03-Stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl \n",
    "\n",
    "## agg backend is used to create plot as a .png file\n",
    "mpl.use('agg')\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## prepare and load the datafiles we will need to generate the stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [],
   "source": [
    "from manny_modules import normalize_dataset as nd\n",
    "def load_raw_dataset(file_name):\n",
    "    # load raw dataset i.e. before we normalised it\n",
    "    dtypes = {'sentiment': 'int', \n",
    "              'text': 'str' \n",
    "             }\n",
    "\n",
    "    raw_data_sample = pd.read_csv('imdb_dataset/'+file_name, dtype=dtypes)\n",
    "    rds = raw_data_sample.copy()\n",
    "\n",
    "    # normalise the dataset\n",
    "    normalized_dataset = nd.clean_and_return(rds, 'text')\n",
    "    \n",
    "    return raw_data_sample, normalized_dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## prepare and load the data files we will need "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_filename = \"raw_data.csv\"\n",
    "raw_data_sample, normalized_dataset = load_raw_dataset(raw_data_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#bert_distance_stats.to_csv(\"ga_attack_results/ga_results_DISTANCE_BERT_STATS.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load all the results files we generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_stats_results_BERT = \"ga_results_BERT_STATS.csv\"\n",
    "ga_stats_results_DISTANCE = \"ga_results_DISTANCE_STATS.csv\"\n",
    "ga_stats_results_DISTANCE_BERT = \"ga_results_DISTANCE_BERT_STATS.csv\"\n",
    "\n",
    "normalised_dataset_ = \"imdb_dataset/normalized_dataset.csv\"\n",
    "imdb_dataset_ = \"imdb_dataset/raw_data.csv\"\n",
    "\n",
    "\n",
    "bert_stats = pd.read_csv('ga_attack_results/'+ga_stats_results_BERT)\n",
    "distance_stats = pd.read_csv('ga_attack_results/'+ga_stats_results_DISTANCE)\n",
    "bert_distance_stats = pd.read_csv('ga_attack_results/'+ga_stats_results_DISTANCE_BERT)\n",
    "\n",
    "normalized_dataset = pd.read_csv(normalised_dataset_)\n",
    "raw_dataset = pd.read_csv(imdb_dataset_)\n",
    "\n",
    "dataset_size = len(distance_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
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       "      <td>0.05</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   sentiment                                               text     probs  \\\n",
       "0          0  well i must say this is probably the worst fil...  0.025771   \n",
       "1          1  if christopher nolan had made memento before f...  0.769358   \n",
       "2          1  i'm not saying anything new when i say that ra...  0.807952   \n",
       "3          0  i have been watching lost with my family since...  0.476023   \n",
       "4          1  caught this flick as one of a fivefor deal fro...  0.942861   \n",
       "\n",
       "   ga_sentiment                                            ga_text  ga_probs  \\\n",
       "0             1  well i must say this is probably the best film...  0.812936   \n",
       "1             0  if christopher nolan had made memento before f...  0.398513   \n",
       "2             0  i'm not saying anything new when i say that ra...  0.484280   \n",
       "3             1  i have been watching lost with my family since...  0.672474   \n",
       "4             0  caught this flick as one of a fivefor deal fro...  0.456993   \n",
       "\n",
       "   ga_num_changes  ga_lev_ratio ga_flipped_sentiment  ga_percent_change  \\\n",
       "0               5      0.966937                    Y               0.03   \n",
       "1               8      0.968591                    Y               0.03   \n",
       "2               5      0.977343                    Y               0.03   \n",
       "3               4      0.979098                    Y               0.01   \n",
       "4               8      0.959369                    Y               0.05   \n",
       "\n",
       "   ga_generations  ga_time_taken  \n",
       "0               1             42  \n",
       "1               2             87  \n",
       "2               1             43  \n",
       "3               1             44  \n",
       "4               2             84  "
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text</th>\n",
       "      <th>probs</th>\n",
       "      <th>ga_sentiment</th>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>caught this flick as one of a fivefor deal fro...</td>\n",
       "      <td>0.942861</td>\n",
       "      <td>0</td>\n",
       "      <td>caught this flick as somebody of a fivefor dea...</td>\n",
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       "      <td>9</td>\n",
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       "      <td>0.06</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   sentiment                                               text     probs  \\\n",
       "0          0  well i must say this is probably the worst fil...  0.025771   \n",
       "1          1  if christopher nolan had made memento before f...  0.769358   \n",
       "2          1  i'm not saying anything new when i say that ra...  0.807952   \n",
       "3          0  i have been watching lost with my family since...  0.476023   \n",
       "4          1  caught this flick as one of a fivefor deal fro...  0.942861   \n",
       "\n",
       "   ga_sentiment                                            ga_text  ga_probs  \\\n",
       "0             1  well i must say this is arguably the meanest f...  0.762060   \n",
       "1             0  if christopher nolan had made memento before f...  0.448427   \n",
       "2             0  i'm not saying anything new when i tell that r...  0.392656   \n",
       "3             1  i have been watching lost with my family since...  0.799918   \n",
       "4             0  caught this flick as somebody of a fivefor dea...  0.463923   \n",
       "\n",
       "   ga_num_changes  ga_lev_ratio ga_flipped_sentiment  ga_percent_change  \\\n",
       "0              13      0.934823                    Y               0.08   \n",
       "1               5      0.974665                    Y               0.02   \n",
       "2               7      0.970754                    Y               0.04   \n",
       "3               2      0.978627                    Y               0.01   \n",
       "4               9      0.955224                    Y               0.06   \n",
       "\n",
       "   ga_generations  ga_time_taken  \n",
       "0               3             97  \n",
       "1               1             33  \n",
       "2               2             59  \n",
       "3               1             32  \n",
       "4               2             64  "
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# box plots for time taken "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   sentiment                                               text     probs  \\\n",
       "0          0  well i must say this is probably the worst fil...  0.025771   \n",
       "1          1  if christopher nolan had made memento before f...  0.769358   \n",
       "2          1  i'm not saying anything new when i say that ra...  0.807952   \n",
       "3          0  i have been watching lost with my family since...  0.476023   \n",
       "4          1  caught this flick as one of a fivefor deal fro...  0.942861   \n",
       "\n",
       "   ga_sentiment                                            ga_text  ga_probs  \\\n",
       "0             1  well i must say this is probably the meanest f...  0.768538   \n",
       "1             0  if christopher nolan had made memento before f...  0.395876   \n",
       "2             0  i'm not saying somethings newer when i say tha...  0.426562   \n",
       "3             1  i have been watching lost with my family since...  0.637239   \n",
       "4             0  caught this flick as anyone of a fivefor addre...  0.294813   \n",
       "\n",
       "   ga_num_changes  ga_lev_ratio ga_flipped_sentiment  ga_percent_change  \\\n",
       "0              20      0.895522                    Y               0.13   \n",
       "1              10      0.966031                    Y               0.03   \n",
       "2              10      0.958418                    Y               0.05   \n",
       "3               5      0.978884                    Y               0.01   \n",
       "4              29      0.836257                    Y               0.19   \n",
       "\n",
       "   ga_generations  ga_time_taken  \n",
       "0               5            264  \n",
       "1               2            107  \n",
       "2               2            106  \n",
       "3               1             53  \n",
       "4               5            256  "
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert_distance_stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_boxplot(data_method_a, data_method_b, data_method_c, file_name):\n",
    "    \n",
    "    data = [data_method_a,data_method_b,data_method_c] \n",
    "\n",
    "    labels = ['Method A\\nStandard Deviation\\n' + str(np.std(distance_time)),'Method B\\nStandard Deviation\\n' + str(np.std(bert_time)),'Method C\\nStandard Deviation\\n' + str(np.std(bert_distance_time))]\n",
    "\n",
    "    plt.style.use('seaborn')\n",
    "    \n",
    "    # Create a figure instance\n",
    "    fig = plt.figure(1, figsize=(15, 12))\n",
    "\n",
    "    # Create an axes instance\n",
    "    axs = fig.add_subplot(111)\n",
    "\n",
    "    axs.set_xticklabels(labels)\n",
    "\n",
    "    box_plot = axs.boxplot(data, showmeans=True)\n",
    "\n",
    "    h, l = axs.get_legend_handles_labels()\n",
    "    h.append(box_plot.get('means')[0])\n",
    "    h.append(box_plot.get('medians')[0])\n",
    "    h.append(box_plot.get('fliers')[0])\n",
    "\n",
    "\n",
    "    l = ['Mean', 'Median', 'Outliers']\n",
    "    plt.rcParams[\"legend.loc\"] = 'lower right'\n",
    "    plt.legend(h, l)\n",
    "\n",
    "    \n",
    "    plt.xlabel(\"*** Algorithm (A, B or C) ***\")\n",
    "    plt.ylabel(\"*** Time (seconds) ***\")\n",
    "\n",
    "    fig.savefig(file_name, bbox_inches='tight',dpi=300)\n",
    "    plt.show()\n",
    "    \n",
    "    return\n",
    "  \n",
    "\n",
    "def generate_scatterplot(x_axis, y_axis, file_name, graph_title):\n",
    "    \n",
    "    fig = plt.figure(1, figsize=(15, 12))\n",
    "    plt.style.use('seaborn')\n",
    "    plt.xticks(range(len(x_axis)))\n",
    "    plt.title(graph_title, fontsize=14)\n",
    "\n",
    "    plt.xlabel(\"Generation\")\n",
    "    plt.ylabel(\"Num of Words Changed\")\n",
    "    \n",
    "    plt.scatter(x_axis, y_axis,  alpha=0.3)\n",
    "    \n",
    "\n",
    "    fig.savefig(file_name, bbox_inches='tight',dpi=300)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    return\n",
    "\n",
    "def generate_scatterplot_text_length(x_axis, y_axis, file_name, graph_title):\n",
    "    \n",
    "    fig = plt.figure(1, figsize=(15, 12))\n",
    "    #plt.xticks(range(len(x_axis)))\n",
    "    plt.title(graph_title, fontsize=14)\n",
    "\n",
    "    plt.xlabel(\"Length of Review Text\")\n",
    "    plt.ylabel(\"Num of Words Changed\")\n",
    "    \n",
    "    plt.scatter(x_axis, y_axis,  alpha=0.3)\n",
    "    \n",
    "\n",
    "    fig.savefig(file_name, bbox_inches='tight',dpi=300)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    return\n",
    "\n",
    "\n",
    "\n",
    "def words_generations_barchart(words_changed,graph_title, file_name):\n",
    "    \n",
    "    data = words_changed.copy()\n",
    "    max_value = (max(data) + 1)\n",
    "    fig = plt.figure(1, figsize=(15, 20))\n",
    "    plt.style.use('seaborn')\n",
    "    plt.yticks(range(max_value))\n",
    "    plt.xlabel('Data Item')\n",
    "    plt.ylabel('Number of Words Changed')\n",
    "    plt.title(graph_title, fontsize=14)\n",
    "    plt.bar(range(len(data)), data) \n",
    "    \n",
    "    fig.savefig(file_name, bbox_inches='tight',dpi=300)\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "def average_stats_bar_chart(data, labels, x_axis_name, y_axis_name, chart_title, file_name):\n",
    "\n",
    "    fig = plt.figure(1, figsize=(15, 12))\n",
    "    plt.xticks(range(len(data)), labels)\n",
    "    plt.xlabel(x_axis_name)\n",
    "    plt.ylabel(y_axis_name)\n",
    "    plt.title(chart_title)\n",
    "    plt.bar(range(len(data)), data) \n",
    "    fig.savefig(file_name, bbox_inches='tight',dpi=300)\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_percentage_modified(data_sample):\n",
    "\n",
    "    word_total_count = 0\n",
    "    percent_modified_total = 0.0\n",
    "    for i in range(len(data_sample)):\n",
    "        review_before = data_sample.at[i,'text'].split()\n",
    "        review_after = data_sample.at[i,'ga_text'].split()\n",
    "        word_count = 0\n",
    "        for j in range(len(review_before)):\n",
    "            if review_before[j] != review_after[j]:\n",
    "                word_count += 1\n",
    "        data_sample.at[i, 'ga_num_changes'] = word_count\n",
    "        percent_modified_total += (word_count / len(review_before))\n",
    "        data_sample.at[i, 'ga_percent_change'] = round(word_count / len(review_before), 2)\n",
    "        word_total_count += word_count\n",
    "            \n",
    "\n",
    "\n",
    "    print(\"Avg. num of words changed changes made: \", (int)(word_total_count / len(data_sample)))\n",
    "    print(\"Avg. percentage modified: \", (round(percent_modified_total / len(data_sample), 2) * 100),\"%\")\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matching_indexes(data_sample,normalized_dataset):\n",
    "    '''match up index values from data_sample to raw data file'''\n",
    "    index_values_rawdata = []\n",
    "    for i in range(len(data_sample)):\n",
    "        for j in range(len(normalized_dataset)): \n",
    "            if normalized_dataset.at[j,'text'] == data_sample.at[i, 'text']:\n",
    "               # print(raw_data_sample.at[j,'text'],\"\\n\",data_sample.at[i, 'text'],\"\\n\\n\")\n",
    "                index_values_rawdata.append(j)\n",
    "                break\n",
    "    return index_values_rawdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "def print_data_item(raw_dataset,normalized_dataset, method_dataset, indx, item):\n",
    "    print(raw_dataset.at[item,'text'])\n",
    "    print(\"\\n\")\n",
    "    print(normalized_dataset.at[item,'text'])\n",
    "    print(\"\\n\")\n",
    "    print(method_dataset.at[indx,'ga_text'])\n",
    "    return\n",
    "\n",
    "def words_swapped(normalized_dataset, method_dataset, indx, item):\n",
    "    raw_split_words = normalized_dataset.at[item,'text'].split()\n",
    "    data_sample_split_words = method_dataset.at[indx,'ga_text'].split()\n",
    "\n",
    "    len_range = len(data_sample_split_words)\n",
    "    \n",
    "    number_of_words_swapped = 0\n",
    "    for i in range(len_range):\n",
    "        if raw_split_words[i] != data_sample_split_words[i]:\n",
    "            number_of_words_swapped += 1\n",
    "            print(\"BEFORE: \", raw_split_words[i], \" AFTER: \",data_sample_split_words[i])\n",
    "    print(\"\\nTotal number of words swapped: \",number_of_words_swapped)\n",
    "    if method_dataset.at[indx,'sentiment'] != method_dataset.at[indx,'ga_sentiment']:\n",
    "        print(\"Sentiment was changed for this data item\")\n",
    "        print(\"Probability Before: \", method_dataset.at[indx,'probs'])\n",
    "        print(\"Probability After: \", method_dataset.at[indx,'ga_probs'])\n",
    "    else:\n",
    "        print(\"Sentiment was NOT changed for this data item\")\n",
    "        print(\"Probability Before: \", method_dataset.at[indx,'probs'])\n",
    "        print(\"Probability After: \", method_dataset.at[indx,'ga_probs'])\n",
    "    return\n",
    "\n",
    "def sentiment_change_percentage(data_sample):\n",
    "\n",
    "    flipped_count = 0\n",
    "    failed_flipped_count = 0\n",
    "    for i in range(len(data_sample)):\n",
    "        if  data_sample.at[i,'sentiment'] != data_sample.at[i,'ga_sentiment'] and (data_sample.at[i,'ga_percent_change'] <= 0.2):\n",
    "            flipped_count += 1\n",
    "        else:\n",
    "            failed_flipped_count += 1\n",
    "\n",
    "        \n",
    "    print(\"Percentage of reviews where sentiment was changed after attack:\", round(flipped_count/len(data_sample) * 100, 2),\"%\", \"changed sentiment. i.e.\", flipped_count, \" out of \",len(data_sample))\n",
    "    print(\"Percentage of reviews failed to change sentiment: \", round(failed_flipped_count/len(data_sample) * 100, 2), \"%\",\"did not change, i.e.\", failed_flipped_count, \" out of \",len(data_sample))\n",
    "    print(\"\\n\")\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# display boxplot for each method for time taken to run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-482-240a5a506f5a>:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axs.set_xticklabels(labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot data\n",
    "distance_time_box = distance_stats['ga_time_taken'] # method A\n",
    "bert_time_box = bert_stats['ga_time_taken'] # method B\n",
    "bert_distance_time_box = bert_distance_stats['ga_time_taken'] # method C\n",
    "\n",
    "time_box_filename = 'ga_attack_results/box-plot-time.png'\n",
    "\n",
    "# draw and save box plot\n",
    "time_boxplot(distance_time_box,bert_time_box,bert_distance_time_box, time_box_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-482-240a5a506f5a>:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axs.set_xticklabels(labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot data for only successfully flipped sentiments\n",
    "# distance_time_box = distance_stats['ga_time_taken'] # method A\n",
    "distance_time_box = []\n",
    "for i in range(dataset_size):\n",
    "    if (distance_stats.at[i,'sentiment'] != distance_stats.at[i,'ga_sentiment']) and distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        distance_time_box.append(distance_stats.at[i,'ga_time_taken'])\n",
    "\n",
    "\n",
    "#bert_time_box = bert_stats['ga_time_taken'] # method B\n",
    "bert_time_box = []\n",
    "for i in range(dataset_size):\n",
    "    if (bert_stats.at[i,'sentiment'] != bert_stats.at[i,'ga_sentiment']) and bert_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        bert_time_box.append(bert_stats.at[i,'ga_time_taken'])\n",
    "\n",
    "\n",
    "\n",
    "#bert_distance_time_box = bert_distance_stats['ga_time_taken'] # method C\n",
    "bert_distance_time_box = []\n",
    "for i in range(dataset_size):\n",
    "    if (bert_distance_stats.at[i,'sentiment'] != bert_distance_stats.at[i,'ga_sentiment']) and bert_distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        bert_distance_time_box.append(bert_distance_stats.at[i,'ga_time_taken'])\n",
    "\n",
    "\n",
    "time_box_filename = 'ga_attack_results/box-plot-time-successful.png'\n",
    "\n",
    "# draw and save box plot\n",
    "time_boxplot(distance_time,bert_time_box,bert_distance_time_box, time_box_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## look at reviews less than or equal to the mean length of reviews (236 words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-482-240a5a506f5a>:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axs.set_xticklabels(labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot data for only successfully flipped sentiments\n",
    "# distance_time_box = distance_stats['ga_time_taken'] # method A\n",
    "\n",
    "review_lengths = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    review_lengths.append(len(distance_stats.at[i,'ga_text'].split()))\n",
    "\n",
    "mean_length = round(mean(review_lengths))\n",
    "#print(\"Minimum review length: \",min(review_lengths))\n",
    "#print(\"Maximum review length: \",max(review_lengths))\n",
    "#print(\"Mean review length: \",round(mean(review_lengths)))\n",
    "\n",
    "distance_time_box = []\n",
    "for i in range(dataset_size):\n",
    "    if (distance_stats.at[i,'sentiment'] != distance_stats.at[i,'ga_sentiment']) and distance_stats.at[i,'ga_percent_change'] < 0.2 and len(distance_stats.at[i,'text'].split()) <= mean_length:     \n",
    "        distance_time_box.append(distance_stats.at[i,'ga_time_taken'])\n",
    "\n",
    "\n",
    "#bert_time_box = bert_stats['ga_time_taken'] # method B\n",
    "bert_time_box = []\n",
    "for i in range(dataset_size):\n",
    "    if (bert_stats.at[i,'sentiment'] != bert_stats.at[i,'ga_sentiment']) and bert_stats.at[i,'ga_percent_change'] < 0.2 and len(distance_stats.at[i,'text'].split()) <= mean_length:\n",
    "        bert_time_box.append(bert_stats.at[i,'ga_time_taken'])\n",
    "\n",
    "\n",
    "\n",
    "#bert_distance_time_box = bert_distance_stats['ga_time_taken'] # method C\n",
    "bert_distance_time_box = []\n",
    "for i in range(dataset_size):\n",
    "    if (bert_distance_stats.at[i,'sentiment'] != bert_distance_stats.at[i,'ga_sentiment']) and bert_distance_stats.at[i,'ga_percent_change'] < 0.2 and len(distance_stats.at[i,'text'].split()) <= mean_length:\n",
    "        bert_distance_time_box.append(bert_distance_stats.at[i,'ga_time_taken'])\n",
    "\n",
    "\n",
    "time_box_filename = 'ga_attack_results/box-plot-time-successful-small-review-length.png'\n",
    "\n",
    "# draw and save box plot\n",
    "time_boxplot(distance_time,bert_time_box,bert_distance_time_box, time_box_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scatter plot for Method A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "distance_time_box_scatter_gen = distance_stats['ga_generations']\n",
    "distance_time_box_scatter = distance_stats['ga_num_changes']\n",
    "scatter_plot_filename_a = 'ga_attack_results/scatter-plot-method_a.png'\n",
    "graph_title_a = \"Number of Words Changed v Generations (Method A)\"\n",
    "generate_scatterplot(distance_time_box_scatter_gen,distance_time_box_scatter,scatter_plot_filename_a, graph_title_a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scatter plot for Method B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bert_time_box_scatter_gen = bert_stats['ga_generations']\n",
    "bert_time_box_scatter = bert_stats['ga_num_changes']\n",
    "scatter_plot_filename_b = 'ga_attack_results/scatter-plot-method_b.png'\n",
    "graph_title_b = \"Number of Words Changed v Generations (Method B)\"\n",
    "generate_scatterplot(bert_time_box_scatter_gen,bert_time_box_scatter,scatter_plot_filename_b, graph_title_b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scatter plot for Method C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MAXIMUM_ITERATIONS = 20 \n",
    "# bert_distance_gen = bert_distance_stats['ga_generations']\n",
    "# bert_distance_time_num_changed = bert_distance_stats['ga_num_changes']\n",
    "\n",
    "# for i in range(len(bert_distance_time_box_scatter_gen)):\n",
    "#     if bert_distance_time_num_changed[i] > 0 and bert_distance_gen[i] == 0:\n",
    "#         bert_distance_stats.at[i,'ga_generations'] = MAXIMUM_ITERATIONS\n",
    "\n",
    "\n",
    "\n",
    "bert_distance_time_box_scatter_gen = bert_distance_stats['ga_generations']\n",
    "bert_distance_time_box_scatter = bert_distance_stats['ga_num_changes']\n",
    "scatter_plot_filename_c = 'ga_attack_results/scatter-plot-method_c.png'\n",
    "grapht_title_c = \"Number of Words Changed v Generations (Method C)\"\n",
    "\n",
    "generate_scatterplot(bert_distance_time_box_scatter_gen,bert_distance_time_box_scatter,scatter_plot_filename_c, grapht_title_c)\n",
    "# need to clean data results - where generation is equal to zero - should be 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# words changed v data item bar chart - Method A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1KzTu3//+t2rVqqVHH31UNptNd9xxh/r27at//etfxb4mPz8/DRgwQBUqVFDr1q11ww03OPfNypUrFRsbqwYNGqh8+fKKiYlR9erVnWcEnO+l0NBQValSRU8//bTL/bd8+XK1bt1alStXVnh4uG699VatXbu20Jjbb79d5cqVK3RWyuV4/PHHVblyZSUmJpboeRd66KGHVLVqVdWoUUN33nmn6tevr0aNGqlSpUpq1qxZse/b5XA4HJo6daoqVqzoPAth+fLl6tmzpxo1aiR/f3+1b99erVu31urVq9WwYUNVq1ZNGzZskPTnUevzZ0oUtWLFCj311FOqXbu2ypcvr+joaFWvXl3/93//p5YtWzrPNDh58qT++OMPdezY0Xkmz44dO9SqVSvl5OQoOztbQUFB8vPzU9WqVbVy5Uo98cQTJX6thw4d0uzZszVo0KBC2xcvXqzw8HA9//zzeuGFF9SuXTtJ0o033qhbbrlFCQkJSk5OVrdu3TRw4EDnv3kArj0EbQAeV7FiRY0ZM0bTp08vdCpxSVz4vbcKFSpcdP/8qYLSn//Te6FbbrlFx44d04kTJ3T27FkNHjzYeeGau+++W5mZmTpy5IhzfLVq1Yqt4/Dhw7rjjjsKbatdu7Z+//33y3odUVFROnbsmA4cOKDvv/9eQUFBuuOOOxQZGamvv/5a586d03/+8x/VrFlTNWvW1OnTp3XmzJlCawYEBKhatWqFLqR0yy23OG8fOXJEVapUkb+/v3NbrVq1nLdjY2N19uxZRUVFadiwYVq9erXLC3ddeGq4O9WrV3feDgwMdL4veXl5evvtt9WuXTvnvj979myh9+2WW24pVHNgYKCys7N16tQp5ebmFnpf6tev77x9/jTvXr16OeeOioqSv7+/0tLS3O6Porp06aLvvvvO+Z3UpKQkPfTQQ5e9D87Lz89XuXLlLvv1XygrK0tTpkxR69at1bBhQ+eHLsWNd8Xd/jmvJH9jFzp16pRCQkIu+ViPHj306aefKjc3V1lZWdq8efNF+/K3337T3r17C11QKiEhodBFqYq6VK/k5ORIuvTfaK1atZSWliaHw6Hs7GzdfPPNhR4rTm5urj7++GM98sgjzm3dunUr9JUA6c8PFUJCQgpdf+Fy+Pn5aeLEiVqwYIF+/vnnEj33vJtuusl5uyTv26X88MMPhd6Hli1b6n//+58WLVqkSpUqSfrz/Vq4cGGhcVu2bHH+G/rAAw/oiy++kPTnqf9nz55V+/btL1rrt99+09SpUwvN8/PPPystLU0RERHKyMjQ4cOH9fXXX6tp06Zq0qSJdu7cKUnauXOnIiMjValSJT333HMaOXKkevbsqddff/2K9mNKSor69Omjv/zlL+ratWuhx/r06aPdu3crMTFRb731lj744ANJ0vDhwzVz5kzVrFlTFStW1JNPPqkGDRoU+mAJwLXl4sMVAOABHTt21IoVKzR9+nQNHDjQ5dhLBbvzgaW4+64eMwxDAQEBCgwMlCR98MEHuvvuu4t9/oX/A3+5LveiV9WrV1ft2rW1detW/fHHH4qMjJT054cDlSpV0q5du/Sf//znou+wXuro+oVrXng0+lL/Y30+hJyvYd26ddq+fbs2bNig+Ph4ffjhh/rggw8uOqpdq1YtffHFFyooKHC5z13VKUlvvfWW/v3vf+vtt99WeHi4ypUrp2bNml3Wc43//yrP5cuXd267sJbz7+vGjRt14403XvT8S13998L9UVTt2rV1xx13aMOGDWrYsKGOHDmiTp06FTu+OD/88IPzQ5/Lef0XeuWVV/TDDz9o4cKFqlWrlv74448r/mkod/vn/BWuS/I3VlRx793tt9+u6tWr68svv1RWVpaaNGmiypUrX1TfvffeW+haClfDz8/vkvXk5ua6/dso6rPPPtOpU6f08ssva8yYMZL+/PcpJydHu3fvVsOGDQuteyUaNGigRx99VJMmTdL8+fNdji0oKLho29W8b5eqZdWqVZL+3F/dunVTgwYNCn1wERgYqKFDh6p///6XnOPBBx9U7969lZmZqc8//9x59kZRgYGBeuWVVy55tFuSIiIi9O2332r37t1q2rSpIiIiNGHCBGVnZ2vPnj3Os1Kef/55PfbYY0pKSlJSUpLeffddzZgx47L/ZpOTkzVs2DANHz78ou+Xn2ez2dS8eXM98cQTWrRoUbFHzKtVq6Zjx45d1roAfA9HtAF4zdixY/XJJ59o165dhbYHBAQoOzvbef9qLt4j6aIjGr///rtuuukmBQcHKywszHkxm/NK8lM6t95660Wn7x46dOiSVxkvTlRUlL755hvn6Y/ntWzZUl9//bW++eYb5xHMkJAQhYSEFDodMTc3V6mpqbr11lsvOX+VKlV07NixQv9TfuHpo5mZmTp37pxatWqlsWPHasWKFfrvf/97yQvWde7cWSdPnrzk1aPPX/jM3c8aSX+G3Q4dOqhRo0YqV66cDh8+7LxCuzuhoaHy9/dXamqqc9vevXudt6tXry5/f/9C72tBQYHzLAN3++NSHnzwQW3cuFGff/652rVr5zyad7l+++23QkdCS/r6v//+ez3yyCOqXbu2/Pz8lJKSUqL1L+Ru/1ytkJAQl2eqdO/eXevWrdO6desuOm1c+vMK/QcOHCj0/pw4caLQvwklUaNGjYtO3/35559Vs2ZNVa5cWeXLly90Bsuvv/5a7FwrVqxQjx49tGbNGq1evVqrV6/W2rVr1bx580IXRTMMQ6dPny7x1wvOGzZsmPbv369169YV2l7038ZL/VyaVQICAvTKK69o7ty5hf5tuPXWWy/6N/T33393vn933XWXbrrpJm3dulWff/65Hn744UvOf6l5Lvy3+Pzp49u3b9c999yjypUr67rrrtPatWtVu3Zt5wc2DodDVatW1RNPPKEFCxbokUceueQF6y5l165deuGFFxQXF3dRyJ46daomTpxYaJufn5/zA78ZM2YUuiil9OfFFGvUqHFZawPwPQRtAF5TvXp1DRgwQFOnTi20/bbbblNSUpLOnTunvXv3Ok87vFJHjhzRypUrde7cOX355ZfavXu3OnfuLOnPq9kmJiZq//79ysvL07Jly9StW7fLDn29evXS9u3b9fnnnysvL087duzQv//970v+PnZxoqKi9N133+n77793HtGWpMjISK1Zs0ZZWVnOq65Lf353dt68eUpNTVV2drZmzJihihUrOsN4Ua1atVJmZqY++OAD5ebmavPmzYWurDx48GBNmDBBZ86cUUFBgXbt2qXy5csXOv38vGrVqmnw4MEaP368lixZoqysLGVnZ+uzzz7TgAED9MADD1zyaFVR1atX1759+5SZmamff/5ZU6dOVdWqVV2eHnyev7+/7r33Xr3//vs6c+aMfvvtNy1fvtz5eKVKlfTwww8rISFBqampysnJ0axZs9S3b1/l5+e73R+X0qVLF23btk1ffPFFsUfcLiUvL0+bN2/WU089pdatWztDhrvXHxgYqLNnz+ro0aPKyspSjRo1lJKSotzcXO3Zs0dLlixRQEDAZe0v6c9Thv/3v//pzJkzuu6661zun6tVt25dl99Nfuihh7Rjxw7t2bNHHTt2vOjxhx9+WH/88YdmzZqlrKws/f777+rXr5/mzJkj6eJ9486jjz6qJUuW6Mcff1Rubq4WLVqkI0eOOK+r0LJlSy1cuFCnT5/WkSNHtGjRokvOc/jwYW3fvl1PPPGE86sc5/+Ljo7WJ5984gzBP/30k/Lz8y/5XfXLUalSJb300kuaMmVKoe233XabNm3apKysLB0+fFirV6++ovkl6ejRo3rggQdK9J3t5s2bq2vXrnr55ZedvdK7d2+tX79eSUlJysvL07fffqvu3bsXuir5Aw88oPfee09//PGH7r333kvO3bt3b3344YfasWOH8vPz9cUXX+jhhx92XjegZcuW2rZtm44ePercrxEREXrvvfecH1B+9913zu9uG4ahkydP6ueffy72Q8gL5eXlacyYMRo8ePAlj363aNFCH330kTZt2qS8vDzt379fS5cudV7J/+TJk3rllVf022+/KScnR++8845+++039erV67L3LwDfQtAG4FXPPPPMRUcHX375ZX3//fe65557NG3aNP3tb3+7qjU6deqkvXv3KjIyUmPHjtWYMWPUoEEDSX9emK1Dhw568skn1axZM/3rX//S3LlzL/tnWurUqaMpU6Zo5syZatasmSZMmKCxY8fqgQceuOz6mjVrpszMTN10002Fvk8ZGRmpX375Rc2aNXOe7iv9GYzvvvtu9e7dW1FRUdq3b58WLVqk66677pLz33TTTUpISNDChQvVvHlzLV++XH369HE+/uqrr8rhcKh9+/Zq2rSp3nnnHc2cOfOiU3rPe/bZZzVlyhStWbNGrVq1Ups2bTR//nyNHz9ezz333GW95oEDB6pcuXJq1aqVXnjhBfXv31/R0dGaPXv2Zf1s1vjx45WXl6e2bdtqxIgRzq8fnD9NduzYsbr99tvVrVs33Xvvvfrvf/+rOXPmyN/f3+3+uJTbbrtNt956q3799VfnxY+Kc/6npxo2bKgmTZpo2rRp6tOnj2bNmuU8ndjd62/ZsqVq1qypTp06KSkpSS+++KKzF1599VUNHz5c3bt317hx41z+HNR5PXv21NatW9W5c2edO3fO5f65Wq1atdKBAweK/Z3lypUrq2HDhmrbtq0CAgIuejwkJESzZ8/Wl19+qRYtWig6OlrNmjVzXpSq6L5xJyYmRt26ddOgQYMUGRmpTz75RAsXLnR+kDR58mTZbDa1a9dOTz/9tJ588slLzrNixQrVqVOn0Onh5913333y9/fX//3f/0n682e3qlevXugaBSXVtWtX1a5du9C2YcOG6cyZM2rZsqX+/ve/X9W/jefOndPPP/9c4u/5jxgxQqmpqXr33Xcl/fnv1Msvv6wpU6aoSZMmevnllzVixIhCHxp26dJFO3bsUOfOnS/5nkt/fmj55JNP6oUXXlCTJk00c+ZMJSQkOPdBw4YNdfz4cd19993Ov/MmTZro4MGDztPGIyIi9Pe//12jR4/W3Xff7dyHQ4YMkeT6573++9//6sCBA5o+fXqh74k3bNhQqampat++vSZNmqTXXnvN+ZN4Dz30kPPfvFGjRjlPJ2/VqpW++OILvf/++4W+/w/g2uJnnP+yGwAAZUhubq7zf9q/++47xcTEaMeOHQoODvZyZXj88cfVvn17Pfvss94uxSu6d++ubt266S9/+cslH+/QoYOef/559ezZ08OVwZvq1q2rhQsXFjpDCYDv4og2AKDMefnll/XXv/5Vp0+fVkZGhubNm6eIiAhCdikxbNgwLV68WKdPn/Z2KR6XlJSkM2fOFHvkFABwbSBoAwDKnBEjRqhy5crq3LmzOnXqpPz8fE2bNs3bZeH/16pVKz3yyCOaMGGCt0vxqJMnT+of//iHEhIS3F6rYNy4cVd85XiULZMmTbrkVw4A+DZOHQcAAAAAwEQc0QYAAAAAwEQ2bxfgSnp6hrdLKLGwsCA5HJneLgMwFX0NX0NPwxfR1/BF9DVKM7u9+GvDcETbZDbb1f80ClDa0NfwNfQ0fBF9DV9EX6OsImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCJLg3Z2drY6duyoVatWObclJyerbt26Vi4LAAAAAIDXWBq0Z8+erdDQUOf9nJwczZ07V3a73cplAQAAAADwGsuC9k8//aSDBw+qXbt2zm2JiYmKjY1VQECAVcsCAAAAAOBVNqsmjouL07hx47R69WpJ0s8//6x9+/Zp6NChmjZt2mXNERYWJJvN36oSLWO3B3u7BMB09DV8DT0NX0RfwxfR176h6/A1zttrE7oVun+htQndPFWSpSwJ2qtXr1bjxo1Vo0YN57YpU6Zo7NixJZrH4cg0uzTL2e3BSk/P8HYZgKnoa/gaehq+iL6GL6KvfZOr97Qsvd+uPgSyJGhv2rRJhw8f1qZNm3TkyBHZbDaVK1dOL774oiTp2LFj6tOnjxYvXmzF8gAAAAAAeI0lQfuNN95w3p41a5aqVaumnj17Ord16NCBkA0AAAAA8En8jjYAAAAAACay7GJo5w0ePPiibRs2bLB6WQAAAAAAvIIj2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJbFZOnp2drYceekjPPfecatWqpfj4eNlsNgUEBGjatGmqXLmylcsDAAAAAOBxlh7Rnj17tkJDQyVJCxYsUHx8vBYtWqSIiAgtX77cyqUBAAAAAPAKy45o//TTTzp48KDatWsnSZo5c6YkyTAMHT16VE2bNrVqaQAAAAAAvMayoB0XF6dx48Zp9erVzm1ffvmlJk+erNq1a+uRRx5xO0dYWJBsNn+rSrSM3R7s7RIA09HX8DX0NHwRfQ1fRF+XXV2Hr5EkrU3oVmi7q/fUV95vS4L26tWr1bhxY9WoUaPQ9qioKLVp00bTp0/X3LlzNXDgQJfzOByZVpRnKbs9WOnpGd4uAzAVfQ1fQ0/DF9HX8EX0tW8o+h66ek/L0vvt6kMBS4L2pk2bdPjwYW3atElHjhxRQECAKlasqAcffFB+fn66//77NWvWLCuWBgAAAADAqywJ2m+88Ybz9qxZs1StWjXNnj1bt912m+rXr69du3apVq1aViwNAAAAAIBXWfrzXheaPHmyXnnlFfn7+yswMFDx8fGeWhoAAAAAAI+xPGgPHjzYeXvp0qVWLwcAAAAAgFdZ+jvaAAAAAABcawjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJjI0qCdnZ2tjh07atWqVUpLS9PTTz+tPn366Omnn1Z6erqVSwMAAAAA4BWWBu3Zs2crNDRUkvTGG2/o8ccf1+LFi9W5c2ctWLDAyqUBAAAAAPAKm1UT//TTTzp48KDatWsnSZowYYIqVKggSQoLC9OePXusWhoAAAAAAK+xLGjHxcVp3LhxWr16tSQpKChIkpSfn68lS5boueeecztHWFiQbDZ/q0q0jN0e7O0SANPR1/A19DR8EX0NX0Rfl31F30NX76mvvN+WBO3Vq1ercePGqlGjRqHt+fn5GjlypFq2bKnIyEi38zgcmVaUZym7PVjp6RneLgMwFX0NX0NPwxfR1/BF9LVvKPoeunpPy9L77epDAUuC9qZNm3T48GFt2rRJR44cUUBAgG666SatXr1aNWvW1PPPP2/FsgAAAAAAeJ0lQfuNN95w3p41a5aqVaum48ePq3z58hoyZIgVSwIAAAAAUCpY9h3topYsWaKcnBz17dtXknT77bdr4sSJnloeAAAAAACPsDxoDx48WJLUs2dPq5cCAAAAAMDrLP0dbQAAAAAArjUEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMZGnQzs7OVseOHbVq1SpJ0qJFi3TXXXfp7NmzVi4LAAAAAIDXWBq0Z8+erdDQUEnS6tWrdfz4cVWpUsXKJQEAAAAA8CqbVRP/9NNPOnjwoNq1aydJ6tSpkypVqqS1a9datSQAAAAAAF5n2RHtuLg4jRo1ynm/UqVKVi0FAAAAAECpYckR7dWrV6tx48aqUaPGVc0TFhYkm83fpKo8x24P9nYJgOnoa/gaehq+iL6GL6Kvy76i76Gr99RX3m9LgvamTZt0+PBhbdq0SUeOHFFAQIBuuukmtWrVqkTzOByZVpRnKbs9WOnpGd4uAzAVfQ1fQ0/DF9HX8EX0tW8o+h66ek/L0vvt6kMBS4L2G2+84bw9a9YsVatWrcQhGwAAAACAsshjv6M9e/Zs9e3bV+np6erXr5/i4+M9tTQAAAAAAB5j2VXHzxs8eLDz9rPPPmv1cgAAAAAAeJXHjmgDAAAAAHAtIGgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJrI0aGdnZ6tjx45atWqV0tLS1LdvX8XGxmro0KHKzc21cmkAAAAAALzC0qA9e/ZshYaGSpJmzpyp2NhYLVmyRNWqVdPKlSutXBoAAAAAAK+wLGj/9NNPOnjwoNq1aydJ2r59uzp27ChJ6tixo7766iurlgYAAAAAwGtsVk0cFxencePGafXq1ZKkrKwsBQQESJLsdrvS09PdzhEWFiSbzd+qEi1jtwd7uwTAdPQ1fA09DV9EX8MX0ddlX9H30NV76ivvtyVBe/Xq1WrcuLFq1Kjh3Obn5+e8bRjGZc3jcGSaXpvV7PZgpadneLsMwFT0NXwNPQ1fRF/DF9HXvqHoe+jqPS1L77erDwUsCdqbNm3S4cOHtWnTJh05ckQBAQGqWLGisrOzFRgYqKNHj6pKlSpWLA0AAAAAgFdZErTfeOMN5+1Zs2apWrVq+u6777R+/Xp169ZNn332mdq0aWPF0gAAAAAAeJXHfkd78ODBWr16tWJjY3Xq1Cl1797dU0sDAAAAAOAxll0M7bzBgwc7by9YsMDq5QAAAAAA8CqPHdEGAAAAAOBaQNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQ2qybOysrSqFGjdOLECeXk5GjQoEG69dZbNX78ePn5+em2227TxIkTZbNZVgIAAAAAAB5nWcrduHGjwsPD1a9fP6WmpuqZZ55R7dq11b9/f7Vt21ZvvfWWPv30U3Xt2tWqEgAAAAAA8DjLTh3v0qWL+vXrJ0lKS0tT1apV9euvv6pRo0aSpDZt2mjr1q1WLQ8AAAAAgFdYft52TEyMjhw5osTERCUmJmrz5s3q3r27kpOTdfz4cZfPDQsLks3mb3WJprPbg71dAmA6+hq+hp6GL6Kv4Yvo69Kr6/A1zttrE7pddP+8ou+hq/fUV95vy4P20qVLtXfvXo0YMUJz587VxIkTtWrVKjVv3lyGYbh8rsORaXV5prPbg5WenuHtMgBT0dfwNfQ0fBF9DV9EX5cdRd+nC++7eszdPKWZqw8FLAvaKSkpuuGGG3TzzTerfv36ys/PV0BAgObMmSNJSk5O1rFjx6xaHgAAAAAAr7DsO9o7duzQ/PnzJUnHjx9XZmamFi1apE2bNkmSVq1apQ4dOli1PAAAAAAAXmFZ0I6JidHJkycVGxur/v37a/z48XrkkUf05ptvqnfv3qpZs6batWtn1fIAAAAAAHiFZaeOBwYGKiEh4aLtK1eutGpJAAAAAAC8zrIj2gAAAAAAXIsI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYyGbVxFlZWRo1apROnDihnJwcDRo0SJUqVdLrr78um82moKAgxcfHKyQkxKoSAAAAAADwOMuC9saNGxUeHq5+/fopNTVVzzzzjK677jpNnz5dtWvXVmJiopYtW6b+/ftbVQIAAAAAAB5nWdDu0qWL83ZaWpqqVq2q8uXL69SpU5Kk06dPq3bt2lYtDwAAAACAV1gWtM+LiYnRkSNHlJiYKJvNpr59++r6669XSEiIhg8f7vK5YWFBstn8rS7RdHZ7sLdLAExHX8PX0NPwRfQ1fBF9XTYUfZ8uvO/qMXfzlFWWB+2lS5dq7969GjFihCpXrqw333xTTZs2VVxcnJYsWaInn3yy2Oc6HJlWl2c6uz1Y6ekZ3i4DMBV9DV9DT8MX0dfwRfR12VH0fbrwvqvH3M1Tmrn6UMCyq46npKQoLS1NklS/fn3l5+dr+/btatq0qSSpVatWSklJsWp5AAAAAAC8wrKgvWPHDs2fP1+SdPz4cWVmZurOO+/UwYMHJUm7d+9WzZo1rVoeAAAAAACvsOzU8ZiYGI0ZM0axsbHKzs7W+PHjFRoaqrFjx6p8+fIKCQnRa6+9ZtXyAAAAAAB4hWVBOzAwUAkJCRdtX7p0qVVLAgAAAADgdZadOg4AAAAAwLWIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiWxWTZyVlaVRo0bpxIkTysnJ0aBBg/Svf/1LDodDknTq1Ck1btxYkyZNsqoEAAAAAAA8zrKgvXHjRoWHh6tfv35KTU3VM888o/Xr1zsfHz16tB577DGrlgcAAAAAwCssC9pdunRx3k5LS1PVqlWd9w8dOqSMjAw1atTIquUBAAAAAPAKy4L2eTExMTpy5IgSExOd2xYuXKg+ffpYvTQAAAAAAB5nedBeunSp9u7dqxEjRujjjz/WuXPntHPnTk2cONHtc8PCgmSz+Vtdouns9mBvlwCYjr6Gr6Gn4Yvoa/gi+rpsKPo+XXjf1WPu5imrLAvaKSkpuuGGG3TzzTerfv36ys/P18mTJ7Vv377LPmXc4ci0qjzL2O3BSk/P8HYZgKnoa/gaehq+iL6GL6Kvy46i79OF91095m6e0szVhwKW/bzXjh07NH/+fEnS8ePHlZmZqbCwMO3evVv16tWzalkAAAAAALzKsqAdExOjkydPKjY2Vv3799f48eNVrlw5paen64YbbrBqWQAAAAAAvMqyU8cDAwOVkJBw0fZx48ZZtSQAAAAAAF5n2RFtAAAAAACuRQRtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQ2qybOysrSqFGjdOLECeXk5GjQoEFq3bq1Ro0apV9//VXXXXedZs6cqZCQEKtKAAAAAADA4yw7or1x40aFh4dr8eLFeuONNzR16lQtX75cYWFhWrlypbp06aIdO3ZYtTwAAAAAAF5h2RHtLl26OG+npaWpatWq2rhxo4YMGSJJio6OtmppAAAAAAC8xrKgfV5MTIyOHDmixMREvfDCC/rmm280Y8YMXX/99ZowYYJCQ0OLfW5YWJBsNn+rSzSd3R7s7RIA09HX8DX0NHwRfQ1fRF+XDUXfpwvvu3rM3TxlleVBe+nSpdq7d69GjBihgoIC3XzzzXr33Xf19ttva86cOXrppZeKfa7DkWl1eaaz24OVnp7h7TIAU9HX8DX0NHwRfQ1fRF+XHUXfpwvvu3rM3TylmasPBSz7jnZKSorS0tIkSfXr11d+fr7KlSune+65R5LUunVrHTx40KrlAQAAAADwCsuC9o4dOzR//nxJ0vHjx5WZmalu3bopOTlZkrRnzx7VqlXLquUBAAAAAPAKy04dj4mJ0ZgxYxQbG6vs7GyNHz9ekZGRGjNmjFavXq2AgADFxcVZtTwAAAAAAF5hWdAODAxUQkLCRdtff/11q5YEAAAAAMDrLDt1HAAAAACAaxFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAAT2ayaOCsrS6NGjdKJEyeUk5OjQYMGacuWLfruu+903XXXSZL++te/ql27dlaVAAAAAACAx1kWtDdu3Kjw8HD169dPqampeuaZZ9SkSRNNnjxZ9evXt2pZAAAAAAC8yrKg3aVLF+fttLQ0Va1aVWfPnrVqOQAAAAAASgXLgvZ5MTExOnLkiBITEzVt2jS9+eabOnPmjKpWraqxY8cqNDS02OeGhQXJZvO3ukTT2e3B3i4BMB19DV9DT8MX0dfwRfR16dJ1+JpLbi/6Pl1439Vj7uYpqywP2kuXLtXevXs1YsQIDRkyRHfccYdq1aql2bNna9asWRo3blyxz3U4Mq0uz3R2e7DS0zO8XQZgKvoavoaehi+ir+GL6Ouyo+j7dOF9V4+5m6c0c/WhgGVXHU9JSVFaWpokqX79+srPz1eTJk1Uq1YtSVLnzp31448/WrU8AAAAAABeYVnQ3rFjh+bPny9JOn78uDIzMzVmzBj9/vvvkqTt27frzjvvtGp5AAAAAAC8wrJTx2NiYjRmzBjFxsYqOztb48ePV0BAgAYPHqygoCBVrFhRU6ZMsWp5AAAAAAC8wrKgHRgYqISEhIu2t27d2qolAQAAAADwOstOHQcAAAAA4FpE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARJYF7aysLA0dOlR9+vTRY489po0bNzofS05OVt26da1aGgAAAAAAr7FZNfHGjRsVHh6ufv36KTU1Vc8884zat2+vnJwczZ07V3a73aqlAQAAAADwGsuOaHfp0kX9+vWTJKWlpalq1aqSpMTERMXGxiogIMCqpQEAAAAA8BrLjmifFxMToyNHjigxMVE///yz9u3bp6FDh2ratGlunxsWFiSbzd/qEk1ntwd7uwTAdPQ1fA09DV9EX8MX0dee1XX4GufttQndLrpfnKLv04X3XT3mbp6yyvKgvXTpUu3du1cjRozQzTffrLFjx172cx2OTAsrs4bdHqz09AxvlwGYir6Gr6Gn4Yvoa/gi+tq7iu57V++Fq7FXM09p5upDActOHU9JSVFaWpokqX79+jp79qwOHjyoF198UY8//riOHTumPn36WLU8AAAAAABeYdkR7R07dig1NVVjxozR8ePHVVBQoA0bNqhcuT+zfYcOHbR48WKrlgcAAAAAwCssO6IdExOjkydPKjY2Vv3799f48eOdIRsAAAAAAF9l2RHtwMBAJSQkFPv4hg0brFoaAAAAAACv4RAzAAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKbVRNnZWVp1KhROnHihHJycjRo0CCFhoYqPj5eNptNAQEBmjZtmipXrmxVCQAAAAAAeJxlQXvjxo0KDw9Xv379lJqaqmeeeUZ169ZVfHy8atSooTfffFPLly/XwIEDrSoBAAAAAACPsyxod+nSxXk7LS1NVatW1cyZMyVJhmHo6NGjatq0qVXLAwAAAADgFZYF7fNiYmJ05MgRJSYmSpK+/PJLTZ48WbVr19Yjjzxi9fIAAAAAAHiU5UF76dKl2rt3r0aMGKGPP/5YUVFRatOmjaZPn665c+e6PHU8LCxINpu/1SWazm4P9nYJgOnoa/gaehq+iL6GL6Kvzdd1+Brn7bUJ3Qrdv1DRfe/qvXA19mrmKassC9opKSm64YYbdPPNN6t+/frKz8/Xp59+qi5dusjPz0/333+/Zs2a5XIOhyPTqvIsY7cHKz09w9tlAKair+Fr6Gn4Ivoavoi+tp6r/Vv0sSsdezXzlGauPhSw7Oe9duzYofnz50uSjh8/rszMTM2ePVt79+6VJO3atUu1atWyankAAAAAALzCsiPaMTExGjNmjGJjY5Wdna3x48fLbrfrlVdekb+/vwIDAxUfH2/V8gAAAAAAeIVlQTswMFAJCQkXbV+6dKlVSwIAAAAA4HWWnToOAAAAAMC1iKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmMhm1cRZWVkaNWqUTpw4oZycHA0aNEj16tXT6NGjlZeXJ5vNpmnTpslut1tVAgAAAAAAHmdZ0N64caPCw8PVr18/paam6plnnlHjxo31+OOPq0uXLvrggw+0YMECjRw50qoSAAAAAADwOMuCdpcuXZy309LSVLVqVU2YMEEVKlSQJIWFhWnPnj1WLQ8AAAAAgFdYFrTPi4mJ0ZEjR5SYmKigoCBJUn5+vpYsWaLnnnvO5XPDwoJks/lbXaLp7PZgb5cAmI6+hq+hp+GL6Gv4Ivr66nUdvsZ5e21Ct0KPudq/RR+70rFXM09ZZXnQXrp0qfbu3asRI0bo448/VkFBgUaOHKmWLVsqMjLS5XMdjkyryzOd3R6s9PQMb5cBmIq+hq+hp+GL6Gv4IvrafEX3p6v9a9bYq5mnNHP1oYBlVx1PSUlRWlqaJKl+/frKz8/XyZMnNXr0aNWsWVPPP/+8VUsDAAAAAOA1lgXtHTt2aP78+ZKk48ePKzMzU1u3blX58uU1ZMgQq5YFAAAAAMCrLDt1PCYmRmPGjFFsbKyys7M1fvx4zZ07Vzk5Oerbt68k6fbbb9fEiROtKgEAAAAAAI+zLGgHBgYqISGh0LYOHTpYtRwAAAAAAKWCZaeOAwAAAABwLSJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAiy4J2VlaWhg4dqj59+uixxx7Txo0bJUmLFi3SXXfdpbNnz1q1NAAAAAAAXmOzauKNGzcqPDxc/fr1U2pqqp555hmdPn1ax48fV5UqVaxaFgAAAAAAr7IsaHfp0sV5Oy0tTVWrVlWnTp1UqVIlrV271qplAQAAAADwKsuC9nkxMTE6cuSIEhMTValSpRI9NywsSDabv0WVWcduD/Z2CYDp6Gv4Gnoavoi+hi+iry+t6/A1zttrE7q5vH+hovvT1f41a+zVzFNWWR60ly5dqr1792rEiBH6+OOP5efnd9nPdTgyLazMGnZ7sNLTM7xdBmAq+hq+hp6GL6Kv4Yvo68tTdB+52mfeGHs185Rmrj4UsOxiaCkpKUpLS5Mk1a9fX/n5+Tp58qRVywEAAAAAUCpYFrR37Nih+fPnS5KOHz+uzMxMhYWFWbUcAAAAAAClgmVBOyYmRidPnlRsbKz69++v8ePHa86cOerbt6/S09PVr18/xcfHW7U8AAAAAABeYdl3tAMDA5WQkFBoW4cOHfTss89atSQAAAAAAF5n2RFtAAAAAACuRQRtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExks2rirKwsjRo1SidOnFBOTo4GDRqkevXqaeTIkcrPz5fdbte0adMUEBBgVQkAAAAAAHicZUe0N27cqPDwcC1evFhvvPGGpk6dqpkzZyo2NlZLlixRtWrVtHLlSquWBwAAAADAKywL2l26dFG/fv0kSWlpaapataq2b9+ujh07SpI6duyor776yqrlAQAAAADwCstOHT8vJiZGR44cUWJiov7yl784TxW32+1KT093+dywsCDZbP5Wl2g6uz3Y2yUApqOv4Wvoafgi+hq+6Fru667D1zhvr03oVuj+hYruI1f7zBtjr2aessryoL106VLt3btXI0aMkJ+fn3O7YRhun+twZFpZmiXs9mClp2d4uwzAVPQ1fA09DV9EX8MX0df/j6v9UPSx0jb2auYpzVx9KGDZqeMpKSlKS0uTJNWvX1/5+fmqWLGisrOzJUlHjx5VlSpVrFoeAAAAAACvsCxo79ixQ/Pnz5ckHT9+XJmZmWrVqpXWr18vSfrss8/Upk0bq5YHAAAAAMArLDt1PCYmRmPGjFFsbKyys7M1fvx4hYeH66WXXtKyZct0yy23qHv37lYtDwAAAACAV1gWtAMDA5WQkHDR9gULFli1JAAAAAAAXmfZqeMAAAAAAFyLCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmMhm5eTx8fHauXOn8vLyNGDAAN1+++0aP368/Pz8dNttt2nixImy2SwtAQAAAAAAj7Is5W7btk0HDhzQsmXL5HA41KNHD9WvX1/9+/dX27Zt9dZbb+nTTz9V165drSoBAAAAAACPs+zU8WbNmmnGjBmSpJCQEGVlZemXX35Ro0aNJElt2rTR1q1brVoeAAAAAACvsCxo+/v7KygoSJK0YsUKRUVFqW7dutq8ebMkKTk5WcePH7dqeQAAAAAAvMLyL0gnJSVp5cqVmj9/vv744w9NnDhRq1atUvPmzWUYhsvnhoUFyWbzt7pE09ntwd4uATAdfQ1fQ0/DF9HX8EW+3tddh69x3l6b0M15f21Ct0LjXO2Hoo+VtrFXM09ZZWnQTk5OVmJiot555x0FBwcrODhYc+bMcT527Ngxl893ODKtLM8Sdnuw0tMzvF0GYCr6Gr6GnoYvoq/hi661vr7wtRZ93a72Q2kfezXzlGauPhSw7NTxjIwMxcfHa86cOQoNDZUkzZw5U5s2bZIkrVq1Sh06dLBqeQAAAAAAvMKyI9rr1q2Tw+HQsGHDnNsGDx7sDN8tWrRQu3btrFoeAAAAAACvsCxoR0dHKzo6+qLtK1eutGpJAAAAAAC8zrJTxwEAAAAAuBYRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAAT2aycPD4+Xjt37lReXp4GDBigsLAwvf7667LZbAoKClJ8fLxCQkKsLAEAAAAAAI+yLGhv27ZNBw4c0LJly+RwONSjRw9VrlxZ06dPV+3atZWYmKhly5apf//+VpUAAAAAAIDHWRa0mzVrpkaNGkmSQkJClJWVpZCQEJ06dUqSdPr0adWuXduq5QEAAAAA8ArLgra/v7+CgoIkSStWrFBUVJQGDBigvn376vrrr1dISIiGDx/uco6wsCDZbP5WlWgZuz3Y2yUApqOv4Wvoafgi+hq+6Frq6wtfa9HX7Wo/lPaxVzNPWWXpd7QlKSkpSStXrtT8+fM1ePBgvfnmm2ratKni4uK0ZMkSPfnkk8U+1+HItLo809ntwUpPz/B2GYCp6Gv4Gnoavoi+hi+61vr6wtda9HW72g+lfezVzFOaufpQwNKrjicnJysxMVHz5s1TcHCwfvzxRzVt2lSS1KpVK6WkpFi5PAAAAAAAHmdZ0M7IyFB8fLzmzJmj0NBQSdKNN96ogwcPSpJ2796tmjVrWrU8AAAAAABeYdmp4+vWrZPD4dCwYcOc28aPH6+xY8eqfPnyCgkJ0WuvvWbV8gAAAAAAeIVlQTs6OlrR0dEXbV+6dKlVSwIAAAAA4HWWfkcbAAAAAIBrDUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABPZrJw8Pj5eO3fuVF5engYMGKB///vfcjgckqRTp06pcePGmjRpkpUlAAAAAADgUZYF7W3btunAgQNatmyZHA6HevTooU2bNjkfHz16tB577DGrlgcAAAAAwCssC9rNmjVTo0aNJEkhISHKyspSfn6+/P39dejQIWVkZDgfBwAAAADAV1gWtP39/RUUFCRJWrFihaKiouTv7y9JWrhwofr06eN2jrCwINls/laVaBm7PdjbJQCmo6/ha+hp+CL6Gr6otPR11+FrnLfXJnQrdP9CRR9zN/ZCF77Woq/b1X4o7WOvZp6yytLvaEtSUlKSVq5cqfnz50uScnNztXPnTk2cONHtcx2OTIurM5/dHqz09AxvlwGYir6Gr6Gn4Yvoa/ii0trXrmoq+tiVjjVrntIw9mrmKc1cfShgadBOTk5WYmKi3nnnHQUH/1nEN998wynjAAAAAACfZVnQzsjIUHx8vN577z2FhoY6t+/evVv16tWzalkAAAAAALzKsqC9bt06ORwODRs2zLktLi5O6enpuvXWW61aFgAAAAAAr7IsaEdHRys6Ovqi7ePGjbNqSQAAAAAAvK6ctwsAAAAAAMCXELQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMJHNysnj4+O1c+dO5eXlacCAAWrfvr1GjRqlX3/9Vdddd51mzpypkJAQK0sAAAAAAMCjLAva27Zt04EDB7Rs2TI5HA716NFD6enpCgsLU0JCgpYtW6YdO3aoY8eOVpUAAAAAAIDHWRa0mzVrpkaNGkmSQkJClJWVpY0bN2rIkCGSpOjoaKuWBgAAAADAaywL2v7+/goKCpIkrVixQlFRUUpJSdE333yjGTNm6Prrr9eECRMUGhpa7BxhYUGy2fytKtEydnuwt0sATEdfw9fQ0/BF9DV8kZV93XX4GufttQndCt2/0NqEbpddU9HHrnSsWfOUhrFXM09ZZel3tCUpKSlJK1eu1Pz58/XYY4/p5ptv1rvvvqu3335bc+bM0UsvvVTscx2OTKvLM53dHqz09AxvlwGYir6Gr6Gn4Yvoa/giT/a1q3WKPuaJsd5Y06qxVzNPaebqQwFLrzqenJysxMREzZs3T8HBwbrxxht1zz33SJJat26tgwcPWrk8AAAAAAAeZ1nQzsjIUHx8vObMmeM8PTwqKkrJycmSpD179qhWrVpWLQ8AAAAAgFdYdur4unXr5HA4NGzYMOe2uLg4TZ8+XatXr1ZAQIDi4uKsWh4AAAAAAK+wLGhHR0df8srir7/+ulVLAgAAAADgdZZ+RxsAAAAAgGsNQRsAAAAAABMVe+r477//7vKJt9xyi+nFAAAAAABQ1hUbtHv37i0/Pz8ZhqFjx46pUqVKys/PV2Zmpm699VZ99tlnnqwTAAAAAIAyodigvXnzZknS1KlT9cgjj6hBgwaSpF27dmnt2rWeqQ4AAAAAgDLG7Xe0f/zxR2fIlqS7775bBw8etLQoAAAAAADKKrc/75WXl6eEhAQ1bdpUfn5++u6775STk+OJ2gAAAAAAKHPcHtF+4403VK5cOS1dulQffvihzp07pzfeeMMDpQEAAAAAUPa4PaJ9ww036Omnn9b//vc/NWzYUAUFBSpXjl8FAwAAAADgUtwm5n//+9+Kjo7W6NGjJUmTJk3SihUrLC8MAAAAAICyyG3Q/vDDD7VmzRqFhYVJkl566SUtX77c8sIAAAAAACiL3AbtgIAAVaxY0Xk/MDBQ5cuXt7QoAAAAAADKKrff0Q4NDdW//vUv5eTkaM+ePVq3bp0qV67sidoAAAAAAChz3B7RfuWVV7R7926dPXtWY8eOVU5Ojl599VVP1AYAAAAAQJnj9oj29ddfr/Hjx3uiFgAAAAAAyjy3Qbtt27by8/MrtM3f31+1atXSSy+9pDvvvNOy4gAAAAAAKGvcBu1+/fopPT1d9913n/z9/fXZZ58pICBAt99+uyZOnKgPPvig2OfGx8dr586dysvL04ABA7R9+3Z99913uu666yRJf/3rX9WuXTvTXgwAAAAAAN7mNmh//vnnev/9953369Wrp7/97W8aOHCgFi5cWOzztm3bpgMHDmjZsmVyOBzq0aOHIiMjNXnyZNWvX9+c6gEAAAAAKGXcBu1Tp05p//79qlOnjiTpl19+0e+//67U1FT98ccfxT6vWbNmatSokSQpJCREWVlZOnPmjEllAwAAAABQOrkN2n//+981YMAAZWVlSfrz+9mjR4/Wvn37NGjQoGKf5+/vr6CgIEnSihUrFBUVpZMnT+rNN9/UmTNnVLVqVY0dO1ahoaHmvBIAAAAAAEqBy7oY2saNG+VwOGQYhsLCwi66OJorSUlJWrlypebPn69t27bpjjvuUK1atTR79mzNmjVL48aNK/a5YWFBstn8L3ut0sJuD/Z2CYDp6Gv4Gnoavoi+hi+y24PVdfga5/21Cd1c3r+Qq7FrE7pdtI6rGjw91htrWjX2auYpq9wG7QMHDmjFihU6ffq0DMNwbo+Pj3c7eXJyshITE/XOO+8oODhYnTt3dj7WuXNnTZw40eXzHY5Mt2uUNnZ7sNLTM7xdBmAq+hq+hp6GL6Kv4Ysu1dfu7l/uWLPmsWpsaa+vJGOvZp7SzNWHAm6D9rBhw/Tggw+W+AJmGRkZio+P13vvvec8PXzgwIEaP368brnlFm3fvp2fBgMAAAAA+By3QfvGG2/U888/X+KJ161bJ4fDoWHDhjm39erVS4MHD1ZQUJAqVqyoKVOmlHheAAAAAABKM7dBOyoqSlu2bFHz5s1ls/2/4eXKlXP5vOjoaEVHR1+0vXv37iWvEgAAAACAMsJt0J49e/ZFP+Pl5+envXv3WlYUAAAAAABlldugvWPHjou2/fLLL1bUAgAAAABAmec2aOfn52vLli1yOBySpNzcXCUmJmrDhg2WFwcAAAAAQFnjNmiPGDFCp0+f1o8//qgmTZpo165dGjx4sCdqAwAAAACgzHF9RTNJR44c0bvvvqtatWpp5syZWrJkiXbv3u2J2gAAAAAAKHPcBu3z8vLylJOTo2rVqungwYNW1gQAAAAAQJnl9tTxli1bat68eerUqZN69Oghu92ugoICT9QGAAAAAECZ4zZoDxkyRPn5+fL391dERIROnDihe++91xO1AQAAAABQ5rgN2llZWdqyZYvOnDkjwzAkSevWrdOjjz5qeXEAAAAAAJQ1boN2//79ZbPZdNNNNxXaTtAGAAAAAOBiboN2bm6uFi1a5IlaAAAAAAAo89xedbxBgwY6efKkJ2oBAAAAAKDMK/aIdmxsrPz8/JSfn68HHnhAtWvXlr+/v/PxDz74wCMFAgAAAABQlhQbtIcNG+bBMgAAAAAA8A3FBu3mzZtrx44duueee5zbTp06pf3796t58+YeKQ4AAAAAgLKm2O9or1u3TmPGjFFGRoZz26lTp/SPf/xDW7du9UhxAAAAAACUNcUG7ffff1/vv/++goODndtuu+02vfPOO5ozZ45HigMAAAAAoKwpNmhf6rezJemmm26SYRiXNXl8fLyio6PVq1cvffbZZ87tycnJqlu37hWUCwAAAABA6Vbsd7QvPGX8QgUFBXI4HG4n3rZtmw4cOKBly5bJ4XCoR48euu+++5STk6O5c+fKbrdfedUAAAAAAJRSxR7Rbt26tRISElRQUODclpubq8mTJ6tdu3ZuJ27WrJlmzJghSQoJCVFWVpby8/OVmJio2NhYBQQEXH31AAAAAACUMsUe0R46dKhGjhypjh07ql69eiooKNAPP/ygyMhIjR492u3E/v7+CgoKkiStWLFCUVFR+u2337Rv3z4NHTpU06ZNcztHWFiQbDZ/t+NKG7s92P0goIyhr+Fr6Gn4IvoapU3X4Wuct9cmdCt0/0JFH3M1tmifu+p7V2PNmudara8kY69mnrKq2KBdoUIFzZgxQ7/++qsOHjwof39/3XnnnapWrVqJFkhKStLKlSs1f/58DR8+XGPHjr3s5zocmSVaqzSw24OVnn7p0+6Bsoq+hq+hp+GL6GuUdq76s+hjnhjrjTV9qb6SjL2aeUozVx8KFBu0z6tZs6Zq1qx5RQsnJycrMTFR77zzjjIzM3Xo0CG9+OKLkqRjx46pT58+Wrx48RXNDQAAAABAaeQ2aF+pjIwMxcfH67333lNoaKikP49un9ehQwdCNgAAAADA51gWtNetWyeHw6Fhw4Y5t8XFxemWW26xakkAAAAAALzObdD+3//+p6NHj6pp06Zavny5/vvf/+qvf/2rbr/9dpfPi46OVnR0dLGPb9iwoeTVAgAAAABQyhX7817njR49WuXLl9cPP/ygFStW6P7779err77qidoAAAAAAChz3AbtcuXKqVGjRvr888/1xBNPqG3btjIMwxO1AQAAAABQ5rgN2mfPntX333+v9evXKyoqSrm5uTpz5ownagMAAAAAoMxxG7SfeeYZjRs3To8//rgqV66sWbNm6eGHH/ZEbQAAAAAAlDluL4bWpUsXdenSxXn/73//u/z8/CwtCgAAAACAsqrYoN2hQweXgfqLL76wpCAAAAAAAMqyYoP2e++9J0latmyZ7Ha7WrZsqfz8fG3dulWZmZmeqg8AAAAAgDKl2KB96623SpIOHTqkESNGOLffddddGjhwoPWVAQAAAABQBrm9GFpqaqq2bNmizMxMZWdn66uvvlJqaqonagMAAAAAoMxxezG0V155RXFxcdq/f78k6Y477tC4ceMsLwwAAAAAgLLIbdCuUKGCli5d6olaAAAAAAAo89yeOj516lRP1AEAAAAAgE9we0S7WrVq6tu3r+6++26VL1/euX3o0KGWFgYAAAAAQFl0WUG7WrVqnqgFAAAAAIAyz23Qfv7555WZmamff/5Zfn5+qlWrlipWrOiJ2gAAAAAAKHPcBu2kpCRNnDhRN910kwoKCnT8+HFNmjRJbdu29UR9AAAAAACUKW6D9jvvvKOPP/5YlStXliQdPXpUQ4cOvaygHR8fr507dyovL08DBgyQ3W5XfHy8bDabAgICNG3aNOe8AAAAAAD4ArdBu3z58oXCcNWqVQtdFK0427Zt04EDB7Rs2TI5HA716NFDjRo1Unx8vGrUqKE333xTy5cv18CBA6/uFQAAAAAAUIq4DdrXXXed5s+fr1atWkmStmzZouuuu87txM2aNVOjRo0kSSEhIcrKytI///lP+fv7yzAMHT16VE2bNr3K8gEAAAAAKF3cBu3JkydrxowZ+vjjjyVJEREReu2119xO7O/vr6CgIEnSihUrFBUVJX9/f3355ZeaPHmyateurUceecTlHGFhQbLZ/C/ndZQqdnuwt0sATEdfw9fQ0/BF9DVKouvwNZfcvjahW6HHit6/3LFrE7oVGuuqP4s+5omx3ljTl+orydirmaesKjZoDxw4UG3atFFUVJT+8Y9/XPECSUlJWrlypebPny9JioqKUps2bTR9+nTNnTvX5anjDkfmFa/rLXZ7sNLTM7xdBmAq+hq+hp6GL6KvYZaifeSqr1yNNWse6iud9ZVk7NXMU5q5+lCgXHEPPPzww9qzZ4+eeuop3XfffZo0aZI2btyorKysy144OTlZiYmJmjdvnoKDg/X5559Lkvz8/HT//fdr586dJXgZAAAAAACUfsUe0X744Yf18MMPS5J+/fVXffXVV/r444/12muvqVq1anrvvfdcTpyRkaH4+Hi99957Cg0NlSTNmjVL1atXV/369bVr1y7VqlXLtBcCAAAAAEBp4PY72pJUoUIFlS9fXhUqVFBgYOBlTbxu3To5HA4NGzbMuW3cuHF65ZVX5O/vr8DAQMXHx19R0QAAAAAAlFbFBu2kpCT95z//0TfffKPg4GA1b95c3bt31z/+8Q8FBAS4nTg6OlrR0dEXbV+6dOnVVQwAAAAAQClWbNCeMGGCAgMDFR0drQ4dOuiOO+7wZF0AAAAAAJRJxQbtrVu3at++ffrqq68UFxenX3/9VY0aNVJkZKRatmypatWqebJOAAAAAADKBJff0a5Xr57q1aunv/zlLzp37pw2b96sBQsWaOzYsdq7d6+nagQAAAAAoMxwGbSzs7P19ddf66uvvtLWrVt18uRJRUZGXvK71wAAAAAAwEXQ7tu3r1JSUhQeHq7WrVtrypQpuuuuuzxZGwAAAAAAZU6xQfupp55Sy5YtValSJU/WAwAAAABAmVZs0O7UqZMn6wAAAAAAwCeU83YBAAAAAAD4kmKD9kcffSRJWrFihceKAQAAAACgrCv21PHZs2fr3Llzev/99+Xn53fR448++qilhQEAAAAAUBYVG7RHjhypzZs3KyMjQzt37rzocYI2AAAAAAAXKzZo33fffbrvvvu0fv163X///Z6sCQAAAACAMqvYoH1e48aN9fLLL2v37t3y8/NT48aNNWzYMFWuXNkT9QEAAAAAUKa4ver4hAkTdNddd+n111/X9OnTVbt2bb388sueqA0AAAAAgDLH7RHtrKwsPfHEE877derU0YYNGywtCgAAAACAssrtEe2srCwdO3bMef/IkSPKzc21tCgAAAAAAMoqt0e0Bw0apJ49e8put8swDJ08eVKTJ0++rMnj4+O1c+dO5eXlacCAAWrYsKFGjx6tvLw82Ww2TZs2TXa7/apfBAAAAAAApYXboN2uXTslJSXpl19+kSTVqlVLFSpUcDvxtm3bdODAAS1btkwOh0M9evRQixYt9Pjjj6tLly764IMPtGDBAo0cOfKqXwQAAAAAAKWF26AtSYGBgapXr16JJm7WrJkaNWokSQoJCVFWVpYmTJjgDOlhYWHas2dPCcsFAAAAAKB0u6ygfSX8/f0VFBQkSVqxYoWioqKc9/Pz87VkyRI999xzLucICwuSzeZvVYmWsduDvV0CYDr6Gr6GnoYvoq+vDV2Hr5EkrU3o5rx9qfsXutTY4hTtI1d95WqsWfNQX+msryRjr2aesspt0DYMQ35+fle8QFJSklauXKn58+dL+jNkjxw5Ui1btlRkZKTL5zocmVe8rrfY7cFKT8/wdhmAqehr+Bp6Gr6Ivr72FH2/Xb3/3hhLfb5dX0nGXs08pZmrDwXcXnX8ySefvOKFk5OTlZiYqHnz5ik4+M8iRo8erZo1a+r555+/4nkBAAAAACit3B7Rrl+/vmbMmKGIiAiVL1/eud3d0eiMjAzFx8frvffeU2hoqCTp448/Vvny5TVkyJCrqxoAAAAAgFLKbdDeu3evJGnHjh3ObX5+fm6D9rp16+RwODRs2DDntt9//13XX3+9+vbtK0m6/fbbNXHixCsoGwAAAACA0slt0F60aJGkkn9XOzo6WtHR0VdeGQAAAAAAZZDb72jv27dPPXv21IMPPihJeuutt7Rr1y7LCwMAAAAAoCxyG7SnTp2q1157TXa7XZLUpUsXTZkyxfLCAAAAAAAoi9wG7XLlyqlevXrO+7Vq1ZLNZtnPbwMAAAAAUKa5DdqSdPjwYef3szdv3izDMCwtCgAAAACAssrtoemXXnpJgwYN0s8//6wmTZqoevXqiouL80RtAAAAAACUOW6Ddt26dbV27VqdPHlSAQEBqlSpkifqAgAAAACgTHIbtA8ePKhZs2bp4MGD8vPzU506dfT888+rdu3anqgPAAAAAIAyxW3QHjlypGJjYzVkyBBJ0s6dOzVixAh99NFHlhcHAAAAAEBZ4zZoV65cWY8++qjz/u23367169dbWhQAAAAAAGVVsVcdLygoUEFBge655x599tln+uOPP3T27FklJSWpWbNmnqwRAAAAAIAyo9gj2g0aNJCfn98lf8rLZrNp4MCBlhYGAAAAAEBZVGzQ3rdvnyfrAAAAAADAJ7j9jvbRo0f12Wef6cyZM4WObj///POWFgYAAAAAQFlU7He0z+vfv79++OEHnTt3Tnl5ec7/AAAAAADAxdwe0Q4JCdGUKVM8UQsAAAAAAGWe26DduXNnffzxx4qIiJC/v79z+y233GJpYQAAAAAAlEVug/aPP/6otWvXKjQ01LnNz89PmzZtcjt5fHy8du7cqby8PA0YMED33XefFi1apKlTp+rrr7/WdddddzW1AwAAAABQ6rgN2rt27dLXX3+tChUqlGjibdu26cCBA1q2bJkcDod69OihzMxMHT9+XFWqVLniggEAAAAAKM3cBu3w8HDl5uaWOGg3a9ZMjRo1kvTn97yzsrLUsWNHBQcHa+3atVdWLQAAAAAApdxl/bxXhw4ddPvttxf6jvYHH3zg8nn+/v4KCgqSJK1YsUJRUVEKDg6+ynIBAAAAACjd3AbtgQMHXtUCSUlJWrlypebPn1/i54aFBclm83c/sJSx2/lAAb6Hvoavoafhi+hr83UdvsZ5e21Ct0L3L1T0MSvHnlf0/Xb1/ntjLPX5dn0lGXs185RVboN2fn7+FU+enJysxMREvfPOO1d0NNvhyLzitb3Fbg9WenqGt8sATEVfw9fQ0/BF9LX1XO3foo95Yqw31qQ+6ruSsVczT2nm6kMBt0H77bffdt4+d+6cDh48qCZNmigyMtLl8zIyMhQfH6/33nuv0BXLAQAAAADwZW6D9qJFiwrdP3HihBISEtxOvG7dOjkcDg0bNsy5rUWLFtq+fbvS09PVr18/NW7cWCNHjix51QAAAAAAlFJug3ZRN9xwgw4dOuR2XHR0tKKjoy/a/vzzz5d0SQAAAAAAygy3QXvEiBHy8/Nz3k9LS1O5cuUsLQoAAAAAgLLKbdBu1aqV87afn58qVaqke++919KiAAAAAAAoq9wG7R49eniiDgAAAAAAfEKxQbtDhw6FThk3DEN+fn7Kzc3V8ePHtXfvXo8UCAAAAABAWVJs0N6wYcNF25KSkpSQkKBevXpZWhQAAAAAAGXVZV11/JdfftGrr76q8uXLa+7cuapRo4bVdQEAAAAAUCa5DNqZmZl66623tHnzZo0YMUJt27b1VF0AAAAAAJRJxf5O17///W/17NlTISEhWr16NSEbAAAAAIDLUOwR7RdffFG33XabkpOTtWXLFuf28xdFW7hwoUcKBAAAAACgLCk2aH/xxReerAMAAAAAAJ9QbNCuVq2aJ+sAAAAAAMAnFPsdbQAAAAAAUHIEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExU7M97mSE+Pl47d+5UXl6eBgwYoIYNG2rkyJHKz8+X3W7XtGnTFBAQYGUJAAAAAAB4lGVBe9u2bTpw4ICWLVsmh8OhHj16KDIyUrGxsXrwwQcVHx+vlStXKjY21qoSAAAAAADwOMtOHW/WrJlmzJghSQoJCVFWVpa2b9+ujh07SpI6duyor776yqrlAQAAAADwCsuOaPv7+ysoKEiStGLFCkVFRWnLli3OU8XtdrvS09NdzhEWFiSbzd+qEi1jtwd7uwTAdPQ1fA09DV/kzb7uOnyNJGltQjfnbXf33Y29kDfGrk3oVmisq/1b9DFPjPXGmtRHfVcy9mrmKass/Y62JCUlJWnlypWaP3++7r//fud2wzDcPtfhyLSyNEvY7cFKT8/wdhmAqehr+Bp6Gr6otPR10Rpc3Xc3tiTzWjGW+qiP+rz/WkozVx8KWHrV8eTkZCUmJmrevHkKDg5WxYoVlZ2dLUk6evSoqlSpYuXyAAAAAAB4nGVBOyMjQ/Hx8ZozZ45CQ0MlSa1atdL69eslSZ999pnatGlj1fIAAAAAAHiFZaeOr1u3Tg6HQ8OGDXNumzp1qsaOHatly5bplltuUffu3a1aHgAAAAAAr7AsaEdHRys6Ovqi7QsWLLBqSQAAAAAAvM7S72gDAAAAAHCtIWgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCJLg/b+/fvVqVMnLV68WJL0008/6YknnlCfPn00duxY5eXlWbk8AAAAAAAeZ1nQzszM1KRJkxQZGencNn36dPXv31+LFy/WzTffrE8//dSq5QEAAAAA8ArLgnZAQIDmzZunKlWqOLf9+uuvatSokSSpTZs22rp1q1XLAwAAAADgFTbLJrbZZLMVnr5OnTravHmzunfvruTkZB0/ftzlHGFhQbLZ/K0q0TJ2e7C3SwBMR1/D19DT8CVdh6+55Pa1Cd0KPVb0vpljzyv6t+XqvruxlzuPVWOpj/qoz/uvpayyLGhfyksvvaSJEydq1apVat68uQzDcDne4cj0UGXmsduDlZ6e4e0yAFPR1/A19DSuFUX73FXfmzXW3TwlGWtFfSUZS33UR33efy2lmasPBTwatG+++WbNmTNHkpScnKxjx455cnkAAAAAACzn0Z/3mjlzpjZt2iRJWrVqlTp06ODJ5QEAAAAAsJxlR7RTUlIUFxen1NRU2Ww2rV+/Xi+++KImTZqkOXPmqEWLFmrXrp1VywMAAAAA4BWWBe3w8HAtWrToou0rV660akkAAAAAALzOo6eOAwAAAADg6wjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJjI0qC9f/9+derUSYsXL5YkffPNN+rdu7f69u2rAQMG6PTp01YuDwAAAACAx1kWtDMzMzVp0iRFRkY6t02ZMkWTJ0/WokWLFBERoWXLllm1PAAAAAAAXmFZ0A4ICNC8efNUpUoV57awsDCdOnVKknT69GmFhYVZtTwAAAAAAF5hs2xim002W+HpR48erb59++r6669XSEiIhg8f7nKOsLAg2Wz+VpVoGbs92NslAKajr+Fr6GkU1XX4GknS2oRuztuXun8hb4y91GPFKdrnrvrerLHu5inJWCvqK8lY6qM+6vP+aymrLAval/Lqq6/qzTffVNOmTRUXF6clS5boySefLHa8w5HpwerMYbcHKz09w9tlAKair+Fr6Gm4UrQ3XPWKN8aW9fp86bVQH/Vdy/WVZOzVzFOaufpQwKNXHf/xxx/VtGlTSVKrVq2UkpLiyeUBAAAAALCcR4P2jTfeqIMHD0qSdu/erZo1a3pyeQAAAAAALGfZqeMpKSmKi4tTamqqbDab1q9fr1deeUVjx45V+fLlFRISotdee82q5QEAAAAA8ArLgnZ4eLgWLVp00falS5datSQAAAAAAF7n0VPHAQAAAADwdQRtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExE0AYAAAAAwEQEbQAAAAAATETQBgAAAADARARtAAAAAABMRNAGAAAAAMBEBG0AAAAAAExks3Ly/fv3a9CgQXr66afVp08fDRkyRA6HQ5J06tQpNW7cWJMmTbKyBAAAAAAAPMqyoJ2ZmalJkyYpMjLSuW3mzJnO26NHj9Zjjz1m1fIAAAAAAHiFZaeOBwQEaN68eapSpcpFjx06dEgZGRlq1KiRVcsDAAAAAOAVlh3RttlsstkuPf3ChQvVp08fq5YGAAAAAMBrLP2O9qXk5uZq586dmjhxotuxYWFBstn8rS/KZHZ7sLdLAExHX8PX0NP/T9fha5y31yZ0K3T/QkUfu9KxZs1j9tjzivaGq17xxtiyXp8vvRbqo75rub6SjL2aecoqjwftb7755rJPGXc4Mi2uxnx2e7DS0zO8XQZgKvoavoaeLp6r/VL0sSsda9Y81Fc26/Ol10J91Hct11eSsVczT2nm6kMBj/+81+7du1WvXj1PLwsAAAAAgEdYdkQ7JSVFcXFxSk1Nlc1m0/r16zVr1iylp6fr1ltvtWpZAAAAAAC8yrKgHR4erkWLFl20fdy4cVYtCQAAAACA13n81HEAAAAAAHwZQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwkaVBe//+/erUqZMWL14sSTp37pyGDx+uRx99VE899ZROnz5t5fIAAAAAAHicZUE7MzNTkyZNUmRkpHPb8uXLFRYWppUrV6pLly7asWOHVcsDAAAAAOAVlgXtgIAAzZs3T1WqVHFu27hxox555BFJUnR0tDp27GjV8gAAAAAAeIXNsoltNtlshadPTU3VN998oxkzZuj666/XhAkTFBoaWuwcYWFBstn8rSrRMnZ7sLdLAExHX8PXlJae7jp8jfP22oRuLu9fyNXYks5zIVf7pehjVzrWrHmor2zW50uvhfqo71quryRjr2aessqyoH0phmHo5ptv1rvvvqu3335bc+bM0UsvvVTseIcj04PVmcNuD1Z6eoa3ywBMRV/D15TWni5ak6saXY01ax7qoz4r6vOl10J91Hct11eSsVczT2nm6kMBj151/MYbb9Q999wjSWrdurUOHjzoyeUBAAAAALCcR4N2VFSUkpOTJUl79uxRrVq1PLk8AAAAAACWs+zU8ZSUFMXFxSk1NVU2m03r16/X9OnTFRcXp9WrVysgIEBxcXFWLQ8AAAAAgFdYFrTDw8O1aNGii7a//vrrVi0JAAAAAIDXefTUcQAAAAAAfB1BGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATEbQBAAAAADARQRsAAAAAABMRtAEAAAAAMBFBGwAAAAAAExG0AQAAAAAwEUEbAAAAAAATWRq09+/fr06dOmnx4sWSpEmTJqlnz57q27ev+vbtq02bNlm5PAAAAAAAHmezauLMzExNmjRJkZGRhbZNnjxZ9evXt2pZAAAAAAC8yrIj2gEBAZo3b56qVKni3Hb27FmrlgMAAAAAoFSw7Ii2zWaTzVZ4+rNnz+rNN9/UmTNnVLVqVY0dO1ahoaHFzhEWFiSbzd+qEi1jtwd7uwTAdPQ1fM3l9HTX4Wuct9cmdHPev/D2pe5fqCRji9bkqkZXY82ah/qoz4r6fOm1UB/1Xcv1lWTs1cxTVlkWtC8lJiZGd9xxh2rVqqXZs2dr1qxZGjduXLHjHY5MD1ZnDrs9WOnpGd4uAzAVfQ1fcyU9feH4os91NZc3xlIf9ZXm+nzptVAf9V3L9ZVk7NXMU5q5+lDAo1cd79y5s2rVquW8/eOPP3pyeQAAAAAALOfRoD1w4ED9/vvvkqTt27frzjvv9OTyAAAAAABYzrJTx1NSUhQXF6fU1FTZbDatX79evXv31uDBgxUUFKSKFStqypQpVi0PAAAAAIBXWBa0w8PDtWjRoou2d+nSxaolAQAAAADwOo+eOg4AAAAAgK8jaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIoI2AAAAAAAmImgDAAAAAGAigjYAAAAAACYiaAMAAAAAYCKCNgAAAAAAJiJoAwAAAABgIkuD9v79+9WpUyctXry40Pbk5GTVrVvXyqUBAAAAAPAKy4J2ZmamJk2apMjIyELbc3JyNHfuXNntdquWBgAAAADAaywL2gEBAZo3b56qVKlSaHtiYqJiY2MVEBBg1dIAAAAAAHiNzbKJbTbZbIWn//nnn7Vv3z4NHTpU06ZNcztHWFiQbDZ/q0q0jN0e7O0SANPR17hSXYevkSStTejmvH2p+xfyxthLPXahC/8Giv49uPr78MZY6qO+0lyfL70W6qO+a7m+koy9mnnKKsuC9qVMmTJFY8eOvezxDkemhdVYw24PVnp6hrfLAExFX8MMRXvIVU95Y6y7eUoy1or6SjKW+qivNNfnS6+F+qjvWq6vJGOvZp7SzNWHAh676vjRo0d16NAhvfjii3r88cd17Ngx9enTx1PLAwAAAADgER47ol21alUlJSU573fo0OGiq5EDAAAAAFDWWRa0U1JSFBcXp9TUVNlsNq1fv16zZs1SaGioVUsCAAAAAOB1lgXt8PBwLVq0qNjHN2zYYNXSAAAAAAB4jce+ow0AAAAAwLWAoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiQjaAAAAAACYiKANAAAAAICJCNoAAAAAAJiIoA0AAAAAgIkI2gAAAAAAmIigDQAAAACAiSwN2vv371enTp20ePFiSdJ3332n3r17q2/fvvrrX/+qkydPWrk8AAAAAAAeZ1nQzszM1KRJkxQZGenctmDBAsXHx2vRokWKiIjQ8uXLrVoeAAAAAACvsCxoBwQEaN68eapSpYpz28yZM1WjRg0ZhqGjR4/qpptusmp5AAAAAAC8wrKgbbPZFBgYeNH2L7/8Ug888ICOHz+uRx55xKrlAQAAAADwCpunF4yKilKbNm00ffp0zZ07VwMHDix2bFhYkGw2fw9WZw67PdjbJQCmo699T9fha5y31yZ0K3T/QkUfK+nY84r2kKue8sZYd/OUZKwV9ZVkLPVRX2muz5deC/VR37VcX0nGXs08ZZVHg/bnn3+uzp07y8/PT/fff79mzZrlcrzDkemhysxjtwcrPT3D22UApqKvfZ+r97foY1c61qx5vFWfL70W6qM+b9bnS6+F+qjvWq6vJGOvZp7SzNWHAh79ea9Zs2Zp7969kqRdu3apVq1anlweAAAAAADLWXZEOyUlRXFxcUpNTZXNZtP69ev16quv6pVXXpG/v78CAwMVHx9v1fLA/9fe3QdZVd53AP8tXNa4sKMrXLCkylCs1bAxL2OmXV/yMiylTYegtilklcQkJmlQ0mSwCqjVGWrMEpiJQRMclMQsQ2VAKzhtg2MmsXGCOkJqupNatCGZuBJcYBVkFxHc/tHJDiCuXPY55+xePp+/7rnn8Xm+D3tyM997z94FAAAoRGZFu7GxMdra2t7y/AMPPJDVkgAAAFC4XG8dBwAAgGqnaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJKRoAwAAQEKKNgAAACSkaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJKRoAwAAQEKKNgAAACSkaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJJRp0d66dWs0NzfHqlWrIiJi+/btcfXVV8dVV10VV199dXR2dma5PAAAAOQus6Ld3d0dixYtiqampr7nvvWtb8Xf/u3fxqpVq2Lq1Knxve99L6vlAQAAoBCZFe3a2tpYsWJFjB07tu+5W2+9NaZNmxYREQ0NDfHKK69ktTwAAAAUopTZxKVSlEpHTl9XVxcREYcOHYrVq1fHtdde2+8cDQ11USoNzypiZsrl+qIjQHKu68pNn7e+7/EjS2cccXy4o8+d6NhK5zlcfz/fo8+d6NhU8xSVr5r2Ip98Rearpr3IJ9/JnK+SsQOZZ6jKrGi/nUOHDsUNN9wQf/Znf3bEbeXH0tXVnVOqdMrl+ujs3Ft0DEjKdT1w/f37HX3uRMemmke+gY/NIl8lY+WTbzDnq6a9yCffyZyvkrEDmWcw6+9Ngdy/dXzBggUxYcKEuO666/JeGgAAADKXa9HesGFDjBgxIr7yla/kuSwAAADkJrNbx9vb26O1tTU6OjqiVCrFxo0bY9euXXHKKafE7NmzIyJi0qRJcdttt2UVAQAAAHKXWdFubGyMtra2rKYHAACAQSn339EGAACAaqZoAwAAQEKKNgAAACSkaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJKRoAwAAQEKKNgAAACSkaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJKRoAwAAQEKKNgAAACSkaAMAAEBCmRbtrVu3RnNzc6xatarvuba2tpg8eXLs27cvy6UBAACgEJkV7e7u7li0aFE0NTX1Pffwww/Hzp07Y+zYsVktCwAAAIXKrGjX1tbGihUrjijVzc3N8bWvfS1qamqyWhYAAAAKVcps4lIpSqUjpx81alRFczQ01EWpNDxlrFyUy/VFR4DkBuN1PX3e+r7HjyydccTx4Y4+d6JjK53ncP39+x197kTHpppHvoGPzSJfJWPlk28w56umvcgn38mcr5KxA5lnqMqsaKfQ1dVddISKlcv10dm5t+gYkNRQuK77y3f0uRMdm2oe+YZmvmrai3zyFZmvmvYin3wnc75Kxg5knsGsvzcFfOs4AAAAJKRoAwAAQEKZ3Tre3t4era2t0dHREaVSKTZu3BgXXXRR/OxnP4vOzs74whe+EO9///vjhhtuyCoCAAAA5C6zot3Y2BhtbW1vef7LX/5yVksCAABA4dw6DgAAAAkp2gAAAJCQog0AAAAJKdoAAACQkKINAAAACSnaAAAAkJCiDQAAAAkp2gAAAJCQog0AAAAJKdoAAACQkKINAAAACSnaAAAAkJCiDQAAAAkp2gAAAJCQog0AAAAJKdoAAACQUKZFe+vWrdHc3ByrVq2KiIjt27fH7Nmzo6WlJf7+7/8+Dhw4kOXyAAAAkLvMinZ3d3csWrQompqa+p779re/HS0tLbF69ep497vfHevWrctqeQAAAChEZkW7trY2VqxYEWPHju177qmnnoopU6ZERMSUKVNi06ZNWS0PAAAAhShlNnGpFKXSkdP39PREbW1tRESUy+Xo7Ozsd46GhroolYZnFTEz5XJ90RHguE2ft/6Yzz+ydMYR544+zmJspfMcrr//3R197kTHpppHvqGZr5r2Ip98Rearpr3IJ9/JnK+SsQOZZ6jKrGgfS01NTd/j3t7edxzf1dWdZZxMlMv10dm5t+gYMGBHX8f9XdepxhaxpnzyHe881bQX+eQrMl817UU++U7mfJWMHcg8g1l/bwrk+q3jp556auzfvz8iInbs2HHEbeUAAABQDXIt2hdddFFs3LgxIiIeffTRuPTSS/NcHgAAADKX2a3j7e3t0draGh0dHVEqlWLjxo2xZMmSmD9/fqxZsybGjx8fl112WVbLAwAAQCEyK9qNjY3R1tb2lue/973vZbUkAAAAFC7XW8cBAACg2inaAAAAkJCiDQAAAAkp2gAAAJCQog0AAAAJKdoAAACQkKINAAAACSnaAAAAkJCiDQAAAAkp2gAAAJCQog0AAAAJKdoAAACQkKINAAAACSnaAAAAkJCiDQAAAAkp2gAAAJBQKc/F3nzzzbj11lvj+eefjxEjRsRtt90WkyZNyjMCAAAAZCrXT7R/9KMfxd69e+OBBx6I22+/PRYvXpzn8gAAAJC5XIv2r3/967jgggsiIuLss8+Ol156KQ4dOpRnBAAAAMhUrkX73HPPjSeeeCIOHToUv/rVr+K3v/1tdHV15RkBAAAAMpXr72h/5CMfiS1btsSVV14Zf/InfxJ/9Ed/FL29vW87vqGhLkql4TkmTKNcri86AkPU9Hnrj/n8I0tnHHHu8OP+zh3P2Ldz9HXc33WdamwRa8on3/HOU017kU++IvNV017kk+9kzlfJ2IHMM1TlWrQjIr72ta/1PW5ubo7Ro0e/7diuru48IiVVLtdHZ+feomNQZY6+pg4/7u9cpWOPd56sxson32DOV017kU++IvNV017kk+9kzlfJ2IHMM5j196ZArreOP/fcc7FgwYKIiPiP//iPeM973hPDhvkLYwAAAFSPXD/RPvfcc6O3tzdmzpwZ9fX10dramufyAAAAkLlci/awYcPiG9/4Rp5LAgAAQK7ctw0AAAAJKdoAAACQkKINAAAACSnaAAAAkJCiDQAAAAkp2gAAAJCQog0AAAAJKdoAAACQkKINAAAACSnaAAAAkJCiDQAAAAkp2gAAAJCQog0AAAAJKdoAAACQkKINAAAACSnaAAAAkJCiDQAAAAmV8lxs3759ceONN8arr74ab7zxRlx77bVx6aWX5hkBAAAAMpVr0f6Xf/mXmDhxYsybNy927NgRn/nMZ+KHP/xhnhEAAAAgU7neOt7Q0BCvvPJKRETs2bMnGhoa8lweAAAAMpfrJ9p/9Vd/FQ899FBMnTo19uzZE/fcc0+/4xsa6qJUGp5TunTK5fqiI5Cj6fPW9z1+ZOmMfo8Pd6yxb+foa+rw4/7OVTr2eOfJaqx88g3mfNW0F/nkKzJfNe1FPvlO5nyVjB3IPENVrkV7/fr1MX78+Ljvvvviueeei5tuuikefPDBtx3f1dWdY7o0yuX66OzcW3QMCnL0z76/ayHV2Heap5KxWeSrZKx88g3mfNW0F/nkKzJfNe1FPvlO5nyVjB3IPINZf28K5Hrr+JYtW+KSSy6JiIjzzjsvduzYEQcPHswzAgAAAGQq16I9YcKEePbZZyMioqOjI0aOHBmlUq4fqgMAAECmcm25M2fOjIULF8ZVV10VBw8ejNtuuy3P5QEAACBzuRbtkSNHxp133pnnkgAAAJCrXG8dBwAAgGqnaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJKRoAwAAQEKKNgAAACSkaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJKRoAwAAQEKKNgAAACSkaAMAAEBCijYAAAAkpGgDAABAQqU8F1u7dm1s2LCh77i9vT1+/vOf5xkBAAAAMpVr0f7kJz8Zn/zkJyMi4umnn45///d/z3N5AAAAyFxht47ffffdMWfOnKKWBwAAgEzk+on27/3iF7+IP/iDP4hyudzvuIaGuiiVhueUKp1yub7oCCQ2fd76vsePLJ1xxPHhjv7Z93ctpBr7TvNUMjaLfJWMlU++wZyvmvYin3xF5qumvcgn38mcr5KxA5lnqCqkaK9bty4uv/zydxzX1dWdQ5q0yuX66OzcW3QMMtTfz/foc3mMfad5KhmbRb5Kxson32DOV017kU++IvNV017kk+9kzlfJ2IHMM5j196ZAIbeOP/XUU/GBD3ygiKUBAAAgU7kX7R07dsTIkSOjtrY276UBAAAgc7kX7c7OzjjjjDPyXhYAAABykXvRbmxsjHvvvTfvZQEAACAXhf15LwAAAKhGijYAAAAkpGgDAABAQoo2AAAAJKRoAwAAQEKKNgAAACSkaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJKRoAwAAQEKKNgAAACSkaAMAAEBCijYAAAAkpGgDAABAQoo2AAAAJJR70d6wYUN84hOfiCuuuCIef/zxvJcHAACATOVatLu6uuLuu++O1atXx/Lly+Oxxx7Lc3kAAADIXCnPxTZt2hRNTU0xatSoGDVqVCxatCjP5QEAACBzuRbtF198MXp7e+OrX/1qvPzyyzF37txoamp62/ENDXVRKg3PMWEa5XJ90RHIUH8/36PP5TH2neapZGwW+SoZK598gzlfNe1FPvmKzFdNe5FPvpM5XyVjBzLPUJVr0Y6I2LFjR9x1113x0ksvxac//en48Y9/HDU1Nccc29XVnXO6gSuX66Ozc2/RMchQfz/fo8/lMfad5qlkbBb5Khkrn3yDOV817UU++YrMV017kU++kzlfJWMHMs9g1t+bArn+jvbo0aPjAx/4QJRKpTj77LNj5MiRsXv37jwjAAAAQKZyLdqXXHJJPPnkk/Hmm2/G7t27o7u7OxoaGvKMAAAAAJnK9dbxcePGxbRp0+Izn/lM9PT0xM033xzDhvlT3gAAAFSP3H9He9asWTFr1qy8lwUAAIBc+DgZAAAAElK0AQAAICFFGwAAABJStAEAACAhRRsAAAASUrQBAAAgIUUbAAAAElK0AQAAICFFGwAAABJStAEAACAhRRsAAAASUrQBAAAgIUUbAAAAElK0AQAAICFFGwAAABJStAEAACChUp6Ltbe3x5w5c2LChAkREXHuuefGLbfckmcEAAAAyFSuRbu7uzumTZsWN910U57LAgAAQG5yvXV83759eS4HAAAAucu1aHd3d8fmzZvjmmuuiSuvvDKefPLJPJcHAACAzOV66/h5550X1157bUyZMiW2bdsWn/3sZ+PRRx+N2traY45vaKiLUml4nhGTKJfri44QERHT562PiIhHls7oe3ys48MVMXYo5Dtcfz/fo8/lMfad5qlkbBb5Khkrn3yDOV817UU++YrMV017kU++kzlfJWMHMs9QlWvRnjRpUkyaNCkiIiZOnBhjxoyJHTt2xFlnnXXM8V1d3XnGS6Jcro/Ozr1FxzjC0Xn6y1fEWPmyzVdNe5FPviLzVdNe5JOvyHzVtBf55DuZ81UydiDzDGb9vSmQ663j69atix/84AcREdHZ2Rm7du2KcePG5RkBAAAAMpXrJ9pTp06N66+/PjZu3BgHDhyI22677W1vGwcAAIChKNeifdppp8WKFSvyXBIAAAByleut4wAAAFDtFG0AAABISNEGAACAhBRtAAAASEjRBgAAgIQUbQAAAEhI0QYAAICEFG0AAABISNEGAACAhBRtAAAASEjRBgAAgIQUbQAAAEhI0QYAAICEFG0AAABISNEGAACAhBRtAAAASEjRBgAAgIQKKdr79++PKVOmxEMPPVTE8gAAAJCZQor2d7/73Tj99NOLWBoAAAAylXvR/t///d944YUX4qMf/WjeSwMAAEDmSnkv2NraGrfccks8/PDD7zi2oaEuSqXh2YdKrFyuTzbX9Hnr+x4/snTGEceHO/rcI0tnvG2e/vIVMVa+bPNV017kk6/IfNW0F/nkKzJfNe1FPvlO5nyVjB3IPENVrkX74Ycfjve///1x1llnHdf4rq7ujBOlVy7XR2fn3kzm7m/eo88dftzfuUrmyWqsfNnmq6a9yCdfkfmqaS/yyVdkvmrai3zyncz5Khk7kHkGs/7eFMi1aP/kJz+J3/72t/GTn/wkfve730VtbW2ceeaZcdFFF+UZAwAAADKTa9H+1re+1fd42bJl8e53v1vJBgAAoKr4O9oAAACQUO5fhvZ7c+fOLWppAAAAyIxPtAEAACAhRRsAAAASUrQBAAAgIUUbAAAAElK0AQAAICFFGwAAABJStAEAACAhRRsAAAASUrQBAAAgIUUbAAAAElK0AQAAICFFGwAAABJStAEAACAhRRsAAAASUrQBAAAgIUUbAAAAEirluVhPT0/Mnz8/du3aFa+//nrMmTMnPvaxj+UZAQAAADKVa9H+8Y9/HI2NjfGFL3whOjo64nOf+5yiDQAAQFXJtWh//OMf73u8ffv2GDduXJ7LAwAAQOZyLdq/N2vWrPjd734Xy5cv73dcQ0NdlErDc0qVTrlcf8Tx9Hnr+x4/snRGv8eHe2TpjH7n7e/c4cf9natknqzGypdtvmrai3zyFZmvmvYin3xF5qumvcgn38mcr5KxA5lnqCqkaD/wwAPx3//93/EP//APsWHDhqipqTnmuK6u7pyTDVy5XB+dnXvf9vzR5/IYW8Sa8g2efNW0F/nkKzJfNe1FPvmKzFdNe5FPvpM5XyVjBzLPYNbfmwK5fut4e3t7bN++PSIizj///Dh06FDs3r07zwgAAACQqVyL9jPPPBMrV66MiIidO3dGd3d3NDQ05BkBAAAAMpVr0Z41a1bs3r07Wlpa4otf/GL84z/+Ywwb5k95AwAAUD1y/R3td73rXbF06dI8lwQAAIBc+TgZAAAAElK0AQAAICFFGwAAABJStAEAACAhRRsAAAASUrQBAAAgIUUbAAAAElK0AQAAICFFGwAAABJStAEAACAhRRsAAAASUrQBAAAgIUUbAAAAElK0AQAAICFFGwAAABJStAEAACChUt4LLl68ODZv3hwHDx6ML33pS/Hnf/7neUcAAACAzORatJ988sl4/vnnY82aNdHV1RWXX365og0AAEBVybVof+hDH4oLLrggIiJOO+206OnpiUOHDsXw4cPzjAEAAACZybVoDx8+POrq6iIiYu3atfHhD3+435Ld0FAXpdLQKOHT563ve/zI0hlHHB+uXK7v9ziLsUWsKd/gyVdNe5FPviLzVdNe5JOvyHzVtBf55DuZ81UydiDzDFW5/452RMRjjz0W69ati5UrV/Y7rqurO6dEaXV27j3uc3mMLWJN+QZPvmrai3zyFZmvmvYin3xF5qumvcgn38mcr5KxA5lnMOvvTYHci/ZPf/rTWL58edx7771RX18d71YAAADA7+VatPfu3RuLFy+O73//+3H66afnuTQAAADkItei/W//9m/R1dUVX/3qV/uea21tjfHjx+cZAwAAADKTa9GeOXNmzJw5M88lAQAAIFfDig4AAAAA1UTRBgAAgIQUbQAAAEhI0QYAAICEFG0AAABISNEGAACAhBRtAAAASEjRBgAAgIQUbQAAAEhI0QYAAICEFG0AAABISNEGAACAhBRtAAAASEjRBgAAgIQUbQAAAEhI0QYAAICECinaW7dujebm5li1alURywMAAEBmci/a3d3dsWjRomhqasp7aQAAAMhc7kW7trY2VqxYEWPHjs17aQAAAMhcKfcFS6UolXJfFgAAAHIxqBtvQ0NdlErDi45RsXK5/rjP5TG2iDXlGzz5qmkv8slXZL5q2ot88hWZr5r2Ip98J3O+SsYOZJ6halAX7a6u7qIjnJDOzr3HfS6PsUWsKd/gyVdNe5FPviLzVdNe5JOvyHzVtBf55DuZ81UydiDzDGb9vSngz3sBAABAQrl/ot3e3h6tra3R0dERpVIpNm7cGMuWLYvTTz897ygAAACQXO5Fu7GxMdra2vJeFgAAAHLh1nEAAABISNEGAACAhBRtAAAASEjRBgAAgIQUbQAAAEhI0QYAAICEFG0AAABISNEGAACAhBRtAAAASEjRBgAAgIQUbQAAAEhI0QYAAICEFG0AAABISNEGAACAhBRtAAAASEjRBgAAgIQUbQAAAEiolPeCX//61+PZZ5+NmpqaWLhwYVxwwQV5RwAAAIDM5Fq0n3766fjNb34Ta9asiRdeeCEWLFgQa9euzTMCAAAAZCrXW8c3bdoUzc3NERFxzjnnxJ49e+K1117LMwIAAABkqqa3t7c3r8VuueWW+MhHPtJXtltaWuL222+PiRMn5hUBAAAAMpXrJ9pHd/re3t6oqanJMwIAAABkKteiPW7cuNi5c2ff8csvvxxjxozJMwIAAABkKteiffHFF8fGjRsjIuKXv/xljB07NkaNGpVnBAAAAMhUrt86/sEPfjAmT54cs2bNipqamrj11lvzXB4AAAAyl+uXoQEAAEC1y/XWcQAAAKh2ijYAAAAklOvvaFezr3/96/Hss89GTU1NLFy4MC644IKiI0HF2tvbY86cOTFhwoSIiDj33HPjmmuuiRtuuCEOHToU5XI5vvnNb0ZtbW3BSeH4bN26NebMmRNXX311XHXVVbF9+/ZjXs8bNmyI+++/P4YNGxYzZ86Mv/mbvyk6Oryto6/rRYsWxc9//vMYOXJkRER8/vOfj49+9KOua4aMxYsXx+bNm+PgwYPxpS99Kd773vd6rWbIU7QTePrpp+M3v/lNrFmzJl544YVYsGBBrF27tuhYULHu7u6YNm1a3HTTTX3PLViwIFpaWuIv//IvY/HixbFu3bpoaWkpMCUcn+7u7li0aFE0NTX1Pfftb3/7LdfzZZddFnfffXesW7cuRowYEZdddlk0NzfH6aefXlx4eBvHuq67u7vj9ttvj/PPP/+I51zXDAVPPvlkPP/887FmzZro6uqKyy+/PJqamrxWM+S5dTyBTZs2RXNzc0REnHPOObFnz5547bXXCk4Fldu3b99bnnvqqadiypQpERExZcqU2LRpU96x4ITU1tbGihUrYuzYsX3PHet6fvbZZ+O9731v1NfXx7ve9a648MILY8uWLUXFhn4d67o+1mu365qh4kMf+lDceeedERFx2mmnRU9Pj9dqqoJPtBPYuXNnTJ48ue949OjR0dnZ6W+EM+R0d3fH5s2b45prromenp6YO3du9PT09N0qXi6Xo7Ozs+CUcHxKpVKUSkf+39yxruedO3fGGWec0TdmzJgxrnMGrWNd1/v27Yu77ror9uzZE+PGjYubb77Zdc2QMXz48Kirq4uIiLVr18aHP/zheOKJJ7xWM+Qp2gkc/RfSent7o6ampqA0cOLOO++8uPbaa2PKlCmxbdu2+OxnPxsHDx7sO++vATLUHf7a/Pvr2Ws4Q92sWbPinHPOiYkTJ8Z3v/vdWLZsWbzvfe87YozrmsHusccei3Xr1sXKlStj2rRpfc97rWaocut4AuPGjYudO3f2Hb/88ssxZsyYAhPBiZk0aVLfrVoTJ06MMWPGxJ49e2L//v0REbFjx44jbleEoebUU099y/V8rNfwcrlcVESo2NSpU2PixIl9j//nf/7Hdc2Q8tOf/jSWL18eK1asiPr6eq/VVAVFO4GLL744Nm7cGBERv/zlL2Ps2LFuG2dIWrduXfzgBz+IiIjOzs7YtWtXXHHFFX3X96OPPhqXXnppkRFhQC666KK3XM/ve9/74r/+679iz549sW/fvtiyZUtceOGFBSeF4/d3f/d38dJLL0XE/38PwR//8R+7rhky9u7dG4sXL4577rmn74vNvFZTDWp63QuaxJIlS+KZZ56JmpqauPXWW+O8884rOhJU7NVXX43rr78+uru748CBA3HdddfF+eefHzfeeGO8/vrrMX78+LjjjjtixIgRRUeFd9Te3h6tra3R0dERpVIpxo0bF0uWLIn58+e/5Xr+4Q9/GPfdd1/U1NTEVVddFZ/4xCeKjg/HdKzr+lOf+lTcd999UVdXF6eeemrccccdMXr0aNc1Q8KaNWti2bJlfXdlRER84xvfiJtvvtlrNUOaog0AAAAJuXUcAAAAElK0AQAAICFFGwAAABJStAEAACAhRRsAAAASUrQBYJB48cUXo7GxMWbPnh2zZ8+OWbNmxZIlS6Knp+cd/9v169dXtNbs2bPjZz/7WUREPP744/HKK6+cSGQA4BgUbQAYRM4444xoa2uLtra2uP/++2Pfvn0xb968fv+bQ4cOxXe+850TXvP73/9+vPrqqyf83wMARyoVHQAAOLZTTjklFi5cGNOmTYsXXnghxo8fHzfeeGO88sorsW/fvviLv/iL+OIXvxgLFy6Mjo6O+NznPhcrV66MO++8MzZt2hQREWeeeWZ885vfjBEjRhxzjdWrV8czzzwT119/fdxxxx1x8ODBaG1tjd7e3njzzTdj/vz58Z73vCdmz54dF154YfziF7+IX//617Fw4cJ4+OGH4/nnn48ZM2bEl7/85Tz/aQBgUFO0AWAQGzFiRDQ2NsbWrVvjlFNOiSlTpsRll10WBw4ciKampmhpaYm5c+fGpk2bYuXKlXHw4ME49dRTY/Xq1TFs2LD4/Oc/H0888UR87GMfO+b8LS0tce+998aSJUtiwoQJMX369Lj77rvj7LPPjueeey4WLlwYDz30UERE9Pb2xn333RfLli2LJUuWxPr16+Pll1+Oyy+/XNEGgMMo2gAwyO3duzeGDRsWo0ePjs2bN8cDDzwQI0aMiNdff/0tv1tdKpVi2LBh0dLSEqVSKX71q19FV1fXca2za9eu2LZtW9x00019z7322mvx5ptvRkTEBz/4wYj4/0/JJ0+eHLW1tXHmmWfGnj170mwUAKqEog0Ag1hPT08899xzMXny5Lj//vvjwIED8c///M9RU1MTf/qnf/qW8Zs3b44HH3wwHnzwwairq4uvfOUrx73WKaecEiNGjIi2trZjni+VSsd8DAAcyZehAcAg9cYbb8Q//dM/xdSpU+Oss86KXbt2xVlnnRU1NTXxox/9KPbv3x8HDhyIYcOGxeuvvx4R//+p9JgxY6Kuri46OjriP//zP+PAgQP9rlNTUxP79++PUaNGxR/+4R/G448/HhER27Zti7vuuivzfQJAtVG0AWAQ2b17d8yePTuuvPLKaGlpiXK5HDfffHNERPz1X/91/Ou//mt8+tOfjhdffDGmT58e119/fYwdOzbGjRsXV1xxRTQ1NcUbb7wRn/rUp+Kee+6JuXPnxvLly2Pbtm1vu+Yll1wS1113XWzZsiVaW1vjnnvuiSuvvDLmz58fF198cV5bB4CqUdPb29tbdAgAAACoFj7RBgAAgIQUbQAAAEhI0QYAAICEFG0AAABISNEGAACAhBRtAAAASEjRBgAAgIT+D2WgaCMLHVDrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "distance_num_words_changed = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (distance_stats.at[i,'sentiment'] != distance_stats.at[i,'ga_sentiment']) and distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        distance_num_words_changed.append(distance_stats.at[i,'ga_num_changes'])\n",
    "\n",
    "distance_num_words_changed.sort()\n",
    "\n",
    "\n",
    "bar_chart_filename_a = 'ga_attack_results/bar_chart_method_a.png'\n",
    "graph_title_a = \"Number of Words Changed v Data Item (Method A) [Num. Reviews: \"+str(len(distance_num_words_changed))+\"]\"\n",
    "\n",
    "words_generations_barchart(distance_num_words_changed,  graph_title_a, bar_chart_filename_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# words changed v data item bar chart - Method B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#bert_num_words_changed = bert_stats['ga_num_changes']\n",
    "\n",
    "bert_num_words_changed = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (bert_stats.at[i,'sentiment'] != bert_stats.at[i,'ga_sentiment']) and bert_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        bert_num_words_changed.append(bert_stats.at[i,'ga_num_changes'])\n",
    "\n",
    "bert_num_words_changed.sort()\n",
    "\n",
    "bar_chart_filename_b = 'ga_attack_results/bar_chart_method_b.png'\n",
    "graph_title_b = \"Number of Words Changed v Data Item (Method B) [Num. Reviews: \"+str(len(bert_num_words_changed))+\"]\"\n",
    "\n",
    "\n",
    "words_generations_barchart(bert_num_words_changed, graph_title_b, bar_chart_filename_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# words changed v data item bar chart - Method C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bert_distance_num_words_changed = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (bert_distance_stats.at[i,'sentiment'] != bert_distance_stats.at[i,'ga_sentiment']) and bert_distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        bert_distance_num_words_changed.append(bert_distance_stats.at[i,'ga_num_changes'])\n",
    "\n",
    "bert_distance_num_words_changed.sort()\n",
    "\n",
    "\n",
    "bar_chart_filename_c = 'ga_attack_results/bar_chart_method_c.png'\n",
    "graph_title_c = \"Number of Words Changed v Data Item (Method C) [Num. Reviews: \"+str(len(bert_distance_num_words_changed))+\"]\"\n",
    "\n",
    "words_generations_barchart(bert_distance_num_words_changed, graph_title_c, bar_chart_filename_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method A summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. num of words changed changes made:  16\n",
      "Avg. percentage modified:  9.0 %\n"
     ]
    }
   ],
   "source": [
    "\n",
    "set_percentage_modified(distance_stats)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method B summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. num of words changed changes made:  14\n",
      "Avg. percentage modified:  8.0 %\n"
     ]
    }
   ],
   "source": [
    "\n",
    "set_percentage_modified(bert_stats)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method C summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. num of words changed changes made:  35\n",
      "Avg. percentage modified:  18.0 %\n"
     ]
    }
   ],
   "source": [
    "\n",
    "set_percentage_modified(bert_distance_stats)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creat lists from above summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_words_changed = [16,14,35]\n",
    "percentage_changed = [9.0,8.0,18.0]\n",
    "chart_labels = [\"Method A\", \"Mathod B\", \"Method C\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# bar chart for average words swapped for each method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x_axis_name = 'Methods (A, B and C)'\n",
    "y_axis_name = 'Average Words Swapped'\n",
    "avg_bar_title = 'Average Words Swapped by Each Method'\n",
    "file_name_avg_words_swapped = 'ga_attack_results/bar_chart_avg_words_changed.png'\n",
    "average_stats_bar_chart(num_words_changed, chart_labels, x_axis_name, y_axis_name, avg_bar_title, file_name_avg_words_swapped)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# bar chart for average percentage change for all reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x_axis_name = 'Methods (A, B and C)'\n",
    "y_axis_name = 'Average Percentage(%) Change'\n",
    "avg_bar_title = 'Average Percentage of Text Changed by Each Method'\n",
    "file_name_avg_words_swapped = 'ga_attack_results/bar_chart_avg_percentage_changed.png'\n",
    "average_stats_bar_chart(percentage_changed, chart_labels, x_axis_name, y_axis_name, avg_bar_title, file_name_avg_words_swapped)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# match indexes of data sample to raw data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# distance_stats\n",
    "# bert_stats\n",
    "# bert_distance_stats\n",
    "# these should all be the same values - but to make sure we generate all of them and check\n",
    "index_values_method_A = matching_indexes(distance_stats,normalized_dataset)\n",
    "index_values_method_B = matching_indexes(bert_stats,normalized_dataset)\n",
    "index_values_method_C = matching_indexes(bert_distance_stats,normalized_dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[42497, 47656, 27691, 5, 48736, 6408, 37435, 32752, 41844, 9491, 19940, 37731, 17343, 7291, 44252, 43838, 45796, 16987, 431, 34684, 18507, 11760, 40433, 10740, 14220, 10551, 46803, 17102, 20040, 46297, 14532, 30696, 32756, 10867, 38335, 25832, 38021, 40511, 42958, 3572, 19237, 11213, 3697, 42863, 48914, 14883, 25740, 7744, 9762, 46436, 3813, 24925, 45108, 46288, 26409, 44155, 15568, 45978, 21670, 38793, 41861, 7764, 35827, 22585, 3723, 34784, 16418, 39053, 27991, 41877, 28272, 36993, 26831, 40810, 7411, 46218, 3360, 12162, 11151, 25764, 10156, 48566, 42735, 42509, 2027, 31772, 20008, 17808, 45973, 36027, 42063, 24105, 32007, 16252, 38179, 44095, 3880, 23377, 29581, 29312, 38306, 14697, 20081, 31526, 21145, 13555, 33459, 19286, 26741, 716, 49820, 5765, 26734, 27897, 42292, 37084, 4369, 49144, 13028, 44059, 8553, 6906, 30805, 19612, 4307, 43153, 4329, 1059, 4536, 14037, 5524, 48334, 20194, 26852, 46102, 3584, 47358, 48028, 41479, 18653, 25920, 39594, 28546, 27092, 19560, 10377, 11651, 42304, 30651, 5220, 14650, 45353, 13814, 20317, 2114, 47488, 4086, 6883, 46566, 15648, 13817, 42368, 30646, 3928, 23977, 30512, 17646, 3207, 7745, 16625, 20401, 46256, 7504, 9637, 16626, 12817, 42591, 3656, 7710, 3887, 23402, 48362, 10901, 37751, 31846, 33289, 41229, 309, 22457, 46118, 2097, 18810, 38159, 6350, 44499, 6861, 41983, 733, 1293, 31492, 38484, 5538, 23134, 18697, 48529, 5422, 27037, 39402, 24741, 39265, 10070, 29159, 49, 46248, 47075, 48791, 18890, 25291, 13449, 42483, 20671, 32571, 38388, 2347, 4511, 46929, 31850, 36634, 35225, 40366, 21779, 21456, 17334, 1965, 2411, 10096, 22851, 3213, 13498, 933, 32469, 27443, 20781, 807, 25017, 11221, 13978, 5740, 23569, 7390]\n"
     ]
    }
   ],
   "source": [
    "print(index_values_method_A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check all the indexes in the lists generated are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equal\n"
     ]
    }
   ],
   "source": [
    "if index_values_method_A == index_values_method_B == index_values_method_C:\n",
    "    print(\"Equal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## randomly select 2 items form the list and we will compare the same values for each method (A, B and C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "item_one, item_two = random.sample(index_values_method_A, 2)\n",
    "index_one = 0\n",
    "index_two = 0\n",
    "\n",
    "# get the index value for the randomly selected items\n",
    "for i in range(len(index_values_method_A)):\n",
    "    if index_values_method_A[i] == item_one:\n",
    "        index_one = i\n",
    "    if index_values_method_A[i] == item_two:\n",
    "        index_two = i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## print first data item selected for method A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the superb `Prime Suspect' series reaches part four there is no loss of momentum at all, this in itself a considerable achievement.' Prime Suspect IV: The Lost Child' has the solid supporting cast that we take for granted in these British dramas but of course the beautiful Helen Mirren easily dominates; our eyes never leave her while she's on-screen.<br /><br />The search for the lost child of the title leads Superintendent Jane Tennison's CID team to a prime suspect who turns out to be a convicted pedophile now living with a single mother and her two young daughters. The insight we are given into the workings of his mind is one of the emotional highlights of this mini-series but it may be too strong for many stomachs.<br /><br />The action sequences are brilliantly handled with the hand-held camera thrusting us right into the middle of the excitement and there's gripping tension during the climactic siege.<br /><br />Altogether this is another magnificent police procedural drama.\n",
      "\n",
      "\n",
      "as the superb prime suspect' series reaches part four there is no loss of momentum at all  this in itself a considerable achievement ' prime suspect iv  the lost child' has the solid supporting cast that we take for granted in these british dramas but of course the beautiful helen mirren easily dominates  our eyes never leave her while she's onscreen the search for the lost child of the title leads superintendent jane tennison's cid team to a prime suspect who turns out to be a convicted pedophile now living with a single mother and her two young daughters  the insight we are given into the workings of his mind is one of the emotional highlights of this miniseries but it may be too strong for many stomachs the action sequences are brilliantly handled with the handheld camera thrusting us right into the middle of the excitement and there's gripping tension during the climactic siege altogether this is another magnificent police procedural drama \n",
      "\n",
      "\n",
      "as the superb prima suspect' number reaches parties oven there is no loosing of momentum at all this in itself a considerable achievement ' first suspect d the lost child' has the solid supporting cast that we take for granted in these british dramas but of courses the handsome jolene mirren easily dominates our eyeballs never leave her while she's onscreen the search for the lost child of the title leads headmaster jane tennison's cid machine to a prime suspect who turns out to be a convicted pedophilia now living with a single mother and her trois young female the insight we are given into the workings of his mind is somebody of the emotional emphasize of this miniseries but it may be too strong for many stomachs the action sequencing are magnificently processed with the handheld cameras thrusting us right into the middle of the agitation and there's gripping voltage during the climactic siege altogether this is another marvellous police procedural drama\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print_data_item(raw_dataset,normalized_dataset, distance_stats, index_one, item_one)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## print first data item selected for method B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the superb `Prime Suspect' series reaches part four there is no loss of momentum at all, this in itself a considerable achievement.' Prime Suspect IV: The Lost Child' has the solid supporting cast that we take for granted in these British dramas but of course the beautiful Helen Mirren easily dominates; our eyes never leave her while she's on-screen.<br /><br />The search for the lost child of the title leads Superintendent Jane Tennison's CID team to a prime suspect who turns out to be a convicted pedophile now living with a single mother and her two young daughters. The insight we are given into the workings of his mind is one of the emotional highlights of this mini-series but it may be too strong for many stomachs.<br /><br />The action sequences are brilliantly handled with the hand-held camera thrusting us right into the middle of the excitement and there's gripping tension during the climactic siege.<br /><br />Altogether this is another magnificent police procedural drama.\n",
      "\n",
      "\n",
      "as the superb prime suspect' series reaches part four there is no loss of momentum at all  this in itself a considerable achievement ' prime suspect iv  the lost child' has the solid supporting cast that we take for granted in these british dramas but of course the beautiful helen mirren easily dominates  our eyes never leave her while she's onscreen the search for the lost child of the title leads superintendent jane tennison's cid team to a prime suspect who turns out to be a convicted pedophile now living with a single mother and her two young daughters  the insight we are given into the workings of his mind is one of the emotional highlights of this miniseries but it may be too strong for many stomachs the action sequences are brilliantly handled with the handheld camera thrusting us right into the middle of the excitement and there's gripping tension during the climactic siege altogether this is another magnificent police procedural drama \n",
      "\n",
      "\n",
      "as the superb prime suspect' night reaches part ii there is no loss of momentum at all this in itself a considerable achievement of s suspect iv the lost child' has the biggest acting cast that we take for free in these holy isles but of course the beautiful helen mirren now dominates our eyes not leave her while she's running the charity for the lost child of the title leads superintendent jane to cid team to a prime suspect who turns out to be a convicted adult now living with a single mother and her two young daughters the insight we are given into the workings of his brain is capable of the emotional consequences of this miniseries but it would be too hot for many stomachs the action sequences are generally associated with the movie camera thrusting us right into the middle of the excitement and there's no silence during the climactic stage altogether this is another magnificent police procedural drama\n"
     ]
    }
   ],
   "source": [
    "print_data_item(raw_dataset,normalized_dataset, bert_stats, index_one, item_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## print first data item selected for method C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the superb `Prime Suspect' series reaches part four there is no loss of momentum at all, this in itself a considerable achievement.' Prime Suspect IV: The Lost Child' has the solid supporting cast that we take for granted in these British dramas but of course the beautiful Helen Mirren easily dominates; our eyes never leave her while she's on-screen.<br /><br />The search for the lost child of the title leads Superintendent Jane Tennison's CID team to a prime suspect who turns out to be a convicted pedophile now living with a single mother and her two young daughters. The insight we are given into the workings of his mind is one of the emotional highlights of this mini-series but it may be too strong for many stomachs.<br /><br />The action sequences are brilliantly handled with the hand-held camera thrusting us right into the middle of the excitement and there's gripping tension during the climactic siege.<br /><br />Altogether this is another magnificent police procedural drama.\n",
      "\n",
      "\n",
      "as the superb prime suspect' series reaches part four there is no loss of momentum at all  this in itself a considerable achievement ' prime suspect iv  the lost child' has the solid supporting cast that we take for granted in these british dramas but of course the beautiful helen mirren easily dominates  our eyes never leave her while she's onscreen the search for the lost child of the title leads superintendent jane tennison's cid team to a prime suspect who turns out to be a convicted pedophile now living with a single mother and her two young daughters  the insight we are given into the workings of his mind is one of the emotional highlights of this miniseries but it may be too strong for many stomachs the action sequences are brilliantly handled with the handheld camera thrusting us right into the middle of the excitement and there's gripping tension during the climactic siege altogether this is another magnificent police procedural drama \n",
      "\n",
      "\n",
      "as the superb prima suspect' instalment obtains parties four there is no loosing of dynamic at all this in itself a sizable successes ' first suspect iv the outof child' has the solid supporting thrown that we picked for granted in these british opera but of paths the beautiful jolene mirren easy dominates our eyelids not letting her while she's internet the searches for the outof infantile of the designation directors governing jane tennison's cid machine to a first suspect who rotates out to be a convicted perverts now living with a sole mom and her ii youngsters girls the insight we are handed into the exmrs of his mind is anybody of the psychological emphasize of this miniseries but it perhaps be too influential for numerous stomachs the action timeline are magnificently handled with the laptop camcorder thrusting ourselves ok into the middle of the excitement and there's fascinating pressure during the climactic seating altogether this is another magnificent police procedural drama\n"
     ]
    }
   ],
   "source": [
    "print_data_item(raw_dataset,normalized_dataset, bert_distance_stats,  index_one, item_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## print second data item selected for method A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There's some very clever humour in this film, which is both a parody of and a tribute to actors. However, after a while it just seems an exercise in style (notwithstanding great gags such as Balasko continuing the part of Dussolier, and very good acting by all involved) and I was wondering why Blier made this film. All is revealed in the ending, when Blier, directing Claude Brasseur, gets a phone call from his dad (Bernard Blier) - from heaven, and gets the chance to say how much he misses him. An effective emotional capper and obviously heartfelt. But there isn't really sufficient dramatic tension or emotional involvement to keep the rest of the film interesting throughout it's entire running time. Some really nice scenes and sequences, however, and anyone who likes these 'mosntres sacrés' of the French cinema should get a fair amount of enjoyment out of this film.\n",
      "\n",
      "\n",
      "there's some very clever humour in this film  which is both a parody of and a tribute to actors  however  after a while it just seems an exercise in style notwithstanding great gags such as balasko continuing the part of dussolier  and very good acting by all involved and i was wondering why blier made this film  all is revealed in the ending  when blier  directing claude brasseur  gets a phone call from his dad bernard blier  from heaven  and gets the chance to say how much he misses him  an effective emotional capper and obviously heartfelt  but there isn't really sufficient dramatic tension or emotional involvement to keep the rest of the film interesting throughout it's entire running time  some really nice scenes and sequences  however  and anyone who likes these 'mosntres sacrés' of the french cinema should get a fair amount of enjoyment out of this film \n",
      "\n",
      "\n",
      "there's some very clever mood in this film which is both a travesty of and a tribute to agents however after a while it just seems an exercise in style notwithstanding grand jaws such as balasko continuing the parties of dussolier and very alright behaving by all involved and i was wondering why blier made this film all is revealed in the ceases when blier directing claude brasseur gets a phone call from his dad barnard blier from heaven and gets the chance to say how much he misses him an effective sentimental capper and obviously heartfelt but there isn't really sufficient dramatic voltage or emotional involvement to keep the rest of the film interesting during it's entire run time some really handsome image and sequences however and anyone who likes these 'mosntres sacrés' of the french cinema should get a fair amounts of enjoyment out of this film\n"
     ]
    }
   ],
   "source": [
    "print_data_item(raw_dataset,normalized_dataset, distance_stats,  index_two, item_two)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## print second data item selected for method B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There's some very clever humour in this film, which is both a parody of and a tribute to actors. However, after a while it just seems an exercise in style (notwithstanding great gags such as Balasko continuing the part of Dussolier, and very good acting by all involved) and I was wondering why Blier made this film. All is revealed in the ending, when Blier, directing Claude Brasseur, gets a phone call from his dad (Bernard Blier) - from heaven, and gets the chance to say how much he misses him. An effective emotional capper and obviously heartfelt. But there isn't really sufficient dramatic tension or emotional involvement to keep the rest of the film interesting throughout it's entire running time. Some really nice scenes and sequences, however, and anyone who likes these 'mosntres sacrés' of the French cinema should get a fair amount of enjoyment out of this film.\n",
      "\n",
      "\n",
      "there's some very clever humour in this film  which is both a parody of and a tribute to actors  however  after a while it just seems an exercise in style notwithstanding great gags such as balasko continuing the part of dussolier  and very good acting by all involved and i was wondering why blier made this film  all is revealed in the ending  when blier  directing claude brasseur  gets a phone call from his dad bernard blier  from heaven  and gets the chance to say how much he misses him  an effective emotional capper and obviously heartfelt  but there isn't really sufficient dramatic tension or emotional involvement to keep the rest of the film interesting throughout it's entire running time  some really nice scenes and sequences  however  and anyone who likes these 'mosntres sacrés' of the french cinema should get a fair amount of enjoyment out of this film \n",
      "\n",
      "\n",
      "there's some very bad humour in this book which is both a member of and a tribute to actors however after a while it just seems an exercise in style notwithstanding some gags such as balasko continuing the part of dussolier and very good acting by all involved and i was wondering why blier made this film all is revealed in the ending when blier directing la brasseur gets a phone call from his dad bernard blier from heaven and gets the chance to say how much he misses him an effective show capper and obviously not but there isn't really no dramatic tension or emotional involvement to keep the rest of the film interesting throughout it's entire running time some rather nice scenes and sequences anything and anyone who likes these 'mosntres sacrés' of the french cinema should make a reasonable amount of enjoyment out of this film\n"
     ]
    }
   ],
   "source": [
    "print_data_item(raw_dataset,normalized_dataset, bert_stats, index_two, item_two)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## print second data item selected for method C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There's some very clever humour in this film, which is both a parody of and a tribute to actors. However, after a while it just seems an exercise in style (notwithstanding great gags such as Balasko continuing the part of Dussolier, and very good acting by all involved) and I was wondering why Blier made this film. All is revealed in the ending, when Blier, directing Claude Brasseur, gets a phone call from his dad (Bernard Blier) - from heaven, and gets the chance to say how much he misses him. An effective emotional capper and obviously heartfelt. But there isn't really sufficient dramatic tension or emotional involvement to keep the rest of the film interesting throughout it's entire running time. Some really nice scenes and sequences, however, and anyone who likes these 'mosntres sacrés' of the French cinema should get a fair amount of enjoyment out of this film.\n",
      "\n",
      "\n",
      "there's some very clever humour in this film  which is both a parody of and a tribute to actors  however  after a while it just seems an exercise in style notwithstanding great gags such as balasko continuing the part of dussolier  and very good acting by all involved and i was wondering why blier made this film  all is revealed in the ending  when blier  directing claude brasseur  gets a phone call from his dad bernard blier  from heaven  and gets the chance to say how much he misses him  an effective emotional capper and obviously heartfelt  but there isn't really sufficient dramatic tension or emotional involvement to keep the rest of the film interesting throughout it's entire running time  some really nice scenes and sequences  however  and anyone who likes these 'mosntres sacrés' of the french cinema should get a fair amount of enjoyment out of this film \n",
      "\n",
      "\n",
      "there's some very smart mood in this film which is both a parody of and a compliments to actresses however after a while it just occurs an exercising in style although great jaws such as balasko continuing the part of dussolier and very ok behaving by all involved and i was asks why blier made this films all is disclosed in the ending when blier directing claude brasseur gets a telephones call from his pappy barnard blier from heaven and gets the chances to says how much he lack him an efficient emotional evilness and clearly candid but there isn't frankly suffice whopping pressure or thrills involved to keep the remainder of the movie interesting in it's entire running timeframe some really pleasant imagery and sequences however and anyone who likes these 'mosntres sacrés' of the frenchmen cinema should get a fair amount of realisation out of this film\n"
     ]
    }
   ],
   "source": [
    "print_data_item(raw_dataset,normalized_dataset, bert_distance_stats,  index_two, item_two)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get list of words swapped for first data item using method A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE:  prime  AFTER:  prima\n",
      "BEFORE:  series  AFTER:  number\n",
      "BEFORE:  part  AFTER:  parties\n",
      "BEFORE:  four  AFTER:  oven\n",
      "BEFORE:  loss  AFTER:  loosing\n",
      "BEFORE:  prime  AFTER:  first\n",
      "BEFORE:  iv  AFTER:  d\n",
      "BEFORE:  course  AFTER:  courses\n",
      "BEFORE:  beautiful  AFTER:  handsome\n",
      "BEFORE:  helen  AFTER:  jolene\n",
      "BEFORE:  eyes  AFTER:  eyeballs\n",
      "BEFORE:  superintendent  AFTER:  headmaster\n",
      "BEFORE:  team  AFTER:  machine\n",
      "BEFORE:  pedophile  AFTER:  pedophilia\n",
      "BEFORE:  two  AFTER:  trois\n",
      "BEFORE:  daughters  AFTER:  female\n",
      "BEFORE:  one  AFTER:  somebody\n",
      "BEFORE:  highlights  AFTER:  emphasize\n",
      "BEFORE:  sequences  AFTER:  sequencing\n",
      "BEFORE:  brilliantly  AFTER:  magnificently\n",
      "BEFORE:  handled  AFTER:  processed\n",
      "BEFORE:  camera  AFTER:  cameras\n",
      "BEFORE:  excitement  AFTER:  agitation\n",
      "BEFORE:  tension  AFTER:  voltage\n",
      "BEFORE:  magnificent  AFTER:  marvellous\n",
      "\n",
      "Total number of words swapped:  25\n",
      "Sentiment was changed for this data item\n",
      "Probability Before:  0.9986020922660828\n",
      "Probability After:  0.3767103850841522\n"
     ]
    }
   ],
   "source": [
    "words_swapped(normalized_dataset, distance_stats,  index_one, item_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get list of words swapped for first data item using method B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE:  series  AFTER:  night\n",
      "BEFORE:  four  AFTER:  ii\n",
      "BEFORE:  '  AFTER:  of\n",
      "BEFORE:  prime  AFTER:  s\n",
      "BEFORE:  solid  AFTER:  biggest\n",
      "BEFORE:  supporting  AFTER:  acting\n",
      "BEFORE:  granted  AFTER:  free\n",
      "BEFORE:  british  AFTER:  holy\n",
      "BEFORE:  dramas  AFTER:  isles\n",
      "BEFORE:  easily  AFTER:  now\n",
      "BEFORE:  never  AFTER:  not\n",
      "BEFORE:  onscreen  AFTER:  running\n",
      "BEFORE:  search  AFTER:  charity\n",
      "BEFORE:  tennison's  AFTER:  to\n",
      "BEFORE:  pedophile  AFTER:  adult\n",
      "BEFORE:  mind  AFTER:  brain\n",
      "BEFORE:  one  AFTER:  capable\n",
      "BEFORE:  highlights  AFTER:  consequences\n",
      "BEFORE:  may  AFTER:  would\n",
      "BEFORE:  strong  AFTER:  hot\n",
      "BEFORE:  brilliantly  AFTER:  generally\n",
      "BEFORE:  handled  AFTER:  associated\n",
      "BEFORE:  handheld  AFTER:  movie\n",
      "BEFORE:  gripping  AFTER:  no\n",
      "BEFORE:  tension  AFTER:  silence\n",
      "BEFORE:  siege  AFTER:  stage\n",
      "\n",
      "Total number of words swapped:  26\n",
      "Sentiment was changed for this data item\n",
      "Probability Before:  0.9986020922660828\n",
      "Probability After:  0.35181382298469543\n"
     ]
    }
   ],
   "source": [
    "words_swapped(normalized_dataset, bert_stats,  index_one, item_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get list of words swapped for first data item using method C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE:  prime  AFTER:  prima\n",
      "BEFORE:  series  AFTER:  instalment\n",
      "BEFORE:  reaches  AFTER:  obtains\n",
      "BEFORE:  part  AFTER:  parties\n",
      "BEFORE:  loss  AFTER:  loosing\n",
      "BEFORE:  momentum  AFTER:  dynamic\n",
      "BEFORE:  considerable  AFTER:  sizable\n",
      "BEFORE:  achievement  AFTER:  successes\n",
      "BEFORE:  prime  AFTER:  first\n",
      "BEFORE:  lost  AFTER:  outof\n",
      "BEFORE:  cast  AFTER:  thrown\n",
      "BEFORE:  take  AFTER:  picked\n",
      "BEFORE:  dramas  AFTER:  opera\n",
      "BEFORE:  course  AFTER:  paths\n",
      "BEFORE:  helen  AFTER:  jolene\n",
      "BEFORE:  easily  AFTER:  easy\n",
      "BEFORE:  eyes  AFTER:  eyelids\n",
      "BEFORE:  never  AFTER:  not\n",
      "BEFORE:  leave  AFTER:  letting\n",
      "BEFORE:  onscreen  AFTER:  internet\n",
      "BEFORE:  search  AFTER:  searches\n",
      "BEFORE:  lost  AFTER:  outof\n",
      "BEFORE:  child  AFTER:  infantile\n",
      "BEFORE:  title  AFTER:  designation\n",
      "BEFORE:  leads  AFTER:  directors\n",
      "BEFORE:  superintendent  AFTER:  governing\n",
      "BEFORE:  team  AFTER:  machine\n",
      "BEFORE:  prime  AFTER:  first\n",
      "BEFORE:  turns  AFTER:  rotates\n",
      "BEFORE:  pedophile  AFTER:  perverts\n",
      "BEFORE:  single  AFTER:  sole\n",
      "BEFORE:  mother  AFTER:  mom\n",
      "BEFORE:  two  AFTER:  ii\n",
      "BEFORE:  young  AFTER:  youngsters\n",
      "BEFORE:  daughters  AFTER:  girls\n",
      "BEFORE:  given  AFTER:  handed\n",
      "BEFORE:  workings  AFTER:  exmrs\n",
      "BEFORE:  one  AFTER:  anybody\n",
      "BEFORE:  emotional  AFTER:  psychological\n",
      "BEFORE:  highlights  AFTER:  emphasize\n",
      "BEFORE:  may  AFTER:  perhaps\n",
      "BEFORE:  strong  AFTER:  influential\n",
      "BEFORE:  many  AFTER:  numerous\n",
      "BEFORE:  sequences  AFTER:  timeline\n",
      "BEFORE:  brilliantly  AFTER:  magnificently\n",
      "BEFORE:  handheld  AFTER:  laptop\n",
      "BEFORE:  camera  AFTER:  camcorder\n",
      "BEFORE:  us  AFTER:  ourselves\n",
      "BEFORE:  right  AFTER:  ok\n",
      "BEFORE:  gripping  AFTER:  fascinating\n",
      "BEFORE:  tension  AFTER:  pressure\n",
      "BEFORE:  siege  AFTER:  seating\n",
      "\n",
      "Total number of words swapped:  52\n",
      "Sentiment was changed for this data item\n",
      "Probability Before:  0.9986020922660828\n",
      "Probability After:  0.4587118327617645\n"
     ]
    }
   ],
   "source": [
    "words_swapped(normalized_dataset, bert_distance_stats,  index_one, item_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get list of words swapped for second data item using method A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE:  humour  AFTER:  mood\n",
      "BEFORE:  parody  AFTER:  travesty\n",
      "BEFORE:  actors  AFTER:  agents\n",
      "BEFORE:  great  AFTER:  grand\n",
      "BEFORE:  gags  AFTER:  jaws\n",
      "BEFORE:  part  AFTER:  parties\n",
      "BEFORE:  good  AFTER:  alright\n",
      "BEFORE:  acting  AFTER:  behaving\n",
      "BEFORE:  ending  AFTER:  ceases\n",
      "BEFORE:  bernard  AFTER:  barnard\n",
      "BEFORE:  emotional  AFTER:  sentimental\n",
      "BEFORE:  tension  AFTER:  voltage\n",
      "BEFORE:  throughout  AFTER:  during\n",
      "BEFORE:  running  AFTER:  run\n",
      "BEFORE:  nice  AFTER:  handsome\n",
      "BEFORE:  scenes  AFTER:  image\n",
      "BEFORE:  amount  AFTER:  amounts\n",
      "\n",
      "Total number of words swapped:  17\n",
      "Sentiment was changed for this data item\n",
      "Probability Before:  0.9913209080696106\n",
      "Probability After:  0.393478661775589\n"
     ]
    }
   ],
   "source": [
    "words_swapped(normalized_dataset, distance_stats,  index_two, item_two)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get list of words swapped for second data item using method B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE:  clever  AFTER:  bad\n",
      "BEFORE:  film  AFTER:  book\n",
      "BEFORE:  parody  AFTER:  member\n",
      "BEFORE:  great  AFTER:  some\n",
      "BEFORE:  claude  AFTER:  la\n",
      "BEFORE:  emotional  AFTER:  show\n",
      "BEFORE:  heartfelt  AFTER:  not\n",
      "BEFORE:  sufficient  AFTER:  no\n",
      "BEFORE:  really  AFTER:  rather\n",
      "BEFORE:  however  AFTER:  anything\n",
      "BEFORE:  get  AFTER:  make\n",
      "BEFORE:  fair  AFTER:  reasonable\n",
      "\n",
      "Total number of words swapped:  12\n",
      "Sentiment was changed for this data item\n",
      "Probability Before:  0.9913209080696106\n",
      "Probability After:  0.38834109902381897\n"
     ]
    }
   ],
   "source": [
    "words_swapped(normalized_dataset, bert_stats,  index_two, item_two)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get list of words swapped for second data item using method C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE:  clever  AFTER:  smart\n",
      "BEFORE:  humour  AFTER:  mood\n",
      "BEFORE:  tribute  AFTER:  compliments\n",
      "BEFORE:  actors  AFTER:  actresses\n",
      "BEFORE:  seems  AFTER:  occurs\n",
      "BEFORE:  exercise  AFTER:  exercising\n",
      "BEFORE:  notwithstanding  AFTER:  although\n",
      "BEFORE:  gags  AFTER:  jaws\n",
      "BEFORE:  good  AFTER:  ok\n",
      "BEFORE:  acting  AFTER:  behaving\n",
      "BEFORE:  wondering  AFTER:  asks\n",
      "BEFORE:  film  AFTER:  films\n",
      "BEFORE:  revealed  AFTER:  disclosed\n",
      "BEFORE:  phone  AFTER:  telephones\n",
      "BEFORE:  dad  AFTER:  pappy\n",
      "BEFORE:  bernard  AFTER:  barnard\n",
      "BEFORE:  chance  AFTER:  chances\n",
      "BEFORE:  say  AFTER:  says\n",
      "BEFORE:  misses  AFTER:  lack\n",
      "BEFORE:  effective  AFTER:  efficient\n",
      "BEFORE:  capper  AFTER:  evilness\n",
      "BEFORE:  obviously  AFTER:  clearly\n",
      "BEFORE:  heartfelt  AFTER:  candid\n",
      "BEFORE:  really  AFTER:  frankly\n",
      "BEFORE:  sufficient  AFTER:  suffice\n",
      "BEFORE:  dramatic  AFTER:  whopping\n",
      "BEFORE:  tension  AFTER:  pressure\n",
      "BEFORE:  emotional  AFTER:  thrills\n",
      "BEFORE:  involvement  AFTER:  involved\n",
      "BEFORE:  rest  AFTER:  remainder\n",
      "BEFORE:  film  AFTER:  movie\n",
      "BEFORE:  throughout  AFTER:  in\n",
      "BEFORE:  time  AFTER:  timeframe\n",
      "BEFORE:  nice  AFTER:  pleasant\n",
      "BEFORE:  scenes  AFTER:  imagery\n",
      "BEFORE:  french  AFTER:  frenchmen\n",
      "BEFORE:  enjoyment  AFTER:  realisation\n",
      "\n",
      "Total number of words swapped:  37\n",
      "Sentiment was changed for this data item\n",
      "Probability Before:  0.9913209080696106\n",
      "Probability After:  0.4697327613830566\n"
     ]
    }
   ],
   "source": [
    "words_swapped(normalized_dataset, bert_distance_stats,  index_two, item_two)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method A\n",
      "Percentage of reviews where sentiment was changed after attack: 95.2 % changed sentiment. i.e. 238  out of  250\n",
      "Percentage of reviews failed to change sentiment:  4.8 % did not change, i.e. 12  out of  250\n",
      "\n",
      "\n",
      "Method B\n",
      "Percentage of reviews where sentiment was changed after attack: 98.4 % changed sentiment. i.e. 246  out of  250\n",
      "Percentage of reviews failed to change sentiment:  1.6 % did not change, i.e. 4  out of  250\n",
      "\n",
      "\n",
      "Method C\n",
      "Percentage of reviews where sentiment was changed after attack: 55.2 % changed sentiment. i.e. 138  out of  250\n",
      "Percentage of reviews failed to change sentiment:  44.8 % did not change, i.e. 112  out of  250\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Method A\")\n",
    "sentiment_change_percentage(distance_stats)\n",
    "print(\"Method B\")\n",
    "sentiment_change_percentage(bert_stats)\n",
    "print(\"Method C\")\n",
    "sentiment_change_percentage(bert_distance_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## percentage success and mean words swapped percentage stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Method A\n",
    "distance_num_words_changed = []\n",
    "distance_length_of_text = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (distance_stats.at[i,'sentiment'] != distance_stats.at[i,'ga_sentiment']) and distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        distance_num_words_changed.append(distance_stats.at[i,'ga_num_changes'])\n",
    "        distance_length_of_text.append(len(distance_stats.at[i,'text'].split()))\n",
    "        \n",
    "# Method B\n",
    "bert_num_words_changed = []\n",
    "bert_length_of_text = []\n",
    "for i in range(dataset_size):\n",
    "    if (bert_stats.at[i,'sentiment'] != bert_stats.at[i,'ga_sentiment']) and bert_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        bert_num_words_changed.append(bert_stats.at[i,'ga_num_changes'])\n",
    "        bert_length_of_text.append(len(bert_stats.at[i,'text'].split()))\n",
    "   \n",
    "        \n",
    "# Method C       \n",
    "bert_distance_num_words_changed = []\n",
    "bert_distance_length_of_text = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (bert_distance_stats.at[i,'sentiment'] != bert_distance_stats.at[i,'ga_sentiment']) and bert_distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        bert_distance_num_words_changed.append(bert_distance_stats.at[i,'ga_num_changes'])\n",
    "        bert_distance_length_of_text.append(len(bert_distance_stats.at[i,'text'].split()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method A:\n",
      "Success (%):  94.0 6.550408529468128 \n",
      "\n",
      "Method B:\n",
      "Success (%):  97.2 5.423175965665236 \n",
      "\n",
      "Method C:\n",
      "Success (%):  52.400000000000006 9.43929972512076 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from statistics import mean, median, mode, stdev\n",
    "\n",
    "print(\"Method A:\\nSuccess (%): \", (len(distance_num_words_changed)/ dataset_size) * 100 ,  \n",
    "      (mean(distance_num_words_changed) / mean(distance_length_of_text)) * 100, \"\\n\")\n",
    "\n",
    "print(\"Method B:\\nSuccess (%): \", (len(bert_num_words_changed)/ dataset_size) * 100 ,  \n",
    "      (mean(bert_num_words_changed) / mean(bert_length_of_text)) * 100, \"\\n\")\n",
    "\n",
    "print(\"Method C:\\nSuccess (%): \", (len(bert_distance_num_words_changed)/ dataset_size) * 100 ,  \n",
    "      (mean(bert_distance_num_words_changed) / mean(bert_distance_length_of_text)) * 100, \"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scatterplot of number of words changed v length of review text (successful reviews only)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "number_of_words_swapped = []\n",
    "length_of_review_text = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (distance_stats.at[i,'sentiment'] != distance_stats.at[i,'ga_sentiment']) and distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        number_of_words_swapped.append(distance_stats.at[i,'ga_num_changes'])\n",
    "        length_of_review_text.append(len(distance_stats.at[i,'text'].split()))\n",
    "\n",
    "\n",
    "scatter_plot_filename_a = 'ga_attack_results/scatter-plot-changed-v-length-method_a.png'\n",
    "graph_title_a = \"Number of Words Changed v Length of Review Text (Method A)\"\n",
    "generate_scatterplot_text_length(length_of_review_text,number_of_words_swapped,scatter_plot_filename_a, graph_title_a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_of_words_swapped = []\n",
    "length_of_review_text = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (bert_stats.at[i,'sentiment'] != bert_stats.at[i,'ga_sentiment']) and bert_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        number_of_words_swapped.append(bert_stats.at[i,'ga_num_changes'])\n",
    "        length_of_review_text.append(len(bert_stats.at[i,'text'].split()))\n",
    "\n",
    "\n",
    "scatter_plot_filename_a = 'ga_attack_results/scatter-plot-changed-v-length-method_b.png'\n",
    "graph_title_a = \"Number of Words Changed v Length of Review Text (Method B)\"\n",
    "generate_scatterplot_text_length(length_of_review_text,number_of_words_swapped,scatter_plot_filename_a, graph_title_a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_of_words_swapped = []\n",
    "length_of_review_text = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (bert_distance_stats.at[i,'sentiment'] != bert_distance_stats.at[i,'ga_sentiment']) and bert_distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        number_of_words_swapped.append(bert_distance_stats.at[i,'ga_num_changes'])\n",
    "        length_of_review_text.append(len(bert_distance_stats.at[i,'text'].split()))\n",
    "\n",
    "\n",
    "scatter_plot_filename_a = 'ga_attack_results/scatter-plot-changed-v-length-method_c.png'\n",
    "graph_title_a = \"Number of Words Changed v Length of Review Text (Method C)\"\n",
    "generate_scatterplot_text_length(length_of_review_text,number_of_words_swapped,scatter_plot_filename_a, graph_title_a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scatter plot of words changed against generations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "successful_generations = []\n",
    "successful_num_words_swapped  = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (distance_stats.at[i,'sentiment'] != distance_stats.at[i,'ga_sentiment']) and distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        successful_generations.append(distance_stats.at[i,'ga_generations'])\n",
    "        successful_num_words_swapped.append(distance_stats.at[i,'ga_num_changes'])\n",
    "        \n",
    "\n",
    "scatter_plot_filename_a = 'ga_attack_results/scatter-plot-success-method_a.png'\n",
    "graph_title_a = \"Number of Words Changed v Generations (Method A)\"\n",
    "generate_scatterplot(successful_generations,successful_num_words_swapped,scatter_plot_filename_a, graph_title_a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "successful_generations = []\n",
    "successful_num_words_swapped  = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (bert_stats.at[i,'sentiment'] != bert_stats.at[i,'ga_sentiment']) and bert_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        successful_generations.append(bert_stats.at[i,'ga_generations'])\n",
    "        successful_num_words_swapped.append(bert_stats.at[i,'ga_num_changes'])\n",
    "        \n",
    "\n",
    "scatter_plot_filename_a = 'ga_attack_results/scatter-plot-success-method_b.png'\n",
    "graph_title_a = \"Number of Words Changed v Generations (Method B)\"\n",
    "generate_scatterplot(successful_generations,successful_num_words_swapped,scatter_plot_filename_a, graph_title_a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+bsXFxY1qDofDikQiqqioUCwWa/S47LHHHqn/btjmfcIJJzSqJRAI6Icffmjx/tjckUceqfvuu0/r16/XDjvsoPnz5+u8885r8nsNw9jihGdnn312KuRZlqWbbrpJ48eP13fffadbb7210ZZ427YbPY6b3n9dunRRNBqVVP+8q6io0F577dXotizLSt2HOTk5ysvLS123fv163XTTTXr33XcbnZBw0/u8OU09z/v169dsreFwWJIUiUQ0ZMgQDR06VOPGjdOBBx6o4cOH67jjjmvyg4qGQwu6deu2xXV5eXkaOXKkXnrpJZ155pl68cUXddRRRzX6QGhbX9Ob27z2hvv5+++/10477ZTqR6p/fdfW1qqiokJlZWWNXmsN90vDFv6mWJbV7HWbMk1TXbt2bXQOBwDwA4I2ALSjMWPGaM6cObrjjjs0derUrX5vU8HONM2tfr2162zbVigUSr15fvLJJ7XPPvs0+/ObBrJttbU32pvaaaedtPPOO+udd97Rxo0bNWTIEEn1Hw7k5ORo6dKlWrhwoUaOHNno55paXd/0NjcNH00FqYYg0VDDyy+/rCVLlui1117TzJkz9fTTT+vJJ5/cYlW7X79+WrBggSzL2up9vrU6Jem+++7TSy+9pN///vfac889ZZqmDjjggG36Wft/Zzbf9IOATWtpeFxff/119ejRY4uf//jjj7e4bNP7Y3M777yzdt11V7322mvaa6+9tHr16mY/IOrXr98WJ9p66KGHUv89evToVNAKh8O6/vrrdeSRRzZ7283dx5mZmerXr5/+8Y9/NPuzmz9vL730UgUCAf3lL39RcXGxPvvsMx177LHN/vymDMNo8vHY9LnVXK2hUEgPPPCAPvvsMy1YsEB/+ctf9NBDD+nZZ59V7969t+n2Gxx33HH63e9+pzPPPFN/+9vf9Nvf/rbR9dv6mt5cc8+1rV0Xj8ebfG3FYrFmf6Zfv36tGtu1tboAwKvYOg4A7ezqq6/W3//+dy1durTR5aFQSJFIJPX1ypUrt+t2vvnmm0Zfl5aWqmfPnsrNzVX37t23OGvxto4JkqQ+ffqktvM2WL58eZNnGW/OyJEj9d577+n999/X0KFDU5cPHjxY7777rt57773UtvGuXbuqa9euqa2tUv0b+1WrVqlPnz5N/v7CwkKtWbOm0Uraplvra2trFY/HNXToUF199dWaM2eOPvrooyZPWDd27FiVl5frxRdf3OK6hhOfbctIoo8//lijR4/W3nvvLdM0tXLlytQZ2lvSrVs3BQIBrVq1KnXZpvOWd9ppJwUCgUaPq2VZKi0tldTy/dGUcePG6fXXX9err76qgw8+uNGJujZ15JFH6qOPPtIHH3zQ5PWb3mafPn3a/NwrKSnRqlWrGq1MV1ZWNjpJ1+b+85//aMKECakV2E8++WSbbkuSevfu3eg5J9W/rrbleZ5IJFRVVaXdd99d559/vp5//nnl5uY2eXhF165dJanRSfM2NXz4cK1bt04vvfSSgsGgfvKTnzS63onX9KZ69+6t77//vtEHMcuXL1d2drZ22GEHFRYWNlrVl7b+XDriiCP08ccf67333tviun/+85+aMmVK6oMk27ZVWVnZ5Mo/AHgZQRsA2tlOO+2kc889V7feemujy/v27av58+crHo/r008/1YIFC7brdlavXq25c+cqHo/rX//6lz7++GONHTtWkjRx4kQ98MAD+uKLL5RIJDR79mwde+yx2xz6TjjhBC1ZskSvvvqqEomE3n//fb300ktNzsduzsiRI/Xhhx/qP//5T2pFW5KGDBmiF154QXV1damzrkv1x6c/9NBDWrVqlSKRiO6++2516dIlFcY3N3ToUNXW1urJJ59ULBbTm2++qX//+9+p6y+88EJdd911qqqqkmVZWrp0qTIyMrbYEivVb6O/8MILde211+qpp55SXV2dIpGI5s2bp3PPPVdHHHGEunTp0mLPO+20kz777DPV1tbqm2++0a233qqioiKVlZW1+LOBQEDDhg3TY489pqqqKn333Xd69tlnU9fn5OTo6KOP1p133qlVq1YpGo1q1qxZmjJlipLJZIv3R1OOPPJILV68WAsWLNjqCvSgQYM0efJknXfeeXr55ZcVi8WUTCb13//+VxdffLEqKytTW7AnTpyop59+Wu+//76SyaQWLFigo48+WsuXL2/xPhg+fLgKCgp08803q7q6WuXl5br88su3Ol6td+/eWrp0qeLxuBYtWqR58+ZJUuo+z8zMVGlpqaqqqrbY/n7iiSfqqaee0ueff65YLKYnnnhCq1ev1rhx41qs9ZFHHtGUKVNSYfebb75RZWVlkx8M9erVS1lZWc0elxwMBnX00Ufrt7/9bbOr8S29pjMzM/Xdd99t9UOJBiNGjFBeXp7uuusuxWIxff/993rwwQc1fvx4maapkSNH6vPPP9f8+fMVi8U0d+7crYb6/fffXyeddJLOP//81PNj48aNmjNnjmbMmKHjjz8+tYr99ddfK5lMNnm8OgB4GVvHAaADnHnmmXrhhRcanZDpyiuv1LXXXquf/vSn2n///fXzn/98izDeGoceeqg+/fRT3XrrrcrKytJVV12lgQMHSqo/MVt1dbX+7//+T9FoVAMGDNCDDz7Y6NjWrenfv79uueUW3XPPPZo2bZqKi4t19dVX64gjjtjm+g444ADV1taquLi40bHmQ4YM0YwZMzR8+PBGx4heeOGFqqqq0sSJExWJRLTXXnvpiSeeUHZ2dpO/v2fPnrrzzjt1xx136M4779SwYcM0efJkvfHGG5KkG2+8Udddd50OOeQQWZalfv366Z577lF+fn6Tv+8Xv/iF+vTpo8cff1y33367gsGgdtllF1177bXbFLwkaerUqbr00ks1dOhQ9e3bV9dff73efvtt3X///du0gnfttddq+vTpGjVqlPr376/zzjtP55xzTmr78tVXX60bbrghFcb22msv/eEPf1AgEGjx/mhK37591adPH61YsUIHH3zwVmu75pprtMcee+jRRx/VVVddJan+nAIjR47U3//+99QHGCeccIJWr16tSy+9VFVVVerbt6/uvPNO7bzzzi32HwwG9fvf/1433nijhg8fruzsbI0aNSp1e83dZ9dcc43mzp2rAw44QDfffLOmT5+un//853r66ad1zDHH6JVXXtHBBx+sF154odHPnnLKKfrhhx903nnnqaKiQrvttpsef/zxJj+M2dwZZ5yh1atX6+STT1ZNTY0KCgp01llnNbn9PiMjQwcddJAWL16sww8/vMnfd9xxx+nxxx/Xz372syavb+k1PWHCBP3ud7/TwoUL9dJLL2219lAopFmzZum2227T0KFDlZubq3HjxumSSy6RJO2zzz665pprdOONN6qqqkrjxo3Tz372s0bjBTd3ww03aI899tCDDz6oK6+8UpmZmRo4cKDuu+++RjtalixZop122qnR8eMA4AeG3bB3BwAApJ1YLKZQKCRJ+vDDD3XKKafo/fffV25ursuVYXu8+eabuuyyy/TGG280++FRZzB+/Hgde+yxOuOMM9wuBQAcxdZxAADS1JVXXqmzzjordVzyQw89pEGDBhGyfaBhl8Ljjz/udimumT9/vqqqqnTKKae4XQoAOI6gDQBAmrr88suVn5+vsWPH6tBDD1UymdTtt9/udllwyB133KEnn3yy0UnuOovy8nL95je/0Z133rlN5zsAAK9h6zgAAAAAAA5iRRsAAAAAAAcRtAEAAAAAcFBaj/dau7bl2Y/ponv3LG3YUOt2GdvNL31I/umFPtKLX/qQ/NMLfaQXv/Qh+acX+kgvfulD8k8v9JFevNRHQUHzJydlRdshwWDA7RIc4Zc+JP/0Qh/pxS99SP7phT7Si1/6kPzTC32kF7/0IfmnF/pIL37pg6ANAAAAAICDCNoAAAAAADiIoA0AAAAAgIMI2gAAAAAAOIigDQAAAACAgwjaAAAAAAA4iKANAAAAAICDCNoAAAAAADiIoA0AAAAAgIMI2gAAAAAAOIigDQAAAACAgwjaAAAAAAA4iKANAAAAAICDCNoAAAAAADiIoA0AAAAAgIMI2gAAAAAAOIigDQAAAACAgwjaAAAAAAA4iKANAAAAAICDCNoAAAAAADiIoA0AAAAAgIMI2gAAAAAAOIigDQAAAACAgwjaAAAAAAA4iKANAAAAAICDCNoAAAAAADiIoA0AAAAAHmZZtiLRhCzLdrsU/E/Q7QIAAAAAAK1n27ZWlFWrvCqirOywamsiys8Lq6QoV4ZhuF1ep8aKNgAAAAB4UH3Ijso0TGVmBGQapsqrolpRVu12aZ0eQRsAAAAAPMaybJVXRWRutnJtGobKqyJsI3cZQRsAAAAAPCaesJRINh2mE0lb8YTVwRVhUwRtAAAAAPCYjKCpYKDp47CDAUMZQaKem7j3AQAAAMBjTNNQfl5Ylt14VduybeXnhWWanAzNTZx1HAAAAAA8qKQoV5JUXhVRNJ6UZVups47DXQRtAAAAAPAgwzDUt2ee+hTmqmu3LFVW1LKSnSbYOg4AAAAAHmaahsKZQUJ2GiFoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOCrbXL66pqdH06dNVWVmpeDyu888/X7vuuqumTZumZDKpgoIC3X777QqFQu1VAgAAAAAAHa7dVrT/+te/ql+/fnriiSd0991366abbtI999yjSZMm6amnnlKvXr00d+7c9rp5AAAAAABc0W5Bu3v37qqoqJAkVVVVqXv37lqyZInGjBkjSRozZowWLVrUXjcPAAAAAIAr2i1oH3XUUSotLdXYsWM1efJkTZ8+XXV1damt4gUFBVq7dm173TwAAAAAAK5ot2O0X3jhBRUXF+uRRx7RZ599pquuukqGYaSut227xd/RvXuWgsFAe5XouIKCXLdLcIRf+pD80wt9pBe/9CH5pxf6SC9+6UPyTy/0kV780ofkn17oI734oY92C9r//ve/NXz4cEnS7rvvrrKyMnXp0kWRSEThcFhlZWUqLCzc6u/YsKG2vcpzXEFBrtaurXa7jO3mlz4k//RCH+nFL31I/umFPtKLX/qQ/NMLfaQXv/Qh+acX+kgvXupjax8ItNvW8ZKSEi1dulSStGrVKmVnZ2vo0KF65ZVXJEnz5s3TiBEj2uvmAQAAAABwRbutaE+YMEFXXnmlJk+erEQioV//+tfaZZddNH36dM2ePVvFxcUaP358e908AAAAAACuaLegnZ2drbvvvnuLyx999NH2ukkAAAAAAFzXblvHAQAAAADojAjaAAAAAAA4iKANAAAAAICDCNoAAAAAADiIoA0AAAAAgIMI2gAAAAAAOIigDQAAAACAgwjaAAAAADoly7IViSZkWbbbpcBngm4XAAAAAAAdybZtrSirVnlVRFnZYdXWRJSfF1ZJUa4Mw3C7PPgAK9oAAAAAOpX6kB2VaZjKzAjINEyVV0W1oqza7dLgEwRtAAAAAJ2GZdkqr4rI3Gzl2jQMlVdF2EYORxC0AQAAAHQa8YSlRLLpMJ1I2oonrA6uCH5E0AYAAADQaWQETQUDTR+HHQwYyggSkbD9eBYBAAAA6DRM01B+XliW3XhV27Jt5eeFZZqcDA3bj7OOAwAAAOhUSopyJUnlVRFF40lZtpU66zjgBII2AAAAgE7FMAz17ZmnPoW56totS5UVtaxkw1FsHQcAAADQKZmmoXBmkJANxxG0AQAAAABwEEEbAAAAAAAHEbQBAAAAAHAQQRsAAAAAAAcRtAEAAAAAcBBBGwAAAAAABxG0AQAAAABwEEEbAAAAAAAHEbQBAAAAAHAQQRsAAAAAAAcRtAEAAAAAcBBBGwAAAAAABxG0AQAAAABwEEEbAAAAAAAHEbQBAAAAAHAQQRsAAAAAAAcRtAEAAAAAcBBBGwAAAAAABxG0AQAAAABwEEEbAAAAAAAHEbQBAAAAAHAQQRsAAAAAPMyybEWiCVmW7XYp+J+g2wUAAAAAAFrPtm2tKKtWeVVEWdlh1dZElJ8XVklRrgzDcLu8To0VbQAAAADwoPqQHZVpmMrMCMg0TJVXRbWirNrt0jo9gjYAAAAAeIxl2SqvisjcbOXaNAyVV0XYRu4ygjYAAAAAeEw8YSmRbDpMJ5K24gmrgyvCpgjaAAAAAOAxGUFTwUDTx2EHA4YygkQ9N3HvAwAAAIDHmKah/LywLLvxqrZl28rPC8s0ORmamzjrOAAAAAB4UElRriSpvCqiaDwpy7ZSZx2HuwjaAAAAAOBBhmGob8889SnMVdduWaqsqGUlO02wdRwAAAAAPMw0DYUzg4TsNELQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAACA6yzLViSakGXZbpey3ZijDQAAAABwjW3bWlFWrfKqiLKyw6qtiSg/L6ySolwZhjdHlrGiDQAAAABwTX3Ijso0TGVmBGQapsqrolpRVu12aW1G0AYAAAAAuMKybJVXRWRutnJtGobKqyKe3UZO0AYAAAAAuCKesJRINh2mE0lb8YTVwRU5g6ANAAAAAHBFRtBUMND0cdjBgKGMoDcjqzerBgAAAAB4nmkays8Ly7Ibr2pbtq38vLBM05snQ+Os4wAAAAAA15QU5UqSyqsiisaTsmwrddZxryJoAwAAAABcYxiG+vbM0049ctQlJ1N1G6MKenTLeAOCNgAAAADANczRBgAAAADAQczRBgAAAADAIczRBgAAAADAQczRBgAAAADAQczRBgAAAADAQczRBgAAAADAYczRBgAAAADAQQ1ztPsU5qprtyxVVtR6diW7AVvHAQAAAACuM01D4cyg50O2RNAGAAAAAMBRBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAK1iWbYi0YQsy3a7FCAtBd0uAAAAAIA32LatFWXVKq+KKCs7rNqaiPLzwiopypVhGG6XB6QNVrQBAAAAbJP6kB2VaZjKzAjINEyVV0W1oqza7dKAtELQBgAAANAiy7JVXhWRudnKtWkYKq+KsI0c2ARBGwAAAECL4glLiWTTYTqRtBVPWB1cEZC+CNoAAAAAWpQRNBUMNH0cdjBgKCNItAAa8GoAAAAA0CLTNJSfF5ZlN17Vtmxb+XlhmSYnQwMacNZxAAAAANukpChXklReFVE0npRlW6mzjgP4EUEbAAAAwDYxDEN9e+apT2GuunbLUmVFLSvZQBPYOg4AAACgVUzTUDgzSMgGmkHQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHBRsz1/+4osv6uGHH1YwGNTFF1+s/v37a9q0aUomkyooKNDtt9+uUCjUniUAAAAAANCh2m1Fe8OGDbrvvvv01FNP6YEHHtD8+fN1zz33aNKkSXrqqafUq1cvzZ07t71uHgAAAAAAV7Rb0F60aJGGDBminJwcFRYW6oYbbtCSJUs0ZswYSdKYMWO0aNGi9rp5AAAAAABc0W5bx7///nvZtq1LLrlEa9as0YUXXqi6urrUVvGCggKtXbu2vW4eAAAAAABXtOsx2mVlZbr33ntVWlqq//u//5NhGKnrbNtu8ee7d89SMBhozxIdVVCQ63YJjvBLH5J/eqGP9OKXPiT/9EIf6cUvfUj+6YU+0otf+pD80wt9pBc/9NFuQXuHHXbQoEGDFAwG1adPH2VnZysQCCgSiSgcDqusrEyFhYVb/R0bNtS2V3mOKyjI1dq11W6Xsd380ofkn17oI734pQ/JP73QR3rxSx+Sf3qhj/Tilz4k//RCH+nFS31s7QOBdjtGe/jw4Vq8eLEsy1J5eblqa2s1dOhQvfLKK5KkefPmacSIEe118wAAAAAAuKLdVrSLiop0+OGH67TTTlNdXZ2uvvpq7bXXXpo+fbpmz56t4uJijR8/vr1uHgAAAAAAV7TrMdqnnHKKTjnllEaXPfroo+15kwAAAAAAuKrdto4DAAAAANAZEbQBAAAAAHAQQRsAAAAAAAcRtAEAAAAAcBBBGwAAAAAABxG0AQAAAABwEEEbAAAAAAAHEbQBAACQ1izLViSakGXZbpeC/+ExAbYu6HYBAAAAQFNs29aKsmqVV0WUlR1WbU1E+XlhlRTlyjAMt8vrlHhMgG3DijYAAADSUn2gi8o0TGVmBGQapsqrolpRVu12aZ0WjwmwbQjaAAAASDuWZau8KiJzs1VS0zBUXhVhy7ILeEyAbUfQBgAAQNqJJywlkk0Ht0TSVjxhdXBF4DEBth1BGwAAAGknI2gqGGj6mN9gwFBGkLexHY3HBNh2vBoAAACQdkzTUH5eWJbdeAXVsm3l54Vlmpx4q6PxmADbjrOOAwAAIC2VFOVKksqrIorGk7JsK3WGa7iDxwTYNgRtAAAApCXDMNS3Z5526pGjLjmZqtsYVdDD25M3nT3t1dXfhsekT2GuunbLUmVFrWd7kfzxmEj+6cNPCNoAAABIS36Z2eyXPjZlmobCmUFVezTU+eUx8UsffuTdjwQBAADga36Z2eyXPvzEL4+JX/rwI4I2AAAA0o5fZjb7pQ8/8ctj4pc+/IqgDQAAgLTjl5nNfunDT/zymPilD78iaAMAACDt+GVms1/68BO/PCZ+6cOvuPcBAACQdvwys9kvffiJXx4Tv/ThV5x1HAAAAGnJLzOb/dKHn/jlMfFLH35E0AYAAEBa8svMZr/04Sd+eUz80ocfsXUcAAAAaa1hZrPXA4Rf+vATvzwmfunDTwjaAAAAAAA4iKANAAAAAICDCNoAAAAAADiIoA0AAAAAgIMI2gAAAAAAOIigDQAAAHQAy7IViSZkWbbbpQBoZ8zRBgAAANqRbdtaUVat8qqIsrLDqq2JKD8vrJKiXBkG45gAP2JFGwAAAGhH9SE7KtMwlZkRkGmYKq+KakVZtdulAWgnBG0AAACgnViWrfKqiMzNVq5Nw1B5VYRt5IBPEbQBAACAdhJPWEokmw7TiaSteMLq4IoAdASCNgAAANBOMoKmgoGmj8MOBgxlBHk7DvgRr2wAAACgnZimofy8sCy78aq2ZdvKzwvLNDkZGuBHnHUcAAAAaEclRbmSpPKqiKLxpCzbSp11HIA/EbQBAACAdmQYhvr2zFOfwlx17ZalyopaVrIBn2PrOAAAANABTNNQODNIyAY6AYI2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgoGbnaJeWlm71B4uLix0vBgAAAAAAr2s2aE+cOFGGYci2ba1Zs0Y5OTlKJpOqra1Vnz59NG/evI6sEwAAAAAAT2g2aL/55puSpFtvvVU/+9nPNHDgQEnS0qVL9be//a1jqgMAAAAAwGNaPEb7888/T4VsSdpnn3301VdftWtRAAAAAAB4VbMr2g0SiYTuvPNO7b///jIMQx9++KGi0WhH1AYAAAAAgOe0uKJ91113yTRNPfPMM3r66acVj8d11113dUBpAAAAAAB4T4sr2jvssINOP/10ff/999prr71kWZZMk6lgAAAAAAA0pcXE/NJLL2nChAmaMWOGJOmGG27QnDlz2r0wAAAAwE8sy1YkmpBl2W6XAqCdtRi0n376ab3wwgvq3r27JGn69Ol69tln270wAAAAwA9s29a3q6v00Vdr9d6nZfroq7X6dnWVbJvADfhVi0E7FAqpS5cuqa/D4bAyMjLatSgAAADAL1aUVau8KirTMJWZEZBpmCqvimpFWbXbpQFoJy0eo92tWzf99a9/VTQa1X//+1+9/PLLys/P74jaAAAAAE+zLFvlVRGZRuP1LdMwVF4VUZ/CXJmm4VJ1ANpLiyva119/vT7++GPV1NTo6quvVjQa1Y033tgRtQEAAACeFk9YSiSb3iKeSNqKJ6wOrghAR2hxRTsvL0/XXnttR9QCAAAA+EpG0FQw0PSKdTBgKCPINB/Aj1oM2qNGjZJhNP7HIRAIqF+/fpo+fbp22223disOAAAA8DLTNJSfF/7fMdo/vqe2bFv5eWG2jQM+1WLQPvvss7V27VoddthhCgQCmjdvnkKhkHbZZRf9+te/1pNPPtkRdQIAAACeVFKUK0kqr4ooGk/Ksi3l54VTlwPwnxaD9quvvqrHHnss9fXuu++un//855o6daoef/zxdi0OAADADZvOO/byiqNf+vA6wzDUt2ee+hTmqmu3LFVW1PJ4AD7XYtCuqKjQF198of79+0uSvv32W5WWlmrVqlXauHFjuxcIAADQUWzb/t8opoiyssOqrYmkVh43P5QunfmlD78xTUPhzKCqCdmA77UYtH/5y1/q3HPPVV1dnaT647NnzJihzz77TOedd167FwgAANBRNp93HPnfvGNJ6tszz+Xqtp1f+gAAr9qmk6G9/vrr2rBhg2zbVvfu3fkkFAAA+I5f5h37pQ8A8LIWg/aXX36pOXPmqLKyUrb94wzAmTNntmthAAAAHalh3nGoiXdHDfOOM0OBji+slfzSBwB4WYtB+5JLLtG4ceO0xx57dEQ9AAAArvDLvGO/9AEAXtZi0O7Ro4cuuOCCjqgFAADANX6Zd+yXPgDAy1r8SHPkyJF6++23FYvFZFlW6n8AAAB+U1KUq/y8TFm2tcm840zPzTv2Sx8A4FUtrmjff//9W4zxMgxDn376absVBQAA4Aa/zDv2Sx9+w1xzoPNoMWi///77W1z27bfftkctAAAAacEv84790ofXMdcc6HxaDNrJZFJvv/22NmzYIEmKxWJ64IEH9Nprr7V7cQAAAIDXMdcc6HxaDNqXX365Kisr9fnnn2u//fbT0qVLdeGFF3ZEbQAAAICnMdcc6JxaPBna6tWr9cgjj6hfv36655579NRTT+njjz/uiNoAAAAAT2uYa96UhrnmAPxnmwcpJhIJRaNR9erVS1999VV71gQAAAD4AnPNgc6pxa3jgwcP1kMPPaRDDz1Uxx13nAoKChjvBQAAAGwD5poDnVOLQfuiiy5SMplUIBDQoEGDtH79eg0bNqwjagMAAAA8r2F+eXlVZJO55mHmmgM+1mLQrqur09tvv62qqirZdv3xJS+//LJOPPHEdi8OAAAAbcfc5vTAXHOg82kxaJ9zzjkKBoPq2bNno8sJ2gAAAOmJuc3pibnmQOfRYtCOxWJ64oknOqIWAAAAOIC5zQDgrhZPczhw4ECVl5d3RC0AAADYTj/ObW68atowt9mymh41BQBwTrMr2pMmTZJhGEomkzriiCO08847KxAIpK5/8sknO6RAAAAAbLuGuc2hJt7lNcxtzgwFtrwSAOCYZoP2JZdc0oFlAAAAwAnMbQYA9zUbtA888EC9//77+ulPf5q6rKKiQl988YUOPPDADikOAAAArcPcZgBwX7Mfab788su66qqrVF1dnbqsoqJCv/nNb/TOO+90SHEAAABovZKiXOXnZcqyrU3mNmcytxkAOkizK9qPPfaYHnvsMeXm/vgPct++ffXwww9r2rRpGjZsWIcUCAAAgNZhbjMAuKvZFe2mZmdLUs+ePWXbnK0SAAAg3TXMbSZkA0DHajZob7plfFOWZWnDhg3tVhAAAAAAAF7WbNAePny47rzzTlmWlbosFovppptu0sEHH9wRtQEAAAAA4DnNHqN98cUXa9q0aRozZox23313WZalZcuWaciQIZoxY0ZH1ggAAAAAgGc0G7QzMzN19913a8WKFfrqq68UCAS02267qVevXh1ZHwAAAAAAntJs0G5QUlKikpKSjqgFAAAAAADPa/YYbQAAAAAA0HoEbQAAAAAAHNRi0P7+++/1wQcfSJKeffZZXXnllfr666/bvTAAAAAAALyoxaA9Y8YMZWRkaNmyZZozZ44OP/xw3XjjjR1RGwAAAAAAntNi0DZNU3vvvbdeffVVnXrqqRo1apRs2+6I2gAAAAAA8JwWg3ZNTY3+85//6JVXXtHIkSMVi8VUVVXVEbUBAAAAAOA5LQbtM888U9dcc41OPvlk5efna9asWTr66KM7ojYAAAAAADynxTnaRx55pI488sjU17/85S9lGEa7FgUAAAAAgFc1G7RHjx691UC9YMGCdikIAIDOxrJsRaIJWZYt0+TDbAAAvK7ZoP2nP/1JkjR79mwVFBRo8ODBSiaTeuedd1RbW9tR9QEA4Fu2bWtFWbXKqyLKyg6rtiai/LywSopy2T0GAICHNRu0+/TpI0lavny5Lr/88tTlP/nJTzR16tT2rwwAAJ+rD9lRmYapzIyAIoap8qqoJKlvzzyXqwMAAG3V4snQVq1apbffflu1tbWKRCJatGiRVq1a1RG1AQDgW5Zlq7wqInOzlWvTMFReFZFlMUoTAACvavFkaNdff71uu+02ffHFF5KkXXfdVddcc027FwYAgJ/FE5YSSVuhJv4SJ5K24glLmaFAxxcGAAC2W4tBOzMzU88880xH1AIAQKeRETQVDDR9HHYwYCgj2OKmMwAAkKZa/Ct+6623dkQdAAB0KqZpKD8vLMtuvEXcsm3l54U5+zgAAB7W4op2r169NGXKFO2zzz7KyMhIXX7xxRe3a2EAAPhdSVGuJKm8KqJoPCnLtlJnHQcAAN61TUG7V69eHVELAACdimEY6tszT30Kc9W1W5YqK2o9vZKdSFiqqI4okbAU9PjWd2abAwC2R4tB+4ILLlBtba2++eYbGYahfv36qUuXLh1RGwAAnYJpGgpnBlXt0UBnWZYWLytT6doaBUJBJWMJFRdka/DAIpmmtwI3s80BAE5oMWjPnz9fv/71r9WzZ09ZlqV169bphhtu0KhRozqiPgAAkOYWLytT6boamaaprMygahKWStfVaPGyMg3dc0e3y2sVZpsDAJzQYtB++OGH9eKLLyo/P1+SVFZWposvvpigDQAAlEhYKl1bs8XKtWmYKl1b46lt5D/ONt+8l/rZ5n0Kc9lGDgDYJi3+5cvIyEiFbEkqKipqdFI0AADQedVGE4onrSaviyct1UYTHVxR2zXMNm9Kw2xzAAC2RYsr2tnZ2frjH/+ooUOHSpLefvttZWdnt3thAAAg/WVlBpURaPpz+4xA/VZyr2C2OQDAKS3+9bvpppt0991368UXX5QkDRo0SDfffHO7FwYAANJfMGiquCC7/hjtTbZcW7al4oJsz2wbl36cbV5/jPaPgZvZ5gCA1mo2aE+dOlUjRozQyJEj9Zvf/KYjawIAAB4yeGBR6qzjtdGELMtKnXXca/w225wxZQDgjmaD9tFHH62FCxfqkUceUTAY1IgRIzR8+HANHjyY8V4AACDFNE0N3XNHJRKWuuRkqm5j1FMr2Zvyy2xzxpQBgLu2GrSPPvpoSdKKFSu0aNEivfjii7r55pvVq1cv/elPf+qoGgEAgAcEg6a65YYVj8TdLmW7eX22OWPKAMBd2/Rxc2ZmpjIyMpSZmalwONzeNQEAAKCNfhxT1vhDgoYxZZbV9JnVAQDOaXZFe/78+Vq4cKHee+895ebm6sADD9T48eP1m9/8RqFQqCNrBAAAwDZqGFMWauJdXsOYssxQoOMLA4BOpNmgfd111ykcDmvChAkaPXq0dt11146sCwAAAG3AmDIAcF+zQfudd97RZ599pkWLFum2227TihUrtPfee2vIkCEaPHiwevXq1ZF1AgAAYBswpgwA3LfVOdq77767dt99d51xxhmKx+N688039eijj+rqq6/Wp59+2lE1AgAAoBX8NqYMALxmq0E7Eono3Xff1aJFi/TOO++ovLxcQ4YM0YQJEzqqPgAA4BHMbE4fDWPKivOzFQgHlYxkK8Rx2QDQYZoN2lOmTNEnn3yiPffcU8OHD9ctt9yin/zkJx1ZGwAA8ABmNqcfy7K0eFmZStfWKBAKKhlLqLggW4MHFsk0OUYbANpbs0H7tNNO0+DBg5WTk9OR9QAAAI9hZnP6WbysTKXramSaprIyg6pJWCpdV6PFy8o0dM8d3S4PAHyv2Y80Dz30UEI2AADYKmY2p59EwlLp2hqZRuO3eaZhqnRtjRIJy6XKAKDzYO8QAABos4aZzU1pmNmMjlUbTSiebPp+jyct1UYTHVwRAHQ+zQbt5557TpI0Z86cDisGAAB4CzOb009WZlAZgabv94xA/VZyAED7avZf2vvvv1/xeFyPPfZYkycyOfHEE9u1MAAAkP6Y2Zx+gkFTxQXZ9cdob7J93LItFRdkK8iHHwDQ7poN2tOmTdObb76p6upqffDBB1tcT9AGAAASM5vT0eCBRamzjtdGE7IsK3XWcQBA+2s2aB922GE67LDD9Morr+jwww/vyJoAAICHNMxs7lOYq67dslRZUctKtstM09TQPXdUImGpS06m6jZGWckGgA7U4kE6++67r6688kp9/PHHMgxD++67ry655BLl5+d3RH0AAMAjTNNQODOoakJ22ggGTXXLDSseibtdCgB0Ki1+tHndddfpJz/5iX7729/qjjvu0M4776wrr7yyI2oDAAAAAMBzWlzRrqur06mnnpr6un///nrttdfatSgAAAAAALyqxRXturo6rVmzJvX16tWrFYvF2rUoAAAAAAC8qsUV7fPOO0/HH3+8CgoKZNu2ysvLddNNN3VEbQAAAAAAeE6LQfvggw/W/Pnz9e2330qS+vXrp8zMzPauCwAAAAAAT2oxaEtSOBzW7rvv3t61AAAAAADgeQxUBAAA8CnLshWJJmRZttulAECnsk0r2gAAAPAO27a1oqxa5VURZWWHVVsTUX5eWCVFuTIM5pwDQHtrMWiXlZVp3rx5qqqqkm3/+GnoBRdc0K6FAQAAoG3qQ3ZUpmEqMyOgiGGqvCoqSerbM8/l6gDA/1rcOn7OOedo2bJlisfjSiQSqf8BAAAg/ViWrfKqiMzNVq5Nw1B5VYRt5ADQAVpc0e7atatuueWWjqgFAAAA2ymesJRI2go18S4vkbQVT1jKDAU6vjAA6ERaDNpjx47Viy++qEGDBikQ+PEf5eLi4nYtDAAAAK2XETQVDDR9HHYwYCgjyLlwAaC9tRi0P//8c/3tb39Tt27dUpcZhqE33nijHcsCAABAW5imofy88P+O0f4xcFu2rfy8sEyTk6EBQHtrMWgvXbpU7777rjIzMzuiHgAAOp1YLKnV6zcqGUsq5OEtvYmEpYrqiBIJS0FWTV1VUpQrSSqviigaT8qyrdRZx71o0zFlfFAAwAtaDNp77rmnYrFYm4J2JBLRUUcdpfPPP19DhgzRtGnTlEwmVVBQoNtvv12hUKhNRQMA4AfJZFIvLVqhlWUbZZiGbMtW76IcHT2kpNHhWunOsiwtXlam0rU1CoSCSsYSKi7I1uCBRTJNArcbDMNQ35556lOYq67dslRZUevJgMqYMgBe1eJfv7KyMo0ePVqnnHKKTj311NT/tsX999+f2nJ+zz33aNKkSXrqqafUq1cvzZ07d7sKBwDA615atEIr11QrYJrqEs5QwDS1ck21Xlq0wu3SWmXxsjKVrquRaZrKygzKNE2VrqvR4mVlbpfW6ZmmoXBm0JMhW9pyTJn5vzFlK8qq3S4NALaqxRXtqVOntukXf/311/rqq6908MEHS5KWLFmi66+/XpI0ZswY/elPf9KkSZPa9LsBAPC6WCyplWUbFTAbr1wHjIBWlm1UzCPbyBMJS6Vra7ZYuTYNU6Vra9hGjjb7cUzZ5s+t+jFlfQpzPfsBAgD/azFoJ5PJNv3i2267Tddcc42ef/55SVJdXV1qq3hBQYHWrl3b4u/o3j1LwWD6v8loUFDgzeOeNueXPiT/9EIf6cUvfUj+6cWLfaxeX79dPDP845/ihv+ui8QVCAdVsEOOW+Vts4rqiAKhoLIyf+wjO7v+731tNKEuOZnqlht2q7zt5sXnVlO82EckmlBWdliZGT++F+zatYskKRpPqmu3LIUzW3wrm5a8+Hg0xy+90Ed68UMfLf7r9Pvf/z713/F4XF999ZX2228/DRkypNmfef7557Xvvvuqd+/eqcs2PY7Gtu1tKm7Dhtpt+r50UFCQq7Vrvb+NyS99SP7phT7Si1/6kPzTi1f7SMaSsi1b0UhCUn3Ibvhv27KVjCQ80VciYSkZS6gmYUmqD9k1NTFJ9cdu122MKh6Ju1lim3n1ubU5r/ZhWbZqayKK/G9Fu2vXLqqsrKu/zrZUWVGrag+uaHv18WiKX3qhj/TipT629oFAi0H7iSeeaPT1+vXrdeedd271Z9544w2tXLlSb7zxhlavXq1QKKQuXbooEokoHA6rrKxMhYWF21g+AAD+EwoF1Lsop/4YbePHFbuknVTvolxPbBuXpGDQVHFBdv0x2pts8bVsS8UF2WwbR5sxpgyAl7V6v80OO+yg5cuXb/V77rrrrtR/z5o1S7169dKHH36oV155Rccee6zmzZunESNGtLpYAAD85OghJamzjtdF4v8763iujh5S4nZprTJ4YFHqrOO10YQsy0qddRzYHn4bUwag82gxaF9++eWNtn3/8MMPbRrVceGFF2r69OmaPXu2iouLNX78+Fb/DgAA/CQQCOjY4TsrEknICpoyE5bCYe8dc2qapobuuaNisaQC4aCSkYRnVuSR3vwypgxA59PiX/OhQ4em/tswDOXk5GjYsGHbfAMXXnhh6r8fffTRVpYHAIB/+WVGsF/6QPpqGFPmxWOyAXROLQbt4447riPqAACg09l8RnDkfzOCJalvzzyXq9t2fukDAACnNBu0R48evcWZwg3DUCwW07p16/Tpp592SIEAAPiRX2YE+6UPAACc1GzQfu2117a4bP78+brzzjt1wgkntGtRAAD4XTxhKZG0FWriL3EiaSuesJTpgeOc/dIHAABO2qYzrnz77be68cYblZGRoQcffLDRfGwAANB6GUFTwUDTK73BgKEMj4zF8ksfAAA4aatBu7a2Vvfdd5/efPNNXX755Ro1alRH1QUAgK/5ZUawX/oAAMBJzX7M/NJLL+n4449X165d9fzzzxOyAQBwWElRrvLzMmXZ1iYzgjM9NyPYL30AAOCUZle0L7vsMvXt21dvvfWW3n777dTlDSdFe/zxxzukQAAA/MovM4Ib+tipR4665GSqbmNUQbaMAwA6sWaD9oIFCzqyDgAAOi2vzwhmjjYAAI01G7R79erVkXUAAACPYo42AACNsa8LAAC02Y9ztBuvXDfM0bYs26XKAABwD0EbAAC0WcMc7aY0zNEGAKCzIWgDAIA2Y442AABb4q8fAABos4Y52pbdeFWbOdoAgM6s2ZOhAQAAbIuGednlVZFN5miHmaMNAOi0CNoAAGC7+GUeOAAATmHrOAAAcETDPHBCNgCgsyNoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgB0QpZlKxJNyLJst0vZLvQBAADSEXO0AaATsW1bK8qqVV4VUVZ2WLU1EeXnhVVSlCvD8M5IJvoAAADpjBVtAOhE6kNdVKZhKjMjINMwVV4V1YqyardLaxX6AAAA6YygDQCdhGXZKq+KyNxspdQ0DJVXRTyzbZk+AABAuiNoA0AnEU9YSiSbDm+JpK14wurgitqGPgAAQLojaANAJ5ERNBUMNH3cbzBgKCPojT8J9AEAANIdf8UBoJMwTUP5eWFZduNVVMu2lZ8Xlml64+Rb9AEAANIdZx0HgE6kpChXklReFVE0npRlW6mzXHsJfQAAgHRG0AaATsQwDPXtmac+hbnq2i1LlRW1nlw5beijOD9bgXBQyUi2QqGA22W1ml8eDwAA0BhBGwA6IdM0FM4Mqtqjoc6yLC1eVqbStTUKhIJKxhIqLsjW4IFFMk3vHRXl9ccDAAA05r13IwCATm/xsjKVrquRaZrKygzKNE2VrqvR4mVlbpcGAABA0AYAeEsiYal0bY1Mo/GfMNMwVbq2RgnGYgEAAJcRtAEAnlIbTSiebDpMx5OWaqOJDq4IAACgMYI2AMBTsjKDygg0/ecrI1C/lRwAAMBNBG0AgKcEg6aKC7Jl2Y1XtS3bUnFBtoJB/rQBAAB38W4EAOA5gwcWqbhHtiyrfqu4ZVkq7lF/1nEAAAC3sb8OAOA5pmlq6J47KhZL/m+OdsKTc7QBAIA/EbQBAJ5j27ZWlFWrvCqirOywamsiys8Lq6QoV4bBLGoAAOAuto4DADynPmRHZRqmMjMCMg1T5VVRrSirdrs0AAAAgjYAwFssy1Z5VUTmZivXpmGovCoiy7JdqgwAAKAeQRsA4CnxhKVEsukwnUjaiieanrENAADQUQjaAABPyQiaCgaaPg47GDCUwXgvAADgMt6NAAA8xTQN5eeFZdmNV7Ut21Z+XlimycnQAACAuzjrOADAc0qKciVJ5VURReNJWbaVOus4AACA2wjaANAJJRKWKqojSiQsBT241dowDPXtmaedeuSoS06m6jZGPdkHAADwJ4I2AHQilmVp8bIyla6tUSAUVDKWUHFBtgYPLJJpeieoMkcbAACkM++8qwIAbLfFy8pUuq5GpmkqKzMo0zRVuq5Gi5eVuV1aqzBHGwAApDOCNgB0EomEpdK1NTKNxv/0m4ap0rU1SnhkLBZztAEAQLojaANAJ1EbTSiebDpMx5OWaqOJDq6obZijDQAA0h1BGwA6iazMoDICTf+znxGo30ruBczRBgAA6Y53IwDQSQSDpooLsmXZjVd8LdtScUG2Z87azRxtAACQ7rzxrgoA4IjBA4tU3CNbllW/VdyyLBX3qD/ruJeUFOUqPy9Tlm1tMkc7kznaAAAgLXhjnyAAwBGmaWronjsqEknICpoyE5bCYe/9KWiYo92nMFddu2WpsqKWlWwAAJA2vPfuCgDQZn6Zo93ANA2FM4OqJmQDAIA04r13VQCANvPLHG0AAIB0RtAGgE7CL3O0AQAA0h1BGwA6Cb/M0QYAAEh3BG0A6CT8MkcbAAAg3RG0AaCT8MscbQAAgHTHuyoA6ET8Mke7QSJhqaI6wvHlAAAgrbBPEAA6kYY52omEpS45marbGPXkSrbfxpQBAAB/4d0IAHRCwaCpbrlhT4ZsiTFlAAAgvXnzHRYAoNNiTBkAAEh3BG0AgKcwpgwAAKQ7gjYAwFMYUwYAANIdQRsA4CmMKQMAAOmOdyMAAM/x25gyAADgL+yvA4BttOnMZq+vmlqWrUg0IcuyZZqG2+W0ml/GlAEAAH8iaANAC/w0s9m2ba0oq1Z5VURZ2WHV1kSUnxdWSVGuDMN7gbthTFk8Ene7FAAAgBRvvUMEABf4aWZzfciOyjRMZWYEZBqmyquiWlFW7XZpAAAAvkHQBoCt8NPMZsuyVV4VkbnZyrVpGCqvisiybJcqAwAA8BeCNgBshZ9mNscTlhLJpsN0Imkr7qEPDQAAANIZQRsAtsJPM5szgqaCgaaPww4GDGVwMjEAAABH8K4KALbCTzObTdNQfl5Ylt14VduybeXnhT159nEAAIB05J13iADgEj/NbC4pylV+XqYs21I0npRlW8rPy1RJUa7bpQEAAPiGd/Y8AvAsZjanD8Mw1LdnnorzsxUIB5WMZCsUCrhdVqfn9dcIAABojKANoN0wszn9+GkmuB/47TUCAADq8a4KQLthZnP68dNMcD/gNQIAgD8RtAG0C2Y2px8/zQT3A14jAAD4F0EbQLtgZnP68dNMcD/gNQIAgH8RtAG0C2Y2px8/zQT3A14jAAD4F3/FAbQLZjanHz/NBPcDXiMAAPgX76oAtBtmNqcfP80E9wNeIwAA+BP7BAG0m4aZzX0Kc9W1W5YqK2pZpXNZw0zwWCz5vznaCU/P0fb6/GleIwAA+BNBG0C7M01D4cygqgkQrvPL3Ga/9NGA1wgAAP7C1nEA6ET8MrfZL30AAAB/ImgDQCfhl7nNfukDAAD4F0EbADoJv8xt9ksfAADAvwjaANBJ+GVus1/6AAAA/sW7EQDoJPwyt9kvfQAAAP8iaANod5uOYPIyP/TRMLc5YSVVXRtTwkp6cm4z86cBAEA6Y7wXgHbjlxFMfumjEVsyjPr/9yLmTwMAgHTGijaAduOXEUx+6UP6sZdgIKCcLiEFAwHP9iL9OH+akA0AANIJQRtAu/DLCCa/9CH5qxcAAIB0RtAG0C78MoLJL31I/uoFAAAgnRG0AbQLv4xg8ksfkr96AQAASGe8qwLQLvwygskvfUj+6gUAACCdcdZxAO2mYdRSeVVkkxFMYc+NYPJLH5K/egEAAEhXBG0A7cYvI5j80ofkr14AAADSFVvHAbQ7v4xg8ksfkr96AQAASDcEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQDYRomEpYrqiBIJy+1S4DOWZSsSTciybLdLAQAADgi6XQAApDvLsrR4WZlK19YoEAoqGUuouCBbgwcWyTT5vBJtZ9u2VpRVq7wqoqzssGprIsrPC6ukKFeGwYxzAAC8ineIANCCxcvKVLquRqZpKiszKNM0VbquRouXlbldGjyuPmRHZRqmMjMCMg1T5VVRrSirdrs0AACwHQjaALAViYSl0rU1Mo3G/1yahqnStTVsI0ebWZat8qqIzM1Wrk3DUHlVhG3kAAB4GEEbALaiNppQPNl0mI4nLdVGEx1cEfwinrCUSDYdphNJW3E+xAEAwLMI2gCwFVmZQWUEmv6nMiNQv5UcaIuMoKlgoOnjsIMBQxlB/kQDAOBV/BUHgK0IBk0VF2TLshuvLlq2peKCbAUJQ2gj0zSUnxeWZTde1bZsW/l5YZkmJ0MDAMCreIcIAC0YPLBIxT2yZVn1W8Uty1Jxj/qzjgPbo6QoV/l5mbJsS9F4UpZtKT8vUyVFuW6XBgAAtgN7HgGgBaZpauieOyoWSyoQDioZSSgUCrhdFnzAMAz17ZmnPoW56totS5UVtaxkAwDgAwRtAGgBs47R3kzTUDgzqGpCNgAAvsDWcQBoAbOOAQAA0BoEbQDYCmYdAwAAoLUI2gCwFcw6BgAAQGsRtAFgK5h1DAAAgNbiHSIAbAWzjgEAANBaBG0gjVmWrUg04fnjgGOxpFav36hYLOl2KW3CrGMAAAC0BuO9gDTkl3FSyWRSLy1aoZVlG2WYhmzLVu+iHB09pESBgHfmUDPrGAAAAK3BijaQhvwyTuqlRSu0ck21AqapLuEMBUxTK9dU66VFK9wurU0aZh0TsgEAALA1BG0gzfhlnFQsltTKso0KGI1XrgNGQCvLvLuNHAAAAGgJQRtIM34ZJ1VZG1M82XSt8aSlytpYB1cEAAAAdAyCNpBm/DJOqmtWSBmBpmvNCJjqmhXq4IoAAACAjuGNd+xAJ+KXcVKhUEC9i3KUtBtvEU/aSfUuylEo5J2ToQEAAACtQdAG0pBfxkkdPaREvQtzlbQs1UXiSlqWehfm6ughJW6XBgAAALQbxnsBaahhnFRxfrYC4aCSkWxPrgAHAgEdO3xnRSIJWUFTZsJSOMw/OwAAAPA33vECaciyLC1eVqbStTUKhIJKxhIqLsjW4IFFMk3vbETxyzxwAAAAoDW8844d6EQWLytT6boamaaprMygTNNU6boaLV5W5nZpreKXeeAAAABAaxC0gTSTSFgqXVsj02j88jQNU6Vra5TwyHgvv8wDBwAAAFqLoA2kmdpoYqvzp2ujiQ6uqG38Mg8cAAAAaC2CNpBmsjKDW50/nZXpjVMr+GUeOAAAANBavNMF0kwwaKq4IFuW3XjF17ItFRdkK+iRgOqXeeAAAABAa3njHTvQyQweWKTiHtmyrPqt4pZlqbhH/VnHvcQv88ABAACA1vDGHlSgkzFNU0P33FGxWPJ/c7QTnpyj7Zd54AAAAEBrELSBNOSX+dN+mQcOAAAAtAbvdIE05Jf5036ZBw4AAAC0BkEbSDN+mT/tl3ngAAAAQGsRtIE045f5036ZBw4AAAC0FkEbSDN+mT/tl3ngAAAAQGt54x070In4Zf60X+aBAwAAAK3FO10gDfll/rRf5oEDAAAArdGuezdnzpypDz74QIlEQueee6722msvTZs2TclkUgUFBbr99tsVCoXaswTAkxrmT/cpzFXXblmqrKj1zEr2pvwyDxwAAABojXYL2osXL9aXX36p2bNna8OGDTruuOM0ZMgQTZo0SePGjdPMmTM1d+5cTZo0qb1KADzPNA2FM4Oq9mDIlvwzDxwAAABojXbbOn7AAQfo7rvvliR17dpVdXV1WrJkicaMGSNJGjNmjBYtWtReNw8gDfhlHjgAAADQGu0WtAOBgLKysiRJc+bM0ciRI1VXV5faKl5QUKC1a9e2180DcJlf5oEDAAAArdXu83Xmz5+vuXPn6o9//KMOP/zw1OW23fKb7O7dsxQMeud4zoICb52oqjl+6UPyTy9e7CMSTSgrO6zMjB9fw127dpEkReNJde2WpbBHR3x58fFojl96oY/04pc+JP/0Qh/pxS99SP7phT7Six/6aNd3uW+99ZYeeOABPfzww8rNzVWXLl0UiUQUDodVVlamwsLCrf78hg217VmeowoKcrV2rfe3w/qlD8k/vXi1D8uyVVsTUcSo3zjTtWsXVVbW1V9nW6qsqPXksedefTya4pde6CO9+KUPyT+90Ed68Usfkn96oY/04qU+tvaBQLttHa+urtbMmTP1hz/8Qd26dZMkDR06VK+88ookad68eRoxYkR73TwAl/llHjgAAADQWu22ov3yyy9rw4YNuuSSS1KX3Xrrrbr66qs1e/ZsFRcXa/z48e1184AvxGJJrV6/UclY0pNjsRrmfpdXRTaZBx723DxwAAAAoDXaLWhPmDBBEyZM2OLyRx99tL1uEvCNZDKplxat0MqyjTJMQ7Zlq3dRjo4eUqJAwDuB2y/zwAEAAIDWaLet4wDa7qVFK7RyTbUCpqku4QwFTFMr11TrpUUr3C6tTRrmgROyAQAA0BkQtIE0E4sltbJsowJG45XrgBHQyrKNisWSLlUGAAAAYFsQtIE0U1kbUzxpNXldPGmpsjbWwRUBAAAAaA2CNpBmumaFlBFo+qWZETDVNSvUwRUBAAAAaA2CNpBmQqGAehflKGk33iKetJPqXZTjybOPAwAAAJ0JQRtIQ0cPKVHvwlwlLUt1kbiSlqXehbk6ekiJ26UBAAAAaEG7jfcC3GRZtiLRhCzL9uSZrgOBgI4dvrOqqqKqiCXULRRUXl6m22W1mdcfDwAAAKA1CNrwFdu2taKsWuVVEWVlh1VbE1F+XlglRbkyDO8EvEQioT+/+qVWlm2UDEm21LsoR5PH7qZg0DsvW788HgAAAEBrsHUcvlIf6qIyDVOZGQGZhqnyqqhWlFW7XVqr/PnVL7VyTbWCAVPhzAwFA/VztP/86pdul9Yqfnk8AAAAgNYgaMM3LMtWeVVE5mYrpaZhqLwqIsuyXaqsdSKRhFaWbVTQbHzSs6BZP0c7Ekm4VFnr+OXxAAAAAFqLoA3fiCcsJZJNh7dE0lY80fRs6nSzvjqiRDNztBNJS+urIx1cUdv45fEAAAAAWougDd/ICJoKBpo+7jcYMJQR9MbTfYfcsILNzNEOBkztkBvu4Iraxi+PBwAAANBavNOFb5imofy8sCy78SqqZdvKzwt75mzX4XBQvYtylLAaz9FOWPVztMNhb5wMzS+PBwAAANBaBG34SklRrvLzMmXZlqLxpCzbUn5epkqKct0urVUmj91NvQtzlUhaikTjSiTr52hPHrub26W1il8eDwAAAKA1vLE0BmwjwzDUt2ee+hTmqmu3LFVW1Hpy5TQYDOr0cXto48aYNiYt5QRM5eSE3C6r1fzyeAAAAACtQdCGL5mmoXBmUNUeDXWWZWnxsjKVrq1RIBRUMpZQcUG2Bg8skml6byOK1x8PAAAAoDW8944d6AQWLytT6boamaaprMygTNNU6boaLV5W5nZpAAAAAFpA0AbSTCJhqXRtjUyj8cvTNEyVrq1RgrFYAAAAQFojaANppjaaULyZOdrxpKXaaKKDKwIAAADQGgRtIM1kZQaV0cwc7YxA/VZyAAAAAOmLoA2kmWDQVHFBtiy78aq2ZVsqLshWMMjLFgAAAEhnvGMH0tDggUUq7pEty6rfKm5Zlop71J91HAAAAEB6Yw8qfMmybEWiCVmW7cm5zaZpauieOyoWSyoQDioZSSgUCrhdVpt5/fEAAAAAWoOgDV+xbVsryqpVXhVRVnZYtTUR5eeFVVKUK8PwTsCjDwAAAMC72DoOX6kPdVGZhqnMjIBMw1R5VVQryqrdLq1V6AMAAADwLoI2fMOybJVXRWRutlJqGobKqyKyLNulylqHPgAAAABvI2jDN+IJS4lk0+EtkbQVTzQ9mzrd0AcAAADgbQRt+EZG0FQw0PRxv8GAoQyPjMWiDwAAAMDbeKcL3zBNQ/l5YVl241VUy7aVnxf2zNmu6QMAAADwNs46Dl8pKcqVJK1eV6vqNVXKkKGePbJSl3tFQ71rKmpVXlWnZDKhwm7e7aO8KqJoPCnLtlJnHQcAAAD8iqANX0kmk3rjw1X6bnW1bEmGpD49czV57G4KBr3zdLdtW6XravT9mo0ygwFZiaQSCVt9CnM8NRbLMAz17ZmnPoW56totS5UVtaxkAwAAwPfYOg5f+fOrX2rlmmplBAPK6hJSRjCglWuq9edXv3S7tFZZvKxMpetqFAwElJsVUjAQUOm6Gi1eVuZ2aW1imobCmUFCNgAAADoFgjZ8IxJJaGXZRgXNQKPLg2ZAK8s2KhJJuFRZ6yQSlkrX1sg0Gr88TcNU6doaJThbNwAAAJDWCNrwjfXVESWSTYfQRNLS+upIB1fUNrXRhOLN9BFPWqqNeuMDAwAAAKCzImjDN3bIDSsYaPopHQyY2iE33MEVtU1WZlAZzfSRETCVlemdY80BAACAzoigDd8Ih4PqXZSjhJVsdHnCSqp3UY7CYW8E1GDQVHFBtiy78aq2ZVsqLshWkPnTAAAAQFrjHTt8ZfLY3dS7MFeJpKVINK5E0lLvwvqzjnvJ4IFFKu6RLcuq3ypuWZaKe2Rr8MAit0sDAAAA0AJvLPEB2ygYDOr0cXto48aYNiYt5QRM5eSE3C6r1UzT1NA9d1QsllQgHFQyklAoFGj5BwEAAAC4jqANX7Esq3401toaBUJBJWMJFRfUrwSbpnc2cNi2rRVl1SqviigrO6zamojy88IqKcr11BxtAAAAoDPyTvIAtkHD/GnTrD9pmGmanpw/XR+yozINU5kZAZmGqfKqqFaUVbtdGgAAAIAWELThG36ZP21ZtsqrIjI3W7k2DUPlVRFZlu1SZQAAAAC2BUEbvuGX+dPxhKVEsukwnUjainvkAwMAAACgsyJowzf8Mn86I2gqGGj6OOxgwFAG470AAACAtMY7dviGX+ZPm6ah/LywLLvxqrZl28rPC8s0ORkaAAAAkM68kTyAbeSX+dMlRbnKz8uUZVuKxpOybEv5eZkqKcp1uzQAAAAALfDGXlpgG/ll/rRhGOrbM099CnPVtVuWKitqWckGAAAAPIKgDV/x2/xp0zQUzgyqmpANAAAAeAZbx+ErzJ8GAAAA4DaCNnyD+dMAAAAA0gFBG77B/GkAAAAA6YCgDd9g/jQAAACAdEDygG8wfxoAAABAOuCs4/CVhjnT5VWRTeZPh5k/DQAAAKDDELSREosltXr9RiVjSU/Onpb8N3/asmxFoglZlu3pPgAAAIDOhKANJZNJvbRohVaWbZRhGrItW72LcnT0kBIFAt4M3F6fP+23eeAAAABAZ8Ix2qgP2WuqFTBNdQlnKGCaWrmmWi8tWuF2aZ0W88ABAAAA7yJod3KxWFIryzYqYDReuQ4YAa0s26hYLOlSZZ0X88ABAAAAbyNod3KVtTHFk03Pl44nLVXWxjq4IjAPHAAAAPA2gnYn1zUrpIxA00+DjICprlmhDq4IzAMHAAAAvI137J1cKBRQ76IcJe3GW8STdlK9i3I8e/ZxL2MeOAAAAOBtBG3o6CEl6l2Yq3giqaqaqOKJpHoX5uroISVul9ZmiYSliuqIEh7dZl1SlKv8vExZtrXJPPBM5oEDAAAAHsB4L8gwDBV066K6aFxJ21DAsFXQrYsnx0hZlqXFy8pUurZGgVBQyVhCxQXZGjywSKbpnc+V/DYPHAAAAOhMvJM80G4WLytT6boahYIZ6tEtS6FghkrX1WjxsjK3S2u1hl5M01RWZlCmaXq2F+nHeeCEbAAAAMA7CNqdXCJhqXRtjUyj8VPBNEyVrq3x1NZrP/UCAAAAwLsI2p1cbTSx1fFetdFEB1fUdn7qBQAAAIB3EbQ7uazM4FbHe2Vleucwfj/1AgAAAMC7CNqdXDBoqrggW5bdeCXYsi0VF2Qr6KGZzX7qBQAAAIB3kTygwQOLVNwjW5ZVv73asiwV96g/U7fX+KkXAAAAAN7EXlrINE0N3XNHbdwY08akpZyAqZyckNtltUlDL5FIQlbQlJmwFA7zNAcAAADQcUggUCKR0J9f/VIryzZKhiRb6l2Uo8ljd1Mw6K2niF/maAMAAADwLpIH6kP2mmoFA6bCmRkKBkytXFOtP7/6pdultZrf5mgDAAAA8B6CdicXiSS0smyjgmag0eVBM6CVZRsViXhnJBZztAEAAACkA4J2J7e+OqJEM7OnE0lL66sjHVxR2zFHGwAAAEA6IGh3cjvkhhVsZvZ0MGBqh9xwB1fUdszRBgAAAJAOCNqdXDgcVO+iHCWsZKPLE1ZSvYtyPHXGbuZoAwAAAEgHJA9o8tjd1LswV4mkpUg0rkTSUu/CXE0eu5vbpbUac7QBAAAAuM07y5VoN8FgUKeP20MVFRGtq4upR5eQunXzzpbxTTXM0U4kLHXJyVTdxigr2QAAAAA6FEEbvpqj3SAYNNUtN6x4JO52KQAAAAA6GZb64Ks52gAAAADgNoJ2J+enOdoAAAAAkA4I2p2cn+ZoAwAAAEA6IGh3cn6aow0AAAAA6YCg3cn5aY42AAAAAKQDgjZ8NUcbAAAAANzGciVSc7QjkYSsoCkzYbGSDQAAAABtxIo2UsLhoEp27ErIBgAAAIDtQNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtpCQSliqqI0okLLdLAQAAAADPYo4TZFmWFi8rU+naGgVCQSVjCRUXZGvwwCKZJp/FAAAAAEBrkKJQH7LX1cg0TWVlBmWapkrX1WjxsjK3SwMAAAAAzyFod3KJhKXStTUyjcZPBdMwVbq2hm3kAAAAANBKBO1OrjaaUDzZdJiOJy3VRhMdXBEAAAAAeBtBu5PLygwqI9D00yAjUL+VHAAAAACw7QjanVwwaKq4IFuW3XhV27ItFRdkKxjkKQIAAAAArUGKggYPLFJxj2xZVv1WccuyVNyj/qzjAAAAAIDWYV8wZJqmhu65oxIJS11yMlW3McpKNgAAAAC0EWkKKcGgqW65YUI2AAAAAGwHEhUAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggrYD1qyp0cvvfK01a2rcLgUAAAAA4LKg2wV4WU1NjaY9+K7qonbqsi6Zhmaec6Cys7NdrAwAAAAA4BZWtLdDQ8g2DMk0JMOQ6qK2pj34rtulAQAAAABcQtBuozVralIhe1MNYZtt5AAAAADQORG02+i/K8u363oAAAAAgD8RtNvoJ73zt+t6AAAAAIA/EbTbqLAwW10yDdl248ttu/6EaIWFnAwNAAAAADojgvZ2mHnOgamwbdk/huyZ5xzodmkAAAAAAJcw3ms7ZGdn675LD9GaNTVaUVGjkm7ZrGQDAAAAQCdH0HZAYWG2fvKTnlq7ttrtUgAAAAAALmPrOAAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIII2AAAAAAAOImgDAAAAAOAggjYAAAAAAA4iaAMAAAAA4CCCNgAAAAAADiJoAwAAAADgIMO2bdvtIgAAAAAA8AtWtAEAAAAAcBBBGwAAAAAABxG0AQAAAABwEEEbAAAAAAAHEbQBAAAAAHAQQRsAAAAAAAcRtB3wxRdf6NBDD9Wf//xnt0vZLjNnztSECRN0wgknaN68eW6X0yZ1dXW6+OKLNXnyZJ100kl6/fXX3S5pu0QiEY0ZM0Z/+ctf3C6lzT755BONHDlSU6ZM0ZQpU3TDDTe4XVKbvfjii/rZz36m448/Xm+++abb5bTJnDlzUo/FlClTNGjQILdLapOamhpdcMEFmjJlik455RS99dZbbpfUZpZl6ZprrtEpp5yiKVOm6Ouvv3a7pFbZ/G/gDz/8oClTpmjSpEm6+OKLFYvFXK5w2zX19/yJJ57QT37yE9XU1LhYWes09Zicfvrpmjx5sk4//XStXbvW5Qq3zeZ9fPjhh5o4caKmTJmis846S+Xl5S5XuG2ae5/41ltvacCAAS5V1Tab93LDDTfo+OOPT/1NeeONN9wtcBtt3kc8HtevfvUrnXjiiTrttNNUWVnpcoXbZvM+LrrootRjccwxx+iaa65xucJts3kf7733Xuq1fu6553rm8dhc0O0CvK62tlY33HCDhgwZ4nYp22Xx4sX68ssvNXv2bG3YsEHHHXecDjvsMLfLarXXX39de+65p84++2ytWrVKZ555pg455BC3y2qz+++/X926dXO7jO1SW1urww8/XFdddZXbpWyXDRs26L777tNzzz2n2tpazZo1S6NGjXK7rFY76aSTdNJJJ0mS3n33Xf3jH/9wuaK2+etf/6p+/frpV7/6lcrKynTaaafpn//8p9tltcmCBQtUXV2tZ555Rt99951uuukm/eEPf3C7rG3S1N/Ae+65R5MmTdK4ceM0c+ZMzZ07V5MmTXKxym3TVC/PP/+81q1bp8LCQhcra52m+rjrrrt08skn68gjj9STTz6pRx99VNOmTXOxypY11cejjz6qmTNnqnfv3rr33nv17LPPaurUqS5W2bLm3idGo1E9+OCDKigocKmy1muql9raWt10003aY489XKysdZrq49lnn1X37t115513avbs2Xr//fc1ZswYF6tsWXP//jaYMWNG6u99Omuqj1tuuUV33HGHdt55Zz3wwAOaPXu2zjnnHBerbBtWtLdTKBTSQw895Kk/wk054IADdPfdd0uSunbtqrq6OiWTSZerar0jjzxSZ599tqT6T/CLiopcrqjtvv76a3311Vc6+OCD3S5lu3hpFWhrFi1apCFDhignJ0eFhYWeXplvcN999+m8885zu4w26d69uyoqKiRJVVVV6t69u7sFbYdvv/1We++9tySpT58+Ki0t9cy/v039DVyyZEnqDeqYMWO0aNEit8prlaZ6OfTQQ3XppZfKMAwXK2udpvq47rrrdPjhh0tq/NpJZ031cc8996h3796ybVtlZWXq2bOnixVum+beJz7wwAOaNGmSQqGQS5W1XlO9ePFvfFN9vP766/rZz34mSZowYULah2xp6xlk+fLlqq6uTv1tSWdN9bHpv1OVlZWe/RtP0N5OwWBQ4XDY7TK2WyAQUFZWlqT6raUjR45UIBBwuaq2O+WUU3TZZZfpyiuvdLuUNrvtttt0xRVXuF3GdqutrdUHH3ygn//85zr11FO1ePFit0tqk++//162beuSSy7RpEmTPBMemvOf//xHO+64o6dWUzZ11FFHqbS0VGPHjtXkyZM1ffp0t0tqs/79++vtt99WMpnU8uXLtXLlSm3YsMHtsrZJU38D6+rqUuGhoKDAM9uUm+olJyfHpWrarqk+srKyFAgElEwm9dRTT+mYY45xqbpt19z7q3/961864ogjtG7dulQwSmdN9fHNN9/os88+07hx41yqqm2a6qWmpkb33nuvpkyZossuu8wTH+I01ceqVav03nvv6ayzztKll17q2T4aPP7445o8eXIHV9Q2TfUxY8YMnX/++Tr88MP1wQcf6LjjjnOpuu1D0EYj8+fP19y5c3Xttde6Xcp2eeaZZ3T//ffr8ssvl23bbpfTas8//7z23Xdf9e7d2+1Sttvuu++u888/Xw8//LBuvPFGXXHFFZ46ZnNTZWVluuOOO3TrrbdqxowZnnxuNZg7d65n/3BJ0gsvvKDi4mK9+uqreuyxxzy9w2DUqFHaa6+9dOqpp+qxxx7Tzjvv7Onn1qarv17uw2+SyaSmTZumwYMHe/pwt5EjR+qf//yndt55Zz344INul9Mmt9xyi2bMmOF2GY5oWNh44okntMsuu2jWrFlul9Qmtm1rxx131COPPKLddtvNM4fvNCUWi+mDDz7Q4MGD3S6lzW688Ubde++9euWVV7T//vvrqaeecrukNiFoI+Wtt97SAw88oIceeki5ublul9Mmn3zyiX744QdJ0h577KFkMumZk6Vs6o033tCCBQt08skna86cOfr973+vhQsXul1Wm+yyyy6pLVj9+vVTjx49VFZW5nJVrbfDDjto0KBBCgaD6tOnj7Kzsz353GqwZMkSz54ITZL+/e9/a/jw4ZLqP8wpKytTIpFwuaq2u/TSS/XMM8/o+uuvV1VVlXbYYQe3S2qzLl26KBKJSKr/cMrrh1b5xYwZM1RSUqILLrjA7VLa7NVXX5VU/2FOw0qX15SVlWn58uW67LLLdPLJJ2vNmjWeWXlsytixY9WvX7/Uf3/++ecuV9Q2PXr00E9/+lNJ0vDhw/XVV1+5XFHbvffee57YMr41n3/+ufbff39J0tChQ/XJJ5+4XFHbELQhSaqurtbMmTP1hz/8wdMn33r//ff1xz/+UZK0bt061dbWevK4jrvuukvPPfecnn32WZ100kk677zzNHToULfLapO5c+fq8ccflyStXbtW69ev9+Sx88OHD9fixYtlWZbKy8s9+9yS6t/oZWdne+rYwM2VlJRo6dKlkuq3/GVnZysY9Ob5PT/77LPU6ta//vUvDRw4UKbp3T/PQ4cO1SuvvCJJmjdvnkaMGOFyRXjxxReVkZGhiy66yO1StsusWbP06aefSpKWLl2aCnheUlRUpPnz5+vZZ5/Vs88+q8LCQk9PrZk6dapKS0sl1X+Au9tuu7lcUduMHDkyNb3iv//9ryefWw0+/vhj7b777m6XsV169OiR+rDj448/VklJicsVtY1hs69ru3zyySe67bbbtGrVKgWDQRUVFWnWrFmeC6uzZ8/WrFmzGv3Dctttt6m4uNjFqlovEonoqquu0g8//KBIJKILLrhAo0ePdrus7TJr1iz16tVLxx9/vNultEllZaUuu+wy1dbWKhaL6YILLvDk2bql+kMS/v73v6uurk6/+MUvPHGylKZ88sknuuuuu/Twww+7XUqb1dTU6Morr9T69euVSCR08cUXe3Y7rGVZuvLKK/XNN98oNzdXt912m2dWtJv6G3jHHXfoiiuuUDQaVXFxsW655RZlZGS4XWqLmupl6NChWrhwoT766CPttdde2nfffdP+bN1N9bF+/XplZmamjjnfZZdd9Otf/9rdQlvQVB+XX365br75ZgUCAYXDYc2cOTPtXystvU8cPXq0XnvtNXeL3EZN9TJx4kQ98sgjysrKUpcuXXTLLbd48jG54447dNttt2nt2rUKhUK67bbb1KNHD7dL3armnluzZs3S/vvvryOPPNLtErdJU31ceumlmjlzpjIyMtS1a1fdfPPNysvLc7vUViNoAwAAAADgIO/uTQMAAAAAIA0RtAEAAAAAcBBBGwAAAAAABxG0AQAAAABwEEEbAAAAAAAHEbQBAHBZZWWlrr32Wh177LGaNGmSjj32WD322GOu1fPCCy9Ikj799FPdcMMNrtUBAIBXMd4LAAAX2batk08+Wccdd5wmTZokSVq3bp1OP/10XXjhhTr88MM7tJ6ysjJdcsklevrppzv0dgEA8BOCNgAALlq4cKHuuecePfPMM40uj8ViCoVCqqys1HXXXacNGzYoFotp0qRJOuaYYzRr1ixVVFSorKxM3377rQ466CBdc801kqTf/va3+ve//y3DMLTnnntq2rRpevfdd3X//fcrFArpsMMO08EHH6xp06YpkUho48aN+r//+z+NHz9ekydP1qeffqoxY8bohBNO0F133aWnn35a33zzja677jrZtq1EIqFf/epX+ulPf6orrrhChYWF+uKLL/TNN9/oxBNP1Nlnn+3GXQkAQNoIul0AAACd2Zdffqk999xzi8tDoZAk6a677tKIESN0wgknqLa2Vscee6yGDRsmSVq2bJn+/Oc/Kx6Pa8iQIbrooou0cOFClZWV6c9//rMk6fzzz9frr7+u7Oxsffzxx1qwYIG6deumZcuW6dRTT9WYMWO0Zs0aHXPMMRo/frwuvPBC3XXXXZo5c6aWLFmSqufGG2/UxIkTNW7cOH3++ec677zztGDBAknSypUr9cADD2jVqlX62c9+RtAGAHR6BG0AAFwUCASUTCZTX8+ePVsvvfSSotGoevbsqa+++koff/yxnn/+eUlSMBjU999/L0naf//9FQgEFAgE1L17d1VWVmrJkiX66KOPNGXKFElSdXW1vv/+ew0YMED9+vVTt27dJEmFhYV6+OGH9fDDDysQCKiiomKrdS5dulS/+93vJEkDBgzQxo0bVV5eLkk68MADJUm9evXSxo0blUwmFQgEnLqLAADwHII2AAAuGjBggJ577rnU1xMmTNCECRO0ZMkS3XXXXQqFQrruuuu01157Nfq5N998c4swa9u2QqGQTj75ZJ111lmNrluyZIkyMjJSX991110qKSnRb3/7W9XU1Gi//fbbap2GYTR7WTDY+O0ER6UBADo7zjoOAICLDjjgAHXr1k1/+MMfUpfF43G988476tKli/bff3/94x//kCRFIhH9+te/ViKRaPb37b///nr11VdT33Pvvffq22+/3eL71q1bpz59+kiSXnrpJZmmqVgsJtM0FY1Gt/j+ffbZR2+//bak+i3r3bp1U/fu3dvcNwAAfkbQBgDAZffff7/Wr1+vY489VqeeeqomTJiguro63XHHHbrgggu0YsUKTZw4UaeeeqoGDhy4xQrypg477DANGjRIp5xyik4++WStX79evXv33uL7Jk+erAceeEBnnHGGsrOzNWTIEP3qV7/SrrvuqoqKCp1xxhmNvv+aa67Rs88+qylTpuiGG27QzJkzHb8fAADwC846DgAAAACAg1jRBgAAAADAQQRtAAAAAAAcRNAGAAAAAMBBBG0AAAAAABxE0AYAAAAAwEEEbQAAAAAAHETQBgAAAADAQQRtAAAAAAAc9P80zWRp30RowgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "successful_generations = []\n",
    "successful_num_words_swapped  = []\n",
    "\n",
    "for i in range(dataset_size):\n",
    "    if (bert_distance_stats.at[i,'sentiment'] != bert_distance_stats.at[i,'ga_sentiment']) and bert_distance_stats.at[i,'ga_percent_change'] < 0.2:\n",
    "        successful_generations.append(bert_distance_stats.at[i,'ga_generations'])\n",
    "        successful_num_words_swapped.append(bert_distance_stats.at[i,'ga_num_changes'])\n",
    "        \n",
    "\n",
    "scatter_plot_filename_a = 'ga_attack_results/scatter-plot-success-method_c.png'\n",
    "graph_title_a = \"Number of Words Changed v Generations (Method C)\"\n",
    "generate_scatterplot(successful_generations,successful_num_words_swapped,scatter_plot_filename_a, graph_title_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum review length:  37\n",
      "Maximum review length:  945\n",
      "Mean review length:  236\n",
      "Number words less than mean size:  167\n",
      "Words >= 300:  60\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_lengths = []\n",
    "count_less_mean = 0\n",
    "count_gt_mean = 0\n",
    "for i in range(dataset_size):\n",
    "    review_lengths.append(len(distance_stats.at[i,'ga_text'].split()))\n",
    "    if review_lengths[i] <= 236:\n",
    "        count_less_mean += 1\n",
    "    if review_lengths[i] >= 300:\n",
    "        count_gt_mean += 1\n",
    "\n",
    "\n",
    "print(\"Minimum review length: \",min(review_lengths))\n",
    "print(\"Maximum review length: \",max(review_lengths))\n",
    "print(\"Mean review length: \",round(mean(review_lengths)))\n",
    "print(\"Number words less than mean size: \", count_less_mean)\n",
    "print(\"Words >= 300: \", count_gt_mean)\n",
    "len(review_lengths)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}