face extraction.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting and Classifying Face Images\n",
    "\n",
    "https://dev.to/azure/opencv-detect-and-blur-faces-using-dnn-40ab\n",
    "\n",
    "https://towardsdatascience.com/face-detection-models-which-to-use-and-why-d263e82c302c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "c:\\Users\\wamik\\miniconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "c:\\Users\\wamik\\miniconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "c:\\Users\\wamik\\miniconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
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     "execution_count": 1,
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   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import image\n",
    "from sklearn.metrics import classification_report, get_scorer_names\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from mtcnn.mtcnn import MTCNN\n",
    "from PIL import Image\n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "\n",
    "# We import the data set from tensorflow and build the model there\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from keras.utils import np_utils\n",
    "import keras\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Import the inception model  \n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "\n",
    "import torch\n",
    "# If you do not have a GPU or do not want to run this code on your GPU, comment out the following lines\n",
    "torch.cuda.empty_cache()\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting the Data\n",
    "\n",
    "The images stored in the combined dataset are extracted into separate train and test set paths.\n",
    "\n",
    "NOTE: Before running this file, please unzip the Distacted Driver Dataset that contains the combined dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    {
     "name": "stdout",
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      "1579\n"
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   ],
   "source": [
    "CLASS = [[\"c0\", \"Safe Driving\"], [\"c1\", \"Text\"], [\"c2\", \"Phone\"], \n",
    "         [\"c3\", \"Adjusting Radio\"], [\"c4\", \"Drinking\"], \n",
    "         [\"c5\", \"Reaching Behind\"], [\"c6\", \"Hair or Makeup\"], \n",
    "         [\"c7\", \"Talking to Passenger\"]]\n",
    "TEST_CLS = [os.path.join(os.getcwd(), \"Distracted Driver Dataset\", \"Combined\", \"test\", cls[0]) for cls in CLASS]\n",
    "TRAIN_CLS = [os.path.join(os.getcwd(), \"Distracted Driver Dataset\", \"Combined\", \"train\", cls[0]) for cls in CLASS]\n",
    "for cls_path in TEST_CLS:\n",
    "  print(cls_path)\n",
    "  print(len(os.listdir(cls_path)))\n",
    "for cls_path in TRAIN_CLS:\n",
    "  print(cls_path)\n",
    "  print(len(os.listdir(cls_path)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each class, the path of the training and test data is stored in separate lists. The corresponding labels are also stored in a different list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_paths = []\n",
    "test_paths = []\n",
    "for cls in range(8):\n",
    "  for train_instance in os.listdir(TRAIN_CLS[cls]):\n",
    "    train_paths.append(os.path.join(TRAIN_CLS[cls], train_instance))\n",
    "  for test_instance in os.listdir(TEST_CLS[cls]):\n",
    "    test_paths.append(os.path.join(TEST_CLS[cls], test_instance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = []\n",
    "test_classes = []\n",
    "for cls in range(8):\n",
    "  for train_instance in os.listdir(TRAIN_CLS[cls]):\n",
    "    classes.append(cls)\n",
    "  for test_instance in os.listdir(TEST_CLS[cls]):\n",
    "    test_classes.append(cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then separate dataframes storing the train and test paths with their corresponding labels are created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df = pd.DataFrame({\n",
    "  'filename': train_paths,\n",
    "  'class': classes\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_test = pd.DataFrame({\n",
    "  'filename': test_paths,\n",
    "  'class': test_classes\n",
    "})\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------------------\n",
    "# Face Detection using DNN Net\n",
    "# -----------------------------------------------\n",
    "# detect faces using a DNN model \n",
    "# download model and prototxt from https://github.com/spmallick/learnopencv/tree/master/FaceDetectionComparison/models\n",
    "\n",
    "def detectFaceOpenCVDnn(net, frame):\n",
    "    \n",
    "    frameHeight = frame.shape[0]\n",
    "    frameWidth = frame.shape[1]\n",
    "    blob = cv2.dnn.blobFromImage(frame, 1.0, mean = [104, 117, 123])\n",
    "\n",
    "    net.setInput(blob)\n",
    "    detections = net.forward()\n",
    "    conf_threshold = 0.1\n",
    "    box = None\n",
    "    for i in range(detections.shape[2]):\n",
    "        confidence = detections[0, 0, i, 2]\n",
    "        if confidence > conf_threshold:\n",
    "            conf_threshold = confidence\n",
    "            x1 = int(detections[0, 0, i, 3] * frameWidth)\n",
    "            y1 = int(detections[0, 0, i, 4] * frameHeight)\n",
    "            x2 = int(detections[0, 0, i, 5] * frameWidth)\n",
    "            y2 = int(detections[0, 0, i, 6] * frameHeight)\n",
    "            box = [x1, y1, x2, y2]\n",
    "\n",
    "    if box == None:\n",
    "        return []\n",
    "    # extract the face\n",
    "    face_boundary = frame[box[1]:box[3], box[0]:box[2]]\n",
    "\n",
    "    # resize pixels to the model size\n",
    "    # face_image = Image.fromarray(face_boundary)\n",
    "    # face_array = np.asarray(face_image)\n",
    "\n",
    "    return face_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AtZevcnjrgKqomaxPwJlXWdZViLscPcRZgh6M6LmbbCgTkBKkmL2uYEgk4iBiRtlrXXqnGYv+yO98iMX0mElZ4IxlOBzonPUlfdQNNcaAOPi+H3g/NnvZKSZcULcy5Y0bp6F4nC/wYlhMZxhX0LYN65vrinEjYC0Boe17fOGJMTAeDPDZ0908f5q//r//j/niJ97Oh3/zX/PFZ2/wlWevUReGyagmhJa+D4QEizbgTcV4fYPx+hoX73uQu+6+n3P33sPm5dOUowHOej7xwQ9TYPFi8MYh3rBx+tR39CxPHmPOFSg2hln+YMA5z2SyzmRtncsP3A8ixJiIfSA0DdPplOPplKP9Az7z0Y8xbxpCSt/4oPxbTObcOQzZ+CzDEhFSSsznM3Zu73J4dMCgqthcW+PWrdskgb29QyZrG5SFJ5QNi6MZKfX0iwWLpmfW9gQMhddwe5kgWpuMObW9zblzZxkNhjhNNWKceowmgTPL4CuRMuhv844dRVMApTgKIk5a7GKfGy98lS986EtcuvC3mYwHpGwkTVLc0VpzsmJzdlmSYFbYiWZ9Ffuqubmzy6/8o1/nqc+/SOprnBjavmXzzBb33X8vt27c5PkvP8f6+haC0M5m9O0Rb3vsTfz03/wpLlzchmaGOQ4gUY9lHMtlLRIxxpNMxmCToXKO+9/xDrAJOZxy/bnrhMEWD7Q9EwNiAy4ETQjUcN4P2BiXtN2Ao0Vg/yhyUARsqqmSgcVCr5ugz9YotGKBNFtQjNcgGegjbd/ie4cz4EuFVqIIQiSZiJAw/OkwyhUOmcPnAkdlS6qi1pdFdEMjT8glXimrD8CKGklEceoVWSHKHZGIQEqITRQZasnrmeVS37t9m49+6EMaFkvCe0dHYho66sGQtmsorOVoNuXht76Rh976EL5wJ+eSQELCJD0XOsAJ0gSSM4rzOkfoAlVZk3LGm5Q0dEaw3tEZKCfKKMiEC0Rgbe0U73n/j/PKlauUJcynxzSzOYX3rK2vs3FqizPnzzEajnHe4rxlUNTU5QBTe8y4BKeOwbXrVzEexIMUBmsd4/XJdxYj3AHwyh0vrR7UklWRo7alt+msxxWeYlhTbW1wBlgcTnnhmWfxR1MmmXXzjcZrwlCKoACxMaSU6LuW6fExB/v7TA+P6Jo51iSGqWZrc8J01nA8X9DFxGQyQGLL7Z2G2WxG1/a0TYf1BXVd5mx1xFnPZDzhzOnTnD59mnowwLriBC8ie4wI1locFoylVzgNH7JzaWUZgVOHntNVx+U3XeKLR9e4+VzBzeevMn7jfTh/AoCZ5eIz2a0w6j0aaxCTH24hiLFgK57+9Bf5lX/6q1zdnYHUmGRoU+T+hx5gczLgS5/+NMeLlno0ous7usWMQSn8xF//y3z/j7+fqgSZH2JjUE9CLE4sUSJYcEYwKUHsUKMZ9AYE9TZD02Fjohe456E3UpY1pt3HmKiGDUgm4RyMK8O4rNkYWC5sWGJax5UFhV+ajeySG5aMKn11voCywGZXe1DVSEqk4wa7ViOhxxpH8p4Q4ncceiNmFT5DRpbzY3DG4k2JM7oM1JAYiCABjEMX+Oq9d0AARl61UpNRj1LyhUYjUIIbFiSjBvYEZBU+9fGPcfv2bQauoE89VVVwvGiwviLFHul73KDiuF3w3e/9HqVbwYlVMLKKkkwwEA1SQootIVliNIgT2tAjRUGUhMdixdK3jX6UtRTW4pKjn/dY72kXkfn+EZPhGjfafQbVGo+9421Yb/V+OUdMAV84jEnsHezjQ8IZRyDSm46ytCzdva5pOT44RIzDuIIkwmR9g+FodDIZvsPx9d5tMCuD//XmjLnjSfqy4O4HH+aFZ1/i7e9+xzc91mvCUGIgIkiINO2Co6N9dvf3aZoWYo9xkELH8dEhp0+dYtFcpxc4OpqyMRkppuM98/mCPiSc8xRFgbWaivHZSJ49e45Tp05R1zVLjtoJLqUZPFnCT2gCKd3pCWafxGEo6BnLjEvrBf3BVX73w7/D4WHD4vAIFg345YIyujKTJWKwHiBpdherFhdBKOj7xMc/+EE+8NsfYe84QaywCZx33P/gvbSzQz7zpc9hioJhXZNCoJnPuP/+S/zM3/oZ7n/oMi51mK7TEF9j/WwdnCaWSHrMBMRlOOaUdiMBocd4wZQOGRjOXNjEdHNM7FB3yyl2S1ru1yRjsYWhRNTrs9lly/GoSFSv2dtVxCASEQfGC6GPWLG4qiKlRj3g0BFTZFAV2LI48ey+zbVlVv+fZIbJz9cai3MFztkTpgGa7CGC6UFKoLzDyt4BYy4TQmIU93OrRI3gTGKwPsTaE9uW8nvm0ykf/sAH8NicRDA4awltx2C8Ttu0lN4RQ2JtbZP3fPf3IZ1gCoV7lpu7KYzivQ7iTDmefdfQJUFSJMZeE2bWK0SQvcq2afHOKYXGFix2D7m9iLiixhqP97Bx9hT11T0evHwZ4ywpRKIEmhSw1pCS5ejoEGMNviihDyRMptpEjKgxmh0cMD88pvAlznssltOnT2PtnyI6MF87Dcw3+d3JUL7niecfe6Eox9z9wBu46/57vukhXxuGUoTU9fRty/Ron+n0kNS2FCIYb5BkMSL0i5a2nDMej7m1t8eVK1eI3YLKwmLRKAXReUqvRhJjcc6zNlnn3NlzbG5uUZYlGKe4Dko6N8ZkIurSqwWM4PUL1oKQFPA14E1kHGecLhZUs4APHX/x7W9jb/+Yc1tjwvSIoqqUDiSC8eo9WhymKqFwhMxVM9YgznN42PBbv/Kv+Oxnn2YRHQWeEBPj7VNsnT3NrSsvcXz7NoPBkOQsse2wbcOP/+D38EN//ccZb40hdZAi0naYNiJ+6caBpIgVDWEFw9KOOrGAJ0UhhcDsaM7R/j4xwiOPP85itsd8tuD0eKBesktYU+h98QorpNRjsViTF6Sot0FacmLVskhISoB3hlRaXGGRWvmWNgG1w9iaZBJ2MsLESHdjly57QH82cy3HbtnJdN7iSneyujIyYjAQBemU8G19JvZYlkRB/TibDRAJbxTeCSEiBMrhGJM3YvWb9T48/eTnefmZr1K6giCJwXCoGzIWUiKEnqosOZ4vePSxd3F68yzSgokgjlU2XSDj2Up3SSECiXZ2jPPQ9guccxTWskztG4S2meOc1UCmqJlMNhDjcbbGeQsmEPtE27RsOuV6xhQIfaAs1MU+bltSipSuVOjG2szvzO56VG98b2cXiQFTlsp0kMCpM6dX5/+n8ym//bGKCUSY7h8znzacv+usbjjfZLwmDKWI0LWtYn/eI6GnsIaYhJQxvRgSEnqO9g5Z296kcMJsPmN3J1EVjtC3+KJUgrcxOGepqprxaMzZs+fYWN+gqkqcsZjsAVhcrjhAs4JWOXMOgxfBI8QUEXFglGBekBhIw4UqcWroKbzF1xXv+O7HcxVFxCzDTSEbZIPIsgRFPUknBpMiyXuuvPAKv/JLv8GzV26RxJJCoHQFl+6+AB5efuYpQtvjB0OCWLrDGWc2B/zM3/5ZHn33W/CVRWIHJmJi1MoJqwR5giYAlJybEG+UThUDMUW6AIdHM2bTOddeeZmDoyn7+1PufcPDPPTWB3jyUx9junvI2mjMbDFl0QWS8SSBovSUhcOmyLgqGRY1k3rAg3efY1AWkJKC7NYqZGEtTiD1EaKeK87gS4tpIzjB1hWpa8A6ugMlcMcYV97Ytz238lfzNV8R9Sh94bFfU+lickJNYRND6hKpT1ixqw/M+yxGEsuKo5Q0Cll0LX6twJaZ3iVLdNXQty0f/q1/rRtaTuaVg5rdnR2q4YB535JQIvRs3vGe93wfOzf2KaqK4aCiqgtsZaEyJzQhgMJAL4QE0+MZeMtiscB6jRbUl9KwdNE0+LLQ2VgOGIwmJBwpOLpuji/hcG+Kt562mWt4n121nkToO2IMOG8xxUmIa636aikmDSoS7Fy/ibNWk00WDJa1zc0/VXLuz2LEPnKwc8jBwSGPvuPhOyiJX3+8JgxliIlGhMJZhhvr1O2MZj6nTx1xyQQVDVIktCxmR6wPSmZHR3SNBQqc8ThncTiMtZR1xcbGBqdOnWJ9fYOqKHHWnFRFZC6SM5qgWYZTGlItw2E1KpL/IREnPbP967DtqbfWNXx1PofuGchMAjEiSQ0WWJKFJIlySenwQiwsn/3Yp/iNX/sQN44CfXIEoPQFpy9epGsW3H7hFZwYynLIrGsJiwVvfcsj/Ox/8h9y5tIapp+RotLgzTJzZ+wKWzMmQdCFvOgS0/1jjnaPOLh+i1s7t9k9mDGd9bRdIDiDiKUcjPnhx76LvdvX2D2YcjRb0NuS0XCdydAxb3vaEPHlkMI6TOqQaFiI5fbuDue3N6gmDps3h5QEseQKK4tY9WZi22HrGmMt3fwIM59TVGu6gU0XuCZQlhXJ+ZNEyXcwXpXEWX6f9y1rHa5wJ/jjq6M4nQ9LouWK7Csn7lDSIoSQEn1UelBvhLWNQc7ZGSQKqY240nPl2ef48pOfUyJ9CgyKEhcVmhDraMOcwli6PnDmzHnuvucBdnYOiCHgrGFQVmxsr7G2vYarPGQc1ThLt2gw4jmatgQxNG2HK0vEaAZ+idzN2oaN9QlRoBqNSCI0TYMESzKB0lRcv36DebuAab+KtJZVUc6ajFHqJhyNfnaIEe89KUaIQuwDN69dwzqnDog1FEXBeH1CAtySD/hvaMgd37eLloODI6q6YjAZnMyRbzBeE4ay7Tr29g/ZWhtDMmxvbnGr7Yk2IbFHUAwzSiTFQHfYESVRWEfhC6wCf1jncDjKsmRtY51Tp0+zvr5OURSstt+8WIxRIFxy2aSxua42c6oiyyhNF7e1Qpk6zPFtbnz5SabhiEs//gPYwmlO3eRaZQfLUkyTsa9kBVdUOOdIhceMatoY+d1f/QAf/O0/oosDjCkwEjmzscX21hbXrl9jf++AqhpixBDmM8q04Kf/xl/lff/+T1AMPSk2eFNjQiRJgDLX8pZquJVKYNi78grPfvJzvLK3x9F0RkzgrMfgsVXJmVNbGOdIznFrZ5e3vuNtjIae3/74x7CFo+8D3WKBaToG5YCN0Yhys+L0+jqbg4JhYWhT4rmbB+x1HfvHDeOqplDeACHTV1yuvArW4ALUxitFyjlcVWGqmiiRaMAbgxvWpL2DVTXUd5TQMX/8W50CSo+ylpyZ/9qxypnekcBB7+kScFRuE0Y0Fk/ZWCY0VAeQIOy8sk83bdg8u8Ynf+/3aQ6PKErFawdVwWJ6jDOOvm2xAmVZcXg447G3vwXBkbLXmlJidjzneDbF37rO2toaZVngvWG4VtItZjiExaJDxNB1gaKsEAOJlD1LSx8jZVXTp0RZV8zbhiBgjWNvd4dz5QX29w4oS8eylmWpMmatUtiWTkVIKZf7WkIKRCl1/wiRECMH+4ckqxGUFahHI8p68G9FCHe58SHC9OCYtm+4cM9ZrDPfwky+RgxlionbN29z9tQ2g8oT+4bJeEzsIiYFohGCRLrY0/e9hsoJiqJSQ+ndCnMaVkPWNzbYOnOKwWSiFQ4ZN7GYO6oyll6B0bIwY0jLl04S1hgEK4kydWyYlnvv2+JNZ9/Fb/76b/GlGwfce/c5xoUCxKYsWXIiUxLcoMR4g8UivoRhjR0N2bl9wK/84j/nyc8+g5gRDktMcOHcWcbDipde/CrHTYOvS0g9s9mMC6c3+A9+/ud5+C88iikCtC2IhmjWgIkJQ0RSwJReqSM5AXV484DF7pS3fd/7qMqC0bCmrCq8q9RQVBbxnluv3OC3P/BB3vjGB2huvsxmkTCu5MyZU1w+d4auC0xnDbNuzuJwwY3jYw4Lx2BQQFVxazpnGuGw6dnuI6UGkQRnkQiFU189GsHPOri1T3F6k27eULsSWzoYVlhfQhdIsyOMy+Wkf8ZzLuZKFefcCbYGJ2GFWocT7FrMKomysh75DSlpgiqFoF69RGIbcFVJPw8sdhc447jx4hU+8ZGPUHmdH6ZUTuM8dNhqSNcstDY6JsRY3v6Od9KnREQyziuIUdJ+6AO3rl3jYP8AkZ6trSF3X9yikEDfLjAJ5rM5o+FYT1h6RIQkjtgFvNMEWde2XLn6Cn0fiG3AFx5jLtOFSFE7QhSslVwUkRNd2eglUcQ7SEJE6wmKGPDOkVJkerDH9GiKr0pqa5EY2djaVA/z3wZAuXpeid3b+xwcHfDGtz+0IhF8s/HaMJQiHM/nzJqOi3ffxd6t6wwGHbNqRkwWCYkYOqTXbK7gKIqasqworCdF3emqQcXaxiZb29uMJhOsL1aeiBgtzPfYVQbbZgB3GS0rR07fYbF4kRw+RSax5f4t2KoXHPaJ/a7hX33kD3nX7v18/7sfz96GgxRJhSqY4DU0wnkoSmRY8fxXX+CX/+4v8+yVQyIjbIRkExfuvgRdy7UrL9N0DeWgRNoeczznu7/rrfzU3/4POH1+G0SxXMVCc/YuVxkQM95qLeK07PLwyk0ODo85tp7LjzyCiXNM3+CshoQKEQh7r1zhg7/xYS7d9QCbhSeFOd/78L3sHM0orCOEGcOyYK0e4uwafYB5L0zbjv15Tz+PWDGse0/lHfNFQ29RkrQI4mwm26PZ7+gIQajLkhogGeX69T39dI6rBvRRvRzn/jSB99d4kqtJh1ZbFVY9/zv+WCGLHH1k0sByJSlx2yyjWCTlyROhMBZfGEK2p0SY7S8gWUxp+eKTT7Fz6zbOWCQFqrLCVBWl8bRdJMVEVRSEvuPM6TOcv3iJkKLO4RRXqj0Y6JqWLz31eZrFjJ2b19kY1Vz6mb+iAilth7iCpm3Z2NzKGKNeQwwdSSK+8Fr1JomjgyOc08hoc3ObebNQaMjq+3zSTL6gyUdvc2VaVu/qg2BsgbNafrzUZti7vUvog372wBETbJ06lRV7vs6D+lpjtQwA74BOXuXdf+O3fUMP0QB9Ezg+PMJ5w/r65Bv85avHa8JQGpSY/fLLr/DImx/hDW94Iy8+8wXabsrh7Ig2BEKfkCg4LK4o8WVJ4T0OQ2kdZV2xtrHB9qlTDEZDfOFfheQbkysYdPPPEmsKtqcsFWWz9xARrbyxSlUv44IzZs4pEm7WMgzCA2e2uXZzhyEG0ybEO2QRMN5gapcXl4o9pMLBcMjnPvkU/+SXfpXbN+cYalJM1OMh586cwfuCG9duEBY9ZTXgeDajIvKXfu4n+eG/+ZNUI4P0rWYTRTTEFw21HQaSQ2KPMR6sQ0JPiJHrL7zIl57+MtODY+LePmURlKKUHMSAidC3gSc++ikOm8B7HngD8eiAIvVUTrAmUJYlzVR5q2J6hr5gYB1FaRkMB0xjzeGipz2csTkaYK2hy7CGF4EgSOxJRhAPzhdYsZooW59oyWcCyhpSp7h/sngpoRojrvhjSZk/+dxazjCl8WjGk5VAhNadiwqTGMgcqpVXadKrP80s55NFsccYdcNJicJ7Iil7bgmSsjGS1Qz0E5/5OEEi1mi4P64HNEZLVUPX6rP0jtl0ynvf/zhlWdO2SrUxApIMkoS+jzTzhiBCE3pu7eywObkfkmPWSr73AawhSmR39zZN22RMNTEaD2n6hsIarHNYp3J6EhPjyTo7ewd5fr3a6GhCTaOymJQNYIy+mgSMzaxYZxFruHXzpr47CcZ6kjVsnzmzMoDfGJ6UV8HJr/o2kZkE3/QDXvUec+crIhztTzk+PubM2dP4slCo5N9GCeO3PbLn1rUNo0HFe9/3PcyOb3I02yHZxKIPWhpGjXeesixwTne2whdUVc3G1iabG5sMBkOlBknGCU3OOIvJ2WitqVaKjCjFMcmrcCpnAJPok6HqO87bGfdc9FhpkeSoBo4f+4H3ITjqYYWtvKr8WK9xsEUTBN6TygHBl/z+Bz7Ir/3a79IEi5gCmyIb6xNOnTlN1yyU3Iyh9hVHR4dsrdf8R/+H/5g3v++7MD5B32Cyxyox6sLOkm1LiGCZXTe5nt2JYW0yYb2q2DxT09+6jhs6KDwpJKTv8cYzPZgyny744R/6EcZ1wfHhbdakwRDAJVxpGI4q+hDpup6m6wjOYYtSQ1ir8EfvoBEhFZ7eGpWxMyokYozFOo91StvyzpH6nuQN/tIZzHGDtALicG5AunXI8e4xYeGR9GevXxizd3uiRwiKXaNRwKpIgFeF4svv5c5VeCIzhTUGmwxlXeYwXRAbuHr1JV546Tn9WYSqLOljYrboaWLmkmYpP7Gex976diSkrDQVTwx7dlV9WXDfGx5m5/oNPvF7H+HRN76RRd+QjBBTYt4sqOua27du0nX9KvnkrGM0rNnb26EoBzj3POcuXMKVNaETynKNG7eucNQ0HNMgvSAmMFkbMxoMdK2KEJIWZljJsINAiEEl2LLHeO3aVS08MIA12MIzWVtb3bY/tu2Zr028cQcWnF9e3vg7nNJvNi/u/BtB4bWd3X2mszkPPfYwZKvwrUDK14Sh9N4hJmEtfP6zn+XMmS32p8c0fWRYD9jtDjBY9USyCKyzBu8LqnrA5uYmG5ub1FWFdy5ntTPdTTKILUIyGq4aEknMSsjX2ZPQSw2MYKPFETmTjnnjXROGaxEjBclYnHOMknLeooNQOJwvNaPrs4CGNURf0CThX/7iL/PRjz7BLBQUYqDvOH/5POWgZH/nKkdHMwpbkfqebj7lkYfv42f/j3+bC/edI80PMSZhstdrUNUd8gQlJ6KIUek4q+tQr+n8fffyY/fcSzKCS0omJ0TMcYNxHkmGYAsGm2coXIFvG4rUYy14X5PSdEXrMc7jS0ufopKa+0jTRHoPXaccyUVqcWZdEQezxNUgWZQDaPSY1lkER98nisFIeYIhIglksSB2AWtLUteuwt8/E0O5XBD5Xjpn1TvMOKic3D3uPOrKE11mv3PIvRLWRXSDtoZk0ioj7IzFmshTn3uCxfGUwugzGk1G3D44onclIRhiiNRVwWLRcvmeB9g+c4YYlt6qfn4COqLyTAtHaR3NrGVYD7j38gXmx0dcu36d3d0dDqZT+tBrrbhk3FOEnp4Qe5w3jMeBa1df4tbtG1CUXLrrATqTeP76bW7s3MoOtgMbGA8r3vjgvayPB7qWYq8Oh1RY63EZd1TesaUPPbu7Oyzd0oSwsbGh5PTlbf2arPdSi5blxh8NdNmP1TqHVwmAIKz+/s7xKgMqr7aAseuZHx9TVJ7TZ76uRu/XHa8JQ1kUBUkCIQZefPFlfusDHyQSONqfszhqKFC1bVdoZYsxBl+WjEZjNja3WVtfo6rrVVIGdOLrTcwTPbv7YkRd9zzBHQLerm6oiBAxGAkMwhEP3jNmcKpYeaXWq5eEczphvdHMp/UarjsBPOJKpvtzfv0f/hP+6NNP00lFHy04yz33X6Zvprz83PMYp9nn2HZ0i2Pe/4Pfw0/+7/4mo9NDYmywokrgsjLmmk3Q2ue8gIIqqZsMmC9zeGLAVF5FGiQitsi8P4cpIyRousD1oym3jmeYp7/MOx68C4NwvAgESXTJ0SX1ghUCTSTjEBQK8aLRqlhDMarxvqC2Suvz2cMSm3Ero96V8QXWJNreMX3ukKI7hekGhINdiHN8YemCobMeU3FHKufPLgOwXJQuq0Z9bZima9h8zXvyKWS1IzL9yTq7omghQugD+9d2iSHRzxoW00O+8NlPKwwhQlGWdH1PGwKYgr7TREtV1xzcusVffOztRCwhxqwAn0ioKAwpIJm+bpPhrrsu8zM/+9d5+KHLfP4zH+eZZ59jOu/oWlXUSillfmc26gZNkvaqdTkZC6RIbGbs7lzhxZeeo+tarKt048dgbMm8j3zpqy9w6dwpLp7bxrssG4cmMi25JNRqpnxnZ5f5bE5dOIpSnYjt7W2c+/omZ+kpLjckMKQ2QJvymrMnaxhelQy607t/laPwNUaSJHz+k09w/eoNojX4IjMT+JZR/GvDUIoI4+GA2XTO7rxlPm+oBxWha5C2Z1gOtELHgnEGZwtGowmbm9usra9TlGUO6bTCRnfg7BWsPHlZKZZDTtJkReZlyLVcGD2RKh7zwF0Ttu7bxnpBrF9l/rAGSo+1AlElxLRqwxARXFFz7YUb/Iu/+8t89cVb9H1JiIn1zTEXL5xn9+Z19m/fpCiUwBxDj7QNP/m3/ho/9Ld+kqpMSsi2JRRJJbWW19W1GG+yZ5a5cSGod2MtRvzygrGlkikl9LigmQcJIKlHvAoGL/b2ee6pz9EfdcxT4urYsTYqKJzHeC1xEzHZUKugwcAYagMmiSaFrKGPWhxQ5Ky6s3aV9DDGZoUgDb+TLbDiaBcGO6uIt0tcvc10f4/addihJ3Yt7XxB0wdi3MZ8x5TzrzPfWKpap1e3VTCclHAvQ5LsPYq6RfoMxGjSTzTpl0xCrIaekpKK58ZI30UKHF99+gsc7dyiQFWjiqJk3vaI86t56euakISqGvDmR95K6iVrs2oCJ0liqdoquRTRSWRYGYZ+wJc/9xlefv45QtciIWjyJyVEUoYZ1MqkfGnGKi3PzeesjywDb9m//hIf+sCvMN64m/ViTDS5asuZlZr/zf0pi77j4pnTjAdOBV9cpKJQ7N8q9n/z2g2aTrUwnfNYMWxtbq+Spd/IOJnM/JY+EhcdNlkFir3ef03ALr3JbFxNfj7fYB81ObJp53OuP/8CV2/epBiPc17ixPh+s/GaMJQxRTbHE9ppw2y+IMWITUNc4TBFgVddMIy3+KJkMl5jc22T9ckaVVkqeJzxSJNOAHtjLEbSSqDTwIrukTKobpDsep3cYd/NeOBszT1vuQtTJpIvkbpGBZJVMUFy6GCCCuxKShrqVyVf/fxX+Yd/5x9w7eaMvnN0nXD2/Fk2L25z7eo1jnZ2qZx6qanrSIue/+A//Vt877//Y1AmjK0gFHQhYk2HjRp7WpsQK1r/7n2eVEZbTKAXLX0PkGkq2emhxsagWfEkmLaB0INxrA0K3vOj30/fGcaDIVVpKIzFOgsO+qOpKkCHpJVSSSiTLloTtfJGjGoxmpzpsE4Fio0vcrVTFj5WLhKStLTTe8OsWdAf7mJboHC4YoB3wtr2Gcbrc+rYc7i99meHUGZjmGLOJq/wSU721Tsz2/nnE5fTvGoxLuUAT0JwVl6nFZVJ+9Qn/wikz3JoulnM5g2UqkBeOCW9T+dz7rvnPrbXNnXjkVWMT4oBAYKoh2iTAD1OOqa7N7jx0gsQg8IyKeUWDOpJgsIwxgiS5eEkVxHNFy2F8axPhhTW0LQzjm6/wOPvfA9b2xd45plXWMQEZUlvDb0It487mn6Pi2e2Ob1V4kXoo1B4gzUOScKNG7dIAm1MWF/hnWdzbf3rBwTCSSidxRbiItAtWpxR7QC31KkLvHq/XFX8yEnY+DXHUMgKdm7epq6HjCbr3PWG+xSykj9ZjPKaMJSIMD88onSWzlvqqmRQV+CM9rUIqoRdFRXj8ZjNzS3GozFlWaqgQd5kFODKOzHoTZAsMr2EQ0RyNhvIJFxE1DOQhOkbLq3DQ4/fi9+qCC5hqzFuNIauAemQqGWQxjgIBiXn6O70+d/7OP/kF/4x128taEUVXy7cc5lzly/x3IvPsph3JFcTUsTGRDtr+JG//hO87+d+Els0JFdBeRrMacrkgB5JU4QjJM6Qfgad0VA/i8can/fElJQWZNVjUp1cJdYjmjmWoIA7QQVzXeU4tbGGHa6Rdg/wEqFTzUiKhI+tYpRVjctZXrtoc3lkXHnULmnoaTFK4QodyYItarCWLK+oAidGDa13Cd8nPEcMbcW03Se0M+KohNEaVAVFGylXXt93Ziy/1mMQffSYLIrxtUZ4tdZWbzR3YJsZNxRdnNp3RVZ9V/IOjSRw3vPcs1/ilVdewHtDikJRFvQovCOYVcuEYV3TdC1ve/ztqouaD7jcAi2aMDECPmnIm4jc3rnClee+TGgWhKjEd2eUuWGNwRtDSKqcn53hlUGJSQs6pmlGUXlwlvlixvF8hyee+D3+2k//HO9777t44smnuL4/ReqhJiLLmlYS125PWbQtF85uURQVlahUSgiR69dvIFmc0FpLURTUVQnOZAI8JyIi+bZJSFpf3we66ZzQNAQDuBqcXoNZtebILm4yaiyXFC/zx2BJfWwCN65eJ0Ro2o6777k3l1x+a7I5vEYMpcEQU4AYmAxr6skQVyt5W9qAKyyVL5hMxkzW1xlNJpRFgbF2qfqFleVGk9XSV6CvUoFOgPpMjs3HlTxpvLHEfsGGb3jsPY9QXRggHnxVKeBGi5QeVT3XD0i5FpjsUT7xgQ/xj/77/y/zzhIoEDxn775MNR7wxS9+ltn+IaVxWGvwSYhY6s0JP/q/+WvYodGacpvrg2nBVnqWtsRISZK5KoO7GrIwhaSkoXeMsBImyL13RHTyZVVz3Sgi4i3G1TkBpHqYxkTcpCS1nYZTpkCcgWpBDAlz4SwmCrZpMG0HAaTvkWahNduCYkkpIl3IYaN6m7YscL5UGTkxq2cyHliqYGj7Obeuf5F2McVvjxmMx5hJDS7hMZS+0sD7O2Apyx3f3JmikSiQpfRg+TvJs4KTzXf5pmXMmpImf5LWzsdcl23uIGFrV0yDNYnPff5TtO2cyulcG0xG7BwcIM4R+khIgnOOPgbKouLhN72ZTpS0vkTQwtKjTUm9YREciW5+xAvPfpn50QEpCiEFdQK8xeNQ5ROhSELAkDK2rZehlLw+JsVX9w9JYjhazOhCz/Gi4V/881/iP/yb/xHf/fgjPP3Cy7xy+5AmWrCa0U8G9qeBpt2jOwuDy0MihtS13N69jXVG1+6wYrA+opgMV0baLCGNGDXKSUJqO2IImCS0syNm82OiBI6uHgHaqqIqKsq6pChKqsEQX5UUdalzzzmMdyuvPy0jHKCbLzi+tYsxlqIsOH3m1Kum0r8TobegvCzrPT7jjQbNSnvnKJxjMhqzvrlBORxgC0/MdJ7ljrDkQi1BXGOMij7niQWZpoCWjvUmUWRPwQAptlTMeez9b2bt4QtZKsyCL/WrWfodua46PwCxQmwTf/grv8k//3u/xHQqBISyrLjn0mX2Z1OuX7lFWCyoCo9NQiRinMHGjjc89ihbd2+D67MCuSByhLDImGgC6SH0WJvlY8QjOQQxzqmx9x4T06qfCQbNTFYWa1wOuxNGXF7wBrwDyuydJGxdItYgXsV1xTtkbY1+7wBbFiokMtApI0lrf03TQt+TmpaUEkYMJgjEhI1LebUS8R5TVlAN9LqsYqSVLSiNZ5gyx3KtwhYO6oqUAmVVMh6NM0b57Utzma/5uvx+uUDtSgftVRPyxDjeOZZwRvYgtQGdsEyFW4eqI1nBizCb7fPFpz6LNwZJUdshFwVdH3FlSeo7bEoM6or5bMaDDzzMZH2D2K/OMnMylwrvikdbBGuEF559lhvXriOhR0QxcmsNznucLyidxwajczak7P3m/ja57SwC0VhmTc/xfEHTdRSlxfmGp5/+Ih/83Q/wN37uf817L7yVveOOT37+JXb3G030ZB5mMrAzDQz3G3xR0RwfMTs6pqg93juSM6yfPkVZD5FGMEFZHH2KEIJ2KEUTjq4uMd4xcBO61JHmPdWyVNk5RCLNYk7bNhwdHWXmSsp7mqGoa3xVsXZqm2pzbeVdHt3eI7Qds0XD2fPnqAeD1WRYsru+2XhNGMqUhD5p9tsUWlQfjKFwnqosWRuPWZusUQ5qyJ7kkhOlts+sFI2XAqlK0j3pxSMZizRoQmhVx2tAYiJ1Rzzynvs499b7kQL1CPAIfmUg1UjGO80kfdvx27/4z/jtf/xrTOeJNiXGa2Mun7/M3t4BN25ch5SojMWKSrhFtP47Ni33PHC/YkfGYHzBUpd5WQqnJepaWQQ2u8IeYyMinRpw7/UaUwLxKqRmwFBgY/Yqs2eUrENsxIhFXE6wgHqXMeByIokoBCAOLc2tQwblKKu0h5NsgDEwGKhRDLnXBUY9mRi1JW3bQm4LakzCFnnzMRaxSo7HewprkcrD2lCFbn0BKWKaSFFWmFcBU3/6EfPiNK9aJfmbhFJT7uBNkjPHypbIe40kUq7zxih+mySoso4zfPELT3J0sKt0sSiM1kbM503erAzEqOWxzrE3W/D4Y+/QKp6YNfVzaaB6R0KMfcbIhUU748UXnqdZdKhgjEONtWLWpSQtUy0s1pZAIMQem0TFO5L2gw8h0HWBrk90fcrrKlJIoiwif/Sxj1JNRpy59CYmW5c5d/ES9XBBFxKuLrBFhbOWQWEpKouUA/aPb3LcJkY2MSyH+GA4v3kau+iIIRL6CCnS9Q3WOXzl8aXDFTXGCKlrSO2C2dEBi3nDbD4Ho54yTildpS+p6wHeF/iixDjPcDDC1wMVZa4qFazJSdBbV2/gi5KDW7d4+O1v+WOKUd9qvCYMJUBRVTjvc+ip1IPSOYajMeO1NerhMHtPJwCsxZJVrIA8p2VZgZP5hZCJNNkwCiAWh2DRnY12wUNvusj9f+ERKGoMLreIAAU9spqgcfnMdPV0845f/Xv/kA/+09+gnSXtiX3qFOcvXObqy9c5PDjK4bShT5ECS4mC/M5YGoHR2TOIy5m9ZTYQxzIdpwY+rsjsukEqLrMyHil7u1kwV0vylqs5YVzOjseIFQ8VIIZknerwiWBCWAH86rkIPgGuIqarSDSaPc0Yr/W5349B6VFFoccwqpQkKWJjDU0DXY+NKYdbmpWlqDFloQ3anCUVHqoSimJVQWUxJONg2Yjj24+8vyakOsnapJSyR25f9VuTCxUkyYoXqM9AM8hGBImaSU45flSMUrPTijkmYuz52Cf+kC72VNarXqMrub17QOEq+txwzXhHlyLjyYSHH3wI+qT9jbJBjikSJKgIr0QNya3hytWX2dndwZlcCZTntouAU3lADFRlRekdKRn6Xg1j33X0KdGFQAyBtmv1uiOkTMMJARZtS0yBP/j93+U9P3SKjctvpiyHrG1sgVXR4KXNR5Rl11s4++AD/NBP/wyf/OgfYMoRBsf6YETqGnqJuMkAi8e1Oo8MgsRAmrVaAhyjKrznksg+qS7tslRXJBEXC7p5g3eOajxi89w53GREHyLTvUP8bM7W9ja+KOj6nls3bhINRBJnz599dc7nTzCnXhOG0hh14XOwS2kd48GA0XDEaG2iIrhZiHe5Oo2xmET2JIGUWJaQKh6nIXamaK9CcnW1E4WAMwG6hnOnS978vrdih2uQEzC6xHqEwMkK1X8CtNMp//x//Lv89q9+CJInxcSlC5fYPnOGL3/1WY6PppoBRBSDEf20hMVaR4XBiqEsKjX4q3/mJLmQs/2y7LHDiRiUwSOiYWOyKYcuSwEJszKSyYApvJ67SyfJ/SwzJqDiwZmDqapH6p2YvsdbTz0pcS6t3kPfQ9eqB2skJ47UGKeuV9k5hNg16oF1PYRIWrQkidlBs9jBmHKygXUqPid1gRlV+nliiN7RtYlZU7K9fvHkvnybQ1D+4KpNmUDKpYbL0GvV6ztjZ2Z5f5Iaxtw5jIQaxZg1PpcK7iKJmMNJY+CVV17mK898BaxK51VlSRsNXRIGpSFlQ1nUNYezGQ/c8yCT8RqhCwTSKsxXzy+qYU4BJOHF8NwLX6WPQZ1fIKSAQ+la2fRg2w4jMKxrfM59LNdH6AN9r1qfErUMySo9REnzCF3fYzAc7h3ysd/7ABvb55mcfgNtBF8YvFVBbec8MZk8NRJ9GzDlPTz6uGN66xkCwvMvv0Q9Ktk6s8W4gsIYbIqE+UJbsFirxSSlKtoX1nJmMmFj3jDZ3Wc2nzFbzAhBIYzhaMLWmTNsnD5NlMTujRvs3d6hFMfaaMKgqLB9BOs43NmnD5E2dGxub7C2sf5tb7ivDUOZ8UhrDHXhGVcDxuMxw7EayRUpOBtKgayDRy4JW5qwE/mn5ZpemjbFkpI+EIOG5rFl7Ba89S88SrU9QAtc0+oTTkjdOpZGd757yD/8b/5HPvG7H0eCo+0TFy5cYm1rg6e+9GWmTaPy+2Log3Z+qwSq4YjOWFy+ViOGoirpjRq4nCNc4SqajzIZn5OM5QCSNLwVA1Fw2dNQ3E2NXeo1RE6p07a0fVTvue+QXnmXxITpepCECSqkG/tekxJRE0SmC7TXbnF47aaKj8DKQBjRlgNds1Dmj7HErsenvChzQi3Fpbes/MMQAimH3rYasXbqHL7SplR2c6CcTWuxdUUUR2/Wv0MTCUuf0nzNSymXBUrmQ5JpKSoUonCM9s6J2XhGUtKwOsZEwmpjr2V4lyeew2KN4ROf+CP60OG9YnmuLJg3DWINbeyJRJZ1VG3T8c53votgEtFqUkZhFEMXepJEQuxJudJm2sy4desGMfR6asZmhSzF+j1OK8/QxEhhIKFFAsv7EUMiBu1OKDmTnJbPloyLJmi7QIyWdOsav/Uvf4k3vP1HkOoMvhjgxOCBGAJREiEKfaebiMUyltts+EiUnt/9vQ/jS4e12iL21NYWFy9eZPvUKc5eushwMsHVpWavczKmKkuqQcdoskbsOhZtS0D7gxdVRTKG23sHTI8OGDjH9uY2I1dT1ZWKsISIFIkbV67grONoOuXSfZcoCnfHqv6TjdeEocSokauKgslwyPp4jeFohKsqrQ826kGmnIPQh2pW+JJqS+qlq/STYdmG2yStl3Xk5APqTUSJlP2cd33XfZy6sKEeK9qvRVd4DstXroaGukc7+/zi/+u/47MfeZI+FKQEl++5B1OXfO7LXyH2gZQcohLCIB3/9//q/8YXPvlxPvjBP0K8Zh57k7A+Ym9dpX/y05juWBdd1N5B1oiKVmTyMCRi22NyF73Qt6vFbVIWG14qikPOdlut6lFMAssym6rAhayEfs3qOXhOwl6T6+PdbIF3x3hXYzAku/yd3tORG6oWYd68nEs4Ek7UeIpzJGuJxmAieFQ8IVpLF3sODvaoxmsMB5uUXuk6yejW4YxmzFdB86t2EVaxdUZbXj2t8qM7Sejc8QdJNyZv3cpr1K+CSQYyX3EZcksMGYJR7c+u72maGe18ynx2xHR2zNF8xnzWsDie8/mnnlhBQBjBFY7F0bEiICT6lDQREoW6qLh27Qa/cfO36LpASD2h7Yh9JIRI13dI0jA5pkDhYb6Ys9qELIjR/ju6EWkpqBWHFaFBwGuDsWViSESIMWRc1RDMCYNEkyMahSWJBDqaxnDj6nPUkz/ivje+nxgUvxevIjWEDlJPPXSE3PJjfnBAZfd59C2P8PRXvsDe4RFnzp6nKi1Hs12+8tJNrIXBqGZjc50L589x4eJ5Tp0+zcbmplb0WIsdDMAXSEwc7x2wt/MKIUR8UTIcDdjYGDMeDqiLGl/XRG9pZzPa6TF1HHKws4uxlkXTcN8D92sicTkPhFdPkm8wXhuGEsOwqtgYDVlbW6MaDaEsEV+QZCX3kInjeeGT+9wYm7PaufQQq1QhMVmpXEsNg0mUsixVTPg455FzFWeGgXjtBeLxbWJokdBq5rhvcV1LzNnclAy705Zf/cDH+MJTLxNTwSIk7rv/PkpnePorX1Z1HVwWKtW+PLYXfu83f4NmMVUuXYpYl+hMBqWvvgwf28eZSDZPkJMMK/uVPRbVBLYZe1XvOgXFrFiKeuSQTVOwGczwWntOyt4RRr3nlMN0y4lnhVnJteWD0yNUTssvJelrKd9dY1KueNKqpGAcQVoqY1fiCeIt3lu8UUZAn7RnTx+y7y8LbYEgPVtb6/lChR6BofZd16e7BMQAliRvVoRvfU5acSOh1wqZoGLPvi4ZTkbqZfQJ0zUUznD1q8/y0rNfoZnPOJ7PmM+m9O2Cpp3TLBpi09E0C7qmoW0X9F1HGwLzrkdiwEnEpkSbAk0IhC4yLCsWTUuh2TNELIu2RWLAFyWLRUcIiclkwqmtM8zKhheevaJhu1cealpWfIkK9i4ZEIlI4aFJiShKa1syKTQqE4rlM4nKz24MgKOL6nGmEBAJWKuOQMKQctJQh86NmKdKIuK6niQ9V154islkm/vf9BeZHTtCH4hRE0MYPVdroS48N44OuL3zVR5604O87/vezz/7lV9lNm2Iw5KyqjC2QCzMFpH5Yo+rr9yisF9gWNesbWxw9twZLp4/x6AsaWYzQKgGA05tncIYR1k4BoOSejSgGGiPKnEGkzej46ZhenRM3/UkEQZlydbW6Tx7vr3o+zVhKK21rE8mrI3GDIYDbFGC09A1oKGNFVGvMKnRdLlKIOYUpCGTbNEFvwy1DaLiDEBLwuGwfcepeMTp+YLuMy/gxiNS12KD9p0hgROfvdkenOGgL/mtjz7JF145oOkdIcF9Dz2MMfDMM89geqG0ns5YKArG5Qi6iLEFH/v089SFw9UVpm/wxpCSlgNOxkNqX2CXj+JOKojNqKTYExUkJBPNVbrKOsuq02LO0kpS3DFme2iTaN9nsQTxKiZsl2lwsyrVU6V3NCkkCcSScCRX00pJEAdGe5RbMasQK+Rkk7H5HJeUFEJWz7G0eaNzZYF12oLWB7BBM+SdRJr5MSKBPjpiMcSfe4Bi7TRFKumanqsvPEtoVGA2dD1939I2DYumYdYc08xndM2c2C3oFg2xi0hI9E1DPfTUQ8dsdkDfdCyOA20jHBxMaUJPL5GQNMw1mc5TWod3inUvxX1DUk84GYNLZNQ55UBaM8azxYxFr/QjZxySYDFXL+/C2XNQFIQkXL77HqpqwK0bN+naLje1y9GRRKzLz1JDrqw3kGinB7RBDZRYh0t6b6NRbVIRiEHwRjTctgZjou6hOVGl5Y0Qkm5megx1PDDKRAHJ8wtiDHgszcGUr37xCcrhNhun3kxInpidAhM7gjgCBkLP0dERoWn5+Mf+iJ/6yZ/gXe98J1/80oss2p5WAoWv8YVGDACuqPDW0gdhZ/eQo+kxx0dT3viGB9jYWKcucydH57HGUnjlRNqyUKqby1VgxmBtwdHhMdP9Q7yv2N3f5+z5C1RV/W3jk/AaMZTOWibjNaqh3jjsSSrNnTgQOlGMho1hGXLlnUwgl9MZjFUST7QGg8Ot/sbRdYFL4wn3pxlr4QBnIzRRa5sJJ7E9KlZhrGWvS/zKR5/kM6/ss+gcYHj0LY8i1vHFp76A9EIyigsZLOPxhIsX76LwnoShnc8xSTheHHK8cwMHBLE421AV9lX8PGNM7ghnTsDWZZsKOYEZBNFkVAYTVvLsmbwrKWm7XrfEz/RGWmNz/2qBICfHzakO0lJFO9tdYwhJqK2j8LUyGa2awWUfI+sKzc4mwZcFrqggRqLRjSxaQ4gdVhJOhND3eFfjR6WSriXz6CzsHRzQrF+A4Tm6W1Ouf+wpQhs4ahpe/PIzSIi0szmx7wgx0PaBRdPTxxYRpdvE0JFiUjI1QFJPR5FSQ2Gh9l5x1MJoF0MKajPBUIBT/l9dGKqBxTtPVVYMqyFra5sUZc3hYsH+8TGX7rpEVai2Z5jPuXLlCju7u2xONqkLzxe/9BR70xltB2I8g7U1ikFNVQxZH24wmx0zHg9gXCNRxZRVuCVl5Z8cKuf/UxeY7/cK8RirKLpBGRwoFa5PqPnO0Rgm4k2uQslJp5i0sCpoNk9n0VJY2ZiVc6GPOdEbZZnYLrI42uGZp/6Qt7/3LMXwbkzS/uxGHEEcpXH4tABX0uO5fv0mn/7kJ3nH2x/j6a++yLxZ4KIjeqECfFGhV6BiQUYSpddiALGW8dqEtbXRqruqZJK+NWRMEzIGhySYH8+4/sp1mnnL4cGUwXDArGl56NIlEqKSo/8uJnOcc5TDAb6qcjOunJoRcFnpZzl0zWu4naUfWXaZI50s3qVxsNaoARMIbU9dFnzX976b+KUP0169glgNI60TXA+lXVKOVOlmbw7/+GOf5ws3p7StZYDjzPlzbE8mfO7pL9OESLB25dXiLF3T4qxjMBgxHA4pTxtc6Ukp0E7v4urLL/PKlRtUCJX3SFRMzIjogw/5AnJXO8IyBauUJ7PkMfYKOEj2ggx2ZfyMMzrRelWxdl6PYXPWV4+JqrLHHskSdCLas1mFji1BLCn3JBejOKNY9d7JPW+M85hlhO8skiKhSyAlXbLM2wXzcMDxbIdmNqVrIu1CmM0DixA57rS9RNu3TLuOaS/0QTPENosnC0YVbazLjzZhnV1JxRXGr9gD3pZ4ZygqS1E6yspSlSOsHzCoxqyNR3T9lD4uuOeBSwyGQzwVdA5J6hF1KTKfHyJhzmy2QIDYJQ73dmjani4GeizPP/VFUoraOK4oqYYj1iZb3H36HPfefZFHHr6fZ1+8wpNffA4/GFONR/QiDCcTur6lbRdEG5XwLznzLJo4UkELFeqNSRAiKQhtiETjFJPMhjUhSvCXzIU0ZAMouKS83AQ5Yx/1OZNZAAAi2vCMvF4Md6wjiC6LMZMwbcti7zrPffEjPPj4OsZsksRlKEyjvz4kLt/3KM9/6YjY3+LJzz3FhUt38xfe9Vb+5W/8DoN6jKkCqdfWuNVgTFnVef1nlaMkHC8amr5XyADomk5VlXL0Yr3Dek8bAtPZjMXxgv2dXYUsshhITIlkhNPnzn7d/kh/ksTOa8JQGmvxRUE0ZlVuSM71nuSxJScQ9DdLkWMtxTKrniJkw2rJvA8iyWqo4b3h+37o+zj/lge4Nn+eV555kmE1YPuBN1BuTdj/3GdZD43ioCZxq4Nf+eRX+MyNhsPWUfsBg+GIcrzBi9ducv32Pn20GmoYKIxgQ0/bdxRhwdjUVDZgnccYwVlLub6moq6hYVIYBibTH7HqyS15jMaSol6kzZuHZM8ziVEQfllfLYrXhtzWQSSBV71ISVojnKwjGqeNvYBB4aiJmryxuqMLSguhMAQJYB19XxBLx34H+0GN2XG7YN50tG1HCMKi71m0DbPFMfPFnEWzILSBdhGJ0dJFIUggyDK8LEi59tgYg3cGX7rcx60CM6AYDCkKQ13CeOjzplhRD9YoqgFlVVKUTnVJrcUbi8/VG846rLO4jMs6r7MoZq586CNNP6KJnttX9nBxF5PL/CJa/+3qinIwYH3tLGunK9q2xRjHeH2dajRiPBlzfLCHx3D/o49iy4pyMKLwBU/+/kcwe/vc/+C93Nq7RdMlms4Ri5rDxTGT4YB6ULN/6yZdaFX5HcnTVXHIuBTtwBNxRASlfhraXj1Ch8EnFXdJFhxK/wopKiadjUSftEZ6JapFDsEl822XwcsqUaYGdEmuW22gy0itT8iiYe/K09w6dZYL930vi36EmKQRYDTEFBmMtnjjo+/lmS/8PrOww8c+8XF+7Ef/El+97xm+8pXnII6oBmDKitAtctLLU/gy6wNotHBrZ18ZItapR2kMURLz+YyrV6/wwvMv0LYdk8ka21tbAAyGAyRGXOGYN3NG4yHD4XAVfX6tU/mtjOVrwlACLEmQ2XcADCnvlCbbPIXPBCMphwY2Z0fVOC5LmRwmV+9o1i6KELvA5niLW69c4+xd2wzPXuQpGfLYe74fuXyJlz7/WYamZhpmeCfsRMO//MxXefLalKNGw0uqIVMsW1XFV19+iS5ZYnKaZjCJsxtDbHNAb+Gxey8wHE64cnBItAXaP1azgdInfKamHAfdQa3YTGjXxIwWgeg1RjEkq7WsgURvDLYuKQcDfDVA7IBUVkhRKhdNpWtw3mESOOcR5zHDIW5UEWZ77N66zv5nP4O0Pc0i0LWJxWLBbDFj3s44WMw5nje0i8hxiDTLRIkYorH0STQJjCMafVZFYXEWKqOFA+I8ripxrqQwlkldMhwMGA7XcNUYMxhgSsewMkx8oio9phjg/YSqnuRooCNKS4oBR0nXdigNRquXJeV5kFLmaPak2ENUmMFbjxGLK0v8qEaspRChCD1rzjJeW2NzY4v17dMUwxGuKKkHAwajAX44wJda9REzxckpDwpJib/33/7X9IuW7/4rf1mJ2tniPPjImzh44QU277uPNBry8U89qYyComDNbjBeW6ftApghZVnjXBaWFo1ugqQsiqEGykbFg0Ew3YK+e0nzXSRcSpn/CJk8tnIwllBVELTax2bvakmLWqpsWCVzK2Vt+Qn5f1H4y2ThX23CpxxLNzvm2jOfYTy5wOjUI4So+HkSg3OGyWSIm0x48+M/zBee/CDXdnb50pe+yI/9yA9z69Y/4ui4oRzA+vpYsVWTSKGjDz3WlyrWKyWYkkhB7IWmmXPl2nWeff5ZXnzheY6PjzAYzmyfwlExHPSIFVxZ0rct2xvrHE4PuXD5EpiMuf67ilEa0LBSNDlgWCZgTe6pnE7quDnRslOo2qyy+yupJqvJHJMMJIONQmgMt5s5169/DpzhXd/9CG/4wZ+k39zg9/7Jv2BDIhdLx7wXmh5+6/PP89T1BfPOUXhDNRpydDzjrssX2Du8TYgtKfZYLH0MvOHBu/lfvf/d1NKwM19we++I/WBocZRW290u62z7TliIJ118kLUf+TmW+ZjmYB9JkXp9E+dyK1exGlb6Eld4+tjTGyGUjhh7FvM57fGC/aMjdvb3OTrcYXG4z/HBPs3smObgiNnhEc1iwfF8RtsvkNBQxF6TEUlxLaxTj8JpQkIKjzEOlxymGIA3lIXDFyXeFUyKitpXVOUQW1b4ylNVnkHpGZYlRVXjixpjvNbre4txQlGAFQupJCRLI5EgPd70ugFGkDBlNj/KLYNVKQljsV7wYjXJ4gvtNlkUmLKiqGoG9ZBBNaAoS6rRgGo0pJ6MKYqKshoo6J83UKXb9ATRcDTFgMwXtM2cvd190i6Erid0HdtnznHq/AXKaqD3CSGEnlvXbnLx7Fm6+UL9rj5gCs/mmW3q0LKYzXj66a+wf3QM5QBXekpbE82ITsDUEw15S6HvO4piTKLSstNlUkkMMUuuCYFufo0uwtBZkokEr7YuCPRmuQ4gpoA3PrdF0cZ6FiWmg6z4FSZrF/S55Ncbe9I/JhtFg1AaVOtUFJZqidhekN1dXvzyJ3jknaex7ix9smA66tJSFgViHMPtCzzw5u/h+S/8AV94+hnuufd+3vu+9/Hhj36SPvbcvH2Lzc11Cl9AVLjheNEogb8PHBzOmM9ann/2eXZ2dpgeTeljjy8GrK0XOOsQ49ndP+KV6zcphyX33H2ZjckIBJrFggvnzus159D7TxJu3zleE4aSjK+ZHAosgWsHJ4kIkw0h+qIsMZSkWBp5twPNKC+ptdoEqcKO1sGNKFLg+l6itet84tkbDMsdzPA00Qk7sScJ/P6nP8Mz12dMF2DFMygrjo5mgKFvenZ2b9E1Pc4Y1buUBKMRj//032BtbYKvB+zu7vIP/+7fp0YJ4c4pKN6J0DWREDzF9nnC1iXmoaeZL1gMPId7U9qXDjk+PmB/7xZH+3scHexzuL/P4njGfDpjPjumbeak2BL7jth1LAVgLYIVofSWwnssRvueZx39wjt8McaVjsrXuKLElRW+KijqimpQ4QtPWVeURcmgHFBWFWVVqqqVqAficTgxWeVa1Z9UmUapVCkXKVtJFMngonp/XTIYB94EnC8Y+wrsgKosKSrPcDiiHo0pR2Oq8QDjCw3DqxpblFlB255k0HP9b0qBvlNstA8dMfbEruXoYJ/54QGz2VQ3lfmcplnQ9oHZvKFrW9rZjBQ7TN9BUo6jBrka2Thfcvnuuzh79izOO5qupw2R6y+8hG97fuN//p/xwPb6GsPJhMFwyGw24/qNXZ57+SVMUYKtGFQTmt6T7DrleASllhSKiYjt8cM1MKUS20OftVVzIkoSiUAM+/SpJeVKtmQMJrlcXRS1WZoBjGKtTgSbUk78LHmiKpdHUsZJEg3btahDiCv4Skt5bYZIlssvAUShCwnjOma7L3P1+Se58MD7gQEkqEcDnFXObS+GrTN3Y9703bzy3Kf46Mc/wV/+sR/l2ZeucXPvmLCYsr+3Tz2oGda1VrTFRNc19F3DR37/D4i9PhPrHN6VWFfQh46u76lrS0yJV15+kUXbUk9GVKXn0pm3ENqeoqi0Te4d3R9PClfVZP470a5WH7jN01LDBvUuMx6XdzgR5YlhbKZKLPmSGdw3RqsMMj4ZA/ShwNVbFOOzGDvGEJn1kfkx1OM1FvM5txcFpx64lwffeB+/9sv/kM9d2afvelyyDIcTDudzYjKa3bSOpo8azhgFvXtjmCfL5MwlApGjxZwOy5mLFzi6cg1EM+49gR6YxR6c8IUnPs7f+okfJzQtbd/TNwGX1LgY0+NLk9uDWry1OOMpnMcCAwu+sJhqAHYD7zx1MaDKBq2oC8rKMxhU1PWQohhSVUO89xS+wDtLYQXvvIpxOJd1Gnokl+op5KuguItAiNrsHjWU3nmc83jvsd7iaq2YqAdrWixQeFxZUJWlRgfe4wpHWRWUVYUraygqjC/wVmXJINH1kbbrCX1LXMxp9nY4ahe6cPqeGBNN29J1LX3XEUJPaBu6RUO/6Om6liRB+ZMoZ1OLFST3UcoU0zxXbIq6OF0NDvySRGAM4gxdFJ578WWef/4FtDRen+fRwRGLjU2uvvgS66MBRYq0izntaMyVm7fY2TvKNeHaDqEoB0xbGFYbxOTxJdhC8XCTkkI41lIUAwb1kLCsoFrKuaWOVA9QOqnk6CquGAvK28zJzTuut0+CdxqdpSiEmH3KzBAjpKXEgmpFptwNIC1bIlulmmWqEQathY+BEA1tM+fGS19g++w9lOv30kWhqkrEq6NisIjxrJ+9j2QsN178NJ/93Gd593d/Nx/48CdJxYCmmbM/nXJ8fEhdq7hz1wetmYiikBUG57X7p7OOoqzVPsTEjRsvE9s5VV0RY8NoNGBY1xwcHNCJ4ZOf+Rzvee+7gD8eef9JIvHXhKGEJa4CyxIL5aSlVZnXUl9Si/+FJTl2WddsMzVCczqRlIS2sVCsU62fw9QDrCkpnMW6nts7HePJZfYPX+LRd7ybuy+f4X/+xV/gD3//D0ghUVjD5sYaO7uHNFEzvaSOnf3b9EQtn0QgJrxzHN3a5f/xX/5f2N2/RbOYExcN5za2uO/yZRqEsi4xyTAYOe5+4AEaEznY2yUdTamwVM7h1ioK7ylKhy88zhUUXo1fVTrqQc2grphMJpSl1qSvb0yYjAZ4U5Giz5WJkUggpj5bhCWV3eTNRTmpyWp1iLGWqtaqG2dHFN5TlsplJS/ysiwpyoK6rijKAjcY4coKW2rrYFc4/HCgWfAs4dXFnq5Xg6WyXokYWuZ9y/RoTzUC53MW8zldMyc0LaHp6dsGSRGJPTF0kMnUSWymq+R5Qo5ErCFaMgzjKFyJiCO5AjEGF02W29M6bEmCjQmbjHpXqDFJGNVTzN87q5t3lEgUhyPTtJIhRRiNxhRFQVEUDEcTiqqmHoxousD+0TFdlpkzxlENKq7cvsHa1hvw1RiJJgtwaA23saoBkDKW6L3H+0Tfd4DggRQ9oRgiSfszidGkibMq4gsWI+pFGmOwKEyQSbJ5nRl6DF2mBYGWc2JzLJeN5LIYQTVfZcXjVdjT4pw6KyFG2hCx01tc/erHuf/xNbwbEvqCqlTVdCTQh44QoR6e5/SZR3j+pWs0ssto8xzH0xnerjHxm4Rmj6adaQRpPYmoLXWtyfXuib6PlHWBM57JZIQrPNPjY45CoCdRWeHs6dOIhUUI3D6Yctz3fN8PvTdbm69J4/wJLOVrxlBaWYbQikP2krKoboKVYtAdwbeo2RQjSHLYpEIM4hPRWOaNZePMQ9z7yNvYPL+t4g7eUhYKxB/vH3Pq4v089l2PU7qeX/g7/w2feeJjVM7QRctwOGDvcErbR7BeJ54vmHc9IShVZpk9G1U13WzOR37ng9rpUaCyFn8uct9dd/OWt72V05cucHT1Ju1iwaW7LrN5bpuPf+yjnNs6xamNU6yvbzGajBlNapIErIPSlxRORTM0mx1yGWIOcaMS763R6g/vHG2rHRSdc5RVTWE9RVFmordijIN6SFFWFKWjHo3wwyGuHuC8p/C5x4nTlr5a3KNEXnLpZNc19KFj1jb0R0ektqWfzenmC9r5nNAt6PtOk2hB2xOkpM2xJEV91pIbX2Ey3StXFKEGIKFQiitLrNHoQtIy5rDq1eXdVXICImVZvSWXXpKhN8ukWP7blE4qkkTz/IaIMzm3gUYJThI25iRbUq9FS1+TKit5z/kL56jqkrIsskyYx3jH/s4uTRdygZNSlkKM9EHY2DxHlCHWQ+EKYoykoLihNt5SB0EMOG8oq1qpQilhUomL21hTIxIylWtpNGVVtBQyV9aKks4LAddHrbTJEmu96M+ZYIEz2TDmea3bkN5nsoeqzAvJLVHInF09/+QMBzsvsXPli5y96830HXSmZzBeg5FDGk9qE0Vds33uPg53a1o7ZLQ5Iro5bjYjLGb4wtE2Q/pugZgI2Uh7W2QV/UiK2pjOe6vdJOsxj7z93ezv7XDl5WfYGBVsbGzQ9B2Lpqc57njo4fspyiU+eRJnrwRkvoWxfM0YStBJLMt4wGpFxFIq7Q54ktULojJM6u0ZyBO56UvqyT3c+8h3U0/WWMxhMrRUA08MkRdeusLFM+s8+IbTSFzw9/+nX+B3f+u38KmnXzSsrY2YzhsWrRCToTCZxC2etjOE5HKzLcF6SzGo2VjbYLQY4q1jY2uDza0thnWFGLj63HM8/Zkn6Ps2V8tYqsLxw9//fhwWE8D4UhMXQZjUI23OZTX0tnh84anqksGoZjgcUA+HlPWQsh6QsqqzwWRjlCjLUkNabzHe5i6IGqQtHYzQa3nebDGnP9wjtC2xawhti43aZbGPkRBTVpnpiV0PSXk2fYwnPVwALyr2EdBSRbJkGctSQ4Py7USwxuGder9KnheCFbq8HVrRYgFBSeurysvlPCGHxmQjISrkkDL5z66Sg4Z+meVFDbRSppbKolnIFlHDlsFvm9QDbZLQxkjXdViTqCwMyoLYL3AZKjBWiwvb0NHNEntHBwS0QEkTCI7dvUMm65fZXD/Lce+ZtQ3RaJWJ8QUpe5/WWLyBPvQkBF94jPFaihkCg/E6RTHApmOUH25JIWGcU1pbWhb/O40udIdZZepTVKUjk183gHNLaCuvrHxfdeMQyAk/s6TvidFEKWQoLNAYj2nmXH/pSba3TzPeuAdC4PjokGq4xqCscJJouxZXBnAz1idjyjDA+gnzQUPfHJP6nkHQvlQhdlpX3/U449TDNxBRFaWchaQ3JclOuPf+S7zhwTczP7hGVdUcH0/pWmF6MGVc17rZOLUTOv7kmpSvCUOpbMcsWiEnWoBL2adkl4tNRQCsUaKxJn9AXCKZgEW1Yo1f402PvZ1zl9cJAaz1TNYti3nH7u0jLt69zX13TyB2/KP/5X/h1/7pL1EmaNqe9bU1ptNDFn3ClSOGA8/aoMIa4WB2TDNvVHEFwTjD9vY2W1tbXDp/QXfbQjG5waBmOKgZDipCijR9Tx+ChrMiFN7RL3oGwzFveftb2dzeIJFycmWo6ixFiSm1usYaA4WWLFqjBqGPidj12K4jhI7ZXJM8fdsR8r/YBGLX0fYtkpTgSwxZIQhCVDKuckAyPmxyEkC0L5AsuXmy5BmoTmSUE60lJ4ptFSanQETU+0KNm7ZIWAoK54od7lB5kqxBKiYr4WRZBsnyaCZXDEkOEfN8CLnAIImSqMUYQhavWGqPqjeOVh2RDSpo0iaH0lG0nC/FlHv+aIlgFwNNF3DeMxqW1IUHCVgr2kPeCG3X0rSqzN+HRNt3YFQWUIx6mrERtrbuxtgBVa0anH3TrcJbb+2qB5ISt7Xdg5G8uVmlYJWDMcVgQDjeB5tW3OJc2wtGnwW5BQgYpW+J0rn6tPScoSCzQ0Q0gsvpKxE9/hIHNaJ9b5RtYnIuQH8Xk4HcS6mUwOJ4h73rL3Dx3BsQGSGuIPWBJA1lPaAoap785O9h2it816M/ws5xIrgN3OgU3iaccblwQIjL2WFsrt8PDGrtTJpiD50Qu56u6Si84/TWJpNK2DU9RoRmETic9dzaO+Lv/+IvcXXnBj/6Yz/A+sYYzKuVwb5VGvxbGkpjzN8H/hJwS0TenF/bAv4JcA/wIvAzIrJvtN/rfwf8GDAH/paIfOZbHQN0MSy3NGudJmnzgtDFwVKeUXuG2Py0c8LBkAi9waQhb337O3nj2y4xmnhS0MV4e2fK7u6Uhx4+w+aWB4n80//fP+Of/dIvUzgIXeLM2bv5Sz/xY9y8/jKz+YyirCldQWkdg6qkCw03b95AREuo9o/2GY4mbKxvMiiHjEcjzl44x0NveQvjrS18XeELx+Gt2xzt7oK11PWQclDjvCUt5qTplJSEtXvvJcSeRTPXDPj+DvPZjK5dIEnVqdumgRCIbUfftnRdByliTczJiWWbhywrZyCIan1ap/Jt1loK5xTii5JBf1iJYhitp4+yDHcDbdvStx1F4fGFU9Ub1ItaYschppxUi1i0z8uSMr18fsloL2oRo8kJc8LYs2Ra0tI4y3I62IwHm6zmo5hbWva6zvxDSVr2GlKiS5EoUbtT9hEJkZh/16fIotNsaZJEt1DNTOc9KXuTzjmcU5qM9Z7hcIgxhqqocE65on0IpL6nix3GsGrL2vYxX4029kpGM7/RVWyduZfoKrDCuBpznI5pFwutiDEm4/NWbZxxOOOzIc+9ucXR9p61zXPcOrqqawBNSy+FYXI2JwMURknXNovWJ9SwyYlyVCaUKASSPXIVy2Al/+ok08+tJpFSLttJFjqT8GIpYkLahHjL1Vee441vnnLh3vsJKDWpbVpCrw3lZof7VLLgM5/8OAvWufuxH2faVpTOKSXNOVxh89TJ2GrUgg1noaihrgwul+3GmGjmajAP53uUXpDQs2gjb3r87bzte97L01/+Cr/9oU/wqU98ln/vZ3+Cxx5/M2X5Z+tR/i/A/wD8gzte+y+AD4nI/9MY81/kn/9z4EeBB/O/7wL+p/z1mw4DK1wJo8of6lUvFYAyjGCVQqQqMbBUV12GAbEVNre2ePQt97N+2uMstDO4fv2QxfyYtz52jvWhwxuIyXLPvZfY3N5i90bPd7373Wysb5NMybmzF3j5pedwJmkJZdZpPHfuNHWptIxAZDDRrFtRegbjAXfddZmzp06zVpaMvMcWJbauGD/0EBeXyZ8MJCUSaXHM0SsvM719i9/+5X/M7OhQRYWt1TadGhhSeDUCOj/V8GmopRhNYR0iFsFpZ73lP5QVYExu55trPqNEDbtMhi6MZL6cYSUrJ3p727bleHpMVZWMRjVd3550tlz+qcT8LLTiuBcF/CVFfSXbNyNaNx6XHonRD1FJMDXWgYBIJIqKGIWgnmwMidirxFmIHW3fEWJPCImu65X8Lprh7giEFHGgtKSYJc2MagT0ItqIyqrErTeqqOOdU7gjJ4+GaxP8YAQ4UghYZ6jqkr7VqGL39g69RDY2N7DOM5ysZY8tc4Hz7h5SohxuYwebSFHjbMQ7x9b2NtevX6dNiUFR5WSax4shBD1nhyghPSWC6AZ27wOP8uJXP8lwYFV2T3GInN8yK9Ufi+LBKp6vepRJ0rJr8Up7Muv0YaK2m80PdWVFl3X9RsgVVWq0liNKwoWe0Flal8DN+cJTH+OBN78ZihHWOtr5gK5JLKaHWCmYLQzPvHCTBx65zLkza4y6grYNdJ3gncUVZtUGhYj2U1JKKLZISjGrVEDYOccoOCTU3H7+BkWp1UutODYvXGCytc7pu+/mwUfewqf/6MP893/nH/CWRx/kr/3MX+Wuuy5+3bLGrx3f0lCKyB8YY+75mpf/KvC+/P0vAr+HGsq/CvwDUUDo48aYDWPMeRG5/q2OE7My9NIwWpfbxyKrTJuRZU21VhKQ76NNWlY1Ho6o68DGmqf0sJgnrr98wGDkueeeCwxKs0IlnIX3vPfdPPaWt/BHf/BxnnvmOSbDIRITW+ub3ChKTKFlcKZLmNjnWlq15k5gbTBUeo0SmeiNcPr+e9g8f17bHCyTIKQs9xWzCDH08zlxMefWzRt85iN/QAqRwhVYUyoDw5pVmGPInR/zBZssVb1sBaGymTlks5B9caXkpJNJoCVy+jubQ1kNh00+z/xeQelXGHxZce7iOlsb6xTOcvX6FUKMSssQVsR/krZBCEkIYrKgQ09K2vgqClq5kVtOhBDpYxbCDYkUoet6mq7FZF5kjKourxupxVCw6lpktWJEEz2GYFxWlXGUtsQj2u4ji0AAVM6rMc11ziHfPG8MDqsUJVIORy3ddEbXJcp6SAyBwWhAVVV88QufZzAYsLuzQ2eF6FQJavvMWUL2yrJvByKkAOP1UxSDEW3QDok2WarKcvHiBXb3pxg8VeG1nNM6umS0rl0SRVYHVKWfxH1veJQP/84EkVZ7vYeIMcsmZJZEyvNFMf6Yq9eiRCJR6Ug2V9rkqG3JiDBWyyAlGEzS7pwiS2m/ZR8q9fCsUZk3dYS1rDCKbtJXXvwKL734Ze5/0zt1w1oDN7S4YsLb3/2jfPnpJzleHHL+nrfgvGN9AG3wdJ0qH2VUGmcsMTvcvgRfQFnqRmQMmri0QlUaonTsXH+OS1sl867DDIaUoxHFwDKoDWtbFzlz8ad5+C3v4tMf+xj/7//6F3nf9z3OD/zF72FjY/JN7dN3ilGevcP43QDO5u8vAq/c8XdX8mvf3FDKMpe5BJLveHhk6CXzJVfCo0l7WFsMoY8MvOPU1pjoEt1ijpSeF1+8xfb6hAsXJioychIJKq7mPetbG7zlbW/l5Zeu8PKVazx0/4MEsfhqxKydEZ3VcrEohEVLaa1mFWOWORPLxsY6jzz+DjYunGPrrkt0iznXvvxlZtMpoetoZzOOj6bUruTN73wHKQa+8pnP8Mpzz9K1jSaEivKEwmLtiShB9irVIT3B7pZtjXXHt6skmHPaoXFZnxuWXsMysaJYhYas2cDaJe8ut4IQWZlaBOhmgen0kND36tGlRN+rkW3bFpMSXbcg9FEl9yMk6RHp8qLSfi2C4LMhjwmcV/qKNdop0otj4CqkMERbEkWbaVmiAvmuwBhtgbGcJ5o6Epxo5j+JJhySZI1EDMF6YtIGdkZQJfhlcsKpOpA3jtB3tF2LRZRxUFUEk5jPpkgSRlWJCBxOZ9y4uaOK7t7ip3PWh5OsbNMvp7TeQ+MIwXLu1BmG4xLTgWTpq2QTdVlwttig69Pqudsc3seUtGhgBTmp0NT2xhu4657HuPHipygKtE963rVUWUrJ8nZZZSMCJuqcTag2Qu6ztIo2WDYRkfy6yVS7vIGuyh3V87fWqSQbJ2wUAxCj9kRyPU98/I+4+/5HKUcDFamuhLpyPDi+n/sev4/5PFCW2k88JSgtDCbLDUZfA02K9TFReauJVa/5CysmlysaKuD2tRscXnuJwxcPOOg67n3rX6CsLEYSVWGpKsP6oGZ97UHOX7rElz7/FT76Rx/jiSee5od+7Hu/qYn6UydzRETMyt35kw9jzM8DPw+wsba+2iHy71ZGQgwnDZ3yQk8idCFgs2KJQzi1tc5oUNBbyyuvXCXdPODyvRfZXK/xK5ATchJvdTxB2Nze4O577+b5Z16gKkfsT/fZ2j7L8QvPkgpZ4XFdPidtw6CTDBH2d3b5w9/5Hd78zrfTNAtuX3uF57/yJY729jKJXiWjJpN1PvJrV5geHWILrwkSZzN0kCXiVt7yUjNS5aeWJOIl6OyMWVn+ZDWMtTnpk8SRokWiRegzxy4nMlLKCtiiuF1c8hyVupNizEaG3K0vZxdFSDEQYqDpOubzFmMdZVlSlwXNTAVSrVU1F+tUoNgYh/cVDL02zTIJiQbnc1/2nHXWyAClmqDP3Ioh4bFlTeE9lbU55MtiD1Hokoboy0x+SrrQ+qhyblZyX+wseLxMRNhc5lp4iykrxDjmbQtOPcs29DDrqOqKohrQdZG9o32wgi1KEp1OA7GECFGM6mymO8Jbq95/l+DyPecZr4PrIQaFG7wTBhU4Z+kDhGRwhaEo0QZhQZCg0NKKNYBgxPMXf/xH+Xv/zRNUOVKOYYAVT11UWEosngItOQwxEGRBCMfa9lgiIqrYnmLAuJQ7czpNk5vEUo90KUIhGSdGljVLNpcbmxXHkiQ5QuigKLjx4rPs3HyBBx59o5YZxkTjDIOBUNcWk0r6CLOFsJhB1yZq7ygLQ1GB8fosU4DYW1KAkLR1RWGgcIIzkVIicrQPOy/xnscf4fb+Pld3d7n//kucP1tQOHAmaaKxhOEpw+Z4yNlTj/HA/Xfz6U8/wS/909//pvbqOzWUN5chtTHmPHArv34VuHzH313Kr/2xISK/AP9/9v40yrb0vO/Dfu/77uHsM9VcdeehZzSAxkBiJAiSIAmSYkRZkSzKS1qyaNmSEvmDs5IVOysf/CWKleSDl+ys2JG9bEnURJFSJNmSKHEUJxAECKAxdKOne7vvfG9NZ97DO+XD8566DUoEKDmCWmtl97pdt+pWnao6Z+9nP8/zn/hLAJfOX4hCEl+PgL/D5kmlnJvEr5NbTRS36hDoD0pGg5JeLhEMR8fHfOiTzzIalqzveW8/zFnVlMfsD3psbY7YHA+Znp7SH/XEVl7neOcJWmODwyEGrjEKomqiILW6KDFlzo1Xvs5v/cavYBT0q1IcLbM87Yc0q6alrVccHx2ytb1FiIKcFiaXwh04I9fG1PEpIphCinsWCNERlZhTqMSScMGnZbx4McZEynU+CNLtA7bzCecJEjXgLRgtyZcJMNAJMJHOEpTRZ0SKtdcnxlBWFUV/SEz8Tk1ksLFBmQnyK/eSFm2SKiNIdxCCRA/Izyavo9JQlAUhRiazOc4HCqOT5hgyrRj2eqJztwGXxruuk0IV0mVr1NrpXSdisgRmRSIZURb3Si40IVenVEWTU+YFMapE+PagNCbTeO9YrJbkPmBMju0sR6en6LygS6a86ygFY3JZKVh3tsaISvbCKtdcub5FMYbWK6wHZ2VKGlRQlbLXnK88bevZ3CzICoV1Busej5goT4wGbwMf/9738uJnvos3X3yJ7cEmJt+CUKDQeIlQEm6otwQltm39YLG2w3Y1Plq0iVjX4mNDbU+JyqJMYp2o9Q56LfpQaX+bduxB2CdJAyDTSwhkXuOsx5cOE1a8+OKv8dSHnyVELaBPgMLAqAdlchTsjxTTWWQ+g+gjzkdKlPAejXxMeYXrwDsjWfLzOZX29PqGwWaf4mDEnrrKqy+/jtEF42rI8ugQXS8Zjvoi1WTtkaUwPcjPKarxNhu738f1J9/N//D/+j/+rgXvX7VQ/gPg3wf+Qnr799/28f9YKfW3EBBn+nvZT37jjiw1fkm6tUbw1ONPRilFrrWYl9oOl0fK3CQ0WrNcLETpkr7kcbsrhqgqGr7hQbViZ3eLzc0BpycP2Dt4juA7tvZ3uffgPkZpWYpbR56Z1KFJF6gS39F5S9NafFIFWZvR1i3D0QYYQW6ts3il2dzeBe8ITYMe9PF5cm5ZsyGQu7pKCFegw4UEhjhJ/dM6nnUFShmIOSoags/woSUoTxcbXOMIToZ3pcxZjESMKcTMS/GVhkGD0RRFRZ4Z8jxPBI14Jo0jdRc+yAntnMe1jaDj3hKdow3yu3Ztjfc2lTJ11s37dJHp5F+ZhwKtM7KsJM8UWa8kLzImp8cMi5KrVy5ydHjMfLWkdR7r5fkqtGagJGLCotJo6qWrCf4Mp8hyw9bWmPl8zpr+JBGvkUKtncA1JiGoEZGN1k2LVxHXNJjM0ytL4QEaI3ShqsISGAwHDIcD1si8NLhr8rZivNXn3LkxuhCgq4tgvezi+oWil4lb0MbQ0HphfCgtF2eeWD+5luCy4AKnkwVf/dzrFGGXC1vvQ/ucue3ofENwnaxYAsL/wstoHtflriDPM/IY8cESVY9Mjxj196ndKavuFEcNyuKVT+OXObuOZH0i76uU1hgiaZcunb4PAes8Kre8/vKXmR8+ZPPCeWIMVFGLnFInlR2KfgZUiiwabNIdFJmiyEDlYGMgQ9NTkdVkjlocM8hF9rk4rNFqj1lnuffGmyzbFp0ZxqM+5/c2KVXALVf4qMirArIMB6ySqXPR0+we5JTFxW9ao34v9KC/iQA3u0qpO8B/jhTIv62U+lPAW8AfSZ/+jxBq0OsIPegnvtXjs74Q31YoZWeyrmWpgJBGj7imDEX6vZxWORaLFT5ApiOZguV8ST1d0SvHZyFj0kO+/f9vMy1Vkb1zu/T6GYcP7jCb7FH2cnb2d7j74C4qBDLEnMDoXPTDSjJi1mysiPwKvUK8HeermiwrxN9Qei9ZE6TAL60zhhsbFFWOKeTK0BiIQorvulpOykxQvejBth5r13xGh4+NVIJgIDhU1CgCIVhInDRTZlDKM6nTYjPLcxn5JSWZiMfahuVygbWOuqtZLKxM9kbTqyryLKdrO2bzOU3Xyl1eS+Z2ZhRFkWNyGdNq61ktO6F0GEOWyVtDho+BTMWzfBelZd8sQB5Y35IFQ9tYVnXD3t4eCxc5mi+pVzUxKgqTU2YZuVao4IQqFaV4+6hRSlMmFFmHyPbuBsNhRbNcyQkfZVRee1UJ6R1UluFbQAfatsN6LyCFc/i6o13V7O3uYNCM+iMGwwHlqE+/KumbDNvZ9Nqkc8spDs4dUGyOKXNNpgKFUmQoWgVt8GTapEE5UmjoaalxHmgRKmQI4GrP3ZuHfPYXvsBv/9rLLE9ajCepVSTZUeOTfNQJaOeFYC43O5dsy6LsHbUYxwQlHXg3s1SjHYZbe0wWD1l2j1C6PaOCoUjFVs779QrjDFRhLRLQQlB3oHzAz1e8/vmv8PEfOo9N8KMK4JVkMQWX2BiCXaEj2A6alNljPOigsYsOd3qMXk0ZltIMvfTqDe4dzjDVBtoYykxTZIpce7Jc8ejRA3b2NtgdbtKeHGOrAjPcwpk+jVI4LdlUznGm2vndjt8L6v3v/S7/9P3/gs+NwJ/7Vo/5z3+TdTF7PBKvP6ZSUYxnBSn5TsZIr5fTqzJu37wl+s+qIKpIFgPTowmb+2Ng/ThAyvpOyMjbOk1FNagYjCq0DjTNgugzisKwMx6xmK9AKxZtS94riMago/DLVNoROhVwCj77W59nuVih0Ozt7/Pcu95FNSjRWtHTBqVy8n6fLEv2bWVG3SxY1iva2uEcad/mUEo4cHleUfVH9KuMjY08GR0b8iKn6g1QqoQY6LoVtqvpuhrbWWazJctlTdtamsZinRcpXehw0VPkBb2yIMbA6ekJbdsx6A/JswKjEmctKyjLkul0ymQyRWlDvxpQZIYsS2YXWvwHQ5RAJ2c7ovdoUmdQKDKtyJUBki+YEphKJbchAKsV3kNjOzSag4Nz7Ozucv/+AybTOYaMXpFRmIiOFusCjesIEUpjyNMU4ZWocRTyPC3qOYvlqaDkRrq+tfrEK7BenvQ8Ew2zUlCWPar+EO88d+/dwznhh+5tbbG9tYW1nuF4SF4VDPo9SiWj+nq3rhAP0yzLOX/hnDhNIY5YhZyK5GVGplKWGiqJLOQs94jTeD1refnFN/i1n/0Cr3/5Fn6VIvSsxsSAi1aUKkkiGqKXXfA6ZC6ZAscQwUbZCZP2uQS8TrttrVnMl+R1wXh0kV5vxPH8Ni7W6DVdbz2iBeFUukRl0wn4UevHDQEfPCFk5Ebzuc/9Oh/+3k9AXtEGaXzEGUzRdLJDjkphXSR0CltLp9oqRa4Cpl0QZ4eMCzC55rW3HvLarbtUW/s8/4mP0R+OadqGEFqcb/AWQhtZdHN++6u3MPVLbBSGS5fPMbAezIByMMYMChotYJH/FiXqHaHMkR2MWnOJiWiRXKUNWWQduCRF0rYNmVaUufgfPjCRejljY7BPBDJlqZsT4MoZ8Tp9Jx6XZEGQ14O5NhnjjQ20UtRNR5UVLKYzNrY2mC8XBKVo2xbNWIxz0w+eTkMyo3njxltQFHz/D38PR48OuXXnNj//z36J7/ue72FrOJZ1gFGUVU4Mngf379Ib9Ln2xFNUgxF4cM6iVKCuVzgnJNouRFZNS7OqaU9OqJuGxWpB3ayom5bWerq6xjspHugMo03yF5QxsA1BAsCUIisyAgGtYNDvMej36Q9HbGzmooxQhs5arHfs7+5ijObmrVsoYxiPNimzgiyTrKMQgpz0SdXim45u1YJSZJmmMIYCiQdAiUsPadcco0LFDHmlJeRK6xwVoShzhqMh8+WKw8NjTIz0SkWeCVJfO0fbiSKjyiUGIqbzQ6c/Pmoa4U6RJ4aAjohOfw0Mhoj1TjipRjOsSkajMXle0rQtxyenLGZLnHdC38lzqkGFX9ZEFD2jGRiZFE6XS7lxxjWoFnl49JBP/OAnz3bvIEXCcPau9J8x6WIS/ebkaMEv/9KL/Oo//TLHb55SUpKZAdpYbLcixg4bRdbonSyr16a8pP0r6Ux3yCQmTcdj1ZJKcsSQil3PZOAc3WJJtbnFzkbByewOKkyJyqGVCBWSebqsOqTVPHt8+R0iJhg6J2uVhw8e8ObNm1x68t04L6Bd7ETZFq3CdiFd45IZlCtF6CIZjlifUpmGamSYTGZ87dU3OWkqnnzhk1x54hxFz0iaiR/jxReEIodBAdpEuuC5f/shr33xJe596XWevLzDuf0D8q4h2BHZYCSNT/+b16h3RKGMMe0kk2QKktWTFquooB9Tgry3tM0KnedCVDY5uclYTKcU584RjaGMnmY1JYYW62pOHz0iNz3yakAk0tqW3YMLvL1wGm3YHA1lBMKjo2M2nXL52lXGwxHzxQyXRsZKywsKpPwYoZncvHGTH/6RH+PalWtsfGSL6WLC//XP/1948Ytf4lOf/B7ZxWZikHDnzltcv36NouzzpS99heP7jyiyjP5ogI2WW/fuMJvNKU0JmcEFkSu2TUvbdXTOk2eiksFIEh8IKbnfzyUITCsKlVYZ3gsRzWgh9GqdnssWgPF4hM4M3kWWqyVHJ8dsbozY3tzixs0btM4z3hhR5BlF2huL12eKMAjirdhZK0a4RjwCswB5oi+tnWx0QkolX4X0enu8j6ysI9dixrC495CmayGFlkWzfgyw3goCbcQL06/NG5Abqo5CtSJG2UmD7GfX6q8oF7jzHTF6srIvI3uv4srly2xvbbJcLTk8PqGzooL68Ie+g9F4yOHRMWXwtK4F3Us0GUVnhXMYPGRG3G7mkyld27IGjaWZXs9G+myfKT9gxDWOX/+5L/PTf/OXOL7vGZc7bGTnca7DJpJ9SLr7aMUSLwZhNZA07S56IZOHt/VJQTK9lU7AXDICRil5fowwEExm6LqO7viUwXjEhc1nOZ69QedPMKnpEO1RCveKgZi4lI+v5xRT4sE1gZgHvvRbX+Li1ecEWlMCP5gQyaNCZxrrAlFz1u3r2KDsjFFf1k1ffe0eNx/O2Tz3FO+9+gSjQQ9UIDORvFA4D7YJWKfIMkVZQs+AxzB46iLn9w/48udf4Ss3X+X2o1OeunKOnb09Yr1EVxuY6ptXyndEoQRp14WWpd72pCcnFUR32riGZlUTvQUnJ4xSmqrX4/DRQ9Rzz9KrCubNijdffolHb73OCx96P75rifmQr/7yr2LKnJsP7vDjf+bPkRW9RLeQ8WMwGpBnCh1aohW53mw25eLBOb4+ndJhWdRLNqpKdjwxpnAy6X03t3e4fvUJQoBHh8eMt8a8933v40uf+xxt26GHAwKKB/dvM94Ycff+fb7+9dexDqpeH10UFL2K6dGC6axm9+ASednj5OSI2ckJtrGgNXk1YrvqU5iMpmnpUsyutxalobWW4ShPXLggY7BHEESjyMucvjG0VuFjTvCO5XKFGY3R2tCrKp559hkuXbxAvVjx4OEheVGRF2Xi5gnKaaNcfMZHdBRp4Mpa7BqLjtLJtF4QaS1ws0jg0hSReFZSPJRgB421tJ1wMNEak+XELMMn8rmKgsybzDCbzZientLUDUprer0e21tb9Moq8SkDEU0Iwik9O7eUdEAmFRijIC9yMm04evQA3yx46umnGA56wuXMMobDPtZ2SeGkk5sQYAwhkFIf9ZnzEUGe+5uvv8Ez73rmG7rKdbl8DDUqJkcL/sp/+7f53C9+nX5xhfPjHVRwdG6FDQ3OthCsUMqDqJQIwlsU42SIWrTuEqMrgOg64z6qxDFKbxScEeydl7jbEBXagHcr6qljMNpmc+MSp9NI8FPQ9m1AqHBuz6hrpBvQmpcbPMFbemXB6y9/ldDOGG1vC5UtyKpBeVlhlVE05cFB6DwbY020Ba+9epM3bp+iB7u872PvpxqLnNSklspbAcUciFxSB5oQabwmU4pcCfpvxhnf8d3Pc/1d13jlK6/wxa/f4MpkxtXL58k7R1jU37Q+vSMKpRjtyrO/3p+sB+T1xSaxB0u8dwz7FfOTU7RRbGwOsRfP8eUvfomsX7K1s8NgtMGjw0es6iUvfu5Fxv0+u70+z1++wG9+6UscHR6ymi8ph4qsKBA0PFKUBVlhIDo626AUzCYTzm9uYYKj7lrmq5pBf0hMgWUhBEiWZBcuXOb2nbtcvXodS6RuW1yI1G1H23UUXgrzcrmiX/V48atfQ5ke1595hs47vO/Ixj3q4w7dL5mtlpzevstqWRNiYDQcy+83HGGMoV4seXh4RFTQK0tqW2O7GjSMxiNG4zGFEiusLM8ohkOyfo8rFy9wfPceJydTQe2zHm3XMJst2N7eZmt3m43NMVEpvv7GDVZdYFz1yU2Bj9AKI50ucRRNCCjvWbaWOtGpjILCSHaOT7zYmCINhGoSMUnR5EOgqWtWrYWsZDQYSgcUxLVIBUE4gxFCeXCOtrHMlydMTk8JXi52rTW2tSznS/b29hhvbafQOeRsilH05UEKhFGyM4whkhtN1SsxypArWC3mvPTlFxkMRtB1LJdL5tOJdDxZgVISlSFu7kb8FkPAKyc3T5WdlcCHjx4SYkxmD5zVRilWssZZTFf8N/+3/5HP/bOXOL/zDKPBHqFzdHZB0y1xzqFCm6hcnuidAHhEUdOQdn0AyonzPuGMZhdCyr6J+gxthmS8QQqgCzlBRYyJlLnC2hWrlafMNtjbusbhyQ1cPJWbS3oujcmkUz5jrqwls8LfDNFAsCxPH3H/rVf54IWPEpFdrDg0SHEOQcLf2gYePjrmOC5xNnLscl74xIfZ2B2hc5VqAQSnJMTMCf8yRkmeFEa3orZgspiUW+LI1C8jxfkB480PcPPGJW5+/Qb3XrzDU5cWXDrY/KY16h1RKAUx1qlzTIamCLfPpOXvarFkPptSDSrmqwXLruH49JjtrSEbmxt89Ls/ztF0yoXLVwmhJi8qvvPDH8G1juN795lPjzi8dZOj2YQrl67yy//oZ/nqK6/wR//4H6NdLVnNpozyjI1qQNvUwpcMHh09tllSZJrM6uS0nIQKSqcoBA0+cungPJ/7rc8ym805d/ECr918gy988UsMRmNUnhMUOOcIEU5Pp3gP+3u73L33gMnxERfP79E/t09bW04nS1bNKdEF8izn3N4+O9tbFGUpoMliwaPDR/jgJIZTGbLM8ODBnNJoQrBCqs40Jhohf8eA9p6H9+4xPzkWPD4AMZDpjNVyyWK+4MGDhxijqVc1Lip6vQqNxrdiWCx1R5x7XBr9bNexalqs9WSZ7EJT6ykXEQqdCe9QxeTe4yzWW1bLFYvZLHUhkTK7kJRKnqrIINnNheT+453n8OiExXIhGc/rLjetAgiBw4eHFL2K/ngMHlT0ZzxRFUlgh+zWXPqZcgW5VuRaockhRtq6lT2xtTIa+4DOInkpMcNiqqRoG5skgXLjxGhxhjcGZ10asdeejzGtEMQ30tvAT//Vf8Cv/5PP8/TFDzLsnaNpPE2zoLO1SDA9ScJrJd8nOBm5k/Gv6OODGI8g9nE6CgXGAaj1jWktAQ7JTEN6YKXlOlvr8qOWCGLrGvwsMt48YHvrCsezDuIqFdz11+t1gyn9Tkhshiju5N4FcmP4+le+zIc//J2YzGBInS0pfsJEgoEiVzz59B6uFZ7xu/o5hVmnGIBLzVTCpugcLGtN20axmwPyYr1lekwtTLwPMg15X/Hs8/scXN7lxqv3efnlr3H3wek3rVHviEIJ6cVRKVOYNYKm8MmsdXIy5XhyjDqNXLv+BFmv5O7Dh1w42GdUwfbmBsPxiGo8YjaZMupXfPkzn+W5Z59nNByhs8iXX/4y3jmayQzdOOqTU/7P/8n/jt3NTYZFwSc//jGqosdisWDYN/QLRZaX3Ht4h6IquLS7S66MoHxr74AgmSRKaUye89yzT/Haqy/x87/wcyybGqMV/eFAbgDBYWKgTGjxlatP8p7v/BDlcIxrVlzY3eDuG68wPXqAq5cQc3pVxf7+AZubm5SZQQGHjx7Rth2L+YwnnniC8c42befY2NxgNOpx683XhcwdJdUvpm4mixrVebzz5HkpSprIGd2qyHPqpmE+nyWna43OMmxTs7KOLssIyVlbq5TlEjydc3ROvCq1UhgDmTKiHoqic/cxsuo66tVSRj6t6LzHhkDXtfi2I0+SO9u2ZMZQ9Up6eYZRimi05Fk7R9M2ArDJWlts07TQizJjZBz1jm61ZHtni85ZkcCejYkRn0yPTVYyKArOH5ynzDOWsxm5ziVjJkRa66g7YQkoo2Tcti3oDG8jts2IyZgDLZZ0Wpsz3TMxMnhbTOraFnc9McWo+K1fe5F/8FO/zPXz72PY26OuG9q2weIJOkcDOtZinBxlnCUEUVJFn2SvCaxJK4wYH3NIpHaJvt2sd6SJhhSUSqCaIMwyLGhckPgGYiB2LfX8hP7GNsPeeWarOyglKishWQk9yCiV0lGR3y5KRvz6ZvK1r75EvVyysbGx5uuxlkyuLTYKHaFQhCI7e6bWRmGgRGWXfkenFF0BJofGKRor6HsvhyoT0nq2fgbWuwEFqEDfKPqbmt33X+TaxR1efu1fqIs5O94xhVLxWCYlT3BM+6yM3e0t2huvMptOaWzDxtYWeVGybFbM5yt2+kN2+iNWyxW/+k9/lv5gRFs3PLxzD79cYPKcw+NDmmVD07TcvHmHJ55+jlxr7t+6zUbZY7CxySuvvMKFy5fY2tziiUtXaGzLdD7hF37+n7Bz/gLPvesF6daQnVv04rAu5gVC6uiVGc898xTXrlzh0ckJL37tK7R1y3y+oCrHBOcxOmMwGPH8+97P+z7xXTTW0XY1N156kV/41X/G4WxK0CUGCTbLM4OPomppVyuid2yORjy8/4BeVUlXJzAGxkiPa7SBoFBk5L2C9zz7HBtVn8nxEaumZraYsZjPaeoV1olFmtKafr9PZu1ZZ6aCIKXeejon0adCMBY+aIwBryI2BMosozQZOtf0TEawFhs9q66hXq2IbUf0jtFwyKA/YJhlRGNYLJacHh4RfCAvS0xREHOxi1nHo26ORpxOJ6CgbWp8cMKxUWsXdlm6+RAQn/CI7Rp812CT6iZZKWDKguHWgH7VI88K2VWajMsXLjApCk4nE2Rw1Xg8nfe0viPaZArno8TT6hxSoXI++WFGec7F6NjjrOPcwcGZnv4bzvcIt28e81N/+Zc5v/UejC+ZzuYCDqkObcBiib7GKIv3HuddQrRDypMRAYICMqVSZymdnlES/mZEAyvXlWQMi+M80qVlUTJ3MmNBaVyUCGKlUma894RuhV0V9Ps7NEVH7e5RnK0TUrFM8tJ1lARGYoWtN6hgaGcTvvbyS3zsIx8lbaXfBqeu/y+d5tsL7hkS9rZnDyCLJGMOKY42l3/JgAL1jcVt7XmAOuuANZBncPlcj529J79pfXpHFEp5ssSZJ2qDCrL0r5ucZ57/MO97/zVu3X2Zt976OjubI3q5Yndzk7eOpzT1isl0Qj0/5cqVS2QxgLf0egWLesl0Pmdze5uy6tN5QWO74PiNz/wGd+894JmnnubC+Yv0+xVt13B0+IjtjU1u373DfLlg3qxYOo8/nnC1tpjKEBQUqVvKjIjuQ+pUjDLo3JBnBdVgwObWDrdu36FuHV3UWC8nqA8dP/+P/z5/+2/+JMu2o+lalosZZa9Hf7jNfLFKO7SIbzsaAp1WLGZTdrd3OTo6Iisyjk6OWNVLQvCUufAIh6NNqsEmrY9sl31+9A//r3n/hz/Eyc03+cwv/jyNbWTUzDMiFarrRA8cAjpCudahR6GxxGSWbPJcFDCIyWzwjoh0WgLWSCfpfKRpJAys6RpChH5RMdrcxWQ6GSsEYoK8+6MBWVEkgnpOVpZnXWdZFOxtbBNj5NA6keahhUKSYkK0Nm9DcqFLtJ8Nk9NajwuRXlmyv3/+LKdbn4XZKXKjMTpyeHTMoKrYyXJOT06FOhQ89WrFqutAK/q9EuUBa1GZlOQ2RJoQsWeUJ5UQZ3Gc39zZAkj7d7lYQ4Cbr9zlr/4//yeW9zt06NDFlHJkkiAg0noLdUOzrLHW4SI4HSVjJzhUlKgEHSOZV8K7QUZ0p1ISaVoHrN3JpQ7FM737mjhODHRR/i0kYvm611NkOB+ga9GlYTg4RzubAPV6bya0phDRmUrfA9Yplj6KA5gOmle+9jIf/fBHkjsWrG0HlUpEeNLjwdtK6dmncfbg6a8aKJC1SVjXkrju2XlbfVWp5qY96ts+nKnI+FtYU74jCqXUerk7myTjq13GwYXn+I6Pfoir1ysuXr3MZz+j0BQUpmAxnVLmhvliwf7WFq2LvPLaG4w3N7Gdo1cVBCLT2ZzR5jY6zxPoIsajFy5f4Id+3+/j9dfeYHt7h+lkws7eAZpI3XVo1k7YinwwYLJcsepayjInixkRnRbZybYsvQDRIONRymUZDgZcunSZVd3IKsF1KB04np7y8hs3GQ5HbGyM2etJsFiRZ7z+6mssvBPSre/Iu5YCL6hworRMpyfM5qfMpico5/EaTCFZ4BcvXiHmBSS0Myxm+KNDiTHoV0zmE6KRETovcjrv6DqfXMcTCq3AaBmf8zKjzHIx8kAypMUDQUzPtBLWgm066rrGd17G5yJne2cPnQmVS7oon+SD69ddZKBFbx18K0p87z2rtsNbS1O3OOfAgPOW3GSUWYZzNu1sYoq6kL2Zj4A2jLe28Em7bhSURSZqm9SxGGPItSaLHhXAuo5VGynLHjv7u0ymM5pmyaLtpIOMgM7E8DcIcRwj0llJLASV8muCF+5omUG3mNAuTjBlH5WVxM7zmX/6W/yt/+FncSvPC+++xvPPX+bC+U3QjmW94ni+YNl1tK1jPl0wPVry6tff4vhkKWwBaaPlWTSGoGSnL89gyh1a2+8g9cJHlzpALcUzXX0KUCpLr3+a7NIfpSDPhQ7nvbhFlXnF3uY1TqdvEmMjihz9eMSNiQuqoxKFkBLDDBUV88ksdcTJheFtFevtFKPHP/Xv/NvvfCd9LY/H97f/e3zb56zXH+pt/ygkgH/uAf+54x1RKAEsARMzlI80DorePh/+7o9w+ckRRRl4z/vex9/96b9D3VgmxwuuX72A71pWqxWzZUMvz5lOFlTDTQKevPAMBgNOTk64ePkqxmSURY92uSDLc9qu4+XXXiVg2LpwkRu372KygjLTVKVOuxxFoQ0b/T7T0wmrxZTNQV8W1CYmcEL2NCqKd2XbtkynomDJix7K5JJa2FlMjIR6Rb80HB0uqPpDrl17gs1BdWagMJkcM51NcNajsgznPcvVDG8LykFFZuDNm2+wmM0IVoovStQ6URm8jzy4/4B+NaSq+jTNgq9+/rfYqkqOZzOOTk6omw4bFZ1SdM7ReE/trMgro3BKFYrMSBpjmWVkyeQ3MXuS3Z1Qp4P3LJcrFtM50QeKXsnW/jYR6chctGgt26J1/grKCOAT12TrKGCTSh0rWqIuoiQkllnq3EMgKzK2trZ5dHQoJHWzBimkCzFKsbe3x2gwZLVaEoNnPBwwPTnCprGZuM75EVnhWk6pckNe9hgNRwxHG9jTE5HsAb2ihzEZ3ltxjlegjMbZ7kzm6nVMqpRAoSDLNF/6rd/iyhNXGG5FvGu4+eIN/s5//zd5/uln+eSnvoPt7RJlLcpJUmWJxjUa30WicQyHoFXJu8trvPTV13l0/4Rc9UCJmYdHrNZI++YYpVhKtyjdpI8REhK/zlwX6eE6wVSW7mvNdrqzyetiRJbovSfaOXEZ6A/H9Mo96tVdslxWZSaTjHAdRIywTqdYd61N0zLe2EDpZEvzDVXr//dHTLsOAcLXXerbjoRAKSDwzY93RqFUEJVP0L8hmDEf/b5P8ux7z1FV8ovs7h+wtbPF4njKcrpgdnpKmWuauqXpWrK84K2796gGfc4f7OOsZTgY8uD+kdhtFRm9ssdkNqUsCxarlrsPH/Kf//n/gp39PWb1krs33mAw3KKxDdvb20TvKaNn6+GYR+oBtlnhncNpjQmGTMkLbpDXIYTA3ft3KXsVruvom5x+ryI0DXlupFNuLXmvxHpP1S+pygylIlkURPzNN2+yqhvQhTjoxCihXlpDDDjvmM1m4COaTPJQcgleCi6gY6TtVjy8d5cr164SPExnp/zDf/wPOZnMyHUm6GeeYaNnOl/SdUI7IYSz75nnJUWvJ9EHZm2YnHZRSsABgLbrOD09oVmtKIucsl8RDJwsTijynLIoJftbCdIc1Prk1YnpEBIyjniMympa4ie8I8s1scghEZxDWiwNt7dwRnN0fIL3QiNTaRWyMd7gvc8/T13X5MZwcG6f8XjMrVu3yY2i805AJ6XRkqEhN4m1Qa51TCZTJtM5s8mEXIsqfrMqcU5GYAkmi5R5TnQOHUKiQWlxeIo+Wd8ZFkvLX/7vfhKFxq0Cs/sTfviHPsETzz7Bo5NDXn79LqeHR2QoLp67wJNPXKVXVQw6R3QdURu63JH14ennL2Ntw+RRTWYKwCQNtxNurxM7QhU5o0atUeJ1R4USH8c19h4AiyIljqGjDNzKR8l7Jwft0h7TE1yNrSv61QHezlEsQaV9veLM5BclpidKK+qmIe/nfOoHvv8bDHD+dR9nBXANbqmzXvbxJ8W3daO/y/HOKJQRccZBUYeCD33sE7zwoScYVikKImquXL3ChUsHfPXBfareLoNqAFgmJ1PRtmaa3f09brx1kwvn93De0av6LOsVq7pmmA8Z9Ct6eYEKcO/BEePdPcpBRTno8xN/9j8C3/HG177K//zTP0Ov1xeVQ71kszfg2ctXefryNWoXsJ07y9VeP98+RKx15FmOMRm/+uu/QVUN+IEf+uGzpEhNTDKrHNdZhrmhFMsaLPDmW29y5/Y9QkwGHybt6coeWZYMLLx0HSpotOlhjMHicM4n4rwYLCxmE2J3gDYZq7Yhlj0Y9HGdmLy2ztG1LfVsjvOOvMzJirWOvJDuSZvkEIPwFNXZTRiV0PTlcsVisSQvxPVmsZphO0duxA1IKyP2hikhUIBZ4beZNe8PucnIRRtomg6lNbvbW2gVaJuG6D1tcCxXS0bDIb28x87GFlUujj7BevJMMRz2ee/z72E2m+O6lizPmZxOODk9FbggUYGkqQopckJWATliRqvTn6jEr9MYQV61gs520kkrGd9zY2hW9dmMF4m0XZdEMYpuuSKczoT7qkrqieVdz72XkGt+5n/6hzw4WmCdIc8yMp3ztRuvce6N+3zkQy9QDit8dLiVOLVnRlH0A888/wS/Pf0awXagijV8zdr9SYW0kwR8Wg0JJensckvA0+PdX0Ds5VRinqw3hARxnYoqWe9FUaY716BDSa8csWpqjAZSV77+UUgplYvFDFNk/Lk/+x9z+fLbXRj/9R9vg4TObgrrdMm3K8O+laPuO6NQAiEo2q7g2rUP8v7veJ7xMNFz0ytZFj2uXLnKy194kaatmS9XlL0MZXIaaxl4z2hzzL17b9F0HXleEPOcvNdjtlgwGA/Js5wiy8izHGc7er2SwXAod/7cYPKS3nAoIUxWYhti0PQHIxbzBfs7e9x+8Ai0EnmgMYI4n6mHYDTeoCh7/PF//yfoDQZMTk8I84jJNZ5A56yY3zYN5Xh4lgR4485tXnntVUIQ3XCmFUp50AXWRZq6pihlfF8n7CmlcDHQRZEuKqUIPlKEgHYRYx2myGibjizvEaOg1l2UBMK27Wg7S1Zk9Ks+vSKjl+UYJYassnKK+BCSqQOJagTeWtq2w3eWqlfR2YbWWzKtoIPZcoavA9sHJTrP0smphBwd1kRgKZwhFa4Q18askYvnz9Erc5SK2LZjdjrBOA8mw/gAylMVBf1ej3Wc17Df4/zBHsdHR5wcT8jLkoAEiq3R2Ejk3t27KODCufMorVnWDdE7tsZjCiOdv1byHDkfIOnTVZ7TeQ+ZZBblJsMoTeu9KHGUIleaumuwPqKCwbUd/arEty1N5ynMmKBzfv2zX8FTsLv3FEEVKJ1jtCJ4x6Jb8muff4UX3nOFrfEAqwKNt0mzbRiNBlx/8jKvvPQWRS7xFxGV7JkFfRfGSCqIal0UUmBcAm2kMEZ0gHKt2FkvJ9OuUiKBhY8ZggJvErrdEoIiL4fEdkqkYT3cinGypa0blNK87/0v8Kf+9J/i3e99z7e1mzzzBU2/kUn7a7fe0MaIQdDxfzs6SiTKdHv/It/50feyNY7EtpanPqz1u4F3v/sF/snf+0d03vHqG2/w7nc/j8l7LFc1g2FDWeYU/T5v3b0HSsT+TiuOpxP6owFlmaG1GD4c7O+xtbVBVVXpTivr4PHWDpgM27ZU/SFL66Dfx4xGKCWuQUobNoYjoYbwuEhqYxgOR4zGG5T9QXICXxKQMayNDhdalHLUtmWzPKANmlu33uQrr36N6A0qZuTaoJTEK9j5kqocMh4M2BwMxbSh1+Pug4ccTWZ0SosKRMKlWUNLQYOpKmKvZNVZzHxFWVSAwkZPs1px8uiIGAO7uzvsH+xL4qN3qWMN8vsZLRdg2msRxTl8NZ9LgcGgQqQqKoyO1MsldWfpQqBvNNb5VPQVRnHmUi9+iOvFvzqzPVMxMhoNxeVHBYzWlP0++aqltY6i7IGKWG+x3mPynKpfoZWi7VpeevklrO2oqgEhSRa9nGLoxGvMsiwZFUuHcTI5ZTGZMHz2WbIiS5QvCFY69+gDa+XYOo6DiDyOkl2q2EEoJscnfPnFF9nY3ebJq0/y5ms3eOLpa5y7dJXjRwu2tgZ87dUvoss+589dJdITnbo2iZua4fICZxu+8rUHPP3EHrvbYwY+gVYh0FnP+fP73HrrPl1nRThwVqZkpUBI/pPaJOAscR7XlJ6okn3aGuRIf2LaKa4XjKTiqQxRO2ywlLoQPqqPBDKU6eF8g1aRru3wKIY7G3zkuz/O7/+xH+Xd73k3var37SsnbzvWZR94m+JP0SEO/8Rk7PwtCvg7olDGGLFdzfGDV/h7P/MqWewIvsNGQeggEqxlPplw/uAcVVkCMJsvAUVd19y/f0/MHkzOnXsPsM7ReoftHPfu3ufll1/i09///RRFSdtZzh/ss1wtuX3zBtVoJKBFUZDnBRsbG1jbkRlD9J5+WaG3dxkO+kLu9Y7J8TGb4xE6CAq8Nrj1zrJcLXHeUzcrgrXoqFOusseHDqU80XcoFXnz1i2+9vLLeAxaFWeL7raz9MuCD37gPeyMNtnd2mC0MRItt7PMVjX3Hh3x9TducOPuXYIT/0lixAbHeHNMNewzGlbExYrTkxmHywe0XSv+i96jtaHs9aiXK5rVirZtaJqa4B2ZkudSlTk60+LHCTRdx/17d4ltQ1H2CMqQ9/uYLGcxnVDPV9Rtx2A4Iu/1hDngvWTlaNYmX/i0syQqojKyQ0MlmZ0QxlVW0HadeGS6Fpt0yTL+y+OErsXZTox6vSPTEb3mX4KcN/Uc59cdaMX+7j4ZkBnpLi6cO4D9XfJCOqwsk9iI2FqyZCbRK3OCteR5xtopO8+NOIh74XQGY7h17w7ewAsf+ABPXHuK1lpUppgvW7ooz1XQjsCKvBfxNkKQDt5o0c9nORjVIzjNa6/fx1/fY3dnLH6bXuFtS94PnL+0w5uvHibfgUR/iTG54IOUhMcj9tqFay01FEMu6URtYg4YJUYq6cpMVUat53UCDqcMmSrkn31GmY9Y1QuIHaPRgJ/43/wZvvtTn2Q0HkiYXjp+Zyn69vWW3/g9hdSlsEpyzk+X7mzn/rsd75xC2azomhW+a4jOygWExmuVdK4iVRv2+mdoXlPXNG1HU9fMFzPmsxlN3QgymhmJeNjeYf/ggM3xmPsP7gtJWs4oXNvyX//f/x+czmbkWc6gqhgOB7h6xdZwzHAwwKbI1eAdV5+6zsJbQlWi25bY1GJQqhQmxjQ2elbLOYvFnHUmeYY4BoVkruucwjnP8dFD3rh5KyHm5ozE3dYNe3s7fPIT34XxkSxIpMNiOaPxjkcnJ9y+e5/T6YKi1+eZJ5/g5PCEyWSKj5GiV3L16iV6ZUaWZ3TOYYqSIjVGq8UM7zzewGo24+j0hNt3bgMBozRlmdErKjrnabylKDK2RyM2RmPqzlJ3nsJU6N6AS5cvYbIc23VMjg7puo5cGXY2ttnf36HslRK3Gv1Znoq1HdZaJEpCujwTfKIlaYLrmE8noJUUSic8znxNZFaPzZtVguENEpegEOJzVIq6bXn04AHTkylEKPKC3Z0dLpw7T5Zn6CBhZybP0CpDvC40eVaA0lgrF39mDFVViCQxWajlWjM0Gb4RZ6CYooAXixl7O5tkRDZHY8qiz8npCUZ7djb3yfKKg4NLvHHzdep6QZGXqbj5NB4KgzFoecwQC268dZ+suMiw38d2gdwqumg5uLDH3RvHeOvQqsD7FKGiApKRTgonU9hkTizO+I+LQlSJfG0kRVRknUIyIskatUkISPCoKK5IqECmAKfp9YY4W0G0tF3LL/7iz/Ou976Lrc0R8Ng4A/i2jt7//KHOyFE5UvyMhlDlrNpvvqR8xxRK17aAQutMfO+RMc2Qsjm8x3WWer5kOp2yXC5ZLpesupYIFGXJxsYGly5fYWtjzMbGkKon5GKFRIB626U9myQI4iPdqqaIiuO797m/WlEUOb2q5J7RvPbaawyGI7Z3tqmKHKsVV5+4zs4T14mzKa998Qt0zeqxScCZEcF6TIuJhK6wXjYjmSrpGmhqx2R6B5ynUPnZVd7ULYOqx0c/+hG0MVhrhd/WdUSnmDcrvvbaG5xO5lgbQC0wWjPsDzi3t4/zjiuXzrM5rBiu+Z824JRi5T3LuiE40cW2XYspCqrRmI2NEQd7e1RlgbMdOiqMyVg2K5bzOc18QRwoJpM5wUfywvChD32IrZ0durZlPpmgvOPh0RHzZU3eL9nY2abIcwwhjfROgrdCxDoJKWvqlbgeESXnJspyzXedjIZRIkvXQW6cFUhQ6cJWUYEKac8kctIQIqcnJ0xPJyjv0SrHu8jR4RGDfsH+7g5KCe0pRCmQIUJZVOR5AcHTLGdEX5PnFXkWOV0usEERdY7REpUxW9WEKKsDguLKteusVjNeeeVVnn/2/ZTVkH4Qg4it/oBoFUU2wHaW4Dt0EQgYcXlKIJlREHSkU5GgDNoXvHnzAe9/95NUVY+29XSuYNCH8Waf00crNAaPkf16BJK1naw1OFO7qSg0L83bzHYRFkIIazWPFuQ8SRj9GhVWGWsf1/VLEWIUJyCVEb087t07d/jJv/xX+BN/8k/w5NPfXPHy7TzeXqIjiBxSKVQOZf5vQUeZZB7ygmlxHgl4OmtpVjWzkwnzyZT5dEabOsbcGDY2xly4eI6NrW2G4zE6yzBGkYnvL857dFQUWYYOj4nONkqmR+sc1jlijMxmM7qmTYisODQXvYqyX9F7cI9+WTLsV5RVn+ZXfoWNquLSxfPymJrk3LLmAwI+Jo6lSMuKLGd2ukDrkrqxrBpHZz1G5ygEMdZKEb3lXc++QK8oCC6kjJwglmZacXgy4WQyT8IPg0jOAvO6ocgMG4M+m4MBO6MBVZbxoG5wzrGoOx4dHxG6TuIDnGcw3qA/HNDrlWxvb7ExHtPLc3Y2NzHaMFnMGHcj3OYmOI+LkdYHlqsFx0cP+Z///t+lqgZcv3adfn+A0obt3V02dzVaa+q2pbNiRJxroRn5pOIwylDpnCIrabtaFDxNKwmXyAipQmR6OmG8uYk28hwbs45RTf6KKl3qMT42nUiB9l3TihbcCCChlEMZzelixtbOFsSkBjOGGCLD0YitjTE6eFazKRe2Bpx795MpLVKzaBwn0yWPTmeYLNBmkVV0NEQUhlxn7O+dJzcXiBiOFivUoE9sanztMbpgWjui7pFlJbPZhNHgQMAm0QBKsYlCDo9KdoDRR5bLJW/desi1y9s0hcHaDJc5tvc3OT6ai9SQKGqnqPHey/OdViYxKDHVINkDqgToBGFKmASmibGxFJB1OqZPjuYhRkwmIGIIEjOBkbk+NyWt08JHjTCfzPhbf+1v8Cf/w/+AS5cvpa5yveuWQ6b6b1+HueZYrPeWkbV2fB04+Lsf74hCKQUsEWa9ZzmfcXx8yPHpMaenc7pWrO7LLGNjY4PzBwfs7u0yqEqyTMuaOi2llRKzgyxl0DxGAxU56syiKoRI13XEGKjrmgsXL/DgwQOuXbvGvbfeoiwLMIYHjx5y6gMbwwG/+Eu/xIVLl7lw/gLtbM6lCxcI65AuxG7f20BURqg6aa+jEJVL3Vh0YWh8l/aaOXmWA0EujAAH+/ucPziQ2FZP0lhbdKZRmWZyOie6iLOeGDQehzeRwsiif+ZnvPL1BbgW5zsaF2gDqKJic2uDIhvRLFcYNBub2/SHAzKtGA8HxCAGFvPZHJUZjFbo3JCRowvpesdbm1x/8jrHJxMWp1N0iPR6A/bOHVBVJcYYPEoyZ2zyDEVszLLM4FUQ9/Ta0jox7iizCmNyXNbHdR3BW1m/KMfu9pZctAp0Zs6ccQiKmECiEOJZ16RRKG1QaHJl6GkjQV4qkmWQaeipDF87jqZHDMdDev2K7e0t9nfHlAoy53nmqSvs7Wxz8+5tbr11i85anAuc297l2hPniSZnoQzziaUIEZSnUCV11xJzg2tWDMcVP/wdn+Zv/KW/zu74HKsusAwSodurRkymEy5eCGijzjh+XkHrRSWlQiQLGh8MLpbcun/ChXNj+lWGdYbOGobjgSDxgs8IcEhIMnh5XJ9WjTpoUKJMI10TUQsDIVOSZSRtvGjofRDyeJEJ4yEAOggbRCvxeSRG8Gt9u0ZZSQF1XcdiOudv/OTf4Cf+w59gb3/v7Rf8t7VAnh2Kx+UwrlOzBPUO/NvQUQJ103Lv0QMOHx0ym57SNrV0ZMEwGoy4eP4Cly6ep+r1KIo8hYYl+/kgFyOKpF/VoDWSOigXj9g6paD4kAjeq5rCGI4mx+zt7nFub4eL5w/Y3xqzvbNN2a8kT7zrpPBqTeO9mOfGyJs3bmCdZdCvyI3BoeiCIIwxjeFrQ0BtMqrRgMY2NG2LE9iUmBlsKiqZ0uxe3KXIMpF+Rc4S7YiBaCPz+RKURpvECwuBaB1NZymyDGU0OuYUZYmOObtVj7ysRJesINiWS9ev8uS1J3BOHIMyFakqyfWZTmfUTSucTwLBdinvW+Cqza1txqNNdjY3KJSWLG6APKlAYkBZGbONipgiJzNG3NiVoouO0mR0eYkuWtrW4juHdlIEy6KHJkCyDnPOJtMPGY+FV+sxOhUBHwhRYaPkjmdak+UlzgWKomAZIzEoqrIgw1EYzbBX4n2HwpEry9WDcxwc7GBUoKcUT119mrBcMRpUPHH+HO954hpd23D3/n0eHB5x8ugtWuvp9be4urnB4bLlpGnxaKxvMKakaRt2zm3z3g+8m2tPX2P2cEHMoNAFrnMMR1vcvXeDullQ5htn3MWgRSauUJioiUElJyODCzl37k+4fm2PrG7JM01Z5WDWckVxX1JR0hCT8Cm9QIqzlL6EcIfEAiBqlM7kG8eQ8nBkZ6wzjdYSwOZiIF9H5aWVhYmR6EFh0CojRosPnqauGZQVJw8P+Vt/XYrlcDxO315949tv87GmC8mzLO/rfxs6ylXd8Ou/+VmmizkuOPCBIsvY3z3H9SuXOL+/T1mUibAsG5W45nylW7FZG+kh2d6oTFQBCKk0Qy6k4OWiM8awubnBxsaYJ69eZjwakZlMtrsKOVmcwxhDUfQBAWtU1LgYUS7QuFZI5iEXjXcING1LnmfJmNRxeHzMbNVy8coTSX3kWcyXRO/ENFhHOu8I1pMpGPeHEMVuv/PiDt46L4Vea+quw0uLis40tAEVwXnp1LTXEnpmSkyesagbdCtuQEWeMap6nN/ZpV9WrEJNZqAqcspcs7G5yRNPPcF0ueL112+wnM6Ss45INfMsp5d2jiE6br11l/FwzNb2FtjkFRk8xgkAte4EI/I7zudztDaJVhPIVSSrckKe0XUeY53QNbQiyypROylBppXWAtik9UYMIvk8o7WkfPEsy8jLHl99+WW6ELhy/Tqz+QzfNfRUwXuevc7l8/tsb2+yfbDPwYXzlFoK891bb5FF6EWHqip8XZMHy9G9I6xrqcqcp564ikNxMl2wajw2QFWNORcUs2XDzMstptzscWF/m8V8wie+7xP81E/+FEWd0etts4qKqjcGMk5OTzjY6wPFYwMLYrInizjvxK5NaVA97j5ccHBxh6zIMaahX/Xo9Qp8E5NkUCfOqkZFIxr0mDpvFVjbQagoRr76zEsnE47sWqOaXrcQAzGKvS/aELXBRXEoX8sU8UHayyishq6z1KsVC50zHI95842b/O2f+tv8e3/8j1FV1dl1H89WJ9+mYw3ir99Jx+/lJ3hHFMq2bTk5mRAJVGXB1csXeOraNXa3togmprD1xG1D4Z2w1mKilGhl1nMuShu0NpIimMZxEpfO6IxBT8ZdVeRoo5P9lhgiONfJ+KJUCntP6XXI41gf8ErWMiqkXJDgxCxCyY3bOYtzHZPJCU0nRhqb23tEHWm7hqZuqJcrMm0oMyPKmiynqR2taymKIoVABbzzySXaE5wiaCm0Tz/5JPfuP2C1ajC9gq72ROVxVnLDnZfgMJRmOB4RUUxOj6nnU2qjqYqSqj/kay+/zPVr1yh2NoleUy8WeO/Y2t3hkx//CNPTGUdHR8zmc+aLuSQvmgwVFTZkLFvP5m6FNusdcMAqhdOKYHRSZ0ix9C7wxhtvcunCBbY2xmc+njFGTF4yKEuc96InDuIEhFYYo8l1Jl20Ei24CBHE5Dkk4CyPYreFitx46xYxBF544X3isqMcxjZ85N3P8dSlPdrVKYPRgEznzO7cJOaGG2++wfHxEd/1ie8li4rZfMXk5ITJ9IiizBmNRjRNg61bFk2HNjnaJkcdYKPX52B3k3iww6OTY+4dHvPX/qu/SOht8/R7PsRq2fHma7/Ns0+/i6wYo7OC7a1zWCsu7lqLw46KaZcYxRjZE5NcEpQydCHn/oMJl/ZH5HlDnnt6VcmyCQSvkvZf1hwxiNGwQhHTDjSxhuRaiTJaayUF7qz58LA2RkGtCXqJg3mGmMcztF92ncJLVAFcJ/6cy9USlKIc9fnC57/AxuYWf+AP/gGMyVIz+e3tKNXbv9+3kuL8juMdUShjjAwHFU9cv8ZT164y7vcwKcLAKQnPUiEkU9Rk/xQ1OpmvoiFqGTWMEduszJjH6LOSk0GItFFCwlyD9zrdqcVPUq9hQJXUoAkFtEqL1C39rBqFCqmQhYjKe4nuElitlrzyymuYMifr93jj5k3y/B6f/MQGbdtK3ESmyY2hMjnaGDpt6DKDdQ6M3P2j87hOindwnqgFicy14uTwEavplNo6dH9AVvXkbt7JyGtjIFhF1yhUpijygiIrROcaPTfefIOTxYLn3v0CGzs7dN6hOocPwt+cTU7x3lH2Boy3ttk+OEBrQ72Yo7xDAT4Gtne2sD7QdQ1tbUU3HRQtAhZkWqSACo0ymmeffY5eKRsh56QLdM5RN01yEBLwTJs8jY0SvRqCPI7RIXU/Mjw4L8+LgBMC5pyeHnP/zm2eevIpwmrGaJBz/coFnr1yia0sokLN6MIBx4+OOH14lzKvuHn7LV67+Sof++hHCCEyWSy4e/cBPog7kcky7j98RK/qMxwN6YWA9Z7cKLIqZ2dnh0DGGzfu8ZWvv8rD4xNal6PLbT7x6e+np3e4cP5pjh6e8trrr3D9iWfIizGj0TbBe6wN9IpEvg9a7NPWnbMgm/JWS7E8PlxyZX+LLDOYvCArczw1WpcyqiNgl8RdeExQ2CBjvZQH/TbgMbEGEiUoMdoSyBWJRskePLg01gdpTJKJMSo1MSGgYupevaZeiVlyiFDblrxX8k/+4c9y4dw5PvJdHwMlHpjf1mKpftd3vuXxjiiUg0GfT3//9zIYVMlxO+DgbLQirP3zND7Zl5mUy6IIYgqglGQlG0NhZJ/o0uObNbUEKXYhCLE2pFEhqJSVrFL2shZjgBhCOrn0maX+OjQrRokQKHp9impENB3WNhwv5tw7OmQwGNIeHWEby+x0wasvv8b2zhZllVMWPfJcfBdDDGd6W+8d8Jgn6J3DWoe1nmg00WgChrsPHmJbifDCNCgj0b2+yGmbBksk847cKhZLzaBvcC7gmo4iMxT9EafTOb/6K7/EqF9x9eJFzp87x2C4SVGW0kJ4x3RxzO37D/FIh1JmhqrIGI76lP2S4XBIf7QJWc58PmN1csxqMsE3KxbLFbZzidajUDhitBidU2QFqECRKbxrCd7jOs9iNpFUzcGA0XjEsF8xGIxQWYEPIrUjCugXYkxJksJQiHiCa/ncZ77GlYNdntwfcWF3gyevX2Cz36c+PYHGkVcF7WzJfL6iv7HNS197iTt3bvPEk89gXcQFeOPGTTAZrW3Y3tjkaHpK1atwMfLo8EiKetnD25r5suHR8Ywbt+5z694xqyYnK86DGfDeD34X463rnC4WuM4zHAy4deMWm5tbjLcMRTkgz3MiBnHv0QmdFqfwoJVcBxKuI5d2UKway3SxEvd301H2c7yfEekRTYZExnqCb6VjT/lEKpoztHfdT8Uo0kSFkrhYJdkzUcmOWkUwMcg4n9B/lMJoMVbWwZPhcaFBxw6CFTMND946rOoSQh4oypy/+9M/w+UrV7h09dur+f5ferwjCmXV6zEeVGd7EeCM9Lq2mCeBFzrRSzIlhhlaa0wmHYtOaPcaGwXJSTkLcIfHHUhcR4U+XuaGEDExnHUnCimqa6RwbU8FgrTXnSNohwuRvFeR9UquP/s8N28/4NGDB8nUV5NnJfcfPqI/GFD1+mRZzmAk8bAxOKpBhXOBXi57InkeJErAeYd10lUZJbZnWZ5jOyc3A++kI0ZTVhWegHWO4BwaAQIy5zEmx+ucpe3IUOgsxwfPYjnj1dcX3Lpzh/HmLps7e2xubjKsSjKt8FE62Xa5YmZbopc4WqWTHlwrlJG8Z4PwPbuVOMkHFCbLRSfuG5596irOR+7eu4/C8+/+R/8Bk4f3+cqXv0zbWLY393nhhfcRIzw4fMStO3c5Pr7P0cmEzgXwMsaHteOPEgOIrq25fv0c73rmOj/+6Q+Te0dG4H3PXiZTsJo8ErR7POLerTvsXrjM/uYuX3nxq0wfHXJ5d5erewfcuX2XZbWFXazSmidwGk/pnMO5pXw/rSmKAuvhrbfu0wXD0bzh9oMFq2aEzvfp4ojx9g5Xn30f86ahsY68LNnY2ORd73ov2zv7zBuHc45i0EeREZLxrY8epcTEwiDxO5K4KKyGLBpsUNx9cMQz1/YpCsPG5pBb4T46S0UwmQcHaS0lOykm1/Ao/OQ1USaka2gdFHeGDEdJplQhPlbzJKmlTFxyjdkITjl8bPDakxnBx1WIBOvpgiWL8poF77C247/7b//f/B/+T/8p4zW482/B8Y4olHG9ZY2Jgxi1RBAolaCZtRlrctLWBoOW0S7T4ke4ptMhRTbGkMZt6Q7RafVCSEXv7VQBMQsw6W3Ac+YAjTyWUerMPzGtZLCdZXdnj5PDIy5euYzJDHa4xQ/96I/xlRe/xGuvvMSF8we8fvNNMTawHZ1zgpr3+2LwawNFVTEKmuVsSte18jMn9VHwMn766Ik2UBYCUulMClVQ6xFNdnu9Xh9XL3Fti/ViA5ZrTXSeXp7jjSDpwVqh1WjheNoYOZ1NOJlNMSZjNByxubHJcDgiz8WcIvh0o9Am8U0j+EC9WNG0Fuc9XbtiozCc399hYzwkOJF7DoY9PvWRD/Dil7/Icx9/N1WvYK8fUFnHpY0CW2k++B3vJssD89kC3Uxop49oFys2y5yTesHKiqtQCJHMiPyyMIbtgx1+9FMfITYLXrz5Isum5ru/+5OYZoElwzYt461Njk9OOZnM2NisuXHjLe4+uEfRy7l+9RI6BOrlEh8cBwd73L5zm6Iw7FU9VJ5zsliy6hwxwny6YFm3WAoWzvNo0bGyQ0x+nhjHLJqOj33gwzhtsFEI33lRMRof4MpNOiuyTq8ApKP0IRJxKapBvE1Vcm+PBJyXcVyHiI85RycznrgEWmnG4yFSImXmATEh8QnUcai0MhLWRyR1hzFF2q6nUI1ovBEusVDsIjZGVHIWEq/K5IoeAk7Jz+1iK0BUTOmKIYHsOuBcMg1BeMyvfP1V/v7/5+/xx/7EH/8de8NvHIf/zWDi/+LjHVEoSYC1kionC/vk2JIT0TqejSIql4Q/Q8oFTh3imjMW18TvhOOEEBOwseaOSTcC+iyMXie7pRiCdJzIzdOHwFnSSoxn/Esf5eSreiXf9eEP0TYNh6fH9HpiKxaj473vfz+XL1/kyy9+gf5wIKTpIhM9bZZJJAEwHI2pF3MmhxOapmXVrGRxrkFFkUQ62+KBnEIyUIyRNEbnIdFktFJ45zGZospLXACCp1/1yJRi1bY4b6n6PVCe1XKVDFnFnGJ3b0soHz6SaVFvTGZzZvMFxhjKPKcoJOtcaYUOHu0d3kVqF+hCpKsXfPiF5/j9P/BxSrsi9y3RdnRBzC2im9DzCy5uXuTRw4c8ev1VHty5x8ObN9nf2UE1KxbzlruPHnLzxps8e/UpNsdbdDEy3h5JpnN6FY0WICyL0t/P7t/lM7/2a9y+dZs/8u/+YbJoiE7G/UwbvId7t++gs5z5asHpfIKNns3xFsONMXfu3AEdOZ3PaLqOeb1iKx8wGvdpOourV2yPtlAm53S5YrGombct96Y1c9cjZDtov411msvXL3L5ylUWbQNRzDNcllH2B3g0obNUVYEueihTEpSR+FklJHMdZV0Rg/xdRfEcCETwDkcUldHJnNG4x3jQpyhzoguoJF/0PuBThEeMEnWh0u5YpU4yJgu2EDwqKDLECCMoyeMJ0QmfE86KtkRKCNUu1xk6BFxohW6VHvsxyJacoZwVdRaZMJuN4hd+7uf53k99L5d+h+3aunl5JxVJeKcUSh6HCZlMJxBGJ6RTCllQJHNV6fIyOLPNWifrSTfq052PpGlVciKmPGlS4VRJXigIqrygAtZIgdRKgY/ivK6kKPuktvExsKo7rl66ggmRjV6FH444Xc7JjJJIgdWSwWDId33043zpxRd5+dXXabZXlMZQ9ios0kkSAndeewPbdaDE4COEgNYKo0FHOcF9CGhrZeenNCYv8GkHpdL4FLQU+2GvotOaznbk/Yq67uicRxtD49yZE3XoHHkuvpJFkZPlBVmKfwjK4KNosaMPeGdpY6BpI97W7FYFH3rhvThnuf3okJt33+KjH3+eP/gj30ffaNzcsrr3iMXRIbbt6FV9+qMRsam5+fprHE9mKJOzbBuKXs7OzpiH9+6Q9Qe8eeMmOxubvPfZp1E+YMoclUeInZwbQfig0YLOMo4ePeRzn/kMdx884Hu//wfpVwPaFDFMDORln7t3brNazDl/4Tyua/C2pVSajcGQECKHh4dok1P0CibzJXXdsbe1RQRmizllVZIXYpsWV0t2BwNu3JuxWg3Q8TKF2Sai8XHOtaeviFIrgSQZhqqs0EqysFulKfICU1ZoU9LWHb5zxNgQo0UjMSOyN5RdvNFiuGKjxwVFDIH7R8dsbV9hUBUUVU6zSK5AKR0zJjmnbPrltQxBHMtj8OIzmq61dWqjThNcTP8mjYY5ay8CMVHfZIrBd6jYomKH0jJxRBWx0aITCKe0RgVFSJ6pRmfMpjN+49d/nT/y4z/+b4xP+S9zvCMKpVIi81PJvYRUEOX+pM+oCmvOlUZCsMw6m2NN7D4bI9aJIObsxVbKpLuUONaoECAZyQpQE3Fa42MQVVaMCTeIdClpLyYgATTzxZKLFy7KaJ6iQ8UBWtRBd+/eoZ3P+c73v58f+tSnGQ02+M3f/k1mp6e8/wMfRAGL2Yw3X3sDt1zhEb1614mywRiN0frMml+FiI0+XUDJTAJNcA6lAw51dsPIjKaLkJU9IR93HQTR7OY6J8dQFQV1s0KhKPKc1XJJUXrKvIfOc0wWkWz7TAqT9+A8ne3YHPT4E3/oh+jnmrduvM6TF6/zAx95hiff9RRl5sFaiqKgqSqy0QZtnLNoGkyRs71zwNdeu0kb4XzIWbSR3nCT49mS4VAz6FW8/uqr/OAP/gCdXdI2NSPdk9jR1G4EJ3p6U/aYTU55+eWvMhhV/Pgf/UO0y5pmcUpOIGrxeWzahru33mQ07FMVGcumRStNkRl6eUnbWo5PJly+eg1lch4dHhPQ5HmFbx1VVlBkCuc6jian+Ahlb4S3Q3IuotU1ah9pukM6teLg4gFd58Uk2Rmiz8mUIuuV5PkY22uAiCl7tF2kszVd3RFCjXNzumZO8AGtM4aDMXlRiSxTWVz0+CD+N6ezhaDvuSYvMxYTOW/k/A+Q1k8Kc7bjVCkp0q8pWHmOT7tGAd6SATRJxw3pmpJ1lTbqjH3ig8PR0YUWr9L5H3W6JtI1pEQoQIw4JwmQ2hl6ZcUv/eIv84Of/jRb29v/2mvM/9LjHVEoSeOkvKjxLKFNacRs4IxK8DiOICLI3Hq/uH5BY9R4JUg1ShGikcU0opRxIeLXToQJXZZJIhKdT4VVnZkYBpD9oLNCjdAa6yA3GQcHB8IjyzRL24quVvzC6BvDS1/7GkbBhz7+cZ585mmGW5v8/C/9AtOV2MN9+YtfQVlPYTIGGyNUrpnMBS3O+iUqz8WnEAU+iJpHGwo00XWyq1SGprN459BFJoReA8E7Ll65TrNaEpyjsx2N8/ScI9d9iqqEXGGtZV7XrOqaIs/ZHG8yHm/QUyWFtLQEFRkYze6ox7C3wQc+8C4Ohhm3b91ienqMipYr+/vUb73FJHiUMZR5ycMHJzT1Sp63AE0nJHsbZKRztqFbLdna3uTBvdv0ih7To1NODk/Jyenqhrqt6ZcZWUDURUZSLpXOaVY1b7zxBm/eusWnv/9TtPMFXS1UFGcQc+Ui4/Zb92jqhr2dLXFtjwbbdRS5ppdpltM5zgb6/RH1vGY1m9HrVRRFkc66SIiwdJFZ3bHRG6HpoxjgOces8dhmStMecvHpHbIsx1uP8gHlAxrxhMxzTb8I2KwgRk1rHe1qwWq1YlVPCXZG8HOir1ktW1zXcUhkMBgw2tzBFH20MngtphSrLnA4mTMeiAHKSZihzZr8I3tGo8Arif3IQ0euS8QhVdx/xGQ3FVBF6mA1JBMXiXLQ+LDOL0yRskGSRINa4UOLMlpEHyoBoKgkeYToPN5EMEaSPkMgOsXxo2O+/OKX+eT3fc/6Uc9G7ndaj/mOKJQKGaNNInmrs8wBRZHGaH92yqbGIjWQGnmB11QgnTpPMYkFkhRLONxeTAC0xoaY0G8ZF3yU8TZEsdJSbq0sUbJ4tykXRWuWreX8+Qv0q76QnLXoYoPOWNolbWd54V3Ps6kNv/XVr/CPf+7nePbZ59ja2eFHft+P8ujRfepVw/UnnySkJjXTsFouWK6WzOsl5aDAFJKu55XCKXHcDs5SZAYbHEWesbezTTkasFzWHJ9OxEChXlK3NWjPfLmgdh5vNM411J2nZ3v0i5LcaLxN8jcljueHx4f46NktdjEukuvI0xf3efLcPhs5lMbRY8bxnSl9rRn2chaTKScmZ5lnnCxmKKDICpaLJUcnxxwc7BNsS1mWwiXQCus9y3oF1jIsS44Oj7h2/Ulu3blHVMKDJSJk8yjjHwDeQ1SsmpbDo2NeeeU1Lu6fp58XHJ8eS6448pqFCKu65vadu2RFQd7LQFlM5nG2ocpLev2SB0dHFFnOxnDMnYePcDGyMRpgdGTRrRKwYqjnDTiJymjsgHkDXafx7QJtZ8RuycXz78HaQNd5CA6lI1kaufNMRlHXRZrGUjdL5vNTlospTTPHdTMUHXlR0ev3sWbFbHbM9OFd9NF9Njd3GA020eVIXIUiHD2csPvMFlvjMXc4JVMRF7xwGpNzJmq9MooYJSOzCj6tsdJ1pB/vLJUS71Sz5vSGgCJDqUTP05IEGmiIuoXYQZAYCAIok6ZBJVzLEIWNEtP15oNHeY0pMr7421/gE9/9CXSWnU0M78RJ/Fs5oH9bDqUgS/EK63FTa6H7RCUvoE6fUygoFEjclOxXxN5duJRRrWmsMZF4Qlpix7NFtPIO4yPGSydpnWiZo/coHyE4UB6iw1sxaRApl4wsTdvyxBNPys+dZRiTUbcdaEnk62zH115/jcHuLj/wIz+CUZpbN26io2bYq9ishijn2RyOGG+Oyfs9kSB2FrTiZDYhqECWKfIiIxoBsgIR750YxrqOj3zgBbLg2R5vsjkeo4JnfnrM3u42z77rOQbDDT79g/8OT1x/mswo8iwjBMVy1RCJlCi2B0Nyk+G8ooviNnM6PWU1nzAqNd//sQ/yh37gu/nQ01fYGhbUruHo9IjTw0fMTo/Z6FfoGDg6OebwdMqbt+5yMpkxmc7QJsf5yHy5ovWeyWolI5gX56YA7O7t0XQd01VNtbHBvcNHVIMBLojSKHgvDuPI+BaaDlu3TE9nvPnWbdqm47mnnqWrOzEQ6SxN29K0HdY5JrMF8+USnRmUhqzQZAaKzFBV4htwenLEoNejKntMZjNUkZH3CzrXoLyll8tFvFysQGc0pseNR4pZ06PrHFlsyWnQeKrBkEVdM68bJssls+WcRb2iCxEXMhqb0bRip7dYzFjMJzhXE4NFA0VeisEHkJUV23vn2djcI3jD/bsPeHD/FqePbmEXx8Su5fhkQtNa+lWBVl6y1r0/0y6LgbHE6Xql6FQg+A5YNxAR5eVcT7o3vLOsF18qCo9Tr4UZSeMdogPtiLSY1L2nrxAkFJE/BgSl9dGLQGDNaEd6nZs3b9I0zdn779TjHdNRrhk+KtFw1uiX1mtKTmrO07uSKK2ShCotvNOtyCckWyUKS4jSKYYQJXgpkZeFqLzmbQaJCgiBkIhEMYi+24VI8EI7knCiyNWrV9E6AU/pTpsriTK4d/cun/3MZxgUBe99//v56Ec+yvHhEePNTZSKPDh8RMwybGfROuf05ITl6Sm9XMwsFqsa5z251vR6BdmykTtakLzwmGVU/R67e7vcunuXr7/0CjZYxqM+P/C9n2A0GHF4MqUNhkuXnyTUE/pqwWtv3KI2EPFUQ7H/CsFTFDnBOwkBa2pUFnnyYIcf++Hv49n3PEPmOrqpYaQv4HSBr1fko8DJ6Smxs2xsbnAymWFti8oMD49P2N3ZReWRoj/g1t17XLpwjtmyJmQl9x4dMtjYoGkaDvb3efnLX2bQG+A6z3y5YrAxIsszyqIgek2ucwjSJdrOMZkteXA84cat22yOt2SH6sTZvLUOZQTI0J1jvmqkkzIZ0SlUyPHe4QJUwwFBQxM8g+GYedOwWi4ZFgVVZjAEchS9LGe2arDO4o3heNXj5n2FdYbgW/ANIToa7+hcJK5W2E4Kp8o0/f6IvFeyWDa0baBtLMvFksl0StM2IttEClGvV9E2Dc5aBoM+Os8oioqN8T6z01NOpoc0qyPsfEZ/uEne73M6PWW8ORKfhBjEkzKZYri45p6GpJsXSacK0tlLJMZ6BSWmvEoptBGcmoAExGFSMF4yzIgBjSNEyamHtaZDYoXlr0m+GiMZ+hvaMpnSFZOTCXfv3OWZ5579/xfKb3kkKk+SBst+JSFxnO0mU4VMiwyfuJGiQZUiKZ2/jA82SsofCM3Gh0QVilFUCyhcKokuFVKCF4Kt4swU1kcvXxtl79k2HecOzrO9vZPGE322FvDB03lH1eslPTi89vVXWa5qLl27xsnkFB89K+/IhwOWpxNi03D66CHvfu4Z5rM50+mUpvMs5ivGgwG9PCM34u7tSDJKpcj7fV586SWWtRTRZ599F+95z7sIXcdyuWJ/f5/Xbt7ky1/5DTbywMF4QH71gJdvPWDWeTIlgE/dtBRVH93UFBm8+5mn+diH38eHP/xBDs7toGNL6BratmE6mTKbLmmXS3SuUHlPlDIaNvelaJui4OjhI7oA2xuePM95cHjC1tY2o0GfxgaWq5YLl7dZLGseTU95MD1hY7iFdQEXAmWRY50lRk9mJCYiKk3TeaaLJUeTKYenUx4enfDU9etY71HKYEOkC5E8hjTiRRprsc4L+htzDD3wDmNydGGwwdOFwMagz9K22LZhq79Bkc5HbXKUyVmsZjS2w1SbHB6VLCYZ0XZAi4+Ozlo672htR2jmGCzPPfs0/f6Qh4enHJ3Oma062tbTth31ak7dtgkVzsAHlC6xXcC1HZOTQ8b9S/R7A1Q0BK04dzBmvLXLfHZMPT9iMZ9i6gWTk132di8StcJ6RcSglEv2hVo4xdETtLgKGe/JjOxfSbnfEcT1HjEl8YnHHGIUHm6AqOUaCa7DZA4XGjxOrimE97luFx9vHNX6Ek7gzuPGJUeahbfefJNnnnv221Rs/tWOd0ahZN1Nyt+N0smyTuGSV59JhW8dAZApnazy5WtiCMmXUt4PCaFW6a33YnKihEMktlxI9xlV2jH6pPxJagRiIASSxZgU6raped97XwDkrqsyzXI+palXkBxuDnZ2GQyHtE3DZr9iuVpxePiIYW+Q9jiBMs/Z297m9VdeBWc5d36P09kJ89VKxu+TBeNSxuJeUdBmrahdfKDFYbThaDKnlxV836c+xm9+4YtcmS25cv4Cs9mb/PTP/AzT6SlZYXj+2We4tDVi2C+5emGXl968R46i8wrXBTbHJe//8Id44bnrfPB9z3D+4g55plH1irha0M1nTA6PWc5rutDSqkC9aIjR0y9zQgy0RBrryMjo9Yccn07J84peT7qak/mSajCiW7UsW8981fLW7dtUvafxPpDlJT5EirxIDjSdjKJFQdN1zOYLFqsli7rmZLni9r37Z8/NYrmgzIuzkC7nY8riNjjrU1Jm4tU6iwqOcb/HuOrj6oY8KmJn8bbFBYcpixSqJqbMq86yajtQOXU74uhIQ6fQ3oIKsiYIHcQWHTqOH52yf7BP3QYeHD7kZLJitmzpvKdtW7qupmvn0pVJzisqT1prOrIyJ8/h1a9/hatPPE1RbeCsTmFqFcOtc1Dk1KcntM2Ct268yoXzl0ArnA3J9iwkUxJQ3qJjJ48fPRmazGTikZw8FEKIYFTKcJeddUS4k0EZcW/STjKfsBA7XKhByw4SLbtNndZbOo3g0atkdagfT4uJ6xx9RGeK6XT67Ss0/4rHO2JHCZwBOI/VL493GevxOKa7UkCJqiHGM2WBI3WGQRIEdRTwwyfuGDJV44I4nIeYwBwvbt+4IM4oXrhrMUjbqtLJRBDD2ExnXL50RUw0jEgn5/MZi/mMdrVEOc/GaMRHP/pROiedRtXr0bYti+UCY/SZLDOEQFmVHFy6ALl4/3mgc5HTyYIQIDMZVb+Sr0txES4EfFDUdcelS1f5sf/VH+Tpp59h/2AfTOTnfvHnmU3nZJTkjLh154iH8war+2wOt7i2f0D0lsZ2jDY3ePqZp7ly+RJ7u3vUq5b58Qy3aLCrmtV8wcnpjOl8hfdRAsyUwvnA0fEJddvRtB2HxxOm84a2c5RFj7brODmdUHeWoj/g0ckpS2s5PpmiTcHD0wnzVc1qVaN1xqrrqLuO4aCP7Vo620GEo6Mj7j94wPHphMWqYbpqWNQtxycnjAd9CLJHrlc1PrEWVIrbVTHiOyuve/ToLBD8isx4tkZ9tgYDZpMZRV5SaE1wlqDECCLGiImKMpM4jq6zKNXjdFYxXQhAIjfYiPcWFTpyLFns6Oo5Vb9PawMuGFoLbedpmxWuW+LsEucaYrAQRGabmYyiKNGmJKqCsj/E+pbX3niJ6fyQuj1hVZ8wX86oO4/KR/Q29siqAaenp7jVkjzT+JC8UJXsDcW/04o5RnQUIdAzAgRplxDo8NiEQ+QWgRCcUIV0TE2hjNmSIeRRxhGiPWs2RKQRJb/9TC/MGWMveLk2BdAJjxsjBcdHR4nuFn/Hn3fO8Y7oKCNSAA3qTEUT0zguvZw49ggFUj8unOtPILleK7kZnhVa1qanIT0OAgwE8U4BEZDF4FKxVWfSyah88o9EDAtSXvP5SxfY3tyi0JLhbLRhMpvTek/WdayWS5S1XLh6hR/5sd/Pb/zarxJiYGs8ol1NGO5ts93vs2qWtG6Fdy1l0ePhrdvMjo6I3rFYLpgqJMt7VNGrCqp+j1XT0nSihimqkl6/T9nf5sbNe1y/fJWdzTG/+iu/wvHxKUVepYSNltBF7t95SHH+AkZpNrf3aCNsn9/iiSefY2djj0fzOTc/8xW0W7DVK3juycu89/nruGXDYtGyahw+eHywNE1D5zzO5Nw7mbO9tc2ynrFatcTWU/Z7mCyntY7lqibPS05PF9y7/4i2dUK7cR1FmdP4gKpKat/QhZaNjTGz6SnBBmbLFa+/8QY2RDa2t4kmZ9lYjk6n1F3HdraJc5FSyWgaYyRDXMKNlsxz23bkKRoiAtZZyDT94ZCj0zm1DeIIVOZrRz6ic2QqUOQKnWsBo4KnY4sHR5rgCnwUwxRnLdZ2ROdorKcNsLV/gMkLOiuSxyyLGNMRmgXBNwTbgG+TyKHAZAaT5eSFwXgtIGJvwPb+BWLo8HaFtxAxWFoyV4HOiDEj72/SxcB0tqAsClZK9oOOgNG5pIRaj9GB6CKaDLQmBnG7IhHOtTFAwETxOQggGn5t5JmLDhU70WxnHhsXgF8vJhOAkybBM2CHJP01cj2vaX9ptRWVEOJv376drObeMX3bP3d8y0KplLoM/FXgADnX/lKM8S8qpbaBnwKuAW8CfyTGeKqEFf4Xgd8HrIA/GWP8wjf9Hum/tRHv2q0c1JmiZk3+XmNjKo3IKsSzghqjePeFRP1B6aSXDrKIjvI16+8YY1o+hjWZPamASIttlcLSjXz+qm25/tRTRJN2oQqU1qzqBmUyGu9xwNG9e3zmM7/Bd374Q5y/cJHT01MGozHDzS3m8xWXLl6g7WZsbD3Nay99lc3xPg/vPqKtHZcuXRFU+PSUw5MTzvXPk2UFvXJI2YP++CLD8S6D0RbVYET0gc9/5Q3On9ugbhRf+NJLyfTWYLJIVJ7WOlh5Tleeg4Nz7OzsM9reI6u2ONi7wqi/zen0lFt3vs7xo9eZz6fc/JXP88WvfIVPf89HWEyXBOtpbY0LntWqwQUIOuf49Ji8P8SicAFq3+GVouoPmUymFL0+JiuIGFZ1R2cdSits19Hr5ZycThj0K2aTCcvlkuFwQJHndF3HfCVo+XRVY/pDhkMx8pjPF5iihDyndoE8Q1YxPoKJ5ImTa62TTjBGdAxkWX52wdoopsh5r0cVIlVZUPtwptZSIaCUwUc4mS7wlEzmJU1XSrRENHjf4V2DDh0gMbfL1YyL158SlHy5pG0DwVs0jug7unqF8y3WtyJBVWDyHtpkZKZEmQyjwbmWohqhosW2LdPpDKNyTF6gfURnPeH7oinyDVYrJaFovsYojWMtgxTQ0itAaXpGdP0R6QTX+g6ISaYrnbI2GeJmBEY5om/RymNUwNMRfYvREZKE+GzOXxvSrIUSpGTIKOqg9S4zrJVAWjOZTBNF7fdSsv7NHL+XjtIB//sY4xeUUiPgt5VSPwf8SeAXYox/QSn1nwH/GfCfAj8CPJ3+fAT4b9Lb3/UQOVTAJx/ILGmv/bpzJJ7ZPK3pBQLMpA4g7Tx8lD9iXabO7NkkHTE5Yafd8tquiyh3PA3oKNJFEwTkCRjQnoDQLZTSPPvcczgVMEVOl8b8xXyBUuL8HLVmUFUsJ1N++7Of5dl3v4fn3vU8RhmyssQGh1KKXOe4uuU9zz3P7VsPaRwElXN8eErTNlS54Xi2ZLMN9IsBB+fPsXexgnyMKTcoeyOKXl9iYiePWDnN8nTBk898kHt3b/Po0X1CtCjTYzDa4tr157h0+Ul2ds8x3tylHIzoVduUeR+tDaN8i+uDbarxHndvfZWujbz46iu0XcvHP/ACy0WD9S0ueJrGEULAaY0yBQ8PT8h7JR0wHI6ItqMsSkCzXNWUZQ9txHHHeo/SBu9afITlqmE8GGFbz2pRc7C7Ta/XJ2CorcVUPWbHJ+SLGeNeTqUUblXTzwpyk9Naiy0MGUZyhgioXApqax0uyMinU+fTdB1F0SMmCzNixFtLyLS45ADeJwWJNlgH1mnqruLouMC1BTatcKIPRN+iQovyLSo2NKspJoBrHSEa6uWSuqnF29L7xztzF/FOHPEHIyPekplGqxzo0x8EiJHVckrrOjA9EQW4Dh0gKyJRZaDlHF/WtdjBexm7Jagu4oOTDjEqdJbhVExjsBbz4TT+rg0zlMkSuCPEfJAESaWEl6lNwMVmPbqlC1iddZRrPfgaodVnvrBiDExKg4yJQZJrw3IpK5iyLN+xxfJbFsoY433gfvr7XCn1MnAR+APA96ZP+yvALyOF8g8AfzXKouI3lVKbSqnz6XF+90NrnFobAQiRPJCKYowQ3+ZsnYCYmPaMAIk59Fi1EyQrRT6ekueiP2sqxS1bvq8KoKKX3Gi17ig0ay8hrcWS/9y58wyGYz7/hc8z3t7mA8+/B+c808kM0+8Jzy/A1u4u/84f/kP87M/+Y774+c/zQz/0o3z8Yx/hlde+KhZwHhYLy8//ws9hU+HBGIiaZrEU6Vg1YOVyVLbNzu6TFNUWQQ9wqo8pR/T7G/R6PaKK9Dd2ufPm14lWceWJ97F37gkOH93F+5b+cIvBaJ/x1nn29i8xHu+QFZV0f61j1czJtOx9u2jojy5w4bLhvlcs5lN++6VXuX71Kpl3CdyKGJXhohNStdJY58T5e1UzqipGVY+us5RFzrKuiZEzaZ31bg0nE4Oi6zxt58hMycnplP39ffrDMSTD36oaQtQcHh6zVfWkK/QenZUQwTpHay1ZSgeMIZ45w1sfiJkRpFcJ2dsCWZbT1pbJcsG4GrGMMwHxCBSFXBLOe7TJWaw62g4m05LFYkB0WZpgxLAkhBYVWyCFqGUFhJzVssXkOVlmCMFR10tBevNcHJ9chg8OXBBXfpOhkuTW5IY8Kso+BDKyckjRNDTFEh0smc5RSkZindZUs8WCNclbRttkIxijxIYog46ywwRN1IZARtTye+QhAkact1Qmo3dMCaDBYzJEEURHG2vQyTOBIOoxzRmQs85al8s1oMkSh5mzlRjq8fXb1jXz+ZzNrc2zneb6mn6nHP9SO0ql1DXgA8BngYO3Fb8HyGgOUkRvv+3L7qSPfUOhVEr9aeBPA2yNRokCIgqGQMRHcYRc10GfiqaOj9U5MQE/Yogh3YCOnLX24W3dY0y5I+uhQxB2GRmCIpllRBwiFfQoYjKdzRQs24annr1E0e/xi7/4C5wcnfC//TN/lmeefIqua6n6PaJ1qBiY1zWqV/KpH/xBvvjZz/Fbv/Kr7I42KAaVcECVRhclTz//Xu7ev8/qwQMKYzBVSeg6YjT0+5tcuPpuhltP0R9foxhsgKkIsaTXH9MfDCnyDOs66rYmqgIbWsq8ZLA5pBhuEoKlrMb0qi1GG3uiTyajayxN02Fdi1YdWgVQBhc03kHUJRt7F3h0eJNYVLx68zbPXbuMq2vRjDuPj+Ly7Z0nelFqaJ2xqmvKWGCdQxlFiB7nLVmWJdK+8FFtiORodAwslw3D8QazxZLj6ZyNrZ0kE9BkOmN7c5tbd+9w88FDLp67SD4YCJWIdfxwxKZoYpQSlHw2RZel3Ai1lsgNK7SgiKaqBviTOU3XUZYFRVHIfjlNNUSxMGvbGmd7zGYF3peJU6jQqqH1HT5YTHQoJaa/m1sH4Epwcg7nvYzN7S3CJLBcCIjogpzjaEVWFNLZUaBUIf6pOqa4SI0xEuerzYos76Nw6BQ61nknXpXeMJ1bbBcJSuJJYlpdhaTOMWi054z3G+PbEhpjPLuRrFkksgULeC97Vhc0xgR87AhYiQFGnRXLdZCfWo/xqRCuW5fH2ThrKpG8VmtwZz5f/MuUom/78XsulEqpIfB3gP8kxjh7eyhQjDEq9S8XQhFj/EvAXwK4fO4gqhgxa76VemyZpkJaDicOpVII4EIqlikHGlK2DskrL4rf3tqaV2yjQiKhpy2lSrb5Zr0bVTgPwWTJSSiN5VoxOXnAX/trP8nBuXN86Ds+xIuf/232t3dZrmq6GCij2FXFGLh16zan0wm7m1v8+B/5ozy4exdUZGf/Enfu3GJ+cozScDI95c7d2xgtNBZQmDKnyPqgS3YPrlOOLpH1z2HKPjEaMp2TmUIMipXCpzG+zEtss8R6yHSOzjMyA3mvT1b1xc2mXcnFECKt9XStBW/ReEIArzQRSwgdaMPOzjlOH9zgjVv3eOLqVQGS2hrvgtBn1hdD+t1D8HRWsQgurYINpijEjMMYcTJfTwPI/k8pQ912VFWfqDNOZwsuHuyjTVJc+UCvrBgMxjw6PKbfGzMajDg9PUU5jwFwEas9RgtoYGLKWW9byjInBk9WlFgbGPWG5EVFt5qRmZx79x+w1e+xvTEiEMhz8QZYm7BYF6nbPotVRYxa9ndRoYIXQCTITs8GjykqNjcPUMGQq5JVs8BHh9OBPO8xGBmc7Sh6w2TGbMUqLi/QeZYs0cRaLTMlMWqUyogqR+kCU7RYv5TzP0Qyp2mtJUYlJPa6Fad/5cWpJ0tBc0p4mlpLVxnSxObwwuzQhqhEj661QulwxkdWBEyKSlGQOmjhD6v4GC8Q8QWPAbEEFEUtG4Gz7lfaynShJ0g1Rubz2ePasK45/zIF5V/z8XsqlEqpHCmSfz3G+HfThx+uR2ql1HngUfr4XeDtJnOX0se+6WESnUOuu7RU15q1TltWjjJCpQ0IKqZVieKMehAUBC/uKQGZaBMgztp+LURQIaF5qQZrLWFMa/2rIH9yR1Q+sjieYOcz/uv/8r/kL/z5/4If/dQPUuQFL9+8QRsD/fRNrLd86fOfRwXFw7Lk7lu3+eB3fICyHDAa9jg+PeTB4SNWTcO9u3dROgUtKY2LQFES0BRlH5UNyKstgupjvYEARSESzyzLyAqJpy3ygl5R0uV5Ml9VafRSWAuRDmdFwhhDwLqA9bK4x3XgO9nxKkkBJFhi58lMjtYF09mC6bKTzHLnk45a8tJVkB2wC+Fsl9z6iNZG4ni1x1pLoRQuilx0HSXsfERnms55mq4jLwrazvLo6Jhzu9vYEFg0DRbFYLxJ21huv3WXy5cuUPVK1FrDHALep/z0TFRSRVFweHTE5vYOoAjaYJPTe912LGaL/y91/x1te5redWKfN/3SDuecm++t2JWruquz1GpAAhTwMARpQEjIBmTWEOzFwmNmkERYxIGFmfEMNmPW2HjwGmbGNgJmBDJIIJCEYre6q9WxunK8+Z58dvilN/iP593nVnW3SiJIq9haV133nHP3OWfv3+95n+f5JnzvuXr1OvPHHpLuKkZcjkK2xogrlHIsVgXr0WVJnhRjkb8EkpeLcwyBex56kKpq6BYeh2FcDXSrNakA7Vy+ji3aKkqjAC/Xm3GgjORoZ7BDKbHZS0phEkQlXbgK4oCudQQXsVozxo6eyOBDvocCEDBY6d+iytLDjX2MFEOrpRlIURO1TE5G5ckqBcnJQZgmSjt8WuPTIOYXIuoWrmV2PBe2pM7T3oaZkq8p5BrXSqGszr6bCP85RE5O7hbKd+Pjl8XjM4r9d4DnUkr/9Vs+9cPA9+b//l7gH7/l439AyeMbgONfdj8p8zEmSaysyYCNSBWzkQ8C8pCicN1SgigGowmRZwWlGKNcVClfMOPG8FSTvS2FmoCOKCMXpUHjtMZpKHWi0BIIr3OxbjvhCtau4GR3n//zX/8v8aOELR0dHggQlWSPaYzFWkfbdazWLfsHh3z6mWfoh57VYp80dvhuze7N64R+YFj3DKueth9IWvKSRx+YzLdBFygcJycLjo9OTsEG5yxFYbBWYZ2haSa4Qjoe+VWzg7aXzsL3A75v6VZL1qsV69WSdr1k6NeMw5owLIndMak9wq8OGZZHtKtjVqslIQoAs+5bPNAnxZA7thDEeT4iqo7NIZayNtgoBcELnzHBmBDQCkdK0tmaHC3Qj5512zIMI4v1mjuHh7x5+w77izWrYQTtmG2fwVQF12/fZrJ1hmQK2nGkj5EBJfzYTDuxpWOMnq7vUNqgTMl68AwhcHR8SFNXGGtYdz2DD7R+JIRAYRxGWYwyKGWwRU1UjhDl4Im584qiaUVLc8c6JO576EnGJCDUMCZUKrCpInaRk/1DxnZNSqP0cjF7SyZJL5R3TYtMdtMwKHFeVcqilUOrAm0brK1xphL6jzbZHwFU9BBHTvmPOk9TmNzW5aKlIgbJtrd5dxiRSGCdFDomtPfYCDYZGdyNh9SK2YZS2XtSGhwxF5b+T4yDVL5/ZQcpYJpQUzZiuxTloI4+ZA7lW0rBu/DxK+kofz3w+4EvKqU+lz/2Z4D/E/D3lVL/MfAG8F35cz+CUINeRuhBf/CX+wZJSTHUpGxUK+dTiMKJCwoZsbKGdMw0BpUNdiUgTAjnkLciOftmzM9nksSZpk2nKMeZjDlK6CNRRWyKBBRW525JKw7bFfc98ijXX3+TlJa8+vIrfO6LX+Qbf903cLS/L1yxDZXCGB58+CE+84ufYxxH8ZmsCj7xyU+dGmfEFFmvl4SoGYeASkbGOTeilWYIHrSlLGeShQ1MJhMUU5y1OGcxVmOMjErGarS2giT6rKrIoFUIITvWiPlqjIkgEiW80sSxQ/VriCM+jiTEWafrTlgsDvE+0A8Dy+WSeVPkZb/OUb3SpXsviKlVYmqStGZUCpVJxtoaAooh/7HG5UzonqKQoulDwBnNweEhs9kUpRV1XWWllOxBC2O5cPYie3u77O8fsH1mLjQUBTGMoK2M/0DUCoqSo/WKR598L9oW7O0esbOzQ5Ht6/YP9jDWcrhYsDWfUBhLYSwxJrxS+ISMxSZlqaETh/IwEtKISgEdPUEHgrbsXLqHISoCiWHsqJsJ2hvGdoSQWBweUDUOZR0+F3bjSqxxEB0GIztSGX/IduS5T9M442RSCONps6B8xEQwMWEVeKGH5Pk3m8ugIGnGfE9sQBcgpwNkWlySeqpSkGZFGUIulOgW/FocyhPEqATAQWG1RukcSkZ2RM9UP6ISJ/28VlBRpkDJXJKDPYaQs97VaaV8N43d8CtDvX+WX/rn/pav8fUJ+GP/uj/Ixl1c5f2J0joDNOmU2iEJilIgU+b5xBjEGCPTgPLPIBuWPLbLfkVSGVNKYO6O88rIfs2nhEfTJoXyEg6PDgze44qKp5/+AM1si89++hmGds3+4pg+BnYP9jHWEsJmX5V4/PEneOGll1l3HauhRS01ZdPw2vXrXDx//pQ4jrYMcJrB3Pe9OAPFxP7+HoVzxABFKTEM2ggiqq055ZnK66HyqJgkVzyOpzdb4C4SGXxgY3Ul5F5FGHp8v2Ic1oy+RyVPGNd03YJ2ecTYDYDm6GhNU5b4IDQrnzsDlcD7lHdpiYCMYzFIxx9NiTaWkAzDOFDWE1xViVtOUiRrKbVhaFtBcpXh8HhBN3p20EybGqMD4+hZDwOFsZy/eIG9o30OFsdcOLtDIGCJGYgxRAwxQFNNOT44JI4BbeDixQv4KDEJ3aqjb3tmszn9MLJet1RFIRZ8YSBZzRA949CjU5H3blI0xABXxl/rNCkIh7FqanxUFKWhXS8lIgHppjUFlpH+eMkYe3AaVRQ4M0eFiojNWfLCawxRvAMkC0n+qCTXvk8imQyxx/sOP3SslkvCEDCmBgwqCXlcq0BKHp9y1N7mYEki4pC0ZxnvvYoMRFQYKfUmI4psq9ZK0qJOmQ1C1vDIfSeuqfnfxDyqJpU/CgFx7ScXV5I0OIZsvp1FAe+2Arl5vCuUOWpDA8pIp3Aa895RCdIdYsJnvzuTieabm17+rZx0SWf45i0cA5Xy2IQiRn9q5WYk4g6QjtWHjcU9RJXwOrAeO86cPY9SjgcfeZw3rl0jOctDjz3Kol1zdHJCs7Mt8jnkArTO8uGPfIif/KmfAhLLkxPm0zkqaQpbEEfxxYxJAIgQRmkggiJpg9Gwt3sDpUamk/NMqhJXaIwV2zlJZxQH9mEYGIYBoy3WWNrOn6KexgoPLkThzgXviX4kBI+1Rsx0uxXjuMYPLTEMDN0S36/o2iXDIC42MRmWfWA1xKyBJ5sUkzlxmSsXIy4aSlXI/lBFvDaiv06Ko2GN1lJYUtJUpsI2MwwRPQpFyVQ149CzGiEcr1l1AztzSa6MRNbBo7rAZPsc3dCz6CPalCibVVVE0TKjqG1BbwvGvmXaVKzbFW0rFJ0QoF2vqYoS7TTLYcQ1DUP01GVBYwUA8cOA0ka4hFrn0TFm96qUncIVGMUwjiQt3EHKxGq5ZLnsRJCQMviTEviROI6kQTOkiNUum7xkHbaMRFkJ5QkhyH8Hz5g83reMw4rBd3Rty3L/mHE5UDEFDGMUAr5KQe6NzCq3SfbyJik5TtJbeMhqs/cX/XlURjK6jUHpER+WwCCTU9p4MehNZyOPDfgaOZWRZi4eYgCdvRQyMBCjAEWwMb3hlOb3bnu8KwrlaWeS8giupHjGGDAq66JDwOd5UsXM8k8yUoQY71KG8mm16eE3pHSVC3CK8hzaKIiKEMVVRqeIJVLrSBsz4hcjw7rjwgPnIWoaXfKt3/jNXHngXs6eP8/xyUIcm7Vm9KOcqtm66t777uPJxx/npRdeQBvN0f4u+tGHSUQuX7nEy6++TFnVeZ8aICqxBMw3SdMU+L6n2aooyxJXmdy5BoZxzCNppOs61us1wzjS5QxwhYAkmToAIchoGjy+b2V07KM4nw8tY1gTfEu/XtKvlwzdimHoM6VKnIq8UrRREb0YhsSk8F6AmrCRiGpFWWpqJ699248MIeCTZ4zQB0OlLH4t3oe9USQ6tic1k9l5sT9LoE1N2/WAxg+Rdm/J1qySdUgS6td63aNNCUqz6AMei7cKbBRydPbvVEazvbNFZKQoNVU9wbqSO7sSQ1u6Eq0t3RBYtT3GGOqqQiEHZ0gJ5xQoL9SbRKaeaSQU1+Ix9F0nbu4FUmAq8ek0vqdIib7v6L0Qz7XROF3gU6AfOqxvpWNUFpM0hbbZ3V+yb7yXxMxh7PFxJA49fugYuo7F4ZLu2FNT4TL/0SMiiZDEudwrA9kAWRS/2ZsV6SoTELSG6HEIfS2Z7MquoWoSO7MdjpZr1qtOzHuVkWvw9M9G1SMJnZssq80fMop+lwedUCYSo0xFZVn+WlWbf6PHu6NQktEvEmgxhhCXM4VOQlIOJEmjS+IRqZGOSkrqhj6UZWyK04/pJLZRQg6WFt9pi1ZSmEWTkFHbmBf62hBTYAiByhTcc+ECKjounjvP2fNn0I3DWcf1/X20MTmpLtOSOMWm+NAHP8ysmZL8yOc++1kOdu9w36VLPP3Ue/nUJz6J1eJBaHIUrHUW42TPmKJ0M7L4lOB5lRLJe+hawjgyjiPtes1qvRLLLmMldMz3bORrJCmIYqgZUHhS7InZEqwdlqi0pjSRk3aP9XrFGEbJS1easqqYzufYuuR4vcb7MY+eyDI+yigWM7KZgoxw3vusk9dsxAI2Jlk4axmRQ0ocLntOFp3ETqiYuzZpUQpvcVoMm/0qUDiDQTEOkRBqhnWJs4qzFzWpcfgkI/oURWnlBm2mE1AwqSsmTQnG0I2wWLdgLSrnuWgMYQhMm4ayrui1pke8SatKkVJPigM6d8tyHBVEVRBoUSly7dUXue+RJxlTQdQG5RzFpETbUWI9hkSgzCOtJ6yX1Pnn80OLzpLEXkv3LDHFXsyLN2smPxD9QLtcsjpe4hcDE1WjkqDqiXgayUB+LVWyJDRioqazp2Q29UCmsADYvKLSWrpSoxXGRJras7VVcO7svazbjhs377DspbtUysr9dkrZyzQgMlc0JlQQVRtReJZJCStFY/K9ophMZr8mdebf9PGuKJQpwRAFsdxwq1JIENKp8WdICYKXUzbJqKxCXlxn5yFxTclaUzgdzRXxVLq4IQwnJCNcikcgKU8fBlk2I6POSbvGNRPe88TDXDx3hbgepdNVCaMtN6/fZNOvgqwKDu7cpm5mNJMZCXj0sScwOtENPYcHh0yaCfPZFmfPXeDg6JjSGgpnmO3MqbYmHJ4c0a6XHC0OuHXnBpfueYpuHHCqoECjlcjyvBpZtiu6rmcYxRl9Nt9C6UTfGSDTeDbu7BnGUloJnJU8IXS0ywN8f8CH3/coH33iPj7z2c+xv1gRk9CJiqKkqh3aQO+96MZR6AgblohWotnVShb71lpQ4o6kkFqfYkKHiNESPwwCCiUjXfgAUmWjHDrGiL1X5wMxeg5XPcGLOqhfJ1Tawrkps5mFleU4doxty7hacXbecHFnwqyAK2fmrNs1585eoi4tbS+KIh9A2QKMkT10WUrkRgJdOAIwCo+MslBoBnzopUPKRGqFI2kHSQrzlz/zKS5evEystqRTyodFUAblCgxi8xayOqVqJsL9JF/n0edQrlFG7hSkU0Nev+g9cRzolytOdg9gTFQU6Jy86I2AindXTRtKXIIk01mAU6/JjaonJulA46l5iM5UoUBhE850mW+Z2JnWzB59gNeu3eTopCdpUaNtxuW8osz3YnZGz/eq7CEjUcvBFCErhWA6nW6qwVsqw7tnCH93FEqku0sgYUxy1QgfUiXGrNXWWWETs1RNJQlP2pCo0ya3g3yBGJ2NeKX1l0NWCqQHYvCAR6nIEEcGYETl6E3Y3z/gF7/0c/zwP/8XPP7IY/y+3/M9fOyDH8mIHRweHshPH0SV/rnPf56XX3uFqprw3iffz3ve8xBBSRbPY089yUsvvIwuCrxS1LMZ6eiEoq5o6galEst2JT+lAmUCn//SJ7nvPe9jOr9IowXZFr6ux4+jqBlykZo0U9SkoSgc+/u7DP0SkigrvPckFTFGEviSgqg8mpEwLMF3NC7y1MOXKfTIz/3icyyWvbhfC8QJUWJ7BR2VaAoNGYDIyCmJwmjxWNTqNNKAGImjJ+iIza+/vBmiPzbWnsreLOJHajIXdLVakZKV/TIaHz22mBKTYesibG0p+sWKRdvRdQPtas3e8ZKrdzSXdyrOnNvBDB1GGdpVR9KWoR8ZB48rCnn9rGHUClc4hpQYRs+kKPAYuuSpK4WznrEbRMsMmKgAg1YF1pQQYLU45tkvfI5H3v8RQgTvxQszoojaARGVRvCe6AdJNNSCCCtUpgrFTKPJHj55Vx9Hj+8H2uMl7dESO1qMLkBZiAqHCAYCeb2ElvckgcpcU2XEIm+zi1TJnIKnJEWygvZrld8DHVC6w9qB0gm/VCVPWRQ8+vCDPPviG7S9kPc2AOFGAbcxA44p5qVXnvrQWRUn8HdKCussk0nDrzY5aPPs6iv+/taP/VKPd0WhhLy3yCiMz6hiShLDAIJlxhQJSrhZmz5O1DibZ9mgkZKdgxLPyITww4z8A6IaReuq4lt88iwxeOLgZTwgsnuwj3MO7eHlF17mL/3Vv8x3/+7v5vd9z+9j6FoOjo8yn86zt7vPq6+9SjIW70ee/cJn6VdLnnzqSTDgbMF7HnyA48URYxzpu5bSSX7LEIbcnmmskW1qZRT7u6+xt/8mdTNHpVIOFJUY+o7VYkWKEVdILETpZIzU1nG8OKFtjxiHE8a+J4aEcqKkkZiAJKFWqyP6fo11hudefpWnHrnE9naNs5axXwvimsjcUEPp3FvUNXJYoVSmlCScVSRkxI8Roja5e5JRawwerW2Wpya2Zw2z6fRUECBjmERq6Exob/ueBFgUulJMZzOsbXA4Lp2f0Z8cyYicNFgriiStuXjfPWzPKwYqXFyzagcKbdCmYBjXjDFirCWFgDUKos9AQ6LrekpjxdEGRVMnnBuI7YiOFrDUyTIQQBWMaYJRDc6O7N05wHz5BS7edxmsSGZ9Cngf8RmUUQQhk6eEH0dM7qpUlFXGZqNHkrygNAS6Zcv6eEloe0zI5hlJNN9KZQmvj1m8IGa5arN6Qug4XmVvn7uGCORtkRS56HOgWMgHV6AdD5nFgHM7OCevV4wRUxiuXDnHK6/fFN4lsjMPWt5vg8Ek+UZGCXdZ3t+YeZcbNkuiairqSZVVPZsr4Vehm/zKSrn5Tr+C+vyuKJQJcTnZBMYTRRMbNjk3pLv6Va2QjJqYDTEUBrkASLIfIQh/jNy9SC62yui4fK2JUciuJEbviQHGfiD5SLKwbNcoZaVQWkXyAR8jP/RP/ynf8m2/BWMUB0dHzLZmDH7k5ddfk2ydKN5/qnC8/OrztH7NBz74YVKAw6MFL778KnUtuShVVQFJLliVX4mUTVOjojKO2K7QYQNeBTG17Vohc1uLcQbrNMooQvAMQ0eMnnW7YmzXxGGN0RajhTtocsfg/cB6vSIFzzh43jg+5OBwzayuuefieY6XPUcnK8bR43ygRMYorRWMHo0UUWuM/NGWwmmc1XT9GpSlrqbZ6iwQhpGqKHn4oQepqwLnHLPZhK5r6dtO3netUNZQKCO/r9bosmS5OCEGjy4TXWyZNDNcKhjaXkbbpPBhZDqZcP7MDpOqxjrNatVytFzz0NnzHCw7mrJivTjm1sERa58kyzopdEiAJ4y9pEcu1ijvMdZgE6Q4MCvhKA4k40haE002b4kWrWpGs0UcR3QP+2/scbh3xPxcTT0rUUZoVTF5YhhkKE6yq0sBlJejPoWUw7yy6mcM9OuO1WLN2AVSAK0qdF4zocQZaLMHNkkiaO+6boFXOXhvA3UmATh19j0QebxGHMplr2sQbjFpYBjWpOiAROUMymnGkFBGcWY+4019C7IlHRsa2il/JTNR4FTqqrTwL0MS8UeMkfsuXaQsq7fUr1+dkfsrn3XTbG2A4Hd6vCsKJcieJDvQy0WEtO0h59iovAyL4RTblnGBRCTkcUFhksp7SC2dTxB3FC0aAinAPqKSnIwhK1i8z67nAClwfLDi93/nHwQNn/nSp3j+1RfxSfG+Jz5AUrB/eCjjjFZ0/cCtW7el002yAxyGAessr7/xBj4qnnziSYpKDG3bdYsylhQk10TnEzSRCEE00ymKJf/QLimMKDPadsNlUxgn3opGK4JP9O2arltzfHJAuzph6Fq69RoTO7AFpnCnZGZjJHrUdwM2JozRdDFx+3BBjIrts2c5c9JzsFwTEcOJlIAYaYqSR9/zHi5cvCA3lbH5RpGCD4kXX36eg8MTQtcxdj2Fc1y6714mkxlNWeXdWGLoPE05oSkalJReucmjKDYiiUuXL5IunSeGQLtecePGLsvbgcqNxNLTjUuGMGCMou/WEvUAuOCorGEIikCBH3piHDg4OmKxaok5y1phSCpSWovThtJYKMXgV1tN8AOKjnljMGQgyyhJxoyRpIVdWTDB6w7jB4yLDMdr7hzfAjNQVVtMJucISq6NGALee3wKog7TsoM0yIE8jgOhH8Ankhf3HWckCxxVQAoZjIw5R0kUQ1YrrL5LmUNwKuJp4ZKiqNXG/1U65qQCGdPK7BCF0ZGQOmDA2AZXlBijqZxIQXGGcjLlzM4We7vHcm/GnIKqZGUWUsSqvIMVgACNfFxpTSQxjCMPPvSQUPV+tR9fo/4m/j0qlJubMKur8m4yR2kmTtHkzdh3CsjkzyWtiFmeGK06ldKRuEtNSEJ6lWgIcZsMQbrK0UuUps+Gv8Yn9vb2+Jmf+yne+9STfPTpD/DrP/YxvHLMts6ws73DCy88J6d6gpOTExYnx6BN1kcb0IohRbR1vHH1KiEGptMZbSuIdfBigiExoNLtyuomA1LGMPqRLz//eZ543zehhxGfogAg+UJX2UC4bzsWiyOWy0MWiz2OD++wPN4nJdESG+3QtkLbgo2SXRkHXkZx2XsqlmtPWQZsPeOBB9/DtZu36YdeTDBCRDtFYQ17d25TloLebm5KpSQF0VrD5Uv30dQLQJNiZDppMGVJH+JGakxhLUYrsR6zDh+8cFGzn6EpXB4eFChDMg4TDQ/cW3B0cMzJ4YIYEmOMaOcoC0thLUVV41zJfDJh3lRcPn+GYViSMCyOT+iGgRA8isyGIOJcKdzFoNAu4ApN2yXA5HhQz3xaYVSQ72cVWhs8FkXEaYNWNdHOwO+S0pJCOVy0hH5kWJ5g20Zs8rJcERyjl11xjB5FzAYWCU+QAz8qjM4GulkR5fO1Lw3BxkgmAzZK3ID0xpIw+2paLe95JKu1TsnkSniTG3SaLEVUgB6JtPggajLxF5XedNLUeOUYteLBB+/n1p3PvmUPmXWKOrNQVMpeCtm3QW3I7nn14D0Xzp/PIOOvPnzzVQUxJTw5WuYdHu+KQslbqCZh826yySOOqHS324tJtLzkMX1DYBVNeCIQ7kr7xD9NZHXk5867tegHfIJ2EN/CKPMBGlh0La0f+dRnP83PfvJnKZzl27/jP+I/+G3fTllURO+5ce165jVGbt28mQtfIJmIsiXGiMegNpa6LsSuKyecpdFLLKjJRZJT+qSsGrKGtzCGW7tv8Nxzn+bhR7+OarJFzIR6J6RQum7NerFksThif/86R/vXWS33iXHEWoept7HFlKLexmSumnEKU5SMqccWcP7SWa6/dsxqNTCfenxaUFVz7r1yiddef4MUIkM3MqkqtDa0XUc/jNRVfcoCUDrvFNcdlSs5f+HKabeslGjxVYj0/UBSsnNU2mZFVaCsCmql8aPkDI1Djx9H+q6TZMMk5nfBR8Io+dyQ2J6eYba1xaUL56mrMkeFiAZ54iy39m7RHtzi8tYEFeQmLowW+atW2ZREE/pAUnKdGeeoqAgRTJaLTqYGbUdiH0neYzC4VAooYoScba1i9GvSuAAlWTg6OYgj7brDlbNT+af0bRadAlp5xD5NwEqdPIB0vVriGJLOHpJJtNUJlXPtN85Ycv9oJcR7MRzPwImClKSBGBEUXqYyLZSg092kyWqdSFkrpk3FMM6ZzyYUhZPiu/Fx9SOutGzNasCDsvKMuWirlLI4YnOJJzbGQZu9aErgvef8hQt5N/lr89h0kFm/QkDWMO/0eHcUSiAkTchcvOx9ITy8EDMZN2tJI9nXUGEybShpI07KWorgGHuM1ogJlxRKkETFjftyCGKYMYbsDxiTUI40HB8dShZxMyMoh+87/r8/+Pd59pVX+ZN/4k/iguLO7i5Jy1h6crKgcgV+9Hkch7J2mKIQuSQiT/TrNRaNU1IglFI5oEoAKa2d7BKD7E6ryoAf+cXP/AuKsuChRz5K0AXWWWIKdOsVx8eHtH3Lul2yXK7ouhatFE0zxbqKyfQMdbNNWU7RRUEiYs2IdQU+et77+JM8/d7H+JE7u4Qh0ria4+WCYR04f+4s169dJ6FOqUGuLHBVRQiRrZ1t+nZNPwzC+0uRZtpQuSo7iyeMTiJq1Ia6KNmabZFSdhUPnr7ruHN0h65bE/wob77RGGWkQ3TiruRsIbu5wlCdaXDGMp/NmW3NsWWJ0/L1ycjKZXlwyPMvvsTh8R4XtiYE41AoSleQ/Cic1beMf0qlzJ2W4mKtpVusKUtN4RyTxqBVSyLifYczdRYtaDFiiQplG1R5nrHzWL3OjASFNpouLBi9oiinKF2I3j558QJFWB2ibtLoIGsiMRcpRP2TC2upALJTOtIvSCWSvHqjNFGfOhrcLZRBkGyRrmZDEpUlwnl3rZIsqIxKVC4ynzua5hLTpkLjMSrhrCjHrIp0qxOm8zM89uD9vPjaNYytRMOvjKiBIC8CxeiDmPeg6KzYSVRVxcMPP4wU7l+7Ynk6cea/+/TOlfJdUSgTGY1FbRqst38SlZf2OepB7FtkFE0Bk6zY7GuFspB0wCaNxQHigq2VkYsZJWFQSYoaQfZAJJFYdTEyqSd86P2XeO7ll1kwMqQS1RteeOFV9vcPKa3mZLmgnk9Z9x0R2NraZugGolG4wmILQ9Qy7vgxZoJylJ1iDCJzU9IJW52tqYS2J2qKYeDeK1dYuiUHh7v83M/+E+pmh/OXHyZqhSIQhyAIIg5nG86fvcz2tCEOa7GNUxZblLiikmU70oFvYmGt0ZQqUOuBsoy0/RJInKxWHLUDCZhuzTg8PMGHQDeOVIiF2cHBAZcvX6QsC8qqAK0yuusZw4APQjpPWV1FIsv9hN+ZvHRcKUWc1ZTzGafSwATaWKZ1w9Z8zmw+p6gqhq6TZMsw4oxla77NbDpnDIGD/UMOu47DxTGHx8cMixVntqa8933v59KlcwxHu7S+J4YBjEWRGIKX4rwBDElo06CVBatxVmEIFCoxqRRlqVgOkbHr0GiCk0KkeylUSWsoZqjUkYYOrUZBfJ0C6+njHsG3KDXFB/Hwl9FXSKkp3U2Pkscmfll8JDVi+iE7/AzY5L24SgqXVTFijZZZH0n2n1FnHU5SmLCxNRQqUdL6bpsVBaUubKI0idpqdBwwKYkZi1GghOGglMGoyEP3XKJbD9y4cwAqK3tkIXraPuqsY99wK1UGd2azKefOnZPfFTJH9S1F9t/x46sBHdmbmq8qPG9/vCsKJZAJqhmkUZB07h61IQR5c3yMGVmWdjMl0R775GUp7yPKC1k56URSw6nGVKlAzDKE0ftcnYWuEZPwBLXStOuW2dYW3/Zb/ld85GMf50vPfpGr166SMDz2yJM4p7l1+yZD8JREhrbHOYfXI9o5tMtAS4qEPvsNKovPv19QmaBtxP0HJTsSF7OTX/IoHbAalodLktJYp1iv93jmmR/j13/T76CqzzKttyicI7gShWVSNSizTdvOxTothdPbLSmNT4E0jCTfE/oFKkBhNX5xh93XPGlosc6gy5IhDDiXuHFzj6QKTJarxTAyBI9WJT4EPv+Fz7M1nYBSmLLIVKw8bqNktxlDDvvS+TASWaFzBbWrKV3BZDKhbhrqpqSuSqqqoSxr6mqSu9meru8YslJJKRi8Z29vn1deeZ0333iDo+UK7z33XL7M1nzGPe95kLJwLJZr3vjUL3LOet5zfpvdOwsqbfBDRzf0aJNwRkwhnHXSkY1e9OlE1qs19WTOrIlc2IGTdcClSOqXhCBZPVZpBq0YtcJQoNxZfOoY/CFWDWgDNoj5LcMKTESpCp0ygoIn+h5NJuhjwdR5Hy1jrCEKiJM7xZikCGl9NwVg89BKAE7yM0alT4G8jfGGEi47URlhWqSQQTZDMCOxFKqPAnFJwkoXq2WfFxHRhSFh0sDTT76HxepY/AAQJy2NXOsbDD4kAR9T5lmOw8DjDzxOWZWkX+OOctNtpwROqezc+Us/3jWFUn7otzj+ZFmcyqddTNn5Jkr3l7IONsZAFC2iLLmVJplA1BBUvEsPUuJ7GJIY1G5cor0XYVcKgkCuVit+4Qtf5PZiye/97u/iQx/9Oh5/6inqYsL2bIet+YxnnvkkkIg+Mg4jyuRxxypiVm6kkC+PQF4pSNi7ViIXc1bcn42SLtJaR2kNSidWfiQZzbobKOoaUxS45Ll54yWef/bnuXTxEe6/70mskRHUJA3OUDQ1VTVh1Z7Qj2t8GAijOOCE0TMMa8b+hDAcE+KKqm64evU2u1evo10FCSZ1jQqBy+fO09iGV67elEgHJWuGYRjRZcBUBWHo0c6hIugAFk1dV1T1hLpuqKqC6aShqWsmTcN0MqGsS0xZYIsCa0uK7NiziWzY5Dsb60hIZ318uEe/27F/sM/h3h7HB0e0bccYI8payumU89MJk2bC2Z0zGK24cfMWXdsiMciBe84VGEaqpmBxcizFUFlsXpxZo7HaYDN6XE1m3Nk7ZhgCQwxYPfDIe0pu7K0Yxhn9oiMNLaM2xGTRtsZ4LWYuOJS5wOANfTjEEOS6NokYe0hD9oB0YiJNwJqEEVnYqeGLBnTc7A4zx3LTqamN8uYuzUXn6hdTImz4rcoQM4+YTbeWxPkok5Jk76/lOk1I6qKzERU942Cg0BQBOcSVwpUWYzR+HFGqYFIbbGl44pH7+dQXXkRZkztGTsE50ZfnnzVFUtL0Q8/7P/B+If7/Gj82JfnuhuDfk44SRDUSN0BOuutaHWPEx3BaJFXed2zcrVPuMGOMhMwTzIYpFFaMWAVQSIwpMCRROqgQRQKtZLE9+sDJcolPmk9++jMoZ/kjf+gPU+gCG2WsCaPn2rVrWG0IY7gLQqkkkRKR04/fXWKnzDcUInBIotYpXEHlCiwWrS3OGnwaJf/FSGpjTBHrCooEpJHXX/kct66/Tgot99zzuOy7UiCFEu0t1lnUOhGHjrFfMLQr1usF3bDEj23OiF5i1Mh0a4uV7wgkrtx7H1fuvczx4QH9Yo07aymMY2c+46Rd03UdISr84FE+Yo1hCJ5v/53fzvbWDkVRUZQlRivQVtQ5Sg62EAS8ijESh5G+71j1HV1/RNu2dF1PP/QM3cDY9fRjxzj29N1A1/ZiHReC7A+jdEOFrgCPJ1LWdY4mgDt3dunaXlB6Oq203gAA9phJREFUJXtIufENVo3MJ46bN05IWPGLLB2FggIorcYaLTvWMbG3e8Sley7kTihw4bzl4kXPcggwnYjRsG+JsUXFFmcmKFMDGkeDLRLdkBj8ApU1n1o3Aj2qbAyhxUDXKEtUVhz6lQTNpSiApdEmK3W1EL5TkDyn7B6eMnk7aZHmBiV/NsVUIzv9u+uXmOWnIRu9evKWE9RIXXjOTUua2sjaKCW6fqApDPiAdoLQOGeBSGmA0HL5/DbbswlHS4m4IAXRmgMoMWnOCbfoPE195CMf+coy8Kv2uLvQePtH/72hB4EUQ+kUubuvUyp7RUZ8tjEjCRooOmaRiGVxKTFIQSXl2FkNwUjEps6a2kggafCjP6UYqZyFsu5blm2HUhJG9YlPfILoA3/sD/8xYgBrFMPYcnR8JB1viHgf6YdBYlgBE/XdpStSGJPmlABPStTOUZclzlmsNhht0UpRFpoQnezxlMLHgfvuu5ednTMsl0teefVlfFiyWK750rM/gw9LmmqGVgVRlZhySllWDMOK46NbLFcHdKtDhmHN0K/x4yBqiDgSfY8rLe994klK65jM5gxjyxe//BzdamDvaMn+0SFDDHzdhz7Eyy+/zMHhEX4M+NFjncSMPv/ii0yaGd0w4EfP2PcSNeFHUvTE4KXQ+SD0EC87Zp8CTpu8ExNzEIXcRCobcqekEAu3WpR6pCw6SBJJEcU6rl+cAAprCxSivrHOCOqvDS4OTCYaq1tKa3j0nsvcvHNMHzwhiQ2c0gptVB4vYX/vAE/ElFZoSCqiUsvD9xXc2Vtz5B22mhF8AeOCNC4J/Qptt6DYIWpQrqJU50idIYRjrBplH50MQWsBPBLouJmmDCFTyqKXfZ1ELJu8jxfLwZity07nqHyX+yjoeMxkcq1kx6oJosnO7laKTRaV3DcJSQcIKDADzTxRVkL5cpXGIWT25L2MQDHm8Vthk+xZU/AUWnP/5UscPv8a2E0nmcno+aaW7lc4t9vb21y6cuW0BvxqjN4bauHp34GNjPPtn/v3YvTOhNR09xc45UimTJbNapyUvRW9D0KJCNn2KwS5gDL4o1IUUwGLqA60xmgktD0IvchnMbIGktacrNcUVcMsKk6WC7QyfObTz/DjD/843/Kbv5UI3NnbZbVcU9Y1ygi9YRy9yASTRFkIUi8RrSlfIJsLXiXxSyw29I5cHWxhqKqC7e0Zb9y4RoieS5fu5f77rtAPHWfPTqmaJ/jis89BhOVqny9+4edoioppM8fVc4pmTt3MCGng+GSfk8UBoV/jxx7vB1IMOK05e2aHptlhPp9w5colTo4X3Ni7w3J5Qtv2LJctt59/kcn2lPmk4d577mH/9h0WJ0vKumYMHtXLWPfJX3iG8xcvA6KGMnoTDUBWYShIxV1LLifKKJ07fpV1yXGT65KlI5K5ok55fTZTcJI2xBjp2xWrxSHHR7sMfsBWNY8//hQ68w79KE5CIQTKlCiyUUeKka2mxl207K9XDNGjtCWqxOBHmmqCcpb9k2Owhmg1k2rCerWiKC3ntyKPvqfkmS+u0WqOsw1aFZBK0rhgWC9JPqGKCWiH0iWu2MH4Aj8cMaQea7QIIGIiaXvKGRZPJbA0hDigc6cna5wsN9SijvKZcyn3ThBqjjbZZSo73qsk4BVkVF/01QIcidR0Y5FGAqsDddVz7nyFUSPOKJQVfrNN4JSYFnddD0WBLevTrlWEIIEr53d49oWXTwuwNpCIbMIIBYUXFsgHPvBetra35OOnleDu49+2bG4ArxgD4zDk3HB9KoEGuX+dNb/s93qXFEoZWTd4l6Ch/i5HKMTTsduHIPk0KdGt18SQqUNiiHgXMEvSkcTRI7SxSGlF7y3La+GlEYXH1w8RrOV9H/ggL7/yCpBYLJZEFfjxH/8xvuFjX09TNdy+tSuyvkbhg2fwHj8E0iBcg2hl53MauKQT2ujMLROUSmsjumytUcZQlAVF5aibmgtndqiLgnbw3L51m9/2H/5WTmND0Wxtn+VnfubnMSoRxo7l0LJcHaFdRTOd4YzDaIUfxfk6jGLTpfNONzBiTOSp9z7K6viYq2++xqLtGIfEwcER63YJWKJRLLseUuRLX/4y12/dEXK6Myij6H3AK8V6jJhyAkmfIrYxm2dkoXh+Rw0packQ38zPQsxDKQFFFNnLUAVRq4TA4cE+fmxRMdGve0xdMfqRo9u3SX2LVol13zIen6CAoqhwrkApQ+FEKrkzt2gVZF8XE6XV1GXijK7pvaftR/psIDLWsl45XCxFSWUMtnCYXnbmhdPceyHx5tmOa7fLU3BJFRMwFaUfGMaO2K8xhSUaQ7IOrc6g9ZQ+nTDEHhMyXzImyeHJcYUaS2G26OMRJgVpAlSQ9Y0yufIkVI5cTpvCl3f8Oh++KbswJWWFe0lAa/FhTWEkRomySNl1HDVi3cCFc4aq8IjlnRUKkUoUWuOMRhlLny0Rq9JlY2e5a3VKbE8cl8/OuH6wAlfmvWhCRTHpCNmpfb1e8nVf91EBXr+iGrwF9/538EisVyuuvvkmTz711NsKsoLMQUz4fnjHZ3l3FMoko2kIcsJt1AIx5diHvODftM2RxHK9QpEwVnF0dAJo2ZFZh9I6O9cI6UP5AR0jBIldIJNyQ5TgK41iGAcu3nMPZ89fpKwb9PMF4xtXadsVd/Z2uXr1DR564BHefONN8h1OQC6YGCI2GZJWjGrjyg4mSkqe1UYW87lrRimS0Whnqcpa5GFVQVHXxKTYmm3T7x+yWrX89M/8HPfd94DQAIBJNeODTz/Ns19+lhhHtJLnTqFj6CLjhqOW3VuUzh1zBHJA1mtvXOX+hx/kd3377+Kf/PAP8fxnnqFdjygvnLq42S2OIydDzzOf+wJKW6y1NIiaSRgEMAwBVImtJlIoUyDRMvSdoPJ5vFHayppijPg4MPiWhCcMPWEMosbKNB20wliLK0qc1TT1hLKwGGMo6gprLM8c3KTtRobR86GPf4zv+K7fw3Q6y0FrktNduAJbWG6+8nle+rH/hUprQjDSzVpNqUukt7As1iuacoIrGq7evgPGMq0qtssSUzj6pPAjlM5Q6BM++r4ZB8cHLFY1DCXeJ9DCfzXW4VIiKAkRCzGiY4nSFqdnQIMeR7QfGMNIxBKTzuR8S9I1gWXmDktuk7RiOpNZwimVR5Ii5WDyUQ4kkSiK7DfGMStv4inyHYNHRWkgko4oE6mKgbNnIpcuVBTWY3VCJZ9jRnLuDkkcqEJgNpmjVZSkRq3BOpxWKBP48NOPs/7si+wvZY2lMyAr3GGN0lDVFR/+6Id5e1ncTISnf/sanebdDvj071/xFW8rLQmKouSee+7h+PgEP3h8CIwhsl6tODnYZ3V8RPHvAz0okdFsJeg26W56W0gxG+NmugGJ5Ef6oWU6mbG7t8uN6zcoXUHTTJjP56JrNkLHSDFHNEQptDHvJsknr87daNcPXLxwEWU1k/mUp554Er8euHn9Kv0w4INnPa5549qbGKOJcfNzZ/VQto+KWXap8uhtrRMFjs5mBQqwOtuLSVE3xuCMY/SJO0dHTLbmmJMFUWleeuV1zl64N4NShhQUly/dRz2Z8LnPf5a270/D670X44lTzToqywTFoszofNxEzWuvvM5/8zf/Jl/40hcxTYPTBS7vCWVsipBkEBw9pGz72o4jzmgMQmD2IXCwfxvtnKwhhoGuX9KtW6bTKU3dCGhV19R1TT2tKett6voi02nDNKPV0+mMqqqomko8MKuKummYNI105BkyjUAYBv763h4/95M/yWro+YZv/Ebe/5GPsvGrjZlZYEn0R7vsfekZ5mVmHPgom5cgB5dFU00bJlvbtCdLrt/cY911zKcTdrZnNGWBjoGmrLizf0woAkaNXNgeeeIRyy9+/pjIWenYw8gmksQH6YpLrQmBU6cll8TyLlhDMlPwolJyMaFjEDqZgaQk/C1lc9ugjESJIMKI7N+U/8iKIiYxFZETWfTAOqeJbswvNmO60J8GlAo413Nme+CBSzMaNwrlJ+ZVgHaYKA0HlWMkUlQOoxNpHHDZ2k12kgCG7dmEj77vCZ559gWO1x1GOaHrYYhBE33HuUs7XLx4FrhLCzrFJ/ga5e8Uu4hiP5wg+Shpp11H23WMq5auXYsAY9XRtSN929O1K7q+pV13+EHAQW0M3bqjW7eslidMp/U71qh3RaEEsrFujuokcyU5VTMKKTgBMdK2a0rn8GHk9u4u9XRKu1rTHh4wek9ZV1STCdoK5VzHza4iGwDnN9QoSWYMPtFUE0pbohIYDPPJhKeeeJTdm9fYPneRC+cuEENgsTymqpqMNspp65xl6Ac5zSHvVOPdsdvISe5jELqTlrRGYy3aGkmL7AfGfmDwPUlrqqYhLFu6buSNN9/kPQ8+QjJWtL8msXPuEh//Db+Jl196md3dXcZhJMTE4CNlURC9UDkSCMFaZWJ9jDR1w81rN0kWpmfOEtGEYZT3ATI5XrTG2XMO8qHQrjpCYamswxpH8ANFofmN3/KNzOdzZrMpk9mUqqwoigJnHdbZHJBWYAsRASh+eaQRON1txc3OC9knf/Q3fCPvefIp2vWa93/wQ3L0ZVRDJ4WKPeubr3Dw3Ofxt95gWgkPMBoknzyJAQkklu2S63v7zOsJEGnqgumsYT5tsptOYlZX3Bh3OTg65J6LZ9Cp4/H7Jlx9M7F7CEEX+CFTb5RERUjOTSKlHujuuv6gicniU0FUVjKgyDk1xhJVQcRwGs71lq5RRznETHbpSVp8O2NCxmr1loYjF1IQn0mUEKuTdAxY19JMei5cLDg3q2isR/lAMiKAwBp8CgzDQFmVOGsI0TOpGlxSWba7EU6kU66NUpFpbXj84fv43JdfYvADmOzkFSUW45t+03dQTbYIQXaIYggT8aOnb0farhVnqb5j6AdOFksWixVt29O2a9IwEMdAPwb6HMtcKY0PHWPyuKKmaz3Oirhg3S2pqhKVv5ezFm8tq7aVOGL1zlEU75pCGSBbq8nCXTwic2eTu78YhCLUrVsm8wm7B4eEmKimU/aPjqiKEmU0BwcHzFOirGpMISTitAF+NHkvNEh3EhVd2/PY4++lsC7rwSH4ER8GpvMZv/0//J2ooNm/c0eUEUb2OiqBMYqyLuiGnhQ8GjGdNVZhCyMWbXIXo5TGmWyckZU5IQZ8H4QKopQEh0VwRY2zAasS1157g93b+7z3vU8zmU7YvXEbVzi2t7Z5zwOP8sADj9L3HcvFMV27IoSBO3t3xJwj6s3iF2XU6ccKW5MMJKuFKWCSuFijKZTJki6R1WkjnanRmjQGxpiwlct0J8Nsa85v+52//dSQ961dwVdSMb76v35lj4wBCLDjLP/Bb/utGfiTz2YnTBl9/Yrh6nPc/sWfpdSKQkVMvp5sXi0IHUae1BgwJgGeptRsTyeUk4qiLAQ1V1ooYlXBtddfY741oZk3zMqORx5xHD97wGq5g3ZVZmtoXIiYFBlTJPg1Ki5RaWDwQV5LNBErqH6GcGJyaLVNHx1RFUIK15KX7tNm754PfUQ/L1HP0l9a0qncVydQRksEbvJEgmjrI1gdKaqWc+cjO2cdTQ2FEp25ijJlKaWEg9t7mkYEASEENhGzUsSzDt1oQpJFlHQKnsnUoQ87Hnvkfn7x2ZewpkJFn5ma4OyUn/iXn2JxvGC1XjH2nbBY+gBJMcaADwOjH+i7DpRmPt2Ww3lsWa8XhJTohx5CZGsyYTUMRJWYnjsHumRaGJyRhmg2m+V9bhDsIkE9rZh2oySopvCO19+7pFCq05tr03/rrNPeZLGE3FGs2xadlR4H+/tMp1Npu2Pk7HxGSvKinN3Z5tbN28S6ZjKRzgCjs6QrnCbTScRr4ty58/lmlOV8M59x5vxZnv7Ah3DGsXP2DM8990WMdWA2mcSyg5xN56xaMYrQXhBb5SzaKpSVqzlFhVOO0liccUC26YqiOw8hk+2VIoSIdZayrPBdjzKGcRy5s3eLYimhVPPKMAwdVVVTNQ1nzm+zs/MYOzvbzOdTPveFz/KPf+gfMy+3NvxkEqKWkdfaYCLEoNjannPvg/ezWix4+bkXUSEgSRyy/C8KIwU2y89Mkj2YMYbaGm7dupkPkA16qN7WMUon+K9bGu92k2+5TO4W4Yz4qnwIkhQ6BWK7j7/1IvrwFovDPfRs6/Rn0VpntVCONk5i9OyUZns2Be+5sLXNtHAIrpRwWiguQY5ytFIsly1bdYWKkbOzlqcft7z+xjG7+wMxNETtTjv4MAyEOJJiQKeASl6mYr1x0FHZaFqTKICKECaAznK/bMGaZNzeaHc21mVp83orMaQwWkC1SMx7ZPkig8KlhEoDcMJs3rFzzuJcIEQY8t6i1g6tjPA1g6cuDPOqlsKrFKYoSEkyyZVWssOIEWs1SVmZ2IRzhyMwb2qauqLzYsQUEdOYq1f3uXb106QUGX1PDCMP3Hs/3XogJfEWCElAJWsqKlfhVImOGlPMMOWEaCCFETP2fOjpp/h7/9P/SDd4PrRzBXSNWARGvFLEZMWdKdk8NQWGoaPvO3FvV+9s8/auKJTi5pxt63MfEoWuJUUmF0kfA4vVkqqpOVqcMAwj57bPcu3WTcqiYDadsjg85N5778UVBScZCU0xUlUl1rkcC5FR9iQ67O3tizz8wJOC0KrEGJe0fYvSBZPZFvP5lKQi12/cpLClXEiSp4A1Fl05tnZ22N89AJ9b0rxRjZuTVykKW1DXJcbYbF0mS3aUYgybPBuVxyyNKx0pBNox8vj7nuKP/fE/Sj2vmUwmTJsGa6wkBlqdWUY59Q7FN33zN3J8fMwzP/UpcdVJUXZTRpzON0l4IUUWixV/4H/7vTz25BM88+ln6Ncdy5MVu7u7vPHGazz7pS9wfHTIvJlinRNdsbY4o7GFKF329/a4ePni297Xu0XzK3DMt2iA735os6dKb/v726+TzSt7t1eNm2/ke1ZXnyfdeYlJ6licLGhXLZUrGGNARaEuDaMYFcecxKm0JaLZms7p+zbzV8X+y5YFlVWMyOF5dnsL4wPtumX3yLAzaZiowH3zkUtPG55/cY9rdyoWfoZPheTe0JK0J2hNSAUmOVS+HoxOAqowElVAJXGgshnFzhy57HspeelJxdw/5jE/cw3IPeqpqxY275l1jvTQ2KiJoUWZFdNGYRiFAJ5kL2qMuOT7UShvzhqaZooxRihhmaIVgyfFhMsrHSnU4nOpkSA8gmJWTzjpBi6f3eHNW/tELzZw9z38FPPtixgc2mpEWm8wSjOrZyg0PqpsfhPvvts5+CqQCBqCkgnz2S9+mddfeJG9vQO6cWCxXtBsVWidiGkkZLJ91y1xVjO0PXt7exwd7rE9n2ALR11V71ij3hWFEoSHpZGTM2RD3ZRBiZQBl1XbE5XGuILDo2OKspRc635gZ2cbnaB0jjNndnjplVfwKTKpa5ZHR+g4xUw1QcvFYFB471mt13zzb/42yqbEB49xhu3JWVxREqPEFmgltKTd/X2RcGnh5GlrcUb++1xZoTEc3NmDFNDanlp4yb8xWKtZtytu7d5kDJIvPplMmc1mQjpHb7YMAjwRaUNP0IaLVy7ygQ8/LQqUzRfI1cPdkrThBiTqpuGP/x/+OH/61e9n985tjBDa0FkZlPIu2ChFt1zzV//Mn+e/+m/+L3zrN/8mQv4ZVBQD2zdff53/13/3d/iFn/sETdVQWIMrLUVZYEuHXw4cHBxw8dLFu3uqtw3duQhyt9gp9bXpxV+rQL7t88DGsBmy6sR3rK8/hz26RmkjaYTDowOMjvTdAEkTQxLE0we54TNfUxl9CshV9YQuSnfqigljiqzbnqIpKcqCrcmUQlvW6x5TlqS4ojKKwgYat+AbPjTjzTuWX3zhkN29Cq0KCqfxyRAlzEJoMmlDf/PoJPEfSQ+QZAedEAu+cdy8u5vfNw+u+aAxucVOiGk1mWhOksKl8ic3IWI+eVSKOJWwKmGTp8BQmFLct0j4fiSMHmUduiowZcGIUL6s1gx9RwqRwlpxRsrjyl0poOxhdVJUWrPoOx66fI5utebOwQlBGR5/6mmayRlClMKvNvlKSWMx+GyqIteC2bRObGSQKQXC6DFWEPWnHn+KV1/4snSiwRDtDFPMWR3dJPkThuApnJUYZmOIITJpIkoV1KWV2OLo3vG6e3cUysTdophEPB/ZqDMghMgwjhwtTqiqinXfs39wyNmz5zg8PgIFk+mU5eKY7a05USX29vconJUM6L7D1SV6HDl/bof1eo1zmlXosU7z2BOPURWNuDQTMTryoz/yI9y+tcvHP/7reeLJJ9nfFyuwra05TenE3MJaFGKumhRcunKB0mr29/YxTuSE2lqxsCKxv9jj3IXz/KE/8If54Ic+REqJT3zik/zEv/xxlicrZtMZGsXYD6yHjtl8xh/93v8dH//Gb+ToZCGqCnW3ML61Ufuq8pLg8uXL/B//1H/Gn/0zf5owphyKpoSftwHIgGldc7R/yF/683+B/+Jv/Jdcvu+evKqAwjgefexRfuBPfT9/6c/9RV788nNY25CMQlktRqxE2RWpt/aOuSs6fajTKvnVu8tf0SVy97mV8AjTuKLfv0l7/VWK9SGFE4WND5GbN24zbeYM64BJWvKSEmTrEeHWGnnmUsvH+5ToY6BteyobsNbw+vXXuPeeS1woKrz3VJMpR6s1xRiJfoTSszUpM0d05J5zgTNnz/CF5054+ZV9op9hqbFJzCQ2hhIJcbsSY94oiHAyOF0IcBXF2DmRbQVjxJhs3RAzYIgUx4iQqLURQEU8Ee6+0ApQOqJ1RNFTVYq61BRWkjY1mugjPgyY6HHGUFaO0lm65QLtDNo6eu+x1lJURTYDlvvT5vWHj36zF5FOeOipSFw8O6Nyj/CZ515hrRwXLlwUV3adxLQ4KAjCSGjHnhAGkpLd6HKxAhJb8wl7e7cpC42xMAydNCkh0q/XKC1+pUM3cnKwZGd+hbDqeO2VL3HSrXn8iccoLcTBC8kqwaSsCSiGqLl1uHzH6+/dUShlbryr3VYQtSJk70nRWI/oGCnLgjeuXQXniMawXK2pq0pO+sFz9sw5DvYPWa9bzp2/SDtIR2aKihihrmuGrpMRwsP9976HsqjFEgxQWhHTyJe/+CwhJr74+c/z9HvfyxuvvYYrHNPZjLIoJC0ve/ttiO5JKS6eP0fh5OcyOTdaoThZHPHYU0/wX/2Nv8GVK1fYtIUf/w2/ge/8rt/DX/srf40vfeELaGV5+JFH+I7f9e18/Td8AxcunifZu6Tc04FVfeVI+9X7PFA8/aEP8p/+yT/JX/8rf41COenktBK1h5buIypFOWl49bXX+LN/+s/yl//qf859D9wPKt+MwM6ZM3zvH/xe/vT3fb9otzfvVUBcfd7y86m3/DhfczP5lR/cfO1XSMrSW34nlYAwEseO1C9Iq0Pi8R3Gw5vUsUetB6K2qMJyfLDP6mjJmdlF2nAstK24MUUmI9MKbR0qBUw2SLFkzqJWrNYLLl66RBcSN2/vsbV9lsV6hZ1UDKs17eBR1rJeD0LGdhVGaQo8TXXCr/tgw1bd8tyLB5wsJ6QoqhCdCeAhermuN/ODEmAxIhncEbHJg1EI+tqAkjH5rdvfLGsgIaPvGEcBGTO5PiUYc7GMeLTqmMwiVSXfY/CQYg5wUxHnLJOyoqgrEXWMntIYCshelPLd4oZhkEnkKdu/bSqzeI72bE0nGAI705KPfehJjvvIyc1XWXlDFwXUUtqicFhjBUk34CqLHnvqxmCSZ1jtUusWBimstZEwNAWoQnHx/BkKp1m/9DI/8S/+AdPJNk88ei+37lxj1XU8/PCDmFigtWK5WrF75zZHxwdUkyt89Ot+L+1qG/hzX+tqBd4thTKBytxJ+asSeyjEIi0mT7taMKkqwjhycHjEZD5j3bbElJjPZiQfqKqa6XTGul0zmTQ00wm7d3aZTCbopJjWDb4TtQpW0/c9Tzz5VKYi5SV01Ozt7bJaramqhnvuvUKKI/t7d9je3qFppnmBfddOH2XyaiigjEgEXeE4XiyJXjrl3nu+7/t/gMtXrtxFhLNJ7AMPPshf/it/mR//8Z+gmUz4lm/5FqbTSQZgyJ3Z1xpVv/pjb/1IQoxSf9Nv/s3cvnaTv/t3/nsKV8h+TDlSGNnEoiqgqWpeeO4F/tT3/QB/7i/+eR5/8olTyVlC8egTj3PfAw9w6+oNMUgeRf/rfTg1ADltY9LdzvGtRV69vfy97WfdfD6dftXGAMWzvnOLsHsdEzsK6zGhJ64XFGGgcAbqghQi49BxtL9PaS3GpgzCyDWFglM/RK3zqyWoflQJHQI2JbHKMw5jod7a5tzZM7x55zZjhEuXL3Jn/4joA8Fqeq85XIxsbTcYElYJnadMa97/xBnufeAefv5T17lxa0mIlayYosGoBFpkiSElVIinsQ5BIWmeg5YcdBNRmKy24bRIbVRmKgM/KcQ8gouaxiZFwAgbQMEQPJVNlGUgxoFxsAKaKk9RV1TWUVuDsY6AADamrlHOSpCd1kh8sTjapzx2b5gpG815SqLpT95Tb5WyjycwcYbjgz2W7ZL7HnkfanaZqCuJ8rWygiiKkrJqKMuSOzevcefG6yi/plByhFhr2LjexOyUZAw4pzl3dpuLi7OM8TZVGXjz2kucHB9htGP/9hHJn2E2uY9nX/hZ1os3KSvD5599gXvvKdmeP/SOJerdUSiRjG6JOc0jOAI2pBQYxp71esXZs+e4fmeXECNV1XDn+BZFYZlsz1gdn/DEww8zn5TsbD2IVpHDhdAOLmzvMPYD57a3siTSo4oCYw33338/3gdM5nmFEGimUx5/6ilu377DAw8+yKpdsWpbtrZ3cEXBMPSnxUPpTYbxxg1Gbv/pfIKPkcPDYxKJsih46OGHvuYOTgEXLl7ke77newg5++d02FSn9PiveMXu/u/dDeVXP1SSLJvv/J7v5uj4mH/49/8hdVljlMrmCVIuQhRT4WndcO2Nq/zAn/x+/txf+PN89Ou/7nSkbiYNDz/8EDevXiOlyDD00k2kRF03GUSAr8z//FqwzEY19bZfRl5B+WsCFTzj+ohrX/gMaf8ms9Cyc2aGnZSgNb7vT12jlB8gBrqu5+jkmK2d7bvfPL+exiisNjImBgF0nBUT2ejFj9EosFqKQrtaoxO8cfUqZ8+dZXtnTt8HnIYQBiIGZw1jGOn6jqaYYLQhRtBGCpy2LefPDcynmqPjFUfHir53pJht/2L2A1CyZ4w65jFa+LdaaSxCnSPl8K60McTQ8n/aCI8yv5QhX4fGKlTc/PuITwN1HXE6gY9YEzHWUNUTjC2wakNn8qQQqMoSV1iSlgM9qbtAWwwRZU1+xzaXqspqoUTftTgngF8fRURijWGnqTg+XrL7+vNcfqLk133bb6dL2YMhjXTLNTfevMmNV25w/c2r1KUjjAMrP2K0xlrLcr3M0lJRizm7cV2Cpq6ZNTXWah56+Amef+4lFouRZ597nfvvfYzSfRPDeIP18Brrdo1zFcv2Ofru+te8fzaPd0mhhBBGQE6sjYpGRdm1rFdrjHX4ENnb36eZNAxjz2q9Ymd7G6UVTVNxbmeLxllCVqAsTxayg7GakgJXFiwXCzldfeD8hYvUzZR+GHFW4woLRlFPGr7j279DDBUKx2p9gjaaqijR1jIEjzWbMKVsU5sixpgsagRiYmtrmxjh8PCIcRi5dvUq2zs7X/W7b7qnjOW8rbNic1Oo0y+Wh3pLfeGXKpOn2ApFWfC9f+gPsn94yI//s3/BpKrRWuesoGzhldMUK1twvH/En/r+H+CP/u//KL/9d/4OmknDYrnkcH+faVNjjc6BbJ7JZMKZne23/BCJdy6Vd0v7W34dQoJ+veDN517kmX/1k8x04L2P3EulBohrTpYLFCPbYYZShpO9A67evMXxasF2U3D2zA5lWXJ4suD8xSnrrjsFrZTKjt4puzxphTWim475vZOuSA5LoiKOI+cmM04OD9Fa3IjGAKawLFdrquBQzuFswTD2DL2lquqNMzO9D+wfHdB2t7l0dof5ZODMWYVSDetlx3qdWC4Tq0GGaB8LovYovzFu2MQ2SAqm2AdssrizKz4qB3gJ7Qkjh3fMqZiaTOjWA9Z0TGdQlobCWWxZyt5bW0bv8dFTGCibGldI9IYxKgsQ0ikIqDONTWfu7wbAUTE76Aeh3sxnUzaTmlUGHQOVUVBZyjJxdPUF/sF/9zqf+/JLAt4SCT6hAlQTS1WVOON4/bU3aOqGnZ0dZvM5tpB8eesM2ijGccR7T13LNb2zfYaua3nphZcIo5fIjhDYP7lGNblJUcPF6Xmuvf4KWsOdg09y6ewV3unxriiUCdlJpig+jSQpkjolun5kuWjZmc85Xi7ph54L53e4ces2SilmsxnduuP+CxdoXCk7NWvZPzpmvV5jnWXwI1uzOclqToaOZA1DN/DUUw/lxk1UAToE4UjmsbG0kgR4dHhEUTiauhZTgHEEFTM1QxOTlvjbvMhOSTLpYoKmnnJ0dMJqteZLX3qW9z799Fd1lQreBnzICS02buu2p56Up2Tut3zFr/CRoRUFzXTCf/In/hP8OPCzP/GvKKuGEO8+WzytvonSOsZ+5G/+1/9XfuETn+S7vvu7+Ff/8sd57gtfYmd7m+Clk4spcub8Gaq6hjyCbYbrr2gsTyvj6ZpN5QMhikpmvVryhU99gh/7Bz/IA/OGD3z8w+wUAWcU7Vqxt1gJ+ViLSODO3gGHJ0v6sUeFEaUt2zuOdgjsHR4zqSfEJGT5u9EJclOnTLtRG7Q4yR5zs3sVC7dAVKCrQiaKrW0KpZm6gsVwjO97opaoWKM0wYtLjS4LSIr1ELl6/Q4hRvr1CTaMXJ7PKeuAuqjxUcb2vcXIamU5OlKsWjlmPBFrLGwMfrTBq83vsCn8YkRhEPs1lMoeD1JslBI7u2gimpZJ1bE9t1S1EQAn5kC4ZYdWkTOzmsZZbI5qSFoEEErpbLbLKeB610tAgJzTtGIS4+jRRqOtGImQc8NTFB5pVWgKJ4j3thp46NKcZQBtS2IQQr61hhQS4xBw1rFcLpnNpmgtNUKh8MOIsXkXqxRt1xFCwLiCiZ0ynU3QSTMGz0hkub7DtZt/mzGc8Oj9l7l8cUa9dQbtLMN67x3vondFoQSxtk+bwKScDx2jWPGDKEDu3L5zykFsVytm9ZStZsrJ0RFnt89gbcGqa7FFxcHJim70nJnOGLxntr1FNw4Mo6eZTOjWA/fdex9D3+dlf2IcA1YV4uxDvvhi5OBwn+3tbaqiZNmKTdbGeV1hMElO3RAzJSPr1rUGZyOuKPld3/m7+dZv+9Zfgh94t6JoZNf0+muv80/+fz/Cp575LH/tv/irXLnn0ulXb2gfb3+Ou49TqsbbABVBIufbc77vB76PqnT88x/9sUyDIpNWN/CQDHFOW6x1fPZTn+H5z38ph2w1+JDNYrXBastTTz5FVdVsMl+ExD/KGiVGopdkxb7v6dqe1XLJuhX7t3694s6tm7z+6iu8+uLzhNUx3/bxD/ORRx9g3ihKl6lMKaIZadsVt3ZHTk7WDIPk7jgknbAbI+0Y6ENiOFnhigYSmCimyjGvBsRUWVYtWhvGEOiHka7rCWFTRANj9IwpMt+a0fcjRDDasjWds797QApSGBOOIYyYcaBxOzK2h8De/gld1zNpDMbCmfkWddkACVNYhiA+BsFB0p51F2m7IK7m2aMgZZ8DSYgERRTBBSJH3aTsKJXE+Dd3okZpYvIoFUCPaLNkaxbRqacfoIsio9VFgdJC93KukIkuiCM72uQMp0B2q8zhYRm8zPaGigwQZlmoDyOzrZmg90llAE0I/lqDsZWsafxApTWXz864ufasO4g+YKxiGDxGW1zpeOChB3jl5Ze5cfM6IXm2ds7irKHQYvsWsjQ4xZh18pkxEKNgCSnlAg1PfuwxHnjwPRztHnDpwlneuPYmr730Eg/fd8871qd3RaFUCADrs6uazy3+EAPLbkUzrWn7luVqxZlz51ger9DRsDOdY31gVlZorXnz9h3euHaVdvT03UhE8mtIsiNcL1diQhoSTVlx4fx5TvlpMYjVVUYdN+NuSsK1a2ZTCSKLHmPyNkhJURBTYLIsWvSsKYg+19vA/Q/ez/f9qe/n7Plzv/SLkMD7kVs3b/NjP/rP+dF/+iPcunWbD33dR5jNmtxlvh37vtsnvR02eTuXWz630T6lFGmmNb/ve/83rFYrfuZf/TRV2eQ0SFl1ROLG5AiVFIUpYBSTBW8lWVH8DTXduqe0NT/94z/LMAYhdLcji5MFXdsy5BztEMS1hWweolRi7Nfs3b7OnRtXwXtmZeJ3/9bfyP0XztA0lrK2QgxPUBYlzWxGf7hPHAMmgm97lDICkBiND5HlusUnxcH+HmfPnxeT2bhRCknyoEaz8Tz1YZAUySBrB+dMBkMMtRL3I5UUZ86cYRwGFm1PMBZVlIQ40g4DOknGUZciyhmCUawXIzdv3KTSgaawNE3NZL6FsYlh7Bh8pB9E1SIgkPAbDYkRESGIfWA6NTOxogCXd/4t1moxSfFKG8ZI0qe7Q6sCTvU412P0muQ1pqxwTUkcBsLYSmMQJVu919lTYfRIjpRcQYKhKAhR4k42Mbr5OpRuPDAMPbOtufz8KQOeAkGhVMLYgpDEkNvpbOa8XsLJmvnkLKshYpQjB/biQ6JpKh54zwPs7e4SYsIYLftKLV6YTlsCYtIiOEcGBxWEnAVETJSuYT65zCd+6tPcuvE6s+0Jv+V3/FZIiaceeuwda9S7olCCOJzndZm4N0eRK3o/UMwmXL96Da011jkWizsUtmBWNqwOj6inDbuH+3z+y8+x6jqcdfmFKdAozmxvo0IidD2FUqS+57GHH8WoxI1bt7BWU5aOatIgBqlieKGtYdVtYkc1wcvbZzadV0Is+Yliu4iBCFFFMRbQwAi3bt3k+eef5/31B5nNpqe/81vpMDeuXef//T/8T/zMz/wsYQxsb834s3/uT/MNH/96Sqs4OTzE+zGf6tJZxBBk0e7EgGKjTT59frhbYDNyToLgA2fPXeBP/Gffx8Wzl/hH/+iH0NqQqXGAyQwE6QiNErOScfSMJBgHdDZ3aJo5i6M1n/zZZwRkSAk2sR35d4wx5PERfPQsF8csF0csT46JYaBQEVTPb/z4xzg/dcwcFDES2l46QmPQ2lKYCoLFI1p4bX3ekwZUEFFA7DqU1hwdn7BedxTOiuxSiR5Z5Yx3Rcqvf6IsnexptcQubH73kFUhBAFTOu+JStEOI0Xd0K+XOWxN4ijmW1sELVkyy9WKoe2Y1CVlkmumHUZ8P9IPnm7lRRPeFLjCYcuAskXeA97t6CSxcERFcFpn31bRA0bkEM/sJlLS2QHeEJJkBZkEpR45c9axvT2lNiKrHYcWFTw6JZwtiGPPoIHCssm4MZkRoRUMXpRMNgOA1iRRvnBqK8o4jBSlywodhXEWomQt+RQI44j34uSutcZq0MbhYs94eIBVmvNnL3K07GDY1ANxC6qqgnvvu2cjZIMcFRNSPLWhCz7lXHGVPU+FxhRSzJ+PXL16je2tLbYmj7Ecl/zcL3yaj3/k19P5dy6F74pCmVIiELOaNp9R3jOu1jRlRQqR1XLFfGtLjFbHnrOzGdYoVqPn4pkdok4s2xUpSZbHuG7ZmjSovmerqUl+xPuBqq5YLlZsNxP+2T/5p1y6934eevghytJhlcb7QVQLtgAFq8Ui95iBIbuob7S3Rkll8SkSgyCIShk29JZN/s+kavi5n/gZjvYO+eZv+U0EP7JeLxn8SN8NfOZTz/A//4Mf4sa1Gzz+xON88MMf5onHH+HBB+5jcec2JwqGPqCNZTKb4YoCa012fVeMq5H1yVpu8KxAWbUr1l2bc2cG1ss1fd/Tdx1d19O1AySFc3O+4dd9E5/57Kdo1yucETMGc1cgnsUAgsYyBlSQgmiU5cL5SyxOOrQWgr2xCmMsRhmstaL4MIqYBvbu3GRv/xbHB3vEYZDn15phHHjgwhkeue8SZWoJfcdoHSEHwxkHKQrPdYyit9Y6Ya3Gx5GUpXspeoIXh/V+PUi3O3eEmBjjSGEUKQUJJVMKl9EzMf0QMxONJsQkxfd0HZQIwdN1A/0IdVlwHKEdPD71zIuSpq4Zo2LReaq64vj4hEltuHR+hjWRwim0ljRD7QzlNGdgl4YQFYHEjV2Zbjb53kk5OYijLAetiojHk5JuL3dpkZSt9BRabWSRkajFGq+eerbPQlVaMfwnynQEqAA2BVGgIVZ5WmtsrgwSzqeJSaI3opHxO2WASeeYk3Eccc4waRrgbhyuT4khBFZdR4qyd3U5csMkMCmcpnB2x8fURcWZ2Zyb+2t8EA51DCm7D4lwIAQP1jEmSWZVKMbRn9LtjDGoGE+DA7UxWFMSfeRk/ypjGglx5D3veZjFuufkYJ/5hQvvWKPeFYUSBCgUvbd0Wd4PDN2ara0txnHk4qXLeGu4tbsnMsXplKHvSYVjsr3Ni6+/TIiR7a0doXtozaSZEGKgqmtWqxW9D5RKTttPf/5zPP7UUzz9wfcTfaAwEgsraGFiHDriAO1yQWUtPuU3iAw/powYo06X2CR9igKaTCg3EWZVw4tf+jLORx66dJnpbIIrLM4YfviHfph/8IP/kIv3PcDv+wN/gN/8zd9EVTpCLwXuuBtZtT2rtqVtc5Fb94z9yDAEgveEcWQMA8YaiZZFyPsxB6ql7L+Ygvh7brqPGAZC6kErnnriA9y+dYcb16+jdMRZIS2rJL+fMBHEDT6FyOgjH/nI1/Hgg48SkjrtWCWHSPQvRiXabsn1q29w/eprjN1alE9EisRpx6DGnvc9/DAn+3s052fi/enFcNZohfFWVjF+wCcvN5gyGGcwQcvNFD3jmDkiSpgIRycnVE0tfEMkmyeGu0YrKmflDH123MmHwziOkjMfNoF2UW68rgMfcUlx7lzDwcFNbAjYUGbDCIMrKk66kVW34PLFHaZlgSMxqStcWZBIjBGGuOE3KmrtqEt4TWuy1ElenJyL4zM1Z+MeL07hb1m/5A5YW+n+UAGFBxNAr9FVD2rAaCfdVoinXeiGcF9aQ6kV2skBp3IxVGx8oDVK5SkhSh+pVBJjwfwazWZbjFGe2/se76WwSw6QdKo63+wqbiJyNc5aLl04y60btxhPDpjNGnbmBYeLFh8VIYkLEom8E5UJ1GhDYawcakhMjDYm/zxJlHkotLGEHNkyjoHWt3R9y4svvcid23t83dPv4/O/8PPvWJ9+2UKplKqAnwbK/PX/MKX0F5RS7wH+HnAW+Azw+1NKg1KqBP4H4CPAPvDdKaXXf7nvQ0ziG5my7+FyKVkWVhPGyM7WnHvuf4Db53e5df0GjXXs7R0w3doCo7l1exelNbPZjMP9fbGFUlDNZnTA0nsGFOvR04VI7zueeO9TEpOg8jibC7VK0j1FDW/evEHTiOmsSVBY0YbeLZRywRrtSBEJpNLiUgRZJackDsEVFX0/cnv3NQ6PD7l15w7/+J/+KPc99Ai/4Zu+Fa01//Kf/ThxHEgRTpa9hIZZRyIXvSRWYUQtjkMxkNKAT0FO14RE2CqV2UuybNfKYI0VipN2WFeckpOTUhS25olHLW+++RrPv/h5FstdrDNsAtjSBhH2CWsrnv7Ah7j/gUfoB1mgay0msQotUcHjwJvXXuPVV18g+FZCrpLYdOmUvUGjEPRNikwnFWO/wjCXgpBCFiFIN+lDoB9FpuZDwmpNDFJMUwzErFWOSXLalTHc3jtg5/x5iYvVkAbk58jqpBGxkvAhiHFGNiLu+3wgZoK30eAUVNaIfNUaWh+4qSLT+ZSt+QyCpykMY4pcffMqjTPUhcbZxKSuaeqGhKLte1ZdTx+RqAUMqVASa+wLQsivN6K28WRa0N0GnxQVOsk1ldk3+XORlIwoVlTAEqhsj/IL1sctBQ1JW9q2JfiRui5omgJCOA3jK53F5HYyaaEa+SyH1CrrrYHCWEHlY8T7kfnWnNF71oMAYqURpQ3ZbUqeUziaOgg4pI0SQ18VmU1qxq0Z7XKNPzni/MWLlCYyjIGQFEPvGby4J4lhToCYxNcVYWz4HGQWU8xuR5oYAl23ZuhHunXL2I/0sUerxJDgYPcOy+WCZz77i/92hRLogW9OKS2VUg74WaXUjwL/KfA3Ukp/Tyn1fwf+Y+C/zf97mFJ6RCn1e4G/Dnz3O32DBIQNWTkl+n7g+GTJZDYl5NhWFSMzrZldusLum1dx0ykXLl1CW8Ph/j7Be5qqQWvNMPTsbM0Zh5GLl+YkHxj7gbqqZbQaAk889ihn53Px2MvcsA3KqJXspHxKPPfKKzz6yCNM59s4JznjymiGUaRiAnBnwnm+Yjc7nphyVITWDCnyC5/+NF/44uc4PD5k2a5Z9wNKWerJFq+9+ppQHvwgY0xRobTjzp1bVFWFc5YkHGWKbNVmjJDmrXUUtpSuwDkmZYUzVugcSbwuYxCtNypPclExZHOJFJOEbKnIhcv3ceHyvRwe3uL23pvs7d1mGDtCjJSm5MqZizz80ONUVSMRwjoJCj56NAGjI8cnh7z4wpc5OdrH2vzqxIAK0hmF/NoEyDdpIIQe65SMvyrrm5O4XxuQzk/J2Ne1HWUer1IYSX4kWrl+Igk0VE3N7Tt3uP+B+ygLLdVOSxHSyIGy8XeULHgpniiwxp5GhBiV7UeVFGFrrdBNuo6nHn1IDi8FhStJpuTatZv4fs3OuS0mZUkza3BFyWrwnCxaVuueFCLTpqHUBToC/Uhha3RQkBQe2X0LEV1WOWIIIV26ytK9qBCnHiN7TTmWpdOTKJyBohxoZg5jRo6PTsQp30hU72zWUFlDGAeGbkBbhzYWEAs9pQQsMWqjS8+80yCH0cnJCeM4cO78efGSjYG6KLHWir2pj7LTzL9Lkp9O/AoSkGOexblIc+bsWfZjInQtLI5oiBzt3uTSpctcvOceonLs7p9w6/CIzkd8CAzjIJzmfM34GCicmEq3bcdytaYfRtbrtVCeYiRm71gVE6XWtF3P5fsegM/8wr95oUzS128U4y7/ScA3A//r/PG/C/zFXCi/Pf83wD8E/m9KKZW+Mjfyq76PEI5ThGXbkYxF2VLcqHP06yuvvEyKcGZ7B1s3tOuWlCJh9Fy+cAmUpus6nHNMJg3dsqWpa4ZBYloLbejaDkLg6aeewoLwwzb0hjymeoRDdnR0yN6tO6xO1rzUvMS9997LpUsXKAqH1tAPIyp3eUnprLmVvZbScodFrXLnCXv7u5wcH0okhLUU1YR7rtxPUc1IuQqaosQ0Bf0gF8DFe64wn89Rxp7a+VvEgWaT3aOUGB+TO+MQVS7gMoIKHUZnowTZm24oJeTCJGdyIMaR6BOTapv33DvhgfseJqSeEDzRK0gFw6pnPDkm6siQ87ML5zA28sUXv8idWzelwGihOvmUICpMhFEFuZHze66SB4To7LTYbSmVGEOmQal0GsTmjKGpSnw/kLzHqhxy5j06uIwIi8xtNmm4MQ68/tqrPPLQA/RBkYzBI672OirQ0qVbFbLHKRRa1O3OOjFPiAGjpFsJ2kr3bSzndgrCtubg+ABbOsZo2N9dsHfzFpNZwfbWRPimydC2nsWiZ7FYkxI0pcMaiCpkE+GI0gVDigSrCBtneTTaWPwoxVpFpFCmCIxZ4msIMZ128ySx3IhEgg24HUt5dk5tZxIjoYQuY41h8AP94HHaUFQNVT3BGn2Xp2mMqNhIRO8Zc0S0xKdEvPfsnDtL0rKWKp0TfwMUaRQ/yaStuDNt7otcPDZ2gFG6JAGtjKVoGlYnx8TVMbOdMxzsH3JwcEIYEg8++ABnGsVsco6rtw+4dXAii5wocSpKS/QKKBbLJe26Y911DKPPr0ikjyPJ90wR/XqREu3JktD/OzDuVeJq+RngEeBvAa8ARykln7/kGrAhIt0DXJXil7xS6hgZz/e+4jn/CPBHACZ1LVZmMTL4kUXXYcqSIQZ830pwEhGjpEAoY1gvFnR9zzAMJK0oy0p2I8GzNZ8RU6Sez1DG0HXdhhxDPw5Mt+dcue9eYjZi3aAzKVedHG/C4d4+y+MTxi6yWra8ceMGTz72CO978klKW0CCYRzFYTrrTvWmu8wUjo2bdQKqSU0zq6mamul8m6QcFy5coV30uVA6XNlQ1A1huYIUhYqknRgN5wwVn2N9ZYQU9xgxrxWEOZyGUUn7KLu2ET8ObMxngw94HxjGkYQn4TMI43CuQmHlexiD06VYcVmNkuOFRCNxqArKsqTv1/ziZz7J7u4NbLay2nSFwhBIbKIpYt5jScEGVzgq65hP6+ypqVHWEaIXdxrrZOqIPYUrKKsKteFo5ueXjiZzQbOiqi4Kjvb3OdqesrOzw5BfI5sQxZaBqDVeCaigx7hpPHMx0RAE2R0z9WbjzuOMSOmayQzlKg73jnj5ldexwJXz55nP5wxjoO1GVuuBk+MTSX+sHGVp0CYx+A5lNdpWdL1j3Y1y4EUBUSJyMPoozvzCjVSZcSDvscT8ympFp5yJk8QpQbsRpTz9AHgY40jjrKwrgielRFnWEuNsk6DUme8YQmTsWvGhJKK1psqplMMwsFqtOHNmB200KUUKYyhyomJMiWQ0hSvxMdL7EaXEcpBMfTLY/PMnxm4QEDJlYJSIioEYPffc/wCf/+JzpBde4sxsyqQp6I6PeODCDsvVipPWgxKqUlWWJKXo+4GToyP6bmTwQn5P0TMG4fPiI6NWdH2P1orD3dtcPLPzb18ok/T1H1RKbQM/BDzxK/l3v8xz/m3gbwOc295OMY6nlmjLdsX29g7jOGCM4d777uXSxYt8+dkvsXdnD1eWKOvAOazRdMOADxLt2ZQVblKzalu2L5wjpcQ4DBTGyggXIw899BBFXRH6HmtFKytJGoLg6SQn3KuvvgZo+n4ktD2egS987guMbc8Hnn6aKtOPOj/iM8Kt8o0U8smuFafu0A8+eD8f+4aP8dIrL3P95m2MsRwfnWBUITIrFfFjS+eHrCgxJAykvMuKGxecSIojEc8YZJ8ZfSB5n2NZhTaEdqgkF6TVhqJQFFbjrMbUJcY0jGEk6axGSTpna0vXoJViNm2YTGqcM1SVparFm7GZTdma71A6yzOf/jT//f/zb9OeHFO5Aj+GLIcUlFmpjR44SSeXQFyLZO9ZbU2IRnO8WKGBatLknVM2b/XCIfRBQIGirGTf5CPt4FmuO2rnMqIr72FpHdOm4WRxzOL4hLquKaoq77DE1V4FWR0YoxHHyMiYghCjzXBqaovSaCNxrc45gkqs+payaQi64s7xmudeeZNQOK5cvoKrS4IXpU5SMKQBVcBse0q3WmQFGZRJYbUlmYrbeyN9a9Fe1g+b01ormzO6PT7JeGmz6UXKEjKdcnEnoLUUSsvIpBgphhPSiSeVDldZnJK4Xq2gKGrQhrbt8EDXdRA9KcpIuymOZVGcdoHDMNB1HWfPnJG4ZcBoQ5n34mMeHFVObjRaoZ1iHMNpegCIt4CPiSFE4uAliVNrKZRRsoC6fsUD911maz7ny5/7AjevX+N9730Sv14QQuTy2R0WV2+jjRN1Vz/SjVLEx65HJSitZQgjKXhMTDgPbTfS2ch0PkENhlffeJVv+Y3f8o716l8L9U4pHSmlfhL4OLCtlLK5q7wX2KjKrwP3AdeUUhbYQkCdd3zE3Pms1idUpUWnwLBYQmG4dvsmXd8SfeTylSvMZjNu7e/T9oMQdguLjgISGKXYms85e85wfLKgtYYxBqqioh880Qfe9/gTInU0BqeN6FmjnKIpG1wonbh27SrWGLzPTtJJM8SR5154kfXxio98+APUswlJK1LnRaeqN6YFufAadSp1bNuBftnh254YAoROzIKrOdbV2eVZcbI4IZKYzWYcH+7x5qtHNE2Dc46qynJGkP2ksRRFgakLlCrE+MFojJGxLeiCLAHONlkCSE2aCVVTMN+eUNQlVV3TTCdUdUVZFlRlgbVa9j3OSad3qkWX9+ql51/i//G3/y4//VM/hTPi9j5mCapoyAMxCTBilNBudNp0wFF4dFE4q+3gIQROTta06xa0JhiFtRZsOP2eMUSWy7VYuwVBTsexo06TDdhJiGCco5lIoezHgcEPFJQC/ysFKaCSFEiVR8KQYnbmUTmQLuYdqUGHgK4STlkMmllVMqI4XHa8/Po1kjWcP3sWDCzbAROliNimZnamwHUdi65luVyji5omZ7knBV4VvHGjJ6YaQ5CCqCRKVyWXi/Ug+/Fk5KdS4q6lxZzybTvAiCaonu1t2DnTYEuRgVqrM/jlxerNeEJMrIcOGxQqmexZAFVdU1hNYS1Jy/dqu46+65k1M/GjVInKOukUEzmaWMx1NXkllFKm7Aja7oPwKgOJEJLs+tFEraS4BtkDhwh2BNsPXN6eY9//Pl5/7Q3akAi2YHF0wvl7z2PSbclFRxNHz7DuGMcBbWUf6b0XlZUfgUTnR9Zjz7oPnLvnAucvX+HLX36ep973gX+7QqmUOg+MuUjWwLchAM1PAt+JIN/fC/zj/E9+OP/9E/nzP/HL7ieBPv8SbdcyrSa0iyXXr19D1wVh1/BmUTCtG7Z2djhZi+LDI4ycFLLTkJZ9zcFySVNKomLfCxChtGYcA81kwv333y8aXucoyzLHmAqyGmLKGuulmDAYgwpyYdqMMKaYuH79FiR43wffx/zMNnVlGbqRcZRthEJl+VTWUmiN8pGjg0OqYgbhmJO2k71ibJlMd9DakUjMZtsMwaOUYTKZMZtvUVSVJD4WDqXAFQXOWilmVmOdZTqdMN+aM502TBopfq4paCYTmlr+vclxEVoLh1FrnalZZD763ajbDLZmkEveqQjs7+3xP//g3+cH/z8/yHq5Zlo3eUGfcmcfs3Ik5Js304BSymOjyaCOxB8cHx3A6Fm3K1RdUWHF1NUrbIjo5LJuWEa1pq7woz8NSxsGzzB67Ea0kBTaaCbTCcYYyVQaPX70kjUepPBanZMOlRhkGAwhyU3sg1CGRB0TMECRpKNCGXThWA6RG7dvE8eWs5OarcIwKXP35SOjTxzu7bHuxf+0Kkuq2Rl2j1cUrqMqK9kDliW7RysBAqPQn5IWdYrsCjVaiSonKolhviu/ivkYViIYSEp8J8sRN0kkIyutlEZUVJn2A8aVRGXpQ6LzYmFm4kjlrBj6aolPGKMmDHLQjCFSVRPIH7dWMcbsepTEYDuEkRAlp12kjunUCzTm5IKAeDtopSidY2OrOAYRVFgjZHSn5UBFK7bP7DC5fYfQ9yhXcLC6w9y3DKElBtnLK+9Zr9d4FMoIwyX4wNgODIOn9SOrviV42bs+//prfOd/9Lt4/4c/yvPPvfiOdfBX0lFeBv5u3lNq4O+nlP6JUurLwN9TSv0V4LPA38lf/3eA/1Ep9TJwAPzeX+4bJARJWy0XZB4Ce8dHTM9sY8uSV66+KalpdY09OKA2jmlZ5Wxsk9FO+W2UEo5W37Y46xj7AWcFrev6jvc//V6mswnrTkKFmrrGIBkppzs7k3jz6i6j9xhbZpK5lmyRIIWZBDdu7XL885/iY7/u6zl37qwUlBgYxkyDyRZaKiOqWsO1W7eoihl1WdN5ePK9H+douWS9Pmb0I1orzp07h+k7+q5j58w2v/6bvp7JVoW1hrIsKMuC6WzKpGmEQvWWleTm5haCyeb/3y14X/m6Q0aK1d2v2YAim3+x2e9265ZP/cKn+G//1t/i5RdeZFpPmdYTUgxyGKnMuUwxFxj5dynHlLLZVypBvVOW5K1WLQdHC6Yu4km0fiCmxJCBGotiUhWUhRXrL2vY2Z6yWq3FcGIMtP1I0SRUpkMZq2iahrqqCWFg6HpoJpBk1PbDIF29gsI5SpsD01QBRrPuuxyuJlNEjJrgLSYj6OsRbh8suLO7y87WlLONY1a4HOsAymn6mBidkwNjDAxdizeGoDTHq56ymlFVW7z46ho/VigPpIQTWE3olJmS4xTYlPBJTCYiyL5U30WUTcq5Uzpgq5GoB2KKNEXBpCpxSmGSISRFlzSLPrJc9ywWHSEFrGlpSsO8NJTOELVmPQSSHzhzZou6quh8oBsD1qhTfmmKUiRj3o9unJhs9goISRzBNtefsWJPp62YiYyjF5CIHCIYZTfvCssQAkPbMYwjq+UJLkXU4KmSYm4UH3n0fmwzoShq+rbjjTdv8OzLr3MSEm2IjF2PHiNdN9IGz6BGJIcrcLJc8b/8o3/E+XMXOD5avGON+pWg3l8APvQ1Pv4q8PVf4+Md8Ht+ued960MBjJ5xsaapK4aUWI0Dly9dYv/omH6UfN7ZRGJpV8MCP53irKXKf5I2mNpQVAVpHNAkxrwjKuuK0Y8EP/LeJ5+SJba1TMuK2roc7enwLtBphU+GN9+8yugD2gaUE46eTglry8x5E7Biter45Cc+zQc/9D6uXLlCNSlJrYSWiXGKFH6fR77VeqCZncUPjrP3JC5c2eLe8gJnL8yYNTXTacN8PpUoCevY2ZozqctTHt3XfI9+6VcV/TU/t/kKdfpfm+d463OlBH4cePXll/nEz/08P/0TP8VLL7yE0Zr5ZIsURbamNKdgksoBUNGnnHWUeTg5NdFn0rFJkhpIBB8NQ7IoY3IkwUgi0fYjy9VadszWMGkq6tIxqQucs3Rtz8lixRgSy9Wa2faWSNiUAF1VKYFzq+Xi/0/dfwbrll73feDvCXvvN510872dIzoA3ciBAEiQoChGkxpTpqhgjzWSRqqaqSmPWJpy1bhqPnhmSrZKsqpUGo1KomyZlEhrBIpBJGgSAAWSSCRCowE0uhuNjjffk9+w937Cmg/r2e+5LREtuewPzVMF9A3nnHve/e69nrX+6x9IXU/seg2GrSuqyuOsJl6u2pa+7RmXLl2VHDW10xxrXyk1alxP6LslfYrsL1dcv3KdMztbnDk9Y1ZVNKbEf1SWtte0Rd+M2Z5skmOij4lVjCy7noW3LLsVXVdx9XKNpC0Sjk4yPmusg1cQiIghJdWBewrB3Gj2URIlrSMgKeMFjG3Z3ITJhmU2qhnVFc45UlTZ3/EicPV4wbWDOYeHc5aLVVF1JSwJ7+HMmU3Onz/NdDrG+Qm7xwFzuKKpKipjqJ3BGcFVRhd+owrnGvV1NSUJMiqsIkOQmzH4IVayOKWTErbYwaWc9QALkVg3OKwqy45WvPrqq2xOxoSUuHnzFpPJWGN3JbJRQzMyHLWBu09N2H7Ho/zGZ7+MyxWSNI45ZsWetZvUhqLyjqaq6VcrNibTN6xRbwpljgBtH8kZmqrhcHeXaTG6uLm/SxZh4mvG3pP7oFSUuuLw6IjOOvqqohlPGNUjNqYbjCvH0d4uMWVCDhqg1fVsbsx45OGHdaOYi1rA2pImh4ajW4PNgZdeegWMoY8BX2kXUlmPEU+KgMvraIqcE1956mmyhUsXLjCaNHRtQNAFlZhh8+uIMeFHOzzy4JP80I89yf2XttWkwdxeuGRtRVYIQLyuhJnbLhxaQF9fQ+XkE78D6CG3/aLwzk/eixC5fPUq/+aTn+Izn/4033rmGSRkaucZl+zqWLpIQUox1IfCZNY+orczCIxRDqU1GQquNhh1pBjZ3dvl1IVtpXJZKdvQQAr6cC9jT4yZtvG0XUdVqRnG/vFc6R+hU2MLrGq0nW6ON7Y2aFdzECHFgKmrYmQieFdR+wpvHW0IHLatLlmcp6mUMJ1tphlVTJoRoUtEHH2M7O/uM3GOs5sbeG8JCH0foc+k1BOzoe8CJmVC1ykrgqIscYaNUUXKhht7gcPlmF5qoqjtmXdWqUBlu53FKulaMv42QxJQh5zBtzRjiUTGdWA6GdRVeg/Mu8RyGTncO+LqzX2uHy6Yx0QWpQs5Ml4sCU+fDa/sthy3N7jn4mlObW+o6w8CMSh3Uyod463o/1JWtVCyZKPEb4PyeHGaADBQ8WyhCkkx+ZWswHKKUXFoIDcjFn1gOV9yfLSg8RX33nc/h4sl8+WSe8+fJZG1W8+B2K8IoWV//wYX776f09Mx1/Z6JFlCUKJ6GBIdS8dhvFIcXFVpHPMbfLw5CqXAwWKBVBUxJLr5gs3tbebzOYvlCgNszqas5gtyTmxsnsJYSxcD1biiRZjPjxnFTIiB6aRhXHtsaf+RTN+1PPH4E5w7e5bj48PikFwpP7EQsS0W66A/XnHtxg1M8YQMOYB3+HGN9TWmAlJgOp7hcSyXK4IEvvLVp8ki3HHxEuPJCGuVqjBsxLG60bxy5TlWLNkcv3uND+rYu7bw/Xfqm3zH31A4lbf/5XdoPf+oa29O3Mbnx3Oe/spT/Pqv/zqf++znOT6cMxmPqOsRONECGIpXI3pKK8k+69ZeVFrn0ZHbiCsZegXkLw/ksAHP+gtsZXjtyqs8etdpdR2vKmpj8b5m3IxYdR3z5ZK2C2qwkDJV5fBOC1zKidpUqBWbWxs/+8qytTVjb1cf1AGr1hz1pFtdb2kq1aNXlSO0PTH0hLbFVTW2VnhnlVd0ISO2Yr7sWCzmbI/H5L5j1UcNxMu6yLPWFIK4vvbZhsoXTYFG1M3GsIjCjQNPn7weMoAfMBQpqaRo3HEq7ubJym3QykkSI1kpL+I7NmeBphYkWfokLOKKG4dLbh4s2Ns7ZNVlghi1ZUMKrzXqcyCGJI6U4OCoJXeX6S9ucfeFM2xMFf4xxuGNxTMoawzW+tJJ6vOcsy5wbAmgG+CBmCNOVFs/bN5y2Q90XSCmzHS6wbLtONrfp10smE1n3H3pLrquZTFfMG48WxtTnDM0o4a6qvW+cZXSAZdzHr77Aru736QNCWJe09MwhdkybPTHIyYbM5r6j0Fcbc6ZvuvZnExYLluSsdhRw96VW0jO1FXFbLbB1atXsJVjtr3JjavXMMawubPN4eEhMSeanFjO54xrhx1VavpZV+Qirn/n25/EW0tT1TTWMK6b0k0qDSWbzKiyPPv8FY4Xc4xtMFmLSJcyKfdUtcFWqOLEZlbLlq7rsU2FsY6vPf0NUh+55847GZXuJbU9VgzeGaINpHiL3atLTEy6jeKPwBDlRPKrJON/q41cf9VJWf23C2xhYvzbDej6NxEIIXOtdI+/+Rsf58Xnv4U1hslowqnNTXXHThoelaOUzXIuhZBiGqE4lSnb9UH6Z41BsuJB1mpnrfSRk3iDjEVcxbOvXOZdTz7Cdm2JJlHVnpFzVM7ijBD6jhiFVdex7HomYx3Dq6pmZRZUlWLVRnRPrcsD0ZjZUzvM5+r0IwaqqqLvlUDtrWHkvR5iYmjqiuwsfUpQKaSzWnY4D9QjFn3k+t4+k9mEs2dPq2FKWaxIIWkbo9v0bFAhgh22zUIyxc6u123wYpFRNV4mSkaSqMbe2IJFinItk8XmpBLbsu8evHuG5U5FxvrAdBQhRxYrz62+49reHtduHhKDaMGVwZ1frfOyMYgZqctOeU9zFnpr2e8iy1f32D9ccd8dZzh7aovxxFHVypu0Xr1ZReFeNUExhhDLzykD8W64A3X7LUnx6dB1dCEQRLS7r2oODw85XBxhLZw6vc1sNqXtl0iGbrVge2Os9nH1COfGSBbaZYcBPVgXhzzx6F287fEHee6FK3z1mW/z6uUbuJCJVU1VNVSVxi1P6krx76Z5wxr1JimUJ8z8w6NDxk2DxMTh0REGmE2n9DnQhRUbsxkSA23bMhqPGDdjdvtdphsTmqqhsrC5PcFkBYYnpmGxmFNZ3X6+9PIrbG1tcOr0aSqSbrJVrKw3ioNnX/g2QQrPDqWNhKyuRU3fMp00GKnIznPqzA77+/t0IehIZxw3b95kczZle2uL0XhELlpVrREGS2Rzc6J4qhlM/YceQXmYrRFCGcZHAvVtt9t6E23UU1F/ffJ3rwcaTwpoGXpJAm3b88zT3+DX/tWv8JlPf5r5wT6jumFSj7HFb1NiVMlXGZNSVknmwBEdKrCgNmxqkKojmhQ3KJESM4EhS1JMrZCOMUPGo2EVDc+9eo0PPHYfpltCCogYHducxTuHmB5jDF2f6MKCVVNhfQXWkWMihB6smjroA59wpuL0zim6tiPESBcDdVNT1zUmZVIf1KXIWZxXMED1w2qsa5wlFtPc1Ef2D+fMj4/ZOrNDCJ3imZUqtXRQ1mJpkzr5ZKdTiZQQuj4k+pzIAfYXhv0j9UFNKZcin5TBQaNbfrHk7JFUrpSob6sUgq4UB3JxGWs6JlWH9ZF5yOwdz7m2u8/RcqmFrCRugh6+1gyLPEPM6pxkpTj/lD8PxhPE0h8GurjLcSuc3p5wamMMkxE2W8ZNhW8c1ihzIGbB+awRtAIMHTyiWfF90EO0cljvGfkK6ToOj45ZLJc45zh/apPpdEqSTNcFRuMJXViRUsvWzjmESL9YEeKCWOpH5T2NNWqy3LcYn3nsobt46MH7ODg45LnnX+Cp575NFw22qnGuxhtP7TyN/2MxemfqUcOya+ljz+lT29w6PFA+lDFszmYcHx1T+Yad2RZh0SJR2N7com1XiDFsbm+xmq/Yns2w3tAtg47dRuj7xDve8W5+6Id+mJdf/ja7uzfZ3dvjzMYGF89fwFVeMcuypf3a15/BuoqclBCmbinFTJVcRlChk55V1XLx0iVee+1VbOUZTaZcvHQR69SCvnaO8XgE0imRPCtW+dqrV3n18lUe3bgfwawND4bNcMYQM7Rdwo0tDpUpOu2XhmEWzedDC4Mpio3b0M7yVySgF1isIn/wmT/kY//sF/jiFz6HkcRk3DCbTIpm1haFSymMaXCnAe0GdLM54K5DpMLtgWhDHIGQMMapnj5DJpGGOAM3uHEL1gi+bvj6cy/wwScfo8qprDAEcejDbUsJMQbvK7oQOFqu2JiMGM1mrJZzQh+pneZSa8OkkQjT0YTTp89w/cZ1Do+PaUYjRt7hnKdbrSBEvKm0WFpV45gskFStMl+0OF+zXHXcvHGDceWpvPqTBpQ7S9ICK5IxErFlkZdzIiTlbKacWAX1o1wtI1f3LUdHFYjDGynORolk3frQMcUPtBfR4DGEZEvqpySqQThrEtn35FHkOPXs3zhgd39JSJCTdsvDOwRSDCQUE1U75WL0W/TwYtRJK5qCgwrsL3rStV36GIkhENuOzemUKjudqlyFjSo/jTGoMYxRJzCMSmRrXzEdN9R1QxBYLVccHs3BwGjUaKJq1vTM1fGCDEymG9Su4nixy9bGhNp5iCowSVHUI9VmJFuWfVcEH5BDJKQF1lZsNoYPv/ttvOOJx/j8U9/gqedeRIwjpsjuzVvkOIgM/+iPN0mhFJwT5kdHqpu1hltHh2QRZs2YSdVwsNxlMpryEz/+H/PL/+qXmdZjNsZTrt28QTNuMMZirWF7e4dMoutXTKsxISbaEHn4kccRY7jj4h1M73uEZtJw6/p1ru3dImRhZ2ubjcmURbfilVcv60gsihta67Ai5KiAdR8iiKPCcbh3zOJ4hXEVztTkDDunTjMbj1itliwXS6bjGdPJmNRqBgjGUpnAx3/jN3nwof8jMVlWXWRrowIzdCZgcyHN4lhpOQUoZq7ltL7tw8jQm6phAqKysIyhz4nnn/82/+PP/hyf/9TvQbtiVlUY3+ArxYvEUAqhrAugFklT1BKoCUQZu4ducMBWTQGiBoqHtaWw5nItfTl4kDL2FcMMoxK9g6M513cPuLgxorKqVA8l8EwMOLFEicQsGOfJObE3X1B7R+3HzOcrKlfRVLV6JmLo+8DIWnY2NmgX8+LRucQ1DbauGY8ndN0KsYbaN8X0VguarbSPb1xFxLJcrOgXS86ePQ2SyNmSM4QQdPGTSwQBCeOVH9inhBh1elI/S/W1TAJtrInBY1M52kq3mF1J3jT62o0TLQw2ab43inOYgW6VlRKUbMut+S6rwz2W/ZKAxRj1LXVZyf1KU1MprAz5VHbAixmgdNVyF/inspCT0GU4WPbEG3vU1TlqX9F3RxweHrG5s8nG5hhXWcQoS0VdnAy1VPji7qSmGJZsNDQNX1FNxspj7gPSdzhbulJr2RiPGI8qlss5k8pybmdL84OSjvApZRKKTccYODw+Ynt7oywZ1VO073qctfSdsDGe8NEPvZv77r+bT33mD9jbu04zmjJqJm9Yo94UhdIY5e4tjo/Z3DnF8XLF0fECEmxvbrJczDEZ3vue9/F93/dR/uW//BjTUaNpb6Hn/PY5csnCMXVFu2jJOePriuPjJZPxmLc/+XjhzhlwFc5VXLh0LxfvuZc2LDm4dYub+zf4nc/8G+bHC6xrEPTU0vWdbutyznSFyBp9TVM3xD4q3cE6xhsTDvYPWB2ri7PxDtMtGU+mTCcTWtPTh57JpOZrX3maq9d3mW6eKqOs+vcts6E2mjc9GXsyEEWdbCwqWRO0QzRldLdl82lKx6aRGoYuCft7+/zax36Zf/HPf552fqwncq3bfluyTqJkPYVTKrIas+Y0Z9KwX1h3lzpSU3TFWrU1lkCISbtEJJfinnWrmFWpRHkNTrR3zjmB0aJ++eYed5x+gMXxLlYyy9CzaHtCH+lTpu0DUSjcu6jph8CsqsjSETgkV5bJqFE8Lwk5BJpRw8UzZ9nb2yMsV3QxYWPC1zUj51TlBVhfqXrEqMGIJNXA91HY299nczphXDnIkZhKVLFoeqhZG4zodUrF6DlKKptX7TpHo4Y2G9oeUvR66BhDNFLibgtUUYxVhjRFL6JGtJJQgSB6MJFJObBY7rLIt4i2V2MXo5OBlaoEqSn+ObjPYwxOLDZnJakbjcdVFy+lcUXJWKeCjlSo1Mdt4vmXrxLvOM99ZzbxNrO3t8+1m9d1nK1rnPE4V2Yjyev7UxV4UoQOxSDZGaq6om5qNc/uA1XjqZ1lMvHk2NL3cza3NjHO0HWRENRcO2dVElkD7WpBt1yydedFjbnIOmW1IRbalmPVzgnLY+6+sMNP/fgPsnfc8Qdffor9/cM3rFFvikJpjaVbdlgcdd1w48YNEEPjKqbjEXu7u3hn+Mmf+tN884XnOTzc59T2DovFXDG8UcPR4RGnT+2AaNdmnCUYoe077r3jbu66cJ7nX3iWw+Oe0fgMTT0iRY31vHTnaR6+/2GchZ/9uX8GRqNx1dQhKQBYbmaMIZHKljLi8VTOk3JmtVziKsPVy1e4cPYczWRE6gN7BwfUdc2li3cwmnjsSrvccNTxlS8+zUd/9KPUhaKz6IWjYBg3MK2004hRI0waD8mfYI196R8Tauk0pP0khD5bVm3mD//gi/yTv///5bVnnmMyqrFujJA1JQ/19UuSSaYYTKRcqEKDbj0XOEDHaivDyE0ZnfXvNYNdyia8nC1FNSJGFw9WwGUtJk7AOzV1GLAzZxu+9PSzPHT3XcR5S+Xg6HiuKwtfk62wjEsQoV8ulX9XOtNgM2Itues4OD7Ge8dkwA6NYnr1eMTOmdPMjw5IIajxc0q4usJVGtkqfWA0mqgHqHckI4ROOFoeY5xhe3ODuvFKP8p6IYbERsoGWJdo6lqesv5P9EbXMKwkrDrL3n4gRYsxiWR6ekmFOuOLgYjed2q6XDyJESpRTiXGEBGCMbQ50MYF2QTFkI0uVaxotEmwkJzRySjrwWWKkielPHjJ6SJalMQTk/p45iQY1IldjUEsbcx8+7XrmLDkofvuYFaNGbLeQ9vSRwHncdYrCd+o4MI7R1Or/0DKicloqt6gfUfftdSTCRsbM9q+VVI6hmUfGE83qZopyzYQS60wviLG0s1L4mhxzObONk3TsFwca6aPUfpZVTlC6GlXrR4sknGjKTsbIz76wfcwPzzkt7/y9HesUW+KQgmGg8Nj6tGYPiYODw7JkplubJIydF3P/Xffw50XL/BLv/QxohH6HFitWibTKdmArypmTY2JkRwCflSxCj19irzrne9le3uHL3/5D/n4b3yJO859N9s7d+FrT9tFnP0S//X/+z8l5n2efeF5slW3ESkmsLq5LYTvYkYgBoJEiMqRkyRYbwmx5/DogNj3GK8P3/H8iLe97QlMtjgvjCYVeSVMmszvfurTfOQHPoxtavoER8eZVSoxE62hW4haYTWe3BhiBcYWzLHUcO8MjYfaqXmEiOF4HvjVj32cf/azf5+wOmY0UZqUSSiFRiKD+VQSHaulbIYENPcGAFkXzKHzGcZxjD5EOafhLMFg8EYKbmrWeSZGtLPw2RYzBSFHHcMVmwWy48q1Xb75witMTU9dGabjMSZnDo/mLJc9oesQI/SpxxS6TxbF4Yz1mCwc3Nhl4iqa7U1wqvXvu5a6UvpINZ7g6qgYYzFy8Bi8q8gpIl2kqitWy5ZYefqQOd7f586zp9ma1BpqJRmbNYdlODSkWJCdXMcSK1Gs5mLKmt1kLJER7WpQawsdEIr2PJUNvKqZwGQL4sjZrYukMZAsKJqbSDkSJRNMRv2swIsvnalijkMYmWYjKYy0pm8Zu9aQmyFLHtZ8WDVZGahIkI2lJfPN67sci/DInXdw59YmrqrJVac4rEAwJ1i6IKSYsSlQV56qUi/MGHoMwvbGpmYTdStGzQhnDDdvHdCMJpjccHCwIoae6biiamr6pNinJdOtOsbesbVziqP5gr5rmU6m2ujkhMmZdrVivlox8gZptWPu8wrrHKc3/phk5iy7jp2zp9k/PqLtW4wxzLY2mC9XWOv54Ac/yO/97qf57Oc/QzKwDIEuJs6MJ/Rty2w8wRroVi0uC7WxzMtD9b4PfxBX1fz0n/0LnNp5jN/91GWMP82F8w9yuL+g7b+J9zXPP/sy12/dAFtD2eQO/o1QOqisRTKWtLqcI7FXVxyHpRLdqLahI61WWGuZjiZcPHdex9CUMVXNdOTIIXHl5Ve4dfUG4/vu4KAzzBcGiZnsDMerTL9ibXxqsmjcAdD3QowZXxnMCMLIkCuDM4686PmF//7n+Z9+7ueYeHWBTsWSzDlbLLaSFkpR09eBe1j2tmWkT4Bu1sseHKR4YQ6mEbnwI60liZK8bYzqEj/s553CBrUBctHfpsR4OmF7ewdrHK+9+irO14ipWbQR31i2tnaQGLl29TLX9/YRLOONKdYa7rjrDmyRnF67dZODg0M2NzYYj2rqasTRoiUJNLXHSGY8HitsYax2TE4THvUtNuRs6WPGoqqabCzWOdply9Hxgrsunmd7VFFXem1yRjFrnwixxLTGtB69U0jr2I0QM0F0yWUzJGtYrAyxrxFRInSwlj5lGlCfTFskf2l4Rzw2WXzBJ5MTOqsmvzknTOqRJAypHA6DyydBWxqdYDW6wVAYDIobG2Mh27JB1/vYoZzOXCCRnNEuGl0u6YHpSGbKQVfxzEs3iBcTF87OmI1HzBqhQr1dQ1J3KkkqebRJyLEnhETtHU1JVOxXSzUPtpbF/IjQR3zdkLKwWhxji2DB+YZshRDUDzbHnsoK440ZkhLdaoV3pmSNZ5Lo/dbFjpharHO4AMZYat8Q+hVDvuV3+nhTFMqcM85rq7R/eEAWYVRXeGvZnS84v7PD8fEhX33maearJcZ5ur7HWse0GdHNj9k5dRZjDKu+p/KeCktue+68dJHHnniU1XyFsyP+9E//CE+88yr/+B9+kue/FbFmymNPnmG65Xn6a18jFeBHymk+kLGl0FpE0DGlkItz1tnIWEcOQgyJVGXFgiwYa3ns0UfYnM1ou46ULClaGufZmMLuwR4vvvgqZ+6+k/2DRHtkqK2FTWhGUFcQoiMFSJH12NuvwKCUltqAJOXIpa7n5//RP+Ff/vOfZ1IbchAcDi+QjNDHoBZoWYrNWUJMIYGX7i5nDe8aTInzGgPV0S8nwYiWTmt0wzwob4YH0haLNCQTu542BqpRw+ntbe6//34eefxxtna2Wa5aHn3kUX71V3+Nf/2vfx3nDIeLOZvTHb769WepjOPo8IjDYgX2+J338NbH3kJcHBMXR7icOHd6h2s3btF1Pcuu42gRaLtA5RzjpqaqXckTstTO4Ss1Qc5ZKTneOWLBPEeTCctVx8GyJRvY3T9gOp2CMbR9T8oWV6map6o8zmUwsbz2tPbFDKUTzEGVTtFm7bBTYpkcV68aJG6QI5oLM1inFZr5oJdXzDnpIkbUHR5jCcbQW5DcQ2pBVoC67tsM1peZvSxnwKp+XjLWWPD6/iVBsW1QupZRb1PN89FO1FjtdG1OVOipmQouO2qmJDfmIEe+cf0W+2HBfad3OD8ZMbFOw8VMidqwioc6P0xkXqGBrIFvYh25V3u0qnHU1YgYM31scc4wqio2xjN85Zl3LZCpvC01xJKz0K4WeAMj53Bl++2do8+RrmsZe6sHgGRiDhixeATTd29Yo94UhTKlxHQ8ZrVYslouMRi2phuEVUuMgb7v+OznPsvN+RFdymw0FX3bsjWb4UKitp6qqujaSIiJejyhjxkTDW+55yGqusa4DnHqnv3+97yFM2dP87P/6Hf49guv8viTHyGGBU899SW81ERBSdO5bIOLRYEUKohBT6vBadxi8WIxWEwQTDJkq5vcZIXdgz3uvOsSq27O9Rt7LFYdDz9wP2NXcWp7k6effo5H3/UBVkei4LobOgMlWyd1ICMVP8qUhG6lI7hrhF6UntJI5rd/4+P80i/+c0a16pQr60FKrGfK5YQtA+9A4zHDIXCiXgCKllhf8+DS7ilgKsU1nVwkmGWkKzklXd8iOTGbTrjr/vt5+OG3cP/9D3Dx3Hm2trYYTSaMRg3eaMH5a3/1r7IxnfCLv/jP2D04IEni5o19coZVSLowiZFPf/oLfP1rz/HBD7yH7fGYxeqA6ajhzJlT5K6n7TqO5nOWh0fMyYxHjmgbul79Dp331HVJEawdKWvMgzeG7Ax1XZGwLI7mXL1+gxgCs+mE+XzOkqx5RCUSwVvd2ldVpebN1tJ3Qe3EjOCSRXrNlu5L3nSMsDcXDudCimXCMJ4Kh7VZOZNFv3qiZNIoBsl57fcYi47epFbt+sySaLqyUPJK4bJDoqN29rEMSMaotHEY7cHqCCtgRZ2+1zRxBTIxJuNNyRhCSAaM94y2NumBEIVVdrx2a8X+fs+FzSkXdjaYTkdQOSjpja7QpjR6OdM4hWECSfnMUQhdR9v1ONco48QIdWWZTkZUTpMiu67FVq7ECysDpJuvkBxpJrU61ZdlngFi1+IFaqc+AcYoyZ/U423FIPz4Th9vikIJQtPU3Lh+jZAStbHMxmP2Dg7IGNqYmLdLjtsV3tfYrGPvqK5YrZbMtreJQNf3VEYzZJb9ihQy586e4/c/8TvU1nHq9CkefvxxTF3xwGOX+C/+7z/Opz/5eZ548g4OD4549tnncQW8Hoi5GL1X1BlHO8th3ZjJeOtVB+4qBddjLEsdj28qchauXr/OPXffze6t67z84kvce9/9bIxrCDAdjdm9vkvshMo6jNft+s0rgVHlGE8cXSfkhHZ52ZAi9J3BeX3wYoDKC1defZmf/Yf/AGekGL/adZTumjg+aMnW65mBJynl16qXFtRNG1H8dcAlo8m4IZegdJgphKLTjdRVzemzp7nv/vt46P77uOuOu5jNNmjqEdPJlLryNKOGqq6ZTad4U9gEIvzn//u/yObmJv/053+O+aIjhExEt/u1tzRYDDXX9vb52K/8Bme3t7h07hxnz5zm9M6MrY0pzXiFtZlKIu1qxdF8ycSOEBQWqFxFNqls6VVuaMuMq9it1cyd0ZjQB0BdrVqEUVPTm0AuxpfOWjVXNi1Yp8ugLNTOI8YSQofBqIVgEvoeut5wNK/ourrIB5UvarKDrO4/WZJOMsYRs26m1d9TLcesLVNMShAjzmZyjvpe6VtZGBCmjO7lni0/szGURkApZ7lEl6g93uA+pXeEGf4nRouzmKKwyow3LnDhvse4/Nq3kdTRZ0vA0yY4uLXkpcMls3HN5qRmczJiOqrwmMKISFiT6VeRSpQXTF1hXU093aAik2OClBh5x3Rc0zgDEum7lqauqUYaFbNqW0KhE42ahmnt8VhiNsSUaKOaEY9rXSytESFABo7rv6dCvSkKpbG6BDleHCMIk9EYAyz6DmM9ftRweLBAMEybESZlGq+M+tB1zCZjtfpKOgJ3IdKHxHRjg5/6s3+OixcucHDrFrt7+3z9K1+lrmsuXLrE2Uvn+cn/3Q+QuyVfevobXNm9iRiv4K9oJKp1il+ZlIhG85OlLDV0EZJwYvS/RWkSY9TNtFHfyL5Xb8lTp87x4rdf5d6772e6ucPXv/oMbR7xg9/zIyxXeuJLEJyF5SLB2IPPdAEkovQOrx1t0ueC0EGqYOSF3/r1X2N1vM+0HmmmcZIy/t6mIR+IzJSxe1CzlQXEsLhJxUpOyczDQDiQ4SH0HaG48Zza3OTRt76Vtzz8MPffdx/jTR1Vu1aDtLq249aNXe679z7Obm6yMZ1oZlBdr/NUKAuVP/UT/zHf+taLfO6zn6XyjsoI3inwLs7ijKcyhtQFbu0dc/PWEYZvMduc8PjD9/LI/Zc4d8dd7DU3aW/cYG++ZLfNSgsT2JrOOLOzxfbWTF2PUlAzlBRxQNd36vLTBy6cPY0hkWJAUtJFz0CbKsT0lBJiHNlqcfMlDsGWBUaXAm2GNkDbJpatYT4f0/eNutiT1cw4szbrsCIksay9QYtTEFmwXjt6J6molzJYdVwq6U+KrUupq9auD8bKmcGPRKESW5ZQWTTV0ZYxlkEXr0sdU7KkhuINkTYlfuD7f4x77n+E3V/+efp2SSaoRBJBvKPFkjpYhY79ZU/TVGyOGybWMLLCqIJJ3VAV2lNynmgdbYjYHCFFxnXFuGmo7WDJFrCVB+9YLXtCKDnkVqgqT+UNzlk8ji4J87anR2jc4ByfSVYxXIs6WKWk7vFv9PGmKJTOWo4Xc/rQ44xhYzphsVhoeNH2Burikxj5WuNiW912x5xpRg2Nq+hCT4yJUTMih0Rc9TzyxKOcvXCBsGoZ1Q2PvuUtWOc5Op5z7epVrr3yEqNRw9mLZ/niF79I12Wct2WraFXHakBcJrm8jjXNBd9S/C4TUtRRx3kNORpUEIWMXFUVT3/9Gzzw4MNcuud+ViLsh8iZBx7iHe/9MG97/3ex7JRsrFkthsm0KmRZCEHhJrLBK0eXmDNkQ530AVscHvK7n/ok49pjRLGynDMxRc3WGVbLt90Q1priApSwllK0lGMnCXzRKFujoLiaIEc2plMeuudeHn/8cd761rdy1513Mp3MAGhXLe1iyWrVknEY55lOp1w8ex4jwtbmhh6MOdPFiPdepaWvvsJXnnqK55/7Ftdv7WokBaaEipk1x1QKT7QaQqwK6fjgaMkXvvINvv3SS7zn7W/joQfuY+vcJa5fu8LiYJ9lt2Cx6jncu8ne3pR777qTC2dP0Ywqsljt6GIghUBlRW3DpiOctcTQE0NAki7EQkqs2o5YOu0+JhKmSHGBVAxDstKeVhHIXo0fWkvXbZNSpfzRZEBcKXEWU6YGQEPZTCHw24FlkMAmKgMSA0Im2J5oey2sRu9JinFGzAkraoJrRHHKJJrpNNCZcAN3cqB2aIEdMoKSFD9WLS1KPRrVvPv7fpjN2UW+8Hu/z1G7ZDnfQ1LWZM4ybeWShSNG49CsFapxjcsZaTtip+7rrvLkFIkSCX1gUgmzccO4rjSBMxtWKdNHZbwsD1d4VzFqxpAzlTWM6xpf6QKqT4ZVCgQEcW4dmyGSy9a+JA9I8fO0fxxGb2PYPz5CRBhVNePxmGtH13HGcPrUFtdv7QEwm87IolrY0WhE7AOnT53GiObiCHrfZcmkHHnvB97PeDJm1XZY64rhbmZzOmXr8cdIIhzv73L5xkt84lOfovGFY1g5XHFDURJ1xuQIGHzlydbQhY6UAqCE7Cyqm5WsQUu6cSvytgxHh0c8+8w3OH3qFGfOnOIn//yf4/ylO+mT4agVQh+KGqHS1L9elyajqd64KQm5E9xE40jrytAHwVrDZGp45kvfZPf6NTZGNbEQnSWnkweAUghF1h6Vw17aiV0rcU5Y5lpU+9BhEc6fO8Pjj72Xd73rnTz0wAOc3tghpqgW+0YNP0AB92Y8YWsyxdU1xjmapmE0ajBAEKFdtewf7nPlyhW++fWv842vf50XXnyRW/u74ByTZoL1mpFthm5qrRAypUMuRckoR69CvSxv3Dzit3779/n615/nAx94J/fcfT+L2XWuXXmVjanq7lOCm9cvc3TrKucvnmPz1DYj77F1RVN55ZfGAVNMeKspf6lQouq6ZmMyJWflAmZR3DdmIRYn9UgmRf3ZfRRyl+hNojUVObh1ZK+mzOq100gFpeGYpC5MUZSONsRwFC4UKUdsUnZEkI5I0CJZ+LCmrL91660YnuYgaQHUGGPAyprSZMqMbopiKqMVTvUH+jNaC4s+8sh738sDb3mMFBre+vb388XDayyXC5Be1VQ4jEk41EjFZdW/d11iVRtM3ZDE0ZPwVnAp4yTisWyPx0wmHl+swhd9T9+nkusNxmdGjfo8hBBwFsaNxjUnYwg5E2JklZRorl25mou40uRkUcVblkzIQnidA9e/+/GmKJQpJZadPrSbkwlt37GKPRvjCSb19O2CUVWxOZuyt7dLNW5UNmEMbtSwSpE2J6zXyNGQFct557vfBWj+RxZUkpYypqoAhzOwub1DvVkTO10KWZ8RZzSWoWidTdHxGmOw3hMl0UunHUQJBxGjuTvO+oIngskZFSsrcNSvVlx++UU+9N0f4L577kKsYSKwMTKcnVnmy0i/yrSd5ejIkDuDmRQT4GKKUBXnIqlVTjiaGEbjzFef+hLkTOw1lyQXovGwrRY5CfgaPgwlezxnNSIuGm/nnI4kMfGOt7+LP/Nn/jRnT+0wrjUSNsfE0dEROWfqxjMdj9maTiALwVnyqMY4D9bj6oaQEvPlips3bvDNZ57hqa9+leeff57d3Zt0yyW+ZOPMJmPFyEqBHLh9KuAZPAw5YdznEkFQOjAxaklmyLz62lWu/epv8o63Pcq7n3yU83c6rl1+VQOmrMW7Bkvi5vWb3Ly5x2hUM2kappMxTVXR1DXOOkxlqf1oHfM6FIxUNmsWIaWgXM2USblRNgSZnBzZaDKoSZmYLe3LEHqnB75NiM2IiWTrkDgQc1AMTxKucDYFSFGLikgi9Ctq8eA9beqU02vAGreGSBj61MLmSqWLAqNL7xMYXrmua8OBAtRkPYhN8UvVa5yIzvADP/wTNFVNTJZ3vve7ePbpz9Mu9mkXJR6r3EvRgPiiIkqqomlTAvGkDpaLYzbGns2RY2SE2kMIPYt5j3WikbfZ4K2qfSpn8ZVhUlfkGJEUGG/MGDmnt4QY2i4yXy6JOTFuxspvdYOLU4muyEqP6zO0QT0J3ujjzVEocybmjDeGUTPicD4ni7C9uUFfLPs3N7fUGzJGJhszYorMJmPlusWebKCuK2JMBEmcvXSJex98AF/Xiu85B1V5ucaQ+xWx7TGV59XLV3jptZeg9uA0hwZjyKm4NRsKsdUXV+9eRfQyuPeYta415VjeDIvxFgmJkEUtu8qD//VvPsuyXTEeTzAGKpOpKst42yNb0EZYtLB/Uwi9GnxUtmI0snivZ4TK3cDXcHy4z6c++dtYp1zGVLpDyal0KYPCBoYljoapRT1ds7qv26IGUfqPWqRduXqN0XjKdLrBbDTCiRBWS2ZVo7KzsapaLGqE4CqLyYrPHS8XfPuZZ3jmmW/y1Fee4qUXX+To8BCRjK88rnI0s8naBV45nZr/PayXUlk4SOnQKYYkJqnkUkQddjCZhOCMbobFWxYx8ftffIrnX3qZ7/7g+zh331u4eeMqq9WCSVXh8WyMJuo+3rUsQmK56kgxaEExhqoZMR43jOqKUd3gvaPyVSGUF0OSkkXUeIMYrwdVzoRe0zIrb8B5UqpZLAMpeZCESEB14Q41PnZQuhxH2cqKYoQGKcmGkZiCFm0gm0SQQDYZh3oArN2citO4Q+lHUXSzXRunTkFlUZMFDcgrXyNFmePsCY1YvEqcsoXpzimefPt7sWIJfWL77CXue+TdLA9vEpZHSsDPvfKBvXpo9kZzeVIMSKdYZ+Vrln3m+rVXuXR6kzvObGFEre+slMbE6WHDIFxwBkmR1GXq2uPrMcZZupRKJymsuh4RmDYjalcMO4p0MyE01uOMJ8ZIHwNJhMr/cSCc54w3urGiqui6jrH3TKcTrty4ieCZTmesFgu88+o23fc022PKnISVhDHQ9oHVqueDH3qS02fOkvoSEmXdsL5GbyU9u8Vknvna1+hWLdV4puNy2fZaKc4q3mGduuD0qddOzTlsKhJAo+l9ttyUphDTTRQNwHIKio+aii4k/vO/9BeZjcfrEx0UVzEo+bnysLFhuHU146PDJU2CzI0MkxeEknSXA//0Z/8JV1+9wrSuCCkQs9pYOatGqcrfKyOZIgHEFNWQsnh1asqiciujZE3Bs4aXX3mJj/3Sv+Sv/aW/QtM0bIxq3OYUU75fnxN9DLQxsmpbFoslL1+5xnPf+Caf/8IX+NaLLxK7toy2jtFsjK5msuKiBRWNOeuirPhdrgtBFvURskBKuJQxIUF5b2SgbYnSqRLlwSKXba7j6u4RH/vXn+Tdb3+Cxx69H3e8y+JgH1M1VM4yaqByDX0fiJLwTUNlPd2q5eDgmCvXb9D1nd6jdU3laprJGFc3GvjmbaHl2NJI6ZY6hkDoF7Rd4rhP7B7V7N46jeBwJV45Y7Bl0aQdoSDZkARiYVtY0XvXOogkkKimKMYQTEeSXg9DO8TB6phNSUeUXFIQKS7lkjDi1jxJIa837QN3FhF8mfeVrZRxpmIVe5582/vY3LmDlKCqLdkY3vvhj/DiN79MbPc4vnWLmB0mJ6psSQntgo12w2IM2EDM0Ew2ODg44tkXr3B0cMg9F89w4fQWzivlLIuS+L2XNZZZ+UrvJyx9SMznXVlMwWrVIiLMmppx5TVZtQSZWVH9e18mqJD0GR/XXpkEb/DxpiiUTVXz4PmLYNWabFw3TCZjDWFfdYyrilFVsd+1NE2DL3hRU9WI5IIjqXxt+N/73vNuddo2GVtV5D4QlyvqyRisJYa0PqW/8IUvYK2C7Vb0TclIKSzlVMuZ0AU1f7XaRSJa2PTktkXuVTaKhadhS0jSaDpmNpnQ7gW2t07pphclIUs2xKDO3TE5OgzLeaZfLqloGBlY9S2yMNTe4wjYLmGrzNVnX+LjH/slam+JkshGb4w+Fd1wcSAyohQj9dZUbNWLK3wySCkWwy79HP3pLN5ZfvfTn+aJRx/jx3/kh+n6QOPUfLYLgWXbcfXGNb790ou88MILPP+tF3jl6hW6xYpV22J9xaiaKQzntMNJ+uTopreojmJSegoGXZjdtnUyWEyUkl9tFc3IxehWP0H5eWUJR5mQfcHcxHj6JHzmi09xY3eXj3zgHWw2Y65fuYLNHtdUGOtxtQHJ646tGdU04zEbeUbbtiznc1IfmR8vSAf7GOPW8cBDumHGqEVfofNI7ukTLJLnaDGiT41KGeUED1YZozrqxCyQNdMd9Dqn4T40+j5JjlTGgRUGISrDthstbKbECw+hXgq7lPuWotkeNtTFw0AP6sE/NFNoxKrmQbvOEB3f+30/gjENfZfxtebNnzp1nseeeD9f+sw13PERNmjMRIqJpD5uGGtINtMTyLKgrjWJ3U2m9PM5L++3XDl4ia1Jw/3nznHuzA5bmzO800O7LtEtKQqHbVuUOZbKV+SI0oBQ2mAzrsiSWYVex26nP2cfoQsr9YEtETAOq03DG3y8KQqlcZatUzsqcxJhOpvhCin1zM42VV2XMRKmk6luxcZjKu/VoUUyddWQQkRSYmM24/HHH8fUqo7JIdPHHm91rLHWYZ1FsmE+n/O1p7+GdZ6uj4hEjVF1rtAObNGftrR9p1GmOekNK8XmClM6Gr3hBB2bnHGMxw3jjQmudvQhsjHaIhz0XH7mW3RpATisNLRHClhvnb6Dalwz7RJ3ba4w5hArEVNp9o/tOhwByY7YCl/81G+QlkfU00mRJCbVXgv0ZGzMyhWjxFqkqEscZ9fjuRYbKZpltVmzxmKtuumkGPmf/sW/4JGHHub+u+/k5mLBtRs3+eazz/LcC9/i5Vdf5fDwUPPIRen5ZLB1jWAIRQWUkxZs7aRKdyyar5Kz5mc7VEEkhdtnBSo0UldK9xuBZK2SvEv3s47cHcKGikGOsyCiJOlsDM+/9BoSAh/90Hs5e+Gi4pbOUXmLrypqXzDRpJnQMalF12wyYjpqCH3keLnUBMikGKURwYlowJ2vEOs1JiH15OQJZFLwxH5CjrVi2UaLlBpU6A+bTS4KHTXQ1eWJLrDEWJI15BBLGJ4hmUyQHh0oi5myWO0mGShfZYoo76kSH+SkqArloGdNIzOl08xl+jAZ7S69MGm2ue++x+lb0cPN6NeFYHjyPR/kmW98gX5xSLdUY8CYVdqpmeSA0+IdQk+OHoUaAD9isQoY4zk+itxaXOPU7pwH772TmsCkdjS1YgEVhtoaKudoRjV9Tto5xkzTVNQjR7ZGpaNA4ytCioSup+sDrqlx3pX3wRb+6h+DZY4AndXOzAKNc0hKVLVjfLouXZBw/tw5vK/o246N2QxjLe1qBdZhnCP2gdAH3vLo49z/lgehspjKYmponEX6DN5BZXC5Rqxw5ZUXuHzlsrqpGCHGni5qKJi1lrqqIGuhzbkUGlEpo2I5JStZtOBTlC3NqGE8HjOeFL9H1DRh7BxN3+MPjpmMLeOtkbqsbExIGWJqWSxvkI8P8F3HYtWyWrasVksW8zntap92dUzbJZI4PvuZz1B5q3ptBsK4rDtJst7oOSWVFxpb/AaFlDUcXgt7wcGMUm8q75Ue5fQkv37rFv/6Nz/OudOneP7557hxc5fd3V36GOhiWC+7Ui7LoaTwhjUaAhaLYxGla8yUmlawSdwgBwWTDVW5gY2oNZspndaAo1GK8doD02p3LzqP6+sEBt9LK0WbbSwvXL5B+t0/4Ac/8kHuuPs+Xn35ebY2Z2w0I/38pGqj8XjMKnbEPpBDpq4bJhsz/LiYBS/U1iuHQMxKxFe+pbqOu4JjpmyIoSH1YyRpLG4WpfaYgqsKAYtitDqplEVOHg4NxYApEbtiIftMSB2xKHd0ypHhJQNSIBhDLprx4RDXvytpiSlRY8s5MxDE1ArPYkoWvPIhn3jH93D67EXaSDl8BSOZurHYU+d59/s/yu99/Aaj2SHzo56+LfdCTPpeWZ0YnDcahWKsOg0lELR4icl0GNqjBVy9xcVTm+wdH7OxOWE0adiqrRZK3yDZ0vYdIpFJ45jUDsmG0AVCFlLKLLolEkKRXDa4qiKXhQ5GEKvX740+3hSFEhksqxj+b72J01hLjcYcWS1EfjImxsjR8TFt6KlHI0LM5Ax9CLzjXe9iurEBUQuAswZTewzpBLeRTCTzta89zWK+oJnM9JT3ikWmPtJ3HX27UgK2SDmJ9Tay1haPP4vJCUxJmnOW6XTKbDIZFqQM20fJcPbiaaqx0wTJ6wvaOOfo6Cartme56ulWkeV8zsHhLbAw3dhkOtlgOt5kOpmxc/4So1FFMxnR5cgv/cavqpFpGbHIqENQTMr7C7Ludo2cdBlIXm+8DcXn0hq81ygF6/SkN6XrE2v5zU98AiO6BDLGKOWKE5Pf212GkKLhL5tUQcm9Tgb5mHJSsfp3UsbkmAUvYDVIRuVuScrixKw12pJjKTSsG8jhQR8WEBnWxiXKNFHf9A7Hc69ew3/683z0Q+9i+8w5dm9dp6o8Yzd0c/pzjcZjovN0q462D0gf8XXNdDphPGropxPmR8d0q5VCHGgcagACqr8P4unjlJjHqmkmYos9XZl6sTnhjcWSyRK0YGe9921pl6UIIYx1iDUkG+hTr5Qg8ror0nCzgX5+2xKMrHSMdfiZvkqbleQeRMDZguAPzvlqHGycoV0lvuvD34cfjTALKUXZlCKtz9ljj72Xr37+92mXuyxWc1IXlOaVlZamcRMGjFNJJUqxSmhmVZ86ndKKNHj/6Ej1+iLMbxxy6swO1awhmUQbOpVVOqGpNASu7wMhZdoUSEXuW1tP3YyxVqN4RfS5kJDAG4yDf8/S+81RKA3gy8mJ1Tf3dkmRiHYf1hSsBFm7SVe+whhLiklPxrrhiSefUKpDVJfwYoUCtVVYsJzKtvF8/gufw1eVbluN1e24scq5LKNpLlkxAwEXOfEXXFvrGGE0rtmabTGq6zUuZMvrUeVJpm0P+f3PfYrGVWxOZuzsbHL27Dkms4bxeINJs0VOjpA1aVCkJ/U9oTes+kCbEstVz958wdef/yYHix5fjUtxymWLrRk3apdfMKnyd5QxLBUfSmcUD6u80/HImPXCKDMUHeX7hZyxRhdZWvzK86z8HVWBAFa085Msa2qLNtqy3l4bdFzNKSPO6qhXChylI7boF2YxxWPRrgu8UlnKdne4R0SLiZLtixIDo2YhqHdlFF2UeGN54ZVX2XxqzIc++B4OFy03do84v73J2DtdVPWByuiW2449LiaOFwvCcsl4NFLu3nRGU1Us5gvaVUvfdThryQZCSMRo6FNN346I2SI24iSW6FZlsirbySHJFqywFCGj7jvlt+VWU9dy/btITyAVkrmx2tW/frQeTo2y0S7O85iTrbhxRmk85uR5VKaBGgmLXnZG423e8e73Y5yl8qIx0EmQpHLa2hjMaIN3vfcj/NZvvMR4fMxq1ZFMV16lKYqYMuUU7L9PkShaLJMZeMuK3dK1VIdHnD91itBmDo87JBrGtcP7nnFj2agqrHHElSChgxzIzlJVTQmo8xgjZKPcXETIKarG33qFMoY2+jt8vCkKJQxdhb5TOpaVm7yo+IeOiAJs6xcVDWqv4HsKidM7p3jg4YfWmleDJYeCkXhtM4yxSMoczec89dRXNIyq4JFJCl/NWbxtiCmw6lLpWoYzGt2AFLWEwTDbmLGzs1NMKFi76JjSBUvOpBC5eOEcP/ETP4KEiGSHdQ191xJ7jY349vVXuLa7z+7BgnYRCcfHrFbHLELPcbdiFTpCq4fF5SuXcXZKjKVj0vtf6Skl6S5nu9Z4m/IaBNXq5hQx3tHYSrEaKdQiALQjNYUssC6Mthi72qpQaGQ9vuUCRQiFRpeHbvKkQIuhhLBR8FzV5IayVfJkoimSxYJrqvPNQJMxii9LRpKa44oINuX1v6Xlx6LmyoYkupm1diBja4eVHXz56Wd47eoNPvLdH2B/2XJwdIzd2sQ5DZuLy47N6QbOQW09G9axOD7i4OYt8myT8XTKaDTB1zX1csVyMSdFJVpXtiI6YTmvyKEqru49Luv6JkgGvP581AjFlRmla+l7pR2eMw5nK6zpMLiyvQ1k8lqtNEAZg8NQFlXHGFugCvS9zGTFMa2s4ySCpdir2fUIrzzWcq2z8Ohj72DUnCamrNh91MMsBCGETFV7HI7H3vpOvvG1zxFXc5bLOV3f6bJQSqeYNCkg5ljs6aJir4YCS5T7sLyvh/Mlo3rE6c1N2uUCU1UE6zBd5uqtY2pvmI48W+OKiRMaB9475TinRIhRcW9nyMZCTjTOUTtPVZqs2L1xpXwTFcpyo5dN3GAQqxkbJ6dkQb6GpwithspPjCLcdc/dXLx4UVvpslg2vUBIJO+xTt8sXObKqy9x5dpVfD0l9zpSJhvpJZOdPtB9iMVJRbTwlJ/HGQWAK6e5wtNmXIi9CZdV7WJRukjOlMKVuHjxTj7/+S/RrZakIHhX0XY9y77nyrWrXL5yhWY8QX16PCkkJEeC9KxST9v2SBByjKSgpg19CkVcxrqj1OB5JT5LGrJvIErUbke1IxiM0jaQIufKJ9hr8Z3MKRd3IIcMMjgizjqFTFLBxShsrYItUlQl3lAWDTojS9bFinGsr6fJavY7IEWpMAckafepTIeMpKgPKQWkKQV26DJLc6tdsL6qMp3oxDA0rWJ0yWSA67v7fPKTn+adjz3I0eExJgnTzSm+qcELvSQq63HGUCt3i5Qze3uH1F1isjGlaRyTyYhRoyFkQbJCP1nYX3hitkDAF/gmiUbPqq5aD2hMxpgS1JaVzmKtsjM8hmgdBk82lt4l2qSO5kakOKsLlAC3zLCUUdPg4fizWa3UBqZGuTQaPpal0IHKUtKqaMhZy3Fa8b4PfYTYNyQy47GFKEigwCuWLkawanDzwQ//IFdffYHNjUPa5YoQNXRNJKmZcc7kCCHGtdRWHYwUoslFRZZQI+KDoyOmoxEi0HUrnPcYW9ObzMHRHH/csz92nJnVbI0tTYK+0/u7riqlQcVA4x3eWWrvyQLLKCgl+o8B4VyBZfXAy8NbWh4uC+vtci5jb5ayZTYl9Y4CosfIAw89RF0yesVKUdl4HT2Gf86AaTxfeuortCkxc6qx7dtIlJ4W1T/HENaabuGko7QCzjpqX9G4Gu8cVUngo0SzDuBTSgrYG1TV8/y3XuCVV14qnDshxkhKmVWMGO9ZzOeMRmNm001yNnRtr3IrCXRZN+218XjrVBebe2IKWhySjp6DDjplvZpDlzXQQoYO0zmrCwjJpKzFf8BhRQTvnT4wBWMU1K4NwET9rkPnDqwLUeI253RjNfe6VFIxTonhhdumY7KsNeWKqRWJWVE+6WityyqLrKGQ4RQycqI6Gt6rtB7PB/JM2SMNnZoMt4I+jNcODvi9P/wyjz9wL+m4I+GZzSxVbem7HtsozCNiwXua2QapSxzN56xix3RSsz2bMqobjKsIORO7QB+Erivc3ByANfNTyfJG3XiSAsj6WoyodZ/Vn83chiMq/9EQJBByj2h0KE5UlZbKddZDa1gMCSK23JK6FR8wc4VFCkwxLIQ4QZUobv7NZJPv+vD3EkNmNT8kb4yJUeiXQXXhkkkx4B0Yidx56U4ee/xJvvaVA0ZHR8oACMUsRpT8rrnu5ddAzIOR9MlBmyWRjKHvLft7++xsbpDbiLiAbRz1aEQfAn3fcrCMLFcdW2PP9rhmc1QzqT3GC+NaTXoxjq4PzJeBvmDbZlBdvcHHm6JQCgX4Lj0AFIykuPXAMDiUragZCtfg/TwkriUee/xRXOUK0FLedpXKUCZIDRmTyJe/8iWaUVO+v96sISZi0twRlfTpT1NuKbyx1LairhT7sBS3Eq8pkIJRA4Py80qKWKObTmMtbdcRYqEe+YrpxiZi4PjGTWpjGY0nnDt3Huc8OWZms5nyDBGiqFsNKZNjJGPoQ9TxNGn3ZTGFh6fLplRuSH09Rb+LqOO4r8qIWjqJcrMYizpb20EzXuCKsjzxRke0YcGlo54Sl80w/oqOj7E86MrKtAraW6VYOcwJ9mYVr7K3kV0SaqhgzICa3faeJsHkpHK0VDwUS1EcChEUxRS8jjKTygJLyjIB4+lR+edXX3yNey+coRqP6Q6P2JiONbfaJ7zXrnKEpa5GNDvbRBEW7YrDg2XxQNR8H2vAOMNq6ZjPg4oPyjiZy5E7XDdtyzM6xjhMBptVhJAYKEIBR4WxiWgifVqd5KszRI3JGiKhvGZXtuiuvH59FHTAlnLoYYbe0pT/F8XsrH5dlwKPPvYBJuOz3Lq5ZDk/wsQesZ7Y92WyKUephxR7OpN58om389rLz3B8vEfo53ShU318wSNzuR5RZEBpypTAmvg+SFQFoQ09qz4wGjUcrpaKP9Y1fjw6iYJ2jr2u56hV0UrjPZOmoa4sda0xw67g5c7aNRNjDal9h483TaHsZdCl6hhmC80lomPJ7R55MHQOup3zIjjv6azh0t133oYdggTlFJpaW2sjKO6xd4tnv/o0O9MNYhQ6JwTvkGQwJdbBWsNAPBdRo9bKempbFfNWA1aVO5SFRCxW99b5NZ5mMFrcUuLGzVvcc+89hJBIWLJ11E3FeDLTpL6csa7W/BaTiSEABlc2J4rdGfIAIRQaUE46thhTVDjFAizqwFqsh8vnAaOqwju/7qxEhMig3FGKhkjxwSwSwQzadVoV9CRRRVLKsubJaUFVmEINGE6KljWoesSasgTSXs9Yty5gcEI1H7bhZSLEaUtIyrffAzrG67KtdKhlezRMA4PpwzqCF5TSZU5GfYyjF8vhKvHMi1fYP17xlgfuJi8Cx6uWbckagIahKhJA39ScOrODPXIsF8e0bceobmj7BRlLtp69Y+j6BqP8pdLNJoqHONZUYBxZPDKYk8B6kspGfUGdUXK2MRR4qFXcrXzu8BxhLNYMr9uu829KD6n3PyitxxTpZ1banDl5pxQfLHU8Z+G7vuv7aVcJayIu9Sz2V1DXKiTIkRB6fe98UYSlgHOexx57kv29y6yO90htR5RCQxuKNBTjGFkf1PqHhiHVU4VoQiWwf3TMubohC3RtC0YblXo0pu962hD0OomwTEbpB6sAWaWREw87k4qdqWdc24Ggha+aN6xRb4pCaVBzUhlGolIETcGvhkyjoTMYLPKNMUhIbG5u8vAD9/M7v/+7684hozywPBSaocsUsJXn8pXL7N/aY7axSegCtXWa3+ENi3ZFKg9aHwMpKZ7kbOkizbC7A7H6vTMoFkjZypuy8bZF3iB6w7VtSx+CjnAiHB0d4ZwlR11ApKSh7q58mcovdXyyRm20Qk5AJOWOFDtC1k5SjG6Hh7EzGyHmqMU+57VKo/IaZZDSsCktuGPK61HLlnHWDVvWgb9ntBvE6ohtjV5oKWO4ASXwYvAFc85m6AcFk08wUCiwykDSL//iUFqBYi5bXr9+xTrILFl9vaF0J+v7qbSLgxRP4YCTgkIpqFpPtThbccXA1dKK5cXrh9w8epYHL53mvjvOszzuaI+WzGZT6pG6EGWJxb1pjDFCv1yR0eiBNmTaZDk8tiQZFR293sRaqCJOEoZaExuGlgo9fEI52JLRaxKsKP8XoTctIa/0Bi8ZQGLyyUNi1ldXR2310MNZC8lgxTLYVA6pkUlyoeToRGCNTkZiYDLb5v573sLuzV0sGSeB0Hek3Kl7uajZRUqag2PrCmOULXLv3ffzyh330s0PiW0gxU5lmgUaWr9P5dmWYWmIHmY5K43Mp0T0isEvDo84tb1N13Zk6/Cjhqau1MYuJN3CO6/fYx1zAUEyB53Q9oHFEqYjR1M76rpiNPpjMHobQTNdKPhW2XwNWzBbOghbRrV1XylaeN71J76fszvb/Jvf/z1uXrmq1J4y/rp6xLD7WfekzvKFL3yeiMqYxo3VLBkyOVjmZSSqqgrr1Lk6hFC26Po9QKWN1qHAMqZwCfU1DRELURRb0oUD+KqmLz6Mxiq7r2s7YtQuNIuwu79H5b1uzRk8L3slQhv1Lsxk+tipuwq61ZVCJD7BdIVoivlFVCpQ7Wpq54ueHb1R8wkuJAl9TXZQcQBla62/0p5j6Oo0PmBYAmg3akpHJ5kTAvN6tJeTTs8WPHegyZQHXeMLUimQrCWhUjrHlG5f4kE0J0U6Z33IhyUgKLatiqDBlWgozMNTOSyHTqCfDLRdz+Erl3nl1j4PnD/DvRfPgZuwd7QgIXgj1LXDe0vTjDHZMF8s2NyYUdeeg8PMamE18K3ICW1B4h0DGGGJGSWSW0HUobl04oLDImLV4q2qEDJtmhOlR6OCjXaq5uRwSblMHhh1cy9d9frJKd3mgNU6p7G3Q5G1Zdx3Tmlqb3/ru5lOT9Eli7dgbYN1hmXfUlW1yn9DX5qc4fpa6qpGEN773g/x6isv4Zs5pgtQLOyGJ2mYJtZ8URnmxvLeI6qsiYFR1XDYtZjVUqeCpAmLvvKMRzVdr/lMaoKsy1RrjYayZY0tDcaxO4/c2F9hTaSpDRuT0RvWqDdFoZRSDLIZbtPhVFFfPnJeA8YKMFsd3wS6FPjQ932E1HWMmoZP/9Yn+N7v/5NMt7aUB9knzMijk4uBmOiWLV/8gz/Q8dg6xfMk0hthHjvarlOQ16kXVS5GGa4sB4xB3berGldZdR6RwaRC1guTWJj/rnSPUsbP3YM9ptMp21s75CR0baAPPePxGCGxXC3Y2tyiGTVqZmAsVVZXZsQqpkOmjYYorsjT5MQMAlEcqCxFckpYRHE1X+lmsYD6MRbTkNL9UWACKfPf8MDKYJbAIHdTQrLNGQohfehoy92tY6JRqy3tpAqqPIzn664yMxiAQVlolP/q3w+Ecj0kktFmChkw13LPlM/P5aAb+qpcOllTCqUqgWwZx9Gib3Rx5IzBo0UtJw3+una04vD4Fb718hXuPHeaOy9eYGdnk65f0ocOZ6GpG6pmTOpbQuix3tMHS2wztVNmwYCrqkzU4E0F4hGTyaZH47VVn5+t/jweh0QVRwSbgUCblojL5VqhPEdTlnWSS8etnUFGX9vwup11ZckjIBlTqEWuLHIsZu3UPxyi7/uu7wdTU1ceX+mU4eueuMh45/GV16KUHDknfIGdjGi0xJlzd/CWx57kxq3r2Nrr8lFY3y/Dlj2vx3GF4FLBDtfO5DFi6wZxlt3DA0ZNsw7Ic7VjIETmrA7sdV0X9gClKTEFSoCcDCFXtEnIbeTVvcM3rFFvkkLJmg9XkJTyRikVzxQ6iN4Iw2YQUsxcPHeRJ9/+TlyOvPcD7+Ppp5/iV37xF/mJn/ppTEi0x3M2z57Bjmuo9XscHh7wzWdf4MK5B6iqhsOjq4SktIU2Kuk15UwOsTxwFm+8gsXlIa4NjCqL1LbI8PL6wVVcKOlIlLyaCZOxWRhXNefPn6Zre4yx+Lri0r1nias5B4dHWFszO3WRzVMXQCwjX+GaMdl6vK2Kr15NVcFn/uCLpLiraXJ2WCblkwJZOtyxr6iNpynYjuSBfmVP1DrlnVCisXZcst6aSzm9KSDvcPIrXdoZU3TmuqRAThgMMvzZ8P5xMmqXzU8pqlkXD0NXJ0JMJS/coN2jxOHbIIP0MWkxMWgxiSLr72Fu26CKbozKIZJ16mDAvrXc5uGgGLrO8rV9zkSENgUOXr3Gc1ducGZni3vuOMP5U1ukEOj7JU1d01RWTS9yUA9FmwixxQB1UczEyhF90iWFsWWCEiSFNddTp4ZyCIpgbY+RjjYdq6kE+mO6ApGkNaaXy/LQgQwZ6uXQsFoALcNWueDAWfAWTMmJz2JU824y5y/dzdue+CDWbmonWpaWZjRGrCWEHl81WFuRsxp25JjwRiEpM/KEvubD3/sn+daLz/Pyt57BOZ2g1hJVPf+1ITE6qSnZXYPSjNEGJCZdtNbeE3NmtWrpY2YyGbOxOaWuPXVVE2sIqxYbtaNUaUrGSMJ6hRec1+c2hAyiMNQbfbwpCqUzhtqUdLdhDCy8sFywFCOmvOknhPO+63nnh97FztYpJLb8n3/mZ/gHf+/v8Tuf+iSVq3j88ce5/5FHsONaH8ykXeILL3yLo/mCH/zB78X7ii9+8VO8+MIeJgqpV5035d+SXLZjxqhaIENIiejiuoDEYszhiz4ZqzdbjlmDlEg4L3Rd5NyFe7n/gYd45bVX6aMlp4rUVhg5DZPTbG2c5r3f/SdxzTaH+3OIiaZpEKOvtw9LQsxgOnw9LcejrLuULCcdImXJ0viKxjVrFc2goEkprzsOjGJTuvBQmMBYMLa8H0U3q5rYEj1qtBsThLweo/XbQV4rPdwaoyzj4G2jloXbJIPl4THqZ5nQn0Ws5pqYcq8YQ8mzVmNim81tG2z9d4afRtCOY/hYSx3lhKs7/My6PMlr4cOwkrdl6x6MJQqsgnB0fY8re3ucmo159N67Ob0xpjuaM5401LXHWajGhsmO4eBWwIsuWZIDI7aQrwNZIj7qQsN41oyOYEqnnSOOiJWOkI4JzElGD2ErhXsqA0yixtFGlANbOY+VYtVb8L6BHzng/Urkt0gu3lFGecrWwTwEfuDDfwL8CONMOag91mmgnvUjDg8PMdYxHtXE1BK6Fd4nTE5Yr05PdT1ixIQf/bH/hH/w9/9bTGixKWCjFvooSvEatO16fpYJpnhAiNEJrQ29Dj1G5cpGXwDeWWwe4a1j7HTRKl1QD4dyAGpFSWSrhwbOUBkPKVE11RvWqDdFocwpUQ1t8TBylQcpDd1NYdEpUK8XNsTAd3/3d0MSrGu474GH+Bv/1X/Fv/6VX+XZbz+LtYkb115j68wZ7r7/QTa3N7GTEV/+ylcQhH/6c/+Itz3+ViaNw2SDxMSaOW1K82StOl77mtCFNfjci2BCoDZgnCL0xmt8ppKdyzcRlQquQmRhLA+++6PMZnfyvkfOIcYjtiYlj7EjBIt1nl4MprNszBqWh3uEGNWCLkZCH8kZqsqyuXGKW8NDUjqpQW89KIMqV2Ntpd15Vtcgyg2ZrS6GZPi6gbJhM1JAcIs+vBrcpgqPnBNWiu1r6bhVTqjtnzVDN1tGXxkyqtXxZogiQIZTcE19XuNzqWy4DazpPK5sMwf3oQH4Rwo1yFBwbFlDAGte7nD4DkUQ1pvwAbcc/kx/jBM0OqHf3+YyrqKPXR/heH/JjcNvcvfpbe49f4bsa45Cy2Q0htTSVCs2NxpWyxE512iURcIZwVlPCIKII6LdnCvPQWUEMREjHTEuELuiz4f0ZgE+KaXFGC2SAw/Vlhwh9L13ZjgADCblNUUol39rfR0oB5NVelbjBOsM3m/xwY/8GKPxBha1C8wYXDFLcdWIbCwpqWtPIw2tNaS+VYpaVTGkRprseezRJ/mhH/ox/tXHfg7j3Vp2e4K3FW6tLbp1e2IyMxTMLiYyvUor0a8PQSOtR3VFZZRMTlOzXKxYL4RF76soUiJslZGiOLdaMb7Rx5uiUCYR9g4PmEynjOtGXcpzVCJqPrmJh5vUGfU03Nna4t3vfa9eYFshxnL6/AX+wl/+S1y9/BrPPP1Vrr70CntXr3HlmWepN2fsXDzPl7/wOSbjGm+EZ7/5FWpv8K6iTyU4TNsrKP/WZDRiXDe0Yog56thnLKs+QhamkwbnivU4esKrXVhiEQIH88Slux7nP/qRP8P9D7+PbtXQzDZZFV+82AZsynpjZ4O3FpwlmURwUgpVS0hLUlyW5U5iY6tBbECS8kjVDUbWMEBVeZUnDjEKIipJBJJoBrpusE3BjDSDWgQVs5XNf5mT112Lwl9K3QjYEj86vJtlA37772RgvJ7QfYbRNq07ufIQF5TfFd25jr95vZmn4FhZ8pqhaUTW3SdoJzZQugb2ge65yt9zUiQxg//mekBlnSuU83orPMBDplwTnWwcGUsXM6vre1w/POb+uy5wZmcTQ8Blw2wqzDYSbZ/ZP1xxcAimG1PlEZmaPnk6k4nGYbMnpwUhHhNzR8o9xgQiKwIrWlmQfI+Y4d8fgh3KIyLaOapMUTBWXc8Lv+5kxOWkSurXC87pgqw2FRWGPna876Pfz90PPUwODSShqksr6k7w5/GkJvQR5yBH8LamD63mc/c9rq5IMdB4Sw6BD7zne7n8yst85nOfUO6vNUgaXkU5pNZHtC6ijLWve99CimCs0vJE3+/YayiZsRasxY4bXM70bY8Ro9DEQGhH8EazxgsCgz05qv/Ij//gQmk0VOIPgcsi8qPGmPuAXwBOA18E/oKI9MaYBvinwLuAXeCnROSlN/reGaFFyG1LnRLj0QRHISWX3cDw3g43RQyBdzzxTk7vnMI6h6k84vQVGQN33HsPl+65m3a5YvfKFV547jlSCnzus7/Li88+U7a1kcprR9hLUkqGGTDS4njiDKPRiNpXpJAUx8HoaKSosFqCFdxNl5CWlIWjVWQRKn74J/4yf+KHfhoY0XVQbzf0SR9OjyoqolOmo3WFoCHqZJ1yxNuCQMeg5JmUSUR2ts8qwdrosmWwjTLG0NSqGHIFV5VhQYJ+Xhd6Ukylcy5xqYM6xJR3JdtiY2bWyx1TjCl05AZj89CP4IoFS5S8Xv4oTal0Z/nkEdXN9lAktdBaCn7K7QsZwRmrdC2jF1i5hkWwV8bjXHBNM4xs5b0oOx+9z2R4fWbNqaQsFIYO0q0hguFeOykqw8Eh5WcbMFYBOiy3usTq5atcOFrw4B3n2Bo12JwwZOX49S3zVSb3pxi5O8ih0aVNFfES6dIBB8uXmcerpKQjpneujLCZbAdidKHIQYF/Mpi8dpDSS5sLFnybBZuBLAO39TYLNSmFwlmMeIyDNmZ+7E/9Keppw+JQSf2p0ylvOHwshtAHuq6lD61ejpCYzxekfqm0nqYmpQgp4I1huTjiAx/+Xg6Xezz91S8SslHsuxyiAzfWlvsDyn1hbZHSnkxMVVUXIyw11mj7HvEWV9cK01UVqeuRnHWJZcwafgohEHNSZkuJiXijj/8lHeX/BXgG2Cy//5vA3xGRXzDG/APg/wD8f8p/90XkQWPMnymf91Nv+J0FQp+wjapR4nLF5nSKLXnKw4158l8IfeR7vud7sClh6jL6utePFBhDPZ1w6aEHufDgfeQYePrpr/LOt79DPenEcPnaNV557TVSH6hHnhkT7Kol9D0pB3zj8MVZx3oP9aCdzuUJtIWDpltUmwxdH1m0PbOdS/zQ9/80H/7on6Y3Duc9lRNCzJoHLagnpETECn3oSdmoYkag73tMBLx2lTlHPQLFkKPl7nsexbgR5AWgIypZc9ErX62xP+0VtOiLZPrQ0/adOgcZTadU+o1+QUaVO/ryBr39UHTKgqd0hQNPcdi2w0lmj8EWtsLQ6cl6kaQEYxge+ix5gHcBpVUNW+9hM5qG0Rp9hqLkMmqnNaZ9u2zx9kXOUBjt0KmW+0MKzmrK3+ViFjx87vD6hk7z9lFdM2jURSflTBA4jrC6dcCtoxWXzpxie7JJ6HqO5ksOFisyU7zfILq7qdyYPrzEJLWYHLi1fIEb/atk02Kc0qwcjoqaiuJGz2ADJ2sTDH0ryntkinOQoZiU5ILrutdjyFlKsqMWUBUbCM7BisDZO+/hnrseRRYBWfa0iwWhi/iqwjqVcla+JvQrVt0xbb/UYtpH5kf7rFYHrFZHhLgip0C7WNK3LcfzXRarI5wRzp8/x96tXebzOTEWV3vJ5OInYMoUk1Mx2y6y3+GwdIWVkvpMHyOm6xBjGBk1wKmrCmlGrJbL0lwMh6BVilkB4nUH8b9BR2mMuRP4EeD/CfxfjR6z3wf82fIp/wPw/0AL5Y+XXwP8/4C/Z4wx8jra/es/rDFU2ZDbHldrwuL8eMF0OsYPucopFT6e4pS+bnjHO9657mawGuA+3NQIhS4zQByWvk9cfe0qdT3GlYXLxnSDe++9l72DXQ4Pj5j6ikPjWFpH2+qJrlgXYC3R6gOqShghY1n0HYZEVdcQIfXCg/ffzQe+50/STC+RoyF7c2L6YApfDl0IiVXemxMtoL1oalwIrW560wA9GM3OMQZxju0zF5lsbXOwv9ANsQiNr7QLKb1HthAL79AbT0yRVegJkhHruF0DPiAOAxHYlsCqJAWzLPZEJwWuvJ5hAbPu5E6s1QbSt25jCxUHxRut8YW+lG8r0qXAcdLNxTxYu53Yh63pP0a7ISkd7jC6n5iqlAJf7rUhqnT4s4Kwvg6jPPEaKMVGhiXH8GdKztYxXJVKsfA3JWd6gb5v6VLkzMZ5Do9v0tRTvL2Ir++gqu7B2hlir3DcX+G4OyTllr1wmWh6pewYSCUwDTS8zMpJURx2uVJIV7Z0mEps1xF5yHS0pUsbFD9Wt3SlEFOWZ8MFyay6JT/8wQ9w/eXnWR58EWcCse8AR58Mo8k2uAbEkVPLqj/i4Gifo+NjVouOrl2Q05IQVhBLwmcM9GHFqluo6CImxrVnazbFA8fHS/oYy7UvWwnRn9qW1+ecJ8ZE1/fEKNR1jfeVQmEps+p7nei8UtWcrXDe4ZwnhFRgIynPc5kq+p66qpQ3/AYf/6Ed5X8H/A1go/z+NHAgMvA1eA24o/z6DuDVctNFY8xh+fxb3/nbqydiioHcdVSNGlksVys2RjW+8rr1FMFYQ+giDz/8CJcuXtIHMWfV1N2OM5jX/Q6D4erlK+wdHbG9sYkBveAh4Izl9M4ptmYzjg+XxKz4Xu21XXel3ccorSAWmzBrVK/bd0HzWZy66Vy6dAeb0xGXX/gmk9NjHnjyu9XKLesInXtBoiiPuxR+rSeuKF7UzDRKKoFRqtyJhcKkgWc1VDV33vMIB/tXGHhzTd2o/14pSDEPix0d1LrQ0fYdxmsI19rbccAPs3bLpmjFxYhSTcrnrpcdrLVTqBmydosDvGTWozDr/yGsnW5Ugx4KOf2k+K2LLcNYK7p8Qk46oVLETmz3WBe6QXEzdJOmLH5yzoXWZNbxIbcvbIaOUZVgQ1eqUIxBF45rrCzntYN6Yiiu5TBJZt3Bd3FE5d/G5sYInKN2p0k2k/MBbfwmy+UzLOOrdGlFn1cE02PFreWLw1Y6pIR1rngnqhzVrk8Avf658FmdlXW8gTr/nFwXZXIUutDa3Fevo8/FhzVm7rn7Hv7qX/vL7GyfYnnzKnbZ0fi6dOCOL33t61zb26ftIuRMTD39ck48mrM6XrBaLlisjui6lm4VSH1SJ/bUAxptkUtR9taws7XF1myDmDLLrmPVdyVXPWFMVkqS1xG5XbX0fUdIHSEEvK/Ke6iwSNcHnO/JGKqqvLOVX/sBUNRy6jOrh2AIEflfWyiNMT8K3BCRLxpjPvLv+/z/0A9jzF8B/gpA5Tyu8urUnBMxdDSmKAOWHdXI4wtB2BlHTD3f9f4P4H1FDBHvIqZSLz9xWhCGjmTdOYjw3DPfIKeESCKJoYuRVMY3ssX7EbNTNXdPxhweHjI/Pqbv+zXhvaosORpSNEpkxSjlyBbzgahLhPMXz7E6XCB9xeNvfYKjo2u8/MJLPPaO96t5a9YiKTnhK4ezhsWiVbWH9yXLw5FDRCRja1e6vMIllITBk7BcuHAn30ii19BaRlVDbSwikZgLDceYksIX6VMgSMIbd1tXBVDs1IZCVQptykI2aa3sKO/durCkUgGHg0R/q92P/vr1BHSLHi5DYFYhDKxvdMphqHyfEwUJpTAPiqF1bSzcy+EfGH42oOi+cyHol0I7fHEZyfkjiqWsO+SsfduwVMpraxYG8YOU0dusIYFC0Bfowh5teo3NyWNkEl18gVX/Km33bfp+j5hXZHoSgSzqE8QguigF2yLloU6IdQxOUGsZbdZ/Wbul0lVKUraCCJq3ox3U7bV1DQozQCeCc5lFWPGnfvInufvue3RBGE+xt/8iu3u7HO4fMT865rlvf5tvvfoq88WCxWpJHwIxhpNVjDFYb3HOM56OqLZduUcMOauh8eBFEFJPCkPBzVTjhnEansmi/bdSXKgMMo4YSRwvy2GWNX0gFkd8gK7tMCljRoLxFbay1OJJIeh9gIa7DZh4yaF4w4//kI7yg8B/ZIz5YWCEYpR/F9g2xvjSVd4JXC6ffxm4C3jNGOOBLXSp87oPEfmHwD8EmDRjcdZAVZGDQWImdD1VXZOdoesCk60NrLP0q57ae1575WXmR0dsTiekGDEh4LIvuAXrB0DfOkCEb3zt69SV4n8JpRfJ8Anl5LXWMRpPaEYjNjY2uHXrFsvlsuA/WgBGVOsHwlijlCxBFS9+wvbsNHvX9tk6d4YvfeUT/N7v/h7SV+yc+5ucuXAPg1ciRg0hctIxwqS+YIJ601d1Reg0Zy9aoSeBc+Reit8ljEfj9VhbFZURKUPWLZ8bZJWiCZd9CAxqh4EfeDuAngY8r1wze9vGUS/r8PknHZgpkMcJnjd0mrd1M5SuuRSqodANEMHwdg0d/PDrAfbIIkqpMYP4AMjFHahgjM4WukcpgNECxY0ddHueC3dUnC0a9BPsUWGCE5x7nXtT8DKzrqb63zzcWyf39Po6RQMwZ9l/idq+QtetaMOCZToiSq+HktLfSxOu0IwOiGZ93Y0pEt6UEZMwrirshiJEGZBHW6haw2sdukZjwFo1C5bBbKaYhxjdOluv1ndjZxglWFy7yj/6b/8bjg72OT7ap1u2gNA0DbONTZrJlDOnt9nc2iAZoWoaqqqmD5G26xQGykLq1dkqxE6LaYjaxSadwlLWWGQjSo43ZGrncQ6NnyipA2p4kkl9T8qRi2dOs9POODg+IiTFp3s1laQWRzSGXljbH1rroeRwEbWgqqtS2ajnEzz8O338ewuliPyXwH9Z3rSPAD8jIn/OGPMvgJ9EN9//GfDL5Ut+pfz+s+XvP/lG+KTeDZC9BdFtTMxB/fxionIObx19FhrvcE2NGMMnPvUJjIW/8dd/hulkggvgcsMwcN8m+sAAR4eHvPjCC4zqhpzKKZTy68Y9GL5GTWqnGzMmkwn7h4fc3L1FjBHrPaYyJ6RYIOeIyZkKmNVjtnc2CbLkU5/8NfrQEaLl+/7kf8aDDz3IwVGnhgheH+phyZCiOjw3Ta26cxGMdWo/JoJxGtSVJOO9pyoGpFXliCkxqkrXnfS0zllIIWIl42o17Wj7npBUZ+6ENTfClP9Xt6ByBbSCrIthSmn98AJr3TUy8AtLYRNOrs1t13WI/j2hgbMuPMNn2dvKjllvp4eCdFsXm9UsQgpO7K3ReALncc6Vf0+x5IjgB1pIzmBlPZ4P7kW3F8pBo1/udx27sxLthyAzPQP0QRvgits/fyiYSQKHi8v0/U00czwTpDjBu/LCMhisMhsKNJH0wSOJih2EMhnkXKg5J3LPk9Is6/gUW/5moHBR7Pcqa/AUY1wU4sllE+ycYbla8f3f/338wI//OBtbW2xubjCdTmiPjsnzObWziLWkDE9/41m++PVnWK5ackr0nTqRHx/PtVjGBCmR+0CfArEUNBUIWAYPD1VyDd1zWofTxRLmZ/KASyeUWQF1XdPUNVky+8fHuohDdJmE1kJndCNvncPUyvu0zkHQQjkc6EoBA+/+V47eb/DxfwN+wRjzXwNfBv5x+fN/DPyPxphvAXvAn/n3fqfSUiQpvCrniMW6qTOGSvxatL8xndJZw4jEJ37nExgr/I2f+RtMa68CefHltD+5qQGuvnaZw/0Dzp06rd1GCsQYTzA3c/LAmGEDJmCd5dTODhubG1y/dYujW3uYuixIrCtjtF13GMt4xO986jfpVyuNPx2PuHm04uK9b8GKR+KqYJNCjpGYInVVa+djHSmqfX8u9KGqajBWSB0E44gUTbKSLnFeN4HOuBKRqiNcFt0W+3Jtowh9uZGqsqDSS2/Wo9fQJa7fk/L7lNLtf7SWKq5pNgxLhJMCORS218EfA3MhszatyMUJ43VEcKOjVlnwk6SIEaIqS1JWykvl7TrzHefxTr0cSeqU1MdIn5T2FSUTDGCLnVgW7GDMMLyONT6qo3UuP/OQMDkcHOWGwZcpPg/fqBwCg8kIYgkxYazyAcWpD6MeKqXDHrSIqE+kd0qAHlgAGJUnWmMIOWFj1Ndg7W1a/Ns7/vIeiiZZOjNc2wFVtjhRGpQVXWha57BWqEYN/6e//l/w6BNPaM58iPRtRxsyLz37Lfr5nMOjI5bLFbtHC7781NdYLHtgyIsvaYbFrcjmsrke1GspaeELiarytLEn5qx5V3EITlOaEiZjbKZPLQahck5x4aFLNobZbMYyBFLfYoMhSFob4pgcscli+l4PmUpTRV1Vkfqgy1PnwJTtv/vf0OFcRH4H+J3y628D7/0jPqcF/vT/ku+rrXApUM6QkhKF+5SIfSL2HWnVYlOiEhhPGhrTsLTwm7/1W1jn+Ot//WcYTSaU5Kj1SD2cvF996iuAytm8rxh87rTI6A1uTYlJRe/i4YwRFOu4dOECpzc2uHX9GkfzBc47YkjkpPhJFljGgIjB24YkaqrQZ+H0mbPkqERukaQnrLU44/FVpVx1q+qBvg+EPmJRTNJh8M6zypluucA5gxt7TS41+j0qWxVcUAe6qNVovQ2VnDVYymhnpJvcQkw2xcNyUCUVjO3EcWb93up/sxSnl9v+rnyN4YSvqF8/+MIPhVmvarEo0K3uuhMrFKMSB6Ld+pAUqQ977RyTUUPT1DhvaLyj8Y5QhniHIfeR2AWwTt3osQSbMDJo0oeSoQ9c1BeHsyeHhQz9rT05ENZbN0r3a4bO9+QhG7rPbHTEzCSSgMUr9inlcCr0qCwlFWfYVpeOXAn3GplA+bdVkRS1CysZR7gyRgNeNPPaoN21N06VPmgWehBVYhlx63dEV0cZ58FI4t/81sd5+kt/SN9Fcp9wKZO6jutXLhNyZBF62lXHfNFyPD8kRkPTjAihJ4oWvLqu6LoWkww5CL6qFOYSStZOxlioKlXpMxo28IJzTheZsaMNqzVlyZbpJScDkrDWUHnP9tYW8Ug47jqiMWrEkUt4YI64pE2MbsM91oPNdu2QlAqOLyeAzh/58aZQ5hjQhUkZg5W9oKL8ECO+uD13suI4CZ5NphPFRazz/M8f/zjf9f4P8KM/8qMY78DV680oQN91fO3przIa1TpyZB27h+ZgePBvx8qMsaoIgfWIiYHZdMbs3ns5ODhif/+AZW5PiLJGTUSDUeuvykAMGd/ssLFzrsjuVEft6gbvFTeZzxdYydRNU05Y0VEtqdRNJJFSRxeWdGHJyDY6hgfBSoUztXoMZuVCJjJ9jugVsDgcIaf1EiVk3aQrBqkPfyKXjrpkz5gTJYp2UqyLn3Xl8ZKBmVd4i0UXjylyyFIib2/tNU62FAxzIlvV+2DYaBfjipxwQGMtk/GYyWRC7TxNXeO81+9l9GvcMBZnwKkpceh6fMxK+k4Wn7Sji0l/DlvXRNFDRLfW2u0NeLTeB6VQlp9xcLhZXxduW/aU7lOyUq90LIeQhEqbFzJSDJatdjNGTZIp10KLZyZHHQnVCV8LiqAFtDKOxnpsmSrUD8HiKVp4NBOHrOzW8kMWj1GDFDkgAE5fa0KjGH77tz/JuXPnWBwdk5YdcdXS9pFF3xEQ1UmLHjK1dSSx9Dko9u2VwZFTovYV4gymqXDOUTMud0o5KKTY8Vk1EbFoocwpkGJP7FeIJI0XiXl9YFgLiYTPBsmaYTSdTDg8Pqa0OEhKBBG8sUQbqazFrl3wwVdWzagF1nShgXnxHT7eFIVSbxK7Tl4TawqHLKNRpWqCUAnElLm5uw9mm/FkggMqY3jo3nvJoWAS3mOqE8RrcXzEay+/xMg3ih3lpGoBSqsvpVsquJySpNE/X/dDssY9ra3Y3N6hmUzZ29vj4PBQHzJsIcWmQpExxCA8/LZ3snnmLClGokTEKScMDH3stXA6VVHEGJGsSghJGkiackfXrcCBrxWXzCkiOO654z42N7aQbkmUotAoVvvKI9QHJKWIZE3PU9WDLqL+nXG7FEQdmfQKDiOpKc7xQzaQNp+34RvlcDqRiZl1Vz+MhMPJfXK46NfEtQWafn9rYNRUbE9nbI0nTMZjXOG71VVNNRrRx6RgvjWE0GraXlRs29Q1HotxiZwCJqpgQHIm2FQe+sK/TBlrEmvpI7fRoMq9OcCzUuz+EE4UR7ddw4GM7kthi1bTEFOW9f24vlyGAqnr9cx5MCQpmCgau+GtozJGC2Nx2W98jXcGX5rZlPWg1pHdFNhiIG3rv23L/Wms5u4oYV3UVSk7au+5fvkG+3tHhckmNFVNnQ3eTZSEL+Cbej0RjZoarKEejxloYpT7TnXWg+CgdNAFQstZtPsf8GPRhqAPHaFvIfeklEkCfadTlbUW4/T9iUbVQd4YpnXD1mRKXHUqwS33pRFdcFZlslAMU7fvQ4jZCSz0xiXqzVEoEWzWuIOI8gedNTg8RopDNyCS+Av/6Z/ny09/lS9++Q85NZthJeFIPP/8czz40CPkNmJ9wLgaMBgLL7/wbeKyxW6OIJflSQgYtAAbqzeWLRdfSmdz+ykjYtcXNJWvc03F2QvnmGxMuXHzBsfLJV0QaueY1k6TBY3hcO8YCZkQEu1qRTZKnDVkUgrE2OFdRUxRqUbWkHJAYktKPTGu6FYL2tUCQk+XI8lEojUsVnv0oUNyxBnd7MacTrpXU/J2CtHaGstgnTAsMoaPYSkxYI0D6D1glLXz6wFaoY3SVZUid+Iur98nrR9Qo5jVuqvUMXDANZXikTAZmqpiYzpjNh6zNZuxMZlSVTo9DIXcOcdkOqVtW1arlpQzLcVl3hRZpgW8dmR1hlwp7htSJoekUamoByqmmMaKBqFlMeWQVmK5iLpYDUVwILoPRWjYng46cMrrstbgslH3cjMcDmhg2IANDQUFdcBJqOHIsJTSjCZLZQ2194gCalS1BsxV6+jeQAi6RRaTBuBCvSOL/lmsX+dlDy5Q1grW6QY8i3D21GnOXDzPM88/T101iK9JztB3eqAbjEpfMXrQWLuWSEoeOvKCm5e7QcqoLFnIMRHzEHGiaiawVN6pgicsSbHHxKxNg4GYE1EEh8OKlixrrJqro1lG29MNFscLln3HqKqY1hWjqsZZXfBJ0ukklu5ZSbCZqiS4Yv9YpDCWEUwG1YSSZ72zGvOZIt7AqY0ZH3zPu/muD72fn/+FGU6EpvJ89nOf4+/+3b/LmTMXePd73o9NGekCVGoH/9Uvfxnv1T2cnIkx6IhrQAY5FGXBIFo4h6KxtqcvnL5ho2luexi2NzaZzWZc3rvJ/e94N5PRjE//+q8xcpnO9GzYDLGjbwOr1THGepyvqCqPIFTe07btms5hJHNw6zqhOybnQAyBdrUkhRUmRqx1xNzSS8fnf+836cJKiy5awgZvT4xRjSwnSXfq6pMKH1HWS5TbZX6mUErW707ZeqeBgiP/LqJz+4HssGtC9+D0A6xliFlrky6aisHEyFdsT6ecPbXNdDrBVR7rPU0zpqpHgMGmTNt19F3P4fFcM4FSScxE1EylD7ocyLqMGMZqxTp1CdenTCyal4yaFKvoaOigsy6MjFnfC0aKKXEp/nLbNRgOm9vlmsP9cbKkKkPnv9W62NKFmfK53il+aK3+2qG0odprx+esxxhX7NQssU90qdfXU7irTgbXpzIZOYd1npCyUoWM4BHWKTllMdT3Pffdfy8f/cj38q3n/q5aCJbChqHQqQaVvSjLIkYwTgthKosZbelYSaILLRaoq0qLY9ZLYb0jORWaSBJC1xL7QAqREGNhKKhJSypCh1QgjJIxSioGVF7UVHhne5vJasXEV9QFXuhFrRutV9GEE6HC0gBONJxOKkeu3rgUvikKpRKXT9pgJaaW5Yo1ZONwwCOPPExdab7zX/zzf47xaMR4MuORt72Nf/IPf5a/87f/Fv/N3/7vuPve+3SicZau7/jms8+ovLCMmiFGBXAH7iSmYDu3nfFrDMkUM4eT7muYUAfgPaMdyblTZ0hHC7ploBH0JhTLPXfeSRtXHC/nrLo53tdIR0lKVNpj3wdc5dSWP7SsVvuEbkGWQOwDi8NDkE6Dy6hwPXTtLV555RsIsTzEnCxZZMjR0RNZzXz19cTS9bze9efkQb9dm337x2DdhhmMik8K5Os8HGFNMhy6r6FYDxthVyIDJlXF9sYG25ubzCYjmlFN1dQ61lpPFxLz5SHtsiX2UYtYSsSoUcIpJ7q+K9LE8npE37OUU8FfpUgMlZIVc4EmBiwU1qofZ10B93V0SzmVw7HgkAAllhduk1Tyuku5xjBPMM5/9+9uv14CZdlm8U7THh26eLOFI6lWb+o1oIyNYgzihOT1uwxZR7ZIFMU5jNfpyotAjmrB5lTyi3OliKhV2ff/yA/z0KU7eMt99/ONl17SKJUyWVgLJyog0QiT8mw0VV2WMFGLuLVghbqu9BCylso1eo8ah60q3KhhMhrz6ksvEeZLcp/KxCdKMk+5sF3KHCMgsaR0rkmtVmGlpM/wpBlRoSF4WRT+ieV+G5JQvVF6kvEUKljCyh+DZY5g1vwpdbGxODwULEy5XpF3vvvd9DGoiShG2/Ou4we+76Ncv3KNX/3lX+Vv/52/xf/rb/4tZpMJxMitG9e5+tplps0YpBgspMJJU92Dxncq4HYblqTlsByA659U/0qpFQM2NfAHG+tI+/ssUuSOs6e4efOIeRDOXriLxWLO4njOaj5HSmHWGmKV/qL3MMFkUmxZzQ/IqdPRu+vo5ruQWj1CxeO94bXL3yR0R0jJ+8YaYi6b2KLozDmvu8GTg4A1Fvn6rmd4ebdTpU6WGoOEcaCbGHvyfYaRcwhTS4XovjYJLtfSiD7MlffsbG5yanOL6bihqWuMMbQxcdTNS2xwJqx6UkjEkOiLa4z+HCcORcqtLNG6IidFAl1OxfLzWXM7tJBvq/IDnUeIUdb4IDIslfRhdYVYv6Y/ldd/+4Gyvl7lPsEotqZKJNbFdmABrHUxty2D4DaNeSnSMWR6a2iMKritGQxpy/cTJWhbtXgiGYUojHPl2Rq6SMEVT1ApHaxFSLHn4Qfu4wPf+93kRceP/NAP8fI//sf0WYPXVKElurAp8Yy+rrBZFyTJOJxrqJ2j8p7JeMLGdMZkPGY6nbC9c4rZ5iaTyZSqHusuwVokR37+f/jveXnvW9DHMr3pAaGMjNtO4ywYW8y8rS6sUkqErAa+isGzzoy3xhDRiGnvlCrldCFAEOhElU8VGW9P3sM/6uNNUSihjCBWBtUSkiO+uGYLmdl4xn333IPEjDFeXZrF4MVgY+anf/qneOHbL/DlL36eX/1XH+On/uyfxYTEi889T+h6TDMhi3LxbM5F5lfGiQL85oI/2uGmRm/W1zmLmJPlBsKJVG/ANp0uTLZPn6ZptmimC06fOUWaL2mPjiBqB9PFxMBEyhlszvjKIRKJscWRNLs7BEgBZ1R6mQeummRuXnuNkLp1kNKAOcrATaSM1DmvWQXlJSBZZZy2PJhrfI2Th3YwuhjA8WFJY4ua4aRrNScyQymapfLaEpkcC2ZmLI1zjJuG2WzG9vY2la8IkunbpY7NIdOnRB8jMSlpnlgyyd1gj5bWXeqgKhp+bt3J5fXrfN0ZUF5XzsPPUygipVBaGaSKw4h820E5jJ7luV0bY9x2oLxuATRAH1JCrm47ZJw5Ud3knNad/nAYDaKJoWdKg+uRQCcaUOaNwZeseovevO62IpmtwXivWu+ggghjVQk2LBr16xLJQQqBhx54gMl0SrW1w90PPMBb3/IIX/76N9YHDkX5FFLmzPnzbGxuMx5tsLGxTTMaMx6NaUYjRnXNIw+/he3NDQYj5dB3dH1gtVzRtT0hJlJQuODSxbt56fkXVKckatf3/2/vzYMty64yv9/ae59z733v5TxVZmXWPFdJJZWGkgQIISQkYQd2N3QHbRwmCDDu0cbY0Q2mTYMd0TZyhN3QbptWgDHRQFvNJAQyjYSYJTSUpFKpBmVVZU1ZWZXDy8w333vP2YP/WOuceytVUpmOcOWr8FsVGfXefdO+5+yz9lrf+ta3pOhziZ/dO1W7KmTniUUjzpIyHqH2zrIdva7OaZNGxlHE9aNSXMJwFo1sRUxWJL8GIkpBR12Cbs2UwXuHFJ2ZIrlwy403sHdxUcv8TijOWtNSIcdMPQz88A/+AP/dT/00v/xLv8B9b3oTN99yE1/6whfxoaKjTGebry2WQjpbQN9+1mWQYt9vFTuV7p+Rs1OKSLYCjz3ARZR7WYsnF1hcGjAajbjw3En2jpYosaGdZopTWhMoZpraqKd/gZxbXMnEdkrOUcc1iI0+sHnhroJmssHK5WV11t7bw1tm0ZHYHCKLJucjxP7sLEom76OYjrM45zS1iGYFi9KJ46r76KI288j9Aw+zlFVEC6u1cwzqmt31kIXhSOGHSctUlFoyaaZ91beJUVNNW0/vgOz6q1bnHEZo//VpvjnwVFS8YqYhqQ5CaTvF1jyLCLVt035joacvdZRFcpcGdvBCJyRt+8CiRW0Ptf4YgzpyUWfnxJmghmHcIsQU+3uia7Hqe/971PF6Lc1goboeTtlGA4sjiEaQbSk0JfUHhxNtXQxFq91ZtGBYnBAlsrGxyfvf+x6m4zEPP/hl3vyOd3D85pu49957+dJXH6NFCIMRVTWgHixwza693HPvfbhQ41CoQHwgRYVj2ibz7DMvko47vHfEptF9GCOT8ZTpZEqKiZgi03bCkWuuxVUDUoyorqD2Xms1XKNy7zvxmNwzVlxRxS2PTnENFqxoxb2YXojWGCIFijCoPUFgUDIxRTppP89ropij1ADApOCNH5czA6dTB++55y4KKmgr2RHQ7oppipQUqJrMbTdez/d891/j//zlX+GXPvQhfvyn/wlf/epJ3emiD05MNvQLTZn705/uGVH+ppTZ6YQVJXo2XR+5FLQ9YoaNxaIpWWVT8byHC8+fZGX5RfYdvZmlA8doC5SiD0tsNXIe1DUFhR580JMOJ7jKayWzGqp6jBe8jzz/3PM07aSPjAr02BGgr5nTaK1q2BUNwNCdrmo996+/IxYJKR2ZPjQrXYptTqRTQO8EKLrf1x0wlfcMq4qlwYCFwYDaaZvp1taUVFQIJOVMm1pVCerSeKy4Z45bIz3rgLIUOptaUdca13VVKQyBztKh9JSU7iAspXtthovrm5lhvDOHORctSncmzCLHLmoUsJlOWDse1iGl16LjQ2oaM7ve2pjj+lRTN9aMj6r706LjbBQf30WcFhXjqX2thdzSjRDO2svnNMIurtCpJ5Ehex2FMB1v8rb738pP/czP8PGP/i6/+xu/xevvexN7rznCrXffyd/5e/+A1h1gbWWdtbUVptMpK2trNK3TA55srbRDMAjJFZ29vTIYsrCwoF1wrR6I02lD20ZijKS2YaudUC0scuTYtTx36gkdAWGdXwgafHinfek5W6unCaSIECRoShaAYPQnBBGT5nOOSoJxV1HOKpng9UBSqpDru7S+nm0LR6kb1dS0UaJ3kUJyBVJiUAduvflmpk1LCJ7KZlW3qUWkkNup1gYbz3e859386af+gk9//s/5zV/7VdYvXGQwGll6nYil7TsxFAouvZKI7iqNTdD92hc8VDYMo1U4QCkmOK+yBvbEdSm7Usb13Q0Ghaa9xJmnNllaO8+x6+6hqgZ9iloNKlwdIJq4LptUAwet4H2FixEZLjAdr3D2xWc5/fTjrF08a8rNwU7eznlki0SYcRfLDJuDGX52ZdGmcxrznzPnTLOI9YXoqR2NpaCOMs9SdyPwD3zFsK5ZGNSMqprgPNOkjIOYEk1ORumxCDbbdRYxMWEjS7tOmSe/BBPUw8711XVFfC0ENH5eR2y3ENEcfkfI6SFWMEhB6UDZDkLMwc4crVJLpN8/XeqNvQ9xrocodIql/p7gtWdGo/Zk0zdU9qxPtEtUXU47lEJXCBIo1uHlXOnvrXYwCeIqfBhA25JTQ3IJcUrLSVnwJlGmO1xTcCeQU+TE0SP88A//IPuPHOZd7/sAf/aJP+TRL32RN7ztHVx7401cvLCChAHDxcKllcsamXkhGy6epRBxlDImuIAvGsFnYHn5Ant376HJmWkbSTHRtpGUCk0zpYlTmpzYGm9w5NoTvHj6OfIkgmV8daXK7qmYkIsPOF/RZTS1N82CknXEhXGnvXFFfQgm3Iz2dAPFqehIm6Jee+8pRUjdAfl1bFs4SgARrydDUSwhGojtBI4eO8rePbuUzyeOEGycbIpUTnDJK42jSewaLfA3/vpf54P/yz/jX/2bD7N3737caIQHrQ0VwbtAxDpz3Eyzr3MU3XQ7StcCOJNy0B5sdGB91mJTR1x1FsZLymTR2SXJbqKEwiJbjJdP8ejyOe68991UC4cp4nF+UXG3knE+kEoF1RJBQEqinZ7jqZMP8OSpR7V4E6eQVBVdZ8XoiFrsXfT91G6eFD4rNMxX78WKDR30MIspmfudFmHyUiebzeHN90lbJya1D4yGQ4aVjqRoU2baRHWOFunOV+JBZnhiH+HOojbmtDC7KM+ZZmaHz3Zr7qI+jSz0Z/u/U7qvQYcaYPdVdR5VIHgGT1jCYMFY52b1GnR7d44x0f+dmcPrMOFubnpfPBMxrNDgAhHabJmIc9qI4KzQ1zEwfCGLidkWR9G5DNqkgHVY2WKCRVdk65MKwQokIKIFjPe/99tIk00mm1scPnaUd3zrO/m9X/917n7DfRy7/jo+/xef5tLaWRZ271U1q8mEvaZYFYvqo1Kc4ulepeByUkx82o4ZlwhS0aZE2zbar18c06Yltg05RiabW4g4Dh67hvPPP4tklWlDlGtcW1bsTPEpWEZZU0gkcjfszBcFJ0KlB1V3n0WM5WD3yAkdLzpTkCoodvsNbNs4ypw7Z1RUg1cczgVy23LbrbcRY9QqroBPjpCFNmq6Jt5D8kiC6daEe+5+HXfccTuPPvE0W9Mxi2mJGBXTdD3Hz9JPy5pT6ZyaVUy753N+z3dPRtJCiz452SIG48PZw1dEK6a5KxaYCEMQ2Nxa49EvfJGD19yJ+MCu0ZLOPUF/ZZtbc5xTTp78HM+c+jLjzYu0eQwlIhSS9QH3M2PmU9QuEjEM8kqaDzBzjHOp7pXWbTKZ+97575930DOMTR/uyvse+5zGqLJbWWdvdx0kui6LvOcisz6KAk2cjcd3paPvK95JGaTOKe9R11RMneil6kDd7+/3nT0+YhvCOaEYvahz3ha8zaJwc+Bdgcphvdmoo3Qwo7tdsdZ+HFj3i60LSMcyOBs/W0zg2JnAU/d1TR2ViqVqRCKOJjXEHKlUD8j2tJsd/EaWLzZ1sIgwXFxi18KIRx87xete90aeO3mSW+97I+/+zvfzT/+rT/L4g1/m7vvfyq333s3FBx4jVMK+fXtoNzahibTtFk1umLYtqW2Ik8YEdRvaZkJqW5b2HeDANdcTwiK5CDFOgEyOhbZRilPbtkxjy9ZkwmhhyOJoSJpskUgWXev71MNDrE3T6RxylwhiejJBYS5Fn3IfIOjlywzrATE2lBSR4qicCmQUcSRxxv38+rY9HGUxok7HvRM0hstCcJ6bb7qJpmlJPhGKqnUPgvZTu+yYNo6creorGT8IvO/b38Ozp36BtLVJ2LsPordtpFXTDn/rm29Eq7cFrb4Xe8i6B6jr5aZov3RHdenS01xyj4PMK2d3mJSITnRoY6adZC4+f5azj29oISC3uMrWLoVde5Zo8xbPn32Crel5pEzxOROw8b0ZigW0qWhk3c+CMUfmve+LMy/nKOnf9pzgw/zr9o8rfrYr7KR5Jzl3HfCOqqrwdSCj6tyzwomlpGJ4puGcHaGYPOslR2ZE9SuW1lsv9Fv0YMpZ+kNCiyGziLGrKPd9MF1Ry6T2xIShe8EM517+74qYDmSXwNt16cV+Ox6g6wVlfcfdm/lGdcYoxDSoVTAjReWpdtczW6FCSqFyqizkknb6iGUSGatGe6Htrq8l9EIhOB0jkQWadkKoKvbvOcC+fQcZ1AMurG7y5NPP4Zxw/R23ceS6E7zjW97Jxz78YW679/XcdvftPPfCC5w/+zw0iT1LkUcfeYzzy+eZTLdwsSWlSGM6j0IhxhZf12y1W1CNWFw8RC6emBuKKfykWCi5ZdJOabY2ufTiGSYrF2A6xQvgtbvH5YL4gASVHPTeZv84VXAfGue02PXodaKtYaUUDcIKqlgUfG372kj0JeMyfQ//17Pt4Si70x8MH5pVE685fJA9u/cQYwsEtD0qEqPVtrxuEsTRThLOg5A4vG8fw0FgvDkmp4acA01XtZSiA9oxUYi+5xkTbTCszDb8vABERito/Rgbo3OYhrNFDVrx7EevdqlZKmxNWrZWM66toVlDBHbvW2KjmXD/W97I1voF3nr/G/j1j/xrtsZniDnawxdJRB1jkE1dG8W7uqiqazV0zllb4eyhe9mrbmntrNVwDse8Inrrf6aoEnrK6SW/t/v5QVVR1TUiOokymmKOY9bOqJ0uilN0UU8qxa4gSoGSrslSLVuE18MEVkTqwv35CHqe8qQXRN1aV4iaCQkXc0auFx/OBru8RPZtLm0X6GlWVfcGRDoegLYYdvskJZq2IfhAGMxSu07rs5uaee2x67h48RLr65u2743f6pz2dxetVIurlD9cQMTR5gLWRZRxeGdsEawjz1LOlDKDhSHvfNs3sbSwyAunz9A2kcWlmmuO7+eBBz/Hzdddw3NPnOTWe9/AOz/wAf7Hf/SPOPngl7jzrW/iluuu5XN/+HGmW1usbq5zdvkiW5tj8qRhOtmkia3xRB21D9pVlFt8VXPx3BncNUMyAx2RkZMW7clMm3UuvvAiF58/TRqvUwWNDBOOmoLvVMBCQFVFlBtZxFF7FUvJOTLJejRU1pii1W4loouIKk1Vynxpk0b8OaV+KmjpuLLfwLaJo4QUI7mIqZloGhljy4033tAXJdq21ddbRysF72qmbcQbr6PY6QKOJ599hrXphKWlBTanYxaHI5qYqLxT4rHNr9FN7636qFGCKvBotOOkw0vRyDIXlZRPSSk+3YPXPUz2ucgMqypFhRpSU9hYi0y3wOVN3vCmW1nfWOGt99/P5770ZV73+hM0k118+tMf59RTD1PKVNPVXEglKh5jLWTKAIhGNnd9JOXEGQ1lFu0w5xiudAAwcwpXtjPOp/DzPxNLYt465xG8V0zSeaU1dYKKRSOabnJlV3EGMdzZ+Ibz65iLykuecRC7g8KZ4+vW2HWvayHupRFy51x7tfa59+PE9W2qL7Eyo1L1UWqPh9Lju7bgXvlK83fsvtsQtblrn7ouL+hZAs89dxolm5uOqEWMzvbbaLhECEPaJtGEQl0FQh3Yu7jErt27OHDwMLfffielmfDbv/lhcoyEylMNKvbs3sWJ48d5y/1vY3H3Xh75ysM005bYNDRbW6zGMefPvcATTzxOzIkbbr2dYzfdyH1vv59/+9u/wa33vo7rb7yJpd17aNqWe978Fm6KLY88+DC7Q00VhMGgYjgY4QZDRrt2E0Lgs5/6DJcvX6IZr7G5dYkw2k/OXgunpeHixRe5cPpJtlbWqUTwAyF7ve++CMGyq0KBCrLXPVmJoxL1F1NRfFH7wAVCwOVIzir4vTBYINNlXSp3p81a2pDhDGLJMX3t/b/CtoWj1IfPIiChV77xznHDjTcov8rPNmvKiWR9u5Wzn4ngScQilCh89eTj7FragyQltpJFUxsUP+uqkjmJSboZX405Dp0h+d0jW4xg7HKh6hxIti4RQLwSYRGnBNaueJAzbYqMx5HJRsbnwGjJcccbrmM63c8XPv+XPPXk8zTfch8lTHnsqcdIzinhulhEaU4S6KvEKRl7cD5HNIyMMpfSXnGt4WudojPoof8+5ooeV9pckaIrXjiEQagYhKAV8WRdFt23W7ptsZL+v0t1bc3KR5yjFxk+1zutubXOg0ovUV8vpe9Y6d5HnnO0VxaExHUFn/mOrLlrNXdNOuGPrngz2xmz6yClKyJ5gsskbxJ+838bdbjeV3Rq5cF7mrax+6lalKUoXj8c7eb7/uMfomm19DOoHKVMaacbXLhwlnbasPzCWQ4d2s/73vs+SmqoB57BaKAFr6blzKnH2djaYHVzi43xOoJn0ESmW1MqCTxx6hTt5iY33H43t973Zt7zH3wXH/xvfozHv/IQ97zxDfyX//jHEV8hoSKmxBc/8xm+8sADeOMBNzEyTYkUW5rYcsONJ1hfvUROUy5dPMOBawaQA9P1dc49/ySrl84QJFLVFUW8ZnVZxX5LLjReVcG8oxfrdc4jUaGbbrZUTlq00oMqMqgrHBWUbG2suR93nIuQolPMKutcoWSKU6/gJ7eJoxRIwSKElHGVEqj3jxbZt3uRGCdI8UpsTa4f/9qNT/Ai5NyQskNCYLq1xbmz51gcLNBOp2xuTaxwU0gxEpQkoTQCMWpJKb2MWJckCbMHqFgUEK0lUESVZjrenWX0RpTWlApx5KRtVpPJhK21RLvp8TmztrrKpJmwtrnJQ48/zO7FvSxfeoG2bDLNWxpBZv17xW46nROHWbsac4WUbh0dVmnV3kwnhvG1bm/+lZdGWjIjm3ffKVf8jEVc3nmTA1OQvI065VAn2xWT++oKIP2xAx30UeautdCBTOaA6Inclq33EEhXLYeuW2qOC1pmqXWPNXcOuLthBrF0XTQdobzzz84qKN18JGeVaGe/I0lXiJG59SstZRAqBqmibSJVFYhJhW31HjmOXnOcO++4i6+efIxLly6wsDBia3miVXc3W58rcO7ceU49fZrbbr+HzZVLPPH4k1SuoW02WV2/zObGBr7A6SdrHTFSMlkSiUhOkWFVszAase/AHm66+TjXXHuIixfW2b2wiyMH97Nv/y4KcPLhR/iNX/tV/uHrXsexG67j9fe9iU985KPcftfd1AsLEGpygRACd7/udTz18FdoNja06OJMMd723r49e7nm0GHOnjtHO9lgsnqO8caU5TPP45oNFl0kSqGIUqW0K9IObCck5xDvcV7l5bqgBSe0JFwuqqaUC+JFtWlFYZtozpYOn5SOvlYoUaP6YlFkTLnvL/9Gti0cJQXanCg5qUKz8kc4sm8fW5vrlNEQPxpSsnanJAqt9zjXSWopsdQZ9nV2eZmN8YTRYKQhejvVjEj0AdWBREEfXOtN7hxDKsZT66IhmKWu9nUl9aqAQdcF0pGMkzky7TuOpFY7FbZWWpoVT9kSCpGWhssXVvnqUydpUsub7n89Z148DS5y+fIldA5S1OlyFhkWw+i8pXeVCzjnaGPLpGnM2bsZlppS7xiuTK9hrmBj/59PzV/CpdQfBGZRmXKeFdcb1CNtIbNUMuVsFKCEd75nAoBNFbQocraUWUFJHY4dfl31Ur+jV28C+r7rbIWyIqrJqP6u9NCDQinGBjB/2GOy1nLTRZ3z10c5rroPQ1UZNquFiAIcOnAAXxzr62uMFkZsTcc0KQGJvfsOcsddr+fUk09zeP9Bbrv1Fn7nd34L8lSVz3EcP3Y9Rw4d5VN/8Slibrm8MsaLw4vvOhi1LOOgdp6Hv/IFjhw5RO0Sk/Eym9NNLl68wCTqpE5fClFUfDc1DQsLNc10kx/90R/hzW9+M8OFJZoUaVvVG0jJs7BrH/sPHaReGNDGwsXzF/iZn/wnnHrkEe583d2869/7AD/3T/8HnnrkYW697014n3E4EjDcvYtbbr+DL376U3jvcTalACAWvRdHT5xgeeUy481NXnjqJM3mlFpg4AqIZnUiHuedVfVLDx1l8fpcl0IQKCWRHTZHSGyYX2Y4rBHnibnQtBbEgFEBoZRumqbyOFOjBbYYEx1FqCToByV9HdsejhK0b7OUfph55T1hMOD0mbMcOnSAECokaFynOGWL9AowkEKg1IEAPPX0M1RVBaIqKdPJmJJU8bsjw7pKQ/qIYoid6nTnGDqOHnQPm55KTdvSNA11qLFx131xJZdCU1TOnuwpptY8XU+kyw63gUa+RIqLrK+usDRcQFrPqa8+x7HrDnLomoM4V1GYahHZO035UTKzE6Hyjiqok8Q5mkZFTtuklKWuLNJHmnOY40uu+fz/+8h55kBl7nv6VNbw2JyV4+qLY1QN2LtnN1sba1ao0XSoawnsOuVzKj180Dkx+9N6+hcbVeE02tcpjJCz8SVl5qhnRapZxDvPNuhZc53jtI/VQdpPlEIIA3SedYuTYjCKynI5HyAV9u7eg/eelUuXSRLJORPqikFVMWmn3HX3XTzz7LOsrK4oN7CdsL455v5v+jb2LC4RyFT1gLadaovraMD11x9nZfUSItrDHHyNqzxkCHWgLYVp25BSJNBy9tmTfORXnkdKQ4kNQSCb00JU7CKJpuauFNLYcfTEUW667Q784hKjvfvYVQ8IlSdUFaEaqrOyAEEQ9h26hvf/h3+N3/rwh/mxu36SG2+7hbvuvJNP/M5Huemee3AumBKRfv91t97KZ/78z1mcqzxLR+cRoaoqXI5M11agTYyCV1m1HAFHHTTAyabyLlaoQbw5pqzRZEmIaJunp5stFLRnPkHbtj0WqQ+qkKNGiikl1WctnfKUBkopJYWzSrf/XiOOUpM0A9VLxlcV4j0rG1tIqFhaWsRT2fhQT86JFI2Eaj3hJCGnyDPPPENd1YgLqm5d1Jn54GmnSTso+oKCRmGpZFyWvvrZRR2lFBty35GkbcBVSYCz0xyLTPQYi6au0U4bpltTtlYLk7WCb1uQTA6ZeuhIbcOBPXs4tH8Pp599hqWlipQnRmtIBK/ixVglVYs0it1UocJ5pxGEqOPsyN9KZZpzIVcUcF7y+fxrc44GvtZZzqJJb8UwCM6zUA/Zt3sfsWkZT8bmzL1G36nr+XY2bsPS8d5Ndk7d0nJ7j12hreif6Ytts1aLecyv9B0+UuYUf7qvi93X0hWKDJvKhapa4Ni111MHfYBfeOE003aCGOcwF6WcHDp8kOnWmMlkzDQlzp4/SxFHzsJWFt7yjm9jdXWNZjplYWHEtcevZzJpuHThPOPxBs57GmN0tJMt/u0ffEyLKrFVAQenzka8il3EWPre94IWKCYlquBwihZ5KgXLuUJwhWPXHGV9bV3HxIqwtGcvt9/7Rqp6QBHHYDhSmbucmWxNWLt8meXzF7h47jznXjjNhXMv8tQTj/PYQw/x/Pf/R5y47nre850f4Gc/+D/x5KOPcftdd+NHggsViLC0exeLoyE0LVBsnIRif5ONDR747GdZPn8WV3lK5Uh4U2JXmCbYuIy+FdN3xS+hWGdREaEKoiK7zhNCBQSaWJgmVY7PJeNF+77bNpGz6mimmPuahgZUmbZk4x/nvl1W98Wc8M3L2DZxlDOcqBQ9Yffu3oWYI9iaTFhZXcftXqD2CzoQPqn0qmsAiVAqWoHNyZSVy6sMBgukAnoLrb1OupFWhke5TouwwyidEX2lj5z61M8qo52SS7fWjneXOrzLdRMeVUR2vAnjjURKGSTqyZcTo3qEE8e5F1/A+TGrWxf4s8+8QFumGrUA3gXV7kNTE2/RZBDp54hTIDpPXdmYWwxT66C6K4o5X3Pl577+Uic6K1d0Uabig077awUG9YB9u/dx8MABnA/UgxHjaUOR1jZetiq9/RbrMrFbrhihzWDR9Nn+gjhEtCkgC5Db2QPVQ4yKaXdzfZq2ncMnZ3tJ9JutY8gEMkzUVazbYGm0BCVx8cJlSip4CVq1LxGyMB6PmYzHDOqa8XjLlNkFX4pig6eeZro6IcZE00zZ2trg03/+p0ynU6atQiJFZsrzLkLbTLXS7Ry+ONqiPewheHKbmDYJsoo1FDJNzkQivggiFS5UOqMI1Vw8fuwIP/Xf/zSf+9zn+a3f/Ah1XdOOG/7rH/67LJ89z9Hjx/iWd72T5fMXuXRhmUsr5xhPthhPpmxuNRw6eIA7b7uFt3/bt3L0+hN87CMf4W///b/PDTffyE233coffPR3ufnGmygpEXbvAecIsXBk/wFefOGM3qdScFI4c/oMn/3LzzKdTnH1gCxZx08UvYfeiRarDAupnE659Cg1Kzi9HrhALokmJhaHC3iElIUmZdpUaLJCLzrBMeFypG0jTRbLUCAlve6gz2osyrpQceY8B7m8BniU6oC0PbEUHYK+b2kXo2ENwdOmxPLyMqHsZSHYAHjxqoCS9NRJEcQLZ89fYNK07Bp6laESZeBnK7w4TMreCcpFsmjTu1kE1VVM6OoKqjLT9ShrhdY6Voo+zC1AKsQMbco005ZmM9NsFOIkIqJ95gnFNTc3J7zw7HkuXl7m8tYya+0GrVW2u7UWFJ9xoietB1UG9x4njhQt8i0WYVqfMaIzdyizKPCl13v2+Twm+dLv7dxjmaXJWQs3vjhqX3PowCH2HThAVVeMp1MGowWqyYQ0bozsLabEXV7yN6STvhJBnKcKNYNhTSkO74P28HptS9vcWufs8jkVUbB1aBFUsaZuvfOVbWzlzgRqxRylt/7fNirVKpiuQDvdYnl5mY2NNcXCZEbmLqUwnkSefu5ZdXhWHPIdId3BpZXzLF88p22KOSpO6/WqzS6nYt+hn9hos5noSPl6AIeSCEX3ZeV08k5KQouydUf1gMrUzlMT0bbXwuWLl/jR//xHmDQNxQV8Fbi8usLTzz1HLZ6nnzvFwSN7OXrsOu584+vZd+QgB48c4toTNzAYLVJVFXUIEBxrq5f54D/+CU6fPs2JE8d59/vez7/82X/OM08+yS233ML4/Fmq4QIXn36awXCgky1josTIww89wsOPPIpznsFwoHAKztS3UF6yQ5srQiGIp6SsykdFZyW5nAlFHZyvK4pzTArEqNFiSnZ/UtHWyagRd0xRaUC59OLUuVPYF0yMBHTap6bm0hX4XqbQOW/bwlECvXKMQ1gYDTh8YD/1cACDocrh58jWxhYrLrBnzy5YBOcHCIpZdFy7M2fP4eoBeE8QYTKd0hOvLYfrCLvaI5vAu75A46WnjmOFTECj0ta6EHSCYfdA6rzlriujC/nTFPLYI9NEnUFypkVB5SLCdAJPPPEUfuDYislGNSiNwTuPiFaevVjJRDDaiFV4kwL32u3pqOsBfuyJbaNcUitezBMfXs5pfj3rcM7u8HCIih7gqaXi8P6DHD50mDAcMm6mxKwScIuLS+TY0sQGgd7RK8+yohoMCKEmI4RQUQ+GDIYjnNeUSyuQHRUssbG1Rds2OlOn6Os2T08PsO69WQGn66AqhhWLiBbvSiESrZPE5mw7T4qbnD7zFFvjrb463rcnlkIpWkVNhr94cX27YuV1lEcXKeIKWbwhAwYPCEiZYd3Y9ezYEUptMqyuZLqhXN18nJyj7nFUrKPNLbmNxDhFlfm9TRQVXIo4Cb1jct6RUkNY3MWb3vqt/IOf+G/Zt/+QShSKRfc5MZ1M2NpYZ62JTNbWeP7pp0lrm/zJx36fv/Wf/gB33HY7N15/PX/8+x/jpr/9n8Gk4eyTT/PCiy+ysbZK5T2r6+s88MADPHf6DFVd9xi/WASp4h1K9xFpcUabcjlR+UqjQu/IDqYp9qMvpjmpdmUupLYQWyWL5xQNK1dGSMyd/oNO79QhhTpPXBGdua6pkl/yXHSFvm9k28NRCsZ3UkLpvj17OXhgv4Lbg4FOYIuRGhhPxqTcstQusXvPLp3OhwOnrPtnnz+Drwd0Q+J1AL0jGgmXYoIX5gE6QdRMNnyy9J0TXcbYBwUpmdp1MXVtVEpL0DQwFWLSudylEWgDIx9IvlIyfXG0ZayRZemq6NDktvfIOrdHR23WCHUB7wPFdy11XaslSp0QSFKoFkZUm0Fl8UsHMMxSZ/jaNHweJXwJX5BZNdyh+om1r9VRimfPrj0cOnKI4XBIkzNN0/Y/ORou4oqwNdm0AowwrGuGwxFVqHRCZudMUL5hNwmTkq0C6Yh2bZ0fIK5C4sxJZUrfGaTthkKwFs7W9DfBcEhQp9AJhMzesT5YJFbWL2sKWNe9fmExLDQne6REf49KoWWbBaSvxxytIKLvQ4nMKgnYxeWgPxN8sB54vebea1HOmbBswQqbPulEwZKhaPuq+Fk0DfTFLucrktPXvQ84cVR1YM/eXdx//5v43u/7Pm689XYAXnj6adaWL7G2usLKymW21jdoxhsEIotLuxmEIU+fehrXNnz+z/+E93/gvRzafw3vfd938KH/7V/w7BNPctP1NyAl8+KLLzIdT7hw5kU+9dm/ZGVjnXpY6VvrGAgiRHtfnkzlYOC94q4W6efcaIE2Jqg9flDRJp2+GAu00YaSNZnYZFUXy5Ei3VgI5UhrIDTr8U8p9a2JJQkxp7k2Twxac68dR9lNpBPb8Nddd5zde3dZ6Od1Cp0McAJNjEwmE1bW1lSfLgv1wgLFeda3pqyuj1natccKOMXoI4ph+VCDdDJTgqSsNxRMHqtzHuostQtML2JKhdxEVWxBhXtz8YpN+gI5URqlKOw9cIgTR4+xemaZy89fZCKBGAODJpMa3fSxRCWrp2LpHnpzSRoNiKP2ntpa01SD0FlPMQQP3imHjKqmFRhWA6bjCUmKDYLqNkBX9LDrLYrhdOdBr4xTNHLRKN0hxVO7WiXCLJobjIbsPbiPsFCRcmKyOVZZrL4Y5KiHC9TDYU/TcU7FXemq0HROxYDKNEv3pRSN8vUusDha4sSR62jGY23Rqzxtbrm0epnJdIJzwnAw0FQ0F2gaUqMi0F1RyXmv2O0V4h/RxHS17VTxrB47FcWsXRX6lktnhPhuPk8hKhaaO2dq3FyLDsXI0t4OBFeMVN/rpM1MDzfXNwtg0n9Kc/EMqoD36vArcf0Y2yJCDo7sofZQhYAPAwaDIbsWRzx/6lk++dH/m8NHHqAOgVGoyW3DxuaGRViOrfV1Ll16gQsXL7K51ZJz4cSJQ9xzw5088Bd/xvve/13cfstNnDh+nE9+/OOc+IEfYs/+A0hdc+rRx/iTP/tTGiKDQaXpc+5Yx84q8Xrfh167akrJpOJIMTMIojUGKQzrmoTOj2qSFl2amGhjJsZMbBPZIgzJ2tabKSiHXA+wjvifC9bgAH1HmEFl+poJydj17xTEvp5tC0eppzuE4BiGiuPHjlJXAZw5DKdhuILyBRkOGG9ssLa5YX3BsFTtYfnsWdXvNEUXciI4TWWTcQqz1Vt97riQAGLpUbdRu+pstzpRRxix0Q/6NNljo2TwlGnahK+XeNOb383CcER77BJrN53l9DPPsXxhlWZjytJ0gTIdUJpMkswkTNiULetRdT3nsKoCC8OakCLDoFXtejhCnOf8+XPs33uA73jPuwnBMS2F3/79j1PXWmRJKdk2fek1fjnrnlel9ARG9ZClhREL1SKTjSlSlK7VpJbRcMCB/QdYHC2Q2sjmhqpVV8OBRVkavdtIlX7kgWpDKuTgrJOqaz/sRVptlW1q6RiQGkkV9i0u0VYVl1YvMWknpJKUHibaraEshN7H6fux90TR69H21fcZ+dxbJNcxBdoYSW523wvgnNfoNc/0BzqREzG4wNnkRPHW3eWdjijp/eGsxt8pm/dpuOhsGwtxCOIZSqAOgnOqK+BCYCCOQQjkemgFkID3gVAF/KACrzCNiAo9O8PkNtY2WFm+RHCe1dVVLp5b5vKlS6ysXKZQqEJNii0iEecdwVUsDIc8+8wpbrn5XYynU1bXLrO4uIt3f/u38Su//K84c/o5jp24ntW1NT72iT/AD2p8XZGKToCEThRb9TNrZ8pORIvglPdZC5QUdd568TRtom0TKRYmRTt8FJfUAyyY4HGLs/EdOn2zjdmYLModVhhPN4ETMf0Lw7KvwMs77dEcXwvFHPTklZy45tBRdi8uKqE5OAKGCYWAeMfICYMqsBg8G+vrbE3GLF++RBgMOHvmBUaDWnE9saJN0ZOmnU6RxV3693LW7grBOqftRogO8Ow01nquXkFHSESj3zhN34vxtlJJlAQxB97x9ncxWDpAKhX10gIHFnYzOnCIE+NNLp6/yNqlVTYubTJeG5OmE3KJuCaw5Ad472mjyqjdfPxajuzbzZE9u9i3azeDwYgwGLKxtcVHfuejVCQO7llCKKyNJ0iODAc1PgRcmcmvzfMp++ttm6VrxXPOMaxGHD5whP179jGqB9SuZuXCKlubm7SxZWnXEkt7lhiMBrQx0jYNsY34EPTk7lXHs1Vm3Yw4XYoq3GAdE6KcSnWaHUhQ+p9v2imT6YRpamhjJLeRdjKlacbgZCb8YF5ecSc7CEvpR2NQiqnZ00d83b/u6znNZtZodKjXTR294KyvHKdtdmKONIi3aq21w1I6thmgR66jK5YZP9TeZ/CVzQFX4nxdVwRLx7XLSRh4GI0GuBAI9YhaHBWeCV7XUpTU3UnK5agq8QXF85wUSjNhGDwPPvQQ4mH3rt1MJy3jzTFSVXgn7N69ixtOnOChL39Zr4t34DWFf/ALD/I3v/tv8vBDD/KWt97PHbfdxuFDh/jkH32So9ddzy/+4i/g6xoXKmIWJGkPde1DH0R4oC4FySokM42qy+kLEAq+CsQC0zbStoV2qroIrbUppwyp6DUstp/b3FqPPris2UBEOcxJbI6TYWZdAwllxnfu0u6ZFupcx9bXsW3hKKEgkgilcP3xoyqzpOgYXgz0dRj3ysQfvKP2jksrq2xsbbF86RLLF5ephiO8V6XyWDLidW7HtGmQnKjQ07aTdeh6fQOiw9DnLli2kCQX7bkuKLWk2JUvVkVrUmZrK3P7HW/m4OGbmcRIPaiAmtwEwnDEYjVhMDjIoWON3uxpQ2wmtNMVXjxzitxOcCK8ePYsFy9c4LqjR7j9hutY8FCHGh9q2gJtihw6dIBrj13DsK6RkljfaAlOqOow63kGw9BevoDTORrvPQf27efIwaOMBov4ohPrcs4s7l2gXqx0Rol3OotZCk3b0ratkoedGAlI6TwlFq1eOztwnJ7aXe+7s0pwAbq5MBrU24sxsbK8zNp4naZEYkkaxVsa701z0ttU6q6yWUruKR/d76b7dyVwj0Y+vQal2Axt6yLyXsnurihPtJuNrsKHJmyRCwM7UNuesq26pOK8/r6gzrIKrmcqKG4XEF/jvTCoKrz3VMETgqlyOw8kxNruyI62eKO2pF7EoX+fqRg2rYWhQfCE4KhGnspBbFuc94RBRcGzNZ7a4ZFZ3Vjj0ZNfJePIKeN8png96M6dvcCTTzzJoWOHWVtbZWlxF9/yre/kQ7/wi5w9fwGcUAdHKrEfbOa8ygXmnPDeJN5QTiQZXHFMi5B9QCTTTKLSqmJh3Cam0eYYRT3Aksntxa7VBr2lOacOJDMhkdwjOM5e60ahJJPv0+vVjReZE0jp2AzfwLaPoyQxHNQcO3qE+SQqOJ0/LSWjw9edSqmJYxAG7C67WN/Y5PnzZ9mKU3aHJRVvFWjJlODJbjbgyWINkkAyEGhgIgQqwqtnocIq2SqrmWkzIUkii1ceFgBaSZu0mYV9R7jj3vuZRkFcpdL0UsiVx8kSLixQ2CA1Y3JODEYjFka7GPjdtJtrTKaXES+srA+pVwNLu5bwdYV41aCMNuN4Elv2HdzPgYMHVVfTZLaqQU1bPHVVMW0aI8S/1HrxB3NOo+GIgwcPcXD/QYKrIOt1KmTlr+UGA9YoZJ29E3Vj55jxTkn8KniheoRk4zcmgU4jU0rHmaeyQk7vxNGIwBmdJklmEqdMm4nNTsIcUMAFT8mqd5hSNmkzzbG6jhqLlfv3PIvqFIfuKtZenOKXon/bi6dy+ji4UiimU9nN8E5d+pyhxuM9DBy44PDBGaXJqxCsD7hQUYXAoArUwWuxydZSfE2WQBfrFps/nkvXg5wRe++kjLPebSXje52JPnfg6LUbGJyheKwPKhKDZFJJNG0mXlpHssO7Wp1XTsSi6WpxQkl6kGm2BNVwgc986Yv8jVu/m2effY677riHhcVFXjynJHIfHMFnJEMsSZ9NZ4Uqk0hLuWsAUDm7DEgItBloC7lV7LGosIHe36L3NRr1T1EYvbPexnAkhJY5ucOsA8ZEMNpeVu1Wyyi0zpP7fYIJk3Q93s57iF1R8mttmzhKjeaOHDzIrl1LZDrsUG+kd14pPNhMYidkU4QeLQxpKKyeOwf1rPrX5khr1J9IpimJ9emYhXqkEathkl1qJC5gBVczLYQUox8kMtHwpoKJLxQd3dkWx/1v+WZSvUDKCUkqV1+8OplUCpID1EvUYchka4MUW5CWJkakhsrViBRGw4p9+3YzGNaqHp10WDsSSQLj1CJ1AK9VvIANWHMOycJoOGJ9Y6OrDtjcH+mjKzGc8OD+Axw8eJilxSUcnpK7B6/Q5kzOUUH01Frrl9IxSsaUi3Rej4vSV3c9jsqF2fhXr5iptgTqAeS6hmvXpcBW8FHRShwRFzIqnlwIQefupJQUQ4xJ7xeA9QUDNpVTnW5wStIPFsFmq7qGqlKoICdVyRaZVVBzgawUJOccrtLOJ50/rU7fe40UXYHKOQa1x1WOqgoMfNCItB7CcERK9DzHzm2nnK0LrCi9xRSCcoq9KpE6f9NWNK1UbVUt4AqSPK7orJ0U45wSvF3SYul9huCgRPBJ+8dzKbiigi6DKpBF2SAx6QhkV3wvYFwK7N6/n/F0zJNPP8M1B49wYXmZn//5D9GmyHDkEZ/JRRBsrX2Mp2toU1FHn4yFUDlahDhtyG1kkqZKC0KgqH6pYOI1ZBAdtqe/TxsYUtIMry2JJMZ6KCriW9ACndJ/pG92UNV6Q8Kdtnh2xZzcyd69ZloYxXH8+HGTVCqI11kzUy/4EAiSqEXn9DqwNA4FuodDLq6tgK+0dzunfkyCq7yK+wbPOLaEwZDghFCs/9dASAEbC5t7KX6PRiElZQWMrcAAtumtM+DGm27lwOFjTKKmMwHIJEhJI8GoQh+FSE4T8J2WZEuJjakg6c2unbBQByogt5lppx7kILpCE1sGo6Gmdeb/2pJV2FU0ovTOIbkT0O+278yC9+zZvZvRaIGSTe2oOFWfyolC0g2aHbEtxLZRGf1OBxLjZ0oG7ymuwz3FhFmNsuO9dTHNIi/nINoD3vEWg9MoRogMXGbfqCJOHNMMPlTU1YDWupW6Ns5OCb0bO4wxGULRyr2Kq2iENimF5JQkXoxr55xSVwZOaUHeeRBHFQb4Wsn7VQgMgiPUFfVoiIRgTl2QXCgx05CVglDor1k7VsFYZxSjVPTwUamvRBBnfELTtgyhx8qKFyKZUBTvwwUiaESeNOV2aHTmQhexFQ0crAOllJmyvSPjq0I9GjKZTslNJIijqh2DhRGra+tMY4MvAUSHSUSgbVqGTYtzwkNf+QqHv/Uw//zn/ldOPn6S0e5FyyBMUTwE+hZUMSzaIn4Kqv8qQpsj06IKXjkpF7SN1gqsQrRIMUiFCZmkYxqsp125qkKy585lnZvji+o9tM7r8DPdKf3B74wyVMpMNb/Hs7Gs5BUwSvl/S0D+/9JEZB04ebXX8e9gB4Hlq72Iv6LtrPnVs9fiuv//vObrSymHXu4L2yWiPFlKefPVXsRf1UTkgdfaunfW/OrZa3HdO2t+efvGkhk7tmM7tmM7tuMod2zHdmzHXsm2i6P80NVewL+jvRbXvbPmV89ei+veWfPL2LYo5uzYju3Yjm1n2y4R5Y7t2I7t2La1q+4oReT9InJSRJ4UkR+72uvpTET+DxE5LyIPz722X0Q+ISJP2P/32esiIj9n7+EhEbnvKq35hIj8sYg8KiKPiMh/8RpZ91BEPiciX7Z1/7S9fqOIfNbW92ERqe31gX3+pH39hquxbluLF5EvicjvvRbWLCLPiMhXRORBEXnAXtvu+2OviPyGiHxVRB4Tkbe/6mvuZ6xchX+oktkp4CagBr4M3HU11zS3tncC9wEPz732QeDH7OMfA37GPv5O4PdRbvfbgM9epTUfBe6zj3cBjwN3vQbWLcCSfVwBn7X1/Bvge+31nwf+jn38d4Gft4+/F/jwVdwnPwr8GvB79vm2XjPwDHDwite2+/74ZeCH7OMa2Ptqr/mqbK65C/B24A/mPv9x4Mev5pquWN8NVzjKk8BR+/goyv8E+JfA33q577vK6/8d4L2vpXUDC8AXgftREnG4cq8AfwC83T4O9n1yFdZ6HPgk8G7g9+zh3O5rfjlHuW33B7AHePrKa/Vqr/lqp97XAqfnPn/eXtuudqSU8qJ9fBY4Yh9vu/dhqd0b0ehs26/bUtgHgfPAJ9BMY6WUEl9mbf267eurwIFXdcFq/wz4h9BPpDjA9l9zAT4uIl8QkR+217bz/rgRuAD8kkEcvyAii7zKa77ajvI1a0WPq21JGRCRJeA3gR8ppazNf227rruUkkopb0CjtLcCd1zdFX1jE5F/HzhfSvnC1V7LX9G+uZRyH/AB4O+JyDvnv7gN90dAIbD/vZTyRmATTbV7ezXWfLUd5RngxNznx+217WrnROQogP3/vL2+bd6HiFSok/zVUspv2cvbft2dlVJWgD9G09a9ItK12c6vrV+3fX0PcPHVXSnfBHyXiDwD/F9o+v2zbO81U0o5Y/8/D/w2eiht5/3xPPB8KeWz9vlvoI7zVV3z1XaUnwdutUphjYLcH73Ka/pG9lHg++3j70cxwO71/8Qqbm8DVufSglfNRESAXwQeK6X8z3Nf2u7rPiQie+3jEYqrPoY6zO+xb7ty3d37+R7gjyyqeNWslPLjpZTjpZQb0H37R6WU72Mbr1lEFkVkV/cx8B3Aw2zj/VFKOQucFpHb7aVvBx591df8aoPJLwPWfidanT0F/MTVXs/cuv418CI6svt54AdRTOmTwBPAHwL77XsF+Bf2Hr4CvPkqrfmb0RTkIeBB+/edr4F1vx74kq37YeAn7fWbgM8BTwK/Dgzs9aF9/qR9/aarvFfexazqvW3XbGv7sv17pHveXgP74w3AA7Y/PgLse7XXvNOZs2M7tmM79gp2tVPvHduxHduxbW87jnLHdmzHduwVbMdR7tiO7diOvYLtOMod27Ed27FXsB1HuWM7tmM79gq24yh3bMd2bMdewXYc5Y7t2I7t2CvYjqPcsR3bsR17Bft/AAq3osslFrkxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load face detection model\n",
    "modelFile = \"models/res10_300x300_ssd_iter_140000_fp16.caffemodel\"\n",
    "configFile = \"models/deploy.prototxt\"\n",
    "net = cv2.dnn.readNetFromCaffe(configFile, modelFile)\n",
    "net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)\n",
    "net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)\n",
    "\n",
    "img_orig = cv2.imread(df['filename'].iloc[10000])\n",
    "plt.imshow(img_orig)\n",
    "plt.show()\n",
    "frame = cv2.resize(img_orig, (640, 480))\n",
    "\n",
    "extracted_face = detectFaceOpenCVDnn(net, frame)\n",
    "plt.imshow(extracted_face)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  135 images. Continuing...\n",
      "Processed  270 images. Continuing...\n",
      "Processed  405 images. Continuing...\n",
      "Processed  540 images. Continuing...\n",
      "Processed  675 images. Continuing...\n",
      "Processed  810 images. Continuing...\n",
      "Processed  945 images. Continuing...\n",
      "Processed  1080 images. Continuing...\n",
      "Processed  1215 images. Continuing...\n",
      "Processed  1350 images. Continuing...\n",
      "Processed  1485 images. Continuing...\n",
      "Processed  1620 images. Continuing...\n",
      "Processed  1755 images. Continuing...\n",
      "Processed  1890 images. Continuing...\n",
      "Processed  2025 images. Continuing...\n",
      "Processed  2160 images. Continuing...\n",
      "Processed  2295 images. Continuing...\n",
      "Processed  2430 images. Continuing...\n",
      "Processed  2565 images. Continuing...\n",
      "Processed  2700 images. Continuing...\n",
      "Processed  2835 images. Continuing...\n",
      "Processed  2970 images. Continuing...\n",
      "Processed  3105 images. Continuing...\n",
      "Processed  3240 images. Continuing...\n",
      "Processed  3375 images. Continuing...\n",
      "Processed  3510 images. Continuing...\n",
      "Processed  3645 images. Continuing...\n",
      "Processed  3780 images. Continuing...\n",
      "Processed  3915 images. Continuing...\n",
      "Processed  4050 images. Continuing...\n",
      "Processed  4185 images. Continuing...\n",
      "Processed  4320 images. Continuing...\n",
      "Processed  4455 images. Continuing...\n",
      "Processed  4590 images. Continuing...\n",
      "Processed  4725 images. Continuing...\n",
      "Processed  4860 images. Continuing...\n",
      "Processed  4995 images. Continuing...\n",
      "Processed  5130 images. Continuing...\n",
      "Processed  5265 images. Continuing...\n",
      "Processed  5400 images. Continuing...\n",
      "Processed  5535 images. Continuing...\n",
      "Processed  5670 images. Continuing...\n",
      "Processed  5805 images. Continuing...\n",
      "Processed  5940 images. Continuing...\n",
      "Processed  6075 images. Continuing...\n",
      "Processed  6210 images. Continuing...\n",
      "Processed  6345 images. Continuing...\n",
      "Processed  6480 images. Continuing...\n",
      "Processed  6615 images. Continuing...\n",
      "Processed  6750 images. Continuing...\n",
      "Processed  6885 images. Continuing...\n",
      "Processed  7020 images. Continuing...\n",
      "Processed  7155 images. Continuing...\n",
      "Processed  7290 images. Continuing...\n",
      "Processed  7425 images. Continuing...\n",
      "Processed  7560 images. Continuing...\n",
      "Processed  7695 images. Continuing...\n",
      "Processed  7830 images. Continuing...\n",
      "Processed  7965 images. Continuing...\n",
      "Processed  8100 images. Continuing...\n",
      "Processed  8235 images. Continuing...\n",
      "Processed  8370 images. Continuing...\n",
      "Processed  8505 images. Continuing...\n",
      "Processed  8640 images. Continuing...\n",
      "Processed  8775 images. Continuing...\n",
      "Processed  8910 images. Continuing...\n",
      "Processed  9045 images. Continuing...\n",
      "Processed  9180 images. Continuing...\n",
      "Processed  9315 images. Continuing...\n",
      "Processed  9450 images. Continuing...\n",
      "Processed  9585 images. Continuing...\n",
      "Processed  9720 images. Continuing...\n",
      "Processed  9855 images. Continuing...\n",
      "Processed  9990 images. Continuing...\n",
      "Processed  10125 images. Continuing...\n",
      "Processed  10260 images. Continuing...\n",
      "Processed  10395 images. Continuing...\n",
      "Processed  10530 images. Continuing...\n",
      "Processed  10665 images. Continuing...\n",
      "Processed  10800 images. Continuing...\n",
      "Processed  10935 images. Continuing...\n",
      "Processed  11070 images. Continuing...\n",
      "Processed  11205 images. Continuing...\n",
      "Processed  11340 images. Continuing...\n",
      "Processed  11475 images. Continuing...\n",
      "Processed  11610 images. Continuing...\n",
      "Processed  11745 images. Continuing...\n",
      "Processed  11880 images. Continuing...\n",
      "Processed  12015 images. Continuing...\n",
      "Processed  12150 images. Continuing...\n",
      "Processed  12285 images. Continuing...\n",
      "Processed  12420 images. Continuing...\n",
      "Processed  12555 images. Continuing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "X_train_final = []\n",
    "Y_train = []\n",
    "for index, path in df.iterrows():\n",
    "    img_orig = cv2.imread(path[0])\n",
    "    frame = cv2.resize(img_orig, (640, 480))\n",
    "    extracted_face = detectFaceOpenCVDnn(net, frame)\n",
    "    if (index+1) % 135 == 0:\n",
    "        print(\"Processed \", index + 1, \"images. Continuing...\")\n",
    "    if len(extracted_face) > 0:\n",
    "        X_train_final.append(np.asarray(extracted_face))\n",
    "        Y_train.append(path[1])\n",
    "\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11827\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wamik\\miniconda3\\lib\\site-packages\\numpy\\lib\\npyio.py:521: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  arr = np.asanyarray(arr)\n"
     ]
    }
   ],
   "source": [
    "np.save(\"Data/extracted faces/processed_train_imgs.npy\", X_train_final)\n",
    "np.save(\"Data/extracted faces/train_labels.npy\", Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  135 images. Continuing...\n",
      "Processed  270 images. Continuing...\n",
      "Processed  405 images. Continuing...\n",
      "Processed  540 images. Continuing...\n",
      "Processed  675 images. Continuing...\n",
      "Processed  810 images. Continuing...\n",
      "Processed  945 images. Continuing...\n",
      "Processed  1080 images. Continuing...\n",
      "Processed  1215 images. Continuing...\n",
      "Processed  1350 images. Continuing...\n",
      "Processed  1485 images. Continuing...\n",
      "Processed  1620 images. Continuing...\n",
      "Processed  1755 images. Continuing...\n",
      "Processed  1890 images. Continuing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "X_test_final = []\n",
    "Y_test = []\n",
    "for index, path in df_test.iterrows():\n",
    "    img_orig = cv2.imread(path[0])\n",
    "    frame = cv2.resize(img_orig, (640, 480))\n",
    "    extracted_face = detectFaceOpenCVDnn(net, frame)\n",
    "    if (index+1) % 135 == 0:\n",
    "        print(\"Processed \", index + 1, \"images. Continuing...\")\n",
    "    if len(extracted_face) > 0:\n",
    "        X_test_final.append(np.asarray(extracted_face))\n",
    "        Y_test.append(path[1])\n",
    "\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1800\n"
     ]
    }
   ],
   "source": [
    "print(len(X_test_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"Data/extracted faces/processed_test_data.npy\", X_test_final)\n",
    "np.save(\"Data/extracted faces/test_labels.npy\", Y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Faces Train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load(\"Data/extracted faces/processed_train_imgs.npy\", allow_pickle=True)\n",
    "Y_train = np.load(\"Data/extracted faces/train_labels.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.load(\"Data/extracted faces/processed_test_data.npy\", allow_pickle=True)\n",
    "Y_test = np.load(\"Data/extracted faces/test_labels.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720,)\n",
      "(720,)\n"
     ]
    }
   ],
   "source": [
    "(X_val, X_test_final, Y_val, Y_test_final) = train_test_split(X_test, Y_test, test_size=0.6, stratify=Y_test, random_state=42)\n",
    "X_val = np.asarray(X_val)\n",
    "Y_val = np.asarray(Y_val)\n",
    "X_test_final = np.asarray(X_test_final)\n",
    "Y_test_final = np.asarray(Y_test_final)\n",
    "print(X_val.shape)\n",
    "print(Y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11797, 100, 75, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_resized = []\n",
    "Y_train_resized = []\n",
    "i = 0\n",
    "for x in X_train:\n",
    "    if len(x)!=0 and len(x[0])!=0:\n",
    "        img = cv2.resize(x, (75, 100))\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "        # img_blur = cv2.GaussianBlur(img, (3,3), 0)\n",
    "        # edges = cv2.Canny(image=img_blur, threshold1=100, threshold2=200)\n",
    "        # edges = np.asarray(edges)\n",
    "        # edges = edges.reshape(120, 80, 1)\n",
    "        X_train_resized.append(img/255.0)\n",
    "        Y_train_resized.append(Y_train[i])\n",
    "    i+=1\n",
    "X_train_resized = np.asarray(X_train_resized)\n",
    "X_train_resized = X_train_resized.reshape(-1, 100, 75, 1)\n",
    "X_train_resized.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An \"interface\" to matplotlib.axes.Axes.hist() method\n",
    "n, bins, patches = plt.hist(x=Y_train_resized, bins='auto', color='#0504aa')\n",
    "plt.grid(axis='y', alpha=0.75)\n",
    "plt.xlabel('Labels')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = []\n",
    "for i in range(0, 8):\n",
    "    counts.append(Y_train_resized.count(i))\n",
    "np.argmin(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>img</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11792</th>\n",
       "      <td>11792</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11793</th>\n",
       "      <td>11793</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11794</th>\n",
       "      <td>11794</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11795</th>\n",
       "      <td>11795</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11796</th>\n",
       "      <td>11796</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11797 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         img  class\n",
       "0          0      0\n",
       "1          1      0\n",
       "2          2      0\n",
       "3          3      0\n",
       "4          4      0\n",
       "...      ...    ...\n",
       "11792  11792      7\n",
       "11793  11793      7\n",
       "11794  11794      7\n",
       "11795  11795      7\n",
       "11796  11796      7\n",
       "\n",
       "[11797 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_resized_train = pd.DataFrame({\"img\": range(11797), \"class\": Y_train_resized})\n",
    "df_resized_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6392, 100, 75, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "X_train_sampled = []\n",
    "Y_train_sampled = []\n",
    "for c in range(0, 8):\n",
    "    to_sample = df_resized_train[df_resized_train['class'] == c]\n",
    "    downsample = resample(to_sample,\n",
    "             replace=True,\n",
    "             n_samples=counts[5],\n",
    "             random_state=42)\n",
    "    for index, val in downsample['img'].items():\n",
    "        X_train_sampled.append(X_train_resized[val])\n",
    "        Y_train_sampled.append(c)\n",
    "X_train_sampled = np.asarray(X_train_sampled)\n",
    "X_train_sampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New y_train shape:  (6392, 8)\n"
     ]
    }
   ],
   "source": [
    "Y_train_sampled = np_utils.to_categorical(Y_train_sampled, 8)\n",
    "print('New y_train shape: ', Y_train_sampled.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(719, 100, 75, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val_resized = []\n",
    "Y_val_resized = []\n",
    "i = 0\n",
    "for x in X_val:\n",
    "    if len(x)!=0 and len(x[0])!=0:\n",
    "        img = cv2.resize(x, (75, 100))\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "        # img_blur = cv2.GaussianBlur(img, (3,3), 0)\n",
    "        # edges = cv2.Canny(image=img_blur, threshold1=100, threshold2=200)\n",
    "        # edges = np.asarray(edges)\n",
    "        # edges = edges.reshape(120, 80, 1)\n",
    "        X_val_resized.append(img/255)\n",
    "        Y_val_resized.append(Y_train[i])\n",
    "    i+=1\n",
    "X_val_resized = np.asarray(X_val_resized)\n",
    "X_val_resized = X_val_resized.reshape(-1, 100, 75, 1)\n",
    "X_val_resized.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New y_train shape:  (719, 8)\n"
     ]
    }
   ],
   "source": [
    "Y_val_resized = np_utils.to_categorical(Y_val_resized, 8)\n",
    "print('New y_train shape: ', Y_val_resized.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_2 (Conv2D)           (None, 99, 74, 16)        80        \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 99, 74, 16)       64        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 33, 24, 16)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 32, 23, 32)        2080      \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 32, 23, 32)       128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 8, 5, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 1280)              0         \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 1280)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 8)                 10248     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,600\n",
      "Trainable params: 12,504\n",
      "Non-trainable params: 96\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(16, (2, 2), activation='relu', input_shape=(100, 75, 1)))\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.MaxPooling2D((3, 3)))\n",
    "model.add(layers.Conv2D(32, (2, 2), activation='relu'))\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.MaxPooling2D((4, 4)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dropout(0.5))\n",
    "# model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(8, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "128/128 [==============================] - 4s 27ms/step - loss: 2.7033 - accuracy: 0.2317 - val_loss: 2.3337 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 2.0950 - accuracy: 0.3198 - val_loss: 2.7774 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 1.8437 - accuracy: 0.3702 - val_loss: 2.8641 - val_accuracy: 0.0111\n",
      "Epoch 4/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 1.7045 - accuracy: 0.4108 - val_loss: 2.8616 - val_accuracy: 0.0167\n",
      "Epoch 5/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 1.6201 - accuracy: 0.4363 - val_loss: 2.9245 - val_accuracy: 0.0612\n",
      "Epoch 6/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 1.5165 - accuracy: 0.4603 - val_loss: 3.0343 - val_accuracy: 0.0584\n",
      "Epoch 7/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 1.4583 - accuracy: 0.4865 - val_loss: 2.8344 - val_accuracy: 0.0987\n",
      "Epoch 8/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 1.3890 - accuracy: 0.5059 - val_loss: 3.3446 - val_accuracy: 0.0431\n",
      "Epoch 9/30\n",
      "128/128 [==============================] - 3s 27ms/step - loss: 1.3557 - accuracy: 0.5197 - val_loss: 3.2440 - val_accuracy: 0.0542\n",
      "Epoch 10/30\n",
      "128/128 [==============================] - 4s 29ms/step - loss: 1.2902 - accuracy: 0.5485 - val_loss: 3.5384 - val_accuracy: 0.0751\n",
      "Epoch 11/30\n",
      "128/128 [==============================] - 4s 29ms/step - loss: 1.2637 - accuracy: 0.5593 - val_loss: 2.8459 - val_accuracy: 0.1530\n",
      "Epoch 12/30\n",
      "128/128 [==============================] - 4s 31ms/step - loss: 1.2444 - accuracy: 0.5635 - val_loss: 3.2846 - val_accuracy: 0.0779\n",
      "Epoch 13/30\n",
      "128/128 [==============================] - 4s 29ms/step - loss: 1.2034 - accuracy: 0.5781 - val_loss: 4.1472 - val_accuracy: 0.0223\n",
      "Epoch 14/30\n",
      "128/128 [==============================] - 4s 29ms/step - loss: 1.1580 - accuracy: 0.5950 - val_loss: 3.1015 - val_accuracy: 0.1099\n",
      "Epoch 15/30\n",
      "128/128 [==============================] - 4s 29ms/step - loss: 1.1562 - accuracy: 0.5945 - val_loss: 3.7373 - val_accuracy: 0.0459\n",
      "Epoch 16/30\n",
      "128/128 [==============================] - 4s 28ms/step - loss: 1.1216 - accuracy: 0.6040 - val_loss: 3.1686 - val_accuracy: 0.1377\n",
      "Epoch 17/30\n",
      "128/128 [==============================] - 4s 29ms/step - loss: 1.1118 - accuracy: 0.6081 - val_loss: 3.6198 - val_accuracy: 0.0584\n",
      "Epoch 18/30\n",
      "128/128 [==============================] - 4s 29ms/step - loss: 1.0874 - accuracy: 0.6237 - val_loss: 3.9212 - val_accuracy: 0.0278\n",
      "Epoch 19/30\n",
      "128/128 [==============================] - 4s 31ms/step - loss: 1.0644 - accuracy: 0.6236 - val_loss: 3.4702 - val_accuracy: 0.0821\n",
      "Epoch 20/30\n",
      "128/128 [==============================] - 4s 28ms/step - loss: 1.0420 - accuracy: 0.6355 - val_loss: 3.5748 - val_accuracy: 0.0640\n",
      "Epoch 21/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 1.0103 - accuracy: 0.6489 - val_loss: 3.1271 - val_accuracy: 0.1669\n",
      "Epoch 22/30\n",
      "128/128 [==============================] - 4s 27ms/step - loss: 1.0232 - accuracy: 0.6438 - val_loss: 3.4991 - val_accuracy: 0.0876\n",
      "Epoch 23/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 0.9897 - accuracy: 0.6528 - val_loss: 3.6581 - val_accuracy: 0.0779\n",
      "Epoch 24/30\n",
      "128/128 [==============================] - 3s 27ms/step - loss: 0.9770 - accuracy: 0.6518 - val_loss: 3.6717 - val_accuracy: 0.0890\n",
      "Epoch 25/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 0.9725 - accuracy: 0.6580 - val_loss: 3.4953 - val_accuracy: 0.0723\n",
      "Epoch 26/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 0.9545 - accuracy: 0.6688 - val_loss: 3.3095 - val_accuracy: 0.1349\n",
      "Epoch 27/30\n",
      "128/128 [==============================] - 3s 26ms/step - loss: 0.9627 - accuracy: 0.6613 - val_loss: 4.1188 - val_accuracy: 0.0376\n",
      "Epoch 28/30\n",
      "128/128 [==============================] - 4s 28ms/step - loss: 0.9161 - accuracy: 0.6768 - val_loss: 4.0141 - val_accuracy: 0.0876\n",
      "Epoch 29/30\n",
      "128/128 [==============================] - 3s 27ms/step - loss: 0.9194 - accuracy: 0.6862 - val_loss: 3.7280 - val_accuracy: 0.0932\n",
      "Epoch 30/30\n",
      "128/128 [==============================] - 3s 27ms/step - loss: 0.9266 - accuracy: 0.6790 - val_loss: 3.2503 - val_accuracy: 0.1947\n"
     ]
    }
   ],
   "source": [
    "callbacks_list = [\n",
    "    keras.callbacks.EarlyStopping(monitor='accuracy', patience=2)\n",
    "]\n",
    "\n",
    "opt = keras.optimizers.SGD(learning_rate=0.01)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "                optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "BATCH_SIZE = 50\n",
    "EPOCHS = 30\n",
    "\n",
    "history = model.fit(X_train_sampled, Y_train_sampled, epochs=EPOCHS, batch_size=BATCH_SIZE,callbacks=callbacks_list,\n",
    "                    validation_data=(X_val_resized, Y_val_resized), shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy and loss\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot(history.history['accuracy'], \"g--\", label=\"Accuracy of training data\")\n",
    "plt.plot(history.history['val_accuracy'], \"g\", label=\"Accuracy of validation data\")\n",
    "plt.plot(history.history['loss'], \"r--\", label=\"Loss of training data\")\n",
    "plt.plot(history.history['val_loss'], \"r\", label=\"Loss of validation data\")\n",
    "plt.title('Model Accuracy and Loss')\n",
    "plt.ylabel('Accuracy and Loss')\n",
    "plt.xlabel('Training Epoch')\n",
    "plt.ylim(0)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1069, 100, 75, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_resized = []\n",
    "Y_test_resized = []\n",
    "i = 0\n",
    "for x in X_test_final:\n",
    "    if len(x)!=0 and len(x[0])!=0:\n",
    "        img = cv2.resize(x, (75, 100))\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "        X_test_resized.append(img/255)\n",
    "        Y_test_resized.append(Y_test_final[i])\n",
    "    i+=1\n",
    "X_test_resized = np.asarray(X_test_resized)\n",
    "X_test_resized = X_test_resized.reshape(-1, 100, 75, 1)\n",
    "X_test_resized.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New y_train shape:  (1069, 8)\n"
     ]
    }
   ],
   "source": [
    "Y_test_resized = np_utils.to_categorical(Y_test_resized, 8)\n",
    "print('New y_train shape: ', Y_test_resized.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34/34 [==============================] - 0s 10ms/step - loss: 2.1416 - accuracy: 0.3124\n",
      "\n",
      "Accuracy on test data: 0.31\n",
      "\n",
      "Loss on test data: 2.14\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test_resized, Y_test_resized, verbose=1)\n",
    "\n",
    "print(\"\\nAccuracy on test data: %0.2f\" % score[1])\n",
    "print(\"\\nLoss on test data: %0.2f\" % score[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "LABELS = [0, 1, 2, 3, 4, 5, 6, 7]\n",
    "def show_confusion_matrix(validations, predictions):\n",
    "\n",
    "    matrix = metrics.confusion_matrix(validations, predictions)\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(matrix,\n",
    "                cmap=\"coolwarm\",\n",
    "                linecolor='white',\n",
    "                linewidths=1,\n",
    "                xticklabels=LABELS,\n",
    "                yticklabels=LABELS,\n",
    "                annot=True,\n",
    "                fmt=\"d\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34/34 [==============================] - 0s 7ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Classification report for test data ---\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.29      0.29       202\n",
      "           1       0.26      0.18      0.21       215\n",
      "           2       0.42      0.26      0.32       186\n",
      "           3       0.19      0.25      0.21       102\n",
      "           4       0.12      0.15      0.13        81\n",
      "           5       0.23      0.50      0.32        82\n",
      "           6       0.28      0.24      0.26        82\n",
      "           7       0.75      0.75      0.75       119\n",
      "\n",
      "    accuracy                           0.31      1069\n",
      "   macro avg       0.32      0.33      0.31      1069\n",
      "weighted avg       0.33      0.31      0.31      1069\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = model.predict(X_test_resized)\n",
    "# Take the class with the highest probability from the test predictions\n",
    "max_y_pred_test = np.argmax(y_pred_test, axis=1)\n",
    "max_y_test = np.argmax(Y_test_resized, axis=1)\n",
    "\n",
    "show_confusion_matrix(max_y_test, max_y_pred_test)\n",
    "\n",
    "print(\"\\n--- Classification report for test data ---\\n\")\n",
    "\n",
    "print(classification_report(max_y_test, max_y_pred_test))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Data For Transfer Learning\n",
    "### Saving Data As Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load(\"Data/extracted faces/processed_train_imgs.npy\", allow_pickle=True)\n",
    "Y_train = np.load(\"Data/extracted faces/train_labels.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(X_train.shape[0]):\n",
    "    x = X_train[i]\n",
    "    if len(x)!=0 and len(x[0])!=0:   \n",
    "        cv2.imwrite(\"Data/extracted faces/train/\" + str(Y_train[i]) + \"/\" + str(i) + \".jpg\", x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.load(\"Data/extracted faces/processed_test_data.npy\", allow_pickle=True)\n",
    "Y_test = np.load(\"Data/extracted faces/test_labels.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(X_test.shape[0]):\n",
    "    x_test = X_test[i]\n",
    "    if len(x_test)!=0 and len(x_test[0])!=0:   \n",
    "        cv2.imwrite(\"Data/extracted faces/test/\" + str(Y_test[i]) + \"/\" + str(i) + \".jpg\", x_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning with TensorFlow Keras\n",
    "\n",
    "https://github.com/tejanirla/image_classification/blob/master/transfer_learning.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11797 images belonging to 8 classes.\n",
      "Found 1788 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "train_dir = \"Data/extracted faces/train\"\n",
    "validation_dir = \"Data/extracted faces/test\"\n",
    "# Add our data-augmentation parameters to ImageDataGenerator\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255.,\n",
    "                                   rotation_range = 30,\n",
    "                                   width_shift_range = 0.2,\n",
    "                                   height_shift_range = 0.2,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "# Note that the validation data should not be augmented!\n",
    "test_datagen = ImageDataGenerator( rescale = 1.0/255. )\n",
    "\n",
    "# Flow training images in batches of 40 using train_datagen generator\n",
    "train_generator = train_datagen.flow_from_directory(train_dir,\n",
    "                                                    batch_size = 40,\n",
    "                                                    class_mode = 'categorical', \n",
    "                                                    target_size = (100, 75))     \n",
    "\n",
    "# Flow validation images in batches of 40 using test_datagen generator\n",
    "validation_generator =  test_datagen.flow_from_directory( validation_dir,\n",
    "                                                          batch_size  = 40,\n",
    "                                                          class_mode  = 'categorical', \n",
    "                                                          target_size = (100, 75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_trained_model = InceptionV3(input_shape = (100, 75, 3), # Shape of our images\n",
    "                                include_top = False, # Leave out the last fully connected layer\n",
    "                                weights = 'imagenet')\n",
    "for layer in pre_trained_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/tejanirla/image_classification/blob/master/transfer_learning.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_layer = pre_trained_model.get_layer('mixed4') #Only use layers including and above 'mixed4'\n",
    "last_op = last_layer.output\n",
    "# Flatten the output layer to 1 dimension\n",
    "x = layers.Flatten()(last_op)\n",
    "# # Add a fully connected layer with 1,024 hidden units and ReLU activation\n",
    "x = layers.Dense(512, activation='relu')(x)\n",
    "# Add a dropout rate of 0.5\n",
    "x = layers.Dropout(0.5)(x)                  \n",
    "# Add a final sigmoid layer for classification\n",
    "x = layers.Dense  (8, activation='softmax')(x)           \n",
    "\n",
    "model = keras.Model(pre_trained_model.input, x) \n",
    "\n",
    "model.compile(optimizer = keras.optimizers.SGD(learning_rate=0.01), \n",
    "              loss = 'categorical_crossentropy', \n",
    "              metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs={}):\n",
    "    if(logs.get('acc')>0.85):\n",
    "      print(\"\\nReached 85% accuracy so cancelling training!\")\n",
    "      self.model.stop_training = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "50/50 - 8s - loss: 2.0955 - acc: 0.2600 - val_loss: 1.8793 - val_acc: 0.2387 - 8s/epoch - 168ms/step\n",
      "Epoch 2/150\n",
      "50/50 - 6s - loss: 1.8266 - acc: 0.3130 - val_loss: 1.7623 - val_acc: 0.3413 - 6s/epoch - 111ms/step\n",
      "Epoch 3/150\n",
      "50/50 - 6s - loss: 1.7186 - acc: 0.3700 - val_loss: 1.7372 - val_acc: 0.3425 - 6s/epoch - 114ms/step\n",
      "Epoch 4/150\n",
      "50/50 - 6s - loss: 1.6855 - acc: 0.3700 - val_loss: 1.7554 - val_acc: 0.3212 - 6s/epoch - 125ms/step\n",
      "Epoch 5/150\n",
      "50/50 - 6s - loss: 1.6460 - acc: 0.3810 - val_loss: 1.6539 - val_acc: 0.3638 - 6s/epoch - 126ms/step\n",
      "Epoch 6/150\n",
      "50/50 - 6s - loss: 1.6404 - acc: 0.4070 - val_loss: 1.6485 - val_acc: 0.3787 - 6s/epoch - 123ms/step\n",
      "Epoch 7/150\n",
      "50/50 - 6s - loss: 1.5677 - acc: 0.4291 - val_loss: 1.7589 - val_acc: 0.3575 - 6s/epoch - 122ms/step\n",
      "Epoch 8/150\n",
      "50/50 - 6s - loss: 1.5673 - acc: 0.4240 - val_loss: 1.6530 - val_acc: 0.3762 - 6s/epoch - 122ms/step\n",
      "Epoch 9/150\n",
      "50/50 - 6s - loss: 1.5542 - acc: 0.4145 - val_loss: 1.7172 - val_acc: 0.3625 - 6s/epoch - 122ms/step\n",
      "Epoch 10/150\n",
      "50/50 - 6s - loss: 1.5288 - acc: 0.4380 - val_loss: 1.7230 - val_acc: 0.3487 - 6s/epoch - 122ms/step\n",
      "Epoch 11/150\n",
      "50/50 - 6s - loss: 1.5217 - acc: 0.4195 - val_loss: 1.7933 - val_acc: 0.3537 - 6s/epoch - 121ms/step\n",
      "Epoch 12/150\n",
      "50/50 - 6s - loss: 1.5371 - acc: 0.4225 - val_loss: 1.6011 - val_acc: 0.3925 - 6s/epoch - 124ms/step\n",
      "Epoch 13/150\n",
      "50/50 - 6s - loss: 1.5042 - acc: 0.4447 - val_loss: 1.6215 - val_acc: 0.4025 - 6s/epoch - 121ms/step\n",
      "Epoch 14/150\n",
      "50/50 - 6s - loss: 1.4463 - acc: 0.4770 - val_loss: 1.6816 - val_acc: 0.3475 - 6s/epoch - 123ms/step\n",
      "Epoch 15/150\n",
      "50/50 - 6s - loss: 1.4854 - acc: 0.4510 - val_loss: 1.6670 - val_acc: 0.3775 - 6s/epoch - 123ms/step\n",
      "Epoch 16/150\n",
      "50/50 - 6s - loss: 1.4778 - acc: 0.4520 - val_loss: 1.6266 - val_acc: 0.3900 - 6s/epoch - 122ms/step\n",
      "Epoch 17/150\n",
      "50/50 - 6s - loss: 1.4348 - acc: 0.4597 - val_loss: 1.6737 - val_acc: 0.3875 - 6s/epoch - 124ms/step\n",
      "Epoch 18/150\n",
      "50/50 - 6s - loss: 1.4042 - acc: 0.4740 - val_loss: 1.6967 - val_acc: 0.3587 - 6s/epoch - 123ms/step\n",
      "Epoch 19/150\n",
      "50/50 - 7s - loss: 1.4398 - acc: 0.4505 - val_loss: 1.5558 - val_acc: 0.3613 - 7s/epoch - 130ms/step\n",
      "Epoch 20/150\n",
      "50/50 - 6s - loss: 1.4374 - acc: 0.4682 - val_loss: 1.6816 - val_acc: 0.3550 - 6s/epoch - 122ms/step\n",
      "Epoch 21/150\n",
      "50/50 - 6s - loss: 1.4533 - acc: 0.4610 - val_loss: 1.6732 - val_acc: 0.3275 - 6s/epoch - 120ms/step\n",
      "Epoch 22/150\n",
      "50/50 - 6s - loss: 1.4413 - acc: 0.4775 - val_loss: 1.6183 - val_acc: 0.4050 - 6s/epoch - 124ms/step\n",
      "Epoch 23/150\n",
      "50/50 - 6s - loss: 1.4386 - acc: 0.4702 - val_loss: 1.6262 - val_acc: 0.3525 - 6s/epoch - 121ms/step\n",
      "Epoch 24/150\n",
      "50/50 - 6s - loss: 1.4039 - acc: 0.4807 - val_loss: 1.7229 - val_acc: 0.3950 - 6s/epoch - 123ms/step\n",
      "Epoch 25/150\n",
      "50/50 - 6s - loss: 1.3885 - acc: 0.4797 - val_loss: 1.6313 - val_acc: 0.3800 - 6s/epoch - 122ms/step\n",
      "Epoch 26/150\n",
      "50/50 - 6s - loss: 1.3406 - acc: 0.5010 - val_loss: 1.5889 - val_acc: 0.3887 - 6s/epoch - 122ms/step\n",
      "Epoch 27/150\n",
      "50/50 - 6s - loss: 1.3892 - acc: 0.4885 - val_loss: 1.6889 - val_acc: 0.3550 - 6s/epoch - 121ms/step\n",
      "Epoch 28/150\n",
      "50/50 - 6s - loss: 1.3567 - acc: 0.5070 - val_loss: 1.6319 - val_acc: 0.3438 - 6s/epoch - 121ms/step\n",
      "Epoch 29/150\n",
      "50/50 - 6s - loss: 1.3817 - acc: 0.4830 - val_loss: 1.6490 - val_acc: 0.3787 - 6s/epoch - 125ms/step\n",
      "Epoch 30/150\n",
      "50/50 - 6s - loss: 1.3828 - acc: 0.4830 - val_loss: 1.6737 - val_acc: 0.3537 - 6s/epoch - 122ms/step\n",
      "Epoch 31/150\n",
      "50/50 - 6s - loss: 1.3436 - acc: 0.5015 - val_loss: 1.6426 - val_acc: 0.3675 - 6s/epoch - 122ms/step\n",
      "Epoch 32/150\n",
      "50/50 - 6s - loss: 1.3272 - acc: 0.5090 - val_loss: 1.5993 - val_acc: 0.3625 - 6s/epoch - 124ms/step\n",
      "Epoch 33/150\n",
      "50/50 - 6s - loss: 1.3681 - acc: 0.4992 - val_loss: 1.6571 - val_acc: 0.3837 - 6s/epoch - 122ms/step\n",
      "Epoch 34/150\n",
      "50/50 - 6s - loss: 1.3441 - acc: 0.5160 - val_loss: 1.6174 - val_acc: 0.3938 - 6s/epoch - 123ms/step\n",
      "Epoch 35/150\n",
      "50/50 - 6s - loss: 1.3625 - acc: 0.4995 - val_loss: 1.6062 - val_acc: 0.3700 - 6s/epoch - 121ms/step\n",
      "Epoch 36/150\n",
      "50/50 - 6s - loss: 1.3415 - acc: 0.5000 - val_loss: 1.6412 - val_acc: 0.3675 - 6s/epoch - 123ms/step\n",
      "Epoch 37/150\n",
      "50/50 - 6s - loss: 1.3272 - acc: 0.5070 - val_loss: 1.6120 - val_acc: 0.4000 - 6s/epoch - 124ms/step\n",
      "Epoch 38/150\n",
      "50/50 - 6s - loss: 1.3086 - acc: 0.5180 - val_loss: 1.5397 - val_acc: 0.4013 - 6s/epoch - 121ms/step\n",
      "Epoch 39/150\n",
      "50/50 - 6s - loss: 1.3172 - acc: 0.5060 - val_loss: 1.6779 - val_acc: 0.3700 - 6s/epoch - 122ms/step\n",
      "Epoch 40/150\n",
      "50/50 - 6s - loss: 1.3368 - acc: 0.5090 - val_loss: 1.5388 - val_acc: 0.4075 - 6s/epoch - 121ms/step\n",
      "Epoch 41/150\n",
      "50/50 - 6s - loss: 1.3518 - acc: 0.4990 - val_loss: 1.5665 - val_acc: 0.4087 - 6s/epoch - 124ms/step\n",
      "Epoch 42/150\n",
      "50/50 - 6s - loss: 1.3346 - acc: 0.5063 - val_loss: 1.5516 - val_acc: 0.4125 - 6s/epoch - 123ms/step\n",
      "Epoch 43/150\n",
      "50/50 - 6s - loss: 1.3189 - acc: 0.5240 - val_loss: 1.5674 - val_acc: 0.3862 - 6s/epoch - 122ms/step\n",
      "Epoch 44/150\n",
      "50/50 - 6s - loss: 1.3180 - acc: 0.5225 - val_loss: 1.5948 - val_acc: 0.3862 - 6s/epoch - 125ms/step\n",
      "Epoch 45/150\n",
      "50/50 - 6s - loss: 1.2604 - acc: 0.5475 - val_loss: 1.5115 - val_acc: 0.4087 - 6s/epoch - 124ms/step\n",
      "Epoch 46/150\n",
      "50/50 - 6s - loss: 1.2998 - acc: 0.5415 - val_loss: 1.6121 - val_acc: 0.3600 - 6s/epoch - 123ms/step\n",
      "Epoch 47/150\n",
      "50/50 - 6s - loss: 1.2974 - acc: 0.5290 - val_loss: 1.7727 - val_acc: 0.3575 - 6s/epoch - 128ms/step\n",
      "Epoch 48/150\n",
      "50/50 - 6s - loss: 1.3086 - acc: 0.5215 - val_loss: 1.6055 - val_acc: 0.3913 - 6s/epoch - 122ms/step\n",
      "Epoch 49/150\n",
      "50/50 - 6s - loss: 1.2975 - acc: 0.5260 - val_loss: 1.5956 - val_acc: 0.3875 - 6s/epoch - 124ms/step\n",
      "Epoch 50/150\n",
      "50/50 - 6s - loss: 1.2910 - acc: 0.5213 - val_loss: 1.6419 - val_acc: 0.3887 - 6s/epoch - 121ms/step\n",
      "Epoch 51/150\n",
      "50/50 - 6s - loss: 1.3143 - acc: 0.5275 - val_loss: 1.6294 - val_acc: 0.3212 - 6s/epoch - 126ms/step\n",
      "Epoch 52/150\n",
      "50/50 - 6s - loss: 1.2683 - acc: 0.5450 - val_loss: 1.5859 - val_acc: 0.4062 - 6s/epoch - 122ms/step\n",
      "Epoch 53/150\n",
      "50/50 - 6s - loss: 1.3073 - acc: 0.5115 - val_loss: 1.6895 - val_acc: 0.3575 - 6s/epoch - 123ms/step\n",
      "Epoch 54/150\n",
      "50/50 - 6s - loss: 1.2992 - acc: 0.5225 - val_loss: 1.6037 - val_acc: 0.3675 - 6s/epoch - 122ms/step\n",
      "Epoch 55/150\n",
      "50/50 - 6s - loss: 1.2785 - acc: 0.5415 - val_loss: 1.6030 - val_acc: 0.3738 - 6s/epoch - 124ms/step\n",
      "Epoch 56/150\n",
      "50/50 - 6s - loss: 1.2616 - acc: 0.5380 - val_loss: 1.6499 - val_acc: 0.3850 - 6s/epoch - 125ms/step\n",
      "Epoch 57/150\n",
      "50/50 - 6s - loss: 1.2797 - acc: 0.5345 - val_loss: 1.5626 - val_acc: 0.3738 - 6s/epoch - 122ms/step\n",
      "Epoch 58/150\n",
      "50/50 - 6s - loss: 1.2367 - acc: 0.5373 - val_loss: 1.5345 - val_acc: 0.4013 - 6s/epoch - 124ms/step\n",
      "Epoch 59/150\n",
      "50/50 - 6s - loss: 1.2685 - acc: 0.5420 - val_loss: 1.6202 - val_acc: 0.4062 - 6s/epoch - 124ms/step\n",
      "Epoch 60/150\n",
      "50/50 - 6s - loss: 1.2487 - acc: 0.5435 - val_loss: 1.6062 - val_acc: 0.3750 - 6s/epoch - 122ms/step\n",
      "Epoch 61/150\n",
      "50/50 - 6s - loss: 1.2283 - acc: 0.5418 - val_loss: 1.6199 - val_acc: 0.3925 - 6s/epoch - 126ms/step\n",
      "Epoch 62/150\n",
      "50/50 - 6s - loss: 1.2577 - acc: 0.5450 - val_loss: 1.6087 - val_acc: 0.3875 - 6s/epoch - 123ms/step\n",
      "Epoch 63/150\n",
      "50/50 - 7s - loss: 1.2569 - acc: 0.5465 - val_loss: 1.5833 - val_acc: 0.3812 - 7s/epoch - 133ms/step\n",
      "Epoch 64/150\n",
      "50/50 - 6s - loss: 1.2197 - acc: 0.5580 - val_loss: 1.5976 - val_acc: 0.3738 - 6s/epoch - 125ms/step\n",
      "Epoch 65/150\n",
      "50/50 - 6s - loss: 1.2159 - acc: 0.5690 - val_loss: 1.5418 - val_acc: 0.4025 - 6s/epoch - 124ms/step\n",
      "Epoch 66/150\n",
      "50/50 - 6s - loss: 1.2839 - acc: 0.5370 - val_loss: 1.5578 - val_acc: 0.4175 - 6s/epoch - 125ms/step\n",
      "Epoch 67/150\n",
      "50/50 - 6s - loss: 1.2094 - acc: 0.5665 - val_loss: 1.5595 - val_acc: 0.4025 - 6s/epoch - 128ms/step\n",
      "Epoch 68/150\n",
      "50/50 - 6s - loss: 1.2451 - acc: 0.5335 - val_loss: 1.5276 - val_acc: 0.4000 - 6s/epoch - 125ms/step\n",
      "Epoch 69/150\n",
      "50/50 - 6s - loss: 1.2423 - acc: 0.5465 - val_loss: 1.6795 - val_acc: 0.3975 - 6s/epoch - 124ms/step\n",
      "Epoch 70/150\n",
      "50/50 - 6s - loss: 1.2524 - acc: 0.5490 - val_loss: 1.5669 - val_acc: 0.3650 - 6s/epoch - 124ms/step\n",
      "Epoch 71/150\n",
      "50/50 - 6s - loss: 1.2324 - acc: 0.5430 - val_loss: 1.6464 - val_acc: 0.3862 - 6s/epoch - 126ms/step\n",
      "Epoch 72/150\n",
      "50/50 - 6s - loss: 1.2361 - acc: 0.5483 - val_loss: 1.5768 - val_acc: 0.4062 - 6s/epoch - 123ms/step\n",
      "Epoch 73/150\n",
      "50/50 - 6s - loss: 1.2260 - acc: 0.5573 - val_loss: 1.5264 - val_acc: 0.3938 - 6s/epoch - 125ms/step\n",
      "Epoch 74/150\n",
      "50/50 - 6s - loss: 1.2017 - acc: 0.5680 - val_loss: 1.5470 - val_acc: 0.4225 - 6s/epoch - 127ms/step\n",
      "Epoch 75/150\n",
      "50/50 - 6s - loss: 1.2270 - acc: 0.5548 - val_loss: 1.5728 - val_acc: 0.3925 - 6s/epoch - 122ms/step\n",
      "Epoch 76/150\n",
      "50/50 - 6s - loss: 1.1953 - acc: 0.5535 - val_loss: 1.5776 - val_acc: 0.4062 - 6s/epoch - 126ms/step\n",
      "Epoch 77/150\n",
      "50/50 - 7s - loss: 1.1859 - acc: 0.5695 - val_loss: 1.6029 - val_acc: 0.3837 - 7s/epoch - 131ms/step\n",
      "Epoch 78/150\n",
      "50/50 - 6s - loss: 1.1774 - acc: 0.5680 - val_loss: 1.5417 - val_acc: 0.4013 - 6s/epoch - 124ms/step\n",
      "Epoch 79/150\n",
      "50/50 - 6s - loss: 1.1974 - acc: 0.5610 - val_loss: 1.6568 - val_acc: 0.3975 - 6s/epoch - 125ms/step\n",
      "Epoch 80/150\n",
      "50/50 - 6s - loss: 1.1876 - acc: 0.5625 - val_loss: 1.5897 - val_acc: 0.3988 - 6s/epoch - 125ms/step\n",
      "Epoch 81/150\n",
      "50/50 - 6s - loss: 1.2185 - acc: 0.5615 - val_loss: 1.6331 - val_acc: 0.3950 - 6s/epoch - 123ms/step\n",
      "Epoch 82/150\n",
      "50/50 - 6s - loss: 1.1652 - acc: 0.5815 - val_loss: 1.6138 - val_acc: 0.3913 - 6s/epoch - 127ms/step\n",
      "Epoch 83/150\n",
      "50/50 - 6s - loss: 1.1843 - acc: 0.5715 - val_loss: 1.6069 - val_acc: 0.3925 - 6s/epoch - 121ms/step\n",
      "Epoch 84/150\n",
      "50/50 - 6s - loss: 1.1838 - acc: 0.5835 - val_loss: 1.6629 - val_acc: 0.3800 - 6s/epoch - 124ms/step\n",
      "Epoch 85/150\n",
      "50/50 - 6s - loss: 1.2126 - acc: 0.5575 - val_loss: 1.5845 - val_acc: 0.3938 - 6s/epoch - 126ms/step\n",
      "Epoch 86/150\n",
      "50/50 - 6s - loss: 1.2042 - acc: 0.5658 - val_loss: 1.5398 - val_acc: 0.4263 - 6s/epoch - 122ms/step\n",
      "Epoch 87/150\n",
      "50/50 - 6s - loss: 1.1646 - acc: 0.5794 - val_loss: 1.6395 - val_acc: 0.3738 - 6s/epoch - 127ms/step\n",
      "Epoch 88/150\n",
      "50/50 - 6s - loss: 1.1763 - acc: 0.5765 - val_loss: 1.6436 - val_acc: 0.3950 - 6s/epoch - 128ms/step\n",
      "Epoch 89/150\n",
      "50/50 - 6s - loss: 1.2058 - acc: 0.5565 - val_loss: 1.6308 - val_acc: 0.3812 - 6s/epoch - 124ms/step\n",
      "Epoch 90/150\n",
      "50/50 - 6s - loss: 1.1615 - acc: 0.5785 - val_loss: 1.6789 - val_acc: 0.3725 - 6s/epoch - 121ms/step\n",
      "Epoch 91/150\n",
      "50/50 - 6s - loss: 1.1615 - acc: 0.5775 - val_loss: 1.5729 - val_acc: 0.3963 - 6s/epoch - 121ms/step\n",
      "Epoch 92/150\n",
      "50/50 - 6s - loss: 1.1872 - acc: 0.5660 - val_loss: 1.5837 - val_acc: 0.3975 - 6s/epoch - 125ms/step\n",
      "Epoch 93/150\n",
      "50/50 - 6s - loss: 1.2013 - acc: 0.5520 - val_loss: 1.5685 - val_acc: 0.4263 - 6s/epoch - 125ms/step\n",
      "Epoch 94/150\n",
      "50/50 - 6s - loss: 1.1574 - acc: 0.5739 - val_loss: 1.5552 - val_acc: 0.4013 - 6s/epoch - 122ms/step\n",
      "Epoch 95/150\n",
      "50/50 - 6s - loss: 1.1654 - acc: 0.5749 - val_loss: 1.6014 - val_acc: 0.4150 - 6s/epoch - 124ms/step\n",
      "Epoch 96/150\n",
      "50/50 - 6s - loss: 1.1490 - acc: 0.5740 - val_loss: 1.6873 - val_acc: 0.4000 - 6s/epoch - 119ms/step\n",
      "Epoch 97/150\n",
      "50/50 - 6s - loss: 1.1702 - acc: 0.5628 - val_loss: 1.6100 - val_acc: 0.4062 - 6s/epoch - 122ms/step\n",
      "Epoch 98/150\n",
      "50/50 - 6s - loss: 1.1425 - acc: 0.5719 - val_loss: 1.6353 - val_acc: 0.4100 - 6s/epoch - 123ms/step\n",
      "Epoch 99/150\n",
      "50/50 - 6s - loss: 1.1693 - acc: 0.5795 - val_loss: 1.6197 - val_acc: 0.3975 - 6s/epoch - 122ms/step\n",
      "Epoch 100/150\n",
      "50/50 - 6s - loss: 1.1798 - acc: 0.5710 - val_loss: 1.5631 - val_acc: 0.3975 - 6s/epoch - 119ms/step\n",
      "Epoch 101/150\n",
      "50/50 - 6s - loss: 1.1710 - acc: 0.5775 - val_loss: 1.6203 - val_acc: 0.4212 - 6s/epoch - 121ms/step\n",
      "Epoch 102/150\n",
      "50/50 - 6s - loss: 1.1579 - acc: 0.5905 - val_loss: 1.6090 - val_acc: 0.4162 - 6s/epoch - 123ms/step\n",
      "Epoch 103/150\n",
      "50/50 - 6s - loss: 1.1334 - acc: 0.5834 - val_loss: 1.6825 - val_acc: 0.3950 - 6s/epoch - 124ms/step\n",
      "Epoch 104/150\n",
      "50/50 - 6s - loss: 1.1620 - acc: 0.5695 - val_loss: 1.6916 - val_acc: 0.4062 - 6s/epoch - 122ms/step\n",
      "Epoch 105/150\n",
      "50/50 - 6s - loss: 1.1648 - acc: 0.5725 - val_loss: 1.5804 - val_acc: 0.3988 - 6s/epoch - 121ms/step\n",
      "Epoch 106/150\n",
      "50/50 - 6s - loss: 1.1552 - acc: 0.5770 - val_loss: 1.6574 - val_acc: 0.4100 - 6s/epoch - 124ms/step\n",
      "Epoch 107/150\n",
      "50/50 - 6s - loss: 1.1508 - acc: 0.5704 - val_loss: 1.6451 - val_acc: 0.3738 - 6s/epoch - 122ms/step\n",
      "Epoch 108/150\n",
      "50/50 - 6s - loss: 1.2004 - acc: 0.5615 - val_loss: 1.7638 - val_acc: 0.3787 - 6s/epoch - 123ms/step\n",
      "Epoch 109/150\n",
      "50/50 - 6s - loss: 1.1497 - acc: 0.5730 - val_loss: 1.6173 - val_acc: 0.4038 - 6s/epoch - 122ms/step\n",
      "Epoch 110/150\n",
      "50/50 - 6s - loss: 1.1487 - acc: 0.5814 - val_loss: 1.6026 - val_acc: 0.4187 - 6s/epoch - 120ms/step\n",
      "Epoch 111/150\n",
      "50/50 - 6s - loss: 1.1420 - acc: 0.5819 - val_loss: 1.6530 - val_acc: 0.4013 - 6s/epoch - 121ms/step\n",
      "Epoch 112/150\n",
      "50/50 - 6s - loss: 1.1073 - acc: 0.6045 - val_loss: 1.6525 - val_acc: 0.3988 - 6s/epoch - 122ms/step\n",
      "Epoch 113/150\n",
      "50/50 - 6s - loss: 1.1093 - acc: 0.5955 - val_loss: 1.7519 - val_acc: 0.4200 - 6s/epoch - 122ms/step\n",
      "Epoch 114/150\n",
      "50/50 - 6s - loss: 1.1455 - acc: 0.5900 - val_loss: 1.5846 - val_acc: 0.4200 - 6s/epoch - 130ms/step\n",
      "Epoch 115/150\n",
      "50/50 - 6s - loss: 1.1258 - acc: 0.5980 - val_loss: 1.6087 - val_acc: 0.4050 - 6s/epoch - 121ms/step\n",
      "Epoch 116/150\n",
      "50/50 - 7s - loss: 1.1491 - acc: 0.5839 - val_loss: 1.6950 - val_acc: 0.4025 - 7s/epoch - 135ms/step\n",
      "Epoch 117/150\n",
      "50/50 - 6s - loss: 1.1388 - acc: 0.5895 - val_loss: 1.7943 - val_acc: 0.3700 - 6s/epoch - 121ms/step\n",
      "Epoch 118/150\n",
      "50/50 - 6s - loss: 1.1152 - acc: 0.6050 - val_loss: 1.6750 - val_acc: 0.3887 - 6s/epoch - 123ms/step\n",
      "Epoch 119/150\n",
      "50/50 - 7s - loss: 1.1267 - acc: 0.6015 - val_loss: 1.6666 - val_acc: 0.4150 - 7s/epoch - 138ms/step\n",
      "Epoch 120/150\n",
      "50/50 - 6s - loss: 1.1064 - acc: 0.6025 - val_loss: 1.7316 - val_acc: 0.3950 - 6s/epoch - 120ms/step\n",
      "Epoch 121/150\n",
      "50/50 - 6s - loss: 1.1167 - acc: 0.6009 - val_loss: 1.5924 - val_acc: 0.4300 - 6s/epoch - 123ms/step\n",
      "Epoch 122/150\n",
      "50/50 - 6s - loss: 1.0782 - acc: 0.5944 - val_loss: 1.6526 - val_acc: 0.4150 - 6s/epoch - 124ms/step\n",
      "Epoch 123/150\n",
      "50/50 - 6s - loss: 1.1015 - acc: 0.5939 - val_loss: 1.6531 - val_acc: 0.3762 - 6s/epoch - 127ms/step\n",
      "Epoch 124/150\n",
      "50/50 - 6s - loss: 1.1162 - acc: 0.6030 - val_loss: 1.7067 - val_acc: 0.3675 - 6s/epoch - 123ms/step\n",
      "Epoch 125/150\n",
      "50/50 - 6s - loss: 1.0976 - acc: 0.6040 - val_loss: 1.6722 - val_acc: 0.3650 - 6s/epoch - 121ms/step\n",
      "Epoch 126/150\n",
      "50/50 - 6s - loss: 1.1326 - acc: 0.5800 - val_loss: 1.6436 - val_acc: 0.4150 - 6s/epoch - 128ms/step\n",
      "Epoch 127/150\n",
      "50/50 - 6s - loss: 1.0899 - acc: 0.6140 - val_loss: 1.6407 - val_acc: 0.4075 - 6s/epoch - 127ms/step\n",
      "Epoch 128/150\n",
      "50/50 - 6s - loss: 1.0769 - acc: 0.6255 - val_loss: 1.6192 - val_acc: 0.4263 - 6s/epoch - 125ms/step\n",
      "Epoch 129/150\n",
      "50/50 - 6s - loss: 1.1122 - acc: 0.5980 - val_loss: 1.6473 - val_acc: 0.4075 - 6s/epoch - 123ms/step\n",
      "Epoch 130/150\n",
      "50/50 - 6s - loss: 1.0701 - acc: 0.6180 - val_loss: 1.6185 - val_acc: 0.3787 - 6s/epoch - 122ms/step\n",
      "Epoch 131/150\n",
      "50/50 - 6s - loss: 1.1215 - acc: 0.6055 - val_loss: 1.7020 - val_acc: 0.4050 - 6s/epoch - 122ms/step\n",
      "Epoch 132/150\n",
      "50/50 - 6s - loss: 1.1420 - acc: 0.5850 - val_loss: 1.6467 - val_acc: 0.3750 - 6s/epoch - 127ms/step\n",
      "Epoch 133/150\n",
      "50/50 - 6s - loss: 1.1124 - acc: 0.5915 - val_loss: 1.5974 - val_acc: 0.4162 - 6s/epoch - 120ms/step\n",
      "Epoch 134/150\n",
      "50/50 - 6s - loss: 1.0771 - acc: 0.6174 - val_loss: 1.6861 - val_acc: 0.4400 - 6s/epoch - 120ms/step\n",
      "Epoch 135/150\n",
      "50/50 - 6s - loss: 1.0614 - acc: 0.6170 - val_loss: 1.6579 - val_acc: 0.4050 - 6s/epoch - 123ms/step\n",
      "Epoch 136/150\n",
      "50/50 - 6s - loss: 1.0701 - acc: 0.6080 - val_loss: 1.5268 - val_acc: 0.4238 - 6s/epoch - 124ms/step\n",
      "Epoch 137/150\n",
      "50/50 - 6s - loss: 1.0955 - acc: 0.6025 - val_loss: 1.6002 - val_acc: 0.3638 - 6s/epoch - 125ms/step\n",
      "Epoch 138/150\n",
      "50/50 - 6s - loss: 1.0758 - acc: 0.6110 - val_loss: 1.6177 - val_acc: 0.4125 - 6s/epoch - 122ms/step\n",
      "Epoch 139/150\n",
      "50/50 - 6s - loss: 1.1218 - acc: 0.5960 - val_loss: 1.6896 - val_acc: 0.3825 - 6s/epoch - 121ms/step\n",
      "Epoch 140/150\n",
      "50/50 - 6s - loss: 1.0757 - acc: 0.6035 - val_loss: 1.7114 - val_acc: 0.4137 - 6s/epoch - 124ms/step\n",
      "Epoch 141/150\n",
      "50/50 - 6s - loss: 1.0917 - acc: 0.6105 - val_loss: 1.6527 - val_acc: 0.3913 - 6s/epoch - 127ms/step\n",
      "Epoch 142/150\n",
      "50/50 - 6s - loss: 1.0795 - acc: 0.5975 - val_loss: 1.6341 - val_acc: 0.3950 - 6s/epoch - 122ms/step\n",
      "Epoch 143/150\n",
      "50/50 - 6s - loss: 1.1081 - acc: 0.5925 - val_loss: 1.6267 - val_acc: 0.4050 - 6s/epoch - 125ms/step\n",
      "Epoch 144/150\n",
      "50/50 - 6s - loss: 1.0729 - acc: 0.6070 - val_loss: 1.6885 - val_acc: 0.3825 - 6s/epoch - 121ms/step\n",
      "Epoch 145/150\n",
      "50/50 - 6s - loss: 1.0739 - acc: 0.6065 - val_loss: 1.6337 - val_acc: 0.4075 - 6s/epoch - 125ms/step\n",
      "Epoch 146/150\n",
      "50/50 - 6s - loss: 1.0721 - acc: 0.6089 - val_loss: 1.7167 - val_acc: 0.4150 - 6s/epoch - 120ms/step\n",
      "Epoch 147/150\n",
      "50/50 - 6s - loss: 1.0893 - acc: 0.6010 - val_loss: 1.6044 - val_acc: 0.4200 - 6s/epoch - 121ms/step\n",
      "Epoch 148/150\n",
      "50/50 - 6s - loss: 1.0711 - acc: 0.6020 - val_loss: 1.6780 - val_acc: 0.4087 - 6s/epoch - 125ms/step\n",
      "Epoch 149/150\n",
      "50/50 - 6s - loss: 1.0495 - acc: 0.6180 - val_loss: 1.6578 - val_acc: 0.4175 - 6s/epoch - 122ms/step\n",
      "Epoch 150/150\n",
      "50/50 - 6s - loss: 1.1040 - acc: 0.6050 - val_loss: 1.7155 - val_acc: 0.4162 - 6s/epoch - 125ms/step\n"
     ]
    }
   ],
   "source": [
    "callbacks_list = [\n",
    "    keras.callbacks.EarlyStopping(monitor='acc', patience=2)\n",
    "]\n",
    "\n",
    "callbacks = myCallback()\n",
    "history = model.fit(\n",
    "            train_generator,\n",
    "            validation_data = validation_generator,\n",
    "            steps_per_epoch = 50,\n",
    "            epochs = 150,\n",
    "            validation_steps = 20,\n",
    "            verbose = 2,\n",
    "            callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45/45 [==============================] - 2s 41ms/step - loss: 1.7656 - acc: 0.4055\n",
      "\n",
      "Accuracy on test data: 0.41\n",
      "\n",
      "Loss on test data: 1.77\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(validation_generator, verbose=1)\n",
    "\n",
    "print(\"\\nAccuracy on test data: %0.2f\" % score[1])\n",
    "print(\"\\nLoss on test data: %0.2f\" % score[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "LABELS = [0, 1, 2, 3, 4, 5, 6, 7]\n",
    "def show_confusion_matrix(validations, predictions):\n",
    "\n",
    "    matrix = metrics.confusion_matrix(validations, predictions)\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(matrix,\n",
    "                cmap=\"coolwarm\",\n",
    "                linecolor='white',\n",
    "                linewidths=1,\n",
    "                xticklabels=LABELS,\n",
    "                yticklabels=LABELS,\n",
    "                annot=True,\n",
    "                fmt=\"d\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45/45 [==============================] - 2s 38ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Classification report for test data ---\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.18      0.12      0.15       336\n",
      "           1       0.25      0.14      0.18       358\n",
      "           2       0.18      0.15      0.16       315\n",
      "           3       0.11      0.17      0.14       170\n",
      "           4       0.11      0.11      0.11       135\n",
      "           5       0.09      0.07      0.08       137\n",
      "           6       0.11      0.20      0.14       138\n",
      "           7       0.11      0.19      0.13       199\n",
      "\n",
      "    accuracy                           0.14      1788\n",
      "   macro avg       0.14      0.14      0.14      1788\n",
      "weighted avg       0.16      0.14      0.15      1788\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = model.predict(validation_generator)\n",
    "# Take the class with the highest probability from the test predictions\n",
    "max_y_pred_test = np.argmax(y_pred_test, axis=1)\n",
    "max_y_test = validation_generator.classes\n",
    "\n",
    "show_confusion_matrix(max_y_test, max_y_pred_test)\n",
    "\n",
    "print(\"\\n--- Classification report for test data ---\\n\")\n",
    "\n",
    "print(classification_report(max_y_test, max_y_pred_test))"
   ]
  }
 ],
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