[194805]=a[f>>0]|0;a[f>>0]=0;c[46973]=f;f:while(1){switch(e|0){case 1:{m=27;break a}case 12:{m=53;break a}case 13:{m=55;break a}case 14:{m=57;break a}case 15:{m=61;break a}case 16:{m=65;break a}case 17:{m=67;break a}case 18:{m=69;break a}case 19:{m=72;break a}case 20:{m=75;break a}case 21:{m=78;break a}case 23:{m=86;break a}case 33:{m=118;break a}case 39:case 38:case 37:case 36:{l=0;break a}case 2:{m=29;break b}case 3:{m=32;break b}case 4:{m=35;break b}case 5:{m=37;break b}case 6:{m=39;break b}case 7:{m=42;break b}case 8:{m=44;break b}case 9:{m=47;break b}case 10:{m=49;break b}case 11:{m=51;break b}case 22:{m=83;break b}case 24:{m=89;break b}case 25:{m=92;break b}case 26:{m=95;break b}case 27:{m=98;break b}case 28:{m=101;break b}case 29:{m=104;break b}case 30:{m=109;break b}case 31:{m=112;break b}case 32:{m=115;break b}case 34:{m=121;break b}case 0:break f;case 35:break;default:{m=134;break a}}h=c[46964]|0;a[f>>0]=a[194805]|0;e=c[46972]|0;g=c[e>>2]|0;if(!(c[g+44>>2]|0)){n=c[g+16>>2]|0;c[46977]=n;c[g>>2]=c[46962];g=c[e>>2]|0;c[g+44>>2]=1;e=n}else e=c[46977]|0;if((c[46973]|0)>>>0<=((c[g+4>>2]|0)+e|0)>>>0){m=128;break e}switch(yD()|0){case 0:{m=132;break d}case 2:{m=133;break e}case 1:break;default:continue a}c[46973]=c[46964];e=(((c[46971]|0)+-1|0)/2|0)+36|0}a[f>>0]=a[194805]|0;f=c[46975]|0;e=c[46974]|0}if((m|0)==128){m=0;g=i-h|0;h=c[46964]|0;f=h+(g+-1)|0;c[46973]=f;e=wD()|0;i=xD(e)|0;j=c[46964]|0;if(!i)continue;else{m=129;break}}else if((m|0)==133){m=0;f=(c[(c[c[46972]>>2]|0)+4>>2]|0)+(c[46977]|0)|0;c[46973]=f;e=wD()|0;j=c[46964]|0;continue}}if((m|0)==129){m=0;f=h+g|0;c[46973]=f;e=i;continue}else if((m|0)==132){m=0;f=(c[46964]|0)+(i-h+-1)|0;c[46973]=f;e=wD()|0;j=c[46964]|0;continue}}switch(m|0){case 29:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;c[4725]=(c[4725]|0)+1;continue a}case 32:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;c[46971]=3;continue a}case 35:{m=0;e=c[46965]|0;if(!e)continue a;c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;continue a}case 37:{m=0;e=c[46965]|0;if(!e)continue a;c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;continue a}case 39:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;c[46971]=1;continue a}case 42:{m=0;e=c[46965]|0;if(!e)continue a;c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;continue a}case 44:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;qD();continue a}case 47:{m=0;e=c[46965]|0;if(!e)continue a;c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;continue a}case 49:{m=0;e=c[46965]|0;if(!e)continue a;c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;continue a}case 51:{m=0;e=c[46965]|0;if(!e)continue a;c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;continue a}case 83:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;c[46971]=5;sD();continue a}case 89:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;uD(98744);continue a}case 92:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;uD(96616);continue a}case 95:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;c[4725]=(c[4725]|0)+1;continue a}case 98:{m=0;f=c[46965]|0;e=c[46964]|0;if(f|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[e+(f+-1)>>0]|0)==10&1;uD(e);continue a}case 101:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;c[46971]=7;c[46976]=1;sD();continue a}case 104:{m=0;e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;n=(c[46976]|0)+-1|0;c[46976]=n;if(!n){m=108;break a}uD(c[46964]|0);continue a}case 109:{m=0;f=c[46965]|0;e=c[46964]|0;if(f|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[e+(f+-1)>>0]|0)==10&1;c[46976]=(c[46976]|0)+1;uD(e);continue a}case 112:{m=0;f=c[46965]|0;e=c[46964]|0;if(f|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[e+(f+-1)>>0]|0)==10&1;uD(e);c[4725]=(c[4725]|0)+1;continue a}case 115:{m=0;f=c[46965]|0;e=c[46964]|0;if(f|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[e+(f+-1)>>0]|0)==10&1;uD(e);continue a}case 121:{m=0;f=c[46965]|0;e=c[46964]|0;if(f|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[e+(f+-1)>>0]|0)==10&1;U4(e,f,1,c[46963]|0)|0;continue a}}}switch(m|0){case 27:{e=c[46965]|0;if(!e)l=-1;else{c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;l=-1}break}case 53:{e=c[46965]|0;if(!e)l=259;else{c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;l=259}break}case 55:{e=c[46965]|0;if(!e)l=260;else{c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;l=260}break}case 57:{e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;if(!(c[46969]|0)){c[46969]=258;l=258}else l=258;break}case 61:{e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;if(!(c[46969]|0)){c[46969]=261;l=261}else l=261;break}case 65:{e=c[46965]|0;if(!e)l=263;else{c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;l=263}break}case 67:{e=c[46965]|0;if(!e)l=262;else{c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;l=262}break}case 69:{e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;l=(c[46969]|0)==261?264:45;break}case 72:{e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;l=(c[46969]|0)==258?264:45;break}case 75:{f=c[46965]|0;e=c[46964]|0;if(f|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[e+(f+-1)>>0]|0)==10&1;c[46992]=fD(c[46942]|0,e)|0;l=267;break}case 78:{e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;if(rD()|0){m=(c[46965]|0)+-1|0;a[f>>0]=a[194805]|0;n=j+m|0;c[46973]=n;c[46964]=j;c[46965]=m;a[194805]=a[n>>0]|0;a[n>>0]=0;c[46973]=n}c[46992]=fD(c[46942]|0,c[46964]|0)|0;l=267;break}case 86:{e=c[46965]|0;if(e|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[(c[46964]|0)+(e+-1)>>0]|0)==10&1;c[46971]=1;tD();l=268;break}case 108:{c[46971]=1;vD();l=268;break}case 118:{f=c[46965]|0;e=c[46964]|0;if(f|0)c[(c[c[46972]>>2]|0)+28>>2]=(a[e+(f+-1)>>0]|0)==10&1;l=a[e>>0]|0;break}case 134:{zD(96619);break}}return l|0}function nD(){var a=0,b=0,d=0;b=c[46972]|0;do if(!b){a=GD(4)|0;c[46972]=a;if(!a)zD(96977);else{c[a>>2]=0;a=1;d=9;break}}else{a=c[46983]|0;if(0>=(a+-1|0)>>>0){a=a+8|0;b=AD(b,a<<2)|0;c[46972]=b;if(!b)zD(96977);else{d=b+(c[46983]<<2)|0;c[d>>2]=0;c[d+4>>2]=0;c[d+8>>2]=0;c[d+12>>2]=0;c[d+16>>2]=0;c[d+20>>2]=0;c[d+24>>2]=0;c[d+28>>2]=0;d=9;break}}}while(0);if((d|0)==9)c[46983]=a;return}function oD(a,b){a=a|0;b=b|0;var d=0;d=GD(48)|0;if(!d)zD(96932);c[d+12>>2]=b;b=GD(b+2|0)|0;c[d+4>>2]=b;if(!b)zD(96932);else{c[d+20>>2]=1;CD(d,a);return d|0}return 0}function pD(){var b=0,d=0;d=c[46972]|0;b=c[d>>2]|0;c[46977]=c[b+16>>2];b=c[b+8>>2]|0;c[46973]=b;c[46964]=b;c[46962]=c[c[d>>2]>>2];a[194805]=a[b>>0]|0;return}function qD(){var b=0,d=0,e=0,f=0,g=0,h=0,i=0;g=l;l=l+32|0;f=g;d=g+16|0;e=g+12|0;h=c[46964]|0;b=h+1|0;i=(Q2(b,96916,4)|0)==0;b=i?h+5|0:b;c[f>>2]=e;c[f+4>>2]=g+20;c[f+8>>2]=d;f=b5(b,96921,f)|0;if((f|0)>0?(c[4725]=(c[e>>2]|0)+-1,(f|0)!=1):0){d=b+(c[d>>2]|0)|0;b=d;a:while(1){e=a[b>>0]|0;switch(e<<24>>24){case 0:case 34:break a;default:{}}b=b+1|0}if((b|0)!=(d|0)&e<<24>>24==34){a[b>>0]=0;FD(d,b-d|0)}}l=g;return}function rD(){var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;j=l;l=l+2080|0;i=j+8|0;f=j;b=j+1056|0;d=j+32|0;h=j+16|0;k=a[(c[46964]|0)+((c[46965]|0)+-1)>>0]|0;e=k<<24>>24==46;if(e|((k&255)+-48|0)>>>0<10)if(e?(ED()|0)!=0:0)g=4;else b=0;else g=4;if((g|0)==4){k=c[46966]|0;kA(h,1024,b);nA(h,96827)|0;nA(h,c[46964]|0)|0;c[f>>2]=c[4725];m4(d,96871,f)|0;nA(h,d)|0;nA(h,(k|0)==0?96821:k)|0;nA(h,96888)|0;d=h+4|0;b=c[d>>2]|0;if(b>>>0>=(c[h+8>>2]|0)>>>0){lA(h,1)|0;b=c[d>>2]|0}a[b>>0]=0;b=c[h>>2]|0;c[d>>2]=b;c[i>>2]=b;gA(0,96913,i)|0;oA(h);b=1}l=j;return b|0}function sD(){var b=0;b=c[46978]|0;if(!b){b=T1(1024)|0;c[46978]=b;c[46980]=b+1024}c[46979]=b;a[b>>0]=0;return}function tD(){c[46992]=fD(c[46942]|0,c[46978]|0)|0;a[c[46978]>>0]=0;return}function uD(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;e=c[46979]|0;d=c[46978]|0;if(e>>>0>d>>>0){e=e+-1|0;f=3}a:while(1){if((f|0)==3)c[46979]=e;f=c[46980]|0;g=f;do{h=b;b=b+1|0;h=a[h>>0]|0;i=e;e=e+1|0;a[i>>0]=h;if(!(h<<24>>24))break a}while(e>>>0<f>>>0);c[46979]=e;f=d;i=g-f<<1;d=W1(d,i)|0;c[46978]=d;c[46980]=d+i;e=d+(e-f)|0;f=3}c[46979]=e;return}function vD(){c[46992]=gD(c[46942]|0,c[46978]|0)|0;a[c[46978]>>0]=0;return}function wD(){var d=0,e=0,f=0,g=0,h=0;h=c[46973]|0;g=c[46964]|0;e=(c[(c[c[46972]>>2]|0)+28>>2]|0)+(c[46971]|0)|0;while(1){if(g>>>0>=h>>>0)break;d=a[g>>0]|0;if(!(d<<24>>24))d=1;else d=c[18904+((d&255)<<2)>>2]|0;if(b[63856+(e<<1)>>1]|0){c[46974]=e;c[46975]=g}a:while(1){f=d&255;do{d=f+(b[64042+(e<<1)>>1]|0)|0;if((e|0)==(b[64252+(d<<1)>>1]|0))break a;d=b[64788+(e<<1)>>1]|0;e=d<<16>>16}while(d<<16>>16<=92);d=c[19928+(f<<2)>>2]|0}g=g+1|0;e=b[64998+(d<<1)>>1]|0}return e|0}function xD(a){a=a|0;var d=0;if(b[63856+(a<<1)>>1]|0){d=c[46973]|0;c[46974]=a;c[46975]=d}while(1){d=(b[64042+(a<<1)>>1]|0)+1|0;if((a|0)==(b[64252+(d<<1)>>1]|0))break;a=b[64788+(a<<1)>>1]|0}d=b[64998+(d<<1)>>1]|0;return (d<<16>>16==92?0:d<<16>>16)|0}function yD(){var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;j=c[46972]|0;g=c[j>>2]|0;i=c[g+4>>2]|0;f=c[46964]|0;l=c[46973]|0;k=l;if(l>>>0>(i+((c[46977]|0)+1)|0)>>>0)zD(96674);h=k-f|0;if(!(c[g+40>>2]|0))b=(h|0)==1?1:2;else{l=h+-1|0;h=i;g=0;while(1){if((g|0)>=(l|0))break;a[h>>0]=a[f>>0]|0;h=h+1|0;f=f+1|0;g=g+1|0}f=c[j>>2]|0;do if((c[f+44>>2]|0)==2){c[46977]=0;c[f+16>>2]=0;e=f;m=17}else{while(1){i=f+12|0;h=c[i>>2]|0;g=h-l+-1|0;if(g|0){m=16;break}g=f+4|0;j=c[g>>2]|0;if(!(c[f+20>>2]|0)){m=12;break}f=h<<1;f=(f|0)==0?(h>>>3)+h|0:f;c[i>>2]=f;f=AD(j,f+2|0)|0;c[g>>2]=f;if(!f)break;j=f+(k-j)|0;c[46973]=j;f=c[c[46972]>>2]|0;k=j}if((m|0)==12)c[g>>2]=0;else if((m|0)==16){d=nb[c[c[(c[46967]|0)+8>>2]>>2]&63](c[46968]|0,(c[f+4>>2]|0)+l|0,g>>>0<8192?g:8192)|0;e=c[c[46972]>>2]|0;c[46977]=d;c[e+16>>2]=d;if(!d){m=17;break}else{b=0;break}}zD(96730)}while(0);do if((m|0)==17)if(!l){BD(c[46962]|0);b=1;d=c[46977]|0;e=c[c[46972]>>2]|0;break}else{c[e+44>>2]=2;b=2;d=0;break}while(0);f=d+l|0;do if(f>>>0>(c[e+12>>2]|0)>>>0){m=AD(c[e+4>>2]|0,f+(d>>>1)|0)|0;d=c[46972]|0;c[(c[d>>2]|0)+4>>2]=m;d=c[(c[d>>2]|0)+4>>2]|0;if(!d)zD(96774);else{n=c[46977]|0;o=d;break}}else{n=d;o=c[e+4>>2]|0}while(0);n=n+l|0;c[46977]=n;a[o+n>>0]=0;o=c[46972]|0;a[(c[(c[o>>2]|0)+4>>2]|0)+((c[46977]|0)+1)>>0]=0;c[46964]=c[(c[o>>2]|0)+4>>2]}return b|0}function zD(a){a=a|0;var b=0,d=0;b=l;l=l+16|0;d=c[15712]|0;c[b>>2]=a;w4(d,96670,b)|0;Sa(2)}function AD(a,b){a=a|0;b=b|0;return W1(a,b)|0}function BD(a){a=a|0;var b=0,d=0,e=0;d=c[46972]|0;if((d|0)!=0?(b=c[d>>2]|0,(b|0)!=0):0)e=4;else{nD();b=oD(c[46962]|0,16384)|0;d=c[46972]|0;c[d>>2]=b;if(!d)b=0;else e=4}CD(b,a);pD();return}function CD(a,b){a=a|0;b=b|0;var d=0;d=c[(d2()|0)>>2]|0;DD(a);c[a>>2]=b;c[a+40>>2]=1;b=c[46972]|0;if(!b)b=0;else b=c[b>>2]|0;if((b|0)!=(a|0)){c[a+32>>2]=1;c[a+36>>2]=0}c[a+24>>2]=0;c[(d2()|0)>>2]=d;return}function DD(b){b=b|0;var d=0;if(b|0){c[b+16>>2]=0;d=b+4|0;a[c[d>>2]>>0]=0;a[(c[d>>2]|0)+1>>0]=0;c[b+8>>2]=c[d>>2];c[b+28>>2]=1;c[b+44>>2]=0;d=c[46972]|0;if(!d)d=0;else d=c[d>>2]|0;if((d|0)==(b|0))pD()}return}function ED(){var b=0,d=0;d=c[46964]|0;b=(c[46965]|0)+-2|0;while(1){if((b|0)<=-1){b=0;break}if((a[d+b>>0]|0)==46){b=1;break}else b=b+-1|0}return b|0}function FD(a,b){a=a|0;b=b|0;var d=0;d=c[46981]|0;if((d|0)<(b|0)){if(!d)d=T1(b+1|0)|0;else d=W1(c[46982]|0,b+1|0)|0;c[46982]=d;c[46981]=b}else d=c[46982]|0;r3(d,a)|0;c[46966]=c[46982];return}function GD(a){a=a|0;return T1(a)|0}function HD(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+2112|0;k=m+32|0;i=m+24|0;h=m+16|0;g=m+8|0;f=m;d=m+56|0;j=m+40|0;kA(j,1024,m+1080|0);e=c[46966]|0;if(e|0){nA(j,e)|0;nA(j,97027)|0}nA(j,b)|0;c[f>>2]=c[4725];m4(d,97030,f)|0;nA(j,d)|0;a:do if(!(a[c[46964]>>0]|0))switch(((c[46971]|0)+-1|0)/2|0|0){case 2:{c[g>>2]=16384;m4(d,97050,g)|0;nA(j,d)|0;b=c[46978]|0;if(!(a[b>>0]|0))break 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hE(a,b){a=a|0;b=b|0;c[46987]=(c[46987]|0)+-1;if((iE(a,b)|0)==-1)a=-1;else a=((jE(a,b,111008)|0)==-1)<<31>>31;return a|0}function iE(a,b){a=a|0;b=b|0;var d=0;d=c[46987]|0;while(1){if((d|0)<=0){d=0;break}if((jE(a,b,153597)|0)==-1){d=-1;break}else d=d+-1|0}return d|0}function jE(a,b,d){a=a|0;b=b|0;d=d|0;return lb[c[(c[(c[a+64>>2]|0)+8>>2]|0)+4>>2]&127](b,d)|0}function kE(a,b){a=a|0;b=b|0;a=OD(a)|0;while(1){if(!a){a=0;break}if(!(cE(a)|0)){if((fE(a,b,0)|0)==-1){a=-1;break}if((gE(a,b)|0)==-1){a=-1;break}if((hE(a,b)|0)==-1){a=-1;break}}else kE(a,b)|0;a=PD(a)|0}return a|0}function lE(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0;if((bE(a,b)|0)==0?(wE(a,b,c,d)|0)!=0:0)if((xE(a,b)|0)==0?(yE(b)|0)==0:0)e=5;else a=1;else e=5;if((e|0)==5)a=0;return a|0}function mE(a,b,c){a=a|0;b=b|0;c=c|0;var d=0;d=xC(a)|0;do if((iE(d,b)|0)!=-1?(pE(a,b)|0)!=-1:0){if((rE(a)|0)==0?(sE(a,b,c)|0)==-1:0){a=-1;break}a=jE(d,b,97330)|0}else a=-1;while(0);return a|0}function nE(a,b){a=a|0;b=b|0;a=OD(a)|0;while(1){if(!a){a=1;break}if((cE(a)|0)==0?mB(a,b,0)|0:0){a=0;break}a=PD(a)|0}return a|0}function oE(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;e=c[a>>2]&3;g=c[((e|0)==3?a:a+48|0)+40>>2]|0;e=c[((e|0)==2?a:a+-48|0)+40>>2]|0;f=xC(g)|0;do if((((((iE(f,b)|0)!=-1?(pE(g,b)|0)!=-1:0)?(qE(a,b,c[46988]|0)|0)!=-1:0)?(g=(xB(xC(g)|0)|0)!=0,(jE(f,b,g?100079:100084)|0)!=-1):0)?(pE(e,b)|0)!=-1:0)?(qE(a,b,c[46989]|0)|0)!=-1:0){if(!(rE(a)|0)){if((sE(a,b,d)|0)==-1){a=-1;break}}else if((tE(a,b,1)|0)==-1){a=-1;break}a=jE(f,b,97330)|0}else a=-1;while(0);return a|0}function pE(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;i=l;l=l+32|0;g=i;d=i+8|0;e=KB(a)|0;f=xC(a)|0;if(!e){j=a+8|0;a=c[j+4>>2]|0;e=g;c[e>>2]=c[j>>2];c[e+4>>2]=a;m4(d,97350,g)|0;if((jE(f,b,d)|0)==-1)a=-1;else h=4}else if((uE(f,b,e)|0)==-1)a=-1;else h=4;if((h|0)==4)a=0;l=i;return a|0}function qE(b,c,d){b=b|0;c=c|0;d=d|0;var e=0,f=0;a:do if((d|0)!=0?(e=xC(b)|0,f=RA(b,d)|0,(a[f>>0]|0)!=0):0)if((jE(e,c,107984)|0)==-1)b=-1;else{do if(iD(f)|0){if((uE(e,c,f)|0)==-1){b=-1;break a}}else{b=R2(f,58)|0;if(!b)if((vE(e,c,f,0)|0)==-1){b=-1;break a}else break;a[b>>0]=0;if((vE(e,c,f,0)|0)==-1){b=-1;break a}if((jE(e,c,107984)|0)==-1){b=-1;break a}if((vE(e,c,b+1|0,0)|0)==-1){b=-1;break a}a[b>>0]=58}while(0);b=0}else b=0;while(0);return b|0}function rE(a){a=a|0;return (c[a>>2]|0)>>>3&1|0}function sE(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;a:do if(!(c[a>>2]&2)){e=0;k=4}else{switch(tE(a,b,0)|0){case -1:{e=-1;break a}case 0:{e=0;k=4;break a}default:{}}e=1;k=4}while(0);b:do if((k|0)==4){f=wA(a)|0;j=xC(a)|0;c:do if(f){i=f+12|0;h=nb[c[d>>2]&63](d,0,128)|0;while(1){if(!h)break c;do if(c[a>>2]&2){g=c[46988]|0;if(g|0?(c[h+16>>2]|0)==(c[g+16>>2]|0):0)break;g=c[46989]|0;if(!((g|0)!=0?(c[h+16>>2]|0)==(c[g+16>>2]|0):0))k=12}else k=12;while(0);if((k|0)==12){k=0;f=h+16|0;if((c[(c[i>>2]|0)+(c[f>>2]<<2)>>2]|0)!=(c[h+12>>2]|0)){g=e+1|0;if(e){if((jE(j,b,97345)|0)==-1){e=-1;break b}if((iE(j,b)|0)==-1){e=-1;break b}}else{if((iE(j,b)|0)==-1){e=-1;break b}if((jE(j,b,97342)|0)==-1){e=-1;break b}c[46987]=(c[46987]|0)+1}if((uE(j,b,c[h+8>>2]|0)|0)==-1){e=-1;break b}if((jE(j,b,97348)|0)==-1){e=-1;break b}if((uE(j,b,c[(c[i>>2]|0)+(c[f>>2]<<2)>>2]|0)|0)==-1){e=-1;break b}else e=g}}h=nb[c[d>>2]&63](d,h,8)|0}}while(0);if((e|0)>0){if((jE(j,b,97340)|0)==-1){e=-1;break}c[46987]=(c[46987]|0)+-1}c[a>>2]=c[a>>2]|8;e=0}while(0);return e|0}function tE(b,c,d){b=b|0;c=c|0;d=d|0;var e=0,f=0;e=KB(b)|0;b=xC(b)|0;if((e|0)!=0?(a[e>>0]|0)!=0:0)if((jE(b,c,97333)|0)!=-1?(uE(b,c,e)|0)!=-1:0)if((d|0)!=0?(jE(b,c,97340)|0)==-1:0)b=-1;else{b=1;f=7}else b=-1;else{b=0;f=7}return b|0}function uE(a,b,c){a=a|0;b=b|0;c=c|0;return vE(a,b,c,1)|0}function vE(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;if(!d)c=_D(c,aE(c)|0)|0;else c=$D(c)|0;return jE(a,b,c)|0}function wE(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;a:do if(0<e>>>0|(0==(e|0)?(c[b>>2]|0)>>>4>>>0<d>>>0:0))b=0;else{b=_A(a,b)|0;while(1){if(!b){b=1;break a}if(0<e>>>0|(0==(e|0)?(c[c[b+40>>2]>>2]|0)>>>4>>>0<d>>>0:0)){b=0;break a}b=$A(a,b)|0}}while(0);return b|0}function xE(a,b){a=a|0;b=b|0;if(!(_A(a,b)|0))a=(XA(a,b)|0)==0&1;else a=0;return a|0}function yE(a){a=a|0;var b=0,d=0,e=0;a=wA(a)|0;a:do if(!a)a=0;else{d=a+8|0;e=c[d>>2]|0;b=a+12|0;a=nb[c[e>>2]&63](e,0,128)|0;while(1){if(!a){a=0;break a}if((c[(c[b>>2]|0)+(c[a+16>>2]<<2)>>2]|0)!=(c[a+12>>2]|0)){a=1;break a}e=c[d>>2]|0;a=nb[c[e>>2]&63](e,a,8)|0}}while(0);return a|0}function zE(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=rA(a,0)|0;if(e)if(((AE(a,b,111494,c[e+16>>2]|0,d)|0)!=-1?(AE(a,b,111481,c[e+8>>2]|0,d)|0)!=-1:0)?(AE(a,b,111450,c[e+12>>2]|0,d)|0)!=-1:0)f=5;else a=-1;else f=5;if((f|0)==5)a=0;return a|0}function AE(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;m=(g|0)!=0;if(m)l=0;else l=_z(f,0)|0;k=(l|0)==0;j=nb[c[f>>2]&63](f,0,128)|0;g=0;while(1){if(!j){n=23;break}i=j+12|0;n=c[i>>2]|0;if((n|0)!=0?(a[n>>0]|0)!=0:0)n=12;else n=7;do if((n|0)==7){n=0;if(!(a[j+22>>0]|0)){if(!k){h=nb[c[l>>2]&63](l,j,4)|0;o=c[h+12>>2]|0;if(o|0?a[o>>0]|0:0){n=12;break}if(!(a[h+22>>0]|0))n=12}}else n=12}while(0);if((n|0)==12){n=0;h=g+1|0;if(g){if((jE(b,d,97345)|0)==-1){g=-1;break}if((iE(b,d)|0)==-1){g=-1;break}}else{if((iE(b,d)|0)==-1){g=-1;break}if((jE(b,d,e)|0)==-1){g=-1;break}if((jE(b,d,97342)|0)==-1){g=-1;break}c[46987]=(c[46987]|0)+1}if((uE(b,d,c[j+8>>2]|0)|0)==-1){g=-1;break}if((jE(b,d,97348)|0)==-1){g=-1;break}if((uE(b,d,c[i>>2]|0)|0)==-1){g=-1;break}else g=h}j=nb[c[f>>2]&63](f,j,8)|0}do if((n|0)==23){if((g|0)>0){c[46987]=(c[46987]|0)+-1;if((g|0)!=1){if((jE(b,d,153599)|0)==-1){g=-1;break}if((iE(b,d)|0)==-1){g=-1;break}}g=(jE(b,d,97377)|0)==-1;if(m|g){g=g<<31>>31;break}}else if(m){g=0;break}_z(f,l)|0;g=0}while(0);return g|0}function BE(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;switch(c[b>>2]&3){case 0:{g=82;break}case 1:{g=83;break}case 3:case 2:{g=84;break}default:{}}b=lb[g&127](a,b)|0;if(!b)b=-1;else{FE(a,b,d,e,g,f);b=0}return b|0}function CE(a,b){a=a|0;b=b|0;if((xC(b)|0)!=(a|0))b=mB(a,b,0)|0;return b|0}function DE(a,b){a=a|0;b=b|0;if((xC(b)|0)!=(a|0))b=oC(a,b,0)|0;return b|0}function EE(a,b){a=a|0;b=b|0;return a|0}function FE(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;h=(f|0)!=0;if(h)vb[c&127](a,b,d);g=OD(a)|0;while(1){if(!g)break;i=lb[e&127](g,b)|0;if(i|0)FE(g,i,c,d,e,f);g=PD(g)|0}if(!h)vb[c&127](a,b,d);return}function GE(){var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=l;l=l+1200|0;r=t+800|0;g=t;c[46990]=0;c[46991]=-2;f=r;i=200;n=g;p=0;q=0;o=r;a:while(1){b[o>>1]=p;if((f+(i<<1)+-2|0)>>>0>o>>>0)k=i;else{m=o-f>>1;e=m+1|0;if(i>>>0>9999){s=75;break}k=i<<1;k=k>>>0<1e4?k:1e4;h=T1(k*6|3)|0;if(h){W6(h|0,f|0,e<<1|0)|0;i=h+(k>>>1<<2)|0;W6(i|0,n|0,e<<2|0)|0;if((f|0)==(r|0)){e=0;f=h}else{U1(f);e=0;f=h}}else{e=4;i=n}j=(e|0)==0;h=f+(m<<1)|0;switch((j?((f+(k<<1)+-2|0)>>>0>h>>>0?0:9):e)&15){case 9:{e=1;s=76;break a}case 4:{s=75;break a}case 0:{g=j?i+(m<<2)|0:g;n=i;o=j?h:o;break}default:{e=0;break a}}}if((p|0)==6){e=0;s=76;break}e=a[97381+p>>0]|0;j=e<<24>>24;do if(e<<24>>24!=-18){h=c[46991]|0;if((h|0)==-2){h=mD()|0;c[46991]=h}if((h|0)>=1)if(h>>>0<269)i=d[97461+h>>0]|0;else i=2;else{c[46991]=0;i=0}h=i+j|0;if(h>>>0<=59?(i|0)==(d[97730+h>>0]|0):0){p=a[97790+h>>0]|0;e=p<<24>>24;if(p<<24>>24<1){h=0-e|0;s=23;break}else{c[46991]=-2;g=g+4|0;c[g>>2]=c[46992];j=(q|0)==0?0:q+-1|0;h=o;break}}else s=22}else s=22;while(0);if((s|0)==22){s=0;p=a[97850+p>>0]|0;h=p&255;if(!(p<<24>>24)){h=c[46991]|0;b:do switch(q|0){case 0:{c[46990]=(c[46990]|0)+1;HD(143761);h=o;break}case 3:if((h|0)<1)if(!h){e=1;s=76;break a}else{h=o;break b}else{c[46991]=-2;h=o;break b}default:h=o}while(0);while(1){if(e<<24>>24==18)break;if((h|0)==(f|0)){e=1;s=76;break a}e=h+-2|0;g=g+-4|0;h=e;e=a[97381+(b[e>>1]|0)>>0]|0}g=g+4|0;c[g>>2]=c[46992];e=1;j=3}else s=23}do if((s|0)==23){s=0;i=d[97930+h>>0]|0;e=c[g+(1-i<<2)>>2]|0;c:do switch(h|0){case 2:{HE();IE();break}case 3:{if(c[46993]|0){HE();IE();sB(c[46993]|0)|0;c[46942]=0;c[46993]=0}break}case 6:{JE(c[g>>2]|0,c[g+-4>>2]|0,c[g+-8>>2]|0);break}case 7:{e=c[g>>2]|0;break}case 8:{e=0;break}case 9:{e=1;break}case 10:{e=0;break}case 11:{e=0;break}case 12:{e=1;break}case 21:if(!(c[g+-4>>2]|0)){LE();break c}else{KE();break c}case 24:{ME();break}case 25:{ME();break}case 26:{e=1;break}case 27:{e=0;break}case 30:{NE(c[g>>2]|0,0,0);break}case 31:{NE(c[g+-8>>2]|0,c[g>>2]|0,0);break}case 32:{NE(c[g+-16>>2]|0,c[g+-8>>2]|0,c[g>>2]|0);break}case 33:{OE(c[g+-8>>2]|0,c[g+-4>>2]|0);break}case 34:{OE(258,0);break}case 35:{e=258;break}case 36:{e=259;break}case 37:{e=260;break}case 38:{e=c[g+-4>>2]|0;break}case 39:{e=0;break}case 48:{PE(c[g+-8>>2]|0,c[g>>2]|0);break}case 49:{PE(c[g>>2]|0,0);break}case 51:{QE(c[g>>2]|0);break}case 52:{RE();break}case 53:{e=c[g>>2]|0;break}case 54:{e=0;break}case 55:{e=0;break}case 59:{e=c[g>>2]|0;break}case 60:{e=c[g>>2]|0;break}case 61:{e=c[g>>2]|0;break}case 62:{e=SE(c[g+-8>>2]|0,c[g>>2]|0)|0;break}default:{}}while(0);i=0-i|0;p=g+(i<<2)|0;i=o+(i<<1)|0;g=p+4|0;c[p+4>>2]=e;e=(d[97993+h>>0]|0)+-24|0;p=b[i>>1]|0;h=p+(a[98056+e>>0]|0)|0;if(h>>>0<60?(d[97730+h>>0]|0)==(p|0):0){e=a[97790+h>>0]|0;j=q;h=i;break}e=a[98091+e>>0]|0;j=q;h=i}while(0);i=k;p=e;q=j;o=h+2|0}if((s|0)==75){HD(140558);e=2;s=76}if((s|0)==76)if((f|0)!=(r|0))U1(f);l=t;return e|0}function HE(){var a=0,b=0;a=c[46994]|0;while(1){if(!a)break;_E(a+8|0);_E((c[46994]|0)+24|0);_E((c[46994]|0)+16|0);b=TE(c[46994]|0)|0;c[46994]=b;a=b}return}function IE(){ID();TB(c[46993]|0);return}function JE(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;h=l;l=l+16|0;g=h;f=c[46993]|0;if(!f){a[187980]=d&1|(e&255)<<1&2|a[187980]&-12|8;f=c[46996]|0;a[g>>0]=a[187980]|0;a[g+1>>0]=a[187981]|0;a[g+2>>0]=a[187982]|0;a[g+3>>0]=a[187983]|0;f=oB(b,g,f)|0;c[46993]=f}c[46942]=f;c[46994]=UE(c[46994]|0,f)|0;hD(0,b)|0;l=h;return}function KE(){var b=0,d=0,e=0,f=0,g=0,h=0;ZE(2);e=c[46994]|0;f=a[98126]|0;h=0;b=e+24|0;while(1){d=c[b>>2]|0;if(!d)break;if(((c[d>>2]|0)==267?(g=c[d+4>>2]|0,(a[g>>0]|0)==f<<24>>24):0)?(s2(g,98126)|0)==0:0)b=c[d+8>>2]|0;else b=h;h=b;b=d+12|0}b=e+16|0;a:while(1){e=c[b>>2]|0;b=e+12|0;if(!(c[b>>2]|0))break;d=e+4|0;if((c[e>>2]|0)==262){e=c[d>>2]|0;d=jC(e)|0;while(1){if(!d)continue a;g=oC(c[c[46994]>>2]|0,d,0)|0;fF(g,0,c[b>>2]|0,h);d=kC(e,d)|0}}else while(1){d=c[d>>2]|0;if(!d)continue a;fF(c[d+4>>2]|0,c[d+8>>2]|0,c[b>>2]|0,h);d=d+12|0}}_E((c[46994]|0)+8|0);_E((c[46994]|0)+16|0);_E((c[46994]|0)+24|0);c[(c[46994]|0)+4>>2]=0;return}function LE(){var a=0;ZE(1);a=(c[46994]|0)+8|0;while(1){a=c[a>>2]|0;if(!a)break;eF(c[a+4>>2]|0);a=a+12|0}_E((c[46994]|0)+8|0);_E((c[46994]|0)+24|0);_E((c[46994]|0)+16|0);c[(c[46994]|0)+4>>2]=0;return}function ME(){var a=0,b=0;a=c[46994]|0;b=c[a+8>>2]|0;if(!b){b=c[a+4>>2]|0;if(!b)b=0;else{b=dF(b)|0;a=c[46994]|0}c[a+4>>2]=0}else{b=cF(b)|0;a=c[46994]|0;c[a+12>>2]=0;c[a+8>>2]=0}if(b|0)WE(a+16|0,b);return}function NE(a,b,d){a=a|0;b=b|0;d=d|0;if(d)b=aF(b,d)|0;d=bF(tC(c[c[46994]>>2]|0,a,1)|0,b)|0;WE((c[46994]|0)+8|0,d);hD(c[46993]|0,a)|0;return}function OE(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;if(d|0)YE();d=(c[46994]|0)+24|0;while(1){d=c[d>>2]|0;if(!d)break;if(!(c[d+8>>2]|0))YE();d=d+12|0}g=(b|0)==260?2:(b|0)==259&1;ZE(g);b=c[46994]|0;d=b+24|0;while(1){f=c[d>>2]|0;if(!f)break;do if((c[f>>2]|0)!=267){d=c[f+4>>2]|0;e=c[b>>2]|0;if(!((a[d+21>>0]|0)!=0&(e|0)==(c[46993]|0))){d=GA(e,g,c[d+8>>2]|0,c[f+8>>2]|0)|0;b=c[46994]|0;if((c[b>>2]|0)!=(c[46993]|0))break}a[d+22>>0]=1}while(0);d=f+12|0}_E(b+24|0);return}function PE(a,b){a=a|0;b=b|0;b=VE(a,b)|0;WE((c[46994]|0)+24|0,b);return}function QE(a){a=a|0;var b=0;b=c[46994]|0;c[46994]=UE(b,ND(c[b>>2]|0,a,1)|0)|0;hD(c[46993]|0,a)|0;return}function RE(){var a=0,b=0;a=c[46994]|0;b=c[a>>2]|0;a=TE(a)|0;c[46994]=a;c[a+4>>2]=b;return}function SE(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;g=l;l=l+1024|0;f=g;d=l2(a)|0;d=d+1+(l2(b)|0)|0;if(d>>>0<1025)d=f;else d=T1(d)|0;r3(d,a)|0;F4(d,b)|0;e=fD(c[46993]|0,d)|0;hD(c[46993]|0,a)|0;hD(c[46993]|0,b)|0;if((d|0)!=(f|0))U1(d);l=g;return e|0}function TE(a){a=a|0;var b=0;b=c[(c[46994]|0)+32>>2]|0;eC(c[46993]|0,a);return b|0}function UE(a,b){a=a|0;b=b|0;var d=0;d=dC(c[46993]|0,36)|0;c[d+32>>2]=a;c[d>>2]=b;return d|0}function VE(a,b){a=a|0;b=b|0;return XE(267,a,b)|0}function WE(a,b){a=a|0;b=b|0;var d=0,e=0;d=a+4|0;e=c[d>>2]|0;if(e|0)c[e+12>>2]=b;c[d>>2]=b;if(!(c[a>>2]|0))c[a>>2]=b;return}function XE(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=dC(c[46993]|0,16)|0;c[e>>2]=a;c[e+4>>2]=b;c[e+8>>2]=d;return e|0}function YE(){var a=0;a=l;l=l+16|0;gA(0,98130,a)|0;l=a;return}function ZE(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;g=(b|0)==2;d=(c[46994]|0)+24|0;while(1){d=c[d>>2]|0;if(!d)break;e=d+4|0;f=c[e>>2]|0;if(!((g?(a[f>>0]|0)==(a[98126]|0):0)?!(s2(f,98126)|0):0)){h=GA(c[c[46994]>>2]|0,b,f,0)|0;c[e>>2]=h;if(!h)c[e>>2]=GA(c[c[46994]>>2]|0,b,f,195059)|0;c[d>>2]=266;hD(c[46993]|0,f)|0}d=d+12|0}return}function _E(a){a=a|0;$E(c[a>>2]|0);c[a+4>>2]=0;c[a>>2]=0;return}function $E(a){a=a|0;var b=0;while(1){if(!a)break;b=c[a+12>>2]|0;switch(c[a>>2]|0){case 265:{$E(c[a+4>>2]|0);break}case 266:case 267:{hD(c[46993]|0,c[a+8>>2]|0)|0;break}default:{}}eC(c[46993]|0,a);a=b}return}function aF(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+1040|0;f=h;g=h+8|0;d=l2(a)|0;d=d+2+(l2(b)|0)|0;if(d>>>0<1025)e=g;else e=T1(d)|0;c[f>>2]=a;c[f+4>>2]=b;m4(e,98163,f)|0;d=fD(c[46993]|0,e)|0;hD(c[46993]|0,a)|0;hD(c[46993]|0,b)|0;if((e|0)!=(g|0))U1(e);l=h;return d|0}function bF(a,b){a=a|0;b=b|0;return XE(259,a,b)|0}function cF(a){a=a|0;return XE(265,a,0)|0}function dF(a){a=a|0;return XE(262,a,0)|0}function eF(a){a=a|0;var b=0,d=0;b=(c[46994]|0)+24|0;while(1){b=c[b>>2]|0;if(!b)break;if((c[b>>2]|0)==266?(d=c[b+4>>2]|0,d|0):0)KA(a,d,c[b+8>>2]|0)|0;b=b+12|0}return}function fF(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;f=d+4|0;a:do if((c[d>>2]|0)==262){d=c[f>>2]|0;f=jC(d)|0;while(1){if(!f)break a;gF(a,b,oC(c[c[46994]>>2]|0,f,0)|0,0,e);f=kC(d,f)|0}}else while(1){f=c[f>>2]|0;if(!f)break a;d=oC(c[c[46994]>>2]|0,c[f+4>>2]|0,0)|0;gF(a,b,d,c[f+8>>2]|0,e);f=f+12|0}while(0);return}function gF(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;d=iB(c[c[46994]>>2]|0,a,d,f,1)|0;if(d|0){g=c[d>>2]&3;f=c[((g|0)==2?d:d+-48|0)+40>>2]|0;a=(f|0)==(a|0)?(c[((g|0)==3?d:d+48|0)+40>>2]|0)!=(f|0):0;hF(d,111455,a?e:b);hF(d,111464,a?b:e);eF(d)}return}function hF(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;if(d|0){e=GA(c[c[46994]>>2]|0,2,b,0)|0;if(!e)e=GA(c[c[46994]>>2]|0,2,b,195059)|0;KA(a,e,d)|0}return}function iF(a,b,d){a=a|0;b=b|0;d=d|0;c[46962]=b;c[46993]=a;c[46942]=0;a=d|0?d:18600;c[46996]=a;lD(a,b);GE()|0;a=c[46942]|0;if(!a){KD();a=c[46942]|0}return a|0}function jF(a,b){a=a|0;b=b|0;return iF(0,a,b)|0}function kF(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;j=k+8|0;i=k;d=c[b+76>>2]|0;f=c[b>>2]|0;if((d|0)!=0?(e=c[d>>2]|0,(e|0)!=0):0){tb[e&127](b);g=12}else g=4;do if((g|0)==4)if((c[b+40>>2]|0)==0?(h=b+36|0,(c[h>>2]|0)==0):0){if(a[f+13>>0]|0)lF(b);d=b+32|0;e=c[d>>2]|0;if(!e){c[h>>2]=c[15679];g=12;break}f=$4(e,106512)|0;c[h>>2]=f;if(!f){j=c[(c[b+12>>2]|0)+16>>2]|0;h=c[d>>2]|0;d=t2(c[(d2()|0)>>2]|0)|0;c[i>>2]=h;c[i+4>>2]=d;ub[j&63](98169,i);d=1}else g=12}else g=12;while(0);if((g|0)==12)if(!(c[b+152>>2]&1024))d=0;else{ub[c[(c[b+12>>2]|0)+16>>2]&63](98207,j);d=1}l=k;return d|0}function lF(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;h=l;l=l+112|0;e=h;g=h+4|0;d=c[b+24>>2]|0;if(!d)a[g>>0]=0;else{c[e>>2]=d+1;m4(g,98225,e)|0}e=c[b+20>>2]|0;e=e|0?e:98229;d=l2(e)|0;i=l2(g)|0;f=b+52|0;i=d+1+i+(l2(c[f>>2]|0)|0)|0;d=i+11|0;if((c[46997]|0)>>>0<(i+1|0)>>>0){c[46997]=d;d=W1(c[46998]|0,d)|0;c[46998]=d}else d=c[46998]|0;r3(d,e)|0;F4(c[46998]|0,g)|0;d=c[46998]|0;d=d+(l2(d)|0)|0;a[d>>0]=46;a[d+1>>0]=0;d=D4(c[f>>2]|0)|0;f=c[46998]|0;while(1){e=E4(d,58)|0;if(!e)break;F4(f,e+1|0)|0;i=c[46998]|0;g=i+(l2(i)|0)|0;a[g>>0]=46;a[g+1>>0]=0;a[e>>0]=0;f=i}F4(f,d)|0;U1(d);c[b+32>>2]=c[46998];l=h;return}function mF(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;h=l;l=l+16|0;g=h+8|0;f=h;if((b|0)!=0&(d|0)!=0){if(c[a+152>>2]&1024|0){ub[c[(c[a+12>>2]|0)+16>>2]&63](98207,f);Sa(1)}if((nF(a,b,d)|0)==(d|0))e=d;else{f=c[(c[a+12>>2]|0)+16>>2]|0;c[g>>2]=d;ub[f&63](98239,g);Sa(1)}}else e=0;l=h;return e|0}function nF(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;n=o;f=c[(c[b>>2]|0)+104>>2]|0;do if(!f){j=b+40|0;h=c[j>>2]|0;if(!h){e=U4(d,1,e,c[b+36>>2]|0)|0;break}f=b+44|0;i=b+48|0;g=c[i>>2]|0;do if(((c[f>>2]|0)+-1-g|0)>>>0<e>>>0){g=e+4096+g&-4096;c[f>>2]=g;f=W1(h,g)|0;c[j>>2]=f;if(!f){ub[c[(c[b+12>>2]|0)+16>>2]&63](98264,n);Sa(1)}else{k=f;m=c[i>>2]|0;break}}else{k=h;m=g}while(0);W6(k+m|0,d|0,e|0)|0;n=(c[i>>2]|0)+e|0;c[i>>2]=n;a[(c[j>>2]|0)+n>>0]=0}else e=nb[f&63](b,d,e)|0;while(0);l=o;return e|0}function oF(a,b){a=a|0;b=b|0;var c=0;c=l2(b)|0;b=(mF(a,b,c)|0)==(c|0);return (b?1:-1)|0}function pF(b,c){b=b|0;c=c|0;var d=0,e=0;d=l;l=l+16|0;e=d;a[e>>0]=c;b=(mF(b,e,1)|0)==1;l=d;return (b?c:-1)|0}function qF(b){b=b|0;var d=0;d=c[b+36>>2]|0;if(((d|0)!=0?(a[b+144>>0]|0)==0:0)?(c[(c[b>>2]|0)+104>>2]|0)==0:0)b=M4(d)|0;else b=0;return b|0}function rF(a){a=a|0;var b=0,d=0;b=c[a+76>>2]|0;if(b|0?(d=c[b+4>>2]|0,d|0):0)tb[d&127](a);qF(a)|0;return}function sF(a){a=a|0;var b=0,d=0,e=0;e=l;l=l+16|0;d=c[a+76>>2]|0;if(c[a+152>>2]&1024|0){ub[c[(c[a+12>>2]|0)+16>>2]&63](98291,e);Sa(1)}if((d|0)!=0?(b=c[d+8>>2]|0,(b|0)!=0):0)tb[b&127](a);else{qF(a)|0;tF(a)}l=e;return}function tF(b){b=b|0;var d=0,e=0,f=0;d=b+32|0;if((c[d>>2]|0?(e=b+36|0,f=c[e>>2]|0,(f|0)!=(c[15679]|0)):0)?(a[b+144>>0]|0)==0:0){if(f|0){I4(f)|0;c[e>>2]=0}c[d>>2]=0}return}function uF(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=l;l=l+1056|0;f=i;h=i+24|0;g=i+8|0;c[g>>2]=d;e=G3(h,1024,b,g)|0;if((e|0)>=0){if((e|0)>1023){e=KK(e+1|0)|0;c[g>>2]=d;f=e;e=n4(e,b,g)|0}else f=h;mF(a,f,e)|0;if((f|0)!=(h|0))U1(f)}else{c[f>>2]=t2(c[(d2()|0)>>2]|0)|0;gA(1,98308,f)|0}l=i;return}function vF(a,b){a=a|0;b=+b;var d=0,e=0,f=0;d=l;l=l+16|0;e=d;f=wF(e,b)|0;mF(a,f,c[e>>2]|0)|0;l=d;return}function wF(b,d){b=b|0;d=+d;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;if(!(d<-1.0e15))if(!(d>1.0e15)){d=d*1.0e4;f=~~((d<0.0?-.5:.5)+d);if(!f){f=138394;e=1}else{l=(f|0)<0;k=4;i=0;e=194826;f=l?0-f|0:f;while(1){if(!((f|0)!=0|(k|0)>0))break;g=(f|0)%10|0;j=(f|0)/10|0;h=e+-1|0;if((g|0)==0&i<<24>>24==0)f=0;else{a[h>>0]=g|48;f=1;e=h}if((k|0)==1)if(!(f<<24>>24))f=1;else{e=e+-1|0;a[e>>0]=46;f=1}k=k+-1|0;i=f;f=j}if(l){e=e+-1|0;a[e>>0]=45}f=e;e=194826-e|0}}else{f=98323;e=18}else{f=98322;e=19}c[b>>2]=e;return f|0}function xF(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=l;l=l+16|0;e=d;f=wF(e,+h[b>>3])|0;mF(a,f,c[e>>2]|0)|0;mF(a,102604,1)|0;b=wF(e,+h[b+8>>3])|0;mF(a,b,c[e>>2]|0)|0;l=d;return}function yF(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;g=l;l=l+16|0;f=g;e=0;while(1){h=b+(e<<4)|0;c[f>>2]=c[h>>2];c[f+4>>2]=c[h+4>>2];c[f+8>>2]=c[h+8>>2];c[f+12>>2]=c[h+12>>2];xF(a,f);e=e+1|0;if((e|0)>=(d|0))break;mF(a,102604,1)|0}l=g;return}function zF(a,b){a=a|0;b=b|0;var d=0;d=JK(392)|0;if(d|0){c[d>>2]=20156;c[d+16>>2]=23;c[d+32>>2]=a;c[d+36>>2]=b}return d|0}function AF(a){a=a|0;var b=0,d=0,e=0,f=0;b=JK(392)|0;d=b;e=a;f=d+40|0;do{c[d>>2]=c[e>>2];d=d+4|0;e=e+4|0}while((d|0)<(f|0));f=b+60|0;e=a+60|0;c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];c[f+8>>2]=c[e+8>>2];c[f+12>>2]=c[e+12>>2];c[f+16>>2]=c[e+16>>2];f=b+80|0;e=a+80|0;c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];c[f+8>>2]=c[e+8>>2];c[f+12>>2]=c[e+12>>2];c[f+16>>2]=c[e+16>>2];return b|0}function BF(a){a=a|0;IF(a);U1(a);return}function CF(a,b){a=a|0;b=b|0;var d=0,e=0;e=a+160|0;d=c[e>>2]|0;if(d){e=c[46999]|0;if(e){d=c[e+4>>2]|0;if(!d){d=JK(632)|0;c[(c[46999]|0)+4>>2]=d}}}else{d=JK(632)|0;c[e>>2]=d;c[a+164>>2]=d}c[46999]=d;c[d+32>>2]=b;c[d>>2]=a;return}function DF(a,b){a=a|0;b=b|0;var d=0,e=0;e=a+160|0;d=c[e>>2]|0;if(d){e=c[47e3]|0;if(e){d=c[e+4>>2]|0;if(!d){d=JK(632)|0;c[(c[47e3]|0)+4>>2]=d}}}else{d=JK(632)|0;c[e>>2]=d;c[a+164>>2]=d}c[47e3]=d;c[d+52>>2]=b;c[d>>2]=a;return (LF(a,3,b)|0)!=0|0}function EF(a){a=a|0;var b=0;b=c[a+160>>2]|0;c[a+164>>2]=b;return b|0}function FF(a){a=a|0;var b=0,d=0,e=0;a=a+164|0;b=c[a>>2]|0;d=c[b+4>>2]|0;if(d|0?(e=d+52|0,(c[e>>2]|0)==0):0)c[e>>2]=c[b+52>>2];c[a>>2]=d;return d|0}function GF(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=a+8|0;f=b+10|0;if((c[e>>2]|0)>(b|0))a=c[a>>2]|0;else{c[e>>2]=f;f=MK(c[a>>2]|0,f<<2)|0;c[a>>2]=f;a=f}c[a+(b<<2)>>2]=d;return}function HF(a){a=a|0;var b=0;b=c[a>>2]|0;if(b|0)U1(b);c[a>>2]=0;c[a+8>>2]=0;c[a+4>>2]=0;return}function IF(a){a=a|0;var b=0,d=0,e=0;d=a+160|0;b=c[d>>2]|0;while(1){if(!b)break;e=c[b+4>>2]|0;HF(b+604|0);HF(b+592|0);U1(c[b+584>>2]|0);U1(c[b+588>>2]|0);U1(b);b=e}c[47e3]=0;c[46999]=0;c[a+192>>2]=0;c[a+164>>2]=0;c[d>>2]=0;c[a+28>>2]=0;return}function JF(b,d,e,f,g,h){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,m=0,n=0;n=l;l=l+128|0;k=n+64|0;m=n;i4(k,e,63)|0;i=R2(k,58)|0;if(i|0)a[i>>0]=0;b=b+60+(d<<2)|0;while(1){d=c[b>>2]|0;if(!d){d=b;b=0;break}i4(m,c[d+4>>2]|0,63)|0;d=R2(m,58)|0;if(d|0)a[d>>0]=0;if((s2(k,m)|0)<1){j=9;break}b=c[b>>2]|0}while(1){if((j|0)==9){d=b;b=c[b>>2]|0}if(!b)break;i4(m,c[b+4>>2]|0,63)|0;b=R2(m,58)|0;if(b|0)a[b>>0]=0;if(s2(k,m)|0)break;b=c[d>>2]|0;if((c[b+8>>2]|0)<=(f|0))break;j=9}m=KK(20)|0;c[m>>2]=c[d>>2];c[d>>2]=m;c[m+4>>2]=e;c[m+8>>2]=f;c[m+12>>2]=g;c[m+16>>2]=h;l=n;return 1}function KF(a,b){a=a|0;b=b|0;b=l;l=l+16|0;gA(1,98396,b)|0;l=b;return 0}function LF(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+144|0;r=s;o=s+80|0;p=s+16|0;n=(d+-3|0)>>>0<2?0:d;i4(o,e,63)|0;e=R2(o,58)|0;if(e){f=e+1|0;a[e>>0]=0;e=R2(f,58)|0;if(!e)m=0;else{a[e>>0]=0;m=e+1|0}}else{m=0;f=0}i=(f|0)!=0;j=(m|0)==0;k=(n|0)==(d|0);h=b+60+(d<<2)|0;a:while(1){e=c[h>>2]|0;if(!e){e=0;break}i4(p,c[e+4>>2]|0,63)|0;e=R2(p,58)|0;if(!e)e=0;else{a[e>>0]=0;e=e+1|0}do if(!(s2(p,o)|0)){g=(e|0)!=0;if(i&g?s2(e,f)|0:0)break;if(!j?s2(m,c[(c[(c[h>>2]|0)+12>>2]|0)+8>>2]|0)|0:0)break;if(k|g^1){q=16;break a}if(LF(b,n,e)|0){q=16;break a}}while(0);h=c[h>>2]|0}do if((q|0)==16){e=c[h>>2]|0;if(e){f=e+16|0;if((c[f>>2]|0)==0?(KF(0,0)|0,(c[f>>2]|0)==0):0){e=0;break}if((c[b+8>>2]|0)>0){q=c[15712]|0;o=c[e+4>>2]|0;p=c[(c[e+12>>2]|0)+8>>2]|0;c[r>>2]=c[20168+(d<<2)>>2];c[r+4>>2]=o;c[r+8>>2]=p;w4(q,98427,r)|0}}else e=0}while(0);c[b+80+(d<<2)>>2]=e;l=s;return e|0}function MF(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;do if(!e)e=0;else{if(c[5047]|0){kA(188004,0,0);c[5047]=0}h=D4(e)|0;e=R2(h,58)|0;if(e){a[e>>0]=0;f=b+60+(d<<2)|0;e=1;g=f;while(1){b=c[g>>2]|0;if(!b)break;d=D4(c[b+4>>2]|0)|0;b=R2(d,58)|0;if(b|0)a[b>>0]=0;if(!((a[h>>0]|0)!=0?(c3(h,d)|0)!=0:0)){e=c[47002]|0;if(e>>>0>=(c[47003]|0)>>>0){lA(188004,1)|0;e=c[47002]|0}c[47002]=e+1;a[e>>0]=32;nA(188004,c[(c[g>>2]|0)+4>>2]|0)|0;e=c[47002]|0;if(e>>>0>=(c[47003]|0)>>>0){lA(188004,1)|0;e=c[47002]|0}c[47002]=e+1;a[e>>0]=58;nA(188004,c[(c[(c[g>>2]|0)+12>>2]|0)+8>>2]|0)|0;e=0}U1(d);g=c[g>>2]|0}U1(h);if(e<<24>>24){g=0;i=19}}else{U1(h);g=0;f=b+60+(d<<2)|0;e=1;i=19}if((i|0)==19){while(1){b=c[f>>2]|0;if(!b)break;d=D4(c[b+4>>2]|0)|0;b=R2(d,58)|0;if(b|0)a[b>>0]=0;if(!((g|0)!=0?(c3(g,d)|0)==0:0)){e=c[47002]|0;if(e>>>0>=(c[47003]|0)>>>0){lA(188004,1)|0;e=c[47002]|0}c[47002]=e+1;a[e>>0]=32;nA(188004,d)|0;e=0}g=d;f=c[f>>2]|0;i=19}if(e<<24>>24){e=195059;break}}e=c[47002]|0;if(e>>>0>=(c[47003]|0)>>>0){lA(188004,1)|0;e=c[47002]|0}a[e>>0]=0;e=c[47001]|0;c[47002]=e}while(0);return e|0}function NF(b){b=b|0;var d=0;if(!b)qa(111208,98444,573,98458);if(!(a[b>>0]|0))qa(111213,98444,574,98458);d=c[47005]|0;if(!d)b=0;else b=nb[c[d>>2]&63](d,b,512)|0;return b|0}function OF(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;j=k+8|0;i=k;if(!b)qa(111312,98444,589,98475);g=b+8|0;e=c[g>>2]|0;if(!e)qa(111315,98444,590,98475);if(!(a[e>>0]|0))qa(111324,98444,591,98475);h=b+20|0;f=c[h>>2]|0;do if(!f){d=mP(e)|0;if(!d){c[i>>2]=c[g>>2];gA(0,98499,i)|0;d=0;break}i=$4(d,138821)|0;c[h>>2]=i;if(!i){c[j>>2]=t2(c[(d2()|0)>>2]|0)|0;c[j+4>>2]=d;gA(0,98524,j)|0;d=0;break}d=c[47006]|0;if((d|0)>49){a[b+17>>0]=1;d=1;break}else{c[47006]=d+1;d=1;break}}else{n5(f,0,0)|0;if(!(c[h>>2]|0))qa(98545,98444,614,98475);else d=1}while(0);l=k;return d|0}function PF(b){b=b|0;var d=0,e=0;if(a[b+17>>0]|0?(d=b+20|0,e=c[d>>2]|0,e|0):0){I4(e)|0;c[d>>2]=0}return}function QF(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0.0;if(!b){e=-1;d=-1}else{e=c[b+48>>2]|0;f=+(e|0);if(!e){g=+h[d>>3];f=+h[d+8>>3]}else{h[d+8>>3]=f;h[d>>3]=f;g=f}e=~~(+((c[b+40>>2]|0)*72|0)/g);d=~~(+((c[b+44>>2]|0)*72|0)/f)}c[a>>2]=e;c[a+4>>2]=d;return}function RF(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0.0;m=l;l=l+48|0;f=m+16|0;j=m;k=m+32|0;if((e|0)!=0?(a[e>>0]|0)!=0:0){if((0==0?(g=c[47133]|0,(c[47007]|0)!=(g|0)):0)?(c[47007]=g,i=c[47005]|0,i|0):0){Rz(i)|0;c[47005]=0}n=+h[(c[(c[d+16>>2]|0)+8>>2]|0)+24>>3];n=!(n>=1.0)?96.0:n;h[j+8>>3]=n;h[j>>3]=n;d=SF(e)|0;c[f>>2]=c[j>>2];c[f+4>>2]=c[j+4>>2];c[f+8>>2]=c[j+8>>2];c[f+12>>2]=c[j+12>>2];QF(k,d,f);d=c[k>>2]|0;f=c[k+4>>2]|0}else{d=-1;f=-1}c[b>>2]=d;c[b+4>>2]=f;l=m;return}function SF(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+16|0;e=f;if(!a)qa(111208,98444,638,98551);if(!(c[47005]|0))c[47005]=Vz(20192,c[4589]|0)|0;b=NF(a)|0;a:do if(!b){b=JK(64)|0;if(!b)b=0;else{d=b+8|0;c[d>>2]=fD(0,a)|0;if(!((OF(b)|0)<<24>>24)){TF(b);b=0;break}if(!(c[b+20>>2]|0))qa(98545,98444,653,98551);do switch(UF(b)|0){case 0:{a=iN(c[d>>2]|0)|0;c[b+52>>2]=a;if(!a){c[e>>2]=c[d>>2];gA(0,98568,e)|0;TF(b);b=0;break a}break}case 2:{VF(b);break}case 3:{WF(b);break}case 1:{XF(b);break}case 4:{YF(b);break}case 6:{ZF(b);break}case 11:{_F(b);break}case 8:{$F(b);break}case 5:{aG(b);break}case 12:{bG(b);break}default:{}}while(0);PF(b);e=c[47005]|0;nb[c[e>>2]&63](e,b,1)|0}}else PF(b);while(0);l=f;return b|0}function TF(a){a=a|0;var b=0;b=c[a+8>>2]|0;if(b|0)hD(0,b)|0;U1(a);return}function UF(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+224|0;f=j+200|0;g=j;h=a+20|0;b=c[h>>2]|0;a:do if((b|0)!=0?(x6(f,1,20,b)|0)==20:0){d=0;while(1){if(d>>>0>=10){i=13;break a}if(!($3(f,c[20228+(d<<4)>>2]|0,c[20228+(d<<4)+4>>2]|0)|0))break;d=d+1|0}e=a+28|0;c[e>>2]=c[20228+(d<<4)+12>>2];b=c[20228+(d<<4)+8>>2]|0;a=a+24|0;c[a>>2]=b;switch(d|0){case 7:break;case 8:{if($3(f+8|0,98784,4)|0)break a;c[e>>2]=98789;c[a>>2]=11;b=11;break a}default:break a}while(1){if(!(c4(g,200,c[h>>2]|0)|0)){i=11;break}if(!($3(g,98779,4)|0)){i=8;break}}if((i|0)==8){c[e>>2]=101803;c[a>>2]=8;b=8;break}else if((i|0)==11){b=c[a>>2]|0;break}}else i=13;while(0);if((i|0)==13){c[a+28>>2]=98794;c[a+24>>2]=0;b=0}l=j;return b|0}function VF(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+16|0;b=f+4|0;d=f;c[a+48>>2]=0;e=a+20|0;n5(c[e>>2]|0,6,0)|0;if((kG(c[e>>2]|0,2,b)|0)<<24>>24?(kG(c[e>>2]|0,2,d)|0)<<24>>24:0){c[a+40>>2]=c[b>>2];c[a+44>>2]=c[d>>2]}l=f;return}function WF(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+16|0;b=f+4|0;d=f;c[a+48>>2]=0;e=a+20|0;n5(c[e>>2]|0,16,0)|0;if((cG(c[e>>2]|0,4,b)|0)<<24>>24?(cG(c[e>>2]|0,4,d)|0)<<24>>24:0){c[a+40>>2]=c[b>>2];c[a+44>>2]=c[d>>2]}l=f;return}function XF(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;b=h+12|0;d=h+8|0;e=h+4|0;f=h;c[a+48>>2]=0;g=a+20|0;n5(c[g>>2]|0,16,0)|0;if((((kG(c[g>>2]|0,2,b)|0)<<24>>24?(kG(c[g>>2]|0,2,d)|0)<<24>>24:0)?(kG(c[g>>2]|0,2,e)|0)<<24>>24:0)?(kG(c[g>>2]|0,2,f)|0)<<24>>24:0){c[a+40>>2]=c[b>>2]<<16|c[d>>2];c[a+44>>2]=c[e>>2]<<16|c[f>>2]}l=h;return}function YF(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+32|0;b=m+16|0;e=m+12|0;f=m+8|0;h=m+4|0;i=m;c[a+48>>2]=0;j=a+20|0;while(1){if(!((cG(c[j>>2]|0,1,b)|0)<<24>>24))break;d=c[b>>2]|0;if((d|0)==255)continue;if(R2(98767,d)|0)continue;if((d|0)==192){k=6;break}g=c[j>>2]|0;if((d|0)==194){k=11;break}if(!((cG(g,2,e)|0)<<24>>24))break;n5(c[j>>2]|0,(c[e>>2]|0)+-2|0,1)|0}if((k|0)==6){if(((cG(c[j>>2]|0,3,i)|0)<<24>>24?(cG(c[j>>2]|0,2,f)|0)<<24>>24:0)?(cG(c[j>>2]|0,2,h)|0)<<24>>24:0){c[a+44>>2]=c[f>>2];c[a+40>>2]=c[h>>2]}}else if((((k|0)==11?(cG(g,3,i)|0)<<24>>24:0)?(cG(c[j>>2]|0,2,f)|0)<<24>>24:0)?(cG(c[j>>2]|0,2,h)|0)<<24>>24:0){c[a+44>>2]=c[f>>2];c[a+40>>2]=c[h>>2]}l=m;return}function ZF(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+1056|0;j=m;b=m+32|0;e=m+28|0;f=m+24|0;g=m+20|0;h=m+16|0;c[a+48>>2]=72;i=a+20|0;n5(c[i>>2]|0,0,0)|0;while(1){if(!(c4(b,1024,c[i>>2]|0)|0))break;d=d4(b,98752)|0;if(!d)continue;c[j>>2]=e;c[j+4>>2]=f;c[j+8>>2]=g;c[j+12>>2]=h;if((b5(d,104311,j)|0)==4){k=5;break}}if((k|0)==5){j=c[e>>2]|0;c[a+32>>2]=j;k=c[f>>2]|0;c[a+36>>2]=k;c[a+40>>2]=(c[g>>2]|0)-j;c[a+44>>2]=(c[h>>2]|0)-k}l=m;return}function _F(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;g=l;l=l+16|0;b=g+4|0;d=g;c[a+48>>2]=0;e=a+20|0;n5(c[e>>2]|0,15,0)|0;h=(D6(c[e>>2]|0)|0)==88;f=c[e>>2]|0;if(h){n5(f,24,0)|0;if((kG(c[e>>2]|0,4,b)|0)<<24>>24?(kG(c[e>>2]|0,4,d)|0)<<24>>24:0){c[a+40>>2]=c[b>>2];c[a+44>>2]=c[d>>2]}}else{n5(f,26,0)|0;if((kG(c[e>>2]|0,2,b)|0)<<24>>24?(kG(c[e>>2]|0,2,d)|0)<<24>>24:0){c[a+40>>2]=c[b>>2];c[a+44>>2]=c[d>>2]}}l=g;return}function $F(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0;F=l;l=l+352|0;C=F+80|0;B=F+72|0;E=F+64|0;D=F+56|0;A=F+48|0;d=F+40|0;s=F+32|0;t=F+24|0;u=F+16|0;v=F+8|0;w=F;x=F+328|0;y=F+128|0;z=F+96|0;if(!(c[47008]|0)){if(F5(188036,98637,1)|0){c[d>>2]=98637;gA(1,98664,d)|0}c[47008]=188036}m=b+20|0;n5(c[m>>2]|0,0,0)|0;n=z+12|0;o=z+20|0;p=z+8|0;q=z+16|0;r=z+4|0;d=0;g=0;f=0;e=0;a:while(1){if(!(c4(y,200,c[m>>2]|0)|0))break;if(g<<24>>24==0|d<<24>>24==0){i=y;k=e}else break;b:while(1){j=f;c:while(1){while(1){if(r6(188036,i,4,z,0)|0){f=j;e=k;continue a}a[i+(c[n>>2]|0)>>0]=0;a[i+(c[o>>2]|0)>>0]=0;e=i+(c[p>>2]|0)|0;f=i+(c[q>>2]|0)|0;i=i+((c[r>>2]|0)+1)|0;if(!(s2(e,141116)|0))break;if(!(s2(e,141048)|0))break c;if(s2(e,98711)|0)continue;c[C>>2]=t;c[C+4>>2]=u;c[C+8>>2]=v;c[C+12>>2]=w;if((b5(f,98719,C)|0)==4)break b}c[A>>2]=s;c[A+4>>2]=x;if((b5(f,98701,A)|0)!=2){c[D>>2]=s;if((b5(f,101209,D)|0)==1){e=1;f=jG(+h[s>>3],98708)|0}else{e=g;f=j}}else{e=1;f=jG(+h[s>>3],x)|0}if(!(d<<24>>24)){d=0;g=e;j=f}else{g=e;e=k;continue a}}c[E>>2]=s;c[E+4>>2]=x;if((b5(f,98701,E)|0)!=2){c[B>>2]=s;if((b5(f,101209,B)|0)==1){d=1;e=jG(+h[s>>3],98708)|0}else e=k}else{d=1;e=jG(+h[s>>3],x)|0}if(!(g<<24>>24)){g=0;f=j;k=e}else{f=j;continue a}}d=1;g=1;f=~~(+h[v>>3]+1.0-+h[t>>3])>>>0;e=~~(+h[w>>3]+1.0-+h[u>>3])>>>0}c[b+48>>2]=0;c[b+40>>2]=f;c[b+44>>2]=e;l=F;return}function aG(a){a=a|0;var b=0,d=0,e=0,f=0.0,g=0.0;d=l;l=l+32|0;b=d;c[a+48>>2]=0;e=a+20|0;n5(c[e>>2]|0,0,0)|0;if(!(dG(c[e>>2]|0,b)|0)){g=+h[b>>3];c[a+32>>2]=~~g;f=+h[b+8>>3];c[a+36>>2]=~~f;c[a+40>>2]=~~(+h[b+16>>3]-g);c[a+44>>2]=~~(+h[b+24>>3]-f)}l=d;return}function bG(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+16|0;b=f+4|0;d=f;c[a+48>>2]=0;e=a+20|0;n5(c[e>>2]|0,6,0)|0;if((cG(c[e>>2]|0,1,b)|0)<<24>>24?(cG(c[e>>2]|0,1,d)|0)<<24>>24:0){c[a+40>>2]=c[b>>2];c[a+44>>2]=c[d>>2]}l=f;return}function cG(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;c[d>>2]=0;e=0;while(1){if(e>>>0>=b>>>0){e=1;break}f=D6(a)|0;if(H4(a)|0){e=0;break}c[d>>2]=c[d>>2]<<8|f;e=e+1|0}return e|0}function dG(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+1040|0;d=h;e=h+16|0;while(1){if(!(c4(e,1024,a)|0)){a=1;break}f=d4(e,98627)|0;if(f|0){g=4;break}}if((g|0)==4){c[d+4>>2]=e;c[d>>2]=f+9;c[d+8>>2]=a;a=eG(d,b)|0}l=h;return a|0}function eG(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;g=l;l=l+1024|0;f=g;fG(b);e=a[c[b>>2]>>0]|0;if(!(e<<24>>24))e=(gG(b)|0)&255;else e=e<<24>>24;if((((e|0)==91?(c[b>>2]=(c[b>>2]|0)+1,hG(b,f),(iG(f,d)|0)==0):0)?(hG(b,f),(iG(f,d+8|0)|0)==0):0)?(hG(b,f),(iG(f,d+16|0)|0)==0):0){hG(b,f);e=(iG(f,d+24|0)|0)!=0&1}else e=1;l=g;return e|0}function fG(b){b=b|0;var d=0;d=c[b>>2]|0;while(1){d=a[d>>0]|0;if(!(d<<24>>24))d=(gG(b)|0)&255;else d=d<<24>>24;if(!((d&255)<<24>>24))break;if(!(S2(d&255)|0))break;d=(c[b>>2]|0)+1|0;c[b>>2]=d}return}function gG(b){b=b|0;var d=0;d=b+4|0;if(!(c4(c[d>>2]|0,1024,c[b+8>>2]|0)|0))b=0;else{d=c[d>>2]|0;c[b>>2]=d;b=a[d>>0]|0}return b|0}function hG(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;fG(b);e=0;f=c[b>>2]|0;while(1){f=a[f>>0]|0;if(!(f<<24>>24))g=(gG(b)|0)&255;else g=f<<24>>24;f=g<<24>>24;if(!f)break;if(!((f|0)==46|(f+-48|0)>>>0<10))break;a[d+e>>0]=g;e=e+1|0;f=(c[b>>2]|0)+1|0;c[b>>2]=f;if((e|0)==1023){e=1023;break}}a[d+e>>0]=0;return}function iG(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0;e=l;l=l+16|0;f=e;d=+s5(a,f);if((c[f>>2]|0)==(a|0))a=1;else{h[b>>3]=d;a=0}l=e;return a|0}function jG(a,b){a=+a;b=b|0;var c=0.0;do if(!(s2(b,98735)|0)){a=a*72.0;c=a;a=a>=0.0?.5:-.5}else{if(!(s2(b,98738)|0)){a=a*.75;c=a;a=a>=0.0?.5:-.5;break}if(!(s2(b,98741)|0)){a=a*12.0;c=a;a=a>=0.0?.5:-.5;break}if(s2(b,98708)|0?s2(b,98744)|0:0)if(!(s2(b,98746)|0)){a=a*28.346456664;c=a;a=a>=0.0?.5:-.5;break}else{b=(s2(b,98749)|0)==0;c=a*2.8346456663999997;return (b?~~(c+(c>=0.0?.5:-.5)):0)|0}c=a>=0.0?.5:-.5}while(0);return ~~(a+c)|0}function kG(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;c[d>>2]=0;e=0;while(1){if(e>>>0>=b>>>0){e=1;break}f=D6(a)|0;if(H4(a)|0){e=0;break}c[d>>2]=c[d>>2]|f<<(e<<3);e=e+1|0}return e|0}function lG(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;a=c[b+20>>2]|0;if(a|0)I4(a)|0;if(c[b+52>>2]|0?(e=c[b+60>>2]|0,e|0):0)tb[e&127](b);U1(b);return}function mG(){var a=0;GA(0,1,108224,105493)|0;a=zF(0,1)|0;aT(a,0);return a|0}function nG(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0.0,n=0.0,o=0.0,p=0.0;k=l;l=l+304|0;j=k+24|0;i=k+8|0;f=k;g=k+40|0;if((vS(b,e)|0)!=999)if((wS(b,d)|0)==-1)b=-1;else{b=c[d+16>>2]|0;e=b+16|0;if(!(a[(c[b+8>>2]|0)+81>>0]|0)){p=+h[e>>3];o=+h[b+24>>3];n=+h[b+32>>3];m=+h[b+40>>3];c[j>>2]=~~(p+(p>=0.0?.5:-.5));c[j+4>>2]=~~(o+(o>=0.0?.5:-.5));c[j+8>>2]=~~(n+(n>=0.0?.5:-.5));c[j+12>>2]=~~(m+(m>=0.0?.5:-.5));m4(g,98899,j)|0}else{m=+h[b+24>>3];n=+h[e>>3];o=+h[b+40>>3];p=+h[b+32>>3];c[i>>2]=~~(m+(m>=0.0?.5:-.5));c[i+4>>2]=~~(n+(n>=0.0?.5:-.5));c[i+8>>2]=~~(o+(o>=0.0?.5:-.5));c[i+12>>2]=~~(p+(p>=0.0?.5:-.5));m4(g,98899,i)|0}TA(d,102671,g,195059)|0;b=0}else{b=MF(b,1,e)|0;c[f>>2]=e;c[f+4>>2]=b;gA(1,111233,f)|0;b=-1}l=k;return b|0}function oG(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;i=l;l=l+16|0;g=i;f=c[b+60>>2]|0;DF(a,d)|0;b=c[a+164>>2]|0;c[b+56>>2]=JR(b,c[b+52>>2]|0)|0;if((TC(f,137483,0,1)|0)!=0?(c[(c[f+16>>2]|0)+8>>2]|0)!=0:0)h=5;else if(!(c[b+152>>2]&67108864)){jA(98911,g);b=-1}else h=5;if((h|0)==5){c[b+36>>2]=e;if(!e){h=b+152|0;c[h>>2]=c[h>>2]|134217728}h=SJ(a,f)|0;LR(b);IF(a);b=h}l=i;return b|0}function pG(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;i=l;l=l+16|0;g=i;f=c[b+60>>2]|0;DF(a,d)|0;b=c[a+164>>2]|0;c[b+56>>2]=JR(b,c[b+52>>2]|0)|0;if((TC(f,137483,0,1)|0)!=0?(c[(c[f+16>>2]|0)+8>>2]|0)!=0:0)h=5;else if(!(c[b+152>>2]&67108864)){jA(98911,g);b=-1}else h=5;if((h|0)==5){CF(a,e);h=SJ(a,f)|0;LR(b);sF(b);IF(a);b=h}l=i;return b|0}function qG(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+16|0;h=m+8|0;g=m;b=c[b+60>>2]|0;DF(a,d)|0;j=c[a+164>>2]|0;c[j+56>>2]=JR(j,c[j+52>>2]|0)|0;if((TC(b,137483,0,1)|0)!=0?(c[(c[b+16>>2]|0)+8>>2]|0)!=0:0)k=5;else if(!(c[j+152>>2]&67108864)){jA(98911,g);b=-1}else k=5;do if((k|0)==5){if(e|0?(i=T1(4096)|0,c[e>>2]=i,i|0):0){g=j+40|0;c[g>>2]=i;c[j+44>>2]=4096;d=j+48|0;c[d>>2]=0;b=SJ(a,b)|0;LR(j);if(!b){c[e>>2]=c[g>>2];c[f>>2]=c[d>>2]}IF(a);break}gA(1,98932,h)|0;b=-1}while(0);l=m;return b|0}function rG(a,b){a=a|0;b=b|0;_S(a,0,b);return}function sG(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0;H=l;l=l+4288|0;E=H+8|0;D=H;F=4;G=T1(40)|0;c[G>>2]=0;A=H+4152|0;B=H+4148|0;C=H+4128|0;j=H+4112|0;k=H+16|0;o=0;i=$(24,b|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;a:do if((h|0)!=1){if(!i){c[d>>2]=0;g=0;break}o=0;n=ca(29,e|0,B|0,A|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)!=1){o=0;m=$(23,40)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)!=1){o=0;na(7,C|0,j|0,k|0,24,85);g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)!=1){o=0;e=$(25,b|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)!=1){j=C+16|0;i=c[j>>2]|0;g=e;while(1){if(!g)break;o=0;aa(i|0,g|0,0)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){e=V6(c[h>>2]|0,G|0,F|0)|0;if(!e)bb(h|0,p|0);z=p}else e=-1;if((e|0)==1){y=0;x=j;w=m;v=n;g=z;m=15;break a}o=0;g=aa(86,b|0,g|0)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){e=V6(c[h>>2]|0,G|0,F|0)|0;if(!e)bb(h|0,p|0);z=p}else e=-1;if((e|0)==1){y=0;x=j;w=m;v=n;g=z;m=15;break a}}G=U6(188068,1,G|0,F|0)|0;F=z;o=0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){y=m;x=j;w=m;v=n;g=z;m=15}else{y=m;x=j;w=m;v=n;g=0;m=15}}else{y=0;x=0;w=m;v=n;g=z;m=15}}else{y=0;x=0;w=m;v=n;g=z;m=15}}else{y=0;x=0;w=m;v=n;g=z;m=15}}else{y=0;x=0;w=0;v=n;g=z;m=15}}else{y=0;x=0;w=0;v=0;g=z;m=15}while(0);do if((m|0)==15){b:while(1){c:do if(!g){o=0;e=$(25,b|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}u=v+(c[B>>2]|0)|0;k=0;m=0;j=0;while(1){if(!e)break;o=0;g=aa(c[x>>2]|0,e|0,-1)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){i=V6(c[h>>2]|0,G|0,F|0)|0;if(!i)bb(h|0,p|0);z=p}else i=-1;if((i|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}if((g|0)==0?(a[(c[e+16>>2]|0)+119>>0]|0)==3:0){if(!m){o=0;c[D>>2]=j;ca(30,u|0,137395,D|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}o=0;i=ca(31,b|0,v|0,1)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}o=0;da(53,i|0,137483,280,1)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}c[y+(j<<2)>>2]=i;k=1;j=j+1|0}else i=m;o=0;da(54,b|0,e|0,i|0,C|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}}else i=m;o=0;e=aa(86,b|0,e|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}m=i}o=0;e=$(25,b|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}i=10;n=y;q=w;r=w;t=w;while(1){if(!e){m=0;i=r;e=t;break c}o=0;g=aa(c[x>>2]|0,e|0,-1)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){m=V6(c[h>>2]|0,G|0,F|0)|0;if(!m)bb(h|0,p|0);z=p}else m=-1;if((m|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}if(!g){o=0;c[E>>2]=j;ca(30,u|0,137395,E|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}o=0;s=ca(31,b|0,v|0,1)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}o=0;da(53,s|0,137483,280,1)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){g=V6(c[h>>2]|0,G|0,F|0)|0;if(!g)bb(h|0,p|0);z=p}else g=-1;if((g|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}o=0;da(54,b|0,e|0,s|0,C|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}if((j|0)==(i|0)){i=j<<1;o=0;m=aa(87,q|0,j<<3|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}g=m;q=m;r=m;h=m}else{g=n;h=t}c[g+(j<<2)>>2]=s;n=g;j=j+1|0;m=h}else m=t;o=0;e=aa(86,b|0,e|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}t=m}}else{m=1;k=0;n=y;j=0;i=w;e=w}while(0);o=0;ia(108,C|0);g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue}if((v|0)!=(A|0)){o=0;ia(105,v|0);g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue}}if(!m){o=0;g=aa(87,e|0,j<<2|0)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){e=V6(c[h>>2]|0,G|0,F|0)|0;if(!e)bb(h|0,p|0);z=p}else e=-1;if((e|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue}else{m=58;break}}c[d>>2]=0;g=0;while(1){if((g|0)>=(j|0))break;o=0;$(26,c[n+(g<<2)>>2]|0)|0;h=o;o=0;if((h|0)!=0&(p|0)!=0){e=V6(c[h>>2]|0,G|0,F|0)|0;if(!e)bb(h|0,p|0);z=p}else e=-1;if((e|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15;continue b}g=g+1|0}o=0;ia(105,i|0);g=o;o=0;if((g|0)!=0&(p|0)!=0){h=V6(c[g>>2]|0,G|0,F|0)|0;if(!h)bb(g|0,p|0);z=p}else h=-1;if((h|0)==1){s=v;t=w;u=x;m=y;g=z;v=s;w=t;x=u;y=m;m=15}else{m=56;break}}if((m|0)==56){g=0;break}else if((m|0)==58){c[d>>2]=j;a[f>>0]=k;break}}while(0);U1(G|0);l=H;return g|0}function tG(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;if(!((a|0)!=0?(CG(a)|0)!=0:0))a=98988;e=l2(a)|0;if((e|0)>=104){d=KK(e+25|0)|0;if(!d)d=0;else f=6}else f=6;if((f|0)==6){r3(d,a)|0;c[b>>2]=e}return d|0}function uG(a,b){a=a|0;b=b|0;oC(b,a,1)|0;return}function vG(b,d){b=b|0;d=d|0;var e=0;b=(c[b+16>>2]|0)+157|0;e=a[b>>0]|0;if((d|0)>=0)a[b>>0]=d;return e|0}function wG(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;c[b>>2]=d;c[b+4>>2]=d+4096;c[b+12>>2]=0;c[b+8>>2]=0;c[a>>2]=b;c[a+4>>2]=b;c[a+8>>2]=d;c[a+12>>2]=e;c[a+16>>2]=f;return}function xG(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;AG(e,b);i=e+12|0;j=e+16|0;b=0;a:while(1){k=BG(e)|0;if(!k)break;b=b+1|0;f=c[i>>2]|0;if(f|0)ub[f&63](k,d);h=aB(a,k)|0;while(1){if(!h)continue a;g=c[h>>2]&3;f=c[((g|0)==3?h:h+48|0)+40>>2]|0;if((f|0)==(k|0))f=c[((g|0)==2?h:h+-48|0)+40>>2]|0;if(!(lb[c[j>>2]&127](f,-1)|0))AG(e,f);h=bB(a,h,k)|0}}return b|0}function yG(a){a=a|0;var b=0;a=c[(c[a>>2]|0)+12>>2]|0;while(1){if(!a)break;b=c[a+12>>2]|0;zG(a);a=b}return}function zG(a){a=a|0;U1(c[a>>2]|0);U1(a);return}function AG(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;j=k+8|0;f=k;g=a+8|0;i=a+4|0;d=c[i>>2]|0;if((c[g>>2]|0)==(c[d+4>>2]|0)){d=c[d+12>>2]|0;do if(!d){e=KK(16)|0;if(!e){gA(1,98969,f)|0;bb(188068,1)}c[e+8>>2]=c[i>>2];c[e+12>>2]=0;d=KK(4e6)|0;c[e>>2]=d;if(!d){gA(1,98969,j)|0;bb(188068,1)}else{c[e+4>>2]=d+4e6;c[(c[i>>2]|0)+12>>2]=e;h=e;break}}else h=d;while(0);c[i>>2]=h;c[g>>2]=c[h>>2]}lb[c[a+16>>2]&127](b,1)|0;j=c[g>>2]|0;c[g>>2]=j+4;c[j>>2]=b;l=k;return}function BG(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;f=a+8|0;b=c[f>>2]|0;d=a+4|0;e=c[d>>2]|0;if((b|0)==(c[e>>2]|0))if((e|0)==(c[a>>2]|0))b=0;else{b=c[e+8>>2]|0;c[d>>2]=b;b=c[b+4>>2]|0;c[f>>2]=b;g=4}else g=4;if((g|0)==4){b=b+-4|0;c[f>>2]=b;b=c[b>>2]|0}return b|0}function CG(b){b=b|0;var c=0;a:while(1){c=b;b=b+1|0;c=a[c>>0]|0;switch(c<<24>>24){case 95:continue a;case 0:{b=1;break a}default:{}}if(!(a3(c&255)|0)){b=0;break}}return b|0}function DG(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,A=0,B=0;B=l;l=l+4272|0;x=B;y=4;A=T1(40)|0;c[A>>2]=0;u=B+4144|0;v=B+4140|0;w=B+4120|0;h=B+4104|0;i=B+8|0;o=0;f=$(24,a|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){e=V6(c[g>>2]|0,A|0,y|0)|0;if(!e)bb(g|0,p|0);z=p}else e=-1;a:do if((e|0)!=1)if(f){o=0;k=ca(29,d|0,v|0,u|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)!=1){o=0;j=$(23,40)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)!=1){o=0;na(7,w|0,h|0,i|0,24,85);e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)!=1){o=0;f=$(25,a|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)!=1){h=w+16|0;g=c[h>>2]|0;e=f;while(1){if(!e)break;o=0;aa(g|0,e|0,0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){f=V6(c[d>>2]|0,A|0,y|0)|0;if(!f)bb(d|0,p|0);z=p}else f=-1;if((f|0)==1){t=h;s=j;r=k;e=z;j=14;break a}o=0;e=aa(86,a|0,e|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){f=V6(c[d>>2]|0,A|0,y|0)|0;if(!f)bb(d|0,p|0);z=p}else f=-1;if((f|0)==1){t=h;s=j;r=k;e=z;j=14;break a}}A=U6(188068,1,A|0,y|0)|0;y=z;o=0;e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)==1){t=h;s=j;r=k;e=z;j=14}else{t=h;s=j;r=k;e=0;j=14}}else{t=0;s=j;r=k;e=z;j=14}}else{t=0;s=j;r=k;e=z;j=14}}else{t=0;s=j;r=k;e=z;j=14}}else{t=0;s=0;r=k;e=z;j=14}}else{d=0;e=0}else{t=0;s=0;r=0;e=z;j=14}while(0);b:do if((j|0)==14){c:while(1){if(e|0){o=0;ia(108,w|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue}o=0;ia(105,s|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue}if((r|0)==(u|0)){d=0;e=0;break b}o=0;ia(105,r|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue}else{j=19;break}}o=0;f=$(25,a|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,A|0,y|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue}q=r+(c[v>>2]|0)|0;h=10;k=s;e=0;j=s;n=s;while(1){if(!f)break;o=0;d=aa(c[t>>2]|0,f|0,-1)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){i=V6(c[g>>2]|0,A|0,y|0)|0;if(!i)bb(g|0,p|0);z=p}else i=-1;if((i|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue c}if(!d){o=0;c[x>>2]=e;ca(30,q|0,137395,x|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,A|0,y|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue c}o=0;m=ca(31,a|0,r|0,1)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,A|0,y|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue c}o=0;da(53,m|0,137483,280,1)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,A|0,y|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue c}o=0;da(54,a|0,f|0,m|0,w|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,A|0,y|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue c}if((e|0)==(h|0)){h=e<<1;o=0;i=aa(87,j|0,e<<3|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,A|0,y|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue c}g=i;j=i;d=i}else{g=k;d=n}c[g+(e<<2)>>2]=m;e=e+1|0;k=g;i=d}else i=n;o=0;f=aa(86,a|0,f|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){g=V6(c[d>>2]|0,A|0,y|0)|0;if(!g)bb(d|0,p|0);z=p}else g=-1;if((g|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue c}n=i}o=0;ia(108,w|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){f=V6(c[d>>2]|0,A|0,y|0)|0;if(!f)bb(d|0,p|0);z=p}else f=-1;if((f|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue}o=0;d=aa(87,n|0,e<<2|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,A|0,y|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14;continue}if((r|0)==(u|0))break b;o=0;ia(105,r|0);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,A|0,y|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){n=r;q=s;j=t;e=z;r=n;s=q;t=j;j=14}else{j=39;break}}if((j|0)==19){d=0;e=0;break}else if((j|0)==39)break}while(0);c[b>>2]=e;U1(A|0);l=B;return d|0}function EG(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=0;f=jC(a)|0;while(1){if(!f)break;e=XA(b,f)|0;while(1){if(!e)break;if(oC(a,c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0,0)|0){mB(a,e,1)|0;d=d+1|0}e=ZA(b,e)|0}f=kC(a,f)|0}return d|0}function FG(a){a=a|0;a=RC(a,98993,0)|0;if(!a)qa(98998,99001,529,99010);else return c[a+8>>2]|0;return 0}function GG(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0;u=l;l=l+4320|0;s=u+24|0;r=u+8|0;q=u;t=u+4184|0;p=u+4160|0;f=u+40|0;if(tB(b)|0){YC(b,0,99019,-12,0);YC(b,1,99031,16,0);o=tG(e,f,t)|0;k=HG(b)|0;m=KK((tB(k)|0)<<2)|0;wG(p,u+4144|0,u+48|0,24,88);h=jC(k)|0;n=p+16|0;i=o+(c[f>>2]|0)|0;j=c[15712]|0;e=0;while(1){if(!h)break;if(!(lb[c[n>>2]&127](h,-1)|0)){c[q>>2]=e;m4(i,99933,q)|0;v=ND(k,o,1)|0;w=ND(b,o,1)|0;TC(w,99019,12,0)|0;a[(RC(w,99019,0)|0)+8>>0]=1;f=xG(k,h,v,p)|0;JG(v,w);g=KG(w)|0;LG(b,w);c[m+(e<<2)>>2]=w;wC(k,v)|0;if(0){c[r>>2]=e;c[r+4>>2]=f;c[r+8>>2]=g;w4(j,99043,r)|0}e=e+1|0}h=kC(k,h)|0}if(0){r=tB(b)|0;v=uB(b)|0;w=KB(b)|0;c[s>>2]=r;c[s+4>>2]=v;c[s+8>>2]=e;c[s+12>>2]=w;w4(j,99073,s)|0}sB(k)|0;ZC(b,0,99019);ZC(b,1,99031);yG(p);f=MK(m,e<<2)|0;if((o|0)!=(t|0))U1(o)}else{f=0;e=0}c[d>>2]=e;l=u;return f|0}function HG(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;a[h>>0]=a[18596]|0;a[h+1>>0]=a[18597]|0;a[h+2>>0]=a[18598]|0;a[h+3>>0]=a[18599]|0;h=oB(99120,h,0)|0;OG(h,b);d=jC(b)|0;while(1){if(!d)break;if(!(c[(RC(d,99031,0)|0)+12>>2]|0)){g=tC(h,KB(d)|0,1)|0;TC(g,99031,16,1)|0;c[(c[g+16>>2]|0)+12>>2]=d;c[(RC(d,99031,0)|0)+12>>2]=g}d=kC(b,d)|0}e=jC(b)|0;while(1){if(!e)break;f=c[(RC(e,99031,0)|0)+12>>2]|0;d=XA(b,e)|0;while(1){if(!d)break;g=c[(RC(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0,99031,0)|0)+12>>2]|0;do if((g|0)!=(f|0))if(g>>>0>f>>>0){iB(h,f,g,0,1)|0;break}else{iB(h,g,f,0,1)|0;break}while(0);d=ZA(b,d)|0}e=kC(b,e)|0}l=i;return h|0}function IG(b,d){b=b|0;d=d|0;var e=0;b=(c[b+16>>2]|0)+8|0;e=a[b>>0]|0;if((d|0)>=0)a[b>>0]=d;return e|0}function JG(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;e=jC(a)|0;while(1){if(!e)break;f=c[(c[e+16>>2]|0)+12>>2]|0;a:do if((c[f>>2]&3|0)==1)oC(b,f,1)|0;else{d=jC(f)|0;while(1){if(!d)break a;oC(b,d,1)|0;d=kC(f,d)|0}}while(0);e=kC(a,e)|0}return}function KG(a){a=a|0;return EG(a,c[a+60>>2]|0)|0}function LG(a,b){a=a|0;b=b|0;MG(a,b,0);return}function MG(b,c,d){b=b|0;c=c|0;d=d|0;var e=0,f=0,g=0;g=(d|0)==0;b=OD(b)|0;while(1){if(!b)break;if((a[(RC(b,99019,0)|0)+8>>0]|0)==0?(f=NG(b,c,d)|0,f|0):0){if(g)e=(Q2(KB(b)|0,108006,7)|0)==0;else e=1;MG(b,f,e&1)}b=PD(b)|0}return}function NG(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;e=0;g=jC(a)|0;while(1){if(!g)break;f=tC(b,KB(g)|0,0)|0;if(f){if(!e)e=ND(b,KB(a)|0,1)|0;oC(e,f,1)|0}g=kC(a,g)|0}if((d|0)!=0&(e|0)==0)e=ND(b,KB(a)|0,1)|0;if(e|0?(EG(e,a)|0,UA(a,e)|0,(Q2(KB(e)|0,108006,7)|0)==0):0)c[(TC(e,98993,12,0)|0)+8>>2]=a;return e|0}function OG(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;h=l;l=l+16|0;g=h;d=OD(b)|0;e=c[15712]|0;while(1){if(!d)break;a:do if(!(Q2(KB(d)|0,108006,7)|0)){f=tC(a,KB(d)|0,1)|0;TC(f,99031,16,1)|0;c[(c[f+16>>2]|0)+12>>2]=d;b=jC(d)|0;while(1){if(!b)break a;if(c[(RC(b,99031,0)|0)+12>>2]|0){k=KB(b)|0;j=KB(d)|0;i=KB(c[(RC(b,99031,0)|0)+12>>2]|0)|0;c[g>>2]=k;c[g+4>>2]=j;c[g+8>>2]=i;w4(e,99123,g)|0}c[(RC(b,99031,0)|0)+12>>2]=f;b=kC(d,b)|0}}else OG(a,d);while(0);d=PD(d)|0}l=h;return}function PG(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0.0;n=+h[a>>3];j=~~(n+(n>=0.0?.5:-.5));n=+h[a+8>>3];a=~~(n+(n>=0.0?.5:-.5));n=+h[b>>3];g=~~(n+(n>=0.0?.5:-.5));n=+h[b+8>>3];f=~~(n+(n>=0.0?.5:-.5));l=g-j|0;i=((l|0)>-1?l:0-l|0)<<1;l=l>>31|1;m=f-a|0;k=((m|0)>-1?m:0-m|0)<<1;m=m>>31|1;a:do if((i|0)>(k|0)){e=0-i|0;d=k-(i>>1)|0;b=j;while(1){QL(c,b,a);if((b|0)==(g|0))break a;j=(d|0)>-1;d=d+k+(j?e:0)|0;b=b+l|0;a=(j?m:0)+a|0}}else{e=0-k|0;d=j;b=i-(k>>1)|0;while(1){QL(c,d,a);if((a|0)==(f|0))break a;k=(b|0)>-1;d=(k?l:0)+d|0;b=b+i+(k?e:0)|0;a=a+m|0}}while(0);return}function QG(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;k=n;j=n+4|0;do if((a|0)<1)d=0;else{f=e+16|0;if((c[f>>2]|0)>>>0<4){d=RG(a,b,d,e)|0;break}m=KK(a<<5)|0;d=0;while(1){if((d|0)==(a|0))break;g=c[b+(d<<2)>>2]|0;BP(g);h=m+(d<<5)|0;g=(c[g+16>>2]|0)+16|0;c[h>>2]=c[g>>2];c[h+4>>2]=c[g+4>>2];c[h+8>>2]=c[g+8>>2];c[h+12>>2]=c[g+12>>2];c[h+16>>2]=c[g+16>>2];c[h+20>>2]=c[g+20>>2];c[h+24>>2]=c[g+24>>2];c[h+28>>2]=c[g+28>>2];d=d+1|0}if((c[f>>2]|0)==4){h=e+28|0;a:do if(c[h>>2]&2|0){f=e+24|0;c[f>>2]=JK(a<<2)|0;d=0;while(1){if((d|0)==(a|0))break a;g=QA(c[b+(d<<2)>>2]|0,99190)|0;if(g|0?(c[k>>2]=j,g=(b5(g,137395,k)|0)>0,i=c[j>>2]|0,g&(i|0)>-1):0)c[(c[f>>2]|0)+(d<<2)>>2]=i;d=d+1|0}}while(0);d=SG(a,m,e)|0;if(c[h>>2]&2)U1(c[e+24>>2]|0)}else d=0;U1(m)}while(0);l=n;return d|0}function RG(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0.0,I=0.0,J=0.0,K=0.0;G=l;l=l+144|0;B=G+136|0;A=G+64|0;F=G+48|0;v=G+40|0;u=G;t=G+120|0;x=G+104|0;C=G+96|0;D=c[f+20>>2]|0;c[x>>2]=0;c[x+4>>2]=0;c[x+8>>2]=0;c[x+12>>2]=0;a:do if((b|0)>=1){o=(D|0)==0;E=c[15712]|0;p=t+4|0;q=t+8|0;r=t+12|0;y=x+4|0;w=x+8|0;z=x+12|0;n=0;g=0;while(1){if((n|0)==(b|0))break;s=c[d+(n<<2)>>2]|0;BP(s);if(!o?(a[D+n>>0]|0)!=0:0){m=c[s+16>>2]|0;H=+h[m+16>>3];i=~~(H+(H>=0.0?.5:-.5));c[t>>2]=i;H=+h[m+24>>3];j=~~(H+(H>=0.0?.5:-.5));c[p>>2]=j;H=+h[m+32>>3];k=~~(H+(H>=0.0?.5:-.5));c[q>>2]=k;H=+h[m+40>>3];m=~~(H+(H>=0.0?.5:-.5));c[r>>2]=m;if(!g){c[x>>2]=c[t>>2];c[x+4>>2]=c[t+4>>2];c[x+8>>2]=c[t+8>>2];c[x+12>>2]=c[t+12>>2]}else{c[x>>2]=c[((c[x>>2]|0)>(i|0)?t:x)>>2];c[y>>2]=c[((c[y>>2]|0)>(j|0)?t:x)+4>>2];c[w>>2]=c[((c[w>>2]|0)<(k|0)?t:x)+8>>2];c[z>>2]=c[((c[z>>2]|0)<(m|0)?t:x)+12>>2]}g=g+1|0}if(0>2){m=KB(s)|0;s=c[s+16>>2]|0;K=+h[s+16>>3];J=+h[s+24>>3];I=+h[s+32>>3];H=+h[s+40>>3];c[u>>2]=m;h[u+8>>3]=K;h[u+16>>3]=J;h[u+24>>3]=I;h[u+32>>3]=H;w4(E,99257,u)|0}n=n+1|0}q=KK(b<<5)|0;g=0;while(1){if((g|0)==(b|0))break;u=q+(g<<5)|0;t=(c[(c[d+(g<<2)>>2]|0)+16>>2]|0)+16|0;c[u>>2]=c[t>>2];c[u+4>>2]=c[t+4>>2];c[u+8>>2]=c[t+8>>2];c[u+12>>2]=c[t+12>>2];c[u+16>>2]=c[t+16>>2];c[u+20>>2]=c[t+20>>2];c[u+24>>2]=c[t+24>>2];c[u+28>>2]=c[t+28>>2];g=g+1|0}o=f+8|0;p=VG(b,q,c[o>>2]|0)|0;if(0){c[v>>2]=p;w4(E,99285,v)|0}if((p|0)>=1){n=(D|0)!=0;if(n){i=((c[w>>2]|0)+(c[x>>2]|0)|0)/2|0;g=((c[z>>2]|0)+(c[y>>2]|0)|0)/2|0}else{i=0;g=0}c[C+4>>2]=g;c[C>>2]=i;m=JK(b<<4)|0;k=f+16|0;g=0;while(1){if((g|0)>=(b|0))break;i=c[d+(g<<2)>>2]|0;j=m+(g<<4)|0;c[m+(g<<4)+12>>2]=g;if((c[k>>2]|0)==3){x=(c[i+16>>2]|0)+16|0;y=c[o>>2]|0;z=KB(i)|0;c[A>>2]=c[x>>2];c[A+4>>2]=c[x+4>>2];c[A+8>>2]=c[x+8>>2];c[A+12>>2]=c[x+12>>2];c[A+16>>2]=c[x+16>>2];c[A+20>>2]=c[x+20>>2];c[A+24>>2]=c[x+24>>2];c[A+28>>2]=c[x+28>>2];c[B>>2]=c[C>>2];c[B+4>>2]=c[C+4>>2];WG(A,j,p,y,B,z)}else{c[B>>2]=c[C>>2];c[B+4>>2]=c[C+4>>2];XG(e,i,j,p,f,B)}g=g+1|0}j=JK(b<<2)|0;g=0;while(1){if((g|0)>=(b|0))break;c[j+(g<<2)>>2]=m+(g<<4);g=g+1|0}o4(j,b,4,89);k=KL()|0;g=JK(b<<3)|0;b:do if(n){i=0;while(1){if((i|0)>=(b|0)){i=0;break}if(a[D+i>>0]|0){e=c[j+(i<<2)>>2]|0;f=g+(c[e+12>>2]<<3)|0;c[B>>2]=c[C>>2];c[B+4>>2]=c[C+4>>2];ZG(e,k,f,B)}i=i+1|0}while(1){if((i|0)>=(b|0))break b;if(!(a[D+i>>0]|0)){C=c[j+(i<<2)>>2]|0;_G(i,C,k,g+(c[C+12>>2]<<3)|0,p,c[o>>2]|0,q)}i=i+1|0}}else{i=0;while(1){if((i|0)>=(b|0))break b;D=c[j+(i<<2)>>2]|0;_G(i,D,k,g+(c[D+12>>2]<<3)|0,p,c[o>>2]|0,q);i=i+1|0}}while(0);U1(j);i=0;while(1){if((i|0)>=(b|0))break;U1(c[m+(i<<4)+4>>2]|0);i=i+1|0}U1(m);NL(k);U1(q);if(0>1){i=0;while(1){if((i|0)>=(b|0))break a;C=c[g+(i<<3)>>2]|0;D=c[g+(i<<3)+4>>2]|0;c[F>>2]=i;c[F+4>>2]=C;c[F+8>>2]=D;w4(E,99301,F)|0;i=i+1|0}}}else g=0}else g=0;while(0);l=G;return g|0}function SG(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0.0,j=0,k=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0;z=l;l=l+16|0;g=z;x=JK(a<<3)|0;f=c[d+4>>2]|0;y=d+28|0;e=(f|0)>0;do if(!(c[y>>2]&1))if(e){k=1;w=(a+-1+f|0)/(f|0)|0;break}else{f=~~+N(+(+C(+(+(a|0)))));k=1;w=(a+-1+f|0)/(f|0)|0;break}else if(e){k=0;w=f;f=(a+-1+f|0)/(f|0)|0;break}else{f=~~+N(+(+C(+(+(a|0)))));k=0;w=f;f=(a+-1+f|0)/(f|0)|0;break}while(0);if(0){v=c[15712]|0;c[g>>2]=k|0?99196:99206;c[g+4>>2]=w;c[g+8>>2]=f;w4(v,99219,g)|0}t=JK((f<<3)+8|0)|0;u=JK((w<<3)+8|0)|0;v=JK(a*24|0)|0;j=d+8|0;e=0;g=v;while(1){if((e|0)>=(a|0))break;p=+h[b+(e<<5)+8>>3];o=+h[b+(e<<5)+24>>3];q=+((c[j>>2]|0)>>>0);h[g>>3]=+h[b+(e<<5)+16>>3]-+h[b+(e<<5)>>3]+q;h[g+8>>3]=o-p+q;c[g+16>>2]=e;e=e+1|0;g=g+24|0}s=JK(a<<2)|0;e=0;while(1){if((e|0)>=(a|0))break;c[s+(e<<2)>>2]=v+(e*24|0);e=e+1|0}e=c[d+24>>2]|0;if(!e){if(!(c[y>>2]&64))o4(s,a,4,91)}else{c[47056]=e;o4(s,a,4,90)}r=(k|0)==0;e=0;g=0;j=0;while(1){if((j|0)>=(a|0)){i=0.0;e=0;break}k=c[s+(j<<2)>>2]|0;d=t+(g<<3)|0;q=+h[d>>3];p=+h[k>>3];h[d>>3]=q>p?q:p;d=u+(e<<3)|0;p=+h[d>>3];q=+h[k+8>>3];h[d>>3]=p>q?p:q;if(r){e=e+1|0;d=(e|0)==(w|0);e=d?0:e;g=g+(d&1)|0}else{g=g+1|0;d=(g|0)==(f|0);e=e+(d&1)|0;g=d?0:g}j=j+1|0}while(1){if((e|0)>(f|0)){i=0.0;e=w;break}d=t+(e<<3)|0;q=+h[d>>3];h[d>>3]=i;i=q+i;e=e+1|0}while(1){if((e|0)<=0)break;d=e+-1|0;q=+h[u+(d<<3)>>3];h[u+(e<<3)>>3]=i;i=q+i;e=d}h[u>>3]=i;e=0;g=0;d=0;while(1){if((d|0)>=(a|0))break;k=c[(c[s+(d<<2)>>2]|0)+16>>2]|0;i=+h[b+(k<<5)>>3];m=-i;p=+h[b+(k<<5)+8>>3];q=-p;n=+h[b+(k<<5)+16>>3];o=+h[b+(k<<5)+24>>3];j=c[y>>2]|0;do if(!(j&4))if(!(j&8)){i=(m-n+ +h[t+(g<<3)>>3]+ +h[t+(g+1<<3)>>3])*.5;break}else{i=i-n+ +h[t+(g+1<<3)>>3];break}else i=+h[t+(g<<3)>>3];while(0);c[x+(k<<3)>>2]=~~i;do if(!(j&16))if(!(j&32)){i=(q-o+ +h[u+(e<<3)>>3]+ +h[u+(e+1<<3)>>3])*.5;break}else{i=+h[u+(e+1<<3)>>3];break}else i=p-o+ +h[u+(e<<3)>>3];while(0);c[x+(k<<3)+4>>2]=~~i;if(r){e=e+1|0;k=(e|0)==(w|0);e=k?0:e;g=g+(k&1)|0}else{g=g+1|0;k=(g|0)==(f|0);e=e+(k&1)|0;g=k?0:g}d=d+1|0}U1(v);U1(s);U1(t);U1(u);l=z;return x|0}function TG(a,b){a=a|0;b=b|0;var d=0;d=c[47056]|0;a=c[d+(c[(c[a>>2]|0)+16>>2]<<2)>>2]|0;b=c[d+(c[(c[b>>2]|0)+16>>2]<<2)>>2]|0;return ((a|0)>(b|0)?1:((a|0)<(b|0))<<31>>31)|0}function UG(a,b){a=a|0;b=b|0;var d=0.0,e=0.0;a=c[a>>2]|0;b=c[b>>2]|0;e=+h[a>>3]+ +h[a+8>>3];d=+h[b>>3]+ +h[b+8>>3];return (e<d?1:(e>d)<<31>>31)|0}function VG(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0.0,i=0.0,j=0.0,k=0,m=0,n=0.0,o=0.0,p=0,q=0,r=0,s=0;s=l;l=l+96|0;q=s+80|0;r=s+48|0;p=s+8|0;m=s;k=(a*100|0)+-1|0;e=+(d<<1|0);n=0.0;o=0.0;d=0;while(1){if((d|0)>=(a|0))break;j=+h[b+(d<<5)+16>>3]-+h[b+(d<<5)>>3]+e;i=+h[b+(d<<5)+24>>3]-+h[b+(d<<5)+8>>3]+e;n=n-j-i;o=o-i*j;d=d+1|0}i=+(k|0);j=n*n-i*4.0*o;if(!(j<0.0)){e=+C(+j);g=i*2.0;f=(e-n)/g;g=(-n-e)/g;d=~~f;d=(d|0)==0?1:d;if(0>2){m=c[15712]|0;U4(99444,27,1,m)|0;h[p>>3]=i;h[p+8>>3]=n;h[p+16>>3]=o;h[p+24>>3]=j;h[p+32>>3]=e;w4(m,99472,p)|0;c[r>>2]=d;h[r+8>>3]=f;c[r+16>>2]=~~g;h[r+24>>3]=g;w4(m,99498,r)|0;h[q>>3]=(f*i+n)*f+o;h[q+8>>3]=(g*i+n)*g+o;w4(m,99520,q)|0}}else{h[m>>3]=j;gA(1,99417,m)|0;d=-1}l=s;return d|0}function WG(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0.0,w=0.0;u=l;l=l+32|0;t=u+16|0;s=u;p=+h[a>>3];q=+h[a+8>>3];n=+h[a+16>>3];o=+h[a+24>>3];r=KL()|0;j=c[f>>2]|0;a=j-e|0;k=c[f+4>>2]|0;i=k-e|0;j=e-~~(p+(p>=0.0?.5:-.5))+j+~~(n+(n>=0.0?.5:-.5))|0;k=e-~~(q+(q>=0.0?.5:-.5))+k+~~(o+(o>=0.0?.5:-.5))|0;if((a|0)>-1)f=(a|0)/(d|0)|0;else f=((a+1|0)/(d|0)|0)+-1|0;if((i|0)>-1)m=(i|0)/(d|0)|0;else m=((i+1|0)/(d|0)|0)+-1|0;if((j|0)>-1)j=(j|0)/(d|0)|0;else j=((j+1|0)/(d|0)|0)+-1|0;if((k|0)>-1)i=(k|0)/(d|0)|0;else i=((k+1|0)/(d|0)|0)+-1|0;while(1){if((f|0)>(j|0))break;else a=m;while(1){if((a|0)>(i|0))break;QL(r,f,a);a=a+1|0}f=f+1|0}k=b+4|0;c[k>>2]=UL(r)|0;j=TL(r)|0;m=b+8|0;c[m>>2]=j;w=+(e<<1|0);v=+(d|0);i=~~+N(+((n+w-p)/v));a=~~+N(+((o+w-q)/v));c[b>>2]=a+i;a:do if(0>2){f=c[15712]|0;c[s>>2]=g;c[s+4>>2]=j;c[s+8>>2]=i;c[s+12>>2]=a;w4(f,99376,s)|0;a=0;while(1){if((a|0)>=(c[m>>2]|0))break a;g=c[k>>2]|0;s=c[g+(a<<3)+4>>2]|0;c[t>>2]=c[g+(a<<3)>>2];c[t+4>>2]=s;w4(f,99403,t)|0;a=a+1|0}}while(0);NL(r);l=u;return}function XG(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,O=0,P=0,Q=0.0,R=0.0;P=l;l=l+128|0;F=P+112|0;E=P+104|0;O=P+16|0;M=P;H=P+64|0;I=P+56|0;z=P+40|0;A=P+96|0;y=P+88|0;v=P+24|0;w=P+80|0;x=P+72|0;K=c[f+8>>2]|0;G=c[f+12>>2]|0;C=(a|0)==0?b:a;L=KL()|0;J=b+16|0;B=c[J>>2]|0;t=+h[B+16>>3];D=(c[g>>2]|0)-~~(t+(t>=0.0?.5:-.5))|0;t=+h[B+24>>3];B=(c[g+4>>2]|0)-~~(t+(t>=0.0?.5:-.5))|0;a:do if((c[f+16>>2]|0)==1){v=KK((tB(b)|0)<<2)|0;a=0;g=jC(b)|0;while(1){if(!g)break;x=g+16|0;c[v+(a<<2)>>2]=c[(c[x>>2]|0)+112>>2];c[(c[x>>2]|0)+112>>2]=0;a=a+1|0;g=kC(b,g)|0}o=D-K|0;p=B-K|0;q=D+K|0;r=B+K|0;n=1;while(1){a=c[J>>2]|0;if((n|0)>(c[a+180>>2]|0))break;m=c[(c[a+184>>2]|0)+(n<<2)>>2]|0;i=c[m+16>>2]|0;t=+h[i+16>>3];a=~~(t+(t>=0.0?.5:-.5));t=+h[i+24>>3];g=~~(t+(t>=0.0?.5:-.5));t=+h[i+32>>3];f=~~(t+(t>=0.0?.5:-.5));t=+h[i+40>>3];i=~~(t+(t>=0.0?.5:-.5));b:do if((f|0)>(a|0)&(i|0)>(g|0)){a=o+a|0;j=p+g|0;f=q+f|0;k=r+i|0;if((a|0)>-1)g=(a|0)/(e|0)|0;else g=((a+1|0)/(e|0)|0)+-1|0;if((j|0)>-1)j=(j|0)/(e|0)|0;else j=((j+1|0)/(e|0)|0)+-1|0;if((f|0)>-1)i=(f|0)/(e|0)|0;else i=((f+1|0)/(e|0)|0)+-1|0;if((k|0)>-1)f=(k|0)/(e|0)|0;else f=((k+1|0)/(e|0)|0)+-1|0;while(1){if((g|0)>(i|0))break;else a=j;while(1){if((a|0)>(f|0))break;QL(L,g,a);a=a+1|0}g=g+1|0}a=jC(m)|0;while(1){if(!a)break b;c[(c[a+16>>2]|0)+212>>2]=m;a=kC(m,a)|0}}while(0);n=n+1|0}r=z+8|0;s=H+4|0;t=+(K|0);u=I+4|0;q=A+4|0;p=y+4|0;o=jC(b)|0;while(1){if(!o)break;jP(z,o);R=+h[z>>3];Q=+h[r>>3];m=D+~~(R+(R>=0.0?.5:-.5))|0;c[H>>2]=m;n=B+~~(Q+(Q>=0.0?.5:-.5))|0;c[s>>2]=n;g=o+16|0;a=c[g>>2]|0;c:do if(!(c[a+212>>2]|0)){c[I>>2]=~~((+h[a+96>>3]+ +h[a+88>>3])*.5+t);c[u>>2]=~~(+h[a+80>>3]*.5+t);c[E>>2]=c[H>>2];c[E+4>>2]=c[H+4>>2];c[F>>2]=c[I>>2];c[F+4>>2]=c[I+4>>2];aH(A,E,F);a=c[A>>2]|0;f=c[q>>2]|0;c[E>>2]=c[H>>2];c[E+4>>2]=c[H+4>>2];c[F>>2]=c[I>>2];c[F+4>>2]=c[I+4>>2];bH(y,E,F);i=c[y>>2]|0;j=c[p>>2]|0;if((a|0)>-1)g=(a|0)/(e|0)|0;else g=((a+1|0)/(e|0)|0)+-1|0;if((f|0)>-1)k=(f|0)/(e|0)|0;else k=((f+1|0)/(e|0)|0)+-1|0;if((i|0)>-1)i=(i|0)/(e|0)|0;else i=((i+1|0)/(e|0)|0)+-1|0;if((j|0)>-1)f=(j|0)/(e|0)|0;else f=((j+1|0)/(e|0)|0)+-1|0;while(1){if((g|0)>(i|0))break;else a=k;while(1){if((a|0)>(f|0))break;QL(L,g,a);a=a+1|0}g=g+1|0}if((m|0)>-1)a=(m|0)/(e|0)|0;else a=((m+1|0)/(e|0)|0)+-1|0;c[H>>2]=a;if((n|0)>-1)a=(n|0)/(e|0)|0;else a=((n+1|0)/(e|0)|0)+-1|0;c[s>>2]=a;a=XA(C,o)|0;while(1){if(!a)break c;c[F>>2]=c[H>>2];c[F+4>>2]=c[H+4>>2];cH(a,F,L,D,B,e,G);a=ZA(C,a)|0}}else{if((m|0)>-1)a=(m|0)/(e|0)|0;else a=((m+1|0)/(e|0)|0)+-1|0;c[H>>2]=a;if((n|0)>-1)a=(n|0)/(e|0)|0;else a=((n+1|0)/(e|0)|0)+-1|0;c[s>>2]=a;a=XA(C,o)|0;while(1){if(!a)break c;if((c[(c[g>>2]|0)+212>>2]|0)!=(c[(c[(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0)){c[F>>2]=c[H>>2];c[F+4>>2]=c[H+4>>2];cH(a,F,L,D,B,e,G)}a=ZA(C,a)|0}}while(0);o=kC(b,o)|0}a=0;g=jC(b)|0;while(1){if(!g)break;c[(c[g+16>>2]|0)+112>>2]=c[v+(a<<2)>>2];a=a+1|0;g=kC(b,g)|0}U1(v)}else{r=v+8|0;s=H+4|0;t=+(K|0);u=I+4|0;q=w+4|0;p=x+4|0;o=jC(b)|0;while(1){if(!o)break a;jP(v,o);Q=+h[v>>3];R=+h[r>>3];m=D+~~(Q+(Q>=0.0?.5:-.5))|0;c[H>>2]=m;n=B+~~(R+(R>=0.0?.5:-.5))|0;c[s>>2]=n;a=c[o+16>>2]|0;c[I>>2]=~~((+h[a+96>>3]+ +h[a+88>>3])*.5+t);c[u>>2]=~~(+h[a+80>>3]*.5+t);c[E>>2]=c[H>>2];c[E+4>>2]=c[H+4>>2];c[F>>2]=c[I>>2];c[F+4>>2]=c[I+4>>2];aH(w,E,F);a=c[w>>2]|0;f=c[q>>2]|0;c[E>>2]=c[H>>2];c[E+4>>2]=c[H+4>>2];c[F>>2]=c[I>>2];c[F+4>>2]=c[I+4>>2];bH(x,E,F);i=c[x>>2]|0;j=c[p>>2]|0;if((a|0)>-1)g=(a|0)/(e|0)|0;else g=((a+1|0)/(e|0)|0)+-1|0;if((f|0)>-1)k=(f|0)/(e|0)|0;else k=((f+1|0)/(e|0)|0)+-1|0;if((i|0)>-1)i=(i|0)/(e|0)|0;else i=((i+1|0)/(e|0)|0)+-1|0;if((j|0)>-1)f=(j|0)/(e|0)|0;else f=((j+1|0)/(e|0)|0)+-1|0;while(1){if((g|0)>(i|0))break;else a=k;while(1){if((a|0)>(f|0))break;QL(L,g,a);a=a+1|0}g=g+1|0}if((m|0)>-1)a=(m|0)/(e|0)|0;else a=((m+1|0)/(e|0)|0)+-1|0;c[H>>2]=a;if((n|0)>-1)a=(n|0)/(e|0)|0;else a=((n+1|0)/(e|0)|0)+-1|0;c[s>>2]=a;a=XA(C,o)|0;while(1){if(!a)break;c[F>>2]=c[H>>2];c[F+4>>2]=c[H+4>>2];cH(a,F,L,D,B,e,G);a=ZA(C,a)|0}o=kC(b,o)|0}}while(0);i=d+4|0;c[i>>2]=UL(L)|0;j=d+8|0;c[j>>2]=TL(L)|0;g=c[J>>2]|0;Q=+(K<<1|0);R=+(e|0);a=~~+N(+((+h[g+32>>3]+Q-+h[g+16>>3])/R));g=~~+N(+((+h[g+40>>3]+Q-+h[g+24>>3])/R));c[d>>2]=g+a;d:do if(0>2){f=c[15712]|0;d=KB(b)|0;b=c[j>>2]|0;c[M>>2]=d;c[M+4>>2]=b;c[M+8>>2]=a;c[M+12>>2]=g;w4(f,99376,M)|0;a=0;while(1){if((a|0)>=(c[j>>2]|0))break d;b=c[i>>2]|0;M=c[b+(a<<3)+4>>2]|0;c[O>>2]=c[b+(a<<3)>>2];c[O+4>>2]=M;w4(f,99403,O)|0;a=a+1|0}}while(0);NL(L);l=P;return}function YG(a,b){a=a|0;b=b|0;return (c[c[b>>2]>>2]|0)-(c[c[a>>2]>>2]|0)|0}function ZG(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;j=l;l=l+32|0;h=j+16|0;i=j;k=c[a+4>>2]|0;g=c[a+8>>2]|0;c[d>>2]=0-(c[e>>2]|0);f=d+4|0;c[f>>2]=0-(c[e+4>>2]|0);a=0;e=k;while(1){if((a|0)>=(g|0))break;c[h>>2]=c[e>>2];c[h+4>>2]=c[e+4>>2];OL(b,h);a=a+1|0;e=e+8|0}if(0>1){k=c[15712]|0;b=c[d>>2]|0;d=c[f>>2]|0;c[i>>2]=g;c[i+4>>2]=b;c[i+8>>2]=d;w4(k,99350,i)|0}l=j;return}function _G(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,l=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0;k=c[b+12>>2]|0;l=+h[i+(k<<5)>>3];m=+h[i+(k<<5)+8>>3];n=+h[i+(k<<5)+16>>3];o=+h[i+(k<<5)+24>>3];if(!((a|0)==0?(q=+(g<<1|0),p=+(f|0),($G((~~+N(+((q-l+n)/p))|0)/-2|0,(~~+N(+((q-m+o)/p))|0)/-2|0,b,d,e,f,i)|0)!=0):0))j=3;a:do if((j|0)==3?($G(0,0,b,d,e,f,i)|0)==0:0)if((~~+N(+(n-l))|0)<(~~+N(+(o-m))|0)){k=1;while(1){j=0-k|0;a=0;while(1){if((a|0)<=(j|0)){g=j;break}if($G(j,a,b,d,e,f,i)|0)break a;a=a+-1|0}while(1){if((g|0)>=(k|0))break;if($G(g,a,b,d,e,f,i)|0)break a;g=g+1|0}while(1){if((a|0)>=(k|0))break;if($G(g,a,b,d,e,f,i)|0)break a;a=a+1|0}while(1){if((g|0)<=(j|0))break;if($G(g,a,b,d,e,f,i)|0)break a;g=g+-1|0}while(1){if((a|0)<=0)break;if(!($G(g,a,b,d,e,f,i)|0))a=a+-1|0;else break a}k=k+1|0}}else{k=1;while(1){j=0-k|0;a=0;while(1){if((a|0)>=(k|0)){g=j;break}if($G(a,j,b,d,e,f,i)|0)break a;a=a+1|0}while(1){if((g|0)>=(k|0))break;if($G(a,g,b,d,e,f,i)|0)break a;g=g+1|0}while(1){if((a|0)<=(j|0))break;if($G(a,g,b,d,e,f,i)|0)break a;a=a+-1|0}while(1){if((g|0)<=(j|0))break;if($G(a,g,b,d,e,f,i)|0)break a;g=g+-1|0}while(1){if((a|0)>=0)break;if(!($G(a,g,b,d,e,f,i)|0))a=a+1|0;else break a}k=k+1|0}}while(0);return}function $G(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0.0,x=0.0;t=l;l=l+48|0;r=t+32|0;s=t+8|0;q=t;m=d+4|0;o=c[d+8>>2]|0;p=q+4|0;j=c[m>>2]|0;k=0;while(1){if((k|0)>=(o|0)){n=5;break}v=j;u=c[v+4>>2]|0;c[q>>2]=(c[v>>2]|0)+a;c[p>>2]=u+b;c[r>>2]=c[q>>2];c[r+4>>2]=c[q+4>>2];if(RL(e,r)|0){j=0;break}j=j+8|0;k=k+1|0}if((n|0)==5){d=c[d+12>>2]|0;x=+h[i+(d<<5)>>3];w=+h[i+(d<<5)+8>>3];c[f>>2]=(O(g,a)|0)-~~(x+(x>=0.0?.5:-.5));d=f+4|0;c[d>>2]=(O(g,b)|0)-~~(w+(w>=0.0?.5:-.5));j=c[m>>2]|0;k=0;while(1){if((k|0)>=(o|0))break;u=j;v=c[u+4>>2]|0;c[q>>2]=(c[u>>2]|0)+a;c[p>>2]=v+b;c[r>>2]=c[q>>2];c[r+4>>2]=c[q+4>>2];OL(e,r);j=j+8|0;k=k+1|0}if(0>1){j=c[15712]|0;u=c[f>>2]|0;v=c[d>>2]|0;c[s>>2]=o;c[s+4>>2]=a;c[s+8>>2]=b;c[s+12>>2]=u;c[s+16>>2]=v;w4(j,99316,s)|0;j=1}else j=1}l=t;return j|0}function aH(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=(c[b+4>>2]|0)-(c[d+4>>2]|0)|0;c[a>>2]=(c[b>>2]|0)-(c[d>>2]|0);c[a+4>>2]=e;return}function bH(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=(c[d+4>>2]|0)+(c[b+4>>2]|0)|0;c[a>>2]=(c[d>>2]|0)+(c[b>>2]|0);c[a+4>>2]=e;return}function cH(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0.0,k=0.0,m=0,n=0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;A=l;l=l+112|0;x=A+96|0;w=A+80|0;r=A+48|0;s=A+32|0;y=A+16|0;z=A;n=A+64|0;h[y>>3]=+(c[b>>2]|0);t=y+8|0;h[t>>3]=+(c[b+4>>2]|0);a:do if((i|0)!=0?(u=a+16|0,m=c[(c[u>>2]|0)+8>>2]|0,(m|0)!=0):0){q=+(e|0);p=+(f|0);o=1.0/+(g|0);n=z+8|0;i=0;while(1){if((i|0)>=(c[m+4>>2]|0))break a;f=c[m>>2]|0;a=c[f+(i*48|0)>>2]|0;b=c[f+(i*48|0)+4>>2]|0;g=c[f+(i*48|0)+8>>2]|0;e=c[f+(i*48|0)+12>>2]|0;m=f+(i*48|0)+16|0;c[r>>2]=c[m>>2];c[r+4>>2]=c[m+4>>2];c[r+8>>2]=c[m+8>>2];c[r+12>>2]=c[m+12>>2];f=f+(i*48|0)+32|0;c[s>>2]=c[f>>2];c[s+4>>2]=c[f+4>>2];c[s+8>>2]=c[f+8>>2];c[s+12>>2]=c[f+12>>2];if(!g){c[y>>2]=c[a>>2];c[y+4>>2]=c[a+4>>2];c[y+8>>2]=c[a+8>>2];c[y+12>>2]=c[a+12>>2];m=a+16|0;c[z>>2]=c[m>>2];c[z+4>>2]=c[m+4>>2];c[z+8>>2]=c[m+8>>2];c[z+12>>2]=c[m+12>>2];m=2}else{c[y>>2]=c[r>>2];c[y+4>>2]=c[r+4>>2];c[y+8>>2]=c[r+8>>2];c[y+12>>2]=c[r+12>>2];c[z>>2]=c[a>>2];c[z+4>>2]=c[a+4>>2];c[z+8>>2]=c[a+8>>2];c[z+12>>2]=c[a+12>>2];m=1}j=+h[y>>3]+q;h[y>>3]=j;k=+h[t>>3]+p;h[t>>3]=k;if(!(j>=0.0))j=(j+1.0)*o+-1.0;else j=j*o;h[y>>3]=j;if(!(k>=0.0))j=(k+1.0)*o+-1.0;else j=k*o;h[t>>3]=j;j=+h[z>>3]+q;h[z>>3]=j;k=+h[n>>3]+p;h[n>>3]=k;if(!(j>=0.0))j=(j+1.0)*o+-1.0;else j=j*o;h[z>>3]=j;if(!(k>=0.0))j=(k+1.0)*o+-1.0;else j=k*o;h[n>>3]=j;c[w>>2]=c[y>>2];c[w+4>>2]=c[y+4>>2];c[w+8>>2]=c[y+8>>2];c[w+12>>2]=c[y+12>>2];c[x>>2]=c[z>>2];c[x+4>>2]=c[z+4>>2];c[x+8>>2]=c[z+8>>2];c[x+12>>2]=c[z+12>>2];PG(w,x,d);while(1){if((m|0)>=(b|0))break;c[y>>2]=c[z>>2];c[y+4>>2]=c[z+4>>2];c[y+8>>2]=c[z+8>>2];c[y+12>>2]=c[z+12>>2];g=a+(m<<4)|0;c[z>>2]=c[g>>2];c[z+4>>2]=c[g+4>>2];c[z+8>>2]=c[g+8>>2];c[z+12>>2]=c[g+12>>2];j=+h[z>>3]+q;h[z>>3]=j;k=+h[n>>3]+p;h[n>>3]=k;if(!(j>=0.0))j=(j+1.0)*o+-1.0;else j=j*o;h[z>>3]=j;if(!(k>=0.0))j=(k+1.0)*o+-1.0;else j=k*o;h[n>>3]=j;c[w>>2]=c[y>>2];c[w+4>>2]=c[y+4>>2];c[w+8>>2]=c[y+8>>2];c[w+12>>2]=c[y+12>>2];c[x>>2]=c[z>>2];c[x+4>>2]=c[z+4>>2];c[x+8>>2]=c[z+8>>2];c[x+12>>2]=c[z+12>>2];PG(w,x,d);m=m+1|0}if(e|0){c[y>>2]=c[z>>2];c[y+4>>2]=c[z+4>>2];c[y+8>>2]=c[z+8>>2];c[y+12>>2]=c[z+12>>2];c[z>>2]=c[s>>2];c[z+4>>2]=c[s+4>>2];c[z+8>>2]=c[s+8>>2];c[z+12>>2]=c[s+12>>2];j=+h[z>>3]+q;h[z>>3]=j;k=+h[n>>3]+p;h[n>>3]=k;if(!(j>=0.0))j=(j+1.0)*o+-1.0;else j=j*o;h[z>>3]=j;if(!(k>=0.0))j=(k+1.0)*o+-1.0;else j=k*o;h[n>>3]=j;c[w>>2]=c[y>>2];c[w+4>>2]=c[y+4>>2];c[w+8>>2]=c[y+8>>2];c[w+12>>2]=c[y+12>>2];c[x>>2]=c[z>>2];c[x+4>>2]=c[z+4>>2];c[x+8>>2]=c[z+8>>2];c[x+12>>2]=c[z+12>>2];PG(w,x,d)}i=i+1|0;m=c[(c[u>>2]|0)+8>>2]|0}}else v=4;while(0);if((v|0)==4){jP(n,c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0);c[z>>2]=c[n>>2];c[z+4>>2]=c[n+4>>2];c[z+8>>2]=c[n+8>>2];c[z+12>>2]=c[n+12>>2];j=+h[z>>3]+ +(e|0);h[z>>3]=j;m=z+8|0;k=+h[m>>3]+ +(f|0);h[m>>3]=k;if(!(j>=0.0))j=(j+1.0)/+(g|0)+-1.0;else j=j/+(g|0);h[z>>3]=j;if(!(k>=0.0))j=(k+1.0)/+(g|0)+-1.0;else j=k/+(g|0);h[m>>3]=j;c[w>>2]=c[y>>2];c[w+4>>2]=c[y+4>>2];c[w+8>>2]=c[y+8>>2];c[w+12>>2]=c[y+12>>2];c[x>>2]=c[z>>2];c[x+4>>2]=c[z+4>>2];c[x+8>>2]=c[z+8>>2];c[x+12>>2]=c[z+12>>2];PG(w,x,d)}l=A;return}function dH(a,b,d){a=a|0;b=b|0;d=d|0;a:do if((a|0)<1)a=0;else switch(c[d+16>>2]|0){case 4:{a=SG(a,b,d)|0;break a}case 3:{a=eH(a,b,d)|0;break a}default:{a=0;break a}}while(0);return a|0}function eH(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;n=l;l=l+80|0;g=n+64|0;f=n+24|0;m=n+8|0;e=n;h=n+56|0;j=d+8|0;k=VG(a,b,c[j>>2]|0)|0;if(0){i=c[15712]|0;c[e>>2]=k;w4(i,99285,e)|0}a:do if((k|0)>=1){c[h+4>>2]=0;c[h>>2]=0;i=JK(a<<4)|0;d=0;while(1){if((d|0)>=(a|0))break;c[i+(d<<4)+12>>2]=d;o=b+(d<<5)|0;e=c[j>>2]|0;c[f>>2]=c[o>>2];c[f+4>>2]=c[o+4>>2];c[f+8>>2]=c[o+8>>2];c[f+12>>2]=c[o+12>>2];c[f+16>>2]=c[o+16>>2];c[f+20>>2]=c[o+20>>2];c[f+24>>2]=c[o+24>>2];c[f+28>>2]=c[o+28>>2];c[g>>2]=c[h>>2];c[g+4>>2]=c[h+4>>2];WG(f,i+(d<<4)|0,k,e,g,195059);d=d+1|0}f=JK(a<<2)|0;d=0;while(1){if((d|0)>=(a|0))break;c[f+(d<<2)>>2]=i+(d<<4);d=d+1|0}o4(f,a,4,89);g=KL()|0;d=JK(a<<3)|0;e=0;while(1){if((e|0)>=(a|0))break;o=c[f+(e<<2)>>2]|0;_G(e,o,g,d+(c[o+12>>2]<<3)|0,k,c[j>>2]|0,b);e=e+1|0}U1(f);e=0;while(1){if((e|0)>=(a|0))break;U1(c[i+(e<<4)+4>>2]|0);e=e+1|0}U1(i);NL(g);if(0>1){f=c[15712]|0;e=0;while(1){if((e|0)>=(a|0))break a;b=c[d+(e<<3)>>2]|0;o=c[d+(e<<3)+4>>2]|0;c[m>>2]=e;c[m+4>>2]=b;c[m+8>>2]=o;w4(f,99301,m)|0;e=e+1|0}}}else d=0;while(0);l=n;return d|0}function fH(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,l=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0,s=0,t=0;a:do if((a|0)<1)f=0-a|0;else{s=(e|0)==0;q=(f|0)==0;j=0;while(1){if((j|0)==(a|0)){f=0;break a}r=c[b+(j<<2)>>2]|0;g=s?r:e;k=c[d+(j<<3)>>2]|0;l=c[d+(j<<3)+4>>2]|0;m=+(k|0);n=m*.013888888888888888;o=+(l|0);p=o*.013888888888888888;i=jC(r)|0;while(1){if(!i)break;f=c[i+16>>2]|0;t=c[f+132>>2]|0;h[t>>3]=+h[t>>3]+n;t=t+8|0;h[t>>3]=+h[t>>3]+p;t=f+16|0;h[t>>3]=+h[t>>3]+m;t=f+24|0;h[t>>3]=+h[t>>3]+o;f=c[f+108>>2]|0;if(f|0){t=f+56|0;h[t>>3]=+h[t>>3]+m;t=f+64|0;h[t>>3]=+h[t>>3]+o}b:do if(!q){f=XA(g,i)|0;while(1){if(!f)break b;gH(f,k,l);f=ZA(g,f)|0}}while(0);i=kC(r,i)|0}hH(r,k,l);j=j+1|0}}while(0);return f|0}function gH(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0.0,k=0,l=0.0,m=0,n=0;e=c[a+16>>2]|0;a=c[e+96>>2]|0;if(a|0){m=a+56|0;h[m>>3]=+h[m>>3]+ +(b|0);m=a+64|0;h[m>>3]=+h[m>>3]+ +(d|0)}a=c[e+108>>2]|0;if(a|0){m=a+56|0;h[m>>3]=+h[m>>3]+ +(b|0);m=a+64|0;h[m>>3]=+h[m>>3]+ +(d|0)}a=c[e+100>>2]|0;if(a|0){m=a+56|0;h[m>>3]=+h[m>>3]+ +(b|0);m=a+64|0;h[m>>3]=+h[m>>3]+ +(d|0)}a=c[e+104>>2]|0;if(a|0){m=a+56|0;h[m>>3]=+h[m>>3]+ +(b|0);m=a+64|0;h[m>>3]=+h[m>>3]+ +(d|0)}m=c[e+8>>2]|0;a:do if(m|0){l=+(b|0);j=+(d|0);b=c[m+4>>2]|0;a=0;while(1){if((a|0)>=(b|0))break a;k=c[m>>2]|0;d=c[k+(a*48|0)>>2]|0;f=c[k+(a*48|0)+4>>2]|0;g=c[k+(a*48|0)+8>>2]|0;i=c[k+(a*48|0)+12>>2]|0;e=0;while(1){if((e|0)>=(f|0))break;n=d+(e<<4)|0;h[n>>3]=+h[n>>3]+l;n=d+(e<<4)+8|0;h[n>>3]=+h[n>>3]+j;e=e+1|0}if(g|0){n=k+(a*48|0)+16|0;h[n>>3]=+h[n>>3]+l;n=k+(a*48|0)+24|0;h[n>>3]=+h[n>>3]+j}if(i|0){n=k+(a*48|0)+32|0;h[n>>3]=+h[n>>3]+l;n=k+(a*48|0)+40|0;h[n>>3]=+h[n>>3]+j}a=a+1|0}}while(0);return}function hH(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0,k=0.0,l=0.0,m=0,n=0.0,o=0;j=b+16|0;b=c[j>>2]|0;o=b+16|0;m=b+24|0;f=b+32|0;b=b+40|0;g=+(d|0);l=+h[f>>3]+g;i=+(e|0);n=+h[m>>3]+i;k=+h[b>>3]+i;h[o>>3]=+h[o>>3]+g;h[m>>3]=n;h[f>>3]=l;h[b>>3]=k;b=c[j>>2]|0;f=c[b+12>>2]|0;if((f|0)!=0?(a[f+81>>0]|0)!=0:0){o=f+56|0;h[o>>3]=+h[o>>3]+g;f=f+64|0;h[f>>3]=+h[f>>3]+i;f=1}else f=1;while(1){if((f|0)>(c[b+180>>2]|0))break;hH(c[(c[b+184>>2]|0)+(f<<2)>>2]|0,d,e);f=f+1|0;b=c[j>>2]|0}return}function iH(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;f=QG(a,b,d,e)|0;if(!f)a=1;else{a=fH(a,b,f,d,c[e+12>>2]|0)|0;U1(f)}return a|0}function jH(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;p=l;l=l+32|0;o=p;n=iH(a,b,d,e)|0;if(!n){BP(d);m=(c[d+16>>2]|0)+16|0;c[o>>2]=c[m>>2];c[o+4>>2]=c[m+4>>2];c[o+8>>2]=c[m+8>>2];c[o+12>>2]=c[m+12>>2];c[o+16>>2]=c[m+16>>2];c[o+20>>2]=c[m+20>>2];c[o+24>>2]=c[m+24>>2];c[o+28>>2]=c[m+28>>2];f=o+8|0;g=o+16|0;i=o+24|0;e=0;while(1){if((e|0)>=(a|0))break;k=c[(c[b+(e<<2)>>2]|0)+16>>2]|0;j=c[k+180>>2]|0;k=k+184|0;d=1;while(1){if((d|0)>(j|0))break;r=(c[(c[(c[k>>2]|0)+(d<<2)>>2]|0)+16>>2]|0)+16|0;h[o>>3]=+h[(+h[o>>3]<+h[r>>3]?o:r)>>3];r=c[(c[(c[k>>2]|0)+(d<<2)>>2]|0)+16>>2]|0;q=r+16|0;h[f>>3]=+h[(+h[f>>3]<+h[r+24>>3]?o:q)+8>>3];h[g>>3]=+h[(+h[g>>3]>+h[r+32>>3]?o:q)+16>>3];h[i>>3]=+h[(+h[i>>3]>+h[r+40>>3]?o:q)+24>>3];d=d+1|0}e=e+1|0}c[m>>2]=c[o>>2];c[m+4>>2]=c[o+4>>2];c[m+8>>2]=c[o+8>>2];c[m+12>>2]=c[o+12>>2];c[m+16>>2]=c[o+16>>2];c[m+20>>2]=c[o+20>>2];c[m+24>>2]=c[o+24>>2];c[m+28>>2]=c[o+28>>2]}l=p;return n|0}function kH(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;g=l;l=l+16|0;f=g;if(!e)qa(99534,99540,1400,99547);d=lH(a,d,d)|0;c[e+8>>2]=d;if(0){h=c[15712]|0;c[f>>2]=d;w4(h,99559,f)|0}c[e+12>>2]=0;c[e+20>>2]=0;mH(a,b,e)|0;l=g;return c[e+16>>2]|0}function lH(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;f=i+4|0;g=QA(b,99689)|0;a:do if(g){c[h>>2]=f;h=(b5(g,137395,h)|0)==1;b=c[f>>2]|0;if(h&(b|0)>-1)d=b;else{switch(a[g>>0]|0){case 84:case 116:break;default:break a}d=e}}while(0);l=i;return d|0}function mH(a,b,c){a=a|0;b=b|0;c=c|0;return nH(QA(a,99572)|0,b,c)|0}function nH(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0.0;u=l;l=l+64|0;r=u+40|0;q=u+32|0;t=u+24|0;s=u+16|0;m=u+8|0;k=u;i=u+48|0;j=u+44|0;if(!e)qa(99534,99540,1292,99581);o=e+28|0;c[o>>2]=0;p=e+16|0;c[p>>2]=d;n=e+4|0;c[n>>2]=0;c[e+24>>2]=0;a:do if(b|0?(f=a[b>>0]|0,f<<24>>24):0)switch(f<<24>>24|0){case 97:if(!(Q2(b,99599,5)|0)){c[p>>2]=4;m=oH(b+5|0,e)|0;c[k>>2]=j;m=(b5(m,137395,k)|0)>0;d=c[j>>2]|0;if(!(m&(d|0)>0))break a;c[n>>2]=d;break a}else{if(Q2(b,99605,6)|0)break a;c[p>>2]=5;c[m>>2]=i;m=(b5(b+5|0,99612,m)|0)>0;v=+g[i>>2];g[e>>2]=m&v>0.0?v:1.0;break a}case 99:{if(f<<24>>24!=99)break a;if(s2(b,108006)|0)break a;c[p>>2]=1;break a}case 103:{if(f<<24>>24!=103)break a;if(s2(b,111494)|0)break a;c[p>>2]=3;break a}case 110:{if(f<<24>>24!=110)break a;if(s2(b,111481)|0)break a;c[p>>2]=2;break a}default:break a}while(0);if(0){d=c[15712]|0;U4(99615,11,1,d)|0;c[s>>2]=pH(c[p>>2]|0)|0;w4(d,99627,s)|0;if((c[p>>2]|0)==5){h[t>>3]=+g[e>>2];w4(d,99640,t)|0}c[q>>2]=c[n>>2];w4(d,99653,q)|0;c[r>>2]=c[o>>2];w4(d,99666,r)|0}l=u;return c[p>>2]|0}function oH(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;a:do if((a[b>>0]|0)==95){f=d+28|0;e=1;while(1){b=b+1|0;b:while(1){if(!e)break a;d=a[b>>0]|0;if(!(d<<24>>24))break a;switch(d<<24>>24|0){case 99:{g=10;break b}case 105:{g=9;break b}case 117:{g=11;break b}case 116:{g=12;break b}case 98:{d=32;break b}case 108:{g=7;break b}case 114:{g=8;break b}default:e=0}}if((g|0)==7){g=0;d=4}else if((g|0)==8){g=0;d=8}else if((g|0)==9){g=0;d=64}else if((g|0)==10){g=0;d=1}else if((g|0)==11){g=0;d=2}else if((g|0)==12){g=0;d=16}c[f>>2]=c[f>>2]|d}}while(0);return b|0}function pH(a){a=a|0;switch(a|0){case 1:{a=108006;break}case 2:{a=111481;break}case 3:{a=111494;break}case 4:{a=99599;break}case 5:{a=99605;break}default:a=99679}return a|0}function qH(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+96|0;k=n;m=n+80|0;do if(a){if(!e){e=V1(1,20)|0;c[e+4>>2]=d>>>0>80?d:80}d=c[e>>2]|0;j=c[e+4>>2]|0;if(!d){h=100;d=a;i=V1(100,j)|0}else{h=d+100|0;i=W1(c[e+8>>2]|0,O(h,j)|0)|0;Y6(i+(O(j,d)|0)|0,0,j*100|0)|0;d=a}while(1){d=rH(k,d,b,m)|0;if(!d)break;a=c[e>>2]|0;f=h<<1;g=O(h,j)|0;if((a|0)==(h|0)){i=W1(i,O(f,j)|0)|0;Y6(i+g|0,0,g|0)|0;a=c[e>>2]|0}else f=h;a=i+(O(a,j)|0)|0;g=k;h=a+80|0;do{c[a>>2]=c[g>>2];a=a+4|0;g=g+4|0}while((a|0)<(h|0));c[e>>2]=(c[e>>2]|0)+1;h=f}if(c[m>>2]|0){m=e+16|0;c[m>>2]=c[m>>2]|1}d=c[e>>2]|0;if(!d){U1(i);U1(e);e=0;break}else{c[e+8>>2]=W1(i,O(d,j)|0)|0;break}}while(0);l=n;return e|0}function rH(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;j=l;l=l+80|0;h=j+64|0;i=j;c[f>>2]=0;do{g=a[d>>0]|0;d=d+1|0}while((S2(g)|0)!=0);a:do switch(g|0){case 69:{c[b>>2]=0;d=sH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e>>2];break}case 101:{c[b>>2]=1;d=sH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e>>2];break}case 80:{c[b>>2]=2;d=tH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+4>>2];break}case 112:{c[b>>2]=3;d=tH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+4>>2];break}case 98:{c[b>>2]=4;d=tH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+8>>2];break}case 66:{c[b>>2]=5;d=tH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+8>>2];break}case 99:{d=uH(d,h)|0;if(!d){c[f>>2]=1;d=0;break a}g=vH(c[h>>2]|0,i)|0;c[h>>2]=g;if(!g){c[f>>2]=1;d=0;break a}if(!(c[i>>2]|0)){c[b>>2]=9;c[b+8>>2]=c[i+8>>2];if(!e)break a;c[b+72>>2]=c[e+24>>2];break a}else{c[b>>2]=14;f=b+8|0;g=i;h=f+64|0;do{c[f>>2]=c[g>>2];f=f+4|0;g=g+4|0}while((f|0)<(h|0));if(!e)break a;c[b+72>>2]=c[e+40>>2];break a}}case 67:{d=uH(d,h)|0;if(!d){c[f>>2]=1;d=0;break a}g=vH(c[h>>2]|0,i)|0;c[h>>2]=g;if(!g){c[f>>2]=1;d=0;break a}if(!(c[i>>2]|0)){c[b>>2]=8;c[b+8>>2]=c[i+8>>2];if(!e)break a;c[b+72>>2]=c[e+20>>2];break a}else{c[b>>2]=13;f=b+8|0;g=i;h=f+64|0;do{c[f>>2]=c[g>>2];f=f+4|0;g=g+4|0}while((f|0)<(h|0));if(!e)break a;c[b+72>>2]=c[e+40>>2];break a}}case 76:{c[b>>2]=6;d=tH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+12>>2];break}case 84:{c[b>>2]=7;d=wH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}d=wH(d,b+16|0)|0;if(!d){c[f>>2]=1;d=0;break a}d=xH(d,b+24|0)|0;if(!d){c[f>>2]=1;d=0;break a}d=wH(d,b+32|0)|0;if(!d){c[f>>2]=1;d=0;break a}d=uH(d,b+40|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+16>>2];break}case 70:{c[b>>2]=10;d=wH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}d=uH(d,b+16|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+28>>2];break}case 83:{c[b>>2]=11;d=uH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+32>>2];break}case 73:{c[b>>2]=12;d=sH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}d=uH(d,b+40|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+36>>2];break}case 116:{c[b>>2]=15;d=yH(d,b+8|0)|0;if(!d){c[f>>2]=1;d=0;break a}if(e)c[b+72>>2]=c[e+44>>2];break}case 0:{d=0;break}default:{c[f>>2]=1;d=0}}while(0);l=j;return d|0}function sH(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0;i=l;l=l+16|0;e=i;h[b>>3]=+s5(a,e);f=c[e>>2]|0;if(((f|0)!=(a|0)?(h[b+8>>3]=+s5(f,e),g=c[e>>2]|0,(f|0)!=(g|0)):0)?(h[b+16>>3]=+s5(g,e),d=c[e>>2]|0,(g|0)!=(d|0)):0){h[b+24>>3]=+s5(d,e);a=c[e>>2]|0;a=(d|0)==(a|0)?0:a}else a=0;l=i;return a|0}function tH(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0;k=l;l=l+16|0;j=k+4|0;g=k;a=zH(a,j)|0;do if(a){d=c[j>>2]|0;i=V1(d,24)|0;c[b>>2]=d;e=i;f=0;while(1){if((f|0)>=(d|0)){d=9;break}h[e>>3]=+s5(a,g);d=c[g>>2]|0;if((a|0)==(d|0)){d=5;break}h[e+8>>3]=+s5(d,g);a=c[g>>2]|0;if((d|0)==(a|0)){d=7;break}h[e+16>>3]=0.0;e=e+24|0;d=c[b>>2]|0;f=f+1|0}if((d|0)==5){c[j>>2]=f;U1(i);a=0;break}else if((d|0)==7){c[j>>2]=f;U1(i);a=0;break}else if((d|0)==9){c[j>>2]=f;c[b+4>>2]=i;break}}else a=0;while(0);l=k;return a|0}function uH(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+16|0;i=j;b=zH(b,i)|0;f=c[i>>2]|0;do if(!((b|0)==0|(f|0)<1)){while(1){h=a[b>>0]|0;e=h<<24>>24==0;if(e^h<<24>>24!=45)b=b+1|0;else break}if(!e){h=V1(f+1|0,1)|0;g=h;e=f;while(1){b=b+1|0;if((e|0)<=0){e=9;break}e=a[b>>0]|0;if(!(e<<24>>24)){e=8;break}a[g>>0]=e;e=(c[i>>2]|0)+-1|0;c[i>>2]=e;g=g+1|0}if((e|0)==8){U1(h);b=0;break}else if((e|0)==9){a[g>>0]=0;c[d>>2]=h;break}}else b=0}else b=0;while(0);l=j;return b|0}function vH(b,d){b=b|0;d=d|0;var e=0;e=a[b>>0]|0;switch(e|0){case 91:{b=AH(b+1|0,d)|0;break}case 40:{b=BH(b+1|0,d)|0;break}case 47:case 35:{c[d>>2]=0;c[d+8>>2]=b;break}default:if(!(a3(e)|0))b=0;else{c[d>>2]=0;c[d+8>>2]=b}}return b|0}function wH(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0;f=l;l=l+16|0;d=f;e=+s5(a,d);d=c[d>>2]|0;if((d|0)==(a|0))d=0;else h[b>>3]=e;l=f;return d|0}function xH(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;a=zH(a,e)|0;e=c[e>>2]|0;c[b>>2]=(e|0)<0?0:e|0?2:1;l=d;return a|0}function yH(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[b>>2]=T2(a,e,10)|0;b=c[e>>2]|0;l=d;return ((b|0)==(a|0)?0:b)|0}function zH(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[b>>2]=y4(a,e,10)|0;b=c[e>>2]|0;l=d;return ((b|0)==(a|0)?0:b)|0}function AH(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;m=o;c[b>>2]=1;n=b+8|0;i=wH(a,n)|0;do if(((((i|0)!=0?(j=wH(i,b+16|0)|0,(j|0)!=0):0)?(e=wH(j,b+24|0)|0,(e|0)!=0):0)?(f=wH(e,b+32|0)|0,(f|0)!=0):0)?(k=b+40|0,d=zH(f,k)|0,(d|0)!=0):0){b=c[k>>2]|0;f=V1(b,8)|0;e=0;while(1){if((e|0)>=(b|0)){d=13;break}d=wH(d,m)|0;if(!d){d=9;break}g[f+(e<<3)>>2]=+h[m>>3];d=uH(d,f+(e<<3)+4|0)|0;if(!d){d=12;break}e=e+1|0;b=c[k>>2]|0}if((d|0)==9){U1(f);a=0;break}else if((d|0)==12){U1(f);a=0;break}else if((d|0)==13){c[n+36>>2]=f;break}}else a=0;while(0);l=o;return a|0}function BH(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+16|0;n=p;c[b>>2]=2;k=wH(a,b+8|0)|0;do if(((((((k|0)!=0?(m=wH(k,b+16|0)|0,(m|0)!=0):0)?(e=wH(m,b+24|0)|0,(e|0)!=0):0)?(f=wH(e,b+32|0)|0,(f|0)!=0):0)?(i=wH(f,b+40|0)|0,(i|0)!=0):0)?(j=wH(i,b+48|0)|0,(j|0)!=0):0)?(o=b+56|0,d=zH(j,o)|0,(d|0)!=0):0){e=c[o>>2]|0;i=V1(e,8)|0;f=0;while(1){if((f|0)>=(e|0)){d=15;break}d=wH(d,n)|0;if(!d){d=11;break}g[i+(f<<3)>>2]=+h[n>>3];d=uH(d,i+(f<<3)+4|0)|0;if(!d){d=14;break}f=f+1|0;e=c[o>>2]|0}if((d|0)==11){U1(i);a=0;break}else if((d|0)==14){U1(i);a=0;break}else if((d|0)==15){c[b+60>>2]=i;break}}else a=0;while(0);l=p;return a|0}function CH(a,b,c){a=a|0;b=b|0;c=c|0;return qH(a,b,c,0)|0}function DH(a){a=a|0;return CH(a,0,0)|0}function EH(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;e=c[a+12>>2]|0;if(a|0){f=c[a+8>>2]|0;g=a+4|0;h=(e|0)==0;b=0;while(1){if((b|0)>=(c[a>>2]|0))break;d=f+(O(c[g>>2]|0,b)|0)|0;if(!h)tb[e&127](d);FH(d);b=b+1|0}U1(f);U1(a)}return}function FH(a){a=a|0;switch(c[a>>2]|0){case 3:case 2:{U1(c[a+8+4>>2]|0);break}case 5:case 4:{U1(c[a+8+4>>2]|0);break}case 6:{U1(c[a+8+4>>2]|0);break}case 7:{U1(c[a+40>>2]|0);break}case 9:case 8:{U1(c[a+8>>2]|0);break}case 14:case 13:{GH(a+8|0);break}case 10:{U1(c[a+16>>2]|0);break}case 11:{U1(c[a+8>>2]|0);break}case 12:{U1(c[a+40>>2]|0);break}default:{}}return}function GH(a){a=a|0;var b=0,d=0,e=0;switch(c[a>>2]|0){case 1:{e=a+40|0;b=a+8+36|0;a=0;while(1){d=c[b>>2]|0;if((a|0)>=(c[e>>2]|0))break;U1(c[d+(a<<3)+4>>2]|0);a=a+1|0}U1(d);break}case 2:{e=a+56|0;d=a+60|0;a=0;while(1){b=c[d>>2]|0;if((a|0)>=(c[e>>2]|0))break;U1(c[b+(a<<3)+4>>2]|0);a=a+1|0}U1(b);break}default:{}}return}function HH(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;k=o+4|0;n=o;c[d>>2]=0;c[e>>2]=(xB(xC(b)|0)|0)!=0&1;f=c[47184]|0;a:do if(f|0?(h=RA(b,f)|0,i=a[h>>0]|0,i<<24>>24):0){f=20388;while(1){g=c[f>>2]|0;if(!g)break a;if(i<<24>>24==(a[g>>0]|0)?(s2(h,g)|0)==0:0)break;f=f+12|0}c[d>>2]=c[f+4>>2];c[e>>2]=c[f+8>>2]}while(0);f=c[47195]|0;if((f|0?(c[e>>2]|0)==1:0)?(j=RA(b,f)|0,a[j>>0]|0):0)IH(j,e);f=c[47196]|0;if((f|0?(c[d>>2]|0)==1:0)?(m=RA(b,f)|0,a[m>>0]|0):0)IH(m,d);if(a[(c[b+16>>2]|0)+153>>0]|0){j=b+-48|0;i=xC(c[((c[b>>2]&3|0)==2?b:j)+40>>2]|0)|0;m=c[b>>2]&3;HH(iB(i,c[((m|0)==2?b:j)+40>>2]|0,c[((m|0)==3?b:b+48|0)+40>>2]|0,0,0)|0,k,n);c[e>>2]=c[k>>2]|c[e>>2];c[d>>2]=c[n>>2]|c[d>>2]}l=o;return}function IH(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;m=o;k=o+4|0;j=0;e=0;a:while(1){c[d>>2]=e;g=(j|0)<4;h=(j|0)==3;i=(j|0)==0;while(1){if(!(g&(a[b>>0]|0)!=0))break a;c[k>>2]=0;f=JH(b,k)|0;e=c[k>>2]|0;if(!e){n=5;break a}b=(e|0)==8;if(!(h&b)){if(!(i&b))break;if(a[f>>0]|0)break}c[k>>2]=0;b=f}e=c[d>>2]|e<<(j<<3);j=j+1|0;b=f}if((n|0)==5){c[m>>2]=b;gA(0,99694,m)|0}l=o;return}function JH(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=l;l=l+16|0;e=f;c[e>>2]=0;d=KH(a,20448,e)|0;if((d|0)==(a|0)){while(1){d=KH(a,20464,e)|0;if((a|0)==(d|0))break;else a=d}d=KH(a,20512,e)|0}a=c[e>>2]|0;if((a|0)!=0&(a&15|0)==0){a=a|1;c[e>>2]=a}c[b>>2]=c[b>>2]|a;l=f;return d|0}function KH(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;while(1){e=c[b>>2]|0;if(!e)break;f=l2(e)|0;if(!(Q2(a,e,f)|0)){g=4;break}b=b+8|0}if((g|0)==4){c[d>>2]=c[d>>2]|c[b+4>>2];a=a+f|0}return a|0}function LH(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0,g=0,i=0;g=0;d=0.0;while(1){if((g|0)==4)break;f=b>>(g<<3)&15;e=1400;while(1){if(!(c[e+16>>2]|0))break;if((f|0)==(c[e>>2]|0)){i=6;break}e=e+24|0}if((i|0)==6){i=0;d=+h[e+8>>3]+d}g=g+1|0}return +(d*10.0*+_O(a,c[47188]|0,1.0,0.0))}function MH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=g|0;var i=0.0,j=0.0,k=0.0,m=0,n=0.0,o=0,p=0;p=l;l=l+96|0;m=p+80|0;o=p;k=f>4.0?f*.0875:.35;i=+h[d+8>>3];n=i*k;j=+h[d>>3];k=j*k;e=+h[b>>3];j=e+j;h[m>>3]=j;f=+h[b+8>>3];i=i+f;h[m+8>>3]=i;d=o+64|0;if(!(g&32)){c[d>>2]=c[m>>2];c[d+4>>2]=c[m+4>>2];c[d+8>>2]=c[m+8>>2];c[d+12>>2]=c[m+12>>2];c[o>>2]=c[m>>2];c[o+4>>2]=c[m+4>>2];c[o+8>>2]=c[m+8>>2];c[o+12>>2]=c[m+12>>2];m=o+32|0;c[m>>2]=c[b>>2];c[m+4>>2]=c[b+4>>2];c[m+8>>2]=c[b+8>>2];c[m+12>>2]=c[b+12>>2];f=i;e=j}else{c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];c[o>>2]=c[b>>2];c[o+4>>2]=c[b+4>>2];c[o+8>>2]=c[b+8>>2];c[o+12>>2]=c[b+12>>2];b=o+32|0;c[b>>2]=c[m>>2];c[b+4>>2]=c[m+4>>2];c[b+8>>2]=c[m+8>>2];c[b+12>>2]=c[m+12>>2]}h[o+16>>3]=e+n;h[o+24>>3]=f-k;h[o+48>>3]=e-n;h[o+56>>3]=f+k;do if(!(g&64))if(!(g&128)){nS(a,o+16|0,3,g>>>4&1^1);break}else{nS(a,o+32|0,3,g>>>4&1^1);break}else nS(a,o,3,g>>>4&1^1);while(0);l=p;return}function NH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=g|0;var i=0.0,j=0.0,k=0.0,m=0.0,n=0,o=0.0,p=0.0,q=0.0,r=0.0,s=0.0,t=0.0,u=0.0,v=0.0,w=0,x=0,y=0;x=l;l=l+160|0;n=x+144|0;w=x;q=e*4.0;y=(g&32|0)==0;q=!(q<f)|y?.45:f/q*.45;s=!(f>1.0)|y?0.0:(f+-1.0)*.05/e;u=+h[d+8>>3];r=-u;v=q*r;t=+h[d>>3];q=t*q;r=s*r;s=t*s;p=+h[b>>3];e=p+t;h[n>>3]=e;o=+h[b+8>>3];f=u+o;h[n+8>>3]=f;t=t*.5+p;u=u*.5+o;d=w+128|0;if(y){c[d>>2]=c[n>>2];c[d+4>>2]=c[n+4>>2];c[d+8>>2]=c[n+8>>2];c[d+12>>2]=c[n+12>>2];c[w>>2]=c[n>>2];c[w+4>>2]=c[n+4>>2];c[w+8>>2]=c[n+8>>2];c[w+12>>2]=c[n+12>>2];y=w+64|0;c[y>>2]=c[b>>2];c[y+4>>2]=c[b+4>>2];c[y+8>>2]=c[b+8>>2];c[y+12>>2]=c[b+12>>2];i=o;j=p;k=o;m=p;f=o;e=p}else{c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];c[w>>2]=c[b>>2];c[w+4>>2]=c[b+4>>2];c[w+8>>2]=c[b+8>>2];c[w+12>>2]=c[b+12>>2];y=w+64|0;c[y>>2]=c[n>>2];c[y+4>>2]=c[n+4>>2];c[y+8>>2]=c[n+8>>2];c[y+12>>2]=c[n+12>>2];i=f+s;j=e+r;k=f-s;m=e-r}h[w+16>>3]=e-v;h[w+24>>3]=f-q;h[w+32>>3]=t-r;h[w+40>>3]=u-s;h[w+48>>3]=m;h[w+56>>3]=k;h[w+80>>3]=j;h[w+88>>3]=i;h[w+96>>3]=t+r;h[w+104>>3]=u+s;h[w+112>>3]=e+v;h[w+120>>3]=f+q;do if(!(g&64))if(!(g&128)){nS(a,w,9,1);break}else{nS(a,w+48|0,6,1);break}else nS(a,w,6,1);while(0);l=x;return}function OH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=g|0;var i=0.0,j=0.0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0.0;q=l;l=l+64|0;p=q;t=+h[d+8>>3];s=+h[d>>3];k=+h[b>>3];e=k+s;m=+h[b+8>>3];f=m+t;i=k+s*.2;j=m+t*.2;k=k+s*.6;m=m+t*.6;h[p>>3]=i-t;h[p+8>>3]=j+s;n=p+16|0;h[n>>3]=i+t;o=p+24|0;h[o>>3]=j-s;h[p+32>>3]=k+t;h[p+40>>3]=m-s;h[p+48>>3]=k-t;h[p+56>>3]=m+s;if(!(g&64)){if(g&128|0){d=2;g=1;r=3}}else{d=3;g=0;r=3}if((r|0)==3){h[p+(g<<4)>>3]=i;h[p+(g<<4)+8>>3]=j;h[p+(d<<4)>>3]=k;h[p+(d<<4)+8>>3]=m}nS(a,p,4,1);c[p>>2]=c[b>>2];c[p+4>>2]=c[b+4>>2];c[p+8>>2]=c[b+8>>2];c[p+12>>2]=c[b+12>>2];h[n>>3]=e;h[o>>3]=f;qS(a,p,2);l=q;return}function PH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=g|0;var i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0.0,s=0.0,t=0.0;o=l;l=l+64|0;n=o;j=+h[d+8>>3];r=j*-.4;i=+h[d>>3];q=i*.4;t=+h[b>>3];e=t+i*.8;s=+h[b+8>>3];f=s+j*.8;i=t+i;j=s+j;h[n>>3]=t+r;k=n+8|0;h[k>>3]=s+q;d=n+16|0;h[d>>3]=t-r;m=n+24|0;h[m>>3]=s-q;h[n+32>>3]=e-r;h[n+40>>3]=f-q;h[n+48>>3]=e+r;h[n+56>>3]=f+q;if(!(g&64)){if(g&128|0){c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];d=2;p=5}}else{c[n>>2]=c[b>>2];c[n+4>>2]=c[b+4>>2];c[n+8>>2]=c[b+8>>2];c[n+12>>2]=c[b+12>>2];d=3;p=5}if((p|0)==5){h[n+(d<<4)>>3]=e;h[n+(d<<4)+8>>3]=f}nS(a,n,4,g>>>4&1^1);h[n>>3]=e;h[k>>3]=f;h[n+16>>3]=i;h[m>>3]=j;qS(a,n,2);l=o;return}function QH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=g|0;var i=0,j=0,k=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0;j=l;l=l+80|0;i=j;n=+h[d+8>>3];k=n*-.3333333333333333;p=+h[d>>3];f=p*.3333333333333333;q=+h[b>>3];m=q+p*.5;o=+h[b+8>>3];e=o+n*.5;d=i+64|0;h[i+64>>3]=q+p;h[i+72>>3]=o+n;c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];h[i+16>>3]=m+k;h[i+24>>3]=e+f;d=i+32|0;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];h[i+48>>3]=m-k;h[i+56>>3]=e-f;do if(!(g&64)){d=g>>>4&1^1;if(!(g&128)){nS(a,i,4,d);break}else{nS(a,i,3,d);break}}else nS(a,d,3,g>>>4&1^1);while(0);l=j;return}function RH(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=+d;e=+e;f=f|0;var g=0,i=0,j=0.0,k=0.0,m=0.0;g=l;l=l+32|0;i=g;k=+h[c>>3];e=+h[c+8>>3];d=+C(+(e*e+k*k))*.5;m=+h[b>>3];k=k*.5;h[i>>3]=m+k-d;j=+h[b+8>>3];e=e*.5;h[i+8>>3]=j-d+e;h[i+16>>3]=m+d+k;h[i+24>>3]=j+d+e;mS(a,i,2,f>>>4&1^1);l=g;return}function SH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=g|0;var i=0.0,j=0.0,k=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0,s=0,t=0;t=l;l=l+112|0;q=t+48|0;r=t+16|0;s=t;n=f>4.0?f*.125:.5;p=+h[b>>3];o=+h[d>>3];i=+h[b+8>>3];m=+h[d+8>>3];e=m*n;f=o*n;c[r>>2]=c[b>>2];c[r+4>>2]=c[b+4>>2];c[r+8>>2]=c[b+8>>2];c[r+12>>2]=c[b+12>>2];h[r+16>>3]=o+p;h[r+24>>3]=m+i;h[q>>3]=n*(o-m)+p;j=n*(m+o);k=j+i;h[q+8>>3]=k;h[q+48>>3]=j+p;i=n*(m-o)+i;h[q+56>>3]=i;o=e*-.95;m=o+f+p;n=f*1.3333333333333333;if(!(g&32)){e=e*1.3333333333333333;j=f*-.33333333333333326;i=i-e;f=k-e;e=m-n}else{e=e*1.3333333333333333;j=f*2.333333333333333;i=i+e;f=k+e;e=m+n}h[q+16>>3]=e;h[q+24>>3]=f;h[q+32>>3]=j-o+p;h[q+40>>3]=i;qS(a,r,2);if(!(g&64)){if(g&128|0)kP(s,q,3,.5,q,0)}else kP(s,q,3,.5,0,q);pS(a,q,4,0,0,0);l=t;return}function TH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=g|0;var i=0;g=l;l=l+32|0;i=g;e=+h[d>>3]+ +h[b>>3];f=+h[d+8>>3]+ +h[b+8>>3];c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];h[i+16>>3]=e;h[i+24>>3]=f;qS(a,i,2);l=g;return}function UH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0,k=0,m=0,n=0,o=0.0,p=0.0,q=0,r=0,s=0;n=l;l=l+80|0;j=n+72|0;k=n+8|0;m=n;i=+LH(a,g);i=i*i;h[m>>3]=i;c[f+12>>2]=g;g=f+32|0;a=e+3|0;f=b+(a<<4)|0;c[g>>2]=c[f>>2];c[g+4>>2]=c[f+4>>2];c[g+8>>2]=c[f+8>>2];c[g+12>>2]=c[f+12>>2];if((e|0)>(d|0)?(o=+h[b+(e<<4)>>3]-+h[f>>3],p=+h[b+(e<<4)+8>>3]-+h[b+(a<<4)+8>>3],p*p+o*o<i):0)e=e+-3|0;s=k+48|0;r=b+(e<<4)|0;c[s>>2]=c[r>>2];c[s+4>>2]=c[r+4>>2];c[s+8>>2]=c[r+8>>2];c[s+12>>2]=c[r+12>>2];q=k+32|0;a=b+(e+1<<4)|0;c[q>>2]=c[a>>2];c[q+4>>2]=c[a+4>>2];c[q+8>>2]=c[a+8>>2];c[q+12>>2]=c[a+12>>2];f=k+16|0;d=b+(e+2<<4)|0;c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];c[k>>2]=c[g>>2];c[k+4>>2]=c[g+4>>2];c[k+8>>2]=c[g+8>>2];c[k+12>>2]=c[g+12>>2];c[j>>2]=k;c[j+4>>2]=m;$N(j,92,k,1);c[r>>2]=c[s>>2];c[r+4>>2]=c[s+4>>2];c[r+8>>2]=c[s+8>>2];c[r+12>>2]=c[s+12>>2];c[a>>2]=c[q>>2];c[a+4>>2]=c[q+4>>2];c[a+8>>2]=c[q+8>>2];c[a+12>>2]=c[q+12>>2];c[d>>2]=c[f>>2];c[d+4>>2]=c[f+4>>2];c[d+8>>2]=c[f+8>>2];c[d+12>>2]=c[f+12>>2];m=b+(e+3<<4)|0;c[m>>2]=c[k>>2];c[m+4>>2]=c[k+4>>2];c[m+8>>2]=c[k+8>>2];c[m+12>>2]=c[k+12>>2];l=n;return e|0}function VH(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0;f=c[a>>2]|0;d=+h[b>>3]-+h[f>>3];e=+h[b+8>>3]-+h[f+8>>3];return e*e+d*d<=+h[c[a+4>>2]>>3]|0}function WH(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0.0,k=0,m=0,n=0,o=0,p=0.0,q=0.0,r=0,s=0;o=l;l=l+80|0;k=o+72|0;m=o+8|0;n=o;j=+LH(a,g);j=j*j;h[n>>3]=j;c[f+8>>2]=g;f=f+16|0;a=b+(d<<4)|0;c[f>>2]=c[a>>2];c[f+4>>2]=c[a+4>>2];c[f+8>>2]=c[a+8>>2];c[f+12>>2]=c[a+12>>2];if(!((e|0)>(d|0)?(i=d+3|0,p=+h[a>>3]-+h[b+(i<<4)>>3],q=+h[b+(d<<4)+8>>3]-+h[b+(i<<4)+8>>3],q*q+p*p<j):0))i=d;e=b+(i+3<<4)|0;c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];g=m+16|0;d=b+(i+2<<4)|0;c[g>>2]=c[d>>2];c[g+4>>2]=c[d+4>>2];c[g+8>>2]=c[d+8>>2];c[g+12>>2]=c[d+12>>2];r=m+32|0;a=b+(i+1<<4)|0;c[r>>2]=c[a>>2];c[r+4>>2]=c[a+4>>2];c[r+8>>2]=c[a+8>>2];c[r+12>>2]=c[a+12>>2];s=m+48|0;c[s>>2]=c[f>>2];c[s+4>>2]=c[f+4>>2];c[s+8>>2]=c[f+8>>2];c[s+12>>2]=c[f+12>>2];c[k>>2]=s;c[k+4>>2]=n;$N(k,92,m,0);n=b+(i<<4)|0;c[n>>2]=c[s>>2];c[n+4>>2]=c[s+4>>2];c[n+8>>2]=c[s+8>>2];c[n+12>>2]=c[s+12>>2];c[a>>2]=c[r>>2];c[a+4>>2]=c[r+4>>2];c[a+8>>2]=c[r+8>>2];c[a+12>>2]=c[r+12>>2];c[d>>2]=c[g>>2];c[d+4>>2]=c[g+4>>2];c[d+8>>2]=c[g+8>>2];c[d+12>>2]=c[g+12>>2];c[e>>2]=c[m>>2];c[e+4>>2]=c[m+4>>2];c[e+8>>2]=c[m+8>>2];c[e+12>>2]=c[m+12>>2];l=o;return i|0}function XH(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0.0,k=0.0,l=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0.0,t=0.0,u=0,v=0,w=0;u=(g|0)!=0;l=(i|0)!=0;if(!((e|0)==(d|0)&(u&l))){if(l){j=+LH(a,i);k=+h[b+(e<<4)>>3];p=+h[b+(e<<4)+8>>3];l=e+3|0;r=b+(l<<4)|0;n=+h[r>>3];l=b+(l<<4)+8|0;o=+h[l>>3];t=k-n;s=p-o;t=+C(+(s*s+t*t))*.9;j=j>=t?t:j;if(p==o){m=(k<n?-j:j)+n;j=p}else{m=k;j=(p<o?-j:j)+o}w=e+1|0;h[b+(w<<4)>>3]=k;h[b+(w<<4)+8>>3]=p;e=b+(e+2<<4)|0;h[r>>3]=m;h[l>>3]=j;c[e>>2]=c[r>>2];c[e+4>>2]=c[r+4>>2];c[e+8>>2]=c[r+8>>2];c[e+12>>2]=c[r+12>>2];c[f+12>>2]=i;h[f+32>>3]=n;h[f+40>>3]=o}if(u){m=+LH(a,g);l=b+(d<<4)|0;k=+h[l>>3];j=+h[b+(d<<4)+8>>3];w=d+3|0;o=+h[b+(w<<4)>>3];p=+h[b+(w<<4)+8>>3];t=k-o;s=j-p;t=+C(+(s*s+t*t))*.9;m=m>=t?t:m;if(j==p){n=(k<o?m:-m)+k;m=j}else{n=k;m=(j<p?m:-m)+j}w=d+1|0;v=b+(w<<4)|0;h[v>>3]=n;h[b+(w<<4)+8>>3]=m;c[l>>2]=c[v>>2];c[l+4>>2]=c[v+4>>2];c[l+8>>2]=c[v+8>>2];c[l+12>>2]=c[v+12>>2];v=d+2|0;h[b+(v<<4)>>3]=o;h[b+(v<<4)+8>>3]=p;v=16}}else{r=b+(e<<4)|0;s=+h[r>>3];t=+h[b+(e<<4)+8>>3];l=e+3|0;u=b+(l<<4)|0;k=+h[u>>3];l=b+(l<<4)+8|0;j=+h[l>>3];m=+LH(a,g);q=+LH(a,i);n=s-k;p=t-j;n=+C(+(p*p+n*n));w=!(q+m>=n);n=n*.3333333333333333;m=w?m:n;n=w?q:n;if(t==j){w=s<k;p=(w?-n:n)+k;q=(w?m:-m)+s;o=t;m=t}else{w=t<j;p=s;q=s;o=(w?m:-m)+t;m=(w?-n:n)+j}w=e+1|0;v=b+(w<<4)|0;h[v>>3]=q;h[b+(w<<4)+8>>3]=o;c[r>>2]=c[v>>2];c[r+4>>2]=c[v+4>>2];c[r+8>>2]=c[v+8>>2];c[r+12>>2]=c[v+12>>2];v=b+(e+2<<4)|0;h[u>>3]=p;h[l>>3]=m;c[v>>2]=c[u>>2];c[v+4>>2]=c[u+4>>2];c[v+8>>2]=c[u+8>>2];c[v+12>>2]=c[u+12>>2];c[f+12>>2]=i;h[f+32>>3]=s;h[f+40>>3]=t;v=16}if((v|0)==16){c[f+8>>2]=g;h[f+16>>3]=k;h[f+24>>3]=j}return}function YH(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=+d;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0,k=0.0,l=0.0,m=0.0,n=0.0,o=0.0;n=+h[b>>3];k=+h[c>>3]-n;o=+h[b+8>>3];l=+h[c+8>>3]-o;j=d*10.0/(+C(+(k*k+l*l))+.0001);k=j*((k>=0.0?.0001:-.0001)+k);j=((l>=0.0?.0001:-.0001)+l)*j;l=k*.5;m=j*.5;i=n-m;f=o-l;n=m+n;l=o+l;o=i+k;m=f+j;k=n+k;j=l+j;g=o>k?o:k;g=n>g?n:g;d=m>j?m:j;d=l>d?l:d;k=o<k?o:k;k=n<k?n:k;j=m<j?m:j;j=l<j?l:j;h[a>>3]=i<k?i:k;h[a+8>>3]=f<j?f:j;h[a+16>>3]=i>g?i:g;h[a+24>>3]=f>d?f:d;return}function ZH(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;g=+g;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0,s=0.0,t=0.0;q=l;l=l+48|0;m=q+32|0;k=q+16|0;p=q;n=(c[a+16>>2]|0)+12|0;o=c[n>>2]|0;c[n>>2]=b;lS(a,c[(c[a>>2]|0)+336>>2]|0);uS(a,g);t=+h[e>>3]-+h[d>>3];b=e+8|0;s=+h[b>>3]-+h[d+8>>3];r=10.0/(+C(+(t*t+s*s))+.0001);h[e>>3]=r*((t>=0.0?.0001:-.0001)+t);h[b>>3]=((s>=0.0?.0001:-.0001)+s)*r;b=0;while(1){if((b|0)>=4)break;j=i>>(b<<3)&255;if(!j)break;c[k>>2]=c[d>>2];c[k+4>>2]=c[d+4>>2];c[k+8>>2]=c[d+8>>2];c[k+12>>2]=c[d+12>>2];c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];_H(p,a,k,m,f,g,j);c[d>>2]=c[p>>2];c[d+4>>2]=c[p+4>>2];c[d+8>>2]=c[p+8>>2];c[d+12>>2]=c[p+12>>2];b=b+1|0}c[n>>2]=o;l=q;return}function _H(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;g=+g;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0;q=l;l=l+32|0;m=q+16|0;k=q;n=i&15;j=1400;while(1){o=c[j>>2]|0;if(!o)break;if((n|0)==(o|0)){p=4;break}j=j+24|0}if((p|0)==4){r=+h[j+8>>3]*f;h[e>>3]=r*+h[e>>3];o=e+8|0;h[o>>3]=r*+h[o>>3];p=c[j+16>>2]|0;c[k>>2]=c[d>>2];c[k+4>>2]=c[d+4>>2];c[k+8>>2]=c[d+8>>2];c[k+12>>2]=c[d+12>>2];c[m>>2]=c[e>>2];c[m+4>>2]=c[e+4>>2];c[m+8>>2]=c[e+8>>2];c[m+12>>2]=c[e+12>>2];wb[p&15](b,k,m,f,g,i);h[d>>3]=+h[e>>3]+ +h[d>>3];p=d+8|0;h[p>>3]=+h[o>>3]+ +h[p>>3]};c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];l=q;return}function $H(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;g=i+8|0;f=i;d=QA(b,99796)|0;if((d|0)!=0?(a[d>>0]|0)!=0:0){e=d;h=5}else{d=QA(b,99808)|0;if((d|0)!=0?(a[d>>0]|0)!=0:0){e=d;h=5}else d=0}if((h|0)==5){d=CH(e,0,120)|0;if(!d){c[f>>2]=KB(b)|0;gA(0,99815,f)|0;c[g>>2]=e;gA(3,99868,g)|0}}l=i;return d|0}function aI(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;g=l;l=l+16|0;f=JK(304)|0;if(!f)gA(1,99876,g)|0;e=a+16|0;a=c[e>>2]|0;c[f>>2]=a;c[e>>2]=f;if(!a){c[f+144>>2]=3;c[f+148>>2]=0;h[f+152>>3]=1.0}else{b=f+16|0;d=a+16|0;e=b+40|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0));b=f+56|0;d=a+56|0;e=b+40|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0));c[f+144>>2]=c[a+144>>2];c[f+148>>2]=c[a+148>>2];h[f+152>>3]=+h[a+152>>3];c[f+136>>2]=c[a+136>>2];b=f+96|0;d=a+96|0;e=b+40|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0))}l=g;return f|0}function bI(a){a=a|0;var b=0;a=a+16|0;b=c[a>>2]|0;if(!b)qa(99902,99906,117,99913);else{U1(c[b+212>>2]|0);U1(c[b+208>>2]|0);U1(c[b+216>>2]|0);U1(c[b+220>>2]|0);U1(c[b+224>>2]|0);U1(c[b+228>>2]|0);U1(c[b+232>>2]|0);U1(c[b+236>>2]|0);U1(c[b+240>>2]|0);U1(c[b+244>>2]|0);U1(c[b+248>>2]|0);U1(c[b+252>>2]|0);U1(c[b+256>>2]|0);U1(c[b+272>>2]|0);U1(c[b+284>>2]|0);U1(c[b+280>>2]|0);c[a>>2]=c[b>>2];U1(b);return}}function cI(d,e,f,g,h,i,j){d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;var k=0,l=0;l=c[d+16>>2]|0;k=c[d+152>>2]|0;if((e|0)!=0&(k&32768|0)!=0)c[l+192>>2]=e;if(((k&65536|0)!=0?(c[l+212>>2]=EK(i,j)|0,(f|0)!=0):0)?(a[f>>0]|0)!=0:0){c[l+208>>2]=EK(f,j)|0;d=1}else d=0;do if(k&4194304){if(g|0?a[g>>0]|0:0){c[l+228>>2]=EK(g,j)|0;d=l+260|0;b[d>>1]=b[d>>1]|1;d=1;break}e=c[l+192>>2]|0;if(e){c[l+228>>2]=D4(e)|0;d=1}}while(0);if((h|0)!=0&(k&8388608|0)!=0?(a[h>>0]|0)!=0:0){c[l+244>>2]=EK(h,j)|0;d=1}return d|0}function dI(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;j=l;l=l+80|0;i=j;h=j+8|0;g=c[(c[b>>2]|0)+168>>2]|0;f=c[(c[(c[g+16>>2]|0)+8>>2]|0)+92>>2]|0;eI(b,e);b=QA(d,141060)|0;if((b|0)!=0?(a[b>>0]|0)!=0:0){nA(e,b)|0;f=e+4|0;b=c[f>>2]|0;if(b>>>0>=(c[e+8>>2]|0)>>>0){lA(e,1)|0;b=c[f>>2]|0}a[b>>0]=0;b=c[e>>2]|0;c[f>>2]=b}else{if((g|0)!=(d|0)&(f|0)!=0){nA(e,f)|0;f=e+4|0;b=c[f>>2]|0;if(b>>>0>=(c[e+8>>2]|0)>>>0){lA(e,1)|0;b=c[f>>2]|0}c[f>>2]=b+1;a[b>>0]=95}switch(IC(d)|0){case 0:{f=(g|0)==(d|0)?111494:99927;b=(c[d>>2]|0)>>>4;break}case 1:{f=111481;b=(c[d>>2]|0)>>>4;break}case 2:{f=111450;b=(c[d>>2]|0)>>>4;break}default:{f=0;b=0}}nA(e,f)|0;c[i>>2]=b;m4(h,99933,i)|0;nA(e,h)|0;f=e+4|0;b=c[f>>2]|0;if(b>>>0>=(c[e+8>>2]|0)>>>0){lA(e,1)|0;b=c[f>>2]|0}a[b>>0]=0;b=c[e>>2]|0;c[f>>2]=b}l=j;return b|0}function eI(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=l;l=l+144|0;h=i;g=i+8|0;e=c[b+160>>2]|0;if((e|0)>1?c[b+152>>2]&64|0:0){nA(d,c[(c[(c[b>>2]|0)+308>>2]|0)+(e<<2)>>2]|0)|0;f=d+4|0;e=c[f>>2]|0;if(e>>>0>=(c[d+8>>2]|0)>>>0){lA(d,1)|0;e=c[f>>2]|0}c[f>>2]=e+1;a[e>>0]=95}f=c[b+196>>2]|0;e=c[b+200>>2]|0;if((f|0)>0|(e|0)>0){c[h>>2]=f;c[h+4>>2]=e;m4(g,99937,h)|0;nA(d,g)|0}l=i;return}function fI(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,i=0.0,j=0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0.0,s=0;s=l;l=l+48|0;p=s+16|0;f=s+32|0;q=s;r=+h[(c[a+16>>2]|0)+152>>3];o=gI(d,0,f)|0;if((o+-1|0)>>>0>=2){e=+h[b+16>>3];k=(e+ +h[b>>3])*.5;h[q>>3]=k;i=+h[b+24>>3];m=(i+ +h[b+8>>3])*.5;h[q+8>>3]=m;k=e-k;m=i-m;n=r>.5;if(n)uS(a,.5);j=c[f>>2]|0;i=0.0;f=c[j+8>>2]|0;while(1){d=c[f>>2]|0;if(!d)break;b=f+4|0;if(+g[b>>2]==0.0)e=i;else{jS(a,d);if(!(c[f+12>>2]|0))e=6.283185307179586;else e=+g[b>>2]*6.283185307179586+i;c[p>>2]=c[q>>2];c[p+4>>2]=c[q+4>>2];c[p+8>>2]=c[q+8>>2];c[p+12>>2]=c[q+12>>2];b=IT(p,k,m,i,e)|0;pS(a,c[b>>2]|0,c[b+4>>2]|0,0,0,1);EY(b)}i=e;f=f+12|0}if(n)uS(a,r);hI(j)}l=s;return o|0}function gI(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,h=0,i=0,j=0.0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0;r=l;l=l+16|0;o=r+8|0;n=r;q=JK(12)|0;i=D4(b)|0;a:do if(!d){d=1;f=i;while(1){switch(a[f>>0]|0){case 0:break a;case 58:{d=d+1|0;break}default:{}}f=f+1|0}}while(0);c[q+4>>2]=i;p=JK((d*12|0)+12|0)|0;c[q+8>>2]=p;m=1.0;h=0;d=0;f=i;while(1){f=A4(f,107984)|0;if(!f){f=20;break}j=+iI(f);if(!(j>=0.0)){f=17;break}k=j-m;if(k>0.0)if((c[5156]|0)!=0?!(k<1.0e-05&k>-1.0e-05):0){c[n>>2]=b;gA(0,99948,n)|0;c[5156]=0;j=m;d=3}else j=m;k=m-j;if(j>0.0)a[p+(h*12|0)+8>>0]=1;if(a[f>>0]|0)c[p+(h*12|0)>>2]=f;f=h+1|0;g[p+(h*12|0)+4>>2]=j;if(k<1.0e-05&k>-1.0e-05){h=f;f=30;break}else{m=k;h=f;f=0}}b:do if((f|0)==17){if(!(c[5156]|0))d=1;else{c[o>>2]=b;gA(1,99983,o)|0;c[5156]=0;d=2}hI(q)}else if((f|0)==20)if(m>0.0){f=0;i=0;while(1){if((f|0)>=(h|0))break;o=i+(+g[p+(f*12|0)+4>>2]==0.0&1)|0;f=f+1|0;i=o}if((i|0)<=0){f=p+((h+-1|0)*12|0)+4|0;g[f>>2]=m+ +g[f>>2];f=30;break}j=m/+(i|0);f=0;while(1){if((f|0)>=(h|0)){f=30;break b}i=p+(f*12|0)+4|0;if(+g[i>>2]==0.0)g[i>>2]=j;f=f+1|0}}else f=30;while(0);if((f|0)==30){while(1){f=h+-1|0;if((h|0)<=0)break;if(+g[p+(f*12|0)+4>>2]>0.0)break;else h=f}c[p+(h*12|0)>>2]=0;c[q>>2]=h;c[e>>2]=q}l=r;return d|0}function hI(a){a=a|0;U1(c[a+4>>2]|0);U1(c[a+8>>2]|0);U1(a);return}function iI(b){b=b|0;var d=0.0,e=0,f=0,g=0;f=l;l=l+16|0;e=f;b=R2(b,59)|0;if(!b)d=0.0;else{g=b+1|0;a[b>>0]=0;d=+s5(g,e);d=(d>=0.0?(c[e>>2]|0)!=(g|0):0)?d:-1.0}l=f;return +d}function jI(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,i=0,j=0,k=0,m=0.0,n=0.0,o=0,p=0,q=0,r=0,s=0.0,t=0;t=l;l=l+80|0;i=t+64|0;r=t;s=+h[(c[a+16>>2]|0)+152>>3];q=gI(d,0,i)|0;if((q+-1|0)>>>0>=2){if(!e){c[r>>2]=c[b>>2];c[r+4>>2]=c[b+4>>2];c[r+8>>2]=c[b+8>>2];c[r+12>>2]=c[b+12>>2];p=r+16|0;o=b+16|0;c[p>>2]=c[o>>2];c[p+4>>2]=c[o+4>>2];c[p+8>>2]=c[o+8>>2];c[p+12>>2]=c[o+12>>2];p=r+32|0;o=b+32|0;c[p>>2]=c[o>>2];c[p+4>>2]=c[o+4>>2];c[p+8>>2]=c[o+8>>2];c[p+12>>2]=c[o+12>>2];p=r+48|0;o=b+48|0;c[p>>2]=c[o>>2];c[p+4>>2]=c[o+4>>2];c[p+8>>2]=c[o+8>>2];c[p+12>>2]=c[o+12>>2]}else{p=b+32|0;c[r>>2]=c[p>>2];c[r+4>>2]=c[p+4>>2];c[r+8>>2]=c[p+8>>2];c[r+12>>2]=c[p+12>>2];p=r+16|0;o=b+48|0;c[p>>2]=c[o>>2];c[p+4>>2]=c[o+4>>2];c[p+8>>2]=c[o+8>>2];c[p+12>>2]=c[o+12>>2];p=r+32|0;c[p>>2]=c[b>>2];c[p+4>>2]=c[b+4>>2];c[p+8>>2]=c[b+8>>2];c[p+12>>2]=c[b+12>>2];p=r+48|0;o=b+16|0;c[p>>2]=c[o>>2];c[p+4>>2]=c[o+4>>2];c[p+8>>2]=c[o+8>>2];c[p+12>>2]=c[o+12>>2]}k=r+16|0;m=+h[k>>3];f=+h[r>>3];n=m-f;o=r+32|0;h[o>>3]=f;h[k>>3]=f;p=s>.5;if(p)uS(a,.5);i=c[i>>2]|0;j=r+48|0;e=c[i+8>>2]|0;while(1){d=c[e>>2]|0;if(!d)break;b=e+4|0;if(!(+g[b>>2]==0.0)){jS(a,d);if(!(c[e+12>>2]|0))f=m;else f=n*+g[b>>2]+ +h[r>>3];h[o>>3]=f;h[k>>3]=f;nS(a,r,4,1);f=+h[k>>3];h[j>>3]=f;h[r>>3]=f}e=e+12|0}if(p)uS(a,s);hI(i)}l=t;return q|0}function kI(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;d=c[a+16>>2]|0;e=c[a+152>>2]|0;if(e&4259840|0){f=e&131072;g=f>>>16^2;c[d+264>>2]=g;h=d+268|0;c[h>>2]=g+2;g=d+272|0;U1(c[g>>2]|0);d=JK(c[h>>2]<<4)|0;c[g>>2]=d;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];g=d+16|0;b=b+16|0;c[g>>2]=c[b>>2];c[g+4>>2]=c[b+4>>2];c[g+8>>2]=c[b+8>>2];c[g+12>>2]=c[b+12>>2];if(!(e&8192))NR(a,d,d,2)|0;if(!f)jK(d)}return}function lI(a,b){a=a|0;b=b|0;var c=0,d=0.0,e=0,f=0,g=0,i=0.0,j=0.0,k=0.0,m=0.0,n=0.0,o=0,p=0,q=0;q=l;l=l+144|0;c=q+80|0;e=q+16|0;f=q;k=+h[b>>3];o=a+16|0;m=+h[o>>3];if(!(((((((((!(k>m)?(n=+h[a>>3],!(k<n)):0)?(d=+h[b+8>>3],i=+h[a+24>>3],!(d>i)):0)?(j=+h[a+8>>3],!(d<j)):0)?(k=+h[b+16>>3],!(k>m|k<n)):0)?(k=+h[b+24>>3],!(k>i|k<j)):0)?(k=+h[b+32>>3],!(k>m|k<n)):0)?(k=+h[b+40>>3],!(k>i|k<j)):0)?(k=+h[b+48>>3],!(k>m|k<n)):0)?(n=+h[b+56>>3],!(n>i|n<j)):0))p=11;a:do if((p|0)==11){if(!(mI(b)|0)){kP(f,b,3,.5,c,e);lI(a,c);lI(a,e);break}f=a+24|0;g=a+8|0;e=0;while(1){if((e|0)==4)break a;d=+h[b+(e<<4)>>3];if(!(d>+h[o>>3])){if(d<+h[a>>3]){c=a;p=16}}else{c=o;p=16}if((p|0)==16){p=0;h[c>>3]=d}d=+h[b+(e<<4)+8>>3];if(!(d>+h[f>>3])){if(d<+h[g>>3]){c=a;p=19}}else{c=o;p=19}if((p|0)==19){p=0;h[c+8>>3]=d}e=e+1|0}}while(0);l=q;return}function mI(a){a=a|0;var b=0,d=0,e=0,f=0,g=0.0,h=0,i=0;b=l;l=l+48|0;d=b+32|0;e=b+16|0;f=b;i=a+48|0;h=a+16|0;c[f>>2]=c[a>>2];c[f+4>>2]=c[a+4>>2];c[f+8>>2]=c[a+8>>2];c[f+12>>2]=c[a+12>>2];c[e>>2]=c[i>>2];c[e+4>>2]=c[i+4>>2];c[e+8>>2]=c[i+8>>2];c[e+12>>2]=c[i+12>>2];c[d>>2]=c[h>>2];c[d+4>>2]=c[h+4>>2];c[d+8>>2]=c[h+8>>2];c[d+12>>2]=c[h+12>>2];g=+oK(f,e,d);h=a+32|0;c[f>>2]=c[a>>2];c[f+4>>2]=c[a+4>>2];c[f+8>>2]=c[a+8>>2];c[f+12>>2]=c[a+12>>2];c[e>>2]=c[i>>2];c[e+4>>2]=c[i+4>>2];c[e+8>>2]=c[i+8>>2];c[e+12>>2]=c[i+12>>2];c[d>>2]=c[h>>2];c[d+4>>2]=c[h+4>>2];c[d+8>>2]=c[h+8>>2];c[d+12>>2]=c[h+12>>2];a=g<4.0&+oK(f,e,d)<4.0&1;l=b;return a|0}function nI(b,d){b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0.0,G=0.0,H=0.0,I=0.0,J=0,K=0;D=l;l=l+272|0;u=D+256|0;v=D+224|0;w=D+192|0;x=D+128|0;y=D+64|0;z=D;A=D+160|0;B=D+96|0;C=D+32|0;t=c[d+16>>2]|0;s=c[t+144>>2]|0;r=t+16|0;c[x>>2]=c[r>>2];c[x+4>>2]=c[r+4>>2];c[x+8>>2]=c[r+8>>2];c[x+12>>2]=c[r+12>>2];c[x+16>>2]=c[r+16>>2];c[x+20>>2]=c[r+20>>2];c[x+24>>2]=c[r+24>>2];c[x+28>>2]=c[r+28>>2];t=c[(c[t+8>>2]|0)+88>>2]|0;c[z>>2]=0;c[z+4>>2]=0;c[z+8>>2]=0;c[z+12>>2]=0;c[z+16>>2]=0;c[z+20>>2]=0;c[z+24>>2]=0;c[z+28>>2]=0;if(!t){c[b>>2]=c[x>>2];c[b+4>>2]=c[x+4>>2];c[b+8>>2]=c[x+8>>2];c[b+12>>2]=c[x+12>>2];c[b+16>>2]=c[x+16>>2];c[b+20>>2]=c[x+20>>2];c[b+24>>2]=c[x+24>>2];c[b+28>>2]=c[x+28>>2]}else{d=x+16|0;if(+h[x>>3]==+h[d>>3]?(e=x+8|0,f=x+24|0,+h[e>>3]==+h[f>>3]):0){h[e>>3]=1797693134862315708145274.0e284;h[x>>3]=1797693134862315708145274.0e284;h[f>>3]=-1797693134862315708145274.0e284;h[d>>3]=-1797693134862315708145274.0e284}j=v+8|0;k=v+16|0;m=v+24|0;n=y+16|0;o=y+24|0;p=s+144|0;q=w+16|0;r=t+12|0;d=0;f=c[t+8>>2]|0;e=0;g=0.0;i=0;while(1){if((i|0)>=(c[t>>2]|0))break;c[y>>2]=c[z>>2];c[y+4>>2]=c[z+4>>2];c[y+8>>2]=c[z+8>>2];c[y+12>>2]=c[z+12>>2];c[y+16>>2]=c[z+16>>2];c[y+20>>2]=c[z+20>>2];c[y+24>>2]=c[z+24>>2];c[y+28>>2]=c[z+28>>2];switch(c[f>>2]|0){case 1:case 0:{H=+h[f+8>>3];I=+h[f+24>>3];h[v>>3]=H-I;F=+h[f+16>>3];G=+h[f+32>>3];h[j>>3]=F-G;h[k>>3]=I+H;h[m>>3]=G+F;E=f+80|0;c[E>>2]=c[v>>2];c[E+4>>2]=c[v+4>>2];c[E+8>>2]=c[v+8>>2];c[E+12>>2]=c[v+12>>2];E=f+96|0;c[E>>2]=c[k>>2];c[E+4>>2]=c[k+4>>2];c[E+8>>2]=c[k+8>>2];c[E+12>>2]=c[k+12>>2];c[u>>2]=c[v>>2];c[u+4>>2]=c[v+4>>2];c[u+8>>2]=c[v+8>>2];c[u+12>>2]=c[v+12>>2];oI(x,u);c[u>>2]=c[k>>2];c[u+4>>2]=c[k+4>>2];c[u+8>>2]=c[k+8>>2];c[u+12>>2]=c[k+12>>2];oI(x,u);break}case 3:case 2:{E=f+80|0;J=f+8|0;pI(A,c[J+4>>2]|0,c[J>>2]|0,x);c[E>>2]=c[A>>2];c[E+4>>2]=c[A+4>>2];c[E+8>>2]=c[A+8>>2];c[E+12>>2]=c[A+12>>2];c[E+16>>2]=c[A+16>>2];c[E+20>>2]=c[A+20>>2];c[E+24>>2]=c[A+24>>2];c[E+28>>2]=c[A+28>>2];break}case 5:case 4:{J=f+80|0;E=f+8|0;pI(B,c[E+4>>2]|0,c[E>>2]|0,x);c[J>>2]=c[B>>2];c[J+4>>2]=c[B+4>>2];c[J+8>>2]=c[B+8>>2];c[J+12>>2]=c[B+12>>2];c[J+16>>2]=c[B+16>>2];c[J+20>>2]=c[B+20>>2];c[J+24>>2]=c[B+24>>2];c[J+28>>2]=c[B+28>>2];break}case 6:{J=f+80|0;E=f+8|0;pI(C,c[E+4>>2]|0,c[E>>2]|0,x);c[J>>2]=c[C>>2];c[J+4>>2]=c[C+4>>2];c[J+8>>2]=c[C+8>>2];c[J+12>>2]=c[C+12>>2];c[J+16>>2]=c[C+16>>2];c[J+20>>2]=c[C+20>>2];c[J+24>>2]=c[C+24>>2];c[J+28>>2]=c[C+28>>2];break}case 7:{J=f+112|0;c[J>>2]=JK(56)|0;E=D4(c[f+40>>2]|0)|0;K=c[J>>2]|0;c[K>>2]=E;a[K+48>>0]=a[100029+(c[f+24>>2]|0)>>0]|0;c[y>>2]=e;h[n>>3]=g;c[o>>2]=c[o>>2]&-128|d&127;K=c[p>>2]|0;K=nb[c[K>>2]&63](K,y,1)|0;E=c[J>>2]|0;c[E+4>>2]=K;LO(u,s,E);qI(w,+h[f+8>>3],+h[f+16>>3],c[J>>2]|0);J=f+80|0;c[J>>2]=c[w>>2];c[J+4>>2]=c[w+4>>2];c[J+8>>2]=c[w+8>>2];c[J+12>>2]=c[w+12>>2];c[J+16>>2]=c[w+16>>2];c[J+20>>2]=c[w+20>>2];c[J+24>>2]=c[w+24>>2];c[J+28>>2]=c[w+28>>2];c[u>>2]=c[w>>2];c[u+4>>2]=c[w+4>>2];c[u+8>>2]=c[w+8>>2];c[u+12>>2]=c[w+12>>2];oI(x,u);c[u>>2]=c[q>>2];c[u+4>>2]=c[q+4>>2];c[u+8>>2]=c[q+8>>2];c[u+12>>2]=c[q+12>>2];oI(x,u);if(!(c[r>>2]|0))c[r>>2]=109;break}case 10:{e=c[f+16>>2]|0;g=+h[f+8>>3];break}case 15:{d=c[f+8>>2]|0;break}default:{}}f=f+120|0;i=i+1|0}c[b>>2]=c[x>>2];c[b+4>>2]=c[x+4>>2];c[b+8>>2]=c[x+8>>2];c[b+12>>2]=c[x+12>>2];c[b+16>>2]=c[x+16>>2];c[b+20>>2]=c[x+20>>2];c[b+24>>2]=c[x+24>>2];c[b+28>>2]=c[x+28>>2]}l=D;return}function oI(a,b){a=a|0;b=b|0;var c=0.0,d=0;c=+h[b>>3];d=a+16|0;if(c>+h[d>>3])h[d>>3]=c;if(c<+h[a>>3])h[a>>3]=c;c=+h[b+8>>3];b=a+24|0;if(c>+h[b>>3])h[b>>3]=c;b=a+8|0;if(c<+h[b>>3])h[b>>3]=c;return}function pI(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;r=l;l=l+48|0;k=r+32|0;n=r;f=+h[b>>3];o=n+16|0;h[o>>3]=f;h[n>>3]=f;f=+h[b+8>>3];p=n+24|0;h[p>>3]=f;m=n+8|0;h[m>>3]=f;j=1;while(1){if((j|0)>=(d|0))break;i=b+24|0;f=+h[i>>3];if(!(f<+h[n>>3])){if(f>+h[o>>3]){g=o;q=5}}else{g=n;q=5}if((q|0)==5){q=0;h[g>>3]=f}f=+h[b+32>>3];if(!(f<+h[m>>3])){if(f>+h[p>>3]){b=o;q=8}}else{b=n;q=8}if((q|0)==8){q=0;h[b+8>>3]=f}j=j+1|0;b=i}c[k>>2]=c[n>>2];c[k+4>>2]=c[n+4>>2];c[k+8>>2]=c[n+8>>2];c[k+12>>2]=c[n+12>>2];oI(e,k);c[k>>2]=c[o>>2];c[k+4>>2]=c[o+4>>2];c[k+8>>2]=c[o+8>>2];c[k+12>>2]=c[o+12>>2];oI(e,k);c[a>>2]=c[n>>2];c[a+4>>2]=c[n+4>>2];c[a+8>>2]=c[n+8>>2];c[a+12>>2]=c[n+12>>2];c[a+16>>2]=c[n+16>>2];c[a+20>>2]=c[n+20>>2];c[a+24>>2]=c[n+24>>2];c[a+28>>2]=c[n+28>>2];l=r;return}function qI(b,c,d,e){b=b|0;c=+c;d=+d;e=e|0;var f=0.0,g=0,i=0,j=0,k=0.0,m=0,n=0;n=l;l=l+16|0;i=n+8|0;j=n;f=+h[e+32>>3];k=+h[e+40>>3];switch(a[e+48>>0]|0){case 108:{h[i>>3]=c;f=f+c;g=j;m=5;break}case 110:{f=f*.5;h[i>>3]=c-f;f=f+c;g=j;m=5;break}case 114:{h[j>>3]=c;f=c-f;g=i;m=5;break}default:{c=0.0;f=0.0}}if((m|0)==5){h[g>>3]=f;c=+h[i>>3];f=+h[j>>3]}d=+h[e+16>>3]+d;h[b>>3]=c;h[b+8>>3]=d-k;h[b+16>>3]=f;h[b+24>>3]=d;l=n;return}function rI(a){a=a|0;if((c[a>>2]|0)==7)BK(c[a+112>>2]|0,1);return}function sI(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0.0,j=0,k=0,m=0,n=0.0;m=l;l=l+16|0;k=m;j=c[b+152>>2]|0;n=+h[b+432>>3];i=+h[b+352>>3]*.013888888888888888;f=i*n;h[b+488>>3]=f;g=+h[b+440>>3];i=i*g;h[b+496>>3]=i;h[b+520>>3]=n*.013888888888888888;e=b+528|0;h[e>>3]=g*.013888888888888888;if(j&4096|0)h[e>>3]=g*-.013888888888888888;e=(c[b+360>>2]|0)==0;n=+((c[b+448>>2]|0)>>>0);g=+((c[b+452>>2]|0)>>>0);h[b+368>>3]=(e?n:g)/f;h[b+376>>3]=(e?g:n)/i;rS(b,aP(d,GA(d,0,101522,0)|0,195059)|0);c[b+160>>2]=0;tI(b,d);if(j&2|0)uI(b,d);e=jC(d)|0;while(1){if(!e)break;a[(c[e+16>>2]|0)+116>>0]=0;e=kC(d,e)|0}vI(b,k);while(1){if(!((wI(b)|0)<<24>>24))break;if((xI(b)|0)>1)SR(b);yI(b);while(1){if(!((zI(b)|0)<<24>>24))break;AI(b,d);BI(b)}if((xI(b)|0)>1)TR(b);CI(b,k)}DI(b);l=m;return}function tI(a,b){a=a|0;b=b|0;var d=0;d=aI(a)|0;c[d+4>>2]=0;c[d+8>>2]=b;c[d+12>>2]=0;CJ(a,c[(c[b+16>>2]|0)+12>>2]|0,b);OR(a,b);return}function uI(b,c){b=b|0;c=c|0;var d=0,e=0,f=0,g=0;jS(b,134800);d=QA(c,140977)|0;if(d|0?a[d>>0]|0:0)jS(b,d);d=QA(c,101446)|0;if(d|0?a[d>>0]|0:0)hS(b,d);OJ(b,c);g=jC(c)|0;while(1){if(!g)break;d=QA(g,140823)|0;if(d|0?a[d>>0]|0:0)hS(b,d);d=QA(g,137729)|0;if(d|0?a[d>>0]|0:0)jS(b,d);d=QA(g,101418)|0;do if(d|0?a[d>>0]|0:0){if(!(R2(d,58)|0)){hS(b,d);break}e=D4(d)|0;d=e;while(1){d=A4(d,107984)|0;if(!d)break;if(!(a[d>>0]|0)){d=0;continue}hS(b,d);d=0}U1(e)}while(0);d=QA(g,101446)|0;if(d|0?a[d>>0]|0:0)hS(b,d);f=XA(c,g)|0;while(1){if(!f)break;d=QA(f,140823)|0;do if(d|0?a[d>>0]|0:0){if(!(R2(d,58)|0)){hS(b,d);break}e=D4(d)|0;d=e;while(1){d=A4(d,107984)|0;if(!d)break;if(!(a[d>>0]|0)){d=0;continue}hS(b,d);d=0}U1(e)}while(0);d=QA(f,101446)|0;if(d|0?a[d>>0]|0:0)hS(b,d);f=ZA(c,f)|0}g=kC(c,g)|0}return}function vI(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;e=i+8|0;g=i;h=c[a>>2]|0;d=c[h+312>>2]|0;f=a+156|0;c[f>>2]=d;h=c[h+316>>2]|0;if(!h)if((d|0)>1?(c[a+152>>2]&64|0)==0:0){c[e>>2]=c[a+52>>2];gA(0,100567,e)|0;c[f>>2]=1;e=1;d=0}else{e=1;d=0}else{d=h+4|0;if((c[h>>2]|0)>1?(c[a+152>>2]&64|0)==0:0){c[g>>2]=c[a+52>>2];gA(0,100567,g)|0;c[h+8>>2]=(c[f>>2]|0)+1}e=c[d>>2]|0;d=h+8|0}c[a+160>>2]=e;c[b>>2]=d;l=i;return}function wI(a){a=a|0;return (c[a+160>>2]|0)<=(c[a+156>>2]|0)|0}function xI(a){a=a|0;var b=0;b=c[(c[a>>2]|0)+316>>2]|0;return c[((b|0)==0?a+156|0:b)>>2]|0}function yI(a){a=a|0;var b=0,d=0;d=a+172|0;b=c[d+4>>2]|0;a=a+196|0;c[a>>2]=c[d>>2];c[a+4>>2]=b;return}function zI(a){a=a|0;var b=0,d=0;d=c[a+196>>2]|0;if(((d|0)>-1?(d|0)<(c[a+164>>2]|0):0)?(b=c[a+200>>2]|0,(b|0)>-1):0)a=(b|0)<(c[a+168>>2]|0)&1;else a=0;return a|0}function AI(d,e){d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;p=l;l=l+176|0;k=p;f=p+48|0;m=p+32|0;n=c[d+16>>2]|0;o=c[d+152>>2]|0;if((c[d+160>>2]|0)<=1?(c[d+196>>2]|0)<=0:0)h=0;else{kA(m,128,f);g=n+212|0;h=c[g>>2]|0;eI(d,m);nA(m,h)|0;i=m+4|0;f=c[i>>2]|0;if(f>>>0>=(c[m+8>>2]|0)>>>0){lA(m,1)|0;f=c[i>>2]|0}a[f>>0]=0;f=c[m>>2]|0;c[i>>2]=f;c[g>>2]=f}HT(QA(e,100032)|0);FI(d);QR(d);hS(d,137314);jS(d,134800);do if(o&4259840|0){if((c[n+208>>2]|0)==0?(b[n+260>>1]&1)==0:0)break;if(o&655360){i=o&131072;c[n+264>>2]=i>>>16^2;f=JK((i>>>12^32)+32|0)|0;g=d+288|0;c[f>>2]=c[g>>2];c[f+4>>2]=c[g+4>>2];c[f+8>>2]=c[g+8>>2];c[f+12>>2]=c[g+12>>2];g=f+16|0;q=d+304|0;c[g>>2]=c[q>>2];c[g+4>>2]=c[q+4>>2];c[g+8>>2]=c[q+8>>2];c[g+12>>2]=c[q+12>>2];if(!i){jK(f);g=4}else g=2}else{f=0;g=0}if(!(o&8192))NR(d,f,f,g)|0;c[n+272>>2]=f;c[n+268>>2]=g}while(0);if(o&32768|0?(j=c[(c[e+16>>2]|0)+12>>2]|0,j|0):0)c[n+192>>2]=c[j>>2];g=(o&4|0)!=0;do if(!g){f=n+208|0;if((c[f>>2]|0)==0?(b[n+260>>1]&1)==0:0)break;q=d+256|0;c[k>>2]=c[q>>2];c[k+4>>2]=c[q+4>>2];c[k+8>>2]=c[q+8>>2];c[k+12>>2]=c[q+12>>2];c[k+16>>2]=c[q+16>>2];c[k+20>>2]=c[q+20>>2];c[k+24>>2]=c[q+24>>2];c[k+28>>2]=c[q+28>>2];kI(d,k);cS(d,c[f>>2]|0,c[n+228>>2]|0,c[n+244>>2]|0,c[n+212>>2]|0)}while(0);GI(d,e);f=c[(c[e+16>>2]|0)+12>>2]|0;if(f|0)DK(d,4,f);do if(!g){if((c[n+208>>2]|0)==0?(b[n+260>>1]&1)==0:0)break;dS(d)}while(0);HI(d,e,o);RR(d);if(h|0){oA(m);c[n+212>>2]=h}l=p;return}function BI(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;h=l;l=l+32|0;d=h+24|0;b=h+16|0;j=h+8|0;f=h;g=a+196|0;i=a+188|0;c[b>>2]=c[g>>2];c[b+4>>2]=c[g+4>>2];c[d>>2]=c[i>>2];c[d+4>>2]=c[i+4>>2];EI(j,b,d);i=c[j+4>>2]|0;e=g;c[e>>2]=c[j>>2];c[e+4>>2]=i;if(!((zI(a)|0)<<24>>24)){e=a+180|0;if(!(c[a+184>>2]|0))c[a+200>>2]=c[a+176>>2];else c[g>>2]=c[a+172>>2];c[b>>2]=c[g>>2];c[b+4>>2]=c[g+4>>2];c[d>>2]=c[e>>2];c[d+4>>2]=c[e+4>>2];EI(f,b,d);i=c[f+4>>2]|0;j=g;c[j>>2]=c[f>>2];c[j+4>>2]=i}l=h;return}function CI(a,b){a=a|0;b=b|0;var d=0;d=c[b>>2]|0;if(!d){d=a+160|0;c[d>>2]=(c[d>>2]|0)+1}else{c[a+160>>2]=c[d>>2];c[b>>2]=d+4}return}function DI(a){a=a|0;PR(a);bI(a);return}function EI(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=(c[d+4>>2]|0)+(c[b+4>>2]|0)|0;c[a>>2]=(c[d>>2]|0)+(c[b>>2]|0);c[a+4>>2]=e;return}function FI(a){a=a|0;var b=0.0,d=0.0,e=0.0,f=0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0.0;t=l;l=l+48|0;n=t+32|0;f=t+24|0;j=t+8|0;k=t+16|0;m=t;r=a+196|0;q=c[r>>2]|0;r=c[r+4>>2]|0;p=f;c[p>>2]=q;c[p+4>>2]=r;p=a+164|0;i=c[p>>2]|0;p=c[p+4>>2]|0;s=j;c[s>>2]=i;c[s+4>>2]=p;s=a+360|0;if(!(c[s>>2]|0)){k=q;j=r}else{c[n>>2]=c[f>>2];c[n+4>>2]=c[f+4>>2];NJ(k,n);r=k;k=c[r>>2]|0;r=c[r+4>>2]|0;p=f;c[p>>2]=k;c[p+4>>2]=r;c[n>>2]=c[j>>2];c[n+4>>2]=c[j+4>>2];NJ(m,n);p=m;i=c[p>>2]|0;p=c[p+4>>2]|0;c[j>>2]=i;c[j+4>>2]=p;j=r}d=+h[a+320>>3];g=d*+(k|0)-+h[a+240>>3];h[a+288>>3]=g;e=+h[a+328>>3];o=e*+(j|0)-+h[a+248>>3];h[a+296>>3]=o;h[a+304>>3]=d+g;h[a+312>>3]=e+o;f=a+472|0;if(!(c[(c[a+12>>2]|0)+28>>2]|0)){r=a+456|0;c[f>>2]=c[r>>2];c[f+4>>2]=c[r+4>>2];c[f+8>>2]=c[r+8>>2];c[f+12>>2]=c[r+12>>2]}else{q=a+456|0;c[f>>2]=c[((c[f>>2]|0)<(c[q>>2]|0)?f:q)>>2];r=a+476|0;c[r>>2]=c[((c[r>>2]|0)<(c[a+460>>2]|0)?f:q)+4>>2];r=a+480|0;c[r>>2]=c[((c[r>>2]|0)>(c[a+464>>2]|0)?f:q)+8>>2];r=a+484|0;c[r>>2]=c[((c[r>>2]|0)>(c[a+468>>2]|0)?f:q)+12>>2]}m=c[a+152>>2]|0;b=+h[a+336>>3];if(!(m&128)){b=(+(k|0)-+(i|0)*.5)*d+b;h[a+256>>3]=b;g=(+(j|0)-+(p|0)*.5)*e+ +h[a+344>>3];o=g;d=d+b;g=g+e}else{d=+h[a+368>>3]*.5;e=b-d;h[a+256>>3]=e;g=+h[a+344>>3];u=+h[a+376>>3]*.5;o=g-u;d=d+b;g=u+g;b=e}h[a+264>>3]=o;h[a+272>>3]=d;h[a+280>>3]=g;if(!(c[s>>2]|0)){e=+h[a+352>>3];h[a+504>>3]=+h[a+384>>3]/e-b;if(!(m&4096)){d=+h[a+392>>3]/e;b=o}else{d=-g;b=+h[a+392>>3]/e}h[a+512>>3]=d-b}else{f=a+384|0;e=+h[a+352>>3];h[a+512>>3]=-g-+h[a+392>>3]/e;if(!(m&4096))d=+h[f>>3]/e;else{d=-d;b=+h[f>>3]/e}h[a+504>>3]=d-b}l=t;return}function GI(b,d){b=b|0;d=d|0;var e=0,f=0.0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+48|0;q=s;o=s+40|0;p=s+36|0;r=s+32|0;e=QA(d,140977)|0;if(!e){j=1;e=137198}else{i=(a[e>>0]|0)==0;j=i&1;e=i?137198:e}h=c[b+152>>2]|0;i=(a[e>>0]|0)==116;if(!(h&256))if(i){m=(s2(e,137379)|0)==0;k=m?137198:e;m=m?1:j;n=8}else{k=e;m=j;n=8}else if(!(i?!(s2(e,137379)|0):0)){k=e;m=j;n=8}if((n|0)==8?!((m|0)!=0&(h&33554432|0)!=0):0)if(!((JJ(k,o,p)|0)<<24>>24)){jS(b,k);hS(b,137379);r=b+256|0;c[q>>2]=c[r>>2];c[q+4>>2]=c[r+4>>2];c[q+8>>2]=c[r+8>>2];c[q+12>>2]=c[r+12>>2];c[q+16>>2]=c[r+16>>2];c[q+20>>2]=c[r+20>>2];c[q+24>>2]=c[r+24>>2];c[q+28>>2]=c[r+28>>2];oS(b,q,1)}else{c[r>>2]=0;i=c[o>>2]|0;jS(b,i);hS(b,137379);IJ(d,r)|0;e=c[o+4>>2]|0;h=ZO(d,c[47143]|0,0,0)|0;f=+g[p>>2];if(!e)kS(b,137314,h,f);else kS(b,e,h,f);r=(c[r>>2]|0)>>>1&1|2;p=b+256|0;c[q>>2]=c[p>>2];c[q+4>>2]=c[p+4>>2];c[q+8>>2]=c[p+8>>2];c[q+12>>2]=c[p+12>>2];c[q+16>>2]=c[p+16>>2];c[q+20>>2]=c[p+20>>2];c[q+24>>2]=c[p+24>>2];c[q+28>>2]=c[p+28>>2];oS(b,q,r);U1(i)}e=c[(c[(c[d+16>>2]|0)+8>>2]|0)+88>>2]|0;if(e|0)LJ(b,e);l=s;return}function HI(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;g=(c[a>>2]|0)+28|0;c[g>>2]=(c[g>>2]|0)+1;g=(d&4|0)!=0;if(!g)II(a,b,d);a:do if(!(d&1)){if(d&16|0){YR(a);f=jC(b)|0;while(1){if(!f)break;e=XA(b,f)|0;while(1){if(!e)break;KI(a,e);e=ZA(b,e)|0}f=kC(b,f)|0}ZR(a);WR(a);e=jC(b)|0;while(1){if(!e)break;JI(a,e);e=kC(b,e)|0}XR(a);break}if(!(d&8)){f=jC(b)|0;while(1){if(!f)break a;JI(a,f);e=XA(b,f)|0;while(1){if(!e)break;JI(a,c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0);KI(a,e);e=ZA(b,e)|0}f=kC(b,f)|0}}WR(a);e=jC(b)|0;while(1){if(!e)break;if((LI(b,e)|0)<<24>>24)JI(a,e);e=kC(b,e)|0}XR(a);YR(a);f=jC(b)|0;while(1){if(!f)break;e=XA(b,f)|0;while(1){if(!e)break;if((MI(b,e)|0)<<24>>24)KI(a,e);e=ZA(b,e)|0}f=kC(b,f)|0}ZR(a)}else{WR(a);e=jC(b)|0;while(1){if(!e)break;JI(a,e);e=kC(b,e)|0}XR(a);YR(a);f=jC(b)|0;while(1){if(!f)break;e=XA(b,f)|0;while(1){if(!e)break;KI(a,e);e=ZA(b,e)|0}f=kC(b,f)|0}ZR(a)}while(0);if(g)II(a,b,d);return}function II(e,f,i){e=e|0;f=f|0;i=i|0;var j=0,k=0,m=0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0;P=l;l=l+128|0;w=P+72|0;N=P+64|0;H=P+112|0;I=P;J=P+104|0;K=f+16|0;L=(i&4|0)!=0;M=e+16|0;x=I+32|0;y=I+16|0;A=I+8|0;B=I+24|0;C=I+48|0;D=I+40|0;E=I+56|0;F=(i&8|0)==0;G=J+4|0;v=1;while(1){j=c[K>>2]|0;if((v|0)>(c[j+180>>2]|0))break;u=c[(c[j+184>>2]|0)+(v<<2)>>2]|0;if((GJ(e,u)|0)<<24>>24){if(L)II(e,u,i);HJ(e,u);r=c[M>>2]|0;s=r+208|0;if(!(c[s>>2]|0))t=(b[r+260>>1]&1)!=0;else t=1;HT(QA(u,100032)|0);if(!(L|t^1)){q=(c[u+16>>2]|0)+16|0;c[w>>2]=c[q>>2];c[w+4>>2]=c[q+4>>2];c[w+8>>2]=c[q+8>>2];c[w+12>>2]=c[q+12>>2];c[w+16>>2]=c[q+16>>2];c[w+20>>2]=c[q+20>>2];c[w+24>>2]=c[q+24>>2];c[w+28>>2]=c[q+28>>2];kI(e,w);cS(e,c[s>>2]|0,c[r+228>>2]|0,c[r+244>>2]|0,c[r+212>>2]|0)}c[H>>2]=0;j=IJ(u,H)|0;if(!j)p=0;else{lS(e,j);p=c[H>>2]&1}q=u+16|0;j=d[(c[q>>2]|0)+112>>0]|0;do if(!(j&1)){if(j&2|0){j=bP(u,0,106256)|0;k=bP(u,0,106264)|0;O=29;break}if(j&8|0){j=bP(u,0,106272)|0;k=bP(u,0,106280)|0;O=29;break}if(j&4|0){j=bP(u,0,106288)|0;k=bP(u,0,106296)|0;O=29;break}j=QA(u,140823)|0;if(!j)k=0;else k=(a[j>>0]|0)==0?0:j;j=QA(u,137729)|0;if(!j)j=k;else j=(a[j>>0]|0)==0?k:j;m=QA(u,101418)|0;if(m)k=(a[m>>0]|0)==0?k:m;if(!((p|0)!=0&(k|0)!=0)){m=QA(u,140977)|0;if(m|0?a[m>>0]|0:0){k=m;O=29;break}j=(j|0)==0?137314:j;n=(k|0)==0?134800:k;c[J>>2]=0;if(!p)k=0;else O=31}else O=29}else{j=bP(u,0,106240)|0;k=bP(u,0,106248)|0;O=29}while(0);if((O|0)==29){c[J>>2]=0;j=(j|0)==0?137314:j;n=(k|0)==0?134800:k;O=31}if((O|0)==31){O=0;if(!((JJ(n,J,w)|0)<<24>>24)){jS(e,n);k=1}else{jS(e,c[J>>2]|0);k=c[G>>2]|0;m=ZO(u,c[47143]|0,0,0)|0;o=+g[w>>2];if(!k)kS(e,137314,m,o);else kS(e,k,m,o);k=(c[H>>2]|0)>>>1&1|2}}m=c[47142]|0;if((m|0?(z=RA(u,m)|0,z|0):0)?a[z>>0]|0:0)uS(e,+_O(u,c[47142]|0,1.0,0.0));p=c[H>>2]|0;do if(p&4){m=ZO(u,c[47141]|0,1,0)|0;if(m|k|0){n=c[q>>2]|0;Q=n+16|0;c[I>>2]=c[Q>>2];c[I+4>>2]=c[Q+4>>2];c[I+8>>2]=c[Q+8>>2];c[I+12>>2]=c[Q+12>>2];n=n+32|0;c[x>>2]=c[n>>2];c[x+4>>2]=c[n+4>>2];c[x+8>>2]=c[n+8>>2];c[x+12>>2]=c[n+12>>2];h[y>>3]=+h[x>>3];h[B>>3]=+h[A>>3];h[C>>3]=+h[I>>3];h[E>>3]=+h[D>>3];if(!m)hS(e,137379);else hS(e,j);TM(e,I,4,p,k)}}else{if(!(p&64)){if(ZO(u,c[47141]|0,1,0)|0){hS(e,j);Q=(c[q>>2]|0)+16|0;c[w>>2]=c[Q>>2];c[w+4>>2]=c[Q+4>>2];c[w+8>>2]=c[Q+8>>2];c[w+12>>2]=c[Q+12>>2];c[w+16>>2]=c[Q+16>>2];c[w+20>>2]=c[Q+20>>2];c[w+24>>2]=c[Q+24>>2];c[w+28>>2]=c[Q+28>>2];oS(e,w,k);break}if(!k)break;hS(e,137379);Q=(c[q>>2]|0)+16|0;c[w>>2]=c[Q>>2];c[w+4>>2]=c[Q+4>>2];c[w+8>>2]=c[Q+8>>2];c[w+12>>2]=c[Q+12>>2];c[w+16>>2]=c[Q+16>>2];c[w+20>>2]=c[Q+20>>2];c[w+24>>2]=c[Q+24>>2];c[w+28>>2]=c[Q+28>>2];oS(e,w,k);break}Q=c[q>>2]|0;p=Q+16|0;c[I>>2]=c[p>>2];c[I+4>>2]=c[p+4>>2];c[I+8>>2]=c[p+8>>2];c[I+12>>2]=c[p+12>>2];Q=Q+32|0;c[x>>2]=c[Q>>2];c[x+4>>2]=c[Q+4>>2];c[x+8>>2]=c[Q+8>>2];c[x+12>>2]=c[Q+12>>2];h[y>>3]=+h[x>>3];h[B>>3]=+h[A>>3];h[C>>3]=+h[I>>3];h[E>>3]=+h[D>>3];if(!(ZO(u,c[47141]|0,1,0)|0))hS(e,137379);else hS(e,j);if((jI(e,I,n,0)|0)>1){c[N>>2]=KB(u)|0;gA(3,100400,N)|0}Q=(c[q>>2]|0)+16|0;c[w>>2]=c[Q>>2];c[w+4>>2]=c[Q+4>>2];c[w+8>>2]=c[Q+8>>2];c[w+12>>2]=c[Q+12>>2];c[w+16>>2]=c[Q+16>>2];c[w+20>>2]=c[Q+20>>2];c[w+24>>2]=c[Q+24>>2];c[w+28>>2]=c[Q+28>>2];oS(e,w,0)}while(0);U1(c[J>>2]|0);j=c[(c[q>>2]|0)+12>>2]|0;if(j|0)DK(e,5,j);if(t){if(L){Q=(c[q>>2]|0)+16|0;c[w>>2]=c[Q>>2];c[w+4>>2]=c[Q+4>>2];c[w+8>>2]=c[Q+8>>2];c[w+12>>2]=c[Q+12>>2];c[w+16>>2]=c[Q+16>>2];c[w+20>>2]=c[Q+20>>2];c[w+24>>2]=c[Q+24>>2];c[w+28>>2]=c[Q+28>>2];kI(e,w);cS(e,c[s>>2]|0,c[r+228>>2]|0,c[r+244>>2]|0,c[r+212>>2]|0)}dS(e)}a:do if(!F){k=jC(u)|0;while(1){if(!k)break a;JI(e,k);j=XA(u,k)|0;while(1){if(!j)break;KI(e,j);j=ZA(u,j)|0}k=kC(u,k)|0}}while(0);KJ(e,f);if(!L)II(e,u,i)}v=v+1|0}l=P;return}function JI(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;j=l;l=l+32|0;e=j;h=c[b>>2]|0;i=d+16|0;a:do if(((c[(c[i>>2]|0)+8>>2]|0?(yJ(b,xC(d)|0,d)|0)<<24>>24:0)?(k=b+256|0,c[e>>2]=c[k>>2],c[e+4>>2]=c[k+4>>2],c[e+8>>2]=c[k+8>>2],c[e+12>>2]=c[k+12>>2],c[e+16>>2]=c[k+16>>2],c[e+20>>2]=c[k+20>>2],c[e+24>>2]=c[k+24>>2],c[e+28>>2]=c[k+28>>2],(zJ(d,e)|0)<<24>>24):0)?(f=(c[i>>2]|0)+116|0,g=c[h+28>>2]|0,(g|0)!=(a[f>>0]|0)):0){a[f>>0]=g;rS(b,KB(d)|0);e=aP(d,c[47169]|0,195059)|0;if(a[e>>0]|0)rS(b,e);e=aP(d,c[47157]|0,195059)|0;b:do if(a[e>>0]|0){PI(e)|0;e=188228;while(1){f=e;e=e+4|0;f=c[f>>2]|0;if(!f)break b;if((a[f>>0]|0)!=105)continue;if(!(s2(f,134335)|0))break a}}while(0);AJ(b,d);ub[c[(c[(c[(c[i>>2]|0)+8>>2]|0)+4>>2]|0)+20>>2]&63](b,d);e=c[(c[i>>2]|0)+108>>2]|0;if(e|0?a[e+81>>0]|0:0)DK(b,10,e);BJ(b)}while(0);l=j;return}function KI(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;h=l;l=l+32|0;g=h;e=b+256|0;c[g>>2]=c[e>>2];c[g+4>>2]=c[e+4>>2];c[g+8>>2]=c[e+8>>2];c[g+12>>2]=c[e+12>>2];c[g+16>>2]=c[e+16>>2];c[g+20>>2]=c[e+20>>2];c[g+24>>2]=c[e+24>>2];c[g+28>>2]=c[e+28>>2];a:do if((NI(d,g)|0)<<24>>24?(f=d+-48|0,(OI(b,d)|0)<<24>>24):0){i=d+48|0;e=l2(KB(c[((c[d>>2]&3|0)==3?d:i)+40>>2]|0)|0)|0;e=T1(e+3+(l2(KB(c[((c[d>>2]&3|0)==2?d:f)+40>>2]|0)|0)|0)|0)|0;r3(e,KB(c[((c[d>>2]&3|0)==3?d:i)+40>>2]|0)|0)|0;i=(xB(xC(c[((c[d>>2]&3|0)==2?d:f)+40>>2]|0)|0)|0)==0;g=e+(l2(e)|0)|0;if(i){a[g>>0]=a[141747]|0;a[g+1>>0]=a[141748]|0;a[g+2>>0]=a[141749]|0}else{a[g>>0]=a[137738]|0;a[g+1>>0]=a[137739]|0;a[g+2>>0]=a[137740]|0}F4(e,KB(c[((c[d>>2]&3|0)==2?d:f)+40>>2]|0)|0)|0;rS(b,e);U1(e);e=aP(d,c[47191]|0,195059)|0;if(a[e>>0]|0)rS(b,e);e=aP(d,c[47185]|0,195059)|0;b:do if(!(a[e>>0]|0))e=0;else{PI(e)|0;e=188228;while(1){f=e;e=e+4|0;f=c[f>>2]|0;if(!f){e=188228;break b}if((a[f>>0]|0)!=105)continue;if(!(s2(f,134335)|0))break a}}while(0);QI(b,d,e);RI(b,d,e);SI(b)}while(0);l=h;return}function LI(a,b){a=a|0;b=b|0;var d=0,e=0;d=a+16|0;a=1;while(1){e=c[d>>2]|0;if((a|0)>(c[e+180>>2]|0)){a=1;break}if(!(HC(c[(c[e+184>>2]|0)+(a<<2)>>2]|0,b)|0))a=a+1|0;else{a=0;break}}return a|0}function MI(a,b){a=a|0;b=b|0;var d=0,e=0;d=a+16|0;a=1;while(1){e=c[d>>2]|0;if((a|0)>(c[e+180>>2]|0)){a=1;break}if(!(HC(c[(c[e+184>>2]|0)+(a<<2)>>2]|0,b)|0))a=a+1|0;else{a=0;break}}return a|0}function NI(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+64|0;i=j+32|0;f=j;g=b+16|0;b=c[g>>2]|0;e=c[b+8>>2]|0;if((e|0)!=0?(e=e+8|0,c[f>>2]=c[e>>2],c[f+4>>2]=c[e+4>>2],c[f+8>>2]=c[e+8>>2],c[f+12>>2]=c[e+12>>2],c[f+16>>2]=c[e+16>>2],c[f+20>>2]=c[e+20>>2],c[f+24>>2]=c[e+24>>2],c[f+28>>2]=c[e+28>>2],c[i>>2]=c[d>>2],c[i+4>>2]=c[d+4>>2],c[i+8>>2]=c[d+8>>2],c[i+12>>2]=c[d+12>>2],c[i+16>>2]=c[d+16>>2],c[i+20>>2]=c[d+20>>2],c[i+24>>2]=c[d+24>>2],c[i+28>>2]=c[d+28>>2],(xJ(f,i)|0)!=0):0)b=1;else h=3;do if((h|0)==3){e=c[b+96>>2]|0;if(e){c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];c[i+16>>2]=c[d+16>>2];c[i+20>>2]=c[d+20>>2];c[i+24>>2]=c[d+24>>2];c[i+28>>2]=c[d+28>>2];if((iQ(e,i)|0)<<24>>24){b=1;break}b=c[g>>2]|0}b=c[b+108>>2]|0;if((b|0?a[b+81>>0]|0:0)?(c[i>>2]=c[d>>2],c[i+4>>2]=c[d+4>>2],c[i+8>>2]=c[d+8>>2],c[i+12>>2]=c[d+12>>2],c[i+16>>2]=c[d+16>>2],c[i+20>>2]=c[d+20>>2],c[i+24>>2]=c[d+24>>2],c[i+28>>2]=c[d+28>>2],(iQ(b,i)|0)<<24>>24):0){b=1;break}b=0}while(0);l=j;return b|0}function OI(b,d){b=b|0;d=d|0;var e=0,f=0;a:do if((c[b+156>>2]|0)>=2?(e=aP(d,c[47190]|0,195059)|0,(tJ(b,e)|0)<<24>>24==0):0)if(!(a[e>>0]|0)){e=0;while(1){if((e|0)>=2){e=0;break a}f=(e|0)==1;f=aP(c[((c[d>>2]&3|0)==((f?2:3)|0)?d:d+((f?-1:1)*48|0)|0)+40>>2]|0,c[47167]|0,195059)|0;if(!(a[f>>0]|0)){e=1;break a}if(!((tJ(b,f)|0)<<24>>24))e=e+1|0;else{e=1;break}}}else e=0;else e=1;while(0);return e|0}function PI(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+192|0;o=p+24|0;n=p+16|0;m=p+8|0;k=p;g=p+48|0;j=p+32|0;if(a[100282]|0){kA(188492,128,194826);a[100282]=0}kA(j,128,p+56|0);c[g>>2]=b;h=j+4|0;i=j+8|0;d=0;e=0;a:while(1){f=d;b:while(1)switch(rJ(g,j)|0){case 0:{d=21;break a}case 40:if(!(f<<24>>24)){f=1;continue b}else{d=7;break a}case 41:if(!(f<<24>>24)){d=9;break a}else{f=0;continue b}default:break b}if(!(f<<24>>24)){if((e|0)==63){d=12;break}d=c[47124]|0;if(d>>>0>=(c[47125]|0)>>>0){lA(188492,1)|0;d=c[47124]|0}c[47124]=d+1;a[d>>0]=0;c[188228+(e<<2)>>2]=c[47124];e=e+1|0}d=c[h>>2]|0;if(d>>>0>=(c[i>>2]|0)>>>0){lA(j,1)|0;d=c[h>>2]|0}a[d>>0]=0;d=c[j>>2]|0;c[h>>2]=d;nA(188492,d)|0;d=c[47124]|0;if(d>>>0>=(c[47125]|0)>>>0){lA(188492,1)|0;d=c[47124]|0}c[47124]=d+1;a[d>>0]=0;d=f}do if((d|0)==7){c[k>>2]=b;gA(1,100283,k)|0;c[47057]=0;oA(j)}else if((d|0)==9){c[m>>2]=b;gA(1,100317,m)|0;c[47057]=0;oA(j)}else if((d|0)==12){c[n>>2]=b;gA(0,100345,n)|0;c[47120]=0;oA(j)}else if((d|0)==21){if(f<<24>>24){c[o>>2]=b;gA(1,100368,o)|0;c[47057]=0;oA(j);break}c[188228+(e<<2)>>2]=0;oA(j);d=c[47124]|0;if(d>>>0>=(c[47125]|0)>>>0){lA(188492,1)|0;d=c[47124]|0}a[d>>0]=0;c[47124]=c[47123]}while(0);l=p;return 188228}
+function Bb(a){a=a|0;var b=0;b=l;l=l+a|0;l=l+15&-16;return b|0}function Cb(){return l|0}function Db(a){a=a|0;l=a}function Eb(a,b){a=a|0;b=b|0;l=a;m=b}function Fb(a,b){a=a|0;b=b|0;if(!o){o=a;p=b}}function Gb(a){a=a|0;z=a}function Hb(){return z|0}function Ib(a){a=a|0;c[46654]=a;return 0}function Jb(){return c[46654]|0}function Kb(a,b){a=a|0;b=b|0;Oa(0,a|0,b|0)|0;return}function Lb(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;f=i+4|0;g=i;c[f>>2]=0;h=mG()|0;rG(h,10976);rG(h,17220);rG(h,17440);fA(1)|0;eA(18)|0;while(1){a=ZB(a)|0;e=c[f>>2]|0;if(!a)break;if(e|0){a=195059;continue}nG(h,a,d)|0;qG(h,a,b,f,g)|0;a=195059}l=i;return e|0}function Mb(a){a=a|0;var d=0,e=0,f=0,g=0;g=l;l=l+16|0;d=g+4|0;e=g;f=c[(c[a+16>>2]|0)+8>>2]|0;a=a+64|0;switch(c[a>>2]|0){case 0:{JL(f);break}case 1:{if(b[(c[f+16>>2]|0)+136>>1]&1)UP(f);break}case 4:case 5:case 6:{GL(f,e,d);Pb(f,c[e>>2]|0,c[d>>2]|0,c[a>>2]|0);break}default:{}}l=g;return}function Nb(a){a=a|0;var b=0,d=0,e=0,f=0;d=c[(c[a+16>>2]|0)+8>>2]|0;if(!(c[46655]|0)){c[46655]=c[4678];c[46656]=57;c[46657]=19}e=d+64|0;f=(c[e>>2]|0)+8|0;b=c[f>>2]|0;c[f>>2]=186620;switch(c[a+64>>2]|0){case 2:{wL(a,d,a,0);break}case 3:{wL(a,d,a,1);break}case 1:case 0:{if(!(c[a+152>>2]&134217728))dE(d,a)|0;break}case 6:case 5:case 4:{Ob(d);if(!(c[a+152>>2]&134217728))dE(d,a)|0;break}default:{}}c[(c[e>>2]|0)+8>>2]=b;return}function Ob(b){b=b|0;var d=0,e=0;d=c[46659]|0;if((d|0)!=(c[46658]|0)){e=c[c[46690]>>2]|0;if(!e){e=_P(b,0,99808,195059)|0;c[c[46690]>>2]=e;d=c[46659]|0}if(d>>>0>=(c[46660]|0)>>>0){lA(186632,1)|0;d=c[46659]|0}a[d>>0]=0;d=c[46658]|0;c[46659]=d;KA(b,e,d)|0}if(c[(c[b+16>>2]|0)+12>>2]|0){e=c[(c[46690]|0)+4>>2]|0;d=c[46675]|0;if(d>>>0>=(c[46676]|0)>>>0){lA(186696,1)|0;d=c[46675]|0}a[d>>0]=0;d=c[46674]|0;c[46675]=d;KA(b,e,d)|0}TA(b,66753,c[(c[46690]|0)+8236>>2]|0,195059)|0;d=0;while(1){if((d|0)==8)break;oA(186632+(d<<4)|0);d=d+1|0}U1(c[46690]|0);h[1]=1.0;h[5]=1.0;c[46691]=0;c[46695]=0;return}function Pb(d,e,f,g){d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0;i=T1(8240)|0;c[46690]=i;a:do switch(g|0){case 6:{b[i+8232>>1]=14;g=66769;break}case 5:{b[i+8232>>1]=12;g=66773;break}default:{g=QA(d,66753)|0;if((g|0?a[g>>0]|0:0)?(h=Qb(g)|0,(h&65535)>10):0){i=c[46690]|0;b[i+8232>>1]=h;break a}g=Qb(66765)|0;i=c[46690]|0;b[i+8232>>1]=g;g=66765}}while(0);c[i+8236>>2]=g;j=d+16|0;g=c[j>>2]|0;if(!(c[g+180>>2]|0))h=0;else{h=_P(d,0,99808,195059)|0;i=c[46690]|0;g=c[j>>2]|0}c[i>>2]=h;if(!(a[g+113>>0]&8))g=0;else g=_P(d,0,66777,195059)|0;c[(c[46690]|0)+4>>2]=g;i=_P(d,1,99808,195059)|0;c[(c[46690]|0)+8>>2]=i;i=_P(d,1,66777,195059)|0;c[(c[46690]|0)+12>>2]=i;i=_P(d,2,99808,195059)|0;g=c[46690]|0;c[g+16>>2]=i;if(!f)h=0;else{h=_P(d,2,66785,195059)|0;g=c[46690]|0}c[g+20>>2]=h;if(!e)h=0;else{h=_P(d,2,66793,195059)|0;g=c[46690]|0}c[g+24>>2]=h;h=a[(c[j>>2]|0)+113>>0]|0;if(!(h&33))i=0;else{i=_P(d,2,66777,195059)|0;g=c[46690]|0;h=a[(c[j>>2]|0)+113>>0]|0}c[g+28>>2]=i;if(!(h&2))i=0;else{i=_P(d,2,66801,195059)|0;g=c[46690]|0;h=a[(c[j>>2]|0)+113>>0]|0}c[g+32>>2]=i;if(!(h&4))h=0;else{h=_P(d,2,66810,195059)|0;g=c[46690]|0}c[g+36>>2]=h;g=0;while(1){if((g|0)==8)break;kA(186632+(g<<4)|0,1024,(c[46690]|0)+40+(g<<10)|0);g=g+1|0}return}function Qb(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+1040|0;h=j;g=j+8|0;f=0;d=b;a:while(1){do{e=d;d=d+1|0;e=a[e>>0]|0;if(!(e<<24>>24))break a}while(((e<<24>>24)+-48|0)>>>0>=10);if((f|0)>=1023){i=7;break}a[g+f>>0]=e;f=f+1|0}if((i|0)==7){c[h>>2]=b;gA(0,66819,h)|0}a[g+f>>0]=0;i=(q5(g)|0)&65535;l=j;return i|0}function Rb(b){b=b|0;var d=0,e=0;e=c[(c[b+16>>2]|0)+8>>2]|0;d=c[c[46690]>>2]|0;b=c[46663]|0;if(b>>>0>=(c[46664]|0)>>>0){lA(186648,1)|0;b=c[46663]|0}a[b>>0]=0;b=c[46662]|0;c[46663]=b;KA(e,d,b)|0;if(c[(c[e+16>>2]|0)+12>>2]|0){d=c[(c[46690]|0)+4>>2]|0;b=c[46679]|0;if(b>>>0>=(c[46680]|0)>>>0){lA(186712,1)|0;b=c[46679]|0}a[b>>0]=0;b=c[46678]|0;c[46679]=b;KA(e,d,b)|0}h[2]=1.0;h[6]=1.0;c[46692]=0;c[46696]=0;return}function Sb(b){b=b|0;var d=0,e=0;e=c[(c[b+16>>2]|0)+8>>2]|0;b=c[46663]|0;if((c[46662]|0)!=(b|0)){d=c[(c[46690]|0)+8>>2]|0;if(b>>>0>=(c[46664]|0)>>>0){lA(186648,1)|0;b=c[46663]|0}a[b>>0]=0;b=c[46662]|0;c[46663]=b;KA(e,d,b)|0}b=c[46679]|0;if((c[46678]|0)!=(b|0)){d=c[(c[46690]|0)+12>>2]|0;if(b>>>0>=(c[46680]|0)>>>0){lA(186712,1)|0;b=c[46679]|0}a[b>>0]=0;b=c[46678]|0;c[46679]=b;KA(e,d,b)|0}h[9]=1.0;h[11]=1.0;c[46699]=0;c[46701]=0;return}function Tb(b){b=b|0;var d=0,e=0;e=c[(c[b+16>>2]|0)+8>>2]|0;b=c[46663]|0;if((c[46662]|0)!=(b|0)){d=c[(c[46690]|0)+16>>2]|0;if(b>>>0>=(c[46664]|0)>>>0){lA(186648,1)|0;b=c[46663]|0}a[b>>0]=0;b=c[46662]|0;c[46663]=b;KA(e,d,b)|0}b=c[46667]|0;if((c[46666]|0)!=(b|0)){d=c[(c[46690]|0)+24>>2]|0;if(b>>>0>=(c[46668]|0)>>>0){lA(186664,1)|0;b=c[46667]|0}a[b>>0]=0;b=c[46666]|0;c[46667]=b;KA(e,d,b)|0}b=c[46671]|0;if((c[46670]|0)!=(b|0)){d=c[(c[46690]|0)+20>>2]|0;if(b>>>0>=(c[46672]|0)>>>0){lA(186680,1)|0;b=c[46671]|0}a[b>>0]=0;b=c[46670]|0;c[46671]=b;KA(e,d,b)|0}b=c[46679]|0;if((c[46678]|0)!=(b|0)){d=c[(c[46690]|0)+28>>2]|0;if(b>>>0>=(c[46680]|0)>>>0){lA(186712,1)|0;b=c[46679]|0}a[b>>0]=0;b=c[46678]|0;c[46679]=b;KA(e,d,b)|0}b=c[46683]|0;if((c[46682]|0)!=(b|0)){d=c[(c[46690]|0)+36>>2]|0;if(b>>>0>=(c[46684]|0)>>>0){lA(186728,1)|0;b=c[46683]|0}a[b>>0]=0;b=c[46682]|0;c[46683]=b;KA(e,d,b)|0}b=c[46687]|0;if((c[46686]|0)!=(b|0)){d=c[(c[46690]|0)+32>>2]|0;if(b>>>0>=(c[46688]|0)>>>0){lA(186744,1)|0;b=c[46687]|0}a[b>>0]=0;b=c[46686]|0;c[46687]=b;KA(e,d,b)|0}h[10]=1.0;h[12]=1.0;h[3]=1.0;h[4]=1.0;h[7]=1.0;h[8]=1.0;c[46700]=0;c[46702]=0;c[46693]=0;c[46694]=0;c[46697]=0;c[46698]=0;return}function Ub(d,e,f){d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=l;l=l+1056|0;j=t+16|0;s=t+8|0;r=t;k=t+32|0;o=c[(c[d+16>>2]|0)+12>>2]|0;p=c[11416+(o<<2)>>2]|0;nA(p,66916)|0;g=f+4|0;bc(k,+h[(c[g>>2]|0)+16>>3]);nA(p,k)|0;ec(d,195059,c[c[g>>2]>>2]|0);_b(d);q=a[f+48>>0]|0;q=q<<24>>24==108?-1:q<<24>>24==114&1;g=c[g>>2]|0;if(!g)g=0;else g=c[g+24>>2]&127;i=b[(c[46690]|0)+8232>>1]|0;if((i&65535)>14?(m=c[11464+((i&65535)+-15<<2)>>2]&g,n=186764+(o<<2)|0,(c[n>>2]|0)!=(m|0)):0){c[r>>2]=m;m4(k,66919,r)|0;nA(p,k)|0;c[n>>2]=m}r=e+8|0;h[r>>3]=+h[r>>3]+ +h[f+24>>3];nA(p,66925)|0;c[j>>2]=c[e>>2];c[j+4>>2]=c[e+4>>2];c[j+8>>2]=c[e+8>>2];c[j+12>>2]=c[e+12>>2];ac(p,j);c[s>>2]=q;m4(k,102220,s)|0;nA(p,k)|0;bc(k,+h[f+32>>3]);nA(p,k)|0;ec(d,195059,c[f>>2]|0);l=t;return}function Vb(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0;i=l;l=l+1040|0;f=i;g=i+16|0;e=c[(c[a+16>>2]|0)+12>>2]|0;Zb(a);_b(a);if(!d){a=c[11416+(e<<2)>>2]|0;nA(a,66913)|0}else{if((d|1|0)==3)gc(a,d,b,2);else hc(a);a=c[11416+(e<<2)>>2]|0;nA(a,66910)|0};c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];ac(a,f);bc(g,+h[b+16>>3]-+h[b>>3]);nA(a,g)|0;bc(g,+h[b+24>>3]-+h[b+8>>3]);nA(a,g)|0;l=i;return}function Wb(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;Zb(a);_b(a);if(!d)$b(a,112,b,c);else{if((d|1|0)==3)gc(a,d,b,c);else hc(a);$b(a,80,b,c)}return}function Xb(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;Zb(a);_b(a);if(!f)$b(a,66,b,c);else{if((f|1|0)==3)gc(a,f,b,c);else hc(a);$b(a,98,b,c)}return}function Yb(a,b,c){a=a|0;b=b|0;c=c|0;Zb(a);_b(a);$b(a,76,b,c);return}function Zb(b){b=b|0;var d=0,e=0,f=0,g=0.0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+1184|0;i=n;f=n+24|0;m=n+8|0;kA(m,1024,n+152|0);j=b+16|0;d=c[j>>2]|0;g=+h[d+152>>3];e=8+(c[d+12>>2]<<3)|0;if(g!=+h[e>>3]){h[e>>3]=g;nA(m,66882)|0;h[i>>3]=+h[(c[j>>2]|0)+152>>3];m4(f,66896,i)|0;cc(f,0);nA(m,f)|0;f=m+4|0;d=c[f>>2]|0;e=m+8|0;if(d>>>0>=(c[e>>2]|0)>>>0){lA(m,1)|0;d=c[f>>2]|0}c[f>>2]=d+1;a[d>>0]=41;d=c[f>>2]|0;if(d>>>0>=(c[e>>2]|0)>>>0){lA(m,1)|0;d=c[f>>2]|0}a[d>>0]=0;d=c[m>>2]|0;c[f>>2]=d;ec(b,66901,d);d=c[j>>2]|0}d=c[d+160>>2]|0;if(d|0){j=m+4|0;k=m+8|0;a:while(1){i=d+4|0;d=c[d>>2]|0;if(!d)break;switch(a[d>>0]|0){case 102:{if(!(s2(d,111101)|0)){d=i;continue a}break}case 98:{if(!(s2(d,111083)|0)){d=i;continue a}break}case 115:{if(!(s2(d,111088)|0)){d=i;continue a}break}default:{}}nA(m,d)|0;do{f=d;d=d+1|0}while((a[f>>0]|0)!=0);if(a[d>>0]|0){e=c[j>>2]|0;if(e>>>0>=(c[k>>2]|0)>>>0){lA(m,1)|0;e=c[j>>2]|0}c[j>>2]=e+1;a[e>>0]=40;f=0;while(1){if(!(a[d>>0]|0))break;if(f|0){e=c[j>>2]|0;if(e>>>0>=(c[k>>2]|0)>>>0){lA(m,1)|0;e=c[j>>2]|0}c[j>>2]=e+1;a[e>>0]=44}nA(m,d)|0;do{e=d;d=d+1|0}while((a[e>>0]|0)!=0);f=f+1|0}d=c[j>>2]|0;if(d>>>0>=(c[k>>2]|0)>>>0){lA(m,1)|0;d=c[j>>2]|0}c[j>>2]=d+1;a[d>>0]=41}d=c[j>>2]|0;if(d>>>0>=(c[k>>2]|0)>>>0){lA(m,1)|0;d=c[j>>2]|0}a[d>>0]=0;d=c[m>>2]|0;c[j>>2]=d;ec(b,66901,d);d=i}oA(m)}l=n;return}function _b(a){a=a|0;dc((c[a+16>>2]|0)+16|0);ec(a,75834,193758);return}function $b(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0;k=l;l=l+1040|0;j=k;i=k+16|0;h=c[11416+(c[(c[b+16>>2]|0)+12>>2]<<2)>>2]|0;g=h+4|0;b=c[g>>2]|0;if(b>>>0>=(c[h+8>>2]|0)>>>0){lA(h,1)|0;b=c[g>>2]|0}c[g>>2]=b+1;a[b>>0]=d;c[j>>2]=f;m4(i,66846,j)|0;nA(h,i)|0;b=0;while(1){if((b|0)>=(f|0))break;i=e+(b<<4)|0;c[j>>2]=c[i>>2];c[j+4>>2]=c[i+4>>2];c[j+8>>2]=c[i+8>>2];c[j+12>>2]=c[i+12>>2];ac(h,j);b=b+1|0}l=k;return}function ac(a,b){a=a|0;b=b|0;var c=0,d=0;c=l;l=l+1024|0;d=c;bc(d,+h[b>>3]);nA(a,d)|0;bc(d,+vL(+h[b+8>>3]));nA(a,d)|0;l=c;return}function bc(a,b){a=a|0;b=+b;var c=0,d=0;c=l;l=l+16|0;d=c;h[d>>3]=b>-1.0e-08&b<1.0e-08?0.0:b;m4(a,66851,d)|0;cc(a,1);l=c;return}function cc(b,c){b=b|0;c=c|0;var d=0,e=0;d=R2(b,46)|0;if(!d){if(c|0){d=b+(l2(b)|0)|0;e=10}}else{while(1){b=d+1|0;if(!(a[b>>0]|0))break;else d=b}a:while(1){switch(a[d>>0]|0){case 46:{e=5;break a}case 48:break;default:{e=6;break a}}a[d>>0]=0;d=d+-1|0}if((e|0)==5)a[d>>0]=0;else if((e|0)==6)d=d+1|0;if(c|0)e=10}if((e|0)==10){a[d>>0]=32;a[d+1>>0]=0}return}function dc(b){b=b|0;var e=0,f=0,g=0,h=0,i=0,j=0;j=l;l=l+32|0;i=j+16|0;h=j;e=a[b+3>>0]|0;f=d[b>>0]|0;g=d[b+1>>0]|0;b=d[b+2>>0]|0;if(e<<24>>24==-1){c[h>>2]=f;c[h+4>>2]=g;c[h+8>>2]=b;m4(193758,75714,h)|0}else{c[i>>2]=f;c[i+4>>2]=g;c[i+8>>2]=b;c[i+12>>2]=e&255;m4(193758,66864,i)|0}l=j;return}function ec(a,b,d){a=a|0;b=b|0;d=d|0;fc(c[11416+(c[(c[a+16>>2]|0)+12>>2]<<2)>>2]|0,b,d);return}function fc(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;f=l;l=l+1040|0;h=f;g=f+8|0;i=l2(e)|0;c[h>>2]=d;c[h+4>>2]=i;m4(g,66857,h)|0;nA(b,g)|0;nA(b,e)|0;e=b+4|0;d=c[e>>2]|0;if(d>>>0>=(c[b+8>>2]|0)>>>0){lA(b,1)|0;d=c[e>>2]|0}c[e>>2]=d+1;a[d>>0]=32;l=f;return}function gc(b,d,f,i){b=b|0;d=d|0;f=f|0;i=i|0;var j=0.0,k=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0.0;x=l;l=l+1120|0;t=x+64|0;w=x+80|0;k=x+32|0;r=x+16|0;s=x;u=c[b+16>>2]|0;j=+(c[u+136>>2]|0)*.017453292519943295;if((e[(c[46690]|0)+8232>>1]|0)<14)hc(b);else{kA(w,1024,x+96|0);v=(d|0)==2;if(v){pQ(f,k,i,j,2);f=w+4|0;d=c[f>>2]|0;if(d>>>0>=(c[w+8>>2]|0)>>>0){lA(w,1)|0;d=c[f>>2]|0}c[f>>2]=d+1;a[d>>0]=91;c[t>>2]=c[k>>2];c[t+4>>2]=c[k+4>>2];c[t+8>>2]=c[k+8>>2];c[t+12>>2]=c[k+12>>2];ac(w,t);s=k+16|0;c[t>>2]=c[s>>2];c[t+4>>2]=c[s+4>>2];c[t+8>>2]=c[s+8>>2];c[t+12>>2]=c[s+12>>2];ac(w,t)}else{pQ(f,k,i,0.0,3);q=+h[k+24>>3];o=+h[k>>3];if(j==0.0){n=+h[k+8>>3];p=q*.25;j=n;m=o}else{p=q*.25;y=p;m=+E(+j)*y+o;n=+h[k+8>>3];j=+F(+j)*y+n}h[r>>3]=m;h[r+8>>3]=j;h[s>>3]=o;h[s+8>>3]=n;f=w+4|0;d=c[f>>2]|0;if(d>>>0>=(c[w+8>>2]|0)>>>0){lA(w,1)|0;d=c[f>>2]|0}c[f>>2]=d+1;a[d>>0]=40;c[t>>2]=c[r>>2];c[t+4>>2]=c[r+4>>2];c[t+8>>2]=c[r+8>>2];c[t+12>>2]=c[r+12>>2];ac(w,t);ic(w,p);c[t>>2]=c[s>>2];c[t+4>>2]=c[s+4>>2];c[t+8>>2]=c[s+8>>2];c[t+12>>2]=c[s+12>>2];ac(w,t);ic(w,q)}nA(w,66907)|0;d=u+140|0;j=+g[d>>2];f=u+56|0;if(j>0.0){jc(w,j,f);jc(w,+g[d>>2],u+96|0)}else{jc(w,0.0,f);jc(w,1.0,u+96|0)}pA(w)|0;k=w+4|0;d=c[k>>2]|0;i=w+8|0;f=d>>>0>=(c[i>>2]|0)>>>0;if(v){if(f){lA(w,1)|0;d=c[k>>2]|0}c[k>>2]=d+1;a[d>>0]=93}else{if(f){lA(w,1)|0;d=c[k>>2]|0}c[k>>2]=d+1;a[d>>0]=41}d=c[k>>2]|0;if(d>>>0>=(c[i>>2]|0)>>>0){lA(w,1)|0;d=c[k>>2]|0}a[d>>0]=0;v=c[w>>2]|0;c[k>>2]=v;ec(b,66904,v);oA(w)}l=x;return}function hc(a){a=a|0;dc((c[a+16>>2]|0)+56|0);ec(a,66904,193758);return}function ic(a,b){a=a|0;b=+b;var c=0,d=0;c=l;l=l+1024|0;d=c;bc(d,b);nA(a,d)|0;l=c;return}function jc(a,b,c){a=a|0;b=+b;c=c|0;var d=0,e=0,f=0;d=l;l=l+1040|0;f=d;e=d+8|0;h[f>>3]=b;m4(e,94168,f)|0;cc(e,1);dc(c);fc(a,e,193758);l=d;return}function kc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0;e=l;l=l+1040|0;i=e;f=e+16|0;g=c[11416+(c[(c[a+16>>2]|0)+12>>2]<<2)>>2]|0;nA(g,67013)|0;c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];ac(g,i);bc(f,+h[d+16>>3]-+h[d>>3]);nA(g,f)|0;bc(f,+h[d+24>>3]-+h[d+8>>3]);nA(g,f)|0;ec(a,195059,c[b+8>>2]|0);l=e;return}function lc(a){a=a|0;var b=0;switch(c[a+64>>2]|0){case 0:{b=AF(c[a>>2]|0)|0;oG(b,c[(c[a+16>>2]|0)+8>>2]|0,67797,0)|0;BF(b);break}case 1:{JL(c[(c[a>>2]|0)+168>>2]|0);break}default:{}}return}function mc(b){b=b|0;var d=0,e=0,f=0,g=0;f=l;l=l+16|0;d=f;e=c[(c[b+16>>2]|0)+8>>2]|0;if(!(c[46703]|0)){c[46703]=c[4678];c[46704]=57;c[46705]=19}c[(c[e+64>>2]|0)+8>>2]=186812;nc(e,1);c[d>>2]=0;a[d+4>>0]=(a[(c[e+16>>2]|0)+115>>0]|0)==1&1;g=c[b+64>>2]|0;a[d+5>>0]=((g|0)==0|(g|0)==3)&1;a[d+6>>0]=0;oc(e,b,1,d);l=f;return}function nc(a,b){a=a|0;b=b|0;var d=0;c[a>>2]=c[a>>2]&-9;d=OD(a)|0;while(1){if(!d)break;nc(d,0);d=PD(d)|0}a:do if(b|0){b=jC(a)|0;while(1){if(!b)break a;c[b>>2]=c[b>>2]&-9;d=XA(a,b)|0;while(1){if(!d)break;c[d>>2]=c[d>>2]&-9;d=ZA(a,d)|0}b=kC(a,b)|0}}while(0);return}function oc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+16|0;p=q+8|0;o=q;n=(d|0)!=0;if(n){m=Vz(11836,c[4581]|0)|0;YC(a,1,141060,12,0);YC(a,2,141060,12,0);YC(a,0,141060,-12,0);f=pc(a,0,m)|0;j=jC(a)|0;i=0;g=0;while(1){if(!j)break;if(!(Q2(KB(j)|0,108006,7)|0)){k=g;g=qc(m,KB(j)|0)|0}else{k=g+1|0;g=g+f|0}c[(RC(j,141060,0)|0)+8>>2]=g;h=XA(a,j)|0;g=i;while(1){if(!h)break;c[(RC(h,141060,0)|0)+8>>2]=g;h=ZA(a,h)|0;g=g+1|0}j=kC(a,j)|0;i=g;g=k}Rz(m)|0}else f=0;m=c[e>>2]|0;c[e>>2]=m+1;rc(b,m);oF(b,97374)|0;sc(a,b,d,e);tc(a,b,e);oF(b,97345)|0;rc(b,c[e>>2]|0);if(n){c[o>>2]=f;uF(b,67016,o)}else{c[p>>2]=c[(RC(a,141060,0)|0)+8>>2];uF(b,67036,p)}vc(a,b,d,uc(a,b,d,e)|0,e);wc(a,b,d,e);oF(b,153599)|0;p=(c[e>>2]|0)+-1|0;c[e>>2]=p;rc(b,p);if(n)oF(b,111008)|0;else oF(b,75357)|0;l=q;return}function pc(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;if((yC(a)|0)!=(a|0)){e=b+1|0;c[(RC(a,141060,0)|0)+8>>2]=b;if(!(Q2(KB(a)|0,108006,7)|0)){b=KB(a)|0;Ic(d,b,c[(RC(a,141060,0)|0)+8>>2]|0);b=e}else b=e}e=OD(a)|0;while(1){if(!e)break;a=pc(e,b,d)|0;e=PD(e)|0;b=a}return b|0}function qc(a,b){a=a|0;b=b|0;a=nb[c[a>>2]&63](a,b,512)|0;if(!a)a=-1;else a=c[a+12>>2]|0;return a|0}function rc(a,b){a=a|0;b=b|0;while(1){if((b|0)<=0)break;oF(a,90574)|0;b=b+-1|0}return}function sc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;h=l;l=l+32|0;g=h+16|0;f=h+8|0;i=h;j=KB(a)|0;rc(b,c[e>>2]|0);c[i>>2]=zc(j,e)|0;uF(b,67124,i);if(d|0){oF(b,97345)|0;rc(b,c[e>>2]|0);j=(xB(a)|0)!=0;c[f>>2]=j?107652:137308;uF(b,67738,f);rc(b,c[e>>2]|0);j=(zB(a)|0)!=0;c[g>>2]=j?107652:137308;uF(b,67755,g)}l=h;return}function tc(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;n=o+8|0;m=o;i=yC(b)|0;j=c[b>>2]&3;f=LA(i,j,0)|0;a:do if(f|0){k=e+5|0;while(1){if(!f)break a;g=RA(b,f)|0;do if(g|0){h=f+8|0;if(!(a[g>>0]|0)){g=c[h>>2]|0;if((a[g>>0]|0)!=108)break;if(s2(g,108224)|0)break}oF(d,97345)|0;rc(d,c[e>>2]|0);c[m>>2]=zc(c[h>>2]|0,e)|0;uF(d,67174,m);if(a[k>>0]|0?Bc(c[h>>2]|0)|0:0){Cc(RA(b,f)|0,d,e);break}c[n>>2]=zc(RA(b,f)|0,e)|0;uF(d,67181,n)}while(0);f=LA(i,j,f)|0}}while(0);l=o;return}function uc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;f=OD(a)|0;if(f){oF(b,97345)|0;g=c[e>>2]|0;c[e>>2]=g+1;rc(b,g);g=(d|0)!=0;if(g){oF(b,67098)|0;a=0;d=f}else{oF(b,67158)|0;rc(b,c[e>>2]|0);a=0;d=f}while(1){if(!d)break;if(!a)a=1;else oF(b,97345)|0;if(g)Ac(d,b,e);else{c[h>>2]=c[(RC(d,141060,0)|0)+8>>2];uF(b,137395,h)}d=PD(d)|0}if(g)a=1;else{c[e>>2]=(c[e>>2]|0)+-1;oF(b,153599)|0;rc(b,c[e>>2]|0);oF(b,97340)|0;a=1}}else a=0;l=i;return a|0}function vc(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;g=jC(a)|0;if(!g){if((d|0)!=0&(e|0)!=0){c[f>>2]=(c[f>>2]|0)+-1;oF(b,153599)|0;rc(b,c[f>>2]|0);oF(b,97340)|0}}else{oF(b,97345)|0;h=(d|0)!=0;if(h)if(!e){e=c[f>>2]|0;c[f>>2]=e+1;rc(b,e);oF(b,67098)|0;e=0}else e=0;else{e=c[f>>2]|0;c[f>>2]=e+1;rc(b,e);oF(b,67112)|0;rc(b,c[f>>2]|0);e=0}while(1){if(!g)break;if(Q2(KB(g)|0,108006,7)|0){do if(e)if(h){oF(b,97345)|0;break}else{oF(b,100999)|0;break}else e=1;while(0);yc(g,b,d,f)}g=kC(a,g)|0}c[f>>2]=(c[f>>2]|0)+-1;oF(b,153599)|0;rc(b,c[f>>2]|0);oF(b,97340)|0}return}function wc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;f=jC(a)|0;a:do if(f|0){while(1){if(!f)break a;if(XA(a,f)|0)break;f=kC(a,f)|0}oF(b,97345)|0;i=c[e>>2]|0;c[e>>2]=i+1;rc(b,i);oF(b,67048)|0;i=(d|0)!=0;if(i)g=0;else{rc(b,c[e>>2]|0);g=0}while(1){if(!f)break;h=XA(a,f)|0;while(1){if(!h)break;do if(g)if(i){oF(b,97345)|0;break}else{oF(b,100999)|0;break}else g=1;while(0);xc(h,b,d,e);h=ZA(a,h)|0}f=kC(a,f)|0}c[e>>2]=(c[e>>2]|0)+-1;oF(b,153599)|0;rc(b,c[e>>2]|0);oF(b,97340)|0}while(0);return}function xc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;j=l;l=l+32|0;i=j+24|0;h=j+16|0;g=j+8|0;f=j;if(!d){c[i>>2]=c[(RC(a,141060,0)|0)+8>>2];uF(b,137395,i)}else{i=c[e>>2]|0;c[e>>2]=i+1;rc(b,i);oF(b,97374)|0;rc(b,c[e>>2]|0);c[f>>2]=c[(RC(a,141060,0)|0)+8>>2];uF(b,67060,f);rc(b,c[e>>2]|0);c[g>>2]=c[(RC(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0,141060,0)|0)+8>>2];uF(b,67074,g);rc(b,c[e>>2]|0);c[h>>2]=c[(RC(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0,141060,0)|0)+8>>2];uF(b,67087,h);tc(a,b,e);oF(b,153599)|0;i=(c[e>>2]|0)+-1|0;c[e>>2]=i;rc(b,i);oF(b,75357)|0}l=j;return}function yc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;i=l;l=l+32|0;h=i+16|0;g=i+8|0;f=i;if(!d){c[h>>2]=c[(RC(a,141060,0)|0)+8>>2];uF(b,137395,h)}else{h=c[e>>2]|0;c[e>>2]=h+1;rc(b,h);oF(b,97374)|0;rc(b,c[e>>2]|0);c[f>>2]=c[(RC(a,141060,0)|0)+8>>2];uF(b,67060,f);rc(b,c[e>>2]|0);c[g>>2]=zc(KB(a)|0,e)|0;uF(b,67124,g);tc(a,b,e);oF(b,153599)|0;h=(c[e>>2]|0)+-1|0;c[e>>2]=h;rc(b,h);oF(b,75357)|0}l=i;return}function zc(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;g=d+4|0;if(a[g>>0]|0)b=eQ(b)|0;if(!(c[46706]|0)){kA(186824,1024,0);e=b}else e=b;a:while(1){f=a[e>>0]|0;switch(f<<24>>24){case 0:break a;case 34:{nA(186824,67137)|0;break}case 92:{nA(186824,96616)|0;break}case 47:{nA(186824,67140)|0;break}case 8:{nA(186824,67143)|0;break}case 12:{nA(186824,67146)|0;break}case 10:{nA(186824,67149)|0;break}case 13:{nA(186824,67152)|0;break}case 9:{nA(186824,67155)|0;break}default:{d=c[46707]|0;if(d>>>0>=(c[46708]|0)>>>0){lA(186824,1)|0;d=c[46707]|0}c[46707]=d+1;a[d>>0]=f}}e=e+1|0}d=c[46707]|0;if(d>>>0>=(c[46708]|0)>>>0){lA(186824,1)|0;d=c[46707]|0}a[d>>0]=0;d=c[46706]|0;c[46707]=d;if(a[g>>0]|0)U1(b);return d|0}function Ac(a,b,c){a=a|0;b=b|0;c=c|0;oc(a,b,0,c);a=OD(a)|0;while(1){if(!a)break;oF(b,97345)|0;Ac(a,b,c);a=PD(a)|0}return}function Bc(b){b=b|0;var c=0;c=b+1|0;a:do if((a[b>>0]|0)==95){b=(a[c>>0]|0)+-100<<24>>24;switch(((b&255)>>>2|b<<6&255)<<24>>24){case 0:{if(!(s2(c,67695)|0)){b=1;break a}break}case 2:{if(!(s2(c,67701)|0)){b=1;break a}break}case 1:{if(!(s2(c,67708)|0)){b=1;break a}return (s2(c,67722)|0)==0|0}case 4:{if(!(s2(c,67715)|0)){b=1;break a}b=(s2(c,67730)|0)==0&1;break a}default:{b=0;break a}}b=0}else b=0;while(0);return b|0}function Cc(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;i=l;l=l+16|0;f=i;do if(b|0?a[b>>0]|0:0){h=DH(b)|0;if(!h){c[f>>2]=b;gA(0,67186,f)|0;break}oF(d,153599)|0;g=c[e>>2]|0;c[e>>2]=g+1;rc(d,g);oF(d,67213)|0;g=h+8|0;b=0;f=0;while(1){if((f|0)>=(c[h>>2]|0))break;if(!b)b=1;else oF(d,97345)|0;Dc((c[g>>2]|0)+(f*80|0)|0,d,e);f=f+1|0}c[e>>2]=(c[e>>2]|0)+-1;oF(d,153599)|0;rc(d,c[e>>2]|0);oF(d,97340)|0;EH(h)}while(0);l=i;return}function Dc(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0.0,C=0.0,D=0.0;A=l;l=l+240|0;y=A+224|0;x=A+216|0;w=A+208|0;v=A+200|0;u=A+192|0;t=A+184|0;s=A+176|0;r=A+152|0;q=A+136|0;p=A+128|0;o=A+112|0;n=A+104|0;m=A+96|0;k=A+88|0;j=A+72|0;i=A+48|0;z=A+40|0;g=A+8|0;f=A;e=c[d>>2]|0;c[d>>2]=e+1;rc(b,e);oF(b,97374)|0;rc(b,c[d>>2]|0);e=c[a>>2]|0;a:do switch(e|0){case 1:case 0:{c[f>>2]=(e|0)==0?69:101;uF(b,67216,f);rc(b,c[d>>2]|0);D=+h[a+16>>3];C=+h[a+24>>3];B=+h[a+32>>3];h[g>>3]=+h[a+8>>3];h[g+8>>3]=D;h[g+16>>3]=C;h[g+24>>3]=B;uF(b,67229,g);break}case 3:case 2:{c[z>>2]=(e|0)==2?80:112;uF(b,67216,z);rc(b,c[d>>2]|0);Ec(b,a+8|0);break}case 5:case 4:{c[i>>2]=(e|0)==4?66:98;uF(b,67216,i);rc(b,c[d>>2]|0);Ec(b,a+8|0);break}case 6:{uF(b,67264,A+56|0);rc(b,c[d>>2]|0);Ec(b,a+8|0);break}case 7:{uF(b,67276,A+64|0);rc(b,c[d>>2]|0);D=+h[a+16>>3];h[j>>3]=+h[a+8>>3];h[j+8>>3]=D;uF(b,67288,j);rc(b,c[d>>2]|0);z=c[a+24>>2]|0;c[k>>2]=(z|0)==0?108:(z|0)==1?99:114;uF(b,67310,k);rc(b,c[d>>2]|0);h[m>>3]=+h[a+32>>3];uF(b,67326,m);rc(b,c[d>>2]|0);c[n>>2]=zc(c[a+40>>2]|0,d)|0;uF(b,67343,n);break}case 9:case 8:{c[o>>2]=(e|0)==8?67:99;uF(b,67216,o);rc(b,c[d>>2]|0);uF(b,67357,A+120|0);rc(b,c[d>>2]|0);c[p>>2]=zc(c[a+8>>2]|0,d)|0;uF(b,67374,p);break}case 13:case 14:{c[q>>2]=(e|0)==13?67:99;uF(b,67216,q);rc(b,c[d>>2]|0);switch(c[a+8>>2]|0){case 0:{uF(b,67357,A+144|0);rc(b,c[d>>2]|0);c[r>>2]=zc(c[a+16>>2]|0,d)|0;uF(b,67374,r);break a}case 1:{uF(b,67389,A+160|0);rc(b,c[d>>2]|0);Fc(b,a+16|0,d);break a}default:{uF(b,67408,A+168|0);rc(b,c[d>>2]|0);Gc(b,a+16|0,d);break a}}}case 10:{uF(b,67427,s);rc(b,c[d>>2]|0);h[t>>3]=+h[a+8>>3];uF(b,67439,t);rc(b,c[d>>2]|0);c[u>>2]=zc(c[a+16>>2]|0,d)|0;uF(b,67455,u);break}case 11:{uF(b,67469,v);rc(b,c[d>>2]|0);c[w>>2]=zc(c[a+8>>2]|0,d)|0;uF(b,67481,w);break}case 15:{uF(b,67496,x);rc(b,c[d>>2]|0);c[y>>2]=c[a+8>>2];uF(b,67508,y);break}default:{}}while(0);z=(c[d>>2]|0)+-1|0;c[d>>2]=z;rc(b,z);oF(b,75357)|0;l=A;return}function Ec(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0.0;j=l;l=l+48|0;i=j+32|0;g=j+16|0;f=j+8|0;e=c[b>>2]|0;d=c[b+4>>2]|0;uF(a,67669,j);b=0;while(1){if((b|0)>=(e|0))break;if((b|0)>0)uF(a,100999,f);k=+h[d+(b*24|0)+8>>3];h[g>>3]=+h[d+(b*24|0)>>3];h[g+8>>3]=k;uF(a,67681,g);b=b+1|0}uF(a,67622,i);l=j;return}function Fc(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0;e=l;l=l+32|0;f=e+16|0;i=e;rc(a,c[d>>2]|0);g=+h[b+8>>3];h[i>>3]=+h[b>>3];h[i+8>>3]=g;uF(a,67625,i);rc(a,c[d>>2]|0);g=+h[b+24>>3];h[f>>3]=+h[b+16>>3];h[f+8>>3]=g;uF(a,67647,f);rc(a,c[d>>2]|0);Hc(a,c[b+32>>2]|0,c[b+36>>2]|0,d);l=e;return}function Gc(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0;e=l;l=l+48|0;f=e+24|0;j=e;rc(a,c[d>>2]|0);g=+h[b+8>>3];i=+h[b+16>>3];h[j>>3]=+h[b>>3];h[j+8>>3]=g;h[j+16>>3]=i;uF(a,67524,j);rc(a,c[d>>2]|0);i=+h[b+32>>3];g=+h[b+40>>3];h[f>>3]=+h[b+24>>3];h[f+8>>3]=i;h[f+16>>3]=g;uF(a,67552,f);rc(a,c[d>>2]|0);Hc(a,c[b+48>>2]|0,c[b+52>>2]|0,d);l=e;return}function Hc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,i=0,j=0,k=0,m=0,n=0,o=0.0;m=l;l=l+48|0;k=m+32|0;j=m+16|0;i=m+8|0;uF(a,67580,m);f=0;while(1){if((f|0)>=(b|0))break;if((f|0)>0)uF(a,100999,i);o=+g[d+(f<<3)>>2];n=zc(c[d+(f<<3)+4>>2]|0,e)|0;h[j>>3]=o;c[j+8>>2]=n;uF(a,67591,j);f=f+1|0}uF(a,67622,k);l=m;return}function Ic(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;g=l;l=l+16|0;f=g;e=nb[c[a>>2]&63](a,b,512)|0;if(e){if((c[e+12>>2]|0)!=(d|0)){c[f>>2]=b;gA(0,67768,f)|0}}else{f=V1(1,16)|0;c[f+8>>2]=D4(b)|0;c[f+12>>2]=d;nb[c[a>>2]&63](a,f,1)|0}l=g;return}function Jc(a,b,d){a=a|0;b=b|0;d=d|0;U1(c[b+8>>2]|0);U1(b);return}function Kc(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;b=l;l=l+32|0;d=b+24|0;e=b+16|0;g=b;f=c[a+16>>2]|0;oF(a,68623)|0;j=c[c[a+12>>2]>>2]|0;i=c[j+4>>2]|0;h=c[j+8>>2]|0;c[g>>2]=c[j>>2];c[g+4>>2]=i;c[g+8>>2]=h;uF(a,68633,g);c[e>>2]=KB(c[f+8>>2]|0)|0;uF(a,68668,e);c[d>>2]=O(c[a+168>>2]|0,c[a+164>>2]|0)|0;uF(a,68681,d);oF(a,68694)|0;oF(a,68704)|0;oF(a,68712)|0;oF(a,68720)|0;oF(a,68728)|0;oF(a,68736)|0;oF(a,68744)|0;oF(a,68748)|0;oF(a,68753)|0;l=b;return}function Lc(a){a=a|0;oF(a,68604)|0;return}function Mc(a){a=a|0;c[46710]=2;return}function Nc(a){a=a|0;c[46710]=1;return}function Oc(a){a=a|0;c[46710]=2;return}function Pc(a){a=a|0;c[46710]=0;return}function Qc(a){a=a|0;c[46710]=2;return}function Rc(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0,k=0.0,m=0,n=0,o=0,p=0,q=0.0,r=0.0,s=0;p=l;l=l+80|0;o=p;m=c[(c[b+16>>2]|0)+16>>2]|0;n=c[46710]|0;f=c[e+4>>2]|0;g=+h[b+352>>3]*+h[f+16>>3];i=c[b+360>>2]|0?1.5707963267948966:0.0;j=c[e>>2]|0;k=g*.3333333333333333*+((l2(j)|0)>>>0);f=c[f+8>>2]|0;if(!f)f=-1;else f=c[f+20>>2]|0;s=a[e+48>>0]|0;r=+h[d>>3];q=+h[d+8>>3];e=$c(j)|0;c[o>>2]=4;c[o+4>>2]=s<<24>>24==108?0:s<<24>>24==114?2:1;c[o+8>>2]=m;c[o+12>>2]=n;c[o+16>>2]=0;c[o+20>>2]=f;h[o+24>>3]=g;h[o+32>>3]=i;c[o+40>>2]=6;h[o+48>>3]=g;h[o+56>>3]=k;c[o+64>>2]=~~(r+(r>=0.0?.5:-.5));c[o+68>>2]=~~(q+(q+-72.0>=0.0?-71.5:-72.5));c[o+72>>2]=e;uF(b,68549,o);l=p;return}function Sc(b,e){b=b|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;n=l;l=l+32|0;j=n;i=n+20|0;m=e+32|0;a:do switch(c[m>>2]|0){case 5:{f=0;while(1){g=c[12724+(f<<2)>>2]|0;if((f|0)==8)break a;h=c[e>>2]|0;if((a[g>>0]|0)==(a[h>>0]|0)?(s2(g,h)|0)==0:0){k=10;break a}f=f+1|0}}case 1:{g=e+1|0;h=e+2|0;f=(_c(i,d[e>>0]|0,d[g>>0]|0,d[h>>0]|0)|0)+32|0;if(!(c[i>>2]|0))k=10;else{o=d[e>>0]|0;i=d[g>>0]|0;k=d[h>>0]|0;c[j>>2]=0;c[j+4>>2]=f;c[j+8>>2]=o;c[j+12>>2]=i;c[j+16>>2]=k;uF(b,68492,j);k=10}break}default:qa(138394,67889,163,67920)}while(0);if((k|0)==10)c[e>>2]=f;c[m>>2]=6;l=n;return}function Tc(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0,k=0,m=0.0,n=0.0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0;e=l;l=l+112|0;f=e+8|0;u=e+104|0;t=e;k=c[a+16>>2]|0;s=~~+h[k+152>>3];r=c[k+16>>2]|0;q=c[k+56>>2]|0;p=c[46710]|0;Yc(k,u,t);n=+h[b>>3];k=~~(n+(n>=0.0?.5:-.5));m=+h[b+8>>3];j=~~(m+(m>=0.0?.5:-.5));i=+h[b+16>>3];n=i-n;g=+h[b+24>>3];m=g-m;b=c[u>>2]|0;o=+h[t>>3];c[f>>2]=1;c[f+4>>2]=1;c[f+8>>2]=b;c[f+12>>2]=s;c[f+16>>2]=r;c[f+20>>2]=q;c[f+24>>2]=p;c[f+28>>2]=0;c[f+32>>2]=d|0?20:-1;h[f+40>>3]=o;c[f+48>>2]=0;h[f+56>>3]=0.0;c[f+64>>2]=k;c[f+68>>2]=j;c[f+72>>2]=~~(n+(n>=0.0?.5:-.5));c[f+76>>2]=~~(m+(m>=0.0?.5:-.5));c[f+80>>2]=k;c[f+84>>2]=j;c[f+88>>2]=~~(i+(i>=0.0?.5:-.5));c[f+92>>2]=~~(g+(g>=0.0?.5:-.5));uF(a,68427,f);l=e;return}function Uc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;f=l;l=l+96|0;g=f+8|0;o=f+80|0;p=f;q=c[a+16>>2]|0;n=~~+h[q+152>>3];m=c[q+16>>2]|0;k=c[q+56>>2]|0;j=c[46710]|0;Yc(q,o,p);o=c[o>>2]|0;i=+h[p>>3];c[g>>2]=2;c[g+4>>2]=3;c[g+8>>2]=o;c[g+12>>2]=n;c[g+16>>2]=m;c[g+20>>2]=k;c[g+24>>2]=j;c[g+28>>2]=0;c[g+32>>2]=e|0?20:-1;h[g+40>>3]=i;c[g+48>>2]=0;c[g+52>>2]=0;c[g+56>>2]=0;c[g+60>>2]=0;c[g+64>>2]=0;c[g+68>>2]=d+1;uF(a,68331,g);Zc(a,b,d,1);l=f;return}function Vc(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0.0,E=0.0,F=0;C=l;l=l+192|0;B=C+176|0;z=C+168|0;A=C+104|0;y=C+96|0;i=C+88|0;u=C+180|0;v=C+80|0;w=C+16|0;x=C;f=a+16|0;e=c[f>>2]|0;q=~~+h[e+152>>3];r=c[e+16>>2]|0;s=c[46710]|0;if((d|0)<=3)qa(67882,67889,360,67909);t=T1((d*140|0)+140|0)|0;Yc(e,u,v);if(!g){o=4;j=0;k=-1}else{o=5;j=c[(c[f>>2]|0)+56>>2]|0;k=20}E=+h[b>>3];n=w+48|0;h[n>>3]=E;D=+h[b+8>>3];h[w+56>>3]=D;c[i>>2]=~~(E+(E>=0.0?.5:-.5));c[i+4>>2]=~~(D+(D>=0.0?.5:-.5));m=x+8|0;e=t+(m4(t,81099,i)|0)|0;p=1;g=0;while(1){i=g+3|0;if((i|0)>=(d|0))break;c[w>>2]=c[n>>2];c[w+4>>2]=c[n+4>>2];c[w+8>>2]=c[n+8>>2];c[w+12>>2]=c[n+12>>2];f=1;while(1){if((f|0)==4){f=1;break}F=f+g|0;h[w+(f<<4)>>3]=+h[b+(F<<4)>>3];h[w+(f<<4)+8>>3]=+h[b+(F<<4)+8>>3];f=f+1|0}while(1){if((f|0)==7)break;kP(x,w,3,+(f|0)*.16666666666666666,0,0);D=+h[x>>3];E=+h[m>>3];c[y>>2]=~~(D+(D>=0.0?.5:-.5));c[y+4>>2]=~~(E+(E>=0.0?.5:-.5));f=f+1|0;e=e+(m4(e,81099,y)|0)|0}p=p+6|0;g=i}f=c[u>>2]|0;E=+h[v>>3];c[A>>2]=3;c[A+4>>2]=o;c[A+8>>2]=f;c[A+12>>2]=q;c[A+16>>2]=r;c[A+20>>2]=j;c[A+24>>2]=s;c[A+28>>2]=0;c[A+32>>2]=k;h[A+40>>3]=E;c[A+48>>2]=0;c[A+52>>2]=0;c[A+56>>2]=0;c[A+60>>2]=p;uF(a,68382,A);c[z>>2]=t;uF(a,81106,z);U1(t);f=p+-1|0;e=0;while(1){if((e|0)>=(p|0))break;c[B>>2]=((e|0)%(f|0)|0|0)!=0&1;uF(a,81111,B);e=e+1|0}oF(a,153599)|0;l=C;return}function Wc(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0;e=l;l=l+96|0;f=e+8|0;m=e+80|0;n=e;o=c[a+16>>2]|0;k=~~+h[o+152>>3];j=c[o+16>>2]|0;i=c[46710]|0;Yc(o,m,n);m=c[m>>2]|0;g=+h[n>>3];c[f>>2]=2;c[f+4>>2]=1;c[f+8>>2]=m;c[f+12>>2]=k;c[f+16>>2]=j;c[f+20>>2]=0;c[f+24>>2]=i;c[f+28>>2]=0;c[f+32>>2]=0;h[f+40>>3]=g;c[f+48>>2]=0;c[f+52>>2]=0;c[f+56>>2]=0;c[f+60>>2]=0;c[f+64>>2]=0;c[f+68>>2]=d;uF(a,68331,f);Zc(a,b,d,0);l=e;return}function Xc(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=b;uF(a,68325,e);l=d;return}function Yc(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0;switch(c[a+144>>2]|0){case 1:{e=10.0;a=1;break}case 2:{e=10.0;a=2;break}default:{e=0.0;a=0}}c[b>>2]=a;h[d>>3]=e;return}function Zc(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0,m=0.0;j=l;l=l+16|0;i=j+8|0;g=j;f=0;while(1){if((f|0)>=(d|0))break;m=+h[b+(f<<4)>>3];k=+h[b+(f<<4)+8>>3];c[g>>2]=~~(m+(m>=0.0?.5:-.5));c[g+4>>2]=~~(k+(k>=0.0?.5:-.5));uF(a,81099,g);f=f+1|0}if(e|0){k=+h[b>>3];m=+h[b+8>>3];c[i>>2]=~~(k+(k>=0.0?.5:-.5));c[i+4>>2]=~~(m+(m>=0.0?.5:-.5));uF(a,81099,i)}oF(a,153599)|0;l=j;return}function _c(a,d,e,f){a=a|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;c[a>>2]=0;k=c[46711]|0;h=195075;g=0;i=-1;while(1){if((g|0)>=(k|0)){l=6;break}m=(b[190680+(g<<1)>>1]|0)-d|0;n=(b[191192+(g<<1)>>1]|0)-e|0;j=(b[191704+(g<<1)>>1]|0)-f|0;j=(O(n,n)|0)+(O(m,m)|0)+(O(j,j)|0)|0;if((j|0)<(h|0))if(!j)break;else{h=j;i=g}g=g+1|0}if((l|0)==6){c[46711]=k+1;if((k|0)==256)g=i;else{b[190680+(g<<1)>>1]=d;b[191192+(g<<1)>>1]=e;b[191704+(g<<1)>>1]=f;c[a>>2]=1}}return g|0}function $c(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;d=c[46712]|0;if(!d){c[46713]=64;d=T1(64)|0;c[46712]=d;e=0}else e=0;while(1){g=b+1|0;f=a[b>>0]|0;if(!(f<<24>>24))break;b=c[46713]|0;if((e|0)>(b+-8|0)){d=b<<1;c[46713]=d;d=W1(c[46712]|0,d)|0;c[46712]=d;d=d+e|0}if(f<<24>>24<=-1){a[d>>0]=92;c[h>>2]=f&255;m4(d+1|0,81224,h)|0;b=g;d=d+4|0;e=e+4|0;continue}if(f<<24>>24==92){a[d>>0]=92;e=e+1|0;d=d+1|0}a[d>>0]=f;b=g;d=d+1|0;e=e+1|0}a[d>>0]=0;l=i;return c[46712]|0}function ad(b){b=b|0;var d=0,e=0,f=0;e=c[b+16>>2]|0;switch(c[b+64>>2]|0){case 0:{oF(b,68220)|0;d=e+208|0;e=c[d>>2]|0;if(e|0?a[e>>0]|0:0){oF(b,68234)|0;oF(b,FK(c[d>>2]|0)|0)|0;oF(b,153599)|0}break}case 1:{d=e+208|0;f=c[d>>2]|0;if(f|0?a[f>>0]|0:0){oF(b,68234)|0;oF(b,FK(c[d>>2]|0)|0)|0;oF(b,102604)|0;oF(b,FK(KB(c[e+8>>2]|0)|0)|0)|0;oF(b,153599)|0}break}case 3:{f=FK(KB(c[e+8>>2]|0)|0)|0;oF(b,68243)|0;oF(b,f)|0;oF(b,68253)|0;oF(b,f)|0;oF(b,68208)|0;break}default:{}}return}function bd(a){a=a|0;var b=0;b=c[a+16>>2]|0;switch(c[a+64>>2]|0){case 2:{dd(a,c[b+264>>2]|0,c[b+272>>2]|0,c[b+268>>2]|0,c[b+208>>2]|0,c[b+228>>2]|0,c[b+244>>2]|0,c[b+212>>2]|0);break}case 3:{dd(a,c[b+264>>2]|0,c[b+272>>2]|0,c[b+268>>2]|0,c[b+208>>2]|0,c[b+228>>2]|0,c[b+244>>2]|0,c[b+212>>2]|0);oF(a,68212)|0;break}default:{}}return}function cd(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;g=c[a+16>>2]|0;dd(a,c[g+264>>2]|0,c[g+272>>2]|0,c[g+268>>2]|0,b,d,e,f);return}function dd(b,d,e,f,g,i,j,k){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;var m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0.0;y=l;l=l+128|0;x=y+120|0;u=y+112|0;t=y+96|0;s=y+80|0;r=y+56|0;w=y+48|0;q=y+40|0;v=y+24|0;p=y;a:do if((e|0)!=0&(f|0)!=0){m=f+10|0;if((c[46714]|0)<(f|0)){c[46714]=m;o=W1(c[46715]|0,m<<3)|0;c[46715]=o}else o=c[46715]|0;m=0;while(1){if((m|0)>=(f|0))break;z=+h[e+(m<<4)>>3];c[o+(m<<3)>>2]=~~(z+(z>=0.0?.5:-.5));z=+h[e+(m<<4)+8>>3];c[o+(m<<3)+4>>2]=~~(z+(z>=0.0?.5:-.5));m=m+1|0}n=b+64|0;m=c[n>>2]|0;e=(g|0)!=0;if(e&(m|0)==0){if(!(a[g>>0]|0))break;switch(d|0){case 0:{v=c[o>>2]|0;w=c[o+12>>2]|0;x=c[o+8>>2]|0;f=c[o+4>>2]|0;c[p>>2]=g;c[p+4>>2]=v;c[p+8>>2]=w;c[p+12>>2]=x;c[p+16>>2]=f;uF(b,67954,p);break a}case 1:{w=c[o>>2]|0;x=c[o+4>>2]|0;f=(c[o+8>>2]|0)-w|0;c[v>>2]=g;c[v+4>>2]=w;c[v+8>>2]=x;c[v+12>>2]=f;uF(b,67975,v);break a}case 2:{c[q>>2]=g;uF(b,67995,q);m=0;while(1){if((m|0)>=(f|0))break;v=c[46715]|0;x=c[v+(m<<3)+4>>2]|0;c[w>>2]=c[v+(m<<3)>>2];c[w+4>>2]=x;uF(b,68003,w);m=m+1|0}oF(b,153599)|0;break a}default:qa(138394,68010,63,68030)}}if(e&(m|0)==1){if(!(a[g>>0]|0))break;if(!d){w=c[o+12>>2]|0;x=c[o+8>>2]|0;f=c[o+4>>2]|0;c[r>>2]=c[o>>2];c[r+4>>2]=w;c[r+8>>2]=x;c[r+12>>2]=f;c[r+16>>2]=g;c[r+20>>2]=i;uF(b,68047,r);break}else qa(138394,68010,75,68030)}if((m&-2|0)==2){switch(d|0){case 1:{oF(b,68080)|0;break}case 0:{oF(b,68101)|0;break}case 2:{oF(b,68120)|0;break}default:qa(138394,68010,91,68030)}if(k|0?a[k>>0]|0:0){oF(b,73600)|0;oF(b,IK(k)|0)|0;oF(b,98744)|0}if(e?a[g>>0]|0:0){oF(b,68139)|0;oF(b,IK(g)|0)|0;oF(b,98744)|0}if(j|0?a[j>>0]|0:0){oF(b,74559)|0;oF(b,FK(j)|0)|0;oF(b,98744)|0}if(i|0?a[i>>0]|0:0){oF(b,68147)|0;oF(b,FK(i)|0)|0;oF(b,98744)|0}oF(b,68156)|0;oF(b,68164)|0;b:do switch(d|0){case 1:{f=c[46715]|0;w=c[f>>2]|0;x=c[f+4>>2]|0;f=(c[f+8>>2]|0)-w|0;c[s>>2]=w;c[s+4>>2]=x;c[s+8>>2]=f;uF(b,68174,s);break}case 0:{v=c[46715]|0;w=c[v+12>>2]|0;x=c[v+8>>2]|0;f=c[v+4>>2]|0;c[t>>2]=c[v>>2];c[t+4>>2]=w;c[t+8>>2]=x;c[t+12>>2]=f;uF(b,68183,t);break}case 2:{w=c[46715]|0;m=c[w+4>>2]|0;c[u>>2]=c[w>>2];c[u+4>>2]=m;uF(b,68195,u);m=1;while(1){if((m|0)>=(f|0))break b;v=c[46715]|0;w=c[v+(m<<3)+4>>2]|0;c[x>>2]=c[v+(m<<3)>>2];c[x+4>>2]=w;uF(b,68201,x);m=m+1|0}}default:{}}while(0);if((c[n>>2]|0)==3){oF(b,73424)|0;break}else{oF(b,68208)|0;break}}}while(0);l=y;return}function ed(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;b=l;l=l+32|0;d=b+24|0;e=b+16|0;g=b;f=c[a+16>>2]|0;oF(a,68623)|0;j=c[c[a+12>>2]>>2]|0;i=c[j+4>>2]|0;h=c[j+8>>2]|0;c[g>>2]=c[j>>2];c[g+4>>2]=i;c[g+8>>2]=h;uF(a,68633,g);c[e>>2]=KB(c[f+8>>2]|0)|0;uF(a,68668,e);c[d>>2]=O(c[a+168>>2]|0,c[a+164>>2]|0)|0;uF(a,68681,d);oF(a,68694)|0;oF(a,68704)|0;oF(a,68712)|0;oF(a,68720)|0;oF(a,68728)|0;oF(a,68736)|0;oF(a,68744)|0;oF(a,68748)|0;oF(a,68753)|0;l=b;return}function fd(a){a=a|0;oF(a,68604)|0;return}function gd(a){a=a|0;c[46716]=2;return}function hd(a){a=a|0;c[46716]=1;return}function id(a){a=a|0;c[46716]=2;return}function jd(a){a=a|0;c[46716]=0;return}function kd(a){a=a|0;c[46716]=2;return}function ld(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0,k=0,m=0,n=0,o=0.0,p=0.0,q=0;n=l;l=l+80|0;m=n;j=c[(c[b+16>>2]|0)+16>>2]|0;k=c[46716]|0;f=c[e+4>>2]|0;g=+h[b+352>>3]*+h[f+16>>3];i=c[b+360>>2]|0?1.5707963267948966:0.0;f=c[f+8>>2]|0;if(!f)f=-1;else f=c[f+20>>2]|0;q=a[e+48>>0]|0;p=+h[d>>3];o=+h[d+8>>3];e=vd(c[e>>2]|0)|0;c[m>>2]=4;c[m+4>>2]=q<<24>>24==108?0:q<<24>>24==114?2:1;c[m+8>>2]=j;c[m+12>>2]=k;c[m+16>>2]=0;c[m+20>>2]=f;h[m+24>>3]=g;h[m+32>>3]=i;c[m+40>>2]=4;h[m+48>>3]=0.0;h[m+56>>3]=0.0;c[m+64>>2]=~~(p+(p>=0.0?.5:-.5));c[m+68>>2]=~~(o+(o>=0.0?.5:-.5));c[m+72>>2]=e;uF(b,68549,m);l=n;return}function md(b,e){b=b|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;n=l;l=l+32|0;j=n;i=n+20|0;m=e+32|0;a:do switch(c[m>>2]|0){case 5:{f=0;while(1){g=c[12724+(f<<2)>>2]|0;if((f|0)==8)break a;h=c[e>>2]|0;if((a[g>>0]|0)==(a[h>>0]|0)?(s2(g,h)|0)==0:0){k=10;break a}f=f+1|0}}case 1:{g=e+1|0;h=e+2|0;f=(ud(i,d[e>>0]|0,d[g>>0]|0,d[h>>0]|0)|0)+32|0;if(!(c[i>>2]|0))k=10;else{o=d[e>>0]|0;i=d[g>>0]|0;k=d[h>>0]|0;c[j>>2]=0;c[j+4>>2]=f;c[j+8>>2]=o;c[j+12>>2]=i;c[j+16>>2]=k;uF(b,68492,j);k=10}break}case 0:{f=0;k=10;break}default:qa(138394,68513,168,68532)}while(0);if((k|0)==10)c[e>>2]=f;c[m>>2]=6;l=n;return}function nd(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0,k=0,m=0.0,n=0.0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0;e=l;l=l+112|0;f=e+8|0;u=e+104|0;t=e;k=c[a+16>>2]|0;s=~~+h[k+152>>3];r=c[k+16>>2]|0;q=c[k+56>>2]|0;p=c[46716]|0;sd(k,u,t);n=+h[b>>3];k=~~(n+(n>=0.0?.5:-.5));m=+h[b+8>>3];j=~~(m+(m>=0.0?.5:-.5));i=+h[b+16>>3];n=i-n;g=+h[b+24>>3];m=g-m;b=c[u>>2]|0;o=+h[t>>3];c[f>>2]=1;c[f+4>>2]=1;c[f+8>>2]=b;c[f+12>>2]=s;c[f+16>>2]=r;c[f+20>>2]=q;c[f+24>>2]=p;c[f+28>>2]=0;c[f+32>>2]=d|0?20:-1;h[f+40>>3]=o;c[f+48>>2]=0;h[f+56>>3]=0.0;c[f+64>>2]=k;c[f+68>>2]=j;c[f+72>>2]=~~(n+(n>=0.0?.5:-.5));c[f+76>>2]=~~(m+(m>=0.0?.5:-.5));c[f+80>>2]=k;c[f+84>>2]=j;c[f+88>>2]=~~(i+(i>=0.0?.5:-.5));c[f+92>>2]=~~(g+(g>=0.0?.5:-.5));uF(a,68427,f);l=e;return}function od(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;f=l;l=l+96|0;g=f+8|0;o=f+80|0;p=f;q=c[a+16>>2]|0;n=~~+h[q+152>>3];m=c[q+16>>2]|0;k=c[q+56>>2]|0;j=c[46716]|0;sd(q,o,p);o=c[o>>2]|0;i=+h[p>>3];c[g>>2]=2;c[g+4>>2]=3;c[g+8>>2]=o;c[g+12>>2]=n;c[g+16>>2]=m;c[g+20>>2]=k;c[g+24>>2]=j;c[g+28>>2]=0;c[g+32>>2]=e|0?20:-1;h[g+40>>3]=i;c[g+48>>2]=0;c[g+52>>2]=0;c[g+56>>2]=0;c[g+60>>2]=0;c[g+64>>2]=0;c[g+68>>2]=d+1;uF(a,68331,g);td(a,b,d,1);l=f;return}function pd(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0.0,E=0.0,F=0;C=l;l=l+192|0;B=C+176|0;z=C+168|0;A=C+104|0;y=C+96|0;f=C+88|0;u=C+180|0;v=C+80|0;w=C+16|0;x=C;e=a+16|0;p=c[e>>2]|0;q=~~+h[p+152>>3];r=c[p+16>>2]|0;s=c[46716]|0;t=T1((d*140|0)+140|0)|0;sd(p,u,v);if(!g){o=4;j=0;k=-1}else{o=5;j=c[(c[e>>2]|0)+56>>2]|0;k=20}E=+h[b>>3];n=w+48|0;h[n>>3]=E;D=+h[b+8>>3];h[w+56>>3]=D;c[f>>2]=~~(E+(E>=0.0?.5:-.5));c[f+4>>2]=~~(D+(D>=0.0?.5:-.5));m=x+8|0;e=t+(m4(t,81099,f)|0)|0;p=1;g=0;while(1){i=g+3|0;if((i|0)>=(d|0))break;c[w>>2]=c[n>>2];c[w+4>>2]=c[n+4>>2];c[w+8>>2]=c[n+8>>2];c[w+12>>2]=c[n+12>>2];f=1;while(1){if((f|0)==4){f=1;break}F=f+g|0;h[w+(f<<4)>>3]=+h[b+(F<<4)>>3];h[w+(f<<4)+8>>3]=+h[b+(F<<4)+8>>3];f=f+1|0}while(1){if((f|0)==7)break;kP(x,w,3,+(f|0)*.16666666666666666,0,0);D=+h[x>>3];E=+h[m>>3];c[y>>2]=~~(D+(D>=0.0?.5:-.5));c[y+4>>2]=~~(E+(E>=0.0?.5:-.5));f=f+1|0;e=e+(m4(e,81099,y)|0)|0}p=p+6|0;g=i}f=c[u>>2]|0;E=+h[v>>3];c[A>>2]=3;c[A+4>>2]=o;c[A+8>>2]=f;c[A+12>>2]=q;c[A+16>>2]=r;c[A+20>>2]=j;c[A+24>>2]=s;c[A+28>>2]=0;c[A+32>>2]=k;h[A+40>>3]=E;c[A+48>>2]=0;c[A+52>>2]=0;c[A+56>>2]=0;c[A+60>>2]=p;uF(a,68382,A);c[z>>2]=t;uF(a,81106,z);U1(t);f=p+-1|0;e=0;while(1){if((e|0)>=(p|0))break;c[B>>2]=((e|0)%(f|0)|0|0)!=0&1;uF(a,81111,B);e=e+1|0}oF(a,153599)|0;l=C;return}function qd(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0;e=l;l=l+96|0;f=e+8|0;m=e+80|0;n=e;o=c[a+16>>2]|0;k=~~+h[o+152>>3];j=c[o+16>>2]|0;i=c[46716]|0;sd(o,m,n);m=c[m>>2]|0;g=+h[n>>3];c[f>>2]=2;c[f+4>>2]=1;c[f+8>>2]=m;c[f+12>>2]=k;c[f+16>>2]=j;c[f+20>>2]=0;c[f+24>>2]=i;c[f+28>>2]=0;c[f+32>>2]=0;h[f+40>>3]=g;c[f+48>>2]=0;c[f+52>>2]=0;c[f+56>>2]=0;c[f+60>>2]=0;c[f+64>>2]=0;c[f+68>>2]=d;uF(a,68331,f);td(a,b,d,0);l=e;return}function rd(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=b;uF(a,68325,e);l=d;return}function sd(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0;switch(c[a+144>>2]|0){case 1:{e=10.0;a=1;break}case 2:{e=10.0;a=2;break}default:{e=0.0;a=0}}c[b>>2]=a;h[d>>3]=e;return}function td(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0,m=0.0;j=l;l=l+16|0;i=j+8|0;g=j;f=0;while(1){if((f|0)>=(d|0))break;m=+h[b+(f<<4)>>3];k=+h[b+(f<<4)+8>>3];c[g>>2]=~~(m+(m>=0.0?.5:-.5));c[g+4>>2]=~~(k+(k>=0.0?.5:-.5));uF(a,81099,g);f=f+1|0}if(e|0){k=+h[b>>3];m=+h[b+8>>3];c[i>>2]=~~(k+(k>=0.0?.5:-.5));c[i+4>>2]=~~(m+(m>=0.0?.5:-.5));uF(a,81099,i)}oF(a,153599)|0;l=j;return}function ud(a,d,e,f){a=a|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;c[a>>2]=0;k=c[46717]|0;h=195075;g=0;i=-1;while(1){if((g|0)>=(k|0)){l=6;break}m=(b[192216+(g<<1)>>1]|0)-d|0;n=(b[192728+(g<<1)>>1]|0)-e|0;j=(b[193240+(g<<1)>>1]|0)-f|0;j=(O(n,n)|0)+(O(m,m)|0)+(O(j,j)|0)|0;if((j|0)<(h|0))if(!j)break;else{h=j;i=g}g=g+1|0}if((l|0)==6){c[46717]=k+1;if((k|0)==256)g=i;else{b[192216+(g<<1)>>1]=d;b[192728+(g<<1)>>1]=e;b[193240+(g<<1)>>1]=f;c[a>>2]=1}}return g|0}function vd(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;d=c[46718]|0;if(!d){c[46719]=64;d=T1(64)|0;c[46718]=d;e=0}else e=0;while(1){g=b+1|0;f=a[b>>0]|0;if(!(f<<24>>24))break;b=c[46719]|0;if((e|0)>(b+-8|0)){d=b<<1;c[46719]=d;d=W1(c[46718]|0,d)|0;c[46718]=d;d=d+e|0}if(f<<24>>24<=-1){a[d>>0]=92;c[h>>2]=f&255;m4(d+1|0,81224,h)|0;b=g;d=d+4|0;e=e+4|0;continue}if(f<<24>>24==92){a[d>>0]=92;e=e+1|0;d=d+1|0}a[d>>0]=f;b=g;d=d+1|0;e=e+1|0}a[d>>0]=0;l=i;return c[46718]|0}function wd(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;d=l;l=l+16|0;b=d;oF(a,73332)|0;if((c[a+64>>2]|0)==2)oF(a,73347)|0;else oF(a,153599)|0;g=c[c[a+12>>2]>>2]|0;f=c[g+4>>2]|0;e=c[g+8>>2]|0;c[b>>2]=c[g>>2];c[b+4>>2]=f;c[b+8>>2]=e;uF(a,73358,b);l=d;return}function xd(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;g=l;l=l+32|0;f=g+8|0;e=g;oF(a,73286)|0;d=a+64|0;b=a+12|0;if((c[d>>2]|0)!=2){c[e>>2]=c[(c[b>>2]|0)+28>>2];uF(a,73297,e)}if((c[(c[b>>2]|0)+20>>2]|0)==0?(c[d>>2]|0)!=2:0){b=c[a+476>>2]|0;d=c[a+480>>2]|0;e=c[a+484>>2]|0;c[f>>2]=c[a+472>>2];c[f+4>>2]=b;c[f+8>>2]=d;c[f+12>>2]=e;uF(a,69666,f)}oF(a,73312)|0;oF(a,73325)|0;l=g;return}function yd(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;k=l;l=l+32|0;j=k+24|0;f=k+8|0;i=k;g=c[b+16>>2]|0;a[193768]=0;e=b+12|0;h=g+8|0;if(!(c[(c[e>>2]|0)+28>>2]|0)){c[i>>2]=KB(c[h>>2]|0)|0;uF(b,69597,i);d=b+64|0;if((c[d>>2]|0)==2)oF(b,69630)|0;else oF(b,69612)|0;do if(!(c[(c[e>>2]|0)+20>>2]|0))if((c[d>>2]|0)==2){n=c[b+460>>2]|0;m=c[b+464>>2]|0;d=c[b+468>>2]|0;c[f>>2]=c[b+456>>2];c[f+4>>2]=n;c[f+8>>2]=m;c[f+12>>2]=d;uF(b,69666,f);break}else{oF(b,69642)|0;break}while(0);oF(b,69696)|0;yM(b,c[(c[e>>2]|0)+24>>2]|0,13e3);AM(b);d=c[(c[e>>2]|0)+20>>2]|0;if(d|0){c[i>>2]=c[d>>2];c[i+4>>2]=0;yM(b,0,i)}}c[46720]=(a[(c[(c[h>>2]|0)+16>>2]|0)+115>>0]|0)==1?1:-1;if(!(a[193768]|0)){oF(b,69716)|0;a[193768]=1}d=c[g+208>>2]|0;if(d|0){c[j>>2]=BM(d,c[46720]|0)|0;uF(b,69729,j)}l=k;return}function zd(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;b=l;l=l+16|0;f=b;c[f>>2]=d;c[f+4>>2]=e;uF(a,69581,f);l=b;return}function Ad(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0.0,v=0.0,w=0.0;s=l;l=l+144|0;r=s+128|0;q=s+112|0;p=s+72|0;o=s+56|0;n=s+40|0;m=s+32|0;e=s+24|0;b=s+8|0;d=s;f=c[a+456>>2]|0;j=c[a+460>>2]|0;g=c[a+464>>2]|0;i=c[a+468>>2]|0;k=a+12|0;t=(c[(c[k>>2]|0)+28>>2]|0)+1|0;c[d>>2]=t;c[d+4>>2]=t;uF(a,69208,d);if(!(c[(c[k>>2]|0)+20>>2]|0)){c[b>>2]=f;c[b+4>>2]=j;c[b+8>>2]=g;c[b+12>>2]=i;uF(a,69225,b)}d=a+360|0;c[e>>2]=c[d>>2]|0?69259:69269;uF(a,69278,e);b=a+64|0;if((c[b>>2]|0)==1){c[m>>2]=g;c[m+4>>2]=i;uF(a,69303,m)}m=c[a+200>>2]|0;t=c[a+204>>2]|0;c[n>>2]=c[a+196>>2];c[n+4>>2]=m;c[n+8>>2]=t;uF(a,69342,n);if(!(c[(c[k>>2]|0)+20>>2]|0)){c[o>>2]=f;c[o+4>>2]=j;c[o+8>>2]=g-f;c[o+12>>2]=i-j;uF(a,69362,o)}w=+h[a+496>>3];t=c[d>>2]|0;v=+h[a+504>>3];u=+h[a+512>>3];h[p>>3]=+h[a+488>>3];h[p+8>>3]=w;c[p+16>>2]=t;h[p+24>>3]=v;h[p+32>>3]=u;uF(a,69402,p);if((c[b>>2]|0)==1){if((g|0)>14399|(i|0)>14399){t=c[(c[k>>2]|0)+16>>2]|0;c[q>>2]=g;c[q+4>>2]=i;c[q+8>>2]=14400;ub[t&63](69445,q)}c[r>>2]=f;c[r+4>>2]=j;c[r+8>>2]=g;c[r+12>>2]=i;uF(a,69540,r)}l=s;return}function Bd(a){a=a|0;var b=0,d=0,e=0;e=l;l=l+16|0;d=e;b=a+12|0;if(c[(c[b>>2]|0)+20>>2]|0){oF(a,69132)|0;yM(a,0,(c[(c[b>>2]|0)+20>>2]|0)+4|0)}oF(a,69149)|0;oF(a,69176)|0;c[d>>2]=c[(c[b>>2]|0)+28>>2];uF(a,69191,d);l=e;return}function Cd(a){a=a|0;var b=0,d=0;b=l;l=l+16|0;d=b;c[d>>2]=KB(c[(c[a+16>>2]|0)+8>>2]|0)|0;uF(a,69125,d);oF(a,69118)|0;l=b;return}function Dd(a){a=a|0;oF(a,84552)|0;return}function Ed(a){a=a|0;oF(a,69118)|0;return}function Fd(a){a=a|0;oF(a,84552)|0;return}function Gd(a){a=a|0;oF(a,69118)|0;return}function Hd(a){a=a|0;oF(a,84552)|0;return}function Id(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;e=l;l=l+16|0;d=e;if(b|0?(g=(c[a+16>>2]|0)+272|0,c[g>>2]|0):0){oF(a,69014)|0;yF(a,c[g>>2]|0,2);oF(a,69025)|0;c[d>>2]=BM(b,c[46720]|0)|0;uF(a,69029,d)}l=e;return}function Jd(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0,j=0,k=0,m=0,n=0;m=l;l=l+32|0;j=m+8|0;k=m;f=c[b+16>>2]|0;if(!(+h[f+40>>3]<.5)){Qd(b,f+16|0);i=e+4|0;vF(b,+h[(c[i>>2]|0)+16>>3]);c[j>>2]=c[c[i>>2]>>2];uF(b,68973,j);i=BM(c[e>>2]|0,c[46720]|0)|0;switch(a[e+48>>0]|0){case 114:{n=e+32|0;f=n;g=+h[n>>3];break}case 108:{f=e+32|0;g=0.0;break}default:{n=e+32|0;f=n;g=+h[n>>3]*.5}}h[d>>3]=+h[d>>3]-g;n=d+8|0;h[n>>3]=+h[n>>3]+ +h[e+24>>3];c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];xF(b,j);oF(b,68988)|0;vF(b,+h[f>>3]);c[k>>2]=i;uF(b,68997,k)}l=m;return}function Kd(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;g=l;l=l+32|0;f=g;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];h[f+16>>3]=+h[b+16>>3]-+h[b>>3];h[f+24>>3]=+h[b+24>>3]-+h[b+8>>3];b=a+16|0;if(d|0?(e=c[b>>2]|0,+h[e+80>>3]>.5):0){Qd(a,e+56|0);yF(a,f,2);oF(a,68931)|0}if(+h[(c[b>>2]|0)+40>>3]>.5){Rd(a);Qd(a,(c[b>>2]|0)+16|0);yF(a,f,2);oF(a,68951)|0}l=g;return}function Ld(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0;j=l;l=l+16|0;i=j;g=a+16|0;if(e|0?(f=c[g>>2]|0,+h[f+80>>3]>.5):0){Qd(a,f+56|0);oF(a,68852)|0;c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];xF(a,i);oF(a,68861)|0;e=1;while(1){if((e|0)>=(d|0))break;f=b+(e<<4)|0;c[i>>2]=c[f>>2];c[i+4>>2]=c[f+4>>2];c[i+8>>2]=c[f+8>>2];c[i+12>>2]=c[f+12>>2];xF(a,i);oF(a,68870)|0;e=e+1|0}oF(a,68897)|0}if(+h[(c[g>>2]|0)+40>>3]>.5){Rd(a);Qd(a,(c[g>>2]|0)+16|0);oF(a,68852)|0;c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];xF(a,i);oF(a,68861)|0;e=1;while(1){if((e|0)>=(d|0))break;g=b+(e<<4)|0;c[i>>2]=c[g>>2];c[i+4>>2]=c[g+4>>2];c[i+8>>2]=c[g+8>>2];c[i+12>>2]=c[g+12>>2];xF(a,i);oF(a,68870)|0;e=e+1|0}oF(a,68913)|0}l=j;return}function Md(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0;k=l;l=l+16|0;f=k;i=a+16|0;if(g|0?(j=c[i>>2]|0,+h[j+80>>3]>.5):0){Qd(a,j+56|0);oF(a,68852)|0;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];xF(a,f);oF(a,68861)|0;e=1;while(1){if((e|0)>=(d|0))break;yF(a,b+(e<<4)|0,3);oF(a,68887)|0;e=e+3|0}oF(a,68897)|0}if(+h[(c[i>>2]|0)+40>>3]>.5){Rd(a);Qd(a,(c[i>>2]|0)+16|0);oF(a,68852)|0;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];xF(a,f);oF(a,68861)|0;e=1;while(1){if((e|0)>=(d|0))break;yF(a,b+(e<<4)|0,3);oF(a,68887)|0;e=e+3|0}oF(a,68879)|0}l=k;return}function Nd(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0;g=l;l=l+16|0;f=g;e=a+16|0;if(+h[(c[e>>2]|0)+40>>3]>.5){Rd(a);Qd(a,(c[e>>2]|0)+16|0);oF(a,68852)|0;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];xF(a,f);oF(a,68861)|0;e=1;while(1){if((e|0)>=(d|0))break;i=b+(e<<4)|0;c[f>>2]=c[i>>2];c[f+4>>2]=c[i+4>>2];c[f+8>>2]=c[i+8>>2];c[f+12>>2]=c[i+12>>2];xF(a,f);oF(a,68870)|0;e=e+1|0}oF(a,68879)|0}l=g;return}function Od(a,b){a=a|0;b=b|0;oF(a,68849)|0;oF(a,b)|0;oF(a,153599)|0;return}function Pd(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+32|0;g=n+16|0;m=n+8|0;k=n;i=a+16|0;if(f|0?(j=c[i>>2]|0,+h[j+80>>3]>.5):0){Qd(a,j+56|0);oF(a,84447)|0;yF(a,d,e);oF(a,102604)|0;c[g>>2]=c[d>>2];c[g+4>>2]=c[d+4>>2];c[g+8>>2]=c[d+8>>2];c[g+12>>2]=c[d+12>>2];xF(a,g);c[k>>2]=e;c[k+4>>2]=b;uF(a,68766,k)}if(+h[(c[i>>2]|0)+40>>3]>.5){Rd(a);Qd(a,(c[i>>2]|0)+16|0);oF(a,84447)|0;yF(a,d,e);oF(a,102604)|0;c[g>>2]=c[d>>2];c[g+4>>2]=c[d+4>>2];c[g+8>>2]=c[d+8>>2];c[g+12>>2]=c[d+12>>2];xF(a,g);c[m>>2]=e;c[m+4>>2]=b;uF(a,68782,m)}l=n;return}function Qd(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0.0,i=0.0;f=l;l=l+32|0;e=f;if(b|0){switch(c[(c[a+16>>2]|0)+4>>2]|0){case 1:case 0:{d=111494;break}case 2:{d=111481;break}case 3:{d=111450;break}default:d=68818}i=+h[b+8>>3];g=+h[b+16>>3];h[e>>3]=+h[b>>3];h[e+8>>3]=i;h[e+16>>3]=g;c[e+24>>2]=d;uF(a,68825,e)}l=f;return}function Rd(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0;m=l;l=l+16|0;k=m+8|0;j=m;g=b+16|0;i=c[g>>2]|0;d=c[i+160>>2]|0;vF(b,+h[i+152>>3]);oF(b,68799)|0;while(1){if(!d)break;i=d+4|0;f=c[d>>2]|0;if(!f)break;if(!(s2(f,111088)|0)){d=i;continue}else e=f;while(1){d=e+1|0;if(!(a[e>>0]|0))break;else e=d}a:while(1){if(!(a[d>>0]|0))break;c[j>>2]=d;uF(b,68814,j);while(1){e=d+1|0;if(!(a[d>>0]|0)){d=e;continue a}else d=e}}if(!(s2(f,134335)|0))h[(c[g>>2]|0)+152>>3]=0.0;c[k>>2]=f;uF(b,96670,k);d=i}l=m;return}function Sd(b){b=b|0;var d=0;oF(b,74838)|0;d=QA(c[(c[b>>2]|0)+168>>2]|0,74894)|0;if(d|0?a[d>>0]|0:0){oF(b,74905)|0;oF(b,d)|0;oF(b,74929)|0}oF(b,74950)|0;oF(b,74998)|0;oF(b,75052)|0;d=b+12|0;oF(b,FK(c[c[c[d>>2]>>2]>>2]|0)|0)|0;oF(b,78181)|0;oF(b,FK(c[(c[c[d>>2]>>2]|0)+4>>2]|0)|0)|0;oF(b,78191)|0;oF(b,FK(c[(c[c[d>>2]>>2]|0)+8>>2]|0)|0)|0;oF(b,75434)|0;oF(b,75458)|0;return}function Td(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0.0,k=0.0,m=0.0;i=l;l=l+48|0;g=i+16|0;f=i+8|0;e=i;d=c[b+16>>2]|0;oF(b,74673)|0;d=d+8|0;if(a[(KB(c[d>>2]|0)|0)>>0]|0){oF(b,75397)|0;oF(b,FK(KB(c[d>>2]|0)|0)|0)|0}c[e>>2]=O(c[b+168>>2]|0,c[b+164>>2]|0)|0;uF(b,74678,e);e=c[b+452>>2]|0;c[f>>2]=c[b+448>>2];c[f+4>>2]=e;uF(b,74694,f);m=+h[b+392>>3];k=+h[b+400>>3];j=+h[b+408>>3];h[g>>3]=+h[b+384>>3];h[g+8>>3]=m;h[g+16>>3]=k;h[g+24>>3]=j;uF(b,74727,g);oF(b,74758)|0;oF(b,74794)|0;oF(b,76388)|0;l=i;return}function Ud(a){a=a|0;oF(a,74665)|0;return}function Vd(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;qe(a,b,0,101510,c[(c[a+16>>2]|0)+8>>2]|0);oF(a,76388)|0;return}function Wd(a){a=a|0;oF(a,74513)|0;return}function Xd(b){b=b|0;var d=0,e=0,f=0,g=0;e=l;l=l+16|0;f=e;g=c[b+16>>2]|0;d=g+8|0;qe(b,c[g+212>>2]|0,0,111494,c[d>>2]|0);oF(b,74617)|0;vF(b,+h[b+488>>3]);oF(b,102604)|0;vF(b,+h[b+496>>3]);c[f>>2]=0-(c[b+360>>2]|0);uF(b,74636,f);vF(b,+h[b+504>>3]);oF(b,102604)|0;vF(b,-+h[b+512>>3]);oF(b,74660)|0;if(a[(KB(c[d>>2]|0)|0)>>0]|0){oF(b,74569)|0;oF(b,FK(KB(c[d>>2]|0)|0)|0)|0;oF(b,74577)|0}l=e;return}function Yd(a){a=a|0;oF(a,74513)|0;return}function Zd(a){a=a|0;var b=0,d=0;d=c[a+16>>2]|0;b=d+8|0;qe(a,c[d+212>>2]|0,0,108006,c[b>>2]|0);oF(a,76388)|0;oF(a,74569)|0;oF(a,FK(KB(c[b>>2]|0)|0)|0)|0;oF(a,74577)|0;return}function _d(a){a=a|0;oF(a,74513)|0;return}function $d(a){a=a|0;var b=0,d=0,e=0;d=c[a+16>>2]|0;b=c[a+160>>2]|0;if((b|0)>1)b=c[(c[(c[a>>2]|0)+308>>2]|0)+(b<<2)>>2]|0;else b=0;e=d+8|0;qe(a,c[d+212>>2]|0,b,111481,c[e>>2]|0);oF(a,76388)|0;oF(a,74569)|0;oF(a,FK(KB(c[e>>2]|0)|0)|0)|0;oF(a,74577)|0;return}function ae(a){a=a|0;oF(a,74513)|0;return}function be(a){a=a|0;var b=0,d=0;d=c[a+16>>2]|0;b=d+8|0;qe(a,c[d+212>>2]|0,0,111450,c[b>>2]|0);oF(a,76388)|0;oF(a,74569)|0;b=EK(102095,c[b>>2]|0)|0;oF(a,FK(b)|0)|0;U1(b);oF(a,74577)|0;return}function ce(a){a=a|0;oF(a,74513)|0;return}function de(b,c,d,e,f){b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;oF(b,74519)|0;if(f|0){oF(b,74522)|0;oF(b,FK(f)|0)|0;oF(b,98744)|0}oF(b,74412)|0;oF(b,76349)|0;if(c|0?a[c>>0]|0:0){oF(b,74530)|0;oF(b,c)|0;oF(b,98744)|0}if(d|0?a[d>>0]|0:0){oF(b,74544)|0;oF(b,GK(d,1)|0)|0;oF(b,98744)|0}if(e|0?a[e>>0]|0:0){oF(b,74559)|0;oF(b,FK(e)|0)|0;oF(b,98744)|0}oF(b,76388)|0;return}function ee(a){a=a|0;oF(a,76343)|0;oF(a,74513)|0;return}function fe(e,f,g){e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0.0,H=0,I=0,J=0,K=0,L=0,M=0;M=l;l=l+176|0;L=M+160|0;K=M+152|0;F=M+136|0;J=M+128|0;E=M+120|0;D=M+112|0;C=M+104|0;B=M+96|0;A=M+88|0;y=M+80|0;w=M+72|0;v=M+64|0;u=M+56|0;t=M+48|0;i=M+40|0;q=M+32|0;s=M+24|0;r=M+16|0;p=M+8|0;o=M;I=c[e+16>>2]|0;oF(e,74044)|0;switch(a[g+48>>0]|0){case 108:{oF(e,74050)|0;break}case 114:{oF(e,74071)|0;break}default:oF(e,74090)|0}H=f+8|0;G=+h[H>>3]+ +h[g+24>>3];h[H>>3]=G;H=I+260|0;if(!(b[H>>1]&512)){oF(e,74112)|0;vF(e,+h[f>>3]);oF(e,74117)|0;vF(e,-G);oF(e,98744)|0}x=g+4|0;f=c[x>>2]|0;n=c[f+8>>2]|0;if(n){switch(c[(c[(c[(c[e>>2]|0)+168>>2]|0)+16>>2]|0)+244>>2]|0){case 1:{m=n;f=n+16|0;i=n+8|0;break}case 2:{m=n+24|0;f=n+32|0;i=n+28|0;break}default:{m=n+4|0;f=n+16|0;i=n+8|0}}j=c[f>>2]|0;f=c[i>>2]|0;k=c[n+12>>2]|0;c[o>>2]=c[m>>2];uF(e,74123,o);i=c[n+24>>2]|0;if(i|0){c[p>>2]=i;uF(e,74140,p)}oF(e,98744)|0;if(f|0){c[r>>2]=f;uF(e,74144,r)}if(k|0){c[s>>2]=k;uF(e,74162,s)}if(!j)j=0;else{c[q>>2]=j;uF(e,74181,q)}}else{c[i>>2]=c[f>>2];uF(e,74198,i);j=0;f=0}i=c[x>>2]|0;if(i|0?(z=c[i+24>>2]|0,z&127|0):0){if(!((f|0)!=0|(z&1|0)==0))uF(e,74216,t);if(!((j|0)!=0|(z&2|0)==0))uF(e,74236,u);if(z&100|0){uF(e,74257,v);if(!(z&4))f=0;else{uF(e,105986,w);f=1}if(z&64){c[y>>2]=f|0?100999:195059;uF(e,74276,y);f=1}if(z&32|0){c[A>>2]=f|0?100999:195059;uF(e,74287,A)}uF(e,98744,B)}if(z&8|0)uF(e,74302,C);if(z&16|0)uF(e,74326,D)}h[E>>3]=+h[(c[x>>2]|0)+16>>3];uF(e,74348,E);f=I+16|0;switch(c[I+48>>2]|0){case 5:{f=c[f>>2]|0;if(c3(f,137314)|0){c[J>>2]=f;uF(e,74366,J)}break}case 1:{E=d[f+1>>0]|0;J=d[f+2>>0]|0;c[F>>2]=d[f>>0];c[F+4>>2]=E;c[F+8>>2]=J;uF(e,74377,F);break}default:qa(138394,73558,486,74399)}oF(e,74412)|0;if(b[H>>1]&512){c[K>>2]=FK(c[I+212>>2]|0)|0;uF(e,74414,K);oF(e,74463)|0;vF(e,-G);oF(e,74481)|0}oF(e,GK(c[g>>2]|0,1)|0)|0;if(b[H>>1]&512)uF(e,74484,L);oF(e,74504)|0;l=M;return}function ge(a,b,c){a=a|0;b=b|0;c=c|0;var d=0;switch(c|0){case 2:{d=ne(a,b,2)|0;break}case 3:{d=oe(a)|0;break}default:d=0}oF(a,74008)|0;le(a,c,d);oF(a,74017)|0;vF(a,+h[b>>3]);oF(a,74023)|0;c=b+8|0;vF(a,-+h[c>>3]);oF(a,74030)|0;vF(a,+h[b+16>>3]-+h[b>>3]);oF(a,74037)|0;vF(a,+h[b+24>>3]-+h[c>>3]);oF(a,73424)|0;return}function he(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0;switch(d|0){case 2:{e=ne(a,b,c)|0;break}case 3:{e=oe(a)|0;break}default:e=0}oF(a,73999)|0;le(a,d,e);oF(a,73414)|0;e=0;while(1){if((e|0)>=(c|0))break;vF(a,+h[b+(e<<4)>>3]);oF(a,100999)|0;vF(a,-+h[b+(e<<4)+8>>3]);oF(a,102604)|0;e=e+1|0}vF(a,+h[b>>3]);oF(a,100999)|0;vF(a,-+h[b+8>>3]);oF(a,73424)|0;return}function ie(a,d,e,f,g,h){a=a|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;g=c[a+16>>2]|0;switch(h|0){case 2:{f=ne(a,d,e)|0;break}case 3:{f=oe(a)|0;break}default:f=0}oF(a,73594)|0;if(b[g+260>>1]&512){oF(a,73600)|0;oF(a,FK(c[g+212>>2]|0)|0)|0;oF(a,73606)|0}le(a,h,f);oF(a,73611)|0;pe(a,d,e);oF(a,73424)|0;return}function je(a,b,c){a=a|0;b=b|0;c=c|0;var d=0;oF(a,73404)|0;le(a,0,0);oF(a,73414)|0;d=0;while(1){if((d|0)>=(c|0))break;vF(a,+h[b+(d<<4)>>3]);oF(a,100999)|0;vF(a,-+h[b+(d<<4)+8>>3]);oF(a,102604)|0;d=d+1|0}oF(a,73424)|0;return}function ke(a,b){a=a|0;b=b|0;oF(a,73398)|0;oF(a,FK(b)|0)|0;oF(a,75458)|0;return}function le(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+96|0;k=q+48|0;p=q+40|0;n=q+32|0;o=q+24|0;j=q+16|0;g=q+8|0;f=q;m=c[b+16>>2]|0;oF(b,73429)|0;a:do switch(d|0){case 2:{c[f>>2]=e;uF(b,73437,f);break}case 3:{c[g>>2]=e;uF(b,73448,g);break}case 0:{oF(b,135709)|0;break}default:{d=m+56|0;f=k;g=d;i=f+40|0;do{c[f>>2]=c[g>>2];f=f+4|0;g=g+4|0}while((f|0)<(i|0));me(b,k);if((c[m+88>>2]|0)==1){d=a[d+3>>0]|0;switch(d<<24>>24){case -1:case 0:break a;default:{}}h[j>>3]=+(d&255)*.00392156862745098;uF(b,73459,j)}}}while(0);oF(b,73478)|0;e=m+16|0;f=k;g=e;i=f+40|0;do{c[f>>2]=c[g>>2];f=f+4|0;g=g+4|0}while((f|0)<(i|0));me(b,k);d=m+152|0;if(+h[d>>3]!=1.0){oF(b,73489)|0;vF(b,+h[d>>3])}switch(c[m+144>>2]|0){case 1:{c[o>>2]=73529;uF(b,73506,o);break}case 2:{c[n>>2]=73533;uF(b,73506,n);break}default:{}}b:do if((c[m+48>>2]|0)==1){d=a[e+3>>0]|0;switch(d<<24>>24){case -1:case 0:break b;default:{}}h[p>>3]=+(d&255)*.00392156862745098;uF(b,73537,p)}while(0);oF(b,98744)|0;l=q;return}function me(b,e){b=b|0;e=e|0;var f=0,g=0,h=0,i=0;g=l;l=l+16|0;f=g;a:do switch(c[e+32>>2]|0){case 5:{oF(b,c[e>>2]|0)|0;break}case 1:if(!(a[e+3>>0]|0)){oF(b,137379)|0;break a}else{i=d[e+1>>0]|0;h=d[e+2>>0]|0;c[f>>2]=d[e>>0];c[f+4>>2]=i;c[f+8>>2]=h;uF(b,75714,f);break a}default:qa(138394,73558,117,73578)}while(0);l=g;return}function ne(b,d,e){b=b|0;d=d|0;e=e|0;var f=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=l;l=l+112|0;o=t+64|0;r=t+56|0;n=t+48|0;m=t+40|0;i=t+32|0;k=t;q=c[46722]|0;c[46722]=q+1;p=c[b+16>>2]|0;f=+(c[p+136>>2]|0)*.017453292519943295;c[k>>2]=0;c[k+4>>2]=0;c[k+8>>2]=0;c[k+12>>2]=0;c[k+16>>2]=0;c[k+20>>2]=0;c[k+24>>2]=0;c[k+28>>2]=0;pQ(d,k,e,f,0);c[o>>2]=q;uF(b,73831,o);oF(b,73896)|0;vF(b,+h[k>>3]);oF(b,73901)|0;vF(b,+h[k+8>>3]);oF(b,73908)|0;vF(b,+h[k+16>>3]);oF(b,73915)|0;vF(b,+h[k+24>>3]);oF(b,73922)|0;k=p+140|0;f=+g[k>>2];if(f>0.0){h[i>>3]=f+-.001;uF(b,73927,i)}else oF(b,73703)|0;d=p+56|0;e=o;i=d;j=e+40|0;do{c[e>>2]=c[i>>2];e=e+4|0;i=i+4|0}while((e|0)<(j|0));me(b,o);oF(b,73739)|0;a:do if((c[p+88>>2]|0)==1){d=a[d+3>>0]|0;switch(d<<24>>24){case -1:case 0:{s=7;break a}default:{}}h[m>>3]=+(d&255)*.00392156862745098;uF(b,99612,m)}else s=7;while(0);if((s|0)==7)oF(b,73754)|0;oF(b,73757)|0;f=+g[k>>2];if(f>0.0){h[n>>3]=f;uF(b,73927,n)}else oF(b,73763)|0;d=p+96|0;e=o;i=d;j=e+40|0;do{c[e>>2]=c[i>>2];e=e+4|0;i=i+4|0}while((e|0)<(j|0));me(b,o);oF(b,73739)|0;b:do if((c[p+128>>2]|0)==1){d=a[d+3>>0]|0;switch(d<<24>>24){case -1:case 0:{s=14;break b}default:{}}h[r>>3]=+(d&255)*.00392156862745098;uF(b,99612,r)}else s=14;while(0);if((s|0)==14)oF(b,73754)|0;oF(b,73967)|0;l=t;return q|0}function oe(b){b=b|0;var d=0,e=0.0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0;q=l;l=l+64|0;k=q+16|0;o=q+8|0;j=q;n=c[46721]|0;c[46721]=n+1;m=c[b+16>>2]|0;r=+(c[m+136>>2]|0)*.017453292519943295;e=r;if(r==0.0){f=50;d=50}else{f=~~((+E(+e)+1.0)*50.0);d=~~((1.0-+F(+e))*50.0)}c[k>>2]=n;c[k+4>>2]=f;c[k+8>>2]=d;uF(b,73619,k);oF(b,73703)|0;d=m+56|0;f=k;g=d;i=f+40|0;do{c[f>>2]=c[g>>2];f=f+4|0;g=g+4|0}while((f|0)<(i|0));me(b,k);oF(b,73739)|0;a:do if((c[m+88>>2]|0)==1){d=a[d+3>>0]|0;switch(d<<24>>24){case -1:case 0:{p=6;break a}default:{}}h[j>>3]=+(d&255)*.00392156862745098;uF(b,99612,j)}else p=6;while(0);if((p|0)==6)oF(b,73754)|0;oF(b,73757)|0;oF(b,73763)|0;d=m+96|0;f=k;g=d;i=f+40|0;do{c[f>>2]=c[g>>2];f=f+4|0;g=g+4|0}while((f|0)<(i|0));me(b,k);oF(b,73739)|0;b:do if((c[m+128>>2]|0)==1){d=a[d+3>>0]|0;switch(d<<24>>24){case -1:case 0:{p=10;break b}default:{}}h[o>>3]=+(d&255)*.00392156862745098;uF(b,99612,o)}else p=10;while(0);if((p|0)==10)oF(b,73754)|0;oF(b,73799)|0;l=q;return n|0}function pe(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0;i=l;l=l+16|0;g=i;e=77;f=0;while(1){if((f|0)>=(d|0))break;c[g>>2]=e;uF(a,73616,g);vF(a,+h[b+(f<<4)>>3]);oF(a,100999)|0;vF(a,-+h[b+(f<<4)+8>>3]);e=(f|0)==0?67:32;f=f+1|0}l=i;return}function qe(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0;j=l;l=l+16|0;i=j+8|0;h=j;oF(b,74587)|0;oF(b,FK(d)|0)|0;if(e|0){c[h>>2]=FK(e)|0;uF(b,74595,h)}c[i>>2]=f;uF(b,74599,i);d=QA(g,74611)|0;if(d|0?a[d>>0]|0:0){oF(b,102604)|0;oF(b,FK(d)|0)|0}oF(b,98744)|0;l=j;return}function re(a){a=a|0;var b=0;oF(a,75418)|0;b=a+12|0;oF(a,Be(c[c[c[b>>2]>>2]>>2]|0)|0)|0;oF(a,78181)|0;oF(a,Be(c[(c[c[b>>2]>>2]|0)+4>>2]|0)|0)|0;oF(a,78191)|0;oF(a,Be(c[(c[c[b>>2]>>2]|0)+8>>2]|0)|0)|0;oF(a,75434)|0;return}function se(b){b=b|0;var d=0,e=0,f=0;f=l;l=l+16|0;e=f;d=c[b+16>>2]|0;oF(b,75395)|0;d=d+8|0;if(a[(KB(c[d>>2]|0)|0)>>0]|0){oF(b,75397)|0;oF(b,Be(KB(c[d>>2]|0)|0)|0)|0}c[e>>2]=O(c[b+168>>2]|0,c[b+164>>2]|0)|0;uF(b,75406,e);c[46723]=0;l=f;return}function te(a){a=a|0;c[46723]=1;return}function ue(a){a=a|0;c[46723]=-1;return}function ve(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+48|0;i=n+8|0;m=n;f=c[b+16>>2]|0;if(c[f+144>>2]|0?(k=e+4|0,j=~~(+h[b+352>>3]*+h[(c[k>>2]|0)+16>>3]),j|0):0){Ce(b);oF(b,75334)|0;g=d+8|0;h[g>>3]=+h[g>>3]-+(j|0)*.55;c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];xF(b,i);oF(b,75348)|0;oF(b,c[e>>2]|0)|0;oF(b,75357)|0;oF(b,75155)|0;g=i;d=f+16|0;f=g+40|0;do{c[g>>2]=c[d>>2];g=g+4|0;d=d+4|0}while((g|0)<(f|0));De(b,i);oF(b,75359)|0;i=c[k>>2]|0;k=c[i+8>>2]|0;k=c[((k|0)==0?i:k+4|0)>>2]|0;oF(b,98744)|0;oF(b,k)|0;oF(b,98744)|0;c[m>>2]=j;uF(b,75368,m);switch(a[e+48>>0]|0){case 108:{oF(b,75373)|0;break}case 114:{oF(b,75384)|0;break}default:{}}Ee(b);oF(b,153599)|0}l=n;return}function we(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0.0,m=0.0,n=0.0;j=l;l=l+48|0;f=j;g=c[a+16>>2]|0;i=g+144|0;if(c[i>>2]|0){n=+h[b>>3];m=+h[b+24>>3];e=b+8|0;k=+h[e>>3];h[b>>3]=n-+h[b+16>>3]+n;h[e>>3]=k-m+k;Ce(a);oF(a,75320)|0;yF(a,b,2);oF(a,75155)|0;do if(!d)if(!(c[46723]|0)){oF(a,97309)|0;break}else{oF(a,137198)|0;break}else{b=f;d=g+56|0;e=b+40|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0));De(a,f)}while(0);if((c[46723]|0)==1)c[46723]=0;oF(a,75266)|0;vF(a,+h[g+152>>3]);oF(a,75309)|0;b=f;d=g+16|0;e=b+40|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0));De(a,f);b=c[i>>2]|0;if((b|0)==1){oF(a,75163)|0;b=c[i>>2]|0}if((b|0)==2)oF(a,75172)|0;Ee(a);oF(a,153599)|0}l=j;return}function xe(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0;j=l;l=l+48|0;f=j;g=c[a+16>>2]|0;i=g+144|0;if(c[i>>2]|0){Ce(a);oF(a,75292)|0;yF(a,b,d);oF(a,75155)|0;do if(!e)if(!(c[46723]|0)){oF(a,97309)|0;break}else{oF(a,137198)|0;break}else{b=f;d=g+56|0;e=b+40|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0));De(a,f)}while(0);if((c[46723]|0)==1)c[46723]=0;oF(a,75266)|0;vF(a,+h[g+152>>3]);oF(a,75309)|0;b=f;d=g+16|0;e=b+40|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0));De(a,f);b=c[i>>2]|0;if((b|0)==1){oF(a,75163)|0;b=c[i>>2]|0}if((b|0)==2)oF(a,75172)|0;Ee(a);oF(a,153599)|0}l=j;return}function ye(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0;m=l;l=l+48|0;i=m;j=c[a+16>>2]|0;k=j+144|0;if(c[k>>2]|0){Ce(a);oF(a,75141)|0;yF(a,b,d);oF(a,75155)|0;e=i;f=j+16|0;g=e+40|0;do{c[e>>2]=c[f>>2];e=e+4|0;f=f+4|0}while((e|0)<(g|0));De(a,i);oF(a,75266)|0;vF(a,+h[j+152>>3]);e=c[k>>2]|0;if((e|0)==1){oF(a,75163)|0;e=c[k>>2]|0}if((e|0)==2)oF(a,75172)|0;oF(a,75275)|0;Ee(a);oF(a,153599)|0}l=m;return}function ze(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=l;l=l+48|0;g=i;e=c[a+16>>2]|0;h=e+144|0;if(c[h>>2]|0){Ce(a);oF(a,75141)|0;yF(a,b,d);oF(a,75155)|0;f=g;b=e+16|0;d=f+40|0;do{c[f>>2]=c[b>>2];f=f+4|0;b=b+4|0}while((f|0)<(d|0));De(a,g);b=c[h>>2]|0;if((b|0)==1){oF(a,75163)|0;b=c[h>>2]|0}if((b|0)==2)oF(a,75172)|0;Ee(a);oF(a,153599)|0}l=i;return}function Ae(a,b){a=a|0;b=b|0;oF(a,75138)|0;oF(a,Be(b)|0)|0;oF(a,153599)|0;return}function Be(a){a=a|0;return a|0}function Ce(b){b=b|0;if(!(a[b+144>>0]|0))oF(b,75263)|0;else oF(b,c[b+148>>2]|0)|0;return}function De(b,e){b=b|0;e=e|0;var f=0,g=0,h=0,i=0;g=l;l=l+16|0;f=g;a:do switch(c[e+32>>2]|0){case 5:{oF(b,c[e>>2]|0)|0;break}case 1:if(!(a[e+3>>0]|0)){oF(b,97309)|0;break a}else{i=d[e+1>>0]|0;h=d[e+2>>0]|0;c[f>>2]=d[e>>0];c[f+4>>2]=i;c[f+8>>2]=h;uF(b,75714,f);break a}default:qa(138394,75181,49,75245)}while(0);l=g;return}function Ee(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;g=h;f=c[a+16>>2]|0;switch(c[f+12>>2]|0){case 8:{b=1;d=111481;e=f+8|0;break}case 10:{b=0;d=111481;e=f+8|0;break}case 3:case 2:case 9:{b=1;d=111450;e=f+8|0;break}case 7:case 6:case 11:{b=0;d=111450;e=f+8|0;break}case 0:{b=1;d=111494;e=f+8|0;break}case 4:{b=0;d=75217;e=f+8|0;break}case 1:{b=1;d=111494;e=f+8|0;break}case 5:{b=0;d=111494;e=f+8|0;break}default:qa(138394,75181,106,75200)}f=c[(c[e>>2]|0)+8>>2]|0;c[g>>2]=b;c[g+4>>2]=d;c[g+8>>2]=f;uF(a,75229,g);l=h;return}function Fe(a){a=a|0;var b=0;oF(a,78153)|0;oF(a,78161)|0;b=a+12|0;oF(a,Qe(c[c[c[b>>2]>>2]>>2]|0)|0)|0;oF(a,78181)|0;oF(a,Qe(c[(c[c[b>>2]>>2]|0)+4>>2]|0)|0)|0;oF(a,78191)|0;oF(a,Qe(c[(c[c[b>>2]>>2]|0)+8>>2]|0)|0)|0;oF(a,78194)|0;return}function Ge(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0;j=l;l=l+32|0;i=j+24|0;g=j+16|0;f=j+8|0;e=j;d=c[b+16>>2]|0;c[46727]=~~(+h[b+232>>3]-+h[b+216>>3]);c[46726]=~~(+h[b+224>>3]-+h[b+208>>3]);oF(b,76840)|0;oF(b,76847)|0;d=KB(c[d+8>>2]|0)|0;if(a[d>>0]|0){oF(b,76916)|0;oF(b,Qe(d)|0)|0;oF(b,76924)|0}c[e>>2]=O(c[b+168>>2]|0,c[b+164>>2]|0)|0;uF(b,76933,e);oF(b,76953)|0;oF(b,76988)|0;oF(b,77016)|0;oF(b,77022)|0;oF(b,77065)|0;oF(b,77106)|0;oF(b,77125)|0;oF(b,77142)|0;oF(b,77190)|0;oF(b,77243)|0;oF(b,77319)|0;oF(b,77395)|0;oF(b,77404)|0;oF(b,77427)|0;oF(b,77454)|0;oF(b,77507)|0;oF(b,77529)|0;oF(b,77574)|0;oF(b,77586)|0;oF(b,77596)|0;oF(b,77622)|0;oF(b,77507)|0;oF(b,77674)|0;oF(b,77574)|0;oF(b,77586)|0;oF(b,77718)|0;oF(b,77427)|0;oF(b,77454)|0;oF(b,77507)|0;oF(b,77674)|0;oF(b,77574)|0;oF(b,77586)|0;oF(b,77596)|0;oF(b,77622)|0;oF(b,77507)|0;oF(b,77529)|0;oF(b,77574)|0;oF(b,77586)|0;oF(b,77731)|0;oF(b,77739)|0;oF(b,77745)|0;oF(b,77759)|0;oF(b,77767)|0;oF(b,77800)|0;e=(c[46727]|0)+10|0;c[f>>2]=c[46726];c[f+4>>2]=e;uF(b,77877,f);oF(b,77907)|0;oF(b,77916)|0;oF(b,77974)|0;oF(b,77984)|0;oF(b,78049)|0;f=c[46727]|0;c[g>>2]=c[46726];c[g+4>>2]=f;uF(b,78086,g);g=c[46727]|0;c[i>>2]=c[46726];c[i+4>>2]=g;uF(b,78114,i);l=j;return}function He(a){a=a|0;oF(a,76391)|0;oF(a,76403)|0;oF(a,76411)|0;oF(a,76474)|0;oF(a,76403)|0;oF(a,76519)|0;oF(a,76568)|0;oF(a,76622)|0;oF(a,76403)|0;oF(a,76722)|0;oF(a,76771)|0;oF(a,76403)|0;oF(a,76823)|0;return}function Ie(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0;j=l;l=l+32|0;i=j+16|0;h=j+8|0;g=j;oF(b,76349)|0;if(d|0?a[d>>0]|0:0){c[g>>2]=Qe(d)|0;uF(b,76352,g)}if(e|0?a[e>>0]|0:0){c[h>>2]=Qe(e)|0;uF(b,76363,h)}if(f|0?a[f>>0]|0:0){c[i>>2]=Qe(f)|0;uF(b,76375,i)}oF(b,76388)|0;l=j;return}function Je(a){a=a|0;oF(a,76343)|0;return}function Ke(b,e,f){b=b|0;e=e|0;f=f|0;var g=0,i=0.0,j=0.0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0.0;A=l;l=l+112|0;y=A+96|0;z=A+88|0;w=A+80|0;q=A+72|0;v=A+64|0;t=A+56|0;s=A+48|0;r=A+40|0;p=A+24|0;o=A+8|0;n=A;x=c[b+16>>2]|0;switch(a[f+48>>0]|0){case 108:{j=+h[e>>3];m=+h[f+32>>3];break}case 114:{m=+h[f+32>>3];j=+h[e>>3]-m;break}default:{m=+h[f+32>>3];j=+h[e>>3]-m*.5}}g=f+40|0;i=+h[g>>3];u=f+4|0;k=+h[(c[u>>2]|0)+16>>3];if(i<k){i=k*1.1+1.0;h[g>>3]=i}B=j+-8.0;k=+((c[46727]|0)>>>0)-+h[e+8>>3]-i+k*.2+(k<12.0?1.4:2.0);uF(b,76005,n);h[o>>3]=B;h[o+8>>3]=k;uF(b,75942,o);h[p>>3]=j+8.0+m-B;h[p+8>>3]=i;uF(b,75966,p);oF(b,76040)|0;oF(b,76074)|0;g=c[u>>2]|0;e=c[g+8>>2]|0;if(e){c[r>>2]=c[e+4>>2];uF(b,76164,r);g=c[e+8>>2]|0;if(g|0){c[s>>2]=g;uF(b,76183,s)}g=c[e+12>>2]|0;if(g|0){c[t>>2]=g;uF(b,76200,t)}g=c[e+16>>2]|0;if(g|0){c[v>>2]=g;uF(b,76218,v)}}else{c[q>>2]=c[g>>2];uF(b,76164,q)}h[w>>3]=+h[(c[u>>2]|0)+16>>3];uF(b,76234,w);g=x+16|0;switch(c[x+48>>2]|0){case 5:{g=c[g>>2]|0;if(c3(g,137314)|0){c[z>>2]=g;uF(b,76254,z)}break}case 1:{x=d[g+1>>0]|0;z=d[g+2>>0]|0;c[y>>2]=d[g>>0];c[y+4>>2]=x;c[y+8>>2]=z;uF(b,76264,y);break}default:qa(138394,75728,438,76285)}oF(b,76298)|0;oF(b,Qe(c[f>>2]|0)|0)|0;oF(b,76309)|0;oF(b,76332)|0;l=A;return}function Le(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0,k=0.0,m=0.0;e=l;l=l+32|0;f=e+16|0;j=e;oF(a,75906)|0;m=+h[b>>3];i=+h[b+16>>3]-m;k=+h[b+24>>3];g=k-+h[b+8>>3];k=+((c[46727]|0)>>>0)-k;h[j>>3]=m-i;h[j+8>>3]=k;uF(a,75942,j);h[f>>3]=i*2.0;h[f+8>>3]=g*2.0;uF(a,75966,f);Ue(a,d);oF(a,75788)|0;Se(a);oF(a,75994)|0;l=e;return}function Me(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0,i=0.0,j=0,k=0,m=0,n=0;m=l;l=l+48|0;k=m+24|0;j=m+8|0;g=m;oF(a,75511)|0;n=c[46727]|0;c[g>>2]=c[46726];c[g+4>>2]=n;uF(a,75764,g);Ue(a,e);oF(a,75788)|0;Se(a);oF(a,75791)|0;g=d+-1|0;e=0;while(1){if((e|0)>=(d|0))break;i=+h[b+(e<<4)>>3];f=+((c[46727]|0)>>>0)-+h[b+(e<<4)+8>>3];if(!e){oF(a,75818)|0;h[j>>3]=i;h[j+8>>3]=f;uF(a,75884,j);oF(a,75895)|0}else{h[k>>3]=i;h[k+8>>3]=f;uF(a,75884,k)}if((e|0)==(g|0))oF(a,75898)|0;e=e+1|0}oF(a,75627)|0;l=m;return}function Ne(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0;f=l;l=l+16|0;e=f;oF(a,75511)|0;h=c[46727]|0;c[e>>2]=c[46726];c[e+4>>2]=h;uF(a,75764,e);Ue(a,g);oF(a,75788)|0;Se(a);oF(a,75791)|0;Ve(a,b,d);oF(a,75804)|0;l=f;return}function Oe(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0.0;k=l;l=l+48|0;j=k+24|0;i=k+8|0;f=k;oF(a,75511)|0;g=c[46727]|0;c[f>>2]=c[46726];c[f+4>>2]=g;uF(a,75548,f);oF(a,75588)|0;f=d+-1|0;g=b+8|0;e=0;while(1){if((e|0)>=(d|0))break;if(!e){oF(a,75600)|0;m=+((c[46727]|0)>>>0)-+h[g>>3];h[i>>3]=+h[b>>3];h[i+8>>3]=m;uF(a,75604,i);oF(a,75615)|0}else{m=+((c[46727]|0)>>>0)-+h[b+(e<<4)+8>>3];h[j>>3]=+h[b+(e<<4)>>3];h[j+8>>3]=m;uF(a,75604,j)}if((e|0)==(f|0))oF(a,75619)|0;e=e+1|0}oF(a,75623)|0;Se(a);oF(a,75627)|0;l=k;return}function Pe(a,b){a=a|0;b=b|0;oF(a,75446)|0;oF(a,Qe(b)|0)|0;oF(a,75458)|0;return}function Qe(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+16|0;e=u;d=c[46724]|0;if(!d){c[46725]=64;d=KK(64)|0;c[46724]=d}s=e+15|0;r=e+14|0;g=0;q=0;o=0;e=0;f=0;a:while(1){n=(o|0)!=0;k=g;p=b;j=e;b:while(1){if(!p){t=32;break a}e=a[p>>0]|0;if(!(e<<24>>24)){t=32;break a}b=c[46725]|0;if((q|0)>(b+-8|0)){d=b<<1;c[46725]=d;d=MK(c[46724]|0,d)|0;c[46724]=d;d=d+q|0;e=a[p>>0]|0}switch(e<<24>>24){case 60:{e=4;b=102153;t=26;break b}case 62:{e=4;b=102136;t=27;break b}case 38:{t=10;break b}case 45:{e=5;b=102147;i=j;break b}default:{}}if(n&e<<24>>24==32){t=12;break}switch(e<<24>>24){case 34:{e=6;b=102116;t=26;break b}case 39:{e=5;b=102141;t=27;break b}default:{}}if(e<<24>>24<0){i=127;b=0}else{e=1;b=p;t=26;break}while(1){g=i&255;h=e&255;if(g>>>0>=h>>>0)break;e=i&h&255;i=g>>>1;b=b+1|0}if((b|0)<=1){g=R6(j|0,f|0,6)|0;g=J6(g|0,z|0,e&255|0,0)|0;f=z;m=k+-1|0;if((k|0)>1){b=m;e=g}else{t=21;break}}else{b=b+-1|0;e=e&255;f=0}k=b;p=p+1|0;j=e}if((t|0)==10){t=0;b=(Re(p)|0)==0;e=b?5:1;b=b?102130:p;i=j}else if((t|0)==12){t=0;b=(a[o>>0]|0)==32;e=b?6:1;b=b?102123:p;i=j}else if((t|0)==21){t=0;a[s>>0]=59;e=3;h=r;i=g;j=f;while(1){b=O6(i|0,j|0,10,0)|0;g=h+-1|0;a[h>>0]=b|48;b=i;i=N6(i|0,j|0,10,0)|0;f=j;j=z;o=e;e=e+1|0;if((o|0)>11){t=23;break a}if(!(f>>>0>0|(f|0)==0&b>>>0>9))break;else h=g}b=h+-2|0;a[g>>0]=35;a[b>>0]=38;k=m;f=j}else if((t|0)==26){t=0;i=j}else if((t|0)==27){t=0;i=j}h=d+e|0;g=e;while(1){if(!g)break;a[d>>0]=a[b>>0]|0;g=g+-1|0;b=b+1|0;d=d+1|0}g=k;b=p+1|0;q=e+q|0;o=p;d=h;e=i}if((t|0)==23){U4(75464,46,1,c[15712]|0)|0;Sa(1)}else if((t|0)==32){a[d>>0]=0;l=u;return c[46724]|0}return 0}function Re(b){b=b|0;var c=0,d=0;d=b+1|0;c=a[d>>0]|0;a:do if(c<<24>>24==35){d=b+2|0;c=a[d>>0]|0;switch(c<<24>>24){case 88:case 120:break;default:{b=d;while(1){d=b+1|0;if((c+-48&255)>=10)break a;b=d;c=a[d>>0]|0}}}d=b+3|0;while(1){c=a[d>>0]|0;if((c+-48&255)>=10)switch(c<<24>>24){case 65:case 66:case 67:case 68:case 69:case 70:case 97:case 98:case 99:case 100:case 101:case 102:break;default:break a}d=d+1|0}}else while(1){b=d+1|0;if(((c&-33)+-65&255)>=26)break a;d=b;c=a[b>>0]|0}while(0);return c<<24>>24==59|0}function Se(a){a=a|0;var b=0.0,d=0,e=0,f=0,g=0,i=0,j=0,k=0;k=l;l=l+48|0;d=k+8|0;j=k;i=c[a+16>>2]|0;oF(a,75639)|0;e=d;f=i+16|0;g=e+40|0;do{c[e>>2]=c[f>>2];e=e+4|0;f=f+4|0}while((e|0)<(g|0));Te(a,d);b=+h[i+152>>3];if(b!=1.0){h[j>>3]=b;uF(a,75657,j)}switch(c[i+144>>2]|0){case 1:{oF(a,75674)|0;break}case 2:{oF(a,75692)|0;break}default:{}}oF(a,75709)|0;l=k;return}function Te(b,e){b=b|0;e=e|0;var f=0,g=0,h=0,i=0;g=l;l=l+16|0;f=g;a:do switch(c[e+32>>2]|0){case 5:{oF(b,c[e>>2]|0)|0;break}case 1:if(!(a[e+3>>0]|0)){oF(b,135709)|0;break a}else{i=d[e+1>>0]|0;h=d[e+2>>0]|0;c[f>>2]=d[e>>0];c[f+4>>2]=i;c[f+8>>2]=h;uF(b,75714,f);break a}default:qa(138394,75728,94,75748)}while(0);l=g;return}function Ue(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;g=l;l=l+48|0;f=g;if(!b)oF(a,75867)|0;else{d=c[a+16>>2]|0;oF(a,75837)|0;b=f;d=d+56|0;e=b+40|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0));Te(a,f);oF(a,75864)|0}l=g;return}function Ve(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0.0,k=0.0;i=l;l=l+32|0;g=i;e=75818;f=0;while(1){if((f|0)>=(d|0))break;k=+h[b+(f<<4)>>3];j=+((c[46727]|0)>>>0)-+h[b+(f<<4)+8>>3];c[g>>2]=e;h[g+8>>3]=k;h[g+16>>3]=j;uF(a,75821,g);e=(f|0)==0?75834:195059;f=f+1|0}oF(a,98744)|0;l=i;return}function We(a){a=a|0;var b=0,d=0,e=0;b=l;l=l+16|0;d=b+8|0;e=b;oF(a,80451)|0;oF(a,80466)|0;oF(a,80505)|0;oF(a,80554)|0;c[e>>2]=137314;c[e+4>>2]=80642;uF(a,80623,e);c[d>>2]=137198;c[d+4>>2]=80648;uF(a,80623,d);l=b;return}function Xe(a){a=a|0;var b=0,d=0,e=0.0,f=0.0,g=0.0,i=0.0,j=0;b=l;l=l+64|0;d=b+8|0;j=b;c[j>>2]=KB(c[(c[a+16>>2]|0)+8>>2]|0)|0;uF(a,79404,j);i=+h[a+368>>3]*.5*+h[a+488>>3];g=+h[a+376>>3]*.5*+h[a+496>>3];f=+J(+(i*2.0000000949949026e-03))*180.0*.6366197723675814;e=+J(+(g*2.0000000949949026e-03))*180.0*.6366197723675814;h[d>>3]=i;h[d+8>>3]=g;h[d+16>>3]=-500.0;h[d+24>>3]=i;h[d+32>>3]=g;h[d+40>>3]=0.0;h[d+48>>3]=(f>e?f:e)*1.2;uF(a,79426,d);oF(a,79573)|0;oF(a,80403)|0;l=b;return}function Ye(a){a=a|0;oF(a,79387)|0;return}function Ze(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,h=0;f=l;l=l+16|0;h=f;c[h>>2]=b;c[h+4>>2]=d;c[h+8>>2]=e;uF(a,79357,h);g[46728]=+(O(d,-10)|0);l=f;return}function _e(a){a=a|0;oF(a,79340)|0;return}function $e(a){a=a|0;oF(a,79322)|0;return}function af(a){a=a|0;oF(a,79306)|0;return}function bf(a){a=a|0;oF(a,79285)|0;g[46728]=+g[46728]+-2.0;return}function cf(a){a=a|0;oF(a,79266)|0;return}function df(a){a=a|0;var b=0,d=0;b=l;l=l+16|0;d=b;c[d>>2]=KB(c[(c[a+16>>2]|0)+8>>2]|0)|0;uF(a,79244,d);l=b;return}function ef(a){a=a|0;oF(a,79228)|0;return}function ff(a){a=a|0;oF(a,79210)|0;g[46728]=+g[46728]+-5.0;return}function gf(a){a=a|0;oF(a,79194)|0;g[46728]=+g[46728]+5.0;return}function hf(b,d,e){b=b|0;d=d|0;e=e|0;var f=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0.0,u=0.0;r=l;l=l+144|0;o=r+104|0;q=r+56|0;m=r+32|0;j=r+24|0;n=r;p=e+4|0;i=c[p>>2]|0;f=+h[i+16>>3];i=c[i>>2]|0;c[n>>2]=c[e>>2];h[n+8>>3]=f;c[n+16>>2]=i;uF(b,79040,n);g[46729]=+g[46728]+-9.0;switch(a[e+48>>0]|0){case 108:{f=+h[d>>3];break}case 114:{i=d;f=+h[d>>3]-+h[e+32>>3];k=5;break}default:{i=d;f=+h[d>>3]-+h[e+32>>3]*.5;k=5}}if((k|0)==5)h[i>>3]=f;u=+h[b+488>>3];t=(+h[b+504>>3]+f)*u;s=(+h[b+512>>3]+ +h[d+8>>3])*+h[b+496>>3];h[j>>3]=+h[(c[p>>2]|0)+16>>3]*u;n=of(b,79092,j)|0;f=+(c[b+360>>2]|0);h[m>>3]=0.0;h[m+8>>3]=0.0;h[m+16>>3]=f;i=of(b,78309,m)|0;f=+g[46729];h[o>>3]=t;h[o+8>>3]=s;h[o+16>>3]=f;d=of(b,78341,o)|0;j=o;k=(c[b+16>>2]|0)+16|0;m=j+40|0;do{c[j>>2]=c[k>>2];j=j+4|0;k=k+4|0}while((j|0)<(m|0));o=pf(b,o,0.0)|0;e=c[e>>2]|0;c[q>>2]=c[c[p>>2]>>2];h[q+8>>3]=.25;h[q+16>>3]=0.0;c[q+24>>2]=e;c[q+28>>2]=79179;c[q+32>>2]=n;c[q+36>>2]=i;c[q+40>>2]=d;c[q+44>>2]=o;q=of(b,79104,q)|0;oF(b,q)|0;U1(q);U1(i);U1(o);U1(d);U1(n);l=r;return}function jf(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,i=0.0,j=0.0,k=0.0,m=0,n=0,o=0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0;z=l;l=l+240|0;x=z+192|0;y=z+152|0;t=z+128|0;r=z+104|0;o=z+80|0;n=z+48|0;e=z+24|0;m=z;oF(a,78917)|0;g[46729]=+g[46728]+-6.0;i=+h[b>>3];j=+h[a+488>>3];p=(+h[a+504>>3]+i)*j;A=+h[b+8>>3];k=+h[a+496>>3];q=(+h[a+512>>3]+A)*k;j=(+h[b+16>>3]-i)*j;k=(+h[b+24>>3]-A)*k;s=a+16|0;A=k+j;i=+h[(c[s>>2]|0)+152>>3]/A*2.5;h[m>>3]=j;h[m+8>>3]=A*.25;h[m+16>>3]=k;b=of(a,78277,m)|0;m=a+360|0;A=+(c[m>>2]|0);h[e>>3]=90.0;h[e+8>>3]=0.0;h[e+16>>3]=A;e=of(a,78309,e)|0;A=+g[46729];h[x>>3]=p;h[x+8>>3]=q;h[x+16>>3]=A;f=of(a,78341,x)|0;u=x;v=(c[s>>2]|0)+16|0;w=u+40|0;do{c[u>>2]=c[v>>2];u=u+4|0;v=v+4|0}while((u|0)<(w|0));v=pf(a,x,0.0)|0;h[n>>3]=1.0;h[n+8>>3]=i;c[n+16>>2]=b;c[n+20>>2]=e;c[n+24>>2]=f;c[n+28>>2]=v;w=of(a,78932,n)|0;oF(a,w)|0;U1(b);U1(e);U1(f);U1(v);U1(w);if(d|0){h[o>>3]=j;h[o+8>>3]=k;h[o+16>>3]=1.0;f=of(a,78277,o)|0;A=+(c[m>>2]|0);h[r>>3]=0.0;h[r+8>>3]=0.0;h[r+16>>3]=A;e=of(a,78309,r)|0;A=+g[46729];h[t>>3]=p;h[t+8>>3]=q;h[t+16>>3]=A;b=of(a,78341,t)|0;u=x;v=(c[s>>2]|0)+56|0;w=u+40|0;do{c[u>>2]=c[v>>2];u=u+4|0;v=v+4|0}while((u|0)<(w|0));x=pf(a,x,0.0)|0;h[y>>3]=0.0;h[y+8>>3]=0.0;h[y+16>>3]=0.0;c[y+24>>2]=f;c[y+28>>2]=e;c[y+32>>2]=b;c[y+36>>2]=x;y=of(a,78978,y)|0;oF(a,y)|0;U1(f);U1(e);U1(b);U1(x);U1(y)}l=z;return}function kf(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0.0,M=0,N=0.0;K=l;l=l+336|0;B=K+296|0;J=K+288|0;I=K+272|0;H=K+264|0;G=K+240|0;D=K+232|0;A=K+208|0;z=K+184|0;w=K+160|0;v=K+152|0;u=K+136|0;t=K+128|0;s=K+96|0;r=K+88|0;q=K+56|0;f=K+48|0;m=K+24|0;k=K;oF(a,78782)|0;g[46729]=+g[46728]+-2.0;o=a+488|0;p=a+496|0;L=+h[p>>3];h[k>>3]=+h[o>>3];h[k+8>>3]=L;h[k+16>>3]=1.0;k=of(a,78277,k)|0;y=a+360|0;L=+(c[y>>2]|0);h[m>>3]=0.0;h[m+8>>3]=0.0;h[m+16>>3]=L;m=of(a,78309,m)|0;L=+g[46729]+-2.0;h[B>>3]=0.0;h[B+8>>3]=0.0;h[B+16>>3]=L;n=of(a,78341,B)|0;C=a+16|0;i=B;j=(c[C>>2]|0)+16|0;x=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(x|0));j=pf(a,B,0.0)|0;c[f>>2]=78404;c[f+4>>2]=d+1;E=a+504|0;F=a+512|0;i=0;f=of(a,78373,f)|0;while(1){if((i|0)>=(d|0))break;N=+h[F>>3]+ +h[b+(i<<4)+8>>3];L=+h[(c[C>>2]|0)+152>>3];h[q>>3]=+h[E>>3]+ +h[b+(i<<4)>>3];h[q+8>>3]=N;h[q+16>>3]=0.0;h[q+24>>3]=L;M=of(a,78418,q)|0;c[r>>2]=f;c[r+4>>2]=M;x=of(a,78447,r)|0;U1(M);U1(f);i=i+1|0;f=x}L=+h[F>>3]+ +h[b+8>>3];N=+h[(c[C>>2]|0)+152>>3];h[s>>3]=+h[E>>3]+ +h[b>>3];h[s+8>>3]=L;h[s+16>>3]=0.0;h[s+24>>3]=N;x=of(a,78418,s)|0;c[t>>2]=f;c[t+4>>2]=x;M=of(a,78447,t)|0;U1(x);U1(f);c[u>>2]=k;c[u+4>>2]=m;c[u+8>>2]=n;c[u+12>>2]=j;x=of(a,78797,u)|0;c[v>>2]=M;c[v+4>>2]=x;M=of(a,78502,v)|0;U1(x);oF(a,M)|0;U1(k);U1(m);U1(n);U1(j);U1(M);if(e|0){N=+h[p>>3];h[w>>3]=+h[o>>3];h[w+8>>3]=N;h[w+16>>3]=1.0;n=of(a,78277,w)|0;N=+(c[y>>2]|0);h[z>>3]=0.0;h[z+8>>3]=0.0;h[z+16>>3]=N;m=of(a,78309,z)|0;N=+g[46729]+-2.0;h[A>>3]=0.0;h[A+8>>3]=0.0;h[A+16>>3]=N;k=of(a,78341,A)|0;i=B;j=(c[C>>2]|0)+56|0;x=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(x|0));j=pf(a,B,.25)|0;c[D>>2]=d;i=0;f=of(a,78842,D)|0;while(1){if((i|0)>=(d|0))break;N=+h[F>>3]+ +h[b+(i<<4)+8>>3];h[G>>3]=+h[E>>3]+ +h[b+(i<<4)>>3];h[G+8>>3]=N;h[G+16>>3]=0.0;D=of(a,78857,G)|0;c[H>>2]=f;c[H+4>>2]=D;M=of(a,78879,H)|0;U1(D);U1(f);i=i+1|0;f=M}c[I>>2]=n;c[I+4>>2]=m;c[I+8>>2]=k;c[I+12>>2]=j;I=of(a,78889,I)|0;c[J>>2]=f;c[J+4>>2]=I;M=of(a,78502,J)|0;U1(I);oF(a,M)|0;U1(n);U1(m);U1(k);U1(j);U1(M)}l=K;return}function lf(a,b,d,e,f,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0.0,D=0.0;B=l;l=l+208|0;e=B+160|0;A=B+152|0;z=B+136|0;y=B+128|0;x=B+96|0;w=B+88|0;v=B+56|0;k=B+48|0;s=B+24|0;r=B;oF(a,78709)|0;g[46729]=+g[46728]+-4.0;C=+h[a+496>>3];h[r>>3]=+h[a+488>>3];h[r+8>>3]=C;h[r+16>>3]=1.0;r=of(a,78277,r)|0;C=+(c[a+360>>2]|0);h[s>>3]=0.0;h[s+8>>3]=0.0;h[s+16>>3]=C;s=of(a,78309,s)|0;C=+g[46729]+-2.0;h[e>>3]=0.0;h[e+8>>3]=0.0;h[e+16>>3]=C;t=of(a,78341,e)|0;u=a+16|0;f=e;i=(c[u>>2]|0)+56|0;j=f+40|0;do{c[f>>2]=c[i>>2];f=f+4|0;i=i+4|0}while((f|0)<(j|0));n=pf(a,e,0.0)|0;c[k>>2]=78723;c[k+4>>2]=d+2;o=a+504|0;p=a+512|0;q=d+-1|0;m=0;e=of(a,78373,k)|0;while(1){if((m|0)>=(d|0))break;f=b+(m<<4)|0;i=b+(m<<4)+8|0;D=+h[p>>3]+ +h[i>>3];C=+h[(c[u>>2]|0)+152>>3];h[v>>3]=+h[o>>3]+ +h[f>>3];h[v+8>>3]=D;h[v+16>>3]=0.0;h[v+24>>3]=C;k=of(a,78418,v)|0;c[w>>2]=e;c[w+4>>2]=k;j=of(a,78447,w)|0;U1(k);U1(e);if((m|0)==0|(m|0)==(q|0)){C=+h[p>>3]+ +h[i>>3];D=+h[(c[u>>2]|0)+152>>3];h[x>>3]=+h[o>>3]+ +h[f>>3];h[x+8>>3]=C;h[x+16>>3]=0.0;h[x+24>>3]=D;k=of(a,78418,x)|0;c[y>>2]=j;c[y+4>>2]=k;e=of(a,78447,y)|0;U1(k);U1(j)}else e=j;m=m+1|0}c[z>>2]=r;c[z+4>>2]=s;c[z+8>>2]=t;c[z+12>>2]=n;b=of(a,78732,z)|0;c[A>>2]=e;c[A+4>>2]=b;d=of(a,78502,A)|0;U1(b);oF(a,d)|0;U1(r);U1(s);U1(t);U1(n);U1(d);l=B;return}function mf(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0.0,x=0,y=0.0;v=l;l=l+160|0;e=v+120|0;u=v+112|0;t=v+96|0;s=v+88|0;r=v+56|0;m=v+48|0;n=v+24|0;q=v;oF(a,78261)|0;g[46729]=+g[46728]+-6.0;w=+h[a+496>>3];h[q>>3]=+h[a+488>>3];h[q+8>>3]=w;h[q+16>>3]=1.0;q=of(a,78277,q)|0;w=+(c[a+360>>2]|0);h[n>>3]=0.0;h[n+8>>3]=0.0;h[n+16>>3]=w;n=of(a,78309,n)|0;w=+g[46729];h[e>>3]=0.0;h[e+8>>3]=0.0;h[e+16>>3]=w;o=of(a,78341,e)|0;p=a+16|0;f=e;i=(c[p>>2]|0)+16|0;j=f+40|0;do{c[f>>2]=c[i>>2];f=f+4|0;i=i+4|0}while((f|0)<(j|0));i=pf(a,e,0.0)|0;c[m>>2]=78404;c[m+4>>2]=d;j=a+504|0;k=a+512|0;f=0;e=of(a,78373,m)|0;while(1){if((f|0)>=(d|0))break;y=+h[k>>3]+ +h[b+(f<<4)+8>>3];w=+h[(c[p>>2]|0)+152>>3];h[r>>3]=+h[j>>3]+ +h[b+(f<<4)>>3];h[r+8>>3]=y;h[r+16>>3]=0.0;h[r+24>>3]=w;x=of(a,78418,r)|0;c[s>>2]=e;c[s+4>>2]=x;m=of(a,78447,s)|0;U1(x);U1(e);f=f+1|0;e=m}c[t>>2]=q;c[t+4>>2]=n;c[t+8>>2]=o;c[t+12>>2]=i;t=of(a,78456,t)|0;c[u>>2]=e;c[u+4>>2]=t;x=of(a,78502,u)|0;U1(t);oF(a,x)|0;U1(q);U1(n);U1(o);U1(i);U1(x);l=v;return}function nf(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=b;uF(a,78242,e);l=d;return}function of(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=l;l=l+1056|0;h=i;e=i+24|0;f=i+8|0;c[f>>2]=d;g=G3(e,1024,b,f)|0;do if((g|0)>=0)if((g|0)>1023){a=T1(g+1|0)|0;c[f>>2]=d;n4(a,b,f)|0;break}else{a=D4(e)|0;break}else{a=c[(c[a+12>>2]|0)+16>>2]|0;c[h>>2]=t2(c[(d2()|0)>>2]|0)|0;ub[a&63](78687,h);a=D4(195059)|0}while(0);l=i;return a|0}function pf(a,b,e){a=a|0;b=b|0;e=+e;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0,s=0.0;q=l;l=l+112|0;o=q+104|0;j=q+96|0;i=q+64|0;n=q+48|0;p=q+32|0;m=q+16|0;k=q;g=c[b+32>>2]|0;a:do switch(g|0){case 5:{f=c[b>>2]|0;if(!(s2(f,136302)|0)){c[k>>2]=78524;h[k+8>>3]=e;f=of(a,78507,k)|0;break a}if(!(s2(f,133432)|0)){c[m>>2]=78528;h[m+8>>3]=e;f=of(a,78507,m)|0;break a}if(!(s2(f,131431)|0)){c[p>>2]=78534;h[p+8>>3]=e;f=of(a,78507,p)|0;break a}else{c[n>>2]=f;h[n+8>>3]=e;f=of(a,78507,n)|0;break a}}case 1:{s=+(d[b+1>>0]|0)*.00390625;r=+(d[b+2>>0]|0)*.00390625;h[i>>3]=+(d[b>>0]|0)*.00390625;h[i+8>>3]=s;h[i+16>>3]=r;h[i+24>>3]=e;f=of(a,78539,i)|0;break}default:{p=c[15712]|0;n=c[b>>2]|0;c[j>>2]=g;c[j+4>>2]=n;w4(p,78578,j)|0;qa(138394,78628,436,78648)}}while(0);c[o>>2]=f;p=of(a,78665,o)|0;U1(f);l=q;return p|0}function qf(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;b=l;l=l+32|0;d=b+24|0;e=b+16|0;g=b;f=c[a+16>>2]|0;h=c[c[a+12>>2]>>2]|0;j=c[h>>2]|0;i=c[h+4>>2]|0;h=c[h+8>>2]|0;c[g>>2]=81094;c[g+4>>2]=j;c[g+8>>2]=i;c[g+12>>2]=h;uF(a,83704,g);f=KB(c[f+8>>2]|0)|0;c[e>>2]=81094;c[e+4>>2]=f;uF(a,83736,e);c[d>>2]=81094;uF(a,83750,d);l=b;return}function rf(a){a=a|0;var b=0,d=0;b=l;l=l+16|0;d=b;c[d>>2]=81094;uF(a,83652,d);l=b;return}function sf(a){a=a|0;var b=0.0,d=0.0,e=0.0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0;W=l;l=l+304|0;V=W+296|0;U=W+288|0;T=W+272|0;R=W+264|0;Q=W+256|0;P=W+248|0;O=W+240|0;N=W+232|0;M=W+224|0;L=W+216|0;K=W+208|0;J=W+200|0;I=W+192|0;H=W+184|0;G=W+176|0;F=W+168|0;E=W+160|0;C=W+152|0;A=W+144|0;z=W+136|0;y=W+128|0;x=W+120|0;w=W+112|0;v=W+104|0;u=W+96|0;t=W+88|0;s=W+80|0;r=W+72|0;q=W+64|0;p=W+56|0;o=W+48|0;S=W+40|0;B=W+24|0;n=W+16|0;m=W;g=c[a+456>>2]|0;i=c[a+460>>2]|0;j=c[a+464>>2]|0;k=c[a+468>>2]|0;f=a+360|0;a:do if(c[4025]|0){switch(c[f>>2]|0){case 90:case 0:break a;default:{}}Ef();c[4025]=0}while(0);e=(+(k|0)-+(i|0))*.013888888888888888;b=(+(j|0)-+(g|0))*.013888888888888888;k=(c[f>>2]|0)==90;d=k?e:b;e=k?b:e;h[m>>3]=d;h[m+8>>3]=e;uF(a,81281,m);c[n>>2]=81094;uF(a,81296,n);if(d>0.0){b=+A2(d);h[19214]=b;b=b+3.0-+(~~b|0)}else b=3.0;b=+D(10.0,+b);h[19214]=b;h[B>>3]=b;h[B+8>>3]=b;uF(a,81465,B);c[S>>2]=81094;uF(a,81500,S);c[o>>2]=81094;uF(a,81558,o);c[p>>2]=81094;uF(a,81618,p);c[q>>2]=81094;uF(a,81667,q);c[r>>2]=81094;uF(a,81706,r);c[s>>2]=81094;uF(a,81757,s);c[t>>2]=81094;uF(a,81819,t);c[u>>2]=81094;uF(a,81929,u);c[v>>2]=81094;uF(a,82020,v);c[w>>2]=81094;uF(a,82074,w);c[x>>2]=81094;uF(a,82143,x);uF(a,82179,y);c[z>>2]=81094;uF(a,82304,z);c[A>>2]=81094;uF(a,82333,A);uF(a,82373,C);c[E>>2]=81094;uF(a,82466,E);c[F>>2]=81094;uF(a,82525,F);c[G>>2]=81094;uF(a,82606,G);uF(a,82676,H);c[I>>2]=81094;uF(a,82765,I);c[J>>2]=81094;uF(a,82870,J);c[K>>2]=81094;uF(a,82946,K);c[L>>2]=81094;uF(a,82985,L);c[M>>2]=81094;uF(a,83084,M);c[N>>2]=81094;uF(a,83130,N);uF(a,83219,O);c[P>>2]=81094;uF(a,83260,P);c[Q>>2]=81094;uF(a,83343,Q);c[R>>2]=81094;uF(a,83444,R);h[T>>3]=e;h[T+8>>3]=d;uF(a,83508,T);uF(a,83536,U);uF(a,83544,V);l=W;return}function tf(a){a=a|0;var b=0;b=l;l=l+16|0;uF(a,81274,b);l=b;return}function uf(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0.0,k=0,m=0,n=0.0,o=0,p=0,q=0,r=0;r=l;l=l+48|0;q=r+24|0;p=r+8|0;o=r;switch(a[e+48>>0]|0){case 108:{k=d;i=+h[e+32>>3];j=+h[d>>3];break}case 114:{i=+h[e+32>>3];g=i;f=5;break}default:{i=+h[e+32>>3];g=i*.5;f=5}}if((f|0)==5){j=+h[d>>3]-g;h[d>>3]=j;k=d}m=e+4|0;f=c[m>>2]|0;g=+h[f+16>>3];d=d+8|0;n=+h[d>>3]+g*.004629629629629629;h[d>>3]=n;i=j+i*.006944444444444444;h[k>>3]=i;f=c[f>>2]|0;do if(f){d=c[46730]|0;if(d|0?(s2(d,f)|0)==0:0)break;c[o>>2]=Cf(f)|0;uF(b,81173,o);o=c[m>>2]|0;c[46730]=c[o>>2];g=+h[o+16>>3]}while(0);f=~~g;f=(f|0)>1?f:1;if((f|0)!=(c[46731]|0)){j=+h[19214];c[p>>2]=f;h[p+8>>3]=j;uF(b,81181,p);c[46731]=f}c[q>>2]=Df(c[e>>2]|0)|0;h[q+8>>3]=i;h[q+16>>3]=n;uF(b,81202,q);l=r;return}function vf(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0.0,k=0.0;e=l;l=l+48|0;f=e;i=+h[b>>3];k=(+h[b+16>>3]-i)*.027777777777777776;g=+h[b+8>>3];j=(+h[b+24>>3]-g)*.027777777777777776;c[f>>2]=1;c[f+4>>2]=d|0?81115:195059;h[f+8>>3]=k;h[f+16>>3]=j;h[f+24>>3]=i*.013888888888888888;h[f+32>>3]=g*.013888888888888888;uF(a,81121,f);l=e;return}function wf(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;e=l;l=l+16|0;Af(c[a+16>>2]|0,e+8|0,e);Bf(a,b,d,1);l=e;return}function xf(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0.0,v=0.0,w=0;t=l;l=l+128|0;s=t+112|0;q=t+104|0;p=t+96|0;e=t+88|0;n=t+16|0;o=t;k=c[a+16>>2]|0;j=T1((d*140|0)+140|0)|0;Af(k,t+116|0,t+80|0);v=+h[b>>3];k=n+48|0;h[k>>3]=v;u=+h[b+8>>3];h[n+56>>3]=u;c[e>>2]=~~(v+(v>=0.0?.5:-.5));c[e+4>>2]=~~(u+(u>=0.0?.5:-.5));i=o+8|0;e=j+(m4(j,81099,e)|0)|0;r=1;g=0;while(1){m=g+3|0;if((m|0)>=(d|0))break;c[n>>2]=c[k>>2];c[n+4>>2]=c[k+4>>2];c[n+8>>2]=c[k+8>>2];c[n+12>>2]=c[k+12>>2];f=1;while(1){if((f|0)==4){f=1;break}w=f+g|0;h[n+(f<<4)>>3]=+h[b+(w<<4)>>3];h[n+(f<<4)+8>>3]=+h[b+(w<<4)+8>>3];f=f+1|0}while(1){if((f|0)==7)break;kP(o,n,3,+(f|0)*.16666666666666666,0,0);u=+h[o>>3];v=+h[i>>3];c[p>>2]=~~(u+(u>=0.0?.5:-.5));c[p+4>>2]=~~(v+(v>=0.0?.5:-.5));f=f+1|0;e=e+(m4(e,81099,p)|0)|0}r=r+6|0;g=m}c[q>>2]=j;uF(a,81106,q);U1(j);f=r+-1|0;e=0;while(1){if((e|0)>=(r|0))break;c[s>>2]=((e|0)%(f|0)|0|0)!=0&1;uF(a,81111,s);e=e+1|0}oF(a,153599)|0;l=t;return}function yf(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=l;l=l+16|0;Af(c[a+16>>2]|0,e+8|0,e);Bf(a,b,d,0);l=e;return}function zf(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=81094;c[e+4>>2]=b;uF(a,81087,e);l=d;return}function Af(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0;switch(c[a+144>>2]|0){case 1:{e=10.0;a=1;break}case 2:{e=10.0;a=2;break}default:{e=0.0;a=0}}c[b>>2]=a;h[d>>3]=e;return}function Bf(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0,m=0.0;j=l;l=l+16|0;i=j+8|0;g=j;f=0;while(1){if((f|0)>=(d|0))break;m=+h[b+(f<<4)>>3];k=+h[b+(f<<4)+8>>3];c[g>>2]=~~(m+(m>=0.0?.5:-.5));c[g+4>>2]=~~(k+(k>=0.0?.5:-.5));uF(a,81099,g);f=f+1|0}if(e|0){k=+h[b>>3];m=+h[b+8>>3];c[i>>2]=~~(k+(k>=0.0?.5:-.5));c[i+4>>2]=~~(m+(m>=0.0?.5:-.5));uF(a,81099,i)}oF(a,153599)|0;l=j;return}function Cf(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;f=h;a:while(1){d=15828;while(1){e=c[d+4>>2]|0;if(!e)break;if(!(s2(e,b)|0)){g=6;break a}d=d+8|0}c[f>>2]=81255;c[f+4>>2]=b;gA(1,81229,f)|0;d=E4(b,45)|0;if(!d){d=81272;break}a[d>>0]=0}l=h;return d|0}function Df(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i;d=c[46732]|0;if(!d){c[46733]=64;d=T1(64)|0;c[46732]=d;e=0}else e=0;while(1){g=b+1|0;f=a[b>>0]|0;if(!(f<<24>>24))break;b=c[46733]|0;if((e|0)>(b+-8|0)){d=b<<1;c[46733]=d;d=W1(c[46732]|0,d)|0;c[46732]=d;d=d+e|0}if(f<<24>>24<=-1){a[d>>0]=92;c[h>>2]=f&255;m4(d+1|0,81224,h)|0;b=g;d=d+4|0;e=e+4|0;continue}if(f<<24>>24==92){a[d>>0]=92;e=e+1|0;d=d+1|0}a[d>>0]=f;b=g;d=d+1|0;e=e+1|0}a[d>>0]=0;l=i;return c[46732]|0}function Ef(){var a=0,b=0;a=l;l=l+16|0;b=a;c[b>>2]=81255;c[b+4>>2]=91269;gA(0,83634,b)|0;l=a;return}function Ff(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0,k=0;e=l;l=l+32|0;f=e+8|0;j=e;b=b+8|0;k=c[b>>2]|0;c[j>>2]=k;c[j+4>>2]=k;uF(a,84223,j);i=(+h[d>>3]+ +h[d+16>>3])*.5;g=(+h[d+8>>3]+ +h[d+24>>3])*.5;c[f>>2]=c[b>>2];h[f+8>>3]=i;h[f+16>>3]=g;uF(a,84265,f);l=e;return}function Gf(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0.0,k=0.0;e=l;l=l+48|0;f=e;i=+h[d>>3];k=+h[d+16>>3]-i;g=+h[d+24>>3];j=g-+h[d+8>>3];g=+(~~(+h[a+232>>3]-+h[a+216>>3])>>>0)-g;c[f>>2]=c[b+8>>2];h[f+8>>3]=k;h[f+16>>3]=j;h[f+24>>3]=i;h[f+32>>3]=g;uF(a,84310,f);oF(a,84402)|0;l=e;return}function Hf(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return}function If(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0.0;t=l;l=l+160|0;s=t+152|0;r=t+136|0;q=t+120|0;k=t+112|0;n=t+104|0;m=t+88|0;j=t+72|0;i=t+64|0;o=t;if(!a)qa(111282,84407,212,84426);if(!b)qa(111312,84407,213,84426);p=b+8|0;if(!(c[p>>2]|0))qa(111315,84407,214,84426);if(c[b+52>>2]|0){c[o>>2]=c[d>>2];c[o+4>>2]=c[d+4>>2];c[o+8>>2]=c[d+8>>2];c[o+12>>2]=c[d+12>>2];b=o+32|0;d=d+16|0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];f=+h[o>>3];h[o+16>>3]=f;h[o+24>>3]=+h[o+40>>3];h[o+48>>3]=+h[b>>3];g=+h[o+8>>3];h[o+56>>3]=g;if(e<<24>>24){uF(a,84447,i);b=0;while(1){if((b|0)==4)break;u=+h[o+(b<<4)+8>>3];h[j>>3]=+h[o+(b<<4)>>3];h[j+8>>3]=u;uF(a,84450,j);b=b+1|0}h[m>>3]=f;h[m+8>>3]=g;uF(a,84450,m);m=c[p>>2]|0;c[n>>2]=4;c[n+4>>2]=m;uF(a,84457,n)}uF(a,84447,k);b=0;while(1){if((b|0)==4)break;u=+h[o+(b<<4)+8>>3];h[q>>3]=+h[o+(b<<4)>>3];h[q+8>>3]=u;uF(a,84450,q);b=b+1|0}h[r>>3]=f;h[r+8>>3]=g;uF(a,84450,r);r=c[p>>2]|0;c[s>>2]=4;c[s+4>>2]=r;uF(a,84472,s)}l=t;return}function Jf(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0.0;o=l;l=l+112|0;n=o+24|0;m=o+16|0;k=o;i=o+28|0;if(!b)qa(111282,84407,153,84488);if(!d)qa(111312,84407,154,84488);if(!(c[d+8>>2]|0))qa(111315,84407,155,84488);j=d+52|0;if(c[j>>2]|0){f=d+60|0;g=c[f>>2]|0;if((g|0)==100)g=18;else{tb[g&127](d);c[j>>2]=0;c[f>>2]=0;c[d+56>>2]=0;g=10}}else g=10;do if((g|0)==10){if(!((OF(d)|0)<<24>>24))break;f=A6(c[d+20>>2]|0)|0;if((c[d+24>>2]&-2|0)==6){Z4(f,i)|0;i=c[i+36>>2]|0;c[d+56>>2]=i;f=j2(0,i,1,1,f,0)|0;c[j>>2]=f;a[d+16>>0]=1}else f=c[j>>2]|0;if(f|0)c[d+60>>2]=100;PF(d);if(c[j>>2]|0)g=18}while(0);if((g|0)==18){p=+h[e+8>>3]-+(c[d+36>>2]|0);h[k>>3]=+h[e>>3]-+(c[d+32>>2]|0);h[k+8>>3]=p;uF(b,84506,k);if(!(a[d+16>>0]|0)){c[m>>2]=c[d+12>>2];uF(b,84537,m)}else zM(b,d);uF(b,84552,n)}l=o;return}function Kf(a){a=a|0;h2(c[a+52>>2]|0,c[a+56>>2]|0)|0;return}function Lf(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;g=l;l=l+80|0;f=g+48|0;if(!a)qa(111282,84407,121,84562);e=c[a+16>>2]|0;if(!e)qa(99902,84407,123,84562);if(!b)qa(111312,84407,124,84562);d=b+8|0;if(!(c[d>>2]|0))qa(111315,84407,125,84562);if(!(c[e+8>>2]|0))qa(138879,84407,128,84562);else{uF(a,84582,g);uF(a,84591,g+8|0);uF(a,84618,g+16|0);uF(a,84643,g+24|0);uF(a,84672,g+32|0);uF(a,84700,g+40|0);c[f>>2]=c[d>>2];uF(a,84707,f);uF(a,84746,g+56|0);uF(a,111008,g+64|0);l=g;return}}function Mf(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0.0;i=l;l=l+128|0;g=i+80|0;f=i;if(!a)qa(111282,84407,97,84751);if(!b)qa(111312,84407,98,84751);e=c[b+8>>2]|0;if(!e)qa(111315,84407,99,84751);else{m=+h[d>>3];j=~~(m+(m>=0.0?.5:-.5));m=+h[d+8>>3];b=~~(m+(m>=0.0?.5:-.5));m=+h[d+16>>3];k=~~(m+(m>=0.0?.5:-.5));m=+h[d+24>>3];d=~~(m+(m>=0.0?.5:-.5));c[f>>2]=2;c[f+4>>2]=5;c[f+8>>2]=0;c[f+12>>2]=0;c[f+16>>2]=0;c[f+20>>2]=-1;c[f+24>>2]=1;c[f+28>>2]=-1;c[f+32>>2]=0;h[f+40>>3]=0.0;c[f+48>>2]=0;c[f+52>>2]=0;c[f+56>>2]=0;c[f+60>>2]=0;c[f+64>>2]=0;c[f+68>>2]=5;c[f+72>>2]=0;c[f+76>>2]=e;uF(a,84770,f);c[g>>2]=j;c[g+4>>2]=b;c[g+8>>2]=j;c[g+12>>2]=d;c[g+16>>2]=k;c[g+20>>2]=d;c[g+24>>2]=k;c[g+28>>2]=b;c[g+32>>2]=j;c[g+36>>2]=b;uF(a,84828,g);l=i;return}}function Nf(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0,k=0,m=0,n=0,o=0;o=l;l=l+96|0;n=o+56|0;m=o+32|0;k=o;i=+h[d>>3];j=+h[d+16>>3]-i;f=+h[d+24>>3];g=f-+h[d+8>>3];if(!a)qa(111282,84407,53,84860);if(!b)qa(111312,84407,54,84860);e=b+8|0;if(!(c[e>>2]|0))qa(111315,84407,55,84860);oF(a,84879)|0;oF(a,c[e>>2]|0)|0;e=a+360|0;f=-f;if(!(c[e>>2]|0)){h[n>>3]=j;h[n+8>>3]=g;h[n+16>>3]=i;h[n+24>>3]=f;uF(a,85008,n)}else{h[k>>3]=g;h[k+8>>3]=j;h[k+16>>3]=i;h[k+24>>3]=f;uF(a,84899,k);c[m>>2]=c[e>>2];h[m+8>>3]=i;h[m+16>>3]=f;uF(a,84978,m)}oF(a,85087)|0;l=o;return}function Of(a){a=a|0;var b=0,c=0;b=jC(a)|0;while(1){if(!b)break;Pf(b);b=kC(a,b)|0}c=jC(a)|0;while(1){if(!c)break;b=XA(a,c)|0;while(1){if(!b)break;Qf(b);b=ZA(a,b)|0}c=kC(a,c)|0}return}function Pf(a){a=a|0;var b=0,d=0;TC(a,137447,304,1)|0;sP(a);qQ(a,c[(c[(xC(a)|0)+16>>2]|0)+116>>2]&1);a=a+16|0;c[(c[a>>2]|0)+176>>2]=0;b=JK(20)|0;d=c[a>>2]|0;c[d+172>>2]=b;c[d+184>>2]=0;d=JK(20)|0;b=c[a>>2]|0;c[b+180>>2]=d;c[b+200>>2]=0;b=JK(12)|0;d=c[a>>2]|0;c[d+196>>2]=b;c[d+192>>2]=0;d=JK(12)|0;b=c[a>>2]|0;c[b+188>>2]=d;c[b+208>>2]=0;b=JK(12)|0;a=c[a>>2]|0;c[a+204>>2]=b;c[a+216>>2]=1;return}function Qf(d){d=d|0;var e=0,f=0,g=0,h=0,i=0;TC(d,137460,176,1)|0;tP(d)|0;h=ZO(d,c[47174]|0,1,0)|0;g=d+16|0;c[(c[g>>2]|0)+156>>2]=h;h=aP(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0,c[47168]|0,195059)|0;i=aP(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0,c[47168]|0,195059)|0;e=c[g>>2]|0;f=e+154|0;b[f>>1]=1;b[e+168>>1]=1;if((h|0)==(i|0)&(a[h>>0]|0)!=0){b[f>>1]=1e3;i=e+156|0;c[i>>2]=(c[i>>2]|0)*100}if(Mk(d)|0){i=c[g>>2]|0;b[i+154>>1]=0;c[i+156>>2]=0}i=(ZO(d,c[47187]|0,0,0)|0)&255;a[(c[g>>2]|0)+152>>0]=i;i=(ZO(d,c[47175]|0,1,0)|0)&65535;b[(c[g>>2]|0)+170>>1]=i;return}function Rf(a){a=a|0;var b=0,d=0;Sf(c[(c[a+16>>2]|0)+192>>2]|0);d=jC(a)|0;while(1){if(!d)break;b=XA(a,d)|0;while(1){if(!b)break;YP(b);b=ZA(a,b)|0}Tf(d);d=kC(a,d)|0}Uf(a);return}function Sf(b){b=b|0;var d=0,e=0,f=0,g=0;while(1){if(!b)break;f=b+16|0;g=c[(c[f>>2]|0)+164>>2]|0;Vf(b);d=c[f>>2]|0;if((a[d+156>>0]|0)!=1){b=g;continue}e=c[d+180>>2]|0;if(e){U1(e);d=c[f>>2]|0}e=c[d+172>>2]|0;if(e){U1(e);d=c[f>>2]|0}U1(d);U1(b);b=g}return}function Tf(a){a=a|0;var b=0,d=0,e=0;e=a+16|0;b=c[e>>2]|0;d=c[b+172>>2]|0;if(d){U1(d);b=c[e>>2]|0}d=c[b+180>>2]|0;if(d){U1(d);b=c[e>>2]|0}d=c[b+188>>2]|0;if(d){U1(d);b=c[e>>2]|0}d=c[b+196>>2]|0;if(d){U1(d);b=c[e>>2]|0}d=c[b+204>>2]|0;if(d){U1(d);b=c[e>>2]|0}CK(c[b+104>>2]|0);CK(c[(c[e>>2]|0)+108>>2]|0);b=c[(c[e>>2]|0)+8>>2]|0;if(b|0)tb[c[(c[b+4>>2]|0)+4>>2]&127](a);VC(a,137447)|0;return}function Uf(a){a=a|0;var d=0,e=0,f=0;d=OD(a)|0;while(1){if(!d)break;Uf(d);d=PD(d)|0}if(TC(a,137483,0,1)|0){f=a+16|0;d=c[f>>2]|0;e=c[d+184>>2]|0;if(e){U1(e);d=c[f>>2]|0}e=c[d+268>>2]|0;if(e){U1(e);d=c[f>>2]|0}e=c[d+216>>2]|0;if(e){U1(e);d=c[f>>2]|0}do if(c[d+196>>2]|0){e=b[d+236>>1]|0;while(1){if((e|0)>(b[d+238>>1]|0))break;U1(c[(c[d+196>>2]|0)+(e<<6)+12>>2]|0);e=e+1|0;d=c[f>>2]|0}e=c[d+196>>2]|0;if((b[d+236>>1]|0)==-1){U1(e+-64|0);break}else{U1(e);break}}while(0);if((yC(a)|0)!=(a|0)){CK(c[(c[f>>2]|0)+12>>2]|0);VC(a,137483)|0}}return}function Vf(a){a=a|0;var b=0,d=0,e=0;e=a+16|0;d=c[e>>2]|0;b=c[d+176>>2]|0;while(1){a=b+-1|0;if((b|0)<=0)break;b=c[(c[d+172>>2]|0)+(a<<2)>>2]|0;ih(b);U1(c[b+16>>2]|0);U1(b);b=a;d=c[e>>2]|0}b=c[d+184>>2]|0;while(1){a=b+-1|0;if((b|0)<=0)break;b=c[(c[(c[e>>2]|0)+180>>2]|0)+(a<<2)>>2]|0;ih(b);U1(c[b+16>>2]|0);U1(b);b=a}return}function Wf(a){a=a|0;if(tB(a)|0)Xf(a);tM(a);return}function Xf(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;h=l;l=l+48|0;g=h+32|0;f=h;b=lH(a,-1,8)|0;d=mH(a,0,f)|0;kH(a,2,8,f)|0;d=(d|0)==0;e=(b|0)<0;if(e&d)Yf(a);else{if(d)c[f+16>>2]=3;else b=e?8:b;c[f+8>>2]=b;c[f+20>>2]=0;e=GG(a,g,0)|0;b=c[g>>2]|0;do if((b|0)==1){Yf(a);b=0}else{if(c[(c[(c[a+16>>2]|0)+8>>2]|0)+84>>2]|0){Yf(a);b=0;break}c[f+12>>2]=1;d=0;while(1){if((d|0)>=(b|0))break;b=c[e+(d<<2)>>2]|0;Zf(b,a);Yf(b);d=d+1|0;b=c[g>>2]|0}_f(a);jH(c[g>>2]|0,e,a,f)|0;$f(a);ag(c[g>>2]|0,e,a);b=0}while(0);while(1){if((b|0)>=(c[g>>2]|0))break;f=e+(b<<2)|0;U1(c[(c[(c[f>>2]|0)+16>>2]|0)+8>>2]|0);Uf(c[f>>2]|0);wC(a,c[f>>2]|0)|0;b=b+1|0}U1(e)}l=h;return}function Yf(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;m=l;l=l+48|0;k=m+40|0;e=m;i=ZO(a,GA(a,0,85102,0)|0,-1,1)|0;oQ(a,10);d=Lk(a,e)|0;cg(a,a);Of(a);f=(i|0)==1;g=e+32|0;h=(i|0)==2;i=(i|0)==3;j=e+28|0;e=e+24|0;while(1){xj(a,d);if(f){d=3;break}if(c[g>>2]|0){gA(0,85108,k)|0;c[e>>2]=0;d=0}Hh(a,(d|0)!=0&1);if(h){d=7;break}Ki(a,d);if(i){d=9;break}n=(c[j>>2]|0)+-1|0;c[j>>2]=n;if(!((n|0)!=0&(c[e>>2]|0)!=0)){d=11;break}}if((d|0)==3)dg(a,1);else if((d|0)==7)dg(a,2);else if((d|0)==9)dg(a,2);else if((d|0)==11){if(b[(c[a+16>>2]|0)+136>>1]&16)eg(a);rk(a);ig(a);if((dP(QA(a,85199)|0)|0)<<24>>24)ll(a)}l=m;return}function Zf(b,d){b=b|0;d=d|0;var e=0,f=0;TC(b,137483,280,1)|0;e=JK(96)|0;b=c[b+16>>2]|0;c[b+8>>2]=e;d=c[d+16>>2]|0;f=c[d+8>>2]|0;h[e>>3]=+h[f>>3];h[e+24>>3]=+h[f+24>>3];c[b+144>>2]=c[d+144>>2];a[b+115>>0]=a[d+115>>0]|0;c[b+116>>2]=c[d+116>>2];c[b+248>>2]=c[d+248>>2];c[b+252>>2]=c[d+252>>2];c[b+244>>2]=c[d+244>>2];return}function _f(a){a=a|0;var b=0,d=0,e=0;b=JK((tB(a)|0)<<4)|0;d=jC(a)|0;while(1){if(!d)break;e=c[d+16>>2]|0;c[e+132>>2]=b;h[b>>3]=+h[e+16>>3]*.013888888888888888;h[b+8>>3]=+h[e+24>>3]*.013888888888888888;b=b+16|0;d=kC(a,d)|0}return}function $f(a){a=a|0;var b=0,d=0,e=0,f=0;e=c[(c[(jC(a)|0)+16>>2]|0)+132>>2]|0;b=e;d=jC(a)|0;while(1){if(!d)break;f=c[d+16>>2]|0;c[f+132>>2]=0;h[f+16>>3]=+h[b>>3]*72.0;h[f+24>>3]=+h[b+8>>3]*72.0;b=b+16|0;d=kC(a,d)|0}U1(e);return}function ag(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;e=0;f=0;while(1){if((e|0)>=(a|0))break;i=(c[(c[(c[b+(e<<2)>>2]|0)+16>>2]|0)+180>>2]|0)+f|0;e=e+1|0;f=i}i=d+16|0;c[(c[i>>2]|0)+180>>2]=f;g=JK((f<<2)+4|0)|0;c[(c[i>>2]|0)+184>>2]=g;g=0;e=1;while(1){if((g|0)>=(a|0))break;h=(c[b+(g<<2)>>2]|0)+16|0;f=1;while(1){d=c[h>>2]|0;if((f|0)>(c[d+180>>2]|0))break;d=FG(c[(c[d+184>>2]|0)+(f<<2)>>2]|0)|0;c[(c[(c[i>>2]|0)+184>>2]|0)+(e<<2)>>2]=d;bg(c[(c[(c[h>>2]|0)+184>>2]|0)+(f<<2)>>2]|0,d);f=f+1|0;e=e+1|0}g=g+1|0}return}function bg(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;TC(d,137483,280,1)|0;g=d+16|0;d=(c[g>>2]|0)+16|0;f=b+16|0;b=(c[f>>2]|0)+16|0;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];c[d+16>>2]=c[b+16>>2];c[d+20>>2]=c[b+20>>2];c[d+24>>2]=c[b+24>>2];c[d+28>>2]=c[b+28>>2];b=c[f>>2]|0;d=c[g>>2]|0;a[d+275>>0]=a[b+275>>0]|0;d=d+48|0;b=b+48|0;e=d+64|0;do{c[d>>2]=c[b>>2];d=d+4|0;b=b+4|0}while((d|0)<(e|0));b=c[(c[f>>2]|0)+180>>2]|0;c[(c[g>>2]|0)+180>>2]=b;d=JK((b<<2)+4|0)|0;c[(c[g>>2]|0)+184>>2]=d;d=1;while(1){e=c[f>>2]|0;if((d|0)>(b|0))break;e=FG(c[(c[e+184>>2]|0)+(d<<2)>>2]|0)|0;c[(c[(c[g>>2]|0)+184>>2]|0)+(d<<2)>>2]=e;bg(c[(c[(c[f>>2]|0)+184>>2]|0)+(d<<2)>>2]|0,e);d=d+1|0}f=e+12|0;c[(c[g>>2]|0)+12>>2]=c[f>>2];c[f>>2]=0;return}function cg(a,b){a=a|0;b=b|0;if((yC(a)|0)!=(a|0))TC(a,137483,280,1)|0;if((a|0)==(b|0))c[(c[(yC(a)|0)+16>>2]|0)+188>>2]=a;a=OD(a)|0;while(1){if(!a)break;cg(a,b);a=PD(a)|0}return}function dg(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+1040|0;k=m+8|0;j=m;e=m+16|0;f=GA(a,1,87171,195059)|0;g=GA(a,1,85242,195059)|0;h=(b|0)>0;d=(b|0)==1;b=jC(a)|0;while(1){if(!b)break;if(h?(i=b+16|0,c[j>>2]=c[(c[i>>2]|0)+232>>2],m4(e,137395,j)|0,KA(b,f,e)|0,!d):0){c[k>>2]=c[(c[i>>2]|0)+236>>2];m4(e,137395,k)|0;KA(b,g,e)|0}b=kC(a,b)|0}l=m;return}function eg(a){a=a|0;var b=0,c=0,d=0;c=ND(a,86675,0)|0;if(c|0){b=jC(c)|0;while(1){if(!b)break;d=kC(c,b)|0;qh(a,b);fg(a,b);Tf(b);vC(a,b)|0;b=d}RD(a,c)|0}return}function fg(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;j=c[(c[b+16>>2]|0)+232>>2]|0;i=a+16|0;a=c[(c[i>>2]|0)+196>>2]|0;d=c[a+(j<<6)>>2]|0;h=a+(j<<6)+4|0;g=0;e=0;while(1){if((e|0)>=(d|0)){f=7;break}f=c[(c[h>>2]|0)+(e<<2)>>2]|0;e=e+1|0;if((f|0)==(b|0)){f=4;break}else g=f}if((f|0)==4){while(1){if((e|0)>=(d|0))break;f=c[a+(j<<6)+4>>2]|0;c[f+(e+-1<<2)>>2]=c[f+(e<<2)>>2];f=c[(c[i>>2]|0)+196>>2]|0;e=e+1|0;d=c[f+(j<<6)>>2]|0;a=f;f=4}c[a+(j<<6)>>2]=d+-1}else if((f|0)==7?(g|0)!=(b|0):0)qa(85208,85215,248,85225);return}function gg(a){a=a|0;return c[(c[(yC(a)|0)+16>>2]|0)+188>>2]|0}function hg(b,c){b=b|0;c=c|0;var d=0;d=a[b+28>>0]|0;if(a[c+28>>0]|0)if(d<<24>>24){d=~~(+h[b>>3]-+h[c>>3]);if(!d)d=~~(+h[b+8>>3]-+h[c+8>>3])}else d=-1;else d=d<<24>>24!=0&1;return d|0}function ig(a){a=a|0;jg(a,1);return}function jg(d,e){d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0.0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0,X=0,Y=0,Z=0,_=0.0;Z=l;l=l+656|0;O=Z+592|0;N=Z+552|0;f=Z+544|0;T=Z+96|0;U=Z;Y=Z+632|0;V=d+16|0;X=b[(c[V>>2]|0)+136>>1]&14;P=X&65535;Q=T+16|0;c[Q>>2]=Z+368;R=U+16|0;c[R>>2]=Z+192;do if(X<<16>>16){S=X<<16>>16==4;do if(S){kg(d);if(!(a[(c[(c[d+60>>2]|0)+16>>2]|0)+113>>0]&1))A=9;else{gA(0,85248,f)|0;A=9}}else if(X<<16>>16==8){kg(d);if(!(a[(c[(c[d+60>>2]|0)+16>>2]|0)+113>>0]&1)){rQ(d,0);n=0;m=0;k=0;break}else{lg(d);rQ(d,1);n=0;m=0;k=0;break}}else A=9;while(0);if((A|0)==9){jl(d);if(FM()|0)break;W=JK(96)|0;K=c[(c[V>>2]|0)+248>>2]|0;c[Y+8>>2]=(K|0)/4|0;c[Y+12>>2]=K;k=JK(512)|0;F=Y+4|0;c[F>>2]=0;c[Y>>2]=0;j=c[V>>2]|0;C=b[j+236>>1]|0;D=0;L=0;M=k;E=k;m=k;n=k;o=k;H=0;I=0;a:while(1){if((C|0)>(b[j+238>>1]|0))break;p=c[j+196>>2]|0;i=c[p+(C<<6)>>2]|0;G=i+D|0;g=c[p+(C<<6)+4>>2]|0;f=c[g>>2]|0;if(!f)f=H;else{_=+(H|0);f=c[f+16>>2]|0;y=+h[f+16>>3]-+h[f+88>>3];f=~~(y>_?_:y)}if((i|0)!=0?(J=c[g+(i+-1<<2)>>2]|0,(J|0)!=0):0){y=+(I|0);g=c[J+16>>2]|0;_=+h[g+96>>3]+ +h[g+16>>3];g=~~(_<y?y:_)}else g=I;B=f+-16|0;z=g+16|0;x=0;t=L;r=M;q=j;u=E;v=m;g=k;f=p;while(1){if((x|0)>=(i|0))break;k=c[(c[f+(C<<6)+4>>2]|0)+(x<<2)>>2]|0;w=k+16|0;f=c[w>>2]|0;i=c[f+112>>2]|0;if(i){i=i+16|0;j=c[(c[i>>2]|0)+96>>2]|0;if(!j){A=21;break a}s=j+56|0;f=f+16|0;c[s>>2]=c[f>>2];c[s+4>>2]=c[f+4>>2];c[s+8>>2]=c[f+8>>2];c[s+12>>2]=c[f+12>>2];a[(c[(c[i>>2]|0)+96>>2]|0)+81>>0]=1;f=c[w>>2]|0}if((a[f+156>>0]|0)!=0?(kb[c[17300>>2]&63](k)|0)<<24>>24==0:0){q=t;f=r;j=u;k=v;i=o}else{s=0;q=t;j=u;k=v;A=25}b:do if((A|0)==25){while(1){A=0;p=c[w>>2]|0;i=c[(c[p+180>>2]|0)+(s<<2)>>2]|0;if(!i)break;switch(a[(c[i+16>>2]|0)+112>>0]|0){case 6:case 4:{f=q;i=r;break}default:{mg(i,1,16,64);f=q+1|0;c[r+(q<<2)>>2]=i;if(!(f&127)){g=MK(g,(q<<2)+516|0)|0;i=g;j=g;k=g;n=g;o=g}else i=r}}s=s+1|0;q=f;r=i;A=25}f=c[p+188>>2]|0;c:do if(!f)i=o;else{s=0;i=o;while(1){f=c[f+(s<<2)>>2]|0;if(!f)break c;mg(f,2,0,128);o=q+1|0;c[r+(q<<2)>>2]=f;if(!(o&127)){g=MK(i,(q<<2)+516|0)|0;f=g;j=g;k=g;n=g;i=g}else f=r;p=c[w>>2]|0;s=s+1|0;q=o;r=f;f=c[p+188>>2]|0}}while(0);f=c[p+204>>2]|0;if(!f)f=r;else{if(!(a[p+156>>0]|0)){v=p+96|0;_=+h[v>>3];s=p+240|0;h[v>>3]=+h[s>>3];h[s>>3]=_;s=0;p=r}else{s=0;p=r}while(1){f=c[f+(s<<2)>>2]|0;if(!f){f=p;break b}mg(f,0,0,128);o=q+1|0;c[p+(q<<2)>>2]=f;if(!(o&127)){g=MK(n,(q<<2)+516|0)|0;f=g;j=g;k=g;n=g;i=g}else f=p;s=s+1|0;q=o;p=f;f=c[(c[w>>2]|0)+204>>2]|0}}}while(0);u=c[V>>2]|0;w=c[u+196>>2]|0;x=x+1|0;t=q;r=f;q=u;u=j;v=k;o=i;i=c[w+(C<<6)>>2]|0;f=w}C=C+1|0;D=G;L=t;M=r;E=u;j=q;m=v;k=g;H=B;I=z}if((A|0)==21)qa(85316,85329,313,85342);c[Y>>2]=H;c[F>>2]=I;o4(E,L,4,58);c[W+84>>2]=JK((D<<5)+11520|0)|0;c[Y+16>>2]=JK(C<<5)|0;d:do if(X<<16>>16==2){f=(c[V>>2]|0)+192|0;while(1){g=c[f>>2]|0;if(!g)break d;i=g+16|0;f=c[i>>2]|0;if((a[f+156>>0]|0)==1?(c[f+104>>2]|0)!=0:0){og(g);f=c[i>>2]|0}f=f+164|0}}while(0);z=U+48|0;A=U+-48|0;B=T+48|0;C=T+-48|0;y=+(K|0);x=0;e:while(1){if((x|0)>=(L|0))break;v=M+(x<<2)|0;r=c[v>>2]|0;s=pg(r)|0;t=r+16|0;g=c[t>>2]|0;if(!(a[g+44>>0]|0)){g=(a[g+84>>0]|0)==0?s:r;f=g;g=c[g+16>>2]|0}else f=r;i=f+16|0;if(c[g+164>>2]&32){g=c[Q>>2]|0;W6(g|0,c[i>>2]|0,176)|0;n=T;o=f;p=n+48|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));c[Q>>2]=g;o=c[f>>2]&3;n=c[T>>2]&3;c[((n|0)==3?T:B)+40>>2]=c[((o|0)==2?f:f+-48|0)+40>>2];c[((n|0)==2?T:C)+40>>2]=c[((o|0)==3?f:f+48|0)+40>>2];n=g+16|0;o=(c[i>>2]|0)+56|0;p=n+40|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));n=g+56|0;o=(c[i>>2]|0)+16|0;p=n+40|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));a[g+112>>0]=1;c[g+116>>2]=f;f=T}q=f+16|0;w=1;f=x;while(1){u=f+1|0;if((u|0)>=(L|0))break;j=M+(u<<2)|0;f=c[j>>2]|0;if((s|0)!=(pg(f)|0))break;if(!(a[(c[t>>2]|0)+113>>0]|0)){k=f+16|0;g=c[k>>2]|0;if(!(a[g+44>>0]|0)){g=(a[g+84>>0]|0)==0?s:f;f=g;g=c[g+16>>2]|0}i=f+16|0;if(c[g+164>>2]&32){g=c[R>>2]|0;W6(g|0,c[i>>2]|0,176)|0;n=U;o=f;p=n+48|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));c[R>>2]=g;o=c[f>>2]&3;n=c[U>>2]&3;c[((n|0)==3?U:z)+40>>2]=c[((o|0)==2?f:f+-48|0)+40>>2];c[((n|0)==2?U:A)+40>>2]=c[((o|0)==3?f:f+48|0)+40>>2];n=g+16|0;o=(c[i>>2]|0)+56|0;p=n+40|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));n=g+56|0;o=(c[i>>2]|0)+16|0;p=n+40|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));a[g+112>>0]=1;c[g+116>>2]=f}i=c[q>>2]|0;f=g+16|0;n=N;o=i+16|0;p=n+40|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));n=O;o=f;p=n+40|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));if(hg(N,O)|0)break;f=g+56|0;n=N;o=i+56|0;p=n+40|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));n=O;o=f;p=n+40|0;do{c[n>>2]=c[o>>2];n=n+4|0;o=o+4|0}while((n|0)<(p|0));if(hg(N,O)|0)break;K=c[t>>2]|0;if((c[K+164>>2]&15|0)==2?(c[K+96>>2]|0)!=(c[(c[k>>2]|0)+96>>2]|0):0)break;if(c[(c[(c[j>>2]|0)+16>>2]|0)+164>>2]&64|0)break}w=w+1|0;f=u}if(S){if((w|0)==1)f=O;else f=JK(w<<2)|0;c[f>>2]=pg(c[v>>2]|0)|0;g=1;while(1){if((g|0)>=(w|0))break;c[f+(g<<2)>>2]=c[v+(g<<2)>>2];g=g+1|0}PM(d,f,w,P,17296);if((w|0)>1)U1(f);x=u;continue}f=c[r>>2]&3;K=c[((f|0)==3?r:r+48|0)+40>>2]|0;f=c[((f|0)==2?r:r+-48|0)+40>>2]|0;g=c[K+16>>2]|0;i=c[g+232>>2]|0;if((K|0)!=(f|0))if((i|0)==(c[(c[f+16>>2]|0)+232>>2]|0)){qg(d,Y,W,M,x,w,P);x=u;continue}else{rg(d,Y,W,M,x,w,P);x=u;continue}f=c[V>>2]|0;do if((i|0)==(b[f+238>>1]|0))if((i|0)>0){f=~~(+h[(c[(c[c[(c[f+196>>2]|0)+(i+-1<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]-+h[g+24>>3]);break}else{f=~~+h[g+80>>3];break}else if((i|0)==(b[f+236>>1]|0)){f=~~(+h[g+24>>3]-+h[(c[(c[c[(c[f+196>>2]|0)+(i+1<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]);break}else{f=c[f+196>>2]|0;_=+h[g+24>>3];K=~~(+h[(c[(c[c[f+(i+-1<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]-_);f=~~(_-+h[(c[(c[c[f+(i+1<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]);f=(K|0)<(f|0)?K:f;break}while(0);lO(W,M,x,w,y,+((f|0)/2|0|0),17296);f=0;while(1){if((f|0)>=(w|0)){x=u;continue e}g=c[(c[(c[M+(f+x<<2)>>2]|0)+16>>2]|0)+96>>2]|0;if(g|0)zP(d,g);f=f+1|0}}f=(c[V>>2]|0)+192|0;while(1){g=c[f>>2]|0;if(!g)break;i=g+16|0;f=c[i>>2]|0;if((a[f+156>>0]|0)==1?(c[f+104>>2]|0)!=0:0){og(g);zP(d,c[(c[i>>2]|0)+104>>2]|0);f=c[i>>2]|0}f=f+164|0}if(!e){n=W;k=W}else{sg(d);n=W;k=W}}f:do if((c[47197]|0)!=0|(c[47198]|0)!=0?(c[47203]|0)!=0|(c[47202]|0)!=0:0){j=jC(d)|0;while(1){if(!j)break f;g:do if(c[47197]|0){f=_A(d,j)|0;while(1){if(!f)break g;g=f+-48|0;i=(c[f>>2]&3|0)==2?f:g;if(c[(c[i+16>>2]|0)+100>>2]|0){tO(i,1)|0;zP(d,c[(c[((c[f>>2]&3|0)==2?f:g)+16>>2]|0)+100>>2]|0)}f=$A(d,f)|0}}while(0);h:do if(c[47198]|0){f=XA(d,j)|0;while(1){if(!f)break h;g=f+16|0;if(c[(c[g>>2]|0)+104>>2]|0?tO(f,0)|0:0)zP(d,c[(c[g>>2]|0)+104>>2]|0);f=ZA(d,f)|0}}while(0);j=kC(d,j)|0}}while(0);switch(X&15){case 4:case 8:break;default:{U1(m);U1(c[n+84>>2]|0);U1(k);U1(c[Y+16>>2]|0);GM()}}c[47138]=1;c[47139]=1}while(0);l=Z;return}function kg(a){a=a|0;var b=0,d=0,e=0.0,f=0;b=jC(a)|0;while(1){if(!b)break;d=c[b+16>>2]|0;if(c[d+204>>2]|0){f=d+96|0;e=+h[f>>3];d=d+240|0;h[f>>3]=+h[d>>3];h[d>>3]=e}b=kC(a,b)|0}return}function lg(b){b=b|0;var d=0,e=0,f=0,g=0;d=(c[b+16>>2]|0)+192|0;a:while(1){f=c[d>>2]|0;if(!f){d=12;break}g=f+16|0;d=c[g>>2]|0;do if((a[d+156>>0]|0)==1){e=c[d+112>>2]|0;if(!e){e=c[d+104>>2]|0;if(!e)break;og(f);d=e}else{e=c[(c[e+16>>2]|0)+96>>2]|0;if(!e){d=6;break a}f=e+56|0;d=d+16|0;c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];a[e+81>>0]=1;d=e}zP(b,d);d=c[g>>2]|0}while(0);d=d+164|0}if((d|0)==6)qa(85516,85329,234,85535);else if((d|0)==12)return}function mg(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0;do if(!d){g=c[b>>2]&3;d=c[((g|0)==3?b:b+48|0)+40>>2]|0;g=c[((g|0)==2?b:b+-48|0)+40>>2]|0;if((d|0)!=(g|0)){d=(c[(c[d+16>>2]|0)+232>>2]|0)==(c[(c[g+16>>2]|0)+232>>2]|0)?2:1;break}d=c[b+16>>2]|0;if(!(a[d+44>>0]|0))d=(a[d+84>>0]|0)==0?8:4;else d=4}while(0);a:do if(!e)switch(d|0){case 1:{e=c[b>>2]&3;e=(c[(c[(c[((e|0)==3?b:b+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)<(c[(c[(c[((e|0)==2?b:b+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)?16:32;break a}case 2:{e=c[b>>2]&3;e=(c[(c[(c[((e|0)==3?b:b+48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)<(c[(c[(c[((e|0)==2?b:b+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)?16:32;break a}default:{e=16;break a}}while(0);c[(c[b+16>>2]|0)+164>>2]=d|f|e;return}function ng(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0;D=l;l=l+624|0;z=D+584|0;y=D+544|0;s=D+368|0;e=D+192|0;t=D+96|0;v=D;u=t+16|0;c[u>>2]=s;w=v+16|0;c[w>>2]=e;C=c[b>>2]|0;B=c[d>>2]|0;x=C+16|0;q=c[x>>2]|0;d=c[q+164>>2]|0;f=d&15;A=B+16|0;b=c[A>>2]|0;g=c[b+164>>2]&15;do if((g|0)==(f|0)){p=pg(C)|0;r=pg(B)|0;n=c[p>>2]|0;g=n&3;f=c[(c[((g|0)==3?p:p+48|0)+40>>2]|0)+16>>2]|0;g=c[(c[((g|0)==2?p:p+-48|0)+40>>2]|0)+16>>2]|0;i=(c[f+232>>2]|0)-(c[g+232>>2]|0)|0;o=c[r>>2]|0;m=o&3;k=c[(c[((m|0)==3?r:r+48|0)+40>>2]|0)+16>>2]|0;m=c[(c[((m|0)==2?r:r+-48|0)+40>>2]|0)+16>>2]|0;j=(c[k+232>>2]|0)-(c[m+232>>2]|0)|0;i=(i|0)>-1?i:0-i|0;j=(j|0)>-1?j:0-j|0;if((i|0)!=(j|0)){b=i-j|0;break}g=~~(+h[f+16>>3]-+h[g+16>>3]);g=(g|0)>-1?g:0-g|0;f=~~(+h[k+16>>3]-+h[m+16>>3]);f=(f|0)>-1?f:0-f|0;if((g|0)!=(f|0)){b=g-f|0;break}g=n>>>4;f=o>>>4;if((g|0)!=(f|0)){b=g-f|0;break}if(!(a[q+44>>0]|0)){d=(a[q+84>>0]|0)==0?p:C;g=d;d=c[(c[d+16>>2]|0)+164>>2]|0}else g=C;f=g+16|0;if(d&32){W6(s|0,c[f>>2]|0,176)|0;i=t;j=g;k=i+48|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));c[u>>2]=s;j=c[g>>2]&3;i=c[t>>2]&3;c[((i|0)==3?t:t+48|0)+40>>2]=c[((j|0)==2?g:g+-48|0)+40>>2];c[((i|0)==2?t:t+-48|0)+40>>2]=c[((j|0)==3?g:g+48|0)+40>>2];i=s+16|0;j=(c[f>>2]|0)+56|0;k=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));i=s+56|0;j=(c[f>>2]|0)+16|0;k=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));a[s+112>>0]=1;c[s+116>>2]=g;g=t;b=c[A>>2]|0}if(!(a[b+44>>0]|0)){b=(a[b+84>>0]|0)==0?r:B;f=b;b=c[b+16>>2]|0}else f=B;d=f+16|0;if(!(c[b+164>>2]&32))e=b;else{W6(e|0,c[d>>2]|0,176)|0;i=v;j=f;k=i+48|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));c[w>>2]=e;j=c[f>>2]&3;i=c[v>>2]&3;c[((i|0)==3?v:v+48|0)+40>>2]=c[((j|0)==2?f:f+-48|0)+40>>2];c[((i|0)==2?v:v+-48|0)+40>>2]=c[((j|0)==3?f:f+48|0)+40>>2];i=e+16|0;j=(c[d>>2]|0)+56|0;k=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));i=e+56|0;j=(c[d>>2]|0)+16|0;k=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));a[e+112>>0]=1;c[e+116>>2]=f}d=c[g+16>>2]|0;b=e+16|0;i=y;j=d+16|0;k=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));i=z;j=b;k=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));b=hg(y,z)|0;if(!b){b=e+56|0;i=y;j=d+56|0;k=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));i=z;j=b;k=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(k|0));b=hg(y,z)|0;if(!b){d=c[(c[x>>2]|0)+164>>2]&192;b=c[(c[A>>2]|0)+164>>2]&192;if((d|0)==(b|0)){b=((c[C>>2]|0)>>>4)-((c[B>>2]|0)>>>4)|0;break}else{b=d-b|0;break}}}}else b=g-f|0;while(0);l=D;return b|0}function og(b){b=b|0;var d=0,e=0,f=0,g=0.0,i=0.0;f=b+16|0;d=c[f>>2]|0;if(c[d+176>>2]|0){d=c[d+180>>2]|0;while(1){d=(c[d>>2]|0)+16|0;e=c[d>>2]|0;if(!(a[e+112>>0]|0))break;else d=e+116|0}e=c[e+96>>2]|0;g=+h[e+24>>3];i=+h[e+32>>3];e=(c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&1|0)!=0;b=c[f>>2]|0;f=c[(c[d>>2]|0)+96>>2]|0;h[f+56>>3]=(e?i:g)*.5+ +h[b+16>>3];h[f+64>>3]=+h[b+24>>3];a[f+81>>0]=1}return}function pg(a){a=a|0;var b=0,d=0;while(1){b=c[a+16>>2]|0;d=c[b+172>>2]|0;if(!d)break;else a=d}while(1){b=c[b+116>>2]|0;if(!b)break;a=b;b=c[b+16>>2]|0}return a|0}function qg(b,d,e,f,g,i,j){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;var k=0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0.0,w=0,x=0,y=0,z=0,A=0,B=0,C=0.0,D=0.0,E=0.0,F=0.0,G=0.0;B=l;l=l+1712|0;x=B+1664|0;s=B+1488|0;t=B+1392|0;A=B+1696|0;y=B+696|0;z=B;p=t+16|0;c[p>>2]=s;q=c[f+(g<<2)>>2]|0;r=q+16|0;k=c[r>>2]|0;u=a[k+113>>0]|0;if(c[k+164>>2]&32){W6(s|0,k|0,176)|0;k=t;n=q;o=k+48|0;do{c[k>>2]=c[n>>2];k=k+4|0;n=n+4|0}while((k|0)<(o|0));c[p>>2]=s;k=c[t>>2]&3;c[((k|0)==3?t:t+48|0)+40>>2]=c[((c[q>>2]&3|0)==2?q:q+-48|0)+40>>2];c[((k|0)==2?t:t+-48|0)+40>>2]=c[((c[q>>2]&3|0)==3?q:q+48|0)+40>>2];k=s+16|0;n=(c[r>>2]|0)+56|0;o=k+40|0;do{c[k>>2]=c[n>>2];k=k+4|0;n=n+4|0}while((k|0)<(o|0));k=s+56|0;n=(c[r>>2]|0)+16|0;o=k+40|0;do{c[k>>2]=c[n>>2];k=k+4|0;n=n+4|0}while((k|0)<(o|0));a[s+112>>0]=1;c[s+116>>2]=q;q=t}k=1;while(1){if((k|0)>=(i|0)){w=6;break}if(!(a[(c[(c[f+(k+g<<2)>>2]|0)+16>>2]|0)+113>>0]|0))k=k+1|0;else{w=7;break}}a:do if((w|0)==6)if(!(u<<24>>24)){k=c[q+16>>2]|0;if(c[k+96>>2]|0){Qg(b,d,e,q,j);break}if((j|0)==2){A=c[q>>2]&3;Rg(c[((A|0)==3?q:q+48|0)+40>>2]|0,c[((A|0)==2?q:q+-48|0)+40>>2]|0,f,g,i,2);break}t=a[k+49>>0]|0;u=a[k+89>>0]|0;if(!(t<<24>>24==1&u<<24>>24!=4)?!(t<<24>>24!=4&u<<24>>24==1):0){p=c[q>>2]&3;o=c[((p|0)==3?q:q+48|0)+40>>2]|0;p=c[((p|0)==2?q:q+-48|0)+40>>2]|0;k=c[o+16>>2]|0;n=c[k+232>>2]|0;if((n|0)>0){u=c[(c[b+16>>2]|0)+196>>2]|0;t=n+~(a[(c[(c[b+60>>2]|0)+16>>2]|0)+113>>0]&1)|0;m=+h[(c[(c[c[u+(t<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]-+h[u+(t<<6)+16>>3]-+h[k+24>>3]-+h[u+(n<<6)+24>>3]}else m=+(c[(c[b+16>>2]|0)+252>>2]|0);C=+(i+1|0);v=+(c[d+12>>2]|0)/C;m=m/C;Tg(b,d,e,o,q,y,1);Tg(b,d,e,p,q,z,0);s=y+52|0;t=z+52|0;p=(j|0)==10;q=e+80|0;k=0;while(1){if((k|0)>=(i|0))break a;r=c[f+(k+g<<2)>>2]|0;o=c[s>>2]|0;n=o+-1|0;G=+h[y+56+(n<<5)>>3];F=+h[y+56+(n<<5)+16>>3];E=+h[y+56+(n<<5)+24>>3];h[19215]=G;h[19216]=E;k=k+1|0;C=+(k|0);D=v*C;h[19217]=F+D;C=E+m*C;h[19218]=C;h[19219]=G;h[19220]=C;n=(c[t>>2]|0)+-1|0;G=+h[z+56+(n<<5)+16>>3];h[19221]=G;h[19222]=C+m;E=+h[z+56+(n<<5)>>3];F=+h[z+56+(n<<5)+24>>3];h[19225]=G;h[19224]=F;h[19223]=E-D;h[19226]=C;n=0;while(1){if((n|0)>=(o|0)){n=0;break}o=y+56+(n<<5)|0;c[x>>2]=c[o>>2];c[x+4>>2]=c[o+4>>2];c[x+8>>2]=c[o+8>>2];c[x+12>>2]=c[o+12>>2];c[x+16>>2]=c[o+16>>2];c[x+20>>2]=c[o+20>>2];c[x+24>>2]=c[o+24>>2];c[x+28>>2]=c[o+28>>2];fO(e,x);n=n+1|0;o=c[s>>2]|0}while(1){if((n|0)==3)break;j=153720+(n<<5)|0;c[x>>2]=c[j>>2];c[x+4>>2]=c[j+4>>2];c[x+8>>2]=c[j+8>>2];c[x+12>>2]=c[j+12>>2];c[x+16>>2]=c[j+16>>2];c[x+20>>2]=c[j+20>>2];c[x+24>>2]=c[j+24>>2];c[x+28>>2]=c[j+28>>2];fO(e,x);n=n+1|0}o=c[t>>2]|0;while(1){n=o+-1|0;if((o|0)<=0)break;o=z+56+(n<<5)|0;c[x>>2]=c[o>>2];c[x+4>>2]=c[o+4>>2];c[x+8>>2]=c[o+8>>2];c[x+12>>2]=c[o+12>>2];c[x+16>>2]=c[o+16>>2];c[x+20>>2]=c[o+20>>2];c[x+24>>2]=c[o+24>>2];c[x+28>>2]=c[o+28>>2];fO(e,x);o=n}if(p)n=HM(e,A)|0;else n=NM(e,A)|0;o=c[A>>2]|0;if(!o)break a;dO(r,c[((c[r>>2]&3|0)==2?r:r+-48|0)+40>>2]|0,n,o,17296);c[q>>2]=0}}Sg(b,d,e,f,g,i,q,(j|0)==10&1)}else w=7;while(0);if((w|0)==7)Pg(b,f,g,i,q,j);l=B;return}function rg(b,d,e,f,g,i,j){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;var k=0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0,X=0,Y=0,Z=0,_=0,$=0,aa=0,ba=0,ca=0;ba=l;l=l+2576|0;S=ba+2528|0;_=ba+2568|0;k=ba+2352|0;n=ba+2176|0;t=ba+1904|0;Y=ba+1808|0;$=ba+1712|0;T=ba+1016|0;U=ba+320|0;V=ba+256|0;X=ba+2564|0;Z=ba+2560|0;z=ba+288|0;A=ba+224|0;L=ba+192|0;M=ba+160|0;N=ba+128|0;P=ba+96|0;Q=ba+64|0;R=ba+32|0;W=ba;s=t+16|0;c[s>>2]=k;o=Y+16|0;c[o>>2]=n;aa=$+16|0;c[aa>>2]=ba+2e3;if(!(c[46734]|0)){c[46734]=KK(32e3)|0;c[46735]=KK(32e3)|0;c[46736]=2e3;c[46737]=2e3}u=c[f+(g<<2)>>2]|0;K=c[u>>2]&3;p=u+48|0;q=u+-48|0;K=(c[(c[(c[((K|0)==3?u:p)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((K|0)==2?u:q)+40>>2]|0)+16>>2]|0)+232>>2]|0)|0;r=u+16|0;if((((K|0)>-1?K:0-K|0)|0)<=1){k=c[r>>2]|0;if(!(c[k+164>>2]&32)){K=u;n=0}else{n=c[s>>2]|0;W6(n|0,k|0,176)|0;w=t;x=u;y=w+48|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));c[s>>2]=n;w=c[t>>2]&3;c[((w|0)==3?t:t+48|0)+40>>2]=c[((c[u>>2]&3|0)==2?u:q)+40>>2];c[((w|0)==2?t:t+-48|0)+40>>2]=c[((c[u>>2]&3|0)==3?u:p)+40>>2];w=n+16|0;x=(c[r>>2]|0)+56|0;y=w+40|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));k=c[s>>2]|0;w=k+56|0;x=(c[r>>2]|0)+16|0;y=w+40|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));a[k+112>>0]=1;n=0;J=12}}else{W6(k|0,c[r>>2]|0,176)|0;w=t;x=u;y=w+48|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));w=t+48|0;x=p;y=w+48|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));c[s>>2]=k;k=c[r>>2]|0;if(!(c[k+164>>2]&32)){W6(n|0,k|0,176)|0;w=Y;x=u;y=w+48|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));c[o>>2]=n;o=c[t>>2]|0;c[((o&3|0)==3?t:t+48|0)+40>>2]=c[((c[u>>2]&3|0)==3?u:p)+40>>2];w=Y+48|0;x=p;y=w+48|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0))}else{n=c[o>>2]|0;W6(n|0,k|0,176)|0;w=Y;x=u;y=w+48|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));c[o>>2]=n;w=c[Y>>2]&3;c[((w|0)==3?Y:Y+48|0)+40>>2]=c[((c[u>>2]&3|0)==2?u:q)+40>>2];c[((w|0)==2?Y:Y+-48|0)+40>>2]=c[((c[u>>2]&3|0)==3?u:p)+40>>2];w=n+16|0;x=(c[r>>2]|0)+56|0;y=w+40|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));k=c[o>>2]|0;w=k+56|0;x=(c[r>>2]|0)+16|0;y=w+40|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));a[k+112>>0]=1;c[k+116>>2]=u;o=c[t>>2]|0;c[((o&3|0)==3?t:t+48|0)+40>>2]=c[((c[u>>2]&3|0)==2?u:q)+40>>2];w=(c[s>>2]|0)+16|0;x=(c[r>>2]|0)+56|0;y=w+40|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0))}k=pg(u)|0;while(1){n=c[(c[k+16>>2]|0)+172>>2]|0;if(!n)break;else k=n}c[((o&3|0)==2?t:t+-48|0)+40>>2]=c[((c[k>>2]&3|0)==2?k:k+-48|0)+40>>2];k=c[s>>2]|0;a[k+84>>0]=0;a[k+112>>0]=1;n=k+56|0;c[n>>2]=0;c[n+4>>2]=0;c[n+8>>2]=0;c[n+12>>2]=0;n=1;J=12}if((J|0)==12){c[k+116>>2]=u;K=t}H=(j|0)==2;if(H?(v=vg(b,K,c[46734]|0,_)|0,c[Z>>2]=v,(v|0)!=0):0)J=63;else J=15;a:do if((J|0)==15){G=(j|0)==10;c[Z>>2]=0;o=c[K>>2]&3;k=c[((o|0)==3?K:K+48|0)+40>>2]|0;o=c[((o|0)==2?K:K+-48|0)+40>>2]|0;c[_>>2]=o;wg(z,b,d,k,0,K);c[T>>2]=c[z>>2];c[T+4>>2]=c[z+4>>2];c[T+8>>2]=c[z+8>>2];c[T+12>>2]=c[z+12>>2];c[T+16>>2]=c[z+16>>2];c[T+20>>2]=c[z+20>>2];c[T+24>>2]=c[z+24>>2];c[T+28>>2]=c[z+28>>2];c[V>>2]=c[z>>2];c[V+4>>2]=c[z+4>>2];c[V+8>>2]=c[z+8>>2];c[V+12>>2]=c[z+12>>2];c[V+16>>2]=c[z+16>>2];c[V+20>>2]=c[z+20>>2];gO(e,K,1,T,xg(k)|0);B=T+52|0;I=(c[B>>2]|0)+-1|0;D=V+24|0;h[D>>3]=+h[T+56+(I<<5)+24>>3];E=V+8|0;h[E>>3]=+h[T+56+(I<<5)+8>>3];I=c[k+16>>2]|0;F=b+16|0;m=+h[I+24>>3]-+h[(c[(c[F>>2]|0)+196>>2]|0)+(c[I+232>>2]<<6)+16>>3];c[S>>2]=c[V>>2];c[S+4>>2]=c[V+4>>2];c[S+8>>2]=c[V+8>>2];c[S+12>>2]=c[V+12>>2];c[S+16>>2]=c[V+16>>2];c[S+20>>2]=c[V+20>>2];c[S+24>>2]=c[V+24>>2];c[S+28>>2]=c[V+28>>2];yg(A,S,1,m);c[V>>2]=c[A>>2];c[V+4>>2]=c[A+4>>2];c[V+8>>2]=c[A+8>>2];c[V+12>>2]=c[A+12>>2];c[V+16>>2]=c[A+16>>2];c[V+20>>2]=c[A+20>>2];c[V+24>>2]=c[A+24>>2];c[V+28>>2]=c[A+28>>2];A=V+16|0;if(+h[V>>3]<+h[A>>3]?+h[E>>3]<+h[D>>3]:0){I=c[B>>2]|0;c[B>>2]=I+1;I=T+56+(I<<5)|0;c[I>>2]=c[V>>2];c[I+4>>2]=c[V+4>>2];c[I+8>>2]=c[V+8>>2];c[I+12>>2]=c[V+12>>2];c[I+16>>2]=c[V+16>>2];c[I+20>>2]=c[V+20>>2];c[I+24>>2]=c[V+24>>2];c[I+28>>2]=c[V+28>>2]}w=b+60|0;C=U+52|0;x=e+56|0;y=e+69|0;j=e+16|0;z=e+29|0;I=K;q=0;r=-1;b:while(1){s=k;t=0;k=0;u=I;while(1){p=o;v=o;if((a[(c[p+16>>2]|0)+156>>0]|0)!=1)break b;if((kb[c[17300>>2]&63](p)|0)<<24>>24)break b;o=t|1;ca=153720+(t<<5)|0;zg(L,d,b,c[(c[s+16>>2]|0)+232>>2]|0);c[ca>>2]=c[L>>2];c[ca+4>>2]=c[L+4>>2];c[ca+8>>2]=c[L+8>>2];c[ca+12>>2]=c[L+12>>2];c[ca+16>>2]=c[L+16>>2];c[ca+20>>2]=c[L+20>>2];c[ca+24>>2]=c[L+24>>2];c[ca+28>>2]=c[L+28>>2];if(!k){q=Ag(p)|0;k=(q|0)<((a[(c[(c[w>>2]|0)+16>>2]|0)+113>>0]<<1&2)+3&255|0);q=k?q:q+-2|0;r=k?r:1;k=(k^1)&1}if(!((k|0)==0|(r|0)>0))break;ca=153720+(o<<5)|0;wg(M,b,d,p,u,c[c[(c[p+16>>2]|0)+180>>2]>>2]|0);c[ca>>2]=c[M>>2];c[ca+4>>2]=c[M+4>>2];c[ca+8>>2]=c[M+8>>2];c[ca+12>>2]=c[M+12>>2];c[ca+16>>2]=c[M+16>>2];c[ca+20>>2]=c[M+20>>2];c[ca+24>>2]=c[M+24>>2];c[ca+28>>2]=c[M+28>>2];ca=c[c[(c[v+16>>2]|0)+180>>2]>>2]|0;o=c[ca>>2]&3;s=c[((o|0)==3?ca:ca+48|0)+40>>2]|0;o=c[((o|0)==2?ca:ca+-48|0)+40>>2]|0;c[_>>2]=o;t=t+2|0;r=r+-1|0;u=ca}wg(N,b,d,p,u,c[c[(c[p+16>>2]|0)+180>>2]>>2]|0);c[U>>2]=c[N>>2];c[U+4>>2]=c[N+4>>2];c[U+8>>2]=c[N+8>>2];c[U+12>>2]=c[N+12>>2];c[U+16>>2]=c[N+16>>2];c[U+20>>2]=c[N+20>>2];c[U+24>>2]=c[N+24>>2];c[U+28>>2]=c[N+28>>2];jO(e,u,1,U,xg(c[((c[u>>2]&3|0)==2?u:u+-48|0)+40>>2]|0)|0);ca=U+56+((c[C>>2]|0)+-1<<5)|0;t=c[v+16>>2]|0;m=+h[(c[(c[F>>2]|0)+196>>2]|0)+(c[t+232>>2]<<6)+24>>3]+ +h[t+24>>3];c[S>>2]=c[ca>>2];c[S+4>>2]=c[ca+4>>2];c[S+8>>2]=c[ca+8>>2];c[S+12>>2]=c[ca+12>>2];c[S+16>>2]=c[ca+16>>2];c[S+20>>2]=c[ca+20>>2];c[S+24>>2]=c[ca+24>>2];c[S+28>>2]=c[ca+28>>2];yg(V,S,4,m);if(+h[V>>3]<+h[A>>3]?+h[E>>3]<+h[D>>3]:0){ca=c[C>>2]|0;c[C>>2]=ca+1;ca=U+56+(ca<<5)|0;c[ca>>2]=c[V>>2];c[ca+4>>2]=c[V+4>>2];c[ca+8>>2]=c[V+8>>2];c[ca+12>>2]=c[V+12>>2];c[ca+16>>2]=c[V+16>>2];c[ca+20>>2]=c[V+20>>2];c[ca+24>>2]=c[V+24>>2];c[ca+28>>2]=c[V+28>>2]}h[x>>3]=1.5707963267948966;a[y>>0]=1;Bg(e,I,u,T,U,o);if(!G){k=NM(e,X)|0;o=c[X>>2]|0;if(H&(o|0)>4){ca=k+16|0;c[ca>>2]=c[k>>2];c[ca+4>>2]=c[k+4>>2];c[ca+8>>2]=c[k+8>>2];c[ca+12>>2]=c[k+12>>2];ca=k+32|0;o=k+(o+-1<<4)|0;c[ca>>2]=c[o>>2];c[ca+4>>2]=c[o+4>>2];c[ca+8>>2]=c[o+8>>2];c[ca+12>>2]=c[o+12>>2];X6(k+48|0,o|0,16)|0;c[X>>2]=4;o=4}else J=33}else{k=HM(e,X)|0;o=c[X>>2]|0;J=33}if((J|0)==33){J=0;if(!o)break a}u=c[Z>>2]|0;p=u+o|0;if((p|0)>(c[46736]|0)){c[46736]=p<<1;t=MK(c[46734]|0,p<<5)|0;c[46734]=t;o=c[X>>2]|0}else t=c[46734]|0;s=0;p=u;while(1){if((s|0)>=(o|0))break;ca=t+(p<<4)|0;u=k+(s<<4)|0;c[ca>>2]=c[u>>2];c[ca+4>>2]=c[u+4>>2];c[ca+8>>2]=c[u+8>>2];c[ca+12>>2]=c[u+12>>2];s=s+1|0;p=p+1|0}c[Z>>2]=p;o=Cg(c[c[(c[v+16>>2]|0)+180>>2]>>2]|0,q,t,Z)|0;Dg(I,e);p=c[o>>2]&3;k=c[((p|0)==3?o:o+48|0)+40>>2]|0;p=c[((p|0)==2?o:o+-48|0)+40>>2]|0;c[_>>2]=p;I=k+16|0;wg(P,b,d,k,c[c[(c[I>>2]|0)+172>>2]>>2]|0,o);c[T>>2]=c[P>>2];c[T+4>>2]=c[P+4>>2];c[T+8>>2]=c[P+8>>2];c[T+12>>2]=c[P+12>>2];c[T+16>>2]=c[P+16>>2];c[T+20>>2]=c[P+20>>2];c[T+24>>2]=c[P+24>>2];c[T+28>>2]=c[P+28>>2];gO(e,o,1,T,xg(k)|0);ca=T+56+((c[B>>2]|0)+-1<<5)|0;I=c[I>>2]|0;m=+h[I+24>>3]-+h[(c[(c[F>>2]|0)+196>>2]|0)+(c[I+232>>2]<<6)+16>>3];c[S>>2]=c[ca>>2];c[S+4>>2]=c[ca+4>>2];c[S+8>>2]=c[ca+8>>2];c[S+12>>2]=c[ca+12>>2];c[S+16>>2]=c[ca+16>>2];c[S+20>>2]=c[ca+20>>2];c[S+24>>2]=c[ca+24>>2];c[S+28>>2]=c[ca+28>>2];yg(V,S,1,m);if(+h[V>>3]<+h[A>>3]?+h[E>>3]<+h[D>>3]:0){ca=c[B>>2]|0;c[B>>2]=ca+1;ca=T+56+(ca<<5)|0;c[ca>>2]=c[V>>2];c[ca+4>>2]=c[V+4>>2];c[ca+8>>2]=c[V+8>>2];c[ca+12>>2]=c[V+12>>2];c[ca+16>>2]=c[V+16>>2];c[ca+20>>2]=c[V+20>>2];c[ca+24>>2]=c[V+24>>2];c[ca+28>>2]=c[V+28>>2]}h[j>>3]=-1.5707963267948966;a[z>>0]=1;I=o;o=p}k=t|1;r=153720+(t<<5)|0;zg(Q,d,b,c[(c[s+16>>2]|0)+232>>2]|0);c[r>>2]=c[Q>>2];c[r+4>>2]=c[Q+4>>2];c[r+8>>2]=c[Q+8>>2];c[r+12>>2]=c[Q+12>>2];c[r+16>>2]=c[Q+16>>2];c[r+20>>2]=c[Q+20>>2];c[r+24>>2]=c[Q+24>>2];c[r+28>>2]=c[Q+28>>2];wg(R,b,d,p,u,0);c[U>>2]=c[R>>2];c[U+4>>2]=c[R+4>>2];c[U+8>>2]=c[R+8>>2];c[U+12>>2]=c[R+12>>2];c[U+16>>2]=c[R+16>>2];c[U+20>>2]=c[R+20>>2];c[U+24>>2]=c[R+24>>2];c[U+28>>2]=c[R+28>>2];c[V>>2]=c[R>>2];c[V+4>>2]=c[R+4>>2];c[V+8>>2]=c[R+8>>2];c[V+12>>2]=c[R+12>>2];c[V+16>>2]=c[R+16>>2];c[V+20>>2]=c[R+20>>2];c[V+24>>2]=c[R+24>>2];c[V+28>>2]=c[R+28>>2];r=(n|0)!=0;s=u+-48|0;jO(e,r?Y:u,1,U,xg(c[((c[u>>2]&3|0)==2?u:s)+40>>2]|0)|0);ca=(c[C>>2]|0)+-1|0;h[D>>3]=+h[U+56+(ca<<5)+24>>3];h[E>>3]=+h[U+56+(ca<<5)+8>>3];ca=c[v+16>>2]|0;m=+h[(c[(c[F>>2]|0)+196>>2]|0)+(c[ca+232>>2]<<6)+24>>3]+ +h[ca+24>>3];c[S>>2]=c[V>>2];c[S+4>>2]=c[V+4>>2];c[S+8>>2]=c[V+8>>2];c[S+12>>2]=c[V+12>>2];c[S+16>>2]=c[V+16>>2];c[S+20>>2]=c[V+20>>2];c[S+24>>2]=c[V+24>>2];c[S+28>>2]=c[V+28>>2];yg(W,S,4,m);c[V>>2]=c[W>>2];c[V+4>>2]=c[W+4>>2];c[V+8>>2]=c[W+8>>2];c[V+12>>2]=c[W+12>>2];c[V+16>>2]=c[W+16>>2];c[V+20>>2]=c[W+20>>2];c[V+24>>2]=c[W+24>>2];c[V+28>>2]=c[W+28>>2];if(+h[V>>3]<+h[A>>3]?+h[E>>3]<+h[D>>3]:0){ca=c[C>>2]|0;c[C>>2]=ca+1;ca=U+56+(ca<<5)|0;c[ca>>2]=c[V>>2];c[ca+4>>2]=c[V+4>>2];c[ca+8>>2]=c[V+8>>2];c[ca+12>>2]=c[V+12>>2];c[ca+16>>2]=c[V+16>>2];c[ca+20>>2]=c[V+20>>2];c[ca+24>>2]=c[V+24>>2];c[ca+28>>2]=c[V+28>>2]}Bg(e,I,u,T,U,k);if(G)q=HM(e,X)|0;else q=NM(e,X)|0;k=c[X>>2]|0;if(!(H&(k|0)>4)){if(!k)break}else{ca=q+16|0;c[ca>>2]=c[q>>2];c[ca+4>>2]=c[q+4>>2];c[ca+8>>2]=c[q+8>>2];c[ca+12>>2]=c[q+12>>2];ca=q+32|0;k=q+(k+-1<<4)|0;c[ca>>2]=c[k>>2];c[ca+4>>2]=c[k+4>>2];c[ca+8>>2]=c[k+8>>2];c[ca+12>>2]=c[k+12>>2];X6(q+48|0,k|0,16)|0;c[X>>2]=4;k=4}o=c[Z>>2]|0;n=o+k|0;if((n|0)>(c[46736]|0)){c[46736]=n<<1;p=MK(c[46734]|0,n<<5)|0;c[46734]=p;k=c[X>>2]|0}else p=c[46734]|0;n=0;while(1){if((n|0)>=(k|0))break;ca=p+(o<<4)|0;X=q+(n<<4)|0;c[ca>>2]=c[X>>2];c[ca+4>>2]=c[X+4>>2];c[ca+8>>2]=c[X+8>>2];c[ca+12>>2]=c[X+12>>2];n=n+1|0;o=o+1|0}c[Z>>2]=o;Dg(I,e);if(r)k=(c[Y>>2]&3|0)==2?Y:Y+-48|0;else k=(c[u>>2]&3|0)==2?u:s;c[_>>2]=c[k+40>>2];v=o;J=63}while(0);c:do if((J|0)==63){if((i|0)==1){dO(K,c[_>>2]|0,c[46734]|0,v,17296);break}t=d+12|0;u=v+-1|0;m=+((O(c[t>>2]|0,i+-1|0)|0)/2|0|0);n=c[46734]|0;k=1;while(1){if((k|0)>=(u|0))break;ca=n+(k<<4)|0;h[ca>>3]=+h[ca>>3]-m;k=k+1|0}k=c[46736]|0;if((k|0)>(c[46737]|0)){c[46737]=k;o=MK(c[46735]|0,k<<4)|0;c[46735]=o;n=c[46734]|0}else o=c[46735]|0;k=0;while(1){if((k|0)>=(v|0))break;ca=o+(k<<4)|0;d=n+(k<<4)|0;c[ca>>2]=c[d>>2];c[ca+4>>2]=c[d+4>>2];c[ca+8>>2]=c[d+8>>2];c[ca+12>>2]=c[d+12>>2];k=k+1|0}dO(K,c[_>>2]|0,o,v,17296);r=$+48|0;s=$+-48|0;q=1;while(1){if((q|0)>=(i|0))break c;o=c[f+(q+g<<2)>>2]|0;p=o+16|0;k=c[p>>2]|0;if(c[k+164>>2]&32){n=c[aa>>2]|0;W6(n|0,k|0,176)|0;w=$;x=o;y=w+48|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));c[aa>>2]=n;w=c[$>>2]&3;c[((w|0)==3?$:r)+40>>2]=c[((c[o>>2]&3|0)==2?o:o+-48|0)+40>>2];c[((w|0)==2?$:s)+40>>2]=c[((c[o>>2]&3|0)==3?o:o+48|0)+40>>2];w=n+16|0;x=(c[p>>2]|0)+56|0;y=w+40|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));k=c[aa>>2]|0;w=k+56|0;x=(c[p>>2]|0)+16|0;y=w+40|0;do{c[w>>2]=c[x>>2];w=w+4|0;x=x+4|0}while((w|0)<(y|0));a[k+112>>0]=1;c[k+116>>2]=o;o=$}p=c[46734]|0;k=1;while(1){if((k|0)>=(u|0))break;ca=p+(k<<4)|0;h[ca>>3]=+h[ca>>3]+ +(c[t>>2]|0);k=k+1|0}n=c[46735]|0;k=0;while(1){if((k|0)>=(v|0))break;ca=n+(k<<4)|0;_=p+(k<<4)|0;c[ca>>2]=c[_>>2];c[ca+4>>2]=c[_+4>>2];c[ca+8>>2]=c[_+8>>2];c[ca+12>>2]=c[_+12>>2];k=k+1|0}dO(o,c[((c[o>>2]&3|0)==2?o:o+-48|0)+40>>2]|0,n,v,17296);q=q+1|0}}while(0);l=ba;return}function sg(a){a=a|0;var b=0,d=0,e=0;b=jC(a)|0;while(1){if(!b)break;d=XA(a,b)|0;while(1){if(!d)break;if((kb[c[4324]&63](d)|0)<<24>>24?(e=c[(c[d+16>>2]|0)+8>>2]|0,e|0):0)tg(e);d=ZA(a,d)|0}b=kC(a,b)|0}return}function tg(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;f=c[a+4>>2]|0;g=KK(f*48|0)|0;b=g;d=0;e=(c[a>>2]|0)+((f+-1|0)*48|0)|0;while(1){if((d|0)>=(f|0)){b=0;break}ug(e,b);b=b+48|0;d=d+1|0;e=e+-48|0}while(1){d=c[a>>2]|0;if((b|0)>=(f|0))break;U1(c[d+(b*48|0)>>2]|0);b=b+1|0}U1(d);c[a>>2]=g;return}function ug(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;g=c[a+4>>2]|0;h=KK(g<<4)|0;d=h;e=0;f=(c[a>>2]|0)+(g+-1<<4)|0;while(1){if((e|0)>=(g|0))break;c[d>>2]=c[f>>2];c[d+4>>2]=c[f+4>>2];c[d+8>>2]=c[f+8>>2];c[d+12>>2]=c[f+12>>2];d=d+16|0;e=e+1|0;f=f+-16|0}c[b>>2]=h;c[b+4>>2]=g;c[b+8>>2]=c[a+12>>2];c[b+12>>2]=c[a+8>>2];h=b+16|0;g=a+32|0;c[h>>2]=c[g>>2];c[h+4>>2]=c[g+4>>2];c[h+8>>2]=c[g+8>>2];c[h+12>>2]=c[g+12>>2];h=b+32|0;g=a+16|0;c[h>>2]=c[g>>2];c[h+4>>2]=c[g+4>>2];c[h+8>>2]=c[g+8>>2];c[h+12>>2]=c[g+12>>2];return}function vg(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0.0,j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;x=l;l=l+96|0;q=x+80|0;s=x+64|0;r=x+48|0;u=x+32|0;v=x+16|0;w=x;m=d;while(1){t=m+16|0;g=c[t>>2]|0;if(!(a[g+112>>0]|0))break;m=c[g+116>>2]|0}g=c[m>>2]&3;o=c[((g|0)==2?m:m+-48|0)+40>>2]|0;g=c[((g|0)==3?m:m+48|0)+40>>2]|0;m=o+16|0;n=g+16|0;y=(c[(c[m>>2]|0)+232>>2]|0)-(c[(c[n>>2]|0)+232>>2]|0)|0;switch(((y|0)>-1?y:0-y|0)|0){case 1:{g=0;break}case 2:{if(!(a[(c[(c[b+60>>2]|0)+16>>2]|0)+113>>0]&1))p=6;else g=0;break}default:p=6}do if((p|0)==6){if((c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)==(g|0)){c[f>>2]=o;y=(c[n>>2]|0)+16|0;p=(c[t>>2]|0)+16|0;c[s>>2]=c[y>>2];c[s+4>>2]=c[y+4>>2];c[s+8>>2]=c[y+8>>2];c[s+12>>2]=c[y+12>>2];c[q>>2]=c[p>>2];c[q+4>>2]=c[p+4>>2];c[q+8>>2]=c[p+8>>2];c[q+12>>2]=c[p+12>>2];Ng(u,s,q);p=(c[m>>2]|0)+16|0;y=(c[t>>2]|0)+56|0;c[s>>2]=c[p>>2];c[s+4>>2]=c[p+4>>2];c[s+8>>2]=c[p+8>>2];c[s+12>>2]=c[p+12>>2];c[q>>2]=c[y>>2];c[q+4>>2]=c[y+4>>2];c[q+8>>2]=c[y+8>>2];c[q+12>>2]=c[y+12>>2];Ng(v,s,q)}else{c[f>>2]=g;y=(c[m>>2]|0)+16|0;p=(c[t>>2]|0)+56|0;c[s>>2]=c[y>>2];c[s+4>>2]=c[y+4>>2];c[s+8>>2]=c[y+8>>2];c[s+12>>2]=c[y+12>>2];c[q>>2]=c[p>>2];c[q+4>>2]=c[p+4>>2];c[q+8>>2]=c[p+8>>2];c[q+12>>2]=c[p+12>>2];Ng(u,s,q);p=(c[n>>2]|0)+16|0;y=(c[t>>2]|0)+16|0;c[s>>2]=c[p>>2];c[s+4>>2]=c[p+4>>2];c[s+8>>2]=c[p+8>>2];c[s+12>>2]=c[p+12>>2];c[q>>2]=c[y>>2];c[q+4>>2]=c[y+4>>2];c[q+8>>2]=c[y+8>>2];c[q+12>>2]=c[y+12>>2];Ng(v,s,q)}g=c[(c[t>>2]|0)+96>>2]|0;if(!g){g=e+16|0;c[e>>2]=c[u>>2];c[e+4>>2]=c[u+4>>2];c[e+8>>2]=c[u+8>>2];c[e+12>>2]=c[u+12>>2];c[g>>2]=c[u>>2];c[g+4>>2]=c[u+4>>2];c[g+8>>2]=c[u+8>>2];c[g+12>>2]=c[u+12>>2];g=e+48|0;y=e+32|0;c[y>>2]=c[v>>2];c[y+4>>2]=c[v+4>>2];c[y+8>>2]=c[v+8>>2];c[y+12>>2]=c[v+12>>2];c[g>>2]=c[v>>2];c[g+4>>2]=c[v+4>>2];c[g+8>>2]=c[v+8>>2];c[g+12>>2]=c[v+12>>2];g=4;break}j=+h[g+24>>3];i=+h[g+32>>3];p=(c[(c[(xC(o)|0)+16>>2]|0)+116>>2]&1|0)==0;k=p?i:j;y=(c[(c[t>>2]|0)+96>>2]|0)+56|0;c[w>>2]=c[y>>2];c[w+4>>2]=c[y+4>>2];c[w+8>>2]=c[y+8>>2];c[w+12>>2]=c[y+12>>2];c[r>>2]=c[v>>2];c[r+4>>2]=c[v+4>>2];c[r+8>>2]=c[v+8>>2];c[r+12>>2]=c[v+12>>2];c[s>>2]=c[u>>2];c[s+4>>2]=c[u+4>>2];c[s+8>>2]=c[u+8>>2];c[s+12>>2]=c[u+12>>2];c[q>>2]=c[y>>2];c[q+4>>2]=c[y+4>>2];c[q+8>>2]=c[y+8>>2];c[q+12>>2]=c[y+12>>2];y=(Og(r,s,q)|0)==0;i=(p?j:i)*.5;j=+h[w>>3];if(y){h[w>>3]=j-i;y=w+8|0;h[y>>3]=+h[y>>3]+k*.5}else{h[w>>3]=j+i;y=w+8|0;h[y>>3]=+h[y>>3]-k*.5}g=e+16|0;c[e>>2]=c[u>>2];c[e+4>>2]=c[u+4>>2];c[e+8>>2]=c[u+8>>2];c[e+12>>2]=c[u+12>>2];c[g>>2]=c[u>>2];c[g+4>>2]=c[u+4>>2];c[g+8>>2]=c[u+8>>2];c[g+12>>2]=c[u+12>>2];g=e+32|0;y=e+48|0;u=e+64|0;c[u>>2]=c[w>>2];c[u+4>>2]=c[w+4>>2];c[u+8>>2]=c[w+8>>2];c[u+12>>2]=c[w+12>>2];c[y>>2]=c[w>>2];c[y+4>>2]=c[w+4>>2];c[y+8>>2]=c[w+8>>2];c[y+12>>2]=c[w+12>>2];c[g>>2]=c[w>>2];c[g+4>>2]=c[w+4>>2];c[g+8>>2]=c[w+8>>2];c[g+12>>2]=c[w+12>>2];g=e+80|0;y=e+96|0;c[y>>2]=c[v>>2];c[y+4>>2]=c[v+4>>2];c[y+8>>2]=c[v+8>>2];c[y+12>>2]=c[v+12>>2];c[g>>2]=c[v>>2];c[g+4>>2]=c[v+4>>2];c[g+8>>2]=c[v+8>>2];c[g+12>>2]=c[v+12>>2];g=7}while(0);l=x;return g|0}function wg(b,d,e,f,g,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0.0,k=0,l=0,m=0.0,n=0,o=0.0,p=0.0,q=0;q=c[f+16>>2]|0;p=+h[q+16>>3];j=p-+h[q+88>>3];m=j+-4.0;l=Jg(d,f,g,i,-1)|0;if(!l){n=~~(j+(m>=0.0?-3.5:-4.5));k=c[e>>2]|0;k=(k|0)>(n|0)?n:k}else{k=Kg(d,f,l)|0;do if(!k){n=c[l+16>>2]|0;j=+h[n+240>>3]+ +h[n+16>>3];if(!(a[n+156>>0]|0)){j=+(c[(c[d+16>>2]|0)+248>>2]|0)*.5+j;break}else{j=j+ +(c[e+8>>2]|0);break}}else j=+h[(c[k+16>>2]|0)+32>>3]+ +(c[e+8>>2]|0);while(0);o=j<m?j:m;k=~~(o+(o>=0.0?.5:-.5))}o=+(k|0);n=(a[q+156>>0]|0)==1;if(n?(c[q+104>>2]|0)!=0:0)m=p+10.0;else m=p+4.0+ +h[q+96>>3];l=Jg(d,f,g,i,1)|0;if(!l){f=~~(m+(m>=0.0?.5:-.5));k=c[e+4>>2]|0;k=(k|0)<(f|0)?f:k}else{k=Kg(d,f,l)|0;do if(!k){f=c[l+16>>2]|0;j=+h[f+16>>3]-+h[f+88>>3];if(!(a[f+156>>0]|0)){j=j-+(c[(c[d+16>>2]|0)+248>>2]|0)*.5;break}else{j=j-+(c[e+8>>2]|0);break}}else j=+h[(c[k+16>>2]|0)+16>>3]-+(c[e+8>>2]|0);while(0);m=j>m?j:m;k=~~(m+(m>=0.0?.5:-.5))}j=+(k|0);if(n?(c[q+104>>2]|0)!=0:0){j=j-+h[q+96>>3];if(j<o)j=p}p=+h[q+24>>3];d=c[(c[d+16>>2]|0)+196>>2]|0;q=c[q+232>>2]|0;m=p-+h[d+(q<<6)+16>>3];p=+h[d+(q<<6)+24>>3]+p;h[b>>3]=o;h[b+8>>3]=m;h[b+16>>3]=j;h[b+24>>3]=p;return}function xg(b){b=b|0;b=c[b+16>>2]|0;if((a[b+156>>0]|0)==1)if((c[b+176>>2]|0)>1)b=1;else b=(c[b+184>>2]|0)>1&1;else b=0;return b|0}function yg(a,b,d,e){a=a|0;b=b|0;d=d|0;e=+e;var f=0,g=0;g=l;l=l+32|0;f=g;switch(d|0){case 1:{Ig(f,+h[b>>3],e,+h[b+16>>3],+h[b+8>>3]);break}case 4:{Ig(f,+h[b>>3],+h[b+24>>3],+h[b+16>>3],e);break}default:{}}c[a>>2]=c[f>>2];c[a+4>>2]=c[f+4>>2];c[a+8>>2]=c[f+8>>2];c[a+12>>2]=c[f+12>>2];c[a+16>>2]=c[f+16>>2];c[a+20>>2]=c[f+20>>2];c[a+24>>2]=c[f+24>>2];c[a+28>>2]=c[f+28>>2];l=g;return}function zg(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0,k=0,l=0,m=0,n=0,o=0;l=c[b+16>>2]|0;k=l+(e<<5)|0;j=+h[k>>3];m=l+(e<<5)+8|0;g=+h[m>>3];n=l+(e<<5)+16|0;f=+h[n>>3];l=l+(e<<5)+24|0;i=+h[l>>3];if(j==f){d=c[(c[d+16>>2]|0)+196>>2]|0;o=e+1|0;j=+(c[b>>2]|0);g=+h[d+(o<<6)+24>>3]+ +h[(c[(c[c[d+(o<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3];f=+(c[b+4>>2]|0);i=+h[(c[(c[c[d+(e<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]-+h[d+(e<<6)+16>>3];h[k>>3]=j;h[m>>3]=g;h[n>>3]=f;h[l>>3]=i}h[a>>3]=j;h[a+8>>3]=g;h[a+16>>3]=f;h[a+24>>3]=i;return}function Ag(b){b=b|0;var d=0,e=0;b=c[b+16>>2]|0;e=b+16|0;d=0;while(1){b=c[c[b+180>>2]>>2]|0;b=c[(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)+16>>2]|0;if((a[b+156>>0]|0)!=1)break;if((c[b+184>>2]|0)!=1)break;if((c[b+176>>2]|0)!=1)break;if(+h[b+16>>3]!=+h[e>>3])break;d=d+1|0}return d|0}function Bg(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;k=l;l=l+32|0;j=k;h=Fg(b,-1)|0;b=Fg(b,1)|0;if(!((h|0)!=0?!(uO(h)|0):0))i=3;do if((i|0)==3){if(b|0?(uO(b)|0)==0:0)break;h=Gg(d,-1)|0;b=Gg(d,1)|0;if(h|0?(uO(h)|0)==0:0)break;if(b|0?(uO(b)|0)==0:0)break;h=e+52|0;b=0;while(1){if((b|0)>=(c[h>>2]|0))break;d=e+56+(b<<5)|0;c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];c[j+16>>2]=c[d+16>>2];c[j+20>>2]=c[d+20>>2];c[j+24>>2]=c[d+24>>2];c[j+28>>2]=c[d+28>>2];fO(a,j);b=b+1|0}h=c[a+80>>2]|0;i=g+-3|0;b=0;while(1){if((b|0)>=(g|0))break;e=153720+(b<<5)|0;c[j>>2]=c[e>>2];c[j+4>>2]=c[e+4>>2];c[j+8>>2]=c[e+8>>2];c[j+12>>2]=c[e+12>>2];c[j+16>>2]=c[e+16>>2];c[j+20>>2]=c[e+20>>2];c[j+24>>2]=c[e+24>>2];c[j+28>>2]=c[e+28>>2];fO(a,j);b=b+1|0}d=h+1|0;i=i+d|0;h=c[f+52>>2]|0;while(1){b=h+-1|0;if((h|0)<=0)break;h=f+56+(b<<5)|0;c[j>>2]=c[h>>2];c[j+4>>2]=c[h+4>>2];c[j+8>>2]=c[h+8>>2];c[j+12>>2]=c[h+12>>2];c[j+16>>2]=c[h+16>>2];c[j+20>>2]=c[h+20>>2];c[j+24>>2]=c[h+24>>2];c[j+28>>2]=c[h+28>>2];fO(a,j);h=b}Hg(a,d,i)}while(0);l=k;return}function Cg(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;f=c[e>>2]|0;while(1){if(!b)break;a=c[c[(c[(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+180>>2]>>2]|0;b=b+-1|0}c[e>>2]=f+1;g=d+(f<<4)|0;b=d+(f+-1<<4)|0;c[g>>2]=c[b>>2];c[g+4>>2]=c[b+4>>2];c[g+8>>2]=c[b+8>>2];c[g+12>>2]=c[b+12>>2];f=c[e>>2]|0;c[e>>2]=f+1;f=d+(f<<4)|0;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];f=d+(c[e>>2]<<4)|0;e=(c[(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+16|0;c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];c[f+8>>2]=c[e+8>>2];c[f+12>>2]=c[e+12>>2];return a|0}function Dg(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0,j=0,k=0,l=0,m=0;m=d+84|0;l=d+80|0;e=0;d=b;a:while(1){j=c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0;k=j+16|0;if((a[(c[k>>2]|0)+156>>0]|0)!=1)break;if((kb[c[17300>>2]&63](j)|0)<<24>>24)break;d=c[l>>2]|0;while(1){if((e|0)>=(d|0))break a;if(!(+h[(c[m>>2]|0)+(e<<5)+8>>3]>+h[(c[k>>2]|0)+24>>3]))break;e=e+1|0}d=c[m>>2]|0;b=c[k>>2]|0;do if(!(+h[d+(e<<5)+24>>3]<+h[b+24>>3])){g=+h[d+(e<<5)>>3];i=~~g;f=+h[d+(e<<5)+16>>3];if(!(c[b+104>>2]|0)){Eg(j,i,~~((f+g)*.5),~~f);break}else{Eg(j,i,~~f,~~(+h[b+96>>3]+f));break}}while(0);d=c[c[(c[k>>2]|0)+180>>2]>>2]|0}return}function Eg(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;a=c[a+16>>2]|0;h[a+16>>3]=+(d|0);h[a+88>>3]=+(d-b|0);h[a+96>>3]=+(e-d|0);return}function Fg(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;h=c[a>>2]&3;i=c[(c[(c[((h|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+180>>2]|0;h=((h|0)==2?a:a+-48|0)+40|0;g=0;a=0;while(1){e=c[i+(g<<2)>>2]|0;if(!e)break;f=c[(c[(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;do if((O(f-(c[(c[(c[h>>2]|0)+16>>2]|0)+236>>2]|0)|0,b)|0)>=1){d=c[e+16>>2]|0;if(!(c[d+8>>2]|0)){d=c[d+116>>2]|0;if(!d)break;if(!(c[(c[d+16>>2]|0)+8>>2]|0))break}if(a|0?(O((c[(c[(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)-f|0,b)|0)<=0:0)break;a=e}while(0);g=g+1|0}return a|0}function Gg(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;h=c[a>>2]&3;i=c[(c[(c[((h|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+172>>2]|0;h=((h|0)==3?a:a+48|0)+40|0;g=0;a=0;while(1){e=c[i+(g<<2)>>2]|0;if(!e)break;f=c[(c[(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;do if((O(f-(c[(c[(c[h>>2]|0)+16>>2]|0)+236>>2]|0)|0,b)|0)>=1){d=c[e+16>>2]|0;if(!(c[d+8>>2]|0)){d=c[d+116>>2]|0;if(!d)break;if(!(c[(c[d+16>>2]|0)+8>>2]|0))break}if(a|0?(O((c[(c[(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)-f|0,b)|0)<=0:0)break;a=e}while(0);g=g+1|0}return a|0}function Hg(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0,j=0,k=0,l=0,m=0.0;l=a+84|0;i=b+-1|0;while(1){if((i|0)>(d|0))break;f=c[l>>2]|0;j=f+(i<<5)|0;e=+h[j>>3];if(!(i-b&1)){f=f+(i<<5)+16|0;m=+h[f>>3];g=~~((m+e)*.5);if(e>=m){h[j>>3]=+(g+-8|0);h[f>>3]=+(g+8|0)}}else{f=f+(i<<5)+16|0;m=+h[f>>3];g=~~((m+e)*.5);if(e+16.0>m){h[j>>3]=+(g+-8|0);h[f>>3]=+(g+8|0)}}i=i+1|0}k=(c[a+80>>2]|0)+-1|0;g=0;while(1){if((g|0)>=(k|0))break;i=c[l>>2]|0;f=i+(g<<5)|0;j=g+1|0;a=i+(j<<5)|0;if(!((g|0)<(b|0)|(g|0)>(d|0))?(g-b&1|0)==0:0){e=+h[f>>3]+16.0;f=i+(j<<5)+16|0;if(e>+h[f>>3])h[f>>3]=e;e=+h[i+(g<<5)+16>>3]+-16.0;if(!(e<+h[a>>3])){g=j;continue}h[a>>3]=e;g=j;continue}if(!((g|0)<(d|0)&(j|0)>=(b|0))){g=j;continue}if(j-b&1|0){g=j;continue}e=+h[i+(j<<5)+16>>3];if(+h[f>>3]+16.0>e)h[f>>3]=e+-16.0;f=i+(g<<5)+16|0;e=+h[a>>3];if(!(+h[f>>3]+-16.0<e)){g=j;continue}h[f>>3]=e+16.0;g=j}return}function Ig(a,b,c,d,e){a=a|0;b=+b;c=+c;d=+d;e=+e;h[a>>3]=b;h[a+8>>3]=c;h[a+16>>3]=d;h[a+24>>3]=e;return}function Jg(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;k=c[(c[b+16>>2]|0)+196>>2]|0;h=c[d+16>>2]|0;j=c[h+232>>2]|0;i=k+(j<<6)|0;j=k+(j<<6)+4|0;h=c[h+236>>2]|0;a:do{h=h+g|0;if((h|0)<=-1){b=0;break}if((h|0)>=(c[i>>2]|0)){b=0;break}b=c[(c[j>>2]|0)+(h<<2)>>2]|0;k=c[b+16>>2]|0;switch(a[k+156>>0]|0){case 0:break a;case 1:{if(c[k+104>>2]|0)break a;break}default:{}}}while((Mg(b,d,e,f)|0)<<24>>24!=0);return b|0}function Kg(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;d=c[d+16>>2]|0;if(!(a[d+156>>0]|0)){i=c[d+212>>2]|0;h=i}else{i=c[(c[(c[c[d+180>>2]>>2]|0)+16>>2]|0)+116>>2]|0;g=c[i>>2]&3;h=c[(c[(c[((g|0)==2?i:i+-48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0;i=c[(c[(c[((g|0)==3?i:i+48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0}d=c[e+16>>2]|0;if(!(a[d+156>>0]|0)){e=c[d+212>>2]|0;e=(e|0)==(b|0)?0:e;return ((e|0)==(h|0)|((e|0)==0|(e|0)==(i|0))?0:e)|0}f=c[(c[(c[c[d+180>>2]>>2]|0)+16>>2]|0)+116>>2]|0;g=c[f>>2]&3;d=c[(c[(c[((g|0)==3?f:f+48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0;d=(d|0)==(b|0)?0:d;if(!(!((d|0)==(h|0)|((d|0)==0|(d|0)==(i|0)))?(Lg(d,e)|0)!=0:0)){d=c[(c[(c[((g|0)==2?f:f+-48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0;d=(d|0)==(b|0)?0:d;if((d|0)==(h|0)|((d|0)==0|(d|0)==(i|0)))d=0;else{e=(Lg(d,e)|0)==0;return (e?0:d)|0}}return d|0}function Lg(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0;d=c[a+16>>2]|0;a=c[b+16>>2]|0;f=+h[a+16>>3];if((+h[d+16>>3]<=f?f<=+h[d+32>>3]:0)?(e=+h[a+24>>3],+h[d+24>>3]<=e):0)a=e<=+h[d+40>>3]&1;else a=0;return a|0}function Mg(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;i=c[b+16>>2]|0;j=(c[i+236>>2]|0)>(c[(c[d+16>>2]|0)+236>>2]|0);a:do if((c[i+184>>2]|0)==1){b:do if(f|0){h=0;b=c[c[i+180>>2]>>2]|0;while(1){if((h|0)>=2)break b;b=c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0;d=c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0;if((b|0)==(d|0))break b;g=c[b+16>>2]|0;b=c[d+16>>2]|0;if(j^(c[g+236>>2]|0)>(c[b+236>>2]|0)){b=1;break a}if((c[g+184>>2]|0)!=1)break b;if(!(a[g+156>>0]|0))break b;if((c[b+184>>2]|0)!=1)break b;if(!(a[b+156>>0]|0))break b;h=h+1|0;f=c[c[b+180>>2]>>2]|0;b=c[c[g+180>>2]>>2]|0}}while(0);if(e|0?(c[i+176>>2]|0)==1:0){g=0;d=e;b=c[c[i+172>>2]>>2]|0;while(1){if((g|0)>=2){b=0;break a}b=c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0;d=c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0;if((b|0)==(d|0)){b=0;break a}f=c[b+16>>2]|0;b=c[d+16>>2]|0;if(j^(c[f+236>>2]|0)>(c[b+236>>2]|0)){b=1;break a}if((c[f+176>>2]|0)!=1){b=0;break a}if(!(a[f+156>>0]|0)){b=0;break a}if((c[b+176>>2]|0)!=1){b=0;break a}if(!(a[b+156>>0]|0)){b=0;break a}g=g+1|0;d=c[c[b+172>>2]>>2]|0;b=c[c[f+172>>2]>>2]|0}}else b=0}else b=0;while(0);return b|0}function Ng(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[c+8>>3]+ +h[b+8>>3];h[a>>3]=+h[c>>3]+ +h[b>>3];h[a+8>>3]=d;return}function Og(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0;d=+h[b+8>>3];e=+h[b>>3];return (~~((+h[c>>3]-e)*(+h[a+8>>3]-d)-(+h[a>>3]-e)*(+h[c+8>>3]-d))|0)>0|0}function Pg(b,d,e,f,g,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0,u=0,v=0.0,w=0.0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0;R=l;l=l+256|0;D=R+232|0;C=R+216|0;P=R+88|0;O=R+80|0;K=R+64|0;L=R+200|0;M=R+184|0;N=R;E=R+168|0;F=R+152|0;G=R+136|0;H=R+120|0;I=R+104|0;B=c[g>>2]&3;J=c[((B|0)==3?g:g+48|0)+40>>2]|0;B=c[((B|0)==2?g:g+-48|0)+40>>2]|0;do if((WM(J)|0)!=2?(WM(B)|0)!=2:0){g=0;j=0;k=0;while(1){if((k|0)>=(f|0))break;A=c[(c[d+(k+e<<2)>>2]|0)+16>>2]|0;g=g+((c[A+96>>2]|0)!=0&1)|0;if(!((a[A+44>>0]|0)==0?(a[A+84>>0]|0)==0:0))j=1;k=k+1|0}if(!j)if(!g){Rg(J,B,d,e,f,i);break}else{Wg(J,B,d,e,f,i,g);break}y=JK(156)|0;z=Xg(b,y)|0;t=ND(z,85478,1)|0;TC(t,137483,280,1)|0;SA(t,87171,87185)|0;m=+h[(c[B+16>>2]|0)+16>>3];s=+h[(c[J+16>>2]|0)+16>>3];A=b+16|0;q=(c[(c[A>>2]|0)+116>>2]&1|0)==0;r=q?B:J;q=q?J:B;t=Yg(t,q)|0;u=Yg(z,r)|0;g=0;p=0;while(1){if((p|0)>=(f|0))break;j=d+(p+e<<2)|0;while(1){o=c[j>>2]|0;n=o+16|0;j=c[n>>2]|0;if(!(a[j+112>>0]|0))break;else j=j+116|0}if((c[((c[o>>2]&3|0)==3?o:o+48|0)+40>>2]|0)==(q|0))k=Zg(z,t,u,o)|0;else k=Zg(z,u,t,o)|0;j=c[n>>2]|0;c[j+120>>2]=k;if(!g)if((a[j+44>>0]|0)==0?(a[j+84>>0]|0)==0:0){c[(c[k+16>>2]|0)+120>>2]=o;g=k}else g=0;p=p+1|0}if(!g)x=iB(z,t,u,0,1)|0;else x=g;KA(x,c[47174]|0,85482)|0;g=z+16|0;n=c[g>>2]|0;c[n+144>>2]=c[(c[A>>2]|0)+144>>2];c[n+188>>2]=z;oQ(z,i);Of(z);xj(z,0);Hh(z,0);Ki(z,0);q=q+16|0;i=c[q>>2]|0;n=c[r+16>>2]|0;r=t+16|0;o=c[r>>2]|0;p=o+16|0;v=+(~~m|0);w=+(~~((+h[(c[u+16>>2]|0)+16>>3]+ +h[p>>3])*.5)|0);m=+(~~s|0);s=+(~~((+h[i+16>>3]-+h[i+96>>3]+ +h[n+16>>3]+ +h[n+88>>3])*.5)|0);n=o+24|0;g=(c[g>>2]|0)+192|0;while(1){j=c[g>>2]|0;if(!j)break;do if((j|0)!=(t|0)){g=c[j+16>>2]|0;k=g+24|0;if((j|0)==(u|0)){h[k>>3]=m;h[g+16>>3]=w;break}else{h[k>>3]=s;break}}else{h[n>>3]=v;h[p>>3]=w;g=o}while(0);g=g+164|0}rk(z);jg(z,0);tM(z);k=c[q>>2]|0;g=c[r>>2]|0;if(!(c[(c[A>>2]|0)+116>>2]&1)){j=g+16|0;m=+h[k+24>>3]-+h[g+24>>3]}else{j=g+24|0;m=+h[g+16>>3]+ +h[k+24>>3]}h[K>>3]=+h[k+16>>3]-+h[j>>3];h[K+8>>3]=m;i=N+16|0;t=N+32|0;u=N+48|0;r=0;while(1){if((r|0)>=(f|0))break;g=d+(r+e<<2)|0;while(1){j=c[g>>2]|0;q=j+16|0;g=c[q>>2]|0;if(!(a[g+112>>0]|0))break;else g=g+116|0}o=c[g+120>>2]|0;p=o+16|0;g=c[p>>2]|0;if(!((o|0)==(x|0)&(c[g+120>>2]|0)==0)){n=c[c[g+8>>2]>>2]|0;o=n+4|0;j=cO(j,c[o>>2]|0)|0;c[j+8>>2]=c[n+8>>2];g=j+16|0;k=n+16|0;S=c[(c[A>>2]|0)+116>>2]&1;c[C>>2]=c[k>>2];c[C+4>>2]=c[k+4>>2];c[C+8>>2]=c[k+8>>2];c[C+12>>2]=c[k+12>>2];c[D>>2]=c[K>>2];c[D+4>>2]=c[K+4>>2];c[D+8>>2]=c[K+8>>2];c[D+12>>2]=c[K+12>>2];_g(L,C,D,S);c[g>>2]=c[L>>2];c[g+4>>2]=c[L+4>>2];c[g+8>>2]=c[L+8>>2];c[g+12>>2]=c[L+12>>2];c[j+12>>2]=c[n+12>>2];g=j+32|0;S=n+32|0;k=c[(c[A>>2]|0)+116>>2]&1;c[C>>2]=c[S>>2];c[C+4>>2]=c[S+4>>2];c[C+8>>2]=c[S+8>>2];c[C+12>>2]=c[S+12>>2];c[D>>2]=c[K>>2];c[D+4>>2]=c[K+4>>2];c[D+8>>2]=c[K+8>>2];c[D+12>>2]=c[K+12>>2];_g(M,C,D,k);c[g>>2]=c[M>>2];c[g+4>>2]=c[M+4>>2];c[g+8>>2]=c[M+8>>2];c[g+12>>2]=c[M+12>>2];g=0;while(1){if((g|0)>=(c[o>>2]|0))break;k=(c[j>>2]|0)+(g<<4)|0;T=(c[n>>2]|0)+(g<<4)|0;S=c[(c[A>>2]|0)+116>>2]&1;c[C>>2]=c[T>>2];c[C+4>>2]=c[T+4>>2];c[C+8>>2]=c[T+8>>2];c[C+12>>2]=c[T+12>>2];c[D>>2]=c[K>>2];c[D+4>>2]=c[K+4>>2];c[D+8>>2]=c[K+8>>2];c[D+12>>2]=c[K+12>>2];_g(E,C,D,S);c[k>>2]=c[E>>2];c[k+4>>2]=c[E+4>>2];c[k+8>>2]=c[E+8>>2];c[k+12>>2]=c[E+12>>2];c[N>>2]=c[E>>2];c[N+4>>2]=c[E+4>>2];c[N+8>>2]=c[E+8>>2];c[N+12>>2]=c[E+12>>2];k=g+1|0;if((k|0)>=(c[o>>2]|0)){Q=48;break}S=(c[j>>2]|0)+(k<<4)|0;k=(c[n>>2]|0)+(k<<4)|0;T=c[(c[A>>2]|0)+116>>2]&1;c[C>>2]=c[k>>2];c[C+4>>2]=c[k+4>>2];c[C+8>>2]=c[k+8>>2];c[C+12>>2]=c[k+12>>2];c[D>>2]=c[K>>2];c[D+4>>2]=c[K+4>>2];c[D+8>>2]=c[K+8>>2];c[D+12>>2]=c[K+12>>2];_g(F,C,D,T);c[S>>2]=c[F>>2];c[S+4>>2]=c[F+4>>2];c[S+8>>2]=c[F+8>>2];c[S+12>>2]=c[F+12>>2];c[i>>2]=c[F>>2];c[i+4>>2]=c[F+4>>2];c[i+8>>2]=c[F+8>>2];c[i+12>>2]=c[F+12>>2];S=g+2|0;T=(c[j>>2]|0)+(S<<4)|0;S=(c[n>>2]|0)+(S<<4)|0;k=c[(c[A>>2]|0)+116>>2]&1;c[C>>2]=c[S>>2];c[C+4>>2]=c[S+4>>2];c[C+8>>2]=c[S+8>>2];c[C+12>>2]=c[S+12>>2];c[D>>2]=c[K>>2];c[D+4>>2]=c[K+4>>2];c[D+8>>2]=c[K+8>>2];c[D+12>>2]=c[K+12>>2];_g(G,C,D,k);c[T>>2]=c[G>>2];c[T+4>>2]=c[G+4>>2];c[T+8>>2]=c[G+8>>2];c[T+12>>2]=c[G+12>>2];c[t>>2]=c[G>>2];c[t+4>>2]=c[G+4>>2];c[t+8>>2]=c[G+8>>2];c[t+12>>2]=c[G+12>>2];T=g+3|0;k=(c[n>>2]|0)+(T<<4)|0;S=c[(c[A>>2]|0)+116>>2]&1;c[C>>2]=c[k>>2];c[C+4>>2]=c[k+4>>2];c[C+8>>2]=c[k+8>>2];c[C+12>>2]=c[k+12>>2];c[D>>2]=c[K>>2];c[D+4>>2]=c[K+4>>2];c[D+8>>2]=c[K+8>>2];c[D+12>>2]=c[K+12>>2];_g(H,C,D,S);c[u>>2]=c[H>>2];c[u+4>>2]=c[H+4>>2];c[u+8>>2]=c[H+8>>2];c[u+12>>2]=c[H+12>>2];lI((c[A>>2]|0)+16|0,N);g=T}if((Q|0)==48)Q=0;g=c[(c[q>>2]|0)+96>>2]|0;if(g|0){T=g+56|0;p=(c[(c[p>>2]|0)+96>>2]|0)+56|0;S=c[(c[A>>2]|0)+116>>2]&1;c[C>>2]=c[p>>2];c[C+4>>2]=c[p+4>>2];c[C+8>>2]=c[p+8>>2];c[C+12>>2]=c[p+12>>2];c[D>>2]=c[K>>2];c[D+4>>2]=c[K+4>>2];c[D+8>>2]=c[K+8>>2];c[D+12>>2]=c[K+12>>2];_g(I,C,D,S);c[T>>2]=c[I>>2];c[T+4>>2]=c[I+4>>2];c[T+8>>2]=c[I+8>>2];c[T+12>>2]=c[I+12>>2];T=c[(c[q>>2]|0)+96>>2]|0;a[T+81>>0]=1;zP(b,T)}}r=r+1|0}$g(z,y)}else Q=3;while(0);if((Q|0)==3?(c[46738]|0)==0:0){c[46738]=1;gA(0,85355,O)|0;Q=KB(J)|0;S=(xB(b)|0)!=0;T=KB(B)|0;c[P>>2]=Q;c[P+4>>2]=S?137738:141747;c[P+8>>2]=T;gA(3,85461,P)|0}l=R;return}function Qg(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0,B=0.0,C=0.0,D=0.0,E=0.0;z=l;l=l+1600|0;u=z+1552|0;n=z+1536|0;t=z+840|0;v=z+144|0;w=z+1584|0;j=z+32|0;s=z+16|0;o=z;q=c[f>>2]&3;p=c[((q|0)==3?f:f+48|0)+40>>2]|0;x=f+-48|0;q=c[((q|0)==2?f:x)+40>>2]|0;r=f+16|0;k=c[r>>2]|0;i=c[k+172>>2]|0;while(1){m=c[(c[i+16>>2]|0)+172>>2]|0;if(!m)break;else i=m}k=(c[k+96>>2]|0)+56|0;i=(c[((c[i>>2]&3|0)==3?i:i+48|0)+40>>2]|0)+16|0;m=(c[i>>2]|0)+16|0;c[k>>2]=c[m>>2];c[k+4>>2]=c[m+4>>2];c[k+8>>2]=c[m+8>>2];c[k+12>>2]=c[m+12>>2];k=c[r>>2]|0;a[(c[k+96>>2]|0)+81>>0]=1;if((g|0)!=2){k=c[i>>2]|0;C=+h[k+16>>3];E=C-+h[k+88>>3];C=+h[k+96>>3]+C;B=+h[k+24>>3];D=+h[k+80>>3]*.5+B;k=c[(c[b+16>>2]|0)+196>>2]|0;m=c[p+16>>2]|0;j=c[m+232>>2]|0;B=+(~~(+(~~(B-+h[k+(j<<6)+16>>3]-+h[m+24>>3]+ +h[k+(j<<6)+24>>3])|0)*.16666666666666666)|0);B=D-(B<5.0?5.0:B);Tg(b,d,e,p,f,t,1);Tg(b,d,e,q,f,v,0);j=t+52|0;k=c[j>>2]|0;m=k+-1|0;A=+h[t+56+(m<<5)>>3];h[19215]=A;h[19216]=+h[t+56+(m<<5)+24>>3];h[19217]=E;h[19218]=B;h[19219]=A;h[19220]=B;m=v+52|0;i=(c[m>>2]|0)+-1|0;A=+h[v+56+(i<<5)+16>>3];h[19221]=A;h[19222]=D;h[19223]=C;h[19226]=B;h[19224]=+h[v+56+(i<<5)+24>>3];h[19225]=A;i=0;while(1){if((i|0)>=(k|0)){i=0;break}k=t+56+(i<<5)|0;c[u>>2]=c[k>>2];c[u+4>>2]=c[k+4>>2];c[u+8>>2]=c[k+8>>2];c[u+12>>2]=c[k+12>>2];c[u+16>>2]=c[k+16>>2];c[u+20>>2]=c[k+20>>2];c[u+24>>2]=c[k+24>>2];c[u+28>>2]=c[k+28>>2];fO(e,u);i=i+1|0;k=c[j>>2]|0}while(1){if((i|0)==3)break;t=153720+(i<<5)|0;c[u>>2]=c[t>>2];c[u+4>>2]=c[t+4>>2];c[u+8>>2]=c[t+8>>2];c[u+12>>2]=c[t+12>>2];c[u+16>>2]=c[t+16>>2];c[u+20>>2]=c[t+20>>2];c[u+24>>2]=c[t+24>>2];c[u+28>>2]=c[t+28>>2];fO(e,u);i=i+1|0}j=c[m>>2]|0;while(1){i=j+-1|0;if((j|0)<=0)break;j=v+56+(i<<5)|0;c[u>>2]=c[j>>2];c[u+4>>2]=c[j+4>>2];c[u+8>>2]=c[j+8>>2];c[u+12>>2]=c[j+12>>2];c[u+16>>2]=c[j+16>>2];c[u+20>>2]=c[j+20>>2];c[u+24>>2]=c[j+24>>2];c[u+28>>2]=c[j+28>>2];fO(e,u);j=i}if((g|0)==10)j=HM(e,w)|0;else j=NM(e,w)|0;i=c[w>>2]|0;if(i|0)y=17}else{i=(c[p+16>>2]|0)+16|0;v=k+16|0;c[n>>2]=c[i>>2];c[n+4>>2]=c[i+4>>2];c[n+8>>2]=c[i+8>>2];c[n+12>>2]=c[i+12>>2];c[u>>2]=c[v>>2];c[u+4>>2]=c[v+4>>2];c[u+8>>2]=c[v+8>>2];c[u+12>>2]=c[v+12>>2];Ng(s,n,u);v=(c[q+16>>2]|0)+16|0;i=(c[r>>2]|0)+56|0;c[n>>2]=c[v>>2];c[n+4>>2]=c[v+4>>2];c[n+8>>2]=c[v+8>>2];c[n+12>>2]=c[v+12>>2];c[u>>2]=c[i>>2];c[u+4>>2]=c[i+4>>2];c[u+8>>2]=c[i+8>>2];c[u+12>>2]=c[i+12>>2];Ng(o,n,u);i=c[(c[r>>2]|0)+96>>2]|0;D=+h[i+56>>3];E=+h[i+64>>3]-+h[i+32>>3]*.5;i=j+16|0;c[j>>2]=c[s>>2];c[j+4>>2]=c[s+4>>2];c[j+8>>2]=c[s+8>>2];c[j+12>>2]=c[s+12>>2];c[i>>2]=c[s>>2];c[i+4>>2]=c[s+4>>2];c[i+8>>2]=c[s+8>>2];c[i+12>>2]=c[s+12>>2];i=j+32|0;v=j+48|0;y=j+64|0;h[j+64>>3]=D;h[j+72>>3]=E;c[v>>2]=c[y>>2];c[v+4>>2]=c[y+4>>2];c[v+8>>2]=c[y+8>>2];c[v+12>>2]=c[y+12>>2];c[i>>2]=c[y>>2];c[i+4>>2]=c[y+4>>2];c[i+8>>2]=c[y+8>>2];c[i+12>>2]=c[y+12>>2];i=j+80|0;y=j+96|0;c[y>>2]=c[o>>2];c[y+4>>2]=c[o+4>>2];c[y+8>>2]=c[o+8>>2];c[y+12>>2]=c[o+12>>2];c[i>>2]=c[o>>2];c[i+4>>2]=c[o+4>>2];c[i+8>>2]=c[o+8>>2];c[i+12>>2]=c[o+12>>2];c[w>>2]=7;i=7;y=17}if((y|0)==17)dO(f,c[((c[f>>2]&3|0)==2?f:x)+40>>2]|0,j,i,17296);l=z;return}function Rg(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0,s=0,t=0.0,u=0,v=0,w=0,x=0,y=0,z=0.0,A=0.0,B=0.0,C=0.0,D=0.0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0;N=l;l=l+352|0;x=N+336|0;w=N+320|0;L=N+128|0;u=N+304|0;y=N+288|0;M=N+112|0;E=N+96|0;F=N+80|0;G=N+64|0;H=N+48|0;I=N+32|0;J=N+16|0;K=N;a=a+16|0;q=(c[a>>2]|0)+16|0;v=(c[d+(e<<2)>>2]|0)+16|0;s=(c[v>>2]|0)+16|0;c[w>>2]=c[q>>2];c[w+4>>2]=c[q+4>>2];c[w+8>>2]=c[q+8>>2];c[w+12>>2]=c[q+12>>2];c[x>>2]=c[s>>2];c[x+4>>2]=c[s+4>>2];c[x+8>>2]=c[s+8>>2];c[x+12>>2]=c[s+12>>2];Ng(u,w,x);C=+h[u>>3];D=+h[u+8>>3];u=(c[b+16>>2]|0)+16|0;v=(c[v>>2]|0)+56|0;c[w>>2]=c[u>>2];c[w+4>>2]=c[u+4>>2];c[w+8>>2]=c[u+8>>2];c[w+12>>2]=c[u+12>>2];c[x>>2]=c[v>>2];c[x+4>>2]=c[v+4>>2];c[x+8>>2]=c[v+8>>2];c[x+12>>2]=c[v+12>>2];Ng(y,w,x);A=+h[y>>3];B=+h[y+8>>3];if((f|0)>1){z=+h[(c[a>>2]|0)+80>>3];i=z*.5;z=z/+(f+-1|0)}else{i=0.0;z=0.0}p=(g|8|0)==10;m=L+8|0;q=L+16|0;r=(A+C*2.0)*.3333333333333333;s=L+32|0;t=(A*2.0+C)*.3333333333333333;n=L+24|0;u=L+48|0;v=L+64|0;w=L+80|0;x=L+96|0;y=L+112|0;k=L+128|0;o=L+136|0;i=D-i;b=0;while(1){if((b|0)>=(f|0))break;j=c[d+(b+e<<2)>>2]|0;h[L>>3]=C;h[m>>3]=D;if(p){Vg(M,r,i);c[q>>2]=c[M>>2];c[q+4>>2]=c[M+4>>2];c[q+8>>2]=c[M+8>>2];c[q+12>>2]=c[M+12>>2];Vg(E,t,i);c[s>>2]=c[E>>2];c[s+4>>2]=c[E+4>>2];c[s+8>>2]=c[E+8>>2];c[s+12>>2]=c[E+12>>2];a=4;g=3}else{h[q>>3]=C;h[n>>3]=D;Vg(F,r,i);c[s>>2]=c[F>>2];c[s+4>>2]=c[F+4>>2];c[s+8>>2]=c[F+8>>2];c[s+12>>2]=c[F+12>>2];Vg(G,r,i);c[u>>2]=c[G>>2];c[u+4>>2]=c[G+4>>2];c[u+8>>2]=c[G+8>>2];c[u+12>>2]=c[G+12>>2];Vg(H,r,i);c[v>>2]=c[H>>2];c[v+4>>2]=c[H+4>>2];c[v+8>>2]=c[H+8>>2];c[v+12>>2]=c[H+12>>2];Vg(I,t,i);c[w>>2]=c[I>>2];c[w+4>>2]=c[I+4>>2];c[w+8>>2]=c[I+8>>2];c[w+12>>2]=c[I+12>>2];Vg(J,t,i);c[x>>2]=c[J>>2];c[x+4>>2]=c[J+4>>2];c[x+8>>2]=c[J+8>>2];c[x+12>>2]=c[J+12>>2];Vg(K,t,i);c[y>>2]=c[K>>2];c[y+4>>2]=c[K+4>>2];c[y+8>>2]=c[K+8>>2];c[y+12>>2]=c[K+12>>2];h[k>>3]=A;h[o>>3]=B;a=10;g=9}h[L+(g<<4)>>3]=A;h[L+(g<<4)+8>>3]=B;dO(j,c[((c[j>>2]&3|0)==2?j:j+-48|0)+40>>2]|0,L,a,17296);i=i+z;b=b+1|0}l=N;return}function Sg(a,d,e,f,g,i,j,k){a=a|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;var m=0,n=0.0,o=0,p=0,q=0,r=0,s=0.0,t=0,u=0,v=0,w=0,x=0,y=0,z=0.0,A=0.0,B=0.0,C=0.0,D=0.0;x=l;l=l+1440|0;t=x+1392|0;v=x+1424|0;w=x+696|0;u=x;r=c[j>>2]&3;q=c[((r|0)==3?j:j+48|0)+40>>2]|0;r=c[((r|0)==2?j:j+-48|0)+40>>2]|0;m=c[q+16>>2]|0;o=c[m+232>>2]|0;p=c[a+16>>2]|0;if((o|0)<(b[p+238>>1]|0)){y=c[p+196>>2]|0;p=o+1|0;n=+h[m+24>>3]-+h[y+(o<<6)+32>>3]-+h[(c[(c[c[y+(p<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]-+h[y+(p<<6)+40>>3]}else n=+(c[p+252>>2]|0);z=+(i+1|0);s=+(c[d+12>>2]|0)/z;n=n/z;Ug(a,d,e,q,j,w,1);Ug(a,d,e,r,j,u,0);r=w+52|0;j=u+52|0;a=(k|0)==0;d=e+80|0;m=0;while(1){if((m|0)>=(i|0))break;q=c[f+(m+g<<2)>>2]|0;p=c[r>>2]|0;o=p+-1|0;D=+h[w+56+(o<<5)>>3];B=+h[w+56+(o<<5)+8>>3];C=+h[w+56+(o<<5)+16>>3];h[19215]=D;h[19218]=B;m=m+1|0;z=+(m|0);A=s*z;h[19217]=C+A;z=B-n*z;h[19216]=z;h[19219]=D;h[19222]=z;o=(c[j>>2]|0)+-1|0;D=+h[u+56+(o<<5)+16>>3];h[19221]=D;h[19220]=z-n;B=+h[u+56+(o<<5)>>3];C=+h[u+56+(o<<5)+8>>3];h[19225]=D;h[19226]=C;h[19223]=B-A;h[19224]=z;o=0;while(1){if((o|0)>=(p|0)){o=0;break}p=w+56+(o<<5)|0;c[t>>2]=c[p>>2];c[t+4>>2]=c[p+4>>2];c[t+8>>2]=c[p+8>>2];c[t+12>>2]=c[p+12>>2];c[t+16>>2]=c[p+16>>2];c[t+20>>2]=c[p+20>>2];c[t+24>>2]=c[p+24>>2];c[t+28>>2]=c[p+28>>2];fO(e,t);o=o+1|0;p=c[r>>2]|0}while(1){if((o|0)==3)break;y=153720+(o<<5)|0;c[t>>2]=c[y>>2];c[t+4>>2]=c[y+4>>2];c[t+8>>2]=c[y+8>>2];c[t+12>>2]=c[y+12>>2];c[t+16>>2]=c[y+16>>2];c[t+20>>2]=c[y+20>>2];c[t+24>>2]=c[y+24>>2];c[t+28>>2]=c[y+28>>2];fO(e,t);o=o+1|0}p=c[j>>2]|0;while(1){o=p+-1|0;if((p|0)<=0)break;p=u+56+(o<<5)|0;c[t>>2]=c[p>>2];c[t+4>>2]=c[p+4>>2];c[t+8>>2]=c[p+8>>2];c[t+12>>2]=c[p+12>>2];c[t+16>>2]=c[p+16>>2];c[t+20>>2]=c[p+20>>2];c[t+24>>2]=c[p+24>>2];c[t+28>>2]=c[p+28>>2];fO(e,t);p=o}if(a)o=NM(e,v)|0;else o=HM(e,v)|0;p=c[v>>2]|0;if(!p)break;dO(q,c[((c[q>>2]&3|0)==2?q:q+-48|0)+40>>2]|0,o,p,17296);c[d>>2]=0}l=x;return}function Tg(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0.0;n=l;l=l+128|0;j=n+96|0;k=n+64|0;o=n+32|0;m=n;wg(o,a,b,e,0,f);c[g>>2]=c[o>>2];c[g+4>>2]=c[o+4>>2];c[g+8>>2]=c[o+8>>2];c[g+12>>2]=c[o+12>>2];c[g+16>>2]=c[o+16>>2];c[g+20>>2]=c[o+20>>2];c[g+24>>2]=c[o+24>>2];c[g+28>>2]=c[o+28>>2];c[k>>2]=c[o>>2];c[k+4>>2]=c[o+4>>2];c[k+8>>2]=c[o+8>>2];c[k+12>>2]=c[o+12>>2];c[k+16>>2]=c[o+16>>2];c[k+20>>2]=c[o+20>>2];c[k+24>>2]=c[o+24>>2];c[k+28>>2]=c[o+28>>2];c[g+48>>2]=4;if(!(i<<24>>24))jO(d,f,2,g,0);else gO(d,f,2,g,0);b=g+52|0;f=(c[b>>2]|0)+-1|0;o=k+24|0;h[o>>3]=+h[g+56+(f<<5)+24>>3];i=k+8|0;h[i>>3]=+h[g+56+(f<<5)+8>>3];e=c[e+16>>2]|0;p=+h[(c[(c[a+16>>2]|0)+196>>2]|0)+(c[e+232>>2]<<6)+24>>3]+ +h[e+24>>3];c[j>>2]=c[k>>2];c[j+4>>2]=c[k+4>>2];c[j+8>>2]=c[k+8>>2];c[j+12>>2]=c[k+12>>2];c[j+16>>2]=c[k+16>>2];c[j+20>>2]=c[k+20>>2];c[j+24>>2]=c[k+24>>2];c[j+28>>2]=c[k+28>>2];yg(m,j,4,p);c[k>>2]=c[m>>2];c[k+4>>2]=c[m+4>>2];c[k+8>>2]=c[m+8>>2];c[k+12>>2]=c[m+12>>2];c[k+16>>2]=c[m+16>>2];c[k+20>>2]=c[m+20>>2];c[k+24>>2]=c[m+24>>2];c[k+28>>2]=c[m+28>>2];if(+h[k>>3]<+h[k+16>>3]?+h[i>>3]<+h[o>>3]:0){o=c[b>>2]|0;c[b>>2]=o+1;o=g+56+(o<<5)|0;c[o>>2]=c[k>>2];c[o+4>>2]=c[k+4>>2];c[o+8>>2]=c[k+8>>2];c[o+12>>2]=c[k+12>>2];c[o+16>>2]=c[k+16>>2];c[o+20>>2]=c[k+20>>2];c[o+24>>2]=c[k+24>>2];c[o+28>>2]=c[k+28>>2]}l=n;return}function Ug(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0.0;n=l;l=l+128|0;j=n+96|0;k=n+64|0;o=n+32|0;m=n;wg(o,a,b,e,0,f);c[g>>2]=c[o>>2];c[g+4>>2]=c[o+4>>2];c[g+8>>2]=c[o+8>>2];c[g+12>>2]=c[o+12>>2];c[g+16>>2]=c[o+16>>2];c[g+20>>2]=c[o+20>>2];c[g+24>>2]=c[o+24>>2];c[g+28>>2]=c[o+28>>2];c[k>>2]=c[o>>2];c[k+4>>2]=c[o+4>>2];c[k+8>>2]=c[o+8>>2];c[k+12>>2]=c[o+12>>2];c[k+16>>2]=c[o+16>>2];c[k+20>>2]=c[o+20>>2];c[k+24>>2]=c[o+24>>2];c[k+28>>2]=c[o+28>>2];c[g+48>>2]=1;if(!(i<<24>>24))jO(d,f,2,g,0);else gO(d,f,2,g,0);b=g+52|0;f=(c[b>>2]|0)+-1|0;o=k+24|0;h[o>>3]=+h[g+56+(f<<5)+24>>3];i=k+8|0;h[i>>3]=+h[g+56+(f<<5)+8>>3];e=c[e+16>>2]|0;p=+h[e+24>>3]-+h[(c[(c[a+16>>2]|0)+196>>2]|0)+(c[e+232>>2]<<6)+24>>3];c[j>>2]=c[k>>2];c[j+4>>2]=c[k+4>>2];c[j+8>>2]=c[k+8>>2];c[j+12>>2]=c[k+12>>2];c[j+16>>2]=c[k+16>>2];c[j+20>>2]=c[k+20>>2];c[j+24>>2]=c[k+24>>2];c[j+28>>2]=c[k+28>>2];yg(m,j,1,p);c[k>>2]=c[m>>2];c[k+4>>2]=c[m+4>>2];c[k+8>>2]=c[m+8>>2];c[k+12>>2]=c[m+12>>2];c[k+16>>2]=c[m+16>>2];c[k+20>>2]=c[m+20>>2];c[k+24>>2]=c[m+24>>2];c[k+28>>2]=c[m+28>>2];if(+h[k>>3]<+h[k+16>>3]?+h[i>>3]<+h[o>>3]:0){o=c[b>>2]|0;c[b>>2]=o+1;o=g+56+(o<<5)|0;c[o>>2]=c[k>>2];c[o+4>>2]=c[k+4>>2];c[o+8>>2]=c[k+8>>2];c[o+12>>2]=c[k+12>>2];c[o+16>>2]=c[k+16>>2];c[o+20>>2]=c[k+20>>2];c[o+24>>2]=c[k+24>>2];c[o+28>>2]=c[k+28>>2]}l=n;return}function Vg(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b;h[a+8>>3]=c;return}function Wg(b,d,e,f,g,i,j){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;var k=0,m=0,n=0,o=0,p=0.0,q=0.0,r=0.0,s=0.0,t=0.0,u=0.0,v=0.0,w=0.0,x=0.0,y=0,z=0,A=0.0,B=0.0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0,X=0,Y=0,Z=0.0;Y=l;l=l+288|0;R=Y+256|0;Q=Y+240|0;P=Y+224|0;V=Y+280|0;W=Y+272|0;X=Y+32|0;S=Y+16|0;T=Y;m=Y+208|0;n=Y+192|0;o=c[e+(f<<2)>>2]|0;U=JK(g<<2)|0;k=0;while(1){if((k|0)>=(g|0))break;c[U+(k<<2)>>2]=c[e+(k+f<<2)>>2];k=k+1|0}o4(U,g,4,59);e=b+16|0;f=(c[e>>2]|0)+16|0;o=o+16|0;C=(c[o>>2]|0)+16|0;c[Q>>2]=c[f>>2];c[Q+4>>2]=c[f+4>>2];c[Q+8>>2]=c[f+8>>2];c[Q+12>>2]=c[f+12>>2];c[R>>2]=c[C>>2];c[R+4>>2]=c[C+4>>2];c[R+8>>2]=c[C+8>>2];c[R+12>>2]=c[C+12>>2];Ng(m,Q,R);c[S>>2]=c[m>>2];c[S+4>>2]=c[m+4>>2];c[S+8>>2]=c[m+8>>2];c[S+12>>2]=c[m+12>>2];C=d+16|0;f=(c[C>>2]|0)+16|0;o=(c[o>>2]|0)+56|0;c[Q>>2]=c[f>>2];c[Q+4>>2]=c[f+4>>2];c[Q+8>>2]=c[f+8>>2];c[Q+12>>2]=c[f+12>>2];c[R>>2]=c[o>>2];c[R+4>>2]=c[o+4>>2];c[R+8>>2]=c[o+8>>2];c[R+12>>2]=c[o+12>>2];Ng(n,Q,R);c[T>>2]=c[n>>2];c[T+4>>2]=c[n+4>>2];c[T+8>>2]=c[n+8>>2];c[T+12>>2]=c[n+12>>2];w=+h[(c[e>>2]|0)+96>>3]+ +h[S>>3];x=+h[T>>3]-+h[(c[C>>2]|0)+88>>3];u=x+w;v=u*.5;C=c[U>>2]|0;c[X>>2]=c[S>>2];c[X+4>>2]=c[S+4>>2];c[X+8>>2]=c[S+8>>2];c[X+12>>2]=c[S+12>>2];o=X+16|0;c[o>>2]=c[S>>2];c[o+4>>2]=c[S+4>>2];c[o+8>>2]=c[S+8>>2];c[o+12>>2]=c[S+12>>2];e=X+32|0;c[e>>2]=c[T>>2];c[e+4>>2]=c[T+4>>2];c[e+8>>2]=c[T+8>>2];c[e+12>>2]=c[T+12>>2];f=X+48|0;c[f>>2]=c[T>>2];c[f+4>>2]=c[T+4>>2];c[f+8>>2]=c[T+8>>2];c[f+12>>2]=c[T+12>>2];dO(C,c[((c[C>>2]&3|0)==2?C:C+-48|0)+40>>2]|0,X,4,17296);C=c[(c[C+16>>2]|0)+96>>2]|0;h[C+56>>3]=v;z=S+8|0;r=+h[z>>3];s=+h[C+32>>3];h[C+64>>3]=(s+6.0)*.5+r;a[C+81>>0]=1;r=r+3.0;B=+h[C+24>>3];A=(u-B)*.5;B=(B+u)*.5;C=X+24|0;D=X+40|0;E=X+56|0;F=X+64|0;G=T+8|0;H=X+72|0;I=X+80|0;J=X+88|0;K=X+96|0;L=X+104|0;M=X+112|0;N=X+120|0;O=V+4|0;n=(i|0)==6&1;q=0.0;p=0.0;s=s+r;m=1;while(1){if((m|0)>=(j|0)){y=6;break}d=c[U+(m<<2)>>2]|0;if(!(m&1)){c[X>>2]=c[S>>2];c[X+4>>2]=c[S+4>>2];c[X+8>>2]=c[S+8>>2];c[X+12>>2]=c[S+12>>2];h[o>>3]=A;h[C>>3]=+h[z>>3];h[e>>3]=A;h[D>>3]=s;h[f>>3]=B;h[E>>3]=s;h[F>>3]=B;t=+h[G>>3];h[H>>3]=t;Z=+h[T>>3];h[I>>3]=Z;h[J>>3]=t;h[K>>3]=Z;Z=s+6.0;h[L>>3]=Z;h[M>>3]=+h[S>>3];h[N>>3]=Z;s=+h[(c[(c[d+16>>2]|0)+96>>2]|0)+32>>3];t=Z+s*.5;s=Z+s}else{k=d+16|0;b=c[(c[k>>2]|0)+96>>2]|0;if((m|0)==1){p=+h[b+24>>3];q=(p+u)*.5;p=(u-p)*.5}r=r+-6.0-+h[b+32>>3];c[X>>2]=c[S>>2];c[X+4>>2]=c[S+4>>2];c[X+8>>2]=c[S+8>>2];c[X+12>>2]=c[S+12>>2];h[o>>3]=+h[S>>3];t=r+-6.0;h[C>>3]=t;h[e>>3]=+h[T>>3];h[D>>3]=t;c[f>>2]=c[T>>2];c[f+4>>2]=c[T+4>>2];c[f+8>>2]=c[T+8>>2];c[f+12>>2]=c[T+12>>2];h[F>>3]=q;h[H>>3]=+h[G>>3];h[I>>3]=q;h[J>>3]=r;h[K>>3]=p;h[L>>3]=r;h[M>>3]=p;h[N>>3]=+h[z>>3];t=+h[(c[(c[k>>2]|0)+96>>2]|0)+32>>3]*.5+r}c[O>>2]=8;c[V>>2]=X;c[P>>2]=c[S>>2];c[P+4>>2]=c[S+4>>2];c[P+8>>2]=c[S+8>>2];c[P+12>>2]=c[S+12>>2];c[Q>>2]=c[T>>2];c[Q+4>>2]=c[T+4>>2];c[Q+8>>2]=c[T+8>>2];c[Q+12>>2]=c[T+12>>2];c[R>>2]=c[V>>2];c[R+4>>2]=c[V+4>>2];k=DM(P,Q,R,W,n)|0;b=c[W>>2]|0;if(!b)break;i=c[(c[d+16>>2]|0)+96>>2]|0;h[i+56>>3]=v;h[i+64>>3]=t;a[i+81>>0]=1;dO(d,c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0,k,b,17296);m=m+1|0}a:do if((y|0)==6){v=(x+w*2.0)*.3333333333333333;u=(x*2.0+w)*.3333333333333333;while(1){if((m|0)>=(g|0))break;d=c[U+(m<<2)>>2]|0;if(!(m&1)){c[X>>2]=c[S>>2];c[X+4>>2]=c[S+4>>2];c[X+8>>2]=c[S+8>>2];c[X+12>>2]=c[S+12>>2];h[o>>3]=A;h[C>>3]=+h[z>>3];h[e>>3]=A;h[D>>3]=s;h[f>>3]=B;h[E>>3]=s;h[F>>3]=B;t=+h[G>>3];h[H>>3]=t;Z=+h[T>>3];h[I>>3]=Z;h[J>>3]=t;h[K>>3]=Z;Z=s+6.0;h[L>>3]=Z;h[M>>3]=+h[S>>3];s=Z;t=p;p=Z}else{y=(m|0)==1;t=y?v:p;q=y?u:q;p=r+-6.0;c[X>>2]=c[S>>2];c[X+4>>2]=c[S+4>>2];c[X+8>>2]=c[S+8>>2];c[X+12>>2]=c[S+12>>2];h[o>>3]=+h[S>>3];r=r+-12.0;h[C>>3]=r;h[e>>3]=+h[T>>3];h[D>>3]=r;c[f>>2]=c[T>>2];c[f+4>>2]=c[T+4>>2];c[f+8>>2]=c[T+8>>2];c[f+12>>2]=c[T+12>>2];h[F>>3]=q;h[H>>3]=+h[G>>3];h[I>>3]=q;h[J>>3]=p;h[K>>3]=t;h[L>>3]=p;h[M>>3]=t;r=p;p=+h[z>>3]}h[N>>3]=p;c[O>>2]=8;c[V>>2]=X;c[P>>2]=c[S>>2];c[P+4>>2]=c[S+4>>2];c[P+8>>2]=c[S+8>>2];c[P+12>>2]=c[S+12>>2];c[Q>>2]=c[T>>2];c[Q+4>>2]=c[T+4>>2];c[Q+8>>2]=c[T+8>>2];c[Q+12>>2]=c[T+12>>2];c[R>>2]=c[V>>2];c[R+4>>2]=c[V+4>>2];k=DM(P,Q,R,W,n)|0;b=c[W>>2]|0;if(!b)break a;dO(d,c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0,k,b,17296);m=m+1|0;p=t}U1(U)}while(0);l=Y;return}function Xg(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0;g=l;l=l+16|0;f=g;j=(xB(b)|0)==0;j=j?18592:18584;a[f>>0]=a[j>>0]|0;a[f+1>>0]=a[j+1>>0]|0;a[f+2>>0]=a[j+2>>0]|0;a[f+3>>0]=a[j+3>>0]|0;f=oB(85493,f,0)|0;TC(f,137483,280,1)|0;GA(f,0,87171,195059)|0;j=JK(96)|0;e=c[f+16>>2]|0;c[e+8>>2]=j;i=c[b+16>>2]|0;k=c[i+8>>2]|0;h[j>>3]=+h[k>>3];h[j+24>>3]=+h[k+24>>3];a[e+115>>0]=a[i+115>>0]|0;c[e+116>>2]=c[i+116>>2]&1^1;c[e+248>>2]=c[i+248>>2];c[e+252>>2]=c[i+252>>2];e=LA(yC(b)|0,1,0)|0;while(1){if(!e)break;GA(f,1,c[e+8>>2]|0,c[e+12>>2]|0)|0;e=LA(yC(b)|0,1,e)|0}e=LA(yC(b)|0,2,0)|0;while(1){if(!e)break;GA(f,2,c[e+8>>2]|0,c[e+12>>2]|0)|0;e=LA(yC(b)|0,2,e)|0}if(!(GA(f,2,111464,0)|0))GA(f,2,111464,195059)|0;if(!(GA(f,2,111455,0)|0))GA(f,2,111455,195059)|0;ah(f,d);l=g;return f|0}function Yg(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;e=l;l=l+16|0;d=e;a=tC(a,KB(b)|0,1)|0;TC(a,137447,304,1)|0;UA(b,a)|0;if((WM(b)|0)==2){f=b+16|0;b=KK((l2(c[c[(c[f>>2]|0)+104>>2]>>2]|0)|0)+3|0)|0;c[d>>2]=c[c[(c[f>>2]|0)+104>>2]>>2];m4(b,85488,d)|0;SA(a,108224,b)|0}l=e;return a|0}function Zg(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;c=iB(a,b,c,0,1)|0;TC(c,137460,176,1)|0;UA(d,c)|0;return c|0}function _g(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0.0;i=l;l=l+32|0;g=i+16|0;f=i;if(e|0){j=+h[b>>3];e=b+8|0;h[b>>3]=+h[e>>3];h[e>>3]=-j};c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];c[g>>2]=c[d>>2];c[g+4>>2]=c[d+4>>2];c[g+8>>2]=c[d+8>>2];c[g+12>>2]=c[d+12>>2];Ng(a,f,g);l=i;return}function $g(a,b){a=a|0;b=b|0;c[47189]=c[b>>2];c[47193]=c[b+4>>2];c[47194]=c[b+8>>2];c[47174]=c[b+12>>2];c[47175]=c[b+16>>2];c[47181]=c[b+20>>2];c[47180]=c[b+24>>2];c[47179]=c[b+28>>2];c[47205]=c[b+32>>2];c[47197]=c[b+36>>2];c[47182]=c[b+40>>2];c[47192]=c[b+44>>2];c[47201]=c[b+48>>2];c[47200]=c[b+52>>2];c[47199]=c[b+56>>2];c[47204]=c[b+60>>2];c[47198]=c[b+64>>2];c[47183]=c[b+68>>2];c[47145]=c[b+72>>2];c[47146]=c[b+76>>2];c[47147]=c[b+80>>2];c[47157]=c[b+84>>2];c[47150]=c[b+88>>2];c[47151]=c[b+92>>2];c[47152]=c[b+96>>2];c[47154]=c[b+100>>2];c[47155]=c[b+104>>2];c[47158]=c[b+108>>2];c[47161]=c[b+112>>2];c[47159]=c[b+116>>2];c[47160]=c[b+120>>2];c[47163]=c[b+124>>2];c[47162]=c[b+128>>2];c[47164]=c[b+132>>2];c[47165]=c[b+136>>2];c[47156]=c[b+140>>2];c[47168]=c[b+144>>2];c[47140]=c[b+148>>2];c[47138]=c[b+152>>2];U1(b);Rf(a);sB(a)|0;return}function ah(a,b){a=a|0;b=b|0;c[b>>2]=c[47189];c[b+4>>2]=c[47193];c[b+8>>2]=c[47194];c[b+12>>2]=c[47174];c[b+16>>2]=c[47175];c[b+20>>2]=c[47181];c[b+24>>2]=c[47180];c[b+28>>2]=c[47179];c[b+32>>2]=c[47205];c[b+36>>2]=c[47197];c[b+40>>2]=c[47182];c[b+44>>2]=c[47192];c[b+48>>2]=c[47201];c[b+52>>2]=c[47200];c[b+56>>2]=c[47199];c[b+60>>2]=c[47204];c[b+64>>2]=c[47198];c[b+68>>2]=c[47183];c[b+72>>2]=c[47145];c[b+76>>2]=c[47146];c[b+80>>2]=c[47147];c[b+84>>2]=c[47157];c[b+88>>2]=c[47150];c[b+92>>2]=c[47151];c[b+96>>2]=c[47152];c[b+100>>2]=c[47154];c[b+104>>2]=c[47155];c[b+108>>2]=c[47158];c[b+112>>2]=c[47161];c[b+116>>2]=c[47159];c[b+120>>2]=c[47160];c[b+124>>2]=c[47163];c[b+128>>2]=c[47162];c[b+132>>2]=c[47164];c[b+136>>2]=c[47165];c[b+140>>2]=c[47156];c[b+144>>2]=c[47168];c[b+152>>2]=c[47138];c[b+148>>2]=c[47140];c[47189]=0;c[47193]=GA(a,2,87412,0)|0;c[47194]=GA(a,2,87421,0)|0;b=GA(a,2,101541,0)|0;c[47174]=b;if(!b)c[47174]=GA(a,2,101541,195059)|0;c[47175]=0;c[47181]=0;c[47180]=GA(a,2,101437,0)|0;c[47179]=GA(a,2,101428,0)|0;c[47205]=GA(a,2,101717,0)|0;c[47197]=0;c[47182]=GA(a,2,108224,0)|0;c[47192]=GA(a,2,85498,0)|0;c[47201]=0;c[47200]=GA(a,2,101617,0)|0;c[47199]=GA(a,2,101603,0)|0;c[47204]=GA(a,2,101708,0)|0;c[47198]=0;c[47183]=0;c[47145]=GA(a,1,141048,0)|0;c[47146]=GA(a,1,141116,0)|0;c[47147]=GA(a,1,108230,0)|0;c[47157]=0;c[47150]=GA(a,1,101428,0)|0;c[47151]=GA(a,1,101437,0)|0;c[47152]=0;c[47154]=GA(a,1,108224,0)|0;c[47155]=0;c[47158]=0;c[47161]=GA(a,1,101409,0)|0;c[47159]=GA(a,1,141076,0)|0;c[47160]=GA(a,1,101472,0)|0;c[47163]=GA(a,1,101484,0)|0;c[47162]=GA(a,1,101348,0)|0;c[47164]=GA(a,1,101489,0)|0;c[47165]=GA(a,1,85510,0)|0;c[47156]=0;c[47168]=0;c[47140]=GA(a,0,101409,0)|0;return}function bh(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0.0,i=0.0;d=c[(c[(c[a>>2]|0)+16>>2]|0)+96>>2]|0;a=c[(c[(c[b>>2]|0)+16>>2]|0)+96>>2]|0;b=(a|0)!=0;if(d)if(b?(f=+h[d+24>>3],i=+h[d+32>>3],e=+h[a+24>>3],g=+h[a+32>>3],!(f>e)):0)if(!(f<e))if(i>g)a=-1;else a=i<g&1;else a=1;else a=-1;else a=b&1;return a|0}function ch(a){a=a|0;var b=0,d=0,e=0;while(1){b=c[(c[a+16>>2]|0)+116>>2]|0;if(!b)break;else a=b}b=c[a>>2]&3;d=c[(c[((b|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0;e=c[d+232>>2]|0;a=c[(c[((b|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0;b=c[a+232>>2]|0;if((e|0)<=(b|0))if((e|0)<(b|0))a=1;else a=(c[d+236>>2]|0)<(c[a+236>>2]|0)&1;else a=0;return a|0}function dh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;d=l;l=l+16|0;e=d+8|0;f=d;h=(c[a+16>>2]|0)+180|0;g=(c[b+16>>2]|0)+172|0;c[f>>2]=c[h>>2];c[f+4>>2]=c[h+4>>2];c[e>>2]=c[g>>2];c[e+4>>2]=c[g+4>>2];b=eh(a,f,b,e)|0;l=d;return b|0}function eh(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;f=c[b+4>>2]|0;a:do if((f|0)>0?(g=c[e+4>>2]|0,(g|0)>0):0)if((f|0)<(g|0)){f=c[b>>2]|0;e=0;while(1){b=c[f+(e<<2)>>2]|0;if(!b){b=0;break a}if((c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)==(d|0))break;else e=e+1|0}}else{f=c[e>>2]|0;e=0;while(1){b=c[f+(e<<2)>>2]|0;if(!b){b=0;break a}if((c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)==(a|0))break;else e=e+1|0}}else b=0;while(0);return b|0}function fh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;d=l;l=l+16|0;e=d+8|0;f=d;h=(c[a+16>>2]|0)+188|0;g=(c[b+16>>2]|0)+196|0;c[f>>2]=c[h>>2];c[f+4>>2]=c[h+4>>2];c[e>>2]=c[g>>2];c[e+4>>2]=c[g+4>>2];b=eh(a,f,b,e)|0;l=d;return b|0}function gh(a){a=a|0;var b=0,d=0,e=0,f=0;e=a+48|0;d=c[(c[((c[a>>2]&3|0)==3?a:e)+40>>2]|0)+16>>2]|0;b=c[d+180>>2]|0;if(!b)b=KK((c[d+184>>2]<<2)+8|0)|0;else b=MK(b,(c[d+184>>2]<<2)+8|0)|0;c[(c[(c[((c[a>>2]&3|0)==3?a:e)+40>>2]|0)+16>>2]|0)+180>>2]=b;f=c[(c[((c[a>>2]&3|0)==3?a:e)+40>>2]|0)+16>>2]|0;d=c[f+180>>2]|0;f=f+184|0;b=c[f>>2]|0;c[f>>2]=b+1;c[d+(b<<2)>>2]=a;e=c[(c[((c[a>>2]&3|0)==3?a:e)+40>>2]|0)+16>>2]|0;c[(c[e+180>>2]|0)+(c[e+184>>2]<<2)>>2]=0;e=a+-48|0;b=c[(c[((c[a>>2]&3|0)==2?a:e)+40>>2]|0)+16>>2]|0;d=c[b+172>>2]|0;if(!d)b=KK((c[b+176>>2]<<2)+8|0)|0;else b=MK(d,(c[b+176>>2]<<2)+8|0)|0;c[(c[(c[((c[a>>2]&3|0)==2?a:e)+40>>2]|0)+16>>2]|0)+172>>2]=b;b=c[(c[((c[a>>2]&3|0)==2?a:e)+40>>2]|0)+16>>2]|0;d=c[b+172>>2]|0;b=b+176|0;f=c[b>>2]|0;c[b>>2]=f+1;c[d+(f<<2)>>2]=a;f=c[(c[((c[a>>2]&3|0)==2?a:e)+40>>2]|0)+16>>2]|0;c[(c[f+172>>2]|0)+(c[f+176>>2]<<2)>>2]=0;return a|0}function hh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;e=a+4|0;f=c[e>>2]|0;d=0;while(1){if((d|0)>=(f|0))break;g=c[a>>2]|0;h=g+(d<<2)|0;if((c[h>>2]|0)==(b|0)){i=4;break}else d=d+1|0}if((i|0)==4){i=f+-1|0;c[e>>2]=i;c[h>>2]=c[g+(i<<2)>>2];c[(c[a>>2]|0)+(i<<2)>>2]=0}return}function ih(a){a=a|0;if(!a)qa(85551,85561,117,85570);else{hh((c[(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+180|0,a);hh((c[(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+172|0,a);return}}function jh(a){a=a|0;var b=0,d=0,e=0,f=0;e=a+48|0;d=c[(c[((c[a>>2]&3|0)==3?a:e)+40>>2]|0)+16>>2]|0;b=c[d+204>>2]|0;if(!b)b=KK((c[d+208>>2]<<2)+8|0)|0;else b=MK(b,(c[d+208>>2]<<2)+8|0)|0;c[(c[(c[((c[a>>2]&3|0)==3?a:e)+40>>2]|0)+16>>2]|0)+204>>2]=b;f=c[(c[((c[a>>2]&3|0)==3?a:e)+40>>2]|0)+16>>2]|0;b=c[f+204>>2]|0;f=f+208|0;d=c[f>>2]|0;c[f>>2]=d+1;c[b+(d<<2)>>2]=a;e=c[(c[((c[a>>2]&3|0)==3?a:e)+40>>2]|0)+16>>2]|0;c[(c[e+204>>2]|0)+(c[e+208>>2]<<2)>>2]=0;return}function kh(a){a=a|0;lh(a,(c[(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+204|0);return}function lh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;h=b+4|0;e=c[h>>2]|0;f=c[b>>2]|0;d=0;while(1){if((d|0)>=(e|0)){g=4;break}if((c[f+(d<<2)>>2]|0)==(a|0))break;else d=d+1|0}if((g|0)==4){if(!f)d=KK((e<<2)+8|0)|0;else d=MK(f,(e<<2)+8|0)|0;c[b>>2]=d;f=c[h>>2]|0;g=f+1|0;c[h>>2]=g;c[d+(f<<2)>>2]=a;c[(c[b>>2]|0)+(g<<2)>>2]=0}return}function mh(d,e,f){d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;n=JK(96)|0;g=n+48|0;c[g>>2]=c[g>>2]|3;c[n>>2]=c[n>>2]&-4|2;h=JK(176)|0;m=n+16|0;c[m>>2]=h;c[((c[n>>2]&3|0)==3?n:g)+40>>2]=d;j=n+-48|0;c[((c[n>>2]&3|0)==2?n:j)+40>>2]=e;a[h+112>>0]=1;if(!f){c[h+156>>2]=1;b[h+154>>1]=1;b[h+168>>1]=1;b[h+170>>1]=1}else{c[n>>2]=c[n>>2]&15|c[f>>2]&-16;c[g>>2]=c[g>>2]&15|c[f>>2]&-16;l=f+16|0;i=c[l>>2]|0;b[h+168>>1]=b[i+168>>1]|0;b[h+154>>1]=b[i+154>>1]|0;c[h+156>>2]=c[i+156>>2];b[h+170>>1]=b[i+170>>1]|0;d=c[((c[n>>2]&3|0)==3?n:g)+40>>2]|0;e=c[f>>2]&3;k=f+48|0;if((d|0)!=(c[((e|0)==3?f:k)+40>>2]|0)){g=f+-48|0;if((d|0)==(c[((e|0)==2?f:g)+40>>2]|0)){h=h+16|0;d=i+56|0;e=h+40|0;do{c[h>>2]=c[d>>2];h=h+4|0;d=d+4|0}while((h|0)<(e|0))}}else{h=h+16|0;d=i+16|0;e=h+40|0;do{c[h>>2]=c[d>>2];h=h+4|0;d=d+4|0}while((h|0)<(e|0));g=f+-48|0}d=c[((c[n>>2]&3|0)==2?n:j)+40>>2]|0;e=c[f>>2]&3;if((d|0)!=(c[((e|0)==2?f:g)+40>>2]|0)){if((d|0)==(c[((e|0)==3?f:k)+40>>2]|0)){h=(c[m>>2]|0)+56|0;d=(c[l>>2]|0)+16|0;e=h+40|0;do{c[h>>2]=c[d>>2];h=h+4|0;d=d+4|0}while((h|0)<(e|0))}}else{h=(c[m>>2]|0)+56|0;d=(c[l>>2]|0)+56|0;e=h+40|0;do{c[h>>2]=c[d>>2];h=h+4|0;d=d+4|0}while((h|0)<(e|0))}d=(c[l>>2]|0)+172|0;if(!(c[d>>2]|0))c[d>>2]=n;c[(c[m>>2]|0)+116>>2]=f}return n|0}function nh(a,b,c){a=a|0;b=b|0;c=c|0;return gh(mh(a,b,c)|0)|0}function oh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;a=(c[a+16>>2]|0)+192|0;f=c[a>>2]|0;d=c[b+16>>2]|0;c[d+164>>2]=f;e=f;if(f|0)c[(c[e+16>>2]|0)+168>>2]=b;c[a>>2]=b;c[d+168>>2]=0;if((e|0)==(b|0))qa(85587,85561,215,85603);else return}function ph(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;if((a|0)==(b|0))qa(85613,85561,220,85620);f=c[b+16>>2]|0;d=f+164|0;if(c[d>>2]|0)qa(85633,85561,221,85620);e=(c[a+16>>2]|0)+164|0;c[d>>2]=c[e>>2];d=c[e>>2]|0;if(d|0)c[(c[d+16>>2]|0)+168>>2]=b;c[f+168>>2]=a;c[e>>2]=b;return}function qh(a,b){a=a|0;b=b|0;var d=0,e=0;if(!(rh(a,b)|0))qa(85652,85561,231,85673);b=c[b+16>>2]|0;d=c[b+164>>2]|0;e=d;b=b+168|0;if(d|0)c[(c[d+16>>2]|0)+168>>2]=c[b>>2];b=c[b>>2]|0;if(!b)c[(c[a+16>>2]|0)+192>>2]=e;else c[(c[b+16>>2]|0)+164>>2]=e;return}function rh(a,b){a=a|0;b=b|0;a=(c[a+16>>2]|0)+192|0;while(1){a=c[a>>2]|0;if((a|0)==0|(a|0)==(b|0))break;a=(c[a+16>>2]|0)+164|0}return a|0}function sh(b){b=b|0;var d=0,e=0,f=0,g=0;d=JK(64)|0;c[d>>2]=c[d>>2]&-4|1;e=d+16|0;c[e>>2]=JK(304)|0;c[d+24>>2]=yC(b)|0;g=c[e>>2]|0;a[g+156>>0]=1;h[g+96>>3]=1.0;h[g+88>>3]=1.0;h[g+80>>3]=1.0;c[g+216>>2]=1;c[g+176>>2]=0;g=JK(20)|0;f=c[e>>2]|0;c[f+172>>2]=g;c[f+184>>2]=0;f=JK(20)|0;c[(c[e>>2]|0)+180>>2]=f;oh(b,d);b=(c[b+16>>2]|0)+232|0;c[b>>2]=(c[b>>2]|0)+1;return d|0}function th(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0;g=d+48|0;e=c[(c[((c[d>>2]&3|0)==3?d:g)+40>>2]|0)+16>>2]|0;f=c[e+188>>2]|0;if(!f)e=KK((c[e+192>>2]<<2)+8|0)|0;else e=MK(f,(c[e+192>>2]<<2)+8|0)|0;c[(c[(c[((c[d>>2]&3|0)==3?d:g)+40>>2]|0)+16>>2]|0)+188>>2]=e;h=c[(c[((c[d>>2]&3|0)==3?d:g)+40>>2]|0)+16>>2]|0;f=c[h+188>>2]|0;h=h+192|0;e=c[h>>2]|0;c[h>>2]=e+1;c[f+(e<<2)>>2]=d;g=c[(c[((c[d>>2]&3|0)==3?d:g)+40>>2]|0)+16>>2]|0;c[(c[g+188>>2]|0)+(c[g+192>>2]<<2)>>2]=0;g=d+-48|0;e=c[(c[((c[d>>2]&3|0)==2?d:g)+40>>2]|0)+16>>2]|0;f=c[e+196>>2]|0;if(!f)e=KK((c[e+200>>2]<<2)+8|0)|0;else e=MK(f,(c[e+200>>2]<<2)+8|0)|0;c[(c[(c[((c[d>>2]&3|0)==2?d:g)+40>>2]|0)+16>>2]|0)+196>>2]=e;e=c[(c[((c[d>>2]&3|0)==2?d:g)+40>>2]|0)+16>>2]|0;f=c[e+196>>2]|0;e=e+200|0;h=c[e>>2]|0;c[e>>2]=h+1;c[f+(h<<2)>>2]=d;h=c[(c[((c[d>>2]&3|0)==2?d:g)+40>>2]|0)+16>>2]|0;c[(c[h+196>>2]|0)+(c[h+200>>2]<<2)>>2]=0;a[(c[b+16>>2]|0)+240>>0]=1;a[(c[(gg(b)|0)+16>>2]|0)+240>>0]=1;return}function uh(a){a=a|0;var b=0,d=0;if(!a)qa(85551,85561,269,85690);b=c[(c[a+16>>2]|0)+116>>2]|0;if(b|0?(d=(c[b+16>>2]|0)+172|0,(c[d>>2]|0)==(a|0)):0)c[d>>2]=0;hh((c[(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+188|0,a);hh((c[(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+196|0,a);return}function vh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=l;l=l+16|0;d=(c[a+16>>2]|0)+172|0;e=c[d>>2]|0;do if((e|0)!=(b|0))if(!e){c[d>>2]=b;wh(a,b);break}else qa(87593,85561,340,85728);else gA(0,85707,f)|0;while(0);l=f;return}function wh(a,d){a=a|0;d=d|0;var f=0,g=0,h=0,i=0;f=(c[d+16>>2]|0)+170|0;i=c[a+16>>2]|0;a=b[i+170>>1]|0;if((e[f>>1]|0)<(a&65535))b[f>>1]=a;g=i+168|0;h=i+154|0;f=i+156|0;a=d;while(1){if(!a)break;d=c[a+16>>2]|0;i=d+168|0;b[i>>1]=(e[i>>1]|0)+(e[g>>1]|0);i=d+154|0;b[i>>1]=(e[i>>1]|0)+(e[h>>1]|0);i=d+156|0;c[i>>2]=(c[i>>2]|0)+(c[f>>2]|0);a=c[d+172>>2]|0}return}function xh(d){d=d|0;var e=0,f=0,g=0,h=0;f=d+16|0;e=c[(c[f>>2]|0)+172>>2]|0;a:while(1){if(!e)break;yh(e,d);h=c[e+16>>2]|0;g=c[h+172>>2]|0;if(!(b[h+168>>1]|0))zh(e);while(1){if((a[(c[e+16>>2]|0)+112>>0]|0)!=1){e=g;continue a}e=c[(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0;if((a[e+156>>0]|0)!=1){e=g;continue a}if((c[e+184>>2]|0)!=1){e=g;continue a}e=c[c[e+180>>2]>>2]|0;yh(e,d)}}c[(c[f>>2]|0)+172>>2]=0;return}function yh(a,d){a=a|0;d=d|0;var f=0;f=c[d+16>>2]|0;d=c[a+16>>2]|0;a=d+168|0;b[a>>1]=(e[a>>1]|0)-(e[f+168>>1]|0);a=d+154|0;b[a>>1]=(e[a>>1]|0)-(e[f+154>>1]|0);d=d+156|0;c[d>>2]=(c[d>>2]|0)-(c[f+156>>2]|0);return}function zh(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;if(!a)qa(85551,85561,128,85741);g=a+48|0;d=0;while(1){b=c[a>>2]|0;e=(c[(c[((b&3|0)==3?a:g)+40>>2]|0)+16>>2]|0)+180|0;f=c[(c[e>>2]|0)+(d<<2)>>2]|0;if(!f)break;if((f|0)==(a|0))hh(e,a);d=d+1|0}g=a+-48|0;f=0;while(1){d=(c[(c[((b&3|0)==2?a:g)+40>>2]|0)+16>>2]|0)+172|0;e=c[(c[d>>2]|0)+(f<<2)>>2]|0;if(!e)break;if((e|0)==(a|0)){hh(d,a);b=c[a>>2]|0}f=f+1|0}return}function Ah(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;p=l;l=l+1040|0;n=p+1032|0;o=p;m=p+8|0;k=d+16|0;d=c[k>>2]|0;j=b[d+236>>1]|0;while(1){if((j|0)>(b[d+238>>1]|0))break;i=c[d+196>>2]|0;h=i+(j<<6)|0;i=i+(j<<6)+4|0;g=0;d=0;while(1){if((g|0)>=(c[h>>2]|0))break;e=c[(c[i>>2]|0)+(g<<2)>>2]|0;f=e+16|0;if(c[(c[f>>2]|0)+112>>2]|0){if(!d){a[n>>0]=a[18588]|0;a[n+1>>0]=a[18589]|0;a[n+2>>0]=a[18590]|0;a[n+3>>0]=a[18591]|0;d=oB(85763,n,0)|0}c[o>>2]=g;m4(m,137395,o)|0;t=tC(d,m,1)|0;TC(t,91164,24,1)|0;q=c[(c[f>>2]|0)+180>>2]|0;r=c[q>>2]|0;r=c[(c[(c[((c[r>>2]&3|0)==2?r:r+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;q=c[q+4>>2]|0;q=c[(c[(c[((c[q>>2]&3|0)==2?q:q+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;s=(r|0)>(q|0);f=c[t+16>>2]|0;c[f+12>>2]=s?q:r;c[f+16>>2]=s?r:q;c[f+20>>2]=e}g=g+1|0}if(d|0){if((tB(d)|0)>1)Bh(d,h);sB(d)|0}j=j+1|0;d=c[k>>2]|0}l=p;return}function Bh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;d=0;h=jC(a)|0;a:while(1){if(!h)break;i=kC(a,h)|0;j=h+16|0;g=i;while(1){if(!g){h=i;continue a}f=c[g+16>>2]|0;e=c[j>>2]|0;if((c[f+16>>2]|0)>(c[e+12>>2]|0)){if((c[e+16>>2]|0)<=(c[f+12>>2]|0))iB(a,h,g,0,1)|0}else{iB(a,g,h,0,1)|0;d=1}g=kC(a,g)|0}}do if(d|0){h=ND(a,85766,1)|0;j=JK((tB(a)|0)<<2)|0;i=JK((tB(a)|0)<<2)|0;f=b+4|0;e=jC(a)|0;b:while(1){if(!e){d=23;break}if((c[(c[e+16>>2]|0)+8>>2]|0)==0?vB(a,e,1,1)|0:0){c:do if(Ch(a,e,h,i)|0){g=tB(h)|0;if((Dh(a,h,j)|0)!=(g|0)){d=17;break b}o4(i,g,4,60);d=0;while(1){if((d|0)>=(g|0))break c;b=i+(d<<2)|0;k=c[j+(d<<2)>>2]|0;c[(c[k+16>>2]|0)+236>>2]=c[b>>2];c[(c[f>>2]|0)+(c[b>>2]<<2)>>2]=k;d=d+1|0}}while(0);Fh(h)}e=kC(a,e)|0}if((d|0)==17)qa(85771,85781,265,85792);else if((d|0)==23){U1(j);break}}while(0);return}function Ch(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;h=c[b+16>>2]|0;c[h+8>>2]=1;h=c[(c[(c[h+20>>2]|0)+16>>2]|0)+236>>2]|0;c[e+((tB(d)|0)<<2)>>2]=h;oC(d,b,1)|0;h=XA(a,b)|0;f=0;while(1){if(!h)break;j=c[h>>2]&3;g=c[((j|0)==2?h:h+-48|0)+40>>2]|0;i=c[g+16>>2]|0;f=f+((c[(c[(c[i+20>>2]|0)+16>>2]|0)+236>>2]|0)>(c[(c[(c[(c[(c[((j|0)==3?h:h+48|0)+40>>2]|0)+16>>2]|0)+20>>2]|0)+16>>2]|0)+236>>2]|0)&1)|0;if(!(c[i+8>>2]|0))f=(Ch(a,g,d,e)|0)+f|0;h=ZA(a,h)|0}h=_A(a,b)|0;while(1){if(!h)break;i=c[h>>2]&3;g=c[((i|0)==3?h:h+48|0)+40>>2]|0;j=c[g+16>>2]|0;f=f+((c[(c[(c[(c[(c[((i|0)==2?h:h+-48|0)+40>>2]|0)+16>>2]|0)+20>>2]|0)+16>>2]|0)+236>>2]|0)>(c[(c[(c[j+20>>2]|0)+16>>2]|0)+236>>2]|0)&1)|0;if(!(c[j+8>>2]|0))f=(Ch(a,g,d,e)|0)+f|0;h=$A(a,h)|0}return f|0}function Dh(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;f=0;while(1){e=Gh(a,b)|0;if(!e)break;c[d+(f<<2)>>2]=c[(c[e+16>>2]|0)+20>>2];vC(b,e)|0;e=XA(a,e)|0;while(1){if(!e)break;g=ZA(a,e)|0;lB(a,e)|0;e=g}f=f+1|0}return f|0}function Eh(a,b){a=a|0;b=b|0;return (c[a>>2]|0)-(c[b>>2]|0)|0}function Fh(a){a=a|0;var b=0,c=0;b=jC(a)|0;while(1){if(!b)break;c=kC(a,b)|0;vC(a,b)|0;b=c}return}function Gh(a,b){a=a|0;b=b|0;var c=0;c=jC(b)|0;while(1){if(!c){c=0;break}if(!(vB(a,c,1,0)|0))break;c=kC(b,c)|0}return c|0}function Hh(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;Ih(b);i=b+16|0;f=0;e=0;while(1){if((f|0)>=(c[(c[i>>2]|0)+220>>2]|0))break;Jh(b,f);f=f+1|0;e=(Kh(b,0,d)|0)+e|0}Lh(b);h=1;while(1){f=c[i>>2]|0;g=c[f+180>>2]|0;if((h|0)>(g|0))break;g=(Mh(c[(c[f+184>>2]|0)+(h<<2)>>2]|0,d)|0)+e|0;h=h+1|0;e=g}do if((g|0)>0){f=QA(b,85806)|0;if(f|0?(dP(f)|0)<<24>>24==0:0)break;jl(b);a[193769]=1;e=Kh(b,2,d)|0}while(0);Nh(b,e);return}function Ih(d){d=d|0;var e=0;if(0)TO();a[193769]=0;c[46741]=d;e=((uB(gg(d)|0)|0)<<2)+4|0;c[46740]=JK(e)|0;c[46739]=JK(e)|0;Di(d);e=d+16|0;if(b[(c[e>>2]|0)+136>>1]&16)Ei(d);Sk(d);Gl(d,1);Fi(d);Uh(d);e=c[e>>2]|0;c[46742]=b[e+236>>1];c[46743]=b[e+238>>1];return}function Jh(a,d){a=a|0;d=d|0;var e=0,f=0,g=0;a=c[a+16>>2]|0;c[a+192>>2]=c[(c[a+216>>2]|0)+(d<<2)>>2];a:do if((d|0)>0){d=b[a+238>>1]|0;e=a+196|0;a=b[a+236>>1]|0;while(1){if((a|0)>(d|0))break a;f=c[e>>2]|0;g=f+(a<<6)+4|0;f=f+(a<<6)|0;c[g>>2]=(c[g>>2]|0)+(c[f>>2]<<2);c[f>>2]=0;a=a+1|0}}while(0);return}function Kh(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+32|0;n=o;if((b|0)>1){e=ii(0)|0;ji(a)}else e=2147483647;m=c[15712]|0;k=b;f=0;b=e;while(1){if((k|0)>=3)break;f=c[47136]|0;if((k|0)==2)if((e|0)>(b|0)){li(a);e=b}else e=b;else{e=(f|0)<4?f:4;if((gg(a)|0)==(a|0))ki(a,k);if(!k)Vh(a);Wh(a);g=ii(0)|0;if((g|0)>(b|0)){f=e;e=g}else{ji(a);f=e;e=g;b=g}}g=0;j=0;while(1){if((j|0)>=(f|0))break;if(0){c[n>>2]=k;c[n+4>>2]=j;c[n+8>>2]=g;c[n+12>>2]=e;c[n+16>>2]=b;w4(m,86199,n)|0}i=g+1|0;if((e|0)==0|(g|0)>=(c[46744]|0))break;mi(a,j);e=ii(0)|0;if((e|0)>(b|0))g=i;else{ji(a);g=+h[23215]*+(b|0)>+(e|0)?0:i;b=e}j=j+1|0}if(!e){e=0;break}else k=k+1|0}if((e|0)>(b|0))li(a);if((b|0)>0){ni(a,0);e=ii(0)|0}else e=b;a:do if(d|0){b=0;while(1){if((b|0)>=(f|0))break a;oi(a);b=b+1|0}}while(0);l=o;return e|0}function Lh(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+16|0;n=p;hi(a);k=a+16|0;d=c[k>>2]|0;m=c[15712]|0;j=b[d+236>>1]|0;while(1){if((j|0)>(b[d+238>>1]|0))break;e=c[d+196>>2]|0;f=c[e+(j<<6)+8>>2]|0;c[e+(j<<6)>>2]=f;g=c[e+(j<<6)+12>>2]|0;c[e+(j<<6)+4>>2]=g;i=0;while(1){if((i|0)>=(f|0))break;h=c[g+(i<<2)>>2]|0;if(!h){o=6;break}c[(c[h+16>>2]|0)+236>>2]=i;i=i+1|0}if((o|0)==6){o=0;if(0){e=KB(a)|0;d=c[(c[(c[k>>2]|0)+196>>2]|0)+(j<<6)>>2]|0;c[n>>2]=e;c[n+4>>2]=j;c[n+8>>2]=i;c[n+12>>2]=d;w4(m,86149,n)|0;d=c[k>>2]|0;e=c[d+196>>2]|0}c[e+(j<<6)>>2]=i}j=j+1|0}l=p;return}function Mh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;dl(a);Uh(a);Vh(a);Wh(a);f=a+16|0;d=Kh(a,2,b)|0;e=1;while(1){g=c[f>>2]|0;if((e|0)>(c[g+180>>2]|0))break;d=(Mh(c[(c[g+184>>2]|0)+(e<<2)>>2]|0,b)|0)+d|0;e=e+1|0}Xh(a);return d|0}function Nh(d,e){d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0;q=l;l=l+16|0;p=q;f=c[46739]|0;if(f|0){U1(f);c[46739]=0}f=c[46740]|0;if(f|0){U1(f);c[46740]=0}o=d+16|0;f=1;while(1){g=c[o>>2]|0;if((f|0)>(c[g+180>>2]|0))break;Oh(c[(c[g+184>>2]|0)+(f<<2)>>2]|0);f=f+1|0}n=b[g+236>>1]|0;f=g;while(1){if((n|0)>(b[f+238>>1]|0))break;else m=0;while(1){f=c[f+196>>2]|0;if((m|0)>=(c[f+(n<<6)>>2]|0))break;k=(c[(c[f+(n<<6)+4>>2]|0)+(m<<2)>>2]|0)+16|0;f=c[k>>2]|0;c[f+236>>2]=m;f=c[f+188>>2]|0;a:do if(f|0){g=0;while(1){i=c[f+(g<<2)>>2]|0;if(!i)break a;j=i+16|0;if((a[(c[j>>2]|0)+112>>0]|0)==4){uh(i);U1(c[j>>2]|0);U1(i);g=g+-1|0;f=c[(c[k>>2]|0)+188>>2]|0}g=g+1|0}}while(0);m=m+1|0;f=c[o>>2]|0}Ph(c[f+(n<<6)+56>>2]|0);n=n+1|0;f=c[o>>2]|0}if(0){o=c[15712]|0;d=KB(d)|0;r=+UO();c[p>>2]=d;c[p+4>>2]=e;h[p+8>>3]=r;w4(o,85817,p)|0}l=q;return}function Oh(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;g=a+16|0;d=1;while(1){f=c[g>>2]|0;if((d|0)>(c[f+180>>2]|0))break;Oh(c[(c[f+184>>2]|0)+(d<<2)>>2]|0);d=d+1|0}a:do if(c[f+268>>2]|0){e=b[f+236>>1]|0;d=f;while(1){if((e|0)>(b[d+238>>1]|0))break a;j=c[(c[d+268>>2]|0)+(e<<2)>>2]|0;i=Qh(a,j,-1)|0;j=Qh(a,j,1)|0;c[(c[(c[g>>2]|0)+268>>2]|0)+(e<<2)>>2]=i;k=c[(c[(c[(gg(a)|0)+16>>2]|0)+196>>2]|0)+(e<<6)+4>>2]|0;i=c[(c[i+16>>2]|0)+236>>2]|0;f=c[g>>2]|0;h=c[f+196>>2]|0;c[h+(e<<6)+4>>2]=k+(i<<2);c[h+(e<<6)>>2]=(c[(c[j+16>>2]|0)+236>>2]|0)+1-i;e=e+1|0;d=f}}while(0);return}function Ph(a){a=a|0;if(a|0){U1(c[a+8>>2]|0);U1(a)}return}function Qh(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0;d=b;while(1){b=Rh(b,c)|0;if(!b)break;if(Sh(a,b)|0){d=b;continue}e=(Th(a,b)|0)==0;d=e?d:b}return d|0}function Rh(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;if(!a)qa(85856,85781,1023,85858);if((b|0)<0){a=c[a+16>>2]|0;e=c[a+236>>2]|0;if((e|0)>0){a=(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(c[a+232>>2]<<6)+4|0;d=e+-1|0;f=7}else g=0}else{a=c[a+16>>2]|0;e=c[a+236>>2]|0;a=(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(c[a+232>>2]<<6)+4|0;d=e+1|0;f=7}if((f|0)==7){a=c[(c[a>>2]|0)+(d<<2)>>2]|0;if(a)if((O((c[(c[a+16>>2]|0)+236>>2]|0)-e|0,b)|0)>0)g=a;else qa(85867,85781,1029,85858);else g=0}return g|0}function Sh(b,d){b=b|0;d=d|0;if(!(a[(c[d+16>>2]|0)+156>>0]|0))b=(HC(b,d)|0)!=0&1;else b=0;return b|0}function Th(b,d){b=b|0;d=d|0;var e=0,f=0;d=c[d+16>>2]|0;if(((a[d+156>>0]|0)==1?(c[d+176>>2]|0)==1:0)?(c[d+184>>2]|0)==1:0){d=c[d+180>>2]|0;while(1){d=c[d>>2]|0;e=c[d+16>>2]|0;if(!(a[e+112>>0]|0))break;else d=e+116|0}if(HC(b,d)|0)d=1;else f=7}else f=7;if((f|0)==7)d=0;return d|0}function Uh(b){b=b|0;var d=0,e=0,f=0;f=l;l=l+16|0;e=f;d=c[47140]|0;a:do if((d|0)!=0|(c[47161]|0)!=0){d=aP(b,d,0)|0;if(d|0){switch(a[d>>0]|0){case 0:break a;case 111:{if(!(s2(d,86069)|0)){ci(b,1);break a}break}case 105:{if(!(s2(d,98735)|0)){ci(b,0);break a}break}default:{}}c[e>>2]=d;gA(1,86073,e)|0;break}d=OD(b)|0;while(1){if(!d)break;if(!(Pj(d)|0))Uh(d);d=PD(d)|0}if(c[47161]|0)di(b)}while(0);l=f;return}function Vh(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=d+16|0;e=c[k>>2]|0;j=b[e+236>>1]|0;while(1){if((j|0)>(b[e+238>>1]|0))break;else{f=0;i=0}while(1){g=c[e+196>>2]|0;h=c[g+(j<<6)>>2]|0;if((i|0)>=(h|0))break;g=c[(c[(c[g+(j<<6)+4>>2]|0)+(i<<2)>>2]|0)+16>>2]|0;a[g+158>>0]=0;a[g+157>>0]=0;c[g+284>>2]=i;if((f|0)==0&(c[g+192>>2]|0)>0){f=ai(h,h)|0;e=c[k>>2]|0;c[(c[e+196>>2]|0)+(j<<6)+56>>2]=f;f=1}i=i+1|0}a:do if(f){i=0;f=h;while(1){if((i|0)>=(f|0))break a;h=c[(c[g+(j<<6)+4>>2]|0)+(i<<2)>>2]|0;if(!(a[(c[h+16>>2]|0)+157>>0]|0)){bi(d,h);e=c[k>>2]|0;f=c[e+196>>2]|0;g=f;f=c[f+(j<<6)>>2]|0}i=i+1|0}}while(0);j=j+1|0}return}function Wh(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;s=d+16|0;e=c[s>>2]|0;if(a[e+240>>0]|0){h=0;r=b[e+236>>1]|0;q=0;i=0;a:while(1){if((r|0)>(b[e+238>>1]|0))break;f=c[e+196>>2]|0;g=c[f+(r<<6)>>2]|0;if(!g){g=q;f=i}else{f=c[f+(r<<6)+4>>2]|0;o=c[(c[(c[f>>2]|0)+16>>2]|0)+236>>2]|0;e=0;while(1){if((e|0)>=(g|0))break;a[(c[(c[f+(e<<2)>>2]|0)+16>>2]|0)+157>>0]=0;e=e+1|0}e=(e<<2)+4|0;if(!h)p=KK(e)|0;else p=MK(i,e)|0;m=0;n=0;while(1){e=c[s>>2]|0;f=c[e+196>>2]|0;g=c[f+(r<<6)>>2]|0;if((g|0)<=(n|0))break;j=c[(c[f+(r<<6)+4>>2]|0)+(((c[e+116>>2]&1|0)==0?g+~n|0:n)<<2)>>2]|0;l=j+16|0;e=0;k=0;while(1){i=c[l>>2]|0;if((e|0)>=(c[i+200>>2]|0)){f=0;g=0;e=i;break}i=k+((Yh(d,c[(c[i+196>>2]|0)+(e<<2)>>2]|0)|0)!=0&1)|0;e=e+1|0;k=i}while(1){if((g|0)>=(c[e+192>>2]|0))break;i=f+((Yh(d,c[(c[e+188>>2]|0)+(g<<2)>>2]|0)|0)!=0&1)|0;f=i;g=g+1|0;e=c[l>>2]|0}if(f|k)if((k|0)==0&(a[e+157>>0]|0)==0)e=(Zh(d,j,p+(m<<2)|0,r)|0)+m|0;else e=m;else{c[p+(m<<2)>>2]=j;e=m+1|0}m=e;n=n+1|0}b:do if(m){c:do if(!(c[e+116>>2]&1)){f=p;e=p+(m<<2)|0;while(1){e=e+-4|0;if(f>>>0>=e>>>0){g=0;break c}n=c[f>>2]|0;c[f>>2]=c[e>>2];c[e>>2]=n;f=f+4|0}}else g=0;while(0);while(1){e=c[s>>2]|0;i=c[e+196>>2]|0;f=c[i+(r<<6)>>2]|0;if((g|0)>=(f|0)){m=0;g=i;break}n=c[p+(g<<2)>>2]|0;c[(c[i+(r<<6)+4>>2]|0)+(g<<2)>>2]=n;c[(c[n+16>>2]|0)+236>>2]=g+o;g=g+1|0}while(1){if((m|0)>=(f|0))break b;l=(c[(c[g+(r<<6)+4>>2]|0)+(m<<2)>>2]|0)+16|0;i=c[(c[l>>2]|0)+188>>2]|0;if(i){k=0;f=i;while(1){g=c[f+(k<<2)>>2]|0;e=c[s>>2]|0;if(!g)break;j=c[g>>2]&3;i=c[(c[(c[((j|0)==2?g:g+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;j=c[(c[(c[((j|0)==3?g:g+48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;if(!(c[e+116>>2]&1))if((i|0)<(j|0))t=36;else e=k;else if((i|0)>(j|0))t=36;else e=k;if((t|0)==36){t=0;if(Yh(d,g)|0){t=37;break a}uh(g);_h(d,g);e=k+-1|0;f=c[(c[l>>2]|0)+188>>2]|0}k=e+1|0}f=c[e+196>>2]|0;g=f;f=c[f+(r<<6)>>2]|0}m=m+1|0}}while(0);a[(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(r<<6)+49>>0]=0;h=p;g=p;f=p}r=r+1|0;q=g;i=f}if((t|0)==37)qa(85915,85781,1561,85954);if(h|0)U1(q)}return}function Xh(a){a=a|0;var d=0,e=0;e=a+16|0;a=c[e>>2]|0;a:do if(c[a+268>>2]|0){d=b[a+236>>1]|0;while(1){if((d|0)>(b[a+238>>1]|0))break a;c[(c[a+268>>2]|0)+(d<<2)>>2]=c[c[(c[a+196>>2]|0)+(d<<6)+4>>2]>>2];d=d+1|0;a=c[e>>2]|0}}while(0);return}function Yh(a,b){a=a|0;b=b|0;if((c[(c[b+16>>2]|0)+156>>2]|0)!=0?($h(a,c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0)!=0:0)a=($h(a,c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0)!=0&1;else a=0;return a|0}function Zh(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0;l=d+16|0;g=c[l>>2]|0;a[g+157>>0]=1;a:do if((c[g+192>>2]|0)>0){h=0;j=0;while(1){i=c[(c[g+188>>2]|0)+(j<<2)>>2]|0;if(!i)break a;if((Yh(b,i)|0)!=0?(k=c[((c[i>>2]&3|0)==2?i:i+-48|0)+40>>2]|0,(a[(c[k+16>>2]|0)+157>>0]|0)==0):0)h=(Zh(b,k,e+(h<<2)|0,f)|0)+h|0;j=j+1|0;g=c[l>>2]|0}}else h=0;while(0);if((c[g+232>>2]|0)==(f|0)){c[e+(h<<2)>>2]=d;return h+1|0}else qa(85967,85781,1490,85983);return 0}function _h(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;i=c[d>>2]&3;j=c[((i|0)==2?d:d+-48|0)+40>>2]|0;f=c[(c[j+16>>2]|0)+188>>2]|0;a:do if(!f){e=d+48|0;m=15}else{l=d+48|0;g=((i|0)==3?d:l)+40|0;e=0;while(1){h=c[f+(e<<2)>>2]|0;if(!h){e=l;m=15;break a}if((c[((c[h>>2]&3|0)==2?h:h+-48|0)+40>>2]|0)==(c[g>>2]|0))break;else e=e+1|0}vh(d,h);e=(c[d+16>>2]|0)+172|0;if(!(c[e>>2]|0))c[e>>2]=h;e=c[h+16>>2]|0;if((a[e+112>>0]|0)==4?(k=e+116|0,(c[k>>2]|0)==0):0)c[k>>2]=d;e=c[(c[((c[d>>2]&3|0)==3?d:l)+40>>2]|0)+16>>2]|0;f=c[e+204>>2]|0;if(!f)e=KK((c[e+208>>2]<<2)+8|0)|0;else e=MK(f,(c[e+208>>2]<<2)+8|0)|0;c[(c[(c[((c[d>>2]&3|0)==3?d:l)+40>>2]|0)+16>>2]|0)+204>>2]=e;i=c[(c[((c[d>>2]&3|0)==3?d:l)+40>>2]|0)+16>>2]|0;j=c[i+204>>2]|0;i=i+208|0;k=c[i>>2]|0;c[i>>2]=k+1;c[j+(k<<2)>>2]=d;d=c[(c[((c[d>>2]&3|0)==3?d:l)+40>>2]|0)+16>>2]|0;c[(c[d+204>>2]|0)+(c[d+208>>2]<<2)>>2]=0}while(0);if((m|0)==15){m=mh(j,c[((i|0)==3?d:e)+40>>2]|0,d)|0;l=c[d+16>>2]|0;d=c[m+16>>2]|0;a[d+112>>0]=(a[l+112>>0]|0)==4?4:3;c[d+96>>2]=c[l+96>>2];th(b,m)}return}function $h(a,b){a=a|0;b=b|0;var c=0;c=Sh(a,b)|0;return Th(a,b)|0|c|0}function ai(a,b){a=a|0;b=b|0;var d=0;d=JK(12)|0;c[d>>2]=a;c[d+4>>2]=b;c[d+8>>2]=JK(O(b,a)|0)|0;return d|0}function bi(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;o=d+16|0;l=c[o>>2]|0;k=c[(c[(c[b+16>>2]|0)+196>>2]|0)+(c[l+232>>2]<<6)+56>>2]|0;a[l+157>>0]=1;a[l+158>>0]=1;l=(c[(c[(gg(b)|0)+16>>2]|0)+180>>2]|0)>0;e=c[o>>2]|0;f=c[e+188>>2]|0;a:do if(f){m=k+4|0;n=k+8|0;d=0;b:while(1){j=c[f+(d<<2)>>2]|0;if(!j){p=e;break a}if(l){if((HC(b,c[((c[j>>2]&3|0)==3?j:j+48|0)+40>>2]|0)|0)!=0?(HC(b,c[((c[j>>2]&3|0)==2?j:j+-48|0)+40>>2]|0)|0)!=0:0)q=7}else q=7;do if((q|0)==7){q=0;h=j+16|0;if(c[(c[h>>2]|0)+156>>2]|0){e=c[j>>2]&3;g=j+-48|0;r=c[(c[((e|0)==2?j:g)+40>>2]|0)+16>>2]|0;i=c[r+284>>2]|0;f=(i|0)<(c[k>>2]|0);if((a[r+158>>0]|0)==1){if(!f){q=10;break b}e=c[(c[(c[((e|0)==3?j:j+48|0)+40>>2]|0)+16>>2]|0)+284>>2]|0;f=c[m>>2]|0;if((e|0)>=(f|0)){q=12;break b}a[(c[n>>2]|0)+((O(f,i)|0)+e)>>0]=1;uh(j);d=d+-1|0;if((a[(c[h>>2]|0)+112>>0]|0)==4)break;_h(b,j);break}else{if(!f){q=16;break b}e=c[(c[(c[((e|0)==3?j:j+48|0)+40>>2]|0)+16>>2]|0)+284>>2]|0;f=c[m>>2]|0;if((e|0)>=(f|0)){q=18;break b}a[(c[n>>2]|0)+((O(f,e)|0)+i)>>0]=1;e=c[((c[j>>2]&3|0)==2?j:g)+40>>2]|0;if(a[(c[e+16>>2]|0)+157>>0]|0)break;bi(b,e);break}}}while(0);f=c[o>>2]|0;d=d+1|0;e=f;f=c[f+188>>2]|0}if((q|0)==10)qa(85993,85781,1251,86025);else if((q|0)==12)qa(86037,85781,1252,86025);else if((q|0)==16)qa(85993,85781,1260,86025);else if((q|0)==18)qa(86037,85781,1261,86025)}else p=e;while(0);a[p+158>>0]=0;return}function ci(a,b){a=a|0;b=b|0;var c=0;c=jC(a)|0;while(1){if(!c)break;ei(a,c,b);c=kC(a,c)|0}return}function di(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;g=l;l=l+16|0;f=g;d=jC(b)|0;while(1){if(!d)break;e=aP(d,c[47161]|0,0)|0;a:do if(e|0){switch(a[e>>0]|0){case 0:break a;case 111:{if(!(s2(e,86069)|0)){ei(b,d,1);break a}break}case 105:{if(!(s2(e,98735)|0)){ei(b,d,0);break a}break}default:{}}h=KB(d)|0;c[f>>2]=e;c[f+4>>2]=h;gA(1,86104,f)|0}while(0);d=kC(b,d)|0}l=g;return}function ei(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;j=c[46740]|0;h=d+16|0;f=c[h>>2]|0;a:do if(!(c[f+212>>2]|0)){i=(e|0)!=0;b:do if(i){g=0;d=0;e=f;while(1){f=c[(c[e+180>>2]|0)+(g<<2)>>2]|0;if(!f)break b;if(!(fi(f)|0)){c[j+(d<<2)>>2]=f;d=d+1|0;e=c[h>>2]|0}g=g+1|0}}else{g=0;d=0;e=f;while(1){f=c[(c[e+172>>2]|0)+(g<<2)>>2]|0;if(!f)break b;if(!(fi(f)|0)){c[j+(d<<2)>>2]=f;d=d+1|0;e=c[h>>2]|0}g=g+1|0}}while(0);if((d|0)>=2){c[j+(d<<2)>>2]=0;o4(j,d,4,61);h=i?2:3;g=i?-1:1;f=1;while(1){d=c[j+(f<<2)>>2]|0;if(!d)break a;e=c[j+(f+-1<<2)>>2]|0;e=c[((c[e>>2]&3|0)==(h|0)?e:e+(g*48|0)|0)+40>>2]|0;d=c[((c[d>>2]&3|0)==(h|0)?d:d+(g*48|0)|0)+40>>2]|0;if(fh(e,d)|0)break a;i=mh(e,d,0)|0;a[(c[i+16>>2]|0)+112>>0]=4;th(b,i);f=f+1|0}}}while(0);return}function fi(a){a=a|0;var b=0;while(1){b=c[(c[a+16>>2]|0)+116>>2]|0;if(!b)break;else a=b}b=c[a>>2]&3;return (c[(c[(c[((b|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0)!=(c[(c[(c[((b|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0)|0}function gi(a,b){a=a|0;b=b|0;return ((c[c[a>>2]>>2]|0)>>>4)-((c[c[b>>2]>>2]|0)>>>4)|0}function hi(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;f=c[a+16>>2]|0;g=f+220|0;h=c[g>>2]|0;if((h|0)>=2){i=f+216|0;a=0;e=0;while(1){if((e|0)>=(h|0))break;d=c[(c[i>>2]|0)+(e<<2)>>2]|0;if(a|0)c[(c[a+16>>2]|0)+164>>2]=d;j=c[d+16>>2]|0;c[j+168>>2]=a;a=d;d=j;while(1){d=c[d+164>>2]|0;if(!d)break;a=d;d=c[d+16>>2]|0}e=e+1|0}c[g>>2]=1;c[f+192>>2]=c[c[f+216>>2]>>2];b[f+236>>1]=c[46742];b[f+238>>1]=c[46743]}return}function ii(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0;i=c[46741]|0;j=i+16|0;f=c[j>>2]|0;g=0;h=b[f+236>>1]|0;while(1){if((h|0)>=(b[f+238>>1]|0))break;d=c[f+196>>2]|0;if(!(a[d+(h<<6)+49>>0]|0)){e=Bi(i,h)|0;d=c[j>>2]|0;f=c[d+196>>2]|0;c[f+(h<<6)+52>>2]=e;a[f+(h<<6)+49>>0]=1}else{e=c[d+(h<<6)+52>>2]|0;d=f}g=e+g|0;h=h+1|0;f=d}return g|0}function ji(a){a=a|0;var d=0,e=0,f=0,g=0,i=0,j=0;a=c[a+16>>2]|0;g=b[a+238>>1]|0;i=a+196|0;a=b[a+236>>1]|0;while(1){if((a|0)>(g|0))break;f=c[i>>2]|0;e=c[f+(a<<6)>>2]|0;f=f+(a<<6)+4|0;d=0;while(1){if((d|0)>=(e|0))break;j=c[(c[(c[f>>2]|0)+(d<<2)>>2]|0)+16>>2]|0;h[j+16>>3]=+(c[j+236>>2]|0);d=d+1|0}a=a+1|0}return}function ki(d,e){d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;r=l;l=l+16|0;m=r;n=d+16|0;o=VO(c[(c[n>>2]|0)+232>>2]|0)|0;j=c[n>>2]|0;f=j+192|0;g=f;while(1){g=c[g>>2]|0;if(!g)break;g=c[g+16>>2]|0;a[g+157>>0]=0;g=g+164|0}h=b[j+238>>1]|0;i=j+196|0;g=b[j+236>>1]|0;while(1){if((g|0)>(h|0))break;c[(c[i>>2]|0)+(g<<6)>>2]=0;g=g+1|0}i=(e|0)==0;while(1){g=c[f>>2]|0;if(!g)break;h=g+16|0;f=c[h>>2]|0;if((c[c[(i?f+172|0:f+180|0)>>2]>>2]|0)==0?(k=f+157|0,(a[k>>0]|0)==0):0){a[k>>0]=1;XO(o,g);while(1){f=YO(o)|0;if(!f)break;if((a[(c[f+16>>2]|0)+159>>0]|0)==7){il(d,f,e,o);continue}else{zi(d,f);Ai(o,f,e);continue}}f=c[h>>2]|0}f=f+164|0}if(YO(o)|0)gA(1,86312,m)|0;f=c[n>>2]|0;j=b[f+236>>1]|0;while(1){if((j|0)>(b[f+238>>1]|0))break;a[(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(j<<6)+49>>0]=0;if((c[f+116>>2]&1|0)!=0?(p=c[f+196>>2]|0,q=c[p+(j<<6)>>2]|0,(q|0)>0):0){g=c[p+(j<<6)+4>>2]|0;h=q+-1|0;i=(h|0)/2|0;f=0;while(1){if((f|0)>(i|0))break;ti(c[g+(f<<2)>>2]|0,c[g+(h-f<<2)>>2]|0);f=f+1|0}f=c[n>>2]|0}j=j+1|0}if((gg(d)|0)==(d|0)?(ii(0)|0)>0:0)ni(d,0);WO(o);l=r;return}function li(d){d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;n=d+16|0;d=c[n>>2]|0;f=b[d+236>>1]|0;e=b[d+238>>1]|0;l=e<<16>>16;m=d+196|0;g=f;while(1){if((g|0)>(l|0))break;k=c[m>>2]|0;j=c[k+(g<<6)>>2]|0;k=k+(g<<6)+4|0;i=0;while(1){if((i|0)>=(j|0))break;o=c[(c[(c[k>>2]|0)+(i<<2)>>2]|0)+16>>2]|0;c[o+236>>2]=~~+h[o+16>>3];i=i+1|0}g=g+1|0}while(1){if((f|0)>(e<<16>>16|0))break;a[(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(f<<6)+49>>0]=0;o=c[d+196>>2]|0;o4(c[o+(f<<6)+4>>2]|0,c[o+(f<<6)>>2]|0,4,62);o=c[n>>2]|0;f=f+1|0;e=b[o+238>>1]|0;d=o}return}function mi(a,d){a=a|0;d=d|0;var e=0,f=0,g=0;g=((d|0)%4|0|0)<2&1;e=c[a+16>>2]|0;if(!(d&1)){d=b[e+236>>1]|0;f=1;e=b[e+238>>1]|0;d=(d<<16>>16<=(b[(c[(c[46741]|0)+16>>2]|0)+236>>1]|0)&1)+(d<<16>>16)|0}else{d=b[e+238>>1]|0;f=-1;e=b[e+236>>1]|0;d=((d<<16>>16>=(b[(c[(c[46741]|0)+16>>2]|0)+238>>1]|0))<<31>>31)+(d<<16>>16)|0}e=f+e|0;while(1){if((d|0)==(e|0))break;wi(a,d,g,(vi(a,d,d-f|0)|0)&255);d=d+f|0}ni(a,g^1);return}function ni(d,e){d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;k=d+16|0;g=c[k>>2]|0;i=b[g+236>>1]|0;j=b[g+238>>1]|0;h=g+196|0;f=i<<16>>16;while(1){if((f|0)>(j|0)){h=i;break}a[(c[h>>2]|0)+(f<<6)+48>>0]=1;f=f+1|0}while(1){f=0;h=h<<16>>16;while(1){if((h|0)>(b[g+238>>1]|0))break;if(a[(c[g+196>>2]|0)+(h<<6)+48>>0]|0){f=(ui(d,h,e)|0)+f|0;g=c[k>>2]|0}h=h+1|0}if((f|0)<=0)break;h=b[g+236>>1]|0}return}function oi(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;o=d+16|0;e=c[o>>2]|0;n=b[e+238>>1]|0;a:while(1){if((n|0)<(b[e+236>>1]|0)){e=16;break}f=c[e+196>>2]|0;a[f+(n<<6)+48>>0]=0;p=(n|0)>0;q=n+1|0;g=0;b:while(1){l=(c[f+(n<<6)>>2]|0)+-1|0;m=f+(n<<6)+4|0;k=f+(q<<6)|0;while(1){if((g|0)>=(l|0))break b;j=c[m>>2]|0;i=c[j+(g<<2)>>2]|0;g=g+1|0;j=c[j+(g<<2)>>2]|0;if((c[(c[i+16>>2]|0)+236>>2]|0)>=(c[(c[j+16>>2]|0)+236>>2]|0)){e=7;break a}if(pi(d,i,j)|0)continue;if(p){f=qi(i,j)|0;h=qi(j,i)|0}else{f=0;h=0}if((c[k>>2]|0)>0){f=(ri(i,j)|0)+f|0;h=(ri(j,i)|0)+h|0}if((h|0)<=(f|0))break}si(d,n,i,j);e=c[o>>2]|0;f=c[e+196>>2]|0}n=n+-1|0}if((e|0)==7)qa(86263,85781,721,86289);else if((e|0)==16)return}function pi(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;j=c[d+16>>2]|0;i=c[j+212>>2]|0;f=c[e+16>>2]|0;g=c[f+212>>2]|0;h=(i|0)!=(g|0);do if(!(a[193769]|0))if((g|0)==0|((i|0)==0|h^1))k=9;else{if((a[j+159>>0]|0)==7?(a[j+156>>0]|0)==1:0){b=0;break}if((a[f+159>>0]|0)==7?(a[f+156>>0]|0)==1:0){b=0;break}b=1}else if(h)b=1;else k=9;while(0);if((k|0)==9){f=c[b+16>>2]|0;b=c[(c[f+196>>2]|0)+(c[j+232>>2]<<6)+56>>2]|0;if(!b)b=0;else{j=(c[f+116>>2]&1|0)==0;k=O(c[b+4>>2]|0,c[(c[(j?d:e)+16>>2]|0)+284>>2]|0)|0;b=a[(c[b+8>>2]|0)+((c[(c[(j?e:d)+16>>2]|0)+284>>2]|0)+k)>>0]|0}}return b|0}function qi(a,d){a=a|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0;l=a+16|0;j=c[(c[d+16>>2]|0)+172>>2]|0;d=0;while(1){a=c[j>>2]|0;if(!a)break;i=c[a+16>>2]|0;k=b[i+154>>1]|0;g=c[(c[(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;i=i+16|0;f=c[(c[l>>2]|0)+172>>2]|0;a=d;while(1){d=c[f>>2]|0;if(!d)break;e=(c[(c[(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)-g|0;if((e|0)<=0){if((e|0)==0?(m=c[d+16>>2]|0,+h[m+16>>3]>+h[i>>3]):0){d=m;n=9}}else{d=c[d+16>>2]|0;n=9}if((n|0)==9){n=0;a=(O(b[d+154>>1]|0,k)|0)+a|0}f=f+4|0}j=j+4|0;d=a}return d|0}function ri(a,d){a=a|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0;l=a+16|0;j=c[(c[d+16>>2]|0)+180>>2]|0;d=0;while(1){a=c[j>>2]|0;if(!a)break;i=c[a+16>>2]|0;k=b[i+154>>1]|0;g=c[(c[(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;i=i+56|0;f=c[(c[l>>2]|0)+180>>2]|0;a=d;while(1){d=c[f>>2]|0;if(!d)break;e=(c[(c[(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)-g|0;if((e|0)<=0){if((e|0)==0?(m=c[d+16>>2]|0,+h[m+56>>3]>+h[i>>3]):0){d=m;n=9}}else{d=c[d+16>>2]|0;n=9}if((n|0)==9){n=0;a=(O(b[d+154>>1]|0,k)|0)+a|0}f=f+4|0}j=j+4|0;d=a}return d|0}function si(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;i=a[(c[e+16>>2]|0)+156>>0]|0;if(i<<24>>24!=(a[(c[f+16>>2]|0)+156>>0]|0)){j=b+16|0;l=c[(c[j>>2]|0)+196>>2]|0;k=c[l+(d<<6)>>2]|0;l=l+(d<<6)+4|0;b=0;g=0;h=0;while(1){if((b|0)>=(k|0))break;o=(a[(c[(c[(c[l>>2]|0)+(b<<2)>>2]|0)+16>>2]|0)+156>>0]|0)==0;b=b+1|0;g=g+(o&1)|0;h=h+((o^1)&1)|0}i=i<<24>>24==0;h=(g|0)<(h|0)?(i?e:f):i?f:e;i=0;b=0;while(1){if((b|0)>=(k|0))break;i=(c[(c[l>>2]|0)+(b<<2)>>2]|0)==(h|0)?b:i;b=b+1|0}o=(a[(c[h+16>>2]|0)+156>>0]|0)==0&1;n=0;g=i;while(1){b=g+-1|0;if((g|0)<=0){m=0;b=i;break}if((a[(c[(c[(c[l>>2]|0)+(b<<2)>>2]|0)+16>>2]|0)+156>>0]|0)!=(o|0)){m=0;b=i;break}n=n+1|0;g=b}while(1){b=b+1|0;if((b|0)>=(k|0))break;if((a[(c[(c[(c[l>>2]|0)+(b<<2)>>2]|0)+16>>2]|0)+156>>0]|0)!=(o|0))break;m=m+1|0}ti(e,f);j=c[(c[j>>2]|0)+196>>2]|0;k=c[j+(d<<6)>>2]|0;j=j+(d<<6)+4|0;b=0;while(1){if((b|0)>=(k|0)){h=0;g=i;break}i=(c[(c[j>>2]|0)+(b<<2)>>2]|0)==(h|0)?b:i;b=b+1|0}while(1){b=g+-1|0;if((g|0)<=0){g=0;b=i;break}if((a[(c[(c[(c[j>>2]|0)+(b<<2)>>2]|0)+16>>2]|0)+156>>0]|0)!=(o|0)){g=0;b=i;break}h=h+1|0;g=b}while(1){b=b+1|0;if((b|0)>=(k|0))break;if((a[(c[(c[(c[j>>2]|0)+(b<<2)>>2]|0)+16>>2]|0)+156>>0]|0)!=(o|0))break;g=g+1|0}d=h-g|0;o=n-m|0;if((((d|0)>-1?d:0-d|0)|0)>(((o|0)>-1?o:0-o|0)|0))ti(e,f)}return}function ti(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;f=c[a+16>>2]|0;e=c[f+232>>2]|0;f=f+236|0;d=c[f>>2]|0;g=b+16|0;h=c[(c[g>>2]|0)+236>>2]|0;c[f>>2]=h;f=(c[46741]|0)+16|0;c[(c[(c[(c[f>>2]|0)+196>>2]|0)+(e<<6)+4>>2]|0)+(h<<2)>>2]=a;c[(c[g>>2]|0)+236>>2]=d;c[(c[(c[(c[f>>2]|0)+196>>2]|0)+(e<<6)+4>>2]|0)+(d<<2)>>2]=b;return}function ui(d,e,f){d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;q=d+16|0;n=c[(c[q>>2]|0)+196>>2]|0;a[n+(e<<6)+48>>0]=0;r=(e|0)>0;s=e+1|0;o=(f|0)!=0;p=e+-1|0;g=0;i=0;f=n;a:while(1){l=(c[f+(e<<6)>>2]|0)+-1|0;m=f+(e<<6)+4|0;h=f+(s<<6)|0;n=g;while(1){if((n|0)>=(l|0)){f=17;break a}k=c[m>>2]|0;j=c[k+(n<<2)>>2]|0;n=n+1|0;k=c[k+(n<<2)>>2]|0;if((c[(c[j+16>>2]|0)+236>>2]|0)>=(c[(c[k+16>>2]|0)+236>>2]|0)){f=5;break a}if(pi(d,j,k)|0)continue;if(r){f=qi(j,k)|0;g=qi(k,j)|0}else{f=0;g=0}if((c[h>>2]|0)>0){f=(ri(j,k)|0)+f|0;g=(ri(k,j)|0)+g|0}if((f|0)>(g|0))break;if((f|0)==(g|0)&(o&(f|0)>0)){f=g;break}}ti(j,k);i=f-g+i|0;g=c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0;a[g+(e<<6)+49>>0]=0;h=c[q>>2]|0;f=c[h+196>>2]|0;a[f+(e<<6)+48>>0]=1;if((b[h+236>>1]|0)<(e|0)){a[g+(p<<6)+49>>0]=0;a[f+(p<<6)+48>>0]=1}if((b[h+238>>1]|0)<=(e|0)){g=n;continue}a[g+(s<<6)+49>>0]=0;a[f+(s<<6)+48>>0]=1;g=n}if((f|0)==5)qa(86263,85781,770,86297);else if((f|0)==17)return i|0;return 0}function vi(a,e,f){a=a|0;e=e|0;f=f|0;var g=0,i=0.0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;p=c[46739]|0;q=a+16|0;a=c[(c[q>>2]|0)+196>>2]|0;r=c[a+(e<<6)+4>>2]|0;n=(f|0)>(e|0);o=p+4|0;m=0;while(1){f=c[a+(e<<6)>>2]|0;if((m|0)>=(f|0)){a=0;j=0;break}l=(c[r+(m<<2)>>2]|0)+16|0;a=c[l>>2]|0;a:do if(n){k=c[a+180>>2]|0;a=0;j=0;while(1){f=c[k+(j<<2)>>2]|0;if(!f)break a;g=c[f+16>>2]|0;if((b[g+154>>1]|0)>0){c[p+(a<<2)>>2]=c[(c[(c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]<<8|d[g+88>>0];a=a+1|0}j=j+1|0}}else{k=c[a+172>>2]|0;j=0;a=0;while(1){f=c[k+(j<<2)>>2]|0;if(!f)break a;g=c[f+16>>2]|0;if((b[g+154>>1]|0)>0){c[p+(a<<2)>>2]=c[(c[(c[((c[f>>2]&3|0)==3?f:f+48|0)+40>>2]|0)+16>>2]|0)+236>>2]<<8|d[g+48>>0];a=a+1|0}j=j+1|0}}while(0);b:do switch(a|0){case 0:{i=-1.0;break}case 1:{i=+(c[p>>2]|0);break}case 2:{i=+(((c[o>>2]|0)+(c[p>>2]|0)|0)/2|0|0);break}default:{o4(p,a,4,60);f=(a|0)/2|0;if(a&1|0){i=+(c[p+(f<<2)>>2]|0);break b}j=c[p+(f<<2)>>2]|0;g=(c[p+(a+-1<<2)>>2]|0)-j|0;a=c[p+(f+-1<<2)>>2]|0;f=a-(c[p>>2]|0)|0;if((f|0)==(g|0)){i=+((a+j|0)/2|0|0);break b}else{i=+(((O(f,j)|0)+(O(a,g)|0)|0)/(f+g|0)|0|0);break b}}}while(0);h[(c[l>>2]|0)+240>>3]=i;m=m+1|0;a=c[(c[q>>2]|0)+196>>2]|0}while(1){if((j|0)>=(f|0))break;g=c[r+(j<<2)>>2]|0;p=c[g+16>>2]|0;if((c[p+184>>2]|0)==0?(c[p+176>>2]|0)==0:0){a=(xi(g)|0|a&255)&255;f=c[(c[(c[q>>2]|0)+196>>2]|0)+(e<<6)>>2]|0}j=j+1|0}return a|0}function wi(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,l=0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0;o=c[(c[b+16>>2]|0)+196>>2]|0;s=c[o+(d<<6)+4>>2]|0;o=c[o+(d<<6)>>2]|0;q=(e|0)!=0;p=(f|e|0)==0;f=0;e=o;o=s+(o<<2)|0;while(1){n=e+-1|0;if((e|0)>0)e=s;else break;a:while(1){b:while(1){if(e>>>0>=o>>>0)break a;while(1){if(e>>>0>=o>>>0)break a;l=c[e>>2]|0;m=+h[(c[l+16>>2]|0)+240>>3];if(!(m<0.0))break;e=e+4|0}k=0;while(1){g=k<<24>>24==0;while(1){e=e+4|0;if(e>>>0>=o>>>0)break a;if(g){g=11;break}i=c[e>>2]|0;if(!(c[(c[i+16>>2]|0)+212>>2]|0)){g=13;break}}if((g|0)==11){i=c[e>>2]|0;j=i}else if((g|0)==13)j=i;if(pi(b,l,j)|0)continue b;g=c[i+16>>2]|0;if(+h[g+240>>3]>=0.0)break;k=(c[g+212>>2]|0)==0?k:1}g=~~m;k=~~+h[(c[i+16>>2]|0)+240>>3];if((g|0)>(k|0)|q&(g|0)==(k|0))break}ti(l,j);f=f+1|0}e=n;o=p?o+-4|0:o}if(f|0?(r=c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0,a[r+(d<<6)+49>>0]=0,(d|0)>0):0)a[r+(d+-1<<6)+49>>0]=0;return}function xi(a){a=a|0;var b=0.0,d=0,e=0,f=0,g=0;g=c[a+16>>2]|0;if((c[g+200>>2]|0)>0){f=c[g+196>>2]|0;a=c[f>>2]|0;a=c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0;d=1;while(1){e=c[f+(d<<2)>>2]|0;if(!e)break;e=c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0;a=(c[(c[e+16>>2]|0)+236>>2]|0)>(c[(c[a+16>>2]|0)+236>>2]|0)?e:a;d=d+1|0}b=+h[(c[a+16>>2]|0)+240>>3];if(!(b>=0.0))a=1;else{h[g+240>>3]=b+1.0;a=0}}else if((c[g+192>>2]|0)>0){e=c[g+188>>2]|0;d=c[e>>2]|0;a=1;d=c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0;while(1){f=c[e+(a<<2)>>2]|0;if(!f)break;f=c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0;a=a+1|0;d=(c[(c[f+16>>2]|0)+236>>2]|0)<(c[(c[d+16>>2]|0)+236>>2]|0)?f:d}b=+h[(c[d+16>>2]|0)+240>>3];if(b>0.0){h[g+240>>3]=b+-1.0;a=0}else a=1}else a=1;return a|0}function yi(a,b){a=a|0;b=b|0;return (c[(c[(c[a>>2]|0)+16>>2]|0)+236>>2]|0)-(c[(c[(c[b>>2]|0)+16>>2]|0)+236>>2]|0)|0}function zi(a,d){a=a|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+96|0;o=p+64|0;n=p+48|0;h=p+24|0;f=p;j=d+16|0;k=c[(c[j>>2]|0)+232>>2]|0;m=a+16|0;e=c[(c[m>>2]|0)+196>>2]|0;i=c[e+(k<<6)>>2]|0;do if((c[e+(k<<6)+8>>2]|0)>=1){c[(c[e+(k<<6)+4>>2]|0)+(i<<2)>>2]=d;c[(c[j>>2]|0)+236>>2]=i;a=c[m>>2]|0;f=c[a+196>>2]|0;e=f+(k<<6)|0;g=c[e>>2]|0;c[e>>2]=g+1;if((g|0)>=(c[f+(k<<6)+8>>2]|0))qa(86377,85781,1346,86413);g=c[(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(k<<6)+8>>2]|0;if((i|0)>(g|0)){m=KB(d)|0;n=c[(c[j>>2]|0)+236>>2]|0;o=c[(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(k<<6)+8>>2]|0;c[h>>2]=1359;c[h+4>>2]=m;c[h+8>>2]=n;c[h+12>>2]=k;c[h+16>>2]=o;gA(1,86429,h)|0;break}e=b[a+236>>1]|0;a=b[a+238>>1]|0;if((k|0)<(e|0)|(k|0)>(a<<16>>16|0)){c[n>>2]=1364;c[n+4>>2]=k;c[n+8>>2]=e;c[n+12>>2]=a<<16>>16;gA(1,86502,n)|0;break}if(((c[f+(k<<6)+4>>2]|0)+(i<<2)|0)>>>0>((c[f+(k<<6)+12>>2]|0)+(g<<2)|0)>>>0){i=KB(d)|0;n=c[(c[m>>2]|0)+196>>2]|0;m=(c[n+(k<<6)+4>>2]|0)+(c[(c[j>>2]|0)+236>>2]<<2)|0;n=(c[n+(k<<6)+12>>2]|0)+(c[(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(k<<6)+8>>2]<<2)|0;c[o>>2]=1370;c[o+4>>2]=k;c[o+8>>2]=i;c[o+12>>2]=m;c[o+16>>2]=k;c[o+20>>2]=k;c[o+24>>2]=n;gA(1,86563,o)|0}}else{n=KB(a)|0;o=KB(d)|0;c[f>>2]=1339;c[f+4>>2]=n;c[f+8>>2]=o;c[f+12>>2]=k;c[f+16>>2]=i;gA(1,86322,f)|0}while(0);l=p;return}function Ai(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;h=d+16|0;a:do if(!e){g=0;while(1){d=c[h>>2]|0;if((g|0)>=(c[d+184>>2]|0))break a;d=c[(c[d+180>>2]|0)+(g<<2)>>2]|0;e=d+-48|0;f=(c[(c[((c[d>>2]&3|0)==2?d:e)+40>>2]|0)+16>>2]|0)+157|0;if(!(a[f>>0]|0)){a[f>>0]=1;XO(b,c[((c[d>>2]&3|0)==2?d:e)+40>>2]|0)}g=g+1|0}}else{g=0;while(1){d=c[h>>2]|0;if((g|0)>=(c[d+176>>2]|0))break a;d=c[(c[d+172>>2]|0)+(g<<2)>>2]|0;e=d+48|0;f=(c[(c[((c[d>>2]&3|0)==3?d:e)+40>>2]|0)+16>>2]|0)+157|0;if(!(a[f>>0]|0)){a[f>>0]=1;XO(b,c[((c[d>>2]&3|0)==3?d:e)+40>>2]|0)}g=g+1|0}}while(0);return}function Bi(d,e){d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;v=l;l=l+16|0;u=v;g=d+16|0;f=c[(c[g>>2]|0)+196>>2]|0;s=c[f+(e<<6)+4>>2]|0;t=e+1|0;d=c[(c[(c[(c[46741]|0)+16>>2]|0)+196>>2]|0)+(t<<6)>>2]|0;if((c[46745]|0)>(d|0))q=c[46746]|0;else{d=d+1|0;c[46745]=d;f=c[46746]|0;if(!f)d=KK(d<<2)|0;else d=MK(f,d<<2)|0;c[46746]=d;f=c[(c[g>>2]|0)+196>>2]|0;q=d}r=f+(t<<6)|0;d=0;while(1){if((d|0)>=(c[r>>2]|0))break;c[q+(d<<2)>>2]=0;d=d+1|0}p=f+(e<<6)|0;o=0;h=0;d=0;j=q;while(1){k=c[p>>2]|0;if((o|0)>=(k|0))break;n=c[(c[(c[s+(o<<2)>>2]|0)+16>>2]|0)+180>>2]|0;a:do if((h|0)>0){m=0;while(1){g=c[n+(m<<2)>>2]|0;if(!g)break a;k=g+16|0;i=c[(c[(c[((c[g>>2]&3|0)==2?g:g+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;while(1){g=i+1|0;if((i|0)>=(h|0))break;i=g;d=(O(c[j+(g<<2)>>2]|0,b[(c[k>>2]|0)+154>>1]|0)|0)+d|0}m=m+1|0}}while(0);i=0;while(1){g=c[n+(i<<2)>>2]|0;if(!g)break;j=c[(c[(c[((c[g>>2]&3|0)==2?g:g+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;m=q+(j<<2)|0;c[m>>2]=(c[m>>2]|0)+(b[(c[g+16>>2]|0)+154>>1]|0);h=(j|0)>(h|0)?j:h;i=i+1|0;j=q}o=o+1|0}i=f+(e<<6)+4|0;h=0;while(1){if((h|0)>=(k|0))break;g=c[(c[(c[i>>2]|0)+(h<<2)>>2]|0)+16>>2]|0;if(a[g+145>>0]|0){e=g+180|0;c[u>>2]=c[e>>2];c[u+4>>2]=c[e+4>>2];d=(Ci(u,1)|0)+d|0}h=h+1|0}i=c[r>>2]|0;h=f+(t<<6)+4|0;g=0;while(1){if((g|0)>=(i|0))break;f=c[(c[(c[h>>2]|0)+(g<<2)>>2]|0)+16>>2]|0;if(a[f+145>>0]|0){t=f+172|0;c[u>>2]=c[t>>2];c[u+4>>2]=c[t+4>>2];d=(Ci(u,-1)|0)+d|0}g=g+1|0}l=v;return d|0}function Ci(a,d){a=a|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0;n=(d|0)<1;l=c[a>>2]|0;d=0;a=0;a:while(1){m=c[l+(d<<2)>>2]|0;if(!m)break;d=d+1|0;if(n){j=m+48|0;k=m+16|0;i=d;while(1){e=c[l+(i<<2)>>2]|0;if(!e)continue a;f=c[e+16>>2]|0;g=c[k>>2]|0;if((+h[f+56>>3]-+h[g+56>>3])*+((c[(c[(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)-(c[(c[(c[((c[m>>2]&3|0)==3?m:j)+40>>2]|0)+16>>2]|0)+236>>2]|0)|0)<0.0)a=(O(b[f+154>>1]|0,b[g+154>>1]|0)|0)+a|0;i=i+1|0}}else{j=m+-48|0;k=m+16|0;i=d;while(1){e=c[l+(i<<2)>>2]|0;if(!e)continue a;f=c[e+16>>2]|0;g=c[k>>2]|0;if((+h[f+16>>3]-+h[g+16>>3])*+((c[(c[(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)-(c[(c[(c[((c[m>>2]&3|0)==2?m:j)+40>>2]|0)+16>>2]|0)+236>>2]|0)|0)<0.0)a=(O(b[f+154>>1]|0,b[g+154>>1]|0)|0)+a|0;i=i+1|0}}}return a|0}function Di(a){a=a|0;var b=0.0,d=0.0;c[46744]=8;c[47136]=24;h[23215]=.995;a=QA(a,86685)|0;if(a|0?(b=+r5(a),b>0.0):0){d=b*+(c[46744]|0);c[46744]=~~(d>1.0?d:1.0);b=b*+(c[47136]|0);c[47136]=~~(b>1.0?b:1.0)}return}function Ei(a){a=a|0;var d=0,e=0;e=(b[(c[a+16>>2]|0)+238>>1]|0)+2|0;d=JK(e<<2)|0;Gi(a,d,e,0)|0;U1(d);return}function Fi(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;h=a+16|0;i=JK((b[(c[h>>2]|0)+238>>1]<<2)+8|0)|0;g=jC(a)|0;while(1){if(!g)break;f=i+(c[(c[g+16>>2]|0)+232>>2]<<2)|0;c[f>>2]=(c[f>>2]|0)+1;f=XA(a,g)|0;while(1){if(!f)break;j=c[f>>2]&3;d=c[(c[(c[((j|0)==3?f:f+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0;j=c[(c[(c[((j|0)==2?f:f+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0;k=(d|0)>(j|0);e=k?d:j;d=k?j:d;while(1){d=d+1|0;if((d|0)>=(e|0))break;k=i+(d<<2)|0;c[k>>2]=(c[k>>2]|0)+1}f=ZA(a,f)|0}g=kC(a,g)|0}f=JK((b[(c[h>>2]|0)+238>>1]<<6)+128|0)|0;e=c[h>>2]|0;c[e+196>>2]=f;d=b[e+236>>1]|0;while(1){if((d|0)>(b[e+238>>1]|0))break;j=i+(d<<2)|0;e=c[j>>2]|0;c[f+(d<<6)>>2]=e;c[f+(d<<6)+8>>2]=e;j=JK((c[j>>2]<<2)+4|0)|0;e=c[h>>2]|0;k=c[e+196>>2]|0;c[k+(d<<6)+4>>2]=j;c[k+(d<<6)+12>>2]=j;d=d+1|0;f=k}U1(i);return}function Gi(a,d,e,f){a=a|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,l=0,m=0;m=a+16|0;i=1;while(1){g=c[m>>2]|0;if((i|0)>(c[g+180>>2]|0))break;l=Gi(c[(c[g+184>>2]|0)+(i<<2)>>2]|0,d,e,f)|0;i=i+1|0;f=l}a:do if((gg(a)|0)!=(a|0)){Y6(d|0,0,e<<2|0)|0;g=jC(a)|0;while(1){if(!g)break;k=g+16|0;c[d+(c[(c[k>>2]|0)+232>>2]<<2)>>2]=1;i=XA(a,g)|0;while(1){if(!i)break;l=i+-48|0;j=c[(c[k>>2]|0)+232>>2]|0;while(1){e=j+1|0;if((j|0)>=(c[(c[(c[((c[i>>2]&3|0)==2?i:l)+40>>2]|0)+16>>2]|0)+232>>2]|0))break;c[d+(e<<2)>>2]=1;j=e}i=ZA(a,i)|0}g=kC(a,g)|0}g=c[m>>2]|0;i=b[g+236>>1]|0;while(1){if((i|0)>(b[g+238>>1]|0))break a;if(!(c[d+(i<<2)>>2]|0)){if(!f)f=ND(gg(a)|0,86675,1)|0;g=tC(f,0,1)|0;TC(g,137447,304,1)|0;l=g+16|0;j=c[l>>2]|0;c[j+232>>2]=i;h[j+96>>3]=.5;h[j+88>>3]=.5;h[j+80>>3]=1.0;c[j+216>>2]=1;c[j+176>>2]=0;j=JK(20)|0;k=c[l>>2]|0;c[k+172>>2]=j;c[k+184>>2]=0;k=JK(20)|0;c[(c[l>>2]|0)+180>>2]=k;oC(a,g,1)|0;g=c[m>>2]|0}i=i+1|0}}while(0);return f|0}function Hi(a){a=a|0;var b=0,d=0;Xh(a);b=a+16|0;a=1;while(1){d=c[b>>2]|0;if((a|0)>(c[d+180>>2]|0))break;Hi(c[(c[d+184>>2]|0)+(a<<2)>>2]|0);a=a+1|0}return}function Ii(a){a=a|0;var b=0,d=0;b=c[a>>2]&3;d=Ji(c[((b|0)==3?a:a+48|0)+40>>2]|0)|0;b=c[17308+(d*12|0)+((Ji(c[((b|0)==2?a:a+-48|0)+40>>2]|0)|0)<<2)>>2]|0;a=(c[a+16>>2]|0)+156|0;c[a>>2]=O(c[a>>2]|0,b)|0;return}function Ji(b){b=b|0;b=c[b+16>>2]|0;if((a[b+156>>0]|0)==1)b=2;else b=(a[b+160>>0]|0)<2&1;return b|0}function Ki(b,d){b=b|0;d=d|0;if(c[(c[b+16>>2]|0)+192>>2]|0){jl(b);Li(b);if(a[194954]|0)xl(b);Mi(b);if(Pl(b)|0)Li(b);Ni(b);if(uL(b,2,Oi(b)|0)|0?(Pi(b),uL(b,2,Oi(b)|0)|0):0)qa(86693,86721,134,86732);Qi(b);Ri(b,d);Si(b)}return}function Li(d){d=d|0;var e=0,f=0,g=0,i=0,j=0.0,k=0.0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0.0,z=0.0;v=d+16|0;e=c[v>>2]|0;w=c[e+196>>2]|0;t=b[e+236>>1]|0;while(1){if((t|0)>(b[e+238>>1]|0))break;n=w+(t<<6)|0;o=w+(t<<6)+4|0;p=w+(t<<6)+40|0;q=w+(t<<6)+24|0;r=w+(t<<6)+32|0;s=w+(t<<6)+16|0;m=0;while(1){if((m|0)>=(c[n>>2]|0))break;l=(c[(c[o>>2]|0)+(m<<2)>>2]|0)+16|0;e=c[l>>2]|0;j=+h[e+80>>3]*.5;i=c[e+204>>2]|0;a:do if(i){g=0;while(1){f=c[i+(g<<2)>>2]|0;if(!f)break a;x=c[f>>2]&3;if((c[((x|0)==3?f:f+48|0)+40>>2]|0)==(c[((x|0)==2?f:f+-48|0)+40>>2]|0)?(u=c[(c[f+16>>2]|0)+96>>2]|0,(u|0)!=0):0){k=+h[u+32>>3]*.5;j=j>k?j:k}g=g+1|0}}while(0);if(+h[p>>3]<j){h[q>>3]=j;h[p>>3]=j}if(+h[r>>3]<j){h[s>>3]=j;h[r>>3]=j}f=c[e+212>>2]|0;if(f|0){if((f|0)==(d|0))g=0;else{g=ZO(f,c[47144]|0,8,0)|0;e=c[l>>2]|0}i=c[e+232>>2]|0;f=c[f+16>>2]|0;e=f+128|0;j=j+ +(g|0);if((i|0)==(b[f+236>>1]|0)){k=+h[e>>3];h[e>>3]=k>j?k:j}e=f+120|0;if((i|0)==(b[f+238>>1]|0)){k=+h[e>>3];h[e>>3]=k>j?k:j}}m=m+1|0}t=t+1|0;e=c[v>>2]|0}m=rj(d)|0;g=c[v>>2]|0;e=b[g+238>>1]|0;h[(c[(c[c[w+(e<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]=+h[w+(e<<6)+16>>3];f=b[g+236>>1]|0;i=g+252|0;k=0.0;while(1){l=e+-1|0;if((e|0)<=(f|0))break;y=+h[w+(l<<6)+32>>3]+ +h[w+(e<<6)+40>>3]+ +(c[i>>2]|0);j=+h[w+(e<<6)+24>>3]+8.0+ +h[w+(l<<6)+16>>3];j=y>j?y:j;if((c[w+(l<<6)>>2]|0)>0)h[(c[(c[c[w+(l<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]=+h[(c[(c[c[w+(e<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]+j;k=k>j?k:j;e=l}b:do if((m|0)!=0?(c[g+116>>2]&1|0)!=0:0){sj(d,0);g=c[v>>2]|0;if(a[g+276>>0]|0){e=b[g+238>>1]|0;f=b[g+236>>1]|0;j=+h[(c[(c[c[w+(e<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3];k=0.0;while(1){i=e+-1|0;if((e|0)<=(f|0))break b;z=+h[(c[(c[c[w+(i<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3];y=z-j;j=z;k=k>y?k:y;e=i}}}while(0);c:do if(a[g+276>>0]|0){i=b[g+236>>1]|0;f=b[g+238>>1]|0;while(1){e=f+-1|0;if((f|0)<=(i|0))break c;if((c[w+(e<<6)>>2]|0)<=0){f=e;continue}h[(c[(c[c[w+(e<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]=+h[(c[(c[c[w+(f<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]+k;f=e}}while(0);e=g+192|0;while(1){e=c[e>>2]|0;if(!e)break;e=c[e+16>>2]|0;h[e+24>>3]=+h[(c[(c[c[w+(c[e+232>>2]<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3];e=e+164|0}return}function Mi(a){a=a|0;var b=0,d=0,e=0;nj(a);b=(c[a+16>>2]|0)+192|0;while(1){b=c[b>>2]|0;if(!b)break;e=b+16|0;b=c[e>>2]|0;d=c[b+224>>2]|0;if(d){oj(a,d);b=c[e>>2]|0}d=c[b+228>>2]|0;if(!d)d=b;else{oj(a,d);d=c[e>>2]|0}e=c[d+204>>2]|0;if(e|0){b=0;while(1)if(!(c[e+(b<<2)>>2]|0))break;else b=b+1|0}b=d+164|0}return}function Ni(a){a=a|0;$i(a);aj(a);bj(a);cj(a);dj(a);return}function Oi(a){a=a|0;var b=0,c=0.0;b=QA(a,86921)|0;if(!b)a=2147483647;else{c=+r5(b);a=~~(c*+(tB(a)|0))}return a|0}function Pi(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;r=d+16|0;e=c[r>>2]|0;q=b[e+236>>1]|0;while(1){p=b[e+238>>1]|0;if((q|0)>(p|0)){s=19;break}n=c[e+196>>2]|0;o=n+(q<<6)|0;m=c[o>>2]|0;n=n+(q<<6)+4|0;l=0;f=0;a:while(1){if((l|0)>=(m|0)){s=14;break}f=c[(c[n>>2]|0)+(l<<2)>>2]|0;h=c[f+16>>2]|0;i=c[h+256>>2]|0;b:do if(i|0){g=0;while(1){j=c[i+(g<<2)>>2]|0;if(!j)break b;k=c[j>>2]&3;if((c[(c[(c[((k|0)==2?j:j+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)>(q|0))break a;if((c[(c[(c[((k|0)==3?j:j+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)>(q|0))break a;else g=g+1|0}}while(0);h=c[h+248>>2]|0;c:do if(h|0){g=0;while(1){i=c[h+(g<<2)>>2]|0;if(!i)break c;j=c[i>>2]&3;if((c[(c[(c[((j|0)==3?i:i+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)>(q|0))break a;if((c[(c[(c[((j|0)==2?i:i+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)>(q|0))break a;else g=g+1|0}}while(0);l=l+1|0}if((s|0)==14){s=0;if(f){f=c[c[n>>2]>>2]|0;e=c[c[o+(((q|0)<(p|0)?1:-1)<<6)+4>>2]>>2]|0;if(!e){s=16;break}o=sh(d)|0;p=o+16|0;a[(c[p>>2]|0)+156>>0]=2;Zi(o,f,0.0,0)|0;Zi(o,e,0.0,0)|0;c[(c[p>>2]|0)+232>>2]=c[(c[((c[(c[f+16>>2]|0)+232>>2]|0)<(c[(c[e+16>>2]|0)+232>>2]|0)?f:e)+16>>2]|0)+232>>2];e=c[r>>2]|0}}q=q+1|0}if((s|0)==16)qa(86829,86721,111,86832);else if((s|0)==19)return}function Qi(a){a=a|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0;a=c[a+16>>2]|0;i=c[a+196>>2]|0;e=b[a+238>>1]|0;a=b[a+236>>1]|0;while(1){if((a|0)>(e|0))break;f=c[i+(a<<6)>>2]|0;g=i+(a<<6)+4|0;d=0;while(1){if((d|0)>=(f|0))break;k=c[(c[(c[g>>2]|0)+(d<<2)>>2]|0)+16>>2]|0;j=k+232|0;h[k+16>>3]=+(c[j>>2]|0);c[j>>2]=a;d=d+1|0}a=a+1|0}return}function Ri(a,d){a=a|0;d=d|0;var e=0,f=0.0,g=0,i=0.0,j=0,k=0,l=0.0,m=0,n=0,o=0,p=0;Ti(a,a);g=a+16|0;k=c[g>>2]|0;a:do if((b[k+238>>1]|0)>0?(j=c[k+8>>2]|0,e=c[j+84>>2]|0,e|0):0){o=~~(+h[k+32>>3]-+h[k+16>>3]);m=~~(+h[k+40>>3]-+h[k+24>>3]);p=(c[k+116>>2]&1|0)==0;n=p?m:o;m=p?o:m;if((e|0)==4){e=Ui(a)|0;k=c[g>>2]|0;j=c[k+8>>2]|0}else e=(e|0)==2&1;b:do if(!(e<<24>>24)){switch(c[j+84>>2]|0){case 5:{f=+h[j+64>>3];if(f<=0.0)break a;i=f/+h[k+32>>3];l=+h[j+72>>3]/+h[k+40>>3];f=i<l?i:l;if(i>1.0&l>1.0){l=f;break b}else break a}case 1:break;default:break a}i=+h[j+16>>3];f=+(n|0)/+(m|0);if(f<i){l=1.0;f=i/f;break}else{l=f/i;f=1.0;break}}else{f=+h[j+64>>3];if(f<=0.0)break a;i=f/+(m|0);f=+h[j+72>>3]/+(n|0);if(i<1.0|f<1.0)if(i<f){l=1.0;f=f/i;break}else{l=i/f;f=1.0;break}else l=i}while(0);e=(c[k+116>>2]&1|0)==0;i=e?f:l;f=e?l:f;e=k+192|0;while(1){e=c[e>>2]|0;if(!e)break;e=c[e+16>>2]|0;p=e+16|0;l=+h[p>>3]*f;h[p>>3]=+(~~(l+(l>=0.0?.5:-.5))|0);p=e+24|0;l=+h[p>>3]*i;h[p>>3]=+(~~(l+(l>=0.0?.5:-.5))|0);e=e+164|0}Vi(a,f,i)}while(0);if(d|0)Wi(a,d);return}function Si(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;h=b+16|0;b=(c[h>>2]|0)+192|0;while(1){b=c[b>>2]|0;if(!b)break;g=b+16|0;d=0;while(1){b=c[g>>2]|0;f=c[b+180>>2]|0;e=c[f+(d<<2)>>2]|0;if(!e)break;U1(c[e+16>>2]|0);U1(e);d=d+1|0}if(f){U1(f);b=c[g>>2]|0}d=c[b+172>>2]|0;if(d){U1(d);b=c[g>>2]|0}f=b+256|0;e=c[f+4>>2]|0;b=b+180|0;c[b>>2]=c[f>>2];c[b+4>>2]=e;b=c[g>>2]|0;e=b+248|0;f=c[e+4>>2]|0;b=b+172|0;c[b>>2]=c[e>>2];c[b+4>>2]=f;b=(c[g>>2]|0)+164|0}b=0;d=c[(c[h>>2]|0)+192>>2]|0;a:while(1){g=(b|0)==0;f=b+16|0;b=d;while(1){if(!b)break a;e=c[b+16>>2]|0;d=c[e+164>>2]|0;if((a[e+156>>0]|0)!=2)continue a;if(g)c[(c[h>>2]|0)+192>>2]=d;else c[(c[f>>2]|0)+164>>2]=d;U1(e);U1(b);b=d}}c[(c[(c[(c[h>>2]|0)+192>>2]|0)+16>>2]|0)+168>>2]=0;return}function Ti(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;e=a+16|0;d=1;while(1){f=c[e>>2]|0;if((d|0)>(c[f+180>>2]|0))break;Ti(c[(c[f+184>>2]|0)+(d<<2)>>2]|0,b);d=d+1|0}Yi(a,b);return}function Ui(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0.0,k=0.0,m=0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0;s=l;l=l+80|0;e=s+64|0;d=s+48|0;f=s+16|0;m=s;r=s+32|0;a=a+16|0;b=c[(c[a>>2]|0)+8>>2]|0;t=b+48|0;c[f>>2]=c[t>>2];c[f+4>>2]=c[t+4>>2];c[f+8>>2]=c[t+8>>2];c[f+12>>2]=c[t+12>>2];if((!(+h[f>>3]<.001)?(g=f+8|0,!(+h[g>>3]<.001)):0)?(i=b+32|0,c[m>>2]=c[i>>2],c[m+4>>2]=c[i+4>>2],c[m+8>>2]=c[i+8>>2],c[m+12>>2]=c[i+12>>2],c[d>>2]=c[f>>2],c[d+4>>2]=c[f+4>>2],c[d+8>>2]=c[f+8>>2],c[d+12>>2]=c[f+12>>2],c[e>>2]=c[i>>2],c[e+4>>2]=c[i+4>>2],c[e+8>>2]=c[i+8>>2],c[e+12>>2]=c[i+12>>2],Xi(r,d,e),c[f>>2]=c[r>>2],c[f+4>>2]=c[r+4>>2],c[f+8>>2]=c[r+8>>2],c[f+12>>2]=c[r+12>>2],c[d>>2]=c[r>>2],c[d+4>>2]=c[r+4>>2],c[d+8>>2]=c[r+8>>2],c[d+12>>2]=c[r+12>>2],c[e>>2]=c[m>>2],c[e+4>>2]=c[m+4>>2],c[e+8>>2]=c[m+8>>2],c[e+12>>2]=c[m+12>>2],Xi(f,d,e),i=c[a>>2]|0,j=+h[i+32>>3],k=+h[i+40>>3],n=+h[f>>3],o=n/j,p=+h[g>>3],q=p/k,!(o>=1.0&q>=1.0)):0){o=o<q?o:q;o=o>.5?o:.5;q=+N(+(o*k/p))*p/k;p=+N(+(o*j/n))*n/j*j;a=c[i+8>>2]|0;h[a+64>>3]=p;h[a+72>>3]=q*k;a=1}else a=0;l=s;return a|0}function Vi(a,b,d){a=a|0;b=+b;d=+d;var e=0,f=0;e=a+16|0;a=1;while(1){f=c[e>>2]|0;if((a|0)>(c[f+180>>2]|0))break;Vi(c[(c[f+184>>2]|0)+(a<<2)>>2]|0,b,d);a=a+1|0}e=f+16|0;h[e>>3]=+h[e>>3]*b;e=f+24|0;h[e>>3]=+h[e>>3]*d;e=f+32|0;h[e>>3]=+h[e>>3]*b;f=f+40|0;h[f>>3]=+h[f>>3]*d;return}function Wi(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0,g=0,i=0.0,j=0,k=0,m=0,n=0;m=l;l=l+32|0;j=m+24|0;g=m+16|0;f=m;n=c[a+16>>2]|0;e=+h[n+32>>3]-+h[n+16>>3];d=+h[n+40>>3]-+h[n+24>>3];i=e/d;if(0){n=c[15712]|0;h[f>>3]=i;h[f+8>>3]=e*.0001*d;w4(n,86745,f)|0;c[g>>2]=xk(a)|0;w4(n,86770,g)|0}d=+h[b>>3];do if(!(i>d*1.1)){a=b+24|0;if(!(i<=d*.8)){c[a>>2]=0;k=9;break}c[a>>2]=-1;if(0){U4(86780,34,1,c[15712]|0)|0;k=9}}else{c[b+24>>2]=~~(d*+((c[b+20>>2]|0)-(c[b+16>>2]|0)|0)/i);k=9}while(0);if((k|0)==9?0:0){n=c[15712]|0;c[j>>2]=c[b+24>>2];w4(n,86815,j)|0}l=m;return}function Xi(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[b+8>>3]-+h[c+8>>3];h[a>>3]=+h[b>>3]-+h[c>>3];h[a+8>>3]=d;return}function Yi(d,e){d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0,k=0,l=0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0.0;s=(gg(d)|0)==(d|0);v=d+16|0;w=c[v>>2]|0;a:do if(s){f=b[w+236>>1]|0;d=b[w+238>>1]|0;q=d<<16>>16;r=w+196|0;s=w+196|0;p=f<<16>>16;i=-2147483647.0;g=2147483647.0;while(1){if((p|0)>(q|0))break;j=c[r>>2]|0;o=c[j+(p<<6)>>2]|0;if((o|0)!=0?(t=c[j+(p<<6)+4>>2]|0,u=c[t>>2]|0,(u|0)!=0):0){l=1;j=u;while(1){j=c[j+16>>2]|0;k=a[j+156>>0]|0;if(!((l|0)<(o|0)&k<<24>>24!=0))break;j=c[t+(l<<2)>>2]|0;l=l+1|0}if(!(k<<24>>24)){m=+h[j+16>>3]-+h[j+88>>3];n=g<m;l=c[(c[s>>2]|0)+(p<<6)+4>>2]|0;k=o+-2|0;j=o+-1|0;while(1){j=c[(c[l+(j<<2)>>2]|0)+16>>2]|0;if(!(a[j+156>>0]|0))break;else{j=k;k=k+-1|0}}x=+h[j+96>>3]+ +h[j+16>>3];i=i>x?i:x;g=n?g:m}}p=p+1|0}k=c[w+180>>2]|0;l=w+184|0;j=1;while(1){if((j|0)>(k|0))break a;u=c[(c[(c[l>>2]|0)+(j<<2)>>2]|0)+16>>2]|0;x=+h[u+16>>3]+-8.0;m=+h[u+32>>3]+8.0;j=j+1|0;i=i>m?i:m;g=g<x?g:x}}else{i=+(c[(c[(c[w+260>>2]|0)+16>>2]|0)+232>>2]|0);g=+(c[(c[(c[w+256>>2]|0)+16>>2]|0)+232>>2]|0);f=b[w+236>>1]|0;d=b[w+238>>1]|0}while(0);e=c[(c[e+16>>2]|0)+196>>2]|0;m=+h[(c[(c[c[e+(d<<16>>16<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]-+h[w+120>>3];x=+h[w+128>>3]+ +h[(c[(c[c[e+(f<<16>>16<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3];h[w+16>>3]=g;h[w+24>>3]=m;e=c[v>>2]|0;h[e+32>>3]=i;h[e+40>>3]=x;return}function Zi(a,d,e,f){a=a|0;d=d|0;e=+e;f=f|0;var g=0,h=0,i=0,j=0;i=JK(96)|0;j=i+48|0;c[j>>2]=c[j>>2]|3;c[i>>2]=c[i>>2]&-4|2;g=JK(176)|0;h=i+16|0;c[h>>2]=g;c[((c[i>>2]&3|0)==3?i:j)+40>>2]=a;c[((c[i>>2]&3|0)==2?i:i+-48|0)+40>>2]=d;if(e>65535.0){_i(e);e=65535.0;g=c[h>>2]|0}b[g+170>>1]=~~(e+(e>=0.0?.5:-.5));c[g+156>>2]=f;gh(i)|0;return i|0}function _i(a){a=+a;var b=0,d=0;b=l;l=l+16|0;d=b;h[d>>3]=a;c[d+8>>2]=65535;gA(1,86845,d)|0;l=b;return}function $i(a){a=a|0;var b=0,d=0,e=0,f=0;a=(c[a+16>>2]|0)+192|0;while(1){a=c[a>>2]|0;if(!a)break;f=a+16|0;e=c[f>>2]|0;a=e+172|0;d=c[a+4>>2]|0;e=e+248|0;c[e>>2]=c[a>>2];c[e+4>>2]=d;e=c[f>>2]|0;d=e+180|0;a=c[d+4>>2]|0;e=e+256|0;c[e>>2]=c[d>>2];c[e+4>>2]=a;e=c[f>>2]|0;a=c[e+180>>2]|0;d=0;while(1)if(!(c[a+(d<<2)>>2]|0))break;else d=d+1|0;b=c[e+172>>2]|0;a=0;while(1)if(!(c[b+(a<<2)>>2]|0))break;else a=a+1|0;c[e+176>>2]=0;a=JK((a+d<<2)+16|0)|0;e=c[f>>2]|0;c[e+172>>2]=a;c[e+184>>2]=0;e=JK(16)|0;a=c[f>>2]|0;c[a+180>>2]=e;a=a+164|0}return}function aj(d){d=d|0;var f=0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0.0,q=0,r=0,s=0,t=0.0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0,B=0.0;z=l;l=l+16|0;w=z;x=d+16|0;u=c[x>>2]|0;y=c[u+196>>2]|0;d=(a[(c[(c[d+60>>2]|0)+16>>2]|0)+113>>0]&1)!=0;v=c[u+248>>2]|0;c[w>>2]=v;c[w+4>>2]=d?5:v;v=b[u+236>>1]|0;d=u;while(1){if((v|0)>(b[d+238>>1]|0))break;r=y+(v<<6)+4|0;c[(c[(c[c[r>>2]>>2]|0)+16>>2]|0)+232>>2]=0;s=y+(v<<6)|0;t=+(c[w+((v&1)<<2)>>2]|0);p=0.0;k=0;a:while(1){if((k|0)>=(c[s>>2]|0))break;f=c[r>>2]|0;m=c[f+(k<<2)>>2]|0;u=m+16|0;d=c[u>>2]|0;i=+h[d+96>>3];h[d+240>>3]=i;if((c[d+208>>2]|0)>0){j=0;f=0;while(1){g=c[(c[d+204>>2]|0)+(j<<2)>>2]|0;if(!g)break;q=c[g>>2]&3;if((c[((q|0)==3?g:g+48|0)+40>>2]|0)==(c[((q|0)==2?g:g+-48|0)+40>>2]|0)){f=(kO(g)|0)+f|0;d=c[u>>2]|0}j=j+1|0}q=d+96|0;i=+h[q>>3]+ +(f|0);h[q>>3]=i;f=c[r>>2]|0}q=k+1|0;f=c[f+(q<<2)>>2]|0;if(f){o=f+16|0;i=i+t+ +h[(c[o>>2]|0)+88>>3];Zi(m,f,i,0)|0;d=~~(i+p);c[(c[o>>2]|0)+232>>2]=d;p=+(d|0);d=c[u>>2]|0}f=c[d+112>>2]|0;if(f){j=c[d+256>>2]|0;k=c[j>>2]|0;j=c[j+4>>2]|0;d=(c[(c[(c[((c[k>>2]&3|0)==2?k:k+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0)>(c[(c[(c[((c[j>>2]&3|0)==2?j:j+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0);g=d?j:k;j=d?k:j;k=f+16|0;d=c[k>>2]|0;i=+((O(c[(c[x>>2]|0)+248>>2]|0,e[d+170>>1]|0)|0)/2|0|0);o=c[g>>2]&3;f=c[((o|0)==2?g:g+-48|0)+40>>2]|0;g=c[((o|0)==3?g:g+48|0)+40>>2]|0;if(!(lj(g,f)|0))Zi(f,g,+(~~(+h[(c[f+16>>2]|0)+96>>3]+i+ +h[(c[g+16>>2]|0)+88>>3])|0),c[d+156>>2]|0)|0;d=c[j>>2]&3;f=c[((d|0)==3?j:j+48|0)+40>>2]|0;d=c[((d|0)==2?j:j+-48|0)+40>>2]|0;if(!(lj(d,f)|0)){Zi(f,d,+(~~(+h[(c[f+16>>2]|0)+96>>3]+i+ +h[(c[d+16>>2]|0)+88>>3])|0),c[(c[k>>2]|0)+156>>2]|0)|0;o=0}else o=0}else o=0;while(1){d=c[u>>2]|0;if((o|0)>=(c[d+192>>2]|0)){k=q;continue a}m=c[(c[d+188>>2]|0)+(o<<2)>>2]|0;k=c[m>>2]&3;g=c[((k|0)==3?m:m+48|0)+40>>2]|0;k=c[((k|0)==2?m:m+-48|0)+40>>2]|0;j=(c[(c[g+16>>2]|0)+236>>2]|0)<(c[(c[k+16>>2]|0)+236>>2]|0);f=j?g:k;g=j?k:g;i=+h[(c[g+16>>2]|0)+88>>3]+ +h[(c[f+16>>2]|0)+96>>3];k=m+16|0;j=~~(i+ +(O(c[(c[x>>2]|0)+248>>2]|0,e[(c[k>>2]|0)+170>>1]|0)|0));n=dh(f,g)|0;if(!n){d=c[k>>2]|0;if(!(c[d+96>>2]|0))Zi(f,g,+(j|0),c[d+156>>2]|0)|0}else{A=+(j|0);d=c[k>>2]|0;B=+h[d+136>>3];i=i+ +(c[(c[x>>2]|0)+248>>2]|0)+ +(~~(B+(B>=0.0?.5:-.5))|0);f=~~(i<A?A:i);if((f|0)>65535){_i(+(f|0));f=65535;d=c[k>>2]|0}k=c[n+16>>2]|0;j=k+170|0;g=e[j>>1]|0;b[j>>1]=(f|0)<(g|0)?g:f;k=k+156|0;c[k>>2]=c[(c[((c[k>>2]|0)>(c[d+156>>2]|0)?n:m)+16>>2]|0)+156>>2]}o=o+1|0}}v=v+1|0;d=c[x>>2]|0}l=z;return}function bj(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0;d=(c[b+16>>2]|0)+192|0;while(1){d=c[d>>2]|0;if(!d)break;g=d+16|0;d=c[g>>2]|0;e=c[d+256>>2]|0;a:do if(e){f=0;while(1){e=c[e+(f<<2)>>2]|0;if(!e)break a;n=sh(b)|0;d=n+16|0;a[(c[d>>2]|0)+156>>0]=2;l=e+16|0;k=c[l>>2]|0;i=~~(+h[k+56>>3]-+h[k+16>>3]);m=(i|0)>0;j=m?0:0-i|0;i=m?i:0;m=e+48|0;Zi(n,c[((c[e>>2]&3|0)==3?e:m)+40>>2]|0,+(i+1|0),c[k+156>>2]|0)|0;k=e+-48|0;Zi(n,c[((c[e>>2]&3|0)==2?e:k)+40>>2]|0,+(j+1|0),c[(c[l>>2]|0)+156>>2]|0)|0;l=c[e>>2]&3;i=(c[(c[(c[((l|0)==3?e:m)+40>>2]|0)+16>>2]|0)+232>>2]|0)-i|0;e=(c[(c[(c[((l|0)==2?e:k)+40>>2]|0)+16>>2]|0)+232>>2]|0)-j|0;c[(c[d>>2]|0)+232>>2]=((i|0)<(e|0)?i:e)+-1;d=c[g>>2]|0;f=f+1|0;e=c[d+256>>2]|0}}while(0);d=d+164|0}return}function cj(a){a=a|0;if((c[(c[a+16>>2]|0)+180>>2]|0)>0){gj(a);hj(a);ij(a);jj(a)}return}function dj(a){a=a|0;var b=0.0,d=0.0,e=0,f=0;e=a+16|0;f=c[(c[e>>2]|0)+8>>2]|0;if((c[f+84>>2]|0)==3?(b=+h[f+64>>3],d=+h[f+72>>3],!(d*b<=1.0)):0){ej(a);f=c[e>>2]|0;d=(c[f+116>>2]&1|0)==0?b:d;Zi(c[f+256>>2]|0,c[f+260>>2]|0,d<65535.0?d:65535.0,1e3)|0}return}function ej(a){a=a|0;var d=0,e=0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;m=n;e=ZO(a,c[47144]|0,8,0)|0;fj(a);i=a+16|0;g=c[i>>2]|0;j=c[g+256>>2]|0;k=c[g+260>>2]|0;f=+(e|0);e=b[g+236>>1]|0;while(1){if((e|0)>(b[g+238>>1]|0))break;d=c[g+196>>2]|0;do if(c[d+(e<<6)>>2]|0){d=c[c[d+(e<<6)+4>>2]>>2]|0;if(!d){c[m>>2]=KB(a)|0;c[m+4>>2]=e;gA(1,86929,m)|0;break}else{Zi(j,d,+h[(c[d+16>>2]|0)+88>>3]+f+ +h[g+96>>3],0)|0;g=c[i>>2]|0;d=c[g+196>>2]|0;d=c[(c[d+(e<<6)+4>>2]|0)+((c[d+(e<<6)>>2]|0)+-1<<2)>>2]|0;Zi(d,k,+h[(c[d+16>>2]|0)+96>>3]+f+ +h[g+64>>3],0)|0;break}}while(0);e=e+1|0;g=c[i>>2]|0}l=n;return}function fj(b){b=b|0;var d=0,e=0,f=0;d=b+16|0;if(!(c[(c[d>>2]|0)+256>>2]|0)){f=sh(gg(b)|0)|0;a[(c[f+16>>2]|0)+156>>0]=2;e=sh(gg(b)|0)|0;a[(c[e+16>>2]|0)+156>>0]=2;if((c[(c[d>>2]|0)+12>>2]|0?(gg(b)|0)!=(b|0):0)?(c[(c[(yC(b)|0)+16>>2]|0)+116>>2]&1|0)==0:0){b=c[d>>2]|0;Zi(f,e,+(~~+h[b+48+((+h[b+48>>3]>+h[b+80>>3]?0:2)<<4)>>3]|0),0)|0}d=c[d>>2]|0;c[d+256>>2]=f;c[d+260>>2]=e}return}function gj(a){a=a|0;var b=0,d=0;do if((gg(a)|0)!=(a|0)){ej(a);a=a+16|0;b=c[a>>2]|0;b=dh(c[b+256>>2]|0,c[b+260>>2]|0)|0;if(!b){d=c[a>>2]|0;Zi(c[d+256>>2]|0,c[d+260>>2]|0,1.0,128)|0;break}else{d=(c[b+16>>2]|0)+156|0;c[d>>2]=(c[d>>2]|0)+128;break}}else a=a+16|0;while(0);b=1;while(1){d=c[a>>2]|0;if((b|0)>(c[d+180>>2]|0))break;gj(c[(c[d+184>>2]|0)+(b<<2)>>2]|0);b=b+1|0}return}function hj(d){d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0.0,n=0,o=0;k=ZO(d,c[47144]|0,8,0)|0;o=d+16|0;e=c[o>>2]|0;m=+(k|0);k=b[e+236>>1]|0;while(1){if((k|0)>(b[e+238>>1]|0)){f=1;break}e=c[e+196>>2]|0;a:do if(c[e+(k<<6)>>2]|0?(l=c[c[e+(k<<6)+4>>2]>>2]|0,l|0):0){i=l+16|0;f=c[(c[i>>2]|0)+236>>2]|0;while(1){if((f|0)<=0)break;f=f+-1|0;j=c[(c[(c[(c[(gg(d)|0)+16>>2]|0)+196>>2]|0)+(k<<6)+4>>2]|0)+(f<<2)>>2]|0;g=j+16|0;e=c[g>>2]|0;if(!(a[e+156>>0]|0)){n=10;break}if(kj(d,j)|0){n=9;break}}if((n|0)==9){e=c[g>>2]|0;n=10}if((n|0)==10){n=0;Zi(j,c[(c[o>>2]|0)+256>>2]|0,+h[e+96>>3]+m,0)|0}f=(c[(c[(c[o>>2]|0)+196>>2]|0)+(k<<6)>>2]|0)+(c[(c[i>>2]|0)+236>>2]|0)|0;while(1){if((f|0)>=(c[(c[(c[(gg(d)|0)+16>>2]|0)+196>>2]|0)+(k<<6)>>2]|0))break a;i=c[(c[(c[(c[(gg(d)|0)+16>>2]|0)+196>>2]|0)+(k<<6)+4>>2]|0)+(f<<2)>>2]|0;g=i+16|0;e=c[g>>2]|0;if(!(a[e+156>>0]|0))break;if(!(kj(d,i)|0))f=f+1|0;else{n=15;break}}if((n|0)==15){n=0;e=c[g>>2]|0}Zi(c[(c[o>>2]|0)+260>>2]|0,i,+h[e+88>>3]+m,0)|0}while(0);k=k+1|0;e=c[o>>2]|0}while(1){if((f|0)>(c[e+180>>2]|0))break;hj(c[(c[e+184>>2]|0)+(f<<2)>>2]|0);f=f+1|0;e=c[o>>2]|0}return}function ij(a){a=a|0;var b=0,d=0.0,e=0,f=0,g=0;e=ZO(a,c[47144]|0,8,0)|0;fj(a);b=a+16|0;d=+(e|0);a=1;while(1){e=c[b>>2]|0;if((a|0)>(c[e+180>>2]|0))break;e=c[(c[e+184>>2]|0)+(a<<2)>>2]|0;fj(e);f=c[b>>2]|0;g=e+16|0;Zi(c[f+256>>2]|0,c[(c[g>>2]|0)+256>>2]|0,+h[f+96>>3]+d,0)|0;f=c[b>>2]|0;Zi(c[(c[g>>2]|0)+260>>2]|0,c[f+260>>2]|0,+h[f+64>>3]+d,0)|0;ij(e);a=a+1|0}return}function jj(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0.0,n=0,o=0;d=ZO(a,c[47144]|0,8,0)|0;n=a+16|0;a=1;while(1){f=c[n>>2]|0;e=c[f+180>>2]|0;if((a|0)>(e|0))break;fj(c[(c[f+184>>2]|0)+(a<<2)>>2]|0);a=a+1|0}m=+(d|0);l=1;a=e;d=f;while(1){if((l|0)>(a|0))break;k=l+1|0;j=k;while(1){e=c[d+184>>2]|0;f=c[e+(l<<2)>>2]|0;if((j|0)>(a|0))break;e=c[e+(j<<2)>>2]|0;g=(b[(c[f+16>>2]|0)+236>>1]|0)>(b[(c[e+16>>2]|0)+236>>1]|0);i=g?e:f;e=g?f:e;f=c[i+16>>2]|0;g=c[e+16>>2]|0;o=b[g+236>>1]|0;h=o<<16>>16;if((b[f+238>>1]|0)>=o<<16>>16){a=(c[(c[(c[c[(c[f+196>>2]|0)+(h<<6)+4>>2]>>2]|0)+16>>2]|0)+236>>2]|0)<(c[(c[(c[c[(c[g+196>>2]|0)+(h<<6)+4>>2]>>2]|0)+16>>2]|0)+236>>2]|0);Zi(c[(c[(a?i:e)+16>>2]|0)+260>>2]|0,c[(c[(a?e:i)+16>>2]|0)+256>>2]|0,m,0)|0;a=c[n>>2]|0;d=a;a=c[a+180>>2]|0}j=j+1|0}jj(f);d=c[n>>2]|0;l=k;a=c[d+180>>2]|0}return}function kj(b,d){b=b|0;d=d|0;var e=0;d=c[d+16>>2]|0;if((a[d+156>>0]|0)==1){d=c[c[d+256>>2]>>2]|0;while(1){e=c[(c[d+16>>2]|0)+116>>2]|0;if(!e)break;else d=e}if(!(HC(b,c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)|0))d=(HC(b,c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)|0)==0&1;else d=0}else d=0;return d|0}function lj(a,b){a=a|0;b=b|0;return mj(a,b)|0}function mj(a,b){a=a|0;b=b|0;var d=0,e=0;a:do if((a|0)==(b|0))a=1;else{d=c[(c[a+16>>2]|0)+180>>2]|0;a=0;while(1){e=c[d+(a<<2)>>2]|0;if(!e){a=0;break a}if(!(mj(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0,b)|0))a=a+1|0;else{a=1;break}}}while(0);return a|0}function nj(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=d+16|0;d=c[k>>2]|0;j=b[d+236>>1]|0;while(1){if((j|0)>(b[d+238>>1]|0))break;h=c[d+196>>2]|0;g=c[h+(j<<6)>>2]|0;h=h+(j<<6)+4|0;i=0;f=0;while(1){if((f|0)>=(g|0))break;e=c[(c[(c[h>>2]|0)+(f<<2)>>2]|0)+16>>2]|0;c[e+236>>2]=i;if((a[e+159>>0]|0)==6)e=c[e+216>>2]|0;else e=1;i=e+i|0;f=f+1|0}if((i|0)>(g|0)){d=c[h>>2]|0;if(!d)d=KK((i<<2)+4|0)|0;else d=MK(d,(i<<2)+4|0)|0;f=c[(c[k>>2]|0)+196>>2]|0;c[f+(j<<6)+4>>2]=d;e=c[f+(j<<6)>>2]|0;while(1){d=e+-1|0;if((e|0)<=0)break;h=c[f+(j<<6)+4>>2]|0;e=c[h+(d<<2)>>2]|0;c[h+(c[(c[e+16>>2]|0)+236>>2]<<2)>>2]=e;e=d;f=c[(c[k>>2]|0)+196>>2]|0}c[f+(j<<6)>>2]=i;c[(c[f+(j<<6)+4>>2]|0)+(i<<2)>>2]=0;d=c[k>>2]|0}j=j+1|0}return}function oj(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;o=l;l=l+48|0;n=o+32|0;m=o+8|0;d=o+24|0;k=o+16|0;j=o;e=b+16|0;f=c[e>>2]|0;a:do if((c[f+216>>2]|0)>=2){c[m>>2]=~~(+h[f+16>>3]-+h[f+88>>3]);c[m+4>>2]=~~+h[f+24>>3];c[n>>2]=c[m>>2];c[n+4>>2]=c[m+4>>2];pj(d,b,n);g=d;i=c[g+4>>2]|0;d=m;c[d>>2]=c[g>>2];c[d+4>>2]=i;d=c[e>>2]|0;if((c[d+184>>2]|0)>0){j=c[c[d+180>>2]>>2]|0;d=(c[d+236>>2]|0)+1|0;j=_A(a,c[((c[j>>2]&3|0)==2?j:j+-48|0)+40>>2]|0)|0;while(1){if(!j)break a;i=(c[j>>2]&3|0)==2?j:j+-48|0;e=i+48|0;f=c[((c[i>>2]&3|0)==3?i:e)+40>>2]|0;if((f|0)!=(b|0)?(fP(f)|0)==(b|0):0){f=c[((c[i>>2]&3|0)==3?i:e)+40>>2]|0;c[n>>2]=c[m>>2];c[n+4>>2]=c[m+4>>2];qj(k,a,f,n,d);f=k;e=c[f+4>>2]|0;g=m;c[g>>2]=c[f>>2];c[g+4>>2]=e;xh(i);g=i+-48|0;e=c[(c[((c[i>>2]&3|0)==2?i:g)+40>>2]|0)+16>>2]|0;f=c[e+172>>2]|0;if(!f)e=KK((c[e+176>>2]<<2)+8|0)|0;else e=MK(f,(c[e+176>>2]<<2)+8|0)|0;c[(c[(c[((c[i>>2]&3|0)==2?i:g)+40>>2]|0)+16>>2]|0)+172>>2]=e;p=c[(c[((c[i>>2]&3|0)==2?i:g)+40>>2]|0)+16>>2]|0;e=c[p+172>>2]|0;p=p+176|0;f=c[p>>2]|0;c[p>>2]=f+1;c[e+(f<<2)>>2]=i;i=c[(c[((c[i>>2]&3|0)==2?i:g)+40>>2]|0)+16>>2]|0;c[(c[i+172>>2]|0)+(c[i+176>>2]<<2)>>2]=0;d=d+1|0}j=$A(a,j)|0}}else{i=c[c[d+172>>2]>>2]|0;d=(c[d+236>>2]|0)+1|0;i=XA(a,c[((c[i>>2]&3|0)==3?i:i+48|0)+40>>2]|0)|0;while(1){if(!i)break a;e=i+-48|0;f=c[((c[i>>2]&3|0)==2?i:e)+40>>2]|0;if((f|0)!=(b|0)?(fP(f)|0)==(b|0):0){f=c[((c[i>>2]&3|0)==2?i:e)+40>>2]|0;c[n>>2]=c[m>>2];c[n+4>>2]=c[m+4>>2];qj(j,a,f,n,d);f=j;e=c[f+4>>2]|0;g=m;c[g>>2]=c[f>>2];c[g+4>>2]=e;xh(i);g=i+48|0;e=c[(c[((c[i>>2]&3|0)==3?i:g)+40>>2]|0)+16>>2]|0;f=c[e+180>>2]|0;if(!f)e=KK((c[e+184>>2]<<2)+8|0)|0;else e=MK(f,(c[e+184>>2]<<2)+8|0)|0;c[(c[(c[((c[i>>2]&3|0)==3?i:g)+40>>2]|0)+16>>2]|0)+180>>2]=e;f=c[(c[((c[i>>2]&3|0)==3?i:g)+40>>2]|0)+16>>2]|0;k=c[f+180>>2]|0;f=f+184|0;p=c[f>>2]|0;c[f>>2]=p+1;c[k+(p<<2)>>2]=i;p=c[(c[((c[i>>2]&3|0)==3?i:g)+40>>2]|0)+16>>2]|0;c[(c[p+180>>2]|0)+(c[p+184>>2]<<2)>>2]=0;d=d+1|0}i=ZA(a,i)|0}}}while(0);l=o;return}function pj(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0;qQ(b,c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&1);e=c[b+16>>2]|0;h[e+24>>3]=+(c[d+4>>2]|0);f=+h[e+88>>3]+ +(c[d>>2]|0);h[e+16>>3]=f;f=f+ +h[e+96>>3];c[d>>2]=~~(f+ +(c[(c[(xC(b)|0)+16>>2]|0)+248>>2]|0));e=d;b=c[e+4>>2]|0;d=a;c[d>>2]=c[e>>2];c[d+4>>2]=b;return}function qj(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;i=l;l=l+16|0;h=i;b=gg(b)|0;g=fP(d)|0;if((g|0)!=(d|0))ph(g,d);j=c[d+16>>2]|0;c[j+236>>2]=f;g=c[(c[g+16>>2]|0)+232>>2]|0;c[j+232>>2]=g;c[(c[(c[(c[b+16>>2]|0)+196>>2]|0)+(g<<6)+4>>2]|0)+(f<<2)>>2]=d;c[h>>2]=c[e>>2];c[h+4>>2]=c[e+4>>2];pj(a,d,h);l=i;return}function rj(a){a=a|0;var d=0.0,e=0.0,f=0,g=0,i=0,j=0.0,k=0,l=0,m=0,n=0.0;m=c[(c[(gg(a)|0)+16>>2]|0)+196>>2]|0;if((gg(a)|0)==(a|0))j=8.0;else j=+(ZO(a,c[47144]|0,8,0)|0);l=a+16|0;g=c[l>>2]|0;f=0;k=1;e=+h[g+120>>3];d=+h[g+128>>3];while(1){if((k|0)>(c[g+180>>2]|0))break;i=c[(c[g+184>>2]|0)+(k<<2)>>2]|0;f=rj(i)|0|f;i=c[i+16>>2]|0;g=c[l>>2]|0;if((b[i+238>>1]|0)==(b[g+238>>1]|0)){n=+h[i+120>>3]+j;e=e>n?e:n}if((b[i+236>>1]|0)==(b[g+236>>1]|0)){n=+h[i+128>>3]+j;d=d>n?d:n}k=k+1|0}if((gg(a)|0)!=(a|0)?(c[(c[l>>2]|0)+12>>2]|0)!=0:0)if(!(c[(c[(yC(a)|0)+16>>2]|0)+116>>2]&1)){k=c[l>>2]|0;f=1;e=+h[k+56>>3]+e;d=+h[k+88>>3]+d}else f=1;k=c[l>>2]|0;h[k+120>>3]=e;h[k+128>>3]=d;if((gg(a)|0)!=(a|0)){l=c[l>>2]|0;a=m+(b[l+236>>1]<<6)+24|0;n=+h[a>>3];h[a>>3]=n>d?n:d;m=m+(b[l+238>>1]<<6)+16|0;n=+h[m>>3];h[m>>3]=n>e?n:e}return f|0}function sj(a,d){a=a|0;d=d|0;var e=0,f=0.0,g=0.0,i=0,j=0,k=0.0,l=0,m=0,n=0,o=0,p=0.0,q=0.0;o=c[(c[(gg(a)|0)+16>>2]|0)+196>>2]|0;if((gg(a)|0)==(a|0))e=0;else e=ZO(a,c[47144]|0,8,0)|0;l=a+16|0;i=c[l>>2]|0;m=e+d|0;k=+(e|0);j=1;f=+h[i+128>>3];g=+h[i+120>>3];e=i;while(1){if((j|0)>(c[e+180>>2]|0))break;i=c[(c[e+184>>2]|0)+(j<<2)>>2]|0;sj(i,m);i=c[i+16>>2]|0;e=c[l>>2]|0;if((b[i+238>>1]|0)==(b[e+238>>1]|0)){q=+h[i+120>>3]+k;g=g>q?g:q}if((b[i+236>>1]|0)==(b[e+236>>1]|0)){q=+h[i+128>>3]+k;f=f>q?f:q}j=j+1|0}h[e+120>>3]=g;h[e+128>>3]=f;if(((gg(a)|0)!=(a|0)?(n=c[l>>2]|0,c[n+12>>2]|0):0)?(p=-g-f+ +h[n+48+((+h[n+104>>3]>+h[n+72>>3]?3:1)<<4)+8>>3]-+h[(c[(c[c[o+(b[n+236>>1]<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]+ +h[(c[(c[c[o+(b[n+238>>1]<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3],p>0.0):0)tj(a,~~p,d);if((gg(a)|0)!=(a|0)){n=c[l>>2]|0;m=o+(b[n+236>>1]<<6)+24|0;q=+h[m>>3];p=+h[n+128>>3];h[m>>3]=q>p?q:p;o=o+(b[n+238>>1]<<6)+16|0;p=+h[o>>3];q=+h[n+120>>3];h[o>>3]=p>q?p:q}return}function tj(a,d,e){a=a|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0.0,k=0,l=0,m=0,n=0.0,o=0.0,p=0,q=0,r=0;k=c[(gg(a)|0)+16>>2]|0;q=c[k+196>>2]|0;p=c[a+16>>2]|0;a=b[p+238>>1]|0;f=b[p+236>>1]|0;l=(d+1|0)/2|0;m=p+120|0;n=+h[m>>3];o=+(l|0);i=+(e|0);e=~~(o+i+n-+h[q+(a<<6)+16>>3]);if((e|0)>0){g=+(e|0);while(1){if((a|0)<(f|0))break;if((c[q+(a<<6)>>2]|0)>0){r=(c[(c[c[q+(a<<6)+4>>2]>>2]|0)+16>>2]|0)+24|0;h[r>>3]=+h[r>>3]+g}a=a+-1|0}j=+h[p+128>>3];a=e;g=j+ +(d-l|0)}else{j=+h[p+128>>3];a=d-l|0;g=j}a=~~(+(a|0)+i+g-+h[q+(f<<6)+24>>3]);a:do if((a|0)>0){e=b[k+236>>1]|0;g=+(a|0);while(1){a=f+-1|0;if((f|0)<=(e|0))break a;if((c[q+(a<<6)>>2]|0)<=0){f=a;continue}f=(c[(c[c[q+(a<<6)+4>>2]>>2]|0)+16>>2]|0)+24|0;h[f>>3]=+h[f>>3]+g;f=a}}while(0);h[p+128>>3]=j+ +(d-l|0);h[m>>3]=n+o;return}function uj(b,d){b=b|0;d=d|0;var e=0,f=0;e=c[b+16>>2]|0;f=a[e+84>>0]|0;b=c[d+16>>2]|0;do if(f<<24>>24==(a[b+84>>0]|0)){if(+h[e+56>>3]==+h[b+56>>3]){if(!(f<<24>>24==0?1:+h[e+64>>3]==+h[b+64>>3])){b=0;break}}else if(f<<24>>24){b=0;break}if(+h[e+16>>3]==+h[b+16>>3]?+h[e+24>>3]==+h[b+24>>3]:0){b=1;break}b=(a[e+44>>0]|0)==0&1}else b=0;while(0);return b|0}function vj(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=a+16|0;d=c[i>>2]|0;b[d+236>>1]=32767;b[d+238>>1]=-1;d=0;h=jC(a)|0;while(1){e=c[i>>2]|0;if(!h)break;f=e+238|0;g=c[(c[h+16>>2]|0)+232>>2]|0;if((g|0)>(b[f>>1]|0))b[f>>1]=g;e=e+236|0;if((g|0)<(b[e>>1]|0))b[e>>1]=g;if(!d)d=h;else d=(g|0)<(c[(c[d+16>>2]|0)+232>>2]|0)?h:d;h=kC(a,h)|0}c[e+264>>2]=d;return}function wj(a){a=a|0;var b=0,d=0,e=0,f=0,g=0.0;b=QA(a,86974)|0;if(!b)b=2147483647;else{g=+r5(b);b=~~(g*+(tB(a)|0))}e=a+16|0;d=0;while(1){f=c[e>>2]|0;if((d|0)>=(c[f+220>>2]|0))break;c[f+192>>2]=c[(c[f+216>>2]|0)+(d<<2)>>2];uL(a,(c[f+180>>2]|0)==0&1,b)|0;d=d+1|0}return}function xj(a,d){a=a|0;d=d|0;var e=0,f=0,g=0;f=l;l=l+16|0;e=f;if(!(QA(a,86983)|0))zj(a,d);else{g=(c[a+16>>2]|0)+136|0;b[g>>1]=b[g>>1]|16;yj(a,d)}if(0){g=c[15712]|0;a=c[a+16>>2]|0;d=b[a+236>>1]|0;c[e>>2]=b[a+238>>1];c[e+4>>2]=d;w4(g,86991,e)|0}l=f;return}function yj(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0.0;i=l;l=l+16|0;h=i;c[46747]=0;a[h>>0]=a[18588]|0;a[h+1>>0]=a[18589]|0;a[h+2>>0]=a[18590]|0;a[h+3>>0]=a[18591]|0;h=oB(87201,h,0)|0;TC(h,87230,280,1)|0;FC(h,17404,17344);Aj(b);e=QA(b,86974)|0;if(!e)f=2147483647;else{j=+r5(e);f=~~(j*+(tB(b)|0))}Qj(b,0);Rj(b,h);Sj(b,h);Tj(b,h,0,0);Uj(h);g=Vj(h)|0;Wj(h);if(d|0){Kk(h);yk(h)}e=QA(b,102586)|0;if(!e)e=-1;else e=q5(e)|0;NK(h,1,f,e)|0;Xj(b,h,g);sB(h)|0;l=i;return}function zj(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;g=l;l=l+32|0;f=g+16|0;e=g+8|0;d=g;Aj(a);do if(!b){Bj(a,a);Nk(a);Cj(d,a);h=d;d=c[h+4>>2]|0;b=e;c[b>>2]=c[h>>2];c[b+4>>2]=d;Gl(a,0);b=0}else{Kk(a);yk(a);Bj(a,a);Nk(a);Cj(d,a);i=d;d=c[i+4>>2]|0;h=e;c[h>>2]=c[i>>2];c[h+4>>2]=d;Gl(a,0);h=c[a+16>>2]|0;if((c[h+220>>2]|0)<=1?(c[h+180>>2]|0)<=0:0)break;c[b+32>>2]=1;b=0}while(0);vk(a);c[f>>2]=c[e>>2];c[f+4>>2]=c[e+4>>2];if(Dj(a,f)|0)Gl(a,0);if(!b)wj(a);else zk(a,b);Ej(a,b);Fj(a);l=g;return}function Aj(d){d=d|0;var f=0,g=0,h=0;if(a[(c[(c[d+60>>2]|0)+16>>2]|0)+113>>0]&1){g=jC(d)|0;while(1){if(!g)break;f=XA(d,g)|0;while(1){if(!f)break;h=(c[f+16>>2]|0)+170|0;b[h>>1]=(e[h>>1]|0)<<1;f=ZA(d,f)|0}g=kC(d,g)|0}h=(c[d+16>>2]|0)+252|0;c[h>>2]=((c[h>>2]|0)+1|0)/2|0}return}function Bj(a,b){a=a|0;b=b|0;var d=0;b=OD(b)|0;while(1){if(!b)break;d=Nj(b)|0;do if(d)if((d|0)==7&(c[47135]|0)==100){Jj(a,b);break}else{Oj(a,b,d);break}else Bj(a,b);while(0);b=PD(b)|0}return}function Cj(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;i=d+16|0;e=c[i>>2]|0;d=c[e+228>>2]|0;e=c[e+224>>2]|0;f=(e|0)==0;if(!d)if(f){j=0;k=0}else g=4;else if(f)g=5;else g=4;if((g|0)==4){d=fP(e)|0;e=c[i>>2]|0;c[e+224>>2]=d;d=c[e+228>>2]|0;if(!d){h=0;d=e;g=12}else g=5}do if((g|0)==5){d=fP(d)|0;e=c[i>>2]|0;c[e+228>>2]=d;if(d){e=d+16|0;d=c[e>>2]|0;f=(a[d+159>>0]|0)==5&1;while(1){d=c[c[d+180>>2]>>2]|0;if(!d){g=11;break}h=c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0;if((h|0)!=(fP(h)|0)){g=9;break}uk(d);d=c[e>>2]|0}if((g|0)==9)qa(87094,87034,346,87126);else if((g|0)==11){h=f;d=c[i>>2]|0;g=12;break}}else{h=0;d=e;g=12}}while(0);a:do if((g|0)==12){d=c[d+224>>2]|0;if(!d){j=0;k=h}else{e=d+16|0;d=c[e>>2]|0;f=(a[d+159>>0]|0)==3&1;while(1){d=c[c[d+172>>2]>>2]|0;if(!d){j=f;k=h;break a}i=c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0;if((i|0)!=(fP(i)|0))break;uk(d);d=c[e>>2]|0}qa(87139,87034,353,87126)}}while(0);c[b>>2]=j;c[b+4>>2]=k;return}function Dj(a,d){a=a|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;j=a+16|0;i=c[j>>2]|0;if((c[i+228>>2]|0)==0?(c[i+224>>2]|0)==0:0)d=0;else e=3;a:do if((e|0)==3){g=jC(a)|0;i=d+4|0;h=c[d>>2]&65535;d=0;while(1){if(!g)break a;if((g|0)==(fP(g)|0)){f=g+16|0;e=c[f>>2]|0;if((c[e+184>>2]|0)==0?(k=c[(c[j>>2]|0)+228>>2]|0,!((k|0)==0|(g|0)==(k|0))):0){d=nh(g,k,0)|0;e=c[d+16>>2]|0;b[e+170>>1]=c[i>>2];c[e+156>>2]=0;e=c[f>>2]|0}if((c[e+176>>2]|0)==0?(l=c[(c[j>>2]|0)+224>>2]|0,!((l|0)==0|(g|0)==(l|0))):0){d=nh(l,g,0)|0;f=c[d+16>>2]|0;b[f+170>>1]=h;c[f+156>>2]=0}}g=kC(a,g)|0}}while(0);return (d|0)!=0|0}function Ej(d,e){d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;g=jC(d)|0;k=d+16|0;f=c[k>>2]|0;a:do if(g){b[f+236>>1]=32767;b[f+238>>1]=-1;j=(e|0)==0;while(1){if(!g)break;f=fP(g)|0;i=c[g+16>>2]|0;e=c[i+232>>2]|0;do if((f|0)!=(g|0)){if(!j)if(!e)e=0;else break;e=e+(c[(c[f+16>>2]|0)+232>>2]|0)|0;c[i+232>>2]=e}while(0);f=c[k>>2]|0;h=f+238|0;if((e|0)>(b[h>>1]|0))b[h>>1]=e;f=f+236|0;if((e|0)<(b[f>>1]|0))b[f>>1]=e;switch(a[i+159>>0]|0){case 6:case 0:break;default:hP(g)}g=kC(d,g)|0}if((gg(d)|0)==(d|0)){if((c[47135]|0)==100)e=1;else{Ij(d);break}while(1){f=c[k>>2]|0;if((e|0)>(c[f+180>>2]|0))break a;Hj(c[(c[f+184>>2]|0)+(e<<2)>>2]|0);e=e+1|0}}}else{b[f+238>>1]=0;b[f+236>>1]=0}while(0);return}function Fj(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;j=b+16|0;e=0;while(1){d=c[j>>2]|0;if((e|0)>=(c[d+220>>2]|0))break;i=c[(c[d+216>>2]|0)+(e<<2)>>2]|0;c[d+192>>2]=i;d=i;while(1){if(!d)break;i=d+16|0;Gj((c[i>>2]|0)+172|0);Gj((c[i>>2]|0)+180|0);i=c[i>>2]|0;a[i+157>>0]=0;d=c[i+164>>2]|0}e=e+1|0}i=jC(b)|0;while(1){if(!i)break;h=XA(b,i)|0;while(1){if(!h)break;f=h+16|0;d=c[f>>2]|0;g=c[d+172>>2]|0;if((g|0)!=0?(k=g+16|0,(h|0)==(c[(c[k>>2]|0)+116>>2]|0)):0){d=jC(b)|0;while(1){if(!d)break;e=XA(b,d)|0;while(1){if(!e)break;if((h|0)!=(e|0)?(l=(c[e+16>>2]|0)+172|0,m=c[l>>2]|0,(m|0)!=0&(g|0)==(m|0)):0)c[l>>2]=0;e=ZA(b,e)|0}d=kC(b,d)|0}U1(c[k>>2]|0);U1(g);d=c[f>>2]|0}c[d+172>>2]=0;h=ZA(b,h)|0}i=kC(b,i)|0}U1(c[(c[j>>2]|0)+216>>2]|0);m=c[j>>2]|0;c[m+216>>2]=0;c[m+220>>2]=0;return}function Gj(a){a=a|0;var b=0,d=0;d=a+4|0;b=c[d>>2]|0;while(1){if((b|0)<=-1)break;c[(c[a>>2]|0)+(b<<2)>>2]=0;b=b+-1|0}c[d>>2]=0;return}function Hj(a){a=a|0;var d=0,f=0,g=0;d=a+16|0;f=c[d>>2]|0;g=c[(c[(c[f+264>>2]|0)+16>>2]|0)+232>>2]|0;a=f+236|0;b[a>>1]=g+(e[a>>1]|0);a=f+238|0;b[a>>1]=g+(e[a>>1]|0);a=1;while(1){if((a|0)>(c[f+180>>2]|0))break;Hj(c[(c[f+184>>2]|0)+(a<<2)>>2]|0);a=a+1|0;f=c[d>>2]|0}return}function Ij(b){b=b|0;var d=0;d=OD(gg(b)|0)|0;while(1){if(!d)break;if((a[(c[d+16>>2]|0)+274>>0]|0)==7)Jj(b,d);d=PD(d)|0}return}function Jj(a,b){a=a|0;b=b|0;var d=0;d=(c[b+16>>2]|0)+200|0;do if((c[d>>2]|0)==0?(c[d>>2]=a,Kj(a,b),jC(b)|0):0){Lj(a,b);if((c[47135]|0)==100){zj(b,0);Mj(b);break}else{vj(b);break}}while(0);return}function Kj(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;h=b+16|0;g=jC(d)|0;while(1){if(!g)break;i=kC(d,g)|0;j=g+16|0;if(!(a[(c[j>>2]|0)+159>>0]|0))f=1;else{wC(d,g)|0;g=i;continue}while(1){e=c[h>>2]|0;b=c[e+180>>2]|0;if((f|0)>=(b|0))break;if(HC(c[(c[e+184>>2]|0)+(f<<2)>>2]|0,g)|0){k=7;break}f=f+1|0}if((k|0)==7){k=0;b=c[(c[h>>2]|0)+180>>2]|0}if((f|0)<(b|0))wC(d,g)|0;c[(c[j>>2]|0)+212>>2]=0;g=i}e=jC(d)|0;while(1){if(!e)break;b=XA(gg(d)|0,e)|0;while(1){if(!b)break;if(HC(d,c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0)mB(d,b,1)|0;b=ZA(gg(d)|0,b)|0}e=kC(d,e)|0}return}function Lj(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;e=a+16|0;d=c[e>>2]|0;g=d+180|0;a=c[g>>2]|0;f=a+1|0;c[g>>2]=f;d=c[d+184>>2]|0;if(!d)a=JK((a<<2)+8|0)|0;else a=LK(d,a+2|0,4,f)|0;c[(c[e>>2]|0)+184>>2]=a;c[a+(f<<2)>>2]=b;uK(b);return}function Mj(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;g=c[b+16>>2]|0;d=g+192|0;h=0;while(1){d=c[d>>2]|0;if(!d)break;f=c[d+16>>2]|0;if(!(c[f+232>>2]|0))e=(a[f+156>>0]|0)==0?d:h;else e=h;d=f+164|0;h=e}if(!h)qa(87019,87034,238,87041);c[g+264>>2]=h;d=jC(b)|0;while(1){if(!d){d=13;break}e=d+16|0;if(!((d|0)==(h|0)?1:(c[(c[e>>2]|0)+216>>2]|0)<2)){d=11;break}gP(d,h)|0;a[(c[e>>2]|0)+159>>0]=7;d=kC(b,d)|0}if((d|0)==11)qa(87056,87034,242,87041);else if((d|0)==13)return}function Nj(b){b=b|0;var d=0;if(!(Pj(b)|0)){d=pP(QA(b,87171)|0,17356,17380)|0;a[(c[b+16>>2]|0)+274>>0]=d;b=d}else b=7;return b|0}function Oj(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;g=jC(d)|0;a:do if(g|0){j=e&255;i=g+16|0;f=g;h=j;while(1){a[(c[f+16>>2]|0)+159>>0]=h;f=kC(d,f)|0;if(!f)break;gP(g,f)|0;h=a[(c[i>>2]|0)+159>>0]|0}switch(e|0){case 3:case 2:{b=b+16|0;f=c[b>>2]|0;h=c[f+224>>2]|0;if(h){g=gP(h,g)|0;f=c[b>>2]|0}c[f+224>>2]=g;break}case 5:case 4:{b=b+16|0;f=c[b>>2]|0;h=c[f+228>>2]|0;if(h){g=gP(h,g)|0;f=c[b>>2]|0}c[f+228>>2]=g;break}default:break a}switch(e|0){case 3:{f=f+224|0;break}case 5:{f=f+228|0;break}default:break a}a[(c[(c[f>>2]|0)+16>>2]|0)+159>>0]=j}while(0);return}function Pj(a){a=a|0;return (Q2(KB(a)|0,108006,7)|0)==0|0}function Qj(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+16|0;g=m;if(!(jk(b)|0)){do if(FP(b)|0)if(!d){c[(c[b+16>>2]|0)+204>>2]=0;d=b;break}else{c[(c[b+16>>2]|0)+204>>2]=(c[(c[d+16>>2]|0)+204>>2]|0)+1;kk(b,d);d=b;break}while(0);e=OD(b)|0;while(1){if(!e)break;Qj(e,d);e=PD(e)|0}a:do if(FP(b)|0){e=jC(b)|0;while(1){if(!e)break a;f=(c[e+16>>2]|0)+212|0;if(!(c[f>>2]|0))c[f>>2]=b;e=kC(b,e)|0}}while(0);switch(lk(b)|0){case 3:{d=d+16|0;a[(c[d>>2]|0)+241>>0]=1;k=19;break}case 2:{d=d+16|0;k=19;break}case 5:{d=d+16|0;a[(c[d>>2]|0)+242>>0]=1;k=21;break}case 4:{d=d+16|0;k=21;break}case 1:{mk(b)|0;break}case 6:break;default:{e=KB(b)|0;f=QA(b,87171)|0;c[g>>2]=e;c[g+4>>2]=f;gA(0,87354,g)|0}}if((k|0)==19){k=mk(b)|0;k=nk(k,c[(c[d>>2]|0)+208>>2]|0)|0;c[(c[d>>2]|0)+208>>2]=k}else if((k|0)==21){k=mk(b)|0;k=nk(k,c[(c[d>>2]|0)+212>>2]|0)|0;c[(c[d>>2]|0)+212>>2]=k}if((FP(b)|0?(h=b+16|0,i=c[h>>2]|0,j=c[i+208>>2]|0,j|0):0)?(j|0)==(c[i+212>>2]|0):0){j=mk(b)|0;k=c[h>>2]|0;c[k+208>>2]=j;c[k+212>>2]=j}}l=m;return}function Rj(a,b){a=a|0;b=b|0;var d=0,e=0;c[46747]=0;d=jC(a)|0;while(1){if(!d)break;if((Yj(d)|0)==(d|0)){e=$j(b,KB(d)|0)|0;c[(c[d+16>>2]|0)+148>>2]=e}d=kC(a,d)|0}d=jC(a)|0;while(1){if(!d)break;b=d+16|0;if(!(c[(c[b>>2]|0)+148>>2]|0)){e=c[(c[(Yj(d)|0)+16>>2]|0)+148>>2]|0;c[(c[b>>2]|0)+148>>2]=e}d=kC(a,d)|0}return}function Sj(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;h=jC(a)|0;while(1){if(!h)break;d=c[(c[h+16>>2]|0)+148>>2]|0;i=XA(a,h)|0;while(1){if(!i)break;do if((ek(i)|0)==0?(j=i+-48|0,k=c[(c[(Yj(c[((c[i>>2]&3|0)==2?i:j)+40>>2]|0)|0)+16>>2]|0)+148>>2]|0,(d|0)!=(k|0)):0){f=c[i>>2]&3;g=i+48|0;e=((f|0)==3?i:g)+40|0;f=c[(c[(c[((f|0)==2?i:j)+40>>2]|0)+16>>2]|0)+212>>2]|0;if(!(fk(i)|0)){if((dk(c[(c[(c[e>>2]|0)+16>>2]|0)+212>>2]|0)|0)==0?(dk(f)|0)==0:0){gk(b,d,k,i);break}hk(b,d,k,i);break}else{f=Yj(c[e>>2]|0)|0;e=c[i>>2]&3;if((f|0)!=(c[(c[(c[(c[(c[((e|0)==3?i:g)+40>>2]|0)+16>>2]|0)+212>>2]|0)+16>>2]|0)+212>>2]|0)?(g=Yj(c[((e|0)==2?i:j)+40>>2]|0)|0,(g|0)!=(c[(c[(c[(c[(c[((c[i>>2]&3|0)==2?i:j)+40>>2]|0)+16>>2]|0)+212>>2]|0)+16>>2]|0)+208>>2]|0)):0)e=k;else{e=d;d=k}gk(b,d,e,i);break}}while(0);i=ZA(a,i)|0}h=kC(a,h)|0}return}function Tj(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;if((FP(a)|0)!=0?(dk(a)|0)!=0:0){g=jC(a)|0;while(1){if(!g)break;if(!(_A(a,g)|0)){f=c[(c[(Yj(g)|0)+16>>2]|0)+148>>2]|0;if(!d)d=$j(b,87252)|0;iB(b,d,f,0,1)|0}if(!(XA(a,g)|0)){f=c[(c[(Yj(g)|0)+16>>2]|0)+148>>2]|0;if(!e)e=$j(b,87257)|0;iB(b,f,e,0,1)|0}g=kC(a,g)|0}if((e|0)!=0&(d|0)!=0){ck(iB(b,d,e,0,1)|0,0,1e3);f=d}else f=d}else f=d;d=OD(a)|0;while(1){if(!d)break;Tj(d,b,f,e);d=PD(d)|0}return}function Uj(b){b=b|0;var d=0,e=0;d=jC(b)|0;while(1){if(!d)break;e=c[d+16>>2]|0;a[e+158>>0]=0;a[e+157>>0]=0;d=kC(b,d)|0}d=jC(b)|0;while(1){if(!d)break;ak(b,d);d=kC(b,d)|0}return}function Vj(a){a=a|0;var b=0,d=0,e=0,f=0;b=jC(a)|0;while(1){if(!b)break;c[(c[b+16>>2]|0)+128>>2]=0;b=kC(a,b)|0}b=0;e=jC(a)|0;while(1){if(!e)break;d=b+1|0;if(!(c[(c[e+16>>2]|0)+128>>2]|0)){_j(a,e,d);b=d}e=kC(a,e)|0}a:do if((b|0)>1){f=$j(a,87246)|0;d=1;e=jC(a)|0;while(1){if(!e)break a;if((c[(c[e+16>>2]|0)+128>>2]|0)==(d|0)){iB(a,f,e,0,1)|0;d=d+1|0}e=kC(a,e)|0}}while(0);return b|0}function Wj(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0;g=jC(a)|0;while(1){if(!g)break;h=g+16|0;f=XA(a,g)|0;while(1){if(!f)break;b=c[h>>2]|0;d=c[b+180>>2]|0;if(!d)b=KK((c[b+184>>2]<<2)+8|0)|0;else b=MK(d,(c[b+184>>2]<<2)+8|0)|0;d=c[h>>2]|0;c[d+180>>2]=b;d=d+184|0;e=c[d>>2]|0;c[d>>2]=e+1;c[b+(e<<2)>>2]=f;e=c[h>>2]|0;c[(c[e+180>>2]|0)+(c[e+184>>2]<<2)>>2]=0;e=f+-48|0;b=c[(c[((c[f>>2]&3|0)==2?f:e)+40>>2]|0)+16>>2]|0;d=c[b+172>>2]|0;if(!d)b=KK((c[b+176>>2]<<2)+8|0)|0;else b=MK(d,(c[b+176>>2]<<2)+8|0)|0;c[(c[(c[((c[f>>2]&3|0)==2?f:e)+40>>2]|0)+16>>2]|0)+172>>2]=b;i=c[(c[((c[f>>2]&3|0)==2?f:e)+40>>2]|0)+16>>2]|0;b=c[i+172>>2]|0;i=i+176|0;d=c[i>>2]|0;c[i>>2]=d+1;c[b+(d<<2)>>2]=f;e=c[(c[((c[f>>2]&3|0)==2?f:e)+40>>2]|0)+16>>2]|0;c[(c[e+172>>2]|0)+(c[e+176>>2]<<2)>>2]=0;f=ZA(a,f)|0}g=kC(a,g)|0}return}function Xj(a,d,f){a=a|0;d=d|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;n=a+16|0;o=c[n>>2]|0;b[o+236>>1]=32767;b[o+238>>1]=-1;a:do if((f|0)>1){h=JK((f<<2)+4|0)|0;g=f+1|0;f=1;while(1){if((f|0)==(g|0)){m=h;o=h;break a}c[h+(f<<2)>>2]=32767;f=f+1|0}}else{m=0;o=0}while(0);i=(m|0)==0;h=jC(a)|0;while(1){if(!h)break;j=c[(c[(c[(Yj(h)|0)+16>>2]|0)+148>>2]|0)+16>>2]|0;k=c[j+232>>2]|0;l=c[h+16>>2]|0;c[l+232>>2]=k;f=c[n>>2]|0;g=f+238|0;if((k|0)>(b[g>>1]|0))b[g>>1]=k;f=f+236|0;if((k|0)<(b[f>>1]|0))b[f>>1]=k;if(!i){j=c[j+128>>2]|0;c[l+128>>2]=j;l=m+(j<<2)|0;j=c[l>>2]|0;c[l>>2]=(j|0)<(k|0)?j:k}h=kC(a,h)|0}j=(m|0)!=0;b:do if(!j){m=b[(c[n>>2]|0)+236>>1]|0;g=m<<16>>16;if(m<<16>>16>0){f=jC(a)|0;while(1){if(!f)break;m=(c[f+16>>2]|0)+232|0;c[m>>2]=(c[m>>2]|0)-g;f=kC(a,f)|0}f=c[n>>2]|0;n=f+236|0;b[n>>1]=(e[n>>1]|0)-g;f=f+238|0;b[f>>1]=(e[f>>1]|0)-g;f=0}else f=0}else{f=jC(a)|0;while(1){if(!f){f=1;break b}l=c[f+16>>2]|0;n=l+232|0;c[n>>2]=(c[n>>2]|0)-(c[m+(c[l+128>>2]<<2)>>2]|0);f=kC(a,f)|0}}while(0);Zj(a,f);i=jC(d)|0;while(1){if(!i)break;g=i+16|0;f=c[g>>2]|0;h=c[f+172>>2]|0;if(h){U1(h);f=c[g>>2]|0}f=c[f+180>>2]|0;if(f|0)U1(f);i=kC(d,i)|0}U1(c[(c[(jC(a)|0)+16>>2]|0)+112>>2]|0);f=jC(a)|0;while(1){if(!f)break;c[(c[f+16>>2]|0)+112>>2]=0;f=kC(a,f)|0}if(j)U1(o);return}function Yj(a){a=a|0;var b=0,d=0,e=0;b=a+16|0;d=(c[b>>2]|0)+152|0;e=c[d>>2]|0;if(e){if((e|0)!=(a|0)){e=Yj(e)|0;c[(c[b>>2]|0)+152>>2]=e;return e|0}}else c[d>>2]=a;return a|0}function Zj(a,d){a=a|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=a+16|0;e=1;while(1){f=c[i>>2]|0;if((e|0)>(c[f+180>>2]|0))break;Zj(c[(c[f+184>>2]|0)+(e<<2)>>2]|0,0);e=e+1|0}if((d|0)!=0|(c[f+200>>2]|0)!=0){b[f+236>>1]=32767;b[f+238>>1]=-1;e=0;h=jC(a)|0;while(1){if(!h)break;g=c[(c[h+16>>2]|0)+232>>2]|0;d=c[i>>2]|0;f=d+238|0;if((g|0)>(b[f>>1]|0))b[f>>1]=g;d=d+236|0;if((g|0)<(b[d>>1]|0)){b[d>>1]=g;e=h}h=kC(a,h)|0}c[(c[i>>2]|0)+264>>2]=e}return}function _j(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=(c[b+16>>2]|0)+128|0;a:do if(!(c[e>>2]|0)){c[e>>2]=d;e=XA(a,b)|0;while(1){if(!e)break;_j(a,c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0,d);e=ZA(a,e)|0}e=_A(a,b)|0;while(1){if(!e)break a;_j(a,c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0,d);e=$A(a,e)|0}}while(0);return}function $j(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=tC(a,b,1)|0;b=f+16|0;c[(c[b>>2]|0)+176>>2]=0;e=JK(20)|0;d=c[b>>2]|0;c[d+172>>2]=e;c[d+184>>2]=0;d=JK(20)|0;b=c[b>>2]|0;c[b+180>>2]=d;d=c[46747]|0;e=b+168|0;if(!d){c[e>>2]=0;c[(c[a+16>>2]|0)+192>>2]=f}else{c[e>>2]=d;c[(c[d+16>>2]|0)+164>>2]=f}c[46747]=f;c[b+164>>2]=0;return f|0}function ak(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0;h=d+16|0;e=c[h>>2]|0;f=e+157|0;if(!(a[f>>0]|0)){a[f>>0]=1;a[e+158>>0]=1;d=XA(b,d)|0;while(1){if(!d)break;e=ZA(b,d)|0;f=c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0;g=c[f+16>>2]|0;if(a[g+158>>0]|0){bk(b,d);d=e;continue}if(a[g+157>>0]|0){d=e;continue}ak(b,f);d=e}a[(c[h>>2]|0)+158>>0]=0}return}function bk(a,b){a=a|0;b=b|0;var d=0,f=0,g=0;d=c[b>>2]&3;g=b+-48|0;f=b+48|0;d=iB(a,c[((d|0)==2?b:g)+40>>2]|0,c[((d|0)==3?b:f)+40>>2]|0,0,0)|0;if(!d){d=c[b>>2]&3;d=iB(a,c[((d|0)==2?b:g)+40>>2]|0,c[((d|0)==3?b:f)+40>>2]|0,0,1)|0}g=c[b+16>>2]|0;ck(d,e[g+170>>1]|0,c[g+156>>2]|0);wC(a,b)|0;return}function ck(a,d,f){a=a|0;d=d|0;f=f|0;var g=0,h=0;a=c[a+16>>2]|0;g=a+170|0;h=e[g>>1]|0;b[g>>1]=(h|0)>(d|0)?h:d;d=a+156|0;c[d>>2]=(c[d>>2]|0)+f;return}function dk(a){a=a|0;return (eP(QA(a,87262)|0,0)|0)&255|0}function ek(b){b=b|0;var d=0,e=0;d=c[47189]|0;if((((d|0)!=0?(e=RA(b,d)|0,(e|0)!=0):0)?(a[e>>0]|0)!=0:0)?(dP(e)|0)<<24>>24==0:0)b=1;else b=0;return b|0}function fk(a){a=a|0;var b=0,d=0;d=c[a>>2]&3;b=c[(c[(c[((d|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0;a=c[(c[(c[((d|0)==2?a:a+-48|0)+40>>2]|0)+16>>2]|0)+212>>2]|0;if((b|0)==(a|0))return 1;else{d=ik(b,a)|0;return ((d|0)==(b|0)|(d|0)==(a|0))&1|0}return 0}function gk(a,b,d,f){a=a|0;b=b|0;d=d|0;f=f|0;var g=0,h=0,i=0,j=0;j=l;l=l+16|0;h=j;g=iB(a,b,d,0,0)|0;if(!g){g=iB(a,d,b,0,0)|0;if(!g){g=iB(a,b,d,0,1)|0;if(!g){b=KB(b)|0;f=KB(d)|0;c[h>>2]=b;c[h+4>>2]=f;gA(1,87279,h)|0}else i=4}else i=4}else i=4;if((i|0)==4){i=c[f+16>>2]|0;ck(g,e[i+170>>1]|0,c[i+156>>2]|0)}l=j;return}function hk(a,d,f,g){a=a|0;d=d|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0;m=l;l=l+112|0;j=m;i=m+4|0;h=_A(a,d)|0;while(1){if(!h){k=6;break}n=XA(a,c[((c[h>>2]&3|0)==3?h:h+48|0)+40>>2]|0)|0;if(n|0?(c[((c[n>>2]&3|0)==2?n:n+-48|0)+40>>2]|0)==(f|0):0)break;h=$A(a,h)|0}if((k|0)==6){n=c[46748]|0;c[46748]=n+1;c[j>>2]=n;m4(i,87270,j)|0;i=$j(a,i)|0;n=iB(a,i,d,0,1)|0;i=iB(a,i,f,0,1)|0;f=c[g+16>>2]|0;k=f+156|0;n=(c[n+16>>2]|0)+156|0;c[n>>2]=(c[n>>2]|0)+((c[k>>2]|0)*1e3|0);n=c[i+16>>2]|0;j=n+170|0;b[j>>1]=b[(c[((e[j>>1]|0)>(e[f+170>>1]|0)?i:g)+16>>2]|0)+170>>1]|0;n=n+156|0;c[n>>2]=(c[n>>2]|0)+(c[k>>2]|0)}l=m;return}function ik(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;a:while(1){e=a+16|0;while(1){if((a|0)==(b|0))break a;f=c[e>>2]|0;d=c[b+16>>2]|0;if((c[f+204>>2]|0)>=(c[d+204>>2]|0))break;b=c[d+200>>2]|0}a=c[f+200>>2]|0}return a|0}function jk(a){a=a|0;return (jC(a)|0)==0|0}function kk(a,b){a=a|0;b=b|0;c[(c[a+16>>2]|0)+200>>2]=b;Lj(b,a);Kj(b,a);return}function lk(b){b=b|0;var c=0;b=QA(b,87171)|0;if((b|0)!=0?(a[b>>0]|0)!=0:0)if(s2(b,87181)|0)if(s2(b,87185)|0)if(s2(b,87192)|0)if(s2(b,87196)|0)if(!(s2(b,87176)|0))b=1;else c=8;else b=5;else b=4;else b=3;else b=2;else c=8;if((c|0)==8)b=6;return b|0}function mk(a){a=a|0;var b=0,c=0;c=jC(a)|0;a:do if(!c)b=0;else{b=Yj(c)|0;while(1){c=kC(a,c)|0;if(!c)break a;nk(b,c)|0}}while(0);return b|0}function nk(a,b){a=a|0;b=b|0;if(b){a=Yj(a)|0;c[(c[(Yj(b)|0)+16>>2]|0)+152>>2]=a}return a|0}function ok(a,b,d){a=a|0;b=b|0;d=d|0;TC(b,87230,c[d>>2]|0,1)|0;return}function pk(a,b,d){a=a|0;b=b|0;d=d|0;TC(b,87397,c[d+4>>2]|0,1)|0;return}function qk(a,b,d){a=a|0;b=b|0;d=d|0;TC(b,87382,c[d+8>>2]|0,1)|0;return}function rk(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+240|0;m=p+120|0;n=p;c[47193]=GA(b,2,87412,0)|0;k=GA(b,2,87421,0)|0;c[47194]=k;a:do if((k|0)!=0|(c[47193]|0)!=0){k=jC(b)|0;while(1){if(!k)break a;j=0;d=0;i=aB(b,k)|0;while(1){if(!i){f=0;break}e=c[i>>2]|0;g=e&3;f=c[((g|0)==2?i:i+-48|0)+40>>2]|0;h=i+48|0;b:do if((f|0)!=(c[((g|0)==3?i:h)+40>>2]|0)){g=c[47193]|0;do if((f|0)==(k|0)&(g|0)!=0){f=RA(i,g)|0;if(!(a[f>>0]|0)){e=c[i>>2]|0;break}else{e=j;d=sk(m,d,k,i,f)|0;break b}}while(0);f=c[47194]|0;if((f|0?(c[((e&3|0)==3?i:h)+40>>2]|0)==(k|0):0)?(o=RA(i,f)|0,(a[o>>0]|0)!=0):0)e=sk(n,j,k,i,o)|0;else e=j}else e=j;while(0);j=e;i=bB(b,i,k)|0}while(1){if((f|0)>=(d|0)){e=0;break}e=m+(f*24|0)+4|0;if((c[m+(f*24|0)+8>>2]|0)>1)tk(k,e);e=c[e>>2]|0;if(e|0)U1(e);f=f+1|0}while(1){if((e|0)>=(j|0))break;d=n+(e*24|0)+4|0;if((c[n+(e*24|0)+8>>2]|0)>1)tk(k,d);d=c[d>>2]|0;if(d|0)U1(d);e=e+1|0}k=kC(b,k)|0}}while(0);l=p;return}function sk(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;p=l;l=l+16|0;k=p;m=p+12|0;n=p+8|0;o=0;while(1){if((o|0)>=(d|0)){j=10;break}j=c[b+(o*24|0)>>2]|0;if((a[j>>0]|0)==(a[g>>0]|0)?(s2(j,g)|0)==0:0){j=5;break}o=o+1|0}do if((j|0)==5){k=b+(o*24|0)+4|0;j=c[k>>2]|0;if(!j){j=b+(o*24|0)+8|0;g=j;j=KK((c[j>>2]<<2)+8|0)|0}else{q=b+(o*24|0)+8|0;g=q;j=MK(j,(c[q>>2]<<2)+8|0)|0}c[k>>2]=j;r=c[g>>2]|0;q=r+1|0;c[g>>2]=q;c[j+(r<<2)>>2]=f;c[(c[k>>2]|0)+(q<<2)>>2]=0;j=13}else if((j|0)==10)if((d|0)>4){r=KB(e)|0;c[k>>2]=5;c[k+4>>2]=r;gA(1,87430,k)|0;break}else{s=b+(o*24|0)+8|0;c[s>>2]=0;k=JK(8)|0;r=b+(o*24|0)+4|0;c[r>>2]=k;q=c[s>>2]|0;j=q+1|0;c[s>>2]=j;c[k+(q<<2)>>2]=f;c[(c[r>>2]|0)+(j<<2)>>2]=0;c[b+(o*24|0)>>2]=g;c[b+(o*24|0)+12>>2]=0;h[b+(o*24|0)+16>>3]=0.0;d=d+1|0;j=13;break}while(0);if((j|0)==13){HH(f,m,n);j=(c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0)==(e|0)?c[n>>2]|0:c[m>>2]|0;if(j){r=b+(o*24|0)+12|0;s=c[r>>2]|0;c[r>>2]=s+1;if(!s)i=+LH(f,j);else i=0.0;h[b+(o*24|0)+16>>3]=i}}l=p;return d|0}function tk(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0.0,k=0.0,m=0.0,n=0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0.0;x=l;l=l+80|0;t=x+68|0;u=x+64|0;r=x;v=d+4|0;n=c[v>>2]|0;w=b+16|0;i=0;p=0.0;q=0.0;while(1){if((i|0)>=(n|0))break;f=c[(c[d>>2]|0)+(i<<2)>>2]|0;g=c[f>>2]&3;e=c[((g|0)==2?f:f+-48|0)+40>>2]|0;if((e|0)==(b|0))e=c[((g|0)==3?f:f+48|0)+40>>2]|0;g=c[e+16>>2]|0;s=c[w>>2]|0;k=+h[g+16>>3]-+h[s+16>>3];m=+h[g+24>>3]-+h[s+24>>3];o=+L2(k,m);i=i+1|0;p=k/o+p;q=m/o+q}j=+L2(p,q);s=c[w>>2]|0;k=+h[s+16>>3];m=+h[s+24>>3];y=+h[s+96>>3]+ +h[s+88>>3];o=+h[s+80>>3];s=y>o+ +(c[(c[(xC(b)|0)+16>>2]|0)+252>>2]|0);e=c[w>>2]|0;if(s)o=+h[e+96>>3]+ +h[e+88>>3];else{o=+h[e+80>>3];o=o+ +(c[(c[(xC(b)|0)+16>>2]|0)+252>>2]|0);e=c[w>>2]|0}y=+h[e+16>>3]+o*(p/j);j=+h[e+24>>3]+o*(q/j);h[r>>3]=k;n=r+8|0;h[n>>3]=m;h[r+16>>3]=(y+k*2.0)*.3333333333333333;h[r+24>>3]=(j+m*2.0)*.3333333333333333;h[r+32>>3]=(y*2.0+k)*.3333333333333333;h[r+40>>3]=(j*2.0+m)*.3333333333333333;h[r+48>>3]=y;h[r+56>>3]=j;aO(b,r);s=c[w>>2]|0;j=+h[r>>3]-+h[s+16>>3];k=+h[n>>3]-+h[s+24>>3];j=+(~~(j+(j>=0.0?.5:-.5))|0);k=+(~~(k+(k>=0.0?.5:-.5))|0);y=+h[s+88>>3];s=~~((y+j)*256.0/(+h[s+96>>3]+y))&255;r=0;while(1){if((r|0)>=(c[v>>2]|0))break;n=c[(c[d>>2]|0)+(r<<2)>>2]|0;HH(n,t,u);while(1){if(!n)break;else g=n;while(1){if(!g){i=n;break}e=c[g>>2]|0;i=g+-48|0;if((c[((e&3|0)==2?g:i)+40>>2]|0)==(b|0)){e=c[g+16>>2]|0;h[e+56>>3]=j;h[e+64>>3]=k;h[e+72>>3]=0.0;c[e+80>>2]=0;a[e+84>>0]=1;a[e+85>>0]=0;a[e+86>>0]=0;a[e+87>>0]=0;a[e+88>>0]=s;a[e+89>>0]=0;c[e+92>>2]=0;e=c[g>>2]|0}f=g+16|0;if((c[((e&3|0)==3?g:g+48|0)+40>>2]|0)==(b|0)){e=c[f>>2]|0;h[e+16>>3]=j;h[e+24>>3]=k;h[e+32>>3]=0.0;c[e+40>>2]=0;a[e+44>>0]=1;a[e+45>>0]=0;a[e+46>>0]=0;a[e+47>>0]=0;a[e+48>>0]=s;a[e+49>>0]=0;c[e+52>>2]=0}if((a[(c[f>>2]|0)+112>>0]|0)!=1){g=0;continue}e=c[(c[((c[g>>2]&3|0)==2?g:i)+40>>2]|0)+16>>2]|0;if((a[e+156>>0]|0)!=1){g=0;continue}if((c[e+184>>2]|0)!=1){g=0;continue}g=c[c[e+180>>2]>>2]|0}while(1){if(!i)break;e=c[i>>2]|0;if((c[((e&3|0)==2?i:i+-48|0)+40>>2]|0)==(b|0)){e=c[i+16>>2]|0;h[e+56>>3]=j;h[e+64>>3]=k;h[e+72>>3]=0.0;c[e+80>>2]=0;a[e+84>>0]=1;a[e+85>>0]=0;a[e+86>>0]=0;a[e+87>>0]=0;a[e+88>>0]=s;a[e+89>>0]=0;c[e+92>>2]=0;e=c[i>>2]|0}f=i+48|0;g=i+16|0;if((c[((e&3|0)==3?i:f)+40>>2]|0)==(b|0)){e=c[g>>2]|0;h[e+16>>3]=j;h[e+24>>3]=k;h[e+32>>3]=0.0;c[e+40>>2]=0;a[e+44>>0]=1;a[e+45>>0]=0;a[e+46>>0]=0;a[e+47>>0]=0;a[e+48>>0]=s;a[e+49>>0]=0;c[e+52>>2]=0}if((a[(c[g>>2]|0)+112>>0]|0)!=1){i=0;continue}e=c[(c[((c[i>>2]&3|0)==3?i:f)+40>>2]|0)+16>>2]|0;if((a[e+156>>0]|0)!=1){i=0;continue}if((c[e+176>>2]|0)!=1){i=0;continue}i=c[c[e+172>>2]>>2]|0}n=c[(c[n+16>>2]|0)+172>>2]|0}r=r+1|0}a[(c[w>>2]|0)+145>>0]=1;l=x;return}function uk(a){a=a|0;var b=0,d=0,e=0;ih(a);b=c[a>>2]&3;d=a+-48|0;e=a+48|0;b=dh(c[((b|0)==2?a:d)+40>>2]|0,c[((b|0)==3?a:e)+40>>2]|0)|0;if(!b){b=c[a>>2]&3;nh(c[((b|0)==2?a:d)+40>>2]|0,c[((b|0)==3?a:e)+40>>2]|0,a)|0}else vh(a,b);return}function vk(b){b=b|0;var d=0,e=0,f=0,g=0;f=b+16|0;e=0;while(1){b=c[f>>2]|0;if((e|0)>=(c[b+220>>2]|0))break;d=c[(c[b+216>>2]|0)+(e<<2)>>2]|0;c[b+192>>2]=d;b=d;d=b;while(1){if(!d)break;g=c[d+16>>2]|0;a[g+157>>0]=0;d=c[g+164>>2]|0}while(1){if(!b)break;wk(b);b=c[(c[b+16>>2]|0)+164>>2]|0}e=e+1|0}return}function wk(b){b=b|0;var d=0,e=0,f=0,g=0;g=b+16|0;d=c[g>>2]|0;b=d+157|0;if(!(a[b>>0]|0)){a[b>>0]=1;a[d+158>>0]=1;b=0;while(1){f=c[(c[d+180>>2]|0)+(b<<2)>>2]|0;if(!f)break;d=c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0;e=c[d+16>>2]|0;if(!(a[e+158>>0]|0)){if(!(a[e+157>>0]|0))wk(d)}else{uk(f);b=b+-1|0}b=b+1|0;d=c[g>>2]|0}a[d+158>>0]=0}return}function xk(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;e=jC(a)|0;b=0;while(1){if(!e)break;d=XA(a,e)|0;while(1){if(!d)break;g=c[d>>2]&3;h=c[(c[(c[((g|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0;g=c[(c[(c[((g|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0;f=h-g|0;d=ZA(a,d)|0;b=((h|0)==(g|0)?0:((f|0)>-1?f:0-f|0)+-1|0)+b|0}e=kC(a,e)|0}return b|0}function yk(b){b=b|0;var d=0,e=0,f=0,g=0;e=jC(b)|0;while(1){if(!e)break;g=c[e+16>>2]|0;f=c[g+176>>2]|0;g=g+172|0;d=0;while(1){if((d|0)>=(f|0))break;a[(c[(c[(c[g>>2]|0)+(d<<2)>>2]|0)+16>>2]|0)+112>>0]=0;d=d+1|0}e=kC(b,e)|0}return}function zk(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+16|0;o=q;j=c[b+24>>2]|0;Ak(a);k=(j|0)==-1;m=b+8|0;i=k^1;n=c[15712]|0;f=1797693134862315708145274.0e284;g=0;while(1){if(!(k|(g|0)<(j|0)))break;d=jC(a)|0;while(1){if(!d)break;c[(c[d+16>>2]|0)+232>>2]=0;d=kC(a,d)|0}wj(a);e=+Bk(a);h[m>>3]=e;if(0){h[o>>3]=e;w4(n,87482,o)|0;e=+h[m>>3]}if(!(!(e<=+h[b>>3])&(!(f<=e)|i))){p=9;break}Ck(a);f=e;g=g+1|0}if((p|0)==9){p=b+20|0;c[b+16>>2]=c[p>>2];c[p>>2]=g}wj(a);Dk(a);Ek();h[m>>3]=+Bk(a);l=q;return}function Ak(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0.0,m=0.0,n=0;c[46752]=KK((tB(a)|0)*24|0)|0;c[46751]=0;b=jC(a)|0;while(1){if(!b)break;c[(c[b+16>>2]|0)+120>>2]=-1;b=kC(a,b)|0}i=jC(a)|0;while(1){if(!i)break;j=i+16|0;do if(!(c[(c[j>>2]|0)+216>>2]|0)){f=JK(4)|0;g=c[46752]|0;k=c[46751]|0;c[g+(k*24|0)>>2]=f;c[f>>2]=i;c[g+(k*24|0)+4>>2]=1;j=c[j>>2]|0;h[g+(k*24|0)+8>>3]=+h[j+32>>3];h[g+(k*24|0)+16>>3]=+h[j+40>>3];c[j+120>>2]=k;c[46751]=k+1}else{g=fP(i)|0;k=g+16|0;b=c[k>>2]|0;d=c[b+120>>2]|0;if((d|0)>-1){g=c[46752]|0;f=c[g+(d*24|0)>>2]|0;e=g+(d*24|0)+4|0;k=c[e>>2]|0;c[e>>2]=k+1;c[f+(k<<2)>>2]=i;k=c[j>>2]|0;j=g+(d*24|0)+8|0;h[j>>3]=+h[j>>3]+ +h[k+32>>3];j=g+(d*24|0)+16|0;l=+h[j>>3];m=+h[k+40>>3];h[j>>3]=l<m?m:l;c[k+120>>2]=d;break}d=JK(c[b+216>>2]<<2)|0;e=c[46752]|0;f=c[46751]|0;b=e+(f*24|0)|0;c[b>>2]=d;c[d>>2]=g;if((g|0)==(i|0)){h[e+(f*24|0)+8>>3]=+h[(c[k>>2]|0)+32>>3];d=1;b=i}else{c[(c[b>>2]|0)+4>>2]=i;n=c[k>>2]|0;b=c[j>>2]|0;h[e+(f*24|0)+8>>3]=+h[b+32>>3]+ +h[n+32>>3];d=2;b=+h[n+40>>3]<+h[b+40>>3]?i:g}c[e+(f*24|0)+4>>2]=d;h[e+(f*24|0)+16>>3]=+h[(c[b+16>>2]|0)+40>>3];c[(c[k>>2]|0)+120>>2]=f;c[(c[j>>2]|0)+120>>2]=f;c[46751]=f+1}while(0);i=kC(a,i)|0}return}function Bk(a){a=a|0;var b=0.0,d=0.0,e=0,f=0,g=0,i=0.0,j=0.0;Dk(a);g=c[46749]|0;e=c[a+16>>2]|0;b=+(O(c[e+252>>2]|0,g+-1|0)|0);f=c[46750]|0;e=e+248|0;a=0;d=0.0;while(1){if((a|0)>=(g|0))break;i=+h[f+(a*40|0)+24>>3]+ +(O(c[e>>2]|0,c[f+(a*40|0)+16>>2]|0)|0);j=+h[f+(a*40|0)+32>>3]+b;a=a+1|0;b=j;d=d<i?i:d}return +(d/b)}function Ck(a){a=a|0;var b=0;c[46753]=JK((tB(a)|0)<<2)|0;b=0;while(1){if((b|0)>=(tB(a)|0))break;c[(c[46753]|0)+(b<<2)>>2]=b;b=b+1|0}Dk(a);Fk(a);Gk(a);return}function Dk(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0.0,k=0,l=0;c[46749]=0;b=c[46750]|0;if(b|0){e=0;while(1){if((e|0)>=(c[46751]|0))break;d=c[b+(e*40|0)+4>>2]|0;if(d){U1(d);b=c[46750]|0}d=c[b+(e*40|0)+8>>2]|0;if(d){U1(d);b=c[46750]|0}e=e+1|0}U1(b)}c[46750]=JK((c[46751]|0)*40|0)|0;b=0;while(1){d=c[46751]|0;if((b|0)>=(d|0))break;k=JK(d<<2)|0;c[(c[46750]|0)+(b*40|0)+4>>2]=k;k=JK(c[46751]<<2)|0;l=c[46750]|0;c[l+(b*40|0)+8>>2]=k;c[l+(b*40|0)>>2]=b;c[l+(b*40|0)+12>>2]=0;c[l+(b*40|0)+16>>2]=0;l=l+(b*40|0)+24|0;c[l>>2]=0;c[l+4>>2]=0;c[l+8>>2]=0;c[l+12>>2]=0;b=b+1|0}e=jC(a)|0;while(1){if(!e)break;d=XA(a,e)|0;while(1){if(!d)break;i=c[d>>2]|0;f=d+-48|0;g=c[46750]|0;b=c[(c[(c[((i&3|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0;while(1){b=b+1|0;if((b|0)>=(c[(c[(c[((i&3|0)==2?d:f)+40>>2]|0)+16>>2]|0)+232>>2]|0))break;i=g+(b*40|0)+16|0;c[i>>2]=(c[i>>2]|0)+1;i=c[d>>2]|0}d=ZA(a,d)|0}e=kC(a,e)|0}i=c[46751]|0;k=c[46752]|0;l=c[46750]|0;d=a+16|0;b=0;while(1){if((b|0)>=(i|0))break;e=(c[c[k+(b*24|0)>>2]>>2]|0)+16|0;f=c[(c[e>>2]|0)+232>>2]|0;if((f|0)>=(c[46749]|0))c[46749]=f+1;g=l+(f*40|0)+24|0;j=+h[g>>3];h[g>>3]=j+ +h[k+(b*24|0)+8>>3]*72.0+(j>0.0?+(c[(c[d>>2]|0)+248>>2]|0):0.0);g=l+(f*40|0)+32|0;j=+h[k+(b*24|0)+16>>3]*72.0;if(+h[g>>3]<j)h[g>>3]=j;c[(c[l+(f*40|0)+4>>2]|0)+(c[l+(f*40|0)+12>>2]<<2)>>2]=k+(b*24|0);a=l+((c[(c[e>>2]|0)+232>>2]|0)*40|0)+12|0;c[a>>2]=(c[a>>2]|0)+1;b=b+1|0}return}function Ek(){var a=0,b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;k=c[46749]|0;l=c[46750]|0;j=0;b=0;a=0;while(1){if((j|0)>=(k|0))break;d=l+(j*40|0)|0;i=c[l+(j*40|0)+12>>2]|0;e=(b|0)==0;a:do if(i)if(!e){if((c[d>>2]|0)>(a|0)){f=l+(j*40|0)+4|0;d=0;while(1){if((d|0)>=(i|0))break a;g=c[(c[f>>2]|0)+(d<<2)>>2]|0;h=c[g+4>>2]|0;e=0;while(1){if((e|0)>=(h|0))break;m=(c[(c[(c[g>>2]|0)+(e<<2)>>2]|0)+16>>2]|0)+232|0;c[m>>2]=(c[m>>2]|0)-b;e=e+1|0}d=d+1|0}}}else b=0;else{if(e)a=c[d>>2]|0;b=b+1|0}while(0);j=j+1|0}return}function Fk(a){a=a|0;var b=0;b=c[46753]|0;o4(b,tB(a)|0,4,63);return}function Gk(b){b=b|0;var d=0.0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0.0,n=0,o=0,p=0,q=0,r=0.0,s=0;j=c[46749]|0;k=c[46750]|0;g=c[46753]|0;i=0;while(1){if((i|0)>=(j|0)){e=0;d=0.0;break}e=c[g+(i<<2)>>2]|0;f=i+1|0;if((c[k+(e*40|0)+12>>2]|0)<2)i=f;else{l=4;break}}if((l|0)==4)if((j|0)>(f|0))d=+h[k+((c[g+(f<<2)>>2]|0)*40|0)+24>>3];else d=0.0;a:do if((i|0)!=(j|0)){o4(c[k+(e*40|0)+4>>2]|0,c[k+(e*40|0)+12>>2]|0,4,64);g=c[46750]|0;r=+h[g+(e*40|0)+24>>3];r=!(d<=r*.25)&!(d>=r*.75)?d:r*.5;s=c[g+(e*40|0)+12>>2]|0;q=b+16|0;n=0;o=0;d=0.0;f=0;p=0;while(1){if((p|0)>=(f+s|0))break a;do if(!(c[(c[g+(e*40|0)+8>>2]|0)+(p<<2)>>2]|0)){l=c[(c[g+(e*40|0)+4>>2]|0)+(p<<2)>>2]|0;b=l+8|0;m=+h[b>>3]*72.0+d+(d>0.0?+(c[(c[q>>2]|0)+248>>2]|0):0.0);i=(o|0)!=0;if(!(i&!(m<=r))){j=i?n:l;i=i?o:1;d=m;break}j=n+4|0;k=l+4|0;i=0;while(1){if((i|0)<(c[j>>2]|0))g=0;else break;while(1){if((g|0)>=(c[k>>2]|0))break;a[(c[(nh(c[(c[n>>2]|0)+(i<<2)>>2]|0,c[(c[l>>2]|0)+(g<<2)>>2]|0,0)|0)+16>>2]|0)+112>>0]=1;g=g+1|0}i=i+1|0}g=c[46750]|0;c[(c[g+(e*40|0)+8>>2]|0)+(p<<2)>>2]=1;j=g+(e*40|0)+12|0;c[j>>2]=(c[j>>2]|0)+-1;j=g+(e*40|0)+16|0;c[j>>2]=(c[j>>2]|0)+1;j=g+(e*40|0)+24|0;h[j>>3]=+h[b>>3]*-72.0-+(c[(c[q>>2]|0)+248>>2]|0)+ +h[j>>3];j=n;i=o}else{j=n;i=o;f=f+1|0}while(0);n=j;o=i;p=p+1|0}}while(0);return}function Hk(a,b){a=a|0;b=b|0;a=Ik(c[a>>2]|0)|0;b=Ik(c[b>>2]|0)|0;return ((b|0)<(a|0)&1)-((b|0)>(a|0)&1)|0}function Ik(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;f=a+4|0;d=0;b=0;while(1){if((d|0)>=(c[f>>2]|0))break;e=c[(c[a>>2]|0)+(d<<2)>>2]|0;g=xC(e)|0;e=XA(g,e)|0;while(1){if(!e)break;e=ZA(g,e)|0;b=b+1|0}d=d+1|0}return b|0}function Jk(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0;f=c[46750]|0;e=+h[f+((c[b>>2]|0)*40|0)+24>>3];d=+h[f+((c[a>>2]|0)*40|0)+24>>3];return (e>d&1)-(e<d&1)|0}function Kk(a){a=a|0;var b=0;b=jC(a)|0;while(1){if(!b)break;c[(c[b+16>>2]|0)+216>>2]=0;b=kC(a,b)|0}return}function Lk(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;g=l;l=l+32|0;f=g+16|0;e=g+8|0;d=g+20|0;c[d>>2]=5;a=QA(a,99605)|0;if(a|0?(c[e>>2]=g,c[e+4>>2]=d,(b5(a,87497,e)|0)>=1):0)gA(0,87504,f)|0;c[b+24>>2]=0;c[b+32>>2]=0;l=g;return 0}function Mk(b){b=b|0;var d=0,e=0;d=c[47189]|0;if((((d|0)!=0?(e=RA(b,d)|0,(e|0)!=0):0)?(a[e>>0]|0)!=0:0)?(dP(e)|0)<<24>>24==0:0)b=1;else b=0;return b|0}function Nk(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0;gl(a);b=jC(a)|0;while(1){if(!b)break;d=XA(a,b)|0;while(1){if(!d)break;do if(((c[(c[d+16>>2]|0)+172>>2]|0)==0?(Mk(d)|0)==0:0)?(e=d+48|0,f=fP(c[((c[d>>2]&3|0)==3?d:e)+40>>2]|0)|0,g=d+-48|0,h=fP(c[((c[d>>2]&3|0)==2?d:g)+40>>2]|0)|0,(f|0)!=(h|0)):0){if((c[(c[f+16>>2]|0)+212>>2]|0)==0?(c[(c[h+16>>2]|0)+212>>2]|0)==0:0){i=dh(f,h)|0;if(!i){nh(f,h,d)|0;break}else{vh(d,i);break}}i=c[d>>2]&3;Ok(a,c[((i|0)==3?d:e)+40>>2]|0,c[((i|0)==2?d:g)+40>>2]|0,d)}while(0);d=ZA(a,d)|0}b=kC(a,b)|0}return}function Ok(b,d,f,g){b=b|0;d=d|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=c[g>>2]&3;h=c[(c[((j|0)==3?g:g+48|0)+40>>2]|0)+16>>2]|0;i=c[h+212>>2]|0;if(!i)k=0;else k=(c[h+232>>2]|0)-(c[(c[(c[(c[i+16>>2]|0)+264>>2]|0)+16>>2]|0)+232>>2]|0)|0;h=c[(c[((j|0)==2?g:g+-48|0)+40>>2]|0)+16>>2]|0;i=c[h+212>>2]|0;if(!i)h=0;else h=(c[(c[(c[(c[i+16>>2]|0)+264>>2]|0)+16>>2]|0)+232>>2]|0)-(c[h+232>>2]|0)|0;j=g+16|0;k=h+k+(e[(c[j>>2]|0)+170>>1]|0)|0;b=sh(b)|0;a[(c[b+16>>2]|0)+156>>0]=2;i=fP(d)|0;d=fP(f)|0;f=Zi(b,i,(k|0)>0?0.0:+(0-k|0),(c[(c[j>>2]|0)+156>>2]|0)*10|0)|0;c[(c[(Zi(b,d,+(((k|0)>0?k:0)|0),c[(c[j>>2]|0)+156>>2]|0)|0)+16>>2]|0)+116>>2]=g;c[(c[f+16>>2]|0)+116>>2]=g;return}function Pk(a,d,f,g){a=a|0;d=d|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=c[d>>2]&3;i=(j|0)==3?d:d+48|0;j=(j|0)==2?d:d+-48|0;j=c[(c[(c[((c[(c[(c[i+40>>2]|0)+16>>2]|0)+232>>2]|0)>(c[(c[(c[j+40>>2]|0)+16>>2]|0)+232>>2]|0)?i:j)+40>>2]|0)+16>>2]|0)+232>>2]|0;i=d+16|0;d=(c[i>>2]|0)+172|0;if(c[d>>2]|0)qa(87593,87615,148,87624);c[d>>2]=f;h=(g|0)==0;do{d=c[i>>2]|0;if(h)g=c[f+16>>2]|0;else{g=c[f+16>>2]|0;k=g+168|0;b[k>>1]=(e[k>>1]|0)+(e[d+168>>1]|0)}k=g+154|0;b[k>>1]=(e[k>>1]|0)+(e[d+154>>1]|0);g=g+156|0;c[g>>2]=(c[g>>2]|0)+(c[d+156>>2]|0);d=f+-48|0;g=c[((c[f>>2]&3|0)==2?f:d)+40>>2]|0;if((c[(c[g+16>>2]|0)+232>>2]|0)==(j|0))break;Qk(a,g);f=c[c[(c[(c[((c[f>>2]&3|0)==2?f:d)+40>>2]|0)+16>>2]|0)+180>>2]>>2]|0}while((f|0)!=0);return}function Qk(a,b){a=a|0;b=b|0;var d=0.0;d=+((c[(c[a+16>>2]|0)+248>>2]|0)/2|0|0);b=c[b+16>>2]|0;a=b+88|0;h[a>>3]=+h[a>>3]+d;b=b+96|0;h[b>>3]=+h[b>>3]+d;return}function Rk(a,b){a=a|0;b=b|0;var d=0,e=0;if(((((a|0)!=0&(b|0)!=0?(e=c[a>>2]&3,d=c[b>>2]&3,(c[((e|0)==3?a:a+48|0)+40>>2]|0)==(c[((d|0)==3?b:b+48|0)+40>>2]|0)):0)?(c[((e|0)==2?a:a+-48|0)+40>>2]|0)==(c[((d|0)==2?b:b+-48|0)+40>>2]|0):0)?(c[(c[a+16>>2]|0)+96>>2]|0)==(c[(c[b+16>>2]|0)+96>>2]|0):0)?(uj(a,b)|0)!=0:0)a=1;else a=0;return a|0}function Sk(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;r=b+16|0;d=c[r>>2]|0;c[d+192>>2]=0;c[d+232>>2]=0;gl(b);d=1;while(1){e=c[r>>2]|0;if((d|0)>(c[e+180>>2]|0))break;hl(b,c[(c[e+184>>2]|0)+(d<<2)>>2]|0);d=d+1|0}h=jC(b)|0;while(1){if(!h)break;g=XA(b,h)|0;while(1){if(!g)break;d=c[g>>2]|0;e=(c[(c[((d&3|0)==2?g:g+-48|0)+40>>2]|0)+16>>2]|0)+160|0;f=a[e>>0]|0;if(f<<24>>24<3){a[e>>0]=f+1<<24>>24;d=c[g>>2]|0}d=(c[(c[((d&3|0)==3?g:g+48|0)+40>>2]|0)+16>>2]|0)+160|0;e=a[d>>0]|0;if(e<<24>>24<3)a[d>>0]=e+1<<24>>24;g=ZA(b,g)|0}h=kC(b,h)|0}m=jC(b)|0;while(1){if(!m)break;if((c[(c[m+16>>2]|0)+212>>2]|0)==0?(m|0)==(fP(m)|0):0){oh(b,m);l=(c[r>>2]|0)+232|0;c[l>>2]=(c[l>>2]|0)+1}d=0;l=XA(b,m)|0;while(1){if(!l)break;k=l+16|0;h=c[k>>2]|0;a:do if(!(c[h+172>>2]|0)){if(Tk(l)|0){if(!(Rk(d,l)|0)){Uk(b,l);d=l;break}e=c[(c[d+16>>2]|0)+172>>2]|0;if(e|0){Pk(b,l,e,0);jh(l);break}k=c[l>>2]&3;if((c[(c[(c[((k|0)==3?l:l+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)!=(c[(c[(c[((k|0)==2?l:l+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0))break;vh(l,d);jh(l);break}if(d){i=c[l>>2]&3;e=l+48|0;f=c[((i|0)==3?l:e)+40>>2]|0;g=c[d>>2]&3;if((f|0)==(c[((g|0)==3?d:d+48|0)+40>>2]|0)?(n=c[((i|0)==2?l:l+-48|0)+40>>2]|0,(n|0)==(c[((g|0)==2?d:d+-48|0)+40>>2]|0)):0){if((c[(c[f+16>>2]|0)+232>>2]|0)==(c[(c[n+16>>2]|0)+232>>2]|0)){vh(l,d);jh(l);break}if(((c[h+96>>2]|0)==0?(o=d+16|0,(c[(c[o>>2]|0)+96>>2]|0)==0):0)?(uj(l,d)|0)!=0:0)if(!(a[194954]|0)){Pk(b,l,c[(c[o>>2]|0)+172>>2]|0,1);jh(l);break}else{a[(c[k>>2]|0)+112>>0]=6;break}else i=e}else i=e}else i=l+48|0;h=c[l>>2]&3;e=c[((h|0)==3?l:i)+40>>2]|0;j=l+-48|0;if((e|0)==(c[((h|0)==2?l:j)+40>>2]|0)){jh(l);d=l;break}e=fP(e)|0;f=fP(c[((c[l>>2]&3|0)==2?l:j)+40>>2]|0)|0;h=c[l>>2]&3;if((c[((h|0)==3?l:i)+40>>2]|0)==(e|0)?(c[((h|0)==2?l:j)+40>>2]|0)==(f|0):0){g=c[(c[e+16>>2]|0)+232>>2]|0;h=c[(c[f+16>>2]|0)+232>>2]|0;if((g|0)==(h|0)){th(b,l);d=l;break}if((h|0)>(g|0)){Vk(b,e,f,l);d=l;break}e=iB(b,f,e,0,0)|0;do if(e|0?(p=c[e>>2]&3,q=c[((p|0)==2?e:e+-48|0)+40>>2]|0,(q|0)!=(c[((c[l>>2]&3|0)==2?l:j)+40>>2]|0)):0){f=e+16|0;if(!(c[(c[f>>2]|0)+172>>2]|0))Vk(b,c[((p|0)==3?e:e+48|0)+40>>2]|0,q,e);if((c[(c[k>>2]|0)+96>>2]|0)==0?(c[(c[f>>2]|0)+96>>2]|0)==0:0){if(!(uj(l,e)|0))break;if(!(a[194954]|0)){jh(l);Pk(b,l,c[(c[f>>2]|0)+172>>2]|0,1);break a}else{a[(c[k>>2]|0)+112>>0]=6;a[(c[f>>2]|0)+153>>0]=1;break a}}}while(0);d=c[l>>2]&3;Vk(b,c[((d|0)==2?l:j)+40>>2]|0,c[((d|0)==3?l:i)+40>>2]|0,l);d=l}}else d=l;while(0);l=ZA(b,l)|0}m=kC(b,m)|0}if((gg(b)|0)!=(b|0)){d=c[(c[r>>2]|0)+216>>2]|0;if(!d)d=KK(4)|0;else d=MK(d,4)|0;r=c[r>>2]|0;c[r+216>>2]=d;c[d>>2]=c[r+192>>2]}return}function Tk(b){b=b|0;var d=0;d=c[b>>2]&3;if((a[(c[(c[((d|0)==3?b:b+48|0)+40>>2]|0)+16>>2]|0)+159>>0]|0)==7)b=1;else b=(a[(c[(c[((d|0)==2?b:b+-48|0)+40>>2]|0)+16>>2]|0)+159>>0]|0)==7&1;return b|0}function Uk(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;f=Yk(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)|0;g=Yk(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)|0;i=(c[(c[f+16>>2]|0)+232>>2]|0)>(c[(c[g+16>>2]|0)+232>>2]|0);e=i?f:g;f=i?g:f;g=f+16|0;i=e+16|0;a:do if((c[(c[g>>2]|0)+212>>2]|0)!=(c[(c[i>>2]|0)+212>>2]|0)){h=dh(f,e)|0;if(h|0){Pk(b,d,h,1);break}if((c[(c[g>>2]|0)+232>>2]|0)!=(c[(c[i>>2]|0)+232>>2]|0)){Vk(b,f,e,d);e=(c[d+16>>2]|0)+172|0;while(1){e=c[e>>2]|0;if(!e)break a;f=e+-48|0;if((c[(c[(c[((c[e>>2]&3|0)==2?e:f)+40>>2]|0)+16>>2]|0)+232>>2]|0)>(c[(c[i>>2]|0)+232>>2]|0))break a;a[(c[e+16>>2]|0)+112>>0]=5;e=c[(c[(c[((c[e>>2]&3|0)==2?e:f)+40>>2]|0)+16>>2]|0)+180>>2]|0}}}while(0);return}function Vk(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;k=e+16|0;f=c[k>>2]|0;if(!(c[f+96>>2]|0))j=-1;else j=((c[(c[d+16>>2]|0)+232>>2]|0)+(c[(c[b+16>>2]|0)+232>>2]|0)|0)/2|0;if(c[f+172>>2]|0)qa(87636,87615,89,87661);i=d+16|0;h=c[(c[b+16>>2]|0)+232>>2]|0;while(1){g=h+1|0;f=c[(c[i>>2]|0)+232>>2]|0;if((h|0)>=(f|0))break;if((g|0)<(f|0)){if((g|0)==(j|0))f=Wk(a,e)|0;else f=Xk(a)|0;c[(c[f+16>>2]|0)+232>>2]=g}else f=d;Ii(nh(b,f,e)|0);h=g;b=f}if(!(c[(c[k>>2]|0)+172>>2]|0))qa(87672,87615,103,87661);else return}function Wk(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0,j=0.0;i=d+16|0;g=c[(c[i>>2]|0)+96>>2]|0;e=+h[g+24>>3];f=+h[g+32>>3];g=sh(b)|0;d=g+16|0;c[(c[d>>2]|0)+104>>2]=c[(c[i>>2]|0)+96>>2];j=+(c[(c[(yC(g)|0)+16>>2]|0)+248>>2]|0);h[(c[d>>2]|0)+88>>3]=j;if(!(a[(c[i>>2]|0)+114>>0]|0)){b=(c[(c[(yC(b)|0)+16>>2]|0)+116>>2]&1|0)==0;i=c[d>>2]|0;h[i+80>>3]=b?f:e;h[i+96>>3]=b?e:f}return g|0}function Xk(a){a=a|0;var b=0;b=sh(a)|0;Qk(a,b);return b|0}function Yk(b){b=b|0;var d=0;d=c[b+16>>2]|0;if((a[d+159>>0]|0)==7)b=c[(c[(c[(c[d+212>>2]|0)+16>>2]|0)+268>>2]|0)+(c[d+232>>2]<<2)>>2]|0;else b=fP(b)|0;return b|0}function Zk(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;j=gg(a)|0;h=jC(a)|0;while(1){if(!h)break;b=aB(j,h)|0;i=0;while(1){if(!b)break;l=bB(j,b,h)|0;if(HC(a,b)|0){k=i;b=l;i=k;continue}k=(c[b>>2]&3|0)==2?b:b+-48|0;m=(Rk(i,k)|0)==0;e=c[k>>2]&3;b=c[((e|0)==3?k:k+48|0)+40>>2]|0;d=c[(c[b+16>>2]|0)+232>>2]|0;e=c[((e|0)==2?k:k+-48|0)+40>>2]|0;f=c[(c[e+16>>2]|0)+232>>2]|0;g=(d|0)==(f|0);if(!m){c[(c[k+16>>2]|0)+172>>2]=g?i:0;b=c[(c[i+16>>2]|0)+172>>2]|0;if(!b){m=i;b=l;i=m;continue}Pk(a,k,b,0);kh(k);m=i;b=l;i=m;continue}if(!g)if((f|0)>(d|0)){_k(b,e,k);b=l;i=k;continue}else{_k(e,b,k);b=l;i=k;continue}b=fh(b,e)|0;if(!b){th(j,k);b=l;i=k;continue}if((k|0)==(b|0)){m=i;b=l;i=m;continue}kh(k);if(c[(c[k+16>>2]|0)+172>>2]|0){m=i;b=l;i=m;continue}vh(k,b);m=i;b=l;i=m}h=kC(a,h)|0}return}function _k(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;f=$k(a)|0;e=$k(b)|0;al(f,e,d,c[(c[d+16>>2]|0)+172>>2]|0,(f|0)==(a|0)&(e|0)==(b|0)?1:5);return}function $k(b){b=b|0;var d=0,e=0,f=0;d=c[b+16>>2]|0;e=c[d+212>>2]|0;if((e|0)!=0?(f=c[e+16>>2]|0,(a[f+272>>0]|0)==0):0)b=c[(c[f+268>>2]|0)+(c[d+232>>2]<<2)>>2]|0;return b|0}function al(d,e,f,g,h){d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0;n=d+16|0;k=c[(c[n>>2]|0)+232>>2]|0;o=e+16|0;i=c[(c[o>>2]|0)+232>>2]|0;if((i|0)<=(k|0))qa(87697,87725,81,87735);m=c[g>>2]&3;if(!((c[((m|0)==3?g:g+48|0)+40>>2]|0)==(d|0)?(c[((m|0)==2?g:g+-48|0)+40>>2]|0)==(e|0):0))l=5;a:do if((l|0)==5){if((b[(c[g+16>>2]|0)+168>>1]|0)>1){c[(c[f+16>>2]|0)+172>>2]=0;if(((i-k|0)==1?(j=dh(d,e)|0,j|0):0)?uj(f,j)|0:0){vh(f,j);if(a[(c[n>>2]|0)+156>>0]|0)break;if(a[(c[o>>2]|0)+156>>0]|0)break;jh(f);break}m=h&255;h=d;l=c[(c[n>>2]|0)+232>>2]|0;while(1){i=c[(c[o>>2]|0)+232>>2]|0;if((l|0)>=(i|0))break a;if((l|0)<(i+-1|0)){i=gg(d)|0;j=g+-48|0;i=bl(i,c[((c[g>>2]&3|0)==2?g:j)+40>>2]|0)|0;k=g}else{i=e;j=g+-48|0;k=g}a[(c[(nh(h,i,f)|0)+16>>2]|0)+112>>0]=m;n=(c[g+16>>2]|0)+168|0;b[n>>1]=(b[n>>1]|0)+-1<<16>>16;h=i;l=l+1|0;g=c[c[(c[(c[((c[k>>2]&3|0)==2?g:j)+40>>2]|0)+16>>2]|0)+180>>2]>>2]|0}}do if((i-k|0)==1){g=dh(d,e)|0;if(g|0?uj(f,g)|0:0){c[(c[f+16>>2]|0)+172>>2]=g;m=c[g+16>>2]|0;a[m+112>>0]=h;m=m+168|0;b[m>>1]=(b[m>>1]|0)+1<<16>>16;if(a[(c[n>>2]|0)+156>>0]|0)break;if(a[(c[o>>2]|0)+156>>0]|0)break;jh(f);break}c[(c[f+16>>2]|0)+172>>2]=0;g=nh(d,e,f)|0;a[(c[g+16>>2]|0)+112>>0]=h}while(0);i=c[(c[o>>2]|0)+232>>2]|0;if((i-(c[(c[n>>2]|0)+232>>2]|0)|0)>1){if((c[((c[g>>2]&3|0)==3?g:g+48|0)+40>>2]|0)==(d|0))l=i;else{n=f+16|0;c[(c[n>>2]|0)+172>>2]=0;l=nh(d,c[((c[g>>2]&3|0)==2?g:g+-48|0)+40>>2]|0,f)|0;c[(c[n>>2]|0)+172>>2]=l;ih(g);g=l;l=c[(c[o>>2]|0)+232>>2]|0}while(1){i=c[g>>2]&3;j=c[((i|0)==2?g:g+-48|0)+40>>2]|0;k=c[j+16>>2]|0;if((c[k+232>>2]|0)==(l|0))break;g=c[c[k+180>>2]>>2]|0}if((j|0)!=(e|0)){a[(c[(nh(c[((i|0)==3?g:g+48|0)+40>>2]|0,e,f)|0)+16>>2]|0)+112>>0]=h;ih(g)}}}while(0);return}function bl(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=b+16|0;b=c[d>>2]|0;e=c[b+232>>2]|0;cl(a,e,c[b+236>>2]|0,2);b=sh(a)|0;d=c[d>>2]|0;f=c[b+16>>2]|0;h[f+88>>3]=+h[d+88>>3];h[f+96>>3]=+h[d+96>>3];c[f+232>>2]=c[d+232>>2];d=(c[d+236>>2]|0)+1|0;c[f+236>>2]=d;c[(c[(c[(c[a+16>>2]|0)+196>>2]|0)+(e<<6)+4>>2]|0)+(d<<2)>>2]=b;return b|0}function cl(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;h=a+16|0;f=c[(c[h>>2]|0)+196>>2]|0;i=c[f+(b<<6)+4>>2]|0;a:do if((e|0)<1){d=d-e|0;while(1){g=d+1|0;a=c[f+(b<<6)>>2]|0;if((g|0)>=(a|0))break;a=c[i+(g<<2)>>2]|0;f=d+e|0;c[(c[a+16>>2]|0)+236>>2]=f;c[i+(f<<2)>>2]=a;d=g;f=c[(c[h>>2]|0)+196>>2]|0}g=e+-1|0;d=g+a|0;while(1){if((d|0)>=(a|0))break a;c[i+(d<<2)>>2]=0;f=c[(c[h>>2]|0)+196>>2]|0;d=d+1|0;a=c[f+(b<<6)>>2]|0}}else{g=e+-1|0;a=c[f+(b<<6)>>2]|0;while(1){a=a+-1|0;if((a|0)<=(d|0))break;j=c[i+(a<<2)>>2]|0;f=g+a|0;c[(c[j+16>>2]|0)+236>>2]=f;c[i+(f<<2)>>2]=j}f=e+d|0;a=d;while(1){a=a+1|0;if((a|0)>=(f|0))break;c[i+(a<<2)>>2]=0}a=c[(c[h>>2]|0)+196>>2]|0;f=a;a=c[a+(b<<6)>>2]|0}while(0);c[f+(b<<6)>>2]=g+a;return}function dl(a){a=a|0;var b=0;Sk(a);b=c[a+16>>2]|0;c[b+220>>2]=1;c[c[b+216>>2]>>2]=c[b+192>>2];Fi(a);ki(a,0);el(a);Zk(a);fl(a);return}function el(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;k=gg(d)|0;l=d+16|0;f=c[l>>2]|0;j=b[f+236>>1]|0;e=j<<16>>16;m=k+16|0;if(j<<16>>16>0)a[(c[(c[m>>2]|0)+196>>2]|0)+(e+-1<<6)+49>>0]=0;j=e;while(1){if((j|0)>(b[f+238>>1]|0))break;i=c[(c[(c[(c[f+268>>2]|0)+(j<<2)>>2]|0)+16>>2]|0)+236>>2]|0;cl(k,j,i,c[(c[f+196>>2]|0)+(j<<6)>>2]|0);g=0;h=i;while(1){f=c[l>>2]|0;e=c[f+196>>2]|0;if((g|0)>=(c[e+(j<<6)>>2]|0))break;e=c[(c[e+(j<<6)+4>>2]|0)+(g<<2)>>2]|0;c[(c[(c[(c[m>>2]|0)+196>>2]|0)+(j<<6)+4>>2]|0)+(h<<2)>>2]=e;f=c[e+16>>2]|0;c[f+236>>2]=h;if((a[f+156>>0]|0)==1)c[e+24>>2]=yC(k)|0;qh(d,e);oh(k,e);f=(c[m>>2]|0)+232|0;c[f>>2]=(c[f>>2]|0)+1;g=g+1|0;h=h+1|0}h=c[(c[m>>2]|0)+196>>2]|0;c[e+(j<<6)+4>>2]=(c[h+(j<<6)+4>>2]|0)+(i<<2);a[h+(j<<6)+49>>0]=0;j=j+1|0}e=c[m>>2]|0;if((j|0)<(b[e+238>>1]|0))a[(c[e+196>>2]|0)+(j<<6)+49>>0]=0;a[f+272>>0]=1;return}function fl(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=a+16|0;d=c[i>>2]|0;h=b[d+236>>1]|0;while(1){if((h|0)>(b[d+238>>1]|0))break;f=c[(c[d+268>>2]|0)+(h<<2)>>2]|0;g=f+16|0;while(1){d=c[g>>2]|0;e=c[c[d+180>>2]>>2]|0;if(!e)break;ih(e)}while(1){d=c[c[d+172>>2]>>2]|0;if(!d)break;ih(d);d=c[g>>2]|0}qh(gg(a)|0,f);c[(c[(c[i>>2]|0)+268>>2]|0)+(h<<2)>>2]=0;h=h+1|0;d=c[i>>2]|0}return}function gl(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;n=o;f=jC(b)|0;while(1){if(!f)break;e=f+16|0;d=c[e>>2]|0;if((a[d+159>>0]|0)==7){hP(f);d=c[e>>2]|0}c[d+212>>2]=0;f=kC(b,f)|0}m=b+16|0;k=1;while(1){d=c[m>>2]|0;if((k|0)>(c[d+180>>2]|0))break;h=c[(c[d+184>>2]|0)+(k<<2)>>2]|0;i=h+16|0;d=jC(h)|0;a:while(1){if(!d)break;j=kC(h,d)|0;e=d+16|0;if(a[(c[e>>2]|0)+159>>0]|0){f=KB(d)|0;g=KB(b)|0;c[n>>2]=f;c[n+4>>2]=g;gA(0,87744,n)|0;wC(h,d)|0;d=j;continue}iP(d,c[(c[i>>2]|0)+264>>2]|0);g=c[e>>2]|0;c[g+212>>2]=h;a[g+159>>0]=7;g=XA(h,d)|0;while(1){if(!g){d=j;continue a}d=c[(c[g+16>>2]|0)+172>>2]|0;b:do if(d|0)do{e=d+-48|0;f=c[(c[((c[d>>2]&3|0)==2?d:e)+40>>2]|0)+16>>2]|0;if((a[f+156>>0]|0)!=1)break b;c[f+212>>2]=h;d=c[c[(c[(c[((c[d>>2]&3|0)==2?d:e)+40>>2]|0)+16>>2]|0)+180>>2]>>2]|0}while((d|0)!=0);while(0);g=ZA(h,g)|0}}k=k+1|0}l=o;return}function hl(d,e){d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;k=e+16|0;g=JK((b[(c[k>>2]|0)+238>>1]<<2)+8|0)|0;f=c[k>>2]|0;c[f+268>>2]=g;g=0;h=b[f+236>>1]|0;while(1){if((h|0)>(b[f+238>>1]|0))break;f=sh(d)|0;c[(c[(c[k>>2]|0)+268>>2]|0)+(h<<2)>>2]=f;j=c[f+16>>2]|0;c[j+232>>2]=h;a[j+159>>0]=7;c[j+212>>2]=e;if(g|0){j=(c[(nh(g,f,0)|0)+16>>2]|0)+154|0;b[j>>1]=(b[j>>1]|0)*1e3}g=f;h=h+1|0;f=c[k>>2]|0}j=jC(e)|0;while(1){f=c[k>>2]|0;if(!j)break;h=(c[(c[f+268>>2]|0)+(c[(c[j+16>>2]|0)+232>>2]<<2)>>2]|0)+16|0;f=(c[h>>2]|0)+216|0;c[f>>2]=(c[f>>2]|0)+1;f=XA(e,j)|0;while(1){if(!f)break;i=c[f>>2]|0;d=f+-48|0;g=c[(c[(c[((i&3|0)==3?f:f+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0;while(1){if((g|0)>=(c[(c[(c[((i&3|0)==2?f:d)+40>>2]|0)+16>>2]|0)+232>>2]|0))break;i=(c[(c[c[(c[h>>2]|0)+180>>2]>>2]|0)+16>>2]|0)+168|0;b[i>>1]=(b[i>>1]|0)+1<<16>>16;g=g+1|0;i=c[f>>2]|0}f=ZA(e,f)|0}j=kC(e,j)|0}d=b[f+238>>1]|0;i=f+268|0;f=b[f+236>>1]|0;while(1){if((f|0)>(d|0))break;g=(c[(c[(c[i>>2]|0)+(f<<2)>>2]|0)+16>>2]|0)+216|0;h=c[g>>2]|0;if((h|0)>1)c[g>>2]=h+-1;f=f+1|0}return}function il(d,e,f,g){d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;k=(c[(c[e+16>>2]|0)+212>>2]|0)+16|0;e=c[k>>2]|0;j=f+1|0;if((j|0)!=(a[e+273>>0]|0)){i=b[e+236>>1]|0;while(1){h=b[e+238>>1]|0;if((i|0)>(h<<16>>16|0))break;zi(d,c[(c[e+268>>2]|0)+(i<<2)>>2]|0);i=i+1|0;e=c[k>>2]|0}i=b[e+236>>1]|0;while(1){if((i|0)>(h<<16>>16|0))break;Ai(g,c[(c[e+268>>2]|0)+(i<<2)>>2]|0,f);d=c[k>>2]|0;i=i+1|0;h=b[d+238>>1]|0;e=d}a[e+273>>0]=j}return}function jl(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;h=jC(b)|0;while(1){if(!h)break;c[(c[h+16>>2]|0)+212>>2]=0;g=XA(b,h)|0;while(1){if(!g)break;d=c[(c[g+16>>2]|0)+172>>2]|0;a:do if(d|0)do{e=d+-48|0;f=c[(c[((c[d>>2]&3|0)==2?d:e)+40>>2]|0)+16>>2]|0;if((a[f+156>>0]|0)!=1)break a;c[f+212>>2]=0;d=c[c[(c[(c[((c[d>>2]&3|0)==2?d:e)+40>>2]|0)+16>>2]|0)+180>>2]>>2]|0}while((d|0)!=0);while(0);g=ZA(b,g)|0}h=kC(b,h)|0}kl(b);return}function kl(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;e=b+16|0;d=1;while(1){f=c[e>>2]|0;if((d|0)>(c[f+180>>2]|0))break;kl(c[(c[f+184>>2]|0)+(d<<2)>>2]|0);d=d+1|0}i=jC(b)|0;while(1){if(!i)break;d=(c[i+16>>2]|0)+212|0;if(!(c[d>>2]|0))c[d>>2]=b;h=XA(b,i)|0;while(1){if(!h)break;d=c[(c[h+16>>2]|0)+172>>2]|0;a:do if(d|0)do{e=c[d>>2]|0;g=d+-48|0;f=c[(c[((e&3|0)==2?d:g)+40>>2]|0)+16>>2]|0;if((a[f+156>>0]|0)!=1)break a;f=f+212|0;if(!(c[f>>2]|0)){c[f>>2]=b;e=c[d>>2]|0}d=c[c[(c[(c[((e&3|0)==2?d:g)+40>>2]|0)+16>>2]|0)+180>>2]>>2]|0}while((d|0)!=0);while(0);h=ZA(b,h)|0}i=kC(b,i)|0}return}function ll(a){a=a|0;var b=0,c=0,d=0;d=IP(a)|0;c=jC(a)|0;while(1){if(!c)break;b=XA(a,c)|0;while(1){if(!b)break;ml(b,d);b=ZA(a,b)|0}c=kC(a,c)|0}Rz(d)|0;return}function ml(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0;M=l;l=l+320|0;H=M+296|0;G=M+280|0;x=M+136|0;w=M+120|0;n=M+104|0;i=M+88|0;f=M+80|0;E=M+16|0;z=M;j=M+264|0;k=M+248|0;m=M+232|0;o=M+216|0;A=M+200|0;B=M+184|0;y=M+168|0;D=M+152|0;g=nl(QA(a,87798)|0,b)|0;q=nl(QA(a,87804)|0,b)|0;r=(q|0)!=0;b=(g|0)!=0;do if(b|r?(L=a+16|0,e=c[(c[L>>2]|0)+8>>2]|0,e|0):0){if((c[e+4>>2]|0)>1){K=KB(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)|0;L=KB(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0)|0;c[f>>2]=K;c[f+4>>2]=L;gA(0,87810,f)|0;break}I=c[e>>2]|0;e=c[I+4>>2]|0;p=c[a>>2]&3;s=a+-48|0;t=c[((p|0)==2?a:s)+40>>2]|0;u=a+48|0;p=c[((p|0)==3?a:u)+40>>2]|0;J=KK(48)|0;v=I+12|0;c[J+12>>2]=c[v>>2];C=I+8|0;c[J+8>>2]=c[C>>2];do if(b){h=(c[g+16>>2]|0)+16|0;g=(c[t+16>>2]|0)+16|0;c[H>>2]=c[g>>2];c[H+4>>2]=c[g+4>>2];c[H+8>>2]=c[g+8>>2];c[H+12>>2]=c[g+12>>2];if(!(ol(H,h)|0)){o=KB(c[((c[a>>2]&3|0)==3?a:u)+40>>2]|0)|0;F=KB(c[((c[a>>2]&3|0)==2?a:s)+40>>2]|0)|0;K=QA(a,87798)|0;c[i>>2]=o;c[i+4>>2]=F;c[i+8>>2]=K;gA(0,87851,i)|0;K=26;break}b=c[I>>2]|0;c[H>>2]=c[b>>2];c[H+4>>2]=c[b+4>>2];c[H+8>>2]=c[b+8>>2];c[H+12>>2]=c[b+12>>2];if(ol(H,h)|0){F=(c[p+16>>2]|0)+16|0;c[H>>2]=c[F>>2];c[H+4>>2]=c[F+4>>2];c[H+8>>2]=c[F+8>>2];c[H+12>>2]=c[F+12>>2];if(ol(H,h)|0){o=KB(c[((c[a>>2]&3|0)==3?a:u)+40>>2]|0)|0;F=KB(c[((c[a>>2]&3|0)==2?a:s)+40>>2]|0)|0;K=QA(a,87798)|0;c[n>>2]=o;c[n+4>>2]=F;c[n+8>>2]=K;gA(0,87894,n)|0;K=26;break}if(!(c[C>>2]|0))qa(87936,87947,369,87958);o=I+16|0;c[G>>2]=c[b>>2];c[G+4>>2]=c[b+4>>2];c[G+8>>2]=c[b+8>>2];c[G+12>>2]=c[b+12>>2];c[H>>2]=c[o>>2];c[H+4>>2]=c[o+4>>2];c[H+8>>2]=c[o+8>>2];c[H+12>>2]=c[o+12>>2];pl(z,G,H,h);F=(c[I>>2]|0)+48|0;c[F>>2]=c[z>>2];c[F+4>>2]=c[z+4>>2];c[F+8>>2]=c[z+8>>2];c[F+12>>2]=c[z+12>>2];F=(c[I>>2]|0)+16|0;c[G>>2]=c[z>>2];c[G+4>>2]=c[z+4>>2];c[G+8>>2]=c[z+8>>2];c[G+12>>2]=c[z+12>>2];c[H>>2]=c[o>>2];c[H+4>>2]=c[o+4>>2];c[H+8>>2]=c[o+8>>2];c[H+12>>2]=c[o+12>>2];ql(j,G,H);c[F>>2]=c[j>>2];c[F+4>>2]=c[j+4>>2];c[F+8>>2]=c[j+8>>2];c[F+12>>2]=c[j+12>>2];F=c[I>>2]|0;b=F+16|0;c[G>>2]=c[b>>2];c[G+4>>2]=c[b+4>>2];c[G+8>>2]=c[b+8>>2];c[G+12>>2]=c[b+12>>2];c[H>>2]=c[o>>2];c[H+4>>2]=c[o+4>>2];c[H+8>>2]=c[o+8>>2];c[H+12>>2]=c[o+12>>2];ql(k,G,H);c[F>>2]=c[k>>2];c[F+4>>2]=c[k+4>>2];c[F+8>>2]=c[k+8>>2];c[F+12>>2]=c[k+12>>2];F=c[I>>2]|0;b=F+32|0;F=F+16|0;c[G>>2]=c[F>>2];c[G+4>>2]=c[F+4>>2];c[G+8>>2]=c[F+8>>2];c[G+12>>2]=c[F+12>>2];c[H>>2]=c[z>>2];c[H+4>>2]=c[z+4>>2];c[H+8>>2]=c[z+8>>2];c[H+12>>2]=c[z+12>>2];ql(m,G,H);c[b>>2]=c[m>>2];c[b+4>>2]=c[m+4>>2];c[b+8>>2]=c[m+8>>2];c[b+12>>2]=c[m+12>>2];b=c[v>>2]|0;if(!b){F=3;break}F=(UH(a,c[I>>2]|0,0,0,J,b)|0)+3|0;break}g=e+-1|0;b=0;while(1){if((b|0)>=(g|0))break;if(rl((c[I>>2]|0)+(b<<4)|0,h)|0)break;b=b+3|0}e=c[v>>2]|0;f=(e|0)!=0;if((b|0)==(g|0))if(f){F=J+32|0;m=I+32|0;n=(c[I>>2]|0)+(g<<4)|0;c[G>>2]=c[m>>2];c[G+4>>2]=c[m+4>>2];c[G+8>>2]=c[m+8>>2];c[G+12>>2]=c[m+12>>2];c[H>>2]=c[n>>2];c[H+4>>2]=c[n+4>>2];c[H+8>>2]=c[n+8>>2];c[H+12>>2]=c[n+12>>2];pl(o,G,H,h);c[F>>2]=c[o>>2];c[F+4>>2]=c[o+4>>2];c[F+8>>2]=c[o+8>>2];c[F+12>>2]=c[o+12>>2];F=g;break}else qa(87975,87947,387,87958);else{if(f)b=UH(a,c[I>>2]|0,0,b,J,e)|0;F=b+3|0;break}}else K=26;while(0);if((K|0)==26){b=e+-1|0;if(!(c[v>>2]|0))F=b;else{F=J+32|0;o=I+32|0;c[F>>2]=c[o>>2];c[F+4>>2]=c[o+4>>2];c[F+8>>2]=c[o+8>>2];c[F+12>>2]=c[o+12>>2];F=b}}do if(r){f=(c[q+16>>2]|0)+16|0;r=(c[p+16>>2]|0)+16|0;c[H>>2]=c[r>>2];c[H+4>>2]=c[r+4>>2];c[H+8>>2]=c[r+8>>2];c[H+12>>2]=c[r+12>>2];if(!(ol(H,f)|0)){G=KB(c[((c[a>>2]&3|0)==3?a:u)+40>>2]|0)|0;H=KB(c[((c[a>>2]&3|0)==2?a:s)+40>>2]|0)|0;K=QA(a,87804)|0;c[w>>2]=G;c[w+4>>2]=H;c[w+8>>2]=K;gA(0,87986,w)|0;K=52;break}b=(c[I>>2]|0)+(F<<4)|0;c[H>>2]=c[b>>2];c[H+4>>2]=c[b+4>>2];c[H+8>>2]=c[b+8>>2];c[H+12>>2]=c[b+12>>2];if(ol(H,f)|0){E=(c[t+16>>2]|0)+16|0;c[H>>2]=c[E>>2];c[H+4>>2]=c[E+4>>2];c[H+8>>2]=c[E+8>>2];c[H+12>>2]=c[E+12>>2];if(ol(H,f)|0){G=KB(c[((c[a>>2]&3|0)==3?a:u)+40>>2]|0)|0;H=KB(c[((c[a>>2]&3|0)==2?a:s)+40>>2]|0)|0;K=QA(a,87804)|0;c[x>>2]=G;c[x+4>>2]=H;c[x+8>>2]=K;gA(0,88029,x)|0;K=52;break}if(!(c[v>>2]|0))qa(87975,87947,429,87958);x=J+32|0;c[G>>2]=c[b>>2];c[G+4>>2]=c[b+4>>2];c[G+8>>2]=c[b+8>>2];c[G+12>>2]=c[b+12>>2];c[H>>2]=c[x>>2];c[H+4>>2]=c[x+4>>2];c[H+8>>2]=c[x+8>>2];c[H+12>>2]=c[x+12>>2];pl(z,G,H,f);d=F+-3|0;E=(c[I>>2]|0)+(d<<4)|0;c[E>>2]=c[z>>2];c[E+4>>2]=c[z+4>>2];c[E+8>>2]=c[z+8>>2];c[E+12>>2]=c[z+12>>2];E=F+-1|0;b=(c[I>>2]|0)+(E<<4)|0;c[G>>2]=c[z>>2];c[G+4>>2]=c[z+4>>2];c[G+8>>2]=c[z+8>>2];c[G+12>>2]=c[z+12>>2];c[H>>2]=c[x>>2];c[H+4>>2]=c[x+4>>2];c[H+8>>2]=c[x+8>>2];c[H+12>>2]=c[x+12>>2];ql(A,G,H);c[b>>2]=c[A>>2];c[b+4>>2]=c[A+4>>2];c[b+8>>2]=c[A+8>>2];c[b+12>>2]=c[A+12>>2];b=c[I>>2]|0;D=b+(F<<4)|0;b=b+(E<<4)|0;c[G>>2]=c[b>>2];c[G+4>>2]=c[b+4>>2];c[G+8>>2]=c[b+8>>2];c[G+12>>2]=c[b+12>>2];c[H>>2]=c[x>>2];c[H+4>>2]=c[x+4>>2];c[H+8>>2]=c[x+8>>2];c[H+12>>2]=c[x+12>>2];ql(B,G,H);c[D>>2]=c[B>>2];c[D+4>>2]=c[B+4>>2];c[D+8>>2]=c[B+8>>2];c[D+12>>2]=c[B+12>>2];D=c[I>>2]|0;b=D+(F+-2<<4)|0;E=D+(E<<4)|0;c[G>>2]=c[E>>2];c[G+4>>2]=c[E+4>>2];c[G+8>>2]=c[E+8>>2];c[G+12>>2]=c[E+12>>2];c[H>>2]=c[z>>2];c[H+4>>2]=c[z+4>>2];c[H+8>>2]=c[z+8>>2];c[H+12>>2]=c[z+12>>2];ql(y,G,H);c[b>>2]=c[y>>2];c[b+4>>2]=c[y+4>>2];c[b+8>>2]=c[y+8>>2];c[b+12>>2]=c[y+12>>2];b=c[C>>2]|0;if(!b)break;d=WH(a,c[I>>2]|0,d,d,J,b)|0;break}e=F;while(1){if((e|0)>0)b=0;else break;while(1){if((b|0)==4)break;B=E+(b<<4)|0;A=(c[I>>2]|0)+(e-b<<4)|0;c[B>>2]=c[A>>2];c[B+4>>2]=c[A+4>>2];c[B+8>>2]=c[A+8>>2];c[B+12>>2]=c[A+12>>2];b=b+1|0}if(rl(E,f)|0){b=0;K=43;break}e=e+-3|0}a:do if((K|0)==43)while(1){K=0;if((b|0)==4)break a;K=(c[I>>2]|0)+(e-b<<4)|0;B=E+(b<<4)|0;c[K>>2]=c[B>>2];c[K+4>>2]=c[B+4>>2];c[K+8>>2]=c[B+8>>2];c[K+12>>2]=c[B+12>>2];b=b+1|0;K=43}while(0);if(e|0){d=e+-3|0;b=c[C>>2]|0;if(!b)break;d=WH(a,c[I>>2]|0,d,F+-3|0,J,b)|0;break}if(!(c[C>>2]|0))qa(87936,87947,452,87958);else{d=J+16|0;E=I+16|0;a=c[I>>2]|0;c[G>>2]=c[E>>2];c[G+4>>2]=c[E+4>>2];c[G+8>>2]=c[E+8>>2];c[G+12>>2]=c[E+12>>2];c[H>>2]=c[a>>2];c[H+4>>2]=c[a+4>>2];c[H+8>>2]=c[a+8>>2];c[H+12>>2]=c[a+12>>2];pl(D,G,H,f);c[d>>2]=c[D>>2];c[d+4>>2]=c[D+4>>2];c[d+8>>2]=c[D+8>>2];c[d+12>>2]=c[D+12>>2];d=0;break}}else K=52;while(0);if((K|0)==52)if(!(c[C>>2]|0))d=0;else{d=J+16|0;K=I+16|0;c[d>>2]=c[K>>2];c[d+4>>2]=c[K+4>>2];c[d+8>>2]=c[K+8>>2];c[d+12>>2]=c[K+12>>2];d=0}b=F-d+1|0;e=J+4|0;c[e>>2]=b;c[J>>2]=KK(b<<4)|0;b=0;while(1){if((b|0)>=(c[e>>2]|0))break;K=(c[J>>2]|0)+(b<<4)|0;a=(c[I>>2]|0)+(d<<4)|0;c[K>>2]=c[a>>2];c[K+4>>2]=c[a+4>>2];c[K+8>>2]=c[a+8>>2];c[K+12>>2]=c[a+12>>2];d=d+1|0;b=b+1|0}U1(c[I>>2]|0);U1(I);c[c[(c[L>>2]|0)+8>>2]>>2]=J}while(0);l=M;return}function nl(b,d){b=b|0;d=d|0;var e=0,f=0;f=l;l=l+16|0;e=f;if((b|0)!=0?(a[b>>0]|0)!=0:0){d=KP(d,b)|0;if(!d){c[e>>2]=b;gA(0,88149,e)|0;d=0}}else d=0;l=f;return d|0}function ol(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;d=+h[a>>3];if((+h[b>>3]<=d?d<=+h[b+16>>3]:0)?(c=+h[a+8>>3],+h[b+8>>3]<=c):0)a=c<=+h[b+24>>3]&1;else a=0;return a|0}function pl(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0,j=0.0,k=0.0,m=0.0,n=0,o=0.0,p=0.0,q=0.0,r=0.0,s=0.0,t=0.0,u=0,v=0,w=0,x=0,y=0.0,z=0,A=0,B=0,C=0;C=l;l=l+464|0;n=C+48|0;B=C+32|0;u=C+16|0;v=C;w=C+364|0;x=C+264|0;z=C+164|0;A=C+64|0;q=+h[b>>3];r=+h[b+8>>3];s=+h[d>>3];t=+h[d+8>>3];c[u>>2]=c[e>>2];c[u+4>>2]=c[e+4>>2];c[u+8>>2]=c[e+8>>2];c[u+12>>2]=c[e+12>>2];e=e+16|0;c[v>>2]=c[e>>2];c[v+4>>2]=c[e+4>>2];c[v+8>>2]=c[e+8>>2];c[v+12>>2]=c[e+12>>2];k=+h[u>>3];if((s<k?(f=r+ +(~~((k-q)*(r-t)/(q-s))|0),f>=+h[u+8>>3]):0)?f<=+h[v+8>>3]:0){o=f;p=k}else i=4;do if((i|0)==4){j=+h[v>>3];if(s>j){g=r+ +(~~((j-q)*(r-t)/(q-s))|0);f=+h[u+8>>3];if(g>=f?g<=+h[v+8>>3]:0){o=g;p=j;break}}else f=+h[u+8>>3];if(t<f?(m=q+ +(~~((f-r)*(q-s)/(r-t))|0),!(!(m>=k)|!(m<=j))):0){o=f;p=m;break}f=+h[v+8>>3];if(t>f?(y=q+ +(~~((f-r)*(q-s)/(r-t))|0),!(!(y>=k)|!(y<=j))):0){o=f;p=y;break};c[n>>2]=c[b>>2];c[n+4>>2]=c[b+4>>2];c[n+8>>2]=c[b+8>>2];c[n+12>>2]=c[b+12>>2];w=wl(n,w)|0;c[n>>2]=c[d>>2];c[n+4>>2]=c[d+4>>2];c[n+8>>2]=c[d+8>>2];c[n+12>>2]=c[d+12>>2];x=wl(n,x)|0;c[n>>2]=c[u>>2];c[n+4>>2]=c[u+4>>2];c[n+8>>2]=c[u+8>>2];c[n+12>>2]=c[u+12>>2];z=wl(n,z)|0;c[n>>2]=c[v>>2];c[n+4>>2]=c[v+4>>2];c[n+8>>2]=c[v+8>>2];c[n+12>>2]=c[v+12>>2];A=wl(n,A)|0;c[B>>2]=w;c[B+4>>2]=x;c[B+8>>2]=z;c[B+12>>2]=A;gA(1,88071,B)|0;qa(138394,87947,78,88123)}while(0);h[a>>3]=p;h[a+8>>3]=o;l=C;return}function ql(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=(+h[c+8>>3]+ +h[b+8>>3])*.5;h[a>>3]=(+h[c>>3]+ +h[b>>3])*.5;h[a+8>>3]=d;return}function rl(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0.0,g=0,i=0,j=0,k=0,m=0;m=l;l=l+80|0;j=m+16|0;k=m;e=0;while(1){if((e|0)==4)break;i=j+(e<<4)|0;g=a+(e<<4)|0;c[i>>2]=c[g>>2];c[i+4>>2]=c[g+4>>2];c[i+8>>2]=c[g+8>>2];c[i+12>>2]=c[g+12>>2];e=e+1|0}e=b+8|0;i=b+24|0;d=+sl(a,0.0,1.0,+h[b>>3],+h[e>>3],+h[i>>3]);if(d>=0.0&d<2.0)kP(k,j,3,d,a,0);else d=2.0;g=b+16|0;f=+sl(a,0.0,d>1.0?1.0:d,+h[g>>3],+h[e>>3],+h[i>>3]);if(f>=0.0&f<d){kP(k,j,3,f,a,0);d=f}f=+tl(a,0.0,d>1.0?1.0:d,+h[e>>3],+h[b>>3],+h[g>>3]);if(f>=0.0&f<d){kP(k,j,3,f,a,0);d=f}f=+tl(a,0.0,d>1.0?1.0:d,+h[i>>3],+h[b>>3],+h[g>>3]);if(f>=0.0&f<d){kP(k,j,3,f,a,0);d=f}l=m;return d<2.0|0}function sl(a,b,c,d,e,f){a=a|0;b=+b;c=+c;d=+d;e=+e;f=+f;var g=0.0,i=0,j=0,k=0,m=0;m=l;l=l+144|0;j=m+80|0;k=m+16|0;i=m;a:do if(!(b==c)){switch(vl(a,d)|0){case 0:{b=-1.0;break a}case 1:{if(+B(+(+h[a+48>>3]-d))<=.005){b=+h[a+56>>3];b=!(b>=e)|!(b<=f)?-1.0:c;break a}break}default:{}}kP(i,a,3,.5,j,k);g=(c+b)*.5;b=+sl(j,b,g,d,e,f);if(!(b>=0.0))b=+sl(k,g,c,d,e,f)}while(0);l=m;return +b}function tl(a,b,c,d,e,f){a=a|0;b=+b;c=+c;d=+d;e=+e;f=+f;var g=0.0,i=0,j=0,k=0,m=0;m=l;l=l+144|0;j=m+80|0;k=m+16|0;i=m;a:do if(!(b==c)){switch(ul(a,d)|0){case 0:{b=-1.0;break a}case 1:{if(+B(+(+h[a+56>>3]-d))<=.005){b=+h[a+48>>3];b=!(b>=e)|!(b<=f)?-1.0:c;break a}break}default:{}}kP(i,a,3,.5,j,k);g=(c+b)*.5;b=+tl(j,b,g,d,e,f);if(!(b>=0.0))b=+tl(k,g,c,d,e,f)}while(0);l=m;return +b}function ul(a,b){a=a|0;b=+b;var c=0,d=0,e=0,f=0.0,g=0,i=0;f=+h[a+8>>3];e=f<b?-1:f>b&1;c=1;d=e;e=(e|0)==0&1;while(1){if((c|0)==4)break;f=+h[a+(c<<4)+8>>3];i=f<b?-1:f>b&1;g=e+((d|0)!=0&(i|0)!=(d|0)&1)|0;c=c+1|0;d=i;e=g}return e|0}function vl(a,b){a=a|0;b=+b;var c=0,d=0,e=0,f=0.0,g=0,i=0;f=+h[a>>3];e=f<b?-1:f>b&1;c=1;d=e;e=(e|0)==0&1;while(1){if((c|0)==4)break;f=+h[a+(c<<4)>>3];i=f<b?-1:f>b&1;g=e+((d|0)!=0&(i|0)!=(d|0)&1)|0;c=c+1|0;d=i;e=g}return e|0}function wl(a,b){a=a|0;b=b|0;var c=0,d=0,e=0.0;c=l;l=l+16|0;d=c;e=+h[a+8>>3];h[d>>3]=+h[a>>3];h[d+8>>3]=e;m4(b,88137,d)|0;l=c;return b|0}function xl(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,q=0,r=0,s=0,t=0,u=0,v=0;v=l;l=l+16|0;s=v;u=4;t=T1(40)|0;c[t>>2]=0;r=a+16|0;e=c[r>>2]|0;a:do if(((b[e+238>>1]|0)-(b[e+236>>1]|0)|0)>=2){m=1;d=e;e=c[e+196>>2]|0;b:while(1){n=m+1|0;if(!(c[e+(n<<6)>>2]|0)){q=15;break}else k=0;while(1){h=c[e+(m<<6)>>2]|0;if((k|0)>=(h|0)){m=n;continue b}j=c[(c[e+(m<<6)+4>>2]|0)+(k<<2)>>2]|0;o=0;d=$(20,j|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,u|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){d=z;break b}if(d<<24>>24){i=k;d=h;while(1){g=i+1|0;if((g|0)>=(d|0))break;o=0;d=aa(65,j|0,c[(c[e+(m<<6)+4>>2]|0)+(g<<2)>>2]|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,t|0,u|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){d=z;break b}if(!(d<<24>>24))break;d=c[(c[r>>2]|0)+196>>2]|0;i=g;e=d;d=c[d+(m<<6)>>2]|0}if((g-k|0)>1){o=0;na(6,a|0,m|0,k|0,i|0,1);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,t|0,u|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){d=z;break b}}}e=c[r>>2]|0;k=k+1|0;d=e;e=c[e+196>>2]|0}}c:do if((q|0)==15){while(1){if((m|0)>0)k=0;else break;while(1){e=c[d+196>>2]|0;h=c[e+(m<<6)>>2]|0;if((k|0)>=(h|0))break;j=c[(c[e+(m<<6)+4>>2]|0)+(k<<2)>>2]|0;o=0;d=$(21,j|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,u|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){d=z;break c}if(d<<24>>24){i=k;d=h;while(1){g=i+1|0;if((g|0)>=(d|0))break;o=0;d=aa(66,j|0,c[(c[e+(m<<6)+4>>2]|0)+(g<<2)>>2]|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,t|0,u|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){d=z;break c}if(!(d<<24>>24))break;d=c[(c[r>>2]|0)+196>>2]|0;i=g;e=d;d=c[d+(m<<6)>>2]|0}if((g-k|0)>1){o=0;na(6,a|0,m|0,k|0,i|0,0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,t|0,u|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){d=z;break c}}}k=k+1|0;d=c[r>>2]|0}m=m+-1|0;q=15}t=U6(187016,1,t|0,u|0)|0;u=z;o=0;d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,t|0,u|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1)d=z;else d=0}while(0);d:while(1){if(d){o=0;ca(20,3,88177,s|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,t|0,u|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){d=z;continue}else break}else f=1;while(1){d=c[r>>2]|0;if((f|0)>(c[d+180>>2]|0))break a;o=0;ia(101,c[(c[d+184>>2]|0)+(f<<2)>>2]|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,t|0,u|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){d=z;continue d}f=f+1|0}}}while(0);U1(t|0);l=v;return}function yl(b){b=b|0;b=c[b+16>>2]|0;if(((a[b+156>>0]|0)==1?(c[b+176>>2]|0)==1:0)?(c[b+184>>2]|0)==1:0)b=(c[b+104>>2]|0)==0&1;else b=0;return b|0}function zl(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+80|0;h=i+40|0;g=i;e=c[c[(c[a+16>>2]|0)+172>>2]>>2]|0;a=c[c[(c[b+16>>2]|0)+172>>2]>>2]|0;if(((yl(b)|0)<<24>>24!=0?(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0)==(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0):0)?(Fl(e,a)|0)<<24>>24!=0:0){d=(c[a+16>>2]|0)+16|0;f=g;a=(c[e+16>>2]|0)+16|0;b=f+40|0;do{c[f>>2]=c[a>>2];f=f+4|0;a=a+4|0}while((f|0)<(b|0));f=h;a=d;b=f+40|0;do{c[f>>2]=c[a>>2];f=f+4|0;a=a+4|0}while((f|0)<(b|0));a=(hg(g,h)|0)==0&1}else a=0;l=i;return a|0}function Al(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=a+16|0;q=c[(c[(c[(c[t>>2]|0)+196>>2]|0)+(b<<6)+4>>2]|0)+(d<<2)>>2]|0;p=d+1|0;m=(f|0)==1;n=q+16|0;l=p;while(1){if((l|0)>(e|0)){g=e;s=p;break}o=c[(c[(c[(c[t>>2]|0)+196>>2]|0)+(b<<6)+4>>2]|0)+(l<<2)>>2]|0;k=o+16|0;a:do if(m)while(1){d=c[k>>2]|0;j=c[c[d+180>>2]>>2]|0;if(!j)break a;h=c[(c[n>>2]|0)+180>>2]|0;i=j+-48|0;f=0;while(1){d=c[h+(f<<2)>>2]|0;if(!d){r=8;break}if((c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)==(c[((c[j>>2]&3|0)==2?j:i)+40>>2]|0))break;else f=f+1|0}if((r|0)==8){r=0;d=nh(q,c[((c[j>>2]&3|0)==2?j:i)+40>>2]|0,j)|0}while(1){f=c[c[(c[k>>2]|0)+172>>2]>>2]|0;if(!f)break;vh(f,d);ih(f)}ih(j)}else while(1){d=c[k>>2]|0;j=c[c[d+172>>2]>>2]|0;if(!j)break a;h=c[(c[n>>2]|0)+172>>2]|0;i=j+48|0;f=0;while(1){d=c[h+(f<<2)>>2]|0;if(!d){r=17;break}if((c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)==(c[((c[j>>2]&3|0)==3?j:i)+40>>2]|0))break;else f=f+1|0}if((r|0)==17){r=0;d=nh(c[((c[j>>2]&3|0)==3?j:i)+40>>2]|0,q,j)|0}while(1){f=c[c[(c[k>>2]|0)+180>>2]>>2]|0;if(!f)break;vh(f,d);ih(f)}ih(j)}while(0);if((c[d+176>>2]|0)!=(0-(c[d+184>>2]|0)|0)){r=23;break}qh(a,o);l=l+1|0}if((r|0)==23)qa(88313,88357,115,88364);while(1){g=g+1|0;d=c[(c[t>>2]|0)+196>>2]|0;f=d+(b<<6)|0;if((g|0)>=(c[f>>2]|0))break;e=c[d+(b<<6)+4>>2]|0;r=c[e+(g<<2)>>2]|0;c[e+(s<<2)>>2]=r;c[(c[r+16>>2]|0)+236>>2]=s;s=s+1|0}c[f>>2]=s;c[(c[d+(b<<6)+4>>2]|0)+(s<<2)>>2]=0;return}function Bl(b){b=b|0;b=c[b+16>>2]|0;if(((a[b+156>>0]|0)==1?(c[b+184>>2]|0)==1:0)?(c[b+176>>2]|0)==1:0)b=(c[b+104>>2]|0)==0&1;else b=0;return b|0}function Cl(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+80|0;h=i+40|0;g=i;e=c[c[(c[a+16>>2]|0)+180>>2]>>2]|0;a=c[c[(c[b+16>>2]|0)+180>>2]>>2]|0;if(((Bl(b)|0)<<24>>24!=0?(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0)==(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0):0)?(Fl(e,a)|0)<<24>>24!=0:0){d=(c[a+16>>2]|0)+56|0;f=g;a=(c[e+16>>2]|0)+56|0;b=f+40|0;do{c[f>>2]=c[a>>2];f=f+4|0;a=a+4|0}while((f|0)<(b|0));f=h;a=d;b=f+40|0;do{c[f>>2]=c[a>>2];f=f+4|0;a=a+4|0}while((f|0)<(b|0));a=(hg(g,h)|0)==0&1}else a=0;l=i;return a|0}function Dl(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;r=l;l=l+32|0;p=r+16|0;o=r;m=d+16|0;f=c[m>>2]|0;e=b[f+236>>1]|0;while(1){if((e|0)>(b[f+238>>1]|0))break;c[(c[f+268>>2]|0)+(e<<2)>>2]=0;e=e+1|0;f=c[m>>2]|0}vj(d);j=jC(d)|0;while(1){if(!j)break;El(d,j);i=XA(d,j)|0;while(1){if(!i)break;else f=i;while(1){e=c[(c[f+16>>2]|0)+172>>2]|0;if(!e)break;else f=e}h=i+-48|0;while(1){e=f+-48|0;g=c[((c[f>>2]&3|0)==2?f:e)+40>>2]|0;if((c[(c[g+16>>2]|0)+232>>2]|0)>=(c[(c[(c[((c[i>>2]&3|0)==2?i:h)+40>>2]|0)+16>>2]|0)+232>>2]|0))break;El(d,g);f=c[c[(c[(c[((c[f>>2]&3|0)==2?f:e)+40>>2]|0)+16>>2]|0)+180>>2]>>2]|0}i=ZA(d,i)|0}j=kC(d,j)|0}e=c[m>>2]|0;i=b[e+236>>1]|0;while(1){if((i|0)>(b[e+238>>1]|0)){k=1;n=e;break}e=c[(c[e+268>>2]|0)+(i<<2)>>2]|0;j=c[(c[(c[(gg(d)|0)+16>>2]|0)+196>>2]|0)+(i<<6)+4>>2]|0;f=e+16|0;if((c[j+(c[(c[f>>2]|0)+236>>2]<<2)>>2]|0)!=(e|0)){q=17;break}h=c[(c[(c[(gg(d)|0)+16>>2]|0)+196>>2]|0)+(i<<6)+4>>2]|0;e=c[m>>2]|0;f=c[e+196>>2]|0;c[f+(i<<6)+4>>2]=h+(c[(c[(c[(c[e+268>>2]|0)+(i<<2)>>2]|0)+16>>2]|0)+236>>2]<<2);e=-1;h=0;a:while(1){if((h|0)>=(c[f+(i<<6)>>2]|0))break;f=c[(c[f+(i<<6)+4>>2]|0)+(h<<2)>>2]|0;if(!f)break;g=c[f+16>>2]|0;b:do if(!(a[g+156>>0]|0))if(!(HC(d,f)|0))break a;else e=h;else{f=c[c[g+172>>2]>>2]|0;while(1){if(!f)break b;g=c[(c[f+16>>2]|0)+116>>2]|0;if(!g)break;else f=g}if(HC(d,c[((c[f>>2]&3|0)==3?f:f+48|0)+40>>2]|0)|0){j=(HC(d,c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0)|0)==0;e=j?e:h}}while(0);h=h+1|0;f=c[(c[m>>2]|0)+196>>2]|0}if((e|0)==-1){c[p>>2]=KB(d)|0;c[p+4>>2]=i;gA(0,88277,p)|0}j=c[m>>2]|0;c[(c[j+196>>2]|0)+(i<<6)>>2]=e+1;i=i+1|0;e=j}if((q|0)==17){p=KB(e)|0;q=c[(c[f>>2]|0)+236>>2]|0;c[o>>2]=p;c[o+4>>2]=q;c[o+8>>2]=i;gA(1,88219,o)|0;bb(187016,1)}while(1){if((k|0)>(c[n+180>>2]|0))break;Dl(c[(c[n+184>>2]|0)+(k<<2)>>2]|0);k=k+1|0;n=c[m>>2]|0}l=r;return}function El(a,b){a=a|0;b=b|0;var d=0,e=0;d=c[b+16>>2]|0;a=(c[(c[a+16>>2]|0)+268>>2]|0)+(c[d+232>>2]<<2)|0;e=c[a>>2]|0;if(!((e|0)!=0?(c[(c[e+16>>2]|0)+236>>2]|0)<=(c[d+236>>2]|0):0))c[a>>2]=b;return}function Fl(b,d){b=b|0;d=d|0;var e=0,f=0;while(1){f=c[b+16>>2]|0;if(!(a[f+112>>0]|0))break;b=c[f+116>>2]|0}while(1){e=c[d+16>>2]|0;if(!(a[e+112>>0]|0))break;d=c[e+116>>2]|0}if((a[f+153>>0]|0)==0?(a[e+153>>0]|0)==0:0){f=c[d>>2]&3;e=c[b>>2]&3;b=(O((c[(c[(c[((e|0)==3?b:b+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((e|0)==2?b:b+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)|0,(c[(c[(c[((f|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((f|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)|0)|0)>0&1}else b=0;return b|0}function Gl(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+544|0;j=k+528|0;Hl(j,k+512|0,k);e=(a[193770]|0)+1<<24>>24;a[193770]=e<<24>>24==0?1:e;e=c[b+16>>2]|0;c[e+220>>2]=0;c[e+232>>2]=0;e=(d|0)>0;d=jC(b)|0;while(1){if(!d)break;if(e?(h=c[d+16>>2]|0,i=c[h+212>>2]|0,(i|0)!=0):0){g=c[(c[(c[i+16>>2]|0)+268>>2]|0)+(c[h+232>>2]<<2)>>2]|0;f=7}else f=6;if((f|0)==6?(f=0,(d|0)==(fP(d)|0)):0){g=d;f=7}if((f|0)==7?(0,(a[(c[g+16>>2]|0)+157>>0]|0)!=(a[193770]|0)):0){Il(b);Jl(j,b,g);Kl(b)}d=kC(b,d)|0}Ll(j);l=k;return}function Hl(a,b,d){a=a|0;b=b|0;d=d|0;c[b>>2]=d;c[b+4>>2]=d+512;c[b+12>>2]=0;c[b+8>>2]=0;c[a>>2]=b;c[a+4>>2]=b;c[a+8>>2]=d;return}function Il(a){a=a|0;c[(c[a+16>>2]|0)+192>>2]=0;c[46793]=0;return}function Jl(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+32|0;p=q;Ml(b,e);k=p+8|0;m=p+16|0;n=p+24|0;a:while(1){o=Nl(b)|0;if(!o)break;e=o+16|0;if((a[(c[e>>2]|0)+157>>0]|0)==(a[193770]|0))continue;Ol(d,o);h=c[e>>2]|0;g=h+180|0;j=c[g+4>>2]|0;i=p;c[i>>2]=c[g>>2];c[i+4>>2]=j;i=h+172|0;j=c[i+4>>2]|0;g=k;c[g>>2]=c[i>>2];c[g+4>>2]=j;g=h+188|0;j=c[g+4>>2]|0;i=m;c[i>>2]=c[g>>2];c[i+4>>2]=j;h=h+196|0;i=c[h+4>>2]|0;j=n;c[j>>2]=c[h>>2];c[j+4>>2]=i;j=3;while(1){if((j|0)<=-1)continue a;e=c[p+(j<<3)>>2]|0;b:do if(e|0){i=(c[p+(j<<3)+4>>2]|0)+-1|0;h=e+(i<<2)|0;while(1){if((i|0)<=-1)break b;f=c[h>>2]|0;g=c[f>>2]&3;e=c[((g|0)==2?f:f+-48|0)+40>>2]|0;if((e|0)==(o|0))e=c[((g|0)==3?f:f+48|0)+40>>2]|0;if((a[(c[e+16>>2]|0)+157>>0]|0)!=(a[193770]|0)?(e|0)==(fP(e)|0):0)Ml(b,e);h=h+-4|0;i=i+-1|0}}while(0);j=j+-1|0}}l=q;return}function Kl(a){a=a|0;var b=0,d=0,e=0,f=0;d=a+16|0;b=c[d>>2]|0;f=b+220|0;e=c[f>>2]|0;a=e+1|0;c[f>>2]=a;b=c[b+216>>2]|0;if(!b)a=KK(a<<2)|0;else a=MK(b,a<<2)|0;f=c[d>>2]|0;c[f+216>>2]=a;c[a+(e<<2)>>2]=c[f+192>>2];return}function Ll(a){a=a|0;var b=0;a=c[(c[a>>2]|0)+12>>2]|0;while(1){if(!a)break;b=c[a+12>>2]|0;U1(c[a>>2]|0);U1(a);a=b}return}function Ml(b,e){b=b|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;h=k+8|0;g=k;j=b+8|0;f=c[j>>2]|0;i=b+4|0;b=c[i>>2]|0;if((f|0)==(c[b+4>>2]|0)){f=c[b+12>>2]|0;if(!f){f=JK(16)|0;if(!f)gA(1,98969,g)|0;c[f+8>>2]=c[i>>2];c[f+12>>2]=0;b=JK(4e6)|0;c[f>>2]=b;if(!b){gA(1,88377,h)|0;b=c[f>>2]|0}c[f+4>>2]=b+4e6;c[(c[i>>2]|0)+12>>2]=f}c[i>>2]=f;f=c[f>>2]|0;c[j>>2]=f}a[(c[e+16>>2]|0)+157>>0]=(d[193770]|0)+1;c[j>>2]=f+4;c[f>>2]=e;l=k;return}function Nl(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;f=a+8|0;b=c[f>>2]|0;d=a+4|0;e=c[d>>2]|0;if((b|0)==(c[e>>2]|0))if((e|0)==(c[a>>2]|0))b=0;else{b=c[e+8>>2]|0;c[d>>2]=b;b=c[b+4>>2]|0;c[f>>2]=b;g=4}else g=4;if((g|0)==4){b=b+-4|0;c[f>>2]=b;b=c[b>>2]|0}return b|0}function Ol(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;f=c[b+16>>2]|0;g=f+232|0;c[g>>2]=(c[g>>2]|0)+1;g=c[d+16>>2]|0;a[g+157>>0]=a[193770]|0;b=c[46793]|0;e=g+168|0;if(!b){c[e>>2]=0;c[f+192>>2]=d}else{c[e>>2]=b;c[(c[b+16>>2]|0)+164>>2]=d}c[46793]=d;c[g+164>>2]=0;return}function Pl(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0.0,o=0.0;m=b+16|0;d=(c[m>>2]|0)+192|0;while(1){d=c[d>>2]|0;if(!d)break;g=d+16|0;d=c[g>>2]|0;e=c[d+188>>2]|0;a:do if(!e)f=0;else{f=0;while(1){e=c[e+(f<<2)>>2]|0;if(!e){f=0;break a}Ql(e);d=c[g>>2]|0;f=f+1|0;e=c[d+188>>2]|0}}while(0);while(1){if((f|0)>=(c[d+208>>2]|0))break;e=c[(c[d+204>>2]|0)+(f<<2)>>2]|0;l=c[e>>2]&3;if((c[(c[(c[((l|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)==(c[(c[(c[((l|0)==3?e:e+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)){Ql(e);d=c[g>>2]|0}f=f+1|0}d=d+164|0}l=c[m>>2]|0;d=c[l+196>>2]|0;if(!((c[d+56>>2]|0)==0?(c[l+180>>2]|0)<=0:0))i=13;b:do if((i|0)==13){i=c[d+4>>2]|0;g=0;c:while(1){d=c[i+(g<<2)>>2]|0;if(!d)break b;e=c[(c[d+16>>2]|0)+196>>2]|0;d=0;while(1){f=c[e+(d<<2)>>2]|0;if(!f)break;l=c[f+16>>2]|0;if(c[l+96>>2]|0?(a[l+113>>0]|0)==0:0)break c;d=d+1|0}g=g+1|0}Rl(b)}while(0);Hi(b);e=0;d=(c[m>>2]|0)+192|0;while(1){d=c[d>>2]|0;if(!d)break;l=d+16|0;d=c[l>>2]|0;f=c[d+188>>2]|0;d:do if(f){k=0;while(1){g=c[f+(k<<2)>>2]|0;if(!g){k=0;break}i=c[g+16>>2]|0;j=c[i+96>>2]|0;do if(j)if(!(a[i+113>>0]|0)){Sl(g);f=c[l>>2]|0;e=1;d=f;f=c[f+188>>2]|0;break}else{h[i+136>>3]=+h[((c[(c[m>>2]|0)+116>>2]&1|0)==0?j+24|0:j+32|0)>>3];break}while(0);k=k+1|0}while(1){if((k|0)>=(c[d+208>>2]|0))break d;i=c[(c[d+204>>2]|0)+(k<<2)>>2]|0;j=c[i>>2]&3;g=c[((j|0)==3?i:i+48|0)+40>>2]|0;j=c[((j|0)==2?i:i+-48|0)+40>>2]|0;do if(!((g|0)==(j|0)?1:(c[(c[g+16>>2]|0)+232>>2]|0)!=(c[(c[j+16>>2]|0)+232>>2]|0))){f=i;do{j=c[f+16>>2]|0;f=c[j+172>>2]|0}while((f|0)!=0);f=a[j+113>>0]|0;g=c[i+16>>2]|0;a[g+113>>0]=f;g=c[g+96>>2]|0;if(g)if(!(f<<24>>24)){Sl(i);e=1;d=c[l>>2]|0;break}else{o=+h[((c[(c[m>>2]|0)+116>>2]&1|0)==0?g+24|0:g+32|0)>>3];j=j+136|0;n=+h[j>>3];h[j>>3]=o>n?o:n;break}}while(0);k=k+1|0}}while(0);d=d+164|0}if(e|0){Ah(b);Oh(b)}return e|0}function Ql(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;d=c[b>>2]&3;i=c[((d|0)==3?b:b+48|0)+40>>2]|0;f=i+16|0;g=c[(c[f>>2]|0)+236>>2]|0;d=c[(c[(c[((d|0)==2?b:b+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;h=(g|0)<(d|0);e=h?d:g;i=c[(c[(gg(i)|0)+16>>2]|0)+196>>2]|0;f=i+(c[(c[f>>2]|0)+232>>2]<<6)+4|0;d=h?g:d;a:while(1){d=d+1|0;if((d|0)>=(e|0))break;g=c[(c[(c[f>>2]|0)+(d<<2)>>2]|0)+16>>2]|0;switch(a[g+156>>0]|0){case 0:break a;case 1:break;default:continue a}if(c[g+104>>2]|0)break}if((d|0)==(e|0))do{i=c[b+16>>2]|0;a[i+113>>0]=1;b=c[i+172>>2]|0}while((b|0)!=0);return}function Rl(a){a=a|0;var d=0,e=0,f=0,g=0;g=a+16|0;a=c[g>>2]|0;if(b[a+236>>1]|0)qa(88434,88417,190,88453);d=(b[a+238>>1]|0)+3|0;a=c[a+196>>2]|0;if(!a)a=KK(d<<6)|0;else a=MK(a,d<<6)|0;d=a+64|0;a=c[g>>2]|0;c[a+196>>2]=d;a=b[a+238>>1]|0;while(1){if((a|0)<=-1)break;f=a+-1|0;e=d+(a<<6)|0;a=d+(f<<6)|0;d=e+64|0;do{c[e>>2]=c[a>>2];e=e+4|0;a=a+4|0}while((e|0)<(d|0));a=f;d=c[(c[g>>2]|0)+196>>2]|0}c[d+(a<<6)+8>>2]=0;c[d+(a<<6)>>2]=0;e=JK(8)|0;g=c[g>>2]|0;f=c[g+196>>2]|0;c[f+(a<<6)+12>>2]=e;c[f+(a<<6)+4>>2]=e;c[f+(a<<6)+56>>2]=0;h[f+(a<<6)+24>>3]=1.0;h[f+(a<<6)+16>>3]=1.0;h[f+(a<<6)+40>>3]=1.0;h[f+(a<<6)+32>>3]=1.0;g=g+236|0;b[g>>1]=(b[g>>1]|0)+-1<<16>>16;return}function Sl(b){b=b|0;var d=0,e=0,f=0.0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0.0,q=0.0,r=0.0;j=b+16|0;if(c[(c[j>>2]|0)+96>>2]|0){m=b+48|0;k=gg(c[((c[b>>2]&3|0)==3?b:m)+40>>2]|0)|0;d=c[(c[(c[((c[b>>2]&3|0)==3?b:m)+40>>2]|0)+16>>2]|0)+232>>2]|0;l=Tl(k,b)|0;n=k+16|0;e=c[n>>2]|0;g=c[e+196>>2]|0;o=d+-1|0;i=c[c[g+(o<<6)+4>>2]>>2]|0;if(!i)f=+h[g+(d<<6)+24>>3]+ +h[(c[(c[c[g+(d<<6)+4>>2]>>2]|0)+16>>2]|0)+24>>3]+ +(c[e+252>>2]|0);else f=+h[(c[i+16>>2]|0)+24>>3]-+h[g+(o<<6)+16>>3];d=Ul(k,o,l)|0;k=c[(c[j>>2]|0)+96>>2]|0;q=+h[k+24>>3];p=+h[k+32>>3];j=(c[(c[n>>2]|0)+116>>2]&1|0)==0;r=j?p:q;g=d+16|0;e=c[g>>2]|0;h[e+80>>3]=r;l=~~(r*.5);p=(j?q:p)*.5;h[e+96>>3]=p;h[e+88>>3]=p;c[e+104>>2]=k;h[e+24>>3]=+(l+~~f|0);e=nh(d,c[((c[b>>2]&3|0)==3?b:m)+40>>2]|0,b)|0;e=c[e+16>>2]|0;h[e+16>>3]=-+h[(c[g>>2]|0)+88>>3];h[e+56>>3]=+h[(c[(c[((c[b>>2]&3|0)==3?b:m)+40>>2]|0)+16>>2]|0)+96>>3];a[e+112>>0]=4;e=b+-48|0;d=nh(d,c[((c[b>>2]&3|0)==2?b:e)+40>>2]|0,b)|0;g=c[g>>2]|0;d=c[d+16>>2]|0;h[d+16>>3]=+h[g+96>>3];h[d+56>>3]=+h[(c[(c[((c[b>>2]&3|0)==2?b:e)+40>>2]|0)+16>>2]|0)+88>>3];a[d+112>>0]=4;d=c[(c[n>>2]|0)+196>>2]|0;e=d+(o<<6)+16|0;f=+(l|0);if(+h[e>>3]<f)h[e>>3]=f;d=d+(o<<6)+24|0;if(+h[d>>3]<f)h[d>>3]=f;c[g+112>>2]=b}return}
+function QI(d,f,g){d=d|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;A=l;l=l+160|0;y=A+24|0;w=A+20|0;v=A+16|0;o=A;p=A+32|0;x=c[d+152>>2]|0;c[y>>2]=0;c[w>>2]=0;c[v>>2]=0;z=aI(d)|0;c[z+4>>2]=3;c[z+8>>2]=f;c[z+12>>2]=9;t=f+16|0;n=c[(c[t>>2]|0)+96>>2]|0;if((n|0?(a[n+82>>0]|0)==0:0)?(eP(QA(f,100105)|0,0)|0)<<24>>24:0){n=z+260|0;b[n>>1]=b[n>>1]|512}if(g|0?c[(c[t>>2]|0)+8>>2]|0:0)lS(d,g);g=c[47206]|0;if((g|0?(i=RA(f,g)|0,i|0):0)?a[i>>0]|0:0)uS(d,+_O(f,c[47206]|0,1.0,0.0));do if(x&134217728|0){g=f+48|0;if((e[(c[(xC(c[((c[f>>2]&3|0)==3?f:g)+40>>2]|0)|0)+16>>2]|0)+178>>1]|0)>2){m=+h[(c[(c[(c[((c[f>>2]&3|0)==3?f:g)+40>>2]|0)+16>>2]|0)+132>>2]|0)+16>>3]*72.0;h[z+176>>3]=+(~~(m+(m>=0.0?.5:-.5))|0);m=+h[(c[(c[(c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0)+16>>2]|0)+132>>2]|0)+16>>3]*72.0;h[z+184>>3]=+(~~(m+(m>=0.0?.5:-.5))|0);break}else{n=z+176|0;c[n>>2]=0;c[n+4>>2]=0;c[n+8>>2]=0;c[n+12>>2]=0;break}}while(0);if(x&32768|0){n=c[t>>2]|0;g=c[n+96>>2]|0;if(!g)g=c[z+192>>2]|0;else{g=c[g>>2]|0;c[z+192>>2]=g}i=z+196|0;c[i>>2]=g;k=z+204|0;c[k>>2]=g;j=z+200|0;c[j>>2]=g;g=c[n+108>>2]|0;if(g|0)c[i>>2]=c[g>>2];g=c[n+104>>2]|0;if(g|0)c[j>>2]=c[g>>2];g=c[n+100>>2]|0;if(g|0)c[k>>2]=c[g>>2]}if(!(x&65536))j=0;else{kA(o,128,p);c[z+212>>2]=EK(dI(d,f,o)|0,f)|0;oA(o);g=QA(f,141055)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0)q=31;else{g=QA(f,111477)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0)q=31;else i=0}if((q|0)==31)i=EK(g,f)|0;g=QA(f,100118)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0)q=36;else q=34;do if((q|0)==34){g=QA(f,100127)|0;if(g|0?a[g>>0]|0:0){q=36;break}if(i|0){g=D4(i)|0;q=39}}while(0);if((q|0)==36){g=EK(g,f)|0;q=39}if((q|0)==39)c[z+208>>2]=g;g=QA(f,100135)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0)q=44;else q=42;do if((q|0)==42){g=QA(f,100145)|0;if(g|0?a[g>>0]|0:0){q=44;break}if(i|0){g=D4(i)|0;q=47}}while(0);if((q|0)==44){g=EK(g,f)|0;q=47}if((q|0)==47)c[z+216>>2]=g;g=QA(f,100154)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0)q=52;else q=50;do if((q|0)==50){g=QA(f,100163)|0;if(g|0?a[g>>0]|0:0){q=52;break}if(i|0)c[z+220>>2]=D4(i)|0}while(0);if((q|0)==52){c[z+220>>2]=EK(g,f)|0;q=z+260|0;b[q>>1]=b[q>>1]|128}g=QA(f,100171)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0)q=59;else q=57;do if((q|0)==57){g=QA(f,100180)|0;if(g|0?a[g>>0]|0:0){q=59;break}if(i|0)c[z+224>>2]=D4(i)|0}while(0);if((q|0)==59){c[z+224>>2]=EK(g,f)|0;q=z+260|0;b[q>>1]=b[q>>1]|256}j=i}do if(x&8388608){g=QA(f,141088)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0)i=EK(g,f)|0;else i=0;g=QA(f,100188)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0){q=z+260|0;b[q>>1]=b[q>>1]|64;g=EK(g,f)|0;q=72}else q=70;if((q|0)==70?i|0:0){g=D4(i)|0;q=72}if((q|0)==72)c[z+244>>2]=g;g=QA(f,100199)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0){g=EK(g,f)|0;q=78}else q=76;if((q|0)==76?i|0:0){g=D4(i)|0;q=78}if((q|0)==78)c[z+248>>2]=g;g=QA(f,100211)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0){c[z+252>>2]=EK(g,f)|0;p=z+260|0;b[p>>1]=b[p>>1]|16}else q=82;if((q|0)==82?i|0:0)c[z+252>>2]=D4(i)|0;g=QA(f,100222)|0;if(g|0?a[g>>0]|0:0){q=z+260|0;b[q>>1]=b[q>>1]|32;c[z+256>>2]=EK(g,f)|0;break}if(i)c[z+256>>2]=D4(i)|0;else i=0}else i=0;while(0);do if(x&4194304|0){g=QA(f,141101)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0)q=94;else q=92;do if((q|0)==92){g=QA(f,100233)|0;if(g|0?a[g>>0]|0:0){q=94;break}g=c[z+192>>2]|0;if(g|0)c[z+228>>2]=D4(g)|0}while(0);if((q|0)==94){p=jJ(g,f)|0;c[z+228>>2]=EK(p,f)|0;U1(p);p=z+260|0;b[p>>1]=b[p>>1]|1}g=QA(f,100245)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0){p=jJ(g,f)|0;c[z+232>>2]=EK(p,f)|0;U1(p);p=z+260|0;b[p>>1]=b[p>>1]|8}else q=100;if((q|0)==100?(r=c[z+192>>2]|0,r|0):0)c[z+232>>2]=D4(r)|0;g=QA(f,100258)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0){r=jJ(g,f)|0;c[z+236>>2]=EK(r,f)|0;U1(r);r=z+260|0;b[r>>1]=b[r>>1]|2}else q=105;if((q|0)==105?(s=c[z+200>>2]|0,s|0):0)c[z+236>>2]=D4(s)|0;g=QA(f,100270)|0;if(g|0?a[g>>0]|0:0){s=jJ(g,f)|0;c[z+240>>2]=EK(s,f)|0;U1(s);s=z+260|0;b[s>>1]=b[s>>1]|4;break}g=c[z+204>>2]|0;if(g|0)c[z+240>>2]=D4(g)|0}while(0);U1(j);U1(i);do if(x&4259840|0?(u=c[(c[t>>2]|0)+8>>2]|0,u|0):0){if(!(c[z+208>>2]|0)){if((x&524288|0)==0|(c[z+228>>2]|0)==0)break}else if(!(x&524288))break;m=+h[(c[d+16>>2]|0)+152>>3]*.5;m=m>2.0?m:2.0;i=c[u+4>>2]|0;g=0;while(1){if((g|0)>=(i|0))break;kJ(y,w,v,(c[u>>2]|0)+(g*48|0)|0,m);g=g+1|0}j=c[v>>2]|0;c[z+276>>2]=j;k=c[w>>2]|0;c[z+280>>2]=k;if(!(x&8192)){g=0;i=0;while(1){if((i|0)>=(j|0))break;g=(c[k+(i<<2)>>2]|0)+g|0;i=i+1|0}y=c[y>>2]|0;NR(d,y,y,g)|0;g=y}else g=c[y>>2]|0;c[z+284>>2]=g;c[z+264>>2]=2;c[z+272>>2]=g;c[z+268>>2]=c[k>>2]}while(0);aS(d,f);g=c[z+208>>2]|0;if(!((g|0)==0?!(b[z+260>>1]&1):0))cS(d,g,c[z+228>>2]|0,c[z+244>>2]|0,c[z+212>>2]|0);l=A;return}function RI(b,e,f){b=b|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0.0,N=0.0,O=0,P=0,Q=0,R=0,S=0;P=l;l=l+240|0;L=P+224|0;K=P+208|0;x=P+128|0;w=P+112|0;O=P+64|0;C=P+48|0;D=P+32|0;E=P+16|0;F=P;G=P+192|0;H=P+176|0;y=P+160|0;B=P+144|0;c[E>>2]=0;c[E+4>>2]=0;c[E+8>>2]=0;c[E+12>>2]=0;M=+h[(c[b+16>>2]|0)+152>>3];HT(QA(e,100032)|0);J=e+16|0;a:do if(c[(c[J>>2]|0)+8>>2]|0){N=+_O(e,c[47188]|0,1.0,0.0);k=aP(e,c[47176]|0,195059)|0;v=(f|0)!=0;b:do if(v){g=f;while(1){i=g;g=g+4|0;i=c[i>>2]|0;if(!i){o=0;break b}if((a[i>>0]|0)!=116)continue;if(!(s2(i,111117)|0)){o=1;break}}}else o=0;while(0);j=k;q=0;i=0;c:while(1){switch(a[j>>0]|0){case 0:break c;case 58:{g=q+1|0;break}case 59:{g=q;i=i+1|0;break}default:g=q}j=j+1|0;q=g}n=(q|0)!=0;if((i|0)!=0&n)if(!(ZI(b,e,f,k,q+1|0,N,M)|0))break;else m=137314;else m=k;g=d[(c[J>>2]|0)+115>>0]|0;if(!(g&1))if(!(g&2))if(!(g&8))if(!(g&4)){i=m;j=bP(e,c[47177]|0,m)|0}else{j=106296;g=106288;k=188712;i=188712;p=18}else{j=106280;g=106272;k=188712;i=188712;p=18}else{j=106264;g=106256;k=188712;i=188712;p=18}else{j=106248;g=106240;k=188712;i=188712;p=18}if((p|0)==18){i=c[i>>2]|0;i=bP(e,i,_I(m,g)|0)|0;g=bP(e,c[k>>2]|0,j)|0;if((i|0)==(m|0)){i=m;j=g}else{hS(b,i);j=g}}if((j|0)!=(m|0))jS(b,j);if(o<<24>>24){i=(a[i>>0]|0)==0?137314:i;g=(a[j>>0]|0)==0?137314:j;hS(b,137379);jS(b,i);u=O;r=c[c[(c[J>>2]|0)+8>>2]>>2]|0;s=u+48|0;do{c[u>>2]=c[r>>2];u=u+4|0;r=r+4|0}while((u|0)<(s|0));J=zO(O,$I(e)|0,M,0,0)|0;nS(b,c[J+8>>2]|0,c[J>>2]|0,1);aJ(J);hS(b,i);if((g|0)!=(i|0))jS(b,g);g=c[O+8>>2]|0;if(g|0){I=O+16|0;J=c[O>>2]|0;c[K>>2]=c[I>>2];c[K+4>>2]=c[I+4>>2];c[K+8>>2]=c[I+8>>2];c[K+12>>2]=c[I+12>>2];c[L>>2]=c[J>>2];c[L+4>>2]=c[J+4>>2];c[L+8>>2]=c[J+8>>2];c[L+12>>2]=c[J+12>>2];ZH(b,2,K,L,N,M,g)}g=c[O+12>>2]|0;if(!g)break;J=O+32|0;O=(c[O>>2]|0)+((c[O+4>>2]|0)+-1<<4)|0;c[K>>2]=c[J>>2];c[K+4>>2]=c[J+4>>2];c[K+8>>2]=c[J+8>>2];c[K+12>>2]=c[J+12>>2];c[L>>2]=c[O>>2];c[L+4>>2]=c[O+4>>2];c[L+8>>2]=c[O+8>>2];c[L+12>>2]=c[O+12>>2];ZH(b,3,K,L,N,M,g);break}g=c[J>>2]|0;if(!n){do if(!(a[g+115>>0]&3)){if(a[i>>0]|0){hS(b,i);jS(b,j);break}hS(b,137314);if(!(a[j>>0]|0)){jS(b,137314);break}else{jS(b,j);break}}while(0);k=b+152|0;m=O+4|0;n=O+8|0;o=O+12|0;p=O+32|0;q=O+16|0;j=0;while(1){g=c[(c[J>>2]|0)+8>>2]|0;if((j|0)>=(c[g+4>>2]|0))break a;u=O;r=(c[g>>2]|0)+(j*48|0)|0;s=u+48|0;do{c[u>>2]=c[r>>2];u=u+4|0;r=r+4|0}while((u|0)<(s|0));g=c[O>>2]|0;i=c[m>>2]|0;do if(!(c[k>>2]&16384)){pS(b,g,i,0,0,0);g=c[n>>2]|0;if(g|0){I=c[O>>2]|0;c[K>>2]=c[q>>2];c[K+4>>2]=c[q+4>>2];c[K+8>>2]=c[q+8>>2];c[K+12>>2]=c[q+12>>2];c[L>>2]=c[I>>2];c[L+4>>2]=c[I+4>>2];c[L+8>>2]=c[I+8>>2];c[L+12>>2]=c[I+12>>2];ZH(b,2,K,L,N,M,g)}g=c[o>>2]|0;if(g|0){I=(c[O>>2]|0)+((c[m>>2]|0)+-1<<4)|0;c[K>>2]=c[p>>2];c[K+4>>2]=c[p+4>>2];c[K+8>>2]=c[p+8>>2];c[K+12>>2]=c[p+12>>2];c[L>>2]=c[I>>2];c[L+4>>2]=c[I+4>>2];c[L+8>>2]=c[I+8>>2];c[L+12>>2]=c[I+12>>2];ZH(b,3,K,L,N,M,g)}if((c[(c[(c[J>>2]|0)+8>>2]|0)+4>>2]|0)>1){if(!(c[n>>2]|0)){if(!(v&(c[o>>2]|0)!=0))break}else if(!v)break;lS(b,f)}}else pS(b,g,i,c[n>>2]|0,c[o>>2]|0,0);while(0);j=j+1|0}}I=c[(c[g+8>>2]|0)+4>>2]|0;A=I*48|0;z=T1(A)|0;A=T1(A)|0;t=+(q+2|0)*.5;v=O+4|0;p=C+8|0;q=D+8|0;e=E+8|0;f=F+8|0;o=0;while(1){if((o|0)>=(I|0))break;u=O;r=(c[c[(c[J>>2]|0)+8>>2]>>2]|0)+(o*48|0)|0;s=u+48|0;do{c[u>>2]=c[r>>2];u=u+4|0;r=r+4|0}while((u|0)<(s|0));g=c[v>>2]|0;c[z+(o*48|0)+4>>2]=g;c[A+(o*48|0)+4>>2]=g;n=g<<4;m=T1(n)|0;c[z+(o*48|0)>>2]=m;n=T1(n)|0;c[A+(o*48|0)>>2]=n;k=c[O>>2]|0;c[F>>2]=c[k>>2];c[F+4>>2]=c[k+4>>2];c[F+8>>2]=c[k+8>>2];c[F+12>>2]=c[k+12>>2];k=0;while(1){if((k|0)>=(g+-1|0))break;c[C>>2]=c[F>>2];c[C+4>>2]=c[F+4>>2];c[C+8>>2]=c[F+8>>2];c[C+12>>2]=c[F+12>>2];g=k+1|0;j=(c[O>>2]|0)+(g<<4)|0;c[D>>2]=c[j>>2];c[D+4>>2]=c[j+4>>2];c[D+8>>2]=c[j+8>>2];c[D+12>>2]=c[j+12>>2];j=m+(k<<4)|0;if(!k){c[K>>2]=c[C>>2];c[K+4>>2]=c[C+4>>2];c[K+8>>2]=c[C+8>>2];c[K+12>>2]=c[C+12>>2];c[L>>2]=c[D>>2];c[L+4>>2]=c[D+4>>2];c[L+8>>2]=c[D+8>>2];c[L+12>>2]=c[D+12>>2];bJ(G,K,L);c[j>>2]=c[G>>2];c[j+4>>2]=c[G+4>>2];c[j+8>>2]=c[G+8>>2];c[j+12>>2]=c[G+12>>2]}else{c[K>>2]=c[E>>2];c[K+4>>2]=c[E+4>>2];c[K+8>>2]=c[E+8>>2];c[K+12>>2]=c[E+12>>2];c[L>>2]=c[D>>2];c[L+4>>2]=c[D+4>>2];c[L+8>>2]=c[D+8>>2];c[L+12>>2]=c[D+12>>2];bJ(H,K,L);c[j>>2]=c[H>>2];c[j+4>>2]=c[H+4>>2];c[j+8>>2]=c[H+8>>2];c[j+12>>2]=c[H+12>>2]}R=c[O>>2]|0;s=k+2|0;S=R+(s<<4)|0;c[E>>2]=c[S>>2];c[E+4>>2]=c[S+4>>2];c[E+8>>2]=c[S+8>>2];c[E+12>>2]=c[S+12>>2];u=k+3|0;R=R+(u<<4)|0;c[F>>2]=c[R>>2];c[F+4>>2]=c[R+4>>2];c[F+8>>2]=c[R+8>>2];c[F+12>>2]=c[R+12>>2];Q=m+(g<<4)|0;r=m+(s<<4)|0;c[w>>2]=c[C>>2];c[w+4>>2]=c[C+4>>2];c[w+8>>2]=c[C+8>>2];c[w+12>>2]=c[C+12>>2];c[x>>2]=c[D>>2];c[x+4>>2]=c[D+4>>2];c[x+8>>2]=c[D+8>>2];c[x+12>>2]=c[D+12>>2];c[K>>2]=c[S>>2];c[K+4>>2]=c[S+4>>2];c[K+8>>2]=c[S+8>>2];c[K+12>>2]=c[S+12>>2];c[L>>2]=c[R>>2];c[L+4>>2]=c[R+4>>2];c[L+8>>2]=c[R+8>>2];c[L+12>>2]=c[R+12>>2];cJ(y,w,x,K,L);c[r>>2]=c[y>>2];c[r+4>>2]=c[y+4>>2];c[r+8>>2]=c[y+8>>2];c[r+12>>2]=c[y+12>>2];c[Q>>2]=c[y>>2];c[Q+4>>2]=c[y+4>>2];c[Q+8>>2]=c[y+8>>2];c[Q+12>>2]=c[y+12>>2];h[n+(k<<4)>>3]=+h[C>>3]-+h[j>>3]*t;h[n+(k<<4)+8>>3]=+h[p>>3]-+h[m+(k<<4)+8>>3]*t;h[n+(g<<4)>>3]=+h[D>>3]-+h[Q>>3]*t;h[n+(g<<4)+8>>3]=+h[q>>3]-+h[m+(g<<4)+8>>3]*t;h[n+(s<<4)>>3]=+h[E>>3]-+h[r>>3]*t;h[n+(s<<4)+8>>3]=+h[e>>3]-+h[m+(s<<4)+8>>3]*t;k=u;g=c[v>>2]|0}S=m+(k<<4)|0;c[K>>2]=c[E>>2];c[K+4>>2]=c[E+4>>2];c[K+8>>2]=c[E+8>>2];c[K+12>>2]=c[E+12>>2];c[L>>2]=c[F>>2];c[L+4>>2]=c[F+4>>2];c[L+8>>2]=c[F+8>>2];c[L+12>>2]=c[F+12>>2];bJ(B,K,L);c[S>>2]=c[B>>2];c[S+4>>2]=c[B+4>>2];c[S+8>>2]=c[B+8>>2];c[S+12>>2]=c[B+12>>2];h[n+(k<<4)>>3]=+h[F>>3]-+h[S>>3]*t;h[n+(k<<4)+8>>3]=+h[f>>3]-+h[m+(k<<4)+8>>3]*t;o=o+1|0}s=D4(i)|0;f=0;r=i;g=i;j=i;i=s;while(1){i=A4(i,107984)|0;if(!i)break;e=(a[i>>0]|0)==0?137314:i;if((e|0)!=(j|0))if(!(a[(c[J>>2]|0)+115>>0]&3)){hS(b,e);jS(b,e);j=e}else j=e;m=(f|0)==0;n=f>>>0<2;i=0;while(1){if((i|0)>=(I|0))break;o=c[A+(i*48|0)>>2]|0;p=c[z+(i*48|0)>>2]|0;q=c[A+(i*48|0)+4>>2]|0;k=0;while(1){if((k|0)>=(q|0))break;S=o+(k<<4)|0;h[S>>3]=+h[S>>3]+ +h[p+(k<<4)>>3];S=o+(k<<4)+8|0;h[S>>3]=+h[S>>3]+ +h[p+(k<<4)+8>>3];k=k+1|0}pS(b,o,q,0,0,0);i=i+1|0}f=f+1|0;r=m?e:r;g=n?e:g;i=0}i=c[O+8>>2]|0;if(!i)i=0;else{if(g){if(!(a[(c[J>>2]|0)+115>>0]&3)){hS(b,g);jS(b,g)}}else g=0;R=O+16|0;S=c[O>>2]|0;c[K>>2]=c[R>>2];c[K+4>>2]=c[R+4>>2];c[K+8>>2]=c[R+8>>2];c[K+12>>2]=c[R+12>>2];c[L>>2]=c[S>>2];c[L+4>>2]=c[S+4>>2];c[L+8>>2]=c[S+8>>2];c[L+12>>2]=c[S+12>>2];ZH(b,2,K,L,N,M,i);i=g}j=O+12|0;g=c[j>>2]|0;if(g|0){if((i|0)!=(r|0)?(a[(c[J>>2]|0)+115>>0]&3)==0:0){hS(b,r);jS(b,r);g=c[j>>2]|0}R=O+32|0;S=(c[O>>2]|0)+((c[v>>2]|0)+-1<<4)|0;c[K>>2]=c[R>>2];c[K+4>>2]=c[R+4>>2];c[K+8>>2]=c[R+8>>2];c[K+12>>2]=c[R+12>>2];c[L>>2]=c[S>>2];c[L+4>>2]=c[S+4>>2];c[L+8>>2]=c[S+8>>2];c[L+12>>2]=c[S+12>>2];ZH(b,3,K,L,N,M,g)}U1(s);g=0;while(1){if((g|0)>=(I|0))break;U1(c[z+(g*48|0)>>2]|0);U1(c[A+(g*48|0)>>2]|0);g=g+1|0}U1(z);U1(A)}while(0);l=P;return}function SI(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;x=l;l=l+64|0;r=x+48|0;u=x+32|0;t=x+16|0;s=x;w=c[a+16>>2]|0;v=c[w+8>>2]|0;q=w+208|0;if(!((c[q>>2]|0)==0?!(b[w+260>>1]&1):0))d=3;a:do if((d|0)==3?(dS(a),p=w+276|0,e=c[p>>2]|0,e|0):0){h=w+280|0;d=c[h>>2]|0;i=w+268|0;j=w+284|0;k=w+272|0;m=w+228|0;n=w+244|0;o=w+212|0;f=c[d>>2]|0;g=1;while(1){if((g|0)>=(e|0))break a;c[i>>2]=c[d+(g<<2)>>2];c[k>>2]=(c[j>>2]|0)+(f<<4);cS(a,c[q>>2]|0,c[m>>2]|0,c[n>>2]|0,c[o>>2]|0);dS(a);y=c[h>>2]|0;f=(c[y+(g<<2)>>2]|0)+f|0;g=g+1|0;e=c[p>>2]|0;d=y}}while(0);c[w+268>>2]=0;c[w+272>>2]=0;q=v+16|0;d=c[q>>2]|0;e=c[d+8>>2]|0;if(!e)f=w+260|0;else{p=c[e>>2]|0;d=c[p>>2]|0;y=c[p+8>>2]|0;o=p+16|0;c[t>>2]=c[o>>2];c[t+4>>2]=c[o+4>>2];c[t+8>>2]=c[o+8>>2];c[t+12>>2]=c[o+12>>2];p=p+32|0;c[s>>2]=c[p>>2];c[s+4>>2]=c[p+4>>2];c[s+8>>2]=c[p+8>>2];c[s+12>>2]=c[p+12>>2];if(!y){c[u>>2]=c[d>>2];c[u+4>>2]=c[d+4>>2];c[u+8>>2]=c[d+8>>2];c[u+12>>2]=c[d+12>>2]}else{c[u>>2]=c[t>>2];c[u+4>>2]=c[t+4>>2];c[u+8>>2]=c[t+8>>2];c[u+12>>2]=c[t+12>>2]}f=w+260|0;p=b[f>>1]|0;o=c[w+220>>2]|0;c[r>>2]=c[u>>2];c[r+4>>2]=c[u+4>>2];c[r+8>>2]=c[u+8>>2];c[r+12>>2]=c[u+12>>2];TI(a,r,(p<<8&65535)<<16>>16>>15&255,o,(p<<14&65535)<<16>>16>>15&255);p=c[(c[q>>2]|0)+8>>2]|0;o=c[p>>2]|0;p=(c[p+4>>2]|0)+-1|0;d=c[o+(p*48|0)>>2]|0;e=c[o+(p*48|0)+4>>2]|0;y=c[o+(p*48|0)+12>>2]|0;n=o+(p*48|0)+16|0;c[t>>2]=c[n>>2];c[t+4>>2]=c[n+4>>2];c[t+8>>2]=c[n+8>>2];c[t+12>>2]=c[n+12>>2];t=o+(p*48|0)+32|0;c[s>>2]=c[t>>2];c[s+4>>2]=c[t+4>>2];c[s+8>>2]=c[t+8>>2];c[s+12>>2]=c[t+12>>2];if(!y){y=d+(e+-1<<4)|0;c[u>>2]=c[y>>2];c[u+4>>2]=c[y+4>>2];c[u+8>>2]=c[y+8>>2];c[u+12>>2]=c[y+12>>2]}else{c[u>>2]=c[s>>2];c[u+4>>2]=c[s+4>>2];c[u+8>>2]=c[s+8>>2];c[u+12>>2]=c[s+12>>2]}d=b[f>>1]|0;y=c[w+224>>2]|0;c[r>>2]=c[u>>2];c[r+4>>2]=c[u+4>>2];c[r+8>>2]=c[u+8>>2];c[r+12>>2]=c[u+12>>2];TI(a,r,(d<<7&65535)<<16>>16>>15&255,y,(d<<13&65535)<<16>>16>>15&255);d=c[q>>2]|0}e=c[d+96>>2]|0;g=(b[f>>1]<<12&65535)<<16>>16>>15<<16>>16;m=w+216|0;h=c[m>>2]|0;n=w+232|0;i=c[n>>2]|0;o=w+248|0;j=c[o>>2]|0;p=w+212|0;k=c[p>>2]|0;if(!((dP(aP(v,c[47186]|0,137308)|0)|0)<<24>>24))d=0;else d=c[(c[q>>2]|0)+8>>2]|0;UI(a,e,11,g,h,i,j,k,d);j=c[(c[q>>2]|0)+108>>2]|0;k=(b[f>>1]<<12&65535)<<16>>16>>15<<16>>16;i=c[m>>2]|0;h=c[n>>2]|0;e=c[o>>2]|0;g=c[p>>2]|0;if(!((dP(aP(v,c[47186]|0,137308)|0)|0)<<24>>24))d=0;else d=c[(c[q>>2]|0)+8>>2]|0;UI(a,j,11,k,i,h,e,g,d);UI(a,c[(c[q>>2]|0)+100>>2]|0,7,(b[f>>1]<<13&65535)<<16>>16>>15<<16>>16,c[w+224>>2]|0,c[w+240>>2]|0,c[w+256>>2]|0,c[p>>2]|0,0);UI(a,c[(c[q>>2]|0)+104>>2]|0,6,(b[f>>1]<<14&65535)<<16>>16>>15<<16>>16,c[w+220>>2]|0,c[w+236>>2]|0,c[w+252>>2]|0,c[p>>2]|0,0);bS(a);bI(a);l=x;return}function TI(a,d,e,f,g){a=a|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0;j=l;l=l+16|0;h=j;i=c[a+16>>2]|0;if(!(e<<24>>24))f=c[i+208>>2]|0;if(!(g<<24>>24==0?((f|0)==0?(b[i+260>>1]&1)==0:0):0)){c[h>>2]=c[d>>2];c[h+4>>2]=c[d+4>>2];c[h+8>>2]=c[d+8>>2];c[h+12>>2]=c[d+12>>2];YI(a,h)}l=j;return}function UI(b,d,e,f,g,h,i,j,k){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;k=k|0;var m=0,n=0,o=0,p=0,q=0,r=0;r=l;l=l+16|0;o=r;q=c[b+152>>2]|0;if(d|0?a[d+81>>0]|0:0){if(!j)n=0;else{n=JK((l2(j)|0)+11|0)|0;switch(e|0){case 11:{m=108224;break}case 7:{m=101583;break}case 6:{m=101593;break}default:qa(138394,99906,2730,100044)}c[o>>2]=j;c[o+4>>2]=m;m4(n,100060,o)|0}o=b+16|0;j=(c[o>>2]|0)+12|0;p=c[j>>2]|0;c[j>>2]=e;j=(f|0)!=0|(g|0)!=0;m=(q&4|0)==0;if(j&m){VI(b,d);cS(b,g,h,i,n)}DK(b,e,d);if(k|0)WI(b,d,k);if(j){if(!m){VI(b,d);cS(b,g,h,i,n)}dS(b)}U1(n);c[(c[o>>2]|0)+12>>2]=p}l=r;return}function VI(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0;d=c[a+16>>2]|0;e=c[a+152>>2]|0;if(e&4259840|0){f=e&131072;i=f>>>16^2;c[d+264>>2]=i;j=d+268|0;c[j>>2]=i+2;i=d+272|0;U1(c[i>>2]|0);d=JK(c[j>>2]<<4)|0;c[i>>2]=d;i=b+56|0;j=b+24|0;h[d>>3]=+h[i>>3]-+h[j>>3]*.5;g=b+64|0;b=b+32|0;h[d+8>>3]=+h[g>>3]-+h[b>>3]*.5;h[d+16>>3]=+h[j>>3]*.5+ +h[i>>3];h[d+24>>3]=+h[b>>3]*.5+ +h[g>>3];if(!(e&8192))NR(a,d,d,2)|0;if(!f)jK(d)}return}function WI(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0.0;o=l;l=l+96|0;g=o+80|0;i=o+32|0;j=o+16|0;k=o;f=c[d>>2]|0;while(1){m=a[f>>0]|0;if(!(m<<24>>24))break;if(!(S2(m&255)|0)){n=4;break}else f=f+1|0}if((n|0)==4){p=+h[d+24>>3];m=d+56|0;XI(i,+h[m>>3]+p*.5,+h[d+64>>3]-+h[d+32>>3]*.5);n=i+16|0;XI(j,+h[i>>3]-p,+h[i+8>>3]);c[n>>2]=c[j>>2];c[n+4>>2]=c[j+4>>2];c[n+8>>2]=c[j+8>>2];c[n+12>>2]=c[j+12>>2];n=i+32|0;c[g>>2]=c[m>>2];c[g+4>>2]=c[m+4>>2];c[g+8>>2]=c[m+8>>2];c[g+12>>2]=c[m+12>>2];qP(k,e,g);c[n>>2]=c[k>>2];c[n+4>>2]=c[k+4>>2];c[n+8>>2]=c[k+8>>2];c[n+12>>2]=c[k+12>>2];lS(b,c[(c[b>>2]|0)+336>>2]|0);hS(b,c[d+8>>2]|0);qS(b,i,3)}l=o;return}function XI(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b;h[a+8>>3]=c;return}function YI(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0.0,i=0.0,j=0,k=0;d=c[a+16>>2]|0;e=c[a+152>>2]|0;if(e&4259840|0){f=e&131072;j=f>>>16^2;c[d+264>>2]=j;k=d+268|0;c[k>>2]=j+2;j=d+272|0;U1(c[j>>2]|0);d=JK(c[k>>2]<<4)|0;c[j>>2]=d;i=+h[b>>3];h[d>>3]=i+-3.0;g=+h[b+8>>3];h[d+8>>3]=g+-3.0;h[d+16>>3]=i+3.0;h[d+24>>3]=g+3.0;if(!(e&8192))NR(a,d,d,2)|0;if(!f)jK(d)}return}function ZI(a,b,d,e,f,h,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;h=+h;i=+i;var j=0,k=0,m=0.0,n=0.0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0;J=l;l=l+256|0;D=J+224|0;C=J+208|0;j=J+192|0;F=J+144|0;G=J+96|0;H=J+48|0;E=J;k=J+240|0;e=gI(e,f,k)|0;if((e|0)>1){z=b+48|0;A=xC(c[((c[b>>2]&3|0)==3?b:z)+40>>2]|0)|0;z=KB(c[((c[b>>2]&3|0)==3?b:z)+40>>2]|0)|0;A=(xB(A)|0)!=0;B=KB(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0;c[j>>2]=z;c[j+4>>2]=A?100079:100084;c[j+8>>2]=B;gA(3,100089,j)|0;if((e|0)==2)e=1;else I=4}else if((e|0)==1)e=1;else I=4;if((I|0)==4){B=b+16|0;r=c[k>>2]|0;s=r+8|0;t=H+4|0;u=F+8|0;v=F+12|0;w=(d|0)!=0;q=(d|0)==0;x=F+32|0;y=F+4|0;z=F+16|0;A=E+4|0;e=0;p=0;while(1){f=c[(c[B>>2]|0)+8>>2]|0;if((p|0)>=(c[f+4>>2]|0))break;j=F;f=(c[f>>2]|0)+(p*48|0)|0;b=j+48|0;do{c[j>>2]=c[f>>2];j=j+4|0;f=f+4|0}while((j|0)<(b|0));b=1;m=1.0;o=c[s>>2]|0;a:while(1){f=c[o>>2]|0;if(!f)break;k=o+4|0;n=+g[k>>2];do if(n<1.0e-05&n>-1.0e-05)f=b;else{hS(a,f);n=+g[k>>2];m=m-n;e=c[o>>2]|0;if(b|0){hJ(F,n,H,E);k=c[H>>2]|0;pS(a,k,c[t>>2]|0,0,0,0);U1(k);if(m<1.0e-05&m>-1.0e-05){I=11;break a}else{f=0;break}}if(m<1.0e-05&m>-1.0e-05){I=13;break a}j=G;f=E;b=j+48|0;do{c[j>>2]=c[f>>2];j=j+4|0;f=f+4|0}while((j|0)<(b|0));n=+g[k>>2];hJ(G,n/(m+n),H,E);U1(c[G>>2]|0);f=c[H>>2]|0;pS(a,f,c[t>>2]|0,0,0,0);U1(f);f=0}while(0);b=f;o=o+12|0}if((I|0)==11){I=0;U1(c[E>>2]|0)}else if((I|0)==13){I=0;o=c[E>>2]|0;pS(a,o,c[A>>2]|0,0,0,0);U1(o)}if(c[u>>2]|0){hS(a,c[c[s>>2]>>2]|0);jS(a,c[c[s>>2]>>2]|0);k=c[F>>2]|0;o=c[u>>2]|0;c[C>>2]=c[z>>2];c[C+4>>2]=c[z+4>>2];c[C+8>>2]=c[z+8>>2];c[C+12>>2]=c[z+12>>2];c[D>>2]=c[k>>2];c[D+4>>2]=c[k+4>>2];c[D+8>>2]=c[k+8>>2];c[D+12>>2]=c[k+12>>2];ZH(a,2,C,D,h,i,o)}if(c[v>>2]|0){hS(a,e);jS(a,e);k=(c[F>>2]|0)+((c[y>>2]|0)+-1<<4)|0;o=c[v>>2]|0;c[C>>2]=c[x>>2];c[C+4>>2]=c[x+4>>2];c[C+8>>2]=c[x+8>>2];c[C+12>>2]=c[x+12>>2];c[D>>2]=c[k>>2];c[D+4>>2]=c[k+4>>2];c[D+8>>2]=c[k+8>>2];c[D+12>>2]=c[k+12>>2];ZH(a,3,C,D,h,i,o)}do if((c[(c[(c[B>>2]|0)+8>>2]|0)+4>>2]|0)>1){if(!(c[u>>2]|0)){if(!(w&(c[v>>2]|0)!=0))break}else if(q)break;lS(a,d)}while(0);p=p+1|0}hI(r);e=0}l=J;return e|0}function _I(b,d){b=b|0;d=d|0;var e=0,f=0;e=1;a:while(1){switch(a[b>>0]|0){case 0:break a;case 58:{e=e+1|0;break}default:{}}b=b+1|0}f=O((l2(d)|0)+1|0,e)|0;b=f+10|0;if((c[47121]|0)<(f|0)){c[47121]=b;b=W1(c[47122]|0,b)|0;c[47122]=b}else b=c[47122]|0;r3(b,d)|0;while(1){e=e+-1|0;b=c[47122]|0;if(!e)break;f=b+(l2(b)|0)|0;a[f>>0]=58;a[f+1>>0]=0;F4(b,d)|0}return b|0}function $I(b){b=b|0;var d=0,e=0;d=c[47184]|0;a:do if(!d)e=7;else{d=RA(b,d)|0;switch(a[d>>0]|0){case 110:if(!(s2(d,135709)|0)){d=1;break a}else{e=7;break a}case 102:if(!(s2(d,100066)|0)){d=2;break a}else{e=7;break a}case 98:{if(!(s2(d,100074)|0)){d=3;break a}if(!(s2(d,111221)|0)){d=4;break a}else{e=7;break a}}default:{e=7;break a}}}while(0);if((e|0)==7){d=(xB(xC(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0)|0)!=0;d=d?2:1}return d|0}function aJ(a){a=a|0;if(a|0){U1(c[a+8>>2]|0);U1(a)}return}function bJ(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0;e=+h[b>>3]-+h[c>>3];f=+h[b+8>>3]-+h[c+8>>3];d=2.0/+C(+(e*e+.0001+f*f));h[a>>3]=d*f;h[a+8>>3]=-(e*d);return}function cJ(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0;g=+h[c>>3]-+h[d>>3];i=+h[c+8>>3]-+h[d+8>>3];f=+C(+(i*i+g*g));if(f<.0001){j=+h[b>>3]-+h[e>>3];f=+h[b+8>>3]-+h[e+8>>3];i=f;g=j;f=+C(+(j*j+.0001+f*f))}j=2.0/f;h[a>>3]=i*j;h[a+8>>3]=-(j*g);return}function dJ(a,b,c){a=+a;b=+b;c=+c;return +(c*.5*(1.0-a/b))}function eJ(a,b,c){a=+a;b=+b;c=+c;return +(c*.5)}function fJ(a,b,c){a=+a;b=+b;c=+c;return +(c*.5*(a/b))}function gJ(a,b,c){a=+a;b=+b;c=+c;b=a/b;return +((!(b<=.5)?1.0-b:b)*c)}function hJ(a,b,d,e){a=a|0;b=+b;d=d|0;e=e|0;var f=0.0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0,s=0;p=l;l=l+16|0;o=p;n=c[a+4>>2]|0;j=(n+-1|0)/3|0;if((n+-4|0)>>>0<3){c[d+4>>2]=4;c[d>>2]=JK(64)|0;c[e+4>>2]=4;n=JK(64)|0;c[e>>2]=n;kP(o,c[a>>2]|0,3,b,c[d>>2]|0,n)}else{n=JK(j<<3)|0;g=0;i=c[a>>2]|0;f=0.0;while(1){if((g|0)>=(j|0))break;q=+iJ(i);h[n+(g<<3)>>3]=q;g=g+1|0;i=i+48|0;f=q+f}b=f*b;m=0;f=0.0;while(1){if((j|0)<=(m|0))break;f=+h[n+(m<<3)>>3]+f;if(f>=b)break;m=m+1|0}k=m*3|0;g=k+4|0;i=d+4|0;c[i>>2]=g;c[d>>2]=JK(g<<4)|0;g=((j-m|0)*3|0)+1|0;j=e+4|0;c[j>>2]=g;c[e>>2]=JK(g<<4)|0;g=0;while(1){if((g|0)>=(c[i>>2]|0))break;r=(c[d>>2]|0)+(g<<4)|0;s=(c[a>>2]|0)+(g<<4)|0;c[r>>2]=c[s>>2];c[r+4>>2]=c[s+4>>2];c[r+8>>2]=c[s+8>>2];c[r+12>>2]=c[s+12>>2];g=g+1|0}g=g+-4|0;i=0;while(1){if((i|0)>=(c[j>>2]|0))break;s=(c[e>>2]|0)+(i<<4)|0;r=(c[a>>2]|0)+(g<<4)|0;c[s>>2]=c[r>>2];c[s+4>>2]=c[r+4>>2];c[s+8>>2]=c[r+8>>2];c[s+12>>2]=c[r+12>>2];g=g+1|0;i=i+1|0}q=+h[n+(m<<3)>>3];kP(o,(c[a>>2]|0)+(k<<4)|0,3,(b-f+q)/q,(c[d>>2]|0)+(k<<4)|0,c[e>>2]|0);U1(n)}l=p;return}function iJ(a){a=a|0;var b=0.0,c=0.0,d=0.0,e=0.0,f=0.0;e=+h[a+16>>3];d=+h[a>>3]-e;f=+h[a+24>>3];b=+h[a+8>>3]-f;d=+C(+(b*b+d*d));b=+h[a+32>>3];e=e-b;c=+h[a+40>>3];f=f-c;d=+C(+(f*f+e*e))+d;b=b-+h[a+48>>3];c=c-+h[a+56>>3];return +(d+ +C(+(c*c+b*b)))}function jJ(b,d){b=b|0;d=d|0;d=yC(d)|0;if((a[(c[d+16>>2]|0)+115>>0]|0)==1)d=eQ(b)|0;else d=bQ(b,d)|0;return qJ(d)|0}function kJ(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;q=l;l=l+1664|0;m=q+1600|0;n=q+800|0;o=q;p=KK(24)|0;c[p+16>>2]=1;j=((c[e+4>>2]|0)+-1|0)/3|0;h=0;i=p;while(1){if((h|0)>=(j|0))break;k=h*3|0;g=0;while(1){if((g|0)==4)break;r=m+(g<<4)|0;s=(c[e>>2]|0)+(g+k<<4)|0;c[r>>2]=c[s>>2];c[r+4>>2]=c[s+4>>2];c[r+8>>2]=c[s+8>>2];c[r+12>>2]=c[s+12>>2];g=g+1|0}h=h+1|0;i=lJ(m,i)|0}g=0;h=0;i=p;while(1){if(!i){h=p;g=p;break}j=c[i+16>>2]|0;k=n+(g<<4)|0;e=o+(g<<4)|0;mJ(h,i,j,k,e,f);g=g+1|0;if(!((g|0)==50|(j|0)==0)){h=i;i=j;continue}nJ(a,b,d,g,n,o);c[n>>2]=c[k>>2];c[n+4>>2]=c[k+4>>2];c[n+8>>2]=c[k+8>>2];c[n+12>>2]=c[k+12>>2];c[o>>2]=c[e>>2];c[o+4>>2]=c[e+4>>2];c[o+8>>2]=c[e+8>>2];c[o+12>>2]=c[e+12>>2];h=i;g=1;i=j}while(1){if(!h)break;s=c[h+16>>2]|0;U1(g);h=s;g=s}l=q;return}function lJ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;g=l;l=l+144|0;f=g+128|0;d=g+64|0;e=g;if(!(mI(a)|0)){kP(f,a,3,.5,d,e);d=lJ(e,lJ(d,b)|0)|0}else{d=b+16|0;if((c[d>>2]|0)==1){c[d>>2]=0;c[b>>2]=c[a>>2];c[b+4>>2]=c[a+4>>2];c[b+8>>2]=c[a+8>>2];c[b+12>>2]=c[a+12>>2]}d=a+48|0;c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];d=pJ(f,b)|0}l=g;return d|0}function mJ(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=+g;var i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0.0;r=l;l=l+96|0;n=r+80|0;m=r+64|0;k=r+48|0;o=r+32|0;p=r+16|0;q=r;c[o>>2]=c[b>>2];c[o+4>>2]=c[b+4>>2];c[o+8>>2]=c[b+8>>2];c[o+12>>2]=c[b+12>>2];do if(a){c[p>>2]=c[a>>2];c[p+4>>2]=c[a+4>>2];c[p+8>>2]=c[a+8>>2];c[p+12>>2]=c[a+12>>2];if(!d){i=+h[o>>3];h[q>>3]=i*2.0-+h[p>>3];j=+h[o+8>>3];h[q+8>>3]=j*2.0-+h[p+8>>3];break}else{c[q>>2]=c[d>>2];c[q+4>>2]=c[d+4>>2];c[q+8>>2]=c[d+8>>2];c[q+12>>2]=c[d+12>>2];i=+h[o>>3];j=+h[o+8>>3];break}}else{c[q>>2]=c[d>>2];c[q+4>>2]=c[d+4>>2];c[q+8>>2]=c[d+8>>2];c[q+12>>2]=c[d+12>>2];i=+h[o>>3];h[p>>3]=i*2.0-+h[q>>3];j=+h[o+8>>3];h[p+8>>3]=j*2.0-+h[q+8>>3]}while(0);c[k>>2]=c[p>>2];c[k+4>>2]=c[p+4>>2];c[k+8>>2]=c[p+8>>2];c[k+12>>2]=c[p+12>>2];c[m>>2]=c[o>>2];c[m+4>>2]=c[o+4>>2];c[m+8>>2]=c[o+8>>2];c[m+12>>2]=c[o+12>>2];c[n>>2]=c[q>>2];c[n+4>>2]=c[q+4>>2];c[n+8>>2]=c[q+8>>2];c[n+12>>2]=c[q+12>>2];t=+oJ(k,m,n);s=+E(+t)*g;g=+F(+t)*g;h[e>>3]=i+s;h[e+8>>3]=j+g;h[f>>3]=i-s;h[f+8>>3]=j-g;l=r;return}function nJ(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;i=e<<1;j=c[d>>2]|0;h=0;k=0;while(1){if((h|0)>=(j|0))break;l=(c[(c[b>>2]|0)+(h<<2)>>2]|0)+k|0;h=h+1|0;k=l}l=j+1|0;c[d>>2]=l;l=MK(c[b>>2]|0,l<<2)|0;c[b>>2]=l;c[l+(h<<2)>>2]=i;c[a>>2]=MK(c[a>>2]|0,k+i<<4)|0;b=i+-1+k|0;h=0;while(1){if((h|0)>=(e|0))break;l=(c[a>>2]|0)+(h+k<<4)|0;j=f+(h<<4)|0;c[l>>2]=c[j>>2];c[l+4>>2]=c[j+4>>2];c[l+8>>2]=c[j+8>>2];c[l+12>>2]=c[j+12>>2];l=(c[a>>2]|0)+(b-h<<4)|0;j=g+(h<<4)|0;c[l>>2]=c[j>>2];c[l+4>>2]=c[j+4>>2];c[l+8>>2]=c[j+8>>2];c[l+12>>2]=c[j+12>>2];h=h+1|0}return}function oJ(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0;f=+h[b+8>>3];d=+h[b>>3];e=+K(+(+h[c+8>>3]-f),+(+h[c>>3]-d));d=+K(+(+h[a+8>>3]-f),+(+h[a>>3]-d));e=e-d;return +((e>0.0?e+-6.283185307179586:e)*.5+d)}function pJ(a,b){a=a|0;b=b|0;var d=0;d=KK(24)|0;c[d+16>>2]=0;c[d>>2]=c[a>>2];c[d+4>>2]=c[a+4>>2];c[d+8>>2]=c[a+8>>2];c[d+12>>2]=c[a+12>>2];c[b+16>>2]=d;return d|0}function qJ(b){b=b|0;var c=0,d=0,e=0,f=0,g=0;c=b;f=b;a:while(1){e=0;while(1){d=c;c=c+1|0;d=a[d>>0]|0;if(!(d<<24>>24))break a;if(e<<24>>24){g=5;break}if(d<<24>>24!=92)break;else e=1}b:do if((g|0)==5){g=0;switch(d<<24>>24|0){case 114:{d=13;break b}case 108:case 110:{d=10;break b}default:break b}}while(0);a[f>>0]=d;f=f+1|0}a[f>>0]=0;return b|0}function rJ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;e=c[b>>2]|0;while(1){g=a[e>>0]|0;if(!(g<<24>>24)){f=0;break}f=g<<24>>24;if(!(g<<24>>24==44|(S2(f)|0)!=0)){h=5;break}e=e+1|0}a:do if((h|0)==5){switch(f|0){case 0:{f=0;break a}case 41:case 40:{e=e+1|0;break a}default:{}}h=d+4|0;i=d+8|0;while(1){if((sJ(g<<24>>24)|0)<<24>>24){f=1;break a}f=c[h>>2]|0;if(f>>>0>=(c[i>>2]|0)>>>0){lA(d,1)|0;f=c[h>>2]|0}c[h>>2]=f+1;a[f>>0]=g;f=e+1|0;e=f;g=a[f>>0]|0}}while(0);c[b>>2]=e;return f|0}function sJ(a){a=a|0;switch(a|0){case 0:case 44:case 41:case 40:{a=1;break}default:a=0}return a|0}function tJ(a,b){a=a|0;b=b|0;return uJ(c[a>>2]|0,c[a+160>>2]|0,c[a+156>>2]|0,b)|0}function uJ(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+160|0;k=o+20|0;m=o+16|0;n=o;c[k>>2]=0;c[m>>2]=0;kA(n,128,o+24|0);nA(n,f)|0;g=n+4|0;f=c[g>>2]|0;if(f>>>0>=(c[n+8>>2]|0)>>>0){lA(n,1)|0;f=c[g>>2]|0}a[f>>0]=0;h=c[n>>2]|0;c[g>>2]=h;i=b+300|0;j=b+296|0;f=0;g=h;a:while(1){if(f){f=1;break}f=C4(g,c[i>>2]|0,k)|0;if(!f){f=0;break}g=C4(f,c[j>>2]|0,m)|0;h=(g|0)!=0;if(h)f=C4(0,c[j>>2]|0,m)|0;else f=0;switch(((f|0)!=0&1)+(h&1)&3){case 2:{g=vJ(b,g,0)|0;f=vJ(b,f,e)|0;h=(g|0)>(f|0);f=(f&g|0)>-1&(((h?g:f)|0)>=(d|0)&((h?f:g)|0)<=(d|0));g=0;continue a}case 1:{f=(vJ(b,g,d)|0)==(d|0);g=0;continue a}default:{f=0;g=0;continue a}}}oA(n);l=o;return f|0}function vJ(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;h=a[d>>0]|0;if(!(h<<24>>24==97?(s2(d,100396)|0)==0:0))f=3;a:do if((f|0)==3){if((wJ(d)|0)<<24>>24){e=q5(d)|0;break}g=c[b+308>>2]|0;if(!g)e=-1;else{b=c[b+312>>2]|0;e=1;while(1){if((e|0)>(b|0)){e=-1;break a}f=c[g+(e<<2)>>2]|0;if(h<<24>>24==(a[f>>0]|0)?(s2(d,f)|0)==0:0)break a;e=e+1|0}}}while(0);return e|0}function wJ(b){b=b|0;var c=0;while(1){c=a[b>>0]|0;if(!(c<<24>>24)){b=1;break}if(((c&255)+-48|0)>>>0<10)b=b+1|0;else{b=0;break}}return b|0}function xJ(a,b){a=a|0;b=b|0;if((+h[a+16>>3]>=+h[b>>3]?+h[b+16>>3]>=+h[a>>3]:0)?+h[a+24>>3]>=+h[b+8>>3]:0)a=+h[b+24>>3]>=+h[a+8>>3]&1;else a=0;return a|0}function yJ(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0;a:do if((c[b+156>>2]|0)>=2?(f=aP(e,c[47167]|0,195059)|0,(tJ(b,f)|0)<<24>>24==0):0)if(!(a[f>>0]|0))if(!(aB(d,e)|0))f=1;else{f=aB(d,e)|0;while(1){if(!f){f=0;break a}g=aP(f,c[47190]|0,195059)|0;if(!(a[g>>0]|0)){f=1;break a}if((tJ(b,g)|0)<<24>>24){f=1;break a}f=bB(d,f,e)|0}}else f=0;else f=1;while(0);return f|0}function zJ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=l;l=l+64|0;e=d+32|0;f=d;a=(c[a+16>>2]|0)+48|0;c[f>>2]=c[a>>2];c[f+4>>2]=c[a+4>>2];c[f+8>>2]=c[a+8>>2];c[f+12>>2]=c[a+12>>2];c[f+16>>2]=c[a+16>>2];c[f+20>>2]=c[a+20>>2];c[f+24>>2]=c[a+24>>2];c[f+28>>2]=c[a+28>>2];c[e>>2]=c[b>>2];c[e+4>>2]=c[b+4>>2];c[e+8>>2]=c[b+8>>2];c[e+12>>2]=c[b+12>>2];c[e+16>>2]=c[b+16>>2];c[e+20>>2]=c[b+20>>2];c[e+24>>2]=c[b+24>>2];c[e+28>>2]=c[b+28>>2];b=(xJ(f,e)|0)&255;l=d;return b|0}function AJ(a,d){a=a|0;d=d|0;var f=0.0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0.0,r=0,s=0,t=0,u=0.0,v=0.0;r=c[a+152>>2]|0;s=aI(a)|0;c[s+4>>2]=2;c[s+8>>2]=d;c[s+12>>2]=8;if(r&134217728|0){if((e[(c[(xC(d)|0)+16>>2]|0)+178>>1]|0)>2){f=+h[(c[(c[d+16>>2]|0)+132>>2]|0)+16>>3]*72.0;f=+(~~(f+(f>=0.0?.5:-.5))|0)}else f=0.0;h[s+168>>3]=f}k=d+16|0;CJ(a,c[(c[k>>2]|0)+104>>2]|0,d);do if(r&4259840|0){if((c[s+208>>2]|0)==0?(b[s+260>>1]&1)==0:0)break;p=WM(d)|0;l=c[k>>2]|0;f=+h[l+16>>3];q=+h[l+24>>3];l=DJ(d)|0;a:do if((p|2|0)==3){n=c[(c[k>>2]|0)+12>>2]|0;if(!((EJ(n)|0)<<24>>24))g=0;else g=(c[n+4>>2]|l|0)!=0;if(!((r&524288|0)==0|((n|0)==0|g))){i=n+8|0;o=c[i>>2]|0;o=(o|0)<3?1:o;j=n+4|0;m=c[j>>2]|0;m=(m|0)>1?m:1;p=c[n+44>>2]|0;g=QA(d,102705)|0;if(!g)g=0;else g=q5(g)|0;g=(g+-4|0)>>>0>56?20:g;if(!(c[j>>2]|l)){c[s+264>>2]=0;i=JK(32)|0;g=c[k>>2]|0;v=+h[g+88>>3];h[i>>3]=f-v;u=+h[g+80>>3]*.5;h[i+8>>3]=q-u;h[i+16>>3]=v+f;h[i+24>>3]=u+q;g=2;break}i=c[i>>2]|0;if(((i|0)<3?+h[n+32>>3]==0.0:0)?+h[n+24>>3]==0.0:0){i=s+264|0;if(c[n>>2]|0){c[i>>2]=1;i=JK(32)|0;h[i>>3]=f;h[i+8>>3]=q;g=(m<<1)+-1|0;h[i+16>>3]=+h[p+(g<<4)>>3]+f;h[i+24>>3]=+h[p+(g<<4)+8>>3]+q;g=2;break}c[i>>2]=2;i=(m<<1)+-1|0;i=FJ(+h[p+(i<<4)>>3],+h[p+(i<<4)+8>>3],g)|0;j=0;while(1){if((j|0)>=(g|0))break a;p=i+(j<<4)|0;h[p>>3]=+h[p>>3]+f;p=i+(j<<4)+8|0;h[p>>3]=+h[p>>3]+q;j=j+1|0}}m=O(i,m+-1|0)|0;c[s+264>>2]=2;if((i|0)<(g|0)){i=JK(o<<4)|0;g=0;while(1){if((g|0)>=(o|0)){g=o;break a}n=g+m|0;h[i+(g<<4)>>3]=+h[p+(n<<4)>>3]+f;h[i+(g<<4)+8>>3]=+h[p+(n<<4)+8>>3]+q;g=g+1|0}}else{l=(i|0)/(g|0)|0;i=JK(g<<4)|0;j=0;k=0;while(1){if((j|0)>=(g|0))break a;o=k+m|0;h[i+(j<<4)>>3]=+h[p+(o<<4)>>3]+f;h[i+(j<<4)+8>>3]=+h[p+(o<<4)+8>>3]+q;j=j+1|0;k=k+l|0}}}else t=31}else t=31;while(0);if((t|0)==31){c[s+264>>2]=0;i=JK(32)|0;g=c[k>>2]|0;h[i>>3]=f-+h[g+88>>3];v=+h[g+80>>3]*.5;h[i+8>>3]=q-v;h[i+16>>3]=+h[g+96>>3]+f;h[i+24>>3]=v+q;g=2}if(!(r&8192))NR(a,i,i,g)|0;c[s+272>>2]=i;c[s+268>>2]=g}while(0);HT(QA(d,100032)|0);_R(a,d);return}function BJ(a){a=a|0;$R(a);bI(a);return}function CJ(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+144|0;j=m;f=QA(e,141055)|0;i=QA(e,141101)|0;k=QA(e,141088)|0;kA(j,128,m+16|0);if(!d)h=0;else h=c[d>>2]|0;if(!((f|0)!=0?(a[f>>0]|0)!=0:0))f=QA(e,111477)|0;g=dI(b,e,j)|0;if(!i)d=0;else d=jJ(i,e)|0;cI(b,h,f,d,k,g,e)|0;U1(d);oA(j);l=m;return}function DJ(b){b=b|0;var d=0,e=0;b=bP(b,c[47157]|0,195059)|0;a:do if(!(a[b>>0]|0))b=0;else{PI(b)|0;b=0;d=188228;while(1){e=c[d>>2]|0;if(!e)break a;e=(s2(e,111101)|0)==0;b=e?1:b;d=d+4|0}}while(0);return b|0}function EJ(a){a=a|0;var b=0.0;if(((c[a+8>>2]|0)==4?(b=+h[a+16>>3],((~~(b+(b>=0.0?.5:-.5))|0)%90|0|0)==0):0)?+h[a+24>>3]==0.0:0)a=+h[a+32>>3]==0.0&1;else a=0;return a|0}function FJ(a,b,c){a=+a;b=+b;c=c|0;var d=0.0,e=0,f=0.0,g=0;f=6.283185307179586/+(c|0);g=JK(c<<4)|0;d=0.0;e=0;while(1){if((e|0)>=(c|0))break;h[g+(e<<4)>>3]=+E(+d)*a;h[g+(e<<4)+8>>3]=+F(+d)*b;d=d+f;e=e+1|0}return g|0}function GJ(b,d){b=b|0;d=d|0;var e=0;a:do if((c[b+156>>2]|0)>=2?(e=aP(d,GA(d,0,101510,0)|0,195059)|0,(tJ(b,e)|0)<<24>>24==0):0)if(!(a[e>>0]|0)){e=jC(d)|0;while(1){if(!e){e=0;break a}if((yJ(b,d,e)|0)<<24>>24){e=1;break a}e=kC(d,e)|0}}else e=0;else e=1;while(0);return e|0}function HJ(a,b){a=a|0;b=b|0;var d=0;d=aI(a)|0;c[d+4>>2]=1;c[d+8>>2]=b;c[d+12>>2]=1;CJ(a,c[(c[b+16>>2]|0)+12>>2]|0,b);UR(a,b);return}function IJ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0;b=QA(b,141082)|0;a:do if((b|0)!=0?(a[b>>0]|0)!=0:0){PI(b)|0;e=188228;b=0;while(1){while(1){f=c[e>>2]|0;if(!f){e=188228;break a}if(!(s2(f,111101)|0)){g=10;break}if(!(s2(f,106334)|0)){f=e;g=12;break}if(!(s2(f,106341)|0)){f=e;g=14;break}if(!(s2(f,106316)|0)){f=e;g=16;break}e=e+4|0}if((g|0)==10){e=e+4|0;b=b|1;continue}else if((g|0)==12){while(1){h=f;f=f+4|0;g=c[f>>2]|0;c[h>>2]=g;if(!g)break;else g=12}b=b|3;continue}else if((g|0)==14){while(1){g=f;f=f+4|0;h=c[f>>2]|0;c[g>>2]=h;if(!h)break;else g=14}b=b|64;continue}else if((g|0)==16){while(1){g=f;f=f+4|0;h=c[f>>2]|0;c[g>>2]=h;if(!h)break;else g=16}b=b|4;continue}}}else{e=0;b=0}while(0);c[d>>2]=b;return e|0}function JJ(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,h=0,i=0,j=0,k=0,m=0;k=l;l=l+16|0;i=k;j=k+4|0;c[j>>2]=0;m=(gI(b,0,j)|0)==0;j=c[j>>2]|0;if((m?(f=c[j>>2]|0,(f|0)>=2):0)?(h=j+8|0,(c[c[h>>2]>>2]|0)!=0):0){if((f|0)>2)gA(0,100415,i)|0;m=KK((l2(b)|0)+1|0)|0;c[d>>2]=m;r3(m,c[c[h>>2]>>2]|0)|0;if(!(c[(c[h>>2]|0)+12>>2]|0))c[d+4>>2]=0;else{m=c[d>>2]|0;m=m+((l2(m)|0)+1)|0;c[d+4>>2]=m;r3(m,c[(c[h>>2]|0)+12>>2]|0)|0}b=c[h>>2]|0;do if(!(a[b+8>>0]|0))if(!(a[b+20>>0]|0)){g[e>>2]=0.0;break}else{g[e>>2]=1.0-+g[b+16>>2];break}else c[e>>2]=c[b+4>>2];while(0);hI(j);b=1}else{c[d>>2]=0;hI(j);b=0}l=k;return b|0}function KJ(a,b){a=a|0;b=b|0;VR(a,b);bI(a);return}function LJ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,i=0,j=0,k=0.0,m=0.0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;x=l;l=l+96|0;p=x+48|0;s=x+16|0;w=x+8|0;v=x;t=x+80|0;c[t>>2]=1e3;f=KK(16e3)|0;u=a+256|0;e=1;j=1;i=0;q=0;r=c[b+8>>2]|0;while(1){if((q|0)>=(c[b>>2]|0))break;d=c[r>>2]|0;a:do switch(d|0){case 1:case 0:{n=r+80|0;c[s>>2]=c[n>>2];c[s+4>>2]=c[n+4>>2];c[s+8>>2]=c[n+8>>2];c[s+12>>2]=c[n+12>>2];c[s+16>>2]=c[n+16>>2];c[s+20>>2]=c[n+20>>2];c[s+24>>2]=c[n+24>>2];c[s+28>>2]=c[n+28>>2];c[p>>2]=c[u>>2];c[p+4>>2]=c[u+4>>2];c[p+8>>2]=c[u+8>>2];c[p+12>>2]=c[u+12>>2];c[p+16>>2]=c[u+16>>2];c[p+20>>2]=c[u+20>>2];c[p+24>>2]=c[u+24>>2];c[p+28>>2]=c[u+28>>2];if(!(xJ(s,p)|0))d=j;else{z=r+8|0;A=r+24|0;h[f>>3]=+h[z>>3]-+h[A>>3];n=r+16|0;y=r+32|0;h[f+8>>3]=+h[n>>3]-+h[y>>3];h[f+16>>3]=+h[A>>3]+ +h[z>>3];h[f+24>>3]=+h[y>>3]+ +h[n>>3];mS(a,f,2,(d|0)==0?j:0);d=j}break}case 3:case 2:{A=r+80|0;c[s>>2]=c[A>>2];c[s+4>>2]=c[A+4>>2];c[s+8>>2]=c[A+8>>2];c[s+12>>2]=c[A+12>>2];c[s+16>>2]=c[A+16>>2];c[s+20>>2]=c[A+20>>2];c[s+24>>2]=c[A+24>>2];c[s+28>>2]=c[A+28>>2];c[p>>2]=c[u>>2];c[p+4>>2]=c[u+4>>2];c[p+8>>2]=c[u+8>>2];c[p+12>>2]=c[u+12>>2];c[p+16>>2]=c[u+16>>2];c[p+20>>2]=c[u+20>>2];c[p+24>>2]=c[u+24>>2];c[p+28>>2]=c[u+28>>2];if(!(xJ(s,p)|0))d=j;else{d=r+8|0;f=MJ(f,t,c[d+4>>2]|0,c[d>>2]|0)|0;nS(a,f,c[d>>2]|0,(c[r>>2]|0)==2?j:0);d=j}break}case 5:case 4:{A=r+80|0;c[s>>2]=c[A>>2];c[s+4>>2]=c[A+4>>2];c[s+8>>2]=c[A+8>>2];c[s+12>>2]=c[A+12>>2];c[s+16>>2]=c[A+16>>2];c[s+20>>2]=c[A+20>>2];c[s+24>>2]=c[A+24>>2];c[s+28>>2]=c[A+28>>2];c[p>>2]=c[u>>2];c[p+4>>2]=c[u+4>>2];c[p+8>>2]=c[u+8>>2];c[p+12>>2]=c[u+12>>2];c[p+16>>2]=c[u+16>>2];c[p+20>>2]=c[u+20>>2];c[p+24>>2]=c[u+24>>2];c[p+28>>2]=c[u+28>>2];if(!(xJ(s,p)|0))d=j;else{d=r+8|0;f=MJ(f,t,c[d+4>>2]|0,c[d>>2]|0)|0;pS(a,f,c[d>>2]|0,0,0,(c[r>>2]|0)==4?j&255:0);d=j}break}case 6:{A=r+80|0;c[s>>2]=c[A>>2];c[s+4>>2]=c[A+4>>2];c[s+8>>2]=c[A+8>>2];c[s+12>>2]=c[A+12>>2];c[s+16>>2]=c[A+16>>2];c[s+20>>2]=c[A+20>>2];c[s+24>>2]=c[A+24>>2];c[s+28>>2]=c[A+28>>2];c[p>>2]=c[u>>2];c[p+4>>2]=c[u+4>>2];c[p+8>>2]=c[u+8>>2];c[p+12>>2]=c[u+12>>2];c[p+16>>2]=c[u+16>>2];c[p+20>>2]=c[u+20>>2];c[p+24>>2]=c[u+24>>2];c[p+28>>2]=c[u+28>>2];if(!(xJ(s,p)|0))d=j;else{d=r+8|0;f=MJ(f,t,c[d+4>>2]|0,c[d>>2]|0)|0;qS(a,f,c[d>>2]|0);d=j}break}case 7:{A=r+80|0;c[s>>2]=c[A>>2];c[s+4>>2]=c[A+4>>2];c[s+8>>2]=c[A+8>>2];c[s+12>>2]=c[A+12>>2];c[s+16>>2]=c[A+16>>2];c[s+20>>2]=c[A+20>>2];c[s+24>>2]=c[A+24>>2];c[s+28>>2]=c[A+28>>2];c[p>>2]=c[u>>2];c[p+4>>2]=c[u+4>>2];c[p+8>>2]=c[u+8>>2];c[p+12>>2]=c[u+12>>2];c[p+16>>2]=c[u+16>>2];c[p+20>>2]=c[u+20>>2];c[p+24>>2]=c[u+24>>2];c[p+28>>2]=c[u+28>>2];if(!(xJ(s,p)|0))d=j;else{h[f>>3]=+h[r+8>>3];h[f+8>>3]=+h[r+16>>3];d=c[r+112>>2]|0;c[p>>2]=c[f>>2];c[p+4>>2]=c[f+4>>2];c[p+8>>2]=c[f+8>>2];c[p+12>>2]=c[f+12>>2];gS(a,p,d);d=j}break}case 8:{jS(a,c[r+8>>2]|0);d=1;break}case 9:{hS(a,c[r+8>>2]|0);d=1;break}case 13:{if((c[r+8>>2]|0)!=2){d=r+16|0;z=c[d+36>>2]|0;A=c[z+12>>2]|0;o=+g[z+8>>2];d=~~(+K(+(+h[r+40>>3]-+h[r+24>>3]),+(+h[r+32>>3]-+h[d>>3]))*57.29577951308232);jS(a,c[z+4>>2]|0);kS(a,A,d,o);d=2;break a}A=c[r+68>>2]|0;j=c[A+4>>2]|0;n=c[A+12>>2]|0;o=+g[A+8>>2];k=+h[r+40>>3];m=+h[r+16>>3];if(k==m?+h[r+48>>3]==+h[r+24>>3]:0)d=0;else d=~~(+H(+((m-k)/+h[r+32>>3]))*57.29577951308232);jS(a,j);kS(a,n,d,o);d=3;break}case 14:{gA(0,100481,v)|0;d=j;break}case 12:{if(!e){e=0;d=j}else{gA(0,100521,w)|0;e=0;d=j}break}case 11:{PI(c[r+8>>2]|0)|0;lS(a,188228);i=188228;d=j;break}default:d=j}while(0);j=d;q=q+1|0;r=r+120|0}if(i|0)lS(a,c[(c[a>>2]|0)+336>>2]|0);U1(f);l=x;return}function MJ(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;g=c[b>>2]|0;f=g<<1;f=(f|0)>(e|0)?f:e;if((g|0)<(e|0)){a=MK(a,f<<4)|0;c[b>>2]=f}b=0;while(1){if((b|0)>=(e|0))break;h[a+(b<<4)>>3]=+h[d+(b*24|0)>>3];h[a+(b<<4)+8>>3]=+h[d+(b*24|0)+8>>3];b=b+1|0}return a|0}function NJ(a,b){a=a|0;b=b|0;var d=0;d=c[b>>2]|0;c[a>>2]=c[b+4>>2];c[a+4>>2]=d;return}function OJ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;g=d+16|0;f=1;while(1){d=c[g>>2]|0;if((f|0)>(c[d+180>>2]|0))break;e=c[(c[d+184>>2]|0)+(f<<2)>>2]|0;OJ(b,e);d=QA(e,140823)|0;if(d|0?a[d>>0]|0:0)hS(b,d);d=QA(e,137729)|0;if(d|0?a[d>>0]|0:0)hS(b,d);d=QA(e,140977)|0;if(d|0?a[d>>0]|0:0)hS(b,d);d=QA(e,101418)|0;if(d|0?a[d>>0]|0:0)jS(b,d);d=QA(e,101446)|0;if(d|0?a[d>>0]|0:0)hS(b,d);f=f+1|0}return}function PJ(a){a=a|0;var b=0,d=0,e=0;b=c[47127]|0;if(!b){b=Vz(20628,c[4581]|0)|0;c[47127]=b}if(!(nb[c[b>>2]&63](b,a,4)|0)){d=c[47127]|0;e=c[d>>2]|0;b=D4(a)|0;nb[e&63](d,b,1)|0;b=1}else b=0;return b|0}function QJ(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function RJ(a){a=a|0;var b=0;b=c[47128]|0;if(!a){if((b|0)>0?(b=b+-1|0,c[47128]=b,(b|0)==0):0){Z3(1,c[47129]|0)|0;U1(c[47129]|0)}}else{c[47128]=b+1;if(!b){c[47129]=D4(Z3(1,0)|0)|0;Z3(1,153563)|0}}return}function SJ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0.0;u=l;l=l+96|0;r=u+72|0;q=u+56|0;p=u+48|0;t=u+32|0;s=u+24|0;e=u+8|0;d=u;if(0)TO();do if((TC(b,137483,0,1)|0)!=0?(o=b+16|0,(c[(c[o>>2]|0)+8>>2]|0)!=0):0){TJ(b);UJ(a,b);VJ(a,b);RJ(1);j=a+56|0;k=a+184|0;m=a+192|0;n=a+28|0;i=EF(a)|0;a:while(1){if(!i){g=33;break}d=c[j>>2]|0;if(!d){c[i+20>>2]=0;d=0}else{c[i+20>>2]=c[d+8>>2];d=c[d+12>>2]|0}c[i+24>>2]=d;c[i+12>>2]=a;c[i+28>>2]=c[k>>2];c[i+620>>2]=25764;c[i+624>>2]=14;if(!(c[(c[o>>2]|0)+8>>2]|0)){g=13;break}e=i+52|0;g=JR(i,c[e>>2]|0)|0;c[i+56>>2]=g;switch(g|0){case 999:{g=16;break a}case 21:{d=1;break}case 24:{d=520;break}default:d=WJ(b)|0}f=i+152|0;c[f>>2]=c[f>>2]|d;d=c[m>>2]|0;do if(!d)g=24;else{if(c[d+152>>2]&32|0?(s2(c[e>>2]|0,c[d+52>>2]|0)|0)==0:0){d=c[47130]|0;if(!d){g=27;break}c[d+8>>2]=i;c[i+36>>2]=c[d+36>>2];g=29;break}LR(d);c[m>>2]=0;c[n>>2]=0;g=24}while(0);if((g|0)==24){c[47130]=0;g=27}if((g|0)==27?(g=0,(KR(i)|0)==0):0){c[m>>2]=i;g=29}if((g|0)==29){c[i+8>>2]=0;c[i+104>>2]=25876;XJ(i);YJ(i);ZJ(i,b);_J(i,b);$J(i,b);if(!(c[f>>2]&128))sI(i,b);c[47130]=i}i=FF(a)|0}if((g|0)==13){gA(1,100679,s)|0;RJ(0);if(!0){d=-1;break}d=c[15712]|0;s=KB(b)|0;v=+UO();c[t>>2]=s;h[t+8>>3]=v;w4(d,100650,t)|0;d=-1;break}else if((g|0)==16){c[p>>2]=c[e>>2];gA(1,100700,p)|0;RJ(0);if(!0){d=-1;break}d=c[15712]|0;t=KB(b)|0;v=+UO();c[q>>2]=t;h[q+8>>3]=v;w4(d,100650,q)|0;d=-1;break}else if((g|0)==33){RJ(0);if(!0){d=0;break}d=c[15712]|0;t=KB(b)|0;v=+UO();c[r>>2]=t;h[r+8>>3]=v;w4(d,100650,r)|0;d=0;break}}else g=5;while(0);if((g|0)==5){gA(1,100602,d)|0;if(!0)d=-1;else{d=c[15712]|0;t=KB(b)|0;v=+UO();c[e>>2]=t;h[e+8>>3]=v;w4(d,100650,e)|0;d=-1}}l=u;return d|0}function TJ(a){a=a|0;var b=0;b=jC(a)|0;while(1){if(!b)break;eK(a,b);b=kC(a,b)|0}return}function UJ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0.0;o=l;l=l+32|0;k=o+24|0;f=o+16|0;j=o+8|0;n=o;c[b+168>>2]=d;g=b+293|0;a[g>>0]=0;e=QA(d,105496)|0;if(e|0?(c[f>>2]=j,c[f+4>>2]=n,i=b5(e,105503,f)|0,(i|0)>0):0){p=+h[j>>3]*72.0;e=b+208|0;h[e>>3]=p;h[b+200>>3]=p;if((i|0)!=1)h[e>>3]=+h[n>>3]*72.0;a[g>>0]=1}f=b+292|0;a[f>>0]=0;e=QA(d,101102)|0;if(e|0?(c[k>>2]=j,c[k+4>>2]=n,m=b5(e,105503,k)|0,(m|0)>0):0){p=+h[j>>3]*72.0;e=b+224|0;h[e>>3]=p;h[b+216>>3]=p;if((m|0)!=1)h[e>>3]=+h[n>>3]*72.0;a[f>>0]=1}e=b+294|0;a[e>>0]=0;f=b+232|0;g=d+16|0;n=(c[(c[g>>2]|0)+8>>2]|0)+48|0;c[f>>2]=c[n>>2];c[f+4>>2]=c[n+4>>2];c[f+8>>2]=c[n+8>>2];c[f+12>>2]=c[n+12>>2];f=c[(c[g>>2]|0)+8>>2]|0;if(+h[f+48>>3]>.001?+h[f+56>>3]>.001:0)a[e>>0]=1;c[b+288>>2]=a[f+81>>0]|0?90:0;e=b+196|0;c[e>>2]=101106;f=QA(d,101109)|0;if(f|0?a[f>>0]|0:0)c[e>>2]=f;n=b+256|0;m=(c[g>>2]|0)+16|0;c[n>>2]=c[m>>2];c[n+4>>2]=c[m+4>>2];c[n+8>>2]=c[m+8>>2];c[n+12>>2]=c[m+12>>2];c[n+16>>2]=c[m+16>>2];c[n+20>>2]=c[m+20>>2];c[n+24>>2]=c[m+24>>2];c[n+28>>2]=c[m+28>>2];c[47141]=GA(d,0,101472,0)|0;c[47142]=GA(d,0,101463,0)|0;c[b+320>>2]=bP(0,c[47151]|0,107994)|0;h[b+328>>3]=+_O(0,c[47150]|0,14.0,1.0);c[b+336>>2]=20664;c[b+188>>2]=KB(d)|0;l=o;return}function VJ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;e=b+304|0;f=c[e>>2]|0;if(f|0){U1(f);c[e>>2]=0}f=b+308|0;e=c[f>>2]|0;if(e|0){U1(e);c[f>>2]=0}g=b+316|0;e=c[g>>2]|0;if(e|0){U1(e);c[g>>2]=0}e=QA(d,100853)|0;if(e){c[b+312>>2]=cK(b,d,e)|0;e=QA(d,100860)|0;if(e|0?a[e>>0]|0:0)c[g>>2]=dK(b,e)|0}else{c[f>>2]=0;c[b+312>>2]=1}return}function WJ(b){b=b|0;b=QA(b,100821)|0;a:do if(!b)b=0;else switch(a[b>>0]|0){case 110:{if(!(s2(b+1|0,100833)|0))b=1;else{b=0;break a}break}case 101:{if(!(s2(b+1|0,100843)|0))b=16;else{b=0;break a}break}default:{b=0;break a}}while(0);return b|0}function XJ(b){b=b|0;var d=0.0,e=0;e=c[b>>2]|0;if(!(a[e+292>>0]|0)){if((c[b+56>>2]|0)==300)d=+h[(c[b+68>>2]|0)+8>>3];else d=4.0;h[b+248>>3]=d;h[b+240>>3]=d}else{b=b+240|0;e=e+216|0;c[b>>2]=c[e>>2];c[b+4>>2]=c[e+4>>2];c[b+8>>2]=c[e+8>>2];c[b+12>>2]=c[e+12>>2]}return}function YJ(b){b=b|0;var d=0;d=c[b>>2]|0;a:do if(!(a[d+293>>0]|0))switch(c[b+56>>2]|0){case 300:{d=b+416|0;b=(c[b+84>>2]|0)+8|0;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];break a}case 30:case 21:case 22:case 4:case 3:case 2:{h[b+424>>3]=36.0;h[b+416>>3]=36.0;break a}default:{d=b+416|0;c[d>>2]=0;c[d+4>>2]=0;c[d+8>>2]=0;c[d+12>>2]=0;break a}}else{b=b+416|0;d=d+200|0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2]}while(0);return}function ZJ(b,d){b=b|0;d=d|0;var e=0.0,f=0;f=c[(c[b>>2]|0)+192>>2]|0;e=+h[(c[(c[d+16>>2]|0)+8>>2]|0)+24>>3];do if(!(e!=0.0)){if(f|0?a[f+128>>0]|0:0){b=b+432|0;f=f+112|0;c[b>>2]=c[f>>2];c[b+4>>2]=c[f+4>>2];c[b+8>>2]=c[f+8>>2];c[b+12>>2]=c[f+12>>2];break}d=b+432|0;if((c[b+56>>2]|0)==300){b=(c[b+84>>2]|0)+40|0;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];break}else{h[b+440>>3]=96.0;h[d>>3]=96.0;break}}else{h[b+440>>3]=e;h[b+432>>3]=e}while(0);return}function _J(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0.0,j=0.0,k=0.0,m=0,n=0,o=0.0,p=0.0,q=0.0,r=0.0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;A=l;l=l+112|0;t=A+80|0;u=A+56|0;s=A+40|0;v=A+32|0;w=A+24|0;x=A+16|0;y=A+8|0;z=A;m=c[b>>2]|0;o=+h[m+272>>3];q=+h[m+280>>3];p=+h[m+256>>3];r=+h[m+264>>3];f=+h[b+240>>3];e=p-f;h[b+208>>3]=e;i=+h[b+248>>3];j=r-i;h[b+216>>3]=j;f=f+o;h[b+224>>3]=f;i=i+q;h[b+232>>3]=i;e=f-e;j=i-j;h[x>>3]=1.0;g=c[(c[d+16>>2]|0)+8>>2]|0;i=+h[g+64>>3];do if(i>.001?(k=+h[g+72>>3],k>.001):0){e=e==0.0?i:e;f=j==0.0?k:j;if(!(i<e|k<f)?!(k>f&(i>e&(a[g+80>>0]|0)!=0)):0){k=e;j=f;i=1.0;break}n=i/e<k/f;i=(n?i:k)/(n?e:f);h[x>>3]=i;k=e;j=f}else{k=e;i=1.0}while(0);f=(p+o)*.5;h[y>>3]=f;e=(r+q)*.5;h[z>>3]=e;c[b+360>>2]=c[m+288>>2];k=i*k;h[v>>3]=k;j=i*j;h[w>>3]=j;g=QA(d,100752)|0;if(g){m=T1((l2(g)|0)+1|0)|0;n=T1((l2(g)|0)+1|0)|0;c[s>>2]=v;c[s+4>>2]=w;c[s+8>>2]=x;c[s+12>>2]=m;do if((b5(g,100761,s)|0)==4){g=tC(c[d+60>>2]|0,m,0)|0;if(g|0){u=c[g+16>>2]|0;h[y>>3]=+h[u+16>>3];h[z>>3]=+h[u+24>>3]}}else{c[u>>2]=v;c[u+4>>2]=w;c[u+8>>2]=x;c[u+12>>2]=m;c[u+16>>2]=n;if((b5(g,100781,u)|0)!=4){c[t>>2]=v;c[t+4>>2]=w;c[t+8>>2]=x;c[t+12>>2]=y;c[t+16>>2]=z;b5(g,100801,t)|0;break}g=tC(c[d+60>>2]|0,m,0)|0;if(g|0){u=c[g+16>>2]|0;h[y>>3]=+h[u+16>>3];h[z>>3]=+h[u+24>>3]}}while(0);U1(m);U1(n);k=+h[v>>3];j=+h[w>>3];i=+h[x>>3];f=+h[y>>3];e=+h[z>>3]}h[b+368>>3]=k;h[b+376>>3]=j;h[b+352>>3]=i;h[b+336>>3]=f;h[b+344>>3]=e;l=A;return}function $J(b,d){b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0,k=0.0,m=0.0,n=0.0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0.0,P=0.0,Q=0.0;M=l;l=l+224|0;J=M+200|0;z=M+168|0;E=M+144|0;I=M+120|0;F=M+96|0;G=M+64|0;e=M+184|0;v=M+176|0;w=M+160|0;y=M+136|0;x=M+112|0;A=M+80|0;B=M+48|0;C=M+32|0;D=M+16|0;K=M+8|0;L=M;u=c[b>>2]|0;c[G>>2]=0;c[G+4>>2]=0;c[G+8>>2]=0;c[G+12>>2]=0;H=b+368|0;c[I>>2]=c[H>>2];c[I+4>>2]=c[H+4>>2];c[I+8>>2]=c[H+8>>2];c[I+12>>2]=c[H+12>>2];H=b+360|0;if(c[H>>2]|0){c[J>>2]=c[I>>2];c[J+4>>2]=c[I+4>>2];c[J+8>>2]=c[I+8>>2];c[J+12>>2]=c[I+12>>2];aK(e,J);c[I>>2]=c[e>>2];c[I+4>>2]=c[e+4>>2];c[I+8>>2]=c[e+8>>2];c[I+12>>2]=c[e+12>>2]}t=b+416|0;c[F>>2]=c[t>>2];c[F+4>>2]=c[t+4>>2];c[F+8>>2]=c[t+8>>2];c[F+12>>2]=c[t+12>>2];if((a[u+294>>0]|0)!=0?(c[b+152>>2]&32|0)!=0:0){i=+h[u+232>>3]-+h[F>>3]*2.0;h[E>>3]=i;k=+h[u+240>>3]-+h[F+8>>3]*2.0;p=E+8|0;h[p>>3]=k;if(!(i<.0001)){o=+h[I>>3];e=~~(o/i);f=b+164|0;c[f>>2]=e;if(o-i*+(e|0)>.0001){e=e+1|0;c[f>>2]=e}}else{c[b+164>>2]=1;e=1}if(!(k<.0001)){g=+h[I+8>>3];f=~~(g/k);j=b+168|0;c[j>>2]=f;if(g-k*+(f|0)>.0001){f=f+1|0;c[j>>2]=f}}else{c[b+168>>2]=1;f=1;g=+h[I+8>>3]}c[b+204>>2]=O(f,e)|0;o=+h[(+h[I>>3]<i?I:E)>>3];h[I>>3]=o;e=p;f=I;m=+h[(g<k?I:E)+8>>3];g=o;j=21}else{if(c[b+68>>2]|0){e=c[b+84>>2]|0;i=+h[e+24>>3]-+h[F>>3]*2.0;i=i<0.0?0.0:i;h[E>>3]=i;o=+h[e+32>>3]-+h[F+8>>3]*2.0;e=E+8|0;h[e>>3]=o;if(o<0.0){f=E;j=17}else e=E}else{h[E+8>>3]=0.0;f=E;e=E;j=17}if((j|0)==17){h[e>>3]=0.0;e=f;i=+h[f>>3]}c[b+204>>2]=1;c[b+168>>2]=1;c[b+164>>2]=1;g=+h[I>>3];if(i<g){h[e>>3]=g;i=g}e=E+8|0;k=+h[e>>3];m=+h[I+8>>3];if(k<m){f=E;j=21}else{r=e;m=k}}if((j|0)==21){h[f+8>>3]=m;r=e;m=+h[e>>3]}n=+h[F>>3];s=b+432|0;k=+h[s>>3]*.013888888888888888*(n*2.0+i);c[b+448>>2]=~~(k+(k>=0.0?.5:-.5));q=F+8|0;k=+h[q>>3];t=b+440|0;o=+h[t>>3]*.013888888888888888*(k*2.0+m);c[b+452>>2]=~~(o+(o>=0.0?.5:-.5));j=b+188|0;p=b+180|0;e=b+172|0;f=u+196|0;c[e>>2]=0;c[e+4>>2]=0;c[e+8>>2]=0;c[e+12>>2]=0;c[e+16>>2]=0;c[e+20>>2]=0;bK(v,b,a[c[f>>2]>>0]|0);u=v;v=c[u+4>>2]|0;e=p;c[e>>2]=c[u>>2];c[e+4>>2]=v;bK(w,b,a[(c[f>>2]|0)+1>>0]|0);e=w;w=c[e>>2]|0;e=c[e+4>>2]|0;v=j;c[v>>2]=w;c[v+4>>2]=e;w=(c[p>>2]|0)+w|0;if(!((((w|0)>-1?w:0-w|0)|0)==1?(w=(c[b+184>>2]|0)+e|0,(((w|0)>-1?w:0-w|0)|0)==1):0)){bK(y,b,66);v=y;y=c[v+4>>2]|0;w=p;c[w>>2]=c[v>>2];c[w+4>>2]=y;bK(x,b,76);w=x;x=c[w+4>>2]|0;y=j;c[y>>2]=c[w>>2];c[y+4>>2]=x;c[z>>2]=c[f>>2];gA(0,100732,z)|0}if(a[(c[(c[d+16>>2]|0)+8>>2]|0)+82>>0]|0){if(i>g){m=(i-g)*.5;h[G>>3]=m}else m=0.0;i=+h[r>>3];o=+h[I+8>>3];if(i>o){i=(i-o)*.5;h[G+8>>3]=i}else i=0.0}else{i=0.0;m=0.0}if(!(c[H>>2]|0))j=1;else{c[J>>2]=c[I>>2];c[J+4>>2]=c[I+4>>2];c[J+8>>2]=c[I+8>>2];c[J+12>>2]=c[I+12>>2];aK(A,J);c[I>>2]=c[A>>2];c[I+4>>2]=c[A+4>>2];c[I+8>>2]=c[A+8>>2];c[I+12>>2]=c[A+12>>2];c[J>>2]=c[E>>2];c[J+4>>2]=c[E+4>>2];c[J+8>>2]=c[E+8>>2];c[J+12>>2]=c[E+12>>2];aK(B,J);c[E>>2]=c[B>>2];c[E+4>>2]=c[B+4>>2];c[E+8>>2]=c[B+8>>2];c[E+12>>2]=c[B+12>>2];c[J>>2]=c[F>>2];c[J+4>>2]=c[F+4>>2];c[J+8>>2]=c[F+8>>2];c[J+12>>2]=c[F+12>>2];aK(C,J);c[F>>2]=c[C>>2];c[F+4>>2]=c[C+4>>2];c[F+8>>2]=c[C+8>>2];c[F+12>>2]=c[C+12>>2];c[J>>2]=c[G>>2];c[J+4>>2]=c[G+4>>2];c[J+8>>2]=c[G+8>>2];c[J+12>>2]=c[G+12>>2];aK(D,J);c[G>>2]=c[D>>2];c[G+4>>2]=c[D+4>>2];c[G+8>>2]=c[D+8>>2];c[G+12>>2]=c[D+12>>2];n=+h[F>>3];m=+h[G>>3];k=+h[q>>3];i=+h[G+8>>3];g=+h[I>>3];j=(c[H>>2]|0)==0}o=m+n;h[b+384>>3]=o;P=i+k;h[b+392>>3]=P;N=m+n+g;h[b+400>>3]=N;Q=+h[I+8>>3];n=i+k+Q;h[b+408>>3]=n;m=+h[b+352>>3];h[b+320>>3]=g/m;h[b+328>>3]=Q/m;m=+h[s>>3];o=o*.013888888888888888*m;f=b+456|0;c[f>>2]=~~(o+(o>=0.0?.5:-.5));o=+h[t>>3];k=P*.013888888888888888*o;c[b+460>>2]=~~(k+(k>=0.0?.5:-.5));m=N*.013888888888888888*m;e=b+464|0;c[e>>2]=~~(m+(m>=0.0?.5:-.5));o=n*.013888888888888888*o;c[b+468>>2]=~~(o+(o>=0.0?.5:-.5));if(!j){c[J>>2]=c[f>>2];c[J+4>>2]=c[f+4>>2];NJ(K,J);I=K;K=c[I+4>>2]|0;b=f;c[b>>2]=c[I>>2];c[b+4>>2]=K;c[J>>2]=c[e>>2];c[J+4>>2]=c[e+4>>2];NJ(L,J);b=L;K=c[b+4>>2]|0;L=e;c[L>>2]=c[b>>2];c[L+4>>2]=K}l=M;return}function aK(a,b){a=a|0;b=b|0;var c=0.0;c=+h[b>>3];h[a>>3]=+h[b+8>>3];h[a+8>>3]=c;return}function bK(a,b,d){a=a|0;b=b|0;d=d|0;d=(d<<24>>24)+-66|0;switch(d>>>1|d<<31|0){case 9:{c[b+176>>2]=(c[b+168>>2]|0)+-1;b=0;d=-1;break}case 0:{b=0;d=1;break}case 5:{b=1;d=0;break}case 8:{c[b+172>>2]=(c[b+164>>2]|0)+-1;b=-1;d=0;break}default:{b=0;d=0}}c[a>>2]=b;c[a+4>>2]=d;return}function cK(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;j=l;l=l+16|0;g=j;f=QA(d,100973)|0;i=b+296|0;c[i>>2]=(f|0)==0?100982:f;d=QA(d,100986)|0;f=b+300|0;d=(d|0)==0?100999:d;c[f>>2]=d;d=G4(c[i>>2]|0,d)|0;if(d|0){c[g>>2]=a[d>>0];gA(0,101001,g)|0;c[f>>2]=195059}d=D4(e)|0;c[b+304>>2]=d;h=b+308|0;f=0;b=0;while(1){e=A4(d,c[i>>2]|0)|0;if(!e)break;g=b+1|0;if((b|0)<(f|0))d=c[h>>2]|0;else{f=f+128|0;d=c[h>>2]|0;if(!d)d=KK(f<<2)|0;else d=MK(d,f<<2)|0;c[h>>2]=d}c[d+(g<<2)>>2]=e;b=g;d=0}if(b|0){i=MK(c[h>>2]|0,(b<<2)+8|0)|0;c[h>>2]=i;c[i>>2]=0;c[(c[h>>2]|0)+(b+1<<2)>>2]=0}l=j;return b|0}function dK(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;j=k;i=a+312|0;d=KK((c[i>>2]<<2)+8|0)|0;e=0;h=1;while(1){g=c[i>>2]|0;if((h|0)>(g|0))break;f=e+1|0;if((uJ(a,h,g,b)|0)<<24>>24){c[d+(f<<2)>>2]=h;e=f}h=h+1|0}if(!e){c[j>>2]=b;gA(0,100872,j)|0;U1(d);d=0}else{c[d>>2]=e;c[d+(e+1<<2)>>2]=(c[i>>2]|0)+1}l=k;return d|0}function eK(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0.0;d=c[b+16>>2]|0;g=+h[d+16>>3];h[d+48>>3]=g-+h[d+88>>3];e=+h[d+24>>3];f=+h[d+80>>3]*.5;h[d+56>>3]=e-f;h[d+64>>3]=+h[d+96>>3]+g;h[d+72>>3]=f+e;b=XA(a,b)|0;while(1){if(!b)break;fK(b);b=ZA(a,b)|0}return}function fK(a){a=a|0;a=c[(c[a+16>>2]|0)+8>>2]|0;if(a|0)gK(a);return}function gK(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0;C=l;l=l+240|0;v=C+192|0;u=C+176|0;w=C+64|0;x=C+32|0;y=C;z=C+144|0;A=C+112|0;B=a+4|0;if((c[B>>2]|0)<=0)qa(101140,99906,3994,101154);b=c[a>>2]|0;d=w;e=b;f=d+48|0;do{c[d>>2]=c[e>>2];d=d+4|0;e=e+4|0}while((d|0)<(f|0));d=v;e=b;f=d+48|0;do{c[d>>2]=c[e>>2];d=d+4|0;e=e+4|0}while((d|0)<(f|0));hK(x,v);i=x+8|0;j=y+8|0;k=x+16|0;m=y+16|0;n=x+24|0;o=y+24|0;p=w+8|0;q=w+12|0;r=w+32|0;s=w+4|0;t=w+16|0;g=0;while(1){if((g|0)>=(c[B>>2]|0))break;if((g|0)>0){b=(c[a>>2]|0)+(g*48|0)|0;d=w;e=b;f=d+48|0;do{c[d>>2]=c[e>>2];d=d+4|0;e=e+4|0}while((d|0)<(f|0));d=v;e=b;f=d+48|0;do{c[d>>2]=c[e>>2];d=d+4|0;e=e+4|0}while((d|0)<(f|0));hK(y,v);h[x>>3]=+h[(+h[x>>3]<+h[y>>3]?x:y)>>3];h[i>>3]=+h[(+h[i>>3]<+h[j>>3]?x:y)+8>>3];h[k>>3]=+h[(+h[k>>3]>+h[m>>3]?x:y)+16>>3];h[n>>3]=+h[(+h[n>>3]>+h[o>>3]?x:y)+24>>3]}b=c[p>>2]|0;if(b|0){f=c[w>>2]|0;c[u>>2]=c[t>>2];c[u+4>>2]=c[t+4>>2];c[u+8>>2]=c[t+8>>2];c[u+12>>2]=c[t+12>>2];c[v>>2]=c[f>>2];c[v+4>>2]=c[f+4>>2];c[v+8>>2]=c[f+8>>2];c[v+12>>2]=c[f+12>>2];YH(z,u,v,1.0,b);c[y>>2]=c[z>>2];c[y+4>>2]=c[z+4>>2];c[y+8>>2]=c[z+8>>2];c[y+12>>2]=c[z+12>>2];c[y+16>>2]=c[z+16>>2];c[y+20>>2]=c[z+20>>2];c[y+24>>2]=c[z+24>>2];c[y+28>>2]=c[z+28>>2];h[x>>3]=+h[(+h[x>>3]<+h[y>>3]?x:y)>>3];h[i>>3]=+h[(+h[i>>3]<+h[j>>3]?x:y)+8>>3];h[k>>3]=+h[(+h[k>>3]>+h[m>>3]?x:y)+16>>3];h[n>>3]=+h[(+h[n>>3]>+h[o>>3]?x:y)+24>>3]}b=c[q>>2]|0;if(b|0){f=(c[w>>2]|0)+((c[s>>2]|0)+-1<<4)|0;c[u>>2]=c[r>>2];c[u+4>>2]=c[r+4>>2];c[u+8>>2]=c[r+8>>2];c[u+12>>2]=c[r+12>>2];c[v>>2]=c[f>>2];c[v+4>>2]=c[f+4>>2];c[v+8>>2]=c[f+8>>2];c[v+12>>2]=c[f+12>>2];YH(A,u,v,1.0,b);c[y>>2]=c[A>>2];c[y+4>>2]=c[A+4>>2];c[y+8>>2]=c[A+8>>2];c[y+12>>2]=c[A+12>>2];c[y+16>>2]=c[A+16>>2];c[y+20>>2]=c[A+20>>2];c[y+24>>2]=c[A+24>>2];c[y+28>>2]=c[A+28>>2];h[x>>3]=+h[(+h[x>>3]<+h[y>>3]?x:y)>>3];h[i>>3]=+h[(+h[i>>3]<+h[j>>3]?x:y)+8>>3];h[k>>3]=+h[(+h[k>>3]>+h[m>>3]?x:y)+16>>3];h[n>>3]=+h[(+h[n>>3]>+h[o>>3]?x:y)+24>>3]}g=g+1|0}B=a+8|0;c[B>>2]=c[x>>2];c[B+4>>2]=c[x+4>>2];c[B+8>>2]=c[x+8>>2];c[B+12>>2]=c[x+12>>2];c[B+16>>2]=c[x+16>>2];c[B+20>>2]=c[x+20>>2];c[B+24>>2]=c[x+24>>2];c[B+28>>2]=c[x+28>>2];l=C;return}function hK(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0.0,p=0.0,q=0.0,r=0.0,s=0.0,t=0;n=l;l=l+48|0;i=n+32|0;j=n;k=c[b+4>>2]|0;if((k|0)<=0)qa(101170,99906,3968,101182);if(((k>>>0)%3|0|0)!=1)qa(101192,99906,3969,101182);m=j+16|0;d=c[b>>2]|0;c[m>>2]=c[d>>2];c[m+4>>2]=c[d+4>>2];c[m+8>>2]=c[d+8>>2];c[m+12>>2]=c[d+12>>2];c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];e=i+8|0;f=j+8|0;g=j+24|0;b=1;while(1){if((b|0)>=(k|0))break;p=+h[d+(b<<4)+8>>3];t=b+1|0;o=+h[d+(t<<4)+8>>3];r=(+h[d+(t<<4)>>3]+ +h[d+(b<<4)>>3])*.5;h[i>>3]=r;p=(o+p)*.5;h[e>>3]=p;o=+h[(+h[j>>3]<r?j:i)>>3];h[j>>3]=o;s=+h[(+h[f>>3]<p?j:i)+8>>3];h[f>>3]=s;r=+h[(+h[m>>3]>r?m:i)>>3];h[m>>3]=r;p=+h[(+h[g>>3]>p?m:i)+8>>3];h[g>>3]=p;t=d+(b+2<<4)|0;c[i>>2]=c[t>>2];c[i+4>>2]=c[t+4>>2];c[i+8>>2]=c[t+8>>2];c[i+12>>2]=c[t+12>>2];q=+h[i>>3];h[j>>3]=+h[(o<q?j:i)>>3];o=+h[e>>3];h[f>>3]=+h[(s<o?j:i)+8>>3];h[m>>3]=+h[(r>q?m:i)>>3];h[g>>3]=+h[(p>o?m:i)+8>>3];b=b+3|0}c[a>>2]=c[j>>2];c[a+4>>2]=c[j+4>>2];c[a+8>>2]=c[j+8>>2];c[a+12>>2]=c[j+12>>2];c[a+16>>2]=c[j+16>>2];c[a+20>>2]=c[j+20>>2];c[a+24>>2]=c[j+24>>2];c[a+28>>2]=c[j+28>>2];l=n;return}function iK(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0.0,f=0.0,g=0.0,i=0,j=0.0,k=0,l=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0.0;q=+h[a>>3];r=+h[c>>3];k=!(q>=r);if((!k?q<=+h[c+16>>3]:0)?(e=+h[a+8>>3],e>=+h[c+8>>3]):0)i=e<=+h[c+24>>3];else i=0;e=+h[b>>3];if((e>=r?e<=+h[c+16>>3]:0)?(f=+h[b+8>>3],f>=+h[c+8>>3]):0){d=f<=+h[c+24>>3];if(!(i^d))if(i&d)d=1;else l=11;else d=0}else if(i)d=0;else l=11;a:do if((l|0)==11){p=+h[a+8>>3];do if(q==e){r=+h[c+8>>3];if(!(k|p>=r^+h[b+8>>3]>=r^1)?q<=+h[c+16>>3]:0){d=0;break a}}else{j=+h[b+8>>3];if(p==j){if(!(q>=r^e>=r))break;if(!(p>=+h[c+8>>3]))break;if(!(p<=+h[c+24>>3]))break;else{d=0;break a}}o=(j-p)/(e-q);l=q<e;g=l?q:e;e=l?e:q;f=(r-q)*o+p;m=+h[c+8>>3];if(!(!(r>=g)|!(r<=e)|!(f>=m))?f<=+h[c+24>>3]:0){d=0;break a}n=+h[c+16>>3];f=(n-r)*o+f;if(f>=m?!(!(n<=e)|(n>=g?!(f<=+h[c+24>>3]):1)):0){d=0;break a}l=p<j;g=l?p:j;f=l?j:p;e=(m-p)/o+q;if(e>=r?!(!(m<=f)|(!(m>=g)|!(e<=n))):0){d=0;break a}p=+h[c+24>>3];q=(p-m)/o+e;if(q>=r?!(!(p<=f)|(!(p>=g)|!(q<=n))):0){d=0;break a}}while(0);d=-1}while(0);return d|0}function jK(a){a=a|0;var b=0,c=0.0;b=a+16|0;c=+h[b>>3];h[a+32>>3]=c;h[a+48>>3]=c;h[a+40>>3]=+h[a+24>>3];h[a+56>>3]=+h[a+8>>3];h[b>>3]=+h[a>>3];return}function kK(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0.0,i=0.0;if((c[47131]|0)!=(d|0)){N2(+(d|0)*.15915494309189535,186328,186336);c[47131]=d}g=+h[b>>3];e=+h[23292];f=+h[b+8>>3];i=+h[23291];h[a>>3]=e*g-i*f;h[a+8>>3]=i*g+f*e;return}function lK(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0.0,i=0,j=0,k=0;k=l;l=l+16|0;f=k;e=+h[b>>3];i=b+8|0;g=+h[i>>3];a:do switch(d|0){case 0:{j=11;break}case 90:{h[b>>3]=g;e=-e;j=10;break}case 180:{e=-g;j=10;break}case 270:{h[b>>3]=g;j=10;break}default:{if((d|0)<0){c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];mK(a,f,0-d|0);break a}if((d|0)>360){c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];lK(a,f,(d>>>0)%360|0);break a}else{c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];kK(a,f,d);break a}}}while(0);if((j|0)==10){h[i>>3]=e;j=11}if((j|0)==11){c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2]}l=k;return}function mK(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0.0,i=0,j=0,k=0;k=l;l=l+16|0;f=k;e=+h[b>>3];i=b+8|0;g=+h[i>>3];a:do switch(d|0){case 0:{j=11;break}case 90:{h[b>>3]=-g;j=10;break}case 180:{e=-g;j=10;break}case 270:{h[b>>3]=g;j=10;break}default:{if((d|0)<0){c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];lK(a,f,0-d|0);break a}if((d|0)>360){c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];mK(a,f,(d>>>0)%360|0);break a}else{c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];kK(a,f,360-d|0);break a}}}while(0);if((j|0)==10){h[i>>3]=e;j=11}if((j|0)==11){c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2]}l=k;return}function nK(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0,g=0.0;g=+h[c>>3];d=+h[c+8>>3];f=d+ +h[b>>3];e=g+ +h[b+24>>3];d=d+ +h[b+16>>3];h[a>>3]=g+ +h[b+8>>3];h[a+8>>3]=f;h[a+16>>3]=e;h[a+24>>3]=d;return}function oK(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0,g=0.0;f=+h[a>>3];d=+h[b>>3]-f;g=+h[a+8>>3];e=+h[b+8>>3]-g;f=(+h[c+8>>3]-g)*d-(+h[c>>3]-f)*e;f=f*f;return +(f<1.0e-10?0.0:f/(e*e+d*d))}function pK(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;g=l;l=l+16|0;f=g+8|0;e=g;a=QA(a,b)|0;if(a|0?(c[f>>2]=e,(b5(a,101209,f)|0)>0):0)h[d>>3]=+h[e>>3];l=g;return}function qK(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;g=n+8|0;i=n;e=JK(96)|0;m=b+16|0;c[(c[m>>2]|0)+8>>2]=e;e=QA(b,101213)|0;if(!e){e=Ta(101222)|0;if(e|0)k=3}else k=3;if((k|0)==3)cb(101234,e|0,1)|0;e=(rK(b)|0)&255;a[(c[m>>2]|0)+115>>0]=e;if(0==0?(e=QA(b,101245)|0,c[47133]=e,(e|0)==0):0)c[47133]=c[47132];f=+_O(b,GA(b,0,101255,0)|0,0.0,0.0);h[c[(c[m>>2]|0)+8>>2]>>3]=f;e=QA(b,101263)|0;a:do if(!e)e=0;else{switch(a[e>>0]|0){case 76:{if(!(s2(e,101271)|0)){e=1;break a}break}case 66:{if(!(s2(e,101274)|0)){e=2;break a}break}case 82:{e=(s2(e,101277)|0)==0;e=e?3:0;break a}default:{e=0;break a}}e=0}while(0);c[(c[m>>2]|0)+116>>2]=e<<2|(d<<24>>24==0?0:e);f=+_O(b,GA(b,0,101280,0)|0,.25,.02);h[i>>3]=f;f=f*72.0;c[(c[m>>2]|0)+248>>2]=~~(f+(f>=0.0?.5:-.5));e=aP(b,GA(b,0,101288,0)|0,0)|0;if(e){c[g>>2]=i;if(b5(e,101209,g)|0){f=+h[i>>3];if(f<.02){h[i>>3]=.02;f=.02}}else{h[i>>3]=.5;f=.5}if(d4(e,101296)|0)a[(c[m>>2]|0)+276>>0]=1}else{h[i>>3]=.5;f=.5}f=f*72.0;c[(c[m>>2]|0)+252>>2]=~~(f+(f>=0.0?.5:-.5));i=(ZO(b,GA(b,0,101304,0)|0,0,0)|0)&255;a[(c[m>>2]|0)+243>>0]=i;i=pP(aP(b,GA(b,0,101314,0)|0,0)|0,20676,20692)|0;c[(c[m>>2]|0)+244>>2]=i;sK(b);i=tK(b,101324,(c[(c[m>>2]|0)+8>>2]|0)+64|0)|0;e=c[(c[m>>2]|0)+8>>2]|0;a[e+80>>0]=i;tK(b,101329,e+48|0)|0;e=dP(QA(b,101334)|0)|0;a[(c[(c[m>>2]|0)+8>>2]|0)+82>>0]=e;e=QA(b,101341)|0;do if(!e){e=QA(b,101348)|0;if(e|0){e=(a[e>>0]|32)<<24>>24==108&1;k=27;break}e=QA(b,101360)|0;if(e|0){e=dP(e)|0;k=27}}else{e=(q5(e)|0)==90&1;k=27}while(0);if((k|0)==27)a[(c[(c[m>>2]|0)+8>>2]|0)+81>>0]=e;c[47135]=pP(QA(b,101370)|0,20708,20724)|0;a[194954]=dP(QA(b,101382)|0)|0;c[47138]=0;c[47139]=0;h[(c[(c[m>>2]|0)+8>>2]|0)+24>>3]=0.0;e=QA(b,101394)|0;if((e|0)!=0?(a[e>>0]|0)!=0:0)k=32;else k=30;if(((k|0)==30?(j=QA(b,101398)|0,j|0):0)?a[j>>0]|0:0){e=j;k=32}if((k|0)==32){f=+r5(e);h[(c[(c[m>>2]|0)+8>>2]|0)+24>>3]=f}uK(b);h[23295]=1.e+37;c[47140]=GA(b,0,101409,0)|0;c[47143]=GA(b,0,141034,0)|0;c[47144]=GA(b,0,105496,0)|0;c[47145]=GA(b,1,141048,0)|0;c[47146]=GA(b,1,141116,0)|0;c[47147]=GA(b,1,108230,0)|0;c[47148]=GA(b,1,140823,0)|0;c[47149]=GA(b,1,101418,0)|0;c[47157]=GA(b,1,141082,0)|0;c[47150]=GA(b,1,101428,0)|0;c[47151]=GA(b,1,101437,0)|0;c[47152]=GA(b,1,101446,0)|0;k=GA(b,1,108224,0)|0;c[47154]=k;if(!k)c[47154]=GA(b,1,108224,105493)|0;c[47155]=GA(b,1,101456,0)|0;c[47158]=GA(b,1,101304,0)|0;c[47172]=GA(b,1,101463,0)|0;c[47161]=GA(b,1,101409,0)|0;c[47153]=GA(b,1,105496,0)|0;c[47159]=GA(b,1,141076,0)|0;c[47160]=GA(b,1,101472,0)|0;c[47163]=GA(b,1,101484,0)|0;c[47162]=GA(b,1,101348,0)|0;c[47164]=GA(b,1,101489,0)|0;c[47165]=GA(b,1,141024,0)|0;c[47166]=GA(b,1,137297,0)|0;c[47156]=GA(b,1,101500,0)|0;c[47167]=GA(b,1,101510,0)|0;c[47168]=GA(b,1,101516,0)|0;c[47169]=GA(b,1,101522,0)|0;c[47170]=GA(b,1,101530,0)|0;c[47171]=GA(b,1,101539,0)|0;c[47173]=GA(b,1,141034,0)|0;c[47174]=GA(b,2,101541,0)|0;c[47176]=GA(b,2,140823,0)|0;c[47177]=GA(b,2,101418,0)|0;c[47179]=GA(b,2,101428,0)|0;c[47180]=GA(b,2,101437,0)|0;c[47181]=GA(b,2,101446,0)|0;c[47182]=GA(b,2,108224,0)|0;c[47183]=GA(b,2,101456,0)|0;c[47192]=GA(b,2,101548,0)|0;c[47184]=GA(b,2,101559,0)|0;c[47195]=GA(b,2,101563,0)|0;c[47196]=GA(b,2,101573,0)|0;c[47197]=GA(b,2,101583,0)|0;c[47198]=GA(b,2,101593,0)|0;c[47199]=GA(b,2,101603,0)|0;c[47200]=GA(b,2,101617,0)|0;c[47201]=GA(b,2,101631,0)|0;c[47202]=GA(b,2,101646,0)|0;c[47203]=GA(b,2,101660,0)|0;c[47175]=GA(b,2,101671,0)|0;c[47187]=GA(b,2,101304,0)|0;c[47185]=GA(b,2,141082,0)|0;c[47186]=GA(b,2,101678,0)|0;c[47188]=GA(b,2,101687,0)|0;c[47189]=GA(b,2,101697,0)|0;c[47190]=GA(b,2,101510,0)|0;c[47191]=GA(b,2,101522,0)|0;c[47204]=GA(b,2,101708,0)|0;c[47205]=GA(b,2,101717,0)|0;c[47206]=GA(b,2,101463,0)|0;e=$H(b)|0;c[(c[(c[m>>2]|0)+8>>2]|0)+88>>2]=e;e=QA(b,141060)|0;if(e|0?a[e>>0]|0:0){b=EK(e,b)|0;c[(c[(c[m>>2]|0)+8>>2]|0)+92>>2]=b}l=n;return}function rK(a){a=a|0;var b=0,d=0;d=l;l=l+16|0;b=d;a=bP(a,GA(a,0,101807,0)|0,101815)|0;if(((((((c3(a,101821)|0)!=0?(c3(a,101829)|0)!=0:0)?(c3(a,101836)|0)!=0:0)?(c3(a,101839)|0)!=0:0)?(c3(a,101850)|0)!=0:0)?(c3(a,101861)|0)!=0:0)?(c3(a,101871)|0)!=0:0)if((c3(a,101882)|0)!=0?(c3(a,101888)|0)!=0:0)if((c3(a,101815)|0)!=0?(c3(a,101893)|0)!=0:0){c[b>>2]=a;gA(0,101898,b)|0;a=0}else a=0;else a=2;else a=1;l=d;return a|0}function sK(b){b=b|0;var d=0.0,e=0,f=0;e=QA(b,101765)|0;a:do if(e|0?(f=a[e>>0]|0,f<<24>>24):0)switch(f<<24>>24|0){case 97:{if(f<<24>>24!=97)break a;if(s2(e,101771)|0)break a;c[(c[(c[b+16>>2]|0)+8>>2]|0)+84>>2]=4;break a}case 99:{if(f<<24>>24!=99)break a;if(s2(e,101776)|0)break a;c[(c[(c[b+16>>2]|0)+8>>2]|0)+84>>2]=3;break a}case 101:{if(f<<24>>24!=101)break a;if(s2(e,101785)|0)break a;c[(c[(c[b+16>>2]|0)+8>>2]|0)+84>>2]=5;break a}case 102:{if(f<<24>>24!=102)break a;if(s2(e,101792)|0)break a;c[(c[(c[b+16>>2]|0)+8>>2]|0)+84>>2]=2;break a}default:{d=+r5(e);if(!(d>0.0))break a;b=c[(c[b+16>>2]|0)+8>>2]|0;c[b+84>>2]=1;h[b+16>>3]=d;break a}}while(0);return}function tK(b,d,e){b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+48|0;m=o+32|0;i=o+16|0;k=o+8|0;j=o;n=o+40|0;a[n>>0]=0;b=QA(b,d)|0;do if(b){c[i>>2]=k;c[i+4>>2]=j;c[i+8>>2]=n;i=(b5(b,101749,i)|0)>1;g=+h[k>>3];f=+h[j>>3];if(i&g>0.0&f>0.0){g=g*72.0;h[e>>3]=+(~~(g+(g>=0.0?.5:-.5))|0);g=f*72.0;h[e+8>>3]=+(~~(g+(g>=0.0?.5:-.5))|0);b=(a[n>>0]|0)==33;break}a[n>>0]=0;c[m>>2]=k;c[m+4>>2]=n;m=(b5(b,101759,m)|0)>0;f=+h[k>>3];if(m&f>0.0){g=f*72.0;g=+(~~(g+(g>=0.0?.5:-.5))|0);h[e>>3]=g;h[e+8>>3]=g;b=(a[n>>0]|0)==33}else b=0}else b=0;while(0);l=o;return b&1|0}function uK(b){b=b|0;var d=0,e=0,f=0.0,g=0.0,i=0;d=QA(b,108224)|0;do if(d|0?a[d>>0]|0:0){e=(c[(c[b+60>>2]|0)+16>>2]|0)+113|0;a[e>>0]=a[e>>0]|8;e=(iD(d)|0)!=0;g=+_O(b,GA(b,0,101428,0)|0,14.0,1.0);i=bP(b,GA(b,0,101437,0)|0,107994)|0;d=zK(b,d,e?2:0,g,i,bP(b,GA(b,0,101446,0)|0,137314)|0)|0;i=b+16|0;c[(c[i>>2]|0)+12>>2]=d;d=QA(b,105628)|0;e=(d|0)!=0;do if((yC(b)|0)==(b|0)){if(e?(a[d>>0]|0)==116:0){d=1;break}d=0}else{if(e?(a[d>>0]|0)==98:0){d=0;break}d=1}while(0);e=QA(b,101726)|0;a:do if(e)switch(a[e>>0]|0){case 108:{d=d|2;break a}case 114:{d=d|4;break a}default:break a}while(0);a[(c[i>>2]|0)+275>>0]=d;if((yC(b)|0)!=(b|0)){d=c[(c[i>>2]|0)+12>>2]|0;f=+h[d+24>>3]+16.0;g=+h[d+32>>3]+8.0;b=(c[(c[(yC(b)|0)+16>>2]|0)+116>>2]&1|0)==0;d=c[i>>2]|0;e=a[d+275>>0]<<1&2;if(b){b=e&255;h[d+48+(b<<4)>>3]=f;h[d+48+(b<<4)+8>>3]=g;break}else{b=(e^3)&255;h[d+48+(b<<4)>>3]=g;h[d+48+(b<<4)+8>>3]=f;break}}}while(0);return}function vK(a){a=a|0;var b=0,d=0,e=0;e=a+16|0;d=c[e>>2]|0;b=c[d+8>>2]|0;do if(!b)b=d;else{d=c[b+88>>2]|0;if(d){EH(d);d=c[e>>2]|0;b=c[d+8>>2]|0;if(!b){b=d;break}}U1(c[b+92>>2]|0);b=c[e>>2]|0}while(0);U1(c[b+8>>2]|0);e=c[e>>2]|0;c[e+8>>2]=0;CK(c[e+12>>2]|0);ZC(a,0,137483);return}function wK(a){a=a|0;var b=0,d=0;d=l;l=l+16|0;b=d;switch(a|0){case 0:{a=101971;break}case 1:{a=101839;break}case 2:{a=101977;break}default:{c[b>>2]=a;gA(1,101941,b)|0;a=101971}}l=d;return a|0}function xK(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;f=c[d>>2]|0;l=d+24|0;c[l>>2]=0;c[l+4>>2]=0;c[l+8>>2]=0;c[l+12>>2]=0;if(a[f>>0]|0){i=KK((l2(f)|0)+1|0)|0;a[i>>0]=0;j=d+12|0;e=i;a:while(1){b:while(1){h=f+1|0;g=a[f>>0]|0;if(!(g<<24>>24))break a;if(!(g<<24>>24==-1|((g&255)<161|(c[j>>2]|0)!=2))){a[e>>0]=g;h=a[h>>0]|0;g=e+2|0;a[e+1>>0]=h;if(!(h<<24>>24)){e=g;break a}else{e=g;f=f+2|0;continue}}switch(g<<24>>24){case 92:break b;case 10:{k=12;break b}default:{}}a[e>>0]=g;e=e+1|0;f=h}if((k|0)==12){k=0;f=e+1|0;a[e>>0]=0;yK(b,d,i,110);e=f;i=f;f=h;continue}g=a[h>>0]|0;switch(g<<24>>24|0){case 114:case 108:case 110:{g=e+1|0;a[e>>0]=0;yK(b,d,i,a[h>>0]|0);e=g;break}default:{a[e>>0]=g;e=e+1|0;g=i}}i=g;f=(a[h>>0]|0)==0?h:f+2|0}if((i|0)!=(e|0)){a[e>>0]=0;yK(b,d,i,110)}d=d+40|0;c[d>>2]=c[l>>2];c[d+4>>2]=c[l+4>>2];c[d+8>>2]=c[l+8>>2];c[d+12>>2]=c[l+12>>2]}return}function yK(d,e,f,g){d=d|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+32|0;q=s+16|0;p=s;r=e+76|0;k=b[r>>1]|0;o=e+72|0;m=c[o>>2]|0;if(!m)n=JK((k*56|0)+112|0)|0;else n=LK(m,k+2|0,56,k+1|0)|0;c[o>>2]=n;k=b[r>>1]|0;m=n+(k*56|0)|0;c[m>>2]=f;a[n+(k*56|0)+48>>0]=g;if((f|0)!=0?(a[f>>0]|0)!=0:0){c[46594]=c[e+4>>2];h[23299]=+h[e+16>>3];g=c[d+144>>2]|0;c[n+(k*56|0)+4>>2]=nb[c[g>>2]&63](g,186376,1)|0;LO(p,d,m);j=+h[p>>3];h[q>>3]=j;i=+h[p+8>>3]}else{h[q>>3]=0.0;i=+(~~(+h[e+16>>3]*1.2)|0);h[n+(k*56|0)+40>>3]=i;j=0.0}b[r>>1]=(b[r>>1]|0)+1<<16>>16;r=e+24|0;h[r>>3]=+h[(+h[r>>3]>j?r:q)>>3];r=e+32|0;h[r>>3]=+h[r>>3]+i;l=s;return}function zK(b,e,f,g,i,j){b=b|0;e=e|0;f=f|0;g=+g;i=i|0;j=j|0;var k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+32|0;r=u+16|0;q=u+8|0;p=u;t=JK(88)|0;switch(IC(b)|0){case 0:{m=0;s=c[b+60>>2]|0;n=b;o=0;break}case 1:{m=0;s=yC(xC(b)|0)|0;n=0;o=b;break}case 2:{m=b;s=yC(xC(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0)|0;n=0;o=0;break}default:{m=0;s=0;n=0;o=0}}c[t+4>>2]=i;c[t+8>>2]=j;h[t+16>>3]=g;k=s+16|0;j=t+12|0;c[j>>2]=d[(c[k>>2]|0)+115>>0];a:do if(f&4){c[t>>2]=D4(e)|0;if(f&2|0)a[t+82>>0]=1}else switch(f|0){case 2:{c[t>>2]=D4(e)|0;a[t+82>>0]=1;if(!(BU(b,t)|0))break a;switch(IC(b)|0){case 0:{c[p>>2]=KB(n)|0;gA(3,101983,p)|0;break a}case 1:{c[q>>2]=KB(o)|0;gA(3,102005,q)|0;break a}case 2:{p=KB(c[((c[m>>2]&3|0)==3?m:m+48|0)+40>>2]|0)|0;q=(xB(s)|0)!=0;s=KB(c[((c[m>>2]&3|0)==2?m:m+-48|0)+40>>2]|0)|0;c[r>>2]=p;c[r+4>>2]=q?137738:141747;c[r+8>>2]=s;gA(3,102026,r)|0;break a}default:break a}}case 0:{i=AK(e,b,0)|0;c[t>>2]=i;if((c[j>>2]|0)==1)i=eQ(i)|0;else i=bQ(i,s)|0;U1(c[t>>2]|0);c[t>>2]=i;xK(c[(c[k>>2]|0)+144>>2]|0,t);break a}default:qa(102053,102069,166,102078)}while(0);l=u;return t|0}function AK(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;switch(IC(d)|0){case 0:{i=KB(d)|0;g=l2(i)|0;f=c[(c[d+16>>2]|0)+12>>2]|0;if(f){f=c[f>>2]|0;if(!b){v=0;t=2;u=2;s=2;h=2;y=102101;m=102098;j=105493;o=195059;q=195059;p=0;r=0;n=2;w=f;x=102095}else{v=0;t=2;u=2;s=2;h=2;y=102101;m=102098;j=105493;o=195059;q=195059;p=0;r=0;n=l2(f)|0;w=f;x=102095}}else{v=0;t=2;u=2;s=2;h=2;y=102101;m=102098;j=105493;o=195059;q=195059;p=0;r=0;n=2;w=102089;x=102095}break}case 1:{i=KB(xC(d)|0)|0;g=l2(i)|0;j=KB(d)|0;h=l2(j)|0;f=c[(c[d+16>>2]|0)+104>>2]|0;if(f){f=c[f>>2]|0;if(!b){v=0;t=2;u=2;s=2;y=102101;m=102098;o=195059;q=195059;p=0;r=0;n=2;w=f;x=102095}else{v=0;t=2;u=2;s=2;y=102101;m=102098;o=195059;q=195059;p=0;r=0;n=l2(f)|0;w=f;x=102095}}else{v=0;t=2;u=2;s=2;y=102101;m=102098;o=195059;q=195059;p=0;r=0;n=2;w=102089;x=102095}break}case 2:{h=d+48|0;i=KB(yC(xC(c[((c[d>>2]&3|0)==3?d:h)+40>>2]|0)|0)|0)|0;g=l2(i)|0;l=KB(c[((c[d>>2]&3|0)==3?d:h)+40>>2]|0)|0;k=l2(l)|0;f=d+16|0;q=c[(c[f>>2]|0)+52>>2]|0;if(!q)r=0;else r=l2(q)|0;m=KB(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)|0;f=c[f>>2]|0;o=c[f+92>>2]|0;if(!o)p=0;else p=l2(o)|0;j=l2(m)|0;f=c[f+96>>2]|0;if(f){f=c[f>>2]|0;if(!b)n=2;else n=l2(f)|0}else{n=2;f=102089}x=(xB(yC(xC(c[((c[d>>2]&3|0)==3?d:h)+40>>2]|0)|0)|0)|0)==0;v=1;t=k;u=j;s=k+2+(r|0?r+1|0:0)+j+(p|0?p+1|0:0)|0;h=2;y=l;j=105493;w=f;x=x?141747:137738;break}default:{v=0;t=2;u=2;s=2;h=2;g=2;y=102101;m=102098;j=105493;i=102092;o=195059;q=195059;p=0;r=0;n=2;w=102089;x=102095}}e=(e|0)==0;l=0;f=b;a:while(1){d=f+1|0;b:do switch(a[f>>0]|0){case 0:break a;case 92:{f=f+2|0;switch(a[d>>0]|0){case 78:{k=h;break b}case 71:{k=g;break b}case 69:{k=s;break b}case 72:{k=u;break b}case 84:{k=t;break b}case 76:{k=n;break b}case 92:{if(!e){k=1;break b}break}default:{}}k=2;break}default:{k=1;f=d}}while(0);l=l+k|0}s=KK(l+1|0)|0;n=(v|0)==0;l=(r|0)==0;d=(p|0)==0;f=s;g=b;c:while(1){k=g+1|0;h=a[g>>0]|0;switch(h<<24>>24){case 0:break c;case 92:break;default:{a[f>>0]=h;f=f+1|0;g=k;continue c}}h=g+2|0;g=a[k>>0]|0;switch(g<<24>>24|0){case 71:{g=i;while(1){b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24)){g=h;continue c}g=g+1|0;f=f+1|0}}case 78:{g=j;while(1){b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24)){g=h;continue c}g=g+1|0;f=f+1|0}}case 69:{if(n){g=h;continue c}else g=y;while(1){b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24))break;g=g+1|0;f=f+1|0}if(l)g=x;else{a[f>>0]=58;g=q;while(1){f=f+1|0;b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24)){g=x;break}else g=g+1|0}}while(1){b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24)){g=m;break}g=g+1|0;f=f+1|0}while(1){b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24))break;g=g+1|0;f=f+1|0}if(d){g=h;continue c}a[f>>0]=58;g=o;while(1){f=f+1|0;b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24)){g=h;continue c}else g=g+1|0}}case 84:{g=y;while(1){b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24)){g=h;continue c}g=g+1|0;f=f+1|0}}case 72:{g=m;while(1){b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24)){g=h;continue c}f=f+1|0;g=g+1|0}}case 76:{g=w;while(1){b=a[g>>0]|0;a[f>>0]=b;if(!(b<<24>>24)){g=h;continue c}f=f+1|0;g=g+1|0}}case 92:{if(!e){a[f>>0]=92;f=f+1|0;g=h;continue c}break}default:{}}a[f>>0]=92;a[f+1>>0]=g;f=f+2|0;g=h}a[f>>0]=0;return s|0}function BK(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;if(a|0){d=a;e=0;while(1){if((e|0)>=(b|0))break;if(!e)U1(c[d>>2]|0);g=c[d+8>>2]|0;if(g|0?(f=c[d+12>>2]|0,f|0):0)tb[f&127](g);d=d+56|0;e=e+1|0}U1(a)}return}function CK(d){d=d|0;var e=0;if(d|0){U1(c[d>>2]|0);e=d+72|0;if(a[d+82>>0]|0){e=c[e>>2]|0;if(e|0)mU(e,1)}else BK(c[e>>2]|0,b[d+76>>1]|0);U1(d)}return}function DK(d,e,f){d=d|0;e=e|0;f=f|0;var g=0.0,i=0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+32|0;q=u+16|0;r=u;k=c[d+16>>2]|0;s=k+12|0;t=c[s>>2]|0;c[s>>2]=e;if(!(a[f+82>>0]|0)){p=f+76|0;if((b[p>>1]|0)>=1){eS(d,0);hS(d,c[f+8>>2]|0);switch(a[f+80>>0]|0){case 116:{j=+h[f+64>>3];g=+h[f+48>>3]*.5+j;break}case 98:{j=+h[f+64>>3];g=+h[f+48>>3]*-.5+j+ +h[f+32>>3];break}default:{j=+h[f+64>>3];g=+h[f+32>>3]*.5+j}}g=g-+h[f+16>>3];o=r+8|0;h[o>>3]=g;if(b[k+260>>1]&512)h[o>>3]=g-j;m=f+72|0;n=f+56|0;i=f+40|0;e=0;while(1){if((e|0)>=(b[p>>1]|0))break;k=c[m>>2]|0;switch(a[k+(e*56|0)+48>>0]|0){case 108:{g=+h[n>>3]-+h[i>>3]*.5;break}case 114:{g=+h[i>>3]*.5+ +h[n>>3];break}default:g=+h[n>>3]}h[r>>3]=g;c[q>>2]=c[r>>2];c[q+4>>2]=c[r+4>>2];c[q+8>>2]=c[r+8>>2];c[q+12>>2]=c[r+12>>2];gS(d,q,k+(e*56|0)|0);h[o>>3]=+h[o>>3]-+h[(c[m>>2]|0)+(e*56|0)+40>>3];e=e+1|0}fS(d);i=18}}else{TT(d,c[f+72>>2]|0,f);i=18}if((i|0)==18)c[s>>2]=t;l=u;return}function EK(a,b){a=a|0;b=b|0;return AK(a,b,1)|0}function FK(a){a=a|0;return GK(a,0)|0}function GK(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;e=c[47207]|0;if(!e){c[47208]=64;e=KK(64)|0;c[47207]=e}j=d<<24>>24==0;i=0;f=0;h=b;while(1){if(!h)break;d=a[h>>0]|0;if(!(d<<24>>24))break;b=c[47208]|0;if((i|0)>(b+-8|0)){e=b<<1;c[47208]=e;e=MK(c[47207]|0,e)|0;c[47207]=e;e=e+i|0;d=a[h>>0]|0}a:do switch(d<<24>>24){case 38:{if(j?(HK(h)|0)!=0:0)k=18;else{f=5;d=102130}break}case 60:{f=4;d=102153;break}case 62:{f=4;d=102136;break}case 45:{f=5;d=102147;break}default:{if((f|0)!=0&d<<24>>24==32)if((a[f>>0]|0)==32){f=6;d=102123;break a}else{k=18;break a}switch(d<<24>>24){case 34:{f=6;d=102116;break a}case 39:{f=5;d=102141;break a}case 10:if(j){k=18;break a}else{f=5;d=102110;break a}default:{k=18;break a}}}}while(0);if((k|0)==18){k=0;d=j|d<<24>>24!=13;f=d?1:5;d=d?h:102104}g=e+f|0;b=f;while(1){if(!b)break;a[e>>0]=a[d>>0]|0;b=b+-1|0;d=d+1|0;e=e+1|0}b=h;i=f+i|0;e=g;h=h+1|0;f=b}a[e>>0]=0;return c[47207]|0}function HK(b){b=b|0;var c=0,d=0;d=b+1|0;c=a[d>>0]|0;a:do if(c<<24>>24==35){d=b+2|0;c=a[d>>0]|0;switch(c<<24>>24){case 88:case 120:break;default:{b=d;while(1){d=b+1|0;if((c+-48&255)>=10)break a;b=d;c=a[d>>0]|0}}}d=b+3|0;while(1){c=a[d>>0]|0;if((c+-48&255)>=10)switch(c<<24>>24){case 65:case 66:case 67:case 68:case 69:case 70:case 97:case 98:case 99:case 100:case 101:case 102:break;default:break a}d=d+1|0}}else while(1){b=d+1|0;if(((c&-33)+-65&255)>=26)break a;d=b;c=a[b>>0]|0}while(0);return c<<24>>24==59|0}function IK(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;d=c[47209]|0;if(!d){c[47210]=64;d=KK(64)|0;c[47209]=d;i=0}else i=0;while(1){if(!b)break;e=a[b>>0]|0;if(!(e<<24>>24))break;f=c[47210]|0;if((i|0)>(f+-8|0)){d=f<<1;c[47210]=d;d=MK(c[47209]|0,d)|0;c[47209]=d;d=d+i|0;e=a[b>>0]|0}switch(e<<24>>24){case 38:{if(!(HK(b)|0)){g=5;e=102130}else j=12;break}case 60:{g=4;e=102153;break}case 62:{g=4;e=102136;break}case 34:{g=6;e=102116;break}case 39:{g=5;e=102141;break}default:j=12}if((j|0)==12){j=0;g=1;e=b}h=d+g|0;f=g;while(1){if(!f)break;a[d>>0]=a[e>>0]|0;f=f+-1|0;e=e+1|0;d=d+1|0}i=g+i|0;d=h;b=b+1|0}a[d>>0]=0;return c[47209]|0}function JK(a){a=a|0;var b=0;if(!a)a=0;else{b=KK(a)|0;Y6(b|0,0,a|0)|0;a=b}return a|0}function KK(a){a=a|0;if(a){a=T1(a)|0;if(!a){U4(111740,14,1,c[15712]|0)|0;a=0}}else a=0;return a|0}function LK(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;a=W1(a,O(d,b)|0)|0;if(!((b|0)!=0&(a|0)==0)){if(b>>>0>e>>>0)Y6(a+(O(e,d)|0)|0,0,O(b-e|0,d)|0)|0}else U4(111740,14,1,c[15712]|0)|0;return a|0}function MK(a,b){a=a|0;b=b|0;a=W1(a,b)|0;if((b|0)!=0&(a|0)==0)U4(111740,14,1,c[15712]|0)|0;return a|0}function NK(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0,m=0,n=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+80|0;r=u+40|0;q=u+32|0;n=u+24|0;m=u;s=4;t=T1(40)|0;c[t>>2]=0;f=u+68|0;g=u+64|0;if(0){o=0;ka(62,a|0,f|0,g|0);i=o;o=0;if((i|0)!=0&(p|0)!=0){j=V6(c[i>>2]|0,t|0,s|0)|0;if(!j)bb(i|0,p|0);z=p}else j=-1;if((j|0)!=1){j=c[15712]|0;i=c[f>>2]|0;f=c[g>>2]|0;o=0;c[m>>2]=102202;c[m+4>>2]=i;c[m+8>>2]=f;c[m+12>>2]=d;c[m+16>>2]=b;ca(32,j|0,102158,m|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)!=1){o=0;ha(1);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)!=1)j=6;else{f=z;j=15}}else{f=z;j=15}}else{f=z;j=15}}else j=6;do if((j|0)==6){o=0;f=$(27,a|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){i=V6(c[g>>2]|0,t|0,s|0)|0;if(!i)bb(g|0,p|0);z=p}else i=-1;if((i|0)!=1){if(!f){o=0;ha(2);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;break}}if((d|0)<1){o=0;ha(3);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;break}f=0;break}else{c[47211]=(e|0)>-1?e:30;t=U6(188848,1,t|0,s|0)|0;s=z;o=0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;break}f=0;j=15;break}}else{f=z;j=15}}while(0);a:do if((j|0)==15){b:while(1){if(f|0){f=2;break a}o=0;ha(4);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue}a=c[15712]|0;m=0;while(1){o=0;j=_(2)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}if(!j)break;o=0;f=$(28,j|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){i=V6(c[g>>2]|0,t|0,s|0)|0;if(!i)bb(g|0,p|0);z=p}else i=-1;if((i|0)==1){f=z;j=15;continue b}o=0;ja(25,j|0,f|0);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}m=m+1|0;do if(((m|0)%100|0|0)==0&0!=0){f=(m|0)%1e3|0;if((f|0)==100){o=0;aa(93,102202,a|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}o=0;c[n>>2]=m;ca(32,a|0,102220,n|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}break}o=0;c[q>>2]=m;ca(32,a|0,102220,q|0)|0;g=o;o=0;if((g|0)!=0&(p|0)!=0){i=V6(c[g>>2]|0,t|0,s|0)|0;if(!i)bb(g|0,p|0);z=p}else i=-1;if((i|0)==1){f=z;j=15;continue b}if(!f){o=0;aa(94,10,a|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}}}while(0);if((m|0)>=(d|0))break}switch(b|0){case 1:{o=0;ha(5);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}break}case 2:{o=0;ha(6);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}break}default:{o=0;ha(7);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}o=0;ha(3);f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue b}}}if(!0){f=0;break a}if((m|0)>99){o=0;aa(94,10,a|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15;continue}}f=c[47251]|0;g=c[47252]|0;o=0;k=+X(1);i=o;o=0;if((i|0)!=0&(p|0)!=0){j=V6(c[i>>2]|0,t|0,s|0)|0;if(!j)bb(i|0,p|0);z=p}else j=-1;if((j|0)==1){f=z;j=15;continue}o=0;c[r>>2]=102202;c[r+4>>2]=f;c[r+8>>2]=g;c[r+12>>2]=m;h[r+16>>3]=k;ca(32,a|0,102224,r|0)|0;f=o;o=0;if((f|0)!=0&(p|0)!=0){g=V6(c[f>>2]|0,t|0,s|0)|0;if(!g)bb(f|0,p|0);z=p}else g=-1;if((g|0)==1){f=z;j=15}else break}f=0}while(0);U1(t|0);l=u;return f|0}function OK(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;a=(c[a+16>>2]|0)+192|0;e=0;h=0;while(1){a=c[a>>2]|0;if(!a)break;g=c[a+16>>2]|0;f=c[g+180>>2]|0;a=0;while(1){if(!(c[f+(a<<2)>>2]|0))break;a=a+1|0;e=e+1|0}a=g+164|0;h=h+1|0}c[b>>2]=h;c[d>>2]=e;return}function PK(b){b=b|0;var d=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0;c[47254]=b;c[47261]=0;c[47252]=0;c[47251]=0;i=b+16|0;b=(c[i>>2]|0)+192|0;h=0;f=0;while(1){b=c[b>>2]|0;if(!b)break;g=c[b+16>>2]|0;a[g+157>>0]=0;h=h+1|0;c[47251]=h;d=c[g+180>>2]|0;b=0;while(1){if(!(c[d+(b<<2)>>2]|0))break;k=f+1|0;c[47252]=k;b=b+1|0;f=k}b=g+164|0}b=c[47262]|0;if(!b)b=KK(h<<2)|0;else b=MK(b,h<<2)|0;c[47262]=b;c[47263]=0;b=c[47256]|0;if(!b)b=KK(c[47251]<<2)|0;else b=MK(b,c[47251]<<2)|0;c[47256]=b;c[47255]=0;d=1;b=(c[i>>2]|0)+192|0;while(1){b=c[b>>2]|0;if(!b)break;k=b+16|0;i=c[k>>2]|0;j=i+292|0;c[j>>2]=0;i=c[i+172>>2]|0;h=d;g=0;b=0;while(1){d=c[i+(g<<2)>>2]|0;if(!d)break;f=b+1|0;c[j>>2]=f;b=c[d+16>>2]|0;c[b+160>>2]=0;c[b+164>>2]=-1;if(!h)b=0;else{l=c[d>>2]&3;b=((c[(c[(c[((l|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((l|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)|0)<(e[b+170>>1]|0|0)?0:h}h=b;g=g+1|0;b=f}b=JK((g<<2)+4|0)|0;d=c[k>>2]|0;c[d+264>>2]=b;c[d+268>>2]=0;d=c[d+180>>2]|0;b=0;do{l=b;b=b+1|0}while((c[d+(l<<2)>>2]|0)!=0);d=JK(b<<2)|0;b=c[k>>2]|0;c[b+272>>2]=d;c[b+276>>2]=0;d=h;b=b+164|0}return d|0}function QK(){var a=0,b=0,d=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;n=o+8|0;k=o;m=VO(c[47251]|0)|0;a=(c[(c[47254]|0)+16>>2]|0)+192|0;while(1){b=c[a>>2]|0;if(!b){j=0;break}d=b+16|0;a=c[d>>2]|0;if(!(c[a+292>>2]|0)){XO(m,b);a=c[d>>2]|0}a=a+164|0}while(1){a=YO(m)|0;if(!a)break;i=a+16|0;a=c[i>>2]|0;f=a+232|0;c[f>>2]=0;d=c[a+172>>2]|0;b=0;h=0;while(1){g=c[d+(b<<2)>>2]|0;if(!g){f=0;break}g=(c[(c[(c[((c[g>>2]&3|0)==3?g:g+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)+(e[(c[g+16>>2]|0)+170>>1]|0)|0;g=(h|0)>(g|0)?h:g;c[f>>2]=g;b=b+1|0;h=g}while(1){b=c[(c[a+180>>2]|0)+(f<<2)>>2]|0;if(!b)break;d=b+-48|0;g=(c[(c[((c[b>>2]&3|0)==2?b:d)+40>>2]|0)+16>>2]|0)+292|0;h=c[g>>2]|0;c[g>>2]=h+-1;if((h|0)<2){XO(m,c[((c[b>>2]&3|0)==2?b:d)+40>>2]|0);a=c[i>>2]|0}f=f+1|0}j=j+1|0}a:do if((j|0)!=(c[47251]|0)){gA(1,102556,k)|0;a=(c[(c[47254]|0)+16>>2]|0)+192|0;while(1){b=c[a>>2]|0;if(!b)break a;d=b+16|0;a=c[d>>2]|0;if(c[a+292>>2]|0){k=KB(b)|0;a=c[(c[d>>2]|0)+292>>2]|0;c[n>>2]=k;c[n+4>>2]=a;gA(3,102578,n)|0;a=c[d>>2]|0}a=a+164|0}}while(0);WO(m);l=o;return}function RK(){var b=0,d=0,e=0;b=(c[(c[47254]|0)+16>>2]|0)+192|0;while(1){b=c[b>>2]|0;if(!b)break;e=b+16|0;b=c[e>>2]|0;d=c[b+264>>2]|0;if(d){U1(d);b=c[e>>2]|0}d=c[b+272>>2]|0;if(d){U1(d);b=c[e>>2]|0}a[b+157>>0]=0;b=b+164|0}return}function SK(){var a=0,b=0,d=0,e=0,f=0;a=(c[(c[47254]|0)+16>>2]|0)+192|0;while(1){a=c[a>>2]|0;if(!a)break;a=c[a+16>>2]|0;c[a+280>>2]=0;a=a+164|0}f=JK(c[47251]<<2)|0;e=0;a=(c[(c[47254]|0)+16>>2]|0)+192|0;while(1){b=c[a>>2]|0;if(!b)break;d=b+16|0;a=c[d>>2]|0;if(!(c[a+280>>2]|0)){c[f+(e<<2)>>2]=dL(b)|0;b=e+1|0;a=c[d>>2]|0}else b=e;e=b;a=a+164|0}a=eL(f,e)|0;while(1){if((fL(a)|0)<=1)break;jL(a,c[(iL(hL(gL(a)|0)|0)|0)+8>>2]|0)}U1(a);a=0;while(1){if((a|0)>=(e|0))break;U1(c[f+(a<<2)>>2]|0);a=a+1|0}U1(f);if((c[47255]|0)==((c[47251]|0)+-1|0)){kL();return}else qa(102301,102331,523,102336)}function TK(){var a=0,b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;i=c[47261]|0;f=c[47255]|0;j=c[47256]|0;k=c[47211]|0;b=0;a=0;g=i;while(1){if((g|0)>=(f|0)){h=8;break}e=c[j+(g<<2)>>2]|0;d=c[(c[e+16>>2]|0)+160>>2]|0;if((d|0)<0){if(!a)a=e;else a=(c[(c[a+16>>2]|0)+160>>2]|0)>(d|0)?e:a;b=b+1|0;if((b|0)>=(k|0))break}e=g+1|0;c[47261]=e;g=e}if((h|0)==8)if((i|0)>0){f=0;while(1){if((f|0)>=(i|0))break;d=c[j+(f<<2)>>2]|0;e=c[(c[d+16>>2]|0)+160>>2]|0;if((e|0)<0){if(!a)a=d;else a=(c[(c[a+16>>2]|0)+160>>2]|0)>(e|0)?d:a;b=b+1|0;if((b|0)>=(k|0))break}f=f+1|0}c[47261]=f}return a|0}function UK(a){a=a|0;var b=0,d=0;b=c[a>>2]&3;d=c[((b|0)==3?a:a+48|0)+40>>2]|0;a=c[((b|0)==2?a:a+-48|0)+40>>2]|0;b=(c[(c[d+16>>2]|0)+288>>2]|0)<(c[(c[a+16>>2]|0)+288>>2]|0);a=b?d:a;c[47257]=0;c[47258]=2147483647;d=c[a+16>>2]|0;c[47259]=c[d+284>>2];c[47260]=c[d+288>>2];if(b)cL(a);else bL(a);return c[47257]|0}function VK(a,b){a=a|0;b=b|0;var d=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;n=o;g=c[b>>2]&3;m=b+-48|0;j=b+48|0;k=b+16|0;g=(c[(c[(c[((g|0)==2?b:m)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((g|0)==3?b:j)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[(c[k>>2]|0)+170>>1]|0)|0;do if((g|0)>0){d=c[a>>2]&3;h=c[((d|0)==3?a:a+48|0)+40>>2]|0;i=c[h+16>>2]|0;if(((c[i+276>>2]|0)+(c[i+268>>2]|0)|0)==1){ZK(h,g);break}d=c[((d|0)==2?a:a+-48|0)+40>>2]|0;f=c[d+16>>2]|0;if(((c[f+276>>2]|0)+(c[f+268>>2]|0)|0)==1){ZK(d,0-g|0);break}if((c[i+288>>2]|0)<(c[f+288>>2]|0)){ZK(h,g);break}else{ZK(d,0-g|0);break}}while(0);d=a+16|0;f=c[(c[d>>2]|0)+160>>2]|0;g=c[b>>2]&3;g=_K(c[((g|0)==3?b:j)+40>>2]|0,c[((g|0)==2?b:m)+40>>2]|0,f,1)|0;i=c[b>>2]&3;if((_K(c[((i|0)==2?b:m)+40>>2]|0,c[((i|0)==3?b:j)+40>>2]|0,f,0)|0)==(g|0)){c[(c[k>>2]|0)+160>>2]=0-f;c[(c[d>>2]|0)+160>>2]=0;$K(a,b);n=c[g+16>>2]|0;aL(g,c[n+280>>2]|0,c[n+284>>2]|0)|0;l=o;return}else{gA(1,102262,n)|0;bb(188848,1)}}function WK(){var b=0,d=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;YK();m=JK((c[47253]<<2)+4|0)|0;d=c[47253]|0;b=0;while(1){if((b|0)>(d|0))break;c[m+(b<<2)>>2]=0;b=b+1|0}b=(c[(c[47254]|0)+16>>2]|0)+192|0;d=b;while(1){d=c[d>>2]|0;if(!d)break;d=c[d+16>>2]|0;if(!(a[d+156>>0]|0)){l=m+(c[d+232>>2]<<2)|0;c[l>>2]=(c[l>>2]|0)+1}d=d+164|0}while(1){b=c[b>>2]|0;if(!b)break;l=b+16|0;b=c[l>>2]|0;if(!(a[b+156>>0]|0)){h=c[47253]|0;f=c[b+172>>2]|0;k=0;j=0;d=0;while(1){g=c[f+(d<<2)>>2]|0;if(!g)break;n=c[g+16>>2]|0;i=(c[(c[(c[((c[g>>2]&3|0)==3?g:g+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)+(e[n+170>>1]|0)|0;k=(c[n+156>>2]|0)+k|0;j=(j|0)>(i|0)?j:i;d=d+1|0}g=c[b+180>>2]|0;i=0;d=0;while(1){f=c[g+(d<<2)>>2]|0;if(!f)break;o=c[f+16>>2]|0;n=(c[(c[(c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[o+170>>1]|0)|0;i=(c[o+156>>2]|0)+i|0;h=(h|0)<(n|0)?h:n;d=d+1|0}d=(j|0)>0?j:0;if((k|0)==(i|0)){g=d;f=d;while(1){d=f+1|0;if((f|0)>=(h|0))break;g=(c[m+(d<<2)>>2]|0)<(c[m+(g<<2)>>2]|0)?d:g;f=d}o=b+232|0;n=m+(c[o>>2]<<2)|0;c[n>>2]=(c[n>>2]|0)+-1;n=m+(g<<2)|0;c[n>>2]=(c[n>>2]|0)+1;c[o>>2]=g}d=c[b+264>>2]|0;if(d){U1(d);b=c[l>>2]|0}d=c[b+272>>2]|0;if(d){U1(d);b=c[l>>2]|0}a[b+157>>0]=0}b=b+164|0}U1(m);return}function XK(){var a=0,b=0,d=0,f=0,g=0,h=0;b=0;while(1){if((b|0)>=(c[47255]|0))break;a=c[(c[47256]|0)+(b<<2)>>2]|0;do if(((c[(c[a+16>>2]|0)+160>>2]|0)==0?(d=UK(a)|0,d|0):0)?(f=c[d>>2]&3,f=(c[(c[(c[((f|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((f|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[(c[d+16>>2]|0)+170>>1]|0)|0,(f|0)>=2):0){h=c[a>>2]&3;g=c[((h|0)==3?a:a+48|0)+40>>2]|0;a=c[((h|0)==2?a:a+-48|0)+40>>2]|0;if((c[(c[g+16>>2]|0)+288>>2]|0)<(c[(c[a+16>>2]|0)+288>>2]|0)){ZK(g,f>>>1);break}else{ZK(a,(f|0)/-2|0);break}}while(0);b=b+1|0}RK();return}function YK(){var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;i=l;l=l+16|0;h=i;c[h>>2]=0;c[h>>2]=2147483647;c[47253]=-2147483647;b=(c[(c[47254]|0)+16>>2]|0)+192|0;e=b;g=2147483647;f=-2147483647;while(1){d=c[e>>2]|0;if(!d)break;e=c[d+16>>2]|0;if(!(a[e+156>>0]|0)){j=c[e+232>>2]|0;d=(g|0)<(j|0)?g:j;c[h>>2]=d;f=(f|0)>(j|0)?f:j;c[47253]=f}else d=g;e=e+164|0;g=d}if(g|0){while(1){b=c[b>>2]|0;if(!b)break;b=c[b+16>>2]|0;j=b+232|0;c[j>>2]=(c[j>>2]|0)-g;b=b+164|0}c[47253]=f-g;c[h>>2]=0}l=i;return}function ZK(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=a+16|0;a=c[f>>2]|0;e=a+232|0;c[e>>2]=(c[e>>2]|0)-b;e=0;while(1){d=c[(c[a+272>>2]|0)+(e<<2)>>2]|0;if(!d){e=0;break}if((d|0)!=(c[a+280>>2]|0)){ZK(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0,b);a=c[f>>2]|0}e=e+1|0}while(1){d=c[(c[a+264>>2]|0)+(e<<2)>>2]|0;if(!d)break;if((d|0)!=(c[a+280>>2]|0)){ZK(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0,b);a=c[f>>2]|0}e=e+1|0}return}function _K(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;g=(e|0)==0&1;h=0-d|0;f=c[(c[b+16>>2]|0)+288>>2]|0;b=a;while(1){a=c[b+16>>2]|0;if((c[a+284>>2]|0)<=(f|0)?(f|0)<=(c[a+288>>2]|0):0)break;a=c[a+280>>2]|0;i=a+48|0;j=(c[a+16>>2]|0)+160|0;c[j>>2]=(((b|0)==(c[((c[a>>2]&3|0)==3?a:i)+40>>2]|0)?e:g)|0?d:h)+(c[j>>2]|0);j=c[a>>2]&3;i=c[((j|0)==3?a:i)+40>>2]|0;a=c[((j|0)==2?a:a+-48|0)+40>>2]|0;b=(c[(c[i+16>>2]|0)+288>>2]|0)>(c[(c[a+16>>2]|0)+288>>2]|0)?i:a}return b|0}function $K(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;i=a+16|0;e=(c[i>>2]|0)+164|0;c[(c[b+16>>2]|0)+164>>2]=c[e>>2];c[(c[47256]|0)+(c[e>>2]<<2)>>2]=b;c[(c[i>>2]|0)+164>>2]=-1;i=(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)+16|0;e=c[i>>2]|0;g=e+276|0;f=c[g>>2]|0;j=f+-1|0;c[g>>2]=j;g=e+272|0;h=0;while(1){if((h|0)>=(f|0)){k=3;break}d=c[g>>2]|0;if((c[d+(h<<2)>>2]|0)==(a|0))break;h=h+1|0}if((k|0)==3)d=c[e+272>>2]|0;c[d+(h<<2)>>2]=c[d+(j<<2)>>2];c[(c[(c[i>>2]|0)+272>>2]|0)+(j<<2)>>2]=0;i=(c[((c[a>>2]&3|0)==2?a:a+-48|0)+40>>2]|0)+16|0;e=c[i>>2]|0;g=e+268|0;f=c[g>>2]|0;j=f+-1|0;c[g>>2]=j;g=e+264|0;h=0;while(1){if((h|0)>=(f|0)){k=8;break}d=c[g>>2]|0;if((c[d+(h<<2)>>2]|0)==(a|0))break;h=h+1|0}if((k|0)==8)d=c[e+264>>2]|0;c[d+(h<<2)>>2]=c[d+(j<<2)>>2];c[(c[(c[i>>2]|0)+264>>2]|0)+(j<<2)>>2]=0;k=(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)+16|0;a=c[k>>2]|0;j=c[a+272>>2]|0;a=a+276|0;i=c[a>>2]|0;c[a>>2]=i+1;c[j+(i<<2)>>2]=b;k=c[k>>2]|0;c[(c[k+272>>2]|0)+(c[k+276>>2]<<2)>>2]=0;k=(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)+16|0;i=c[k>>2]|0;j=c[i+264>>2]|0;i=i+268|0;a=c[i>>2]|0;c[i>>2]=a+1;c[j+(a<<2)>>2]=b;k=c[k>>2]|0;c[(c[k+264>>2]|0)+(c[k+268>>2]<<2)>>2]=0;return}function aL(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;g=a+16|0;a=c[g>>2]|0;c[a+280>>2]=b;c[a+284>>2]=d;f=0;while(1){e=c[(c[a+272>>2]|0)+(f<<2)>>2]|0;if(!e){f=0;break}if((e|0)!=(b|0)){d=aL(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0,e,d)|0;a=c[g>>2]|0}f=f+1|0}while(1){e=c[(c[a+264>>2]|0)+(f<<2)>>2]|0;if(!e)break;if((e|0)!=(b|0)){d=aL(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0,e,d)|0;a=c[g>>2]|0}f=f+1|0}c[a+288>>2]=d;return d+1|0}function bL(a){a=a|0;var b=0,d=0,f=0,g=0,h=0,i=0,j=0;i=a+16|0;g=0;while(1){a=c[i>>2]|0;d=c[(c[a+180>>2]|0)+(g<<2)>>2]|0;if(!d){d=0;break}f=c[d+16>>2]|0;if((c[f+164>>2]|0)<0){a=c[d>>2]&3;b=c[(c[((a|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0;j=c[b+288>>2]|0;if((c[47259]|0)>(j|0)|(j|0)>(c[47260]|0)?(h=(c[b+232>>2]|0)-(c[(c[(c[((a|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[f+170>>1]|0)|0,(h|0)<(c[47258]|0)|(c[47257]|0)==0):0){c[47257]=d;c[47258]=h}}else{b=c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0;if((c[(c[b+16>>2]|0)+288>>2]|0)<(c[a+288>>2]|0))bL(b)}g=g+1|0}while(1){b=c[(c[a+264>>2]|0)+(d<<2)>>2]|0;if(!((b|0)!=0&(c[47258]|0)>0))break;b=c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0;if((c[(c[b+16>>2]|0)+288>>2]|0)<(c[a+288>>2]|0)){bL(b);a=c[i>>2]|0}d=d+1|0}return}function cL(a){a=a|0;var b=0,d=0,f=0,g=0,h=0,i=0,j=0;i=a+16|0;g=0;while(1){a=c[i>>2]|0;d=c[(c[a+172>>2]|0)+(g<<2)>>2]|0;if(!d){d=0;break}f=c[d+16>>2]|0;if((c[f+164>>2]|0)<0){a=c[d>>2]&3;b=c[(c[((a|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0;j=c[b+288>>2]|0;if((c[47259]|0)>(j|0)|(j|0)>(c[47260]|0)?(h=(c[(c[(c[((a|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[b+232>>2]|0)-(e[f+170>>1]|0)|0,(h|0)<(c[47258]|0)|(c[47257]|0)==0):0){c[47257]=d;c[47258]=h}}else{b=c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0;if((c[(c[b+16>>2]|0)+288>>2]|0)<(c[a+288>>2]|0))cL(b)}g=g+1|0}while(1){b=c[(c[a+272>>2]|0)+(d<<2)>>2]|0;if(!((b|0)!=0&(c[47258]|0)>0))break;b=c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0;if((c[(c[b+16>>2]|0)+288>>2]|0)<(c[a+288>>2]|0)){cL(b);a=c[i>>2]|0}d=d+1|0}return}function dL(a){a=a|0;var b=0;b=JK(16)|0;c[b>>2]=a;c[b+4>>2]=tL(a,b)|0;c[b+12>>2]=b;return b|0}function eL(a,b){a=a|0;b=b|0;var d=0,e=0;e=JK(8)|0;c[e>>2]=a;c[e+4>>2]=b;d=0;while(1){if((d|0)>=(b|0))break;c[(c[a+(d<<2)>>2]|0)+8>>2]=d;d=d+1|0}d=(b|0)/2|0;while(1){if((d|0)<=-1)break;jL(e,d);d=d+-1|0}return e|0}function fL(a){a=a|0;return c[a+4>>2]|0}function gL(a){a=a|0;var b=0,d=0,e=0,f=0;f=c[a>>2]|0;b=c[f>>2]|0;c[b+8>>2]=-1;d=a+4|0;e=(c[d>>2]|0)+-1|0;c[f>>2]=c[f+(e<<2)>>2];f=c[a>>2]|0;c[(c[f>>2]|0)+8>>2]=0;c[f+(e<<2)>>2]=b;c[d>>2]=e;jL(a,0);return b|0}function hL(a){a=a|0;return sL(c[a>>2]|0,0,0)|0}function iL(a){a=a|0;var b=0,d=0,f=0,g=0,h=0,i=0;b=a+16|0;if((c[(c[b>>2]|0)+164>>2]|0)>-1)qa(102350,102331,463,102364);g=a+48|0;d=oL(c[((c[a>>2]&3|0)==3?a:g)+40>>2]|0)|0;i=a+-48|0;f=oL(c[((c[a>>2]&3|0)==2?a:i)+40>>2]|0)|0;h=c[a>>2]&3;b=(c[(c[(c[((h|0)==2?a:i)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((h|0)==3?a:g)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[(c[b>>2]|0)+170>>1]|0)|0;if((c[d+8>>2]|0)==-1)pL(c[d>>2]|0,0,b);else pL(c[f>>2]|0,0,0-b|0);qL(a);return rL(d,f)|0}function jL(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;h=c[a>>2]|0;g=c[a+4>>2]|0;while(1){a=b<<1;f=a+2|0;a=a|1;if(!((a|0)<(g|0)?(c[(c[h+(a<<2)>>2]|0)+4>>2]|0)<(c[(c[h+(b<<2)>>2]|0)+4>>2]|0):0))a=b;if((f|0)>=(g|0))break;d=h+(f<<2)|0;e=c[d>>2]|0;if((f|0)==(b|0)?1:(c[e+4>>2]|0)>=(c[(c[h+(a<<2)>>2]|0)+4>>2]|0))break;i=h+(b<<2)|0;a=c[i>>2]|0;c[i>>2]=e;c[d>>2]=a;c[(c[i>>2]|0)+8>>2]=b;c[a+8>>2]=f;b=f}return}function kL(){aL(c[(c[(c[47254]|0)+16>>2]|0)+192>>2]|0,0,1)|0;lL(c[(c[(c[47254]|0)+16>>2]|0)+192>>2]|0,0);return}function lL(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=a+16|0;d=0;while(1){a=c[f>>2]|0;e=c[(c[a+272>>2]|0)+(d<<2)>>2]|0;if(!e){e=0;break}if((e|0)!=(b|0))lL(c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0,e);d=d+1|0}while(1){d=c[(c[a+264>>2]|0)+(e<<2)>>2]|0;if(!d)break;if((d|0)!=(b|0)){lL(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0,d);a=c[f>>2]|0}e=e+1|0}if(b|0)mL(b);return}function mL(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0;b=c[a>>2]&3;d=c[((b|0)==3?a:a+48|0)+40>>2]|0;e=c[d+16>>2]|0;if((c[e+280>>2]|0)==(a|0))i=1;else{e=c[((b|0)==2?a:a+-48|0)+40>>2]|0;i=-1;d=e;e=c[e+16>>2]|0}g=c[e+180>>2]|0;b=0;f=0;while(1){h=c[g+(f<<2)>>2]|0;if(!h)break;b=(nL(h,d,i)|0)+b|0;f=f+1|0}g=c[e+172>>2]|0;e=0;while(1){f=c[g+(e<<2)>>2]|0;if(!f)break;b=(nL(f,d,i)|0)+b|0;e=e+1|0}c[(c[a+16>>2]|0)+160>>2]=b;return}function nL(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;h=c[a>>2]&3;i=(h|0)==3?a:a+48|0;e=c[i+40>>2]|0;if((e|0)==(b|0))e=c[((h|0)==2?a:a+-48|0)+40>>2]|0;g=c[b+16>>2]|0;f=c[(c[e+16>>2]|0)+288>>2]|0;if((c[g+284>>2]|0)<=(f|0)?(f|0)<=(c[g+288>>2]|0):0){f=c[a+16>>2]|0;if((c[f+164>>2]|0)>-1)e=c[f+160>>2]|0;else e=0;g=1;e=e-(c[f+156>>2]|0)|0}else{g=0;e=c[(c[a+16>>2]|0)+156>>2]|0}i=(c[((d|0)>0?((h|0)==2?a:a+-48|0):i)+40>>2]|0)==(b|0)?1:-1;return (((g?i:0-i|0)|0)<0?0-e|0:e)|0}function oL(a){a=a|0;var b=0,d=0;a=c[(c[a+16>>2]|0)+280>>2]|0;while(1){b=a+12|0;d=c[b>>2]|0;if(!((d|0)!=(a|0)&(d|0)!=0))break;a=c[d+12>>2]|0;if(!a){a=d;continue}c[b>>2]=a}return a|0}function pL(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;h=a+16|0;e=c[h>>2]|0;g=e+232|0;c[g>>2]=(c[g>>2]|0)+d;g=0;while(1){f=c[(c[e+264>>2]|0)+(g<<2)>>2]|0;if(!f){g=0;break}f=c[((c[f>>2]&3|0)==3?f:f+48|0)+40>>2]|0;if((f|0)!=(b|0)){pL(f,a,d);e=c[h>>2]|0}g=g+1|0}while(1){f=c[(c[e+272>>2]|0)+(g<<2)>>2]|0;if(!f)break;f=c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0;if((f|0)!=(b|0)){pL(f,a,d);e=c[h>>2]|0}g=g+1|0}return}function qL(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;h=l;l=l+32|0;d=(c[b+16>>2]|0)+164|0;if((c[d>>2]|0)>-1){gA(1,102453,h)|0;bb(188848,1)}g=c[47255]|0;c[d>>2]=g;d=c[47256]|0;c[47255]=g+1;c[d+(g<<2)>>2]=b;d=c[b>>2]|0;g=b+48|0;e=c[((d&3|0)==3?b:g)+40>>2]|0;if(!(a[(c[e+16>>2]|0)+157>>0]|0)){f=c[47262]|0;d=c[47263]|0;c[47263]=d+1;c[f+(d<<2)>>2]=e;d=c[b>>2]|0}f=b+-48|0;e=c[((d&3|0)==2?b:f)+40>>2]|0;if(!(a[(c[e+16>>2]|0)+157>>0]|0)){i=c[47262]|0;d=c[47263]|0;c[47263]=d+1;c[i+(d<<2)>>2]=e;d=c[b>>2]|0}i=(c[((d&3|0)==3?b:g)+40>>2]|0)+16|0;d=c[i>>2]|0;a[d+157>>0]=1;e=c[d+272>>2]|0;d=d+276|0;g=c[d>>2]|0;c[d>>2]=g+1;c[e+(g<<2)>>2]=b;g=c[i>>2]|0;c[(c[g+272>>2]|0)+(c[g+276>>2]<<2)>>2]=0;i=c[i>>2]|0;if(!(c[(c[i+180>>2]|0)+((c[i+276>>2]|0)+-1<<2)>>2]|0)){gA(1,102487,h+8|0)|0;bb(188848,1)}i=(c[((c[b>>2]&3|0)==2?b:f)+40>>2]|0)+16|0;e=c[i>>2]|0;a[e+157>>0]=1;f=c[e+264>>2]|0;e=e+268|0;g=c[e>>2]|0;c[e>>2]=g+1;c[f+(g<<2)>>2]=b;b=c[i>>2]|0;c[(c[b+264>>2]|0)+(c[b+268>>2]<<2)>>2]=0;i=c[i>>2]|0;if(!(c[(c[i+172>>2]|0)+((c[i+268>>2]|0)+-1<<2)>>2]|0)){gA(1,102522,h+16|0)|0;bb(188848,1)}else{l=h;return}}function rL(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;while(1){g=a+12|0;d=c[g>>2]|0;if((d|0)==0|(d|0)==(a|0))break;else a=d}while(1){f=b+12|0;d=c[f>>2]|0;if((d|0)==0|(d|0)==(b|0))break;else b=d}if((a|0)!=(b|0)){e=c[a+8>>2]|0;d=c[b+8>>2]|0;do if((e|0)>-1)if((d|0)==-1)h=a;else j=10;else if((d|0)>-1)if((e|0)==-1){h=b;break}else{j=10;break}else qa(102376,102331,327,102423);while(0);if((j|0)==10)h=(c[b+4>>2]|0)<(c[a+4>>2]|0)?a:b;c[f>>2]=h;c[g>>2]=h;c[h+4>>2]=(c[b+4>>2]|0)+(c[a+4>>2]|0);if((c[h+8>>2]|0)>-1)i=h;else qa(102434,102331,335,102423)}else i=a;return i|0}function sL(a,b,d){a=a|0;b=b|0;d=d|0;var f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;l=oL(a)|0;if(d){k=c[d>>2]&3;if(((c[(c[(c[((k|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((k|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)|0)!=(e[(c[d+16>>2]|0)+170>>1]|0|0))f=3}else{d=0;f=3}a:do if((f|0)==3){k=a+16|0;j=0;while(1){f=c[k>>2]|0;i=c[(c[f+180>>2]|0)+(j<<2)>>2]|0;if(!i){j=0;break}f=i+16|0;g=i+-48|0;h=c[((c[i>>2]&3|0)==2?i:g)+40>>2]|0;do if((c[(c[f>>2]|0)+164>>2]|0)>-1){if((h|0)!=(b|0))d=sL(h,a,d)|0}else if((oL(h)|0)!=(l|0)){if(d|0?(m=c[i>>2]&3,h=c[d>>2]&3,((c[(c[(c[((m|0)==2?i:g)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((m|0)==3?i:i+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[(c[f>>2]|0)+170>>1]|0)|0)>=((c[(c[(c[((h|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((h|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[(c[d+16>>2]|0)+170>>1]|0)|0)):0)break;d=i}while(0);j=j+1|0}while(1){f=c[(c[f+172>>2]|0)+(j<<2)>>2]|0;if(!f)break a;g=f+16|0;h=f+48|0;i=c[((c[f>>2]&3|0)==3?f:h)+40>>2]|0;do if((c[(c[g>>2]|0)+164>>2]|0)>-1){if((i|0)!=(b|0))d=sL(i,a,d)|0}else if((oL(i)|0)!=(l|0)){if(d|0?(i=c[f>>2]&3,m=c[d>>2]&3,((c[(c[(c[((i|0)==2?f:f+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((i|0)==3?f:h)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[(c[g>>2]|0)+170>>1]|0)|0)>=((c[(c[(c[((m|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[(c[(c[((m|0)==3?d:d+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(e[(c[d+16>>2]|0)+170>>1]|0)|0)):0)break;d=f}while(0);j=j+1|0;f=c[k>>2]|0}}while(0);return d|0}function tL(a,b){a=a|0;b=b|0;var d=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;l=a+16|0;d=c[l>>2]|0;c[d+280>>2]=b;a=1;h=0;while(1){g=c[(c[d+172>>2]|0)+(h<<2)>>2]|0;if(!g){h=0;break}f=c[g+16>>2]|0;if(((c[f+164>>2]|0)<=-1?(i=c[g>>2]&3,j=g+48|0,k=c[(c[((i|0)==3?g:j)+40>>2]|0)+16>>2]|0,(c[k+280>>2]|0)==0):0)?((c[(c[(c[((i|0)==2?g:g+-48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)-(c[k+232>>2]|0)|0)==(e[f+170>>1]|0|0):0){qL(g);a=(tL(c[((c[g>>2]&3|0)==3?g:j)+40>>2]|0,b)|0)+a|0;d=c[l>>2]|0}h=h+1|0}while(1){f=c[(c[d+180>>2]|0)+(h<<2)>>2]|0;if(!f)break;g=c[f+16>>2]|0;if(((c[g+164>>2]|0)<=-1?(m=c[f>>2]&3,n=f+-48|0,o=c[(c[((m|0)==2?f:n)+40>>2]|0)+16>>2]|0,(c[o+280>>2]|0)==0):0)?((c[o+232>>2]|0)-(c[(c[(c[((m|0)==3?f:f+48|0)+40>>2]|0)+16>>2]|0)+232>>2]|0)|0)==(e[g+170>>1]|0|0):0){qL(f);a=(tL(c[((c[f>>2]&3|0)==2?f:n)+40>>2]|0,b)|0)+a|0;d=c[l>>2]|0}h=h+1|0}return a|0}function uL(a,b,c){a=a|0;b=b|0;c=c|0;var d=0;d=QA(a,102586)|0;if(!d)d=30;else d=q5(d)|0;return NK(a,b,c,d)|0}function vL(a){a=+a;return +(0==0?a:+h[23301]-a)}function wL(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0.0,u=0;r=l;l=l+16|0;q=r;c[47264]=c[(c[(c[d+64>>2]|0)+8>>2]|0)+4>>2];xL(d);i=c[d+16>>2]|0;t=+h[i+32>>3];s=+h[i+40>>3];yL(e,102597,+h[b+352>>3]);yL(e,102604,t*.013888888888888888);yL(e,102604,s*.013888888888888888);zL(e);i=jC(d)|0;while(1){if(!i)break;g=i+16|0;if(!(a[(c[g>>2]|0)+118>>0]|0)){AL(e,102606,$D(KB(i)|0)|0);p=(c[g>>2]|0)+16|0;c[q>>2]=c[p>>2];c[q+4>>2]=c[p+4>>2];c[q+8>>2]=c[p+8>>2];c[q+12>>2]=c[p+12>>2];BL(e,q);if(!(a[(c[(c[g>>2]|0)+104>>2]|0)+82>>0]|0)){b=xC(i)|0;b=CL(b,c[c[(c[g>>2]|0)+104>>2]>>2]|0)|0}else b=$D(RA(i,c[47154]|0)|0)|0;yL(e,102604,+h[(c[g>>2]|0)+32>>3]);yL(e,102604,+h[(c[g>>2]|0)+40>>3]);AL(e,102604,b);AL(e,102604,bP(i,c[47157]|0,111067)|0);AL(e,102604,c[c[(c[g>>2]|0)+8>>2]>>2]|0);AL(e,102604,bP(i,c[47148]|0,137314)|0);b=bP(i,c[47149]|0,195059)|0;if(!(a[b>>0]|0))b=bP(i,c[47148]|0,134800)|0;AL(e,102604,b);zL(e)}i=kC(d,i)|0}o=f<<24>>24==0;n=jC(d)|0;while(1){if(!n)break;m=XA(d,n)|0;while(1){if(!m)break;if(o){f=195059;j=195059}else{j=QA(m,111455)|0;f=QA(m,111464)|0;f=f|0?f:195059;j=j|0?j:195059}p=m+16|0;b=c[p>>2]|0;k=c[b+8>>2]|0;a:do if(k){i=c[k+4>>2]|0;b=0;g=0;while(1){if((b|0)>=(i|0))break;u=(c[(c[k>>2]|0)+(b*48|0)+4>>2]|0)+g|0;b=b+1|0;g=u}AL(e,0,111450);DL(e,c[((c[m>>2]&3|0)==3?m:m+48|0)+40>>2]|0,j);DL(e,c[((c[m>>2]&3|0)==2?m:m+-48|0)+40>>2]|0,f);EL(e,g);f=0;while(1){b=c[p>>2]|0;g=c[b+8>>2]|0;if((f|0)>=(c[g+4>>2]|0))break a;i=c[g>>2]|0;g=c[i+(f*48|0)>>2]|0;i=c[i+(f*48|0)+4>>2]|0;b=0;while(1){if((b|0)>=(i|0))break;u=g+(b<<4)|0;c[q>>2]=c[u>>2];c[q+4>>2]=c[u+4>>2];c[q+8>>2]=c[u+8>>2];c[q+12>>2]=c[u+12>>2];BL(e,q);b=b+1|0}f=f+1|0}}while(0);if(c[b+96>>2]|0){u=xC(c[((c[m>>2]&3|0)==3?m:m+48|0)+40>>2]|0)|0;AL(e,102604,CL(u,c[c[(c[p>>2]|0)+96>>2]>>2]|0)|0);u=(c[(c[p>>2]|0)+96>>2]|0)+56|0;c[q>>2]=c[u>>2];c[q+4>>2]=c[u+4>>2];c[q+8>>2]=c[u+8>>2];c[q+12>>2]=c[u+12>>2];BL(e,q)}AL(e,102604,bP(m,c[47185]|0,111067)|0);AL(e,102604,bP(m,c[47176]|0,137314)|0);zL(e);m=ZA(d,m)|0}n=kC(d,n)|0}FL(102612,e);l=r;return}function xL(a){a=a|0;var b=0.0;if(0){a=c[a+16>>2]|0;b=+h[a+24>>3]+ +h[a+40>>3];h[23301]=b;h[23302]=b*.013888888888888888}return}function yL(a,b,c){a=a|0;b=b|0;c=+c;var d=0,e=0,f=0;f=l;l=l+1040|0;e=f;d=f+8|0;if(b|0)FL(b,a);h[e>>3]=c;m4(d,102618,e)|0;FL(d,a);l=f;return}function zL(b){b=b|0;a[194955]=10;lb[c[47264]&127](b,194955)|0;return}function AL(a,b,c){a=a|0;b=b|0;c=c|0;if(b|0)FL(b,a);FL(c,a);return}function BL(a,b){a=a|0;b=b|0;var c=0.0;yL(a,102604,+h[b>>3]*.013888888888888888);if(!0)c=+h[b+8>>3];else c=+h[23301]-+h[b+8>>3];yL(a,102604,c*.013888888888888888);return}function CL(a,b){a=a|0;b=b|0;var c=0;c=fD(a,b)|0;b=$D(c)|0;hD(a,c)|0;return b|0}function DL(b,d,e){b=b|0;d=d|0;e=e|0;var f=0;if(!(a[(c[d+16>>2]|0)+118>>0]|0))d=$D(KB(d)|0)|0;else{f=xC(d)|0;d=CL(f,(R2(KB(d)|0,58)|0)+1|0)|0}AL(b,102604,d);if(e|0?a[e>>0]|0:0)AL(b,107984,$D(e)|0);return}function EL(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=l;l=l+1040|0;f=d;e=d+8|0;FL(102604,a);c[f>>2]=b;m4(e,137395,f)|0;FL(e,a);l=d;return}function FL(a,b){a=a|0;b=b|0;lb[c[47264]&127](b,a)|0;return}function GL(d,f,g){d=d|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0.0,n=0,o=0.0,p=0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0,X=0,Y=0;Y=l;l=l+2288|0;W=Y+208|0;V=Y+192|0;U=Y+176|0;T=Y+160|0;S=Y+144|0;R=Y+128|0;P=Y+112|0;O=Y+96|0;N=Y+80|0;M=Y+64|0;L=Y+56|0;K=Y+48|0;X=Y+32|0;Q=Y+24|0;J=Y;G=Y+1264|0;H=Y+224|0;I=d+16|0;C=(e[(c[I>>2]|0)+178>>1]|0)>2;RJ(1);xL(d);kA(H,1024,Y+240|0);_P(d,1,102623,195059)|0;_P(d,1,102627,195059)|0;c[47146]=_P(d,1,141116,195059)|0;c[47145]=_P(d,1,141048,195059)|0;_P(d,2,102623,195059)|0;i=a[(c[I>>2]|0)+113>>0]|0;if(i&16){_P(d,1,102633,195059)|0;i=a[(c[I>>2]|0)+113>>0]|0}if(i&1){_P(d,2,102637,195059)|0;i=a[(c[I>>2]|0)+113>>0]|0}if(i&32){_P(d,2,102633,195059)|0;i=a[(c[I>>2]|0)+113>>0]|0}if(i&2){_P(d,2,102640,195059)|0;i=a[(c[I>>2]|0)+113>>0]|0}if(i&4){_P(d,2,102648,195059)|0;i=a[(c[I>>2]|0)+113>>0]|0}if(!(i&8)){v=0;w=0;x=0}else{x=_P(d,0,102637,195059)|0;w=_P(d,0,102656,195059)|0;v=_P(d,0,102663,195059)|0}z=_P(d,0,102671,195059)|0;A=H+4|0;B=H+8|0;u=0;i=0;y=jC(d)|0;while(1){if(!y)break;t=y+16|0;k=c[t>>2]|0;o=+h[k+16>>3];j=0==0;if(C){if(j)m=+h[k+24>>3];else m=+h[23301]-+h[k+24>>3];q=+h[(c[k+132>>2]|0)+16>>3]*72.0;h[J>>3]=o;h[J+8>>3]=m;h[J+16>>3]=q;m4(G,102674,J)|0;nA(H,G)|0;j=3;while(1){if((j|0)>=(e[(c[I>>2]|0)+178>>1]|0))break;h[Q>>3]=+h[(c[(c[t>>2]|0)+132>>2]|0)+(j<<3)>>3]*72.0;m4(G,102689,Q)|0;nA(H,G)|0;j=j+1|0}j=c[A>>2]|0;if(j>>>0>=(c[B>>2]|0)>>>0){lA(H,1)|0;j=c[A>>2]|0}a[j>>0]=0;s=c[H>>2]|0;c[A>>2]=s;SA(y,102623,s)|0;s=y}else{if(j)m=+h[k+24>>3];else m=+h[23301]-+h[k+24>>3];h[X>>3]=o;h[X+8>>3]=m;m4(G,102695,X)|0;SA(y,102623,G)|0;s=y}h[K>>3]=+h[(c[t>>2]|0)+80>>3]*.013888888888888888;m4(G,102618,K)|0;KA(s,c[47145]|0,G)|0;j=c[t>>2]|0;h[L>>3]=(+h[j+96>>3]+ +h[j+88>>3])*.013888888888888888;m4(G,102618,L)|0;KA(s,c[47146]|0,G)|0;j=c[t>>2]|0;k=c[j+108>>2]|0;if((k|0)!=0?(a[k+81>>0]|0)!=0:0){q=+h[k+64>>3];q=0==0?q:+h[23301]-q;h[M>>3]=+h[k+56>>3];h[M+8>>3]=q;m4(G,102695,M)|0;SA(s,102633,G)|0;j=c[t>>2]|0}if(s2(c[c[j+8>>2]>>2]|0,106220)|0){if(c[47170]|0?(hN(y)|0)<<24>>24:0){k=c[(c[t>>2]|0)+12>>2]|0;r=k+8|0;j=c[r>>2]|0;if((j|0)<3){j=QA(s,102705)|0;if(!j)j=8;else j=q5(j)|0;j=(j|0)<3?8:j}p=k+44|0;q=1.0/+(j|0);n=0;while(1){if((n|0)>=(j|0))break;if((n|0)>0){k=c[A>>2]|0;if(k>>>0>=(c[B>>2]|0)>>>0){lA(H,1)|0;k=c[A>>2]|0}c[A>>2]=k+1;a[k>>0]=32}if((c[r>>2]|0)>2){k=c[p>>2]|0;if(!0)m=+h[k+(n<<4)+8>>3]*.013888888888888888;else m=+h[23302]-+h[k+(n<<4)+8>>3]*.013888888888888888;h[N>>3]=+h[k+(n<<4)>>3]*.013888888888888888;h[N+8>>3]=m;m4(G,102718,N)|0}else{k=c[t>>2]|0;m=q*+(n|0)*6.283185307179586;o=+h[k+32>>3]*.5*+E(+m);if(!0)m=+h[k+40>>3]*.5*+F(+m);else m=+h[23302]-+h[k+40>>3]*.5*+F(+m);h[O>>3]=o;h[O+8>>3]=m;m4(G,102718,O)|0}nA(H,G)|0;n=n+1|0}k=c[47170]|0;j=c[A>>2]|0;if(j>>>0>=(c[B>>2]|0)>>>0){lA(H,1)|0;j=c[A>>2]|0}a[j>>0]=0;t=c[H>>2]|0;c[A>>2]=t;KA(s,k,t)|0}}else{HL(y,c[j+12>>2]|0,H);pA(H)|0;j=c[A>>2]|0;if(j>>>0>=(c[B>>2]|0)>>>0){lA(H,1)|0;j=c[A>>2]|0}a[j>>0]=0;t=c[H>>2]|0;c[A>>2]=t;SA(s,102627,t)|0}a:do if((c[47138]|0)>0){t=XA(d,y)|0;j=u;while(1){if(!t)break a;s=t+16|0;k=c[s>>2]|0;do if((a[k+112>>0]|0)!=6?(D=c[k+8>>2]|0,(D|0)!=0):0){r=0;k=D;while(1){if((r|0)>=(c[k+4>>2]|0))break;if((r|0)>0){k=c[A>>2]|0;if(k>>>0>=(c[B>>2]|0)>>>0){lA(H,1)|0;k=c[A>>2]|0}c[A>>2]=k+1;a[k>>0]=59;k=c[(c[s>>2]|0)+8>>2]|0}k=c[k>>2]|0;if(c[k+(r*48|0)+8>>2]|0){if(!0)m=+h[k+(r*48|0)+24>>3];else m=+h[23301]-+h[k+(r*48|0)+24>>3];h[P>>3]=+h[k+(r*48|0)+16>>3];h[P+8>>3]=m;m4(G,102728,P)|0;nA(H,G)|0;i=1;k=c[c[(c[s>>2]|0)+8>>2]>>2]|0}if(c[k+(r*48|0)+12>>2]|0){if(!0)m=+h[k+(r*48|0)+40>>3];else m=+h[23301]-+h[k+(r*48|0)+40>>3];h[R>>3]=+h[k+(r*48|0)+32>>3];h[R+8>>3]=m;m4(G,102741,R)|0;nA(H,G)|0;j=1}p=0;while(1){k=c[(c[s>>2]|0)+8>>2]|0;n=c[k>>2]|0;if((p|0)>=(c[n+(r*48|0)+4>>2]|0))break;if((p|0)>0){k=c[A>>2]|0;if(k>>>0>=(c[B>>2]|0)>>>0){lA(H,1)|0;k=c[A>>2]|0}c[A>>2]=k+1;a[k>>0]=32;k=c[c[(c[s>>2]|0)+8>>2]>>2]|0}else k=n;u=c[k+(r*48|0)>>2]|0;q=+h[u+(p<<4)+8>>3];q=0==0?q:+h[23301]-q;h[S>>3]=+h[u+(p<<4)>>3];h[S+8>>3]=q;m4(G,102695,S)|0;nA(H,G)|0;p=p+1|0}r=r+1|0}k=c[A>>2]|0;if(k>>>0>=(c[B>>2]|0)>>>0){lA(H,1)|0;k=c[A>>2]|0}a[k>>0]=0;k=c[H>>2]|0;c[A>>2]=k;SA(t,102623,k)|0;k=c[s>>2]|0;n=c[k+96>>2]|0;if(n){q=+h[n+64>>3];q=0==0?q:+h[23301]-q;h[T>>3]=+h[n+56>>3];h[T+8>>3]=q;m4(G,102695,T)|0;SA(t,102637,G)|0;k=c[s>>2]|0}n=c[k+108>>2]|0;do if(n){if(!(a[n+81>>0]|0))break;q=+h[n+64>>3];q=0==0?q:+h[23301]-q;h[U>>3]=+h[n+56>>3];h[U+8>>3]=q;m4(G,102695,U)|0;SA(t,102633,G)|0;k=c[s>>2]|0}while(0);n=c[k+100>>2]|0;if(n){q=+h[n+64>>3];q=0==0?q:+h[23301]-q;h[V>>3]=+h[n+56>>3];h[V+8>>3]=q;m4(G,102695,V)|0;SA(t,102640,G)|0;k=c[s>>2]|0}k=c[k+104>>2]|0;if(!k)break;q=+h[k+64>>3];q=0==0?q:+h[23301]-q;h[W>>3]=+h[k+56>>3];h[W+8>>3]=q;m4(G,102695,W)|0;SA(t,102648,G)|0}while(0);t=ZA(d,t)|0}}else j=u;while(0);u=j;y=kC(d,y)|0}IL(d,z,x,w,v);oA(H);if(b[(c[I>>2]|0)+136>>1]&1)UP(d);c[f>>2]=i;c[g>>2]=u;RJ(0);l=Y;return}function HL(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0.0,j=0,k=0,m=0,n=0,o=0.0;n=l;l=l+1056|0;k=n;j=n+32|0;m=b+48|0;if(!(c[m>>2]|0)){e=c[a+16>>2]|0;i=+h[e+16>>3];if(!0){g=+h[e+24>>3];f=g+ +h[b+24>>3];g=g+ +h[b+40>>3]}else{g=+h[e+24>>3];f=+h[23301]-+h[b+24>>3]-g;g=+h[23301]-+h[b+40>>3]-g}o=i+ +h[b+32>>3];h[k>>3]=i+ +h[b+16>>3];h[k+8>>3]=f;h[k+16>>3]=o;h[k+24>>3]=g;m4(j,102779,k)|0;nA(d,j)|0}b=b+56|0;e=0;while(1){if((e|0)>=(c[m>>2]|0))break;HL(a,c[(c[b>>2]|0)+(e<<2)>>2]|0,d);e=e+1|0}l=n;return}function IL(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0;s=l;l=l+1088|0;o=s+56|0;q=s+48|0;p=s+32|0;j=s;n=s+64|0;r=b+16|0;i=c[r>>2]|0;if(!0){k=+h[i+24>>3];m=+h[i+40>>3]}else{m=+h[23301];k=m-+h[i+24>>3];m=m-+h[i+40>>3]}t=+h[i+32>>3];h[j>>3]=+h[i+16>>3];h[j+8>>3]=k;h[j+16>>3]=t;h[j+24>>3]=m;m4(n,102754,j)|0;KA(b,d,n)|0;i=c[(c[r>>2]|0)+12>>2]|0;if((i|0)!=0?(a[c[i>>2]>>0]|0)!=0:0){t=+h[i+64>>3];t=0==0?t:+h[23301]-t;h[p>>3]=+h[i+56>>3];h[p+8>>3]=t;m4(n,102695,p)|0;KA(b,e,n)|0;i=c[(c[r>>2]|0)+12>>2]|0;t=+h[i+32>>3];h[q>>3]=+h[i+24>>3]*.013888888888888888;m4(n,102774,q)|0;KA(b,f,n)|0;h[o>>3]=t*.013888888888888888;m4(n,102774,o)|0;KA(b,g,n)|0;i=1}else i=1;while(1){j=c[r>>2]|0;if((i|0)>(c[j+180>>2]|0))break;IL(c[(c[j+184>>2]|0)+(i<<2)>>2]|0,d,e,f,g);i=i+1|0}l=s;return}function JL(a){a=a|0;var b=0;b=l;l=l+16|0;GL(a,b,b+4|0);l=b;return}function KL(){return Vz(20740,c[4581]|0)|0}function LL(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function ML(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;a=c[b>>2]|0;e=c[d>>2]|0;if((a|0)<=(e|0))if((a|0)<(e|0))a=-1;else{b=c[b+4>>2]|0;d=c[d+4>>2]|0;return ((b|0)>(d|0)?1:((b|0)<(d|0))<<31>>31)|0}else a=1;return a|0}function NL(a){a=a|0;Rz(a)|0;return}function OL(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=c[b>>2];c[e+4>>2]=c[b+4>>2];b=PL(e)|0;if((nb[c[a>>2]&63](a,b,1)|0)!=(b|0))U1(b);l=d;return}function PL(a){a=a|0;var b=0,d=0,e=0;b=JK(16)|0;e=a;d=c[e+4>>2]|0;a=b+8|0;c[a>>2]=c[e>>2];c[a+4>>2]=d;return b|0}function QL(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;e=l;l=l+16|0;f=e+8|0;g=e;c[g>>2]=b;c[g+4>>2]=d;c[f>>2]=c[g>>2];c[f+4>>2]=c[g+4>>2];b=PL(f)|0;if((nb[c[a>>2]&63](a,b,1)|0)!=(b|0))U1(b);l=e;return}function RL(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;d=l;l=l+16|0;e=d;g=b;f=c[g+4>>2]|0;b=e+8|0;c[b>>2]=c[g>>2];c[b+4>>2]=f;b=(nb[c[a>>2]&63](a,e,4)|0)!=0&1;l=d;return b|0}function SL(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=l;l=l+16|0;f=e;c[f+8>>2]=b;c[f+12>>2]=d;d=(nb[c[a>>2]&63](a,f,4)|0)!=0&1;l=e;return d|0}function TL(a){a=a|0;return Xz(a)|0}function UL(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;d=JK((Xz(a)|0)<<3)|0;b=d;a=Tz(a)|0;while(1){if(!a)break;g=a+8|0;f=c[g+4>>2]|0;e=b;c[e>>2]=c[g>>2];c[e+4>>2]=f;b=b+8|0;a=c[a>>2]|0}return d|0}function VL(){var a=0,b=0,d=0,e=0;a=KK(40)|0;b=a;d=20776;e=b+36|0;do{c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}while((b|0)<(e|0));c[a+36>>2]=0;return Vz(a,c[4581]|0)|0}function WL(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;d=d+36|0;a=c[d>>2]|0;if(!a)a=KK(20)|0;else c[d>>2]=c[a>>2];f=b+8|0;e=c[f+4>>2]|0;d=a+8|0;c[d>>2]=c[f>>2];c[d+4>>2]=e;c[a+16>>2]=c[b+16>>2];return a|0}function XL(a,b,d){a=a|0;b=b|0;d=d|0;d=d+36|0;c[b>>2]=c[d>>2];c[d>>2]=b;return}function YL(a){a=a|0;nb[c[a>>2]&63](a,0,64)|0;return}function ZL(a){a=a|0;var b=0,d=0;b=c[a+4>>2]|0;Rz(a)|0;a=c[b+36>>2]|0;while(1){if(!a)break;d=c[a>>2]|0;U1(a);a=d}U1(b);return}function _L(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;f=l;l=l+32|0;g=f;c[g+8>>2]=b;c[g+12>>2]=d;c[g+16>>2]=e;e=c[(nb[c[a>>2]&63](a,g,1)|0)+16>>2]|0;l=f;return e|0}function $L(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0.0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=l;l=l+160|0;q=t+144|0;d=t+48|0;j=t+128|0;k=t+112|0;m=t+96|0;n=t+80|0;o=t+32|0;p=t+16|0;r=t;s=a+16|0;a=c[s>>2]|0;e=+h[a+16>>3];f=+h[a+24>>3];g=+h[a+32>>3];i=+h[a+40>>3];if((b+-1|0)>>>0<2){aM(j,e,i);c[q>>2]=c[j>>2];c[q+4>>2]=c[j+4>>2];c[q+8>>2]=c[j+8>>2];c[q+12>>2]=c[j+12>>2];bM(d,q);p=d+16|0;aM(k,g,f);c[q>>2]=c[k>>2];c[q+4>>2]=c[k+4>>2];c[q+8>>2]=c[k+8>>2];c[q+12>>2]=c[k+12>>2];bM(m,q);c[p>>2]=c[m>>2];c[p+4>>2]=c[m+4>>2];c[p+8>>2]=c[m+8>>2];c[p+12>>2]=c[m+12>>2]}else{aM(n,e,f);c[q>>2]=c[n>>2];c[q+4>>2]=c[n+4>>2];c[q+8>>2]=c[n+8>>2];c[q+12>>2]=c[n+12>>2];bM(d,q);n=d+16|0;aM(o,g,i);c[q>>2]=c[o>>2];c[q+4>>2]=c[o+4>>2];c[q+8>>2]=c[o+8>>2];c[q+12>>2]=c[o+12>>2];bM(p,q);c[n>>2]=c[p>>2];c[n+4>>2]=c[p+4>>2];c[n+8>>2]=c[p+8>>2];c[n+12>>2]=c[p+12>>2]}a=(c[s>>2]|0)+16|0;c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];c[a+16>>2]=c[d+16>>2];c[a+20>>2]=c[d+20>>2];c[a+24>>2]=c[d+24>>2];c[a+28>>2]=c[d+28>>2];a=c[(c[s>>2]|0)+12>>2]|0;if(!a)a=1;else{a=a+56|0;c[q>>2]=c[a>>2];c[q+4>>2]=c[a+4>>2];c[q+8>>2]=c[a+8>>2];c[q+12>>2]=c[a+12>>2];bM(r,q);c[a>>2]=c[r>>2];c[a+4>>2]=c[r+4>>2];c[a+8>>2]=c[r+8>>2];c[a+12>>2]=c[r+12>>2];a=1}while(1){d=c[s>>2]|0;if((a|0)>(c[d+180>>2]|0))break;$L(c[(c[d+184>>2]|0)+(a<<2)>>2]|0,b);a=a+1|0}l=t;return}function aM(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b;h[a+8>>3]=c;return}function bM(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;d=l;l=l+32|0;g=d+16|0;e=d;f=(c[47265]|0)*90|0;c[g>>2]=c[b>>2];c[g+4>>2]=c[b+4>>2];c[g+8>>2]=c[b+8>>2];c[g+12>>2]=c[b+12>>2];mK(e,g,f);c[b>>2]=c[e>>2];c[b+4>>2]=c[e+4>>2];c[b+8>>2]=c[e+8>>2];c[b+12>>2]=c[e+12>>2];h[b>>3]=+h[b>>3]-+h[23303];e=b+8|0;h[e>>3]=+h[e>>3]-+h[23304];c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];l=d;return}function cM(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0.0,k=0.0,m=0,n=0.0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0;w=l;l=l+1168|0;s=w+144|0;v=w+96|0;u=w+64|0;t=w+48|0;o=w+32|0;p=w+16|0;q=w;c[t>>2]=0;c[t+4>>2]=0;c[t+8>>2]=0;c[t+12>>2]=0;r=b+16|0;m=c[(c[r>>2]|0)+116>>2]|0;c[47265]=m&3;m=m&1;a[194957]=m;if(!(m<<24>>24))eM(b);else dM(b);fM(b);m=c[r>>2]|0;e=c[m+12>>2]|0;do if(e|0?(a[e+81>>0]|0)==0:0){g=e+24|0;c[t>>2]=c[g>>2];c[t+4>>2]=c[g+4>>2];c[t+8>>2]=c[g+8>>2];c[t+12>>2]=c[g+12>>2];n=+h[t>>3]+16.0;h[t>>3]=n;g=t+8|0;f=+h[g>>3]+8.0;h[g>>3]=f;g=(a[m+275>>0]&1)!=0;if(a[194957]|0){if(g){i=m+32|0;h[i>>3]=+h[i>>3]+f}else{i=m+16|0;h[i>>3]=+h[i>>3]-f}g=m+40|0;f=+h[g>>3];i=m+24|0;j=+h[i>>3];k=f-j;if(!(n>k))break;n=(n-k)*.5;h[i>>3]=j-n;h[g>>3]=f+n;break}i=(c[47265]|0)==0;do if(g)if(i){i=m+40|0;h[i>>3]=+h[i>>3]+f;break}else{i=m+24|0;h[i>>3]=+h[i>>3]-f;break}else if(i){i=m+24|0;h[i>>3]=+h[i>>3]-f;break}else{i=m+40|0;h[i>>3]=+h[i>>3]+f;break}while(0);g=m+32|0;f=+h[g>>3];i=m+16|0;j=+h[i>>3];k=f-j;if(n>k){n=(n-k)*.5;h[i>>3]=j-n;h[g>>3]=f+n}}while(0);if(d){switch(c[47265]|0){case 0:{q=m+16|0;c[46606]=c[q>>2];c[46607]=c[q+4>>2];c[46608]=c[q+8>>2];c[46609]=c[q+12>>2];break}case 1:{aM(o,-+h[m+40>>3],+h[m+16>>3]);c[46606]=c[o>>2];c[46607]=c[o+4>>2];c[46608]=c[o+8>>2];c[46609]=c[o+12>>2];break}case 2:{aM(p,+h[m+16>>3],-+h[m+40>>3]);c[46606]=c[p>>2];c[46607]=c[p+4>>2];c[46608]=c[p+8>>2];c[46609]=c[p+12>>2];break}case 3:{aM(q,+h[m+24>>3],+h[m+16>>3]);c[46606]=c[q>>2];c[46607]=c[q+4>>2];c[46608]=c[q+8>>2];c[46609]=c[q+12>>2];break}default:{}}gM(b);e=c[(c[r>>2]|0)+12>>2]|0}if(e|0?(a[e+81>>0]|0)==0:0){c[s>>2]=c[t>>2];c[s+4>>2]=c[t+4>>2];c[s+8>>2]=c[t+8>>2];c[s+12>>2]=c[t+12>>2];hM(b,s)}if(!0){l=w;return}if(!(a[194957]|0)){n=+h[23304];k=+h[23303];h[v>>3]=n;h[v+8>>3]=k;h[v+16>>3]=n;h[v+24>>3]=k;h[v+32>>3]=-k;h[v+40>>3]=-n;m4(s,103015,v)|0}else{k=+h[23303];n=+h[23304];h[u>>3]=k;h[u+8>>3]=n;h[u+16>>3]=k;h[u+24>>3]=n;m4(s,102800,u)|0}D4(s)|0;ab()}function dM(b){b=b|0;var d=0,e=0.0,f=0.0,g=0,i=0,j=0;j=b+16|0;if(((yC(b)|0)!=(b|0)?(g=c[j>>2]|0,i=c[g+12>>2]|0,(i|0)!=0):0)?(a[i+81>>0]|0)==0:0){d=a[g+275>>0]|0;if(!(d&1)){f=+h[g+16>>3]+ +h[g+96>>3]*.5;b=g+104|0}else{f=+h[g+32>>3]-+h[g+64>>3]*.5;b=g+72|0}e=+h[b>>3];b=d<<24>>24;do if(!(b&4))if(!(b&2)){e=(+h[g+40>>3]+ +h[g+24>>3])*.5;break}else{e=+h[g+40>>3]-e*.5;break}else e=+h[g+24>>3]+e*.5;while(0);h[i+56>>3]=f;h[i+64>>3]=e;a[(c[(c[j>>2]|0)+12>>2]|0)+81>>0]=1;b=1}else b=1;while(1){d=c[j>>2]|0;if((b|0)>(c[d+180>>2]|0))break;dM(c[(c[d+184>>2]|0)+(b<<2)>>2]|0);b=b+1|0}return}function eM(b){b=b|0;var d=0,e=0.0,f=0.0,g=0.0,i=0,j=0,k=0;k=b+16|0;if(((yC(b)|0)!=(b|0)?(i=c[k>>2]|0,j=c[i+12>>2]|0,(j|0)!=0):0)?(a[j+81>>0]|0)==0:0){d=a[i+275>>0]|0;if(!(d&1)){g=+h[i+24>>3]+ +h[i+56>>3]*.5;b=i+48|0}else{g=+h[i+40>>3]-+h[i+88>>3]*.5;b=i+80|0}f=+h[b>>3];b=d<<24>>24;do if(!(b&4)){e=+h[i+16>>3];if(!(b&2)){e=(+h[i+32>>3]+e)*.5;break}else{e=e+f*.5;break}}else e=+h[i+32>>3]-f*.5;while(0);h[j+56>>3]=e;h[j+64>>3]=g;a[(c[(c[k>>2]|0)+12>>2]|0)+81>>0]=1;b=1}else b=1;while(1){d=c[k>>2]|0;if((b|0)>(c[d+180>>2]|0))break;eM(c[(c[d+184>>2]|0)+(b<<2)>>2]|0);b=b+1|0}return}function fM(e){e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0;S=l;l=l+544|0;L=S+504|0;R=S+416|0;Q=S+376|0;K=S+352|0;J=S+312|0;I=S+288|0;H=S+248|0;M=S+200|0;B=S+152|0;O=S+96|0;p=S+488|0;C=S+456|0;D=S+424|0;E=S+384|0;F=S+360|0;G=S+320|0;w=S+296|0;x=S+256|0;y=S+232|0;z=S+168|0;A=S+136|0;f=S+56|0;N=S+16|0;P=S;o=e+16|0;u=c[o>>2]|0;g=b[u+136>>1]&14;u=d[u+113>>0]|0;if(!((u&54|0)==0?(u&1|0)==0|(c[47139]|0)!=0:0)){u=g<<16>>16==0;n=jC(e)|0;h=0;j=0;i=0;while(1){if(!n)break;g=c[(c[n+16>>2]|0)+108>>2]|0;do if(g)if(!(a[g+81>>0]|0)){i=i+1|0;break}else{h=h+1|0;break}while(0);m=XA(e,n)|0;g=j;while(1){if(!m)break;k=c[m+16>>2]|0;j=c[k+108>>2]|0;do if(j){if(a[j+81>>0]|0){h=h+1|0;break}if(!u)g=g+((c[k+8>>2]|0)!=0&1)|0}while(0);j=c[k+100>>2]|0;do if(j){if(a[j+81>>0]|0){h=h+1|0;break}if(!u)g=g+((c[k+8>>2]|0)!=0&1)|0}while(0);j=c[k+104>>2]|0;do if(j){if(a[j+81>>0]|0){h=h+1|0;break}if(!u)g=g+((c[k+8>>2]|0)!=0&1)|0}while(0);j=c[k+96>>2]|0;do if(j){if(a[j+81>>0]|0){h=h+1|0;break}if(!u)g=g+((c[k+8>>2]|0)!=0&1)|0}while(0);m=ZA(e,m)|0}n=kC(e,n)|0;j=g}if(!(a[(c[o>>2]|0)+113>>0]&8))o=0;else o=jM(e)|0;t=j+i|0;if(t|0){q=h+j+o+(tB(e)|0)|0;r=JK(q*40|0)|0;s=JK(t*40|0)|0;aM(M,2147483647.0,2147483647.0);g=M+16|0;aM(p,-2147483647.0,-2147483647.0);c[g>>2]=c[p>>2];c[g+4>>2]=c[p+4>>2];c[g+8>>2]=c[p+8>>2];c[g+12>>2]=c[p+12>>2];g=s;i=r;n=jC(e)|0;while(1){if(!n)break;c[L>>2]=c[M>>2];c[L+4>>2]=c[M+4>>2];c[L+8>>2]=c[M+8>>2];c[L+12>>2]=c[M+12>>2];c[L+16>>2]=c[M+16>>2];c[L+20>>2]=c[M+20>>2];c[L+24>>2]=c[M+24>>2];c[L+28>>2]=c[M+28>>2];kM(C,n,i,L);c[M>>2]=c[C>>2];c[M+4>>2]=c[C+4>>2];c[M+8>>2]=c[C+8>>2];c[M+12>>2]=c[C+12>>2];c[M+16>>2]=c[C+16>>2];c[M+20>>2]=c[C+20>>2];c[M+24>>2]=c[C+24>>2];c[M+28>>2]=c[C+28>>2];h=c[(c[n+16>>2]|0)+108>>2]|0;do if(h)if(!(a[h+81>>0]|0)){c[L>>2]=c[B>>2];c[L+4>>2]=c[B+4>>2];c[L+8>>2]=c[B+8>>2];c[L+12>>2]=c[B+12>>2];mM(h,i,g,0,L);g=g+40|0;break}else{i=i+40|0;c[L>>2]=c[M>>2];c[L+4>>2]=c[M+4>>2];c[L+8>>2]=c[M+8>>2];c[L+12>>2]=c[M+12>>2];c[L+16>>2]=c[M+16>>2];c[L+20>>2]=c[M+20>>2];c[L+24>>2]=c[M+24>>2];c[L+28>>2]=c[M+28>>2];lM(D,h,i,L);c[M>>2]=c[D>>2];c[M+4>>2]=c[D+4>>2];c[M+8>>2]=c[D+8>>2];c[M+12>>2]=c[D+12>>2];c[M+16>>2]=c[D+16>>2];c[M+20>>2]=c[D+20>>2];c[M+24>>2]=c[D+24>>2];c[M+28>>2]=c[D+28>>2];break}while(0);m=XA(e,n)|0;i=i+40|0;while(1){if(!m)break;k=m+16|0;h=c[k>>2]|0;j=c[h+96>>2]|0;a:do if(!j)v=52;else{do if(!(a[j+81>>0]|0)){if(!u?c[h+8>>2]|0:0){vO(F,e,m);c[L>>2]=c[F>>2];c[L+4>>2]=c[F+4>>2];c[L+8>>2]=c[F+8>>2];c[L+12>>2]=c[F+12>>2];mM(j,i,g,1,L);g=g+40|0;break}c[H>>2]=c[j>>2];gA(0,103827,H)|0;break a}else{c[L>>2]=c[M>>2];c[L+4>>2]=c[M+4>>2];c[L+8>>2]=c[M+8>>2];c[L+12>>2]=c[M+12>>2];c[L+16>>2]=c[M+16>>2];c[L+20>>2]=c[M+20>>2];c[L+24>>2]=c[M+24>>2];c[L+28>>2]=c[M+28>>2];lM(E,j,i,L);c[M>>2]=c[E>>2];c[M+4>>2]=c[E+4>>2];c[M+8>>2]=c[E+8>>2];c[M+12>>2]=c[E+12>>2];c[M+16>>2]=c[E+16>>2];c[M+20>>2]=c[E+20>>2];c[M+24>>2]=c[E+24>>2];c[M+28>>2]=c[E+28>>2]}while(0);i=i+40|0;h=c[k>>2]|0;v=52}while(0);b:do if((v|0)==52){v=0;j=c[h+104>>2]|0;if(j){do if(!(a[j+81>>0]|0)){if(!u?c[h+8>>2]|0:0){nM(w,m);c[L>>2]=c[w>>2];c[L+4>>2]=c[w+4>>2];c[L+8>>2]=c[w+8>>2];c[L+12>>2]=c[w+12>>2];mM(j,i,g,1,L);g=g+40|0;break}c[I>>2]=c[j>>2];gA(0,103862,I)|0;break b}else{c[L>>2]=c[M>>2];c[L+4>>2]=c[M+4>>2];c[L+8>>2]=c[M+8>>2];c[L+12>>2]=c[M+12>>2];c[L+16>>2]=c[M+16>>2];c[L+20>>2]=c[M+20>>2];c[L+24>>2]=c[M+24>>2];c[L+28>>2]=c[M+28>>2];lM(G,j,i,L);c[M>>2]=c[G>>2];c[M+4>>2]=c[G+4>>2];c[M+8>>2]=c[G+8>>2];c[M+12>>2]=c[G+12>>2];c[M+16>>2]=c[G+16>>2];c[M+20>>2]=c[G+20>>2];c[M+24>>2]=c[G+24>>2];c[M+28>>2]=c[G+28>>2]}while(0);i=i+40|0;h=c[k>>2]|0}j=c[h+100>>2]|0;if(j){do if(!(a[j+81>>0]|0)){if(!u?c[h+8>>2]|0:0){oM(y,m);c[L>>2]=c[y>>2];c[L+4>>2]=c[y+4>>2];c[L+8>>2]=c[y+8>>2];c[L+12>>2]=c[y+12>>2];mM(j,i,g,1,L);g=g+40|0;break}c[J>>2]=c[j>>2];gA(0,103902,J)|0;break b}else{c[L>>2]=c[M>>2];c[L+4>>2]=c[M+4>>2];c[L+8>>2]=c[M+8>>2];c[L+12>>2]=c[M+12>>2];c[L+16>>2]=c[M+16>>2];c[L+20>>2]=c[M+20>>2];c[L+24>>2]=c[M+24>>2];c[L+28>>2]=c[M+28>>2];lM(x,j,i,L);c[M>>2]=c[x>>2];c[M+4>>2]=c[x+4>>2];c[M+8>>2]=c[x+8>>2];c[M+12>>2]=c[x+12>>2];c[M+16>>2]=c[x+16>>2];c[M+20>>2]=c[x+20>>2];c[M+24>>2]=c[x+24>>2];c[M+28>>2]=c[x+28>>2]}while(0);i=i+40|0;h=c[k>>2]|0}j=c[h+108>>2]|0;if(j){do if(!(a[j+81>>0]|0)){if(!u?c[h+8>>2]|0:0){vO(A,e,m);c[L>>2]=c[A>>2];c[L+4>>2]=c[A+4>>2];c[L+8>>2]=c[A+8>>2];c[L+12>>2]=c[A+12>>2];mM(j,i,g,1,L);g=g+40|0;break}c[K>>2]=c[j>>2];gA(0,103942,K)|0;break b}else{c[L>>2]=c[M>>2];c[L+4>>2]=c[M+4>>2];c[L+8>>2]=c[M+8>>2];c[L+12>>2]=c[M+12>>2];c[L+16>>2]=c[M+16>>2];c[L+20>>2]=c[M+20>>2];c[L+24>>2]=c[M+24>>2];c[L+28>>2]=c[M+28>>2];lM(z,j,i,L);c[M>>2]=c[z>>2];c[M+4>>2]=c[z+4>>2];c[M+8>>2]=c[z+8>>2];c[M+12>>2]=c[z+12>>2];c[M+16>>2]=c[z+16>>2];c[M+20>>2]=c[z+20>>2];c[M+24>>2]=c[z+24>>2];c[M+28>>2]=c[z+28>>2]}while(0);i=i+40|0}}while(0);m=ZA(e,m)|0}n=kC(e,n)|0}if(o|0){c[f>>2]=c[M>>2];c[f+4>>2]=c[M+4>>2];c[f+8>>2]=c[M+8>>2];c[f+12>>2]=c[M+12>>2];c[f+16>>2]=c[M+16>>2];c[f+20>>2]=c[M+20>>2];c[f+24>>2]=c[M+24>>2];c[f+28>>2]=c[M+28>>2];c[f+32>>2]=i;h=L;g=h+40|0;do{c[h>>2]=c[f>>2];h=h+4|0;f=f+4|0}while((h|0)<(g|0));pM(N,e,L);c[M>>2]=c[N>>2];c[M+4>>2]=c[N+4>>2];c[M+8>>2]=c[N+8>>2];c[M+12>>2]=c[N+12>>2];c[M+16>>2]=c[N+16>>2];c[M+20>>2]=c[N+20>>2];c[M+24>>2]=c[N+24>>2];c[M+28>>2]=c[N+28>>2]}a[O+32>>0]=cP(e,GA(e,0,103978,0)|0,1)|0;c[O>>2]=c[M>>2];c[O+4>>2]=c[M+4>>2];c[O+8>>2]=c[M+8>>2];c[O+12>>2]=c[M+12>>2];c[O+16>>2]=c[M+16>>2];c[O+20>>2]=c[M+20>>2];c[O+24>>2]=c[M+24>>2];c[O+28>>2]=c[M+28>>2];hT(r,q,s,t,O)|0;if(0)qM(r,q,s,t,O);f=0;g=0;h=s;while(1){if((g|0)>=(t|0))break;if(a[h+36>>0]|0){O=c[h+32>>2]|0;a[O+81>>0]=1;N=O+56|0;rM(P,h);c[N>>2]=c[P>>2];c[N+4>>2]=c[P+4>>2];c[N+8>>2]=c[P+8>>2];c[N+12>>2]=c[P+12>>2];zP(e,O);f=f+1|0}g=g+1|0;h=h+40|0}if(!0){if((f|0)!=(t|0)){c[R>>2]=f;c[R+4>>2]=t;gA(0,104023,R)|0}}else{R=c[15712]|0;c[Q>>2]=f;c[Q+4>>2]=t;w4(R,103990,Q)|0}U1(r);U1(s)}}l=S;return}function gM(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0;i=l;l=l+48|0;e=i+32|0;f=i+16|0;g=i;if(+h[23303]!=0.0|+h[23304]!=0.0|(c[47265]|0)!=0){d=jC(a)|0;while(1){if(!d)break;if(c[47265]|0)qQ(d,0);b=d+16|0;j=(c[b>>2]|0)+16|0;c[e>>2]=c[j>>2];c[e+4>>2]=c[j+4>>2];c[e+8>>2]=c[j+8>>2];c[e+12>>2]=c[j+12>>2];bM(f,e);c[j>>2]=c[f>>2];c[j+4>>2]=c[f+4>>2];c[j+8>>2]=c[f+8>>2];c[j+12>>2]=c[f+12>>2];b=c[(c[b>>2]|0)+108>>2]|0;if(b|0){j=b+56|0;c[e>>2]=c[j>>2];c[e+4>>2]=c[j+4>>2];c[e+8>>2]=c[j+8>>2];c[e+12>>2]=c[j+12>>2];bM(g,e);c[j>>2]=c[g>>2];c[j+4>>2]=c[g+4>>2];c[j+8>>2]=c[g+8>>2];c[j+12>>2]=c[g+12>>2]}a:do if((c[47138]|0)==1){b=XA(a,d)|0;while(1){if(!b)break a;iM(b);b=ZA(a,b)|0}}while(0);d=kC(a,d)|0}$L(a,c[(c[a+16>>2]|0)+116>>2]&3)}l=i;return}function hM(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0,j=0;i=b+16|0;j=c[i>>2]|0;g=a[j+275>>0]|0;b=g<<24>>24;do if(!(b&4)){e=+h[j+16>>3];if(!(b&2)){f=(+h[j+32>>3]+e)*.5;break}else{f=+h[d>>3]*.5+e;break}}else f=+h[j+32>>3]-+h[d>>3]*.5;while(0);if(!(g&1))e=+h[d+8>>3]*.5+ +h[j+24>>3];else e=+h[j+40>>3]-+h[d+8>>3]*.5;j=c[j+12>>2]|0;h[j+56>>3]=f;h[j+64>>3]=e;a[(c[(c[i>>2]|0)+12>>2]|0)+81>>0]=1;return}function iM(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;s=l;l=l+144|0;q=s+120|0;e=s+16|0;i=s+104|0;j=s+88|0;k=s+72|0;m=s+56|0;n=s+40|0;o=s+24|0;r=s;p=b+16|0;d=c[p>>2]|0;f=c[d+8>>2]|0;if(!f){if((a[194954]|0)==0?(a[d+112>>0]|0)!=6:0){q=KB(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0;r=KB(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0;c[e>>2]=q;c[e+4>>2]=r;gA(1,103810,e)|0}}else{h=0;b=f;while(1){if((h|0)>=(c[b+4>>2]|0))break;g=c[b>>2]|0;b=c[g+(h*48|0)>>2]|0;e=c[g+(h*48|0)+4>>2]|0;f=c[g+(h*48|0)+8>>2]|0;g=c[g+(h*48|0)+12>>2]|0;d=0;while(1){if((d|0)>=(e|0))break;t=b+(d<<4)|0;c[q>>2]=c[t>>2];c[q+4>>2]=c[t+4>>2];c[q+8>>2]=c[t+8>>2];c[q+12>>2]=c[t+12>>2];bM(i,q);c[t>>2]=c[i>>2];c[t+4>>2]=c[i+4>>2];c[t+8>>2]=c[i+8>>2];c[t+12>>2]=c[i+12>>2];d=d+1|0}if(f|0){t=(c[c[(c[p>>2]|0)+8>>2]>>2]|0)+(h*48|0)+16|0;c[q>>2]=c[t>>2];c[q+4>>2]=c[t+4>>2];c[q+8>>2]=c[t+8>>2];c[q+12>>2]=c[t+12>>2];bM(j,q);c[t>>2]=c[j>>2];c[t+4>>2]=c[j+4>>2];c[t+8>>2]=c[j+8>>2];c[t+12>>2]=c[j+12>>2]}if(g|0){t=(c[c[(c[p>>2]|0)+8>>2]>>2]|0)+(h*48|0)+32|0;c[q>>2]=c[t>>2];c[q+4>>2]=c[t+4>>2];c[q+8>>2]=c[t+8>>2];c[q+12>>2]=c[t+12>>2];bM(k,q);c[t>>2]=c[k>>2];c[t+4>>2]=c[k+4>>2];c[t+8>>2]=c[k+8>>2];c[t+12>>2]=c[k+12>>2]}d=c[p>>2]|0;h=h+1|0;b=c[d+8>>2]|0}b=c[d+96>>2]|0;if(b){d=b+56|0;c[q>>2]=c[d>>2];c[q+4>>2]=c[d+4>>2];c[q+8>>2]=c[d+8>>2];c[q+12>>2]=c[d+12>>2];bM(m,q);c[d>>2]=c[m>>2];c[d+4>>2]=c[m+4>>2];c[d+8>>2]=c[m+8>>2];c[d+12>>2]=c[m+12>>2];d=c[p>>2]|0}b=c[d+108>>2]|0;if(b){d=b+56|0;c[q>>2]=c[d>>2];c[q+4>>2]=c[d+4>>2];c[q+8>>2]=c[d+8>>2];c[q+12>>2]=c[d+12>>2];bM(n,q);c[d>>2]=c[n>>2];c[d+4>>2]=c[n+4>>2];c[d+8>>2]=c[n+8>>2];c[d+12>>2]=c[n+12>>2];d=c[p>>2]|0}b=c[d+100>>2]|0;if(b){d=b+56|0;c[q>>2]=c[d>>2];c[q+4>>2]=c[d+4>>2];c[q+8>>2]=c[d+8>>2];c[q+12>>2]=c[d+12>>2];bM(o,q);c[d>>2]=c[o>>2];c[d+4>>2]=c[o+4>>2];c[d+8>>2]=c[o+8>>2];c[d+12>>2]=c[o+12>>2];d=c[p>>2]|0}d=c[d+104>>2]|0;if(d|0){t=d+56|0;c[q>>2]=c[t>>2];c[q+4>>2]=c[t+4>>2];c[q+8>>2]=c[t+8>>2];c[q+12>>2]=c[t+12>>2];bM(r,q);c[t>>2]=c[r>>2];c[t+4>>2]=c[r+4>>2];c[t+8>>2]=c[r+8>>2];c[t+12>>2]=c[r+12>>2]}}l=s;return}function jM(b){b=b|0;var d=0,e=0,f=0;f=b+16|0;if((yC(b)|0)!=(b|0)?(d=c[(c[f>>2]|0)+12>>2]|0,(d|0)!=0):0){e=1;b=(a[d+81>>0]|0)!=0&1}else{e=1;b=0}while(1){d=c[f>>2]|0;if((e|0)>(c[d+180>>2]|0))break;d=(jM(c[(c[d+184>>2]|0)+(e<<2)>>2]|0)|0)+b|0;e=e+1|0;b=d}return b|0}function kM(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0.0,k=0.0,m=0,n=0;g=l;l=l+32|0;i=g;n=(a[194957]|0)==0;d=d+16|0;m=c[d>>2]|0;k=+h[(n?m+32|0:m+40|0)>>3];h[e+16>>3]=k*72.0;j=+h[(n?m+40|0:m+32|0)>>3];h[e+24>>3]=j*72.0;d=(c[d>>2]|0)+16|0;c[e>>2]=c[d>>2];c[e+4>>2]=c[d+4>>2];c[e+8>>2]=c[d+8>>2];c[e+12>>2]=c[d+12>>2];h[e>>3]=+h[e>>3]-k*36.0;d=e+8|0;h[d>>3]=+h[d>>3]-j*36.0;c[i>>2]=c[f>>2];c[i+4>>2]=c[f+4>>2];c[i+8>>2]=c[f+8>>2];c[i+12>>2]=c[f+12>>2];c[i+16>>2]=c[f+16>>2];c[i+20>>2]=c[f+20>>2];c[i+24>>2]=c[f+24>>2];c[i+28>>2]=c[f+28>>2];sM(b,e,i);l=g;return}function lM(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0.0,k=0.0,m=0,n=0;g=l;l=l+32|0;i=g;n=(a[194957]|0)==0;m=d+24|0;k=+h[(n?m:d+32|0)>>3];h[e+16>>3]=k;j=+h[(n?d+32|0:m)>>3];h[e+24>>3]=j;d=d+56|0;c[e>>2]=c[d>>2];c[e+4>>2]=c[d+4>>2];c[e+8>>2]=c[d+8>>2];c[e+12>>2]=c[d+12>>2];h[e>>3]=+h[e>>3]-k*.5;d=e+8|0;h[d>>3]=+h[d>>3]-j*.5;c[i>>2]=c[f>>2];c[i+4>>2]=c[f+4>>2];c[i+8>>2]=c[f+8>>2];c[i+12>>2]=c[f+12>>2];c[i+16>>2]=c[f+16>>2];c[i+20>>2]=c[f+20>>2];c[i+24>>2]=c[f+24>>2];c[i+28>>2]=c[f+28>>2];sM(b,e,i);l=g;return}function mM(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;if(f|0){f=d+16|0;c[f>>2]=0;c[f+4>>2]=0;c[f+8>>2]=0;c[f+12>>2]=0;c[d>>2]=c[g>>2];c[d+4>>2]=c[g+4>>2];c[d+8>>2]=c[g+8>>2];c[d+12>>2]=c[g+12>>2]}if(!(a[194957]|0)){g=b+24|0;c[e>>2]=c[g>>2];c[e+4>>2]=c[g+4>>2];c[e+8>>2]=c[g+8>>2];c[e+12>>2]=c[g+12>>2]}else{h[e>>3]=+h[b+32>>3];h[e+8>>3]=+h[b+24>>3]}c[e+32>>2]=b;a[e+36>>0]=0;c[d+32>>2]=e;return}function nM(a,b){a=a|0;b=b|0;b=uO(b)|0;do if(b){b=c[b>>2]|0;if(!(c[b+8>>2]|0)){b=c[b>>2]|0;c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];break}else{b=b+16|0;c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];break}}else{c[a>>2]=0;c[a+4>>2]=0;c[a+8>>2]=0;c[a+12>>2]=0}while(0);return}function oM(a,b){a=a|0;b=b|0;var d=0;b=uO(b)|0;do if(b){d=c[b>>2]|0;b=(c[b+4>>2]|0)+-1|0;if(!(c[d+(b*48|0)+12>>2]|0)){d=(c[d+(b*48|0)>>2]|0)+((c[d+(b*48|0)+4>>2]|0)+-1<<4)|0;c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];break}else{d=d+(b*48|0)+32|0;c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];break}}else{c[a>>2]=0;c[a+4>>2]=0;c[a+8>>2]=0;c[a+12>>2]=0}while(0);return}function pM(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+112|0;k=q+72|0;n=q+32|0;o=q;p=d+16|0;j=1;while(1){f=c[p>>2]|0;if((j|0)>(c[f+180>>2]|0))break;f=c[(c[f+184>>2]|0)+(j<<2)>>2]|0;g=k;h=e;i=g+40|0;do{c[g>>2]=c[h>>2];g=g+4|0;h=h+4|0}while((g|0)<(i|0));pM(n,f,k);g=e;h=n;i=g+40|0;do{c[g>>2]=c[h>>2];g=g+4|0;h=h+4|0}while((g|0)<(i|0));j=j+1|0}if(((yC(d)|0)!=(d|0)?(m=c[(c[p>>2]|0)+12>>2]|0,m|0):0)?a[m+81>>0]|0:0){p=e+32|0;n=c[p>>2]|0;c[k>>2]=c[e>>2];c[k+4>>2]=c[e+4>>2];c[k+8>>2]=c[e+8>>2];c[k+12>>2]=c[e+12>>2];c[k+16>>2]=c[e+16>>2];c[k+20>>2]=c[e+20>>2];c[k+24>>2]=c[e+24>>2];c[k+28>>2]=c[e+28>>2];lM(o,m,n,k);c[e>>2]=c[o>>2];c[e+4>>2]=c[o+4>>2];c[e+8>>2]=c[o+8>>2];c[e+12>>2]=c[o+12>>2];c[e+16>>2]=c[o+16>>2];c[e+20>>2]=c[o+20>>2];c[e+24>>2]=c[o+24>>2];c[e+28>>2]=c[o+28>>2];c[p>>2]=(c[p>>2]|0)+40}g=b;h=e;i=g+40|0;do{c[g>>2]=c[h>>2];g=g+4|0;h=h+4|0}while((g|0)<(i|0));l=q;return}function qM(a,b,e,f,g){a=a|0;b=b|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0.0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+160|0;r=s+96|0;p=s+48|0;o=s;q=c[15712]|0;n=d[g+32>>0]|0;i=+h[g>>3];j=+h[g+8>>3];k=+h[g+16>>3];m=+h[g+24>>3];c[o>>2]=b;c[o+4>>2]=f;c[o+8>>2]=n;h[o+16>>3]=i;h[o+24>>3]=j;h[o+32>>3]=k;h[o+40>>3]=m;w4(q,104065,o)|0;a:do if(0>=2){U4(104125,8,1,q)|0;o=0;while(1){if((o|0)>=(b|0))break;g=c[a+32>>2]|0;i=+h[a>>3];j=+h[a+8>>3];k=+h[a+16>>3];m=+h[a+24>>3];if(!g)n=195059;else n=c[c[g+32>>2]>>2]|0;c[p>>2]=o;h[p+8>>3]=i;h[p+16>>3]=j;h[p+24>>3]=k;h[p+32>>3]=m;c[p+40>>2]=g;c[p+44>>2]=n;w4(q,104134,p)|0;o=o+1|0;a=a+40|0}U4(104177,8,1,q)|0;g=0;while(1){if((g|0)>=(f|0))break a;b=d[e+36>>0]|0;i=+h[e+16>>3];j=+h[e+24>>3];k=+h[e>>3];m=+h[e+8>>3];p=c[c[e+32>>2]>>2]|0;c[r>>2]=g;c[r+4>>2]=e;c[r+8>>2]=b;h[r+16>>3]=i;h[r+24>>3]=j;h[r+32>>3]=k;h[r+40>>3]=m;c[r+48>>2]=p;w4(q,104186,r)|0;e=e+40|0;g=g+1|0}}while(0);l=s;return}function rM(a,b){a=a|0;b=b|0;var c=0.0;c=+h[b+8>>3]*.5+ +h[b+24>>3];h[a>>3]=+h[b>>3]*.5+ +h[b+16>>3];h[a+8>>3]=c;return}function sM(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0.0,j=0.0;e=l;l=l+16|0;f=e;j=+h[b>>3];h[d>>3]=+h[(+h[d>>3]<j?d:b)>>3];g=d+8|0;i=+h[b+8>>3];h[g>>3]=+h[(+h[g>>3]<i?d:b)+8>>3];j=+h[b+16>>3]+j;h[f>>3]=j;i=+h[b+24>>3]+i;h[f+8>>3]=i;g=d+16|0;h[g>>3]=+h[(+h[g>>3]>j?g:f)>>3];b=d+24|0;h[b>>3]=+h[(+h[b>>3]>i?g:f)+8>>3];c[a>>2]=c[d>>2];c[a+4>>2]=c[d+4>>2];c[a+8>>2]=c[d+8>>2];c[a+12>>2]=c[d+12>>2];c[a+16>>2]=c[d+16>>2];c[a+20>>2]=c[d+20>>2];c[a+24>>2]=c[d+24>>2];c[a+28>>2]=c[d+28>>2];l=e;return}function tM(a){a=a|0;cM(a,1);return}function uM(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;e=l;l=l+16|0;d=e;b=mP(QA(a,105526)|0)|0;if(b){b=vM(b)|0;if(b|0){f=c[b+40>>2]|0;d=c[b+44>>2]|0;g=a+16|0;a=c[g>>2]|0;h[a+32>>3]=+(f|0)*.013888888888888888;h[a+40>>3]=+(d|0)*.013888888888888888;a=JK(12)|0;c[(c[g>>2]|0)+12>>2]=a;c[a>>2]=c[b+12>>2];c[a+4>>2]=((f|0)/-2|0)-(c[b+32>>2]|0);c[a+8>>2]=((d|0)/-2|0)-(c[b+36>>2]|0)}}else{c[d>>2]=KB(a)|0;gA(0,104234,d)|0}l=e;return}function vM(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;p=l;l=l+1152|0;i=p+24|0;h=p+8|0;e=p;f=p+120|0;j=p+44|0;k=p+40|0;m=p+36|0;n=p+32|0;o=p+28|0;d=c[47266]|0;if(!d){d=Vz(20812,c[4581]|0)|0;c[47266]=d}d=nb[c[d>>2]&63](d,b,512)|0;do if(!d){g=$4(b,138821)|0;if(!g){c[e>>2]=b;gA(0,104283,e)|0;d=0;break}else{d=0;e=0}while(1){if(!(c4(f,1024,g)|0)){f=11;break}c[h>>2]=k;c[h+4>>2]=m;c[h+8>>2]=n;c[h+12>>2]=o;q=(b5(f,104311,h)|0)==4;e=q?1:e;if((a[f>>0]|0)!=37){q=(d4(f,104340)|0)==0;d=q?d:1}if((e|0)!=0&(d|0)!=0){f=12;break}}if((f|0)==11)if(!e){c[i>>2]=b;gA(0,104345,i)|0;d=0}else f=12;if((f|0)==12){q=KK(64)|0;k=c[k>>2]|0;c[q+32>>2]=k;m=c[m>>2]|0;c[q+40>>2]=(c[n>>2]|0)-k;c[q+36>>2]=(c[o>>2]|0)-m;c[q+8>>2]=b;o=c[47267]|0;c[47267]=o+1;c[q+12>>2]=o;Z4(A6(g)|0,j)|0;o=c[j+36>>2]|0;n=KK(o+1|0)|0;c[q+52>>2]=n;n5(g,0,0)|0;x6(n,o,1,g)|0;a[n+o>>0]=0;o=c[47266]|0;nb[c[o>>2]&63](o,q,1)|0;a[q+16>>0]=d;d=q}I4(g)|0}while(0);l=p;return d|0}function wM(a,b,d){a=a|0;b=b|0;d=d|0;U1(c[b+52>>2]|0);return}function xM(a){a=a|0;U1(c[(c[a+16>>2]|0)+12>>2]|0);return}function yM(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;m=n+8|0;k=n;j=(d|0)!=0;a:do if(j){h=0;f=0;b:while(1)while(1){if(h)break a;g=c[d+(f<<2)>>2]|0;if(!g){i=6;break a}f=f+1|0;if(!(a[g>>0]|0)){h=1;continue b}}}else i=6;while(0);c:do if((i|0)==6)while(1){f=c[e>>2]|0;if(!f)break c;oF(b,f)|0;oF(b,153599)|0;e=e+4|0;i=6}while(0);d:do if(j){h=0;while(1){f=c[d+(h<<2)>>2]|0;if(!f)break d;do if(a[f>>0]|0){e=mP(f)|0;if(!e){c[k>>2]=f;gA(0,104384,k)|0;break}g=$4(e,138821)|0;if(!g){c[m>>2]=e;gA(0,104412,m)|0;break}while(1){f=lP(g)|0;if(!f)break;oF(b,f)|0}oF(b,153599)|0;I4(g)|0}while(0);h=h+1|0}}while(0);l=n;return}function zM(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;d=c[d+52>>2]|0;a:while(1){e=a[d>>0]|0;b:do switch(e<<24>>24){case 0:break a;case 37:{if((a[d+1>>0]|0)==37){e=d+2|0;if((((j3(e,104440,3)|0)!=0?(j3(e,104444,5)|0)!=0:0)?(j3(e,104450,3)|0)!=0:0)?(j3(e,104454,7)|0)!=0:0){e=37;break b}else g=37;c:while(1){switch(g<<24>>24){case 13:{f=10;break c}case 10:case 0:{f=12;break c}default:{}}g=d+1|0;d=g;g=a[g>>0]|0}if((f|0)==10){e=d+1|0;if((a[e>>0]|0)==10){d=d+2|0;continue a}}else if((f|0)==12)e=d+1|0;d=g<<24>>24==0?d:e;continue a}else e=37;break}default:{}}while(0);d:while(1){switch(e<<24>>24){case 10:case 13:case 0:break d;default:{}}pF(b,e<<24>>24)|0;g=d+1|0;d=g;e=a[g>>0]|0}f=d+1|0;if(e<<24>>24==13?(a[f>>0]|0)==10:0)d=d+2|0;else d=e<<24>>24==0?d:f;pF(b,10)|0}return}function AM(b){b=b|0;var d=0,e=0,f=0,g=0;f=l;l=l+16|0;e=f;d=c[47266]|0;a:do if(d|0){d=nb[c[d>>2]&63](d,0,128)|0;while(1){if(!d)break a;if(!(a[d+16>>0]|0)){c[e>>2]=c[d+12>>2];uF(b,104462,e);oF(b,104480)|0;zM(b,d);oF(b,104498)|0;oF(b,104513)|0}g=c[47266]|0;d=nb[c[g>>2]&63](g,d,8)|0}}while(0);l=f;return}function BM(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0;g=l;l=l+16|0;e=g;a:do switch(d|0){case 0:{f=b;break}case 1:{f=fQ(b)|0;break}default:{switch(CM(b)|0){case 2:break;case 1:{f=fQ(b)|0;break a}default:{f=b;break a}}if(!(c[47268]|0)){gA(0,104525,e)|0;c[47268]=1;f=b}else f=b}}while(0);if(!(c[47269]|0))kA(189076,0,0);d=c[47270]|0;if(d>>>0>=(c[47271]|0)>>>0){lA(189076,1)|0;d=c[47270]|0}c[47270]=d+1;a[d>>0]=40;e=f;b:while(1){switch(a[e>>0]|0){case 0:break b;case 92:case 41:case 40:{d=c[47270]|0;if(d>>>0>=(c[47271]|0)>>>0){lA(189076,1)|0;d=c[47270]|0}c[47270]=d+1;a[d>>0]=92;break}default:{}}d=c[47270]|0;if(d>>>0>=(c[47271]|0)>>>0){lA(189076,1)|0;d=c[47270]|0}h=a[e>>0]|0;c[47270]=d+1;a[d>>0]=h;e=e+1|0}d=c[47270]|0;if(d>>>0>=(c[47271]|0)>>>0){lA(189076,1)|0;d=c[47270]|0}c[47270]=d+1;a[d>>0]=41;if((f|0)!=(b|0))U1(f);d=c[47270]|0;if(d>>>0>=(c[47271]|0)>>>0){lA(189076,1)|0;d=c[47270]|0}a[d>>0]=0;h=c[47269]|0;c[47270]=h;l=g;return h|0}function CM(b){b=b|0;var c=0,d=0;c=0;a:while(1){while(1){d=a[b>>0]|0;if(!(d<<24>>24))break a;if((d&255)<127)b=b+1|0;else break}if((d&-4)<<24>>24==-64){c=1;b=b+2|0}else{c=2;break}}return c|0}function DM(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;n=l;l=l+96|0;i=n+80|0;j=n+72|0;m=n+64|0;g=n+32|0;k=n;h[g>>3]=+h[a>>3];h[g+8>>3]=+h[a+8>>3];h[g+16>>3]=+h[b>>3];h[g+24>>3]=+h[b+8>>3];do if((iY(d,g,j)|0)>=0){if(!f){b=d+4|0;a=c[b>>2]|0;f=c[47274]|0;if((a|0)>(c[47273]|0)){if(!f)a=KK(a<<5)|0;else a=MK(f,a<<5)|0;c[47274]=a;g=c[b>>2]|0;c[47273]=g;f=a}else g=a;b=c[d>>2]|0;a=0;while(1){if((a|0)>=(g|0))break;o=f+(a<<5)|0;d=b+(a<<4)|0;c[o>>2]=c[d>>2];c[o+4>>2]=c[d+4>>2];c[o+8>>2]=c[d+8>>2];c[o+12>>2]=c[d+12>>2];o=f+(a<<5)+16|0;d=a+1|0;p=b+(((d|0)%(g|0)|0)<<4)|0;c[o>>2]=c[p>>2];c[o+4>>2]=c[p+4>>2];c[o+8>>2]=c[p+8>>2];c[o+12>>2]=c[p+12>>2];a=d}c[k>>2]=0;c[k+4>>2]=0;c[k+8>>2]=0;c[k+12>>2]=0;c[k+16>>2]=0;c[k+20>>2]=0;c[k+24>>2]=0;c[k+28>>2]=0;c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];if((OX(f,g,i,k,m)|0)<0){a=0;break}}else{c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];FY(i,m)}a=m+4|0;if(!(EM(c[a>>2]|0)|0)){g=c[a>>2]|0;a=c[47275]|0;f=c[m>>2]|0;b=0;while(1){if((b|0)>=(g|0))break;p=a+(b<<4)|0;o=f+(b<<4)|0;c[p>>2]=c[o>>2];c[p+4>>2]=c[o+4>>2];c[p+8>>2]=c[o+8>>2];c[p+12>>2]=c[o+12>>2];b=b+1|0}c[e>>2]=g}else a=0}else a=0;while(0);l=n;return a|0}function EM(a){a=a|0;var b=0,d=0,e=0;e=l;l=l+16|0;d=e;b=c[47276]|0;do if((b|0)<(a|0)){a=a+300-((a|0)%300|0)+b|0;b=MK(c[47275]|0,a<<4)|0;c[47275]=b;if(!b){gA(1,104615,d)|0;a=1;break}else{c[47276]=a;a=0;break}}else a=0;while(0);l=e;return a|0}function FM(){var a=0,b=0,d=0;b=l;l=l+16|0;a=b;d=c[47277]|0;c[47277]=d+1;do if((d|0)<=0){d=KK(4800)|0;c[47275]=d;if(!d){gA(1,104638,a)|0;a=1;break}c[47276]=300;c[47278]=0;c[47279]=0;if(0){TO();a=0}else a=0}else a=0;while(0);l=b;return a|0}function GM(){var a=0,b=0,d=0,e=0.0,f=0,g=0;b=l;l=l+16|0;a=b;d=c[47277]|0;c[47277]=d+-1;if((d|0)<=1?(U1(c[47275]|0),0):0){d=c[15712]|0;g=c[47278]|0;f=c[47279]|0;e=+UO();c[a>>2]=g;c[a+4>>2]=f;h[a+8>>3]=e;w4(d,104676,a)|0}l=b;return}function HM(a,b){a=a|0;b=b|0;return IM(a,b,0)|0}function IM(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0;L=l;l=l+176|0;G=L+168|0;K=L+128|0;x=L+120|0;v=L+112|0;i=L+104|0;s=L+88|0;n=L+72|0;g=L+64|0;t=L+160|0;I=L+152|0;y=L+144|0;u=L+32|0;w=L;J=L+136|0;c[47278]=(c[47278]|0)+1;H=c[b+80>>2]|0;c[47279]=(c[47279]|0)+H;f=b+88|0;while(1){C=c[f>>2]|0;if(!C){D=4;break}f=c[C+16>>2]|0;if(!(a[f+112>>0]|0)){D=5;break}else f=f+116|0}do if((D|0)==4){gA(1,104719,g)|0;f=0}else if((D|0)==5){B=c[b+84>>2]|0;if(!(JM(H,B,b)|0)){g=H<<3;if((g|0)>(c[47280]|0)){f=c[47281]|0;if(!f)f=KK(H<<7)|0;else f=MK(f,H<<7)|0;c[47281]=f;c[47280]=g}a:do if((H|0)>1?+h[B+8>>3]>+h[B+40>>3]:0){f=0;while(1){if((f|0)==(H|0)){p=1;break a}z=B+(f<<5)+24|0;m=+h[z>>3];A=B+(f<<5)+8|0;h[z>>3]=-+h[A>>3];h[A>>3]=-m;f=f+1|0}}else p=0;while(0);r=c[C>>2]&3;z=C+48|0;f=c[((r|0)==3?C:z)+40>>2]|0;A=C+-48|0;if((f|0)==(c[((r|0)==2?C:A)+40>>2]|0)){c[i>>2]=KB(f)|0;gA(1,104826,i)|0;f=0;break}o=H+-1|0;q=c[47281]|0;r=q;f=0;j=0;b:while(1){if((j|0)>=(H|0)){j=o;n=f;break}if((j|0)>0)g=+h[B+(j<<5)+8>>3]>+h[B+(j+-1<<5)+8>>3]?-1:1;else g=0;if((j|0)<(o|0))i=+h[B+(j+1<<5)+8>>3]>+h[B+(j<<5)+8>>3]?1:-1;else i=0;c:do if((g|0)!=(i|0))if((g|0)==1|(i|0)==-1){g=B+(j<<5)|0;h[q+(f<<4)>>3]=+h[g>>3];i=f+1|0;h[q+(f<<4)+8>>3]=+h[B+(j<<5)+24>>3];h[q+(i<<4)>>3]=+h[g>>3];g=B+(j<<5)+8|0;D=29;break}else{g=B+(j<<5)+16|0;h[q+(f<<4)>>3]=+h[g>>3];i=f+1|0;h[q+(f<<4)+8>>3]=+h[B+(j<<5)+8>>3];h[q+(i<<4)>>3]=+h[g>>3];g=B+(j<<5)+24|0;D=29;break}else{switch(g|0){case -1:break c;case 0:break;default:{D=28;break b}}g=B+(j<<5)|0;h[q+(f<<4)>>3]=+h[g>>3];i=f+1|0;h[q+(f<<4)+8>>3]=+h[B+(j<<5)+24>>3];h[q+(i<<4)>>3]=+h[g>>3];g=B+(j<<5)+8|0;D=29}while(0);if((D|0)==29){D=0;h[q+(i<<4)+8>>3]=+h[g>>3];f=f+2|0}j=j+1|0}if((D|0)==28){c[n>>2]=g;c[n+4>>2]=g;c[n+8>>2]=480;gA(1,104761,n)|0;f=0;break}d:while(1){if((j|0)<=-1)break;if((j|0)<(o|0))f=+h[B+(j<<5)+8>>3]>+h[B+(j+1<<5)+8>>3]?-1:1;else f=0;if((j|0)>0)g=+h[B+(j+-1<<5)+8>>3]>+h[B+(j<<5)+8>>3]?1:-1;else g=0;e:do if((f|0)!=(g|0))if((f|0)==1|(g|0)==-1){f=B+(j<<5)|0;h[q+(n<<4)>>3]=+h[f>>3];g=n+1|0;h[q+(n<<4)+8>>3]=+h[B+(j<<5)+24>>3];h[q+(g<<4)>>3]=+h[f>>3];f=B+(j<<5)+8|0;i=2;break}else{f=B+(j<<5)+16|0;h[q+(n<<4)>>3]=+h[f>>3];g=n+1|0;h[q+(n<<4)+8>>3]=+h[B+(j<<5)+8>>3];h[q+(g<<4)>>3]=+h[f>>3];f=B+(j<<5)+24|0;i=2;break}else switch(f|0){case 0:{f=B+(j<<5)+16|0;h[q+(n<<4)>>3]=+h[f>>3];g=n+1|0;h[q+(n<<4)+8>>3]=+h[B+(j<<5)+8>>3];h[q+(g<<4)>>3]=+h[f>>3];f=B+(j<<5)+24|0;i=2;break e}case -1:{N=B+(j<<5)+16|0;h[q+(n<<4)>>3]=+h[N>>3];f=B+(j<<5)+8|0;i=n+1|0;h[q+(n<<4)+8>>3]=+h[f>>3];h[q+(i<<4)>>3]=+h[N>>3];N=B+(j<<5)+24|0;M=n+2|0;h[q+(i<<4)+8>>3]=+h[N>>3];i=B+(j<<5)|0;h[q+(M<<4)>>3]=+h[i>>3];g=n+3|0;h[q+(M<<4)+8>>3]=+h[N>>3];h[q+(g<<4)>>3]=+h[i>>3];i=4;break e}default:{D=42;break d}}while(0);h[q+(g<<4)+8>>3]=+h[f>>3];j=j+-1|0;n=n+i|0}if((D|0)==42){c[s>>2]=f;c[s+4>>2]=f;c[s+8>>2]=513;gA(1,104761,s)|0;f=0;break}f:do if(!p)f=0;else{f=0;while(1){if((f|0)>=(H|0)){f=0;break}M=B+(f<<5)+24|0;m=+h[M>>3];N=B+(f<<5)+8|0;h[M>>3]=-+h[N>>3];h[N>>3]=-m;f=f+1|0}while(1){if((f|0)>=(n|0)){f=0;break f}N=q+(f<<4)+8|0;h[N>>3]=-+h[N>>3];f=f+1|0}}while(0);while(1){if((f|0)>=(H|0))break;h[B+(f<<5)>>3]=2147483647.0;h[B+(f<<5)+16>>3]=-2147483648.0;f=f+1|0}c[t>>2]=r;i=t+4|0;c[i>>2]=n;h[u>>3]=+h[b>>3];h[u+8>>3]=+h[b+8>>3];h[u+16>>3]=+h[b+40>>3];h[u+24>>3]=+h[b+48>>3];if((iY(t,u,I)|0)<0){gA(1,104865,v)|0;f=0;break}if(!e){g=c[i>>2]|0;f=c[47274]|0;if((g|0)>(c[47273]|0)){if(!f)f=KK(g<<5)|0;else f=MK(f,g<<5)|0;c[47274]=f;g=c[i>>2]|0;c[47273]=g}j=c[47281]|0;i=0;while(1){if((i|0)>=(g|0))break;M=f+(i<<5)|0;N=j+(i<<4)|0;c[M>>2]=c[N>>2];c[M+4>>2]=c[N+4>>2];c[M+8>>2]=c[N+8>>2];c[M+12>>2]=c[N+12>>2];M=f+(i<<5)+16|0;N=i+1|0;v=j+(((N|0)%(g|0)|0)<<4)|0;c[M>>2]=c[v>>2];c[M+4>>2]=c[v+4>>2];c[M+8>>2]=c[v+8>>2];c[M+12>>2]=c[v+12>>2];i=N}if(!(a[b+29>>0]|0)){k=0.0;m=0.0}else{m=+h[b+16>>3];k=+E(+m);m=+F(+m)}h[w+8>>3]=m;h[w>>3]=k;if(!(a[b+69>>0]|0)){k=0.0;m=0.0}else{m=+h[b+56>>3];k=-+E(+m);m=-+F(+m)}h[w+24>>3]=m;h[w+16>>3]=k;c[G>>2]=c[I>>2];c[G+4>>2]=c[I+4>>2];if((OX(f,g,G,w,y)|0)<0){gA(1,104904,x)|0;f=0;break}}else{c[G>>2]=c[I>>2];c[G+4>>2]=c[I+4>>2];FY(G,y)}n=y+4|0;if(!(EM(c[n>>2]|0)|0)){f=0;while(1){if((f|0)>=(H|0))break;h[B+(f<<5)>>3]=2147483647.0;h[B+(f<<5)+16>>3]=-2147483648.0;f=f+1|0}i=c[n>>2]|0;j=c[47275]|0;g=c[y>>2]|0;f=0;while(1){if((f|0)>=(i|0)){i=1;f=10;g=0;break}N=j+(f<<4)|0;M=g+(f<<4)|0;c[N>>2]=c[M>>2];c[N+4>>2]=c[M+4>>2];c[N+8>>2]=c[M+8>>2];c[N+12>>2]=c[M+12>>2];f=f+1|0}while(1){if(!((g|0)<15&i<<24>>24!=0))break;KM(B,H,c[47275]|0,c[n>>2]|0,f);j=0;while(1){if((j|0)>=(H|0))break;if(+h[B+(j<<5)>>3]==2147483647.0){D=82;break}if(+h[B+(j<<5)+16>>3]==-2147483648.0){D=82;break}j=j+1|0}if((D|0)==82){D=0;N=f<<1;f=N;g=(N|0)>(2147483647/(H|0)|0|0)?15:g}i=(j|0)==(H|0)?0:i;g=g+1|0}if(i<<24>>24){M=KB(c[((c[C>>2]&3|0)==3?C:z)+40>>2]|0)|0;N=KB(c[((c[C>>2]&3|0)==2?C:A)+40>>2]|0)|0;c[K>>2]=M;c[K+4>>2]=N;gA(0,104942,K)|0;c[G>>2]=c[I>>2];c[G+4>>2]=c[I+4>>2];FY(G,J);KM(B,H,c[J>>2]|0,c[J+4>>2]|0,10);U1(c[J>>2]|0)}c[d>>2]=c[n>>2];f=c[47275]|0}else f=0}else f=0}while(0);l=L;return f|0}function JM(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0,j=0,k=0,m=0.0,n=0.0,o=0,p=0.0,q=0.0,r=0.0,s=0.0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0.0,L=0,M=0,N=0,O=0.0,P=0,Q=0,R=0;R=l;l=l+32|0;I=R+16|0;H=R+8|0;k=R;e=0;j=0;while(1){if((j|0)>=(a|0))break;i=b+(j<<5)|0;s=+h[b+(j<<5)+8>>3]-+h[b+(j<<5)+24>>3];if(!((s>=0.0?s:-s)<.01)?(s=+h[i>>3]-+h[b+(j<<5)+16>>3],!((s>=0.0?s:-s)<.01)):0){if((e|0)!=(j|0)){P=b+(e<<5)|0;c[P>>2]=c[i>>2];c[P+4>>2]=c[i+4>>2];c[P+8>>2]=c[i+8>>2];c[P+12>>2]=c[i+12>>2];c[P+16>>2]=c[i+16>>2];c[P+20>>2]=c[i+20>>2];c[P+24>>2]=c[i+24>>2];c[P+28>>2]=c[i+28>>2]}e=e+1|0}j=j+1|0}J=b+16|0;do if(!(+h[b>>3]>+h[J>>3])?(L=b+8|0,N=b+24|0,!(+h[L>>3]>+h[N>>3])):0){P=e+-1|0;M=c[15712]|0;w=0;while(1){if((w|0)>=(P|0))break;D=w+1|0;E=b+(D<<5)|0;f=+h[E>>3];F=b+(D<<5)+16|0;g=+h[F>>3];if(f>g){Q=16;break}A=b+(D<<5)+8|0;m=+h[A>>3];B=b+(D<<5)+24|0;n=+h[B>>3];if(m>n){Q=16;break}G=b+(w<<5)+16|0;v=+h[G>>3]<f;e=v&1;C=b+(w<<5)|0;u=+h[C>>3]>g;a=u&1;z=b+(w<<5)+24|0;t=+h[z>>3]<m;j=t&1;y=b+(w<<5)+8|0;k=+h[y>>3]>n;i=k&1;x=a+e+j+i|0;o=(x|0)!=0;if(!(0==0|o^1)){c[I>>2]=w;c[I+4>>2]=D;w4(M,105142,I)|0;LM(d)}a:do if(o){if(!v)if(!u)if(!t){if(k){k=0;o=e;e=B;i=y;Q=24}}else{k=i;j=0;o=e;e=A;i=z;Q=24}else{k=i;a=0;o=e;e=F;i=C;Q=24}else{k=i;o=0;e=E;i=G;Q=24}if((Q|0)==24){Q=0;w=~~+h[i>>3];h[i>>3]=+h[e>>3];h[e>>3]=+(w|0);i=k;e=o}o=x+-1|0;k=0;while(1){if((k|0)>=(o|0))break a;do if((e|0)!=1){if((a|0)==1){s=+(~~((+h[F>>3]+ +h[C>>3])*.5+.5)|0);h[F>>3]=s;h[C>>3]=s;a=0;break}if((j|0)==1){s=+(~~((+h[A>>3]+ +h[z>>3])*.5+.5)|0);h[A>>3]=s;h[z>>3]=s;j=0;break}if((i|0)==1){s=+(~~((+h[B>>3]+ +h[y>>3])*.5+.5)|0);h[B>>3]=s;h[y>>3]=s;i=0}}else{s=+(~~((+h[E>>3]+ +h[G>>3])*.5+.5)|0);h[E>>3]=s;h[G>>3]=s;e=0}while(0);k=k+1|0}}while(0);p=+h[C>>3];q=+h[G>>3];r=+h[E>>3];s=+h[F>>3];i=MM(~~p,~~q,~~r,~~s)|0;n=+h[y>>3];m=+h[z>>3];g=+h[A>>3];f=+h[B>>3];e=MM(~~n,~~m,~~g,~~f)|0;if(!((i|0)!=0&(e|0)!=0)){w=D;continue}if((i|0)<(e|0)){B=q-p>s-r;w=q<s;h[(B?(w?G:C):w?E:F)>>3]=B?(w?r:s):w?q:p;w=D;continue}else{B=m-n>f-g;w=m<f;h[(B?(w?G:C):w?E:F)+8>>3]=B?(w?g:f):w?m:n;w=D;continue}}if((Q|0)==16){c[H>>2]=D;gA(1,105096,H)|0;LM(d);e=1;break}f=+h[d>>3];g=+h[b>>3];if(!(((!(f<g)?!(f>+h[J>>3]):0)?(K=+h[d+8>>3],!(K<+h[L>>3])):0)?!(K>+h[N>>3]):0)){if(0){U4(105185,42,1,M)|0;LM(d);g=+h[b>>3];f=+h[d>>3]}if(f<g)h[d>>3]=g;else g=f;f=+h[J>>3];if(g>f)h[d>>3]=f;e=d+8|0;g=+h[e>>3];f=+h[L>>3];if(g<f){h[e>>3]=f;g=f}f=+h[N>>3];if(g>f)h[e>>3]=f}i=d+40|0;f=+h[i>>3];e=b+(P<<5)|0;g=+h[e>>3];if(((!(f<g)?!(f>+h[b+(P<<5)+16>>3]):0)?(O=+h[d+48>>3],!(O<+h[b+(P<<5)+8>>3])):0)?!(O>+h[b+(P<<5)+24>>3]):0){e=0;break}if(0){U4(105228,39,1,M)|0;LM(d);g=+h[e>>3];f=+h[i>>3]}if(f<g){h[i>>3]=g;f=g}g=+h[b+(P<<5)+16>>3];if(f>g)h[i>>3]=g;e=d+48|0;f=+h[e>>3];g=+h[b+(P<<5)+8>>3];if(f<g)h[e>>3]=g;else g=f;f=+h[b+(P<<5)+24>>3];if(g>f){h[e>>3]=f;e=0}else e=0}else Q=12;while(0);if((Q|0)==12){gA(1,105051,k)|0;LM(d);e=1}l=R;return e|0}function KM(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,l=0,m=0.0,n=0.0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0,v=0.0,w=0.0,x=0.0,y=0.0,z=0.0;s=O(e,b)|0;t=1.0/+(s|0);e=0;a:while(1){u=e+3|0;if((u|0)>=(d|0))break;o=c+(e<<4)|0;p=c+(e<<4)+8|0;r=e+1|0;q=c+(r<<4)|0;r=c+(r<<4)+8|0;j=e+2|0;i=c+(j<<4)|0;j=c+(j<<4)+8|0;k=c+(u<<4)|0;l=c+(u<<4)+8|0;g=0;while(1){if((g|0)>(s|0)){e=u;continue a}v=t*+(g|0);m=+h[o>>3];n=+h[p>>3];y=+h[q>>3];w=+h[r>>3];z=+h[i>>3];x=+h[j>>3];m=(y-m)*v+m;n=(w-n)*v+n;y=(z-y)*v+y;w=(x-w)*v+w;m=(y-m)*v+m;n=(w-n)*v+n;m=((z-y+(+h[k>>3]-z)*v)*v+y-m)*v+m;n=((x-w+(+h[l>>3]-x)*v)*v+w-n)*v+n;f=0;while(1){if((f|0)>=(b|0))break;if(n<=+h[a+(f<<5)+24>>3]+.0001?n>=+h[a+(f<<5)+8>>3]+-.0001:0){e=a+(f<<5)|0;if(+h[e>>3]>m)h[e>>3]=m;e=a+(f<<5)+16|0;if(+h[e>>3]<m)h[e>>3]=m}f=f+1|0}g=g+1|0}}return}function LM(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0.0,o=0.0,p=0.0,q=0.0,r=0;m=l;l=l+112|0;j=m+80|0;k=m+48|0;i=m+8|0;g=m;e=c[15712]|0;f=b+80|0;c[g>>2]=c[f>>2];w4(e,105268,g)|0;g=b+84|0;d=0;while(1){if((d|0)>=(c[f>>2]|0))break;r=c[g>>2]|0;q=+h[r+(d<<5)>>3];p=+h[r+(d<<5)+8>>3];o=+h[r+(d<<5)+16>>3];n=+h[r+(d<<5)+24>>3];c[i>>2]=d;h[i+8>>3]=q;h[i+16>>3]=p;h[i+24>>3]=o;h[i+32>>3]=n;w4(e,105279,i)|0;d=d+1|0}q=+h[b+8>>3];p=+h[b+16>>3];r=a[b+29>>0]|0?105310:105322;h[k>>3]=+h[b>>3];h[k+8>>3]=q;h[k+16>>3]=p;c[k+24>>2]=r;w4(e,105338,k)|0;p=+h[b+48>>3];q=+h[b+56>>3];r=a[b+69>>0]|0?105310:105322;h[j>>3]=+h[b+40>>3];h[j+8>>3]=p;h[j+16>>3]=q;c[j+24>>2]=r;w4(e,105389,j)|0;l=m;return}function MM(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;do if((b|0)>(c|0)&(d|0)>(a|0)){if(!((c|0)>(a|0)|(d|0)<(a|0))){a=d-a|0;break}if((b|0)<(c|0)|(b|0)>(d|0)){b=b-a|0;a=d-c|0;a=(b|0)<(a|0)?b:a;break}else{a=b-c|0;break}}else a=0;while(0);return a|0}function NM(a,b){a=a|0;b=b|0;return IM(a,b,1)|0}function OM(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;j=l;l=l+80|0;g=j;i=1;f=b;while(1){h=f;f=c[(c[f+16>>2]|0)+172>>2]|0;if(!((f|0)!=0&(h|0)!=(f|0)))break;else i=i+1|0}if((i|0)>=21)g=JK(i<<2)|0;h=0;f=b;while(1){if((h|0)>=(i|0))break;c[g+(h<<2)>>2]=f;h=h+1|0;f=c[(c[f+16>>2]|0)+172>>2]|0}PM(a,g,i,d,e);if((i|0)>20)U1(g);l=j;return}function PM(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0.0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0.0,M=0.0,N=0.0,P=0,Q=0;K=l;l=l+304|0;A=K+272|0;z=K+208|0;G=K+96|0;H=K+32|0;I=K+16|0;J=K;r=K+192|0;y=K+176|0;i=K+160|0;B=K+296|0;D=K+288|0;j=c[d>>2]|0;w=c[j>>2]&3;k=j+-48|0;E=c[((w|0)==2?j:k)+40>>2]|0;t=G+16|0;w=(c[(c[((w|0)==3?j:j+48|0)+40>>2]|0)+16>>2]|0)+16|0;x=j+16|0;F=(c[x>>2]|0)+16|0;c[z>>2]=c[w>>2];c[z+4>>2]=c[w+4>>2];c[z+8>>2]=c[w+8>>2];c[z+12>>2]=c[w+12>>2];c[A>>2]=c[F>>2];c[A+4>>2]=c[F+4>>2];c[A+8>>2]=c[F+8>>2];c[A+12>>2]=c[F+12>>2];QM(r,z,A);c[G>>2]=c[r>>2];c[G+4>>2]=c[r+4>>2];c[G+8>>2]=c[r+8>>2];c[G+12>>2]=c[r+12>>2];c[t>>2]=c[r>>2];c[t+4>>2]=c[r+4>>2];c[t+8>>2]=c[r+8>>2];c[t+12>>2]=c[r+12>>2];c[I>>2]=c[r>>2];c[I+4>>2]=c[r+4>>2];c[I+8>>2]=c[r+8>>2];c[I+12>>2]=c[r+12>>2];r=G+32|0;F=G+48|0;w=(c[E+16>>2]|0)+16|0;x=(c[x>>2]|0)+56|0;c[z>>2]=c[w>>2];c[z+4>>2]=c[w+4>>2];c[z+8>>2]=c[w+8>>2];c[z+12>>2]=c[w+12>>2];c[A>>2]=c[x>>2];c[A+4>>2]=c[x+4>>2];c[A+8>>2]=c[x+8>>2];c[A+12>>2]=c[x+12>>2];QM(y,z,A);c[F>>2]=c[y>>2];c[F+4>>2]=c[y+4>>2];c[F+8>>2]=c[y+8>>2];c[F+12>>2]=c[y+12>>2];c[r>>2]=c[y>>2];c[r+4>>2]=c[y+4>>2];c[r+8>>2]=c[y+8>>2];c[r+12>>2]=c[y+12>>2];c[J>>2]=c[y>>2];c[J+4>>2]=c[y+4>>2];c[J+8>>2]=c[y+8>>2];c[J+12>>2]=c[y+12>>2];a:do if((e|0)!=1&(a[194954]|0)==0){m=+h[G>>3];n=+h[F>>3];L=m-n;o=+h[G+8>>3];p=+h[G+56>>3];s=o-p;q=s*s;if(q+L*L<1.0e-06){c[t>>2]=c[G>>2];c[t+4>>2]=c[G+4>>2];c[t+8>>2]=c[G+8>>2];c[t+12>>2]=c[G+12>>2];c[r>>2]=c[F>>2];c[r+4>>2]=c[F+4>>2];c[r+8>>2]=c[F+8>>2];c[r+12>>2]=c[F+12>>2];y=G+24|0;x=G+40|0;n=0.0;m=0.0}else{M=n-m;L=+C(+(q+M*M));w=c[(c[(c[b+60>>2]|0)+16>>2]|0)+248>>2]|0;N=+((O(w,e+-1|0)|0)/2|0|0);q=s*N/L;h[t>>3]=q+m;m=M*N/L;y=G+24|0;h[y>>3]=m+o;h[r>>3]=q+n;x=G+40|0;h[x>>3]=p+m;m=+(0-w|0);n=M*m/L;m=s*m/L}f=(f|0)==6;u=D+4|0;v=B+4|0;j=0;while(1){if((j|0)>=(e|0))break a;w=c[d+(j<<2)>>2]|0;k=w+-48|0;b:do if((c[((c[w>>2]&3|0)==2?w:k)+40>>2]|0)==(E|0)){c[I>>2]=c[G>>2];c[I+4>>2]=c[G+4>>2];c[I+8>>2]=c[G+8>>2];c[I+12>>2]=c[G+12>>2];c[J>>2]=c[F>>2];c[J+4>>2]=c[F+4>>2];c[J+8>>2]=c[F+8>>2];c[J+12>>2]=c[F+12>>2];i=0;while(1){if((i|0)==4)break b;P=H+(i<<4)|0;Q=G+(i<<4)|0;c[P>>2]=c[Q>>2];c[P+4>>2]=c[Q+4>>2];c[P+8>>2]=c[Q+8>>2];c[P+12>>2]=c[Q+12>>2];i=i+1|0}}else{c[I>>2]=c[F>>2];c[I+4>>2]=c[F+4>>2];c[I+8>>2]=c[F+8>>2];c[I+12>>2]=c[F+12>>2];c[J>>2]=c[G>>2];c[J+4>>2]=c[G+4>>2];c[J+8>>2]=c[G+8>>2];c[J+12>>2]=c[G+12>>2];i=0;while(1){if((i|0)==4)break b;Q=H+(3-i<<4)|0;P=G+(i<<4)|0;c[Q>>2]=c[P>>2];c[Q+4>>2]=c[P+4>>2];c[Q+8>>2]=c[P+8>>2];c[Q+12>>2]=c[P+12>>2];i=i+1|0}}while(0);if(f){c[u>>2]=4;c[D>>2]=z;i=0;while(1){if((i|0)==4)break;Q=z+(i<<4)|0;P=H+(i<<4)|0;c[Q>>2]=c[P>>2];c[Q+4>>2]=c[P+4>>2];c[Q+8>>2]=c[P+8>>2];c[Q+12>>2]=c[P+12>>2];i=i+1|0}c[A>>2]=c[D>>2];c[A+4>>2]=c[D+4>>2];FY(A,B);dO(w,c[((c[w>>2]&3|0)==2?w:k)+40>>2]|0,c[B>>2]|0,c[v>>2]|0,g)}else dO(w,c[((c[w>>2]&3|0)==2?w:k)+40>>2]|0,H,4,g);c[z>>2]=c[I>>2];c[z+4>>2]=c[I+4>>2];c[z+8>>2]=c[I+8>>2];c[z+12>>2]=c[I+12>>2];c[A>>2]=c[J>>2];c[A+4>>2]=c[J+4>>2];c[A+8>>2]=c[J+8>>2];c[A+12>>2]=c[J+12>>2];yO(b,w,z,A);h[t>>3]=+h[t>>3]+m;h[y>>3]=+h[y>>3]+n;h[r>>3]=+h[r>>3]+m;h[x>>3]=+h[x>>3]+n;j=j+1|0}}else{if((f|0)==4){RM(i,b);c[A>>2]=c[i>>2];c[A+4>>2]=c[i+4>>2];c[A+8>>2]=c[i+8>>2];c[A+12>>2]=c[i+12>>2];SM(G,A)}dO(j,c[((c[j>>2]&3|0)==2?j:k)+40>>2]|0,G,4,g);c[z>>2]=c[I>>2];c[z+4>>2]=c[I+4>>2];c[z+8>>2]=c[I+8>>2];c[z+12>>2]=c[I+12>>2];c[A>>2]=c[J>>2];c[A+4>>2]=c[J+4>>2];c[A+8>>2]=c[J+8>>2];c[A+12>>2]=c[J+12>>2];yO(b,j,z,A)}while(0);l=K;return}function QM(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[c+8>>3]+ +h[b+8>>3];h[a>>3]=+h[c>>3]+ +h[b>>3];h[a+8>>3]=d;return}function RM(a,b){a=a|0;b=b|0;b=c[b+16>>2]|0;h[23305]=(+h[b+32>>3]+ +h[b+16>>3])*.5;h[23306]=(+h[b+40>>3]+ +h[b+24>>3])*.5;c[a>>2]=c[46610];c[a+4>>2]=c[46611];c[a+8>>2]=c[46612];c[a+12>>2]=c[46613];return}function SM(a,b){a=a|0;b=b|0;var c=0.0,d=0.0,e=0.0,f=0.0,g=0.0,i=0.0;f=+h[a>>3];d=+h[a+48>>3];i=(d+f)*.5;g=+h[a+8>>3];c=+h[a+56>>3];e=(c+g)*.5;f=d-f;g=c-g;f=+C(+(g*g+f*f))*.2;g=+h[b>>3]-i;c=+h[b+8>>3]-e;d=+C(+(c*c+g*g));if(!(d==0.0)){g=i-g/d*f;i=e-c/d*f;h[a+32>>3]=g;h[a+16>>3]=g;h[a+40>>3]=i;h[a+24>>3]=i}return}function TM(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0.0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,D=0,E=0,F=0.0,G=0,H=0.0,I=0.0,J=0.0;E=l;l=l+208|0;t=E+192|0;s=E+176|0;B=E+32|0;w=E+16|0;x=E;y=E+160|0;z=E+144|0;u=E+128|0;v=E+112|0;g=e&2130706432;A=(e&8|0)==0?((g|0)==0?4:g):8;if((A|0)==436207616)UM(a,b,d,f);else{D=JK((d<<6)+64|0)|0;p=d+-1|0;q=x+8|0;r=w+8|0;o=12.0;e=0;while(1){if((e|0)>=(d|0))break;n=b+(e<<4)|0;c[w>>2]=c[n>>2];c[w+4>>2]=c[n+4>>2];c[w+8>>2]=c[n+8>>2];c[w+12>>2]=c[n+12>>2];if((e|0)<(p|0)){e=e+1|0;n=b+(e<<4)|0;c[x>>2]=c[n>>2];c[x+4>>2]=c[n+4>>2];c[x+8>>2]=c[n+8>>2];c[x+12>>2]=c[n+12>>2]}else{c[x>>2]=c[b>>2];c[x+4>>2]=c[b+4>>2];c[x+8>>2]=c[b+8>>2];c[x+12>>2]=c[b+12>>2];e=e+1|0}m=+h[x>>3]-+h[w>>3];F=+h[q>>3]-+h[r>>3];m=+C(+(F*F+m*m))*.3333333333333333;o=o<m?o:m}k=(g|134217728|0)==83886080;n=(A|0)==4;j=(g|0)==134217728;e=0;i=0;while(1){if((i|0)>=(d|0))break;g=b+(i<<4)|0;c[w>>2]=c[g>>2];c[w+4>>2]=c[g+4>>2];c[w+8>>2]=c[g+8>>2];c[w+12>>2]=c[g+12>>2];if((i|0)<(p|0)){g=b+(i+1<<4)|0;c[x>>2]=c[g>>2];c[x+4>>2]=c[g+4>>2];c[x+8>>2]=c[g+8>>2];c[x+12>>2]=c[g+12>>2]}else{c[x>>2]=c[b>>2];c[x+4>>2]=c[b+4>>2];c[x+8>>2]=c[b+8>>2];c[x+12>>2]=c[b+12>>2]}m=+h[x>>3]-+h[w>>3];F=+h[q>>3]-+h[r>>3];m=o/+C(+(F*F+m*m));m=k?m*.3333333333333333:j?m*.5:m;g=D+(e<<4)|0;if(n){c[s>>2]=c[w>>2];c[s+4>>2]=c[w+4>>2];c[s+8>>2]=c[w+8>>2];c[s+12>>2]=c[w+12>>2];c[t>>2]=c[x>>2];c[t+4>>2]=c[x+4>>2];c[t+8>>2]=c[x+8>>2];c[t+12>>2]=c[x+12>>2];VM(y,m*.5,s,t);c[g>>2]=c[y>>2];c[g+4>>2]=c[y+4>>2];c[g+8>>2]=c[y+8>>2];c[g+12>>2]=c[y+12>>2]}else{c[g>>2]=c[w>>2];c[g+4>>2]=c[w+4>>2];c[g+8>>2]=c[w+8>>2];c[g+12>>2]=c[w+12>>2]}g=D+(e+1<<4)|0;c[s>>2]=c[w>>2];c[s+4>>2]=c[w+4>>2];c[s+8>>2]=c[w+8>>2];c[s+12>>2]=c[w+12>>2];c[t>>2]=c[x>>2];c[t+4>>2]=c[x+4>>2];c[t+8>>2]=c[x+8>>2];c[t+12>>2]=c[x+12>>2];VM(z,m,s,t);c[g>>2]=c[z>>2];c[g+4>>2]=c[z+4>>2];c[g+8>>2]=c[z+8>>2];c[g+12>>2]=c[z+12>>2];g=e+3|0;G=D+(e+2<<4)|0;c[s>>2]=c[w>>2];c[s+4>>2]=c[w+4>>2];c[s+8>>2]=c[w+8>>2];c[s+12>>2]=c[w+12>>2];c[t>>2]=c[x>>2];c[t+4>>2]=c[x+4>>2];c[t+8>>2]=c[x+8>>2];c[t+12>>2]=c[x+12>>2];VM(u,1.0-m,s,t);c[G>>2]=c[u>>2];c[G+4>>2]=c[u+4>>2];c[G+8>>2]=c[u+8>>2];c[G+12>>2]=c[u+12>>2];if(n){G=D+(g<<4)|0;c[s>>2]=c[w>>2];c[s+4>>2]=c[w+4>>2];c[s+8>>2]=c[w+8>>2];c[s+12>>2]=c[w+12>>2];c[t>>2]=c[x>>2];c[t+4>>2]=c[x+4>>2];c[t+8>>2]=c[x+8>>2];c[t+12>>2]=c[x+12>>2];VM(v,1.0-m*.5,s,t);c[G>>2]=c[v>>2];c[G+4>>2]=c[v+4>>2];c[G+8>>2]=c[v+8>>2];c[G+12>>2]=c[v+12>>2];e=e+4|0}else e=g;i=i+1|0}k=D+(e<<4)|0;c[k>>2]=c[D>>2];c[k+4>>2]=c[D+4>>2];c[k+8>>2]=c[D+8>>2];c[k+12>>2]=c[D+12>>2];k=D+(e+1<<4)|0;j=D+16|0;c[k>>2]=c[j>>2];c[k+4>>2]=c[j+4>>2];c[k+8>>2]=c[j+8>>2];c[k+12>>2]=c[j+12>>2];e=D+(e+2<<4)|0;k=D+32|0;c[e>>2]=c[k>>2];c[e+4>>2]=c[k+4>>2];c[e+8>>2]=c[k+8>>2];c[e+12>>2]=c[k+12>>2];e=A&2147483647;a:do if((e|0)<201326592){if((e|0)>=83886080){if((e|0)<134217728){if((e|0)<100663296){switch(e|0){case 83886080:break;default:break a}if((d|0)==4){G=JK(192)|0;c[G>>2]=c[b>>2];c[G+4>>2]=c[b+4>>2];c[G+8>>2]=c[b+8>>2];c[G+12>>2]=c[b+12>>2];p=G+16|0;t=b+16|0;c[p>>2]=c[t>>2];c[p+4>>2]=c[t+4>>2];c[p+8>>2]=c[t+8>>2];c[p+12>>2]=c[t+12>>2];p=D+48|0;t=D+64|0;o=+h[t>>3];n=G+32|0;h[n>>3]=o;q=D+56|0;r=D+72|0;H=+h[r>>3];z=G+40|0;h[z>>3]=H;F=+h[p>>3]+o-+h[k>>3];u=G+48|0;h[u>>3]=F;J=+h[q>>3]+H-+h[D+40>>3];x=G+56|0;h[x>>3]=J;I=+h[t>>3]+F-+h[p>>3];p=G+64|0;h[p>>3]=I;m=+h[r>>3]+J-+h[q>>3];q=G+72|0;h[q>>3]=m;r=G+80|0;h[r>>3]=o+I-F;h[G+88>>3]=H+m-J;t=D+96|0;v=D+80|0;J=+h[v>>3];d=G+144|0;h[d>>3]=J;w=D+104|0;s=D+88|0;m=+h[s>>3];h[G+152>>3]=m;H=+h[t>>3]+J-+h[D+112>>3];y=G+128|0;h[y>>3]=H;F=+h[w>>3]+m-+h[D+120>>3];A=G+136|0;h[A>>3]=F;I=+h[v>>3]+H-+h[t>>3];t=G+112|0;h[t>>3]=I;o=+h[s>>3]+F-+h[w>>3];w=G+120|0;h[w>>3]=o;s=G+96|0;h[s>>3]=J+I-H;v=G+104|0;h[v>>3]=m+o-F;k=G+160|0;j=b+32|0;c[k>>2]=c[j>>2];c[k+4>>2]=c[j+4>>2];c[k+8>>2]=c[j+8>>2];c[k+12>>2]=c[j+12>>2];k=G+176|0;b=b+48|0;c[k>>2]=c[b>>2];c[k+4>>2]=c[b+4>>2];c[k+8>>2]=c[b+8>>2];c[k+12>>2]=c[b+12>>2];nS(a,G,12,f);c[B>>2]=c[n>>2];c[B+4>>2]=c[n+4>>2];c[B+8>>2]=c[n+8>>2];c[B+12>>2]=c[n+12>>2];F=+h[n>>3];o=+h[u>>3];F=F+F-o;u=B+16|0;h[u>>3]=F;m=+h[z>>3];H=+h[x>>3];m=m+m-H;x=B+24|0;h[x>>3]=m;z=B+32|0;h[z>>3]=+h[p>>3]+F-o;b=B+40|0;h[b>>3]=+h[q>>3]+m-H;f=B+48|0;c[f>>2]=c[r>>2];c[f+4>>2]=c[r+4>>2];c[f+8>>2]=c[r+8>>2];c[f+12>>2]=c[r+12>>2];qS(a,B,4);c[B>>2]=c[s>>2];c[B+4>>2]=c[s+4>>2];c[B+8>>2]=c[s+8>>2];c[B+12>>2]=c[s+12>>2];H=+h[s>>3];m=+h[t>>3];H=H-(m-H);h[u>>3]=H;o=+h[v>>3];F=+h[w>>3];o=o-(F-o);h[x>>3]=o;h[z>>3]=+h[y>>3]+H-m;h[b>>3]=+h[A>>3]+o-F;c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];qS(a,B,4);U1(G);break}else qa(105438,105449,737,105458)}if((e|0)<117440512){switch(e|0){case 100663296:break;default:break a}y=d+5|0;G=JK(y<<4)|0;z=b+16|0;H=+h[z>>3];H=(+h[b>>3]-H)*.625+H;h[G>>3]=H;A=b+40|0;J=+h[A>>3];d=b+24|0;v=D+48|0;w=D+56|0;x=D+72|0;J=(+h[d>>3]-J)*.5+J+(+h[w>>3]-+h[x>>3])*1.5;h[G+8>>3]=J;I=+h[z>>3];I=(+h[b>>3]-I)*.25+I;h[G+16>>3]=I;h[G+24>>3]=J;h[G+32>>3]=I;F=+h[A>>3];h[G+40>>3]=(+h[d>>3]-F)*.5+F;I=(+h[k>>3]-+h[v>>3])*.5+I;h[G+48>>3]=I;F=+h[A>>3];h[G+56>>3]=(+h[d>>3]-F)*.5+F;h[G+64>>3]=I;I=+h[A>>3];I=(+h[d>>3]-I)*.5+I+(+h[w>>3]-+h[x>>3]);h[G+72>>3]=I;h[G+80>>3]=H;h[G+88>>3]=I;h[G+96>>3]=H;I=I-(+h[w>>3]-+h[x>>3])*.25;h[G+104>>3]=I;h[G+112>>3]=+h[k>>3]+H-+h[v>>3];h[G+120>>3]=(+h[w>>3]-+h[x>>3])*.5+I;h[G+128>>3]=+h[G>>3];h[G+136>>3]=(+h[w>>3]-+h[x>>3])*.25+J;nS(a,G,y,f);h[B>>3]=+h[z>>3];J=+h[A>>3];h[B+8>>3]=(+h[d>>3]-J)*.5+J;h[B+16>>3]=+h[b>>3];h[B+24>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}else{switch(e|0){case 117440512:break;default:break a}d=d+1|0;G=JK(d<<4)|0;h[G>>3]=+h[j>>3];z=D+56|0;A=D+72|0;h[G+8>>3]=+h[D+24>>3]-(+h[z>>3]-+h[A>>3])*.5;h[G+16>>3]=+h[D+48>>3];J=+h[z>>3];h[G+24>>3]=J-(J-+h[A>>3])*.5;h[G+32>>3]=+h[b+32>>3];B=b+40|0;h[G+40>>3]=(+h[z>>3]-+h[A>>3])*.5+ +h[B>>3];h[G+48>>3]=+h[j>>3];h[G+56>>3]=(+h[z>>3]-+h[A>>3])*.5+ +h[B>>3];J=+h[b+8>>3];h[G+72>>3]=J-(J-+h[b+56>>3])*.5;h[G+64>>3]=+h[b>>3];nS(a,G,d,f);U1(G);break}}if((e|0)<1342177280)if((e|0)<150994944){switch(e|0){case 134217728:break;default:break a}y=d+4|0;G=JK(y<<4)|0;z=b+16|0;F=+h[z>>3];x=D+48|0;F=(+h[b>>3]-F)*.5+F+(+h[k>>3]-+h[x>>3])*.25;h[G>>3]=F;A=b+40|0;J=+h[A>>3];d=b+24|0;J=(+h[d>>3]-J)*.5+J;h[G+8>>3]=J;h[G+16>>3]=F;v=D+56|0;w=D+72|0;H=(+h[v>>3]-+h[w>>3])*.5+J;h[G+24>>3]=H;F=(+h[k>>3]-+h[x>>3])*.5+F;h[G+32>>3]=F;h[G+40>>3]=H;h[G+48>>3]=F;F=(+h[v>>3]-+h[w>>3])*.5+H;h[G+56>>3]=F;I=+h[z>>3];I=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[x>>3])*-.75;h[G+64>>3]=I;h[G+72>>3]=F;h[G+80>>3]=I;h[G+88>>3]=H;I=+h[z>>3];I=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[x>>3])*-.25;h[G+96>>3]=I;h[G+104>>3]=H;h[G+112>>3]=I;h[G+120>>3]=J;nS(a,G,y,f);h[B>>3]=+h[z>>3];J=+h[A>>3];h[B+8>>3]=(+h[d>>3]-J)*.5+J;h[B+16>>3]=+h[b>>3];h[B+24>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}else{switch(e|0){case 150994944:break;default:break a}y=d+2|0;G=JK(y<<4)|0;z=b+16|0;H=+h[z>>3];x=D+48|0;H=(+h[b>>3]-H)*.5+H+(+h[k>>3]-+h[x>>3])*.75;h[G>>3]=H;A=b+40|0;J=+h[A>>3];d=b+24|0;J=(+h[d>>3]-J)*.5+J;h[G+8>>3]=J;h[G+16>>3]=H;v=D+56|0;w=D+72|0;H=(+h[v>>3]-+h[w>>3])*.25+J;h[G+24>>3]=H;I=+h[z>>3];h[G+32>>3]=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[x>>3])*.25;I=(+h[v>>3]-+h[w>>3])*.5+H;h[G+40>>3]=I;F=+h[z>>3];h[G+48>>3]=(+h[b>>3]-F)*.5+F+(+h[k>>3]-+h[x>>3])*-.25;h[G+56>>3]=I;I=+h[z>>3];I=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[x>>3])*-.75;h[G+64>>3]=I;h[G+72>>3]=H;h[G+80>>3]=I;h[G+88>>3]=J;nS(a,G,y,f);h[B>>3]=+h[z>>3];J=+h[A>>3];h[B+8>>3]=(+h[d>>3]-J)*.5+J;h[B+16>>3]=+h[b>>3];h[B+24>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}else if((e|0)<184549376){switch(e|0){case 1342177280:break;default:break a}y=d+1|0;G=JK(y<<4)|0;z=b+16|0;H=+h[z>>3];v=D+48|0;H=+h[k>>3]+H+(+h[b>>3]-H)*.5-+h[v>>3];h[G>>3]=H;A=b+40|0;J=+h[A>>3];d=b+24|0;w=D+56|0;x=D+72|0;J=(+h[d>>3]-J)*.5+J+(+h[w>>3]-+h[x>>3])*.25;h[G+8>>3]=J;H=H-+h[k>>3]+ +h[v>>3];h[G+16>>3]=H;h[G+24>>3]=+h[w>>3]+J-+h[x>>3];h[G+32>>3]=H;H=(+h[w>>3]-+h[x>>3])*.5+J;h[G+40>>3]=H;I=+h[z>>3];I=(+h[b>>3]-I)*.25+I;h[G+48>>3]=I;h[G+56>>3]=H;h[G+64>>3]=I;h[G+72>>3]=J;nS(a,G,y,f);h[B>>3]=+h[z>>3];J=+h[A>>3];h[B+8>>3]=(+h[d>>3]-J)*.5+J;h[B+16>>3]=+h[b>>3];h[B+24>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}else{switch(e|0){case 184549376:break;default:break a}z=d+4|0;G=JK(z<<4)|0;y=b+16|0;H=+h[y>>3];I=+h[b>>3]-H;u=D+48|0;I=I*.125+H+(+h[k>>3]-+h[u>>3]+I)*.5;h[G>>3]=I;w=b+40|0;H=+h[w>>3];x=b+24|0;d=D+56|0;v=D+72|0;H=(+h[x>>3]-H)*.5+H+(+h[d>>3]-+h[v>>3])*.25;h[G+8>>3]=H;J=+h[y>>3];J=(+h[b>>3]-J)*.375+J;h[G+16>>3]=J;h[G+24>>3]=H;h[G+32>>3]=J;H=(+h[d>>3]-+h[v>>3])*.5+H;h[G+40>>3]=H;J=J-(+h[k>>3]-+h[u>>3])*.5;h[G+48>>3]=J;h[G+56>>3]=H;A=G+64|0;h[A>>3]=J;J=+h[w>>3];J=(+h[x>>3]-J)*.5+J+(+h[d>>3]-+h[v>>3])*-.25;h[G+72>>3]=J;H=I-(+h[k>>3]-+h[u>>3])*.5;h[G+80>>3]=H;h[G+88>>3]=J;h[G+96>>3]=H;J=J-(+h[d>>3]-+h[v>>3])*.5;h[G+104>>3]=J;v=G+112|0;h[v>>3]=I;h[G+120>>3]=J;nS(a,G,z,f);h[B>>3]=+h[y>>3];J=+h[w>>3];y=B+8|0;h[y>>3]=(+h[x>>3]-J)*.5+J;z=B+16|0;h[z>>3]=+h[A>>3];A=b+8|0;d=b+56|0;f=B+24|0;h[f>>3]=(+h[A>>3]-+h[d>>3])*.5+J;qS(a,B,2);h[B>>3]=+h[v>>3];J=+h[w>>3];h[y>>3]=(+h[x>>3]-J)*.5+J;h[z>>3]=+h[b>>3];h[f>>3]=(+h[A>>3]-+h[d>>3])*.5+J;qS(a,B,2);U1(G);break}}if((e|0)<33554432){if((e|0)<8){switch(e|0){case 4:break;default:break a}i=KK((d*96|0)+32|0)|0;e=0;g=0;while(1){j=g|1;k=i+(g<<4)|0;if((e|0)>=(d|0))break;b=e<<2;G=D+(b<<4)|0;c[k>>2]=c[G>>2];c[k+4>>2]=c[G+4>>2];c[k+8>>2]=c[G+8>>2];c[k+12>>2]=c[G+12>>2];G=i+(j<<4)|0;B=D+((b|1)<<4)|0;c[G>>2]=c[B>>2];c[G+4>>2]=c[B+4>>2];c[G+8>>2]=c[B+8>>2];c[G+12>>2]=c[B+12>>2];G=i+(g+2<<4)|0;c[G>>2]=c[B>>2];c[G+4>>2]=c[B+4>>2];c[G+8>>2]=c[B+8>>2];c[G+12>>2]=c[B+12>>2];G=i+(g+3<<4)|0;B=D+((b|2)<<4)|0;c[G>>2]=c[B>>2];c[G+4>>2]=c[B+4>>2];c[G+8>>2]=c[B+8>>2];c[G+12>>2]=c[B+12>>2];G=i+(g+4<<4)|0;c[G>>2]=c[B>>2];c[G+4>>2]=c[B+4>>2];c[G+8>>2]=c[B+8>>2];c[G+12>>2]=c[B+12>>2];G=i+(g+5<<4)|0;b=D+((b|3)<<4)|0;c[G>>2]=c[b>>2];c[G+4>>2]=c[b+4>>2];c[G+8>>2]=c[b+8>>2];c[G+12>>2]=c[b+12>>2];e=e+1|0;g=g+6|0}c[k>>2]=c[i>>2];c[k+4>>2]=c[i+4>>2];c[k+8>>2]=c[i+8>>2];c[k+12>>2]=c[i+12>>2];d=i+(j<<4)|0;G=i+16|0;c[d>>2]=c[G>>2];c[d+4>>2]=c[G+4>>2];c[d+8>>2]=c[G+8>>2];c[d+12>>2]=c[G+12>>2];pS(a,G,j,0,0,f&255);U1(i);break}if((e|0)<134217728){switch(e|0){case 8:break;default:break a}nS(a,b,d,f);g=B+16|0;e=0;while(1){if((e|0)>=(d|0))break a;G=e*3|0;f=D+(G+2<<4)|0;c[B>>2]=c[f>>2];c[B+4>>2]=c[f+4>>2];c[B+8>>2]=c[f+8>>2];c[B+12>>2]=c[f+12>>2];G=D+(G+4<<4)|0;c[g>>2]=c[G>>2];c[g+4>>2]=c[G+4>>2];c[g+8>>2]=c[G+8>>2];c[g+12>>2]=c[G+12>>2];qS(a,B,2);e=e+1|0}}switch(e|0){case 134217728:break;default:break a}g=d+1|0;i=JK(g<<4)|0;e=1;while(1){if((e|0)>=(d|0))break;G=i+(e<<4)|0;A=b+(e<<4)|0;c[G>>2]=c[A>>2];c[G+4>>2]=c[A+4>>2];c[G+8>>2]=c[A+8>>2];c[G+12>>2]=c[A+12>>2];e=e+1|0}b=d*3|0;A=D+(b+1<<4)|0;c[i>>2]=c[A>>2];c[i+4>>2]=c[A+4>>2];c[i+8>>2]=c[A+8>>2];c[i+12>>2]=c[A+12>>2];d=i+(d<<4)|0;G=D+(b+-1<<4)|0;c[d>>2]=c[G>>2];c[d+4>>2]=c[G+4>>2];c[d+8>>2]=c[G+8>>2];c[d+12>>2]=c[G+12>>2];nS(a,i,g,f);U1(i);c[B>>2]=c[G>>2];c[B+4>>2]=c[G+4>>2];c[B+8>>2]=c[G+8>>2];c[B+12>>2]=c[G+12>>2];G=B+16|0;c[G>>2]=c[A>>2];c[G+4>>2]=c[A+4>>2];c[G+8>>2]=c[A+8>>2];c[G+12>>2]=c[A+12>>2];f=B+32|0;h[f>>3]=+h[B>>3]+ +h[G>>3]-+h[D+(b<<4)>>3];h[B+40>>3]=+h[B+8>>3]+ +h[B+24>>3]-+h[D+(b<<4)+8>>3];qS(a,G,2);c[G>>2]=c[f>>2];c[G+4>>2]=c[f+4>>2];c[G+8>>2]=c[f+8>>2];c[G+12>>2]=c[f+12>>2];qS(a,B,2);break}else{if((e|0)<50331648){switch(e|0){case 33554432:break;default:break a}g=d+2|0;i=JK(g<<4)|0;c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];j=i+16|0;c[j>>2]=c[k>>2];c[j+4>>2]=c[k+4>>2];c[j+8>>2]=c[k+8>>2];c[j+12>>2]=c[k+12>>2];j=D+48|0;d=D+64|0;h[i+32>>3]=(+h[j>>3]-+h[d>>3])*.3333333333333333+ +h[k>>3];G=D+56|0;e=D+72|0;h[i+40>>3]=(+h[G>>3]-+h[e>>3])*.3333333333333333+ +h[D+40>>3];J=+h[j>>3];h[i+48>>3]=(J-+h[d>>3])*.3333333333333333+J;J=+h[G>>3];h[i+56>>3]=(J-+h[e>>3])*.3333333333333333+J;e=4;while(1){if((e|0)>=(g|0))break;G=i+(e<<4)|0;d=b+(e+-2<<4)|0;c[G>>2]=c[d>>2];c[G+4>>2]=c[d+4>>2];c[G+8>>2]=c[d+8>>2];c[G+12>>2]=c[d+12>>2];e=e+1|0}nS(a,i,g,f);U1(i);c[B>>2]=c[j>>2];c[B+4>>2]=c[j+4>>2];c[B+8>>2]=c[j+8>>2];c[B+12>>2]=c[j+12>>2];G=B+16|0;c[G>>2]=c[k>>2];c[G+4>>2]=c[k+4>>2];c[G+8>>2]=c[k+8>>2];c[G+12>>2]=c[k+12>>2];qS(a,B,2);break}if((e|0)>=67108864){switch(e|0){case 67108864:break;default:break a}if((d|0)==4){G=JK(96)|0;c[G>>2]=c[b>>2];c[G+4>>2]=c[b+4>>2];c[G+8>>2]=c[b+8>>2];c[G+12>>2]=c[b+12>>2];d=G+16|0;c[d>>2]=c[k>>2];c[d+4>>2]=c[k+4>>2];c[d+8>>2]=c[k+8>>2];c[d+12>>2]=c[k+12>>2];d=G+32|0;A=D+64|0;c[d>>2]=c[A>>2];c[d+4>>2]=c[A+4>>2];c[d+8>>2]=c[A+8>>2];c[d+12>>2]=c[A+12>>2];d=G+48|0;b=b+32|0;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];b=G+64|0;d=D+128|0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];b=G+80|0;z=D+160|0;c[b>>2]=c[z>>2];c[b+4>>2]=c[z+4>>2];c[b+8>>2]=c[z+8>>2];c[b+12>>2]=c[z+12>>2];nS(a,G,6,f);U1(G);h[B>>3]=+h[D+176>>3]+ +h[j>>3]-+h[D>>3];h[B+8>>3]=+h[D+184>>3]+ +h[D+24>>3]-+h[D+8>>3];G=B+16|0;c[G>>2]=c[A>>2];c[G+4>>2]=c[A+4>>2];c[G+8>>2]=c[A+8>>2];c[G+12>>2]=c[A+12>>2];qS(a,B,2);c[G>>2]=c[d>>2];c[G+4>>2]=c[d+4>>2];c[G+8>>2]=c[d+8>>2];c[G+12>>2]=c[d+12>>2];qS(a,B,2);c[G>>2]=c[D>>2];c[G+4>>2]=c[D+4>>2];c[G+8>>2]=c[D+8>>2];c[G+12>>2]=c[D+12>>2];qS(a,B,2);break}else qa(105438,105449,714,105458)}switch(e|0){case 50331648:break;default:break a}g=d+3|0;i=JK(g<<4)|0;c[i>>2]=c[b>>2];c[i+4>>2]=c[b+4>>2];c[i+8>>2]=c[b+8>>2];c[i+12>>2]=c[b+12>>2];J=+h[b>>3];h[i+16>>3]=J-(J-+h[j>>3])*.25;e=D+56|0;J=(+h[e>>3]-+h[D+72>>3])*.3333333333333333+ +h[b+8>>3];h[i+24>>3]=J;I=+h[b>>3];h[i+32>>3]=I-(I-+h[j>>3])*2.0;h[i+40>>3]=J;J=+h[b>>3];h[i+48>>3]=J-(J-+h[j>>3])*2.25;h[i+56>>3]=+h[e>>3];h[i+64>>3]=+h[D+48>>3];h[i+72>>3]=+h[e>>3];e=4;while(1){if((e|0)>=(g|0))break;G=i+(e<<4)|0;d=b+(e+-3<<4)|0;c[G>>2]=c[d>>2];c[G+4>>2]=c[d+4>>2];c[G+8>>2]=c[d+8>>2];c[G+12>>2]=c[d+12>>2];e=e+1|0}nS(a,i,g,f);U1(i);break}}else if((e|0)<318767104){if((e|0)<251658240){if((e|0)<218103808){switch(e|0){case 201326592:break;default:break a}G=d<<4;u=JK(G)|0;v=b+16|0;J=+h[v>>3];h[u>>3]=J;z=b+40|0;I=+h[z>>3];A=b+24|0;y=D+48|0;w=D+56|0;x=D+72|0;I=(+h[A>>3]-I)*.5+I+(+h[w>>3]-+h[x>>3])*.125;h[u+8>>3]=I;H=(+h[k>>3]-+h[y>>3])*2.0+J;h[u+16>>3]=H;h[u+24>>3]=I;h[u+32>>3]=H;I=(+h[w>>3]-+h[x>>3])*.5+I;h[u+40>>3]=I;h[u+48>>3]=J;h[u+56>>3]=I;nS(a,u,d,f);U1(u);G=JK(G)|0;I=+h[k>>3]+ +h[v>>3]-+h[y>>3];h[G>>3]=I;J=+h[z>>3];J=(+h[A>>3]-J)*.5+J+(+h[w>>3]-+h[x>>3])*-.625;h[G+8>>3]=J;H=+h[k>>3]-+h[y>>3]+I;y=G+16|0;h[y>>3]=H;h[G+24>>3]=J;h[G+32>>3]=H;J=(+h[w>>3]-+h[x>>3])*.5+J;h[G+40>>3]=J;h[G+48>>3]=I;h[G+56>>3]=J;nS(a,G,d,f);h[B>>3]=+h[y>>3];J=+h[z>>3];h[B+8>>3]=(+h[A>>3]-J)*.5+J;h[B+16>>3]=+h[b>>3];h[B+24>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}if((e|0)<234881024){switch(e|0){case 218103808:break;default:break a}G=d<<4;w=JK(G)|0;J=+h[b>>3];h[w>>3]=J;y=b+40|0;I=+h[y>>3];z=b+24|0;x=D+56|0;A=D+72|0;I=(+h[z>>3]-I)*.5+I+(+h[x>>3]-+h[A>>3])*.125;h[w+8>>3]=I;h[w+16>>3]=J;H=(+h[x>>3]-+h[A>>3])*.5+I;h[w+24>>3]=H;J=J-(+h[x>>3]-+h[A>>3])*2.0;h[w+32>>3]=J;h[w+40>>3]=H;h[w+48>>3]=J;h[w+56>>3]=I;nS(a,w,d,f);U1(w);G=JK(G)|0;I=+h[b>>3]-+h[k>>3]+ +h[D+48>>3];h[G>>3]=I;J=+h[y>>3];J=(+h[z>>3]-J)*.5+J+(+h[x>>3]-+h[A>>3])*-.625;h[G+8>>3]=J;h[G+16>>3]=I;H=(+h[x>>3]-+h[A>>3])*.5+J;h[G+24>>3]=H;I=I-(+h[x>>3]-+h[A>>3]);h[G+32>>3]=I;h[G+40>>3]=H;A=G+48|0;h[A>>3]=I;h[G+56>>3]=J;nS(a,G,d,f);h[B>>3]=+h[b+16>>3];J=+h[y>>3];h[B+8>>3]=(+h[z>>3]-J)*.5+J;h[B+16>>3]=+h[A>>3];h[B+24>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}else{switch(e|0){case 234881024:break;default:break a}G=d<<4;y=JK(G)|0;u=b+16|0;I=+h[u>>3];v=D+48|0;I=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[v>>3])*-1.125;h[y>>3]=I;w=b+40|0;J=+h[w>>3];x=b+24|0;A=D+56|0;z=D+72|0;J=(+h[x>>3]-J)*.5+J+(+h[A>>3]-+h[z>>3])*.125;h[y+8>>3]=J;H=+h[k>>3]-+h[v>>3]+I;h[y+16>>3]=H;h[y+24>>3]=J;h[y+32>>3]=H;J=(+h[A>>3]-+h[z>>3])*.5+J;h[y+40>>3]=J;h[y+48>>3]=I;h[y+56>>3]=J;nS(a,y,d,f);U1(y);y=JK(G)|0;J=+h[u>>3];J=(+h[b>>3]-J)*.5+J+(+h[k>>3]-+h[v>>3])*-1.125;h[y>>3]=J;I=+h[w>>3];I=(+h[x>>3]-I)*.5+I+(+h[A>>3]-+h[z>>3])*-.625;h[y+8>>3]=I;H=+h[k>>3]-+h[v>>3]+J;h[y+16>>3]=H;h[y+24>>3]=I;h[y+32>>3]=H;I=(+h[A>>3]-+h[z>>3])*.5+I;h[y+40>>3]=I;h[y+48>>3]=J;h[y+56>>3]=I;nS(a,y,d,f);U1(y);y=JK(G)|0;I=+h[u>>3];I=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[v>>3])*.125;h[y>>3]=I;J=+h[w>>3];J=(+h[x>>3]-J)*.5+J+(+h[A>>3]-+h[z>>3])*-.625;h[y+8>>3]=J;H=+h[k>>3]-+h[v>>3]+I;h[y+16>>3]=H;h[y+24>>3]=J;h[y+32>>3]=H;J=(+h[A>>3]-+h[z>>3])*.5+J;h[y+40>>3]=J;h[y+48>>3]=I;h[y+56>>3]=J;nS(a,y,d,f);U1(y);G=JK(G)|0;J=+h[u>>3];J=(+h[b>>3]-J)*.5+J+(+h[k>>3]-+h[v>>3])*.125;h[G>>3]=J;I=+h[w>>3];I=(+h[x>>3]-I)*.5+I+(+h[A>>3]-+h[z>>3])*.125;h[G+8>>3]=I;H=+h[k>>3]-+h[v>>3]+J;y=G+16|0;h[y>>3]=H;h[G+24>>3]=I;h[G+32>>3]=H;I=(+h[A>>3]-+h[z>>3])*.5+I;h[G+40>>3]=I;h[G+48>>3]=J;h[G+56>>3]=I;nS(a,G,d,f);h[B>>3]=+h[y>>3];I=+h[w>>3];y=B+8|0;h[y>>3]=(+h[x>>3]-I)*.5+I;z=B+16|0;h[z>>3]=+h[b>>3];A=b+8|0;d=b+56|0;f=B+24|0;h[f>>3]=(+h[A>>3]-+h[d>>3])*.5+I;qS(a,B,2);I=+h[u>>3];h[B>>3]=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[v>>3])*-1.125;J=+h[w>>3];h[y>>3]=(+h[x>>3]-J)*.5+J;h[z>>3]=I;h[f>>3]=(+h[A>>3]-+h[d>>3])*.5+J;qS(a,B,2);U1(G);break}}if((e|0)<285212672)if((e|0)<268435456){switch(e|0){case 251658240:break;default:break a}G=d<<4;u=JK(G)|0;v=b+16|0;J=+h[v>>3];y=D+48|0;J=J-+h[k>>3]+(+h[b>>3]-J)*.5+ +h[y>>3];h[u>>3]=J;w=b+40|0;I=+h[w>>3];x=b+24|0;A=D+56|0;z=D+72|0;I=(+h[x>>3]-I)*.5+I+(+h[A>>3]-+h[z>>3])*.125;h[u+8>>3]=I;H=(+h[k>>3]-+h[y>>3])*2.0+J;h[u+16>>3]=H;h[u+24>>3]=I;h[u+32>>3]=H;I=(+h[A>>3]-+h[z>>3])*.5+I;h[u+40>>3]=I;h[u+48>>3]=J;h[u+56>>3]=I;nS(a,u,d,f);U1(u);G=JK(G)|0;I=+h[v>>3];I=I-+h[k>>3]+(+h[b>>3]-I)*.5+ +h[y>>3];h[G>>3]=I;J=+h[w>>3];J=(+h[x>>3]-J)*.5+J+(+h[A>>3]-+h[z>>3])*-.625;h[G+8>>3]=J;H=(+h[k>>3]-+h[y>>3])*2.0+I;y=G+16|0;h[y>>3]=H;h[G+24>>3]=J;h[G+32>>3]=H;J=(+h[A>>3]-+h[z>>3])*.5+J;h[G+40>>3]=J;h[G+48>>3]=I;h[G+56>>3]=J;nS(a,G,d,f);h[B>>3]=+h[y>>3];J=+h[w>>3];y=B+8|0;h[y>>3]=(+h[x>>3]-J)*.5+J;z=B+16|0;h[z>>3]=+h[b>>3];A=b+8|0;d=b+56|0;f=B+24|0;h[f>>3]=(+h[A>>3]-+h[d>>3])*.5+J;qS(a,B,2);h[B>>3]=+h[v>>3];J=+h[w>>3];h[y>>3]=(+h[x>>3]-J)*.5+J;h[z>>3]=+h[G>>3];h[f>>3]=(+h[A>>3]-+h[d>>3])*.5+J;qS(a,B,2);U1(G);break}else{switch(e|0){case 268435456:break;default:break a}G=JK(d<<4)|0;h[G>>3]=+h[b>>3];v=D+48|0;x=D+56|0;y=D+72|0;h[G+8>>3]=+h[D+24>>3]-(+h[x>>3]-+h[y>>3])*.5;h[G+16>>3]=+h[v>>3];J=+h[x>>3];h[G+24>>3]=J-(J-+h[y>>3])*.5;h[G+32>>3]=+h[b+32>>3];z=b+40|0;h[G+40>>3]=(+h[x>>3]-+h[y>>3])*.5+ +h[z>>3];h[G+48>>3]=+h[b>>3];h[G+56>>3]=(+h[x>>3]-+h[y>>3])*.5+ +h[z>>3];nS(a,G,d,f);w=b+16|0;J=(+h[k>>3]-+h[v>>3])*.25;I=J+ +h[w>>3];h[B>>3]=I;H=+h[z>>3];u=b+24|0;F=+h[x>>3]-+h[y>>3];H=(+h[u>>3]-H)*.5+H+F*.125;A=B+8|0;h[A>>3]=H;d=B+16|0;h[d>>3]=J+I;f=B+24|0;h[f>>3]=H-F*.25;qS(a,B,2);F=(+h[k>>3]-+h[v>>3])*.25;H=F+ +h[w>>3];h[B>>3]=H;I=+h[z>>3];J=+h[x>>3]-+h[y>>3];I=(+h[u>>3]-I)*.5+I+J*-.125;h[A>>3]=I;h[d>>3]=F+H;h[f>>3]=J*.25+I;qS(a,B,2);I=(+h[k>>3]-+h[v>>3])*.25;h[B>>3]=I+ +h[w>>3];J=(+h[x>>3]-+h[y>>3])*.75+ +h[z>>3];h[A>>3]=J;h[d>>3]=+h[b>>3]-I;h[f>>3]=J;qS(a,B,2);U1(G);break}else if((e|0)<301989888){switch(e|0){case 285212672:break;default:break a}y=JK(d<<4)|0;v=b+16|0;I=+h[v>>3];z=D+48|0;I=(+h[k>>3]-+h[z>>3]+(+h[b>>3]-I))*.5+I;h[y>>3]=I;w=b+40|0;F=+h[w>>3];x=b+24|0;F=(+h[k>>3]-+h[z>>3]+(+h[x>>3]-F))*.5+F;h[y+8>>3]=F;h[y+16>>3]=I;I=+h[w>>3];o=+h[k>>3]-+h[z>>3];I=(+h[x>>3]-I-o)*.5+I;h[y+24>>3]=I;J=+h[v>>3];J=(+h[b>>3]-J-o)*.5+J;h[y+32>>3]=J;h[y+40>>3]=I;h[y+48>>3]=J;h[y+56>>3]=F;nS(a,y,d,f);U1(y);F=+h[v>>3];F=(+h[b>>3]-F)*.5+F;J=+h[k>>3]-+h[z>>3];I=J*.75;o=F+I;h[B>>3]=o;H=+h[w>>3];H=(+h[x>>3]-H)*.5+H;I=H+I;y=B+8|0;h[y>>3]=I;A=B+16|0;h[A>>3]=o;J=J*-.75;H=H+J;G=B+24|0;h[G>>3]=H;J=F+J;h[B+32>>3]=J;h[B+40>>3]=H;h[B+48>>3]=J;h[B+56>>3]=I;d=B+64|0;c[d>>2]=c[B>>2];c[d+4>>2]=c[B+4>>2];c[d+8>>2]=c[B+8>>2];c[d+12>>2]=c[B+12>>2];qS(a,B,5);I=+h[v>>3];J=+h[b>>3];h[B>>3]=(J-I)*.5+I+(+h[k>>3]-+h[z>>3])*.75;I=+h[w>>3];h[y>>3]=(+h[x>>3]-I)*.5+I;h[A>>3]=J;d=b+8|0;f=b+56|0;h[G>>3]=(+h[d>>3]-+h[f>>3])*.5+I;qS(a,B,2);I=+h[v>>3];h[B>>3]=I;J=+h[w>>3];h[y>>3]=(+h[x>>3]-J)*.5+J;h[A>>3]=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[z>>3])*-.75;h[G>>3]=(+h[d>>3]-+h[f>>3])*.5+J;qS(a,B,2);break}else{switch(e|0){case 301989888:break;default:break a}A=d+12|0;G=JK(A<<4)|0;x=b+16|0;m=+h[x>>3];s=D+48|0;m=(+h[b>>3]-m)*.5+m+(+h[k>>3]-+h[s>>3])*.25;h[G>>3]=m;y=b+40|0;F=+h[y>>3];z=b+24|0;v=D+56|0;w=D+72|0;F=(+h[v>>3]-+h[w>>3]+(+h[z>>3]-F))*.5+F;u=G+8|0;h[u>>3]=F;h[G+16>>3]=m;F=(+h[v>>3]-+h[w>>3])*.125+F;t=G+24|0;h[t>>3]=F;H=m-(+h[k>>3]-+h[s>>3])*.125;d=G+32|0;h[d>>3]=H;o=(+h[v>>3]-+h[w>>3])*.125+F;h[G+40>>3]=o;h[G+48>>3]=m;I=(+h[v>>3]-+h[w>>3])*.125+o;h[G+56>>3]=I;h[G+64>>3]=m;m=(+h[v>>3]-+h[w>>3])*.125+I;h[G+72>>3]=m;h[G+80>>3]=H;h[G+88>>3]=m;H=+h[x>>3];H=(+h[b>>3]-H)*.5+H;h[G+96>>3]=H;h[G+104>>3]=I;J=H-(+h[k>>3]-+h[s>>3])*.125;h[G+112>>3]=J;h[G+120>>3]=m;J=J-(+h[k>>3]-+h[s>>3])*.125;h[G+128>>3]=J;h[G+136>>3]=m;h[G+144>>3]=J;h[G+152>>3]=I;I=(+h[k>>3]-+h[s>>3])*.125+J;h[G+160>>3]=I;h[G+168>>3]=o;h[G+176>>3]=J;h[G+184>>3]=F;h[G+192>>3]=J;J=+h[u>>3];h[G+200>>3]=J;h[G+208>>3]=I;h[G+216>>3]=J;u=G+224|0;h[u>>3]=H;h[G+232>>3]=+h[t>>3];h[G+240>>3]=+h[d>>3];h[G+248>>3]=J;nS(a,G,A,f);J=+h[u>>3];h[B>>3]=J;H=+h[y>>3];H=(+h[z>>3]-H)*.5+H;A=B+8|0;h[A>>3]=H;d=B+16|0;h[d>>3]=J;f=B+24|0;h[f>>3]=(+h[v>>3]-+h[w>>3])*.125+H;qS(a,B,2);H=+h[u>>3];h[B>>3]=H;J=+h[y>>3];I=+h[v>>3]-+h[w>>3];J=(+h[z>>3]-J)*.5+J+I*.25;h[A>>3]=J;h[d>>3]=H;h[f>>3]=I*.125+J;qS(a,B,2);h[B>>3]=+h[x>>3];J=+h[y>>3];h[A>>3]=(+h[z>>3]-J)*.5+J;h[d>>3]=+h[b>>3];h[f>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}}else{if((e|0)<369098752){if((e|0)<335544320){switch(e|0){case 318767104:break;default:break a}A=d+4|0;G=JK(A<<4)|0;x=b+16|0;H=+h[x>>3];d=D+48|0;H=(+h[b>>3]-H)*.5+H+(+h[k>>3]-+h[d>>3])*.125;h[G>>3]=H;y=b+40|0;J=+h[y>>3];z=b+24|0;v=D+56|0;w=D+72|0;J=(+h[v>>3]-+h[w>>3]+(+h[z>>3]-J))*.5+J;h[G+8>>3]=J;o=(+h[k>>3]-+h[d>>3])*.125+H;h[G+16>>3]=o;I=(+h[v>>3]-+h[w>>3])*.125+J;h[G+24>>3]=I;h[G+32>>3]=o;o=(+h[v>>3]-+h[w>>3])*.25+I;h[G+40>>3]=o;h[G+48>>3]=H;F=(+h[v>>3]-+h[w>>3])*.125+o;h[G+56>>3]=F;H=H-(+h[k>>3]-+h[d>>3])*.25;h[G+64>>3]=H;h[G+72>>3]=F;F=H-(+h[k>>3]-+h[d>>3])*.125;h[G+80>>3]=F;h[G+88>>3]=o;h[G+96>>3]=F;h[G+104>>3]=I;h[G+112>>3]=H;h[G+120>>3]=J;nS(a,G,A,f);J=+h[x>>3];J=(+h[b>>3]-J)*.5+J;h[B>>3]=J;H=+h[y>>3];H=(+h[z>>3]-H)*.5+H;A=B+8|0;h[A>>3]=H;d=B+16|0;h[d>>3]=J;f=B+24|0;h[f>>3]=(+h[v>>3]-+h[w>>3])*.125+H;qS(a,B,2);H=+h[x>>3];H=(+h[b>>3]-H)*.5+H;h[B>>3]=H;J=+h[y>>3];I=+h[v>>3]-+h[w>>3];J=(+h[z>>3]-J)*.5+J+I*.25;h[A>>3]=J;h[d>>3]=H;h[f>>3]=I*.125+J;qS(a,B,2);h[B>>3]=+h[x>>3];J=+h[y>>3];h[A>>3]=(+h[z>>3]-J)*.5+J;h[d>>3]=+h[b>>3];h[f>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}if((e|0)<352321536){switch(e|0){case 335544320:break;default:break a}x=d+12|0;G=JK(x<<4)|0;y=b+16|0;m=+h[y>>3];u=D+48|0;m=(+h[b>>3]-m)*.5+m+(+h[k>>3]-+h[u>>3])*.25;h[G>>3]=m;z=b+40|0;F=+h[z>>3];A=b+24|0;s=D+56|0;t=D+72|0;F=(+h[s>>3]-+h[t>>3]+(+h[A>>3]-F))*.5+F;d=G+8|0;h[d>>3]=F;h[G+16>>3]=m;F=(+h[s>>3]-+h[t>>3])*.125+F;v=G+24|0;h[v>>3]=F;I=m-(+h[k>>3]-+h[u>>3])*.125;w=G+32|0;h[w>>3]=I;o=(+h[s>>3]-+h[t>>3])*.125+F;h[G+40>>3]=o;h[G+48>>3]=m;H=(+h[s>>3]-+h[t>>3])*.125+o;h[G+56>>3]=H;h[G+64>>3]=m;m=(+h[s>>3]-+h[t>>3])*.125+H;h[G+72>>3]=m;h[G+80>>3]=I;h[G+88>>3]=m;I=+h[y>>3];I=(+h[b>>3]-I)*.5+I;h[G+96>>3]=I;h[G+104>>3]=H;J=I-(+h[k>>3]-+h[u>>3])*.125;h[G+112>>3]=J;h[G+120>>3]=m;J=J-(+h[k>>3]-+h[u>>3])*.125;h[G+128>>3]=J;h[G+136>>3]=m;h[G+144>>3]=J;h[G+152>>3]=H;H=(+h[k>>3]-+h[u>>3])*.125+J;h[G+160>>3]=H;h[G+168>>3]=o;h[G+176>>3]=J;h[G+184>>3]=F;h[G+192>>3]=J;J=+h[d>>3];h[G+200>>3]=J;h[G+208>>3]=H;h[G+216>>3]=J;d=G+224|0;h[d>>3]=I;h[G+232>>3]=+h[v>>3];h[G+240>>3]=+h[w>>3];h[G+248>>3]=J;nS(a,G,x,f);c[B>>2]=c[d>>2];c[B+4>>2]=c[d+4>>2];c[B+8>>2]=c[d+8>>2];c[B+12>>2]=c[d+12>>2];d=B+16|0;h[d>>3]=+h[B>>3];J=+h[z>>3];f=B+24|0;h[f>>3]=(+h[A>>3]-J)*.5+J;qS(a,B,2);h[B>>3]=+h[y>>3];J=+h[z>>3];h[B+8>>3]=(+h[A>>3]-J)*.5+J;h[d>>3]=+h[b>>3];h[f>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}else{switch(e|0){case 352321536:break;default:break a}A=d+4|0;G=JK(A<<4)|0;x=b+16|0;I=+h[x>>3];w=D+48|0;I=(+h[b>>3]-I)*.5+I+(+h[k>>3]-+h[w>>3])*.125;h[G>>3]=I;y=b+40|0;J=+h[y>>3];z=b+24|0;u=D+56|0;v=D+72|0;J=(+h[u>>3]-+h[v>>3]+(+h[z>>3]-J))*.5+J;d=G+8|0;h[d>>3]=J;o=(+h[k>>3]-+h[w>>3])*.125+I;h[G+16>>3]=o;H=(+h[u>>3]-+h[v>>3])*.125+J;h[G+24>>3]=H;h[G+32>>3]=o;o=(+h[u>>3]-+h[v>>3])*.25+H;h[G+40>>3]=o;h[G+48>>3]=I;F=(+h[u>>3]-+h[v>>3])*.125+o;h[G+56>>3]=F;I=I-(+h[k>>3]-+h[w>>3])*.25;h[G+64>>3]=I;h[G+72>>3]=F;F=I-(+h[k>>3]-+h[w>>3])*.125;h[G+80>>3]=F;h[G+88>>3]=o;h[G+96>>3]=F;h[G+104>>3]=H;h[G+112>>3]=I;h[G+120>>3]=J;nS(a,G,A,f);J=+h[x>>3];J=(+h[b>>3]-J)*.5+J;h[B>>3]=J;A=B+8|0;h[A>>3]=+h[d>>3];d=B+16|0;h[d>>3]=J;J=+h[y>>3];f=B+24|0;h[f>>3]=(+h[z>>3]-J)*.5+J;qS(a,B,2);h[B>>3]=+h[x>>3];J=+h[y>>3];h[A>>3]=(+h[z>>3]-J)*.5+J;h[d>>3]=+h[b>>3];h[f>>3]=(+h[b+8>>3]-+h[b+56>>3])*.5+J;qS(a,B,2);U1(G);break}}if((e|0)<402653184)if((e|0)<385875968){switch(e|0){case 369098752:break;default:break a}d=d+5|0;G=JK(d<<4)|0;B=D+48|0;h[G>>3]=+h[j>>3]-(+h[k>>3]-+h[B>>3])*.5;y=D+56|0;A=D+72|0;h[G+8>>3]=+h[D+24>>3]-(+h[y>>3]-+h[A>>3])*.5;h[G+16>>3]=+h[B>>3];J=+h[y>>3];h[G+24>>3]=J-(J-+h[A>>3])*.5;h[G+32>>3]=+h[b+32>>3];z=b+40|0;h[G+40>>3]=+h[z>>3];J=+h[k>>3];h[G+48>>3]=(J-+h[B>>3])*.5+J;h[G+56>>3]=+h[z>>3];J=+h[k>>3];h[G+64>>3]=(J-+h[B>>3])*.5+J;h[G+72>>3]=(+h[y>>3]-+h[A>>3])*.5+ +h[z>>3];h[G+80>>3]=+h[j>>3]-(+h[k>>3]-+h[B>>3])*.5;h[G+88>>3]=(+h[y>>3]-+h[A>>3])*.5+ +h[z>>3];h[G+96>>3]=+h[j>>3]-(+h[k>>3]-+h[B>>3])*.5;z=b+56|0;h[G+104>>3]=+h[z>>3];A=b+8|0;J=+h[A>>3];h[G+120>>3]=J-(J-+h[z>>3])*.5;h[G+112>>3]=+h[b>>3];h[G+136>>3]=+h[A>>3];h[G+128>>3]=+h[j>>3]-(+h[k>>3]-+h[B>>3])*.5;nS(a,G,d,f);U1(G);break}else{switch(e|0){case 385875968:break;default:break a}d=d+3|0;G=JK(d<<4)|0;B=D+48|0;h[G>>3]=+h[j>>3]-(+h[k>>3]-+h[B>>3])*.5;y=D+56|0;A=D+72|0;h[G+8>>3]=+h[D+24>>3]-(+h[y>>3]-+h[A>>3])*.5;h[G+16>>3]=+h[B>>3];J=+h[y>>3];h[G+24>>3]=J-(J-+h[A>>3])*.5;h[G+32>>3]=+h[b+32>>3];z=b+40|0;h[G+40>>3]=(+h[y>>3]-+h[A>>3])*.5+ +h[z>>3];h[G+48>>3]=+h[j>>3]-(+h[k>>3]-+h[B>>3])*.5;h[G+56>>3]=(+h[y>>3]-+h[A>>3])*.5+ +h[z>>3];h[G+64>>3]=+h[j>>3]-(+h[k>>3]-+h[B>>3])*.5;z=b+56|0;h[G+72>>3]=+h[z>>3];A=b+8|0;J=+h[A>>3];h[G+88>>3]=J-(J-+h[z>>3])*.5;h[G+80>>3]=+h[b>>3];h[G+104>>3]=+h[A>>3];h[G+96>>3]=+h[j>>3]-(+h[k>>3]-+h[B>>3])*.5;nS(a,G,d,f);U1(G);break}else if((e|0)<419430400){switch(e|0){case 402653184:break;default:break a}d=d+3|0;G=JK(d<<4)|0;h[G>>3]=+h[b>>3];y=b+8|0;z=D+48|0;A=D+56|0;B=D+72|0;h[G+8>>3]=+h[y>>3]-(+h[A>>3]-+h[B>>3])*.5;J=+h[k>>3];h[G+16>>3]=(J-+h[z>>3])*.5+J;h[G+24>>3]=+h[y>>3]-(+h[A>>3]-+h[B>>3])*.5;J=+h[k>>3];h[G+32>>3]=(J-+h[z>>3])*.5+J;h[G+40>>3]=+h[D+40>>3];h[G+48>>3]=+h[b+16>>3];J=+h[b+24>>3];y=b+40|0;h[G+56>>3]=J-(J-+h[y>>3])*.5;J=+h[k>>3];h[G+64>>3]=(J-+h[z>>3])*.5+J;h[G+72>>3]=+h[y>>3];h[G+88>>3]=(+h[A>>3]-+h[B>>3])*.5+ +h[y>>3];J=+h[k>>3];h[G+80>>3]=(J-+h[z>>3])*.5+J;h[G+104>>3]=(+h[A>>3]-+h[B>>3])*.5+ +h[b+56>>3];h[G+96>>3]=+h[b>>3];nS(a,G,d,f);U1(G);break}else{switch(e|0){case 419430400:break;default:break a}d=d+5|0;G=JK(d<<4)|0;h[G>>3]=+h[b>>3];B=b+8|0;A=D+48|0;y=D+56|0;z=D+72|0;h[G+8>>3]=+h[B>>3]-(+h[y>>3]-+h[z>>3])*.5;J=+h[k>>3];h[G+16>>3]=(J-+h[A>>3])*.5+J;h[G+24>>3]=+h[B>>3]-(+h[y>>3]-+h[z>>3])*.5;J=+h[k>>3];h[G+32>>3]=(J-+h[A>>3])*.5+J;h[G+40>>3]=+h[D+40>>3];h[G+48>>3]=+h[b+16>>3];J=+h[b+24>>3];B=b+40|0;h[G+56>>3]=J-(J-+h[B>>3])*.5;J=+h[k>>3];h[G+64>>3]=(J-+h[A>>3])*.5+J;h[G+72>>3]=+h[B>>3];h[G+88>>3]=(+h[y>>3]-+h[z>>3])*.5+ +h[B>>3];J=+h[k>>3];h[G+80>>3]=(J-+h[A>>3])*.5+J;B=b+56|0;h[G+104>>3]=(+h[y>>3]-+h[z>>3])*.5+ +h[B>>3];h[G+96>>3]=+h[j>>3]-(+h[k>>3]-+h[A>>3])*.5;h[G+112>>3]=+h[j>>3]-(+h[k>>3]-+h[A>>3])*.5;h[G+120>>3]=+h[B>>3];h[G+128>>3]=+h[b+48>>3];h[G+136>>3]=+h[B>>3];nS(a,G,d,f);U1(G);break}}while(0);U1(D)}l=E;return}function UM(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0;f=l;l=l+112|0;g=f;k=+h[b+8>>3]*2.0;c[g>>2]=c[b>>2];c[g+4>>2]=c[b+4>>2];c[g+8>>2]=c[b+8>>2];c[g+12>>2]=c[b+12>>2];h[g+16>>3]=+h[b+16>>3];h[g+24>>3]=k-+h[b+24>>3];h[g+32>>3]=+h[b+32>>3];h[g+40>>3]=k-+h[b+40>>3];h[g+48>>3]=+h[b+48>>3];h[g+56>>3]=k-+h[b+56>>3];h[g+64>>3]=+h[b+64>>3];h[g+72>>3]=k-+h[b+72>>3];h[g+80>>3]=+h[b+80>>3];h[g+88>>3]=k-+h[b+88>>3];i=g+96|0;j=b+96|0;c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];pS(a,b,d,0,0,e&255);pS(a,g,7,0,0,0);l=f;return}function VM(a,b,c,d){a=a|0;b=+b;c=c|0;d=d|0;var e=0.0,f=0.0;f=+h[c>>3];e=+h[c+8>>3];e=(+h[d+8>>3]-e)*b+e;h[a>>3]=(+h[d>>3]-f)*b+f;h[a+8>>3]=e;return}function WM(a){a=a|0;a=c[(c[a+16>>2]|0)+8>>2]|0;if(a){a=c[c[a+4>>2]>>2]|0;if((a|0)!=91)if((a|0)==94)a=2;else return ((a|0)==93?3:(a|0)==96?4:0)|0;else a=1}else a=0;return a|0}function XM(b){b=b|0;var d=0,e=0,f=0.0,g=0,i=0,j=0,k=0.0,m=0.0,n=0.0,o=0.0,p=0,q=0.0,r=0.0,s=0.0,t=0.0,u=0.0,v=0.0,w=0.0,x=0.0,y=0.0,z=0.0,A=0,D=0,G=0,H=0,I=0,J=0,L=0,M=0,N=0,P=0.0,Q=0.0,R=0,S=0,T=0.0,U=0.0,V=0.0,W=0,X=0,Y=0,Z=0,_=0,$=0,aa=0,ba=0.0;aa=l;l=l+144|0;J=aa+112|0;N=aa+104|0;G=aa+96|0;H=aa+88|0;i=aa+80|0;Y=aa+64|0;_=aa+48|0;R=aa+40|0;S=aa+32|0;j=aa+24|0;p=aa+16|0;D=aa+136|0;A=aa+128|0;L=aa;$=JK(48)|0;Z=b+16|0;W=c[(c[(c[Z>>2]|0)+8>>2]|0)+8>>2]|0;g=(W|0)==1616;X=c[W>>2]|0;d=c[W+4>>2]|0;e=c[W+8>>2]|0;m=+h[W+16>>3];n=+h[W+32>>3];o=+h[W+24>>3];X=X|(dP(QA(b,105511)|0)|0)&255;do if(!g){if(!X){W=c[Z>>2]|0;k=+h[W+32>>3]*72.0;f=+h[W+40>>3]*72.0;f=+(~~(f+(f>=0.0?.5:-.5))|0);k=+(~~(k+(k>=0.0?.5:-.5))|0);break}k=+fN(b);if(k>0.0)f=k;else{W=c[Z>>2]|0;f=+h[W+32>>3];k=+h[W+40>>3];k=(f<k?f:k)*72.0;k=+(~~(k+(k>=0.0?.5:-.5))|0);f=k}}else{f=0.0;k=0.0}while(0);W=ZO(b,c[47160]|0,d,0)|0;V=+_O(b,c[47162]|0,0.0,-360.0)+m;if(!e){U=+_O(b,c[47163]|0,0.0,-100.0);I=ZO(b,c[47159]|0,4,0)|0;T=+_O(b,c[47164]|0,0.0,-100.0)}else{I=e;U=n;T=o}d=c[(c[Z>>2]|0)+104>>2]|0;e=d+24|0;c[Y>>2]=c[e>>2];c[Y+4>>2]=c[e+4>>2];c[Y+8>>2]=c[e+8>>2];c[Y+12>>2]=c[e+12>>2];q=+h[Y>>3];if(q>0.0){if(!g)M=11}else if(!(g|!(+h[Y+8>>3]>0.0)))M=11;if((M|0)==11){d=QA(b,105496)|0;do if(d){h[p>>3]=0.0;h[j>>3]=0.0;c[i>>2]=j;c[i+4>>2]=p;d=b5(d,105503,i)|0;m=+h[j>>3];if(m<0.0){h[j>>3]=0.0;m=0.0}n=+h[p>>3];if(n<0.0){h[p>>3]=0.0;o=0.0}else o=n;if((d|0)<=0){n=q+16.0;h[Y>>3]=n;m=8.0;break}m=m*72.0;m=+(~~(m+(m>=0.0?.5:-.5))<<1|0);n=q+m;h[Y>>3]=n;if((d|0)!=1){m=o*72.0;m=+(~~(m+(m>=0.0?.5:-.5))<<1|0)}}else{n=q+16.0;h[Y>>3]=n;m=8.0}while(0);d=Y+8|0;h[d>>3]=+h[d>>3]+m;d=c[(c[Z>>2]|0)+104>>2]|0;q=n}s=+h[d+24>>3];m=+h[c[(c[(xC(b)|0)+16>>2]|0)+8>>2]>>3];if(m>0.0){P=m*72.0;P=+(~~(P+(P>=0.0?.5:-.5))|0);Q=+gN(q,P);h[Y>>3]=Q;p=Y+8|0;h[p>>3]=+gN(+h[p>>3],P)}else Q=q;d=c[(c[Z>>2]|0)+8>>2]|0;do if(!(a[d+12>>0]|0)){e=QA(b,109025)|0;if((e|0)!=0?(a[e>>0]|0)!=0:0){RF(A,xC(b)|0,e);d=c[A>>2]|0;g=c[A+4>>2]|0;if((g&d|0)==-1){d=KB(b)|0;c[G>>2]=e;c[G+4>>2]=d;gA(0,105587,G)|0;e=0;d=0;break}else{a[(c[(xC(b)|0)+16>>2]|0)+114>>0]=1;e=d+2|0;d=g+2|0;break}}else{e=0;d=0}}else{d=c[d>>2]|0;if((a[d>>0]|0)==99?(s2(d,105519)|0)==0:0){e=QA(b,105526)|0;RF(D,xC(b)|0,e);d=c[D>>2]|0;g=c[D+4>>2]|0;if((g&d|0)==-1){d=KB(b)|0;c[H>>2]=e|0?e:105536;c[H+4>>2]=d;gA(0,105542,H)|0;e=0;d=0;break}else{a[(c[(xC(b)|0)+16>>2]|0)+114>>0]=1;e=d+2|0;d=g+2|0;break}}else{e=0;d=0}}while(0);P=+(e|0);h[_>>3]=Q>P?Q:P;P=+h[Y+8>>3];r=+(d|0);d=_+8|0;h[d>>3]=P>r?P:r;e=(I|0)<3&(T!=0.0|U!=0.0)?120:I;g=QA(b,105628)|0;if(!g)i=99;else{i=a[g>>0]|0;i=i<<24>>24==116?116:i<<24>>24==98?98:99}g=c[Z>>2]|0;a[(c[g+104>>2]|0)+80>>0]=i;if((e|0)==4?(T==0.0?((~~(V+(V>=0.0?.5:-.5))|0)%90|0|0)==0:0)&U==0.0:0)p=1;else M=39;do if((M|0)==39){g=c[(c[(c[g+8>>2]|0)+8>>2]|0)+44>>2]|0;if(g|0){p=c[g>>2]|0;c[J>>2]=c[_>>2];c[J+4>>2]=c[_+4>>2];c[J+8>>2]=c[_+8>>2];c[J+12>>2]=c[_+12>>2];ub[p&63](L,J);c[_>>2]=c[L>>2];c[_+4>>2]=c[L+4>>2];c[_+8>>2]=c[L+8>>2];c[_+12>>2]=c[L+12>>2];p=0;break}m=+h[d>>3];n=m*1.4142135623730951;if(f>n&i<<24>>24==99){o=m/f;o=+C(+(1.0/(1.0-o*o)));o=+h[_>>3]*o;h[_>>3]=o}else{o=+h[_>>3]*1.4142135623730951;h[_>>3]=o;h[d>>3]=n;m=n}if((e|0)>2){z=+E(+(3.141592653589793/+(e|0)));h[_>>3]=o/z;h[d>>3]=m/z;p=0}else p=0}while(0);m=+h[d>>3];g=aP(b,c[47165]|0,137308)|0;if((a[g>>0]|0)==115?(s2(g,108230)|0)==0:0){h[_>>3]=k;h[d>>3]=f;N=$+40|0;c[N>>2]=c[N>>2]|2048}else{if(!((dP(g)|0)<<24>>24)){z=+h[_>>3];k=k>z?k:z;h[_>>3]=k;z=+h[d>>3];f=f>z?f:z}else{M=c[(c[Z>>2]|0)+104>>2]|0;if(!(!(k<+h[M+24>>3])?!(f<+h[M+32>>3]):0)){L=KB(b)|0;M=KB(xC(b)|0)|0;c[N>>2]=L;c[N+4>>2]=M;gA(0,105637,N)|0}h[_>>3]=k}h[d>>3]=f}if(!X){z=f;y=k}else{y=k>f?k:f;h[d>>3]=y;h[_>>3]=y;z=y}do if(!((dP(aP(b,c[47156]|0,137308)|0)|0)<<24>>24)){if(p){f=+h[(Q>+h[_>>3]?Y:_)>>3];break}f=+h[d>>3];if(P<f){f=+h[_>>3]*+C(+(1.0-P*P/(f*f)));f=Q>f?Q:f}else f=Q}else f=Q;while(0);g=c[(c[Z>>2]|0)+104>>2]|0;h[g+40>>3]=s-q+f;A=$+40|0;if(!(c[A>>2]&2048)){x=+h[d>>3]-m;h[g+48>>3]=(P<r?r-P+x:x)+P}j=(W|0)>1;g=j?W:1;a:do if((e|0)<3){g=JK(g<<5)|0;k=+h[_>>3]*.5;f=+h[d>>3]*.5;h[g>>3]=-k;h[g+8>>3]=-f;h[g+16>>3]=k;h[g+24>>3]=f;if(j){e=1;i=2;while(1){if((e|0)==(W|0))break;y=k+4.0;z=f+4.0;h[g+(i<<4)>>3]=-y;h[g+(i<<4)+8>>3]=-z;S=i|1;h[g+(S<<4)>>3]=y;h[g+(S<<4)+8>>3]=z;e=e+1|0;i=i+2|0;k=y;f=z}h[_>>3]=k*2.0;h[d>>3]=f*2.0;e=2}else e=2}else{g=JK(O(g<<4,e)|0)|0;i=c[(c[(c[(c[Z>>2]|0)+8>>2]|0)+8>>2]|0)+44>>2]|0;b:do if(!i){q=6.283185307179586/+(e|0);t=q*.5;r=+F(+t);s=+L2(+B(+U)+ +B(+T),1.0);t=T*1.4142135623730951/+E(+t);u=U*.5;N2((q+-3.141592653589793)*.5,R,S);v=V*.017453292519943295;k=0.0;f=0.0;m=0.0;i=0;n=+h[S>>3]*.5;o=+h[R>>3]*.5;while(1){if((i|0)>=(e|0))break b;m=m+q;N2(m,R,S);n=+h[S>>3]*r+n;o=+h[R>>3]*r+o;x=(o*t+s)*n+u*o;N2(+K(+o,+x)+v,R,S);x=+L2(x,o);w=+h[S>>3]*x*+h[_>>3];x=+h[R>>3]*x*+h[d>>3];ba=+B(+w);f=ba>f?ba:f;ba=+B(+x);k=ba>k?ba:k;h[g+(i<<4)>>3]=w;h[g+(i<<4)+8>>3]=x;if(p)break;else i=i+1|0}ba=-w;h[g+16>>3]=ba;h[g+24>>3]=x;h[g+32>>3]=ba;ba=-x;h[g+40>>3]=ba;h[g+48>>3]=w;h[g+56>>3]=ba}else{ub[c[i+4>>2]&63](g,_);k=+h[d>>3]*.5;f=+h[_>>3]*.5}while(0);f=f*2.0;k=k*2.0;y=y>f?y:f;h[_>>3]=y;ba=z>k?z:k;h[d>>3]=ba;f=y/f;k=ba/k;i=0;while(1){if((i|0)>=(e|0))break;N=g+(i<<4)|0;b=g+(i<<4)+8|0;ba=+h[b>>3]*k;h[N>>3]=+h[N>>3]*f;h[b>>3]=ba;i=i+1|0}if(j){j=e+-1|0;k=+h[g>>3];m=+h[g+8>>3];f=+K(+(m-+h[g+(j<<4)+8>>3]),+(k-+h[g+(j<<4)>>3]));j=0;c:while(1){if((j|0)>=(e|0))break;p=j+1|0;i=(p|0)%(e|0)|0;n=+h[g+(i<<4)>>3];o=+h[g+(i<<4)+8>>3];q=+K(+(o-m),+(n-k));z=(f+3.141592653589793-q)*.5;ba=4.0/+F(+z);N2(f-z,R,S);h[R>>3]=+h[R>>3]*ba;h[S>>3]=+h[S>>3]*ba;i=1;f=m;while(1){if((i|0)==(W|0)){f=q;j=p;k=n;m=o;continue c}z=+h[S>>3]+k;ba=+h[R>>3]+f;b=(O(i,e)|0)+j|0;h[g+(b<<4)>>3]=z;h[g+(b<<4)+8>>3]=ba;i=i+1|0;k=z;f=ba}}j=O(e,W+-1|0)|0;i=0;while(1){if((i|0)>=(e|0))break a;S=i+j|0;z=+h[g+(S<<4)+8>>3];y=+B(+(+h[g+(S<<4)>>3]))*2.0;ba=+h[_>>3];h[_>>3]=y>ba?y:ba;z=+B(+z)*2.0;ba=+h[d>>3];h[d>>3]=z>ba?z:ba;i=i+1|0}}}while(0);c[$>>2]=X;c[$+4>>2]=W;c[$+8>>2]=e;h[$+16>>3]=V;h[$+32>>3]=U;h[$+24>>3]=T;c[$+44>>2]=g;f=+h[_>>3];if(!(c[A>>2]&2048)){e=c[Z>>2]|0;h[e+32>>3]=f*.013888888888888888}else{e=c[Z>>2]|0;h[e+32>>3]=+h[(Q>f?Y:_)>>3]*.013888888888888888;d=(P>+h[d>>3]?Y:_)+8|0}h[e+40>>3]=+h[d>>3]*.013888888888888888;c[e+12>>2]=$;l=aa;return}function YM(a){a=a|0;var b=0,d=0,e=0,f=0,g=0.0,i=0.0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+64|0;j=o+40|0;e=o+32|0;k=o+16|0;n=o;d=(c[(c[(xC(a)|0)+16>>2]|0)+116>>2]|0)>>>2&1^1;m=a+16|0;f=c[c[(c[m>>2]|0)+104>>2]>>2]|0;c[47282]=f;f=l2(f)|0;f=JK(((f|0)>1?f:1)+1|0)|0;b=_M(a,d,1,f)|0;if(!b){c[e>>2]=c[c[(c[m>>2]|0)+104>>2]>>2];gA(1,105472,e)|0;c[47282]=105493;b=_M(a,d,1,f)|0}U1(f);$M(j,a,b);d=c[m>>2]|0;i=+h[d+32>>3]*72.0;i=+(~~(i+(i>=0.0?.5:-.5))|0);h[n>>3]=i;g=+h[d+40>>3]*72.0;g=+(~~(g+(g>=0.0?.5:-.5))|0);d=n+8|0;h[d>>3]=g;if(!((dP(aP(a,c[47165]|0,137308)|0)|0)<<24>>24)){i=+h[(+h[b>>3]>i?b:n)>>3];h[n>>3]=i;e=b+8|0;g=+h[(+h[e>>3]>g?b:n)+8>>3];h[d>>3]=g;d=e;e=b}else{d=b+8|0;e=b}a=(dP(aP(a,c[47156]|0,137308)|0)|0)&255;c[j>>2]=c[n>>2];c[j+4>>2]=c[n+4>>2];c[j+8>>2]=c[n+8>>2];c[j+12>>2]=c[n+12>>2];aN(b,j,a);bN(k,i*-.5,g*.5);c[j>>2]=c[k>>2];c[j+4>>2]=c[k+4>>2];c[j+8>>2]=c[k+8>>2];c[j+12>>2]=c[k+12>>2];cN(b,j,15);n=c[m>>2]|0;h[n+32>>3]=+h[e>>3]*.013888888888888888;h[n+40>>3]=(+h[d>>3]+1.0)*.013888888888888888;c[n+12>>2]=b;l=o;return}function ZM(a){a=a|0;var b=0.0,d=0,e=0.0,f=0.0,g=0,i=0,j=0,k=0,l=0.0,m=0;j=JK(48)|0;k=a+16|0;g=c[(c[(c[(c[k>>2]|0)+8>>2]|0)+8>>2]|0)+4>>2]|0;f=+_O(a,c[47146]|0,1797693134862315708145274.0e284,0.0);b=+_O(a,c[47145]|0,1797693134862315708145274.0e284,0.0);f=f<b?f:b;f=b==1797693134862315708145274.0e284&f==1797693134862315708145274.0e284?.05:f>0.0?(f>.0003?f:.0003):f;i=c[k>>2]|0;h[i+40>>3]=f;h[i+32>>3]=f;b=f*72.0;g=ZO(a,c[47160]|0,g,0)|0;i=JK((g|0)<1?32:g<<5)|0;f=f*36.0;e=-f;h[i>>3]=e;h[i+8>>3]=e;h[i+16>>3]=f;h[i+24>>3]=f;if((g|0)>1){a=1;d=2;e=f;b=f;while(1){if((a|0)==(g|0))break;l=e+4.0;f=b+4.0;h[i+(d<<4)>>3]=-l;h[i+(d<<4)+8>>3]=-f;m=d|1;h[i+(m<<4)>>3]=l;h[i+(m<<4)+8>>3]=f;a=a+1|0;d=d+2|0;e=l;b=f}b=e*2.0}c[j>>2]=1;c[j+4>>2]=g;c[j+8>>2]=2;m=j+16|0;c[m>>2]=0;c[m+4>>2]=0;c[m+8>>2]=0;c[m+12>>2]=0;c[m+16>>2]=0;c[m+20>>2]=0;c[j+44>>2]=i;l=b*.013888888888888888;m=c[k>>2]|0;h[m+32>>3]=l;h[m+40>>3]=l;c[m+12>>2]=j;return}function _M(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0;g=JK(72)|0;n=c[(c[b+16>>2]|0)+104>>2]|0;m=0;i=1;j=c[47282]|0;a:while(1){k=a[j>>0]|0;switch(k<<24>>24){case 0:break a;case 92:{j=j+1|0;k=a[j>>0]|0;switch(k<<24>>24){case 124:case 125:case 123:case 92:{l=m;break}default:E=4}break}default:E=4}if((E|0)==4){E=0;switch(k<<24>>24){case 123:{l=m+1|0;break}case 125:{l=m+-1|0;break}default:{l=m;i=i+((m|0)==0&k<<24>>24==124&1)|0}}if((l|0)<0)break}m=l;j=j+1|0}B=g+56|0;c[B>>2]=JK(i<<2)|0;a[g+64>>0]=d;y=(d|0)==0&1;z=n+82|0;A=f+1|0;v=(e|0)!=0;w=n+16|0;x=n+4|0;u=n+8|0;k=0;m=0;d=0;t=0;l=0;e=0;n=f;i=0;j=0;b:while(1){p=k;k=0;c:while(1){d:while(1){s=e;e:while(1){r=k;o=i;f:while(1){q=m;g:while(1){while(1){if(t){E=78;break b}m=c[47282]|0;i=a[m>>0]|0;if((i+-1&255)>=31)break;c[47282]=m+1}switch(i<<24>>24|0){case 92:{E=53;break d}case 0:case 124:case 125:break e;case 60:break f;case 62:break g;case 123:break;default:{k=d;i=m;break d}}i=m+1|0;c[47282]=i;if(l|0){E=34;break b}if(!(a[i>>0]|0)){E=34;break b}e=_M(b,y,0,f)|0;c[(c[B>>2]|0)+(q<<2)>>2]=e;if(!e){E=36;break b}else{q=q+1|0;l=4}}if(a[z>>0]|0){k=d;i=m;break d}if(!(l&16)){E=27;break b}if(o>>>0>A>>>0?(C=o+-1|0,(C|0)!=(s|0)):0)i=(a[C>>0]|0)==32?C:o;else i=o;a[i>>0]=0;r=D4(f)|0;c[47282]=(c[47282]|0)+1;m=q;l=l&-17;o=i}if(l&6|0){E=22;break b}if(a[z>>0]|0){k=d;i=m;break d}c[47282]=m+1;m=q;l=l|18;k=r;s=f;i=f}if(!((l&16|0)==0&(v|i<<24>>24!=0))){E=38;break b}if(!(l&4)){k=JK(72)|0;c[(c[B>>2]|0)+(q<<2)>>2]=k;m=q+1|0}else{k=p;m=q}if(r|0)c[k+60>>2]=r;if(!(l&5)){a[f+j>>0]=32;l=l|1;j=j+1|0}i=f+j|0;if(l&1){if((j|0)>1?(D=i+-1|0,(D|0)!=(n|0)):0)i=(a[D>>0]|0)==32?D:i;a[i>>0]=0;n=D4(f)|0;c[k+52>>2]=zK(b,n,a[z>>0]|0?2:0,+h[w>>3],c[x>>2]|0,c[u>>2]|0)|0;a[k+64>>0]=1;n=f;j=0}i=c[47282]|0;switch(a[i>>0]|0){case 0:{t=1;e=s;i=o;continue b}case 125:{E=51;break b}default:{}}c[47282]=i+1;p=k;l=0;k=0;e=s;i=o}h:do if((E|0)==53){E=0;k=f+j|0;i=m+1|0;switch(a[i>>0]|0){case 0:{k=d;i=m;break h}case 62:case 60:case 124:case 125:case 123:{k=d;break}case 32:{if(!(a[z>>0]|0))k=1;else E=55;break}default:E=55}if((E|0)==55){E=0;a[k>>0]=92;k=d;l=l|9;j=j+1|0}c[47282]=i}while(0);d=f+j|0;if(l&4|0?(a[i>>0]|0)!=32:0){E=59;break b}if(!(l&24))l=(a[i>>0]|0)==32?l:l|9;if(!(l&8))if(!(l&16))e=s;else{m=a[i>>0]|0;d=(k|0)!=0;if(!(d|m<<24>>24!=32))if((o|0)!=(f|0))if((a[o+-1>>0]|0)==32)m=o;else E=73;else m=f;else E=73;if((E|0)==73){E=0;a[o>>0]=m;m=o+1|0;i=c[47282]|0}e=d?m+-1|0:s;o=m}else{m=a[i>>0]|0;e=(k|0)!=0;if(!((!(e|m<<24>>24!=32)?(a[d+-1>>0]|0)==32:0)?(a[z>>0]|0)==0:0)){a[d>>0]=m;j=j+1|0}if(e){e=s;n=f+j+-1|0}else e=s}i=i+1|0;c[47282]=i;while(1){if((a[i>>0]|0)>=0){m=q;d=k;k=r;i=o;continue c}s=i+1|0;c[47282]=s;a[f+j>>0]=a[i>>0]|0;j=j+1|0;i=s}}}if((E|0)==22){dN(g,r);g=0}else if((E|0)==27){dN(g,r);g=0}else if((E|0)==34){dN(g,r);g=0}else if((E|0)==36){dN(g,r);g=0}else if((E|0)==38){dN(g,r);g=0}else if((E|0)==51){c[47282]=i+1;c[g+48>>2]=m}else if((E|0)==59){dN(g,r);g=0}else if((E|0)==78)c[g+48>>2]=q;return g|0}function $M(b,d,e){b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+64|0;k=u+48|0;m=u+40|0;n=u+32|0;t=u+16|0;s=u;j=c[e+52>>2]|0;a:do if(!j){n=t+8|0;o=e+48|0;p=e+56|0;q=e+64|0;r=s+8|0;c[t>>2]=0;c[t+4>>2]=0;c[t+8>>2]=0;c[t+12>>2]=0;j=0;while(1){if((j|0)>=(c[o>>2]|0))break a;$M(s,d,c[(c[p>>2]|0)+(j<<2)>>2]|0);if(!(a[q>>0]|0)){h[n>>3]=+h[n>>3]+ +h[r>>3];k=t;m=+h[t>>3]>+h[s>>3]?t:s}else{h[t>>3]=+h[t>>3]+ +h[s>>3];k=n;m=(+h[n>>3]>+h[r>>3]?t:s)+8|0}h[k>>3]=+h[m>>3];j=j+1|0}}else{g=+h[j+24>>3];f=+h[j+32>>3];if(g>0.0|f>0.0){j=QA(d,105496)|0;do if(j){c[k>>2]=m;c[k+4>>2]=n;j=b5(j,105503,k)|0;if((j|0)<=0){i=8.0;g=g+16.0;break}i=+h[m>>3]*72.0;i=+(~~(i+(i>=0.0?.5:-.5))<<1|0);g=g+i;if((j|0)!=1){i=+h[n>>3]*72.0;i=+(~~(i+(i>=0.0?.5:-.5))<<1|0)}}else{i=8.0;g=g+16.0}while(0);f=i+f}h[t>>3]=g;h[t+8>>3]=f}while(0);c[e>>2]=c[t>>2];c[e+4>>2]=c[t+4>>2];c[e+8>>2]=c[t+8>>2];c[e+12>>2]=c[t+12>>2];c[b>>2]=c[t>>2];c[b+4>>2]=c[t+4>>2];c[b+8>>2]=c[t+8>>2];c[b+12>>2]=c[t+12>>2];l=u;return}function aN(b,d,e){b=b|0;d=d|0;e=e|0;var f=0.0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0.0,s=0;s=l;l=l+32|0;o=s+16|0;p=s;q=+h[d>>3];i=q-+h[b>>3];r=+h[d+8>>3];f=r-+h[b+8>>3];c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];d=c[b+52>>2]|0;if(!((e|0)!=0|(d|0)==0)){n=d+40|0;h[n>>3]=+h[n>>3]+i;n=d+48|0;h[n>>3]=+h[n>>3]+f}m=b+48|0;d=c[m>>2]|0;a:do if(d|0){n=b+64|0;f=((a[n>>0]|0)==0?f:i)/+(d|0);k=b+56|0;j=0;while(1){if((j|0)>=(d|0))break a;b=c[(c[k>>2]|0)+(j<<2)>>2]|0;g=j+1|0;d=~~(f*+(g|0))-~~(f*+(j|0))|0;if(!(a[n>>0]|0))bN(p,q,+h[b+8>>3]+ +(d|0));else bN(p,+h[b>>3]+ +(d|0),r);c[o>>2]=c[p>>2];c[o+4>>2]=c[p+4>>2];c[o+8>>2]=c[p+8>>2];c[o+12>>2]=c[p+12>>2];aN(b,o,e);j=g;d=c[m>>2]|0}}while(0);l=s;return}function bN(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b;h[a+8>>3]=c;return}function cN(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0,s=0.0;q=l;l=l+48|0;j=q+32|0;n=q+16|0;m=q;a[b+65>>0]=e;k=b+16|0;s=+h[d>>3];p=d+8|0;r=+h[p>>3];bN(n,s,r-+h[b+8>>3]);c[k>>2]=c[n>>2];c[k+4>>2]=c[n+4>>2];c[k+8>>2]=c[n+8>>2];c[k+12>>2]=c[n+12>>2];k=b+32|0;bN(m,+h[b>>3]+s,r);c[k>>2]=c[m>>2];c[k+4>>2]=c[m+4>>2];c[k+8>>2]=c[m+8>>2];c[k+12>>2]=c[m+12>>2];k=c[b+48>>2]|0;m=k+-1|0;n=(e|0)==0;o=b+56|0;i=b+64|0;g=0;while(1){if((g|0)>=(k|0))break;do if(!n){b=(g|0)==0;f=(g|0)==(m|0);if(!(a[i>>0]|0))if(b){b=f?15:14;break}else{b=f?11:10;break}else if(b){b=f?15:13;break}else{b=f?7:5;break}}else b=0;while(0);f=c[(c[o>>2]|0)+(g<<2)>>2]|0;c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];cN(f,j,b&e);if(!(a[i>>0]|0))h[p>>3]=+h[p>>3]-+h[(c[(c[o>>2]|0)+(g<<2)>>2]|0)+8>>3];else h[d>>3]=+h[c[(c[o>>2]|0)+(g<<2)>>2]>>3]+ +h[d>>3];g=g+1|0}l=q;return}function dN(a,b){a=a|0;b=b|0;eN(a);U1(b);return}function eN(a){a=a|0;var b=0,d=0,e=0;d=a+48|0;e=a+56|0;b=0;while(1){if((b|0)>=(c[d>>2]|0))break;eN(c[(c[e>>2]|0)+(b<<2)>>2]|0);b=b+1|0}U1(c[a+60>>2]|0);CK(c[a+52>>2]|0);U1(c[e>>2]|0);U1(a);return}function fN(a){a=a|0;var b=0.0,d=0.0;d=+_O(a,c[47146]|0,0.0,.01);b=+_O(a,c[47145]|0,0.0,.02);b=(d>b?d:b)*72.0;return +(+(~~(b+(b>=0.0?.5:-.5))|0))}function gN(a,b){a=+a;b=+b;var c=0;c=~~(a/b);return +(+((+(c|0)*b+1.0e-05<a&1)+c|0)*b)}function hN(a){a=a|0;a=c[(c[a+16>>2]|0)+8>>2]|0;if(!a)a=0;else a=(c[c[a+4>>2]>>2]|0)==91&1;return a|0}function iN(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;f=c[47283]|0;a:do if(!f)d=0;else{g=c[47284]|0;e=0;while(1){if((e|0)>=(g|0)){d=0;break a}d=c[f+(e<<2)>>2]|0;h=c[d>>2]|0;if((a[h>>0]|0)==(a[b>>0]|0)?(s2(h,b)|0)==0:0)break a;e=e+1|0}}while(0);return d|0}function jN(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;if(mP(QA(d,105526)|0)|0)if((a[b>>0]|0)==101?(s2(b,105685)|0)==0:0){d=101;g=6}else{b=105519;g=4}else g=4;if((g|0)==4){d=a[b>>0]|0;if(d<<24>>24==99)if(!(s2(b,105519)|0))g=11;else{d=99;g=6}else g=6}a:do if((g|0)==6){e=20848;while(1){f=c[e>>2]|0;if(!f){g=11;break a}if((a[f>>0]|0)==d<<24>>24?(s2(f,b)|0)==0:0){b=e;break a}e=e+16|0}}while(0);if((g|0)==11)b=kN(b)|0;return b|0}function kN(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;g=h;d=iN(b)|0;if(!d){f=c[47284]|0;d=f+1|0;c[47284]=d;e=c[47283]|0;if(!e)d=KK(d<<2)|0;else d=MK(e,d<<2)|0;c[47283]=d;d=JK(16)|0;c[(c[47283]|0)+(f<<2)>>2]=d;c[d>>2]=c[5212];c[d+4>>2]=c[5213];c[d+8>>2]=c[5214];c[d+12>>2]=c[5215];e=D4(b)|0;c[d>>2]=e;do if(!0){if((a[b>>0]|0)==99?(s2(b,105519)|0)==0:0){e=1;break}c[g>>2]=c[5212];c[g+4>>2]=e;gA(0,105690,g)|0;e=0}else e=1;while(0);a[d+12>>0]=e}l=h;return d|0}function lN(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;d=+h[b>>3]*.5257311121191336;c=+h[b+8>>3]*.8944271909999159;c=d>c?d:c;h[a>>3]=c*4.97979656976556;h[a+8>>3]=c*4.73606797749979;return}function mN(a,b){a=a|0;b=b|0;var c=0.0,d=0.0,e=0.0,f=0,g=0.0,i=0.0,j=0.0,k=0,l=0,m=0.0;d=+h[b>>3];k=b+8|0;e=+h[k>>3];c=e/d;if(!(c>.9510565162951536))if(c<.9510565162951536){j=d;e=d*.9510565162951536}else j=d;else j=e*1.0514622242382672;d=j*.5257311121191336;g=j*.20081141588622725;i=j*.05020285397155681;c=.3141592653589793;f=0;while(1){if((f|0)>=10)break;h[a+(f<<4)>>3]=+E(+c)*d;h[a+(f<<4)+8>>3]=+F(+c)*d-i;m=c+.6283185307179586;l=f|1;h[a+(l<<4)>>3]=+E(+m)*g;h[a+(l<<4)+8>>3]=+F(+m)*g-i;c=c+1.2566370614359172;f=f+2|0}h[b>>3]=j;h[k>>3]=e;return}function nN(a){a=a|0;a=c[(c[a+16>>2]|0)+12>>2]|0;if(a|0){U1(c[a+44>>2]|0);U1(a)}return}function oN(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+80|0;k=o+40|0;g=o;n=o+64|0;h=o+56|0;if(!(a[e>>0]|0)){g=4208;h=b+40|0;do{c[b>>2]=c[g>>2];b=b+4|0;g=g+4|0}while((b|0)<(h|0))}else{i=(f|0)==0?106356:f;c[n>>2]=15;j=d+16|0;f=c[j>>2]|0;do if(a[(c[f+104>>2]|0)+82>>0]|0){f=pU(d,e,n)|0;if(!f){f=c[j>>2]|0;m=8;break}if(CN(d,f,g,i,c[n>>2]|0,0)|0){c[k>>2]=KB(d)|0;c[k+4>>2]=e;c[k+8>>2]=i;gA(0,106358,k)|0}}else m=8;while(0);if((m|0)==8){if((c[(c[f+8>>2]|0)+8>>2]|0)==1664)h=0;else{c[h>>2]=d;c[h+4>>2]=0}if(CN(d,0,g,e,c[n>>2]|0,h)|0)DN(d,e)}c[g+36>>2]=0;h=b+40|0;do{c[b>>2]=c[g>>2];b=b+4|0;g=g+4|0}while((b|0)<(h|0))}l=o;return}function pN(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0,g=0,i=0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0;r=l;l=l+112|0;n=r+96|0;m=r+80|0;p=r+64|0;k=r+48|0;q=r+32|0;o=r;f=r+16|0;if(!a){c[47287]=0;a=0}else{g=c[a+4>>2]|0;a=c[a>>2]|0;i=(c[(c[(xC(a)|0)+16>>2]|0)+116>>2]&3)*90|0;c[n>>2]=c[b>>2];c[n+4>>2]=c[b+4>>2];c[n+8>>2]=c[b+8>>2];c[n+12>>2]=c[b+12>>2];mK(f,n,i);c[q>>2]=c[f>>2];c[q+4>>2]=c[f+4>>2];c[q+8>>2]=c[f+8>>2];c[q+12>>2]=c[f+12>>2];a:do if(g){d=+h[g+8>>3];e=+h[g+24>>3];s=+h[q>>3];if((+h[g>>3]<=s?s<=+h[g+16>>3]:0)?(j=+h[q+8>>3],d<=j):0)a=j<=e&1;else a=0}else{if((a|0)==(c[47287]|0)){f=c[47288]|0;g=c[47290]|0;i=c[47289]|0}else{g=c[(c[a+16>>2]|0)+12>>2]|0;c[47291]=g;i=c[g+44>>2]|0;c[47289]=i;f=c[g+8>>2]|0;c[47288]=f;g=O((c[g+4>>2]|0)+-1|0,f)|0;g=(g|0)>0?g:0;c[47290]=g;c[47287]=a}a=0;b=0;while(1){if((a|0)>=(f|0)){a=1;break a}t=i+(g+a<<4)|0;c[o>>2]=c[t>>2];c[o+4>>2]=c[t+4>>2];c[o+8>>2]=c[t+8>>2];c[o+12>>2]=c[t+12>>2];t=i+(g+((a+4|0)%(f|0)|0)<<4)|0;c[k>>2]=c[q>>2];c[k+4>>2]=c[q+4>>2];c[k+8>>2]=c[q+8>>2];c[k+12>>2]=c[q+12>>2];c[p>>2]=c[46614];c[p+4>>2]=c[46615];c[p+8>>2]=c[46616];c[p+12>>2]=c[46617];c[m>>2]=c[o>>2];c[m+4>>2]=c[o+4>>2];c[m+8>>2]=c[o+8>>2];c[m+12>>2]=c[o+12>>2];c[n>>2]=c[t>>2];c[n+4>>2]=c[t+4>>2];c[n+8>>2]=c[t+8>>2];c[n+12>>2]=c[t+12>>2];b=b+((BN(k,p,m,n)|0)==0&1)|0;if((b|0)==2){a=0;break}else a=a+2|0}}while(0)}l=r;return a|0}function qN(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;e=c[b+16>>2]|0;if((a[(c[e+104>>2]|0)+82>>0]|0)!=0?(a[e+145>>0]|0)!=0:0)b=sU(b,d,0,f,g)|0;else b=0;return b|0}function rN(e,f){e=e|0;f=f|0;var i=0,j=0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0.0,C=0.0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,P=0,Q=0,R=0;P=l;l=l+48|0;H=P+24|0;G=P+16|0;A=P+8|0;z=P;L=P+32|0;n=P+28|0;M=c[e+16>>2]|0;N=M+208|0;i=c[N>>2]|0;if((i|0)==0?(b[M+260>>1]&1)==0:0)J=0;else E=4;if((E|0)==4)if(!(c[e+152>>2]&4)){cS(e,i,c[M+228>>2]|0,c[M+244>>2]|0,c[M+212>>2]|0);J=1}else J=1;I=f+16|0;i=c[I>>2]|0;r=c[i+12>>2]|0;F=c[r+44>>2]|0;K=c[r+8>>2]|0;r=c[r+4>>2]|0;if((c[47285]|0)<(K|0)){i=K+5|0;c[47285]=i;j=c[47286]|0;if(!j)i=KK(i<<4)|0;else i=MK(j,i<<4)|0;c[47286]=i;i=c[I>>2]|0}D=(c[i+104>>2]|0)+56|0;i=i+16|0;c[D>>2]=c[i>>2];c[D+4>>2]=c[i+4>>2];c[D+8>>2]=c[i+8>>2];c[D+12>>2]=c[i+12>>2];D=c[I>>2]|0;B=+h[D+32>>3]*72.0;B=(+h[D+96>>3]+ +h[D+88>>3])/+(~~(B+(B>=0.0?.5:-.5))|0);C=+h[D+40>>3]*72.0;C=+h[D+80>>3]/+(~~(C+(C>=0.0?.5:-.5))|0);D=sN(e,f)|0;c[L>>2]=0;i=d[(c[I>>2]|0)+117>>0]|0;do if(!(i&1)){if(i&2|0){s=bP(f,0,106256)|0;hS(e,s);jS(e,bP(f,0,106264)|0);j=0;i=1;break}if(i&8|0){s=bP(f,0,106272)|0;hS(e,s);jS(e,bP(f,0,106280)|0);j=0;i=1;break}if(i&4|0){s=bP(f,0,106288)|0;hS(e,s);jS(e,bP(f,0,106296)|0);j=0;i=1;break}if(!(D&1))if(!(D&576)){j=0;i=0}else{j=tN(f)|0;i=1}else{j=tN(f)|0;if(!((JJ(j,L,n)|0)<<24>>24)){jS(e,j);i=1}else{jS(e,c[L>>2]|0);i=c[L+4>>2]|0;m=ZO(f,c[47173]|0,0,0)|0;k=+g[n>>2];if(!i)kS(e,137314,m,k);else kS(e,i,m,k);i=(D&2)>>>1|2}}s=uN(e,f)|0}else{s=bP(f,0,106240)|0;hS(e,s);jS(e,bP(f,0,106248)|0);j=0;i=1}while(0);m=c[(c[I>>2]|0)+8>>2]|0;if(a[m+12>>0]|0){m=c[m>>2]|0;if((a[m>>0]|0)==99){m=(s2(m,105519)|0)==0;E=33}else u=1}else{m=1;E=33}if((E|0)==33){m=m^1;if((r|0)!=0|i<<24>>24==0|m)u=m;else{hS(e,137379);r=1;u=0}}v=(K|0)<3;w=(D&512|0)!=0;y=(D&8|0)==0;x=(D&64|0)==0;p=(D&1024|0)==0;q=(D&2130706444|0)==0;t=0;while(1){if((t|0)>=(r|0))break;n=O(t,K)|0;o=c[47286]|0;m=0;while(1){if((m|0)>=(K|0))break;R=m+n|0;k=+h[F+(R<<4)+8>>3];Q=c[I>>2]|0;h[o+(m<<4)>>3]=+h[Q+16>>3]+ +h[F+(R<<4)>>3]*B;h[o+(m<<4)+8>>3]=+h[Q+24>>3]+k*C;m=m+1|0}do if(v){if(w&(t|0)==0?(R2(j,58)|0)!=0:0)if((fI(e,o,j)|0)>1){c[z>>2]=KB(f)|0;gA(3,106304,z)|0;i=0}else i=0;mS(e,c[47286]|0,K,i&255);if(!y)vN(e,f)}else{if(!x){if((t|0)==0?(jI(e,o,j,1)|0)>1:0){c[A>>2]=KB(f)|0;gA(3,106304,A)|0}nS(e,c[47286]|0,K,0);break}if(!p){hS(e,137379);nS(e,c[47286]|0,K,i&255);hS(e,s);qS(e,(c[47286]|0)+32|0,2);break}i=i&255;if(q){nS(e,o,K,i);break}else{TM(e,o,K,D,i);break}}while(0);t=t+1|0;i=0}m=c[(c[I>>2]|0)+8>>2]|0;if(!(a[m+12>>0]|0)){m=QA(f,109025)|0;if(m|0?a[m>>0]|0:0){o=m;E=65}}else{m=c[m>>2]|0;if((a[m>>0]|0)==99?(s2(m,105519)|0)==0:0){m=QA(f,105526)|0;if(m|0?a[m>>0]|0:0){o=m;E=65}}else{o=m;E=65}}if((E|0)==65){n=c[47286]|0;m=0;while(1){if((m|0)>=(K|0))break;k=+h[F+(m<<4)+8>>3];R=c[I>>2]|0;h[n+(m<<4)>>3]=+h[R+16>>3]+ +h[F+(m<<4)>>3]*B;h[n+(m<<4)+8>>3]=+h[R+24>>3]+k*C;m=m+1|0}m=i&255;do if(!(u|i<<24>>24==0)){if(v){if(w&(t|0)==0?(R2(j,58)|0)!=0:0)if((fI(e,n,j)|0)>1){c[G>>2]=KB(f)|0;gA(3,106304,G)|0;i=0}else i=0;mS(e,c[47286]|0,K,i&255);if(y)break;vN(e,f);break}if(x)if(!(D&12)){nS(e,n,K,m);break}else{TM(e,n,K,D,m);break}else{if((jI(e,n,j,1)|0)>1){c[H>>2]=KB(f)|0;gA(3,106304,H)|0}nS(e,c[47286]|0,K,0);break}}while(0);R=c[47286]|0;sS(e,o,R,K,i,aP(f,c[47166]|0,137308)|0)}U1(c[L>>2]|0);DK(e,10,c[(c[I>>2]|0)+104>>2]|0);if(J){if(c[e+152>>2]&4|0)cS(e,c[N>>2]|0,c[M+228>>2]|0,c[M+244>>2]|0,c[M+212>>2]|0);dS(e)}l=P;return}function sN(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0;h=l;l=l+16|0;f=h;e=yN(d,f)|0;if(e|0)lS(b,e);e=c[47172]|0;if((e|0?(g=RA(d,e)|0,g|0):0)?a[g>>0]|0:0)uS(b,+_O(d,c[47172]|0,1.0,0.0));l=h;return c[f>>2]|0}function tN(a){a=a|0;return xN(a,134800)|0}function uN(b,d){b=b|0;d=d|0;d=bP(d,c[47148]|0,195059)|0;d=a[d>>0]|0?d:137314;hS(b,d);return d|0}function vN(a,b){a=a|0;b=b|0;var d=0,e=0,f=0.0,g=0,i=0.0,j=0,k=0,m=0;d=l;l=l+96|0;j=d+80|0;k=d+64|0;e=d+32|0;m=d+16|0;g=d;b=c[b+16>>2]|0;f=+h[b+80>>3];h[m+8>>3]=f*.375;i=+h[b+96>>3];h[m>>3]=i*.6614;b=b+16|0;c[k>>2]=c[m>>2];c[k+4>>2]=c[m+4>>2];c[k+8>>2]=c[m+8>>2];c[k+12>>2]=c[m+12>>2];c[j>>2]=c[b>>2];c[j+4>>2]=c[b+4>>2];c[j+8>>2]=c[b+8>>2];c[j+12>>2]=c[b+12>>2];wN(g,k,j);c[e>>2]=c[g>>2];c[e+4>>2]=c[g+4>>2];c[e+8>>2]=c[g+8>>2];c[e+12>>2]=c[g+12>>2];g=e+8|0;b=e+24|0;h[b>>3]=+h[g>>3];h[e+16>>3]=+h[e>>3]-i*1.3228;qS(a,e,2);f=+h[g>>3]-f*.75;h[g>>3]=f;h[b>>3]=f;qS(a,e,2);l=d;return}function wN(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[c+8>>3]+ +h[b+8>>3];h[a>>3]=+h[c>>3]+ +h[b>>3];h[a+8>>3]=d;return}function xN(b,d){b=b|0;d=d|0;var e=0;e=bP(b,c[47149]|0,195059)|0;if(!(a[e>>0]|0)){e=bP(b,c[47148]|0,195059)|0;return ((a[e>>0]|0)==0?d:e)|0}else return e|0;return 0}function yN(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0;e=bP(b,c[47157]|0,195059)|0;a:do if(!(a[e>>0]|0)){g=0;e=0}else{g=PI(e)|0;h=g;e=0;b:while(1){f=c[h>>2]|0;if(!f)break a;switch(a[f>>0]|0){case 102:{if(!(s2(f,111101)|0)){h=h+4|0;e=e|1;continue b}break}case 114:{if(!(s2(f,106316)|0)){f=h;do{j=f;f=f+4|0;i=c[f>>2]|0;c[j>>2]=i}while((i|0)!=0);j=h;e=e|4;h=j;continue b}if(!(s2(f,106334)|0)){f=h;do{i=f;f=f+4|0;j=c[f>>2]|0;c[i>>2]=j}while((j|0)!=0);j=h;e=e|3;h=j;continue b}break}case 100:{if(!(s2(f,106324)|0)){f=h;do{i=f;f=f+4|0;j=c[f>>2]|0;c[i>>2]=j}while((j|0)!=0);j=h;e=e|8;h=j;continue b}break}case 105:{if(!(s2(f,134335)|0)){h=h+4|0;e=e|32;continue b}break}case 115:{if((s2(f,106341)|0)==0?zN(b)|0:0){f=h;do{i=f;f=f+4|0;j=c[f>>2]|0;c[i>>2]=j}while((j|0)!=0);j=h;e=e|64;h=j;continue b}break}case 119:{if((s2(f,106349)|0)==0?AN(b)|0:0){f=h;do{i=f;f=f+4|0;j=c[f>>2]|0;c[i>>2]=j}while((j|0)!=0);j=h;e=e|512;h=j;continue b}break}default:{}}h=h+4|0}}while(0);f=c[(c[(c[b+16>>2]|0)+8>>2]|0)+8>>2]|0;if(f)e=c[f+40>>2]|e;c[d>>2]=e;return g|0}function zN(a){a=a|0;var b=0.0;a=c[(c[(c[a+16>>2]|0)+8>>2]|0)+8>>2]|0;if((((a|0)!=0?(c[a+8>>2]|0)==4:0)?(b=+h[a+16>>3],((~~(b+(b>=0.0?.5:-.5))|0)%90|0|0)==0):0)?+h[a+24>>3]==0.0:0)a=+h[a+32>>3]==0.0&1;else a=0;return a|0}function AN(a){a=a|0;a=c[(c[(c[a+16>>2]|0)+8>>2]|0)+8>>2]|0;if(!a)a=0;else a=(c[a+8>>2]|0)<3&1;return a|0}function BN(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0.0,f=0.0,g=0.0,i=0.0;i=+h[c+8>>3];g=-(+h[d+8>>3]-i);f=+h[c>>3];e=+h[d>>3]-f;f=e*i+f*g;return (+h[a+8>>3]*e+ +h[a>>3]*g-f>=0.0^+h[b>>3]*g-f+ +h[b+8>>3]*e>=0.0^1)&1|0}function CN(b,d,e,f,g,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0.0,r=0.0,s=0.0,t=0.0,u=0.0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0;G=l;l=l+192|0;B=G+176|0;E=G+160|0;m=G+144|0;z=G+128|0;A=G+112|0;n=G+96|0;o=G+80|0;p=G+64|0;v=G+48|0;w=G+32|0;x=G+16|0;D=G;if(!d){c[E>>2]=0;c[E+4>>2]=0;c[E+8>>2]=0;c[E+12>>2]=0;m=(c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&1|0)==0;C=c[b+16>>2]|0;q=+h[C+80>>3]*.5;j=+h[C+88>>3];t=m?q:j;q=m?j:q;m=0;C=E;j=0.0;k=-q;r=t;F=E+8|0;s=0.0;t=-t}else{k=+h[d>>3];t=+h[d+8>>3];q=+h[d+16>>3];r=+h[d+24>>3];bN(m,(q+k)*.5,(r+t)*.5);c[E>>2]=c[m>>2];c[E+4>>2]=c[m+4>>2];c[E+8>>2]=c[m+8>>2];c[E+12>>2]=c[m+12>>2];H=E+8|0;m=1;C=E;j=+h[E>>3];F=H;s=+h[H>>3]}u=(q>r?q:r)*4.0;a:do if((f|0)!=0?(y=a[f>>0]|0,y<<24>>24!=0):0){f=f+1|0;switch(y<<24>>24|0){case 99:{p=1;g=0;f=0;n=0;j=0.0;o=0;break a}case 101:{if(a[f>>0]|0){p=1;g=0;f=0;n=0;j=0.0;o=1;break a}if(!i)h[C>>3]=q;else{EN(z,i,s,u);c[E>>2]=c[z>>2];c[E+4>>2]=c[z+4>>2];c[E+8>>2]=c[z+8>>2];c[E+12>>2]=c[z+12>>2]}p=0;g=g&2;f=0;n=1;j=0.0;o=0;m=1;break a}case 115:{h[F>>3]=t;switch(a[f>>0]|0){case 0:{if(!i)h[C>>3]=j;else{EN(A,i,-u,j);c[E>>2]=c[A>>2];c[E+4>>2]=c[A+4>>2];c[E+8>>2]=c[A+8>>2];c[E+12>>2]=c[A+12>>2]}p=0;g=g&1;f=0;n=1;j=-1.5707963267948966;o=0;m=1;break a}case 101:{if(!i)h[C>>3]=q;else{EN(n,i,-u,u);c[E>>2]=c[n>>2];c[E+4>>2]=c[n+4>>2];c[E+8>>2]=c[n+8>>2];c[E+12>>2]=c[n+12>>2]}p=0;g=g&3;f=0;n=1;j=-.7853981633974483;o=0;m=1;break a}case 119:{if(!i)h[C>>3]=k;else{u=-u;EN(o,i,u,u);c[E>>2]=c[o>>2];c[E+4>>2]=c[o+4>>2];c[E+8>>2]=c[o+8>>2];c[E+12>>2]=c[o+12>>2]}p=0;g=g&9;f=0;n=1;j=-2.356194490192345;o=0;m=1;break a}default:{h[F>>3]=s;p=1;g=0;f=0;n=0;j=0.0;o=1;break a}}}case 119:{if(a[f>>0]|0){p=1;g=0;f=0;n=0;j=0.0;o=1;break a}if(!i)h[C>>3]=k;else{EN(p,i,s,-u);c[E>>2]=c[p>>2];c[E+4>>2]=c[p+4>>2];c[E+8>>2]=c[p+8>>2];c[E+12>>2]=c[p+12>>2]}p=0;g=g&8;f=0;n=1;j=3.141592653589793;o=0;m=1;break a}case 110:{h[F>>3]=r;switch(a[f>>0]|0){case 0:{if(!i)h[C>>3]=j;else{EN(v,i,u,j);c[E>>2]=c[v>>2];c[E+4>>2]=c[v+4>>2];c[E+8>>2]=c[v+8>>2];c[E+12>>2]=c[v+12>>2]}p=0;g=g&4;f=0;n=1;j=1.5707963267948966;o=0;m=1;break a}case 101:{if(!i)h[C>>3]=q;else{EN(w,i,u,u);c[E>>2]=c[w>>2];c[E+4>>2]=c[w+4>>2];c[E+8>>2]=c[w+8>>2];c[E+12>>2]=c[w+12>>2]}p=0;g=g&6;f=0;n=1;j=.7853981633974483;o=0;m=1;break a}case 119:{if(!i)h[C>>3]=k;else{EN(x,i,u,-u);c[E>>2]=c[x>>2];c[E+4>>2]=c[x+4>>2];c[E+8>>2]=c[x+8>>2];c[E+12>>2]=c[x+12>>2]}p=0;g=g&12;f=0;n=1;j=2.356194490192345;o=0;m=1;break a}default:{h[F>>3]=s;p=1;g=0;f=0;n=0;j=0.0;o=1;break a}}}case 95:{p=1;f=1;n=0;j=0.0;o=0;break a}default:{p=1;g=0;f=0;n=0;j=0.0;o=1;break a}}}else{p=1;g=0;f=0;n=0;j=0.0;o=0}while(0);H=(c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&3)*90|0;c[B>>2]=c[E>>2];c[B+4>>2]=c[E+4>>2];c[B+8>>2]=c[E+8>>2];c[B+12>>2]=c[E+12>>2];lK(D,B,H);c[E>>2]=c[D>>2];c[E+4>>2]=c[D+4>>2];c[E+8>>2]=c[D+8>>2];c[E+12>>2]=c[D+12>>2];if(!(f<<24>>24))g=FN(g,c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&3)|0;a[e+33>>0]=g;c[e+24>>2]=d;k=+h[C>>3];h[e>>3]=+(~~(k+(k>=0.0?.5:-.5))|0);k=+h[F>>3];h[e+8>>3]=+(~~(k+(k>=0.0?.5:-.5))|0);h[e+16>>3]=+GN(j,c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&3);k=+h[C>>3];j=+h[F>>3];if(k==0.0&j==0.0)g=-128;else{u=+K(+j,+k);t=u+4.71238898038469;g=~~((!(t>=6.283185307179586)?t:u+-1.5707963267948966)*40.74366543152521)&255}a[e+32>>0]=g;a[e+29>>0]=n;a[e+28>>0]=m;a[e+30>>0]=p;a[e+31>>0]=f;l=G;return o|0}function DN(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=KB(a)|0;c[e+4>>2]=b;gA(0,106419,e)|0;l=d;return}function EN(a,b,d,e){a=a|0;b=b|0;d=+d;e=+e;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;p=l;l=l+128|0;g=p+112|0;j=p+48|0;k=p+16|0;m=p+32|0;n=p;o=c[b>>2]|0;i=c[(c[(xC(o)|0)+16>>2]|0)+116>>2]&3;h[k>>3]=e;h[k+8>>3]=d;f=(i|0)!=0;if(f){c[g>>2]=c[k>>2];c[g+4>>2]=c[k+4>>2];c[g+8>>2]=c[k+8>>2];c[g+12>>2]=c[k+12>>2];lK(m,g,i*90|0);c[k>>2]=c[m>>2];c[k+4>>2]=c[m+4>>2];c[k+8>>2]=c[m+8>>2];c[k+12>>2]=c[m+12>>2]}m=j+48|0;q=j+32|0;c[j>>2]=0;c[j+4>>2]=0;c[j+8>>2]=0;c[j+12>>2]=0;c[j+16>>2]=0;c[j+20>>2]=0;c[j+24>>2]=0;c[j+28>>2]=0;c[q>>2]=c[k>>2];c[q+4>>2]=c[k+4>>2];c[q+8>>2]=c[k+8>>2];c[q+12>>2]=c[k+12>>2];c[m>>2]=c[k>>2];c[m+4>>2]=c[k+4>>2];c[m+8>>2]=c[k+8>>2];c[m+12>>2]=c[k+12>>2];$N(b,c[(c[(c[(c[o+16>>2]|0)+8>>2]|0)+4>>2]|0)+12>>2]|0,j,1);if(f){c[g>>2]=c[j>>2];c[g+4>>2]=c[j+4>>2];c[g+8>>2]=c[j+8>>2];c[g+12>>2]=c[j+12>>2];mK(n,g,i*90|0);c[j>>2]=c[n>>2];c[j+4>>2]=c[n+4>>2];c[j+8>>2]=c[n+8>>2];c[j+12>>2]=c[n+12>>2]};c[a>>2]=c[j>>2];c[a+4>>2]=c[j+4>>2];c[a+8>>2]=c[j+8>>2];c[a+12>>2]=c[j+12>>2];l=p;return}function FN(a,b){a=a|0;b=b|0;a:do switch(b|0){case 3:switch(a|0){case 4:{a=2;break a}case 1:{a=8;break a}case 8:{a=1;break a}case 2:{a=4;break a}default:break a}case 2:switch(a|0){case 4:{a=1;break a}case 1:{a=4;break a}default:break a}case 1:switch(a|0){case 4:{a=2;break a}case 1:{a=8;break a}case 8:{a=4;break a}case 2:{a=1;break a}default:break a}default:{}}while(0);return a|0}function GN(a,b){a=+a;b=b|0;switch(b|0){case 3:{if(!(a==3.141592653589793))if(!(a==2.356194490192345))if(!(a==1.5707963267948966))if(!(a==0.0))if(!(a==-.7853981633974483)){if(a==-1.5707963267948966)a=3.141592653589793}else a=2.356194490192345;else a=1.5707963267948966;else a=0.0;else a=-.7853981633974483;else a=-1.5707963267948966;break}case 2:{a=-a;break}case 1:{a=a+-1.5707963267948966;break}default:{}}return +a}function HN(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0.0,g=0.0,i=0,j=0,k=0;e=l;l=l+32|0;k=e+16|0;i=e;a=c[a>>2]|0;j=(c[(c[(xC(a)|0)+16>>2]|0)+116>>2]&3)*90|0;c[k>>2]=c[b>>2];c[k+4>>2]=c[b+4>>2];c[k+8>>2]=c[b+8>>2];c[k+12>>2]=c[b+12>>2];mK(i,k,j);d=+h[i>>3];g=+h[i+8>>3];a=c[a+16>>2]|0;f=+h[a+80>>3]*.5;if(!(!(g>=-f)|!(g<=f))?d>=-+h[a+88>>3]:0)a=d<=+h[a+96>>3]&1;else a=0;l=e;return a|0}function IN(a,d){a=a|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0.0;n=l;l=l+32|0;m=n;i=c[a+16>>2]|0;j=i+208|0;g=c[j>>2]|0;if(!g)k=(b[i+260>>1]&1)!=0;else k=1;e=d+16|0;d=c[e>>2]|0;f=c[d+12>>2]|0;if(f|0){if(k?(c[a+152>>2]&4|0)==0:0){cS(a,g,c[i+228>>2]|0,c[i+244>>2]|0,c[i+212>>2]|0);d=c[e>>2]|0}g=c[a+36>>2]|0;p=+h[d+24>>3]+ +(c[f+8>>2]|0);o=c[f>>2]|0;h[m>>3]=+h[d+16>>3]+ +(c[f+4>>2]|0);h[m+8>>3]=p;c[m+16>>2]=o;w4(g,106450,m)|0;g=c[e>>2]|0;m=(c[g+104>>2]|0)+56|0;g=g+16|0;c[m>>2]=c[g>>2];c[m+4>>2]=c[g+4>>2];c[m+8>>2]=c[g+8>>2];c[m+12>>2]=c[g+12>>2];DK(a,10,c[(c[e>>2]|0)+104>>2]|0);if(k){if(c[a+152>>2]&4|0)cS(a,c[j>>2]|0,c[i+228>>2]|0,c[i+244>>2]|0,c[i+212>>2]|0);dS(a)}}l=n;return}function JN(a){a=a|0;eN(c[(c[a+16>>2]|0)+12>>2]|0);return}function KN(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0;m=l;l=l+64|0;k=m+40|0;h=m;if(!(a[f>>0]|0)){h=4208;g=b+40|0;do{c[b>>2]=c[h>>2];b=b+4|0;h=h+4|0}while((b|0)<(g|0))}else{g=(g|0)==0?106356:g;i=c[(c[e+16>>2]|0)+12>>2]|0;j=QN(i,f)|0;if(!j){if(CN(e,i+16|0,h,f,15,0)|0)DN(e,f)}else if(CN(e,j+16|0,h,g,d[j+65>>0]|0,0)|0){c[k>>2]=KB(e)|0;c[k+4>>2]=f;c[k+8>>2]=g;gA(0,106358,k)|0}g=b+40|0;do{c[b>>2]=c[h>>2];b=b+4|0;h=h+4|0}while((b|0)<(g|0))}l=m;return}function LN(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0,g=0,i=0,j=0.0,k=0,m=0,n=0.0;k=l;l=l+32|0;m=k+16|0;i=k;f=c[a+4>>2]|0;a=c[a>>2]|0;g=(c[(c[(xC(a)|0)+16>>2]|0)+116>>2]&3)*90|0;c[m>>2]=c[b>>2];c[m+4>>2]=c[b+4>>2];c[m+8>>2]=c[b+8>>2];c[m+12>>2]=c[b+12>>2];mK(i,m,g);c[b>>2]=c[i>>2];c[b+4>>2]=c[i+4>>2];c[b+8>>2]=c[i+8>>2];c[b+12>>2]=c[i+12>>2];if(!f){f=c[(c[a+16>>2]|0)+12>>2]|0;g=f+16|0;a=f+24|0;i=f+32|0;f=f+40|0}else{g=f;a=f+8|0;i=f+16|0;f=f+24|0}e=+h[a>>3];d=+h[f>>3];n=+h[b>>3];if((+h[g>>3]<=n?n<=+h[i>>3]:0)?(j=+h[b+8>>3],e<=j):0)a=j<=d&1;else a=0;l=k;return a|0}function MN(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0,u=0,v=0.0;u=l;l=l+80|0;r=u+64|0;q=u+32|0;t=u;a:do if(!(a[d+28>>0]|0))e=0;else{i=+h[d>>3];p=b+16|0;m=c[(c[p>>2]|0)+12>>2]|0;k=m+48|0;m=m+56|0;d=0;while(1){if((d|0)>=(c[k>>2]|0))break a;n=(c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&1|0)==0;o=c[(c[m>>2]|0)+(d<<2)>>2]|0;j=+(~~+h[(n?o+16|0:o+24|0)>>3]|0);if(i>=j?(s=+(~~+h[(n?o+32|0:o+40|0)>>3]|0),i<=s):0)break;d=d+1|0}if(!(c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&1)){t=c[p>>2]|0;v=+h[t+16>>3];h[f>>3]=v+j;j=+h[t+24>>3];i=+h[t+80>>3];h[f+8>>3]=j-i*.5;h[f+16>>3]=v+s}else{o=(c[(c[m>>2]|0)+(d<<2)>>2]|0)+16|0;b=(c[p>>2]|0)+16|0;c[q>>2]=c[o>>2];c[q+4>>2]=c[o+4>>2];c[q+8>>2]=c[o+8>>2];c[q+12>>2]=c[o+12>>2];c[q+16>>2]=c[o+16>>2];c[q+20>>2]=c[o+20>>2];c[q+24>>2]=c[o+24>>2];c[q+28>>2]=c[o+28>>2];c[r>>2]=c[b>>2];c[r+4>>2]=c[b+4>>2];c[r+8>>2]=c[b+8>>2];c[r+12>>2]=c[b+12>>2];nK(t,q,r);c[f>>2]=c[t>>2];c[f+4>>2]=c[t+4>>2];c[f+8>>2]=c[t+8>>2];c[f+12>>2]=c[t+12>>2];c[f+16>>2]=c[t+16>>2];c[f+20>>2]=c[t+20>>2];c[f+24>>2]=c[t+24>>2];c[f+28>>2]=c[t+28>>2];t=c[p>>2]|0;i=+h[t+80>>3];j=+h[t+24>>3]}h[f+24>>3]=i*.5+j;c[g>>2]=1}while(0);l=u;return e|0}function NN(d,e){d=d|0;e=e|0;var f=0,i=0,j=0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0.0;w=l;l=l+144|0;n=w+96|0;p=w+64|0;r=w;s=w+128|0;t=c[d+16>>2]|0;u=t+208|0;f=c[u>>2]|0;if(!f)v=(b[t+260>>1]&1)!=0;else v=1;m=e+16|0;q=c[m>>2]|0;o=c[q+12>>2]|0;j=o+16|0;c[p>>2]=c[j>>2];c[p+4>>2]=c[j+4>>2];c[p+8>>2]=c[j+8>>2];c[p+12>>2]=c[j+12>>2];c[p+16>>2]=c[j+16>>2];c[p+20>>2]=c[j+20>>2];c[p+24>>2]=c[j+24>>2];c[p+28>>2]=c[j+28>>2];x=+h[q+16>>3];h[p>>3]=+h[p>>3]+x;k=+h[q+24>>3];q=p+8|0;h[q>>3]=+h[q>>3]+k;q=p+16|0;h[q>>3]=+h[q>>3]+x;j=p+24|0;h[j>>3]=+h[j>>3]+k;if(v?(c[d+152>>2]&4|0)==0:0)cS(d,f,c[t+228>>2]|0,c[t+244>>2]|0,c[t+212>>2]|0);f=sN(d,e)|0;uN(d,e)|0;c[s>>2]=0;if(!(f&1))i=0;else{i=tN(e)|0;if(!((JJ(i,s,n)|0)<<24>>24)){jS(d,i);i=1}else{jS(d,c[s>>2]|0);i=c[s+4>>2]|0;j=ZO(e,c[47173]|0,0,0)|0;k=+g[n>>2];if(!i)kS(d,137314,j,k);else kS(d,i,j,k);i=f>>>1&1|2}}j=c[c[(c[m>>2]|0)+8>>2]>>2]|0;if((a[j>>0]|0)==77){m=(s2(j,106227)|0)==0;f=m?f|4:f}if(!(f&2130706444)){c[n>>2]=c[p>>2];c[n+4>>2]=c[p+4>>2];c[n+8>>2]=c[p+8>>2];c[n+12>>2]=c[p+12>>2];c[n+16>>2]=c[p+16>>2];c[n+20>>2]=c[p+20>>2];c[n+24>>2]=c[p+24>>2];c[n+28>>2]=c[p+28>>2];oS(d,n,i)}else{c[r>>2]=c[p>>2];c[r+4>>2]=c[p+4>>2];c[r+8>>2]=c[p+8>>2];c[r+12>>2]=c[p+12>>2];p=r+32|0;c[p>>2]=c[q>>2];c[p+4>>2]=c[q+4>>2];c[p+8>>2]=c[q+8>>2];c[p+12>>2]=c[q+12>>2];h[r+16>>3]=+h[p>>3];h[r+24>>3]=+h[r+8>>3];h[r+48>>3]=+h[r>>3];h[r+56>>3]=+h[r+40>>3];TM(d,r,4,f,i)}ON(d,e,o);U1(c[s>>2]|0);if(v){if(c[d+152>>2]&4|0)cS(d,c[u>>2]|0,c[t+228>>2]|0,c[t+244>>2]|0,c[t+212>>2]|0);dS(d)}l=w;return}function ON(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+144|0;p=u+128|0;o=u+112|0;q=u+16|0;r=u;g=u+96|0;i=u+80|0;s=u+64|0;t=u+48|0;j=e+52|0;f=c[j>>2]|0;if(!f)f=d+16|0;else{n=f+56|0;m=e+16|0;f=e+32|0;c[o>>2]=c[m>>2];c[o+4>>2]=c[m+4>>2];c[o+8>>2]=c[m+8>>2];c[o+12>>2]=c[m+12>>2];c[p>>2]=c[f>>2];c[p+4>>2]=c[f+4>>2];c[p+8>>2]=c[f+8>>2];c[p+12>>2]=c[f+12>>2];PN(g,o,p);f=d+16|0;m=(c[f>>2]|0)+16|0;c[o>>2]=c[g>>2];c[o+4>>2]=c[g+4>>2];c[o+8>>2]=c[g+8>>2];c[o+12>>2]=c[g+12>>2];c[p>>2]=c[m>>2];c[p+4>>2]=c[m+4>>2];c[p+8>>2]=c[m+8>>2];c[p+12>>2]=c[m+12>>2];wN(i,o,p);c[n>>2]=c[i>>2];c[n+4>>2]=c[i+4>>2];c[n+8>>2]=c[i+8>>2];c[n+12>>2]=c[i+12>>2];DK(b,10,c[j>>2]|0);uN(b,d)|0}k=(c[f>>2]|0)+16|0;c[r>>2]=c[k>>2];c[r+4>>2]=c[k+4>>2];c[r+8>>2]=c[k+8>>2];c[r+12>>2]=c[k+12>>2];k=e+48|0;m=e+64|0;n=q+16|0;j=e+56|0;e=q+16|0;i=0;while(1){if((i|0)>=(c[k>>2]|0))break;if((i|0)>0){f=(c[j>>2]|0)+(i<<2)|0;g=c[f>>2]|0;if(!(a[m>>0]|0)){g=g+32|0;c[n>>2]=c[g>>2];c[n+4>>2]=c[g+4>>2];c[n+8>>2]=c[g+8>>2];c[n+12>>2]=c[g+12>>2];h[q>>3]=+h[(c[f>>2]|0)+16>>3];f=n;g=0}else{g=g+16|0;c[q>>2]=c[g>>2];c[q+4>>2]=c[g+4>>2];c[q+8>>2]=c[g+8>>2];c[q+12>>2]=c[g+12>>2];h[e>>3]=+h[q>>3];f=(c[f>>2]|0)+32|0;g=1}h[q+(g<<4)+8>>3]=+h[f+8>>3];c[o>>2]=c[q>>2];c[o+4>>2]=c[q+4>>2];c[o+8>>2]=c[q+8>>2];c[o+12>>2]=c[q+12>>2];c[p>>2]=c[r>>2];c[p+4>>2]=c[r+4>>2];c[p+8>>2]=c[r+8>>2];c[p+12>>2]=c[r+12>>2];wN(s,o,p);c[q>>2]=c[s>>2];c[q+4>>2]=c[s+4>>2];c[q+8>>2]=c[s+8>>2];c[q+12>>2]=c[s+12>>2];c[o>>2]=c[n>>2];c[o+4>>2]=c[n+4>>2];c[o+8>>2]=c[n+8>>2];c[o+12>>2]=c[n+12>>2];c[p>>2]=c[r>>2];c[p+4>>2]=c[r+4>>2];c[p+8>>2]=c[r+8>>2];c[p+12>>2]=c[r+12>>2];wN(t,o,p);c[n>>2]=c[t>>2];c[n+4>>2]=c[t+4>>2];c[n+8>>2]=c[t+8>>2];c[n+12>>2]=c[t+12>>2];qS(b,q,2)}ON(b,d,c[(c[j>>2]|0)+(i<<2)>>2]|0);i=i+1|0}l=u;return}function PN(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=(+h[c+8>>3]+ +h[b+8>>3])*.5;h[a>>3]=(+h[c>>3]+ +h[b>>3])*.5;h[a+8>>3]=d;return}function QN(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;e=c[b+60>>2]|0;if(!(((e|0)!=0?(a[e>>0]|0)==(a[d>>0]|0):0)?(s2(e,d)|0)==0:0))f=4;a:do if((f|0)==4){g=b+56|0;f=c[b+48>>2]|0;e=0;while(1){if((e|0)>=(f|0)){b=0;break a}b=QN(c[(c[g>>2]|0)+(e<<2)>>2]|0,d)|0;if(!b)e=e+1|0;else break}}while(0);return b|0}function RN(a,b){a=a|0;b=b|0;var d=0;d=b+8|0;h[d>>3]=+h[d>>3]*1.375;c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];return}function SN(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0.0,i=0.0,j=0.0,k=0.0,l=0.0,m=0.0;k=+h[b>>3];f=k*.5;m=+h[b+8>>3];i=m*.5;h[a>>3]=f;e=m*.40909090909090906;h[a+8>>3]=e;h[a+16>>3]=f;g=m*.4592530909090909;h[a+24>>3]=g;j=k*.275892;h[a+32>>3]=j;h[a+40>>3]=i;h[a+48>>3]=0.0;h[a+56>>3]=i;k=k*-.275892;h[a+64>>3]=k;h[a+72>>3]=i;l=-f;h[a+80>>3]=l;h[a+88>>3]=g;d=a+96|0;h[d>>3]=l;h[a+104>>3]=e;b=a+112|0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];b=a+128|0;h[b>>3]=l;h[a+136>>3]=m*-.40909090909090906;d=a+144|0;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];h[a+160>>3]=l;g=-g;h[a+168>>3]=g;h[a+176>>3]=k;i=-i;h[a+184>>3]=i;h[a+192>>3]=0.0;h[a+200>>3]=i;h[a+208>>3]=j;h[a+216>>3]=i;h[a+224>>3]=f;h[a+232>>3]=g;d=a+240|0;h[d>>3]=f;h[a+248>>3]=-e;b=a+256|0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];b=a+288|0;d=a+272|0;c[d>>2]=c[a>>2];c[d+4>>2]=c[a+4>>2];c[d+8>>2]=c[a+8>>2];c[d+12>>2]=c[a+12>>2];c[b>>2]=c[a>>2];c[b+4>>2]=c[a+4>>2];c[b+8>>2]=c[a+8>>2];c[b+12>>2]=c[a+12>>2];return}function TN(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0.0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0.0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0;x=l;l=l+144|0;u=x+112|0;t=x+96|0;v=x+80|0;s=x+64|0;w=x+32|0;q=x+16|0;r=x;g=x+48|0;do if(a){j=c[a+4>>2]|0;k=c[a>>2]|0;o=(c[(c[(xC(k)|0)+16>>2]|0)+116>>2]&3)*90|0;c[u>>2]=c[b>>2];c[u+4>>2]=c[b+4>>2];c[u+8>>2]=c[b+8>>2];c[u+12>>2]=c[b+12>>2];mK(g,u,o);c[w>>2]=c[g>>2];c[w+4>>2]=c[g+4>>2];c[w+8>>2]=c[g+8>>2];c[w+12>>2]=c[g+12>>2];if(j|0){d=+h[w>>3];if(!(+h[j>>3]<=d)){a=0;break}if(!(d<=+h[j+16>>3])){a=0;break}d=+h[w+8>>3];if(!(+h[j+8>>3]<=d)){a=0;break}a=d<=+h[j+24>>3];break}if((k|0)==(c[47292]|0)){d=+h[23309];e=+h[23310];i=+h[23311]}else{a=k+16|0;b=c[(c[a>>2]|0)+12>>2]|0;c[47293]=b;c[47294]=c[b+44>>2];c[47295]=c[b+8>>2];if(!(c[b+40>>2]&2048)){n=(c[(c[(xC(k)|0)+16>>2]|0)+116>>2]&1|0)==0;o=c[a>>2]|0;h[(n?186496:186504)>>3]=+h[o+96>>3]+ +h[o+88>>3];h[(n?186504:186496)>>3]=+h[o+80>>3];i=+h[o+32>>3]*72.0;f=+h[o+40>>3]*72.0;f=+(~~(f+(f>=0.0?.5:-.5))|0);i=+(~~(i+(i>=0.0?.5:-.5))|0)}else{yP(u,b);i=+h[u+16>>3]-+h[u>>3];f=+h[u+24>>3]-+h[u+8>>3];o=(c[(c[(xC(k)|0)+16>>2]|0)+116>>2]&1|0)==0;h[(o?186496:186504)>>3]=i;h[(o?186504:186496)>>3]=f}d=+h[23312];if(d==0.0){h[23312]=1.0;d=1.0}e=+h[23313];if(e==0.0){h[23313]=1.0;e=1.0}d=i/d;h[23309]=d;e=f/e;h[23310]=e;i=i*.5;h[23311]=i;h[23314]=f*.5;o=O((c[(c[47293]|0)+4>>2]|0)+-1|0,c[47295]|0)|0;c[47296]=(o|0)>0?o:0;c[47292]=k}f=+h[w>>3]*d;h[w>>3]=f;o=w+8|0;d=+h[o>>3]*e;h[o>>3]=d;if(!(+B(+f)>i)?(e=+B(+d),p=+h[23314],!(e>p)):0){o=c[47295]|0;if((o|0)<3){a=+L2(f/i,d/p)<1.0;break}b=(c[47297]|0)%(o|0)|0;a=(b+1|0)%(o|0)|0;k=c[47294]|0;m=c[47296]|0;n=k+(m+b<<4)|0;c[q>>2]=c[n>>2];c[q+4>>2]=c[n+4>>2];c[q+8>>2]=c[n+8>>2];c[q+12>>2]=c[n+12>>2];n=k+(m+a<<4)|0;c[r>>2]=c[n>>2];c[r+4>>2]=c[n+4>>2];c[r+8>>2]=c[n+8>>2];c[r+12>>2]=c[n+12>>2];c[s>>2]=c[w>>2];c[s+4>>2]=c[w+4>>2];c[s+8>>2]=c[w+8>>2];c[s+12>>2]=c[w+12>>2];c[v>>2]=c[46630];c[v+4>>2]=c[46631];c[v+8>>2]=c[46632];c[v+12>>2]=c[46633];c[t>>2]=c[q>>2];c[t+4>>2]=c[q+4>>2];c[t+8>>2]=c[q+8>>2];c[t+12>>2]=c[q+12>>2];c[u>>2]=c[n>>2];c[u+4>>2]=c[n+4>>2];c[u+8>>2]=c[n+8>>2];c[u+12>>2]=c[n+12>>2];if(BN(s,v,t,u)|0){c[s>>2]=c[w>>2];c[s+4>>2]=c[w+4>>2];c[s+8>>2]=c[w+8>>2];c[s+12>>2]=c[w+12>>2];c[v>>2]=c[q>>2];c[v+4>>2]=c[q+4>>2];c[v+8>>2]=c[q+8>>2];c[v+12>>2]=c[q+12>>2];c[t>>2]=c[r>>2];c[t+4>>2]=c[r+4>>2];c[t+8>>2]=c[r+8>>2];c[t+12>>2]=c[r+12>>2];c[u>>2]=c[46630];c[u+4>>2]=c[46631];c[u+8>>2]=c[46632];c[u+12>>2]=c[46633];n=(BN(s,v,t,u)|0)!=0;if(n?(c[s>>2]=c[w>>2],c[s+4>>2]=c[w+4>>2],c[s+8>>2]=c[w+8>>2],c[s+12>>2]=c[w+12>>2],c[v>>2]=c[r>>2],c[v+4>>2]=c[r+4>>2],c[v+8>>2]=c[r+8>>2],c[v+12>>2]=c[r+12>>2],c[t>>2]=c[46630],c[t+4>>2]=c[46631],c[t+8>>2]=c[46632],c[t+12>>2]=c[46633],c[u>>2]=c[q>>2],c[u+4>>2]=c[q+4>>2],c[u+8>>2]=c[q+8>>2],c[u+12>>2]=c[q+12>>2],(BN(s,v,t,u)|0)!=0):0){a=1;break}else{j=b;b=1}while(1){if((b|0)>=(o|0)){b=31;break}if(n){g=a;a=(a+1|0)%(o|0)|0}else{g=(j+-1+o|0)%(o|0)|0;a=j}q=k+(m+g<<4)|0;r=k+(m+a<<4)|0;c[s>>2]=c[w>>2];c[s+4>>2]=c[w+4>>2];c[s+8>>2]=c[w+8>>2];c[s+12>>2]=c[w+12>>2];c[v>>2]=c[46630];c[v+4>>2]=c[46631];c[v+8>>2]=c[46632];c[v+12>>2]=c[46633];c[t>>2]=c[q>>2];c[t+4>>2]=c[q+4>>2];c[t+8>>2]=c[q+8>>2];c[t+12>>2]=c[q+12>>2];c[u>>2]=c[r>>2];c[u+4>>2]=c[r+4>>2];c[u+8>>2]=c[r+8>>2];c[u+12>>2]=c[r+12>>2];if(!(BN(s,v,t,u)|0)){b=30;break}else{j=g;b=b+1|0}}if((b|0)==30){c[47297]=g;a=0;break}else if((b|0)==31){c[47297]=j;a=1;break}}else a=0}else a=0}else{c[47292]=0;a=0}while(0);l=x;return a&1|0}function UN(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0,g=0.0,i=0,j=0,k=0;j=l;l=l+32|0;f=j+16|0;i=j;if(a){a=c[a>>2]|0;k=(c[(c[(xC(a)|0)+16>>2]|0)+116>>2]&3)*90|0;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];mK(i,f,k);g=+h[i>>3];e=+h[i+8>>3];if((a|0)==(c[47300]|0))d=+h[23317];else{i=c[(c[a+16>>2]|0)+12>>2]|0;k=(c[i+4>>2]<<1)+-2|0;d=+h[(c[i+44>>2]|0)+((((k|0)>0?k:0)|1)<<4)>>3];h[23317]=d;c[47300]=a}if(+B(+g)>d|+B(+e)>d)a=0;else a=+L2(g,e)<=d&1}else{c[47300]=0;a=0}l=j;return a|0}function VN(e,f){e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0.0,v=0,w=0;t=l;l=l+16|0;j=t;r=c[e+16>>2]|0;s=r+208|0;g=c[s>>2]|0;if((g|0)==0?(b[r+260>>1]&1)==0:0)q=0;else if(!(c[e+152>>2]&4)){cS(e,g,c[r+228>>2]|0,c[r+244>>2]|0,c[r+212>>2]|0);q=1}else q=1;n=f+16|0;k=c[(c[n>>2]|0)+12>>2]|0;o=c[k+44>>2]|0;p=c[k+8>>2]|0;k=c[k+4>>2]|0;if((c[47298]|0)<(p|0)){g=p+2|0;c[47298]=g;i=c[47299]|0;if(!i)g=KK(g<<4)|0;else g=MK(i,g<<4)|0;c[47299]=g}yN(f,j)|0;if(!(c[j>>2]&32))lS(e,22020);else lS(e,22016);g=c[47172]|0;if(g|0)uS(e,+_O(f,g,1.0,0.0));g=d[(c[n>>2]|0)+117>>0]|0;do if(!(g&1)){if(g&2|0){hS(e,bP(f,0,106256)|0);g=bP(f,0,106264)|0;jS(e,g);break}if(g&8|0){hS(e,bP(f,0,106272)|0);g=bP(f,0,106280)|0;jS(e,g);break}if(!(g&4)){g=xN(f,137314)|0;jS(e,g);uN(e,f)|0;break}else{hS(e,bP(f,0,106288)|0);g=bP(f,0,106296)|0;jS(e,g);break}}else{hS(e,bP(f,0,106240)|0);g=bP(f,0,106248)|0;jS(e,g)}while(0);if(!k)if(!(a[g>>0]|0))i=1;else{hS(e,g);i=1}else i=k;j=1;f=0;while(1){if((f|0)>=(i|0))break;k=O(f,p)|0;m=c[47299]|0;g=0;while(1){if((g|0)>=(p|0))break;w=g+k|0;u=+h[o+(w<<4)+8>>3];v=c[n>>2]|0;h[m+(g<<4)>>3]=+h[v+16>>3]+ +h[o+(w<<4)>>3];h[m+(g<<4)+8>>3]=+h[v+24>>3]+u;g=g+1|0}mS(e,m,p,j);j=0;f=f+1|0}if(q){if(c[e+152>>2]&4|0)cS(e,c[s>>2]|0,c[r+228>>2]|0,c[r+244>>2]|0,c[r+212>>2]|0);dS(e)}l=t;return}function WN(a,b,e,f){a=a|0;b=b|0;e=e|0;f=f|0;var g=0,h=0;g=l;l=l+48|0;h=g;e=XN(b,e,f)|0;c[h+36>>2]=c[f+36>>2];CN(b,c[f+24>>2]|0,h,e,d[f+33>>0]|0,0)|0;b=h;e=a+40|0;do{c[a>>2]=c[b>>2];a=a+4|0;b=b+4|0}while((a|0)<(e|0));l=g;return}function XN(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0.0,k=0,m=0.0,n=0.0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0,B=0.0,C=0;z=l;l=l+32|0;y=z;g=z+24|0;k=z+16|0;x=c[(c[(c[(xC(b)|0)+60>>2]|0)+16>>2]|0)+116>>2]&3;f=b+16|0;w=(c[f>>2]|0)+16|0;c[y>>2]=c[w>>2];c[y+4>>2]=c[w+4>>2];c[y+8>>2]=c[w+8>>2];c[y+12>>2]=c[w+12>>2];YN(g,y,x);w=(c[d+16>>2]|0)+16|0;c[y>>2]=c[w>>2];c[y+4>>2]=c[w+4>>2];c[y+8>>2]=c[w+8>>2];c[y+12>>2]=c[w+12>>2];YN(k,y,x);x=a[e+33>>0]|0;y=x&255;a:do switch(x<<24>>24){case 0:case 15:{d=0;break}default:{d=c[e+24>>2]|0;if(!d){x=(c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&1|0)==0;w=c[f>>2]|0;B=+h[w+80>>3]*.5;A=-B;m=+h[w+88>>3];j=-m;n=x?j:A;i=x?m:B;m=x?B:m;j=x?A:j}else{n=+h[d>>3];i=+h[d+16>>3];m=+h[d+24>>3];j=+h[d+8>>3]}x=c[g>>2]|0;v=c[g+4>>2]|0;w=c[k>>2]|0;s=c[k+4>>2]|0;t=~~j;u=~~((i+n)*.5);r=~~i;q=~~((m+j)*.5);p=~~m;o=~~n;g=0;k=0;d=0;e=0;b=0;while(1){if((k|0)==4)break a;if(1<<k&y){switch(k|0){case 0:{e=u;b=t;break}case 1:{e=r;b=q;break}case 2:{e=u;b=p;break}case 3:{e=o;b=q;break}default:{}}e=x+e|0;b=v+b|0;f=e-w|0;C=b-s|0;f=(O(C,C)|0)+(O(f,f)|0)|0;if((d|0)==0|(f|0)<(g|0))d=c[22028+(k<<2)>>2]|0;else f=g}else f=g;g=f;k=k+1|0}}}while(0);l=z;return d|0}function YN(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0;switch(d|0){case 0:{f=+h[b>>3];e=+h[b+8>>3];break}case 2:{f=+h[b>>3];e=-+h[b+8>>3];break}case 1:{f=-+h[b+8>>3];e=+h[b>>3];break}case 3:{f=+h[b+8>>3];e=+h[b>>3];break}default:{f=0.0;e=0.0}}c[a>>2]=~~(f+(f>=0.0?.5:-.5));c[a+4>>2]=~~(e+(e>=0.0?.5:-.5));return}function ZN(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+80|0;f=i+40|0;h=i;g=b+16|0;e=c[g>>2]|0;d=e+16|0;if(a[e+47>>0]|0){e=c[b>>2]&3;WN(f,c[((e|0)==3?b:b+48|0)+40>>2]|0,c[((e|0)==2?b:b+-48|0)+40>>2]|0,d);e=f;f=d+40|0;do{c[d>>2]=c[e>>2];d=d+4|0;e=e+4|0}while((d|0)<(f|0));e=c[g>>2]|0}d=e+56|0;if(a[e+87>>0]|0){e=c[b>>2]&3;WN(h,c[((e|0)==2?b:b+-48|0)+40>>2]|0,c[((e|0)==3?b:b+48|0)+40>>2]|0,d);e=h;f=d+40|0;do{c[d>>2]=c[e>>2];d=d+4|0;e=e+4|0}while((d|0)<(f|0))}l=i;return}function _N(){var a=0,b=0,d=0;a=l;l=l+32|0;b=a+16|0;d=a;c[d>>2]=0;c[d+4>>2]=0;c[d+8>>2]=0;c[d+12>>2]=0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];TN(0,b)|0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];UN(0,b)|0;c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];pN(0,b)|0;l=a;return}function $N(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0.0,q=0,r=0.0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;z=l;l=l+192|0;s=z+176|0;x=z+112|0;y=z+48|0;t=z+32|0;u=z+24|0;v=z+16|0;w=z;if(!(e<<24>>24)){k=d+48|0;c[t>>2]=c[k>>2];c[t+4>>2]=c[k+4>>2];c[t+8>>2]=c[k+8>>2];c[t+12>>2]=c[k+12>>2];k=u;m=v;n=x;o=0}else{c[t>>2]=c[d>>2];c[t+4>>2]=c[d+4>>2];c[t+8>>2]=c[d+8>>2];c[t+12>>2]=c[d+12>>2];k=v;m=u;n=0;o=x}h[u>>3]=0.0;h[v>>3]=1.0;q=t+8|0;f=0.0;g=1.0;j=0;p=+h[t>>3];while(1){r=+h[q>>3];f=(f+g)*.5;kP(w,d,3,f,n,o);c[t>>2]=c[w>>2];c[t+4>>2]=c[w+4>>2];c[t+8>>2]=c[w+8>>2];c[t+12>>2]=c[w+12>>2];c[s>>2]=c[w>>2];c[s+4>>2]=c[w+4>>2];c[s+8>>2]=c[w+8>>2];c[s+12>>2]=c[w+12>>2];a:do if(!((lb[b&127](a,s)|0)<<24>>24)){e=0;while(1){if((e|0)==4){e=k;j=1;break a}j=y+(e<<4)|0;A=x+(e<<4)|0;c[j>>2]=c[A>>2];c[j+4>>2]=c[A+4>>2];c[j+8>>2]=c[A+8>>2];c[j+12>>2]=c[A+12>>2];e=e+1|0}}else e=m;while(0);h[e>>3]=f;i=+h[t>>3];p=p-i;if(!((p>=0.0?p:-p)>.5)?(r=r-+h[q>>3],!((r>=0.0?r:-r)>.5)):0)break;f=+h[u>>3];g=+h[v>>3];p=i}b:do if(!(j<<24>>24)){e=0;while(1){if((e|0)==4)break b;A=d+(e<<4)|0;y=x+(e<<4)|0;c[A>>2]=c[y>>2];c[A+4>>2]=c[y+4>>2];c[A+8>>2]=c[y+8>>2];c[A+12>>2]=c[y+12>>2];e=e+1|0}}else{e=0;while(1){if((e|0)==4)break b;A=d+(e<<4)|0;x=y+(e<<4)|0;c[A>>2]=c[x>>2];c[A+4>>2]=c[x+4>>2];c[A+8>>2]=c[x+8>>2];c[A+12>>2]=c[x+12>>2];e=e+1|0}}while(0);l=z;return}function aO(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0.0;m=l;l=l+48|0;d=m+16|0;e=m;f=m+32|0;g=a+16|0;i=c[g>>2]|0;j=c[i+8>>2]|0;if(j|0?(k=j+4|0,c[(c[k>>2]|0)+12>>2]|0):0){c[f>>2]=a;c[f+4>>2]=0;n=+h[i+96>>3];h[e>>3]=+h[b>>3]-+h[i+16>>3];h[e+8>>3]=+h[b+8>>3]-+h[i+24>>3];k=c[(c[k>>2]|0)+12>>2]|0;c[d>>2]=c[e>>2];c[d+4>>2]=c[e+4>>2];c[d+8>>2]=c[e+8>>2];c[d+12>>2]=c[e+12>>2];k=lb[k&127](f,d)|0;h[(c[g>>2]|0)+96>>3]=n;bO(f,a,b,k)}l=m;return}function bO(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0.0,m=0,n=0;n=l;l=l+64|0;m=n;j=b+16|0;g=c[j>>2]|0;k=+h[g+96>>3];i=g+16|0;f=g+24|0;b=0;while(1){if((b|0)==4)break;h[m+(b<<4)>>3]=+h[d+(b<<4)>>3]-+h[i>>3];h[m+(b<<4)+8>>3]=+h[d+(b<<4)+8>>3]-+h[f>>3];b=b+1|0}$N(a,c[(c[(c[g+8>>2]|0)+4>>2]|0)+12>>2]|0,m,e);b=0;while(1){if((b|0)==4)break;e=c[j>>2]|0;h[d+(b<<4)>>3]=+h[e+16>>3]+ +h[m+(b<<4)>>3];h[d+(b<<4)+8>>3]=+h[e+24>>3]+ +h[m+(b<<4)+8>>3];b=b+1|0}h[(c[j>>2]|0)+96>>3]=k;l=n;return}function cO(b,d){b=b|0;d=d|0;var e=0,f=0;while(1){f=b+16|0;b=c[f>>2]|0;if(!(a[b+112>>0]|0))break;b=c[b+116>>2]|0}b=c[b+8>>2]|0;if(!b){b=JK(40)|0;c[(c[f>>2]|0)+8>>2]=b}e=c[b>>2]|0;if(!e)b=KK(((c[b+4>>2]|0)*48|0)+48|0)|0;else b=MK(e,((c[b+4>>2]|0)*48|0)+48|0)|0;f=c[(c[f>>2]|0)+8>>2]|0;c[f>>2]=b;f=f+4|0;e=c[f>>2]|0;c[f>>2]=e+1;f=b+(e*48|0)|0;c[f>>2]=JK(d<<4)|0;c[b+(e*48|0)+4>>2]=d;b=b+(e*48|0)+8|0;e=b+40|0;do{c[b>>2]=0;b=b+4|0}while((b|0)<(e|0));return f|0}function dO(b,e,f,g,i){b=b|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0.0,F=0.0;D=l;l=l+96|0;A=D+16|0;u=D;x=D+92|0;y=D+88|0;v=D+80|0;m=c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0;z=xC(m)|0;B=cO(b,g)|0;n=b;while(1){o=c[n+16>>2]|0;if(!(a[o+112>>0]|0))break;n=c[o+116>>2]|0}if((a[i+8>>0]|0)==0?(j=c[m+16>>2]|0,k=c[e+16>>2]|0,(c[j+232>>2]|0)==(c[k+232>>2]|0)):0){p=(c[j+236>>2]|0)>(c[k+236>>2]|0);w=p?m:e;m=p?e:m}else w=e;if((m|0)==(c[((c[n>>2]&3|0)==3?n:n+48|0)+40>>2]|0)){j=o+80|0;k=o+40|0;p=d[o+86>>0]|0;e=d[o+46>>0]|0}else{j=o+40|0;k=o+80|0;p=d[o+46>>0]|0;e=d[o+86>>0]|0}o=c[j>>2]|0;j=c[k>>2]|0;if(((e|0)!=0?(r=m+16|0,q=c[(c[r>>2]|0)+8>>2]|0,(q|0)!=0):0)?(c[(c[q+4>>2]|0)+12>>2]|0)!=0:0){c[v>>2]=m;c[v+4>>2]=j;j=g+-4|0;e=u+8|0;k=0;while(1){if((k|0)>=(j|0))break;n=k+3|0;q=c[r>>2]|0;h[u>>3]=+h[f+(n<<4)>>3]-+h[q+16>>3];h[e>>3]=+h[f+(n<<4)+8>>3]-+h[q+24>>3];q=c[(c[(c[q+8>>2]|0)+4>>2]|0)+12>>2]|0;c[A>>2]=c[u>>2];c[A+4>>2]=c[u+4>>2];c[A+8>>2]=c[u+8>>2];c[A+12>>2]=c[u+12>>2];if(!((lb[q&127](v,A)|0)<<24>>24))break;else k=n}c[x>>2]=k;bO(v,m,f+(k<<4)|0,1)}else{c[x>>2]=0;k=0}if(((p|0)!=0?(t=w+16|0,s=c[(c[t>>2]|0)+8>>2]|0,(s|0)!=0):0)?(c[(c[s+4>>2]|0)+12>>2]|0)!=0:0){c[v>>2]=w;c[v+4>>2]=o;m=g+-4|0;e=u+8|0;j=m;while(1){if((j|0)<=0)break;g=c[t>>2]|0;h[u>>3]=+h[f+(j<<4)>>3]-+h[g+16>>3];h[e>>3]=+h[f+(j<<4)+8>>3]-+h[g+24>>3];g=c[(c[(c[g+8>>2]|0)+4>>2]|0)+12>>2]|0;c[A>>2]=c[u>>2];c[A+4>>2]=c[u+4>>2];c[A+8>>2]=c[u+8>>2];c[A+12>>2]=c[u+12>>2];if(!((lb[g&127](v,A)|0)<<24>>24))break;j=j+-3|0}c[y>>2]=j;bO(v,w,f+(j<<4)|0,0)}else{m=g+-4|0;c[y>>2]=m;j=m}while(1){if((k|0)>=(m|0))break;e=k+3|0;E=+h[f+(k<<4)>>3]-+h[f+(e<<4)>>3];F=+h[f+(k<<4)+8>>3]-+h[f+(e<<4)+8>>3];if(F*F+E*E<1.0e-06)k=e;else break}c[x>>2]=k;while(1){if((j|0)<=0)break;v=j+3|0;F=+h[f+(j<<4)>>3]-+h[f+(v<<4)>>3];E=+h[f+(j<<4)+8>>3]-+h[f+(v<<4)+8>>3];if(!(E*E+F*F<1.0e-06))break;j=j+-3|0}c[y>>2]=j;eO(b,w,f,x,y,B,i);r=c[x>>2]|0;n=c[y>>2]|0;m=n+4|0;n=n+3|0;o=A+16|0;p=A+32|0;q=A+48|0;k=z+16|0;j=r;while(1){if((j|0)>=(m|0))break;z=(c[B>>2]|0)+(j-r<<4)|0;e=f+(j<<4)|0;c[z>>2]=c[e>>2];c[z+4>>2]=c[e+4>>2];c[z+8>>2]=c[e+8>>2];c[z+12>>2]=c[e+12>>2];c[A>>2]=c[e>>2];c[A+4>>2]=c[e+4>>2];c[A+8>>2]=c[e+8>>2];c[A+12>>2]=c[e+12>>2];e=j+1|0;if((j|0)>=(n|0)){C=38;break}y=(c[B>>2]|0)+(e-r<<4)|0;z=f+(e<<4)|0;c[y>>2]=c[z>>2];c[y+4>>2]=c[z+4>>2];c[y+8>>2]=c[z+8>>2];c[y+12>>2]=c[z+12>>2];c[o>>2]=c[z>>2];c[o+4>>2]=c[z+4>>2];c[o+8>>2]=c[z+8>>2];c[o+12>>2]=c[z+12>>2];z=j+2|0;y=(c[B>>2]|0)+(z-r<<4)|0;z=f+(z<<4)|0;c[y>>2]=c[z>>2];c[y+4>>2]=c[z+4>>2];c[y+8>>2]=c[z+8>>2];c[y+12>>2]=c[z+12>>2];c[p>>2]=c[z>>2];c[p+4>>2]=c[z+4>>2];c[p+8>>2]=c[z+8>>2];c[p+12>>2]=c[z+12>>2];z=j+3|0;y=f+(z<<4)|0;c[q>>2]=c[y>>2];c[q+4>>2]=c[y+4>>2];c[q+8>>2]=c[y+8>>2];c[q+12>>2]=c[y+12>>2];lI((c[k>>2]|0)+16|0,A);j=z}c[B+4>>2]=m-r;l=D;return}function eO(b,d,e,f,g,h,i){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+16|0;m=p+4|0;n=p;o=b;while(1){j=c[(c[o+16>>2]|0)+116>>2]|0;if(!j)break;else o=j}if(!(a[i+8>>0]|0))j=(kb[c[i>>2]&63](o)|0)&255;else j=0;HH(o,m,n);k=i+4|0;if((kb[c[k>>2]&63](d)|0)<<24>>24)c[n>>2]=0;if((kb[c[k>>2]&63](c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0)<<24>>24)c[m>>2]=0;if(j|0){d=c[m>>2]|0;c[m>>2]=c[n>>2];c[n>>2]=d}if(!(a[i+9>>0]|0)){j=c[m>>2]|0;if(j|0)c[f>>2]=WH(o,e,c[f>>2]|0,c[g>>2]|0,h,j)|0;j=c[n>>2]|0;if(j|0)c[g>>2]=UH(o,e,c[f>>2]|0,c[g>>2]|0,h,j)|0}else{k=c[n>>2]|0;j=c[m>>2]|0;if(j|k|0)XH(o,e,c[f>>2]|0,c[g>>2]|0,h,j,k)}l=p;return}function fO(a,b){a=a|0;b=b|0;var d=0,e=0;if(+h[b>>3]<+h[b+16>>3]?+h[b+8>>3]<+h[b+24>>3]:0){d=c[a+84>>2]|0;e=a+80|0;a=c[e>>2]|0;c[e>>2]=a+1;a=d+(a<<5)|0;c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];c[a+16>>2]=c[b+16>>2];c[a+20>>2]=c[b+20>>2];c[a+24>>2]=c[b+24>>2];c[a+28>>2]=c[b+28>>2]}return}function gO(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0.0,z=0.0;x=l;l=l+112|0;u=x+72|0;o=x+56|0;n=x+16|0;q=x;j=c[d>>2]&3;p=d+48|0;w=c[((j|0)==3?d:p)+40>>2]|0;t=d+16|0;r=c[t>>2]|0;i=r+16|0;if(a[r+47>>0]|0){WN(n,w,c[((j|0)==2?d:d+-48|0)+40>>2]|0,i);j=n;n=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(n|0))}r=w+16|0;j=c[r>>2]|0;i=c[j+8>>2]|0;if(!i)n=0;else n=c[(c[i+4>>2]|0)+16>>2]|0;i=j+16|0;j=(c[t>>2]|0)+16|0;c[o>>2]=c[i>>2];c[o+4>>2]=c[i+4>>2];c[o+8>>2]=c[i+8>>2];c[o+12>>2]=c[i+12>>2];c[u>>2]=c[j>>2];c[u+4>>2]=c[j+4>>2];c[u+8>>2]=c[j+8>>2];c[u+12>>2]=c[j+12>>2];hO(q,o,u);c[b>>2]=c[q>>2];c[b+4>>2]=c[q+4>>2];c[b+8>>2]=c[q+8>>2];c[b+12>>2]=c[q+12>>2];if(!(g<<24>>24)){i=c[t>>2]|0;if(!(a[i+45>>0]|0))i=0;else{k=+h[i+32>>3];v=9}}else{k=+iO(c[((c[d>>2]&3|0)==3?d:p)+40>>2]|0);v=9}if((v|0)==9){h[b+16>>3]=k;i=1}a[b+29>>0]=i;c[b+80>>2]=0;c[b+88>>2]=d;q=f+32|0;c[q>>2]=c[b>>2];c[q+4>>2]=c[b+4>>2];c[q+8>>2]=c[b+8>>2];c[q+12>>2]=c[b+12>>2];switch(e|0){case 1:{j=c[r>>2]|0;if((a[j+156>>0]|0)==0?(q=a[(c[t>>2]|0)+49>>0]|0,s=q&255,q<<24>>24!=0):0){c[u>>2]=c[f>>2];c[u+4>>2]=c[f+4>>2];c[u+8>>2]=c[f+8>>2];c[u+12>>2]=c[f+12>>2];c[u+16>>2]=c[f+16>>2];c[u+20>>2]=c[f+20>>2];c[u+24>>2]=c[f+24>>2];c[u+28>>2]=c[f+28>>2];do if(!(s&4)){if(s&1|0){c[f+48>>2]=1;i=u+24|0;h[i>>3]=+h[(+h[i>>3]>+h[b+8>>3]?u+16|0:b)+8>>3];i=f+56|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=1;break}i=f+48|0;if(!(s&8)){c[i>>2]=2;h[u>>3]=+h[b>>3];h[u+8>>3]=+h[j+24>>3]-+h[j+80>>3]*.5;h[u+24>>3]=+h[b+8>>3];i=f+56|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=1;break}else{c[i>>2]=8;h[u+16>>3]=+h[b>>3];h[u+8>>3]=+h[j+24>>3]-+h[j+80>>3]*.5;h[u+24>>3]=+h[b+8>>3];i=f+56|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=1;break}}else{c[f+48>>2]=4;k=+h[u>>3];if(+h[b>>3]<+h[j+16>>3]){z=k+-1.0;y=+h[b+8>>3];b=u+16|0;k=+h[b>>3];m=+h[j+80>>3]*.5+ +h[j+24>>3];m=m+ +((c[(c[(xC(w)|0)+16>>2]|0)+252>>2]|0)/2|0|0);i=c[r>>2]|0;h[b>>3]=+h[i+16>>3]-+h[i+88>>3];h[u+24>>3]=y;h[u+8>>3]=+h[i+24>>3]-+h[i+80>>3]*.5;h[u>>3]=z;h[f+56>>3]=z;h[f+64>>3]=y;h[f+72>>3]=k;h[f+80>>3]=m;i=f+88|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=2;break}else{m=+h[b+8>>3];i=u+16|0;y=+h[i>>3]+1.0;z=+h[j+80>>3]*.5+ +h[j+24>>3];z=z+ +((c[(c[(xC(w)|0)+16>>2]|0)+252>>2]|0)/2|0|0);b=c[r>>2]|0;h[u>>3]=+h[b+96>>3]+ +h[b+16>>3];h[u+24>>3]=m;h[u+8>>3]=+h[b+24>>3]-+h[b+80>>3]*.5;h[i>>3]=y;h[f+56>>3]=k;h[f+64>>3]=m;h[f+72>>3]=y;h[f+80>>3]=z;i=f+88|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=2;break}}while(0);c[f+52>>2]=i;while(1){i=c[d+16>>2]|0;if(!(a[i+112>>0]|0))break;d=c[i+116>>2]|0}a[((w|0)==(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)?i+16|0:i+56|0)+30>>0]=0}else{j=1;v=47}break}case 2:{s=a[(c[t>>2]|0)+49>>0]|0;j=s&255;if(!(s<<24>>24))v=46;else{c[u>>2]=c[f>>2];c[u+4>>2]=c[f+4>>2];c[u+8>>2]=c[f+8>>2];c[u+12>>2]=c[f+12>>2];c[u+16>>2]=c[f+16>>2];c[u+20>>2]=c[f+20>>2];c[u+24>>2]=c[f+24>>2];c[u+28>>2]=c[f+28>>2];do if(!(j&4)){if(j&1|0)if((c[f+48>>2]|0)==4){i=c[r>>2]|0;z=+h[i+24>>3]-+h[i+80>>3]*.5;i=u+16|0;y=+h[i>>3]+1.0;k=+h[b>>3];m=z-+((c[(c[(xC(w)|0)+16>>2]|0)+252>>2]|0)/2|0|0);b=c[r>>2]|0;h[u>>3]=+h[b+96>>3]+ +h[b+16>>3];h[u+8>>3]=z;h[u+24>>3]=+h[b+80>>3]*.5+ +h[b+24>>3];h[i>>3]=y;h[f+56>>3]=k;h[f+64>>3]=m;h[f+72>>3]=y;h[f+80>>3]=z;i=f+88|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=2;break}else{i=u+24|0;h[i>>3]=+h[(+h[i>>3]>+h[b+8>>3]?u+16|0:b)+8>>3];i=f+56|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=1;break}k=+h[b>>3];if(!(j&8)){h[u>>3]=k;e=c[r>>2]|0;k=+h[e+24>>3];m=+h[e+80>>3]*.5;if((c[f+48>>2]|0)==4){h[u+24>>3]=m+k;k=+h[b+8>>3];i=u}else{h[u+8>>3]=k-m;k=+h[b+8>>3]+1.0;i=u+16|0}h[i+8>>3]=k;i=f+56|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=1;break}else{i=u+16|0;h[i>>3]=k+1.0;e=c[r>>2]|0;k=+h[e+24>>3];m=+h[e+80>>3]*.5;if((c[f+48>>2]|0)==4){h[u+24>>3]=m+k;k=+h[b+8>>3]+-1.0;i=u}else{h[u+8>>3]=k-m;k=+h[b+8>>3]+1.0}h[i+8>>3]=k;i=f+56|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=1;break}}else{i=u+8|0;h[i>>3]=+h[(+h[i>>3]<+h[b+8>>3]?u:b)+8>>3];i=f+56|0;c[i>>2]=c[u>>2];c[i+4>>2]=c[u+4>>2];c[i+8>>2]=c[u+8>>2];c[i+12>>2]=c[u+12>>2];c[i+16>>2]=c[u+16>>2];c[i+20>>2]=c[u+20>>2];c[i+24>>2]=c[u+24>>2];c[i+28>>2]=c[u+28>>2];i=1}while(0);c[f+52>>2]=i;while(1){i=c[d+16>>2]|0;if(!(a[i+112>>0]|0))break;d=c[i+116>>2]|0}a[((w|0)==(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)?i+16|0:i+56|0)+30>>0]=0;c[f+48>>2]=j}break}default:v=46}if((v|0)==46){j=c[f+48>>2]|0;v=47}a:do if((v|0)==47){if(n){d=f+56|0;i=f+52|0;j=pb[n&127](w,(c[t>>2]|0)+16|0,j,d,i)|0;if(j){c[f+48>>2]=j;break}}else{i=f+52|0;d=f+56|0};c[d>>2]=c[f>>2];c[d+4>>2]=c[f+4>>2];c[d+8>>2]=c[f+8>>2];c[d+12>>2]=c[f+12>>2];c[d+16>>2]=c[f+16>>2];c[d+20>>2]=c[f+20>>2];c[d+24>>2]=c[f+24>>2];c[d+28>>2]=c[f+28>>2];c[i>>2]=1;switch(e|0){case 8:{qa(138394,106514,569,106524);break}case 2:{h[((c[f+48>>2]|0)==4?f+56|0:f+72|0)+8>>3]=+h[b+8>>3];break a}case 1:{h[f+80>>3]=+h[b+8>>3];c[f+48>>2]=1;break a}default:break a}}while(0);l=x;return}function hO(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[c+8>>3]+ +h[b+8>>3];h[a>>3]=+h[c>>3]+ +h[b>>3];h[a+8>>3]=d;return}function iO(a){a=a|0;var b=0.0,d=0,e=0,f=0.0,g=0,i=0,j=0,k=0.0,l=0.0;i=c[a+16>>2]|0;j=c[i+172>>2]|0;f=0.0;g=0;while(1){a=c[j+(g<<2)>>2]|0;if(!a)break;f=+h[(c[(c[((c[a>>2]&3|0)==3?a:a+48|0)+40>>2]|0)+16>>2]|0)+16>>3]+f;g=g+1|0}e=c[i+180>>2]|0;b=0.0;a=0;while(1){d=c[e+(a<<2)>>2]|0;if(!d)break;b=+h[(c[(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)+16>>2]|0)+16>>3]+b;a=a+1|0}k=+h[i+16>>3];l=+h[i+24>>3];j=c[j>>2]|0;f=+K(+(l-+h[(c[(c[((c[j>>2]&3|0)==3?j:j+48|0)+40>>2]|0)+16>>2]|0)+24>>3]),+(k-f/+(g|0)));j=c[e>>2]|0;return +((+K(+(+h[(c[(c[((c[j>>2]&3|0)==2?j:j+-48|0)+40>>2]|0)+16>>2]|0)+24>>3]-l),+(b/+(a|0)-k))+f)*.5)}function jO(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0,B=0.0;z=l;l=l+112|0;w=z+72|0;p=z+56|0;n=z+16|0;t=z;j=c[d>>2]&3;r=d+-48|0;y=c[((j|0)==2?d:r)+40>>2]|0;u=d+16|0;s=c[u>>2]|0;i=s+56|0;if(a[s+87>>0]|0){WN(n,y,c[((j|0)==3?d:d+48|0)+40>>2]|0,i);j=n;n=i+40|0;do{c[i>>2]=c[j>>2];i=i+4|0;j=j+4|0}while((i|0)<(n|0))}s=y+16|0;j=c[s>>2]|0;i=c[j+8>>2]|0;if(!i)o=0;else o=c[(c[i+4>>2]|0)+16>>2]|0;n=b+40|0;i=j+16|0;j=(c[u>>2]|0)+56|0;c[p>>2]=c[i>>2];c[p+4>>2]=c[i+4>>2];c[p+8>>2]=c[i+8>>2];c[p+12>>2]=c[i+12>>2];c[w>>2]=c[j>>2];c[w+4>>2]=c[j+4>>2];c[w+8>>2]=c[j+8>>2];c[w+12>>2]=c[j+12>>2];hO(t,p,w);c[n>>2]=c[t>>2];c[n+4>>2]=c[t+4>>2];c[n+8>>2]=c[t+8>>2];c[n+12>>2]=c[t+12>>2];if(!(g<<24>>24)){i=c[u>>2]|0;if(!(a[i+85>>0]|0))q=0;else{h[b+56>>3]=+h[i+72>>3];q=1}}else{m=+iO(c[((c[d>>2]&3|0)==2?d:r)+40>>2]|0)+3.141592653589793;h[b+56>>3]=m;if(m<6.283185307179586)q=1;else qa(106534,106514,606,106558)}a[b+69>>0]=q;t=f+32|0;c[t>>2]=c[n>>2];c[t+4>>2]=c[n+4>>2];c[t+8>>2]=c[n+8>>2];c[t+12>>2]=c[n+12>>2];switch(e|0){case 1:{j=c[s>>2]|0;if((a[j+156>>0]|0)==0?(t=a[(c[u>>2]|0)+89>>0]|0,v=t&255,t<<24>>24!=0):0){c[w>>2]=c[f>>2];c[w+4>>2]=c[f+4>>2];c[w+8>>2]=c[f+8>>2];c[w+12>>2]=c[f+12>>2];c[w+16>>2]=c[f+16>>2];c[w+20>>2]=c[f+20>>2];c[w+24>>2]=c[f+24>>2];c[w+28>>2]=c[f+28>>2];do if(!(v&4))if(!(v&1)){i=f+48|0;if(!(v&8)){c[i>>2]=2;h[w>>3]=+h[n>>3];h[w+24>>3]=+h[j+80>>3]*.5+ +h[j+24>>3];h[w+8>>3]=+h[b+48>>3];i=f+56|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=1;break}else{c[i>>2]=8;h[w+16>>3]=+h[n>>3];h[w+24>>3]=+h[j+80>>3]*.5+ +h[j+24>>3];h[w+8>>3]=+h[b+48>>3];i=f+56|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=1;break}}else{c[f+48>>2]=1;k=+h[w>>3];if(+h[n>>3]<+h[j+16>>3]){B=k+-1.0;m=+h[b+48>>3];b=w+16|0;k=+h[b>>3];A=+h[j+80>>3]*-.5+ +h[j+24>>3];A=A-+((c[(c[(xC(y)|0)+16>>2]|0)+252>>2]|0)/2|0|0);i=c[s>>2]|0;h[b>>3]=+h[i+16>>3]-+h[i+88>>3];h[w+8>>3]=m;h[w+24>>3]=+h[i+80>>3]*.5+ +h[i+24>>3];h[w>>3]=B;h[f+56>>3]=B;h[f+64>>3]=A;h[f+72>>3]=k;h[f+80>>3]=m;i=f+88|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=2;break}else{B=+h[b+48>>3];i=w+16|0;A=+h[i>>3]+1.0;m=+h[j+80>>3]*-.5+ +h[j+24>>3];m=m-+((c[(c[(xC(y)|0)+16>>2]|0)+252>>2]|0)/2|0|0);b=c[s>>2]|0;h[w>>3]=+h[b+96>>3]+ +h[b+16>>3];h[w+8>>3]=B;h[w+24>>3]=+h[b+80>>3]*.5+ +h[b+24>>3];h[i>>3]=A;h[f+56>>3]=k;h[f+64>>3]=m;h[f+72>>3]=A;h[f+80>>3]=B;i=f+88|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=2;break}}else{c[f+48>>2]=4;i=w+8|0;h[i>>3]=+h[(+h[i>>3]<+h[b+48>>3]?w:n)+8>>3];i=f+56|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=1}while(0);c[f+52>>2]=i;while(1){i=c[d+16>>2]|0;if(!(a[i+112>>0]|0))break;d=c[i+116>>2]|0}a[((y|0)==(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)?i+56|0:i+16|0)+30>>0]=0;c[f+48>>2]=v}else{j=4;x=47}break}case 2:{v=a[(c[u>>2]|0)+89>>0]|0;j=v&255;if(!(v<<24>>24))x=46;else{c[w>>2]=c[f>>2];c[w+4>>2]=c[f+4>>2];c[w+8>>2]=c[f+8>>2];c[w+12>>2]=c[f+12>>2];c[w+16>>2]=c[f+16>>2];c[w+20>>2]=c[f+20>>2];c[w+24>>2]=c[f+24>>2];c[w+28>>2]=c[f+28>>2];do if(!(j&4)){if(j&1|0)if((c[f+48>>2]|0)==4){k=+h[w>>3]+-1.0;i=c[s>>2]|0;B=+h[i+24>>3]-+h[i+80>>3]*.5;A=+h[n>>3];m=B-+((c[(c[(xC(y)|0)+16>>2]|0)+252>>2]|0)/2|0|0);i=c[s>>2]|0;h[w+16>>3]=+h[i+16>>3]+-2.0-+h[i+88>>3];h[w+8>>3]=B;h[w+24>>3]=+h[i+80>>3]*.5+ +h[i+24>>3];h[w>>3]=k;h[f+56>>3]=k;h[f+64>>3]=m;h[f+72>>3]=A;h[f+80>>3]=B;i=f+88|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=2;break}else{i=w+24|0;h[i>>3]=+h[(+h[i>>3]>+h[b+8>>3]?w+16|0:b)+8>>3];i=f+56|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=1;break}k=+h[n>>3];if(!(j&8)){h[w>>3]=k+-1.0;e=c[s>>2]|0;k=+h[e+24>>3];m=+h[e+80>>3]*.5;if((c[f+48>>2]|0)==4){h[w+24>>3]=m+k;k=+h[b+48>>3]+-1.0;i=w}else{h[w+8>>3]=k-m;k=+h[b+48>>3];i=w+16|0}h[i+8>>3]=k;i=f+56|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=1;break}else{i=w+16|0;h[i>>3]=k+1.0;e=c[s>>2]|0;k=+h[e+24>>3];m=+h[e+80>>3]*.5;if((c[f+48>>2]|0)==4){h[w+24>>3]=m+k;k=+h[b+48>>3]+-1.0;i=w}else{h[w+8>>3]=k-m;k=+h[b+48>>3]+1.0}h[i+8>>3]=k;i=f+56|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=1;break}}else{i=w+8|0;h[i>>3]=+h[(+h[i>>3]<+h[b+48>>3]?w:n)+8>>3];i=f+56|0;c[i>>2]=c[w>>2];c[i+4>>2]=c[w+4>>2];c[i+8>>2]=c[w+8>>2];c[i+12>>2]=c[w+12>>2];c[i+16>>2]=c[w+16>>2];c[i+20>>2]=c[w+20>>2];c[i+24>>2]=c[w+24>>2];c[i+28>>2]=c[w+28>>2];i=1}while(0);c[f+52>>2]=i;while(1){i=c[d+16>>2]|0;if(!(a[i+112>>0]|0))break;d=c[i+116>>2]|0}a[((y|0)==(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)?i+56|0:i+16|0)+30>>0]=0;c[f+48>>2]=j}break}default:x=46}if((x|0)==46){j=c[f+48>>2]|0;x=47}a:do if((x|0)==47){if(o){d=f+56|0;i=f+52|0;j=pb[o&127](y,(c[u>>2]|0)+56|0,j,d,i)|0;if(j){c[f+48>>2]=j;break}}else{i=f+52|0;d=f+56|0};c[d>>2]=c[f>>2];c[d+4>>2]=c[f+4>>2];c[d+8>>2]=c[f+8>>2];c[d+12>>2]=c[f+12>>2];c[d+16>>2]=c[f+16>>2];c[d+20>>2]=c[f+20>>2];c[d+24>>2]=c[f+24>>2];c[d+28>>2]=c[f+28>>2];c[i>>2]=1;switch(e|0){case 8:{qa(138394,106514,765,106558);break}case 2:{h[((c[f+48>>2]|0)==4?f+56|0:f+72|0)+8>>3]=+h[b+48>>3];break a}case 1:{h[f+64>>3]=+h[b+48>>3];c[f+48>>2]=4;break a}default:break a}}while(0);l=z;return}function kO(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0;g=c[b+16>>2]|0;i=c[g+96>>2]|0;if((a[g+44>>0]|0)==0?(a[g+84>>0]|0)==0:0)j=6;else{d=a[g+49>>0]|0;e=d&255;if(((e&8|0)==0?(f=a[g+89>>0]|0,(f&8)==0):0)?((e&5|0)==0?1:d<<24>>24!=f<<24>>24):0)j=6;else b=0}if((j|0)==6)if(!i)b=18;else{b=(c[(c[(xC(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0)+16>>2]|0)+116>>2]&1|0)!=0;b=~~(+h[(b?i+32|0:i+24|0)>>3]+18.0)}return b|0}function lO(b,d,e,f,g,h,i){b=b|0;d=d|0;e=e|0;f=f|0;g=+g;h=+h;i=i|0;var j=0,k=0,l=0,m=0;b=c[(c[d+(e<<2)>>2]|0)+16>>2]|0;if((a[b+44>>0]|0)==0?(a[b+84>>0]|0)==0:0)l=6;else l=3;do if((l|0)==3){j=a[b+49>>0]|0;k=j&255;if(!(k&8)){m=a[b+89>>0]|0;if((m&8)==0?((k&5|0)==0?1:j<<24>>24!=m<<24>>24):0){l=6;break}if(!(a[b+89>>0]&8)){if(k&4|0){nO(d,e,f,g,h,i);break}if(!(k&1))qa(138394,106514,1231,106566);else{pO(d,e,f,g,h,i);break}}}if((j&2)==0?(a[b+89>>0]&2)==0:0){oO(d,e,f,g,h,i);break}nO(d,e,f,g,h,i)}while(0);if((l|0)==6)mO(d,e,f,g,h,i);return}function mO(b,e,f,g,i,j){b=b|0;e=e|0;f=f|0;g=+g;i=+i;j=j|0;var k=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0,s=0,t=0,u=0,v=0.0,w=0,x=0,y=0,z=0.0,A=0,B=0,C=0.0,D=0,E=0.0,F=0.0,G=0.0,H=0.0,I=0.0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0.0,R=0.0,S=0;P=l;l=l+16080|0;L=P+80|0;M=P+64|0;N=P+48|0;O=P+32|0;J=P+16|0;K=P;B=c[b+(e<<2)>>2]|0;n=i*.5/+(f|0);n=n>2.0?n:2.0;D=(c[((c[B>>2]&3|0)==3?B:B+48|0)+40>>2]|0)+16|0;A=c[D>>2]|0;C=+h[A+16>>3];H=+h[A+24>>3];B=c[B+16>>2]|0;m=+h[B+16>>3];E=m+C;F=+h[B+24>>3]+H;i=+h[B+56>>3];G=i+C;H=+h[B+64>>3]+H;k=!(F>=H)?-1:1;p=+h[A+96>>3];switch(qO(d[B+49>>0]|0,d[B+89>>0]|0)|0){case 65:case 32:{if(F==H)k=0-k|0;break}default:{}}Q=(p-m)*3.0;m=(p-i)*3.0;v=n*+(k|0);u=L+8|0;w=L+16|0;x=L+32|0;y=L+48|0;z=(H+F)*.5;A=L+64|0;B=L+80|0;s=L+96|0;t=L+104|0;r=0;o=0.0;i=p;n=p<Q?p:Q;m=p<m?p:m;while(1){if((r|0)>=(f|0))break;q=e+1|0;e=c[b+(e<<2)>>2]|0;p=i+g;n=n+g;m=m+g;o=o+v;h[L>>3]=E;h[u>>3]=F;Q=o+F;rO(M,n*.3333333333333333+E,Q);c[w>>2]=c[M>>2];c[w+4>>2]=c[M+4>>2];c[w+8>>2]=c[M+8>>2];c[w+12>>2]=c[M+12>>2];R=p+C;rO(N,R,Q);c[x>>2]=c[N>>2];c[x+4>>2]=c[N+4>>2];c[x+8>>2]=c[N+8>>2];c[x+12>>2]=c[N+12>>2];rO(O,R,z);c[y>>2]=c[O>>2];c[y+4>>2]=c[O+4>>2];c[y+8>>2]=c[O+8>>2];c[y+12>>2]=c[O+12>>2];Q=H-o;rO(J,R,Q);c[A>>2]=c[J>>2];c[A+4>>2]=c[J+4>>2];c[A+8>>2]=c[J+8>>2];c[A+12>>2]=c[J+12>>2];rO(K,m*.3333333333333333+G,Q);c[B>>2]=c[K>>2];c[B+4>>2]=c[K+4>>2];c[B+8>>2]=c[K+8>>2];c[B+12>>2]=c[K+12>>2];h[s>>3]=G;h[t>>3]=H;k=e+16|0;if((c[(c[k>>2]|0)+96>>2]|0)!=0?(S=(c[(c[(xC(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0)|0)+16>>2]|0)+116>>2]&1|0)==0,k=c[(c[k>>2]|0)+96>>2]|0,I=+h[(S?k+24|0:k+32|0)>>3],S=c[D>>2]|0,h[k+56>>3]=I*.5+p+ +h[S+16>>3],h[k+64>>3]=+h[S+24>>3],a[k+81>>0]=1,I>g):0)i=I+i;else i=p;dO(e,c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0,L,7,j);e=q;r=r+1|0}l=P;return}function nO(b,e,f,g,i,j){b=b|0;e=e|0;f=f|0;g=+g;i=+i;j=j|0;var k=0.0,m=0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0.0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0.0,B=0,C=0,D=0.0,E=0,F=0.0,G=0.0,H=0.0,I=0.0,J=0.0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0.0,S=0;Q=l;l=l+16080|0;M=Q+80|0;N=Q+64|0;O=Q+48|0;P=Q+32|0;K=Q+16|0;L=Q;C=c[b+(e<<2)>>2]|0;q=g*.5/+(f|0);q=q>2.0?q:2.0;E=(c[((c[C>>2]&3|0)==3?C:C+48|0)+40>>2]|0)+16|0;m=c[E>>2]|0;H=+h[m+16>>3];D=+h[m+24>>3];C=c[C+16>>2]|0;k=+h[C+16>>3];p=+h[C+24>>3];F=k+H;G=p+D;g=+h[C+56>>3];o=+h[C+64>>3];H=g+H;I=o+D;n=!(F>=H)?-1.0:1.0;s=+h[m+80>>3]*.5;switch(qO(d[C+49>>0]|0,d[C+89>>0]|0)|0){case 15:{k=(q-g+ +h[m+96>>3])*n;break}case 38:{k=(g+q+ +h[m+88>>3])*n;break}case 41:{k=(q-k+ +h[m+96>>3])*n;break}case 48:{k=(q-k+ +h[m+96>>3])*n;break}case 58:case 57:case 51:case 47:case 37:case 14:{k=n*.3333333333333333*(k-g+ +h[m+88>>3]+ +h[m+96>>3]);break}case 73:{k=(k+q+ +h[m+88>>3])*n;break}case 83:{k=(+h[m+88>>3]+k)*n;break}case 84:{k=((k-g+ +h[m+88>>3]+ +h[m+96>>3])*.5+q)*n;break}case 85:case 75:case 74:{k=((k-g+ +h[m+88>>3]+ +h[m+96>>3])*.5+q*2.0)*n;break}default:k=0.0}p=(s-p)*3.0;o=(s-o)*3.0;q=n*q;w=M+8|0;x=M+16|0;y=M+32|0;z=M+48|0;A=(H+F)*.5;B=M+64|0;C=M+80|0;u=M+96|0;v=M+104|0;t=0;g=s;p=s<p?s:p;n=s<o?s:o;while(1){if((t|0)>=(f|0))break;r=e+1|0;e=c[b+(e<<2)>>2]|0;o=g+i;p=p+i;n=n+i;k=k+q;h[M>>3]=F;h[w>>3]=G;s=k+F;rO(N,s,p*.3333333333333333+G);c[x>>2]=c[N>>2];c[x+4>>2]=c[N+4>>2];c[x+8>>2]=c[N+8>>2];c[x+12>>2]=c[N+12>>2];R=o+D;rO(O,s,R);c[y>>2]=c[O>>2];c[y+4>>2]=c[O+4>>2];c[y+8>>2]=c[O+8>>2];c[y+12>>2]=c[O+12>>2];rO(P,A,R);c[z>>2]=c[P>>2];c[z+4>>2]=c[P+4>>2];c[z+8>>2]=c[P+8>>2];c[z+12>>2]=c[P+12>>2];s=H-k;rO(K,s,R);c[B>>2]=c[K>>2];c[B+4>>2]=c[K+4>>2];c[B+8>>2]=c[K+8>>2];c[B+12>>2]=c[K+12>>2];rO(L,s,n*.3333333333333333+I);c[C>>2]=c[L>>2];c[C+4>>2]=c[L+4>>2];c[C+8>>2]=c[L+8>>2];c[C+12>>2]=c[L+12>>2];h[u>>3]=H;h[v>>3]=I;m=e+16|0;if((c[(c[m>>2]|0)+96>>2]|0)!=0?(S=(c[(c[(xC(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0)|0)+16>>2]|0)+116>>2]&1|0)==0,m=c[(c[m>>2]|0)+96>>2]|0,J=+h[(S?m+32|0:m+24|0)>>3],S=c[E>>2]|0,h[m+64>>3]=J*.5+o+ +h[S+24>>3],h[m+56>>3]=+h[S+16>>3],a[m+81>>0]=1,J>i):0)g=J+g;else g=o;dO(e,c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0,M,7,j);e=r;t=t+1|0}l=Q;return}function oO(b,e,f,g,i,j){b=b|0;e=e|0;f=f|0;g=+g;i=+i;j=j|0;var k=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0,s=0,t=0,u=0,v=0.0,w=0,x=0,y=0,z=0.0,A=0,B=0,C=0.0,D=0,E=0.0,F=0.0,G=0.0,H=0.0,I=0.0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0.0,R=0.0,S=0;P=l;l=l+16080|0;L=P+80|0;M=P+64|0;N=P+48|0;O=P+32|0;J=P+16|0;K=P;B=c[b+(e<<2)>>2]|0;n=i*.5/+(f|0);n=n>2.0?n:2.0;D=(c[((c[B>>2]&3|0)==3?B:B+48|0)+40>>2]|0)+16|0;A=c[D>>2]|0;C=+h[A+16>>3];H=+h[A+24>>3];B=c[B+16>>2]|0;m=+h[B+16>>3];E=m+C;F=+h[B+24>>3]+H;i=+h[B+56>>3];G=i+C;H=+h[B+64>>3]+H;k=!(F>=H)?-1:1;p=+h[A+88>>3];switch(qO(d[B+49>>0]|0,d[B+89>>0]|0)|0){case 67:case 12:{if(F==H)k=0-k|0;break}default:{}}Q=(m+p)*3.0;m=(i+p)*3.0;v=n*+(k|0);u=L+8|0;w=L+16|0;x=L+32|0;y=L+48|0;z=(H+F)*.5;A=L+64|0;B=L+80|0;s=L+96|0;t=L+104|0;r=0;o=0.0;i=p;n=p<Q?p:Q;m=p<m?p:m;while(1){if((r|0)>=(f|0))break;q=e+1|0;e=c[b+(e<<2)>>2]|0;p=i+g;n=n+g;m=m+g;o=o+v;h[L>>3]=E;h[u>>3]=F;Q=o+F;rO(M,E-n*.3333333333333333,Q);c[w>>2]=c[M>>2];c[w+4>>2]=c[M+4>>2];c[w+8>>2]=c[M+8>>2];c[w+12>>2]=c[M+12>>2];R=C-p;rO(N,R,Q);c[x>>2]=c[N>>2];c[x+4>>2]=c[N+4>>2];c[x+8>>2]=c[N+8>>2];c[x+12>>2]=c[N+12>>2];rO(O,R,z);c[y>>2]=c[O>>2];c[y+4>>2]=c[O+4>>2];c[y+8>>2]=c[O+8>>2];c[y+12>>2]=c[O+12>>2];Q=H-o;rO(J,R,Q);c[A>>2]=c[J>>2];c[A+4>>2]=c[J+4>>2];c[A+8>>2]=c[J+8>>2];c[A+12>>2]=c[J+12>>2];rO(K,G-m*.3333333333333333,Q);c[B>>2]=c[K>>2];c[B+4>>2]=c[K+4>>2];c[B+8>>2]=c[K+8>>2];c[B+12>>2]=c[K+12>>2];h[s>>3]=G;h[t>>3]=H;k=e+16|0;if((c[(c[k>>2]|0)+96>>2]|0)!=0?(S=(c[(c[(xC(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0)|0)+16>>2]|0)+116>>2]&1|0)==0,k=c[(c[k>>2]|0)+96>>2]|0,I=+h[(S?k+24|0:k+32|0)>>3],S=c[D>>2]|0,h[k+56>>3]=I*-.5-p+ +h[S+16>>3],h[k+64>>3]=+h[S+24>>3],a[k+81>>0]=1,I>g):0)i=I+i;else i=p;dO(e,c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0,L,7,j);e=q;r=r+1|0}l=P;return}function pO(b,e,f,g,i,j){b=b|0;e=e|0;f=f|0;g=+g;i=+i;j=j|0;var k=0.0,m=0.0,n=0.0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0.0,w=0,x=0.0,y=0.0,z=0.0,A=0.0,B=0.0,C=0,D=0,E=0,F=0.0,G=0,H=0,I=0.0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0.0,R=0.0,S=0;P=l;l=l+16080|0;L=P+80|0;M=P+64|0;N=P+48|0;O=P+32|0;J=P+16|0;K=P;C=c[b+(e<<2)>>2]|0;B=g*.5/+(f|0);w=(c[((c[C>>2]&3|0)==3?C:C+48|0)+40>>2]|0)+16|0;D=c[w>>2]|0;z=+h[D+16>>3];v=+h[D+24>>3];C=c[C+16>>2]|0;m=+h[C+24>>3];x=+h[C+16>>3]+z;y=m+v;n=+h[C+64>>3];z=+h[C+56>>3]+z;A=n+v;u=!(x>=z)?-1:1;o=+h[D+80>>3]*.5;C=(qO(d[C+49>>0]|0,d[C+89>>0]|0)|0)==67;m=(m+o)*3.0;n=(n+o)*3.0;B=(B>2.0?B:2.0)*+((C?0-u|0:u)|0);u=L+8|0;C=L+16|0;D=L+32|0;E=L+48|0;F=(z+x)*.5;G=L+64|0;H=L+80|0;s=L+96|0;t=L+104|0;g=o;k=0.0;m=o<m?o:m;n=o<n?o:n;q=0;while(1){if((q|0)>=(f|0))break;r=e+1|0;p=c[b+(e<<2)>>2]|0;o=g+i;m=m+i;n=n+i;k=k+B;h[L>>3]=x;h[u>>3]=y;Q=k+x;rO(M,Q,y-m*.3333333333333333);c[C>>2]=c[M>>2];c[C+4>>2]=c[M+4>>2];c[C+8>>2]=c[M+8>>2];c[C+12>>2]=c[M+12>>2];R=v-o;rO(N,Q,R);c[D>>2]=c[N>>2];c[D+4>>2]=c[N+4>>2];c[D+8>>2]=c[N+8>>2];c[D+12>>2]=c[N+12>>2];rO(O,F,R);c[E>>2]=c[O>>2];c[E+4>>2]=c[O+4>>2];c[E+8>>2]=c[O+8>>2];c[E+12>>2]=c[O+12>>2];Q=z-k;rO(J,Q,R);c[G>>2]=c[J>>2];c[G+4>>2]=c[J+4>>2];c[G+8>>2]=c[J+8>>2];c[G+12>>2]=c[J+12>>2];rO(K,Q,A-n*.3333333333333333);c[H>>2]=c[K>>2];c[H+4>>2]=c[K+4>>2];c[H+8>>2]=c[K+8>>2];c[H+12>>2]=c[K+12>>2];h[s>>3]=z;h[t>>3]=A;e=p+16|0;if((c[(c[e>>2]|0)+96>>2]|0)!=0?(S=(c[(c[(xC(c[((c[p>>2]&3|0)==3?p:p+48|0)+40>>2]|0)|0)+16>>2]|0)+116>>2]&1|0)==0,e=c[(c[e>>2]|0)+96>>2]|0,I=+h[(S?e+32|0:e+24|0)>>3],S=c[w>>2]|0,h[e+64>>3]=I*-.5-o+ +h[S+24>>3],h[e+56>>3]=+h[S+16>>3],a[e+81>>0]=1,I>i):0)g=I+g;else g=o;dO(p,c[((c[p>>2]&3|0)==2?p:p+-48|0)+40>>2]|0,L,7,j);e=r;q=q+1|0}l=P;return}function qO(a,b){a=a|0;b=b|0;var d=0;d=0;while(1){if((d|0)>=8){b=-1;break}if((c[22044+(d<<2)>>2]|0)==(b|0)){b=d;break}d=d+1|0}d=0;while(1){if((d|0)>=8){d=-1;break}if((c[22044+(d<<2)>>2]|0)==(a|0))break;d=d+1|0}if((d|b|0)<0)d=0;else d=c[22076+(d<<5)+(b<<2)>>2]|0;return d|0}function rO(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b;h[a+8>>3]=c;return}function sO(b){b=b|0;var d=0,e=0;if((c[47203]|0)!=0|(c[47202]|0)!=0){d=b+16|0;e=c[(c[d>>2]|0)+100>>2]|0;if((e|0?(a[e+81>>0]|0)==0:0)?tO(b,1)|0:0){e=xC(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0;zP(e,c[(c[d>>2]|0)+100>>2]|0)}e=c[(c[d>>2]|0)+104>>2]|0;if((e|0?(a[e+81>>0]|0)==0:0)?tO(b,0)|0:0){e=xC(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0;zP(e,c[(c[d>>2]|0)+104>>2]|0)}}return}function tO(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0;q=l;l=l+96|0;o=q+32|0;m=q+16|0;n=q;i=b+16|0;do if((a[(c[i>>2]|0)+112>>0]|0)!=6){e=c[47203]|0;if(!((e|0)!=0?(a[(RA(b,e)|0)>>0]|0)!=0:0)){e=c[47202]|0;if(!e){e=0;break}if(!(a[(RA(b,e)|0)>>0]|0)){e=0;break}}d=d<<24>>24==0;p=c[i>>2]|0;p=c[(d?p+104|0:p+100|0)>>2]|0;e=uO(b)|0;if(e){k=c[e>>2]|0;do if(d){if(c[k+8>>2]|0){e=c[k>>2]|0;j=+h[e>>3];g=+h[k+16>>3];e=e+8|0;f=+h[k+24>>3];break}e=c[k>>2]|0;g=+h[e>>3];f=+h[e+8>>3];e=0;while(1){if((e|0)==4)break;n=o+(e<<4)|0;i=(c[k>>2]|0)+(e<<4)|0;c[n>>2]=c[i>>2];c[n+4>>2]=c[i+4>>2];c[n+8>>2]=c[i+8>>2];c[n+12>>2]=c[i+12>>2];e=e+1|0}kP(m,o,3,.1,0,0);j=+h[m>>3];e=m+8|0}else{d=(c[e+4>>2]|0)+-1|0;i=k+(d*48|0)|0;if(c[k+(d*48|0)+12>>2]|0){o=c[i>>2]|0;e=(c[k+(d*48|0)+4>>2]|0)+-1|0;j=+h[o+(e<<4)>>3];g=+h[k+(d*48|0)+32>>3];e=o+(e<<4)+8|0;f=+h[k+(d*48|0)+40>>3];break}m=c[i>>2]|0;d=k+(d*48|0)+4|0;e=(c[d>>2]|0)+-1|0;g=+h[m+(e<<4)>>3];f=+h[m+(e<<4)+8>>3];e=0;while(1){if((e|0)==4)break;m=o+(e<<4)|0;k=(c[i>>2]|0)+(e+-4+(c[d>>2]|0)<<4)|0;c[m>>2]=c[k>>2];c[m+4>>2]=c[k+4>>2];c[m+8>>2]=c[k+8>>2];c[m+12>>2]=c[k+12>>2];e=e+1|0}kP(n,o,3,.9,0,0);j=+h[n>>3];e=n+8|0}while(0);r=+K(+(+h[e>>3]-f),+(j-g));r=+_O(b,c[47203]|0,-25.0,-180.0)*.017453292519943295+r;j=+_O(b,c[47202]|0,1.0,0.0)*10.0;h[p+56>>3]=j*+E(+r)+g;h[p+64>>3]=+F(+r)*j+f;a[p+81>>0]=1;e=1}else e=0}else e=0;while(0);l=q;return e|0}function uO(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;f=h;d=b;while(1){e=c[d+16>>2]|0;d=c[e+8>>2]|0;if(d|0)break;if(!(a[e+112>>0]|0)){g=5;break}d=c[e+116>>2]|0}if((g|0)==5){g=KB(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0;d=KB(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0;c[f>>2]=g;c[f+4>>2]=d;gA(1,106579,f)|0;d=0}l=h;return d|0}function vO(a,d,e){a=a|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0.0;s=l;l=l+96|0;m=s+80|0;n=s+64|0;o=s+48|0;p=s+32|0;q=s+16|0;r=s;k=b[(c[d+16>>2]|0)+136>>1]|0;d=e+16|0;wO(c[(c[d>>2]|0)+8>>2]|0,p,q);f=+h[p>>3];g=+h[q>>3];t=f-g;i=+h[p+8>>3];j=+h[q+8>>3];u=i-j;a:do if(u*u+t*t<1.0e-06){c[o>>2]=c[p>>2];c[o+4>>2]=c[p+4>>2];c[o+8>>2]=c[p+8>>2];c[o+12>>2]=c[p+12>>2]}else switch(k&14){case 4:case 10:{h[n>>3]=(g+f)*.5;h[n+8>>3]=(j+i)*.5;q=c[(c[d>>2]|0)+8>>2]|0;c[m>>2]=c[n>>2];c[m+4>>2]=c[n+4>>2];c[m+8>>2]=c[n+8>>2];c[m+12>>2]=c[n+12>>2];qP(r,q,m);c[o>>2]=c[r>>2];c[o+4>>2]=c[r+4>>2];c[o+8>>2]=c[r+8>>2];c[o+12>>2]=c[r+12>>2];break a}default:{xO(o,c[(c[d>>2]|0)+8>>2]|0,p,q);break a}}while(0);c[a>>2]=c[o>>2];c[a+4>>2]=c[o+4>>2];c[a+8>>2]=c[o+8>>2];c[a+12>>2]=c[o+12>>2];l=s;return}function wO(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;h=l;l=l+32|0;g=h+16|0;f=h;j=c[a>>2]|0;e=c[j>>2]|0;i=c[j+8>>2]|0;k=j+16|0;c[g>>2]=c[k>>2];c[g+4>>2]=c[k+4>>2];c[g+8>>2]=c[k+8>>2];c[g+12>>2]=c[k+12>>2];j=j+32|0;c[f>>2]=c[j>>2];c[f+4>>2]=c[j+4>>2];c[f+8>>2]=c[j+8>>2];c[f+12>>2]=c[j+12>>2];if(!i){c[b>>2]=c[e>>2];c[b+4>>2]=c[e+4>>2];c[b+8>>2]=c[e+8>>2];c[b+12>>2]=c[e+12>>2]}else{c[b>>2]=c[g>>2];c[b+4>>2]=c[g+4>>2];c[b+8>>2]=c[g+8>>2];c[b+12>>2]=c[g+12>>2]}i=c[a>>2]|0;j=(c[a+4>>2]|0)+-1|0;e=c[i+(j*48|0)>>2]|0;a=c[i+(j*48|0)+4>>2]|0;k=c[i+(j*48|0)+12>>2]|0;b=i+(j*48|0)+16|0;c[g>>2]=c[b>>2];c[g+4>>2]=c[b+4>>2];c[g+8>>2]=c[b+8>>2];c[g+12>>2]=c[b+12>>2];j=i+(j*48|0)+32|0;c[f>>2]=c[j>>2];c[f+4>>2]=c[j+4>>2];c[f+8>>2]=c[j+8>>2];c[f+12>>2]=c[j+12>>2];if(!k){k=e+(a+-1<<4)|0;c[d>>2]=c[k>>2];c[d+4>>2]=c[k+4>>2];c[d+8>>2]=c[k+8>>2];c[d+12>>2]=c[k+12>>2]}else{c[d>>2]=c[f>>2];c[d+4>>2]=c[f+4>>2];c[d+8>>2]=c[f+8>>2];c[d+12>>2]=c[f+12>>2]}l=h;return}function xO(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0,j=0,k=0,l=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0.0;r=c[b+4>>2]|0;g=0.0;f=0;while(1){if((f|0)>=(r|0))break;l=c[b>>2]|0;k=c[l+(f*48|0)>>2]|0;l=c[l+(f*48|0)+4>>2]|0;i=3;j=0;while(1){if((i|0)>=(l|0))break;q=+h[k+(j<<4)>>3]-+h[k+(i<<4)>>3];p=+h[k+(j<<4)+8>>3]-+h[k+(i<<4)+8>>3];i=i+3|0;j=j+3|0;g=+C(+(p*p+q*q))+g}f=f+1|0}l=0;g=g*.5;a:while(1){if((l|0)>=(r|0)){f=15;break}k=c[b>>2]|0;j=c[k+(l*48|0)>>2]|0;k=c[k+(l*48|0)+4>>2]|0;f=3;i=0;while(1){if((f|0)>=(k|0))break;m=+h[j+(i<<4)>>3];p=+h[j+(i<<4)+8>>3];n=+h[j+(f<<4)>>3];o=+h[j+(f<<4)+8>>3];q=m-n;s=p-o;q=+C(+(s*s+q*q));if(q>=g){f=12;break a}f=f+3|0;i=i+3|0;g=g-q}l=l+1|0}if((f|0)==12){h[d>>3]=m;h[d+8>>3]=p;h[e>>3]=n;h[e+8>>3]=o;s=q-g;h[a>>3]=(s*m+n*g)/q;h[a+8>>3]=(s*p+o*g)/q;return}else if((f|0)==15)qa(138712,106514,1316,106641)}function yO(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;sO(b);return}function zO(b,d,e,f,g){b=b|0;d=d|0;e=+e;f=f|0;g=g|0;var i=0.0,j=0.0,k=0.0,l=0.0,m=0.0,n=0,o=0,p=0.0,q=0.0,r=0.0,s=0,t=0.0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0.0,C=0.0,D=0,G=0,H=0,I=0,J=0,K=0,L=0;J=AO(b)|0;I=c[J+4>>2]|0;K=c[J>>2]|0;H=I+-1|0;B=+h[K+(H<<6)+16>>3];C=+(I|0);D=(g|0)==2;G=(f|0)!=0;n=0;j=0.0;i=0.0;l=0.0;m=0.0;while(1){if((n|0)>=(I|0))break;b=~~+BO(+(n+-1|0),C);A=n+1|0;L=~~+BO(+(A|0),C);o=K+(n<<6)|0;m=+h[o>>3];z=K+(n<<6)+8|0;l=+h[z>>3];s=K+(n<<6)+16|0;t=+h[s>>3];u=K+(n<<6)+24|0;v=K+(n<<6)+32|0;w=K+(n<<6)+40|0;x=K+(n<<6)+48|0;y=K+(n<<6)+56|0;k=+CO(+h[K+(L<<6)+8>>3]-l,+h[K+(L<<6)>>3]-m);q=+CO(+h[K+(b<<6)+8>>3]-l,+h[K+(b<<6)>>3]-m);r=+hb[d&7](t,B,e);b=(n|0)==0;do if(b|(n|0)==(H|0))if(b){i=k+1.5707963267948966;if(!D){b=0;p=i;j=r;break}b=0;p=i;j=r;l=l-+F(+k)*r;m=m-+E(+k)*r;break}else{i=q+-1.5707963267948966;if(!D){b=0;p=i;j=r;break}b=0;p=i;j=r;l=l-+F(+q)*r;m=m-+E(+q)*r;break}else{p=k-q;p=1.5707963267948966-(p<0.0?p+6.283185307179586:p)*.5;j=+E(+p);j=j==0.0?0.0:r/j;i=k+1.5707963267948966;k=p+i;if(G|j>r*10.0){q=+BO(q+-1.5707963267948966,6.283185307179586);b=1;p=+BO(i,6.283185307179586);j=r;i=q}else{b=0;p=k;i=k}}while(0);h[o>>3]=m;h[z>>3]=l;h[s>>3]=t;a[u>>0]=108;h[v>>3]=i;h[w>>3]=j;c[x>>2]=b;h[y>>3]=p;n=A}o=JK(12)|0;b=0;while(1){if((b|0)>=(I|0))break;m=+h[K+(b<<6)>>3];l=+h[K+(b<<6)+8>>3];i=+h[K+(b<<6)+32>>3];j=+h[K+(b<<6)+40>>3];L=c[K+(b<<6)+48>>2]|0;k=+h[K+(b<<6)+56>>3];DO(o,+E(+i)*j+m,+F(+i)*j+l);if(L|0)EO(m,l,j,1,i,k,f,o);b=b+1|0}n=(g|0)==1;k=i+3.141592653589793;if(n)FO(o,m,l,j,i,k);else{DO(o,+E(+k)*j+m,+F(+k)*j+l);i=k}b=I+-2|0;k=m;while(1){if((b|0)<=-1)break;m=+h[K+(b<<6)>>3];l=+h[K+(b<<6)+8>>3];j=+h[K+(b<<6)+40>>3];L=c[K+(b<<6)+48>>2]|0;i=+h[K+(b<<6)+32>>3]+3.141592653589793;k=+h[K+(b<<6)+56>>3]+3.141592653589793;DO(o,+E(+k)*j+m,+F(+k)*j+l);if(L|0)EO(m,l,j,0,i,k,f,o);b=b+-1|0;k=m}if(n)FO(o,k,l,j,i,i+3.141592653589793);GO(J);return o|0}function AO(a){a=a|0;var b=0.0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0;q=l;l=l+144|0;i=q+128|0;h=q+112|0;j=q+96|0;k=q+64|0;m=q;n=q+80|0;o=HO()|0;p=c[a+4>>2]|0;f=c[a>>2]|0;c[i>>2]=c[f>>2];c[i+4>>2]=c[f+4>>2];c[i+8>>2]=c[f+8>>2];c[i+12>>2]=c[f+12>>2];IO(o,i,0.0);g=m+48|0;c[g>>2]=c[f>>2];c[g+4>>2]=c[f+4>>2];c[g+8>>2]=c[f+8>>2];c[g+12>>2]=c[f+12>>2];d=0;b=0.0;a:while(1){e=d+3|0;if((e|0)>=(p|0))break;c[m>>2]=c[g>>2];c[m+4>>2]=c[g+4>>2];c[m+8>>2]=c[g+8>>2];c[m+12>>2]=c[g+12>>2];a=1;while(1){if((a|0)==4)break;r=m+(a<<4)|0;s=f+(a+d<<4)|0;c[r>>2]=c[s>>2];c[r+4>>2]=c[s+4>>2];c[r+8>>2]=c[s+8>>2];c[r+12>>2]=c[s+12>>2];a=a+1|0}c[j>>2]=c[m>>2];c[j+4>>2]=c[m+4>>2];c[j+8>>2]=c[m+8>>2];c[j+12>>2]=c[m+12>>2];a=1;while(1){if((a|0)==21){d=e;continue a}kP(n,m,3,+(a|0)*.05,0,0);c[k>>2]=c[n>>2];c[k+4>>2]=c[n+4>>2];c[k+8>>2]=c[n+8>>2];c[k+12>>2]=c[n+12>>2];c[h>>2]=c[j>>2];c[h+4>>2]=c[j+4>>2];c[h+8>>2]=c[j+8>>2];c[h+12>>2]=c[j+12>>2];c[i>>2]=c[n>>2];c[i+4>>2]=c[n+4>>2];c[i+8>>2]=c[n+8>>2];c[i+12>>2]=c[n+12>>2];t=+JO(h,i)+b;c[i>>2]=c[k>>2];c[i+4>>2]=c[k+4>>2];c[i+8>>2]=c[k+8>>2];c[i+12>>2]=c[k+12>>2];IO(o,i,t);c[j>>2]=c[k>>2];c[j+4>>2]=c[k+4>>2];c[j+8>>2]=c[k+8>>2];c[j+12>>2]=c[k+12>>2];a=a+1|0;b=t}}KO(o);l=q;return o|0}function BO(a,b){a=+a;b=+b;var c=0.0;c=a-+A(+(a/b))*b;return +(!(a<0.0)&!(a>=b)?a:c)}function CO(a,b){a=+a;b=+b;if(!(a==0.0&b==0.0)){a=+K(+a,+b);if(!(a>=0.0))a=a+6.283185307179586}else a=0.0;return +a}function DO(a,b,d){a=a|0;b=+b;d=+d;var e=0,f=0;e=c[a>>2]|0;f=a+4|0;if((e|0)<(c[f>>2]|0))f=c[a+8>>2]|0;else{c[f>>2]=2e3;e=a+8|0;f=MK(c[e>>2]|0,32e3)|0;c[e>>2]=f;e=c[a>>2]|0}c[a>>2]=e+1;h[f+(e<<4)>>3]=b;h[f+(e<<4)+8>>3]=d;return}function EO(a,b,c,d,e,f,g,h){a=+a;b=+b;c=+c;d=d|0;e=+e;f=+f;g=g|0;h=h|0;var i=0.0;d=(d|0)==0;i=d?e:f;do if((g|0)==1){e=(d?f:e)-i;e=!(e<=1.7453292519943296e-03)?e:e+6.283185307179586;if(e<3.141592653589793){FO(h,a,b,c,e+i,i);break}else{DO(h,+E(+i)*c+a,+F(+i)*c+a);break}}else DO(h,+E(+i)*c+a,+F(+i)*c+a);while(0);return}function FO(a,b,c,d,e,f){a=a|0;b=+b;c=+c;d=+d;e=+e;f=+f;var g=0,h=0.0;DO(a,+E(+e)*d+b,+F(+e)*d+c);a:do if(!(d==0.0)){while(1){if(!(f>e))break;f=f+-6.283185307179586}f=e-f;while(1){if(!(f>6.283185307179586))break;f=f+-6.283185307179586}f=f*.05263157894736842;g=1;while(1){if((g|0)==20)break a;h=e-f*+(g|0);DO(a,+E(+h)*d+b,+F(+h)*d+c);g=g+1|0}}while(0);return}function GO(a){a=a|0;U1(c[a>>2]|0);U1(a);return}function HO(){var a=0;a=JK(12)|0;c[a+4>>2]=0;c[a+8>>2]=2e3;c[a>>2]=JK(128e3)|0;return a|0}function IO(a,b,d){a=a|0;b=b|0;d=+d;var e=0,f=0,g=0,i=0;i=a+4|0;e=c[i>>2]|0;f=a+8|0;g=c[f>>2]|0;if((e|0)<(g|0))a=c[a>>2]|0;else{c[f>>2]=g<<1;e=MK(c[a>>2]|0,g<<7)|0;c[a>>2]=e;a=e;e=c[i>>2]|0}h[a+(e<<6)>>3]=+h[b>>3];h[a+(e<<6)+8>>3]=+h[b+8>>3];c[i>>2]=e+1;h[a+(e<<6)+16>>3]=d;return}function JO(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a>>3]-+h[b>>3];d=+h[a+8>>3]-+h[b+8>>3];return +(+C(+(d*d+c*c)))}function KO(a){a=a|0;var b=0;b=c[a+4>>2]|0;if((c[a+8>>2]|0)>(b|0))c[a>>2]=MK(c[a>>2]|0,b<<6)|0;return}function LO(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;j=k+8|0;i=k;g=k+12|0;c[g>>2]=0;h=c[d+4>>2]|0;if(!h)qa(106658,106669,203,106680);e=c[h>>2]|0;if(!e)qa(106694,106669,206,106680);f=h+8|0;if(!(c[f>>2]|0))c[f>>2]=MO(e)|0;if(!0)e=0;else{e=(PJ(c[h>>2]|0)|0)==0;e=e?0:g}if(!((dT(b,d,e)|0)<<24>>24))NO(d,e);do if(e|0){f=c[g>>2]|0;b=c[15712]|0;e=c[h>>2]|0;if(!f){c[j>>2]=e;w4(b,106737,j)|0;break}else{c[i>>2]=e;c[i+4>>2]=f;w4(b,106705,i)|0;break}}while(0);j=d+32|0;c[a>>2]=c[j>>2];c[a+4>>2]=c[j+4>>2];c[a+8>>2]=c[j+8>>2];c[a+12>>2]=c[j+12>>2];l=k;return}function MO(a){a=a|0;var b=0;b=c[47301]|0;if((b|0)!=0?(c3(b,a)|0)==0:0)a=c[47310]|0;else{U1(b);c[47301]=D4(a)|0;a=u5(189204,22332,35,36,95)|0;c[47310]=a}return a|0}function NO(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0.0,j=0,k=0.0;j=c[b+4>>2]|0;e=c[j>>2]|0;i=+h[j+16>>3];j=b+32|0;h[j>>3]=0.0;h[b+40>>3]=i*1.2;h[b+16>>3]=0.0;h[b+24>>3]=i*.1;c[b+8>>2]=0;c[b+12>>2]=0;if(j3(e,106771,4)|0)if((j3(e,106776,5)|0)!=0?(j3(e,106782,9)|0)!=0:0){g=4248;e=106792}else{g=8344;e=106828}else{g=6296;e=106809}if(d|0)c[d>>2]=e;e=c[b>>2]|0;if(e|0){f=0.0;while(1){b=a[e>>0]|0;if(!(b<<24>>24))break;k=f+ +h[g+((b&255)<<3)>>3];h[j>>3]=k;e=e+1|0;f=k}h[j>>3]=f*i}return}function OO(a,b){a=a|0;b=b|0;return c3(c[a>>2]|0,c[b>>2]|0)|0}function PO(a){a=a|0;var b=0;b=a+108|0;c[b>>2]=0;c[a+112>>2]=32;c[a+116>>2]=-1;c[a+120>>2]=33;c[a+124>>2]=63;c[a+128>>2]=55;c[a+132>>2]=0;c[a+136>>2]=0;c[a+140>>2]=0;b=Vz(b,c[4581]|0)|0;c[a+144>>2]=b;return b|0}function QO(a,b,d){a=a|0;b=b|0;d=d|0;d=V1(1,32)|0;a=c[b>>2]|0;if(a|0)c[d>>2]=D4(a)|0;a=c[b+4>>2]|0;if(a|0)c[d+4>>2]=D4(a)|0;c[d+24>>2]=c[b+24>>2]&127;h[d+16>>3]=+h[b+16>>3];c[d+8>>2]=c[b+8>>2];return d|0}function RO(a,b,d){a=a|0;b=b|0;d=d|0;U1(c[b>>2]|0);U1(c[b+4>>2]|0);U1(b);return}function SO(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0,j=0;a=c[b>>2]|0;e=c[d>>2]|0;i=(e|0)==0;if(!a)if(i)j=5;else a=-1;else if(!i){a=s2(a,e)|0;if(!a)j=5}else a=1;do if((j|0)==5){a=c[b+4>>2]|0;e=c[d+4>>2]|0;i=(e|0)==0;if(!a){if(!i){a=-1;break}}else{if(i){a=1;break}a=s2(a,e)|0;if(a|0)break}a=(c[b+24>>2]&127)-(c[d+24>>2]&127)|0;if(!a){g=+h[b+16>>3];f=+h[d+16>>3];if(g<f)a=-1;else a=g>f&1}}while(0);return a|0}function TO(){eb(189244)|0;return}function UO(){var a=0,b=0;b=l;l=l+16|0;a=b;eb(a|0)|0;l=b;return +(+((c[a+4>>2]|0)+(c[a>>2]|0)-(c[47311]|0)-(c[47312]|0)|0)*.016666666666666666)}function VO(a){a=a|0;var b=0,d=0;b=JK(16)|0;a=(a|0)>2?a:2;d=JK(a<<2)|0;c[b>>2]=d;c[b+12>>2]=d;c[b+8>>2]=d;c[b+4>>2]=d+(a<<2);return b|0}function WO(a){a=a|0;U1(c[a>>2]|0);U1(a);return}function XO(a,b){a=a|0;b=b|0;var d=0,e=0;d=a+12|0;e=c[d>>2]|0;c[d>>2]=e+4;c[e>>2]=b;if((c[d>>2]|0)>>>0>=(c[a+4>>2]|0)>>>0)c[d>>2]=c[a>>2];return}function YO(a){a=a|0;var b=0,d=0,e=0;d=a+8|0;b=c[d>>2]|0;if((b|0)!=(c[a+12>>2]|0)){e=b+4|0;c[d>>2]=e;b=c[b>>2]|0;if(e>>>0>=(c[a+4>>2]|0)>>>0)c[d>>2]=c[a>>2]}else b=0;return b|0}function ZO(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;i=l;l=l+16|0;g=i;if(((d|0)!=0?(h=RA(b,d)|0,(h|0)!=0):0)?(a[h>>0]|0)!=0:0){d=y4(h,g,10)|0;e=(h|0)==(c[g>>2]|0)?e:(d|0)<(f|0)?f:d}l=i;return e|0}function _O(b,d,e,f){b=b|0;d=d|0;e=+e;f=+f;var g=0,h=0,i=0,j=0.0;i=l;l=l+16|0;g=i;if(((b|0)!=0&(d|0)!=0?(h=RA(b,d)|0,(h|0)!=0):0)?(a[h>>0]|0)!=0:0){j=+s5(h,g);e=(h|0)==(c[g>>2]|0)?e:j<f?f:j}l=i;return +e}function $O(a){a=a|0;var b=0.0;b=+h[23293];if(!(b>0.0)){b=+_O(a,GA(a,0,107638,0)|0,-1.0,0.0);b=b==0.0?72.0:b}return +b}function aP(a,b,c){a=a|0;b=b|0;c=c|0;if((a|0)!=0&(b|0)!=0)c=RA(a,b)|0;return c|0}function bP(b,c,d){b=b|0;c=c|0;d=d|0;b=aP(b,c,d)|0;if((b|0)!=0?(a[b>>0]|0)!=0:0)d=b;return d|0}function cP(a,b,c){a=a|0;b=b|0;c=c|0;if(!b)a=c&255;else a=dP(RA(a,b)|0)|0;return a|0}function dP(a){a=a|0;return eP(a,0)|0}function eP(b,c){b=b|0;c=c|0;var d=0;if((b|0)!=0?(d=a[b>>0]|0,d<<24>>24!=0):0)if((c3(b,137308)|0)!=0?(c3(b,107649)|0)!=0:0)if((c3(b,107652)|0)!=0?(c3(b,107657)|0)!=0:0){if(((d<<24>>24)+-48|0)>>>0<10)c=(q5(b)|0)&255}else c=1;else c=0;return c|0}function fP(a){a=a|0;var b=0,d=0;while(1){b=(c[a+16>>2]|0)+220|0;d=c[b>>2]|0;if(!((d|0)!=(a|0)&(d|0)!=0))break;a=c[(c[d+16>>2]|0)+220>>2]|0;if(!a){a=d;continue}c[b>>2]=a}return a|0}function gP(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;if((a|0)!=(b|0)){d=c[a+16>>2]|0;e=d+220|0;if(!(c[e>>2]|0)){c[e>>2]=a;c[d+216>>2]=1}else a=fP(a)|0;d=c[b+16>>2]|0;e=d+220|0;if(!(c[e>>2]|0)){c[e>>2]=b;c[d+216>>2]=1}else{d=fP(b)|0;b=d;d=c[d+16>>2]|0}g=a+16|0;e=c[g>>2]|0;f=b+16|0;h=(c[e+120>>2]|0)>(c[d+120>>2]|0);a=h?b:a;c[(h?e:d)+220>>2]=a;e=(c[(h?f:g)>>2]|0)+216|0;c[e>>2]=(c[e>>2]|0)+(c[(c[(h?g:f)>>2]|0)+216>>2]|0)}return a|0}function hP(b){b=b|0;b=c[b+16>>2]|0;c[b+216>>2]=1;c[b+220>>2]=0;a[b+159>>0]=0;return}function iP(a,b){a=a|0;b=b|0;if((fP(a)|0)==(a|0)){a=c[a+16>>2]|0;c[a+220>>2]=b;b=(c[b+16>>2]|0)+216|0;c[b>>2]=(c[b>>2]|0)+(c[a+216>>2]|0);return}else qa(107669,107661,194,107685)}function jP(a,b){a=a|0;b=b|0;var d=0.0;b=c[(c[b+16>>2]|0)+132>>2]|0;d=+h[b+8>>3]*72.0;h[a>>3]=+h[b>>3]*72.0;h[a+8>>3]=d;return}function kP(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=f|0;g=g|0;var i=0,j=0,k=0,m=0.0,n=0,o=0,p=0;o=l;l=l+576|0;n=o;i=0;while(1){if((i|0)>(d|0))break;k=n+(i<<4)|0;j=b+(i<<4)|0;c[k>>2]=c[j>>2];c[k+4>>2]=c[j+4>>2];c[k+8>>2]=c[j+8>>2];c[k+12>>2]=c[j+12>>2];i=i+1|0}m=1.0-e;i=1;while(1){if((i|0)>(d|0))break;j=d-i|0;k=i+-1|0;b=0;while(1){if((b|0)>(j|0))break;p=b+1|0;h[n+(i*96|0)+(b<<4)>>3]=+h[n+(k*96|0)+(p<<4)>>3]*e+ +h[n+(k*96|0)+(b<<4)>>3]*m;h[n+(i*96|0)+(b<<4)+8>>3]=+h[n+(k*96|0)+(p<<4)+8>>3]*e+ +h[n+(k*96|0)+(b<<4)+8>>3]*m;b=p}i=i+1|0}a:do if(f|0){i=0;while(1){if((i|0)>(d|0))break a;p=f+(i<<4)|0;k=n+(i*96|0)|0;c[p>>2]=c[k>>2];c[p+4>>2]=c[k+4>>2];c[p+8>>2]=c[k+8>>2];c[p+12>>2]=c[k+12>>2];i=i+1|0}}while(0);b:do if(g|0){i=0;while(1){if((i|0)>(d|0))break b;p=g+(i<<4)|0;f=n+((d-i|0)*96|0)+(i<<4)|0;c[p>>2]=c[f>>2];c[p+4>>2]=c[f+4>>2];c[p+8>>2]=c[f+8>>2];c[p+12>>2]=c[f+12>>2];i=i+1|0}}while(0);p=n+(d*96|0)|0;c[a>>2]=c[p>>2];c[a+4>>2]=c[p+4>>2];c[a+8>>2]=c[p+8>>2];c[a+12>>2]=c[p+12>>2];l=o;return}function lP(b){b=b|0;var d=0,e=0,f=0,g=0;e=0;do{d=c[47315]|0;f=d+1024|0;if((d-e|0)<1024){c[47315]=f;f=MK(c[47316]|0,f)|0;c[47316]=f;d=c[47315]|0}else f=c[47316]|0;d=c4(f+e|0,d-e|0,b)|0;if(!d){g=6;break}e=(l2(d)|0)+e|0;d=c[47316]|0}while((a[d+(e+-1)>>0]|0)!=10);if((g|0)==6)d=c[47316]|0;return ((e|0)>0?d:0)|0}function mP(b){b=b|0;var d=0,e=0,f=0;f=l;l=l+16|0;do if((b|0)!=0?(a[b>>0]|0)!=0:0)if(!0){d=c[47133]|0;e=c[47318]|0;if((c[47317]|0)==(d|0))d=e;else{if(e){U1(c[e>>2]|0);U1(c[47318]|0);c[47318]=0;d=c[47133]|0}c[47317]=d;if(!d)break;if(!(a[d>>0]|0))break;d=nP(d)|0;c[47318]=d}if(!((a[b>>0]|0)!=47&(d|0)!=0))break;b=oP(d,c[47319]|0,b)|0;break}else S();else b=0;while(0);l=f;return b|0}function nP(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;e=0;f=0;g=0;b=D4(a)|0;d=0;while(1){b=A4(b,107984)|0;if(!b)break;a=(g<<2)+8|0;if(!f)a=KK(a)|0;else a=MK(d,a)|0;c[a+(g<<2)>>2]=b;f=l2(b)|0;e=e>>>0>f>>>0?e:f;f=a;g=g+1|0;b=0;d=a}c[f+(g<<2)>>2]=0;c[47319]=e;return f|0}function oP(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;g=l;l=l+16|0;e=g;h=c[47320]|0;c[47320]=W1(h,b+2+(l2(d)|0)|0)|0;while(1){b=c[a>>2]|0;if(!b){a=0;break}h=c[47320]|0;c[e>>2]=b;c[e+4>>2]=107982;c[e+8>>2]=d;m4(h,107975,e)|0;if(!(_4(c[47320]|0,4)|0)){f=4;break}else a=a+4|0}if((f|0)==4)a=c[47320]|0;l=g;return a|0}function pP(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;g=(b|0)==0;f=0;while(1){h=c[d+(f<<2)>>2]|0;if(!h)break;if((!g?(a[b>>0]|0)==(a[h>>0]|0):0)?(s2(b,h)|0)==0:0)break;f=f+1|0}return c[e+(f<<2)>>2]|0}function qP(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0.0,k=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,C=0;z=l;l=l+80|0;x=z+16|0;y=z;u=c[b+4>>2]|0;v=c[b>>2]|0;w=d+8|0;f=0;e=-1;g=1.e+38;b=-1;while(1){if((f|0)>=(u|0))break;s=c[v+(f*48|0)>>2]|0;t=c[v+(f*48|0)+4>>2]|0;r=0;while(1){if((r|0)>=(t|0))break;q=+h[s+(r<<4)>>3]-+h[d>>3];p=+h[s+(r<<4)+8>>3]-+h[w>>3];q=p*p+q*q;A=(e|0)==-1|q<g;C=A?r:e;r=r+1|0;b=A?f:b;e=C;g=A?q:g}f=f+1|0}f=c[v+(b*48|0)>>2]|0;e=e+(((e|0)==((c[v+(b*48|0)+4>>2]|0)+-1|0))<<31>>31)|0;e=e-((e|0)%3|0)|0;b=0;while(1){if((b|0)==4)break;C=b+e|0;h[x+(b<<4)>>3]=+h[f+(C<<4)>>3];h[x+(b<<4)+8>>3]=+h[f+(C<<4)+8>>3];b=b+1|0}q=+h[d>>3];k=+h[x>>3]-q;o=+h[w>>3];p=+h[x+8>>3]-o;j=+h[x+48>>3]-q;n=+h[x+56>>3]-o;b=y+8|0;g=1.0;i=0.0;j=n*n+j*j;k=p*p+k*k;while(1){p=(g+i)*.5;kP(y,x,3,p,0,0);m=+h[y>>3];n=+h[b>>3];if(+B(+(k-j))<1.0)break;if(+B(+(g-i))<1.0e-05)break;C=k<j;m=m-q;n=n-o;n=n*n+m*m;g=C?p:g;i=C?i:p;j=C?n:j;k=C?k:n}h[a>>3]=m;h[a+8>>3]=n;l=z;return}function rP(){return 0}function sP(b){b=b|0;var d=0.0,e=0,f=0,g=0,i=0,j=0,k=0,l=0;d=+_O(b,c[47146]|0,.75,.01);g=b+16|0;h[(c[g>>2]|0)+32>>3]=d;d=+_O(b,c[47145]|0,.5,.02);h[(c[g>>2]|0)+40>>3]=d;l=jN(bP(b,c[47147]|0,107986)|0,b)|0;c[(c[g>>2]|0)+8>>2]=l;l=RA(b,c[47154]|0)|0;d=+_O(b,c[47150]|0,14.0,1.0);e=bP(b,c[47151]|0,107994)|0;f=bP(b,c[47152]|0,137314)|0;i=(iD(l)|0)!=0;k=(WM(b)|0)==2;i=zK(b,l,(k?4:0)|(i?2:0),d,e,f)|0;c[(c[g>>2]|0)+104>>2]=i;i=c[47155]|0;if((i|0?(j=RA(b,i)|0,j|0):0)?a[j>>0]|0:0){l=(iD(j)|0)!=0;l=zK(b,j,l?2:0,d,e,f)|0;c[(c[g>>2]|0)+108>>2]=l;l=(c[(xC(b)|0)+16>>2]|0)+113|0;a[l>>0]=a[l>>0]|16}k=(ZO(b,c[47158]|0,0,0)|0)&255;l=c[g>>2]|0;a[l+144>>0]=k;tb[c[c[(c[l+8>>2]|0)+4>>2]>>2]&127](b);return}function tP(b){b=b|0;var d=0,e=0,f=0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;v=l;l=l+112|0;m=v+96|0;n=v+80|0;s=v+40|0;u=v;r=b+48|0;p=xC(c[((c[b>>2]&3|0)==3?b:r)+40>>2]|0)|0;g=m+8|0;c[g>>2]=0;o=n+8|0;c[o>>2]=0;d=c[47182]|0;if(((d|0)!=0?(e=RA(b,d)|0,(e|0)!=0):0)?(a[e>>0]|0)!=0:0){uP(b,m);t=(iD(e)|0)!=0;i=+h[m>>3];f=c[g>>2]|0;d=c[m+12>>2]|0;e=zK(b,e,t?2:0,i,f,d)|0;t=b+16|0;c[(c[t>>2]|0)+96>>2]=e;e=(c[p+16>>2]|0)+113|0;a[e>>0]=a[e>>0]|1;e=dP(aP(b,c[47192]|0,137308)|0)|0;a[(c[t>>2]|0)+114>>0]=e;t=1;e=f}else{t=0;d=0;i=0.0;e=0}f=c[47183]|0;if((f|0?(j=RA(b,f)|0,j|0):0)?a[j>>0]|0:0){if(!e){uP(b,m);i=+h[m>>3];e=c[g>>2]|0;d=c[m+12>>2]|0}g=(iD(j)|0)!=0;j=zK(b,j,g?2:0,i,e,d)|0;c[(c[b+16>>2]|0)+108>>2]=j;j=(c[p+16>>2]|0)+113|0;a[j>>0]=a[j>>0]|32}d=c[47197]|0;if(((d|0)!=0?(k=RA(b,d)|0,(k|0)!=0):0)?(a[k>>0]|0)!=0:0){vP(b,m,n);j=(iD(k)|0)!=0;i=+h[n>>3];d=c[o>>2]|0;e=c[n+12>>2]|0;k=zK(b,k,j?2:0,i,d,e)|0;c[(c[b+16>>2]|0)+100>>2]=k;k=(c[p+16>>2]|0)+113|0;a[k>>0]=a[k>>0]|2}else{e=0;i=0.0;d=0}f=c[47198]|0;if((f|0?(q=RA(b,f)|0,q|0):0)?a[q>>0]|0:0){if(!d){vP(b,m,n);e=c[n+12>>2]|0;i=+h[n>>3];d=c[o>>2]|0}o=(iD(q)|0)!=0;q=zK(b,q,o?2:0,i,d,e)|0;c[(c[b+16>>2]|0)+104>>2]=q;q=(c[p+16>>2]|0)+113|0;a[q>>0]=a[q>>0]|4}d=QA(b,111455)|0;d=d|0?d:195059;if(a[d>>0]|0)a[(c[(c[((c[b>>2]&3|0)==3?b:r)+40>>2]|0)+16>>2]|0)+145>>0]=1;g=b+16|0;f=(c[g>>2]|0)+16|0;e=c[((c[b>>2]&3|0)==3?b:r)+40>>2]|0;wP(s,c[(c[(c[(c[e+16>>2]|0)+8>>2]|0)+4>>2]|0)+8>>2]|0,e,d);d=s;e=f+40|0;do{c[f>>2]=c[d>>2];f=f+4|0;d=d+4|0}while((f|0)<(e|0));if((xP(b,c[47204]|0)|0)<<24>>24)a[(c[g>>2]|0)+46>>0]=0;d=QA(b,111464)|0;d=d|0?d:195059;if(!(a[d>>0]|0))e=b+-48|0;else{e=b+-48|0;a[(c[(c[((c[b>>2]&3|0)==2?b:e)+40>>2]|0)+16>>2]|0)+145>>0]=1}f=(c[g>>2]|0)+56|0;e=c[((c[b>>2]&3|0)==2?b:e)+40>>2]|0;wP(u,c[(c[(c[(c[e+16>>2]|0)+8>>2]|0)+4>>2]|0)+8>>2]|0,e,d);d=u;e=f+40|0;do{c[f>>2]=c[d>>2];f=f+4|0;d=d+4|0}while((f|0)<(e|0));if((xP(b,c[47205]|0)|0)<<24>>24)a[(c[g>>2]|0)+86>>0]=0;l=v;return t|0}function uP(a,b){a=a|0;b=b|0;h[b>>3]=+_O(a,c[47179]|0,14.0,1.0);c[b+8>>2]=bP(a,c[47180]|0,107994)|0;c[b+12>>2]=bP(a,c[47181]|0,137314)|0;return}function vP(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=b+8|0;if(!(c[e>>2]|0))uP(a,b);h[d>>3]=+_O(a,c[47199]|0,+h[b>>3],1.0);c[d+8>>2]=bP(a,c[47200]|0,c[e>>2]|0)|0;c[d+12>>2]=bP(a,c[47201]|0,c[b+12>>2]|0)|0;return}function wP(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+128|0;k=m+80|0;h=m+40|0;g=m;if((f|0)!=0?(j=R2(f,58)|0,(j|0)!=0):0){a[j>>0]=0;i=j+1|0;xb[d&31](h,e,f,i);e=k;g=h;d=e+36|0;do{c[e>>2]=c[g>>2];e=e+4|0;g=g+4|0}while((e|0)<(d|0));a[j>>0]=58;f=i}else{xb[d&31](g,e,f,0);e=k;d=e+36|0;do{c[e>>2]=c[g>>2];e=e+4|0;g=g+4|0}while((e|0)<(d|0))}e=b;g=k;d=e+36|0;do{c[e>>2]=c[g>>2];e=e+4|0;g=g+4|0}while((e|0)<(d|0));c[b+36>>2]=f;l=m;return}function xP(b,c){b=b|0;c=c|0;var d=0;if(((c|0)!=0?(d=RA(b,c)|0,(d|0)!=0):0)?(a[d>>0]|0)!=0:0)b=(dP(d)|0)<<24>>24==0&1;else b=0;return b|0}function yP(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0.0,o=0.0;k=l;l=l+32|0;i=k;j=c[b+8>>2]|0;d=c[b+4>>2]|0;d=(c[b+44>>2]|0)+((O((d|0)>1?d+-1|0:0,j)|0)<<4)|0;e=i+16|0;c[e>>2]=c[d>>2];c[e+4>>2]=c[d+4>>2];c[e+8>>2]=c[d+8>>2];c[e+12>>2]=c[d+12>>2];c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];f=i+8|0;g=i+24|0;b=1;while(1){if((b|0)>=(j|0))break;m=d+(b<<4)|0;o=+h[m>>3];h[i>>3]=+h[(+h[i>>3]<o?i:m)>>3];n=+h[m+8>>3];h[f>>3]=+h[(+h[f>>3]<n?i:m)+8>>3];h[e>>3]=+h[(+h[e>>3]>o?e:m)>>3];h[g>>3]=+h[(+h[g>>3]>n?e:m)+8>>3];b=b+1|0}c[a>>2]=c[i>>2];c[a+4>>2]=c[i+4>>2];c[a+8>>2]=c[i+8>>2];c[a+12>>2]=c[i+12>>2];c[a+16>>2]=c[i+16>>2];c[a+20>>2]=c[i+20>>2];c[a+24>>2]=c[i+24>>2];c[a+28>>2]=c[i+28>>2];l=k;return}function zP(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;d=l;l=l+64|0;g=d+32|0;e=d;f=c[a+16>>2]|0;a=f+16|0;f=c[f+116>>2]&1;c[g>>2]=c[a>>2];c[g+4>>2]=c[a+4>>2];c[g+8>>2]=c[a+8>>2];c[g+12>>2]=c[a+12>>2];c[g+16>>2]=c[a+16>>2];c[g+20>>2]=c[a+20>>2];c[g+24>>2]=c[a+24>>2];c[g+28>>2]=c[a+28>>2];AP(e,g,b,f);c[a>>2]=c[e>>2];c[a+4>>2]=c[e+4>>2];c[a+8>>2]=c[e+8>>2];c[a+12>>2]=c[e+12>>2];c[a+16>>2]=c[e+16>>2];c[a+20>>2]=c[e+20>>2];c[a+24>>2]=c[e+24>>2];c[a+28>>2]=c[e+28>>2];l=d;return}function AP(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0,k=0.0,l=0.0,m=0.0;g=+h[d+56>>3];l=+h[d+64>>3];j=e<<24>>24==0;k=+h[d+24>>3];i=+h[d+32>>3];m=(j?k:i)*.5;f=g-m;g=m+g;if(f<+h[b>>3])h[b>>3]=f;e=b+16|0;if(g>+h[e>>3])h[e>>3]=g;g=(j?i:k)*.5;f=l-g;g=g+l;e=b+8|0;if(f<+h[e>>3])h[e>>3]=f;e=b+24|0;if(g>+h[e>>3])h[e>>3]=g;c[a>>2]=c[b>>2];c[a+4>>2]=c[b+4>>2];c[a+8>>2]=c[b+8>>2];c[a+12>>2]=c[b+12>>2];c[a+16>>2]=c[b+16>>2];c[a+20>>2]=c[b+20>>2];c[a+24>>2]=c[b+24>>2];c[a+28>>2]=c[b+28>>2];return}function BP(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0.0,N=0.0,O=0.0,P=0.0;L=l;l=l+448|0;H=L+416|0;t=L+384|0;u=L+304|0;K=L+256|0;G=L+192|0;B=L+144|0;C=L+96|0;d=L+400|0;e=L+368|0;f=L+352|0;g=L+336|0;v=L+288|0;w=L+224|0;x=L+160|0;y=L+112|0;z=L+64|0;A=L+32|0;I=L;J=b+16|0;if((tB(b)|0)==0?(c[(c[J>>2]|0)+180>>2]|0)==0:0){CP(d,0.0,0.0);c[K>>2]=c[d>>2];c[K+4>>2]=c[d+4>>2];c[K+8>>2]=c[d+8>>2];c[K+12>>2]=c[d+12>>2];K=K+16|0;CP(e,0.0,0.0);c[K>>2]=c[e>>2];c[K+4>>2]=c[e+4>>2];c[K+8>>2]=c[e+8>>2];c[K+12>>2]=c[e+12>>2]}else{CP(f,2147483647.0,2147483647.0);c[K>>2]=c[f>>2];c[K+4>>2]=c[f+4>>2];c[K+8>>2]=c[f+8>>2];c[K+12>>2]=c[f+12>>2];F=K+16|0;CP(g,-2147483647.0,-2147483647.0);c[F>>2]=c[g>>2];c[F+4>>2]=c[g+4>>2];c[F+8>>2]=c[g+8>>2];c[F+12>>2]=c[g+12>>2];o=C+8|0;p=u+16|0;D=K+8|0;q=u+8|0;E=K+24|0;r=u+24|0;s=B+8|0;n=jC(b)|0;while(1){if(!n)break;jP(B,n);d=n+16|0;m=c[d>>2]|0;h[C>>3]=(+h[m+96>>3]+ +h[m+88>>3])*.5;h[o>>3]=+h[m+80>>3]*.5;c[t>>2]=c[B>>2];c[t+4>>2]=c[B+4>>2];c[t+8>>2]=c[B+8>>2];c[t+12>>2]=c[B+12>>2];c[H>>2]=c[C>>2];c[H+4>>2]=c[C+4>>2];c[H+8>>2]=c[C+8>>2];c[H+12>>2]=c[C+12>>2];DP(u,t,H);c[t>>2]=c[B>>2];c[t+4>>2]=c[B+4>>2];c[t+8>>2]=c[B+8>>2];c[t+12>>2]=c[B+12>>2];c[H>>2]=c[C>>2];c[H+4>>2]=c[C+4>>2];c[H+8>>2]=c[C+8>>2];c[H+12>>2]=c[C+12>>2];EP(v,t,H);c[p>>2]=c[v>>2];c[p+4>>2]=c[v+4>>2];c[p+8>>2]=c[v+8>>2];c[p+12>>2]=c[v+12>>2];h[K>>3]=+h[(+h[K>>3]<+h[u>>3]?K:u)>>3];h[D>>3]=+h[(+h[D>>3]<+h[q>>3]?K:u)+8>>3];h[F>>3]=+h[(+h[F>>3]>+h[p>>3]?K:u)+16>>3];h[E>>3]=+h[(+h[E>>3]>+h[r>>3]?K:u)+24>>3];d=c[(c[d>>2]|0)+108>>2]|0;if(d|0?a[d+81>>0]|0:0){m=c[(c[J>>2]|0)+116>>2]&1;c[H>>2]=c[K>>2];c[H+4>>2]=c[K+4>>2];c[H+8>>2]=c[K+8>>2];c[H+12>>2]=c[K+12>>2];c[H+16>>2]=c[K+16>>2];c[H+20>>2]=c[K+20>>2];c[H+24>>2]=c[K+24>>2];c[H+28>>2]=c[K+28>>2];AP(w,H,d,m);c[K>>2]=c[w>>2];c[K+4>>2]=c[w+4>>2];c[K+8>>2]=c[w+8>>2];c[K+12>>2]=c[w+12>>2];c[K+16>>2]=c[w+16>>2];c[K+20>>2]=c[w+20>>2];c[K+24>>2]=c[w+24>>2];c[K+28>>2]=c[w+28>>2]}m=XA(b,n)|0;while(1){if(!m)break;k=m+16|0;d=c[k>>2]|0;j=d+8|0;e=c[j>>2]|0;if(e|0){i=0;while(1){if((i|0)<(c[e+4>>2]|0))g=0;else break;while(1){f=c[e>>2]|0;if((g|0)>=(c[f+(i*48|0)+4>>2]|0))break;e=(c[f+(i*48|0)>>2]|0)+(g<<4)|0;c[B>>2]=c[e>>2];c[B+4>>2]=c[e+4>>2];c[B+8>>2]=c[e+8>>2];c[B+12>>2]=c[e+12>>2];N=+h[B>>3];h[K>>3]=+h[(+h[K>>3]<N?K:B)>>3];M=+h[s>>3];h[D>>3]=+h[(+h[D>>3]<M?K:B)+8>>3];h[F>>3]=+h[(+h[F>>3]>N?F:B)>>3];h[E>>3]=+h[(+h[E>>3]>M?F:B)+8>>3];g=g+1|0;e=c[j>>2]|0}i=i+1|0}e=c[d+96>>2]|0;if((e|0)!=0?(a[e+81>>0]|0)!=0:0){d=c[(c[J>>2]|0)+116>>2]&1;c[H>>2]=c[K>>2];c[H+4>>2]=c[K+4>>2];c[H+8>>2]=c[K+8>>2];c[H+12>>2]=c[K+12>>2];c[H+16>>2]=c[K+16>>2];c[H+20>>2]=c[K+20>>2];c[H+24>>2]=c[K+24>>2];c[H+28>>2]=c[K+28>>2];AP(x,H,e,d);c[K>>2]=c[x>>2];c[K+4>>2]=c[x+4>>2];c[K+8>>2]=c[x+8>>2];c[K+12>>2]=c[x+12>>2];c[K+16>>2]=c[x+16>>2];c[K+20>>2]=c[x+20>>2];c[K+24>>2]=c[x+24>>2];c[K+28>>2]=c[x+28>>2];d=c[k>>2]|0}e=c[d+100>>2]|0;if((e|0)!=0?(a[e+81>>0]|0)!=0:0){d=c[(c[J>>2]|0)+116>>2]&1;c[H>>2]=c[K>>2];c[H+4>>2]=c[K+4>>2];c[H+8>>2]=c[K+8>>2];c[H+12>>2]=c[K+12>>2];c[H+16>>2]=c[K+16>>2];c[H+20>>2]=c[K+20>>2];c[H+24>>2]=c[K+24>>2];c[H+28>>2]=c[K+28>>2];AP(y,H,e,d);c[K>>2]=c[y>>2];c[K+4>>2]=c[y+4>>2];c[K+8>>2]=c[y+8>>2];c[K+12>>2]=c[y+12>>2];c[K+16>>2]=c[y+16>>2];c[K+20>>2]=c[y+20>>2];c[K+24>>2]=c[y+24>>2];c[K+28>>2]=c[y+28>>2];d=c[k>>2]|0}e=c[d+104>>2]|0;if((e|0)!=0?(a[e+81>>0]|0)!=0:0){d=c[(c[J>>2]|0)+116>>2]&1;c[H>>2]=c[K>>2];c[H+4>>2]=c[K+4>>2];c[H+8>>2]=c[K+8>>2];c[H+12>>2]=c[K+12>>2];c[H+16>>2]=c[K+16>>2];c[H+20>>2]=c[K+20>>2];c[H+24>>2]=c[K+24>>2];c[H+28>>2]=c[K+28>>2];AP(z,H,e,d);c[K>>2]=c[z>>2];c[K+4>>2]=c[z+4>>2];c[K+8>>2]=c[z+8>>2];c[K+12>>2]=c[z+12>>2];c[K+16>>2]=c[z+16>>2];c[K+20>>2]=c[z+20>>2];c[K+24>>2]=c[z+24>>2];c[K+28>>2]=c[z+28>>2];d=c[k>>2]|0}d=c[d+108>>2]|0;if(d|0?a[d+81>>0]|0:0){k=c[(c[J>>2]|0)+116>>2]&1;c[H>>2]=c[K>>2];c[H+4>>2]=c[K+4>>2];c[H+8>>2]=c[K+8>>2];c[H+12>>2]=c[K+12>>2];c[H+16>>2]=c[K+16>>2];c[H+20>>2]=c[K+20>>2];c[H+24>>2]=c[K+24>>2];c[H+28>>2]=c[K+28>>2];AP(A,H,d,k);c[K>>2]=c[A>>2];c[K+4>>2]=c[A+4>>2];c[K+8>>2]=c[A+8>>2];c[K+12>>2]=c[A+12>>2];c[K+16>>2]=c[A+16>>2];c[K+20>>2]=c[A+20>>2];c[K+24>>2]=c[A+24>>2];c[K+28>>2]=c[A+28>>2]}}m=ZA(b,m)|0}n=kC(b,n)|0}d=c[J>>2]|0;f=c[d+180>>2]|0;g=d+184|0;i=G+8|0;j=G+16|0;k=G+24|0;e=1;while(1){if((e|0)>(f|0))break;P=+h[(c[(c[(c[g>>2]|0)+(e<<2)>>2]|0)+16>>2]|0)+16>>3];h[G>>3]=P;C=c[(c[(c[g>>2]|0)+(e<<2)>>2]|0)+16>>2]|0;O=+h[C+24>>3];h[i>>3]=O;M=+h[C+32>>3];h[j>>3]=M;N=+h[C+40>>3];h[k>>3]=N;h[K>>3]=+h[(+h[K>>3]<P?K:G)>>3];h[D>>3]=+h[(+h[D>>3]<O?K:G)+8>>3];h[F>>3]=+h[(+h[F>>3]>M?K:G)+16>>3];h[E>>3]=+h[(+h[E>>3]>N?K:G)+24>>3];e=e+1|0}e=c[d+12>>2]|0;if((e|0)!=0?(a[e+81>>0]|0)!=0:0){d=c[d+116>>2]&1;c[H>>2]=c[K>>2];c[H+4>>2]=c[K+4>>2];c[H+8>>2]=c[K+8>>2];c[H+12>>2]=c[K+12>>2];c[H+16>>2]=c[K+16>>2];c[H+20>>2]=c[K+20>>2];c[H+24>>2]=c[K+24>>2];c[H+28>>2]=c[K+28>>2];AP(I,H,e,d);c[K>>2]=c[I>>2];c[K+4>>2]=c[I+4>>2];c[K+8>>2]=c[I+8>>2];c[K+12>>2]=c[I+12>>2];c[K+16>>2]=c[I+16>>2];c[K+20>>2]=c[I+20>>2];c[K+24>>2]=c[I+24>>2];c[K+28>>2]=c[I+28>>2];d=c[J>>2]|0}J=d+16|0;c[J>>2]=c[K>>2];c[J+4>>2]=c[K+4>>2];c[J+8>>2]=c[K+8>>2];c[J+12>>2]=c[K+12>>2];c[J+16>>2]=c[K+16>>2];c[J+20>>2]=c[K+20>>2];c[J+24>>2]=c[K+24>>2];c[J+28>>2]=c[K+28>>2]}l=L;return}function CP(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b;h[a+8>>3]=c;return}function DP(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[b+8>>3]-+h[c+8>>3];h[a>>3]=+h[b>>3]-+h[c>>3];h[a+8>>3]=d;return}function EP(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[c+8>>3]+ +h[b+8>>3];h[a>>3]=+h[c>>3]+ +h[b>>3];h[a+8>>3]=d;return}function FP(a){a=a|0;if((c[a+60>>2]|0)==(a|0))a=1;else a=(j3(KB(a)|0,108006,7)|0)==0&1;return a|0}function GP(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;a:do if(!e)switch(IC(b)|0){case 0:{e=GA(a,0,c,195059)|0;break a}case 1:{e=GA(a,1,c,195059)|0;break a}case 2:{e=GA(a,2,c,195059)|0;break a}default:{e=0;break a}}while(0);KA(b,e,d)|0;return e|0}function HP(d){d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+144|0;g=k;i=IP(d)|0;h=Vz(23592,c[4581]|0)|0;j=ND(d,108014,1)|0;TC(j,137483,280,1)|0;kA(g,128,k+16|0);e=jC(d)|0;while(1){if(!e)break;a:do if(!(a[(c[e+16>>2]|0)+118>>0]|0)){f=XA(d,e)|0;while(1){if(!f)break a;JP(f,j,g,h,i);f=ZA(d,f)|0}}while(0);e=kC(d,e)|0}oA(g);Rz(h)|0;f=tB(j)|0;e=jC(j)|0;while(1){if(!e)break;h=kC(j,e)|0;wC(d,e)|0;e=h}sB(j)|0;if(f|0){j=(c[d+16>>2]|0)+136|0;b[j>>1]=b[j>>1]|1}Rz(i)|0;l=k;return f|0}function IP(a){a=a|0;var b=0;b=Vz(23628,c[4581]|0)|0;SP(a,b);return b|0}function JP(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=l;l=l+48|0;q=t+32|0;p=t+24|0;s=t+16|0;r=t+8|0;k=t;n=c[b>>2]&3;m=c[((n|0)==3?b:b+48|0)+40>>2]|0;n=c[((n|0)==2?b:b+-48|0)+40>>2]|0;do if(!(a[(c[n+16>>2]|0)+118>>0]|0)){if(!(Q2(KB(m)|0,108006,7)|0))o=KP(g,KB(m)|0)|0;else o=0;if(!(Q2(KB(n)|0,108006,7)|0))i=KP(g,KB(n)|0)|0;else i=0;h=(o|0)!=0;j=(i|0)!=0;if(h|j){if((o|0)==(i|0)){r=KB(m)|0;s=KB(m)|0;c[k>>2]=r;c[k+4>>2]=s;gA(0,108029,k)|0;break}g=LP(f,b)|0;if(g|0){MP(b,c[g+16>>2]|0,c[g+20>>2]|0)|0;break}if(!j)if(!(HC(o,n)|0)){OP(f,m,n,MP(b,NP(m,o,e,d)|0,n)|0);break}else{r=KB(n)|0;s=KB(o)|0;c[q>>2]=r;c[q+4>>2]=s;gA(0,108184,q)|0;break}if(!h)if(!(HC(i,m)|0)){OP(f,m,n,MP(b,m,NP(n,i,e,d)|0)|0);break}else{r=KB(m)|0;s=KB(i)|0;c[p>>2]=r;c[p+4>>2]=s;gA(0,108147,p)|0;break}if(HC(i,o)|0){q=KB(o)|0;s=KB(i)|0;c[r>>2]=q;c[r+4>>2]=s;gA(0,108067,r)|0;break}if(!(HC(o,i)|0)){s=NP(m,o,e,d)|0;OP(f,m,n,MP(b,s,NP(n,i,e,d)|0)|0);break}else{q=KB(i)|0;r=KB(o)|0;c[s>>2]=q;c[s+4>>2]=r;gA(0,108107,s)|0;break}}}while(0);l=t;return}function KP(a,b){a=a|0;b=b|0;a=nb[c[a>>2]&63](a,b,512)|0;if(!a)a=0;else a=c[a+12>>2]|0;return a|0}function LP(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=l;l=l+16|0;e=d;f=c[b>>2]&3;c[e>>2]=c[((f|0)==3?b:b+48|0)+40>>2];c[e+4>>2]=c[((f|0)==2?b:b+-48|0)+40>>2];b=nb[c[a>>2]&63](a,e,512)|0;l=d;return b|0}function MP(a,b,c){a=a|0;b=b|0;c=c|0;c=iB(xC(b)|0,b,c,0,1)|0;TC(c,137460,176,1)|0;UA(a,c)|0;return c|0}function NP(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0;k=l;l=l+112|0;g=k;j=k+4|0;nA(e,108221)|0;i=c[47321]|0;c[47321]=i+1;c[g>>2]=i;m4(j,137395,g)|0;nA(e,j)|0;j=e+4|0;g=c[j>>2]|0;i=e+8|0;if(g>>>0>=(c[i>>2]|0)>>>0){lA(e,1)|0;g=c[j>>2]|0}c[j>>2]=g+1;a[g>>0]=58;nA(e,KB(d)|0)|0;h=yC(d)|0;g=c[j>>2]|0;if(g>>>0>=(c[i>>2]|0)>>>0){lA(e,1)|0;g=c[j>>2]|0}a[g>>0]=0;e=c[e>>2]|0;c[j>>2]=e;j=tC(h,e,1)|0;TC(j,137447,304,1)|0;a[(c[j+16>>2]|0)+118>>0]=1;oC(d,j,1)|0;oC(f,b,1)|0;f=xC(j)|0;c[47154]=GP(f,j,108224,195059,c[47154]|0)|0;f=xC(j)|0;c[47157]=GP(f,j,141082,134335,c[47157]|0)|0;f=xC(j)|0;c[47147]=GP(f,j,108230,108236,c[47147]|0)|0;l=k;return j|0}function OP(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;f=l;l=l+32|0;g=f;n=g+8|0;c[n>>2]=b;m=g+12|0;c[m>>2]=d;o=c[e>>2]&3;i=e+48|0;j=g+16|0;c[j>>2]=c[((o|0)==3?e:i)+40>>2];k=e+-48|0;h=g+20|0;c[h>>2]=c[((o|0)==2?e:k)+40>>2];nb[c[a>>2]&63](a,g,1)|0;c[n>>2]=d;c[m>>2]=b;d=c[e>>2]&3;c[j>>2]=c[((d|0)==2?e:k)+40>>2];c[h>>2]=c[((d|0)==3?e:i)+40>>2];nb[c[a>>2]&63](a,g,1)|0;l=f;return}function PP(a,b,d){a=a|0;b=b|0;d=d|0;d=JK(24)|0;c[d+8>>2]=c[b+8>>2];c[d+12>>2]=c[b+12>>2];c[d+16>>2]=c[b+16>>2];c[d+20>>2]=c[b+20>>2];return d|0}function QP(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function RP(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;a=c[b>>2]|0;e=c[d>>2]|0;if(a>>>0>=e>>>0)if(a>>>0>e>>>0)a=1;else{b=c[b+4>>2]|0;d=c[d+4>>2]|0;return (b>>>0<d>>>0?-1:b>>>0>d>>>0&1)|0}else a=-1;return a|0}function SP(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;h=l;l=l+16|0;g=h;f=a+16|0;e=1;while(1){a=c[f>>2]|0;if((e|0)>(c[a+180>>2]|0))break;a=c[(c[a+184>>2]|0)+(e<<2)>>2]|0;d=KB(a)|0;if(!(nb[c[b>>2]&63](b,d,512)|0)){i=JK(16)|0;c[i+8>>2]=d;c[i+12>>2]=a;nb[c[b>>2]&63](b,i,1)|0}else{c[g>>2]=d;gA(0,108240,g)|0}SP(a,b);e=e+1|0}l=h;return}function TP(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function UP(a){a=a|0;var b=0,c=0,d=0;d=ND(a,108014,1)|0;TC(d,137483,280,1)|0;b=jC(a)|0;while(1){if(!b)break;c=XA(a,b)|0;while(1){if(!c)break;VP(c,d);c=ZA(a,c)|0}b=kC(a,b)|0}b=jC(d)|0;while(1){if(!b)break;c=kC(d,b)|0;WP(b);wC(a,b)|0;b=c}sB(d)|0;return}function VP(b,d){b=b|0;d=d|0;var e=0,f=0;e=c[b>>2]&3;f=c[((e|0)==3?b:b+48|0)+40>>2]|0;e=c[((e|0)==2?b:b+-48|0)+40>>2]|0;if(!((a[(c[f+16>>2]|0)+118>>0]|0)==0?!(a[(c[e+16>>2]|0)+118>>0]|0):0)){f=XP(f,d)|0;e=MP(b,f,XP(e,d)|0)|0;f=c[b+16>>2]|0;d=f+8|0;e=c[e+16>>2]|0;c[e+8>>2]=c[d>>2];c[d>>2]=0;d=f+96|0;c[e+96>>2]=c[d>>2];c[d>>2]=0;d=f+108|0;c[e+108>>2]=c[d>>2];c[d>>2]=0;d=f+100|0;c[e+100>>2]=c[d>>2];c[d>>2]=0;f=f+104|0;c[e+104>>2]=c[f>>2];c[f>>2]=0;YP(b)}return}function WP(a){a=a|0;var b=0,d=0,e=0;e=a+16|0;b=c[e>>2]|0;d=c[b+132>>2]|0;if(d){U1(d);b=c[e>>2]|0}d=c[b+8>>2]|0;if(d){tb[c[(c[d+4>>2]|0)+4>>2]&127](a);b=c[e>>2]|0}CK(c[b+104>>2]|0);CK(c[(c[e>>2]|0)+108>>2]|0);VC(a,137447)|0;return}function XP(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;f=xC(b)|0;a:do if(a[(c[b+16>>2]|0)+118>>0]|0){oC(d,b,1)|0;b=R2(KB(b)|0,58)|0;if(!b)qa(111208,107661,1225,108292);d=b+1|0;b=tC(f,d,0)|0;if(!b){b=tC(f,d,1)|0;TC(b,137447,304,1)|0;d=LA(f,1,0)|0;while(1){if(!d)break a;g=RA(b,d)|0;e=c[d+12>>2]|0;if((g|0)!=(e|0))KA(b,d,e)|0;d=LA(f,1,d)|0}}}while(0);return b|0}function YP(a){a=a|0;var b=0;b=a+16|0;U1(c[(c[b>>2]|0)+144>>2]|0);ZP(a);CK(c[(c[b>>2]|0)+96>>2]|0);CK(c[(c[b>>2]|0)+108>>2]|0);CK(c[(c[b>>2]|0)+100>>2]|0);CK(c[(c[b>>2]|0)+104>>2]|0);VC(a,137460)|0;return}function ZP(a){a=a|0;var b=0,d=0,e=0;e=a+16|0;a=c[e>>2]|0;b=c[a+8>>2]|0;if(b){d=0;while(1){a=c[b>>2]|0;if((d|0)>=(c[b+4>>2]|0))break;U1(c[a+(d*48|0)>>2]|0);d=d+1|0;b=c[(c[e>>2]|0)+8>>2]|0}U1(a);U1(c[(c[e>>2]|0)+8>>2]|0);a=c[e>>2]|0}c[a+8>>2]=0;return}function _P(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0;e=GA(a,b,c,0)|0;if(!e)e=GA(a,b,c,d)|0;return e|0}function $P(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+32|0;i=o;h=o+8|0;j=o+16|0;m=R2(b,59)|0;n=d+4|0;e=c[n>>2]|0;k=d+8|0;if(e>>>0>=(c[k>>2]|0)>>>0){lA(d,1)|0;e=c[n>>2]|0}c[n>>2]=e+1;a[e>>0]=38;if(((m|0)!=0?(f=m-b|0,(f+-2|0)>>>0<=6):0)?(i4(j,b,f)|0,a[j+f>>0]=0,c[h>>2]=j,g=u5(h,23664,252,8,96)|0,(g|0)!=0):0){c[i>>2]=c[g+4>>2];m4(j,137395,i)|0;b=c[n>>2]|0;if(b>>>0>=(c[k>>2]|0)>>>0){lA(d,1)|0;b=c[n>>2]|0}c[n>>2]=b+1;a[b>>0]=35;nA(d,j)|0;b=c[n>>2]|0;if(b>>>0>=(c[k>>2]|0)>>>0){lA(d,1)|0;b=c[n>>2]|0}c[n>>2]=b+1;a[b>>0]=59;b=m+1|0}l=o;return b|0}function aQ(a,b){a=a|0;b=b|0;return s2(c[a>>2]|0,c[b>>2]|0)|0}function bQ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+1072|0;k=q+8|0;j=q;i=q+16|0;o=q+24|0;c[i>>2]=b;if((c[47322]|0)!=(d|0)){c[47322]=d;a[194958]=0}kA(o,1024,q+40|0);p=o+4|0;n=o+8|0;while(1){f=b+1|0;c[i>>2]=f;e=a[b>>0]|0;if(!(e<<24>>24))break;do if((e&255)<192)if(e<<24>>24==38){f=dQ(i)|0;if(!f)e=38;else{if(f>>>0<127){e=f&255;break}b=c[p>>2]|0;e=b>>>0>=(c[n>>2]|0)>>>0;if(f>>>0<2047){if(e){lA(o,1)|0;b=c[p>>2]|0}c[p>>2]=b+1;a[b>>0]=f>>>6|192;e=(f&63|128)&255;break}if(e){lA(o,1)|0;b=c[p>>2]|0}c[p>>2]=b+1;a[b>>0]=f>>>12|224;b=c[p>>2]|0;if(b>>>0>=(c[n>>2]|0)>>>0){lA(o,1)|0;b=c[p>>2]|0}c[p>>2]=b+1;a[b>>0]=f>>>6&63|128;e=(f&63|128)&255}}else{h=0;m=9}else if((e&255)>=224)if((e&255)>=240)if((e&255)<248){h=3;m=9}else{if(!(a[194958]|0)){c[j>>2]=KB(d)|0;gA(0,109707,j)|0;a[194958]=1}h=-1;e=cQ(e,o)|0;m=9}else{h=2;m=9}else{h=1;m=9}while(0);do if((m|0)==9){m=0;g=0;while(1){if((g|0)>=(h|0)){m=34;break}if((a[f>>0]&-64)<<24>>24!=-128)break;b=c[p>>2]|0;if(b>>>0>=(c[n>>2]|0)>>>0){lA(o,1)|0;b=c[p>>2]|0}c[p>>2]=b+1;a[b>>0]=e;g=g+1|0;e=a[f>>0]|0;f=f+1|0}if((m|0)==34){m=0;c[i>>2]=f;break}c[i>>2]=f;if(!(a[194958]|0)){g=KB(d)|0;c[k>>2]=h+1;c[k+4>>2]=g;gA(0,109828,k)|0;a[194958]=1}e=cQ(e,o)|0}while(0);b=c[p>>2]|0;if(b>>>0>=(c[n>>2]|0)>>>0){lA(o,1)|0;b=c[p>>2]|0}c[p>>2]=b+1;a[b>>0]=e;b=c[i>>2]|0}b=c[p>>2]|0;if(b>>>0>=(c[n>>2]|0)>>>0){lA(o,1)|0;b=c[p>>2]|0}a[b>>0]=0;n=c[o>>2]|0;c[p>>2]=n;p=D4(n)|0;oA(o);l=q;return p|0}function cQ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;j=l;l=l+16|0;g=j;a[g>>0]=b;a[g+1>>0]=0;g=eQ(g)|0;h=d+4|0;i=d+8|0;e=l2(g)|0;f=g;while(1){if((e|0)<=1)break;b=c[h>>2]|0;if(b>>>0>=(c[i>>2]|0)>>>0){lA(d,1)|0;b=c[h>>2]|0}k=a[f>>0]|0;c[h>>2]=b+1;a[b>>0]=k;e=e+-1|0;f=f+1|0}k=a[f>>0]|0;U1(g);l=j;return k|0}function dQ(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+32|0;h=i;d=i+8|0;e=c[b>>2]|0;a:do if((a[e>>0]|0)!=35){c[h>>2]=d;g=0;b:while(1){if((g|0)>=8){d=0;break a}f=a[e+g>>0]|0;switch(f<<24>>24){case 0:{d=0;break a}case 59:break b;default:{}}a[d>>0]=f;d=d+1|0;g=g+1|0}a[d>>0]=0;d=u5(h,23664,252,8,96)|0;if(!d)d=0;else{e=e+(g+1)|0;d=c[d+4>>2]|0}}else{h=a[e+1>>0]|0;d=h&255;c:do if((h|32)<<24>>24==120){h=0;g=2;while(1){if((g|0)>=8){f=h;break c}f=a[e+g>>0]|0;d=f&255;if((f+-65&255)>=6)if((f+-97&255)>=6)if((f+-48&255)<10)f=-48;else{f=h;break c}else f=-87;else f=-55;d=f+d|0;h=d+(h<<4)|0;g=g+1|0}}else{f=0;g=1;while(1){if((g|0)>=8)break c;h=a[e+g>>0]|0;d=h&255;if((h+-48&255)>=10)break c;f=(f*10|0)+-48+d|0;g=g+1|0}}while(0);d=(d|0)==59;e=d?e+(g+1)|0:e;d=d?f:0}while(0);c[b>>2]=e;l=i;return d|0}function eQ(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+1056|0;g=k;i=k+8|0;c[g>>2]=b;kA(i,1024,k+24|0);j=i+4|0;h=i+8|0;while(1){c[g>>2]=b+1;d=a[b>>0]|0;if(!(d<<24>>24))break;b=d&255;if(d<<24>>24==38){b=dQ(g)|0;if(!b){b=38;f=6}else f=5}else f=5;do if((f|0)==5){f=0;if(b>>>0<127)f=6;else{d=c[j>>2]|0;e=d>>>0>=(c[h>>2]|0)>>>0;if(b>>>0<2047){if(e){lA(i,1)|0;d=c[j>>2]|0}c[j>>2]=d+1;a[d>>0]=b>>>6|192;d=c[j>>2]|0;if(d>>>0>=(c[h>>2]|0)>>>0){lA(i,1)|0;d=c[j>>2]|0}b=b&63|128;break}if(e){lA(i,1)|0;d=c[j>>2]|0}c[j>>2]=d+1;a[d>>0]=b>>>12|224;d=c[j>>2]|0;if(d>>>0>=(c[h>>2]|0)>>>0){lA(i,1)|0;d=c[j>>2]|0}c[j>>2]=d+1;a[d>>0]=b>>>6&63|128;d=c[j>>2]|0;if(d>>>0>=(c[h>>2]|0)>>>0){lA(i,1)|0;d=c[j>>2]|0}b=b&63|128}}while(0);if((f|0)==6){d=c[j>>2]|0;if(d>>>0>=(c[h>>2]|0)>>>0){lA(i,1)|0;d=c[j>>2]|0}}c[j>>2]=d+1;a[d>>0]=b;b=c[g>>2]|0}b=c[j>>2]|0;if(b>>>0>=(c[h>>2]|0)>>>0){lA(i,1)|0;b=c[j>>2]|0}a[b>>0]=0;h=c[i>>2]|0;c[j>>2]=h;j=D4(h)|0;oA(i);l=k;return j|0}function fQ(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+1040|0;g=i;kA(g,1024,i+16|0);h=g+4|0;f=g+8|0;while(1){d=b+1|0;e=a[b>>0]|0;if(!(e<<24>>24))break;if((e&255)<127){b=c[h>>2]|0;if(b>>>0>=(c[f>>2]|0)>>>0){lA(g,1)|0;b=c[h>>2]|0}c[h>>2]=b+1;a[b>>0]=e;b=d;continue}else{e=a[d>>0]&63|e<<6&255;d=c[h>>2]|0;if(d>>>0>=(c[f>>2]|0)>>>0){lA(g,1)|0;d=c[h>>2]|0}c[h>>2]=d+1;a[d>>0]=e;b=b+2|0;continue}}b=c[h>>2]|0;if(b>>>0>=(c[f>>2]|0)>>>0){lA(g,1)|0;b=c[h>>2]|0}a[b>>0]=0;f=c[g>>2]|0;c[h>>2]=f;h=D4(f)|0;oA(g);l=i;return h|0}function gQ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+80|0;e=n+48|0;d=n+32|0;f=n+64|0;g=n+16|0;i=n;j=b+16|0;k=a+16|0;m=c[k>>2]|0;if(((+h[j>>3]>=+h[m+48>>3]?+h[m+64>>3]>=+h[b>>3]:0)?+h[b+24>>3]>=+h[m+56>>3]:0)?+h[m+72>>3]>=+h[b+8>>3]:0){m=m+16|0;c[d>>2]=c[j>>2];c[d+4>>2]=c[j+4>>2];c[d+8>>2]=c[j+8>>2];c[d+12>>2]=c[j+12>>2];c[e>>2]=c[b>>2];c[e+4>>2]=c[b+4>>2];c[e+8>>2]=c[b+8>>2];c[e+12>>2]=c[b+12>>2];hQ(g,d,e);c[d>>2]=c[m>>2];c[d+4>>2]=c[m+4>>2];c[d+8>>2]=c[m+8>>2];c[d+12>>2]=c[m+12>>2];c[e>>2]=c[g>>2];c[e+4>>2]=c[g+4>>2];c[e+8>>2]=c[g+8>>2];c[e+12>>2]=c[g+12>>2];DP(i,d,e);c[f>>2]=a;c[f+4>>2]=0;d=c[(c[(c[(c[k>>2]|0)+8>>2]|0)+4>>2]|0)+12>>2]|0;c[e>>2]=c[i>>2];c[e+4>>2]=c[i+4>>2];c[e+8>>2]=c[i+8>>2];c[e+12>>2]=c[i+12>>2];d=lb[d&127](f,e)|0}else d=0;l=n;return d|0}function hQ(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=(+h[c+8>>3]+ +h[b+8>>3])*.5;h[a>>3]=(+h[c>>3]+ +h[b>>3])*.5;h[a+8>>3]=d;return}function iQ(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0.0,g=0,i=0.0,j=0,k=0,m=0,n=0;e=l;l=l+80|0;j=e+64|0;k=e+48|0;m=e;n=e+32|0;g=e+16|0;h[m>>3]=+h[a+24>>3]*.5;h[m+8>>3]=+h[a+32>>3]*.5;a=a+56|0;c[k>>2]=c[a>>2];c[k+4>>2]=c[a+4>>2];c[k+8>>2]=c[a+8>>2];c[k+12>>2]=c[a+12>>2];c[j>>2]=c[m>>2];c[j+4>>2]=c[m+4>>2];c[j+8>>2]=c[m+8>>2];c[j+12>>2]=c[m+12>>2];DP(n,k,j);i=+h[n>>3];f=+h[n+8>>3];c[k>>2]=c[a>>2];c[k+4>>2]=c[a+4>>2];c[k+8>>2]=c[a+8>>2];c[k+12>>2]=c[a+12>>2];c[j>>2]=c[m>>2];c[j+4>>2]=c[m+4>>2];c[j+8>>2]=c[m+8>>2];c[j+12>>2]=c[m+12>>2];EP(g,k,j);d=+h[g+8>>3];if((+h[b+16>>3]>=i?+h[g>>3]>=+h[b>>3]:0)?+h[b+24>>3]>=f:0)a=d>=+h[b+8>>3]&1;else a=0;l=e;return a|0}function jQ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+80|0;k=n+48|0;j=n;h=a+16|0;a=c[h>>2]|0;i=c[a+8>>2]|0;a:do if((i|0)!=0?(g=i+8|0,c[j>>2]=c[g>>2],c[j+4>>2]=c[g+4>>2],c[j+8>>2]=c[g+8>>2],c[j+12>>2]=c[g+12>>2],c[j+16>>2]=c[g+16>>2],c[j+20>>2]=c[g+20>>2],c[j+24>>2]=c[g+24>>2],c[j+28>>2]=c[g+28>>2],c[k>>2]=c[b>>2],c[k+4>>2]=c[b+4>>2],c[k+8>>2]=c[b+8>>2],c[k+12>>2]=c[b+12>>2],c[k+16>>2]=c[b+16>>2],c[k+20>>2]=c[b+20>>2],c[k+24>>2]=c[b+24>>2],c[k+28>>2]=c[b+28>>2],(kQ(j,k)|0)!=0):0){d=i+4|0;a=0;while(1){if((a|0)>=(c[d>>2]|0))break;e=j;f=(c[i>>2]|0)+(a*48|0)|0;g=e+48|0;do{c[e>>2]=c[f>>2];e=e+4|0;f=f+4|0}while((e|0)<(g|0));c[k>>2]=c[b>>2];c[k+4>>2]=c[b+4>>2];c[k+8>>2]=c[b+8>>2];c[k+12>>2]=c[b+12>>2];c[k+16>>2]=c[b+16>>2];c[k+20>>2]=c[b+20>>2];c[k+24>>2]=c[b+24>>2];c[k+28>>2]=c[b+28>>2];if(!((lQ(j,k)|0)<<24>>24))a=a+1|0;else{a=1;break a}}a=c[h>>2]|0;m=7}else m=7;while(0);do if((m|0)==7){a=c[a+96>>2]|0;if(a|0?(c[k>>2]=c[b>>2],c[k+4>>2]=c[b+4>>2],c[k+8>>2]=c[b+8>>2],c[k+12>>2]=c[b+12>>2],c[k+16>>2]=c[b+16>>2],c[k+20>>2]=c[b+20>>2],c[k+24>>2]=c[b+24>>2],c[k+28>>2]=c[b+28>>2],(iQ(a,k)|0)<<24>>24):0){a=1;break}a=0}while(0);l=n;return a|0}function kQ(a,b){a=a|0;b=b|0;if((+h[a+16>>3]>=+h[b>>3]?+h[b+16>>3]>=+h[a>>3]:0)?+h[a+24>>3]>=+h[b+8>>3]:0)a=+h[b+24>>3]>=+h[a+8>>3]&1;else a=0;return a|0}function lQ(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+96|0;k=n+64|0;j=n+48|0;i=n+32|0;f=n+16|0;g=n;m=a+4|0;d=c[m>>2]|0;if(!d)qa(109937,107661,1637,109945);e=c[a>>2]|0;c[g>>2]=c[e>>2];c[g+4>>2]=c[e+4>>2];c[g+8>>2]=c[e+8>>2];c[g+12>>2]=c[e+12>>2];e=1;while(1){if((e|0)>=(d|0)){h=7;break}d=(c[a>>2]|0)+(e<<4)|0;c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];c[i>>2]=c[d>>2];c[i+4>>2]=c[d+4>>2];c[i+8>>2]=c[d+8>>2];c[i+12>>2]=c[d+12>>2];c[j>>2]=c[g>>2];c[j+4>>2]=c[g+4>>2];c[j+8>>2]=c[g+8>>2];c[j+12>>2]=c[g+12>>2];c[k>>2]=c[b>>2];c[k+4>>2]=c[b+4>>2];c[k+8>>2]=c[b+8>>2];c[k+12>>2]=c[b+12>>2];c[k+16>>2]=c[b+16>>2];c[k+20>>2]=c[b+20>>2];c[k+24>>2]=c[b+24>>2];c[k+28>>2]=c[b+28>>2];if((iK(i,j,k)|0)!=-1){d=1;break};c[g>>2]=c[f>>2];c[g+4>>2]=c[f+4>>2];c[g+8>>2]=c[f+8>>2];c[g+12>>2]=c[f+12>>2];e=e+1|0;d=c[m>>2]|0}do if((h|0)==7){d=c[a+8>>2]|0;if(d|0?(g=a+16|0,h=c[a>>2]|0,c[i>>2]=c[g>>2],c[i+4>>2]=c[g+4>>2],c[i+8>>2]=c[g+8>>2],c[i+12>>2]=c[g+12>>2],c[j>>2]=c[h>>2],c[j+4>>2]=c[h+4>>2],c[j+8>>2]=c[h+8>>2],c[j+12>>2]=c[h+12>>2],c[k>>2]=c[b>>2],c[k+4>>2]=c[b+4>>2],c[k+8>>2]=c[b+8>>2],c[k+12>>2]=c[b+12>>2],c[k+16>>2]=c[b+16>>2],c[k+20>>2]=c[b+20>>2],c[k+24>>2]=c[b+24>>2],c[k+28>>2]=c[b+28>>2],(mQ(i,j,d,k)|0)<<24>>24):0){d=1;break}d=c[a+12>>2]|0;if(d|0?(h=a+32|0,m=(c[a>>2]|0)+((c[m>>2]|0)+-1<<4)|0,c[i>>2]=c[h>>2],c[i+4>>2]=c[h+4>>2],c[i+8>>2]=c[h+8>>2],c[i+12>>2]=c[h+12>>2],c[j>>2]=c[m>>2],c[j+4>>2]=c[m+4>>2],c[j+8>>2]=c[m+8>>2],c[j+12>>2]=c[m+12>>2],c[k>>2]=c[b>>2],c[k+4>>2]=c[b+4>>2],c[k+8>>2]=c[b+8>>2],c[k+12>>2]=c[b+12>>2],c[k+16>>2]=c[b+16>>2],c[k+20>>2]=c[b+20>>2],c[k+24>>2]=c[b+24>>2],c[k+28>>2]=c[b+28>>2],(mQ(i,j,d,k)|0)<<24>>24):0){d=1;break}d=0}while(0);l=n;return d|0}function mQ(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0.0;m=l;l=l+160|0;g=m+144|0;f=m+128|0;n=m+96|0;i=m+64|0;j=m+32|0;k=m;o=+h[e+16>>3];c[f>>2]=c[a>>2];c[f+4>>2]=c[a+4>>2];c[f+8>>2]=c[a+8>>2];c[f+12>>2]=c[a+12>>2];c[g>>2]=c[b>>2];c[g+4>>2]=c[b+4>>2];c[g+8>>2]=c[b+8>>2];c[g+12>>2]=c[b+12>>2];YH(n,f,g,1.0,d);if(((o>=+h[n>>3]?(c[f>>2]=c[a>>2],c[f+4>>2]=c[a+4>>2],c[f+8>>2]=c[a+8>>2],c[f+12>>2]=c[a+12>>2],c[g>>2]=c[b>>2],c[g+4>>2]=c[b+4>>2],c[g+8>>2]=c[b+8>>2],c[g+12>>2]=c[b+12>>2],YH(i,f,g,1.0,d),+h[i+16>>3]>=+h[e>>3]):0)?(o=+h[e+24>>3],c[f>>2]=c[a>>2],c[f+4>>2]=c[a+4>>2],c[f+8>>2]=c[a+8>>2],c[f+12>>2]=c[a+12>>2],c[g>>2]=c[b>>2],c[g+4>>2]=c[b+4>>2],c[g+8>>2]=c[b+8>>2],c[g+12>>2]=c[b+12>>2],YH(j,f,g,1.0,d),o>=+h[j+8>>3]):0)?(c[f>>2]=c[a>>2],c[f+4>>2]=c[a+4>>2],c[f+8>>2]=c[a+8>>2],c[f+12>>2]=c[a+12>>2],c[g>>2]=c[b>>2],c[g+4>>2]=c[b+4>>2],c[g+8>>2]=c[b+8>>2],c[g+12>>2]=c[b+12>>2],YH(k,f,g,1.0,d),+h[k+24>>3]>=+h[e+8>>3]):0)f=1;else f=0;l=m;return f|0}function nQ(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;g=l;l=l+16|0;f=g;a:do if((b|0)!=0?(e=a[b>>0]|0,e<<24>>24!=0):0){do switch(e<<24>>24|0){case 48:{d=2;break a}case 57:case 56:case 55:case 54:case 53:case 52:case 51:case 50:case 49:{d=10;break a}case 67:case 99:{e=b+1|0;if(!(c3(e,109960)|0)){d=4;break a}if(!(c3(e,109966)|0)){d=12;break a}break}case 70:case 102:{if(!(c3(b+1|0,109974)|0)){d=2;break a}break}case 76:case 108:{if(!(c3(b+1|0,109979)|0)){d=2;break a}break}case 78:case 110:{e=b+1|0;if(!(c3(e,109983)|0)){d=0;break a}if(!(c3(e,109987)|0)){d=2;break a}break}case 79:case 111:{if(!(c3(b+1|0,109989)|0)){d=8;break a}break}case 80:case 112:{if(!(c3(b+1|0,109994)|0)){d=6;break a}break}case 83:case 115:{if(!(c3(b+1|0,110002)|0)){d=10;break a}break}case 84:case 116:{if(!(c3(b+1|0,110008)|0)){d=10;break a}break}case 89:case 121:{if(!(c3(b+1|0,110012)|0)){d=10;break a}break}default:{}}while(0);c[f>>2]=b;gA(0,110015,f)|0}while(0);l=g;return d|0}function oQ(d,f){d=d|0;f=f|0;var g=0;g=QA(d,110056)|0;if(g)if(!(a[g>>0]|0))f=0;else f=nQ(g,f)|0;d=(c[d+16>>2]|0)+136|0;b[d>>1]=f|e[d>>1];return}function pQ(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=+d;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0,k=0.0,m=0,n=0,o=0.0,p=0,q=0.0,r=0,s=0.0,t=0,u=0,v=0,w=0;v=l;l=l+32|0;p=v+16|0;r=v;t=e&1;u=e&2;a:do if((c|0)==2){s=+h[a+16>>3];f=+h[a>>3];j=+h[a+24>>3];i=+h[a+8>>3];f=f-(s-f);h[p>>3]=f;h[r>>3]=s;i=i-(j-i);h[p+8>>3]=i;h[r+8>>3]=j}else{g=+h[a>>3];h[r>>3]=g;h[p>>3]=g;j=+h[a+8>>3];m=r+8|0;h[m>>3]=j;n=p+8|0;h[n>>3]=j;e=0;f=g;i=j;while(1){if((e|0)>=(c|0)){s=g;break a}w=a+(e<<4)|0;q=+h[w>>3];k=+h[(q<f?w:p)>>3];h[p>>3]=k;s=+h[a+(e<<4)+8>>3];o=+h[(s<i?w:p)+8>>3];h[n>>3]=o;q=+h[(q>g?w:r)>>3];h[r>>3]=q;s=+h[(s>j?w:r)+8>>3];h[m>>3]=s;e=e+1|0;f=k;i=o;g=q;j=s}}while(0);g=(s-f)*.5;q=g+f;o=(j-i)*.5;k=o+i;if(!t){f=j-k;j=d;i=+F(+j);j=+E(+j);if(!u){g=i*f-k;f=-k-o*i}else{f=i*f;g=k-f;f=f+k}h[b+8>>3]=g;h[b+24>>3]=f;d=j*(s-q);h[b>>3]=q-d;h[b+16>>3]=d+q}else{d=+C(+(o*o+g*g));h[b+8>>3]=u|0?k:-k;h[b>>3]=q;h[b+16>>3]=d*.25;h[b+24>>3]=d}l=v;return}function qQ(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0;e=a+16|0;f=c[e>>2]|0;if(!(b<<24>>24)){d=+h[f+32>>3]*36.0;h[f+88>>3]=d;a=f+40|0}else{d=+h[f+40>>3]*36.0;h[f+88>>3]=d;a=f+32|0}h[f+96>>3]=d;h[(c[e>>2]|0)+80>>3]=+h[a>>3]*72.0;return}
+function Tl(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+32|0;j=n+8|0;f=n+4|0;g=n;d=c[b>>2]&3;e=c[((d|0)==3?b:b+48|0)+40>>2]|0;k=(c[(c[e+16>>2]|0)+232>>2]|0)+-1|0;a=c[(c[a+16>>2]|0)+196>>2]|0;h=c[a+(k<<6)+4>>2]|0;a=c[a+(k<<6)>>2]|0;k=j+8|0;c[k>>2]=-1;c[j>>2]=-1;m=j+12|0;c[m>>2]=a;i=j+4|0;c[i>>2]=a;Vl(e,c[((d|0)==2?b:b+-48|0)+40>>2]|0,f,g);f=c[f>>2]|0;g=c[g>>2]|0;d=0;e=a;b=-1;while(1){if((d|0)>=(e|0))break;e=e+-1|0;Wl(c[h+(d<<2)>>2]|0,j,f,g);if((d|0)!=(e|0))Wl(c[h+(e<<2)>>2]|0,j,f,g);a=c[i>>2]|0;b=c[j>>2]|0;if((a-b|0)<2)break;else d=d+1|0}if((b|0)>(a|0))a=(c[m>>2]|0)+(c[k>>2]|0)|0;else a=a+b|0;l=n;return (a+1|0)/2|0|0}function Ul(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;g=a+16|0;f=c[(c[g>>2]|0)+196>>2]|0;e=f+(b<<6)|0;f=c[f+(b<<6)+4>>2]|0;if(!f)f=KK((c[e>>2]<<2)+8|0)|0;else f=MK(f,(c[e>>2]<<2)+8|0)|0;e=c[(c[g>>2]|0)+196>>2]|0;c[e+(b<<6)+4>>2]=f;e=c[e+(b<<6)>>2]|0;while(1){if((e|0)<=(d|0))break;h=e+-1|0;i=c[f+(h<<2)>>2]|0;c[f+(e<<2)>>2]=i;i=(c[i+16>>2]|0)+236|0;c[i>>2]=(c[i>>2]|0)+1;e=h}h=sh(a)|0;i=f+(d<<2)|0;c[i>>2]=h;h=c[h+16>>2]|0;c[h+236>>2]=d;c[h+232>>2]=b;g=(c[(c[g>>2]|0)+196>>2]|0)+(b<<6)|0;h=(c[g>>2]|0)+1|0;c[g>>2]=h;c[f+(h<<2)>>2]=0;return c[i>>2]|0}function Vl(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;a=c[(c[a+16>>2]|0)+236>>2]|0;b=c[(c[b+16>>2]|0)+236>>2]|0;f=(a|0)>(b|0);c[d>>2]=f?b:a;c[e>>2]=f?a:b;return}function Wl(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0;m=l;l=l+16|0;h=m+4|0;g=m;b=c[b+16>>2]|0;do if((a[b+156>>0]|0)==1){k=c[b+236>>2]|0;if(c[b+176>>2]|0){i=c[b+180>>2]|0;b=0;g=0;h=0;while(1){j=c[i+(h<<2)>>2]|0;if(!j)break;n=c[(c[(c[((c[j>>2]&3|0)==2?j:j+-48|0)+40>>2]|0)+16>>2]|0)+236>>2]|0;j=(n|0)>(e|0);b=j?((n|0)<(f|0)?b:1):b;g=j?g:1;h=h+1|0}if(g<<24>>24!=0&b<<24>>24==0){c[d>>2]=k+1;break}if(!(g<<24>>24==0&b<<24>>24!=0))break;c[d+4>>2]=k+-1;break}if((c[b+184>>2]|0)!=2)qa(88397,88417,63,88424);i=c[b+180>>2]|0;n=c[i>>2]|0;i=c[i+4>>2]|0;Vl(c[((c[n>>2]&3|0)==2?n:n+-48|0)+40>>2]|0,c[((c[i>>2]&3|0)==2?i:i+-48|0)+40>>2]|0,h,g);i=c[g>>2]|0;if((i|0)<=(e|0)){c[d>>2]=k;c[d+8>>2]=k;break}b=c[h>>2]|0;if((b|0)>=(f|0)){c[d+4>>2]=k;c[d+12>>2]=k;break}g=(b|0)<(e|0);h=(i|0)>(f|0);if(!(h&g)){if(!(!g?!((i|0)<(f|0)&(b|0)==(e|0)):0))c[d+8>>2]=k;if(!h?!((i|0)==(f|0)&(b|0)>(e|0)):0)break;c[d+12>>2]=k}}while(0);l=m;return}function Xl(a){a=a|0;c[47134]=1;pm(a);c[47134]=0;return}function Yl(a){a=a|0;c[47134]=2;pm(a);c[47134]=0;return}function Zl(a){a=a|0;var b=0;TC(a,137447,304,1)|0;sP(a);b=JK((e[(c[(xC(a)|0)+16>>2]|0)+176>>1]|0)<<3)|0;c[(c[a+16>>2]|0)+132>>2]=b;qQ(a,c[(c[(xC(a)|0)+16>>2]|0)+116>>2]&1);return}function _l(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0.0;s=l;l=l+64|0;j=s+48|0;o=s+40|0;k=s+24|0;i=s+8|0;q=s+56|0;m=s;do if((b|0)!=0?(r=e+16|0,p=c[(c[r>>2]|0)+132>>2]|0,g=RA(e,b)|0,(a[g>>0]|0)!=0):0){a[q>>0]=0;b=p+8|0;if((c[47137]|0)>2?(c[i>>2]=p,c[i+4>>2]=b,c[i+8>>2]=p+16,c[i+12>>2]=q,(b5(g,88535,i)|0)>2):0){a[(c[r>>2]|0)+119>>0]=1;g=c[47137]|0;a:do if(+h[23293]>0.0){b=0;while(1){if((b|0)>=(g|0))break a;o=p+(b<<3)|0;h[o>>3]=+h[o>>3]/+h[23293];b=b+1|0}}while(0);if((g|0)>3)Fn(e,f,3);if((a[q>>0]|0)!=33){if(!d){b=1;break}if(!((dP(RA(e,d)|0)|0)<<24>>24)){b=1;break}}a[(c[r>>2]|0)+119>>0]=3;b=1;break}c[k>>2]=p;c[k+4>>2]=b;c[k+8>>2]=q;if((b5(g,101749,k)|0)<=1){c[j>>2]=KB(e)|0;c[j+4>>2]=g;gA(1,88549,j)|0;b=0;break}a[(c[r>>2]|0)+119>>0]=1;g=c[47137]|0;b:do if(+h[23293]>0.0){b=0;while(1){if((b|0)>=(g|0))break b;k=p+(b<<3)|0;h[k>>3]=+h[k>>3]/+h[23293];b=b+1|0}}while(0);do if((g|0)>2){b=c[47171]|0;if((b|0?(n=RA(e,b)|0,n|0):0)?(c[o>>2]=m,(b5(n,101209,o)|0)==1):0){u=+h[23293];t=+h[m>>3];h[p+16>>3]=u>0.0?t/u:t;Fn(e,f,3);break}Gn(e,f)}while(0);if((a[q>>0]|0)!=33){if(!d){b=1;break}if(!((dP(RA(e,d)|0)|0)<<24>>24)){b=1;break}}a[(c[r>>2]|0)+119>>0]=3;b=1}else b=0;while(0);l=s;return b|0}function $l(a){a=a|0;var b=0,c=0;c=jC(a)|0;while(1){if(!c)break;b=XA(a,c)|0;while(1){if(!b)break;YP(b);b=ZA(a,b)|0}WP(c);c=kC(a,c)|0}am(a);return}function am(a){a=a|0;if((c[47134]|0)!=0|(c[46795]|0)<0)Dn(a);if((yC(a)|0)!=(a|0))ZC(a,0,137483);return}function bm(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+48|0;m=q+32|0;o=q;k=GA(b,0,102637,0)|0;e=GA(b,0,102671,0)|0;p=(eP(QA(b,88593)|0,0)|0)<<24>>24!=0;if(!e)e=GA(b,0,102671,195059)|0;Cn(b)|0;n=b+16|0;i=0;while(1){j=c[(c[(c[n>>2]|0)+152>>2]|0)+(i<<2)>>2]|0;if(!j){f=12;break}g=j+16|0;f=c[g>>2]|0;if(!(a[f+119>>0]|0)){if(Q2(KB(j)|0,108006,7)|0){f=8;break}f=c[g>>2]|0}f=c[f+108>>2]|0;if(f|0)cm(j,f,102633);i=i+1|0}a:do if((f|0)==8){p=KB(j)|0;e=KB(b)|0;c[m>>2]=p;c[m+4>>2]=e;gA(1,88605,m)|0;e=-1}else if((f|0)==12){dm(b,k,e);j=em(b)|0;e=c[(c[n>>2]|0)+8>>2]|0;if(!(c[e+88>>2]|0))e=0;else{c[e+84>>2]=0;e=1}g=(d|0)!=0;i=(e|0)!=0;if(!(i|g&(c[47134]|0)==1^1)?(or(b)|0)!=0:0){f=c[(c[n>>2]|0)+12>>2]|0;if(!f)f=1;else{a[f+81>>0]=0;f=1}}else f=0;BP(b);if(i){d=(c[n>>2]|0)+16|0;nI(o,b);c[d>>2]=c[o>>2];c[d+4>>2]=c[o+4>>2];c[d+8>>2]=c[o+8>>2];c[d+12>>2]=c[o+12>>2];c[d+16>>2]=c[o+16>>2];c[d+20>>2]=c[o+20>>2];c[d+24>>2]=c[o+24>>2];c[d+28>>2]=c[o+28>>2]}if(!g){c[47138]=1;f=jC(b)|0;while(1){if(!f)break a;p=c[f+16>>2]|0;o=c[p+132>>2]|0;h[p+16>>3]=+h[o>>3]*72.0;h[p+24>>3]=+h[o+8>>3]*72.0;f=kC(b,f)|0}}do if(!(p|i)){p=c[n>>2]|0;if(!(+h[p+16>>3]!=0.0)?!(+h[p+24>>3]!=0.0):0)break;an(b)}while(0);if(!((j|0)!=0&(f|(_m(b)|0)<<24>>24!=0))){if((j|0)==2){c[47138]=1;break}}else fm(b);Zm(b,0)}while(0);l=q;return e|0}function cm(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0;k=l;l=l+48|0;j=k+32|0;f=k+24|0;g=k+16|0;i=k;b=QA(b,e)|0;if(b|0?(c[j>>2]=f,c[j+4>>2]=g,(b5(b,105503,j)|0)==2):0){j=d+56|0;im(i,+h[f>>3],+h[g>>3]);c[j>>2]=c[i>>2];c[j+4>>2]=c[i+4>>2];c[j+8>>2]=c[i+8>>2];c[j+12>>2]=c[i+12>>2];a[d+81>>0]=1}l=k;return}function dm(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0;m=l;l=l+48|0;k=m+32|0;f=m+24|0;g=m+16|0;i=m;j=b+16|0;if((d|0?(c[(c[j>>2]|0)+12>>2]|0)!=0:0)?(n=RA(b,d)|0,c[k>>2]=f,c[k+4>>2]=g,(b5(n,105503,k)|0)==2):0){n=(c[(c[j>>2]|0)+12>>2]|0)+56|0;im(i,+h[f>>3],+h[g>>3]);c[n>>2]=c[i>>2];c[n+4>>2]=c[i+4>>2];c[n+8>>2]=c[i+8>>2];c[n+12>>2]=c[i+12>>2];a[(c[(c[j>>2]|0)+12>>2]|0)+81>>0]=1}a:do if(e|0){f=OD(b)|0;while(1){if(!f)break a;jm(f,b,d,e);f=PD(f)|0}}while(0);l=m;return}function em(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;if(uB(a)|0){f=GA(a,2,102623,0)|0;if(!((f|0)==0|(c[47134]|0)<2)){b=0;e=jC(a)|0;while(1){if(!e)break;d=XA(a,e)|0;while(1){if(!d)break;g=b+((gm(f,d)|0)!=0&1)|0;d=ZA(a,d)|0;b=g}e=kC(a,e)|0}if(b){b=(b|0)==(uB(a)|0);b=b?2:1}else b=0}else b=0}else b=2;return b|0}function fm(a){a=a|0;var b=0,d=0,e=0;d=jC(a)|0;while(1){if(!d)break;b=XA(a,d)|0;while(1){if(!b)break;ZP(b);e=b+16|0;CK(c[(c[e>>2]|0)+96>>2]|0);CK(c[(c[e>>2]|0)+108>>2]|0);CK(c[(c[e>>2]|0)+100>>2]|0);CK(c[(c[e>>2]|0)+104>>2]|0);b=ZA(a,b)|0}d=kC(a,d)|0}return}function gm(b,d){b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0;B=l;l=l+96|0;z=B+72|0;x=B+56|0;A=B+48|0;y=B+32|0;w=B+16|0;r=B+88|0;s=B+8|0;t=B;u=B+84|0;v=B+80|0;b=RA(d,b)|0;do if(a[b>>0]|0){HH(d,u,v);q=1;f=0;e=0;j=0.0;i=0.0;k=0.0;g=0.0;a:while(1){c[w>>2]=s;c[w+4>>2]=t;c[w+8>>2]=r;if((b5(b,88642,w)|0)==2){p=1;b=b+(c[r>>2]|0)|0;i=+h[s>>3];g=+h[t>>3]}else p=e;c[y>>2]=s;c[y+4>>2]=t;c[y+8>>2]=r;if((b5(b,88654,y)|0)==2){o=1;b=b+(c[r>>2]|0)|0;j=+h[s>>3];k=+h[t>>3]}else o=f;m=hm(b)|0;if(!((m|0)>3&((m|0)%3|0|0)==1)){e=8;break}n=KK(m<<4)|0;e=n;f=m;while(1){if(!f)break;c[x>>2]=s;c[x+4>>2]=t;c[x+8>>2]=r;if((b5(b,88724,x)|0)<2){e=13;break a}C=b+(c[r>>2]|0)|0;h[e>>3]=+h[s>>3];h[e+8>>3]=+h[t>>3];e=e+16|0;f=f+-1|0;b=C}while(1){e=a[b>>0]|0;f=b+1|0;if(!(S2(e<<24>>24)|0))break;else b=f}C=e<<24>>24==0;b=C?b:f;q=C?0:q;f=cO(d,m)|0;if(p|0){c[f+8>>2]=c[u>>2];h[f+16>>3]=i;h[f+24>>3]=g}if(o|0){c[f+12>>2]=c[v>>2];h[f+32>>3]=j;h[f+40>>3]=k}e=0;while(1){if((e|0)>=(m|0))break;C=(c[f>>2]|0)+(e<<4)|0;D=n+(e<<4)|0;c[C>>2]=c[D>>2];c[C+4>>2]=c[D+4>>2];c[C+8>>2]=c[D+8>>2];c[C+12>>2]=c[D+12>>2];e=e+1|0}U1(n);if(!q){e=26;break}else{f=o;e=p}}if((e|0)==8){ZP(d);if(a[193771]|0){b=0;break}a[193771]=1;D=KB(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)|0;b=KB(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)|0;c[A>>2]=D;c[A+4>>2]=b;gA(0,88667,A)|0;b=0;break}else if((e|0)==13){if(!(a[193771]|0)){a[193771]=1;C=KB(c[((c[d>>2]&3|0)==3?d:d+48|0)+40>>2]|0)|0;D=KB(c[((c[d>>2]&3|0)==2?d:d+-48|0)+40>>2]|0)|0;c[z>>2]=C;c[z+4>>2]=D;gA(0,88734,z)|0}U1(n);ZP(d);b=0;break}else if((e|0)==26){f=d+16|0;b=c[f>>2]|0;e=c[b+96>>2]|0;if(e){cm(d,e,102637);b=c[f>>2]|0}e=c[b+108>>2]|0;if(e){cm(d,e,102633);b=c[f>>2]|0}e=c[b+100>>2]|0;if(e){cm(d,e,102640);b=c[f>>2]|0}b=c[b+104>>2]|0;if(!b){b=1;break}cm(d,b,102648);b=1;break}}else b=0;while(0);l=B;return b|0}function hm(b){b=b|0;var c=0,d=0;d=0;do{while(1){c=a[b>>0]|0;if(!(S2(c&255)|0))break;else b=b+1|0}a:do if(!(c<<24>>24))c=0;else{d=d+1|0;while(1){if(!(c<<24>>24)){c=0;break a}if(c<<24>>24==59|(S2(c&255)|0)!=0)break a;c=b+1|0;b=c;c=a[c>>0]|0}}while(0)}while((S2(c&255)|0)!=0);return d|0}function im(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b;h[a+8>>3]=c;return}function jm(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;h=l;l=l+32|0;f=h;if((Q2(KB(a)|0,108006,7)|0)==0?(km(a,e,f)|0)!=0:0){TC(a,137483,280,1)|0;i=(c[a+16>>2]|0)+16|0;c[i>>2]=c[f>>2];c[i+4>>2]=c[f+4>>2];c[i+8>>2]=c[f+8>>2];c[i+12>>2]=c[f+12>>2];c[i+16>>2]=c[f+16>>2];c[i+20>>2]=c[f+20>>2];c[i+24>>2]=c[f+24>>2];c[i+28>>2]=c[f+28>>2];lm(b,a);dm(a,d,e)}else g=4;a:do if((g|0)==4){a=OD(a)|0;while(1){if(!a)break a;jm(a,b,d,e);a=PD(a)|0}}while(0);l=h;return}function km(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0,j=0,k=0;i=l;l=l+48|0;j=i+32|0;g=i;k=RA(a,b)|0;a=g+8|0;b=g+24|0;c[j>>2]=g;c[j+4>>2]=a;c[j+8>>2]=g+16;c[j+12>>2]=b;if((b5(k,88782,j)|0)==4){e=+h[a>>3];f=+h[b>>3];if(e>f){h[a>>3]=f;h[b>>3]=e};c[d>>2]=c[g>>2];c[d+4>>2]=c[g+4>>2];c[d+8>>2]=c[g+8>>2];c[d+12>>2]=c[g+12>>2];c[d+16>>2]=c[g+16>>2];c[d+20>>2]=c[g+20>>2];c[d+24>>2]=c[g+24>>2];c[d+28>>2]=c[g+28>>2];a=1}else a=0;l=i;return a|0}function lm(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;e=a+16|0;d=c[e>>2]|0;g=d+180|0;a=c[g>>2]|0;f=a+1|0;c[g>>2]=f;d=c[d+184>>2]|0;if(!d)a=JK((a<<2)+8|0)|0;else a=LK(d,a+2|0,4,f)|0;c[(c[e>>2]|0)+184>>2]=a;c[a+(f<<2)>>2]=b;uK(b);return}function mm(b,e,f){b=b|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+48|0;m=n+8|0;k=n;i=n+16|0;j=n+12|0;h=QA(b,88798)|0;do if((h|0)!=0?(g=a[h>>0]|0,g<<24>>24!=0):0){g=g&255;if(!(b3(g)|0))if((e|0)==2|(g+-48|0)>>>0<10)e=h;else break;else{if(!(Q2(h,88804,4)|0)){e=0;break}if(!(Q2(h,105511,7)|0)){e=1;break}if(Q2(h,88809,6)|0)if((e|0)==2)e=h;else break;else e=h+6|0}if(!(((d[e>>0]|0)+-48|0)>>>0<10?(c[k>>2]=j,(b5(e,99933,k)|0)>=1):0)){k=X4()|0;k=(db(0)|0)^k;c[j>>2]=k;c[m>>2]=k;m4(i,99933,m)|0;SA(b,88798,i)|0}c[f>>2]=c[j>>2];e=2}while(0);l=n;return e|0}function nm(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;f=l;l=l+16|0;e=f+4|0;c[e>>2]=1;d=mm(a,d,e)|0;if((d|0)!=2&(c[46796]|0)!=0)gA(0,88816,f)|0;if((d|0)==1)om(a,b);z5(c[e>>2]|0);l=f;return d|0}function om(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0.0,j=0,k=0,l=0.0;g=+(d|0);i=6.283185307179586/g;e=jC(b)|0;f=0.0;while(1){if(!e)break;l=+E(+f)*g;j=c[e+16>>2]|0;k=c[j+132>>2]|0;h[k>>3]=l;h[k+8>>3]=+F(+f)*g;a[j+119>>0]=1;if((c[47137]|0)>2)Gn(e,d);e=kC(b,e)|0;f=f+i}return}function pm(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0;r=l;l=l+80|0;d=r+24|0;n=r+32|0;k=r;p=r+28|0;m=r+64|0;q=+h[23293];do if(c[47134]|0){h[23293]=72.0;qm(b);rm(b);if((bm(b,1)|0)<0){gA(3,88864,d)|0;break}else{cM(b,0);f=32;break}}else{o=eP(QA(b,88593)|0,0)|0;h[23293]=+$O(b);qm(b);g=sm(b)|0;Nq(b,k,0)|0;i=tm(b)|0;j=mH(b,0,n)|0;d=lH(b,-1,8)|0;c[46795]=d;e=(d|0)<0;do if(!j){if((g|0)!=0&e){c[46795]=8;c[n+16>>2]=2;f=11;break}c[n+16>>2]=2;if((d|0)<=-1){um(b,g,i);Qq(b,k)|0;rm(b);if(!(o<<24>>24)){fn(b);break}else{vm(b);break}}else f=11}else if(e){c[46795]=8;f=11}else f=11;while(0);if((f|0)==11){j=sG(b,p,88892,m)|0;d=c[p>>2]|0;do if((d|0)<=1){um(b,g,i);Qq(b,k)|0;if(!(o<<24>>24)){fn(b);break}else{vm(b);break}}else{f=o<<24>>24==0;e=0;while(1){if((e|0)>=(d|0))break;d=c[j+(e<<2)>>2]|0;KG(d)|0;um(d,g,i);Qq(d,k)|0;oQ(d,2);if(f)fn(d);else vm(d);e=e+1|0;d=c[p>>2]|0}if(!(a[m>>0]|0))e=0;else{e=JK(d)|0;a[e>>0]=1;d=c[p>>2]|0}c[n+8>>2]=c[46795];c[n+20>>2]=e;c[n+12>>2]=1;iH(d,j,b,n)|0;U1(e)}while(0);BP(b);rm(b);d=0;while(1){if((d|0)>=(c[p>>2]|0))break;n=c[j+(d<<2)>>2]|0;Dn(n);VC(n,137483)|0;wC(b,n)|0;d=d+1|0}U1(j)}cM(b,o<<24>>24==0&1);f=32}while(0);if((f|0)==32)h[23293]=q;l=r;return}function qm(a){a=a|0;var d=0,e=0,f=0,g=0;oQ(a,2);f=ZO(a,GA(a,0,91368,0)|0,2,2)|0;d=(ZO(a,GA(a,0,91374,0)|0,f,2)|0)&65535;b[(c[(yC(a)|0)+16>>2]|0)+176>>1]=d;d=c[(c[a+60>>2]|0)+16>>2]|0;g=d+176|0;e=b[g>>1]|0;e=(e&65535)<10?e:10;b[g>>1]=e;e=e&65535;c[47137]=e;b[d+178>>1]=(f|0)<(e|0)?f:e;Fm(a);return}function rm(a){a=a|0;var b=0,d=0,e=0,f=0;f=l;l=l+1040|0;e=f;d=f+8|0;a:do if((c[47137]|0)>2&(c[47171]|0)!=0){b=jC(a)|0;while(1){if(!b)break a;h[e>>3]=+h[(c[(c[b+16>>2]|0)+132>>2]|0)+16>>3]*72.0;m4(d,101209,e)|0;KA(b,c[47171]|0,d)|0;b=kC(a,b)|0}}while(0);l=f;return}function sm(b){b=b|0;var d=0,e=0,f=0;f=l;l=l+16|0;e=f;d=QA(b,89577)|0;a:do if(!d)b=1;else{switch(a[d>>0]|0){case 0:{b=1;break a}case 75:{if(!(s2(d,89582)|0)){b=0;break a}break}case 109:{if(!(s2(d,89585)|0)){b=1;break a}break}case 104:{if(!(s2(d,89591)|0)){b=2;break a}break}default:{}}b=KB(b)|0;c[e>>2]=d;c[e+4>>2]=b;gA(0,89596,e)|0;b=1}while(0);l=f;return b|0}function tm(b){b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+32|0;g=h+16|0;f=h+8|0;e=h;d=QA(b,89360)|0;a:do if(!d)b=0;else{switch(a[d>>0]|0){case 0:{b=0;break a}case 99:{if(!(s2(d,89366)|0)){b=1;break a}break}case 115:{if(!(s2(d,89374)|0)){b=2;break a}if(!(s2(d,89381)|0)){b=0;break a}break}case 109:{if(!(s2(d,89391)|0)){if(GA(b,2,92493,0)|0){b=3;break a}c[e>>2]=KB(b)|0;gA(0,89395,e)|0;gA(3,89458,f)|0;b=0;break a}break}default:{}}b=KB(b)|0;c[g>>2]=d;c[g+4>>2]=b;gA(0,89515,g)|0;b=0}while(0);l=h;return b|0}function um(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=QA(a,91061)|0;if(!e)if((b|0)==1)e=200;else e=(tB(a)|0)*100|0;else e=q5(e)|0;c[47136]=e;e=wn(a,b)|0;do if(!((e|0)<2|(c[47136]|0)<0))if(!b){xm(a,e,d);break}else{wm(a,e,b,d,c[47137]|0);break}while(0);return}function vm(a){a=a|0;BP(a);Zm(a,1);return}function wm(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+64|0;r=u+40|0;o=u+24|0;n=u;p=u+48|0;t=u+44|0;q=(d|0)==2;i=nm(a,b,q?0:2)|0;j=Em(a)|0;k=j|4;s=KK(f<<2)|0;c[s>>2]=KK(O(b<<3,f)|0)|0;g=c[47137]|0;f=1;while(1){if((f|0)>=(g|0))break;c[s+(f<<2)>>2]=(c[s>>2]|0)+((O(f,b)|0)<<3);f=f+1|0}g=(i|0)==0;f=g?k:j;if(0){k=c[15712]|0;j=c[47136]|0;m=+h[23294];c[n>>2]=e;c[n+4>>2]=g&1;c[n+8>>2]=f&3;c[n+12>>2]=j;h[n+16>>3]=m;w4(k,89173,n)|0;U4(89230,15,1,k)|0;TO();U4(89246,13,1,k)|0}n=Am(a,b,p,d,e,t)|0;if(0){k=c[15712]|0;m=+UO();c[o>>2]=b;h[o+8>>3]=m;w4(k,89260,o)|0}if((d|0)!=1){m=+_O(a,GA(a,0,89279,0)|0,0.0,-1797693134862315708145274.0e284);if(q){f=Br(n,b,c[p>>2]|0,s,c[t>>2]|0,c[47137]|0,f,e,c[47136]|0,m)|0;g=12}else g=14}else{f=on(n,b,c[p>>2]|0,s,c[t>>2]|0,c[47137]|0,f,e,c[47136]|0)|0;g=12}if((g|0)==12)if((f|0)<0)gA(3,89289,r)|0;else g=14;a:do if((g|0)==14){g=jC(a)|0;while(1){if(!g)break a;k=c[g+16>>2]|0;i=c[k+120>>2]|0;j=c[47137]|0;k=k+132|0;f=0;while(1){if((f|0)>=(j|0))break;h[(c[k>>2]|0)+(f<<3)>>3]=+h[(c[s+(f<<2)>>2]|0)+(i<<3)>>3];f=f+1|0}g=kC(a,g)|0}}while(0);as(n);U1(c[s>>2]|0);U1(s);U1(c[t>>2]|0);l=u;return}function xm(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0.0,j=0;g=l;l=l+48|0;f=g+32|0;e=g;switch(d|0){case 2:{ym(a,b);break}case 1:{if(!(xr(a,b)|0)){c[e>>2]=KB(a)|0;gA(0,88902,e)|0;gA(3,93364,g+8|0)|0;gA(3,88954,g+16|0)|0;gA(3,89026,g+24|0)|0;Un(a,b)}break}default:{Un(a,b);if((d|0)==3)zm(a)}}In(a,b);Jn(a,b);if(0){e=c[15712]|0;j=c[47136]|0;i=+h[23294];c[f>>2]=d;c[f+4>>2]=j;h[f+8>>3]=i;w4(e,89064,f)|0;TO()}Kn(a,b);l=g;return}function ym(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0;j=l;l=l+16|0;g=Am(a,b,j,0,2,0)|0;i=ct(g,b)|0;e=a+16|0;a=0;while(1){if((a|0)>=(b|0))break;f=i+(a<<2)|0;d=0;while(1){if((d|0)==(b|0))break;h[(c[(c[(c[e>>2]|0)+160>>2]|0)+(a<<2)>>2]|0)+(d<<3)>>3]=+(c[(c[f>>2]|0)+(d<<2)>>2]|0);d=d+1|0}a=a+1|0}U1(c[i>>2]|0);U1(i);as(g);l=j;return}function zm(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0.0;f=a+16|0;d=jC(a)|0;while(1){if(!d)break;b=XA(a,d)|0;while(1){if(!b)break;g=c[b>>2]&3;e=(c[c[((g|0)==3?b:b+48|0)+40>>2]>>2]|0)>>>4;g=(c[c[((g|0)==2?b:b+-48|0)+40>>2]>>2]|0)>>>4;if((e|0)!=(g|0)){j=+h[(c[b+16>>2]|0)+136>>3];i=c[(c[f>>2]|0)+160>>2]|0;h[(c[i+(g<<2)>>2]|0)+(e<<3)>>3]=j;h[(c[i+(e<<2)>>2]|0)+(g<<3)>>3]=j}b=ZA(a,b)|0}d=kC(a,d)|0}return}function Am(a,b,d,e,f,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;i=i|0;var j=0,k=0,l=0,m=0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0.0,I=0;j=uB(a)|0;G=VL()|0;if((f|0)==2){k=0;D=0}else{D=GA(a,2,92493,0)|0;k=(c[47174]|0)!=0&1;D=(D|0)!=0}A=(e|1|0)==3;E=KK(b*20|0)|0;F=KK(b<<2)|0;l=(j<<1)+b<<2;m=KK(l)|0;z=A|D;if(z)f=KK(l)|0;else f=0;C=(k|0)!=0;if(C)j=KK(l)|0;else j=0;if(A)k=KK(l)|0;else k=0;l=0;x=jC(a)|0;y=0;while(1){if(!x)break;YL(G);if((c[(c[x+16>>2]|0)+120>>2]|0)!=(y|0)){B=12;break}c[F+(y<<2)>>2]=x;u=E+(y*20|0)+4|0;c[u>>2]=m;v=E+(y*20|0)+8|0;c[v>>2]=z?f:0;w=E+(y*20|0)+12|0;c[w>>2]=C?j:0;c[E+(y*20|0)+16>>2]=A?k:0;r=1;t=aB(a,x)|0;s=1;q=l;m=m+4|0;f=z?f+4|0:f;j=C?j+4|0:j;k=A?k+4|0:k;while(1){if(!t)break;n=c[t>>2]&3;p=t+-48|0;l=t+48|0;do if((c[((n|0)==2?t:p)+40>>2]|0)==(c[((n|0)==3?t:l)+40>>2]|0)){n=r;p=s;l=q}else{n=Bm(G,t,r)|0;if((n|0)!=(r|0)){if(C){p=(c[w>>2]|0)+(n<<2)|0;g[p>>2]=+h[(c[t+16>>2]|0)+128>>3]+ +g[p>>2]}if(!D){n=r;p=s;l=q;break}n=(c[v>>2]|0)+(n<<2)|0;H=+h[(c[t+16>>2]|0)+136>>3];o=+(~~+g[n>>2]|0);g[n>>2]=H>o?H:o;n=r;p=s;l=q;break}I=c[t>>2]&3;l=(I|0)==3?t:l;q=q+1|0;n=r+1|0;r=m+4|0;c[m>>2]=c[(c[(c[((c[l+40>>2]|0)==(x|0)?((I|0)==2?t:p):l)+40>>2]|0)+16>>2]|0)+120>>2];if(C){g[j>>2]=+h[(c[t+16>>2]|0)+128>>3];j=j+4|0}if(D){l=f+4|0;g[f>>2]=+h[(c[t+16>>2]|0)+136>>3];if(A){f=l;B=27}else f=l}else if(A){g[f>>2]=1.0;f=f+4|0;B=27}if((B|0)==27){B=0;l=QA(t,101559)|0;if((l|0)!=0?(Q2(l,135709,4)|0)==0:0)o=0.0;else o=(x|0)==(c[((c[t>>2]&3|0)==2?t:p)+40>>2]|0)?1.0:-1.0;g[k>>2]=o;k=k+4|0}p=s+1|0;l=q;m=r}while(0);r=n;t=bB(a,t,x)|0;s=p;q=l}c[E+(y*20|0)>>2]=s;c[c[u>>2]>>2]=y;l=q;x=kC(a,x)|0;y=y+1|0}if((B|0)==12)qa(89103,89118,819,89130);if(A)Cm(E,b,e,F);n=(l|0)/2|0;a:do if((n|0)!=(uB(a)|0)){l=(n<<1)+b<<2;k=MK(c[E+4>>2]|0,l)|0;if(D)f=MK(c[E+8>>2]|0,l)|0;if(C){m=0;j=MK(c[E+12>>2]|0,l)|0}else m=0;while(1){if((m|0)>=(b|0))break a;l=c[E+(m*20|0)>>2]|0;c[E+(m*20|0)+4>>2]=k;if(D){c[E+(m*20|0)+8>>2]=f;f=f+(l<<2)|0}if(C){c[E+(m*20|0)+12>>2]=j;j=j+(l<<2)|0}m=m+1|0;k=k+(l<<2)|0}}while(0);c[d>>2]=n;if(!i)U1(F);else c[i>>2]=F;ZL(G);return E|0}function Bm(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;f=c[b>>2]&3;e=c[(c[(c[((f|0)==3?b:b+48|0)+40>>2]|0)+16>>2]|0)+120>>2]|0;b=c[(c[(c[((f|0)==2?b:b+-48|0)+40>>2]|0)+16>>2]|0)+120>>2]|0;f=(e|0)>(b|0);return _L(a,f?b:e,f?e:b,d)|0}function Cm(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;g=0;while(1){if((g|0)>=(d|0)){g=0;break}h=c[(c[f+(g<<2)>>2]|0)+16>>2]|0;a[h+157>>0]=0;a[h+158>>0]=0;g=g+1|0}while(1){if((g|0)>=(d|0))break;if(!(a[(c[(c[f+(g<<2)>>2]|0)+16>>2]|0)+157>>0]|0))Dm(b,g,e,f);g=g+1|0}return}function Dm(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0.0,p=0,q=0;q=(c[f+(d<<2)>>2]|0)+16|0;l=c[q>>2]|0;a[l+157>>0]=1;a[l+158>>0]=1;l=b+(d*20|0)|0;m=b+(d*20|0)+16|0;n=b+(d*20|0)+4|0;o=(e|0)==3?-1.0:1.0;k=1;a:while(1){if((k|0)>=(c[l>>2]|0)){h=14;break}h=(c[m>>2]|0)+(k<<2)|0;do if(!(+g[h>>2]==1.0)){p=c[(c[n>>2]|0)+(k<<2)>>2]|0;i=c[(c[f+(p<<2)>>2]|0)+16>>2]|0;if(!(a[i+158>>0]|0)){if(a[i+157>>0]|0)break;Dm(b,p,e,f);break}g[h>>2]=o;i=b+(p*20|0)+4|0;j=c[b+(p*20|0)>>2]|0;h=1;while(1){if((h|0)>=(j|0)){h=9;break a}if((c[(c[i>>2]|0)+(h<<2)>>2]|0)==(d|0))break;h=h+1|0}g[(c[b+(p*20|0)+16>>2]|0)+(h<<2)>>2]=-1.0}while(0);k=k+1|0}if((h|0)==9)qa(89144,89118,721,89164);else if((h|0)==14){a[(c[q>>2]|0)+158>>0]=0;return}}function Em(a){a=a|0;var b=0,d=0;d=l;l=l+16|0;b=d;a=ZO(a,GA(a,0,89305,0)|0,2,0)|0;if((a|0)==0|(a|0)>2){c[b>>2]=89305;gA(0,89314,b)|0;a=2}l=d;return a|0}function Fm(a){a=a|0;var b=0,d=0,e=0;d=tB(a)|0;c[46796]=GA(a,1,102623,0)|0;e=GA(a,1,90660,0)|0;b=jC(a)|0;while(1){if(!b)break;Zl(b);_l(c[46796]|0,e,b,d)|0;b=kC(a,b)|0}d=jC(a)|0;while(1){if(!d)break;b=XA(a,d)|0;while(1){if(!b)break;Gm(b);b=ZA(a,b)|0}d=kC(a,d)|0}return}function Gm(a){a=a|0;var b=0.0;TC(a,137460,176,1)|0;tP(a)|0;b=+_O(a,c[47174]|0,1.0,1.0);h[(c[a+16>>2]|0)+128>>3]=b;return}function Hm(a,d,e){a=a|0;d=d|0;e=e|0;var f=0,g=0;a=JK(64)|0;e=a+8|0;f=d+8|0;g=e+48|0;do{c[e>>2]=c[f>>2];e=e+4|0;f=f+4|0}while((e|0)<(g|0));d=c[d+56>>2]|0;c[a+56>>2]=d;b[(c[d+16>>2]|0)+168>>1]=1;return a|0}function Im(a,b,c){a=a|0;b=b|0;c=c|0;U1(b);return}function Jm(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;a=c[b>>2]|0;e=c[d>>2]|0;if(a>>>0<=e>>>0)if(a>>>0>=e>>>0){e=c[b+24>>2]|0;a=c[d+24>>2]|0;if(e>>>0<=a>>>0)if(e>>>0>=a>>>0){a=~~(+h[b+8>>3]-+h[d+8>>3]);if(!a){a=~~(+h[b+16>>3]-+h[d+16>>3]);if(!a){a=~~(+h[b+32>>3]-+h[d+32>>3]);if(!a)a=~~(+h[b+40>>3]-+h[d+40>>3])}}}else a=-1;else a=1}else a=-1;else a=1;return a|0}function Km(d,e,f){d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0.0;k=l;l=l+16|0;g=k;h=e+16|0;i=b[(c[h>>2]|0)+168>>1]|0;j=i<<16>>16;if(i<<16>>16!=1&(a[194954]|0)==0){i=KK(j<<2)|0;g=0;while(1){if((g|0)>=(j|0))break;c[i+(g<<2)>>2]=e;e=c[(c[e+16>>2]|0)+172>>2]|0;g=g+1|0}m=+(f|0);lO(d,i,0,j,m,m,17792);e=0;while(1){if((e|0)>=(j|0))break;g=c[i+(e<<2)>>2]|0;h=g+16|0;if(c[(c[h>>2]|0)+96>>2]|0){f=xC(c[((c[g>>2]&3|0)==3?g:g+48|0)+40>>2]|0)|0;zP(f,c[(c[h>>2]|0)+96>>2]|0)}sO(g);e=e+1|0}U1(i)}else{c[g>>2]=e;m=+(f|0);lO(d,g,0,1,m,m,17792);if(c[(c[h>>2]|0)+96>>2]|0){j=xC(c[((c[e>>2]&3|0)==3?e:e+48|0)+40>>2]|0)|0;zP(j,c[(c[h>>2]|0)+96>>2]|0)}sO(e)}l=k;return}function Lm(a){a=a|0;return 0}function Mm(a){a=a|0;return 0}function Nm(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,i=0,j=0,k=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0.0,t=0,u=0,v=0,w=0.0,x=0.0,y=0.0,z=0,A=0,B=0,D=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0;Q=l;l=l+384|0;O=Q+368|0;P=Q+112|0;j=Q+32|0;r=Q+336|0;K=Q+320|0;L=Q+304|0;M=Q+288|0;N=Q+272|0;i=Q+256|0;H=Q+240|0;I=Q+224|0;J=Q+208|0;t=Q+192|0;u=Q+176|0;v=Q+160|0;z=Q+144|0;A=Q+128|0;B=Q+96|0;D=Q+16|0;G=Q;a:do switch(WM(b)|0){case 3:case 1:{f=JK(8)|0;z=b+16|0;b=c[z>>2]|0;i=c[b+12>>2]|0;do if(!(e<<24>>24)){b=c[i+8>>2]|0;if((b|0)>2){y=0.0;j=c[i+44>>2]|0;e=0;p=+g[d>>2];q=+g[d+4>>2];break}else{y=+B5()*.01;b=8;j=0;e=1;p=0.0;q=0.0;break}}else if(!(c[i+40>>2]&2048)){q=+h[b+88>>3];p=-q;y=+h[b+80>>3];x=y*-.5;h[j>>3]=p;h[j+8>>3]=x;h[j+16>>3]=q;h[j+24>>3]=x;y=y*.5;h[j+32>>3]=q;h[j+40>>3]=y;h[j+48>>3]=p;h[j+56>>3]=y;y=0.0;b=4;e=0;p=0.0;q=0.0;break}else{yP(r,i);p=+h[r>>3];x=+h[r+8>>3];q=+h[r+16>>3];y=+h[r+24>>3];h[j>>3]=p;h[j+8>>3]=x;h[j+16>>3]=q;h[j+24>>3]=x;h[j+32>>3]=q;h[j+40>>3]=y;h[j+48>>3]=p;h[j+56>>3]=y;y=0.0;b=4;e=0;p=0.0;q=0.0;break}while(0);c[f+4>>2]=b;r=JK(b<<4)|0;c[f>>2]=r;s=1.0/+(b|0);t=d+8|0;u=d+4|0;v=(b|0)==4;w=-p;x=-q;i=0;while(1){if((b|0)<=(i|0))break a;do if(e){k=+(i|0)*6.283185307179586*s+y;m=+E(+k);k=+F(+k);if(!(a[t>>0]|0)){P=c[z>>2]|0;n=k*+g[u>>2]*+h[P+80>>3];k=m*+g[d>>2]*(+h[P+96>>3]+ +h[P+88>>3])}else{P=c[z>>2]|0;n=(+h[P+80>>3]+ +g[u>>2])*k;k=(+h[P+96>>3]+ +h[P+88>>3]+ +g[d>>2])*m}o=k*.5;k=n*.5}else{if(!(a[t>>0]|0)){o=+h[j+(i<<4)>>3]*p;k=+h[j+(i<<4)+8>>3]*q;break}if(!v){o=+h[j+(i<<4)>>3];k=+h[j+(i<<4)+8>>3];n=+C(+(k*k+o*o));o=(p/n+1.0)*o;k=(q/n+1.0)*k;break}switch(i|0){case 0:{m=q;k=p;break}case 1:{m=q;k=w;break}case 2:{m=x;k=w;break}case 3:{m=x;k=p;break}default:{m=0.0;k=0.0}}o=+h[j+(i<<4)>>3]+k;k=+h[j+(i<<4)+8>>3]+m}while(0);O=c[z>>2]|0;P=b-i+-1|0;h[r+(P<<4)>>3]=+h[O+16>>3]+o;h[r+(P<<4)+8>>3]=+h[O+24>>3]+k;i=i+1|0}}case 2:{G=b+16|0;f=c[(c[G>>2]|0)+12>>2]|0;k=+h[f+16>>3];o=+h[f+24>>3];m=+h[f+32>>3];n=+h[f+40>>3];f=JK(8)|0;c[f+4>>2]=4;b=JK(64)|0;c[f>>2]=b;G=(c[G>>2]|0)+16|0;c[P>>2]=c[G>>2];c[P+4>>2]=c[G+4>>2];c[P+8>>2]=c[G+8>>2];c[P+12>>2]=c[G+12>>2];if(!(a[d+8>>0]|0)){c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Pm(i,k,o,O,d);c[b>>2]=c[i>>2];c[b+4>>2]=c[i+4>>2];c[b+8>>2]=c[i+8>>2];c[b+12>>2]=c[i+12>>2];N=(c[f>>2]|0)+16|0;c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Pm(H,k,n,O,d);c[N>>2]=c[H>>2];c[N+4>>2]=c[H+4>>2];c[N+8>>2]=c[H+8>>2];c[N+12>>2]=c[H+12>>2];N=(c[f>>2]|0)+32|0;c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Pm(I,m,n,O,d);c[N>>2]=c[I>>2];c[N+4>>2]=c[I+4>>2];c[N+8>>2]=c[I+8>>2];c[N+12>>2]=c[I+12>>2];N=(c[f>>2]|0)+48|0;c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Pm(J,m,o,O,d);c[N>>2]=c[J>>2];c[N+4>>2]=c[J+4>>2];c[N+8>>2]=c[J+8>>2];c[N+12>>2]=c[J+12>>2];break a}else{y=k-+g[d>>2];J=d+4|0;x=o-+g[J>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Om(K,y,x,O);c[b>>2]=c[K>>2];c[b+4>>2]=c[K+4>>2];c[b+8>>2]=c[K+8>>2];c[b+12>>2]=c[K+12>>2];K=(c[f>>2]|0)+16|0;x=k-+g[d>>2];y=n+ +g[J>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Om(L,x,y,O);c[K>>2]=c[L>>2];c[K+4>>2]=c[L+4>>2];c[K+8>>2]=c[L+8>>2];c[K+12>>2]=c[L+12>>2];L=(c[f>>2]|0)+32|0;y=m+ +g[d>>2];x=n+ +g[J>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Om(M,y,x,O);c[L>>2]=c[M>>2];c[L+4>>2]=c[M+4>>2];c[L+8>>2]=c[M+8>>2];c[L+12>>2]=c[M+12>>2];M=(c[f>>2]|0)+48|0;x=m+ +g[d>>2];y=o-+g[J>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Om(N,x,y,O);c[M>>2]=c[N>>2];c[M+4>>2]=c[N+4>>2];c[M+8>>2]=c[N+8>>2];c[M+12>>2]=c[N+12>>2];break a}}case 4:{b=b+16|0;f=JK(8)|0;c[f+4>>2]=4;i=JK(64)|0;c[f>>2]=i;j=c[b>>2]|0;N=j+16|0;c[P>>2]=c[N>>2];c[P+4>>2]=c[N+4>>2];c[P+8>>2]=c[N+8>>2];c[P+12>>2]=c[N+12>>2];k=-+h[j+88>>3];if(!(a[d+8>>0]|0)){x=-+h[j+80>>3];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Pm(A,k,x,O,d);c[i>>2]=c[A>>2];c[i+4>>2]=c[A+4>>2];c[i+8>>2]=c[A+8>>2];c[i+12>>2]=c[A+12>>2];N=(c[f>>2]|0)+16|0;M=c[b>>2]|0;x=-+h[M+88>>3];y=+h[M+80>>3];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Pm(B,x,y,O,d);c[N>>2]=c[B>>2];c[N+4>>2]=c[B+4>>2];c[N+8>>2]=c[B+8>>2];c[N+12>>2]=c[B+12>>2];N=(c[f>>2]|0)+32|0;M=c[b>>2]|0;y=+h[M+96>>3];x=+h[M+80>>3];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Pm(D,y,x,O,d);c[N>>2]=c[D>>2];c[N+4>>2]=c[D+4>>2];c[N+8>>2]=c[D+8>>2];c[N+12>>2]=c[D+12>>2];N=(c[f>>2]|0)+48|0;M=c[b>>2]|0;x=+h[M+96>>3];y=-+h[M+80>>3];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Pm(G,x,y,O,d);c[N>>2]=c[G>>2];c[N+4>>2]=c[G+4>>2];c[N+8>>2]=c[G+8>>2];c[N+12>>2]=c[G+12>>2];break a}else{y=k-+g[d>>2];M=d+4|0;x=-+h[j+80>>3]-+g[M>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Om(t,y,x,O);c[i>>2]=c[t>>2];c[i+4>>2]=c[t+4>>2];c[i+8>>2]=c[t+8>>2];c[i+12>>2]=c[t+12>>2];N=(c[f>>2]|0)+16|0;L=c[b>>2]|0;x=-+h[L+88>>3]-+g[d>>2];y=+h[L+80>>3]+ +g[M>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Om(u,x,y,O);c[N>>2]=c[u>>2];c[N+4>>2]=c[u+4>>2];c[N+8>>2]=c[u+8>>2];c[N+12>>2]=c[u+12>>2];N=(c[f>>2]|0)+32|0;L=c[b>>2]|0;y=+h[L+96>>3]+ +g[d>>2];x=+h[L+80>>3]+ +g[M>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Om(v,y,x,O);c[N>>2]=c[v>>2];c[N+4>>2]=c[v+4>>2];c[N+8>>2]=c[v+8>>2];c[N+12>>2]=c[v+12>>2];N=(c[f>>2]|0)+48|0;L=c[b>>2]|0;x=+h[L+96>>3]+ +g[d>>2];y=-+h[L+80>>3]-+g[M>>2];c[O>>2]=c[P>>2];c[O+4>>2]=c[P+4>>2];c[O+8>>2]=c[P+8>>2];c[O+12>>2]=c[P+12>>2];Om(z,x,y,O);c[N>>2]=c[z>>2];c[N+4>>2]=c[z+4>>2];c[N+8>>2]=c[z+8>>2];c[N+12>>2]=c[z+12>>2];break a}}default:f=0}while(0);l=Q;return f|0}function Om(a,b,c,d){a=a|0;b=+b;c=+c;d=d|0;c=+h[d+8>>3]+c;h[a>>3]=+h[d>>3]+b;h[a+8>>3]=c;return}function Pm(a,b,c,d,e){a=a|0;b=+b;c=+c;d=d|0;e=e|0;c=+g[e+4>>2]*c+ +h[d+8>>3];h[a>>3]=+g[e>>2]*b+ +h[d>>3];h[a+8>>3]=c;return}function Qm(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;o=l;l=l+112|0;j=o+88|0;i=o+72|0;k=o+32|0;m=o+16|0;n=o;f=o+56|0;g=o+40|0;h=b+48|0;r=(c[(c[((c[b>>2]&3|0)==3?b:h)+40>>2]|0)+16>>2]|0)+16|0;p=b+16|0;q=(c[p>>2]|0)+16|0;c[i>>2]=c[r>>2];c[i+4>>2]=c[r+4>>2];c[i+8>>2]=c[r+8>>2];c[i+12>>2]=c[r+12>>2];c[j>>2]=c[q>>2];c[j+4>>2]=c[q+4>>2];c[j+8>>2]=c[q+8>>2];c[j+12>>2]=c[q+12>>2];Rm(f,i,j);c[m>>2]=c[f>>2];c[m+4>>2]=c[f+4>>2];c[m+8>>2]=c[f+8>>2];c[m+12>>2]=c[f+12>>2];f=b+-48|0;q=(c[(c[((c[b>>2]&3|0)==2?b:f)+40>>2]|0)+16>>2]|0)+16|0;p=(c[p>>2]|0)+56|0;c[i>>2]=c[q>>2];c[i+4>>2]=c[q+4>>2];c[i+8>>2]=c[q+8>>2];c[i+12>>2]=c[q+12>>2];c[j>>2]=c[p>>2];c[j+4>>2]=c[p+4>>2];c[j+8>>2]=c[p+8>>2];c[j+12>>2]=c[p+12>>2];Rm(g,i,j);c[n>>2]=c[g>>2];c[n+4>>2]=c[g+4>>2];c[n+8>>2]=c[g+8>>2];c[n+12>>2]=c[g+12>>2];if(!e){g=-1111;f=-1111}else{r=c[b>>2]&3;g=c[(c[(c[((r|0)==2?b:f)+40>>2]|0)+16>>2]|0)+288>>2]|0;f=c[(c[(c[((r|0)==3?b:h)+40>>2]|0)+16>>2]|0)+288>>2]|0};c[i>>2]=c[m>>2];c[i+4>>2]=c[m+4>>2];c[i+8>>2]=c[m+8>>2];c[i+12>>2]=c[m+12>>2];c[j>>2]=c[n>>2];c[j+4>>2]=c[n+4>>2];c[j+8>>2]=c[n+8>>2];c[j+12>>2]=c[n+12>>2];MX(d,i,f,j,g,k)|0;p=k;q=c[p+4>>2]|0;r=a;c[r>>2]=c[p>>2];c[r+4>>2]=q;l=o;return}function Rm(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[c+8>>3]+ +h[b+8>>3];h[a>>3]=+h[c>>3]+ +h[b>>3];h[a+8>>3]=d;return}function Sm(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;u=l;l=l+144|0;p=u+104|0;q=u+88|0;o=u+80|0;n=u+72|0;j=u+64|0;s=u+128|0;k=u+32|0;m=u+124|0;t=u+16|0;r=u;i=u+120|0;h=(c[b+16>>2]|0)+144|0;g=c[h>>2]|0;h=c[h+4>>2]|0;v=j;c[v>>2]=g;c[v+4>>2]=h;v=g;c[t>>2]=c[v>>2];c[t+4>>2]=c[v+4>>2];c[t+8>>2]=c[v+8>>2];c[t+12>>2]=c[v+12>>2];h=g+(h+-1<<4)|0;c[r>>2]=c[h>>2];c[r+4>>2]=c[h+4>>2];c[r+8>>2]=c[h+8>>2];c[r+12>>2]=c[h+12>>2];a:do if(!(f<<24>>24)){g=-1111;f=-1111}else{g=-1111;f=-1111;h=0;while(1){if((h|0)>=(e|0))break a;if((f|0)==-1111){f=c[d+(h<<2)>>2]|0;c[q>>2]=c[f>>2];c[q+4>>2]=c[f+4>>2];c[p>>2]=c[t>>2];c[p+4>>2]=c[t+4>>2];c[p+8>>2]=c[t+8>>2];c[p+12>>2]=c[t+12>>2];f=(NX(q,p)|0)==0;f=f?-1111:h}if((g|0)==-1111){g=c[d+(h<<2)>>2]|0;c[q>>2]=c[g>>2];c[q+4>>2]=c[g+4>>2];c[p>>2]=c[r>>2];c[p+4>>2]=c[r+4>>2];c[p+8>>2]=c[r+8>>2];c[p+12>>2]=c[r+12>>2];g=(NX(q,p)|0)==0;g=g?-1111:h}h=h+1|0}}while(0);Tm(d,e,f,g,i,m);c[k>>2]=0;c[k+4>>2]=0;c[k+8>>2]=0;c[k+12>>2]=0;c[k+16>>2]=0;c[k+20>>2]=0;c[k+24>>2]=0;c[k+28>>2]=0;h=c[i>>2]|0;v=c[m>>2]|0;c[p>>2]=c[j>>2];c[p+4>>2]=c[j+4>>2];if((OX(h,v,p,k,s)|0)<0){t=KB(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0;v=KB(c[((c[b>>2]&3|0)==2?b:b+-48|0)+40>>2]|0)|0;c[n>>2]=t;c[n+4>>2]=v;gA(1,89657,n)|0}else{if(0>1){g=c[15712]|0;n=KB(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0;f=b+-48|0;v=KB(c[((c[b>>2]&3|0)==2?b:f)+40>>2]|0)|0;c[o>>2]=n;c[o+4>>2]=v;w4(g,89705,o)|0;g=b}else{f=b+-48|0;g=b}dO(b,c[((c[g>>2]&3|0)==2?b:f)+40>>2]|0,c[s>>2]|0,c[s+4>>2]|0,17792);U1(h);c[q>>2]=c[t>>2];c[q+4>>2]=c[t+4>>2];c[q+8>>2]=c[t+8>>2];c[q+12>>2]=c[t+12>>2];c[p>>2]=c[r>>2];c[p+4>>2]=c[r+4>>2];c[p+8>>2]=c[r+8>>2];c[p+12>>2]=c[r+12>>2];yO(a,b,q,p)}l=u;return}function Tm(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;i=0;h=0;while(1){if((i|0)>=(b|0))break;if(!((i|0)==(d|0)|(i|0)==(e|0)))h=(c[(c[a+(i<<2)>>2]|0)+4>>2]|0)+h|0;i=i+1|0}o=KK(h<<5)|0;i=0;n=0;while(1){if((n|0)>=(b|0))break;a:do if(!((n|0)==(d|0)|(n|0)==(e|0))){m=a+(n<<2)|0;l=0;while(1){j=c[m>>2]|0;k=c[j+4>>2]|0;if((l|0)>=(k|0))break a;p=l+1|0;q=o+(i<<5)|0;j=(c[j>>2]|0)+(l<<4)|0;c[q>>2]=c[j>>2];c[q+4>>2]=c[j+4>>2];c[q+8>>2]=c[j+8>>2];c[q+12>>2]=c[j+12>>2];j=o+(i<<5)+16|0;k=(c[c[m>>2]>>2]|0)+(((p|0)<(k|0)?p:0)<<4)|0;c[j>>2]=c[k>>2];c[j+4>>2]=c[k+4>>2];c[j+8>>2]=c[k+8>>2];c[j+12>>2]=c[k+12>>2];l=p;i=i+1|0}}while(0);n=n+1|0}if((i|0)==(h|0)){c[f>>2]=o;c[g>>2]=h;return}else qa(89719,89726,77,89741)}function Um(a,d,e){a=a|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;n=l;l=l+32|0;k=n+12|0;f=n;pr(f,a);c[k>>2]=c[f>>2];c[k+4>>2]=c[f+4>>2];c[k+8>>2]=c[f+8>>2];f=jC(a)|0;while(1){if(!f)break;g=XA(a,f)|0;while(1){if(!g)break;ZN(g);g=ZA(a,g)|0}f=kC(a,f)|0}h=Vz(17756,c[4581]|0)|0;f=jC(a)|0;while(1){if(!f)break;g=XA(a,f)|0;while(1){if(!g)break;if((c[47134]|0)>1?(i=c[g+16>>2]|0,(c[i+8>>2]|0)!=0):0){o=i+168|0;b[o>>1]=(b[o>>1]|0)+1<<16>>16}else m=14;if((m|0)==14?(m=0,j=Vm(h,g)|0,(j|0)!=(g|0)):0){o=c[j+16>>2]|0;p=o+168|0;b[p>>1]=(b[p>>1]|0)+1<<16>>16;o=o+172|0;c[(c[g+16>>2]|0)+172>>2]=c[o>>2];c[o>>2]=g}g=ZA(a,g)|0}f=kC(a,f)|0}Rz(h)|0;if(!(nb[d&63](a,k,e)|0)){c[47138]=1;f=0}else f=1;l=n;return f|0}function Vm(a,b){a=a|0;b=b|0;var d=0,e=0,f=0.0,g=0.0,i=0,j=0.0,k=0.0,m=0.0,n=0.0,o=0,p=0,q=0;q=l;l=l+64|0;p=q;e=c[b>>2]&3;i=c[((e|0)==3?b:b+48|0)+40>>2]|0;e=c[((e|0)==2?b:b+-48|0)+40>>2]|0;do if(i>>>0>=e>>>0){d=b+16|0;if(i>>>0>e>>>0){d=c[d>>2]|0;o=e;e=i;k=+h[d+16>>3];m=+h[d+24>>3];g=+h[d+56>>3];f=+h[d+64>>3];break}o=c[d>>2]|0;j=+h[o+56>>3];n=+h[o+64>>3];g=+h[o+16>>3];f=+h[o+24>>3];if(!(g<j))if(!(g>j)){d=f>n;if(f<n){o=i;e=i;k=j;m=n}else{o=i;e=i;k=g;m=f;g=d?j:g;f=d?n:f}}else{o=i;e=i;k=g;m=f;g=j;f=n}else{o=i;e=i;k=j;m=n}}else{d=c[b+16>>2]|0;o=i;k=+h[d+56>>3];m=+h[d+64>>3];g=+h[d+16>>3];f=+h[d+24>>3]}while(0);c[p+8>>2]=o;h[p+16>>3]=g;h[p+24>>3]=f;c[p+32>>2]=e;h[p+40>>3]=k;h[p+48>>3]=m;c[p+56>>2]=b;p=c[(nb[c[a>>2]&63](a,p,1)|0)+56>>2]|0;l=q;return p|0}function Wm(a,b){a=a|0;b=b|0;return Um(a,21,b)|0}function Xm(d,e,f){d=d|0;e=e|0;f=f|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0.0,H=0;F=l;l=l+112|0;B=F+88|0;A=F+72|0;t=F+40|0;q=F+8|0;p=F;u=F+64|0;C=F+48|0;D=F+24|0;i=(c[47134]|0)>1&1;do if((f|0)>5){s=JK((tB(d)|0)<<2)|0;o=(f|0)==8;n=o&1;m=jC(d)|0;r=0;while(1){if(!m)break;j=Nm(m,e,n)|0;k=(c[m+16>>2]|0)+288|0;if(!j){c[k>>2]=-1111;j=r}else{c[k>>2]=r;c[s+(r<<2)>>2]=j;j=r+1|0}m=kC(d,m)|0;r=j}if(s){j=lt(s,r)|0;if(j|0){if(o){y=0;z=s;x=s;w=1;break}y=JX(s,r)|0;z=s;x=s;w=1;break}if(o){gA(0,89755,p)|0;j=0;y=0;z=s;x=s;w=1;break}else{G=+g[e+4>>2];h[q>>3]=+g[e>>2];h[q+8>>3]=G;gA(0,89833,q)|0;j=0;y=0;z=s;x=s;w=1;break}}else{j=0;y=0;z=0;x=0;w=0}}else{j=0;y=0;z=0;r=0;x=0;w=0}while(0);if(0){k=c[15712]|0;if((f|0)==8&(j|0)!=0)m=89939;else m=(y|0)==0?89925:(f|0)==10?110056:89915;c[t>>2]=m;w4(k,89956,t)|0}v=(y|0)!=0;a:do if(!v){if((f|0)==8&(j|0)!=0){rQ(d,0);i=1}}else{k=jC(d)|0;while(1){if(!k)break a;j=XA(d,k)|0;while(1){if(!j)break;t=(c[j+16>>2]|0)+144|0;Qm(u,j,y,1,0,0);q=u;s=c[q+4>>2]|0;c[t>>2]=c[q>>2];c[t+4>>2]=s;j=ZA(d,j)|0}k=kC(d,k)|0}}while(0);p=(i|0)==0;q=d+60|0;s=(f|0)==10;i=0;o=jC(d)|0;j=0;while(1){if(!o)break;t=o+16|0;e=XA(d,o)|0;while(1){if(!e)break;n=c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0;k=e+16|0;m=c[k>>2]|0;if(!p?(c[m+8>>2]|0)!=0:0){H=(c[t>>2]|0)+16|0;u=m+16|0;c[A>>2]=c[H>>2];c[A+4>>2]=c[H+4>>2];c[A+8>>2]=c[H+8>>2];c[A+12>>2]=c[H+12>>2];c[B>>2]=c[u>>2];c[B+4>>2]=c[u+4>>2];c[B+8>>2]=c[u+8>>2];c[B+12>>2]=c[u+12>>2];Rm(C,A,B);n=(c[n+16>>2]|0)+16|0;u=(c[k>>2]|0)+56|0;c[A>>2]=c[n>>2];c[A+4>>2]=c[n+4>>2];c[A+8>>2]=c[n+8>>2];c[A+12>>2]=c[n+12>>2];c[B>>2]=c[u>>2];c[B+4>>2]=c[u+4>>2];c[B+8>>2]=c[u+8>>2];c[B+12>>2]=c[u+12>>2];Rm(D,A,B);c[A>>2]=c[C>>2];c[A+4>>2]=c[C+4>>2];c[A+8>>2]=c[C+8>>2];c[A+12>>2]=c[C+12>>2];c[B>>2]=c[D>>2];c[B+4>>2]=c[D+4>>2];c[B+8>>2]=c[D+8>>2];c[B+12>>2]=c[D+12>>2];yO(d,e,A,B)}else E=35;b:do if((E|0)==35){E=0;H=b[m+168>>1]|0;k=H<<16>>16;if(H<<16>>16){if((o|0)==(n|0)){if(!i){j=JK(96)|0;c[j+84>>2]=JK(((tB(d)|0)<<5)+11520|0)|0;i=j}Km(i,e,c[(c[(c[q>>2]|0)+16>>2]|0)+248>>2]|0);break}if(!v){OM(d,e,f,17792);break}k=a[194954]|0?1:k;m=e;n=0;while(1){if((n|0)>=(k|0))break b;if(s)Sm(d,m,z,r,1);else Ym(d,m);m=c[(c[m+16>>2]|0)+172>>2]|0;n=n+1|0}}}while(0);e=ZA(d,e)|0}o=kC(d,o)|0}if(v)LX(y);if(i|0){U1(c[i+84>>2]|0);U1(j)}if(w){i=0;while(1){if((i|0)>=(r|0))break;U1(c[z+(i<<2)>>2]|0);i=i+1|0}U1(x)}l=F;return 0}function Ym(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;k=l;l=l+96|0;g=k+64|0;f=k+48|0;d=k+40|0;h=k+80|0;e=k+32|0;i=k+16|0;j=k;m=(c[b+16>>2]|0)+144|0;n=c[m>>2]|0;m=c[m+4>>2]|0;o=e;c[o>>2]=n;c[o+4>>2]=m;o=n;c[i>>2]=c[o>>2];c[i+4>>2]=c[o+4>>2];c[i+8>>2]=c[o+8>>2];c[i+12>>2]=c[o+12>>2];m=n+(m+-1<<4)|0;c[j>>2]=c[m>>2];c[j+4>>2]=c[m+4>>2];c[j+8>>2]=c[m+8>>2];c[j+12>>2]=c[m+12>>2];c[g>>2]=c[e>>2];c[g+4>>2]=c[e+4>>2];FY(g,h);if(0>1){o=c[15712]|0;m=KB(c[((c[b>>2]&3|0)==3?b:b+48|0)+40>>2]|0)|0;e=b+-48|0;n=KB(c[((c[b>>2]&3|0)==2?b:e)+40>>2]|0)|0;c[d>>2]=m;c[d+4>>2]=n;w4(o,89981,d)|0;d=e;e=b}else{d=b+-48|0;e=b}dO(b,c[((c[e>>2]&3|0)==2?b:d)+40>>2]|0,c[h>>2]|0,c[h+4>>2]|0,17792);c[f>>2]=c[i>>2];c[f+4>>2]=c[i+4>>2];c[f+8>>2]=c[i+8>>2];c[f+12>>2]=c[i+12>>2];c[g>>2]=c[j>>2];c[g+4>>2]=c[j+4>>2];c[g+8>>2]=c[j+8>>2];c[g+12>>2]=c[j+12>>2];yO(a,b,f,g);l=k;return}function Zm(a,d){a=a|0;d=d|0;var e=0;e=b[(c[a+16>>2]|0)+136>>1]&14;if(d<<24>>24)_m(a)|0;if(e<<16>>16)Wm(a,e&65535)|0;return}function _m(a){a=a|0;var b=0,d=0,e=0,f=0;d=$m(a)|0;b=jC(a)|0;while(1){if(!b)break;e=c[b+16>>2]|0;f=c[e+132>>2]|0;h[e+16>>3]=+h[f>>3]*72.0;h[e+24>>3]=+h[f+8>>3]*72.0;b=kC(a,b)|0}return d|0}function $m(a){a=a|0;var b=0,d=0,e=0,f=0.0,g=0.0,i=0.0,j=0,k=0;a:do if((c[a+60>>2]|0)==(a|0)?(d=a+16|0,e=c[d>>2]|0,(c[(c[e+8>>2]|0)+84>>2]|0)!=0):0){if(!(+h[e+16>>3]!=0.0)?!(+h[e+24>>3]!=0.0):0)b=0;else{an(a);b=1;e=c[d>>2]|0}j=(c[e+116>>2]&1|0)==0;if(!j){k=e+32|0;i=+h[k>>3];d=e+40|0;h[k>>3]=+h[d>>3];h[d>>3]=i}d=c[e+8>>2]|0;b:do switch(c[d+84>>2]|0){case 2:{f=+h[d+64>>3];if(f<=0.0)break a;g=f/+h[e+32>>3];f=+h[d+72>>3]/+h[e+40>>3];if(g<1.0|f<1.0)if(g<f){i=1.0;f=f/g;break b}else{i=g/f;f=1.0;break b}else i=g;break}case 5:{f=+h[d+64>>3];if(f<=0.0)break a;g=f/+h[e+32>>3];f=+h[d+72>>3]/+h[e+40>>3];if(!(g>1.0&f>1.0))break a;f=g<f?g:f;i=f;break}case 1:{g=+h[d+16>>3];f=+h[e+40>>3]/+h[e+32>>3];if(f<g){i=1.0;f=g/f;break b}else{i=f/g;f=1.0;break b}}default:break a}while(0);g=j?f:i;f=j?i:f;c:do if((c[47134]|0)>1){d=jC(a)|0;while(1){if(!d)break c;b=XA(a,d)|0;while(1){if(!b)break;if(c[(c[b+16>>2]|0)+8>>2]|0)bn(b,f,g);b=ZA(a,b)|0}d=kC(a,d)|0}}while(0);b=jC(a)|0;while(1){if(!b)break;k=c[(c[b+16>>2]|0)+132>>2]|0;h[k>>3]=+h[k>>3]*f;k=k+8|0;h[k>>3]=+h[k>>3]*g;b=kC(a,b)|0}cn(a,f,g);b=1}else b=0;while(0);return b|0}function an(b){b=b|0;var d=0,e=0,f=0.0,g=0.0,i=0.0,j=0.0,k=0,m=0,n=0,o=0;n=l;l=l+32|0;k=n+16|0;m=n;d=(c[b+16>>2]|0)+16|0;c[m>>2]=c[d>>2];c[m+4>>2]=c[d+4>>2];c[m+8>>2]=c[d+8>>2];c[m+12>>2]=c[d+12>>2];f=+h[m>>3];g=f*.013888888888888888;i=+h[m+8>>3];j=i*.013888888888888888;d=jC(b)|0;while(1){if(!d)break;e=c[d+16>>2]|0;o=c[e+132>>2]|0;h[o>>3]=+h[o>>3]-g;o=o+8|0;h[o>>3]=+h[o>>3]-j;e=c[e+108>>2]|0;if(e|0?a[e+81>>0]|0:0){o=e+56|0;h[o>>3]=+h[o>>3]-f;o=e+64|0;h[o>>3]=+h[o>>3]-i}d=kC(b,d)|0}e=jC(b)|0;while(1){if(!e)break;d=XA(b,e)|0;while(1){if(!d)break;if(c[(c[d+16>>2]|0)+8>>2]|0){c[k>>2]=c[m>>2];c[k+4>>2]=c[m+4>>2];c[k+8>>2]=c[m+8>>2];c[k+12>>2]=c[m+12>>2];dn(d,k)}d=ZA(b,d)|0}e=kC(b,e)|0}c[k>>2]=c[m>>2];c[k+4>>2]=c[m+4>>2];c[k+8>>2]=c[m+8>>2];c[k+12>>2]=c[m+12>>2];en(b,k);l=n;return}function bn(b,d,e){b=b|0;d=+d;e=+e;var f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0.0,p=0,q=0.0,r=0.0,s=0.0,t=0.0,u=0;p=c[b>>2]&3;i=c[(c[(c[((p|0)==2?b:b+-48|0)+40>>2]|0)+16>>2]|0)+132>>2]|0;s=(d+-1.0)*72.0;q=s*+h[i>>3];t=(e+-1.0)*72.0;r=t*+h[i+8>>3];p=c[(c[(c[((p|0)==3?b:b+48|0)+40>>2]|0)+16>>2]|0)+132>>2]|0;s=s*+h[p>>3];t=t*+h[p+8>>3];p=c[b+16>>2]|0;i=c[p+8>>2]|0;j=c[i+4>>2]|0;k=j+-1|0;b=0;i=c[i>>2]|0;while(1){if((b|0)>=(j|0))break;l=c[i+4>>2]|0;m=l+-1|0;n=(b|0)==(k|0);f=0;g=c[i>>2]|0;while(1){if((f|0)>=(l|0))break;do if(f|b){o=+h[g>>3];if((f|0)==(m|0)&n){h[g>>3]=o+q;u=g+8|0;h[u>>3]=+h[u>>3]+r;break}else{h[g>>3]=o*d;u=g+8|0;h[u>>3]=+h[u>>3]*e;break}}else{h[g>>3]=+h[g>>3]+s;u=g+8|0;h[u>>3]=+h[u>>3]+t}while(0);f=f+1|0;g=g+16|0}if(c[i+8>>2]|0){u=i+16|0;h[u>>3]=+h[u>>3]+s;u=i+24|0;h[u>>3]=+h[u>>3]+t}if(c[i+12>>2]|0){u=i+32|0;h[u>>3]=+h[u>>3]+q;u=i+40|0;h[u>>3]=+h[u>>3]+r}b=b+1|0;i=i+48|0}b=c[p+96>>2]|0;if(b|0?a[b+81>>0]|0:0){u=b+56|0;h[u>>3]=+h[u>>3]*d;u=b+64|0;h[u>>3]=+h[u>>3]*e}b=c[p+100>>2]|0;if(b|0?a[b+81>>0]|0:0){u=b+56|0;h[u>>3]=+h[u>>3]+q;u=b+64|0;h[u>>3]=+h[u>>3]+r}b=c[p+104>>2]|0;if(b|0?a[b+81>>0]|0:0){u=b+56|0;h[u>>3]=+h[u>>3]+s;u=b+64|0;h[u>>3]=+h[u>>3]+t}return}function cn(b,d,e){b=b|0;d=+d;e=+e;var f=0,g=0,i=0;i=b+16|0;g=c[i>>2]|0;b=g+32|0;h[b>>3]=+h[b>>3]*d;b=g+40|0;h[b>>3]=+h[b>>3]*e;b=g+16|0;h[b>>3]=+h[b>>3]*d;b=g+24|0;h[b>>3]=+h[b>>3]*e;b=c[g+12>>2]|0;if((b|0)!=0?(a[b+81>>0]|0)!=0:0){f=b+56|0;h[f>>3]=+h[f>>3]*d;f=b+64|0;h[f>>3]=+h[f>>3]*e;f=1;b=g}else{f=1;b=g}while(1){if((f|0)>(c[b+180>>2]|0))break;cn(c[(c[b+184>>2]|0)+(f<<2)>>2]|0,d,e);f=f+1|0;b=c[i>>2]|0}return}function dn(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0;k=c[b+16>>2]|0;b=c[k+8>>2]|0;j=c[b+4>>2]|0;l=d+8|0;b=c[b>>2]|0;g=0;while(1){if((g|0)>=(j|0))break;i=c[b+4>>2]|0;e=c[b>>2]|0;f=0;while(1){if((f|0)>=(i|0))break;h[e>>3]=+h[e>>3]-+h[d>>3];m=e+8|0;h[m>>3]=+h[m>>3]-+h[l>>3];e=e+16|0;f=f+1|0}if(c[b+8>>2]|0){m=b+16|0;h[m>>3]=+h[m>>3]-+h[d>>3];m=b+24|0;h[m>>3]=+h[m>>3]-+h[l>>3]}if(c[b+12>>2]|0){m=b+32|0;h[m>>3]=+h[m>>3]-+h[d>>3];m=b+40|0;h[m>>3]=+h[m>>3]-+h[l>>3]}b=b+48|0;g=g+1|0}b=c[k+96>>2]|0;if(b|0?a[b+81>>0]|0:0){m=b+56|0;h[m>>3]=+h[m>>3]-+h[d>>3];m=b+64|0;h[m>>3]=+h[m>>3]-+h[l>>3]}b=c[k+108>>2]|0;if(b|0?a[b+81>>0]|0:0){m=b+56|0;h[m>>3]=+h[m>>3]-+h[d>>3];m=b+64|0;h[m>>3]=+h[m>>3]-+h[l>>3]}b=c[k+100>>2]|0;if(b|0?a[b+81>>0]|0:0){m=b+56|0;h[m>>3]=+h[m>>3]-+h[d>>3];m=b+64|0;h[m>>3]=+h[m>>3]-+h[l>>3]}b=c[k+104>>2]|0;if(b|0?a[b+81>>0]|0:0){m=b+56|0;h[m>>3]=+h[m>>3]-+h[d>>3];m=b+64|0;h[m>>3]=+h[m>>3]-+h[l>>3]}return}function en(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0,j=0,k=0,m=0;k=l;l=l+16|0;j=k;g=+h[d>>3];i=b+16|0;b=c[i>>2]|0;e=b+32|0;h[e>>3]=+h[e>>3]-g;f=+h[d+8>>3];e=b+40|0;h[e>>3]=+h[e>>3]-f;e=b+16|0;h[e>>3]=+h[e>>3]-g;e=b+24|0;h[e>>3]=+h[e>>3]-f;e=c[b+12>>2]|0;if((e|0)!=0?(a[e+81>>0]|0)!=0:0){m=e+56|0;h[m>>3]=+h[m>>3]-g;e=e+64|0;h[e>>3]=+h[e>>3]-f;e=1}else e=1;while(1){if((e|0)>(c[b+180>>2]|0))break;m=c[(c[b+184>>2]|0)+(e<<2)>>2]|0;c[j>>2]=c[d>>2];c[j+4>>2]=c[d+4>>2];c[j+8>>2]=c[d+8>>2];c[j+12>>2]=c[d+12>>2];en(m,j);e=e+1|0;b=c[i>>2]|0}l=k;return}function fn(a){a=a|0;var b=0,d=0,e=0,f=0.0,g=0.0,i=0,j=0;i=l;l=l+16|0;d=i;BP(a);e=a+16|0;b=c[e>>2]|0;f=+h[b+16>>3]*.013888888888888888;g=+h[b+24>>3]*.013888888888888888;b=jC(a)|0;while(1){if(!b)break;j=c[(c[b+16>>2]|0)+132>>2]|0;h[j>>3]=+h[j>>3]-f;j=j+8|0;h[j>>3]=+h[j>>3]-g;b=kC(a,b)|0}j=(c[e>>2]|0)+16|0;c[d>>2]=c[j>>2];c[d+4>>2]=c[j+4>>2];c[d+8>>2]=c[j+8>>2];c[d+12>>2]=c[j+12>>2];gn(a,d);Zm(a,1);l=i;return}function gn(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0.0,j=0.0;g=l;l=l+16|0;f=g;d=a+16|0;a=1;while(1){e=c[d>>2]|0;if((a|0)>(c[e+180>>2]|0))break;e=c[(c[e+184>>2]|0)+(a<<2)>>2]|0;c[f>>2]=c[b>>2];c[f+4>>2]=c[b+4>>2];c[f+8>>2]=c[b+8>>2];c[f+12>>2]=c[b+12>>2];gn(e,f);a=a+1|0}j=+h[b>>3];f=e+32|0;h[f>>3]=+h[f>>3]-j;i=+h[b+8>>3];b=e+40|0;h[b>>3]=+h[b>>3]-i;b=e+16|0;h[b>>3]=+h[b>>3]-j;b=e+24|0;h[b>>3]=+h[b>>3]-i;l=g;return}function hn(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0.0;p=(e|0)>2;n=0;o=0;j=c[f+4>>2]|0;b=c[f>>2]|0;while(1){if((o|0)>=(d|0)){b=0;break}i=c[(c[g+(o<<2)>>2]|0)+16>>2]|0;l=a[i+119>>0]|0;a:do if(!(l<<24>>24)){k=b+8|0;h[b>>3]=+B5();i=j+8|0;h[j>>3]=+B5();if(p){b=2;while(1){if((b|0)==(e|0)){j=n;b=k;break a}q=+B5();h[(c[f+(b<<2)>>2]|0)+(o<<3)>>3]=q;b=b+1|0}}else{j=n;b=k}}else{k=c[i+132>>2]|0;m=b+8|0;h[b>>3]=+h[k>>3];i=j+8|0;h[j>>3]=+h[k+8>>3];b:do if(p){j=2;b=k+16|0;while(1){if((j|0)==(e|0))break b;h[(c[f+(j<<2)>>2]|0)+(o<<3)>>3]=+h[b>>3];j=j+1|0;b=b+8|0}}while(0);j=(l&255)>1?1:n;b=m}while(0);n=j;o=o+1|0;j=i}while(1){if((b|0)>=(e|0))break;Ht(d,c[f+(b<<2)>>2]|0);b=b+1|0}return n|0}function jn(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,i=0,j=0,k=0,l=0.0,m=0,n=0,o=0,p=0;d=JK(((O(b+1|0,b)|0)/2|0)<<2)|0;n=un(b,b,0.0)|0;o=un(b,b,0.0)|0;a:do if(!(c[a+8>>2]|0)){e=0;while(1){if((e|0)>=(b|0))break a;i=c[a+(e*20|0)>>2]|0;j=a+(e*20|0)+4|0;k=n+(e<<2)|0;f=1;while(1){if((f|0)>=(i|0))break;m=c[(c[j>>2]|0)+(f<<2)>>2]|0;h[(c[n+(m<<2)>>2]|0)+(e<<3)>>3]=-1.0;h[(c[k>>2]|0)+(m<<3)>>3]=-1.0;f=f+1|0}e=e+1|0}}else{e=0;while(1){if((e|0)>=(b|0))break a;i=c[a+(e*20|0)>>2]|0;j=a+(e*20|0)+4|0;k=a+(e*20|0)+8|0;m=n+(e<<2)|0;f=1;while(1){if((f|0)>=(i|0))break;p=c[(c[j>>2]|0)+(f<<2)>>2]|0;l=-1.0/+g[(c[k>>2]|0)+(f<<2)>>2];h[(c[n+(p<<2)>>2]|0)+(e<<3)>>3]=l;h[(c[m>>2]|0)+(p<<3)>>3]=l;f=f+1|0}e=e+1|0}}while(0);b:do if(!(wr(b,n,o)|0)){U1(d);d=0}else{f=0;j=0;m=b;while(1){if((j|0)>=(b|0))break b;k=o+(j<<2)|0;e=j;i=f;while(1){if((e|0)==(b|0))break;if((j|0)==(e|0))l=0.0;else{p=c[k>>2]|0;l=+h[(c[o+(e<<2)>>2]|0)+(e<<3)>>3]+ +h[p+(j<<3)>>3]+ +h[p+(e<<3)>>3]*-2.0}g[d+(i<<2)>>2]=l;e=e+1|0;i=i+1|0}f=f+m|0;j=j+1|0;m=m+-1|0}}while(0);vn(n);vn(o);return d|0}function kn(a,b){a=a|0;b=b|0;var d=0,e=0,f=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0;r=l;l=l+16|0;q=r;if(c[a+8>>2]|0){d=ln(a,b)|0;f=0.0;e=0;p=0;while(1){if((p|0)>=(b|0))break;e=e+p|0;o=c[a+(p*20|0)>>2]|0;k=a+(p*20|0)+4|0;m=(O(p,b)|0)-e|0;n=a+(p*20|0)+8|0;j=1;while(1){if((j|0)>=(o|0))break;i=c[(c[k>>2]|0)+(j<<2)>>2]|0;if((i|0)>=(p|0)){i=d+(m+i<<2)|0;s=+g[(c[n>>2]|0)+(j<<2)>>2];f=f+ +B(+(+g[i>>2]-s));g[i>>2]=s}j=j+1|0}p=p+1|0}if(0){b=c[15712]|0;h[q>>3]=f;w4(b,89997,q)|0}}else d=0;l=r;return d|0}function ln(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+16|0;h=m;i=JK(((O(b+1|0,b)|0)/2|0)<<2)|0;j=JK(b<<2)|0;ur(h,b);d=0;f=0;k=b;while(1){if((d|0)>=(b|0))break;hs(d,a,b,j);e=d;g=f;while(1){if((e|0)==(b|0))break;c[i+(g<<2)>>2]=c[j+(e<<2)>>2];e=e+1|0;g=g+1|0}d=d+1|0;f=f+k|0;k=k+-1|0}U1(j);vr(h);l=m;return i|0}function mn(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;i=n;j=JK(((O(b+1|0,b)|0)/2|0)<<2)|0;k=JK(b<<2)|0;ur(i,b);d=0;f=0;m=b;while(1){if((d|0)>=(b|0))break;qr(d,a,b,k,i);e=d;h=f;while(1){if((e|0)==(b|0))break;g[j+(h<<2)>>2]=+(c[k+(e<<2)>>2]|0);e=e+1|0;h=h+1|0}d=d+1|0;f=f+m|0;m=m+-1|0}U1(k);vr(i);l=n;return j|0}function nn(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,h=0,i=0,j=0,k=0,l=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0;r=a+8|0;d=c[r>>2]|0;e=0;f=0;while(1){if((f|0)>=(b|0))break;e=(c[a+(f*20|0)>>2]|0)+e|0;f=f+1|0}f=JK(e<<2)|0;q=JK(b<<2)|0;e=0;while(1){if((e|0)>=(b|0))break;c[q+(e<<2)>>2]=0;e=e+1|0}if(!(c[r>>2]|0)){h=0;while(1){if((h|0)>=(b|0))break;c[a+(h*20|0)+8>>2]=f;Zs(a,h,q);i=a+(h*20|0)|0;j=c[i>>2]|0;k=a+(h*20|0)+4|0;l=+(j+-1|0);e=1;while(1){if((e|0)>=(j|0))break;p=c[(c[k>>2]|0)+(e<<2)>>2]|0;s=+((c[a+(p*20|0)>>2]|0)+-1|0)+l;g[f+(e<<2)>>2]=s-+((Ys(a,h,p,q)|0)<<1|0);e=e+1|0}_s(a,h,q);f=f+(c[i>>2]<<2)|0;h=h+1|0}f=mn(a,b)|0}else{j=0;while(1){if((j|0)>=(b|0))break;Zs(a,j,q);k=a+(j*20|0)|0;m=c[k>>2]|0;n=a+(j*20|0)+4|0;o=m+-2|0;p=a+(j*20|0)+8|0;i=1;while(1){if((i|0)>=(m|0))break;e=c[(c[n>>2]|0)+(i<<2)>>2]|0;h=o+(c[a+(e*20|0)>>2]|0)|0;s=+(h-((Ys(a,j,e,q)|0)<<1)|0);l=+g[(c[p>>2]|0)+(i<<2)>>2];if(l<s)l=+(h-((Ys(a,j,e,q)|0)<<1)|0);g[f+(i<<2)>>2]=l;i=i+1|0}_s(a,j,q);c[p>>2]=f;f=f+(c[k>>2]<<2)|0;j=j+1|0}f=ln(a,b)|0}U1(q);U1(c[r>>2]|0);c[r>>2]=0;a:do if(d|0){e=0;while(1){if((e|0)>=(b|0))break a;c[a+(e*20|0)+8>>2]=d;d=d+(c[a+(e*20|0)>>2]<<2)|0;e=e+1|0}}while(0);return f|0}function on(a,b,e,f,i,j,k,m,n){a=a|0;b=b|0;e=e|0;f=f|0;i=i|0;j=j|0;k=k|0;m=m|0;n=n|0;var o=0,p=0,q=0,r=0.0,s=0,t=0,u=0,v=0.0,w=0,x=0.0,y=0,z=0,A=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0.0,P=0.0;M=l;l=l+80|0;L=M+56|0;K=M+48|0;A=M+40|0;u=M+32|0;t=M+24|0;s=M+16|0;p=M+8|0;o=M;q=k&4;J=k&3;a:do if((n|0)<0)e=0;else{if(0)TO();k=(m|0)==2;b:do if(k){if(0)U4(93290,24,1,c[15712]|0)|0;e=nn(a,b)|0;w=14}else{switch(m|0){case 1:{e=jn(a,b)|0;if(e|0){I=e;break b}gA(0,93315,o)|0;gA(3,93364,p)|0;w=15;break b}case 3:break;default:{w=15;break b}}if(0)U4(93417,21,1,c[15712]|0)|0;e=kn(a,b)|0;w=14}while(0);if((w|0)==14)if(!e)w=15;else I=e;do if((w|0)==15){if(0)U4(93439,26,1,c[15712]|0)|0;if(!(c[a+8>>2]|0)){I=mn(a,b)|0;break}else{I=ln(a,b)|0;break}}while(0);if(0){H=c[15712]|0;h[s>>3]=+UO();w4(H,93466,s)|0;U4(93478,25,1,H)|0;TO()}c:do if((b|0)>1&(q|0)!=0)if((pn(a,b,f,j,q,J,k&1)|0)<0){u=0;t=0;e=-1;a=0;s=0;q=0;p=0;o=0;m=0;k=0}else{k=0;while(1){if((k|0)>=(j|0)){y=0;w=35;break c}m=f+(k<<2)|0;r=1.0;e=0;while(1){if((e|0)==(b|0))break;x=+B(+(+h[(c[m>>2]|0)+(e<<3)>>3]));r=x>r?x:r;e=e+1|0}r=1.0/r;e=0;while(1){if((e|0)==(b|0)){e=0;break}H=(c[m>>2]|0)+(e<<3)|0;h[H>>3]=+h[H>>3]*r;e=e+1|0}while(1){if((e|0)==(b|0))break;x=(+B5()+-.5)*1.0e-06;H=(c[m>>2]|0)+(e<<3)|0;h[H>>3]=+h[H>>3]+x;e=e+1|0}Ht(b,c[m>>2]|0);k=k+1|0}}else{y=hn(0,b,j,f,i)|0;w=35}while(0);d:do if((w|0)==35){if(0){H=c[15712]|0;h[t>>3]=+UO();w4(H,93504,t)|0}if((b|0)==1|(n|0)==0){e=0;break a}if(0){H=c[15712]|0;h[u>>3]=+UO();w4(H,93466,u)|0;U4(90019,26,1,H)|0;TO()}s=j<<2;H=JK(s)|0;t=b<<2;a=O(t,j)|0;G=JK(a)|0;e=0;while(1){if((e|0)>=(j|0))break;m=G+((O(e,b)|0)<<2)|0;c[H+(e<<2)>>2]=m;o=f+(e<<2)|0;k=0;while(1){if((k|0)>=(b|0))break;g[m+(k<<2)>>2]=+h[(c[o>>2]|0)+(k<<3)>>3];k=k+1|0}e=e+1|0}e:do if(!J){p=b+-1|0;e=0;r=0.0;o=0;while(1){if((o|0)>=(p|0))break e;q=b-o|0;m=e;k=1;while(1){e=m+1|0;if((k|0)>=(q|0))break;m=e;r=+g[I+(e<<2)>>2]+r;k=k+1|0}o=o+1|0}}else r=+(b|0)*.5*+(b+-1|0);while(0);C=(O(b+1|0,b)|0)/2|0;D=(J|0)==2;if(D)bu(C,I);cu(C,I);E=b<<3;m=JK(E)|0;Y6(m|0,0,E|0)|0;F=b+-1|0;e=0;o=0;while(1){if((o|0)>=(F|0)){e=b;k=0;o=0;break}p=b-o|0;v=0.0;k=1;while(1){e=e+1|0;if((k|0)>=(p|0))break;x=+g[I+(e<<2)>>2];z=m+(k+o<<3)|0;h[z>>3]=+h[z>>3]-x;v=v+x;k=k+1|0}z=m+(o<<3)|0;h[z>>3]=+h[z>>3]-v;o=o+1|0}while(1){if((k|0)>=(b|0))break;g[I+(o<<2)>>2]=+h[m+(k<<3)>>3];z=e+o|0;e=e+-1|0;k=k+1|0;o=z}z=JK(s)|0;c[z>>2]=JK(a)|0;e=1;while(1){if((e|0)>=(j|0))break;c[z+(e<<2)>>2]=(c[z>>2]|0)+((O(e,b)|0)<<2);e=e+1|0}p=JK(t)|0;o=JK(t)|0;k=JK(C<<2)|0;if(0){w=c[15712]|0;h[A>>3]=+UO();w4(w,93466,A)|0;U4(90046,15,1,w)|0;TO()}w=(y|0)==0;y=c[15712]|0;e=0;q=0;x=1797693134862315708145274.0e284;while(1){if(!(q<<24>>24==0&(e|0)<(n|0)))break;Y6(m|0,0,E|0)|0;if(D){du(C,I,k);u=0;a=0}else{u=0;a=0}while(1){if((u|0)>=(F|0)){q=b;s=0;a=0;break}t=b-u+-1|0;$t(t,0.0,o);q=0;while(1){if((q|0)>=(j|0))break;A=H+(q<<2)|0;$t(t,+g[(c[A>>2]|0)+(u<<2)>>2],p);Wt(t,p,-1.0,(c[A>>2]|0)+(u<<2)+4|0);bu(t,p);Vt(t,p,o,o);q=q+1|0}eu(t,o);q=0;while(1){if((q|0)>=(t|0))break;s=o+(q<<2)|0;v=+g[s>>2];if(v>=3402823466385288598117041.0e14|v<0.0)g[s>>2]=0.0;q=q+1|0}q=a+1|0;a=u+1|0;f:do if(D){v=0.0;s=0;while(1){if((s|0)>=(t|0))break f;A=k+(q<<2)|0;N=+g[A>>2]*+g[o+(s<<2)>>2];g[A>>2]=N;A=m+(a+s<<3)|0;h[A>>3]=+h[A>>3]-N;v=v+N;q=q+1|0;s=s+1|0}}else{v=0.0;s=0;while(1){if((s|0)>=(t|0))break f;N=+g[o+(s<<2)>>2];g[k+(q<<2)>>2]=N;A=m+(a+s<<3)|0;h[A>>3]=+h[A>>3]-N;v=v+N;q=q+1|0;s=s+1|0}}while(0);A=m+(u<<3)|0;h[A>>3]=+h[A>>3]-v;u=a;a=q}while(1){if((s|0)>=(b|0)){q=0;break}g[k+(a<<2)>>2]=+h[m+(s<<3)>>3];A=q+a|0;q=q+-1|0;s=s+1|0;a=A}while(1){if((q|0)>=(j|0)){v=0.0;q=0;break}Tt(k,b,c[H+(q<<2)>>2]|0,c[z+(q<<2)>>2]|0);q=q+1|0}while(1){if((q|0)>=(j|0))break;v=+Zt(b,c[H+(q<<2)>>2]|0,c[z+(q<<2)>>2]|0)+v;q=q+1|0}v=v*2.0+r;q=0;while(1){if((q|0)>=(j|0))break;A=H+(q<<2)|0;Tt(I,b,c[A>>2]|0,p);v=v-+Zt(b,c[A>>2]|0,p);q=q+1|0}P=x-v;N=+h[23294];q=(v<N?1:(P>=0.0?P:-P)/x<N)&1;a=0;while(1){if((a|0)>=(j|0))break;t=H+(a<<2)|0;s=c[t>>2]|0;g:do if(w){if((Ar(I,s,c[z+(a<<2)>>2]|0,b,.001,b)|0)<0){u=z;t=I;e=-1;a=G;s=H;q=z;break d}}else{Yt(b,s,p);if((Ar(I,p,c[z+(a<<2)>>2]|0,b,.001,b)|0)<0){u=z;t=I;e=-1;a=G;s=H;q=z;break d}else s=0;while(1){if((s|0)>=(b|0))break g;if((d[(c[(c[i+(s<<2)>>2]|0)+16>>2]|0)+119>>0]|0)<=1)c[(c[t>>2]|0)+(s<<2)>>2]=c[p+(s<<2)>>2];s=s+1|0}}while(0);a=a+1|0}if(((e|0)%5|0|0)==0&0!=0?(h[K>>3]=v,w4(y,90371,K)|0,((e+5|0)%50|0|0)==0):0)E6(10,y)|0;e=e+1|0;x=v}if(!0)q=0;else{N=+qn(H,I,j,b,J);P=+UO();h[L>>3]=N;c[L+8>>2]=e;h[L+16>>3]=P;w4(y,90062,L)|0;q=0}while(1){if((q|0)>=(j|0)){u=z;t=I;a=G;s=H;q=z;break d}a=H+(q<<2)|0;t=f+(q<<2)|0;s=0;while(1){if((s|0)>=(b|0))break;h[(c[t>>2]|0)+(s<<3)>>3]=+g[(c[a>>2]|0)+(s<<2)>>2];s=s+1|0}q=q+1|0}}while(0);U1(a);U1(s);U1(t);if(u|0){U1(c[u>>2]|0);U1(q)}U1(p);U1(o);U1(m);U1(k)}while(0);l=M;return e|0}function pn(b,d,e,f,i,j,k){b=b|0;d=d|0;e=e|0;f=f|0;i=i|0;j=j|0;k=k|0;var m=0,n=0,o=0,p=0.0,q=0,r=0,s=0.0,t=0,u=0.0,v=0,w=0,x=0,y=0,z=0,A=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,P=0,Q=0,R=0;R=l;l=l+32|0;o=R+24|0;Q=R+8|0;H=R+4|0;N=R;M=(d|0)<50?d:50;P=KK(M<<2)|0;G=d<<3;n=KK(O(G,M)|0)|0;m=0;while(1){if((m|0)>=(M|0))break;c[P+(m<<2)>>2]=n+((O(m,d)|0)<<3);m=m+1|0}E=M<<1;E=(E|0)>50?E:50;E=(E|0)>(d|0)?d:E;c[o>>2]=0;rs(b,d,E,o,k);ss(c[o>>2]|0,d,E);mu(c[o>>2]|0,E,d,P,M);U1(c[c[o>>2]>>2]|0);U1(c[o>>2]|0);E=d<<2;L=KK(E)|0;m=0;while(1){if((m|0)>=(d|0))break;c[L+(m<<2)>>2]=-1;m=m+1|0}ur(Q,d);J=c[b+8>>2]|0;I=(k|0)!=0;if(I)dt(b,d);C=KK(E)|0;K=KK(160)|0;n=KK(d*160|0)|0;D=KK(160)|0;m=0;while(1){if((m|0)==40)break;c[D+(m<<2)>>2]=n+((O(m,d)|0)<<2);m=m+1|0}m=(x5()|0)%(d|0)|0;c[L+(m<<2)>>2]=0;c[K>>2]=m;n=c[D>>2]|0;if(I){bs(m,b,d,n);n=0;o=0}else{qr(m,b,d,n,Q);n=0;o=0}while(1){if((o|0)>=(d|0)){r=1;break}F=c[(c[D>>2]|0)+(o<<2)>>2]|0;c[C+(o<<2)>>2]=F;A=(F|0)>(n|0);m=A?o:m;n=A?F:n;o=o+1|0}while(1){if((r|0)==40){m=0;break}c[L+(m<<2)>>2]=r;c[K+(r<<2)>>2]=m;t=D+(r<<2)|0;n=c[t>>2]|0;if(I){bs(m,b,d,n);q=0;n=0}else{qr(m,b,d,n,Q);q=0;n=0}while(1){if((q|0)>=(d|0))break;o=C+(q<<2)|0;F=c[o>>2]|0;k=c[(c[t>>2]|0)+(q<<2)>>2]|0;k=(F|0)<(k|0)?F:k;c[o>>2]=k;if((k|0)<=(n|0)){if((k|0)==(n|0)?((x5()|0)%(q+1|0)|0|0)==0:0){n=c[o>>2]|0;m=q}}else{n=k;m=q}q=q+1|0}r=r+1|0}while(1){if((m|0)>=(d|0))break;c[C+(m<<2)>>2]=-1;m=m+1|0}w=KK(E)|0;F=KK(d<<4)|0;x=d+-1|0;y=x<<2;A=0;o=0;k=0;n=0;z=0;while(1){if((z|0)>=(d|0))break;m=L+(z<<2)|0;a:do if((c[m>>2]|0)>-1){t=F+(z<<4)+4|0;c[t>>2]=KK(y)|0;v=KK(y)|0;c[F+(z<<4)+8>>2]=v;c[F+(z<<4)>>2]=x;a[F+(z<<4)+12>>0]=1;r=D+(c[m>>2]<<2)|0;m=0;while(1){if((m|0)==(z|0)){q=z;break}c[(c[t>>2]|0)+(m<<2)>>2]=m;c[v+(m<<2)>>2]=c[(c[r>>2]|0)+(m<<2)>>2];m=m+1|0}while(1){m=q+1|0;if((q|0)==(x|0)){m=x;break a}c[(c[t>>2]|0)+(q<<2)>>2]=m;c[v+(q<<2)>>2]=c[(c[r>>2]|0)+(m<<2)>>2];q=m}}else{if((n|0)<40){k=KK(E)|0;o=KK(E)|0;n=d;m=1}else m=0;a[F+(z<<4)+12>>0]=m;c[F+(z<<4)+4>>2]=k;c[F+(z<<4)+8>>2]=o;c[F+(z<<4)>>2]=40;m=0;while(1){if((m|0)==40)break;c[k+(m<<2)>>2]=c[K+(m<<2)>>2];c[o+(m<<2)>>2]=c[(c[D+(m<<2)>>2]|0)+(z<<2)>>2];m=m+1|0}o=o+160|0;k=k+160|0;n=n+-40|0;m=40}while(0);A=m+A|0;z=z+1|0}U1(C);U1(w);if(D|0){U1(c[D>>2]|0);U1(D)}D=KK(d*20|0)|0;q=A+d<<2;r=KK(q)|0;v=(j|0)==2;q=KK(q)|0;t=0;while(1){if((t|0)>=(d|0))break;c[D+(t*20|0)+4>>2]=r;c[D+(t*20|0)+8>>2]=q;m=(c[F+(t<<4)>>2]|0)+1|0;w=D+(t*20|0)|0;c[w>>2]=m;o=(c[F+(t<<4)+8>>2]|0)+-4|0;k=F+(t<<4)+4|0;b:do if(v){p=0.0;n=1;while(1){if((n|0)>=(m|0))break b;c[r+(n<<2)>>2]=c[(c[k>>2]|0)+(n+-1<<2)>>2];u=+(c[o+(n<<2)>>2]|0);u=-1.0/(u*u);g[q+(n<<2)>>2]=u;p=p-u;n=n+1|0;m=c[w>>2]|0}}else{p=0.0;n=1;while(1){if((n|0)>=(m|0))break b;c[r+(n<<2)>>2]=c[(c[k>>2]|0)+(n+-1<<2)>>2];u=-1.0/+(c[o+(n<<2)>>2]|0);g[q+(n<<2)>>2]=u;p=p-u;n=n+1|0;m=c[w>>2]|0}}while(0);c[r>>2]=t;g[q>>2]=p;E=c[w>>2]|0;q=q+(E<<2)|0;r=r+(E<<2)|0;t=t+1|0}C=KK(f<<2)|0;c[C>>2]=KK(O(f<<3,M)|0)|0;m=1;while(1){if((m|0)>=(f|0))break;c[C+(m<<2)>>2]=(c[C>>2]|0)+((O(m,M)|0)<<3);m=m+1|0}c:do if(i){m=0;while(1){if((m|0)>=(f|0))break;o=C+(m<<2)|0;n=0;while(1){if((n|0)>=(M|0))break;h[(c[o>>2]|0)+(n<<3)>>3]=0.0;n=n+1|0}m=m+1|0}if((f|0)!=2){m=0;while(1){if((m|0)>=(f|0)){m=0;break c}h[(c[C+(m<<2)>>2]|0)+(m<<3)>>3]=1.0;m=m+1|0}}h[c[C>>2]>>3]=1.0;m=C+4|0;if(!((nu(P,M,d,c[m>>2]|0)|0)<<24>>24)){n=c[m>>2]|0;m=0;while(1){if((m|0)>=(M|0))break;h[n+(m<<3)>>3]=0.0;m=m+1|0}h[n+8>>3]=1.0;m=0}else m=0}else{n=0;while(1){if((n|0)>=(f|0)){m=0;break c}o=C+(n<<2)|0;m=0;while(1){if((m|0)>=(M|0))break;u=+(x5()|0)*4.656612875245797e-10;h[(c[o>>2]|0)+(m<<3)>>3]=u;m=m+1|0}n=n+1|0}}while(0);while(1){if((m|0)>=(f|0))break;Rt(P,d,M,c[C+(m<<2)>>2]|0,c[e+(m<<2)>>2]|0);m=m+1|0}c[H>>2]=0;c[N>>2]=0;Gt(D,P,d,M,H);Et(P,c[H>>2]|0,M,d,M,N);U1(c[c[H>>2]>>2]|0);U1(c[H>>2]|0);z=KK(G)|0;A=KK(M<<3)|0;m=0;n=0;p=+rn(e,F,f,d,j);d:while(1){if((n|0)<50&m<<24>>24==0)x=0;else break;while(1){if((x|0)>=(f|0))break;y=e+(x<<2)|0;o=0;while(1){if((o|0)>=(d|0))break;q=z+(o<<3)|0;h[q>>3]=0.0;r=(c[F+(o<<4)+8>>2]|0)+-4|0;t=c[D+(o*20|0)+4>>2]|0;v=c[D+(o*20|0)+8>>2]|0;w=D+(o*20|0)|0;s=0.0;k=1;while(1){if((k|0)>=(c[w>>2]|0))break;m=c[t+(k<<2)>>2]|0;u=+ft(e,f,o,m);if(u>1.0e-30){u=-(+g[v+(k<<2)>>2]*+(c[r+(k<<2)>>2]|0))/u;h[q>>3]=u*+h[(c[y>>2]|0)+(m<<3)>>3]+ +h[q>>3];s=s-u}k=k+1|0}h[q>>3]=+h[q>>3]+ +h[(c[y>>2]|0)+(o<<3)>>3]*s;o=o+1|0}Dt(P,M,d,z,A);m=C+(x<<2)|0;if(zr(c[N>>2]|0,c[m>>2]|0,A,M,.001,M,0)|0){n=-1;break d}Rt(P,d,M,c[m>>2]|0,c[y>>2]|0);x=x+1|0}if(!(n&1)){u=+rn(e,F,f,d,j);p=+B(+(u-p))/(u+1.0e-10);m=p<+h[23294]&1;p=u}else m=0;n=n+1|0}U1(A);U1(z);if(I){et(b,d,J);m=0}else m=0;while(1){if((m|0)>=(d|0))break;if(a[F+(m<<4)+12>>0]|0){U1(c[F+(m<<4)+4>>2]|0);U1(c[F+(m<<4)+8>>2]|0)}m=m+1|0}U1(F);U1(c[D+4>>2]|0);U1(c[D+8>>2]|0);U1(D);U1(L);U1(K);U1(c[C>>2]|0);U1(C);m=c[N>>2]|0;if(m|0){U1(c[m>>2]|0);U1(c[N>>2]|0)}U1(c[P>>2]|0);U1(P);vr(Q);l=R;return n|0}function qn(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var h=0.0,i=0.0,j=0,k=0,l=0.0,m=0,n=0,o=0,p=0,q=0,r=0;q=e+-1|0;o=(f|0)==2;h=0.0;f=0;m=0;while(1){if((m|0)>=(q|0))break;p=e-m|0;n=1;while(1){f=f+1|0;if((n|0)>=(p|0))break;k=n+m|0;i=0.0;j=0;while(1){if((j|0)>=(d|0))break;r=c[a+(j<<2)>>2]|0;l=+g[r+(m<<2)>>2]-+g[r+(k<<2)>>2];i=i+l*l;j=j+1|0}i=+C(+i);l=+g[b+(f<<2)>>2];if(o){i=1.0/+C(+l)-i;i=i*i}else{i=1.0/l-i;i=i*i}n=n+1|0;h=i*l+h}m=m+1|0}return +h}function rn(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0.0,i=0.0,j=0,k=0,l=0,m=0,n=0,o=0,p=0.0,q=0;a:do if((f|0)==2){g=0.0;l=0;while(1){if((l|0)>=(e|0))break a;n=c[b+(l<<4)>>2]|0;o=b+(l<<4)+4|0;m=b+(l<<4)+8|0;k=0;while(1){if((k|0)>=(n|0))break;j=c[(c[o>>2]|0)+(k<<2)>>2]|0;if((j|0)>(l|0)){i=0.0;f=0;while(1){if((f|0)>=(d|0))break;q=c[a+(f<<2)>>2]|0;p=+h[q+(l<<3)>>3]-+h[q+(j<<3)>>3];i=p*p+i;f=f+1|0}i=+C(+i);p=+(c[(c[m>>2]|0)+(k<<2)>>2]|0);i=p-i;g=i*i/(p*p)+g}k=k+1|0}l=l+1|0}}else{l=0;g=0.0;while(1){if((l|0)>=(e|0))break a;m=c[b+(l<<4)>>2]|0;n=b+(l<<4)+4|0;o=b+(l<<4)+8|0;k=0;while(1){if((k|0)>=(m|0))break;j=c[(c[n>>2]|0)+(k<<2)>>2]|0;if((j|0)>(l|0)){i=0.0;f=0;while(1){if((f|0)>=(d|0))break;q=c[a+(f<<2)>>2]|0;p=+h[q+(l<<3)>>3]-+h[q+(j<<3)>>3];i=p*p+i;f=f+1|0}i=+C(+i);p=+(c[(c[o>>2]|0)+(k<<2)>>2]|0);i=p-i;g=i*i/p+g}k=k+1|0}l=l+1|0}}while(0);return +g}function sn(a){a=+a;return +(+C(+a)*a)}function tn(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0.0;g=c[47137]|0;e=0.0;f=0;while(1){if((f|0)>=(g|0))break;i=+h[a+(f<<3)>>3]-+h[b+(f<<3)>>3];h[d+(f<<3)>>3]=i;e=i*i+e;f=f+1|0}return +(+C(+e))}function un(a,b,d){a=a|0;b=b|0;d=+d;var e=0,f=0,g=0,i=0;i=JK(a<<2)|0;f=0;g=JK(O(a<<3,b)|0)|0;while(1){if((f|0)>=(a|0))break;c[i+(f<<2)>>2]=g;e=0;while(1){if((e|0)>=(b|0))break;h[g+(e<<3)>>3]=d;e=e+1|0}f=f+1|0;g=g+(b<<3)|0}return i|0}function vn(a){a=a|0;if(a|0){U1(c[a>>2]|0);U1(a)}return}function wn(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;o=l;l=l+16|0;n=tB(b)|0;m=uB(b)|0;k=GA(b,2,92493,0)|0;a:do if(!d){h[23294]=+(n|0)*.0001;pK(b,108886,186352);g=QA(c[b+60>>2]|0,90100)|0;if(!g)e=.99;else e=+r5(g);h[23296]=e;g=JK((n<<2)+4|0)|0;j=b+16|0;c[(c[j>>2]|0)+152>>2]=g;g=0;e=0.0;i=jC(b)|0;while(1){if(!i)break a;c[(c[(c[j>>2]|0)+152>>2]|0)+(g<<2)>>2]=i;p=c[i+16>>2]|0;c[p+120>>2]=g;c[p+124>>2]=-1;f=+zn(b,i,k)+e;g=g+1|0;e=f;i=kC(b,i)|0}}else{h[23294]=.0001;pK(b,108886,186352);g=0;e=0.0;i=jC(b)|0;while(1){if(!i)break a;c[(c[i+16>>2]|0)+120>>2]=g;f=+zn(b,i,k)+e;g=g+1|0;e=f;i=kC(b,i)|0}}while(0);g=QA(b,90108)|0;if((g|0)!=0?(a[g>>0]|0)!=0:0){e=+h[23294];f=+r5(g);if(!(e>f))e=f}else e=e/+(((m|0)>1?m:1)|0)*+C(+(+(n|0)))+1.0;h[23295]=e;if(!(c[47134]|d)){d=un(n,n,e)|0;p=b+16|0;c[(c[p>>2]|0)+160>>2]=d;d=un(n,n,1.0)|0;c[(c[p>>2]|0)+164>>2]=d;d=un(n,c[47137]|0,1.0)|0;c[(c[p>>2]|0)+168>>2]=d;d=An(n,n,c[47137]|0)|0;c[(c[p>>2]|0)+172>>2]=d}l=o;return n|0}function xn(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0;f=0;e=0;j=aB(a,b)|0;a:while(1){if(!j)break;h=c[j>>2]&3;g=c[((h|0)==2?j:j+-48|0)+40>>2]|0;h=c[((h|0)==3?j:j+48|0)+40>>2]|0;do if((g|0)!=(h|0)){i=(h|0)==(b|0);if((e|0)!=1){f=i?g:h;c[d>>2]=f;e=e+1|0;break}if(!((g|0)==(f|0)&i)?!((g|0)==(b|0)&(h|0)==(f|0)):0){e=2;break a}else e=1}while(0);j=bB(a,j,b)|0}return e|0}function yn(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;h=l;l=l+16|0;f=h;g=a+60|0;a:while(1){b:while(1){if(!b)break a;switch(xn(a,b,f)|0){case 0:{e=5;break b}case 1:{e=8;break b}default:b=0}}if((e|0)==5){if((d|0)==(b|0))d=kC(a,d)|0;wC(c[g>>2]|0,b)|0;b=0;continue}else if((e|0)==8){if((d|0)==(b|0))d=kC(a,d)|0;wC(c[g>>2]|0,b)|0;b=c[f>>2]|0;continue}}l=h;return d|0}function zn(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0,j=0,k=0;k=l;l=l+32|0;i=k+8|0;g=k;b=XA(a,b)|0;e=0.0;while(1){if(!b)break;switch(Bn(b,d,g)|0){case 0:{f=+h[g>>3];break}case 2:{c[i>>2]=KB(a)|0;h[i+8>>3]=1.0;gA(3,90120,i)|0;j=6;break}default:j=6}if((j|0)==6){j=0;h[g>>3]=1.0;f=1.0}h[(c[b+16>>2]|0)+136>>3]=f;b=ZA(a,b)|0;e=f+e}l=k;return +e}function An(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,l=0;j=JK((a<<2)+4|0)|0;k=(b<<2)+4|0;l=d<<3;g=0;while(1){if((g|0)>=(a|0))break;i=j+(g<<2)|0;c[i>>2]=JK(k)|0;f=0;while(1){if((f|0)>=(b|0))break;e=JK(l)|0;c[(c[i>>2]|0)+(f<<2)>>2]=e;e=0;while(1){if((e|0)>=(d|0))break;h[(c[(c[i>>2]|0)+(f<<2)>>2]|0)+(e<<3)>>3]=0.0;e=e+1|0}f=f+1|0}c[(c[i>>2]|0)+(f<<2)>>2]=0;g=g+1|0}c[j+(g<<2)>>2]=0;return j|0}function Bn(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0,j=0,k=0;k=l;l=l+16|0;j=k+8|0;i=k;do if((d|0)!=0?(f=RA(b,d)|0,(a[f>>0]|0)!=0):0){c[i>>2]=e;if(((b5(f,101209,i)|0)>=1?(g=+h[e>>3],!(g<0.0)):0)?g!=0.0|(c[47134]|0)!=0:0){b=0;break}c[j>>2]=f;gA(0,90147,j)|0;b=2}else b=1;while(0);l=k;return b|0}function Cn(a){a=a|0;return wn(a,0)|0}function Dn(a){a=a|0;a=a+16|0;U1(c[(c[a>>2]|0)+152>>2]|0);if(!(c[47134]|0)){vn(c[(c[a>>2]|0)+160>>2]|0);vn(c[(c[a>>2]|0)+164>>2]|0);vn(c[(c[a>>2]|0)+168>>2]|0);En(c[(c[a>>2]|0)+172>>2]|0);c[(c[a>>2]|0)+172>>2]=0}return}function En(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;if(a|0){f=0;while(1){g=a+(f<<2)|0;b=c[g>>2]|0;if(!b)break;else e=0;while(1){d=c[b+(e<<2)>>2]|0;if(!d)break;U1(d);e=e+1|0;b=c[g>>2]|0}U1(b);f=f+1|0}U1(a)}return}function Fn(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0;e=+(b|0);b=a+16|0;while(1){if((d|0)>=(c[47137]|0))break;f=+B5()*e;h[(c[(c[b>>2]|0)+132>>2]|0)+(d<<3)>>3]=f;d=d+1|0}return}function Gn(a,b){a=a|0;b=b|0;Fn(a,b,2);return}function Hn(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0;e=+(b|0);f=+B5()*e;d=a+16|0;h[c[(c[d>>2]|0)+132>>2]>>3]=f;e=+B5()*e;h[(c[(c[d>>2]|0)+132>>2]|0)+8>>3]=e;if((c[47137]|0)>2)Gn(a,b);return}function In(b,d){b=b|0;d=d|0;var e=0,f=0;f=l;l=l+16|0;e=f;if(0)U4(90165,26,1,c[15712]|0)|0;d=nm(b,d,2)|0;a:do if((d|0)!=1){if(!(c[46797]|d)){gA(0,90192,e)|0;c[46797]=1}e=b+16|0;d=0;while(1){b=c[(c[(c[e>>2]|0)+152>>2]|0)+(d<<2)>>2]|0;if(!b)break a;if(!(a[(c[b+16>>2]|0)+119>>0]|0))Hn(b,1);d=d+1|0}}while(0);l=f;return}function Jn(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0;q=l;l=l+96|0;p=q+80|0;n=q;if(0){U4(90241,25,1,c[15712]|0)|0;TO()}o=a+16|0;f=c[o>>2]|0;k=c[f+164>>2]|0;f=c[f+160>>2]|0;m=0;while(1){if((m|0)>=(b|0))break;i=f+(m<<2)|0;j=k+(m<<2)|0;e=0;while(1){if((e|0)==(m|0))break;g=+h[(c[i>>2]|0)+(e<<3)>>3];g=1.0/(g*g);d=c[(c[o>>2]|0)+152>>2]|0;d=iB(a,c[d+(m<<2)>>2]|0,c[d+(e<<2)>>2]|0,0,0)|0;if(d)g=+h[(c[d+16>>2]|0)+128>>3]*g;h[(c[k+(e<<2)>>2]|0)+(m<<3)>>3]=g;h[(c[j>>2]|0)+(e<<3)>>3]=g;e=e+1|0}m=m+1|0}a=c[47137]|0;e=0;while(1){if((e|0)<(b|0))d=0;else break;while(1){if((d|0)>=(a|0))break;h[(c[(c[(c[o>>2]|0)+168>>2]|0)+(e<<2)>>2]|0)+(d<<3)>>3]=0.0;d=d+1|0}e=e+1|0}i=0;while(1){d=c[(c[(c[o>>2]|0)+152>>2]|0)+(i<<2)>>2]|0;if(!d)break;a=d+16|0;d=0;while(1){if((d|0)>=(b|0))break;a:do if((i|0)!=(d|0)){g=+tn(c[(c[a>>2]|0)+132>>2]|0,c[(c[(c[(c[(c[o>>2]|0)+152>>2]|0)+(d<<2)>>2]|0)+16>>2]|0)+132>>2]|0,n);f=c[47137]|0;g=1.0/g;e=0;while(1){if((e|0)>=(f|0))break a;m=c[o>>2]|0;r=+h[n+(e<<3)>>3];r=(r-+h[(c[(c[m+160>>2]|0)+(i<<2)>>2]|0)+(d<<3)>>3]*r*g)*+h[(c[(c[m+164>>2]|0)+(i<<2)>>2]|0)+(d<<3)>>3];h[(c[(c[(c[m+172>>2]|0)+(i<<2)>>2]|0)+(d<<2)>>2]|0)+(e<<3)>>3]=r;m=(c[(c[m+168>>2]|0)+(i<<2)>>2]|0)+(e<<3)|0;h[m>>3]=+h[m>>3]+r;e=e+1|0}}while(0);d=d+1|0}i=i+1|0}if(0){o=c[15712]|0;h[p>>3]=+UO();w4(o,90267,p)|0}l=q;return}function Kn(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0.0,k=0;i=l;l=l+32|0;g=i+24|0;f=i+8|0;e=i;j=+h[23294];h[23216]=j*j;while(1){d=Ln(a,b)|0;if(!d)break;Mn(a,b,d)}if(!0)b=a+16|0;else{d=c[15712]|0;h[e>>3]=+Nn(a,b);w4(d,90277,e)|0;b=a+16|0;k=c[(c[b>>2]|0)+156>>2]|0;e=(k|0)==(c[47136]|0)?90291:195059;j=+UO();c[f>>2]=k;c[f+4>>2]=e;h[f+8>>3]=j;w4(d,90293,f)|0}b=c[(c[b>>2]|0)+156>>2]|0;if((b|0)==(c[47136]|0)){k=KB(a)|0;c[g>>2]=b;c[g+4>>2]=k;gA(0,90320,g)|0}l=i;return}function Ln(a,b){a=a|0;b=b|0;var e=0.0,f=0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0;r=l;l=l+16|0;q=r;o=(c[46799]|0)+1|0;c[46799]=o;a=c[a+16>>2]|0;if((c[a+156>>2]|0)<(c[47136]|0)){m=c[47137]|0;n=a+152|0;k=a+168|0;a=0;e=0.0;j=0;while(1){if((j|0)>=(b|0))break;g=c[(c[n>>2]|0)+(j<<2)>>2]|0;if((d[(c[g+16>>2]|0)+119>>0]|0)<=1){i=0.0;f=0;while(1){if((f|0)>=(m|0))break;s=+h[(c[(c[k>>2]|0)+(j<<2)>>2]|0)+(f<<3)>>3];i=s*s+i;f=f+1|0}if(i>e){a=g;e=i}}j=j+1|0}if(!(e<+h[23216])){if(0!=0&((o|0)%100|0|0)==0?(p=c[15712]|0,h[q>>3]=+C(+e),w4(p,90371,q)|0,((c[46799]|0)%1e3|0|0)==0):0)E6(10,p)|0}else a=0}else a=0;l=r;return a|0}function Mn(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;n=o;k=d+16|0;m=c[(c[k>>2]|0)+120>>2]|0;f=c[46798]|0;if(!f){g=c[47137]|0;g=KK(O(g<<3,g)|0)|0}else{g=c[47137]|0;g=MK(f,O(g<<3,g)|0)|0}c[46798]=g;On(a,b,m,g);i=c[47137]|0;j=a+16|0;f=0;while(1){if((f|0)>=(i|0))break;h[185736+(f<<3)>>3]=-+h[(c[(c[(c[j>>2]|0)+168>>2]|0)+(m<<2)>>2]|0)+(f<<3)>>3];f=f+1|0}Xu(g,185816,185736,i);f=0;while(1){if((f|0)>=(c[47137]|0))break;e=+h[23296];e=(1.0-e)*2.0*+B5()+e;i=185816+(f<<3)|0;e=e*+h[i>>3];h[i>>3]=e;i=(c[(c[k>>2]|0)+132>>2]|0)+(f<<3)|0;h[i>>3]=+h[i>>3]+e;f=f+1|0}k=(c[j>>2]|0)+156|0;c[k>>2]=(c[k>>2]|0)+1;Pn(a,b,m);if(rP()|0){g=c[47137]|0;e=0.0;f=0;while(1){if((f|0)>=(g|0))break;e=+B(+(+h[185816+(f<<3)>>3]))+e;f=f+1|0}e=+C(+e);m=c[15712]|0;c[n>>2]=KB(d)|0;h[n+8>>3]=e;w4(m,90362,n)|0}l=o;return}function Nn(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0.0;q=b+-1|0;n=a+16|0;o=c[47137]|0;g=0;d=0.0;a:while(1){if((g|0)>=(q|0))break;m=c[n>>2]|0;p=c[m+152>>2]|0;j=g+1|0;k=(c[p+(g<<2)>>2]|0)+16|0;l=m+164|0;m=m+160|0;i=j;while(1){if((i|0)>=(b|0)){g=j;continue a}f=(c[p+(i<<2)>>2]|0)+16|0;a=0;e=0.0;while(1){if((a|0)>=(o|0))break;r=+h[(c[(c[k>>2]|0)+132>>2]|0)+(a<<3)>>3]-+h[(c[(c[f>>2]|0)+132>>2]|0)+(a<<3)>>3];a=a+1|0;e=r*r+e}r=+h[(c[(c[m>>2]|0)+(g<<2)>>2]|0)+(i<<3)>>3];r=((+C(+e)*-2.0+r)*r+e)*+h[(c[(c[l>>2]|0)+(g<<2)>>2]|0)+(i<<3)>>3]+d;i=i+1|0;d=r}}return +d}function On(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0.0,o=0.0,p=0.0,q=0.0,r=0.0,s=0,t=0,u=0,v=0,w=0,x=0;x=l;l=l+80|0;w=x;v=c[a+16>>2]|0;k=c[v+164>>2]|0;m=c[v+160>>2]|0;v=c[v+152>>2]|0;j=c[v+(d<<2)>>2]|0;g=c[47137]|0;a=0;while(1){if((a|0)>=(g|0))break;i=O(g,a)|0;f=0;while(1){if((f|0)==(g|0))break;h[e+(i+f<<3)>>3]=0.0;f=f+1|0}a=a+1|0}u=c[47137]|0;t=j+16|0;s=k+(d<<2)|0;k=m+(d<<2)|0;j=0;while(1){if((j|0)>=(b|0))break;a:do if((j|0)!=(d|0)){f=(c[v+(j<<2)>>2]|0)+16|0;r=0.0;a=0;while(1){if((a|0)>=(u|0))break;q=+h[(c[(c[t>>2]|0)+132>>2]|0)+(a<<3)>>3]-+h[(c[(c[f>>2]|0)+132>>2]|0)+(a<<3)>>3];h[w+(a<<3)>>3]=q;r=q*q+r;a=a+1|0}n=1.0/+sn(r);i=0;while(1){if((i|0)>=(u|0))break a;f=(c[s>>2]|0)+(j<<3)|0;g=(c[k>>2]|0)+(j<<3)|0;o=+h[w+(i<<3)>>3];a=0;while(1){p=+h[f>>3];q=+h[g>>3];if((a|0)==(i|0))break;m=e+((O(u,a)|0)+i<<3)|0;h[m>>3]=+h[m>>3]+p*n*q*o*+h[w+(a<<3)>>3];a=a+1|0}m=e+((O(u,i)|0)+i<<3)|0;h[m>>3]=(1.0-q*n*(r-o*o))*p+ +h[m>>3];i=i+1|0}}while(0);j=j+1|0}f=c[47137]|0;i=1;while(1){if((i|0)>=(f|0))break;g=O(f,i)|0;a=0;while(1){if((a|0)==(i|0))break;h[e+(g+a<<3)>>3]=+h[e+((O(f,a)|0)+i<<3)>>3];a=a+1|0}i=i+1|0}l=x;return}function Pn(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0.0,p=0.0,q=0,r=0,s=0;m=l;l=l+80|0;k=m;j=a+16|0;f=c[j>>2]|0;g=c[(c[f+152>>2]|0)+(d<<2)>>2]|0;e=c[47137]|0;f=f+168|0;a=0;while(1){if((a|0)>=(e|0))break;h[(c[(c[f>>2]|0)+(d<<2)>>2]|0)+(a<<3)>>3]=0.0;a=a+1|0}f=g+16|0;a=0;while(1){if((a|0)>=(b|0))break;a:do if((a|0)!=(d|0)){i=+tn(c[(c[f>>2]|0)+132>>2]|0,c[(c[(c[(c[(c[j>>2]|0)+152>>2]|0)+(a<<2)>>2]|0)+16>>2]|0)+132>>2]|0,k);g=c[47137]|0;i=1.0/i;e=0;while(1){if((e|0)>=(g|0))break a;n=c[j>>2]|0;q=c[n+172>>2]|0;r=(c[(c[q+(d<<2)>>2]|0)+(a<<2)>>2]|0)+(e<<3)|0;o=+h[k+(e<<3)>>3];o=(o-+h[(c[(c[n+160>>2]|0)+(d<<2)>>2]|0)+(a<<3)>>3]*o*i)*+h[(c[(c[n+164>>2]|0)+(d<<2)>>2]|0)+(a<<3)>>3];h[r>>3]=o;n=c[n+168>>2]|0;s=(c[n+(d<<2)>>2]|0)+(e<<3)|0;h[s>>3]=+h[s>>3]+o;q=(c[(c[q+(a<<2)>>2]|0)+(d<<2)>>2]|0)+(e<<3)|0;o=+h[q>>3];p=-+h[r>>3];h[q>>3]=p;n=(c[n+(a<<2)>>2]|0)+(e<<3)|0;h[n>>3]=p-o+ +h[n>>3];e=e+1|0}}while(0);a=a+1|0}l=m;return}function Qn(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0;d=a+16|0;g=c[46800]|0;b=c[(c[d>>2]|0)+124>>2]|0;while(1){if((b|0)<=0)break;i=(b+-1|0)/2|0;j=g+(i<<2)|0;e=c[j>>2]|0;f=e+16|0;if(+h[(c[f>>2]|0)+136>>3]<=+h[(c[d>>2]|0)+136>>3])break;c[j>>2]=a;c[(c[d>>2]|0)+124>>2]=i;c[g+(b<<2)>>2]=e;c[(c[f>>2]|0)+124>>2]=b;b=i}return}function Rn(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0;i=a+16|0;j=c[46801]|0;k=c[46800]|0;g=c[(c[i>>2]|0)+124>>2]|0;while(1){e=g<<1|1;if((e|0)>=(j|0))break;d=e+1|0;if((d|0)<(j|0)){f=c[k+(d<<2)>>2]|0;b=c[k+(e<<2)>>2]|0;if(+h[(c[f+16>>2]|0)+136>>3]<+h[(c[b+16>>2]|0)+136>>3]){e=d;b=f}else l=6}else{b=c[k+(e<<2)>>2]|0;l=6}if((l|0)==6)l=0;d=b+16|0;if(+h[(c[i>>2]|0)+136>>3]<=+h[(c[d>>2]|0)+136>>3])break;c[k+(e<<2)>>2]=a;c[(c[i>>2]|0)+124>>2]=e;c[k+(g<<2)>>2]=b;c[(c[d>>2]|0)+124>>2]=g;g=e}return}function Sn(a){a=a|0;var b=0,d=0;b=(c[a+16>>2]|0)+124|0;if((c[b>>2]|0)>=0)qa(90377,90397,643,90405);d=c[46801]|0;c[46801]=d+1;c[b>>2]=d;c[(c[46800]|0)+(d<<2)>>2]=a;if((d|0)>0)Qn(a);return}function Tn(){var a=0,b=0,d=0,e=0;b=c[46801]|0;if(!b)a=0;else{e=c[46800]|0;a=c[e>>2]|0;d=b+-1|0;c[46801]=d;d=c[e+(d<<2)>>2]|0;c[e>>2]=d;c[(c[d+16>>2]|0)+124>>2]=0;if((b|0)>2)Rn(d);c[(c[a+16>>2]|0)+124>>2]=-1}return a|0}function Un(a,b){a=a|0;b=b|0;var d=0,e=0;e=l;l=l+16|0;d=e;c[46800]=JK((b<<2)+4|0)|0;if(0){U4(90419,28,1,c[15712]|0)|0;TO()}b=jC(a)|0;while(1){if(!b)break;Vn(a,b);b=kC(a,b)|0}if(0){a=c[15712]|0;h[d>>3]=+UO();w4(a,90267,d)|0}U1(c[46800]|0);l=e;return}function Vn(a,b){a=a|0;b=b|0;var d=0,e=0,f=0.0,g=0,i=0,j=0,k=0;e=c[(c[a+16>>2]|0)+152>>2]|0;f=+h[23295];d=0;while(1){g=c[e+(d<<2)>>2]|0;if(!g)break;h[(c[g+16>>2]|0)+136>>3]=f;d=d+1|0}c[46802]=b;k=c[b+16>>2]|0;h[k+136>>3]=0.0;c[k+128>>2]=0;Sn(b);a:while(1){k=Tn()|0;if(!k)break;d=c[46802]|0;j=k+16|0;if((k|0)!=(d|0))Wn(a,d,k,+h[(c[j>>2]|0)+136>>3]);i=aB(a,k)|0;while(1){if(!i)continue a;d=c[i>>2]&3;b=c[((d|0)==3?i:i+48|0)+40>>2]|0;if((b|0)==(k|0))b=c[((d|0)==2?i:i+-48|0)+40>>2]|0;d=c[j>>2]|0;f=+h[(c[i+16>>2]|0)+136>>3]+ +h[d+136>>3];e=c[b+16>>2]|0;g=e+136|0;do if(+h[g>>3]>f){h[g>>3]=f;if((c[e+124>>2]|0)>-1){Qn(b);break}else{c[e+128>>2]=(c[d+128>>2]|0)+1;Sn(b);break}}while(0);i=bB(a,i,k)|0}}return}function Wn(a,b,d,e){a=a|0;b=b|0;d=d|0;e=+e;b=c[(c[b+16>>2]|0)+120>>2]|0;d=c[(c[d+16>>2]|0)+120>>2]|0;a=c[(c[a+16>>2]|0)+160>>2]|0;h[(c[a+(d<<2)>>2]|0)+(b<<3)>>3]=e;h[(c[a+(b<<2)>>2]|0)+(d<<3)>>3]=e;return}function Xn(a){a=a|0;oQ(a,2);b[(c[a+16>>2]|0)+176>>1]=2;c[47137]=2;Yn(a);return}function Yn(a){a=a|0;var b=0,d=0,e=0,f=0;b=tB(a)|0;e=JK(b*56|0)|0;b=JK((b<<2)+4|0)|0;f=a+16|0;c[(c[f>>2]|0)+152>>2]=b;b=jC(a)|0;d=0;while(1){if(!b)break;Zl(b);c[(c[b+16>>2]|0)+112>>2]=e+(d*56|0);c[(c[(c[f>>2]|0)+152>>2]|0)+(d<<2)>>2]=b;b=kC(a,b)|0;d=d+1|0}d=jC(a)|0;while(1){if(!d)break;b=XA(a,d)|0;while(1){if(!b)break;Zn(b);b=ZA(a,b)|0}d=kC(a,d)|0}return}function Zn(a){a=a|0;var b=0.0;TC(a,137460,176,1)|0;tP(a)|0;b=+_O(a,c[47174]|0,1.0,0.0);h[(c[a+16>>2]|0)+128>>3]=b;return}function _n(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+80|0;m=u+32|0;i=u+24|0;g=u+16|0;j=u;t=u+72|0;p=u+40|0;if(tB(b)|0){Xn(b);f=QA(b,95738)|0;if(f)if(a[f>>0]|0){d=tC(b,f,0)|0;if(!d){c[g>>2]=f;gA(0,90448,g)|0;gA(3,90488,i)|0;d=0;s=1}else s=0}else{d=0;s=1}else{d=0;s=0}n=GA(b,1,95738,0)|0;o=(n|0)!=0;f=QA(b,140742)|0;if((f|0?a[f>>0]|0:0)?(k=j+8|0,c[m>>2]=j,c[m+4>>2]=k,(b5(f,105503,m)|0)==1):0)h[k>>3]=+h[j>>3];if(tB(b)|0){m=DG(b,t,0)|0;if((c[t>>2]|0)==1){g=(d|0)!=0;do if(g)e=d;else{if(o?(e=$n(b,n)|0,e|0):0)break;e=0}while(0);f=Zu(b,e)|0;if(!((e|0)!=0|o^1))KA(f,n,142463)|0;r=(jC(b)|0)+16|0;U1(c[(c[r>>2]|0)+112>>2]|0);c[(c[r>>2]|0)+112>>2]=0;or(b)|0;fn(b);d=(s|0)==0|g?d:f}else{kH(b,2,8,p)|0;c[p+12>>2]=0;k=(s|0)==0;j=0;while(1){if((j|0)>=(c[t>>2]|0))break;g=c[m+(j<<2)>>2]|0;e=(d|0)!=0;if(e?(HC(g,d)|0)!=0:0)f=d;else r=23;do if((r|0)==23){r=0;if(o?(q=$n(g,n)|0,q|0):0){f=q;break}f=0}while(0);KG(g)|0;i=Zu(g,f)|0;d=k|e?d:i;if(o?(f|0)==0|(f|0)==(d|0):0)KA(i,n,142463)|0;or(g)|0;j=j+1|0}r=(jC(b)|0)+16|0;U1(c[(c[r>>2]|0)+112>>2]|0);c[(c[r>>2]|0)+112>>2]=0;jH(c[t>>2]|0,m,b,p)|0;fn(b)}e=0;while(1){if((e|0)>=(c[t>>2]|0))break;wC(b,c[m+(e<<2)>>2]|0)|0;e=e+1|0}U1(m)}if(s|0)SA(b,95738,KB(d)|0)|0;tM(b)}l=u;return}function $n(a,b){a=a|0;b=b|0;var c=0;c=jC(a)|0;while(1){if(!c){c=0;break}if((dP(RA(c,b)|0)|0)<<24>>24)break;c=kC(a,c)|0}return c|0}function ao(a){a=a|0;var b=0,c=0;b=jC(a)|0;if(b|0){while(1){if(!b)break;c=XA(a,b)|0;while(1){if(!c)break;YP(c);c=ZA(a,c)|0}WP(b);b=kC(a,b)|0}bo(a)}return}function bo(a){a=a|0;U1(c[(c[a+16>>2]|0)+152>>2]|0);if((yC(a)|0)!=(a|0))ZC(a,0,137483);return}function co(a){a=a|0;eo(a);if(!((tB(a)|0)==0?!(c[(c[a+16>>2]|0)+180>>2]|0):0)){nv(a);tM(a)}return}function eo(a){a=a|0;c[47147]=GA(a,1,108230,108236)|0;oQ(a,2);b[(c[a+16>>2]|0)+176>>1]=2;c[47137]=2;fo(a,0);go(a);return}function fo(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;g=i;h=(b|0)==0;if(h){jo(g);b=g}d=OD(a)|0;while(1){if(!d)break;if(!(Q2(KB(d)|0,108006,7)|0)){TC(d,137483,280,1)|0;ko(b,d);fo(d,0)}else fo(d,b);d=PD(d)|0}if(h?(e=c[g+8>>2]|0,f=a+16|0,c[(c[f>>2]|0)+180>>2]=e,e|0):0){h=MK(c[g>>2]|0,(e<<2)+4|0)|0;c[(c[f>>2]|0)+184>>2]=h}l=i;return}function go(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;f=JK((tB(a)|0)<<2)|0;b=JK(((tB(a)|0)<<2)+4|0)|0;g=a+16|0;c[(c[g>>2]|0)+152>>2]=b;b=jC(a)|0;d=0;while(1){if(!b)break;TC(b,137447,304,1)|0;c[(c[b+16>>2]|0)+112>>2]=f+(d<<2);c[(c[(c[g>>2]|0)+152>>2]|0)+(d<<2)>>2]=b;ho(b);e=XA(a,b)|0;while(1){if(!e)break;io(e);e=ZA(a,e)|0}b=kC(a,b)|0;d=d+1|0}return}function ho(a){a=a|0;SA(a,108230,108236)|0;return}function io(a){a=a|0;TC(a,137460,304,1)|0;return}function jo(a){a=a|0;c[a>>2]=0;c[a+4>>2]=0;c[a+8>>2]=0;return}function ko(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;g=a+8|0;d=(c[g>>2]|0)+1|0;c[g>>2]=d;e=a+4|0;h=c[e>>2]|0;f=h+10|0;if((d|0)<(h|0))a=c[a>>2]|0;else{c[e>>2]=f;d=MK(c[a>>2]|0,f<<2)|0;c[a>>2]=d;a=d;d=c[g>>2]|0}c[a+(d<<2)>>2]=b;return}function lo(a){a=a|0;var b=0,d=0;b=jC(a)|0;if(b|0){U1(c[(c[b+16>>2]|0)+112>>2]|0);while(1){if(!b)break;d=XA(a,b)|0;while(1){if(!d)break;YP(d);d=ZA(a,d)|0}WP(b);b=kC(a,b)|0}mo(a)}return}function mo(a){a=a|0;U1(c[(c[a+16>>2]|0)+152>>2]|0);if((yC(a)|0)!=(a|0))ZC(a,0,137483);return}function no(a){a=a|0;var d=0,e=0,f=0;oo(a);po(a,0);qo(a,0);ro(a,0);d=c[a+16>>2]|0;if(!(c[(c[d+8>>2]|0)+84>>2]|0)){d=b[d+136>>1]&14;if(d<<16>>16)Wm(a,d&65535)|0}else{d=jC(a)|0;while(1){if(!d)break;f=c[d+16>>2]|0;e=c[f+132>>2]|0;h[e>>3]=+h[f+16>>3]*.013888888888888888;h[e+8>>3]=+h[f+24>>3]*.013888888888888888;d=kC(a,d)|0}Zm(a,1)}tM(a);return}function oo(a){a=a|0;var d=0,e=0;oQ(a,2);b[(c[a+16>>2]|0)+176>>1]=2;c[47137]=2;d=jC(a)|0;while(1){if(!d)break;Zl(d);d=kC(a,d)|0}e=jC(a)|0;while(1){if(!e)break;d=XA(a,e)|0;while(1){if(!d)break;TC(d,137460,176,1)|0;tP(d)|0;d=ZA(a,d)|0}e=kC(a,e)|0}return}function po(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;g=i;h=(b|0)==0;if(h){wo(g);b=g}d=OD(a)|0;while(1){if(!d)break;if(!(Q2(KB(d)|0,108006,7)|0)){TC(d,137483,280,1)|0;uK(d);xo(b,d);po(d,0)}else po(d,b);d=PD(d)|0}if(h?(e=c[g+8>>2]|0,f=a+16|0,c[(c[f>>2]|0)+180>>2]=e,e|0):0){h=MK(c[g>>2]|0,(e<<2)+4|0)|0;c[(c[f>>2]|0)+184>>2]=h}l=i;return}function qo(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0.0,j=0.0,k=0.0,m=0.0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0;Q=l;l=l+448|0;G=Q+400|0;F=Q+384|0;P=Q+344|0;O=Q+304|0;N=Q+248|0;D=Q+192|0;x=Q+152|0;y=Q+96|0;o=Q+72|0;d=Q+64|0;I=Q+32|0;J=Q;B=Q+416|0;u=Q+328|0;w=Q+288|0;K=Q+232|0;L=Q+176|0;M=Q+136|0;H=Q+80|0;n=c[a+60>>2]|0;if(0>1){so(b);E=c[15712]|0;c[d>>2]=KB(a)|0;w4(E,90723,d)|0}E=a+16|0;f=b+1|0;d=1;e=0;while(1){g=c[E>>2]|0;if((d|0)>(c[g+180>>2]|0))break;C=c[(c[g+184>>2]|0)+(d<<2)>>2]|0;qo(C,f);d=d+1|0;e=(tB(C)|0)+e|0}p=(tB(a)|0)-e|0;d=c[E>>2]|0;C=(c[d+180>>2]|0)+p|0;v=(C|0)==0;if(v?(c[d+12>>2]|0)==0:0){P=d+16|0;c[P>>2]=0;c[P+4>>2]=0;c[P+8>>2]=0;c[P+12>>2]=0;h[d+40>>3]=18.0;h[d+32>>3]=18.0}else{d=B+16|0;do if((kH(a,4,4,B)|0)>>>0>=3)if((c[d>>2]|0)==4?(c[B+28>>2]&2|0)!=0:0){e=GA(n,0,99190,0)|0;d=GA(n,1,99190,0)|0;if((e|0)!=0|(d|0)!=0){c[B+24>>2]=JK(C<<2)|0;break}else{c[o>>2]=KB(a)|0;gA(0,90577,o)|0;d=0;e=0;break}}else{d=0;e=0}else{c[d>>2]=3;d=0;e=0}while(0);z=JK(C<<5)|0;A=JK(C<<2)|0;s=B+24|0;n=(e|0)!=0;r=0;g=1;while(1){f=c[E>>2]|0;if((g|0)>(c[f+180>>2]|0))break;f=c[(c[f+184>>2]|0)+(g<<2)>>2]|0;t=z+(r<<5)|0;q=(c[f+16>>2]|0)+16|0;c[t>>2]=c[q>>2];c[t+4>>2]=c[q+4>>2];c[t+8>>2]=c[q+8>>2];c[t+12>>2]=c[q+12>>2];c[t+16>>2]=c[q+16>>2];c[t+20>>2]=c[q+20>>2];c[t+24>>2]=c[q+24>>2];c[t+28>>2]=c[q+28>>2];if(n&(c[s>>2]|0)!=0){t=ZO(f,e,0,0)|0;c[(c[s>>2]|0)+(r<<2)>>2]=t}c[A+(r<<2)>>2]=f;r=r+1|0;g=g+1|0}a:do if((p|0)>0){o=I+16|0;p=I+24|0;q=(d|0)!=0;n=jC(a)|0;e=r;while(1){if(!n)break a;f=c[n+16>>2]|0;g=f+112|0;if(!(c[g>>2]|0)){c[g>>2]=a;c[I>>2]=0;c[I+4>>2]=0;c[I+8>>2]=0;c[I+12>>2]=0;h[o>>3]=+h[f+96>>3]+ +h[f+88>>3];h[p>>3]=+h[f+80>>3];t=z+(e<<5)|0;c[t>>2]=c[I>>2];c[t+4>>2]=c[I+4>>2];c[t+8>>2]=c[I+8>>2];c[t+12>>2]=c[I+12>>2];c[t+16>>2]=c[I+16>>2];c[t+20>>2]=c[I+20>>2];c[t+24>>2]=c[I+24>>2];c[t+28>>2]=c[I+28>>2];if(q&(c[s>>2]|0)!=0){t=ZO(n,d,0,0)|0;c[(c[s>>2]|0)+(e<<2)>>2]=t}c[A+(e<<2)>>2]=n;e=e+1|0}n=kC(a,n)|0}}while(0);t=dH(C,z,B)|0;d=c[s>>2]|0;if(d|0)U1(d);to(J,2147483647.0,2147483647.0);s=J+16|0;to(u,-2147483647.0,-2147483647.0);c[s>>2]=c[u>>2];c[s+4>>2]=c[u+4>>2];c[s+8>>2]=c[u+8>>2];c[s+12>>2]=c[u+12>>2];g=I+16|0;n=I+8|0;o=I+24|0;p=J+8|0;q=J+24|0;r=c[15712]|0;d=0;while(1){if((d|0)>=(C|0))break;j=+(c[t+(d<<3)>>2]|0);m=+(c[t+(d<<3)+4>>2]|0);e=z+(d<<5)|0;c[I>>2]=c[e>>2];c[I+4>>2]=c[e+4>>2];c[I+8>>2]=c[e+8>>2];c[I+12>>2]=c[e+12>>2];c[I+16>>2]=c[e+16>>2];c[I+20>>2]=c[e+20>>2];c[I+24>>2]=c[e+24>>2];c[I+28>>2]=c[e+28>>2];i=+h[I>>3]+j;h[I>>3]=i;j=+h[g>>3]+j;h[g>>3]=j;k=+h[n>>3]+m;h[n>>3]=k;m=+h[o>>3]+m;h[o>>3]=m;h[J>>3]=+h[(+h[J>>3]<i?J:I)>>3];h[p>>3]=+h[(+h[p>>3]<k?J:I)+8>>3];h[s>>3]=+h[(+h[s>>3]>j?J:I)+16>>3];h[q>>3]=+h[(+h[q>>3]>m?J:I)+24>>3];e=c[A+(d<<2)>>2]|0;f=e+16|0;if((d|0)<(c[(c[E>>2]|0)+180>>2]|0)){u=(c[f>>2]|0)+16|0;c[u>>2]=c[I>>2];c[u+4>>2]=c[I+4>>2];c[u+8>>2]=c[I+8>>2];c[u+12>>2]=c[I+12>>2];c[u+16>>2]=c[I+16>>2];c[u+20>>2]=c[I+20>>2];c[u+24>>2]=c[I+24>>2];c[u+28>>2]=c[I+28>>2];if(0>1){so(b);c[y>>2]=KB(e)|0;h[y+8>>3]=i;h[y+16>>3]=k;h[y+24>>3]=j;h[y+32>>3]=m;w4(r,90556,y)|0}}else{u=(c[f>>2]|0)+16|0;c[F>>2]=c[I>>2];c[F+4>>2]=c[I+4>>2];c[F+8>>2]=c[I+8>>2];c[F+12>>2]=c[I+12>>2];c[G>>2]=c[g>>2];c[G+4>>2]=c[g+4>>2];c[G+8>>2]=c[g+8>>2];c[G+12>>2]=c[g+12>>2];uo(w,F,G);c[u>>2]=c[w>>2];c[u+4>>2]=c[w+4>>2];c[u+8>>2]=c[w+8>>2];c[u+12>>2]=c[w+12>>2];if(0>1){so(b);u=KB(e)|0;f=c[f>>2]|0;k=+h[f+16>>3];m=+h[f+24>>3];c[x>>2]=u;h[x+8>>3]=k;h[x+16>>3]=m;w4(r,90544,x)|0}}d=d+1|0}e=c[E>>2]|0;d=c[e+12>>2]|0;if(d|0){i=+h[d+24>>3];if(v){j=+h[d+32>>3];c[J>>2]=0;c[J+4>>2]=0;c[J+8>>2]=0;c[J+12>>2]=0;h[s>>3]=i;h[q>>3]=j;j=0.0;k=i}else{j=+h[J>>3];k=+h[s>>3]}m=i-(k-j);i=m*.5;if(m>0.0){h[J>>3]=j-i;h[s>>3]=k+i}}if((b|0)>0)i=+((c[B+8>>2]|0)>>>0)*.5;else i=0.0;j=+h[J>>3]-i;h[J>>3]=j;k=+h[s>>3]+i;h[s>>3]=k;m=-i-+h[e+56>>3]+ +h[p>>3];h[p>>3]=m;i=+h[e+88>>3]+i+ +h[q>>3];h[q>>3]=i;if(0>1){so(b);c[D>>2]=KB(a)|0;h[D+8>>3]=j;h[D+16>>3]=m;h[D+24>>3]=k;h[D+32>>3]=i;w4(r,90556,D)|0}d=0;while(1){if((d|0)>=(C|0))break;e=c[A+(d<<2)>>2]|0;f=e+16|0;if((d|0)<(c[(c[E>>2]|0)+180>>2]|0)){D=(c[f>>2]|0)+16|0;c[I>>2]=c[D>>2];c[I+4>>2]=c[D+4>>2];c[I+8>>2]=c[D+8>>2];c[I+12>>2]=c[D+12>>2];c[I+16>>2]=c[D+16>>2];c[I+20>>2]=c[D+20>>2];c[I+24>>2]=c[D+24>>2];c[I+28>>2]=c[D+28>>2];c[F>>2]=c[D>>2];c[F+4>>2]=c[D+4>>2];c[F+8>>2]=c[D+8>>2];c[F+12>>2]=c[D+12>>2];c[G>>2]=c[J>>2];c[G+4>>2]=c[J+4>>2];c[G+8>>2]=c[J+8>>2];c[G+12>>2]=c[J+12>>2];vo(I,F,G);c[F>>2]=c[g>>2];c[F+4>>2]=c[g+4>>2];c[F+8>>2]=c[g+8>>2];c[F+12>>2]=c[g+12>>2];c[G>>2]=c[J>>2];c[G+4>>2]=c[J+4>>2];c[G+8>>2]=c[J+8>>2];c[G+12>>2]=c[J+12>>2];vo(K,F,G);c[g>>2]=c[K>>2];c[g+4>>2]=c[K+4>>2];c[g+8>>2]=c[K+8>>2];c[g+12>>2]=c[K+12>>2];D=(c[f>>2]|0)+16|0;c[D>>2]=c[I>>2];c[D+4>>2]=c[I+4>>2];c[D+8>>2]=c[I+8>>2];c[D+12>>2]=c[I+12>>2];c[D+16>>2]=c[I+16>>2];c[D+20>>2]=c[I+20>>2];c[D+24>>2]=c[I+24>>2];c[D+28>>2]=c[I+28>>2];if(0>1){so(b);D=KB(e)|0;i=+h[I>>3];j=+h[n>>3];k=+h[g>>3];m=+h[o>>3];c[N>>2]=D;h[N+8>>3]=i;h[N+16>>3]=j;h[N+24>>3]=k;h[N+32>>3]=m;w4(r,90556,N)|0}}else{D=(c[f>>2]|0)+16|0;c[F>>2]=c[D>>2];c[F+4>>2]=c[D+4>>2];c[F+8>>2]=c[D+8>>2];c[F+12>>2]=c[D+12>>2];c[G>>2]=c[J>>2];c[G+4>>2]=c[J+4>>2];c[G+8>>2]=c[J+8>>2];c[G+12>>2]=c[J+12>>2];vo(L,F,G);c[D>>2]=c[L>>2];c[D+4>>2]=c[L+4>>2];c[D+8>>2]=c[L+8>>2];c[D+12>>2]=c[L+12>>2];if(0>1){so(b);D=KB(e)|0;B=c[f>>2]|0;k=+h[B+16>>3];m=+h[B+24>>3];c[O>>2]=D;h[O+8>>3]=k;h[O+16>>3]=m;w4(r,90544,O)|0}}d=d+1|0}c[F>>2]=c[s>>2];c[F+4>>2]=c[s+4>>2];c[F+8>>2]=c[s+8>>2];c[F+12>>2]=c[s+12>>2];c[G>>2]=c[J>>2];c[G+4>>2]=c[J+4>>2];c[G+8>>2]=c[J+8>>2];c[G+12>>2]=c[J+12>>2];vo(M,F,G);c[s>>2]=c[M>>2];c[s+4>>2]=c[M+4>>2];c[s+8>>2]=c[M+8>>2];c[s+12>>2]=c[M+12>>2];c[F>>2]=c[J>>2];c[F+4>>2]=c[J+4>>2];c[F+8>>2]=c[J+8>>2];c[F+12>>2]=c[J+12>>2];c[G>>2]=c[J>>2];c[G+4>>2]=c[J+4>>2];c[G+8>>2]=c[J+8>>2];c[G+12>>2]=c[J+12>>2];vo(H,F,G);c[J>>2]=c[H>>2];c[J+4>>2]=c[H+4>>2];c[J+8>>2]=c[H+8>>2];c[J+12>>2]=c[H+12>>2];O=(c[E>>2]|0)+16|0;c[O>>2]=c[J>>2];c[O+4>>2]=c[J+4>>2];c[O+8>>2]=c[J+8>>2];c[O+12>>2]=c[J+12>>2];c[O+16>>2]=c[J+16>>2];c[O+20>>2]=c[J+20>>2];c[O+24>>2]=c[J+24>>2];c[O+28>>2]=c[J+28>>2];if(0>1){so(b);O=KB(a)|0;i=+h[J>>3];j=+h[p>>3];k=+h[s>>3];m=+h[q>>3];c[P>>2]=O;h[P+8>>3]=i;h[P+16>>3]=j;h[P+24>>3]=k;h[P+32>>3]=m;w4(r,90556,P)|0}U1(z);U1(A);U1(t)}l=Q;return}function ro(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0.0,k=0.0,m=0.0,n=0.0,o=0,p=0.0,q=0.0,r=0,s=0,t=0,u=0;t=l;l=l+80|0;s=t+32|0;i=t+8|0;d=t;r=a+16|0;o=c[r>>2]|0;p=+h[o+16>>3];q=+h[o+24>>3];if(0>1){so(b);o=c[15712]|0;c[d>>2]=KB(a)|0;w4(o,90529,d)|0}o=(b|0)!=0;a:do if(o){d=jC(a)|0;e=c[15712]|0;while(1){if(!d)break a;f=d+16|0;g=c[f>>2]|0;if((c[g+112>>2]|0)==(a|0)?(u=g+16|0,h[u>>3]=+h[u>>3]+p,g=g+24|0,h[g>>3]=+h[g>>3]+q,0>1):0){so(b);u=KB(d)|0;g=c[f>>2]|0;m=+h[g+16>>3];n=+h[g+24>>3];c[i>>2]=u;h[i+8>>3]=m;h[i+16>>3]=n;w4(e,90544,i)|0}d=kC(a,d)|0}}else e=c[15712]|0;while(0);i=b+1|0;a=1;while(1){d=c[r>>2]|0;if((a|0)>(c[d+180>>2]|0))break;g=c[(c[d+184>>2]|0)+(a<<2)>>2]|0;if(o){f=g+16|0;d=c[f>>2]|0;j=+h[d+16>>3]+p;k=+h[d+24>>3]+q;m=+h[d+32>>3]+p;n=+h[d+40>>3]+q;if(0>1){so(b);c[s>>2]=KB(g)|0;h[s+8>>3]=j;h[s+16>>3]=k;h[s+24>>3]=m;h[s+32>>3]=n;w4(e,90556,s)|0;d=c[f>>2]|0}h[d+16>>3]=j;h[d+24>>3]=k;h[d+32>>3]=m;h[d+40>>3]=n}ro(g,i);a=a+1|0}l=t;return}function so(a){a=a|0;var b=0;b=c[15712]|0;while(1){if((a|0)<=0)break;T4(90574,b)|0;a=a+-1|0}return}function to(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b;h[a+8>>3]=c;return}function uo(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=(+h[c+8>>3]+ +h[b+8>>3])*.5;h[a>>3]=(+h[c>>3]+ +h[b>>3])*.5;h[a+8>>3]=d;return}function vo(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0;d=+h[b+8>>3]-+h[c+8>>3];h[a>>3]=+h[b>>3]-+h[c>>3];h[a+8>>3]=d;return}function wo(a){a=a|0;c[a>>2]=0;c[a+4>>2]=0;c[a+8>>2]=0;return}function xo(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;g=a+8|0;d=(c[g>>2]|0)+1|0;c[g>>2]=d;e=a+4|0;h=c[e>>2]|0;f=h+10|0;if((d|0)<(h|0))a=c[a>>2]|0;else{c[e>>2]=f;d=MK(c[a>>2]|0,f<<2)|0;c[a>>2]=d;a=d;d=c[g>>2]|0}c[a+(d<<2)>>2]=b;return}function yo(a){a=a|0;var b=0;b=jC(a)|0;while(1){if(!b)break;WP(b);b=kC(a,b)|0}zo(a);return}function zo(a){a=a|0;var b=0,d=0,e=0;b=a+16|0;a=1;while(1){e=c[b>>2]|0;d=c[e+184>>2]|0;if((a|0)>(c[e+180>>2]|0))break;e=c[d+(a<<2)>>2]|0;CK(c[(c[e+16>>2]|0)+12>>2]|0);zo(e);a=a+1|0}U1(d);return}function Ao(a){a=a|0;var b=0,d=0,e=0;YC(a,1,137447,304,1);HP(a)|0;b=JK(((tB(a)|0)<<2)+4|0)|0;e=a+16|0;c[(c[e>>2]|0)+152>>2]=b;b=0;d=jC(a)|0;while(1){if(!d)break;Bo(d);c[(c[(c[e>>2]|0)+152>>2]|0)+(b<<2)>>2]=d;c[(c[d+16>>2]|0)+120>>2]=b;b=b+1|0;d=kC(a,d)|0}e=GA(a,2,92493,0)|0;d=jC(a)|0;while(1){if(!d)break;b=XA(a,d)|0;while(1){if(!b)break;Co(b,e);b=ZA(a,b)|0}d=kC(a,d)|0}Do(a);return}function Bo(a){a=a|0;var b=0;sP(a);b=JK((e[(c[(xC(a)|0)+16>>2]|0)+176>>1]|0)<<3)|0;c[(c[a+16>>2]|0)+132>>2]=b;qQ(a,c[(c[(xC(a)|0)+16>>2]|0)+116>>2]&1);return}function Co(a,b){a=a|0;b=b|0;var d=0,e=0.0;TC(a,137460,176,1)|0;e=+_O(a,c[47174]|0,1.0,0.0);d=a+16|0;h[(c[d>>2]|0)+128>>3]=e;e=+_O(a,b,+h[1371],0.0);h[(c[d>>2]|0)+136>>3]=e;tP(a)|0;return}function Do(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;r=l;l=l+32|0;q=r+16|0;p=r;m=r+24|0;n=GA(b,1,102623,0)|0;a:do if(n|0){o=GA(b,1,90660,0)|0;i=b+16|0;j=(o|0)==0;k=c[15712]|0;g=0;while(1){e=c[(c[(c[i>>2]|0)+152>>2]|0)+(g<<2)>>2]|0;if(!e)break a;b=RA(e,n)|0;do if(a[b>>0]|0){f=e+16|0;d=c[(c[f>>2]|0)+132>>2]|0;a[m>>0]=0;c[p>>2]=d;c[p+4>>2]=d+8;c[p+8>>2]=m;if((b5(b,101749,p)|0)<=1){c[q>>2]=KB(e)|0;c[q+4>>2]=b;w4(k,90664,q)|0;break}b:do if(+h[23293]>0.0){b=0;while(1){if((b|0)==2)break b;s=d+(b<<3)|0;h[s>>3]=+h[s>>3]/+h[23293];b=b+1|0}}while(0);b=c[f>>2]|0;a[b+119>>0]=1;if((a[m>>0]|0)!=33){if(j)break;if(!((dP(RA(e,o)|0)|0)<<24>>24))break;b=c[f>>2]|0}a[b+119>>0]=3}while(0);g=g+1|0}}while(0);l=r;return}function Eo(a){a=a|0;var b=0,c=0;c=jC(a)|0;while(1){if(!c)break;b=XA(a,c)|0;while(1){if(!b)break;YP(b);b=ZA(a,b)|0}WP(c);c=kC(a,c)|0}Fo(a);return}function Fo(a){a=a|0;Go(a);a=a+16|0;U1(c[(c[a>>2]|0)+152>>2]|0);U1(c[(c[a>>2]|0)+140>>2]|0);return}function Go(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;g=a+16|0;f=1;while(1){e=c[g>>2]|0;b=c[e+184>>2]|0;if((f|0)>(c[e+180>>2]|0))break;e=c[b+(f<<2)>>2]|0;b=e+16|0;CK(c[(c[b>>2]|0)+12>>2]|0);d=c[(c[b>>2]|0)+140>>2]|0;if(d|0){U1(c[d>>2]|0);U1(c[(c[b>>2]|0)+140>>2]|0)}Go(e);f=f+1|0}U1(b);if((yC(a)|0)!=(a|0))VC(a,137483)|0;return}function Ho(a,b){a=a|0;b=b|0;c[b+4>>2]=GA(a,0,90716,0)|0;c[b+8>>2]=GA(a,0,141116,0)|0;c[b+12>>2]=GA(a,0,141048,0)|0;c[b>>2]=a;c[b+16>>2]=0;c[b+36>>2]=kH(a,2,4,b+20|0)|0;return}function Io(a){a=a|0;var d=0,e=0;oQ(a,2);d=JK(56)|0;e=a+16|0;c[(c[e>>2]|0)+140>>2]=d;d=(ZO(a,GA(a,0,91374,0)|0,2,2)|0)&65535;d=(d&65535)<10?d:10;b[(c[e>>2]|0)+176>>1]=d;c[47137]=d&65535;Jo(a,0,a);fp(a);Ao(a);return}function Jo(a,d,e){a=a|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;m=l;l=l+16|0;j=m;k=(d|0)==0;if(k){Ko(j);d=j}g=e+16|0;f=OD(a)|0;while(1){if(!f)break;if(!(Q2(KB(f)|0,108006,7)|0)){TC(f,137483,280,1)|0;n=JK(56)|0;p=c[f+16>>2]|0;c[p+140>>2]=n;o=c[g>>2]|0;b[p+176>>1]=b[o+176>>1]|0;c[n+44>>2]=(c[(c[o+140>>2]|0)+44>>2]|0)+1;c[n+48>>2]=e;Lo(d,f);Jo(f,0,f)}else Jo(f,d,e);f=PD(f)|0}if(k?(h=c[j+8>>2]|0,i=a+16|0,c[(c[i>>2]|0)+180>>2]=h,h|0):0){p=MK(c[j>>2]|0,(h<<2)+4|0)|0;c[(c[i>>2]|0)+184>>2]=p}l=m;return}function Ko(a){a=a|0;c[a>>2]=0;c[a+4>>2]=0;c[a+8>>2]=0;return}function Lo(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;g=a+8|0;d=(c[g>>2]|0)+1|0;c[g>>2]=d;e=a+4|0;h=c[e>>2]|0;f=h+10|0;if((d|0)<(h|0))a=c[a>>2]|0;else{c[e>>2]=f;d=MK(c[a>>2]|0,f<<2)|0;c[a>>2]=d;a=d;d=c[g>>2]|0}c[a+(d<<2)>>2]=b;return}function Mo(a){a=a|0;var b=0,c=0;b=l;l=l+64|0;c=b;Ho(a,c);No(a,c);Oo(a);Po(a,a);Qo(a);l=b;return}function No(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0.0,v=0,w=0.0,x=0.0;s=l;l=l+64|0;r=s+48|0;e=s+40|0;o=s+56|0;n=s+52|0;m=s;if(0){q=c[15712]|0;c[e>>2]=KB(b)|0;w4(q,90723,e)|0}e=jC(b)|0;while(1){if(!e)break;c[(c[e+16>>2]|0)+164>>2]=0;e=kC(b,e)|0}p=Ro(b,d)|0;q=Iv(p,o,n)|0;e=q;while(1){k=e+4|0;f=c[e>>2]|0;if(!f)break;gp(f,m);e=jC(f)|0;while(1){if(!e)break;g=kC(f,e)|0;i=e+16|0;j=c[i>>2]|0;if(c[j+212>>2]|0){v=So(e,f)|0;No(v,d);v=c[(c[v+16>>2]|0)+140>>2]|0;u=+h[v+24>>3];j=c[i>>2]|0;h[j+32>>3]=u;t=+h[v+32>>3];h[j+40>>3]=t;u=u*36.0;h[j+88>>3]=u;h[j+96>>3]=u;h[j+80>>3]=t*72.0;e=g;continue}if(c[(c[j+112>>2]|0)+8>>2]|0){e=g;continue}wC(f,e)|0;e=g}if((tB(f)|0)<=1){e=k;continue}if((c[d>>2]|0)==(b|0))Lq(f)|0;tp(f,m);e=k}e=c[o>>2]|0;if((e|0)<=1)if((e|0)==1){BP(c[q>>2]|0);e=0}else e=0;else{if(!(c[n>>2]|0))f=0;else{f=JK(e)|0;a[f>>0]=1;e=c[o>>2]|0}c[d+40>>2]=f;e=QG(e,q,0,d+20|0)|0;U1(f)}To(p,c[o>>2]|0,q,e,b,d);U1(e);f=jC(p)|0;while(1){if(!f)break;g=c[f+16>>2]|0;e=c[g+212>>2]|0;if(!e){e=c[(c[g+112>>2]|0)+8>>2]|0;if(e|0){o=c[g+132>>2]|0;v=c[(c[e+16>>2]|0)+132>>2]|0;h[v>>3]=+h[o>>3];h[v+8>>3]=+h[o+8>>3]}}else{o=c[g+132>>2]|0;x=+h[g+32>>3];w=+h[o>>3]-x*.5;v=c[(c[e+16>>2]|0)+140>>2]|0;h[v+8>>3]=w;t=+h[g+40>>3];u=+h[o+8>>3]-t*.5;h[v+16>>3]=u;h[v+24>>3]=x+w;h[v+32>>3]=t+u}f=kC(p,f)|0}v=(c[(c[b+16>>2]|0)+140>>2]|0)+8|0;o=(c[(c[p+16>>2]|0)+140>>2]|0)+8|0;c[v>>2]=c[o>>2];c[v+4>>2]=c[o+4>>2];c[v+8>>2]=c[o+8>>2];c[v+12>>2]=c[o+12>>2];c[v+16>>2]=c[o+16>>2];c[v+20>>2]=c[o+20>>2];c[v+24>>2]=c[o+24>>2];c[v+28>>2]=c[o+28>>2];Uo(p,q);U1(q);if(0){v=c[15712]|0;c[r>>2]=KB(b)|0;w4(v,90734,r)|0}l=s;return}function Oo(b){b=b|0;var d=0,e=0,f=0.0,g=0.0,i=0.0,j=0.0,k=0;d=jC(b)|0;while(1){if(!d)break;e=c[d+16>>2]|0;if(a[e+118>>0]|0){k=c[(c[(c[e+212>>2]|0)+16>>2]|0)+140>>2]|0;j=+h[k+24>>3]-+h[k+8>>3];i=+h[k+32>>3]-+h[k+16>>3];g=j*36.0;f=i*36.0;k=c[e+132>>2]|0;h[k>>3]=j*.5;h[k+8>>3]=i*.5;h[e+32>>3]=j;h[e+40>>3]=i;h[e+96>>3]=g;h[e+88>>3]=g;h[e+80>>3]=i*72.0;e=c[(c[e+12>>2]|0)+44>>2]|0;h[e>>3]=g;h[e+8>>3]=f;i=-g;h[e+16>>3]=i;h[e+24>>3]=f;h[e+32>>3]=i;f=-f;h[e+40>>3]=f;h[e+48>>3]=g;h[e+56>>3]=f}d=kC(b,d)|0}return}function Po(a,b){a=a|0;b=b|0;var d=0,e=0,f=0.0,g=0.0,i=0,j=0,k=0.0,l=0,m=0.0,n=0,o=0.0,p=0;i=a+16|0;j=c[(c[i>>2]|0)+140>>2]|0;f=+h[j+8>>3];g=+h[j+16>>3];j=(a|0)!=(b|0);a:do if(j){d=jC(a)|0;while(1){if(!d){a=1;break a}e=c[d+16>>2]|0;if((c[e+212>>2]|0)==(a|0)){e=c[e+132>>2]|0;h[e>>3]=+h[e>>3]+f;e=e+8|0;h[e>>3]=+h[e>>3]+g}d=kC(a,d)|0}}else a=1;while(0);while(1){d=c[i>>2]|0;if((a|0)>(c[d+180>>2]|0))break;d=c[(c[d+184>>2]|0)+(a<<2)>>2]|0;if(j){e=c[(c[d+16>>2]|0)+140>>2]|0;p=e+8|0;n=e+16|0;l=e+24|0;e=e+32|0;o=+h[n>>3]+g;m=+h[l>>3]+f;k=+h[e>>3]+g;h[p>>3]=+h[p>>3]+f;h[n>>3]=o;h[l>>3]=m;h[e>>3]=k}Po(d,b);a=a+1|0}return}function Qo(a){a=a|0;var b=0,d=0,e=0.0,f=0.0,g=0.0;b=a+16|0;a=c[b>>2]|0;d=c[a+140>>2]|0;g=+h[d+16>>3]*72.0;f=+h[d+24>>3]*72.0;e=+h[d+32>>3]*72.0;h[a+16>>3]=+h[d+8>>3]*72.0;h[a+24>>3]=g;h[a+32>>3]=f;h[a+40>>3]=e;a=1;while(1){d=c[b>>2]|0;if((a|0)>(c[d+180>>2]|0))break;Qo(c[(c[d+184>>2]|0)+(a<<2)>>2]|0);a=a+1|0}return}function Ro(d,e){d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;y=l;l=l+160|0;s=y+24|0;t=y+8|0;g=y;f=y+56|0;w=e+16|0;x=c[w>>2]|0;c[w>>2]=x+1;c[s>>2]=x;m4(f,90796,s)|0;if(0>1){x=c[15712]|0;w=KB(d)|0;c[g>>2]=f;c[g+4>>2]=w;w4(x,90803,g)|0};a[s>>0]=a[18588]|0;a[s+1>>0]=a[18589]|0;a[s+2>>0]=a[18590]|0;a[s+3>>0]=a[18591]|0;x=oB(91156,s,0)|0;TC(x,137483,280,1)|0;v=JK(56)|0;w=x+16|0;o=c[w>>2]|0;c[o+140>>2]=v;v=d+16|0;b[o+176>>1]=b[(c[v>>2]|0)+176>>1]|0;_o(d,x,92507);_o(d,x,93112);_o(d,x,91202);o=e+4|0;p=s+16|0;q=s+8|0;r=s+24|0;j=0;n=1;while(1){f=c[v>>2]|0;if((n|0)>(c[f+180>>2]|0))break;c[s>>2]=c[332];c[s+4>>2]=c[333];c[s+8>>2]=c[334];c[s+12>>2]=c[335];c[s+16>>2]=c[336];c[s+20>>2]=c[337];c[s+24>>2]=c[338];c[s+28>>2]=c[339];g=c[(c[f+184>>2]|0)+(n<<2)>>2]|0;uK(g);i=$o(x,KB(g)|0)|0;k=i+16|0;f=c[k>>2]|0;c[f+212>>2]=g;m=j+1|0;c[f+120>>2]=j;if(c[o>>2]|0)ap(g,i,e,s);f=jC(g)|0;while(1){if(!f)break;c[(c[f+16>>2]|0)+164>>2]=i;f=kC(g,f)|0}f=c[k>>2]|0;if(a[f+119>>0]|0){k=c[f+132>>2]|0;h[k>>3]=(+h[p>>3]+ +h[s>>3])*.5;h[k+8>>3]=(+h[r>>3]+ +h[q>>3])*.5}j=m;n=n+1|0}k=jC(d)|0;while(1){if(!k)break;f=k+16|0;g=c[f>>2]|0;if(!(c[g+164>>2]|0)){i=g+212|0;s=c[i>>2]|0;if(s|0?(s|0)!=(c[(c[(c[v>>2]|0)+140>>2]|0)+48>>2]|0):0){u=18;break}c[i>>2]=d;if(!(a[g+118>>0]|0)){i=$o(x,KB(k)|0)|0;g=c[f>>2]|0;c[g+164>>2]=i;i=c[i+16>>2]|0;c[i+120>>2]=j;h[i+32>>3]=+h[g+32>>3];h[i+40>>3]=+h[g+40>>3];h[i+88>>3]=+h[g+88>>3];h[i+96>>3]=+h[g+96>>3];h[i+80>>3]=+h[g+80>>3];c[i+8>>2]=c[g+8>>2];c[i+12>>2]=c[g+12>>2];f=a[g+119>>0]|0;if(f<<24>>24){e=c[g+132>>2]|0;s=c[i+132>>2]|0;h[s>>3]=+h[e>>3];h[s+8>>3]=+h[e+8>>3];a[i+119>>0]=f}c[(c[i+112>>2]|0)+8>>2]=k;f=j+1|0}else f=j}else f=j;k=kC(d,k)|0;j=f}if((u|0)==18){e=KB(k)|0;s=KB(d)|0;u=KB(c[(c[f>>2]|0)+212>>2]|0)|0;c[t>>2]=e;c[t+4>>2]=s;c[t+8>>2]=u;gA(1,90826,t)|0;bb(187212,1)}m=jC(d)|0;while(1){if(!m)break;n=c[(c[m+16>>2]|0)+164>>2]|0;o=n+16|0;i=XA(d,m)|0;while(1){if(!i)break;f=c[(c[(c[((c[i>>2]&3|0)==2?i:i+-48|0)+40>>2]|0)+16>>2]|0)+164>>2]|0;if((f|0)!=(n|0)){if(f>>>0>n>>>0)k=iB(x,n,f,0,1)|0;else k=iB(x,f,n,0,1)|0;TC(k,137460,176,1)|0;g=c[i+16>>2]|0;u=c[k+16>>2]|0;h[u+136>>3]=+h[g+136>>3];h[u+128>>3]=+h[g+128>>3];f=c[(c[f+16>>2]|0)+112>>2]|0;g=f+4|0;c[g>>2]=(c[g>>2]|0)+1;g=c[(c[o>>2]|0)+112>>2]|0;t=g+4|0;c[t>>2]=(c[t>>2]|0)+1;if(!(c[u+172>>2]|0)){c[f>>2]=(c[f>>2]|0)+1;c[g>>2]=(c[g>>2]|0)+1}bp(k,i)}i=ZA(d,i)|0}m=kC(d,m)|0}f=c[(c[v>>2]|0)+140>>2]|0;g=c[f>>2]|0;if(g|0){i=JK((c[f+4>>2]<<4)+16|0)|0;c[c[(c[w>>2]|0)+140>>2]>>2]=i;f=0;while(1){if(!(c[g>>2]|0))break;m=c[(c[(c[g+4>>2]|0)+16>>2]|0)+164>>2]|0;if(m){cp(d,g);n=$o(x,193772)|0;o=n+16|0;c[(c[o>>2]|0)+120>>2]=j;if(n>>>0>m>>>0)k=iB(x,m,n,0,1)|0;else k=iB(x,n,m,0,1)|0;TC(k,137460,176,1)|0;u=c[g>>2]|0;t=c[u+16>>2]|0;v=c[k+16>>2]|0;h[v+136>>3]=+h[t+136>>3];h[v+128>>3]=+h[t+128>>3];bp(k,u);u=c[(c[o>>2]|0)+112>>2]|0;v=u+4|0;c[v>>2]=(c[v>>2]|0)+1;v=c[(c[m+16>>2]|0)+112>>2]|0;t=v+4|0;c[t>>2]=(c[t>>2]|0)+1;c[u>>2]=(c[u>>2]|0)+1;c[v>>2]=(c[v>>2]|0)+1;c[i+4>>2]=n;h[i+8>>3]=+h[g+8>>3];c[i>>2]=k;f=f+1|0;i=i+16|0;j=j+1|0}g=g+16|0}c[(c[(c[w>>2]|0)+140>>2]|0)+4>>2]=f}l=y;return x|0}function So(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0,g=0,i=0,j=0,k=0,l=0;l=c[a+16>>2]|0;k=c[l+212>>2]|0;l=c[(c[l+112>>2]|0)+4>>2]|0;do if(l|0){j=JK((l<<4)+16|0)|0;f=Xo(a,b)|0;g=f+8|0;b=0;e=f;while(1){if(!(c[e>>2]|0))break;i=e+24|0;if(!(c[i>>2]|0))d=+h[g>>3]+6.283185307179586;else d=+h[e+32>>3];b=Yo(a,e,j,b,d)|0;e=i}if((b|0)==(l|0)){a=c[(c[k+16>>2]|0)+140>>2]|0;c[a>>2]=j;c[a+4>>2]=l;U1(f);break}else qa(90742,90752,766,90761)}while(0);return k|0}function To(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0.0,j=0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0.0,H=0.0;B=l;l=l+32|0;x=B+16|0;A=B;m=c[g+8>>2]|0;j=c[g+12>>2]|0;y=(c[g>>2]|0)==(f|0);z=(b|0)!=0;a:do if(z){p=c[(c[d>>2]|0)+16>>2]|0;k=+h[p+16>>3];m=~~(k+(k>=0.0?.5:-.5));c[A>>2]=m;k=+h[p+24>>3];g=~~(k+(k>=0.0?.5:-.5));u=A+4|0;c[u>>2]=g;k=+h[p+32>>3];n=~~(k+(k>=0.0?.5:-.5));v=A+8|0;c[v>>2]=n;k=+h[p+40>>3];p=~~(k+(k>=0.0?.5:-.5));w=A+12|0;c[w>>2]=p;if((b|0)>1){q=c[e>>2]|0;t=c[e+4>>2]|0;m=q+m|0;c[A>>2]=m;b=t+g|0;c[u>>2]=b;n=q+n|0;c[v>>2]=n;q=x+4|0;r=x+8|0;s=x+12|0;o=e;g=d;t=t+p|0;p=b;while(1){g=g+4|0;j=o+8|0;c[w>>2]=t;b=c[g>>2]|0;if(!b){j=0;r=p;s=t;break a}E=c[b+16>>2]|0;G=+h[E+16>>3];i=+h[E+24>>3];k=+h[E+32>>3];H=+h[E+40>>3];E=~~(H+(H>=0.0?.5:-.5));c[s>>2]=E;b=c[j>>2]|0;F=c[o+12>>2]|0;D=b+~~(G+(G>=0.0?.5:-.5))|0;c[x>>2]=D;C=F+~~(i+(i>=0.0?.5:-.5))|0;c[q>>2]=C;b=b+~~(k+(k>=0.0?.5:-.5))|0;c[r>>2]=b;E=F+E|0;c[s>>2]=E;D=c[((m|0)<(D|0)?A:x)>>2]|0;c[A>>2]=D;C=c[((p|0)<(C|0)?A:x)+4>>2]|0;c[u>>2]=C;b=c[((n|0)>(b|0)?A:x)+8>>2]|0;c[v>>2]=b;o=j;t=c[((t|0)>(E|0)?A:x)+12>>2]|0;m=D;p=C;n=b}}else{j=0;r=g;s=p}}else{c[A>>2]=0;c[A+4>>2]=0;n=ZO(f,m,54,3)|0;c[A+8>>2]=n;s=ZO(f,j,36,3)|0;c[A+12>>2]=s;j=1;m=0;r=0}while(0);b=f+16|0;o=c[b>>2]|0;g=c[o+12>>2]|0;if(g){H=+h[g+24>>3];g=m-n+~~(H+(H>=0.0?.5:-.5))|0;if((g|0)>0){g=g>>>1;m=m-g|0;c[A>>2]=m;n=n+g|0;c[A+8>>2]=n;g=0}else g=0}else g=j;if(y|(g|0)!=0){j=0;g=o}else{j=ZO(a,c[47144]|0,8,0)|0;g=c[b>>2]|0}q=j-m|0;p=~~(+h[g+56>>3]+ +(j-r|0));c[A>>2]=0;c[A+4>>2]=0;o=q+j+n|0;c[A+8>>2]=o;b=~~(+h[g+88>>3]+ +(j+p|0)+ +(s|0));c[A+12>>2]=b;b:do if(z){g=e;c:while(1){n=d+4|0;j=c[d>>2]|0;if(!j)break b;if(!g){m=0;d=q;g=p}else{m=g+8|0;d=(c[g>>2]|0)+q|0;g=(c[g+4>>2]|0)+p|0}k=+(d|0)*.013888888888888888;i=+(g|0)*.013888888888888888;d=jC(j)|0;while(1){if(!d){d=n;g=m;continue c}F=c[(c[d+16>>2]|0)+132>>2]|0;h[F>>3]=+h[F>>3]+k;F=F+8|0;h[F>>3]=+h[F>>3]+i;d=kC(j,d)|0}}}while(0);F=c[(c[a+16>>2]|0)+140>>2]|0;E=F+8|0;c[E>>2]=0;c[E+4>>2]=0;c[E+8>>2]=0;c[E+12>>2]=0;h[F+24>>3]=+(o|0)*.013888888888888888;h[F+32>>3]=+(b|0)*.013888888888888888;l=B;return}function Uo(a,b){a=a|0;b=b|0;var d=0,e=0;while(1){d=c[b>>2]|0;if(!d)break;Vo(d);VC(d,137483)|0;b=b+4|0}b=c[c[(c[a+16>>2]|0)+140>>2]>>2]|0;if(b|0)U1(b);Vo(a);VC(a,137483)|0;d=jC(a)|0;while(1){if(!d)break;e=kC(a,d)|0;b=XA(a,d)|0;while(1){if(!b)break;U1(c[(c[b+16>>2]|0)+172>>2]|0);VC(b,137460)|0;b=ZA(a,b)|0}Wo(d);d=e}sB(a)|0;return}function Vo(a){a=a|0;U1(c[(c[a+16>>2]|0)+140>>2]|0);return}function Wo(a){a=a|0;var b=0;b=a+16|0;U1(c[(c[b>>2]|0)+112>>2]|0);U1(c[(c[b>>2]|0)+132>>2]|0);VC(a,137447)|0;return}function Xo(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0,g=0.0,i=0,j=0,k=0,l=0,m=0;k=a+16|0;l=c[c[(c[k>>2]|0)+112>>2]>>2]|0;m=JK((l*24|0)+24|0)|0;i=0;j=aB(b,a)|0;while(1){if(!j)break;f=c[j>>2]&3;e=c[((f|0)==2?j:j+-48|0)+40>>2]|0;if((e|0)==(a|0))e=c[((f|0)==3?j:j+48|0)+40>>2]|0;e=c[(c[e+16>>2]|0)+132>>2]|0;f=c[(c[k>>2]|0)+132>>2]|0;g=+h[e>>3]-+h[f>>3];d=+h[e+8>>3]-+h[f+8>>3];c[m+(i*24|0)>>2]=j;h[m+(i*24|0)+8>>3]=+K(+d,+g);h[m+(i*24|0)+16>>3]=d*d+g*g;i=i+1|0;j=bB(b,j,a)|0}if((i|0)!=(l|0))qa(90775,90752,642,90784);o4(m,l,24,67);a:do if((l|0)>1){j=l+-1|0;e=0;b:while(1){if((e|0)>=(j|0))break a;g=+h[m+(e*24|0)+8>>3];f=e+1|0;i=f;while(1){if((i|0)>=(l|0))break;if(!(+h[m+(i*24|0)+8>>3]==g))break;i=i+1|0}if((i|0)==(f|0)){e=f;continue}if((i|0)==(l|0))d=3.141592653589793;else d=+h[m+(i*24|0)+8>>3];d=(d-g)/+(i-e|0);d=d>.03490658503988659?.03490658503988659:d;g=0.0;while(1){if((e|0)>=(i|0))continue b;k=m+(e*24|0)+8|0;h[k>>3]=+h[k>>3]+g;g=g+d;e=e+1|0}}}while(0);return m|0}function Yo(a,d,e,f,g){a=a|0;d=d|0;e=e|0;f=f|0;g=+g;var i=0,j=0,k=0,l=0.0,m=0,n=0,o=0,p=0.0,q=0;k=c[d>>2]|0;n=c[k+16>>2]|0;m=b[n+168>>1]|0;o=m<<16>>16;j=c[k>>2]&3;i=c[((j|0)==2?k:k+-48|0)+40>>2]|0;if((i|0)==(a|0))i=c[((j|0)==3?k:k+48|0)+40>>2]|0;p=+h[d+8>>3];g=(g-p)/+(m<<16>>16);g=g>.03490658503988659?.03490658503988659:g;m=i>>>0>a>>>0;l=m?g:-g;d=m?1:-1;k=0;i=c[n+172>>2]|0;j=m?f:f+-1+o|0;g=m?p:g*+(o+-1|0)+p;while(1){if((k|0)>=(o|0))break;n=c[i>>2]|0;c[e+(j<<4)>>2]=n;m=c[n>>2]&3;q=(m|0)==3?n:n+48|0;c[e+(j<<4)+4>>2]=c[((c[(c[(c[q+40>>2]|0)+16>>2]|0)+164>>2]|0)==(a|0)?q:(m|0)==2?n:n+-48|0)+40>>2];h[e+(j<<4)+8>>3]=g;k=k+1|0;i=i+4|0;j=j+d|0;g=g+l}return o+f|0}function Zo(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a+8>>3];d=+h[b+8>>3];if(!(c>d))if(!(c<d)){d=+h[a+16>>3];c=+h[b+16>>3];if(d>c)a=1;else a=(d<c)<<31>>31}else a=-1;else a=1;return a|0}function _o(a,b,c){a=a|0;b=b|0;c=c|0;var d=0;d=GA(a,0,c,0)|0;do if(d|0){a=RA(a,d)|0;d=GA(b,0,c,0)|0;if(!d){GA(b,0,c,a)|0;break}else{KA(b,d,a)|0;break}}while(0);return}function $o(a,b){a=a|0;b=b|0;var d=0,f=0;b=tC(a,b,1)|0;TC(b,137447,304,1)|0;f=JK(32)|0;d=b+16|0;c[(c[d>>2]|0)+112>>2]=f;a=KK((e[(c[a+16>>2]|0)+176>>1]|0)<<3)|0;c[(c[d>>2]|0)+132>>2]=a;return b|0}function ap(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+80|0;n=q+56|0;m=q+32|0;p=q;o=q+64|0;i=c[e+4>>2]|0;k=RA(b,i)|0;do if(a[k>>0]|0){if((c[e>>2]|0)!=(b|0)){e=RA(QD(b)|0,i)|0;if((e|0)==(k|0))break;if(!(s2(k,e)|0))break}a[o>>0]=0;e=p+8|0;i=p+16|0;j=p+24|0;c[m>>2]=p;c[m+4>>2]=e;c[m+8>>2]=i;c[m+12>>2]=j;c[m+16>>2]=o;if((b5(k,90937,m)|0)<=3){c[n>>2]=KB(b)|0;c[n+4>>2]=k;gA(0,90955,n)|0;break}g=+h[23293];if(g>0.0){h[p>>3]=+h[p>>3]/g;h[e>>3]=+h[e>>3]/g;h[i>>3]=+h[i>>3]/g;h[j>>3]=+h[j>>3]/g}e=a[o>>0]|0;if(e<<24>>24==33)a[(c[d+16>>2]|0)+119>>0]=3;else a[(c[d+16>>2]|0)+119>>0]=e<<24>>24==63?2:1;c[f>>2]=c[p>>2];c[f+4>>2]=c[p+4>>2];c[f+8>>2]=c[p+8>>2];c[f+12>>2]=c[p+12>>2];c[f+16>>2]=c[p+16>>2];c[f+20>>2]=c[p+20>>2];c[f+24>>2]=c[p+24>>2];c[f+28>>2]=c[p+28>>2]}while(0);l=q;return}function bp(a,d){a=a|0;d=d|0;var e=0,f=0,g=0;g=a+16|0;f=c[g>>2]|0;a=b[f+168>>1]|0;f=c[f+172>>2]|0;if(!f){a=a<<16>>16;e=a;a=KK((a<<2)+4|0)|0}else{a=a<<16>>16;e=a;a=MK(f,(a<<2)+4|0)|0}c[a+(e<<2)>>2]=d;d=c[g>>2]|0;c[d+172>>2]=a;d=d+168|0;b[d>>1]=(b[d>>1]|0)+1<<16>>16;return}function cp(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;i=l;l=l+48|0;h=i+24|0;g=i;e=c[b>>2]|0;d=c[e>>2]&3;f=c[((d|0)==2?e:e+-48|0)+40>>2]|0;d=c[((d|0)==3?e:e+48|0)+40>>2]|0;b=l2(KB(a)|0)|0;j=l2(KB(f)|0)|0;j=(b+8+j+(l2(KB(d)|0)|0)|0)>999;b=KB(a)|0;if(j){d=KB(d)|0;j=KB(f)|0;h=(c[e>>2]|0)>>>4;c[g>>2]=b;c[g+4>>2]=d;c[g+8>>2]=j;j=g+16|0;c[j>>2]=h;c[j+4>>2]=0;m4(193772,90895,g)|0}else{d=c[(c[d+16>>2]|0)+120>>2]|0;j=c[(c[f+16>>2]|0)+120>>2]|0;g=(c[e>>2]|0)>>>4;c[h>>2]=b;c[h+4>>2]=d;c[h+8>>2]=j;j=h+16|0;c[j>>2]=g;c[j+4>>2]=0;m4(193772,90914,h)|0}l=i;return}function dp(a){a=a|0;var d=0,e=0,f=0.0,g=0.0,i=0,j=0,k=0;j=4;i=T1(40)|0;c[i>>2]=0;g=+h[23293];o=0;f=+Z(1,a|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,i|0,j|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)!=1){h[23293]=f;o=0;ia(102,a|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,i|0,j|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)!=1){i=U6(187212,1,i|0,j|0)|0;j=z;o=0;e=o;o=0;if((e|0)!=0&(p|0)!=0){d=V6(c[e>>2]|0,i|0,j|0)|0;if(!d)bb(e|0,p|0);z=p}else d=-1;if((d|0)!=1)d=0;else d=z}else d=z}else d=z;while(1){if(d|0)break;o=0;ia(103,a|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,i|0,j|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){d=z;continue}o=0;$(22,a|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,i|0,j|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){d=z;continue}if(b[(c[a+16>>2]|0)+136>>1]&14){o=0;ia(104,a|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,i|0,j|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){d=z;continue}}o=0;ja(22,a|0,0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,i|0,j|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1)d=z;else{k=12;break}}if((k|0)==12)h[23293]=g;U1(i|0);return}function ep(a){a=a|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;g=i;e=a+16|0;f=b[(c[e>>2]|0)+136>>1]&14;d=f&65535;if((f&65535)>8){if(f<<16>>16==12){if(Um(a,22,10)|0){c[47134]=2;h=5}}else h=5;do if((h|0)==5)if(!(b[(c[e>>2]|0)+136>>1]&1)){Wm(a,d)|0;break}else{gA(0,90998,g)|0;d=2;break}while(0);c[47134]=0}if((c[47138]|0)<1)Wm(a,d)|0;l=i;return}function fp(a){a=a|0;var b=0.0,d=0.0,e=0;e=l;l=l+16|0;c[46474]=1;c[46475]=1;c[46477]=-1;c[46479]=50;h[23245]=0.0;h[23240]=0.0;h[23241]=1.0;c[46478]=ZO(a,GA(a,0,91061,0)|0,600,0)|0;d=+_O(a,GA(a,0,91202,0)|0,.3,0.0);h[23242]=d;h[1371]=d;if(-1.0==-1.0)b=+_O(a,GA(a,0,91069,0)|0,-1.0,0.0);else b=-1.0;h[23243]=b;c[46476]=1;a=mm(a,2,185904)|0;c[46488]=a;if(!a){gA(0,91072,e)|0;c[46476]=2}c[46504]=(O(c[46478]|0,c[46479]|0)|0)/100|0;d=+h[23242];h[23247]=d*d;if(c[46474]|0){b=+h[23245];if(b<=0.0){b=d*3.0;h[23245]=b}h[23246]=b*b}l=e;return}function gp(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0.0,g=0,i=0,j=0;j=l;l=l+16|0;d=j;g=c[c[(c[a+16>>2]|0)+140>>2]>>2]|0;i=hp(a,b)|0;ip(d,a,g);e=+h[d>>3];f=+h[d+8>>3];a:do if(!(c[46474]|0)){b=0;while(1){if((b|0)>=(c[46505]|0))break a;lp(a,+jp(b),g);b=b+1|0}}else{d=Kv(tB(a)|0)|0;Pv(d,tB(a)|0);b=0;while(1){if((b|0)>=(c[46505]|0))break;kp(a,+jp(b),g,d);b=b+1|0}Rv(d)}while(0);b:do if(e!=0.0|f!=0.0){b=jC(a)|0;while(1){if(!b)break b;g=c[(c[b+16>>2]|0)+132>>2]|0;h[g>>3]=+h[g>>3]+e;g=g+8|0;h[g>>3]=+h[g>>3]+f;b=kC(a,b)|0}}while(0);if(i|0)mp();l=j;return}function hp(a,b){a=a|0;b=b|0;var d=0.0,e=0,f=0,g=0,i=0,j=0;if(+h[23243]==-1.0){i=tB(a)|0;d=+h[23242];h[23243]=+h[23241]*.2*d*+C(+(+(i|0)));i=1}else{i=0;d=+h[23242]}g=c[46504]|0;h[b+8>>3]=+jp(g);h[b+16>>3]=d;h[b+24>>3]=+h[23240];e=c[46478]|0;a=e-g|0;c[b>>2]=a;f=c[46477]|0;do if((f|0)>-1){if((f|0)<=(g|0)){c[46505]=f;a=0;j=10;break}if((f|0)<=(e|0)){c[46505]=g;a=f-g|0;j=10}}else{c[46505]=g;j=10}while(0);if((j|0)==10)c[b+32>>2]=a;return i|0}function ip(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0.0,k=0,l=0.0,m=0.0,n=0,o=0.0,p=0.0,q=0,r=0,s=0.0,t=0.0,u=0,v=0,w=0.0,x=0,y=0,z=0;v=tB(d)|0;x=c[(c[(c[d+16>>2]|0)+140>>2]|0)+4>>2]|0;u=jC(d)|0;q=0;r=0;g=0;k=0;n=0;while(1){if(!u)break;f=c[u+16>>2]|0;if(!(a[f+119>>0]|0))f=r;else{f=c[f+132>>2]|0;i=+h[f>>3];if(!q){k=~~i;g=~~+h[f+8>>3];n=g;f=k}else{p=+(r|0);s=+h[f+8>>3];t=+(g|0);w=+(k|0);o=+(n|0);n=~~(s>o?s:o);f=~~(i<p?i:p);g=~~(s<t?s:t);k=~~(i>w?i:w)}q=q+1|0}u=kC(d,u)|0;r=f}i=+h[23242]*(+C(+(+(v-x|0)))+1.0);s=i*.6;h[23249]=s;h[23248]=s;if((q|0)!=1)if((q|0)>1){t=+(k+r|0)*.5;w=+(n+g|0)*.5;o=+(k-r|0);p=o*1.2;l=+(n-g|0);m=l*1.2;i=i*2.4*s;j=m*p/i;do if(!(j>=1.0)){if(j>0.0){i=+C(+j)*2.0;j=p/i;h[23248]=j;i=m/i;z=20;break}if(p>0.0){j=o*.6;h[23248]=j;i=i/p*.5;z=20;break}if(m>0.0){j=i/m*.5;h[23248]=j;i=l*.6;z=20}else{i=s;j=s}}else{j=o*.6;h[23248]=j;i=l*.6;z=20}while(0);if((z|0)==20)h[23249]=i;s=+K(+i,+j);j=j/+E(+s);h[23248]=j;i=i/+F(+s);h[23249]=i}else{t=0.0;w=0.0;j=s;i=s}else{t=+(r|0);w=+(g|0);j=s;i=s}h[23250]=j*j;h[23251]=i*i;if((c[46488]|0)==2)f=c[46476]|0;else{f=X4()|0;f=(db(0)|0)^f}z5(f);a:do if(!e){e=jC(d)|0;if(!q)while(1){if(!e)break a;s=+h[23248];s=(+B5()*2.0+-1.0)*s;z=e+16|0;h[c[(c[z>>2]|0)+132>>2]>>3]=s;s=+h[23249];s=(+B5()*2.0+-1.0)*s;h[(c[(c[z>>2]|0)+132>>2]|0)+8>>3]=s;e=kC(d,e)|0}while(1){if(!e)break a;f=e+16|0;g=c[f>>2]|0;if(!(a[g+119>>0]|0)){s=+h[23248];s=(+B5()*2.0+-1.0)*s;h[c[(c[f>>2]|0)+132>>2]>>3]=s;s=+h[23249];s=(+B5()*2.0+-1.0)*s;h[(c[(c[f>>2]|0)+132>>2]|0)+8>>3]=s}else{z=c[g+132>>2]|0;h[z>>3]=+h[z>>3]-t;z=z+8|0;h[z>>3]=+h[z>>3]-w}e=kC(d,e)|0}}else{while(1){if(!(c[e>>2]|0))break;u=e+8|0;s=+E(+(+h[u>>3]))*+h[23248]+t;x=c[(c[e+4>>2]|0)+16>>2]|0;v=c[x+132>>2]|0;h[v>>3]=s;h[v+8>>3]=+F(+(+h[u>>3]))*+h[23249]+w;a[x+119>>0]=1;e=e+16|0}l=t*.1;m=w*.1;r=jC(d)|0;while(1){if(!r)break a;q=r+16|0;e=c[q>>2]|0;if(!((c[(c[e+112>>2]|0)+8>>2]|0)==0?!(c[e+212>>2]|0):0))z=32;do if((z|0)==32){z=0;if(a[e+119>>0]|0){x=c[e+132>>2]|0;h[x>>3]=+h[x>>3]-t;x=x+8|0;h[x>>3]=+h[x>>3]-w;break}n=aB(d,r)|0;e=0;j=0.0;i=0.0;while(1){if(!n)break;k=c[n>>2]&3;f=(k|0)==2?n:n+-48|0;g=c[f+40>>2]|0;k=(k|0)==3?n:n+48|0;do if((g|0)!=(c[k+40>>2]|0)?(y=c[(c[((g|0)==(r|0)?k:f)+40>>2]|0)+16>>2]|0,(a[y+119>>0]|0)!=0):0)if(!e){x=c[y+132>>2]|0;e=1;j=+h[x>>3];i=+h[x+8>>3];break}else{p=+(e|0);x=c[y+132>>2]|0;e=e+1|0;s=+(e|0);j=(+h[x>>3]+j*p)/s;i=(+h[x+8>>3]+i*p)/s;break}while(0);n=bB(d,n,r)|0}do if((e|0)<=1)if((e|0)==1){f=c[q>>2]|0;e=c[f+132>>2]|0;h[e>>3]=j*.98+l;i=i*.9+m;break}else{i=+B5()*6.283185307179586;s=+B5()*.9;p=s*+h[23248]*+E(+i);f=c[q>>2]|0;e=c[f+132>>2]|0;h[e>>3]=p;i=+h[23249]*s*+F(+i);break}else{f=c[q>>2]|0;e=c[f+132>>2]|0;h[e>>3]=j}while(0);h[e+8>>3]=i;a[f+119>>0]=1}while(0);r=kC(d,r)|0}}while(0);h[b>>3]=t;h[b+8>>3]=w;return}function jp(a){a=a|0;var b=0;b=c[46478]|0;return +(+h[23243]*+(b-a|0)/+(b|0))}function kp(a,b,d,e){a=a|0;b=+b;d=d|0;e=e|0;var f=0,g=0,i=0,j=0.0;if(!(b<=0.0)){Qv(e);f=jC(a)|0;while(1){if(!f)break;i=c[f+16>>2]|0;g=(c[i+112>>2]|0)+16|0;c[g>>2]=0;c[g+4>>2]=0;c[g+8>>2]=0;c[g+12>>2]=0;i=c[i+132>>2]|0;j=+h[23245];g=~~+A(+(+h[i>>3]/j));Tv(e,g,~~+A(+(+h[i+8>>3]/j)),f);f=kC(a,f)|0}g=jC(a)|0;while(1){if(!g)break;f=XA(a,g)|0;while(1){if(!f)break;i=c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0;if((g|0)!=(i|0))op(g,i,f);f=ZA(a,f)|0}g=kC(a,g)|0}Vv(e,23);pp(a,b,d)}return}function lp(a,b,d){a=a|0;b=+b;d=d|0;var e=0,f=0,g=0;if(!(b<=0.0)){e=jC(a)|0;while(1){if(!e)break;g=(c[(c[e+16>>2]|0)+112>>2]|0)+16|0;c[g>>2]=0;c[g+4>>2]=0;c[g+8>>2]=0;c[g+12>>2]=0;e=kC(a,e)|0}g=jC(a)|0;while(1){if(!g)break;else e=g;while(1){e=kC(a,e)|0;if(!e)break;np(g,e)}e=XA(a,g)|0;while(1){if(!e)break;f=c[((c[e>>2]&3|0)==2?e:e+-48|0)+40>>2]|0;if((g|0)!=(f|0))op(g,f,e);e=ZA(a,e)|0}g=kC(a,g)|0}pp(a,b,d)}return}function mp(){h[23243]=-1.0;return}function np(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0,g=0;g=c[(c[b+16>>2]|0)+132>>2]|0;f=c[(c[a+16>>2]|0)+132>>2]|0;d=+h[g>>3]-+h[f>>3];e=+h[g+8>>3]-+h[f+8>>3];qp(a,b,d,e,e*e+d*d);return}function op(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0.0,i=0.0,j=0,k=0;j=b+16|0;k=c[(c[j>>2]|0)+132>>2]|0;a=a+16|0;b=c[(c[a>>2]|0)+132>>2]|0;e=+h[k>>3]-+h[b>>3];f=+h[k+8>>3]-+h[b+8>>3];g=e;i=f;e=e*e;f=f*f;while(1){e=e+f;if(!(e==0.0))break;e=+(5-((x5()|0)%10|0)|0);f=+(5-((x5()|0)%10|0)|0);g=e;i=f;e=e*e;f=f*f}f=+C(+e);b=c[d+16>>2]|0;e=+h[b+128>>3];if(!(c[46475]|0))e=e*f/+h[b+136>>3];else e=(f-+h[b+136>>3])*e/f;g=e*g;k=c[(c[j>>2]|0)+112>>2]|0;j=k+16|0;h[j>>3]=+h[j>>3]-g;i=e*i;k=k+24|0;h[k>>3]=+h[k>>3]-i;k=c[(c[a>>2]|0)+112>>2]|0;j=k+16|0;h[j>>3]=+h[j>>3]+g;k=k+24|0;h[k>>3]=+h[k>>3]+i;return}function pp(b,d,e){b=b|0;d=+d;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0,k=0,l=0,m=0,n=0,o=0.0;o=d*d;n=(e|0)==0;m=jC(b)|0;while(1){if(!m)break;l=c[m+16>>2]|0;if(!(a[l+119>>0]&2)){k=c[l+112>>2]|0;f=+h[k+16>>3];i=+h[k+24>>3];g=i*i+f*f;if(g<o){e=c[l+132>>2]|0;j=+h[e>>3]+f;f=+h[e+8>>3]+i}else{g=d/+C(+g);e=c[l+132>>2]|0;j=+h[e>>3]+g*f;f=+h[e+8>>3]+g*i}do if(!n){g=+C(+(f*f/+h[23251]+j*j/+h[23250]));if((c[k+8>>2]|0)==0?(c[l+212>>2]|0)==0:0){h[e>>3]=j/g;f=f/g;break}if(!(g>=1.0)){h[e>>3]=j;break}else{h[e>>3]=j*.95/g;f=f*.95/g;break}}else h[e>>3]=j;while(0);h[e+8>>3]=f}m=kC(b,m)|0}return}function qp(a,b,d,e,f){a=a|0;b=b|0;d=+d;e=+e;f=+f;var g=0.0,i=0,j=0;g=d;d=f;while(1){if(!(d==0.0))break;d=+(5-((x5()|0)%10|0)|0);e=+(5-((x5()|0)%10|0)|0);g=d;d=e*e+d*d}if(!(c[46475]|0))d=+h[23247]/d;else{f=+C(+d);d=+h[23247]/(f*d)}j=c[a+16>>2]|0;a=c[j+112>>2]|0;if(((c[a+8>>2]|0)==0?(c[j+212>>2]|0)==0:0)?(i=c[b+16>>2]|0,(c[(c[i+112>>2]|0)+8>>2]|0)==0):0)d=(c[i+212>>2]|0)==0?d*10.0:d;f=d*g;j=c[(c[b+16>>2]|0)+112>>2]|0;b=j+16|0;h[b>>3]=+h[b>>3]+f;g=d*e;j=j+24|0;h[j>>3]=+h[j>>3]+g;j=a+16|0;h[j>>3]=+h[j>>3]-f;j=a+24|0;h[j>>3]=+h[j>>3]-g;return}function rp(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;f=c[b+8>>2]|0;g=c[b>>2]|0;e=c[b+4>>2]|0;b=f;while(1){if(!b)break;a=f;while(1){if(!a)break;if((b|0)!=(a|0))np(c[b>>2]|0,c[a>>2]|0);a=c[a+4>>2]|0}b=c[b+4>>2]|0}h=g+-1|0;a=e+-1|0;sp(d,h,a,f);sp(d,h,e,f);b=e+1|0;sp(d,h,b,f);sp(d,g,a,f);sp(d,g,b,f);g=g+1|0;sp(d,g,a,f);sp(d,g,e,f);sp(d,g,b,f);return 0}function sp(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0,k=0,l=0,m=0;a=Wv(a,b,d)|0;if((e|0)!=0&(a|0)!=0){k=a+8|0;do{d=c[e>>2]|0;j=d+16|0;a=k;while(1){a=c[a>>2]|0;if(!a)break;b=c[a>>2]|0;m=c[(c[b+16>>2]|0)+132>>2]|0;l=c[(c[j>>2]|0)+132>>2]|0;f=+h[m>>3]-+h[l>>3];g=+h[m+8>>3]-+h[l+8>>3];i=g*g+f*f;if(i<+h[23246])qp(d,b,f,g,i);a=a+4|0}e=c[e+4>>2]|0}while((e|0)!=0)}return}function tp(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;h=l;l=l+16|0;g=h;e=QA(b,92507)|0;if(0)U4(91116,8,1,c[15712]|0)|0;if(!((e|0)!=0?(a[e>>0]|0)!=0:0))e=91125;f=R2(e,58)|0;do if(!f)f=0;else{if((f|0)!=(e|0)?((a[e>>0]|0)+-48|0)>>>0>=10:0){f=0;break}i=q5(e)|0;e=f+1|0;f=(i|0)>0?i:0}while(0);if(0){i=c[15712]|0;c[g>>2]=f;c[g+4>>2]=e;w4(i,91133,g)|0}if(!((f|0)!=0?!(up(b,d,f)|0):0))nr(b,e)|0;l=h;return}function up(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,i=0,j=0,k=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0,r=0,s=0,t=0;t=l;l=l+64|0;r=t;j=t+40|0;s=tB(b)|0;k=uB(b)|0;lr(j,b);c[46842]=c[j>>2];c[46843]=c[j+4>>2];c[46844]=c[j+8>>2];if(a[187376]|0){g[46842]=+g[46842]*.013888888888888888;g[46843]=+g[46843]*.013888888888888888}f=vp(b)|0;a:do if(!f)f=0;else{j=r;i=j+40|0;do{c[j>>2]=c[d>>2];j=j+4|0;d=d+4|0}while((j|0)<(i|0));j=r+16|0;q=+h[j>>3];o=+(k|0)*2.0;p=1.0/+(O(s+-1|0,s)|0);i=0;n=q;while(1){if(!((f|0)!=0&(i|0)<(e|0)))break a;wp(s,r);m=+h[23253]*+h[170];h[23254]=m;h[23255]=o*m*p;d=0;while(1){if((d|0)>=(c[46512]|0))break;m=+xp(d);if(m<=0.0)break;f=yp(b,m)|0;if(!f){f=0;break}else d=d+1|0}m=n+q;h[j>>3]=m;i=i+1|0;n=m}}while(0);l=t;return f|0}function vp(a){a=a|0;var b=0,c=0,d=0,e=0;b=0;e=jC(a)|0;while(1){if(!e)break;else d=e;while(1){c=kC(a,d)|0;if(!c)break;d=c;b=(Bp(e,c)|0)+b|0}e=kC(a,e)|0}return b|0}function wp(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0.0;e=+h[b+16>>3];h[171]=e;c[344]=c[b>>2];f=+h[b+8>>3];h[23257]=f;c[46512]=c[b+32>>2];d=+h[b+24>>3];if(d>0.0)h[170]=d;h[23253]=e*e;if(f==0.0)h[23257]=+C(+(+(a|0)))*.2*e;return}function xp(a){a=a|0;var b=0;b=c[344]|0;return +(+h[23257]*+(b-a|0)/+(b|0))}function yp(b,d){b=b|0;d=+d;var e=0,f=0,g=0,i=0.0,j=0,k=0.0,l=0.0,m=0.0;e=jC(b)|0;while(1){if(!e)break;j=(c[(c[e+16>>2]|0)+112>>2]|0)+16|0;c[j>>2]=0;c[j+4>>2]=0;c[j+8>>2]=0;c[j+12>>2]=0;e=kC(b,e)|0}e=0;j=jC(b)|0;while(1){if(!j)break;else g=j;while(1){f=kC(b,g)|0;if(!f)break;g=f;e=(zp(j,f)|0)+e|0}f=XA(b,j)|0;while(1){if(!f)break;Ap(j,c[((c[f>>2]&3|0)==2?f:f+-48|0)+40>>2]|0);f=ZA(b,f)|0}j=kC(b,j)|0}a:do if(!e)e=0;else{m=d*d;g=jC(b)|0;while(1){if(!g)break a;f=c[g+16>>2]|0;if((a[f+119>>0]|0)!=3){j=c[f+112>>2]|0;k=+h[j+16>>3];i=+h[j+24>>3];l=i*i+k*k;if(l<m){f=c[f+132>>2]|0;h[f>>3]=+h[f>>3]+k}else{l=+C(+l);f=c[f+132>>2]|0;h[f>>3]=+h[f>>3]+k*d/l;i=i*d/l}j=f+8|0;h[j>>3]=+h[j>>3]+i}g=kC(b,g)|0}}while(0);return e|0}function zp(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0,g=0;g=c[(c[b+16>>2]|0)+132>>2]|0;f=c[(c[a+16>>2]|0)+132>>2]|0;d=+h[g>>3]-+h[f>>3];e=+h[g+8>>3]-+h[f+8>>3];return Dp(a,b,d,e,e*e+d*d)|0}function Ap(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0,g=0.0,i=0,j=0.0,k=0.0,l=0,m=0;if(!(Bp(a,b)|0)){i=c[b+16>>2]|0;m=c[i+132>>2]|0;f=c[a+16>>2]|0;l=c[f+132>>2]|0;e=+h[m>>3]-+h[l>>3];d=+h[m+8>>3]-+h[l+8>>3];g=+C(+(d*d+e*e));j=+Cp(a);j=+Cp(b)+j;k=g-j;g=k*k/((+h[171]+j)*g);e=g*e;b=c[i+112>>2]|0;a=b+16|0;h[a>>3]=+h[a>>3]-e;d=g*d;b=b+24|0;h[b>>3]=+h[b>>3]-d;b=c[f+112>>2]|0;a=b+16|0;h[a>>3]=+h[a>>3]+e;b=b+24|0;h[b>>3]=+h[b>>3]+d}return}function Bp(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,i=0.0,j=0.0,k=0,l=0;k=c[d+16>>2]|0;l=c[k+132>>2]|0;d=c[b+16>>2]|0;b=c[d+132>>2]|0;i=+h[l>>3]-+h[b>>3];j=+h[l+8>>3]-+h[b+8>>3];j=j<0.0?-j:j;b=(a[187376]|0)==0;e=+h[d+32>>3];if(b){f=+g[46842];e=e*.5*f;f=+h[k+32>>3]*.5*f}else{f=+g[46842];e=e*.5+f;f=+h[k+32>>3]*.5+f}if(!((i<0.0?-i:i)<=f+e))d=0;else{e=+h[d+40>>3];if(b){i=+g[46843];f=e*.5*i;e=+h[k+40>>3]*.5*i}else{i=+g[46843];f=e*.5+i;e=+h[k+40>>3]*.5+i}d=j<=e+f&1}return d|0}function Cp(b){b=b|0;var d=0.0,e=0.0;b=c[b+16>>2]|0;d=+h[b+32>>3];if(!(a[187376]|0)){e=d*.5*+g[46842];d=+h[b+40>>3]*.5*+g[46843]}else{e=d*.5+ +g[46842];d=+h[b+40>>3]*.5+ +g[46843]}return +(+C(+(d*d+e*e)))}function Dp(a,b,d,e,f){a=a|0;b=b|0;d=+d;e=+e;f=+f;var g=0,i=0;while(1){if(!(f==0.0))break;f=+(5-((x5()|0)%10|0)|0);e=+(5-((x5()|0)%10|0)|0);d=f;f=e*e+f*f}g=Bp(a,b)|0;f=((g|0)==0?+h[23255]:+h[23254])/f;d=f*d;b=c[(c[b+16>>2]|0)+112>>2]|0;i=b+16|0;h[i>>3]=+h[i>>3]+d;f=f*e;b=b+24|0;h[b>>3]=+h[b>>3]+f;b=c[(c[a+16>>2]|0)+112>>2]|0;a=b+16|0;h[a>>3]=+h[a>>3]-d;b=b+24|0;h[b>>3]=+h[b>>3]-f;return g|0}function Ep(a){a=a|0;oQ(a,2);b[(c[a+16>>2]|0)+176>>1]=2;c[47137]=2;Fp(a);return}function Fp(a){a=a|0;var b=0,d=0,e=0,f=0;e=JK((tB(a)|0)<<2)|0;b=JK(((tB(a)|0)<<2)+4|0)|0;f=a+16|0;c[(c[f>>2]|0)+152>>2]=b;b=jC(a)|0;d=0;while(1){if(!b)break;Zl(b);c[(c[b+16>>2]|0)+112>>2]=e+(d<<2);c[(c[(c[f>>2]|0)+152>>2]|0)+(d<<2)>>2]=b;b=kC(a,b)|0;d=d+1|0}d=jC(a)|0;while(1){if(!d)break;b=XA(a,d)|0;while(1){if(!b)break;Gp(b);b=ZA(a,b)|0}d=kC(a,d)|0}return}function Gp(a){a=a|0;var b=0.0;TC(a,137460,176,1)|0;tP(a)|0;b=+_O(a,c[47174]|0,1.0,0.0);h[(c[a+16>>2]|0)+128>>3]=b;return}function Hp(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;m=n+4|0;k=n;a[m>>0]=a[18596]|0;a[m+1>>0]=a[18597]|0;a[m+2>>0]=a[18598]|0;a[m+3>>0]=a[18599]|0;m=oB(91156,m,0)|0;TC(m,91164,280,1)|0;c[(c[b+16>>2]|0)+140>>2]=m;e=jC(b)|0;while(1){if(!e)break;f=e+16|0;if(!(c[c[(c[f>>2]|0)+112>>2]>>2]|0)){j=Ip(m,KB(e)|0,e)|0;c[c[(c[f>>2]|0)+112>>2]>>2]=j}e=kC(b,e)|0}f=jC(b)|0;while(1){if(!f)break;e=XA(b,f)|0;while(1){if(!e)break;h=c[e>>2]&3;g=c[c[(c[(c[((h|0)==3?e:e+48|0)+40>>2]|0)+16>>2]|0)+112>>2]>>2]|0;h=c[c[(c[(c[((h|0)==2?e:e+-48|0)+40>>2]|0)+16>>2]|0)+112>>2]>>2]|0;if((g|0)!=(h|0))TC(iB(m,g,h,0,1)|0,137460,176,1)|0;e=ZA(b,e)|0}f=kC(b,f)|0}j=DG(m,k,0)|0;f=0;while(1){if((f|0)>=(c[k>>2]|0))break;h=c[j+(f<<2)>>2]|0;e=jC(h)|0;while(1){if(!e)break;g=XA(b,c[c[(c[e+16>>2]|0)+112>>2]>>2]|0)|0;while(1){if(!g)break;i=c[c[(c[(c[((c[g>>2]&3|0)==2?g:g+-48|0)+40>>2]|0)+16>>2]|0)+112>>2]>>2]|0;if((e|0)!=(i|0)){i=iB(m,e,i,0,1)|0;TC(i,137460,176,1)|0;mB(h,i,1)|0}g=ZA(b,g)|0}e=kC(h,e)|0}f=f+1|0}e=jC(m)|0;while(1){if(!e)break;f=XA(m,e)|0;while(1){if(!f)break;b=JK(8)|0;c[(c[f+16>>2]|0)+120>>2]=b;f=ZA(m,f)|0}e=kC(m,e)|0}c[d>>2]=c[k>>2];l=n;return j|0}function Ip(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;b=tC(a,b,1)|0;TC(b,137447,304,1)|0;e=JK(40)|0;a=b+16|0;c[(c[a>>2]|0)+112>>2]=e;e=JK(c[47137]<<3)|0;a=c[a>>2]|0;c[a+132>>2]=e;e=c[d+16>>2]|0;h[a+88>>3]=+h[e+88>>3];h[a+96>>3]=+h[e+96>>3];h[a+80>>3]=+h[e+80>>3];c[c[a+112>>2]>>2]=d;return b|0}function Jp(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0;h=l;l=l+48|0;b=h+32|0;e=h;if(tB(a)|0){g=Hp(a,b)|0;f=c[b>>2]|0;b=c[g>>2]|0;if((f|0)==1){bx(b,a);Kp(c[g>>2]|0);or(a)|0}else{d=c[b+60>>2]|0;kH(a,2,8,e)|0;b=0;while(1){if((b|0)>=(f|0))break;i=c[g+(b<<2)>>2]|0;bx(i,a);or(i)|0;b=b+1|0}jH(f,g,d,e)|0;b=0;while(1){if((b|0)>=(f|0))break;Kp(c[g+(b<<2)>>2]|0);b=b+1|0}}U1(g)}l=h;return}function Kp(a){a=a|0;var b=0,d=0,e=0;b=jC(a)|0;while(1){if(!b)break;d=c[b+16>>2]|0;e=c[d+132>>2]|0;d=c[(c[(c[c[d+112>>2]>>2]|0)+16>>2]|0)+132>>2]|0;h[d>>3]=+h[e>>3];h[d+8>>3]=+h[e+8>>3];b=kC(a,b)|0}return}function Lp(a){a=a|0;if(tB(a)|0){Ep(a);Jp(a);U1(c[(c[(jC(a)|0)+16>>2]|0)+112>>2]|0);fn(a);tM(a)}return}function Mp(a){a=a|0;var b=0,d=0,e=0;b=jC(a)|0;if(b|0){e=a+16|0;Np(c[(c[e>>2]|0)+140>>2]|0);while(1){if(!b)break;d=XA(a,b)|0;while(1){if(!d)break;YP(d);d=ZA(a,d)|0}WP(b);b=kC(a,b)|0}U1(c[(c[e>>2]|0)+152>>2]|0);if((yC(a)|0)!=(a|0))ZC(a,0,137483)}return}function Np(a){a=a|0;var b=0,d=0;d=jC(a)|0;while(1){if(!d)break;b=XA(a,d)|0;while(1){if(!b)break;U1(c[(c[b+16>>2]|0)+120>>2]|0);b=ZA(a,b)|0}b=d+16|0;U1(c[(c[b>>2]|0)+112>>2]|0);U1(c[(c[b>>2]|0)+132>>2]|0);d=kC(a,d)|0}sB(a)|0;return}function Op(b){b=b|0;var d=0,e=0.0,f=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+112|0;m=s+40|0;n=s+16|0;r=s+100|0;o=s;j=s+88|0;p=s+56|0;Pp(b);i=(c[47137]|0)==2;d=i&1;if(tB(b)|0){q=Yp()|0;Qp(b,q);Nq(b,n,0)|0;if(i&(c[n>>2]|0)==18){c[q+124>>2]=c[n+8>>2];h[q+144>>3]=+h[n+16>>3];lr(j,b);if(!(a[j+8>>0]|0)){f=.05555555555555555;e=.05555555555555555}else{f=+g[j+4>>2]*.013888888888888888;e=+g[j>>2]*.013888888888888888}h[o>>3]=e;h[o+8>>3]=f;d=0}else c[q+124>>2]=-1;if(0)_p(q);k=DG(b,r,0)|0;if((c[r>>2]|0)==1){c[m>>2]=c[o>>2];c[m+4>>2]=c[o+4>>2];c[m+8>>2]=c[o+8>>2];c[m+12>>2]=c[o+12>>2];Rp(b,q,m);if(d|0)Qq(b,n)|0;fn(b);d=0}else{kH(b,2,8,p)|0;c[p+12>>2]=1;j=(d|0)==0;i=0;while(1){d=c[r>>2]|0;if((i|0)>=(d|0))break;d=c[k+(i<<2)>>2]|0;KG(d)|0;c[m>>2]=c[o>>2];c[m+4>>2]=c[o+4>>2];c[m+8>>2]=c[o+8>>2];c[m+12>>2]=c[o+12>>2];Rp(d,q,m);if(!j)Qq(d,n)|0;oQ(d,2);fn(d);i=i+1|0}jH(d,k,b,p)|0;d=0}while(1){if((d|0)>=(c[r>>2]|0))break;wC(b,c[k+(d<<2)>>2]|0)|0;d=d+1|0}U1(k);Zp(q)}tM(b);l=s;return}function Pp(a){a=a|0;var d=0,f=0;oQ(a,2);d=ZO(a,GA(a,0,91368,0)|0,2,2)|0;f=(ZO(a,GA(a,0,91374,0)|0,d,2)|0)&65535;b[(c[(yC(a)|0)+16>>2]|0)+176>>1]=f;if((e[(c[(yC(a)|0)+16>>2]|0)+176>>1]|0)<10)f=b[(c[(yC(a)|0)+16>>2]|0)+176>>1]|0;else f=10;b[(c[(yC(a)|0)+16>>2]|0)+176>>1]=f;f=f&65535;c[47137]=f;b[(c[(yC(a)|0)+16>>2]|0)+178>>1]=(d|0)<(f|0)?d:f;Vp(a);return}function Qp(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;g=l;l=l+16|0;f=g+8|0;d=g+12|0;e=b+108|0;c[d>>2]=c[e>>2];if((mm(a,2,d)|0)!=2)gA(0,91169,g)|0;c[e>>2]=c[d>>2];h[b+24>>3]=+_O(a,GA(a,0,91202,0)|0,-1.0,0.0);h[b>>3]=-+_O(a,GA(a,0,91204,0)|0,1.0001234,0.0);c[b+40>>2]=ZO(a,GA(a,0,91219,0)|0,2147483647,0)|0;c[b+120>>2]=Tp(a,GA(a,0,91226,0)|0)|0;c[b+132>>2]=Up(a,GA(a,0,91236,0)|0)|0;c[b+136>>2]=0;c[b+112>>2]=(eP(QA(a,91245)|0,0)|0)&255;c[b+128>>2]=(eP(QA(a,91254)|0,1)|0)&255;h[b+152>>3]=+_O(a,GA(a,0,91269,0)|0,0.0,-1797693134862315708145274.0e284);d=ZO(a,GA(a,0,91278,0)|0,0,0)|0;a=b+160|0;c[a>>2]=d;if((d|0)>4){c[f>>2]=d;gA(0,91291,f)|0;c[a>>2]=0}l=g;return}function Rp(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+48|0;f=q;n=q+32|0;j=q+28|0;g=q+24|0;p=q+20|0;o=q+16|0;c[g>>2]=0;c[p>>2]=0;c[o>>2]=0;i=b+136|0;e=c[47137]|0;if((c[i>>2]|0)==1)m=Kq(a,e,o)|0;else m=Kq(a,e,0)|0;do if((c[b+124>>2]|0)>-1)if((c[b+160>>2]|0)>0){c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];k=Jq(a,f,g,p)|0;break}else{c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];k=Jq(a,f,0,0)|0;break}else k=0;while(0);e=Sp(a)|0;c[n>>2]=e;switch(c[i>>2]|0){case 1:case 0:{wq(c[47137]|0,m,c[o>>2]|0,b,0,k,e,c[g>>2]|0,c[p>>2]|0,j);break}case 5:{Iq(c[47137]|0,m,e,j);break}case 4:{e=c[o>>2]|0;if(!e)e=Qw(m)|0;else e=Aw(e,0)|0;c[o>>2]=e;Dq(c[47137]|0,m,e,n,1,200,.001,j);break}default:{}}e=jC(a)|0;while(1){if(!e)break;d=c[47137]|0;g=c[e+16>>2]|0;b=(c[n>>2]|0)+((O(c[g+120>>2]|0,d)|0)<<3)|0;g=g+132|0;f=0;while(1){if((f|0)>=(d|0))break;h[(c[g>>2]|0)+(f<<3)>>3]=+h[b+(f<<3)>>3];f=f+1|0}e=kC(a,e)|0}U1(k);U1(c[n>>2]|0);qw(m);e=c[o>>2]|0;if(e|0)qw(e);e=c[p>>2]|0;if(e|0)U1(e);l=q;return}function Sp(b){b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0;j=c[47137]|0;j=JK(O(j<<3,tB(b)|0)|0)|0;a:do if(GA(b,1,102623,0)|0){f=jC(b)|0;while(1){if(!f)break a;d=c[f+16>>2]|0;b:do if(a[d+119>>0]|0){g=c[47137]|0;i=O(g,c[d+120>>2]|0)|0;e=d+132|0;d=0;while(1){if((d|0)>=(g|0))break b;h[j+(i+d<<3)>>3]=+h[(c[e>>2]|0)+(d<<3)>>3];d=d+1|0}}while(0);f=kC(b,f)|0}}while(0);return j|0}function Tp(b,c){b=b|0;c=c|0;do if(c){b=RA(b,c)|0;c=a[b>>0]|0;if((c+-48|0)>>>0<10){b=q5(b)|0;b=(b|0)<5?b:0;break}if(b3(c)|0)if(c3(b,91330)|0)if(c3(b,91339)|0)if(c3(b,135709)|0)if(!(c3(b,91350)|0))b=3;else{b=(c3(b,91361)|0)==0;b=b?4:0}else b=0;else b=1;else b=2;else b=0}else b=0;while(0);return b|0}function Up(b,c){b=b|0;c=c|0;do if(c){b=RA(b,c)|0;c=a[b>>0]|0;if((c+-48|0)>>>0<10){b=q5(b)|0;b=b>>>0<3?b:1;break}if(b3(c)|0)if((c3(b,135709)|0)!=0?(c3(b,137308)|0)!=0:0)if(((c3(b,99730)|0)!=0?(c3(b,107652)|0)!=0:0)?(c3(b,107657)|0)!=0:0){b=(c3(b,91325)|0)==0;b=b?2:1}else b=1;else b=0;else b=1}else b=1;while(0);return b|0}function Vp(a){a=a|0;var b=0,c=0;b=jC(a)|0;while(1){if(!b)break;Zl(b);b=kC(a,b)|0}c=jC(a)|0;while(1){if(!c)break;b=XA(a,c)|0;while(1){if(!b)break;Wp(b);b=ZA(a,b)|0}c=kC(a,c)|0}return}function Wp(a){a=a|0;TC(a,137460,176,1)|0;tP(a)|0;return}function Xp(a){a=a|0;var b=0,c=0;c=jC(a)|0;while(1){if(!c)break;b=XA(a,c)|0;while(1){if(!b)break;YP(b);b=ZA(a,b)|0}WP(c);c=kC(a,c)|0}return}function Yp(){var a=0,b=0;a=KK(168)|0;h[a>>3]=-1.0001234;h[a+8>>3]=1.0;c[a+16>>2]=1;h[a+24>>3]=-1.0;h[a+32>>3]=.2;c[a+40>>2]=0;c[a+44>>2]=4;c[a+48>>2]=1;c[a+52>>2]=45;c[a+56>>2]=10;h[a+64>>3]=.6;h[a+72>>3]=.001;c[a+80>>2]=500;h[a+88>>3]=.9;h[a+96>>3]=.1;c[a+104>>2]=1;c[a+108>>2]=123;b=a+112|0;c[b>>2]=0;c[b+4>>2]=0;c[b+8>>2]=0;c[b+12>>2]=0;c[a+128>>2]=1;c[a+132>>2]=3;c[a+136>>2]=0;h[a+144>>3]=-4.0;h[a+152>>3]=0.0;c[a+160>>2]=0;return a|0}function Zp(a){a=a|0;U1(a);return}function _p(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;b=l;l=l+176|0;d=b+160|0;f=b+152|0;g=b+128|0;k=b+112|0;j=b+88|0;m=b+64|0;n=b+56|0;o=b+40|0;q=b+24|0;p=b+16|0;r=b;e=c[15712]|0;U4(91378,27,1,e)|0;i=+h[a+8>>3];h[r>>3]=+h[a>>3];h[r+8>>3]=i;w4(e,91406,r)|0;r=c[a+108>>2]|0;c[p>>2]=c[a+16>>2];c[p+4>>2]=r;w4(e,91457,p)|0;i=+h[a+32>>3];h[q>>3]=+h[a+24>>3];h[q+8>>3]=i;w4(e,91484,q)|0;q=c[a+44>>2]|0;p=c[a+48>>2]|0;c[o>>2]=c[a+40>>2];c[o+4>>2]=q;c[o+8>>2]=p;w4(e,91507,o)|0;o=c[a+56>>2]|0;c[n>>2]=c[a+52>>2];c[n+4>>2]=o;w4(e,91558,n)|0;i=+h[a+72>>3];n=c[a+80>>2]|0;h[m>>3]=+h[a+64>>3];h[m+8>>3]=i;c[m+16>>2]=n;w4(e,91591,m)|0;i=+h[a+96>>3];m=c[a+104>>2]|0;h[j>>3]=+h[a+88>>3];h[j+8>>3]=i;c[j+16>>2]=m;w4(e,91649,j)|0;j=c[a+116>>2]|0;i=+h[a+152>>3];c[k>>2]=c[a+112>>2];c[k+4>>2]=j;h[k+8>>3]=i;w4(e,91695,k)|0;k=c[a+124>>2]|0;j=c[a+128>>2]|0;i=+h[a+144>>3];c[g>>2]=c[17804+(c[a+120>>2]<<2)>>2];c[g+4>>2]=k;c[g+8>>2]=j;h[g+16>>3]=i;w4(e,91748,g)|0;g=c[17848+(c[a+136>>2]<<2)>>2]|0;c[f>>2]=c[17832+(c[a+132>>2]<<2)>>2];c[f+4>>2]=g;w4(e,91813,f)|0;c[d>>2]=c[a+160>>2];w4(e,91843,d)|0;l=b;return}function $p(a){a=a|0;U1(a);return}function aq(a){a=a|0;var b=0;b=KK(184)|0;c[b>>2]=a;c[b+176>>2]=0;return b|0}function bq(a,b){a=a|0;b=+b;var d=0,e=0,f=0;e=c[a>>2]|0;h[a+8+(e<<3)>>3]=b;f=a+176|0;a:do switch(c[f>>2]|0){case 0:{d=c[a>>2]|0;if((d|0)==20){c[f>>2]=-1;d=19}else{c[f>>2]=1;d=(d|0)>19?20:d+1|0}c[a>>2]=d;break}case 1:{d=c[a>>2]|0;if((d|0)<20?+h[a+8+(e+-1<<3)>>3]>b:0){c[a>>2]=d+1;break a}else{c[a>>2]=d+-1;c[f>>2]=-1;break a}}default:{d=c[a>>2]|0;if((d|0)>0?+h[a+8+(e+1<<3)>>3]>b:0){c[a>>2]=d+-1;break a}else{c[a>>2]=d+1;c[f>>2]=1;break a}}}while(0);return}function cq(a){a=a|0;return c[a>>2]|0}function dq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0.0;o=c[a+20>>2]|0;p=c[a+24>>2]|0;m=c[a>>2]|0;n=c[o+(m<<2)>>2]|0;if(!n)e=1.0;else{a=0;e=0.0;a:while(1){if((a|0)>=(m|0))break;j=a+1|0;k=c[o+(j<<2)>>2]|0;l=O(a,b)|0;i=c[o+(a<<2)>>2]|0;while(1){if((i|0)>=(k|0)){a=j;continue a}g=p+(i<<2)|0;a=0;f=0.0;while(1){if((a|0)>=(b|0))break;q=+h[d+(a+l<<3)>>3]-+h[d+((O(c[g>>2]|0,b)|0)<<3)>>3];a=a+1|0;f=q*q+f}i=i+1|0;e=+C(+f)+e}}e=e/+(n|0)}return +e}function eq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=b+10|0;if((c[d>>2]|0)<=(b|0)){c[d>>2]=e;c[a>>2]=MK(c[a>>2]|0,e<<3)|0}return}function fq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=b+10|0;if((c[d>>2]|0)<=(b|0)){c[d>>2]=e;c[a>>2]=MK(c[a>>2]|0,e<<2)|0}return}function gq(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0.0,f=0.0,g=0.0,i=0,j=0,k=0;k=l;l=l+16|0;j=k;i=O(d,b)|0;b=O(c,b)|0;d=0;while(1){if((d|0)==2)break;h[j+(d<<3)>>3]=+h[a+(d+i<<3)>>3]-+h[a+(d+b<<3)>>3];d=d+1|0}g=+h[j>>3];f=+h[j+8>>3];do if(!((g>=0.0?g:-g)<=(f>=0.0?f:-f)*1.0e-05)){e=+J(+(f/g));if(g>0.0){if(!(f<0.0))break;e=e+6.283185307179586;break}else{if(!(g<0.0))break;e=e+3.141592653589793;break}}else e=f>0.0?1.5707963267948966:4.71238898038469;while(0);l=k;return +e}function hq(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;d=+h[a>>3];c=+h[b>>3];return (d>c?1:(d<c)<<31>>31)|0}function iq(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0.0,k=0,m=0,n=0.0,o=0,p=0.0,q=0.0,r=0,s=0,t=0,u=0,v=0.0,w=0,x=0,y=0,z=0.0,A=0.0,B=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0.0,N=0,P=0,Q=0,R=0.0,S=0,T=0.0,U=0,V=0,W=0,X=0,Y=0,Z=0,_=0,$=0,aa=0.0;_=l;l=l+80|0;Z=_+32|0;Y=_;p=+h[d>>3];m=d+24|0;n=+h[m>>3];o=d+32|0;j=+h[o>>3];R=+h[d+72>>3];S=c[d+80>>2]|0;T=+h[d+88>>3];q=+h[d+96>>3];U=c[d+104>>2]|0;V=d+56|0;i=c[V>>2]|0;do if(!((b|0)==0|(S|0)<1)?(W=c[b+4>>2]|0,!((a|0)<1|(W|0)<1)):0){Q=c[b>>2]|0;X=aq(i)|0;c[g>>2]=0;if((Q|0)!=(W|0)){c[g>>2]=-100;$p(X);c[V>>2]=i;break}N=vw(b,1)|0;P=c[N+20>>2]|0;Q=c[N+24>>2]|0;a:do if(c[d+16>>2]|0){w5(c[d+108>>2]|0);k=O(W,a)|0;i=0;while(1){if((i|0)>=(k|0))break a;h[f+(i<<3)>>3]=+Sw();i=i+1|0}}while(0);if(n<0.0){n=+dq(N,a,f);h[m>>3]=n}if(j<0.0){h[o>>3]=.2;j=.2}if(p>=0.0){h[d>>3]=-1.0;p=-1.0}M=+D(+n,+(1.0-p));A=+D(+j,+((2.0-p)*.3333333333333333))/n;B=O(W,a<<3)|0;E=KK(B)|0;F=KK(B)|0;G=d+116|0;H=d+64|0;I=c[15712]|0;J=N+8|0;K=Y+8|0;L=Y+16|0;y=0;z=0.0;v=q;while(1){W6(E|0,f|0,B|0)|0;w=cq(X)|0;x=hw(a,W,w,f,(c[G>>2]|0)==0?0:e)|0;cw(x,F,f,+h[H>>3],p,M,Y,g);m=0;b:while(1){if((m|0)>=(W|0)){q=0.0;k=0;break}o=O(m,a)|0;r=F+(o<<3)|0;s=m+1|0;t=P+(s<<2)|0;k=c[P+(m<<2)>>2]|0;while(1){if((k|0)>=(c[t>>2]|0)){m=s;continue b}u=Q+(k<<2)|0;i=c[u>>2]|0;c:do if((i|0)!=(m|0)){j=+_w(f,a,m,i)*A;i=0;while(1){if((i|0)>=(a|0))break c;q=j*(+h[f+(i+o<<3)>>3]-+h[f+((O(c[u>>2]|0,a)|0)+i<<3)>>3]);$=r+(i<<3)|0;h[$>>3]=+h[$>>3]-q;i=i+1|0}}while(0);k=k+1|0}}while(1){if((k|0)>=(W|0))break;m=O(k,a)|0;o=F+(m<<3)|0;j=0.0;i=0;while(1){if((i|0)>=(a|0))break;aa=+h[o+(i<<3)>>3];j=aa*aa+j;i=i+1|0}j=+C(+j);q=j+q;d:do if(j>0.0){j=1.0/j;i=0;while(1){if((i|0)>=(a|0)){i=0;break d}$=o+(i<<3)|0;h[$>>3]=+h[$>>3]*j;i=i+1|0}}else i=0;while(0);while(1){if((i|0)>=(a|0))break;$=f+(i+m<<3)|0;h[$>>3]=+h[$>>3]+ +h[o+(i<<3)>>3]*v;i=i+1|0}k=k+1|0}y=y+1|0;if(!x){if(0){$=c[J>>2]|0;c[Z>>2]=y;h[Z+8>>3]=v;h[Z+16>>3]=q;c[Z+24>>2]=$;h[Z+32>>3]=n;w4(I,92099,Z)|0}}else{ow(x);bq(X,+h[K>>3]*.85+ +h[Y>>3]+ +h[L>>3]*3.3)}v=+jq(U,v,q,z,T);if(!((y|0)<(S|0)&v>R))break;else z=q}if(c[d+112>>2]|0)kq(a,N,f);$p(X);c[V>>2]=w;U1(E);if((N|0)!=(b|0))qw(N);U1(F)}while(0);l=_;return}function jq(a,b,c,d,e){a=a|0;b=+b;c=+c;d=+d;e=+e;do if(a){if(c>=d){b=e*b;break}if(!(d*.95<c))b=b*.99/e}else b=e*b;while(0);return +b}function kq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0;D=l;l=l+16|0;v=D+12|0;y=D+8|0;z=D+4|0;A=D;B=c[b>>2]|0;C=c[b+20>>2]|0;t=c[b+24>>2]|0;c[v>>2]=10;c[A>>2]=10;u=KK(B<<2)|0;f=KK(80)|0;c[y>>2]=f;e=KK(40)|0;c[z>>2]=e;b=0;while(1){if((b|0)>=(B|0)){n=0;r=f;k=e;o=e;m=f;break}c[u+(b<<2)>>2]=0;b=b+1|0}while(1){if((n|0)>=(B|0))break;b=c[C+(n<<2)>>2]|0;a:do if((((c[C+(n+1<<2)>>2]|0)-b|0)==1?(c[u+(n<<2)>>2]|0)==0:0)?(w=c[t+(b<<2)>>2]|0,x=u+(w<<2)|0,(c[x>>2]|0)==0):0){c[x>>2]=1;q=C+(w+1<<2)|0;n=0;s=0;g=0.0;p=c[C+(w<<2)>>2]|0;k=o;b=r;while(1){if((p|0)>=(c[q>>2]|0))break;e=t+(p<<2)|0;f=c[e>>2]|0;if(((c[C+(f+1<<2)>>2]|0)-(c[C+(f<<2)>>2]|0)|0)==1){c[u+(f<<2)>>2]=1;fq(z,s,v);g=+_w(d,a,w,c[e>>2]|0)+g;k=c[z>>2]|0;c[k+(s<<2)>>2]=c[e>>2];e=n;f=s+1|0}else{eq(y,n,A);j=+gq(d,a,w,c[e>>2]|0);b=c[y>>2]|0;h[b+(n<<3)>>3]=j;e=n+1|0;f=s;m=b}n=e;s=f;p=p+1|0}j=g/+(s|0);if((n|0)>0){lq(n,m);b=m}g=(s|0)>1?6.283185307179586/+(s+-1|0):0.0;e=0;i=0.0;while(1){if((e|0)>=(s|0)){o=k;f=k;break a}mq(d,a,j,i,w,c[k+(e<<2)>>2]|0);e=e+1|0;i=i+g}}else{e=n;f=k;b=r}while(0);n=e+1|0;r=b;k=f}U1(u);U1(r);U1(k);l=D;return}function lq(a,b){a=a|0;b=b|0;o4(b,a,8,68);return}function mq(a,b,c,d,e,f){a=a|0;b=b|0;c=+c;d=+d;e=e|0;f=f|0;var g=0.0;g=+E(+d)*c;e=O(e,b)|0;f=O(f,b)|0;h[a+(f<<3)>>3]=g+ +h[a+(e<<3)>>3];d=+F(+d)*c;h[a+(f+1<<3)>>3]=d+ +h[a+(e+1<<3)>>3];return}function nq(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0.0,l=0.0,m=0,n=0,o=0.0,p=0,q=0,r=0.0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0.0,A=0,B=0.0,E=0.0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,P=0.0,Q=0,R=0.0,S=0,T=0,U=0,V=0,W=0.0;o=+h[d>>3];m=d+24|0;l=+h[m>>3];n=d+32|0;k=+h[n>>3];P=+h[d+72>>3];Q=c[d+80>>2]|0;R=+h[d+88>>3];r=+h[d+96>>3];S=c[d+104>>2]|0;i=c[d+56>>2]|0;U4(92198,32,1,c[15712]|0)|0;if(!((b|0)==0|(Q|0)<1)?(T=c[b>>2]|0,j=c[b+4>>2]|0,!((a|0)<1|(j|0)<1)):0){p=a<<3;U=KK(O(p,j)|0)|0;if((j|0)<(c[d+52>>2]|0)){I=0;J=0;K=0;L=0;M=0;N=0}else{aq(i)|0;L=KK(a*80|0)|0;M=KK(80)|0;N=KK(80)|0;I=N;J=M;K=L}c[g>>2]=0;if((T|0)==(j|0)){F=vw(b,1)|0;G=c[F+20>>2]|0;H=c[F+24>>2]|0;a:do if(c[d+16>>2]|0){w5(c[d+108>>2]|0);j=O(T,a)|0;i=0;while(1){if((i|0)>=(j|0))break a;h[f+(i<<3)>>3]=+Sw();i=i+1|0}}while(0);if(l<0.0){l=+dq(F,a,f);h[m>>3]=l}if(k<0.0){h[n>>3]=.2;k=.2}if(o>=0.0){h[d>>3]=-1.0;o=-1.0}B=1.0-o;E=+D(+l,+B);z=+D(+k,+((2.0-o)*.3333333333333333))/l;A=KK(p)|0;u=O(T,p)|0;v=KK(u)|0;w=O(T,a)|0;x=d+116|0;y=(e|0)!=0;t=o==-1.0;s=0;o=0.0;while(1){i=0;while(1){if((i|0)>=(w|0))break;h[U+(i<<3)>>3]=0.0;i=i+1|0}W6(v|0,f|0,u|0)|0;p=0;while(1){if((p|0)<(T|0))i=0;else{n=0;break}while(1){if((i|0)>=(a|0))break;h[A+(i<<3)>>3]=0.0;i=i+1|0}n=O(p,a)|0;b:do if(y&(c[x>>2]|0)!=0){i=0;while(1){if((i|0)==(T|0)){i=0;break b}c:do if((i|0)!=(p|0)){l=+Zw(f,a,p,i);g=e+(i<<3)|0;m=O(i,a)|0;k=+D(+l,+B);k=1.0/(t?l*l:k);j=0;while(1){if((j|0)>=(a|0))break c;q=A+(j<<3)|0;h[q>>3]=+h[g>>3]*E*(+h[f+(j+n<<3)>>3]-+h[f+(j+m<<3)>>3])*k+ +h[q>>3];j=j+1|0}}while(0);i=i+1|0}}else{i=0;while(1){if((i|0)==(T|0)){i=0;break b}d:do if((i|0)!=(p|0)){l=+Zw(f,a,p,i);g=O(i,a)|0;k=+D(+l,+B);k=1.0/(t?l*l:k);j=0;while(1){if((j|0)>=(a|0))break d;q=A+(j<<3)|0;h[q>>3]=(+h[f+(j+n<<3)>>3]-+h[f+(j+g<<3)>>3])*E*k+ +h[q>>3];j=j+1|0}}while(0);i=i+1|0}}while(0);while(1){if((i|0)>=(a|0))break;q=U+(i+n<<3)|0;h[q>>3]=+h[q>>3]+ +h[A+(i<<3)>>3];i=i+1|0}p=p+1|0}e:while(1){if((n|0)<(T|0))i=0;else{l=0.0;j=0;break}while(1){if((i|0)>=(a|0))break;h[A+(i<<3)>>3]=0.0;i=i+1|0}p=n+1|0;g=G+(p<<2)|0;q=O(n,a)|0;j=c[G+(n<<2)>>2]|0;while(1){if((j|0)>=(c[g>>2]|0)){i=0;break}m=H+(j<<2)|0;i=c[m>>2]|0;f:do if((i|0)!=(n|0)){k=+_w(f,a,n,i)*z;i=0;while(1){if((i|0)>=(a|0))break f;l=k*(+h[f+(i+q<<3)>>3]-+h[f+((O(c[m>>2]|0,a)|0)+i<<3)>>3]);V=A+(i<<3)|0;h[V>>3]=+h[V>>3]-l;i=i+1|0}}while(0);j=j+1|0}while(1){if((i|0)>=(a|0)){n=p;continue e}V=U+(i+q<<3)|0;h[V>>3]=+h[V>>3]+ +h[A+(i<<3)>>3];i=i+1|0}}while(1){if((j|0)>=(T|0))break;g=O(j,a)|0;i=0;while(1){if((i|0)>=(a|0)){k=0.0;i=0;break}h[A+(i<<3)>>3]=+h[U+(i+g<<3)>>3];i=i+1|0}while(1){if((i|0)>=(a|0))break;W=+h[A+(i<<3)>>3];k=W*W+k;i=i+1|0}k=+C(+k);l=k+l;g:do if(k>0.0){k=1.0/k;i=0;while(1){if((i|0)>=(a|0)){i=0;break g}V=A+(i<<3)|0;h[V>>3]=+h[V>>3]*k;i=i+1|0}}else i=0;while(0);while(1){if((i|0)>=(a|0))break;V=f+(i+g<<3)|0;h[V>>3]=+h[V>>3]+ +h[A+(i<<3)>>3]*r;i=i+1|0}j=j+1|0}s=s+1|0;r=+jq(S,r,l,o,R);if(!((s|0)<(Q|0)&r>P))break;else o=l}if(c[d+112>>2]|0)kq(a,F,f);U1(v);if((F|0)!=(b|0))qw(F);U1(A)}else c[g>>2]=-100;if(K|0)U1(L);if(J|0)U1(M);if(I|0)U1(N);U1(U)}return}function oq(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0.0,s=0.0,t=0.0,u=0.0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,E=0.0,F=0,G=0,H=0.0,I=0.0,J=0,K=0,L=0,M=0,N=0.0,P=0.0,Q=0,R=0,S=0,T=0,U=0,V=0.0,W=0,X=0.0,Y=0,Z=0,_=0,$=0,aa=0,ba=0,ca=0,da=0,ea=0,fa=0,ga=0,ha=0.0,ia=0;ga=l;l=l+32|0;_=ga+24|0;$=ga+20|0;da=ga+16|0;ea=ga+12|0;fa=ga+8|0;U=ga;s=+h[d>>3];k=d+24|0;r=+h[k>>3];m=d+32|0;q=+h[m>>3];V=+h[d+72>>3];W=c[d+80>>2]|0;X=+h[d+88>>3];t=+h[d+96>>3];Y=c[d+104>>2]|0;c[_>>2]=0;c[$>>2]=10;c[da>>2]=0;c[ea>>2]=0;c[fa>>2]=0;h[U>>3]=0.0;ca=d+56|0;n=c[ca>>2]|0;if(!((b|0)==0|(W|0)<1)?(Z=c[b>>2]|0,i=c[b+4>>2]|0,!((a|0)<1|(i|0)<1)):0){if((i|0)<(c[d+52>>2]|0)){ba=0;aa=0}else{ba=aq(n)|0;c[da>>2]=KK(a*80|0)|0;c[ea>>2]=KK(80)|0;c[fa>>2]=KK(80)|0;aa=1}c[g>>2]=0;a:do if((Z|0)==(i|0)){o=vw(b,1)|0;Q=c[o+20>>2]|0;R=c[o+24>>2]|0;b:do if(c[d+16>>2]|0){w5(c[d+108>>2]|0);j=O(Z,a)|0;i=0;while(1){if((i|0)>=(j|0))break b;h[f+(i<<3)>>3]=+Sw();i=i+1|0}}while(0);if(r<0.0){r=+dq(o,a,f);h[k>>3]=r}if(q<0.0){h[m>>3]=.2;q=.2}if(s>=0.0){h[d>>3]=-1.0;s=-1.0}N=1.0-s;P=+D(+r,+N);I=+D(+q,+((2.0-s)*.3333333333333333))/r;J=a<<3;S=KK(J)|0;J=O(Z,J)|0;T=KK(J)|0;K=(aa|0)!=0;L=d+116|0;M=d+64|0;F=s==-1.0;G=(e|0)!=0;H=1.0/+(Z|0);i=0;B=0;E=0.0;while(1){B=B+1|0;W6(T|0,f|0,J|0)|0;if(K){i=cq(ba)|0;n=i;i=hw(a,Z,i,f,(c[L>>2]|0)==0?0:e)|0}u=0.0;s=0.0;v=0;r=0.0;c:while(1){if((v|0)<(Z|0))j=0;else break;while(1){if((j|0)>=(a|0))break;h[S+(j<<3)>>3]=0.0;j=j+1|0}z=v+1|0;m=Q+(z<<2)|0;A=O(v,a)|0;k=c[Q+(v<<2)>>2]|0;while(1){if((k|0)>=(c[m>>2]|0))break;p=R+(k<<2)|0;j=c[p>>2]|0;d:do if((j|0)!=(v|0)){q=+_w(f,a,v,j)*I;j=0;while(1){if((j|0)>=(a|0))break d;ha=q*(+h[f+(j+A<<3)>>3]-+h[f+((O(c[p>>2]|0,a)|0)+j<<3)>>3]);y=S+(j<<3)|0;h[y>>3]=+h[y>>3]-ha;j=j+1|0}}while(0);k=k+1|0}e:do if(!K)if(G&(c[L>>2]|0)!=0){j=0;while(1){if((j|0)==(Z|0))break e;f:do if((j|0)!=(v|0)){ha=+Zw(f,a,v,j);m=e+(j<<3)|0;p=O(j,a)|0;q=+D(+ha,+N);q=1.0/(F?ha*ha:q);k=0;while(1){if((k|0)>=(a|0))break f;y=S+(k<<3)|0;h[y>>3]=+h[m>>3]*P*(+h[f+(k+A<<3)>>3]-+h[f+(k+p<<3)>>3])*q+ +h[y>>3];k=k+1|0}}while(0);j=j+1|0}}else{j=0;while(1){if((j|0)==(Z|0))break e;g:do if((j|0)!=(v|0)){ha=+Zw(f,a,v,j);m=O(j,a)|0;q=+D(+ha,+N);q=1.0/(F?ha*ha:q);k=0;while(1){if((k|0)>=(a|0))break g;y=S+(k<<3)|0;h[y>>3]=(+h[f+(k+A<<3)>>3]-+h[f+(k+m<<3)>>3])*P*q+ +h[y>>3];k=k+1|0}}while(0);j=j+1|0}}else{bw(i,+h[M>>3],f+(A<<3)|0,v,_,$,da,ea,fa,U,g);u=+h[U>>3]+u;y=c[_>>2]|0;s=s+ +(y|0);if(c[g>>2]|0){m=S;k=T;i=S;j=T;p=67;break a}m=c[fa>>2]|0;p=c[ea>>2]|0;v=c[da>>2]|0;j=0;while(1){if((j|0)>=(y|0))break e;ha=+h[m+(j<<3)>>3];ha=ha>1.0e-15?ha:1.0e-15;w=p+(j<<3)|0;x=O(j,a)|0;q=+D(+ha,+N);q=1.0/(F?ha*ha:q);k=0;while(1){if((k|0)>=(a|0))break;ia=S+(k<<3)|0;h[ia>>3]=+h[w>>3]*P*(+h[f+(k+A<<3)>>3]-+h[v+(k+x<<3)>>3])*q+ +h[ia>>3];k=k+1|0}j=j+1|0}}while(0);q=0.0;j=0;while(1){if((j|0)>=(a|0))break;ha=+h[S+(j<<3)>>3];q=ha*ha+q;j=j+1|0}q=+C(+q);r=q+r;h:do if(q>0.0){q=1.0/q;j=0;while(1){if((j|0)>=(a|0)){j=0;break h}ia=S+(j<<3)|0;h[ia>>3]=+h[ia>>3]*q;j=j+1|0}}else j=0;while(0);while(1){if((j|0)>=(a|0)){v=z;continue c}ia=f+(j+A<<3)|0;h[ia>>3]=+h[ia>>3]+ +h[S+(j<<3)>>3]*t;j=j+1|0}}if(i|0){ow(i);bq(ba,s*H*5.0+u*H)}t=+jq(Y,t,r,E,X);if(!((B|0)<(W|0)&t>V))break;else E=r}if(!(c[d+112>>2]|0)){m=S;k=T;j=T;i=S;p=66}else{kq(a,o,f);m=S;k=T;j=T;i=S;p=66}}else{c[g>>2]=-100;o=b;m=0;k=0;j=0;i=0;p=66}while(0);if((p|0)==66)if(aa)p=67;if((p|0)==67){$p(ba);c[ca>>2]=n}if(k|0)U1(j);if((o|0)!=(b|0))qw(o);if(m|0)U1(i);i=c[da>>2]|0;if(i|0)U1(i);i=c[ea>>2]|0;if(i|0)U1(i);i=c[fa>>2]|0;if(i|0)U1(i)}l=ga;return}function pq(a,b,d,e,f,g,i,j){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=+i;j=j|0;var k=0,m=0,n=0,o=0,p=0.0,q=0,r=0.0,s=0,t=0.0,u=0.0,v=0.0,w=0,x=0,y=0.0,z=0,A=0,B=0.0,E=0,F=0.0,G=0.0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0,X=0,Y=0.0,Z=0,_=0.0,$=0,aa=0,ba=0,ca=0,da=0,ea=0,fa=0,ga=0,ha=0.0,ia=0;ga=l;l=l+48|0;o=ga+8|0;ba=ga+40|0;ca=ga+36|0;W=ga+32|0;ea=ga+28|0;fa=ga+24|0;X=ga;p=+h[e>>3];q=e+32|0;r=+h[q>>3];Y=+h[e+72>>3];Z=c[e+80>>2]|0;_=+h[e+88>>3];t=+h[e+96>>3];$=c[e+104>>2]|0;c[ba>>2]=0;c[ca>>2]=10;c[W>>2]=0;c[ea>>2]=0;c[fa>>2]=0;h[X>>3]=0.0;if(!((b|0)==0|(Z|0)<1)?(aa=c[b>>2]|0,n=c[b+4>>2]|0,!((a|0)<1|(n|0)<1)):0){if((c[e+132>>2]|0)!=0?(n|0)>=(c[e+52>>2]|0):0){m=KK(a*80|0)|0;c[W>>2]=m;c[ea>>2]=KK(80)|0;c[fa>>2]=KK(80)|0;s=1;k=m}else{s=0;k=0;m=0}c[j>>2]=0;if((aa|0)==(n|0)){V=vw(b,1)|0;if(!d){U=0;k=V}else{U=c[d+28>>2]|0;k=d}T=c[k+24>>2]|0;S=c[k+20>>2]|0;if(i<0.0){G=+qq(aa,S,U);F=+(c[S+(aa<<2)>>2]|0);B=+(aa|0);i=F/(B*B-F)*i/+D(+G,+(p+1.0));R=c[15712]|0;h[o>>3]=G;h[o+8>>3]=i;w4(R,92231,o)|0}a:do if(c[e+16>>2]|0){U4(92253,24,1,c[15712]|0)|0;w5(c[e+108>>2]|0);m=O(aa,a)|0;k=0;while(1){if((k|0)>=(m|0))break a;h[g+(k<<3)>>3]=+Sw();k=k+1|0}}while(0);rq(aa,a,g,S,T,U);if(r<0.0)h[q>>3]=.2;if(p>=0.0){h[e>>3]=-1.0;p=-1.0}Q=a<<3;P=KK(Q)|0;Q=O(aa,Q)|0;R=KK(Q)|0;I=(s|0)!=0;J=e+116|0;K=(U|0)==0;L=e+8|0;M=(f|0)!=0;N=p==-1.0;G=1.0-p;H=e+64|0;k=0;E=0;F=0.0;p=1.0;B=t;b:while(1){E=E+1|0;W6(R|0,g|0,Q|0)|0;if(I)k=hw(a,aa,10,g,(c[J>>2]|0)==0?0:f)|0;s=0;t=0.0;c:while(1){if((s|0)<(aa|0))m=0;else break;while(1){if((m|0)>=(a|0))break;h[P+(m<<3)>>3]=0.0;m=m+1|0}A=s+1|0;q=S+(A<<2)|0;z=O(s,a)|0;o=c[S+(s<<2)>>2]|0;while(1){if((o|0)>=(c[q>>2]|0))break;d=T+(o<<2)|0;m=c[d>>2]|0;d:do if((m|0)!=(s|0)){y=+Zw(g,a,s,m);if(!K)p=+h[U+(o<<3)>>3];r=+h[L>>3];e:do if(!(r==2.0))if(r==1.0){r=(y-p)*(1.0/(p*p));u=1.0/y;m=0;while(1){if((m|0)>=(a|0))break e;v=r*(+h[g+(m+z<<3)>>3]-+h[g+((O(c[d>>2]|0,a)|0)+m<<3)>>3])*u;x=P+(m<<3)|0;h[x>>3]=+h[x>>3]-v;m=m+1|0}}else{r=1.0/+D(+p,+(r+1.0));u=y-p;v=1.0/y;m=0;while(1){if((m|0)>=(a|0))break e;ha=+h[g+(m+z<<3)>>3]-+h[g+((O(c[d>>2]|0,a)|0)+m<<3)>>3];ha=r*ha*+D(+u,+(+h[L>>3]))*v;x=P+(m<<3)|0;h[x>>3]=+h[x>>3]-ha;m=m+1|0}}else{r=y-p;r=r*r*(1.0/(p*p*p));u=1.0/y;m=0;while(1){if((m|0)>=(a|0))break e;ha=r*(+h[g+(m+z<<3)>>3]-+h[g+((O(c[d>>2]|0,a)|0)+m<<3)>>3])*u;x=P+(m<<3)|0;h[x>>3]=+h[x>>3]-ha;m=m+1|0}}while(0);if(M&(c[J>>2]|0)!=0){n=f+(o<<3)|0;r=+D(+y,+G);r=1.0/(N?y*y:r);m=0;while(1){if((m|0)>=(a|0))break d;ha=+h[n>>3]*i*(+h[g+(m+z<<3)>>3]-+h[g+((O(c[d>>2]|0,a)|0)+m<<3)>>3])*r;x=P+(m<<3)|0;h[x>>3]=+h[x>>3]-ha;m=m+1|0}}else{r=+D(+y,+G);r=1.0/(N?y*y:r);m=0;while(1){if((m|0)>=(a|0))break d;ha=(+h[g+(m+z<<3)>>3]-+h[g+((O(c[d>>2]|0,a)|0)+m<<3)>>3])*i*r;x=P+(m<<3)|0;h[x>>3]=+h[x>>3]-ha;m=m+1|0}}}while(0);o=o+1|0}f:do if(!I)if(M&(c[J>>2]|0)!=0){m=0;while(1){if((m|0)==(aa|0)){r=0.0;m=0;break f}g:do if((m|0)!=(s|0)){ha=+Zw(g,a,s,m);d=f+(m<<3)|0;o=O(m,a)|0;r=+D(+ha,+G);r=1.0/(N?ha*ha:r);n=0;while(1){if((n|0)>=(a|0))break g;x=P+(n<<3)|0;h[x>>3]=+h[d>>3]*i*(+h[g+(n+z<<3)>>3]-+h[g+(n+o<<3)>>3])*r+ +h[x>>3];n=n+1|0}}while(0);m=m+1|0}}else{m=0;while(1){if((m|0)==(aa|0)){r=0.0;m=0;break f}h:do if((m|0)!=(s|0)){ha=+Zw(g,a,s,m);d=O(m,a)|0;r=+D(+ha,+G);r=1.0/(N?ha*ha:r);n=0;while(1){if((n|0)>=(a|0))break h;x=P+(n<<3)|0;h[x>>3]=(+h[g+(n+z<<3)>>3]-+h[g+(n+d<<3)>>3])*i*r+ +h[x>>3];n=n+1|0}}while(0);m=m+1|0}}else{bw(k,+h[H>>3],g+(z<<3)|0,s,ba,ca,W,ea,fa,X,j);d=c[ba>>2]|0;if(c[j>>2]|0)break b;o=c[fa>>2]|0;q=c[ea>>2]|0;s=c[W>>2]|0;m=0;while(1){if((m|0)>=(d|0)){r=0.0;m=0;break f}ha=+h[o+(m<<3)>>3];ha=ha>1.0e-15?ha:1.0e-15;w=q+(m<<3)|0;x=O(m,a)|0;r=+D(+ha,+G);r=1.0/(N?ha*ha:r);n=0;while(1){if((n|0)>=(a|0))break;ia=P+(n<<3)|0;h[ia>>3]=+h[w>>3]*i*(+h[g+(n+z<<3)>>3]-+h[s+(n+x<<3)>>3])*r+ +h[ia>>3];n=n+1|0}m=m+1|0}}while(0);while(1){if((m|0)>=(a|0))break;ha=+h[P+(m<<3)>>3];r=ha*ha+r;m=m+1|0}r=+C(+r);t=r+t;i:do if(r>0.0){r=1.0/r;m=0;while(1){if((m|0)>=(a|0)){m=0;break i}ia=P+(m<<3)|0;h[ia>>3]=+h[ia>>3]*r;m=m+1|0}}else m=0;while(0);while(1){if((m|0)>=(a|0)){s=A;continue c}ia=g+(m+z<<3)|0;h[ia>>3]=+h[ia>>3]+ +h[P+(m<<3)>>3]*B;m=m+1|0}}if(k|0)ow(k);B=+jq($,B,t,F,_);if(!((E|0)<(Z|0)&B>Y)){da=83;break}else F=t}if((da|0)==83?c[e+112>>2]|0:0)kq(a,V,g);U1(R);if((V|0)!=(b|0))qw(V);U1(P);k=c[W>>2]|0;m=k}else c[j>>2]=-100;if(m|0)U1(k);k=c[ea>>2]|0;if(k|0)U1(k);k=c[fa>>2]|0;if(k|0)U1(k)}l=ga;return}function qq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0,j=0.0;if(!d)e=1.0;else{f=0;e=0.0;a:while(1){if((f|0)>=(a|0))break;g=f+1|0;i=c[b+(g<<2)>>2]|0;f=c[b+(f<<2)>>2]|0;while(1){if((f|0)>=(i|0)){f=g;continue a}j=+h[d+(f<<3)>>3]+e;f=f+1|0;e=j}}e=e/+(c[b+(a<<2)>>2]|0)}return +e}function rq(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0,s=0,t=0,u=0,v=0,w=0.0;v=l;l=l+16|0;u=v;t=(g|0)==0;k=0.0;m=0.0;n=1.0;j=0;a:while(1){if((j|0)>=(a|0))break;r=j+1|0;s=e+(r<<2)|0;q=c[e+(j<<2)>>2]|0;while(1){if((q|0)>=(c[s>>2]|0)){j=r;continue a}i=c[f+(q<<2)>>2]|0;b:do if((i|0)!=(j|0)){o=+Zw(d,b,j,i);if(!t)n=+h[g+(q<<3)>>3];w=1.0/(n*n);p=n*o*w;o=o*o*w;i=0;while(1){if((i|0)>=(b|0))break b;i=i+1|0;k=k+o;m=m+p}}while(0);q=q+1|0}}k=m/k;j=O(b,a)|0;i=0;while(1){if((i|0)>=(j|0))break;b=d+(i<<3)|0;h[b>>3]=+h[b>>3]*k;i=i+1|0}d=c[15712]|0;h[u>>3]=k;w4(d,92278,u)|0;l=v;return}function sq(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,m=0,n=0.0,o=0.0,p=0,q=0,r=0.0,s=0,t=0,u=0,v=0.0,w=0,x=0,y=0,z=0.0,A=0,B=0,E=0,F=0,G=0.0,H=0,I=0.0,J=0,K=0,L=0,M=0.0,N=0.0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0.0,X=0,Y=0.0,Z=0,_=0,$=0,aa=0,ba=0,ca=0,da=0,ea=0,fa=0,ga=0.0,ha=0.0,ia=0;fa=l;l=l+32|0;$=fa+24|0;aa=fa+20|0;ba=fa+16|0;da=fa+12|0;ea=fa+8|0;V=fa;r=+h[e>>3];p=e+24|0;o=+h[p>>3];q=e+32|0;n=+h[q>>3];W=+h[e+72>>3];X=c[e+80>>2]|0;Y=+h[e+88>>3];v=+h[e+96>>3];Z=c[e+104>>2]|0;c[$>>2]=0;c[aa>>2]=10;c[ba>>2]=0;c[da>>2]=0;c[ea>>2]=0;h[V>>3]=0.0;if(!((b|0)==0|(X|0)<1)?(_=c[b>>2]|0,m=c[b+4>>2]|0,!((a|0)<1|(m|0)<1)):0){if((m|0)<(c[e+52>>2]|0)){s=0;j=0;k=0}else{k=KK(a*80|0)|0;c[ba>>2]=k;c[da>>2]=KK(80)|0;c[ea>>2]=KK(80)|0;s=1;j=k}c[i>>2]=0;if((_|0)==(m|0)){Q=vw(b,1)|0;R=c[Q+20>>2]|0;S=c[Q+24>>2]|0;T=c[d+20>>2]|0;U=c[d+24>>2]|0;P=c[d+28>>2]|0;a:do if(c[e+16>>2]|0){w5(c[e+108>>2]|0);k=O(_,a)|0;j=0;while(1){if((j|0)>=(k|0))break a;h[g+(j<<3)>>3]=+Sw();j=j+1|0}}while(0);if(o<0.0){o=+dq(Q,a,g);h[p>>3]=o}if(n<0.0){h[q>>3]=.2;n=.2}if(r>=0.0){h[e>>3]=-1.0;r=-1.0}M=1.0-r;N=+D(+o,+M);I=+D(+n,+((2.0-r)*.3333333333333333))/o;K=a<<3;J=KK(K)|0;K=O(_,K)|0;L=KK(K)|0;E=(s|0)!=0;F=e+116|0;G=I*.2;H=e+64|0;A=r==-1.0;B=(f|0)!=0;j=0;y=0;z=0.0;b:while(1){y=y+1|0;W6(L|0,g|0,K|0)|0;if(E)j=hw(a,_,10,g,(c[F>>2]|0)==0?0:f)|0;s=0;o=0.0;c:while(1){if((s|0)<(_|0))k=0;else break;while(1){if((k|0)>=(a|0))break;h[J+(k<<3)>>3]=0.0;k=k+1|0}w=s+1|0;d=R+(w<<2)|0;x=O(s,a)|0;m=c[R+(s<<2)>>2]|0;while(1){if((m|0)>=(c[d>>2]|0))break;p=S+(m<<2)|0;k=c[p>>2]|0;d:do if((k|0)!=(s|0)){n=+_w(g,a,s,k)*I;k=0;while(1){if((k|0)>=(a|0))break d;r=n*(+h[g+(k+x<<3)>>3]-+h[g+((O(c[p>>2]|0,a)|0)+k<<3)>>3]);u=J+(k<<3)|0;h[u>>3]=+h[u>>3]-r;k=k+1|0}}while(0);m=m+1|0}p=T+(w<<2)|0;d=c[T+(s<<2)>>2]|0;while(1){if((d|0)>=(c[p>>2]|0))break;q=U+(d<<2)|0;k=c[q>>2]|0;e:do if((k|0)!=(s|0)){n=+Zw(g,a,s,k);m=P+(d<<3)|0;r=1.0/n;k=0;while(1){if((k|0)>=(a|0))break e;ha=+h[m>>3];ga=n-ha;ga=ga*ga*G*(+h[g+(k+x<<3)>>3]-+h[g+((O(c[q>>2]|0,a)|0)+k<<3)>>3])*r;u=J+(k<<3)|0;h[u>>3]=(n<ha?ga:-ga)+ +h[u>>3];k=k+1|0}}while(0);d=d+1|0}f:do if(!E)if(B&(c[F>>2]|0)!=0){k=0;while(1){if((k|0)==(_|0)){n=0.0;k=0;break f}g:do if((k|0)!=(s|0)){ha=+Zw(g,a,s,k);d=f+(k<<3)|0;p=O(k,a)|0;n=+D(+ha,+M);n=1.0/(A?ha*ha:n);m=0;while(1){if((m|0)>=(a|0))break g;u=J+(m<<3)|0;h[u>>3]=+h[d>>3]*N*(+h[g+(m+x<<3)>>3]-+h[g+(m+p<<3)>>3])*n+ +h[u>>3];m=m+1|0}}while(0);k=k+1|0}}else{k=0;while(1){if((k|0)==(_|0)){n=0.0;k=0;break f}h:do if((k|0)!=(s|0)){ha=+Zw(g,a,s,k);d=O(k,a)|0;n=+D(+ha,+M);n=1.0/(A?ha*ha:n);m=0;while(1){if((m|0)>=(a|0))break h;u=J+(m<<3)|0;h[u>>3]=(+h[g+(m+x<<3)>>3]-+h[g+(m+d<<3)>>3])*N*n+ +h[u>>3];m=m+1|0}}while(0);k=k+1|0}}else{bw(j,+h[H>>3],g+(x<<3)|0,s,$,aa,ba,da,ea,V,i);d=c[$>>2]|0;if(c[i>>2]|0)break b;p=c[ea>>2]|0;q=c[da>>2]|0;s=c[ba>>2]|0;k=0;while(1){if((k|0)>=(d|0)){n=0.0;k=0;break f}ha=+h[p+(k<<3)>>3];ha=ha>1.0e-15?ha:1.0e-15;t=q+(k<<3)|0;u=O(k,a)|0;n=+D(+ha,+M);n=1.0/(A?ha*ha:n);m=0;while(1){if((m|0)>=(a|0))break;ia=J+(m<<3)|0;h[ia>>3]=+h[t>>3]*N*(+h[g+(m+x<<3)>>3]-+h[s+(m+u<<3)>>3])*n+ +h[ia>>3];m=m+1|0}k=k+1|0}}while(0);while(1){if((k|0)>=(a|0))break;ha=+h[J+(k<<3)>>3];n=ha*ha+n;k=k+1|0}n=+C(+n);o=n+o;i:do if(n>0.0){n=1.0/n;k=0;while(1){if((k|0)>=(a|0)){k=0;break i}ia=J+(k<<3)|0;h[ia>>3]=+h[ia>>3]*n;k=k+1|0}}else k=0;while(0);while(1){if((k|0)>=(a|0)){s=w;continue c}ia=g+(k+x<<3)|0;h[ia>>3]=+h[ia>>3]+ +h[J+(k<<3)>>3]*v;k=k+1|0}}if(j|0)ow(j);v=+jq(Z,v,o,z,Y);if(!((y|0)<(X|0)&v>W)){ca=70;break}else z=o}if((ca|0)==70?c[e+112>>2]|0:0)kq(a,Q,g);U1(L);if((Q|0)!=(b|0))qw(Q);U1(J);j=c[ba>>2]|0;k=j}else c[i>>2]=-100;if(k|0)U1(j);j=c[da>>2]|0;if(j|0)U1(j);j=c[ea>>2]|0;if(j|0)U1(j)}l=fa;return}function tq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;n=c[b+20>>2]|0;o=c[b+24>>2]|0;p=KK(a<<3)|0;m=c[b>>2]|0;k=0;a:while(1){if((k|0)<(m|0))b=0;else break;while(1){if((b|0)>=(a|0))break;h[p+(b<<3)>>3]=0.0;b=b+1|0}l=k+1|0;j=c[n+(l<<2)>>2]|0;i=c[n+(k<<2)>>2]|0;b=0;while(1){if((i|0)>=(j|0))break;e=c[o+(i<<2)>>2]|0;if((e|0)!=(k|0)){g=O(e,a)|0;e=0;while(1){if((e|0)>=(a|0))break;q=p+(e<<3)|0;h[q>>3]=+h[q>>3]+ +h[d+(g+e<<3)>>3];e=e+1|0}b=b+1|0}i=i+1|0}if((b|0)<=0){k=l;continue}f=.5/+(b|0);e=O(k,a)|0;b=0;while(1){if((b|0)>=(a|0)){k=l;continue a}q=d+(b+e<<3)|0;h[q>>3]=+h[p+(b<<3)>>3]*f+ +h[q>>3]*.5;b=b+1|0}}U1(p);return}function uq(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0.0;i=c[a+20>>2]|0;j=c[a+24>>2]|0;h=c[a>>2]|0;k=KK((h<<2)+4|0)|0;a=0;while(1){if((a|0)>(h|0)){d=0;e=0;break}c[k+(a<<2)>>2]=0;a=a+1|0}while(1){if((e|0)>=(h|0))break;f=e+1|0;g=c[i+(f<<2)>>2]|0;a=0;b=c[i+(e<<2)>>2]|0;while(1){if((b|0)>=(g|0))break;a=a+((e|0)!=(c[j+(b<<2)>>2]|0)&1)|0;b=b+1|0}g=k+(a<<2)|0;e=(c[g>>2]|0)+1|0;c[g>>2]=e;d=(d|0)>(e|0)?d:e;e=f}l=+(c[k+4>>2]|0);if(+(d|0)*.8<l?+(h|0)*.3<l:0)a=1;else a=0;U1(k);return a|0}function vq(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0.0;o=l;l=l+48|0;n=o+16|0;j=o;g=O(b,b)|0;e=0;while(1){if((e|0)>=(g|0)){e=0;break}h[n+(e<<3)>>3]=0.0;e=e+1|0}while(1){if((e|0)>=(b|0)){g=0;break}h[j+(e<<3)>>3]=0.0;e=e+1|0}while(1){if((g|0)>=(a|0))break;i=O(g,b)|0;e=0;while(1){if((e|0)>=(b|0))break;m=j+(e<<3)|0;h[m>>3]=+h[m>>3]+ +h[c+(e+i<<3)>>3];e=e+1|0}g=g+1|0}d=1.0/+(a|0);e=0;while(1){if((e|0)>=(b|0)){g=0;break}m=j+(e<<3)|0;h[m>>3]=+h[m>>3]*d;e=e+1|0}while(1){if((g|0)>=(a|0)){i=0;break}i=O(g,b)|0;e=0;while(1){if((e|0)>=(b|0))break;m=c+(e+i<<3)|0;h[m>>3]=+h[m>>3]-+h[j+(e<<3)>>3];e=e+1|0}g=g+1|0}while(1){if((i|0)>=(a|0))break;j=O(i,b)|0;g=0;while(1){if((g|0)>=(b|0))break;k=c+(g+j<<3)|0;m=O(g,b)|0;e=0;while(1){if((e|0)==(b|0))break;p=n+(e+m<<3)|0;h[p>>3]=+h[p>>3]+ +h[c+(e+j<<3)>>3]*+h[k>>3];e=e+1|0}g=g+1|0}i=i+1|0}d=+h[n+8>>3];if(d==0.0)d=0.0;else{q=+h[n>>3];f=+h[n+24>>3];d=-(f-q-+C(+(q*q+d*d*4.0+(q*-2.0+f)*f)))/(d*2.0)}f=+C(+(d*d+1.0));d=d/f;f=1.0/f;e=0;while(1){if((e|0)>=(a|0))break;p=O(e,b)|0;n=c+(p<<3)|0;q=+h[n>>3];p=c+(p+1<<3)|0;r=+h[p>>3];h[n>>3]=r*f+q*d;h[p>>3]=r*d-q*f;e=e+1|0}l=o;return}function wq(a,b,c,d,e,f,g,h,i,j){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;xq(a,b,c,d,e,f,g,h,i,j);return}function xq(a,b,d,e,f,g,i,j,k,m){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;m=m|0;var n=0.0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0;M=l;l=l+192|0;I=M+176|0;H=M+168|0;K=M;W6(K|0,e|0,168)|0;c[m>>2]=0;do if(b|0?(L=c[b+4>>2]|0,!((a|0)<1|(L|0)<1)):0){if((ww(b,0)|0)!=0?(c[b+16>>2]|0)==1:0){if((c[e+136>>2]|0)==1)o=Bw(d)|0;else o=d;J=Bw(b)|0}else t=5;do if((t|0)==5)if((c[e+136>>2]|0)==1){J=Aw(b,0)|0;o=Aw(d,0)|0;break}else{J=Qw(b)|0;o=d;break}while(0);G=e+160|0;p=c[G>>2]|0;if((p|0)==3){if((j|0)>0)t=14}else if((j|0)>0&(p|0)==4)t=14;if((t|0)==14){d=KK(O(a<<3,c[J>>2]|0)|0)|0;L=yq(J,j,k)|0;wq(a,L,0,e,0,0,d,0,0,m);zq(a,J,j,k,i,d);lu(a,J,i,g,c[e+124>>2]|0,+h[e+144>>3],c[G>>2]|0,j,k);qw(L);U1(d);if((J|0)==(b|0))break;qw(J);break}E=fx(c[e+44>>2]|0,c[e+48>>2]|0)|0;c[E+16>>2]=c[e+40>>2];F=tx(J,o,f,E)|0;q=wx(F)|0;if(!(c[q+32>>2]|0))r=i;else r=KK(O(a<<3,c[q+4>>2]|0)|0)|0;p=uq(J)|0;if(+h[e>>3]==-1.0001234)h[e>>3]=(p|0)==0?-1.0:-1.8;v=e+136|0;w=e+24|0;x=e+16|0;y=e+104|0;z=e+96|0;A=a<<3;B=e+132|0;C=c[15712]|0;D=e+80|0;while(1){a:do switch(c[v>>2]|0){case 0:{switch(c[B>>2]|0){case 0:{nq(a,c[q+8>>2]|0,e,c[q+24>>2]|0,r,m);break a}case 2:{p=q+8|0;t=29;break}case 3:{p=q+8|0;s=c[p>>2]|0;if((c[s>>2]|0)>1e4)if(!0)t=29;else{c[H>>2]=1e4;w4(C,92299,H)|0;t=29}else t=30;break}default:{s=c[q+8>>2]|0;t=30}}if((t|0)==29){iq(a,c[p>>2]|0,e,c[q+24>>2]|0,r,m);break a}else if((t|0)==30){oq(a,s,e,c[q+24>>2]|0,r,m);break a}break}case 1:{h[z>>3]=1.0;c[y>>2]=1;u=(c[q+28>>2]|0)==0;n=u?.5:.05;c[D>>2]=u?500:100;u=(c[q+32>>2]|0)==0;p=q+8|0;s=q+12|0;t=q+24|0;pq(a,c[p>>2]|0,c[s>>2]|0,e,c[t>>2]|0,r,n,m);if(u){c[x>>2]=0;h[z>>3]=.05;c[y>>2]=0;pq(a,c[p>>2]|0,c[s>>2]|0,e,c[t>>2]|0,r,n*.5,m);pq(a,c[p>>2]|0,c[s>>2]|0,e,c[t>>2]|0,r,n*.125,m);pq(a,c[p>>2]|0,c[s>>2]|0,e,c[t>>2]|0,r,n*.03125,m)}break}default:{}}while(0);t=c[q+32>>2]|0;if(!t){t=39;break}if(c[m>>2]|0){t=35;break}u=c[q+16>>2]|0;p=c[q+40>>2]|0;if(!(c[t+32>>2]|0))s=i;else s=KK(O(A,c[t+4>>2]|0)|0)|0;Aq(a,c[t+8>>2]|0,u,c[t+20>>2]|0,r,s,p,+h[w>>3]*.001);U1(r);c[x>>2]=0;h[w>>3]=+h[w>>3]*.75;c[y>>2]=0;h[z>>3]=((c[(c[t+28>>2]|0)+40>>2]|0)+-9|0)>>>0<2?1.0:.1;q=t;r=s}if((t|0)==35)U1(r);else if((t|0)==39){Ux(a,J,e,f,i,m);if(0){c[I>>2]=c[e+124>>2];w4(C,92362,I)|0}if((a|0)==2)vq(L,2,i);n=+h[e+152>>3];if(n!=0.0)Bq(L,a,i,n);lu(a,J,i,g,c[e+124>>2]|0,+h[e+144>>3],c[G>>2]|0,j,k)}W6(e|0,K|0,168)|0;if((J|0)!=(b|0))qw(J);if(!((o|0)==0|(o|0)==(d|0)))qw(o);gx(E);hx(F)}while(0);l=M;return}function yq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;y=l;l=l+16|0;x=y;u=c[a+20>>2]|0;v=c[a+24>>2]|0;w=KK(c[a>>2]<<2)|0;e=0;while(1){if((e|0)>=(c[a>>2]|0)){e=0;break}c[w+(e<<2)>>2]=1;e=e+1|0}while(1){if((e|0)>=(b|0)){e=0;d=0;break}c[w+(c[d+(e<<2)>>2]<<2)>>2]=-1;e=e+1|0}while(1){m=c[a>>2]|0;if((d|0)>=(m|0)){d=0;b=0;break}b=w+(d<<2)|0;if((c[b>>2]|0)>0){c[b>>2]=e;e=e+1|0}d=d+1|0}a:while(1){while(1){if((b|0)>=(m|0))break a;if((c[w+(b<<2)>>2]|0)>=0)break;b=b+1|0}j=b+1|0;k=c[u+(j<<2)>>2]|0;i=c[u+(b<<2)>>2]|0;while(1){if((i|0)>=(k|0)){b=j;continue a}f=c[v+(i<<2)>>2]|0;b:do if((c[w+(f<<2)>>2]|0)>-1)d=d+1|0;else{h=c[u+(f+1<<2)>>2]|0;g=c[u+(f<<2)>>2]|0;while(1){if((g|0)>=(h|0))break b;f=c[v+(g<<2)>>2]|0;if((f|0)!=(b|0))d=((c[w+(f<<2)>>2]|0)>>>31^1)+d|0;g=g+1|0}}while(0);i=i+1|0}}b=d<<2;if((d|0)>0){r=KK(b)|0;s=KK(b)|0;p=s;q=r}else{p=0;q=0;r=0;s=0}t=c[15712]|0;b=0;f=0;c:while(1){d=c[a>>2]|0;while(1){if((b|0)>=(d|0))break c;o=w+(b<<2)|0;if((c[o>>2]|0)>=0)break;b=b+1|0}m=b+1|0;n=u+(m<<2)|0;k=c[u+(b<<2)>>2]|0;d=f;while(1){if((k|0)>=(c[n>>2]|0)){b=m;f=d;continue c}f=v+(k<<2)|0;g=c[f>>2]|0;d:do if((c[w+(g<<2)>>2]|0)>-1){c[q+(d<<2)>>2]=c[o>>2];c[p+(d<<2)>>2]=c[w+(c[f>>2]<<2)>>2];d=d+1|0}else{j=u+(g+1<<2)|0;i=c[u+(g<<2)>>2]|0;while(1){if((i|0)>=(c[j>>2]|0))break d;f=v+(i<<2)|0;h=c[f>>2]|0;do if((h|0)!=(b|0)?(c[w+(h<<2)>>2]|0)>-1:0){c[q+(d<<2)>>2]=c[o>>2];h=d+1|0;c[p+(d<<2)>>2]=c[w+(c[f>>2]<<2)>>2];g=c[o>>2]|0;d=c[w+(c[f>>2]<<2)>>2]|0;if((g|0)!=68)if((d|0)==68)d=68;else{d=h;break}c[x>>2]=g;c[x+4>>2]=d;w4(t,92380,x)|0;d=h}while(0);i=i+1|0}}while(0);k=k+1|0}}x=Dw(f,e,e,q,p,0,8,8)|0;U1(r);U1(s);U1(w);l=y;return x|0}function zq(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0,k=0.0,l=0,m=0,n=0,o=0,p=0,q=0;p=KK(c[b>>2]<<2)|0;i=0;while(1){if((i|0)>=(c[b>>2]|0)){i=0;break}c[p+(i<<2)>>2]=1;i=i+1|0}while(1){if((i|0)>=(d|0)){i=0;l=0;break}j=c[e+(i<<2)>>2]|0;if((j|0)>-1?(j|0)<(c[b>>2]|0):0)c[p+(j<<2)>>2]=-1;i=i+1|0}while(1){n=c[b>>2]|0;if((l|0)>=(n|0)){l=0;break}j=p+(l<<2)|0;if((c[j>>2]|0)>-1){c[j>>2]=i;i=i+1|0}l=l+1|0}while(1){if((l|0)>=(n|0))break;i=c[p+(l<<2)>>2]|0;a:do if((i|0)>-1){m=O(l,a)|0;j=O(i,a)|0;i=0;while(1){if((i|0)>=(a|0))break a;h[f+(i+m<<3)>>3]=+h[g+(j+i<<3)>>3];i=i+1|0}}while(0);l=l+1|0}o=b+20|0;g=b+24|0;n=0;while(1){if((n|0)>=(d|0))break;b=c[e+(n<<2)>>2]|0;i=c[o>>2]|0;l=c[i+(b+1<<2)>>2]|0;i=c[i+(b<<2)>>2]|0;m=l-i|0;b=O(b,a)|0;j=0;while(1){if((j|0)>=(a|0))break;h[f+(j+b<<3)>>3]=0.0;j=j+1|0}while(1){if((i|0)<(l|0))j=0;else break;while(1){if((j|0)>=(a|0))break;k=+h[f+((O(c[(c[g>>2]|0)+(i<<2)>>2]|0,a)|0)+j<<3)>>3];q=f+(j+b<<3)|0;h[q>>3]=+h[q>>3]+k;j=j+1|0}i=i+1|0}k=1.0/+(m|0);i=0;while(1){if((i|0)>=(a|0))break;q=f+(i+b<<3)|0;h[q>>3]=+h[q>>3]*k;i=i+1|0}n=n+1|0}U1(p);return}function Aq(a,b,d,e,f,g,i,j){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=+j;var k=0,m=0,n=0,o=0,p=0,q=0.0;o=l;l=l+16|0;n=o;c[n>>2]=g;Gw(d,0,f,0,n,0,a);a:do if((i+-1|0)>>>0<6){tq(a,b,c[n>>2]|0);k=c[e>>2]|0;m=c[e+20>>2]|0;g=c[e+24>>2]|0;b=0;b:while(1){if((b|0)>=(k|0))break a;i=b+1|0;e=m+(i<<2)|0;b=c[m+(b<<2)>>2]|0;c:while(1){b=b+1|0;if((b|0)>=(c[e>>2]|0)){b=i;continue b}f=g+(b<<2)|0;d=0;while(1){if((d|0)>=(a|0))continue c;q=(+Sw()+-.5)*j;p=(c[n>>2]|0)+((O(c[f>>2]|0,a)|0)+d<<3)|0;h[p>>3]=+h[p>>3]+q;d=d+1|0}}}}while(0);l=o;return}function Bq(a,b,c,d){a=a|0;b=b|0;c=c|0;d=+d;var e=0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0.0,o=0.0;k=l;l=l+16|0;j=k;e=0;while(1){if((e|0)>=(b|0)){g=0;break}h[j+(e<<3)>>3]=0.0;e=e+1|0}while(1){if((g|0)>=(a|0))break;i=O(g,b)|0;e=0;while(1){if((e|0)>=(b|0))break;m=j+(e<<3)|0;h[m>>3]=+h[m>>3]+ +h[c+(e+i<<3)>>3];e=e+1|0}g=g+1|0}f=1.0/+(a|0);e=0;while(1){if((e|0)>=(b|0)){g=0;break}m=j+(e<<3)|0;h[m>>3]=+h[m>>3]*f;e=e+1|0}while(1){if((g|0)>=(a|0))break;i=O(g,b)|0;e=0;while(1){if((e|0)>=(b|0))break;m=c+(e+i<<3)|0;h[m>>3]=+h[m>>3]-+h[j+(e<<3)>>3];e=e+1|0}g=g+1|0}d=d*-.017453277777777776;f=+E(+d);d=+F(+d);e=0;while(1){if((e|0)>=(a|0))break;m=O(e,b)|0;j=c+(m<<3)|0;n=+h[j>>3];m=c+(m+1<<3)|0;o=+h[m>>3];h[j>>3]=o*d+n*f;h[m>>3]=o*f-n*d;e=e+1|0}l=k;return}function Cq(a,b,d,e,f,g,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=+g;i=i|0;var j=0,k=0,l=0,m=0;m=(ww(b,0)|0)==0;j=(c[b+16>>2]|0)==1;if(m)if(j)j=Bw(vw(b,0)|0)|0;else k=5;else if(j)j=b;else k=5;if((k|0)==5)j=Qw(b)|0;m=Bw(j)|0;c[i>>2]=0;j=c[m>>2]|0;if(!d){KK(O(a<<3,j)|0)|0;ab()}l=Gx(m,a,0.0,c[d>>2]|0,(e|0)==0?0:2,1)|0;if(!l)c[i>>2]=-1;else{h[l+40>>3]=.1;c[l+24>>2]=5;+Ix(l,a,c[d>>2]|0,f,g);k=O(j,a)|0;e=l+32|0;j=0;while(1){if((j|0)>=(k|0))break;i=(c[d>>2]|0)+(j<<3)|0;h[i>>3]=+h[i>>3]/+h[e>>3];j=j+1|0}Hx(l)}if((m|0)!=(b|0))qw(m);return}function Dq(a,b,c,d,e,f,g,h){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=+g;h=h|0;Cq(a,c,d,e,f,g,h);return}function Eq(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=g|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0.0;v=c[b>>2]|0;w=c[b+20>>2]|0;t=c[b+24>>2]|0;u=c[b+28>>2]|0;if(!(ww(b,0)|0))qa(95795,92387,37,92404);a=KK(56)|0;k=a+20|0;c[k>>2]=0;c[a+24>>2]=2;c[a+12>>2]=0;j=KK(16)|0;c[k>>2]=j;h[j>>3]=e;h[j+8>>3]=f;c[a+16>>2]=105;h[a+40>>3]=.01;c[a+48>>2]=~~+C(+(+(c[b>>2]|0)));j=b+8|0;k=a+4|0;c[k>>2]=rw(v,v,(c[j>>2]|0)+v|0,1,1)|0;j=rw(v,v,(c[j>>2]|0)+v|0,1,1)|0;c[a+8>>2]=j;k=c[k>>2]|0;l=c[k+28>>2]|0;m=c[j+28>>2]|0;if((j|0)==0|(k|0)==0){Fx(a);a=0}else{n=c[k+20>>2]|0;o=c[k+24>>2]|0;p=c[j+20>>2]|0;q=c[j+24>>2]|0;c[p>>2]=0;c[n>>2]=0;b=0;d=0;while(1){if((b|0)>=(v|0))break;r=b+1|0;s=w+(r<<2)|0;e=0.0;f=0.0;i=c[w+(b<<2)>>2]|0;while(1){if((i|0)>=(c[s>>2]|0))break;g=c[t+(i<<2)>>2]|0;if((g|0)!=(b|0)){x=+h[u+(i<<3)>>3];x=x>=0.0?x:-x;x=x>.01?x:.01;c[o+(d<<2)>>2]=g;c[q+(d<<2)>>2]=g;g=l+(d<<3)|0;h[g>>3]=-1.0;h[m+(d<<3)>>3]=-x;e=+h[g>>3]+e;f=f-x;d=d+1|0}i=i+1|0}c[o+(d<<2)>>2]=b;c[q+(d<<2)>>2]=b;h[l+(d<<3)>>3]=-e;h[m+(d<<3)>>3]=-f;d=d+1|0;c[n+(r<<2)>>2]=d;c[p+(r<<2)>>2]=d;b=r}c[k+8>>2]=d;c[j+8>>2]=d}return a|0}function Fq(a){a=a|0;Fx(a);return}function Gq(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return +(+Jx(a,b,c,d,.001))}function Hq(a,b){a=a|0;b=+b;var d=0,e=0,f=0,g=0;if((c[a+16>>2]|0)==1)d=vw(a,0)|0;else d=Qw(a)|0;f=c[d+28>>2]|0;a:do if(b!=1.0){e=c[d+8>>2]|0;a=0;while(1){if((a|0)>=(e|0))break a;g=f+(a<<3)|0;h[g>>3]=+h[g>>3]*b;a=a+1|0}}while(0);return d|0}function Iq(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,l=0.0;j=c[b>>2]|0;c[e>>2]=0;k=O(j,a)|0;e=0;while(1){if((e|0)>=(k|0)){e=1;f=1;break}h[d+(e<<3)>>3]=+Sw()*100.0;e=e+1|0}while(1){if((f|0)>=(j|0))break;i=O(f,a)|0;g=0;while(1){if((g|0)>=(a|0))break;l=+h[d+(g<<3)>>3]-+h[d+(g+i<<3)>>3];if((l>=0.0?l:-l)>1.0e-16){e=0;f=j;break}else g=g+1|0}f=f+1|0}a:do if(e|0){w5(1);e=0;while(1){if((e|0)>=(k|0))break a;h[d+(e<<3)>>3]=+Sw()*100.0;e=e+1|0}}while(0);e=Hq(b,1.0)|0;if(!(ww(e,0)|0))qa(92430,92387,164,92466);else{k=Eq(0,e,0,101.0e5,100.0,0)|0;+Gq(k,a,d,300);Fq(k);k=Eq(0,e,0,101.0e3,100.0,0)|0;+Gq(k,a,d,300);Fq(k);k=Eq(0,e,0,1010.0,100.0,0)|0;+Gq(k,a,d,300);Fq(k);k=Eq(0,e,0,10.1,100.0,0)|0;+Gq(k,a,d,300);Fq(k);ax(0.0,0.0,490.0,700.0,c[b>>2]|0,a,d);qw(e);return}}function Jq(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,l=0,m=0;k=KK((tB(a)|0)<<4)|0;i=(e|0)!=0;j=b+8|0;g=jC(a)|0;f=0;while(1){if(!g)break;if(i)f=f+((Q2(KB(g)|0,92481,11)|0)==0&1)|0;m=c[g+16>>2]|0;l=c[m+120>>2]<<1;h[k+(l<<3)>>3]=+h[m+32>>3]*.5+ +h[b>>3];h[k+((l|1)<<3)>>3]=+h[m+40>>3]*.5+ +h[j>>3];g=kC(a,g)|0}if(i&(f|0)!=0){b=KK(f<<2)|0;g=jC(a)|0;f=0;while(1){if(!g)break;if(!(Q2(KB(g)|0,92481,11)|0)){c[b+(f<<2)>>2]=c[(c[g+16>>2]|0)+120>>2];f=f+1|0}g=kC(a,g)|0}c[e>>2]=b;c[d>>2]=f}return k|0}function Kq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;z=l;l=l+32|0;w=z+16|0;v=z+8|0;u=z;if(a){x=tB(a)|0;y=uB(a)|0;b=jC(a)|0;e=0;while(1){if(!b)break;c[(c[b+16>>2]|0)+120>>2]=e;b=kC(a,b)|0;e=e+1|0}r=y<<2;q=KK(r)|0;r=KK(r)|0;b=y<<3;s=KK(b)|0;o=GA(a,2,101541,0)|0;t=(d|0)!=0;if(t){e=GA(a,2,92493,0)|0;p=JK(b)|0;n=p}else{n=0;e=0;p=0}j=(o|0)==0;k=(e|0)==0;i=jC(a)|0;b=0;while(1){if(!i)break;m=c[(c[i+16>>2]|0)+120>>2]|0;g=XA(a,i)|0;while(1){if(!g)break;c[q+(b<<2)>>2]=m;c[r+(b<<2)>>2]=c[(c[(c[((c[g>>2]&3|0)==2?g:g+-48|0)+40>>2]|0)+16>>2]|0)+120>>2];if(!j?(A=RA(g,o)|0,c[v>>2]=u,(b5(A,101209,v)|0)==1):0)f=+h[u>>3];else{h[u>>3]=1.0;f=1.0}h[s+(b<<3)>>3]=f;if(!k){A=RA(g,e)|0;c[w>>2]=u;if((b5(A,101209,w)|0)==1)f=+h[u>>3];else{h[u>>3]=1.0;f=1.0}h[n+(b<<3)>>3]=f}g=ZA(a,g)|0;b=b+1|0}i=kC(a,i)|0}b=Dw(y,x,x,q,r,s,1,8)|0;if(t)c[d>>2]=Dw(y,x,x,q,r,p,1,8)|0;U1(q);U1(r);U1(s);if(n)U1(p)}else b=0;l=z;return b|0}function Lq(a){a=a|0;var b=0,d=0,e=0.0,f=0,g=0.0,i=0.0,j=0.0,k=0,m=0,n=0,o=0.0,p=0.0;m=l;l=l+16|0;k=m;a:do if(Mq(a,k)|0){b=c[(c[(jC(a)|0)+16>>2]|0)+132>>2]|0;g=+h[b>>3];e=+h[b+8>>3];b=jC(a)|0;while(1){if(!b)break;f=c[(c[b+16>>2]|0)+132>>2]|0;h[f>>3]=+h[f>>3]-g;f=f+8|0;h[f>>3]=+h[f>>3]-e;b=kC(a,b)|0}b=(g!=0.0|e!=0.0)&1;d=jC(a)|0;while(1){if(!d)break a;f=XA(a,d)|0;if(f|0)break;d=kC(a,d)|0}n=c[f>>2]&3;d=c[(c[(c[((n|0)==2?f:f+-48|0)+40>>2]|0)+16>>2]|0)+132>>2]|0;f=c[(c[(c[((n|0)==3?f:f+48|0)+40>>2]|0)+16>>2]|0)+132>>2]|0;i=+h[f+8>>3];j=+h[f>>3];e=+K(+(+h[d+8>>3]-i),+(+h[d>>3]-j));e=+h[k>>3]-e;h[k>>3]=e;if(e!=0.0){g=+E(+e);e=+F(+e);b=jC(a)|0;while(1){if(!b){b=1;break a}k=c[(c[b+16>>2]|0)+132>>2]|0;p=+h[k>>3]-j;n=k+8|0;o=+h[n>>3]-i;h[k>>3]=p*g+j-o*e;h[n>>3]=p*e+i+o*g;b=kC(a,b)|0}}}else b=0;while(0);l=m;return b|0}function Mq(b,d){b=b|0;d=d|0;var e=0.0,f=0,g=0;g=l;l=l+16|0;f=g;b=QA(b,92497)|0;do if((b|0)!=0?(a[b>>0]|0)!=0:0){e=+s5(b,f);if((c[f>>2]|0)==(b|0))if(!((dP(b)|0)<<24>>24)){b=0;break}else e=0.0;while(1){if(!(e>180.0))break;e=e+-360.0}while(1){if(!(e<=-180.0))break;e=e+360.0}h[d>>3]=e*.017453292519943295;b=1}else b=0;while(0);l=g;return b|0}function Nq(a,b,c){a=a|0;b=b|0;c=c|0;var d=0;d=QA(a,92507)|0;return Oq(a,(d|0)==0?(c|0?c:195059):d,b)|0}function Oq(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0.0;n=l;l=l+32|0;m=n+16|0;j=n+8|0;i=n;if((d|0)!=0?(a[d>>0]|0)!=0:0){f=17896;while(1){g=c[f+4>>2]|0;if(!g)break;if(!(j3(d,g,c[f+8>>2]|0)|0)){k=6;break}f=f+16|0}if((k|0)==6){if(!(c[f+12>>2]|0)){c[i>>2]=g;gA(0,92515,i)|0;f=17896}i=c[f>>2]|0;c[e>>2]=i;c[e+4>>2]=c[f+12>>2];if((i|0)==18)Pq(b,d+(c[f+8>>2]|0)|0,e)}if(!(c[f+4>>2]|0)){f=eP(d,63)|0;if(f<<24>>24==63){c[j>>2]=d;gA(0,92557,j)|0;f=0}else f=f&255;f=(f|0)==0;c[e>>2]=f&1;f=f?92604:135709;k=15}}else{c[e>>2]=0;f=135709;k=15}if((k|0)==15)c[e+4>>2]=f;if(0){k=c[15712]|0;j=c[e+8>>2]|0;o=+h[e+16>>3];c[m>>2]=c[e+4>>2];c[m+4>>2]=j;h[m+8>>3]=o;w4(k,92612,m)|0}l=n;return e|0}function Pq(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;e=l;l=l+16|0;f=e;g=e+4|0;c[f>>2]=g;f=(b5(b,137395,f)|0)>0;b=c[g>>2]|0;c[d+8>>2]=f&(b|0)>-1?b:1e3;h[d+16>>3]=+_O(a,GA(a,0,92648,0)|0,-4.0,-1.0e10);l=e;return}function Qq(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0;h=l;l=l+16|0;g=h+8|0;f=h;do if((tB(a)|0)>=2){d=Lq(a)|0;d=(Rq(a)|0)+d|0;e=c[b>>2]|0;if(e){if(0){e=c[15712]|0;j=KB(a)|0;i=c[b+4>>2]|0;c[f>>2]=j;c[f+4>>2]=i;w4(e,92977,f)|0;e=c[b>>2]|0}if(e>>>0>2){switch(e|0){case 3:{e=Sr(a,1)|0;break}case 4:{e=Sr(a,0)|0;break}case 6:case 5:{e=0;break}case 10:case 9:case 7:case 8:case 14:case 13:case 11:case 12:{Cr(a,e)|0;e=0;break}case 15:{e=Sr(a,-1)|0;break}default:{c[g>>2]=c[b+4>>2];gA(0,93e3,g)|0;e=0}}d=e+d|0;break}if(Sq(a)|0){Tq();U1(c[46845]|0);c[46845]=0;break}Uq(a);if((c[b>>2]|0)==2)e=Vq()|0;else e=Wq()|0;if(e|0)Xq();Tq();U1(c[46845]|0);c[46845]=0;d=e+d|0}}else d=0;while(0);l=h;return d|0}function Rq(a){a=a|0;var b=0,d=0.0,e=0,f=0,g=0.0,i=0,j=0,k=0,m=0;m=l;l=l+48|0;i=m+24|0;f=m+16|0;j=m;b=QA(a,140742)|0;a:do if(((b|0)!=0?(k=j+8|0,c[f>>2]=j,c[f+4>>2]=k,e=b5(b,105503,f)|0,(e|0)!=0):0)?(g=+h[j>>3],!(+B(+g)<1.0e-09)):0){if((e|0)!=1){d=+h[k>>3];if(+B(+d)<1.0e-09){b=0;break}}else{h[k>>3]=g;d=g}if(!(d==1.0&g==1.0)){if(0){f=c[15712]|0;h[i>>3]=g;h[i+8>>3]=d;w4(f,93160,i)|0}b=jC(a)|0;while(1){if(!b){b=1;break a}i=c[(c[b+16>>2]|0)+132>>2]|0;h[i>>3]=+h[i>>3]*+h[j>>3];i=i+8|0;h[i>>3]=+h[i>>3]*+h[k>>3];b=kC(a,b)|0}}else b=0}else b=0;while(0);l=m;return b|0}function Sq(b){b=b|0;var d=0,e=0,f=0,i=0.0,j=0.0,k=0,m=0,n=0,o=0;n=l;l=l+16|0;k=n;c[46854]=tB(b)|0;ts();c[46869]=KK((c[46854]|0)*96|0)|0;f=jC(b)|0;d=c[46869]|0;lr(k,b);i=+g[k>>2];j=+g[k+4>>2];k=(a[k+8>>0]|0)==0;j=k?j:j*.013888888888888888;i=k?i:i*.013888888888888888;k=k?2:1;e=0;while(1){if((e|0)>=(c[46854]|0)){d=0;break}o=c[(c[f+16>>2]|0)+132>>2]|0;h[d+8>>3]=+h[o>>3];h[d+16>>3]=+h[o+8>>3];if(mb[k&3](d+40|0,f,i,j)|0){m=4;break}c[d+24>>2]=e;c[d+28>>2]=1;c[d>>2]=f;c[d+88>>2]=0;d=d+96|0;e=e+1|0;f=kC(b,f)|0}if((m|0)==4){U1(c[46869]|0);c[46869]=0;d=1}l=n;return d|0}function Tq(){var a=0,b=0;a=c[46869]|0;b=0;while(1){if((b|0)>=(c[46854]|0))break;pu(a+40|0);a=a+96|0;b=b+1|0}ou();Vs();U1(c[46869]|0);return}function Uq(b){b=b|0;var d=0,e=0,f=0,g=0.0,i=0.0,j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0.0,r=0.0,s=0.0,t=0.0;o=l;l=l+32|0;m=o+16|0;n=o;p=c[46869]|0;i=+h[p+8>>3];k=+h[p+16>>3];f=c[46854]|0;d=p;e=1;g=+h[p+40>>3]+i;i=+h[p+56>>3]+i;j=+h[p+48>>3]+k;k=+h[p+64>>3]+k;while(1){if((e|0)>=(f|0))break;s=+h[d+104>>3];q=+h[d+112>>3];t=+h[d+136>>3]+s;r=+h[d+144>>3]+q;s=+h[d+152>>3]+s;q=+h[d+160>>3]+q;d=d+96|0;e=e+1|0;g=t<g?t:g;i=s>i?s:i;j=r<j?r:j;k=q>k?q:k}d=QA(b,93100)|0;if(d|0?a[d>>0]|0:0)h[173]=+r5(d);s=+h[173];t=s*(k-j);s=s*(i-g);h[m>>3]=g-s;h[m+8>>3]=j-t;h[n>>3]=s+i;h[n+8>>3]=t+k;dr(m,n);l=o;return}function Vq(){var a=0,b=0,d=0,e=0;d=l;l=l+16|0;b=d;if(Yq(0)|0){Zq();a=0;do{kr();a=a+1|0}while((Yq(a)|0)!=0);if(!0)a=1;else{e=c[15712]|0;c[b>>2]=a;w4(e,93028,b)|0;a=1}}else a=0;l=d;return a|0}function Wq(){var a=0,b=0,d=0,e=0,f=0,g=0,h=0,i=0;i=l;l=l+16|0;h=i+8|0;g=i;b=Yq(0)|0;if(!b)a=0;else{Zq();_q(0);Yu(0,1);a=0;e=0;d=0;while(1){ar();d=d+1|0;f=Yq(d)|0;if(!f)break;b=(f|0)<(b|0)?0:e+1|0;c[46846]=1;if(b){br();a=a+1|0}_q(1);Yu(0,1);e=b;b=f}if(0){f=c[15712]|0;c[g>>2]=d;w4(f,93028,g)|0;c[h>>2]=a;w4(f,93055,h)|0}cr();a=1}l=i;return a|0}function Xq(){var a=0,b=0,d=0,e=0;d=c[46854]|0;a=c[46869]|0;b=0;while(1){if((b|0)>=(d|0))break;e=c[(c[(c[a>>2]|0)+16>>2]|0)+132>>2]|0;h[e>>3]=+h[a+8>>3];h[e+8>>3]=+h[a+16>>3];a=a+96|0;b=b+1|0}return}function Yq(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+48|0;o=q+24|0;n=q+8|0;p=q;d=c[46869]|0;e=c[46854]|0;b=0;while(1){if((b|0)>=(e|0)){f=0;b=0;break}c[d+(b*96|0)+32>>2]=0;b=b+1|0}a:while(1){if((b|0)>=(e+-1|0))break;m=d+96|0;b=b+1|0;j=d+8|0;k=d+40|0;i=d+32|0;g=m;h=b;while(1){if((h|0)>=(e|0)){d=m;continue a}e=g+8|0;c[n>>2]=c[j>>2];c[n+4>>2]=c[j+4>>2];c[n+8>>2]=c[j+8>>2];c[n+12>>2]=c[j+12>>2];c[o>>2]=c[e>>2];c[o+4>>2]=c[e+4>>2];c[o+8>>2]=c[e+8>>2];c[o+12>>2]=c[e+12>>2];if(!(yu(n,k,o,g+40|0)|0))d=f;else{c[i>>2]=1;c[g+32>>2]=1;d=f+1|0}g=g+96|0;h=h+1|0;f=d;e=c[46854]|0}}if(0>1){o=c[15712]|0;c[p>>2]=a;c[p+4>>2]=f;w4(o,93081,p)|0}l=q;return f|0}function Zq(){var a=0,b=0,d=0,e=0.0,f=0,g=0.0,i=0,j=0,k=0,l=0,m=0,n=0;ir();k=c[46848]|0;l=c[46869]|0;a=c[46845]|0;a:while(1){i=a;if(a>>>0>=k>>>0)break;b=a+4|0;if(b>>>0>=k>>>0){a=b;continue}f=c[b>>2]|0;d=c[a>>2]|0;g=+h[d>>3];if(+h[f>>3]!=g){a=b;continue}e=+h[d+8>>3];if(+h[f+8>>3]!=e){a=b;continue}j=a+8|0;f=2;while(1){if(j>>>0>=k>>>0)break;d=c[j>>2]|0;if(!(+h[d>>3]==g)){m=11;break}if(!(+h[d+8>>3]==e)){m=11;break}j=j+4|0;f=f+1|0}if((m|0)==11){m=0;d=c[j>>2]|0;if(+h[d+8>>3]==e){e=(+h[d>>3]-g)/+(f|0);d=(((j>>>0>b>>>0?j:b)+-1+(0-i)|0)>>>2)+1|0;a=1;while(1){if((a|0)==(d|0)){a=j;continue a}i=c[b>>2]|0;h[i>>3]=+h[i>>3]+e*+(a|0);a=a+1|0;b=b+4|0}}}while(1){if(b>>>0>=j>>>0){a=j;continue a}f=c[a>>2]|0;n=c[f+16>>2]|0;i=c[b>>2]|0;d=c[i+16>>2]|0;h[i>>3]=(+h[l+(n*96|0)+56>>3]-+h[l+(n*96|0)+40>>3]+ +h[l+(d*96|0)+56>>3]-+h[l+(d*96|0)+40>>3])*.5+ +h[f>>3];b=b+4|0;a=a+4|0}}return}function _q(a){a=a|0;var b=0.0,d=0.0,e=0.0,f=0,g=0.0,i=0,j=0,k=0;if(a|0)ir();i=c[46845]|0;j=c[i>>2]|0;d=+h[j>>3];h[23272]=d;k=c[46854]|0;f=1;g=+h[j>>3];while(1){if((f|0)>=(k|0))break;a=c[i+(f<<2)>>2]|0;b=+h[a>>3];if(b<d){h[23272]=b;e=+h[a>>3]}else{e=b;b=d}f=f+1|0;d=b;g=e>g?e:g}e=+h[j+8>>3];h[23273]=e;h[23275]=+h[(c[i+(k+-1<<2)>>2]|0)+8>>3]-e;h[23274]=g-d;return}function $q(){var a=0;a=c[46847]|0;if(a>>>0<(c[46848]|0)>>>0){c[46847]=a+4;a=c[a>>2]|0}else a=0;return a|0}function ar(){var a=0,b=0,d=0;a=c[46869]|0;er();d=(c[46846]|0)==0;b=0;while(1){if((b|0)>=(c[46854]|0))break;if(!(d?!(c[a+32>>2]|0):0))fr(a);a=a+96|0;b=b+1|0}return}function br(){var a=0,b=0,c=0,d=0.0,e=0.0,f=0.0,g=0.0,i=0.0,j=0.0;a=l;l=l+32|0;c=a+16|0;b=a;j=+h[23268];i=+h[23266];g=+h[23269];e=+h[23267];d=(i-e)*.05;f=(j-g)*.05;h[b>>3]=f+j;h[b+8>>3]=i+d;h[c>>3]=g-f;h[c+8>>3]=e-d;dr(c,b);l=a;return}function cr(){Gs();Is();Ku();ms();return}function dr(a,b){a=a|0;b=b|0;var c=0.0,d=0.0,e=0.0,f=0.0;f=+h[a>>3];h[23269]=f;e=+h[b>>3];h[23268]=e;c=+h[a+8>>3];h[23267]=c;d=+h[b+8>>3];h[23266]=d;h[23258]=f;h[23260]=f;h[23262]=e;h[23264]=e;h[23265]=d;h[23261]=d;h[23263]=c;h[23259]=c;return}function er(){var a=0,b=0.0,d=0.0,e=0.0,f=0.0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0.0,s=0.0,t=0.0,u=0.0;m=c[46869]|0;g=m+8|0;f=+us(g,186064);e=+us(g,186080);d=+us(g,186096);a=1;b=+us(g,186112);g=m;i=m;j=m;k=m;while(1){l=m+96|0;if((a|0)>=(c[46854]|0))break;q=m+104|0;r=+us(q,186064);n=r<f;t=+us(q,186096);p=t<d;s=+us(q,186080);o=s<e;u=+us(q,186112);q=u<b;a=a+1|0;b=q?u:b;d=p?t:d;e=o?s:e;f=n?r:f;g=q?l:g;i=p?l:i;j=o?l:j;k=n?l:k;m=l}Ws(k+8|0,+h[23258],+h[23259]);Ws(i+8|0,+h[23262],+h[23263]);Ws(j+8|0,+h[23260],+h[23261]);Ws(g+8|0,+h[23264],+h[23265]);return}function fr(a){a=a|0;var b=0.0,d=0.0,e=0.0,f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0,s=0;p=l;l=l+64|0;j=p+48|0;i=p+32|0;g=p+16|0;m=p+8|0;n=p;k=c[a+88>>2]|0;o=k+8|0;b=0.0;d=0.0;e=0.0;k=c[k>>2]|0;while(1){f=c[k>>2]|0;if(!f)break;s=k+8|0;r=f+8|0;c[g>>2]=c[o>>2];c[g+4>>2]=c[o+4>>2];c[g+8>>2]=c[o+8>>2];c[g+12>>2]=c[o+12>>2];c[i>>2]=c[s>>2];c[i+4>>2]=c[s+4>>2];c[i+8>>2]=c[s+8>>2];c[i+12>>2]=c[s+12>>2];c[j>>2]=c[r>>2];c[j+4>>2]=c[r+4>>2];c[j+8>>2]=c[r+8>>2];c[j+12>>2]=c[r+12>>2];q=+gr(g,i,j);c[g>>2]=c[o>>2];c[g+4>>2]=c[o+4>>2];c[g+8>>2]=c[o+8>>2];c[g+12>>2]=c[o+12>>2];c[i>>2]=c[s>>2];c[i+4>>2]=c[s+4>>2];c[i+8>>2]=c[s+8>>2];c[i+12>>2]=c[s+12>>2];c[j>>2]=c[r>>2];c[j+4>>2]=c[r+4>>2];c[j+8>>2]=c[r+8>>2];c[j+12>>2]=c[r+12>>2];hr(g,i,j,m,n);b=+h[n>>3]*q+b;d=+h[m>>3]*q+d;e=q+e;k=f}h[a+8>>3]=d/e;h[a+16>>3]=b/e;l=p;return}function gr(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0;d=+h[b+8>>3];f=+h[c+8>>3];e=+h[a+8>>3];return +(+B(+((f-e)*+h[b>>3]+(d-f)*+h[a>>3]+ +h[c>>3]*(e-d)))*.5)}function hr(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;h[d>>3]=(+h[b>>3]+ +h[a>>3]+ +h[c>>3])*.3333333333333333;h[e>>3]=(+h[b+8>>3]+ +h[a+8>>3]+ +h[c+8>>3])*.3333333333333333;return}function ir(){var a=0,b=0,d=0,e=0;a=c[46845]|0;if(!a){a=KK(c[46854]<<2)|0;c[46845]=a;c[46848]=a+(c[46854]<<2)}d=c[46869]|0;Vs();e=c[46854]|0;b=0;while(1){if((b|0)>=(e|0))break;c[a>>2]=d+8;c[d+88>>2]=0;c[d+28>>2]=1;d=d+96|0;a=a+4|0;b=b+1|0}o4(c[46845]|0,e,4,69);c[46847]=c[46845];return}function jr(a,b){a=a|0;b=b|0;var d=0.0,e=0.0;a=c[a>>2]|0;b=c[b>>2]|0;d=+h[a+8>>3];e=+h[b+8>>3];if(!(d<e))if(!(d>e)){e=+h[a>>3];d=+h[b>>3];if(e<d)a=-1;else a=e>d&1}else a=1;else a=-1;return a|0}function kr(){var a=0,b=0,d=0,e=0;d=c[46854]|0;a=0;b=c[46869]|0;while(1){if((a|0)>=(d|0))break;e=b+8|0;h[e>>3]=+h[e>>3]*1.05;e=b+16|0;h[e>>3]=+h[e>>3]*1.05;a=a+1|0;b=b+96|0}return}function lr(b,e){b=b|0;e=e|0;var f=0,i=0,j=0,k=0,m=0,n=0.0,o=0.0;m=l;l=l+48|0;k=m;j=m+24|0;f=QA(e,93112)|0;if(!((f|0)!=0?(mr(f,j,1.0,0.0)|0)!=0:0))i=3;do if((i|0)==3){e=QA(e,93116)|0;if(e|0?mr(e,j,.800000011920929,4.0)|0:0)break;g[j+4>>2]=4.0;g[j>>2]=4.0;a[j+8>>0]=1}while(0);if(0){i=c[15712]|0;o=+g[j>>2];n=+g[j+4>>2];c[k>>2]=d[j+8>>0];h[k+8>>3]=o;h[k+16>>3]=n;w4(i,93121,k)|0};c[b>>2]=c[j>>2];c[b+4>>2]=c[j+4>>2];c[b+8>>2]=c[j+8>>2];l=m;return}function mr(b,d,e,f){b=b|0;d=d|0;e=+e;f=+f;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0;o=l;l=l+16|0;m=o;j=o+12|0;k=o+8|0;while(1){h=a[b>>0]|0;i=b+1|0;if(!(S2(h<<24>>24)|0))break;else b=i}p=h<<24>>24==43;h=d+8|0;a[h>>0]=p&1;c[m>>2]=j;c[m+4>>2]=k;switch(b5(p?i:b,93154,m)|0){case 0:{b=0;break}case 1:{c[k>>2]=c[j>>2];n=5;break}default:n=5}if((n|0)==5){do if(a[h>>0]|0){if(e>1.0){q=+g[j>>2]/e;g[d>>2]=q>f?f:q;e=+g[k>>2]/e;e=e>f?f:e;break}if(e<1.0){q=+g[j>>2]/e;g[d>>2]=q<f?f:q;e=+g[k>>2]/e;e=e<f?f:e;break}else{c[d>>2]=c[j>>2];e=+g[k>>2];break}}else{g[d>>2]=+g[j>>2]/e+1.0;e=+g[k>>2]/e+1.0}while(0);g[d+4>>2]=e;b=1}l=o;return b|0}function nr(a,b){a=a|0;b=b|0;var c=0,d=0;d=l;l=l+32|0;c=d;if((tB(a)|0)<2)a=0;else{Oq(a,b,c)|0;a=Qq(a,c)|0}l=d;return a|0}function or(a){a=a|0;return nr(a,QA(a,92507)|0)|0}function pr(b,e){b=b|0;e=e|0;var f=0,i=0,j=0,k=0,m=0,n=0.0,o=0.0;m=l;l=l+48|0;k=m;j=m+24|0;f=QA(e,93116)|0;if(!((f|0)!=0?(mr(f,j,1.0,0.0)|0)!=0:0))i=3;do if((i|0)==3){e=QA(e,93112)|0;if(e|0?mr(e,j,1.25,3.200000047683716)|0:0)break;g[j+4>>2]=3.200000047683716;g[j>>2]=3.200000047683716;a[j+8>>0]=1}while(0);if(0){i=c[15712]|0;o=+g[j>>2];n=+g[j+4>>2];c[k>>2]=d[j+8>>0];h[k+8>>3]=o;h[k+16>>3]=n;w4(i,93183,k)|0};c[b>>2]=c[j>>2];c[b+4>>2]=c[j+4>>2];c[b+8>>2]=c[j+8>>2];l=m;return}function qr(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+16|0;o=p;h=0;while(1){if((h|0)>=(d|0))break;c[e+(h<<2)>>2]=-1;h=h+1|0}c[e+(a<<2)>>2]=0;rr(f,a);a:do if(!(c[b+8>>2]|0)){h=2147483647;b:while(1){if(!((sr(f,o)|0)<<24>>24))break a;j=c[o>>2]|0;h=c[e+(j<<2)>>2]|0;i=b+(j*20|0)|0;j=b+(j*20|0)+4|0;k=h+1|0;a=1;while(1){if((a|0)>=(c[i>>2]|0))continue b;m=c[(c[j>>2]|0)+(a<<2)>>2]|0;n=e+(m<<2)|0;if((c[n>>2]|0)<0){c[n>>2]=k;tr(f,m)|0}a=a+1|0}}}else{h=2147483647;c:while(1){if(!((sr(f,o)|0)<<24>>24))break a;k=c[o>>2]|0;h=c[e+(k<<2)>>2]|0;i=b+(k*20|0)|0;j=b+(k*20|0)+4|0;k=b+(k*20|0)+8|0;a=1;while(1){if((a|0)>=(c[i>>2]|0))continue c;m=c[(c[j>>2]|0)+(a<<2)>>2]|0;n=e+(m<<2)|0;if((c[n>>2]|0)<0){c[n>>2]=h+~~+g[(c[k>>2]|0)+(a<<2)>>2];tr(f,m)|0}a=a+1|0}}}while(0);a=h+10|0;h=0;while(1){if((h|0)>=(d|0))break;i=e+(h<<2)|0;if((c[i>>2]|0)<0)c[i>>2]=a;h=h+1|0}l=p;return}function rr(a,b){a=a|0;b=b|0;c[c[a>>2]>>2]=b;c[a+12>>2]=0;c[a+8>>2]=1;return}function sr(a,b){a=a|0;b=b|0;var d=0,e=0;d=a+12|0;e=c[d>>2]|0;if((e|0)<(c[a+8>>2]|0)){a=c[a>>2]|0;c[d>>2]=e+1;c[b>>2]=c[a+(e<<2)>>2];a=1}else a=0;return a|0}function tr(a,b){a=a|0;b=b|0;var d=0,e=0;d=a+8|0;e=c[d>>2]|0;if((e|0)<(c[a+4>>2]|0)){a=c[a>>2]|0;c[d>>2]=e+1;c[a+(e<<2)>>2]=b;a=1}else a=0;return a|0}function ur(a,b){a=a|0;b=b|0;c[a>>2]=KK(b<<2)|0;c[a+4>>2]=b;c[a+8>>2]=0;c[a+12>>2]=0;return}function vr(a){a=a|0;U1(c[a>>2]|0);return}function wr(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0;if(!0)g=0;else{U4(93216,25,1,c[15712]|0)|0;g=0}while(1){if((g|0)>=(a|0))break;i=b+(g<<2)|0;f=0;e=0.0;while(1){if((f|0)==(a|0))break;if((g|0)!=(f|0))e=+h[(c[i>>2]|0)+(f<<3)>>3]+e;f=f+1|0}h[(c[i>>2]|0)+(g<<3)>>3]=-e;g=g+1|0}return wt(b,d,a+-1|0)|0}function xr(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0.0;j=un(b,b,0.0)|0;k=un(b,b,0.0)|0;e=jC(a)|0;while(1){if(!e)break;d=aB(a,e)|0;while(1){if(!d)break;g=c[d>>2]&3;f=(c[c[((g|0)==3?d:d+48|0)+40>>2]>>2]|0)>>>4;g=(c[c[((g|0)==2?d:d+-48|0)+40>>2]>>2]|0)>>>4;if((f|0)!=(g|0)){l=-1.0/+h[(c[d+16>>2]|0)+136>>3];h[(c[j+(g<<2)>>2]|0)+(f<<3)>>3]=l;h[(c[j+(f<<2)>>2]|0)+(g<<3)>>3]=l}d=bB(a,d,e)|0}e=kC(a,e)|0}i=wr(b,j,k)|0;a:do if(i|0){f=a+16|0;e=0;while(1){if((e|0)>=(b|0))break a;g=k+(e<<2)|0;d=0;while(1){if((d|0)==(b|0))break;a=c[g>>2]|0;h[(c[(c[(c[f>>2]|0)+160>>2]|0)+(e<<2)>>2]|0)+(d<<3)>>3]=+h[(c[k+(d<<2)>>2]|0)+(d<<3)>>3]+ +h[a+(e<<3)>>3]+ +h[a+(d<<3)>>3]*-2.0;d=d+1|0}e=e+1|0}}while(0);vn(j);vn(k);return i|0}function yr(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=+e;f=f|0;var g=0.0,h=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+16|0;j=s;n=d<<3;o=KK(n)|0;p=KK(n)|0;q=KK(n)|0;k=KK(n)|0;m=KK(n)|0;n=KK(n)|0;Ot(d,c,n);Ht(d,n);Ht(d,b);Jt(a,d,b,k);Lt(d,n,k,o);Ot(d,o,p);i=f+-1|0;c=0;g=+Pt(d,o,o);while(1){if((c|0)>=(f|0)){c=0;break}if(!(+Qt(d,o)>e)){c=0;break}Jt(a,d,p,q);h=+Pt(d,p,q);if(h==0.0){c=0;break}h=g/h;Nt(d,p,h,m);Mt(d,b,m,b);if((c|0)<(i|0)){Nt(d,q,h,q);Lt(d,o,q,o);h=+Pt(d,o,o);if(g==0.0){r=7;break}Nt(d,p,h/g,p);Mt(d,o,p,p);g=h}c=c+1|0}if((r|0)==7){gA(1,93242,j)|0;c=1}U1(o);U1(p);U1(q);U1(k);U1(m);U1(n);l=s;return c|0}function zr(a,b,c,d,e,f,g){a=a|0;b=b|0;c=c|0;d=d|0;e=+e;f=f|0;g=g|0;var h=0.0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+16|0;j=s;o=d<<3;p=KK(o)|0;q=KK(o)|0;k=KK(o)|0;m=KK(o)|0;n=KK(o)|0;o=KK(o)|0;Ot(d,c,o);if(g<<24>>24){Ht(d,o);Ht(d,b)}Kt(a,d,b,m);Lt(d,o,m,p);Ot(d,p,q);g=f+-1|0;c=0;h=+Pt(d,p,p);while(1){if((c|0)>=(f|0)){c=0;break}if(!(+Qt(d,p)>e)){c=0;break}Kt(a,d,q,k);i=+Pt(d,q,k);if(i==0.0){c=0;break}i=h/i;Nt(d,q,i,n);Mt(d,b,n,b);if((c|0)<(g|0)){Nt(d,k,i,k);Lt(d,p,k,p);i=+Pt(d,p,p);if(h==0.0){r=9;break}Nt(d,q,i/h,q);Mt(d,p,q,q);h=i}c=c+1|0}if((r|0)==9){gA(1,93242,j)|0;c=1}U1(p);U1(q);U1(k);U1(m);U1(n);U1(o);l=s;return c|0}function Ar(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=+e;f=f|0;var g=0.0,h=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+16|0;j=q;k=d<<2;m=JK(k)|0;n=JK(k)|0;o=JK(k)|0;k=JK(k)|0;St(d,b);St(d,c);Tt(a,d,b,k);St(d,k);Ut(d,c,k,m);Yt(d,m,n);i=f+-1|0;c=0;g=+Zt(d,m,m);while(1){if((c|0)>=(f|0)){c=0;break}if(!(+au(d,m)>e)){c=0;break}St(d,n);St(d,b);St(d,m);Tt(a,d,n,o);St(d,o);h=+Zt(d,n,o);if(h==0.0){c=0;break}h=g/h;Wt(d,b,h,n);if((c|0)<(i|0)){Wt(d,m,-h,o);h=+Zt(d,m,m);if(g==0.0){p=7;break}Xt(d,n,h/g,n);Vt(d,m,n,n);g=h}c=c+1|0}if((p|0)==7){gA(1,93242,j)|0;c=1}U1(m);U1(n);U1(o);U1(k);l=q;return c|0}function Br(a,b,d,e,f,i,j,k,m,n){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;i=i|0;j=j|0;k=k|0;m=m|0;n=+n;var o=0,p=0,q=0.0,r=0,s=0,t=0,u=0,v=0.0,w=0,x=0,y=0,z=0,A=0.0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0.0;M=l;l=l+48|0;F=M+24|0;E=M+16|0;D=M+8|0;C=M;J=M+40|0;K=M+36|0;H=M+32|0;s=j&4;c[J>>2]=0;c[K>>2]=0;a:do if(c[a+16>>2]|0){p=0;o=0;while(1){if((p|0)>=(b|0))break;t=c[a+(p*20|0)>>2]|0;u=a+(p*20|0)+16|0;r=1;while(1){if((r|0)>=(t|0))break;if(!(o<<24>>24))o=+g[(c[u>>2]|0)+(r<<2)>>2]!=0.0;else o=1;r=r+1|0;o=o&1}p=p+1|0}if(o<<24>>24){z=(s|0)!=0;if(z){r=e+4|0;b:do if((i|0)>2){o=i+-1|0;if((on(a,b,d,r,f,o,j,k,15)|0)<0){o=-1;break a}p=e+(o<<2)|0;o=0;while(1){if((o|0)>=(b|0))break b;h[(c[p>>2]|0)+(o<<3)>>3]=+h[(c[r>>2]|0)+(o<<3)>>3];o=o+1|0}}while(0);y=c[e>>2]|0;w=c[r>>2]|0;if((ju(a,b,w,b)|0)==0?(ty(a,b,.01,.1,w,J,K,H)|0)==0:0){x=c[H>>2]|0;if((x|0)<1){o=on(a,b,d,e,f,i,j,k,m)|0;break}c:do if(n>0.0){s=c[J>>2]|0;t=c[K>>2]|0;u=x+-1|0;q=0.0;r=0;while(1){if((r|0)>=(x|0))break c;o=c[t+(r<<2)>>2]|0;A=n-q-+h[w+(c[s+(o<<2)>>2]<<3)>>3]+ +h[w+(c[s+(o+-1<<2)>>2]<<3)>>3];q=(A<0.0?0.0:A)+q;if((r|0)<(u|0))p=c[t+(r+1<<2)>>2]|0;else p=b;while(1){if((o|0)>=(p|0))break;I=w+(c[s+(o<<2)>>2]<<3)|0;h[I>>3]=+h[I>>3]+q;o=o+1|0}r=r+1|0}}while(0);if((i|0)==2?(Qu(a,b,w,y,+h[23294])|0)!=0:0){p=0;o=-1}else L=33}else{p=0;o=-1}}else{hn(a,b,i,e,f)|0;if(!(ty(a,b,.01,.1,0,J,K,H)|0))L=33;else{p=0;o=-1}}d:do if((L|0)==33){if((b|0)==1){o=0;break a}I=KK(c[H>>2]<<2)|0;if(!m){o=0;break a}if(0)TO();switch(k|0){case 2:{if(0)U4(93290,24,1,c[15712]|0)|0;o=nn(a,b)|0;L=46;break}case 1:{o=jn(a,b)|0;if(!o){gA(0,93315,C)|0;gA(3,93364,D)|0;L=47}else G=o;break}case 3:{if(0)U4(93417,21,1,c[15712]|0)|0;o=kn(a,b)|0;L=46;break}default:L=47}if((L|0)==46)if(!o)L=47;else G=o;if((L|0)==47){if(0)U4(93439,26,1,c[15712]|0)|0;G=mn(a,b)|0}if(0){a=c[15712]|0;h[E>>3]=+UO();w4(a,93466,E)|0;U4(93478,25,1,a)|0;TO()}a=b+-1|0;y=(O(a,b)|0)/2|0;E=y+b|0;e:do if(!z){q=1.0;p=0;while(1){if((p|0)>=(i|0))break;r=e+(p<<2)|0;o=0;while(1){if((o|0)>=(b|0))break;A=+B(+(+h[(c[r>>2]|0)+(o<<3)>>3]));q=A>q?A:q;o=o+1|0}p=p+1|0}q=1.0/q;p=0;while(1){if((p|0)>=(i|0))break e;r=e+(p<<2)|0;o=0;while(1){if((o|0)>=(b|0))break;D=(c[r>>2]|0)+(o<<3)|0;h[D>>3]=+h[D>>3]*(10.0*q);o=o+1|0}p=p+1|0}}while(0);f:do if(n>0.0){v=+(y|0);o=0;q=0.0;s=0;g:while(1){if((s|0)>=(a|0))break;t=s+1|0;r=o;p=t;while(1){o=r+1|0;if((p|0)>=(b|0)){s=t;continue g}A=+ft(e,i,s,p);r=o;q=A/+g[G+(o<<2)>>2]+q;p=p+1|0}}q=q/v;o=0;while(1){if((o|0)>=(E|0)){o=0;break f}D=G+(o<<2)|0;g[D>>2]=+g[D>>2]*q;o=o+1|0}}else o=0;while(0);while(1){if((o|0)>=(i|0))break;Ht(b,c[e+(o<<2)>>2]|0);o=o+1|0}p=c[e+4>>2]|0;q=+h[p>>3];o=0;while(1){if((o|0)>=(b|0))break;D=p+(o<<3)|0;h[D>>3]=+h[D>>3]-q;o=o+1|0}u=i<<2;D=KK(u)|0;x=b<<2;w=O(x,i)|0;r=KK(w)|0;o=0;while(1){if((o|0)>=(i|0))break;s=r+((O(o,b)|0)<<2)|0;c[D+(o<<2)>>2]=s;t=e+(o<<2)|0;p=0;while(1){if((p|0)>=(b|0))break;g[s+(p<<2)>>2]=+h[(c[t>>2]|0)+(p<<3)>>3];p=p+1|0}o=o+1|0}v=+(y|0);if(0){C=c[15712]|0;h[F>>3]=+UO();w4(C,93504,F)|0}bu(E,G);cu(E,G);C=KK(b<<3)|0;_t(b,0.0,C);o=0;r=0;while(1){if((r|0)>=(a|0)){o=b;p=0;r=0;break}s=b-r|0;q=0.0;p=1;while(1){o=o+1|0;if((p|0)>=(s|0))break;A=+g[G+(o<<2)>>2];F=C+(p+r<<3)|0;h[F>>3]=+h[F>>3]-A;q=q+A;p=p+1|0}F=C+(r<<3)|0;h[F>>3]=+h[F>>3]-q;r=r+1|0}while(1){if((p|0)>=(b|0))break;g[G+(r<<2)>>2]=+h[C+(p<<3)>>3];F=o+r|0;o=o+-1|0;p=p+1|0;r=F}k=KK(u)|0;c[k>>2]=KK(w)|0;o=1;while(1){if((o|0)>=(i|0))break;c[k+(o<<2)>>2]=(c[k>>2]|0)+((O(o,b)|0)<<2);o=o+1|0}z=KK(x)|0;f=KK(x)|0;j=KK(E<<2)|0;p=Eu(G,b)|0;d=Ju(G,b,c[J>>2]|0,c[K>>2]|0,c[H>>2]|0)|0;y=k+4|0;r=0;A=1797693134862315708145274.0e284;o=0;while(1){if(!(r<<24>>24==0&(o|0)<(m|0)))break;_t(b,0.0,C);du(E,G,j);w=0;t=0;while(1){if((w|0)>=(a|0)){r=0;s=b;t=0;break}x=b-w+-1|0;$t(b,0.0,f);r=0;while(1){if((r|0)>=(i|0))break;H=D+(r<<2)|0;$t(x,+g[(c[H>>2]|0)+(w<<2)>>2],z);Wt(x,z,-1.0,(c[H>>2]|0)+(w<<2)+4|0);bu(x,z);Vt(x,z,f,f);r=r+1|0}eu(x,f);r=0;while(1){if((r|0)>=(x|0))break;s=f+(r<<2)|0;q=+g[s>>2];if(q>=3402823466385288598117041.0e14|q<0.0)g[s>>2]=0.0;r=r+1|0}u=w+1|0;q=0.0;r=t;s=0;while(1){r=r+1|0;if((s|0)>=(x|0))break;H=j+(r<<2)|0;N=+g[H>>2]*+g[f+(s<<2)>>2];g[H>>2]=N;H=C+(u+s<<3)|0;h[H>>3]=+h[H>>3]-N;q=q+N;s=s+1|0}t=C+(w<<3)|0;h[t>>3]=+h[t>>3]-q;w=u;t=r}while(1){if((r|0)>=(b|0)){r=0;break}g[j+(t<<2)>>2]=+h[C+(r<<3)>>3];H=s+t|0;r=r+1|0;s=s+-1|0;t=H}while(1){if((r|0)>=(i|0)){q=0.0;r=0;break}Tt(j,b,c[D+(r<<2)>>2]|0,c[k+(r<<2)>>2]|0);r=r+1|0}while(1){if((r|0)>=(i|0))break;q=+Zt(b,c[D+(r<<2)>>2]|0,c[k+(r<<2)>>2]|0)+q;r=r+1|0}q=q*2.0+v;r=0;while(1){if((r|0)>=(i|0))break;H=D+(r<<2)|0;Tt(G,b,c[H>>2]|0,z);q=q-+Zt(b,c[H>>2]|0,z);r=r+1|0}N=+B(+(q-A))/+B(+(A+1.0e-10));r=((o|0)>1&q>A|N<+h[23294])&1;s=0;while(1){if((s|0)>=(i|0))break;if((s|0)!=1){if(Ar(G,c[D+(s<<2)>>2]|0,c[k+(s<<2)>>2]|0,b,.001,b)|0){o=-1;break d}}else Fu(d,c[y>>2]|0,D,i,1,15,I,n)|0;s=s+1|0}A=q;o=o+1|0}U1(I);Iu(d);if(D|0){r=0;while(1){if((r|0)>=(i|0))break;t=D+(r<<2)|0;u=e+(r<<2)|0;s=0;while(1){if((s|0)>=(b|0))break;h[(c[u>>2]|0)+(s<<3)>>3]=+g[(c[t>>2]|0)+(s<<2)>>2];s=s+1|0}r=r+1|0}U1(c[D>>2]|0);U1(D)}if(k|0){U1(c[k>>2]|0);U1(k)}U1(z);U1(f);U1(C);U1(G);U1(j)}while(0);U1(c[J>>2]|0);U1(c[K>>2]|0);if(p){U1(c[p>>2]|0);U1(p)}}else L=10}else L=10;while(0);if((L|0)==10)o=on(a,b,d,e,f,i,j,k,m)|0;l=M;return o|0}function Cr(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0;m=l;l=l+48|0;f=m+24|0;g=m+12|0;d=m;i=tB(a)|0;k=KK(i*48|0)|0;lr(d,a);c[g>>2]=c[d>>2];c[g+4>>2]=c[d+4>>2];c[g+8>>2]=c[d+8>>2];d=jC(a)|0;e=k;while(1){if(!d)break;c[f>>2]=c[g>>2];c[f+4>>2]=c[g+4>>2];c[f+8>>2]=c[g+8>>2];Dr(d,e,f);d=kC(a,d)|0;e=e+48|0}a:do if(!(Er(k,i)|0))d=0;else{switch(b|0){case 9:{Gr(a,k,i,70,1);Ir(a,k,i,71,1);d=0;e=k;break}case 10:{Ir(a,k,i,71,1);Gr(a,k,i,70,1);d=0;e=k;break}case 7:{Gr(a,k,i,72,1);Ir(a,k,i,71,1);j=9;break}case 8:{j=9;break}case 13:{j=10;break}case 14:{Ir(a,k,i,71,0);Gr(a,k,i,70,0);d=0;e=k;break}case 12:{Ir(a,k,i,73,0);Gr(a,k,i,70,0);d=0;e=k;break}default:{Gr(a,k,i,72,0);Ir(a,k,i,71,0);d=0;e=k}}if((j|0)==9){Ir(a,k,i,73,1);Gr(a,k,i,70,1);j=10}if((j|0)==10){Gr(a,k,i,70,0);Ir(a,k,i,71,0);d=0;e=k}while(1){if((d|0)>=(i|0)){d=1;break a}g=c[e+16>>2]|0;j=c[(c[(c[e+20>>2]|0)+16>>2]|0)+132>>2]|0;h[j>>3]=+(c[e+12>>2]|0)*1.3888888888888887e-03;h[j+8>>3]=+(g|0)*1.3888888888888887e-03;d=d+1|0;e=e+48|0}}while(0);U1(k);l=m;return d|0}function Dr(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,i=0,j=0,k=0,l=0.0,m=0.0;f=c[b+16>>2]|0;k=c[f+132>>2]|0;l=+h[k>>3]*720.0;j=~~(l+(l>=0.0?.5:-.5));l=+h[k+8>>3]*720.0;k=~~(l+(l>=0.0?.5:-.5));if(!(a[e+8>>0]|0)){m=+h[f+32>>3]*72.0*(+g[e>>2]*5.0);l=+h[f+40>>3]*72.0*(+g[e+4>>2]*5.0);i=~~(m+(m>=0.0?.5:-.5));f=~~(l+(l>=0.0?.5:-.5))}else{l=+h[f+32>>3]*36.0;m=+h[f+40>>3]*36.0;i=~~((+g[e>>2]+ +(~~(l+(l>=0.0?.5:-.5))|0))*10.0);f=~~((+g[e+4>>2]+ +(~~(m+(m>=0.0?.5:-.5))|0))*10.0)}c[d+12>>2]=j;c[d+16>>2]=k;c[d+20>>2]=b;c[d+32>>2]=j-i;c[d+36>>2]=k-f;c[d+40>>2]=i+j;c[d+44>>2]=f+k;return}function Er(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=b+-1|0;d=0;a:while(1){if((d|0)>=(k|0)){a=0;break}j=a+48|0;d=d+1|0;g=a+32|0;h=a+40|0;i=a+36|0;f=a+44|0;a=j;e=d;while(1){if((e|0)>=(b|0)){a=j;continue a}if((((c[g>>2]|0)<=(c[a+40>>2]|0)?(c[a+32>>2]|0)<=(c[h>>2]|0):0)?(c[i>>2]|0)<=(c[a+44>>2]|0):0)?(c[a+36>>2]|0)<=(c[f>>2]|0):0){a=1;break a}a=a+48|0;e=e+1|0}}return a|0}function Fr(a,b){a=a|0;b=b|0;if((c[a+36>>2]|0)>(c[b+44>>2]|0))a=0;else a=(c[b+36>>2]|0)<=(c[a+44>>2]|0)&1;return a|0}function Gr(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;i=Vz(18168,c[4584]|0)|0;g=b;h=0;while(1){if((h|0)>=(d|0))break;c[g+8>>2]=c[g+12>>2];nb[c[i>>2]&63](i,g,1)|0;g=g+48|0;h=h+1|0}if(!f)h=Or(a,i,e,74)|0;else h=Nr(i,e,74)|0;uL(h,2,2147483647)|0;g=0;while(1){if((g|0)>=(d|0))break;f=b+12|0;a=c[(c[(c[b+24>>2]|0)+16>>2]|0)+232>>2]|0;e=a-(c[f>>2]|0)|0;c[f>>2]=a;f=b+32|0;c[f>>2]=(c[f>>2]|0)+e;f=b+40|0;c[f>>2]=(c[f>>2]|0)+e;b=b+48|0;g=g+1|0}Pr(h);Rz(i)|0;return}function Hr(a,b){a=a|0;b=b|0;if((c[a+32>>2]|0)>(c[b+40>>2]|0))a=0;else a=(c[b+32>>2]|0)<=(c[a+40>>2]|0)&1;return a|0}function Ir(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;i=Vz(18168,c[4584]|0)|0;g=b;h=0;while(1){if((h|0)>=(d|0))break;c[g+8>>2]=c[g+16>>2];nb[c[i>>2]&63](i,g,1)|0;g=g+48|0;h=h+1|0}if(!f)h=Or(a,i,e,75)|0;else h=Nr(i,e,75)|0;uL(h,2,2147483647)|0;g=0;while(1){if((g|0)>=(d|0))break;f=b+16|0;a=c[(c[(c[b+24>>2]|0)+16>>2]|0)+232>>2]|0;e=a-(c[f>>2]|0)|0;c[f>>2]=a;f=b+36|0;c[f>>2]=(c[f>>2]|0)+e;f=b+44|0;c[f>>2]=(c[f>>2]|0)+e;b=b+48|0;g=g+1|0}Pr(h);Rz(i)|0;return}function Jr(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;d=a+32|0;e=b+32|0;if((c[a+36>>2]|0)<=(c[b+44>>2]|0)?(c[b+36>>2]|0)<=(c[a+44>>2]|0):0)if((c[a+40>>2]|0)<(c[e>>2]|0))a=1;else{g=Mr(d,e)|0;g=g-(c[b+12>>2]|0)+(c[a+12>>2]|0)|0;b=c[b+16>>2]|0;a=c[a+16>>2]|0;f=(b|0)<(a|0);e=Lr(d,e)|0;a=(g|0)<=(e-(f?a:b)+(f?b:a)|0)&1}else a=0;return a|0}function Kr(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;d=a+32|0;e=b+32|0;if((c[d>>2]|0)<=(c[b+40>>2]|0)?(c[e>>2]|0)<=(c[a+40>>2]|0):0)if((c[a+44>>2]|0)<(c[b+36>>2]|0))a=1;else{g=Lr(d,e)|0;g=g-(c[b+16>>2]|0)+(c[a+16>>2]|0)|0;b=c[b+12>>2]|0;a=c[a+12>>2]|0;f=(b|0)<(a|0);e=Mr(d,e)|0;a=(g|0)<=(e-(f?a:b)+(f?b:a)|0)&1}else a=0;return a|0}function Lr(a,b){a=a|0;b=b|0;return ((c[a+12>>2]|0)-(c[a+4>>2]|0)+(c[b+12>>2]|0)-(c[b+4>>2]|0)|0)/2|0|0}function Mr(a,b){a=a|0;b=b|0;return ((c[a+8>>2]|0)-(c[a>>2]|0)+(c[b+8>>2]|0)-(c[b>>2]|0)|0)/2|0|0}function Nr(d,e,f){d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0;x=l;l=l+16|0;v=x;a[v>>0]=a[18588]|0;a[v+1>>0]=a[18589]|0;a[v+2>>0]=a[18590]|0;a[v+3>>0]=a[18591]|0;w=oB(93515,v,0)|0;TC(w,137483,280,1)|0;g=Tz(d)|0;h=-2147483647;u=0;while(1){if(!g)break;s=c[g+8>>2]|0;t=u+((h|0)!=(s|0)&1)|0;g=c[g>>2]|0;h=s;u=t}s=w+16|0;t=(u<<3)+-4|0;n=0;j=0;g=0;h=0;m=0;r=Tz(d)|0;i=-2147483647;while(1){if(!r)break;q=c[r+8>>2]|0;if((i|0)!=(q|0)){p=tC(w,KB(c[r+20>>2]|0)|0,1)|0;TC(p,137447,304,1)|0;o=p+16|0;g=c[o>>2]|0;c[g+112>>2]=r;if(!h){c[(c[s>>2]|0)+192>>2]=p;n=p}else{c[(c[n+16>>2]|0)+164>>2]=p;n=h}c[g+176>>2]=0;k=j+1|0;i=JK(k<<2)|0;c[(c[o>>2]|0)+172>>2]=i;if(!m){j=k;k=p;m=p;g=p;h=n;i=q}else{i=m+16|0;c[(c[i>>2]|0)+184>>2]=0;j=JK((m|0)==(n|0)?t:u-j<<2)|0;c[(c[i>>2]|0)+180>>2]=j;j=iB(w,m,p,0,1)|0;TC(j,137460,176,1)|0;g=c[j+16>>2]|0;b[g+170>>1]=10;c[g+156>>2]=1;g=c[i>>2]|0;h=c[g+180>>2]|0;if(!h)g=KK((c[g+184>>2]<<2)+8|0)|0;else g=MK(h,(c[g+184>>2]<<2)+8|0)|0;m=c[i>>2]|0;c[m+180>>2]=g;m=m+184|0;h=c[m>>2]|0;c[m>>2]=h+1;c[g+(h<<2)>>2]=j;g=c[i>>2]|0;c[(c[g+180>>2]|0)+(c[g+184>>2]<<2)>>2]=0;g=c[o>>2]|0;h=c[g+172>>2]|0;if(!h)g=KK((c[g+176>>2]<<2)+8|0)|0;else g=MK(h,(c[g+176>>2]<<2)+8|0)|0;h=c[o>>2]|0;c[h+172>>2]=g;h=h+176|0;m=c[h>>2]|0;c[h>>2]=m+1;c[g+(m<<2)>>2]=j;j=c[o>>2]|0;c[(c[j+172>>2]|0)+(c[j+176>>2]<<2)>>2]=0;j=k;k=p;m=p;g=p;h=n;i=q}}else{k=g;g=n}c[r+24>>2]=k;n=g;g=k;r=c[r>>2]|0}m=m+16|0;c[(c[m>>2]|0)+184>>2]=0;g=JK(4)|0;c[(c[m>>2]|0)+180>>2]=g;a[v>>0]=a[18588]|0;a[v+1>>0]=a[18589]|0;a[v+2>>0]=a[18590]|0;a[v+3>>0]=a[18591]|0;m=oB(93562,v,0)|0;g=Tz(d)|0;while(1){if(!g)break;v=tC(m,KB(c[g+20>>2]|0)|0,1)|0;TC(v,137447,304,1)|0;c[g+28>>2]=v;c[(c[v+16>>2]|0)+112>>2]=g;g=c[g>>2]|0}g=0;k=Tz(d)|0;h=-2147483647;a:while(1){if(!k)break;j=c[k+8>>2]|0;b:do if((h|0)==(j|0))j=h;else{g=k;while(1){g=c[g>>2]|0;if(!g)break a;if((c[g+8>>2]|0)!=(j|0))break b}}while(0);i=k+28|0;h=g;while(1){if(!h)break;if(lb[e&127](k,h)|0)iB(m,c[i>>2]|0,c[h+28>>2]|0,0,1)|0;h=c[h>>2]|0}k=c[k>>2]|0;h=j}Qr(m,w,f);sB(m)|0;l=x;return w|0}function Or(d,e,f,g){d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;s=l;l=l+16|0;r=s;a[r>>0]=a[18588]|0;a[r+1>>0]=a[18589]|0;a[r+2>>0]=a[18590]|0;a[r+3>>0]=a[18591]|0;r=oB(93515,r,0)|0;TC(r,137483,280,1)|0;k=r+16|0;h=Tz(e)|0;i=0;while(1){if(!h)break;j=tC(r,KB(c[h+20>>2]|0)|0,1)|0;TC(j,137447,304,1)|0;p=j+16|0;n=c[p>>2]|0;c[n+112>>2]=h;c[h+24>>2]=j;c[n+176>>2]=0;n=JK(4)|0;o=c[p>>2]|0;c[o+172>>2]=n;c[o+184>>2]=0;o=JK(4)|0;c[(c[p>>2]|0)+180>>2]=o;if(!i)c[(c[k>>2]|0)+192>>2]=j;else c[(c[i+16>>2]|0)+164>>2]=j;h=c[h>>2]|0;i=j}m=Tz(e)|0;a:while(1){if(!m)break;n=m+32|0;o=m+24|0;p=m+20|0;h=m;while(1){h=c[h>>2]|0;if(!h)break;if(lb[f&127](m,h)|0){i=lb[g&127](n,h+32|0)|0;j=iB(r,c[o>>2]|0,c[h+24>>2]|0,0,1)|0;TC(j,137460,176,1)|0;if((i|0)>=65536){q=13;break a}k=j+16|0;t=c[k>>2]|0;b[t+170>>1]=i;c[t+156>>2]=1;if(j|0?iB(d,c[p>>2]|0,c[h+20>>2]|0,0,0)|0:0)c[(c[k>>2]|0)+156>>2]=100}}m=c[m>>2]|0}if((q|0)==13)qa(93518,93534,254,93547);m=Tz(e)|0;while(1){if(!m)break;k=c[m+24>>2]|0;n=k+16|0;k=XA(r,k)|0;while(1){if(!k)break;h=c[n>>2]|0;i=c[h+180>>2]|0;if(!i)h=KK((c[h+184>>2]<<2)+8|0)|0;else h=MK(i,(c[h+184>>2]<<2)+8|0)|0;i=c[n>>2]|0;c[i+180>>2]=h;i=i+184|0;j=c[i>>2]|0;c[i>>2]=j+1;c[h+(j<<2)>>2]=k;j=c[n>>2]|0;c[(c[j+180>>2]|0)+(c[j+184>>2]<<2)>>2]=0;j=k+-48|0;h=c[(c[((c[k>>2]&3|0)==2?k:j)+40>>2]|0)+16>>2]|0;i=c[h+172>>2]|0;if(!i)h=KK((c[h+176>>2]<<2)+8|0)|0;else h=MK(i,(c[h+176>>2]<<2)+8|0)|0;c[(c[(c[((c[k>>2]&3|0)==2?k:j)+40>>2]|0)+16>>2]|0)+172>>2]=h;g=c[(c[((c[k>>2]&3|0)==2?k:j)+40>>2]|0)+16>>2]|0;q=c[g+172>>2]|0;g=g+176|0;t=c[g>>2]|0;c[g>>2]=t+1;c[q+(t<<2)>>2]=k;t=c[(c[((c[k>>2]&3|0)==2?k:j)+40>>2]|0)+16>>2]|0;c[(c[t+172>>2]|0)+(c[t+176>>2]<<2)>>2]=0;k=ZA(r,k)|0}m=c[m>>2]|0}l=s;return r|0}function Pr(a){a=a|0;var b=0,d=0,e=0,f=0;f=jC(a)|0;while(1){if(!f)break;d=f+16|0;b=c[d>>2]|0;e=c[b+172>>2]|0;if(e){U1(e);b=c[d>>2]|0}b=c[b+180>>2]|0;if(b|0)U1(b);f=kC(a,f)|0}sB(a)|0;return}function Qr(a,d,e){a=a|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;k=jC(a)|0;while(1){if(!k)break;n=c[(c[k+16>>2]|0)+112>>2]|0;m=c[n+24>>2]|0;n=n+32|0;o=m+16|0;l=XA(a,k)|0;while(1){if(!l)break;h=c[(c[(c[((c[l>>2]&3|0)==2?l:l+-48|0)+40>>2]|0)+16>>2]|0)+112>>2]|0;p=lb[e&127](n,h+32|0)|0;h=c[h+24>>2]|0;i=iB(d,m,h,0,1)|0;TC(i,137460,176,1)|0;j=i+16|0;f=c[j>>2]|0;c[f+156>>2]=1;g=b[f+170>>1]|0;if((p|0)>(g&65535|0)){if(!(g<<16>>16)){f=c[o>>2]|0;g=c[f+180>>2]|0;if(!g)f=KK((c[f+184>>2]<<2)+8|0)|0;else f=MK(g,(c[f+184>>2]<<2)+8|0)|0;q=c[o>>2]|0;c[q+180>>2]=f;q=q+184|0;g=c[q>>2]|0;c[q>>2]=g+1;c[f+(g<<2)>>2]=i;f=c[o>>2]|0;c[(c[f+180>>2]|0)+(c[f+184>>2]<<2)>>2]=0;h=h+16|0;f=c[h>>2]|0;g=c[f+172>>2]|0;if(!g)f=KK((c[f+176>>2]<<2)+8|0)|0;else f=MK(g,(c[f+176>>2]<<2)+8|0)|0;g=c[h>>2]|0;c[g+172>>2]=f;g=g+176|0;q=c[g>>2]|0;c[g>>2]=q+1;c[f+(q<<2)>>2]=i;f=c[h>>2]|0;c[(c[f+172>>2]|0)+(c[f+176>>2]<<2)>>2]=0;f=c[j>>2]|0}b[f+170>>1]=p}l=ZA(a,l)|0}k=kC(a,k)|0}return}function Rr(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;return (c[b>>2]|0)-(c[d>>2]|0)|0}function Sr(b,d){b=b|0;d=d|0;var e=0,f=0,i=0.0,j=0.0,k=0.0,m=0.0,n=0.0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0.0,A=0.0;y=l;l=l+64|0;u=y+24|0;s=y+16|0;r=y+52|0;q=y+40|0;t=y;w=tB(b)|0;x=KK(w*72|0)|0;lr(q,b);k=+g[q>>2];m=+g[q+4>>2];q=(a[q+8>>0]|0)!=0;k=q?k*.013888888888888888:k;m=q?m*.013888888888888888:m;n=k*.5;o=m*.5;e=x;f=jC(b)|0;while(1){if(!f)break;p=c[f+16>>2]|0;i=+h[p+32>>3];if(q){j=+h[p+40>>3]*.5+m;i=i*.5+k}else{j=o*+h[p+40>>3];i=n*i}p=c[p+132>>2]|0;A=+h[p>>3];h[e>>3]=A;z=+h[p+8>>3];h[e+8>>3]=z;h[e+16>>3]=A-i;h[e+24>>3]=z-j;h[e+32>>3]=A+i;h[e+40>>3]=z+j;h[e+48>>3]=i;h[e+56>>3]=j;c[e+64>>2]=f;e=e+72|0;f=kC(b,f)|0}do if((d|0)<0){i=+Tr(x,w);if(i==0.0){U1(x);e=0;break}if(!0){k=i;j=i;v=19}else{v=c[15712]|0;h[s>>3]=i;w4(v,93565,s)|0;k=i;j=i;v=19}}else{f=Ur(x,w,r)|0;e=c[r>>2]|0;if(!e){U1(f);U1(x);e=0;break}if(!d){Wr(t,f,e);i=+h[t>>3];j=+h[t+8>>3]}else{j=+Vr(f,e);i=j}U1(f);if(!0){k=i;v=19}else{v=c[15712]|0;h[u>>3]=i;h[u+8>>3]=j;w4(v,93579,u)|0;k=i;v=19}}while(0);if((v|0)==19){e=0;f=x;while(1){if((e|0)>=(w|0))break;v=c[(c[(c[f+64>>2]|0)+16>>2]|0)+132>>2]|0;h[v>>3]=+h[f>>3]*k;h[v+8>>3]=+h[f+8>>3]*j;e=e+1|0;f=f+72|0}U1(x);e=1}l=y;return e|0}function Tr(a,b){a=a|0;b=b|0;var c=0.0,d=0,e=0.0,f=0.0,g=0.0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;c=0.0;d=0;a:while(1){if((d|0)>=(b|0))break;r=a+72|0;d=d+1|0;m=a+16|0;n=a+8|0;o=a+56|0;p=a+48|0;q=a+32|0;k=a+24|0;l=a+40|0;i=d;j=r;while(1){if((i|0)>=(b|0)){a=r;continue a}if(((+h[m>>3]<=+h[j+32>>3]?+h[j+16>>3]<=+h[q>>3]:0)?+h[k>>3]<=+h[j+40>>3]:0)?+h[j+24>>3]<=+h[l>>3]:0){c=0.0;break a}e=+h[a>>3];f=+h[j>>3];if(e==f)g=t;else{g=+B(+(e-f));g=(+h[j+48>>3]+ +h[p>>3])/g}e=+h[n>>3];f=+h[j+8>>3];if(e==f)e=t;else{e=+B(+(e-f));e=(+h[j+56>>3]+ +h[o>>3])/e}g=e<g?e:g;i=i+1|0;j=j+72|0;c=g>c?g:c}}return +c}function Ur(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0,j=0.0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;k=KK((b<<4)+16|0)|0;m=0;e=0;n=k;o=b;i=k;a:while(1){if((e|0)>=(b|0))break;z=a+72|0;e=e+1|0;y=a+16|0;r=a+32|0;s=a+24|0;u=a+40|0;v=a+8|0;w=a+56|0;x=a+48|0;p=e;q=z;l=i;while(1){if((p|0)==(b|0)){a=z;i=l;continue a}if(((+h[y>>3]<=+h[q+32>>3]?+h[q+16>>3]<=+h[r>>3]:0)?+h[s>>3]<=+h[q+40>>3]:0)?+h[q+24>>3]<=+h[u>>3]:0){i=o+b|0;if((m|0)==(o|0)){k=MK(k,(i<<4)+16|0)|0;n=k;l=k}else i=o;f=+h[a>>3];g=+h[q>>3];if(!(f==g)){f=(+h[q+48>>3]+ +h[x>>3])/+B(+(f-g));if(f<1.0){f=1.0;A=13}}else{f=t;A=13}if((A|0)==13)A=0;g=+h[v>>3];j=+h[q+8>>3];if(!(g==j)){g=(+h[q+56>>3]+ +h[w>>3])/+B(+(g-j));if(g<1.0){g=1.0;A=16}}else{g=t;A=16}if((A|0)==16)A=0;m=m+1|0;h[n+(m<<4)>>3]=f;h[n+(m<<4)+8>>3]=g}else i=o;p=p+1|0;q=q+72|0;o=i}}A=MK(i,(m<<4)+16|0)|0;c[d>>2]=m;return A|0}function Vr(a,b){a=a|0;b=b|0;var c=0,d=0,e=0.0,f=0.0,g=0.0;d=1;e=0.0;while(1){c=a+16|0;if((d|0)>(b|0))break;g=+h[c>>3];f=+h[a+24>>3];f=g<f?g:f;d=d+1|0;e=f>e?f:e;a=c}return +e}function Wr(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0,f=0,g=0,i=0.0;h[b>>3]=1.0;h[b+8>>3]=t;o4(b+16|0,c,16,76);g=KK((c<<4)+16|0)|0;h[g+(c<<4)>>3]=+h[b+(c<<4)>>3];f=c;d=1.0;while(1){h[g+(f<<4)+8>>3]=d;e=f+-1|0;if((f|0)<=0){d=t;e=0;f=0;break}h[g+(e<<4)>>3]=+h[b+(e<<4)>>3];i=+h[(+h[b+(f<<4)+8>>3]>d?b:g)+(f<<4)+8>>3];f=e;d=i}while(1){if((f|0)>(c|0))break;i=+h[g+(f<<4)+8>>3]*+h[g+(f<<4)>>3];b=i<d;d=b?i:d;e=b?f:e;f=f+1|0}if(d<t){i=+h[g+(e<<4)+8>>3];h[a>>3]=+h[g+(e<<4)>>3];h[a+8>>3]=i;return}else qa(93596,93534,832,93616)}function Xr(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a>>3];d=+h[b>>3];if(!(c<d))if(!(c>d)){d=+h[a+8>>3];c=+h[b+8>>3];if(d<c)a=-1;else a=d>c&1}else a=1;else a=-1;return a|0}function Yr(a,b,d){a=a|0;b=b|0;d=d|0;d=l;l=l+16|0;b=d;c[b>>2]=93631;gA(1,93681,b)|0;l=d;return 0}function Zr(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;e=l;l=l+16|0;d=e;c[d>>2]=93631;gA(1,93709,d)|0;l=e;return 0}function _r(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;a:do switch(d|0){case 2:{e=KK(16)|0;f=KK(24)|0;c[f+8>>2]=0;c[f+4>>2]=e;c[f>>2]=2;c[e>>2]=0;c[e+4>>2]=1;d=e+8|0;c[f+16>>2]=d;c[f+20>>2]=0;c[f+12>>2]=2;c[d>>2]=1;c[e+12>>2]=0;break}case 1:{e=KK(4)|0;f=KK(12)|0;c[f+8>>2]=0;c[f+4>>2]=e;c[f>>2]=1;c[e>>2]=0;break}default:{Yr(0,0,0)|0;a=(d|0)>0;if(!e){if(!a){f=0;break a}ab()}else{if(!a){f=0;break a}ab()}}}while(0);return f|0}function $r(a){a=a|0;var b=0;if(a|0){b=c[a+4>>2]|0;if(b|0)U1(b);b=c[a+8>>2]|0;if(b|0)U1(b);U1(a)}return}function as(a){a=a|0;var b=0;if(a|0){b=c[a+4>>2]|0;if(b|0)U1(b);b=c[a+8>>2]|0;if(b|0)U1(b);b=c[a+16>>2]|0;if(b|0)U1(b);U1(a)}return}function bs(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;n=o+8|0;m=o;k=W1(c[46849]|0,d<<2)|0;c[46849]=k;f=0;while(1){if((f|0)>=(d|0))break;c[e+(f<<2)>>2]=2147483647;f=f+1|0}c[e+(a<<2)>>2]=0;h=b+(a*20|0)|0;i=b+(a*20|0)+8|0;j=b+(a*20|0)+4|0;f=1;while(1){if((f|0)>=(c[h>>2]|0))break;c[e+(c[(c[j>>2]|0)+(f<<2)>>2]<<2)>>2]=~~+g[(c[i>>2]|0)+(f<<2)>>2];f=f+1|0}cs(n,a,k,e,d);i=-2147483639;while(1){if(!((ds(n,m,c[46849]|0,e)|0)<<24>>24)){f=0;break}f=c[m>>2]|0;a=c[e+(f<<2)>>2]|0;if((a|0)==2147483647){f=0;break}i=b+(f*20|0)|0;j=b+(f*20|0)+4|0;h=b+(f*20|0)+8|0;f=1;while(1){if((f|0)>=(c[i>>2]|0))break;es(n,c[(c[j>>2]|0)+(f<<2)>>2]|0,a+~~+g[(c[h>>2]|0)+(f<<2)>>2]|0,c[46849]|0,e);f=f+1|0}i=a+10|0}while(1){if((f|0)>=(d|0))break;h=e+(f<<2)|0;if((c[h>>2]|0)==2147483647)c[h>>2]=i;f=f+1|0}fs(n);l=o;return}function cs(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;if((f|0)==1)i=0;else i=KK((f<<2)+-4|0)|0;c[a>>2]=i;j=f+-1|0;c[a+4>>2]=j;g=0;h=0;while(1){if((h|0)>=(f|0))break;if((h|0)!=(b|0)){c[i+(g<<2)>>2]=h;c[d+(h<<2)>>2]=g;g=g+1|0}h=h+1|0}g=(j|0)/2|0;while(1){if((g|0)<=-1)break;gs(a,g,d,e);g=g+-1|0}return}function ds(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;f=a+4|0;if(!(c[f>>2]|0))a=0;else{g=c[a>>2]|0;c[b>>2]=c[g>>2];b=c[g+((c[f>>2]|0)+-1<<2)>>2]|0;c[g>>2]=b;c[d+(b<<2)>>2]=0;c[f>>2]=(c[f>>2]|0)+-1;gs(a,0,d,e);a=1}return a|0}function es(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;g=f+(b<<2)|0;if((c[g>>2]|0)>(d|0)){j=e+(b<<2)|0;i=c[j>>2]|0;c[g>>2]=d;g=c[a>>2]|0;a=i;while(1){if((a|0)<=0)break;h=a>>>1;i=c[g+(h<<2)>>2]|0;if((c[f+(i<<2)>>2]|0)<=(d|0))break;c[g+(a<<2)>>2]=i;c[e+(i<<2)>>2]=a;a=h}c[g+(a<<2)>>2]=b;c[j>>2]=a}return}function fs(a){a=a|0;a=c[a>>2]|0;if(a|0)U1(a);return}function gs(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;i=a+4|0;while(1){f=b<<1;g=f|1;h=c[i>>2]|0;if(!((f|0)<(h|0)?(j=c[a>>2]|0,(c[e+(c[j+(f<<2)>>2]<<2)>>2]|0)<(c[e+(c[j+(b<<2)>>2]<<2)>>2]|0)):0))f=b;if((g|0)<(h|0)){j=c[a>>2]|0;f=(c[e+(c[j+(g<<2)>>2]<<2)>>2]|0)<(c[e+(c[j+(f<<2)>>2]<<2)>>2]|0)?g:f}if((f|0)==(b|0))break;j=c[a>>2]|0;h=j+(f<<2)|0;g=c[h>>2]|0;j=j+(b<<2)|0;c[h>>2]=c[j>>2];c[j>>2]=g;c[d+(c[h>>2]<<2)>>2]=f;c[d+(c[j>>2]<<2)>>2]=b;b=f}return}function hs(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,h=0,i=0,j=0,k=0.0,m=0,n=0,o=0,p=0;p=l;l=l+16|0;m=p+8|0;n=p;c[n>>2]=0;o=KK(d<<2)|0;f=0;while(1){if((f|0)>=(d|0))break;g[e+(f<<2)>>2]=3402823466385288598117041.0e14;f=f+1|0}g[e+(a<<2)>>2]=0.0;h=c[b+(a*20|0)>>2]|0;i=b+(a*20|0)+8|0;j=b+(a*20|0)+4|0;f=1;while(1){if((f|0)>=(h|0))break;c[e+(c[(c[j>>2]|0)+(f<<2)>>2]<<2)>>2]=c[(c[i>>2]|0)+(f<<2)>>2];f=f+1|0}is(m,a,o,e,d);a:while(1){if(!((js(m,n,o,e)|0)<<24>>24))break;f=c[n>>2]|0;k=+g[e+(f<<2)>>2];if(k==3402823466385288598117041.0e14)break;i=b+(f*20|0)|0;j=b+(f*20|0)+4|0;h=b+(f*20|0)+8|0;f=1;while(1){if((f|0)>=(c[i>>2]|0))continue a;ks(m,c[(c[j>>2]|0)+(f<<2)>>2]|0,+g[(c[h>>2]|0)+(f<<2)>>2]+k,o,e);f=f+1|0}}fs(m);U1(o);l=p;return}function is(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;i=f+-1|0;j=KK(i<<2)|0;c[a>>2]=j;c[a+4>>2]=i;g=0;h=0;while(1){if((h|0)>=(f|0))break;if((h|0)!=(b|0)){c[j+(g<<2)>>2]=h;c[d+(h<<2)>>2]=g;g=g+1|0}h=h+1|0}g=(i|0)/2|0;while(1){if((g|0)<=-1)break;ls(a,g,d,e);g=g+-1|0}return}function js(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;f=a+4|0;if(!(c[f>>2]|0))a=0;else{g=c[a>>2]|0;c[b>>2]=c[g>>2];b=c[g+((c[f>>2]|0)+-1<<2)>>2]|0;c[g>>2]=b;c[d+(b<<2)>>2]=0;c[f>>2]=(c[f>>2]|0)+-1;ls(a,0,d,e);a=1}return a|0}function ks(a,b,d,e,f){a=a|0;b=b|0;d=+d;e=e|0;f=f|0;var h=0,i=0,j=0,k=0;h=f+(b<<2)|0;if(!(+g[h>>2]<=d)){k=e+(b<<2)|0;j=c[k>>2]|0;g[h>>2]=d;h=c[a>>2]|0;a=j;while(1){if((a|0)<=0)break;i=a>>>1;j=c[h+(i<<2)>>2]|0;if(!(+g[f+(j<<2)>>2]>d))break;c[h+(a<<2)>>2]=j;c[e+(j<<2)>>2]=a;a=i}c[h+(a<<2)>>2]=b;c[k>>2]=a}return}function ls(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,h=0,i=0,j=0,k=0;j=a+4|0;while(1){f=b<<1;h=f|1;i=c[j>>2]|0;if(!((f|0)<(i|0)?(k=c[a>>2]|0,+g[e+(c[k+(f<<2)>>2]<<2)>>2]<+g[e+(c[k+(b<<2)>>2]<<2)>>2]):0))f=b;if((h|0)<(i|0)?(k=c[a>>2]|0,+g[e+(c[k+(h<<2)>>2]<<2)>>2]<+g[e+(c[k+(f<<2)>>2]<<2)>>2]):0)f=h;if((f|0)==(b|0))break;k=c[a>>2]|0;i=k+(f<<2)|0;h=c[i>>2]|0;k=k+(b<<2)|0;c[i>>2]=c[k>>2];c[k>>2]=h;c[d+(c[i>>2]<<2)>>2]=f;c[d+(c[k>>2]<<2)>>2]=b;b=f}return}function ms(){fu(187400,48);c[46853]=0;return}function ns(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0.0,g=0.0,i=0;i=hu(187400)|0;c[i+32>>2]=a;c[i+36>>2]=b;Pu(a);Pu(b);c[i+24>>2]=0;c[i+28>>2]=0;f=+h[a>>3];g=+h[b>>3]-f;e=+h[a+8>>3];d=+h[b+8>>3]-e;f=d*e+g*f+(d*d+g*g)*.5;b=i+16|0;h[b>>3]=f;if((g>0.0?g:-g)>(d>0.0?d:-d)){h[i>>3]=1.0;e=d/g;d=g;a=i+8|0}else{h[i+8>>3]=1.0;e=g/d;a=i}h[a>>3]=e;h[b>>3]=f/d;b=c[46853]|0;c[i+40>>2]=b;c[46853]=b+1;return i|0}function os(a){a=a|0;var b=0,d=0,e=0.0,f=0.0,g=0.0,i=0.0,j=0.0,k=0.0,l=0.0,m=0;l=+h[a>>3];a:do if(l==1.0){if(!(+h[a+8>>3]>=0.0)){b=c[a+28>>2]|0;d=c[a+24>>2]|0;if(!d)m=9;else m=5}else{b=c[a+24>>2]|0;d=c[a+28>>2]|0;if(!d)m=9;else m=5}do if((m|0)==5){e=+h[d+8>>3];if(e>+h[23266])break a;f=+h[23267];if(!(e>=f)){i=f;e=+h[a+16>>3]-+h[a+8>>3]*f;break}else{i=e;e=+h[d>>3];break}}else if((m|0)==9){f=+h[23267];i=f;e=+h[a+16>>3]-+h[a+8>>3]*f}while(0);do if(b){g=+h[b+8>>3];if(g<f)break a;f=+h[23266];if(!(g<=f)){g=f;f=+h[a+16>>3]-+h[a+8>>3]*f;break}else{f=+h[b>>3];break}}else{f=+h[23266];g=f;f=+h[a+16>>3]-+h[a+8>>3]*f}while(0);j=+h[23268];b=e>j;d=f>j;k=+h[23269];if(!(b&d|e<k&f<k)){if(b){i=(+h[a+16>>3]-j)/+h[a+8>>3];e=j}if(e<k){i=(+h[a+16>>3]-k)/+h[a+8>>3];e=k}if(d){g=(+h[a+16>>3]-j)/+h[a+8>>3];f=j}if(f<k){g=(+h[a+16>>3]-k)/+h[a+8>>3];f=k;m=46}else m=46}}else{d=c[a+28>>2]|0;b=c[a+24>>2]|0;do if(b){e=+h[b>>3];if(e>+h[23268])break a;g=+h[23269];if(!(e>=g)){i=+h[a+16>>3]-g*l;e=g;break}else{i=+h[b+8>>3];break}}else{g=+h[23269];i=+h[a+16>>3]-g*l;e=g}while(0);do if(d){f=+h[d>>3];if(f<g)break a;j=+h[23268];if(!(f<=j)){g=+h[a+16>>3]-l*j;f=j;break}else{g=+h[d+8>>3];break}}else{f=+h[23268];g=+h[a+16>>3]-l*f}while(0);j=+h[23266];b=i>j;d=g>j;k=+h[23267];if(!(b&d|i<k&g<k)){if(b){i=j;e=(+h[a+16>>3]-j)/l}if(i<k){i=k;e=(+h[a+16>>3]-k)/l}if(d){g=j;f=(+h[a+16>>3]-j)/l}if(g<k){g=k;f=(+h[a+16>>3]-k)/l;m=46}else m=46}}while(0);if((m|0)==46)ps(a,e,i,f,g);return}function ps(a,b,d,e,f){a=a|0;b=+b;d=+d;e=+e;f=+f;var g=0;g=a+32|0;Ws(c[g>>2]|0,b,d);Ws(c[g>>2]|0,e,f);a=a+36|0;Ws(c[a>>2]|0,b,d);Ws(c[a>>2]|0,e,f);return}function qs(a,b,d){a=a|0;b=b|0;d=d|0;c[a+24+(b<<2)>>2]=d;Pu(d);if(c[a+24+(1-b<<2)>>2]|0){os(a);Ou(c[a+32>>2]|0);Ou(c[a+36>>2]|0);iu(a,187400)}return}function rs(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;p=l;l=l+16|0;o=p;m=b<<2;h=KK(O(m,d)|0)|0;g=c[e>>2]|0;m=KK(m)|0;n=c[a+8>>2]|0;if(g|0){U1(c[g>>2]|0);U1(g)}k=KK(d<<2)|0;c[e>>2]=k;g=0;while(1){if((g|0)>=(d|0))break;c[k+(g<<2)>>2]=h+((O(g,b)|0)<<2);g=g+1|0}j=(f|0)!=0;if(j)dt(a,b);e=(x5()|0)%(b|0)|0;ur(o,b);g=c[k>>2]|0;if(j){bs(e,a,b,g);f=0;g=0}else{qr(e,a,b,g,o);f=0;g=0}while(1){if((g|0)>=(b|0)){i=1;break}h=c[(c[k>>2]|0)+(g<<2)>>2]|0;c[m+(g<<2)>>2]=h;i=(h|0)>(f|0);f=i?h:f;e=i?g:e;g=g+1|0}while(1){if((i|0)>=(d|0))break;h=k+(i<<2)|0;g=c[h>>2]|0;if(j){bs(e,a,b,g);g=0;f=0}else{qr(e,a,b,g,o);g=0;f=0}while(1){if((g|0)>=(b|0))break;s=m+(g<<2)|0;q=c[s>>2]|0;r=c[(c[h>>2]|0)+(g<<2)>>2]|0;r=(q|0)<(r|0)?q:r;c[s>>2]=r;s=(r|0)>(f|0);q=s?g:e;g=g+1|0;f=s?r:f;e=q}i=i+1|0}U1(m);if(j)et(a,b,n);l=p;return}function ss(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,h=0,i=0.0,j=0,k=0;i=1.0/+(b|0);h=0;while(1){if((h|0)>=(d|0))break;j=a+(h<<2)|0;e=0.0;f=0;while(1){if((f|0)>=(b|0))break;e=e+ +(c[(c[j>>2]|0)+(f<<2)>>2]|0);f=f+1|0}g=~~(e*i);f=0;while(1){if((f|0)>=(b|0))break;k=(c[j>>2]|0)+(f<<2)|0;c[k>>2]=(c[k>>2]|0)-g;f=f+1|0}h=h+1|0}return}function ts(){c[46855]=~~+C(+(+((c[46854]|0)+4|0)));return}function us(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a>>3]-+h[b>>3];d=+h[a+8>>3]-+h[b+8>>3];return +(d*d+c*c)}function vs(a,b,c){a=a|0;b=b|0;c=c|0;h[a>>3]=+h[b>>3]-+h[c>>3];h[a+8>>3]=+h[b+8>>3]-+h[c+8>>3];return}function ws(a,b,c){a=a|0;b=b|0;c=c|0;h[a>>3]=+h[c>>3]+ +h[b>>3];h[a+8>>3]=+h[c+8>>3]+ +h[b+8>>3];return}function xs(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0;d=+h[b+8>>3];e=+h[b>>3];return +((+h[c>>3]-e)*(+h[a+8>>3]-d)-(+h[a>>3]-e)*(+h[c+8>>3]-d))}function ys(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;e=l;l=l+48|0;f=e+32|0;g=e+16|0;h=e;c[h>>2]=c[a>>2];c[h+4>>2]=c[a+4>>2];c[h+8>>2]=c[a+8>>2];c[h+12>>2]=c[a+12>>2];c[g>>2]=c[b>>2];c[g+4>>2]=c[b+4>>2];c[g+8>>2]=c[b+8>>2];c[g+12>>2]=c[b+12>>2];c[f>>2]=c[d>>2];c[f+4>>2]=c[d+4>>2];c[f+8>>2]=c[d+8>>2];c[f+12>>2]=c[d+12>>2];d=+xs(h,g,f)>0.0&1;l=e;return d|0}function zs(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;var f=0.0,g=0.0,i=0.0,j=0.0,k=0.0,l=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0.0;o=+h[a>>3];p=+h[d+8>>3];q=+h[c+8>>3];m=(p-q)*o;n=+h[b>>3];l=+h[d>>3];k=+h[b+8>>3];i=+h[a+8>>3];j=k-i;f=+h[c>>3];g=m+n*(q-p)+j*l+f*(i-k);if(g==0.0)d=0;else{p=(f*(i-p)+m+(q-i)*l)/g;q=-(f*j+((q-k)*o+(i-q)*n))/g;h[e>>3]=p*(n-o)+o;h[e+8>>3]=j*p+i;d=q<=1.0&(q>=0.0&(p>=0.0&p<=1.0))&1}return d|0}function As(a,b,d){a=a|0;b=b|0;d=+d;var e=0,f=0,g=0.0,i=0;c[a+20>>2]=b;Pu(b);i=a+24|0;h[i>>3]=+h[b+8>>3]+d;e=c[46856]|0;e=e+((Bs(a)|0)*40|0)|0;while(1){f=e+32|0;e=c[f>>2]|0;if(!e)break;d=+h[i>>3];g=+h[e+24>>3];if(d>g)continue;if(!(d==g))break;if(!(+h[b>>3]>+h[c[e+20>>2]>>3]))break}c[a+32>>2]=e;c[f>>2]=a;c[46857]=(c[46857]|0)+1;return}function Bs(a){a=a|0;var b=0.0,d=0,e=0.0;d=c[46858]|0;e=+(d|0);b=(+h[a+24>>3]-+h[23273])/+h[23275]*e;do if(!(b<0.0))if(!(b>=e)){a=~~b;break}else{a=d+-1|0;break}else a=0;while(0);if((a|0)<(c[46859]|0))c[46859]=a;return a|0}function Cs(a){a=a|0;var b=0,d=0,e=0;d=a+20|0;if(c[d>>2]|0){b=c[46856]|0;b=b+((Bs(a)|0)*40|0)|0;do{e=b+32|0;b=c[e>>2]|0}while((b|0)!=(a|0));c[e>>2]=c[a+32>>2];c[46857]=(c[46857]|0)+-1;Ou(c[d>>2]|0);c[d>>2]=0}return}function Ds(){return (c[46857]|0)==0|0}function Es(a){a=a|0;var b=0,d=0,e=0,f=0.0;b=c[46856]|0;d=c[46859]|0;while(1){e=c[b+(d*40|0)+32>>2]|0;if(e|0)break;e=d+1|0;c[46859]=e;d=e}f=+h[e+24>>3];h[a>>3]=+h[c[e+20>>2]>>3];h[a+8>>3]=f;return}function Fs(){var a=0,b=0;b=(c[46856]|0)+((c[46859]|0)*40|0)+32|0;a=c[b>>2]|0;c[b>>2]=c[a+32>>2];c[46857]=(c[46857]|0)+-1;return a|0}function Gs(){U1(c[46856]|0);c[46856]=0;return}function Hs(){var a=0,b=0,d=0;c[46857]=0;c[46859]=0;a=c[46855]|0;b=a<<2;c[46858]=b;d=c[46856]|0;if(!d){d=KK(a*160|0)|0;c[46856]=d;b=c[46858]|0}a=0;while(1){if((a|0)>=(b|0))break;c[d+(a*40|0)+32>>2]=0;a=a+1|0}return}function Is(){fu(187448,40);U1(c[46865]|0);c[46865]=0;return}function Js(){var a=0,b=0,d=0;fu(187448,40);a=c[46855]|0;b=a<<1;c[46866]=b;d=c[46865]|0;if(!d){d=KK(a<<3)|0;c[46865]=d;b=c[46866]|0}a=0;while(1){if((a|0)>=(b|0))break;c[d+(a<<2)>>2]=0;a=a+1|0}c[46860]=Ks(0,0)|0;c[46861]=Ks(0,0)|0;c[c[46860]>>2]=0;d=c[46861]|0;b=c[46860]|0;c[b+4>>2]=d;c[d>>2]=b;c[(c[46861]|0)+4>>2]=0;d=c[46865]|0;c[d>>2]=c[46860];c[d+((c[46866]|0)+-1<<2)>>2]=c[46861];return}function Ks(b,d){b=b|0;d=d|0;var e=0;e=hu(187448)|0;c[e+8>>2]=b;a[e+16>>0]=d;c[e+32>>2]=0;c[e+20>>2]=0;c[e+12>>2]=0;return e|0}function Ls(b,d){b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0,j=0.0,k=0,l=0.0,m=0.0,n=0.0,o=0,p=0.0,q=0.0;i=c[b+8>>2]|0;k=c[d+8>>2]|0;do if((!((i|0)==0|(k|0)==0)?(o=c[i+36>>2]|0,e=c[k+36>>2]|0,(o|0)!=(e|0)):0)?(j=+h[i>>3],f=+h[k+8>>3],g=+h[i+8>>3],l=+h[k>>3],m=f*j-l*g,!(m>-1.0e-10&m<1.0e-10)):0){p=+h[i+16>>3];q=+h[k+16>>3];n=(p*f-q*g)/m;j=(q*j-p*l)/m;f=+h[o+8>>3];g=+h[e+8>>3];do if(f<g)e=o;else{if(f==g?+h[o>>3]<+h[e>>3]:0){e=o;break}b=d}while(0);b=a[b+16>>0]|0;if(!(n>=+h[e>>3])){if(b<<24>>24==1){b=0;break}}else if(!(b<<24>>24)){b=0;break}b=Lu()|0;c[b+20>>2]=0;h[b>>3]=n;h[b+8>>3]=j}else b=0;while(0);return b|0}function Ms(b,d){b=b|0;d=d|0;var e=0.0,f=0,g=0.0,i=0.0,j=0.0,k=0,l=0.0,m=0,n=0,o=0.0,p=0;n=c[b+8>>2]|0;k=c[n+36>>2]|0;l=+h[d>>3];o=+h[k>>3];m=l>o;p=a[b+16>>0]|0;if(m)if(!(p<<24>>24))b=1;else f=4;else if(p<<24>>24==1)b=0;else f=4;if((f|0)==4){e=+h[n>>3];a:do if(e==1.0){e=+h[d+8>>3];g=e-+h[k+8>>3];i=l-o;j=+h[n+8>>3];d=j<0.0;do if(m?j>=0.0:d){if(g>=j*i){b=1;break a}}else{b=j*e+l>+h[n+16>>3];if(d)if(b){b=0;break a}else break;else if(b)break;else{b=0;break a}}while(0);o=o-+h[c[n+32>>2]>>3];b=j*(i*i-g*g)<o*g*(i*2.0/o+1.0+j*j);if(d)b=b^1}else{i=+h[n+16>>3]-l*e;j=+h[d+8>>3]-i;o=l-o;l=i-+h[k+8>>3];b=j*j>l*l+o*o}while(0);b=(b^p<<24>>24!=0)&1}return b|0}function Ns(a,b){a=a|0;b=b|0;c[b>>2]=a;a=a+4|0;c[b+4>>2]=c[a>>2];c[c[a>>2]>>2]=b;c[a>>2]=b;return}function Os(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;f=c[46866]|0;b=~~((+h[a>>3]-+h[23272])/+h[23274]*+(f|0));b=(b|0)>0?b:0;f=(b|0)<(f|0)?b:f+-1|0;b=Ps(f)|0;if(!b){d=1;while(1){b=Ps(f-d|0)|0;if(b|0)break;b=Ps(d+f|0)|0;if(b|0)break;d=d+1|0}c[46867]=(c[46867]|0)+d}c[46868]=(c[46868]|0)+1;e=c[46860]|0;d=c[46861]|0;a:do if((b|0)==(e|0))g=9;else{if((b|0)!=(d|0)?(Ms(b,a)|0)!=0:0){g=9;break}while(1){b=c[b>>2]|0;if((b|0)==(e|0))break a;if(Ms(b,a)|0){e=b;break}}}while(0);if((g|0)==9){while(1){b=c[b+4>>2]|0;if((b|0)==(d|0))break;if(!(Ms(b,a)|0))break;else g=9}e=c[b>>2]|0}if((f|0)>0?(f|0)<((c[46866]|0)+-1|0):0){b=(c[46865]|0)+(f<<2)|0;d=c[b>>2]|0;if(d|0){g=d+12|0;c[g>>2]=(c[g>>2]|0)+-1}c[b>>2]=e;g=e+12|0;c[g>>2]=(c[g>>2]|0)+1}return e|0}function Ps(a){a=a|0;var b=0,d=0;if((a|0)>-1&(c[46866]|0)>(a|0)?(d=(c[46865]|0)+(a<<2)|0,b=c[d>>2]|0,(b|0)!=0):0){if((c[b+8>>2]|0)==(-2|0)){c[d>>2]=0;a=b+12|0;d=(c[a>>2]|0)+-1|0;c[a>>2]=d;if(!d){iu(b,187448);b=0}else b=0}}else b=0;return b|0}function Qs(a){a=a|0;var b=0,d=0;b=a+4|0;d=c[a>>2]|0;c[d+4>>2]=c[b>>2];c[c[b>>2]>>2]=d;c[a+8>>2]=-2;return}function Rs(a){a=a|0;return c[a+4>>2]|0}function Ss(a){a=a|0;return c[a>>2]|0}function Ts(b){b=b|0;var d=0;d=c[b+8>>2]|0;if(!d)b=187672;else b=(a[b+16>>0]|0)==0?d+32|0:d+36|0;return c[b>>2]|0}function Us(b){b=b|0;var d=0;d=c[b+8>>2]|0;if(!d)b=187672;else b=(a[b+16>>0]|0)==0?d+36|0:d+32|0;return c[b>>2]|0}function Vs(){fu(187480,24);return}function Ws(a,b,d){a=a|0;b=+b;d=+d;var e=0,f=0,g=0,i=0,j=0;j=l;l=l+32|0;i=j;g=(c[46869]|0)+((c[a+16>>2]|0)*96|0)+88|0;f=c[g>>2]|0;h[i+8>>3]=b;h[i+16>>3]=d;e=Xs(a,i,f)|0;do if(e|0){if((e|0)<0){i=hu(187480)|0;h[i+8>>3]=b;h[i+16>>3]=d;c[i>>2]=f;c[g>>2]=i;break}while(1){e=c[f>>2]|0;g=Xs(a,i,e)|0;if((g|0)>0)f=e;else break}if(g|0){i=hu(187480)|0;h[i+8>>3]=b;h[i+16>>3]=d;c[f>>2]=i;c[i>>2]=e}}while(0);l=j;return}function Xs(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0,f=0.0,g=0.0,i=0.0,j=0.0,k=0.0;do if(c){e=+h[b+8>>3];i=+h[c+8>>3];d=+h[b+16>>3];f=+h[c+16>>3];if(!(e==i&d==f)){k=+h[a>>3];j=e-k;e=+h[a+8>>3];g=d-e;i=i-k;d=f-e;if(!(j>=0.0)){if(i>=0.0){b=1;break}e=d/i;d=g/j;if(d<e){b=-1;break}if(d>e){b=1;break}b=j>i?-1:1;break}if(!(i<0.0)){b=i>0.0;if(!(j>0.0)){if(b){b=!(g<=0.0)?1:-1;break}if(g<d){b=!(d<=0.0)?-1:1;break}else{b=!(g<=0.0)?1:-1;break}}else{if(!b){b=d>0.0?-1:1;break}e=d/i;d=g/j;if(d<e){b=-1;break}if(d>e){b=1;break}b=j<i?-1:1;break}}else b=-1}else b=0}else b=-1;while(0);return b|0}function Ys(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;f=c[a+(d*20|0)>>2]|0;d=a+(d*20|0)+4|0;b=1;a=0;while(1){if((b|0)>=(f|0))break;g=a+((c[e+(c[(c[d>>2]|0)+(b<<2)>>2]<<2)>>2]|0)>0&1)|0;b=b+1|0;a=g}return a|0}function Zs(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=a+(b*20|0)|0;b=a+(b*20|0)+4|0;a=1;while(1){if((a|0)>=(c[e>>2]|0))break;c[d+(c[(c[b>>2]|0)+(a<<2)>>2]<<2)>>2]=1;a=a+1|0}return}function _s(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=a+(b*20|0)|0;b=a+(b*20|0)+4|0;a=1;while(1){if((a|0)>=(c[e>>2]|0))break;c[d+(c[(c[b>>2]|0)+(a<<2)>>2]<<2)>>2]=0;a=a+1|0}return}function $s(a,b){a=a|0;b=b|0;if(!(c[a+8>>2]|0))a=bt(a,b)|0;else a=at(a,b)|0;return a|0}function at(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=b<<2;e=KK(O(f,b)|0)|0;f=KK(f)|0;d=0;while(1){if((d|0)>=(b|0)){d=0;break}c[f+(d<<2)>>2]=e+((O(d,b)|0)<<2);d=d+1|0}while(1){if((d|0)>=(b|0))break;bs(d,a,b,c[f+(d<<2)>>2]|0);d=d+1|0}return f|0}function bt(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;h=l;l=l+16|0;f=h;g=b<<2;e=KK(O(g,b)|0)|0;g=KK(g)|0;d=0;while(1){if((d|0)>=(b|0))break;c[g+(d<<2)>>2]=e+((O(d,b)|0)<<2);d=d+1|0}ur(f,b);d=0;while(1){if((d|0)>=(b|0))break;qr(d,a,b,c[g+(d<<2)>>2]|0,f);d=d+1|0}vr(f);l=h;return g|0}function ct(a,b){a=a|0;b=b|0;var d=0,e=0;e=c[a+8>>2]|0;dt(a,b);d=at(a,b)|0;et(a,b,e);return d|0}function dt(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0;l=KK(b<<2)|0;d=0;e=0;while(1){if((d|0)>=(b|0))break;k=(c[a+(d*20|0)>>2]|0)+e|0;d=d+1|0;e=k}e=KK(e<<2)|0;d=0;while(1){if((d|0)>=(b|0))break;c[l+(d<<2)>>2]=0;d=d+1|0}f=0;while(1){if((f|0)>=(b|0))break;c[a+(f*20|0)+8>>2]=e;Zs(a,f,l);h=a+(f*20|0)|0;i=c[h>>2]|0;j=a+(f*20|0)+4|0;k=i+-2|0;d=1;while(1){if((d|0)>=(i|0))break;m=c[(c[j>>2]|0)+(d<<2)>>2]|0;n=c[a+(m*20|0)>>2]|0;g[e+(d<<2)>>2]=+(k+n-((Ys(a,0,m,l)|0)<<1)|0);d=d+1|0}_s(a,f,l);e=e+(c[h>>2]<<2)|0;f=f+1|0}U1(l);return}function et(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=a+8|0;U1(c[e>>2]|0);c[e>>2]=0;a:do if(d|0){e=0;while(1){if((e|0)>=(b|0))break a;c[a+(e*20|0)+8>>2]=d;f=d+(c[a+(e*20|0)>>2]<<2)|0;e=e+1|0;d=f}}while(0);return}function ft(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0.0,i=0.0,j=0;f=0;g=0.0;while(1){if((f|0)>=(b|0))break;j=c[a+(f<<2)>>2]|0;i=+h[j+(d<<3)>>3]-+h[j+(e<<3)>>3];f=f+1|0;g=i*i+g}return +(+C(+g))}function gt(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;if((e|0)>(d|0)){c[46873]=a;o4(b+(d<<2)|0,1-d+e|0,4,77)}return}function ht(a,b){a=a|0;b=b|0;var d=0.0,e=0.0,f=0;f=c[46873]|0;e=+g[f+(c[a>>2]<<2)>>2];d=+g[f+(c[b>>2]<<2)>>2];return (e<d?-1:e>d&1)|0}function it(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;h=l;l=l+16|0;f=h;if((d|0)<(e|0)){jt(a,b,d,e,f);g=c[f>>2]|0;f=g+-1|0;it(a,b,d,f);g=g+1|0;it(a,b,g,e);if(!(kt(a,b,d,f)|0))it(a,b,d,f);if(!(kt(a,b,g,e)|0))it(a,b,g,e)}l=h;return}function jt(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,l=0.0,m=0,n=0;g=x5()|0;g=b+(((((x5()|0)<<16|g)>>>0)%((1-d+e|0)>>>0)|0)+d<<2)|0;j=c[g>>2]|0;k=b+(d<<2)|0;c[g>>2]=c[k>>2];c[k>>2]=j;l=+h[a+(j<<3)>>3];g=d+1|0;d=e;a:while(1){if((g|0)>=(d|0))break;while(1){if((g|0)>=(d|0))break;if(!(+h[a+(c[b+(g<<2)>>2]<<3)>>3]<=l))break;g=g+1|0}while(1){if((g|0)>=(d|0))continue a;e=b+(d<<2)|0;i=c[e>>2]|0;if(!(+h[a+(i<<3)>>3]>l))break;d=d+-1|0}n=b+(g<<2)|0;m=c[n>>2]|0;c[n>>2]=i;c[e>>2]=m;g=g+1|0;d=d+-1|0}n=g+((+h[a+(c[b+(g<<2)>>2]<<3)>>3]>l)<<31>>31)|0;c[f>>2]=n;n=b+(n<<2)|0;c[k>>2]=c[n>>2];c[n>>2]=j;return}function kt(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0;g=1;while(1){f=d+1|0;if(!((d|0)<(e|0)&(g|0)!=0))break;g=+h[a+(c[b+(d<<2)>>2]<<3)>>3]>+h[a+(c[b+(f<<2)>>2]<<3)>>3]?0:g;d=f}return g|0}function lt(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0.0,i=0.0,j=0.0,k=0.0,m=0,n=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0.0,A=0.0;x=l;l=l+16|0;v=4;w=T1(40)|0;c[w>>2]=0;t=x;o=0;u=$(23,b*40|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,w|0,v|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)!=1){d=0;r=0;while(1){if((d|0)>=(b|0))break;s=(c[(c[a+(d<<2)>>2]|0)+4>>2]|0)+r|0;d=d+1|0;r=s}o=0;s=$(23,r*24|0)|0;d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,w|0,v|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)!=1){d=0;m=0;while(1){if((m|0)>=(b|0))break;n=u+(m*40|0)|0;c[n>>2]=s+(d*24|0);q=a+(m<<2)|0;f=0;g=1797693134862315708145274.0e284;i=-1797693134862315708145274.0e284;j=-1797693134862315708145274.0e284;k=1797693134862315708145274.0e284;while(1){e=c[q>>2]|0;if((f|0)>=(c[e+4>>2]|0))break;e=c[e>>2]|0;A=+h[e+(f<<4)>>3];y=+h[e+(f<<4)+8>>3];h[s+(d*24|0)>>3]=A;h[s+(d*24|0)+8>>3]=y;c[s+(d*24|0)+16>>2]=n;c[s+(d*24|0)+20>>2]=0;f=f+1|0;d=d+1|0;g=g<A?g:A;i=i>A?i:A;j=j>y?j:y;k=k<y?k:y}c[u+(m*40|0)+4>>2]=s+((d+-1|0)*24|0);h[u+(m*40|0)+8>>3]=g;h[u+(m*40|0)+16>>3]=k;h[u+(m*40|0)+24>>3]=i;h[u+(m*40|0)+32>>3]=j;m=m+1|0}c[t>>2]=r;c[t+4>>2]=b;w=U6(187496,1,w|0,v|0)|0;v=z;o=0;d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,w|0,v|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){d=z;q=s;m=s;n=u}else{d=0;q=s;m=s;n=u}}else{d=z;q=0;m=s;n=u}}else{d=z;q=0;m=0;n=0}while(1){if(d|0){o=0;ia(105,u|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,w|0,v|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){f=n;r=m;s=q;d=z;n=f;m=r;q=s;continue}o=0;ia(105,m|0);d=o;o=0;if((d|0)!=0&(p|0)!=0){e=V6(c[d>>2]|0,w|0,v|0)|0;if(!e)bb(d|0,p|0);z=p}else e=-1;if((e|0)==1){f=n;r=m;s=q;d=z;n=f;m=r;q=s;continue}else{e=17;break}}o=0;d=aa(78,q|0,t|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,v|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){f=n;r=m;s=q;d=z;n=f;m=r;q=s;continue}if(!d){o=0;d=ca(24,a|0,b|0,n|0)|0;e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,v|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){f=n;r=m;s=q;d=z;n=f;m=r;q=s;continue}}o=0;ia(105,u|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,v|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){f=n;r=m;s=q;d=z;n=f;m=r;q=s;continue}o=0;ia(105,m|0);e=o;o=0;if((e|0)!=0&(p|0)!=0){f=V6(c[e>>2]|0,w|0,v|0)|0;if(!f)bb(e|0,p|0);z=p}else f=-1;if((f|0)==1){f=n;r=m;s=q;d=z;n=f;m=r;q=s}else{e=24;break}}if((e|0)==17)d=0;else if((e|0)==24)d=(d|0)==0&1;U1(w|0);l=x;return d|0}function mt(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;v=l;l=l+16|0;q=v;o=v+8|0;p=v+4|0;c[b+8>>2]=0;u=KK(c[b>>2]<<2)|0;f=c[b>>2]|0;e=0;while(1){if((e|0)>=(f|0))break;c[u+(e<<2)>>2]=a+(e*24|0);e=e+1|0}o4(u,f,4,79);m=0;g=0;f=0;h=0;a:while(1){if((m|0)>=(c[b>>2]|0)){s=0;d=g;t=h;break}n=u+(m<<2)|0;e=c[n>>2]|0;c[o>>2]=e;a=c[e+16>>2]|0;if((c[a>>2]|0)==(e|0))e=c[a+4>>2]|0;else e=e+-24|0;c[p>>2]=e;k=0;a=g;j=h;while(1){if((k|0)>=2)break;switch(ot(o,p)|0){case -1:{g=0;h=a;while(1){if((g|0)>=(j|0))break;i=pt(c[h>>2]|0,e)|0;if(i|0){s=i;d=a;t=j;break a}g=g+1|0;h=c[h+4>>2]|0}g=KK(12)|0;if(!j){f=0;a=g}else c[f+4>>2]=g;c[g+8>>2]=f;c[g>>2]=e;c[g+4>>2]=0;c[e+20>>2]=g;h=j+1|0;break}case 1:{e=e+20|0;g=c[e>>2]|0;if(!g){r=19;break a}do if((j|0)!=1){if((g|0)==(a|0)){a=c[a+4>>2]|0;c[a+8>>2]=0;break}if((g|0)==(f|0)){f=c[f+8>>2]|0;c[f+4>>2]=0;break}else{i=g+4|0;h=c[g+8>>2]|0;c[h+4>>2]=c[i>>2];c[(c[i>>2]|0)+8>>2]=h;break}}else{a=0;f=0}while(0);U1(g);c[e>>2]=0;g=f;h=j+-1|0;break}default:{g=f;h=j}}e=c[n>>2]|0;f=c[e+16>>2]|0;if((e|0)==(c[f+4>>2]|0))f=c[f>>2]|0;else f=e+24|0;c[p>>2]=f;k=k+1|0;f=g;j=h}m=m+1|0;g=a;h=j}if((r|0)==19){gA(1,93727,q)|0;bb(187496,1)}e=0;while(1){if((e|0)>=(t|0))break;r=c[d+4>>2]|0;U1(d);e=e+1|0;d=r}U1(u);l=v;return s|0}function nt(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0.0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0.0,s=0.0,t=0.0,u=0,v=0.0,w=0.0,x=0.0,y=0.0,z=0,A=0,B=0,C=0,D=0,E=0.0;D=l;l=l+48|0;o=D+24|0;n=D+16|0;u=D;e=0;a:while(1){if((e|0)>=(b|0)){e=0;break}z=c[a+(e<<2)>>2]|0;A=c[z>>2]|0;c[u>>2]=c[A>>2];c[u+4>>2]=c[A+4>>2];c[u+8>>2]=c[A+8>>2];c[u+12>>2]=c[A+12>>2];A=e+1|0;B=d+(e*40|0)+8|0;p=d+(e*40|0)+24|0;q=d+(e*40|0)+32|0;g=d+(e*40|0)+16|0;e=A;while(1){if((e|0)>=(b|0)){e=A;continue a}i=c[a+(e<<2)>>2]|0;j=+h[B>>3];k=+h[d+(e*40|0)+24>>3];m=!(j<=k);f=+h[d+(e*40|0)+8>>3];if((((!(m|!(j>=f))?(r=+h[g>>3],s=+h[d+(e*40|0)+32>>3],r<=s):0)?(t=+h[d+(e*40|0)+16>>3],r>=t):0)?(E=+h[p>>3],!(!(E<=k)|!(E>=f))):0)?(E=+h[q>>3],!(!(E<=s)|!(E>=t))):0){c[n>>2]=c[i>>2];c[n+4>>2]=c[i+4>>2];c[o>>2]=c[u>>2];c[o+4>>2]=c[u+4>>2];c[o+8>>2]=c[u+8>>2];c[o+12>>2]=c[u+12>>2];if(NX(n,o)|0){e=1;break a}}else C=11;if((((((C|0)==11?(C=0,v=+h[p>>3],!(!(f<=v)|!(f>=j))):0)?(w=+h[d+(e*40|0)+16>>3],x=+h[q>>3],w<=x):0)?(y=+h[g>>3],!(!(w>=y)|(!(k<=v)|m))):0)?(E=+h[d+(e*40|0)+32>>3],!(!(E<=x)|!(E>=y))):0)?(m=c[i>>2]|0,c[n>>2]=c[z>>2],c[n+4>>2]=c[z+4>>2],c[o>>2]=c[m>>2],c[o+4>>2]=c[m+4>>2],c[o+8>>2]=c[m+8>>2],c[o+12>>2]=c[m+12>>2],NX(n,o)|0):0){e=1;break a}e=e+1|0}}l=D;return e|0}function ot(a,b){a=a|0;b=b|0;var d=0.0,e=0;e=c[a>>2]|0;a=c[b>>2]|0;d=+h[e>>3]-+h[a>>3];if(!(d!=0.0)){d=+h[e+8>>3]-+h[a+8>>3];if(d==0.0)a=0;else a=d>0.0?1:-1}else a=d>0.0?1:-1;return a|0}function pt(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,m=0;m=l;l=l+64|0;g=m+32|0;i=m+24|0;j=m+16|0;k=m;f=m+48|0;qt(a,b,f);d=f+8|0;e=c[d>>2]|0;do if((e|0)>0)d=0;else{if((e|0)<0){qt(b,a,f);d=c[d>>2]|0;if((d|0)>0){d=0;break}if((d|0)<0)d=3;else{d=c[f>>2]|0;d=rt(b,a,(d|0)>-1?d:0-d|0)|0}if(!(st(a,b,i,j,d)|0)){d=0;break}}else{d=c[f>>2]|0;if((d|0)==(c[f+4>>2]|0)){d=rt(a,b,0)|0;d=(rt(a,b,(d|0)<=(rt(a,b,1)|0)&1)|0)<<1}else d=rt(a,b,(d|0)>-1?d:0-d|0)|0;if(!(st(a,b,i,j,d)|0)){d=0;break}}h[k>>3]=+h[i>>3];h[k+8>>3]=+h[j>>3];c[g>>2]=c[k>>2];c[g+4>>2]=c[k+4>>2];c[g+8>>2]=c[k+8>>2];c[g+12>>2]=c[k+12>>2];d=tt(a,b,g)|0}while(0);l=m;return d|0}function qt(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0.0,j=0.0,k=0.0,l=0.0;i=+h[a>>3];j=+h[a+8>>3];e=c[a+16>>2]|0;if((c[e+4>>2]|0)==(a|0)){a=c[e>>2]|0;e=a}else{e=a+24|0;a=a+24|0}g=+h[e>>3]-i;f=+h[a+8>>3]-j;e=c[b+16>>2]|0;if((c[e+4>>2]|0)==(b|0)){a=c[e>>2]|0;e=a}else{e=b+24|0;a=b+24|0}k=+h[e>>3]-i;l=+h[a+8>>3]-j;j=(+h[b+8>>3]-j)*g-(+h[b>>3]-i)*f;b=j==0.0?0:j>0.0?1:-1;c[d>>2]=b;j=l*g-k*f;a=j==0.0?0:j>0.0?1:-1;c[d+4>>2]=a;c[d+8>>2]=O(a,b)|0;return}function rt(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0.0,i=0.0,j=0.0,k=0.0,l=0.0;k=+h[a>>3];l=+h[a+8>>3];f=c[a+16>>2]|0;if((c[f+4>>2]|0)==(a|0))a=c[f>>2]|0;else a=a+24|0;j=+h[a>>3];i=+h[a+8>>3];do if(d){a=c[b+16>>2]|0;if((c[a+4>>2]|0)==(b|0)){b=c[a>>2]|0;break}else{b=b+24|0;break}}while(0);g=+h[b>>3];e=+h[b+8>>3];if(k==j)if(k==g)b=(vt(l,e,i)|0)!=-1&1;else b=0;else b=vt(k,g,j)|0;return b|0}function st(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0.0,j=0.0,k=0.0,l=0.0,m=0.0,n=0.0,o=0.0,p=0.0;a:do if((f|0)<1)g=0;else{n=+h[a>>3];p=+h[a+8>>3];g=c[a+16>>2]|0;if((c[g+4>>2]|0)==(a|0))g=c[g>>2]|0;else g=a+24|0;l=+h[g>>3];j=+h[g+8>>3];m=+h[b>>3];o=+h[b+8>>3];g=c[b+16>>2]|0;if((c[g+4>>2]|0)==(b|0))g=c[g>>2]|0;else g=b+24|0;k=+h[g>>3];i=+h[g+8>>3];b:do switch(f|0){case 3:{if(n==l){h[d>>3]=n;i=(o-i)/(m-k)*(n-k)+i;break b}if(m==k){h[d>>3]=m;i=(p-j)/(n-l)*(m-l)+j;break b}else{k=(o-i)/(m-k);l=(p-j)/(n-l);o=o-k*m;p=p-l*n;i=k-l;h[d>>3]=(p-o)/i;i=(k*p-o*l)/i;break b}}case 2:{if((rt(a,b,0)|0)==-1)if((rt(b,a,1)|0)==-1){f=(rt(b,a,0)|0)==-1;l=f?l:n;k=m;j=f?j:p;i=o}else{l=k;k=m;j=i;i=o}else if((rt(a,b,1)|0)!=-1)if((rt(b,a,0)|0)==-1){k=n;i=p}else{g=0;break a}else{l=m;j=o}h[d>>3]=(k+l)*.5;i=(i+j)*.5;break}case 1:if((o-p)*(n-l)==(m-n)*(p-j)){h[d>>3]=m;i=o;break b}else{h[d>>3]=k;break b}default:{g=1;break a}}while(0);h[e>>3]=i;g=1}while(0);return g|0}function tt(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0.0,i=0.0,j=0.0,k=0.0,m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0,s=0;s=l;l=l+16|0;q=s;o=+h[a>>3];p=+h[a+8>>3];e=c[a+16>>2]|0;if((c[e+4>>2]|0)==(a|0))e=c[e>>2]|0;else e=a+24|0;m=+h[e>>3];j=+h[e+8>>3];k=+h[b>>3];n=+h[b+8>>3];e=c[b+16>>2]|0;if((c[e+4>>2]|0)==(b|0))e=c[e>>2]|0;else e=b+24|0;i=+h[e>>3];g=+h[e+8>>3];a:do if(!(o!=m&k!=i)){do if(o==m){f=+h[d>>3];if(o==f?p==+h[d+8>>3]:0)break;if(!(m==f)){r=18;break a}if(!(j==+h[d+8>>3])){r=18;break a}}while(0);if(k==i){f=+h[d>>3];if(k==f?n==+h[d+8>>3]:0){e=0;break}if(i==f?g==+h[d+8>>3]:0)e=0;else r=18}else e=0}else r=18;while(0);if((r|0)==18)if(0>1){e=c[15712]|0;p=+h[d+8>>3];h[q>>3]=+h[d>>3];h[q+8>>3]=p;w4(e,93756,q)|0;ut(1,a);ut(2,b);e=1}else e=1;l=s;return e|0}function ut(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0.0,i=0.0,j=0,k=0,m=0.0,n=0.0;k=l;l=l+48|0;j=k;f=c[15712]|0;g=+h[b>>3];i=+h[b+8>>3];d=c[b+16>>2]|0;if((c[d+4>>2]|0)==(b|0)){d=c[d>>2]|0;e=d}else{e=b+24|0;d=b+24|0}n=+h[e>>3];m=+h[d+8>>3];c[j>>2]=a;h[j+8>>3]=g;h[j+16>>3]=i;h[j+24>>3]=n;h[j+32>>3]=m;w4(f,93784,j)|0;l=k;return}function vt(a,b,c){a=+a;b=+b;c=+c;return (a==b|b==c?0:(a<b?b<c:c<b)?1:-1)|0}function wt(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,i=0.0,j=0;a:do if(!(uy(a,d)|0))a=0;else{f=JK(d<<3)|0;a=0;while(1){if((a|0)<(d|0))e=0;else break;while(1){if((e|0)==(d|0))break;h[f+(e<<3)>>3]=0.0;e=e+1|0}h[f+(a<<3)>>3]=1.0;vy(c[b+(a<<2)>>2]|0,f,d);a=a+1|0}U1(f);f=0;while(1){if((f|0)>=(d|0)){a=1;break a}e=b+(f<<2)|0;a=0;while(1){if((a|0)==(f|0))break;j=(c[e>>2]|0)+(a<<3)|0;i=+h[j>>3];g=(c[b+(a<<2)>>2]|0)+(f<<3)|0;h[j>>3]=+h[g>>3];h[g>>3]=i;a=a+1|0}f=f+1|0}}while(0);return a|0}function xt(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var i=0,j=0.0,k=0,l=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;q=b<<3;p=KK(q)|0;q=KK(q)|0;r=b*30|0;n=(d|0)<(b|0)?d:b;o=b+-1|0;m=(g|0)==0;d=0;i=0;a:while(1){if((i|0)>=(n|0))break;k=c[e+(i<<2)>>2]|0;do{b:do if(m)d=0;else{d=0;while(1){if((d|0)==(b|0)){d=0;break b}h[k+(d<<3)>>3]=+((x5()|0)%100|0|0);d=d+1|0}}while(0);while(1){if((d|0)==(i|0))break;g=c[e+(d<<2)>>2]|0;zt(k,0,o,-+yt(g,0,o,k),g);d=d+1|0}j=+At(k,0,o)}while(j<1.0e-10);Bt(k,0,o,1.0/j,k);d=0;do{Ct(q,0,o,k);Dt(a,b,b,k,p);Ct(k,0,o,p);g=0;while(1){if((g|0)==(i|0))break;s=c[e+(g<<2)>>2]|0;zt(k,0,o,-+yt(s,0,o,k),s);g=g+1|0}s=d;d=d+1|0;j=+At(k,0,o);if(!((s|0)<(r|0)&!(j<1.0e-10)))break a;Bt(k,0,o,1.0/j,k);l=+yt(k,0,o,q)}while(+B(+l)<.999);h[f+(i<<3)>>3]=l*j;i=i+1|0}while(1){if((i|0)>=(n|0))break;k=c[e+(i<<2)>>2]|0;g=0;while(1){if((g|0)>=(b|0)){g=0;break}h[k+(g<<3)>>3]=+((x5()|0)%100|0|0);g=g+1|0}while(1){if((g|0)>=(i|0))break;s=c[e+(g<<2)>>2]|0;zt(k,0,o,-+yt(s,0,o,k),s);g=g+1|0}Bt(k,0,o,1.0/+At(k,0,o),k);h[f+(i<<3)>>3]=0.0;i=i+1|0}m=n+-1|0;i=0;while(1){if((i|0)>=(m|0))break;a=f+(i<<3)|0;b=i+1|0;j=+h[a>>3];g=i;k=b;while(1){if((k|0)>=(n|0))break;l=+h[f+(k<<3)>>3];s=j<l;j=s?l:j;g=s?k:g;k=k+1|0}if((g|0)==(i|0)){i=b;continue}k=e+(i<<2)|0;Ct(p,0,o,c[k>>2]|0);s=e+(g<<2)|0;Ct(c[k>>2]|0,0,o,c[s>>2]|0);Ct(c[s>>2]|0,0,o,p);h[f+(g<<3)>>3]=+h[a>>3];h[a>>3]=j;i=b}U1(p);U1(q);return (d|0)<=(r|0)|0}function yt(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0.0;f=0.0;e=1-b+c|0;d=d+(b<<3)|0;c=a+(b<<3)|0;while(1){if(!e)break;f=+h[d>>3]*+h[c>>3]+f;e=e+-1|0;d=d+8|0;c=c+8|0}return +f}function zt(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=+d;e=e|0;var f=0;f=1-b+c|0;e=e+(b<<3)|0;c=a+(b<<3)|0;while(1){if(!f)break;h[c>>3]=+h[c>>3]+ +h[e>>3]*d;f=f+-1|0;e=e+8|0;c=c+8|0}return}function At(a,b,c){a=a|0;b=b|0;c=c|0;return +(+C(+(+yt(a,b,c,a))))}function Bt(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=+d;e=e|0;var f=0;f=1-b+c|0;e=e+(b<<3)|0;c=a+(b<<3)|0;while(1){if(!f)break;h[c>>3]=+h[e>>3]*d;f=f+-1|0;e=e+8|0;c=c+8|0}return}function Ct(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0;e=1-b+c|0;d=d+(b<<3)|0;c=a+(b<<3)|0;while(1){if(!e)break;h[c>>3]=+h[d>>3];e=e+-1|0;d=d+8|0;c=c+8|0}return}function Dt(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0.0,i=0,j=0,k=0;j=0;while(1){if((j|0)>=(b|0))break;k=a+(j<<2)|0;g=0.0;i=0;while(1){if((i|0)>=(d|0))break;g=+h[e+(i<<3)>>3]*+h[(c[k>>2]|0)+(i<<3)>>3]+g;i=i+1|0}h[f+(j<<3)>>3]=g;j=j+1|0}return}function Et(a,b,d,e,f,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;i=i|0;var j=0,k=0,l=0.0,m=0,n=0,o=0,p=0.0;k=c[i>>2]|0;if(!k){o=d<<2;j=T1(O(o,f)|0)|0;o=T1(o)|0}else{o=d<<2;j=W1(c[k>>2]|0,O(o,f)|0)|0;o=W1(k,o)|0}c[i>>2]=o;k=0;while(1){if((k|0)>=(d|0)){i=0;break}c[o+(k<<2)>>2]=j;k=k+1|0;j=j+(f<<2)|0}while(1){if((i|0)>=(d|0))break;m=a+(i<<2)|0;n=o+(i<<2)|0;k=0;while(1){if((k|0)<(f|0)){j=0;l=0.0}else break;while(1){if((j|0)>=(e|0))break;p=+h[(c[m>>2]|0)+(j<<3)>>3]*+g[(c[b+(j<<2)>>2]|0)+(k<<2)>>2]+l;j=j+1|0;l=p}g[(c[n>>2]|0)+(k<<2)>>2]=l;k=k+1|0}i=i+1|0}return}function Ft(a,b,d,e,f,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;i=i|0;var j=0,k=0,l=0.0,m=0,n=0,o=0;k=c[i>>2]|0;if(!k){j=T1(O(d<<3,f)|0)|0;o=T1(d<<2)|0}else{j=W1(c[k>>2]|0,O(d<<3,f)|0)|0;o=W1(k,d<<2)|0}c[i>>2]=o;k=0;while(1){if((k|0)>=(d|0)){i=0;break}c[o+(k<<2)>>2]=j;k=k+1|0;j=j+(f<<3)|0}while(1){if((i|0)>=(d|0))break;m=a+(i<<2)|0;n=o+(i<<2)|0;k=0;while(1){if((k|0)<(f|0)){l=0.0;j=0}else break;while(1){if((j|0)>=(e|0))break;l=+h[(c[m>>2]|0)+(j<<3)>>3]*+g[(c[b+(j<<2)>>2]|0)+(k<<2)>>2]+l;j=j+1|0}h[(c[n>>2]|0)+(k<<3)>>3]=l;k=k+1|0}i=i+1|0}return}function Gt(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var i=0,j=0,k=0.0,l=0,m=0,n=0,o=0,p=0,q=0;j=c[f>>2]|0;if(!j){i=T1(O(d*20|0,e)|0)|0;q=T1(d<<2)|0}else{i=W1(c[j>>2]|0,O(d*20|0,e)|0)|0;q=W1(j,d<<2)|0}c[f>>2]=q;j=0;while(1){if((j|0)>=(d|0)){f=0;break}c[q+(j<<2)>>2]=i;j=j+1|0;i=i+(e<<2)|0}while(1){if((f|0)>=(d|0))break;l=c[a+(f*20|0)+4>>2]|0;m=c[a+(f*20|0)+8>>2]|0;n=c[a+(f*20|0)>>2]|0;o=q+(f<<2)|0;i=0;while(1){if((i|0)>=(e|0))break;p=b+(i<<2)|0;k=0.0;j=0;while(1){if((j|0)>=(n|0))break;k=+h[(c[p>>2]|0)+(c[l+(j<<2)>>2]<<3)>>3]*+g[m+(j<<2)>>2]+k;j=j+1|0}g[(c[o>>2]|0)+(i<<2)>>2]=k;i=i+1|0}f=f+1|0}return}function Ht(a,b){a=a|0;b=b|0;var c=0.0,d=0,e=0;c=0.0;d=b;e=a;while(1){if(!e)break;c=+h[d>>3]+c;d=d+8|0;e=e+-1|0}c=c/+(a|0);while(1){if(!a)break;h[b>>3]=+h[b>>3]-c;b=b+8|0;a=a+-1|0}return}function It(a,b){a=a|0;b=b|0;var c=0;c=0;while(1){if((c|0)>=(a|0))break;h[b+(c<<3)>>3]=+((x5()|0)%500|0|0);c=c+1|0}Ht(a,b);return}function Jt(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,i=0,j=0,k=0,l=0,m=0;j=0;while(1){if((j|0)>=(b|0))break;k=c[a+(j*20|0)>>2]|0;l=a+(j*20|0)+8|0;m=a+(j*20|0)+4|0;f=0.0;i=0;while(1){if((i|0)>=(k|0))break;f=+h[d+(c[(c[m>>2]|0)+(i<<2)>>2]<<3)>>3]*+g[(c[l>>2]|0)+(i<<2)>>2]+f;i=i+1|0}h[e+(j<<3)>>3]=f;j=j+1|0}return}function Kt(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,i=0,j=0,k=0;j=0;while(1){if((j|0)>=(b|0))break;k=a+(j<<2)|0;f=0.0;i=0;while(1){if((i|0)==(b|0))break;f=+h[d+(i<<3)>>3]*+g[(c[k>>2]|0)+(i<<2)>>2]+f;i=i+1|0}h[e+(j<<3)>>3]=f;j=j+1|0}return}function Lt(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0;e=0;while(1){if((e|0)>=(a|0))break;h[d+(e<<3)>>3]=+h[b+(e<<3)>>3]-+h[c+(e<<3)>>3];e=e+1|0}return}function Mt(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0;e=0;while(1){if((e|0)>=(a|0))break;h[d+(e<<3)>>3]=+h[c+(e<<3)>>3]+ +h[b+(e<<3)>>3];e=e+1|0}return}function Nt(a,b,c,d){a=a|0;b=b|0;c=+c;d=d|0;var e=0;e=0;while(1){if((e|0)>=(a|0))break;h[d+(e<<3)>>3]=+h[b+(e<<3)>>3]*c;e=e+1|0}return}function Ot(a,b,c){a=a|0;b=b|0;c=c|0;var d=0;d=0;while(1){if((d|0)>=(a|0))break;h[c+(d<<3)>>3]=+h[b+(d<<3)>>3];d=d+1|0}return}function Pt(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0;d=0.0;e=0;while(1){if((e|0)>=(a|0))break;d=+h[c+(e<<3)>>3]*+h[b+(e<<3)>>3]+d;e=e+1|0}return +d}function Qt(a,b){a=a|0;b=b|0;var c=0,d=0.0,e=0.0;c=0;d=-1.e+50;while(1){if((c|0)>=(a|0))break;e=+B(+(+h[b+(c<<3)>>3]));c=c+1|0;d=e>d?e:d}return +d}function Rt(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0.0,i=0,j=0;j=0;while(1){if((j|0)<(b|0)){g=0.0;i=0}else break;while(1){if((i|0)>=(d|0))break;g=+h[e+(i<<3)>>3]*+h[(c[a+(i<<2)>>2]|0)+(j<<3)>>3]+g;i=i+1|0}h[f+(j<<3)>>3]=g;j=j+1|0}return}function St(a,b){a=a|0;b=b|0;var c=0.0,d=0,e=0;c=0.0;d=b;e=a;while(1){if(!e)break;c=+g[d>>2]+c;d=d+4|0;e=e+-1|0}c=c/+(a|0);while(1){if(!a)break;g[b>>2]=+g[b>>2]-c;b=b+4|0;a=a+-1|0}return}function Tt(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0.0,h=0,i=0,j=0,k=0,l=0.0,m=0,n=0.0,o=0,p=0.0;e=0;while(1){if((e|0)>=(b|0)){h=0;j=0;m=b;break}g[d+(e<<2)>>2]=0.0;e=e+1|0}while(1){if((j|0)>=(b|0))break;l=+g[c+(j<<2)>>2];k=j+1|0;f=+g[a+(h<<2)>>2]*l;i=k;e=h;while(1){e=e+1|0;if((i|0)==(b|0))break;p=+g[a+(e<<2)>>2];n=+g[c+(i<<2)>>2]*p+f;o=d+(i<<2)|0;g[o>>2]=+g[o>>2]+p*l;f=n;i=i+1|0}o=d+(j<<2)|0;g[o>>2]=+g[o>>2]+f;h=h+m|0;j=k;m=m+-1|0}return}function Ut(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0;e=0;while(1){if((e|0)>=(a|0))break;g[d+(e<<2)>>2]=+g[b+(e<<2)>>2]-+g[c+(e<<2)>>2];e=e+1|0}return}function Vt(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0;e=0;while(1){if((e|0)>=(a|0))break;g[d+(e<<2)>>2]=+g[c+(e<<2)>>2]+ +g[b+(e<<2)>>2];e=e+1|0}return}function Wt(a,b,c,d){a=a|0;b=b|0;c=+c;d=d|0;var e=0,f=0;e=0;while(1){if((e|0)>=(a|0))break;f=b+(e<<2)|0;g[f>>2]=+g[d+(e<<2)>>2]*c+ +g[f>>2];e=e+1|0}return}function Xt(a,b,c,d){a=a|0;b=b|0;c=+c;d=d|0;var e=0;e=0;while(1){if((e|0)>=(a|0))break;g[d+(e<<2)>>2]=+g[b+(e<<2)>>2]*c;e=e+1|0}return}function Yt(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=0;while(1){if((e|0)>=(a|0))break;c[d+(e<<2)>>2]=c[b+(e<<2)>>2];e=e+1|0}return}function Zt(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0;d=0.0;e=0;while(1){if((e|0)>=(a|0))break;d=d+ +g[c+(e<<2)>>2]*+g[b+(e<<2)>>2];e=e+1|0}return +d}function _t(a,b,c){a=a|0;b=+b;c=c|0;var d=0;d=0;while(1){if((d|0)>=(a|0))break;h[c+(d<<3)>>3]=b;d=d+1|0}return}function $t(a,b,c){a=a|0;b=+b;c=c|0;var d=0;d=0;while(1){if((d|0)>=(a|0))break;g[c+(d<<2)>>2]=b;d=d+1|0}return}function au(a,b){a=a|0;b=b|0;var c=0.0,d=0,e=0.0;c=-1000000015047466219876688.0e6;d=0;while(1){if((d|0)>=(a|0))break;e=+B(+(+g[b+(d<<2)>>2]));c=e>c?e:c;d=d+1|0}return +c}function bu(a,b){a=a|0;b=b|0;var c=0,d=0,e=0.0;c=0;while(1){if((c|0)>=(a|0))break;d=b+(c<<2)|0;e=+g[d>>2];g[d>>2]=e*e;c=c+1|0}return}function cu(a,b){a=a|0;b=b|0;var c=0,d=0,e=0.0;c=0;while(1){if((c|0)>=(a|0))break;d=b+(c<<2)|0;e=+g[d>>2];if(e!=0.0)g[d>>2]=1.0/e;c=c+1|0}return}function du(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0.0;d=0;while(1){if((d|0)>=(a|0))break;e=+g[b+(d<<2)>>2];if(e>=0.0)g[c+(d<<2)>>2]=+C(+e);d=d+1|0}return}function eu(a,b){a=a|0;b=b|0;var c=0,d=0,e=0.0;c=0;while(1){if((c|0)>=(a|0))break;d=b+(c<<2)|0;e=+g[d>>2];if(e>0.0)g[d>>2]=1.0/+C(+e);c=c+1|0}return}function fu(a,b){a=a|0;b=b|0;var d=0;c[a>>2]=0;if(b&3)if(!(4%(b>>>0)|0))b=4;else b=O(4/((gu(b)|0)>>>0)|0,b)|0;c[a+8>>2]=b;a=a+4|0;b=c[a>>2]|0;a:do if(b|0)while(1){if(!b)break a;d=c[b>>2]|0;U1(c[b+4>>2]|0);U1(b);b=d}while(0);c[a>>2]=0;return}function gu(a){a=a|0;var b=0,c=0;c=4;a:while(1){b=c;while(1){if((b|0)==(a|0))break a;if((b|0)<=(a|0))break;b=b-a|0}c=b;a=a-b|0}return a|0}function hu(a){a=a|0;var b=0,d=0,e=0,f=0;b=c[a>>2]|0;if(!b){d=c[a+8>>2]|0;e=KK(8)|0;f=KK(O(c[46855]|0,d)|0)|0;c[e+4>>2]=f;b=0;while(1){if((b|0)>=(c[46855]|0))break;iu(f+(O(b,d)|0)|0,a);b=b+1|0}b=a+4|0;c[e>>2]=c[b>>2];c[b>>2]=e;b=c[a>>2]|0}c[a>>2]=c[b>>2];return b|0}function iu(a,b){a=a|0;b=b|0;c[a>>2]=c[b>>2];c[b>>2]=a;return}function ju(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,h=0,i=0,j=0,k=0,l=0,m=0;m=JK(b<<3)|0;l=a+8|0;f=c[l>>2]|0;ku(a,b,m);It(b,d);h=0;i=0;while(1){if((i|0)>=(b|0))break;h=(c[a+(i*20|0)>>2]|0)+h|0;i=i+1|0}h=KK(h<<2)|0;j=0;while(1){if((j|0)>=(b|0))break;c[a+(j*20|0)+8>>2]=h;k=c[a+(j*20|0)>>2]|0;g[h>>2]=+(1-k|0);i=1;while(1){if((i|0)>=(k|0))break;g[h+(i<<2)>>2]=1.0;i=i+1|0}h=h+(k<<2)|0;j=j+1|0}i=yr(a,d,m,b,.001,e)|0;U1(c[l>>2]|0);h=0;while(1){if((h|0)>=(b|0))break;c[a+(h*20|0)+8>>2]=f;f=f+(c[a+(h*20|0)>>2]<<2)|0;h=h+1|0}U1(m);return i>>>31|0}function ku(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,i=0,j=0,k=0,l=0,m=0;k=a+16|0;i=0;while(1){if((i|0)>=(b|0))break;if(c[k>>2]|0){l=c[a+(i*20|0)>>2]|0;m=a+(i*20|0)+8|0;j=a+(i*20|0)+16|0;e=0.0;f=1;while(1){if((f|0)>=(l|0))break;e=e+ +g[(c[j>>2]|0)+(f<<2)>>2]*+g[(c[m>>2]|0)+(f<<2)>>2];f=f+1|0}h[d+(i<<3)>>3]=e}i=i+1|0}return}function lu(a,b,d,e,f,g,h,i,j){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=+h;i=i|0;j=j|0;a=l;l=l+16|0;if(!(c[46913]|0)){c[46913]=1;gA(1,93820,a)|0}l=a;return}function mu(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,i=0,j=0.0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0;r=KK(f<<2)|0;i=b<<3;g=0;while(1){if((g|0)>=(f|0))break;c[r+(g<<2)>>2]=KK(i)|0;g=g+1|0}p=KK(f<<3)|0;q=KK(b<<2)|0;g=KK(O(i,b)|0)|0;i=0;while(1){if((i|0)>=(b|0))break;c[q+(i<<2)>>2]=g;g=g+(b<<3)|0;i=i+1|0}k=0;o=1;while(1){if((k|0)>=(b|0))break;l=a+(k<<2)|0;m=q+(k<<2)|0;i=0;while(1){if((i|0)==(o|0))break;n=a+(i<<2)|0;g=0;j=0.0;while(1){if((g|0)>=(d|0))break;s=j+ +(O(c[(c[n>>2]|0)+(g<<2)>>2]|0,c[(c[l>>2]|0)+(g<<2)>>2]|0)|0);g=g+1|0;j=s}h[(c[q+(i<<2)>>2]|0)+(k<<3)>>3]=j;h[(c[m>>2]|0)+(i<<3)>>3]=j;i=i+1|0}k=k+1|0;o=o+1|0}xt(q,b,f,r,p,1)|0;i=0;while(1){if((i|0)>=(f|0)){g=0;break}l=r+(i<<2)|0;m=e+(i<<2)|0;k=0;while(1){if((k|0)<(d|0)){g=0;j=0.0}else break;while(1){if((g|0)>=(b|0))break;s=+h[(c[l>>2]|0)+(g<<3)>>3]*+(c[(c[a+(g<<2)>>2]|0)+(k<<2)>>2]|0)+j;g=g+1|0;j=s}h[(c[m>>2]|0)+(k<<3)>>3]=j;k=k+1|0}i=i+1|0}while(1){if((g|0)>=(f|0))break;U1(c[r+(g<<2)>>2]|0);g=g+1|0}U1(r);U1(p);U1(c[q>>2]|0);U1(q);return}function nu(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0;f=l;l=l+32|0;g=f+16|0;j=f+20|0;i=f+12|0;h=f+8|0;c[g>>2]=e;c[i>>2]=0;c[h>>2]=0;iy(c[a>>2]|0,d,d<<2,j);Gt(c[j>>2]|0,a,d,b,i);Ft(a,c[i>>2]|0,b,d,b,h);U1(c[c[i>>2]>>2]|0);U1(c[i>>2]|0);e=(xt(c[h>>2]|0,b,1,g,f,1)|0)&255;l=f;return e|0}function ou(){c[46914]=0;U1(c[46915]|0);U1(c[46916]|0);U1(c[46917]|0);c[46915]=0;c[46916]=0;c[46917]=0;return}function pu(a){a=a|0;U1(c[a+36>>2]|0);return}function qu(b,d,e,f){b=b|0;d=d|0;e=+e;f=+f;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0.0,u=0.0,v=0.0;r=l;l=l+80|0;o=r+64|0;q=r+68|0;i=r+48|0;j=r+32|0;k=r+16|0;m=r;n=d+16|0;g=c[n>>2]|0;a:do if(!(c[g+212>>2]|0))switch(WM(d)|0){case 1:{g=c[n>>2]|0;j=c[g+12>>2]|0;k=c[j+8>>2]|0;c[q>>2]=k;g=c[c[g+8>>2]>>2]|0;switch(a[g>>0]|0){case 98:{if(!(s2(g,108236)|0))p=11;else p=8;break}case 112:{if((s2(g,105721)|0)==0?(ru(c[j+44>>2]|0,k)|0)!=0:0)p=11;else p=8;break}default:p=8}do if((p|0)==8)if((k|0)<3){c[b+40>>2]=(c[j>>2]|0)==0?0:2;p=17;break}else{g=b+40|0;c[g>>2]=0;break}else if((p|0)==11){g=b+40|0;c[g>>2]=1;if((k|0)<=2)p=17}while(0);if((p|0)==17){i=su(d,q,e,f)|0;p=21;break a}i=KK(k<<4)|0;j=j+44|0;if((c[g>>2]|0)==1){p=c[j>>2]|0;s=e;h[i>>3]=+h[p>>3]*.013888888888888888+s;e=f;h[i+8>>3]=+h[p+8>>3]*.013888888888888888+e;h[i+16>>3]=+h[p+16>>3]*.013888888888888888-s;h[i+24>>3]=+h[p+24>>3]*.013888888888888888+e;h[i+32>>3]=+h[p+32>>3]*.013888888888888888-s;h[i+40>>3]=+h[p+40>>3]*.013888888888888888-e;h[i+48>>3]=+h[p+48>>3]*.013888888888888888+s;h[i+56>>3]=+h[p+56>>3]*.013888888888888888-e;p=21;break a}g=0;while(1){if((g|0)>=(k|0)){p=21;break a}p=c[j>>2]|0;u=+h[p+(g<<4)>>3];s=+h[p+(g<<4)+8>>3];t=+C(+(s*s+u*u));h[i+(g<<4)>>3]=(e/t+1.0)*u*.013888888888888888;h[i+(g<<4)+8>>3]=(f/t+1.0)*s*.013888888888888888;g=g+1|0}}case 2:{c[q>>2]=4;p=KK(64)|0;d=c[(c[n>>2]|0)+12>>2]|0;v=+h[d+32>>3];t=+h[d+40>>3];o=~~+h[d+16>>3];d=~~+h[d+24>>3];u=-e;s=-f;tu(i,o,d,u,s);c[p>>2]=c[i>>2];c[p+4>>2]=c[i+4>>2];c[p+8>>2]=c[i+8>>2];c[p+12>>2]=c[i+12>>2];i=p+16|0;n=~~v;tu(j,n,d,e,s);c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];i=p+32|0;d=~~t;tu(k,n,d,e,f);c[i>>2]=c[k>>2];c[i+4>>2]=c[k+4>>2];c[i+8>>2]=c[k+8>>2];c[i+12>>2]=c[k+12>>2];i=p+48|0;tu(m,o,d,u,f);c[i>>2]=c[m>>2];c[i+4>>2]=c[m+4>>2];c[i+8>>2]=c[m+8>>2];c[i+12>>2]=c[m+12>>2];c[b+40>>2]=1;i=p;p=21;break a}case 3:{c[b+40>>2]=2;i=su(d,q,e,f)|0;p=21;break a}default:{c[o>>2]=c[c[(c[n>>2]|0)+8>>2]>>2];gA(1,93883,o)|0;g=1;break a}}else{c[q>>2]=4;u=+h[g+32>>3]*.5+e;v=+h[g+40>>3]*.5+f;c[b+40>>2]=1;i=KK(64)|0;h[i>>3]=u;h[i+8>>3]=v;t=-u;h[i+16>>3]=t;h[i+24>>3]=v;h[i+32>>3]=t;v=-v;h[i+40>>3]=v;h[i+48>>3]=u;h[i+56>>3]=v;p=21}while(0);if((p|0)==21){c[b+36>>2]=i;g=c[q>>2]|0;c[b+32>>2]=g;uu(i,g,b,b+16|0);if((g|0)>(c[46914]|0)){c[46914]=g;g=0}else g=0}l=r;return g|0}function ru(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;do if((b|0)==4){c=+h[a+8>>3];b=a+16|0;d=+h[a+24>>3];if(c==d){if(!(+h[a+40>>3]==+h[a+56>>3])){b=0;break}if(!(+h[a>>3]==+h[a+48>>3])){b=0;break}b=+h[b>>3]==+h[a+32>>3];break}if((+h[a>>3]==+h[b>>3]?+h[a+32>>3]==+h[a+48>>3]:0)?c==+h[a+56>>3]:0)b=d==+h[a+40>>3];else b=0}else b=0;while(0);return b&1|0}function su(a,b,d,e){a=a|0;b=b|0;d=+d;e=+e;var f=0,g=0.0,i=0,j=0,k=0.0,l=0;f=QA(a,102705)|0;if(!f)f=0;else f=q5(f)|0;i=(f|0)<3?20:f;j=KK(i<<4)|0;a=a+16|0;g=1.0/+(i|0);f=0;while(1){if((f|0)>=(i|0))break;l=c[a>>2]|0;k=g*+(f|0)*6.283185307179586;h[j+(f<<4)>>3]=(+h[l+32>>3]*.5+d)*+E(+k);h[j+(f<<4)+8>>3]=(+h[l+40>>3]*.5+e)*+F(+k);f=f+1|0}c[b>>2]=i;return j|0}function tu(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=+d;e=+e;h[a>>3]=+(b|0)*.013888888888888888+d;h[a+8>>3]=+(c|0)*.013888888888888888+e;return}function uu(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0.0,g=0.0,i=0.0,j=0.0,k=0,l=0.0,m=0.0;j=+h[a>>3];i=+h[a+8>>3];e=1;f=i;g=j;while(1){if((e|0)>=(b|0))break;k=a+16|0;l=+h[k>>3];m=+h[a+24>>3];e=e+1|0;f=m>f?m:f;g=l>g?l:g;i=m<i?m:i;j=l<j?l:j;a=k}h[c>>3]=j;h[c+8>>3]=i;h[d>>3]=g;h[d+8>>3]=f;return}function vu(b,d,e,f){b=b|0;d=d|0;e=+e;f=+f;var g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0.0,u=0.0,v=0.0;r=l;l=l+80|0;n=r+64|0;p=r+68|0;i=r+48|0;j=r+32|0;k=r+16|0;m=r;o=d+16|0;g=c[o>>2]|0;a:do if(!(c[g+212>>2]|0))switch(WM(d)|0){case 1:{m=c[(c[o>>2]|0)+12>>2]|0;n=m+8|0;k=c[n>>2]|0;c[p>>2]=k;b:do if((k|0)>2){i=KK(k<<4)|0;j=m+44|0;g=0;while(1){if((g|0)>=(k|0))break b;d=c[j>>2]|0;h[i+(g<<4)>>3]=+h[d+(g<<4)>>3]*.013888888888888888;h[i+(g<<4)+8>>3]=+h[d+(g<<4)+8>>3]*.013888888888888888;g=g+1|0}}else i=su(d,p,0.0,0.0)|0;while(0);g=c[c[(c[o>>2]|0)+8>>2]>>2]|0;switch(a[g>>0]|0){case 98:{if(!(s2(g,108236)|0))g=1;else q=13;break}case 112:{if((s2(g,105721)|0)==0?(ru(i,c[p>>2]|0)|0)!=0:0)g=1;else q=13;break}default:q=13}do if((q|0)==13){if((c[n>>2]|0)<3?c[m>>2]|0:0){g=2;break}g=0}while(0);c[b+40>>2]=g;q=20;break a}case 2:{c[p>>2]=4;q=KK(64)|0;o=c[(c[o>>2]|0)+12>>2]|0;t=+h[o+16>>3];v=+h[o+24>>3];u=+h[o+32>>3];s=+h[o+40>>3];wu(i,t,v);c[q>>2]=c[i>>2];c[q+4>>2]=c[i+4>>2];c[q+8>>2]=c[i+8>>2];c[q+12>>2]=c[i+12>>2];i=q+16|0;wu(j,u,v);c[i>>2]=c[j>>2];c[i+4>>2]=c[j+4>>2];c[i+8>>2]=c[j+8>>2];c[i+12>>2]=c[j+12>>2];i=q+32|0;wu(k,u,s);c[i>>2]=c[k>>2];c[i+4>>2]=c[k+4>>2];c[i+8>>2]=c[k+8>>2];c[i+12>>2]=c[k+12>>2];i=q+48|0;wu(m,t,s);c[i>>2]=c[m>>2];c[i+4>>2]=c[m+4>>2];c[i+8>>2]=c[m+8>>2];c[i+12>>2]=c[m+12>>2];c[b+40>>2]=1;i=q;q=20;break a}case 3:{c[b+40>>2]=2;i=su(d,p,0.0,0.0)|0;q=20;break a}default:{c[n>>2]=c[c[(c[o>>2]|0)+8>>2]>>2];gA(1,93919,n)|0;g=1;break a}}else{c[p>>2]=4;u=+h[g+32>>3]*.5;v=+h[g+40>>3]*.5;c[b+40>>2]=1;i=KK(64)|0;h[i>>3]=u;h[i+8>>3]=v;t=-u;h[i+16>>3]=t;h[i+24>>3]=v;h[i+32>>3]=t;v=-v;h[i+40>>3]=v;h[i+48>>3]=u;h[i+56>>3]=v;q=20}while(0);if((q|0)==20){g=c[p>>2]|0;if(e!=1.0|f!=1.0)xu(i,g,e,f);c[b+36>>2]=i;c[b+32>>2]=g;uu(i,g,b,b+16|0);if((g|0)>(c[46914]|0)){c[46914]=g;g=0}else g=0}l=r;return g|0}function wu(a,b,c){a=a|0;b=+b;c=+c;h[a>>3]=b*.013888888888888888;h[a+8>>3]=c*.013888888888888888;return}function xu(a,b,c,d){a=a|0;b=b|0;c=+c;d=+d;var e=0,f=0;e=0;while(1){if((e|0)>=(b|0))break;h[a>>3]=+h[a>>3]*c;f=a+8|0;h[f>>3]=+h[f>>3]*d;a=a+16|0;e=e+1|0}return}function yu(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0,t=0.0,u=0.0,v=0;r=l;l=l+128|0;o=r+112|0;n=r+96|0;m=r+80|0;f=r+64|0;p=r+48|0;q=r+32|0;j=r+16|0;k=r;c[n>>2]=c[a>>2];c[n+4>>2]=c[a+4>>2];c[n+8>>2]=c[a+8>>2];c[n+12>>2]=c[a+12>>2];c[o>>2]=c[b>>2];c[o+4>>2]=c[b+4>>2];c[o+8>>2]=c[b+8>>2];c[o+12>>2]=c[b+12>>2];ws(p,n,o);g=b+16|0;c[n>>2]=c[a>>2];c[n+4>>2]=c[a+4>>2];c[n+8>>2]=c[a+8>>2];c[n+12>>2]=c[a+12>>2];c[o>>2]=c[g>>2];c[o+4>>2]=c[g+4>>2];c[o+8>>2]=c[g+8>>2];c[o+12>>2]=c[g+12>>2];ws(q,n,o);c[n>>2]=c[d>>2];c[n+4>>2]=c[d+4>>2];c[n+8>>2]=c[d+8>>2];c[n+12>>2]=c[d+12>>2];c[o>>2]=c[e>>2];c[o+4>>2]=c[e+4>>2];c[o+8>>2]=c[e+8>>2];c[o+12>>2]=c[e+12>>2];ws(j,n,o);i=e+16|0;c[n>>2]=c[d>>2];c[n+4>>2]=c[d+4>>2];c[n+8>>2]=c[d+8>>2];c[n+12>>2]=c[d+12>>2];c[o>>2]=c[i>>2];c[o+4>>2]=c[i+4>>2];c[o+8>>2]=c[i+8>>2];c[o+12>>2]=c[i+12>>2];ws(k,n,o);c[f>>2]=c[p>>2];c[f+4>>2]=c[p+4>>2];c[f+8>>2]=c[p+8>>2];c[f+12>>2]=c[p+12>>2];c[m>>2]=c[q>>2];c[m+4>>2]=c[q+4>>2];c[m+8>>2]=c[q+8>>2];c[m+12>>2]=c[q+12>>2];c[n>>2]=c[j>>2];c[n+4>>2]=c[j+4>>2];c[n+8>>2]=c[j+8>>2];c[n+12>>2]=c[j+12>>2];c[o>>2]=c[k>>2];c[o+4>>2]=c[k+4>>2];c[o+8>>2]=c[k+8>>2];c[o+12>>2]=c[k+12>>2];do if(zu(f,m,n,o)|0){f=c[b+40>>2]|0;if(f&1|0?c[e+40>>2]&1|0:0){f=1;break}if(f&2|0?c[e+40>>2]&2|0:0){s=+h[g>>3]-+h[b>>3]+ +h[i>>3]-+h[e>>3];t=+h[a>>3]-+h[d>>3];u=+h[a+8>>3]-+h[d+8>>3];f=!(u*u+t*t>s*s*.25);break}f=c[46915]|0;if(!f){c[46915]=KK(c[46914]<<4)|0;i=KK(c[46914]<<4)|0;c[46916]=i;f=c[46915]|0}else i=c[46916]|0;v=c[b+36>>2]|0;b=b+32|0;g=c[b>>2]|0;c[o>>2]=c[a>>2];c[o+4>>2]=c[a+4>>2];c[o+8>>2]=c[a+8>>2];c[o+12>>2]=c[a+12>>2];Au(v,g,o,f);a=c[e+36>>2]|0;g=e+32|0;e=c[g>>2]|0;c[o>>2]=c[d>>2];c[o+4>>2]=c[d+4>>2];c[o+8>>2]=c[d+8>>2];c[o+12>>2]=c[d+12>>2];Au(a,e,o,i);if(!(Bu(f,i,c[b>>2]|0,c[g>>2]|0)|0)){f=c[46915]|0;c[m>>2]=c[f>>2];c[m+4>>2]=c[f+4>>2];c[m+8>>2]=c[f+8>>2];c[m+12>>2]=c[f+12>>2];c[n>>2]=c[j>>2];c[n+4>>2]=c[j+4>>2];c[n+8>>2]=c[j+8>>2];c[n+12>>2]=c[j+12>>2];c[o>>2]=c[k>>2];c[o+4>>2]=c[k+4>>2];c[o+8>>2]=c[k+8>>2];c[o+12>>2]=c[k+12>>2];if(Cu(m,n,o)|0?(k=c[46916]|0,v=c[g>>2]|0,c[o>>2]=c[f>>2],c[o+4>>2]=c[f+4>>2],c[o+8>>2]=c[f+8>>2],c[o+12>>2]=c[f+12>>2],Du(k,v,o)|0):0){f=1;break}f=c[46916]|0;c[m>>2]=c[f>>2];c[m+4>>2]=c[f+4>>2];c[m+8>>2]=c[f+8>>2];c[m+12>>2]=c[f+12>>2];c[n>>2]=c[p>>2];c[n+4>>2]=c[p+4>>2];c[n+8>>2]=c[p+8>>2];c[n+12>>2]=c[p+12>>2];c[o>>2]=c[q>>2];c[o+4>>2]=c[q+4>>2];c[o+8>>2]=c[q+8>>2];c[o+12>>2]=c[q+12>>2];if(!(Cu(m,n,o)|0))f=0;else{q=c[46915]|0;v=c[b>>2]|0;c[o>>2]=c[f>>2];c[o+4>>2]=c[f+4>>2];c[o+8>>2]=c[f+8>>2];c[o+12>>2]=c[f+12>>2];f=(Du(q,v,o)|0)!=0}}else f=1}else f=0;while(0);l=r;return f&1|0}function zu(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;if((+h[a>>3]<=+h[d>>3]?+h[c>>3]<=+h[b>>3]:0)?+h[a+8>>3]<=+h[d+8>>3]:0)a=+h[c+8>>3]<=+h[b+8>>3]&1;else a=0;return a|0}function Au(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0;f=c+8|0;e=0;while(1){if((e|0)>=(b|0))break;h[d>>3]=+h[c>>3]+ +h[a>>3];h[d+8>>3]=+h[f>>3]+ +h[a+8>>3];e=e+1|0;a=a+16|0;d=d+16|0}return}function Bu(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0;A=l;l=l+112|0;s=A+96|0;r=A+80|0;q=A+64|0;p=A+48|0;u=A+32|0;v=A+16|0;w=A;x=d+-1|0;y=e+-1|0;z=d<<1;t=e<<1;f=0;g=0;h=0;i=0;while(1){B=a+(f<<4)|0;C=a+(((x+f|0)%(d|0)|0)<<4)|0;c[r>>2]=c[B>>2];c[r+4>>2]=c[B+4>>2];c[r+8>>2]=c[B+8>>2];c[r+12>>2]=c[B+12>>2];c[s>>2]=c[C>>2];c[s+4>>2]=c[C+4>>2];c[s+8>>2]=c[C+8>>2];c[s+12>>2]=c[C+12>>2];vs(u,r,s);o=b+(g<<4)|0;n=b+(((y+g|0)%(e|0)|0)<<4)|0;c[r>>2]=c[o>>2];c[r+4>>2]=c[o+4>>2];c[r+8>>2]=c[o+8>>2];c[r+12>>2]=c[o+12>>2];c[s>>2]=c[n>>2];c[s+4>>2]=c[n+4>>2];c[s+8>>2]=c[n+8>>2];c[s+12>>2]=c[n+12>>2];vs(v,r,s);c[q>>2]=c[46540];c[q+4>>2]=c[46541];c[q+8>>2]=c[46542];c[q+12>>2]=c[46543];c[r>>2]=c[u>>2];c[r+4>>2]=c[u+4>>2];c[r+8>>2]=c[u+8>>2];c[r+12>>2]=c[u+12>>2];c[s>>2]=c[v>>2];c[s+4>>2]=c[v+4>>2];c[s+8>>2]=c[v+8>>2];c[s+12>>2]=c[v+12>>2];j=+xs(q,r,s);c[q>>2]=c[C>>2];c[q+4>>2]=c[C+4>>2];c[q+8>>2]=c[C+8>>2];c[q+12>>2]=c[C+12>>2];c[r>>2]=c[B>>2];c[r+4>>2]=c[B+4>>2];c[r+8>>2]=c[B+8>>2];c[r+12>>2]=c[B+12>>2];c[s>>2]=c[o>>2];c[s+4>>2]=c[o+4>>2];c[s+8>>2]=c[o+8>>2];c[s+12>>2]=c[o+12>>2];k=ys(q,r,s)|0;c[q>>2]=c[n>>2];c[q+4>>2]=c[n+4>>2];c[q+8>>2]=c[n+8>>2];c[q+12>>2]=c[n+12>>2];c[r>>2]=c[o>>2];c[r+4>>2]=c[o+4>>2];c[r+8>>2]=c[o+8>>2];c[r+12>>2]=c[o+12>>2];c[s>>2]=c[B>>2];c[s+4>>2]=c[B+4>>2];c[s+8>>2]=c[B+8>>2];c[s+12>>2]=c[B+12>>2];m=ys(q,r,s)|0;c[p>>2]=c[C>>2];c[p+4>>2]=c[C+4>>2];c[p+8>>2]=c[C+8>>2];c[p+12>>2]=c[C+12>>2];c[q>>2]=c[B>>2];c[q+4>>2]=c[B+4>>2];c[q+8>>2]=c[B+8>>2];c[q+12>>2]=c[B+12>>2];c[r>>2]=c[n>>2];c[r+4>>2]=c[n+4>>2];c[r+8>>2]=c[n+8>>2];c[r+12>>2]=c[n+12>>2];c[s>>2]=c[o>>2];c[s+4>>2]=c[o+4>>2];c[s+8>>2]=c[o+8>>2];c[s+12>>2]=c[o+12>>2];if(zs(p,q,r,s,w)|0){f=1;break}n=(k|0)==0;o=(m|0)==0;do if(j!=0.0|(m|k|0)!=0)if(!(j>=0.0))if(o){f=(f+1|0)%(d|0)|0;h=h+1|0;break}else{g=(g+1|0)%(e|0)|0;i=i+1|0;break}else if(n){g=(g+1|0)%(e|0)|0;i=i+1|0;break}else{f=(f+1|0)%(d|0)|0;h=h+1|0;break}else{f=(f+1|0)%(d|0)|0;h=h+1|0}while(0);if(!((i|0)<(t|0)&((h|0)<(z|0)&((i|0)<(e|0)|(h|0)<(d|0))))){f=0;break}}l=A;return f|0}function Cu(a,b,c){a=a|0;b=b|0;c=c|0;var d=0.0,e=0.0;e=+h[a>>3];if((e<=+h[c>>3]?e>=+h[b>>3]:0)?(d=+h[a+8>>3],d<=+h[c+8>>3]):0)a=d>=+h[b+8>>3]&1;else a=0;return a|0}function Du(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0.0,k=0.0,l=0;e=c[46917]|0;if(!e){e=KK(c[46914]<<4)|0;c[46917]=e}i=d+8|0;g=0;while(1){if((g|0)>=(b|0))break;h[e+(g<<4)>>3]=+h[a+(g<<4)>>3]-+h[d>>3];h[e+(g<<4)+8>>3]=+h[a+(g<<4)+8>>3]-+h[i>>3];g=g+1|0}d=b+-1|0;f=0.0;a=0;a:while(1){if((a|0)>=(b|0)){l=19;break}g=(d+a|0)%(b|0)|0;j=+h[e+(a<<4)+8>>3];i=j==0.0;if(i?+h[e+(g<<4)+8>>3]==0.0:0){if(+h[e+(g<<4)>>3]*+h[e+(a<<4)>>3]<0.0){e=1;break}}else l=11;do if((l|0)==11){l=0;k=+h[e+(g<<4)+8>>3];if(!(j>=0.0)|!(k<=0.0)?!(k>=0.0)|!(j<=0.0):0)break;j=(k*+h[e+(a<<4)>>3]-j*+h[e+(g<<4)>>3])/(k-j);if(j==0.0){e=1;break a}if(j>0.0)if(k==0.0|i){f=f+.5;break}else{f=f+1.0;break}}while(0);a=a+1|0}if((l|0)==19)e=((~~f|0)%2|0|0)==1&1;return e|0}function Eu(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;j=KK(b<<2)|0;i=O(b,b)|0;d=KK(i<<2)|0;c[j>>2]=d;$t(i,0.0,d);d=1;while(1){if((d|0)>=(b|0)){d=0;g=0;i=b;break}c[j+(d<<2)>>2]=(c[j>>2]|0)+((O(d,b)|0)<<2);d=d+1|0}while(1){if((g|0)>=(b|0))break;h=j+(g<<2)|0;e=g;f=d;while(1){if((e|0)==(b|0))break;k=c[a+(f<<2)>>2]|0;c[(c[h>>2]|0)+(e<<2)>>2]=k;c[(c[j+(e<<2)>>2]|0)+(g<<2)>>2]=k;e=e+1|0;f=f+1|0}d=d+i|0;g=g+1|0;i=i+-1|0}return j|0}function Fu(a,b,d,e,f,h,i,j){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;h=h|0;i=i|0;j=+j;var k=0.0,l=0.0,m=0.0,n=0,o=0,p=0,q=0.0,r=0,s=0.0,t=0,u=0,v=0,w=0,x=0,y=0.0,z=0,A=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0.0;M=c[d+(f<<2)>>2]|0;C=c[a+4>>2]|0;D=c[a>>2]|0;E=c[a+48>>2]|0;F=c[a+52>>2]|0;G=c[a+56>>2]|0;a:do if((h|0)<1)d=0;else{Hu(M,C,E,F,G,j);H=c[a+28>>2]|0;I=c[a+32>>2]|0;J=c[a+36>>2]|0;K=c[a+12>>2]|0;L=c[a+16>>2]|0;f=0;e=0;d=-1;while(1){if((f|0)>=(C|0))break;if((f|0)>=(e|0)){d=d+1|0;if((d|0)==(G|0)){e=C;d=G}else e=c[F+(d<<2)>>2]|0}c[L+(c[E+(f<<2)>>2]<<2)>>2]=d;f=f+1|0}z=c[a+40>>2]|0;A=I+(C+-1<<2)|0;d=0;e=0;while(1){if(e<<24>>24==0&(d|0)<(h|0)){k=-1.0e9;f=0;e=1}else break a;b:while(1){x=f;while(1){if((x|0)>=(C|0))break b;f=c[E+(x<<2)>>2]|0;y=+g[M+(f<<2)>>2];l=y;w=x;m=0.0;while(1){g[z+(f<<2)>>2]=m;f=w+1|0;if((f|0)>=(C|0)){v=0;break}l=(c[L+(f<<2)>>2]|0)>(c[L+(w<<2)>>2]|0)?l+j:l;a=c[E+(f<<2)>>2]|0;m=+g[M+(a<<2)>>2];if(+B(+(m-l))>1.0e-09){v=1;break}w=f;m=m-y;f=a}n=x;while(1){if((n|0)>(w|0)){a=0;o=x;break}o=c[E+(n<<2)>>2]|0;p=c[D+(o<<2)>>2]|0;a=0;l=-+g[b+(o<<2)>>2];while(1){if((a|0)>=(C|0))break;if((a|0)!=(o|0))l=+g[M+(a<<2)>>2]*+g[p+(a<<2)>>2]+l;a=a+1|0}g[H+(o<<2)>>2]=l/-+g[p+(o<<2)>>2]-+g[z+(o<<2)>>2];n=n+1|0}c:while(1){if((o|0)>(w|0)){m=0.0;q=0.0;r=0;break}n=c[L+(c[E+(o<<2)>>2]<<2)>>2]|0;if((n|0)==(G|0))r=f;else{r=c[F+(n<<2)>>2]|0;r=(f|0)<(r|0)?f:r}p=o;while(1){if((p|0)>=(r|0)){p=o;break}n=c[E+(p<<2)>>2]|0;if(+g[H+(n<<2)>>2]<y){c[K+(a<<2)>>2]=n;a=a+1|0}p=p+1|0}while(1){if((p|0)>=(r|0))break;n=c[E+(p<<2)>>2]|0;if(+g[H+(n<<2)>>2]==y){c[K+(a<<2)>>2]=n;a=a+1|0}p=p+1|0}while(1){if((o|0)>=(r|0)){o=r;continue c}n=c[E+(o<<2)>>2]|0;if(+g[H+(n<<2)>>2]>y){c[K+(a<<2)>>2]=n;a=a+1|0}o=o+1|0}}while(1){if((r|0)>=(a|0))break;o=c[K+(r<<2)>>2]|0;p=c[D+(o<<2)>>2]|0;l=0.0;n=0;while(1){if((n|0)==(r|0))break;l=l-+g[p+(c[K+(n<<2)>>2]<<2)>>2];n=n+1|0}N=l*2.0;u=p+(o<<2)|0;s=+g[u>>2];l=N+m;s=(N*y+m*q-+g[H+(o<<2)>>2]*s)/(l-s);g[I+(r<<2)>>2]=s;m=l-+g[u>>2];q=s;r=r+1|0}p=(a|0)==(C|0);if(p)g[A>>2]=y;u=a+-1|0;m=0.0;q=0.0;o=u;while(1){if((o|0)<=-1)break;r=c[K+(o<<2)>>2]|0;t=c[D+(r<<2)>>2]|0;l=0.0;n=o;while(1){n=n+1|0;if((n|0)>=(a|0))break;l=l-+g[t+(c[K+(n<<2)>>2]<<2)>>2]}l=l*2.0;t=t+(r<<2)|0;N=+g[t>>2];s=l+m;N=(l*y+m*q-+g[H+(r<<2)>>2]*N)/(s-N);g[J+(o<<2)>>2]=N;m=s-+g[t>>2];q=N;o=o+-1|0}if(p){g[J>>2]=y;s=0.0;n=-1;p=0}else{s=0.0;n=-1;p=0}while(1){if((a|0)<=(p|0))break;q=+g[J+(p<<2)>>2];if((p|0)>0)l=+g[I+(p+-1<<2)>>2];else l=q;do if(q<l){o=l>y;if(q<y){l=o?y:l;m=l;break}if(o){m=q;l=q}else m=q}else m=q;while(0);N=+B(+(m-y))*+(a-p|0)+ +B(+(l-y))*+(p|0);t=N>s;s=t?N:s;n=t?p:n;p=p+1|0}if((n|0)>-1)break;if(v?(c[L+(c[E+(f<<2)>>2]<<2)>>2]|0)>(c[L+(c[E+(w<<2)>>2]<<2)>>2]|0):0){k=+g[M+(c[K+(u<<2)>>2]<<2)>>2]+j;x=f;continue}k=+g[M+(c[K+(u<<2)>>2]<<2)>>2];x=f}m=+g[J+(n<<2)>>2];if(!n)q=m;else q=+g[I+(n+-1<<2)>>2];if(v){t=c[E+(f<<2)>>2]|0;l=+g[M+(t<<2)>>2];l=((c[L+(t<<2)>>2]|0)>(c[L+(c[E+(w<<2)>>2]<<2)>>2]|0)?l-j:l)-+g[z+(c[K+(u<<2)>>2]<<2)>>2]}else l=1.0e9;l=m<l?m:l;k=q>k?q:k;do if(l<k){o=k>y;if(l<y){l=o?y:k;m=l;break}if(o)m=l;else{m=l;l=k}}else{m=l;l=k}while(0);o=0;while(1){if((o|0)>=(n|0))break;t=c[K+(o<<2)>>2]|0;g[M+(t<<2)>>2]=+g[z+(t<<2)>>2]+l;o=o+1|0}while(1){if((n|0)>=(a|0))break;t=c[K+(n<<2)>>2]|0;g[M+(t<<2)>>2]=+g[z+(t<<2)>>2]+m;n=n+1|0}if(v?(c[L+(c[E+(f<<2)>>2]<<2)>>2]|0)>(c[L+(c[E+(w<<2)>>2]<<2)>>2]|0):0)k=+g[M+(c[K+(u<<2)>>2]<<2)>>2]+j;else k=+g[M+(c[K+(u<<2)>>2]<<2)>>2];a=x;while(1){if((a|0)>(w|0))break;c[E+(a<<2)>>2]=c[K+(a-x<<2)>>2];a=a+1|0}if(e<<24>>24!=0?+B(+(l-y))<.01:0)e=+B(+(m-y))<.01;else e=0;e=e&1}St(C,M);Gu(M,E,F,G,i);d=d+1|0}}while(0);return d|0}function Gu(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;g=0;while(1){if((g|0)>=(e|0))break;c[f+(g<<2)>>2]=c[a+(c[b+((c[d+(g<<2)>>2]|0)+-1<<2)>>2]<<2)>>2];g=g+1|0}return}function Hu(a,b,d,e,f,h){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;h=+h;var i=0.0,j=0,k=0,l=0,m=0;i=-1.0e9;k=0;j=-1;m=0;while(1){if((m|0)>=(b|0))break;if((m|0)<(k|0))l=k;else{j=j+1|0;if((j|0)==(f|0))k=b;else k=c[e+(j<<2)>>2]|0;if((m|0)>0)i=+g[a+(c[d+(m+-1<<2)>>2]<<2)>>2]+h;else i=-1.0e9;gt(a,d,m,k+-1|0);l=k}k=a+(c[d+(m<<2)>>2]<<2)|0;if(+g[k>>2]<i)g[k>>2]=i;k=l;m=m+1|0}return}function Iu(a){a=a|0;U1(c[c[a>>2]>>2]|0);U1(c[a>>2]|0);U1(c[a+8>>2]|0);U1(c[a+28>>2]|0);U1(c[a+32>>2]|0);U1(c[a+36>>2]|0);U1(c[a+40>>2]|0);U1(c[a+12>>2]|0);U1(c[a+16>>2]|0);U1(c[a+20>>2]|0);U1(c[a+24>>2]|0);U1(a);return}function Ju(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0;k=KK(60)|0;c[k>>2]=0;l=k+4|0;c[l>>2]=b;c[k+48>>2]=d;c[k+52>>2]=e;c[k+56>>2]=f;c[k>>2]=Eu(a,b)|0;i=b<<2;j=KK(i)|0;c[k+8>>2]=j;h=0;b=0;a=-1;while(1){if((h|0)>=(c[l>>2]|0))break;g=a+1|0;if((h|0)>=(b|0)){b=c[((g|0)==(f|0)?l:e+(g<<2)|0)>>2]|0;a=g}c[j+(c[d+(h<<2)>>2]<<2)>>2]=a;h=h+1|0}c[k+28>>2]=KK(i)|0;c[k+32>>2]=KK(i)|0;c[k+36>>2]=KK(i)|0;c[k+40>>2]=KK(i)|0;c[k+12>>2]=KK(i)|0;c[k+16>>2]=KK(i)|0;c[k+20>>2]=KK(i)|0;c[k+24>>2]=KK(i)|0;return k|0}function Ku(){fu(187676,24);c[46922]=0;return}function Lu(){return hu(187676)|0}function Mu(a,b){a=a|0;b=b|0;var c=0.0,d=0.0;c=+h[a>>3]-+h[b>>3];d=+h[a+8>>3]-+h[b+8>>3];return +(+C(+(d*d+c*c)))}function Nu(a){a=a|0;var b=0;b=c[46922]|0;c[a+16>>2]=b;c[46922]=b+1;return}function Ou(a){a=a|0;var b=0,d=0;d=a+20|0;b=(c[d>>2]|0)+-1|0;c[d>>2]=b;if(!b)iu(a,187676);return}function Pu(a){a=a|0;a=a+20|0;c[a>>2]=(c[a>>2]|0)+1;return}function Qu(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;var i=0.0,j=0,k=0,m=0,n=0.0,o=0,p=0.0,q=0,r=0.0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;x=l;l=l+16|0;o=x;c[o>>2]=e;q=b<<2;v=KK(q)|0;m=b<<3;w=KK(m)|0;u=$s(a,b)|0;a=0;while(1){if((a|0)>=(b|0))break;k=u+(a<<2)|0;j=0;while(1){if((j|0)==(b|0))break;s=(c[k>>2]|0)+(j<<2)|0;c[s>>2]=c[s>>2]<<8;j=j+1|0}a=a+1|0}if(!d)qa(93952,93960,283,93974);s=KK(m)|0;a=0;while(1){if((a|0)>=(b|0))break;h[s+(a<<3)>>3]=+h[d+(a<<3)>>3];a=a+1|0}Ru(s,b);i=0.0;n=0.0;m=1;while(1){if((m|0)>=(b|0))break;j=u+(m<<2)|0;k=d+(m<<3)|0;a=0;while(1){if((a|0)==(m|0))break;y=c[(c[j>>2]|0)+(a<<2)>>2]|0;r=+h[k>>3]-+h[d+(a<<3)>>3];i=r*r*(1.0/+(O(y,y)|0))+i;n=+B(+r)*(1.0/+(y|0))+n;a=a+1|0}m=m+1|0}r=n/i;a=0;while(1){if((a|0)>=(b|0))break;y=d+(a<<3)|0;h[y>>3]=+h[y>>3]*r;a=a+1|0}Su(b,o,f,d,u);k=KK(O(q,b)|0)|0;j=0;while(1){if((j|0)>=(b|0)){k=1;break}m=k+((O(j,b)|0)<<2)|0;c[v+(j<<2)>>2]=m;o=u+(j<<2)|0;i=0.0;a=0;while(1){if((a|0)==(b|0))break;if((a|0)!=(j|0)){p=+(c[(c[o>>2]|0)+(a<<2)>>2]|0);p=-1.0/(p*p);g[m+(a<<2)>>2]=p;i=i-p}a=a+1|0}g[m+(j<<2)>>2]=i;j=j+1|0}while(1){if((k|0)>=(b|0)){j=0;break}i=+h[d+(k<<3)>>3];j=u+(k<<2)|0;a=0;while(1){if((a|0)==(k|0))break;y=(c[j>>2]|0)+(a<<2)|0;n=+(c[y>>2]|0);p=i-+h[d+(a<<3)>>3];p=n*n-p*p;q=~~+C(+p);q=p>0.0?q:0;c[(c[u+(a<<2)>>2]|0)+(k<<2)>>2]=q;c[y>>2]=q;a=a+1|0}k=k+1|0}while(1){if((j|0)>=(b|0)){q=0;a=0;break}n=+h[e+(j<<3)>>3];m=w+(j<<3)|0;h[m>>3]=0.0;o=u+(j<<2)|0;k=v+(j<<2)|0;a=0;i=0.0;while(1){if((a|0)==(b|0))break;if((a|0)!=(j|0)){p=+g[(c[k>>2]|0)+(a<<2)>>2]*+(c[(c[o>>2]|0)+(a<<2)>>2]|0);i=i+(n>=+h[e+(a<<3)>>3]?-p:p);h[m>>3]=i}a=a+1|0}j=j+1|0}while(1){if(!(a<<24>>24==0&(q|0)<200)){t=42;break}if((zr(v,e,w,b,f,b,1)|0)<0){a=1;break}else{a=1;o=0}while(1){if((o|0)>=(b|0))break;p=+h[e+(o<<3)>>3];k=u+(o<<2)|0;m=v+(o<<2)|0;i=0.0;j=0;while(1){if((j|0)==(b|0))break;do if((j|0)!=(o|0)){n=+g[(c[m>>2]|0)+(j<<2)>>2]*+(c[(c[k>>2]|0)+(j<<2)>>2]|0);if(!(p>=+h[e+(j<<3)>>3])){i=i+n;break}else{i=i+-n;break}}while(0);j=j+1|0}j=w+(o<<3)|0;p=+h[j>>3];if(i!=p?+B(+(1.0-i/p))>1.0e-05:0){h[j>>3]=i;a=0}o=o+1|0}q=q+1|0}a:do if((t|0)==42){i=1.0/r;a=0;while(1){if((a|0)>=(b|0)){a=0;break a}y=d+(a<<3)|0;h[y>>3]=+h[y>>3]*i;y=e+(a<<3)|0;h[y>>3]=+h[y>>3]*i;a=a+1|0}}while(0);U1(c[u>>2]|0);U1(u);U1(c[v>>2]|0);U1(v);U1(s);U1(w);l=x;return a|0}function Ru(a,b){a=a|0;b=b|0;var c=0,d=0.0,e=0.0,f=0;c=0;d=0.0;while(1){if((c|0)>=(b|0))break;e=+h[a+(c<<3)>>3]+d;c=c+1|0;d=e}d=d/+(b|0);c=0;while(1){if((c|0)>=(b|0))break;f=a+(c<<3)|0;h[f>>3]=+h[f>>3]-d;c=c+1|0}f=b+-1|0;Bt(a,0,f,1.0/+At(a,0,f),a);return}function Su(a,b,d,e,f){a=a|0;b=b|0;d=+d;e=e|0;f=f|0;var g=0,i=0,j=0,k=0,l=0,m=0;k=Tu(f,a)|0;l=KK(8)|0;if(!e){f=0;j=0}else{g=KK(a<<3)|0;f=0;while(1){if((f|0)>=(a|0))break;h[g+(f<<3)>>3]=+h[e+(f<<3)>>3];f=f+1|0}Ru(g,a);f=g;j=g}Uu(k,a,b,l,f,d);e=0;while(1){if((e|0)==1)break;g=l+(e<<3)|0;i=b+(e<<2)|0;f=0;while(1){if((f|0)>=(a|0))break;d=+C(+(+B(+(+h[g>>3]))));m=(c[i>>2]|0)+(f<<3)|0;h[m>>3]=+h[m>>3]*d;f=f+1|0}e=e+1|0}U1(c[k>>2]|0);U1(k);U1(l);U1(j);return}function Tu(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,h=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0.0;o=l;l=l+16|0;f=o;n=b<<2;e=KK(O(n,b)|0)|0;n=KK(n)|0;d=0;while(1){if((d|0)>=(b|0))break;c[n+(d<<2)>>2]=e+((O(d,b)|0)<<2);d=d+1|0}m=Wu(a,b,f)|0;h=+g[f>>2];e=0;k=1;while(1){if((e|0)>=(b|0))break;f=a+(e<<2)|0;i=m+(e<<2)|0;j=n+(e<<2)|0;d=0;while(1){if((d|0)==(k|0))break;p=+(c[(c[f>>2]|0)+(d<<2)>>2]|0);p=+g[m+(d<<2)>>2]+ +g[i>>2]-p*p-h;g[(c[j>>2]|0)+(d<<2)>>2]=p;g[(c[n+(d<<2)>>2]|0)+(e<<2)>>2]=p;d=d+1|0}e=e+1|0;k=k+1|0}U1(m);l=o;return n|0}function Uu(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=+g;var i=0,j=0,k=0,l=0,m=0.0,n=0.0,o=0,p=0,q=0,r=0,s=0;r=b<<3;q=KK(r)|0;r=KK(r)|0;n=1.0-g;o=(b|0)<1?b:1;p=b+-1|0;k=(f|0)==0;j=0;a:while(1){if((j|0)>=(o|0))break;l=c[d+(j<<2)>>2]|0;do{i=0;while(1){if((i|0)==(b|0))break;h[l+(i<<3)>>3]=+((x5()|0)%100|0|0);i=i+1|0}if(k)i=0;else{zt(l,0,p,-+yt(f,0,p,l),f);i=0}while(1){if((i|0)==(j|0))break;s=d+(i<<2)|0;m=-+yt(c[s>>2]|0,0,p,l);zt(l,0,p,m,c[s>>2]|0);i=i+1|0}g=+At(l,0,p)}while(g<1.0e-10);Bt(l,0,p,1.0/g,l);do{Ct(r,0,p,l);Vu(a,b,b,l,q,f);Ct(l,0,p,q);i=0;while(1){if((i|0)==(j|0))break;s=d+(i<<2)|0;m=-+yt(c[s>>2]|0,0,p,l);zt(l,0,p,m,c[s>>2]|0);i=i+1|0}g=+At(l,0,p);if(g<1.0e-10)break a;Bt(l,0,p,1.0/g,l);m=+yt(l,0,p,r)}while(+B(+m)<n);h[e+(j<<3)>>3]=m*g;j=j+1|0}while(1){if((j|0)>=(o|0))break;k=c[d+(j<<2)>>2]|0;i=0;while(1){if((i|0)>=(b|0)){i=0;break}h[k+(i<<3)>>3]=+((x5()|0)%100|0|0);i=i+1|0}while(1){if((i|0)>=(j|0))break;s=d+(i<<2)|0;n=-+yt(c[s>>2]|0,0,p,k);zt(k,0,p,n,c[s>>2]|0);i=i+1|0}Bt(k,0,p,1.0/+At(k,0,p),k);h[e+(j<<3)>>3]=0.0;j=j+1|0}l=o+-1|0;j=0;while(1){if((j|0)>=(l|0))break;a=e+(j<<3)|0;f=j+1|0;g=+h[a>>3];i=j;k=f;while(1){if((k|0)>=(o|0))break;n=+h[e+(k<<3)>>3];s=g<n;g=s?n:g;i=s?k:i;k=k+1|0}if((i|0)==(j|0)){j=f;continue}b=d+(j<<2)|0;Ct(q,0,p,c[b>>2]|0);s=d+(i<<2)|0;Ct(c[b>>2]|0,0,p,c[s>>2]|0);Ct(c[s>>2]|0,0,p,q);h[e+(i<<3)>>3]=+h[a>>3];h[a>>3]=g;j=f}U1(q);U1(r);return}function Vu(a,b,d,e,f,i){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;i=i|0;var j=0,k=0.0,l=0,m=0;j=0;while(1){if((j|0)>=(b|0))break;m=a+(j<<2)|0;k=0.0;l=0;while(1){if((l|0)>=(d|0))break;k=+h[e+(l<<3)>>3]*+g[(c[m>>2]|0)+(l<<2)>>2]+k;l=l+1|0}h[f+(j<<3)>>3]=k;j=j+1|0}j=b+-1|0;if(i|0)zt(f,0,j,-+yt(f,0,j,i),i);return}function Wu(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0.0,h=0,i=0,j=0,k=0.0,l=0,m=0.0;j=KK(b<<2)|0;k=1.0/+(b|0);e=0.0;i=0;while(1){if((i|0)>=(b|0))break;l=a+(i<<2)|0;f=0.0;h=0;while(1){if((h|0)==(b|0))break;m=+(c[(c[l>>2]|0)+(h<<2)>>2]|0);m=m*m;f=m+f;h=h+1|0;e=m+e}g[j+(i<<2)>>2]=k*f;i=i+1|0}g[d>>2]=e/+(O(b,b)|0);return j|0}function Xu(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0.0,u=0;q=O(d,d)|0;r=KK(q<<3)|0;s=KK(d<<3)|0;e=0;while(1){if((e|0)>=(d|0))break;h[s+(e<<3)>>3]=+h[c+(e<<3)>>3];e=e+1|0}e=0;while(1){if((e|0)>=(q|0))break;h[r+(e<<3)>>3]=+h[a+(e<<3)>>3];e=e+1|0}p=d+-1|0;g=0;o=0;a:while(1){if((o|0)<(p|0)){f=0.0;e=o}else{l=21;break}while(1){if((e|0)>=(d|0))break;t=+B(+(+h[a+((O(e,d)|0)+o<<3)>>3]));m=t<f;n=m?g:e;f=m?f:t;e=e+1|0;g=n}if(f<1.0e-10){l=33;break}i=O(g,d)|0;n=O(o,d)|0;e=o;while(1){if((e|0)>=(d|0))break;l=a+(e+i<<3)|0;t=+h[l>>3];m=a+(e+n<<3)|0;h[l>>3]=+h[m>>3];h[m>>3]=t;e=e+1|0}m=c+(g<<3)|0;t=+h[m>>3];j=c+(o<<3)|0;h[m>>3]=+h[j>>3];h[j>>3]=t;m=o+1|0;k=a+(n+o<<3)|0;e=m;while(1){if((e|0)>=(d|0)){o=m;continue a}l=O(e,d)|0;f=+h[a+(l+o<<3)>>3]/+h[k>>3];i=c+(e<<3)|0;h[i>>3]=+h[i>>3]-+h[j>>3]*f;i=0;while(1){if((i|0)>=(d|0))break;u=a+(i+l<<3)|0;h[u>>3]=+h[u>>3]-+h[a+(i+n<<3)>>3]*f;i=i+1|0}e=e+1|0}}if((l|0)==21){f=+h[a+(q+-1<<3)>>3];if(+B(+f)<1.0e-10)l=33;else{h[b+(p<<3)>>3]=+h[c+(p<<3)>>3]/f;e=0;while(1){if((e|0)>=(p|0)){e=0;break}g=d-e|0;i=g+-2|0;f=+h[c+(i<<3)>>3];j=b+(i<<3)|0;h[j>>3]=f;k=O(i,d)|0;g=g+-1|0;while(1){if((g|0)>=(d|0))break;t=f-+h[b+(g<<3)>>3]*+h[a+(g+k<<3)>>3];h[j>>3]=t;g=g+1|0;f=t}h[j>>3]=f/+h[a+(k+i<<3)>>3];e=e+1|0}while(1){if((e|0)>=(d|0)){e=0;break}h[c+(e<<3)>>3]=+h[s+(e<<3)>>3];e=e+1|0}while(1){if((e|0)>=(q|0))break;h[a+(e<<3)>>3]=+h[r+(e<<3)>>3];e=e+1|0}U1(r);U1(s)}}if((l|0)==33){B6(93989)|0;U1(r);U1(s)}return}function Yu(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;s=l;l=l+16|0;r=s;ms();Ku();Hs();c[46918]=jb[d&3]()|0;Js();q=r+8|0;p=jb[d&3]()|0;e=0.0;f=0.0;a:while(1){n=(p|0)==0;o=p+8|0;while(1){if(!(Ds()|0)){Es(r);e=+h[r>>3];f=+h[q>>3]}if(!n){if(Ds()|0)break;g=+h[o>>3];if(g<f)break;if(g==f?+h[p>>3]<e:0)break}if(Ds()|0)break a;t=Fs()|0;b=Ss(t)|0;v=Rs(t)|0;i=Rs(v)|0;m=Ts(t)|0;u=Us(v)|0;j=c[t+20>>2]|0;Nu(j);qs(c[t+8>>2]|0,a[t+16>>0]|0,j);qs(c[v+8>>2]|0,a[v+16>>0]|0,j);Qs(t);Cs(v);Qs(v);v=+h[m+8>>3]>+h[u+8>>3];t=v&1;k=v?u:m;u=ns(k,v?m:u)|0;m=Ks(u,t)|0;Ns(b,m);qs(u,(t^1)&255,j);Ou(j);j=Ls(b,m)|0;if(j|0){Cs(b);As(b,j,+Mu(j,k))}b=Ls(m,i)|0;if(!b)continue;As(m,b,+Mu(b,k))}b=Os(p)|0;k=Rs(b)|0;i=ns(Us(b)|0,p)|0;m=Ks(i,0)|0;Ns(b,m);j=Ls(b,m)|0;if(j|0){Cs(b);As(b,j,+Mu(j,p))}i=Ks(i,1)|0;Ns(m,i);b=Ls(i,k)|0;if(b|0)As(i,b,+Mu(b,p));p=jb[d&3]()|0}b=c[46860]|0;while(1){b=Rs(b)|0;if((b|0)==(c[46861]|0))break;os(c[b+8>>2]|0)}l=s;return}function Zu(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;g=l;l=l+16|0;f=g+8|0;e=g;do if((tB(a)|0)!=1){_u(a);if(!b)b=$u(a)|0;d=av(a,b)|0;if(0){h=c[15712]|0;c[e>>2]=KB(b)|0;c[e+4>>2]=d;w4(h,94005,e)|0}if((d|0)<0){gA(1,94039,f)|0;break}else{bv(a);cv(a,b);dv(a,b);ev(a,d);break}}else{h=c[(c[(jC(a)|0)+16>>2]|0)+132>>2]|0;c[h>>2]=0;c[h+4>>2]=0;c[h+8>>2]=0;c[h+12>>2]=0}while(0);l=g;return b|0}function _u(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;d=tB(a)|0;d=O(d,d)|0;b=jC(a)|0;while(1){if(!b)break;e=b+16|0;f=c[(c[e>>2]|0)+112>>2]|0;g=f+24|0;c[g>>2]=d;c[g+4>>2]=0;h[f+48>>3]=10.0;f=(mv(a,b)|0)==0;e=c[(c[e>>2]|0)+112>>2]|0;c[e>>2]=f?d:0;c[e+4>>2]=f?0:0;b=kC(a,b)|0}return}function $u(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;e=(tB(a)|0)<3;b=jC(a)|0;a:do if(!e){while(1){if(!b)break;e=c[(c[b+16>>2]|0)+112>>2]|0;if((c[e>>2]|0)==0&(c[e+4>>2]|0)==0)lv(a,b,0);b=kC(a,b)|0}d=0;b=0;e=jC(a)|0;while(1){if(!e)break a;h=c[(c[e+16>>2]|0)+112>>2]|0;g=c[h>>2]|0;h=c[h+4>>2]|0;f=((d|0)<0)<<31>>31;f=h>>>0>f>>>0|(h|0)==(f|0)&g>>>0>d>>>0;d=f?g:d;b=f?e:b;e=kC(a,e)|0}}while(0);return b|0}function av(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;d=c[(c[b+16>>2]|0)+112>>2]|0;f=d+24|0;e=c[f>>2]|0;c[f>>2]=0;c[f+4>>2]=0;c[d+32>>2]=0;iv(a,b);d=jC(a)|0;e=P6(0,e|0,32)|0;f=z;b=0;while(1){if(!d)break;h=(c[(c[d+16>>2]|0)+112>>2]|0)+24|0;g=c[h>>2]|0;h=c[h+4>>2]|0;if((g|0)==(e|0)&(h|0)==(f|0)){b=-1;break}i=((b|0)<0)<<31>>31;b=h>>>0>i>>>0|(h|0)==(i|0)&g>>>0>b>>>0?g:b;d=kC(a,d)|0}return b|0}function bv(a){a=a|0;var b=0,d=0,e=0,f=0;d=jC(a)|0;while(1){if(!d)break;b=c[(c[d+16>>2]|0)+112>>2]|0;e=b+16|0;a:do if((c[e>>2]|0)==0&(c[e+4>>2]|0)==0)while(1){e=b+8|0;f=e;f=J6(c[f>>2]|0,c[f+4>>2]|0,1,0)|0;c[e>>2]=f;c[e+4>>2]=z;b=c[b+32>>2]|0;if(!b)break a;b=c[(c[b+16>>2]|0)+112>>2]|0}while(0);d=kC(a,d)|0}return}function cv(a,b){a=a|0;b=b|0;h[(c[(c[b+16>>2]|0)+112>>2]|0)+40>>3]=6.283185307179586;hv(a,b);return}function dv(a,b){a=a|0;b=b|0;h[(c[(c[b+16>>2]|0)+112>>2]|0)+48>>3]=0.0;gv(a,b);return}function ev(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0.0,k=0.0;i=l;l=l+16|0;f=i;g=fv(a,b)|0;if(0){e=c[15712]|0;T4(94095,e)|0;d=0;while(1){if((d|0)>(b|0))break;h[f>>3]=+h[g+(d<<3)>>3];w4(e,94114,f)|0;d=d+1|0}T4(153599,e)|0}d=jC(a)|0;while(1){if(!d)break;f=c[d+16>>2]|0;e=c[f+112>>2]|0;j=+h[g+(c[e+24>>2]<<3)>>3];e=e+48|0;k=+E(+(+h[e>>3]))*j;f=c[f+132>>2]|0;h[f>>3]=k;h[f+8>>3]=+F(+(+h[e>>3]))*j;d=kC(a,d)|0}U1(g);l=i;return}function fv(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,g=0,i=0.0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;k=n;m=JK((d<<3)+8|0)|0;b=aP(b,GA(c[b+60>>2]|0,0,101288,0)|0,0)|0;a:do if(!b){f=1.0;e=0.0;b=1}else{i=0.0;e=0.0;g=1;b:while(1){if((g|0)>(d|0)){f=i;b=g;break a}f=+s5(b,k);if(!(f>0.0)){f=i;b=g;break a}i=f>.02?f:.02;e=i+e;j=g+1|0;h[m+(g<<3)>>3]=e;b=c[k>>2]|0;while(1){g=a[b>>0]|0;if(!(g<<24>>24)){g=j;continue b}if(!(g<<24>>24==58|(S2(g<<24>>24)|0)!=0)){g=j;continue b}b=b+1|0}}}while(0);while(1){if((b|0)>(d|0))break;i=e+f;h[m+(b<<3)>>3]=i;b=b+1|0;e=i}l=n;return m|0}function gv(a,b){a=a|0;b=b|0;var d=0,e=0.0,f=0,g=0,i=0,j=0.0;d=c[(c[b+16>>2]|0)+112>>2]|0;if(!(c[d+32>>2]|0))e=0.0;else e=+h[d+48>>3]-+h[d+40>>3]*.5;g=aB(a,b)|0;while(1){if(!g)break;d=c[g>>2]&3;f=c[((d|0)==3?g:g+48|0)+40>>2]|0;if((f|0)==(b|0))f=c[((d|0)==2?g:g+-48|0)+40>>2]|0;d=c[(c[f+16>>2]|0)+112>>2]|0;if((c[d+32>>2]|0)==(b|0)?(i=d+48|0,!(+h[i>>3]!=10.0)):0){j=+h[d+40>>3];h[i>>3]=j*.5+e;e=j+e;d=d+16|0;if(!((c[d>>2]|0)==0&(c[d+4>>2]|0)==0))gv(a,f)}g=bB(a,g,b)|0}return}function hv(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0.0,i=0,j=0;e=c[(c[b+16>>2]|0)+112>>2]|0;f=e+8|0;g=+h[e+40>>3]/(+((c[f>>2]|0)>>>0)+4294967296.0*+((c[f+4>>2]|0)>>>0));f=aB(a,b)|0;while(1){if(!f)break;e=c[f>>2]&3;d=c[((e|0)==3?f:f+48|0)+40>>2]|0;if((d|0)==(b|0))d=c[((e|0)==2?f:f+-48|0)+40>>2]|0;e=c[(c[d+16>>2]|0)+112>>2]|0;if(((c[e+32>>2]|0)==(b|0)?(i=e+40|0,!(+h[i>>3]!=0.0)):0)?(j=e+8|0,h[i>>3]=g*(+((c[j>>2]|0)>>>0)+4294967296.0*+((c[j+4>>2]|0)>>>0)),e=e+16|0,!((c[e>>2]|0)==0&(c[e+4>>2]|0)==0)):0)hv(a,d);f=bB(a,f,b)|0}return}function iv(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0;p=l;l=l+16|0;n=p;o=GA(b,2,101541,0)|0;c[n+4>>2]=0;c[n>>2]=0;jv(n,d);j=(o|0)==0;a:while(1){k=kv(n)|0;if(!k)break;m=k+16|0;h=c[(c[(c[m>>2]|0)+112>>2]|0)+24>>2]|0;g=aB(b,k)|0;h=J6(0,h|0,0,1)|0;h=P6(h|0,z|0,32)|0;i=z;while(1){if(!g)continue a;if(!((!j?(a[(RA(g,o)|0)>>0]|0)==48:0)?!(s2(RA(g,o)|0,138394)|0):0)){d=c[g>>2]&3;e=c[((d|0)==3?g:g+48|0)+40>>2]|0;if((e|0)==(k|0))e=c[((d|0)==2?g:g+-48|0)+40>>2]|0;d=c[(c[e+16>>2]|0)+112>>2]|0;f=d+24|0;q=f;r=c[q+4>>2]|0;if(i>>>0<r>>>0|((i|0)==(r|0)?h>>>0<(c[q>>2]|0)>>>0:0)){r=f;c[r>>2]=h;c[r+4>>2]=i;c[d+32>>2]=k;r=(c[(c[m>>2]|0)+112>>2]|0)+16|0;q=r;q=J6(c[q>>2]|0,c[q+4>>2]|0,1,0)|0;c[r>>2]=q;c[r+4>>2]=z;jv(n,e)}}g=bB(b,g,k)|0}}l=p;return}function jv(a,b){a=a|0;b=b|0;var d=0,e=0;d=JK(8)|0;c[d>>2]=b;b=a+4|0;e=c[b>>2]|0;c[((e|0)==0?a:e+4|0)>>2]=d;c[b>>2]=d;return}function kv(a){a=a|0;var b=0,d=0;d=c[a>>2]|0;if(d){b=c[d>>2]|0;c[a>>2]=c[d+4>>2];U1(d);if(!(c[a>>2]|0))c[a+4>>2]=0}else b=0;return b|0}function lv(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;h=c[c[(c[b+16>>2]|0)+112>>2]>>2]|0;g=aB(a,b)|0;h=J6(0,h|0,0,1)|0;h=P6(h|0,z|0,32)|0;i=z;while(1){if(!g)break;f=c[g>>2]&3;e=c[((f|0)==3?g:g+48|0)+40>>2]|0;if((e|0)==(b|0))e=c[((f|0)==2?g:g+-48|0)+40>>2]|0;if((e|0)!=(d|0)?(j=c[(c[e+16>>2]|0)+112>>2]|0,f=j,k=c[f+4>>2]|0,i>>>0<k>>>0|((i|0)==(k|0)?h>>>0<(c[f>>2]|0)>>>0:0)):0){k=j;c[k>>2]=h;c[k+4>>2]=i;lv(a,e,b)}g=bB(a,g,b)|0}return}function mv(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;f=0;g=aB(a,b)|0;while(1){if(!g){d=1;break}e=c[g>>2]&3;d=c[((e|0)==3?g:g+48|0)+40>>2]|0;if((d|0)==(b|0))d=c[((e|0)==2?g:g+-48|0)+40>>2]|0;if((d|0)!=(b|0)){if(f)if((f|0)==(d|0))d=f;else{d=0;break}}else d=f;f=d;g=bB(a,g,b)|0}return d|0}function nv(a){a=a|0;var b=0,d=0,e=0,f=0.0,g=0;b=l;l=l+32|0;e=b;d=GA(a,1,94122,0)|0;g=GA(a,0,94122,0)|0;a=ov(a,g,d,GA(a,0,94127,0)|0)|0;d=a+16|0;f=+C(+(+h[a>>3]+.1));zv(e,0.0,0.0,f,f);c[d>>2]=c[e>>2];c[d+4>>2]=c[e+4>>2];c[d+8>>2]=c[e+8>>2];c[d+12>>2]=c[e+12>>2];c[d+16>>2]=c[e+16>>2];c[d+20>>2]=c[e+20>>2];c[d+24>>2]=c[e+24>>2];c[d+28>>2]=c[e+28>>2];pv(a);qv(a);rv(a);l=b;return}function ov(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0.0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;o=JK(72)|0;c[o+60>>2]=0;c[o+56>>2]=a;k=a+16|0;f=0.0;l=0;i=1;j=0;m=0;while(1){g=c[k>>2]|0;if((i|0)>(c[g+180>>2]|0))break;g=ov(c[(c[g+184>>2]|0)+(i<<2)>>2]|0,b,d,e)|0;f=+h[g>>3]+f;if(j|0)c[j+52>>2]=g;l=l+1|0;i=i+1|0;j=g;m=(m|0)==0?g:m}n=jC(a)|0;g=m;while(1){if(!n)break;i=n+16|0;if(!(c[c[(c[i>>2]|0)+112>>2]>>2]|0)){k=uv(n,d)|0;f=+h[k>>3]+f;if(j|0)c[j+52>>2]=k;c[c[(c[i>>2]|0)+112>>2]>>2]=a;i=l+1|0;j=k;g=(g|0)==0?k:g}else i=l;n=kC(a,n)|0;l=i}c[o+64>>2]=l;if(!l)f=+wv(a,b);else{h[o+8>>3]=f;f=+vv(o,e)}h[o>>3]=f;c[o+48>>2]=g;return o|0}function pv(a){a=a|0;var b=0,d=0,e=0,f=0,g=0.0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0.0,r=0.0,s=0.0,t=0.0,u=0.0;p=l;l=l+176|0;f=p+144|0;n=p+64|0;k=p+32|0;i=p;o=c[a+64>>2]|0;a:do if(o|0){m=JK(o<<2)|0;b=a+48|0;e=0;d=b;while(1){d=c[d>>2]|0;if((e|0)>=(o|0))break;c[m+(e<<2)>>2]=d;e=e+1|0;d=d+52|0}o4(m,o,4,80);j=JK(o<<3)|0;d=0;while(1){if((d|0)>=(o|0))break;h[j+(d<<3)>>3]=+h[c[m+(d<<2)>>2]>>3];d=d+1|0}g=+h[a+8>>3];if(+h[a>>3]==g){i=a+16|0;c[f>>2]=c[i>>2];c[f+4>>2]=c[i+4>>2];c[f+8>>2]=c[i+8>>2];c[f+12>>2]=c[i+12>>2];c[f+16>>2]=c[i+16>>2];c[f+20>>2]=c[i+20>>2];c[f+24>>2]=c[i+24>>2];c[f+28>>2]=c[i+28>>2];i=xv(o,j,f)|0}else{q=+h[a+40>>3];r=+h[a+32>>3];h[i>>3]=+h[a+16>>3];h[i+8>>3]=+h[a+24>>3];s=q-r;g=(r+q-+C(+(g*4.0+s*s)))*.5;h[i+16>>3]=r-g;h[i+24>>3]=q-g;c[f>>2]=c[i>>2];c[f+4>>2]=c[i+4>>2];c[f+8>>2]=c[i+8>>2];c[f+12>>2]=c[i+12>>2];c[f+16>>2]=c[i+16>>2];c[f+20>>2]=c[i+20>>2];c[f+24>>2]=c[i+24>>2];c[f+28>>2]=c[i+28>>2];i=xv(o,j,f)|0}if(0){f=c[15712]|0;q=+h[a+24>>3];r=+h[a+32>>3];s=+h[a+40>>3];h[k>>3]=+h[a+16>>3];h[k+8>>3]=q;h[k+16>>3]=r;h[k+24>>3]=s;w4(f,94174,k)|0}e=c[15712]|0;d=0;while(1){if((d|0)>=(o|0))break;k=(c[m+(d<<2)>>2]|0)+16|0;f=i+(d<<5)|0;c[k>>2]=c[f>>2];c[k+4>>2]=c[f+4>>2];c[k+8>>2]=c[f+8>>2];c[k+12>>2]=c[f+12>>2];c[k+16>>2]=c[f+16>>2];c[k+20>>2]=c[f+20>>2];c[k+24>>2]=c[f+24>>2];c[k+28>>2]=c[f+28>>2];if(0){g=+h[f>>3];r=+h[i+(d<<5)+16>>3];u=r*.5;q=+h[i+(d<<5)+8>>3];s=+h[i+(d<<5)+24>>3];t=s*.5;h[n>>3]=+h[j+(d<<3)>>3];h[n+8>>3]=g-u;h[n+16>>3]=q-t;h[n+24>>3]=u+g;h[n+32>>3]=t+q;h[n+40>>3]=s*r;h[n+48>>3]=g;h[n+56>>3]=q;h[n+64>>3]=r;h[n+72>>3]=s;w4(e,94191,n)|0}d=d+1|0}U1(m);U1(j);U1(i);d=0;while(1){b=c[b>>2]|0;if((d|0)>=(o|0))break a;if(!(c[b+60>>2]|0))pv(b);b=b+52|0;d=d+1|0}}while(0);l=p;return}function qv(a){a=a|0;var b=0,d=0,e=0,f=0,g=0.0,i=0.0,j=0,k=0.0,m=0.0;f=l;l=l+48|0;e=f;if(c[a+60>>2]|0){k=+h[a+24>>3];i=+h[a+32>>3];g=+h[a+40>>3];b=c[a+56>>2]|0;d=b+16|0;j=c[d>>2]|0;h[j+16>>3]=+h[a+16>>3];h[j+24>>3]=k;a=c[d>>2]|0;h[a+32>>3]=i*.013888888888888888;h[a+40>>3]=g*.013888888888888888;qQ(b,c[(c[(xC(b)|0)+16>>2]|0)+116>>2]&1);sv(b);if(0){j=c[15712]|0;a=KB(b)|0;d=c[d>>2]|0;m=+h[d+16>>3];g=+h[d+24>>3];i=+h[d+80>>3];k=+h[d+96>>3]+ +h[d+88>>3];c[e>>2]=a;h[e+8>>3]=m;h[e+16>>3]=g;h[e+24>>3]=i;h[e+32>>3]=k;w4(j,94133,e)|0}}else{b=a+48|0;while(1){b=c[b>>2]|0;if(!b)break;qv(b);b=b+52|0}i=+h[a+32>>3];m=+h[a+40>>3];g=+h[a+16>>3]-i*.5;k=+h[a+24>>3]-m*.5;j=c[(c[a+56>>2]|0)+16>>2]|0;h[j+16>>3]=g;h[j+24>>3]=k;h[j+32>>3]=g+i;h[j+40>>3]=k+m}l=f;return}function rv(a){a=a|0;var b=0,d=0,e=0;e=c[a+64>>2]|0;b=a+48|0;d=0;while(1){b=c[b>>2]|0;if((d|0)>=(e|0))break;rv(b);b=b+52|0;d=d+1|0}U1(a);return}function sv(b){b=b|0;var d=0,e=0,f=0,g=0;g=l;l=l+48|0;f=g;d=g+8|0;e=c[47150]|0;if(e|0?(a[(RA(b,e)|0)>>0]|0)==0:0){h[f>>3]=+h[(c[b+16>>2]|0)+80>>3]*.7;m4(d,94168,f)|0;KA(b,c[47150]|0,d)|0}sP(b);l=g;return}function tv(a,b){a=a|0;b=b|0;var d=0.0;d=+h[c[a>>2]>>3]-+h[c[b>>2]>>3];return (d<0.0?1:(d>0.0)<<31>>31)|0}function uv(a,b){a=a|0;b=b|0;var d=0;d=JK(72)|0;h[d>>3]=+wv(a,b);c[d+60>>2]=1;c[d+56>>2]=a;return d|0}function vv(a,b){a=a|0;b=b|0;var d=0.0;d=+_O(c[a+56>>2]|0,b,0.0,0.0);if(d==0.0)d=+h[a+8>>3];else{d=+C(+(+h[a+8>>3]))+d*2.0;d=d*d}return +d}function wv(a,b){a=a|0;b=b|0;var c=0.0;c=+_O(a,b,1.0,0.0);return +(c==0.0?1.0e3:c*1.0e3)}function xv(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0.0,g=0,i=0,j=0.0;i=l;l=l+32|0;g=i;e=0;f=0.0;while(1){if((e|0)>=(a|0))break;j=+h[b+(e<<3)>>3]+f;e=e+1|0;f=j}if(f>+h[d+24>>3]*+h[d+16>>3]+.001)e=0;else{e=JK(a<<5)|0;c[g>>2]=c[d>>2];c[g+4>>2]=c[d+4>>2];c[g+8>>2]=c[d+8>>2];c[g+12>>2]=c[d+12>>2];c[g+16>>2]=c[d+16>>2];c[g+20>>2]=c[d+20>>2];c[g+24>>2]=c[d+24>>2];c[g+28>>2]=c[d+28>>2];yv(a,b,e,0,0.0,1.0,0.0,1.0,g)}l=i;return e|0}function yv(a,b,d,e,f,g,i,j,k){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;g=+g;i=+i;j=+j;k=k|0;var m=0,n=0.0,o=0.0,p=0,q=0.0,r=0.0,s=0.0,t=0.0,u=0,v=0.0,w=0,x=0,y=0,z=0,A=0,B=0.0;z=l;l=l+112|0;y=z+72|0;u=z+40|0;p=z+32|0;m=z;w=k+16|0;q=+h[w>>3];x=k+24|0;r=+h[x>>3];v=+h[k+16+((!(q<r)&1)<<3)>>3];do if((a|0)>=1){if(0){A=c[15712]|0;B=+h[k+8>>3];h[m>>3]=+h[k>>3];h[m+8>>3]=q;h[m+16>>3]=B;h[m+24>>3]=r;w4(A,94228,m)|0;c[p>>2]=e;w4(A,94274,p)|0}if(!e){i=+h[b>>3];B=v*v;v=i/B;B=B/i;c[y>>2]=c[k>>2];c[y+4>>2]=c[k+4>>2];c[y+8>>2]=c[k+8>>2];c[y+12>>2]=c[k+12>>2];c[y+16>>2]=c[k+16>>2];c[y+20>>2]=c[k+20>>2];c[y+24>>2]=c[k+24>>2];c[y+28>>2]=c[k+28>>2];yv(a,b,d,1,i,i,i,v>B?v:B,y);break}if((a|0)>(e|0)?(t=+h[b+(e<<3)>>3],n=t<f?f:t,s=t>g?g:t,t=t+i,o=t/v,o=o*o,B=o/s,o=n/o,o=B>o?B:o,o<=j):0){c[y>>2]=c[k>>2];c[y+4>>2]=c[k+4>>2];c[y+8>>2]=c[k+8>>2];c[y+12>>2]=c[k+12>>2];c[y+16>>2]=c[k+16>>2];c[y+20>>2]=c[k+20>>2];c[y+24>>2]=c[k+24>>2];c[y+28>>2]=c[k+28>>2];yv(a,b,d,e+1|0,n,s,t,o,y);break}q=i/v;if(0){A=c[15712]|0;c[u>>2]=e;h[u+8>>3]=i;h[u+16>>3]=v;h[u+24>>3]=q;w4(A,94299,u)|0}n=+h[w>>3];if(v==n){o=1.0/q;p=k+8|0;n=+h[k>>3]-n*.5;m=0;while(1){if((m|0)>=(e|0))break;h[d+(m<<5)+24>>3]=q;B=+h[b+(m<<3)>>3]*o;h[d+(m<<5)+16>>3]=B;h[d+(m<<5)+8>>3]=(+h[x>>3]-q)*.5+ +h[p>>3];h[d+(m<<5)>>3]=B*.5+n;n=B+n;m=m+1|0}h[p>>3]=+h[p>>3]-q*.5;m=1}else{o=1.0/q;n=+h[x>>3]*.5+ +h[k+8>>3];m=0;while(1){if((m|0)>=(e|0))break;h[d+(m<<5)+16>>3]=q;B=+h[b+(m<<3)>>3]*o;h[d+(m<<5)+24>>3]=B;h[d+(m<<5)>>3]=(q-+h[w>>3])*.5+ +h[k>>3];h[d+(m<<5)+8>>3]=n-B*.5;n=n-B;m=m+1|0}h[k>>3]=+h[k>>3]+q*.5;m=0}A=k+16+(m<<3)|0;h[A>>3]=+h[A>>3]-q;c[y>>2]=c[k>>2];c[y+4>>2]=c[k+4>>2];c[y+8>>2]=c[k+8>>2];c[y+12>>2]=c[k+12>>2];c[y+16>>2]=c[k+16>>2];c[y+20>>2]=c[k+20>>2];c[y+24>>2]=c[k+24>>2];c[y+28>>2]=c[k+28>>2];yv(a-e|0,b+(e<<3)|0,d+(e<<5)|0,0,0.0,0.0,0.0,1.0,y)}while(0);l=z;return}function zv(a,b,c,d,e){a=a|0;b=+b;c=+c;d=+d;e=+e;h[a>>3]=b;h[a+8>>3]=c;h[a+16>>3]=d;h[a+24>>3]=e;return}function Av(a,d,e){a=a|0;d=d|0;e=e|0;var f=0,i=0,j=0,k=0,m=0.0,n=0.0,o=0.0,p=0.0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0;D=l;l=l+96|0;C=D+48|0;B=D+24|0;A=D+16|0;z=D+8|0;u=D+80|0;v=D;w=a+16|0;x=d+4|0;y=u+4|0;s=jC(a)|0;e=0;f=0;i=0;j=0;while(1){if(!s)break;t=XA(a,s)|0;q=e;r=j;while(1){if(!t)break;e=(b[(c[t+16>>2]|0)+168>>1]|0)==0;do if((s|0)==(c[((c[t>>2]&3|0)==2?t:t+-48|0)+40>>2]|0))if(e){j=q;e=r}else{if(!f){e=JK(96)|0;c[e+84>>2]=JK(((tB(a)|0)<<5)+11520|0)|0;f=e}else e=r;Km(f,t,c[(c[w>>2]|0)+248>>2]|0);j=q}else if(!e){i=Bv(t,d)|0;j=i+8|0;if(lt(c[j>>2]|0,c[i>>2]|0)|0){k=JX(c[j>>2]|0,c[i>>2]|0)|0;if(!k){gA(0,94352,z)|0;j=1;e=r;break}else e=t;while(1){if(!e)break;E=e+16|0;F=(c[E>>2]|0)+144|0;Qm(v,e,k,0,c[j>>2]|0,c[i>>2]|0);H=v;G=c[H+4>>2]|0;c[F>>2]=c[H>>2];c[F+4>>2]=G;Sm(a,e,c[j>>2]|0,c[i>>2]|0,0);e=c[(c[E>>2]|0)+172>>2]|0}Cv(i);j=q;e=r;break}if(!q){lr(u,a);e=lH(a,8,8)|0;gA(0,94436,A)|0;n=+(e|0);o=+g[d>>2];p=+g[x>>2];if(!(o>=n)&!(p>=n)){m=+g[u>>2];n=+g[y>>2];if(!(!(m<=o)&!(n<=p))){h[C>>3]=m;h[C+8>>3]=n;h[C+16>>3]=o;h[C+24>>3]=p;gA(3,94552,C)|0}}else{c[B>>2]=e;h[B+8>>3]=o;h[B+16>>3]=p;gA(3,94502,B)|0}j=1;e=r}else{j=q;e=r}}else{j=q;e=r}while(0);t=ZA(a,t)|0;q=j;r=e}s=kC(a,s)|0;e=q;j=r}Dv(i);if(f|0){U1(c[f+84>>2]|0);U1(j)}l=D;return e|0}function Bv(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;p=l;l=l+16|0;m=p+4|0;n=p;e=c[b>>2]&3;f=c[((e|0)==2?b:b+-48|0)+40>>2]|0;e=c[((e|0)==3?b:b+48|0)+40>>2]|0;q=f+16|0;h=c[(c[q>>2]|0)+212>>2]|0;c[m>>2]=h;i=e+16|0;g=c[(c[i>>2]|0)+212>>2]|0;c[n>>2]=g;k=JK(12)|0;b=h;j=g;if(a[(c[q>>2]|0)+118>>0]|0){b=c[(c[(c[b+16>>2]|0)+140>>2]|0)+48>>2]|0;c[m>>2]=b;f=h;h=b}if(!(a[(c[i>>2]|0)+118>>0]|0))i=j;else{i=c[(c[(c[j+16>>2]|0)+140>>2]|0)+48>>2]|0;c[n>>2]=i;e=g;g=i}h=c[(c[(c[h+16>>2]|0)+140>>2]|0)+44>>2]|0;g=c[(c[(c[g+16>>2]|0)+140>>2]|0)+44>>2]|0;if((h|0)<=(g|0))if((g|0)>(h|0)){Ev(k,g,e,h,n,d);b=c[n>>2]|0;g=b;e=n;o=11}else h=i;else{Ev(k,h,f,g,m,d);b=c[m>>2]|0;f=b;g=e;e=m;o=11}while(1){if((o|0)==11){c[e>>2]=c[(c[(c[b+16>>2]|0)+140>>2]|0)+48>>2];e=g;b=c[m>>2]|0;h=c[n>>2]|0}if((b|0)==(h|0))break;Fv(k,b,0,f,d);Fv(k,h,e,0,d);c[m>>2]=c[(c[(c[b+16>>2]|0)+140>>2]|0)+48>>2];f=b;g=h;e=n;b=h;o=11}Fv(k,h,e,f,d);l=p;return k|0}function Cv(a){a=a|0;c[a>>2]=0;return}function Dv(a){a=a|0;if(a|0){U1(c[a+8>>2]|0);U1(a)}return}function Ev(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0;i=f;while(1){h=c[i>>2]|0;if((b|0)<=(e|0))break;Fv(a,h,d,0,g);b=b+-1|0;i=(c[(c[h+16>>2]|0)+140>>2]|0)+48|0;d=h}c[f>>2]=d;return}function Fv(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0;h=jC(d)|0;while(1){if(!h)break;i=c[h+16>>2]|0;if(!((h|0)==(f|0)|((h|0)==(e|0)?1:(c[i+212>>2]|0)!=(d|0)))?(a[i+118>>0]|0)==0:0)Gv(b,Nm(h,g,0)|0);h=kC(d,h)|0}i=d+16|0;d=1;while(1){h=c[i>>2]|0;if((d|0)>(c[h+180>>2]|0))break;h=c[(c[h+184>>2]|0)+(d<<2)>>2]|0;if(!((h|0)==(e|0)|(h|0)==(f|0)))Gv(b,Hv(h,g)|0);d=d+1|0}return}function Gv(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;e=a+4|0;f=c[e>>2]|0;g=a+8|0;d=c[g>>2]|0;do if((f|0)==(c[a>>2]|0))if(!d){d=KK(400)|0;c[g>>2]=d;c[e>>2]=100;break}else{c[e>>2]=f<<1;d=MK(d,f<<3)|0;c[g>>2]=d;break}while(0);g=c[a>>2]|0;c[a>>2]=g+1;c[d+(g<<2)>>2]=b;return}function Hv(b,d){b=b|0;d=d|0;var e=0.0,f=0.0,i=0.0,j=0.0,k=0.0,l=0.0,m=0,n=0.0,o=0.0,p=0.0;m=JK(8)|0;b=c[b+16>>2]|0;e=+h[b+16>>3];l=+h[b+24>>3];f=+h[b+32>>3];k=+h[b+40>>3];c[m+4>>2]=4;b=JK(64)|0;c[m>>2]=b;if(!(a[d+8>>0]|0)){p=+g[d>>2];o=+g[d+4>>2];i=(f+e)*.5*(p+-1.0);n=(k+l)*.5*(o+-1.0);j=e*p-i;i=f*p-i;f=k*o-n;e=l*o-n}else{i=+g[d>>2];p=+g[d+4>>2];j=e-i;i=f+i;f=k+p;e=l-p}h[b>>3]=j;h[b+8>>3]=e;h[b+16>>3]=j;h[b+24>>3]=f;h[b+32>>3]=i;h[b+40>>3]=f;h[b+48>>3]=i;h[b+56>>3]=e;return m|0}function Iv(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;o=l;l=l+160|0;n=o+16|0;j=o+8|0;g=o;k=o+24|0;m=JK(tB(b)|0)|0;h=b+16|0;f=c[c[(c[h>>2]|0)+140>>2]>>2]|0;a:do if(!f){g=0;f=0}else{p=KB(b)|0;i=c[46923]|0;c[g>>2]=p;c[g+4>>2]=i;m4(k,94612,g)|0;i=ND(b,k,1)|0;TC(i,137483,280,1)|0;g=JK(56)|0;c[(c[i+16>>2]|0)+140>>2]=g;c[g>>2]=f;c[g+4>>2]=c[(c[(c[h>>2]|0)+140>>2]|0)+4>>2];while(1){g=c[f+4>>2]|0;if(!g){g=i;f=1;break a}if(!(a[m+(c[(c[g+16>>2]|0)+120>>2]|0)>>0]|0))Jv(b,g,i,m);f=f+16|0}}while(0);h=0;i=jC(b)|0;while(1){if(!i)break;p=c[i+16>>2]|0;if((a[m+(c[p+120>>2]|0)>>0]|0)==0?(a[p+119>>0]|0)==3:0){if(!g){p=KB(b)|0;g=(c[46923]|0)+f|0;c[j>>2]=p;c[j+4>>2]=g;m4(k,94612,j)|0;g=ND(b,k,1)|0;TC(g,137483,280,1)|0;p=JK(56)|0;c[(c[g+16>>2]|0)+140>>2]=p;f=f+1|0}Jv(b,i,g,m);h=1}i=kC(b,i)|0}if(g|0)KG(g)|0;g=jC(b)|0;while(1){if(!g)break;if(!(a[m+(c[(c[g+16>>2]|0)+120>>2]|0)>>0]|0)){j=KB(b)|0;p=(c[46923]|0)+f|0;c[n>>2]=j;c[n+4>>2]=p;m4(k,94620,n)|0;p=ND(b,k,1)|0;TC(p,137483,280,1)|0;j=JK(56)|0;c[(c[p+16>>2]|0)+140>>2]=j;Jv(b,g,p,m);KG(p)|0;f=f+1|0}g=kC(b,g)|0}U1(m);c[46923]=(c[46923]|0)+f;if(d|0)c[d>>2]=f;if(e|0)c[e>>2]=h;i=JK((f<<2)+4|0)|0;h=i;g=OD(b)|0;while(1){if(!g)break;c[h>>2]=g;h=h+4|0;g=PD(g)|0;f=f+-1|0}if(!f){c[h>>2]=0;l=o;return i|0}else qa(94628,94639,134,94646);return 0}function Jv(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;a[f+(c[(c[d+16>>2]|0)+120>>2]|0)>>0]=1;oC(e,d,1)|0;i=aB(b,d)|0;while(1){if(!i)break;h=c[i>>2]&3;g=c[((h|0)==3?i:i+48|0)+40>>2]|0;if((g|0)==(d|0))g=c[((h|0)==2?i:i+-48|0)+40>>2]|0;if(!(a[f+(c[(c[g+16>>2]|0)+120>>2]|0)>>0]|0))Jv(b,g,e,f);i=bB(b,i,d)|0}return}function Kv(a){a=a|0;var b=0;b=KK(24)|0;c[46924]=b;c[b>>2]=Vz(18204,c[4581]|0)|0;c[b+16>>2]=0;c[b+12>>2]=0;c[b+4>>2]=Lv(a)|0;return b|0}function Lv(a){a=a|0;var b=0,d=0;b=KK(16)|0;c[b+12>>2]=0;d=KK(a*20|0)|0;c[b>>2]=d;c[b+8>>2]=d+(a*20|0);c[b+4>>2]=d;return b|0}function Mv(a,b,d){a=a|0;b=b|0;d=d|0;d=Ov(c[46924]|0)|0;c[d>>2]=c[b>>2];c[d+4>>2]=c[b+4>>2];c[d+8>>2]=0;return d|0}function Nv(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;a=(c[b>>2]|0)-(c[d>>2]|0)|0;if(!a)a=(c[b+4>>2]|0)-(c[d+4>>2]|0)|0;return a|0}function Ov(a){a=a|0;var b=0,d=0,e=0,f=0;f=a+8|0;a=c[f>>2]|0;b=c[a+4>>2]|0;if((b|0)==(c[a+8>>2]|0)){d=a+12|0;e=c[d>>2]|0;if(!e){a=Lv(((b-(c[a>>2]|0)|0)/20|0)<<1)|0;c[d>>2]=a}else a=e;c[f>>2]=a;b=c[a>>2]|0;c[a+4>>2]=b}c[a+4>>2]=b+20;return b|0}function Pv(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;f=a+12|0;d=c[f>>2]|0;if((d|0)<(b|0)){e=d<<1;e=(e|0)<(b|0)?b:e;b=a+16|0;d=c[b>>2]|0;if(d|0)U1(d);c[b>>2]=KK(e<<3)|0;c[f>>2]=e}return}function Qv(a){a=a|0;var b=0;b=c[a>>2]|0;nb[c[b>>2]&63](b,0,64)|0;c[a+20>>2]=c[a+16>>2];b=c[a+4>>2]|0;c[a+8>>2]=b;c[b+4>>2]=c[b>>2];return}function Rv(a){a=a|0;Rz(c[a>>2]|0)|0;Sv(c[a+4>>2]|0);U1(c[a+16>>2]|0);U1(a);return}function Sv(a){a=a|0;var b=0;while(1){if(!a)break;b=c[a+12>>2]|0;U1(c[a>>2]|0);U1(a);a=b}return}function Tv(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;g=l;l=l+32|0;f=g;h=g+12|0;c[h>>2]=b;c[h+4>>2]=d;i=c[a>>2]|0;h=(nb[c[i>>2]&63](i,h,1)|0)+8|0;c[h>>2]=Uv(a,e,c[h>>2]|0)|0;if(0>2){i=c[15712]|0;h=KB(e)|0;c[f>>2]=b;c[f+4>>2]=d;c[f+8>>2]=h;w4(i,94656,f)|0}l=g;return}function Uv(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;e=a+20|0;a=c[e>>2]|0;c[e>>2]=a+8;c[a>>2]=b;c[a+4>>2]=d;return a|0}function Vv(a,b){a=a|0;b=b|0;aA(c[a>>2]|0,b,a)|0;return}function Wv(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=l;l=l+32|0;f=e;c[f>>2]=b;c[f+4>>2]=d;d=c[a>>2]|0;d=nb[c[d>>2]&63](d,f,4)|0;l=e;return d|0}function Xv(a){a=a|0;U1(c[a+8>>2]|0);U1(a);return}function Yv(a){a=a|0;return +(+h[a>>3])}function Zv(a){a=a|0;return c[a+8>>2]|0}function _v(a){a=a|0;return ~~+h[a+16>>3]|0}function $v(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;b=c[b>>2]|0;if((b|0)>=(c[d>>2]|0)){b=b+10|0;c[d>>2]=b;c[e>>2]=MK(c[e>>2]|0,O(a<<3,b)|0)|0;c[f>>2]=MK(c[f>>2]|0,c[d>>2]<<3)|0;c[g>>2]=MK(c[g>>2]|0,c[d>>2]<<3)|0}return}function aw(a,b,d,e,f,g,i,j,k,l,m){a=a|0;b=+b;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;l=l|0;m=m|0;var n=0,o=0,p=0,q=0,r=0.0;h[l>>3]=+h[l>>3]+1.0;a:do if(a|0){q=c[a+16>>2]|0;n=c[a+40>>2]|0;if(n|0)do{$v(q,f,g,i,j,k);if((_v(zy(n)|0)|0)!=(e|0)){p=Zv(zy(n)|0)|0;o=0;while(1){if((o|0)>=(q|0))break;h[(c[i>>2]|0)+((O(c[f>>2]|0,q)|0)+o<<3)>>3]=+h[p+(o<<3)>>3];o=o+1|0}r=+Yv(zy(n)|0);h[(c[j>>2]|0)+(c[f>>2]<<3)>>3]=r;r=+$w(d,p,q);p=c[f>>2]|0;h[(c[k>>2]|0)+(p<<3)>>3]=r;c[f>>2]=p+1}n=Ay(n)|0}while((n|0)!=0);p=a+36|0;if(c[p>>2]|0){r=+$w(c[a+20>>2]|0,d,q);if(!(+h[a+24>>3]<r*b)){o=1<<q;n=0;while(1){if((n|0)>=(o|0))break a;aw(c[(c[p>>2]|0)+(n<<2)>>2]|0,b,d,e,f,g,i,j,k,l,m);n=n+1|0}}$v(q,f,g,i,j,k);o=a+32|0;n=0;while(1){if((n|0)>=(q|0))break;h[(c[i>>2]|0)+((O(c[f>>2]|0,q)|0)+n<<3)>>3]=+h[(c[o>>2]|0)+(n<<3)>>3];n=n+1|0}h[(c[j>>2]|0)+(c[f>>2]<<3)>>3]=+h[a+8>>3];r=+$w(c[o>>2]|0,d,q);j=c[f>>2]|0;h[(c[k>>2]|0)+(j<<3)>>3]=r;c[f>>2]=j+1}}while(0);return}function bw(a,b,d,e,f,g,i,j,k,l,m){a=a|0;b=+b;d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;j=j|0;k=k|0;l=l|0;m=m|0;var n=0;n=c[a+16>>2]|0;h[l>>3]=0.0;c[f>>2]=0;c[m>>2]=0;c[g>>2]=10;if(!(c[i>>2]|0))c[i>>2]=KK(n*80|0)|0;if(!(c[j>>2]|0))c[j>>2]=KK(c[g>>2]<<3)|0;if(!(c[k>>2]|0))c[k>>2]=KK(c[g>>2]<<3)|0;aw(a,b,d,e,f,g,i,j,k,l,m);return}function cw(a,b,d,e,f,g,i,j){a=a|0;b=b|0;d=d|0;e=+e;f=+f;g=+g;i=i|0;j=j|0;var k=0,l=0,m=0;m=c[a>>2]|0;l=c[a+16>>2]|0;k=0;while(1){if((k|0)==4)break;h[i+(k<<3)>>3]=0.0;k=k+1|0}c[j>>2]=0;l=O(l,m)|0;k=0;while(1){if((k|0)>=(l|0))break;h[b+(k<<3)>>3]=0.0;k=k+1|0}dw(a,a,d,b,e,f,g,i);ew(a,b,i);e=1.0/+(m|0);k=0;while(1){if((k|0)==4)break;m=i+(k<<3)|0;h[m>>3]=+h[m>>3]*e;k=k+1|0}return}function dw(a,b,d,e,f,g,i,j){a=a|0;b=b|0;d=d|0;e=e|0;f=+f;g=+g;i=+i;j=j|0;var k=0,l=0.0,m=0,n=0.0,o=0,p=0,q=0,r=0.0,s=0,t=0,u=0,v=0,w=0,x=0;a:do if((a|0)!=0&(b|0)!=0){if((c[a>>2]|0)>0?(c[b>>2]|0)>0:0){w=c[a+16>>2]|0;p=c[a+40>>2]|0;v=b+40|0;o=c[v>>2]|0;k=a+32|0;m=b+32|0;r=+$w(c[k>>2]|0,c[m>>2]|0,w);l=+h[a+24>>3];n=+h[b+24>>3];if(n+l<r*f){h[j>>3]=+h[j>>3]+1.0;q=c[k>>2]|0;n=+h[a+8>>3];p=fw(a,w)|0;o=c[m>>2]|0;l=+h[b+8>>3];m=fw(b,w)|0;if(!(r>0.0))qa(94798,94681,191,94764);n=n*i*l;l=+D(+r,+(1.0-g));l=1.0/(g==-1.0?r*r:l);k=0;while(1){if((k|0)>=(w|0))break a;i=n*(+h[q+(k<<3)>>3]-+h[o+(k<<3)>>3])*l;e=p+(k<<3)|0;h[e>>3]=i+ +h[e>>3];e=m+(k<<3)|0;h[e>>3]=+h[e>>3]-i;k=k+1|0}}m=(p|0)!=0;k=(o|0)!=0;u=(a|0)==(b|0);if(m&k){s=j+8|0;t=g==-1.0;f=1.0-g;m=p;while(1){if(!m)break a;p=Zv(zy(m)|0)|0;r=+Yv(zy(m)|0);q=_v(zy(m)|0)|0;a=gw(e,q,m,w)|0;r=r*i;o=c[v>>2]|0;while(1){if(!o)break;b=Zv(zy(o)|0)|0;l=+Yv(zy(o)|0);k=_v(zy(o)|0)|0;j=gw(e,k,o,w)|0;b:do if(!((q|0)==(k|0)|u&(k|0)<(q|0))){h[s>>3]=+h[s>>3]+1.0;g=+Zw(d,w,q,k);l=r*l;n=+D(+g,+f);n=1.0/(t?g*g:n);k=0;while(1){if((k|0)>=(w|0))break b;g=l*(+h[p+(k<<3)>>3]-+h[b+(k<<3)>>3])*n;x=a+(k<<3)|0;h[x>>3]=g+ +h[x>>3];x=j+(k<<3)|0;h[x>>3]=+h[x>>3]-g;k=k+1|0}}while(0);o=Ay(o)|0}m=Ay(m)|0}}if(u){q=1<<w;o=a+36|0;m=0;while(1){if((m|0)>=(q|0))break a;p=c[(c[o>>2]|0)+(m<<2)>>2]|0;k=m;while(1){if((k|0)==(q|0))break;dw(p,c[(c[o>>2]|0)+(k<<2)>>2]|0,d,e,f,g,i,j);k=k+1|0}m=m+1|0}}if(!(m|!(l>n))){o=1<<w;m=a+36|0;k=0;while(1){if((k|0)>=(o|0))break a;dw(c[(c[m>>2]|0)+(k<<2)>>2]|0,b,d,e,f,g,i,j);k=k+1|0}}if(!(k|!(n>l))){o=1<<w;m=b+36|0;k=0;while(1){if((k|0)>=(o|0))break a;dw(c[(c[m>>2]|0)+(k<<2)>>2]|0,a,d,e,f,g,i,j);k=k+1|0}}if(!m){o=1<<w;m=a+36|0;k=0;while(1){if((k|0)>=(o|0))break a;dw(c[(c[m>>2]|0)+(k<<2)>>2]|0,b,d,e,f,g,i,j);k=k+1|0}}if(k)qa(138394,94681,273,94764);o=1<<w;m=b+36|0;k=0;while(1){if((k|0)>=(o|0))break a;dw(c[(c[m>>2]|0)+(k<<2)>>2]|0,a,d,e,f,g,i,j);k=k+1|0}}qa(94739,94681,175,94764)}while(0);return}function ew(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0.0,i=0.0,j=0,k=0,l=0,m=0,n=0,o=0;e=c[a+40>>2]|0;m=c[a+16>>2]|0;g=+h[a+8>>3];n=fw(a,m)|0;if(!(g>0.0))qa(94673,94681,289,94692);l=d+16|0;h[l>>3]=+h[l>>3]+1.0;a:do if(!e){l=1<<m;k=a+36|0;g=1.0/g;a=0;while(1){if((a|0)>=(l|0))break a;f=c[(c[k>>2]|0)+(a<<2)>>2]|0;if(f|0){if((c[f>>2]|0)<=0)break;j=fw(f,m)|0;i=+h[f+8>>3]*g;e=0;while(1){if((e|0)>=(m|0))break;o=j+(e<<3)|0;h[o>>3]=+h[o>>3]+ +h[n+(e<<3)>>3]*i;e=e+1|0}ew(f,b,d)}a=a+1|0}qa(94728,94681,307,94692)}else{i=1.0/g;while(1){if(!e)break a;f=gw(b,_v(zy(e)|0)|0,e,m)|0;g=+Yv(zy(e)|0)*i;a=0;while(1){if((a|0)>=(m|0))break;o=f+(a<<3)|0;h[o>>3]=+h[o>>3]+ +h[n+(a<<3)>>3]*g;a=a+1|0}e=Ay(e)|0}}while(0);return}function fw(a,b){a=a|0;b=b|0;var d=0;d=a+48|0;a=c[d>>2]|0;a:do if(!a){a=KK(b<<3)|0;c[d>>2]=a;d=0;while(1){if((d|0)>=(b|0))break a;h[a+(d<<3)>>3]=0.0;d=d+1|0}}while(0);return a|0}function gw(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;f=c[(zy(d)|0)+24>>2]|0;if(!f){f=a+((O(e,b)|0)<<3)|0;c[(zy(d)|0)+24>>2]=f;f=c[(zy(d)|0)+24>>2]|0}return f|0}function hw(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;var f=0,g=0,i=0.0,j=0,k=0,l=0,m=0,n=0,o=0.0,p=0;m=a<<3;k=KK(m)|0;l=KK(m)|0;m=KK(m)|0;if((k|0)!=0&(l|0)!=0&(m|0)!=0){f=0;while(1){if((f|0)>=(a|0)){f=0;break}h[k+(f<<3)>>3]=+h[d+(f<<3)>>3];f=f+1|0}while(1){if((f|0)>=(a|0)){g=1;break}h[l+(f<<3)>>3]=+h[d+(f<<3)>>3];f=f+1|0}while(1){if((g|0)>=(b|0))break;j=O(g,a)|0;f=0;while(1){if((f|0)>=(a|0))break;n=k+(f<<3)|0;i=+h[n>>3];p=d+(f+j<<3)|0;o=+h[p>>3];h[n>>3]=i<o?i:o;n=l+(f<<3)|0;o=+h[n>>3];i=+h[p>>3];h[n>>3]=o>i?o:i;f=f+1|0}g=g+1|0}i=+h[l>>3]-+h[k>>3];f=0;while(1){if((f|0)>=(a|0))break;p=k+(f<<3)|0;n=l+(f<<3)|0;h[m+(f<<3)>>3]=(+h[n>>3]+ +h[p>>3])*.5;o=+h[n>>3]-+h[p>>3];i=i>o?i:o;f=f+1|0}f=iw(a,m,i==0.0?5.2e-06:i*.52,c)|0;a:do if(!e){g=0;while(1){if((g|0)>=(b|0))break a;f=jw(f,d+((O(g,a)|0)<<3)|0,1.0,g)|0;g=g+1|0}}else{g=0;while(1){if((g|0)>=(b|0))break a;p=d+((O(g,a)|0)<<3)|0;f=jw(f,p,+h[e+(g<<3)>>3],g)|0;g=g+1|0}}while(0);U1(k);U1(l);U1(m)}else{U1(k);U1(l);U1(m);f=0}return f|0}function iw(a,b,d,e){a=a|0;b=b|0;d=+d;e=e|0;var f=0,g=0,i=0;g=KK(56)|0;c[g+16>>2]=a;c[g>>2]=0;i=KK(a<<3)|0;c[g+20>>2]=i;f=0;while(1){if((f|0)>=(a|0))break;h[i+(f<<3)>>3]=+h[b+(f<<3)>>3];f=f+1|0}if(d>0.0){h[g+24>>3]=d;h[g+8>>3]=0.0;c[g+32>>2]=0;c[g+36>>2]=0;c[g+40>>2]=0;c[g+44>>2]=e;c[g+48>>2]=0;return g|0}else qa(94896,94681,410,94906);return 0}function jw(a,b,c,d){a=a|0;b=b|0;c=+c;d=d|0;if(!a)a=0;else kw(a,b,c,d,0)|0;return a|0}function kw(a,b,d,e,f){a=a|0;b=b|0;d=+d;e=e|0;f=f|0;var g=0,i=0,j=0,k=0.0,l=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=a+16|0;r=c[t>>2]|0;s=c[a+44>>2]|0;m=c[a>>2]|0;do if(!m){c[a>>2]=1;h[a+8>>3]=d;j=KK(r<<3)|0;c[a+32>>2]=j;i=c[t>>2]|0;g=0;while(1){if((g|0)>=(i|0))break;h[j+(g<<3)>>3]=+h[b+(g<<3)>>3];g=g+1|0}g=lw(i,d,b,e)|0;i=a+40|0;if(!(c[i>>2]|0)){c[i>>2]=wy(g)|0;break}else qa(94807,94681,502,94815)}else{if((s|0)<=(f|0)){if(c[a+36>>2]|0)qa(94881,94681,541,94815);g=m+1|0;c[a>>2]=g;i=a+8|0;h[i>>3]=+h[i>>3]+d;i=a+32|0;k=+(g|0);l=1.0/+(m+2|0);g=0;while(1){if((g|0)>=(r|0))break;q=(c[i>>2]|0)+(g<<3)|0;h[q>>3]=(+h[q>>3]*k+ +h[b+(g<<3)>>3])*l;g=g+1|0}g=lw(r,d,b,e)|0;i=a+40|0;j=c[i>>2]|0;if(!j)qa(94891,94681,547,94815);else{c[i>>2]=yy(j,g)|0;break}}i=a+8|0;h[i>>3]=+h[i>>3]+d;i=a+32|0;l=+(m|0);k=1.0/+(m+1|0);g=0;while(1){if((g|0)>=(r|0))break;q=(c[i>>2]|0)+(g<<3)|0;h[q>>3]=(+h[q>>3]*l+ +h[b+(g<<3)>>3])*k;g=g+1|0}q=a+36|0;p=1<<r;a:do if(!(c[q>>2]|0)){c[q>>2]=KK(p<<2)|0;g=0;while(1){if((g|0)>=(p|0))break a;c[(c[q>>2]|0)+(g<<2)>>2]=0;g=g+1|0}}while(0);n=a+20|0;g=c[n>>2]|0;j=mw(r,g,b)|0;if(!((j|0)<(p|0)&(j|0)>-1))qa(94837,94681,515,94815);i=c[(c[q>>2]|0)+(j<<2)>>2]|0;if(!i){i=nw(c[t>>2]|0,g,+h[a+24>>3]*.5,s,j)|0;c[(c[q>>2]|0)+(j<<2)>>2]=i;i=c[(c[q>>2]|0)+(j<<2)>>2]|0}o=f+1|0;e=kw(i,b,d,e,o)|0;c[(c[q>>2]|0)+(j<<2)>>2]=e;if(!(c[(c[q>>2]|0)+(j<<2)>>2]|0))qa(94860,94681,519,94815);b=a+40|0;g=c[b>>2]|0;do if(g|0){m=_v(zy(g)|0)|0;if((c[a>>2]|0)!=1)qa(94871,94681,523,94815);f=Zv(zy(c[b>>2]|0)|0)|0;k=+Yv(zy(c[b>>2]|0)|0);i=c[n>>2]|0;j=mw(r,i,f)|0;if(!((j|0)<(p|0)&(j|0)>-1))qa(94837,94681,527,94815);g=c[(c[q>>2]|0)+(j<<2)>>2]|0;if(!g){g=nw(c[t>>2]|0,i,+h[a+24>>3]*.5,s,j)|0;c[(c[q>>2]|0)+(j<<2)>>2]=g;g=c[(c[q>>2]|0)+(j<<2)>>2]|0}t=kw(g,f,k,m,o)|0;c[(c[q>>2]|0)+(j<<2)>>2]=t;if(!(c[(c[q>>2]|0)+(j<<2)>>2]|0))qa(94860,94681,532,94815);else{xy(c[b>>2]|0,106);c[b>>2]=0;break}}while(0);c[a>>2]=(c[a>>2]|0)+1}while(0);return a|0}function lw(a,b,d,e){a=a|0;b=+b;d=d|0;e=e|0;var f=0,g=0;g=KK(32)|0;h[g>>3]=b;f=KK(a<<3)|0;c[g+8>>2]=f;h[g+16>>3]=+(e|0);e=0;while(1){if((e|0)>=(a|0))break;h[f+(e<<3)>>3]=+h[d+(e<<3)>>3];e=e+1|0}c[g+24>>2]=0;return g|0}function mw(a,b,c){a=a|0;b=b|0;c=c|0;var d=0,e=0;e=0;while(1){d=a+-1|0;if((a|0)<=0)break;a=d;e=e<<1|!(+h[c+(d<<3)>>3]-+h[b+(d<<3)>>3]<0.0)&1}return e|0}function nw(a,b,d,e,f){a=a|0;b=b|0;d=+d;e=e|0;f=f|0;var g=0,i=0,j=0.0;g=iw(a,b,d,e)|0;i=c[g+20>>2]|0;j=-d;e=0;b=f;while(1){if((e|0)>=(a|0))break;f=i+(e<<3)|0;h[f>>3]=+h[f>>3]+((b&1|0)==0?j:d);e=e+1|0;b=(b|0)/2|0}return g|0}function ow(a){a=a|0;var b=0,d=0,e=0,f=0,g=0;if(a|0){b=c[a+16>>2]|0;U1(c[a+20>>2]|0);U1(c[a+32>>2]|0);U1(c[a+48>>2]|0);g=a+36|0;e=c[g>>2]|0;if(e|0){f=1<<b;d=0;b=e;while(1){if((d|0)>=(f|0))break;ow(c[b+(d<<2)>>2]|0);d=d+1|0;b=c[g>>2]|0}U1(b)}xy(c[a+40>>2]|0,106);U1(a)}return}function pw(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0.0,r=0,s=0,t=0;a:do if(!a)b=0;else{l=c[a+20>>2]|0;m=c[a+24>>2]|0;d=c[a+8>>2]|0;n=c[a>>2]|0;e=c[a+4>>2]|0;i=a+16|0;if((c[a+32>>2]|0)!=1)qa(94919,94943,76,94958);b=rw(e,n,d,c[i>>2]|0,1)|0;c[b+8>>2]=d;o=c[b+20>>2]|0;k=c[b+24>>2]|0;d=0;while(1){if((d|0)>(e|0)){d=0;break}c[o+(d<<2)>>2]=0;d=d+1|0}b:while(1){if((d|0)>=(n|0)){d=0;break}f=d+1|0;g=l+(f<<2)|0;d=c[l+(d<<2)>>2]|0;while(1){if((d|0)>=(c[g>>2]|0)){d=f;continue b}j=o+((c[m+(d<<2)>>2]|0)+1<<2)|0;c[j>>2]=(c[j>>2]|0)+1;d=d+1|0}}while(1){if((d|0)>=(e|0))break;j=d+1|0;g=o+(j<<2)|0;c[g>>2]=(c[g>>2]|0)+(c[o+(d<<2)>>2]|0);d=j}c:do switch(c[i>>2]|0){case 1:{g=c[a+28>>2]|0;a=c[b+28>>2]|0;f=0;d:while(1){if((f|0)>=(n|0))break c;i=f+1|0;j=l+(i<<2)|0;d=c[l+(f<<2)>>2]|0;while(1){if((d|0)>=(c[j>>2]|0)){f=i;continue d}r=m+(d<<2)|0;c[k+(c[o+(c[r>>2]<<2)>>2]<<2)>>2]=f;q=+h[g+(d<<3)>>3];r=o+(c[r>>2]<<2)|0;p=c[r>>2]|0;c[r>>2]=p+1;h[a+(p<<3)>>3]=q;d=d+1|0}}}case 2:{g=c[a+28>>2]|0;a=c[b+28>>2]|0;f=0;e:while(1){if((f|0)>=(n|0))break c;i=f+1|0;j=l+(i<<2)|0;d=c[l+(f<<2)>>2]|0;while(1){if((d|0)>=(c[j>>2]|0)){f=i;continue e}r=m+(d<<2)|0;c[k+(c[o+(c[r>>2]<<2)>>2]<<2)>>2]=f;t=d<<1;r=o+(c[r>>2]<<2)|0;p=c[r>>2]|0;s=p<<1;h[a+(s<<3)>>3]=+h[g+(t<<3)>>3];h[a+((s|1)<<3)>>3]=+h[g+((t|1)<<3)>>3];c[r>>2]=p+1;d=d+1|0}}}case 4:{g=c[a+28>>2]|0;a=c[b+28>>2]|0;f=0;f:while(1){if((f|0)>=(n|0))break c;i=f+1|0;j=l+(i<<2)|0;d=c[l+(f<<2)>>2]|0;while(1){if((d|0)>=(c[j>>2]|0)){f=i;continue f}r=m+(d<<2)|0;c[k+(c[o+(c[r>>2]<<2)>>2]<<2)>>2]=f;s=c[g+(d<<2)>>2]|0;r=o+(c[r>>2]<<2)|0;t=c[r>>2]|0;c[r>>2]=t+1;c[a+(t<<2)>>2]=s;d=d+1|0}}}case 8:{f=0;g:while(1){if((f|0)>=(n|0))break c;g=f+1|0;a=l+(g<<2)|0;d=c[l+(f<<2)>>2]|0;while(1){if((d|0)>=(c[a>>2]|0)){f=g;continue g}s=o+(c[m+(d<<2)>>2]<<2)|0;t=c[s>>2]|0;c[s>>2]=t+1;c[k+(t<<2)>>2]=f;d=d+1|0}}}case 16:{qw(b);b=0;break a}default:{qw(b);b=0;break a}}while(0);while(1){d=e+-1|0;if((e|0)<=0)break;c[o+(e<<2)>>2]=c[o+(d<<2)>>2];e=d}c[o>>2]=0}while(0);return b|0}function qw(a){a=a|0;var b=0;if(a|0){b=c[a+20>>2]|0;if(b|0)U1(b);b=c[a+24>>2]|0;if(b|0)U1(b);U1(c[a+28>>2]|0);U1(a)}return}function rw(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;a=tw(a,b,d,sw(d)|0,e)|0;if((c|0)>0)uw(a,c)|0;return a|0}function sw(a){a=a|0;switch(a|0){case 1:{a=8;break}case 2:{a=16;break}case 4:{a=4;break}case 8:{a=0;break}case 16:{a=0;break}default:a=0}return a|0}function tw(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;g=KK(44)|0;c[g>>2]=a;c[g+4>>2]=b;c[g+8>>2]=0;c[g+12>>2]=0;c[g+16>>2]=d;c[g+40>>2]=e;if((f|0)==2)a=0;else a=KK((a<<2)+4|0)|0;c[g+20>>2]=a;c[g+24>>2]=0;c[g+28>>2]=0;c[g+32>>2]=f;c[g+36>>2]=0;return g|0}function uw(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0;h=c[a+32>>2]|0;f=a+28|0;c[f>>2]=0;d=b<<2;e=KK(d)|0;if((h|0)!=2){c[a+24>>2]=e;d=c[a+40>>2]|0;if((b|0)!=0&(d|0)>0)g=4}else{c[a+20>>2]=e;c[a+24>>2]=KK(d)|0;d=c[a+40>>2]|0;g=4}if((g|0)==4)c[f>>2]=KK(O(d,b)|0)|0;c[a+12>>2]=b;return a|0}function vw(a,b){a=a|0;b=b|0;if(!(ww(a,b)|0)){b=pw(a)|0;if(!b)b=0;else{a=yw(a,b)|0;qw(b);b=a+36|0;c[b>>2]=c[b>>2]|3;b=a}}else b=xw(a)|0;return b|0}function ww(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0.0;if(a){if((c[a+32>>2]|0)!=1)qa(94919,94943,187,95074);s=a+36|0;d=c[s>>2]|0;if((d&2|0)==0?(p=(b|0)!=0,(d&1|0)==0|p^1):0)if((c[a>>2]|0)==(c[a+4>>2]|0)?(t=pw(a)|0,(t|0)!=0):0){k=c[a+20>>2]|0;l=c[a+24>>2]|0;m=c[t+20>>2]|0;n=c[t+24>>2]|0;o=c[a>>2]|0;q=KK(o<<2)|0;b=0;while(1){if((b|0)>=(o|0))break;c[q+(b<<2)>>2]=-1;b=b+1|0}a:do switch((p?8:c[a+16>>2]|0)|0){case 1:{g=c[a+28>>2]|0;i=c[t+28>>2]|0;b=0;while(1){if((b|0)>(o|0)){d=0;break}if((c[k+(b<<2)>>2]|0)==(c[m+(b<<2)>>2]|0))b=b+1|0;else{b=0;break a}}b:while(1){if((d|0)>=(o|0)){r=54;break a}e=k+(d<<2)|0;f=d+1|0;a=k+(f<<2)|0;b=c[e>>2]|0;while(1){if((b|0)>=(c[a>>2]|0))break;c[q+(c[l+(b<<2)>>2]<<2)>>2]=b;b=b+1|0}b=c[m+(d<<2)>>2]|0;a=c[m+(f<<2)>>2]|0;d=b;while(1){if((d|0)>=(a|0))break;if((c[q+(c[n+(d<<2)>>2]<<2)>>2]|0)<(c[e>>2]|0)){b=0;break a}else d=d+1|0}while(1){if((b|0)>=(a|0)){d=f;continue b}u=+h[i+(b<<3)>>3]-+h[g+(c[q+(c[n+(b<<2)>>2]<<2)>>2]<<3)>>3];if((u>=0.0?u:-u)>1.0e-07){b=0;break a}else b=b+1|0}}}case 2:{i=c[a+28>>2]|0;j=c[t+28>>2]|0;b=0;while(1){if((b|0)>(o|0)){d=0;break}if((c[k+(b<<2)>>2]|0)==(c[m+(b<<2)>>2]|0))b=b+1|0;else{b=0;break a}}c:while(1){if((d|0)>=(o|0)){r=54;break a}f=k+(d<<2)|0;g=d+1|0;a=k+(g<<2)|0;b=c[f>>2]|0;while(1){if((b|0)>=(c[a>>2]|0))break;c[q+(c[l+(b<<2)>>2]<<2)>>2]=b;b=b+1|0}d=c[m+(d<<2)>>2]|0;e=c[m+(g<<2)>>2]|0;b=d;while(1){if((b|0)>=(e|0)){b=d;break}if((c[q+(c[n+(b<<2)>>2]<<2)>>2]|0)<(c[f>>2]|0)){b=0;break a}else b=b+1|0}while(1){if((b|0)>=(e|0)){d=g;continue c}d=b<<1;a=c[q+(c[n+(b<<2)>>2]<<2)>>2]<<1;u=+h[j+(d<<3)>>3]-+h[i+(a<<3)>>3];if((u>=0.0?u:-u)>1.0e-07){b=0;break a}u=+h[j+((d|1)<<3)>>3]-+h[i+((a|1)<<3)>>3];if((u>=0.0?u:-u)>1.0e-07){b=0;break a}else b=b+1|0}}}case 4:{f=c[a+28>>2]|0;g=c[t+28>>2]|0;b=0;d:while(1){if((b|0)>=(o|0)){r=54;break a}e=k+(b<<2)|0;i=b+1|0;a=k+(i<<2)|0;d=c[e>>2]|0;while(1){if((d|0)>=(c[a>>2]|0))break;c[q+(c[l+(d<<2)>>2]<<2)>>2]=d;d=d+1|0}b=c[m+(b<<2)>>2]|0;a=c[m+(i<<2)>>2]|0;d=b;while(1){if((d|0)>=(a|0))break;if((c[q+(c[n+(d<<2)>>2]<<2)>>2]|0)<(c[e>>2]|0)){b=0;break a}else d=d+1|0}while(1){if((b|0)>=(a|0)){b=i;continue d}if((c[g+(b<<2)>>2]|0)==(c[f+(c[q+(c[n+(b<<2)>>2]<<2)>>2]<<2)>>2]|0))b=b+1|0;else{b=0;break a}}}}case 8:{a=0;e:while(1){if((a|0)>=(o|0)){r=54;break a}e=k+(a<<2)|0;f=a+1|0;d=k+(f<<2)|0;b=c[e>>2]|0;while(1){if((b|0)>=(c[d>>2]|0))break;c[q+(c[l+(b<<2)>>2]<<2)>>2]=b;b=b+1|0}d=c[m+(f<<2)>>2]|0;b=c[m+(a<<2)>>2]|0;while(1){if((b|0)>=(d|0)){a=f;continue e}if((c[q+(c[n+(b<<2)>>2]<<2)>>2]|0)<(c[e>>2]|0)){b=0;break a}else b=b+1|0}}}default:b=0}while(0);if((r|0)==54){d=c[s>>2]|0;if(p)b=1;else{c[s>>2]=d|2;b=3}c[s>>2]=b|d;b=1}U1(q);qw(t)}else b=0;else b=1}else b=0;return b|0}function xw(a){a=a|0;var b=0,d=0,e=0,f=0;if(!a)b=0;else{e=a+8|0;f=a+40|0;b=zw(c[a>>2]|0,c[a+4>>2]|0,c[e>>2]|0,c[a+16>>2]|0,c[f>>2]|0,c[a+32>>2]|0)|0;d=a+20|0;W6(c[b+20>>2]|0,c[d>>2]|0,(c[a>>2]<<2)+4|0)|0;W6(c[b+24>>2]|0,c[a+24>>2]|0,c[(c[d>>2]|0)+(c[a>>2]<<2)>>2]<<2|0)|0;d=c[a+28>>2]|0;if(d|0)W6(c[b+28>>2]|0,d|0,O(c[e>>2]|0,c[f>>2]|0)|0)|0;c[b+36>>2]=c[a+36>>2];c[b+8>>2]=c[e>>2]}return b|0}function yw(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;p=c[a+20>>2]|0;r=c[a+24>>2]|0;s=c[b+20>>2]|0;t=c[b+24>>2]|0;if(!((a|0)!=0&(b|0)!=0))qa(94981,94943,973,94988);q=c[a+32>>2]|0;if(!((q|0)==1?(q|0)==(c[b+32>>2]|0):0))qa(95005,94943,974,94988);e=a+16|0;d=c[e>>2]|0;if((d|0)!=(c[b+16>>2]|0))qa(95055,94943,975,94988);o=c[a>>2]|0;f=c[a+4>>2]|0;if(((o|0)==(c[b>>2]|0)?(f|0)==(c[b+4>>2]|0):0)?(u=rw(o,f,(c[b+8>>2]|0)+(c[a+8>>2]|0)|0,d,1)|0,u|0):0){m=c[u+20>>2]|0;n=c[u+24>>2]|0;q=KK(f<<2)|0;d=0;while(1){if((d|0)>=(f|0))break;c[q+(d<<2)>>2]=-1;d=d+1|0}c[m>>2]=0;a:do switch(c[e>>2]|0){case 1:{l=c[a+28>>2]|0;i=c[b+28>>2]|0;j=c[u+28>>2]|0;d=0;f=0;while(1){if((f|0)>=(o|0))break a;k=f+1|0;e=p+(k<<2)|0;a=c[p+(f<<2)>>2]|0;while(1){if((a|0)>=(c[e>>2]|0))break;g=r+(a<<2)|0;c[q+(c[g>>2]<<2)>>2]=d;c[n+(d<<2)>>2]=c[g>>2];h[j+(d<<3)>>3]=+h[l+(a<<3)>>3];a=a+1|0;d=d+1|0}b=s+(k<<2)|0;g=m+(f<<2)|0;f=c[s+(f<<2)>>2]|0;while(1){if((f|0)>=(c[b>>2]|0))break;a=c[t+(f<<2)>>2]|0;e=c[q+(a<<2)>>2]|0;if((e|0)<(c[g>>2]|0)){c[n+(d<<2)>>2]=a;h[j+(d<<3)>>3]=+h[i+(f<<3)>>3];d=d+1|0}else{e=j+(e<<3)|0;h[e>>3]=+h[e>>3]+ +h[i+(f<<3)>>3]}f=f+1|0}c[m+(k<<2)>>2]=d;f=k}}case 2:{l=c[a+28>>2]|0;k=c[b+28>>2]|0;i=c[u+28>>2]|0;f=0;d=0;while(1){if((f|0)>=(o|0))break a;j=f+1|0;e=p+(j<<2)|0;a=c[p+(f<<2)>>2]|0;while(1){if((a|0)>=(c[e>>2]|0))break;b=r+(a<<2)|0;c[q+(c[b>>2]<<2)>>2]=d;c[n+(d<<2)>>2]=c[b>>2];b=a<<1;g=d<<1;h[i+(g<<3)>>3]=+h[l+(b<<3)>>3];h[i+((g|1)<<3)>>3]=+h[l+((b|1)<<3)>>3];a=a+1|0;d=d+1|0}b=s+(j<<2)|0;g=m+(f<<2)|0;f=c[s+(f<<2)>>2]|0;while(1){if((f|0)>=(c[b>>2]|0))break;a=c[t+(f<<2)>>2]|0;e=c[q+(a<<2)>>2]|0;if((e|0)<(c[g>>2]|0)){c[n+(d<<2)>>2]=a;a=f<<1;e=d<<1;h[i+(e<<3)>>3]=+h[k+(a<<3)>>3];h[i+((e|1)<<3)>>3]=+h[k+((a|1)<<3)>>3];d=d+1|0}else{a=f<<1;e=e<<1;v=i+(e<<3)|0;h[v>>3]=+h[v>>3]+ +h[k+(a<<3)>>3];e=i+((e|1)<<3)|0;h[e>>3]=+h[e>>3]+ +h[k+((a|1)<<3)>>3]}f=f+1|0}c[m+(j<<2)>>2]=d;f=j}}case 4:{l=c[a+28>>2]|0;i=c[b+28>>2]|0;j=c[u+28>>2]|0;f=0;d=0;while(1){if((f|0)>=(o|0))break a;k=f+1|0;e=p+(k<<2)|0;a=c[p+(f<<2)>>2]|0;while(1){if((a|0)>=(c[e>>2]|0))break;v=r+(a<<2)|0;c[q+(c[v>>2]<<2)>>2]=d;c[n+(d<<2)>>2]=c[v>>2];c[j+(d<<2)>>2]=c[l+(a<<2)>>2];a=a+1|0;d=d+1|0}b=s+(k<<2)|0;g=m+(f<<2)|0;f=c[s+(f<<2)>>2]|0;while(1){if((f|0)>=(c[b>>2]|0))break;a=c[t+(f<<2)>>2]|0;e=c[q+(a<<2)>>2]|0;if((e|0)<(c[g>>2]|0)){c[n+(d<<2)>>2]=a;c[j+(d<<2)>>2]=c[i+(f<<2)>>2];d=d+1|0}else{v=j+(e<<2)|0;c[v>>2]=(c[v>>2]|0)+(c[i+(f<<2)>>2]|0)}f=f+1|0}c[m+(k<<2)>>2]=d;f=k}}case 8:{d=0;f=0;while(1){if((f|0)>=(o|0))break a;i=f+1|0;e=p+(i<<2)|0;a=c[p+(f<<2)>>2]|0;while(1){if((a|0)>=(c[e>>2]|0))break;v=r+(a<<2)|0;c[q+(c[v>>2]<<2)>>2]=d;c[n+(d<<2)>>2]=c[v>>2];d=d+1|0;a=a+1|0}b=s+(i<<2)|0;g=m+(f<<2)|0;e=c[s+(f<<2)>>2]|0;while(1){if((e|0)>=(c[b>>2]|0))break;a=c[t+(e<<2)>>2]|0;if((c[q+(a<<2)>>2]|0)<(c[g>>2]|0)){c[n+(d<<2)>>2]=a;d=d+1|0}e=e+1|0}c[m+(i<<2)>>2]=d;f=i}}default:d=0}while(0);c[u+8>>2]=d;U1(q);return u|0}return 0}function zw(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;a=tw(a,b,d,e,f)|0;if((c|0)>0)uw(a,c)|0;return a|0}function Aw(a,b){a=a|0;b=b|0;var d=0;if(!(ww(a,b)|0)){b=pw(a)|0;if(!b)b=0;else{d=yw(a,b)|0;qw(b);b=d+36|0;c[b>>2]=c[b>>2]|3;b=d;d=5}}else{b=xw(a)|0;d=5}if((d|0)==5)b=Bw(b)|0;return b|0}function Bw(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,i=0,j=0,k=0,l=0,m=0;a:do if(!a)a=0;else{k=c[a+20>>2]|0;l=c[a+24>>2]|0;d=c[k>>2]|0;b:do switch(c[a+16>>2]|0){case 1:{j=c[a+28>>2]|0;i=0;b=0;while(1){if((i|0)>=(c[a>>2]|0))break b;f=i+1|0;g=k+(f<<2)|0;while(1){e=c[g>>2]|0;if((d|0)>=(e|0))break;e=c[l+(d<<2)>>2]|0;if((e|0)!=(i|0)){c[l+(b<<2)>>2]=e;h[j+(b<<3)>>3]=+h[j+(d<<3)>>3];b=b+1|0}d=d+1|0}c[g>>2]=b;i=f;d=e}}case 2:{j=c[a+28>>2]|0;i=0;b=0;while(1){if((i|0)>=(c[a>>2]|0))break b;f=i+1|0;g=k+(f<<2)|0;while(1){e=c[g>>2]|0;if((d|0)>=(e|0))break;e=c[l+(d<<2)>>2]|0;if((e|0)!=(i|0)){c[l+(b<<2)>>2]=e;m=d<<1;e=b<<1;h[j+(e<<3)>>3]=+h[j+(m<<3)>>3];h[j+((e|1)<<3)>>3]=+h[j+((m|1)<<3)>>3];b=b+1|0}d=d+1|0}c[g>>2]=b;i=f;d=e}}case 4:{j=c[a+28>>2]|0;i=0;b=0;while(1){if((i|0)>=(c[a>>2]|0))break b;f=i+1|0;g=k+(f<<2)|0;while(1){e=c[g>>2]|0;if((d|0)>=(e|0))break;e=c[l+(d<<2)>>2]|0;if((e|0)!=(i|0)){c[l+(b<<2)>>2]=e;c[j+(b<<2)>>2]=c[j+(d<<2)>>2];b=b+1|0}d=d+1|0}c[g>>2]=b;i=f;d=e}}case 8:{i=0;b=0;while(1){if((i|0)>=(c[a>>2]|0))break b;f=i+1|0;g=k+(f<<2)|0;while(1){e=c[g>>2]|0;if((d|0)>=(e|0))break;e=c[l+(d<<2)>>2]|0;if((e|0)!=(i|0)){c[l+(b<<2)>>2]=e;b=b+1|0}d=d+1|0}c[g>>2]=b;i=f;d=e}}default:{a=0;break a}}while(0);c[a+8>>2]=b}while(0);return a|0}function Cw(a){a=a|0;if((c[a+32>>2]|0)==2)return Dw(c[a+8>>2]|0,c[a>>2]|0,c[a+4>>2]|0,c[a+20>>2]|0,c[a+24>>2]|0,c[a+28>>2]|0,c[a+16>>2]|0,c[a+40>>2]|0)|0;else qa(95100,94943,803,95126);return 0}function Dw(a,b,c,d,e,f,g,h){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;return Ew(a,b,c,d,e,f,g,h,1)|0}
+function J_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;j=b+72|0;k=b+352|0;l=b+356|0;m=b+360|0;i=0;a:while(1){h=f-e|0;if((h|0)<=0){f=-1;break}switch(a[j+(d[e>>0]|0)>>0]|0){case 8:case 1:case 0:{n=16;break a}case 5:{if((h|0)<2){f=-2;break a}if(lb[c[k>>2]&127](b,e)|0){n=6;break a}h=i;e=e+2|0;i=h;continue a}case 6:{if((h|0)<3){f=-2;break a}if(lb[c[l>>2]&127](b,e)|0){n=10;break a}h=i;e=e+3|0;i=h;continue a}case 7:{if((h|0)<4){f=-2;break a}if(lb[c[m>>2]&127](b,e)|0){n=14;break a}h=i;e=e+4|0;i=h;continue a}case 2:{h=e+1|0;if((f-h|0)<=0){f=-1;break a}if((a[h>>0]|0)!=33){o=i;e=h;i=o;continue a}h=e+2|0;if((f-h|0)<=0){f=-1;break a}o=(a[h>>0]|0)==91;i=i+(o&1)|0;e=o?e+3|0:h;continue a}case 4:{h=e+1|0;if((f-h|0)<=0){f=-1;break a}if((a[h>>0]|0)!=93){o=i;e=h;i=o;continue a}h=e+2|0;if((f-h|0)<=0){f=-1;break a}if((a[h>>0]|0)!=62){o=i;e=h;i=o;continue a}e=e+3|0;if(!i){n=26;break a}i=i+-1|0;continue a}default:{o=i;e=e+1|0;i=o;continue a}}}if((n|0)==6){c[g>>2]=e;f=0}else if((n|0)==10){c[g>>2]=e;f=0}else if((n|0)==14){c[g>>2]=e;f=0}else if((n|0)==16){c[g>>2]=e;f=0}else if((n|0)==26){c[g>>2]=e;f=42}return f|0}function K_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;do if(e>>>0<f>>>0){k=f;if((k-e|0)>0){l=b+72|0;i=e;a:while(1){if((k-i|0)<=0){j=24;break}switch(a[l+(d[i>>0]|0)>>0]|0){case 3:{j=8;break a}case 2:{j=11;break a}case 10:{j=12;break a}case 9:{j=15;break a}case 21:{j=19;break a}case 5:{h=2;break}case 6:{h=3;break}case 7:{h=4;break}default:h=1}i=i+h|0}if((j|0)==8)if((i|0)==(e|0)){h=W_(b,e+1|0,f,g)|0;break}else{c[g>>2]=i;h=6;break}else if((j|0)==11){c[g>>2]=i;h=0;break}else if((j|0)==12)if((i|0)==(e|0)){c[g>>2]=e+1;h=7;break}else{c[g>>2]=i;h=6;break}else if((j|0)==15){if((i|0)!=(e|0)){c[g>>2]=i;h=6;break}h=e+1|0;if((k-h|0)<=0){h=-3;break}c[g>>2]=(a[l+(d[h>>0]|0)>>0]|0)==10?e+2|0:h;h=7;break}else if((j|0)==19)if((i|0)==(e|0)){c[g>>2]=e+1;h=39;break}else{c[g>>2]=i;h=6;break}else if((j|0)==24){c[g>>2]=i;h=6;break}}else h=-1}else h=-4;while(0);return h|0}function L_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;do if(e>>>0<f>>>0){k=f;if((k-e|0)>0){l=b+72|0;i=e;a:while(1){if((k-i|0)<=0){j=23;break}switch(a[l+(d[i>>0]|0)>>0]|0){case 3:{j=8;break a}case 30:{j=11;break a}case 10:{j=14;break a}case 9:{j=17;break a}case 5:{h=2;break}case 6:{h=3;break}case 7:{h=4;break}default:h=1}i=i+h|0}if((j|0)==8)if((i|0)==(e|0)){h=W_(b,e+1|0,f,g)|0;break}else{c[g>>2]=i;h=6;break}else if((j|0)==11)if((i|0)==(e|0)){h=X_(b,e+1|0,f,g)|0;h=(h|0)==22?0:h;break}else{c[g>>2]=i;h=6;break}else if((j|0)==14)if((i|0)==(e|0)){c[g>>2]=e+1;h=7;break}else{c[g>>2]=i;h=6;break}else if((j|0)==17){if((i|0)!=(e|0)){c[g>>2]=i;h=6;break}h=e+1|0;if((k-h|0)<=0){h=-3;break}c[g>>2]=(a[l+(d[h>>0]|0)>>0]|0)==10?e+2|0:h;h=7;break}else if((j|0)==23){c[g>>2]=i;h=6;break}}else h=-1}else h=-4;while(0);return h|0}function M_(b,c,d,e){b=b|0;c=c|0;d=d|0;e=e|0;var f=0,g=0;f=d;b=c;while(1){c=a[e>>0]|0;if(!(c<<24>>24)){g=6;break}if((f-b|0)<1){b=0;break}if((a[b>>0]|0)!=c<<24>>24){b=0;break}e=e+1|0;b=b+1|0}if((g|0)==6)b=(b|0)==(d|0)&1;return b|0}function N_(b,c){b=b|0;c=c|0;var e=0,f=0;f=b+72|0;b=c;a:while(1){switch(a[f+(d[b>>0]|0)>>0]|0){case 5:{e=2;break}case 6:{e=3;break}case 7:{e=4;break}case 27:case 26:case 25:case 24:case 23:case 22:case 29:{e=1;break}default:break a}b=b+e|0}return b-c|0}function O_(b,c){b=b|0;c=c|0;b=b+72|0;a:while(1){switch(a[b+(d[c>>0]|0)>>0]|0){case 21:case 9:case 10:break;default:break a}c=c+1|0}return c|0}function P_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;l=b+72|0;j=0;k=0;b=1;a:while(1){h=e+1|0;i=a[h>>0]|0;do switch(a[l+(i&255)>>0]|0){case 5:{if(!b)if((k|0)<(f|0)){c[g+(k<<4)>>2]=h;a[g+(k<<4)+12>>0]=1;b=1}else b=1;h=k;i=j;e=e+2|0;k=h;j=i;continue a}case 6:{if(!b)if((k|0)<(f|0)){c[g+(k<<4)>>2]=h;a[g+(k<<4)+12>>0]=1;b=1}else b=1;h=k;i=j;e=e+3|0;k=h;j=i;continue a}case 7:{if(!b)if((k|0)<(f|0)){c[g+(k<<4)>>2]=h;a[g+(k<<4)+12>>0]=1;b=1}else b=1;h=k;i=j;e=e+4|0;k=h;j=i;continue a}case 24:case 22:case 29:{if(b|0){o=b;n=k;i=j;e=h;b=o;k=n;j=i;continue a}if((k|0)>=(f|0)){n=k;o=j;b=1;e=h;k=n;j=o;continue a}c[g+(k<<4)>>2]=h;a[g+(k<<4)+12>>0]=1;n=k;o=j;b=1;e=h;k=n;j=o;continue a}case 12:{if((b|0)!=2){if((k|0)>=(f|0)){o=k;j=12;b=2;e=h;k=o;continue a}c[g+(k<<4)+4>>2]=e+2;o=k;j=12;b=2;e=h;k=o;continue a}if((j|0)!=12){n=k;o=j;b=2;e=h;k=n;j=o;continue a}if((k|0)<(f|0))c[g+(k<<4)+8>>2]=h;j=12;k=k+1|0;b=0;e=h;continue a}case 13:{if((b|0)!=2){if((k|0)>=(f|0)){o=k;j=13;b=2;e=h;k=o;continue a}c[g+(k<<4)+4>>2]=e+2;o=k;j=13;b=2;e=h;k=o;continue a}if((j|0)!=13){n=k;o=j;b=2;e=h;k=n;j=o;continue a}if((k|0)<(f|0))c[g+(k<<4)+8>>2]=h;j=13;k=k+1|0;b=0;e=h;continue a}case 3:{if((k|0)>=(f|0)){i=b;n=k;o=j;e=h;b=i;k=n;j=o;continue a}a[g+(k<<4)+12>>0]=0;i=b;n=k;o=j;e=h;b=i;k=n;j=o;continue a}case 21:{switch(b|0){case 1:{n=k;o=j;b=0;e=h;k=n;j=o;continue a}case 2:break;default:{i=b;n=k;o=j;e=h;b=i;k=n;j=o;continue a}}if((k|0)>=(f|0)){n=k;o=j;b=2;e=h;k=n;j=o;continue a}b=g+(k<<4)+12|0;if(!(a[b>>0]|0)){n=k;o=j;b=2;e=h;k=n;j=o;continue a}if(((i<<24>>24==32?(h|0)!=(c[g+(k<<4)+4>>2]|0):0)?(m=a[e+2>>0]|0,m<<24>>24!=32):0)?(j|0)!=(d[l+(m&255)>>0]|0):0){n=k;o=j;b=2;e=h;k=n;j=o;continue a}a[b>>0]=0;n=k;o=j;b=2;e=h;k=n;j=o;continue a}case 10:case 9:{switch(b|0){case 1:{n=k;o=j;b=0;e=h;k=n;j=o;continue a}case 2:break;default:{i=b;n=k;o=j;e=h;b=i;k=n;j=o;continue a}}if((k|0)>=(f|0)){n=k;o=j;b=2;e=h;k=n;j=o;continue a}a[g+(k<<4)+12>>0]=0;n=k;o=j;b=2;e=h;k=n;j=o;continue a}case 17:case 11:if((b|0)==2){n=k;o=j;b=2;e=h;k=n;j=o;continue a}else break a;default:{i=b;n=k;o=j;e=h;b=i;k=n;j=o;continue a}}while(0)}return k|0}function Q_(b,c){b=b|0;c=c|0;var d=0,e=0;b=c+2|0;d=a[b>>0]|0;a:do if(d<<24>>24==120){d=c+3|0;b=0;while(1){b:while(1){e=a[d>>0]|0;c=e<<24>>24;if(e<<24>>24==59){e=14;break a}switch(c|0){case 57:case 56:case 55:case 54:case 53:case 52:case 51:case 50:case 49:case 48:{e=7;break b}case 70:case 69:case 68:case 67:case 66:case 65:{e=8;break b}case 102:case 101:case 100:case 99:case 98:case 97:{e=9;break b}default:{}}d=d+1|0}if((e|0)==7){e=0;b=c+-48|b<<4}else if((e|0)==8){e=0;b=(b<<4)+-55+c|0}else if((e|0)==9){e=0;b=(b<<4)+-87+c|0}if((b|0)<1114112)d=d+1|0;else{b=-1;break}}}else{c=b;b=0;while(1){if(d<<24>>24==59){e=14;break a}b=(d<<24>>24)+-48+(b*10|0)|0;if((b|0)>=1114112){b=-1;break a}d=c+1|0;c=d;d=a[d>>0]|0}}while(0);if((e|0)==14)b=A_(b)|0;return b|0}function R_(b,c,d){b=b|0;c=c|0;d=d|0;var e=0;a:do switch(d-c|0){case 2:{if((a[c+1>>0]|0)==116){b=a[c>>0]|0;b=b<<24>>24==108?60:b<<24>>24==103?62:0}else e=14;break}case 3:{if(((a[c>>0]|0)==97?(a[c+1>>0]|0)==109:0)?(a[c+2>>0]|0)==112:0)b=38;else e=14;break}case 4:switch(a[c>>0]|0){case 113:{if((a[c+1>>0]|0)!=117){e=14;break a}if((a[c+2>>0]|0)!=111){e=14;break a}if((a[c+3>>0]|0)==116){b=34;break a}else{e=14;break a}}case 97:{if((a[c+1>>0]|0)!=112){e=14;break a}if((a[c+2>>0]|0)!=111){e=14;break a}if((a[c+3>>0]|0)==115){b=39;break a}else{e=14;break a}}default:{e=14;break a}}default:e=14}while(0);if((e|0)==14)b=0;return b|0}function S_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0;h=b+72|0;i=g+4|0;while(1){if((f-e|0)<=0)break;switch(a[h+(d[e>>0]|0)>>0]|0){case 5:{e=e+2|0;break}case 6:{e=e+3|0;break}case 7:{e=e+4|0;break}case 10:{c[i>>2]=-1;c[g>>2]=(c[g>>2]|0)+1;e=e+1|0;break}case 9:{c[g>>2]=(c[g>>2]|0)+1;b=e+1|0;if((f-b|0)>0)e=(a[h+(d[b>>0]|0)>>0]|0)==10?e+2|0:b;else e=b;c[i>>2]=-1;break}default:e=e+1|0}c[i>>2]=(c[i>>2]|0)+1}return}function T_(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;g=e+-1|0;b=b+72|0;a:while(1){d=d+1|0;if((g-d|0)<=0){d=1;break}e=a[d>>0]|0;switch(a[b+(e&255)>>0]|0){case 23:case 19:case 30:case 33:case 16:case 18:case 10:case 9:case 15:case 14:case 17:case 35:case 34:case 32:case 31:case 13:case 27:case 24:case 25:continue a;case 21:if(e<<24>>24==9){h=7;break a}else continue a;case 22:case 26:{if(e<<24>>24>=0)continue a;break}default:{}}switch(e<<24>>24|0){case 64:case 36:break;default:{h=7;break a}}}if((h|0)==7){c[f>>2]=d;d=0}return d|0}function U_(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0;i=h;while(1){j=c[e>>2]|0;if((j|0)==(f|0)){b=0;break}k=d[j>>0]|0;b=c[g>>2]|0;if(!(k&128)){if((b|0)==(h|0)){b=2;break}c[e>>2]=j+1;j=a[j>>0]|0;k=c[g>>2]|0;c[g>>2]=k+1;a[k>>0]=j;continue}else{if((i-b|0)<2){b=2;break}c[g>>2]=b+1;a[b>>0]=k>>>6|192;j=c[g>>2]|0;c[g>>2]=j+1;a[j>>0]=k&63|128;c[e>>2]=(c[e>>2]|0)+1;continue}}return b|0}function V_(a,e,f,g,h){a=a|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0;while(1){a=c[e>>2]|0;i=a>>>0<f>>>0;j=c[g>>2]|0;if(!(i&j>>>0<h>>>0))break;c[e>>2]=a+1;i=d[a>>0]|0;j=c[g>>2]|0;c[g>>2]=j+2;b[j>>1]=i}return ((j|0)!=(h|0)|i^1?0:2)|0}function W_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;k=f;h=k-e|0;a:do if((h|0)>0){l=b+72|0;switch(a[l+(d[e>>0]|0)>>0]|0){case 29:{c[g>>2]=e;f=0;break a}case 24:case 22:{f=1;break}case 5:{if((h|0)<2){f=-2;break a}if(!(lb[c[b+340>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else f=2;break}case 6:{if((h|0)<3){f=-2;break a}if(!(lb[c[b+344>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else f=3;break}case 7:{if((h|0)<4){f=-2;break a}if(!(lb[c[b+348>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else f=4;break}case 19:{f=Y_(b,e+1|0,f,g)|0;break a}default:{c[g>>2]=e;f=0;break a}}h=b+328|0;i=b+332|0;j=b+336|0;e=e+f|0;b:while(1){f=k-e|0;if((f|0)<=0){f=-1;break a}switch(a[l+(d[e>>0]|0)>>0]|0){case 29:{h=18;break b}case 18:{h=29;break b}case 27:case 26:case 25:case 24:case 22:{f=1;break}case 5:{if((f|0)<2){f=-2;break a}if(!(lb[c[h>>2]&127](b,e)|0)){h=21;break b}else f=2;break}case 6:{if((f|0)<3){f=-2;break a}if(!(lb[c[i>>2]&127](b,e)|0)){h=24;break b}else f=3;break}case 7:{if((f|0)<4){f=-2;break a}if(!(lb[c[j>>2]&127](b,e)|0)){h=27;break b}else f=4;break}default:{h=30;break b}}e=e+f|0}if((h|0)==18){c[g>>2]=e;f=0;break}else if((h|0)==21){c[g>>2]=e;f=0;break}else if((h|0)==24){c[g>>2]=e;f=0;break}else if((h|0)==27){c[g>>2]=e;f=0;break}else if((h|0)==29){c[g>>2]=e+1;f=9;break}else if((h|0)==30){c[g>>2]=e;f=0;break}}else f=-1;while(0);return f|0}function X_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;h=f-e|0;a:do if((h|0)>0){l=b+72|0;switch(a[l+(d[e>>0]|0)>>0]|0){case 29:{c[g>>2]=e;f=0;break a}case 24:case 22:{h=1;break}case 5:{if((h|0)<2){f=-2;break a}if(!(lb[c[b+340>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=2;break}case 6:{if((h|0)<3){f=-2;break a}if(!(lb[c[b+344>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=3;break}case 7:{if((h|0)<4){f=-2;break a}if(!(lb[c[b+348>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=4;break}case 30:case 9:case 10:case 21:{c[g>>2]=e;f=22;break a}default:{c[g>>2]=e;f=0;break a}}i=b+328|0;j=b+332|0;k=b+336|0;e=e+h|0;b:while(1){h=f-e|0;if((h|0)<=0){f=-1;break a}switch(a[l+(d[e>>0]|0)>>0]|0){case 29:{h=18;break b}case 18:{h=29;break b}case 27:case 26:case 25:case 24:case 22:{h=1;break}case 5:{if((h|0)<2){f=-2;break a}if(!(lb[c[i>>2]&127](b,e)|0)){h=21;break b}else h=2;break}case 6:{if((h|0)<3){f=-2;break a}if(!(lb[c[j>>2]&127](b,e)|0)){h=24;break b}else h=3;break}case 7:{if((h|0)<4){f=-2;break a}if(!(lb[c[k>>2]&127](b,e)|0)){h=27;break b}else h=4;break}default:{h=30;break b}}e=e+h|0}if((h|0)==18){c[g>>2]=e;f=0;break}else if((h|0)==21){c[g>>2]=e;f=0;break}else if((h|0)==24){c[g>>2]=e;f=0;break}else if((h|0)==27){c[g>>2]=e;f=0;break}else if((h|0)==29){c[g>>2]=e+1;f=28;break}else if((h|0)==30){c[g>>2]=e;f=0;break}}else f=-1;while(0);return f|0}function Y_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0;i=f;a:do if((i-e|0)>0){h=a[e>>0]|0;if(h<<24>>24==120){b=Z_(b,e+1|0,f,g)|0;break}f=b+72|0;if((a[f+(h&255)>>0]|0)!=25){c[g>>2]=e;b=0;break}b:while(1){b=e+1|0;if((i-b|0)<=0){b=-1;break a}switch(a[f+(d[b>>0]|0)>>0]|0){case 25:{e=b;break}case 18:{f=8;break b}default:{f=9;break b}}}if((f|0)==8){c[g>>2]=e+2;b=10;break}else if((f|0)==9){c[g>>2]=b;b=0;break}}else b=-1;while(0);return b|0}function Z_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0;a:do if((f-e|0)>0){b=b+72|0;if((a[b+(d[e>>0]|0)>>0]&-2)<<24>>24!=24){c[g>>2]=e;f=0;break}b:while(1){h=e+1|0;if((f-h|0)<=0){f=-1;break a}switch(a[b+(d[h>>0]|0)>>0]|0){case 24:case 25:{e=h;break}case 18:{b=6;break b}default:{b=7;break b}}}if((b|0)==6){c[g>>2]=e+2;f=10;break}else if((b|0)==7){c[g>>2]=h;f=0;break}}else f=-1;while(0);return f|0}function __(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;q=f;h=q-e|0;a:do if((h|0)>0){p=b+72|0;switch(a[p+(d[e>>0]|0)>>0]|0){case 29:{c[g>>2]=e;h=0;break a}case 24:case 22:{h=1;break}case 5:{if((h|0)<2){h=-2;break a}if(!(lb[c[b+340>>2]&127](b,e)|0)){c[g>>2]=e;h=0;break a}else h=2;break}case 6:{if((h|0)<3){h=-2;break a}if(!(lb[c[b+344>>2]&127](b,e)|0)){c[g>>2]=e;h=0;break a}else h=3;break}case 7:{if((h|0)<4){h=-2;break a}if(!(lb[c[b+348>>2]&127](b,e)|0)){c[g>>2]=e;h=0;break a}else h=4;break}case 16:{h=e+1|0;if((q-h|0)<=0){h=-1;break a}switch(a[p+(d[h>>0]|0)>>0]|0){case 27:{h=$_(b,e+2|0,f,g)|0;break a}case 20:{h=a$(e+2|0,f,g)|0;break a}default:{c[g>>2]=h;h=0;break a}}}case 15:{h=b$(b,e+1|0,f,g)|0;break a}case 17:{h=c$(b,e+1|0,f,g)|0;break a}default:{c[g>>2]=e;h=0;break a}}j=b+328|0;k=b+332|0;l=b+336|0;m=b+340|0;n=b+344|0;o=b+348|0;i=0;h=e+h|0;b:while(1){e=q-h|0;if((e|0)<=0){h=-1;break a}c:do switch(a[p+(d[h>>0]|0)>>0]|0){case 29:{r=24;break b}case 10:case 9:case 21:{r=50;break b}case 11:{r=64;break b}case 17:break b;case 27:case 26:case 25:case 24:case 22:{e=1;break}case 5:{if((e|0)<2){h=-2;break a}if(!(lb[c[j>>2]&127](b,h)|0)){r=27;break b}else e=2;break}case 6:{if((e|0)<3){h=-2;break a}if(!(lb[c[k>>2]&127](b,h)|0)){r=30;break b}else e=3;break}case 7:{if((e|0)<4){h=-2;break a}if(!(lb[c[l>>2]&127](b,h)|0)){r=33;break b}else e=4;break}case 23:{if(i|0){r=35;break b}i=h+1|0;e=q-i|0;if((e|0)<=0){h=-1;break a}switch(a[p+(d[i>>0]|0)>>0]|0){case 29:{r=38;break b}case 24:case 22:{i=1;e=2;break c}case 5:{if((e|0)<2){h=-2;break a}if(!(lb[c[m>>2]&127](b,i)|0)){r=42;break b}else{i=1;e=3;break c}}case 6:{if((e|0)<3){h=-2;break a}if(!(lb[c[n>>2]&127](b,i)|0)){r=45;break b}else{i=1;e=4;break c}}case 7:{if((e|0)<4){h=-2;break a}if(!(lb[c[o>>2]&127](b,i)|0)){r=48;break b}else{i=1;e=5;break c}}default:{r=49;break b}}}default:{r=69;break b}}while(0);h=h+e|0}d:switch(r|0){case 24:{c[g>>2]=h;h=0;break a}case 27:{c[g>>2]=h;h=0;break a}case 30:{c[g>>2]=h;h=0;break a}case 33:{c[g>>2]=h;h=0;break a}case 35:{c[g>>2]=h;h=0;break a}case 38:{c[g>>2]=i;h=0;break a}case 42:{c[g>>2]=i;h=0;break a}case 45:{c[g>>2]=i;h=0;break a}case 48:{c[g>>2]=i;h=0;break a}case 49:{c[g>>2]=i;h=0;break a}case 50:{e:while(1){r=0;i=h+1|0;e=q-i|0;if((e|0)<=0){h=-1;break a}switch(a[p+(d[i>>0]|0)>>0]|0){case 11:{h=i;r=64;break d}case 17:{h=i;break d}case 10:case 9:case 21:{h=i;r=50;break}case 29:{r=52;break e}case 24:case 22:{i=2;break e}case 5:{r=53;break e}case 6:{r=56;break e}case 7:{r=59;break e}default:{r=62;break e}}}if((r|0)==52){c[g>>2]=i;h=0;break a}else if((r|0)==53){if((e|0)<2){h=-2;break a}if(!(lb[c[m>>2]&127](b,i)|0)){c[g>>2]=i;h=0;break a}else i=3}else if((r|0)==56){if((e|0)<3){h=-2;break a}if(!(lb[c[n>>2]&127](b,i)|0)){c[g>>2]=i;h=0;break a}else i=4}else if((r|0)==59){if((e|0)<4){h=-2;break a}if(!(lb[c[o>>2]&127](b,i)|0)){c[g>>2]=i;h=0;break a}else i=5}else if((r|0)==62){c[g>>2]=i;h=0;break a}h=d$(b,h+i|0,f,g)|0;break a}case 69:{c[g>>2]=h;h=0;break a}}if((r|0)==64){c[g>>2]=h+1;h=2;break}i=h+1|0;if((q-i|0)>0)if((a[i>>0]|0)==62){c[g>>2]=h+2;h=4;break}else{c[g>>2]=i;h=0;break}else h=-1}else h=-1;while(0);return h|0}function $_(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;a:do if((f-e|0)>0){if((a[e>>0]|0)!=45){c[g>>2]=e;f=0;break}h=b+72|0;i=b+352|0;j=b+356|0;k=b+360|0;l=e+1|0;b:while(1){e=f-l|0;if((e|0)<=0){f=-1;break a}switch(a[h+(d[l>>0]|0)>>0]|0){case 8:case 1:case 0:{e=19;break b}case 5:{if((e|0)<2){f=-2;break a}if(lb[c[i>>2]&127](b,l)|0){e=9;break b}l=l+2|0;continue b}case 6:{if((e|0)<3){f=-2;break a}if(lb[c[j>>2]&127](b,l)|0){e=13;break b}l=l+3|0;continue b}case 7:{if((e|0)<4){f=-2;break a}if(lb[c[k>>2]&127](b,l)|0){e=17;break b}l=l+4|0;continue b}case 27:{e=l+1|0;if((f-e|0)<=0){f=-1;break a}if((a[e>>0]|0)==45){e=22;break b}else{l=e;continue b}}default:{l=l+1|0;continue b}}}if((e|0)==9){c[g>>2]=l;f=0;break}else if((e|0)==13){c[g>>2]=l;f=0;break}else if((e|0)==17){c[g>>2]=l;f=0;break}else if((e|0)==19){c[g>>2]=l;f=0;break}else if((e|0)==22){e=l+2|0;if((f-e|0)<=0){f=-1;break}if((a[e>>0]|0)==62){c[g>>2]=l+3;f=13;break}else{c[g>>2]=e;f=0;break}}}else f=-1;while(0);return f|0}function a$(b,d,e){b=b|0;d=d|0;e=e|0;if((d-b|0)>5){d=0;while(1){if((d|0)>=6){d=8;break}if((a[b>>0]|0)!=(a[144300+d>>0]|0)){d=0;break}d=d+1|0;b=b+1|0}c[e>>2]=b}else d=-1;return d|0}function b$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+16|0;p=q;o=f;f=o-e|0;a:do if((f|0)>0){n=b+72|0;switch(a[n+(d[e>>0]|0)>>0]|0){case 29:{c[g>>2]=e;f=0;break a}case 24:case 22:{f=1;break}case 5:{if((f|0)<2){f=-2;break a}if(!(lb[c[b+340>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else f=2;break}case 6:{if((f|0)<3){f=-2;break a}if(!(lb[c[b+344>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else f=3;break}case 7:{if((f|0)<4){f=-2;break a}if(!(lb[c[b+348>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else f=4;break}default:{c[g>>2]=e;f=0;break a}}i=b+328|0;j=b+332|0;k=b+336|0;f=e+f|0;b:while(1){h=o-f|0;if((h|0)<=0){f=-1;break a}switch(a[n+(d[f>>0]|0)>>0]|0){case 29:{m=17;break b}case 10:case 9:case 21:{m=28;break b}case 15:{m=50;break b}case 27:case 26:case 25:case 24:case 22:{h=1;break}case 5:{if((h|0)<2){f=-2;break a}if(!(lb[c[i>>2]&127](b,f)|0)){m=20;break b}else h=2;break}case 6:{if((h|0)<3){f=-2;break a}if(!(lb[c[j>>2]&127](b,f)|0)){m=23;break b}else h=3;break}case 7:{if((h|0)<4){f=-2;break a}if(!(lb[c[k>>2]&127](b,f)|0)){m=26;break b}else h=4;break}default:break b}f=f+h|0}if((m|0)==17){c[g>>2]=f;f=0;break}else if((m|0)==20){c[g>>2]=f;f=0;break}else if((m|0)==23){c[g>>2]=f;f=0;break}else if((m|0)==26){c[g>>2]=f;f=0;break}else if((m|0)==28){if(!(e$(e,f,p)|0)){c[g>>2]=f;f=0;break}i=b+352|0;j=b+356|0;k=b+360|0;h=f+1|0;c:while(1){f=o-h|0;if((f|0)<=0){f=-1;break a}switch(a[n+(d[h>>0]|0)>>0]|0){case 8:case 1:case 0:{m=45;break c}case 5:{if((f|0)<2){f=-2;break a}if(lb[c[i>>2]&127](b,h)|0){m=35;break c}h=h+2|0;continue c}case 6:{if((f|0)<3){f=-2;break a}if(lb[c[j>>2]&127](b,h)|0){m=39;break c}h=h+3|0;continue c}case 7:{if((f|0)<4){f=-2;break a}if(lb[c[k>>2]&127](b,h)|0){m=43;break c}h=h+4|0;continue c}case 15:{f=h+1|0;if((o-f|0)<=0){f=-1;break a}if((a[f>>0]|0)==62){m=48;break c}else{h=f;continue c}}default:{h=h+1|0;continue c}}}if((m|0)==35){c[g>>2]=h;f=0;break}else if((m|0)==39){c[g>>2]=h;f=0;break}else if((m|0)==43){c[g>>2]=h;f=0;break}else if((m|0)==45){c[g>>2]=h;f=0;break}else if((m|0)==48){c[g>>2]=h+2;f=c[p>>2]|0;break}}else if((m|0)==50){if(!(e$(e,f,p)|0)){c[g>>2]=f;f=0;break}h=f+1|0;if((o-h|0)<=0){f=-1;break}if((a[h>>0]|0)==62){c[g>>2]=f+2;f=c[p>>2]|0;break}else f=h}c[g>>2]=f;f=0}else f=-1;while(0);l=q;return f|0}function c$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;h=f-e|0;a:do if((h|0)>0){l=b+72|0;switch(a[l+(d[e>>0]|0)>>0]|0){case 29:{c[g>>2]=e;f=0;break a}case 24:case 22:{h=1;break}case 5:{if((h|0)<2){f=-2;break a}if(!(lb[c[b+340>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=2;break}case 6:{if((h|0)<3){f=-2;break a}if(!(lb[c[b+344>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=3;break}case 7:{if((h|0)<4){f=-2;break a}if(!(lb[c[b+348>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=4;break}default:{c[g>>2]=e;f=0;break a}}i=b+328|0;j=b+332|0;k=b+336|0;h=e+h|0;b:while(1){e=f-h|0;if((e|0)<=0){f=-1;break a}switch(a[l+(d[h>>0]|0)>>0]|0){case 29:{i=17;break b}case 10:case 9:case 21:{i=27;break b}case 11:{i=32;break b}case 23:case 27:case 26:case 25:case 24:case 22:{e=1;break}case 5:{if((e|0)<2){f=-2;break a}if(!(lb[c[i>>2]&127](b,h)|0)){i=20;break b}else e=2;break}case 6:{if((e|0)<3){f=-2;break a}if(!(lb[c[j>>2]&127](b,h)|0)){i=23;break b}else e=3;break}case 7:{if((e|0)<4){f=-2;break a}if(!(lb[c[k>>2]&127](b,h)|0)){i=26;break b}else e=4;break}default:{i=33;break b}}h=h+e|0}if((i|0)==17){c[g>>2]=h;f=0;break}else if((i|0)==20){c[g>>2]=h;f=0;break}else if((i|0)==23){c[g>>2]=h;f=0;break}else if((i|0)==26){c[g>>2]=h;f=0;break}else if((i|0)==27){c:while(1){e=h+1|0;if((f-e|0)<=0){f=-1;break a}switch(a[l+(d[e>>0]|0)>>0]|0){case 10:case 9:case 21:{h=e;i=27;break}case 11:{i=29;break c}default:{i=30;break c}}}if((i|0)==29){c[g>>2]=h+2;f=5;break}else if((i|0)==30){c[g>>2]=e;f=0;break}}else if((i|0)==32){c[g>>2]=h+1;f=5;break}else if((i|0)==33){c[g>>2]=h;f=0;break}}else f=-1;while(0);return f|0}function d$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;y=l;l=l+16|0;v=y;c[v>>2]=e;w=f;r=b+72|0;s=b+328|0;t=b+332|0;u=b+336|0;k=b+340|0;m=b+344|0;n=b+348|0;o=b+352|0;p=b+356|0;q=b+360|0;i=0;a:while(1){h=w-e|0;if((h|0)<=0){e=-1;break}b:do switch(a[r+(d[e>>0]|0)>>0]|0){case 29:{x=4;break a}case 27:case 26:case 25:case 24:case 22:{h=e+1|0;c[v>>2]=h;j=i;e=h;i=j;continue a}case 5:{if((h|0)<2){e=-2;break a}if(!(lb[c[s>>2]&127](b,e)|0)){x=8;break a}h=e+2|0;c[v>>2]=h;j=i;e=h;i=j;continue a}case 6:{if((h|0)<3){e=-2;break a}if(!(lb[c[t>>2]&127](b,e)|0)){x=12;break a}h=e+3|0;c[v>>2]=h;j=i;e=h;i=j;continue a}case 7:{if((h|0)<4){e=-2;break a}if(!(lb[c[u>>2]&127](b,e)|0)){x=16;break a}h=e+4|0;c[v>>2]=h;j=i;e=h;i=j;continue a}case 23:{if(i|0){x=19;break a}h=e+1|0;c[v>>2]=h;i=w-h|0;if((i|0)<=0){e=-1;break a}switch(a[r+(d[h>>0]|0)>>0]|0){case 29:{x=22;break a}case 24:case 22:{j=e+2|0;c[v>>2]=j;i=1;e=j;continue a}case 5:{if((i|0)<2){e=-2;break a}if(!(lb[c[k>>2]&127](b,h)|0)){x=26;break a}j=e+3|0;c[v>>2]=j;i=1;e=j;continue a}case 6:{if((i|0)<3){e=-2;break a}if(!(lb[c[m>>2]&127](b,h)|0)){x=30;break a}j=e+4|0;c[v>>2]=j;i=1;e=j;continue a}case 7:{if((i|0)<4){e=-2;break a}if(!(lb[c[n>>2]&127](b,h)|0)){x=34;break a}j=e+5|0;c[v>>2]=j;i=1;e=j;continue a}default:{x=36;break a}}}case 10:case 9:case 21:while(1){e=e+1|0;c[v>>2]=e;if((w-e|0)<=0){e=-1;break a}switch(a[r+(d[e>>0]|0)>>0]|0){case 14:break b;case 9:case 10:case 21:break;default:{x=39;break a}}}case 14:break;default:{x=92;break a}}while(0);while(1){h=e+1|0;c[v>>2]=h;if((w-h|0)<=0){e=-1;break a}j=a[r+(d[h>>0]|0)>>0]|0;if((j|1)<<24>>24==13)break;switch(j<<24>>24){case 9:case 10:case 21:{e=h;break}default:{x=43;break a}}}e=e+2|0;c[v>>2]=e;c:while(1){h=w-e|0;if((h|0)<=0){e=-1;break a}i=a[r+(d[e>>0]|0)>>0]|0;if(i<<24>>24==j<<24>>24)break;switch(i<<24>>24){case 8:case 1:case 0:{x=60;break a}case 2:{x=65;break a}case 5:{if((h|0)<2){e=-2;break a}if(lb[c[o>>2]&127](b,e)|0){x=50;break a}i=e+2|0;c[v>>2]=i;e=i;continue c}case 6:{if((h|0)<3){e=-2;break a}if(lb[c[p>>2]&127](b,e)|0){x=54;break a}i=e+3|0;c[v>>2]=i;e=i;continue c}case 7:{if((h|0)<4){e=-2;break a}if(lb[c[q>>2]&127](b,e)|0){x=58;break a}i=e+4|0;c[v>>2]=i;e=i;continue c}case 3:{e=W_(b,e+1|0,f,v)|0;if((e|0)<1){x=63;break a}e=c[v>>2]|0;continue c}default:{i=e+1|0;c[v>>2]=i;e=i;continue c}}}h=e+1|0;c[v>>2]=h;if((w-h|0)<=0){e=-1;break}switch(a[r+(d[h>>0]|0)>>0]|0){case 11:{x=86;break a}case 17:{x=87;break a}case 10:case 9:case 21:{e=h;break}default:{x=69;break a}}d:while(1){h=e+1|0;c[v>>2]=h;i=w-h|0;if((i|0)<=0){e=-1;break a}switch(a[r+(d[h>>0]|0)>>0]|0){case 29:{x=72;break a}case 11:{x=86;break a}case 17:{x=87;break a}case 10:case 9:case 21:{e=h;break}case 24:case 22:{x=73;break d}case 5:{x=74;break d}case 6:{x=78;break d}case 7:{x=82;break d}default:{x=91;break a}}}if((x|0)==73){x=0;e=e+2|0;c[v>>2]=e;i=0;continue}else if((x|0)==74){x=0;if((i|0)<2){e=-2;break}if(!(lb[c[k>>2]&127](b,h)|0)){x=76;break}e=e+3|0;c[v>>2]=e;i=0;continue}else if((x|0)==78){x=0;if((i|0)<3){e=-2;break}if(!(lb[c[m>>2]&127](b,h)|0)){x=80;break}e=e+4|0;c[v>>2]=e;i=0;continue}else if((x|0)==82){x=0;if((i|0)<4){e=-2;break}if(!(lb[c[n>>2]&127](b,h)|0)){x=84;break}e=e+5|0;c[v>>2]=e;i=0;continue}}switch(x|0){case 4:{c[g>>2]=e;e=0;break}case 8:{c[g>>2]=e;e=0;break}case 12:{c[g>>2]=e;e=0;break}case 16:{c[g>>2]=e;e=0;break}case 19:{c[g>>2]=e;e=0;break}case 22:{c[g>>2]=h;e=0;break}case 26:{c[g>>2]=h;e=0;break}case 30:{c[g>>2]=h;e=0;break}case 34:{c[g>>2]=h;e=0;break}case 36:{c[g>>2]=h;e=0;break}case 39:{c[g>>2]=e;e=0;break}case 43:{c[g>>2]=h;e=0;break}case 50:{c[g>>2]=e;e=0;break}case 54:{c[g>>2]=e;e=0;break}case 58:{c[g>>2]=e;e=0;break}case 60:{c[g>>2]=e;e=0;break}case 63:{if(!e){c[g>>2]=c[v>>2];e=0}break}case 65:{c[g>>2]=e;e=0;break}case 69:{c[g>>2]=h;e=0;break}case 72:{c[g>>2]=h;e=0;break}case 76:{c[g>>2]=h;e=0;break}case 80:{c[g>>2]=h;e=0;break}case 84:{c[g>>2]=h;e=0;break}case 86:{c[g>>2]=e+2;e=1;break}case 87:{e=h+1|0;c[v>>2]=e;if((w-e|0)>0)if((a[e>>0]|0)==62){c[g>>2]=h+2;e=3;break}else{c[g>>2]=e;e=0;break}else e=-1;break}case 91:{c[g>>2]=h;e=0;break}case 92:{c[g>>2]=e;e=0;break}}l=y;return e|0}function e$(b,d,e){b=b|0;d=d|0;e=e|0;c[e>>2]=11;a:do if((d-b|0)==3){switch(a[b>>0]|0){case 120:{d=0;break}case 88:{d=1;break}default:{d=1;break a}}switch(a[b+1>>0]|0){case 109:break;case 77:{d=1;break}default:{d=1;break a}}switch(a[b+2>>0]|0){case 108:break;case 76:{d=0;break a}default:{d=1;break a}}if(!d){c[e>>2]=12;d=1}else d=0}else d=1;while(0);return d|0}function f$(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0;k=e+72|0;l=e+352|0;m=e+356|0;n=e+360|0;a:while(1){i=g-f|0;if((i|0)<=0){g=-1;break}j=a[k+(d[f>>0]|0)>>0]|0;switch(j<<24>>24){case 8:case 1:case 0:{o=16;break a}case 5:{if((i|0)<2){g=-2;break a}if(lb[c[l>>2]&127](e,f)|0){o=6;break a}f=f+2|0;continue a}case 6:{if((i|0)<3){g=-2;break a}if(lb[c[m>>2]&127](e,f)|0){o=10;break a}f=f+3|0;continue a}case 7:{if((i|0)<4){g=-2;break a}if(lb[c[n>>2]&127](e,f)|0){o=14;break a}f=f+4|0;continue a}case 13:case 12:{f=f+1|0;if((j&255|0)==(b|0)){o=18;break a}else continue a}default:{f=f+1|0;continue a}}}b:do if((o|0)==6){c[h>>2]=f;g=0}else if((o|0)==10){c[h>>2]=f;g=0}else if((o|0)==14){c[h>>2]=f;g=0}else if((o|0)==16){c[h>>2]=f;g=0}else if((o|0)==18)if((g-f|0)>0){c[h>>2]=f;switch(a[k+(d[f>>0]|0)>>0]|0){case 20:case 30:case 11:case 10:case 9:case 21:{g=27;break b}default:{}}g=0}else g=-27;while(0);return g|0}function g$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0;h=f;a:do if((h-e|0)>0){i=b+72|0;switch(a[i+(d[e>>0]|0)>>0]|0){case 27:{b=$_(b,e+1|0,f,g)|0;break a}case 20:{c[g>>2]=e+1;b=33;break a}case 24:case 22:{b:while(1){b=e+1|0;f=h-b|0;if((f|0)<=0){b=-1;break a}switch(a[i+(d[b>>0]|0)>>0]|0){case 24:case 22:{e=b;break}case 30:{j=8;break b}case 10:case 9:case 21:break b;default:{j=12;break b}}}if((j|0)==12){c[g>>2]=b;b=0;break a}c:do if((j|0)==8){if((f|0)<=1){b=-1;break a}switch(a[i+(d[e+2>>0]|0)>>0]|0){case 30:case 10:case 9:case 21:break;default:break c}c[g>>2]=b;b=0;break a}while(0);c[g>>2]=b;b=16;break a}default:{c[g>>2]=e;b=0;break a}}}else b=-1;while(0);return b|0}function h$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;h=f-e|0;a:do if((h|0)>0){l=b+72|0;switch(a[l+(d[e>>0]|0)>>0]|0){case 29:{c[g>>2]=e;f=0;break a}case 24:case 22:{h=1;break}case 5:{if((h|0)<2){f=-2;break a}if(!(lb[c[b+340>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=2;break}case 6:{if((h|0)<3){f=-2;break a}if(!(lb[c[b+344>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=3;break}case 7:{if((h|0)<4){f=-2;break a}if(!(lb[c[b+348>>2]&127](b,e)|0)){c[g>>2]=e;f=0;break a}else h=4;break}default:{c[g>>2]=e;f=0;break a}}i=b+328|0;j=b+332|0;k=b+336|0;e=e+h|0;b:while(1){h=f-e|0;if((h|0)<=0){f=-20;break a}switch(a[l+(d[e>>0]|0)>>0]|0){case 29:{h=17;break b}case 36:case 30:case 11:case 32:case 21:case 10:case 9:{h=28;break b}case 27:case 26:case 25:case 24:case 22:{h=1;break}case 5:{if((h|0)<2){f=-2;break a}if(!(lb[c[i>>2]&127](b,e)|0)){h=20;break b}else h=2;break}case 6:{if((h|0)<3){f=-2;break a}if(!(lb[c[j>>2]&127](b,e)|0)){h=23;break b}else h=3;break}case 7:{if((h|0)<4){f=-2;break a}if(!(lb[c[k>>2]&127](b,e)|0)){h=26;break b}else h=4;break}default:{h=29;break b}}e=e+h|0}if((h|0)==17){c[g>>2]=e;f=0;break}else if((h|0)==20){c[g>>2]=e;f=0;break}else if((h|0)==23){c[g>>2]=e;f=0;break}else if((h|0)==26){c[g>>2]=e;f=0;break}else if((h|0)==28){c[g>>2]=e;f=20;break}else if((h|0)==29){c[g>>2]=e;f=0;break}}else f=-1;while(0);return f|0}function i$(){return 58220}function j$(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;i=l;l=l+16|0;g=i;c[g>>2]=d;h=c[b>>2]|0;a=f-(c[e>>2]|0)|0;if((d-h|0)>(a|0)){a=h+a|0;c[g>>2]=a;f=1}else{f=0;a=d}w_(h,g);h=c[g>>2]|0;d=c[b>>2]|0;g=h-d|0;W6(c[e>>2]|0,d|0,g|0)|0;c[b>>2]=(c[b>>2]|0)+g;c[e>>2]=(c[e>>2]|0)+g;l=i;return (f?2:h>>>0<a>>>0&1)|0}function k$(d,e,f,g,h){d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0;j=d+72|0;k=f;l=h;m=c[e>>2]|0;n=c[g>>2]|0;a:while(1){d=m>>>0<f>>>0;if(!(n>>>0<h>>>0&d)){o=12;break}d=a[m>>0]|0;i=d&255;switch(a[j+i>>0]|0){case 5:{if((k-m|0)<2){d=1;break a}b[n>>1]=(d&31)<<6|a[m+1>>0]&63;m=m+2|0;n=n+2|0;continue a}case 6:{if((k-m|0)<3){d=1;break a}b[n>>1]=(a[m+1>>0]&63)<<6|i<<12|a[m+2>>0]&63;m=m+3|0;n=n+2|0;continue a}case 7:{if((l-n|0)<4){d=2;break a}if((k-m|0)<4){d=1;break a}i=((a[m+1>>0]&63)<<12|(d&7)<<18|(a[m+2>>0]&63)<<6|a[m+3>>0]&63)+-65536|0;b[n>>1]=i>>>10|55296;b[n+2>>1]=i&1023|56320;m=m+4|0;n=n+4|0;continue a}default:{b[n>>1]=d<<24>>24;m=m+1|0;n=n+2|0;continue a}}}if((o|0)==12)d=d?2:0;c[e>>2]=m;c[g>>2]=n;return d|0}function l$(a,b){a=a|0;b=b|0;a=d[b>>0]|0;b=d[b+1>>0]|0;return 1<<(b&31)&c[56940+(((d[144044+(a>>>2&7)>>0]|0)<<3|a<<1&6|b>>>5&1)<<2)>>2]|0}function m$(b,e){b=b|0;e=e|0;var f=0;f=d[e+1>>0]|0;b=d[e+2>>0]|0;return 1<<(b&31)&c[56940+(((d[144044+(f>>>2&15|a[e>>0]<<4&255)>>0]|0)<<3|f<<1&6|b>>>5&1)<<2)>>2]|0}function n$(a,b){a=a|0;b=b|0;return 0}function o$(a,b){a=a|0;b=b|0;a=d[b>>0]|0;b=d[b+1>>0]|0;return 1<<(b&31)&c[56940+(((d[143788+(a>>>2&7)>>0]|0)<<3|a<<1&6|b>>>5&1)<<2)>>2]|0}function p$(b,e){b=b|0;e=e|0;var f=0;f=d[e+1>>0]|0;b=d[e+2>>0]|0;return 1<<(b&31)&c[56940+(((d[143788+(f>>>2&15|a[e>>0]<<4&255)>>0]|0)<<3|f<<1&6|b>>>5&1)<<2)>>2]|0}function q$(a,b){a=a|0;b=b|0;if((d[b>>0]|0)<194)return 1;else{b=d[b+1>>0]|0;return b>>>7^1|(b&192|0)==192|0}return 0}function r$(b,c){b=b|0;c=c|0;var d=0;b=a[c+2>>0]|0;do if(b<<24>>24>-1)b=1;else{d=a[c>>0]|0;if(d<<24>>24==-17?(a[c+1>>0]|0)==-65:0)if((b&255)>189){b=1;break}else{b=191;c=-65}else{if((b&255)>191){b=1;break}c=a[c+1>>0]|0;b=c&255;if(d<<24>>24==-32){b=(c&255)<160|(b&192|0)==192;break}}b=(b&128|0)==0?1:d<<24>>24==-19?(c&255)>159:(b&192|0)==192}while(0);return b&1|0}function s$(b,c){b=b|0;c=c|0;var e=0;e=d[c+3>>0]|0;do if(!((e&128|0)==0|(e&192|0)==192)?(e=d[c+2>>0]|0,!((e&128|0)==0|(e&192|0)==192)):0){e=a[c>>0]|0;b=a[c+1>>0]|0;c=b&255;if(e<<24>>24==-16){b=(b&255)<144|(c&192|0)==192;break}else{b=(c&128|0)==0?1:e<<24>>24==-12?(b&255)>143:(c&192|0)==192;break}}else b=1;while(0);return b&1|0}function t$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0;a:do if(e>>>0<f>>>0){i=e;m=f-i|0;k=(m&1|0)==0;m=m&-2;l=(m|0)==0;m=k|l?f:e+m|0;if(k|l^1){f=a[e+1>>0]|0;if(!(f<<24>>24)){h=a[e>>0]|0;j=h;h=d[b+72+(h&255)>>0]|0}else{h=a[e>>0]|0;j=h;h=J$(f,h)|0}do switch(h|0){case 12:{f=V$(12,b,e+2|0,m,g)|0;break a}case 13:{f=V$(13,b,e+2|0,m,g)|0;break a}case 2:{h=e+2|0;if((m-h|0)<=1){f=-1;break a}f=a[e+3>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[h>>0]|0)>>0]|0;else f=J$(f,a[h>>0]|0)|0;switch(f|0){case 16:{f=W$(b,e+4|0,m,g)|0;break a}case 15:{f=R$(b,e+4|0,m,g)|0;break a}case 7:case 6:case 5:case 29:case 24:case 22:{c[g>>2]=e;f=29;break a}default:{c[g>>2]=h;f=0;break a}}}case 9:{if((e+2|0)==(m|0)){c[g>>2]=m;f=-15;break a}else i=7;break}case 10:case 21:{i=7;break}case 30:{f=L$(b,e+2|0,m,g)|0;break a}case 35:{c[g>>2]=e+2;f=38;break a}case 20:{c[g>>2]=e+2;f=25;break a}case 4:{h=e+2|0;f=m-h|0;if((f|0)<=1){f=-26;break a}if((a[e+3>>0]|0)==0?(a[h>>0]|0)==93:0){if((f|0)<=3){f=-1;break a}if((a[e+5>>0]|0)==0?(a[e+4>>0]|0)==62:0){c[g>>2]=e+6;f=34;break a}}c[g>>2]=h;f=26;break a}case 31:{c[g>>2]=e+2;f=23;break a}case 32:{h=e+2|0;if((m-h|0)<=1){f=-24;break a}f=a[e+3>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[h>>0]|0)>>0]|0;else f=J$(f,a[h>>0]|0)|0;switch(f|0){case 33:{c[g>>2]=e+4;f=36;break a}case 15:{c[g>>2]=e+4;f=35;break a}case 34:{c[g>>2]=e+4;f=37;break a}case 32:case 36:case 35:case 11:case 21:case 10:case 9:{c[g>>2]=h;f=24;break a}default:{c[g>>2]=h;f=0;break a}}}case 36:{c[g>>2]=e+2;f=21;break a}case 11:{c[g>>2]=e+2;f=17;break a}case 19:{f=X$(b,e+2|0,m,g)|0;break a}case 5:{if((m-i|0)<2){f=-2;break a}c[g>>2]=e;f=0;break a}case 6:{if((m-i|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((m-i|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}case 24:case 22:{f=18;i=61;break}case 23:case 27:case 26:case 25:{f=19;i=61;break}case 29:{i=f&255;h=j&255;f=h>>>5;h=1<<(h&31);if(!(h&c[56940+((f|d[143788+i>>0]<<3)<<2)>>2]))if(!(c[56940+((d[144044+i>>0]<<3|f)<<2)>>2]&h))i=64;else{f=19;i=61}else{f=18;i=61}break}default:i=64}while(0);if((i|0)==7){k=m;b=b+72|0;j=e;b:while(1){i=j;j=j+2|0;if((k-j|0)<=1){i=28;break}f=a[i+3>>0]|0;h=a[j>>0]|0;if(!(f<<24>>24))f=d[b+(h&255)>>0]|0;else f=J$(f,h)|0;switch(f|0){case 10:case 21:continue b;case 9:break;default:{i=27;break b}}if((i+4|0)==(m|0)){i=27;break}}if((i|0)==27){c[g>>2]=j;f=15;break}else if((i|0)==28){c[g>>2]=j;f=15;break}}else if((i|0)==61){l=b+72|0;h=e+2|0;c:while(1){e=f&255;d:while(1){k=m-h|0;if((k|0)<=1){i=104;break c}i=a[h+1>>0]|0;j=a[h>>0]|0;if(!(i<<24>>24))b=d[l+(j&255)>>0]|0;else b=J$(i,j)|0;switch(b|0){case 5:{i=73;break c}case 6:{i=74;break c}case 7:{i=76;break c}case 10:case 9:case 21:case 30:case 20:case 36:case 35:case 32:case 11:{i=78;break c}case 34:{i=94;break c}case 33:{i=97;break c}case 15:{i=100;break c}case 29:{k=j&255;if(!(1<<(k&31)&c[56940+((k>>>5|d[144044+(i&255)>>0]<<3)<<2)>>2])){i=71;break c}break}case 27:case 26:case 25:case 24:case 22:break;case 23:{k=h+2|0;switch(e&63){case 41:{f=19;h=k;continue c}case 18:break d;default:{h=k;continue d}}}default:{i=103;break c}}h=h+2|0}b=m-k|0;if((b|0)<=1){f=-1;break a}f=a[h+3>>0]|0;i=a[k>>0]|0;if(!(f<<24>>24))j=d[l+(i&255)>>0]|0;else j=J$(f,i)|0;switch(j|0){case 5:{i=89;break c}case 6:{i=90;break c}case 7:{i=92;break c}case 29:{e=i&255;if(!(1<<(e&31)&c[56940+((e>>>5|d[144044+(f&255)>>0]<<3)<<2)>>2])){i=87;break c}break}case 27:case 26:case 25:case 24:case 22:break;default:{f=19;h=k;continue c}}f=41;h=h+4|0}switch(i|0){case 71:{c[g>>2]=h;f=0;break a}case 73:{c[g>>2]=h;f=0;break a}case 74:{if((k|0)<3){f=-2;break a}c[g>>2]=h;f=0;break a}case 76:{if((k|0)<4){f=-2;break a}c[g>>2]=h;f=0;break a}case 78:{c[g>>2]=h;break a}case 87:{c[g>>2]=k;f=0;break a}case 89:{c[g>>2]=k;f=0;break a}case 90:{if((b|0)<3){f=-2;break a}c[g>>2]=k;f=0;break a}case 92:{if((b|0)<4){f=-2;break a}c[g>>2]=k;f=0;break a}case 94:if((f|0)==19){c[g>>2]=h;f=0;break a}else{c[g>>2]=h+2;f=32;break a}case 97:if((f|0)==19){c[g>>2]=h;f=0;break a}else{c[g>>2]=h+2;f=31;break a}case 100:if((f|0)==19){c[g>>2]=h;f=0;break a}else{c[g>>2]=h+2;f=30;break a}case 103:{c[g>>2]=h;f=0;break a}case 104:{f=0-f|0;break a}}}else if((i|0)==64){c[g>>2]=e;f=0;break}}else f=-1}else f=-4;while(0);return f|0}function u$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;a:do if(e>>>0<f>>>0){h=e;i=f-h|0;k=(i&1|0)==0;i=i&-2;j=(i|0)==0;i=k|j?f:e+i|0;if(k|j^1){f=a[e+1>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[e>>0]|0)>>0]|0;else f=J$(f,a[e>>0]|0)|0;do switch(f|0){case 2:{f=O$(b,e+2|0,i,g)|0;break a}case 3:{f=K$(b,e+2|0,i,g)|0;break a}case 9:{h=e+2|0;if((i-h|0)<=1){f=-3;break a}f=a[e+3>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[h>>0]|0)>>0]|0;else f=J$(f,a[h>>0]|0)|0;c[g>>2]=(f|0)==10?e+4|0:h;f=7;break a}case 10:{c[g>>2]=e+2;f=7;break a}case 4:{h=e+2|0;if((i-h|0)<=1){f=-5;break a}if((a[e+3>>0]|0)==0?(a[h>>0]|0)==93:0){f=e+4|0;if((i-f|0)<=1){f=-5;break a}if((a[e+5>>0]|0)==0?(a[f>>0]|0)==62:0){c[g>>2]=f;f=0;break a}else f=i}else f=i;break}case 5:{f=i;if((f-h|0)<2){f=-2;break a}h=e+2|0;break}case 6:{f=i;if((f-h|0)<3){f=-2;break a}h=e+3|0;break}case 7:{f=i;if((f-h|0)<4){f=-2;break a}h=e+4|0;break}case 8:case 1:case 0:{c[g>>2]=e;f=0;break a}default:{h=e+2|0;f=i}}while(0);j=b+72|0;b=h;b:while(1){e=f-b|0;if((e|0)<=1){h=52;break}h=a[b+1>>0]|0;i=a[b>>0]|0;if(!(h<<24>>24))h=d[j+(i&255)>>0]|0;else h=J$(h,i)|0;switch(h|0){case 10:case 9:case 8:case 1:case 0:case 2:case 3:{h=50;break b}case 5:{b=b+2|0;continue b}case 6:{if((e|0)<3){h=38;break b}b=b+3|0;continue b}case 7:{if((e|0)<4){h=41;break b}b=b+4|0;continue b}case 4:{if((e|0)<=3){h=50;break b}h=b+2|0;if(a[b+3>>0]|0){b=h;continue b}if((a[h>>0]|0)!=93){b=h;continue b}if((e|0)<=5){h=50;break b}i=b+4|0;if(a[b+5>>0]|0){b=h;continue b}if((a[i>>0]|0)==62){h=49;break b}else{b=h;continue b}}default:{b=b+2|0;continue b}}}if((h|0)==38){c[g>>2]=b;f=6;break}else if((h|0)==41){c[g>>2]=b;f=6;break}else if((h|0)==49){c[g>>2]=i;f=0;break}else if((h|0)==50){c[g>>2]=b;f=6;break}else if((h|0)==52){c[g>>2]=b;f=6;break}}else f=-1}else f=-4;while(0);return f|0}function v$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;a:do if(e>>>0<f>>>0){h=e;i=f-h|0;k=(i&1|0)==0;i=i&-2;j=(i|0)==0;i=k|j?f:e+i|0;if(k|j^1){f=a[e+1>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[e>>0]|0)>>0]|0;else f=J$(f,a[e>>0]|0)|0;switch(f|0){case 4:{h=e+2|0;if((i-h|0)<=1){f=-1;break a}if((a[e+3>>0]|0)==0?(a[h>>0]|0)==93:0){f=e+4|0;if((i-f|0)<=1){f=-1;break a}if((a[e+5>>0]|0)==0?(a[f>>0]|0)==62:0){c[g>>2]=e+6;f=40;break a}}break}case 9:{h=e+2|0;if((i-h|0)<=1){f=-1;break a}f=a[e+3>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[h>>0]|0)>>0]|0;else f=J$(f,a[h>>0]|0)|0;c[g>>2]=(f|0)==10?e+4|0:h;f=7;break a}case 10:{c[g>>2]=e+2;f=7;break a}case 5:{f=i;if((f-h|0)<2){f=-2;break a}h=e+2|0;i=f;break}case 6:{f=i;if((f-h|0)<3){f=-2;break a}h=e+3|0;i=f;break}case 7:{f=i;if((f-h|0)<4){f=-2;break a}h=e+4|0;i=f;break}case 8:case 1:case 0:{c[g>>2]=e;f=0;break a}default:h=e+2|0}j=b+72|0;b=h;b:while(1){e=i-b|0;if((e|0)<=1){h=40;break}f=a[b+1>>0]|0;h=a[b>>0]|0;if(!(f<<24>>24))f=d[j+(h&255)>>0]|0;else f=J$(f,h)|0;switch(f|0){case 10:case 9:case 8:case 1:case 0:case 4:{h=38;break b}case 6:{if((e|0)<3){h=35;break b}else f=3;break}case 7:{if((e|0)<4){h=37;break b}else f=4;break}default:f=2}b=b+f|0}if((h|0)==35){c[g>>2]=b;f=6;break}else if((h|0)==37){c[g>>2]=b;f=6;break}else if((h|0)==38){c[g>>2]=b;f=6;break}else if((h|0)==40){c[g>>2]=b;f=6;break}}else f=-1}else f=-4;while(0);return f|0}function w$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;k=f-e|0;k=(k&1|0)==0?f:e+(k&-2)|0;j=b+72|0;i=0;f=e;a:while(1){h=k-f|0;if((h|0)<=1){f=-1;break}b=a[f+1>>0]|0;e=a[f>>0]|0;if(!(b<<24>>24))b=d[j+(e&255)>>0]|0;else b=J$(b,e)|0;switch(b|0){case 8:case 1:case 0:{l=12;break a}case 5:{h=i;f=f+2|0;i=h;continue a}case 6:{if((h|0)<3){f=-2;break a}h=i;f=f+3|0;i=h;continue a}case 7:{if((h|0)<4){f=-2;break a}h=i;f=f+4|0;i=h;continue a}case 2:{b=f+2|0;if((k-b|0)<=1){f=-1;break a}if(a[f+3>>0]|0){h=i;f=b;i=h;continue a}if((a[b>>0]|0)!=33){h=i;f=b;i=h;continue a}b=f+4|0;if((k-b|0)<=1){f=-1;break a}if(a[f+5>>0]|0){h=i;f=b;i=h;continue a}h=(a[b>>0]|0)==91;i=i+(h&1)|0;f=h?f+6|0:b;continue a}case 4:{b=f+2|0;if((k-b|0)<=1){f=-1;break a}if(a[f+3>>0]|0){h=i;f=b;i=h;continue a}if((a[b>>0]|0)!=93){h=i;f=b;i=h;continue a}b=f+4|0;if((k-b|0)<=1){f=-1;break a}if(a[f+5>>0]|0){h=i;f=b;i=h;continue a}if((a[b>>0]|0)!=62){h=i;f=b;i=h;continue a}f=f+6|0;if(!i){l=26;break a}i=i+-1|0;continue a}default:{h=i;f=f+2|0;i=h;continue a}}}if((l|0)==12){c[g>>2]=f;f=0}else if((l|0)==26){c[g>>2]=f;f=42}return f|0}function x$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;do if(e>>>0<f>>>0){k=f;if((k-e|0)>1){l=b+72|0;j=e;a:while(1){if((k-j|0)<=1){i=30;break}h=a[j+1>>0]|0;i=a[j>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;switch(h|0){case 3:{i=11;break a}case 2:{i=14;break a}case 10:{i=15;break a}case 9:{i=18;break a}case 21:{i=25;break a}case 5:{h=2;break}case 6:{h=3;break}case 7:{h=4;break}default:h=2}j=j+h|0}if((i|0)==11)if((j|0)==(e|0)){h=K$(b,e+2|0,f,g)|0;break}else{c[g>>2]=j;h=6;break}else if((i|0)==14){c[g>>2]=j;h=0;break}else if((i|0)==15)if((j|0)==(e|0)){c[g>>2]=e+2;h=7;break}else{c[g>>2]=j;h=6;break}else if((i|0)==18){if((j|0)!=(e|0)){c[g>>2]=j;h=6;break}j=e+2|0;if((k-j|0)<=1){h=-3;break}h=a[e+3>>0]|0;i=a[j>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;c[g>>2]=(h|0)==10?e+4|0:j;h=7;break}else if((i|0)==25)if((j|0)==(e|0)){c[g>>2]=e+2;h=39;break}else{c[g>>2]=j;h=6;break}else if((i|0)==30){c[g>>2]=j;h=6;break}}else h=-1}else h=-4;while(0);return h|0}function y$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;do if(e>>>0<f>>>0){k=f;if((k-e|0)>1){l=b+72|0;j=e;a:while(1){if((k-j|0)<=1){i=29;break}h=a[j+1>>0]|0;i=a[j>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;switch(h|0){case 3:{i=11;break a}case 30:{i=14;break a}case 10:{i=17;break a}case 9:{i=20;break a}case 5:{h=2;break}case 6:{h=3;break}case 7:{h=4;break}default:h=2}j=j+h|0}if((i|0)==11)if((j|0)==(e|0)){h=K$(b,e+2|0,f,g)|0;break}else{c[g>>2]=j;h=6;break}else if((i|0)==14)if((j|0)==(e|0)){h=L$(b,e+2|0,f,g)|0;h=(h|0)==22?0:h;break}else{c[g>>2]=j;h=6;break}else if((i|0)==17)if((j|0)==(e|0)){c[g>>2]=e+2;h=7;break}else{c[g>>2]=j;h=6;break}else if((i|0)==20){if((j|0)!=(e|0)){c[g>>2]=j;h=6;break}j=e+2|0;if((k-j|0)<=1){h=-3;break}h=a[e+3>>0]|0;i=a[j>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;c[g>>2]=(h|0)==10?e+4|0:j;h=7;break}else if((i|0)==29){c[g>>2]=j;h=6;break}}else h=-1}else h=-4;while(0);return h|0}function z$(b,c,d,e){b=b|0;c=c|0;d=d|0;e=e|0;var f=0,g=0;f=d;b=c;while(1){c=a[e>>0]|0;if(!(c<<24>>24)){g=7;break}if((f-b|0)<2){b=0;break}if(a[b+1>>0]|0){b=0;break}if((a[b>>0]|0)!=c<<24>>24){b=0;break}e=e+1|0;b=b+2|0}if((g|0)==7)b=(b|0)==(d|0)&1;return b|0}function A$(b,c){b=b|0;c=c|0;var e=0,f=0,g=0;g=b+72|0;f=c;a:while(1){b=a[f+1>>0]|0;e=a[f>>0]|0;if(!(b<<24>>24))b=d[g+(e&255)>>0]|0;else b=J$(b,e)|0;switch(b|0){case 5:{b=2;break}case 6:{b=3;break}case 7:{b=4;break}case 27:case 26:case 25:case 24:case 23:case 22:case 29:{b=2;break}default:break a}f=f+b|0}return f-c|0}function B$(b,c){b=b|0;c=c|0;var e=0,f=0;f=b+72|0;a:while(1){b=a[c+1>>0]|0;e=a[c>>0]|0;if(!(b<<24>>24))b=d[f+(e&255)>>0]|0;else b=J$(b,e)|0;switch(b|0){case 21:case 9:case 10:break;default:break a}c=c+2|0}return c|0}function C$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;o=b+72|0;m=0;n=0;b=1;a:while(1){l=e+2|0;i=e+3|0;h=a[i>>0]|0;j=h<<24>>24==0;k=a[l>>0]|0;if(j)h=d[o+(k&255)>>0]|0;else h=J$(h,k)|0;do switch(h|0){case 5:{if(b|0){i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}if((n|0)>=(f|0)){j=n;k=m;b=1;e=l;n=j;m=k;continue a}c[g+(n<<4)>>2]=l;a[g+(n<<4)+12>>0]=1;j=n;k=m;b=1;e=l;n=j;m=k;continue a}case 6:{if(b|0){j=b;k=n;l=m;e=i;b=j;n=k;m=l;continue a}if((n|0)>=(f|0)){k=n;l=m;b=1;e=i;n=k;m=l;continue a}c[g+(n<<4)>>2]=l;a[g+(n<<4)+12>>0]=1;k=n;l=m;b=1;e=i;n=k;m=l;continue a}case 7:{if(!b)if((n|0)<(f|0)){c[g+(n<<4)>>2]=l;a[g+(n<<4)+12>>0]=1;b=1}else b=1;k=n;l=m;e=e+4|0;n=k;m=l;continue a}case 24:case 22:case 29:{if(b|0){i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}if((n|0)>=(f|0)){j=n;k=m;b=1;e=l;n=j;m=k;continue a}c[g+(n<<4)>>2]=l;a[g+(n<<4)+12>>0]=1;j=n;k=m;b=1;e=l;n=j;m=k;continue a}case 12:{if((b|0)!=2){if((n|0)>=(f|0)){k=n;m=12;b=2;e=l;n=k;continue a}c[g+(n<<4)+4>>2]=e+4;k=n;m=12;b=2;e=l;n=k;continue a}if((m|0)!=12){j=n;k=m;b=2;e=l;n=j;m=k;continue a}if((n|0)<(f|0))c[g+(n<<4)+8>>2]=l;m=12;n=n+1|0;b=0;e=l;continue a}case 13:{if((b|0)!=2){if((n|0)>=(f|0)){k=n;m=13;b=2;e=l;n=k;continue a}c[g+(n<<4)+4>>2]=e+4;k=n;m=13;b=2;e=l;n=k;continue a}if((m|0)!=13){j=n;k=m;b=2;e=l;n=j;m=k;continue a}if((n|0)<(f|0))c[g+(n<<4)+8>>2]=l;m=13;n=n+1|0;b=0;e=l;continue a}case 3:{if((n|0)>=(f|0)){i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}a[g+(n<<4)+12>>0]=0;i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}case 21:{switch(b|0){case 1:{j=n;k=m;b=0;e=l;n=j;m=k;continue a}case 2:break;default:{i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}}if((n|0)>=(f|0)){j=n;k=m;b=2;e=l;n=j;m=k;continue a}i=g+(n<<4)+12|0;if(!(a[i>>0]|0)){j=n;k=m;b=2;e=l;n=j;m=k;continue a}do if(j&(l|0)!=(c[g+(n<<4)+4>>2]|0)&k<<24>>24==32){h=a[e+5>>0]|0;b=a[e+4>>0]|0;if(!(h<<24>>24)){if(b<<24>>24==32)break;b=d[o+(b&255)>>0]|0}else b=J$(h,b)|0;if((b|0)!=(m|0)){j=n;k=m;b=2;e=l;n=j;m=k;continue a}}while(0);a[i>>0]=0;j=n;k=m;b=2;e=l;n=j;m=k;continue a}case 10:case 9:{switch(b|0){case 1:{j=n;k=m;b=0;e=l;n=j;m=k;continue a}case 2:break;default:{i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}}if((n|0)>=(f|0)){j=n;k=m;b=2;e=l;n=j;m=k;continue a}a[g+(n<<4)+12>>0]=0;j=n;k=m;b=2;e=l;n=j;m=k;continue a}case 17:case 11:if((b|0)==2){j=n;k=m;b=2;e=l;n=j;m=k;continue a}else break a;default:{i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}}while(0)}return n|0}function D$(b,c){b=b|0;c=c|0;var d=0,e=0;b=c+4|0;a:do if((a[c+5>>0]|0)==0?(a[b>>0]|0)==120:0){c=c+6|0;b=0;while(1){b:while(1){if(!(a[c+1>>0]|0)){d=a[c>>0]|0;if(d<<24>>24==59){e=17;break a}d=d<<24>>24;switch(d|0){case 57:case 56:case 55:case 54:case 53:case 52:case 51:case 50:case 49:case 48:{e=8;break b}case 70:case 69:case 68:case 67:case 66:case 65:{e=9;break b}case 102:case 101:case 100:case 99:case 98:case 97:{e=10;break b}default:{}}}c=c+2|0}if((e|0)==8){e=0;b=d+-48|b<<4}else if((e|0)==9){e=0;b=(b<<4)+-55+d|0}else if((e|0)==10){e=0;b=(b<<4)+-87+d|0}if((b|0)<1114112)c=c+2|0;else{b=-1;break}}}else{d=b;b=0;e=13}while(0);c:do if((e|0)==13)while(1){e=0;if(!(a[d+1>>0]|0)){c=a[d>>0]|0;if(c<<24>>24==59){e=17;break c}c=c<<24>>24}else c=-1;b=(b*10|0)+-48+c|0;if((b|0)>=1114112){b=-1;break}else{d=d+2|0;e=13}}while(0);if((e|0)==17)b=A_(b)|0;return b|0}function E$(b,c,d){b=b|0;c=c|0;d=d|0;var e=0;a:do switch((d-c|0)/2|0|0){case 2:{if((a[c+3>>0]|0)==0?(a[c+2>>0]|0)==116:0){if(!(a[c+1>>0]|0))b=a[c>>0]|0;else b=-1;b=(b|0)==108?60:(b|0)==103?62:0}else e=27;break}case 3:{if((((((a[c+1>>0]|0)==0?(a[c>>0]|0)==97:0)?(a[c+3>>0]|0)==0:0)?(a[c+2>>0]|0)==109:0)?(a[c+5>>0]|0)==0:0)?(a[c+4>>0]|0)==112:0)b=38;else e=27;break}case 4:{if(!(a[c+1>>0]|0))switch(a[c>>0]|0){case 113:{if(a[c+3>>0]|0){e=27;break a}if((a[c+2>>0]|0)!=117){e=27;break a}if(a[c+5>>0]|0){e=27;break a}if((a[c+4>>0]|0)!=111){e=27;break a}if(a[c+7>>0]|0){e=27;break a}if((a[c+6>>0]|0)==116){b=34;break a}else{e=27;break a}}case 97:{if(a[c+3>>0]|0){e=27;break a}if((a[c+2>>0]|0)!=112){e=27;break a}if(a[c+5>>0]|0){e=27;break a}if((a[c+4>>0]|0)!=111){e=27;break a}if(a[c+7>>0]|0){e=27;break a}if((a[c+6>>0]|0)==115){b=39;break a}else{e=27;break a}}default:{e=27;break a}}else e=27;break}default:e=27}while(0);if((e|0)==27)b=0;return b|0}function F$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=b+72|0;k=g+4|0;while(1){if((f-e|0)<=1)break;b=a[e+1>>0]|0;h=a[e>>0]|0;if(!(b<<24>>24))b=d[j+(h&255)>>0]|0;else b=J$(b,h)|0;switch(b|0){case 5:{e=e+2|0;break}case 6:{e=e+3|0;break}case 7:{e=e+4|0;break}case 10:{c[k>>2]=-1;c[g>>2]=(c[g>>2]|0)+1;e=e+2|0;break}case 9:{c[g>>2]=(c[g>>2]|0)+1;i=e+2|0;if((f-i|0)>1){b=a[e+3>>0]|0;h=a[i>>0]|0;if(!(b<<24>>24))b=d[j+(h&255)>>0]|0;else b=J$(b,h)|0;e=(b|0)==10?e+4|0:i}else e=i;c[k>>2]=-1;break}default:e=e+2|0}c[k>>2]=(c[k>>2]|0)+1}return}function G$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=f+-2|0;i=b+72|0;f=e;a:while(1){h=f+2|0;if((j-h|0)<=1){f=1;break}f=a[f+3>>0]|0;b=f<<24>>24==0;e=a[h>>0]|0;if(b)f=d[i+(e&255)>>0]|0;else f=J$(f,e)|0;switch(f|0){case 23:case 19:case 30:case 33:case 16:case 18:case 10:case 9:case 15:case 14:case 17:case 35:case 34:case 32:case 31:case 13:case 27:case 24:case 25:{f=h;continue a}case 21:if(b&e<<24>>24==9){k=12;break a}else{f=h;continue a}case 22:case 26:{if(!b){k=12;break a}if(e<<24>>24>=0){f=h;continue a}break}default:if(!b){k=12;break a}}switch(e<<24>>24|0){case 64:case 36:{f=h;break}default:{k=12;break a}}}if((k|0)==12){c[g>>2]=h;f=0}return f|0}function H$(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0;j=c[e>>2]|0;k=j+(f-j&-2)|0;l=k;m=h;a:while(1){if(j>>>0>=k>>>0){b=0;break}i=a[j>>0]|0;b=a[j+1>>0]|0;f=b&255;switch(b<<24>>24){case 0:{if(i<<24>>24>-1){b=c[g>>2]|0;if((b|0)==(h|0)){b=2;break a}c[g>>2]=b+1;a[b>>0]=i;b=j}else n=7;break}case 7:case 6:case 5:case 4:case 3:case 2:case 1:{n=7;break}case -37:case -38:case -39:case -40:{b=c[g>>2]|0;if((m-b|0)<4){b=2;break a}if((l-j|0)<4){b=1;break a}o=i&255;i=(f<<2&12|o>>>6)+1|0;c[g>>2]=b+1;a[b>>0]=i>>>2|240;b=c[g>>2]|0;c[g>>2]=b+1;a[b>>0]=o>>>2&15|i<<4&48|128;b=j+2|0;i=d[b>>0]|0;f=(o<<4&48|i>>>6|a[j+3>>0]<<2&12|128)&255;j=c[g>>2]|0;c[g>>2]=j+1;a[j>>0]=f;j=c[g>>2]|0;c[g>>2]=j+1;a[j>>0]=i&63|128;break}default:{b=c[g>>2]|0;if((m-b|0)<3){b=2;break a}c[g>>2]=b+1;a[b>>0]=f>>>4|224;o=i&255;b=c[g>>2]|0;c[g>>2]=b+1;a[b>>0]=o>>>6|f<<2&60|128;b=c[g>>2]|0;c[g>>2]=b+1;a[b>>0]=o&63|128;b=j}}if((n|0)==7){n=0;b=c[g>>2]|0;if((m-b|0)<2){b=2;break}o=i&255;c[g>>2]=b+1;a[b>>0]=o>>>6|f<<2|192;b=c[g>>2]|0;c[g>>2]=b+1;a[b>>0]=o&63|128;b=j}j=b+2|0}c[e>>2]=j;return b|0}function I$(e,f,g,h,i){e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;var j=0,k=0,l=0;j=c[f>>2]|0;l=g-j&-2;e=j+l|0;if((l|0)>(i-(c[h>>2]|0)|0)){k=e+-2|0;g=(a[k+1>>0]&-8)<<24>>24==-40;l=g&1;e=g?k:e}else l=0;g=j;while(1){j=g>>>0<e>>>0;k=c[h>>2]|0;if(!(j&k>>>0<i>>>0))break;j=((d[g+1>>0]|0)<<8|(d[g>>0]|0))&65535;c[h>>2]=k+2;b[k>>1]=j;k=(c[f>>2]|0)+2|0;c[f>>2]=k;g=k}return ((k|0)!=(i|0)|j^1?l:2)|0}function J$(a,b){a=a|0;b=b|0;var c=0;switch(a<<24>>24){case -37:case -38:case -39:case -40:{a=7;break}case -33:case -34:case -35:case -36:{a=8;break}case -1:{if((b&255)>253)a=0;else c=4;break}default:c=4}if((c|0)==4)a=29;return a|0}function K$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;l=f;j=l-e|0;a:do if((j|0)>1){k=a[e+1>>0]|0;if(!(k<<24>>24)){i=a[e>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{j=i&255;if(!(1<<(j&31)&c[56940+((j>>>5|(d[143788+(k&255)>>0]|0)<<3)<<2)>>2])){c[g>>2]=e;b=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;b=0;break a}case 6:{if((j|0)<3){b=-2;break a}c[g>>2]=e;b=0;break a}case 7:{if((j|0)<4){b=-2;break a}c[g>>2]=e;b=0;break a}case 19:{b=M$(b,e+2|0,f,g)|0;break a}default:{c[g>>2]=e;b=0;break a}}k=b+72|0;j=e;b:while(1){i=j+2|0;e=l-i|0;if((e|0)<=1){b=-1;break a}b=a[j+3>>0]|0;h=a[i>>0]|0;if(!(b<<24>>24))f=d[k+(h&255)>>0]|0;else f=J$(b,h)|0;switch(f|0){case 27:case 26:case 25:case 24:case 22:{j=i;continue b}case 5:{h=23;break b}case 6:{h=24;break b}case 7:{h=26;break b}case 18:{h=28;break b}case 29:break;default:{h=29;break b}}e=h&255;if(!(1<<(e&31)&c[56940+((e>>>5|(d[144044+(b&255)>>0]|0)<<3)<<2)>>2])){h=22;break}else j=i}if((h|0)==22){c[g>>2]=i;b=0;break}else if((h|0)==23){c[g>>2]=i;b=0;break}else if((h|0)==24){if((e|0)<3){b=-2;break}c[g>>2]=i;b=0;break}else if((h|0)==26){if((e|0)<4){b=-2;break}c[g>>2]=i;b=0;break}else if((h|0)==28){c[g>>2]=j+4;b=9;break}else if((h|0)==29){c[g>>2]=i;b=0;break}}else b=-1;while(0);return b|0}function L$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;j=f-e|0;a:do if((j|0)>1){k=a[e+1>>0]|0;if(!(k<<24>>24)){i=a[e>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{l=i&255;if(!(1<<(l&31)&c[56940+((l>>>5|(d[143788+(k&255)>>0]|0)<<3)<<2)>>2])){c[g>>2]=e;f=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;f=0;break a}case 6:{if((j|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((j|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}case 30:case 9:case 10:case 21:{c[g>>2]=e;f=22;break a}default:{c[g>>2]=e;f=0;break a}}l=b+72|0;b=e;b:while(1){k=b+2|0;e=f-k|0;if((e|0)<=1){f=-1;break a}h=a[b+3>>0]|0;i=a[k>>0]|0;if(!(h<<24>>24))j=d[l+(i&255)>>0]|0;else j=J$(h,i)|0;switch(j|0){case 27:case 26:case 25:case 24:case 22:{b=k;continue b}case 5:{h=23;break b}case 6:{h=24;break b}case 7:{h=26;break b}case 18:{h=28;break b}case 29:break;default:{h=29;break b}}e=i&255;if(!(1<<(e&31)&c[56940+((e>>>5|(d[144044+(h&255)>>0]|0)<<3)<<2)>>2])){h=22;break}else b=k}if((h|0)==22){c[g>>2]=k;f=0;break}else if((h|0)==23){c[g>>2]=k;f=0;break}else if((h|0)==24){if((e|0)<3){f=-2;break}c[g>>2]=k;f=0;break}else if((h|0)==26){if((e|0)<4){f=-2;break}c[g>>2]=k;f=0;break}else if((h|0)==28){c[g>>2]=b+4;f=28;break}else if((h|0)==29){c[g>>2]=k;f=0;break}}else f=-1;while(0);return f|0}function M$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;k=f;a:do if((k-e|0)>1){i=a[e+1>>0]|0;h=a[e>>0]|0;do if(!(i<<24>>24))if(h<<24>>24==120){h=N$(b,e+2|0,f,g)|0;break a}else{h=d[b+72+(h&255)>>0]|0;break}else h=J$(i,h)|0;while(0);if((h|0)!=25){c[g>>2]=e;h=0;break}j=b+72|0;b=e;b:while(1){i=b+2|0;if((k-i|0)<=1){h=-1;break a}h=a[b+3>>0]|0;f=a[i>>0]|0;if(!(h<<24>>24))h=d[j+(f&255)>>0]|0;else h=J$(h,f)|0;switch(h|0){case 25:{b=i;break}case 18:{f=15;break b}default:{f=16;break b}}}if((f|0)==15){c[g>>2]=b+4;h=10;break}else if((f|0)==16){c[g>>2]=i;h=0;break}}else h=-1;while(0);return h|0}function N$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0;a:do if((f-e|0)>1){h=a[e+1>>0]|0;if(!(h<<24>>24))h=d[b+72+(d[e>>0]|0)>>0]|0;else h=J$(h,a[e>>0]|0)|0;if((h&-2|0)!=24){c[g>>2]=e;f=0;break}j=b+72|0;b:while(1){i=e+2|0;if((f-i|0)<=1){f=-1;break a}h=a[e+3>>0]|0;b=a[i>>0]|0;if(!(h<<24>>24))h=d[j+(b&255)>>0]|0;else h=J$(h,b)|0;switch(h|0){case 24:case 25:{e=i;break}case 18:{h=13;break b}default:{h=14;break b}}}if((h|0)==13){c[g>>2]=e+4;f=10;break}else if((h|0)==14){c[g>>2]=i;f=0;break}}else f=-1;while(0);return f|0}function O$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;o=f;j=o-e|0;a:do if((j|0)>1){k=a[e+1>>0]|0;if(!(k<<24>>24)){i=a[e>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{m=i&255;if(!(1<<(m&31)&c[56940+((m>>>5|d[143788+(k&255)>>0]<<3)<<2)>>2])){c[g>>2]=e;h=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;h=0;break a}case 6:{if((j|0)<3){h=-2;break a}c[g>>2]=e;h=0;break a}case 7:{if((j|0)<4){h=-2;break a}c[g>>2]=e;h=0;break a}case 16:{i=e+2|0;if((o-i|0)<=1){h=-1;break a}h=a[e+3>>0]|0;if(!(h<<24>>24))h=d[b+72+(d[i>>0]|0)>>0]|0;else h=J$(h,a[i>>0]|0)|0;switch(h|0){case 27:{h=P$(b,e+4|0,f,g)|0;break a}case 20:{h=Q$(e+4|0,f,g)|0;break a}default:{c[g>>2]=i;h=0;break a}}}case 15:{h=R$(b,e+2|0,f,g)|0;break a}case 17:{h=S$(b,e+2|0,f,g)|0;break a}default:{c[g>>2]=e;h=0;break a}}m=b+72|0;l=0;h=e+2|0;b:while(1){c:while(1){i=o-h|0;if((i|0)<=1){h=-1;break a}j=a[h+1>>0]|0;k=a[h>>0]|0;if(!(j<<24>>24))e=d[m+(k&255)>>0]|0;else e=J$(j,k)|0;switch(e|0){case 5:{n=34;break b}case 6:{n=35;break b}case 7:{n=37;break b}case 10:case 9:case 21:{n=55;break b}case 11:{n=69;break b}case 17:break b;case 23:break c;case 29:{e=k&255;if(!(1<<(e&31)&c[56940+((e>>>5|d[144044+(j&255)>>0]<<3)<<2)>>2])){n=32;break b}break}case 27:case 26:case 25:case 24:case 22:break;default:{n=75;break b}}h=h+2|0}if(l|0){n=40;break}k=h+2|0;e=o-k|0;if((e|0)<=1){h=-1;break a}l=a[h+3>>0]|0;i=a[k>>0]|0;if(!(l<<24>>24))j=d[m+(i&255)>>0]|0;else j=J$(l,i)|0;switch(j|0){case 5:{n=49;break b}case 6:{n=50;break b}case 7:{n=52;break b}case 29:{e=i&255;if(!(1<<(e&31)&c[56940+((e>>>5|d[143788+(l&255)>>0]<<3)<<2)>>2])){n=47;break b}break}case 24:case 22:break;default:{n=54;break b}}l=1;h=h+4|0}d:switch(n|0){case 32:{c[g>>2]=h;h=0;break a}case 34:{c[g>>2]=h;h=0;break a}case 35:{if((i|0)<3){h=-2;break a}c[g>>2]=h;h=0;break a}case 37:{if((i|0)<4){h=-2;break a}c[g>>2]=h;h=0;break a}case 40:{c[g>>2]=h;h=0;break a}case 47:{c[g>>2]=k;h=0;break a}case 49:{c[g>>2]=k;h=0;break a}case 50:{if((e|0)<3){h=-2;break a}c[g>>2]=k;h=0;break a}case 52:{if((e|0)<4){h=-2;break a}c[g>>2]=k;h=0;break a}case 54:{c[g>>2]=k;h=0;break a}case 55:{e:while(1){n=0;i=h+2|0;j=o-i|0;if((j|0)<=1){h=-1;break a}k=a[h+3>>0]|0;e=a[i>>0]|0;if(!(k<<24>>24))l=d[m+(e&255)>>0]|0;else l=J$(k,e)|0;switch(l|0){case 11:{h=i;n=69;break d}case 17:{h=i;break d}case 10:case 9:case 21:{h=i;n=55;break}case 29:{n=60;break e}case 24:case 22:break e;case 5:{n=63;break e}case 6:{n=64;break e}case 7:{n=66;break e}default:{n=68;break e}}}if((n|0)==60){o=e&255;if(!(1<<(o&31)&c[56940+((o>>>5|d[143788+(k&255)>>0]<<3)<<2)>>2])){c[g>>2]=i;h=0;break a}}else if((n|0)==63){c[g>>2]=i;h=0;break a}else if((n|0)==64){if((j|0)<3){h=-2;break a}c[g>>2]=i;h=0;break a}else if((n|0)==66){if((j|0)<4){h=-2;break a}c[g>>2]=i;h=0;break a}else if((n|0)==68){c[g>>2]=i;h=0;break a}h=T$(b,h+4|0,f,g)|0;break a}case 75:{c[g>>2]=h;h=0;break a}}if((n|0)==69){c[g>>2]=h+2;h=2;break}i=h+2|0;if((o-i|0)>1){if((a[h+3>>0]|0)==0?(a[i>>0]|0)==62:0){c[g>>2]=h+4;h=4;break}c[g>>2]=i;h=0}else h=-1}else h=-1;while(0);return h|0}function P$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=f;a:do if((j-e|0)>1){if((a[e+1>>0]|0)==0?(a[e>>0]|0)==45:0){i=b+72|0;h=e+2|0;b:while(1){e=j-h|0;if((e|0)<=1){f=-1;break a}f=a[h+1>>0]|0;b=a[h>>0]|0;if(!(f<<24>>24))f=d[i+(b&255)>>0]|0;else f=J$(f,b)|0;switch(f|0){case 8:case 1:case 0:{k=16;break b}case 5:{h=h+2|0;continue b}case 6:{if((e|0)<3){f=-2;break a}h=h+3|0;continue b}case 7:{if((e|0)<4){f=-2;break a}h=h+4|0;continue b}case 27:{f=h+2|0;if((j-f|0)<=1){f=-1;break a}if(a[h+3>>0]|0){h=f;continue b}if((a[f>>0]|0)==45)break b;else{h=f;continue b}}default:{h=h+2|0;continue b}}}if((k|0)==16){c[g>>2]=h;f=0;break}f=h+4|0;if((j-f|0)<=1){f=-1;break}if((a[h+5>>0]|0)==0?(a[f>>0]|0)==62:0){c[g>>2]=h+6;f=13;break}c[g>>2]=f;f=0;break}c[g>>2]=e;f=0}else f=-1;while(0);return f|0}function Q$(b,d,e){b=b|0;d=d|0;e=e|0;if((d-b|0)>11){d=0;while(1){if((d|0)>=6){d=8;break}if(a[b+1>>0]|0){d=0;break}if((a[b>>0]|0)!=(a[144300+d>>0]|0)){d=0;break}d=d+1|0;b=b+2|0}c[e>>2]=b}else d=-1;return d|0}function R$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+16|0;p=q;o=f;j=o-e|0;a:do if((j|0)>1){f=a[e+1>>0]|0;if(!(f<<24>>24)){i=a[e>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e>>0]|0;h=J$(f,i)|0}switch(h|0){case 29:{m=i&255;if(!(1<<(m&31)&c[56940+((m>>>5|d[143788+(f&255)>>0]<<3)<<2)>>2])){c[g>>2]=e;f=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;f=0;break a}case 6:{if((j|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((j|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}default:{c[g>>2]=e;f=0;break a}}m=b+72|0;k=e;b:while(1){f=k+2|0;h=o-f|0;if((h|0)<=1){f=-1;break a}i=a[k+3>>0]|0;j=a[f>>0]|0;if(!(i<<24>>24))b=d[m+(j&255)>>0]|0;else b=J$(i,j)|0;switch(b|0){case 27:case 26:case 25:case 24:case 22:{k=f;continue b}case 5:{n=22;break b}case 6:{n=23;break b}case 7:{n=25;break b}case 10:case 9:case 21:{n=27;break b}case 15:{n=46;break b}case 29:break;default:break b}k=j&255;if(!(1<<(k&31)&c[56940+((k>>>5|d[144044+(i&255)>>0]<<3)<<2)>>2])){n=21;break}else k=f}if((n|0)==21){c[g>>2]=f;f=0;break}else if((n|0)==22){c[g>>2]=f;f=0;break}else if((n|0)==23){if((h|0)<3){f=-2;break}c[g>>2]=f;f=0;break}else if((n|0)==25){if((h|0)<4){f=-2;break}c[g>>2]=f;f=0;break}else if((n|0)==27){if(!(U$(e,f,p)|0)){c[g>>2]=f;f=0;break}j=k+4|0;c:while(1){i=o-j|0;if((i|0)<=1){f=-1;break a}f=a[j+1>>0]|0;h=a[j>>0]|0;if(!(f<<24>>24))f=d[m+(h&255)>>0]|0;else f=J$(f,h)|0;switch(f|0){case 8:case 1:case 0:{n=40;break c}case 5:{j=j+2|0;continue c}case 6:{if((i|0)<3){f=-2;break a}j=j+3|0;continue c}case 7:{if((i|0)<4){f=-2;break a}j=j+4|0;continue c}case 15:{f=j+2|0;if((o-f|0)<=1){f=-1;break a}if(a[j+3>>0]|0){j=f;continue c}if((a[f>>0]|0)==62){n=44;break c}else{j=f;continue c}}default:{j=j+2|0;continue c}}}if((n|0)==40){c[g>>2]=j;f=0;break}else if((n|0)==44){c[g>>2]=j+4;f=c[p>>2]|0;break}}else if((n|0)==46){if(!(U$(e,f,p)|0)){c[g>>2]=f;f=0;break}f=k+4|0;if((o-f|0)<=1){f=-1;break}if((a[k+5>>0]|0)==0?(a[f>>0]|0)==62:0){c[g>>2]=k+6;f=c[p>>2]|0;break}}c[g>>2]=f;f=0}else f=-1;while(0);l=q;return f|0}function S$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;j=f-e|0;a:do if((j|0)>1){k=a[e+1>>0]|0;if(!(k<<24>>24)){i=a[e>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{l=i&255;if(!(1<<(l&31)&c[56940+((l>>>5|(d[143788+(k&255)>>0]|0)<<3)<<2)>>2])){c[g>>2]=e;f=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;f=0;break a}case 6:{if((j|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((j|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}default:{c[g>>2]=e;f=0;break a}}l=b+72|0;b=e;b:while(1){j=b+2|0;e=f-j|0;if((e|0)<=1){f=-1;break a}h=a[b+3>>0]|0;i=a[j>>0]|0;if(!(h<<24>>24))k=d[l+(i&255)>>0]|0;else k=J$(h,i)|0;switch(k|0){case 23:case 27:case 26:case 25:case 24:case 22:{b=j;continue b}case 5:{h=22;break b}case 6:{h=23;break b}case 7:{h=25;break b}case 10:case 9:case 21:{h=27;break b}case 11:{h=34;break b}case 29:break;default:{h=35;break b}}e=i&255;if(!(1<<(e&31)&c[56940+((e>>>5|(d[144044+(h&255)>>0]|0)<<3)<<2)>>2])){h=21;break}else b=j}if((h|0)==21){c[g>>2]=j;f=0;break}else if((h|0)==22){c[g>>2]=j;f=0;break}else if((h|0)==23){if((e|0)<3){f=-2;break}c[g>>2]=j;f=0;break}else if((h|0)==25){if((e|0)<4){f=-2;break}c[g>>2]=j;f=0;break}else if((h|0)==27){c:while(1){k=j+2|0;if((f-k|0)<=1){f=-1;break a}h=a[j+3>>0]|0;i=a[k>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;switch(h|0){case 10:case 9:case 21:{j=k;h=27;break}case 11:{h=32;break c}default:{h=33;break c}}}if((h|0)==32){c[g>>2]=j+4;f=5;break}else if((h|0)==33){c[g>>2]=k;f=0;break}}else if((h|0)==34){c[g>>2]=b+4;f=5;break}else if((h|0)==35){c[g>>2]=j;f=0;break}}else f=-1;while(0);return f|0}function T$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+16|0;q=s;c[q>>2]=e;r=f;o=b+72|0;h=0;a:while(1){b:while(1){m=r-e|0;if((m|0)<=1){e=-1;break a}i=a[e+1>>0]|0;j=a[e>>0]|0;if(!(i<<24>>24))k=d[o+(j&255)>>0]|0;else k=J$(i,j)|0;switch(k|0){case 5:{p=11;break a}case 6:{p=12;break a}case 7:{p=14;break a}case 23:{p=16;break b}case 10:case 9:case 21:{p=31;break b}case 14:{p=37;break b}case 29:{n=j&255;if(!(1<<(n&31)&c[56940+((n>>>5|d[144044+(i&255)>>0]<<3)<<2)>>2])){p=9;break a}break}case 27:case 26:case 25:case 24:case 22:break;default:{p=89;break a}}n=e+2|0;c[q>>2]=n;e=n}c:do if((p|0)==16){p=0;if(h|0){p=17;break a}h=e+2|0;c[q>>2]=h;i=h;j=r-i|0;if((j|0)<=1){e=-1;break a}k=a[e+3>>0]|0;m=a[h>>0]|0;if(!(k<<24>>24))n=d[o+(m&255)>>0]|0;else n=J$(k,m)|0;switch(n|0){case 5:{p=25;break a}case 6:{p=26;break a}case 7:{p=28;break a}case 24:case 22:{h=1;break c}case 29:break;default:{p=30;break a}}n=m&255;if(!(1<<(n&31)&c[56940+((n>>>5|d[143788+(k&255)>>0]<<3)<<2)>>2])){p=24;break a}else h=1}else if((p|0)==31)while(1){p=0;i=e+2|0;c[q>>2]=i;j=i;if((r-j|0)<=1){e=-1;break a}e=a[e+3>>0]|0;h=a[i>>0]|0;if(!(e<<24>>24))e=d[o+(h&255)>>0]|0;else e=J$(e,h)|0;switch(e|0){case 14:{e=i;p=37;break c}case 9:case 10:case 21:{e=i;p=31;break}default:{p=36;break a}}}while(0);d:do if((p|0)==37){while(1){p=0;j=e+2|0;c[q>>2]=j;k=j;if((r-k|0)<=1){e=-1;break a}h=a[e+3>>0]|0;i=a[j>>0]|0;if(!(h<<24>>24))m=d[o+(i&255)>>0]|0;else m=J$(h,i)|0;if((m|1|0)==13)break;switch(m|0){case 9:case 10:case 21:{e=j;p=37;break}default:{p=43;break a}}}e=e+4|0;c[q>>2]=e;e:while(1){j=r-e|0;if((j|0)<=1){e=-1;break a}h=a[e+1>>0]|0;i=a[e>>0]|0;if(!(h<<24>>24))h=d[o+(i&255)>>0]|0;else h=J$(h,i)|0;if((h|0)==(m|0))break;switch(h|0){case 8:case 1:case 0:{p=56;break a}case 2:{p=61;break a}case 5:{n=e+2|0;c[q>>2]=n;e=n;continue e}case 6:{if((j|0)<3){e=-2;break a}n=e+3|0;c[q>>2]=n;e=n;continue e}case 7:{if((j|0)<4){e=-2;break a}n=e+4|0;c[q>>2]=n;e=n;continue e}case 3:{e=K$(b,e+2|0,f,q)|0;if((e|0)<1){p=59;break a}e=c[q>>2]|0;continue e}default:{n=e+2|0;c[q>>2]=n;e=n;continue e}}}i=e+2|0;c[q>>2]=i;k=i;if((r-k|0)<=1){e=-1;break a}h=a[e+3>>0]|0;j=a[i>>0]|0;if(!(h<<24>>24))h=d[o+(j&255)>>0]|0;else h=J$(h,j)|0;switch(h|0){case 11:{p=81;break a}case 17:{p=82;break a}case 10:case 9:case 21:{e=i;break}default:{p=68;break a}}f:while(1){i=e+2|0;c[q>>2]=i;h=i;j=r-h|0;if((j|0)<=1){e=-1;break a}k=a[e+3>>0]|0;m=a[i>>0]|0;if(!(k<<24>>24))n=d[o+(m&255)>>0]|0;else n=J$(k,m)|0;switch(n|0){case 5:{p=76;break a}case 6:{p=77;break a}case 7:{p=79;break a}case 11:{p=81;break a}case 17:{p=82;break a}case 24:case 22:{h=0;break d}case 10:case 9:case 21:{e=i;break}case 29:break f;default:{p=87;break a}}}n=m&255;if(!(1<<(n&31)&c[56940+((n>>>5|d[143788+(k&255)>>0]<<3)<<2)>>2])){p=75;break a}else h=0}while(0);e=e+4|0;c[q>>2]=e}switch(p|0){case 9:{c[g>>2]=e;e=0;break}case 11:{c[g>>2]=e;e=0;break}case 12:{if((m|0)<3)e=-2;else{c[g>>2]=e;e=0}break}case 14:{if((m|0)<4)e=-2;else{c[g>>2]=e;e=0}break}case 17:{c[g>>2]=e;e=0;break}case 24:{c[g>>2]=h;e=0;break}case 25:{c[g>>2]=h;e=0;break}case 26:{if((j|0)<3)e=-2;else{c[g>>2]=h;e=0}break}case 28:{if((j|0)<4)e=-2;else{c[g>>2]=h;e=0}break}case 30:{c[g>>2]=i;e=0;break}case 36:{c[g>>2]=j;e=0;break}case 43:{c[g>>2]=k;e=0;break}case 56:{c[g>>2]=e;e=0;break}case 59:{if(!e){c[g>>2]=c[q>>2];e=0}break}case 61:{c[g>>2]=e;e=0;break}case 68:{c[g>>2]=k;e=0;break}case 75:{c[g>>2]=i;e=0;break}case 76:{c[g>>2]=i;e=0;break}case 77:{if((j|0)<3)e=-2;else{c[g>>2]=i;e=0}break}case 79:{if((j|0)<4)e=-2;else{c[g>>2]=i;e=0}break}case 81:{c[g>>2]=e+4;e=1;break}case 82:{h=i+2|0;c[q>>2]=h;e=h;if((r-e|0)>1){if((a[i+3>>0]|0)==0?(a[h>>0]|0)==62:0){c[g>>2]=i+4;e=3;break}c[g>>2]=e;e=0}else e=-1;break}case 87:{c[g>>2]=h;e=0;break}case 89:{c[g>>2]=e;e=0;break}}l=s;return e|0}function U$(b,d,e){b=b|0;d=d|0;e=e|0;c[e>>2]=11;a:do if((d-b|0)==6?(a[b+1>>0]|0)==0:0){switch(a[b>>0]|0){case 120:{d=0;break}case 88:{d=1;break}default:{d=1;break a}}if(!(a[b+3>>0]|0)){switch(a[b+2>>0]|0){case 109:break;case 77:{d=1;break}default:{d=1;break a}}if(!(a[b+5>>0]|0)){switch(a[b+4>>0]|0){case 108:break;case 76:{d=0;break a}default:{d=1;break a}}if(!d){c[e>>2]=12;d=1}else d=0}else d=1}else d=1}else d=1;while(0);return d|0}function V$(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0;k=e+72|0;a:while(1){b:while(1){j=g-f|0;if((j|0)<=1){f=-1;break a}e=a[f+1>>0]|0;i=a[f>>0]|0;if(!(e<<24>>24))e=d[k+(i&255)>>0]|0;else e=J$(e,i)|0;switch(e|0){case 8:case 1:case 0:{l=10;break a}case 6:{l=9;break b}case 7:{l=19;break b}case 5:{f=f+2|0;continue b}case 13:case 12:{i=f+2|0;if((e|0)==(b|0)){l=12;break a}else{f=i;continue b}}default:{f=f+2|0;continue b}}}if((l|0)==9){l=0;if((j|0)<3){f=-2;break}else{f=f+3|0;continue}}else if((l|0)==19){l=0;if((j|0)<4){f=-2;break}else{f=f+4|0;continue}}}c:do if((l|0)==10){c[h>>2]=f;f=0}else if((l|0)==12)if((g-i|0)>1){c[h>>2]=i;g=a[f+3>>0]|0;f=a[i>>0]|0;if(!(g<<24>>24))f=d[k+(f&255)>>0]|0;else f=J$(g,f)|0;switch(f|0){case 20:case 30:case 11:case 10:case 9:case 21:{f=27;break c}default:{}}f=0}else f=-27;while(0);return f|0}function W$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;k=f;a:do if((k-e|0)>1){h=a[e+1>>0]|0;if(!(h<<24>>24))h=d[b+72+(d[e>>0]|0)>>0]|0;else h=J$(h,a[e>>0]|0)|0;switch(h|0){case 27:{h=P$(b,e+2|0,f,g)|0;break a}case 20:{c[g>>2]=e+2;h=33;break a}case 24:case 22:{j=b+72|0;b:while(1){i=e+2|0;f=k-i|0;if((f|0)<=1){h=-1;break a}h=a[e+3>>0]|0;b=a[i>>0]|0;if(!(h<<24>>24))h=d[j+(b&255)>>0]|0;else h=J$(h,b)|0;switch(h|0){case 24:case 22:{e=i;break}case 30:{l=15;break b}case 10:case 9:case 21:break b;default:{l=22;break b}}}if((l|0)==22){c[g>>2]=i;h=0;break a}c:do if((l|0)==15){if((f|0)<=3){h=-1;break a}b=a[e+5>>0]|0;h=a[e+4>>0]|0;if(!(b<<24>>24))h=d[j+(h&255)>>0]|0;else h=J$(b,h)|0;switch(h|0){case 30:case 10:case 9:case 21:break;default:break c}c[g>>2]=i;h=0;break a}while(0);c[g>>2]=i;h=16;break a}default:{c[g>>2]=e;h=0;break a}}}else h=-1;while(0);return h|0}function X$(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=f-e|0;a:do if((j|0)>1){k=a[e+1>>0]|0;if(!(k<<24>>24)){i=a[e>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{j=i&255;if(!(1<<(j&31)&c[56940+((j>>>5|(d[143788+(k&255)>>0]|0)<<3)<<2)>>2])){c[g>>2]=e;f=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;f=0;break a}case 6:{if((j|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((j|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}default:{c[g>>2]=e;f=0;break a}}b=b+72|0;k=e;b:while(1){h=k;k=k+2|0;e=f-k|0;if((e|0)<=1){f=-20;break a}h=a[h+3>>0]|0;i=a[k>>0]|0;if(!(h<<24>>24))j=d[b+(i&255)>>0]|0;else j=J$(h,i)|0;switch(j|0){case 27:case 26:case 25:case 24:case 22:continue b;case 5:{h=22;break b}case 6:{h=23;break b}case 7:{h=25;break b}case 36:case 30:case 11:case 32:case 21:case 10:case 9:{h=27;break b}case 29:break;default:{h=28;break b}}e=i&255;if(!(1<<(e&31)&c[56940+((e>>>5|(d[144044+(h&255)>>0]|0)<<3)<<2)>>2])){h=21;break}}if((h|0)==21){c[g>>2]=k;f=0;break}else if((h|0)==22){c[g>>2]=k;f=0;break}else if((h|0)==23){if((e|0)<3){f=-2;break}c[g>>2]=k;f=0;break}else if((h|0)==25){if((e|0)<4){f=-2;break}c[g>>2]=k;f=0;break}else if((h|0)==27){c[g>>2]=k;f=20;break}else if((h|0)==28){c[g>>2]=k;f=0;break}}else f=-1;while(0);return f|0}function Y$(b,d,e){b=b|0;d=d|0;e=e|0;e=Z$(e)|0;if((e|0)==-1)e=0;else{a[b+69>>0]=e;c[b>>2]=67;c[b+4>>2]=68;c[b+48>>2]=31;c[b+72>>2]=d;c[d>>2]=b;e=1}return e|0}function Z$(a){a=a|0;var b=0;a:do if(!a)b=6;else{b=0;while(1){if((b|0)>=6){b=-1;break a}if(H0(a,c[60068+(b<<2)>>2]|0)|0)break a;b=b+1|0}}while(0);return b|0}function _$(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return b0(58948,a,0,b,c,d)|0}function $$(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return b0(58948,a,1,b,c,d)|0}function a0(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;S_(58584,b,c,d);return}function b0(b,d,e,f,g,h){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0;a:do if(f>>>0<g>>>0){l=c[d+72>>2]|0;i=f+1|0;b:do if((i|0)==(g|0)){i=a[d+69>>0]|0;if(((i<<24>>24)+-3|0)>>>0<3){i=-1;break a}switch(a[f>>0]|0){case 60:case 0:{i=-1;break a}case -17:case -1:case -2:break;default:break b}if(!((e|0)==1&i<<24>>24==0)){i=-1;break a}}else{k=a[f>>0]|0;i=a[i>>0]|0;j=((k&255)<<8|i&255)&65535;c:do if(j<<16>>16<-2){if(j<<16>>16>=-257){switch(j<<16>>16){case -257:break;default:break c}if((e|0)==1&(a[d+69>>0]|0)==0)break b;c[h>>2]=f+2;c[l>>2]=c[b+16>>2];i=14;break a}switch(j<<16>>16){case -4165:break;default:break c}if((e|0)==1)switch(a[d+69>>0]|0){case 0:case 3:case 4:case 5:break b;default:{}}i=f+2|0;if((i|0)==(g|0)){i=-1;break a}if((a[i>>0]|0)!=-65)break b;c[h>>2]=f+3;c[l>>2]=c[b+8>>2];i=14;break a}else{if(j<<16>>16<15360){switch(j<<16>>16){case -2:break;default:break c}if((e|0)==1&(a[d+69>>0]|0)==0)break b;c[h>>2]=f+2;c[l>>2]=c[b+20>>2];i=14;break a}switch(j<<16>>16){case 15360:break;default:break c}i=a[d+69>>0]|0;if(i<<24>>24==4){if((e|0)==1)break b}else if((e|0)==1&i<<24>>24==3)break b;i=c[b+20>>2]|0;c[l>>2]=i;i=ob[c[i+(e<<2)>>2]&127](i,f,g,h)|0;break a}while(0);if(k<<24>>24){if((e|0)==1|i<<24>>24!=0)break;i=c[b+20>>2]|0;c[l>>2]=i;i=ob[c[i+(e<<2)>>2]&127](i,f,g,h)|0;break a}if((e|0)==1?(a[d+69>>0]|0)==5:0)break;i=c[b+16>>2]|0;c[l>>2]=i;i=ob[c[i+(e<<2)>>2]&127](i,f,g,h)|0;break a}while(0);i=c[b+(a[d+69>>0]<<2)>>2]|0;c[l>>2]=i;i=ob[c[i+(e<<2)>>2]&127](i,f,g,h)|0}else i=-4;while(0);return i|0}function c0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0;a:do if(e>>>0<f>>>0){i=e;m=f-i|0;k=(m&1|0)==0;m=m&-2;l=(m|0)==0;m=k|l?f:e+m|0;if(k|l^1){f=a[e>>0]|0;if(!(f<<24>>24)){h=a[e+1>>0]|0;j=h;h=d[b+72+(h&255)>>0]|0}else{h=a[e+1>>0]|0;j=h;h=J$(f,h)|0}do switch(h|0){case 12:{f=D0(12,b,e+2|0,m,g)|0;break a}case 13:{f=D0(13,b,e+2|0,m,g)|0;break a}case 2:{h=e+2|0;if((m-h|0)<=1){f=-1;break a}f=a[h>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[e+3>>0]|0)>>0]|0;else f=J$(f,a[e+3>>0]|0)|0;switch(f|0){case 16:{f=E0(b,e+4|0,m,g)|0;break a}case 15:{f=z0(b,e+4|0,m,g)|0;break a}case 7:case 6:case 5:case 29:case 24:case 22:{c[g>>2]=e;f=29;break a}default:{c[g>>2]=h;f=0;break a}}}case 9:{if((e+2|0)==(m|0)){c[g>>2]=m;f=-15;break a}else i=7;break}case 10:case 21:{i=7;break}case 30:{f=t0(b,e+2|0,m,g)|0;break a}case 35:{c[g>>2]=e+2;f=38;break a}case 20:{c[g>>2]=e+2;f=25;break a}case 4:{h=e+2|0;f=m-h|0;if((f|0)<=1){f=-26;break a}if((a[h>>0]|0)==0?(a[e+3>>0]|0)==93:0){if((f|0)<=3){f=-1;break a}if((a[e+4>>0]|0)==0?(a[e+5>>0]|0)==62:0){c[g>>2]=e+6;f=34;break a}}c[g>>2]=h;f=26;break a}case 31:{c[g>>2]=e+2;f=23;break a}case 32:{h=e+2|0;if((m-h|0)<=1){f=-24;break a}f=a[h>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[e+3>>0]|0)>>0]|0;else f=J$(f,a[e+3>>0]|0)|0;switch(f|0){case 33:{c[g>>2]=e+4;f=36;break a}case 15:{c[g>>2]=e+4;f=35;break a}case 34:{c[g>>2]=e+4;f=37;break a}case 32:case 36:case 35:case 11:case 21:case 10:case 9:{c[g>>2]=h;f=24;break a}default:{c[g>>2]=h;f=0;break a}}}case 36:{c[g>>2]=e+2;f=21;break a}case 11:{c[g>>2]=e+2;f=17;break a}case 19:{f=F0(b,e+2|0,m,g)|0;break a}case 5:{if((m-i|0)<2){f=-2;break a}c[g>>2]=e;f=0;break a}case 6:{if((m-i|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((m-i|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}case 24:case 22:{f=18;i=61;break}case 23:case 27:case 26:case 25:{f=19;i=61;break}case 29:{i=f&255;h=j&255;f=h>>>5;h=1<<(h&31);if(!(h&c[56940+((f|d[143788+i>>0]<<3)<<2)>>2]))if(!(c[56940+((d[144044+i>>0]<<3|f)<<2)>>2]&h))i=64;else{f=19;i=61}else{f=18;i=61}break}default:i=64}while(0);if((i|0)==7){k=m;b=b+72|0;j=e;b:while(1){i=j;j=j+2|0;if((k-j|0)<=1){i=28;break}f=a[j>>0]|0;h=a[i+3>>0]|0;if(!(f<<24>>24))f=d[b+(h&255)>>0]|0;else f=J$(f,h)|0;switch(f|0){case 10:case 21:continue b;case 9:break;default:{i=27;break b}}if((i+4|0)==(m|0)){i=27;break}}if((i|0)==27){c[g>>2]=j;f=15;break}else if((i|0)==28){c[g>>2]=j;f=15;break}}else if((i|0)==61){l=b+72|0;h=e+2|0;c:while(1){e=f&255;d:while(1){k=m-h|0;if((k|0)<=1){i=104;break c}i=a[h>>0]|0;j=a[h+1>>0]|0;if(!(i<<24>>24))b=d[l+(j&255)>>0]|0;else b=J$(i,j)|0;switch(b|0){case 5:{i=73;break c}case 6:{i=74;break c}case 7:{i=76;break c}case 10:case 9:case 21:case 30:case 20:case 36:case 35:case 32:case 11:{i=78;break c}case 34:{i=94;break c}case 33:{i=97;break c}case 15:{i=100;break c}case 29:{k=j&255;if(!(1<<(k&31)&c[56940+((k>>>5|d[144044+(i&255)>>0]<<3)<<2)>>2])){i=71;break c}break}case 27:case 26:case 25:case 24:case 22:break;case 23:{k=h+2|0;switch(e&63){case 41:{f=19;h=k;continue c}case 18:break d;default:{h=k;continue d}}}default:{i=103;break c}}h=h+2|0}b=m-k|0;if((b|0)<=1){f=-1;break a}f=a[k>>0]|0;i=a[h+3>>0]|0;if(!(f<<24>>24))j=d[l+(i&255)>>0]|0;else j=J$(f,i)|0;switch(j|0){case 5:{i=89;break c}case 6:{i=90;break c}case 7:{i=92;break c}case 29:{e=i&255;if(!(1<<(e&31)&c[56940+((e>>>5|d[144044+(f&255)>>0]<<3)<<2)>>2])){i=87;break c}break}case 27:case 26:case 25:case 24:case 22:break;default:{f=19;h=k;continue c}}f=41;h=h+4|0}switch(i|0){case 71:{c[g>>2]=h;f=0;break a}case 73:{c[g>>2]=h;f=0;break a}case 74:{if((k|0)<3){f=-2;break a}c[g>>2]=h;f=0;break a}case 76:{if((k|0)<4){f=-2;break a}c[g>>2]=h;f=0;break a}case 78:{c[g>>2]=h;break a}case 87:{c[g>>2]=k;f=0;break a}case 89:{c[g>>2]=k;f=0;break a}case 90:{if((b|0)<3){f=-2;break a}c[g>>2]=k;f=0;break a}case 92:{if((b|0)<4){f=-2;break a}c[g>>2]=k;f=0;break a}case 94:if((f|0)==19){c[g>>2]=h;f=0;break a}else{c[g>>2]=h+2;f=32;break a}case 97:if((f|0)==19){c[g>>2]=h;f=0;break a}else{c[g>>2]=h+2;f=31;break a}case 100:if((f|0)==19){c[g>>2]=h;f=0;break a}else{c[g>>2]=h+2;f=30;break a}case 103:{c[g>>2]=h;f=0;break a}case 104:{f=0-f|0;break a}}}else if((i|0)==64){c[g>>2]=e;f=0;break}}else f=-1}else f=-4;while(0);return f|0}function d0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;a:do if(e>>>0<f>>>0){h=e;i=f-h|0;k=(i&1|0)==0;i=i&-2;j=(i|0)==0;i=k|j?f:e+i|0;if(k|j^1){f=a[e>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[e+1>>0]|0)>>0]|0;else f=J$(f,a[e+1>>0]|0)|0;do switch(f|0){case 2:{f=w0(b,e+2|0,i,g)|0;break a}case 3:{f=s0(b,e+2|0,i,g)|0;break a}case 9:{h=e+2|0;if((i-h|0)<=1){f=-3;break a}f=a[h>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[e+3>>0]|0)>>0]|0;else f=J$(f,a[e+3>>0]|0)|0;c[g>>2]=(f|0)==10?e+4|0:h;f=7;break a}case 10:{c[g>>2]=e+2;f=7;break a}case 4:{h=e+2|0;if((i-h|0)<=1){f=-5;break a}if((a[h>>0]|0)==0?(a[e+3>>0]|0)==93:0){f=e+4|0;if((i-f|0)<=1){f=-5;break a}if((a[f>>0]|0)==0?(a[e+5>>0]|0)==62:0){c[g>>2]=f;f=0;break a}else f=i}else f=i;break}case 5:{f=i;if((f-h|0)<2){f=-2;break a}h=e+2|0;break}case 6:{f=i;if((f-h|0)<3){f=-2;break a}h=e+3|0;break}case 7:{f=i;if((f-h|0)<4){f=-2;break a}h=e+4|0;break}case 8:case 1:case 0:{c[g>>2]=e;f=0;break a}default:{h=e+2|0;f=i}}while(0);j=b+72|0;b=h;b:while(1){e=f-b|0;if((e|0)<=1){h=52;break}h=a[b>>0]|0;i=a[b+1>>0]|0;if(!(h<<24>>24))h=d[j+(i&255)>>0]|0;else h=J$(h,i)|0;switch(h|0){case 10:case 9:case 8:case 1:case 0:case 2:case 3:{h=50;break b}case 5:{b=b+2|0;continue b}case 6:{if((e|0)<3){h=38;break b}b=b+3|0;continue b}case 7:{if((e|0)<4){h=41;break b}b=b+4|0;continue b}case 4:{if((e|0)<=3){h=50;break b}h=b+2|0;if(a[h>>0]|0){b=h;continue b}if((a[b+3>>0]|0)!=93){b=h;continue b}if((e|0)<=5){h=50;break b}i=b+4|0;if(a[i>>0]|0){b=h;continue b}if((a[b+5>>0]|0)==62){h=49;break b}else{b=h;continue b}}default:{b=b+2|0;continue b}}}if((h|0)==38){c[g>>2]=b;f=6;break}else if((h|0)==41){c[g>>2]=b;f=6;break}else if((h|0)==49){c[g>>2]=i;f=0;break}else if((h|0)==50){c[g>>2]=b;f=6;break}else if((h|0)==52){c[g>>2]=b;f=6;break}}else f=-1}else f=-4;while(0);return f|0}function e0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;a:do if(e>>>0<f>>>0){h=e;i=f-h|0;k=(i&1|0)==0;i=i&-2;j=(i|0)==0;i=k|j?f:e+i|0;if(k|j^1){f=a[e>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[e+1>>0]|0)>>0]|0;else f=J$(f,a[e+1>>0]|0)|0;switch(f|0){case 4:{h=e+2|0;if((i-h|0)<=1){f=-1;break a}if((a[h>>0]|0)==0?(a[e+3>>0]|0)==93:0){f=e+4|0;if((i-f|0)<=1){f=-1;break a}if((a[f>>0]|0)==0?(a[e+5>>0]|0)==62:0){c[g>>2]=e+6;f=40;break a}}break}case 9:{h=e+2|0;if((i-h|0)<=1){f=-1;break a}f=a[h>>0]|0;if(!(f<<24>>24))f=d[b+72+(d[e+3>>0]|0)>>0]|0;else f=J$(f,a[e+3>>0]|0)|0;c[g>>2]=(f|0)==10?e+4|0:h;f=7;break a}case 10:{c[g>>2]=e+2;f=7;break a}case 5:{f=i;if((f-h|0)<2){f=-2;break a}h=e+2|0;i=f;break}case 6:{f=i;if((f-h|0)<3){f=-2;break a}h=e+3|0;i=f;break}case 7:{f=i;if((f-h|0)<4){f=-2;break a}h=e+4|0;i=f;break}case 8:case 1:case 0:{c[g>>2]=e;f=0;break a}default:h=e+2|0}j=b+72|0;b=h;b:while(1){e=i-b|0;if((e|0)<=1){h=40;break}f=a[b>>0]|0;h=a[b+1>>0]|0;if(!(f<<24>>24))f=d[j+(h&255)>>0]|0;else f=J$(f,h)|0;switch(f|0){case 10:case 9:case 8:case 1:case 0:case 4:{h=38;break b}case 6:{if((e|0)<3){h=35;break b}else f=3;break}case 7:{if((e|0)<4){h=37;break b}else f=4;break}default:f=2}b=b+f|0}if((h|0)==35){c[g>>2]=b;f=6;break}else if((h|0)==37){c[g>>2]=b;f=6;break}else if((h|0)==38){c[g>>2]=b;f=6;break}else if((h|0)==40){c[g>>2]=b;f=6;break}}else f=-1}else f=-4;while(0);return f|0}function f0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;k=f-e|0;k=(k&1|0)==0?f:e+(k&-2)|0;j=b+72|0;i=0;f=e;a:while(1){h=k-f|0;if((h|0)<=1){f=-1;break}b=a[f>>0]|0;e=a[f+1>>0]|0;if(!(b<<24>>24))b=d[j+(e&255)>>0]|0;else b=J$(b,e)|0;switch(b|0){case 8:case 1:case 0:{l=12;break a}case 5:{h=i;f=f+2|0;i=h;continue a}case 6:{if((h|0)<3){f=-2;break a}h=i;f=f+3|0;i=h;continue a}case 7:{if((h|0)<4){f=-2;break a}h=i;f=f+4|0;i=h;continue a}case 2:{b=f+2|0;if((k-b|0)<=1){f=-1;break a}if(a[b>>0]|0){h=i;f=b;i=h;continue a}if((a[f+3>>0]|0)!=33){h=i;f=b;i=h;continue a}b=f+4|0;if((k-b|0)<=1){f=-1;break a}if(a[b>>0]|0){h=i;f=b;i=h;continue a}h=(a[f+5>>0]|0)==91;i=i+(h&1)|0;f=h?f+6|0:b;continue a}case 4:{b=f+2|0;if((k-b|0)<=1){f=-1;break a}if(a[b>>0]|0){h=i;f=b;i=h;continue a}if((a[f+3>>0]|0)!=93){h=i;f=b;i=h;continue a}b=f+4|0;if((k-b|0)<=1){f=-1;break a}if(a[b>>0]|0){h=i;f=b;i=h;continue a}if((a[f+5>>0]|0)!=62){h=i;f=b;i=h;continue a}f=f+6|0;if(!i){l=26;break a}i=i+-1|0;continue a}default:{h=i;f=f+2|0;i=h;continue a}}}if((l|0)==12){c[g>>2]=f;f=0}else if((l|0)==26){c[g>>2]=f;f=42}return f|0}function g0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;do if(e>>>0<f>>>0){k=f;if((k-e|0)>1){l=b+72|0;j=e;a:while(1){if((k-j|0)<=1){i=30;break}h=a[j>>0]|0;i=a[j+1>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;switch(h|0){case 3:{i=11;break a}case 2:{i=14;break a}case 10:{i=15;break a}case 9:{i=18;break a}case 21:{i=25;break a}case 5:{h=2;break}case 6:{h=3;break}case 7:{h=4;break}default:h=2}j=j+h|0}if((i|0)==11)if((j|0)==(e|0)){h=s0(b,e+2|0,f,g)|0;break}else{c[g>>2]=j;h=6;break}else if((i|0)==14){c[g>>2]=j;h=0;break}else if((i|0)==15)if((j|0)==(e|0)){c[g>>2]=e+2;h=7;break}else{c[g>>2]=j;h=6;break}else if((i|0)==18){if((j|0)!=(e|0)){c[g>>2]=j;h=6;break}j=e+2|0;if((k-j|0)<=1){h=-3;break}h=a[j>>0]|0;i=a[e+3>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;c[g>>2]=(h|0)==10?e+4|0:j;h=7;break}else if((i|0)==25)if((j|0)==(e|0)){c[g>>2]=e+2;h=39;break}else{c[g>>2]=j;h=6;break}else if((i|0)==30){c[g>>2]=j;h=6;break}}else h=-1}else h=-4;while(0);return h|0}function h0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;do if(e>>>0<f>>>0){k=f;if((k-e|0)>1){l=b+72|0;j=e;a:while(1){if((k-j|0)<=1){i=29;break}h=a[j>>0]|0;i=a[j+1>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;switch(h|0){case 3:{i=11;break a}case 30:{i=14;break a}case 10:{i=17;break a}case 9:{i=20;break a}case 5:{h=2;break}case 6:{h=3;break}case 7:{h=4;break}default:h=2}j=j+h|0}if((i|0)==11)if((j|0)==(e|0)){h=s0(b,e+2|0,f,g)|0;break}else{c[g>>2]=j;h=6;break}else if((i|0)==14)if((j|0)==(e|0)){h=t0(b,e+2|0,f,g)|0;h=(h|0)==22?0:h;break}else{c[g>>2]=j;h=6;break}else if((i|0)==17)if((j|0)==(e|0)){c[g>>2]=e+2;h=7;break}else{c[g>>2]=j;h=6;break}else if((i|0)==20){if((j|0)!=(e|0)){c[g>>2]=j;h=6;break}j=e+2|0;if((k-j|0)<=1){h=-3;break}h=a[j>>0]|0;i=a[e+3>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;c[g>>2]=(h|0)==10?e+4|0:j;h=7;break}else if((i|0)==29){c[g>>2]=j;h=6;break}}else h=-1}else h=-4;while(0);return h|0}function i0(b,c,d,e){b=b|0;c=c|0;d=d|0;e=e|0;var f=0,g=0;f=d;b=c;while(1){c=a[e>>0]|0;if(!(c<<24>>24)){g=7;break}if((f-b|0)<2){b=0;break}if(a[b>>0]|0){b=0;break}if((a[b+1>>0]|0)!=c<<24>>24){b=0;break}e=e+1|0;b=b+2|0}if((g|0)==7)b=(b|0)==(d|0)&1;return b|0}function j0(b,c){b=b|0;c=c|0;var e=0,f=0,g=0;g=b+72|0;f=c;a:while(1){b=a[f>>0]|0;e=a[f+1>>0]|0;if(!(b<<24>>24))b=d[g+(e&255)>>0]|0;else b=J$(b,e)|0;switch(b|0){case 5:{b=2;break}case 6:{b=3;break}case 7:{b=4;break}case 27:case 26:case 25:case 24:case 23:case 22:case 29:{b=2;break}default:break a}f=f+b|0}return f-c|0}function k0(b,c){b=b|0;c=c|0;var e=0,f=0;f=b+72|0;a:while(1){b=a[c>>0]|0;e=a[c+1>>0]|0;if(!(b<<24>>24))b=d[f+(e&255)>>0]|0;else b=J$(b,e)|0;switch(b|0){case 21:case 9:case 10:break;default:break a}c=c+2|0}return c|0}function l0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;o=b+72|0;m=0;n=0;b=1;a:while(1){l=e+2|0;h=a[l>>0]|0;j=h<<24>>24==0;i=e+3|0;k=a[i>>0]|0;if(j)h=d[o+(k&255)>>0]|0;else h=J$(h,k)|0;do switch(h|0){case 5:{if(b|0){i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}if((n|0)>=(f|0)){j=n;k=m;b=1;e=l;n=j;m=k;continue a}c[g+(n<<4)>>2]=l;a[g+(n<<4)+12>>0]=1;j=n;k=m;b=1;e=l;n=j;m=k;continue a}case 6:{if(b|0){j=b;k=n;l=m;e=i;b=j;n=k;m=l;continue a}if((n|0)>=(f|0)){k=n;l=m;b=1;e=i;n=k;m=l;continue a}c[g+(n<<4)>>2]=l;a[g+(n<<4)+12>>0]=1;k=n;l=m;b=1;e=i;n=k;m=l;continue a}case 7:{if(!b)if((n|0)<(f|0)){c[g+(n<<4)>>2]=l;a[g+(n<<4)+12>>0]=1;b=1}else b=1;k=n;l=m;e=e+4|0;n=k;m=l;continue a}case 24:case 22:case 29:{if(b|0){i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}if((n|0)>=(f|0)){j=n;k=m;b=1;e=l;n=j;m=k;continue a}c[g+(n<<4)>>2]=l;a[g+(n<<4)+12>>0]=1;j=n;k=m;b=1;e=l;n=j;m=k;continue a}case 12:{if((b|0)!=2){if((n|0)>=(f|0)){k=n;m=12;b=2;e=l;n=k;continue a}c[g+(n<<4)+4>>2]=e+4;k=n;m=12;b=2;e=l;n=k;continue a}if((m|0)!=12){j=n;k=m;b=2;e=l;n=j;m=k;continue a}if((n|0)<(f|0))c[g+(n<<4)+8>>2]=l;m=12;n=n+1|0;b=0;e=l;continue a}case 13:{if((b|0)!=2){if((n|0)>=(f|0)){k=n;m=13;b=2;e=l;n=k;continue a}c[g+(n<<4)+4>>2]=e+4;k=n;m=13;b=2;e=l;n=k;continue a}if((m|0)!=13){j=n;k=m;b=2;e=l;n=j;m=k;continue a}if((n|0)<(f|0))c[g+(n<<4)+8>>2]=l;m=13;n=n+1|0;b=0;e=l;continue a}case 3:{if((n|0)>=(f|0)){i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}a[g+(n<<4)+12>>0]=0;i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}case 21:{switch(b|0){case 1:{j=n;k=m;b=0;e=l;n=j;m=k;continue a}case 2:break;default:{i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}}if((n|0)>=(f|0)){j=n;k=m;b=2;e=l;n=j;m=k;continue a}i=g+(n<<4)+12|0;if(!(a[i>>0]|0)){j=n;k=m;b=2;e=l;n=j;m=k;continue a}do if(j&(l|0)!=(c[g+(n<<4)+4>>2]|0)&k<<24>>24==32){h=a[e+4>>0]|0;b=a[e+5>>0]|0;if(!(h<<24>>24)){if(b<<24>>24==32)break;b=d[o+(b&255)>>0]|0}else b=J$(h,b)|0;if((b|0)!=(m|0)){j=n;k=m;b=2;e=l;n=j;m=k;continue a}}while(0);a[i>>0]=0;j=n;k=m;b=2;e=l;n=j;m=k;continue a}case 10:case 9:{switch(b|0){case 1:{j=n;k=m;b=0;e=l;n=j;m=k;continue a}case 2:break;default:{i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}}if((n|0)>=(f|0)){j=n;k=m;b=2;e=l;n=j;m=k;continue a}a[g+(n<<4)+12>>0]=0;j=n;k=m;b=2;e=l;n=j;m=k;continue a}case 17:case 11:if((b|0)==2){j=n;k=m;b=2;e=l;n=j;m=k;continue a}else break a;default:{i=b;j=n;k=m;e=l;b=i;n=j;m=k;continue a}}while(0)}return n|0}function m0(b,c){b=b|0;c=c|0;var d=0,e=0;b=c+4|0;a:do if(!(a[b>>0]|0))if((a[c+5>>0]|0)==120){c=c+6|0;b=0;while(1){b:while(1){if(!(a[c>>0]|0)){d=a[c+1>>0]|0;if(d<<24>>24==59){e=18;break a}d=d<<24>>24;switch(d|0){case 57:case 56:case 55:case 54:case 53:case 52:case 51:case 50:case 49:case 48:{e=8;break b}case 70:case 69:case 68:case 67:case 66:case 65:{e=9;break b}case 102:case 101:case 100:case 99:case 98:case 97:{e=10;break b}default:{}}}c=c+2|0}if((e|0)==8)b=d+-48|b<<4;else if((e|0)==9)b=(b<<4)+-55+d|0;else if((e|0)==10)b=(b<<4)+-87+d|0;if((b|0)<1114112)c=c+2|0;else{b=-1;e=19;break}}}else{c=b;b=0;e=13}else{d=b;b=0;c=-1;e=16}while(0);while(1)if((e|0)==13){if(a[c>>0]|0){d=c;c=-1;e=16;continue}if((a[c+1>>0]|0)==59){e=18;continue}d=c;c=a[c+1>>0]|0;e=16;continue}else if((e|0)==16){b=(b*10|0)+-48+c|0;if((b|0)>=1114112){b=-1;e=19;continue}c=d+2|0;e=13;continue}else if((e|0)==18){b=A_(b)|0;e=19;continue}else if((e|0)==19)return b|0;return 0}function n0(b,c,d){b=b|0;c=c|0;d=d|0;var e=0;a:do switch((d-c|0)/2|0|0){case 2:{if((a[c+2>>0]|0)==0?(a[c+3>>0]|0)==116:0){if(!(a[c>>0]|0))b=a[c+1>>0]|0;else b=-1;b=(b|0)==108?60:(b|0)==103?62:0}else e=27;break}case 3:{if((((((a[c>>0]|0)==0?(a[c+1>>0]|0)==97:0)?(a[c+2>>0]|0)==0:0)?(a[c+3>>0]|0)==109:0)?(a[c+4>>0]|0)==0:0)?(a[c+5>>0]|0)==112:0)b=38;else e=27;break}case 4:{if(!(a[c>>0]|0))switch(a[c+1>>0]|0){case 113:{if(a[c+2>>0]|0){e=27;break a}if((a[c+3>>0]|0)!=117){e=27;break a}if(a[c+4>>0]|0){e=27;break a}if((a[c+5>>0]|0)!=111){e=27;break a}if(a[c+6>>0]|0){e=27;break a}if((a[c+7>>0]|0)==116){b=34;break a}else{e=27;break a}}case 97:{if(a[c+2>>0]|0){e=27;break a}if((a[c+3>>0]|0)!=112){e=27;break a}if(a[c+4>>0]|0){e=27;break a}if((a[c+5>>0]|0)!=111){e=27;break a}if(a[c+6>>0]|0){e=27;break a}if((a[c+7>>0]|0)==115){b=39;break a}else{e=27;break a}}default:{e=27;break a}}else e=27;break}default:e=27}while(0);if((e|0)==27)b=0;return b|0}function o0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=b+72|0;k=g+4|0;while(1){if((f-e|0)<=1)break;b=a[e>>0]|0;h=a[e+1>>0]|0;if(!(b<<24>>24))b=d[j+(h&255)>>0]|0;else b=J$(b,h)|0;switch(b|0){case 5:{e=e+2|0;break}case 6:{e=e+3|0;break}case 7:{e=e+4|0;break}case 10:{c[k>>2]=-1;c[g>>2]=(c[g>>2]|0)+1;e=e+2|0;break}case 9:{c[g>>2]=(c[g>>2]|0)+1;i=e+2|0;if((f-i|0)>1){b=a[i>>0]|0;h=a[e+3>>0]|0;if(!(b<<24>>24))b=d[j+(h&255)>>0]|0;else b=J$(b,h)|0;e=(b|0)==10?e+4|0:i}else e=i;c[k>>2]=-1;break}default:e=e+2|0}c[k>>2]=(c[k>>2]|0)+1}return}function p0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;k=f+-2|0;j=b+72|0;a:while(1){h=e+2|0;if((k-h|0)<=1){f=1;break}f=a[h>>0]|0;i=f<<24>>24==0;b=a[e+3>>0]|0;if(i)f=d[j+(b&255)>>0]|0;else f=J$(f,b)|0;switch(f|0){case 23:case 19:case 30:case 33:case 16:case 18:case 10:case 9:case 15:case 14:case 17:case 35:case 34:case 32:case 31:case 13:case 27:case 24:case 25:{e=h;continue a}case 21:if(i&b<<24>>24==9){l=12;break a}else{e=h;continue a}case 22:case 26:{if(!i){l=12;break a}if(b<<24>>24>=0){e=h;continue a}break}default:if(!i){l=12;break a}}switch(b<<24>>24|0){case 64:case 36:{e=h;break}default:{l=12;break a}}}if((l|0)==12){c[g>>2]=h;f=0}return f|0}function q0(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0;j=c[e>>2]|0;k=j+(f-j&-2)|0;l=k;m=h;a:while(1){if(j>>>0>=k>>>0){b=0;break}i=a[j+1>>0]|0;b=a[j>>0]|0;f=b&255;switch(b<<24>>24){case 0:{if(i<<24>>24>-1){b=c[g>>2]|0;if((b|0)==(h|0)){b=2;break a}c[g>>2]=b+1;a[b>>0]=i;b=j}else n=7;break}case 7:case 6:case 5:case 4:case 3:case 2:case 1:{n=7;break}case -37:case -38:case -39:case -40:{b=c[g>>2]|0;if((m-b|0)<4){b=2;break a}if((l-j|0)<4){b=1;break a}o=i&255;i=(f<<2&12|o>>>6)+1|0;c[g>>2]=b+1;a[b>>0]=i>>>2|240;b=c[g>>2]|0;c[g>>2]=b+1;a[b>>0]=o>>>2&15|i<<4&48|128;b=j+2|0;i=d[j+3>>0]|0;f=(o<<4&48|i>>>6|a[b>>0]<<2&12|128)&255;j=c[g>>2]|0;c[g>>2]=j+1;a[j>>0]=f;j=c[g>>2]|0;c[g>>2]=j+1;a[j>>0]=i&63|128;break}default:{b=c[g>>2]|0;if((m-b|0)<3){b=2;break a}c[g>>2]=b+1;a[b>>0]=f>>>4|224;o=i&255;b=c[g>>2]|0;c[g>>2]=b+1;a[b>>0]=o>>>6|f<<2&60|128;b=c[g>>2]|0;c[g>>2]=b+1;a[b>>0]=o&63|128;b=j}}if((n|0)==7){n=0;b=c[g>>2]|0;if((m-b|0)<2){b=2;break}o=i&255;c[g>>2]=b+1;a[b>>0]=o>>>6|f<<2|192;b=c[g>>2]|0;c[g>>2]=b+1;a[b>>0]=o&63|128;b=j}j=b+2|0}c[e>>2]=j;return b|0}function r0(e,f,g,h,i){e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;var j=0,k=0,l=0;j=c[f>>2]|0;l=g-j&-2;e=j+l|0;if((l|0)>(i-(c[h>>2]|0)|0)){k=e+-2|0;g=(a[k>>0]&-8)<<24>>24==-40;l=g&1;e=g?k:e}else l=0;g=j;while(1){j=g>>>0<e>>>0;k=c[h>>2]|0;if(!(j&k>>>0<i>>>0))break;j=((d[g>>0]|0)<<8|(d[g+1>>0]|0))&65535;c[h>>2]=k+2;b[k>>1]=j;k=(c[f>>2]|0)+2|0;c[f>>2]=k;g=k}return ((k|0)!=(i|0)|j^1?l:2)|0}function s0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;l=f;j=l-e|0;a:do if((j|0)>1){k=a[e>>0]|0;if(!(k<<24>>24)){i=a[e+1>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e+1>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{j=i&255;if(!(1<<(j&31)&c[56940+((j>>>5|(d[143788+(k&255)>>0]|0)<<3)<<2)>>2])){c[g>>2]=e;b=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;b=0;break a}case 6:{if((j|0)<3){b=-2;break a}c[g>>2]=e;b=0;break a}case 7:{if((j|0)<4){b=-2;break a}c[g>>2]=e;b=0;break a}case 19:{b=u0(b,e+2|0,f,g)|0;break a}default:{c[g>>2]=e;b=0;break a}}k=b+72|0;j=e;b:while(1){i=j+2|0;e=l-i|0;if((e|0)<=1){b=-1;break a}b=a[i>>0]|0;f=a[j+3>>0]|0;if(!(b<<24>>24))h=d[k+(f&255)>>0]|0;else h=J$(b,f)|0;switch(h|0){case 27:case 26:case 25:case 24:case 22:{j=i;continue b}case 5:{f=23;break b}case 6:{f=24;break b}case 7:{f=26;break b}case 18:{f=28;break b}case 29:break;default:{f=29;break b}}e=f&255;if(!(1<<(e&31)&c[56940+((e>>>5|(d[144044+(b&255)>>0]|0)<<3)<<2)>>2])){f=22;break}else j=i}if((f|0)==22){c[g>>2]=i;b=0;break}else if((f|0)==23){c[g>>2]=i;b=0;break}else if((f|0)==24){if((e|0)<3){b=-2;break}c[g>>2]=i;b=0;break}else if((f|0)==26){if((e|0)<4){b=-2;break}c[g>>2]=i;b=0;break}else if((f|0)==28){c[g>>2]=j+4;b=9;break}else if((f|0)==29){c[g>>2]=i;b=0;break}}else b=-1;while(0);return b|0}function t0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;j=f-e|0;a:do if((j|0)>1){k=a[e>>0]|0;if(!(k<<24>>24)){i=a[e+1>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e+1>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{l=i&255;if(!(1<<(l&31)&c[56940+((l>>>5|(d[143788+(k&255)>>0]|0)<<3)<<2)>>2])){c[g>>2]=e;f=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;f=0;break a}case 6:{if((j|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((j|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}case 30:case 9:case 10:case 21:{c[g>>2]=e;f=22;break a}default:{c[g>>2]=e;f=0;break a}}l=b+72|0;b=e;b:while(1){k=b+2|0;e=f-k|0;if((e|0)<=1){f=-1;break a}h=a[k>>0]|0;i=a[b+3>>0]|0;if(!(h<<24>>24))j=d[l+(i&255)>>0]|0;else j=J$(h,i)|0;switch(j|0){case 27:case 26:case 25:case 24:case 22:{b=k;continue b}case 5:{h=23;break b}case 6:{h=24;break b}case 7:{h=26;break b}case 18:{h=28;break b}case 29:break;default:{h=29;break b}}e=i&255;if(!(1<<(e&31)&c[56940+((e>>>5|(d[144044+(h&255)>>0]|0)<<3)<<2)>>2])){h=22;break}else b=k}if((h|0)==22){c[g>>2]=k;f=0;break}else if((h|0)==23){c[g>>2]=k;f=0;break}else if((h|0)==24){if((e|0)<3){f=-2;break}c[g>>2]=k;f=0;break}else if((h|0)==26){if((e|0)<4){f=-2;break}c[g>>2]=k;f=0;break}else if((h|0)==28){c[g>>2]=b+4;f=28;break}else if((h|0)==29){c[g>>2]=k;f=0;break}}else f=-1;while(0);return f|0}function u0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;k=f;a:do if((k-e|0)>1){i=a[e>>0]|0;h=a[e+1>>0]|0;do if(!(i<<24>>24))if(h<<24>>24==120){h=v0(b,e+2|0,f,g)|0;break a}else{h=d[b+72+(h&255)>>0]|0;break}else h=J$(i,h)|0;while(0);if((h|0)!=25){c[g>>2]=e;h=0;break}j=b+72|0;b=e;b:while(1){i=b+2|0;if((k-i|0)<=1){h=-1;break a}h=a[i>>0]|0;f=a[b+3>>0]|0;if(!(h<<24>>24))h=d[j+(f&255)>>0]|0;else h=J$(h,f)|0;switch(h|0){case 25:{b=i;break}case 18:{f=15;break b}default:{f=16;break b}}}if((f|0)==15){c[g>>2]=b+4;h=10;break}else if((f|0)==16){c[g>>2]=i;h=0;break}}else h=-1;while(0);return h|0}function v0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0;a:do if((f-e|0)>1){h=a[e>>0]|0;if(!(h<<24>>24))h=d[b+72+(d[e+1>>0]|0)>>0]|0;else h=J$(h,a[e+1>>0]|0)|0;if((h&-2|0)!=24){c[g>>2]=e;f=0;break}j=b+72|0;b:while(1){i=e+2|0;if((f-i|0)<=1){f=-1;break a}h=a[i>>0]|0;b=a[e+3>>0]|0;if(!(h<<24>>24))h=d[j+(b&255)>>0]|0;else h=J$(h,b)|0;switch(h|0){case 24:case 25:{e=i;break}case 18:{h=13;break b}default:{h=14;break b}}}if((h|0)==13){c[g>>2]=e+4;f=10;break}else if((h|0)==14){c[g>>2]=i;f=0;break}}else f=-1;while(0);return f|0}function w0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;o=f;j=o-e|0;a:do if((j|0)>1){k=a[e>>0]|0;if(!(k<<24>>24)){i=a[e+1>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e+1>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{m=i&255;if(!(1<<(m&31)&c[56940+((m>>>5|d[143788+(k&255)>>0]<<3)<<2)>>2])){c[g>>2]=e;h=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;h=0;break a}case 6:{if((j|0)<3){h=-2;break a}c[g>>2]=e;h=0;break a}case 7:{if((j|0)<4){h=-2;break a}c[g>>2]=e;h=0;break a}case 16:{i=e+2|0;if((o-i|0)<=1){h=-1;break a}h=a[i>>0]|0;if(!(h<<24>>24))h=d[b+72+(d[e+3>>0]|0)>>0]|0;else h=J$(h,a[e+3>>0]|0)|0;switch(h|0){case 27:{h=x0(b,e+4|0,f,g)|0;break a}case 20:{h=y0(e+4|0,f,g)|0;break a}default:{c[g>>2]=i;h=0;break a}}}case 15:{h=z0(b,e+2|0,f,g)|0;break a}case 17:{h=A0(b,e+2|0,f,g)|0;break a}default:{c[g>>2]=e;h=0;break a}}m=b+72|0;l=0;h=e+2|0;b:while(1){c:while(1){i=o-h|0;if((i|0)<=1){h=-1;break a}j=a[h>>0]|0;k=a[h+1>>0]|0;if(!(j<<24>>24))e=d[m+(k&255)>>0]|0;else e=J$(j,k)|0;switch(e|0){case 5:{n=34;break b}case 6:{n=35;break b}case 7:{n=37;break b}case 10:case 9:case 21:{n=55;break b}case 11:{n=69;break b}case 17:break b;case 23:break c;case 29:{e=k&255;if(!(1<<(e&31)&c[56940+((e>>>5|d[144044+(j&255)>>0]<<3)<<2)>>2])){n=32;break b}break}case 27:case 26:case 25:case 24:case 22:break;default:{n=75;break b}}h=h+2|0}if(l|0){n=40;break}e=h+2|0;l=o-e|0;if((l|0)<=1){h=-1;break a}i=a[e>>0]|0;j=a[h+3>>0]|0;if(!(i<<24>>24))k=d[m+(j&255)>>0]|0;else k=J$(i,j)|0;switch(k|0){case 5:{n=49;break b}case 6:{n=50;break b}case 7:{n=52;break b}case 29:{l=j&255;if(!(1<<(l&31)&c[56940+((l>>>5|d[143788+(i&255)>>0]<<3)<<2)>>2])){n=47;break b}break}case 24:case 22:break;default:{n=54;break b}}l=1;h=h+4|0}d:switch(n|0){case 32:{c[g>>2]=h;h=0;break a}case 34:{c[g>>2]=h;h=0;break a}case 35:{if((i|0)<3){h=-2;break a}c[g>>2]=h;h=0;break a}case 37:{if((i|0)<4){h=-2;break a}c[g>>2]=h;h=0;break a}case 40:{c[g>>2]=h;h=0;break a}case 47:{c[g>>2]=e;h=0;break a}case 49:{c[g>>2]=e;h=0;break a}case 50:{if((l|0)<3){h=-2;break a}c[g>>2]=e;h=0;break a}case 52:{if((l|0)<4){h=-2;break a}c[g>>2]=e;h=0;break a}case 54:{c[g>>2]=e;h=0;break a}case 55:{e:while(1){n=0;i=h+2|0;j=o-i|0;if((j|0)<=1){h=-1;break a}k=a[i>>0]|0;e=a[h+3>>0]|0;if(!(k<<24>>24))l=d[m+(e&255)>>0]|0;else l=J$(k,e)|0;switch(l|0){case 11:{h=i;n=69;break d}case 17:{h=i;break d}case 10:case 9:case 21:{h=i;n=55;break}case 29:{n=60;break e}case 24:case 22:break e;case 5:{n=63;break e}case 6:{n=64;break e}case 7:{n=66;break e}default:{n=68;break e}}}if((n|0)==60){o=e&255;if(!(1<<(o&31)&c[56940+((o>>>5|d[143788+(k&255)>>0]<<3)<<2)>>2])){c[g>>2]=i;h=0;break a}}else if((n|0)==63){c[g>>2]=i;h=0;break a}else if((n|0)==64){if((j|0)<3){h=-2;break a}c[g>>2]=i;h=0;break a}else if((n|0)==66){if((j|0)<4){h=-2;break a}c[g>>2]=i;h=0;break a}else if((n|0)==68){c[g>>2]=i;h=0;break a}h=B0(b,h+4|0,f,g)|0;break a}case 75:{c[g>>2]=h;h=0;break a}}if((n|0)==69){c[g>>2]=h+2;h=2;break}i=h+2|0;if((o-i|0)>1){if((a[i>>0]|0)==0?(a[h+3>>0]|0)==62:0){c[g>>2]=h+4;h=4;break}c[g>>2]=i;h=0}else h=-1}else h=-1;while(0);return h|0}function x0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=f;a:do if((j-e|0)>1){if((a[e>>0]|0)==0?(a[e+1>>0]|0)==45:0){i=b+72|0;h=e+2|0;b:while(1){e=j-h|0;if((e|0)<=1){f=-1;break a}f=a[h>>0]|0;b=a[h+1>>0]|0;if(!(f<<24>>24))f=d[i+(b&255)>>0]|0;else f=J$(f,b)|0;switch(f|0){case 8:case 1:case 0:{k=16;break b}case 5:{h=h+2|0;continue b}case 6:{if((e|0)<3){f=-2;break a}h=h+3|0;continue b}case 7:{if((e|0)<4){f=-2;break a}h=h+4|0;continue b}case 27:{f=h+2|0;if((j-f|0)<=1){f=-1;break a}if(a[f>>0]|0){h=f;continue b}if((a[h+3>>0]|0)==45)break b;else{h=f;continue b}}default:{h=h+2|0;continue b}}}if((k|0)==16){c[g>>2]=h;f=0;break}f=h+4|0;if((j-f|0)<=1){f=-1;break}if((a[f>>0]|0)==0?(a[h+5>>0]|0)==62:0){c[g>>2]=h+6;f=13;break}c[g>>2]=f;f=0;break}c[g>>2]=e;f=0}else f=-1;while(0);return f|0}function y0(b,d,e){b=b|0;d=d|0;e=e|0;if((d-b|0)>11){d=0;while(1){if((d|0)>=6){d=8;break}if(a[b>>0]|0){d=0;break}if((a[b+1>>0]|0)!=(a[144300+d>>0]|0)){d=0;break}d=d+1|0;b=b+2|0}c[e>>2]=b}else d=-1;return d|0}function z0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0;q=l;l=l+16|0;p=q;o=f;i=o-e|0;a:do if((i|0)>1){j=a[e>>0]|0;if(!(j<<24>>24)){h=a[e+1>>0]|0;f=d[b+72+(h&255)>>0]|0}else{h=a[e+1>>0]|0;f=J$(j,h)|0}switch(f|0){case 29:{m=h&255;if(!(1<<(m&31)&c[56940+((m>>>5|d[143788+(j&255)>>0]<<3)<<2)>>2])){c[g>>2]=e;f=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;f=0;break a}case 6:{if((i|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((i|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}default:{c[g>>2]=e;f=0;break a}}m=b+72|0;k=e;b:while(1){f=k+2|0;h=o-f|0;if((h|0)<=1){f=-1;break a}i=a[f>>0]|0;j=a[k+3>>0]|0;if(!(i<<24>>24))b=d[m+(j&255)>>0]|0;else b=J$(i,j)|0;switch(b|0){case 27:case 26:case 25:case 24:case 22:{k=f;continue b}case 5:{n=22;break b}case 6:{n=23;break b}case 7:{n=25;break b}case 10:case 9:case 21:{n=27;break b}case 15:{n=46;break b}case 29:break;default:break b}k=j&255;if(!(1<<(k&31)&c[56940+((k>>>5|d[144044+(i&255)>>0]<<3)<<2)>>2])){n=21;break}else k=f}if((n|0)==21){c[g>>2]=f;f=0;break}else if((n|0)==22){c[g>>2]=f;f=0;break}else if((n|0)==23){if((h|0)<3){f=-2;break}c[g>>2]=f;f=0;break}else if((n|0)==25){if((h|0)<4){f=-2;break}c[g>>2]=f;f=0;break}else if((n|0)==27){if(!(C0(e,f,p)|0)){c[g>>2]=f;f=0;break}j=k+4|0;c:while(1){i=o-j|0;if((i|0)<=1){f=-1;break a}f=a[j>>0]|0;h=a[j+1>>0]|0;if(!(f<<24>>24))f=d[m+(h&255)>>0]|0;else f=J$(f,h)|0;switch(f|0){case 8:case 1:case 0:{n=40;break c}case 5:{j=j+2|0;continue c}case 6:{if((i|0)<3){f=-2;break a}j=j+3|0;continue c}case 7:{if((i|0)<4){f=-2;break a}j=j+4|0;continue c}case 15:{f=j+2|0;if((o-f|0)<=1){f=-1;break a}if(a[f>>0]|0){j=f;continue c}if((a[j+3>>0]|0)==62){n=44;break c}else{j=f;continue c}}default:{j=j+2|0;continue c}}}if((n|0)==40){c[g>>2]=j;f=0;break}else if((n|0)==44){c[g>>2]=j+4;f=c[p>>2]|0;break}}else if((n|0)==46){if(!(C0(e,f,p)|0)){c[g>>2]=f;f=0;break}f=k+4|0;if((o-f|0)<=1){f=-1;break}if((a[f>>0]|0)==0?(a[k+5>>0]|0)==62:0){c[g>>2]=k+6;f=c[p>>2]|0;break}}c[g>>2]=f;f=0}else f=-1;while(0);l=q;return f|0}function A0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;j=f-e|0;a:do if((j|0)>1){k=a[e>>0]|0;if(!(k<<24>>24)){i=a[e+1>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e+1>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{l=i&255;if(!(1<<(l&31)&c[56940+((l>>>5|(d[143788+(k&255)>>0]|0)<<3)<<2)>>2])){c[g>>2]=e;f=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;f=0;break a}case 6:{if((j|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((j|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}default:{c[g>>2]=e;f=0;break a}}l=b+72|0;b=e;b:while(1){j=b+2|0;e=f-j|0;if((e|0)<=1){f=-1;break a}h=a[j>>0]|0;i=a[b+3>>0]|0;if(!(h<<24>>24))k=d[l+(i&255)>>0]|0;else k=J$(h,i)|0;switch(k|0){case 23:case 27:case 26:case 25:case 24:case 22:{b=j;continue b}case 5:{h=22;break b}case 6:{h=23;break b}case 7:{h=25;break b}case 10:case 9:case 21:{h=27;break b}case 11:{h=34;break b}case 29:break;default:{h=35;break b}}e=i&255;if(!(1<<(e&31)&c[56940+((e>>>5|(d[144044+(h&255)>>0]|0)<<3)<<2)>>2])){h=21;break}else b=j}if((h|0)==21){c[g>>2]=j;f=0;break}else if((h|0)==22){c[g>>2]=j;f=0;break}else if((h|0)==23){if((e|0)<3){f=-2;break}c[g>>2]=j;f=0;break}else if((h|0)==25){if((e|0)<4){f=-2;break}c[g>>2]=j;f=0;break}else if((h|0)==27){c:while(1){k=j+2|0;if((f-k|0)<=1){f=-1;break a}h=a[k>>0]|0;i=a[j+3>>0]|0;if(!(h<<24>>24))h=d[l+(i&255)>>0]|0;else h=J$(h,i)|0;switch(h|0){case 10:case 9:case 21:{j=k;h=27;break}case 11:{h=32;break c}default:{h=33;break c}}}if((h|0)==32){c[g>>2]=j+4;f=5;break}else if((h|0)==33){c[g>>2]=k;f=0;break}}else if((h|0)==34){c[g>>2]=b+4;f=5;break}else if((h|0)==35){c[g>>2]=j;f=0;break}}else f=-1;while(0);return f|0}function B0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+16|0;q=s;c[q>>2]=e;r=f;o=b+72|0;h=0;a:while(1){b:while(1){m=r-e|0;if((m|0)<=1){e=-1;break a}i=a[e>>0]|0;j=a[e+1>>0]|0;if(!(i<<24>>24))k=d[o+(j&255)>>0]|0;else k=J$(i,j)|0;switch(k|0){case 5:{p=11;break a}case 6:{p=12;break a}case 7:{p=14;break a}case 23:{p=16;break b}case 10:case 9:case 21:{p=31;break b}case 14:{p=37;break b}case 29:{n=j&255;if(!(1<<(n&31)&c[56940+((n>>>5|d[144044+(i&255)>>0]<<3)<<2)>>2])){p=9;break a}break}case 27:case 26:case 25:case 24:case 22:break;default:{p=89;break a}}n=e+2|0;c[q>>2]=n;e=n}c:do if((p|0)==16){p=0;if(h|0){p=17;break a}h=e+2|0;c[q>>2]=h;i=h;j=r-i|0;if((j|0)<=1){e=-1;break a}k=a[h>>0]|0;m=a[e+3>>0]|0;if(!(k<<24>>24))n=d[o+(m&255)>>0]|0;else n=J$(k,m)|0;switch(n|0){case 5:{p=25;break a}case 6:{p=26;break a}case 7:{p=28;break a}case 24:case 22:{h=1;break c}case 29:break;default:{p=30;break a}}n=m&255;if(!(1<<(n&31)&c[56940+((n>>>5|d[143788+(k&255)>>0]<<3)<<2)>>2])){p=24;break a}else h=1}else if((p|0)==31)while(1){p=0;i=e+2|0;c[q>>2]=i;j=i;if((r-j|0)<=1){e=-1;break a}h=a[i>>0]|0;e=a[e+3>>0]|0;if(!(h<<24>>24))e=d[o+(e&255)>>0]|0;else e=J$(h,e)|0;switch(e|0){case 14:{e=i;p=37;break c}case 9:case 10:case 21:{e=i;p=31;break}default:{p=36;break a}}}while(0);d:do if((p|0)==37){while(1){p=0;j=e+2|0;c[q>>2]=j;k=j;if((r-k|0)<=1){e=-1;break a}h=a[j>>0]|0;i=a[e+3>>0]|0;if(!(h<<24>>24))m=d[o+(i&255)>>0]|0;else m=J$(h,i)|0;if((m|1|0)==13)break;switch(m|0){case 9:case 10:case 21:{e=j;p=37;break}default:{p=43;break a}}}e=e+4|0;c[q>>2]=e;e:while(1){j=r-e|0;if((j|0)<=1){e=-1;break a}h=a[e>>0]|0;i=a[e+1>>0]|0;if(!(h<<24>>24))h=d[o+(i&255)>>0]|0;else h=J$(h,i)|0;if((h|0)==(m|0))break;switch(h|0){case 8:case 1:case 0:{p=56;break a}case 2:{p=61;break a}case 5:{n=e+2|0;c[q>>2]=n;e=n;continue e}case 6:{if((j|0)<3){e=-2;break a}n=e+3|0;c[q>>2]=n;e=n;continue e}case 7:{if((j|0)<4){e=-2;break a}n=e+4|0;c[q>>2]=n;e=n;continue e}case 3:{e=s0(b,e+2|0,f,q)|0;if((e|0)<1){p=59;break a}e=c[q>>2]|0;continue e}default:{n=e+2|0;c[q>>2]=n;e=n;continue e}}}i=e+2|0;c[q>>2]=i;k=i;if((r-k|0)<=1){e=-1;break a}h=a[i>>0]|0;j=a[e+3>>0]|0;if(!(h<<24>>24))h=d[o+(j&255)>>0]|0;else h=J$(h,j)|0;switch(h|0){case 11:{p=81;break a}case 17:{p=82;break a}case 10:case 9:case 21:{e=i;break}default:{p=68;break a}}f:while(1){i=e+2|0;c[q>>2]=i;h=i;j=r-h|0;if((j|0)<=1){e=-1;break a}k=a[i>>0]|0;m=a[e+3>>0]|0;if(!(k<<24>>24))n=d[o+(m&255)>>0]|0;else n=J$(k,m)|0;switch(n|0){case 5:{p=76;break a}case 6:{p=77;break a}case 7:{p=79;break a}case 11:{p=81;break a}case 17:{p=82;break a}case 24:case 22:{h=0;break d}case 10:case 9:case 21:{e=i;break}case 29:break f;default:{p=87;break a}}}n=m&255;if(!(1<<(n&31)&c[56940+((n>>>5|d[143788+(k&255)>>0]<<3)<<2)>>2])){p=75;break a}else h=0}while(0);e=e+4|0;c[q>>2]=e}switch(p|0){case 9:{c[g>>2]=e;e=0;break}case 11:{c[g>>2]=e;e=0;break}case 12:{if((m|0)<3)e=-2;else{c[g>>2]=e;e=0}break}case 14:{if((m|0)<4)e=-2;else{c[g>>2]=e;e=0}break}case 17:{c[g>>2]=e;e=0;break}case 24:{c[g>>2]=h;e=0;break}case 25:{c[g>>2]=h;e=0;break}case 26:{if((j|0)<3)e=-2;else{c[g>>2]=h;e=0}break}case 28:{if((j|0)<4)e=-2;else{c[g>>2]=h;e=0}break}case 30:{c[g>>2]=i;e=0;break}case 36:{c[g>>2]=j;e=0;break}case 43:{c[g>>2]=k;e=0;break}case 56:{c[g>>2]=e;e=0;break}case 59:{if(!e){c[g>>2]=c[q>>2];e=0}break}case 61:{c[g>>2]=e;e=0;break}case 68:{c[g>>2]=k;e=0;break}case 75:{c[g>>2]=i;e=0;break}case 76:{c[g>>2]=i;e=0;break}case 77:{if((j|0)<3)e=-2;else{c[g>>2]=i;e=0}break}case 79:{if((j|0)<4)e=-2;else{c[g>>2]=i;e=0}break}case 81:{c[g>>2]=e+4;e=1;break}case 82:{h=i+2|0;c[q>>2]=h;e=h;if((r-e|0)>1){if((a[h>>0]|0)==0?(a[i+3>>0]|0)==62:0){c[g>>2]=i+4;e=3;break}c[g>>2]=e;e=0}else e=-1;break}case 87:{c[g>>2]=h;e=0;break}case 89:{c[g>>2]=e;e=0;break}}l=s;return e|0}function C0(b,d,e){b=b|0;d=d|0;e=e|0;c[e>>2]=11;a:do if((d-b|0)==6?(a[b>>0]|0)==0:0){switch(a[b+1>>0]|0){case 120:{d=0;break}case 88:{d=1;break}default:{d=1;break a}}if(!(a[b+2>>0]|0)){switch(a[b+3>>0]|0){case 109:break;case 77:{d=1;break}default:{d=1;break a}}if(!(a[b+4>>0]|0)){switch(a[b+5>>0]|0){case 108:break;case 76:{d=0;break a}default:{d=1;break a}}if(!d){c[e>>2]=12;d=1}else d=0}else d=1}else d=1}else d=1;while(0);return d|0}function D0(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0;k=e+72|0;a:while(1){b:while(1){j=g-f|0;if((j|0)<=1){f=-1;break a}e=a[f>>0]|0;i=a[f+1>>0]|0;if(!(e<<24>>24))i=d[k+(i&255)>>0]|0;else i=J$(e,i)|0;switch(i|0){case 8:case 1:case 0:{l=10;break a}case 6:{l=9;break b}case 7:{l=19;break b}case 5:{f=f+2|0;continue b}case 13:case 12:{e=f+2|0;if((i|0)==(b|0)){l=12;break a}else{f=e;continue b}}default:{f=f+2|0;continue b}}}if((l|0)==9){l=0;if((j|0)<3){f=-2;break}else{f=f+3|0;continue}}else if((l|0)==19){l=0;if((j|0)<4){f=-2;break}else{f=f+4|0;continue}}}c:do if((l|0)==10){c[h>>2]=f;f=0}else if((l|0)==12)if((g-e|0)>1){c[h>>2]=e;g=a[e>>0]|0;f=a[f+3>>0]|0;if(!(g<<24>>24))f=d[k+(f&255)>>0]|0;else f=J$(g,f)|0;switch(f|0){case 20:case 30:case 11:case 10:case 9:case 21:{f=27;break c}default:{}}f=0}else f=-27;while(0);return f|0}function E0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0;k=f;a:do if((k-e|0)>1){h=a[e>>0]|0;if(!(h<<24>>24))h=d[b+72+(d[e+1>>0]|0)>>0]|0;else h=J$(h,a[e+1>>0]|0)|0;switch(h|0){case 27:{h=x0(b,e+2|0,f,g)|0;break a}case 20:{c[g>>2]=e+2;h=33;break a}case 24:case 22:{j=b+72|0;b:while(1){i=e+2|0;f=k-i|0;if((f|0)<=1){h=-1;break a}h=a[i>>0]|0;b=a[e+3>>0]|0;if(!(h<<24>>24))h=d[j+(b&255)>>0]|0;else h=J$(h,b)|0;switch(h|0){case 24:case 22:{e=i;break}case 30:{l=15;break b}case 10:case 9:case 21:break b;default:{l=22;break b}}}if((l|0)==22){c[g>>2]=i;h=0;break a}c:do if((l|0)==15){if((f|0)<=3){h=-1;break a}b=a[e+4>>0]|0;h=a[e+5>>0]|0;if(!(b<<24>>24))h=d[j+(h&255)>>0]|0;else h=J$(b,h)|0;switch(h|0){case 30:case 10:case 9:case 21:break;default:break c}c[g>>2]=i;h=0;break a}while(0);c[g>>2]=i;h=16;break a}default:{c[g>>2]=e;h=0;break a}}}else h=-1;while(0);return h|0}function F0(b,e,f,g){b=b|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0;j=f-e|0;a:do if((j|0)>1){k=a[e>>0]|0;if(!(k<<24>>24)){i=a[e+1>>0]|0;h=d[b+72+(i&255)>>0]|0}else{i=a[e+1>>0]|0;h=J$(k,i)|0}switch(h|0){case 29:{j=i&255;if(!(1<<(j&31)&c[56940+((j>>>5|(d[143788+(k&255)>>0]|0)<<3)<<2)>>2])){c[g>>2]=e;f=0;break a}break}case 24:case 22:break;case 5:{c[g>>2]=e;f=0;break a}case 6:{if((j|0)<3){f=-2;break a}c[g>>2]=e;f=0;break a}case 7:{if((j|0)<4){f=-2;break a}c[g>>2]=e;f=0;break a}default:{c[g>>2]=e;f=0;break a}}b=b+72|0;k=e;b:while(1){h=k;k=k+2|0;e=f-k|0;if((e|0)<=1){f=-20;break 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d=0,e=0,f=0,g=0;a:while(1){f=a[b>>0]|0;g=a[c>>0]|0;f=(f+-97&255)<26?(f&255)+224&255:f;switch((f<<24>>24==((g+-97&255)<26?(g&255)+224&255:g)<<24>>24?(f<<24>>24==0?2:0):1)&3){case 0:{c=c+1|0;b=b+1|0;break}case 1:{e=3;break a}case 2:{d=1;break a}default:{e=5;break a}}}if((e|0)==3)d=0;return d|0}function I0(a,b,c,d,e,f,g,h,i,j){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;return K0(39,a,b,c,d,e,f,g,h,i,j)|0}function J0(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;h=l;l=l+144|0;i=h+4|0;f=h+8|0;g=h;c[i>>2]=d;c[g>>2]=f;pb[c[b+56>>2]&127](b,i,e,g,f+127|0)|0;do if((c[i>>2]|0)==(e|0)){a[c[g>>2]>>0]=0;if(H0(f,144315)|0?(c[b+64>>2]|0)==2:0)break;b=Z$(f)|0;if((b|0)!=-1)b=c[58948+(b<<2)>>2]|0;else b=0}else b=0;while(0);l=h;return b|0}function K0(a,b,d,e,f,g,h,i,j,k,m){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;k=k|0;m=m|0;var n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0;v=l;l=l+16|0;t=v+8|0;q=v+12|0;o=v+4|0;p=v;c[q>>2]=0;c[o>>2]=0;c[p>>2]=0;u=d+64|0;s=c[u>>2]|0;r=e+(s*5|0)|0;c[t>>2]=r;s=f+(0-(s<<1))|0;r=(L0(d,r,s,o,p,q,t)|0)!=0;n=c[o>>2]|0;e=n;a:do if(r&(n|0)!=0){r=d+24|0;f=c[p>>2]|0;do if(!(ob[c[r>>2]&127](d,n,f,144340)|0)){if(!b){c[g>>2]=e;e=0;break a}}else{if(h|0)c[h>>2]=c[q>>2];if(!i)e=c[t>>2]|0;else{e=c[t>>2]|0;c[i>>2]=e}if(!(L0(d,e,s,o,p,q,t)|0)){c[g>>2]=c[t>>2];e=0;break a}f=c[o>>2]|0;if(f|0){e=f;n=f;f=c[p>>2]|0;break}if(!b){e=1;break a}c[g>>2]=c[t>>2];e=0;break a}while(0);if(ob[c[r>>2]&127](d,n,f,144348)|0){f=c[q>>2]|0;e=f;if((((M0(d,f,s)|0)&-33)+-65|0)>>>0>=26){c[g>>2]=e;e=0;break}if(j|0)c[j>>2]=e;e=c[t>>2]|0;if(k|0)c[k>>2]=nb[a&63](d,f,e+(0-(c[u>>2]|0))|0)|0;if(!(L0(d,e,s,o,p,q,t)|0)){c[g>>2]=c[t>>2];e=0;break}e=c[o>>2]|0;if(!e){e=1;break}n=e;f=c[p>>2]|0}if((b|0)!=0|(ob[c[r>>2]&127](d,n,f,144357)|0)==0){c[g>>2]=e;e=0;break}f=c[q>>2]|0;e=c[t>>2]|0;n=f;if(!(ob[c[r>>2]&127](d,f,e+(0-(c[u>>2]|0))|0,107657)|0)){if(!(ob[c[r>>2]&127](d,f,e+(0-(c[u>>2]|0))|0,107649)|0)){c[g>>2]=n;e=0;break}if(m)c[m>>2]=0}else if(m)c[m>>2]=1;while(1){if(!(N0(M0(d,e,s)|0)|0))break;e=e+(c[u>>2]|0)|0}c[t>>2]=e;if((e|0)==(s|0))e=1;else{c[g>>2]=e;e=0}}else{c[g>>2]=c[t>>2];e=0}while(0);l=v;return e|0}function L0(a,b,d,e,f,g,h){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0;a:do if((b|0)!=(d|0)){if(!(N0(M0(a,b,d)|0)|0)){c[h>>2]=b;b=0;break}j=a+64|0;do 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f=0,g=0,h=0,i=0;h=l;l=l+16|0;i=h+4|0;f=h+8|0;g=h;c[i>>2]=d;c[g>>2]=f;pb[c[b+56>>2]&127](b,i,e,g,f+1|0)|0;l=h;return ((c[g>>2]|0)==(f|0)?-1:a[f>>0]|0)|0}function N0(a){a=a|0;switch(a|0){case 9:case 10:case 13:case 32:{a=1;break}default:a=0}return a|0}function O0(){return 60092}function P0(b,d,e){b=b|0;d=d|0;e=e|0;e=Z$(e)|0;if((e|0)==-1)e=0;else{a[b+69>>0]=e;c[b>>2]=69;c[b+4>>2]=70;c[b+48>>2]=31;c[b+72>>2]=d;c[d>>2]=b;e=1}return e|0}function Q0(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return b0(60456,a,0,b,c,d)|0}function R0(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return b0(60456,a,1,b,c,d)|0}function S0(a,b,c,d,e,f,g,h,i,j){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;return K0(40,a,b,c,d,e,f,g,h,i,j)|0}function T0(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0;h=l;l=l+144|0;i=h+4|0;f=h+8|0;g=h;c[i>>2]=d;c[g>>2]=f;pb[c[b+56>>2]&127](b,i,e,g,f+127|0)|0;do if((c[i>>2]|0)==(e|0)){a[c[g>>2]>>0]=0;if(H0(f,144315)|0?(c[b+64>>2]|0)==2:0)break;b=Z$(f)|0;if((b|0)!=-1)b=c[60456+(b<<2)>>2]|0;else 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15:{a=3;break}case 41:case 18:{c[a>>2]=19;a=4;break}default:a=_0(a,b)|0}return a|0}function Z0(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;return 0}function _0(a,b){a=a|0;b=b|0;if((b|0)==28&(c[a+16>>2]|0)==0)a=59;else{c[a>>2]=18;a=-1}return a|0}function $0(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;a:do switch(b|0){case 15:{a=3;break}case 25:{c[a>>2]=20;a=7;break}case 17:{c[a>>2]=21;a=8;break}case 18:{g=f+24|0;if(ob[c[g>>2]&127](f,d,e,144376)|0){c[a>>2]=22;a=3;break a}if(!(ob[c[g>>2]&127](f,d,e,144383)|0))h=8;else{c[a>>2]=23;a=3}break}default:h=8}while(0);if((h|0)==8)a=_0(a,b)|0;return a|0}function a1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;a:do switch(b|0){case -4:case 15:{a=0;break}case 16:{g=f+24|0;h=f+64|0;if(ob[c[g>>2]&127](f,d+(c[h>>2]<<1)|0,e,144390)|0){c[a>>2]=24;a=11;break a}if(ob[c[g>>2]&127](f,d+(c[h>>2]<<1)|0,e,144397)|0){c[a>>2]=25;a=33;break a}if(ob[c[g>>2]&127](f,d+(c[h>>2]<<1)|0,e,144405)|0){c[a>>2]=26;a=39;break a}if(!(ob[c[g>>2]&127](f,d+(c[h>>2]<<1)|0,e,144413)|0))i=14;else{c[a>>2]=27;a=17}break}case 11:{a=55;break}case 13:{a=56;break}case 28:{a=60;break}case 26:{c[a>>2]=28;a=3;break}default:i=14}while(0);if((i|0)==14)a=_0(a,b)|0;return a|0}function b1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;f=b+-11|0;switch(f>>>1|f<<31|0){case 2:{a=0;break}case 0:{a=55;break}case 1:{a=56;break}case 9:{c[a>>2]=18;a=2;break}default:a=_0(a,b)|0}return a|0}function c1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=3;break}case 27:{c[a>>2]=29;a=5;break}default:a=_0(a,b)|0}return a|0}function d1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=3;break}case 27:{c[a>>2]=22;a=6;break}default:a=_0(a,b)|0}return a|0}function e1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=3;break}case 25:{c[a>>2]=20;a=7;break}case 17:{c[a>>2]=21;a=8;break}default:a=_0(a,b)|0}return a|0}function f1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=11;break}case 22:{c[a>>2]=30;a=11;break}case 18:{c[a>>2]=31;a=9;break}default:a=_0(a,b)|0}return a|0}function g1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=33;break}case 41:case 18:{c[a>>2]=32;a=34;break}default:a=_0(a,b)|0}return a|0}function h1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=39;break}case 41:case 18:{c[a>>2]=33;a=40;break}default:a=_0(a,b)|0}return a|0}function i1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=17;break}case 18:{c[a>>2]=34;a=18;break}default:a=_0(a,b)|0}return a|0}function j1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=3;break}case 17:{c[a>>2]=21;a=8;break}default:a=_0(a,b)|0}return a|0}function k1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;a:do switch(b|0){case 15:{a=17;break}case 18:{g=f+24|0;if(ob[c[g>>2]&127](f,d,e,144376)|0){c[a>>2]=35;a=17;break a}if(!(ob[c[g>>2]&127](f,d,e,144383)|0))h=6;else{c[a>>2]=36;a=17}break}default:h=6}while(0);if((h|0)==6)a=_0(a,b)|0;return a|0}function l1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=17;break}case 27:{c[a>>2]=37;c[a+8>>2]=17;a=19;break}default:a=_0(a,b)|0}return a|0}function m1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=17;break}case 27:{c[a>>2]=38;a=21;break}default:a=_0(a,b)|0}return a|0}function n1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=17;break}case 27:{c[a>>2]=37;c[a+8>>2]=17;a=19;break}case 17:{c[a>>2]=c[a+16>>2]|0?20:39;a=20;break}default:a=_0(a,b)|0}return a|0}function o1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=c[a+8>>2]|0;break}case 17:{c[a>>2]=c[a+16>>2]|0?20:39;a=c[a+8>>2]|0;break}default:a=_0(a,b)|0}return a|0}function p1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;switch(b|0){case 33:{c[a>>2]=40;d=0;break}case 34:{d=a+12|0;e=c[d>>2]|0;if(!e)g=7;else{c[d>>2]=e+-1;d=0}break}case 15:{d=0;break}case 26:{g=7;break}case -4:{if(!(c[a+12>>2]|0))d=0;else g=7;break}default:d=a1(a,b,d,e,f)|0}if((g|0)==7)d=_0(a,b)|0;return d|0}function q1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;a:do switch(b|0){case 15:{a=0;break}case 18:{g=f+24|0;if(ob[c[g>>2]&127](f,d,e,144422)|0){c[a>>2]=41;a=0;break a}if(!(ob[c[g>>2]&127](f,d,e,144430)|0))h=6;else{c[a>>2]=42;a=0}break}default:h=6}while(0);if((h|0)==6)a=_0(a,b)|0;return a|0}function r1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=0;break}case 25:{c[a>>2]=39;a=a+12|0;c[a>>2]=(c[a>>2]|0)+1;a=0;break}default:a=_0(a,b)|0}return a|0}function s1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=0;break}case 25:{c[a>>2]=39;a=58;break}default:a=_0(a,b)|0}return a|0}function t1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;a:do switch(b|0){case 15:{a=39;break}case 18:{g=f+24|0;if(ob[c[g>>2]&127](f,d,e,144437)|0){c[a>>2]=37;c[a+8>>2]=39;a=42;break a}if(!(ob[c[g>>2]&127](f,d,e,144443)|0))h=7;else{c[a>>2]=37;c[a+8>>2]=39;a=41}break}case 23:{c[a>>2]=43;c[a+4>>2]=1;a=44;break}default:h=7}while(0);if((h|0)==7)a=_0(a,b)|0;return a|0}function u1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;switch(b|0){case 15:{a=39;break}case 20:{if(!(ob[c[f+24>>2]&127](f,d+(c[f+64>>2]|0)|0,e,144447)|0))g=9;else{c[a>>2]=44;a=43}break}case 23:{c[a+4>>2]=2;c[a>>2]=45;a=44;break}case 41:case 18:{c[a>>2]=46;a=51;break}case 30:{c[a>>2]=46;a=53;break}case 31:{c[a>>2]=46;a=52;break}case 32:{c[a>>2]=46;a=54;break}default:g=9}if((g|0)==9)a=_0(a,b)|0;return a|0}function v1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=39;break}case 24:{c[a>>2]=37;c[a+8>>2]=39;a=45;break}case 36:{c[a>>2]=37;c[a+8>>2]=39;a=46;break}case 21:{c[a>>2]=47;a=39;break}default:a=_0(a,b)|0}return a|0}function w1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=39;break}case 23:{a=a+4|0;c[a>>2]=(c[a>>2]|0)+1;a=44;break}case 41:case 18:{c[a>>2]=46;a=51;break}case 30:{c[a>>2]=46;a=53;break}case 31:{c[a>>2]=46;a=52;break}case 32:{c[a>>2]=46;a=54;break}default:a=_0(a,b)|0}return a|0}function x1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=39;break}case 24:{e=a+4|0;f=(c[e>>2]|0)+-1|0;c[e>>2]=f;if(!f){c[a>>2]=37;c[a+8>>2]=39;a=45}else a=45;break}case 36:{e=a+4|0;f=(c[e>>2]|0)+-1|0;c[e>>2]=f;if(!f){c[a>>2]=37;c[a+8>>2]=39;a=46}else a=46;break}case 35:{e=a+4|0;f=(c[e>>2]|0)+-1|0;c[e>>2]=f;if(!f){c[a>>2]=37;c[a+8>>2]=39;a=47}else a=47;break}case 37:{e=a+4|0;f=(c[e>>2]|0)+-1|0;c[e>>2]=f;if(!f){c[a>>2]=37;c[a+8>>2]=39;a=48}else a=48;break}case 38:{c[a>>2]=45;a=50;break}case 21:{c[a>>2]=45;a=49;break}default:a=_0(a,b)|0}return a|0}function y1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=39;break}case 41:case 18:{c[a>>2]=48;a=51;break}default:a=_0(a,b)|0}return a|0}function z1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=39;break}case 36:{c[a>>2]=37;c[a+8>>2]=39;a=46;break}case 21:{c[a>>2]=47;a=39;break}default:a=_0(a,b)|0}return a|0}function A1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=33;break}case 17:{c[a>>2]=c[a+16>>2]|0?20:39;a=33;break}case 41:case 18:{c[a>>2]=49;a=22;break}default:a=_0(a,b)|0}return a|0}function B1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;a:do switch(b|0){case 15:{g=33;break}case 18:{h=f+24|0;g=0;while(1){i=c[h>>2]|0;if((g|0)>=8)break;if(ob[i&127](f,d,e,c[62304+(g<<2)>>2]|0)|0){j=6;break}g=g+1|0}if((j|0)==6){c[a>>2]=50;g=g+23|0;break a}if(!(ob[i&127](f,d,e,144413)|0))j=10;else{c[a>>2]=51;g=33}break}case 23:{c[a>>2]=52;g=33;break}default:j=10}while(0);if((j|0)==10)g=_0(a,b)|0;return g|0}function C1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0;a:do switch(b|0){case 15:{a=33;break}case 20:{g=f+24|0;h=f+64|0;if(ob[c[g>>2]&127](f,d+(c[h>>2]|0)|0,e,144454)|0){c[a>>2]=32;a=35;break a}if(ob[c[g>>2]&127](f,d+(c[h>>2]|0)|0,e,144462)|0){c[a>>2]=32;a=36;break a}if(!(ob[c[g>>2]&127](f,d+(c[h>>2]|0)|0,e,144471)|0))i=9;else{c[a>>2]=53;a=33}break}case 27:{c[a>>2]=32;a=37;break}default:i=9}while(0);if((i|0)==9)a=_0(a,b)|0;return a|0}function D1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=33;break}case 23:{c[a>>2]=54;a=33;break}default:a=_0(a,b)|0}return a|0}function E1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=33;break}case 41:case 18:case 19:{c[a>>2]=55;a=31;break}default:a=_0(a,b)|0}return a|0}function F1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=33;break}case 24:{c[a>>2]=50;a=33;break}case 21:{c[a>>2]=52;a=33;break}default:a=_0(a,b)|0}return a|0}function G1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=33;break}case 18:{c[a>>2]=56;a=32;break}default:a=_0(a,b)|0}return a|0}function H1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=33;break}case 24:{c[a>>2]=50;a=33;break}case 21:{c[a>>2]=54;a=33;break}default:a=_0(a,b)|0}return a|0}function I1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=33;break}case 27:{c[a>>2]=32;a=38;break}default:a=_0(a,b)|0}return a|0}function J1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=11;break}case 18:{c[a>>2]=57;a=10;break}default:a=_0(a,b)|0}return a|0}function K1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;a:do switch(b|0){case 15:{a=11;break}case 18:{g=f+24|0;if(ob[c[g>>2]&127](f,d,e,144376)|0){c[a>>2]=58;a=11;break a}if(!(ob[c[g>>2]&127](f,d,e,144383)|0))h=7;else{c[a>>2]=59;a=11}break}case 27:{c[a>>2]=37;c[a+8>>2]=11;a=12;break}default:h=7}while(0);if((h|0)==7)a=_0(a,b)|0;return a|0}function L1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=11;break}case 27:{c[a>>2]=60;a=13;break}default:a=_0(a,b)|0}return a|0}function M1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=11;break}case 27:{c[a>>2]=58;a=14;break}default:a=_0(a,b)|0}return a|0}function N1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0;switch(b|0){case 15:{a=11;break}case 17:{c[a>>2]=c[a+16>>2]|0?20:39;a=15;break}case 18:{if(!(ob[c[f+24>>2]&127](f,d,e,144525)|0))g=5;else{c[a>>2]=61;a=11}break}default:g=5}if((g|0)==5)a=_0(a,b)|0;return a|0}function O1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=11;break}case 18:{c[a>>2]=37;c[a+8>>2]=11;a=16;break}default:a=_0(a,b)|0}return a|0}function P1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0;a:do switch(b|0){case 15:{a=11;break}case 18:{g=f+24|0;if(ob[c[g>>2]&127](f,d,e,144376)|0){c[a>>2]=62;a=11;break a}if(!(ob[c[g>>2]&127](f,d,e,144383)|0))h=7;else{c[a>>2]=63;a=11}break}case 27:{c[a>>2]=37;c[a+8>>2]=11;a=12;break}default:h=7}while(0);if((h|0)==7)a=_0(a,b)|0;return a|0}function Q1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=11;break}case 27:{c[a>>2]=64;a=13;break}default:a=_0(a,b)|0}return a|0}function R1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=11;break}case 27:{c[a>>2]=62;a=14;break}default:a=_0(a,b)|0}return a|0}function S1(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;switch(b|0){case 15:{a=11;break}case 17:{c[a>>2]=c[a+16>>2]|0?20:39;a=15;break}default:a=_0(a,b)|0}return a|0}function T1(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0;x=l;l=l+16|0;p=x;do if(a>>>0<245){m=a>>>0<11?16:a+11&-8;a=m>>>3;o=c[47519]|0;d=o>>>a;if(d&3|0){b=(d&1^1)+a|0;a=190116+(b<<1<<2)|0;d=a+8|0;e=c[d>>2]|0;f=e+8|0;g=c[f>>2]|0;if((g|0)==(a|0))c[47519]=o&~(1<<b);else{c[g+12>>2]=a;c[d>>2]=g}w=b<<3;c[e+4>>2]=w|3;w=e+w+4|0;c[w>>2]=c[w>>2]|1;w=f;l=x;return w|0}n=c[47521]|0;if(m>>>0>n>>>0){if(d|0){b=2<<a;b=d<<a&(b|0-b);b=(b&0-b)+-1|0;i=b>>>12&16;b=b>>>i;d=b>>>5&8;b=b>>>d;g=b>>>2&4;b=b>>>g;a=b>>>1&2;b=b>>>a;e=b>>>1&1;e=(d|i|g|a|e)+(b>>>e)|0;b=190116+(e<<1<<2)|0;a=b+8|0;g=c[a>>2]|0;i=g+8|0;d=c[i>>2]|0;if((d|0)==(b|0)){a=o&~(1<<e);c[47519]=a}else{c[d+12>>2]=b;c[a>>2]=d;a=o}w=e<<3;h=w-m|0;c[g+4>>2]=m|3;f=g+m|0;c[f+4>>2]=h|1;c[g+w>>2]=h;if(n|0){e=c[47524]|0;b=n>>>3;d=190116+(b<<1<<2)|0;b=1<<b;if(!(a&b)){c[47519]=a|b;b=d;a=d+8|0}else{a=d+8|0;b=c[a>>2]|0}c[a>>2]=e;c[b+12>>2]=e;c[e+8>>2]=b;c[e+12>>2]=d}c[47521]=h;c[47524]=f;w=i;l=x;return w|0}j=c[47520]|0;if(j){d=(j&0-j)+-1|0;i=d>>>12&16;d=d>>>i;h=d>>>5&8;d=d>>>h;k=d>>>2&4;d=d>>>k;e=d>>>1&2;d=d>>>e;a=d>>>1&1;a=c[190380+((h|i|k|e|a)+(d>>>a)<<2)>>2]|0;d=(c[a+4>>2]&-8)-m|0;e=c[a+16+(((c[a+16>>2]|0)==0&1)<<2)>>2]|0;if(!e){k=a;h=d}else{do{i=(c[e+4>>2]&-8)-m|0;k=i>>>0<d>>>0;d=k?i:d;a=k?e:a;e=c[e+16+(((c[e+16>>2]|0)==0&1)<<2)>>2]|0}while((e|0)!=0);k=a;h=d}i=k+m|0;if(i>>>0>k>>>0){f=c[k+24>>2]|0;b=c[k+12>>2]|0;do if((b|0)==(k|0)){a=k+20|0;b=c[a>>2]|0;if(!b){a=k+16|0;b=c[a>>2]|0;if(!b){d=0;break}}while(1){d=b+20|0;e=c[d>>2]|0;if(e|0){b=e;a=d;continue}d=b+16|0;e=c[d>>2]|0;if(!e)break;else{b=e;a=d}}c[a>>2]=0;d=b}else{d=c[k+8>>2]|0;c[d+12>>2]=b;c[b+8>>2]=d;d=b}while(0);do if(f|0){b=c[k+28>>2]|0;a=190380+(b<<2)|0;if((k|0)==(c[a>>2]|0)){c[a>>2]=d;if(!d){c[47520]=j&~(1<<b);break}}else{c[f+16+(((c[f+16>>2]|0)!=(k|0)&1)<<2)>>2]=d;if(!d)break}c[d+24>>2]=f;b=c[k+16>>2]|0;if(b|0){c[d+16>>2]=b;c[b+24>>2]=d}b=c[k+20>>2]|0;if(b|0){c[d+20>>2]=b;c[b+24>>2]=d}}while(0);if(h>>>0<16){w=h+m|0;c[k+4>>2]=w|3;w=k+w+4|0;c[w>>2]=c[w>>2]|1}else{c[k+4>>2]=m|3;c[i+4>>2]=h|1;c[i+h>>2]=h;if(n|0){e=c[47524]|0;b=n>>>3;d=190116+(b<<1<<2)|0;b=1<<b;if(!(o&b)){c[47519]=o|b;b=d;a=d+8|0}else{a=d+8|0;b=c[a>>2]|0}c[a>>2]=e;c[b+12>>2]=e;c[e+8>>2]=b;c[e+12>>2]=d}c[47521]=h;c[47524]=i}w=k+8|0;l=x;return w|0}else n=m}else n=m}else n=m}else if(a>>>0<=4294967231){a=a+11|0;m=a&-8;k=c[47520]|0;if(k){e=0-m|0;a=a>>>8;if(a)if(m>>>0>16777215)j=31;else{o=(a+1048320|0)>>>16&8;v=a<<o;n=(v+520192|0)>>>16&4;v=v<<n;j=(v+245760|0)>>>16&2;j=14-(n|o|j)+(v<<j>>>15)|0;j=m>>>(j+7|0)&1|j<<1}else j=0;d=c[190380+(j<<2)>>2]|0;a:do if(!d){d=0;a=0;v=57}else{a=0;i=d;h=m<<((j|0)==31?0:25-(j>>>1)|0);d=0;while(1){f=(c[i+4>>2]&-8)-m|0;if(f>>>0<e>>>0)if(!f){e=0;d=i;a=i;v=61;break a}else{a=i;e=f}f=c[i+20>>2]|0;i=c[i+16+(h>>>31<<2)>>2]|0;d=(f|0)==0|(f|0)==(i|0)?d:f;f=(i|0)==0;if(f){v=57;break}else h=h<<((f^1)&1)}}while(0);if((v|0)==57){if((d|0)==0&(a|0)==0){a=2<<j;a=k&(a|0-a);if(!a){n=m;break}o=(a&0-a)+-1|0;i=o>>>12&16;o=o>>>i;h=o>>>5&8;o=o>>>h;j=o>>>2&4;o=o>>>j;n=o>>>1&2;o=o>>>n;d=o>>>1&1;a=0;d=c[190380+((h|i|j|n|d)+(o>>>d)<<2)>>2]|0}if(!d){i=a;h=e}else v=61}if((v|0)==61)while(1){v=0;n=(c[d+4>>2]&-8)-m|0;o=n>>>0<e>>>0;e=o?n:e;a=o?d:a;d=c[d+16+(((c[d+16>>2]|0)==0&1)<<2)>>2]|0;if(!d){i=a;h=e;break}else v=61}if((i|0)!=0?h>>>0<((c[47521]|0)-m|0)>>>0:0){g=i+m|0;if(g>>>0<=i>>>0){w=0;l=x;return w|0}f=c[i+24>>2]|0;b=c[i+12>>2]|0;do if((b|0)==(i|0)){a=i+20|0;b=c[a>>2]|0;if(!b){a=i+16|0;b=c[a>>2]|0;if(!b){b=0;break}}while(1){d=b+20|0;e=c[d>>2]|0;if(e|0){b=e;a=d;continue}d=b+16|0;e=c[d>>2]|0;if(!e)break;else{b=e;a=d}}c[a>>2]=0}else{w=c[i+8>>2]|0;c[w+12>>2]=b;c[b+8>>2]=w}while(0);do if(f){a=c[i+28>>2]|0;d=190380+(a<<2)|0;if((i|0)==(c[d>>2]|0)){c[d>>2]=b;if(!b){e=k&~(1<<a);c[47520]=e;break}}else{c[f+16+(((c[f+16>>2]|0)!=(i|0)&1)<<2)>>2]=b;if(!b){e=k;break}}c[b+24>>2]=f;a=c[i+16>>2]|0;if(a|0){c[b+16>>2]=a;c[a+24>>2]=b}a=c[i+20>>2]|0;if(a){c[b+20>>2]=a;c[a+24>>2]=b;e=k}else e=k}else e=k;while(0);do if(h>>>0>=16){c[i+4>>2]=m|3;c[g+4>>2]=h|1;c[g+h>>2]=h;b=h>>>3;if(h>>>0<256){d=190116+(b<<1<<2)|0;a=c[47519]|0;b=1<<b;if(!(a&b)){c[47519]=a|b;b=d;a=d+8|0}else{a=d+8|0;b=c[a>>2]|0}c[a>>2]=g;c[b+12>>2]=g;c[g+8>>2]=b;c[g+12>>2]=d;break}b=h>>>8;if(b)if(h>>>0>16777215)b=31;else{v=(b+1048320|0)>>>16&8;w=b<<v;u=(w+520192|0)>>>16&4;w=w<<u;b=(w+245760|0)>>>16&2;b=14-(u|v|b)+(w<<b>>>15)|0;b=h>>>(b+7|0)&1|b<<1}else b=0;d=190380+(b<<2)|0;c[g+28>>2]=b;a=g+16|0;c[a+4>>2]=0;c[a>>2]=0;a=1<<b;if(!(e&a)){c[47520]=e|a;c[d>>2]=g;c[g+24>>2]=d;c[g+12>>2]=g;c[g+8>>2]=g;break}a=h<<((b|0)==31?0:25-(b>>>1)|0);d=c[d>>2]|0;while(1){if((c[d+4>>2]&-8|0)==(h|0)){v=97;break}e=d+16+(a>>>31<<2)|0;b=c[e>>2]|0;if(!b){v=96;break}else{a=a<<1;d=b}}if((v|0)==96){c[e>>2]=g;c[g+24>>2]=d;c[g+12>>2]=g;c[g+8>>2]=g;break}else if((v|0)==97){v=d+8|0;w=c[v>>2]|0;c[w+12>>2]=g;c[v>>2]=g;c[g+8>>2]=w;c[g+12>>2]=d;c[g+24>>2]=0;break}}else{w=h+m|0;c[i+4>>2]=w|3;w=i+w+4|0;c[w>>2]=c[w>>2]|1}while(0);w=i+8|0;l=x;return w|0}else n=m}else n=m}else n=-1;while(0);d=c[47521]|0;if(d>>>0>=n>>>0){b=d-n|0;a=c[47524]|0;if(b>>>0>15){w=a+n|0;c[47524]=w;c[47521]=b;c[w+4>>2]=b|1;c[a+d>>2]=b;c[a+4>>2]=n|3}else{c[47521]=0;c[47524]=0;c[a+4>>2]=d|3;w=a+d+4|0;c[w>>2]=c[w>>2]|1}w=a+8|0;l=x;return w|0}i=c[47522]|0;if(i>>>0>n>>>0){u=i-n|0;c[47522]=u;w=c[47525]|0;v=w+n|0;c[47525]=v;c[v+4>>2]=u|1;c[w+4>>2]=n|3;w=w+8|0;l=x;return w|0}if(!(c[47637]|0)){c[47639]=4096;c[47638]=4096;c[47640]=-1;c[47641]=-1;c[47642]=0;c[47630]=0;c[47637]=p&-16^1431655768;a=4096}else a=c[47639]|0;j=n+48|0;k=n+47|0;h=a+k|0;f=0-a|0;m=h&f;if(m>>>0<=n>>>0){w=0;l=x;return w|0}a=c[47629]|0;if(a|0?(o=c[47627]|0,p=o+m|0,p>>>0<=o>>>0|p>>>0>a>>>0):0){w=0;l=x;return w|0}b:do if(!(c[47630]&4)){d=c[47525]|0;c:do if(d){e=190524;while(1){a=c[e>>2]|0;if(a>>>0<=d>>>0?(s=e+4|0,(a+(c[s>>2]|0)|0)>>>0>d>>>0):0)break;a=c[e+8>>2]|0;if(!a){v=118;break c}else e=a}b=h-i&f;if(b>>>0<2147483647){a=Z6(b|0)|0;if((a|0)==((c[e>>2]|0)+(c[s>>2]|0)|0)){if((a|0)!=(-1|0)){h=b;g=a;v=135;break b}}else{e=a;v=126}}else b=0}else v=118;while(0);do if((v|0)==118){d=Z6(0)|0;if((d|0)!=(-1|0)?(b=d,q=c[47638]|0,r=q+-1|0,b=((r&b|0)==0?0:(r+b&0-q)-b|0)+m|0,q=c[47627]|0,r=b+q|0,b>>>0>n>>>0&b>>>0<2147483647):0){s=c[47629]|0;if(s|0?r>>>0<=q>>>0|r>>>0>s>>>0:0){b=0;break}a=Z6(b|0)|0;if((a|0)==(d|0)){h=b;g=d;v=135;break b}else{e=a;v=126}}else b=0}while(0);do if((v|0)==126){d=0-b|0;if(!(j>>>0>b>>>0&(b>>>0<2147483647&(e|0)!=(-1|0))))if((e|0)==(-1|0)){b=0;break}else{h=b;g=e;v=135;break b}a=c[47639]|0;a=k-b+a&0-a;if(a>>>0>=2147483647){h=b;g=e;v=135;break b}if((Z6(a|0)|0)==(-1|0)){Z6(d|0)|0;b=0;break}else{h=a+b|0;g=e;v=135;break b}}while(0);c[47630]=c[47630]|4;v=133}else{b=0;v=133}while(0);if(((v|0)==133?m>>>0<2147483647:0)?(g=Z6(m|0)|0,s=Z6(0)|0,t=s-g|0,u=t>>>0>(n+40|0)>>>0,!((g|0)==(-1|0)|u^1|g>>>0<s>>>0&((g|0)!=(-1|0)&(s|0)!=(-1|0))^1)):0){h=u?t:b;v=135}if((v|0)==135){b=(c[47627]|0)+h|0;c[47627]=b;if(b>>>0>(c[47628]|0)>>>0)c[47628]=b;j=c[47525]|0;do if(j){b=190524;while(1){a=c[b>>2]|0;d=b+4|0;e=c[d>>2]|0;if((g|0)==(a+e|0)){v=143;break}f=c[b+8>>2]|0;if(!f)break;else b=f}if(((v|0)==143?(c[b+12>>2]&8|0)==0:0)?g>>>0>j>>>0&a>>>0<=j>>>0:0){c[d>>2]=e+h;w=(c[47522]|0)+h|0;u=j+8|0;u=(u&7|0)==0?0:0-u&7;v=j+u|0;u=w-u|0;c[47525]=v;c[47522]=u;c[v+4>>2]=u|1;c[j+w+4>>2]=40;c[47526]=c[47641];break}if(g>>>0<(c[47523]|0)>>>0)c[47523]=g;a=g+h|0;b=190524;while(1){if((c[b>>2]|0)==(a|0)){v=151;break}b=c[b+8>>2]|0;if(!b){a=190524;break}}if((v|0)==151)if(!(c[b+12>>2]&8)){c[b>>2]=g;m=b+4|0;c[m>>2]=(c[m>>2]|0)+h;m=g+8|0;m=g+((m&7|0)==0?0:0-m&7)|0;b=a+8|0;b=a+((b&7|0)==0?0:0-b&7)|0;k=m+n|0;i=b-m-n|0;c[m+4>>2]=n|3;do if((j|0)!=(b|0)){if((c[47524]|0)==(b|0)){w=(c[47521]|0)+i|0;c[47521]=w;c[47524]=k;c[k+4>>2]=w|1;c[k+w>>2]=w;break}a=c[b+4>>2]|0;if((a&3|0)==1){h=a&-8;e=a>>>3;d:do if(a>>>0<256){a=c[b+8>>2]|0;d=c[b+12>>2]|0;if((d|0)==(a|0)){c[47519]=c[47519]&~(1<<e);break}else{c[a+12>>2]=d;c[d+8>>2]=a;break}}else{g=c[b+24>>2]|0;a=c[b+12>>2]|0;do if((a|0)==(b|0)){e=b+16|0;d=e+4|0;a=c[d>>2]|0;if(!a){a=c[e>>2]|0;if(!a){a=0;break}else d=e}while(1){e=a+20|0;f=c[e>>2]|0;if(f|0){a=f;d=e;continue}e=a+16|0;f=c[e>>2]|0;if(!f)break;else{a=f;d=e}}c[d>>2]=0}else{w=c[b+8>>2]|0;c[w+12>>2]=a;c[a+8>>2]=w}while(0);if(!g)break;d=c[b+28>>2]|0;e=190380+(d<<2)|0;do if((c[e>>2]|0)!=(b|0)){c[g+16+(((c[g+16>>2]|0)!=(b|0)&1)<<2)>>2]=a;if(!a)break d}else{c[e>>2]=a;if(a|0)break;c[47520]=c[47520]&~(1<<d);break d}while(0);c[a+24>>2]=g;d=b+16|0;e=c[d>>2]|0;if(e|0){c[a+16>>2]=e;c[e+24>>2]=a}d=c[d+4>>2]|0;if(!d)break;c[a+20>>2]=d;c[d+24>>2]=a}while(0);b=b+h|0;f=h+i|0}else f=i;b=b+4|0;c[b>>2]=c[b>>2]&-2;c[k+4>>2]=f|1;c[k+f>>2]=f;b=f>>>3;if(f>>>0<256){d=190116+(b<<1<<2)|0;a=c[47519]|0;b=1<<b;if(!(a&b)){c[47519]=a|b;b=d;a=d+8|0}else{a=d+8|0;b=c[a>>2]|0}c[a>>2]=k;c[b+12>>2]=k;c[k+8>>2]=b;c[k+12>>2]=d;break}b=f>>>8;do if(!b)b=0;else{if(f>>>0>16777215){b=31;break}v=(b+1048320|0)>>>16&8;w=b<<v;u=(w+520192|0)>>>16&4;w=w<<u;b=(w+245760|0)>>>16&2;b=14-(u|v|b)+(w<<b>>>15)|0;b=f>>>(b+7|0)&1|b<<1}while(0);e=190380+(b<<2)|0;c[k+28>>2]=b;a=k+16|0;c[a+4>>2]=0;c[a>>2]=0;a=c[47520]|0;d=1<<b;if(!(a&d)){c[47520]=a|d;c[e>>2]=k;c[k+24>>2]=e;c[k+12>>2]=k;c[k+8>>2]=k;break}a=f<<((b|0)==31?0:25-(b>>>1)|0);d=c[e>>2]|0;while(1){if((c[d+4>>2]&-8|0)==(f|0)){v=192;break}e=d+16+(a>>>31<<2)|0;b=c[e>>2]|0;if(!b){v=191;break}else{a=a<<1;d=b}}if((v|0)==191){c[e>>2]=k;c[k+24>>2]=d;c[k+12>>2]=k;c[k+8>>2]=k;break}else if((v|0)==192){v=d+8|0;w=c[v>>2]|0;c[w+12>>2]=k;c[v>>2]=k;c[k+8>>2]=w;c[k+12>>2]=d;c[k+24>>2]=0;break}}else{w=(c[47522]|0)+i|0;c[47522]=w;c[47525]=k;c[k+4>>2]=w|1}while(0);w=m+8|0;l=x;return w|0}else a=190524;while(1){b=c[a>>2]|0;if(b>>>0<=j>>>0?(w=b+(c[a+4>>2]|0)|0,w>>>0>j>>>0):0)break;a=c[a+8>>2]|0}f=w+-47|0;a=f+8|0;a=f+((a&7|0)==0?0:0-a&7)|0;f=j+16|0;a=a>>>0<f>>>0?j:a;b=a+8|0;d=h+-40|0;u=g+8|0;u=(u&7|0)==0?0:0-u&7;v=g+u|0;u=d-u|0;c[47525]=v;c[47522]=u;c[v+4>>2]=u|1;c[g+d+4>>2]=40;c[47526]=c[47641];d=a+4|0;c[d>>2]=27;c[b>>2]=c[47631];c[b+4>>2]=c[47632];c[b+8>>2]=c[47633];c[b+12>>2]=c[47634];c[47631]=g;c[47632]=h;c[47634]=0;c[47633]=b;b=a+24|0;do{v=b;b=b+4|0;c[b>>2]=7}while((v+8|0)>>>0<w>>>0);if((a|0)!=(j|0)){g=a-j|0;c[d>>2]=c[d>>2]&-2;c[j+4>>2]=g|1;c[a>>2]=g;b=g>>>3;if(g>>>0<256){d=190116+(b<<1<<2)|0;a=c[47519]|0;b=1<<b;if(!(a&b)){c[47519]=a|b;b=d;a=d+8|0}else{a=d+8|0;b=c[a>>2]|0}c[a>>2]=j;c[b+12>>2]=j;c[j+8>>2]=b;c[j+12>>2]=d;break}b=g>>>8;if(b)if(g>>>0>16777215)d=31;else{v=(b+1048320|0)>>>16&8;w=b<<v;u=(w+520192|0)>>>16&4;w=w<<u;d=(w+245760|0)>>>16&2;d=14-(u|v|d)+(w<<d>>>15)|0;d=g>>>(d+7|0)&1|d<<1}else d=0;e=190380+(d<<2)|0;c[j+28>>2]=d;c[j+20>>2]=0;c[f>>2]=0;b=c[47520]|0;a=1<<d;if(!(b&a)){c[47520]=b|a;c[e>>2]=j;c[j+24>>2]=e;c[j+12>>2]=j;c[j+8>>2]=j;break}a=g<<((d|0)==31?0:25-(d>>>1)|0);d=c[e>>2]|0;while(1){if((c[d+4>>2]&-8|0)==(g|0)){v=213;break}e=d+16+(a>>>31<<2)|0;b=c[e>>2]|0;if(!b){v=212;break}else{a=a<<1;d=b}}if((v|0)==212){c[e>>2]=j;c[j+24>>2]=d;c[j+12>>2]=j;c[j+8>>2]=j;break}else if((v|0)==213){v=d+8|0;w=c[v>>2]|0;c[w+12>>2]=j;c[v>>2]=j;c[j+8>>2]=w;c[j+12>>2]=d;c[j+24>>2]=0;break}}}else{w=c[47523]|0;if((w|0)==0|g>>>0<w>>>0)c[47523]=g;c[47631]=g;c[47632]=h;c[47634]=0;c[47528]=c[47637];c[47527]=-1;c[47532]=190116;c[47531]=190116;c[47534]=190124;c[47533]=190124;c[47536]=190132;c[47535]=190132;c[47538]=190140;c[47537]=190140;c[47540]=190148;c[47539]=190148;c[47542]=190156;c[47541]=190156;c[47544]=190164;c[47543]=190164;c[47546]=190172;c[47545]=190172;c[47548]=190180;c[47547]=190180;c[47550]=190188;c[47549]=190188;c[47552]=190196;c[47551]=190196;c[47554]=190204;c[47553]=190204;c[47556]=190212;c[47555]=190212;c[47558]=190220;c[47557]=190220;c[47560]=190228;c[47559]=190228;c[47562]=190236;c[47561]=190236;c[47564]=190244;c[47563]=190244;c[47566]=190252;c[47565]=190252;c[47568]=190260;c[47567]=190260;c[47570]=190268;c[47569]=190268;c[47572]=190276;c[47571]=190276;c[47574]=190284;c[47573]=190284;c[47576]=190292;c[47575]=190292;c[47578]=190300;c[47577]=190300;c[47580]=190308;c[47579]=190308;c[47582]=190316;c[47581]=190316;c[47584]=190324;c[47583]=190324;c[47586]=190332;c[47585]=190332;c[47588]=190340;c[47587]=190340;c[47590]=190348;c[47589]=190348;c[47592]=190356;c[47591]=190356;c[47594]=190364;c[47593]=190364;w=h+-40|0;u=g+8|0;u=(u&7|0)==0?0:0-u&7;v=g+u|0;u=w-u|0;c[47525]=v;c[47522]=u;c[v+4>>2]=u|1;c[g+w+4>>2]=40;c[47526]=c[47641]}while(0);b=c[47522]|0;if(b>>>0>n>>>0){u=b-n|0;c[47522]=u;w=c[47525]|0;v=w+n|0;c[47525]=v;c[v+4>>2]=u|1;c[w+4>>2]=n|3;w=w+8|0;l=x;return w|0}}c[(d2()|0)>>2]=12;w=0;l=x;return w|0}function U1(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0;if(!a)return;d=a+-8|0;f=c[47523]|0;a=c[a+-4>>2]|0;b=a&-8;j=d+b|0;do if(!(a&1)){e=c[d>>2]|0;if(!(a&3))return;h=d+(0-e)|0;g=e+b|0;if(h>>>0<f>>>0)return;if((c[47524]|0)==(h|0)){a=j+4|0;b=c[a>>2]|0;if((b&3|0)!=3){i=h;b=g;break}c[47521]=g;c[a>>2]=b&-2;c[h+4>>2]=g|1;c[h+g>>2]=g;return}d=e>>>3;if(e>>>0<256){a=c[h+8>>2]|0;b=c[h+12>>2]|0;if((b|0)==(a|0)){c[47519]=c[47519]&~(1<<d);i=h;b=g;break}else{c[a+12>>2]=b;c[b+8>>2]=a;i=h;b=g;break}}f=c[h+24>>2]|0;a=c[h+12>>2]|0;do if((a|0)==(h|0)){d=h+16|0;b=d+4|0;a=c[b>>2]|0;if(!a){a=c[d>>2]|0;if(!a){a=0;break}else b=d}while(1){d=a+20|0;e=c[d>>2]|0;if(e|0){a=e;b=d;continue}d=a+16|0;e=c[d>>2]|0;if(!e)break;else{a=e;b=d}}c[b>>2]=0}else{i=c[h+8>>2]|0;c[i+12>>2]=a;c[a+8>>2]=i}while(0);if(f){b=c[h+28>>2]|0;d=190380+(b<<2)|0;if((c[d>>2]|0)==(h|0)){c[d>>2]=a;if(!a){c[47520]=c[47520]&~(1<<b);i=h;b=g;break}}else{c[f+16+(((c[f+16>>2]|0)!=(h|0)&1)<<2)>>2]=a;if(!a){i=h;b=g;break}}c[a+24>>2]=f;b=h+16|0;d=c[b>>2]|0;if(d|0){c[a+16>>2]=d;c[d+24>>2]=a}b=c[b+4>>2]|0;if(b){c[a+20>>2]=b;c[b+24>>2]=a;i=h;b=g}else{i=h;b=g}}else{i=h;b=g}}else{i=d;h=d}while(0);if(h>>>0>=j>>>0)return;a=j+4|0;e=c[a>>2]|0;if(!(e&1))return;if(!(e&2)){if((c[47525]|0)==(j|0)){j=(c[47522]|0)+b|0;c[47522]=j;c[47525]=i;c[i+4>>2]=j|1;if((i|0)!=(c[47524]|0))return;c[47524]=0;c[47521]=0;return}if((c[47524]|0)==(j|0)){j=(c[47521]|0)+b|0;c[47521]=j;c[47524]=h;c[i+4>>2]=j|1;c[h+j>>2]=j;return}f=(e&-8)+b|0;d=e>>>3;do if(e>>>0<256){b=c[j+8>>2]|0;a=c[j+12>>2]|0;if((a|0)==(b|0)){c[47519]=c[47519]&~(1<<d);break}else{c[b+12>>2]=a;c[a+8>>2]=b;break}}else{g=c[j+24>>2]|0;a=c[j+12>>2]|0;do if((a|0)==(j|0)){d=j+16|0;b=d+4|0;a=c[b>>2]|0;if(!a){a=c[d>>2]|0;if(!a){d=0;break}else b=d}while(1){d=a+20|0;e=c[d>>2]|0;if(e|0){a=e;b=d;continue}d=a+16|0;e=c[d>>2]|0;if(!e)break;else{a=e;b=d}}c[b>>2]=0;d=a}else{d=c[j+8>>2]|0;c[d+12>>2]=a;c[a+8>>2]=d;d=a}while(0);if(g|0){a=c[j+28>>2]|0;b=190380+(a<<2)|0;if((c[b>>2]|0)==(j|0)){c[b>>2]=d;if(!d){c[47520]=c[47520]&~(1<<a);break}}else{c[g+16+(((c[g+16>>2]|0)!=(j|0)&1)<<2)>>2]=d;if(!d)break}c[d+24>>2]=g;a=j+16|0;b=c[a>>2]|0;if(b|0){c[d+16>>2]=b;c[b+24>>2]=d}a=c[a+4>>2]|0;if(a|0){c[d+20>>2]=a;c[a+24>>2]=d}}}while(0);c[i+4>>2]=f|1;c[h+f>>2]=f;if((i|0)==(c[47524]|0)){c[47521]=f;return}}else{c[a>>2]=e&-2;c[i+4>>2]=b|1;c[h+b>>2]=b;f=b}a=f>>>3;if(f>>>0<256){d=190116+(a<<1<<2)|0;b=c[47519]|0;a=1<<a;if(!(b&a)){c[47519]=b|a;a=d;b=d+8|0}else{b=d+8|0;a=c[b>>2]|0}c[b>>2]=i;c[a+12>>2]=i;c[i+8>>2]=a;c[i+12>>2]=d;return}a=f>>>8;if(a)if(f>>>0>16777215)a=31;else{h=(a+1048320|0)>>>16&8;j=a<<h;g=(j+520192|0)>>>16&4;j=j<<g;a=(j+245760|0)>>>16&2;a=14-(g|h|a)+(j<<a>>>15)|0;a=f>>>(a+7|0)&1|a<<1}else a=0;e=190380+(a<<2)|0;c[i+28>>2]=a;c[i+20>>2]=0;c[i+16>>2]=0;b=c[47520]|0;d=1<<a;do if(b&d){b=f<<((a|0)==31?0:25-(a>>>1)|0);d=c[e>>2]|0;while(1){if((c[d+4>>2]&-8|0)==(f|0)){a=73;break}e=d+16+(b>>>31<<2)|0;a=c[e>>2]|0;if(!a){a=72;break}else{b=b<<1;d=a}}if((a|0)==72){c[e>>2]=i;c[i+24>>2]=d;c[i+12>>2]=i;c[i+8>>2]=i;break}else if((a|0)==73){h=d+8|0;j=c[h>>2]|0;c[j+12>>2]=i;c[h>>2]=i;c[i+8>>2]=j;c[i+12>>2]=d;c[i+24>>2]=0;break}}else{c[47520]=b|d;c[e>>2]=i;c[i+24>>2]=e;c[i+12>>2]=i;c[i+8>>2]=i}while(0);j=(c[47527]|0)+-1|0;c[47527]=j;if(!j)a=190532;else return;while(1){a=c[a>>2]|0;if(!a)break;else a=a+8|0}c[47527]=-1;return}function V1(a,b){a=a|0;b=b|0;var d=0;if(a){d=O(b,a)|0;if((b|a)>>>0>65535)d=((d>>>0)/(a>>>0)|0|0)==(b|0)?d:-1}else d=0;a=T1(d)|0;if(!a)return a|0;if(!(c[a+-4>>2]&3))return a|0;Y6(a|0,0,d|0)|0;return a|0}function W1(a,b){a=a|0;b=b|0;var d=0,e=0;if(!a){b=T1(b)|0;return b|0}if(b>>>0>4294967231){c[(d2()|0)>>2]=12;b=0;return b|0}d=X1(a+-8|0,b>>>0<11?16:b+11&-8)|0;if(d|0){b=d+8|0;return b|0}d=T1(b)|0;if(!d){b=0;return b|0}e=c[a+-4>>2]|0;e=(e&-8)-((e&3|0)==0?8:4)|0;W6(d|0,a|0,(e>>>0<b>>>0?e:b)|0)|0;U1(a);b=d;return b|0}function X1(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0;l=a+4|0;m=c[l>>2]|0;d=m&-8;i=a+d|0;if(!(m&3)){if(b>>>0<256){a=0;return a|0}if(d>>>0>=(b+4|0)>>>0?(d-b|0)>>>0<=c[47639]<<1>>>0:0)return a|0;a=0;return a|0}if(d>>>0>=b>>>0){d=d-b|0;if(d>>>0<=15)return a|0;k=a+b|0;c[l>>2]=m&1|b|2;c[k+4>>2]=d|3;m=i+4|0;c[m>>2]=c[m>>2]|1;Y1(k,d);return a|0}if((c[47525]|0)==(i|0)){k=(c[47522]|0)+d|0;d=k-b|0;e=a+b|0;if(k>>>0<=b>>>0){a=0;return a|0}c[l>>2]=m&1|b|2;c[e+4>>2]=d|1;c[47525]=e;c[47522]=d;return a|0}if((c[47524]|0)==(i|0)){e=(c[47521]|0)+d|0;if(e>>>0<b>>>0){a=0;return a|0}d=e-b|0;if(d>>>0>15){k=a+b|0;e=a+e|0;c[l>>2]=m&1|b|2;c[k+4>>2]=d|1;c[e>>2]=d;e=e+4|0;c[e>>2]=c[e>>2]&-2;e=k}else{c[l>>2]=m&1|e|2;e=a+e+4|0;c[e>>2]=c[e>>2]|1;e=0;d=0}c[47521]=d;c[47524]=e;return a|0}e=c[i+4>>2]|0;if(e&2|0){a=0;return a|0}j=(e&-8)+d|0;if(j>>>0<b>>>0){a=0;return a|0}k=j-b|0;f=e>>>3;do if(e>>>0<256){e=c[i+8>>2]|0;d=c[i+12>>2]|0;if((d|0)==(e|0)){c[47519]=c[47519]&~(1<<f);break}else{c[e+12>>2]=d;c[d+8>>2]=e;break}}else{h=c[i+24>>2]|0;d=c[i+12>>2]|0;do if((d|0)==(i|0)){f=i+16|0;e=f+4|0;d=c[e>>2]|0;if(!d){d=c[f>>2]|0;if(!d){f=0;break}else g=f}else g=e;while(1){f=d+20|0;e=c[f>>2]|0;if(e|0){d=e;g=f;continue}e=d+16|0;f=c[e>>2]|0;if(!f)break;else{d=f;g=e}}c[g>>2]=0;f=d}else{f=c[i+8>>2]|0;c[f+12>>2]=d;c[d+8>>2]=f;f=d}while(0);if(h|0){d=c[i+28>>2]|0;e=190380+(d<<2)|0;if((c[e>>2]|0)==(i|0)){c[e>>2]=f;if(!f){c[47520]=c[47520]&~(1<<d);break}}else{c[h+16+(((c[h+16>>2]|0)!=(i|0)&1)<<2)>>2]=f;if(!f)break}c[f+24>>2]=h;d=i+16|0;e=c[d>>2]|0;if(e|0){c[f+16>>2]=e;c[e+24>>2]=f}d=c[d+4>>2]|0;if(d|0){c[f+20>>2]=d;c[d+24>>2]=f}}}while(0);if(k>>>0<16){c[l>>2]=j|m&1|2;m=a+j+4|0;c[m>>2]=c[m>>2]|1;return a|0}else{i=a+b|0;c[l>>2]=m&1|b|2;c[i+4>>2]=k|3;m=a+j+4|0;c[m>>2]=c[m>>2]|1;Y1(i,k);return a|0}return 0}function Y1(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;i=a+b|0;d=c[a+4>>2]|0;do if(!(d&1)){f=c[a>>2]|0;if(!(d&3))return;h=a+(0-f)|0;b=f+b|0;if((c[47524]|0)==(h|0)){a=i+4|0;d=c[a>>2]|0;if((d&3|0)!=3)break;c[47521]=b;c[a>>2]=d&-2;c[h+4>>2]=b|1;c[i>>2]=b;return}e=f>>>3;if(f>>>0<256){a=c[h+8>>2]|0;d=c[h+12>>2]|0;if((d|0)==(a|0)){c[47519]=c[47519]&~(1<<e);break}else{c[a+12>>2]=d;c[d+8>>2]=a;break}}g=c[h+24>>2]|0;a=c[h+12>>2]|0;do if((a|0)==(h|0)){e=h+16|0;d=e+4|0;a=c[d>>2]|0;if(!a){a=c[e>>2]|0;if(!a){a=0;break}else d=e}while(1){e=a+20|0;f=c[e>>2]|0;if(f|0){a=f;d=e;continue}e=a+16|0;f=c[e>>2]|0;if(!f)break;else{a=f;d=e}}c[d>>2]=0}else{f=c[h+8>>2]|0;c[f+12>>2]=a;c[a+8>>2]=f}while(0);if(g){d=c[h+28>>2]|0;e=190380+(d<<2)|0;if((c[e>>2]|0)==(h|0)){c[e>>2]=a;if(!a){c[47520]=c[47520]&~(1<<d);break}}else{c[g+16+(((c[g+16>>2]|0)!=(h|0)&1)<<2)>>2]=a;if(!a)break}c[a+24>>2]=g;d=h+16|0;e=c[d>>2]|0;if(e|0){c[a+16>>2]=e;c[e+24>>2]=a}d=c[d+4>>2]|0;if(d){c[a+20>>2]=d;c[d+24>>2]=a}}}else h=a;while(0);a=i+4|0;e=c[a>>2]|0;if(!(e&2)){if((c[47525]|0)==(i|0)){i=(c[47522]|0)+b|0;c[47522]=i;c[47525]=h;c[h+4>>2]=i|1;if((h|0)!=(c[47524]|0))return;c[47524]=0;c[47521]=0;return}if((c[47524]|0)==(i|0)){i=(c[47521]|0)+b|0;c[47521]=i;c[47524]=h;c[h+4>>2]=i|1;c[h+i>>2]=i;return}f=(e&-8)+b|0;d=e>>>3;do if(e>>>0<256){a=c[i+8>>2]|0;b=c[i+12>>2]|0;if((b|0)==(a|0)){c[47519]=c[47519]&~(1<<d);break}else{c[a+12>>2]=b;c[b+8>>2]=a;break}}else{g=c[i+24>>2]|0;b=c[i+12>>2]|0;do if((b|0)==(i|0)){d=i+16|0;a=d+4|0;b=c[a>>2]|0;if(!b){b=c[d>>2]|0;if(!b){d=0;break}else a=d}while(1){d=b+20|0;e=c[d>>2]|0;if(e|0){b=e;a=d;continue}d=b+16|0;e=c[d>>2]|0;if(!e)break;else{b=e;a=d}}c[a>>2]=0;d=b}else{d=c[i+8>>2]|0;c[d+12>>2]=b;c[b+8>>2]=d;d=b}while(0);if(g|0){b=c[i+28>>2]|0;a=190380+(b<<2)|0;if((c[a>>2]|0)==(i|0)){c[a>>2]=d;if(!d){c[47520]=c[47520]&~(1<<b);break}}else{c[g+16+(((c[g+16>>2]|0)!=(i|0)&1)<<2)>>2]=d;if(!d)break}c[d+24>>2]=g;b=i+16|0;a=c[b>>2]|0;if(a|0){c[d+16>>2]=a;c[a+24>>2]=d}b=c[b+4>>2]|0;if(b|0){c[d+20>>2]=b;c[b+24>>2]=d}}}while(0);c[h+4>>2]=f|1;c[h+f>>2]=f;if((h|0)==(c[47524]|0)){c[47521]=f;return}}else{c[a>>2]=e&-2;c[h+4>>2]=b|1;c[h+b>>2]=b;f=b}b=f>>>3;if(f>>>0<256){d=190116+(b<<1<<2)|0;a=c[47519]|0;b=1<<b;if(!(a&b)){c[47519]=a|b;b=d;a=d+8|0}else{a=d+8|0;b=c[a>>2]|0}c[a>>2]=h;c[b+12>>2]=h;c[h+8>>2]=b;c[h+12>>2]=d;return}b=f>>>8;if(b)if(f>>>0>16777215)b=31;else{g=(b+1048320|0)>>>16&8;i=b<<g;e=(i+520192|0)>>>16&4;i=i<<e;b=(i+245760|0)>>>16&2;b=14-(e|g|b)+(i<<b>>>15)|0;b=f>>>(b+7|0)&1|b<<1}else b=0;e=190380+(b<<2)|0;c[h+28>>2]=b;c[h+20>>2]=0;c[h+16>>2]=0;a=c[47520]|0;d=1<<b;if(!(a&d)){c[47520]=a|d;c[e>>2]=h;c[h+24>>2]=e;c[h+12>>2]=h;c[h+8>>2]=h;return}a=f<<((b|0)==31?0:25-(b>>>1)|0);d=c[e>>2]|0;while(1){if((c[d+4>>2]&-8|0)==(f|0)){b=69;break}e=d+16+(a>>>31<<2)|0;b=c[e>>2]|0;if(!b){b=68;break}else{a=a<<1;d=b}}if((b|0)==68){c[e>>2]=h;c[h+24>>2]=d;c[h+12>>2]=h;c[h+8>>2]=h;return}else if((b|0)==69){g=d+8|0;i=c[g>>2]|0;c[i+12>>2]=h;c[g>>2]=h;c[h+8>>2]=i;c[h+12>>2]=d;c[h+24>>2]=0;return}}function Z1(a,b){a=a|0;b=b|0;if(a>>>0<9){b=T1(b)|0;return b|0}else{b=_1(a,b)|0;return b|0}return 0}function _1(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0;a=a>>>0>16?a:16;if(a+-1&a){d=16;while(1)if(d>>>0<a>>>0)d=d<<1;else{a=d;break}}if((-64-a|0)>>>0<=b>>>0){c[(d2()|0)>>2]=12;h=0;return h|0}g=b>>>0<11?16:b+11&-8;d=T1(g+12+a|0)|0;if(!d){h=0;return h|0}f=d+-8|0;do if(a+-1&d){e=(d+a+-1&0-a)+-8|0;b=f;e=(e-b|0)>>>0>15?e:e+a|0;b=e-b|0;a=d+-4|0;i=c[a>>2]|0;d=(i&-8)-b|0;if(!(i&3)){c[e>>2]=(c[f>>2]|0)+b;c[e+4>>2]=d;a=e;b=e;break}else{i=e+4|0;c[i>>2]=d|c[i>>2]&1|2;d=e+d+4|0;c[d>>2]=c[d>>2]|1;c[a>>2]=b|c[a>>2]&1|2;c[i>>2]=c[i>>2]|1;Y1(f,b);a=e;b=e;break}}else{a=f;b=f}while(0);a=a+4|0;d=c[a>>2]|0;if(d&3|0?(h=d&-8,h>>>0>(g+16|0)>>>0):0){i=h-g|0;f=b+g|0;c[a>>2]=g|d&1|2;c[f+4>>2]=i|3;h=b+h+4|0;c[h>>2]=c[h>>2]|1;Y1(f,i)}i=b+8|0;return i|0}function $1(a){a=a|0;var b=0,d=0;b=l;l=l+16|0;d=b;c[d>>2]=e2(c[a+60>>2]|0)|0;a=c2(Ja(6,d|0)|0)|0;l=b;return a|0}function a2(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0;k=l;l=l+32|0;f=k;i=k+16|0;c[i>>2]=d;g=i+4|0;j=b+48|0;m=c[j>>2]|0;c[g>>2]=e-((m|0)!=0&1);h=b+44|0;c[i+8>>2]=c[h>>2];c[i+12>>2]=m;c[f>>2]=c[b+60>>2];c[f+4>>2]=i;c[f+8>>2]=2;f=c2(ya(145,f|0)|0)|0;if((f|0)>=1){i=c[g>>2]|0;if(f>>>0>i>>>0){g=c[h>>2]|0;h=b+4|0;c[h>>2]=g;c[b+8>>2]=g+(f-i);if(!(c[j>>2]|0))f=e;else{c[h>>2]=g+1;a[d+(e+-1)>>0]=a[g>>0]|0;f=e}}}else c[b>>2]=c[b>>2]|f&48^16;l=k;return f|0}function b2(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;f=l;l=l+32|0;g=f;e=f+20|0;c[g>>2]=c[a+60>>2];c[g+4>>2]=0;c[g+8>>2]=b;c[g+12>>2]=e;c[g+16>>2]=d;if((c2(xa(140,g|0)|0)|0)<0){c[e>>2]=-1;a=-1}else a=c[e>>2]|0;l=f;return a|0}function c2(a){a=a|0;if(a>>>0>4294963200){c[(d2()|0)>>2]=0-a;a=-1}return a|0}function d2(){return 190572}function e2(a){a=a|0;return a|0}function f2(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0;g=l;l=l+32|0;f=g;c[b+36>>2]=18;if((c[b>>2]&64|0)==0?(c[f>>2]=c[b+60>>2],c[f+4>>2]=21523,c[f+8>>2]=g+16,Ia(54,f|0)|0):0)a[b+75>>0]=-1;f=g2(b,d,e)|0;l=g;return f|0}function g2(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;n=l;l=l+48|0;k=n+16|0;g=n;f=n+32|0;i=a+28|0;e=c[i>>2]|0;c[f>>2]=e;j=a+20|0;e=(c[j>>2]|0)-e|0;c[f+4>>2]=e;c[f+8>>2]=b;c[f+12>>2]=d;e=e+d|0;h=a+60|0;c[g>>2]=c[h>>2];c[g+4>>2]=f;c[g+8>>2]=2;g=c2(za(146,g|0)|0)|0;a:do if((e|0)!=(g|0)){b=2;while(1){if((g|0)<0)break;e=e-g|0;p=c[f+4>>2]|0;o=g>>>0>p>>>0;f=o?f+8|0:f;b=b+(o<<31>>31)|0;p=g-(o?p:0)|0;c[f>>2]=(c[f>>2]|0)+p;o=f+4|0;c[o>>2]=(c[o>>2]|0)-p;c[k>>2]=c[h>>2];c[k+4>>2]=f;c[k+8>>2]=b;g=c2(za(146,k|0)|0)|0;if((e|0)==(g|0)){m=3;break a}}c[a+16>>2]=0;c[i>>2]=0;c[j>>2]=0;c[a>>2]=c[a>>2]|32;if((b|0)==2)d=0;else d=d-(c[f+4>>2]|0)|0}else m=3;while(0);if((m|0)==3){p=c[a+44>>2]|0;c[a+16>>2]=p+(c[a+48>>2]|0);c[i>>2]=p;c[j>>2]=p}l=n;return d|0}function h2(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=a;c[e+4>>2]=b;b=c2(Ka(91,e|0)|0)|0;l=d;return b|0}function i2(){return}function j2(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0;i=l;l=l+32|0;h=i;do if((g&4095|0)==0&(((g|0)<0)<<31>>31&-4096|0)==0){if(b>>>0>2147483646){c[(d2()|0)>>2]=12;a=-1;break}c[h>>2]=a;c[h+4>>2]=b;c[h+8>>2]=d;c[h+12>>2]=e;c[h+16>>2]=f;c[h+20>>2]=g>>12;a=c2(Aa(192,h|0)|0)|0}else{c[(d2()|0)>>2]=22;a=-1}while(0);l=i;return a|0}function k2(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;f=d&255;a:do if(!f)b=b+(l2(b)|0)|0;else{if(b&3){e=d&255;do{g=a[b>>0]|0;if(g<<24>>24==0?1:g<<24>>24==e<<24>>24)break a;b=b+1|0}while((b&3|0)!=0)}f=O(f,16843009)|0;e=c[b>>2]|0;b:do if(!((e&-2139062144^-2139062144)&e+-16843009))do{g=e^f;if((g&-2139062144^-2139062144)&g+-16843009|0)break b;b=b+4|0;e=c[b>>2]|0}while(!((e&-2139062144^-2139062144)&e+-16843009|0));while(0);e=d&255;while(1){g=a[b>>0]|0;if(g<<24>>24==0?1:g<<24>>24==e<<24>>24)break;else b=b+1|0}}while(0);return b|0}function l2(b){b=b|0;var d=0,e=0,f=0;f=b;a:do if(!(f&3))e=4;else{d=f;while(1){if(!(a[b>>0]|0)){b=d;break a}b=b+1|0;d=b;if(!(d&3)){e=4;break}}}while(0);if((e|0)==4){while(1){d=c[b>>2]|0;if(!((d&-2139062144^-2139062144)&d+-16843009))b=b+4|0;else break}if((d&255)<<24>>24)do b=b+1|0;while((a[b>>0]|0)!=0)}return b-f|0}function m2(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;h=l;l=l+48|0;g=h+16|0;f=h;e=h+32|0;if(!(b&4194368))e=0;else{c[e>>2]=d;i=(c[e>>2]|0)+(4-1)&~(4-1);d=c[i>>2]|0;c[e>>2]=i+4;e=d}c[f>>2]=a;c[f+4>>2]=b|32768;c[f+8>>2]=e;e=Ha(5,f|0)|0;if(!((b&524288|0)==0|(e|0)<0)){c[g>>2]=e;c[g+4>>2]=2;c[g+8>>2]=1;Ea(221,g|0)|0}i=c2(e)|0;l=h;return i|0}function n2(b,e){b=b|0;e=e|0;var f=0,g=0;g=0;while(1){if((d[144531+g>>0]|0)==(b|0)){b=2;break}f=g+1|0;if((f|0)==87){f=144619;g=87;b=5;break}else g=f}if((b|0)==2)if(!g)f=144619;else{f=144619;b=5}if((b|0)==5)while(1){do{b=f;f=f+1|0}while((a[b>>0]|0)!=0);g=g+-1|0;if(!g)break;else b=5}return o2(f,c[e+20>>2]|0)|0}function o2(a,b){a=a|0;b=b|0;return p2(a,b)|0}function p2(a,b){a=a|0;b=b|0;if(!b)b=0;else b=q2(c[b>>2]|0,c[b+4>>2]|0,a)|0;return (b|0?b:a)|0}function q2(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;o=(c[b>>2]|0)+1794895138|0;h=r2(c[b+8>>2]|0,o)|0;f=r2(c[b+12>>2]|0,o)|0;g=r2(c[b+16>>2]|0,o)|0;a:do if((h>>>0<d>>>2>>>0?(n=d-(h<<2)|0,f>>>0<n>>>0&g>>>0<n>>>0):0)?((g|f)&3|0)==0:0){n=f>>>2;m=g>>>2;l=0;while(1){j=h>>>1;k=l+j|0;i=k<<1;g=i+n|0;f=r2(c[b+(g<<2)>>2]|0,o)|0;g=r2(c[b+(g+1<<2)>>2]|0,o)|0;if(!(g>>>0<d>>>0&f>>>0<(d-g|0)>>>0)){f=0;break a}if(a[b+(g+f)>>0]|0){f=0;break a}f=s2(e,b+g|0)|0;if(!f)break;f=(f|0)<0;if((h|0)==1){f=0;break a}else{l=f?l:k;h=f?j:h-j|0}}f=i+m|0;g=r2(c[b+(f<<2)>>2]|0,o)|0;f=r2(c[b+(f+1<<2)>>2]|0,o)|0;if(f>>>0<d>>>0&g>>>0<(d-f|0)>>>0)f=(a[b+(f+g)>>0]|0)==0?b+f|0:0;else f=0}else f=0;while(0);return f|0}function r2(a,b){a=a|0;b=b|0;var c=0;c=T6(a|0)|0;return ((b|0)==0?a:c)|0}function s2(b,c){b=b|0;c=c|0;var d=0,e=0;d=a[b>>0]|0;e=a[c>>0]|0;if(d<<24>>24==0?1:d<<24>>24!=e<<24>>24)b=e;else{do{b=b+1|0;c=c+1|0;d=a[b>>0]|0;e=a[c>>0]|0}while(!(d<<24>>24==0?1:d<<24>>24!=e<<24>>24));b=e}return (d&255)-(b&255)|0}function t2(a){a=a|0;return n2(a,c[(u2()|0)+188>>2]|0)|0}function u2(){return v2()|0}function v2(){return 62976}function w2(a,b,c){a=+a;b=+b;c=c|0;var d=0.0,e=0.0,f=0.0;f=a*a;d=f*(f*f)*(f*1.58969099521155e-10+-2.5050760253406863e-08)+(f*(f*2.7557313707070068e-06+-1.984126982985795e-04)+.00833333333332249);e=f*a;if(!c)a=e*(f*d+-.16666666666666632)+a;else a=a-(e*.16666666666666632+(f*(b*.5-e*d)-b));return +a}function x2(a){a=+a;var b=0,d=0,e=0,f=0,g=0.0,i=0.0,k=0.0,l=0.0,m=0.0;h[j>>3]=a;b=c[j>>2]|0;d=c[j+4>>2]|0;e=(d|0)<0;do if(e|d>>>0<1048576){if((b|0)==0&(d&2147483647|0)==0){a=-1.0/(a*a);break}if(e){a=(a-a)/0.0;break}else{h[j>>3]=a*18014398509481984.0;d=c[j+4>>2]|0;e=-1077;b=c[j>>2]|0;f=9;break}}else if(d>>>0<=2146435071)if((b|0)==0&0==0&(d|0)==1072693248)a=0.0;else{e=-1023;f=9}while(0);if((f|0)==9){f=d+614242|0;c[j>>2]=b;c[j+4>>2]=(f&1048575)+1072079006;m=+h[j>>3]+-1.0;k=m*(m*.5);i=m/(m+2.0);l=i*i;g=l*l;h[j>>3]=m-k;d=c[j+4>>2]|0;c[j>>2]=0;c[j+4>>2]=d;a=+h[j>>3];g=m-a-k+i*(k+(g*(g*(g*.15313837699209373+.22222198432149784)+.3999999999940942)+l*(g*(g*(g*.14798198605116586+.1818357216161805)+.2857142874366239)+.6666666666666735)));l=a*1.4426950407214463;k=+(e+(f>>>20)|0);i=l+k;a=i+(l+(k-i)+(g*1.4426950407214463+(g+a)*1.6751713164886512e-10))}return +a}function y2(a,b){a=+a;b=b|0;var d=0,e=0,f=0;h[j>>3]=a;d=c[j>>2]|0;e=c[j+4>>2]|0;f=Q6(d|0,e|0,52)|0;switch(f&2047){case 0:{if(a!=0.0){a=+y2(a*18446744073709551616.0,b);d=(c[b>>2]|0)+-64|0}else d=0;c[b>>2]=d;break}case 2047:break;default:{c[b>>2]=(f&2047)+-1022;c[j>>2]=d;c[j+4>>2]=e&-2146435073|1071644672;a=+h[j>>3]}}return +a}function z2(a,b){a=+a;b=+b;var c=0.0,d=0.0,e=0.0,f=0.0;c=a*a;d=c*c;e=c*.5;f=1.0-e;return +(f+(1.0-f-e+(c*(c*(c*(c*2.480158728947673e-05+-.001388888888887411)+.0416666666666666)+d*d*(c*(2.087572321298175e-09-c*1.1359647557788195e-11)+-2.7557314351390663e-07))-a*b)))}function A2(a){a=+a;var b=0,d=0,e=0,f=0,g=0.0,i=0.0,k=0.0,l=0.0,m=0.0;h[j>>3]=a;b=c[j>>2]|0;d=c[j+4>>2]|0;e=(d|0)<0;do if(e|d>>>0<1048576){if((b|0)==0&(d&2147483647|0)==0){a=-1.0/(a*a);break}if(e){a=(a-a)/0.0;break}else{h[j>>3]=a*18014398509481984.0;d=c[j+4>>2]|0;e=-1077;b=c[j>>2]|0;f=9;break}}else if(d>>>0<=2146435071)if((b|0)==0&0==0&(d|0)==1072693248)a=0.0;else{e=-1023;f=9}while(0);if((f|0)==9){f=d+614242|0;c[j>>2]=b;c[j+4>>2]=(f&1048575)+1072079006;k=+h[j>>3]+-1.0;i=k*(k*.5);l=k/(k+2.0);m=l*l;g=m*m;h[j>>3]=k-i;d=c[j+4>>2]|0;c[j>>2]=0;c[j+4>>2]=d;a=+h[j>>3];g=k-a-i+l*(i+(g*(g*(g*.15313837699209373+.22222198432149784)+.3999999999940942)+m*(g*(g*(g*.14798198605116586+.1818357216161805)+.2857142874366239)+.6666666666666735)));m=a*.4342944818781689;i=+(e+(f>>>20)|0);l=i*.30102999566361177;k=l+m;a=k+(m+(l-k)+(g*.4342944818781689+(i*3.694239077158931e-13+(g+a)*2.5082946711645275e-11)))}return +a}function B2(a,b){a=+a;b=b|0;var d=0,e=0.0,f=0.0,g=0.0,i=0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0.0;r=l;l=l+48|0;m=r+16|0;n=r;h[j>>3]=a;d=c[j>>2]|0;i=c[j+4>>2]|0;o=Q6(d|0,i|0,63)|0;q=i&2147483647;do if(q>>>0<1074752123)if((i&1048575|0)!=598523){d=(o|0)!=0;if(q>>>0<1073928573)if(d){g=a+1.5707963267341256;k=g+6.077100506506192e-11;h[b>>3]=k;h[b+8>>3]=g-k+6.077100506506192e-11;d=-1;break}else{g=a+-1.5707963267341256;k=g+-6.077100506506192e-11;h[b>>3]=k;h[b+8>>3]=g-k+-6.077100506506192e-11;d=1;break}else if(d){g=a+3.1415926534682512;k=g+1.2154201013012384e-10;h[b>>3]=k;h[b+8>>3]=g-k+1.2154201013012384e-10;d=-2;break}else{g=a+-3.1415926534682512;k=g+-1.2154201013012384e-10;h[b>>3]=k;h[b+8>>3]=g-k+-1.2154201013012384e-10;d=2;break}}else p=21;else{if(q>>>0<1075594812)if(q>>>0<1075183037){if((q|0)==1074977148){p=21;break}if(!o){g=a+-4.712388980202377;k=g+-1.8231301519518578e-10;h[b>>3]=k;h[b+8>>3]=g-k+-1.8231301519518578e-10;d=3;break}else{g=a+4.712388980202377;k=g+1.8231301519518578e-10;h[b>>3]=k;h[b+8>>3]=g-k+1.8231301519518578e-10;d=-3;break}}else{if((q|0)==1075388923){p=21;break}if(!o){g=a+-6.2831853069365025;k=g+-2.430840202602477e-10;h[b>>3]=k;h[b+8>>3]=g-k+-2.430840202602477e-10;d=4;break}else{g=a+6.2831853069365025;k=g+2.430840202602477e-10;h[b>>3]=k;h[b+8>>3]=g-k+2.430840202602477e-10;d=-4;break}}if(q>>>0>=1094263291){if(q>>>0>2146435071){k=a-a;h[b+8>>3]=k;h[b>>3]=k;d=0;break}c[j>>2]=d;c[j+4>>2]=i&1048575|1096810496;a=+h[j>>3];d=0;do{k=+(~~a|0);h[m+(d<<3)>>3]=k;a=(a-k)*134217728.0;d=d+1|0}while((d|0)!=2);h[m+16>>3]=a;if(a==0.0){d=1;while(1)if(+h[m+(d<<3)>>3]==0.0)d=d+-1|0;else break}else d=2;d=C2(m,n,(q>>>20)+-1046|0,d+1|0,1)|0;a=+h[n>>3];if(!o){h[b>>3]=a;h[b+8>>3]=+h[n+8>>3];break}else{h[b>>3]=-a;h[b+8>>3]=-+h[n+8>>3];d=0-d|0;break}}else p=21}while(0);if((p|0)==21){g=a*.6366197723675814+6755399441055744.0+-6755399441055744.0;d=~~g;e=a-g*1.5707963267341256;f=g*6.077100506506192e-11;a=e-f;h[b>>3]=a;h[j>>3]=a;p=Q6(c[j>>2]|0,c[j+4>>2]|0,52)|0;i=q>>>20;if((i-(p&2047)|0)>16){f=g*6.077100506303966e-11;k=e-f;f=g*2.0222662487959506e-21-(e-k-f);a=k-f;h[b>>3]=a;h[j>>3]=a;q=Q6(c[j>>2]|0,c[j+4>>2]|0,52)|0;s=g*2.0222662487111665e-21;e=k-s;g=g*8.4784276603689e-32-(k-e-s);if((i-(q&2047)|0)>49){a=e-g;h[b>>3]=a;f=g}else e=k}h[b+8>>3]=e-a-f}l=r;return d|0}function C2(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0.0,i=0.0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0.0;H=l;l=l+560|0;C=H+480|0;z=H+320|0;G=H+160|0;E=H;D=c[63220+(f<<2)>>2]|0;p=e+-1|0;y=(d+-3|0)/24|0;y=(y|0)>0?y:0;B=O(y,-24)|0;j=d+-24+B|0;if((D+p|0)>=0){n=D+e|0;k=y-p|0;m=0;while(1){if((k|0)<0)g=0.0;else g=+(c[63236+(k<<2)>>2]|0);h[z+(m<<3)>>3]=g;m=m+1|0;if((m|0)==(n|0))break;else k=k+1|0}}o=(e|0)>0;n=0;while(1){if(o){m=n+p|0;g=0.0;k=0;do{g=g+ +h[a+(k<<3)>>3]*+h[z+(m-k<<3)>>3];k=k+1|0}while((k|0)!=(e|0))}else g=0.0;h[E+(n<<3)>>3]=g;if((n|0)<(D|0))n=n+1|0;else break}t=(j|0)>0;u=24-j|0;v=23-j|0;w=(e|0)>0;x=(j|0)==0;k=D;a:while(1){g=+h[E+(k<<3)>>3];o=(k|0)>0;if(o){m=k;n=0;while(1){i=+(~~(g*5.9604644775390625e-08)|0);c[C+(n<<2)>>2]=~~(g-i*134217728.0);s=m;m=m+-1|0;g=+h[E+(m<<3)>>3]+i;if((s|0)<=1)break;else n=n+1|0}}g=+D2(g,j);g=g-+A(+(g*.125))*8.0;m=~~g;g=g-+(m|0);do if(!t)if(!x)if(!(g>=.5)){p=0;s=m;break}else{n=2;F=20;break}else{n=c[C+(k+-1<<2)>>2]>>23;F=19;break}else{s=C+(k+-1<<2)|0;n=c[s>>2]|0;F=n>>u;n=n-(F<<u)|0;c[s>>2]=n;n=n>>v;m=F+m|0;F=19}while(0);if((F|0)==19){F=0;if((n|0)>0)F=20;else{p=n;s=m}}if((F|0)==20){F=0;s=m+1|0;if(o){m=0;r=0;do{p=C+(r<<2)|0;q=c[p>>2]|0;if(!m)if(!q)m=0;else{m=1;o=134217728;F=23}else{o=16777215;F=23}if((F|0)==23){F=0;c[p>>2]=o-q}r=r+1|0}while((r|0)!=(k|0))}else m=0;b:do if(t){switch(j|0){case 1:{o=8388607;break}case 2:{o=4194303;break}default:break b}r=C+(k+-1<<2)|0;c[r>>2]=c[r>>2]&o}while(0);if((n|0)==2){g=1.0-g;if(!m)p=2;else{p=2;g=g-+D2(1.0,j)}}else p=n}if(!(g==0.0)){F=42;break}if((k|0)>(D|0)){m=0;n=k;do{n=n+-1|0;m=c[C+(n<<2)>>2]|m}while((n|0)>(D|0));if(m){F=41;break}else m=1}else m=1;while(1)if(!(c[C+(D-m<<2)>>2]|0))m=m+1|0;else break;o=m+k|0;if((m|0)<=0){k=o;continue}while(1){n=k+1|0;m=k+e|0;h[z+(m<<3)>>3]=+(c[63236+(n+y<<2)>>2]|0);if(w){g=0.0;k=0;do{g=g+ +h[a+(k<<3)>>3]*+h[z+(m-k<<3)>>3];k=k+1|0}while((k|0)!=(e|0))}else g=0.0;h[E+(n<<3)>>3]=g;if((n|0)<(o|0))k=n;else{k=o;continue a}}}if((F|0)==41)while(1){F=0;j=j+-24|0;k=k+-1|0;if(!(c[C+(k<<2)>>2]|0))F=41;else{o=k;break}}else if((F|0)==42){g=+D2(g,0-j|0);if(g>=134217728.0){i=+(~~(g*5.9604644775390625e-08)|0);c[C+(k<<2)>>2]=~~(g-i*134217728.0);j=B+d|0;k=k+1|0;g=i}c[C+(k<<2)>>2]=~~g;o=k}n=(o|0)>-1;if(n){g=+D2(1.0,j);j=o;while(1){h[E+(j<<3)>>3]=g*+(c[C+(j<<2)>>2]|0);if((j|0)>0){g=g*5.9604644775390625e-08;j=j+-1|0}else break}if(n){k=o;while(1){m=o-k|0;j=0;g=0.0;while(1){g=g+ +h[10904+(j<<3)>>3]*+h[E+(j+k<<3)>>3];if((j|0)>=(D|0)|(j|0)>=(m|0))break;else j=j+1|0}h[G+(m<<3)>>3]=g;if((k|0)>0)k=k+-1|0;else break}}}switch(f|0){case 0:{if(n){g=0.0;j=o;while(1){g=g+ +h[G+(j<<3)>>3];if((j|0)>0)j=j+-1|0;else break}}else g=0.0;i=(p|0)==0?g:-g;F=73;break}case 2:case 1:{if(n){g=0.0;j=o;while(1){g=g+ +h[G+(j<<3)>>3];if((j|0)>0)j=j+-1|0;else break}}else g=0.0;k=(p|0)==0;h[b>>3]=k?g:-g;g=+h[G>>3]-g;if((o|0)>=1){j=1;while(1){g=g+ +h[G+(j<<3)>>3];if((j|0)==(o|0))break;else j=j+1|0}}i=k?g:-g;b=b+8|0;F=73;break}case 3:{if((o|0)>0){j=o;g=+h[G+(o<<3)>>3];do{F=j;j=j+-1|0;E=G+(j<<3)|0;i=+h[E>>3];I=g;g=i+g;h[G+(F<<3)>>3]=I+(i-g);h[E>>3]=g}while((F|0)>1);k=(o|0)>1;if(k){j=o;g=+h[G+(o<<3)>>3];do{F=j;j=j+-1|0;E=G+(j<<3)|0;I=+h[E>>3];i=g;g=I+g;h[G+(F<<3)>>3]=i+(I-g);h[E>>3]=g}while((F|0)>2);if(k){j=o;i=0.0;while(1){i=i+ +h[G+(j<<3)>>3];if((j|0)>2)j=j+-1|0;else break}}else i=0.0}else i=0.0}else i=0.0;g=+h[G>>3];if(!p){h[b>>3]=g;g=+h[G+8>>3]}else{h[b>>3]=-g;i=-i;g=-+h[G+8>>3]}h[b+8>>3]=g;b=b+16|0;F=73;break}default:{}}if((F|0)==73)h[b>>3]=i;l=H;return s&7|0}function D2(a,b){a=+a;b=b|0;var d=0,e=0;if((b|0)<=1023){if((b|0)<-1022){a=a*2.2250738585072014e-308;e=(b|0)<-2044;d=b+2044|0;a=e?a*2.2250738585072014e-308:a;b=e?((d|0)>-1022?d:-1022):b+1022|0}}else{a=a*8988465674311579538646525.0e283;d=(b|0)>2046;e=b+-2046|0;a=d?a*8988465674311579538646525.0e283:a;b=d?((e|0)<1023?e:1023):b+-1023|0}d=R6(b+1023|0,0,52)|0;e=z;c[j>>2]=d;c[j+4>>2]=e;return +(a*+h[j>>3])}function E2(a){a=+a;var b=0,d=0,e=0,f=0.0,g=0.0;h[j>>3]=a;d=c[j+4>>2]|0;b=d&2147483647;do if(b>>>0>2146435071)a=a+a;else{if(b>>>0<1048576){h[j>>3]=a*18014398509481984.0;d=c[j+4>>2]|0;b=d&2147483647;if(!b)break;else e=696219795}else e=715094163;c[j>>2]=0;c[j+4>>2]=((b>>>0)/3|0)+e|d&-2147483648;f=+h[j>>3];g=f*f*(f/a);h[j>>3]=(g*(g*g)*(g*.14599619288661245+-.758397934778766)+(g*(g*1.6214297201053545+-1.8849797954337717)+1.87595182427177))*f;d=J6(c[j>>2]|0,c[j+4>>2]|0,-2147483648,0)|0;e=z;c[j>>2]=d&-1073741824;c[j+4>>2]=e;f=+h[j>>3];a=a/(f*f);a=(a-f)/(f+f+a)*f+f}while(0);return +a}function F2(a,b){a=+a;b=+b;var d=0,e=0;h[j>>3]=a;e=c[j>>2]|0;d=c[j+4>>2]|0;h[j>>3]=b;d=c[j+4>>2]&-2147483648|d&2147483647;c[j>>2]=e;c[j+4>>2]=d;return +(+h[j>>3])}function G2(a,b){a=+a;b=+b;return +(+F2(a,b))}function H2(a,b){a=+a;b=b|0;return +(+D2(a,b))}function I2(a,b){a=+a;b=+b;return +(+J2(a,b))}function J2(a,b){a=+a;b=+b;var d=0,e=0,f=0,g=0,i=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0;h[j>>3]=a;i=c[j>>2]|0;l=c[j+4>>2]|0;h[j>>3]=b;n=c[j>>2]|0;o=c[j+4>>2]|0;e=Q6(i|0,l|0,52)|0;e=e&2047;m=Q6(n|0,o|0,52)|0;m=m&2047;p=l&-2147483648;g=R6(n|0,o|0,1)|0;k=z;a:do if(!((g|0)==0&(k|0)==0)?(f=K2(b)|0,d=z&2147483647,!((e|0)==2047|(d>>>0>2146435072|(d|0)==2146435072&f>>>0>0))):0){d=R6(i|0,l|0,1)|0;f=z;if(!(f>>>0>k>>>0|(f|0)==(k|0)&d>>>0>g>>>0))return +((d|0)==(g|0)&(f|0)==(k|0)?a*0.0:a);if(!e){d=R6(i|0,l|0,12)|0;f=z;if((f|0)>-1|(f|0)==-1&d>>>0>4294967295){e=0;do{e=e+-1|0;d=R6(d|0,f|0,1)|0;f=z}while((f|0)>-1|(f|0)==-1&d>>>0>4294967295)}else e=0;i=R6(i|0,l|0,1-e|0)|0;g=z}else g=l&1048575|1048576;if(!m){f=R6(n|0,o|0,12)|0;k=z;if((k|0)>-1|(k|0)==-1&f>>>0>4294967295){d=0;do{d=d+-1|0;f=R6(f|0,k|0,1)|0;k=z}while((k|0)>-1|(k|0)==-1&f>>>0>4294967295)}else d=0;n=R6(n|0,o|0,1-d|0)|0;m=d;l=z}else l=o&1048575|1048576;f=K6(i|0,g|0,n|0,l|0)|0;d=z;k=(d|0)>-1|(d|0)==-1&f>>>0>4294967295;b:do if((e|0)>(m|0)){while(1){if(k){if((f|0)==0&(d|0)==0)break}else{f=i;d=g}i=R6(f|0,d|0,1)|0;g=z;e=e+-1|0;f=K6(i|0,g|0,n|0,l|0)|0;d=z;k=(d|0)>-1|(d|0)==-1&f>>>0>4294967295;if((e|0)<=(m|0))break b}b=a*0.0;break a}while(0);if(k){if((f|0)==0&(d|0)==0){b=a*0.0;break}}else{d=g;f=i}if(d>>>0<1048576|(d|0)==1048576&f>>>0<0)do{f=R6(f|0,d|0,1)|0;d=z;e=e+-1|0}while(d>>>0<1048576|(d|0)==1048576&f>>>0<0);if((e|0)>0){o=J6(f|0,d|0,0,-1048576)|0;d=z;e=R6(e|0,0,52)|0;d=d|z;e=o|e}else{e=Q6(f|0,d|0,1-e|0)|0;d=z}c[j>>2]=e;c[j+4>>2]=d|p;b=+h[j>>3]}else q=3;while(0);if((q|0)==3){b=a*b;b=b/b}return +b}function K2(a){a=+a;var b=0;h[j>>3]=a;b=c[j>>2]|0;z=c[j+4>>2]|0;return b|0}function L2(a,b){a=+a;b=+b;var d=0.0,e=0,f=0,g=0,i=0,k=0.0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;q=l;l=l+32|0;m=q+24|0;n=q+16|0;o=q+8|0;p=q;h[j>>3]=a;t=c[j>>2]|0;g=c[j+4>>2]|0;h[j>>3]=b;e=c[j>>2]|0;g=g&2147483647;f=c[j+4>>2]&2147483647;i=g>>>0<f>>>0|(g|0)==(f|0)&t>>>0<e>>>0;s=i?e:t;r=i?f:g;e=i?t:e;f=i?g:f;g=Q6(s|0,r|0,52)|0;i=Q6(e|0,f|0,52)|0;c[j>>2]=s;c[j+4>>2]=r;a=+h[j>>3];c[j>>2]=e;c[j+4>>2]=f;k=+h[j>>3];do if((i|0)!=2047){if(!((e|0)==0&(f|0)==0|(g|0)==2047)){if((g-i|0)>64){a=a+k;break}if((g|0)>1533){d=5260135901548373507240989.0e186;b=a*1.90109156629516e-211;a=k*1.90109156629516e-211}else{t=(i|0)<573;d=t?1.90109156629516e-211:1.0;b=t?a*5260135901548373507240989.0e186:a;a=t?k*5260135901548373507240989.0e186:k}M2(m,n,b);M2(o,p,a);a=d*+C(+(+h[p>>3]+ +h[n>>3]+ +h[o>>3]+ +h[m>>3]))}}else a=k;while(0);l=q;return +a}function M2(a,b,c){a=a|0;b=b|0;c=+c;var d=0.0,e=0.0;e=c*134217729.0;e=e+(c-e);d=c-e;c=c*c;h[a>>3]=c;h[b>>3]=d*d+(e*e-c+e*2.0*d);return}function N2(a,b,d){a=+a;b=b|0;d=d|0;var e=0.0,f=0,g=0,i=0.0,k=0,m=0;k=l;l=l+16|0;g=k;h[j>>3]=a;f=c[j+4>>2]&2147483647;do if(f>>>0<1072243196)if(f>>>0<1044816030){h[b>>3]=a;h[d>>3]=1.0;break}else{h[b>>3]=+w2(a,0.0,0);h[d>>3]=+z2(a,0.0);break}else{if(f>>>0>2146435071){i=a-a;h[d>>3]=i;h[b>>3]=i;break}f=B2(a,g)|0;m=g+8|0;e=+w2(+h[g>>3],+h[m>>3],1);a=+z2(+h[g>>3],+h[m>>3]);switch(f&3){case 0:{h[b>>3]=e;i=a;break}case 1:{h[b>>3]=a;i=-e;break}case 2:{h[b>>3]=-e;i=-a;break}case 3:{h[b>>3]=-a;i=e;break}default:{}}h[d>>3]=i}while(0);l=k;return}function O2(a,b){a=+a;b=b|0;return +(+y2(a,b))}function P2(a){a=a|0;if(a>>>0<131072)a=(d[146423+((d[146423+(a>>>8)>>0]|0)<<5|a>>>3&31)>>0]|0)>>>(a&7)&1;else a=0;return a|0}function Q2(b,c,e){b=b|0;c=c|0;e=e|0;var f=0,g=0;if(!e)f=0;else{f=a[b>>0]|0;a:do if(!(f<<24>>24))f=0;else while(1){e=e+-1|0;g=a[c>>0]|0;if(!(f<<24>>24==g<<24>>24&((e|0)!=0&g<<24>>24!=0)))break a;b=b+1|0;c=c+1|0;f=a[b>>0]|0;if(!(f<<24>>24)){f=0;break}}while(0);f=(f&255)-(d[c>>0]|0)|0}return f|0}function 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g=0,h=0,i=0,j=0;j=l;l=l+128|0;h=j;c[h>>2]=0;i=h+4|0;c[i>>2]=a;c[h+44>>2]=a;g=h+8|0;c[g>>2]=(a|0)<0?-1:a+2147483647|0;c[h+76>>2]=-1;W2(h,0);d=X2(h,d,1,e,f)|0;if(b|0)c[b>>2]=a+((c[i>>2]|0)+(c[h+108>>2]|0)-(c[g>>2]|0));l=j;return d|0}function W2(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;c[a+104>>2]=b;d=c[a+8>>2]|0;e=c[a+4>>2]|0;f=d-e|0;c[a+108>>2]=f;c[a+100>>2]=(b|0)!=0&(f|0)>(b|0)?e+b|0:d;return}function X2(b,e,f,g,h){b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;a:do if(e>>>0>36){c[(d2()|0)>>2]=22;h=0;g=0}else{r=b+4|0;q=b+100|0;do{i=c[r>>2]|0;if(i>>>0<(c[q>>2]|0)>>>0){c[r>>2]=i+1;i=d[i>>0]|0}else i=Y2(b)|0}while((S2(i)|0)!=0);b:do switch(i|0){case 43:case 45:{i=((i|0)==45)<<31>>31;j=c[r>>2]|0;if(j>>>0<(c[q>>2]|0)>>>0){c[r>>2]=j+1;p=i;i=d[j>>0]|0;break b}else{p=i;i=Y2(b)|0;break b}}default:p=0}while(0);j=(e|0)==0;do if((e|16|0)==16&(i|0)==48){i=c[r>>2]|0;if(i>>>0<(c[q>>2]|0)>>>0){c[r>>2]=i+1;i=d[i>>0]|0}else 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a|0}function i3(a){a=a|0;var b=0;b=a;while(1)if(!(c[b>>2]|0))break;else b=b+4|0;return b-a>>2|0}function j3(b,c,e){b=b|0;c=c|0;e=e|0;var f=0,g=0,h=0;if(!e)b=0;else{f=a[b>>0]|0;a:do if(!(f<<24>>24))b=0;else{h=b;b=f;f=f&255;while(1){e=e+-1|0;g=a[c>>0]|0;if(!((e|0)!=0&g<<24>>24!=0))break a;if(b<<24>>24!=g<<24>>24?(f=d3(f)|0,(f|0)!=(d3(g&255)|0)):0)break a;h=h+1|0;c=c+1|0;f=a[h>>0]|0;if(!(f<<24>>24)){b=0;break}else{b=f;f=f&255}}}while(0);b=d3(b&255)|0;b=b-(d3(d[c>>0]|0)|0)|0}return b|0}function k3(a){a=a|0;if(a>>>0>=255)if((a+-57344|0)>>>0<8185|(a>>>0<8232|(a+-8234|0)>>>0<47062))a=1;else return (a&65534|0)!=65534&(a+-65532|0)>>>0<1048580&1|0;else a=(a+1&127)>>>0>32&1;return a|0}function l3(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0;k=l;l=l+16|0;g=k;i=(f|0)==0?190640:f;f=c[i>>2]|0;a:do if(!d)if(!f)f=0;else j=17;else{h=(b|0)==0?g:b;if(!e)f=-2;else{if(!f){f=a[d>>0]|0;if(f<<24>>24>-1){c[h>>2]=f&255;f=f<<24>>24!=0&1;break}g=(c[c[(p3()|0)+188>>2]>>2]|0)==0;f=a[d>>0]|0;if(g){c[h>>2]=f<<24>>24&57343;f=1;break}f=(f&255)+-194|0;if(f>>>0>50){j=17;break}f=c[62384+(f<<2)>>2]|0;g=e+-1|0;if(g){d=d+1|0;j=11}}else{g=e;j=11}b:do if((j|0)==11){b=a[d>>0]|0;m=(b&255)>>>3;if((m+-16|m+(f>>26))>>>0>7){j=17;break a}while(1){d=d+1|0;f=(b&255)+-128|f<<6;g=g+-1|0;if((f|0)>=0)break;if(!g)break b;b=a[d>>0]|0;if((b&-64)<<24>>24!=-128){j=17;break a}}c[i>>2]=0;c[h>>2]=f;f=e-g|0;break a}while(0);c[i>>2]=f;f=-2}}while(0);if((j|0)==17){c[i>>2]=0;c[(d2()|0)>>2]=84;f=-1}l=k;return f|0}function m3(a,b){a=a|0;b=b|0;if(!a)a=0;else a=n3(a,b,0)|0;return a|0}function n3(b,d,e){b=b|0;d=d|0;e=e|0;do if(b){if(d>>>0<128){a[b>>0]=d;b=1;break}if(!(c[c[(o3()|0)+188>>2]>>2]|0))if((d&-128|0)==57216){a[b>>0]=d;b=1;break}else{c[(d2()|0)>>2]=84;b=-1;break}if(d>>>0<2048){a[b>>0]=d>>>6|192;a[b+1>>0]=d&63|128;b=2;break}if(d>>>0<55296|(d&-8192|0)==57344){a[b>>0]=d>>>12|224;a[b+1>>0]=d>>>6&63|128;a[b+2>>0]=d&63|128;b=3;break}if((d+-65536|0)>>>0<1048576){a[b>>0]=d>>>18|240;a[b+1>>0]=d>>>12&63|128;a[b+2>>0]=d>>>6&63|128;a[b+3>>0]=d&63|128;b=4;break}else{c[(d2()|0)>>2]=84;b=-1;break}}else b=1;while(0);return b|0}function o3(){return v2()|0}function p3(){return v2()|0}function q3(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0;p=l;l=l+272|0;n=p+8|0;o=p;do if(!(a[d>>0]|0)){d=Ta(152833)|0;if(d|0?a[d>>0]|0:0)break;d=Ta(152840+(b*12|0)|0)|0;if(d|0?a[d>>0]|0:0)break;d=Ta(152912)|0;if(d|0?a[d>>0]|0:0)break;d=152917}while(0);e=0;a:do{switch(a[d+e>>0]|0){case 47:case 0:break a;default:{}}e=e+1|0}while(e>>>0<15);f=a[d>>0]|0;if(f<<24>>24!=46?(a[d+e>>0]|0)==0:0)if(f<<24>>24==67)m=15;else{k=d;m=16}else{d=152917;m=15}if((m|0)==15)if(!(a[d+1>>0]|0))m=18;else{k=d;m=16}b:do if((m|0)==16)if((s2(k,152917)|0)!=0?(s2(k,152925)|0)!=0:0){d=c[47661]|0;if(d|0)do{if(!(s2(k,d+8|0)|0))break b;d=c[d+24>>2]|0}while((d|0)!=0);ta(190648);d=c[47661]|0;c:do if(d|0){while(1){if(!(s2(k,d+8|0)|0))break;d=c[d+24>>2]|0;if(!d)break c}La(190648);break b}while(0);d:do if(((c[47646]|0)==0?(g=Ta(152931)|0,(g|0)!=0):0)?(a[g>>0]|0)!=0:0){i=254-e|0;j=e+1|0;f=g;while(1){h=k2(f,58)|0;d=a[h>>0]|0;g=h-f+((d<<24>>24!=0)<<31>>31)|0;if(g>>>0<i>>>0){W6(n|0,f|0,g|0)|0;f=n+g|0;a[f>>0]=47;W6(f+1|0,k|0,e|0)|0;a[n+(j+g)>>0]=0;f=ua(n|0,o|0)|0;if(f|0)break;d=a[h>>0]|0}f=h+(d<<24>>24!=0&1)|0;if(!(a[f>>0]|0)){m=41;break d}}d=T1(28)|0;if(!d){h2(f,c[o>>2]|0)|0;m=41;break}else{c[d>>2]=f;c[d+4>>2]=c[o>>2];o=d+8|0;W6(o|0,k|0,e|0)|0;a[o+e>>0]=0;c[d+24>>2]=c[47661];c[47661]=d;break}}else m=41;while(0);if((m|0)==41){d=T1(28)|0;if(d){c[d>>2]=c[15584];c[d+4>>2]=c[15585];o=d+8|0;W6(o|0,k|0,e|0)|0;a[o+e>>0]=0;c[d+24>>2]=c[47661];c[47661]=d}}La(190648);d=(b|0)==0&(d|0)==0?62336:d}else{d=k;m=18}while(0);do if((m|0)==18){if((b|0)==0?(a[d+1>>0]|0)==46:0){d=62336;break}d=0}while(0);l=p;return d|0}function r3(a,b){a=a|0;b=b|0;s3(a,b)|0;return a|0}function s3(b,d){b=b|0;d=d|0;var e=0,f=0;e=d;a:do if(!((e^b)&3)){if(e&3)do{e=a[d>>0]|0;a[b>>0]=e;if(!(e<<24>>24))break a;d=d+1|0;b=b+1|0}while((d&3|0)!=0);e=c[d>>2]|0;if(!((e&-2139062144^-2139062144)&e+-16843009)){f=b;while(1){d=d+4|0;b=f+4|0;c[f>>2]=e;e=c[d>>2]|0;if((e&-2139062144^-2139062144)&e+-16843009|0)break;else f=b}}f=8}else f=8;while(0);if((f|0)==8){f=a[d>>0]|0;a[b>>0]=f;if(f<<24>>24)do{d=d+1|0;b=b+1|0;f=a[d>>0]|0;a[b>>0]=f}while(f<<24>>24!=0)}return b|0}function t3(a){a=a|0;return ((a|0)==32|(a|0)==9)&1|0}function u3(a){a=a|0;if(!a)a=0;else a=(v3(63500,a)|0)!=0&1;return a|0}function v3(a,b){a=a|0;b=b|0;var d=0;if(!b)a=a+((i3(a)|0)<<2)|0;else{while(1){d=c[a>>2]|0;if((d|0)==0|(d|0)==(b|0))break;else a=a+4|0}a=d|0?a:0}return a|0}function w3(a,b){a=a|0;b=b|0;do switch(b|0){case 1:{a=x3(a)|0;break}case 2:{a=h3(a)|0;break}case 3:{a=y3(a)|0;break}case 4:{a=z3(a)|0;break}case 5:{a=$2(a)|0;break}case 6:{a=A3(a)|0;break}case 7:{a=B3(a)|0;break}case 8:{a=k3(a)|0;break}case 9:{a=P2(a)|0;break}case 10:{a=u3(a)|0;break}case 11:{a=C3(a)|0;break}case 12:{a=D3(a)|0;break}default:a=0}while(0);return a|0}function x3(a){a=a|0;if(!($2(a)|0))a=(h3(a)|0)!=0&1;else a=1;return a|0}function y3(a){a=a|0;return t3(a)|0}function z3(a){a=a|0;return ((a+-65529|0)>>>0<3|((a&-2|0)==8232|(a>>>0<32|(a+-127|0)>>>0<33)))&1|0}function A3(a){a=a|0;if(!(u3(a)|0))a=(k3(a)|0)!=0&1;else a=0;return a|0}function B3(a){a=a|0;return (f3(a)|0)!=(a|0)|0}function C3(a){a=a|0;return (E3(a)|0)!=(a|0)|0}function D3(a){a=a|0;return ((a+-48|0)>>>0<10|((a|32)+-97|0)>>>0<6)&1|0}function E3(a){a=a|0;return g3(a,1)|0}function F3(a){a=a|0;return (a+-97|0)>>>0<26|0}function G3(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+128|0;g=n+124|0;m=n;h=m;i=63588;j=h+124|0;do{c[h>>2]=c[i>>2];h=h+4|0;i=i+4|0}while((h|0)<(j|0));if((d+-1|0)>>>0>2147483646)if(!d){b=g;d=1;k=4}else{c[(d2()|0)>>2]=75;d=-1}else k=4;if((k|0)==4){k=-2-b|0;k=d>>>0>k>>>0?k:d;c[m+48>>2]=k;g=m+20|0;c[g>>2]=b;c[m+44>>2]=b;d=b+k|0;b=m+16|0;c[b>>2]=d;c[m+28>>2]=d;d=H3(m,e,f)|0;if(k){m=c[g>>2]|0;a[m+(((m|0)==(c[b>>2]|0))<<31>>31)>>0]=0}}l=n;return d|0}function H3(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+224|0;n=s+120|0;p=s+80|0;q=s;r=s+136|0;f=p;g=f+40|0;do{c[f>>2]=0;f=f+4|0}while((f|0)<(g|0));c[n>>2]=c[e>>2];if((I3(0,d,n,q,p)|0)<0)e=-1;else{if((c[b+76>>2]|0)>-1)o=J3(b)|0;else o=0;e=c[b>>2]|0;m=e&32;if((a[b+74>>0]|0)<1)c[b>>2]=e&-33;f=b+48|0;if(!(c[f>>2]|0)){g=b+44|0;h=c[g>>2]|0;c[g>>2]=r;i=b+28|0;c[i>>2]=r;j=b+20|0;c[j>>2]=r;c[f>>2]=80;k=b+16|0;c[k>>2]=r+80;e=I3(b,d,n,q,p)|0;if(h){nb[c[b+36>>2]&63](b,0,0)|0;e=(c[j>>2]|0)==0?-1:e;c[g>>2]=h;c[f>>2]=0;c[k>>2]=0;c[i>>2]=0;c[j>>2]=0}}else e=I3(b,d,n,q,p)|0;f=c[b>>2]|0;c[b>>2]=f|m;if(o|0)K3(b);e=(f&32|0)==0?e:-1}l=s;return e|0}function I3(d,e,f,g,i){d=d|0;e=e|0;f=f|0;g=g|0;i=i|0;var j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0;H=l;l=l+64|0;D=H+16|0;E=H;B=H+24|0;F=H+8|0;G=H+20|0;c[D>>2]=e;x=(d|0)!=0;y=B+40|0;A=y;B=B+39|0;C=F+4|0;k=0;j=0;p=0;a:while(1){do if((j|0)>-1)if((k|0)>(2147483647-j|0)){c[(d2()|0)>>2]=75;j=-1;break}else{j=k+j|0;break}while(0);k=a[e>>0]|0;if(!(k<<24>>24)){w=86;break}else m=e;b:while(1){switch(k<<24>>24){case 37:{k=m;w=9;break b}case 0:{k=m;break b}default:{}}v=m+1|0;c[D>>2]=v;k=a[v>>0]|0;m=v}c:do if((w|0)==9)while(1){w=0;if((a[m+1>>0]|0)!=37)break c;k=k+1|0;m=m+2|0;c[D>>2]=m;if((a[m>>0]|0)==37)w=9;else break}while(0);k=k-e|0;if(x)L3(d,e,k);if(k|0){e=m;continue}n=m+1|0;k=(a[n>>0]|0)+-48|0;if(k>>>0<10){v=(a[m+2>>0]|0)==36;t=v?k:-1;p=v?1:p;n=v?m+3|0:n}else t=-1;c[D>>2]=n;k=a[n>>0]|0;v=(k<<24>>24)+-32|0;if(v>>>0>31|(1<<v&75913|0)==0)m=0;else{m=0;do{m=1<<(k<<24>>24)+-32|m;n=n+1|0;c[D>>2]=n;k=a[n>>0]|0;v=(k<<24>>24)+-32|0}while(!(v>>>0>31|(1<<v&75913|0)==0))}if(k<<24>>24==42){o=n+1|0;k=(a[o>>0]|0)+-48|0;if(k>>>0<10?(a[n+2>>0]|0)==36:0){c[i+(k<<2)>>2]=10;k=c[g+((a[o>>0]|0)+-48<<3)>>2]|0;p=1;n=n+3|0}else{if(p|0){j=-1;break}if(x){p=(c[f>>2]|0)+(4-1)&~(4-1);k=c[p>>2]|0;c[f>>2]=p+4;p=0;n=o}else{k=0;p=0;n=o}}c[D>>2]=n;u=(k|0)<0;k=u?0-k|0:k;u=u?m|8192:m}else{k=M3(D)|0;if((k|0)<0){j=-1;break}u=m;n=c[D>>2]|0}do if((a[n>>0]|0)==46){if((a[n+1>>0]|0)!=42){c[D>>2]=n+1;m=M3(D)|0;n=c[D>>2]|0;break}o=n+2|0;m=(a[o>>0]|0)+-48|0;if(m>>>0<10?(a[n+3>>0]|0)==36:0){c[i+(m<<2)>>2]=10;m=c[g+((a[o>>0]|0)+-48<<3)>>2]|0;n=n+4|0;c[D>>2]=n;break}if(p|0){j=-1;break a}if(x){v=(c[f>>2]|0)+(4-1)&~(4-1);m=c[v>>2]|0;c[f>>2]=v+4}else m=0;c[D>>2]=o;n=o}else m=-1;while(0);s=0;while(1){if(((a[n>>0]|0)+-65|0)>>>0>57){j=-1;break a}v=n+1|0;c[D>>2]=v;o=a[(a[n>>0]|0)+-65+(152944+(s*58|0))>>0]|0;q=o&255;if((q+-1|0)>>>0<8){s=q;n=v}else break}if(!(o<<24>>24)){j=-1;break}r=(t|0)>-1;do if(o<<24>>24==19)if(r){j=-1;break a}else w=48;else{if(r){c[i+(t<<2)>>2]=q;r=g+(t<<3)|0;t=c[r+4>>2]|0;w=E;c[w>>2]=c[r>>2];c[w+4>>2]=t;w=48;break}if(!x){j=0;break a}N3(E,q,f)}while(0);if((w|0)==48?(w=0,!x):0){k=0;e=v;continue}n=a[n>>0]|0;n=(s|0)!=0&(n&15|0)==3?n&-33:n;t=u&-65537;u=(u&8192|0)==0?u:t;d:do switch(n|0){case 110:switch((s&255)<<24>>24){case 0:{c[c[E>>2]>>2]=j;k=0;e=v;continue a}case 1:{c[c[E>>2]>>2]=j;k=0;e=v;continue a}case 2:{k=c[E>>2]|0;c[k>>2]=j;c[k+4>>2]=((j|0)<0)<<31>>31;k=0;e=v;continue a}case 3:{b[c[E>>2]>>1]=j;k=0;e=v;continue a}case 4:{a[c[E>>2]>>0]=j;k=0;e=v;continue a}case 6:{c[c[E>>2]>>2]=j;k=0;e=v;continue a}case 7:{k=c[E>>2]|0;c[k>>2]=j;c[k+4>>2]=((j|0)<0)<<31>>31;k=0;e=v;continue a}default:{k=0;e=v;continue a}}case 112:{n=120;m=m>>>0>8?m:8;e=u|8;w=60;break}case 88:case 120:{e=u;w=60;break}case 111:{n=E;e=c[n>>2]|0;n=c[n+4>>2]|0;r=P3(e,n,y)|0;s=A-r|0;o=0;q=153408;m=(u&8|0)==0|(m|0)>(s|0)?m:s+1|0;s=u;w=66;break}case 105:case 100:{n=E;e=c[n>>2]|0;n=c[n+4>>2]|0;if((n|0)<0){e=K6(0,0,e|0,n|0)|0;n=z;o=E;c[o>>2]=e;c[o+4>>2]=n;o=1;q=153408;w=65;break d}else{o=(u&2049|0)!=0&1;q=(u&2048|0)==0?((u&1|0)==0?153408:153410):153409;w=65;break d}}case 117:{n=E;o=0;q=153408;e=c[n>>2]|0;n=c[n+4>>2]|0;w=65;break}case 99:{a[B>>0]=c[E>>2];e=B;o=0;q=153408;r=y;n=1;m=t;break}case 109:{n=t2(c[(d2()|0)>>2]|0)|0;w=70;break}case 115:{n=c[E>>2]|0;n=n|0?n:153418;w=70;break}case 67:{c[F>>2]=c[E>>2];c[C>>2]=0;c[E>>2]=F;r=-1;n=F;w=74;break}case 83:{e=c[E>>2]|0;if(!m){R3(d,32,k,0,u);e=0;w=83}else{r=m;n=e;w=74}break}case 65:case 71:case 70:case 69:case 97:case 103:case 102:case 101:{k=S3(d,+h[E>>3],k,m,u,n)|0;e=v;continue a}default:{o=0;q=153408;r=y;n=m;m=u}}while(0);e:do if((w|0)==60){u=E;t=c[u>>2]|0;u=c[u+4>>2]|0;r=O3(t,u,y,n&32)|0;q=(e&8|0)==0|(t|0)==0&(u|0)==0;o=q?0:2;q=q?153408:153408+(n>>4)|0;s=e;e=t;n=u;w=66}else if((w|0)==65){r=Q3(e,n,y)|0;s=u;w=66}else if((w|0)==70){w=0;u=U2(n,0,m)|0;s=(u|0)==0;e=n;o=0;q=153408;r=s?n+m|0:u;n=s?m:u-n|0;m=t}else if((w|0)==74){w=0;q=n;e=0;m=0;while(1){o=c[q>>2]|0;if(!o)break;m=m3(G,o)|0;if((m|0)<0|m>>>0>(r-e|0)>>>0)break;e=m+e|0;if(r>>>0>e>>>0)q=q+4|0;else break}if((m|0)<0){j=-1;break a}R3(d,32,k,e,u);if(!e){e=0;w=83}else{o=0;while(1){m=c[n>>2]|0;if(!m){w=83;break e}m=m3(G,m)|0;o=m+o|0;if((o|0)>(e|0)){w=83;break e}L3(d,G,m);if(o>>>0>=e>>>0){w=83;break}else 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f=0,g=0,h=0,i=0,j=0,k=0,m=0;m=l;l=l+16|0;j=m;k=e&255;a[j>>0]=k;g=b+16|0;h=c[g>>2]|0;if(!h)if(!(V3(b)|0)){h=c[g>>2]|0;i=4}else f=-1;else i=4;do if((i|0)==4){i=b+20|0;g=c[i>>2]|0;if(g>>>0<h>>>0?(f=e&255,(f|0)!=(a[b+75>>0]|0)):0){c[i>>2]=g+1;a[g>>0]=k;break}if((nb[c[b+36>>2]&63](b,j,1)|0)==1)f=d[j>>0]|0;else f=-1}while(0);l=m;return f|0}function W4(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=l;l=l+16|0;f=e;c[f>>2]=a;c[f+4>>2]=b;c[f+8>>2]=d;d=c2(Fa(3,f|0)|0)|0;l=e;return d|0}function X4(){var a=0,b=0;b=l;l=l+16|0;a=Da(20,b|0)|0;l=b;return a|0}function Y4(b,c){b=b|0;c=c|0;var d=0,e=0,f=0;d=b;e=153565;f=d+15|0;do{a[d>>0]=a[e>>0]|0;d=d+1|0;e=e+1|0}while((d|0)<(f|0));if(!c){a[b+14>>0]=48;a[b+15>>0]=0}else{e=c;d=14;while(1){d=d+1|0;if(e>>>0<10)break;else e=(e>>>0)/10|0}a[b+d>>0]=0;while(1){d=d+-1|0;a[b+d>>0]=(c>>>0)%10|0|48;if(c>>>0<10)break;else c=(c>>>0)/10|0}}return}function Z4(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0;g=l;l=l+48|0;f=g+40|0;e=g+8|0;d=g;c[d>>2]=a;c[d+4>>2]=b;d=Ca(197,d|0)|0;if((d|0)==-9?(c[e>>2]=a,c[e+4>>2]=1,(Ea(221,e|0)|0)>=0):0){Y4(e,a);c[f>>2]=e;c[f+4>>2]=b;b=c2(Ba(195,f|0)|0)|0}else b=c2(d)|0;l=g;return b|0}function _4(a,b){a=a|0;b=b|0;var d=0,e=0;d=l;l=l+16|0;e=d;c[e>>2]=a;c[e+4>>2]=b;b=c2(Ga(33,e|0)|0)|0;l=d;return b|0}function $4(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0;i=l;l=l+48|0;h=i+32|0;g=i+16|0;e=i;if(U2(153580,a[d>>0]|0,4)|0){f=a5(d)|0;c[e>>2]=b;c[e+4>>2]=f|32768;c[e+8>>2]=438;e=c2(Ha(5,e|0)|0)|0;if((e|0)>=0){if(f&524288|0){c[g>>2]=e;c[g+4>>2]=2;c[g+8>>2]=1;Ea(221,g|0)|0}b=P4(e,d)|0;if(!b){c[h>>2]=e;Ja(6,h|0)|0;b=0}}else b=0}else{c[(d2()|0)>>2]=22;b=0}l=i;return b|0}function a5(b){b=b|0;var c=0,d=0,e=0;d=(R2(b,43)|0)==0;c=a[b>>0]|0;d=d?c<<24>>24!=114&1:2;e=(R2(b,120)|0)==0;d=e?d:d|128;b=(R2(b,101)|0)==0;b=b?d:d|524288;b=c<<24>>24==114?b:b|64;b=c<<24>>24==119?b|512:b;return (c<<24>>24==97?b|1024:b)|0}function b5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=l;l=l+16|0;f=e;c[f>>2]=d;d=c5(a,b,f)|0;l=e;return d|0}function c5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;g=l;l=l+128|0;e=g;f=e;h=f+124|0;do{c[f>>2]=0;f=f+4|0}while((f|0)<(h|0));c[e+32>>2]=41;c[e+44>>2]=a;c[e+76>>2]=-1;c[e+84>>2]=a;h=e5(e,b,d)|0;l=g;return h|0}function d5(a,b,c){a=a|0;b=b|0;c=c|0;return m5(a,b,c)|0}function e5(b,e,f){b=b|0;e=e|0;f=f|0;var i=0,j=0,k=0,m=0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0;P=l;l=l+288|0;J=P+8|0;K=P+17|0;L=P;M=P+16|0;if((c[b+76>>2]|0)>-1)O=J3(b)|0;else O=0;i=a[e>>0]|0;a:do if(i<<24>>24){B=b+4|0;C=b+100|0;D=b+108|0;E=b+8|0;F=K+10|0;G=K+33|0;A=J+4|0;H=K+1|0;I=K+1|0;m=e;t=0;e=0;k=0;j=0;b:while(1){c:do if(!(S2(i&255)|0)){i=i<<24>>24==37;d:do if(i){p=m+1|0;n=a[p>>0]|0;e:do switch(n<<24>>24){case 37:break d;case 42:{y=0;m=m+2|0;break}default:{i=(n&255)+-48|0;if(i>>>0<10?(a[m+2>>0]|0)==36:0){y=f5(f,i)|0;m=m+3|0;break e}m=(c[f>>2]|0)+(4-1)&~(4-1);y=c[m>>2]|0;c[f>>2]=m+4;m=p}}while(0);i=a[m>>0]|0;n=i&255;if((n+-48|0)>>>0<10){p=0;do{p=(p*10|0)+-48+n|0;m=m+1|0;i=a[m>>0]|0;n=i&255}while((n+-48|0)>>>0<10)}else p=0;i=i<<24>>24==109;x=(y|0)!=0;k=i?0:k;j=i?0:j;m=i?m+1|0:m;i=x&i;n=m+1|0;switch(a[m>>0]|0){case 104:{w=(a[n>>0]|0)==104;q=w?-2:-1;m=w?m+2|0:n;break}case 108:{w=(a[n>>0]|0)==108;q=w?3:1;m=w?m+2|0:n;break}case 106:{q=3;m=n;break}case 116:case 122:{q=1;m=n;break}case 76:{q=2;m=n;break}case 110:case 112:case 67:case 83:case 91:case 99:case 115:case 88:case 71:case 70:case 69:case 65:case 103:case 102:case 101:case 97:case 120:case 117:case 111:case 105:case 100:{q=0;break}default:{N=135;break b}}s=d[m>>0]|0;u=(s&47|0)==3;s=u?s|32:s;u=u?1:q;r=s&255;switch(r<<24>>24){case 99:{p=(p|0)>1?p:1;w=t;break}case 91:{w=t;break}case 110:{g5(y,u,t,((t|0)<0)<<31>>31);n=t;break c}default:{W2(b,0);do{n=c[B>>2]|0;if(n>>>0<(c[C>>2]|0)>>>0){c[B>>2]=n+1;n=d[n>>0]|0}else n=Y2(b)|0}while((S2(n)|0)!=0);if(!(c[C>>2]|0))n=c[B>>2]|0;else{n=(c[B>>2]|0)+-1|0;c[B>>2]=n}w=(c[D>>2]|0)+t+n-(c[E>>2]|0)|0}}W2(b,p);n=c[B>>2]|0;q=c[C>>2]|0;if(n>>>0<q>>>0)c[B>>2]=n+1;else{if((Y2(b)|0)<0){N=135;break b}q=c[C>>2]|0}if(q|0)c[B>>2]=(c[B>>2]|0)+-1;f:do switch(r<<24>>24){case 91:case 99:case 115:{v=(s|0)==99;g:do if((s|16|0)==115){Y6(H|0,-1,256)|0;a[K>>0]=0;if((s|0)==115){a[G>>0]=0;a[F>>0]=0;a[F+1>>0]=0;a[F+2>>0]=0;a[F+3>>0]=0;a[F+4>>0]=0}}else{s=m+1|0;n=(a[s>>0]|0)==94;t=n&1;m=n?m+2|0:s;Y6(I|0,n&1|0,256)|0;a[K>>0]=0;n=a[m>>0]|0;switch(n<<24>>24){case 45:{n=46;N=63;break}case 93:{n=94;N=63;break}default:{}}while(1){if((N|0)==63){N=0;a[K+n>>0]=t^1;n=m+1|0;m=n;n=a[n>>0]|0}h:do switch(n<<24>>24){case 0:{N=135;break b}case 93:break g;case 45:{s=m+1|0;n=a[s>>0]|0;switch(n<<24>>24){case 93:case 0:{n=45;break h}default:{}}m=a[m+-1>>0]|0;if((m&255)<(n&255)){r=(t^1)&255;m=m&255;do{m=m+1|0;a[K+m>>0]=r;n=a[s>>0]|0}while((m|0)<(n&255|0));m=s}else m=s;break}default:{}}while(0);n=(n&255)+1|0;N=63}}while(0);n=v?p+1|0:31;s=(u|0)==1;i:do if(s){if(i){j=T1(n<<2)|0;if(!j){k=0;j=0;i=1;N=135;break b}}else j=y;c[J>>2]=0;c[A>>2]=0;r=n;k=0;j:while(1){q=(j|0)==0;do{k:while(1){n=c[B>>2]|0;if(n>>>0<(c[C>>2]|0)>>>0){c[B>>2]=n+1;n=d[n>>0]|0}else n=Y2(b)|0;if(!(a[K+(n+1)>>0]|0))break j;a[M>>0]=n;switch(l3(L,M,1,J)|0){case -1:{k=0;N=135;break b}case -2:break;default:break k}}if(!q){c[j+(k<<2)>>2]=c[L>>2];k=k+1|0}}while(!(i&(k|0)==(r|0)));k=r<<1|1;n=W1(j,k<<2)|0;if(!n){k=0;i=1;N=135;break b}else{u=r;r=k;j=n;k=u}}if(!(h5(J)|0)){k=0;N=135;break b}else{n=k;k=0;r=j}}else{if(i){k=T1(n)|0;if(!k){k=0;j=0;i=1;N=135;break b}else{q=n;j=0}while(1){do{n=c[B>>2]|0;if(n>>>0<(c[C>>2]|0)>>>0){c[B>>2]=n+1;n=d[n>>0]|0}else n=Y2(b)|0;if(!(a[K+(n+1)>>0]|0)){n=j;r=0;j=0;break i}a[k+j>>0]=n;j=j+1|0}while((j|0)!=(q|0));j=q<<1|1;n=W1(k,j)|0;if(!n){j=0;i=1;N=135;break b}else{u=q;q=j;k=n;j=u}}}if(!y){k=q;while(1){j=c[B>>2]|0;if(j>>>0<k>>>0){c[B>>2]=j+1;j=d[j>>0]|0}else j=Y2(b)|0;if(!(a[K+(j+1)>>0]|0)){n=0;k=0;r=0;j=0;break i}k=c[C>>2]|0}}else{n=0;k=q;while(1){j=c[B>>2]|0;if(j>>>0<k>>>0){c[B>>2]=j+1;j=d[j>>0]|0}else j=Y2(b)|0;if(!(a[K+(j+1)>>0]|0)){k=y;r=0;j=0;break i}a[y+n>>0]=j;n=n+1|0;k=c[C>>2]|0}}}while(0);if(!(c[C>>2]|0))q=c[B>>2]|0;else{q=(c[B>>2]|0)+-1|0;c[B>>2]=q}q=q-(c[E>>2]|0)+(c[D>>2]|0)|0;if(!q){N=137;break b}if(!((q|0)==(p|0)|v^1)){N=137;break b}do if(i)if(s){c[y>>2]=r;break}else{c[y>>2]=k;break}while(0);if(!v){if(r|0)c[r+(n<<2)>>2]=0;if(!k){k=0;break f}a[k+n>>0]=0}break}case 120:case 88:case 112:{n=16;N=123;break}case 111:{n=8;N=123;break}case 117:case 100:{n=10;N=123;break}case 105:{n=0;N=123;break}case 71:case 103:case 70:case 102:case 69:case 101:case 65:case 97:{o=+i5(b,u,0);if((c[D>>2]|0)==((c[E>>2]|0)-(c[B>>2]|0)|0)){N=137;break b}if(y)switch(u|0){case 0:{g[y>>2]=o;break f}case 1:{h[y>>3]=o;break f}case 2:{h[y>>3]=o;break f}default:break f}break}default:{}}while(0);do if((N|0)==123){N=0;n=X2(b,n,0,-1,-1)|0;if((c[D>>2]|0)==((c[E>>2]|0)-(c[B>>2]|0)|0)){N=137;break b}if(x&(s|0)==112){c[y>>2]=n;break}else{g5(y,u,n,z);break}}while(0);e=e+(x&1)|0;n=(c[D>>2]|0)+w+(c[B>>2]|0)-(c[E>>2]|0)|0;break c}while(0);m=m+(i&1)|0;W2(b,0);i=c[B>>2]|0;if(i>>>0<(c[C>>2]|0)>>>0){c[B>>2]=i+1;i=d[i>>0]|0}else i=Y2(b)|0;if((i|0)!=(d[m>>0]|0)){N=22;break b}n=t+1|0}else{while(1){i=m+1|0;if(!(S2(d[i>>0]|0)|0))break;else m=i}W2(b,0);do{i=c[B>>2]|0;if(i>>>0<(c[C>>2]|0)>>>0){c[B>>2]=i+1;i=d[i>>0]|0}else i=Y2(b)|0}while((S2(i)|0)!=0);if(!(c[C>>2]|0))i=c[B>>2]|0;else{i=(c[B>>2]|0)+-1|0;c[B>>2]=i}n=(c[D>>2]|0)+t+i-(c[E>>2]|0)|0}while(0);m=m+1|0;i=a[m>>0]|0;if(!(i<<24>>24))break a;else t=n}if((N|0)==22){if(c[C>>2]|0)c[B>>2]=(c[B>>2]|0)+-1;if((e|0)!=0|(i|0)>-1)break;else{i=0;N=136}}else if((N|0)==135){i=i&1;if(!e)N=136}else if((N|0)==137)i=i&1;if((N|0)==136)e=-1;if(i){U1(k);U1(j)}}else e=0;while(0);if(O|0)K3(b);l=P;return e|0}function f5(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;e=l;l=l+16|0;d=e;c[d>>2]=c[a>>2];while(1){f=(c[d>>2]|0)+(4-1)&~(4-1);a=c[f>>2]|0;c[d>>2]=f+4;if(b>>>0>1)b=b+-1|0;else break}l=e;return a|0}function g5(d,e,f,g){d=d|0;e=e|0;f=f|0;g=g|0;a:do if(d|0)switch(e|0){case -2:{a[d>>0]=f;break a}case -1:{b[d>>1]=f;break a}case 0:{c[d>>2]=f;break a}case 1:{c[d>>2]=f;break a}case 3:{e=d;c[e>>2]=f;c[e+4>>2]=g;break a}default:break a}while(0);return}function h5(a){a=a|0;if(!a)a=1;else a=(c[a>>2]|0)==0&1;return a|0}function i5(b,e,f){b=b|0;e=e|0;f=f|0;var g=0.0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;switch(e|0){case 0:{l=-149;m=24;j=4;break}case 1:{l=-1074;m=53;j=4;break}case 2:{l=-1074;m=53;j=4;break}default:g=0.0}a:do if((j|0)==4){o=b+4|0;n=b+100|0;do{e=c[o>>2]|0;if(e>>>0<(c[n>>2]|0)>>>0){c[o>>2]=e+1;e=d[e>>0]|0}else e=Y2(b)|0}while((S2(e)|0)!=0);b:do switch(e|0){case 43:case 45:{i=1-(((e|0)==45&1)<<1)|0;e=c[o>>2]|0;if(e>>>0<(c[n>>2]|0)>>>0){c[o>>2]=e+1;h=d[e>>0]|0;break b}else{h=Y2(b)|0;break b}}default:{h=e;i=1}}while(0);e=0;do{if((h|32|0)!=(a[153584+e>>0]|0))break;do if(e>>>0<7){h=c[o>>2]|0;if(h>>>0<(c[n>>2]|0)>>>0){c[o>>2]=h+1;h=d[h>>0]|0;break}else{h=Y2(b)|0;break}}while(0);e=e+1|0}while(e>>>0<8);c:do switch(e|0){case 8:break;case 3:{j=23;break}default:{k=(f|0)!=0;if(k&e>>>0>3)if((e|0)==8)break c;else{j=23;break c}d:do if(!e){e=0;do{if((h|32|0)!=(a[153593+e>>0]|0))break d;do if(e>>>0<2){h=c[o>>2]|0;if(h>>>0<(c[n>>2]|0)>>>0){c[o>>2]=h+1;h=d[h>>0]|0;break}else{h=Y2(b)|0;break}}while(0);e=e+1|0}while(e>>>0<3)}while(0);switch(e|0){case 3:{e=c[o>>2]|0;if(e>>>0<(c[n>>2]|0)>>>0){c[o>>2]=e+1;e=d[e>>0]|0}else e=Y2(b)|0;if((e|0)==40)e=1;else{if(!(c[n>>2]|0)){g=s;break a}c[o>>2]=(c[o>>2]|0)+-1;g=s;break a}while(1){h=c[o>>2]|0;if(h>>>0<(c[n>>2]|0)>>>0){c[o>>2]=h+1;h=d[h>>0]|0}else h=Y2(b)|0;if(!((h+-48|0)>>>0<10|(h+-65|0)>>>0<26)?!((h|0)==95|(h+-97|0)>>>0<26):0)break;e=e+1|0}if((h|0)==41){g=s;break a}h=(c[n>>2]|0)==0;if(!h)c[o>>2]=(c[o>>2]|0)+-1;if(!k){c[(d2()|0)>>2]=22;W2(b,0);g=0.0;break a}if(!e){g=s;break a}while(1){e=e+-1|0;if(!h)c[o>>2]=(c[o>>2]|0)+-1;if(!e){g=s;break a}}}case 0:{if((h|0)==48){e=c[o>>2]|0;if(e>>>0<(c[n>>2]|0)>>>0){c[o>>2]=e+1;e=d[e>>0]|0}else e=Y2(b)|0;if((e|32|0)==120){g=+j5(b,m,l,i,f);break a}if(!(c[n>>2]|0))e=48;else{c[o>>2]=(c[o>>2]|0)+-1;e=48}}else e=h;g=+k5(b,e,m,l,i,f);break a}default:{if(c[n>>2]|0)c[o>>2]=(c[o>>2]|0)+-1;c[(d2()|0)>>2]=22;W2(b,0);g=0.0;break a}}}}while(0);if((j|0)==23){h=(c[n>>2]|0)==0;if(!h)c[o>>2]=(c[o>>2]|0)+-1;if((f|0)!=0&e>>>0>3)do{if(!h)c[o>>2]=(c[o>>2]|0)+-1;e=e+-1|0}while(e>>>0>3)}g=+(i|0)*t}while(0);return +g}function j5(a,b,e,f,g){a=a|0;b=b|0;e=e|0;f=f|0;g=g|0;var h=0.0,i=0,j=0,k=0.0,l=0,m=0,n=0,o=0.0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;y=a+4|0;i=c[y>>2]|0;x=a+100|0;if(i>>>0<(c[x>>2]|0)>>>0){c[y>>2]=i+1;j=d[i>>0]|0;l=0}else{j=Y2(a)|0;l=0}a:while(1){switch(j|0){case 46:{w=8;break a}case 48:break;default:{s=0;n=0;o=1.0;h=0.0;i=0;m=j;t=l;v=0;u=0;l=0;j=0;break a}}i=c[y>>2]|0;if(i>>>0<(c[x>>2]|0)>>>0){c[y>>2]=i+1;j=d[i>>0]|0;l=1;continue}else{j=Y2(a)|0;l=1;continue}}if((w|0)==8){i=c[y>>2]|0;if(i>>>0<(c[x>>2]|0)>>>0){c[y>>2]=i+1;j=d[i>>0]|0}else j=Y2(a)|0;if((j|0)==48){l=0;j=0;do{i=c[y>>2]|0;if(i>>>0<(c[x>>2]|0)>>>0){c[y>>2]=i+1;m=d[i>>0]|0}else m=Y2(a)|0;l=J6(l|0,j|0,-1,-1)|0;j=z}while((m|0)==48);s=1;n=0;o=1.0;h=0.0;i=0;t=1;v=0;u=0}else{s=1;n=0;o=1.0;h=0.0;i=0;m=j;t=l;v=0;u=0;l=0;j=0}}while(1){q=m+-48|0;p=m|32;if(q>>>0>=10){r=(m|0)==46;if(!(r|(p+-97|0)>>>0<6))break;if(r)if(!s){s=1;k=o;r=t;l=u;j=v;q=u;p=v}else{m=46;break}else w=20}else w=20;if((w|0)==20){w=0;m=(m|0)>57?p+-87|0:q;do 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if(!(c[x>>2]|0)){j=0;i=0}else{c[y>>2]=(c[y>>2]|0)+-1;j=0;i=0}l=R6(q|0,p|0,2)|0;l=J6(l|0,z|0,-32,-1)|0;l=J6(l|0,z|0,j|0,i|0)|0;i=z;if(!n){h=+(f|0)*0.0;break}y=0-e|0;g=((y|0)<0)<<31>>31;if((i|0)>(g|0)|(i|0)==(g|0)&l>>>0>y>>>0){c[(d2()|0)>>2]=34;h=+(f|0)*1797693134862315708145274.0e284*1797693134862315708145274.0e284;break}y=e+-106|0;g=((y|0)<0)<<31>>31;if((i|0)<(g|0)|(i|0)==(g|0)&l>>>0<y>>>0){c[(d2()|0)>>2]=34;h=+(f|0)*2.2250738585072014e-308*2.2250738585072014e-308;break}if((n|0)>-1){j=n;do{y=!(h>=.5);j=j<<1|(y^1)&1;h=h+(y?h:h+-1.0);l=J6(l|0,i|0,-1,-1)|0;i=z}while((j|0)>-1);o=h;m=j}else{o=h;m=n}y=((b|0)<0)<<31>>31;e=K6(32,0,e|0,((e|0)<0)<<31>>31|0)|0;i=J6(e|0,z|0,l|0,i|0)|0;e=z;if((e|0)<(y|0)|(e|0)==(y|0)&i>>>0<b>>>0)if((i|0)>0)w=59;else{j=0;i=84;w=61}else{i=b;w=59}if((w|0)==59)if((i|0)<53){j=i;i=84-i|0;w=61}else{k=0.0;h=+(f|0)}if((w|0)==61){h=+(f|0);k=+G2(+D2(1.0,i),h);i=j}f=(m&1|0)==0&(o!=0.0&(i|0)<32);h=(f?0.0:o)*h+(k+h*+((m+(f&1)|0)>>>0))-k;if(!(h!=0.0))c[(d2()|0)>>2]=34;h=+H2(h,l)}while(0);return +h}function k5(a,b,e,f,g,h){a=a|0;b=b|0;e=e|0;f=f|0;g=g|0;h=h|0;var i=0.0,j=0.0,k=0,m=0,n=0,o=0,p=0,q=0,r=0.0,s=0.0,t=0.0,u=0,v=0,w=0,x=0,y=0,A=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0.0;J=l;l=l+512|0;G=J;H=f+e|0;I=0-H|0;D=a+4|0;E=a+100|0;k=0;a:while(1){switch(b|0){case 46:{A=6;break a}case 48:break;default:{v=0;p=k;q=0;o=0;break a}}b=c[D>>2]|0;if(b>>>0<(c[E>>2]|0)>>>0){c[D>>2]=b+1;b=d[b>>0]|0;k=1;continue}else{b=Y2(a)|0;k=1;continue}}if((A|0)==6){b=c[D>>2]|0;if(b>>>0<(c[E>>2]|0)>>>0){c[D>>2]=b+1;b=d[b>>0]|0}else b=Y2(a)|0;if((b|0)==48){k=0;b=0;while(1){k=J6(k|0,b|0,-1,-1)|0;o=z;b=c[D>>2]|0;if(b>>>0<(c[E>>2]|0)>>>0){c[D>>2]=b+1;b=d[b>>0]|0}else b=Y2(a)|0;if((b|0)==48)b=o;else{v=1;p=1;q=k;break}}}else{v=1;p=k;q=0;o=0}}c[G>>2]=0;n=b+-48|0;m=(b|0)==46;b:do if(m|n>>>0<10){C=G+496|0;w=0;k=0;u=0;x=v;y=p;A=n;p=0;n=0;c:while(1){do if(m)if(!x){x=1;q=p;o=n}else break c;else{p=J6(p|0,n|0,1,0)|0;n=z;v=(b|0)!=48;if((k|0)>=125){if(!v)break;c[C>>2]=c[C>>2]|1;break}m=G+(k<<2)|0;if(!w)b=A;else b=b+-48+((c[m>>2]|0)*10|0)|0;c[m>>2]=b;w=w+1|0;y=(w|0)==9;w=y?0:w;k=k+(y&1)|0;u=v?p:u;y=1}while(0);b=c[D>>2]|0;if(b>>>0<(c[E>>2]|0)>>>0){c[D>>2]=b+1;b=d[b>>0]|0}else b=Y2(a)|0;A=b+-48|0;m=(b|0)==46;if(!(m|A>>>0<10)){v=x;m=y;A=29;break b}}b=w;m=(y|0)!=0;A=37}else{w=0;k=0;u=0;m=p;p=0;n=0;A=29}while(0);do if((A|0)==29){C=(v|0)==0;q=C?p:q;o=C?n:o;m=(m|0)!=0;if(!(m&(b|32|0)==101))if((b|0)>-1){b=w;A=37;break}else{b=w;A=39;break}m=l5(a,h)|0;b=z;if((m|0)==0&(b|0)==-2147483648){if(!h){W2(a,0);i=0.0;break}if(!(c[E>>2]|0)){m=0;b=0}else{c[D>>2]=(c[D>>2]|0)+-1;m=0;b=0}}y=J6(m|0,b|0,q|0,o|0)|0;b=w;o=z;A=41}while(0);if((A|0)==37)if(c[E>>2]|0){c[D>>2]=(c[D>>2]|0)+-1;if(m){y=q;A=41}else A=40}else A=39;if((A|0)==39)if(m){y=q;A=41}else A=40;do if((A|0)==40){c[(d2()|0)>>2]=22;W2(a,0);i=0.0}else if((A|0)==41){m=c[G>>2]|0;if(!m){i=+(g|0)*0.0;break}if(((n|0)<0|(n|0)==0&p>>>0<10)&((y|0)==(p|0)&(o|0)==(n|0))?(e|0)>30|(m>>>e|0)==0:0){i=+(g|0)*+(m>>>0);break}a=(f|0)/-2|0;E=((a|0)<0)<<31>>31;if((o|0)>(E|0)|(o|0)==(E|0)&y>>>0>a>>>0){c[(d2()|0)>>2]=34;i=+(g|0)*1797693134862315708145274.0e284*1797693134862315708145274.0e284;break}a=f+-106|0;E=((a|0)<0)<<31>>31;if((o|0)<(E|0)|(o|0)==(E|0)&y>>>0<a>>>0){c[(d2()|0)>>2]=34;i=+(g|0)*2.2250738585072014e-308*2.2250738585072014e-308;break}if(b){if((b|0)<9){n=G+(k<<2)|0;m=c[n>>2]|0;while(1){m=m*10|0;if((b|0)>=8)break;else b=b+1|0}c[n>>2]=m}k=k+1|0}if((u|0)<9?(u|0)<=(y|0)&(y|0)<18:0){if((y|0)==9){i=+(g|0)*+((c[G>>2]|0)>>>0);break}if((y|0)<9){i=+(g|0)*+((c[G>>2]|0)>>>0)/+(c[63712+(8-y<<2)>>2]|0);break}a=e+27+(O(y,-3)|0)|0;b=c[G>>2]|0;if((a|0)>30|(b>>>a|0)==0){i=+(g|0)*+(b>>>0)*+(c[63712+(y+-10<<2)>>2]|0);break}}b=(y|0)%9|0;if(!b){b=0;n=0}else{u=(y|0)>-1?b:b+9|0;p=c[63712+(8-u<<2)>>2]|0;if(k){q=1e9/(p|0)|0;n=0;o=0;m=y;b=0;do{D=G+(b<<2)|0;E=c[D>>2]|0;a=((E>>>0)/(p>>>0)|0)+n|0;c[D>>2]=a;n=O(q,(E>>>0)%(p>>>0)|0)|0;a=(b|0)==(o|0)&(a|0)==0;m=a?m+-9|0:m;o=a?o+1&127:o;b=b+1|0}while((b|0)!=(k|0));if(!n)n=o;else{c[G+(k<<2)>>2]=n;n=o;k=k+1|0}}else{n=0;k=0;m=y}b=0;y=9-u+m|0}d:while(1){u=(y|0)<18;v=(y|0)==18;w=G+(n<<2)|0;while(1){if(!u){if(!v){m=y;break d}if((c[w>>2]|0)>>>0>=9007199){m=18;break d}}m=0;x=k;k=k+127|0;while(1){o=k&127;p=G+(o<<2)|0;k=R6(c[p>>2]|0,0,29)|0;k=J6(k|0,z|0,m|0,0)|0;m=z;if(m>>>0>0|(m|0)==0&k>>>0>1e9){q=N6(k|0,m|0,1e9,0)|0;k=O6(k|0,m|0,1e9,0)|0}else q=0;c[p>>2]=k;a=(o|0)==(n|0);x=(k|0)==0&(((o|0)!=(x+127&127|0)|a)^1)?o:x;if(a)break;else{m=q;k=o+-1|0}}b=b+-29|0;if(q|0)break;else k=x}n=n+127&127;k=x+127&127;m=G+((x+126&127)<<2)|0;if((n|0)==(x|0))c[m>>2]=c[m>>2]|c[G+(k<<2)>>2];else k=x;c[G+(n<<2)>>2]=q;y=y+9|0}e:while(1){w=k+1&127;x=G+((k+127&127)<<2)|0;while(1){q=(m|0)==18;v=(m|0)>27?9:1;y=n;while(1){p=0;while(1){n=p+y&127;if((n|0)==(k|0)){F=2;A=88;break}n=c[G+(n<<2)>>2]|0;o=c[63744+(p<<2)>>2]|0;if(n>>>0<o>>>0){F=2;A=88;break}if(n>>>0>o>>>0)break;n=p+1|0;if((p|0)<1)p=n;else{F=n;A=88;break}}if((A|0)==88?(A=0,q&(F|0)==2):0){i=0.0;o=0;break e}b=v+b|0;if((y|0)==(k|0))y=k;else break}q=(1<<v)+-1|0;u=1e9>>>v;p=0;n=y;o=y;do{D=G+(o<<2)|0;E=c[D>>2]|0;a=(E>>>v)+p|0;c[D>>2]=a;p=O(E&q,u)|0;a=(o|0)==(n|0)&(a|0)==0;m=a?m+-9|0:m;n=a?n+1&127:n;o=o+1&127}while((o|0)!=(k|0));if(!p)continue;if((w|0)!=(n|0))break;c[x>>2]=c[x>>2]|1}c[G+(k<<2)>>2]=p;k=w}do{n=o+y&127;m=k+1&127;if((n|0)==(k|0)){c[G+(m+-1<<2)>>2]=0;k=m}i=i*1.0e9+ 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e=Y2(a)|0;if((b|0)!=0&(e+-48|0)>>>0>9?(c[i>>2]|0)!=0:0)c[h>>2]=(c[h>>2]|0)+-1;break}default:f=0}if((e+-48|0)>>>0>9)if(!(c[i>>2]|0)){f=-2147483648;e=0}else{c[h>>2]=(c[h>>2]|0)+-1;f=-2147483648;e=0}else{g=0;do{g=e+-48+(g*10|0)|0;e=c[h>>2]|0;if(e>>>0<(c[i>>2]|0)>>>0){c[h>>2]=e+1;e=d[e>>0]|0}else e=Y2(a)|0}while((e+-48|0)>>>0<10&(g|0)<214748364);b=((g|0)<0)<<31>>31;if((e+-48|0)>>>0<10)do{b=I6(g|0,b|0,10,0)|0;g=z;e=J6(e|0,((e|0)<0)<<31>>31|0,-48,-1)|0;g=J6(e|0,z|0,b|0,g|0)|0;b=z;e=c[h>>2]|0;if(e>>>0<(c[i>>2]|0)>>>0){c[h>>2]=e+1;e=d[e>>0]|0}else e=Y2(a)|0}while((e+-48|0)>>>0<10&((b|0)<21474836|(b|0)==21474836&g>>>0<2061584302));if((e+-48|0)>>>0<10)do{e=c[h>>2]|0;if(e>>>0<(c[i>>2]|0)>>>0){c[h>>2]=e+1;e=d[e>>0]|0}else e=Y2(a)|0}while((e+-48|0)>>>0<10);if(c[i>>2]|0)c[h>>2]=(c[h>>2]|0)+-1;i=(f|0)!=0;e=K6(0,0,g|0,b|0)|0;f=i?z:b;e=i?e:g}z=f;return e|0}function m5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;e=a+84|0;g=c[e>>2]|0;h=d+256|0;f=U2(g,0,h)|0;f=(f|0)==0?h:f-g|0;d=f>>>0<d>>>0?f:d;W6(b|0,g|0,d|0)|0;c[a+4>>2]=g+d;b=g+f|0;c[a+8>>2]=b;c[e>>2]=b;return d|0}function n5(a,b,c){a=a|0;b=b|0;c=c|0;return o5(a,b,c)|0}function o5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;if((c[a+76>>2]|0)>-1){e=(J3(a)|0)==0;b=p5(a,b,d)|0;if(!e)K3(a)}else b=p5(a,b,d)|0;return b|0}function p5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;if((d|0)==1)b=b-(c[a+8>>2]|0)+(c[a+4>>2]|0)|0;e=a+20|0;f=a+28|0;if((c[e>>2]|0)>>>0>(c[f>>2]|0)>>>0?(nb[c[a+36>>2]&63](a,0,0)|0,(c[e>>2]|0)==0):0)b=-1;else{c[a+16>>2]=0;c[f>>2]=0;c[e>>2]=0;if((nb[c[a+40>>2]&63](a,b,d)|0)<0)b=-1;else{c[a+8>>2]=0;c[a+4>>2]=0;c[a>>2]=c[a>>2]&-17;b=0}}return b|0}function q5(b){b=b|0;var c=0,d=0,e=0,f=0,g=0,h=0;while(1){e=a[b>>0]|0;c=e<<24>>24;f=b+1|0;if(!(S2(c)|0))break;else b=f}switch(c|0){case 45:{b=1;g=5;break}case 43:{b=0;g=5;break}default:{h=0;d=b;b=e}}if((g|0)==5){h=b;d=f;b=a[f>>0]|0}c=(b<<24>>24)+-48|0;if(c>>>0<10){b=0;do{d=d+1|0;b=(b*10|0)-c|0;c=(a[d>>0]|0)+-48|0}while(c>>>0<10)}else b=0;return (h|0?b:0-b|0)|0}function r5(a){a=a|0;return +(+s5(a,0))}function s5(a,b){a=a|0;b=b|0;return +(+t5(a,b,1))}function t5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0.0,f=0,g=0,h=0,i=0;i=l;l=l+128|0;h=i;f=h;g=f+124|0;do{c[f>>2]=0;f=f+4|0}while((f|0)<(g|0));f=h+4|0;c[f>>2]=a;g=h+8|0;c[g>>2]=-1;c[h+44>>2]=a;c[h+76>>2]=-1;W2(h,0);e=+i5(h,d,1);d=(c[f>>2]|0)-(c[g>>2]|0)+(c[h+108>>2]|0)|0;if(b|0)c[b>>2]=d|0?a+d|0:a;l=i;return +e}function u5(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;var f=0,g=0,h=0;a:do if(!c)b=0;else{h=b;while(1){g=c>>>1;b=h+(O(g,d)|0)|0;f=lb[e&127](a,b)|0;if(!f)break a;if((c|0)==1){b=0;break a}f=(f|0)<0;c=f?g:c-g|0;if(!c){b=0;break}else h=f?h:b}}while(0);return b|0}function v5(a,c){a=a|0;c=c|0;var d=0,f=0,g=0;g=a+2|0;f=a+4|0;d=I6((e[c+2>>1]|0)<<16|(e[c>>1]|0)|0,e[c+4>>1]|0|0,(e[g>>1]|0)<<16|(e[a>>1]|0)|0,e[f>>1]|0|0)|0;c=J6(d|0,z|0,e[c+6>>1]|0|0,0)|0;d=z;b[a>>1]=c;a=Q6(c|0,d|0,16)|0;b[g>>1]=a;b[f>>1]=d;z=d&65535;return c|0}function w5(a){a=a|0;var b=0;b=186608;c[b>>2]=a+-1;c[b+4>>2]=0;return}function x5(){var a=0,b=0,d=0;b=186608;b=I6(c[b>>2]|0,c[b+4>>2]|0,1284865837,1481765933)|0;b=J6(b|0,z|0,1,0)|0;a=z;d=186608;c[d>>2]=b;c[d+4>>2]=a;a=Q6(b|0,a|0,33)|0;return a|0}function y5(a){a=a|0;b[96876]=b[32922]|0;b[96877]=b[32923]|0;b[96878]=b[32924]|0;b[32922]=b[a>>1]|0;b[32923]=b[a+2>>1]|0;b[32924]=b[a+4>>1]|0;return 193752}function z5(a){a=a|0;var c=0,d=0;c=l;l=l+16|0;d=c;b[d>>1]=13070;b[d+2>>1]=a;b[d+4>>1]=a>>>16;y5(d)|0;l=c;return}function A5(a){a=a|0;var b=0;b=v5(a,65850)|0;b=R6(b|0,z|0,4)|0;a=z|1072693248;c[j>>2]=b;c[j+4>>2]=a;return +(+h[j>>3]+-1.0)}function B5(){return +(+A5(65844))}function C5(a,b){a=a|0;b=b|0;if(!a)b=V1(1,24)|0;else{c[b>>2]=0;c[b+4>>2]=0;c[b+8>>2]=0;c[b+12>>2]=0;c[b+16>>2]=0;c[b+20>>2]=0}return b|0}function D5(a){a=a|0;var b=0,d=0;b=c[a>>2]|0;if(b|0)do{U1(c[b>>2]|0);d=b;b=c[b+4>>2]|0;U1(d)}while((b|0)!=0);U1(a);return}function E5(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0;h=a+16|0;a:do if(!(c[h>>2]|0)){k=a+12|0;g=c[k>>2]|0;if(g>>>0<f>>>0){do if(b)if(!d){c[h>>2]=1;b=0;break a}else{h=a+8|0;c[h>>2]=d;g=1024;b=d;break}else{g=f<<3;g=g>>>0>1024?g:1024;j=T1(8)|0;if(!j){c[h>>2]=1;b=0;break a}b=T1(g)|0;c[j>>2]=b;d=b;if(!b){U1(j);c[h>>2]=1;b=0;break a}c[j+4>>2]=0;h=a+4|0;i=c[h>>2]|0;if(i|0)c[i+4>>2]=j;if(!(c[a>>2]|0))c[a>>2]=j;c[h>>2]=j;h=a+8|0;c[h>>2]=d}while(0);c[k>>2]=g}else{h=a+8|0;b=c[h>>2]|0;d=b}d=d+f&3;d=((d|0)==0?0:4-d|0)+f|0;c[h>>2]=b+d;c[k>>2]=g-d;if(e)Y6(b|0,0,d|0)|0}else b=0;while(0);return b|0}function F5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0;v=l;l=l+48|0;i=v;t=G5()|0;a:do if(!t)b=12;else{u=C5(0,0)|0;if(!u){H5(t);b=12;break}e=i+8|0;c[e>>2]=0;c[e+4>>2]=0;c[e+8>>2]=0;c[e+12>>2]=0;c[e+16>>2]=0;c[i>>2]=u;c[i+4>>2]=t;c[i+16>>2]=b;c[i+32>>2]=d;e=i+28|0;c[e>>2]=-1;b=I5(i)|0;b:do if(!b){h=i+20|0;f=c[h>>2]|0;c[a>>2]=f+-1;j=c[i+8>>2]|0;b=c[e>>2]|0;if((b|0)<(f|0)){g=V1(1,68)|0;if(g){r=b>>>31^1;c[g+60>>2]=r;c[g+28>>2]=f;if((d&8|0)==0|(r|0)!=0){b=J5(0,t,j,g)|0;if(b|0){h=0;i=0;f=0;e=0;break}f=c[g+40>>2]|0;if((f|0)>0){b=(f<<2)+4|0;e=T1(b)|0;if(!e){h=0;i=0;b=12;f=0;e=0;break}c[g+32>>2]=e;Y6(e|0,-1,b|0)|0}else e=0;r=V1(f<<1|1,4)|0;c[g+36>>2]=r;if(!r){h=0;i=0;b=12;f=0;e=0;break}b=V1(c[h>>2]|0,12)|0;if(!b){h=0;i=0;b=12;f=0;e=0;break}c[g+16>>2]=b;b=J5(u,t,j,g)|0;if(!b)b=e;else{h=0;i=0;f=0;e=0;break}}else b=0;r=i+24|0;b=K5(u,t,j,r,b)|0;if(!b){b=c[r>>2]|0;c[r>>2]=b+1;b=L5(u,0,0,b)|0;if((b|0)!=0?(s=M5(u,j,b)|0,(s|0)!=0):0){b=N5(u,t,s)|0;if(!b){e=c[r>>2]|0;b=e<<2;p=T1(b)|0;if(p){q=T1(b)|0;if(q){if((e|0)>0)Y6(p|0,0,e<<2|0)|0;O5(s,0,p,0)|0;f=c[r>>2]|0;if((f|0)>0){b=0;e=0;do{c[q+(e<<2)>>2]=b;o=p+(e<<2)|0;b=b+1+(c[o>>2]|0)|0;c[o>>2]=0;e=e+1|0}while((e|0)<(f|0));m=b}else m=0;n=V1(m+1|0,32)|0;if(n){c[g>>2]=n;o=g+4|0;c[o>>2]=m;b=O5(s,n,p,q)|0;if(!b){c[g+20>>2]=0;f=s+24|0;b=c[f>>2]|0;e=0;while(1){e=e+1|0;if((c[b>>2]|0)<=-1)break;else b=b+32|0}k=V1(e,32)|0;if(!k){h=p;i=q;b=12;f=p;e=q}else{c[g+8>>2]=k;e=c[f>>2]|0;f=c[e>>2]|0;if((f|0)>-1){b=0;do{c[k+(b<<5)+8>>2]=n+(c[q+(f<<2)>>2]<<5);c[k+(b<<5)+12>>2]=f;i=k+(b<<5)+16|0;c[i>>2]=0;j=e+12|0;h=c[j>>2]|0;if(h|0){f=0;do{w=f;f=f+1|0}while((c[h+(w<<2)>>2]|0)>-1);f=f<<2;h=T1(f)|0;c[i>>2]=h;if(!h){h=p;i=q;b=12;f=p;e=q;break b}W6(h|0,c[j>>2]|0,f|0)|0}c[k+(b<<5)+20>>2]=c[e+16>>2];b=b+1|0;e=e+32|0;f=c[e>>2]|0}while((f|0)>-1)}else b=0;c[k+(b<<5)+8>>2]=0;c[o>>2]=m;c[g+12>>2]=n+(c[q+(c[c[s+28>>2]>>2]<<2)>>2]<<5);c[g+52>>2]=c[r>>2];c[g+56>>2]=d;D5(u);H5(t);U1(p);U1(q);c[a+4>>2]=g;b=0;break a}}else{h=p;i=q;f=p;e=q}}else{h=p;i=q;b=12;f=p;e=q}}else{h=p;i=q;b=12;f=p;e=0}}else{h=p;i=0;b=12;f=0;e=0}}else{h=0;i=0;f=0;e=0}}else{h=0;i=0;b=12;f=0;e=0}}else{h=0;i=0;f=0;e=0}}else{h=0;i=0;b=12;f=0;e=0}}else{g=0;h=0;i=0;b=6;f=0;e=0}}else{g=0;h=0;i=0;f=0;e=0}while(0);D5(u);H5(t);if(h|0)U1(f);if(i|0)U1(e);c[a+4>>2]=g;P5(a)}while(0);l=v;return b|0}function G5(){var a=0,b=0;a=T1(20)|0;do if(a){b=T1(2048)|0;c[a+16>>2]=b;if(!b){U1(a);a=0;break}else{c[a>>2]=512;c[a+4>>2]=1024e3;c[a+8>>2]=128;c[a+12>>2]=0;break}}while(0);return a|0}function H5(a){a=a|0;U1(c[a+16>>2]|0);U1(a);return}function I5(b){b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0;u=l;l=l+16|0;p=u+4|0;q=u;r=c[b+32>>2]&1;s=b+16|0;e=c[s>>2]|0;t=c[b+4>>2]|0;d=T5(t,0)|0;a:do if(!d){m=(r|0)!=0;n=b+8|0;o=b+12|0;d=0;k=1;b:while(1){g=0;h=d;f=0;c:while(1){d=a[e>>0]|0;if(m){if(d<<24>>24==40)break;if((h|0)!=0&d<<24>>24==41)j=13;else j=14}else if(d<<24>>24==92)switch(a[e+1>>0]|0){case 40:break c;case 41:{j=13;break}default:j=14}else j=14;if((j|0)==13){j=0;i=L5(c[b>>2]|0,-1,-1,-1)|0;c[n>>2]=i;if(!i){d=12;break a}else i=f}else if((j|0)==14){j=0;d=g6(b,e)|0;if(d|0)break a;i=f;e=c[o>>2]|0}d:while(1){e:while(1){d=a[e>>0]|0;switch(d<<24>>24){case 42:case 92:break;default:{if(!m)break e;switch(d<<24>>24){case 123:case 63:case 43:break;default:break e}}}d=d<<24>>24==92;if(m&d)break;f=e+1|0;if(d)switch(a[f>>0]|0){case 123:case 63:case 43:break;default:break e}e=d?f:e;if((!m?(e|0)==((c[s>>2]|0)+1|0):0)?(a[e+-1>>0]|0)==94:0)break;if((a[e>>0]|0)==123){e=h6(e+1|0,r,p,q)|0;if(!e){j=28;break b}d=c[q>>2]|0;f=c[b>>2]|0;if(!d)d=L5(f,-1,-1,-1)|0;else j=36}else{c[p>>2]=0;c[q>>2]=-1;d=a[e>>0]|0;if(d<<24>>24==43){c[p>>2]=1;d=a[e>>0]|0}if(d<<24>>24==63){c[q>>2]=1;d=1}else d=-1;e=e+1|0;f=c[b>>2]|0;j=36}if((j|0)==36){j=0;d=b6(f,c[n>>2]|0,c[p>>2]|0,d,0)|0}c[n>>2]=d;if(!d){d=12;break a}}d=M5(c[b>>2]|0,g,c[n>>2]|0)|0;f=a[e>>0]|0;f:do if(m){if(f<<24>>24==124){j=40;break d}if((h|0)!=0&f<<24>>24==41|f<<24>>24==0)j=47;else{g=d;f=i;continue c}}else{switch(f<<24>>24){case 0:{j=47;break f}case 92:break;default:{g=d;f=i;continue c}}switch(a[e+1>>0]|0){case 124:case 41:break;default:{g=d;f=i;continue c}}d=c6(c[b>>2]|0,i,d)|0;if((a[e+1>>0]|0)==124){f=2;break d}if(!h){d=8;break a}e=e+2|0;f=92}while(0);if((j|0)==47){j=0;d=c6(c[b>>2]|0,i,d)|0;e=f<<24>>24==41?e+1|0:e}d=i6(b,d,U5(t)|0)|0;if(d|0)break a;d=f<<24>>24==0;f=(h|0)<1;if(f&d){j=50;break b}if(f|d){d=8;break a}g=V5(t)|0;h=h+-1|0;i=V5(t)|0}if((j|0)==40){f=1;d=c6(c[b>>2]|0,i,d)|0}g=0;e=e+f|0;f=d}d=S5(t,f)|0;if(d|0)break a;d=S5(t,g)|0;if(d|0)break a;d=T5(t,k)|0;if(d|0)break a;d=h+1|0;k=k+1|0;e=m?e+1|0:e+2|0}if((j|0)==28){d=10;break}else if((j|0)==50){c[b+20>>2]=k;d=0;break}}while(0);l=u;return d|0}function J5(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0;x=R5(d)|0;y=(b|0)==0|(f|0)==0;if(!y){c[f+48>>2]=0;c[c[f+36>>2]>>2]=-1}h=c[f+28>>2]|0;z=T1((h<<3)+8|0)|0;do if(!z)g=12;else{c[z>>2]=-1;A=T1((h<<2)+4|0)|0;if(!A){U1(z);g=12;break}c[A>>2]=-1;g=0;while(1)if(g>>>0>h>>>0)break;else g=g+1|0;S5(d,e)|0;g=T5(d,0)|0;a:do if((g|0)!=0|(R5(d)|0)<=(x|0)){e=z;n=0;h=0;m=0;l=-1}else{u=f+32|0;v=f+36|0;w=f+16|0;e=z;n=0;h=0;m=0;t=1;l=-1;s=0;while(1){b:do switch(U5(d)|0){case 6:{i=U5(d)|0;g=0;do{j=e+(g<<2)|0;g=g+1|0}while((c[j>>2]|0)>-1);c[j>>2]=i<<1|1;c[e+(g<<2)>>2]=-1;g=0;while(1)if((c[A+(g<<2)>>2]|0)>-1)g=g+1|0;else break;c[A+(g+-1<<2)>>2]=-1;g=0;j=t;i=s;break}case 0:{q=V5(d)|0;r=q+12|0;j=c[r>>2]|0;if((j|0)>-1){g=0;do{i=e+(g<<2)|0;g=g+1|0}while((c[i>>2]|0)>-1);c[i>>2]=j<<1;c[e+(g<<2)>>2]=-1;if(!y){g=0;while(1)if((c[A+(g<<2)>>2]|0)>-1)g=g+1|0;else break;i=(c[w>>2]|0)+(j*12|0)+8|0;c[i>>2]=0;if((g|0)>0){g=T1((g<<2)+4|0)|0;if(!g){g=12;j=t;i=s;break b}c[i>>2]=g;i=c[A>>2]|0;if((i|0)>-1){k=0;j=g;do{c[j>>2]=i;k=k+1|0;i=c[A+(k<<2)>>2]|0;j=g+(k<<2)|0}while((i|0)>-1);g=j}c[g>>2]=-1}}g=T5(d,c[r>>2]|0)|0;if(g|0){j=t;i=s;break b}g=T5(d,6)|0;if(g|0){j=t;i=s;break b}}c:do switch(c[q>>2]|0){case 0:{p=c[c[q+4>>2]>>2]|0;if((p|0)>-1|(p|0)==-4?(c[e>>2]|0)>-1:0){if(y){c[q+20>>2]=1;g=0}else{g=d6(b,q,m)|0;c[(c[u>>2]|0)+(m<<2)>>2]=s;if((l|0)>-1){j=c[v>>2]|0;i=0;while(1){k=j+(i<<2)|0;o=i+1|0;if((c[k>>2]|0)>-1)i=o;else break}c[k>>2]=m;c[j+(o<<2)>>2]=l;c[j+(i+2<<2)>>2]=-1;h=h+1|0;l=-1}e6(e,f,m)}c[e>>2]=-1;p=t+1|0;o=s;n=n+1|0;m=t}else{p=t;g=0;o=s}break}case 1:{j=c[q+4>>2]|0;k=c[j>>2]|0;j=c[j+4>>2]|0;g=S5(d,q)|0;if(!g){g=T5(d,5)|0;if(!g){g=S5(d,j)|0;if(!g){g=T5(d,0)|0;if(!g){i=k+20|0;g=T5(d,(c[i>>2]|0)+t|0)|0;if(!g){if((c[i>>2]|0)>0){i=(c[j+20>>2]|0)>0;g=i?t:-1;i=t+(i&1)|0}else{g=-1;i=t}g=T5(d,g)|0;if(!g){g=T5(d,4)|0;if(!g){g=S5(d,k)|0;if(!g){p=i;g=T5(d,0)|0;o=s}else{p=i;o=s}}else{p=i;o=s}}else{p=i;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}break}case 2:{i=c[q+4>>2]|0;if(y){if((c[e>>2]|0)>-1)g=1;else g=(a[i+12>>0]&1)!=0;g=T5(d,g&1)|0;if(g|0){p=t;o=s;break c}}else{g=T5(d,m)|0;if(g|0){p=t;o=s;break c}g=T5(d,a[i+12>>0]&1)|0;if(g|0){p=t;o=s;break c}}g=S5(d,q)|0;if(!g){g=T5(d,1)|0;if(!g){g=S5(d,c[i>>2]|0)|0;if(!g){g=T5(d,0)|0;if(!g){if((c[e>>2]|0)<=-1?(a[i+12>>0]&1)==0:0){p=t;g=0;o=0;break c}if(y)g=0;else{g=d6(b,q,m)|0;c[(c[u>>2]|0)+(m<<2)>>2]=(a[i+12>>0]&1)==0?s:1;if((l|0)>-1){j=c[v>>2]|0;i=0;while(1){k=j+(i<<2)|0;o=i+1|0;if((c[k>>2]|0)>-1)i=o;else break}c[k>>2]=m;c[j+(o<<2)>>2]=l;c[j+(i+2<<2)>>2]=-1;h=h+1|0;l=-1}e6(e,f,m)}c[e>>2]=-1;p=t+1|0;o=0;n=n+1|0;m=t}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}break}case 3:{j=c[q+4>>2]|0;k=c[j>>2]|0;j=c[j+4>>2]|0;i=(c[e>>2]|0)>-1;p=t+1|0;g=T5(d,i?p:t)|0;if(!g){g=T5(d,i?t:m)|0;if(!g){g=S5(d,e)|0;if(!g){g=T5(d,(c[e>>2]|0)>>>31^1)|0;if(!g){g=S5(d,q)|0;if(!g){g=S5(d,j)|0;if(!g){g=S5(d,k)|0;if(!g){g=T5(d,3)|0;if(!g){g=S5(d,j)|0;if(!g){g=T5(d,0)|0;if(!g){g=T5(d,2)|0;if(g|0){p=t;o=s;break c}g=S5(d,k)|0;if(g|0){p=t;o=s;break c}g=T5(d,0)|0;if(g|0){p=t;o=s;break c}if((c[e>>2]|0)>-1){if(y)g=0;else{g=d6(b,q,m)|0;c[(c[u>>2]|0)+(m<<2)>>2]=s;if((l|0)>-1){j=c[v>>2]|0;i=0;while(1){k=j+(i<<2)|0;o=i+1|0;if((c[k>>2]|0)>-1)i=o;else break}c[k>>2]=m;c[j+(o<<2)>>2]=l;c[j+(i+2<<2)>>2]=-1;h=h+1|0;l=-1}e6(e,f,m)}c[e>>2]=-1;n=n+1|0;m=t;i=p}else{g=0;i=t}t=(c[q+16>>2]|0)>0;p=t?i+2|0:i;o=s;m=t?i+1|0:m}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}}else{p=t;o=s}break}default:{p=t;g=0;o=s}}while(0);j=c[r>>2]|0;if((j|0)>-1){i=0;do{k=A+(i<<2)|0;i=i+1|0}while((c[k>>2]|0)>-1);c[k>>2]=j;c[A+(i<<2)>>2]=-1;j=p;i=o}else{j=p;i=o}break}case 1:{g=V5(d)|0;if(y){i=c[(c[c[g+4>>2]>>2]|0)+20>>2]|0;c[g+20>>2]=(U5(d)|0)+i;i=0;l=-1}else{q=U5(d)|0;r=U5(d)|0;i=q;l=(q|0)==0?l:r}g=0;j=t;i=y?s:(i|0)==0&1;break}case 4:{i=U5(d)|0;j=U5(d)|0;g=0;m=(i|0)>-1?i:m;i=s;break}case 5:{g=V5(d)|0;if(y){j=c[g+4>>2]|0;c[g+20>>2]=(c[(c[j+4>>2]|0)+20>>2]|0)+(c[(c[j>>2]|0)+20>>2]|0);g=0;j=t;i=s}else{g=0;j=t;i=s}break}case 2:{while(1)if((c[e>>2]|0)>-1)e=e+4|0;else{g=0;j=t;i=s;break}break}case 3:{j=V5(d)|0;o=V5(d)|0;g=V5(d)|0;e=U5(d)|0;if(y){r=c[g+4>>2]|0;s=g+16|0;c[g+20>>2]=(c[(c[r>>2]|0)+20>>2]|0)+e+(c[(c[r+4>>2]|0)+20>>2]|0)+((c[s>>2]|0)>0?2:0);g=s}else g=g+16|0;e=V5(d)|0;i=U5(d)|0;k=U5(d)|0;if((c[g>>2]|0)>0){if(y)g=0;else{g=f6(b,j,i)|0;c[(c[u>>2]|0)+(i<<2)>>2]=1;if(!g)g=f6(b,o,k)|0;c[(c[u>>2]|0)+(k<<2)>>2]=1}n=n+2|0;j=t;i=1}else{g=0;j=t;i=1}break}default:{g=0;j=t;i=s}}while(0);if((g|0)!=0|(R5(d)|0)<=(x|0))break a;else{t=j;s=i}}}while(0);if(!y?(e6(e,f,m),(l|0)>-1):0){i=c[f+36>>2]|0;e=0;while(1){j=i+(e<<2)|0;k=e+1|0;if((c[j>>2]|0)>-1)e=k;else break}c[j>>2]=m;c[i+(k<<2)>>2]=l;c[i+(e+2<<2)>>2]=-1;h=h+1|0}c[f+48>>2]=n;c[f+40>>2]=n;c[f+44>>2]=h;U1(z);U1(A)}while(0);return g|0}function K5(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;y=l;l=l+16|0;t=y+12|0;x=y+8|0;u=y+4|0;v=y;w=R5(b)|0;c[t>>2]=0;c[x>>2]=0;d=S5(b,d)|0;a:do if(!d){d=T5(b,0)|0;if(!d){b:do if((R5(b)|0)>(w|0)){g=0;i=0;j=0;c:while(1){q=U5(b)|0;r=V5(b)|0;d:do switch(q|0){case 0:{switch(c[r>>2]|0){case 0:{d=c[r+4>>2]|0;r=c[d>>2]|0;if(!((r|0)>-1|(r|0)==-4)){d=j;break d}r=d+8|0;d=(c[r>>2]|0)+j|0;c[r>>2]=d;if((d|0)<=(c[x>>2]|0)){d=j;break d}c[x>>2]=d;d=j;break d}case 3:{h=c[r+4>>2]|0;d=S5(b,c[h+4>>2]|0)|0;if(d|0)break b;d=T5(b,0)|0;if(d|0)break b;d=S5(b,c[h>>2]|0)|0;if(d|0)break b;break}case 1:{h=c[r+4>>2]|0;d=S5(b,c[h+4>>2]|0)|0;if(d|0)break b;d=T5(b,0)|0;if(d|0)break b;d=S5(b,c[h>>2]|0)|0;if(d|0)break b;break}case 2:{h=c[r+4>>2]|0;d=T5(b,j)|0;if(d|0)break b;d=S5(b,r)|0;if(d|0)break b;d=T5(b,1)|0;if(d|0)break b;d=S5(b,c[h>>2]|0)|0;if(d|0)break b;d=T5(b,0)|0;if(d|0)break b;if((c[h+4>>2]|0)<=1?(c[h+8>>2]|0)<=1:0)d=j;else{c[t>>2]=0;d=0}i=i+1|0;break d}default:{d=j;break d}}d=T5(b,0)|0;if(!d)d=j;else break b;break}case 1:{p=r+4|0;o=c[p>>2]|0;q=U5(b)|0;c[t>>2]=q;n=o+4|0;j=c[n>>2]|0;if((j|0)<=1)if((c[o+8>>2]|0)>1){c[u>>2]=0;if((j|0)>0)s=30;else{d=q;h=0;s=37}}else d=q;else{c[u>>2]=0;s=30}e:do if((s|0)==30){d=a6(a,b,c[o>>2]|0,(j|0)>1?1:2,t,f,v,x)|0;if(!d){h=0;k=q;m=1}else{s=34;break c}while(1){d=c[v>>2]|0;if(!h)h=d;else h=M5(a,h,d)|0;if(!h){d=12;s=34;break c}j=c[n>>2]|0;if((m|0)>=(j|0)){d=k;s=37;break e}k=c[t>>2]|0;m=m+1|0;d=a6(a,b,c[o>>2]|0,(m|0)<(j|0)?1:2,t,f,v,x)|0;if(d|0){s=34;break c}}}while(0);if((s|0)==37){s=0;n=o+8|0;k=c[n>>2]|0;if((k|0)==-1){k=c[t>>2]|0;d=a6(a,b,c[o>>2]|0,0,t,0,u,x)|0;if(d|0)break c;j=b6(a,c[u>>2]|0,0,-1,0)|0;c[u>>2]=j;if(!j){d=12;break c}else d=k}else if((j|0)<(k|0)){k=j;j=0;while(1){m=c[t>>2]|0;d=a6(a,b,c[o>>2]|0,0,t,0,v,x)|0;if(d|0){s=49;break c}if(!j){d=c[v>>2]|0;c[u>>2]=d}else{d=M5(a,c[v>>2]|0,j)|0;c[u>>2]=d}if(!d){d=12;s=49;break c}j=L5(a,-1,-1,-1)|0;if(!j){d=12;s=49;break c}j=c6(a,j,d)|0;c[u>>2]=j;if(!j){d=12;s=49;break c}k=k+1|0;if((k|0)>=(c[n>>2]|0)){d=m;break}}}else j=0;c[t>>2]=d;if(h){if(j){h=M5(a,h,j)|0;s=54}}else{h=j;s=54}if((s|0)==54){s=0;if(!h){d=12;break c}}c[p>>2]=c[h+4>>2];c[r>>2]=c[h>>2]}i=i+-1|0;h=d-q+g|0;if(!i){c[t>>2]=h;g=h;i=0;d=h}else g=h;break}default:d=j}while(0);if((R5(b)|0)>(w|0))j=d;else{d=0;break b}}break a}else{d=0;g=0}while(0);w=(c[e>>2]|0)+g|0;x=c[x>>2]|0;c[e>>2]=(x|0)>(w|0)?x:w}}while(0);l=y;return d|0}function L5(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0;f=E5(a,0,0,1,20)|0;a=$5(a,0,f)|0;if(!a)a=0;else{c[f>>2]=b;c[f+4>>2]=d;c[f+8>>2]=e}return a|0}function M5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;if(b){e=E5(a,0,0,1,8)|0;a=$5(a,1,e)|0;if(!a)d=0;else{c[e>>2]=b;c[e+4>>2]=d;c[a+16>>2]=(c[d+16>>2]|0)+(c[b+16>>2]|0);d=a}}return d|0}function N5(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0;o=l;l=l+16|0;j=o+4|0;k=o;m=R5(b)|0;d=S5(b,d)|0;a:do if(!d){d=T5(b,0)|0;if(!d)if((R5(b)|0)>(m|0)){b:while(1){h=U5(b)|0;i=V5(b)|0;c:do switch(h|0){case 0:switch(c[i>>2]|0){case 0:{g=c[i+4>>2]|0;f=c[g>>2]|0;if((f|0)==-4){c[i+8>>2]=0;d=g+8|0;h=W5(a,c[d>>2]|0,0,1114111,0,0,-1)|0;c[i+24>>2]=h;if(!h){d=12;break a}h=W5(a,c[d>>2]|0,0,1114111,0,0,c[g+4>>2]|0)|0;c[i+28>>2]=h;if(!h){d=12;break a}else break c}d=i+8|0;if((f|0)<0){c[d>>2]=1;h=X5(a)|0;c[i+24>>2]=h;if(!h){d=12;break a}h=X5(a)|0;c[i+28>>2]=h;if(!h){d=12;break a}else break c}else{c[d>>2]=0;d=g+8|0;e=g+4|0;h=W5(a,c[d>>2]|0,f,c[e>>2]|0,0,0,-1)|0;c[i+24>>2]=h;if(!h){d=12;break a}h=W5(a,c[d>>2]|0,c[g>>2]|0,c[e>>2]|0,c[g+12>>2]|0,c[g+16>>2]|0,-1)|0;c[i+28>>2]=h;if(!h){d=12;break a}else break c}}case 3:{d=S5(b,i)|0;if(d|0)break a;d=T5(b,1)|0;if(d|0)break a;e=i+4|0;d=S5(b,c[(c[e>>2]|0)+4>>2]|0)|0;if(d|0)break a;d=T5(b,0)|0;if(d|0)break a;d=S5(b,c[c[e>>2]>>2]|0)|0;if(d|0)break a;d=T5(b,0)|0;if(!d)break c;else break a}case 1:{d=S5(b,i)|0;if(d|0)break a;d=T5(b,2)|0;if(d|0)break a;e=i+4|0;d=S5(b,c[(c[e>>2]|0)+4>>2]|0)|0;if(d|0)break a;d=T5(b,0)|0;if(d|0)break a;d=S5(b,c[c[e>>2]>>2]|0)|0;if(d|0)break a;d=T5(b,0)|0;if(!d)break c;else break a}case 2:{d=S5(b,i)|0;if(d|0)break a;d=T5(b,3)|0;if(d|0)break a;d=S5(b,c[c[i+4>>2]>>2]|0)|0;if(d|0)break a;d=T5(b,0)|0;if(!d)break c;else break a}default:break c}case 1:{d=c[i+4>>2]|0;e=c[d>>2]|0;f=d+4|0;g=c[f>>2]|0;if(!(c[e+8>>2]|0))h=(c[g+8>>2]|0)!=0;else h=1;c[i+8>>2]=h&1;h=Y5(a,c[e+24>>2]|0,c[g+24>>2]|0,0,0)|0;c[i+24>>2]=h;if(!h){d=12;break a}h=Y5(a,c[(c[d>>2]|0)+28>>2]|0,c[(c[f>>2]|0)+28>>2]|0,0,0)|0;c[i+28>>2]=h;if(!h){d=12;break a}break}case 3:{h=c[i+4>>2]|0;d=c[h>>2]|0;if(!(c[h+4>>2]|0))e=1;else e=(c[d+8>>2]|0)!=0&1;c[i+8>>2]=e;c[i+24>>2]=c[d+24>>2];c[i+28>>2]=c[d+28>>2];break}case 2:{g=c[i+4>>2]|0;d=c[g>>2]|0;e=d+8|0;if(!(c[e>>2]|0))f=0;else f=(c[(c[g+4>>2]|0)+8>>2]|0)!=0;c[i+8>>2]=f&1;if(c[e>>2]|0){d=Z5(b,d,0,0,j)|0;if(d|0)break b;e=T1((c[j>>2]<<2)+4|0)|0;if(!e){d=12;break b}c[e>>2]=-1;c[k>>2]=0;d=Z5(b,c[g>>2]|0,e,k,0)|0;if(d|0){n=45;break b}d=g+4|0;h=i+24|0;c[h>>2]=Y5(a,c[(c[d>>2]|0)+24>>2]|0,c[(c[g>>2]|0)+24>>2]|0,e,c[k>>2]|0)|0;U1(e);if(!(c[h>>2]|0)){d=12;break b}else f=d}else{c[i+24>>2]=c[d+24>>2];f=g+4|0}d=c[f>>2]|0;if(c[d+8>>2]|0){d=Z5(b,d,0,0,j)|0;if(d|0)break b;e=T1((c[j>>2]<<2)+4|0)|0;if(!e){d=12;break b}c[e>>2]=-1;c[k>>2]=0;d=Z5(b,c[f>>2]|0,e,k,0)|0;if(d|0){n=52;break b}i=i+28|0;c[i>>2]=Y5(a,c[(c[g>>2]|0)+28>>2]|0,c[(c[f>>2]|0)+28>>2]|0,e,c[k>>2]|0)|0;U1(e);if(!(c[i>>2]|0)){d=12;break b}}else c[i+28>>2]=c[d+28>>2];break}default:{}}while(0);if((R5(b)|0)<=(m|0)){d=0;break a}}if((n|0)==45)U1(e);else if((n|0)==52)U1(e)}else d=0}while(0);l=o;return d|0}function O5(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;a:while(1){switch(c[a>>2]|0){case 2:{a=c[a+4>>2]|0;if((c[a+8>>2]|0)==-1?(h=c[a>>2]|0,h=Q5(c[h+28>>2]|0,c[h+24>>2]|0,b,d,e)|0,(h|0)!=0):0){a=h;break a}break}case 3:{f=c[a+4>>2]|0;a=O5(c[f>>2]|0,b,d,e)|0;if(a|0)break a;a=f+4|0;break}case 1:{f=c[a+4>>2]|0;g=f+4|0;a=Q5(c[(c[f>>2]|0)+28>>2]|0,c[(c[g>>2]|0)+24>>2]|0,b,d,e)|0;if(a|0)break a;a=O5(c[f>>2]|0,b,d,e)|0;if(!a)a=g;else break a;break}default:{a=0;break a}}a=c[a>>2]|0}return a|0}function P5(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0;h=c[a+4>>2]|0;if(h|0){e=h+4|0;a=c[h>>2]|0;if(c[e>>2]|0){d=0;do{if(c[a+(d<<5)+8>>2]|0){b=c[a+(d<<5)+16>>2]|0;if(b){U1(b);a=c[h>>2]|0}b=c[a+(d<<5)+28>>2]|0;if(b){U1(b);a=c[h>>2]|0}}d=d+1|0}while(d>>>0<(c[e>>2]|0)>>>0)}if(a|0)U1(a);d=h+8|0;a=c[d>>2]|0;if(a|0){if(c[a+8>>2]|0){while(1){b=c[a+16>>2]|0;if(b|0)U1(b);if(!(c[a+40>>2]|0))break;else a=a+32|0}a=c[d>>2]|0}U1(a)}f=h+16|0;a=c[f>>2]|0;if(a|0){g=h+28|0;b=c[g>>2]|0;if(b){e=0;do{d=c[a+(e*12|0)+8>>2]|0;if(d){U1(d);b=c[g>>2]|0;a=c[f>>2]|0}e=e+1|0}while(e>>>0<b>>>0)}U1(a)}a=c[h+32>>2]|0;if(a|0)U1(a);a=c[h+20>>2]|0;if(a|0)U1(a);a=c[h+36>>2]|0;if(a|0)U1(a);U1(h)}return}function Q5(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0;g=c[a>>2]|0;h=(g|0)>-1;a:do if(!d)if(h){h=b+32|0;do{if((c[b>>2]|0)>-1?(y=e+(g<<2)|0,c[y>>2]=(c[y>>2]|0)+1,(c[h>>2]|0)>-1):0){g=h;do{y=e+(c[a>>2]<<2)|0;c[y>>2]=(c[y>>2]|0)+1;g=g+32|0}while((c[g>>2]|0)>-1)}a=a+32|0;g=c[a>>2]|0}while((g|0)>-1);g=0}else g=0;else if(h)while(1){h=c[b>>2]|0;b:do if((h|0)>-1){p=a+4|0;q=a+8|0;r=a+16|0;s=a+20|0;t=a+24|0;u=a+28|0;v=a+12|0;e=-1;g=b;while(1){o=h;while(1){if((o|0)!=(e|0))break;g=g+32|0;h=c[g>>2]|0;if((h|0)>-1)o=h;else break b}l=d+(c[f+(c[a>>2]<<2)>>2]<<5)|0;while(1){h=l+8|0;if(!(c[h>>2]|0))break;else l=l+32|0}c[l+40>>2]=0;c[l>>2]=c[p>>2];c[l+4>>2]=c[q>>2];c[h>>2]=d+(c[f+(o<<2)>>2]<<5);c[l+12>>2]=o;h=c[s>>2]|0;e=c[g+16>>2]|c[r>>2]|(h|0?4:0)|(c[t>>2]|0?8:0);i=l+20|0;c[i>>2]=e;j=c[u>>2]|0;if((j|0)>-1){c[i>>2]=e|256;h=j}c[l+24>>2]=h;e=c[t>>2]|0;if(!e)c[l+28>>2]=0;else{h=0;do{n=h;h=h+1|0}while((c[e+(n<<2)>>2]|0)!=0);h=T1(h<<2)|0;c[l+28>>2]=h;if(!h){g=12;break a}k=c[t>>2]|0;e=c[k>>2]|0;if(e){j=0;i=h;do{c[i>>2]=e;j=j+1|0;e=c[k+(j<<2)>>2]|0;i=h+(j<<2)|0}while((e|0)!=0);h=i}c[h>>2]=0}e=c[v>>2]|0;if(!e)h=0;else{h=0;while(1)if((c[e+(h<<2)>>2]|0)>-1)h=h+1|0;else break}k=g+12|0;i=c[k>>2]|0;if(!i)e=0;else{e=0;while(1)if((c[i+(e<<2)>>2]|0)>-1)e=e+1|0;else break}j=l+16|0;i=c[j>>2]|0;if(i|0)U1(i);c[j>>2]=0;h=e+h|0;if((h|0)>0){n=T1((h<<2)+4|0)|0;c[j>>2]=n;if(!n){g=12;break a}e=c[v>>2]|0;if((e|0)!=0?(w=c[e>>2]|0,(w|0)>-1):0){h=0;i=w;do{c[n+(h<<2)>>2]=i;h=h+1|0;i=c[e+(h<<2)>>2]|0}while((i|0)>-1);m=h}else m=0;j=c[k>>2]|0;if((j|0)!=0?(x=c[j>>2]|0,(x|0)>-1):0){k=(m|0)>0;h=m;i=0;l=x;do{c:do if(k){e=0;while(1){if((c[n+(e<<2)>>2]|0)==(l|0))break c;e=e+1|0;if((e|0)>=(m|0)){y=39;break}}}else y=39;while(0);if((y|0)==39){y=0;c[n+(h<<2)>>2]=l;h=h+1|0}i=i+1|0;l=c[j+(i<<2)>>2]|0}while((l|0)>-1)}else h=m;c[n+(h<<2)>>2]=-1}g=g+32|0;h=c[g>>2]|0;if((h|0)<=-1)break;else e=o}}while(0);a=a+32|0;if((c[a>>2]|0)<=-1){g=0;break}}else g=0;while(0);return g|0}function R5(a){a=a|0;return c[a+12>>2]|0}function S5(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=l;l=l+16|0;e=d+4|0;f=d;c[f>>2]=b;c[e>>2]=c[f>>2];b=_5(a,e)|0;l=d;return b|0}function T5(a,b){a=a|0;b=b|0;var d=0,e=0,f=0;d=l;l=l+16|0;e=d+4|0;f=d;c[f>>2]=b;c[e>>2]=c[f>>2];b=_5(a,e)|0;l=d;return b|0}function U5(a){a=a|0;var b=0,d=0;b=c[a+16>>2]|0;d=a+12|0;a=(c[d>>2]|0)+-1|0;c[d>>2]=a;return c[b+(a<<2)>>2]|0}function V5(a){a=a|0;var b=0,d=0;b=c[a+16>>2]|0;d=a+12|0;a=(c[d>>2]|0)+-1|0;c[d>>2]=a;return c[b+(a<<2)>>2]|0}function W5(a,b,d,e,f,g,h){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;a=E5(a,0,0,1,64)|0;if(!a)a=0;else{c[a>>2]=b;c[a+4>>2]=d;c[a+8>>2]=e;c[a+20>>2]=f;c[a+24>>2]=g;c[a+28>>2]=h;c[a+32>>2]=-1;c[a+36>>2]=-1;c[a+40>>2]=-1}return a|0}function X5(a){a=a|0;a=E5(a,0,0,1,32)|0;if(!a)a=0;else{c[a>>2]=-1;c[a+4>>2]=-1;c[a+8>>2]=-1}return a|0}function Y5(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;o=(e|0)==0;if(o)n=0;else{g=0;while(1)if((c[e+(g<<2)>>2]|0)>-1)g=g+1|0;else{n=g;break}}h=0;while(1)if((c[b+(h<<5)>>2]|0)>-1)h=h+1|0;else{g=0;break}while(1)if((c[d+(g<<5)>>2]|0)>-1)g=g+1|0;else break;g=E5(a,0,0,1,(g+h<<5)+32|0)|0;a:do if(!g)g=0;else{h=c[b>>2]|0;if((h|0)>-1){m=(n|0)>0;i=0;while(1){c[g+(i<<5)>>2]=h;c[g+(i<<5)+4>>2]=c[b+(i<<5)+4>>2];c[g+(i<<5)+8>>2]=c[b+(i<<5)+8>>2];c[g+(i<<5)+16>>2]=c[b+(i<<5)+16>>2]|f;c[g+(i<<5)+20>>2]=c[b+(i<<5)+20>>2];c[g+(i<<5)+24>>2]=c[b+(i<<5)+24>>2];c[g+(i<<5)+28>>2]=c[b+(i<<5)+28>>2];l=b+(i<<5)+12|0;j=c[l>>2]|0;h=(j|0)==0;if(o&h)h=0;else{if(h)k=0;else{h=0;while(1)if((c[j+(h<<2)>>2]|0)>-1)h=h+1|0;else{k=h;break}}h=E5(a,0,0,0,(k+n<<2)+4|0)|0;if(!h){g=0;break a}if((k|0)>0){l=c[l>>2]|0;j=0;do{c[h+(j<<2)>>2]=c[l+(j<<2)>>2];j=j+1|0}while((j|0)!=(k|0))}else k=0;if(m){j=0;do{c[h+(j+k<<2)>>2]=c[e+(j<<2)>>2];j=j+1|0}while((j|0)!=(n|0));j=n}else j=0;c[h+(j+k<<2)>>2]=-1}c[g+(i<<5)+12>>2]=h;i=i+1|0;h=c[b+(i<<5)>>2]|0;if((h|0)<=-1){n=i;break}}}else n=0;i=c[d>>2]|0;h=g+(n<<5)|0;if((i|0)>-1){m=0;l=n;do{c[h>>2]=i;c[g+(l<<5)+4>>2]=c[d+(m<<5)+4>>2];c[g+(l<<5)+8>>2]=c[d+(m<<5)+8>>2];c[g+(l<<5)+16>>2]=c[d+(m<<5)+16>>2];c[g+(l<<5)+20>>2]=c[d+(m<<5)+20>>2];c[g+(l<<5)+24>>2]=c[d+(m<<5)+24>>2];c[g+(l<<5)+28>>2]=c[d+(m<<5)+28>>2];j=d+(m<<5)+12|0;h=c[j>>2]|0;if(!h)h=0;else{i=0;while(1)if((c[h+(i<<2)>>2]|0)>-1)i=i+1|0;else break;h=E5(a,0,0,0,(i<<2)+4|0)|0;if(!h){g=0;break a}if((i|0)>0){k=c[j>>2]|0;j=0;do{c[h+(j<<2)>>2]=c[k+(j<<2)>>2];j=j+1|0}while((j|0)!=(i|0))}else i=0;c[h+(i<<2)>>2]=-1}c[g+(l<<5)+12>>2]=h;m=m+1|0;i=c[d+(m<<5)>>2]|0;l=m+n|0;h=g+(l<<5)|0}while((i|0)>-1)}c[h>>2]=-1}while(0);return g|0}function Z5(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0;o=R5(a)|0;p=(f|0)!=0;if(p)c[f>>2]=0;b=S5(a,b)|0;a:do if(!b){m=(d|0)==0;l=(e|0)==0;if((R5(a)|0)>(o|0))while(1){b=V5(a)|0;b:do switch(c[b>>2]|0){case 0:{b=c[b+4>>2]|0;switch(c[b>>2]|0){case -3:break;case -2:{if(l)break b;c[e>>2]=c[e>>2]|c[b+4>>2];break b}default:break b}h=c[b+4>>2]|0;if((h|0)>-1){c:do if(!m){b=0;while(1){i=d+(b<<2)|0;j=c[i>>2]|0;if((j|0)<=-1)break;if((j|0)==(h|0))break c;else b=b+1|0}c[i>>2]=h;c[d+(b+1<<2)>>2]=-1}while(0);if(p)c[f>>2]=(c[f>>2]|0)+1}break}case 3:{b=c[b+4>>2]|0;h=c[b>>2]|0;if(c[h+8>>2]|0){k=h;n=6;break b}b=c[b+4>>2]|0;if(c[b+8>>2]|0){k=b;n=6}break}case 1:{b=c[b+4>>2]|0;h=S5(a,c[b>>2]|0)|0;if(h|0){g=h;break a}k=c[b+4>>2]|0;n=6;break}case 2:{b=c[c[b+4>>2]>>2]|0;if(c[b+8>>2]|0){k=b;n=6}break}default:{}}while(0);if((n|0)==6?(n=0,g=S5(a,k)|0,g|0):0)break a;if((R5(a)|0)<=(o|0)){g=0;break a}}else g=0}else g=b;while(0);return g|0}function _5(a,b){a=a|0;b=b|0;var d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+16|0;g=k;d=a+12|0;e=c[d>>2]|0;j=c[a>>2]|0;if((e|0)>=(j|0)){d=c[a+4>>2]|0;if((j|0)<(d|0)?(f=(c[a+8>>2]|0)+j|0,f=(f|0)>(d|0)?d:f,h=a+16|0,i=W1(c[h>>2]|0,f<<2)|0,(i|0)!=0):0){c[a>>2]=f;c[h>>2]=i;c[g>>2]=c[b>>2];_5(a,g)|0;d=0}else d=12}else{c[(c[a+16>>2]|0)+(e<<2)>>2]=c[b>>2];c[d>>2]=(c[d>>2]|0)+1;d=0}l=k;return d|0}function $5(a,b,d){a=a|0;b=b|0;d=d|0;a=E5(a,0,0,1,32)|0;if((d|0)!=0&(a|0)!=0){c[a+4>>2]=d;c[a>>2]=b;c[a+8>>2]=-1;c[a+12>>2]=-1}else a=0;return a|0}function a6(b,d,e,f,g,h,i,j){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;var k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0;r=R5(d)|0;S5(d,e)|0;e=T5(d,0)|0;a:do if(!e){q=(f&1|0)==0;p=(f&2|0)!=0;o=0;l=1;e=i;while(1){if((R5(d)|0)>(r|0))n=e;else{f=o;e=0;break a}b:while(1){c:while(1){switch(U5(d)|0){case 1:{i=7;break c}case 0:{e=V5(d)|0;switch(c[e>>2]|0){case 0:{i=9;break b}case 3:{i=19;break b}case 1:{i=26;break b}case 2:{i=33;break c}default:{}}break}default:{}}if((R5(d)|0)<=(r|0)){f=o;e=0;break a}}if((i|0)==7)e=V5(d)|0;else if((i|0)==33){f=c[e+4>>2]|0;e=S5(d,c[f>>2]|0)|0;if(e|0){f=o;break a}e=T5(d,0)|0;if(e|0){f=o;break a}e=b6(b,c[f>>2]|0,c[f+4>>2]|0,c[f+8>>2]|0,a[f+12>>0]&1)|0;c[n>>2]=e;if(!e){f=o;e=12;break a}e=c[e+4>>2]|0}if((R5(d)|0)>(r|0))n=e;else{f=o;e=0;break a}}if((i|0)==9){m=c[e+4>>2]|0;i=c[m+8>>2]|0;f=c[m>>2]|0;e=c[m+4>>2]|0;if(!((f|0)>-1|(f|0)==-4))if(q|(f|0)!=-3)if((f|0)==-3)if(p&(l|0)!=0){c[h+(e<<2)>>2]=1;f=-3;k=o;l=0}else{f=-3;k=o}else k=o;else{e=-1;f=-1;i=-1;k=o}else{i=(c[g>>2]|0)+i|0;k=o+1|0}e=L5(b,f,e,i)|0;c[n>>2]=e;if(!e)e=12;else{e=c[e+4>>2]|0;c[e+12>>2]=c[m+12>>2];c[e+16>>2]=c[m+16>>2];e=0}if((i|0)>(c[j>>2]|0)){c[j>>2]=i;f=n}else f=n}else if((i|0)==19){i=c[e+4>>2]|0;f=i+4|0;e=c6(b,c[i>>2]|0,c[f>>2]|0)|0;c[n>>2]=e;if(!e){f=o;e=12;break a}m=c[e+4>>2]|0;e=S5(d,c[f>>2]|0)|0;if(e|0){f=o;break a}e=T5(d,0)|0;if(e|0){f=o;break a}e=S5(d,m+4|0)|0;if(e|0){f=o;break a}e=T5(d,1)|0;if(e|0){f=o;break a}e=S5(d,c[i>>2]|0)|0;if(e|0){f=o;break a}k=o;f=m;e=T5(d,0)|0}else if((i|0)==26){m=c[e+4>>2]|0;i=m+4|0;e=M5(b,c[m>>2]|0,c[i>>2]|0)|0;c[n>>2]=e;if(!e){f=o;e=12;break a}f=c[e+4>>2]|0;c[f>>2]=0;k=f+4|0;c[k>>2]=0;e=S5(d,c[i>>2]|0)|0;if(e|0){f=o;break a}e=T5(d,0)|0;if(e|0){f=o;break a}e=S5(d,k)|0;if(e|0){f=o;break a}e=T5(d,1)|0;if(e|0){f=o;break a}e=S5(d,c[m>>2]|0)|0;if(e|0){f=o;break a}k=o;e=T5(d,0)|0}if(!e){o=k;e=f}else{f=k;break}}}else f=0;while(0);c[g>>2]=(c[g>>2]|0)+f;return e|0}function b6(b,d,e,f,g){b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0;h=E5(b,0,0,1,16)|0;b=$5(b,2,h)|0;if(!b)b=0;else{c[h>>2]=d;c[h+4>>2]=e;c[h+8>>2]=f;h=h+12|0;a[h>>0]=a[h>>0]&-2|g&1;c[b+16>>2]=c[d+16>>2]}return b|0}function c6(a,b,d){a=a|0;b=b|0;d=d|0;var e=0;if(b){e=E5(a,0,0,1,8)|0;a=$5(a,3,e)|0;if((d|0)!=0&(a|0)!=0){c[e>>2]=b;c[e+4>>2]=d;c[a+16>>2]=(c[d+16>>2]|0)+(c[b+16>>2]|0);d=a}else d=0}return d|0}function d6(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=E5(a,0,0,0,8)|0;if(((e|0)!=0?(d=L5(a,-3,d,-1)|0,c[e>>2]=d,(d|0)!=0):0)?(f=E5(a,0,0,0,32)|0,c[e+4>>2]=f,(f|0)!=0):0){a=b+4|0;c[f+4>>2]=c[a>>2];c[f>>2]=c[b>>2];c[f+8>>2]=-1;c[f+12>>2]=-1;f=f+16|0;c[f>>2]=0;c[f+4>>2]=0;c[f+8>>2]=0;c[f+12>>2]=0;c[a>>2]=e;c[b>>2]=1;a=0}else a=12;return a|0}function e6(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;f=c[a>>2]|0;if((f|0)>-1){g=c[b+16>>2]|0;e=0;b=f;do{f=b>>>1;c[((b&1|0)==0?g+(f*12|0)|0:g+(f*12|0)+4|0)>>2]=d;e=e+1|0;b=c[a+(e<<2)>>2]|0}while((b|0)>-1)}c[a>>2]=-1;return}function f6(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=E5(a,0,0,0,8)|0;if(((e|0)!=0?(d=L5(a,-3,d,-1)|0,c[e+4>>2]=d,(d|0)!=0):0)?(f=E5(a,0,0,0,32)|0,c[e>>2]=f,(f|0)!=0):0){a=b+4|0;c[f+4>>2]=c[a>>2];c[f>>2]=c[b>>2];c[f+8>>2]=-1;c[f+12>>2]=-1;f=f+16|0;c[f>>2]=0;c[f+4>>2]=0;c[f+8>>2]=0;c[f+12>>2]=0;c[a>>2]=e;c[b>>2]=1;a=0}else a=12;return a|0}function g6(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0;m=l;l=l+16|0;j=m;i=b+32|0;h=c[i>>2]|0;g=h&1;a:do switch(a[d>>0]|0){case 91:{e=k6(b,d+1|0)|0;break}case 92:{e=d+1|0;f=l6(e)|0;if(f|0){e=g6(b,f)|0;c[b+12>>2]=d+2;break a}f=a[e>>0]|0;switch(f|0){case 0:{e=5;break a}case 98:{f=L5(c[b>>2]|0,-2,64,-1)|0;break}case 66:{f=L5(c[b>>2]|0,-2,128,-1)|0;break}case 60:{f=L5(c[b>>2]|0,-2,16,-1)|0;break}case 62:{f=L5(c[b>>2]|0,-2,32,-1)|0;break}case 120:{h=d+2|0;i=(a[h>>0]|0)==123;g=i?8:2;h=i?d+3|0:h;e=0;f=0;do{d=m6(a[h+e>>0]|0)|0;if((d|0)<0)break;f=d+(f<<4)|0;e=e+1|0}while((f|0)<1114112&(e|0)<(g|0));e=h+e|0;if(i){if((a[e>>0]|0)!=125){e=9;break a}e=e+1|0}j=c[b>>2]|0;i=b+24|0;k=c[i>>2]|0;c[i>>2]=k+1;f=L5(j,f,f,k)|0;e=e+-1|0;break}case 63:case 43:case 123:if(!g){e=13;break a}else{k=37;break a}case 124:{if(g|0){k=37;break a}e=L5(c[b>>2]|0,-1,-1,-1)|0;k=45;break a}default:{if(!((g|0)==0&(f+-49|0)>>>0<9)){k=37;break a}j=f+-48|0;k=c[b>>2]|0;i=b+24|0;f=c[i>>2]|0;c[i>>2]=f+1;f=L5(k,-4,j,f)|0;k=b+28|0;i=c[k>>2]|0;c[k>>2]=(j|0)<(i|0)?i:j}}d=e+1|0;e=f;k=45;break}case 46:{e=c[b>>2]|0;g=b+24|0;f=c[g>>2]|0;c[g>>2]=f+1;if(h&4){f=L5(e,0,9,f)|0;k=c[b>>2]|0;e=c[g>>2]|0;c[g>>2]=e+1;e=L5(k,11,1114111,e)|0;if((f|0)!=0&(e|0)!=0)e=c6(c[b>>2]|0,f,e)|0;else e=0}else e=L5(e,0,1114111,f)|0;d=d+1|0;k=45;break}case 94:{if((g|0)==0?(c[b+16>>2]|0)!=(d|0):0){e=d;k=37;break a}d=d+1|0;e=L5(c[b>>2]|0,-2,1,-1)|0;k=45;break}case 36:{e=d+1|0;if((g|0)==0?a[e>>0]|0:0){e=d;k=37;break a}d=e;e=L5(c[b>>2]|0,-2,2,-1)|0;k=45;break}case 63:case 43:case 123:case 42:{if(!g){e=d;k=37}else e=13;break}case 124:{if(!g){e=d;k=37}else k=36;break}case 0:{k=36;break}default:{e=d;k=37}}while(0);if((k|0)==36){e=L5(c[b>>2]|0,-1,-1,-1)|0;k=45}else if((k|0)==37){h=R4(j,e,-1)|0;if((h|0)<0)e=2;else{do if(c[i>>2]&2){if((C3(c[j>>2]|0)|0)==0?(B3(c[j>>2]|0)|0)==0:0){k=43;break}i=c[b>>2]|0;n=f3(c[j>>2]|0)|0;g=f3(c[j>>2]|0)|0;f=b+24|0;g=L5(i,n,g,c[f>>2]|0)|0;n=c[b>>2]|0;i=E3(c[j>>2]|0)|0;d=E3(c[j>>2]|0)|0;d=L5(n,i,d,c[f>>2]|0)|0;if((g|0)!=0&(d|0)!=0)g=c6(c[b>>2]|0,g,d)|0;else g=0}else k=43;while(0);if((k|0)==43){g=c[j>>2]|0;f=b+24|0;g=L5(c[b>>2]|0,g,g,c[f>>2]|0)|0}c[f>>2]=(c[f>>2]|0)+1;d=e+h|0;e=g;k=45}}if((k|0)==45)if(!e)e=12;else{c[b+8>>2]=e;c[b+12>>2]=d;e=0}l=m;return e|0}function h6(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0;j=l;l=l+16|0;i=j+4|0;g=j;b=j6(b,i)|0;if((a[b>>0]|0)==44){b=j6(b+1|0,g)|0;h=c[g>>2]|0;g=c[i>>2]|0}else{i=c[i>>2]|0;c[g>>2]=i;h=i;g=i}do if(!(g>>>0>255|((h|0)>255|(h|0)>-1&(h|0)<(g|0)))){if(!d)if((a[b>>0]|0)==92)b=b+1|0;else{b=0;break}if((a[b>>0]|0)==125){c[e>>2]=g;c[f>>2]=h;b=b+1|0}else b=0}else b=0;while(0);l=j;return b|0}function i6(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0;if((c[b+12>>2]|0)>-1){e=L5(c[a>>2]|0,-1,-1,-1)|0;if((e|0)!=0?(f=M5(c[a>>2]|0,e,b)|0,(f|0)!=0):0){e=c[b+16>>2]|0;c[f+16>>2]=e;g=6}else e=12}else{f=b;e=c[b+16>>2]|0;g=6}if((g|0)==6){c[f+12>>2]=d;c[f+16>>2]=e+1;c[a+8>>2]=f;e=0}return e|0}function j6(b,d){b=b|0;d=d|0;var e=0,f=0,g=0;c[d>>2]=-1;if(((a[b>>0]|0)+-48|0)>>>0<10){c[d>>2]=0;e=b;g=0;f=a[b>>0]|0;while(1){g=(g*10|0)+-48+(f<<24>>24)|0;c[d>>2]=g;b=e+1|0;f=a[b>>0]|0;if((g|0)>255|((f<<24>>24)+-48|0)>>>0>9)break;else e=b}}return b|0}function k6(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0;s=l;l=l+288|0;e=s+264|0;g=s;c[e>>2]=c[b>>2];h=e+8|0;c[h>>2]=0;c[e+12>>2]=32;p=T1(128)|0;r=e+4|0;c[r>>2]=p;if(!p)d=12;else{f=g+4|0;c[f>>2]=0;p=(a[d>>0]|0)==94;c[g>>2]=p&1;d=n6(b,p?d+1|0:d,e,g)|0;a:do if(!d){p=(c[g>>2]|0)==0;if(!p){o4(c[r>>2]|0,c[h>>2]|0,4,111);d=p6(e)|0;if(!d){d=12;e=0;break}c[d>>2]=1114112;c[d+4>>2]=1114112;c[d+8>>2]=-1;d=c[f>>2]|0;if(d){e=d<<2;f=E5(c[b>>2]|0,0,0,0,e+4|0)|0;if(!f){d=12;e=0;break}W6(f|0,g+8|0,e|0)|0;c[f+(d<<2)>>2]=0}else f=0}else f=0;m=c[h>>2]|0;if((m|0)>0){n=c[r>>2]|0;o=b+24|0;e=0;d=0;k=0;while(1){j=c[n+(k<<2)>>2]|0;g=c[j>>2]|0;h=j+4|0;i=c[h>>2]|0;do if(!p)if((g|0)>(d|0)){c[j>>2]=d;c[h>>2]=g+-1;d=i+1|0;q=14;break}else{j=i+1|0;d=(j|0)>=(d|0)?j:d;break}else q=14;while(0);if((q|0)==14){q=0;c[j+8>>2]=c[o>>2];c[j+16>>2]=f;j=$5(c[b>>2]|0,0,j)|0;e=c6(c[b>>2]|0,e,j)|0;if(!e){d=12;e=0;break a}}k=k+1|0;if((k|0)>=(m|0)){d=0;break}}}else{d=0;e=0}}else e=0;while(0);U1(c[r>>2]|0);r=b+24|0;c[r>>2]=(c[r>>2]|0)+1;c[b+8>>2]=e}l=s;return d|0}function l6(b){b=b|0;var d=0;d=a[b>>0]|0;b=0;while(1){if((a[63752+(b<<3)>>0]|0)==d<<24>>24)break;b=b+1|0;if((b|0)==12){b=12;break}}return c[63752+(b<<3)+4>>2]|0}function m6(a){a=a|0;var b=0;b=a+-48|0;if(b>>>0<10)return b|0;else{b=a|32;return ((b+-97|0)>>>0<6?b+-87|0:-1)|0}return 0}function n6(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0;t=l;l=l+32|0;o=t;q=t+4|0;i=R4(o,d,-1)|0;g=a[d>>0]|0;a:do if((i|0)>=1){r=f+4|0;n=b+32|0;h=d;j=i;b:while(1){i=(h|0)==(d|0);if(!(i|g<<24>>24!=93)){s=5;break}c:do if(!(i|g<<24>>24!=45)){switch(a[h+1>>0]|0){case 93:break c;case 45:break;default:{g=11;break a}}if((a[h+2>>0]|0)==93){g=11;break a}}while(0);d:do if(g<<24>>24==91){switch(a[h+1>>0]|0){case 61:case 46:{g=3;break a}case 58:break;default:{s=17;break d}}i=h+2|0;g=0;e:while(1){switch(a[i+g>>0]|0){case 0:{s=16;break b}case 58:break e;default:{}}if((g|0)<13)g=g+1|0;else{s=16;break b}}W6(q|0,i|0,g|0)|0;a[q+g>>0]=0;j=X3(q)|0;if(!j){s=16;break b}h=i+(g+2)|0;if((a[i+(g+1)>>0]|0)!=93){s=16;break b}if(!(c[f>>2]|0)){g=1114111;m=0;k=1;s=25}else{g=c[r>>2]|0;if((g|0)>63){g=12;break a}c[r>>2]=g+1;c[f+8+(g<<2)>>2]=j}}else s=17;while(0);if((s|0)==17){s=0;i=c[o>>2]|0;h=h+j|0;if((a[h>>0]|0)==45?(p=h+1|0,(a[p>>0]|0)!=93):0){h=R4(o,p,-1)|0;g=c[o>>2]|0;if((h|0)<1|(i|0)>(g|0)){g=11;break a}j=0;m=i;h=p+h|0;k=0;s=25}else{j=0;g=i;m=i;k=0;s=25}}if((s|0)==25){s=0;i=p6(e)|0;if(!i){g=12;break a}c[i>>2]=m;c[i+4>>2]=g;c[i+12>>2]=j;c[i+8>>2]=-1;if(!(k|(c[n>>2]&2|0)==0)?(q6(e,m,g)|0)!=0:0){g=12;break a}}j=R4(o,h,-1)|0;g=a[h>>0]|0;if((j|0)<1){s=3;break a}}if((s|0)==5){c[b+12>>2]=h+1;g=0;break}else if((s|0)==16){g=4;break}}else s=3;while(0);if((s|0)==3)g=g<<24>>24?2:7;l=t;return g|0}function o6(a,b){a=a|0;b=b|0;return (c[c[a>>2]>>2]|0)-(c[c[b>>2]>>2]|0)|0}function p6(a){a=a|0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0;h=a+8|0;d=c[h>>2]|0;e=a+12|0;f=c[e>>2]|0;if((d|0)>=(f|0))if((f|0)<=32767?(c[e>>2]=f<<1,g=a+4|0,b=W1(c[g>>2]|0,f<<3)|0,(b|0)!=0):0){c[g>>2]=b;d=c[h>>2]|0;i=6}else b=0;else{b=c[a+4>>2]|0;i=6}if((i|0)==6){c[h>>2]=d+1;i=E5(c[a>>2]|0,0,0,1,20)|0;c[b+(d<<2)>>2]=i;b=i}return b|0}function q6(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0,g=0,h=0;a:do if((b|0)>(d|0))b=0;else while(1){b:do if(!(B3(b)|0)){if(!(C3(b)|0)){b=b+1|0;break}f=E3(b)|0;e=f;while(1){g=b+1|0;e=e+1|0;if((b|0)>=(d|0)){h=f;b=g;g=12;break b}if((E3(g)|0)==(e|0))b=g;else{h=f;b=g;g=12;break}}}else{f=f3(b)|0;e=f;while(1){g=b+1|0;e=e+1|0;if((b|0)>=(d|0)){h=f;b=g;g=12;break b}if((f3(g)|0)==(e|0))b=g;else{h=f;b=g;g=12;break}}}while(0);if((g|0)==12){g=0;f=p6(a)|0;if(!f){b=-1;break a}c[f>>2]=h;c[f+4>>2]=e+-1;c[f+8>>2]=-1}if((b|0)>(d|0)){b=0;break}}while(0);return b|0}function r6(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0;n=l;l=l+16|0;k=n;i=c[a+4>>2]|0;j=i+56|0;h=(c[j>>2]&8|0)==0?d:0;a=c[i+40>>2]|0;if((a|0)>0&(h|0)!=0){a=T1(a<<2)|0;if(!a)a=12;else{g=a;d=a;m=3}}else{g=0;d=0;m=3}if((m|0)==3){if(!(c[i+60>>2]|0))a=t6(i,b,g,f,k)|0;else a=s6(i,b,g,f,k)|0;if(!a)u6(h,e,c[j>>2]|0,i,g,c[k>>2]|0);if(g)U1(d)}l=n;return a|0}function s6(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,O=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0;V=l;l=l+16|0;T=V;c[T>>2]=0;o=e&1;R=e&2;S=a+56|0;n=c[S>>2]&4;U=C5(0,0)|0;a:do if(U){p=E5(U,0,0,0,32)|0;if(!p){D5(U);e=12;break}c[p+24>>2]=0;c[p+28>>2]=0;Q=a+40|0;q=c[Q>>2]|0;if(q){e=T1(q<<2)|0;if(!e){m=0;k=0;j=e;e=12;i=0;h=0;g=0}else{j=e;i=e;C=6}}else{j=0;i=0;C=6}b:do if((C|0)==6){e=c[a+28>>2]|0;if(e){e=T1(e<<3)|0;if(!e){m=e;k=0;e=12;h=0;g=0;break}else{m=e;h=e}}else{m=0;h=0}P=a+52|0;e=c[P>>2]|0;if(e){e=T1(e<<2)|0;if(!e){k=e;e=12;g=0;break}else{k=e;g=e}}else{k=0;g=0}K=a+8|0;L=a+12|0;M=(d|0)==0;N=(d|0)!=0;O=a+32|0;J=(n|0)!=0;I=(o|0)==0;s=b;e=1;r=-1;v=-1;n=q;B=0;c:while(1){if((n|0)>0){o=0;do{c[j+(o<<2)>>2]=-1;if(!M){c[d+(o<<2)>>2]=-1;n=c[Q>>2]|0}o=o+1|0}while((o|0)<(n|0))}n=c[P>>2]|0;if((n|0)>0)Y6(g|0,0,n<<2|0)|0;G=e+r|0;e=R4(T,s,4)|0;if((e|0)<1)if((e|0)<0){e=1;break b}else e=1;F=s+e|0;H=c[T>>2]|0;n=c[K>>2]|0;o=n+8|0;if(c[o>>2]|0){w=(G|0)!=0;x=(B|0)==95;y=(G|0)==0;A=J&(B|0)==10;z=(G|0)<1&I;u=n;n=0;t=0;s=o;while(1){r=u+20|0;o=c[r>>2]|0;d:do if(o)if((o&1|0)==0|z|A){if(o&2|0?(E=c[T>>2]|0,!((E|R|0)==0|J&(E|0)==10)):0){q=t;break}do if(o&16|0){if(x){q=t;break d}if(x3(B)|0){q=t;break d}o=c[T>>2]|0;if((o|0)==95)break;if(!(x3(o)|0)){q=t;break d}}while(0);o=c[r>>2]|0;if(o&32){if(x){o=c[T>>2]|0;if((o|0)==95){q=t;break}}else{E=(x3(B)|0)==0;o=c[T>>2]|0;if(E|(o|0)==95){q=t;break}}if(x3(o)|0){q=t;break}o=c[r>>2]|0}q=c[T>>2]|0;do if((q|0)!=0&(w&(o&64|0)!=0)){if(x)o=1;else{o=(x3(B)|0)!=0;q=c[T>>2]|0}if((q|0)==95)if(o){q=t;break d}else break;else if(o^(x3(q)|0)!=0)break;else{q=t;break d}}while(0);if(!(c[r>>2]&128)){C=49;break}o=c[T>>2]|0;if(y|(o|0)==0){q=t;break}if(x)q=1;else{q=(x3(B)|0)!=0;o=c[T>>2]|0}if((o|0)==95)if(q){C=49;break}else{q=t;break}else if(q^(x3(o)|0)!=0){q=t;break}else{C=49;break}}else q=t;else C=49;while(0);do if((C|0)==49){C=0;if(!t){n=c[u+16>>2]|0;q=c[s>>2]|0;break}q=p+28|0;o=c[q>>2]|0;if(!o){o=E5(U,0,0,0,32)|0;if(!o){C=53;break c}c[o+24>>2]=p;c[o+28>>2]=0;E=E5(U,0,0,0,c[Q>>2]<<2)|0;c[o+20>>2]=E;if(!E){C=60;break c}c[q>>2]=o;p=o}else p=o;c[p>>2]=G;c[p+4>>2]=F;c[p+8>>2]=c[s>>2];c[p+12>>2]=c[u+12>>2];c[p+16>>2]=c[T>>2];if((c[Q>>2]|0)>0){q=c[p+20>>2]|0;o=0;do{c[q+(o<<2)>>2]=c[j+(o<<2)>>2];o=o+1|0}while((o|0)<(c[Q>>2]|0))}o=c[u+16>>2]|0;if(!o)q=t;else{q=c[o>>2]|0;if((q|0)<=-1){q=t;break}r=c[p+20>>2]|0;do{o=o+4|0;c[r+(q<<2)>>2]=G;q=c[o>>2]|0}while((q|0)>-1);q=t}}while(0);s=u+40|0;if(!(c[s>>2]|0))break;else{u=u+32|0;t=q}}if(n){o=c[n>>2]|0;if((o|0)>-1)do{c[j+(o<<2)>>2]=G;n=n+4|0;o=c[n>>2]|0}while((o|0)>-1)}else n=0;if(!q)C=171;else{s=G;r=F;w=q;x=n;C=79}}else{n=0;C=171}e:while(1){if((C|0)==79){if((w|0)==(c[L>>2]|0)){if((v|0)>=(s|0)){if(!(N&(v|0)==(s|0))){n=x;C=171;continue}n=c[Q>>2]|0;if(!(w6(n,c[O>>2]|0,j,d)|0)){n=x;C=171;continue}}else{if(M){v=s;n=x;C=171;continue}n=c[Q>>2]|0}if((n|0)>0)n=0;else{v=s;n=x;C=171;continue}while(1){c[d+(n<<2)>>2]=c[j+(n<<2)>>2];n=n+1|0;if((n|0)>=(c[Q>>2]|0)){v=s;n=x;C=171;continue e}}}u=w+8|0;if((c[u>>2]|0)!=0?(c[w+20>>2]&256|0)!=0:0){o=c[w+24>>2]|0;u6(o+1|0,m,c[S>>2]&-9,a,j,s);E=c[m+(o<<3)>>2]|0;o=(c[m+(o<<3)+4>>2]|0)-E|0;if(Q2(b+E|0,r+-1|0,o)|0){n=x;C=171;continue}E=(o|0)==0;n=E&1;q=c[w+12>>2]|0;if(E?c[k+(q<<2)>>2]|0:0){n=x;C=171;continue}c[k+(q<<2)>>2]=n;o=o+-1|0;r=r+o|0;o=o+s|0;q=c[T>>2]|0;n=R4(T,r,4)|0;if((n|0)<1)if((n|0)<0){e=1;break b}else n=1;D=r+n|0;E=n}else{q=c[T>>2]|0;if(!q){n=x;C=171;continue}n=R4(T,r,4)|0;if((n|0)<1)if((n|0)<0){e=1;break b}else n=1;o=s;D=r+n|0;E=n}s=o+e|0;if(!(c[u>>2]|0)){e=E;n=x;C=171;continue}y=(s|0)!=0;z=(q|0)==95;A=(s|0)==0;C=J&(q|0)==10;B=(s|0)<1&I;t=0;n=x;while(1){f:do if((c[w>>2]|0)>>>0<=q>>>0){if((c[w+4>>2]|0)>>>0<q>>>0)break;r=w+20|0;e=c[r>>2]|0;do if(e|0){if(!((e&1|0)==0|B|C))break f;if(e&2|0?(x=c[T>>2]|0,!((x|R|0)==0|J&(x|0)==10)):0)break f;do if(e&16|0){if(z)break f;if(x3(q)|0)break f;e=c[T>>2]|0;if((e|0)==95)break;if(!(x3(e)|0))break f}while(0);e=c[r>>2]|0;if(e&32){if(z){e=c[T>>2]|0;if((e|0)==95)break f}else{x=(x3(q)|0)==0;e=c[T>>2]|0;if(x|(e|0)==95)break f}if(x3(e)|0)break f;e=c[r>>2]|0}o=c[T>>2]|0;do if((o|0)!=0&(y&(e&64|0)!=0)){if(z)e=1;else{e=(x3(q)|0)!=0;o=c[T>>2]|0}if((o|0)==95)if(e)break f;else break;else if(e^(x3(o)|0)!=0)break;else break f}while(0);do if(c[r>>2]&128|0){e=c[T>>2]|0;if(A|(e|0)==0)break f;if(z)o=1;else{o=(x3(q)|0)!=0;e=c[T>>2]|0}if((e|0)==95)if(o)break;else break f;else if(o^(x3(e)|0)!=0)break f;else break}while(0);e=c[r>>2]|0;do if(e&4){if(c[S>>2]&2|0)break;if(!(w3(q,c[w+24>>2]|0)|0))break f;e=c[r>>2]|0}while(0);do if(e&4|0){if(!(c[S>>2]&2))break;x=E3(q)|0;e=w+24|0;if(w3(x,c[e>>2]|0)|0)break;x=f3(q)|0;if(!(w3(x,c[e>>2]|0)|0))break f}while(0);if(!(c[r>>2]&8))break;if(v6(c[w+28>>2]|0,q,c[S>>2]&2)|0)break f}while(0);if(!t){t=c[u>>2]|0;n=c[w+16>>2]|0;break}o=p+28|0;e=c[o>>2]|0;if(!e){e=E5(U,0,0,0,32)|0;if(!e){C=145;break c}c[e+24>>2]=p;c[e+28>>2]=0;x=E5(U,0,0,0,c[Q>>2]<<2)|0;c[e+20>>2]=x;if(!x){C=152;break c}c[o>>2]=e;p=e}else p=e;c[p>>2]=s;c[p+4>>2]=D;c[p+8>>2]=c[u>>2];c[p+12>>2]=c[w+12>>2];c[p+16>>2]=c[T>>2];if((c[Q>>2]|0)>0){o=c[p+20>>2]|0;e=0;do{c[o+(e<<2)>>2]=c[j+(e<<2)>>2];e=e+1|0}while((e|0)<(c[Q>>2]|0))}e=c[w+16>>2]|0;if(!e)break;o=c[e>>2]|0;if((o|0)<=-1)break;r=c[p+20>>2]|0;do{c[r+(o<<2)>>2]=s;e=e+4|0;o=c[e>>2]|0}while((o|0)>-1)}while(0);u=w+40|0;if(!(c[u>>2]|0))break;else w=w+32|0}if(!t){e=E;C=171;continue}if(n){e=c[n>>2]|0;if((e|0)>-1){do{n=n+4|0;c[j+(e<<2)>>2]=s;e=c[n>>2]|0}while((e|0)>-1);q=v;o=D;e=E}else{q=v;o=D;e=E}}else{q=v;o=D;e=E;n=0}}else if((C|0)==171){C=0;r=c[p+24>>2]|0;if(!r)break;t=c[p+8>>2]|0;if(c[t+20>>2]&256|0)c[k+(c[p+12>>2]<<2)>>2]=0;s=c[p>>2]|0;u=c[p+4>>2]|0;c[T>>2]=c[p+16>>2];q=c[Q>>2]|0;if((q|0)>0){p=c[p+20>>2]|0;o=0;do{c[j+(o<<2)>>2]=c[p+(o<<2)>>2];o=o+1|0}while((o|0)<(q|0));p=r;q=v;o=u}else{p=r;q=v;o=u}}r=o;v=q;w=t;x=n;C=79}n=(v|0)>-1;if(n|(c[T>>2]|0)==0){C=180;break}c[T>>2]=H;s=F;r=G;n=c[Q>>2]|0;B=H}if((C|0)==53){D5(U);if(j|0)U1(i);if(m|0)U1(h);if(!k){e=12;break a}U1(g);e=12;break a}else if((C|0)==60){D5(U);if(j|0)U1(i);if(m|0)U1(h);if(!k){e=12;break a}U1(g);e=12;break a}else if((C|0)==145){D5(U);if(j|0)U1(i);if(m|0)U1(h);if(!k){e=12;break a}U1(g);e=12;break a}else if((C|0)==152){D5(U);if(j|0)U1(i);if(m|0)U1(h);if(!k){e=12;break a}U1(g);e=12;break a}else if((C|0)==180){c[f>>2]=v;e=(n^1)&1;break}}while(0);D5(U);if(j|0)U1(i);if(m|0)U1(h);if(k)U1(g)}else e=12;while(0);l=V;return e|0}function t6(a,b,d,e,f){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,H=0,I=0,J=0,K=0,L=0,M=0,N=0,P=0,Q=0,R=0,S=0,T=0,U=0,V=0,W=0;W=l;l=l+16|0;T=W;c[T>>2]=0;k=e&1;Q=e&2;R=a+56|0;j=c[R>>2]&4;if(!d)N=0;else N=c[a+40>>2]|0;P=N<<2;i=c[a+52>>2]|0;h=i<<3;g=h+8|0;e=P+12+h+(g+(O(P,i)|0)<<1)|0;U=T1(e)|0;if(!U)e=12;else{Y6(U|0,0,e|0)|0;e=U+P|0;m=e&3;m=e+((m|0)==0?0:4-m|0)|0;e=m+g|0;n=e&3;n=e+((n|0)==0?0:4-n|0)|0;e=n+g|0;M=e&3;M=e+((M|0)==0?0:4-M|0)|0;e=M+h|0;g=e&3;h=(i|0)>0;if(h){e=e+((g|0)==0?0:4-g|0)|0;g=0;while(1){c[n+(g<<3)+4>>2]=e;e=e+P|0;c[m+(g<<3)+4>>2]=e;g=g+1|0;if((g|0)>=(i|0))break;else e=e+P|0}if(h){e=0;do{c[M+(e<<3)>>2]=-1;e=e+1|0}while((e|0)<(i|0))}}e=R4(T,b,4)|0;if((e|0)<1)if((e|0)<0)e=1;else{i=1;V=11}else{i=e;V=11}a:do if((V|0)==11){L=a+8|0;K=(j|0)!=0;E=(k|0)==0;F=(N|0)>0;G=a+12|0;H=(N|0)<1;I=a+44|0;J=a+32|0;C=a+36|0;D=(N|0)==0;h=0;v=b+i|0;u=0;j=0;e=-1;g=m;A=U;x=m;w=n;while(1){if((e|0)<0){a=c[L>>2]|0;k=a+8|0;b:do if(c[k>>2]|0){p=(u|0)!=0;q=(h|0)==95;r=(u|0)==0;t=K&(h|0)==10;s=(u|0)<1&E;while(1){c:while(1){o=a+12|0;if((c[M+(c[o>>2]<<3)>>2]|0)>=(u|0))break;n=a+20|0;b=c[n>>2]|0;if(!b){V=46;break}d:do if((b&1|0)==0|s|t){if(b&2|0?(B=c[T>>2]|0,!((B|Q|0)==0|K&(B|0)==10)):0)break;do if(b&16|0){if(q)break d;if(x3(h)|0)break d;b=c[T>>2]|0;if((b|0)==95)break;if(!(x3(b)|0))break d}while(0);b=c[n>>2]|0;if(b&32){if(q){b=c[T>>2]|0;if((b|0)==95)break}else{B=(x3(h)|0)==0;b=c[T>>2]|0;if(B|(b|0)==95)break}if(x3(b)|0)break;b=c[n>>2]|0}m=c[T>>2]|0;do if((m|0)!=0&(p&(b&64|0)!=0)){if(q)b=1;else{b=(x3(h)|0)!=0;m=c[T>>2]|0}if((m|0)==95)if(b)break d;else break;else if(b^(x3(m)|0)!=0)break;else break d}while(0);if(!(c[n>>2]&128)){V=46;break c}b=c[T>>2]|0;if(r|(b|0)==0)break;if(q)m=1;else{m=(x3(h)|0)!=0;b=c[T>>2]|0}if((b|0)==95)if(m){V=46;break c}else break;else if(m^(x3(b)|0)!=0)break;else{V=46;break c}}while(0);k=a+40|0;if(!(c[k>>2]|0))break b;else a=a+32|0}if((V|0)==46){V=0;c[g>>2]=c[k>>2];if(F)Y6(c[g+4>>2]|0,-1,P|0)|0;k=c[a+16>>2]|0;if(k|0?(S=c[k>>2]|0,(S|0)>-1):0){m=g+4|0;b=S;do{if((b|0)<(N|0))c[(c[m>>2]|0)+(b<<2)>>2]=u;k=k+4|0;b=c[k>>2]|0}while((b|0)>-1)}k=(c[g>>2]|0)!=(c[G>>2]|0);if(k|H){j=k?j:1;e=k?e:u;k=g+4|0}else{k=g+4|0;j=c[k>>2]|0;e=0;do{c[d+(e<<2)>>2]=c[j+(e<<2)>>2];e=e+1|0}while((e|0)!=(N|0));j=1;e=u}B=c[o>>2]|0;c[M+(B<<3)>>2]=u;c[M+(B<<3)+4>>2]=k;g=g+8|0}k=a+40|0;if(!(c[k>>2]|0))break;else a=a+32|0}}while(0);c[g>>2]=0;g=c[T>>2]|0;if(!g)break;else h=g}else{if(D)break;h=c[T>>2]|0;if(!((g|0)!=(x|0)&(h|0)!=0))break}B=u+i|0;g=R4(T,v,4)|0;if((g|0)<1)if((g|0)<0){e=1;break a}else i=1;else i=g;z=v+i|0;if((j|0)!=0&(c[I>>2]|0)!=0){if(!(c[x>>2]|0))g=w;else{o=c[C>>2]|0;p=c[o>>2]|0;q=(p|0)>-1;n=x;g=w;do{m=n+4|0;e:do if(q){k=0;b=p;do{a=c[o+((k|1)<<2)>>2]|0;if((b|0)>=(N|0))break e;j=c[m>>2]|0;if((c[j+(a<<2)>>2]|0)==(c[d+(a<<2)>>2]|0)?(c[j+(b<<2)>>2]|0)<(c[d+(b<<2)>>2]|0):0)break e;k=k+2|0;b=c[o+(k<<2)>>2]|0}while((b|0)>-1);k=m;V=75}else{k=m;j=c[m>>2]|0;V=75}while(0);if((V|0)==75){V=0;c[g>>2]=c[n>>2];v=g+4|0;y=c[v>>2]|0;c[v>>2]=j;c[k>>2]=y;g=g+8|0}n=n+8|0}while((c[n>>2]|0)!=0)}c[g>>2]=0;y=x;j=0}else{y=w;w=x}k=c[w>>2]|0;if(!k){k=A;g=y}else{r=(B|0)!=0;s=(h|0)==95;t=(B|0)==0;v=K&(h|0)==10;u=(B|0)<1&E;q=w;a=A;g=y;while(1){b=k+8|0;if(c[b>>2]|0){p=q+4|0;o=a;n=b;while(1){f:do if((c[k>>2]|0)>>>0<=h>>>0?(c[k+4>>2]|0)>>>0>=h>>>0:0){m=k+20|0;a=c[m>>2]|0;do if(a|0){if(!((a&1|0)==0|u|v)){a=o;break f}if(a&2|0?(A=c[T>>2]|0,!((A|Q|0)==0|K&(A|0)==10)):0){a=o;break f}do if(a&16|0){if(s){a=o;break f}if(x3(h)|0){a=o;break f}a=c[T>>2]|0;if((a|0)==95)break;if(!(x3(a)|0)){a=o;break f}}while(0);a=c[m>>2]|0;if(a&32){if(s){a=c[T>>2]|0;if((a|0)==95){a=o;break f}}else{A=(x3(h)|0)==0;a=c[T>>2]|0;if(A|(a|0)==95){a=o;break f}}if(x3(a)|0){a=o;break f}a=c[m>>2]|0}b=c[T>>2]|0;do if((b|0)!=0&(r&(a&64|0)!=0)){if(s)a=1;else{a=(x3(h)|0)!=0;b=c[T>>2]|0}if((b|0)==95)if(a){a=o;break f}else break;else if(a^(x3(b)|0)!=0)break;else{a=o;break f}}while(0);do if(c[m>>2]&128|0){a=c[T>>2]|0;if(t|(a|0)==0){a=o;break f}if(s)b=1;else{b=(x3(h)|0)!=0;a=c[T>>2]|0}if((a|0)==95)if(b)break;else{a=o;break f}else if(b^(x3(a)|0)!=0){a=o;break f}else break}while(0);a=c[m>>2]|0;do if(a&4){if(c[R>>2]&2|0)break;if(!(w3(h,c[k+24>>2]|0)|0)){a=o;break f}a=c[m>>2]|0}while(0);do if(a&4|0){if(!(c[R>>2]&2))break;A=E3(h)|0;a=k+24|0;if(w3(A,c[a>>2]|0)|0)break;A=f3(h)|0;if(!(w3(A,c[a>>2]|0)|0)){a=o;break f}}while(0);if(!(c[m>>2]&8))break;if(v6(c[k+28>>2]|0,h,c[R>>2]&2)|0){a=o;break f}}while(0);if(F){b=c[p>>2]|0;a=0;do{c[o+(a<<2)>>2]=c[b+(a<<2)>>2];a=a+1|0}while((a|0)!=(N|0))}a=c[k+16>>2]|0;do if(a|0){b=c[a>>2]|0;if((b|0)<=-1)break;do{if((b|0)<(N|0))c[o+(b<<2)>>2]=B;a=a+4|0;b=c[a>>2]|0}while((b|0)>-1)}while(0);a=c[k+12>>2]|0;b=M+(a<<3)|0;if((c[b>>2]|0)>=(B|0)){a=c[M+(a<<3)+4>>2]|0;b=c[a>>2]|0;if(!(w6(N,c[J>>2]|0,o,b)|0)){a=o;break}c[a>>2]=o;if((c[n>>2]|0)!=(c[G>>2]|0)){a=b;break}if(F)e=0;else{j=1;a=b;e=B;break}while(1){c[d+(e<<2)>>2]=c[o+(e<<2)>>2];e=e+1|0;if((e|0)==(N|0)){j=1;a=b;e=B;break f}}}A=c[n>>2]|0;c[g>>2]=A;x=g+4|0;m=c[x>>2]|0;c[x>>2]=o;c[b>>2]=B;c[M+(a<<3)+4>>2]=x;do if((c[G>>2]|0)==(A|0)){if((e|0)==-1)if(F)e=0;else{e=B;j=1;break}else{if(!F)break;if((c[o>>2]|0)>(c[d>>2]|0))break;else e=0}do{c[d+(e<<2)>>2]=c[o+(e<<2)>>2];e=e+1|0}while((e|0)<(N|0));e=B;j=1}while(0);a=m;g=g+8|0}else a=o;while(0);n=k+40|0;if(!(c[n>>2]|0))break;else{k=k+32|0;o=a}}}q=q+8|0;k=c[q>>2]|0;if(!k){k=a;break}}}c[g>>2]=0;v=z;u=B;A=k;x=y}c[f>>2]=e;e=e>>>31}while(0);U1(U)}l=W;return e|0}function u6(a,b,d,e,f,g){a=a|0;b=b|0;d=d|0;e=e|0;f=f|0;g=g|0;var h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0;if((d&8|0)==0&(g|0)>-1?(m=c[e+16>>2]|0,n=c[e+28>>2]|0,l=(a|0)!=0&(n|0)!=0,l):0){k=c[e+48>>2]|0;j=0;do{d=c[m+(j*12|0)>>2]|0;if((d|0)==(k|0))i=g;else i=c[f+(d<<2)>>2]|0;h=b+(j<<3)|0;c[h>>2]=i;d=c[m+(j*12|0)+4>>2]|0;if((d|0)==(k|0))d=g;else d=c[f+(d<<2)>>2]|0;e=b+(j<<3)+4|0;c[e>>2]=d;if((d|0)==-1|(i|0)==-1){c[e>>2]=-1;c[h>>2]=-1}j=j+1|0}while(j>>>0<a>>>0&j>>>0<n>>>0);if(l){d=0;do{i=b+(d<<3)+4|0;j=c[m+(d*12|0)+8>>2]|0;if(j|0?(o=c[j>>2]|0,(o|0)>-1):0){k=b+(d<<3)|0;h=0;f=o;e=c[k>>2]|0;do{if(!((e|0)>=(c[b+(f<<3)>>2]|0)?(c[i>>2]|0)<=(c[b+(f<<3)+4>>2]|0):0)){c[i>>2]=-1;c[k>>2]=-1;e=-1}h=h+1|0;f=c[j+(h<<2)>>2]|0}while((f|0)>-1)}d=d+1|0}while(d>>>0<a>>>0&d>>>0<n>>>0)}else d=0}else d=0;if(d>>>0<a>>>0)Y6(b+(d<<3)|0,-1,a-d<<3|0)|0;return}function v6(a,b,d){a=a|0;b=b|0;d=d|0;var e=0,f=0;e=c[a>>2]|0;a:do if(!e)d=0;else{f=(d|0)==0;d=e;while(1){if(f){if(w3(b,d)|0){d=1;break a}}else{e=f3(b)|0;if(w3(e,c[a>>2]|0)|0){d=1;break a}e=E3(b)|0;if(w3(e,c[a>>2]|0)|0){d=1;break a}}a=a+4|0;d=c[a>>2]|0;if(!d){d=0;break}}}while(0);return d|0}function w6(a,b,d,e){a=a|0;b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;a:do if((a|0)>0){f=0;while(1){h=c[d+(f<<2)>>2]|0;g=c[e+(f<<2)>>2]|0;if(!(c[b+(f<<2)>>2]|0)){if((h|0)<(g|0)){f=1;break a}if((h|0)>(g|0)){f=0;break a}}else{if((h|0)>(g|0)){f=1;break a}if((h|0)<(g|0)){f=0;break a}}f=f+1|0;if((f|0)>=(a|0)){f=0;break}}}else f=0;while(0);return f|0}function x6(b,d,e,f){b=b|0;d=d|0;e=e|0;f=f|0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0;k=O(e,d)|0;e=(d|0)==0?0:e;if((c[f+76>>2]|0)>-1)j=J3(f)|0;else j=0;g=f+74|0;h=a[g>>0]|0;a[g>>0]=h+255|h;g=f+4|0;h=c[g>>2]|0;m=(c[f+8>>2]|0)-h|0;i=m>>>0<k>>>0?m:k;if((m|0)>0){W6(b|0,h|0,i|0)|0;c[g>>2]=h+i;g=k-i|0;b=b+i|0}else g=k;a:do if(!g)l=13;else{i=f+32|0;while(1){if(_2(f)|0)break;h=nb[c[i>>2]&63](f,b,g)|0;if((h+1|0)>>>0<2)break;g=g-h|0;if(!g){l=13;break a}else b=b+h|0}if(j|0)K3(f);e=((k-g|0)>>>0)/(d>>>0)|0}while(0);if((l|0)==13)if(j)K3(f);return e|0}function y6(a){a=a|0;var b=0;if((c[a+76>>2]|0)>-1){b=(J3(a)|0)==0;a=z6(a)|0}else a=z6(a)|0;return a|0}function z6(a){a=a|0;var b=0;if(!(c[a>>2]&128))b=1;else b=(c[a+20>>2]|0)>>>0>(c[a+28>>2]|0)>>>0?2:1;b=nb[c[a+40>>2]&63](a,0,b)|0;if((b|0)>=0)b=b-(c[a+8>>2]|0)+(c[a+4>>2]|0)+(c[a+20>>2]|0)-(c[a+28>>2]|0)|0;return b|0}function A6(a){a=a|0;(c[a+76>>2]|0)>-1?J3(a)|0:0;return c[a+60>>2]|0}function B6(b){b=b|0;var d=0,e=0,f=0,g=0;f=c[15679]|0;if((c[f+76>>2]|0)>-1)g=J3(f)|0;else g=0;do if((T4(b,f)|0)<0)b=-1;else{if((a[f+75>>0]|0)!=10?(d=f+20|0,e=c[d>>2]|0,e>>>0<(c[f+16>>2]|0)>>>0):0){c[d>>2]=e+1;a[e>>0]=10;b=0;break}b=(V4(f,10)|0)>>31}while(0);if(g|0)K3(f);return b|0}function C6(){var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;k=l;l=l+48|0;i=k+24|0;h=k+16|0;g=k;d=k+28|0;b=d;e=153686;f=b+20|0;do{a[b>>0]=a[e>>0]|0;b=b+1|0;e=e+1|0}while((b|0)<(f|0));e=d+13|0;b=0;while(1){x4(e)|0;c[g>>2]=d;c[g+4>>2]=32962;c[g+8>>2]=384;f=c2(Ha(5,g|0)|0)|0;if((f|0)>-1){j=4;break}if((b|0)<99)b=b+1|0;else{b=0;break}}if((j|0)==4){c[h>>2]=d;wa(10,h|0)|0;b=P4(f,153706)|0;if(!b){c[i>>2]=f;Ja(6,i|0)|0;b=0}}l=k;return b|0}function D6(a){a=a|0;var b=0,e=0,f=0;if((c[a+76>>2]|0)>=0?(J3(a)|0)!=0:0){b=a+4|0;e=c[b>>2]|0;if(e>>>0<(c[a+8>>2]|0)>>>0){c[b>>2]=e+1;b=d[e>>0]|0}else b=Z2(a)|0}else f=3;do if((f|0)==3){b=a+4|0;e=c[b>>2]|0;if(e>>>0<(c[a+8>>2]|0)>>>0){c[b>>2]=e+1;b=d[e>>0]|0;break}else{b=Z2(a)|0;break}}while(0);return b|0}function E6(b,d){b=b|0;d=d|0;var e=0,f=0,g=0,h=0,i=0,j=0,k=0;if((c[d+76>>2]|0)>=0?(J3(d)|0)!=0:0){f=b&255;e=b&255;if((e|0)!=(a[d+75>>0]|0)?(i=d+20|0,j=c[i>>2]|0,j>>>0<(c[d+16>>2]|0)>>>0):0){c[i>>2]=j+1;a[j>>0]=f}else e=V4(d,b)|0;K3(d)}else k=3;do 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c[b+((f<<3)+4)>>2]|0;f=f+1|0}return 0}function W6(b,d,e){b=b|0;d=d|0;e=e|0;var f=0,g=0,h=0;if((e|0)>=8192)return Ra(b|0,d|0,e|0)|0;h=b|0;g=b+e|0;if((b&3)==(d&3)){while(b&3){if(!e)return h|0;a[b>>0]=a[d>>0]|0;b=b+1|0;d=d+1|0;e=e-1|0}e=g&-4|0;f=e-64|0;while((b|0)<=(f|0)){c[b>>2]=c[d>>2];c[b+4>>2]=c[d+4>>2];c[b+8>>2]=c[d+8>>2];c[b+12>>2]=c[d+12>>2];c[b+16>>2]=c[d+16>>2];c[b+20>>2]=c[d+20>>2];c[b+24>>2]=c[d+24>>2];c[b+28>>2]=c[d+28>>2];c[b+32>>2]=c[d+32>>2];c[b+36>>2]=c[d+36>>2];c[b+40>>2]=c[d+40>>2];c[b+44>>2]=c[d+44>>2];c[b+48>>2]=c[d+48>>2];c[b+52>>2]=c[d+52>>2];c[b+56>>2]=c[d+56>>2];c[b+60>>2]=c[d+60>>2];b=b+64|0;d=d+64|0}while((b|0)<(e|0)){c[b>>2]=c[d>>2];b=b+4|0;d=d+4|0}}else{e=g-4|0;while((b|0)<(e|0)){a[b>>0]=a[d>>0]|0;a[b+1>>0]=a[d+1>>0]|0;a[b+2>>0]=a[d+2>>0]|0;a[b+3>>0]=a[d+3>>0]|0;b=b+4|0;d=d+4|0}}while((b|0)<(g|0)){a[b>>0]=a[d>>0]|0;b=b+1|0;d=d+1|0}return h|0}function X6(b,c,d){b=b|0;c=c|0;d=d|0;var 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b7(a){a=a|0;return jb[a&3]()|0}function c7(a,b){a=a|0;b=b|0;return kb[a&63](b|0)|0}function d7(a,b,c){a=a|0;b=b|0;c=c|0;return lb[a&127](b|0,c|0)|0}function e7(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=+d;e=+e;return mb[a&3](b|0,c|0,+d,+e)|0}function f7(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;return nb[a&63](b|0,c|0,d|0)|0}function g7(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;return ob[a&127](b|0,c|0,d|0,e|0)|0}function h7(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;return pb[a&127](b|0,c|0,d|0,e|0,f|0)|0}function i7(a,b,c,d,e,f,g){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;return qb[a&1](b|0,c|0,d|0,e|0,f|0,g|0)|0}function j7(a,b,c,d,e,f,g,h,i,j,k){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;k=k|0;return rb[a&3](b|0,c|0,d|0,e|0,f|0,g|0,h|0,i|0,j|0,k|0)|0}function k7(a){a=a|0;sb[a&15]()}function l7(a,b){a=a|0;b=b|0;tb[a&127](b|0)}function m7(a,b,c){a=a|0;b=b|0;c=c|0;ub[a&63](b|0,c|0)}function n7(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;vb[a&127](b|0,c|0,d|0)}function o7(a,b,c,d,e,f,g){a=a|0;b=b|0;c=c|0;d=d|0;e=+e;f=+f;g=g|0;wb[a&15](b|0,c|0,d|0,+e,+f,g|0)}function p7(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;xb[a&31](b|0,c|0,d|0,e|0)}function q7(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;yb[a&15](b|0,c|0,d|0,e|0,f|0)}function r7(a,b,c,d,e,f,g){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;zb[a&15](b|0,c|0,d|0,e|0,f|0,g|0)}function s7(a,b,c,d,e,f,g,h,i,j){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;Ab[a&0](b|0,c|0,d|0,e|0,f|0,g|0,h|0,i|0,j|0)}function t7(){S(0);return 0.0}function u7(a,b,c){a=+a;b=+b;c=+c;S(1);return 0.0}function v7(a){a=a|0;S(2);return 0.0}function w7(){S(3);return 0}function x7(a){a=a|0;S(4);return 0}function y7(a,b){a=a|0;b=b|0;S(5);return 0}function z7(a,b,c,d){a=a|0;b=b|0;c=+c;d=+d;S(6);return 0}function A7(a,b,c){a=a|0;b=b|0;c=c|0;S(7);return 0}function B7(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;S(8);return 0}function C7(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;S(9);return 0}function D7(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;S(10);return 0}function E7(a,b,c,d,e,f,g,h,i,j){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;j=j|0;S(11);return 0}function F7(){S(12)}function G7(a){a=a|0;S(13)}function H7(a,b){a=a|0;b=b|0;S(14)}function I7(a,b,c){a=a|0;b=b|0;c=c|0;S(15)}function J7(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=+d;e=+e;f=f|0;S(16)}function K7(a,b,c,d){a=a|0;b=b|0;c=c|0;d=d|0;S(17)}function L7(a,b,c,d,e){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;S(18)}function M7(a,b,c,d,e,f){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;S(19)}function N7(a,b,c,d,e,f,g,h,i){a=a|0;b=b|0;c=c|0;d=d|0;e=e|0;f=f|0;g=g|0;h=h|0;i=i|0;S(20)}
+
+// EMSCRIPTEN_END_FUNCS
+var gb=[t7,UO];var hb=[u7,eJ,dJ,fJ,gJ,u7,u7,u7];var ib=[v7,$O];var jb=[w7,$q,TK,KL];var kb=[x7,ch,xg,Lm,Mm,YB,$B,HQ,IQ,zS,AS,BS,CS,DS,ES,FS,GS,$1,Ib,qF,yl,Bl,_m,KK,tB,jC,sB,PK,UK,uB,tQ,JK,yQ,zQ,sY,T1,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7,x7];var lb=[y7,AB,XB,aC,TN,UN,LN,HN,pN,$W,aX,bX,cX,dX,eX,gX,hX,iX,jX,kX,lX,mX,nX,oX,pX,qX,rX,sX,tX,uX,vX,wX,xX,yX,zX,AX,BX,CX,DX,EX,N_,O_,Q_,l$,m$,n$,o$,p$,q$,r$,s$,j0,k0,m0,A$,B$,D$,oF,ng,bh,Eh,gi,yi,Jk,Hk,zl,Cl,Zo,hq,jr,Fr,Hr,Jr,Kr,Mr,Lr,Xr,ht,mt,ot,tv,sx,EE,DE,CE,vG,kC,MK,IG,YG,TG,UG,VH,T4,E6,OO,aQ,QA,eV,XA,ZA,uQ,s2,FT,_W,oY,pY,W1,B_,C_,D_,o6,y7,y7,y7,y7,y7,y7,y7,y7,y7,y7,y7,y7,y7,y7,y7,y7];var mb=[z7,qu,vu,z7];var nb=[A7,Hm,Mv,Dz,Lz,Uz,Zz,WB,_B,WL,PP,QU,R_,n0,E$,a2,b2,f2,g2,W3,gA,Xm,Av,rp,nt,Vx,Xx,_x,$z,tG,m4,ND,w4,QO,SL,HR,lY,Y$,P0,J0,T0,d5,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7,A7];var ob=[B7,Jm,Rr,Nv,Fz,Nz,VA,SD,WA,nB,CB,EB,PB,OB,bC,fC,hC,ML,RP,iR,gR,eT,SU,mV,lV,G_,H_,I_,J_,K_,L_,M_,P_,T_,c0,d0,e0,f0,g0,h0,i0,l0,p0,t$,u$,v$,w$,x$,y$,z$,C$,G$,cA,TC,xG,SO,fZ,iZ,pZ,AZ,JZ,PZ,NZ,QZ,z_,U0,m_,_$,$$,Q0,R0,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7,B7];var pb=[C7,BB,qN,MN,U_,V_,j$,k$,G0,q0,r0,H$,I$,E_,F_,W0,X0,Y0,Z0,$0,a1,b1,c1,d1,f1,g1,h1,i1,j1,e1,J1,K1,A1,t1,k1,l1,m1,o1,n1,p1,q1,r1,s1,u1,v1,w1,x1,y1,z1,B1,C1,D1,E1,I1,G1,F1,H1,P1,L1,M1,N1,O1,Q1,R1,S1,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7,C7];var qb=[D7,RX];var rb=[E7,I0,S0,E7];var sb=[F7,TO,QK,RK,SK,WK,XK,YK,WU,F7,F7,F7,F7,F7,F7,F7];var tb=[G7,Mb,Nb,Rb,Sb,Tb,lc,mc,Kc,Lc,Mc,Nc,Oc,Pc,Qc,ad,bd,ed,fd,gd,hd,id,jd,kd,wd,xd,yd,Ad,Bd,Cd,Dd,Ed,Fd,Gd,Hd,Sd,Td,Ud,Wd,Xd,Yd,Zd,_d,$d,ae,be,ce,ee,re,se,te,ue,Fe,Ge,He,Je,We,Xe,Ye,_e,$e,af,bf,cf,df,ef,ff,gf,qf,rf,sf,tf,Wf,Rf,pm,$l,dp,Eo,Op,Xp,_n,ao,Lp,Mp,Xl,Yl,co,lo,no,yo,FB,XM,nN,ZM,YM,JN,uM,xM,HS,MS,Kf,Dl,Io,Mo,ep,U1,Xv,tA,yG,rI,VU,AR,BQ,NL,nV,QX,jY,kY,qY,G7,G7,G7,G7,G7,G7,G7,G7,G7];var ub=[H7,Sc,Xc,md,rd,Od,ke,Ae,Pe,nf,zf,cC,rN,VN,NN,IN,lN,mN,RN,SN,KS,OS,cM,jA,uG,VK,sQ,RW,PX,mY,nY,rY,tY,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7,H7];var vb=[I7,Ub,Vb,Yb,Jc,Rc,Tc,Wc,ld,nd,qd,Jd,Kd,Nd,fe,ge,je,ve,we,ze,Ke,Le,Oe,hf,jf,mf,uf,vf,yf,ok,pk,qk,Im,Ez,Mz,qA,GB,gC,PC,TD,lG,QJ,LL,XL,wM,QP,TP,hR,fR,IS,JS,LS,NS,PS,RU,EW,DW,JA,jB,uC,XC,_C,OK,RO,QL,wQ,AQ,FW,HW,IW,QW,SW,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7,I7];var wb=[J7,MH,NH,OH,PH,QH,RH,SH,TH,J7,J7,J7,J7,J7,J7,J7];var xb=[K7,Wb,Uc,od,zd,Ld,Vd,he,xe,Me,Ze,kf,wf,Nf,Mf,Lf,Jf,If,Hf,kc,Gf,Ff,DB,oN,KN,S_,o0,F$,o4,vQ,xQ,a0];var yb=[L7,cd,Id,Pd,de,Ie,Al,wG,CQ,L7,L7,L7,L7,L7,L7,L7];var zb=[M7,Xb,Vc,pd,Md,ie,ye,Ne,lf,xf,DQ,M7,M7,M7,M7,M7];var Ab=[N7];return{___errno_location:d2,___muldi3:I6,___udivdi3:N6,___uremdi3:O6,_bitshift64Ashr:P6,_bitshift64Lshr:Q6,_bitshift64Shl:R6,_dtextract:Sz,_free:U1,_htons:a4,_i64Add:J6,_i64Subtract:K6,_llvm_bswap_i16:S6,_llvm_bswap_i32:T6,_malloc:T1,_memalign:Z1,_memcpy:W6,_memmove:X6,_memset:Y6,_realloc:W1,_saveSetjmp:U6,_sbrk:Z6,_testSetjmp:V6,_vizCreateFile:Kb,_vizLastErrorMessage:Jb,_vizRenderFromString:Lb,dynCall_d:_6,dynCall_dddd:$6,dynCall_di:a7,dynCall_i:b7,dynCall_ii:c7,dynCall_iii:d7,dynCall_iiidd:e7,dynCall_iiii:f7,dynCall_iiiii:g7,dynCall_iiiiii:h7,dynCall_iiiiiii:i7,dynCall_iiiiiiiiiii:j7,dynCall_v:k7,dynCall_vi:l7,dynCall_vii:m7,dynCall_viii:n7,dynCall_viiiddi:o7,dynCall_viiii:p7,dynCall_viiiii:q7,dynCall_viiiiii:r7,dynCall_viiiiiiiii:s7,establishStackSpace:Eb,getTempRet0:Hb,runPostSets:G6,setTempRet0:Gb,setThrew:Fb,stackAlloc:Bb,stackRestore:Db,stackSave:Cb}})
+
+
+// EMSCRIPTEN_END_ASM
+(Module.asmGlobalArg,Module.asmLibraryArg,buffer);var ___errno_location=Module["___errno_location"]=asm["___errno_location"];var ___muldi3=Module["___muldi3"]=asm["___muldi3"];var ___udivdi3=Module["___udivdi3"]=asm["___udivdi3"];var ___uremdi3=Module["___uremdi3"]=asm["___uremdi3"];var _bitshift64Ashr=Module["_bitshift64Ashr"]=asm["_bitshift64Ashr"];var _bitshift64Lshr=Module["_bitshift64Lshr"]=asm["_bitshift64Lshr"];var _bitshift64Shl=Module["_bitshift64Shl"]=asm["_bitshift64Shl"];var _dtextract=Module["_dtextract"]=asm["_dtextract"];var _free=Module["_free"]=asm["_free"];var _htons=Module["_htons"]=asm["_htons"];var _i64Add=Module["_i64Add"]=asm["_i64Add"];var _i64Subtract=Module["_i64Subtract"]=asm["_i64Subtract"];var _llvm_bswap_i16=Module["_llvm_bswap_i16"]=asm["_llvm_bswap_i16"];var _llvm_bswap_i32=Module["_llvm_bswap_i32"]=asm["_llvm_bswap_i32"];var _malloc=Module["_malloc"]=asm["_malloc"];var _memalign=Module["_memalign"]=asm["_memalign"];var _memcpy=Module["_memcpy"]=asm["_memcpy"];var _memmove=Module["_memmove"]=asm["_memmove"];var _memset=Module["_memset"]=asm["_memset"];var _realloc=Module["_realloc"]=asm["_realloc"];var _saveSetjmp=Module["_saveSetjmp"]=asm["_saveSetjmp"];var _sbrk=Module["_sbrk"]=asm["_sbrk"];var _testSetjmp=Module["_testSetjmp"]=asm["_testSetjmp"];var _vizCreateFile=Module["_vizCreateFile"]=asm["_vizCreateFile"];var _vizLastErrorMessage=Module["_vizLastErrorMessage"]=asm["_vizLastErrorMessage"];var _vizRenderFromString=Module["_vizRenderFromString"]=asm["_vizRenderFromString"];var establishStackSpace=Module["establishStackSpace"]=asm["establishStackSpace"];var getTempRet0=Module["getTempRet0"]=asm["getTempRet0"];var runPostSets=Module["runPostSets"]=asm["runPostSets"];var setTempRet0=Module["setTempRet0"]=asm["setTempRet0"];var setThrew=Module["setThrew"]=asm["setThrew"];var stackAlloc=Module["stackAlloc"]=asm["stackAlloc"];var stackRestore=Module["stackRestore"]=asm["stackRestore"];var stackSave=Module["stackSave"]=asm["stackSave"];var dynCall_d=Module["dynCall_d"]=asm["dynCall_d"];var dynCall_dddd=Module["dynCall_dddd"]=asm["dynCall_dddd"];var dynCall_di=Module["dynCall_di"]=asm["dynCall_di"];var dynCall_i=Module["dynCall_i"]=asm["dynCall_i"];var dynCall_ii=Module["dynCall_ii"]=asm["dynCall_ii"];var dynCall_iii=Module["dynCall_iii"]=asm["dynCall_iii"];var dynCall_iiidd=Module["dynCall_iiidd"]=asm["dynCall_iiidd"];var dynCall_iiii=Module["dynCall_iiii"]=asm["dynCall_iiii"];var dynCall_iiiii=Module["dynCall_iiiii"]=asm["dynCall_iiiii"];var dynCall_iiiiii=Module["dynCall_iiiiii"]=asm["dynCall_iiiiii"];var dynCall_iiiiiii=Module["dynCall_iiiiiii"]=asm["dynCall_iiiiiii"];var dynCall_iiiiiiiiiii=Module["dynCall_iiiiiiiiiii"]=asm["dynCall_iiiiiiiiiii"];var dynCall_v=Module["dynCall_v"]=asm["dynCall_v"];var dynCall_vi=Module["dynCall_vi"]=asm["dynCall_vi"];var dynCall_vii=Module["dynCall_vii"]=asm["dynCall_vii"];var dynCall_viii=Module["dynCall_viii"]=asm["dynCall_viii"];var dynCall_viiiddi=Module["dynCall_viiiddi"]=asm["dynCall_viiiddi"];var dynCall_viiii=Module["dynCall_viiii"]=asm["dynCall_viiii"];var dynCall_viiiii=Module["dynCall_viiiii"]=asm["dynCall_viiiii"];var dynCall_viiiiii=Module["dynCall_viiiiii"]=asm["dynCall_viiiiii"];var dynCall_viiiiiiiii=Module["dynCall_viiiiiiiii"]=asm["dynCall_viiiiiiiii"];Module["asm"]=asm;Module["ccall"]=ccall;Module["Pointer_stringify"]=Pointer_stringify;Module["UTF8ToString"]=UTF8ToString;if(memoryInitializer){if(!isDataURI(memoryInitializer)){if(typeof Module["locateFile"]==="function"){memoryInitializer=Module["locateFile"](memoryInitializer)}else if(Module["memoryInitializerPrefixURL"]){memoryInitializer=Module["memoryInitializerPrefixURL"]+memoryInitializer}}if(ENVIRONMENT_IS_NODE||ENVIRONMENT_IS_SHELL){var data=Module["readBinary"](memoryInitializer);HEAPU8.set(data,GLOBAL_BASE)}else{addRunDependency("memory initializer");var applyMemoryInitializer=(function(data){if(data.byteLength)data=new Uint8Array(data);HEAPU8.set(data,GLOBAL_BASE);if(Module["memoryInitializerRequest"])delete Module["memoryInitializerRequest"].response;removeRunDependency("memory initializer")});function doBrowserLoad(){Module["readAsync"](memoryInitializer,applyMemoryInitializer,(function(){throw"could not load memory initializer "+memoryInitializer}))}var memoryInitializerBytes=tryParseAsDataURI(memoryInitializer);if(memoryInitializerBytes){applyMemoryInitializer(memoryInitializerBytes.buffer)}else if(Module["memoryInitializerRequest"]){function useRequest(){var request=Module["memoryInitializerRequest"];var response=request.response;if(request.status!==200&&request.status!==0){var data=tryParseAsDataURI(Module["memoryInitializerRequestURL"]);if(data){response=data.buffer}else{console.warn("a problem seems to have happened with Module.memoryInitializerRequest, status: "+request.status+", retrying "+memoryInitializer);doBrowserLoad();return}}applyMemoryInitializer(response)}if(Module["memoryInitializerRequest"].response){setTimeout(useRequest,0)}else{Module["memoryInitializerRequest"].addEventListener("load",useRequest)}}else{doBrowserLoad()}}}function ExitStatus(status){this.name="ExitStatus";this.message="Program terminated with exit("+status+")";this.status=status}ExitStatus.prototype=new Error;ExitStatus.prototype.constructor=ExitStatus;var initialStackTop;dependenciesFulfilled=function runCaller(){if(!Module["calledRun"])run();if(!Module["calledRun"])dependenciesFulfilled=runCaller};function run(args){args=args||Module["arguments"];if(runDependencies>0){return}preRun();if(runDependencies>0)return;if(Module["calledRun"])return;function doRun(){if(Module["calledRun"])return;Module["calledRun"]=true;if(ABORT)return;ensureInitRuntime();preMain();if(Module["onRuntimeInitialized"])Module["onRuntimeInitialized"]();postRun()}if(Module["setStatus"]){Module["setStatus"]("Running...");setTimeout((function(){setTimeout((function(){Module["setStatus"]("")}),1);doRun()}),1)}else{doRun()}}Module["run"]=run;function exit(status,implicit){if(implicit&&Module["noExitRuntime"]&&status===0){return}if(Module["noExitRuntime"]){}else{ABORT=true;EXITSTATUS=status;STACKTOP=initialStackTop;exitRuntime();if(Module["onExit"])Module["onExit"](status)}if(ENVIRONMENT_IS_NODE){process["exit"](status)}Module["quit"](status,new ExitStatus(status))}Module["exit"]=exit;function abort(what){if(Module["onAbort"]){Module["onAbort"](what)}if(what!==undefined){Module.print(what);Module.printErr(what);what=JSON.stringify(what)}else{what=""}ABORT=true;EXITSTATUS=1;throw"abort("+what+"). Build with -s ASSERTIONS=1 for more info."}Module["abort"]=abort;if(Module["preInit"]){if(typeof Module["preInit"]=="function")Module["preInit"]=[Module["preInit"]];while(Module["preInit"].length>0){Module["preInit"].pop()()}}Module["noExitRuntime"]=true;run()
+
+
+
+
+  return Module;
+};
+function Viz(src) {
+  var options = arguments.length <= 1 || arguments[1] === undefined ? {} : arguments[1];
+  var format = options.format === undefined ? "svg" : options.format;
+  var engine = options.engine === undefined ? "dot" : options.engine;
+  var scale = options.scale;
+  var totalMemory = options.totalMemory;
+  var files = options.files === undefined ? [] : options.files;
+  var images = options.images === undefined ? [] : options.images;
+  var i;
+
+  for (i = 0; i < images.length; i++) {
+    files.push({ path: images[i].path, data: "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg width=\"" + images[i].width + "\" height=\"" + images[i].height + "\"></svg>" });
+  }
+
+  if (format == "png-image-element") {
+    return Viz.svgXmlToPngImageElement(render(src, "svg", engine, totalMemory, files), scale);
+  } else {
+    return render(src, format, engine, totalMemory, files);
+  }
+}
+
+function render(src, format, engine, totalMemory, files) {
+  var graphviz = Module({ TOTAL_MEMORY: totalMemory });
+  var i;
+
+  for (i = 0; i < files.length; i++) {
+    graphviz["ccall"]("vizCreateFile", "number", ["string", "string"], [files[i].path, files[i].data]);
+  }
+
+  var resultPointer = graphviz["ccall"]("vizRenderFromString", "number", ["string", "string", "string"], [src, format, engine]);
+  var resultString = graphviz["Pointer_stringify"](resultPointer);
+
+  var errorMessagePointer = graphviz["ccall"]("vizLastErrorMessage", "number", [], []);
+  var errorMessageString = graphviz["Pointer_stringify"](errorMessagePointer);
+
+  if (errorMessageString != "") {
+    throw new Error(errorMessageString);
+  }
+
+  return resultString;
+}
+
+// https://developer.mozilla.org/en-US/docs/Web/API/WindowBase64/Base64_encoding_and_decoding
+function b64EncodeUnicode(str) {
+  return btoa(encodeURIComponent(str).replace(/%([0-9A-F]{2})/g, function(match, p1) {
+    return String.fromCharCode('0x' + p1);
+  }));
+}
+
+Viz.svgXmlToPngImageElement = function(svgXml, scale, callback) {
+  if (scale === undefined) {
+    if ("devicePixelRatio" in window && window.devicePixelRatio > 1) {
+      scale = window.devicePixelRatio;
+    } else {
+      scale = 1;
+    }
+  }
+
+  var pngImage = new Image();
+
+  try {
+    if (typeof fabric === "object" && fabric.loadSVGFromString) {
+      fabric.loadSVGFromString(svgXml, function(objects, options) {
+        // If there's something wrong with the SVG, Fabric may return an empty array of objects. Graphviz appears to give us at least one <g> element back even given an empty graph, so we will assume an error in this case.
+        if (objects.length == 0) {
+          if (callback !== undefined) {
+            callback(new Error("Error loading SVG with Fabric"));
+            return;
+          } else {
+            throw new Error("Error loading SVG with Fabric");
+          }
+        }
+
+        var element = document.createElement("canvas");
+        element.width = options.width;
+        element.height = options.height;
+
+        var canvas = new fabric.Canvas(element, { enableRetinaScaling: false });
+        var obj = fabric.util.groupSVGElements(objects, options);
+        canvas.add(obj).renderAll();
+
+        pngImage.src = canvas.toDataURL({ multiplier: scale });
+        pngImage.width = options.width;
+        pngImage.height = options.height;
+
+        if (callback !== undefined) {
+          callback(null, pngImage);
+        }
+      });
+    } else {
+      var svgImage = new Image();
+
+      svgImage.onload = function() {
+        var canvas = document.createElement("canvas");
+        canvas.width = svgImage.width * scale;
+        canvas.height = svgImage.height * scale;
+
+        var context = canvas.getContext("2d");
+        context.drawImage(svgImage, 0, 0, canvas.width, canvas.height);
+
+        pngImage.src = canvas.toDataURL("image/png");
+        pngImage.width = svgImage.width;
+        pngImage.height = svgImage.height;
+
+        if (callback !== undefined) {
+          callback(null, pngImage);
+        }
+      }
+
+      svgImage.onerror = function(e) {
+        var error;
+
+        if ('error' in e) {
+          error = e.error;
+        } else {
+          error = new Error('Error loading SVG');
+        }
+
+        if (callback !== undefined) {
+          callback(error);
+        } else {
+          throw error;
+        }
+      }
+
+      svgImage.src = "data:image/svg+xml;base64," + b64EncodeUnicode(svgXml);
+    }
+  } catch (e) {
+    if (callback !== undefined) {
+      callback(e);
+    } else {
+      throw e;
+    }
+  }
+
+  if (callback === undefined) {
+    return pngImage;
+  }
+}
+
+Viz.svgXmlToPngBase64 = function(svgXml, scale, callback) {
+  Viz.svgXmlToPngImageElement(svgXml, scale, function(err, image) {
+    if (err) {
+      callback(err);
+    } else {
+      callback(null, image.src.slice("data:image/png;base64,".length));
+    }
+  });
+}
+if (typeof exports === 'object' && typeof module !== 'undefined') {
+  module.exports = Viz;
+} else if (typeof define === 'function' && define.amd) {
+  define(function() { return Viz; });
+} else {
+  global.Viz = Viz;
+}
+
+})(this);
diff --git a/_quarto.yml b/_quarto.yml
index 90dc3a1..af5eadc 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -36,17 +36,17 @@ book:
       - chapters/chapter-03.qmd
       - chapters/chapter-04.qmd
       - chapters/chapter-05.qmd
-      - chapters/chapter-06.qmd
       
     - part: The Design Phase 
       chapters: 
+      - chapters/chapter-06.qmd
       - chapters/chapter-07.qmd
       - chapters/chapter-08.qmd
       - chapters/chapter-09.qmd
-      - chapters/chapter-10.qmd
       
     - part: Estimating Causal Effects 
       chapters: 
+      - chapters/chapter-10.qmd
       - chapters/chapter-11.qmd
       - chapters/chapter-12.qmd
       - chapters/chapter-13.qmd
@@ -56,11 +56,6 @@ book:
       - chapters/chapter-17.qmd
       - chapters/chapter-18.qmd
       - chapters/chapter-19.qmd
-      - chapters/chapter-20.qmd
-      - chapters/chapter-21.qmd
-      - chapters/chapter-22.qmd
-      - chapters/chapter-23.qmd
-      - chapters/chapter-24.qmd
       
     - chapters/references-99.qmd
       
diff --git a/chapters/00-setup.qmd b/chapters/00-setup.qmd
index 4e6f544..cfb4974 100644
--- a/chapters/00-setup.qmd
+++ b/chapters/00-setup.qmd
@@ -1,6 +1,5 @@
 ```{r}
 #| include: false
-source(here::here("R/ggdag-mask.R"))
 source(here::here("R/setup.R"))
 library(tidyverse)
 ```
diff --git a/chapters/chapter-01.html b/chapters/chapter-01.html
new file mode 100644
index 0000000..886a697
--- /dev/null
+++ b/chapters/chapter-01.html
@@ -0,0 +1,870 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta name="generator" content="quarto-1.4.298">
+<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
+<title>Causal Inference in R - 1&nbsp; What is a causal question?</title>
+<style>
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+div.columns{display: flex; gap: min(4vw, 1.5em);}
+div.column{flex: auto; overflow-x: auto;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
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+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+        <nav id="TOC" role="doc-toc" class="toc-active"><h2 id="toc-title">Table of contents</h2>
+   
+  <ul>
+<li><a href="#schr%C3%B6dingers-causality" id="toc-schrödingers-causality" class="nav-link active" data-scroll-target="#schr%C3%B6dingers-causality"><span class="header-section-number">1.1</span> Schrödinger’s Causality</a></li>
+  <li>
+<a href="#description-prediction-and-explanation" id="toc-description-prediction-and-explanation" class="nav-link" data-scroll-target="#description-prediction-and-explanation"><span class="header-section-number">1.2</span> Description, prediction, and explanation</a>
+  <ul class="collapse">
+<li><a href="#description" id="toc-description" class="nav-link" data-scroll-target="#description"><span class="header-section-number">1.2.1</span> Description</a></li>
+  <li><a href="#prediction" id="toc-prediction" class="nav-link" data-scroll-target="#prediction"><span class="header-section-number">1.2.2</span> Prediction</a></li>
+  <li><a href="#causal-inference" id="toc-causal-inference" class="nav-link" data-scroll-target="#causal-inference"><span class="header-section-number">1.2.3</span> Causal Inference</a></li>
+  <li><a href="#sec-pred-or-explain" id="toc-sec-pred-or-explain" class="nav-link" data-scroll-target="#sec-pred-or-explain"><span class="header-section-number">1.2.4</span> Why isn’t the right causal model just the best prediction model?</a></li>
+  </ul>
+</li>
+  <li><a href="#sec-diag" id="toc-sec-diag" class="nav-link" data-scroll-target="#sec-diag"><span class="header-section-number">1.3</span> Diagraming a causal claim</a></li>
+  </ul><div class="toc-actions"><ul><li><a href="https://github.com/malcolmbarrett/causal-inference-in-r/edit/main/chapters/chapter-01.qmd" class="toc-action"><i class="bi bi-github"></i>Edit this page</a></li><li><a href="https://github.com/malcolmbarrett/causal-inference-in-r/issues/new" class="toc-action"><i class="bi empty"></i>Report an issue</a></li></ul></div></nav>
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+<main class="content" id="quarto-document-content"><header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../chapters/chapter-01.html">Asking Causal Questions</a></li><li class="breadcrumb-item"><a href="../chapters/chapter-01.html"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">What is a causal question?</span></a></li></ol></nav><div class="quarto-title">
+<h1 class="title"><span id="sec-causal-question" class="quarto-section-identifier"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">What is a causal question?</span></span></h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+  
+    
+  </div>
+  
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+
+</header><section id="schrödingers-causality" class="level2" data-number="1.1"><h2 data-number="1.1" class="anchored" data-anchor-id="schrödingers-causality">
+<span class="header-section-number">1.1</span> Schrödinger’s Causality</h2>
+<p>The heart of causal analysis is the causal question; it dictates what data we analyze, how we analyze it, and to which populations our inferences apply. This book, being applied in nature, deals primarily with the analysis stage of causal inference. Relative to the complexity of specifying a good causal question, the analysis stage is considerably more straightforward. In the first six chapters of this book, we’ll discuss what a causal question is, how to improve our questions, and consider some examples.</p>
+<p>Causal questions are part of a broader set of questions we can ask with statistical techniques related to the primary tasks of data science: description, prediction, and causal inference <span class="citation" data-cites="hernan2019">(<a href="#ref-hernan2019" role="doc-biblioref">Hernán, Hsu, and Healy 2019</a>)</span>. Unfortunately, these tasks are often muddled by the techniques we use (regression, for instance, is helpful for all three tasks) and how we talk about them. When researchers are interested in causal inference from non-randomized data, we often use euphemistic language like “association” instead of declaring our intent to estimate a causal effect <span class="citation" data-cites="Hernan2018">(<a href="#ref-Hernan2018" role="doc-biblioref">Hernán 2018</a>)</span>.</p>
+<p>In a recent study of the language of analyses in epidemiologic research, for instance, the most common root word describing the estimated effect was “associate,” but many researchers also felt that “associate” implied at least <em>some</em> causal effect (<a href="#fig-word-ranking" class="quarto-xref">Figure&nbsp;<span class="quarto-unresolved-ref">fig-word-ranking</span></a>) <span class="citation" data-cites="haber_causal_language">(<a href="#ref-haber_causal_language" role="doc-biblioref">Haber et al. 2022</a>)</span>. Only around 1% of the studies analyzed used the root word “cause” at all. Yet, a third of studies had action recommendations, and researchers rated 80% of these recommendations as having at least some causal implication. Often, these studies have stronger action recommendations (alluding to causal effects) than those implied by the description of the effect (root words like “associate” and “compare”). Despite how many studies implied that the goal was causal inference, only about 4% used formal causal models like those discussed in this book. However, most discussed how such a cause might be justified by previous research or theory.</p>
+<div class="cell">
+<div class="cell-output-display">
+<div id="fig-word-ranking" class="quarto-figure quarto-figure-center anchored">
+<figure class="figure"><p><img src="chapter-01_files/figure-html/fig-word-ranking-1.png" class="img-fluid figure-img" width="672"></p>
+<figcaption class="figure-caption">Figure&nbsp;1.1: Rankings of causal strength of root words used by researchers. Root words with more Strong rankings have stronger causal implications than those with many None or Weak rankings. Data from Haber et al.</figcaption></figure>
+</div>
+</div>
+</div>
+<p>Instead of clear questions with obvious assumptions and goals, we end up with “Schrödinger’s causal inference”:</p>
+<blockquote class="blockquote">
+<p>Our results suggest that “Schrödinger’s causal inference,” — where studies avoid stating (or even explicitly deny) an interest in estimating causal effects yet are otherwise embedded with causal intent, inference, implications, and recommendations — is common.</p>
+<p>— <span class="citation" data-cites="haber_causal_language">Haber et al. (<a href="#ref-haber_causal_language" role="doc-biblioref">2022</a>)</span></p>
+</blockquote>
+</section><section id="description-prediction-and-explanation" class="level2" data-number="1.2"><h2 data-number="1.2" class="anchored" data-anchor-id="description-prediction-and-explanation">
+<span class="header-section-number">1.2</span> Description, prediction, and explanation</h2>
+<p>An excellent first step to address this problem is recognizing that questions about description, prediction, and explanation are fundamentally different. Data science in industry isn’t quite as burdened by Schrödinger’s causal inference as the academic sciences, but being explicit in the differences in analytic intent is still helpful. For instance, when a stakeholder asks for “drivers” of a particular event, what are they asking? For a model to predict the event? For a deeper understanding of what causes the event? It’s a vague request, but it smacks of causal interest to us. When we’re clear about our goals, we can use all three approaches more effectively (and, as we’ll see, both descriptive analysis and prediction models are still helpful when the goal is to make causal inferences). Moreover, all three approaches are useful decision-making tools.</p>
+<section id="description" class="level3" data-number="1.2.1"><h3 data-number="1.2.1" class="anchored" data-anchor-id="description">
+<span class="header-section-number">1.2.1</span> Description</h3>
+<p>Descriptive analysis aims to describe the distribution of variables, often stratified by key variables of interest. A closely related idea is exploratory data analysis (EDA), although descriptive studies often have more explicit goals than those in EDA.</p>
+<p>Descriptive analyses are usually based on statistical summaries such as measures of centrality (means, medians) and spread (minimums, maximums, quartiles), but they also occasionally use techniques like regression modeling. The goal of applying more advanced techniques like regression is different in descriptive analyses than in either predictive or causal studies. “Adjusting” for a variable in descriptive analyses means that we are removing its associational effect (and thus changing our question), <em>not</em> that we are controlling for confounding.</p>
+<p>In epidemiology, a valuable concept for descriptive analyses is “person, place, and time” – who has what disease, where, and when. This concept is also a good template for descriptive analyses in other fields. Usually, we want to be clear about what population we’re trying to describe, so we need to be as specific as possible. For human health, describing the people involved, the location, and the period are all critical. In other words, focus on the first principles of generating understanding of your data and describe your data accordingly.</p>
+<section id="examples" class="level4" data-number="1.2.1.1"><h4 data-number="1.2.1.1" class="anchored" data-anchor-id="examples">
+<span class="header-section-number">1.2.1.1</span> Examples</h4>
+<p>Counting things is one of the best things we can do with data. EDA benefits both predictive and causal analyses, but descriptive analyses are valuable independent of the other analysis tasks. Ask any data scientist who thought they’d be developing complex machine learning models and found themselves spending most of their time on dashboards. Understanding the distributions of the data, particularly for key analysis goals (say, KPIs in industry or disease incidence in epidemiology), is critical for many types of decision-making.</p>
+<p>One of the best recent examples of descriptive analyses arose from the COVID-19 pandemic <span class="citation" data-cites="Fox2022">(<a href="#ref-Fox2022" role="doc-biblioref">Fox et al. 2022</a>)</span>. In 2020, particularly in the early months of the pandemic, descriptive analyses were vital to understanding risk and allocating resources. Since the coronavirus is similar to other respiratory diseases, we had many public health tools to reduce risk (e.g., distancing and, later, face masks). Descriptive statistics of cases by region were vital for deciding local policies and the strength of those policies.</p>
+<p>A great example of a more complex descriptive analysis during the pandemic was an <a href="https://www.ft.com/content/a2901ce8-5eb7-4633-b89c-cbdf5b386938">ongoing report by the Financial Times of expected deaths vs.&nbsp;observed deaths</a> in various countries and regions<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>. While the calculation of expected deaths is slightly more sophisticated than most descriptive statistics, it provided a tremendous amount of information about current deaths without needing to untangle causal effects (e.g., were they due to COVID-19 directly? Inaccessible healthcare? Cardiovascular events post-COVID?). In this (simplified) recreation of their plot from July 2020, you can see the staggering effect of the pandemic’s early months.</p>
+<div class="cell">
+<div class="cell-output-display">
+<div id="fig-ft-chart" class="quarto-figure quarto-figure-center anchored">
+<figure class="figure"><p><img src="chapter-01_files/figure-html/fig-ft-chart-1.png" class="img-fluid figure-img" width="960"></p>
+<figcaption class="figure-caption">Figure&nbsp;1.2: 2020 excess deaths vs.&nbsp;historical expected deaths from any cause. Data from the Financial Times.</figcaption></figure>
+</div>
+</div>
+</div>
+<p>Here are some other great examples of descriptive analyses.</p>
+<ul>
+<li>Deforestation around the world. Our World in Data <span class="citation" data-cites="owidforestsanddeforestation">(<a href="#ref-owidforestsanddeforestation" role="doc-biblioref">Ritchie and Roser 2021</a>)</span> is a data journalism organization that produces thoughtful, usually descriptive reports on various topics. In this report, they present data visualizations of both absolute change in forest coverage (forest transitions) and relative change (deforestation or reforestation), using basic statistics and forestry theory to present helpful information about the state of forests over time.</li>
+<li>The prevalence of chlamydial and gonococcal infections <span class="citation" data-cites="Miller2004">(<a href="#ref-Miller2004" role="doc-biblioref">Miller 2004</a>)</span>. Measuring the prevalence of disease (how many people currently have a disease, usually expressed as a rate per number of people) is helpful for basic public health (resources, prevention, education) and scientific understanding. In this study, the authors conducted a complex survey meant to be representative of all high schools in the United States (the target population); they used survey weights to address a variety of factors related to their question, then calculated prevalence rates and other statistics. As we’ll see, weights are helpful in causal inference for the same reason: targeting a particular population. That said, not all weighting techniques are causal in nature, and they were not here.</li>
+<li>Estimating race and ethnicity-specific hysterectomy inequalities <span class="citation" data-cites="Gartner2020">(<a href="#ref-Gartner2020" role="doc-biblioref">Gartner et al. 2020</a>)</span>. Descriptive techniques also help us understand disparities in areas like economics and epidemiology. In this study, the authors asked: Does the risk of hysterectomy differ by racial or ethnic background? Although the analysis is stratified by a key variable, it’s still descriptive. Another interesting aspect of this paper is the authors’ work ensuring the research answered questions about the right target population. Their analysis combined several data sources to better estimate the true population prevalence (instead of the prevalence among those in hospitals, as commonly presented). They also adjusted for the prevalence of hysterectomy, e.g., they calculated the incidence (new case) rate only among those who could actually have a hysterectomy (e.g., they hadn’t had one yet).</li>
+</ul></section><section id="validity" class="level4" data-number="1.2.1.2"><h4 data-number="1.2.1.2" class="anchored" data-anchor-id="validity">
+<span class="header-section-number">1.2.1.2</span> Validity</h4>
+<p>There are two critical validity issues in descriptive analyses: measurement and sampling errors.</p>
+<p>Measurement error is when we have mismeasured one or more variables in some capacity. For descriptive analyses, mismeasuring things means we may not get the answer to our question. However, the degree to which that is the case depends on both the severity of the measurement error and the question itself.</p>
+<p>Sampling error is a more nuanced topic in descriptive analyses. It’s related to the population we’re analyzing (who should the analysis produce descriptions about) and uncertainty (how certain are we that the descriptions of those we have data for represent the population we’re trying to describe.).</p>
+<p>The population from which our data come and the population we’re trying to describe must be the same for us to provide valid descriptions. Consider a dataset generated by an online survey. Who are the people who are answering these questions, and how do they relate to the people we want to describe? For many analyses, the people who take the time to answer surveys are different than the people we want to describe, e.g., the group of people who fill out surveys may have a different distribution of variables than those who don’t. Results from data like these are not technically biased because, outside of sample size-related uncertainty and measurement error, the descriptions are accurate—they’re just not for the right group of people! In other words, <em>we’ve gotten an answer to the wrong question</em>.</p>
+<p>Notably, sometimes our data represent the entire population (or close enough) that sampling error is irrelevant. Consider a company with certain data on every customer using their service. For many analyses, this represents the entire population (current customers) about whom we want information. Similarly, in countries with population-wide health registries, the data available for specific practical purposes is close enough to the entire population that no sampling is needed (although researchers might use sampling for simpler computations). In these cases, there’s not really such a thing as uncertainty. Assuming everything is well-measured, the summary statistics we generate <em>are inherently unbiased and precise</em> because we have information from everyone in the population. Of course, in practice, we usually have some mixture of measurement error, missing data, and so on, even in the best of circumstances.</p>
+<p>One crucial detail of descriptive analysis is that confounding bias, one of the chief concerns of this book, is undefined. That’s because confounding is a causal concern. Descriptive analyses cannot be confounded because they are a statistical description of relationships <em>as-is</em>, not the mechanisms behind those relationships.</p>
+</section><section id="relationship-to-causal-inference" class="level4" data-number="1.2.1.3"><h4 data-number="1.2.1.3" class="anchored" data-anchor-id="relationship-to-causal-inference">
+<span class="header-section-number">1.2.1.3</span> Relationship to causal inference</h4>
+<p>Humans see patterns very well. Pattern-finding is a helpful feature of our brains, but it can also lead down a slippery slope of inference when we’re not working with data or methods that can allow us to do that validly. The biggest thing to be cautious of when your goal is to describe is making the leap from description to causation (implicitly or explicitly).</p>
+<p>But, of course, descriptive analysis is helpful when we <em>are</em> estimating causal effects. It helps us understand the population we’re working with, the distribution of the outcomes, exposures (variables we think might be causal), and confounders (variables we need to account for to get unbiased causal effects for the exposure). It also helps us be sure that the data structure we’re using matches the question we’re trying to answer, as we’ll see in <a href="#sec-data-causal" class="quarto-xref">Chapter&nbsp;<span class="quarto-unresolved-ref ref-noprefix">sec-data-causal</span></a>. You should always do descriptive analyses of your data when conducting causal research.</p>
+<p>Finally, as we’ll see in <a href="#sec-trials-std" class="quarto-xref">Chapter&nbsp;<span class="quarto-unresolved-ref ref-noprefix">sec-trials-std</span></a>, there are certain circumstances where we can make causal inferences with basic statistics. Be cautious about the distinction between the causal question and the descriptive component here, too: just because we’re using the same calculation (e.g., a difference in means) doesn’t mean that all descriptions you can generate are causal. Whether a descriptive analysis overlaps with a causal analysis is a function of the data and the question.</p>
+</section></section><section id="prediction" class="level3" data-number="1.2.2"><h3 data-number="1.2.2" class="anchored" data-anchor-id="prediction">
+<span class="header-section-number">1.2.2</span> Prediction</h3>
+<p>The goal of prediction is to use data to make accurate predictions about variables, usually on new data. What this means depends on the question, domain, and so on. Prediction models are used in about every setting imaginable, from peer-reviewed clinical models to bespoke machine learning models embedded in consumer devices. Even large language models like the ones ChatGPT is based on are prediction models: they predict what a response to a prompt would look like.</p>
+<p>Predictive modeling generally uses a different workflow than the workflow for causal modeling we’ll present in this book. Since the goal of prediction is usually related to making predictions on new data, the workflow of this type of modeling focuses on maximizing predictive accuracy while retaining generalization to new data, sometimes called the bias-variance trade-off. In practice, this means splitting your data into training sets (the part of the data you build your model on) and test sets (the part you assess your model with, a proxy for how it would perform on new data). Usually, data scientists use cross-validation or other sampling techniques to reduce further the risk of overfitting your model to the training set.</p>
+<p>There are many excellent texts on predictive modeling, and so we refer you to those for a deeper exploration of the goals and methods of these techniques <span class="citation" data-cites="kuhn2013a harrell2001 Kuhn_Silge_2022 James_Witten_Hastie_Tibshirani_2022">(<a href="#ref-kuhn2013a" role="doc-biblioref">Kuhn and Johnson 2013</a>; <a href="#ref-harrell2001" role="doc-biblioref">Harrell 2001</a>; <a href="#ref-Kuhn_Silge_2022" role="doc-biblioref">Kuhn and Silge 2022</a>; <a href="#ref-James_Witten_Hastie_Tibshirani_2022" role="doc-biblioref">James et al. 2022</a>)</span>.</p>
+<section id="examples-1" class="level4" data-number="1.2.2.1"><h4 data-number="1.2.2.1" class="anchored" data-anchor-id="examples-1">
+<span class="header-section-number">1.2.2.1</span> Examples</h4>
+<p>Prediction is the most popular topic in data science, largely thanks to machine learning applications in industry. Prediction, of course, has a long history in statistics, and many models popular today have been used for decades in and outside academia.</p>
+<p>Let’s look at an example of prediction about COVID-19 <a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a>. In 2021, researchers published the ISARIC 4C Deterioration model, a clinical prognostic model for predicting severe adverse outcomes for acute COVID-19 <span class="citation" data-cites="Gupta2021">(<a href="#ref-Gupta2021" role="doc-biblioref">Gupta et al. 2021</a>)</span>. The authors included a descriptive analysis to understand the population from which this model was developed, particularly the distribution of the outcome and candidate predictors. One helpful aspect of this model is that it uses items commonly measured on day one of COVID-related hospitalization. The authors built this model using cross-validation by region of the UK and then tested the model on data from a hold-out region. The final model included eleven items and a description of their model attributes, relation with the outcome, and so on. Notably, the authors used clinical domain knowledge to select candidate variables but did not fall into the temptation of interpreting the model coefficients as causal. Without question, some of the predictive value of this model stems from the causal structure of the variables as they relate to the outcome, but the researchers had a different goal entirely for this model and stuck to it.</p>
+<p>Here are other good examples from the predictive space:</p>
+<ul>
+<li><p>Some of the most exciting work in predictive modeling is in industry. Netflix regularly shares details on their modeling success and novel strategies in their <a href="https://research.netflix.com/">research blog</a>. They also recently published a paper reviewing their use of deep learning models for recommender systems (in this case, recommending shows and movies to users) <span class="citation" data-cites="steck2021">(<a href="#ref-steck2021" role="doc-biblioref">Steck et al. 2021</a>)</span>. The authors explain their experimentation with models, the details of those models, and many of the challenges they faced, resulting in a practical guide on using such models.</p></li>
+<li><p>In early 2020, researchers experienced with predictive and prognostic modeling in health research published a review of models for diagnosis and prognosis of COVID-19 <span class="citation" data-cites="Wynants2020">(<a href="#ref-Wynants2020" role="doc-biblioref">Wynants et al. 2020</a>)</span>. This review is interesting not just for its breadth but also the astounding number of models that were rated as poor quality: “[232] models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting.” This research is also a <a href="https://www.covprecise.org/">living review</a>.</p></li>
+</ul></section><section id="validity-1" class="level4" data-number="1.2.2.2"><h4 data-number="1.2.2.2" class="anchored" data-anchor-id="validity-1">
+<span class="header-section-number">1.2.2.2</span> Validity</h4>
+<p>The key measure of validity in prediction modeling is predictive accuracy, which can be measured in several ways, such as root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC), and many more. A convenient detail about predictive modeling is that we can often assess if we’re right, which is not true of descriptive statistics for which we only have a subset of data or causal inference for which we don’t know the true causal structure. We aren’t always able to assess against the truth, but it’s almost always required for fitting the initial predictive model <a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a>.</p>
+<p>Measurement error is also a concern for predictive modeling because we usually need accurate data for accurate predictions. Interestingly, measurement error and missingness can be informative in predictive settings. In a causal setting, this might induce bias, but predictive models can consume that information with no issue. For instance, in the famous Netflix Prize, winning models leveraged information about whether or not a customer rated a movie at all to improve recommendation systems.</p>
+<p>Like descriptive analysis, confounding is undefined for predictive modeling. A coefficient in a prediction model cannot be confounded; we only care about whether or not the variable provides predictive information, not if that information is because of a causal relationship or something else.</p>
+</section><section id="relationship-to-causal-inference-1" class="level4" data-number="1.2.2.3"><h4 data-number="1.2.2.3" class="anchored" data-anchor-id="relationship-to-causal-inference-1">
+<span class="header-section-number">1.2.2.3</span> Relationship to causal inference</h4>
+<p>The single biggest risk in prediction is to assume that a given coefficient in a model has a causal interpretation. There is a good chance that this isn’t so. A model may predict well but may also have completely biased coefficients from a causal point of view. We’ll see more about this in <a href="#sec-pred-or-explain" class="quarto-xref"><span class="quarto-unresolved-ref">sec-pred-or-explain</span></a> and the rest of the book.</p>
+<p>Often, people mistakenly use methods for selecting features (variables) for prediction models to select confounders in causal models. Aside from their risk of overfitting, these methods are appropriate for prediction models but not for causal models. Prediction metrics cannot determine the causal structure of your question, and predictive value for the outcome does not make a variable a confounder. In general, background knowledge (not prediction or associational statistics) should help you select variables for causal models <span class="citation" data-cites="robinsImpossible">Robins and Wasserman (<a href="#ref-robinsImpossible" role="doc-biblioref">1999</a>)</span>; we’ll discuss this process in detail in <a href="#sec-dags" class="quarto-xref">Chapter&nbsp;<span class="quarto-unresolved-ref ref-noprefix">sec-dags</span></a> and <a href="#sec-building-models" class="quarto-xref">Chapter&nbsp;<span class="quarto-unresolved-ref ref-noprefix">sec-building-models</span></a>.</p>
+<p>Prediction is nevertheless crucial to causal inference. From a philosophical perspective, we’re comparing predictions from different <em>what if</em> scenarios: What would the outcome had one thing happened vs.&nbsp;if another thing happened? We’ll spend much time on this subject, particularly in <a href="#sec-counterfactuals" class="quarto-xref">Chapter&nbsp;<span class="quarto-unresolved-ref ref-noprefix">sec-counterfactuals</span></a>. We’ll also talk a lot about prediction from a practical perspective: just like in prediction and some description, we’ll use modeling techniques to answer causal questions. Techniques like propensity score methods and g-computation use model predictions to answer causal questions, but the workflow for building and interpreting the models themselves are quite different.</p>
+</section></section><section id="causal-inference" class="level3" data-number="1.2.3"><h3 data-number="1.2.3" class="anchored" data-anchor-id="causal-inference">
+<span class="header-section-number">1.2.3</span> Causal Inference</h3>
+<p>The goal of causal inference is to understand the impact that a variable, sometimes called an exposure, has on another variable, sometimes called an outcome. “Exposure” and “outcome” are the terms we’ll use in this book to describe the causal relationship we’re interested in. Importantly, our goal is to answer this question clearly and precisely. In practice, this means using techniques like study design (e.g., a randomized trial) or statistical methods (like propensity scores) to calculate an unbiased effect of the exposure on the outcome.</p>
+<p>As with prediction and description, it’s better to start with a clear, precise question to get a clear, precise answer. In statistics and data science, particularly as we swim through the ocean of data of the modern world, we often end up with an answer without a question (e.g., <code>42</code>). This, of course, makes interpretation of the answer difficult. In <a href="#sec-diag" class="quarto-xref"><span class="quarto-unresolved-ref">sec-diag</span></a>, we’ll discuss the structure of causal questions. We’ll discuss philosophical and practical ways to sharpen our questions in <a href="#sec-counterfactuals" class="quarto-xref">Chapter&nbsp;<span class="quarto-unresolved-ref ref-noprefix">sec-counterfactuals</span></a> and <a href="#sec-trials-std" class="quarto-xref">Chapter&nbsp;<span class="quarto-unresolved-ref ref-noprefix">sec-trials-std</span></a>.</p>
+<div class="callout callout-style-simple callout-note callout-titled">
+<div class="callout-header d-flex align-content-center">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-title-container flex-fill">
+Causal inference and explanation
+</div>
+</div>
+<div class="callout-body-container callout-body">
+<p>Some authors use the phrases “causal inference” and “explanation” interchangeably. We’re a little more cautious about that. Causal inference always has a relationship to explanation, but we can accurately estimate the effect of one thing on another without understanding how it happens.</p>
+<p>Consider John Snow, the so-called father of epidemiology. In 1854, Snow famously investigated a cholera outbreak in London and identified that specific water sources were to blame for the disease. He was right: contaminated water was a mechanism for cholera transmission. Yet, he didn’t have enough information to explain how: <em>Vibrio cholerae</em>, the bacteria responsible for cholera, wasn’t identified until nearly thirty years later.</p>
+</div>
+</div>
+<section id="examples-2" class="level4" data-number="1.2.3.1"><h4 data-number="1.2.3.1" class="anchored" data-anchor-id="examples-2">
+<span class="header-section-number">1.2.3.1</span> Examples</h4>
+<p>We’ll see many examples of causal inference in this book, but let’s continue with an example related to COVID-19. As the pandemic continued and tools like vaccines and anti-viral treatments became available, policies like universal masking also began to change. In February 2022, the US state of Massachusetts rescinded a statewide policy that required universal masking in public schools <span class="citation" data-cites="Cowger2022">(<a href="#ref-Cowger2022" role="doc-biblioref">Cowger et al. 2022</a>)</span>. In the greater Boston area, some school districts continued the policy while others discontinued it; those that discontinued it also did so at different times over the following weeks after the policy change. This difference in policy allowed researchers to take advantage of the differences in district policies over this period to study the impact of universal masking on COVID-19 cases. The researchers included a descriptive analysis of the school districts to understand the distribution of factors related to COVID-19 and other determinants of health. To estimate the effect of universal masking on cases, the authors used a technique common in policy-related causal inference called difference-in-differences to estimate this effect. Their design alleviates some problematic assumptions needed for causal inference, but they also wisely controlled for potential confounders despite that advantage. The authors found that districts that continued masking saw a drastically lower caseload than those that didn’t; their analysis concluded that almost 12,000 additional cases occurred due to the policy change, nearly 30% of the cases in the districts during the 15 weeks of the study.</p>
+<!-- TODO: add example when code and data are available -->
+<p>Here are a few other interesting examples:</p>
+<ul>
+<li><p>Netflix regularly uses causal inference in their work. In 2022, they published a <a href="https://netflixtechblog.com/a-survey-of-causal-inference-applications-at-netflix-b62d25175e6f">blog post summarizing some causal tasks</a> they have engaged with. One interesting example is localization. Netflix, being worldwide, localizes content through subtitles and dubbing. Randomized experiments were a bad idea because they meant withholding content from users, so researchers at Netflix used several approaches to understand the value of localization while addressing potential confounding. One example is studying the impact of pandemic-related delays in dubbing. Researchers used synthetic controls to simulate the impact on viewership with and without these delays. Presumably, the timing of the pandemic-related delays was unrelated to many factors that would typically be related to dubbing processes, thus reducing some of the potential confounding.</p></li>
+<li><p>The Tuskegee Study is one of modern history’s most infamous examples of medical abuse. It is commonly pointed to as a source of distrust in the medical community from Black Americans. Health economics researchers used a variation of difference-in-difference techniques to assess the effect of the Tuskegee Study on distrust and life expectancy in older Black men <span class="citation" data-cites="Alsan2018">(<a href="#ref-Alsan2018" role="doc-biblioref">Alsan and Wanamaker 2017</a>)</span>. The results are important and disturbing: “We find that the disclosure of the study in 1972 is correlated with increases in medical mistrust and mortality and decreases in both outpatient and inpatient physician interactions for older black men. Our estimates imply life expectancy at age 45 for black men fell by up to 1.5 years in response to the disclosure, accounting for approximately 35% of the 1980 life expectancy gap between black and white men and 25% of the gap between black men and women.”</p></li>
+</ul></section><section id="validity-2" class="level4" data-number="1.2.3.2"><h4 data-number="1.2.3.2" class="anchored" data-anchor-id="validity-2">
+<span class="header-section-number">1.2.3.2</span> Validity</h4>
+<p>Making valid causal inferences requires several assumptions that we’ll discuss in <a href="#sec-assump" class="quarto-xref"><span class="quarto-unresolved-ref">sec-assump</span></a>. Unlike prediction, we generally cannot confirm that our causal models are correct. In other words, most assumptions we need to make are unverifiable. We’ll come back to this topic time and time again in the book—from the basics of these assumptions to practical decision-making to probing our models for problems.</p>
+</section></section><section id="sec-pred-or-explain" class="level3" data-number="1.2.4"><h3 data-number="1.2.4" class="anchored" data-anchor-id="sec-pred-or-explain">
+<span class="header-section-number">1.2.4</span> Why isn’t the right causal model just the best prediction model?</h3>
+<p>At this point, you may wonder why the right causal model isn’t just the best prediction model. It makes sense that the two would be related: naturally, things that cause other things would be predictors. It’s causality all the way down, so any predictive information <em>is</em> related, in some capacity, to the causal structure of the thing we’re predicting. Doesn’t it stand to reason that a model that predicts well is causal, too? It’s true that <em>some</em> predictive models can be great causal models and vice versa. Unfortunately, this is not always the case; causal effects needn’t predict particularly well, and good predictors needn’t be causally unbiased <span class="citation" data-cites="shmueli2010a">(<a href="#ref-shmueli2010a" role="doc-biblioref">Shmueli 2010</a>)</span>. There is no way to know using data alone.</p>
+<p>Let’s look at the causal perspective first because it’s a bit simpler. Consider a causally unbiased model for an exposure but only includes variables related to the outcome <em>and</em> the exposure. In other words, this model provides us with the correct answer for the exposure of interest but doesn’t include other predictors of the outcome (which can sometimes be a good idea, as discussed in <a href="#sec-data-causal" class="quarto-xref"><span class="quarto-unresolved-ref">sec-data-causal</span></a>). If an outcome has many causes, a model that accurately describes the relationship with the exposure likely won’t predict the outcome very well. Likewise, if a true causal effect of the exposure on the outcome is small, it will bring little predictive value. In other words, the predictive ability of a model, whether high or small, can’t help us distinguish if the model is giving us the correct answer. Of course, low predictive power might also indicate that a causal effect isn’t much use from an applied perspective, although that depends on several statistical factors.</p>
+<p>There are two more complex reasons that predictive models won’t always be unbiased causal models. For the first reason, let’s consider an accurate model from a causal perspective: it estimates effects on an outcome, and all of these effects are unbiased. Even in this ideal setting, you might get better predictions using a different model. The reason has to do with the bias-variance trade-off in predictive modeling. When effects are small, data are noisy, predictors are highly correlated, or there’s not much data, using a biased model like penalized regression might make sense. These models intentionally introduce bias in favor of improved variance in out-of-data predictions. Since the goals of prediction and causal inference are different (accurate predictions, usually for out-of-data observations vs.&nbsp;an unbiased effect), the best model for inference is not necessarily the best prediction model.</p>
+<!-- TODO: maybe include a simulation to prove it? -->
+<p>Secondly, variables that are biased from a causal perspective often bring along with them predictive power. We’ll discuss which variables to include and <em>not</em> include in your models in <a href="#sec-dags" class="quarto-xref"><span class="quarto-unresolved-ref">sec-dags</span></a>, but let’s consider a simple example. One of the famous examples of confounded relationships is ice cream sales and crime in the summer. <a href="https://slate.com/news-and-politics/2013/07/warm-weather-homicide-rates-when-ice-cream-sales-rise-homicides-rise-coincidence.html">Descriptively, ice cream sales and crime are related</a>, but this relationship is confounded by weather, e.g., both ice cream sales and crime increases when it’s warmer. (This is simplistic, of course, as weather itself doesn’t cause crime, but it’s on the causal pathway.)</p>
+<p>Consider a thought experiment where you are in a dark room. Your goal is to predict crime, but you don’t know the weather or time of year. You do, however, have information on ice cream sales. A model with ice cream sales on crime would be biased from a causal perspective—ice cream sales do not cause crime, even though the model would show an effect—but would provide some predictive value to your crime model. The reason for both of these conditions is the same: weather and ice cream sales are correlated, and so are weather and crime. Ice cream sales can successfully, if imperfectly, serve as a proxy for weather. That results in a biased effect estimate of the causal impact of ice cream sales on crime but a partially effective prediction of crime. Other variables, too, which are invalid from a causal perspective, either by being biased themselves or by introducing bias into the causal effect estimate, often bring good predictive value. Thus, predictive accuracy is not a good measure of causality.</p>
+<p>A closely related idea is the <em>Table Two Fallacy</em>, so-called because, in health research papers, descriptive analyses are often presented in Table 1, and regression models are often presented in Table 2 <span class="citation" data-cites="Westreich2013">(<a href="#ref-Westreich2013" role="doc-biblioref">Westreich and Greenland 2013</a>)</span>. The Table Two Fallacy is when a researcher presents confounders and other non-effect variables, particularly when they interpret those coefficients as if they, too, were causal. The problem is that in some situations, the model to estimate an unbiased effect of one variable may not be the same model to estimate an unbiased effect of another variable. In other words, we can’t interpret the effects of confounders as causal because they might <em>themselves</em> be confounded by another variable unrelated to the original exposure.</p>
+<p>Descriptive, predictive, and causal analyses will always contain some aspect of each other. A predictive model gains some of its predictive power from the causal structure of the outcome, and a causal model has some predictive power because it contains information about the outcome. However, the same model in the same data with different goals will have different usefulness depending on those goals. <!-- TODO: uncomment this when section is written -- We'll dive more deeply into this topic in @sec-causal-pred-revisit. --></p>
+</section></section><section id="sec-diag" class="level2" data-number="1.3"><h2 data-number="1.3" class="anchored" data-anchor-id="sec-diag">
+<span class="header-section-number">1.3</span> Diagraming a causal claim</h2>
+<p>Each analysis task, whether descriptive, predictive, or inferential, should start with a clear, precise question. Let’s diagram them to understand better the structure of causal questions (to which we’ll return our focus). Diagramming sentences is a grammatical method used to visually represent the structure of a sentence, occasionally taught in grammar school. In this technique, sentences are deconstructed into their constituent parts, such as subjects, verbs, objects, and modifiers, and then displayed using a series of lines and symbols. The arrangement of these elements on the diagram reflects their syntactic roles and how they interact within the sentence’s overall structure. By breaking down sentences into these visual representations, diagramming can help learners grasp the nuances of sentence construction, identify grammatical errors, and appreciate the intricate connections between words. We can apply a similar idea to <em>causal claims</em>. Here is an example of how one might diagram a causal claim. We’ve pulled out the <em>cause</em>, the <em>effect</em>, the <em>subject</em> (for whom?), and the <em>timing</em> (when?).</p>
+<div class="cell">
+<div class="cell-output-display">
+<div id="fig-diagram-1" class="quarto-figure quarto-figure-center anchored">
+<figure class="figure"><p><img src="../images/sentence-diagram-1.png" class="img-fluid figure-img" width="2219"></p>
+<figcaption class="figure-caption">Figure&nbsp;1.3: Example of diagraming a causal claim.</figcaption></figure>
+</div>
+</div>
+</div>
+<p>Let’s start with a basic causal question: <strong>Does smoking cause lung cancer?</strong></p>
+<p>The causal claim here could be that <em>smoking causes lung cancer</em>. <a href="#fig-diagram-2" class="quarto-xref">Figure&nbsp;<span class="quarto-unresolved-ref">fig-diagram-2</span></a> shows a potential diagram of this causal claim.</p>
+<div class="cell">
+<div class="cell-output-display">
+<div id="fig-diagram-2" class="quarto-figure quarto-figure-center anchored">
+<figure class="figure"><p><img src="../images/sentence-diagram-2.png" class="img-fluid figure-img" width="2219"></p>
+<figcaption class="figure-caption">Figure&nbsp;1.4: Diagram of the causal claim “smoking causes lung cancer”.</figcaption></figure>
+</div>
+</div>
+</div>
+<p>Let’s get more specific. A study was published in <em>JAMA</em> (the Journal of the American Medical Association) in 2005 titled “Effect of Smoking Reduction on Lung Cancer Risk.” This study concluded: “Among individuals who smoke 15 or more cigarettes per day, smoking reduction by 50% significantly reduces the risk of lung cancer”. <span class="citation" data-cites="godtfredsen2005effect">(<a href="#ref-godtfredsen2005effect" role="doc-biblioref">Godtfredsen, Prescott, and Osler 2005</a>)</span> The study describes the time frame studied as 5-10 years. Let’s diagram this causal claim. Here, we assume that the eligibility criteria and the target population for the estimated causal effect are the same (individuals who smoke 15 or more cigarettes per day); this need not always be the case. In <a href="#sec-estimands" class="quarto-xref"><span class="quarto-unresolved-ref">sec-estimands</span></a>, we will discuss other potential target populations.</p>
+<div class="cell">
+<div class="cell-output-display">
+<div id="fig-diagram-3" class="quarto-figure quarto-figure-center anchored">
+<figure class="figure"><p><img src="../images/sentence-diagram-3.png" class="img-fluid figure-img" width="2222"></p>
+<figcaption class="figure-caption">Figure&nbsp;1.5: Example diagram of a more specific causal claim based on results from <span class="citation" data-cites="godtfredsen2005effect">Godtfredsen, Prescott, and Osler (<a href="#ref-godtfredsen2005effect" role="doc-biblioref">2005</a>)</span>.</figcaption></figure>
+</div>
+</div>
+</div>
+<p>Translating this idea into asking good causal questions, we can map the following terms that you will see throughout this book to these diagrams: <em>exposure</em> (the cause), <em>outcome</em> (the effect), <em>eligibility criteria</em> (for whom?), <em>time zero</em> (when did the participants begin to be followed?), <em>target population</em>, (who can we estimate an outcome effect for?) and <em>follow-up period</em> (when?).</p>
+<div class="cell">
+<div class="cell-output-display">
+<div id="fig-diagram-4" class="quarto-figure quarto-figure-center anchored">
+<figure class="figure"><p><img src="../images/sentence-diagram-4.png" class="img-fluid figure-img" width="2219"></p>
+<figcaption class="figure-caption">Figure&nbsp;1.6: Example diagram mapped to causal analysis terminology</figcaption></figure>
+</div>
+</div>
+</div>
+<p>Asking good causal questions means we map the <em>question</em> to the observable <em>evidence</em>. Let’s return to the smoking example. Our initial question was: <em>Does smoking cause lung cancer?</em>; The evidence in the study shows: <em>For people who smoke 15+ cigarettes a day, reducing smoking by 50% reduces the risk of lung cancer over 5-10 years</em>. Does the answer match the question? Not quite. Let’s update our question to match what the study actually showed: <em>For people who smoke 15+ cigarettes a day, does reducing smoking by 50% reduce the lung cancer risk over 5-10 years?</em> Honing this skill — asking answerable causal questions — is essential and one we will discuss throughout this book.</p>
+
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+</div>
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+</div>
+</div>
+</section><aside id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"><hr>
+<ol>
+<li id="fn1"><p>John Burn-Murdoch was responsible for many of these presentations and gave a <a href="https://cloud.rstudio.com/resources/rstudioglobal-2021/reporting-on-and-visualising-the-pandemic/">fascinating talk on the subject</a>.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
+<li id="fn2"><p>A natural model here is predicting cases, but infectious disease modeling is complex and usually uses techniques outside the usual predictive modeling workflow.<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
+<li id="fn3"><p>We say model singular, but usually data scientists fit many models for experimentation, and often the best prediction models are some combination of predictions from several models, called a stacked model<a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
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+      };
+      const unselectCodeLines = () => {
+        const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
+        elementsIds.forEach((elId) => {
+          const div = window.document.getElementById(elId);
+          if (div) {
+            div.remove();
+          }
+        });
+        selectedAnnoteEl = undefined;
+      };
+      // Attach click handler to the DT
+      const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
+      for (const annoteDlNode of annoteDls) {
+        annoteDlNode.addEventListener('click', (event) => {
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+            unselectCodeLines();
+            const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
+            if (activeEl) {
+              activeEl.classList.remove('code-annotation-active');
+            }
+            selectCodeLines(clickedEl);
+            clickedEl.classList.add('code-annotation-active');
+          } else {
+            // Unselect the line
+            unselectCodeLines();
+            clickedEl.classList.remove('code-annotation-active');
+          }
+        });
+      }
+  const findCites = (el) => {
+    const parentEl = el.parentElement;
+    if (parentEl) {
+      const cites = parentEl.dataset.cites;
+      if (cites) {
+        return {
+          el,
+          cites: cites.split(' ')
+        };
+      } else {
+        return findCites(el.parentElement)
+      }
+    } else {
+      return undefined;
+    }
+  };
+  var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
+  for (var i=0; i<bibliorefs.length; i++) {
+    const ref = bibliorefs[i];
+    const citeInfo = findCites(ref);
+    if (citeInfo) {
+      tippyHover(citeInfo.el, function() {
+        var popup = window.document.createElement('div');
+        citeInfo.cites.forEach(function(cite) {
+          var citeDiv = window.document.createElement('div');
+          citeDiv.classList.add('hanging-indent');
+          citeDiv.classList.add('csl-entry');
+          var biblioDiv = window.document.getElementById('ref-' + cite);
+          if (biblioDiv) {
+            citeDiv.innerHTML = biblioDiv.innerHTML;
+          }
+          popup.appendChild(citeDiv);
+        });
+        return popup.innerHTML;
+      });
+    }
+  }
+});
+</script><nav class="page-navigation"><div class="nav-page nav-page-previous">
+      <a href="../index.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Preface</span>
+      </a>          
+  </div>
+  <div class="nav-page nav-page-next">
+      <a href="../chapters/chapter-02.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">The whole game: mosquito nets and malaria</span></span> <i class="bi bi-arrow-right-short"></i>
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+</nav>
+</div> <!-- /content -->
+
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diff --git a/chapters/chapter-02.qmd b/chapters/chapter-02.qmd
index cc5f718..64d1956 100644
--- a/chapters/chapter-02.qmd
+++ b/chapters/chapter-02.qmd
@@ -1,4 +1,4 @@
-# The whole game: mosquito nets and malaria {#sec-whole-game}
+# The whole game: mosquito nets and malaria
 
 {{< include 00-setup.qmd >}}
 
diff --git a/chapters/chapter-05.qmd b/chapters/chapter-05.qmd
index 15bcf15..a7b5a16 100644
--- a/chapters/chapter-05.qmd
+++ b/chapters/chapter-05.qmd
@@ -4,99 +4,86 @@
 
 ## Visualizing Causal Assumptions
 
-> Draw your assumptions before your conclusions --@hernan2021
+So you think correlation isn't causation?
+You better be ready to name some proposed confounders!
+In @sec-assump we discuss several assumptions that allow us to estimate unbiased causal effects with our current statistical tools; one of the main assumptions is *exchangeability*, also known as "no unmeasured confounders".
+There is no statistical test that can confirm whether this assumption is met.
+Instead, we often use domain knowledge to construct an assumed world view of how different measured (or unmeasured) factors interact, and then *assuming that world view is correct* determine whether the proposed analysis included any unmeasured confounders.
+One way to communicate one's world view with respect to how different factors interact is via a directed acyclic graph (DAG).
 
-Causal diagrams are a tool to visualize your assumptions about the causal structure of the questions you're trying to answer.
-In a randomized experiment, the causal structure is quite simple.
-While there may be many causes of an outcome, the only cause of the exposure is the randomization process itself (we hope!).
-In many non-randomized settings, however, the structure of your question can be a complex web of causality.
-Causal diagrams help communicate what we think this structure looks like.
-In addition to being open about what we think the causal structure is, causal diagrams have incredible mathematical properties that allow us to identify a way to estimate unbiased causal effects even with observational data.
-
-Causal diagrams are also increasingly common.
-Data collected as a review of causal diagrams in applied health research papers show a drastic increase in use over time [@Tennant2021].
-
-```{r}
-#| label: fig-dag-usage
-#| echo: false
-#| warning: false
-#| fig-cap: "Percentage of health research papers using causal diagrams over time."
-dag_data <- readxl::read_xlsx(here::here("data", "dag_data.xlsx"))
-
-dag_data <- dag_data |> 
-  separate(
-    dag_number, 
-    into = c("dag_number", "dag_location"), 
-    convert = TRUE, 
-    extra = "merge"
-  ) |> 
-  separate(
-    consistent_direction, 
-    into = c("consistent_direction", "dag_ordering"),
-    extra = "merge"
-  ) |> 
-  separate(
-    report_adjset, 
-    into = c("report_adjset", "report_adjset_loc"),
-    extra = "merge"
-  ) |> 
-  mutate(
-    year = str_extract(citation, "\\d{4}") |> as.integer(),
-    used_dag = !is.na(nodes),
-    reported_estimand = !str_detect(report_estimand, "Not reported")
-  )
-
-dag_data |> 
-  count(year, used_dag) |> 
-  mutate(pct = n / sum(n)) |> 
-  filter(used_dag, year < max(year)) |> 
-  ggplot(aes(year, pct)) +
-  geom_line(color = "#0072B2", linewidth = .9) + 
-  scale_y_continuous(
-    name = "percent of papers", 
-    labels = scales::label_percent()
-  )
-```
-
-The type of causal diagrams we use are also called directed acyclic graphs (DAGs)[^1].
-These graphs are directed because they include arrows going in a specific direction.
-They're acyclic because they don't go in circles; a variable can't cause itself, for instance.
-DAGs are used for various problems, but we're specifically concerned with *causal* DAGs.
-This class of DAGs is sometimes called Structural Causal Models (SCMs) because they are a model of the causal structure of a question [@hernan2021; @Pearl_Glymour_Jewell_2021].
-
-[^1]: An essential but rarely observed detail of DAGs is that dag is also an [affectionate Australian insult](https://en.wikipedia.org/wiki/Dag_(slang)) referring to the dung-caked fur of a sheep, a *daglock*.
+::: callout-tip
+## Jargon
+
+Why is it called a *directed acyclic graph*?
+Let's start with the third word: **graph**.
+A graph, as defined here, is a collection of *nodes* (sometimes these are called *vertices*) and *edges* that connect the nodes.
+In mathematics, there is a whole field called *graph theory* which studies these graphs.
+For our use, each *node* is a variable or factor, for example the exposure would be a node and likewise the outcome would be a node.
+Edges between nodes are depicted with *arrows* that imply causality.
+So if we think the exposure causes the outcome we would draw an arrow from the exposure to the outcome.
+This one way connection between nodes (*from* causes *to* effects) makes this graph **directed**.
+Finally, **acyclic** refers to the fact that there are no cycles or loops in the graph.
+This makes sense because when thinking about causes and effects, loops are not possible without breaking the space-time continuum.
+Often when a DAG erroneously includes a "loop" it is because the analyst did not appropriately consider the timing of the factors in question.
+:::
 
-DAGs depict causal relationships between variables.
-Visually, the way they depict variables is as *edges* and *nodes*.
-Edges are the arrows going from one variable to another, sometimes called arcs or just arrows.
-Nodes are the variables themselves, sometimes called vertices, points, or just variables.
-In @fig-dag-basic, there are two nodes, `x` and `y`, and one edge going from `x` to `y`.
-Here, we are saying that `x` causes `y`.
-`y` "listens" to `x` [@Pearl_Glymour_Jewell_2021].
+For example, @fig-dag-ex, adapted from @mcgowan2023causal shows a sample DAG that suggests that `cause` causes `effect` and `other cause` causes both cause and effect.
 
 ```{r}
 #| echo: false
+#| label: fig-dag-ex
 #| message: false
-#| label: fig-dag-basic
-#| fig-width: 3
-#| fig-height: 2
-#| fig-cap: "A causal directed acyclic graph (DAG). DAGs depict causal relationships. In this DAG, the assumption is that `x` causes `y`."
+#| warning: false
+#| fig-cap: "Example DAG adapted from @mcgowan2023causal. Here, there are three nodes representing three factors: `cause`, `other cause`, and `effect`. The arrows demonstrate the causal relationships between these factors such that `cause` causes `effect` and `other cause` causes both `cause` and `effect`."
 library(ggdag)
-dagify(y ~ x, coords = time_ordered_coords()) |> 
-  ggdag() + 
-  theme_dag() + 
-  expand_plot(expand_x = expansion(c(.2, .2)))
-```
+library(ggokabeito)
+
+coords <- list(
+  x = c(cause = 2, Z = 1, effect = 3),
+  y = c(cause = 1, Z = 1.1, effect = 1)
+)
+d_conf <- dagify(
+  cause ~ Z,
+  effect ~ cause + Z,
+  exposure = "cause",
+  outcome = "effect",
+  labels = c(cause = "cause", effect = "effect", Z = "Z"),
+  coords = coords
+)
 
-If we're interested in the causal effect of `x` on `y`, we're trying to estimate a numeric representation of that arrow.
-Usually, though, there are many other variables and arrows in the causal structure of a given question.
-A series of arrows is called a *path*.
-There are three types of paths you'll see in DAGs: forks, chains, and colliders (sometimes called inverse forks).
+d_conf |>
+  tidy_dagitty() |>
+  mutate(name = ifelse(name == "Z", "other\ncause", name)) |>
+  ggplot(
+    aes(x = x, y = y, xend = xend, yend = yend)
+  ) +
+  geom_dag_point(aes(color = label)) +
+  geom_dag_edges() +
+  geom_dag_text(size = 3) +
+  theme_dag() +
+  coord_cartesian(clip = "off") +
+  theme(legend.position = "none")
+```
+
+Thinking about any three nodes in a DAG, there are three ways they can be connected, via a *fork*, a *chain*, or a *collider*.
+Examining @fig-dag-3, the fork describes the scenario where the `q` node has two arrows extending from it, one pointing to `x` and one pointing to `y`.
+This implies that `q` causes both `x` and `y`.
+If `x` were an exposure and `y` and outcome, `q` would be a classic *confounder*.
+If we were trying to quantify the causal relationship between `x` and `y`, the forking caused by `q` provides a potential *backdoor path* from `x` to `y` that could lead to a spurious estimate of the relationship between `x` and `y` if not accounted for (sometimes we refer to this "accounting" as closing the backdoor path).
+Associations can flow through forks.
+The second panel of @fig-dag-3 in @fig-dag-3 displays a chain.
+Here, the `x` node has an arrow to the `q` node which in turn has an arrow to the `y` node.
+If `x` were an exposure and `y` and outcome, `q` would be a classic *mediator*.
+In the final panel of @fig-dag-3, we see the collider.
+Here `y` has an arrow to `q` and `x` has an arrow to `q`.
+Colliders *block* backdoor paths.
+Opposite from confounders, adjusting for a collider can actually *open* a backdoor path.
 
 ```{r}
 #| echo: false
-#| label: fig-dag-path-types
-#| fig-cap: "Three types of causal relationships: forks, chains, and colliders. The direction of the arrows and the relationships of interest dictate which type of path a series of variables is. Forks represent a mutual cause, chains represent direct causes, and colliders represent a mutual descendant."
+#| fig-width: 10
+#| label: fig-dag-3
+#| fig-cap: "Three types of paths connecting three nodes, `x`, `y`, and `q`: a fork, a chain, and a collider."
 coords <- list(x = c(x = 0, y = 2, q = 1), y = c(x = 0, y = 0, q = 1))
 
 fork <- dagify(
@@ -122,347 +109,77 @@ collider <- dagify(
   coords = coords
 )
 
-dag_flows <- map(list(fork = fork, chain = chain, collider = collider), tidy_dagitty) |> 
-  map("data") |> 
-  list_rbind(names_to = "dag") |> 
-  mutate(dag = factor(dag, levels = c("fork", "chain", "collider")))
-
-dag_flows |> 
+map(list(fork = fork, chain = chain, collider = collider), tidy_dagitty, layout = "time_ordered") |>
+  map("data") |>
+  list_rbind(names_to = "dag") |>
+  mutate(dag = factor(dag, levels = c("fork", "chain", "collider"))) |>
   ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
-  geom_dag_edges(edge_width = 1) + 
-  geom_dag_point() + 
-  geom_dag_text() + 
-  facet_wrap(~ dag) +
+  geom_dag_edges(edge_width = 1) +
+  geom_dag_point() +
+  geom_dag_text() +
+  facet_wrap(~dag) +
   expand_plot(
-    expand_x = expansion(c(0.2, 0.2)),
+    expand_x = expansion(c(.1, .1)),
     expand_y = expansion(c(0.2, 0.2))
   ) +
   theme_dag()
 ```
 
-Forks represent a common cause of two variables.
-Here, we're saying that `q` causes both `x` and `y`, the traditional definition of a confounder.
-They're called forks because the arrows from `x` to `y` are in different directions.
-Chains, on the other hand, represent a series of arrows going in the same direction.
-Here, `q` is called a *mediator*: it is along the causal path from `x` to `y`.
-In this diagram, the only path from `x` to `y` is mediated through `q`.
-Finally, a collider is a path where two arrowheads meet at a variable.
-Because causality always goes forward in time, this naturally means that the collider variable is caused by two other variables.
-Here, we're saying that `x` and `y` both cause `q`.
-
-::: callout-tip
-## Are DAGs SEMs?
-
-If you're familiar with structural equation models (SEMs), a modeling technique commonly used in psychology and other social science settings, you may notice some similarities between SEMs and DAGs.
-DAGs are a form of *non-parametric* SEM.
-SEMs estimate entire graphs using parametric assumptions.
-Causal DAGs, on the other hand, don't estimate anything; an arrow going from one variable to another says nothing about the strength or functional form of that relationship, only that we think it exists.
-:::
-
-One of the significant benefits of DAGs is that they help us identify sources of bias and, often, provide clues on how to address them.
-However, talking about an unbiased effect estimate only makes sense when we have a specific causal question in mind.
-Since each arrow represents a cause, it's causality all the way down; no individual arrow is inherently problematic.
-Here, we're interested in the effect of `x` on `y`.
-This question defines which paths we're interested in and which we're not.
-
-These three types of paths have different implications for the statistical relationship between `x` and `y`.
-If we only look at the correlation between the two variables under these assumptions:
-
-1.  In the fork, `x` and `y` will be associated, despite there being no arrow from `x` to `y`.
-2.  In the chain, `x` and `y` are related only through `q`.
-3.  In the collider, `x` and `y` will *not* be related.
-
-Paths that transmit association are called *open paths*.
-Paths that do not transmit association are called *closed paths*.
-Forks and chains are open, while colliders are closed.
-
-So, should we adjust for `q`?
-That depends on the nature of the path.
-Forks are confounding paths.
-Because `q` causes both `x` and `y`, `x` and `y` will have a spurious association.
-They both contain information from `q`, their mutual cause.
-That mutual causal relationship makes `x` and `y` associated statistically.
-Adjusting for `q` will *block* the bias from confounding and give us the true relationship between `x` and `y`.
-
-::: callout-tip
-## Adjustment
-
-We can use a variety of techniques to account for a variable.
-We use the term "adjustment" or "controlling for" to refer to any technique that removes the effect of variables we're not interested in.
-:::
-
-@fig-confounder-scatter depicts this effect visually.
-Here, `x` and `y` are continuous, and by definition of the DAG, they are unrelated.
-`q`, however, causes both.
-The unadjusted effect is biased because it includes information about the open path from `x` to `y` via `q`.
-Within levels of `q`, however, `x` and `y` are unrelated.
-
-```{r}
-#| echo: false
-#| label: fig-confounder-scatter
-#| message: false
-#| fig-cap: "Two scatterplots of the relationship between `x` and `y`. With forks, the relationship is biased by `q`. When accounting for `q`, we see the true null relationship."
-set.seed(123)
-library(patchwork)
-n <- 1000
-
-### q
-q <- rbinom(n, size = 1, prob = .35)
-###
-
-### x
-x <- 2 * q + rnorm(n)
-###
-
-### y
-y <- -3 * q + rnorm(n)
-###
-
-confounder_data <- tibble(x, y, q = as.factor(q))
-
-p1 <- confounder_data |> 
-  ggplot(aes(x, y)) + 
-  geom_point(alpha = .2) + 
-  geom_smooth(method = "lm", se = FALSE, color = "black") + 
-  facet_wrap(~ "not adjusting for `q`\n(biased)")
-
-p2 <- confounder_data |> 
-  ggplot(aes(x, y, color = q)) + 
-  geom_point(alpha = .2) + 
-  geom_smooth(method = "lm", se = FALSE) +
-  facet_wrap(~ "adjusting for `q`\n(unbiased)")
-
-p1 + p2
-```
-
-For chains, whether or not we adjust for mediators depends on the research question.
-Here, adjusting for `q` would result in a null estimate of the effect of `x` on `y`.
-Because the only effect of `x` on `y` is via `q`, no other effect remains.
-The effect of `x` on `y` mediated by `q` is called the *indirect* effect, while the effect of `x` on `y` directly is called the *direct* effect.
-If we're only interested in the direct effect, controlling for `q` might be what we want.
-If we want to know about both effects, we shouldn't try to adjust for `q`.
-We'll learn more about estimating these and other mediation effects in @sec-mediation.
-
-@fig-mediator-scatter shows this effect visually.
-The unadjusted effect of `x` on `y` represents the total effect.
-Since the total effect is due entirely to the path mediated by `q`, when we adjust for `q`, no relationship remains.
-This null effect is the direct effect.
-Neither of these effects is due to bias, but each answers a different research question.
-
-```{r}
-#| echo: false
-#| label: fig-mediator-scatter
-#| message: false
-#| fig-cap: "Two scatterplots of the relationship between `x` and `y`. With chains, whether and how we should account for `q` depends on the research question. Without doing so, we see the impact of the total effect of `x` and `y`, including the indirect effect via `q`. When accounting for `q`, we see the direct (null) effect of `x` on `y`."
-### x
-x <- rnorm(n)
-###
-
-### q
-linear_pred <- 2*x + rnorm(n)
-prob <- 1 / (1 + exp(-linear_pred))
-q <- rbinom(n, size = 1, prob = prob)
-###
-
-### y
-y <- 2 * q + rnorm(n)
-###
-
-mediator_data <- tibble(x, y, q = as.factor(q))
-
-p1 <- mediator_data |> 
-  ggplot(aes(x, y)) + 
-  geom_point(alpha = .2) + 
-  geom_smooth(method = "lm", se = FALSE, color = "black") + 
-  facet_wrap(~ "not adjusting for `q`\n(total effect)")
-
-p2 <- mediator_data |> 
-  ggplot(aes(x, y, color = q)) + 
-  geom_point(alpha = .2) + 
-  geom_smooth(method = "lm", se = FALSE) +
-  facet_wrap(~ "adjusting for `q`\n(direct effect)")
-
-p1 + p2
-```
-
-Colliders are different.
-In the collider DAG of @fig-dag-path-types, `x` and `y` are *not* associated, but both cause `q`.
-Adjusting for `q` has the opposite effect than with confounding: it *opens* a biasing pathway.
-Sometimes, people draw the path opened up by conditioning on a collider connecting `x` and `y`.
-
-Visually, we can see this happen when `x` and `y` are continuous and `q` is binary.
-In @fig-collider-scatter, when we don't include `q`, we find no relationship between `x` and `y`.
-That's the correct result.
-However, when we include `q`, we can detect information about both `x` and `y`, and they appear correlated: across levels of `x`, those with `q = 0` have lower levels of `y`.
-Association seemingly flows back in time.
-Of course, that can't happen from a causal perspective, so controlling for `q` is the wrong thing to do.
-We end up with a biased effect of `x` on `y`.
-
-```{r}
-#| echo: false
-#| label: fig-collider-scatter
-#| message: false
-#| fig-cap: "Two scatterplots of the relationship between `x` and `y`. The unadjusted relationship between the two is unbiased. When accounting for `q`, we open a colliding backdoor path and bias the relationship between `x` and `y`."
-#| 
-### x
-x <- rnorm(n)
-###
-
-### y
-y <- rnorm(n)
-###
-
-### q
-linear_pred <- 2*x + 3*y + rnorm(n)
-prob <- 1 / (1 + exp(-linear_pred))
-q <- rbinom(n, size = 1, prob = prob)
-###
-
-collider_data <- tibble(x, y, q = as.factor(q))
-
-p1 <- collider_data |> 
-  ggplot(aes(x, y)) + 
-  geom_point(alpha = .2) + 
-  geom_smooth(method = "lm", se = FALSE, color = "black") + 
-  facet_wrap(~"not adjusting for `q`\n(unbiased)")
-
-p2 <- collider_data |> 
-  ggplot(aes(x, y, color = q)) + 
-  geom_point(alpha = .2) + 
-  geom_smooth(method = "lm", se = FALSE) + 
-  facet_wrap(~"adjusting for `q`\n(biased)")
-
-p1 + p2
-```
-
-How can this be?
-Since `x` and `y` happen before `q`, `q` can't impact them.
-Let's turn the DAG on its side and consider @fig-collider-time.
-If we break down the two time points, at time point 1, `q` hasn't happened yet, and `x` and `y` are unrelated.
-At time point 2, `q` happens due to `x` and `y`.
-*But causality only goes forward in time*.
-`q` happening later can't change the fact that `x` and `y` happened independently in the past.
+Often, the goal in causal inference is to quantify the relationship between some exposure and some outcome.
+One way this estimate can be biased is if we are actually reporting the *correlation* between these two factors rather than the *causal relationship*.
+Thinking about these DAGs, spurious correlations can often be attributed to *open backdoor paths*, i.e. the relationship between other factors and our exposure and outcome of interest.
+One way to think about these paths is to conceptualize associations *flowing* along the paths.
+Without adjustment, associations flow through forks and chains, and are *blocked* by colliders.
+With adjustment, the opposite is true, associations are blocked by forks and chains if the node in question is adjusted for, however an association *will* flow through a collider if it is adjusted for.
 
-```{r}
-#| echo: false
-#| label: fig-collider-time
-#| fig-cap: "A collider relationship over two points in time. At time point one, there is no relationship between `x` and `y`. Both cause `q` by time point two, but this does not change what already happened at time point one."
+While not strictly necessary, we recommend that DAGs are *time-ordered* from left to right.
+This helps the analyst ensure that they are not erroneously making assumptions that violate the space-time continuum (for example it is impossible for something in the future to cause something from the past).
 
-coords <- list(x = c(x = 0, y = 2, q = 1), y = c(x = 0, y = 0, q = -1))
-collider <- dagify(
-    q ~ x + y,
-    exposure = "x",
-    outcome = "y",
-    coords = time_ordered_coords()
-)
-
-collider_t <- collider |> 
-  tidy_dagitty() |> 
-  mutate(time = "time point 1", direction = NA, to = NA) |> 
-  filter(name != "q")
+How do we use these in practice?
+The basic idea is:
 
-t2 <- collider |> 
- tidy_dagitty() |> 
- mutate(time = "time point 2") |> 
-  pull_dag_data()
+1.  Specify your causal question
+2.  Using domain knowledge:
 
-collider_t$data <- bind_rows(collider_t$data, t2)
-
-collider_t |> 
-  mutate(deemphasize = (name %in% c("x", "y") & time == 
-           "time point 2")) |> 
-  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
-  geom_dag_edges(edge_width = 1, edge_color = "grey85") + 
-  geom_dag_point(aes(color = deemphasize), show.legend = FALSE) + 
-  geom_dag_text() + 
-  facet_wrap(~ time) + 
-  theme_dag() + 
-  scale_color_manual(values = c("TRUE" = "grey85", "FALSE" = "black"))
-```
-
-Causality only goes forward.
-Association, however, is time-agnostic.
-It's just an observation about the numerical relationships between variables.
-When we control for the future, we risk introducing bias.
-It takes time to develop an intuition for this.
-Consider a case where `x` and `y` are the only causes of `q`, and all three variables are binary.
-When *either* `x` or `y` equals 1, then `q` happens.
-If we know `q = 1` and `x = 0` then logically it must be that `y = 1`.
-Thus, knowing about `q` gives us information about `y` via `x`.
-This example is extreme, but it shows how this type of bias, sometimes called *collider-stratification bias* or *selection bias*, occurs: conditioning on `q` provides statistical information about `x` and `y` and distorts their relationship [@Banack2023].
-
-::: callout-tip
-## Exchangeability revisited
-
-We commonly refer to exchangability as the assumption of no confounding.
-Actually, this isn't quite right.
-It's the assumption of no *open, non-causal* paths [@hernan2021].
-Many times, these are confounding pathways.
-However, conditioning on a collider can also open paths.
-Even though these aren't confounders, doing so creates non-exchangeability between the two groups: they are different in a way that matters to the exposure and outcome.
-
-Open, non-causal paths are also called *backdoor paths*.
-We'll use this terminology often because it captures the idea well: these are any open paths biasing the effect we're interested in estimating.
-:::
-
-Correctly identifying the causal structure between the exposure and outcome thus helps us 1) communicate the assumptions we're making about the relationships between variables and 2) identify sources of bias.
-Importantly, in doing 2), we are also often able to identify ways to prevent bias based on the assumptions in 1).
-In the simple case of the three DAGs in @fig-dag-path-types, we know whether or not to control for `q` depending on the nature of the causal structure.
-The set or sets of variables we need to adjust for is called the *adjustment set*.
-DAGs can help us identify adjustment sets even in complex settings [@vanderzander2019].
-
-::: callout-tip
-## What about interaction?
-
-DAGs don't make a statement about interaction or effect estimate modification, even though they are an important part of inference.
-Technically, interaction is a matter of the functional form of the relationships in the DAG.
-Much as we don't need to specify how we will model a variable in the DAG (e.g., with splines), we don't need to determine how variables statistically interact.
-That's a matter for the modeling stage.
-
-There are several ways we use interactions in causal inference.
-In one extreme, they are simply a matter of functional form: interaction terms are included in models but marginalized to get an overall causal effect.
-Conversely, we're interested in *joint causal effects*, where the two variables interacting are both causal.
-In between, we can use interaction terms to identify *heterogeneous causal effects*, which vary by a second variable that is not assumed to be causal.
-As with many tools in causal inference, we use the same statistical technique in many ways to answer different questions.
-We'll revisit this topic in detail in [Chapter -@sec-interaction].
-
-Many people have tried expressing interaction in DAGs using different types of arcs, nodes, and other annotations, but no approach has taken off as the preferred way [@weinberg2007; @Nilsson2021].
-:::
-
-Let's take a look at an example in R.
-We'll learn to build DAGs, visualize them, and identify important information like adjustment sets.
+-   Write all relevant variables as *nodes*
+-   Draw causal pathways as arrows (*edges*)
 
 ## DAGs in R
 
-First, consider a research question: Does listening to a comedy podcast the morning before an exam improve graduate students' test scores?
-We can diagram this using the method described in @sec-diag (@fig-diagram-podcast).
+Let's begin by specifying our causal question: Does listening to a comedy podcast the morning before an exam improve graduate students test scores?
+We can diagram this using the method describe in @sec-diag (@fig-podcast).
 
 ```{r}
 #| echo: false
-#| fig-cap: "A sentence diagram for the question: Does listening to a comedy podcast the morning before an exam improve graduate student test scores? The population is graduate students. The start time is morning, and the outcome time is after the exam."
+#| fig-cap: "Does listening to a comedy podcast the morning before an exam improve graduate students test scores?"
 #| fig-height: 2
-#| label: fig-diagram-podcast
-knitr::include_graphics("../images/podcast-diagram.png")
-```
-
-The tool we'll use for making DAGs is ggdag.
-ggdag is a package that connects ggplot2, the most powerful visualization tool in R, to dagitty, an R package with sophisticated algorithms for querying DAGs.
+#| label: fig-podcast
 
-To create a DAG object, we'll use the `dagify()` function.`dagify()` returns a `dagitty` object that works with both the dagitty and ggdag packages.
-The `dagify()` function takes formulas, separated by commas, that specify causes and effects, with the left element of the formula defining the effect and the right all of the factors that cause it.
-This is just like the type of formula we specify for most regression models in R.
-
-```{r}
-#| eval: false
-dagify(
-  effect1 ~ cause1 + cause2 + cause3,
-  effect2 ~ cause1 + cause4,
-  ...
+data <- data.frame(
+  labels = c("comedy podcast", "test scores", "graduate students", "morning", "graduate students", "after exam"),
+  x = c(1.25, 1.75, 1.25, 1.55, 1.8, 2.05),
+  y = c(1, 1, 0.8, 0.7, 0.8, 0.7),
+  angle = c(0, 0, -30, 0, -30, 0)
 )
-```
 
-What are all of the factors that cause graduate students to listen to a podcast the morning before an exam?
-What are all of the factors that could cause a graduate student to do well on a test?
+ggplot(data, aes(x = x, y = y)) +
+  geom_text(aes(label = labels, angle = angle, vjust = 0),
+    size = 5
+  ) +
+  geom_segment(aes(x = 1, xend = 2, y = 0.95, yend = 0.95)) +
+  geom_segment(aes(x = 1.5, xend = 1.5, y = 0.95, yend = 1.1)) +
+  geom_segment(aes(x = 1, xend = 1.35, y = 0.95, yend = 0.65)) +
+  geom_segment(aes(x = 1.35, xend = 1.65, y = 0.65, yend = 0.65)) +
+  geom_segment(aes(x = 1.55, xend = 1.9, y = 0.95, yend = 0.65)) +
+  geom_segment(aes(x = 1.9, xend = 2.15, y = 0.65, yend = 0.65)) +
+  theme_void()
+```
+
+The first step is to specify your DAG.
+In R, we can use the {ggdag} package for this along with the `dagify()` function.
+The `dagify()` function takes formulas, separated by commas, that specify cause and effect, with the left element of of the formula specifying the effect and the right all of the factors that cause it.
+What are all of the factors that "cause" graduate students to listen to a podcast the morning before an exam?
+What are all of the factors that could "cause" a graduate student to do well on a test?
 Let's posit some here.
 
 ```{r}
@@ -473,41 +190,21 @@ dagify(
 )
 ```
 
-In the code above, we assume that:
-
--   a graduate student's mood, sense of humor, and how prepared they feel for the exam could influence whether they listened to a podcast the morning of the test
--   their mood and how prepared they are also influence their exam score
-
-Notice we *do not* see podcast in the exam equation; this means that we assume that there is **no** causal relationship between podcast and the exam score.
-
-There are some other useful arguments you'll often find yourself supplying to `dagify()`:
-
--   `exposure` and `outcome`: Telling ggdag the variables that are the exposure and outcome of your research question is required for many of the most valuable queries we can make of DAGs.
--   `latent`: This argument lets us tell ggdag that some variables in the DAG are unmeasured. `latent` helps identify valid adjustment sets with the data we actually have.
--   `coords`: Coordinates for the variables. You can choose between algorithmic or manual layouts, as discussed below. We'll use `time_ordered_coords()` here.
--   `labels`: A character vector of labels for the variables.
-
-Let's create a DAG object, `podcast_dag`, with some of these attributes, then visualize the DAG with `ggdag()`.
-`ggdag()` returns a ggplot object, so we can add additional layers to the plot, like themes.
+In the code chunk above, we have posited that a graduate students mood, sense of humor, and how prepared they feel for the exam could influence whether they listened to a podcast the morning of the text.
+Likewise, we posit that their mood and how prepared they are also influences their exam score.
+Notice we *do not* see `podcast` in the `exam` equation -- this means that we assume that there is no causal relationship between podcast and the exam score.
+We can add additional arguments to `dagify()`, for example, we can time order the coordinates, tag the exposure and outcome, and add labels.
+We can save this `dagify` object and use the `ggdag()` function to visualize this DAG.
+This function is a wrapper for a `ggplot2` call, meaning we can add layers like we would to a ggplot object.
+For example, we can update the theme by adding `+ theme_dag()`.
 
 ```{r}
 #| label: fig-dag-podcast
-#| fig-cap: "Proposed DAG to answer the question: Does listening to a comedy podcast the morning before an exam improve graduate students' test scores?"
-#| fig-width: 4
-#| fig-height: 4
+#| fig-cap: "Proposed DAG to answer the question: Does listening to a comedy podcast the morning before an exam improve graduate students test scores?"
 podcast_dag <- dagify(
   podcast ~ mood + humor + prepared,
   exam ~ mood + prepared,
-  coords = time_ordered_coords(
-    list(
-      # time point 1
-      c("prepared", "humor", "mood"), 
-      # time point 2
-      "podcast",  
-      # time point 3
-      "exam"
-    )
-  ),
+  coords = time_ordered_coords(),
   exposure = "podcast",
   outcome = "exam",
   labels = c(
@@ -522,214 +219,41 @@ ggdag(podcast_dag, use_labels = "label", text = FALSE) +
   theme_dag()
 ```
 
-::: callout-note
-For the rest of the chapter, we'll use `theme_dag()`, a ggplot theme from ggdag meant for DAGs.
-
-```{r}
-theme_set(
-  theme_dag() %+replace%
-    # also add some additional styling
-    theme(
-      legend.position = "bottom",
-      strip.text.x = element_text(margin = margin(2, 0, 2, 0, "mm"))
-    )
-)
-```
-:::
-
-::: callout-tip
-## DAG coordinates
-
-You don't need to specify coordinates to ggdag.
-If you don't, it uses algorithms designed for automatic layouts.
-There are many such algorithms, and they focus on different aspects of the layout, e.g., the shape, the space between the nodes, minimizing how many edges cross, etc.
-These layout algorithms usually have a component of randomness, so it's good to use a seed if you want to get the same result.
-
-```{r}
-#| fig-width: 4
-#| fig-height: 4
-#| fig-align: center
-# no coordinates specified
-set.seed(123)
-pod_dag <- dagify(
-  podcast ~ mood + humor + prepared,
-  exam ~ mood + prepared
-)
-
-# automatically determine layouts
-pod_dag |> 
-  ggdag(text_size = 2.8)
-```
-
-We can also ask for a specific layout, e.g., the popular Sugiyama algorithm for DAGs [@sugiyama1981].
-
-```{r}
-#| fig-width: 4
-#| fig-height: 4
-#| fig-align: center
-pod_dag |> 
-  ggdag(layout = "sugiyama", text_size = 2.8)
-```
-
-For causal DAGs, the time-ordered layout algorithm is often best, which we can specify with `time_ordered_coords()` or `layout = "time_ordered"`.
-We'll discuss time ordering in greater detail in @sec-time-ordered.
-Earlier, we explicitly told ggdag which variables were at which time points, but we don't need to.
-Notice, though, that the time ordering algorithm puts `podcast` and `exam` at the same time point since one doesn't cause another (and thus predate it).
-We know that's not the case: listening to the podcast happened before taking the exam.
-
-<!-- TODO: if implementing a better way to use this algorithm while specifying one or more time points, then update this -->
+In the previous section, we discussed *backdoor paths*.
+These are paths between factors that could potentially lead us to drawing spurious conclusions about the relationship between our exposure and outcome.
+The `ggdag_paths()` function will help us identify potential backdoor paths.
 
 ```{r}
-#| fig-width: 4
-#| fig-height: 4
-#| fig-align: center
-pod_dag |> 
-  ggdag(layout = "time_ordered", text_size = 2.8)
+#| label: fig-paths
+ggdag_paths(podcast_dag)
 ```
 
-You can manually specify coordinates using a list or data frame and provide them to the `coords` argument of `dagify()`.
-Additionally, because ggdag is based on dagitty, you can use `dagitty.net` to create and organize a DAG using a graphical interface, then export the result as dagitty code for ggdag to consume.
+In @fig-paths we see two open paths, one through `mood` and one through `prepared`.
+This tells us we need to find a way to account for these open, non-causal paths.
+Some ways to do this include:
 
-Algorithmic layouts are lovely for fast visualization of DAGs or particularly complex graphs.
-Once you want to share your DAG, it's usually best to be more intentional about the layout, perhaps by specifying the coordinates manually.
-`time_ordered_coords()` is often the best of both worlds, and we'll use it for most DAGs in this book.
-:::
+-   Randomization
+-   Stratification, adjustment, weighting, matching, etc.
 
-We've specified the DAG for this question and told ggdag what the exposure and outcome of interest are.
-According to the DAG, there is no direct causal relationship between listening to a podcast and exam scores.
-Are there any other open paths?
-`ggdag_paths()` takes a DAG and visualizes the open paths.
-In @fig-paths-podcast, we see two open paths: `podcast <- mood -> exam"` and `podcast <- prepared -> exam`. These are both forks---*confounding pathways*. Since there is no causal relationship between listening to a podcast and exam scores, the only open paths are *backdoor* paths, these two confounding pathways.
+In this particular scenario, randomization is likely not possible.
+It would be challenging to justify randomizing students to listening to a full podcast prior to taking an exam (and it would likely have lots of issues with adherence!).
+Stratification is similar to what we demonstrated in @sec-group-sum.
+We could stratify the students into all possible mood and prepared categories and analyze the causal effect within each stratum -- again, this might pose challenges depending on the sample size and the number of categories we believe exists in each of these factors.
+The next section will dive into some of these other tools we could use to account for these potential backdoor paths.
 
-```{r}
-#| label: fig-paths-podcast
-#| fig-cap: "`ggdag_paths()` visualizes open paths in a DAG. There are two open paths in `podcast_dag`: the fork from `mood` and the fork from `prepared`." 
-podcast_dag |> 
-  # show the whole dag as a light gray "shadow" 
-  # rather than just the paths
-  ggdag_paths(shadow = TRUE, text = FALSE, use_labels = "label")
-```
-
-::: callout-tip
-`dagify()` returns a `dagitty()` object, but underneath the hood, ggdag converts `dagitty` objects to tidy DAGs, a structure that holds both the `dagitty` object and a `dataframe` about the DAG.
-This is handy if you want to manipulate the DAG programmatically.
+The {ggdag} package can also help us identify adjustment sets.
+In this particular example, this yields the same result as above, since we need to adjust for both `mood` and `prepared` in order to close the backdoor paths.
+It is possible, however, to have different ways to close backdoor paths depending on the number of factors and complexity causal relationships assumed.
 
 ```{r}
-podcast_dag_tidy <- podcast_dag |> 
-  tidy_dagitty()
-
-podcast_dag_tidy
-```
-
-Most of the quick plotting functions transform the `dagitty` object to a tidy DAG if it's not already, then manipulate the data in some capacity.
-For instance, `dag_paths()` underlies `ggdag_paths()`; it returns a tidy DAG with data about the paths.
-You can use several dplyr functions on these objects directly.
-
-```{r}
-podcast_dag_tidy |> 
-  dag_paths() |> 
-  filter(set == 2, path == "open path")
-```
-
-Tidy DAGs are not pure data frames, but you can retrieve either the `dataframe` or `dagitty` object to work with them directly using `pull_dag_data()` or `pull_dag()`.
-`pull_dag()` can be useful when you want to work with dagitty functions:
-
-```{r}
-library(dagitty)
-podcast_dag_tidy |> 
-  pull_dag() |> 
-  paths()
-```
-:::
-
-Backdoor paths pollute the statistical association between `podcast` and `exam`, so we must account for them.
-`ggdag_adjustment_set()` visualizes any valid adjustment sets implied by the DAG.
-@fig-podcast-adustment-set shows adjusted variables as squares.
-Any arrows coming out of adjusted variables are removed from the DAG because the path is longer open at that variable.
-
-```{r}
-#| label: fig-podcast-adustment-set
-#| fig-width: 4
-#| fig-height: 4
-#| fig-align: center
-#| fig-cap: "A visualization of the minimal adjustment set for the podcast-exam DAG. If this DAG is correct, two variables are required to block the backdoor paths: `mood` and `prepared`."
 ggdag_adjustment_set(
-  podcast_dag, 
-  text = FALSE, 
-  use_labels = "label"
-)
-```
-
-@fig-podcast-adustment-set shows the *minimal adjustment set*.
-By default, ggdag returns the set(s) that can close all backdoor paths with the fewest number of variables possible.
-In this DAG, that's just one set: `mood` and `prepared`.
-This set makes sense because there are two backdoor paths, and the only other variables on them besides the exposure and outcome are these two variables.
-So, at minimum, we must account for both to get a valid estimate.
-
-::: callout-tip
-`ggdag()` and friends usually use `tidy_dagitty()` and `dag_*()` or `node_*()` functions to change the underlying data frame.
-Similarly, the quick plotting functions use ggdag's geoms to visualize the resulting DAG(s).
-In other words, you can use the same data manipulation and visualization strategies that you use day-to-day directly with ggdag.
-
-Here's a condensed version of what `ggdag_adjustment_set()` is doing:
-
-```{r}
-#| fig-width: 4.5
-#| fig-height: 4.5
-#| fig-align: center
-podcast_dag_tidy |> 
-  # add adjustment sets to data
-  dag_adjustment_sets() |>
-  ggplot(aes(
-    x = x, y = y, xend = xend, yend = yend,
-    color = adjusted, shape = adjusted
-  )) + 
-  # ggdag's custom geoms: add nodes, edges, and labels
-  geom_dag_point() + 
-  # remove adjusted paths
-  geom_dag_edges_link(data = \(.df) filter(.df, adjusted != "adjusted")) + 
-  geom_dag_label_repel() + 
-  # you can use any ggplot function, too
-  facet_wrap(~ set) +
-  scale_shape_manual(values = c(adjusted = 15, unadjusted = 19))
-```
-:::
-
-Minimal adjustment sets are only one type of valid adjustment set [@vanderzander2019].
-Sometimes, other combinations of variables can get us an unbiased effect estimate.
-Two other options available in ggdag are full adjustment sets and canonical adjustment sets.
-Full adjustment sets are every combination of variables that result in a valid set.
-
-```{r}
-#| label: fig-adustment-set-all
-#| fig-width: 6.5
-#| fig-height: 5
-#| fig-align: center
-#| fig-cap: "All valid adjustment sets for `podcast_dag`."
-ggdag_adjustment_set(
-  podcast_dag, 
-  text = FALSE, 
+  podcast_dag,
   use_labels = "label",
-  # get full adjustment sets
-  type = "all"
+  text = FALSE
 )
 ```
 
-It turns out that we can also control for `humor`.
-
-Canonical adjustment sets are a bit more complex: they are all possible ancestors of the exposure and outcome minus any likely descendants.
-In fully saturated DAGs (DAGs where every node causes anything that comes after it in time), the canonical adjustment set is the minimal adjustment set.
-
-::: callout-tip
-Most of the functions in ggdag use dagitty underneath the hood.
-It's often helpful to call dagitty functions directly.
-
-```{r}
-adjustmentSets(podcast_dag, type = "canonical")
-```
-:::
-
-Using our proposed DAG, let's simulate some data to see how accounting for the minimal adjustment set might occur in practice.
+Using our proposed DAG, let's simulate some data to see how this might occur in practice!
 
 ```{r}
 set.seed(10)
@@ -741,14 +265,13 @@ sim_data <- podcast_dag |>
 sim_data
 ```
 
-Since we have simulated this data, we know that this is a case where *standard methods will succeed* (see @sec-standard) and, therefore, can estimate the causal effect using a basic linear regression model.
+Since we have simulated this data, we know that this is a case where *standard methods will succeed* (see @sec-standard), and therefore can estimate the causal effect using a basic linear regression model.
 @fig-dag-sim shows a forest plot of the simulated data based on our DAG.
-Notice the model that only included the exposure resulted in a spurious effect (an estimate of -0.1 when we know the truth is 0).
-In contrast, the model that adjusted for the two variables as suggested by `ggdag_adjustment_set()` is not spurious (much closer to 0).
+Notice the model that only included the exposure resulted in a spurious effect (an estimate of -0.1 when we know the truth is 0), whereas the model that adjusted for the two variables as suggested by `ggdag_adjustment_set()` is not spurious (0.0).
 
 ```{r}
 #| label: fig-dag-sim
-#| fig-cap: "Forest plot of simulated data based on the DAG described in @fig-dag-podcast."
+#| fig-cap: "Forest plot of simulated data based on the DAG described in @fig-dag-podcast"
 ## Model that does not close backdoor paths
 unadjusted_model <- lm(exam ~ podcast, sim_data) |>
   broom::tidy(conf.int = TRUE) |>
@@ -775,875 +298,8 @@ bind_rows(
   )
 ```
 
-## Structures of Causality
-
-### Advanced Confounding
-
-In `podcast_dag`, `mood` and `prepared` were *direct* confounders: an arrow was going directly from them to `podcast` and `exam`.
-Often, backdoor paths are more complex.
-Let's consider such a case by adding two new variables: `alertness` and `skills_course`.
-`alertness` represents the feeling of alertness from a good mood, thus the arrow from `mood` to `alertness`.
-`skills_course` represents whether the student took a College Skills Course and learned time management techniques.
-Now, `skills_course` is what frees up the time to listen to a podcast as well as being prepared for the exam.
-`mood` and `prepared` are no longer direct confounders: they are two variables along a more complex backdoor path.
-Additionally, we've added an arrow going from `humor` to `mood`.
-Let's take a look at @fig-podcast_dag2.
-
-```{r}
-#| label: fig-podcast_dag2
-#| fig-width: 5
-#| fig-height: 4
-#| fig-cap: "An expanded version of `podcast_dag` that includes two additional variables: `skills_course`, representing a College Skills Course, and `alertness`."
-podcast_dag2 <- dagify(
-    podcast ~ mood + humor + skills_course,
-    alertness ~ mood,
-    mood ~ humor,
-    prepared ~ skills_course,
-    exam ~ alertness + prepared,
-    coords = time_ordered_coords(),
-    exposure = "podcast",
-    outcome = "exam",
-    labels = c(
-        podcast = "podcast",
-        exam = "exam score",
-        mood = "mood",
-        alertness = "alertness",
-        skills_course = "college\nskills course",
-        humor = "humor",
-        prepared = "prepared"
-    )
-)
-
-ggdag(podcast_dag2, use_labels = "label", text = FALSE)
-```
-
-```{r}
-#| echo: false
-paths <- paths(podcast_dag2)
-open_paths <- glue::glue_collapse(glue::glue('`{paths$paths[paths$open]}`'), sep = ", ", last = ", and")
-adj_sets <- unclass(adjustmentSets(podcast_dag2)) |> map_chr(\(.x) glue::glue('{unlist(glue::glue_collapse(.x, sep = " + "))}'))
-adj_sets <- glue::glue("`{adj_sets}`")
-```
-
-Now there are *three* backdoor paths we need to close: `r open_paths`.
-
-```{r}
-#| label: fig-podcast_dag2-paths
-#| fig-width: 11
-#| fig-height: 4.5
-#| fig-cap: "Three open paths in `podcast_dag2`. Since there is no effect of `podcast` on `exam`, all three are backdoor paths that must be closed to get the correct effect."
-ggdag_paths(podcast_dag2, use_labels = "label", text = FALSE, shadow = TRUE)
-```
-
-There are four minimal adjustment sets to close all three paths (and eighteen full adjustment sets!).
-The minimal adjustment sets are `r adj_sets`.
-We can now block the open paths in several ways.
-`mood` and `prepared` still work, but we've got other options now.
-Notably, `prepared` and `alertness` could happen at the same time or even after `podcast`.
-`skills_course` and `mood` still happen before both `podcast` and `exam`, so the idea is still the same: the confounding pathway starts before the exposure and outcome.
-
-```{r}
-#| label: fig-podcast_dag2-set
-#| fig-width: 7
-#| fig-height: 8
-#| fig-cap: "Valid minimal adjustment sets that will close the backdoor paths in @fig-podcast_dag2-paths."
-ggdag_adjustment_set(podcast_dag2, use_labels = "label", text = FALSE)
-```
-
-Deciding between these adjustment sets is a matter of judgment: if all data are perfectly measured, the DAG is correct, and we've modeled them correctly, then it doesn't matter which we use.
-Each adjustment set will result in an unbiased estimate.
-All three of those assumptions are usually untrue to some degree.
-Let's consider the path via `skills_course` and `prepared`.
-It may be that we are better able to assess whether or not someone took the College Skills Course than how prepared for the exam they are.
-In that case, an adjustment set with `skills_course` is a better option.
-But perhaps we better understand the relationship between preparedness and exam results.
-If we have it measured, controlling for that might be better.
-We could get the best of both worlds by including both variables: between the better measurement of `skills_course` and the better modeling of `prepared`, we might have a better chance of minimizing confounding from this path.
-
-### Selection Bias and Mediation
-
-Selection bias is another name for the type of bias that is induced by adjusting for a collider [@lu2022].
-It's called "selection bias" because a common form of collider-induced bias is a variable inherently stratified upon by the design of the study---selection *into* the study.
-Let's consider a case based on the original `podcast_dag` but with one additional variable: whether or not the student showed up to the exam.
-Now, there is an indirect effect of `podcast` on `exam`: listening to a podcast influences whether or not the students attend the exam.
-The true result of `exam` is missing for those who didn't show up; by studying the group of people who *did* show up, we are inherently stratifying on this variable.
-
-```{r}
-#| label: fig-podcast_dag3
-#| fig-width: 4.5
-#| fig-height: 3.5
-#| fig-cap: "Another variant of `podcast_dag`, this time including the inherent stratification on those who appear for the exam. There is still no direct effect of `podcast` on `exam`, but there is an indirect effect via `showed_up`."
-podcast_dag3 <- dagify(
-  podcast ~ mood + humor + prepared,
-  exam ~ mood + prepared + showed_up,
-  showed_up ~ podcast + mood + prepared,
-  coords = time_ordered_coords(
-    list(
-      # time point 1
-      c("prepared", "humor", "mood"), 
-      # time point 2
-      "podcast",  
-      "showed_up",  
-      # time point 3
-      "exam"
-    )
-  ),
-  exposure = "podcast",
-  outcome = "exam",
-  labels = c(
-    podcast = "podcast",
-    exam = "exam score",
-    mood = "mood",
-    humor = "humor",
-    prepared = "prepared",
-    showed_up = "showed up"
-  )
-)
-ggdag(podcast_dag3, use_labels = "label", text = FALSE)
-```
-
-The problem is that `showed_up` is both a collider and a mediator: stratifying on it induces a relationship between many of the variables in the DAG but blocks the indirect effect of `podcast` on `exam`.
-Luckily, the adjustment sets can handle the first problem; because `showed_up` happens *before* `exam`, we're less at risk of collider bias between the exposure and outcome.
-Unfortunately, we cannot calculate the total effect of `podcast` on `exam` because part of the effect is missing: the indirect effect is closed at `showed_up`.
-
-```{r}
-#| label: fig-podcast_dag3-as
-#| fig-width: 4.5
-#| fig-height: 4 
-#| warning: false
-#| fig-cap: "The adjustment set for `podcast_dag3` given that the data are inherently conditioned on showing up to the exam. In this case, there is no way to recover an unbiased estimate of the total effect of `podcast` on `exam`."
-podcast_dag3 |> 
-  adjust_for("showed_up") |> 
-  ggdag_adjustment_set(text = FALSE, use_labels = "label")
-```
-
-Sometimes, you can still estimate effects in this situation by changing the estimate you wish to calculate.
-We can't calculate the total effect because we are missing the indirect effect, but we can still calculate the direct effect of `podcast` on `exam`.
-
-```{r}
-#| label: fig-podcast_dag3-direct
-#| fig-width: 4.5
-#| fig-height: 4 
-#| warning: false
-#| fig-cap: "The adjustment set for `podcast_dag3` when targeting a different effect. There is one minimal adjustment set that we can use to estimate the direct effect of `podcast` on `exam`."
-podcast_dag3 |> 
-  adjust_for("showed_up") |> 
-  ggdag_adjustment_set(effect = "direct", text = FALSE, use_labels = "label")
-```
-
-#### M-Bias and Butterfly Bias
-
-A particular case of selection bias that you'll often see people talk about is *M-bias*.
-It's called M-bias because it looks like an M when arranged top to bottom.
-
-```{r}
-#| label: fig-m-bias
-#| fig-width: 4
-#| fig-height: 4
-#| fig-cap: "A DAG representing M-Bias, a situation where a collider predates the exposure and outcome."
-m_bias() |> 
-  ggdag()
-```
-
-::: callout-tip
-ggdag has several quick-DAGs for demonstrating basic causal structures, including `confounder_triangle()`, `collider_triangle()`, `m_bias()`, and `butterfly_bias()`.
-:::
-
-What's theoretically interesting about M-bias is that `m` is a collider but occurs before `x` and `y`.
-Remember that association is blocked at a collider, so there is no open path between `x` and `y`.
-
-```{r}
-paths(m_bias())
-```
-
-Let's focus on the `mood` path of the podcast-exam DAG.
-What if we were wrong about mood, and the actual relationship was M-shaped?
-Let's say that, rather than causing `podcast` and `exam`, `mood` was itself caused by two mutual causes of `podcast` and `exam`, `u1` and `u2`, as in @fig-podcast_dag4.
-We don't know what `u1` and `u2` are, and we don't have them measured.
-As above, there are no open paths in this subset of the DAG.
-
-```{r}
-#| label: fig-podcast_dag4
-#| fig-width: 5.5
-#| fig-height: 4
-#| fig-cap: "A reconfiguration of @fig-dag-podcast where `mood` is a collider on an M-shaped path."
-podcast_dag4 <- dagify(
-  podcast ~ u1,
-  exam ~ u2,
-  mood ~ u1 + u2,
-  coords = time_ordered_coords(list(
-    c("u1", "u2"),
-    "mood",
-    "podcast", 
-    "exam"
-  )),
-  exposure = "podcast",
-  outcome = "exam",
-  labels = c(
-    podcast = "podcast",
-    exam = "exam score",
-    mood = "mood",
-    u1 = "unmeasured",
-    u2 = "unmeasured"
-  ),
-  # we don't have them measured
-  latent = c("u1", "u2")
-)
-
-ggdag(podcast_dag4, use_labels = "label", text = FALSE)
-```
-
-The problem arises when we think our original DAG is the right DAG: `mood` is in the adjustment set, so we control for it.
-But this induces bias!
-It opens up a path between `u1` and `u2`, thus creating a path from `podcast` to `exam`.
-If we had either `u1` or `u2` measured, we could adjust for them to close this path, but we don't.
-There is no way to close this open path.
-
-```{r}
-#| label: fig-podcast_dag4-as
-#| warning: false
-#| fig-width: 5.5
-#| fig-height: 4.5
-#| fig-cap: "The adjustment set where `mood` is a collider. If we control for `mood` and don't know about or have the unmeasured causes of `mood`, we have no means of closing the backdoor path opened by adjusting for a collider."
-podcast_dag4 |> 
-  adjust_for("mood") |> 
-  ggdag_adjustment_set(use_labels = "label", text = FALSE)
-```
-
-Of course, the best thing to do here is not control for `mood` at all.
-Sometimes, though, that is not an option.
-Imagine if, instead of `mood`, this turned out to be the real structure for `showed_up`: since we inherently control for `showed_up`, and we don't have the unmeasured variables, our study results will always be biased.
-It's essential to understand if we're in that situation so we can address it with sensitivity analysis to understand just how biased the effect would be.
-
-Let's consider a variation on M-bias where `mood` causes `podcast` and `exam` and `u1` and `u2` are mutual causes of `mood` and the exposure and outcome.
-This arrangement is sometimes called butterfly or bowtie bias, again because of its shape.
-
-```{r}
-#| label: fig-butterfly_bias
-#| fig-width: 5
-#| fig-height: 4
-#| fig-cap: "In butterfly bias, `mood` is both a collider and a confounder. Controlling for the bias induced by `mood` opens a new pathway because we've also conditioned on a collider. We can't properly close all backdoor paths without either `u1` or `u2`."
-butterfly_bias(x = "podcast", y = "exam", m = "mood", a = "u1", b = "u2") |> 
-  ggdag(text = FALSE, use_labels = "label")
-```
-
-Now, we're in a challenging position: we need to control for `mood` because it's a confounder, but controlling for `mood` opens up the pathway from `u1` to `u2`.
-Because we don't have either variable measured, we can't then close the path opened from conditioning on `mood`.
-What should we do?
-It turns out that, when in doubt, controlling for `mood` is the better of the two options: confounding bias tends to be worse than collider bias, and M-shaped structures of colliders are sensitive to slight deviations (e.g., if this is not the exact structure, often the bias isn't as bad) [@DingMiratrix2015].
-
-Another common form of selection bias is from *loss to follow-up*: people drop out of a study in a way that is related to the exposure and outcome.
-We'll come back to this topic in [Chapter -@sec-longitudinal].
-
-### Causes of the exposure, causes of the outcome
-
-Let's consider one other type of causal structure that's important: causes of the exposure and not the outcome, and their opposites, causes of the outcome and not the exposure.
-Let's add a variable, `grader_mood`, to the original DAG.
-
-```{r}
-#| label: fig-podcast_dag5
-#| fig-width: 5
-#| fig-height: 4
-#| fig-cap: "A DAG containing a cause of the exposure that is not the cause of the outcome (`humor`) and a cause of the outcome that is not a cause of the exposure (`grader_mood`)."
-podcast_dag5 <- dagify(
-  podcast ~ mood + humor + prepared,
-  exam ~ mood + prepared + grader_mood,
-  coords = time_ordered_coords(
-    list(
-      # time point 1
-      c("prepared", "humor", "mood"), 
-      # time point 2
-      c("podcast", "grader_mood"),  
-      # time point 3
-      "exam"
-    )
-  ),
-  exposure = "podcast",
-  outcome = "exam",
-  labels = c(
-    podcast = "podcast",
-    exam = "exam score",
-    mood = "student\nmood",
-    humor = "humor",
-    prepared = "prepared",
-    grader_mood = "grader\nmood"
-  )
-)
-ggdag(podcast_dag5, use_labels = "label", text = FALSE)
-```
-
-There are now two variables that aren't related to *both* the exposure and the outcome: `humor`, which causes `podcast` but not `exam`, and `grader_mood`, which causes `exam` but not `podcast`.
-Let's start with `humor`.
-
-Variables that cause the exposure but not the outcome are also called *instrumental variables* (IVs).
-IVs are an unusual circumstance where, under certain conditions, controlling for them can make other types of bias worse.
-What's unique about this is that IVs can *also* be used to conduct an entirely different approach to estimating an unbiased effect of the exposure on the outcome.
-IVs are commonly used this way in econometrics and are increasingly popular in other areas.
-In short, IV analysis allows us to estimate the causal effect using a different set of assumptions than the approaches we've talked about thus far.
-Sometimes, a problem intractable using propensity score methods can be addressed using IVs and vice versa.
-We'll talk more about IVs in @sec-iv-friends.
-
-So, if you're *not* using IV methods, should you include an IV in a model meant to address confounding?
-If you're unsure if the variable is an IV or not, you should probably add it to your model: it's more likely to be a confounder than an IV, and, it turns out, the bias from adding an IV is usually small in practice.
-So, like adjusting for a potential M-structure variable, the risk of bias is worse from confounding [@Myers2011].
-
-Now, let's talk about the opposite of an IV: a cause of the outcome that is not the cause of the exposure.
-These variables are sometimes called *competing exposures* (because they also cause the outcome) or *precision variables* (because, as we'll see, they increase the precision of causal estimates).
-We'll call them precision variables because we're concerned about the relationship to the research question at hand, not to another research question where they are exposures [@Brookhart2006].
-
-Like IVs, precision variables do not occur along paths from the exposure to the outcome.
-Thus, including them is not necessary.
-Unlike IVs, including precision variables is beneficial.
-Including other causes of the outcomes helps a statistical model capture some of its variation.
-This doesn't impact the point estimate of the effect, but it does reduce the variance, resulting in smaller standard errors and narrower confidence intervals.
-Thus, we recommend including them when possible.
-
-So, even though we don't need to control for `grader_mood`, if we have it in the data set, we should.
-Similarly, `humor` is not a good addition to the model unless we think it really might be a confounder; if it is a valid instrument, we might want to consider using IV methods to estimate the effect instead.
-
-### Measurement Error and Missingness
-
-DAGs can also help us understand the bias arising from mismeasurements in the data, including the worst mismeasurement: not measuring it at all.
-We'll cover these topics in [Chapter -@sec-missingness], but the basic idea is that by separating the actual value from the observed value, we can better understand how such biases may behave [@Hernán2009].
-Here's a basic example of a bias called *recall bias*.
-Recall bias is when the outcome influences a participant's memory of exposure, so it's a particular problem in retrospective studies where the earlier exposure is not recorded until after the outcome happens.
-An example of when this can occur is a case-control study of cancer.
-Someone *with* cancer may be more motivated to ruminate on their past exposures than someone *without* cancer.
-So, their memory about a given exposure may be more refined than someone without.
-By conditioning on the observed version of the exposure, we open up many collider paths.
-Unfortunately, there is no way to close them all.
-If this is the case, we must investigate how severe the bias would be in practice.
-
-```{r}
-#| label: fig-error_dag
-#| fig-width: 5.5
-#| fig-height: 4
-#| fig-cap: "A DAG representing measurement error in observing the exposure and outcome. In this case, the outcome impacts the participant's memory of the exposure, also known as recall bias."
-error_dag <- dagify(
-  exposure_observed ~ exposure_real + exposure_error,
-  outcome_observed ~ outcome_real + outcome_error,
-  outcome_real ~ exposure_real,
-  exposure_error ~ outcome_real,
-  labels = c(
-    exposure_real = "Exposure\n(truth)",
-    exposure_error = "Measurement Error\n(exposure)",
-    exposure_observed = "Exposure\n(observed)",
-    outcome_real = "Outcome\n(truth)",
-    outcome_error = "Measurement Error\n(outcome)",
-    outcome_observed = "Outcome\n(observed)"
-  ),
-  exposure = "exposure_real",
-  outcome = "outcome_real",
-  coords = time_ordered_coords()
-)
-
-error_dag |> 
-  ggdag(text = FALSE, use_labels = "label")
-```
-
-## Recommendations in building DAGs
-
-In principle, using DAGs is easy: specify the causal relationships you think exist and then query the DAG for information like valid adjustment sets.
-In practice, assembling DAGs takes considerable time and thought.
-Next to defining the research question itself, it's one of the most challenging steps in making causal inferences.
-Very little guidance exists on best practices in assembling DAGs.
-@Tennant2021 collected data on DAGs in applied health research to better understand how researchers used them.
-@tbl-dag-properties shows some information they collected: the median number of nodes and arcs in a DAG, their ratio, the saturation percent of the DAG, and how many were fully saturated.
-Saturating DAGs means adding all possible arrows going forward in time, e.g., in a fully saturated DAG, any given variable at time point 1 has arrows going to all variables in future time points, and so on.
-Most DAGs were only about half saturated, and very few were fully saturated.
-
-Only about half of the papers using DAGs reported the adjustment set used.
-In other words, researchers presented their assumptions about the research question but not the implications about how they should handle the modeling stage or if they did use a valid adjustment set.
-Similarly, the majority of studies did not report the estimand of interest.
-
-::: callout-note
-The estimand is the target of interest in terms of what we're trying to estimate, as discussed briefly in [Chapter -@sec-whole-game].
-We'll discuss estimands in detail in [Chapter -@sec-estimands].
-:::
-
-```{r}
-#| label: tbl-dag-properties
-#| echo: false
-#| message: false
-#| tbl-cap: "A table of DAG properties measured by @Tennant2021. Number of nodes and arcs are the median number of variables and arrows in the analyzed DAGs, while the Node to Arc ratio is their ratio. Saturation proportion is the proportion of all possible arrows going forward in time to other included variables. Fully saturated DAGs are those that include all such arrows. @Tennant2021 also analyzed whether studies reported their estimands and adjustment sets."
-library(gtsummary)
-library(gt)
-dag_data_used <- dag_data |> 
-  filter(used_dag)
-
-as_yes_no <- function(x) {
-  factor(x, c(TRUE, FALSE), labels = c("Yes", "No"))
-}
-
-dag_data_used |> 
-  mutate(
-    saturated = near(saturation, 1),
-    report_adjset = report_adjset == "Yes",
-    across(c(saturated, reported_estimand, report_adjset), as_yes_no)
-  ) |> 
-  select(nodes, arcs, ratio, saturation, saturated, reported_estimand, report_adjset) |> 
-  tbl_summary(label = list(
-    nodes ~ "Number of Nodes",
-    arcs ~ "Number of Arcs",
-    ratio ~ "Node to Arc Ratio",
-    saturation ~ "Saturation Proportion",
-    saturated ~ "Fully Saturated",
-    reported_estimand ~ "Reported Estimand",
-    report_adjset ~ "Reported Adjustment Set"
-  ),
-  digits = list(everything() ~ 0, c(ratio, saturation) ~ 2),
-  type = list(where(is.factor) ~ "categorical")
-) |> 
-  as_gt() |> 
-  tab_row_group(
-    label = md("**DAG properties**"),
-    rows = 1:7,
-    id = "dag_prop"
-  ) |> 
-  tab_row_group(
-    label = md("**Reporting**"),
-    rows = 8:13,
-    id = "reporting"
-  ) |> 
-  row_group_order(c("dag_prop", "reporting"))
-```
-
-In this section, we'll offer some advice from @Tennant2021 and our own experience assembling DAGs.
-
-### Iterate early and often
-
-One of the best things you can do for the quality of your results is to make the DAG before you conduct the study, ideally before you even collect the data.
-If you're already working with your data, at minimum, build your DAG before doing data analysis.
-This advice is similar in spirit to pre-registered analysis plans: declaring your assumptions ahead of time can help clarify what you need to do, reduce the risk of overfitting (e.g., determining confounders incorrectly from the data), and give you time to get feedback on your DAG.
-
-This last benefit is significant: you should ideally democratize your DAG.
-Share it early and often with others who are experts on the data, domain, and models.
-It's natural to create a DAG, present it to your colleagues, and realize you have missed something important.
-Sometimes, you will only agree on some details of the structure.
-That's a good thing: you know now where there is uncertainty in your DAG.
-You can then examine the results from multiple plausible DAGs or address the uncertainty with sensitivity analyses.
-
-If you have more than one candidate DAG, check their adjustment sets.
-If two DAGs have overlapping adjustment sets, focus on those sets; then, you can move forward in a way that satisfies the plausible assumptions you have.
-
-### Consider your question
-
-As we saw in @fig-podcast_dag3, some questions can be challenging to answer with certain data, while others are more approachable.
-You should consider precisely what it is you want to estimate.
-Defining your target estimate is an important topic and the subject of [Chapter -@sec-estimands].
-
-Another important detail about how your DAG relates to your question is the population and time.
-Many causal structures are not static over time and space.
-Consider lung cancer: the distribution of causes of lung cancer was considerably different before the spread of smoking.
-In medieval Japan, before the spread of tobacco from the Americas centuries later, the causal structure for lung cancer would have been practically different from what it is in Japan today, both in terms of tobacco use and other factors (age of the population, etc.).
-
-The same is true for confounders.
-Even if something *can* cause the exposure and outcome, if the prevalence of that thing is zero in the population you're analyzing, it's irrelevant to the causal question.
-It may also be that, in some populations, it doesn't affect one of the two.
-The reverse is also true: something might be unique to the target population.
-The use of tobacco in North America several centuries ago was unique among the world population, even though ceremonial tobacco use was quite different from modern recreational use.
-Many changes won't happen as dramatically as across centuries, but sometimes, they do, e.g., if regulation in one country effectively eliminates the population's exposure to something.
-
-### Order nodes by time {#sec-time-ordered}
-
-As discussed earlier, we recommend ordering your variables by time, either left-to-right or up-to-down.
-There are two reasons for this.
-First, time ordering is an integral part of your assumptions.
-After all, something happening before another thing is a requirement for it to be a cause.
-Thinking this through carefully will clarify your DAG and the variables you need to address.
-
-Second, after a certain level of complexity, it's easier to read a DAG when arranged by time because you have to think less about that dimension; it's inherent to the layout.
-The time ordering algorithm in ggdag automates much of this for you, although, as we saw earlier, it's sometimes helpful to give it more information about the order.
-
-A related topic is feedback loops [@murray2022].
-Often, we think about two things that mutually cause each other as happening in a circle, like global warming and A/C use (A/C use increases global warming, which makes it hotter, which increases A/C use, and so on).
-It's tempting to visualize that relationship like this:
-
-```{r}
-#| label: fig-feedback-loop
-#| fig-width: 4.5
-#| fig-height: 3.5
-#| fig-cap: "A DAG representing the reciprocal relationship between A/C use and global temperature because of global warming. Feedback loops are useful mental shorthands to describe variables that impact each other over time compactly, but they are not true causal diagrams."
-dagify(
-  ac_use ~ global_temp,
-  global_temp ~ ac_use,
-  labels = c(ac_use = "A/C use", global_temp = "Global\ntemperature")
-) |> 
-  ggdag(layout = "circle", edge_type = "arc", text = FALSE, use_labels = "label")
-```
-
-From a DAG perspective, this is a problem because of the *A* part of *DAG*: it's cyclic!
-Importantly, though, it's also not correct from a causal perspective.
-Feedback loops are a shorthand for what really happens, which is that the two variables mutually affect each other *over time*.
-Causality only goes forward in time, so it doesn't make sense to go back and forth like in @fig-feedback-loop.
-
-The real DAG looks something like this:
-
-```{r}
-#| label: fig-feedforward
-#| fig-width: 5
-#| fig-height: 3.5
-#| fig-cap: "A DAG showing the relationship between A/C use and global temperature over time. The true causal relationship in a feedback loop goes *forward*."
-dagify(
-  global_temp_2000 ~ ac_use_1990 + global_temp_1990,
-  ac_use_2000 ~ ac_use_1990 + global_temp_1990,
-  global_temp_2010 ~ ac_use_2000 + global_temp_2000,
-  ac_use_2010 ~ ac_use_2000 + global_temp_2000,
-  global_temp_2020 ~ ac_use_2010 + global_temp_2010,
-  ac_use_2020 ~ ac_use_2010 + global_temp_2010,
-  coords = time_ordered_coords(),
-  labels = c(
-    ac_use_1990 = "A/C use\n(1990)", 
-    global_temp_1990 = "Global\ntemperature\n(1990)",
-    ac_use_2000 = "A/C use\n(2000)", 
-    global_temp_2000 = "Global\ntemperature\n(2000)",
-    ac_use_2010 = "A/C use\n(2010)", 
-    global_temp_2010 = "Global\ntemperature\n(2010)",
-    ac_use_2020 = "A/C use\n(2020)", 
-    global_temp_2020 = "Global\ntemperature\n(2020)"
-  )
-) |> 
-  ggdag(text = FALSE, use_labels = "label")
-```
-
-The two variables, rather than being in a feed*back* loop, are actually in a feed*forward* loop: they co-evolve over time.
-Here, we only show four discrete moments in time (the decades from 1990 to 2020), but of course, we could get much finer depending on the question and data.
-
-As with any DAG, the proper analysis approach depends on the question.
-The effect of A/C use in 2000 on the global temperature in 2020 produces a different adjustment set than the global temperature in 2000 on A/C use in 2020.
-Similarly, whether we also model this change over time or just those two time points depends on the question.
-Often, these feedforward relationships require you to address *time-varying* confounding, which we'll discuss in [Chapter -@sec-longitudinal].
-
-### Consider the whole data collection process
-
-As @fig-podcast_dag3 showed us, it's essential to consider the *way* we collected data as much as the causal structure of the question.
-Considering the whole data collection process is particularly true if you're working with "found" data---a data set not intentionally collected to answer the research question.
-We are always inherently conditioning on the data we have vs. the data we don't have.
-If other variables influenced the data collection process in the causal structure, you need to consider the impact.
-Do you need to control for additional variables?
-Do you need to change the effect you are trying to estimate?
-Can you answer the question at all?
-
-<!-- TODO: revisit this after I've thought it through a bit more -->
-
-<!-- Let's consider an example: workers at a factory are potentially exposed to something (e.g., a carcinogenic) that may increase their risk of dying. Some workers are exposed, and some are not. The problem is that exposure *prior* to the study resulted in only those healthy enough to work being present at the start of the study. If there is another factor, say some important aspect of the worker's health, that also contributes to whether or not they are at work, we have selection bias. We are inherently stratifying on whether or not someone is at work. -->
-
-<!-- ```{r} -->
-
-<!-- dagify( -->
-
-<!--     death ~ health, -->
-
-<!--     exposed ~ exposed_prior + at_work, -->
-
-<!--     at_work ~ exposed_prior + health, -->
-
-<!--     exposure = "exposed", -->
-
-<!--     outcome = "death", -->
-
-<!--     latent = c("health", "exposed_prior"), -->
-
-<!--     coords = time_ordered_coords(list( -->
-
-<!--         c("health", "exposed_prior"), -->
-
-<!--         "at_work", -->
-
-<!-- "exposed", -->
-
-<!--         "death" -->
-
-<!--     )), -->
-
-<!--     labels = c( -->
-
-<!--         exposed_prior = "Exposed\n(prior)", -->
-
-<!--         exposed = "Exposed\n(start)", -->
-
-<!--         at_work = "Working", -->
-
-<!--         health = "Health\nStatus", -->
-
-<!--         death = "Death" -->
-
-<!--     ) -->
-
-<!-- ) |>  -->
-
-<!--   ggdag(text = FALSE, use_labels = "label") -->
-
-<!-- ``` -->
-
-<!-- `at_work` is a collider: both health status and prior exposure have arrows going into it. Controlling for it induces a ... -->
-
-<!-- -   race/shooting (show the `effect` argument of `adjustmentSets` to get direct effect) -->
-
-<!-- -   healthy worker bias -->
-
-::: callout-tip
-## What about case-control studies?
-
-A standard study design in epidemiology is the case-control study.
-Case-control studies are beneficial when the outcome under study is rare or takes a very long time to happen (like many types of cancer).
-Participants are selected into the study based on their outcome: once a person has an event, they are entered as a case and matched with a control who hasn't had the event.
-Often, they are matched on other factors as well.
-
-Matched case-control studies are selection biased by design [@mansournia2013].
-In @fig-case-control, when we condition on selection into the study, we lose the ability to close all backdoor paths, even if we control for `confounder`.
-From the DAG, it would appear that the entire design is invalid!
-
-```{r}
-#| label: fig-case-control
-#| fig-width: 4.5
-#| fig-height: 3.2
-#| fig-cap: "A DAG representing a matched case-control study. In such a study, selection is determined by outcome status and any matched confounders. Selection into the study is thus a collider. Since it is inherently stratified on who is actually in the study, such data are limited in the types of causal effects they can estimate." 
-dagify(
-  outcome ~ confounder + exposure,
-  selection ~ outcome + confounder,
-  exposure ~ confounder,
-  exposure = "exposure",
-  outcome = "outcome",
-  coords = time_ordered_coords()
-) |> 
-  ggdag(edge_type = "arc", text_size = 2.2)
-```
-
-Luckily, this isn't wholly true.
-Case-control studies are limited in the type of causal effects they can estimate (causal odds ratios, which under some circumstances approximate causal risk ratios).
-With careful study design and sampling, the math works out such that these estimates are still valid.
-Exactly how and why case-control studies work is beyond the scope of this book, but they are a remarkably clever design.
-:::
-
-### Include variables you don't have
-
-It's critical that you include *all* variables important to the causal structure, not just the variables you have measured in your data.
-ggdag can mark variables as unmeasured ("latent"); it will then return only usable adjustment sets, e.g., those without the unmeasured variables.
-Of course, the best thing to do is to use DAGs to help you understand what to measure in the first place, but there are many reasons why your data might be different.
-Even data intentionally collected for the research question might not have a variable discovered to be a confounder after data collection.
-
-For instance, if we have a DAG where `exposure` and `outcome` have a confounding pathway consisting of `confounder1` and `confounder2`, we can control for either to successfully debias the estimate:
-
-```{r}
-dagify(
-  outcome ~ exposure + confounder1,
-  exposure ~ confounder2,
-  confounder2 ~ confounder1,
-  exposure = "exposure",
-  outcome = "outcome"
-) |> 
-  adjustmentSets()
-```
-
-Thus, if just one is missing (`latent`), then we are ok:
-
-```{r}
-dagify(
-  outcome ~ exposure + confounder1,
-  exposure ~ confounder2,
-  confounder2 ~ confounder1,
-  exposure = "exposure",
-  outcome = "outcome",
-  latent = "confounder1"
-) |> 
-  adjustmentSets()
-```
-
-But if both are missing, there are no valid adjustment sets.
-
-When you don't have a variable measured, you still have a few options.
-As mentioned above, you may be able to identify alternate adjustment sets.
-If the missing variable is required to close all backdoor paths completely, you can and should conduct a sensitivity analysis to understand the impact of not having it.
-This is the subject of [Chapter -@sec-sensitivity].
-
-Under some lucky circumstances, you can also use a *proxy* confounder [@miao2018].
-A proxy confounder is a variable closely related to the confounder such that controlling for it controls for some of the effects of the missing variable.
-Consider an expansion of the fundamental confounding relationship where `q` has a cause, `p`, as in @fig-proxy-confounder.
-Technically, if we don't have `q`, we can't close the backdoor path, and our effect will be biased.
-Practically, though, if `p` is highly correlated with `q`, it can serve as a method to reduce the confounding from `q`.
-You can think of `p` as a mismeasured version of `q`; it will seldom wholly control for the bias via `q`, but it can help minimize it.
-
-```{r}
-#| label: fig-proxy-confounder
-#| fig-width: 4.5
-#| fig-height: 3.2
-#| fig-cap: "A DAG with a confounder, `q`, and a proxy confounder, `p`. The true adjustment set is `q`. Since `p` causes `q`, it contains information about `q` and can reduce the bias if we don't have `q` measured."
-dagify(
-  y ~ x + q,
-  x ~ q,
-  q ~ p,
-  coords = time_ordered_coords()
-) |> 
-  ggdag(edge_type = "arc")
-```
-
-### Saturate your DAG, then prune
-
-In discussing @tbl-dag-properties, we mentioned *saturated* DAGs.
-These are DAGs where all possible arrows are included based on the time ordering, e.g., every variable causes variables that come after it in time.
-
-*Not* including an arrow is a bigger assumption than including one.
-In other words, your default should be to have an arrow from one variable to a future variable.
-This default is counterintuitive to many people.
-How can it be that we need to be so careful about assessing causal effects yet be so liberal in applying causal assumptions in the DAG?
-The answer to this lies in the strength and prevalence of the cause.
-Technically, an arrow present means that *for at least a single observation*, the prior node causes the following node.
-The arrow similarly says nothing about the strength of the relationship.
-So, a minuscule causal effect on a single individual justifies the presence of an arrow.
-In practice, such a case is probably not relevant.
-There is *effectively* no arrow.
-
-The more significant point, though, is that you should feel confident to add an arrow.
-The bar for justification is much lower than you think.
-Instead, it's helpful to 1) determine your time ordering, 2) saturate the DAG, and 3) prune out implausible arrows.
-
-Let's experiment by working through a saturated version of the podcast-exam DAG.
-
-First, the time-ordering.
-Presumably, the student's sense of humor far predates the day of the exam.
-Mood in the morning, too, predates listening to the podcast or exam score, as does preparation.
-The saturated DAG given this ordering is:
-
-```{r}
-#| label: fig-podcast_dag_sat
-#| fig-width: 5.5
-#| fig-height: 4
-#| echo: false
-#| fig-cap: "A saturated version of `podcast_dag`: variables have all possible arrows going forward to other variables over time."
-podcast_dag_sat <- dagify(
-  podcast ~ mood + humor + prepared,
-  exam ~ mood + prepared + humor,
-  prepared ~ humor,
-  mood ~ humor,
-  coords = time_ordered_coords(
-    list(
-      "humor",
-      c("prepared", "mood"), 
-      "podcast",  
-      "exam"
-    )
-  ),
-  exposure = "podcast",
-  outcome = "exam",
-  labels = c(
-    podcast = "podcast",
-    exam = "exam score",
-    mood = "mood",
-    humor = "humor",
-    prepared = "prepared"
-  )
-)
-
-curvatures <- rep(0, 8)
-curvatures[1] <- .25
-
-podcast_dag_sat |> 
-  tidy_dagitty() |> 
-  ggplot(aes(x, y, xend = xend, yend = yend)) +
-  geom_dag_point() +
-  geom_dag_edges_arc(curvature = curvatures) +
-  geom_dag_label_repel()
-```
-
-There are a few new arrows here.
-Humor now causes the other two confounders, as well as exam score.
-Some of them make sense.
-Sense of humor probably affects mood for some people.
-What about preparedness?
-This relationship seems a little less plausible.
-Similarly, we know that a sense of humor does not affect exam scores in this case because the grading is blinded.
-Let's prune those two.
-
-```{r}
-#| label: fig-podcast_dag_pruned
-#| echo: false
-#| fig-width: 5.5
-#| fig-height: 4
-#| fig-cap: "A pruned version of @fig-podcast_dag_sat: we've removed implausible arrows from the fully saturated DAGs."
-podcast_dag_pruned <- dagify(
-  podcast ~ mood + humor + prepared,
-  exam ~ mood + prepared,
-  mood ~ humor,
-  coords = time_ordered_coords(
-    list(
-      "humor",
-      c("prepared", "mood"), 
-      "podcast",  
-      "exam"
-    )
-  ),
-  exposure = "podcast",
-  outcome = "exam",
-  labels = c(
-    podcast = "podcast",
-    exam = "exam score",
-    mood = "mood",
-    humor = "humor",
-    prepared = "prepared"
-  )
-)
-
-ggdag(podcast_dag_pruned, text = FALSE, use_labels = "label")
-```
-
-This DAG seems more reasonable.
-So, was our original DAG wrong?
-That depends on several factors.
-Notably, both DAGs produce the same adjustment set: controlling for `mood` and `prepared` will give us an unbiased effect if either DAG is correct.
-Even if the new DAG were to produce a different adjustment set, whether the result is meaningfully different depends on the strength of the confounding.
-
-### Include instruments and precision variables
-
-Technically, you do not need to include instrumental and precision variables in your DAG.
-The adjustment sets will be the same with and without them.
-However, adding them is helpful for two reasons.
-Firstly, they demonstrate your assumptions about their relationships and the variables under study.
-As discussed above, *not* including an arrow is a more significant assumption than including one, so it's valuable information about how you think the causal structure operates.
-Secondly, it impacts your modeling decision.
-You should always include precision variables in your model to reduce variability in your estimate so it helps you identify those.
-Instruments are also helpful to see because they may guide alternative or complementary modeling strategies, as we'll discuss in @sec-evidence.
-
-### Focus on the causal structure, then consider measurement bias
-
-As we saw above, missingness and measurement error can be a source of bias.
-As we'll see in [Chapter -@sec-missingness], we have several strategies to approach such a situation.
-Yet, almost everything we measure is inaccurate to some degree.
-The true DAG for the data at hand inherently conditions on the measured version of variables.
-In that sense, your data are always subtly-wrong, a sort of unreliable narrator.
-When should we include this information in the DAG?
-We recommend first focusing on the causal structure of the DAG as if you had perfectly measured each variable [@hernan2021].
-Then, consider how mismeasurement and missingness might affect the realized data, particularly related to the exposure, outcome, and critical confounders.
-You may prefer to present this as an alternative DAG to consider strategies for addressing the bias arising from those sources, e.g., imputation or sensitivity analyses.
-After all, the DAG in \@ fig-error_dag makes you think the question is unanswerable because we have no method to close all backdoor paths.
-As with all open paths, that depends on the severity of the bias and our ability to reckon with it.
-
-<!-- TODO: I don't quite remember what I wanted to cover here, so revisit later to add or delete -->
-
-<!-- ### Be accurate, but focus on clarity -->
-
-### Pick adjustment sets most likely to be successful
-
-One area where measurement error is an important consideration is when picking an adjustment set.
-In theory, if a DAG is correct, any adjustment set will work to create an unbiased result.
-In practice, variables have different levels of quality.
-Pick an adjustment set most likely to succeed because it contains accurate variables.
-Similarly, non-minimal adjustment sets are helpful to consider because, together, several variables with measurement error along a backdoor path may be enough to minimize the practical bias resulting from that path.
-
-What if you don't have certain critical variables measured and thus do not have a valid adjustment set?
-In that case, you should pick the adjustment set with the best chance of minimizing the bias from other backdoor paths.
-All is not lost if you don't have every confounder measured: get the highest quality estimate you can, then conduct a sensitivity analysis about the unmeasured variables to understand the impact.
-
-### Use robustness checks
-
-Finally, we recommend checking your DAG for robustness.
-You can never verify the correctness of your DAG under most conditions, but you can use the implications in your DAG to support it.
-Three types of robustness checks can be helpful depending on the circumstances.
+<!-- ## Common Structures of Bias -->
 
-1.  **Negative controls** [@Lipsitch2010]. These come in two flavors: negative exposure controls and negative outcome controls. The idea is to find something associated with one but not the other, e.g., the outcome but not the exposure, so there should be no effect. Since there should be no effect, you now have a measurement for how well you control for *other* effects (e.g., the difference from null). Ideally, the confounders for negative controls are similar to the research question.
-2.  **DAG-data consistency** [@Textor2016]. Negative controls are an implication of your DAG. An extension of this idea is that there are *many* such implications. Because blocking a path removes statistical dependencies from that path, you can check those assumptions in several places in your DAG.
-3.  **Alternate adjustment sets**. Adjustment sets should give roughly the same answer because, outside of random and measurement errors, they are all sets that block backdoor paths. If more than one adjustment set seems reasonable, you can use that as a sensitivity analysis by checking multiple models.
+<!-- ## Causal Inference is not (just) a statistical problem {#sec-quartets} -->
 
-We'll discuss these in detail in [Chapter -@sec-sensitivity].
-The caveat here is that these should be complementary to your initial DAG, not a way of *replacing* it.
-In fact, if you use more than one adjustment set during your analysis, you should report the results from all of them to avoid overfitting your results to your data.
+<!-- ## Causal and Predictive Models, Revisited {#sec-causal-pred-revisit} -->
diff --git a/chapters/chapter-06.qmd b/chapters/chapter-06.qmd
index ecf74be..07c97e0 100644
--- a/chapters/chapter-06.qmd
+++ b/chapters/chapter-06.qmd
@@ -1,15 +1,408 @@
-# Causal Inference is not (just) a statistical problem {#sec-quartets}
+# Preparing data to answer causal questions {#sec-data-causal}
 
 {{< include 00-setup.qmd >}}
 
+## Introduction to the data {#sec-data}
+
+Throughout this book we will be using data obtained from [Touring Plans](https://touringplans.com).
+Touring Plans is a company that helps folks plan their trips to Disney and Universal theme parks.
+One of their goals is to accurately predict attraction wait times at these theme parks by leveraging data and statistical modeling.
+The `{touringplans}` R package includes several datasets containing information about Disney theme park attractions.
+A summary of the attractions included in the package can be found by running the following:
+
+```{r}
+library(touringplans)
+attractions_metadata
+```
+
+Additionally, this package contains a dataset with raw metadata about the parks, with observations recorded daily.
+This metadata includes information like the Walt Disney World ticket season on the particular day (was it high season -- think Christmas -- or low season -- think right when school started), what the historic temperatures were in the park on that day, and whether there was a special event, such as "extra magic hours" in the park on that day (did the park open early to guests staying in the Walt Disney World resorts?).
+
+```{r}
+parks_metadata_raw
+```
+
+Suppose the causal question of interest is:
+
+**Is there a relationship between whether there were "Extra Magic Hours" in the morning at Magic Kingdom and the average wait time for an attraction called the "Seven Dwarfs Mine Train" the same day between 9am and 10am in 2018?**
+
+Let's begin by diagramming this causal question (@fig-seven-diag).
+
+```{r}
+#| echo: false
+#| fig-cap: "Diagram of the causal question \"Is there a relationship between whether there were \"Extra Magic Hours\" in the morning at Magic Kingdom and the average wait time for an attraction called the \"Seven Dwarfs Mine Train\" the same day between 9am and 10am in 2018?\""
+#| label: fig-seven-diag
+
+data <- data.frame(
+  labels = c("Extra Magic Hours", "Change average wait time", "Magic Kingdom guests", "before the park opens (2018)", "Magic Kingdom guests", "9am and 10am (2018)"),
+  x = c(1, 2, .83, 1.4, 1.88, 2.45),
+  y = c(1, 1, 0.77, 0.7, 0.77, 0.7),
+  angle = c(0, 0, -52, 0, -52, 0)
+)
+
+ggplot(data, aes(x = x, y = y)) +
+  geom_text(aes(label = labels, angle = angle, vjust = 0), size = 4) +
+  geom_segment(aes(x = 0.5, xend = 2.5, y = 0.95, yend = 0.95)) +
+  geom_segment(aes(x = 1.5, xend = 1.5, y = 0.95, yend = 1.1)) +
+  geom_segment(aes(x = 0.5, xend = 1, y = 0.95, yend = 0.65)) +
+  geom_segment(aes(x = 1, xend = 1.5, y = 0.65, yend = 0.65)) +
+  geom_segment(aes(x = 1.55, xend = 2.05, y = 0.95, yend = 0.65)) +
+  geom_segment(aes(x = 2.05, xend = 2.55, y = 0.65, yend = 0.65)) +
+  xlim(c(0.5, 2.75)) +
+  ylim(c(0.5, 1.2)) +
+  theme_void()
+```
+
+Historically, guests who stayed in a Walt Disney World resort hotel could access the park during "Extra Magic Hours," during which the park was closed to all other guests.
+These extra hours could be in the morning or evening.
+The Seven Dwarfs Mine Train is a ride at Walt Disney World's Magic Kingdom.
+Magic Kingdom may or may not be selected each day to have these "Extra Magic Hours." We are interested in examining the relationship between whether there were "Extra Magic Hours" in the morning and the average wait time for the Seven Dwarfs Mine Train on the same day between 9 am and 10 am.
+Below is a proposed DAG for this question.
+
+```{r}
+#| label: fig-dag-magic
+#| echo: false
+#| message: false
+#| warning: false
+#| fig.cap: >
+#|   Proposed DAG for the relationship between Extra Magic Hours
+#|   in the morning at a particular park and the average wait
+#|   time between 9 am and 10 am.
+#|   Here we are saying that we believe 1) Extra Magic Hours impacts average wait time and 2) both Extra Magic Hours and average wait time are determined by the time the park closes, historic high temperatures, and ticket season.
+
+library(tidyverse)
+library(ggdag)
+library(ggokabeito)
+
+coord_dag <- list(
+  x = c(Season = 0, close = 0, weather = -1, x = 1, y = 2),
+  y = c(Season = -1, close = 1, weather = 0, x = 0, y = 0)
+)
+
+labels <- c(
+  x = "Extra Magic Morning",
+  y = "Average wait",
+  Season = "Ticket Season",
+  weather = "Historic high temperature",
+  close = "Time park closed"
+)
+
+dagify(
+  y ~ x + close + Season + weather,
+  x ~ weather + close + Season,
+  coords = coord_dag,
+  labels = labels,
+  exposure = "x",
+  outcome = "y"
+) |>
+  tidy_dagitty() |>
+  node_status() |>
+  ggplot(
+    aes(x, y, xend = xend, yend = yend, color = status)
+  ) +
+  geom_dag_edges_arc(curvature = c(rep(0, 5), .3)) +
+  geom_dag_point() +
+  geom_dag_label_repel(seed = 1630) +
+  scale_color_okabe_ito(na.value = "grey90") +
+  theme_dag() +
+  theme(
+    legend.position = "none",
+    axis.text.x = element_text()
+  ) +
+  coord_cartesian(clip = "off") +
+  scale_x_continuous(
+    limits = c(-1.25, 2.25),
+    breaks = c(-1, 0, 1, 2),
+    labels = c(
+      "\n(one year ago)",
+      "\n(6 months ago)",
+      "\n(3 months ago)",
+      "9am - 10am\n(Today)"
+    )
+  )
+```
+
+Since we are not in charge of Walt Disney World's operations, we cannot randomize dates to have (or not) "Extra Magic Hours", therefore, we need to rely on previously collected observational data and do our best to emulate the *target trial* that we would have created, should it have been possible.
+Here, our observations are *days*.
+Looking at the diagram above, we can map each element of the causal question to elements of our target trial protocol:
+
+-   **Eligibility criteria**: Days must be from 2018
+-   **Exposure definition**: Magic kingdom had "Extra Magic Hours" in the morning
+-   **Assignment procedures**: Observed -- if the historic data suggests there were "Extra Magic Hours" in the morning on a particular day, that day is classified as "exposed" otherwise it is "unexposed"
+-   **Follow-up period**: From park open to 10am.
+-   **Outcome definition**: The average posted wait time between 9am and 10am
+-   **Causal contrast of interest**: Average treatment effect (we will discuss this in @sec-estimands)
+-   **Analysis plan**: We use inverse probability waiting after fitting a propensity score model to estimate the average treatment effect of the exposure on the outcome of interest. We will adjust for variables as determined by our DAG (@fig-dag-magic)
+
+## Data wrangling and recipes
+
+Most of our data manipulation tools come from the `{dplyr}` package (@tbl-dplyr).
+We will also use `{lubridate}` to help us manipulate dates.
+
+| Target trial protocol element | {dplyr} functions                           |
+|-------------------------------|---------------------------------------------|
+| Eligibility criteria          | `filter()`                                  |
+| Exposure definition           | `mutate()`                                  |
+| Assignment procedures         | `mutate()`                                  |
+| Follow-up period              | `mutate()` `pivot_longer()` `pivot_wider()` |
+| Outcome definition            | `mutate()`                                  |
+| Analysis plan                 | `select()` `mutate()`                       |
+
+: Mapping target trial protocol elements to commonly used `{dplyr}` functions {#tbl-dplyr}
+
+To answer this question, we are going to need to manipulate both the `seven_dwarfs_train` dataset as well as the `parks_metadata_raw` dataset.
+Let's start with the `seven_dwarfs_train` data set.
+The Seven Dwarfs Mine Train ride is an attraction at Walt Disney World's Magic Kingdom.
+The `seven_dwarfs_train` dataset in the {touringplans} package contains information about the date a particular wait time was recorded (`park_date`), the time of the wait time (`wait_datetime`), the actual wait time (`wait_minutes_actual`), and the posted wait time (`wait_minutes_posted`).
+Let's take a look at this dataset.
+The {skimr} package is great for getting a quick glimpse at a new dataset.
+
+```{r}
+library(skimr)
+skim(seven_dwarfs_train)
+```
+
+Examining the output above, we learn that this dataset contains four columns and 321,631 rows.
+We also learn that the dates span from 2015 to 2021.
+We can also examine the distribution of each of the variables to detect any potential anomalies.
+Notice anything strange?
+Look at the `p0` (that is the minimum value) for `wait_minutes_actual`.
+It is `-92918`!
+We are not using this variable for this analysis, but we will for future analyses, so this is good to keep in mind.
+
+We need this dataset to calculate our *outcome*.
+Recall from above that our outcome is defined as the average posted wait time between 9am and 10am.
+Additionally, recall our eligibility criteria states that we need to restrict our analysis to days in 2018.
+
+```{r}
+#| message: false
+#| warning: false
+library(dplyr)
+library(lubridate)
+seven_dwarfs_train_2018 <- seven_dwarfs_train |>
+  filter(year(park_date) == 2018) |> # eligibility criteria
+  mutate(hour = hour(wait_datetime)) |> # get hour from wait
+  group_by(park_date, hour) |> # group by date
+  summarise(
+    wait_minutes_posted_avg = mean(wait_minutes_posted, na.rm = TRUE),
+    .groups = "drop"
+  ) |> # get average wait time
+  mutate(
+    wait_minutes_posted_avg =
+      case_when(
+        is.nan(wait_minutes_posted_avg) ~ NA,
+        TRUE ~ wait_minutes_posted_avg
+      )
+  ) |> # if it is NAN make it NA
+  filter(hour == 9) # only keep the average wait time between 9 and 10
+```
+
+```{r}
+seven_dwarfs_train_2018
+```
+
+Now that we have our outcome settled, we need to get our exposure variable, as well as any other park-specific variables about the day in question that may be used as variables that we adjust for.
+Examining @fig-dag-magic, we see that we need data for three proposed confounders: the ticket season, the time the park closed, and the historic high temperature.
+These are in the `parks_metadata_raw` dataset.
+This data will require extra cleaning, since the names are in the original format.
+
+::: callout-tip
+We like to have our variable names follow a clean convention -- one way to do this is to follow Emily Riederer's "Column Names as Contracts" format [@Riederer_2020].
+The basic idea is to predefine a set of words, phrases, or stubs with clear meanings to index information, and use these consistently when naming variables.
+For example, in these data, variables that are specific to a particular wait time are prepended with the term `wait` (e.g. `wait_datetime` and `wait_minutes_actual`), variables that are specific to the park on a particular day, acquired from parks metadata, are prepended with the term `park` (e.g. `park_date` or `park_temperature_high`).
+:::
+
+Let's first decide what variables we will need.
+In practice, this decision may involve an iterative process.
+For example, after drawing our DAG or after conducting diagnostic, we may determine that we need more variables than what we originally cleaned.
+Let's start by skimming this dataframe.
+
+```{r}
+skim(parks_metadata_raw)
+```
+
+This dataset contains many more variables than the one we worked with previously.
+For this analysis, we are going to select `date` (the observation date), `wdw_ticket_season` (the ticket season for the observation), `wdwmaxtemp` (the maximum temperature), `mkclose` (the time Magic Kingdom closed), `mkemhmorn` (whether Magic Kingdom had an "Extra Magic Hour" in the morning).
+
+```{r}
+parks_metadata_clean <- parks_metadata_raw |>
+  ##  based on our analysis plan, we will select the following variables
+  select(date, wdw_ticket_season, wdwmaxtemp, mkclose, mkemhmorn) |>
+  ## based on eligibility criteria, limit to 2018
+  filter(year(date) == 2018) |>
+  ## rename variables
+  rename(
+    park_date = date,
+    park_ticket_season = wdw_ticket_season,
+    park_temperature_high = wdwmaxtemp,
+    park_close = mkclose,
+    park_extra_magic_morning = mkemhmorn
+  )
+```
+
+## Working with multiple data sources
+
+Frequently we find ourselves merging data from multiple sources when attempting to answer causal questions in order to ensure that all of the necessary factors are accounted for.
+The way we can combine datasets is via *joins* -- joining two or more datasets based on a set or sets of common variables.
+We can think of three main types of *joins*: left, right, and inner.
+A *left* join combines data from two datasets based on a common variable and includes all records from the *left* dataset along with matching records from the *right* dataset (in `{dplyr}`, `left_join()`), while a *right* join includes all records from the *right* dataset and their corresponding matches from the *left* dataset (in `{dplyr}` `right_join()`); an inner join, on the other hand, includes only the records with matching values in *both* datasets, excluding non-matching records (in `{dplyr}` `inner_join()`.
+For this analysis, we need to use a left join to pull in the cleaned parks metadata.
+
 ```{r}
-rnorm(5)
+seven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>
+  left_join(parks_metadata_clean, by = "park_date")
 ```
 
-## Causal and Predictive Models, Revisited {#sec-causal-pred-revisit}
+## Recognizing missing data
+
+It is important to recognize whether we have any missing data in our variables.
+The `{visdat}` package is great for getting a quick sense of whether we have any missing data.
 
-<!-- TODO:  -->
-<!-- -   Probably too long, but if possible, condense to a popout -->
-<!-- -   DAGs showing examples where prediction can lean on measured confounders, colliders. It's the amount of information a variable brings, not whether the coeffecient is unbiased affect of variable on outcome. -->
-<!-- -   Not practical to fit a prediction model with future variable -->
-<!-- -   Table 2 Bias examples. Unmeasure confounding of Z-Y relationship. Mediation example. -->
+```{r}
+library(visdat)
+vis_miss(seven_dwarfs_train_2018)
+```
+
+It looks like we only have a few observations (2%) missing our outcome of interest.
+This is not too bad.
+For this first analysis we will ignore the missing values.
+We can explicitly drop them using the `drop_na()` function from `{dplyr}`.
+
+```{r}
+seven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>
+  drop_na()
+```
+
+## Exploring and visualizing data and assumptions
+
+The *positivity* assumption requires that within each level and combination of the study variables used to achieve exchangeability, there are exposed and unexposed subjects (@sec-assump).
+We can explore this by visualizing the distribution of each of our proposed confounders stratified by the exposure.
+
+### Single variable checks for positivity violations
+
+@fig-close shows the distribution of Magic Kingdom park closing time by whether the date had extra magic hours in the morning.
+There is not clear evidence of a lack of positivity here as both exposure levels span the majority of the covariate space.
+
+```{r}
+#| label: fig-close
+#| fig-cap: "Distribution of Magic Kingdom park closing time by whether the date had extra magic hours in the morning"
+ggplot(seven_dwarfs_train_2018, aes(
+  x = factor(park_close),
+  group = factor(park_extra_magic_morning),
+  fill = factor(park_extra_magic_morning)
+)) +
+  geom_bar(position = position_dodge2(width = 0.9, preserve = "single")) +
+  labs(
+    fill = "Extra Magic Morning",
+    x = "Time of Park Close"
+  )
+```
+
+To examine the distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning we can use a mirrored histogram.
+We'll use the {halfmoon} package's `geom_mirror_histogram()` to create one.
+Examining @fig-temp, it does look like there are very few days in the exposed group with maximum temperatures less than 60 degrees, while not necessarily a positivity violation it is worth keeping an eye on, particularly because the dataset is not very large, so this could make it difficult to estimate an average exposure effect across this whole space.
+If we found this to be particularly difficult, we could posit changing our causal question to instead restrict the analysis to warmer days.
+This of course would also restrict which days we could draw conclusions about for the future.
+
+```{r}
+#| label: fig-temp
+#| fig-cap: "Distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning"
+library(halfmoon)
+ggplot(seven_dwarfs_train_2018, aes(
+  x = park_temperature_high,
+  group = factor(park_extra_magic_morning),
+  fill = factor(park_extra_magic_morning)
+)) +
+  geom_mirror_histogram(bins = 20) +
+  labs(
+    fill = "Extra Magic Morning",
+    x = "Historic maximum temperature (degrees F)"
+  )
+```
+
+Finally, let's look at the distribution of ticket season by whether there were extra magic hours in the morning.
+Examining @fig-ticket, we do not see any positivity violations.
+
+```{r}
+#| label: fig-ticket
+#| fig-cap: "Distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning"
+ggplot(seven_dwarfs_train_2018, aes(
+  x = park_ticket_season,
+  group = factor(park_extra_magic_morning),
+  fill = factor(park_extra_magic_morning)
+)) +
+  geom_bar(position = "dodge") +
+  labs(
+    fill = "Extra Magic Morning",
+    x = "Magic Kingdom Ticket Season"
+  )
+```
+
+### Multiple variable checks for positivity violations
+
+We have confirmed that for each of the three confounders, we do not see strong evidence of positivity violations.
+Because we have so few variables here, we can examine this a bit more closely.
+Let's start by discretizing the `park_temperature_high` variable a bit (we will cut it into tertiles).
+
+```{r}
+#| label: fig-positivity
+#| fig-cap: "Check for positivity violations across three confounders: historic high temperature, park close time, and ticket season."
+#| fig-width: 9
+seven_dwarfs_train_2018 |>
+  ## cut park_temperature_high into tertiles
+  mutate(park_temperature_high_bin = cut(park_temperature_high, breaks = 3)) |>
+  ## bin park close time
+  mutate(park_close_bin = case_when(
+    hour(park_close) < 19 & hour(park_close) > 12 ~ "(1) early",
+    hour(park_close) >= 19 & hour(park_close) < 24 ~ "(2) standard",
+    hour(park_close) >= 24 | hour(park_close) < 12 ~ "(3) late"
+  )) |>
+  group_by(park_close_bin, park_temperature_high_bin, park_ticket_season) |>
+  ## calculate the proportion exposed in each bin
+  summarise(prop_exposed = mean(park_extra_magic_morning), .groups = "drop") |>
+  ggplot(aes(x = park_close_bin, y = park_temperature_high_bin, fill = prop_exposed)) +
+  geom_tile() +
+  scale_fill_gradient2(midpoint = 0.5) +
+  facet_wrap(~park_ticket_season) +
+  labs(
+    y = "Historic Maximum Temperature (F)",
+    x = "Magic Kingdom Park Close Time",
+    fill = "Proportion of Days Exposed"
+  )
+```
+
+Interesting!
+@fig-positivity shows an interesting potential violation.
+It looks like 100% of days with lower temperatures (historic highs between 51 and 65 degrees) that are in the peak ticket season have extra magic hours in the morning.
+This actually makes sense if we think a bit about this data set.
+The only days with cold temperatures in Florida that would also be considered a "peak" time to visit Walt Disney World would be over Christmas / New Years.
+During this time there historically were always extra magic hours.
+
+We are going to proceed with the analysis, but we will keep these observations in mind.
+
+## Presenting descriptive statistics
+
+Let's examine a table of the variables of interest in this data frame.
+To do so, we are going to use the `tbl_summary()` function from the `{gtsummary}` package.
+(We'll also use the `{labelled}` package to clean up the variable names for the table.)
+
+```{r}
+#| label: tbl-unweighted-gtsummary
+#| tbl-cap: A descriptive table of Extra Magic Morning in the touringplans dataset. This table shows the distributions of these variables in the observed population.
+library(gtsummary)
+library(labelled)
+seven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>
+  set_variable_labels(
+    park_ticket_season = "Ticket Season",
+    park_close = "Close Time",
+    park_temperature_high = "Historic High Temperature"
+  )
+
+tbl_summary(
+  seven_dwarfs_train_2018,
+  by = park_extra_magic_morning,
+  include = c(park_ticket_season, park_close, park_temperature_high)
+) |>
+  # add an overall column to the table
+  add_overall(last = TRUE)
+```
diff --git a/chapters/chapter-07.qmd b/chapters/chapter-07.qmd
index 07c97e0..6e3d940 100644
--- a/chapters/chapter-07.qmd
+++ b/chapters/chapter-07.qmd
@@ -1,66 +1,83 @@
-# Preparing data to answer causal questions {#sec-data-causal}
+# Building propensity score models {#sec-building-models}
 
 {{< include 00-setup.qmd >}}
 
-## Introduction to the data {#sec-data}
-
-Throughout this book we will be using data obtained from [Touring Plans](https://touringplans.com).
-Touring Plans is a company that helps folks plan their trips to Disney and Universal theme parks.
-One of their goals is to accurately predict attraction wait times at these theme parks by leveraging data and statistical modeling.
-The `{touringplans}` R package includes several datasets containing information about Disney theme park attractions.
-A summary of the attractions included in the package can be found by running the following:
+Often we are interested in how some *exposure* (or treatment) impacts an outcome.
+For example, we could assess how an ad campaign (exposure) impacts sales (outcome), whether a particular medication (exposure) improves patient survival (outcome), or whether opening a theme park early to some visitors (exposure) reduces wait times later in the day (outcome).
+As defined in the @sec-counterfactuals, <!--TODO make sure that this is correct --> an exposure in the context of this book is often a modifiable event or condition that occurs before the outcome.
+In an ideal world, we would simply estimate the correlation between the exposure and outcome as the causal effect of the exposure.
+Randomized trials are the best practical examples of this idealized scenario: participants are randomly assigned to exposure groups.
+If all goes well, this allows for an unbiased estimate of the causal effect between the exposure and outcome.
+In the "real world," outside this randomized trial setting, we are often *exposed* to something based on other factors.
+For example, when deciding what medication to give a diabetic patient, a doctor may consider the patient's medical history, their likelihood to adhere to certain medications, and the severity of their disease.
+The treatment is no longer random; it is *conditional* on factors about that patient, also known as the patient's *covariates*.
+If these covariates also affect the outcome, they are *confounders*.
+
+::: callout-note
+A **confounder** is a common cause of exposure and outcome.
+:::
 
-```{r}
-library(touringplans)
-attractions_metadata
-```
+Suppose we could collect information about all of these factors.
+In that case, we could determine each patient's probability of exposure and use this to inform an analysis assessing the relationship between that exposure and some outcome.
+This probability is the propensity score!
+When used appropriately, modeling with a propensity score can simulate what the relationship between exposure and outcome would have looked like if we had run a randomized trial.
+The correlation between exposure and outcome will estimate the causal effect after applying a propensity score.
+When fitting a *propensity score model* we want to condition on all known confounders.
 
-Additionally, this package contains a dataset with raw metadata about the parks, with observations recorded daily.
-This metadata includes information like the Walt Disney World ticket season on the particular day (was it high season -- think Christmas -- or low season -- think right when school started), what the historic temperatures were in the park on that day, and whether there was a special event, such as "extra magic hours" in the park on that day (did the park open early to guests staying in the Walt Disney World resorts?).
+::: callout-note
+A **propensity score** is the probability of being in the exposure group, conditioned on observed covariates.
+:::
 
-```{r}
-parks_metadata_raw
-```
+@rosenbaum1983central showed in observational studies conditioning on propensity scores can lead to unbiased estimates of the exposure effect as long as certain assumptions hold:
 
-Suppose the causal question of interest is:
+1.  There are no unmeasured confounders
+2.  Every subject has a nonzero probability of receiving either exposure
 
-**Is there a relationship between whether there were "Extra Magic Hours" in the morning at Magic Kingdom and the average wait time for an attraction called the "Seven Dwarfs Mine Train" the same day between 9am and 10am in 2018?**
+## Logistic Regression
 
-Let's begin by diagramming this causal question (@fig-seven-diag).
+There are many ways to estimate the propensity score; typically, people use logistic regression for binary exposures.
+The logistic regression model predicts the exposure using known confounders.
+Each individual's predicted value is the propensity score.
+The `glm()` function will fit a logistic regression model in R.
+Below is pseudo-code.
+The first argument is the model, with the exposure on the left side and the confounders on the right.
+The `data` argument takes the data frame, and the `family = binomial()` argument denotes the model should be fit using logistic regression (as opposed to a different generalized linear model).
 
 ```{r}
-#| echo: false
-#| fig-cap: "Diagram of the causal question \"Is there a relationship between whether there were \"Extra Magic Hours\" in the morning at Magic Kingdom and the average wait time for an attraction called the \"Seven Dwarfs Mine Train\" the same day between 9am and 10am in 2018?\""
-#| label: fig-seven-diag
-
-data <- data.frame(
-  labels = c("Extra Magic Hours", "Change average wait time", "Magic Kingdom guests", "before the park opens (2018)", "Magic Kingdom guests", "9am and 10am (2018)"),
-  x = c(1, 2, .83, 1.4, 1.88, 2.45),
-  y = c(1, 1, 0.77, 0.7, 0.77, 0.7),
-  angle = c(0, 0, -52, 0, -52, 0)
+#| eval: false
+glm(
+  exposure ~ confounder_1 + confounder_2,
+  data = df,
+  family = binomial()
 )
+```
+
+We can extract the propensity scores by pulling out the predictions on the probability scale.
+Using the `augment()` function from the [{`broom`}](https://broom.tidymodels.org/) package, we can extract these propensity scores and add them to our original data frame.
+The argument `type.predict` is set to `"response"` to indicate that we want to extract the predicted values on the *probability* scale.
+By default, these will be on the linear logit scale.
+The `data` argument contains the original data frame.
+This code will output a new data frame consisting of all components in `df` with six additional columns corresponding to the logistic regression model that was fit.
+The `.fitted` column is the propensity score.
 
-ggplot(data, aes(x = x, y = y)) +
-  geom_text(aes(label = labels, angle = angle, vjust = 0), size = 4) +
-  geom_segment(aes(x = 0.5, xend = 2.5, y = 0.95, yend = 0.95)) +
-  geom_segment(aes(x = 1.5, xend = 1.5, y = 0.95, yend = 1.1)) +
-  geom_segment(aes(x = 0.5, xend = 1, y = 0.95, yend = 0.65)) +
-  geom_segment(aes(x = 1, xend = 1.5, y = 0.65, yend = 0.65)) +
-  geom_segment(aes(x = 1.55, xend = 2.05, y = 0.95, yend = 0.65)) +
-  geom_segment(aes(x = 2.05, xend = 2.55, y = 0.65, yend = 0.65)) +
-  xlim(c(0.5, 2.75)) +
-  ylim(c(0.5, 1.2)) +
-  theme_void()
+```{r}
+#| eval: false
+glm(
+  exposure ~ confounder_1 + confounder_2,
+  data = df,
+  family = binomial()
+) |>
+  augment(type.predict = "response", data = df)
 ```
 
-Historically, guests who stayed in a Walt Disney World resort hotel could access the park during "Extra Magic Hours," during which the park was closed to all other guests.
-These extra hours could be in the morning or evening.
-The Seven Dwarfs Mine Train is a ride at Walt Disney World's Magic Kingdom.
-Magic Kingdom may or may not be selected each day to have these "Extra Magic Hours." We are interested in examining the relationship between whether there were "Extra Magic Hours" in the morning and the average wait time for the Seven Dwarfs Mine Train on the same day between 9 am and 10 am.
-Below is a proposed DAG for this question.
+Let's look at an example.
+
+### Extra Magic Hours at Magic Kingdom {#sec-prop-dag}
+
+Recall our causal question of interest from @sec-data: **Is there a relationship between whether there were "Extra Magic Hours" in the morning at Magic Kingdom and the average wait time for an attraction called the "Seven Dwarfs Mine Train" the same day between 9am and 10am in 2018?** Below is a proposed DAG for this question.
 
 ```{r}
-#| label: fig-dag-magic
+#| label: fig-dag-magic-hours-wait
 #| echo: false
 #| message: false
 #| warning: false
@@ -122,287 +139,163 @@ dagify(
   )
 ```
 
-Since we are not in charge of Walt Disney World's operations, we cannot randomize dates to have (or not) "Extra Magic Hours", therefore, we need to rely on previously collected observational data and do our best to emulate the *target trial* that we would have created, should it have been possible.
-Here, our observations are *days*.
-Looking at the diagram above, we can map each element of the causal question to elements of our target trial protocol:
-
--   **Eligibility criteria**: Days must be from 2018
--   **Exposure definition**: Magic kingdom had "Extra Magic Hours" in the morning
--   **Assignment procedures**: Observed -- if the historic data suggests there were "Extra Magic Hours" in the morning on a particular day, that day is classified as "exposed" otherwise it is "unexposed"
--   **Follow-up period**: From park open to 10am.
--   **Outcome definition**: The average posted wait time between 9am and 10am
--   **Causal contrast of interest**: Average treatment effect (we will discuss this in @sec-estimands)
--   **Analysis plan**: We use inverse probability waiting after fitting a propensity score model to estimate the average treatment effect of the exposure on the outcome of interest. We will adjust for variables as determined by our DAG (@fig-dag-magic)
-
-## Data wrangling and recipes
-
-Most of our data manipulation tools come from the `{dplyr}` package (@tbl-dplyr).
-We will also use `{lubridate}` to help us manipulate dates.
-
-| Target trial protocol element | {dplyr} functions                           |
-|-------------------------------|---------------------------------------------|
-| Eligibility criteria          | `filter()`                                  |
-| Exposure definition           | `mutate()`                                  |
-| Assignment procedures         | `mutate()`                                  |
-| Follow-up period              | `mutate()` `pivot_longer()` `pivot_wider()` |
-| Outcome definition            | `mutate()`                                  |
-| Analysis plan                 | `select()` `mutate()`                       |
-
-: Mapping target trial protocol elements to commonly used `{dplyr}` functions {#tbl-dplyr}
-
-To answer this question, we are going to need to manipulate both the `seven_dwarfs_train` dataset as well as the `parks_metadata_raw` dataset.
-Let's start with the `seven_dwarfs_train` data set.
-The Seven Dwarfs Mine Train ride is an attraction at Walt Disney World's Magic Kingdom.
-The `seven_dwarfs_train` dataset in the {touringplans} package contains information about the date a particular wait time was recorded (`park_date`), the time of the wait time (`wait_datetime`), the actual wait time (`wait_minutes_actual`), and the posted wait time (`wait_minutes_posted`).
-Let's take a look at this dataset.
-The {skimr} package is great for getting a quick glimpse at a new dataset.
+In @fig-dag-magic-hours-wait, we propose three confounders: the historic high temperature on the day, the time the park closed, and the ticket season: value, regular, or peak.
+We can build a propensity score model using the `seven_dwarfs_train_2018` data set from the `{touringplans}` package.
+Each row of this dataset contains information about the Seven Dwarfs Mine Train during a particular hour on a given day.
+First, we need to subset the data to only include average wait times between 9 and 10 am.
+Then we will use the `glm()` function to fit the propensity score model, predicting `park_extra_magic_morning` using the four confounders specified above.
+We'll add the propensity scores to the data frame (in a column called `.fitted` as set by the `augment()` function in the `{broom}` package).
 
 ```{r}
-library(skimr)
-skim(seven_dwarfs_train)
-```
-
-Examining the output above, we learn that this dataset contains four columns and 321,631 rows.
-We also learn that the dates span from 2015 to 2021.
-We can also examine the distribution of each of the variables to detect any potential anomalies.
-Notice anything strange?
-Look at the `p0` (that is the minimum value) for `wait_minutes_actual`.
-It is `-92918`!
-We are not using this variable for this analysis, but we will for future analyses, so this is good to keep in mind.
-
-We need this dataset to calculate our *outcome*.
-Recall from above that our outcome is defined as the average posted wait time between 9am and 10am.
-Additionally, recall our eligibility criteria states that we need to restrict our analysis to days in 2018.
-
-```{r}
-#| message: false
-#| warning: false
-library(dplyr)
-library(lubridate)
-seven_dwarfs_train_2018 <- seven_dwarfs_train |>
-  filter(year(park_date) == 2018) |> # eligibility criteria
-  mutate(hour = hour(wait_datetime)) |> # get hour from wait
-  group_by(park_date, hour) |> # group by date
-  summarise(
-    wait_minutes_posted_avg = mean(wait_minutes_posted, na.rm = TRUE),
-    .groups = "drop"
-  ) |> # get average wait time
-  mutate(
-    wait_minutes_posted_avg =
-      case_when(
-        is.nan(wait_minutes_posted_avg) ~ NA,
-        TRUE ~ wait_minutes_posted_avg
-      )
-  ) |> # if it is NAN make it NA
-  filter(hour == 9) # only keep the average wait time between 9 and 10
-```
-
-```{r}
-seven_dwarfs_train_2018
-```
-
-Now that we have our outcome settled, we need to get our exposure variable, as well as any other park-specific variables about the day in question that may be used as variables that we adjust for.
-Examining @fig-dag-magic, we see that we need data for three proposed confounders: the ticket season, the time the park closed, and the historic high temperature.
-These are in the `parks_metadata_raw` dataset.
-This data will require extra cleaning, since the names are in the original format.
-
-::: callout-tip
-We like to have our variable names follow a clean convention -- one way to do this is to follow Emily Riederer's "Column Names as Contracts" format [@Riederer_2020].
-The basic idea is to predefine a set of words, phrases, or stubs with clear meanings to index information, and use these consistently when naming variables.
-For example, in these data, variables that are specific to a particular wait time are prepended with the term `wait` (e.g. `wait_datetime` and `wait_minutes_actual`), variables that are specific to the park on a particular day, acquired from parks metadata, are prepended with the term `park` (e.g. `park_date` or `park_temperature_high`).
-:::
+library(broom)
+library(touringplans)
 
-Let's first decide what variables we will need.
-In practice, this decision may involve an iterative process.
-For example, after drawing our DAG or after conducting diagnostic, we may determine that we need more variables than what we originally cleaned.
-Let's start by skimming this dataframe.
+seven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)
 
-```{r}
-skim(parks_metadata_raw)
+seven_dwarfs_9_with_ps <-
+  glm(
+    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
+    data = seven_dwarfs_9,
+    family = binomial()
+  ) |>
+  augment(type.predict = "response", data = seven_dwarfs_9)
 ```
 
-This dataset contains many more variables than the one we worked with previously.
-For this analysis, we are going to select `date` (the observation date), `wdw_ticket_season` (the ticket season for the observation), `wdwmaxtemp` (the maximum temperature), `mkclose` (the time Magic Kingdom closed), `mkemhmorn` (whether Magic Kingdom had an "Extra Magic Hour" in the morning).
+Let's take a look at these propensity scores.
+@tbl-df-ps shows the propensity scores (in the `.fitted` column) for the first six days in the dataset, as well as the values of each day's exposure, outcome, and confounders.
+The propensity score here is the probability that a given date will have Extra Magic Hours in the morning given the observed confounders, in this case, the historical high temperatures on a given date, the time the park closed, and Ticket Season.
+For example, on January 1, 2018, there was a 30.2% chance that there would be Extra Magic Hours at the Magic Kingdom given the Ticket Season (peak in this case), time of park closure (11 pm), and the historic high temperature on this date (58.6 degrees).
+On this particular day, there were *not* Extra Magic Hours in the morning (as indicated by the 0 in the first row of the `park_extra_magic_morning` column).
 
 ```{r}
-parks_metadata_clean <- parks_metadata_raw |>
-  ##  based on our analysis plan, we will select the following variables
-  select(date, wdw_ticket_season, wdwmaxtemp, mkclose, mkemhmorn) |>
-  ## based on eligibility criteria, limit to 2018
-  filter(year(date) == 2018) |>
-  ## rename variables
-  rename(
-    park_date = date,
-    park_ticket_season = wdw_ticket_season,
-    park_temperature_high = wdwmaxtemp,
-    park_close = mkclose,
-    park_extra_magic_morning = mkemhmorn
-  )
+#| label: tbl-df-ps
+#| tbl-cap: >
+#|   The first six observations in the `seven_dwarfs_9_with_ps` dataset, including their propensity scores in the `.fitted` column.
+seven_dwarfs_9_with_ps |>
+  select(
+    .fitted,
+    park_date,
+    park_extra_magic_morning,
+    park_ticket_season,
+    park_close,
+    park_temperature_high
+  ) |>
+  head() |>
+  knitr::kable()
 ```
 
-## Working with multiple data sources
-
-Frequently we find ourselves merging data from multiple sources when attempting to answer causal questions in order to ensure that all of the necessary factors are accounted for.
-The way we can combine datasets is via *joins* -- joining two or more datasets based on a set or sets of common variables.
-We can think of three main types of *joins*: left, right, and inner.
-A *left* join combines data from two datasets based on a common variable and includes all records from the *left* dataset along with matching records from the *right* dataset (in `{dplyr}`, `left_join()`), while a *right* join includes all records from the *right* dataset and their corresponding matches from the *left* dataset (in `{dplyr}` `right_join()`); an inner join, on the other hand, includes only the records with matching values in *both* datasets, excluding non-matching records (in `{dplyr}` `inner_join()`.
-For this analysis, we need to use a left join to pull in the cleaned parks metadata.
+We can examine the distribution of propensity scores by exposure group.
+A nice way to visualize this is via mirrored histograms.
+We'll use the {halfmoon} package's `geom_mirror_histogram()` to create one.
+The code below creates two histograms of the propensity scores, one on the "top" for the exposed group (the dates with Extra Magic Hours in the morning) and one on the "bottom" for the unexposed group.
+We'll also tweak the y-axis labels to use absolute values (rather than negative values for the bottom histogram) via `scale_y_continuous(labels = abs)`.
 
 ```{r}
-seven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>
-  left_join(parks_metadata_clean, by = "park_date")
+#| label: fig-mirrored-ps
+#| fig.cap: >
+#|   Mirrored histograms of estimated propensity scores for
+#|   Extra Magic Hour days (exposed group, top) and days without
+#|   Extra Magic hours (unexposed group, bottom)
+library(halfmoon)
+ggplot(
+  seven_dwarfs_9_with_ps,
+  aes(.fitted, fill = factor(park_extra_magic_morning))
+) +
+  geom_mirror_histogram(bins = 50) +
+  scale_y_continuous(labels = abs) +
+  labs(x = "propensity score", fill = "extra magic morning")
 ```
 
-## Recognizing missing data
-
-It is important to recognize whether we have any missing data in our variables.
-The `{visdat}` package is great for getting a quick sense of whether we have any missing data.
+Here are some questions to ask to gain diagnostic insights we gain from @fig-mirrored-ps.
 
-```{r}
-library(visdat)
-vis_miss(seven_dwarfs_train_2018)
-```
+<!-- TODO: This section needs to be clarified. -->
 
-It looks like we only have a few observations (2%) missing our outcome of interest.
-This is not too bad.
-For this first analysis we will ignore the missing values.
-We can explicitly drop them using the `drop_na()` function from `{dplyr}`.
+1.  Look for lack of overlap as a potential positivity problem.
+    But too much overlap may indicate a poor model 
+    <!-- (TODO: not necessarily. depends on the relative covariate distributions in the two groups). -->
 
-```{r}
-seven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>
-  drop_na()
-```
+2.  Avg treatment effect among treated is easier to estimate with precision (because of higher counts) than in the control group.
 
-## Exploring and visualizing data and assumptions
+3.  A single outlier in either group concerning range could be a problem and warrant data inspection
+<!-- (TODO: do this here?) *look at the model coefs here*. -->
 
-The *positivity* assumption requires that within each level and combination of the study variables used to achieve exchangeability, there are exposed and unexposed subjects (@sec-assump).
-We can explore this by visualizing the distribution of each of our proposed confounders stratified by the exposure.
+<!-- *TODO* This may be a "nice" example -- should we also show a bad model (maybe only with ticket season or possibly overspecifying it) -->
 
-### Single variable checks for positivity violations
+## Choosing what variables to include {#sec-choosing-vars}
 
-@fig-close shows the distribution of Magic Kingdom park closing time by whether the date had extra magic hours in the morning.
-There is not clear evidence of a lack of positivity here as both exposure levels span the majority of the covariate space.
+The best way to decide what variables to include in your propensity score model is to look at your DAG and have at least a minimal adjustment set of confounders.
+Of course, sometimes, essential variables are missing or measured with error.
+In addition, there is often more than one theoretical adjustment set that debiases your estimate; it may be that one of the minimal adjustment sets is measured well in your data set and another is not.
+If you have confounders on your DAG that you do not have access to, sensitivity analyses can help quantify the potential impact.
+See Chapter 11 for an in-depth discussion of sensitivity analyses.
 
-```{r}
-#| label: fig-close
-#| fig-cap: "Distribution of Magic Kingdom park closing time by whether the date had extra magic hours in the morning"
-ggplot(seven_dwarfs_train_2018, aes(
-  x = factor(park_close),
-  group = factor(park_extra_magic_morning),
-  fill = factor(park_extra_magic_morning)
-)) +
-  geom_bar(position = position_dodge2(width = 0.9, preserve = "single")) +
-  labs(
-    fill = "Extra Magic Morning",
-    x = "Time of Park Close"
-  )
-```
+Accurately specifying a DAG improves our ability to add the correct variables to our models.
+However, confounders are not the only necessary type of variable to consider.
+For example, variables that are predictors of the *outcome* *but not the exposure* can improve the precision of propensity score models.
+Conversely, including variables that are predictors of the *exposure but not the outcome* (instrumental variables) can bias the model.
+Luckily, this bias seems relatively negligible in practice, especially compared to the risk of confounding bias [@Myers2011].
 
-To examine the distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning we can use a mirrored histogram.
-We'll use the {halfmoon} package's `geom_mirror_histogram()` to create one.
-Examining @fig-temp, it does look like there are very few days in the exposed group with maximum temperatures less than 60 degrees, while not necessarily a positivity violation it is worth keeping an eye on, particularly because the dataset is not very large, so this could make it difficult to estimate an average exposure effect across this whole space.
-If we found this to be particularly difficult, we could posit changing our causal question to instead restrict the analysis to warmer days.
-This of course would also restrict which days we could draw conclusions about for the future.
+::: callout-note
+Some estimates, such as the odds and hazard ratios, suffer from an additional problem called *non-collapsibility*.
+For these estimates, adding noise variables (variables unrelated to the exposure or outcome) doesn't reduce precision: they can bias the estimate as well---more the reason to avoid data-driven approaches to selecting variables for causal models.
+:::
 
-```{r}
-#| label: fig-temp
-#| fig-cap: "Distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning"
-library(halfmoon)
-ggplot(seven_dwarfs_train_2018, aes(
-  x = park_temperature_high,
-  group = factor(park_extra_magic_morning),
-  fill = factor(park_extra_magic_morning)
-)) +
-  geom_mirror_histogram(bins = 20) +
-  labs(
-    fill = "Extra Magic Morning",
-    x = "Historic maximum temperature (degrees F)"
-  )
-```
+Another variable to be wary of is a *collider*, a descendant of both the exposure and outcome.
+If you specify your DAG correctly, you can avoid colliders by only using adjustment sets that completely close backdoor paths from the exposure to the outcome.
+However, some circumstances make this difficult: some colliders are inherently stratified by the study's design or the nature of follow-up.
+For example, loss-to-follow-up is a common source of collider-stratification bias; in Chapter XX, we'll discuss this further.
 
-Finally, let's look at the distribution of ticket season by whether there were extra magic hours in the morning.
-Examining @fig-ticket, we do not see any positivity violations.
+A variable can also be both a confounder and a collider, as in the case of so-called butterfly bias:
 
 ```{r}
-#| label: fig-ticket
-#| fig-cap: "Distribution of historic temperature high at Magic Kingdom by whether the date had extra magic hours in the morning"
-ggplot(seven_dwarfs_train_2018, aes(
-  x = park_ticket_season,
-  group = factor(park_extra_magic_morning),
-  fill = factor(park_extra_magic_morning)
-)) +
-  geom_bar(position = "dodge") +
-  labs(
-    fill = "Extra Magic Morning",
-    x = "Magic Kingdom Ticket Season"
-  )
+#| label: fig-butterfly-dag
+#| fig.cap: >
+#|   A causal diagram that displays a variable `m`, which is both a collider and a confounder for `x` and `y`. In situations where you don't have all measured variables in a DAG, you may have to make a tough choice about which type of bias is the least bad.
+#| fig.width: 5
+#| fig.height: 3.5
+ggdag(butterfly_bias()) +
+  theme_dag()
 ```
 
-### Multiple variable checks for positivity violations
-
-We have confirmed that for each of the three confounders, we do not see strong evidence of positivity violations.
-Because we have so few variables here, we can examine this a bit more closely.
-Let's start by discretizing the `park_temperature_high` variable a bit (we will cut it into tertiles).
+Consider @fig-butterfly-dag.
+To estimate the causal effect of `x` on `y`, we need to account for `m` because it's a counfounder.
+However, `m` is also a collider between `a` and `b`, so controlling for it will induce a relationship between those variables, creating a second set of confounders.
+If we have all the variables measured well, we can avoid the bias from adjusting for `m` by adjusting for either `a` or `b` as well.
 
 ```{r}
-#| label: fig-positivity
-#| fig-cap: "Check for positivity violations across three confounders: historic high temperature, park close time, and ticket season."
-#| fig-width: 9
-seven_dwarfs_train_2018 |>
-  ## cut park_temperature_high into tertiles
-  mutate(park_temperature_high_bin = cut(park_temperature_high, breaks = 3)) |>
-  ## bin park close time
-  mutate(park_close_bin = case_when(
-    hour(park_close) < 19 & hour(park_close) > 12 ~ "(1) early",
-    hour(park_close) >= 19 & hour(park_close) < 24 ~ "(2) standard",
-    hour(park_close) >= 24 | hour(park_close) < 12 ~ "(3) late"
-  )) |>
-  group_by(park_close_bin, park_temperature_high_bin, park_ticket_season) |>
-  ## calculate the proportion exposed in each bin
-  summarise(prop_exposed = mean(park_extra_magic_morning), .groups = "drop") |>
-  ggplot(aes(x = park_close_bin, y = park_temperature_high_bin, fill = prop_exposed)) +
-  geom_tile() +
-  scale_fill_gradient2(midpoint = 0.5) +
-  facet_wrap(~park_ticket_season) +
-  labs(
-    y = "Historic Maximum Temperature (F)",
-    x = "Magic Kingdom Park Close Time",
-    fill = "Proportion of Days Exposed"
-  )
+#| label: fig-butterfly-sets
+#| fig.cap: >
+#|   The adjustment sets in the case of butterfly bias: we can get an unbiased effect by accounting for both `m` and the collider bias induced by adjusting for it. But what if we don't have `a` or `b`?
+#| fig.width: 5
+ggdag_adjustment_set(butterfly_bias()) +
+  theme_dag() +
+  theme(legend.position = "bottom")
 ```
 
-Interesting!
-@fig-positivity shows an interesting potential violation.
-It looks like 100% of days with lower temperatures (historic highs between 51 and 65 degrees) that are in the peak ticket season have extra magic hours in the morning.
-This actually makes sense if we think a bit about this data set.
-The only days with cold temperatures in Florida that would also be considered a "peak" time to visit Walt Disney World would be over Christmas / New Years.
-During this time there historically were always extra magic hours.
-
-We are going to proceed with the analysis, but we will keep these observations in mind.
-
-## Presenting descriptive statistics
-
-Let's examine a table of the variables of interest in this data frame.
-To do so, we are going to use the `tbl_summary()` function from the `{gtsummary}` package.
-(We'll also use the `{labelled}` package to clean up the variable names for the table.)
-
-```{r}
-#| label: tbl-unweighted-gtsummary
-#| tbl-cap: A descriptive table of Extra Magic Morning in the touringplans dataset. This table shows the distributions of these variables in the observed population.
-library(gtsummary)
-library(labelled)
-seven_dwarfs_train_2018 <- seven_dwarfs_train_2018 |>
-  set_variable_labels(
-    park_ticket_season = "Ticket Season",
-    park_close = "Close Time",
-    park_temperature_high = "Historic High Temperature"
-  )
-
-tbl_summary(
-  seven_dwarfs_train_2018,
-  by = park_extra_magic_morning,
-  include = c(park_ticket_season, park_close, park_temperature_high)
-) |>
-  # add an overall column to the table
-  add_overall(last = TRUE)
-```
+However, what should we do if we don't have those variables?
+Adjusting for `m` opens a biasing pathway that we cannot block through `a` and `b`(collider-stratification bias), but `m` is also a confounder for `x` and `y`.
+As in the case above, it appears that confounding bias is often the worse of the two options, so we should adjust for `m` unless we have reason to believe it will cause more problems than it solves [@DingMiratrix2015].
+
+### Don't use prediction metrics for causal modeling
+
+By and large, metrics commonly used for building prediction models are inappropriate for building causal models.
+Researchers and data scientists often make decisions about models using metrics like R^2^, AUC, accuracy, and (often inappropriately) p-values.
+However, a causal model's goal is not to predict as much about the outcome as possible [@hernan2021]; the goal is to estimate the relationship between the exposure and outcome accurately.
+A causal model needn't predict particularly well to be unbiased.
+
+These metrics, however, may help identify a model's best *functional form*.
+Generally, we'll use DAGs and our domain knowledge to build the model itself.
+However, we may be unsure of the mathematical relationship between a confounder and the outcome or exposure.
+For instance, we may not know if the relationship is linear.
+Misspecifying this relationship can lead to residual confounding: we may only partially account for the confounder in question, leaving some bias in the estimate.
+Testing different functional forms using prediction-focused metrics can help improve the model's accuracy, potentially allowing for better control.
+
+Another technique researchers sometimes use to determine confounders is to add a variable, then calculate the percent change in the coefficient between the outcome and exposure.
+For instance, we first model `y ~ x` to estimate the relationship between `x` and `y`.
+Then, we model `y ~ x + z` and see how much the coefficient on `x` has changed.
+A common rule is to add a variable if it changes the coefficient of`x` by 10%.
+
+Unfortunately, this technique is unreliable.
+As we've discussed, controlling for mediators, colliders, and instrumental variables all affect the estimate of the relationship between `x` and `y`, and usually, they result in bias.
+In other words, there are many different types of variables besides confounders that can cause a change in the coefficient of the exposure.
+As discussed above, confounding bias is often the most crucial factor, but systematically searching your variables for anything that changes the exposure coefficient can compound many types of bias.
diff --git a/chapters/chapter-08.qmd b/chapters/chapter-08.qmd
index 6e3d940..10ba7d0 100644
--- a/chapters/chapter-08.qmd
+++ b/chapters/chapter-08.qmd
@@ -1,156 +1,49 @@
-# Building propensity score models {#sec-building-models}
+# Using the propensity score {#sec-using-ps}
 
 {{< include 00-setup.qmd >}}
 
-Often we are interested in how some *exposure* (or treatment) impacts an outcome.
-For example, we could assess how an ad campaign (exposure) impacts sales (outcome), whether a particular medication (exposure) improves patient survival (outcome), or whether opening a theme park early to some visitors (exposure) reduces wait times later in the day (outcome).
-As defined in the @sec-counterfactuals, <!--TODO make sure that this is correct --> an exposure in the context of this book is often a modifiable event or condition that occurs before the outcome.
-In an ideal world, we would simply estimate the correlation between the exposure and outcome as the causal effect of the exposure.
-Randomized trials are the best practical examples of this idealized scenario: participants are randomly assigned to exposure groups.
-If all goes well, this allows for an unbiased estimate of the causal effect between the exposure and outcome.
-In the "real world," outside this randomized trial setting, we are often *exposed* to something based on other factors.
-For example, when deciding what medication to give a diabetic patient, a doctor may consider the patient's medical history, their likelihood to adhere to certain medications, and the severity of their disease.
-The treatment is no longer random; it is *conditional* on factors about that patient, also known as the patient's *covariates*.
-If these covariates also affect the outcome, they are *confounders*.
+The propensity score is a *balancing* tool -- we use it to help us make our exposure groups *exchangeable*. There are many ways to incorporate the propensity score into an analysis. Commonly used techniques include stratification (estimating the causal effect within propensity score stratum), matching, weighting, and direct covariate adjustment. In this section, we will focus on *matching* and *weighting*; other techniques will be discussed once we introduce the *outcome model*. Recall at this point in the book we are still in the *design* phase. We have not yet incorporated the outcome into our analysis at all. 
 
-::: callout-note
-A **confounder** is a common cause of exposure and outcome.
-:::
+## Matching
 
-Suppose we could collect information about all of these factors.
-In that case, we could determine each patient's probability of exposure and use this to inform an analysis assessing the relationship between that exposure and some outcome.
-This probability is the propensity score!
-When used appropriately, modeling with a propensity score can simulate what the relationship between exposure and outcome would have looked like if we had run a randomized trial.
-The correlation between exposure and outcome will estimate the causal effect after applying a propensity score.
-When fitting a *propensity score model* we want to condition on all known confounders.
+Ultimately, we want the exposed and unexposed observations to be *exchangeable* with respect to the confounders we have proposed in our DAG (so we can use the observed effect for one to estimate the counterfactual for the other). One way to do this is to ensure that each observation in our analysis sample has at least one observation of the opposite exposure that has *match*ing values for each of these confounders. If we had a small number of binary confounders, for example, we might be able to construct an *exact match* for observations (and only include those for whom such a match exists), but as the number and continuity of confounders increases, exact matching becomes less feasible. This is where the propensity score, a summary measure of all of the confounders, comes in to play. 
 
-::: callout-note
-A **propensity score** is the probability of being in the exposure group, conditioned on observed covariates.
-:::
+Let's setup the data as we did in @sec-building-models.
 
-@rosenbaum1983central showed in observational studies conditioning on propensity scores can lead to unbiased estimates of the exposure effect as long as certain assumptions hold:
-
-1.  There are no unmeasured confounders
-2.  Every subject has a nonzero probability of receiving either exposure
+```{r}
+library(broom)
+library(touringplans)
 
-## Logistic Regression
+seven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)
+```
 
-There are many ways to estimate the propensity score; typically, people use logistic regression for binary exposures.
-The logistic regression model predicts the exposure using known confounders.
-Each individual's predicted value is the propensity score.
-The `glm()` function will fit a logistic regression model in R.
-Below is pseudo-code.
-The first argument is the model, with the exposure on the left side and the confounders on the right.
-The `data` argument takes the data frame, and the `family = binomial()` argument denotes the model should be fit using logistic regression (as opposed to a different generalized linear model).
+We can re-fit the propensity score using the `{MatchIt}` package, as below. Notice here the `matchit` function fit a logistic regression model for our propensity score, as we had in @sec-building-models. There were 60 days in 2018 where the Magic Kingdom had extra magic morning hours. For each of these 60 exposed days, `matchit` found a comparable unexposed day, by implementing a nearest-neighbor match using the constructed propensity score. Examining the output, we also see that the target estimand is an "ATT" (do not worry about this yet, we will discuss this and several other estimands in @sec-estimands).
 
 ```{r}
-#| eval: false
-glm(
-  exposure ~ confounder_1 + confounder_2,
-  data = df,
-  family = binomial()
+library(MatchIt)
+m <- matchit(
+  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
+  data = seven_dwarfs_9
 )
+m
 ```
-
-We can extract the propensity scores by pulling out the predictions on the probability scale.
-Using the `augment()` function from the [{`broom`}](https://broom.tidymodels.org/) package, we can extract these propensity scores and add them to our original data frame.
-The argument `type.predict` is set to `"response"` to indicate that we want to extract the predicted values on the *probability* scale.
-By default, these will be on the linear logit scale.
-The `data` argument contains the original data frame.
-This code will output a new data frame consisting of all components in `df` with six additional columns corresponding to the logistic regression model that was fit.
-The `.fitted` column is the propensity score.
+We can use the `get_matches` function to create a data frame with the original variables that only consists of those who were matched. Notice here our sample size has been reduced from the original 354 days to 120. 
 
 ```{r}
-#| eval: false
-glm(
-  exposure ~ confounder_1 + confounder_2,
-  data = df,
-  family = binomial()
-) |>
-  augment(type.predict = "response", data = df)
+matched_data <- get_matches(m)
+glimpse(matched_data)
 ```
 
-Let's look at an example.
-
-### Extra Magic Hours at Magic Kingdom {#sec-prop-dag}
-
-Recall our causal question of interest from @sec-data: **Is there a relationship between whether there were "Extra Magic Hours" in the morning at Magic Kingdom and the average wait time for an attraction called the "Seven Dwarfs Mine Train" the same day between 9am and 10am in 2018?** Below is a proposed DAG for this question.
+## Weighting
 
-```{r}
-#| label: fig-dag-magic-hours-wait
-#| echo: false
-#| message: false
-#| warning: false
-#| fig.cap: >
-#|   Proposed DAG for the relationship between Extra Magic Hours
-#|   in the morning at a particular park and the average wait
-#|   time between 9 am and 10 am.
-#|   Here we are saying that we believe 1) Extra Magic Hours impacts average wait time and 2) both Extra Magic Hours and average wait time are determined by the time the park closes, historic high temperatures, and ticket season.
+One way to think about matching is as a crude "weight" where everyone who was matched gets a weight of 1 and everyone who was not matched gets a weight of 0 in the final sample. Another option is to allow this weight to be smooth, applying a weight to allow, on average, the covariates of interest to be balanced in the weighted population. To do this, we will construct a weight using the propensity score. There are many different weights that can be applied, depending on your target estimand of interest (see @sec-estimand for details). For this section, we will focus on the "Average Treatment Effect" weights, commonly referred to as an "inverse probability weight". The weight is constructed as follows, where each observation is weighted by the *inverse* of the probability of receiving the exposure they received. 
 
-library(tidyverse)
-library(ggdag)
-library(ggokabeito)
+$$w_{ATE} = \frac{X}{p} + \frac{(1 - X)}{1 - p}$$
 
-coord_dag <- list(
-  x = c(Season = 0, close = 0, weather = -1, x = 1, y = 2),
-  y = c(Season = -1, close = 1, weather = 0, x = 0, y = 0)
-)
-
-labels <- c(
-  x = "Extra Magic Morning",
-  y = "Average wait",
-  Season = "Ticket Season",
-  weather = "Historic high temperature",
-  close = "Time park closed"
-)
-
-dagify(
-  y ~ x + close + Season + weather,
-  x ~ weather + close + Season,
-  coords = coord_dag,
-  labels = labels,
-  exposure = "x",
-  outcome = "y"
-) |>
-  tidy_dagitty() |>
-  node_status() |>
-  ggplot(
-    aes(x, y, xend = xend, yend = yend, color = status)
-  ) +
-  geom_dag_edges_arc(curvature = c(rep(0, 5), .3)) +
-  geom_dag_point() +
-  geom_dag_label_repel(seed = 1630) +
-  scale_color_okabe_ito(na.value = "grey90") +
-  theme_dag() +
-  theme(
-    legend.position = "none",
-    axis.text.x = element_text()
-  ) +
-  coord_cartesian(clip = "off") +
-  scale_x_continuous(
-    limits = c(-1.25, 2.25),
-    breaks = c(-1, 0, 1, 2),
-    labels = c(
-      "\n(one year ago)",
-      "\n(6 months ago)",
-      "\n(3 months ago)",
-      "9am - 10am\n(Today)"
-    )
-  )
-```
-
-In @fig-dag-magic-hours-wait, we propose three confounders: the historic high temperature on the day, the time the park closed, and the ticket season: value, regular, or peak.
-We can build a propensity score model using the `seven_dwarfs_train_2018` data set from the `{touringplans}` package.
-Each row of this dataset contains information about the Seven Dwarfs Mine Train during a particular hour on a given day.
-First, we need to subset the data to only include average wait times between 9 and 10 am.
-Then we will use the `glm()` function to fit the propensity score model, predicting `park_extra_magic_morning` using the four confounders specified above.
-We'll add the propensity scores to the data frame (in a column called `.fitted` as set by the `augment()` function in the `{broom}` package).
+For example, if observation 1 had a very high likelihood of being exposed given their pre-exposure covariates ($p = 0.9$), but they in fact were *not* exposed, their weight would be 10 ($w_1 = 1 / (1 - 0.9)$). Likewise, if observation 2 had a very high likelihood of being exposed given their pre-exposure covariates ($p = 0.9$), and they *were* exposed, their weight would be 1.1 ($w_2 = 1 / 0.9$). Intuitively, we give *more* weight to observations who, based on their measured confounders, appear to have useful information for constructing a counterfactual -- we would have predicted that they were exposed and but by chance they were not, or vice-versa. The `{propensity}` package is useful for implementing propensity score weighting.
 
 ```{r}
-library(broom)
-library(touringplans)
-
-seven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)
+library(propensity)
 
 seven_dwarfs_9_with_ps <-
   glm(
@@ -159,20 +52,19 @@ seven_dwarfs_9_with_ps <-
     family = binomial()
   ) |>
   augment(type.predict = "response", data = seven_dwarfs_9)
+seven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>
+  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))
 ```
 
-Let's take a look at these propensity scores.
-@tbl-df-ps shows the propensity scores (in the `.fitted` column) for the first six days in the dataset, as well as the values of each day's exposure, outcome, and confounders.
-The propensity score here is the probability that a given date will have Extra Magic Hours in the morning given the observed confounders, in this case, the historical high temperatures on a given date, the time the park closed, and Ticket Season.
-For example, on January 1, 2018, there was a 30.2% chance that there would be Extra Magic Hours at the Magic Kingdom given the Ticket Season (peak in this case), time of park closure (11 pm), and the historic high temperature on this date (58.6 degrees).
-On this particular day, there were *not* Extra Magic Hours in the morning (as indicated by the 0 in the first row of the `park_extra_magic_morning` column).
+@tbl-df-wt shows the weights in the first column.
 
 ```{r}
-#| label: tbl-df-ps
+#| label: tbl-df-wt
 #| tbl-cap: >
-#|   The first six observations in the `seven_dwarfs_9_with_ps` dataset, including their propensity scores in the `.fitted` column.
-seven_dwarfs_9_with_ps |>
+#|   The first six observations in the `seven_dwarfs_9_with_wt` dataset, including their propensity scores in the `.fitted` column and weight in the `w_ate` column.
+seven_dwarfs_9_with_wt |>
   select(
+    w_ate,
     .fitted,
     park_date,
     park_extra_magic_morning,
@@ -184,118 +76,6 @@ seven_dwarfs_9_with_ps |>
   knitr::kable()
 ```
 
-We can examine the distribution of propensity scores by exposure group.
-A nice way to visualize this is via mirrored histograms.
-We'll use the {halfmoon} package's `geom_mirror_histogram()` to create one.
-The code below creates two histograms of the propensity scores, one on the "top" for the exposed group (the dates with Extra Magic Hours in the morning) and one on the "bottom" for the unexposed group.
-We'll also tweak the y-axis labels to use absolute values (rather than negative values for the bottom histogram) via `scale_y_continuous(labels = abs)`.
-
-```{r}
-#| label: fig-mirrored-ps
-#| fig.cap: >
-#|   Mirrored histograms of estimated propensity scores for
-#|   Extra Magic Hour days (exposed group, top) and days without
-#|   Extra Magic hours (unexposed group, bottom)
-library(halfmoon)
-ggplot(
-  seven_dwarfs_9_with_ps,
-  aes(.fitted, fill = factor(park_extra_magic_morning))
-) +
-  geom_mirror_histogram(bins = 50) +
-  scale_y_continuous(labels = abs) +
-  labs(x = "propensity score", fill = "extra magic morning")
-```
-
-Here are some questions to ask to gain diagnostic insights we gain from @fig-mirrored-ps.
-
-<!-- TODO: This section needs to be clarified. -->
-
-1.  Look for lack of overlap as a potential positivity problem.
-    But too much overlap may indicate a poor model 
-    <!-- (TODO: not necessarily. depends on the relative covariate distributions in the two groups). -->
-
-2.  Avg treatment effect among treated is easier to estimate with precision (because of higher counts) than in the control group.
-
-3.  A single outlier in either group concerning range could be a problem and warrant data inspection
-<!-- (TODO: do this here?) *look at the model coefs here*. -->
-
-<!-- *TODO* This may be a "nice" example -- should we also show a bad model (maybe only with ticket season or possibly overspecifying it) -->
-
-## Choosing what variables to include {#sec-choosing-vars}
-
-The best way to decide what variables to include in your propensity score model is to look at your DAG and have at least a minimal adjustment set of confounders.
-Of course, sometimes, essential variables are missing or measured with error.
-In addition, there is often more than one theoretical adjustment set that debiases your estimate; it may be that one of the minimal adjustment sets is measured well in your data set and another is not.
-If you have confounders on your DAG that you do not have access to, sensitivity analyses can help quantify the potential impact.
-See Chapter 11 for an in-depth discussion of sensitivity analyses.
-
-Accurately specifying a DAG improves our ability to add the correct variables to our models.
-However, confounders are not the only necessary type of variable to consider.
-For example, variables that are predictors of the *outcome* *but not the exposure* can improve the precision of propensity score models.
-Conversely, including variables that are predictors of the *exposure but not the outcome* (instrumental variables) can bias the model.
-Luckily, this bias seems relatively negligible in practice, especially compared to the risk of confounding bias [@Myers2011].
-
-::: callout-note
-Some estimates, such as the odds and hazard ratios, suffer from an additional problem called *non-collapsibility*.
-For these estimates, adding noise variables (variables unrelated to the exposure or outcome) doesn't reduce precision: they can bias the estimate as well---more the reason to avoid data-driven approaches to selecting variables for causal models.
-:::
-
-Another variable to be wary of is a *collider*, a descendant of both the exposure and outcome.
-If you specify your DAG correctly, you can avoid colliders by only using adjustment sets that completely close backdoor paths from the exposure to the outcome.
-However, some circumstances make this difficult: some colliders are inherently stratified by the study's design or the nature of follow-up.
-For example, loss-to-follow-up is a common source of collider-stratification bias; in Chapter XX, we'll discuss this further.
-
-A variable can also be both a confounder and a collider, as in the case of so-called butterfly bias:
-
-```{r}
-#| label: fig-butterfly-dag
-#| fig.cap: >
-#|   A causal diagram that displays a variable `m`, which is both a collider and a confounder for `x` and `y`. In situations where you don't have all measured variables in a DAG, you may have to make a tough choice about which type of bias is the least bad.
-#| fig.width: 5
-#| fig.height: 3.5
-ggdag(butterfly_bias()) +
-  theme_dag()
-```
-
-Consider @fig-butterfly-dag.
-To estimate the causal effect of `x` on `y`, we need to account for `m` because it's a counfounder.
-However, `m` is also a collider between `a` and `b`, so controlling for it will induce a relationship between those variables, creating a second set of confounders.
-If we have all the variables measured well, we can avoid the bias from adjusting for `m` by adjusting for either `a` or `b` as well.
-
-```{r}
-#| label: fig-butterfly-sets
-#| fig.cap: >
-#|   The adjustment sets in the case of butterfly bias: we can get an unbiased effect by accounting for both `m` and the collider bias induced by adjusting for it. But what if we don't have `a` or `b`?
-#| fig.width: 5
-ggdag_adjustment_set(butterfly_bias()) +
-  theme_dag() +
-  theme(legend.position = "bottom")
-```
-
-However, what should we do if we don't have those variables?
-Adjusting for `m` opens a biasing pathway that we cannot block through `a` and `b`(collider-stratification bias), but `m` is also a confounder for `x` and `y`.
-As in the case above, it appears that confounding bias is often the worse of the two options, so we should adjust for `m` unless we have reason to believe it will cause more problems than it solves [@DingMiratrix2015].
-
-### Don't use prediction metrics for causal modeling
-
-By and large, metrics commonly used for building prediction models are inappropriate for building causal models.
-Researchers and data scientists often make decisions about models using metrics like R^2^, AUC, accuracy, and (often inappropriately) p-values.
-However, a causal model's goal is not to predict as much about the outcome as possible [@hernan2021]; the goal is to estimate the relationship between the exposure and outcome accurately.
-A causal model needn't predict particularly well to be unbiased.
 
-These metrics, however, may help identify a model's best *functional form*.
-Generally, we'll use DAGs and our domain knowledge to build the model itself.
-However, we may be unsure of the mathematical relationship between a confounder and the outcome or exposure.
-For instance, we may not know if the relationship is linear.
-Misspecifying this relationship can lead to residual confounding: we may only partially account for the confounder in question, leaving some bias in the estimate.
-Testing different functional forms using prediction-focused metrics can help improve the model's accuracy, potentially allowing for better control.
 
-Another technique researchers sometimes use to determine confounders is to add a variable, then calculate the percent change in the coefficient between the outcome and exposure.
-For instance, we first model `y ~ x` to estimate the relationship between `x` and `y`.
-Then, we model `y ~ x + z` and see how much the coefficient on `x` has changed.
-A common rule is to add a variable if it changes the coefficient of`x` by 10%.
 
-Unfortunately, this technique is unreliable.
-As we've discussed, controlling for mediators, colliders, and instrumental variables all affect the estimate of the relationship between `x` and `y`, and usually, they result in bias.
-In other words, there are many different types of variables besides confounders that can cause a change in the coefficient of the exposure.
-As discussed above, confounding bias is often the most crucial factor, but systematically searching your variables for anything that changes the exposure coefficient can compound many types of bias.
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diff --git a/chapters/chapter-09.qmd b/chapters/chapter-09.qmd
index 3d8754d..4e9c0bc 100644
--- a/chapters/chapter-09.qmd
+++ b/chapters/chapter-09.qmd
@@ -1,53 +1,183 @@
-# Using the propensity score {#sec-using-ps}
+# Evaluating your propensity score model {#sec-eval-ps-model}
 
 {{< include 00-setup.qmd >}}
 
-The propensity score is a *balancing* tool -- we use it to help us make our exposure groups *exchangeable*. There are many ways to incorporate the propensity score into an analysis. Commonly used techniques include stratification (estimating the causal effect within propensity score stratum), matching, weighting, and direct covariate adjustment. In this section, we will focus on *matching* and *weighting*; other techniques will be discussed once we introduce the *outcome model*. Recall at this point in the book we are still in the *design* phase. We have not yet incorporated the outcome into our analysis at all. 
+Propensity scores are inherently *balancing* scores. The goal is to *balance* the exposure groups across confounders. 
 
-## Matching
+## Calculating the standardized mean difference
 
-Ultimately, we want the exposed and unexposed observations to be *exchangeable* with respect to the confounders we have proposed in our DAG (so we can use the observed effect for one to estimate the counterfactual for the other). One way to do this is to ensure that each observation in our analysis sample has at least one observation of the opposite exposure that has *match*ing values for each of these confounders. If we had a small number of binary confounders, for example, we might be able to construct an *exact match* for observations (and only include those for whom such a match exists), but as the number and continuity of confounders increases, exact matching becomes less feasible. This is where the propensity score, a summary measure of all of the confounders, comes in to play. 
+One way to assess balance is the *standardized mean difference*. This measure helps you assess whether the average value for the confounder is balanced between exposure groups. For example, if you have some continuous confounder, $Z$, and $\bar{z}_{exposed} = \frac{\sum Z_i(X_i)}{\sum X_i}$ is the mean value of $Z$ among the exposed,  $\bar{z}_{unexposed} = \frac{\sum Z_i(1-X_i)}{\sum 1-X_i}$ is the mean value of $Z$ among the unexposed, $s_{exposed}$ is the sample standard deviation of $Z$ among the exposed and $s_{unexposed}$ is the sample standard deviation of $Z$ among the unexposed, then the standardized mean difference can be expressed as follows:
 
-Let's setup the data as we did in @sec-building-models.
+$$
+d =\frac{\bar{z}_{exposed}-\bar{z}_{unexposued}}{\frac{\sqrt{s^2_{exposed}+s^2_{unexposed}}}{2}}
+$$
+In the case of a binary $Z$ (a confounder with just two levels), $\bar{z}$ is replaced with the sample proportion in each group (e.g., $\hat{p}_{exposed}$ or $\hat{p}_{unexposed}$ ) and $s^2=\hat{p}(1-\hat{p})$. In the case where $Z$ is categorical with more than two categories, $\bar{z}$ is the vector of proportions of each category level within a group and the denominator is the multinomial covariance matrix ($S$ below), as the above can be written more generally as:
+
+$$
+d = \sqrt{(\bar{z}_{exposed} - \bar{z}_{unexposed})^TS^{-1}(\bar{z}_{exposed} - \bar{z}_{unexposed})}
+$$
+
+Often, we calculate the standardized mean difference for each confounder in the full, unadjusted, data set and then compare this to an *adjusted* standardized mean difference. If the propensity score is incorporated using *matching*, this adjusted standardized mean difference uses the exact equation as above, but restricts the sample considered to only those that were matched. If the propensity score is incorporated using *weighting*, this adjusted standardized mean difference *weights* each of the above components using the constructed propensity score weight. 
+
+In R, the `{halfmoon}` package has a function `tidy_smd` that will calculate this for a data set.
+
+```{r}
+#| eval: false
+library(halfmoon)
+
+smds <- tidy_smd(
+  df,
+  .vars = c(confounder_1, confounder_2, ...),
+  .group = exposure,
+  .wts = wts # weight is optional
+)
+```
+
+Let's look at an example using the same data as @sec-using-ps. 
 
 ```{r}
 library(broom)
 library(touringplans)
+library(propensity)
 
 seven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)
+
+seven_dwarfs_9_with_ps <-
+  glm(
+    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
+    data = seven_dwarfs_9,
+    family = binomial()
+  ) |>
+  augment(type.predict = "response", data = seven_dwarfs_9)
+seven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>
+  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))
 ```
 
-We can re-fit the propensity score using the `{MatchIt}` package, as below. Notice here the `matchit` function fit a logistic regression model for our propensity score, as we had in @sec-building-models. There were 60 days in 2018 where the Magic Kingdom had extra magic morning hours. For each of these 60 exposed days, `matchit` found a comparable unexposed day, by implementing a nearest-neighbor match using the constructed propensity score. Examining the output, we also see that the target estimand is an "ATT" (do not worry about this yet, we will discuss this and several other estimands in @sec-estimands).
+Now, using the `tidy_smd` function, we can examine the standardized mean difference before and after weighting.
 
 ```{r}
-library(MatchIt)
-m <- matchit(
-  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
-  data = seven_dwarfs_9
-)
-m
+library(halfmoon)
+smds <- 
+  seven_dwarfs_9_with_wt |>
+  mutate(park_close = as.numeric(park_close)) |>
+  tidy_smd(
+    .vars = c(park_ticket_season, park_close, park_temperature_high),
+    .group = park_extra_magic_morning,
+    .wts = w_ate
+  )
+smds
 ```
-We can use the `get_matches` function to create a data frame with the original variables that only consists of those who were matched. Notice here our sample size has been reduced from the original 354 days to 120. 
+
+For example, we see above that the *observed* standardized mean difference (prior to incorporating the propensity score) for ticket season is `r smds |> filter(variable == "park_ticket_season" & method == "observed") |> pull(smd) |> round(2)`, however after incorporating the propensity score weight this is attenuated, now `r smds |> filter(variable == "park_ticket_season" & method == "w_ate") |> pull(smd) |> round(2)`.
+
+One downside of this metric is it only quantifying balance *on the mean*, which may not be sufficient for continuous confounders, as it is possible to be balanced on the mean but severely imbalanced in the tails. At the end of this chapter we will show you a few tools for examining balance across the full distribution of the confounder.
+
+## Visualizing balance
+
+### Love Plots
+
+Let's start by visualizing these standardized mean differences. To do so, we like to use a *Love Plot* (named for Thomas Love, as he was one of the first to popularize them). The `{halfmoon}` package has a function `geom_love` that simplifies this implementation.
 
 ```{r}
-matched_data <- get_matches(m)
-glimpse(matched_data)
+ggplot(
+  data = smds,
+  aes(
+    x = abs(smd), 
+    y = variable, 
+    group = method, 
+    color = method
+  )
+) +  
+  geom_love()
 ```
 
-## Weighting
 
-One way to think about matching is as a crude "weight" where everyone who was matched gets a weight of 1 and everyone who was not matched gets a weight of 0 in the final sample. Another option is to allow this weight to be smooth, applying a weight to allow, on average, the covariates of interest to be balanced in the weighted population. To do this, we will construct a weight using the propensity score. There are many different weights that can be applied, depending on your target estimand of interest (see @sec-estimands for details). For this section, we will focus on the "Average Treatment Effect" weights, commonly referred to as an "inverse probability weight". The weight is constructed as follows, where each observation is weighted by the *inverse* of the probability of receiving the exposure they received. 
+### Boxplots and eCDF plots
 
-$$w_{ATE} = \frac{X}{p} + \frac{(1 - X)}{1 - p}$$
+As mentioned above, one issue with the standardized mean differences is they only quantify balance on a single point for continuous confounders (the mean). It can be helpful to visualize the whole distribution to ensure that there is not residual imbalance in the tails. Let's first use a boxplot. As an example, let's use the `park_temperature_high` variable. When we make boxplots, we prefer to always jitter the points on top to make sure we aren't masking and data anomolies -- we use `geom_jitter` to accomplish this. First, we will make the unweighted boxplot.
 
-For example, if observation 1 had a very high likelihood of being exposed given their pre-exposure covariates ($p = 0.9$), but they in fact were *not* exposed, their weight would be 10 ($w_1 = 1 / (1 - 0.9)$). Likewise, if observation 2 had a very high likelihood of being exposed given their pre-exposure covariates ($p = 0.9$), and they *were* exposed, their weight would be 1.1 ($w_2 = 1 / 0.9$). Intuitively, we give *more* weight to observations who, based on their measured confounders, appear to have useful information for constructing a counterfactual -- we would have predicted that they were exposed and but by chance they were not, or vice-versa. The `{propensity}` package is useful for implementing propensity score weighting.
+```{r}
+#| label: fig-boxplot
+#| fig.cap: "Unweighted boxplot showing the difference in historical high temperature between days that had extra magic hours and those that did not."
+ggplot(
+  seven_dwarfs_9_with_wt, 
+  aes(
+    x = factor(park_extra_magic_morning), 
+    y = park_temperature_high,
+    group = park_extra_magic_morning
+    )
+) + 
+  geom_boxplot(outlier.color = NA) + 
+  geom_jitter() + 
+  labs(x = "Extra magic morning",
+       y = "Temperature high")
+```
 
 ```{r}
-library(propensity)
+#| label: fig-weighted-boxplot
+#| fig.cap: "Weighted boxplot showing the difference in historical high temperature between days that had extra magic hours and those that did not after incorporating the propensity score weight (ATE weight)."
+ggplot(
+  seven_dwarfs_9_with_wt, 
+  aes(
+    x = factor(park_extra_magic_morning), 
+    y = park_temperature_high,
+    group = park_extra_magic_morning,
+    weight = w_ate
+    )
+) + 
+  geom_boxplot(outlier.color = NA) + 
+  geom_jitter() + 
+  labs(x = "Extra magic morning",
+       y = "Historic temperature high")
+```
+
+Similarly, we can also examine the empirical cumulative distribution function (eCDF) for the confounder stratified by each exposure group. The unweighted eCDF can be visualized using `geom_ecdf`
+
+```{r}
+#| label: fig-ecdf
+#| fig.cap: "Unweighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green)."
+
+ggplot(seven_dwarfs_9_with_wt, 
+       aes(x = park_temperature_high,
+           color = factor(park_extra_magic_morning))) + 
+  geom_ecdf() + 
+  scale_color_manual(
+    "Extra Magic Morning",
+    values = c("#5154B8", "#5DB854"),
+    labels = c("Yes", "No")
+  ) + 
+  labs(x = "Historic temperature high",
+       y = "Proportion <= x")
+```
+
+The `{halfmoon}` package allows for the additional `weight` argument to be passed to `geom_ecdf` to display a weighted eCDF plot.
 
+```{r}
+#| label: fig-weighted-ecdf
+#| fig.cap: "Weighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green) after incorporating the propensity score weight (ATE)."
+
+ggplot(seven_dwarfs_9_with_wt, 
+       aes(x = park_temperature_high,
+           color = factor(park_extra_magic_morning))) + 
+  geom_ecdf(aes(weights = w_ate)) + 
+  scale_color_manual(
+    "Extra Magic Morning",
+    values = c("#5154B8", "#5DB854"),
+    labels = c("Yes", "No")
+  ) + 
+  labs(x = "Historic temperature high",
+       y = "Proportion <= x")
+```
+
+Examining @fig-weighted-ecdf, we can notice a few things. First, compared to @fig-ecdf there is improvement in the overlap between the two distributions. In @fig-ecdf, the green line is almost always noticeably above the purple, whereas in @fig-weighted-ecdf the two lines appear to mostly overlap until we reach slightly above 80 degrees. After 80 degrees, the lines appear to diverge in the weighted plot. This is why it can be useful to examine the full distribution rather than a single summary measure. If we had just used the standardized mean difference, for example, we would have likely said these two groups are balanced and moved on. Looking at @fig-weighted-ecdf suggests that perhaps there is a non-linear relationship between the probability of having an extra magic morning and the historic high temperature. Let's try refitting our propensity score model using a natural spline. We can use the function `splines::ns` for this.
+
+<!-- TODO put a callout box here explaining what a natural spline is. -->
+
+```{r}
 seven_dwarfs_9_with_ps <-
   glm(
-    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
+    park_extra_magic_morning ~ park_ticket_season + park_close +
+      splines::ns(park_temperature_high, df = 5), # refit model with a spline
     data = seven_dwarfs_9,
     family = binomial()
   ) |>
@@ -56,26 +186,23 @@ seven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>
   mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))
 ```
 
-@tbl-df-wt shows the weights in the first column.
+Now let's see how that impacts the weighted eCDF plot
 
 ```{r}
-#| label: tbl-df-wt
-#| tbl-cap: >
-#|   The first six observations in the `seven_dwarfs_9_with_wt` dataset, including their propensity scores in the `.fitted` column and weight in the `w_ate` column.
-seven_dwarfs_9_with_wt |>
-  select(
-    w_ate,
-    .fitted,
-    park_date,
-    park_extra_magic_morning,
-    park_ticket_season,
-    park_close,
-    park_temperature_high
-  ) |>
-  head() |>
-  knitr::kable()
+#| label: fig-weighted-ecdf-2
+#| fig.cap: "Weighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green) after incorporating the propensity score weight where historic high temperature was modeled flexibly with a spline."
+
+ggplot(seven_dwarfs_9_with_wt, 
+       aes(x = park_temperature_high,
+           color = factor(park_extra_magic_morning))) + 
+  geom_ecdf(aes(weights = w_ate)) + 
+  scale_color_manual(
+    "Extra Magic Morning",
+    values = c("#5154B8", "#5DB854"),
+    labels = c("Yes", "No")
+  ) + 
+  labs(x = "Historic temperature high",
+       y = "Proportion <= x")
 ```
 
-
-
-
+Now in @fig-weighted-ecdf-2 the lines appear to overlap across the whole space.
diff --git a/chapters/chapter-10.qmd b/chapters/chapter-10.qmd
index 4e9c0bc..816d726 100644
--- a/chapters/chapter-10.qmd
+++ b/chapters/chapter-10.qmd
@@ -1,208 +1,832 @@
-# Evaluating your propensity score model {#sec-eval-ps-model}
+# Causal estimands {#sec-estimands}
 
 {{< include 00-setup.qmd >}}
 
-Propensity scores are inherently *balancing* scores. The goal is to *balance* the exposure groups across confounders. 
+## Estimands, Estimators, Estimates
 
-## Calculating the standardized mean difference
+When analyzing to make causal inferences, we need to keep the causal question that we're interested in close to our chest.
+The causal question helps guide not just the assumptions we need to make but also how we will go about answering the question.
+This chapter deals with three ideas closely related to answering causal questions: the estimand, the estimator, and the estimate.
+The **estimand** is the *target of interest*, the **estimator** is the method by which we approximate this estimand using data, and the **estimate** is the value we get when we plug our data into the estimator.
+You can think of the **estimand** as a glossy picture of a beautiful cake we are trying to bake, the **estimator** as the recipe, and the **estimate** as the cake we pull out of our oven.
 
-One way to assess balance is the *standardized mean difference*. This measure helps you assess whether the average value for the confounder is balanced between exposure groups. For example, if you have some continuous confounder, $Z$, and $\bar{z}_{exposed} = \frac{\sum Z_i(X_i)}{\sum X_i}$ is the mean value of $Z$ among the exposed,  $\bar{z}_{unexposed} = \frac{\sum Z_i(1-X_i)}{\sum 1-X_i}$ is the mean value of $Z$ among the unexposed, $s_{exposed}$ is the sample standard deviation of $Z$ among the exposed and $s_{unexposed}$ is the sample standard deviation of $Z$ among the unexposed, then the standardized mean difference can be expressed as follows:
+So far, we have been estimating the average treatment effect, the effect of the exposure of interest averaged across the whole population.
+The **estimand** here is the expected value of the difference in potential outcomes across all individuals:
 
-$$
-d =\frac{\bar{z}_{exposed}-\bar{z}_{unexposued}}{\frac{\sqrt{s^2_{exposed}+s^2_{unexposed}}}{2}}
-$$
-In the case of a binary $Z$ (a confounder with just two levels), $\bar{z}$ is replaced with the sample proportion in each group (e.g., $\hat{p}_{exposed}$ or $\hat{p}_{unexposed}$ ) and $s^2=\hat{p}(1-\hat{p})$. In the case where $Z$ is categorical with more than two categories, $\bar{z}$ is the vector of proportions of each category level within a group and the denominator is the multinomial covariance matrix ($S$ below), as the above can be written more generally as:
+$$E[Y(1) - Y(0)]$$
 
-$$
-d = \sqrt{(\bar{z}_{exposed} - \bar{z}_{unexposed})^TS^{-1}(\bar{z}_{exposed} - \bar{z}_{unexposed})}
-$$
+The **estimator** we use depends on the method we've chosen.
+For example, in an A/B test or randomized controlled trial, our estimator could just be the average outcome among those who received the exposure A minus the average outcome among those who receive exposure B.
 
-Often, we calculate the standardized mean difference for each confounder in the full, unadjusted, data set and then compare this to an *adjusted* standardized mean difference. If the propensity score is incorporated using *matching*, this adjusted standardized mean difference uses the exact equation as above, but restricts the sample considered to only those that were matched. If the propensity score is incorporated using *weighting*, this adjusted standardized mean difference *weights* each of the above components using the constructed propensity score weight. 
+$$\sum_{i=1}^{N}\frac{Y_i\times X_i}{N_{\textrm{A}}} - \frac{Y_i\times (1-X_i)}{N_{\textrm{B}}}$$
 
-In R, the `{halfmoon}` package has a function `tidy_smd` that will calculate this for a data set.
+Where $X$ is just an indicator for exposure A ($X = 1$ for exposure A and $X = 0$ for exposure B), $N_A$ is the total number in group A and $N_B$ is the total number in group B such that $N_A + N_B = N$.
+Let's motivate this example a bit more.
+Suppose we have two different designs for a call-to-action (often abbreviated as CTA, a written directive for a marketing campaign) and want to assess whether they lead to a different average number of items a user purchases.
+We can create an A/B test where we randomly assign a subset of users to see design A or design B.
+Suppose we have A/B testing data for 100 participants in a dataset called `ab`.
+Here is some code to simulate such a data set.
 
 ```{r}
 #| eval: false
-library(halfmoon)
+library(tidyverse)
+set.seed(928)
+ab <- tibble(
+  # generate the exposure, x, from a binomial distribution
+  # with the probability exposure A of 0.5
+  x = rbinom(100, 1, 0.5),
+  # generate the "true" average treatment effect of 1
+  # we use rnorm(100) to add the random error term that is normally
+  # distributed with a mean of 0 and a standard deviation of 1
+  y = x + rnorm(100)
+)
+```
+
+Here the exposure is `x`, and the outcome is `y`.
+The true average treatment effect is 1, implying that on average seeing call-to-action A increases the number of items purchased by 1.
+
+```{r}
+#| echo: false
+set.seed(928)
+ab <- tibble(
+  x = rbinom(100, 1, 0.5),
+  y = x + rnorm(100)
+)
+ab |>
+  summarise(
+    n_a = sum(x),
+    n_b = sum(1 - x),
+    estimate = sum(
+      (y * x) / n_a -
+        y * (1 - x) / n_b
+    )
+  ) |>
+  pull(estimate) -> estimate
+```
+
+```{r}
+ab
+```
+
+Below are two ways to estimate this in R.
+Using a formula approach, we calculate the difference in `y` between exposure values in the first example.
+
+```{r}
+ab |>
+  summarise(
+    n_a = sum(x),
+    n_b = sum(1 - x),
+    estimate = sum(
+      (y * x) / n_a -
+        y * (1 - x) / n_b
+    )
+  )
+```
+
+Alternatively, we can `group_by()` `x` and `summarize()` the means of `y` for each group, then pivot the results to take their difference.
+
+```{r}
+ab |>
+  group_by(x) |>
+  summarise(avg_y = mean(y)) |>
+  pivot_wider(
+    names_from = x,
+    values_from = avg_y,
+    names_prefix = "x_"
+  ) |>
+  summarise(estimate = x_1 - x_0)
+```
 
-smds <- tidy_smd(
-  df,
-  .vars = c(confounder_1, confounder_2, ...),
-  .group = exposure,
-  .wts = wts # weight is optional
+Because $X$, the exposure, was randomly assigned, this estimator results in an unbiased estimate of our estimand of interest.
+
+What do we mean by unbiased, though?
+Notice that while the "true" causal effect is 1, this estimate is not precisely 1; it is `r round(estimate, 3)`.
+Why the difference?
+This A/B test included a sample of 100 participants, not the whole population.
+As that sample increases, our estimate will get closer to the truth.
+Let's try that.
+Let's simulate the data again but increase the sample size from 100 to 100,000:
+
+```{r}
+set.seed(928)
+ab <- tibble(
+  x = rbinom(100000, 1, 0.5),
+  y = x + rnorm(100000)
 )
+
+ab |>
+  summarise(
+    n_a = sum(x),
+    n_b = sum(1 - x),
+    estimate = sum(
+      (y * x) / n_a -
+        y * (1 - x) / n_b
+    )
+  )
 ```
 
-Let's look at an example using the same data as @sec-using-ps. 
+Notice how the estimate is 1.01, much closer to the actual average treatment effect, 1.
+
+Suppose $X$ had not been randomly assigned.
+In that case, we could use the pre-exposure covariates to estimate the conditional probability of $X$ (the propensity score) and incorporate this probability in a *weight* $(w_i)$ to estimate this causal effect.
+For example, we could use the following *estimator* to *estimate* our average treatment effect *estimand*:
+
+$$\frac{\sum_{i=1}^NY_i\times X_i\times w_i}{\sum_{i=1}^NX_i\times w_i}-\frac{\sum_{i=1}^NY_i\times(1-X_i)\times w_i}{\sum_{i=1}^N(1-X_i)\times w_i}$$
+
+## Estimating treatment effects with specific targets in mind
+
+Depending on the study's goal, or the causal question, we may want to estimate different *estimands*.
+Here, we will outline the most common causal estimands, their target populations, the causal questions they may help answer, and the methods used to estimate them [@greifer2021choosing].
+
+### Average treatment effect
+
+A common estimand is the average treatment effect (ATE).
+The target population is the *total sample* or population of interest.
+The **estimand** here is the expected value of the difference in potential outcomes across all individuals:
+
+$$E[Y(1) - Y(0)]$$
+
+An example research question is "Should a policy be applied to all eligible patients?" [@greifer2021choosing].
+
+Most randomized controlled trials are designed with the ATE as the target estimand.
+In a non-randomized setting, you can estimate the ATE using full matching.
+Each observation in the exposed group is matched to at least one observation in the control group (and vice versa) without replacement.
+Alternatively, the following inverse probability weight will allow you to estimate the ATE using propensity score weighting.
+
+$$w_{ATE} = \frac{X}{p} + \frac{(1 - X)}{1 - p}$$
+
+In other words, the weight is one over the propensity score for those in the exposure group and one over one minus the propensity score for the control group.
+Intuitively, this weight equates to the inverse probability of being in the exposure group in which you actually were.
+
+Let's dig deeper into this causal estimand using the Touring Plans data.
+Recall that in @sec-using-ps, we examined the relationship between whether there were "Extra Magic Hours" in the morning and the average wait time for the Seven Dwarfs Mine Train the same day between 9 am and 10 am.
+Let's reconstruct our data set, `seven_dwarfs` and fit the same propensity score model specified previously.
 
 ```{r}
 library(broom)
 library(touringplans)
+
+seven_dwarfs <- seven_dwarfs_train_2018 |>
+  filter(wait_hour == 9) |>
+  mutate(park_extra_magic_morning = factor(
+    park_extra_magic_morning,
+    labels = c("No Magic Hours", "Extra Magic Hours")
+  ))
+
+seven_dwarfs_with_ps <- glm(
+  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
+  data = seven_dwarfs,
+  family = binomial()
+) |>
+  augment(type.predict = "response", data = seven_dwarfs)
+```
+
+Let's examine a table of the variables of interest in this data frame.
+To do so, we are going to use the `tbl_summary()` function from the `{gtsummary}` package.
+(We'll also use the `{labelled}` package to clean up the variable names for the table.)
+
+```{r}
+#| label: tbl-unweighted-gtsummary
+#| tbl-cap: A descriptive table of Extra Magic Morning in the touringplans dataset. This table shows the distributions of these variables in the observed population.
+library(gtsummary)
+library(labelled)
+seven_dwarfs_with_ps <- seven_dwarfs_with_ps |>
+  set_variable_labels(
+    park_ticket_season = "Ticket Season",
+    park_close = "Close Time",
+    park_temperature_high = "Historic High Temperature"
+  )
+
+tbl_summary(
+  seven_dwarfs_with_ps,
+  by = park_extra_magic_morning,
+  include = c(park_ticket_season, park_close, park_temperature_high)
+) |>
+  # add an overall column to the table
+  add_overall(last = TRUE)
+```
+
+From @tbl-unweighted-gtsummary, 294 days did not have extra magic hours in the morning, and 60 did.
+We also see that 30% of the extra magic mornings were during peak season compared to 20% of the non-extra magic mornings.
+Additionally, days with extra magic mornings were more likely to close at 6 pm (18:00:00) compared to non-magic hour mornings.
+The median high temperature on days with extra magic hour mornings is slightly lower (83 degrees) compared to non-extra magic hour morning days.
+
+Now let's construct our propensity score weight to estimate the ATE.
+The `{propensity}` package has helper functions to estimate weights.
+The `wt_ate()` function will calculate ATE weights.
+
+```{r}
 library(propensity)
+seven_dwarfs_wts <- seven_dwarfs_with_ps |>
+  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))
+```
+
+Let's look at a distribution of these weights.
+
+```{r}
+#| label: fig-sd-ate-hist
+#| fig.cap: >
+#|   A histogram of the average treatment effect (ATE) weights for whether or not a day had extra magic hours. ATE weights can range from 1 to infinity, so paying attention to the actual range of weights is important.
+ggplot(seven_dwarfs_wts, aes(x = w_ate)) +
+  geom_histogram(bins = 50)
+```
+
+Many weights in @fig-sd-ate-hist are close to 1 (the smallest possible value for an ATE weight), with a long tail leading to the largest weight (around 24).
+This distribution highlights a potential problem with ATE weights.
+They range from 1 to infinity; if your weights are too large in small samples, this can lead to bias in your estimates (known as finite sample bias).
+
+::: {.callout-warning}
+## Finite sample bias
+
+We know that not accounting for confounders can bias our causal estimates, but it turns out that even after accounting for all confounders, we may still get a biased estimate in finite samples.
+Many of the properties we tout in statistics rely on *large samples*---how "large" is defined can be opaque.
+Let's look at a quick simulation.
+Here, we have an exposure, $X$, an outcome, $Y$, and one confounder, $Z$.
+We will simulate $Y$, which is only dependent on $Z$ (so the true treatment effect is 0), and $X$, which also depends on $Z$.
 
-seven_dwarfs_9 <- seven_dwarfs_train_2018 |> filter(wait_hour == 9)
+```{r}
+set.seed(928)
+n <- 100
+finite_sample <- tibble(
+  # z is normally distributed with a mean: 0 and sd: 1
+  z = rnorm(n),
+  # x is defined from a probit selection model with normally distributed errors
+  x = case_when(
+    0.5 + z + rnorm(n) > 0 ~ 1,
+    TRUE ~ 0
+  ),
+  # y is continuous, dependent only on z with normally distrbuted errors
+  y = z + rnorm(n)
+)
+```
 
-seven_dwarfs_9_with_ps <-
-  glm(
-    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
-    data = seven_dwarfs_9,
-    family = binomial()
+If we fit a propensity score model using the one confounder $Z$ and calculate the weighted estimator, we should get an unbiased result (which in this case would be 0).
+
+```{r}
+## fit the propensity score model
+finite_sample_wts <- glm(
+  x ~ z,
+  data = finite_sample, family = binomial("probit")
+) |>
+  augment(newdata = finite_sample, type.predict = "response") |>
+  mutate(wts = wt_ate(.fitted, x))
+
+finite_sample_wts |>
+  summarize(
+    effect = sum(y * x * wts) / sum(x * wts) -
+      sum(y * (1 - x) * wts) / sum((1 - x) * wts)
+  )
+```
+
+Ok, here, our effect of $X$ is pretty far from 0, although it's hard to know if this is really bias, or something we are just seeing by chance in this particular simulated sample.
+To explore the potential for finite sample bias, we can rerun this simulation many times at different sample sizes.
+Let's do that.
+
+```{r}
+#| label: sim-finite-sample-bias
+#| warning: false
+#| message: false
+#| cache: true
+sim <- function(n) {
+  ## create a simulated dataset
+  finite_sample <- tibble(
+    z = rnorm(n),
+    x = case_when(
+      0.5 + z + rnorm(n) > 0 ~ 1,
+      TRUE ~ 0
+    ),
+    y = z + rnorm(n)
+  )
+  finite_sample_wts <- glm(
+    x ~ z,
+    data = finite_sample, family = binomial("probit")
   ) |>
-  augment(type.predict = "response", data = seven_dwarfs_9)
-seven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>
-  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))
+    augment(newdata = finite_sample, type.predict = "response") |>
+    mutate(wts = wt_ate(.fitted, x))
+  bias <- finite_sample_wts |>
+    summarize(
+      effect = sum(y * x * wts) / sum(x * wts) -
+        sum(y * (1 - x) * wts) / sum((1 - x) * wts)
+    ) |>
+    pull()
+  tibble(
+    n = n,
+    bias = bias
+  )
+}
+
+## Examine 5 different sample sizes, simulate each 1000 times
+set.seed(1)
+finite_sample_sims <- map_df(rep(c(50, 100, 500, 1000, 5000, 10000),
+  each = 1000
+), sim)
+
+bias <- finite_sample_sims %>%
+  group_by(n) %>%
+  summarise(bias = mean(bias))
+```
+
+Let's plot our results:
+
+```{r}
+#| label: fig-finite-sample-bias
+#| fig-cap: "Finite sample bias present with ATE weights created using correctly specified propensity score model, varying the sample size from n = 50 to n = 10,000"
+ggplot(bias, aes(x = n, y = bias)) +
+  geom_point() +
+  geom_line() +
+  geom_hline(yintercept = 0, lty = 2)
 ```
 
-Now, using the `tidy_smd` function, we can examine the standardized mean difference before and after weighting.
+This is an example of finite sample bias.
+Notice here, even when the sample size is quite large (5,000) we still see some bias away from the "true" effect of 0.
+It isn't until a sample size larger than 10,000 that we see this bias disappear.
+
+Estimands that utilize weights that are unbounded (i.e. that theoretically can be infinitely large) are more likely to suffer from finite sample bias.
+The likelihood of falling into finite sample bias depends on:\
+(1) the estimand you have chosen (i.e. are the weights bounded?)\
+(2) the distribution of the covariates in the exposed and unexposed groups (i.e. is there good overlap? Potential positivity violations, when there is poor overlap, are the regions where weights can become too large)\
+(3) the sample size.
+:::
+
+The `{gtsummary}` package allows the creation of *weighted* tables, which can help us build an intuition for the psuedopopulation created by the weights.
+First, we must load the `{survey}` package and create a survey design object.
+The `{survey}` package supports calculating statistics and models using weights.
+Historically, many researchers applied techniques incorporating weights to survey analyses.
+Even though we are not doing a survey analysis, the same techniques are helpful for our propensity score weights.
 
 ```{r}
+#| label: tbl-weighted-gtsummary
+#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
+#| message: false
+library(survey)
+seven_dwarfs_svy <- svydesign(
+  ids = ~1,
+  data = seven_dwarfs_wts,
+  weights = ~w_ate
+)
+tbl_svysummary(
+  seven_dwarfs_svy,
+  by = park_extra_magic_morning,
+  include = c(park_ticket_season, park_close, park_temperature_high)
+) |>
+  add_overall(last = TRUE)
+```
+
+Notice in @tbl-weighted-gtsummary that the variables are more **balanced** between the groups.
+For example, looking at the variable `park_ticket_season`, in @tbl-unweighted-gtsummary we see that a higher percentage of the days with extra magic hours in the mornings were during the peak season (30%) compared to the percent of days without extra magic hours in the morning (23%).
+In @tbl-weighted-gtsummary this is balanced, with a weighted percent of 22% peak days in the extra magic morning group and 22% in the no extra magic morning group.
+The weights change the variables' distribution by weighting each observation so that the two groups appear balanced.
+Also, notice that the distribution of variables in @tbl-weighted-gtsummary now matches the Overall column of @tbl-unweighted-gtsummary.
+That is what ATE weights do: they make the distribution of variables included in the propensity score for each exposure group look like the overall distribution across the whole sample.
+
+Let's look at another visualization that can help us understand the weighted psuedopopulation created by the ATE weights: a mirrored histogram.
+We will plot the distribution of the propensity score for the days in the exposure group (days with extra magic hours in the morning) on the top half of the plot; then, we will mirror the distribution of the propensity score for the days in the"unexposed group below it. This visualization allows us to compare the two distributions quickly. Then we will overlay weighted histograms to demonstrate how these distributions differ after incorporating the weights. The {halfmoon} package includes a function `geom_mirror_histogram()` to assist with this visualization.
+
+```{r}
+#| label: fig-sd-mirror-hist-ate
+#| fig.cap: >
+#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect (ATE) weight.
 library(halfmoon)
-smds <- 
-  seven_dwarfs_9_with_wt |>
-  mutate(park_close = as.numeric(park_close)) |>
-  tidy_smd(
-    .vars = c(park_ticket_season, park_close, park_temperature_high),
-    .group = park_extra_magic_morning,
-    .wts = w_ate
+ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
+  geom_mirror_histogram(bins = 50) +
+  geom_mirror_histogram(
+    aes(fill = park_extra_magic_morning, weight = w_ate),
+    bins = 50,
+    alpha = 0.5
+  ) +
+  scale_y_continuous(labels = abs) +
+  labs(
+    x = "propensity score",
+    fill = "Extra Magic Morning"
   )
-smds
 ```
 
-For example, we see above that the *observed* standardized mean difference (prior to incorporating the propensity score) for ticket season is `r smds |> filter(variable == "park_ticket_season" & method == "observed") |> pull(smd) |> round(2)`, however after incorporating the propensity score weight this is attenuated, now `r smds |> filter(variable == "park_ticket_season" & method == "w_ate") |> pull(smd) |> round(2)`.
+We can learn a few things from @fig-sd-mirror-hist-ate.
+First, we can see that there are more "unexposed" days---days without extra magic hours in the morning---compared to "exposed" in the observed population.
+We can see this by examining the darker histograms, which show the distributions in the observed sample.
+Similarly, the exposed days are upweighted more substantially, as evidenced by the light blue we see on the top.
+We can also see that after weighting, the two distributions look similar; the shape of the blue weighted distribution on top looks comparable to the orange weighted distribution below.
 
-One downside of this metric is it only quantifying balance *on the mean*, which may not be sufficient for continuous confounders, as it is possible to be balanced on the mean but severely imbalanced in the tails. At the end of this chapter we will show you a few tools for examining balance across the full distribution of the confounder.
+### Average treatment effect among the treated
 
-## Visualizing balance
+The target population to estimate the average treatment effect among the treated (ATT) is the *exposed* (treated) population.
+This causal estimand conditions on those in the exposed group:
 
-### Love Plots
+$$E[Y(1) - Y(0) | X = 1]$$
 
-Let's start by visualizing these standardized mean differences. To do so, we like to use a *Love Plot* (named for Thomas Love, as he was one of the first to popularize them). The `{halfmoon}` package has a function `geom_love` that simplifies this implementation.
+Example research questions where the ATT is of interest could include "Should we stop our marketing campaign to those currently receiving it?" or "Should medical providers stop recommending treatment for those currently receiving it?" [@greifer2021choosing]
+
+The ATT is a common target estimand when using matching; here, all exposed observations are included and "matched" to control observations, some of which may be discarded.
+Alternatively, the ATT can be estimated via weighting.
+The ATT weight is estimated by:
+
+$$w_{ATT} = X + \frac{(1 - X)p}{1 - p}$$
+
+Let's add the ATT weights to the `seven_dwarfs_wts` data frame and look at their distribution.
 
 ```{r}
-ggplot(
-  data = smds,
-  aes(
-    x = abs(smd), 
-    y = variable, 
-    group = method, 
-    color = method
+#| label: fig-sd-att-hist
+#| fig.cap : >
+#|   A histogram of the average treatment effect among the treated (ATT) weights. The range of the ATT weights in this example is more stable than the ATE weights: the range is much smaller.
+seven_dwarfs_wts <- seven_dwarfs_wts |>
+  mutate(w_att = wt_att(.fitted, park_extra_magic_morning))
+
+ggplot(seven_dwarfs_wts, aes(w_att)) +
+  geom_histogram(bins = 50)
+```
+
+Compared to the ATE weights, the weights in @fig-sd-att-hist look more *stable*; the distribution of the weights is not heavily skewed, and the range is small, from ground zero to a bit over one, with many at precisely one.
+These weights are exactly one for all days in the exposed group.
+Theoretically, for unexposed days these weights can range from zero to infinity.
+Still, because we have many more unexposed days compared to exposed in this particular sample, the range is much smaller, meaning the vast majority of unexposed days are "down-weighted" to match the variable distribution of the exposed days.
+Let's take a look at the weighted table to see this a bit more clearly.
+
+```{r}
+#| label: tbl-weighted-att
+#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Treated Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
+#| message: false
+seven_dwarfs_svy <- svydesign(
+  ids = ~1,
+  data = seven_dwarfs_wts,
+  weights = ~w_att
+)
+tbl_svysummary(
+  seven_dwarfs_svy,
+  by = park_extra_magic_morning,
+  include = c(park_ticket_season, park_close, park_temperature_high)
+) |>
+  add_overall(last = TRUE)
+```
+
+We again achieve a balance between groups, but the target values in @tbl-weighted-att differ from the previous tables.
+Recall in our ATE weighted table (@tbl-weighted-gtsummary), when looking at the `wdw_ticket_season` variable, we saw the weighted percent in the peak season balanced close to 22%, the overall percent of peak days in the observed sample.
+We again see balance, but the weighted percent of peak season days is 30% in the exposed and unexposed groups, reflecting the percent in the unweighted exposure group.
+If you compare @tbl-weighted-att to our unweighted table (@tbl-unweighted-gtsummary), you might notice that the columns are most similar to the exposed column from the unweighted table.
+This is because the "target" population is the exposed group, the days with extra magic hours in the morning, so we're trying to make the comparison group (no magic hours) as similar to that group as possible.
+
+We can again create a mirrored histogram to observe the weighted psuedopopulation.
+
+```{r}
+#| label: fig-sd-mirror-hist-att
+#| fig.cap: >
+#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the treated (ATT) weight.
+ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
+  geom_mirror_histogram(bins = 50) +
+  geom_mirror_histogram(
+    aes(fill = park_extra_magic_morning, weight = w_att),
+    bins = 50,
+    alpha = 0.5
+  ) +
+  scale_y_continuous(labels = abs) +
+  labs(
+    x = "propensity score",
+    fill = "Extra Magic Morning"
   )
-) +  
-  geom_love()
 ```
 
+Since every day in the exposed group has a weight of 1, the distribution of the propensity scores (the dark bars above 0 on the graph) in @fig-sd-mirror-hist-att overlaps exactly with the weighted distribution overlaid in blue.
+In the unexposed population, we see that almost all observations are *down weighted*; the dark orange distribution is smaller than the distribution of the propensity scores and more closely matches the exposed distribution above it.
+The ATT is easier to estimate when there are many more observations in the unexposed group compared to the exposed one.
+
+### Average treatment effect among the unexposed
+
+The target population to estimate the average treatment effect among the unexposed (control) (ATU / ATC) is the *unexposed* (control) population.
+This causal estimand conditions on those in the unexposed group.
 
-### Boxplots and eCDF plots
+$$E[Y(1) - Y(0) | X = 0]$$
 
-As mentioned above, one issue with the standardized mean differences is they only quantify balance on a single point for continuous confounders (the mean). It can be helpful to visualize the whole distribution to ensure that there is not residual imbalance in the tails. Let's first use a boxplot. As an example, let's use the `park_temperature_high` variable. When we make boxplots, we prefer to always jitter the points on top to make sure we aren't masking and data anomolies -- we use `geom_jitter` to accomplish this. First, we will make the unweighted boxplot.
+Example research questions where the ATU is of interest could include "Should we send our marketing campaign to those not currently receiving it?" or "Should medical providers begin recommending treatment for those not currently receiving it?" [@greifer2021choosing]
+
+The ATU can be estimated via matching; here, all unexposed observations are included and "matched" to exposed observations, some of which may be discarded.
+Alternatively, the ATU can be estimated via weighting.
+The ATU weight is estimated by:
+
+$$w_{ATU} = \frac{X(1-p)}{p}+ (1-X)$$ Let's add the ATU weights to the `seven_dwarfs_wts` data frame and look at their distribution.
 
 ```{r}
-#| label: fig-boxplot
-#| fig.cap: "Unweighted boxplot showing the difference in historical high temperature between days that had extra magic hours and those that did not."
-ggplot(
-  seven_dwarfs_9_with_wt, 
-  aes(
-    x = factor(park_extra_magic_morning), 
-    y = park_temperature_high,
-    group = park_extra_magic_morning
-    )
-) + 
-  geom_boxplot(outlier.color = NA) + 
-  geom_jitter() + 
-  labs(x = "Extra magic morning",
-       y = "Temperature high")
-```
-
-```{r}
-#| label: fig-weighted-boxplot
-#| fig.cap: "Weighted boxplot showing the difference in historical high temperature between days that had extra magic hours and those that did not after incorporating the propensity score weight (ATE weight)."
-ggplot(
-  seven_dwarfs_9_with_wt, 
-  aes(
-    x = factor(park_extra_magic_morning), 
-    y = park_temperature_high,
-    group = park_extra_magic_morning,
-    weight = w_ate
-    )
-) + 
-  geom_boxplot(outlier.color = NA) + 
-  geom_jitter() + 
-  labs(x = "Extra magic morning",
-       y = "Historic temperature high")
+#| label: fig-sd-atu-hist
+#| fig.cap : >
+#|   A histogram of the average treatment effect among the unexposed (ATU) weights. The range of the ATU weights in this example is very similar to the ATE weights.
+seven_dwarfs_wts <- seven_dwarfs_wts |>
+  mutate(w_atu = wt_atu(.fitted, park_extra_magic_morning))
+
+ggplot(seven_dwarfs_wts, aes(w_atu)) +
+  geom_histogram(bins = 50)
 ```
 
-Similarly, we can also examine the empirical cumulative distribution function (eCDF) for the confounder stratified by each exposure group. The unweighted eCDF can be visualized using `geom_ecdf`
+The distribution of the weights in @fig-sd-atu-hist weights looks very similar to what we saw with the ATE weights---several around 1, with a long tail.
+These weights will be 1 for all observations in the unexposed group; they can range from 0 to infinity for the exposed group.
+Because we have more observations in our unexposed group, our exposed observations are upweighted to match their distribution.
+
+Now let's take a look at the weighted table.
 
 ```{r}
-#| label: fig-ecdf
-#| fig.cap: "Unweighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green)."
+#| label: tbl-weighted-atu
+#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Unexposed Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
 
-ggplot(seven_dwarfs_9_with_wt, 
-       aes(x = park_temperature_high,
-           color = factor(park_extra_magic_morning))) + 
-  geom_ecdf() + 
-  scale_color_manual(
-    "Extra Magic Morning",
-    values = c("#5154B8", "#5DB854"),
-    labels = c("Yes", "No")
-  ) + 
-  labs(x = "Historic temperature high",
-       y = "Proportion <= x")
+seven_dwarfs_svy <- svydesign(
+  ids = ~1,
+  data = seven_dwarfs_wts,
+  weights = ~w_atu
+)
+tbl_svysummary(
+  seven_dwarfs_svy,
+  by = park_extra_magic_morning,
+  include = c(park_ticket_season, park_close, park_temperature_high)
+) |>
+  add_overall(last = TRUE)
 ```
 
-The `{halfmoon}` package allows for the additional `weight` argument to be passed to `geom_ecdf` to display a weighted eCDF plot.
+Now the exposed column of @tbl-weighted-atu is weighted to match the unexposed column of the unweighted table.
+
+We can again create a mirrored histogram to observe the weighted psuedopopulation.
 
 ```{r}
-#| label: fig-weighted-ecdf
-#| fig.cap: "Weighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green) after incorporating the propensity score weight (ATE)."
+#| label: fig-sd-mirror-hist-atu
+#| fig.cap: >
+#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the unexposed (ATU) weight.
+ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
+  geom_mirror_histogram(bins = 50) +
+  geom_mirror_histogram(
+    aes(fill = park_extra_magic_morning, weight = w_atu),
+    bins = 50,
+    alpha = 0.5
+  ) +
+  scale_y_continuous(labels = abs) +
+  labs(
+    x = "propensity score",
+    fill = "Extra Magic Morning"
+  )
+```
+
+The weights for the unexposed days are all 1, so the distribution of the propensity scores for this group exactly overlaps the weighted pseudopopulation in orange.
+The blue bars indicate that the exposed population is largely upweighted to match the unexposed distribution.
+
+### Average treatment effect among the evenly matchable
 
-ggplot(seven_dwarfs_9_with_wt, 
-       aes(x = park_temperature_high,
-           color = factor(park_extra_magic_morning))) + 
-  geom_ecdf(aes(weights = w_ate)) + 
-  scale_color_manual(
-    "Extra Magic Morning",
-    values = c("#5154B8", "#5DB854"),
-    labels = c("Yes", "No")
-  ) + 
-  labs(x = "Historic temperature high",
-       y = "Proportion <= x")
+The target population to estimate the average treatment effect among the evenly matchable (ATM) is the evenly matchable.
+This causal estimand conditions on those deemed "evenly matchable" by some distance metric.
+
+$$E[Y(1) - Y(0) | M_d = 1]$$
+
+Here $d$ denotes a distance measure, and $M_d=1$ indicates that a unit is evenly matchable under that distance measure.[@samuels2017aspects; @d2018improving] Example research questions where the ATM is of interest could include "Should those at clinical equipoise receive the exposure?" [@greifer2021choosing]
+
+The ATM is a common target estimand when using matching.
+Here, some exposed observations are "matched" to control observations, however some observations in both groups may be discarded.
+This is often done via a "caliper", where observations are only matched if they fall within a pre-specified caliper distance of each other.
+Alternatively, the ATM can be estimated via weighting.
+The ATM weight is estimated by:
+
+$$w_{ATM} = \frac{\min \{p, 1-p\}}{Xp + (1-X)(1-p)}$$
+
+Let's add the ATM weights to the `seven_dwarfs_wts` data frame and look at their distribution.
+
+```{r}
+#| label: fig-sd-atm-hist
+#| fig.cap: >
+#|   A histogram of the average treatment effect among the evenly matchable (ATM) weights for whether or not a day had extra magic hours. ATM weights can range from 0 to 1, so they are always stable.
+seven_dwarfs_wts <- seven_dwarfs_wts |>
+  mutate(w_atm = wt_atm(.fitted, park_extra_magic_morning))
+
+ggplot(seven_dwarfs_wts, aes(w_atm)) +
+  geom_histogram(bins = 50)
 ```
 
-Examining @fig-weighted-ecdf, we can notice a few things. First, compared to @fig-ecdf there is improvement in the overlap between the two distributions. In @fig-ecdf, the green line is almost always noticeably above the purple, whereas in @fig-weighted-ecdf the two lines appear to mostly overlap until we reach slightly above 80 degrees. After 80 degrees, the lines appear to diverge in the weighted plot. This is why it can be useful to examine the full distribution rather than a single summary measure. If we had just used the standardized mean difference, for example, we would have likely said these two groups are balanced and moved on. Looking at @fig-weighted-ecdf suggests that perhaps there is a non-linear relationship between the probability of having an extra magic morning and the historic high temperature. Let's try refitting our propensity score model using a natural spline. We can use the function `splines::ns` for this.
+The distribution of the weights in @fig-sd-atm-hist looks very similar to what we saw with the ATT weights.
+That is because, in this particular sample, there are many more unexposed observations compared to exposed ones.
+These weights have the nice property that they range from 0 to 1, making them *always* stable, regardless of the imbalance between exposure groups.
 
-<!-- TODO put a callout box here explaining what a natural spline is. -->
+Now let's take a look at the weighted table.
 
 ```{r}
-seven_dwarfs_9_with_ps <-
-  glm(
-    park_extra_magic_morning ~ park_ticket_season + park_close +
-      splines::ns(park_temperature_high, df = 5), # refit model with a spline
-    data = seven_dwarfs_9,
-    family = binomial()
+#| label: tbl-weighted-atm
+#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Evenly Matchable Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
+
+seven_dwarfs_svy <- svydesign(
+  ids = ~1,
+  data = seven_dwarfs_wts,
+  weights = ~w_atm
+)
+tbl_svysummary(
+  seven_dwarfs_svy,
+  by = park_extra_magic_morning,
+  include = c(park_ticket_season, park_close, park_temperature_high)
+) |>
+  add_overall(last = TRUE)
+```
+
+In this particular sample, the ATM weights resemble the ATT weights, so this table looks similar to the ATT weighted table.
+This similarity is not guaranteed, and the ATM pseudopopulation can look different from the overall, exposed, and unexposed unweighted populations.
+It's thus essential to examine the weighted table when using ATM weights to understand what population inference will ultimately be drawn on.
+
+We can again create a mirrored histogram to observe the ATM weighted psuedopopulation.
+
+```{r}
+#| label: fig-sd-mirror-hist-atm
+#| fig.cap: >
+#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the evenly matchable (ATM) weight.
+ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
+  geom_mirror_histogram(bins = 50) +
+  geom_mirror_histogram(
+    aes(fill = park_extra_magic_morning, weight = w_atm),
+    bins = 50,
+    alpha = 0.5
+  ) +
+  scale_y_continuous(labels = abs) +
+  labs(
+    x = "propensity score",
+    fill = "Extra Magic Morning"
+  )
+```
+
+Again, the ATM weights are bounded by 0 and 1, meaning that all observations will be *down weighted*.
+This removes the potential for finite sample bias due to large weights in small samples and has improved variance properties.
+
+### Average treatment effect among the overlap population
+
+The target population to estimate the average treatment effect among the overlap population (ATO) is the overlap -- this is very similar to the "even matchable" from above.
+Example research questions where the ATO is of interest are the same as those for the ATM, such as "Should those at clinical equipoise receive the exposure?" [@greifer2021choosing]
+
+These weights, again, are pretty similar to the ATM weights but are slightly attenuated, yielding improved variance properties.
+
+The ATO weight is estimated by:
+
+$$w_{ATO} = X(1-p) + (1-X)p$$
+
+Let's add the ATO weights to the `seven_dwarfs_wts` data frame and look at their distribution.
+
+```{r}
+#| label: fig-sd-ato-hist
+#| fig.cap: >
+#|   A histogram of the average treatment effect among the overlap population (ATO) weights for whether or not a day had extra magic hours. Like ATM weights, ATO weights can range from 0 to 1, so they are always stable. ATO weights also have improved variance properties compared to the ATM weights.
+seven_dwarfs_wts <- seven_dwarfs_wts |>
+  mutate(w_ato = wt_ato(.fitted, park_extra_magic_morning))
+
+ggplot(seven_dwarfs_wts, aes(w_ato)) +
+  geom_histogram(bins = 50)
+```
+
+Like the ATM weights, the ATO weights are bounded by 0 and 1, making them more stable than the ATE, ATT, and ATU, regardless of the exposed and unexposed imbalance.
+
+::: {.callout-warning}
+## Finite sample bias - revisited
+
+Let's revisit our finite sample bias simulation, adding in the ATO weights to examine whether they are impacted by this potential for bias.
+
+```{r}
+#| label: sim-finite-sample-bias-2
+#| warning: false
+#| message: false
+#| cache: true
+sim <- function(n) {
+  ## create a simulated dataset
+  finite_sample <- tibble(
+    z = rnorm(n),
+    x = case_when(
+      0.5 + z + rnorm(n) > 0 ~ 1,
+      TRUE ~ 0
+    ),
+    y = z + rnorm(n)
+  )
+  finite_sample_wts <- glm(
+    x ~ z,
+    data = finite_sample, family = binomial("probit")
   ) |>
-  augment(type.predict = "response", data = seven_dwarfs_9)
-seven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>
-  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))
+    augment(newdata = finite_sample, type.predict = "response") |>
+    mutate(
+      wts_ate = wt_ate(.fitted, x),
+      wts_ato = wt_ato(.fitted, x)
+    )
+  bias <- finite_sample_wts |>
+    summarize(
+      effect_ate = sum(y * x * wts_ate) / sum(x * wts_ate) -
+        sum(y * (1 - x) * wts_ate) / sum((1 - x) * wts_ate),
+      effect_ato = sum(y * x * wts_ato) / sum(x * wts_ato) -
+        sum(y * (1 - x) * wts_ato) / sum((1 - x) * wts_ato)
+    )
+  tibble(
+    n = n,
+    bias = c(bias$effect_ate, bias$effect_ato),
+    weight = c("ATE", "ATO")
+  )
+}
+
+## Examine 5 different sample sizes, simulate each 1000 times
+set.seed(1)
+finite_sample_sims <- map_df(rep(c(50, 100, 500, 1000, 5000, 10000),
+  each = 1000
+), sim)
+
+bias <- finite_sample_sims %>%
+  group_by(n, weight) %>%
+  summarise(bias = mean(bias))
+```
+
+Let's plot our results:
+
+```{r}
+#| label: fig-finite-sample-bias-2
+#| fig-cap: "Finite sample bias with ATE weights (orange) and ATO weights (blue) created using correctly specified propensity score model, varying the sample size from n = 50 to n = 10,000"
+ggplot(bias, aes(x = n, y = bias, color = weight)) +
+  geom_point() +
+  geom_line() +
+  geom_hline(yintercept = 0, lty = 2)
 ```
 
-Now let's see how that impacts the weighted eCDF plot
+Notice here the estimate using the ATO weights is almost immediately unbiased, even in fairly small samples.
+:::
+
+Now let's take a look at the weighted table.
 
 ```{r}
-#| label: fig-weighted-ecdf-2
-#| fig.cap: "Weighted eCDF examining the difference in distribution for historic high temperature among days that had extra magic morning hours (purple) compared to those that did not (green) after incorporating the propensity score weight where historic high temperature was modeled flexibly with a spline."
+#| label: tbl-weighted-ato
+#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Overlap Population Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
+
+seven_dwarfs_svy <- svydesign(
+  ids = ~1,
+  data = seven_dwarfs_wts,
+  weights = ~w_ato
+)
+tbl_svysummary(
+  seven_dwarfs_svy,
+  by = park_extra_magic_morning,
+  include = c(park_ticket_season, park_close, park_temperature_high)
+) |>
+  add_overall(last = TRUE)
+```
 
-ggplot(seven_dwarfs_9_with_wt, 
-       aes(x = park_temperature_high,
-           color = factor(park_extra_magic_morning))) + 
-  geom_ecdf(aes(weights = w_ate)) + 
-  scale_color_manual(
-    "Extra Magic Morning",
-    values = c("#5154B8", "#5DB854"),
-    labels = c("Yes", "No")
-  ) + 
-  labs(x = "Historic temperature high",
-       y = "Proportion <= x")
+Like the ATM weights, the ATO pseudopopulation may not resemble the overall, exposed, or unexposed unweighted populations, so it is essential to examine these weighted tables to understand the population on whom we will draw inferences.
+
+We can again create a mirrored histogram to observe the ATO weighted psuedopopulation.
+
+```{r}
+#| label: fig-sd-mirror-hist-ato
+#| fig.cap: >
+#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the overlap population (ATO) weight.
+ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
+  geom_mirror_histogram(bins = 50) +
+  geom_mirror_histogram(
+    aes(fill = park_extra_magic_morning, weight = w_ato),
+    bins = 50,
+    alpha = 0.5
+  ) +
+  scale_y_continuous(labels = abs) +
+  labs(
+    x = "propensity score",
+    fill = "Extra Magic Morning"
+  )
 ```
 
-Now in @fig-weighted-ecdf-2 the lines appear to overlap across the whole space.
+@fig-sd-mirror-hist-ato looks similar to the ATM pseudopopulation, with slight attenuation, as evidenced by the increased down weighting in the exposed population.
+We prefer using the ATO weights when the overlap (or evenly matchable) population is the target, as they have improved variance properties.
+Another benefit is that any variables included in the propensity score model (if fit via logistic regression) will be perfectly balanced on the mean.
+We will discuss this further in @sec-eval-ps-model.
+
+## Choosing estimands
+
+Several factors need to be considered when choosing an estimand for a given analysis.
+First an foremost, the research question at hand will drive this decision.
+For example, returing to the Touring Plans data, suppose Disney was interested in assessing whether they should add additional extra magic hours on days that don't currently have them---here, the ATU will be the estimand of interest.
+Other considerations include the available sample size and the distribution of the exposed and unexposed observations (i.e. might finite sample bias be an issue?).
+Below is a table summarizing the estimands and methods for estimating them (including R functions and example code), along with sample questions extended from Table 2 in @greifer2021choosing.
+
++----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
+| Estimand | Target population                | Example Research Question                                                                                                                                  | Matching Method                               | Weighting Method       |
++==========+==================================+============================================================================================================================================================+===============================================+========================+
+| ATE      | Full population                  | Should we decide whether to have extra magic hours *all* mornings to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                       | Full matching                                 | ATE Weights            |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  | Should a specific policy be applied to all eligible observations?                                                                                          | Fine stratification                           | `propensity::wt_ate()` |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = "ATE")`       | |                      |
++----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
+| ATT      | Exposed (treated) observations   | Should we stop extra magic hours to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                                                        | Pair matching without a caliper               | ATT weights            |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  | Should we stop our marketing campaign to those currently receiving it?                                                                                     | Full matching                                 | `propensity::wt_att()` |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  | Should medical providers stop recommending treatment for those currently receiving it?                                                                     | Fine stratification                           |                        |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = "ATT")`       |                        |
++----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
+| ATU      | Unexposed (control) observations | Should we add extra magic hours for all days to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                                            | Pair matching without a caliper               | ATU weights            |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  | Should we extend our marketing campaign to those not receiving it?                                                                                         | Full matching                                 | `propensity::wt_atu()` |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  | Should medical providers extend treatment to those not currently receiving it?                                                                             | Fine stratification                           |                        |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = "ATC")`       |                        |
++----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
+| ATM      | Evenly matchable                 | Are there some days we should change whether we are offering extra magic hours in order to change the wait time for Seven Dwarfs Mine Train between 5-6pm? | Caliper matching                              | ATM weights            |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  | Is there an effect of the exposure for some observations?                                                                                                  | `MatchIt::matchit(…, caliper = 0.1)`          | `propensity::wt_atm()` |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  | Should those at clinical equipoise receive treatment?                                                                                                      | Coarsened exact matching                      |                        |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  |                                                                                                                                                            | Cardinality matching                          |                        |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, method = "cardinality")` |                        |
++----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
+| ATO      | Overlap population               | Same as ATM                                                                                                                                                |                                               | ATO weights            |
+|          |                                  |                                                                                                                                                            |                                               |                        |
+|          |                                  |                                                                                                                                                            |                                               | `propensity::wt_ato()` |
++----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
+
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 survey
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--- a/chapters/chapter-11.qmd
+++ b/chapters/chapter-11.qmd
@@ -1,832 +1,224 @@
-# Causal estimands {#sec-estimands}
+# Fitting the outcome model {#sec-outcome-model}
 
 {{< include 00-setup.qmd >}}
 
-## Estimands, Estimators, Estimates
+## Using matched data sets
 
-When analyzing to make causal inferences, we need to keep the causal question that we're interested in close to our chest.
-The causal question helps guide not just the assumptions we need to make but also how we will go about answering the question.
-This chapter deals with three ideas closely related to answering causal questions: the estimand, the estimator, and the estimate.
-The **estimand** is the *target of interest*, the **estimator** is the method by which we approximate this estimand using data, and the **estimate** is the value we get when we plug our data into the estimator.
-You can think of the **estimand** as a glossy picture of a beautiful cake we are trying to bake, the **estimator** as the recipe, and the **estimate** as the cake we pull out of our oven.
-
-So far, we have been estimating the average treatment effect, the effect of the exposure of interest averaged across the whole population.
-The **estimand** here is the expected value of the difference in potential outcomes across all individuals:
-
-$$E[Y(1) - Y(0)]$$
-
-The **estimator** we use depends on the method we've chosen.
-For example, in an A/B test or randomized controlled trial, our estimator could just be the average outcome among those who received the exposure A minus the average outcome among those who receive exposure B.
-
-$$\sum_{i=1}^{N}\frac{Y_i\times X_i}{N_{\textrm{A}}} - \frac{Y_i\times (1-X_i)}{N_{\textrm{B}}}$$
-
-Where $X$ is just an indicator for exposure A ($X = 1$ for exposure A and $X = 0$ for exposure B), $N_A$ is the total number in group A and $N_B$ is the total number in group B such that $N_A + N_B = N$.
-Let's motivate this example a bit more.
-Suppose we have two different designs for a call-to-action (often abbreviated as CTA, a written directive for a marketing campaign) and want to assess whether they lead to a different average number of items a user purchases.
-We can create an A/B test where we randomly assign a subset of users to see design A or design B.
-Suppose we have A/B testing data for 100 participants in a dataset called `ab`.
-Here is some code to simulate such a data set.
-
-```{r}
-#| eval: false
-library(tidyverse)
-set.seed(928)
-ab <- tibble(
-  # generate the exposure, x, from a binomial distribution
-  # with the probability exposure A of 0.5
-  x = rbinom(100, 1, 0.5),
-  # generate the "true" average treatment effect of 1
-  # we use rnorm(100) to add the random error term that is normally
-  # distributed with a mean of 0 and a standard deviation of 1
-  y = x + rnorm(100)
-)
-```
-
-Here the exposure is `x`, and the outcome is `y`.
-The true average treatment effect is 1, implying that on average seeing call-to-action A increases the number of items purchased by 1.
-
-```{r}
-#| echo: false
-set.seed(928)
-ab <- tibble(
-  x = rbinom(100, 1, 0.5),
-  y = x + rnorm(100)
-)
-ab |>
-  summarise(
-    n_a = sum(x),
-    n_b = sum(1 - x),
-    estimate = sum(
-      (y * x) / n_a -
-        y * (1 - x) / n_b
-    )
-  ) |>
-  pull(estimate) -> estimate
-```
-
-```{r}
-ab
-```
-
-Below are two ways to estimate this in R.
-Using a formula approach, we calculate the difference in `y` between exposure values in the first example.
-
-```{r}
-ab |>
-  summarise(
-    n_a = sum(x),
-    n_b = sum(1 - x),
-    estimate = sum(
-      (y * x) / n_a -
-        y * (1 - x) / n_b
-    )
-  )
-```
-
-Alternatively, we can `group_by()` `x` and `summarize()` the means of `y` for each group, then pivot the results to take their difference.
-
-```{r}
-ab |>
-  group_by(x) |>
-  summarise(avg_y = mean(y)) |>
-  pivot_wider(
-    names_from = x,
-    values_from = avg_y,
-    names_prefix = "x_"
-  ) |>
-  summarise(estimate = x_1 - x_0)
-```
-
-Because $X$, the exposure, was randomly assigned, this estimator results in an unbiased estimate of our estimand of interest.
-
-What do we mean by unbiased, though?
-Notice that while the "true" causal effect is 1, this estimate is not precisely 1; it is `r round(estimate, 3)`.
-Why the difference?
-This A/B test included a sample of 100 participants, not the whole population.
-As that sample increases, our estimate will get closer to the truth.
-Let's try that.
-Let's simulate the data again but increase the sample size from 100 to 100,000:
-
-```{r}
-set.seed(928)
-ab <- tibble(
-  x = rbinom(100000, 1, 0.5),
-  y = x + rnorm(100000)
-)
-
-ab |>
-  summarise(
-    n_a = sum(x),
-    n_b = sum(1 - x),
-    estimate = sum(
-      (y * x) / n_a -
-        y * (1 - x) / n_b
-    )
-  )
-```
-
-Notice how the estimate is 1.01, much closer to the actual average treatment effect, 1.
-
-Suppose $X$ had not been randomly assigned.
-In that case, we could use the pre-exposure covariates to estimate the conditional probability of $X$ (the propensity score) and incorporate this probability in a *weight* $(w_i)$ to estimate this causal effect.
-For example, we could use the following *estimator* to *estimate* our average treatment effect *estimand*:
-
-$$\frac{\sum_{i=1}^NY_i\times X_i\times w_i}{\sum_{i=1}^NX_i\times w_i}-\frac{\sum_{i=1}^NY_i\times(1-X_i)\times w_i}{\sum_{i=1}^N(1-X_i)\times w_i}$$
-
-## Estimating treatment effects with specific targets in mind
-
-Depending on the study's goal, or the causal question, we may want to estimate different *estimands*.
-Here, we will outline the most common causal estimands, their target populations, the causal questions they may help answer, and the methods used to estimate them [@greifer2021choosing].
-
-### Average treatment effect
-
-A common estimand is the average treatment effect (ATE).
-The target population is the *total sample* or population of interest.
-The **estimand** here is the expected value of the difference in potential outcomes across all individuals:
-
-$$E[Y(1) - Y(0)]$$
-
-An example research question is "Should a policy be applied to all eligible patients?" [@greifer2021choosing].
-
-Most randomized controlled trials are designed with the ATE as the target estimand.
-In a non-randomized setting, you can estimate the ATE using full matching.
-Each observation in the exposed group is matched to at least one observation in the control group (and vice versa) without replacement.
-Alternatively, the following inverse probability weight will allow you to estimate the ATE using propensity score weighting.
-
-$$w_{ATE} = \frac{X}{p} + \frac{(1 - X)}{1 - p}$$
-
-In other words, the weight is one over the propensity score for those in the exposure group and one over one minus the propensity score for the control group.
-Intuitively, this weight equates to the inverse probability of being in the exposure group in which you actually were.
-
-Let's dig deeper into this causal estimand using the Touring Plans data.
-Recall that in @sec-using-ps, we examined the relationship between whether there were "Extra Magic Hours" in the morning and the average wait time for the Seven Dwarfs Mine Train the same day between 9 am and 10 am.
-Let's reconstruct our data set, `seven_dwarfs` and fit the same propensity score model specified previously.
+When fitting an outcome model on matched data sets, we can simply subset the original data to only those who were matched and then fit a model on these data as we would otherwise. For example, re-performing the matching as we did in @sec-using-ps, we can extract the matched observations in a dataset called `matched_data` as follows.
 
 ```{r}
+#| message: false
+#| warning: false
 library(broom)
 library(touringplans)
+library(MatchIt)
 
-seven_dwarfs <- seven_dwarfs_train_2018 |>
-  filter(wait_hour == 9) |>
-  mutate(park_extra_magic_morning = factor(
-    park_extra_magic_morning,
-    labels = c("No Magic Hours", "Extra Magic Hours")
-  ))
+seven_dwarfs_9 <- seven_dwarfs_train_2018 |> 
+  filter(wait_hour == 9) 
 
-seven_dwarfs_with_ps <- glm(
+m <- matchit(
   park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
-  data = seven_dwarfs,
-  family = binomial()
-) |>
-  augment(type.predict = "response", data = seven_dwarfs)
-```
-
-Let's examine a table of the variables of interest in this data frame.
-To do so, we are going to use the `tbl_summary()` function from the `{gtsummary}` package.
-(We'll also use the `{labelled}` package to clean up the variable names for the table.)
-
-```{r}
-#| label: tbl-unweighted-gtsummary
-#| tbl-cap: A descriptive table of Extra Magic Morning in the touringplans dataset. This table shows the distributions of these variables in the observed population.
-library(gtsummary)
-library(labelled)
-seven_dwarfs_with_ps <- seven_dwarfs_with_ps |>
-  set_variable_labels(
-    park_ticket_season = "Ticket Season",
-    park_close = "Close Time",
-    park_temperature_high = "Historic High Temperature"
-  )
-
-tbl_summary(
-  seven_dwarfs_with_ps,
-  by = park_extra_magic_morning,
-  include = c(park_ticket_season, park_close, park_temperature_high)
-) |>
-  # add an overall column to the table
-  add_overall(last = TRUE)
-```
-
-From @tbl-unweighted-gtsummary, 294 days did not have extra magic hours in the morning, and 60 did.
-We also see that 30% of the extra magic mornings were during peak season compared to 20% of the non-extra magic mornings.
-Additionally, days with extra magic mornings were more likely to close at 6 pm (18:00:00) compared to non-magic hour mornings.
-The median high temperature on days with extra magic hour mornings is slightly lower (83 degrees) compared to non-extra magic hour morning days.
-
-Now let's construct our propensity score weight to estimate the ATE.
-The `{propensity}` package has helper functions to estimate weights.
-The `wt_ate()` function will calculate ATE weights.
-
-```{r}
-library(propensity)
-seven_dwarfs_wts <- seven_dwarfs_with_ps |>
-  mutate(w_ate = wt_ate(.fitted, park_extra_magic_morning))
-```
-
-Let's look at a distribution of these weights.
-
-```{r}
-#| label: fig-sd-ate-hist
-#| fig.cap: >
-#|   A histogram of the average treatment effect (ATE) weights for whether or not a day had extra magic hours. ATE weights can range from 1 to infinity, so paying attention to the actual range of weights is important.
-ggplot(seven_dwarfs_wts, aes(x = w_ate)) +
-  geom_histogram(bins = 50)
-```
-
-Many weights in @fig-sd-ate-hist are close to 1 (the smallest possible value for an ATE weight), with a long tail leading to the largest weight (around 24).
-This distribution highlights a potential problem with ATE weights.
-They range from 1 to infinity; if your weights are too large in small samples, this can lead to bias in your estimates (known as finite sample bias).
-
-::: {.callout-warning}
-## Finite sample bias
-
-We know that not accounting for confounders can bias our causal estimates, but it turns out that even after accounting for all confounders, we may still get a biased estimate in finite samples.
-Many of the properties we tout in statistics rely on *large samples*---how "large" is defined can be opaque.
-Let's look at a quick simulation.
-Here, we have an exposure, $X$, an outcome, $Y$, and one confounder, $Z$.
-We will simulate $Y$, which is only dependent on $Z$ (so the true treatment effect is 0), and $X$, which also depends on $Z$.
-
-```{r}
-set.seed(928)
-n <- 100
-finite_sample <- tibble(
-  # z is normally distributed with a mean: 0 and sd: 1
-  z = rnorm(n),
-  # x is defined from a probit selection model with normally distributed errors
-  x = case_when(
-    0.5 + z + rnorm(n) > 0 ~ 1,
-    TRUE ~ 0
-  ),
-  # y is continuous, dependent only on z with normally distrbuted errors
-  y = z + rnorm(n)
+  data = seven_dwarfs_9
 )
+matched_data <- get_matches(m)
 ```
 
-If we fit a propensity score model using the one confounder $Z$ and calculate the weighted estimator, we should get an unbiased result (which in this case would be 0).
+We can then fit the outcome model on this data. For this analysis, we are interested in the impact of extra magic morning hours on the average posted wait time between 9 and 10am. The linear model below will estimate this in the matched cohort.
 
 ```{r}
-## fit the propensity score model
-finite_sample_wts <- glm(
-  x ~ z,
-  data = finite_sample, family = binomial("probit")
-) |>
-  augment(newdata = finite_sample, type.predict = "response") |>
-  mutate(wts = wt_ate(.fitted, x))
-
-finite_sample_wts |>
-  summarize(
-    effect = sum(y * x * wts) / sum(x * wts) -
-      sum(y * (1 - x) * wts) / sum((1 - x) * wts)
-  )
+lm(wait_minutes_posted_avg ~ park_extra_magic_morning, data = matched_data) |>
+  tidy(conf.int = TRUE)
 ```
 
-Ok, here, our effect of $X$ is pretty far from 0, although it's hard to know if this is really bias, or something we are just seeing by chance in this particular simulated sample.
-To explore the potential for finite sample bias, we can rerun this simulation many times at different sample sizes.
-Let's do that.
-
-```{r}
-#| label: sim-finite-sample-bias
-#| warning: false
-#| message: false
-#| cache: true
-sim <- function(n) {
-  ## create a simulated dataset
-  finite_sample <- tibble(
-    z = rnorm(n),
-    x = case_when(
-      0.5 + z + rnorm(n) > 0 ~ 1,
-      TRUE ~ 0
-    ),
-    y = z + rnorm(n)
-  )
-  finite_sample_wts <- glm(
-    x ~ z,
-    data = finite_sample, family = binomial("probit")
-  ) |>
-    augment(newdata = finite_sample, type.predict = "response") |>
-    mutate(wts = wt_ate(.fitted, x))
-  bias <- finite_sample_wts |>
-    summarize(
-      effect = sum(y * x * wts) / sum(x * wts) -
-        sum(y * (1 - x) * wts) / sum((1 - x) * wts)
-    ) |>
-    pull()
-  tibble(
-    n = n,
-    bias = bias
-  )
-}
-
-## Examine 5 different sample sizes, simulate each 1000 times
-set.seed(1)
-finite_sample_sims <- map_df(rep(c(50, 100, 500, 1000, 5000, 10000),
-  each = 1000
-), sim)
+Recall that by default `{MatchIt}` estimates an average treatment effect among the treated. This means among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 7.9 minutes (95% CI: 1.2-14.5). 
 
-bias <- finite_sample_sims %>%
-  group_by(n) %>%
-  summarise(bias = mean(bias))
-```
+## Using weights in outcome models
 
-Let's plot our results:
+Now let's use propensity score weights to estimate this same estimand. We will use the ATT weights so the analysis matches that for matching above.
 
 ```{r}
-#| label: fig-finite-sample-bias
-#| fig-cap: "Finite sample bias present with ATE weights created using correctly specified propensity score model, varying the sample size from n = 50 to n = 10,000"
-ggplot(bias, aes(x = n, y = bias)) +
-  geom_point() +
-  geom_line() +
-  geom_hline(yintercept = 0, lty = 2)
-```
-
-This is an example of finite sample bias.
-Notice here, even when the sample size is quite large (5,000) we still see some bias away from the "true" effect of 0.
-It isn't until a sample size larger than 10,000 that we see this bias disappear.
-
-Estimands that utilize weights that are unbounded (i.e. that theoretically can be infinitely large) are more likely to suffer from finite sample bias.
-The likelihood of falling into finite sample bias depends on:\
-(1) the estimand you have chosen (i.e. are the weights bounded?)\
-(2) the distribution of the covariates in the exposed and unexposed groups (i.e. is there good overlap? Potential positivity violations, when there is poor overlap, are the regions where weights can become too large)\
-(3) the sample size.
-:::
-
-The `{gtsummary}` package allows the creation of *weighted* tables, which can help us build an intuition for the psuedopopulation created by the weights.
-First, we must load the `{survey}` package and create a survey design object.
-The `{survey}` package supports calculating statistics and models using weights.
-Historically, many researchers applied techniques incorporating weights to survey analyses.
-Even though we are not doing a survey analysis, the same techniques are helpful for our propensity score weights.
-
-```{r}
-#| label: tbl-weighted-gtsummary
-#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
 #| message: false
-library(survey)
-seven_dwarfs_svy <- svydesign(
-  ids = ~1,
-  data = seven_dwarfs_wts,
-  weights = ~w_ate
-)
-tbl_svysummary(
-  seven_dwarfs_svy,
-  by = park_extra_magic_morning,
-  include = c(park_ticket_season, park_close, park_temperature_high)
-) |>
-  add_overall(last = TRUE)
-```
-
-Notice in @tbl-weighted-gtsummary that the variables are more **balanced** between the groups.
-For example, looking at the variable `park_ticket_season`, in @tbl-unweighted-gtsummary we see that a higher percentage of the days with extra magic hours in the mornings were during the peak season (30%) compared to the percent of days without extra magic hours in the morning (23%).
-In @tbl-weighted-gtsummary this is balanced, with a weighted percent of 22% peak days in the extra magic morning group and 22% in the no extra magic morning group.
-The weights change the variables' distribution by weighting each observation so that the two groups appear balanced.
-Also, notice that the distribution of variables in @tbl-weighted-gtsummary now matches the Overall column of @tbl-unweighted-gtsummary.
-That is what ATE weights do: they make the distribution of variables included in the propensity score for each exposure group look like the overall distribution across the whole sample.
-
-Let's look at another visualization that can help us understand the weighted psuedopopulation created by the ATE weights: a mirrored histogram.
-We will plot the distribution of the propensity score for the days in the exposure group (days with extra magic hours in the morning) on the top half of the plot; then, we will mirror the distribution of the propensity score for the days in the"unexposed group below it. This visualization allows us to compare the two distributions quickly. Then we will overlay weighted histograms to demonstrate how these distributions differ after incorporating the weights. The {halfmoon} package includes a function `geom_mirror_histogram()` to assist with this visualization.
-
-```{r}
-#| label: fig-sd-mirror-hist-ate
-#| fig.cap: >
-#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect (ATE) weight.
-library(halfmoon)
-ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
-  geom_mirror_histogram(bins = 50) +
-  geom_mirror_histogram(
-    aes(fill = park_extra_magic_morning, weight = w_ate),
-    bins = 50,
-    alpha = 0.5
-  ) +
-  scale_y_continuous(labels = abs) +
-  labs(
-    x = "propensity score",
-    fill = "Extra Magic Morning"
-  )
-```
-
-We can learn a few things from @fig-sd-mirror-hist-ate.
-First, we can see that there are more "unexposed" days---days without extra magic hours in the morning---compared to "exposed" in the observed population.
-We can see this by examining the darker histograms, which show the distributions in the observed sample.
-Similarly, the exposed days are upweighted more substantially, as evidenced by the light blue we see on the top.
-We can also see that after weighting, the two distributions look similar; the shape of the blue weighted distribution on top looks comparable to the orange weighted distribution below.
-
-### Average treatment effect among the treated
-
-The target population to estimate the average treatment effect among the treated (ATT) is the *exposed* (treated) population.
-This causal estimand conditions on those in the exposed group:
-
-$$E[Y(1) - Y(0) | X = 1]$$
-
-Example research questions where the ATT is of interest could include "Should we stop our marketing campaign to those currently receiving it?" or "Should medical providers stop recommending treatment for those currently receiving it?" [@greifer2021choosing]
-
-The ATT is a common target estimand when using matching; here, all exposed observations are included and "matched" to control observations, some of which may be discarded.
-Alternatively, the ATT can be estimated via weighting.
-The ATT weight is estimated by:
-
-$$w_{ATT} = X + \frac{(1 - X)p}{1 - p}$$
-
-Let's add the ATT weights to the `seven_dwarfs_wts` data frame and look at their distribution.
+#| warning: false
+library(propensity)
 
-```{r}
-#| label: fig-sd-att-hist
-#| fig.cap : >
-#|   A histogram of the average treatment effect among the treated (ATT) weights. The range of the ATT weights in this example is more stable than the ATE weights: the range is much smaller.
-seven_dwarfs_wts <- seven_dwarfs_wts |>
+seven_dwarfs_9_with_ps <-
+  glm(
+    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
+    data = seven_dwarfs_9,
+    family = binomial()
+  ) |>
+  augment(type.predict = "response", data = seven_dwarfs_9)
+seven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>
   mutate(w_att = wt_att(.fitted, park_extra_magic_morning))
-
-ggplot(seven_dwarfs_wts, aes(w_att)) +
-  geom_histogram(bins = 50)
 ```
 
-Compared to the ATE weights, the weights in @fig-sd-att-hist look more *stable*; the distribution of the weights is not heavily skewed, and the range is small, from ground zero to a bit over one, with many at precisely one.
-These weights are exactly one for all days in the exposed group.
-Theoretically, for unexposed days these weights can range from zero to infinity.
-Still, because we have many more unexposed days compared to exposed in this particular sample, the range is much smaller, meaning the vast majority of unexposed days are "down-weighted" to match the variable distribution of the exposed days.
-Let's take a look at the weighted table to see this a bit more clearly.
+We can fit a *weighted* outcome model by using the `weights` argument. 
 
 ```{r}
-#| label: tbl-weighted-att
-#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Treated Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
-#| message: false
-seven_dwarfs_svy <- svydesign(
-  ids = ~1,
-  data = seven_dwarfs_wts,
-  weights = ~w_att
-)
-tbl_svysummary(
-  seven_dwarfs_svy,
-  by = park_extra_magic_morning,
-  include = c(park_ticket_season, park_close, park_temperature_high)
-) |>
-  add_overall(last = TRUE)
+lm(wait_minutes_posted_avg ~ park_extra_magic_morning, 
+   data = seven_dwarfs_9_with_wt,
+   weights = w_att) |>
+  tidy()
 ```
 
-We again achieve a balance between groups, but the target values in @tbl-weighted-att differ from the previous tables.
-Recall in our ATE weighted table (@tbl-weighted-gtsummary), when looking at the `wdw_ticket_season` variable, we saw the weighted percent in the peak season balanced close to 22%, the overall percent of peak days in the observed sample.
-We again see balance, but the weighted percent of peak season days is 30% in the exposed and unexposed groups, reflecting the percent in the unweighted exposure group.
-If you compare @tbl-weighted-att to our unweighted table (@tbl-unweighted-gtsummary), you might notice that the columns are most similar to the exposed column from the unweighted table.
-This is because the "target" population is the exposed group, the days with extra magic hours in the morning, so we're trying to make the comparison group (no magic hours) as similar to that group as possible.
-
-We can again create a mirrored histogram to observe the weighted psuedopopulation.
-
-```{r}
-#| label: fig-sd-mirror-hist-att
-#| fig.cap: >
-#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the treated (ATT) weight.
-ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
-  geom_mirror_histogram(bins = 50) +
-  geom_mirror_histogram(
-    aes(fill = park_extra_magic_morning, weight = w_att),
-    bins = 50,
-    alpha = 0.5
-  ) +
-  scale_y_continuous(labels = abs) +
-  labs(
-    x = "propensity score",
-    fill = "Extra Magic Morning"
-  )
-```
+Using weighting, we estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 6.2 minutes. 
+While this approach will get us the desired estimate for the point estimate, the default output using the `lm` function for the uncertainty (the standard errors and confidence intervals) are not correct.
 
-Since every day in the exposed group has a weight of 1, the distribution of the propensity scores (the dark bars above 0 on the graph) in @fig-sd-mirror-hist-att overlaps exactly with the weighted distribution overlaid in blue.
-In the unexposed population, we see that almost all observations are *down weighted*; the dark orange distribution is smaller than the distribution of the propensity scores and more closely matches the exposed distribution above it.
-The ATT is easier to estimate when there are many more observations in the unexposed group compared to the exposed one.
 
-### Average treatment effect among the unexposed
+## Estimating uncertainty
 
-The target population to estimate the average treatment effect among the unexposed (control) (ATU / ATC) is the *unexposed* (control) population.
-This causal estimand conditions on those in the unexposed group.
+There are three ways to estimate the uncertainty:
 
-$$E[Y(1) - Y(0) | X = 0]$$
+1. A bootstrap
+2. A sandwich estimator that only takes into account the outcome model
+3. A sandwich estimator that takes into account the propensity score model
 
-Example research questions where the ATU is of interest could include "Should we send our marketing campaign to those not currently receiving it?" or "Should medical providers begin recommending treatment for those not currently receiving it?" [@greifer2021choosing]
+The first option can be computationally intensive, but should get you the correct estimates.
+The second option is computationally the easiest, but tends to overestimate the variability. There are not many current solutions in R (aside from coding it up yourself) for the third; however, the `{PSW}` package will do this. 
 
-The ATU can be estimated via matching; here, all unexposed observations are included and "matched" to exposed observations, some of which may be discarded.
-Alternatively, the ATU can be estimated via weighting.
-The ATU weight is estimated by:
+### The bootstrap
 
-$$w_{ATU} = \frac{X(1-p)}{p}+ (1-X)$$ Let's add the ATU weights to the `seven_dwarfs_wts` data frame and look at their distribution.
+1. Create a function to run your analysis once on a sample of your data
 
 ```{r}
-#| label: fig-sd-atu-hist
-#| fig.cap : >
-#|   A histogram of the average treatment effect among the unexposed (ATU) weights. The range of the ATU weights in this example is very similar to the ATE weights.
-seven_dwarfs_wts <- seven_dwarfs_wts |>
-  mutate(w_atu = wt_atu(.fitted, park_extra_magic_morning))
-
-ggplot(seven_dwarfs_wts, aes(w_atu)) +
-  geom_histogram(bins = 50)
+fit_ipw <- function(split, ...) {
+  .df <- analysis(split)
+  
+  # fit propensity score model
+  propensity_model <- glm(
+    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
+    data = seven_dwarfs_9,
+    family = binomial()
+  ) 
+  
+  # calculate inverse probability weights
+  .df <- propensity_model |>
+    augment(type.predict = "response", data = .df) |>
+    mutate(wts = wt_att(.fitted, 
+                        park_extra_magic_morning,
+                        exposure_type = "binary"))
+  
+  # fit correctly bootstrapped ipw model
+  lm(wait_minutes_posted_avg ~ park_extra_magic_morning, 
+     data = .df, weights = wts) |>
+    tidy()
+}
 ```
 
-The distribution of the weights in @fig-sd-atu-hist weights looks very similar to what we saw with the ATE weights---several around 1, with a long tail.
-These weights will be 1 for all observations in the unexposed group; they can range from 0 to infinity for the exposed group.
-Because we have more observations in our unexposed group, our exposed observations are upweighted to match their distribution.
 
-Now let's take a look at the weighted table.
+2. Use {rsample} to bootstrap our causal effect
 
 ```{r}
-#| label: tbl-weighted-atu
-#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Unexposed Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
+#| message: false
+#| warning: false
+library(rsample)
 
-seven_dwarfs_svy <- svydesign(
-  ids = ~1,
-  data = seven_dwarfs_wts,
-  weights = ~w_atu
+# fit ipw model to bootstrapped samples
+bootstrapped_seven_dwarfs <- bootstraps(
+  seven_dwarfs_9, 
+  times = 1000, 
+  apparent = TRUE
 )
-tbl_svysummary(
-  seven_dwarfs_svy,
-  by = park_extra_magic_morning,
-  include = c(park_ticket_season, park_close, park_temperature_high)
-) |>
-  add_overall(last = TRUE)
-```
 
-Now the exposed column of @tbl-weighted-atu is weighted to match the unexposed column of the unweighted table.
+ipw_results <- bootstrapped_seven_dwarfs |> 
+  mutate(boot_fits = map(splits, fit_ipw)) 
 
-We can again create a mirrored histogram to observe the weighted psuedopopulation.
-
-```{r}
-#| label: fig-sd-mirror-hist-atu
-#| fig.cap: >
-#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the unexposed (ATU) weight.
-ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
-  geom_mirror_histogram(bins = 50) +
-  geom_mirror_histogram(
-    aes(fill = park_extra_magic_morning, weight = w_atu),
-    bins = 50,
-    alpha = 0.5
-  ) +
-  scale_y_continuous(labels = abs) +
-  labs(
-    x = "propensity score",
-    fill = "Extra Magic Morning"
-  )
+ipw_results
 ```
 
-The weights for the unexposed days are all 1, so the distribution of the propensity scores for this group exactly overlaps the weighted pseudopopulation in orange.
-The blue bars indicate that the exposed population is largely upweighted to match the unexposed distribution.
-
-### Average treatment effect among the evenly matchable
-
-The target population to estimate the average treatment effect among the evenly matchable (ATM) is the evenly matchable.
-This causal estimand conditions on those deemed "evenly matchable" by some distance metric.
 
-$$E[Y(1) - Y(0) | M_d = 1]$$
-
-Here $d$ denotes a distance measure, and $M_d=1$ indicates that a unit is evenly matchable under that distance measure.[@samuels2017aspects; @d2018improving] Example research questions where the ATM is of interest could include "Should those at clinical equipoise receive the exposure?" [@greifer2021choosing]
-
-The ATM is a common target estimand when using matching.
-Here, some exposed observations are "matched" to control observations, however some observations in both groups may be discarded.
-This is often done via a "caliper", where observations are only matched if they fall within a pre-specified caliper distance of each other.
-Alternatively, the ATM can be estimated via weighting.
-The ATM weight is estimated by:
-
-$$w_{ATM} = \frac{\min \{p, 1-p\}}{Xp + (1-X)(1-p)}$$
-
-Let's add the ATM weights to the `seven_dwarfs_wts` data frame and look at their distribution.
-
-```{r}
-#| label: fig-sd-atm-hist
-#| fig.cap: >
-#|   A histogram of the average treatment effect among the evenly matchable (ATM) weights for whether or not a day had extra magic hours. ATM weights can range from 0 to 1, so they are always stable.
-seven_dwarfs_wts <- seven_dwarfs_wts |>
-  mutate(w_atm = wt_atm(.fitted, park_extra_magic_morning))
-
-ggplot(seven_dwarfs_wts, aes(w_atm)) +
-  geom_histogram(bins = 50)
-```
-
-The distribution of the weights in @fig-sd-atm-hist looks very similar to what we saw with the ATT weights.
-That is because, in this particular sample, there are many more unexposed observations compared to exposed ones.
-These weights have the nice property that they range from 0 to 1, making them *always* stable, regardless of the imbalance between exposure groups.
-
-Now let's take a look at the weighted table.
-
-```{r}
-#| label: tbl-weighted-atm
-#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Evenly Matchable Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
-
-seven_dwarfs_svy <- svydesign(
-  ids = ~1,
-  data = seven_dwarfs_wts,
-  weights = ~w_atm
-)
-tbl_svysummary(
-  seven_dwarfs_svy,
-  by = park_extra_magic_morning,
-  include = c(park_ticket_season, park_close, park_temperature_high)
-) |>
-  add_overall(last = TRUE)
-```
-
-In this particular sample, the ATM weights resemble the ATT weights, so this table looks similar to the ATT weighted table.
-This similarity is not guaranteed, and the ATM pseudopopulation can look different from the overall, exposed, and unexposed unweighted populations.
-It's thus essential to examine the weighted table when using ATM weights to understand what population inference will ultimately be drawn on.
-
-We can again create a mirrored histogram to observe the ATM weighted psuedopopulation.
+Let's look at the results.
 
 ```{r}
-#| label: fig-sd-mirror-hist-atm
-#| fig.cap: >
-#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the evenly matchable (ATM) weight.
-ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
-  geom_mirror_histogram(bins = 50) +
-  geom_mirror_histogram(
-    aes(fill = park_extra_magic_morning, weight = w_atm),
-    bins = 50,
-    alpha = 0.5
-  ) +
-  scale_y_continuous(labels = abs) +
-  labs(
-    x = "propensity score",
-    fill = "Extra Magic Morning"
-  )
+ipw_results |>
+  mutate(
+    estimate = map_dbl(
+      boot_fits,
+      \(.fit) .fit |>
+        filter(term == "park_extra_magic_morning") |>
+        pull(estimate)
+    )
+  ) |>
+  ggplot(aes(estimate)) +
+  geom_histogram(bins = 30, fill = "#D55E00FF", color = "white", alpha = 0.8) + 
+  theme_minimal()
 ```
 
-Again, the ATM weights are bounded by 0 and 1, meaning that all observations will be *down weighted*.
-This removes the potential for finite sample bias due to large weights in small samples and has improved variance properties.
-
-### Average treatment effect among the overlap population
-
-The target population to estimate the average treatment effect among the overlap population (ATO) is the overlap -- this is very similar to the "even matchable" from above.
-Example research questions where the ATO is of interest are the same as those for the ATM, such as "Should those at clinical equipoise receive the exposure?" [@greifer2021choosing]
 
-These weights, again, are pretty similar to the ATM weights but are slightly attenuated, yielding improved variance properties.
-
-The ATO weight is estimated by:
-
-$$w_{ATO} = X(1-p) + (1-X)p$$
-
-Let's add the ATO weights to the `seven_dwarfs_wts` data frame and look at their distribution.
+3. Pull out the causal effect
 
 ```{r}
-#| label: fig-sd-ato-hist
-#| fig.cap: >
-#|   A histogram of the average treatment effect among the overlap population (ATO) weights for whether or not a day had extra magic hours. Like ATM weights, ATO weights can range from 0 to 1, so they are always stable. ATO weights also have improved variance properties compared to the ATM weights.
-seven_dwarfs_wts <- seven_dwarfs_wts |>
-  mutate(w_ato = wt_ato(.fitted, park_extra_magic_morning))
-
-ggplot(seven_dwarfs_wts, aes(w_ato)) +
-  geom_histogram(bins = 50)
+# get t-based CIs
+boot_estimate <- int_t(ipw_results, boot_fits) |> 
+  filter(term == "park_extra_magic_morning")
+boot_estimate
 ```
 
-Like the ATM weights, the ATO weights are bounded by 0 and 1, making them more stable than the ATE, ATT, and ATU, regardless of the exposed and unexposed imbalance.
+We estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is `r round(boot_estimate$.estimate, 1)` minutes, 95% CI (`r round(boot_estimate$.lower, 1)`, `r round(boot_estimate$.upper, 1)`). 
 
-::: {.callout-warning}
-## Finite sample bias - revisited
+### The outcome model sandwich
 
-Let's revisit our finite sample bias simulation, adding in the ATO weights to examine whether they are impacted by this potential for bias.
+There are two ways to get the sandwich estimator. The first is to use the same weighted outcome model as above along with the `{sandwich}` package. Using the `sandwich` function, we can get the robust estimate for the variance for the parameter of interest, as shown below.
 
 ```{r}
-#| label: sim-finite-sample-bias-2
-#| warning: false
 #| message: false
-#| cache: true
-sim <- function(n) {
-  ## create a simulated dataset
-  finite_sample <- tibble(
-    z = rnorm(n),
-    x = case_when(
-      0.5 + z + rnorm(n) > 0 ~ 1,
-      TRUE ~ 0
-    ),
-    y = z + rnorm(n)
-  )
-  finite_sample_wts <- glm(
-    x ~ z,
-    data = finite_sample, family = binomial("probit")
-  ) |>
-    augment(newdata = finite_sample, type.predict = "response") |>
-    mutate(
-      wts_ate = wt_ate(.fitted, x),
-      wts_ato = wt_ato(.fitted, x)
-    )
-  bias <- finite_sample_wts |>
-    summarize(
-      effect_ate = sum(y * x * wts_ate) / sum(x * wts_ate) -
-        sum(y * (1 - x) * wts_ate) / sum((1 - x) * wts_ate),
-      effect_ato = sum(y * x * wts_ato) / sum(x * wts_ato) -
-        sum(y * (1 - x) * wts_ato) / sum((1 - x) * wts_ato)
-    )
-  tibble(
-    n = n,
-    bias = c(bias$effect_ate, bias$effect_ato),
-    weight = c("ATE", "ATO")
-  )
-}
-
-## Examine 5 different sample sizes, simulate each 1000 times
-set.seed(1)
-finite_sample_sims <- map_df(rep(c(50, 100, 500, 1000, 5000, 10000),
-  each = 1000
-), sim)
+#| warning: false
+library(sandwich)
+weighted_mod <- lm(wait_minutes_posted_avg ~ park_extra_magic_morning, 
+   data = seven_dwarfs_9_with_wt,
+   weights = w_att) 
 
-bias <- finite_sample_sims %>%
-  group_by(n, weight) %>%
-  summarise(bias = mean(bias))
+sandwich(weighted_mod)
 ```
 
-Let's plot our results:
+Here, our robust variance estimate is `r round(sandwich(weighted_mod)[2,2], 3)`. We can then use this to construct a robust confidence interval.
 
 ```{r}
-#| label: fig-finite-sample-bias-2
-#| fig-cap: "Finite sample bias with ATE weights (orange) and ATO weights (blue) created using correctly specified propensity score model, varying the sample size from n = 50 to n = 10,000"
-ggplot(bias, aes(x = n, y = bias, color = weight)) +
-  geom_point() +
-  geom_line() +
-  geom_hline(yintercept = 0, lty = 2)
+robust_var <- sandwich(weighted_mod)[2, 2]
+point_est <- coef(weighted_mod)[2]
+lb <- point_est - 1.96 * sqrt(robust_var)
+ub <- point_est + 1.96 * sqrt(robust_var)
+lb
+ub
 ```
+We estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is `r round(point_est, 1)` minutes, 95% CI (`r round(lb, 1)`, `r round(ub, 1)`). 
 
-Notice here the estimate using the ATO weights is almost immediately unbiased, even in fairly small samples.
-:::
-
-Now let's take a look at the weighted table.
+Alternatively, we could fit the model using the `{survey}` package. To do this, we need to create a design object, like we did when fitting weighted tables.
 
 ```{r}
-#| label: tbl-weighted-ato
-#| tbl-cap: A descriptive table of Extra Magic Morning hours weighted by the Average Treatment Effect among the Overlap Population Weights. This table shows the distributions of these variables in the psuedopopulation created by these weights.
+#| message: false
+#| warning: false
+library(survey)
 
-seven_dwarfs_svy <- svydesign(
+des <- svydesign(
   ids = ~1,
-  data = seven_dwarfs_wts,
-  weights = ~w_ato
+  weights = ~w_att,
+  data = seven_dwarfs_9_with_wt
 )
-tbl_svysummary(
-  seven_dwarfs_svy,
-  by = park_extra_magic_morning,
-  include = c(park_ticket_season, park_close, park_temperature_high)
-) |>
-  add_overall(last = TRUE)
 ```
 
-Like the ATM weights, the ATO pseudopopulation may not resemble the overall, exposed, or unexposed unweighted populations, so it is essential to examine these weighted tables to understand the population on whom we will draw inferences.
+Then we can use `svyglm` to fit the outcome model.
 
-We can again create a mirrored histogram to observe the ATO weighted psuedopopulation.
+```{r}
+svyglm(wait_minutes_posted_avg ~ park_extra_magic_morning, des) |>
+  tidy(conf.int = TRUE)
+```
+### Sandwich estimator that takes into account the propensity score model
+
+The correct sandwich estimator will also take into account the propensity score model. The `{PSW}` will allow us to do this. This package has some quirks, for example it doesn't work well with categorical variables, so we need to create dummy variables for `park_ticket_season` to pass into the model. *Actually, the code below isn't working because it seems there is a bug in the package. Stay tuned!*
 
 ```{r}
-#| label: fig-sd-mirror-hist-ato
-#| fig.cap: >
-#|   A mirrored histogram of the distribution of propensity scores between exposure groups. The dark bars represent the unweighted distribution, and the colored bars represent the distribution weighted by the average treatment effect among the overlap population (ATO) weight.
-ggplot(seven_dwarfs_wts, aes(.fitted, group = park_extra_magic_morning)) +
-  geom_mirror_histogram(bins = 50) +
-  geom_mirror_histogram(
-    aes(fill = park_extra_magic_morning, weight = w_ato),
-    bins = 50,
-    alpha = 0.5
-  ) +
-  scale_y_continuous(labels = abs) +
-  labs(
-    x = "propensity score",
-    fill = "Extra Magic Morning"
+#| eval: false
+library(PSW)
+
+seven_dwarfs_9 <- seven_dwarfs_9 |>
+  mutate(park_ticket_season_regular = ifelse(park_ticket_season == "regular", 1, 0),
+         park_ticket_season_value = ifelse(park_ticket_season == "value", 1, 0)
   )
+psw(data = seven_dwarfs_9, 
+    form.ps = "park_extra_magic_morning ~ park_ticket_season_regular + park_ticket_season_value + park_close + park_temperature_high",
+    weight = "ATT",
+    wt = TRUE,
+    out.var = "wait_minutes_posted_avg")
 ```
 
-@fig-sd-mirror-hist-ato looks similar to the ATM pseudopopulation, with slight attenuation, as evidenced by the increased down weighting in the exposed population.
-We prefer using the ATO weights when the overlap (or evenly matchable) population is the target, as they have improved variance properties.
-Another benefit is that any variables included in the propensity score model (if fit via logistic regression) will be perfectly balanced on the mean.
-We will discuss this further in @sec-eval-ps-model.
-
-## Choosing estimands
-
-Several factors need to be considered when choosing an estimand for a given analysis.
-First an foremost, the research question at hand will drive this decision.
-For example, returing to the Touring Plans data, suppose Disney was interested in assessing whether they should add additional extra magic hours on days that don't currently have them---here, the ATU will be the estimand of interest.
-Other considerations include the available sample size and the distribution of the exposed and unexposed observations (i.e. might finite sample bias be an issue?).
-Below is a table summarizing the estimands and methods for estimating them (including R functions and example code), along with sample questions extended from Table 2 in @greifer2021choosing.
-
-+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
-| Estimand | Target population                | Example Research Question                                                                                                                                  | Matching Method                               | Weighting Method       |
-+==========+==================================+============================================================================================================================================================+===============================================+========================+
-| ATE      | Full population                  | Should we decide whether to have extra magic hours *all* mornings to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                       | Full matching                                 | ATE Weights            |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  | Should a specific policy be applied to all eligible observations?                                                                                          | Fine stratification                           | `propensity::wt_ate()` |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = "ATE")`       | |                      |
-+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
-| ATT      | Exposed (treated) observations   | Should we stop extra magic hours to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                                                        | Pair matching without a caliper               | ATT weights            |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  | Should we stop our marketing campaign to those currently receiving it?                                                                                     | Full matching                                 | `propensity::wt_att()` |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  | Should medical providers stop recommending treatment for those currently receiving it?                                                                     | Fine stratification                           |                        |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = "ATT")`       |                        |
-+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
-| ATU      | Unexposed (control) observations | Should we add extra magic hours for all days to change the wait time for Seven Dwarfs Mine Train between 5-6pm?                                            | Pair matching without a caliper               | ATU weights            |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  | Should we extend our marketing campaign to those not receiving it?                                                                                         | Full matching                                 | `propensity::wt_atu()` |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  | Should medical providers extend treatment to those not currently receiving it?                                                                             | Fine stratification                           |                        |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, estimand = "ATC")`       |                        |
-+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
-| ATM      | Evenly matchable                 | Are there some days we should change whether we are offering extra magic hours in order to change the wait time for Seven Dwarfs Mine Train between 5-6pm? | Caliper matching                              | ATM weights            |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  | Is there an effect of the exposure for some observations?                                                                                                  | `MatchIt::matchit(…, caliper = 0.1)`          | `propensity::wt_atm()` |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  | Should those at clinical equipoise receive treatment?                                                                                                      | Coarsened exact matching                      |                        |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  |                                                                                                                                                            | Cardinality matching                          |                        |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  |                                                                                                                                                            | `MatchIt::matchit(…, method = "cardinality")` |                        |
-+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
-| ATO      | Overlap population               | Same as ATM                                                                                                                                                |                                               | ATO weights            |
-|          |                                  |                                                                                                                                                            |                                               |                        |
-|          |                                  |                                                                                                                                                            |                                               | `propensity::wt_ato()` |
-+----------+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------+------------------------+
-
diff --git a/chapters/chapter-11_cache/html/__packages b/chapters/chapter-11_cache/html/__packages
deleted file mode 100644
index a9a273e..0000000
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@@ -1,19 +0,0 @@
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diff --git a/chapters/chapter-12.qmd b/chapters/chapter-12.qmd
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+++ b/chapters/chapter-12.qmd
@@ -1,224 +1,284 @@
-# Fitting the outcome model {#sec-outcome-model}
+# Continuous exposures
 
 {{< include 00-setup.qmd >}}
 
-## Using matched data sets
+## Calculating propensity scores for continuous exposures
 
-When fitting an outcome model on matched data sets, we can simply subset the original data to only those who were matched and then fit a model on these data as we would otherwise. For example, re-performing the matching as we did in @sec-using-ps, we can extract the matched observations in a dataset called `matched_data` as follows.
+Propensity scores generalize to many other types of exposures, including continuous exposures.
+At its heart, the workflow is the same: we fit a model where the exposure is the outcome and then use that model to weight a second outcome model.
+For continuous exposures, linear regression is the simplest way to create propensities.
+Instead of probabilities, we use the cumulative density function.
+Then, we use this density to weight the outcome model.
+
+Let's take a look at an example.
+In the `touringplans` data set, we have information about the posted waiting times for rides.
+We also have a limited amount of data on the observed actual times.
+The question we will consider is this: Do posted wait times for the Seven Dwarfs Mine Train at 8 am affect actual wait times at 9 am?
+Here's our DAG:
 
 ```{r}
+#| label: fig-dag-avg-wait
+#| echo: false
 #| message: false
 #| warning: false
-library(broom)
-library(touringplans)
-library(MatchIt)
-
-seven_dwarfs_9 <- seven_dwarfs_train_2018 |> 
-  filter(wait_hour == 9) 
+#| fig.cap: >
+#|    Proposed DAG for the relationship between posted wait
+#|    in the morning at a particular park and the average
+#|    wait time between 5 pm and 6 pm.
+
+library(tidyverse)
+library(ggdag)
+library(ggokabeito)
+
+coord_dag <- list(
+  x = c(Season = -1, close = -1, weather = -2, extra = 0, x = 1, y = 2),
+  y = c(Season = -1, close = 1, weather = 0, extra = 0, x = 0, y = 0)
+)
 
-m <- matchit(
-  park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
-  data = seven_dwarfs_9
+labels <- c(
+  extra = "Extra Magic Morning",
+  x = "Average posted wait ",
+  y = "Average acutal wait",
+  Season = "Ticket Season",
+  weather = "Historic high temperature",
+  close = "Time park closed"
 )
-matched_data <- get_matches(m)
+
+dagify(
+  y ~ x + close + Season + weather + extra,
+  x ~ weather + close + Season + extra,
+  extra ~ weather + close + Season,
+  coords = coord_dag,
+  labels = labels,
+  exposure = "x",
+  outcome = "y"
+) |>
+  tidy_dagitty() |>
+  node_status() |>
+  ggplot(
+    aes(x, y, xend = xend, yend = yend, color = status)
+  ) +
+  geom_dag_edges_arc(curvature = c(rep(0, 7), .2, 0, .2, .2, 0), edge_colour = "grey70") +
+  geom_dag_point() +
+  geom_dag_label_repel(seed = 1602) +
+  scale_color_okabe_ito(na.value = "grey90") +
+  theme_dag() +
+  theme(
+    legend.position = "none",
+    axis.text.x = element_text()
+  ) +
+  coord_cartesian(clip = "off") +
+  scale_x_continuous(
+    limits = c(-2.25, 2.25),
+    breaks = c(-2, -1, 0, 1, 2),
+    labels = c(
+      "\n(one year ago)",
+      "\n(6 months ago)",
+      "\n(3 months ago)",
+      "8am-9am\n(Today)",
+      "9am-10am\n(Today)"
+    )
+  )
 ```
 
-We can then fit the outcome model on this data. For this analysis, we are interested in the impact of extra magic morning hours on the average posted wait time between 9 and 10am. The linear model below will estimate this in the matched cohort.
+In @fig-dag-avg-wait, we're assuming that our primary confounders are when the park closes, historic high temperatures, whether or not the ride has extra magic morning hours, and the ticket season.
+This is the only minimal adjustment set in the DAG, as well.
+The confounders precede the exposure and outcome, and (by definition) the exposure precedes the outcome.
+The average posted wait time is, in theory, a manipulable exposure because the park could post a time different from what they expect.
+The adjustment set
+
+The model is similar to the binary exposure case, but we need to use linear regression, as the posted time is a continuous variable.
+Since we're not using probabilities, we'll calculate denominators for our weights from a normal density.
+We then calculate the denominator using the `dnorm()` function, which calculates the normal density for the `exposure`, using `.fitted` as the mean and `mean(.sigma)` as the SD.
 
 ```{r}
-lm(wait_minutes_posted_avg ~ park_extra_magic_morning, data = matched_data) |>
-  tidy(conf.int = TRUE)
+#| eval: false
+lm(
+  exposure ~ confounder_1 + confounder_2,
+  data = df
+) |>
+  augment(data = df) |>
+  mutate(
+    denominator = dnorm(exposure, .fitted, mean(.sigma, na.rm = TRUE))
+  )
 ```
 
-Recall that by default `{MatchIt}` estimates an average treatment effect among the treated. This means among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 7.9 minutes (95% CI: 1.2-14.5). 
+## Diagnostics and stabilization
 
-## Using weights in outcome models
+Continuous exposure weights, however, are very sensitive to modeling choices.
+One problem, in particular, is the existence of extreme weights, an issue that can also affect other types of exposures.
+When some observations have extreme weights, the propensities are *destabilized,* which results in wider confidence intervals.
+We can stabilize them using the marginal distribution of the exposure.
+A common way to calculate the marginal distribution for propensity scores is to use a regression model with no predictors.
 
-Now let's use propensity score weights to estimate this same estimand. We will use the ATT weights so the analysis matches that for matching above.
+::: callout-caution
+Extreme weights destabilize estimates, resulting in wider confidence intervals.
+Extreme weights can be an issue for any time of weight (including those for binary and other types of exposures) that is not bounded.
+Bounded weights like the ATO (which are bounded to 0 and 1) do not have this problem, however---one of their many benefits.
+:::
 
 ```{r}
-#| message: false
-#| warning: false
-library(propensity)
+#| eval: false
+# for continuous exposures
+lm(
+  exposure ~ 1,
+  data = df
+) |>
+  augment(data = df) |>
+  transmute(
+    numerator = dnorm(exposure, .fitted, mean(.sigma, na.rm = TRUE))
+  )
 
-seven_dwarfs_9_with_ps <-
-  glm(
-    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
-    data = seven_dwarfs_9,
-    family = binomial()
-  ) |>
-  augment(type.predict = "response", data = seven_dwarfs_9)
-seven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>
-  mutate(w_att = wt_att(.fitted, park_extra_magic_morning))
+# for binary exposures
+glm(
+  exposure ~ 1,
+  data = df,
+  family = binomial()
+) |>
+  augment(type.predict = "response", data = df) |>
+  select(numerator = .fitted)
 ```
 
-We can fit a *weighted* outcome model by using the `weights` argument. 
+Then, rather than inverting them, we calculate the weights as `numerator / denominator`.
+Let's try it out on our posted wait times example.
+First, let's wrangle our data to address our question: do posted wait times at 8 affect actual weight times at 9?
+We'll join the baseline data (all covariates and posted wait time at 8) with the outcome (average actual time).
+We also have a lot of missingness for `wait_minutes_actual_avg`, so we'll drop unobserved values for now.
 
 ```{r}
-lm(wait_minutes_posted_avg ~ park_extra_magic_morning, 
-   data = seven_dwarfs_9_with_wt,
-   weights = w_att) |>
-  tidy()
-```
-
-Using weighting, we estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is 6.2 minutes. 
-While this approach will get us the desired estimate for the point estimate, the default output using the `lm` function for the uncertainty (the standard errors and confidence intervals) are not correct.
-
-
-## Estimating uncertainty
-
-There are three ways to estimate the uncertainty:
-
-1. A bootstrap
-2. A sandwich estimator that only takes into account the outcome model
-3. A sandwich estimator that takes into account the propensity score model
-
-The first option can be computationally intensive, but should get you the correct estimates.
-The second option is computationally the easiest, but tends to overestimate the variability. There are not many current solutions in R (aside from coding it up yourself) for the third; however, the `{PSW}` package will do this. 
-
-### The bootstrap
+library(tidyverse)
+library(touringplans)
+eight <- seven_dwarfs_train_2018 |>
+  filter(wait_hour == 8) |>
+  select(-wait_minutes_actual_avg)
 
-1. Create a function to run your analysis once on a sample of your data
+nine <- seven_dwarfs_train_2018 |>
+  filter(wait_hour == 9) |>
+  select(park_date, wait_minutes_actual_avg)
 
-```{r}
-fit_ipw <- function(split, ...) {
-  .df <- analysis(split)
-  
-  # fit propensity score model
-  propensity_model <- glm(
-    park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
-    data = seven_dwarfs_9,
-    family = binomial()
-  ) 
-  
-  # calculate inverse probability weights
-  .df <- propensity_model |>
-    augment(type.predict = "response", data = .df) |>
-    mutate(wts = wt_att(.fitted, 
-                        park_extra_magic_morning,
-                        exposure_type = "binary"))
-  
-  # fit correctly bootstrapped ipw model
-  lm(wait_minutes_posted_avg ~ park_extra_magic_morning, 
-     data = .df, weights = wts) |>
-    tidy()
-}
+wait_times <- eight |>
+  left_join(nine, by = "park_date") |>
+  drop_na(wait_minutes_actual_avg)
 ```
 
-
-2. Use {rsample} to bootstrap our causal effect
+First, let's calculate our denominator model.
+We'll fit a model using `lm()` for `wait_minutes_posted_avg` with our covariates, then use the fitted predictions of `wait_minutes_posted_avg` (`.fitted`) to calculate the density using `dnorm()`.
 
 ```{r}
-#| message: false
-#| warning: false
-library(rsample)
-
-# fit ipw model to bootstrapped samples
-bootstrapped_seven_dwarfs <- bootstraps(
-  seven_dwarfs_9, 
-  times = 1000, 
-  apparent = TRUE
+library(broom)
+denominator_model <- lm(
+  wait_minutes_posted_avg ~
+    park_close + park_extra_magic_morning + park_temperature_high + park_ticket_season,
+  data = wait_times
 )
 
-ipw_results <- bootstrapped_seven_dwarfs |> 
-  mutate(boot_fits = map(splits, fit_ipw)) 
-
-ipw_results
-```
-
-
-Let's look at the results.
-
-```{r}
-ipw_results |>
+denominators <- denominator_model |>
+  augment(data = wait_times) |>
   mutate(
-    estimate = map_dbl(
-      boot_fits,
-      \(.fit) .fit |>
-        filter(term == "park_extra_magic_morning") |>
-        pull(estimate)
+    denominator = dnorm(
+      wait_minutes_posted_avg,
+      .fitted,
+      mean(.sigma, na.rm = TRUE)
     )
   ) |>
-  ggplot(aes(estimate)) +
-  geom_histogram(bins = 30, fill = "#D55E00FF", color = "white", alpha = 0.8) + 
-  theme_minimal()
+  select(park_date, denominator, .fitted)
 ```
 
-
-3. Pull out the causal effect
+When we only use the inverted values of `denominator`, we end up with several extreme weights:
 
 ```{r}
-# get t-based CIs
-boot_estimate <- int_t(ipw_results, boot_fits) |> 
-  filter(term == "park_extra_magic_morning")
-boot_estimate
+#| label: fig-hist-sd-unstable
+#| fig.cap: >
+#|   A histogram of the inverse probability weights for posted waiting time. Weights for continuous exposures are prone to extreme values, which can unstabilize estimates and variance.
+denominators |>
+  mutate(wts = 1 / denominator) |>
+  ggplot(aes(wts)) +
+  geom_histogram(fill = "#E69F00", color = "white", bins = 50) +
+  scale_x_log10(name = "weights")
 ```
 
-We estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is `r round(boot_estimate$.estimate, 1)` minutes, 95% CI (`r round(boot_estimate$.lower, 1)`, `r round(boot_estimate$.upper, 1)`). 
+In @fig-hist-sd-unstable, we see several weights over 100 and one over 10,000; these extreme weights will put undue stress on specific points, complicating the results we will estimate.
 
-### The outcome model sandwich
-
-There are two ways to get the sandwich estimator. The first is to use the same weighted outcome model as above along with the `{sandwich}` package. Using the `sandwich` function, we can get the robust estimate for the variance for the parameter of interest, as shown below.
+Let's now fit the marginal density to use for stabilized weights:
 
 ```{r}
-#| message: false
-#| warning: false
-library(sandwich)
-weighted_mod <- lm(wait_minutes_posted_avg ~ park_extra_magic_morning, 
-   data = seven_dwarfs_9_with_wt,
-   weights = w_att) 
+numerator_model <- lm(
+  wait_minutes_posted_avg ~ 1,
+  data = wait_times
+)
 
-sandwich(weighted_mod)
+numerators <- numerator_model |>
+  augment(data = wait_times) |>
+  mutate(
+    numerator = dnorm(
+      wait_minutes_posted_avg,
+      .fitted,
+      mean(.sigma, na.rm = TRUE)
+    )
+  ) |>
+  select(park_date, numerator)
 ```
 
-Here, our robust variance estimate is `r round(sandwich(weighted_mod)[2,2], 3)`. We can then use this to construct a robust confidence interval.
+We also need to join the fitted values back to our original data set by date, then calculate the stabilized weights (`swts`) using `numerator / denominator`.
 
 ```{r}
-robust_var <- sandwich(weighted_mod)[2, 2]
-point_est <- coef(weighted_mod)[2]
-lb <- point_est - 1.96 * sqrt(robust_var)
-ub <- point_est + 1.96 * sqrt(robust_var)
-lb
-ub
+wait_times_wts <- wait_times |>
+  left_join(numerators, by = "park_date") |>
+  left_join(denominators, by = "park_date") |>
+  mutate(swts = numerator / denominator)
 ```
-We estimate that among days that have extra magic hours, the expected impact of having extra magic hours on the average posted wait time between 9 and 10am is `r round(point_est, 1)` minutes, 95% CI (`r round(lb, 1)`, `r round(ub, 1)`). 
 
-Alternatively, we could fit the model using the `{survey}` package. To do this, we need to create a design object, like we did when fitting weighted tables.
+The stabilized weights are much less extreme.
+Stabilized weights should have a mean close to 1 (in this example, it is `round(mean(wait_times_wts$swts), digits = 2)`); when that is the case, then the pseudo-population (that is, the equivalent number of observations after weighting) is equal to the original sample size.
+If the mean is far from 1, we may have issues with model misspecification or positivity violations [@hernan2021].
 
 ```{r}
-#| message: false
-#| warning: false
-library(survey)
-
-des <- svydesign(
-  ids = ~1,
-  weights = ~w_att,
-  data = seven_dwarfs_9_with_wt
-)
+#| label: fig-hist-sd-stable
+#| fig.cap: >
+#|   A histogram of the stabilized inverse probability weights for posted waiting time. These weights are much more reasonable and will allow the outcome model to behave better.
+ggplot(wait_times_wts, aes(swts)) +
+  geom_histogram(fill = "#E69F00", color = "white", bins = 50) +
+  scale_x_log10(name = "weights")
 ```
 
-Then we can use `svyglm` to fit the outcome model.
+When we compare the exposure---average posted wait times---to the standardized weights, we still have one exceptionally high weight.
+Is this a problem, or is this a valid data point?
 
 ```{r}
-svyglm(wait_minutes_posted_avg ~ park_extra_magic_morning, des) |>
-  tidy(conf.int = TRUE)
+#| label: fig-stabilized-wts-scatter
+#| fig.cap: >
+#|   A scatter of the stabilized inverse probability weights for posted waiting time vs. posted waiting times. Days with more values of `wait_minutes_posted_avg` farther from the mean appear to be downweighted, with a few exceptions. The most unusual weight is for June 23, 2018.
+ggplot(wait_times_wts, aes(wait_minutes_posted_avg, swts)) +
+  geom_point(size = 3, color = "grey80", alpha = 0.7) +
+  geom_point(
+    data = function(x) filter(x, swts > 10),
+    color = "firebrick",
+    size = 3
+  ) +
+  geom_text(
+    data = function(x) filter(x, swts > 10),
+    aes(label = park_date),
+    size = 5,
+    hjust = 0,
+    nudge_x = -15.5,
+    color = "firebrick"
+  ) +
+  scale_y_log10() +
+  labs(x = "Average Posted Wait", y = "Stabilized Weights")
 ```
-### Sandwich estimator that takes into account the propensity score model
-
-The correct sandwich estimator will also take into account the propensity score model. The `{PSW}` will allow us to do this. This package has some quirks, for example it doesn't work well with categorical variables, so we need to create dummy variables for `park_ticket_season` to pass into the model. *Actually, the code below isn't working because it seems there is a bug in the package. Stay tuned!*
 
 ```{r}
-#| eval: false
-library(PSW)
-
-seven_dwarfs_9 <- seven_dwarfs_9 |>
-  mutate(park_ticket_season_regular = ifelse(park_ticket_season == "regular", 1, 0),
-         park_ticket_season_value = ifelse(park_ticket_season == "value", 1, 0)
-  )
-psw(data = seven_dwarfs_9, 
-    form.ps = "park_extra_magic_morning ~ park_ticket_season_regular + park_ticket_season_value + park_close + park_temperature_high",
-    weight = "ATT",
-    wt = TRUE,
-    out.var = "wait_minutes_posted_avg")
+wait_times_wts |>
+  filter(swts > 10) |>
+  select(
+    park_date, wait_minutes_posted_avg, .fitted,
+    park_close, park_extra_magic_morning,
+    park_temperature_high, park_ticket_season
+  ) |>
+  knitr::kable()
 ```
 
+Our model predicted a much lower posted wait time than observed, so this date was upweighted.
+We don't know why the posted time was so high (the actual time was much lower), but we did find an artist rendering from that date of [Pluto digging for Seven Dwarfs Mine Train treasure](https://disneyparks.disney.go.com/blog/2018/06/disney-doodle-pluto-sniffs-out-fun-at-seven-dwarfs-mine-train/).
+
+## Fitting the outcome model for continuous exposures
diff --git a/chapters/chapter-13.qmd b/chapters/chapter-13.qmd
index 9c413aa..3c0cfcd 100644
--- a/chapters/chapter-13.qmd
+++ b/chapters/chapter-13.qmd
@@ -1,284 +1,13 @@
-# Continuous exposures
+# Categorical exposures
 
 {{< include 00-setup.qmd >}}
 
-## Calculating propensity scores for continuous exposures
-
-Propensity scores generalize to many other types of exposures, including continuous exposures.
-At its heart, the workflow is the same: we fit a model where the exposure is the outcome and then use that model to weight a second outcome model.
-For continuous exposures, linear regression is the simplest way to create propensities.
-Instead of probabilities, we use the cumulative density function.
-Then, we use this density to weight the outcome model.
-
-Let's take a look at an example.
-In the `touringplans` data set, we have information about the posted waiting times for rides.
-We also have a limited amount of data on the observed actual times.
-The question we will consider is this: Do posted wait times for the Seven Dwarfs Mine Train at 8 am affect actual wait times at 9 am?
-Here's our DAG:
-
-```{r}
-#| label: fig-dag-avg-wait
-#| echo: false
-#| message: false
-#| warning: false
-#| fig.cap: >
-#|    Proposed DAG for the relationship between posted wait
-#|    in the morning at a particular park and the average
-#|    wait time between 5 pm and 6 pm.
-
-library(tidyverse)
-library(ggdag)
-library(ggokabeito)
-
-coord_dag <- list(
-  x = c(Season = -1, close = -1, weather = -2, extra = 0, x = 1, y = 2),
-  y = c(Season = -1, close = 1, weather = 0, extra = 0, x = 0, y = 0)
-)
-
-labels <- c(
-  extra = "Extra Magic Morning",
-  x = "Average posted wait ",
-  y = "Average acutal wait",
-  Season = "Ticket Season",
-  weather = "Historic high temperature",
-  close = "Time park closed"
-)
-
-dagify(
-  y ~ x + close + Season + weather + extra,
-  x ~ weather + close + Season + extra,
-  extra ~ weather + close + Season,
-  coords = coord_dag,
-  labels = labels,
-  exposure = "x",
-  outcome = "y"
-) |>
-  tidy_dagitty() |>
-  node_status() |>
-  ggplot(
-    aes(x, y, xend = xend, yend = yend, color = status)
-  ) +
-  geom_dag_edges_arc(curvature = c(rep(0, 7), .2, 0, .2, .2, 0), edge_colour = "grey70") +
-  geom_dag_point() +
-  geom_dag_label_repel(seed = 1602) +
-  scale_color_okabe_ito(na.value = "grey90") +
-  theme_dag() +
-  theme(
-    legend.position = "none",
-    axis.text.x = element_text()
-  ) +
-  coord_cartesian(clip = "off") +
-  scale_x_continuous(
-    limits = c(-2.25, 2.25),
-    breaks = c(-2, -1, 0, 1, 2),
-    labels = c(
-      "\n(one year ago)",
-      "\n(6 months ago)",
-      "\n(3 months ago)",
-      "8am-9am\n(Today)",
-      "9am-10am\n(Today)"
-    )
-  )
-```
-
-In @fig-dag-avg-wait, we're assuming that our primary confounders are when the park closes, historic high temperatures, whether or not the ride has extra magic morning hours, and the ticket season.
-This is the only minimal adjustment set in the DAG, as well.
-The confounders precede the exposure and outcome, and (by definition) the exposure precedes the outcome.
-The average posted wait time is, in theory, a manipulable exposure because the park could post a time different from what they expect.
-The adjustment set
-
-The model is similar to the binary exposure case, but we need to use linear regression, as the posted time is a continuous variable.
-Since we're not using probabilities, we'll calculate denominators for our weights from a normal density.
-We then calculate the denominator using the `dnorm()` function, which calculates the normal density for the `exposure`, using `.fitted` as the mean and `mean(.sigma)` as the SD.
-
-```{r}
-#| eval: false
-lm(
-  exposure ~ confounder_1 + confounder_2,
-  data = df
-) |>
-  augment(data = df) |>
-  mutate(
-    denominator = dnorm(exposure, .fitted, mean(.sigma, na.rm = TRUE))
-  )
-```
-
-## Diagnostics and stabilization
-
-Continuous exposure weights, however, are very sensitive to modeling choices.
-One problem, in particular, is the existence of extreme weights, an issue that can also affect other types of exposures.
-When some observations have extreme weights, the propensities are *destabilized,* which results in wider confidence intervals.
-We can stabilize them using the marginal distribution of the exposure.
-A common way to calculate the marginal distribution for propensity scores is to use a regression model with no predictors.
-
-::: callout-caution
-Extreme weights destabilize estimates, resulting in wider confidence intervals.
-Extreme weights can be an issue for any time of weight (including those for binary and other types of exposures) that is not bounded.
-Bounded weights like the ATO (which are bounded to 0 and 1) do not have this problem, however---one of their many benefits.
-:::
-
-```{r}
-#| eval: false
-# for continuous exposures
-lm(
-  exposure ~ 1,
-  data = df
-) |>
-  augment(data = df) |>
-  transmute(
-    numerator = dnorm(exposure, .fitted, mean(.sigma, na.rm = TRUE))
-  )
-
-# for binary exposures
-glm(
-  exposure ~ 1,
-  data = df,
-  family = binomial()
-) |>
-  augment(type.predict = "response", data = df) |>
-  select(numerator = .fitted)
-```
-
-Then, rather than inverting them, we calculate the weights as `numerator / denominator`.
-Let's try it out on our posted wait times example.
-First, let's wrangle our data to address our question: do posted wait times at 8 affect actual weight times at 9?
-We'll join the baseline data (all covariates and posted wait time at 8) with the outcome (average actual time).
-We also have a lot of missingness for `wait_minutes_actual_avg`, so we'll drop unobserved values for now.
-
-```{r}
-library(tidyverse)
-library(touringplans)
-eight <- seven_dwarfs_train_2018 |>
-  filter(wait_hour == 8) |>
-  select(-wait_minutes_actual_avg)
-
-nine <- seven_dwarfs_train_2018 |>
-  filter(wait_hour == 9) |>
-  select(park_date, wait_minutes_actual_avg)
-
-wait_times <- eight |>
-  left_join(nine, by = "park_date") |>
-  drop_na(wait_minutes_actual_avg)
-```
-
-First, let's calculate our denominator model.
-We'll fit a model using `lm()` for `wait_minutes_posted_avg` with our covariates, then use the fitted predictions of `wait_minutes_posted_avg` (`.fitted`) to calculate the density using `dnorm()`.
-
-```{r}
-library(broom)
-denominator_model <- lm(
-  wait_minutes_posted_avg ~
-    park_close + park_extra_magic_morning + park_temperature_high + park_ticket_season,
-  data = wait_times
-)
-
-denominators <- denominator_model |>
-  augment(data = wait_times) |>
-  mutate(
-    denominator = dnorm(
-      wait_minutes_posted_avg,
-      .fitted,
-      mean(.sigma, na.rm = TRUE)
-    )
-  ) |>
-  select(park_date, denominator, .fitted)
-```
-
-When we only use the inverted values of `denominator`, we end up with several extreme weights:
-
-```{r}
-#| label: fig-hist-sd-unstable
-#| fig.cap: >
-#|   A histogram of the inverse probability weights for posted waiting time. Weights for continuous exposures are prone to extreme values, which can unstabilize estimates and variance.
-denominators |>
-  mutate(wts = 1 / denominator) |>
-  ggplot(aes(wts)) +
-  geom_histogram(fill = "#E69F00", color = "white", bins = 50) +
-  scale_x_log10(name = "weights")
-```
-
-In @fig-hist-sd-unstable, we see several weights over 100 and one over 10,000; these extreme weights will put undue stress on specific points, complicating the results we will estimate.
-
-Let's now fit the marginal density to use for stabilized weights:
-
-```{r}
-numerator_model <- lm(
-  wait_minutes_posted_avg ~ 1,
-  data = wait_times
-)
-
-numerators <- numerator_model |>
-  augment(data = wait_times) |>
-  mutate(
-    numerator = dnorm(
-      wait_minutes_posted_avg,
-      .fitted,
-      mean(.sigma, na.rm = TRUE)
-    )
-  ) |>
-  select(park_date, numerator)
-```
-
-We also need to join the fitted values back to our original data set by date, then calculate the stabilized weights (`swts`) using `numerator / denominator`.
-
-```{r}
-wait_times_wts <- wait_times |>
-  left_join(numerators, by = "park_date") |>
-  left_join(denominators, by = "park_date") |>
-  mutate(swts = numerator / denominator)
-```
-
-The stabilized weights are much less extreme.
-Stabilized weights should have a mean close to 1 (in this example, it is `round(mean(wait_times_wts$swts), digits = 2)`); when that is the case, then the pseudo-population (that is, the equivalent number of observations after weighting) is equal to the original sample size.
-If the mean is far from 1, we may have issues with model misspecification or positivity violations [@hernan2021].
-
-```{r}
-#| label: fig-hist-sd-stable
-#| fig.cap: >
-#|   A histogram of the stabilized inverse probability weights for posted waiting time. These weights are much more reasonable and will allow the outcome model to behave better.
-ggplot(wait_times_wts, aes(swts)) +
-  geom_histogram(fill = "#E69F00", color = "white", bins = 50) +
-  scale_x_log10(name = "weights")
-```
-
-When we compare the exposure---average posted wait times---to the standardized weights, we still have one exceptionally high weight.
-Is this a problem, or is this a valid data point?
-
-```{r}
-#| label: fig-stabilized-wts-scatter
-#| fig.cap: >
-#|   A scatter of the stabilized inverse probability weights for posted waiting time vs. posted waiting times. Days with more values of `wait_minutes_posted_avg` farther from the mean appear to be downweighted, with a few exceptions. The most unusual weight is for June 23, 2018.
-ggplot(wait_times_wts, aes(wait_minutes_posted_avg, swts)) +
-  geom_point(size = 3, color = "grey80", alpha = 0.7) +
-  geom_point(
-    data = function(x) filter(x, swts > 10),
-    color = "firebrick",
-    size = 3
-  ) +
-  geom_text(
-    data = function(x) filter(x, swts > 10),
-    aes(label = park_date),
-    size = 5,
-    hjust = 0,
-    nudge_x = -15.5,
-    color = "firebrick"
-  ) +
-  scale_y_log10() +
-  labs(x = "Average Posted Wait", y = "Stabilized Weights")
-```
+## Calculating propensity scores for categorical exposures
 
 ```{r}
-wait_times_wts |>
-  filter(swts > 10) |>
-  select(
-    park_date, wait_minutes_posted_avg, .fitted,
-    park_close, park_extra_magic_morning,
-    park_temperature_high, park_ticket_season
-  ) |>
-  knitr::kable()
+rnorm(5)
 ```
 
-Our model predicted a much lower posted wait time than observed, so this date was upweighted.
-We don't know why the posted time was so high (the actual time was much lower), but we did find an artist rendering from that date of [Pluto digging for Seven Dwarfs Mine Train treasure](https://disneyparks.disney.go.com/blog/2018/06/disney-doodle-pluto-sniffs-out-fun-at-seven-dwarfs-mine-train/).
+## Diagnostics with many categories
 
-## Fitting the outcome model for continuous exposures
+## Fitting the outcome model again
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diff --git a/chapters/chapter-14.qmd b/chapters/chapter-14.qmd
index 3c0cfcd..775efc1 100644
--- a/chapters/chapter-14.qmd
+++ b/chapters/chapter-14.qmd
@@ -1,13 +1,13 @@
-# Categorical exposures
+# Missingness
 
 {{< include 00-setup.qmd >}}
 
-## Calculating propensity scores for categorical exposures
+## Missingness as structural bias
 
 ```{r}
 rnorm(5)
 ```
 
-## Diagnostics with many categories
+## Multiple Imputation
 
-## Fitting the outcome model again
+## Combining MICE and IPW
diff --git a/chapters/chapter-16.qmd b/chapters/chapter-16.qmd
index e2b53cb..079f8d5 100644
--- a/chapters/chapter-16.qmd
+++ b/chapters/chapter-16.qmd
@@ -1,10 +1,13 @@
-# Interaction {#sec-interaction}
+# Causal survival analysis
 
 {{< include 00-setup.qmd >}}
 
-## Functional form, hetereogenous effects, and joint causes
+## Preparing data for survival analysis
 
-## Fitting interaction terms in causal models
+```{r}
+rnorm(5)
+```
 
-<!-- Maybe also: -->
-<!-- ## type ii selection bias, induced by EM [@lu2022] -->
\ No newline at end of file
+## Pooled logistic regression
+
+## Confidence intervals for causal survival models
diff --git a/chapters/chapter-17.qmd b/chapters/chapter-17.qmd
index b8b6ad5..90dd7df 100644
--- a/chapters/chapter-17.qmd
+++ b/chapters/chapter-17.qmd
@@ -1,13 +1,13 @@
-# Missingness {#sec-missingness}
+# Causal mediation analysis
 
 {{< include 00-setup.qmd >}}
 
-## Missingness as structural bias
+## Estimating direct effects
 
 ```{r}
 rnorm(5)
 ```
 
-## Multiple Imputation
+## Estimating indirect effects
 
-## Combining MICE and IPW
+## Allowing for interactions
diff --git a/chapters/chapter-18.qmd b/chapters/chapter-18.qmd
index 124190b..f8a8cee 100644
--- a/chapters/chapter-18.qmd
+++ b/chapters/chapter-18.qmd
@@ -1,12 +1,11 @@
-# Causal inference across time {#sec-longitudinal}
+# Sensitivity analysis  {#sec-sensitivity}
 
-{{< include 00-setup.qmd >}}
+## Quantitative bias analyses
 
-## Loss to follow-up
+{{< include 00-setup.qmd >}}
 
 ```{r}
 rnorm(5)
 ```
 
-## Time-varying confounding and exposures
-
+## Tipping point analyses
diff --git a/chapters/chapter-19.qmd b/chapters/chapter-19.qmd
index 0ce9ebe..ebff54b 100644
--- a/chapters/chapter-19.qmd
+++ b/chapters/chapter-19.qmd
@@ -1,13 +1,15 @@
-# Causal survival analysis {#sec-survival}
+# Other approaches to causal inference
 
 {{< include 00-setup.qmd >}}
 
-## Preparing data for survival analysis
+## Targeted Learning
 
 ```{r}
 rnorm(5)
 ```
 
-## Pooled logistic regression
+## Instrumental variable analysis
 
-## Confidence intervals for causal survival models
+## Regression discontinuity
+
+## Difference-in-Difference
diff --git a/chapters/chapter-20.qmd b/chapters/chapter-20.qmd
deleted file mode 100644
index e8e9533..0000000
--- a/chapters/chapter-20.qmd
+++ /dev/null
@@ -1,13 +0,0 @@
-# Causal mediation analysis {#sec-mediation}
-
-{{< include 00-setup.qmd >}}
-
-## Estimating direct effects
-
-```{r}
-rnorm(5)
-```
-
-## Estimating indirect effects
-
-## Allowing for interactions
diff --git a/chapters/chapter-21.qmd b/chapters/chapter-21.qmd
deleted file mode 100644
index f8a8cee..0000000
--- a/chapters/chapter-21.qmd
+++ /dev/null
@@ -1,11 +0,0 @@
-# Sensitivity analysis  {#sec-sensitivity}
-
-## Quantitative bias analyses
-
-{{< include 00-setup.qmd >}}
-
-```{r}
-rnorm(5)
-```
-
-## Tipping point analyses
diff --git a/chapters/chapter-22.qmd b/chapters/chapter-22.qmd
deleted file mode 100644
index f40701d..0000000
--- a/chapters/chapter-22.qmd
+++ /dev/null
@@ -1,15 +0,0 @@
-# Machine Learning and Causal Inference {#sec-causal-ml}
-
-{{< include 00-setup.qmd >}}
-
-## Prediction and causal inference, again
-
-## Augmented propensity scores
-
-```{r}
-rnorm(5)
-```
-
-## Targeted Learning
-
-
diff --git a/chapters/chapter-23.qmd b/chapters/chapter-23.qmd
deleted file mode 100644
index 13e82b2..0000000
--- a/chapters/chapter-23.qmd
+++ /dev/null
@@ -1,13 +0,0 @@
-# Instrumental variables and friends {#sec-iv-friends}
-
-{{< include 00-setup.qmd >}}
-
-## Instrumental variable analysis
-
-```{r}
-rnorm(5)
-```
-
-## Regression discontinuity
-
-## Difference-in-Difference
diff --git a/chapters/chapter-24.qmd b/chapters/chapter-24.qmd
deleted file mode 100644
index 3ba5d0d..0000000
--- a/chapters/chapter-24.qmd
+++ /dev/null
@@ -1,13 +0,0 @@
-# Evidence {#sec-evidence}
-
-{{< include 00-setup.qmd >}}
-
-## Triangulation
-
-```{r}
-rnorm(5)
-```
-
-## Generalization and transportability
-
-## Ragged evidence
diff --git a/citations.bib b/citations.bib
index 54545a5..5c58f37 100644
--- a/citations.bib
+++ b/citations.bib
@@ -505,200 +505,4 @@ @book{robinsImpossible
     publisher = {AAAI Press, The MIT Press},             
     address   = {Menlo Park, CA, Cambridge, MA},
     pages     = {305--321}
-}
-@article{Tennant2021,
-	title = {Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations},
-	author = {Tennant, Peter W G and Murray, Eleanor J and Arnold, Kellyn F and Berrie, Laurie and Fox, Matthew P and Gadd, Sarah C and Harrison, Wendy J and Keeble, Claire and Ranker, Lynsie R and Textor, Johannes and Tomova, Georgia D and Gilthorpe, Mark S and Ellison, George T H},
-	year = {2020},
-	month = {12},
-	date = {2020-12-17},
-	journal = {International Journal of Epidemiology},
-	pages = {620--632},
-	volume = {50},
-	number = {2},
-	doi = {10.1093/ije/dyaa213},
-	url = {http://dx.doi.org/10.1093/ije/dyaa213},
-	langid = {en}
-}
-
-@book{Pearl_Glymour_Jewell_2021, place={Chichester, West Sussex}, title={Causal inference in statistics: A Primer}, publisher={Wiley}, author={Pearl, Judea and Glymour, Madelyn and Jewell, Nicholas P.}, year={2021}} 
-@article{Banack2023,
-	title = {Collider Stratification Bias I: Principles and Structure},
-	author = {Banack, Hailey R and Mayeda, Elizabeth Rose and Naimi, Ashley I and Fox, Matthew P and Whitcomb, Brian W},
-	year = {2023},
-	month = {10},
-	date = {2023-10-10},
-	journal = {American Journal of Epidemiology},
-	doi = {10.1093/aje/kwad203},
-	url = {http://dx.doi.org/10.1093/aje/kwad203},
-	langid = {en}
-}
-
-@article{vanderzander2019,
-	title = {Separators and adjustment sets in causal graphs: Complete criteria and an algorithmic framework},
-	author = {van der Zander, Benito and {Li{\'{s}}kiewicz}, Maciej and Textor, Johannes},
-	year = {2019},
-	month = {05},
-	date = {2019-05},
-	journal = {Artificial Intelligence},
-	pages = {1--40},
-	volume = {270},
-	doi = {10.1016/j.artint.2018.12.006},
-	url = {http://dx.doi.org/10.1016/j.artint.2018.12.006},
-	langid = {en}
-}
-
-@article{weinberg2007,
-	title = {Can DAGs Clarify Effect Modification?},
-	author = {Weinberg, Clarice R.},
-	year = {2007},
-	month = {09},
-	date = {2007-09},
-	journal = {Epidemiology},
-	pages = {569--572},
-	volume = {18},
-	number = {5},
-	doi = {10.1097/ede.0b013e318126c11d},
-	url = {http://dx.doi.org/10.1097/ede.0b013e318126c11d},
-	langid = {en}
-}
-
-@article{Nilsson2021,
-	title = {A directed acyclic graph for interactions},
-	author = {Nilsson, Anton and Bonander, Carl and {Strömberg}, Ulf and {Björk}, Jonas},
-	year = {2020},
-	month = {11},
-	date = {2020-11-22},
-	journal = {International Journal of Epidemiology},
-	pages = {613--619},
-	volume = {50},
-	number = {2},
-	doi = {10.1093/ije/dyaa211},
-	url = {http://dx.doi.org/10.1093/ije/dyaa211},
-	langid = {en}
-}
-
-@article{sugiyama1981,
-	title = {Methods for Visual Understanding of Hierarchical System Structures},
-	author = {Sugiyama, Kozo and Tagawa, Shojiro and Toda, Mitsuhiko},
-	year = {1981},
-	date = {1981},
-	journal = {IEEE Transactions on Systems, Man, and Cybernetics},
-	pages = {109--125},
-	volume = {11},
-	number = {2},
-	doi = {10.1109/tsmc.1981.4308636},
-	url = {http://dx.doi.org/10.1109/tsmc.1981.4308636}
-}
-
-@article{lu2022,
-	title = {Toward a Clearer Definition of Selection Bias When Estimating Causal Effects},
-	author = {Lu, Haidong and Cole, Stephen R. and Howe, Chanelle J. and Westreich, Daniel},
-	year = {2022},
-	month = {06},
-	date = {2022-06-06},
-	journal = {Epidemiology},
-	pages = {699--706},
-	volume = {33},
-	number = {5},
-	doi = {10.1097/ede.0000000000001516},
-	url = {http://dx.doi.org/10.1097/ede.0000000000001516},
-	langid = {en}
-}
-
-@article{Brookhart2006,
-	title = {Variable Selection for Propensity Score Models},
-	author = {Brookhart, M. Alan and Schneeweiss, Sebastian and Rothman, Kenneth J. and Glynn, Robert J. and Avorn, Jerry and {Stürmer}, Til},
-	year = {2006},
-	month = {04},
-	date = {2006-04-19},
-	journal = {American Journal of Epidemiology},
-	pages = {1149--1156},
-	volume = {163},
-	number = {12},
-	doi = {10.1093/aje/kwj149},
-	url = {http://dx.doi.org/10.1093/aje/kwj149},
-	langid = {en}
-}
-
-@article{Hernán2009,
-	title = {Invited Commentary: Causal Diagrams and Measurement Bias},
-	author = {Hernan, M. A. and Cole, S. R.},
-	year = {2009},
-	month = {09},
-	date = {2009-09-15},
-	journal = {American Journal of Epidemiology},
-	pages = {959--962},
-	volume = {170},
-	number = {8},
-	doi = {10.1093/aje/kwp293},
-	url = {http://dx.doi.org/10.1093/aje/kwp293},
-	langid = {en}
-}
-
-@misc{murray2022,
-	title = {As the Wheel Turns: Causal Inference for Feedback Loops and Bidirectional Effects},
-	author = {Murray, Eleanor J. and Kunicki, Zach},
-	year = {2022},
-	month = {07},
-	date = {2022-07-18},
-	url = {http://dx.doi.org/10.31219/osf.io/9em5q}
-}
-
-@article{mansournia2013,
-	title = {Matched designs and causal diagrams},
-	author = {Mansournia, Mohammad A and {Hernán}, Miguel A and Greenland, Sander},
-	year = {2013},
-	month = {06},
-	date = {2013-06},
-	journal = {International Journal of Epidemiology},
-	pages = {860--869},
-	volume = {42},
-	number = {3},
-	doi = {10.1093/ije/dyt083},
-	url = {http://dx.doi.org/10.1093/ije/dyt083},
-	langid = {en}
-}
-
-@article{miao2018,
-	title = {Identifying causal effects with proxy variables of an unmeasured confounder},
-	author = {Miao, Wang and Geng, Zhi and Tchetgen Tchetgen, Eric J},
-	year = {2018},
-	month = {08},
-	date = {2018-08-13},
-	journal = {Biometrika},
-	pages = {987--993},
-	volume = {105},
-	number = {4},
-	doi = {10.1093/biomet/asy038},
-	url = {http://dx.doi.org/10.1093/biomet/asy038},
-	langid = {en}
-}
-
-@article{Lipsitch2010,
-	title = {Negative Controls},
-	author = {Lipsitch, Marc and Tchetgen Tchetgen, Eric and Cohen, Ted},
-	year = {2010},
-	month = {05},
-	date = {2010-05},
-	journal = {Epidemiology},
-	pages = {383--388},
-	volume = {21},
-	number = {3},
-	doi = {10.1097/ede.0b013e3181d61eeb},
-	url = {http://dx.doi.org/10.1097/EDE.0b013e3181d61eeb},
-	langid = {en}
-}
-
-@article{Textor2016,
-	title = {Robust causal inference using directed acyclic graphs: the R package {\textquoteleft}dagitty{\textquoteright}},
-	author = {Textor, Johannes and van der Zander, Benito and Gilthorpe, Mark S. and {Li{\'{s}}kiewicz}, Maciej and Ellison, George T.H.},
-	year = {2017},
-	month = {01},
-	date = {2017-01-15},
-	journal = {International Journal of Epidemiology},
-	pages = {dyw341},
-	doi = {10.1093/ije/dyw341},
-	url = {http://dx.doi.org/10.1093/ije/dyw341},
-	langid = {en}
-}
+}
\ No newline at end of file
diff --git a/data/dag_data.xlsx b/data/dag_data.xlsx
deleted file mode 100644
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diff --git a/images/podcast-diagram.png b/images/podcast-diagram.png
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new file mode 100644
index 0000000..6048eed
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+  <div class="sidebar-item-container"> 
+  <a href="./chapters/chapter-06.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Preparing data to answer causal questions</span></span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
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+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">Building propensity score models</span></span></a>
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+  <a href="./chapters/chapter-08.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">Using the propensity score</span></span></a>
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+  <a href="./chapters/chapter-09.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Evaluating your propensity score model</span></span></a>
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+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true">
+ <span class="menu-text">Estimating Causal Effects</span></a>
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+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Causal estimands</span></span></a>
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+  <a href="./chapters/chapter-12.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">12</span>&nbsp; <span class="chapter-title">Continuous exposures</span></span></a>
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+ <span class="menu-text"><span class="chapter-number">13</span>&nbsp; <span class="chapter-title">Categorical exposures</span></span></a>
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+ <span class="menu-text"><span class="chapter-number">14</span>&nbsp; <span class="chapter-title">Missingness</span></span></a>
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+ <span class="menu-text"><span class="chapter-number">17</span>&nbsp; <span class="chapter-title">Causal mediation analysis</span></span></a>
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+  <a href="./chapters/chapter-19.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">19</span>&nbsp; <span class="chapter-title">Other approaches to causal inference</span></span></a>
+  </div>
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+  <div class="sidebar-item-container"> 
+  <a href="./chapters/references-99.html" class="sidebar-item-text sidebar-link">
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+  </div>
+</li>
+    </ul>
+</div>
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+<!-- margin-sidebar -->
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+        <nav id="TOC" role="doc-toc" class="toc-active"><h2 id="toc-title">Table of contents</h2>
+   
+  <ul>
+<li>
+<a href="#preface" id="toc-preface" class="nav-link active" data-scroll-target="#preface">Preface</a>
+  <ul class="collapse">
+<li>
+<a href="#conventions" id="toc-conventions" class="nav-link" data-scroll-target="#conventions">Conventions</a>
+  <ul class="collapse">
+<li><a href="#modern-r-features" id="toc-modern-r-features" class="nav-link" data-scroll-target="#modern-r-features">Modern R Features</a></li>
+  </ul>
+</li>
+  <li><a href="#theming" id="toc-theming" class="nav-link" data-scroll-target="#theming">Theming</a></li>
+  </ul>
+</li>
+  </ul><div class="toc-actions"><ul><li><a href="https://github.com/malcolmbarrett/causal-inference-in-r/edit/main/index.qmd" class="toc-action"><i class="bi bi-github"></i>Edit this page</a></li><li><a href="https://github.com/malcolmbarrett/causal-inference-in-r/issues/new" class="toc-action"><i class="bi empty"></i>Report an issue</a></li></ul></div></nav>
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+<!-- main -->
+<main class="content" id="quarto-document-content"><header id="title-block-header" class="quarto-title-block default"><div class="quarto-title">
+<h1 class="title">Causal Inference in R</h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    <div>
+    <div class="quarto-title-meta-heading">Authors</div>
+    <div class="quarto-title-meta-contents">
+             <p>Malcolm Barrett </p>
+             <p>Lucy D’Agostino McGowan </p>
+             <p>Travis Gerke </p>
+          </div>
+  </div>
+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">September 30, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+
+</header><section id="preface" class="level1 unnumbered"><h1 class="unnumbered">Preface</h1>
+<p>Welcome to <em>Causal Inference in R</em>. Answering causal questions is critical for scientific and business purposes, but techniques like randomized clinical trials and A/B testing are not always practical or successful. The tools in this book will allow readers better make causal inferences with observational data with the R programming language. By its end, we hope to help you:</p>
+<ol type="1">
+<li>Ask better causal questions.</li>
+<li>Understand the assumptions needed for causal inference</li>
+<li>Identify the target population for which you want to make inferences</li>
+<li>Fit causal models and check their problems</li>
+<li>Conduct sensitivity analyses where the techniques we use might be imperfect</li>
+</ol>
+<p>This book is for both academic researchers and data scientists. Although the questions may differ between these settings, many techniques are the same: causal inference is as helpful for asking questions about cancer as it is about clicks. We use a mix of examples from medicine, economics, and tech to demonstrate that you need a clear causal question and a willingness to be transparent about your assumptions.</p>
+<p>You’ll learn a lot in this book, but ironically, you won’t learn much about conducting randomized trials, one of the best tools for causal inferences. Randomized trials, and their cousins, A/B tests (standard in the tech world), are compelling because they alleviate many of the assumptions we need to make for valid inferences. They are also sufficiently complex in design to merit their own learning resources. Instead, we’ll focus on observational data where we don’t usually benefit from randomization. If you’re interested in randomization techniques, don’t put away this resource just yet: many causal inference techniques designed for observational data improve randomized analyses, too.</p>
+<p>We’re making a few assumptions about you as a reader:</p>
+<ol type="1">
+<li>You’re familiar with the <a href="https://www.tidyverse.org/">tidyverse</a> ecosystem of R packages and their general philosophy. For instance, we use a lot of dplyr and ggplot2 in this book, but we won’t explain their basic grammar. To learn more about starting with the tidyverse, we recommend <a href="https://r4ds.hadley.nz/">R for Data Science</a>.</li>
+<li>You’re familiar with basic statistical modeling in R. For instance, we’ll fit many models with <code><a href="https://rdrr.io/r/stats/lm.html">lm()</a></code> and <code><a href="https://rdrr.io/r/stats/glm.html">glm()</a></code>, but we won’t discuss how they work. If you want to learn more about R’s powerful modeling functions, we recommend reading <a href="https://www.tmwr.org/base-r.html">“A Review of R Modeling Fundamentals”</a> in <a href="https://www.tmwr.org">Tidy Modeling with R</a>.</li>
+<li>We also assume you have familiarity with other R basics, such as <a href="https://r4ds.hadley.nz/functions.html">writing functions</a>. <a href="https://r4ds.hadley.nz/">R for Data Science</a> is also a good resource for these topics. (For a deeper dive into the R programming language, we recommend <a href="https://adv-r.hadley.nz/index.html">Advanced R</a>, although we don’t assume you have mastered its material for this book).</li>
+</ol>
+<p>We’ll also use tools from the tidymodels ecosystem, a set of R packages for modeling related to the tidyverse. We don’t assume you have used them before. tidymodels also focuses on predictive modeling, so many of its tools aren’t appropriate for this book. Nevertheless, if you are interested in this topic, we recommend <a href="https://www.tmwr.org">Tidy Modeling with R</a>.</p>
+<p>There are also several other excellent books on causal inference. This book is different in its focus on R, but it’s still helpful to see this area from other perspectives. A few books you might like:</p>
+<ul>
+<li><a href="https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"><em>Causal Inference: What If?</em></a></li>
+<li><a href="https://mixtape.scunning.com/"><em>Causal Inference: The Mixtape</em></a></li>
+<li><a href="https://theeffectbook.net/"><em>The Effect</em></a></li>
+</ul>
+<p>The first book is focused on epidemiology. The latter two are focused on econometrics. We also recommend <em>The Book of Why</em> <span class="citation" data-cites="pearl2018why">Pearl and Mackenzie (<a href="#ref-pearl2018why" role="doc-biblioref">2018</a>)</span> for more on causal diagrams.</p>
+<section id="conventions" class="level2"><h2 class="anchored" data-anchor-id="conventions">Conventions</h2>
+<section id="modern-r-features" class="level3"><h3 class="anchored" data-anchor-id="modern-r-features">Modern R Features</h3>
+<p>We use two modern R features in R 4.1.0 and above in this book. The first is the native pipe, <code>|&gt;</code>. This R feature is similar to the tidyverse’s <code>%&gt;%</code>, with which you may be more familiar. In typical cases, the two work interchangeably. One notable difference is that <code>|&gt;</code> uses the <code>_</code> symbol to direct the pipe’s results, e.g., <code>.df |&gt; lm(y ~ x, data = _)</code>. See <a href="https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/">this Tidyverse Blog post</a> for more on this topic.</p>
+<p>Another modern R feature we use is the native lambda, a way of writing short functions that looks like <code>\(.x) do_something(.x)</code>. It is similar to purrr’s <code>~</code> lambda notation. It’s also helpful to realize the native lambda is identical to <code>function(.x) do_something(.x)</code>, where <code>\</code> is shorthand for <code>function</code>. See <a href="https://r4ds.hadley.nz/iteration.html">R for Data Science’s chapter on iteration</a> for more on this topic.</p>
+</section></section><section id="theming" class="level2"><h2 class="anchored" data-anchor-id="theming">Theming</h2>
+<p>The plots in this book use a consistent theme that we don’t include in every code chunk, meaning if you run the code for a visualization, you might get a slightly different-looking result. We set the following defaults related to ggplot2:</p>
+<!-- TODO: make sure these are up to date -->
+<div class="cell">
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/options.html">options</a></span><span class="op">(</span></span>
+<span>  <span class="co"># set default colors in ggplot2 to colorblind-friendly </span></span>
+<span>  <span class="co"># Okabe-Ito and Viridis palettes</span></span>
+<span>  ggplot2.discrete.colour <span class="op">=</span> <span class="fu">ggokabeito</span><span class="fu">::</span><span class="fu"><a href="https://malcolmbarrett.github.io/ggokabeito/reference/palette_okabe_ito.html">palette_okabe_ito</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  ggplot2.discrete.fill <span class="op">=</span> <span class="fu">ggokabeito</span><span class="fu">::</span><span class="fu"><a href="https://malcolmbarrett.github.io/ggokabeito/reference/palette_okabe_ito.html">palette_okabe_ito</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  ggplot2.continuous.colour <span class="op">=</span> <span class="st">"viridis"</span>,</span>
+<span>  ggplot2.continuous.fill <span class="op">=</span> <span class="st">"viridis"</span>,</span>
+<span>  <span class="co"># set theme font and size</span></span>
+<span>  book.base_family <span class="op">=</span> <span class="st">"sans"</span>,</span>
+<span>  book.base_size <span class="op">=</span> <span class="fl">14</span></span>
+<span><span class="op">)</span></span>
+<span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># set default theme</span></span>
+<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme_get.html">theme_set</a></span><span class="op">(</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html">theme_minimal</a></span><span class="op">(</span></span>
+<span>    base_size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html">getOption</a></span><span class="op">(</span><span class="st">"book.base_size"</span><span class="op">)</span>, </span>
+<span>    base_family <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html">getOption</a></span><span class="op">(</span><span class="st">"book.base_family"</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://ggplot2.tidyverse.org/reference/theme_get.html">%+replace%</a></span> </span>
+<span>    <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html">theme</a></span><span class="op">(</span></span>
+<span>      panel.grid.minor <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/element.html">element_blank</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>      legend.position <span class="op">=</span> <span class="st">"bottom"</span></span>
+<span>    <span class="op">)</span></span>
+<span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>We also mask a few functions from ggdag that we like to customize:</p>
+<div class="cell">
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">theme_dag</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="fu">ggdag</span><span class="fu">::</span><span class="fu"><a href="https://r-causal.github.io/ggdag/reference/theme_dag_blank.html">theme_dag</a></span><span class="op">(</span>base_family <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html">getOption</a></span><span class="op">(</span><span class="st">"book.base_family"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="va">geom_dag_label_repel</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">...</span>, <span class="va">seed</span> <span class="op">=</span> <span class="fl">10</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="fu">ggdag</span><span class="fu">::</span><span class="fu"><a href="https://r-causal.github.io/ggdag/reference/repel.html">geom_dag_label_repel</a></span><span class="op">(</span></span>
+<span>    <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, label <span class="op">=</span> <span class="va">label</span><span class="op">)</span>,</span>
+<span>    box.padding <span class="op">=</span> <span class="fl">3.5</span>, </span>
+<span>    inherit.aes <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>    max.overlaps <span class="op">=</span> <span class="cn">Inf</span>, </span>
+<span>    family <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html">getOption</a></span><span class="op">(</span><span class="st">"book.base_family"</span><span class="op">)</span>,</span>
+<span>    seed <span class="op">=</span> <span class="va">seed</span>,</span>
+<span>    label.size <span class="op">=</span> <span class="cn">NA</span>, </span>
+<span>    label.padding <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>    size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html">getOption</a></span><span class="op">(</span><span class="st">"book.base_size"</span><span class="op">)</span> <span class="op">/</span> <span class="fl">3</span>,</span>
+<span>    <span class="va">...</span></span>
+<span>  <span class="op">)</span> </span>
+<span><span class="op">}</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+
+
+<div id="refs" class="references csl-bib-body hanging-indent" role="list">
+<div id="ref-pearl2018why" class="csl-entry" role="listitem">
+Pearl, J., and D. Mackenzie. 2018. <em>The Book of Why: The New Science of Cause and Effect</em>. Penguin Books Limited.
+</div>
+</div>
+</section></section></main><!-- /main --><script id="quarto-html-after-body" type="application/javascript">
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+      interactiveBorder: 10,
+      theme: 'quarto',
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+    if (contentFn) {
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+    if (onTriggerFn) {
+      config.onTrigger = onTriggerFn;
+    }
+    if (onUntriggerFn) {
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+    }
+    window.tippy(el, config); 
+  }
+  const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
+  for (var i=0; i<noterefs.length; i++) {
+    const ref = noterefs[i];
+    tippyHover(ref, function() {
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+      let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
+      try { href = new URL(href).hash; } catch {}
+      const id = href.replace(/^#\/?/, "");
+      const note = window.document.getElementById(id);
+      return note.innerHTML;
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+  const xrefs = window.document.querySelectorAll('a.quarto-xref');
+  const processXRef = (id, note) => {
+    // Strip column container classes
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+        for (const child of el.children) {
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+    stripColumnClz(note)
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+      return note.innerHTML;
+    }
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+  for (var i=0; i<xrefs.length; i++) {
+    const xref = xrefs[i];
+    tippyHover(xref, undefined, function(instance) {
+      instance.disable();
+      let url = xref.getAttribute('href');
+      let hash = undefined; 
+      if (url.startsWith('#')) {
+        hash = url;
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+            parent = el.parentElement.parentElement;
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+            const bottom = lastEl.offsetTop + lastEl.offsetHeight;
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+            // cook up a div (if necessary) and position it 
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+  var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
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+    const citeInfo = findCites(ref);
+    if (citeInfo) {
+      tippyHover(citeInfo.el, function() {
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+        citeInfo.cites.forEach(function(cite) {
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+          citeDiv.classList.add('csl-entry');
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+            citeDiv.innerHTML = biblioDiv.innerHTML;
+          }
+          popup.appendChild(citeDiv);
+        });
+        return popup.innerHTML;
+      });
+    }
+  }
+});
+</script><nav class="page-navigation"><div class="nav-page nav-page-previous">
+  </div>
+  <div class="nav-page nav-page-next">
+      <a href="./chapters/chapter-01.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">What is a causal question?</span></span> <i class="bi bi-arrow-right-short"></i>
+      </a>
+  </div>
+</nav>
+</div> <!-- /content -->
+
+
+
+</body></html>
\ No newline at end of file