src/affectnet/train_emotions-pytorch-afew-vgaf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=0\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_VISIBLE_DEVICES=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from PIL import Image\n",
    "from random import shuffle\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import pickle\n",
    "\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier,ExtraTreesClassifier\n",
    "from sklearn import svm,metrics,preprocessing\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn import preprocessing\n",
    "\n",
    "#from scipy.misc import imread, imresize\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALL_DATA_DIR = '/home/HDD6TB/datasets/emotions/'\n",
    "INPUT_SIZE = (224, 224)\n",
    "#INPUT_SIZE = (299, 299)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch: 1.7.1+cu110\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "from itertools import chain\n",
    "import os\n",
    "import random\n",
    "import zipfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from PIL import Image\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision import datasets, transforms\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "\n",
    "print(f\"Torch: {torch.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True cuda\n"
     ]
    }
   ],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "device = 'cuda' if use_cuda else 'cpu'\n",
    "print(use_cuda,device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_ENET2=False #True #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compose(\n",
      "    Resize(size=(224, 224), interpolation=PIL.Image.BILINEAR)\n",
      "    ToTensor()\n",
      "    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "IMG_SIZE=260 if USE_ENET2 else 224 # 300 # 80 #\n",
    "train_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((IMG_SIZE,IMG_SIZE)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                     std=[0.229, 0.224, 0.225])\n",
    "    ]\n",
    ")\n",
    "\n",
    "test_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((IMG_SIZE,IMG_SIZE)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                     std=[0.229, 0.224, 0.225])\n",
    "    ]\n",
    ")\n",
    "print(test_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../models/affectnet_emotions/enet_b0_8_best_afew.pt\n"
     ]
    }
   ],
   "source": [
    "USE_MTL=False\n",
    "if USE_ENET2:\n",
    "    if False: # 7 emotions\n",
    "        PATH='../../models/affectnet_emotions/enet_b2_7.pt'\n",
    "        model_name='enet2_7_pt'\n",
    "    else:\n",
    "        #PATH='../../models/affectnet_emotions/enet_b2_8.pt'\n",
    "        PATH='../../models/affectnet_emotions/enet_b2_8_best.pt'\n",
    "        model_name='enet2_8_pt'\n",
    "else:\n",
    "    if False: # 7 emotions from AFFECT_IMG_SEVEN_TRAIN_DATA_DIR and AFFECT_IMG_SEVEN_VAL_DATA_DIR\n",
    "        PATH='../../models/affectnet_emotions/enet_b0_7.pt'\n",
    "        model_name='enet0_7_pt'\n",
    "    elif True:\n",
    "        #PATH='../../models/affectnet_emotions/enet_b0_8_best_vgaf.pt'\n",
    "        PATH='../../models/affectnet_emotions/enet_b0_8_best_afew.pt'\n",
    "        model_name='enet0_8_pt'\n",
    "    else:\n",
    "        PATH='../../models/affectnet_emotions/enet_b0_8_va_mtl.pt'\n",
    "        model_name='enet0_8_mtl_pt'\n",
    "        USE_MTL=True\n",
    "print(PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../models/affectnet_emotions/enet_b2_8_best.pt\n"
     ]
    }
   ],
   "source": [
    "# Load\n",
    "print(PATH)\n",
    "model = torch.load(PATH)\n",
    "model=model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.compat.v1.keras.backend import set_session \n",
    "config = tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess=tf.compat.v1.Session(config=config)\n",
    "set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from facial_analysis import FacialImageProcessing\n",
    "imgProcessing=FacialImageProcessing(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vkiaDAUPPZjOw2Wzsu9lWiXnhgS2ekL9f7IkYIt6Zlndi19X5LsMrx2azYZ5nxnFknibu3b/H1WHP2e1bJFVee/AA7zwnpycMmwHJjJQ4t3q+8gwwx40QaO8cu93WNFtmF8Cou2EYTOtIox0zxlaE7XZHSol5nhmGoT4M7zxm+NlWGsNEmEcEZZ4nzKpcWArQZlKfiCCr/7e/x7x7rIXZYIJRbGacxRiJ00SaZwS1XaDz+K5n2Gzw3YDLMApMqJx3DJuBk5MT046A7/zKMPbeZxotQ6AY6/1oUpwUAQx4Lzhvx/WdYxg6hqHLiwIMG9h8hGmqBvX+am8aPAZcntezszN2u50ZgRtbzNvdDgGuLi/ZbAe2uw3eO+7fvwsx8PIXXqLvu3ydtuj6rjNmR5XDfl/n7mnGzRDoriNqYhpHOucz5tNsJMpKSATQEJkPhtdSNJrMO5/50qy5AMH45RQD8zwhQJhGwjTa6ylV3F23cgAWrNji6fK5yqzooq3L58pnuq6j8yZsmTEG5ypk8t1AP2zxvsd3nf1kOOC6Dt93OO8r3Og3A11ePOIcLgtz+U65luUna1g1nO1E8M5lZsYWh/MOIxY0U5FiNoAo4+GK7aZHJKEp0HmHz+dxIgzDwNnZqUEfTbzw4ot02UZ49PAhfbZ/bt26Tdf1jIeD0YXO4To7VkyJYbNdINozCMN4zxwr7Ygxsr+45PTspGqfIigxxmoEFmEynJrYbTekEJnmwK2zU8ZxrIIVgj2ElGk5Afq+Z5r2kDljcS57F7T6RCp7oYux2To1ygIrhuzyneU4BY8nBZzQ+QHf9RkydDjxBnEy/i0aNaVkcGPIx2WBOwVCSf47JapmTYCKI+iyyArlqarG2zupO0mifFcq9JBmUVZuPUZ22x3zNOF7b3CQhPM9XdfjvSmcw3jg7r27fPDwIQ5XV1w+Pme329H1PYfxwL0X7vPa6w/ZX17Sbzf4zuN7s2/6Tc9mu+Xi/IJnAaJvhkCHwNXVFffu3c3bdBaIucFyDQcKBlNSTNVJ4JxUKxtVphiAvnoA+6Fnnqe6dYrzCzOSr6MyCRk/F4MQqMLQCrfPwoBAioaYnRdc50iA8509+K5DXJehhnnaJAHeNF7MO4WLLtsIPvOyRuMVzenzjiW4LHSpMgn4xDymjK/NsdP3PSGEvGMsi8HnG06qqJpGjuozZlboqYrl9u1bXJxfZLhj8EC8adkpzDj1+L4jqnLvxRd49Lpx5ZcXl2x2W8SbwX5yesI0Hoia2G63+M4TY2KaJ05OT7m8uHwmGPpGCDQinN06o88GAlCpsMIWSN7GD4eDEfFdV98f+oGr/aG6vqdponh5Y4zElLi6uqrbL+IwBqNoX1cF186fKidaBLjV1AUfF8tcAZzalowtFu89Xb8xWOCMd11pQLdgdC8J50EGabSrN2+kGO5tvZYoSIykJPnaE6TM1GBG5XYYSGTN3hfczcrFnVSNYszGLkBQUxpdpVFhd7IjoexOzkhJGeeDLThvlJzvPEETJ7dOwQtXF5dsQmQ8jAzbDYdx5OTkhEePz/ExEcYJ8cXQtus4u3WrXsPTjBsh0M4JfTboOm9xByklOrEtfr/fc3J6Wj1UxUovVJ5zwjyOJuRZ23XeMU2G2zQGYsrGYmN8VYycH7QmrQ9xccJo1c6to6WwLYXgE8mGqDi6oafrN4AY1HDeGBjnjB6TWKGTqqJS8KsZkZpZDRpMXHYuG5rnzZGS8fVz1sQCRp11HfM4VicJTvDdch+qiqSEJIMWxTubUiLFSOc78w5G836KdwwnW3Ce8DDgfId0tuMo9rkQA8N2a/eQEi+/9AWoLEigy/M4zzOddtXhM8fAZrc1C/Ypxw0RaF95zspgZCcCmODdvn2bw+HAZrOpeBLI3rtgdJ1LTNNIihE0Mo8T83ywuI0mwKfL2K98XzMXXWi2IrQgtlUXo1GKe1yOnCyLsDnf0fVDxtcWA+Kb8zlXWBOwfFI7jzRCXbRysdmdZLyf0+7KglqMUalYmaSm/QERg0XOezNE65wZtWgjgQqqku+3g4znvfdVgZxfnvPw9df5wEe+jK4fOOxH+t0Gh1GsvTgOo/HXw26HKJzeMrjS+Y6Yc4cLE5NiXDmItDsKzHqH40YINFCDi4pQFyw8jxO7kxNSSozjyOnpqcUw5PiNeZ5JKRpGniameUbjDCSmcU8MAe+PXMONMLfMhcuYdolGK5rRtvOkmVcuBpo4ELdo66JlM2shONqaNy3XagLTNTBmWRxknNzy4NVYJaGEvLvYK7Hg5MxPl52my5y188v1FYjlXDlvrGEBZngLXW8RcOYoipyc7DjsL9lfXHDx8BEnJyeEeSKFwJzSwtKIYx4nZOuJc+D27Ts8eviIy4tLTk5PmeapzoWqIiFYIFhKqO+WyMmnGDdCoDXHcRTgW+g6E9bE6elp5ZfBGIwi+Pv9HnGw3W5IzgMWOJRiYBoPFK3WshR2Tl39XkdmPVqnhWnnxVFR+GUQpPG8+ez8IGt4o84kwwhy6OZyLSllaKFHi6teyrIA8mUBEONCYwKV6jN6zhZlCGGJ82Bx6xePZjGwnXNonbfUMCEWwFQUiQAuJh699oDugy+y3W7YX+yRzqMxMR0OddGQ4du2H9jttjx6/aG5yGWBNYBRe+OIzK6GCz/tuBECLeKIIdA5IUbDteM4QrJttB96rq72FVsXyz0EC/KR5JimGY2BFAJxmpjmAynFRfvmB4kI2ngBjz1UraBXdzaKsOBgM6x8hR0tTYY4VM0wK5hYKmQQVBOOTAv6BfK01+JWgrzQlzGGytn2fY8TqfdYdg+cGVriMxeftXO5vnK8ZTc0mhFcvi6jEMWZYd51HVeXl4g4+k4IYebi9dct+tGZ00aBaRxrzMh0mNjtTnh4+YizszMevfqA/eUFp2dnTPNk2Lz6Fuw5OrG5edpxIwQaSkzzYqzFEPDiDI8hXF5cmM9fzAwL0aAGojiHbYFxIoaZECd70CI4FuEqD1NVVy6lVqALDefdAiU8JTDJV61XhH1hH0xju8qjFx74+DwuMy1Uwb3OOVPmpPwkNVd68RS2NoG5A/NeVNiLjPfhCLJc4/ks3sJUzulte6mRjrtdNQ5JiTDNaExsNgMxRGIIxHlmLC5331sE3njg3r27nJzuOH/4iNEJmiMHvXPM04hTNeZq6N4/jpXFGLTIMO88c5xwXjJzEbm4vOD27duGG0Ut/HOe0ZToN8LhcIHGQJhHNEYkZ2pUL5ksD9XMH1mcFbJE6oks7znnqjY3ONFVLrjVzBbaKSCF+VicH8WNbide2InjHaLGizQP9XihlUi5yo5kHJNyfEmL/UtGiR4d543PoRkGZVuGhNPF9qjHDDNhmglhJoXJ4mVGSx4YD6PNXR+5SoGoytXFOSkGOpTLh69zeucO6mHoN4TLS+YYSNPMtL96/wi0y9SZYTiHJotgO9ntAJimMQfBDIzTITtIYo7TgBAmUphIcUZTIIaJNli8aPXyuzvSsFA8cuZU8WKct8ssi3M+Y+WFXanHzUacaV4hr4Cjcy9bOWDRhFk4j4Wr3UWO/25jHUQWz2Q5rqWSZe2cElETXrp6jCKs19kOxqkX9z5EBZHstE+JcmovoF6Y5mBBWNET59nsATBYFAN+Nm/g4eISQqD3wtV0YN73uH6DOkfnHePVlS2KwwHS+wRylGkNwdiJlJmJYgzO88huu6PrOh49PgDJwhVTxHthngKiFg6a4tzwtYs2dFIwrtB3HZ3zjVCVMFHzyA1dj/MWSwEN83AECSQbi4UdsBiQ9o6WUbQ2sriz34ymOha6QiW2wtlYjxadR8J3vkbuuaNzHZ+vUH6Uf92iicViUJGMxw3fpgpPnLedIYSZEC10VPA5Om8miUNTzAkK3oKyNHK4vGR35pgOwtD3HDJ/rfP0/tHQpkCTGRhi7uzNYNoZTRz2e07ObnF+eWEZE505TVyGCmiqAl08ga2Gq9FpYoZc5zqcaxNW/WJ5NwFA4vwiiCWPMGeAFMaDYtWrImKUWBktXi1Rdsb5aiY89AkhK/w3WUTLcOJylkpqhNw+4jAOvYbCOkse7tzKUFi+l/9uRNneU4u406grpgWKAGMwJnuSSvx1oQpFTOhTmIyKi72dZxjQGOgEpumKePCQItM80TlhHCfmeXr/GIWChSdqCoRgYZD9ZsMcZguKjxaPcTjsEaGm+TjyNlsWQ0pPbOHt78ZSLMFPrdWvWgKAuhxIbzxzhb9FmLNWK3ERJtiufub4nAWf298sTpZrWJaCiRdniaN1vpT/C9ZNMVUuzxxLRlEe07nH8KWcW1mowPKCYDuP07JzLayIiEPJwWLJE6WNm47G4IhF9U3TZElt4gg56tFsGZgPl5ZmVzytaSZMh6OLeWfjRgi05rhl1UQMxauXCCmQwow4w8kxhjzBlu09jgc2Q0/KQTrVvKkPYOG0iyNkEeblqRcsXbM5XHFBFwVsNB1Hwmfc6eKsaMfxVl+EtPDMLedbPreMhXteadCj4y87QMbo2Qos19wet90tWgEv9oWmYkDm1KzYCP415xZZgvMDS9iAZAfPqCWR2JNm26FEFUdOIOgDomZ8SgqImkJ62nEzBDobTA4hasxpSR4HXI17nIhtSSlirJpW3OudY5xnSOsMDqhooKHWpD7s1iAEmr+lamdDCFlbQnWLs4IFRWiPhbcR0ubvlNJCq72BRhJpocii5csiUk0137DOYT2PCaJFrrYsyTEEWp2RAm9Uy8HMyE0aKma3THgHuf6GJsH7VOGK2UBGo3adGYu+z9obg0EWJRmZp9Eyz0VwCoOzYLOnHTdCoMkaLoaZOM/20FJinib2+yu6HLpI5mE1pRoGOo6HXJ9jMZ5qhks9/CJA5rlbew1bISya27Zmt4IRGTlXKuw6rdqeM2UPZQmIMqE8MixbKHDtcamv18AqUSQVl/3aa1iwfFk4x3SgE7l2QWl+DpX7LkbnKgwg03p5Z4kx4JKj810hKglBa2RkmDKW1sVwrovenPsVsskzKGEAN0WgpUS22U8ME1cX55ycnCCacJiV7L1HYySlAKqkFBFSDT66zkmRkuL9WkBazezEDKryejWG3CLM5QGQtXORt7WGXVzspWiLZpAaC72meUs/FqZG8Oz6mwydo/up8GDFflxvN5QtqtXOT37Hrs1KN2h5JcOX5brr/WaFUebLYlYMWkBJbI4M240ppcOYGRdnEX1Ck3BMTSzQaqQ+3bgZAq1ag+9DsGznTmAa93aTqngRSIkU5hqrkXJqvX1k0TptFFrBxi0z0GLMIoTVfZwTVFcQgsUAXOx/8vGKZjQhbOt2FOPPFps91Ot2jjfaTdZTVLyoWneIfFErbV/uv120x4zF+lzZ+CNjaU1mo4iiovm+mtxKm7EKwswYNfampJ5FVWKgZgRJUsRlg5fFyeVY7tdnY/1px43IKVSAXAIr5GIywzDUGhYFL4cwV4MxZuYjpVghRtVmGAJsf4QWS7fRa0UQoOLivBiKZj7WlK2XUNVqfcBinMFauFq+t3WStK7txfP3pAGXUlodt4hoNcRkSa/SBQTbNTTfLbHiZawZlvy5spCvMUYXGrSZQ2lZnnUY7ND3xkvn+JN2F2oXnB2nzP/TjbcUaBH5oyLysoh8d/PafRH56yLyg/n/e/l1EZE/KFbo/LtE5Ge8nYsQsocpzKSY2O12FGHVEiZZqblgsQPBNHrKGgSM0I9hqpFblkfXTHw+l2QoUI08jJIT5xthL1pvjbWBlQCO48jFxUV9v10w6x9WxyivFyG7TnjLuVbnpYEj7ph6vEarN5DjugXTnttOk4W/YO5W018j3HW+Sjx4CdKCSm3adS/30iqWEp5QdsWnHW9HQ/8x4JccvfYtwN9Q1Z8A/I38N8AvBX5C/vmtwH/6di9Ek8Xldp2lyStlO86lsg57YrTihCnOufKR/VgF0UThTYHVZJdyV0XzlDogFF3+hPCViT7iAhqNVoRoGAZu3Vr6SraCcwwlVobf8f1fI4xLXT19QmCNWbhOi2slmKtxd3Qty/maa4K6WK4T/uuuKc9KpfFWkYfZVigG5HF6VbtIYQnUetrxlgKtqn8L6wzVjm9k6bP3bcC/0rz+x9XG3wHuyls3Si8nqgLiuyVeNwSj4y4uLupK1lIHTdUyNNKTuHMxCptEUrWQyDae2jTdEkFXtHN5Pc/BarJb4XfO5TSwozDS9RyufheRlcYqrx9DjSemiLUGv17r2iebP3KMxwI1nlwYxfhrz6s1XLcsHmxmVgspg7YnF0z+dKlFmLLTxebP1e8s8/UsTMJ3jqE/pKqfz7+/BHwo//7Oi50n41i7rq/CPc8TIjCPU32trRMtzUS2wrHChiniSHQONIeclii6EupZxtryXx4i8AQUeHIs19GW2G0FrSy6WiDxmnEszPUYUgy2peyBpsRaqM2gewKToxVCtBq97FSiyz2nlCA1AlzmFMVpZuSLIsgGpVamxD4pkmO6ncXNFFhh7y7eWtPuncG9N5nZL2Y8Ncuhqioib/akrx3SFDy/c7oFFOeFs7NTYjb0VBN917E/XOU0Kst/A0zj+MXIaI67Dk4SqcmzIlLTkUrWyWLYNHg07wVVgx/BjOPzLcJz7MbmCeFcHUOPOZPlmOkaqGCX6iqUON6VirZteebWGFt/NlUGKZUFAGacH8GKsoBoFvlqcTfzVHh28vlLEkTKFVotPgYWGyXPV2O3PM14pxr6CwVK5P9fzq+/rWLnAKr6rar6s1T1Z51sLHN7GHr6oWOeJ0KYKDSSlO0qFQyaiaNG07QwAFGUiDWULZpNc8X4rsZBKywPs3moLZOQr/UJoS4jZcfBddvu+jPXsxul2GIR0hYKlfO1mj3GuPpcq60p93lkxB7vGEUzt1q+BHm195x/ocZ4HC3uowf6hC1SaMpVzLk+aVyjT875Ox3vVKD/K5b+z78B+EvN69+c2Y6fAzxqoMmbjn7oGHKOWQoBckBLcaJY/YhY8XDRqPCkhq4Timm1lDLmFskJnb4lpOt3i0Oh3arb0Rp1WRmuzlk+c6ydj69v9V75vflOm42iumTB5w9Uum4RzFQrL7WLvIU8rTEnag9ejq4jhjXjUuwT3oRirPfczJNIqSRa6L9lfkrLkPW86BNz/U7HW0IOEfnPga8HXhSRzwC/C/i9wJ8Vkd+CteT9lfnjfwX4BuCTwBXwm97ORYhY2GYKgfFwwHdStQ2pxConoGxfpnnzrv2EZrjmHnJwfkfXW1F1lScNN8Ed/S2VbSnHyUes192e4y3uciUE12ng9twWjF/48YKPbTFXblnXTEcRimO74piZEGX12QopymJuovjahfYknMow4miuK+yoU1XsHVeLUoq45rqehW628ZYCraq/+g3e+oXXfFaB/90XexEiguactb7rIGYDpATg0mrBBWIsToIj/hfwSK6+aV6oruvp+gHxvrpZzdhaLwTVJVPbdgK3cokrmqv7kyPwcuWlpDUAh7TGrkWjF03Varly3WutZzAmRaGKnhbDLgtxwbRkSNHUsl7vJIvwSc4Ibz9TDFTT+tKGb9TPNs93he1TVFCL0a7YusyDczV8gGwgqlpupOS4AJdxs5L44q2w68eNcH07EU62W/ou19vQ1DxwqalDbeq/azTbSkOjuexrFmTvcw8P+5Gs9bSYfi0cICe5NhQcRZNnDL44AZYMFBHJbmI7hsqCTe2ra43+Vtq5asqVwbm8XwKH1tt/Qo+OvzZgFwxbj1+P2TwMzReq7fWvGZLlNcPJMcdCt8ewHaNk2Hu8V0KIZcLqIhIxn8y7pqHfjeGc42SzYXQwzRAjq8KMsDz8GFOtIQc2cU4kZ3jnfEDn6L3lrHVdV4XZdV3GzTzhNAEB56zEVXUSuPpeEf51HPLyGFzNLcwas3lQx3G+x1v3eqwNxpWppEqKT+LZ8j3LgH8ytqU59IpZWQzQVqgXHd1+f63tC0Zozl+FWKttIm7JuFdgnkOuU+irMKcULd/mGWSrwE0RaKB3QnQdQaKlEMli+baC1NZshmJkgBej63rvGDpv/Kd39MNAP2yg643vPGINXM7WLtWOrFRBKSTT8rDHFN2RBpQ1ECzXe53t3t7HcWyFpkTSWDFtu/CeYEnSwuAU6GTbhDa/LzwyKR05dDKkWWlge1mz8Ldz0P6+MC1rLrzcb4F+FrTkWRZYBLXMF1QQTWVtPBM1fSMEOs+hOaJFCLqUqyoC3QqTIDkbSmpbiS7XP+68z5XhBys70A9IP+C6nqya60mdFJetr4JcfoqAlBhgVW2CpRrsqgUO+cVdLrISxNI38ViztsJc3fMsdkILp1rDrn5PNbeoyxCqK3Wn81buXBWSYkReB2/K9r+cI+XQjOsz0NtFWg+XXyzP0omFhCLW6aC8llKi60BSsy2k95mGVrU6wZULVcVCGA2rttve4nXKGRDeIIZ3lhQ6ZGHu+gHfb3D9AN6DKx1mUxY4zfC4FDPMWvmI/aju3xgIs9GJ1bmRtVMSwblE1/XZzWtY0Qqbr/FnK8zFgCrMRdHanZMnv7fii6nvtVpUklYitmLt/LtQgo/WLIUZx4vWrqlsst5Jjq+/hTr17wV9VNd3Ce5KsYTXRnMpVsN2WZDPYtwQgTbMPEcrTlK2zBZqHGNBq6zp6TvBi1TN3PU9vu/pNhtcv0W6oSlUWJaBVA1ccw2rVb4IU+VzYymRYIHtlt28hiAhd8Xy3tfiNCoLFehygcmiiYtDBU2ktLAXSZWoWaMXDK7GHZeK/8d4tg2HJallpa8gQzE02zlvYcvx/C6fvZZfX2nztS1QjdpEvcf2WVpic8oQLn//CFo9zbgZAp0ScwhEXWO8tl5yzAFJ1ixIMxWXNXNmMvo+l7Ld7PCbnQmzGIQoxg+seesCM0VsYlMs+W8ZBhTvnF4ff7E4WZoywL5gdWMfhGVRFg49lrSxIthQ20GrZDuhCDprwXkCd+tSqw5ZhLkRs+Y4WrF64bXb4xRdWWjNFm6Uc7eavjqidLEz2uZFIrKqzGp+mrIo100/n8W4EQJtBRjnpecJS6FE57q89WkOLPKGm7MgD31Hn1uMWWOdLNDDFsSZaMrCO68fzpLnZjguVhoug1GKMEjhlxoAaQzGcg8iOY9DtVbTr+KRsibOUCDFWN35RaDbFNHWEDPBKVTdk/O3GKZLMm+5bjO41sZd6XzQHqxgY2kEvzV6Vwasro1lbY5bT13erRTKkcjq4rhaL76nGzdCoAHiFPBDZ6t5FdfczrvkRkDRBDq3e3C+oxs2+H6g227xwwZxHarU/L0FHrTbtB31+OE9iRMz71yIA1fy/sxqX1zTYl42MedMdUfnCLaqycRIkZSPU1KgUjbmWjaGeo2pXIhdw5GAaIYohV1IjQOqSHnFuUcGohaYlwXr2OvZ2jB27HauFu1eND+U+GZHpM0mKjdTnuviureXn16sb4ZA563YJ2+BSF5ypSLylmaassyzc46h7+m73rT0MFiT9s0W1xm7oUhD/VEFrj6rRmkcc62LIK8XVXlg0zwRUyxonMK8lPogZMdKDDFv/4vRVznzvKBSs3gr1HKKlt3K1s0KEhTBKtdUv89yjRXjFiNSoAR0LSq0YOWCadf332rn68aKp2YBNvXzbYUpsj2UsC68rixMm+Bn0V8FbopAA04TcRzRzuHdkFGsDaVUk0+gsVYl9V0u29X1+H5jmLnr0KOYjKo9y57dPGDDrPZCzOxEedDFsZKyW9jlSkqqQoqgyVgPa4EBSa2Gs8bc5jnGaoCaRkyZYlwXfSxJvKrWRaDuIroYs+Ia4VKpwlzusdSlM+ya27tpg3mVI/1nkKrQpRTjrBHSa0fGxKyCnxYcVhY4ml31rd3iPSlYcZlUGRaFZLvDs3B/3wiBrnyv5lDMrJ3N1bbQSJJ/6TLfbJDD1f5/VurWPfFA7EEvHLZzR5pY8z/lydfPh2zk2BYuTffZMmKMxLmkg6Xa+3ue5irQgj1En7OiU772An3a8Fe0sAMg/bKoS8lcrezFsRevmh+AcewplfBZquC2MyPNK08yFcfVmZr/V1DMoEQxDtvj28ckQ6jI0tel4HXJUKjAqSdl44sdN0OgnaMbBkt4rcH5RnglDJN61yFide2cGyrn63vrA0jGm+IEUbcSOvt98aSldPxAimDbeyYzWh+IUVtFuadq1C3UW+aJQySFCClj5BAbobPacykp3hkcsbJjS53qVlNDNrIKPGEx+LQsQMplF2HL9JgaF96yIs4uon5epBScWeZhkah1gsBq6NrmKErCes1gfHM+qLEaiiRFJVdD7TqmEGp3gOXccLSFvKNxMwRahGG3I6Zg3KuWdM38Xu5dkhLEYPjZ53K31iLYWqcZhFi25rVRV9gCk0zJgRaaikZugvBD/l/TUvMtO2ViTKb5sjDbYkm5TkimqjJjUJrM19jkqObW77ImloQ4VomsTxhYpVpRrrVXMPe1uDY7amxBrpMFNC33UPTo2gvZHrM0/IRWyItm5Rphr9CqkU2R4lix+I1SpfTq6qpmEj3rcWMEuvOdPVzTgdWlW4JbcAIRhr5nyDXRSpOeSvc1W++xQLdauHrnVNG4bKU1IyQumSiaTIiNQw6L4DdB9DTZ0kANiIdl27a+JYbJl4Y91nm2bNXlGsr32vlRXUp4tT/lvCue2BU2pcHY+RxFK7fa+5gibJ4MFCihVlmpXGOh+RZGaOHpF/iUsY4TCIA65jlwcXHJ2clJbqOxHOdZjBsh0AAarFh5RPFDj4WBSjYIzTiKKZkw56g41/VIN+BcpvtoHiCsvIxrDJgslpfCb9sCiiEX3y6cMWI1QHI+XJhD7ca1ZIdYRk1pT1baxhVNSVp2BOO5pRp8Ko4YcxqYXZ01CM0dBCIJSZJL8JZPyFLKV9ZZ5lWAjyFVKTncBN4fU2TH1GW5x2XODNJUGFZ2gKPnKM0i4Bohdc6z3ezohyE3YzpG9k83boRApxi5eu0hjx89pN9tuP3hD9Ztu7q/o23xvgQCeeOfXQ4kKhq3pZra7RuWB7TK41MlzDG3uLCflJLlNcaUnT7mlg8hWk9vVWKw3L5EttIBUHO/S+m8lZMNcpF0RNBQmtQLHV0TuGRRdrYb5TZyabmHNvpP6FZwofxeqklBhjF5XqTYA0dwoF3kZVG0tkcdGZq1Wj1fzCpeZNn9dAW9hKxcRPCd4+79u4jmQkLPKCipjJsh0CFyeP0h4eoKSYk0B9TbJHViwfwpY67SmbU0dy9tJsTJtVvnsXGTdJ1oWgQ5zBHNWngOgXmemUMgxMA8B+YYGaeRcT8xTTPzOGeGYzaBEHMm9F3PsNmy2W7YbnoLnqrt31ii9zBt53zuGiu+wp926y67TGH5rNh4ZCkkacKrlQV+kpFoocEyRXL9/LQQpMK0Flpo/X4qOZ7wBGyoDp12JYkZx8ZHL9lGPEOhvhECrZrQeaYTSGEmzBNDv11wMqaxXE6lkpwfuGitciTTgmtst8bRxbFQ8G/MFneMkTQXym1imgPjPLM/HBiniXmemabAPEfjoMXhuw3DdofPDyjFRJgmHj48J4bXEIHNpufu3ds11sR7VzsJJE14NSaga3aUUm1oXbAm1sVQTAbNGFRlnTDc2gvm8FnmwPhfa5AEi7ZtdzT7Lk/YG2UOl92gTmzDDJWE5qVaFTlGp3g+tRDf5Vk5hzRy/zTjRgg0gOtd7gkNYZoYtlvEL8ZPSmnVF1DEqoXmGjyrYx1jwZXB0UKTxrgLs1U9DdPEdBg5TCPjPFv/DzXPlnQd2+2Ok9M7DNsdiDXl1Px9lx/Q4fycw+UlcZqI88wrDx6iMXKy23Dr9hnbbb/EqXgrF4wsPV9UpArIcg/rezNNutznSsDIwpMSUdtXzUgzfnoR/mNI1uYrHmvdVlEUjV9ksUQNFlTcKhYKDl9p68oZXou338m4EQItAm7TIclDCdoR2167rq8PcBgG085dZ4muUhIvtfRBpaWfrmMB8mZOS2ulkLXzNDGOE9M01XQh5x1pnPCbLfdfuIfrNoSkHGbl6nDF1WHMQeseScowdIaXT7d02w0+zAwpMV/uubg4Z5omzs5OOLt1Rt/la40CWKaOQaiYj9k1zo1EjErpLGC719K3ENZtMEpFpHKPpfhOi5FbLrmme1WY0O5o1OOuR8vPN1kwpTdLLoGQZ9xitfM92KabDXRAi4//KceNEGjE4YeNbUGa8M5bsT9v8c2lFbKIFYopwTvQTvJamK8ba/yXeeQszDFY1vmc4zRSiuzHA0nh7NYd/NAzjSPj+RWXh4nHlxOHCOp7zi8uzCso0HuPhomhE3ZDx53TnVGNZ7YDHa4uOT+/Yr+fuHv3Dien22wrlGRgj0NryGV7nyawiZSaavol1avlr4uBtjLkKu+ymovVote1EF9Hpa28muV7Uv53mbKk8tU4lyGHfb7YPBqxZOLmXp/FuBECLSL0u1NrdCmCyy0ofGfB+vVB19SoJcwUqEmzxw6H44d2PGkxWe3iGMy4S7EUbIkkjWyGDa7fcBgnposrrg4TM57gB4Y7d7l96x4PL654fQxEMejSn57hUuRzn/80p4Pj4fljtv2G27sd93ZbTr0w7q+Y54kHDx4A99id7KA0uU+RGJaGQu09Fdi1dohc50TKnHUrnI3B2EKHhWajshPH87fa4Zo5XXB3zo8slf2Xs63Ob/ahQ0i5nISiyYzLxZX2dONGCLTznpNbt2uGhu8cLje/7HrrJ+1i8Xgt1nwZqlpxmGvePRbg6lpuslNIypyNvnmaMqXWWfuxGNhfXqJAnGZ8t+XOR38c3b0X8ZtTrvYzn/zsP6E/ucXZsOHFFz/AyckJd++c8ekfusfLn/sUYX/F48PIOE1sh56d9/SbDd4J0+HAw9deB5SNbun7HrxDdFmgxQnTMh4LxNAnFmvFt2ld6qBE/mjxClLsvnWIwHXC2752Xb3oFqa0i+JY55rwA+KBhDjrOdO2z3vacSMEWkTo+j47GJTTO7eZpxkp3Vy1aARLl6JEydFoDOSIFXjSOKzbdu6zFzJEKaV0t9utUXEJfNcj8wQScF1PNyj+7B73vuLjHPotc3I8fPkz7A8Hdrst0zwhovzQJz/B+aMHHK4ecWvXc/vWLcbDAZ1nghdS1yGS6MSK4UzTgcvzy1qM0vfGrwMrHN1G55V7u+7/FnKU6LVaTKYVthVUe5IVug6SlPGkF9OeW8X7sZzv6DkIiC9KKRfvyR1rjcl6FzC0iPw44I9jJXMV+FZV/QMich/4duBjwKeAX6mqr4vNwB/ASoJdAb9RVf/RW15J0popfHbnDg9fez1Tc7lORuVdF8/Yuo9Iu8TXsdCtA8JnR4Zzji63TDjDEXc7UoxM80xMCd8PDJsdKo5pToxROUiHcz2Dtw6pl+fn9kBRUpr5kR/+QV579VWmq8fEeU+XbnHv9hnb3ZbgHdEJu7t3uHz9dTxCTttlHEemcWLjBYkCTf3kGC2B4Di3ctHaBY5cF1CkKy19fY3otUCnWhphIR/auYwxrvo6FqeMJot9KWwGChojZBhXd88CsDU3bBKjQFWevqUbvD0NHYD/g6r+IxG5BfxDEfnrwG/Eqvj/XhH5FqyK/7/Nuor/12JV/L/2zU8hOBVCUjZ3btHvTsGdI97bYs/RXIjUklKklLOts+NBPK2zoPzfCoBzjkriYsftuh5OqA+8eAIN8vQWEKUwq+PhfiSJ4LsekhU6P9ltURJeoOs9kgJhntCQQB39YAuFBFN0DLfu4/yGw2uvogouJfoUOX/0mG54AecVjVYuwXc9AlWgj4V6gR+L+/jYMEwlobWBKmvqb21zFKEGagzLsW0SYyndYJlDJVIQxVz3AMmK4rj8rOqOeuSWd86Zt5VnYxi+ndp2nwc+n38/F5Hvw4qYfyPw9flj3wZ8BybQ30iu4g/8HRG5KyIf0beoQhpjIopy54X7VhvDuxxhl/P8Mu5dvE5H3DOLqxxZC7b93m7ZUhmTMsFSw0RLCGkOdMqhoF48d33PozHgEeg89+7d4aWXfgxBGfqOcR75wIsvsN10zIc9H/zQh+j7nimlHHfSI/1AfyboPDHF13AqbP2O+WLmsD8Aiu86+sGE0moquxo4VdiNdlwHtapwailsnlYCfMwnl7HEqBxj4SZKL4eydl1n1ZFioutSrr3t0ZDpvKSEGKy8QlZAJemguMLLcxInTzzTdzK+KAwtIh8Dfjrwd/niq/ivBFqaguf3T7ekmOhOtgy7HfMcagAOav2xEzEbFEtc8ErL6PWsRtuhqYySSV60W8raBGda2hIylBRy6hegKXK23eH7LRdxYtN1fPlHPsInP/kDxBjwfc+LH/iglSHrv5KLx4/ovcv9yR2qjrt3X8B3G3AOv9kiXQfByh2cnJxaaGpI4CI+9QtHHCOaQy3fCOte9x45X9Hus9B3C99bWA7NPHIxuJMqFT2YxVdhSHvOECK+M/gRQlwaNKmic0BjROdoOaIFR2tuKV3ZDyxsoVVWTzHetkCLyBnwF4B/XVUfH21VX3QVf1X9VuBbAT7+wXvadQO+G4gpMc9zhRkOiCK58rsU91P1EuZrq0Ew1SAqVnmBKuKqy7elwVRzBy5SfdhWKmGBLNYEx6qZ3tpt6CLsU6I73fETP/ZxfuAHP8F4mOm7nnE8cKoDMSa63rM7OWW82nN2uuUjH/4ozjvmw4zDeOmgyhwCw9DjXRGsBU445xbXelpDgIWbfuJZ2RyX+ShlyipXTU5oN+VQtHF1sDRYvOwMhaEoycFG0yVirkUiGQN77yzwKMzoHJAUcZgJWFglXHGuZJ6aJoH2KcfbEmgR6TFh/lOq+hfzy18oUELeYRX/MpxzDNsN0nWWi5e0luNCCxNhIlvxXH5ohZfNlksWbvMclubyQgk0d1XYi/ZemJBQ7hVBTcBZAv+tJAIoid0wsHEdY0h8zcc/ztX5I37spc9zcX6OF+FweU4MM8PmbsXmX/Hxr2B3dmq9FmdPyo4iTdbn0HeOzTAYW9BkrrTC2SYhHLeOU1jd27FHMAfBLYJJNqTTWtuviqtjWtiEfCkCaQXkTatq2TkBEUWTs4SznL3j1ApXlhxC73IFWaeIykLzvVtJspm1+M+A71PV39+8Var4/16erOL/20Xkz2DG4FtW8bcUrA1RI9P+gObFu2yfmfJp8PMTDhQDbZSm6+3DLsK8QJAMSUqgjrjcPjnjU7XY5dqC2coA5ZiNgLoR3MCw2fKB0x1f+1N+Cnd2Wz792c9xeXWJ14BTZXx8zgv3X+An/uQfz9mt20zBkldJEdGIRzk52eXIUtN03dBT9h4tCzXj5hV1J2K5lyWQqTGA86RcEw9SFskSTFW0cPk/JXJYp817iIGYGgYiLdBBBKT2uTGjNUq+lhDQMONE6Zy3wvXJ+uiIy3CvKKyoWcM/vVC/HQ3984FfD/xTEfnO/Nq/wzOs4l96n0yHiWm/p9ttFoFtd6L8exHgY+61lAxY8hJpBLrk5i0kfkmWNeXvV8cimQs+uUicJ9teQ8S5DnxHN0QSkZiUD5xuufc1X80/++EP89prr/Ho/CFJE9vdjrM7t+mHjW3Bh5E0j6SrC+aLS+Zx4nSz5cs++mXEGHj51VfYbk+gSU5tG2uu5kEWIWrv9ViAy6i19FRRMaMvhlA1fygRh6qEkAUcCPk1S1nLMCgrjM45usqwpFp3UERzj/bWDyA1I6nuCHkh2Wbs0WegpN8Oy/G3eWO0/kyq+Dvn6HZbmPbMhwn6HOSexxMPtHl9de4cTdY6WVLWdwtMKQ+gxBLbn23nrMKvJlW63qPqLaA/BsI04n1PmEaiWrZJ6AeGzYYP3b/FB+6dMR8+ZGGn80RIapngMTKEET/vcWFinkZcKj22bZE5ESvOPgyVgy73XaFVve9WUOzeLP1KcvraAsEoOxilahI519EEO8RoSQwlkSFBVGWaA4dpZpwC+8PeWkCrZec4EYa+49bpCSfbLd4LvfdWmq1z9NmV753U4vQ1C0mbq1dLbSvG59OOm+EpdI7d7VuEFLjcXxBCpBsWjBjTuq5cYTlaq94mbJ2FUSzn4hhQlUoB2shxBeU6at0NK1jatpaQrjMNFWfmw1XDJMAowsF1dJstXT/gUE63Aydbiw4MIRDCzDw5tBdi77nTew6HA9M08vjR6yRN3Lp9i91ui+ROXcWpUgy14swonbzE+VqkfeUW1wgdaAxWXqzISkyZH44Wu50iISSmaWIKgSkmrg4jsyohJfZjYD9OluCKZzjdmbLIi2SMkfl8z6uvP2bTe042A9uh52S74WRjKVZ0S3VYJznuWYsxHqtTRo+e8TsdN0KgQdjeuQWdoK9DIK1wcmsEFku91dhPaOr8YL1fFoUZRMvnl+r8NlJKOVXKV0qw8DbaKSEJThP9sMHJRJinGkOtCIownj8GIJQKoM5ZiQWspaKmSJjHHDQU2G17zm6dGG3lPae3zogpcTgcDE/3PTGZ9iRTbD7XH/G5tof4zv5vmRtxSCJn4MxrGyMl4mwZOSFGxjAzjpbMMCtcjhP7YO7/5Bx+d8Kw3RJCYhztnouZenZ6SocQp5FpGtE0cnlxybTbMW56dkPHdujZ9D3aCeqc9b5Ji4qOaUbIhXvePwJtWrPf7djGW+z3VzkZtVjwqW5GhU5rR2UDMquXm+FVVV6oKufKVr3G2PV/NXrQiwARuqzaMrRIqjg1TtbFSN9bvmDJDLeAe+gE6zsSZtK8J0RLrq0pS/nyNtsNvd/SDQPbsxNc54nzbO0zintfc2+ZmF333tP1JtQle8f5xcvmvCO5CHNTZNysQ2K0opgxzBbzHQL7aTLXfojs58BhDkwIKo6E4+Jiz+HBQ0Kw420qHAqcX42kaebO6Ql3TrZImiEm9ofRQm07z63THXICQoesOjMs3kyb/3faYXA9bo5A52KILmufkl1tGLHQWKlCAFi2LMjwo1n5QC4dUDRyOc+CoWFdhgusGApamq9bDeoaSKOW2yjYIcI4ImJZIeXZzNNMTKYVwzwTw7QIdD6P73r6fkCTeUglJWuoU9rVOWnqbxgD41zW0L1VWvX9QO0c4F01hDVlI7cwE1kTmFEbmKfZUsrCzOEwsR8nphA5xMQYE3NS1Hlc3zOGxOVhBPEMm8EgFwbUfLexY0riwaPHeAcvnJ3QbXriPEFMzFF59OiceRy5d/cOMlipCufdisUyhaPvLw2dUrCH0cRaVFWmxYhba9ZCY7V8bf27GIgNXDFHxZp/rmyC+GIq2mfJlU6dR1UQe4wEKbSgR1xncdTjASXQuRyfHI36ijHguo7ee8Qvpb+c72r2uqoSw8zlhYWJdrk3jO1E9hm8N+dE7vNXNLg4wfWmAApEs9xILM6lJOdiDpGYW+fNNTNnZp4D4xyYk5qRC+A9fhgI8545RjOMRZimiZhxuHPC0A+oWKTkfhqR4Q7b3cB0eUHYj1YlFsccEg9efcC9O7e4ffvMFnYonsLWg/n044YI9MI6QDb4YiQq+L403zHPIdlV7f3S7m3l2dKEK7XWinpS85a1cQytZrcOVvk61ARJ1IovopJjPoyNkGyYBdfh/YBuIsP2hDCPpDkwz6M5FVI0ui4LdlK1SL+M5xNLaYIQIkms+SglzqT0e/Ee73ssrsplwzAbhT7DDe9rMUdwSFRSG6EI1YFTstnt95DDZRPe9xwOI8lbihtODDaJZozv2I8WLx7nwK3TE7rOEyN0uTrSlCJ3tlu8cxy4YP/wUZ5zRwgzr7/+iL7v2O021SZIWVklXWDl04wbIdAG8ZLh1JRrKc+R6NLSiq3R0iKLVi4re3E6LKuitHBILlfHz+Z+S9HBYlwbXi9CXTR5womj6xaPZXRmBIpzFiKZjb/UB9zUE8JsNUTiTC2YmLVaiLFGsZV7953D9R3b7dYortz8R3KV0sUpJHlnsvbOpS+M6/ySKS5WcsFSuUoHKqnFcmIpqBNi3RH7jWecrISDywZmxeZA3zmGoWO32/Lo0SPz+HlbMEmzbaDmBfTeI9ud8UfjjI4HDocD275DNfLo0Xk1aMv9F9vi/QM5lFwbYybln/kwEgX67bY4lOyjjRFRKvCvDqSWBYFSnQH5nSyoumI8yNp8gR9L8m3V/iiIOSHsi57kSyhmvqaCrVl4cxe9dSYALMk1IqJ0Pmd1Y6Go3Wag327M5c0SKN/GHJfjurx4nHcWxpptDNPiBoWIiXgYLXHBiuflkg2xoQJzFwQcSYRxvLLrzoFa3jtu3brFa68/IoTAye6Ej3z0w2y3W/YXl2yGATTRd54OhyNyenKK77wx/9PE7mTHGOZc9y/SDx2Hw4FxnHDOI2LGdkylNcYbuTve/rgRAq2amA57wjQRxgOHiwujglLk1r07xCfglVnJrlbSb96R5iNkSFFsxSrMjUu9wnSlVO8swlR3AV2q0AO16U3pgWIOmqVAY4mQA49p51zkMcZa/lfE5Y4DJ/Rbw6Ip5brOOSBpKfOVmp2oWwpVisV3e9/jvUXn4SP4gPcDve8JMhrWphRvymyIACUkIDetj0nRccQ5IW4GdiennJycsD9MTCFxd7fj3l3Fa8JDbjst+JQY+g27kxOc76vxGFXpOg+pZ7w6Z/Anhqnnmc1uW4OiFvj8PtHQmhLTxQXzeGAa91ydX7HfjyQnkIo7b327TwbMLBnQtYd0ZkZqprKzdhdFlgu1p417vATyHF0hsBiiIfcrtK8XylDMZZ0zOopWRp2Vt3OOk9PT2gfRdR2b3Ql+GMC5XCbYQVK6oakKlW0D26FzhF0x9pxDXEfnl+I73kV0Hum7jpQZo+qpKwyPiHU5yDuLc47Tkx0yTSZkITBfXOBF+NAL9/jsy68S5onPf+6zxHlm8B27EyuwIylCDHz5l3+U07NTK/g+z7ikSAgQE33XoX3POE5sNgNhzkUvWQdcvSuu73djpBg5PHrEPI3sDyMPLycenl/QbwY+EBLWR36h2sp4o9iFIsjSZLGQw82UJb2oMiNaovlKCn5DBR5lI6eUWzN3HUmwzl0pR5TFBJpwuEXYnaMfJEcPgorVwvalVbPxMUbldR7vLcS1vZdyr6a9fYVC2cmfF6PPLn+PE8PWnR/ofEfnfG2DsdgLUIyxzjtOd1t2uy1RrUUdIhAmtrsT5P49Xn10zv4wG8++6zk5PSPFgE+JD3/wy7h3/65p5hAgzjCNaO5zPaeYw2BNoxebIrFUsDr2LbzTcSMEWlPk6vyc/RS4HCMvX868/No5t8+2fDwpvV/HdZSbL86MNnZ30WpUp0rVpmrMh9Fu9oFlQWQ4AqvXij1a8Gs5h+stR9CRq/inaNyq+Kz1oculfgu29YNVTrUIuRzkLm2BmCLe+eyVjmzc+6UmSWYPlsCrDEFywUcndl7vLO66YNRVqC1kl7QtJPG2w8WkmWUREsrt26fcPT3l9cfnXF5ese06NgIvfOiD3D47Y7vdoCkwjwe6FJkOI2E84B10Q08KkIJ5Yr1zDF2/FLrM431F24WkvLKPPJqE87Hj8X5Cux2HKRBDou/buGUbRUiPxxKJl1B1LOWyntTwRh0VyBKpaVrV0aK1seyqZK3m6qOqSI9t22rpYr4z9V+08yKABhES+fNiMQ6sBDjnex0trGOWIzPn+VulQpFaAfV8n13XEZ0VirTE6kWYxTvzcMYsVC6zFj6HqQKQ8NLRdR7vlbNNzwdO7jPNt1FgGLZsNx1OA9OlOZA0TMTDFXoYGbzH7yxsNI7CrIqmgCapfLhkvl2LJnkG40YI9Jgcnx7v8HA/cXl+hYyBnUv028wSSFfjv8uDfrMtqjagyXjYupn6BmZkmJAj3UANq0uBG7E6KlpY43ODT9smR6PqtEN8X8UypUiidKg1ukyhOnQ8VuQ8NxBY6keLklIxSBeNvHDmiyMp5a4DJbbbPhdzoZqi9XOz0bI4WPj33ncESVZDLxu4DrLxLJW/N3ycHVIx0ntP73OMigswXpBG0/Q9AIoMQw6XDYTxQJpmSzPz5k4XIEyZ1tz0eYG10PDpxo0Q6KAdn32k7C8mwmGm1xEne2LXEcKEyKkxGqwLobTbcRkFPxcNVhgIdaZx28+VkMYscaQUMp3kKzwx2Ta+O0V71ClDDCF76/IDKZGBxcsnIvhmhpdg+lxEUVlS+7Nx6fKCO2ZbFman4POFP7fAKslsSqwlBVx+zWfWRETo+5zYqhbbIXEpL0w+vzq3pF3l1wt0c95lhsMK3RS2CVgyujUBxuio2Gf73iPS2a6g5uTBO7qhz995NrJ0IwTaO+GjL96FF25xeXnB4WGgn2e2Q58dEy1H/OT3W09hGeX3NuruiVHhSdaITQvlgrXLMUqaveY8xOJdtFBUVy4kt2V78jrKotBsRAqLW74szPJa+fqqhzcOq1EiZZuiSEH53BI/XSjEjr4fKIutz04q3yVislS3Odf0a6/Ze1/Lq9XrJ4cTVHbCHEpd12UmSql9vdW0veV9mldXc9MnnFWNJS2LwHuDZNc+3C9y3AiBvn37Fj/v5/1PcL2n844f/kd/h9c+90lON/YQy1gebusIWbMBa2EqmvrJ90rRbeOZcynBBmK0bnJYNsTWKF22/SxCsjhXWs+X5oetGrOHPXM2rsRsLFi+3FO5hraW3XoOltE6Ypa+Lw3nnBdZ5y0mgxQtGycvYqAuhqVc8cJGSF7kS8PQfC20wfrNvGjpX249Ep3LEX9i2jzFSJfDa4396GpJg6cdN0Kgfd8z9Vtun+345Hf+Y17+sU+x68D73iLZWOCFjeXm2wd9zEe37EbSJX2/GnjOoILLCbDCwl+3xy10lwhoCWIrgpeKs2ZJh9K0NNhBS6nawo+vF2YxVlf0HOsC5su1aK5TIhUjt/PQOoPqHInQ+d5qb6doHWpjzvZWwFnWzDxP1JK6LMpSvOEbyS+a9zEH6+dHIXXHwHrVWGLi8qZzTStUq3FSgtBK4ydyENXTjhsh0Ii1ddvvr/jU9303H9j0uQJRJCVwvjNjK5esXUfduWwIWbZ00YCSH4JkoRISiD2MYigV54oUUS6CU9sy21goMzCF1jQjorJgFCfOSqsLFrWXE09XbEXJ/+L4ntbXVJgNTaXkb+Tk9Kxm3rTH9c4ZBpby3YLzHS56XIq1D7eZDg4vghcsBiUbhSXp1hxHJmylIVLR0qumTCnVcrxWh6HEfUttMlrvz7nVgnWde1ZJ3zdDoFNS7t17gflqwz/z1V/N/sFniOeJlGZwYn2hkyx0WP2mbXk1j7topyzEFSNHs+itZoRpujLZqWrGhgYrvzUQpGj/tfZr72FJlVpr25YLlyOBXui09julRdqyWLLwoFbLegrozrDsEryUoZXknaYKSMbuRVuqBWpZHUEyfSiUIg3ttS5e12Wsr39ZuMb/2zOg6eOtWaMvi3/p3FVjZZxwTXzDOxo3QqBVlaDK+X7P2Yc/yq0P3KW7uuDB536UkzvbYo4s/NP6ywUh12PZ1ln6dudMFSmarECEDC1cI6AsAtRqv/J/cc9KOV+9Fq2xHHaMxdCr58qOhMJkmJBpxaXlHEv8slYhaYP9dycnDFutpdGMlVkMKkEsYq6U1hLzZraCWOvkQS2FUBZfyIzHCurk3aLMl+piMFvn3jwLKRmlkQT11tFAVWt+ZBF8iwnPsd4CMRUW5+nHjRBogEcPHnD1+CGHALvhDve/6is5efEDnL/0SVKC0ih5DTnWDvGVlmMp9SVSamtUkTMh11wBoxaRAXJORqmvvCyE5aFqxixL96mlLkbF7U01ohiLkZazrWkEVp4sCuNcGyS18M2a6UXvrZaISGkB5yqFaI3lQy5F7CoWvq7if6VTWDLOfaHsWO7blYSIlqMuCqHYNqoWE1Nc/KpWGSJrbZdjt8nHc36JJEy1h/rT444bIdCaEudf+AKPX/08dz/y5dx94cOEvqO/o3SPvgC6z9ymLEYLbdkqrOaDPjkhhmuzgIhlfttD1oxIluwWq3pf3m+31eXhCy3fangxxmgPi2KAag3RzB+s11bCNs0JkjLj1wiHWkATRaOy4NYM+itVaJ5IvzJIwRJp+82Azgcr4tMV4V5z21W4yeWFc4zKPM+UiqXVVHYluSJWR5HqwrBopvNYLezlvkopiWPIoWpwTaoCerpxIwQ6pYRPM3p1we2TE6TzoMI8RaaY2PSCqFhfDlhZzAVyaBa6NSddijE2G5q0eHlpyJOSZJxpx7RQU1s+hrWL4bewEsVQtf8XjWahmGl1fcXIlPLdrLVLv5SFVrAYjyV0tNiumYEha+zaZ0Yq/PDOmWNHreVd6YW+FqBF2BanzYLvC8Ao5ROoTNHaOIbCf+drq+oYrOOsrnaF8nnyvfhsKMZgDzUV585TjrdTCmwL/C1gkz//51X1d4nIx4E/A7wA/EPg16vqJCIbrED6zwQeAL9KVT/1ZudIKbLdbHC+52J/wf27d4lhzin/JYrNJtQXeydj2ZLgTbbG7YAZQEiOnnOKqoVmqph0VyHTsgDIwmRaXTP+zJNQ5fyYWy5lOo9bBUvTZStHLeVjpJqsIOVaS40NgZQDnsqxVIrgFMyZSx+K1L/Le5YgEIhZ4DXnIC71PHxt/9bSkjXeBPLukTNzQrQpscaM9f5TZjDM+7jMR22qqcuOVHj0VYWnHKwVo8WuxGjpa9ftsF/seDsaegR+gapeiBVt/Nsi8leBfwP4j1T1z4jIHwF+C1bc/LcAr6vqV4nINwG/D/hVb3oGhSkmPvDjvpLXL/bsrvZWnuqwpwiqtlZhvfEFgKAWpKNNFF3VOpohSm4gZnxujppLFRWCpPrd0qDoSRvUjEELxi8ggKwZ84NM2nxP0ewybx80zd89JeqtW7EK1UAt91H/d/X6ChTQzBaI+roYayKt93TO0p7a+GOpmlsqPjZP36LByY19Ykx5oZHb0NnacixQBooAU+cR1pq52Delb3jJ3N9fXr2piLzd8XZKgSlwkf/s848CvwD4Nfn1bwN+NybQ35h/B/jzwH8iIqJvsfzGObI7O+HO9pRQSH5V4hzAF9rK5fT8ol0WpqE9fMpB/CW4HW+Cv5SFjdUgsVCihZddYEFxDMASF5INOM114oCSMVP5Z6XWbKvX09B5mmmSqu3FHmrf9yvhLZnc5bxFCxvcKJrZllOqmlGWnaUImVhXMd93+ClraKUyJE0qSyVupHDTdXfLdfBqINVSqbU4WUonrGLQGtyw2VygzTKHIS0FIkWVq4vLJxmsdzDebjldj8GKrwL+EPBDwENVLcUzSlFzaAqeq2oQkUcYLHn16JhLwfN79zlMIy5uLPUoWUSWTJNtd3SLls4woWSxVMhbhCTTZ06cFQoSzdti2z3VrqEE19hxllBPRdFAU2F+7cZuvZIrwc0B/ovFvmiplcGTjSmcYUmXmYpaDWmlkXOsSNVu0uDWIuiFhy735YjN777v6YcBfzjkRZBLnpF58KzJhXUmkCEXo+W8W0qNFUfJooHFuuGy3lFcrgNd+r+snEdxYYFUYZommrff8XhbAq2qEfhpInIX+C+An/S0J9am4PlXfsXHtO86SMo0HyxgpWA4shZ0C2UERTBLscM1tm3pJWBVH64IbUqZb8WoJpVohQRbfllznYrmHMeOlvbBLobNGlq0Gr5qKmfZ27WcV98xDBvL5i7nYNF2rUNHVcjtz2vMdaXAcp24ktFt5cis2pLrPL7rCbG57kwJWpaLkNyy8GynyJBNtTI8Re5KsFVpCV3vttHIBVsfP6MyRymlJTjqGUj0F8VyqOpDEfmbwM8F7opIl7V0W9S8FDz/jIh0wB3MOHzDYZSaYx6nmjh5stkQppGOlNtFWDp+AguVzLAiZRW90Gmlus8ygXVrdM60jpo0xBRzOKRprZRC4yUE46NbITYjrViUi4bJAl2EWFsKSnBuidYrjIZ4KyAj3UDXd2w2G6vLkVmE0uxnYToWOrGaEpJZ83bHaBYVGW+L73F9T9/39tkYQFwppl+erYUZuBIMlefN511CtdKU+WYt6CktCcSVFmQd/ZjSQpGW4KkYo2WDo1ztRzabpoTyU4y3w3J8AJizMO+AX4QZen8T+OUY0/EbWBc8/w3A/y+//9++FX62h2Ru6TiPTPPEIMrh6jGnbgZdtmFNZO9eNljaQ1c24kmSvkS2lV6IZfJSiFZ+1rms2Wk0RQ7CyRjRmI01dadSGA/NbMeifQqmtPPneIpcFEacp+sG+s3AMNhP+0BLkH85DlWY89btpAo/UHecEp5aAo3K+9aIaLAqR1cHVBKdc1XQVHXp5FvKshUnDvn0mQESoVYL1WzHlF1ERCC19oqu5q8Kc7LyZ3GKzNOMG4amWM47H29HQ38E+LaMox3wZ1X1vxaR7wX+jIj8HuAfY1X+yf//CRH5JPAa8E1vfQrBu45Hr72KagDxpI1DdEI0mCtVuywcOZ42a8kCLxZBIhs32YDJm+G6P4nWot0rqq1qmswcYJSaankgi6JahYdCFWYtHwDLAtdS/svXSqTZFrQW0L6n7wacmLtasZp4FSez/J/yzYlr3NJJweXmQDES5ok4T0YbssRY+5o029dZT6oF+eaFaQsfn8sosBh5QI6wyzuBy/efrHBjiR8vNkgLzVS1CnH5P6US6D9ZHcPuXYqHVtXvwjpfHb/+w8DPvub1A/ArvpiLEIGu86RklTG3mw0a9rW1g+oR6W64oXLT9f/lIjKWhqIti/lnaUyp4tOVUNJQZGQolJYeIgXDpgw/Us4MqVkvaO7h52spg1JQsTUkEanZ2xYGumDNco1L9yqWRaIpu91N+gqt5jSRUsw1qEeIVj4gN3nEOUdQcDmFrO8HphBIWZNWo1Ik1wzJD8VZ4mxB7wvb4jKsWlLVVFOtp114d/I8WT08u544W4uLUsYgqpJqpOS7jKG/VCPGyOHqMRpmNIwc4p6zzSmSrNJQitZMvhBrVetqFjoArPuSZvu+at6imWGJR85HKcM5l6FItvqrpqFxYydr0kmDV48wdFlc0ttOspxtYRAWAbLzrLpMheJSTk8cX0PIwuaJWUgVjzhrDBrmGU0BK8WQ0BRJIZig5fWmWCjuMGyYY1mo68KQ9n+BG34R1sKGsOwYhhStZZ3GYE4rLUxJURItlrZdxLLkjZKdDrkQTl6cTztuhEDP44HXX/oMmgKCEOYDYQMuBaTLQpYDgYwevr6OgykyWSAJi6ClZG6VkmhahE0yBq4GXdY+9Xgo42EkxMAwbKt2qnxyE4TkcuC6UY25klF9rFRXcopmvKUUmecDITpCyLHaORgpppBZk1xJqevsKJIbGDlvx5KinaeMhaLtKsmOLxn3FiwmTuiGni70jNNUd4tqxzSwa7EDmjgVkaoYnPeQLP7FFIjLFaOWGQYoXWVLD5eUdz1NiTBZMXdNcXkGTzFuhEBrCkyXj9AYEFHiNPI47TnbCqnrEXwt4ZpWsCATbUWLlS0ayOVDMZEsIaU2ymJoYUrRynWyGyzsnODx1s8wsmDk4/uo9NwS5ZYPvjpXCoEpG2+lHK64YkAKXhxJk2HTEJFc3Nx3ue+57w3DpqLJUxbijKXnyfqWpIQ02t6q+yt+6Dlxp0yPQqX9ijAXtkOyFSjCSqBFiney7HQJjSWVK+HyAqpex+xFKrhZc5yL1dgLVpkVW7S6CtJ5Z+OGCHRivHzMdLhiHq9sMqdTtvdv4W4NyNLeCljzuYuhJ9W9XCp+WhrUevt2ujAcNJq5UHsts9AKYdEo9TXVKrRFm5G3aTLtlskC+wyWTRIVYpoYxz3j/srotGFAfIk6LgSG1EUlsxCcFTp3/YC4YH3AZdGIqlnrx2hYOwZIqSYelBiOQkOenp5ylR0tSyBUYWSaGiRHc9z+vVYHZZ2vF3sxuashmOcwxshhHIm5umtwEdc/vUTfEIGOHC4fEqY9YR7RmNhrhHunS1ZDNsJg3XS9GHtANZaand4+o0tATcksPE5ibYWc/JljbGyCmkjBhKNU0HTZwKsGaNKFBSiXVmi27OXcX14gKK7r2GdNZjy5x/e+BgeVBj2d7+gG643o+4HUGWcu1ShegqRSjGaEaTJNHWMO9gpM0wSYh3Kz3Vov7po9svb05VPXyWxhnjbz5HJQf2bssjGdFvujmd9SMjmhTPOUF7kynQX8tu3H/s7GjRDow37P+YOXzTCOAY2KS4m++6hB2pRqg0fVhd0pgKJoNjNENH9GjKfO5EhL71W2gcUlXYW6CPGRwNffG77ZuVRjFkr5rbJ4Sq75wiMbd+zw9MOGvuvYX5yDWKhmCJF5nLk4vySpcvvOHc5u36bbWmEba5fWM2xGhu2Wrt8sHrnMgNgizO0tNOXWFBaHEeaJcX9gPozgHSEEhmEgjWnF/64UhSyVp1rYsYZqCzxTqMpHnLNGm86RXNHoS5DWPE2EOBMlEe/CrZ98l/A9q+iIdzRuhECLKtPlVU0oGTrP2emO05Nd9fgVy7nGM5Bd3yKNYQYrQy/jZpfjhAur0FrdhWEAtUIy5eE0WeK1NIA9DbvmDAlCCFbeNjMlkDV2E5JZhBnncF7wIpye3SKOI/vLS8ZxJIbEPM7EqwPnrz/mM9//Q3S7DR/98V/J/Q++CCIMQ2CaZvpxYrPZZAPXGY7NtJlkQzVbg6RcdzvME/N+ZBonpOs4jAej5Z6AVsv9LZe/tjmOFz8YdEu5ZHBqOs8WwzrmgvbFETWnwNTNuBd67n31fea7x+EC72zcCIFWVfYXlyiJzcmGj338Y3zkox9m03uDCo1gWGKnNe+xGOcKIipeK8PK01pBlYJD6tZ3JNgWv9w2iF+CcVKy7qn19yZgveWsawuIEjCEXVrSiM9185zzyNAxnJ5xEmZI1sVVg5qgn+zYDQNnuw0XFxe88ukf5bC/5P6HPmSa2ls7iWkcbVdoEwGKDZBjOVJmO8I0EabZfkLAoxwOB6v83zppGmO2eFvNUF5St9phMp2TAFig31IfJFZ+PIRATPaTBoXbjjv376H3Hdz17K8unwELfVMEOiX6LvLCi/f52Mc/xv0X7tN1Pk/WIhglcKgabrTsQYlQo9Ac9fV6nvz/cVDRsbt68Wwt8CQ1B1jFWMiyI5SnWhwGTpWuRrIZO5CysvbbLZt4ixgDdB2Hyz3TYczHEXZ3b3F7HJlDZIwzFxfn7FTZbDaEFDlMWaDJZQLyvuW8zzvWUuIrTCPzONWKTcQI04xH6fuu6efYzK0sc9dCtLW2zosnY32t72fGKMMvg1RWUB2dmT8YmF6MbG9vUQeff+VlBr9ZPat3Om6EQJ+e7vi6n/uzrGB2UuI84d1AUoenxAW3AS+GWTXFqhlSFuJlESwau1rpCIm4UFiF7ssPpmDjRTM326AuD/N4VE9kcT4U5sBb6QGykVUK2iQ1SCCbge3tO7h+wA8busPBCr5kw9KdbOhixM8zISkxRaZ5ou97VMncMzg3154l4mLNeVQ11mMcZ1IIhsN9DsVNS92MEm9SHSKa0GQBY1A2N10MmIbxSDHabtHAF61u7kSIVm86aCBq5NAHxjuJeRs4HTxhjAxsIMobzu8XM26EQG+3G05PT6ojwfe5UmhOBYK8teWor5gLzmSAVkt5aXas2OcX2qsIulY40cRxZLzcTuUSrJ+5bClUVhPbkIXBd0anDZsd3WZD1/U4l5NSnXWsAislwLGr2Aky9KYpO49setzhQMyN5EOK6GzmrM8QIKkSkmZIYdSkNQQyDdqmQKUUmcYD02EEEpve4ji6rkPU5b7fHSVJodyXsTQ5F7MuxjU7ZAqCXJvO1/cs4s9CQq0ldLBsngw3rm4F0mnH4HbMUyQG5fbZbaIuicZPM26EQJctHoy7de5o+8uBMkvGRKbnGvhRRrGyqfEVy2t6pGUXT96TxkizFqz5plv4WhHLKBmGDX1vEWy+y31OXG6nVpJSC9+nS9yEQqa1Um5X4ZHe8iV7TVbZ1wnMWhe1hlgdHpaXkFkctfZrJONziVZDb54mDvsrYgz0vmPozdM4zzOHw8hms8V1vgpha8Cap7PJAayc8zoC0AQ6J9O2PHM0u2CeZ6bZGo/GmJg7cC9uYEiEoJydbgGP8z0vvfLSyph8p+NGCDQAah461xUXcDFQyva5eLsgoy0pjojG0ZKFQWPeMnPDH5cDmo7554L3bAfI2kkc4jJTAJUNsGAda/fQDxuGzZZ+yFq5hl5ai06r15aFpKHF1nAn1ZoUmgOPtPN0yTJCvDf3tgn/MgchhKW/t7eqR7bNmxacxwPjOOJE6Z0Qpj2Hq5kUE30/sDs5MYHMx465mRFSIuWO6cyFglw5nXLcdcy8t4hkjWw0ZAyRKQVinNGkjNuZzYu3cTv47Gd+DOfg1q07fOGVVzi/vDS68SnHjRHoYoOU1CQRareoY2qp0G0lkxlp6lqw2ISOnGNXDDYnkLfRVfqUCiUFRLzgVYjEtYlSNbsZYX0/0PeDsRY5larcR6knRzEIRSit5o6ZgiKkxaIsTS5d8vRFC6rFSaAhVwkl9w4vpRRSNiISMVlAkncCmhgPB+u+5T2u99YT0bnMvmj1IErnl1ukzFGsPHuZx3VRnFTxsmueSwwh92MMFmselVlm5I7H99Ygqes75kPkpYtXiClxsjvDuzfNA3lb40YIdOGKUw6eKYLssndt5cnLo7W6i0FSjBrIcOEJ4dEltLGcWwQRv2y7NWlgqe557Pot/8cUEedJMRHmGTIUcZKzZky67TtNSCUprbTdci+lGlKqYZvOezo1aLG4jpe0slRCMzEBkwyrkkAMS8P7YgdY9SKIGnMyscWNzKGkqNkTKaqh0oItJVjmpbmPlJQUc2faORI0EHK766SJeKp0LwzglGmc2Z5s6d2GcHXF7Tt3cyTg+8RTWBgCKyLoljZk5NrNjZeqHTHGms+2eLIaSolKP6+HYFowv6mqxhNnLWRewEVo6nW2u4Ca4ROjJY361FmLimJE5SI2omoRfFn72kXlc0suy4GVQZDMbuA6pBMCoaZIxVTq2cni3KFuOvmel0D/klliuwU1WAgREiUNKpiNoL5c4hOL1iVF3NpormxQpgcL7x1CKbyTDDdngZ+GSLzn0D6Swsw4HfC9lRw7OdkRw8z55SXzPL0zAWrGjRBosAm3emc5cyFvi2ih5dYeOmg8eCzCviCDvEVqCVK6xumS7Z6WDaFqnHWIajlPjLE2cVdd+q50XU+vPV47RIbqYCl8eRsjAYCHGMX6kqviUjlHwNUdwxOYMqNSDErTol3XWWkBBRykFLKnM1FadyQyU5TnsSTNWsmGPDclBKBWiQInFmcNSlRFktQMn3aui/OkGIQ1cF9tAapa0m04FcbTxOC8QTrvePDgAb3zbLcnhJC4vLhcSqw9xbgRAl0N7NyQUnyXG9poNuSaqkhHW3/BgLB0cTX0nFaOGMgu8FWu21Kyqgyz2A0dtPG55bshzExTThvC4is2mw3DZkMfOouPSNB1yXII+47epASRpcdgYXYkZ7OQrJhkOQ+5uadzniixYVccScxNL9kjV7jhatRSqEYpJQFtl8vBT1LsDoGAZZJITjNLMdL7jr7rs2GTKdHMT+eZz9x9bKhPXV1DgXXqPXEzc3G44Gy6xf0Pvsjd+/eYw0znhBjB5ypi7xvXN5BT7Xsr7Zo1iZbJLHQRa+wMZMFaa+4lHqNM/QIXilt2HbOwaGLnHJqrlVqzeV3tAtaSLHA4HJingFPPsNlwcnrCZrtl2AQ2u8RutzMnRuaMfdfXBkIL3m93Is2lvJI9lByx531nWk5CvX5o0p+cI2lcwQWjelpD2v62WnfmWEFygyMtIQK288zzzMmwpcSHF2pzHWm31O0jB4OltITqxhhNnSiEPrGPew6XV2xONzx+dM7D89d57cGrvHDvLnfu3uPh64+tz3sD797puDkCnftXi7ilBocs9TVa/LqaXEyoy2tdk4hahLp+9g2EuF0MFTs6h3qPz/EM1QjLtNk4juwvDpy/dsF0OHB2esaLH/wAJ7duMew2nJyccHJ6yma7JW0HfK/0g6Kd5RxmHFG9b9UhkhthdiyQqsRslB1INd+vLPeiSp2rUtwFsqJU2+G8K8kErmrswiiWBdbl2tIxWZs4KRMojalYJ1UzZ41h89zhSrHmnWkD824iucALd18gkJjGA48fPqple+dpou86NsPwTkVnNW6IQBdmw3pQ43IalZSHkzCDZ10IEBZtF6JpLLKhWKmwo12s1eIty3CtplZFeqsH5zqPi9F+suOkdz1d8py/ply99pBPvfKArus5vXObs9u32Z2d0G+39FsrV7DZDGx3O3YnJ/iuw9fiL7Y7mYAVwy3XDEkeddF6dzdGsGjAYfSiaqHV2hkVwIMkxIF3Xd4B7F1Vw+/UoE675y6HwZr7PVZNbR9ZCvvY3Bd+Ogftq+bi5TBJQO95wmBZ+7fu3ua1Rw9I0Xa+s7Nb3L93j3kOdF3PbnfCZrN5akm6GQKd8V6pNVGfS6N1wKLWaGI61nEXTcUfv2RS16IrrKFD+/lVEfBm+FwMpgj8AlWEM1XCnUh8IbL/4AUPPvsF9o8uIETmiysePDqn63s2uy0nZ2f028FwtLPefCbIFmTvO083DJzeOrPPDn2N//ZOYDAvn1CYHWt3nJIyjlYHUJ0nZViygmVqJQl8bulWmI6FFTKqsuXwj2nKdRTjulxBap8DtiMEVaYz5eRjHVfnkXglXE57szOGnq7r2G53ANy5c4dXX3mdOQSexbgRAp11Tp7c/NqRdw2KwXP9SGkpKVUa2EjbRhWeeNjtgjh23hxrazTHQIjLLdESOghyy3N2+xZnZ7d5+PKrTJd70piD6acJxpnL/QP80LM9OWGz3aBzInmHekfaj9Ud/vjlV3Hes9ltGTYbtqc7zu7cotsOdJ3HMRCy+zyUdCvIDozMgqR1N6wai1ErIpU6eTSU0LLzlR3iGILV+cl/lxohmqc3qVaaLnag94ThA8qdsw3z5Y795YgmYStiBdVjpB96NrsdL36oZ3dyC+feJwH+ZJ9DNqqXgKSK/xZcW7a0lXWdR4mVKJnJJYIuZxuumI12vNnrtbomhSO2z3S+xw8GG9BTNtsdd194gfFqz3x5YLy64uLhI+arPWEamcaR+fKS+eqKYbtld3aKoweSFZR0Ys2NXOD8Ym/n2HSc3zrl5M4tdmenBkvI61MXF7qI4HsPGDPhjvjzUnejeFULhhZnuNdkUxCaZNhmDupjarV4ZpASWExKDrFVgbBVps4iBz/4wTNcPCNcbZj3CXziCy99nocPHxFCIswwJ+VwNX5xMvMG420LtFjlpH8AfFZVf5k8w4LndnzXRNatBaxqVik17dbao8XFIRi2xEvTKzALtqw/+wQzwjparQblZOMrZcbDecV3HV3fG80oVuEzTpFhtyWdRtIcuHf/BcaLCy4fP+JweUEYZ+ZpZhxnHr36OrvTE/qtBTclsACorqNzjhgC08Wew+We1199nWG35fT2LU5OT01ocn2LOQTmMC/2JQ6VVENMbRTnlBRiwtiNwoasnEdkb+oSXgpZs5dAK/MMmAAngVia0AfSRohnyigTxHvE6Q6XF4F4tef27hYnt3b8sz/xJ3F++RgRxxc+/zJ0XV4U724sx+8Avg+4nf/+fTyrgudQmCWbroLvjig6rtEW5fc2o3jG0eHzcynxDiHHAj/Jd7YCXHjq4x8QOuex/MFuidHTHH/R2efmaUZ7E/6oyrA94fT0Fg5lf3nJ5aNz5sPI/uqKq6sr9vsR8Y5ht8EPA1PKhpcqXqx4TjzMHMaZ8WLPw96M5piStUMrUYDeV4rSiZBycUjK/ap5I02AIzRFFqv9UBRFjYHJgi8lgrFxNEVI5BBXNUiWkmcaBuKpkJLy+c8+Yrzasduccnay4Xy/56AzX3jlFbrecbI7ZXdygjpFek8/9DzteLv1ob8c+BeB/wD4N8Se8DMteF6nMuPJ41GCZKrjwa5rlRpU8LBmIauFvHMnQ6PASjeARYivud8GO7paHktkadTTpv2X83vvYOgIqoh26NyTDvbA/TBw9wO3OL19n3A4MI0jl+fnXJyfc3V5wXQ1wjjjegtLtfsybD+4jpAsyAdNaJ4jcZCcmovcp0WARYikRUNgzS2dz0V0XAPx0pL5I5rvoQaLLqUVUsbPkrV8EiGlnGoliSSec8545XzD2V24fX/DcDIR04wfHLu7Z7jesd32nIynXJ4/4rWHe27dOuPk5IQxWrjp0463q6H/Y+DfAm7lv1/gGRY8f+H2aX7VtKnkYJrj7V+K56rRyMeaOuYwSoChtxiFpFpLDsSYVpppHZ+x/r1E/llIao5xllxlKG8pKQuCAEkUnOCH3lo1JIhM6BQJ0coOuGHLndv3qvCF6cDLn/s8j197wP7qkv3+iv14WW7Iity4DvEe3/fgc/Uj51EUn5NzLcXL5FXjUipA1TJd5hxxN2wG+k2PdA7nMxbOqVmd61a8fcmRLECjlEsr0CXGzPOrZ+9O+Uy4zcuXp2zDzAeuAh/92CO2J7Dddrz22is8fO01Xrh/lylMhBTovCfNM6f9lt6vfQbvdLydcrq/DHhZVf+hiHz905/ShjYFzz/+4RfVtG2Xc/dsChuCosYIZD4uB+bIquZDvl7zUIUZEaXvXJU/42vNe9imVz0JL+rRlnOu58TuISWKX9jc0HnncKapHVqrG8XZKMeUJvYq9JsNruvob9/jqz74YdI4cfnwNR6+8jLnDx/x6OEjzh8/Zro6WKgo4PqO7ekp27NTus0W8Q4Nkelqb+74aMmzMdgCCiVIKEYr6OIsVmY423H3g/fZ3TqxTJly3yWOBtuNrIDmkpiQnxuKwZeYLMNoYuDz0y0+d3nKHHv0MPDyF0Ze+HBks3vAg9deQdTRbzwX+wumcc/5+eu8ePcD3Do5IanyyiuvPdG19p2Mt6Ohfz7wL4vINwBbDEP/AZ5hwXMw4Ywph002RmAZhf9d8aNHTEfhWIvre55nnPQVZpTXl0XD0fHX3kRysE7B0KV/YHk/lW5bHAXEx5RDNT3uZEsA5HJGZys4aTU4DgZlzj0X3tF3Hd4Jt+/dZbfbcXbrFleXl+wvrwyW7PfM88z+4pLD1SG3vlPCOBGD0WAWelsy42FwwuB7khrmd72n2wzIYJ7KeZoQjBcuHtZyD84tbvY1WjQ8bgySI6Kcy5bPPO45zJ5u49EkjIdTXn35krsvbkiyJ0yRYeh58MoDLi9e52Nf+WW45NiebDiMV7huHZfzTsfbKaf7O4HfmR/i1wP/pqr+WhH5czyzguc2XPYQLrHJUBwpx2n0mnnP8nstNRUjKi6HNlqQ/jAM2cVbyahykPr9Jwwklo9mEW4Wz7pswKqSk+ba02pV+13nkN2AxxEvR6b9aAVYxBPB4lc6zzzBpLlRUgx0Q8+t7rY5I7YbTnOcxRysNkcM2UPoPTH2FmGHFT7vnYVmFchRft+cbtncOkGGvmLqthTCSjkgVQmUe1sybcrUOGa35XMXG67iKZocGpVZlb4fePT6hoeveU5PFVTwrifMyv5iYryaeeHefa72B84v99x/8UO1kObTjKfhof9tnlnB81JtyIRgEbDFMCoT2U66E7He1Lo0eaxDhBRhHGecePo+pzOpgqgJeDYOjzF062gpMcqqEe+6fO5seIosfa5reVrN2dJGnwHQOdzJYN4/p8wXe+Jksb8pzXSxAyTXnVvKZalRDAzbDcPO0vznebYE1DnkYoeWIRJTIOTwzYDFOidVolpC8WY30O82DKcbJGvjmsAbLWy1812NRVddB4LV2nhqZaeDdszS8yDc5tMPErMLONdbdJ/3hCg8fnTKK58PdF/ecxgfo7IjcZfNrmN3+uMIacfnXn6Zw+g5hMtc8PHpxhfbY+U7gO/Ivz+zgucFSqjqiot+I1hQvyWyEnAwQzLoutrRNBWe1oqTu1wfQ7g+VvmYnxYtDpac1pVyp6h8WieWv6g5PjCikF25KffgVudw247en9INA/HqwP78gjiNxJztUuiy6w1WQKwCUbcZkM7jYoeEgEsdLgYkxZyaFZDOEzQieLptz7Db4npLw8rtrSw0NZZqq9hu1JYjaHdEu9EMMyCo51J3fOZqw1WIeBeZSHTB0aWOfuhI4wkvvbRhSpGYrnj0eEcIXw4p8oM/0hFmYQ4voJp49eEV0/S9X5zYXDNujKfQ/pMnX14ZfCaUx84RyNshWrM3RJYKPtM8o6IMw0Bb6QdympRq7UVdotvqDiH5Q65s6rboUlMqzD4kNbNLkxVpz1dmHeWyNu+7nv5sixs6khemx5dM41SbGqVQKLNllHOV0mYhBDR7GNUZTYfzRuuJw9ORUJw6fN+xPTEDshjHSdfwqlaAEqM7S7HcZafCBFkTKTlCclzphh9+oHzu8UiMjhgOVoPDOfrYk1JPN2y42u8Ir2wR8bz2QPHO54RcrBWcZk8mkRj+1jsWoTJuhkAX7SBuFVSfGi218M/FWFsEfYEbkkUaShcpzQ6F+WpvlrsvwCafI1+Aax5wi6mtWMyiuaoHqFxJDqgvl20OZNO0KcacxaHVSTHKiBfH4Hv6W6dI3yGXe8JhJIXUUGN5AdsN2rVGi6rTnEKlAtI5S0hIJsziLMTU+Y7Tocf1XY0tzyoYT+O8aubXd74WMbcdwxaTYmGtMUCIyqgDnz0f+NEHE/s4Wsy2KhoD4yGQ4pYwd2xOEjGB708saSNZ4JKwtEwmRvreG6eta4X2TsbNEGiTZoqwtp4rcevttzXEjuMMyoNpsXT5XFK42u852e1whVsWrP+el5pF/oQ7nSUTxntnruX2euwkoMo0zZw/vmCz3bLZbWrWS1SrVZFiQjB67ypB5zu2my3DnTM2ZyfEcSYcZg77vX02G8apUIfeoS733U659h45p9AJ4NFodUR87h7rvLd7rStuHVLQzlstb5YZlMrcYNeQUscBz0sHz/d/9oqr1OO92QExBgvGAjQK4mGeDkwhMYXE3fsfMF49G9fViZLnR0TePylYQDYmrvfcwdoYrFjXmWu4FeBSQah8rvCmiBBi4mq/Z7vd0nlnWlrqh1eCag9R6/GnaULTnpOTs0XoaYJ4sMWz318hnaNL/eIez8dKWGZ0itYqwhgLgxud7+h9h98NDJ0wjZO57HO1pIWb9wjeMHouMKm5O5gg9H0H3uXKTcUZ1fA7jWJo52iZsyUdzU5riiYGOKjj85cd3/1jVzzcO7ZbyXMzWj+XXGg+YMVufEpID+C4ePSIO/fuLxkteUe1uc6l3d5KSN7GuCECLXXGnxBa+6MR6EWwr3eM5MLomRUR56onTFUZpxkFdrsdTqybWCquMFaQPePQJrwUi+irnG3pwumsVly/3fKBD3/YeOCGZlwWlmFrRKpTItSAnNHgingciussnFYVCwDC10ApxNExMIUJp7lqkapx+M7KEkjVhqVfoR3L6j03M98IdhHmlJY6f0mFkISrKLw6d/zTH7vk1celvofk8gcgJFDLg1SxWA/vOzqxc07jgf3lJbuTE+t8FaLtwN5DytuQvl80tBRqKOPCZsZTdlLUCW6a9LR1IaBg7uK8E1aB6818TdOMcx3D0OWutKZttVlU2hiOYLCj89LQerk8QBGUHF/hpavHIC34vgh4LfKSLA8wqmXb1OAgNYOvLORSltdwvGFz5zIUE2/OHrUmmkAOrjJtLSKQjjLmm/k2CjNXlcrnMCFORDW+PQH7JDwIO773Ry958NgUxnYQrHlTAYoWLecEUpxtnqK1mXNRwcPh6soM85gsYz0lXEq4vl8o1accN0OgWWip4sBYjVSMLqH2DMk/VZhTqu3BouYkTVkblqX+cdkmzbDPU1CqzKdUKbiU4kqDUai/Gs9RtufMi4OxDeTYbL/geRHL11syzzF3eC5jMGfeeHFs5N0l5Ar7AqUivKRlN8oHr7CktQFiWd0iFf6INMFfWZvX+aeUfcgKMymjOl459PzjH3qdVx8lUhoQq7mAd45Jl4Ai1ZCdXWXOOsRZhSenjjg7xv2G7e6EaRxpS7M9C2GGGyTQx6Ol5GKMVXtel7VdcHKBJLZ1xlrZH1hrdlVCSKgeUAborf+gOC3MV/G5PGF4tsZTuc5j6GMGpF99r3zW7sHTOSW6QPQWJ+Gir46RlcEryzH06By2whonUHOe615bDSl5QlTjskbSJeuEtY+eVw6ef/xDr/O5V0ac3+K8zXHMDq3sCcc5oes8YbYiNzHOiJthHum6DaRAmmfGw1WuY+JJuYLZPE25pO/7SkObID7pUChQgwpw28r51SA8WuVOXA5Bz0OoWyy5+lBIkXQo2LVHIrVwd0pK1y04fjEEyRBiLbArTd6MJVO7wCGhc6Beid4Tu4jvOgvWz1U62/bBywRR6zijFl2nmQ4sQt+Woy0wZzVHrIW97CuVz1HN/QLhSjtePnj+8Sdf59MvXRICnJ0OIImu25DUEdOiUCSHu5YiN84pKZqXVl1HihNeHGHcc4mw3e6IufKUlT/Q9xPLsdR/KGMJ/LleyxRtCdnRUAXf3NKan/RKwCQbSWI+QlVlDoHDOJnQDr2VyYgJ741HlqRI11mIaF4ILUZthbm9tuNFWbhol93nxdD03tN1qV5L1MQ8WcxGDLFi9pXXruxELHyyFTGn0uTH2vqNRioqFmUOEcVzYMunXo98/2ce8dKDGVXBe0Wc1t3LSe7xrdahS9UyaDLximjCSTLNHCfbWeNkMSxuhE1vntpcJsI2tPeNQGejsBQ1p7AViaJUizAeb53HD1r1ehjQ5t+10EMx+sy238zJiiCSGw5lHOxFcMpSSw9MqTWQoBWcYzrMYLDdlyYLrDq+FuddNRCdd8wugFK397IQyr0e7wo1xJN0rUA/sSiE3KZCSApTcsxs+JEHyic+n3jtYjASTkf6vq8siKaEuty7pizs3PfLnEwpU4rRflIgxYDHuuw6YNwfGIYNyVlWfkkUftpxYwQaAFnCPI9r1qUGK7aGRH3QLFg5xWKYlP3Y4cSMpOPt32eYc5hmEhZzgXQMUgQjVs+WnSfm7+S4kOIbdCWbei3U5X+LPG28czER4eg+rQaZdg4r4Zg1u4Mwm3AX49RRKks9ySeLSDX26vWUworZiZXULD8RR1RhTp7HwfFDXwh87pHnfMyVlpzURUzG185rNjStRIKqLfjCqhS6kBRtQYigwXZF7zpCmPD5GnwbLvwMxo0R6JTZhfZvWLStytKnu1B27dDGCVCYktRY3PCk1uRIGEIIHA5jjusY6Dy4TE25mOtkeE9SK6jopNTSWzjB6/B0jQvR5t4y3Kj33uwqjlzDLpWFA523YCMyvScuR/pdawCuMTPkxVkwdb4OxWjRCceDqw0/+PLI516LTMli0x3a1mqvxwshMGx2FCn3ztkiz02KqvexPI58NkswEFIMiDO7oRs6awWdue+nHTdEoDNdl1LDVuT0+JgqB7u4sfXa7clwtYC6ilFrZkrheZcPH3+ZmBKHabKdQhzboQ3ojxn2aFMUMlfqLLxuuRtdCkAuONtVyNRq0qWGRrMAWITaPutJotaRsBiC+YNF6xfDtSxiJ4UNWgKMrGrRch1Ix5UOfOZR5IdeHnnp9USUHDsumuFIaTbv8y7p6fuO0hMdnwvZeCucMx3mmm+ZME9r6XY7jSOqgni7xpCLxbvu/aahq4KTbPHmvzVHZVWokSGFAriVMQgLhi6/28h8bSNE6/epWLpwuVeHMXv7NpBLy0pKKKEWVzfcmGrWteZ+im2VJaARbF2d1+oqN4kJSVfrDbWs74Ix26Ct1T3UdSlP3JO96jJ7kBMOk0GvgOdicvzY65FPvBJ5fBiIajBHy2LX5Xwxln6NS8faYdis574onGTX7H0OtXUWT+7EMtNJVid60ivmeWK73b7PBBqozomc9hOj5EgvqXHNiuaq82s2JOVOVYX6WRuE2XBpI7kaKNA6Poq2TFjMh6HwTTVIe2+LIyTzzGnDx4p3ON/TsjLr/9fRbWScuzAYa7qyzMlKazvzCqajxWmfXGBPOc9qsacI4oh49trzypXjBz93wWdfSxziFvGSY0ryvOborUTDvatBlzkEeudzcq7WQjxz7t5Vgrza+8odOtAYES+oBlJQvCbmsdzh048bIdBFlwhWKLsE5FR4oQ1soGiMmCPZilOlPOAnYz1W5b+g4u/rtHRWP6SkXO33+XsOwSOiaIiYzWaLxeVFYQm7gVJqCwpMykLps1YtFTqPDNui1VohbDWktejIRm5D460MwdwOmYqwUr0vFUdMjqvo+dy58k8//ZCXH4NzO7u/lCxjJNc3EGTliTXlnrC6qNbyzeVFGVPAS8bIbdaJ5tJi+X6TJHABSVh8TWf3Ok/Rap3o02vpGyHQNgojsRhZKS15cYshuDxIkUIULdxsG6ewwBSz7uORIB87HFoKDoEQA1f7vR1/u0W1Nw3mLaAoqdJ5T+fa9K7Ct1iADiy18VLjOFgMwVR/ynW0YZt15KlpKbpWqNvvCDncMwc3JU0E13EePD/yhT2f+NxjHpyD82dWh8MVL2SOH8zQy7ml6ZEtNvJ9JJzmkhBOQSy6z4kQKvNSnou1rLDFmEgqOM0Tk939znmbq/eLUVh8cCV+IMZUHR+t1lqMxcJ3PulRLFLQ4unSvL6Miq6P2IECXaRa57YTHA6H3BbOEZOgWGHy2pDdgyLZAfmkIyPEgCvtIyjyUu4prq6l5crbxbvQlTxxz+vv5LbMYtF6Ec+o8Oql8AM/+hqfeWXkYjJaUCSaEMvyDKoBiyLqaukDy6G0cxbaMsaQg7tMiJ1vSiK0s53ZEM3xNmYbpTwPgkZF8t9PO26EQBetrJngV0o02uLeteqW9rlipJTJyjx+KWF3jaG4fC+/uJxZlybt5TxWgbPkCGI9QK4OxCRsN1YzTvtE54xfVjWo5DPj0MZ7qGruIpuLlK8gho038uRFLRgdigNjueY3czIZFx+DcHADP/Zo5Ls/9QoPzwPTbLET/bCj63wOfGqPnav6Z0WdctuJMj9mPCckRbwfcoTdArvWRreiGQZp2XnLM8s7khffsFnvEw2dVJjTYvTZaBgPABwllrc8UMkCfyy89vfygNoqQmWUiffe58VhGsg1JX3LCCkyR2u+HuMA2wGRHnw+f0rWNiILsjR14+z+cnwDkh0/sS6q69zmQIVR9tPy5U8KcjtM+Dxjgkez8KlXHvE9n36FfbBsmxBGOtfU/RNF/BqLS/GiNK9VanGZWSuM6fM1YRn463spT3IxwK3CU87uT4kUQ+4jYwbm044bIdDd5oSJHcyX+ILjClYUZY6LJ7CU0xXnayhpocBWBsxxRF75+4i2s4XRGpVNzt7RQpnnmWka0XiCSiJ1js5ZK2Sfo+YWC79xsuRdoFRRWii1YqA2PcQb/ElzXW1NjGOv4Ir1UdjPwuuj47t/5BV+7JXHHILgnSVrJQ22g4jLMGr5vvddpRcNcVkZ3sLnl7lVzNEDJVDM0r5iCBnDS1VO1i6vaOTc7i7FpiFpMvz8BrvUFztuhEDTDXzwq34GL33yO4mHR1hCiLmUNReOs1oViwYoeFLJVTVNpS3BSbqEjK6Mv5Se0AMFptjvqT7k1oPnaiQgXB4O4CDtBnqneEn0yTI4wpxjMY5qTGiGD2Vrbo9fKEMz9KDCo+qUaYWZ2q6DRqepwqw9l1PiR1694hM/+oCXH89EHFYFb4kft2aY1nhTNODdFhWXmYZYF7ldV4dzbb1p6+FoRmQDMTK1UnqLmg1k8+0lZv7enqehl5wkUWJB5H2UgpVUCGcf4iNf/bV87gf+EePVAwYPKkookXO6OCwsL816dZjgAqRc9GXhllcUWB4tjl1+7HutQVceVhX0gvMVxnGy+s3jwDB4tkNP6ntrH5EfkHdppZ2BlQAv507Zq6cGqTC6T9Gqtdd8sz+6dkuTmrTjtb3wfZ9+lU9+9gGX+4i6DpFkFLhKrtmRQCy7JCZzFCkl1NTskuK21kIXiqfrmkZEqjV2o0ARkBxctRjmhR1Z5p6cJZ93RYPleccwevRpx40QaFVlH5Q03OHLfsrP5ZVPfz/nL/0wvVg8QdIAurQ8SNF+TJk2MCMWy/lJyx/WrMYb4VAzSBcvX8kwKTtCMSBjSlwdRkLsrFH7YI19RCwW27mSVkXBDWZktuVrs1As2Sa2YXspMStU2GHXX3aYXGEpJeYkHHTDjz244nt++GU+88ojopZiCskExS1FF02C8v1XqjACxgN7nzNqXOG+hSg5wtBZQFJB0ZVjzhNXoJ7gmkVYIJQ2ceRN/EkbLPIMaA55MwPj3Roicg78wHt8GS9yVPL3+TW86+MrVfUDT3OAG6GhgR9Q1Z/1Xl6AiPyD59dwM67hacbTg5bn4/m4QeO5QD8f76txUwT6W9/rC+D5NZRxE67hHY8bYRQ+H8/Hsxo3RUM/H8/HMxnvuUCLyC8RkR8QkU+KyLd8Cc/zR0XkZRH57ua1+yLy10XkB/P/9/LrIiJ/MF/Td4nIz3hG1/DjRORvisj3isj3iMjveLevQ0S2IvL3ROSf5Gv4d/PrHxeRv5vP9e0iMuTXN/nvT+b3P/a01/AlHcdep3fzB0tt/iHgxwMD8E+Ar/kSneufB34G8N3Na/8h8C35928Bfl/+/RuAv4p5BX4O8Hef0TV8BPgZ+fdbwCeAr3k3ryMf6yz/3gN/Nx/7zwLflF//I8D/Nv/+24A/kn//JuDb30uZecv7e09PDj8X+GvN378T+J1fwvN97EigfwD4SCNsP5B//78Dv/q6zz3j6/lLwC96r64DOAH+EfC1mDOlO34uwF8Dfm7+vcufk/dSbt7s572GHLVJZx5tA893Y3xIVT+ff38J+NC7dV156/7pmIZ8V69DRLyIfCfwMvDXsV3yob6NRqrAI6yR6o0c77VA35ihpoLeFcpHRM6AvwD866r6+N2+DlWNqvrTsP6SPxv4SV/K872b470W6NKks4y2gee7Mb4gIh8ByP+//KW+LhHpMWH+U6r6F9+r6wBQ1YfA38Qgxl2ptYWvbaSKfBGNVN+r8V4L9N8HfkK2sAfM6Piv3sXzlyah8GTz0G/OLMPPAR41kOAdD7EQtP8M+D5V/f3vxXWIyAdE5G7+fYdh+O/DBPuXv8E1lGv7ohqpvifjvQbxmCX/CQzH/R+/hOf5z4HPAzOGEX8LhgX/BvCDwP8HuJ8/K8Afytf0T4Gf9Yyu4eswOPFdwHfmn294N68D+KlYo9TvAr4b+L/k13888PeATwJ/Dtjk17f570/m93/8ey0zb/bz3FP4fLyvxnsNOZ6P5+OZjucC/Xy8r8ZzgX4+3lfjuUA/H++r8Vygn4/31Xgu0M/H+2o8F+jn4301ngv08/G+Gv9/rCTKjyycUFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c3gp4eRhaeNQawDkl08DOqCxaebkO9Zr41I9Zm2OopM3qUX6bc172LQFSBhWIPXJyQrh/l+Fjr8Jf/dStsnM8XgqBHlYbXnvl4zz869/H9rNvcjqZlhBAp0zaT0SFWAwnlgsbYmCeE1fjyOO853IVCK/e5+RjH2W4exfpPE3Pta5du4UGKgK9YDwX5oAv9g4vmqW3akcaY6+12hu8eBttd3BsGgFRw5GCwwuotJsJmR6yDkcY94agVapRjzB/g2mz7TM4gMgp+8PvzMzRPrU57rFh2n62/CYfbHfM8JRra8+cEhCSKxQNHeHklPVH3tGfUsfLIdD9mu2nPsPDv/o93FFFNJHTjLk3IrNAtxkMT2bDmDEGW9QzTCjXOvNEZ7YnG+68cpf+/A6xGxwfJ+duF2ErmsouaDhavt2hIeIww7S1SsHdTqOhpsG4RTh93OrEULvRBx4zv7ELoG3QEM12GD237KdZNW7R4Dc0N46N8yKIrdaVW35zfE63CuXR+RZD8VjwFyP2UMBzKvCjKB4T6n59wnhxxouOl0Kg027kjb/4V1iPW8LahbDcbDL90NMP0aABgZST8a5dx5Qyo2au0szTqMRX77O6f59uY1ADhTwniEXDB8eDhhJjiHZzXUZMsKF4BbOo6+GG2ioCiNuQ2PYHnmuOtKLTiqaKihfw+dq1jIMlXIHYCGAraAfYNZuH8yZBYjSi3L5alNcHNKBmoxNZ8HLOuVJ2RTEsrM0tApsP4Ub7XTvn6ghy9oMQYL2+8dtnjZdCoOftNav1FetVj4hi630iZdPSIhCld+7ZEPSclTxNzDmzTYmreWJ/vuLs1QeE0xNzmJBxzqygU1ST3ezCVIhpY1WpGk5kYTJEpC67EkLV3IsWPYQdRs0VbWdesXqj800e+3jJLuNYwFtNfJtwHH8mSNXCvpPKXtRtjpialo1YXt/UxoaZy/EKNhZ/zg/Pq9XILWZvz/PgvLMbtDkhmkk5E1cnN873WeOlEGjJmR5gEbsFV6F0XU/OhWUwRThnMwRHF+htJ8RX7rK+c8GwWoEI0zwRNBhdh1vRqXCmizMlSDDjKbshSMHSkWqu+W+L1m2Ft3xfOOqFnivauGB36rYx3nTnHkOTVthbY/RAuI4MsFshTrO/27B8YW+e93AVwYRDaqzg7Xa+5Rrno98dz9M+NBao2hzOuOTKogixe59d3+92BISui2hjHIjjWOOPy9Nf3M5CFiFlZVRlp8q4XtE9uEPwgCXNmeBxCTaOL6aS00wgkkQRya6VA+pYutBnOdtOiqCmlBb8Da7ZlTxPzmJEMn6zy9FVabzRzHPRgDe1cHlf5h1CvFVIb9PWtwm7fV6MVGc/DoS2rEzLdarXKB9ChgOoUwS0ZX4Ka+Hfi6rTibkeBy37To2xvgRhqYqxnTabZz6gt42XQqARRT2UM2eIKhXv5ZyInWEodeptzgkVE+oxKzvN6Okp3fk5xGhcpgIqZExYg2bQ0Gi0BA5nJBRt7ZqZQBPSgGgAEZKmg2Vf3GjUtOBOVYzyErPWjwWAEhdS3uM3TqlBSwvbclPb1riRI41dxm1a2BCSfTbP862/aUc751bDHm9T/m+/XwxuoAiqGmNTDUWcsm8YGMChn3sYVd2jyE3r+DnjpRBolz2CL8+zP+Vzmun7FaG32I1xv6cL0QVISGpFNrZB0JM1cVhbBJ2Tx5qz7Veciy3MRVgEO6dEcTsUgSqQI88JJBAoGo3FgRjs4QpCDQRKObttKNUlYcTFwrOWOItCzdnnbmglZ9ifCz0Or13rfSzbHGPeAt1yziR/2Jb9He2QQy1/zFfDIW1aPzsKZ825RAo2hmde4jhy+yBosW2KorF7lVOqv3nR8VIItFSGwD1dFI+U0PU9WZWz01PmlNiPE3NKRAlMKbOdZ/brQHfnnG69BonkrPQY1svHTABUzViX9xIZhgUqtVa+zaksgVqNVDRAUHJxJuuiMcsSrKhRfOU8xcRcxDT5bThVqgFLI5CHvG6Zeytw7dC6jC8Qp9gAt2n9Z2n4ZV+HFKDBp5uftfNpp3SDydDDwCwarlqLNk8zOacKeV50fFECLSLfj1XFScCsqj9VRO5jdZM/gRUR+UWq+vAdd+Y0kDNmIBBjpAuR/W7HerMhdh3jbiJIIGeLd5hQ8mZFf35G1/eHToEQTfOpErJCUGIIph1yJnadH9qWxqCBGE2jpZxNC4MLH1X7mlBMpqELM+IrgzodWFiO5lr5sWwFMINTXdBM59Ul/gifiod8UowsODRMG8E/1M712T0MmNJDXNxSdO22x/st21btegQ7FoMZUnK3vcdzt7h8nif8hzfmnebZwnLr34+8hv67dSmkCFbA+79V1d8sVr7gG4Hn1y0ToBhiKcNsWKvvekiZGCNZTNvOaSaEwJhnssAcQE9WhM3KNWej7TQhRIsxcKOvaCkROXgdEPKc7Pg+Yoyu4g7xLv7bEAKEaPMmOcYvglw470NNBpCSII3P3G44ZsTaxnVeBY4UYS+hpylpFZ6WAD/UhofU3G20WRun3TIvraC1MCWlRjhveB8N0tTYfV3iX+zeuMGoqYFBtmJV4U3q+1Df/Haa8lnjSwE5vgH4Wf76d2Plo96xEF9dqu2NacMgaBSIgaSJrreIuSklZpSRzC4o4WSN9Cb0odxUmuCdLvpn2QS83BBdLPTbDKzj5fzmUp3J2TVkMTj94TTGBA4cLssvgZuCZnHV8cYc2v+lPlhFGBZWpLo1GkjVatZjbN0KczlG2a5Qb8fnfeBl9e0WDb1g4QI76jVsV4+UmdPcOJYcU2dbOVNKFrmohw/oi4wvVqAV+K/FwOF/pJYn+FFVLW0GPg989EV2VAy1LMm0XxchBrILicSIJCfxxdjfXc7MQ2R9dgKxY86ZQMlicWMtHC7D1egg4hH+B9qwFeyUEjGEKiTHVJiNZMyDKiZz4gKdkRjcTV1ox3CDhioCabmIBSMfxi1zcHwqBkqqdDGa10+9NrK68SVL7PbxA3tbnMkxp33MYhzgeQCHPi32Lw+lbXfo4g7l+zSbF1BbaGMQpeyrOmM8VetHUqD/DlX9jIh8BPjjIvLd7Zeqqi7sN4Y0Wd+vxo6clfXJinmePVg+IkGIfWcxz8B2t7UIt2AXZxRlHjr60xO6vr+BW00PLvhOkhCd1rM8PcXiNKTiZC3RP8U4KctdUX/+nZ9fjevIzU33qGlbMuVY8zscEdNoIcQq1IurvBUIKpTKi5+dEIxhyTlZ+ljRuk6VaVZnWWyl0NT4CVXr+RScnu2e0LldoWrZO5RDanUhHdCMZaQ0A43mx+fvZzxrMkh3tCrM80x54AsL0wq0HjEl7zS+KIFW1c/4/2+IyH+BFZh5XUReU9XPichrWDXM235bs76/bljpnDMZYX16xjzOdLEjpcy03XJydkaeZqb96AKZyWTmAGk1oFXoW2E250wbjF+XUYcFJoBaSIpFY3FoJBUDyG7kErNQBM5YRnf/FkweAuKGLsE0te/Bl2CbZU6zZZoj9XOLm/YTKfsoWDoYLFs8njbbFi4cXudicOYG/y7nJ6gTSgJZmUcTsOwTKPsu25uA2XkUuFWu0aLFF0OwzCHNM3meD+JbFlrPstMNOy/nVWKljxMJnjd+2AItIqdAUNWn/vrvA/514A9j9ZJ/s///X77I/qLHNm9WK1JKrNcrMwBFuHr8pApJRkmaGHNi6oRwYnU2FNO+NWbYGaHqnFJdbrgLE6Fd0qksxoEGlkU1F8PHzx9VdQ3jGjMEQugWzOip++IhviXzetHkPj/NRgiqnV+9fcfLvHhsSAgLk3LAWhRj6xD3thjZxmFOo1DyKw+hhogJtglZ6+LON/ZvUMNXubzAh/Jdds3bshYLLx5I6VATH2jpd+FZ+WI09EeB/8JPsgN+r6r+VyLyZ7GK7L8K+AGslvPzhxStY5kZJovL8lUSSMG8cLMmxpwZJbK5c4e4GghdpOs7grfDMz5XXQCMsWiXOmHRAsUgLdkwrVCEEujP8hksxk7wgCUtqUykmrVthWxsn+Jz0cap0+7THC2+goRQsaN4OGubCa45Lefnu6kPonKQiGrzM9wQgpBTtkTg4rqvK0UFaLZKFG7dM24KzDAhcwdVcUnVzG6zWwqWttVrttfpkGvOR0J/7PRZBPpHiLZT1U9x2E6hfP4W1nbghYdA5Xz32x1dF+rSU93BahylZquENAns+4716elSlsBjBEJLP6FV4x7MEz3IKHF26eDi5WzQ5iBu44j1sPzGwnuXY2WQWJ04xZFSDNWS2HvM/zY4A6RQXrlqqCq8zcPK0S/LhoU41LL0a1kdUv2cojDa+iKtZw8OhK0oBTAoImSvp7IIe06pPozLqmH7TKmFIVrhxm2rSb3+ea6OrxcZL4Wn0EwzE+inT5+wGQaA5Yl17AaAwJQzV5JI6x4ZOmIX3BXd0H5BrOqR/74NKKqaSVpYcTjK8ngbLj3YvjwEvtRm7NhBBdEmYUA50KrSCFM7hxaLlvftHI4dHXUaR1DheD/lnI5H+6Ae03jP+z+pIk2+5fFfGzbaauGWETEBlxtKYmE/3p12hpdEoFFlNQxMsxmDxTquwllujgf/JAJ7UeLZhuHshNh1hNghMVhWtEtrzpkYFyFuuViJNzOwWwGogt9sc5ONoOLcAkuyrfmIRFuQj35XIgdp8PRtY6HBbtJm73STj7d51nEqlr9FiMuohtkRFACQvMDCIoRlZW0FuDhcWriWc2aeZ89vhKKpl21KXPQHMJYDgevLK4vR6IIF+YBpZrHgoiDRHCqijApTFxnOzN1NsJhZdYwoalmDEkJlL8yrd8iE1MO3hposLugY4+ES3ODSqs3q7pbPW4hyHDyE4oUnl2Mfa+pWq5Xjthq6HWV+Za7VmDvy4h1M4ZYVoaxYoUAA/5Nc4sUNLpUHuMY8t7xxI7CtQBfGowhwOe6iYDjQylBowAX2vOh4OQTal+AQimDhFy5aYJDWzZhUGRHYDKzPzwzXxVgxZIxLcJEJtAuA3BSG27TxDWPtSIPd9tuy7bK9ucOpGvXwGO1vjleCMpYgn2dDkXZuLbVWHrqDS/wMDVwflGL1uXHZxnm0Auo/MnjVCG07Wu+har5xLmWeN2FSrtvXc/5AamhgTpbIanXcMqpNlBrFy5ZImrkmk9cDq9NTYtchUUrwG1KKGgrVISONcB1f1APm45Zt4NlC3u7n8KYeu9Ll4KaqKhSHR6PZDvP4DjXycZGaY2Fv41KeNfd2vgfv8xJmWjyZx3i63VcrxMcP1PH3bVGZY3tkWfEs4cEehKPk2ndhEMLLItCukfuudwVhxlNdksToIwUmgSvJxLMT+s1QNa+wGIFlyc+NUIlS3esHmjnYCiDcxJG3Cfhty397c+EwBcu0c/nt0SmX1yL+wHrBRZ9TLsVuWBw5cFNLt8J+m7Zv537raiQmyKUqUrtNCeEss7XvisG2wBDUqUV8RdViMxyvXofu9ZarhhKfcjjfwvW/yHg5BLpgqOR14KKQnFYLCDknUlaSwhOduV4HHpyf0ve9nXzGeaSbGqrws0HiAX6u7An+Ow6FdtlmSRC4zdo/rnVhkKdot8O45xi7RajValBoMIJNxCBSFU4ByR6+6t6ynJNjg5vsTIv93wkm3XhI6++orM0icOlgBdLsq4H98ABDl0ydgsEXOm+ZwzF112r3WjFKFxz+LEj2rPFSCHTlO3FSI5tGzX7zUrag8F2auQ6ZuDmhW1kl0RJCeQOfyrLvWFytmmtNi2LQ1Qumh1pzwdyLsB/jvmP24Tb4UuiP1gCq8yxGKJZ+VvB/+T7G6LmRYRG4RnDLPNrzfpGbf3wOyzW4/Vxaqi03xiANLCwxGAcPSuOqPV4V2uMWCm/2rgvtPF9clG28FAItKP3QUeqndF10V6jVeVZVUlZ2KNcBhvMz1qenFpFXBFRLlXvAXcNFqg2zLsIrIhVfm8AGLJGzCEfRuo7pKahIEddaNR4abtyEg1YZ3M6ihBAIHinXwpISk1yZBBEQrcKuHDIZzxLgm4JZYjoOV5vjO1HVqp2Nb5+OcLFBi9m17bFhuMzr9gev/exgn7c8qBk+gI6VQhl1kTnNSEoWbScKaoH5uzzxNM+MQ8fF+Tmrs1NC3xNCNNwZA+rUGSE4ZhYaSFfd3WoygkjrDg9VeFUPhb/FykVrdN1N7HyAzUUo4akHn5U5Frjj41kMQCjhq7mFCYfCept2bgU2BCF7AJUtDMtKcTDkWIMGd10fLv1F8EzQD2OkD44vC+tUCkCW725jZm5bOd7teCkEGkwrrzZr8m5LmhIxGOwICDkEtjlxHRQ5PeH07l1CP7j2EkIMQHCqLtYLG6NV+S8CSy0kJFhLiYKjDc+KGKZtRAuVxuCKxqRAA2luufiLNnreTdEFArXw4xbBLjj/eBxv+6zv4JB+O9zdAguAA8OwCKnFgCRLbwthiYqTZT83DE379AYsK9u2q0vrjLlxvd6lYL8UAi2CpfwH4c7du7z5+psWR5wzyUt97UTZDZHh4ozh5MS452DCXCCEigUYlWU7xKUUVjHOVITgwo8bfYVhUAQrag44XGgDo0LovBines5hsED3Iyy6GEZFVlvIEZaTZoEoz9OyJgCHRcVv2Ay3GK1lWNz1s4TucLS1/WxucqDNVQ+zVG4yO+2DePiwHLIaN13l7UPUPnzvZrwU9aHBguHHKTGcGLfsQV/VmbKVzDQETu7dpV+vlsRRAAkGPWJZynVxqog5OmLsKjwJMVRuOkRrHlS+BxPwNhDHPnNt4u7r8qAcU1HQappbHA+GOiq2Psa0x8txGeq/ve0mHxuox3O2bRYjt/xp2WlzHv6m/u6AxchHAfiqXmdwod3ah3AJxT08v9bIfB70aJ1TLzreUaBF5JtE5A0R+avNZ/dF5I+LyF/3/+/55yIi/75Yf+9vF5Gf8kKzEOglkrLFOStCnmbwp3zWzC6ArnpWFyeEdU/sI7Fke3juUZACOzxYqQhqjAZFYrQYDneJl9oa5S/G6DHVTQD8kcYsOLw+AOEI+2VPBM2H0XA2Qzw5gWZ7rcmgrQa7EdDDYhDW3z5D05W5HxqrZuS2Al3qZRdY0z6IHJ17nf9RXLOqFYQpSiI0f7ZSLsJejOHb8PKRzB2E+94IH3jOeJEtvxn4uUeflczurwP+W38P8A8AX+d/vxr4HS80C4VxmupyJiEs2jaIJcPqzMmdc04vzgldB455a8zxDcMsVIrONHD0ICYT2K7rsRochwbgsWY+1iw5LYIqEip8qTfKBbnEMCyxwmbgki2vrga8a3JGxilKx6dVqEsakhddOZ7Ts7RXqylv+7yM+tBqQ9GZdNc5zPPsLTKOHhosocFWPuP5iyC3WSmH9+XF8HB7P95TgVbVPwm8ffTxN2AZ3fj/P6/5/D9VG98G3BVLw3r+MOhqjK/f+OLXnzVzlSfyquPk7gXrs1ML5D/wxunCfdVlubmhYlFwpkFiFeTy/TGGvS1k1KYpNem1Um8huLYugh3LdasCUDR3LRHrGlzKvD22uWY6Zw+IL0KU2wD/w2UbuAFrjrX2MSw6fl8fjML/1wfoZoTcsr1dkdJKrlyPlnYE6LqOrluuT/tduwKqKuM41qCwdn+3FbZ81vjhGoXPyux+Vo/vz3E05CBJNtJ1HX3s2D69YpomitKYRbmWTH/ngtM7F0aFNYJblrzFwCvU3cJJW07foeC2UWrHfHLRDOWzivdYtHI9dlcMz1JhVElzIs3JKZHFaGre2XH8nWWAQPHSiRRHDDXptQg2Ja0LagwGFcsu2JxgMOrY41YZBhfgcqFLyWBVmz+aauZLXSGa6xZk4ZlbYW1hS+wEyU1hmuXeHwhzOW4R/ONtfkR5aNVnZ3a/w+9qkuzXDitFIPvSFkMg5Zk5mzG47wPDvTusz8/tpuaMFIErFyUsAluMCVsCo9/cRYvUbVi0xXHWRqvFji++CZFtWzRuaYQZUEJISJibm1vIMak5h6oldsJKkFkB04bV0JJp4lBFFy9dnWedY66OH8r/qmi4HU9TNSz14VAgpxL7rNVpUjx4x04VpfDx0R7yEGp4qGFyc9dLsGThNrS2CG9btKZdYYrT6lkr5fPGD1egn5XZ/Y49vm8bggnRuN3ae/FCjJLZkRlXHaf3LpBhVTW0NLjZftM82cGxtQVi3PrkA42wmvEiVanfHnpZf+M3Kkis+Ld0TBUUQkZiT04zJesaDzrq+n5Z4r3KpngMdxHKTMbLj1BSmG5jA2541fLtrePKVa4rkGPksk0xdkuRF3NoKZoyaZpJXrG0MBftKhhKINWNEFFXFM21LnNv35d7cFwIp11N2/v8TuOHK9DPyuz+w8CvFZFvAX468LiBJs8ctpQpXbBErIzVUh5T5jok5PSU03v3rAppt1jJUvByszxZvQqLjRAWYw2R2tBysZ7LQyHEsHCnhRBsL7IJ7MLJWiu5WNmBUG4GmRAsML4Ir9Wc9t/VsFEIIaF5JpG8PIALb9I6l8O6FFornd4w9rInpZbr0Wi3pYpTlWPKElOZFl1q02WvalQqGOWcXSmUOG+HS2FZ6WxVMCeMad5C3WE+g3Jg1cPrmvMNfN4+lDEEK7X8guMdBVpEfh/ws4BXROTTwL+KCfJtmd1/FPh64JPANfCPvehEgi/jEsSSYVH2KFfA6SuvsD61lm6IVCxaMakqS2P5RYva/EuqVIEnDkMajVEE3mF4e/JYPxalNA6CJUQyzaly3hWS0BEC1aVY4jnaYQ9hdsUdCMGEN6UlRFTJJK8yVLB1PbfC7vi5Z2dWRKRy76hRcSEEi1QpGtTnUN3VniWfoRbrSZTQW0thQzq6vqfrhgrnjkNki4Kw6yHkLEvxTZb5t+dQIE357Jjjztky/t8N7HhHgVbVX/KMr25kdqsd+de88NGbUU5wzlYGYAowxkAahItX7iN9Z1i4cWq0mqpYx+ou5Q6tXWKDCp7tR9DD3nzlt3K0r5ZJaNkBmuVaRCy5ILf156TBf7hgHFyjg2NY5kc+YBSUTJonZxuODCJ/itsKSQYXtNbVU/HCj1KqLXkJMg9nbAVKRYmd5T/GaE2W/Lm3lUWtYekweKiBh7u2IbWFHw/BDMAOMcyvSwpWzrkm1YbmmhY4mFIiZSuxoE1ZsJQSemQ7PG+8FK5vWLBcRslB2KaJJzoRL+6xurhAYiT0HchS1Uea36EWj5sk1CAnkhK66K7fYLCCTHIhWLB2SQY1Tdku57loyLzUcpMmJpmENxMqy2WEHMgCFWcIblilGhGY3cgT18hllclqwlyWe3XD0dZwX+oLpMi+WpSHI+WaR5l9yVfJEMxGcLPUHsCGYsuqVRlQHFJBCIPx/UaddZVJKm5Leyj9ulBWAK2rZqRksySLnkwlQMnn3ayIISVizkzzTI6Z7EH9xtW/hxr6R2y4ECXNzCgTyk6U81fu0Z+eWOFGqJ66Y062vlYlqvXz7qIVbBH3JAJI9mVRApLrgkhOBbsVh01rvHigu5ZC34ulr0WrZBOYshQHSuhoy+FajY1SQ6Tkz8VoUpKcq05eP1m9PFY7CjfbRv5VVkOdflOMdkPIZGJoknBDsS/KZZcKGXJWKzAZgiUfV5tjsUNyA1/w88AhXmn/pqo1Bh0RutgTY6VVyHmuHcUU6gMW1B7IyZ1sASG8y5iOl0OgBS/xlUmqJFF2ZMLZCWev3if0EQ2hybC20eLKJT7AipvnlEk6W9Fzr1iUWJbvzhvYH9Q7PjI+FjYhOZ51YT6CJ6UAztKs0wOcFMfGlpJfOGNxCquMFNxQBEqsdYsjXTkfsTrL3CqFKFjD0iYAStVKp1WYkawgTknyKZRmpQizIlGa8zhsjlQcPur88KI9ix3jx2nvDyXJwh76GKxURaVendrL2X4X1Y3TeWYax3cjSS+JQLNABxXD0dd5Zv3KR1ndvcOs0CNL2djGgq+Qg8Vtm91oI7o3jpthmWkeb/CfLV9bsF5wbZi8KiqeS1fpqHKDy/wdophhlColmRutNSenwUK08gtymMpUjp+KUZQVYukQsLARQFO5SFF/GCrF5qwRSatRbL1sIooLebZ4c6UY5QZ7YhwIMRDicn0D5szJ81Kyi6YyUn1gpdR7Un8vSOfMkuN849pd+P0BoZl3uc7Aj4in8L0droGK92mfZsYucP/BXcJ6IEWx8lkq9aYeC99C+UDChE+SXySKFloosBjtZicvZiLV+2daNsZSdNEmWAwbayLfUGKA+ncF62pKjYW/BP7sc/aee2YMFSNYVZsywuJGn+0/iYAKwQ2oUrB9cX376lKTVUHV23GUJR3/DFuFsgJJUEkegJ8q7ACDVSF2hK6j74ca/zJpYpomi41272tuYqMLDlct8dceTx0DJDdylUq94kZzLoYibiOoHq6c72K8HALNAhsmMtc5I3fOOLl3t0bJWTcqT8mhiT/giBIqwlSe8GpzedFHVS+ZQCPkjms96CijqFh7jILZi4tcU/abWjxsJsDFICrGYSmUYhqzzDkTZt9Ptoqe5SEq6Va5kNQuo0mVvl8h3WHvw/oAQ13qcS+qHb/g89h4UxuvnV+yLLPRj1pqZpdEh44UA7kbaySdUgqQZ1QCSc2jWXD+JNSHLAQTrQSoMxn1QexKM01TYCJSPZLeV6mGM8QYmabpheXopRHo7H+jZi6jsrp3wXC6qU+v3RPXSA1bfMxvFsOqxbjZjauWE82FUXBN0IVAFyJzzta7xVkPrxnkGdsF19qNjbEjamCeJzMBjzx6oesN42KGX5ClAuh+no40rQuDsDhPYlww7NEDfOBxK9F980ypzTwnWzFyzmjwTrrl/P2vxhvPxRidPQ1NkDBBCMxxT4gWgLU4aywrP3tByZwX2GUrG2Q1X0IMkXHcu0Hqq+rsTqusqIqnzzVlx+zEKrf+bqLtXiKBziSU62litwk8eHCXHCNEz0KRcFD4ux2tYEjO5hhomA8L5zQabJ5nbz4ZrEst1iBzP05kRhRhN02Ms7ml9/s9V9fX1L577snLOdN3PWdnp4hm5mlENNNHezBiCEzjRJpn+j7S9x1d37FZr+l7qz+S55LjFygtKQ7iRoCud8MpuzF3hPnxh5iyMuRcu0ypCmlMJIxCO7hm/n8IhaO2lWYcrc+LhEDoAlF6wOyIjCJqEM7XHPcMZps/ALOZ3tGFNzY17lg8naaB/eGZxW2Oxg3fPMDvBna8JAJtDMeclb0mhjt3Obl7D+ISxG9P6oKfj+MYoBiFubqLVYG8xBgXYd7vR/bjxOXVlpQVQmCXMvuUudztuLzekXLhhD0WWIS+65jnpYrpNM+cnq147aOvsoqBabsj7hMhK0PsCCmx32+Z5pn1Zo1IZL3Z0A8Dq2GgcxgTPbgnRI/+c1zclYzzspT7qVZvoVoGTUqzBeHlTErTwpJkq7ltvWcONftiOBoVF3QxOMnZ6m1rjzi0CmHpFKDqMSqqBilUyTHUdh/2sC73pM1QWWpANyEIMS5CfCQZNzJ+3mG8HAItxunOwYowbu7dpd9szDCRSKwBSQV1HT61LcVFU7EyN7EIpVVYSolxnNju9jy9uuath4+ZcmabhYdX12jXEYc1/TDYkTqLIpummdPViot759XJ8PjxIy6na3Y5szk9ZSWC7nbM19dINK2sOlhiaQYksxtnskRynui6ROfxvjEGOo2gmdCVnMcFd8bgXb60iXWQEusQmeaRXAyvvKRK5aRMyam9go8b5ifIQiGmlMkSmGvyQWa9WrNen7BardwDCiVpofYfjIGQjR6UkAvHCBwGTKWUKsywR8HHNB0Id3tP3w0HDS+LQKs5Oq7zyLjpuHf3olJOVXjFjJqOtu7DYTZzwcbq3GyxoMt3+92Oh48es9uN7KfE46dXqARWmzVdN/BknNB+ReiHGvex3++Y08z19ZbdtKdb9azXa2KI9MPAfn/Fbj8zPFgRouUxjsl56xiJ/QoN5rnMWUnzxCyC9hnVkh2jQCSKZaNnNQEu3HPhyGtiL96RQPGmRIcP9TzPVUtPk7HvJbRTHLol386Ey+a9T4n9bC7oOc0utI+JMTKs1miG9Wpgs14xDD2RTNcHwmRz6jsPXlKtnsNWoNv4kbbJ0gFV+i4zVI7HSyHQqpByYptmOL3D2tmNGJYOqpYtYpq87/t641rBxtmP2eMBsnOmaZ7Z7/ZcPr1ke3XNdjcyuWewC4blOu/PoqKcna5dmJSr6yekaSKi7LfXvP7Zz/DVX/3VHrdtDTB3uz3zPHMyDMxzot9sGK+eMqZE3/d0MaATSIkxnicg04WVMQsSQRNWG6RfGJjC2UZLAC4LcvEWWgxJOIAixbCap5mUE/Ocmb2UmoiQMlzvtuyniXlWtvuR7Wx2BzFytd9bPEbf0/c9IQi7/WhGcXKBA7oo9CSGLjB0HZozQ99x9+IOq76n6wJdV+YIsQt2P0UKyq6MUPEappQJvlrBAos+eEahUJtpru5eEDYbpAuUtgymQ6DUuShFzFt8ZlrKlsIowphm5mlm2k9sr7fsdzv2+8nZicCsiaAWYbYdJ6KuWPUDo4BU7DyzWa3ZqxoDMk6kaUaT0vXBg4A6hq5jMwxW8alf0yF0KPN+x7jf0/UdfdczA9M0M+dkmS4GHIEmVNUprxgj0kX6obcAns54cbMpvLlQcUYEE5aKZ5vK+im73TAnxnE2dkLh6XbL0+s9V2MihZ7dODKnROh7c7RMmX5QhvWGfRKm3c6vL6RpJgbh1bvn7OeR7dVb5tFTJapwtt5wsrG/i/NTTk43xGg2wtD14ExKyb0sqyzYc906atpsmBcZLxI++k3A/xZ4Q1X/Jv/sNwD/B+ALvtmvV9U/6t/9OuBXYRTkP6Oqf+xFJjIFZQ6RszvnhM4SWqugOvUk6rU23Lruuq4xDhM5B/JkS9s0z4y7kXmaSGkmxo7Npid2E1PO9M6GdMNAd71jr0s+4H4yw2ieZj72kY9wenbKfr/n85/7fGUkUNheXTHEwOl6w3q1snl1iZyE0Ee62VLKxp1ycecO69MztvsdafZ+JTT8dnGkpJlh6FCxGIh+GOiGwYTc+dzCzVqeIuZtzD3TtK/XrF3Kpzmx2++tN433Fb+63nI9JnZT5mras59mO2aG2HVkFZ7uLtnMidV67RFvgZRmpO+43l5xNfa8eveCYd2jc2Lc7thfXbPf7dhdXfEY4er0hPO7F5ycn3B2fkoMnfH1R93EWqxc+oQXgX6vYzm+GfhtwH969PlvVdV/u/1ARH488IuBnwB8BfDfiMjfoKXo7zNGVmWXZ+T8hP58Y6UG3OYrGLp4Eos7eqG3rOF8tZNyMaI6+gH6fuD05KyyHilnxnliTtlWBc103Sm7WXi627G73tkKHsyRsd3tnXGI3D2/wzCs3F2rnK7WdJ1w986Fu2czQ9+jZMZpCznThcDl1VNWq4H1es352TnTPDG7IVTcunPKnvc41Ei4rutru41y3JqIKhaIn0MgT8LMRNf3HtG2eEXHcWScJrJaWdyUZq53e7b7PbNGLnd7trNaAZ4YSRgNN84zu92etx8/YbPZcHZ2ynq9povRslhUud7v0RgImAE8rAbWw0Dabpmut+Rx4vLxY/I8c331lCeP19y9f4/TszPzehampXn4CmxUtVjsd8NwwIvFQ/9JEfnEC+7vG4BvUdU98H0i8kmsGef//E4/3OeMnJ0Q12tEgqX99H3j3y+sxnIRWqxVsrkDka4bWK1OLDfNl2mj7Exbj9PENCWmZNgyK+ymGQ2B/efe4HK/Y316CromJdjvJq7zjs1qTex7w+eaiKHj7GTFxfkpQ99Z+4Y5oxLZ7Sb2l9dM+5HTkzN0zuy2Oy7u3SXGjlEM+y7ZJFafr2RJx6436qwbiLGj6we6rq83vhM1RkJH6NRpto652AJ5CQxKyc59HGemOfnDZJ7RcT8yqzmBzEgr3v7FwNzv95ydndVgpGmcvJyD5X/2cXBmKRGHnj4G+hiYrndsn16y32+dQ1femmbSOHF2cU4/WMJA7bJA61M4pPxedHwxGPrXisivAP4c8H9W1YdYhve3NduUrO8bQ5qs7wchsNdMd3GKrPryffUWaVg0deGAbem3fRlUsCV66oYbcQAlV26a9szTRBxHumkijrNReaqEGBhWG84uLvjM62/y+PqabnBjZ33Cer2ii51BmXli1pnYCa995AEXJxt0Gpn2e8btjvlqy/j0ijRNDMPA6dmph15CFyKrkw05nRyElYYQ6Htrr9F1A7HricE0dD+sqmADtZtsnmdEYJ5gGAZ2aTbN3vfEbmQ/jcwpM6dEmmZzKSPonAiEpd4GAQ+ws1Wii/SdYen09CnBOXjF8HPp1T3Eno7AeugQYL/bMU2jadcQiOsVEQuamqaJk2EgqDJurxn7Ds1KNyxKyx7wEvvCYiv9CAT4/w7g38Ae5n8D+HeAf/zd7ECbrO+v7jodBU7OT8kxWMaJFFhRPf7Om5Zgo+BGotfaQJglIaGzKLWjuGGLmBvJOTGNoxmJu5HdbgfArAPzDGebnvtn5zy6uuZzb77F46eX7J8+ZffkCbFb/G1djLxy/z4rEdJuR55Gxt2Webtn3u7oFDR2zhQEYtcR+56+H2y57XuSrxqqZtkPQ88wOG7u7P8Ye2M+/Dy7rrPY8JwhdjU+RbMSp9HjwDtjJWDJBFEnB730mWBQr+97ptE1tq/6gciwXpkGjdEE3XMo6TLzNDH0A2enZ8ZPS0ZTspzNDKQMyT7rYvCi7iBqHstpt+dxesj65IST81PEef0Y4kE0Xhs1+KLjhyXQqvp6eS0i/0/gj/jbH1bWtwJ5vULWJwTp/Rj1CHadW5dwWGJ1l0pHQtdbImzvVBCNCzxnJXYdaZ5oq/2gasEvWYhRmOfEOkZ+1Ede4StefcBunHjy9IrLy0v2456cjYrbrNecbDZshoDmmVXf0bNGup48rMjpDEWtQlNnsRD90LParOtZxzBUnNh1HX3fcXJyQuw7336g73oXwIiEjhB7ZzUUzTOaErEzGNTFjtx1TNVZExmGnm7qGOfZ3dOB1aonTwnSTBQx58ucCH2AOROjBTeFLnLv7h2j2mZvTeH3YbNZ88q9uwzdgOaRlCZCyug0I+MMKRuv7n+FG08hMPSdYfF54ury0s7XI/ti6LyEm92b4AzOi44flkCLlzDwtz8fKHXv/jDwe0Xkt2BG4dcBf+ad9pcBzk6Rfg0aDf8Fi0XIGSSoBdcUbtbHkupuWRcSAp2tm76EFU+bpU2VUrsxdqYBY08Q4fLpU0QzRPe8KaTdFgTOuo47r95DPvrAnzKtfUMsoi4xjcaXn61Whj0nixqL/cCwXltwT2fW/TRPrpXVb5g9dMMwICJm/EUxyOGa1uCGae0udl7y14L0NWZymNEwE2NPiDOxi3RdpHODM8ZYtZxBm55ZM0MH6wR5s+ZyvyOL5wXOmZmJPggx9gz9wD6PbPd7pmlPh/KRu3e4s95Y6GzywKhppHODrotWb3ue3enivgPEQnYlGc4mJTQEstgxpYcoglWBje9KmOGHn/X9s0TkJ2HK9fuBfxJAVb9DRP4A8J3ADPyad2I4wMI148mG2HeVmy0xxGUUgr4I8HEQDxg+jJ3tQ7GKpiUGpHCzOQsaIzmZ29m05obd5RXXV9fm/tVkOW0K+93W+WYD8iX6roRMpmTBQDkrfW9aZj0MxGg4tBT8tmXdzinWVhpSA4mGYTBPWXDD0HntAhHaklrZWZ++M7gxT3s6hzeFojSNb8K4K1F0YnHesbPiOx95MHC9nxgTXO/XJAnMGaY0k0czmhkS834yRZATpMSDu+d8/JV7dJI8d3OmE6ALDOsB6aIpCKwQDc29NO48QxCLjERgGhmKs8i77/Z9z+DX8T0N8Nfbs77/4+ds/5uA3/TCMwBUoD87JYvYDekWw1BEa/JqiSGg4VnFvYchLNUqzasYvAVIeQBaV2tpmmn47GxYcXZ6xjxNTOPE1eUVV1eX7LZbBMODaTb+NqmB0eBezD70nJyegJpQdkPvGRfKNI5s91eo2o3tukiIi2ezwIwCOYzd8L+6mpTfFcfLsjIFIl1OJjQpMgwDKc9Mo+HvYRgYhol+mlgNK2Y1p40IbELH2VmPhI45ZfMYjjPEjtgN7KaJp5dXFoWYEpKETSdsTs95cOccmfbMWJTdKgS0izU8QWIk55Lkurjml3sWCZ3VNJFgGLst8igirFYr+r6/UTzzncZL4SnMIvTnp26960EAf1YLZ9QSxMsSt9CeaH2pWt8UZqTkjiweJ/c+dX0NC6XPxNWalcL5vQekyeiqNI1Ge02TRdo1TEvnsGCarJooPq/ZG7iPUsruNoJZmJkoVfsU3rXvzSsoMZhh58kNxVsmYjg6hg5fgwixJ8YBOothiSHSdx0MA3vX0Kt+Yp6zJ6EavSkFXpHZDGtOV2uud3vD533PsLoD+oo5XLRkwygxCCtnNWZPZi1UX1aIIULMzEnqvSrXvgp0MTCDWAaLV5QyA7pjtVrZw/0utTO8JAKNCHGzoYCHNtJKalaxj6yoJMsoaQNc0JrCVZJErVJoid1d3MvmNi+tjD1jJKvDFAu7DEPPybDxQCc7foncyznVWImsEDqxUgFqsSOdRDQqcdX7cZcgJMFyC2MnzQ0r6WO21EoMrqFLgL/Xj/M61zW+QW3V6ruenCbznpaSA33Pql+x7yY2qxVoZMbnK5F58vNyeyOgbLoIIoz7HbvdFX3XsY7R3O7BHChpnpFkdN88Wl5mHzubXz8YK6UQ0xKeoM6mlPexN6dR8hQxBDcKe/p+xTAMlR0Ksauc+ouMl0OgQyB6eKJQ4nOLhrbYjBsZGnKU6eH/iwtBWZ4tuGlJai1UXhGUGD0KDKu8pF7yIPvFzroEPXnZbjKm3Tt6z6nLdMEyzbMH6ncxEqJ6TQzb5zSNdN4ttxhNy3Jsr6MXZfc4w1q+15EWMRYO3golCJnkHQo0RMPSXY/kzGq1YtjtPbJtYs6zF6gJqIfSlmseg9APFuq62awYxxGdZ/I0sttemR2hNp+u76ELdF0gihA9jLV6awV62TQQYikRNqdkRm/sGNpSvDHQ9wP9sFp+F0whLSXi33m8HALd4Nni5han3IIHrhvXatkXQvSE0RLI4ssZS7z0gkGWqC17HYh9ZyVjy7FCxAqwmC7P4ulCWQnRspNnj2UQKfHIJuRdiEiwvDzzXhpx3ve9N9kxzG5GrTJ0nXG6QWu6Uwl6r+7uEhNMWWnsHQ6eiuAHEcfFAdFSqzl4ypoJ+Wq1cqotMqXJowQ7xm5mN+6tclKMZux6laUQI+thRResrknGgpzy7MnAHoIaO6GLndUk9BXICsubpi9wY+mE5SWJgywrUDAvb2GCgndGMHhyM87jncZLIdASArEzzdlRKv9YCa/sGc+CEfOCEhQ0lXrIVhuuFmxxo23Bzw0zAvadFs23jCiRLKVOc3bPXkk+TUTx2iC6GDklXiLEjhKM34UFL5abXMIRLBZCStPbmoEDVPghYg8XUJ0maPYK+Vg2SdH6Uowpw9VZLPtlaYJkx8TPe3ADlBDp+0QIpXStCVAscSIs9oZ2nRm73ZJTWbrlpjzXB7Gei1gocAiLU6u1d9pyxtY6xFckkVrkBu/Am2unhA+YUWgewFhhgWle9Xu5VN4sVuFiNC6JoEsVzGzctQuzOU+wGOOyp7yUIqj7gVo376ZV3tb1WKLiCla3NhoeQOXsRZlLYTiMUzX+OEggSlt6obHwadgab5fchlHW/jEiVZhSF2sl0OjF46dpqlq8cMA5Z8JgFZGmZN7VlHMtS1B+a0Z4U48jmBcxFq+dCCF2dGLNnZCyINqcS9FH6n3Ugwe3egCjRVWCx+p4x62clkCltlbJi4yXQqDxE9QiTa7Z6sjqIYViblYP6gkxeOvrjIhVSCJoFaRQVCGAWryHyAJBVPF2zBWoWD2PYMtvu92hZl7yGtW319wKps3fOFV7bw+UBfR04kmifo7HGThQ7IFFoNs/X3a8D81iTAYdDoTT+jQa/FGPtENKgRvMs+hxLpqzw4Xer5lDPr9meOiu4XgvIyzl4S8Vo5ZrZufcrpDU88ossKPebzGolpJF2ZX7XWyeFx0vhUCbwTBb3pyq145YHCiwaGwyhN4vngsZKSMhV41ZVupSfss+z0Cs31M0RgiQvASsLild5pxRF1Spy19N+cLwNRJrnEOs3rFkVfn93MRNfzEC2zS5nVVdXQ6MXqiFJJdotEago9fmgNr5K8aI9D3jfl8fqrIKxRgZ+p5dSpTsHAmBrrdQUapQmze1MEIxxqXRqO+nCFnJNLFEAzsXm+aShFFrVVYhD56U6zDTH6hiGMzuntd8GFb6gYMcnnNkNThYKlOWURegItSOkasRWCSYUgk/1LKvpQiL4UR1QS/Cb8cRd1qUFsIhuGWt5ubGP0tptpvtMcDhAA5I7Uli6f+Wvb1AFn/QcqmwlCrcKOdGORcWQQ0hQLCHpVQhanGnCZK4gbtQezVu2kfOuUa2dV3vV1nwkAk6pTaPbzNEpMGxJYa5fucMB24kF8HG8ntNWYidi7qBXLC676EWsSmrVAjRIwDbe/4Bgxxlxdfy2vze9URaHFadLVqK/xnLsZy4h2PivUz8IpdqP3Y8yx0szPAyD0sUsH1JvblaWsjI4uRYbvriWDCGpkQF2rmUmxPF5pvBKzQJx3qnFZbi+g8hQFyO2f6qwp9gBqTGUIUixo7ZS9ICFasqOC0Y6lIvnpcYY6y/qZkisqxkt/UOrPur1wisykr2Lr+mXA478haOWhe42eD1/O5b9tTxUgg0mEVrFeAXZqAs9SHogYA7T2A5aSjFrd0O46EXTVxK2dp32bV4Ca6XaogCdck8Tv9pNdTipHHczYJ7i5BnVbrYQS5p/+o8e66Qp64w5TroUmm/3ujKu3s2eKXxyv92LqTimRwb72IDT6IXZRQLOqLCukWgF8+l94zsgpW1dcyuulSyMsgjXnpgMdrxVSuGzuBRtZGaFShEVErFpabiVL3PTWmKdzFeEoEucc6GWwXBoq2aXhzJiw1G2772O1GabVJjRFEpr6KJNCdqpU1w5R6qcJGLIXa4VJexJLIu7wEisXq8gmtiRLzyKUjMnpNnQTlkoxtLbl2Ln221sqW6wCNjAcz5UzJ3KoRRKpebgkGO6BpaYkTcECTngxVBglS3uhWqtM/7yiyYa5tClUqBgY1N4/tZjMFQ921tbozJKFx8PU//16Gz1+EzB1UprFnn2WjvFxkvEm33VVg+4Ud9Lr9LVf89EbkP/H7gE1jE3S9S1Ydis/n3sF4r18A/qqp/4fnHYAHFUJftSt01J5RzQvLi428pNSsz1QYghWrUCaBSBMg1imY3QPyY7tUqk1o4V23merh0tuGsh/SePXvJc/wqk6K4G9khFLdpocMbWBmOEOo51mVfBJzxyTk4p98RZgsfTbI8sGWv4t2rCg0YpHgfZYECQF71XpxyNEO2GHYilgXTJFIUeCLOdlgtPIFg5ReqZm6cLKjW4oyo7b8cu9CphdF60fEiGnrGUqz+goicA39eRP448I9i7ZF/s4h8I9Ye+V/ksD3yT8eyW3768w6g4I6RUqTErL6cvT5yUrJXoRdM8xmlZPhWg5p8uhKxi5HcFbsUOxEn/kWze+HE+wNiWhEqBCnL5nJxbxGysNTKENdgZfleDNZDHtXQQjUUQJdQ1JaaUykC2KTyqyzHauBR9olL0dB9T5cmprQERlUBElmCnqJFvUER1uiUpRWqjC6QIUotnFge11XfVa0eQiBraTfRxKk3Aq0NlGqN/AozSsF4pdovJc7mXSjoFwof/RzeCVZVn4rId2F5gt8A/Czf7HcD/z0m0N+At0cGvk1E7sphQsCNUT15DT4tV65g4GInLEFLTeXO7BctBLKXCKiSqkutZRHsIcjOiMQlWKnMoy6dvh62xoppIYNHVnIXikFaWgTbjrD6H+XGaoNhvYyA1pt1+LCIQ2JVB0ayUF6IeHD/shQbQgmoBorbGrWk2j4lUj+x3+0sqAtvZVdGWPbd/i8ipiTAG/2Yl3KeZsfC2VdIqQqok7jMteAh7BnMLCttcg9vwcpL7bpyPZYae6XUcdHaLzLeFYYWy/7+ycCf5j1oj1xHM99CRZUqPYvRJId4ueFFDzGoVO2dNTfBPW484rQZ4h7DUpGJKsxlf2aFc7DvYtWH+llo5r1sZ5rT8HthLEwDWXY0unSH0pxLlx+yBIRM33eLYyX4Qy2tfqsXzIU6Lq5kSc50RPreMmFSShauma1rbbFZytwX+nGppwfULl9WQNKTJzS4jcAiuM15F4EWEati5bx9Eeg6df/L7q1chNzpWZ9hy5C803hhgRaRM+APAf+cqj45oJj03bdHlibr++7pxj5zwcvOCRctVJ5ggyT1oDcYgmOI4HyJCam3hpJQoAEUNuJgqddibJVlsukxUmCGanVBtwE0x+5yRWv7texhqDnPVkg9zzafbMttqacRo4Wj1oi6ulItJWjrA93OCT04jwJBYm9Z49vrrWtpv+l1m8WF3gr0sloaSYOqRRCWe4J6LWfbrrTPoBiPRfmUKk4NJ39sM7QrjibftzYP8HtpFPrF7DFh/j2q+p/7x19Ue2Rtsr6/6pV7Gj3jIRaN5FAhhmJdN4K3qE1gcR23N2LhSm95utXzC127luqetr/sS5xzyhSFfrgfrf9AuYP1phSDz3uylIdPs/XcS/OM5GQC5rUuUOq5loAjKvxZBFSLAetHrjSbz1nVCjGWaxVj9MqhpcOrV2XNmU4NggVZKL5yncv/pljCAm/8dIOWRGS/T7HYDs7weOKEg0l7oMvvfb5JmyqpxVupZTVW5pSd6XlxgX5H89FZi/8Y+C5V/S3NV38Ya4sMN9sj/wqx8bfzIu2RVUnlhKVgVQrKsNBNEUokmnkDl6Xc53nrX6nZ0ZxRNU5cSfsUdOlTQourW62PNQlKpQhkKgzdwWqhdTfeO3GercpRsiztnGbm2WpVzF4Mp+97uqG3vMqwaP1Fa+KMiJuh0jys6qVt81Izu6xqIkLoO3vIvC+iFS8344tM9XC2q0ybgGzJuhbi2eJu/C92y/clNa0VwSBiYbb1+nntQM/0qfDG7Y3SkUBdqWk+ZoGePV5EQ/9M4JcDf0VE/pJ/9ut5D9sjK3iQfHTHQvlGKuaq0WKhhEYeLkWtVmk19g3MybK8WWDSEUZTNeo6OidcKK8q6H7DtPyu8BlUJ4G4NstuvAYRL1GbvLeK/aR0fI29FZEJ0VkFb5SZUiak7DEjFoWmAcTjtpXMNI4mHPNkmdclrFYtd0/FqLLNyQlPxyfWzLPAgAAq4pnuDVvDoiCKkEevz1Gi38p1brFz2UeqvSFDdSqVh6ysEvVaVwOw2CPJO9A67MBKKLzoeBGW409xqOLa8d60R9YlfWoRQDm4yG1wEI7fiFautUTMleX3OLHyUOPYCBJrANABjKEYKokS1FTwa3vTosOiVnvkEsivGG5muZn1pteDWMuHYbCUo9h1eA40gZKlbrmJ8zR58DtoDiSZIZWSC9kLu5u206qp7bjTbA04L+49QFV48ugxvV/zAhGU5aHW5gFuDUWbf8HUuX7X2g3lWtcVIpgBTQZthLIopxovnReYVo5t34Nm4frp5QuL0kvhKVRwh4FrvrAYZ0VQF4YjI+qpWsmKIYYj4V1oMKn7KO9rDIaU4y43pzp0DmbW6vjGceKCakLtQi5WLreEoJa+hinNhqUFCB73raadu9VA9PjhoKVfimsohJQmW3ZROkCCess48foeRaCTZVpreYisKOWU5qoxLx7cY86J/TjRV3i1wKVjJVCUQ41AZBH0MtpAJlg0eLMjVzgBadLmFmH2dhl4Rj3FblgM+8ePHr+zEPl4KQTaDI12SV9OulzMrnqYFGTp91eD+5vla8m6OMbUx/Ra8wCoN3/UUqmpjYIrgu03U5faxjlZrbdQeNacvHpqqLDD8DkIJe/O8/g6L+0l1hK6GImTNyxSDKrEaAKa5onS2aBG9om59K3Ju/U+KX9pmmoeHyGQQ+Ti3gMePXxIcqOwjGMNC9REhUWYy926uW3Lp7dwpD4A0SIQS8mGsl/rge4xHVUrA+p1wBW22+sXlqWXQ6CbJd8uSqmg2WKnNr9MquDWNCUWZqNd4tvPyz5iiECh3WzvSqH5tJAdHrwkdXUQwQw2jzsgqS3x89KcXTzoPqfsKVtUCq/w3GVOOVvPQ4KdQyqC7E3rU06E2Bn8mW2+AfFsc9OwldpymJGzNUdKs1VZLYmwdL3d7Rg5v3eX3W6HREEaA/QYP7fQ4rZ7Vs7jmGE6tGGK4APihYDmeUlAaATfWxbU8F4t1rV+wAL8EakVQmNpYxCWbAXNSpaE6hJXUa3yrHZjpMQ+HzpGCuYDKN1hG3+AH/7wRpTUpwNLvdxUhxmCNcmxZpfKtNuRppl+MyBxQMNyY9z68xSvRatnxSonOcPSxn3kgsWTMovVkkaga4xiVWpjJMHov8qqzDOjl1qwJNSJ1FvVpBAjXe9aUi2eIuCcdGNLHOPpY41crt1tOPr42oJ4Wz4qDWnZPmY4SuHcp4WKLQzQsO5fWJReDoEGximzikpUtVhaDdUQk8ZLCI5ZS55oSfNRZwO4Xdu0FjvuUMn5UJODPyhCjZSzneD03O3MiYh3Auh7W9oLAxhKjETnN1EhzczjWOOO0zyTp5ngFFUGxKsK4XZCyXmMnRmy3VFV1XmaCRhsMWE2uFHK5QrWeiP2VmMj9oOXGhus6WbfoRQteoipC+Rrr2cLL9pxLNDltyXDR5ztUWd4srpb3HG2IzNviJsdriVOTtc3jvWs8VII9Dgl/tr3f4a/+cd/XSXiF+rpyJkCtAK1CLd57doLbmhkucjlfXC6LN+igRA35lg46BbTV6NRpMYTQyAEL6QioGTPt1uyRrJNlnkauXz0BM0z6/UKnSbm6z3j1Y5HDx9xvd3y6le9xtndO8xqLdlm7xA7dD0SLeOkaPGcrVTZlDPzaOUUsvdVsZrQyjxOEGBYrehXA9J1rE5O6S6sAeih0bzAovac23Ebq3GspW9iaz14EA4UDpaFP3sWvV1/qddLuPnwPGu8FAJ9db3jBz//Fj/ub/xaNoPBgjklVOfKcOSDC7JcuJwzodHcKofewgU/H46cUzX82otb4E97D49vqhayS8QqokYXfsfKwQ3CuhPnlSVn8jwxXV8TRRnnkd3Vlvlqx9XbT/n09/0AP/iDP0Q8W/Njf+LfzNf8uL+RYdU7j+18/JxIZQVQx/DZ8Hcai9cteecuYX+949Hbb5NR7r/ywMrurleMYced+/eMdSnBSTF69ksTI3402t4o5RqX18Vn0Dpp6vt8u1Yv11d1+Z1dRYt3SbN1433R8VIIdEqJx0+e8ujJU9bDhS2p6vmB9QleLloIAimTg1otDh9avzdBrXEINNFyuEDmRHEzF8NtwdAtH+771kIrKhV0CIhE1JIODffXqk3q9F1nEFosDHba7pCcmMYd22kk7SfSdmT39AmMIyex4/rxFX/pT30bb77+Bj/2J/8tnN27SyfBKn0CSZdazaVNc5qT04RWdSqNM1dX17z+mc9yfXXF6vSE05MTK3Gwt3qCRh0eB1fhKWdaacxy/iU6sDynx/CjfX+bULcwqUKRUoizWYGzFre5sL3eMo9f+gr+7/nYbrc8vnzKq69cED3frwhY9MD7hZJb4gtSzkhKB4H4hxezvbiNMShQ6bhGy5jBKPUGtjdcjyFMCXP1OOVav9oNy4Bnv7gQJN/nPO6ZdztHLQJR6PvI+mTN6fkpcRugi1y9/ZDv/c7v5M5HXuH87l02pyf0w8pCKtEao2yRaplOYLfd8uZnP8fbr7/B5ePHaEqsTzaInLDf7RlWK4t3iHONvLPYa+fU3disCqC5NixX6AaOPhbslrorGBowgz85RZesN6F6iGgqJY/Req0n7wvzouOlEGijw5TPf+EtvvqrvoKhcw+e6lImy7e9ceFoPFONNinMhlHUJSrOsFnOUre1m3XsLl9GNRLhQGmXm5bcQRPaGhTOSMRgJbZS8h7kKVXYHf1/jUJY9ZzeO6ffrDm9d8HTx48hBIaTNcPJmjRPPHn4JpeXPafnd6zNmj8kBd7st1ve/Oznefj6F7j8wtuMl1fGEGzWbNZrhmHl5+zzySUIq70Ji71wG2PRfv6s0Wri8ldwcTH0UK1hAqZwrIxF8vgYxaqqprQHoB9Wzz1mO14KgVag6wY+//oXeHq5ZQgn9KWPiFrVz4R1GS3LfbHKy/VOnk94KKjtUmlCXT4TN+eXG1dm01JOZXY360No0WZuJGZZQjHxz4r3Mai6A88cR6vNmuudh3OqxX2vz885uRM5vX+Pi+0DpnkynB8CGoUcYJxnrq+eoDrXiMQogf12x1tf+AK7t5+Sr7ecrVZM2TTvcLKmXw30q4HNxsJ0S2SeFXeHTkr9jyM6ExrFUBTCTaFuBbdSoF68ptYlTCXOZcmEN/q8QBBbbYsjqu8iobd5v6cFz38kRmEfnjy54q23HnLndEPAmkoWjJsdMmi5CN6cURHzwhkPV50hrSYvF7qU0K0Jm142DMpxvBponVnR2oex1ksmOQ4vDulBOZIKibaSxBjpVis2d+6CKtdPL8nTDBKR9QaJHWf31gzTvgbxKMp2e8047ZFuZhwnpv0OgrW+0zmzfXpN3k9E4OTkBLIybNbmZVz1dKsVw9B5Fn3ymttSYVPseueg3alUmO6GNq7hn0e2RnuNq0cxW0RdiaSr8SXV6DOhLoH8s/d4Sam4/M2Zoqqsz06ZxumFZemlEOgowroPjCq8/uab/KivfI0hLkExoRFSONQIpXZd6wIv44COo1jo4YY2KZAjBMgajn7jxpcesSf+y4zWQPx2jm0Sr6ph69D1DCenNd55dXrOuNtb+StvQxG7jo6NOVwcH2uM5G0g7/espDSfV/KszONMFCsF1p2asVw8l30QYt+xOT1BumitMxBmgVPvwTL0w9K1t5RXcCjQjmM67jhm45jRaCPrUoul1UNv3ZCdHGYUDO3rAfvZsuStNNmLi+kXk/X9G3iP2iN3ItzbrHiKcnV1xTxnclwcK2UU338VHC3GhyUCFI74Npy3xIVYFF2JqUAtxq0IZAmKaXlVESyyzz/LulBU0i25dMcOmgOrn4B0vWWZhEgcVmzO73gTz1y1kEQrYRt1bZF2aWYFVjY39uR5ZBxHix2ZM1EDGnq6rif35iEka62XocCsiS5nxpSQ2DOcnHB6dkH2JNZSSyR4Wd2cZr9OhwZ2vR52kZ36lHLClryQ7KGoyQROI7bUnqqtsHNKzG4YJi1sh2Xl96FnnEaEgNxkXZ8tSy+wzbOyvuE9ao8cgbvDQJDAfrfjervlbHVW9neQvHk4xMNIFyYi+8Vsy7+22rgYhgv9VOp5RESyVyUrDhOW31hBtubYhkMLC3PMgBScXnS5iJARVCLi1ftFjVcOOVt7NPXUqt6s/nmekSnQKayTMoYdKUYgetHHjnG/N948W4xJTqnOPHi8xDjtSXNi6Dq6zYaTOxec3LmwSqRdqHEsC78vDu+aTq6Nhi2BWIBn0gtpTrWUmF2z8iAsmrwqJAIpZ6Y5M8+JOWeSB6cHhzlKQsQqBKq8h55CfXbW97PGN/Au2yMHEVYSOF2vSNstT5484dW7Zzc0bSvcZWSFzm9AYTrUhbql8g6xX7noJtyhaqmyz1y1f/ltAZQKVjmzlpK96ceqN9AXUAFP03e+VeLifKFEz5kwWvItxsHHDokdKcaK1ycpSaVWJzp2fVMvz3noYEUUo9d93nCGSEC6SLfZMJyeMZxskL5fimOKJfWWpPB6ymLiVc4xIFVIW+h1DEVavrl8XhwoKWemlJnmmZSVpEJSo++CBrohkshkgUl7Hl4eK7Jnjy8m6/tn8kW2Ry6j3P7N+oRZM48ePYSv/ng1IvzYB8JchFcaGk41I7dYxBVvHxmMhSWJMSxQJWcvFRCaG+rfaeHHPRsktBWfbmroiuFZEkOXPEUXlAJX6AgSqkMJIKjQRS8bkDpkNgNuJcFZBPNK5qx+ET2HLxrOlmCpXbHrCJ1nkHcDcVgRup4YOoIucKsI6GELPZ91UMuY0cO/Y2dJyRFss8cVUzDlb0rKNFsaWgKyinP0yqwK0jNnZbufeOPhlk99bvtcuWzHF5P1/UW1R5Ym6/u079ghrDszYMZptHjdox537YUEfKkswuOck2d2H3LRi/YtVvwCQ5aihCVYCaQmoypiGePOotT6GHL0kFUo2YRRpoyKMs8mfH3TIKhkp4iRyfawxGCthX2iotE52Y6u7+n6zgy3GFDtm2g+vxauVYN7LQvkkBCN+A7RBNmb/BSevNSqc6LOq8Bi5+nsRnFdZ0/uXa7nkefvQMgTqcRqq7vnU7b+6klJiiXCeteBTCb2PTvt+Mvf8QM8erzjcpe5nN5Do9CF70bWt36R7ZG1yfp+9exU5fSMCWF1esKURi631wx37kKeccK4amwty39Z5or2VCFI0VYcaPPF9W20W8GKSOmfktxoVKyQI3UFUC0Y3fhstGDHxvgrTgMW7hZtYxjKA2aZK1X3NRFpx3aCMYuelS3WzySgrkFlKSXgNTgK6ggelYeIFUwUMaEO/r+UGOcyL0VCodTEI1Wksh3VRZ1zdVIXoa3CevB/abi0aOw0e/C+isGMZMdYovzKQxLZ7oXPvZm43vV0fc/J+r1lOYRbsr7lvWyPHIT12Sn7cUfoO2KAq+st9+/erZuUJb3UFi4X1qCACXVwTX1MoR3WYDbqDmwpx8vyFte6b2gPSIUOXp+jMRT9GgDUm1bOZTnWcnwBK9goWiFIwfVlqRaRms1RBbv859nVNb5bhM6CSTxx2PG+C3Lhz0u/lVAFenE+LVDMIVEpb1uO2xgHreYl5YNApDadSlhiMRYIUpF2jTtRqDy0uhvfrljg6vKavgusV7Bedxxd9ueOLybr+5fIe9QeWUToNytr2hOEk/XmAAJINC1VKaIb9NhNXueYNmsNvANO2SEAWRuMfoyBbRRNJm604dF9qdGwoWj3vBSbsd+aI6isFEuN5EM3cW211nLaXfBbpeQkRC3azQS1QJ4izFoqD7VshCzCnJvzB2onhOKpK9q7PDypqWqkqZTAPQw8goXKTHPR0lptj3lOlGfGePTcaCUW6jQkch65uAhsTs/oIxxz4s8bX0zW9x99zm/eVXtkiYFhs0JIiCirPtqTrZm+Xyz8hQ47hBFW/LwUjVmukglOEaaw8KpWB8BTsTg4PYVaVbNiVMpNL9DHPGelPnVZPep8CPVmFhiRsX6LzTW68aAdO3RErCZzRol9hwSYJzN+Yyn9JW3F1EXb5bQ4NkpzzxoyK8WWoOL5WtpAC0MkBguq9j00Bsuci7AXr2DLbKRs3l7zABbaLtf3habE7YBu6MnA08vH9N3MahjZrHqG/sWJ6JfCUxhC5OTsjL0oadrTdx3zNPHk6pKPvvIqOU0HPG8xBotRZnaeG4Ysy13f9xjL7UaiOstQt1uMylabZ//MUvixviMVrljIpgrm0nUt3GZG3xyeIaNaS2nBAivajgAH8MjX/qXPd7TQ0BkkdK6VgzE7zQOVJTvp0ZRYyMkM2aCuGIO1zHONXfliP8+UimF7CC1qjIZDjMIt3zQKFydKKtF0qUTUFYVjxqZ4uQNV4Wo7s98LsRuYJnPzB3nWdb05Xg6BjpGLe/d5NE9EbwQpCNfX10xpZuisCr461xzjQt/VxUhb5uKwd0nBkyYjxrsK4vl2CwRosWXLq4aAV6lfOFjENaEaO9Fe8lYz2/vGOdFo5sNEVxtZG/ZDsNhkFYIWV3oASRXOqAi1p0wInuwwLytYg8/FHVHGcqpDpuaBLkxJNq+fCe28/C4vJROAuu/WBjCB1pq5XVpNLwLtBiULxBFVb7DU8fDREy4vJyZmOu/UNU5fAtruSzokEFcb1qdn7LN5ulZDj2rm6dOnvHL/nvsw3LWdFekdLzbUHRSZPMxaPsDMhl9q0cVSBy7bFfaOU8tyWh0xarb/gnnd7SvGC9jnXuOa8lBUhnqBFAWyiJfXrXNaNHb9X0vjHdfsROY5G4XX9W58HV5KVbUKSmoJBiWdLHubNASrQtqsDjlrdaioCHPO4JxxEegFQzfQIjUsSFndDEu4wbdUEs25GIGLowV1A9k3yFl59PiScRyZNJn3cBCG1Qcsp1BCQPs1/ckpzBM6bu0iSuDp06fcvbig76K5iR2PZqy4oarSVjU6pOgOsbaoG2lihQKBqpkKqCyFZ4pAm5YTL4RTaCZ/QMyKWfqiSGnIuaweRTObwViEVTy0Mi2wSRe6MenMPE1WpdR/I7F0uAqELtp8nD5ULUcrBwWClzvIyY9XvDUGuawlW4EZgVztbYWMadM5QU5VWEvJiBZeFFvl0LBd6kBb3EtmztnuWyPs4sFkBTaO84yKsDkd+MjFHbpVpOtKw6MXGy+HQEsgERk2Z0xXV8AecQ5VU+Lq6pr79+5ANO0cG4fLcXxHEep2yV/c4MvSXzjkdtsaqxDaOskKXoXfWATzIJZcx2Nr+QBHl7IAWsqDAUqFN8Uw84lb9Nl+ZN5tmfejCfXc0IFB6FYDq9WKGMxwRtxtrKAYJx1isBZu2dkD7NlrbYYC4RA5wPVuVjqMsEpMhR7NsECPvISDtsakXYPC7duKmtQ8g7g2NkOwFMuxuXVdx+V2x/psQwoTu3FL3idU00FN6XcaL4dAh8Dm7A557Nmc73h6fUmnFlZK17Hb7Uj5DiFG5jzTN8J6sB8RCtvRGnrl/QK45aAm3WLwLcv9Ipj2nRLQpBDM7V2+K5j3MADq0MgskMPYb9fOilWDAubdxLTdM2537K+uYbdn2u2Y9uPBuQ2rgT3CtQTmPDFP1jdROtNws2RC3zGs1qzPThjWG8Rr5mmeTaA1Vg1ugBk0SkOFem0P76EimmvZhHmebUXIrfAu2tqMQHtws/PLYI6UOVmUo5UuWK5vaS4qIlxePuXJo6ecnN8jJ2VzcsrV9RX7/QcsHlokELqeeQwQBwgdKc8Lvs2Z/X7P6ekpXefM2YF6s9Fi0WoEFs2L1UI2MphbicgigMXrBos4ky2QJqhF5WW1qkb40lm1vfcLN1YiHzAgMZi7WU1dst/vSfuJ/eU1+8srpqsteZzRebK6GtNk0WcSiF0kb/e1uGRKyerUTaPh8CCkkIl9x7xZM+239CcnDJsNcejQXBwvzow3Dpjkq0bXdfVBz+6c2u8nGBY+uoYaYNRg5anrilOSCBYtnEscevVCWiShJQ93BLGmnznNnG56NoPw9OkV15c7+r7n9N7pC8vSyyHQYAVQhhUMK4b1CdPl4yrQqonr62vW67VHxrFwv0fMxGHcRnE3hxIugapHlOmyXTE2KcIorkmbCdr3oJIR6cytO+1RWTBeSgmdoTWUbLdWPck0nlpa/jgzbXeMV9fM11vGq2vSbqxVo0rZXoK1qChVSIN7/lK2mnXjPJGDGMSIwpRG9ruReL2lv96xOj1htdnQr+zaZWHR0MH4+3LJpsmKP9bSW/7Fbr83BZ6X1a4EHWWU2QtolsLtWd0t76lvpkTM/jD8YnEmnlGIktnt9+z3O+6dX7A5Gbhzep/tfs/1dsvV48cvLEsvhUCD0gVBhoFpWLE5O2e6fFK/S8mWu3EcWa8Xi/c4Q0UOsOAyUl4SaCsUyPlA+HOj3e19g4XVlsYIVTslzY4Tl98uy7C5e7PHXHQSCRlkSsy7HeNuR77eMV5tSbst89Ygxrjf2W8aTro0Hgoh0PW9LefJ6a4QoAvOWgQ0WwWkOc9MU2a/m9ltd6xONqxOT1ltNl5QPYAapadBCQdFx+2ELZN8gRXmzAk101ydYSnxGWaXljLA5ToVzIwZz3kxFF2ZE5zjf/L0MTnPxKCIzpxtVtw9uyB09+n6yO/5Y//DC0nSSyLQwjAMXO/35KxMbhX3eH27yaK2xmlktV4dOhU4Mg5F6mdFi4uqF04s3j6q17Cl9Yqw4rEWwR0Wrpecdy0cagnWyeS5aEzzLObi1s1YLY45o+PM/PSK3eOnTNtr0nZL2o/gpcA0JaZ5sowPz2XMfh3Qho0BknvbpDPGoxsGpO8J6zXr01Mzmr3Zz6wW9nl9vXNsfUq/WROG3rW1Xa8wTx4EBUiJuZ6hoekqU4IzNeQKM5buVTRGqlYjsPDvxfWtULHz9fU1T548IYTAuN8iOtNJpt+skZyQ+YPGcgSh69cgl3RdTxxWaDBN07t7es4zU5rYTxPdsPaODIKUTOSys5ZSw3G13NaoRqomtA1TbfNWHCdZU91WczaPl3o9upIAivG+OZUoPpjHmXG3Q6fEvJ9gP7N7csV0eYnu9uRxpPf2bEFgPQzVmWSY20py7fcjk5fzytmr9afMrDN46td+2jPOkwlRjFy+bUFMxIgMA91mzcn5OXFj5RBmTfRptgD/2DE5Zx2jMHTRWkK70yfNMyEfOk5K/mTLMSMlYtARsjotqsbva3ES1cCNRbGgWlvgpZQY96M50lSZp5GQwwKBXmC8HALt8QUiwXpDd5Fu6Bmvn3CyWZkumM1DNY471psNq24wodJD3nlxZbT7l+MDVg9VG6TUwpc2NqOGhWr20MdUmYB5TszjTJ6MqXjy9iMev/UWup8IGXS29Kh117Ppevp+xerszA1ci3MehsFTkma63noMomqctsK437K9ujR8LfZwba+vLevbT3rKhqfH/Yii1r9lv2f35DFXb75Ff3HO6s455xLIIbCdJobVCjCOWwFyootNPT4vYFMC81s6s7BGC8OxeHJthTN7xeZnv5umud4P8QqoyQuyn59byt1+u+Pq8opOoO9OzeAOR/fvOeOlEGiAvh8Y1itUEml3xdAPXE+Tr25mYOTZHA7XV1f0Xefg4BAbI4sxdxtHXeIVzInXuMubbcsDscSO4HyFGzrFmeCZ1/vtnidvP2T39JL95ZUJc1Ii1iT+9PSEzWrDalixWg8MQ896taZ4LDcnJ8S+5/T83GKZ+8HKnami88R+e81bb77BtN9z984F0zjy5O2H7LdbSuDTnBMZZdztGMd9fUCud1uutzuu33rI5aMnPHn0hHsf/xgnd+8xeaNOSyMQNDbVQr1MRNHG5fod0pMN/eYOplpzD6rXtWxX4nGMqrNV6Ho7AmqZNTHw4P49Lp8+Zb/bMs8TpxenDOvhheXoReKh18CfBFa+/R9U1X9VRH408C3AA+DPA79cVUcRWWFZ4n8r8BbwD6vq9z/vGIoS+8h6syGhjL1Bjv04st+ZodTHzuoFp8R+v2e33bI5OXFO19zTxyGi5SaU/6sH0Au41OOrxWNUL5uIp0M57hMOeNeSpj/P1qV1e3VJCMK9B/c5+YqvpENI4wQpE4hEhYj1SowxWg3sYNX7RSyjG4Xr/Z7p2jprVefGbM6NHHrohH0Wsgb6zRmxWztdZpyxouSTmTlNhsdzYjPuWV1dcfX0mu1+z9Wbb/HorbdhvWZ994L7H/0I9155wHq9IqlaxyzNFvNC8FJdmBFJ9RE5HVoaAMkNBVCcNuVzlYCEDkG9IWhHyomr6ytyzrUIjgQ4v3PG5qOvcn11yW53TcrvLQ+9B362ql6KZa78KRH5VuBfwLK+v0VEfidWtuB3+P8PVfVrReQXA/8m8A8/7wBBDPd1fU/IA/16zWq9IWXzMHVeJUjAWguHbMtS35vRiBK9U0f1Bvq41ZOYDt3lbkV6BN2Cwc0iLxkbJYJsttDMefbAnMR6s2K4c856fcJqtSGGDklKnmYkKR1CHmcrMDnN5NnwugSxVmdzJql5BlO2aqrl2EB1rfext6U8dIQBJPaQM3OaiVEdjwZC6BCJ7Oc9yEgYerqzNSerHtl2XO/2XD695M2nT3nrC29y/sp9Pv5VX8mD+3dBlc5DdouJnMFXjMLtH9blsIevuOCdhxZqp1n1WPbSETd2lkVzefWkQpmUEsMwQAhsNicMq4EHr77K48dvc1UZr3ceLxIPrUBpQ9T7nwI/G/hH/PPfDfwGTKC/wV8D/EHgt4mI6G18WjOC32BE6IcV5xcXDKsV19db7p2fmRDQGCU5cf30kpPzM/rYuQY+9A6WcRyodGAkFsHGAvcPYMZBOKRa9c9cYIe5v9cnJ1ZDrh/oh5XdLBWImdAPliOoIH0gJKXPA9N2bzkAKSPZIs00qS/8VjxHJBi2FUjaMDdOOZZOtsbyeRZ5VlIMjOPetGgo2SrWUTZ0HUkgRaE720DfE1crpiBsLy/ZnayRKIQoRI6Ught2OJecU1ueoLAYbvgFW+FKya9Fe4vTdFac8+nTJ4iIr1Te+m5WNmcXvPLqq8Q+cnbvHldP32MeWszX++eBrwX+Q+B7gUeqWgr3tpndtde3qs4i8hiDJW8e7bMmyb7yyiuVDguxY7U5QU8v2GxOePr2F8gnJ861luwUdW/ZzPXVFWenp02wvjszmqyPluEoRhS6hHGWL9T/T661qyVf/AwEspp7N4aAdJ31F6ztk71pEPaAVi2fgRDJc0anhKyiQZCUa6Uj9VK5QSw4yShD9+Z5rRBNajEY7okLUeo5lcab+/2Ocdwx55nduDO2Qazqv/QdqYtwukaGAWKkX61QCaZQxGJPUkpIjESHXsCBQshpYTMKu1Fy2kuAYXHLBxfWJSAJVpsN4zwxZ2XoO0LsOD094+69e4zTzFsPH5NDx8XdC9brFXdf/diLiCnwggKtlkL1k0TkLvBfAD/2hY/w7H3WJNmv/TFfq6enp2w1kwQmTYTVhs3ZOQ9f/wz7vZWBLYZZG2dsLuKZbt3557nsf2E+Dgw+T6Gq1rqxEHiWSWk/XEq85uxCVzC4Vaaw2hzRmhuV7PR5nkGTpxhZfG/xsUcRpAvmFAli70MolC4g1vdbDZFKBpXCdRu1aalN1jrOmnFGcppJ42ScsSr76yumNDHlmTnNSDTtrAHCauDeasWMkMPSRyZhEKzz9nJL8XbqygCLAVj45QLTCnRKhflQL1ugMHRdpfa6rmfYrFmdnbK5uCCu17z91ptkiaxOL3jw2sfpup7duGM/jlxebXn85IrNyZcofFRVH4nInwB+BnBXRDrX0m1md8n6/rSIdMAdzDh89n6xJ3q1WjG6kHb9wOnpmfGu00w3DNXBQPC6Eyho5mp7bWlc/bDwmyzERdZl2SzOCfPASlXZxTNYOVjnmUvZq9uMSxFxSOA3ujgkHJZI07U1EoiK9VLBU6PEl/fYQcimgdUqExXP2hI16NF+qnS9dzjw+m+aZ4MZmpnybA2LRAirNdJFhvWK1HdWYcphna0appF7r8fddVbBv7isM4cCXZRENa5rdnfB1u7+xqhXyeoVqQQlMGzWFlV3tSX2kY+89nFe/dhrPHnyhP1+z6c/+zlee+1j9Ks13bCmG3qL8Xg+Wj0YL8JyvApMLswb4O/FDL0/AfwCjOn4lRz2+v6VWKWkXwD8d++Eny1MwGqgIVYkXGLPyfldQjcwzROrBsvCEkKa3Si63m4Nf9dOV16OYEEUiJZ2YTBndVpqIfvVGZCl3Ks23rFDrb8EI8VajLD+4UmgzrGiMGUvs4Dj5WxRbEOMhCxWzUxAaiD8TBZLGCiVO5POvj5kwuAsTAZdd4RByFFYxwuSKAGFrmP22G+LozAIIWp9FYM7cdoQ2rbIZDnP47GcZ0PdFa2es18/6zNYaLqu6wgh8PDhQyaFbhjQ2LFarzm7e5/7w8Bbb73JG2++ZcUlY+CEM1arNZvVe1sf+jXgdzuODsAfUNU/IiLfCXyLiPxG4C9ipQ7w//8zsRJgb2N17p4/xOJhc4KcZmLfo6sNpxf3ODm7YLx+4hQRi4CqNjg5M88j2y2wOal17ayaXEvlLVoVmpDPCkks580P4HK+3NDbQlZzyge9/tq/otmrVzF5CS/MVZ63xngYhrZyXFY/eak+VI7Yeb9CdRotdJHghl4p86VDRLrIlDMJIEqppUTAYiYSboek8hB6bmb1Ai6CfADVjmCH8d+tB7D8JhC74PEb9nnXdaxWA9u9FZrMIvTrFbv9SELo+sz69IyPffwrefPNN3h6tSV0kTEJ9+4NbMf3sDWyqn47Vv7r+PNPYTXrjj/fAb/whWdgv2GeJ/dEdRCU1HXEYc3F3ft89tEXUNXqBkYOhavciGmy+OHVal0t5yL4BW6kGmyvxWvgGRNaCwKVGhVGSR/e4DZxoM7fWQd7iAARd+UuD0zRaBnrFCuSSXkizZaZUjJUUKVzB0cpGTDP81KQRg2/JjBDMwTr0tp3dMOKfrUiDisz6tTzIHOmi43t4XEoFS87uLiNx29fJ+/IVa5tWeGySnWclIjF0uux63vW6zXjPPP06WNCNEfS6vSEHIR+GEgZZoTYD5zdecA0zXRDz9PLS5JE7jx48MKy9NJ4CjNqiZkY7tIQkX7Fg498lB/61HfV5c3IfS1x/AdMhqoyjiNFjfdu5Bh7katDoGjQkpXceRA80lJ+umB2buezRcy4y1kNL+YFnyOhBkT1XcfQGx6c5wAyQZoJXU9QIctM37kbeJ6RNFNKOqhkumAr+pwzYxpNkNViLbIIeRY6tRzMnBJxngl9R9d1ZrhS6Molco9omS2mYkt9vRLTrPV9OVeg7q/9TBHvnBWbwCNzbUeHFHPOvP7GGzx++piLO3eQuWft7aPXJ2fWfkIicX3CKvTIOHPvwQNOdzvefviQc/kANt5cr9bgAplJhK6Drufi3n1CjKQ0owTmnOjo65IMHMQXqJrzY5q8lXCw4JaD1m4unO3vWq66OgGyWfGBm0Jdw0zzwqocL9Fd19EfrRQxRoMPo8U+59Ah/eJ9JCXmeSTNk8evZIKU+srCSpzHSXN1aBjXGMgym8adQEmIJoTeFETGuss5BImxczaHJqYcE+Lm2W3rjohIhVciHk6LY3wBnS22JcSerreeiNoF3vj8F7ja79AQ2KeETpPFa29Oub7ecnqxYk6KSo/0Hate2I2Z0K356Me+iuvt7oXl6KUQaFupjF0oJXC7vmcfI8P5HS7u3me73XJy2i1ZIdzUnq22LvCjG/qmoGJZcpce1gcY2aGIxMM+163DBQ6LP7axDXL0oBxw4U2pL+0zrFYONcyDKGqeRVTJeSZNkwmIZ67knOmC0sWOpIrIRFDrvGudWBUrG2BByiHbA2llHywyr9QFKVBBoK50VIjRmBRFmGX5okAewRxcpfJpEKELgRxhWK9ZnZww5sSjR4/Z7kf61Yr12RmzKrEf2O72rE8v2G73nN6NzNPMfjcRPOKv7wd2u5GLi3Nuq4z1rPFSCHRWiw5rHSASghk9/cBXf83X8gOf/O4amaVZa/GVIjCHWNe0dMqJLs8Mw+qgTkfF1c371nIPfgOr9R+i1aXjUJiPW2a00Wi2gSw0YoE5c+nNLczjSN8PiAvhPFoPcNEMg5Im45encayeuTlbm7Oum5k1M2mu7YTVee3iYgYao9hwbYwLN96O0oToQDsfXde6bYUjYlWo/LghdKy6YBVEx5EvvP0W19trTk5PkBBZn5ySstL3A+M0GY4KMI0j4zTz5Mkja9vcdZx+9Bzd7nn06BFd/wFrGgS3MAghWBGVYeDuqx/lzdc/y9WTJ96/OtWihuW3tXKRG3pgVOA0jRVf98NwyCEf3yQpOW/cMAhbYW17F5bPQ/R8QReCEAJJcy2kHrDsmtyZQWZQwlPDZitRG6QzTZ4ViUoOHZpmhn6FgLdvmJmm2RwnmhnTzOQtHaQLVpgmWjWl0HW1EmkInbnIw2HGvKrHaYB7/ywWI8Z44xot18prDWK9GLMbngqs1muSCp97/fM8vbok9j2xGxinmdXmlDkrFxcXPHxowhtCsDDW2NtK40ZwmmdOz049O+cD1kkWFiwagjXAyTmzlUDsVgybc8YxeWCP1sLbtYSWUJs1AoaBZUmZSmlmu71imiZW63Xdv216+CBVDN2wGa1wHwtzrVgUY3XUSIgmSFXDWV0M64xrECFIoIvRYIHMXnMkQNdbD8FpdrtUMBsu0HksduwmYkpMaSZqps+WiKri+X290XeUiqMSXJjDwcMcaomzhVu3KMRoRV5w+0Es9cAgRrlQ5piKnbXW0K6j7zu2+4nPfP513nrymJOzc9umX0ESxtkycGI/0A0rNqdnbE5PbZ4xV4MSArv9jvV6w2o1sDm5eGE5ejkEunr2Di/2arUh77d0m1MevPoRPvPk7foEqw5mFHFcOenmclrgxDSN9XXf9wfCWX+PVM9U4YFbrd4KdoxexT8sZcTEtXVZMYzKUy9IulCBuT58LPEgDk3SNBE0VqdEoeskWhnISOcuerPqotORGXstXUcOwfKAPU7DkmGbwo5INSgLpg4tVBF3HuHsB1aEMoQCNYzJMIrSUuIeP73kk9/3/eymic3ZOf1qzTgnEuLxIh1ogtAxrDeoBMZpJnYW6NXH6IVplvvVddGZqxcbL4VAlxbELXmfPABnyopK5KNf8RV84TPfzzQnxmlmSDNDHG6gwWfRa4tQW4jmMAw1i/xgWa031n8PB7iyfGeGFgdu8fbYNa9QvdSLqmeeN6DGnQ+lV3wQ6z2IC0tE0JSQJMsU1DJtAIurzv6gqJBErCppF8nR4zWkxfWmrYudUVihwny0IFqdS17ovlCvTfGkxhhRiSSEtx++zSc/9SkePn7MnfsPuH//Vc7v3uXh48cgkadXjzi9uMP65ITdOEGwhF6j9SxftKdntT5hu93X0IMYo8XIvOB4KQQ6ZatRUXjjYRicwhpI88wcIuuzO5yeXXB5dUXWudJcwSFCK5jH/x+zDynN7HZWw3i1Xls2c+vtUqku4VAynZs0oJa9KGyBUPqtLHiylkh34bESBFqF0yhf57tLPIl/WZwUQqALpfloRrI5v61wumHwcgzB4IxGC9Msq4WIeDTj8mAfOHz8IbE5UylMLfRlWBIeZmwV6ocVBGEcZ954620+87nPcr27JsTIyckJ680GRDg7v+D+/QeEfmC1OSGEjpzNNzCnzNOnV5ydn6HR9rVaW8DUNE1kha7vGT9ojTdhKddVcatHs/VDj6QVjBvOLh7w+PGjSmWthmHxznEIHWDRpm2KvjluLNxyt9+SNdF1PaEbXJuJu4WXByCD9elujEKLtAuuMb2Kh0fzVIcEy1wqlKLU58BjWLzIeQmCl0AIHpaJPxih1MJTLydix9WQnT4zqS6hosZ0WK4geJ3mEgbqglsM5yrULN7TUOAE1Lhrgj1YORu/rgJPr6/57Ode5+HjR1xvt0gIXJyfEgjuTQwWiSiR87v3ODu9YLfbkcXyFY1iVa4ut/R9z2rYsN+bY6wbVsxz4up698ErBYYumNRc2BPGA0XrfKqGM4ezM+baDsyCiGKIB5wx3BTslicuMc1lm3EcmeeZ0GWGYVWX4wVLLvRXy3qkhsa7nQm4ieVhSRq4LZHZ3MhLzQ0rxGguZaPmvCQAispSsalWZoqRhFbqrvDTC/lzk9VpHUO3UZISi+Ft4aUWDJb57Gc/y/f94A+xG2f6ofd7Z/cwehGZvuvYbXdMKYEKq83azkWXDPngiiHlTMheOgIYx4l5nt9Vn294SQRaUZ4+vSKlzMnJZmEG1HLRiB05dtx/9aMM6w3TNHtt4Uw4amUMh3TbAa51AWm3CSFYKGYyTdD3nUOeBXKUrPQ2yD16vbeyr/J34JEs59dEs5U8RZtfWeIPW6DVuQcrKaAlX1CU5NsVJobserywGQV2OARCls5bB3M6sBuKJl6699bXhUlyD+GTy0u+/wd/gE9/+tMWx7zeHPSFqYpimhk2dvysypQS+3FkmmcuLu4ap55SfRhhqRsd+x6RSN/3qCq73QfMU1jy+MqNNe/cssxp1zNJpN+ccOfuA95+8/NMU2K/Hw8ixABziLiWQtyS99cCWDffJiXLf4Nb1eO4rwE45UblsPBVLYwxbLr0RLkNx5fKni2Lom4Qih9XS/iqbzNnz9tqIv+s/IxBieQc9tLVqr2YUCqVZha83D7crXcTTJjL6tg6iloos9+OvP7GF/i+7/8+vvDmm4Su5+Lu3XqNFrsCun4wvO0aN6sgEun6wYruYDCoxNJINCgzdNGK9WSl721/bXfaFxnv6FMUkbWI/BkR+csi8h0i8q/5598sIt8nIn/J/36Sfy4i8u+LyCdF5NtF5Ke8yERiDKxWwyJEXjx8tRoQDdAN6LDmzv0HTCmxG0fmpoSVRcuZUdVq6BrQf0S91TZoFCFdvtvtdux2Oyu8PU0HLRfa5NBWsxx/B4UEaTSuFDahnZ9DmLzkLyLUKp0JK5xYsK/ESBx6C9aPxjOr+J8f1Kg5D+LnUJjLtSj/Fy7/gGcPYjWoBeaU+MLbb/Ed3/WdfNd3fxeXV1dMOdH1A/0w+LULfhzxSMeerMKcLXNHRZi8D7iqMgwDIUbOzs84uzj3oK25uuj3+z0CRFdw7wZ2fDFZ3wD/V1X9g0fb/wNYK7evA346ljj70597BMGLMa6sco9zqFOarJtq35NlA0zcffVVuu8d2I+juZFdkArWO/buFaFrK/8UQSpePNvWppKzvU5pZr83rCy91getZQna1svHjEq7/N4KiQqbcUQZGl8Wqdm1hTYLdqGsTp9pZXXnRh2anEmxeI3YlGK4bW71s2gPQ0qJfmUM0ziOPHz4kMeXT/nMZz/LdrtDQmAa98yTV1KNwViY2DGOI72Hiq42a8JgtTQsliaiOi8+BLGYbwkrYoxcXFzw5MmTJU9yVoahp4+9N7h/D1kOtatxW9b3s8Y3AP+p/+7bROSuHPY0vO0YPHnymJVfzNNTq5jTBcuZU8zRMhOIm1NOzy+4fPSwttftus7zrKgUWdmvLeuHWttf3NiuLJmLAWUCvct7upTo+o4Yonn4MM1cYoOXAumHBdRbDrysBCW1Syo+xjGqaeUSDGTt4izjpT6YhcX27cv5lge0OGIoFOJzBLoYe8mrI4UgpHnm8ePHvPX2W7z59ttcba8tPDYExv3oME/chW776fuOy8unnJ563bx+YHWyIamns+VMiKH6FqrDySFbeRBi37G/3haEyJOnj5F3yUO/UBiTiESxHoVvAH9cVf+0f/WbHFb8VrECM9Bkffu4tde3iPxqEflzIvLnrq+u6Puek5MTVquVByoZbxvAI7A6Yjeg/Zp7r7xqT+6c8a5itSRAs3/ABdPpsgOt1Gy3YEuzvpeYB/t8zontfsd2t7Ns5ZJaxU24Uf7a9+3rUs+j9DasAfu5RM254SgY7RasFFbCsk1KXmByY1DxcFIxb1zSkia81PU4uu5L/EmhHf1Mt9fXfObTP8SnPvVJ3nzzC0zzWEnuaTZ2I8/GTfbDii4ObNYbdvsdc5o4OTtl2GzYXFxwducuw2rF2dkZEmC9WlV7ZZ5ThXTt/Vj1g+WP9j3TPFtyr1pBnxcdLyTQqppU9SdhybA/TUT+JuDXYdnffxtwH/gXX/iots/fpao/VVV/6vnFBeM4cn11BVAxk/UqdMGOga4fWG1O+ehrH6fr197MMTPPXnDbdnxz2WfRwFWYb2VCDgWgxW5Fm+x2O653O7Z7F+5acGYR2vqZLu0XjCKc6nY5N91nfSyNdvINzXoMWxaPSoEwllpWbIYa5ukCXHjz8rry6Fi02+uvf55Pf/qHeOMLb1QBSslSvsr5lHPr+8H8A8GKMe73e9brNWdnZ+AwZxpHYuy4e/8eXeys3FmTHLBeb/z+GRfd931lboods8z7SxRtp0vW989V1X/bP96LyH8C/F/8/Qv1+m6HIEzjyFtvvsl6s7HlRwSqs8F54GBF0Vend7j74FWuHz80zlosnNSca5Eb4bNF2I9Gy0oswf43PY1tIkApXDhNE+M0se6t50kLOcpvWygjDnprHeri+ZNDzrxlJcprw5axZqaLeWTsd+Uc/MEpwl2Kj5f5t6yFZoM/19srHj16xNsP32a7u/YZGB2aanX+XF3dxXg7G4YlDNWN0PPzCzIGRbqhZ1it3DkjdEMxII2rnpPZBbHrGaep0nfTPBNCMOeLHzuEwOpddMF6EZbjVbF6HMiS9f3dIvKafybAz+Ow1/evcLbjbwcePw8/g8VyWIrSyG57XXPXDHd21e2sKBoiOfZc3H/AfprZ7ffMc6q98EpTG9tvgx39wgfHmYUJsO08UH055wNt2GrLlmee55ntbst+v2e/3x8wIm3/78pilLlA1aa1UEtuMD00Rh9eUWo5j7Kf0p6iDY4yLWwxHVWQQ6ilxxAhk3j70Vv8wA/+AJ/93Ge43l6iHmq73+/r9S/3IGfLeZzGkazKerMhdr1loEwjEgLrzcYKP3bGIU8p1RWp67oaDHZ6ds6wMq9sVhBPJ5vmuXohwcJdS0VTa6D6YuOLyfr+78RKHAjwl4B/yrf/o8DXA58EroF/7B2PIMI8T5ycbGq6e8WjWL0IVJnF++v1Kx587DU+8wOfYrvf0a0G9vNkTJYutFg7VEv7irJOWxScSHA8XAS6CNZNvF320zIYWZXduGfOiX4YWA0DHbF6FoPHYRRjM1VDVRemg0Mh9gkeNDYywTz8DJrOAweflqi49iGFeZ64vLzk6dMnPHr8iGkaSXnG6gECLM3mLeKtZNvkWj744uKCs9NTb9uBGeZe+ZRgBuacM/N+T4y9YfxsvRWnaSZ2kaAm4IXGa+sRhtDR91RWqe97Vu9lGQN9dtb3z37G9gr8mheeAXZhVquBt99+m35Y0a/WbDYbclameaTvTNvELiKyoheQece9+w/4wmc/7cZWQnsrsRuLGuM2gaTCGFiW45znQ43ecMU0NJvkw31mteTeomVK24yu6+iqNb/UujiAGEZCe4xbWMTP2Y8KY7SI/GG5hLLPdo6VERBzlARnCa6uLnn46CFPnjxmnqfmIbZ2EybER7XodDF6i6YeBnOakJZM9xAj/TCgjqdX5wZrhiHW1QcRtrsdp2dnXLmttNlsSCkzjrli5RBy1ebGTXd03QcsSda0sF2Q119/vTa4OT05oZfAPE3s99tKqxGsr+HdBx/hC5/7LHOaybkjZSW2cR2F/6lMnfqSu0ABa0y5DJGlBKwJoNFNLVSoD0vwGGbsQUk5VcEo8b29pxTF0AReiUfD+dRKR9YSx0CD5asoe5V88Xp3pWmnYE6VlGZ3i6faIzDEyH4cefzkMbtxx/XVFSlPjVZc4FR272SQQBZzSClat03zXJNhp3nmZH1Sr+HZ2Tl37t4jh46YZYknlMB2u6MfVmimOk8GDyozyjOz2+1QzXSdVao+OdlYjLivbsdK6XnjpRDoGCPX2709od7X5OrykqvLSy4uLhiGgdVqXbFp0pnQD1w8eJX16TlpvkZ1oDZ8LHhT8V7ZuCvdA4+q9X/o2BBnCpaG7UthyAMuuXGKlGqaRRMbG2LYb54To/PkQQK9swud13srGSNaeOYirNUodeEvsR6pwAshiK1axuBAmpRp3IOYgb3d79hPI9vdzrB9nsne23yevdsVt7AnWHZN8bzOuXTgGhGxYKJu6D1pwM77/OKc0A+odKxi71p34vT8LuM0McTIk6srQuiYZ/vu/Pys2iNdZ6uICGw2a4Mn41Qzi3L+AAr06dmJVc10Y+Tu3bs3An2Kdy5rx3BySsgTH/n4x/nc9/11xnE0rJXUirxoqmlQJmyu4ZpY5pbNqF5FF7Scb7INx4xEmdMxS7H8r86Vm3aLIq6xIzH27mlsBdt+m4ogO09cSoFZo3qtXsPsEYfjaEbpdm9xKLtxX9mDXOuRuHGaDuM4FpoQ5tngx5zUS9umA849Ng+kYXL7/OzUMrNjiKzPzpE41BQuY0uUfrA4jcJetH2/LcrRFMHgcC0ngzf7/f6Dp6FFAicnJ6SU+MxnPsMP/dAPsdlsuHPnzkEsbIxxaf4YAwwrLh68wme/73uZp2RhiqKkFAi9adcqmAvuOGQbKk0GVu2taK4liKeNrDucd1MmVqrabvZblmzXwDEiafISC/uKcQvj0r5uvXnGkphbe57H2uPFWJadsQ8519iWEhdtrYcLF75MzzBzPnj4ymsLTzWHT1IT6mlOHmJrpW+j03EFU69PTuj6FWN26CjmM+j6nm5YWf2/lFhvTpiThcNOs3kIQ3Tn2Twzp8Q6BOY5MXgL6NVqVav7v8h4SQTa4m6LFT2OI5eX5m1XVTabzYERZK7nnlki53fu8+DVj/HW53+Ik5yZkxCD0rkglRt5IJBHGso/9PdQYEZb6qAcu7yvmrkR5sKktBq7hKAWp8JcIt3UMr+1Nrt3iOQwyR6WXFcNj3SyFLLZwgE0q3PG7h5nibBrQwDKPExLL1FurSC3RmfKlkWePPZ5u99DTpyentA7p6xOx/T9gITIrCDR2jK//sYXuHf/FRAxO2K14vLyClUr5xZDxzjODT42HwNumE/TxGp1Vh+YYXjxHityvIS+H0NEngLf837P412MVzgq4P4Sjw/SXOHZ8/1qVX31nX78Umho4HtU9ae+35N40SEif+6DMt8P0lzhi5/vi9dY+nB8OD4A40OB/nB8WY2XRaB/1/s9gXc5Pkjz/SDNFb7I+b4URuGH48PxXo2XRUN/OD4c78l43wVaRH6uiHyPWFLtN77f8wEQkW8SkTdE5K82n90XkT8uIn/d/7/nn4v8MJKC38O5fpWI/AkR+U6xJOZ/9mWdrzw74fpHi8if9jn9fhEZ/POVv/+kf/+JdzxIS7D/SP8BEWvi+TXAAPxl4Me/n3Pyef2dwE8B/mrz2b8FfKO//kbg3/TXXw98K+b6+NuBP/0jPNfXgJ/ir8+Bvwb8+Jdxvn7MM3/dA3/a5/AHgF/sn/9O4P/or/9p4Hf6618M/P53PMb7LDg/A/hjzftfB/y693NOzVw+cSTQ3wO81gjR9/jr/wj4Jbdt9z7N+7/EkjBe6vkCJ8BfwCoCvAl0xzIB/DHgZ/jrzreT5+33/YYcL5RQ+5KMj+qSefN54KP++qU5B1+SfzKm+V7K+cpRwjXvos028Bhrs/3M8X4L9AdyqKmMl4oeEpEz4A8B/5yqPmm/e5nmq0cJ17wHbbbb8X4L9LtOqH0fx+uy5FG+hmkYeAnOQawA0B8Cfo+q/uf+8Us7X7CEa6wbcW2zfct86lzlBdtsv98C/WeBr3Mrd8CA/x9+n+f0rPGHsZbPcLMV9LtKCn4vh1i42n8MfJeq/paXeb5ye8L1d7G02b5truUcXqjN9stgfH09Zpl/L/Avvd/z8Tn9PuBzwIRhul+FYbf/FvjrwH8D3PdtBfgPff5/BfipP8Jz/TswOPHtWLLyX/Jr+tLNF/hbsDba345VCfhX/POvAf4Mllj9/wZW/vna33/Sv/+adzrGh57CD8eX1Xi/IceH48Pxno4PBfrD8WU1PhToD8eX1fhQoD8cX1bjQ4H+cHxZjQ8F+sPxZTU+FOgPx5fV+FCgPxxfVuP/D/0ZsyUluqPqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fpath='../test_images/20180720_174416.jpg'\n",
    "frame_bgr=cv2.imread(fpath)\n",
    "plt.figure(figsize=(5, 5))\n",
    "frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)\n",
    "plt.imshow(frame)\n",
    "bounding_boxes, points = imgProcessing.detect_faces(frame)\n",
    "points = points.T\n",
    "for bbox,p in zip(bounding_boxes, points):\n",
    "    box = bbox.astype(np.int)\n",
    "    x1,y1,x2,y2=box[0:4]    \n",
    "    face_img=frame[y1:y2,x1:x2,:]\n",
    "    \n",
    "    img_tensor = test_transforms(Image.fromarray(face_img))\n",
    "    img_tensor.unsqueeze_(0)\n",
    "    scores = model(img_tensor.to(device))\n",
    "    scores=scores[0].data.cpu().numpy()\n",
    "    plt.figure(figsize=(3, 3))\n",
    "    plt.imshow(face_img)\n",
    "    if USE_MTL:\n",
    "        plt.title(f\"{idx_to_class[np.argmax(scores[:8])]} V:{scores[8]:.2f} A:{scores[9]:.2f}\")\n",
    "    else:\n",
    "        plt.title(idx_to_class[np.argmax(scores)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgProcessing.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature extractor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_extractor_model = torch.load(PATH)\n",
    "feature_extractor_model.classifier=torch.nn.Identity()\n",
    "feature_extractor_model.eval()\n",
    "feature_extractor_model=feature_extractor_model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AFEW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/HDD6TB/datasets/emotions/EmotiW/AFEW/\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR=os.path.join(ALL_DATA_DIR,'EmotiW/AFEW/')\n",
    "print(DATA_DIR)\n",
    "emotion_to_index = {'Angry':0, 'Disgust':1, 'Fear':2, 'Happy':3, 'Neutral':4, 'Sad':5, 'Surprise':6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d8e79e29359404f9f16f585cb05f117",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/383 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d1e7bbca3494c359ba056721bf0aa40",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/773 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_features(data_dir):\n",
    "    filename2features={}\n",
    "    for filename in tqdm(os.listdir(data_dir)):\n",
    "        frames_dir=os.path.join(data_dir,filename)\n",
    "        X_global_features,X_isface=[],[]\n",
    "        imgs=[]\n",
    "        for img_name in os.listdir(frames_dir):\n",
    "            img = Image.open(os.path.join(frames_dir,img_name))\n",
    "            img_tensor = test_transforms(img)\n",
    "            X_isface.append('noface' not in img_name)\n",
    "                \n",
    "            if img.size:\n",
    "                imgs.append(img_tensor)\n",
    "                if len(imgs)>=16:        \n",
    "                    #global_features,feats,scores=feature_extractor_model.predict(inp)\n",
    "                    scores = feature_extractor_model(torch.stack(imgs, dim=0).to(device))\n",
    "                    scores=scores.data.cpu().numpy()\n",
    "                    #print(scores.shape)\n",
    "            \n",
    "                    #print(global_features.shape,feats.shape,scores.shape)\n",
    "                    if len(X_global_features)==0:\n",
    "                        X_global_features=scores\n",
    "                    else:\n",
    "                        X_global_features=np.concatenate((X_global_features,scores),axis=0)\n",
    "                    \n",
    "                    imgs=[]\n",
    "\n",
    "        if len(imgs)>0:        \n",
    "            scores = feature_extractor_model(torch.stack(imgs, dim=0).to(device))\n",
    "            scores=scores.data.cpu().numpy()\n",
    "            #print(scores.shape)\n",
    "\n",
    "            #print(global_features.shape,feats.shape,scores.shape)\n",
    "            if len(X_global_features)==0:\n",
    "                X_global_features=scores\n",
    "            else:\n",
    "                X_global_features=np.concatenate((X_global_features,scores),axis=0)\n",
    "\n",
    "        X_isface=np.array(X_isface)\n",
    "        #print(X_global_features.shape,X_feats.shape,X_scores.shape)\n",
    "        filename2features[filename]=(X_global_features,X_isface)\n",
    "    return filename2features\n",
    "\n",
    "filename2features_val=get_features(os.path.join(DATA_DIR,'val/AlignedFaces_LBPTOP_Points_Val/frames_mtcnn_cropped/'))\n",
    "filename2features_train=get_features(os.path.join(DATA_DIR,'train/AlignedFaces_LBPTOP_Points/frames_mtcnn_cropped/')) #_cropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enet0_8_pt_feat_emotiw.pickle\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "MODEL2EMOTIW_FEATURES=model_name+'_feat_emotiw.pickle' #'_feat_bgr_emotiw.pickle'\n",
    "\n",
    "#MODEL2EMOTIW_FEATURES='enet0_vggface2_new1_pt_feat_emotiw.pickle'\n",
    "#MODEL2EMOTIW_FEATURES='enet0_vggface2_pt_feat_emotiw.pickle'\n",
    "print(MODEL2EMOTIW_FEATURES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(MODEL2EMOTIW_FEATURES, 'wb') as handle:\n",
    "    pickle.dump([filename2features_train,filename2features_val], handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "773 383\n"
     ]
    }
   ],
   "source": [
    "with open(MODEL2EMOTIW_FEATURES, 'rb') as handle:\n",
    "    filename2features_train,filename2features_val=pickle.load(handle)\n",
    "print(len(filename2features_train),len(filename2features_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(773, 5120) (773,)\n",
      "(383, 5120) (383,)\n"
     ]
    }
   ],
   "source": [
    "def create_dataset(filename2features,data_dir):\n",
    "    x = []\n",
    "    y = []\n",
    "    has_faces=[]\n",
    "    ind=0\n",
    "    for category in emotion_to_index:\n",
    "        for filename in os.listdir(os.path.join(data_dir,category)):\n",
    "            fn=os.path.splitext(filename)[0]\n",
    "            if not fn in filename2features:\n",
    "                continue\n",
    "            features=filename2features[fn]\n",
    "            total_features=None\n",
    "            if True:\n",
    "                #prev=features[0].shape\n",
    "                cur_features=features[0][features[-1]==1]\n",
    "                #print(prev,features.shape)\n",
    "            else:\n",
    "                cur_features=features[0]\n",
    "            if len(cur_features)==0:\n",
    "                has_faces.append(0)\n",
    "                total_features=np.zeros_like(feature)\n",
    "            else:\n",
    "                has_faces.append(1)\n",
    "                #mean_features=features.mean(axis=0)\n",
    "                mean_features = (np.mean(cur_features, axis=0))\n",
    "                std_features = (np.std(cur_features, axis=0))\n",
    "                max_features = (np.max(cur_features, axis=0))\n",
    "                min_features = (np.min(cur_features, axis=0))\n",
    "\n",
    "                # join several features together\n",
    "                feature = np.concatenate((mean_features, std_features, min_features, max_features), axis=None)                    \n",
    "                #feature = np.concatenate((mean_features, std_features, min_features), axis=None)\n",
    "                #feature = np.concatenate((mean_features, min_features, max_features), axis=None)\n",
    "                #feature = np.concatenate((max_features, std_features), axis=None)\n",
    "                #feature=max_features\n",
    "\n",
    "                total_features=feature\n",
    "            \n",
    "            if total_features is not None:\n",
    "                x.append(total_features)\n",
    "                y.append(emotion_to_index[category])\n",
    "    x=np.array(x)\n",
    "    y=np.array(y)\n",
    "    has_faces=np.array(has_faces)\n",
    "    print(x.shape,y.shape)\n",
    "    return x,y,has_faces\n",
    "\n",
    "x_train, y_train, has_faces_train = create_dataset(filename2features_train,os.path.join(DATA_DIR,'train'))\n",
    "x_test, y_test, has_faces_test = create_dataset(filename2features_val,os.path.join(DATA_DIR,'val'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_norm=preprocessing.normalize(x_train,norm='l2')\n",
    "x_test_norm=preprocessing.normalize(x_test,norm='l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5989445910290238\n",
      "Complete accuracy: 0.5926892950391645\n"
     ]
    }
   ],
   "source": [
    "#clf = svm.LinearSVC(C=3.5) #0.5 1.1\n",
    "clf = svm.LinearSVC(C=1.1) \n",
    "#clf = svm.SVC(kernel='rbf')\n",
    "#np.random.seed(1)\n",
    "#clf=RandomForestClassifier(n_estimators=1000,max_depth=7, n_jobs=-1)\n",
    "#clf=KNeighborsClassifier(n_neighbors=3,p=2)\n",
    "\n",
    "#import xgboost as xgb\n",
    "#clf = xgb.XGBClassifier(n_estimators=1000,use_label_encoder=False)\n",
    "\n",
    "if True:    \n",
    "    clf.fit(x_train_norm[has_faces_train==1], y_train[has_faces_train==1])\n",
    "    y_pred = clf.predict(x_test_norm)\n",
    "else:\n",
    "    clf.fit(x_train[has_faces_train==1], y_train[has_faces_train==1])\n",
    "    y_pred = clf.predict(x_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test[has_faces_test==1], y_pred[has_faces_test==1]))\n",
    "print(\"Complete accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adaptive Frame Rate "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "source code for the \"Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction\" accepted at ICML 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(filename2features,data_dir, is_train=False,stride=1):\n",
    "    x = []\n",
    "    y = []\n",
    "    filenames=[]\n",
    "    num_frames=[]\n",
    "    ind=0\n",
    "    for category in emotion_to_index:\n",
    "        for filename in os.listdir(os.path.join(data_dir,category)):\n",
    "            fn=os.path.splitext(filename)[0]\n",
    "            if not fn in filename2features:\n",
    "                continue\n",
    "            features=filename2features[fn]\n",
    "            total_features=None\n",
    "            if False and is_train:\n",
    "                print(fn,features[0].shape,features[0][features[-1]==1].shape)\n",
    "            if True:\n",
    "                #prev=features[0].shape\n",
    "                cur_features=features[0][features[-1]==1]\n",
    "                #print(prev,features.shape)\n",
    "            else:\n",
    "                cur_features=features[0]\n",
    "            if len(cur_features)==0:\n",
    "                cur_features=features[0]\n",
    "                if is_train or True:\n",
    "                    continue\n",
    "\n",
    "            cur_features=cur_features[::stride]\n",
    "            #mean_features=features.mean(axis=0)\n",
    "            mean_features = (np.mean(cur_features, axis=0))\n",
    "            std_features = (np.std(cur_features, axis=0))\n",
    "            max_features = (np.max(cur_features, axis=0))\n",
    "            min_features = (np.min(cur_features, axis=0))\n",
    "\n",
    "            # join several features together\n",
    "            feature = np.concatenate((mean_features, std_features, min_features, max_features), axis=None)                    \n",
    "            #feature = np.concatenate((mean_features, std_features, min_features), axis=None)\n",
    "            #feature = np.concatenate((mean_features, std_features, max_features), axis=None)\n",
    "            #feature = np.concatenate((max_features, std_features), axis=None)\n",
    "            #feature = np.concatenate((mean_features, max_features), axis=None)\n",
    "            \n",
    "            #feature=min_features\n",
    "            #feature=std_features\n",
    "            #feature=max_features\n",
    "            #feature=mean_features\n",
    "\n",
    "            total_features=feature\n",
    "            \n",
    "            if total_features is not None:\n",
    "                x.append(total_features)\n",
    "                y.append(emotion_to_index[category])\n",
    "                num_frames.append(len(cur_features))\n",
    "                filenames.append(os.path.join(category,filename))\n",
    "    x=np.array(x)\n",
    "    y=np.array(y)\n",
    "    num_frames=np.array(num_frames)\n",
    "    #print(x.shape,y.shape)\n",
    "    return x,y,num_frames,filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.5989445910290238 0.5883905013192612 0.6322869955156951\n",
      "2 0.5804749340369393 0.5883905013192612 0.6233183856502242\n",
      "3 0.5725593667546174 0.5883905013192612 0.6143497757847534\n",
      "4 0.5646437994722955 0.5883905013192612 0.6098654708520179\n",
      "5 0.5277044854881267 0.5883905013192612 0.57847533632287\n",
      "6 0.5461741424802111 0.5883905013192612 0.5964125560538116\n",
      "7 0.5303430079155673 0.5883905013192612 0.5829596412556054\n",
      "8 0.5408970976253298 0.5883905013192612 0.5874439461883408\n",
      "9 0.5329815303430079 0.5883905013192612 0.5919282511210763\n",
      "10 0.5013192612137203 0.5883905013192612 0.5605381165919282\n",
      "12 0.5277044854881267 0.5883905013192612 0.5919282511210763\n",
      "15 0.5013192612137203 0.5883905013192612 0.5829596412556054\n"
     ]
    }
   ],
   "source": [
    "C=1.1 #0.6 #2.0 #0.5 #0.8 #0.1 0.7\n",
    "clf = svm.LinearSVC(C=C)\n",
    "x_train, y_train, num_frames_train,_ = create_dataset(filename2features_train,os.path.join(DATA_DIR,'train'),is_train=True, stride=1)\n",
    "x_train_norm=preprocessing.normalize(x_train,norm='l2')\n",
    "clf.fit(x_train_norm, y_train)\n",
    "\n",
    "for stride in [1,2,3,4,5,6,7,8,9,10,12,15]: #[1]: #\n",
    "    x_test, y_test, num_frames_test,_ = create_dataset(filename2features_val,os.path.join(DATA_DIR,'val'),is_train=False, stride=stride)\n",
    "    x_test_norm=preprocessing.normalize(x_test,norm='l2')\n",
    "\n",
    "    if True:\n",
    "        y_pred = clf.predict(x_test_norm)\n",
    "    else:\n",
    "        decision_values = clf.decision_function(x_test_norm)\n",
    "        y_pred=decision_values.argmax(axis=1)\n",
    "        max_decision_values=decision_values.max(axis=1)\n",
    "    print(stride,metrics.accuracy_score(y_test, y_pred), (max_decision_values>0).mean(),(y_test[max_decision_values>0]==y_pred[max_decision_values>0]).mean())\n",
    "    #print(delta,str.format('\\t{0:.4f}\\t{1:.4f}',mord_mse,reg_mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stride 18 0.1\n",
      "best_threshold 0.08612321740995665 115\n",
      "stride 9 0.1\n",
      "best_threshold 0.11035874437126736 124\n",
      "stride 6 0.1\n",
      "best_threshold 0.1073352034266093 121\n",
      "stride 3 0.1\n",
      "best_threshold 0.1169380934718865 123\n",
      "stride 1 0.1\n",
      "best_threshold 0.10636898392038563 122\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "def get_threshold(stride,fpr_corrected):\n",
    "    x_train_all, y_train_all, _, _ = create_dataset(filename2features_train,os.path.join(DATA_DIR,'train'),is_train=True, stride=stride)\n",
    "    x_train_all=preprocessing.normalize(x_train_all,norm='l2')\n",
    "\n",
    "    X_tr, X_val, Y_tr, Y_val = model_selection.train_test_split(x_train_all,y_train_all, test_size=0.5, random_state=42, stratify=y_train_all)\n",
    "    clf_tmp = svm.LinearSVC(C=clf.C)\n",
    "    clf_tmp.fit(X_tr, Y_tr)\n",
    "\n",
    "    decision_values = clf_tmp.decision_function(X_val)\n",
    "    predictions=decision_values.argmax(axis=1)\n",
    "    max_decision_values=decision_values.max(axis=1)\n",
    "    mistakes=max_decision_values[predictions!=Y_val]\n",
    "    best_threshold=-1\n",
    "    for i,threshold in enumerate(sorted(max_decision_values[predictions==Y_val])[::-1]):\n",
    "        tpr=i/len(predictions)\n",
    "        fpr=(mistakes>threshold).sum()/len(predictions)\n",
    "        #print(threshold,fpr,tpr)\n",
    "        if fpr>fpr_corrected:\n",
    "            if best_threshold==-1:\n",
    "                best_threshold=threshold\n",
    "            print('best_threshold',best_threshold,i)\n",
    "            break\n",
    "        best_threshold=threshold\n",
    "    return best_threshold\n",
    "\n",
    "stride2threshold={}\n",
    "strides=[18,9,6,3,1]\n",
    "for s_ind,stride in enumerate(strides):\n",
    "    fpr_corrected=0.1 #0.1*(s_ind+1)/(len(strides)) #0.08 #\n",
    "    print('stride',stride,fpr_corrected)\n",
    "    stride2threshold[stride]=get_threshold(stride,fpr_corrected)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5989445910290238\n"
     ]
    }
   ],
   "source": [
    "x_test, y_test, num_frames_test,_ = create_dataset(filename2features_val,os.path.join(DATA_DIR,'val'),is_train=False, stride=1)\n",
    "x_test_norm=preprocessing.normalize(x_test,norm='l2')\n",
    "decision_values = clf.decision_function(x_test_norm)\n",
    "y_pred=decision_values.argmax(axis=1)\n",
    "print(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18, 9, 3, 1]\n",
      "Total acc 0.6015831134564644  Num processed frames: 15490 0.7759743512674081\n",
      "[18, 9, 1]\n",
      "Total acc 0.6015831134564644  Num processed frames: 13165 0.6595030558060314\n",
      "[18, 6, 1]\n",
      "Total acc 0.6015831134564644  Num processed frames: 13101 0.6562969642320409\n",
      "[9, 3, 1]\n",
      "Total acc 0.5963060686015831  Num processed frames: 15501 0.7765253982566878\n",
      "[6, 3, 1]\n",
      "Total acc 0.5989445910290238  Num processed frames: 15810 0.7920048091373609\n",
      "[18, 1]\n",
      "Total acc 0.5989445910290238  Num processed frames: 12699 0.6361587015329125\n",
      "[9, 1]\n",
      "Total acc 0.5963060686015831  Num processed frames: 13617 0.6821460775473399\n",
      "[6, 1]\n",
      "Total acc 0.5989445910290238  Num processed frames: 13636 0.6830978859833684\n",
      "[3, 1]\n",
      "Total acc 0.5989445910290238  Num processed frames: 15178 0.760344654844204\n",
      "[18]\n",
      "Total acc 0.5145118733509235  Num processed frames: 1286 0.06442240256487326\n",
      "[9]\n",
      "Total acc 0.5329815303430079  Num processed frames: 2391 0.1197775773970544\n",
      "[6]\n",
      "Total acc 0.5461741424802111  Num processed frames: 3478 0.1742310389740507\n",
      "[3]\n",
      "Total acc 0.5725593667546174  Num processed frames: 6785 0.3398958020238453\n",
      "[1]\n",
      "Total acc 0.5989445910290238  Num processed frames: 19962 1.0\n"
     ]
    }
   ],
   "source": [
    "x_test, y_test, num_frames_test,filenames_test = create_dataset(filename2features_val,os.path.join(DATA_DIR,'val'),is_train=False, stride=1)\n",
    "num_frames_total=num_frames_test.sum()\n",
    "all_strides=[\n",
    "    [18,9,3,1],\n",
    "    [18,9,1],\n",
    "    [18,6,1],\n",
    "    [9,3,1],\n",
    "    [6,3,1],\n",
    "    [18,1],\n",
    "    [9,1],\n",
    "    [6,1],\n",
    "    [3,1]\n",
    "]\n",
    "#all_strides=[]\n",
    "for s in stride2threshold.keys():\n",
    "    all_strides.append([s])\n",
    "    \n",
    "for strides in all_strides:\n",
    "    print(strides)\n",
    "    last_stride=strides[-1]    \n",
    "    \n",
    "    indices=None\n",
    "    y_pred_seq=None\n",
    "    total_frames_processed=0\n",
    "    #for stride,threshold in [(10,0.1),(6,0.3),(1,0)]:\n",
    "    #for stride,threshold in [(10,0.15),(1,0)]:\n",
    "    for stride in strides:\n",
    "        threshold=stride2threshold[stride]\n",
    "        x_test, y_test, num_frames_test,_ = create_dataset(filename2features_val,os.path.join(DATA_DIR,'val'),is_train=False, stride=stride)\n",
    "        x_test_norm=preprocessing.normalize(x_test,norm='l2')\n",
    "\n",
    "        decision_values = clf.decision_function(x_test_norm)\n",
    "        y_pred=decision_values.argmax(axis=1)\n",
    "        max_decision_values=decision_values.max(axis=1)\n",
    "        #print(stride,metrics.accuracy_score(y_test, y_pred), (max_decision_values>threshold).mean(),(y_test[max_decision_values>0]==y_pred[max_decision_values>0]).mean())\n",
    "        if y_pred_seq is None:\n",
    "            y_pred_seq=y_pred\n",
    "            indices=np.array(max_decision_values<=threshold) #np.arange(len(y_test))\n",
    "            \n",
    "            if stride==last_stride:\n",
    "                total_frames_processed+=num_frames_test.sum()\n",
    "            else:\n",
    "                total_frames_processed=(num_frames_test[indices==False]).sum()\n",
    "        else:\n",
    "            y_pred_seq[indices]=y_pred[indices]\n",
    "            if stride==last_stride:\n",
    "                total_frames_processed+=(num_frames_test[indices]).sum()\n",
    "            else:\n",
    "                unreliable_indices=np.array(max_decision_values<=threshold)\n",
    "                cur_frames_processed=(num_frames_test[indices & unreliable_indices==False]).sum()\n",
    "                total_frames_processed+=cur_frames_processed\n",
    "                indices=unreliable_indices & indices\n",
    "    print('Total acc',metrics.accuracy_score(y_test, y_pred_seq),' Num processed frames:',total_frames_processed, total_frames_processed/num_frames_total)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18, 9, 3, 1]\n",
      "stride 18 0.075\n",
      "best_threshold 0.15087943410466864 102\n",
      "445 1286\n",
      "stride 9 0.15\n",
      "best_threshold 0.01931227660137802 138\n",
      "stride 3 0.22499999999999998\n",
      "best_threshold -0.11815472314371855 159\n",
      "Total acc 0.5936675461741425  Num processed frames: 13456 0.6740807534315199\n",
      "[18, 9, 1]\n",
      "stride 18 0.09999999999999999\n",
      "best_threshold 0.08611320472305128 115\n",
      "511 1286\n",
      "stride 9 0.19999999999999998\n",
      "best_threshold -0.09436104522927136 156\n",
      "Total acc 0.5910290237467019  Num processed frames: 10615 0.5317603446548442\n",
      "[18, 6, 1]\n",
      "stride 18 0.09999999999999999\n",
      "best_threshold 0.0861118061058318 115\n",
      "511 1286\n",
      "stride 6 0.19999999999999998\n",
      "best_threshold -0.04895691538696789 149\n",
      "Total acc 0.5989445910290238  Num processed frames: 11700 0.5861136158701533\n",
      "[9, 3, 1]\n",
      "stride 9 0.09999999999999999\n",
      "best_threshold 0.1103475825025616 124\n",
      "872 2391\n",
      "stride 3 0.19999999999999998\n",
      "best_threshold -0.06872089280832505 150\n",
      "Total acc 0.5963060686015831  Num processed frames: 13810 0.6918144474501553\n",
      "[6, 3, 1]\n",
      "stride 6 0.09999999999999999\n",
      "best_threshold 0.10733639439809661 121\n",
      "1335 3478\n",
      "stride 3 0.19999999999999998\n",
      "best_threshold -0.06872710306920282 150\n",
      "Total acc 0.5963060686015831  Num processed frames: 14279 0.715309087265805\n",
      "[18, 1]\n",
      "stride 18 0.15\n",
      "best_threshold -0.026719098008566977 134\n",
      "620 1286\n",
      "Total acc 0.5883905013192612  Num processed frames: 11071 0.5546037471195271\n",
      "[9, 1]\n",
      "stride 9 0.15\n",
      "best_threshold 0.019332990136570416 138\n",
      "1049 2391\n",
      "Total acc 0.5963060686015831  Num processed frames: 12322 0.6172728183548742\n",
      "[6, 1]\n",
      "stride 6 0.15\n",
      "best_threshold 0.014934862944395277 135\n",
      "1630 3478\n",
      "Total acc 0.5936675461741425  Num processed frames: 12245 0.6134154894299169\n",
      "[3, 1]\n",
      "stride 3 0.15\n",
      "best_threshold 0.04811246987957618 137\n",
      "2871 6785\n",
      "Total acc 0.5963060686015831  Num processed frames: 14408 0.7217713655946298\n",
      "[18]\n",
      "Total acc 0.5145118733509235  Num processed frames: 1286 0.06442240256487326\n",
      "[9]\n",
      "Total acc 0.5329815303430079  Num processed frames: 2391 0.1197775773970544\n",
      "[6]\n",
      "Total acc 0.5461741424802111  Num processed frames: 3478 0.1742310389740507\n",
      "[3]\n",
      "Total acc 0.5725593667546174  Num processed frames: 6785 0.3398958020238453\n",
      "[1]\n",
      "Total acc 0.5989445910290238  Num processed frames: 19962 1.0\n"
     ]
    }
   ],
   "source": [
    "# Complete example\n",
    "for strides in all_strides:\n",
    "    print(strides)    \n",
    "    last_stride=strides[-1]\n",
    "    \n",
    "    indices=None\n",
    "    y_pred_seq=None\n",
    "    total_frames_processed=0\n",
    "    #for stride,threshold in [(10,0.1),(6,0.3),(1,0)]:\n",
    "    #for stride,threshold in [(10,0.15),(1,0)]:\n",
    "    for s_ind,stride in enumerate(strides):\n",
    "        if stride!=last_stride:\n",
    "            fpr_corrected=0.3*(s_ind+1)/(len(strides)) #0.08 #0.1 #\n",
    "            print('stride',stride,fpr_corrected)\n",
    "            threshold=get_threshold(stride,fpr_corrected)\n",
    "        else:\n",
    "            threshold=0\n",
    "        \n",
    "        x_test, y_test, num_frames_test,_ = create_dataset(filename2features_val,os.path.join(DATA_DIR,'val'),is_train=False, stride=stride)\n",
    "        x_test_norm=preprocessing.normalize(x_test,norm='l2')\n",
    "\n",
    "        decision_values = clf.decision_function(x_test_norm)\n",
    "        y_pred=decision_values.argmax(axis=1)\n",
    "        max_decision_values=decision_values.max(axis=1)\n",
    "        #print(stride,metrics.accuracy_score(y_test, y_pred), (max_decision_values>threshold).mean(),(y_test[max_decision_values>0]==y_pred[max_decision_values>0]).mean())\n",
    "        if y_pred_seq is None:\n",
    "            y_pred_seq=y_pred\n",
    "            indices=np.array(max_decision_values<=threshold) #np.arange(len(y_test))\n",
    "            \n",
    "            if stride==last_stride:\n",
    "                total_frames_processed+=num_frames_test.sum()\n",
    "            else:\n",
    "                total_frames_processed=(num_frames_test[indices==False]).sum()\n",
    "                print(total_frames_processed,num_frames_test.sum())\n",
    "        else:\n",
    "            y_pred_seq[indices]=y_pred[indices]\n",
    "            if stride==last_stride:\n",
    "                total_frames_processed+=(num_frames_test[indices]).sum()\n",
    "            else:\n",
    "                unreliable_indices=np.array(max_decision_values<=threshold)\n",
    "                cur_frames_processed=(num_frames_test[indices & unreliable_indices==False]).sum()\n",
    "                total_frames_processed+=cur_frames_processed\n",
    "                indices=unreliable_indices & indices\n",
    "    print('Total acc',metrics.accuracy_score(y_test, y_pred_seq),' Num processed frames:',total_frames_processed, total_frames_processed/num_frames_total)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18, 9, 3, 1]\n",
      "stride 18 0.075\n",
      "best_threshold 0.15085990615363443 102\n",
      "431 1286\n",
      "stride 9 0.15\n",
      "best_threshold 0.019323074580977817 138\n",
      "stride 3 0.22499999999999998\n",
      "best_threshold -0.11815412994815744 159\n",
      "Total acc 0.5936675461741425  Num processed frames: 13635 0.6830477908025248\n",
      "[18, 9, 1]\n",
      "stride 18 0.09999999999999999\n",
      "best_threshold 0.0861147766938517 115\n",
      "523 1286\n",
      "stride 9 0.19999999999999998\n",
      "best_threshold -0.09436642948726326 156\n",
      "Total acc 0.5989445910290238  Num processed frames: 10697 0.5358681494840196\n",
      "[18, 6, 1]\n",
      "stride 18 0.09999999999999999\n",
      "best_threshold 0.0861147694505695 115\n",
      "523 1286\n",
      "stride 6 0.19999999999999998\n",
      "best_threshold -0.04896769522533084 149\n",
      "Total acc 0.5963060686015831  Num processed frames: 11442 0.5731890592125037\n",
      "[9, 3, 1]\n",
      "stride 9 0.09999999999999999\n",
      "best_threshold 0.11035688133207433 124\n",
      "883 2391\n",
      "stride 3 0.19999999999999998\n",
      "best_threshold -0.06871885488541585 150\n",
      "Total acc 0.5989445910290238  Num processed frames: 13762 0.6894098787696623\n",
      "[6, 3, 1]\n",
      "stride 6 0.09999999999999999\n",
      "best_threshold 0.1073432724448381 121\n",
      "1239 3478\n",
      "stride 3 0.19999999999999998\n",
      "best_threshold -0.06873069937561194 150\n",
      "Total acc 0.5963060686015831  Num processed frames: 14075 0.7050896703737101\n",
      "[18, 1]\n",
      "stride 18 0.15\n",
      "best_threshold -0.02672205621952939 134\n",
      "626 1286\n",
      "Total acc 0.5936675461741425  Num processed frames: 10994 0.5507464181945697\n",
      "[9, 1]\n",
      "stride 9 0.15\n",
      "best_threshold 0.01934027132836258 138\n",
      "1028 2391\n",
      "Total acc 0.5963060686015831  Num processed frames: 12479 0.6251377617473199\n",
      "[6, 1]\n",
      "stride 6 0.15\n",
      "best_threshold 0.014935792957210037 135\n",
      "1538 3478\n",
      "Total acc 0.5963060686015831  Num processed frames: 12689 0.6356577497244765\n",
      "[3, 1]\n",
      "stride 3 0.15\n",
      "best_threshold 0.0481150390745293 137\n",
      "2782 6785\n",
      "Total acc 0.5963060686015831  Num processed frames: 14578 0.7302875463380423\n",
      "[18]\n",
      "Total acc 0.5277044854881267  Num processed frames: 1286 0.06442240256487326\n",
      "[9]\n",
      "Total acc 0.5382585751978892  Num processed frames: 2391 0.1197775773970544\n",
      "[6]\n",
      "Total acc 0.5382585751978892  Num processed frames: 3478 0.1742310389740507\n",
      "[3]\n",
      "Total acc 0.5725593667546174  Num processed frames: 6785 0.3398958020238453\n",
      "[1]\n",
      "Total acc 0.5989445910290238  Num processed frames: 19962 1.0\n"
     ]
    }
   ],
   "source": [
    "#separate classifiers\n",
    "for strides in all_strides:\n",
    "    print(strides)    \n",
    "    last_stride=strides[-1]\n",
    "    \n",
    "    indices=None\n",
    "    y_pred_seq=None\n",
    "    total_frames_processed=0\n",
    "    #for stride,threshold in [(10,0.1),(6,0.3),(1,0)]:\n",
    "    #for stride,threshold in [(10,0.15),(1,0)]:\n",
    "    for s_ind,stride in enumerate(strides):\n",
    "        x_train, y_train, num_frames_train,_ = create_dataset(filename2features_train,os.path.join(DATA_DIR,'train'),is_train=True, stride=stride)\n",
    "        x_train_norm=preprocessing.normalize(x_train,norm='l2')\n",
    "        clf.fit(x_train_norm, y_train)\n",
    "        if stride!=last_stride:\n",
    "            fpr_corrected=0.3*(s_ind+1)/(len(strides)) #0.08 #0.1 #\n",
    "            print('stride',stride,fpr_corrected)\n",
    "            threshold=get_threshold(stride,fpr_corrected)\n",
    "        else:\n",
    "            threshold=0\n",
    "        \n",
    "        x_test, y_test, num_frames_test,_ = create_dataset(filename2features_val,os.path.join(DATA_DIR,'val'),is_train=False, stride=stride)\n",
    "        x_test_norm=preprocessing.normalize(x_test,norm='l2')\n",
    "\n",
    "        decision_values = clf.decision_function(x_test_norm)\n",
    "        y_pred=decision_values.argmax(axis=1)\n",
    "        max_decision_values=decision_values.max(axis=1)\n",
    "        #print(stride,metrics.accuracy_score(y_test, y_pred), (max_decision_values>threshold).mean(),(y_test[max_decision_values>0]==y_pred[max_decision_values>0]).mean())\n",
    "        if y_pred_seq is None:\n",
    "            y_pred_seq=y_pred\n",
    "            indices=np.array(max_decision_values<=threshold) #np.arange(len(y_test))\n",
    "            \n",
    "            if stride==last_stride:\n",
    "                total_frames_processed+=num_frames_test.sum()\n",
    "            else:\n",
    "                total_frames_processed=(num_frames_test[indices==False]).sum()\n",
    "                print(total_frames_processed,num_frames_test.sum())\n",
    "        else:\n",
    "            y_pred_seq[indices]=y_pred[indices]\n",
    "            if stride==last_stride:\n",
    "                total_frames_processed+=(num_frames_test[indices]).sum()\n",
    "            else:\n",
    "                unreliable_indices=np.array(max_decision_values<=threshold)\n",
    "                cur_frames_processed=(num_frames_test[indices & unreliable_indices==False]).sum()\n",
    "                total_frames_processed+=cur_frames_processed\n",
    "                indices=unreliable_indices & indices\n",
    "    print('Total acc',metrics.accuracy_score(y_test, y_pred_seq),' Num processed frames:',total_frames_processed, total_frames_processed/num_frames_total)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VGAF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/HDD6TB/datasets/emotions/EmotiW/VGAF/\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR='/home/HDD6TB/datasets/emotions/EmotiW/VGAF/'\n",
    "print(DATA_DIR)\n",
    "emotion_to_index = {'Positive':1, 'Neutral':2, 'Negative':3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b09828da2b542369a8b1da94f44bc2d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/766 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70da0a94dc3d4a8488ba9c8557a32f64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2661 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_features_scores(data_dir):\n",
    "    videoname2features={}\n",
    "    for videoname in tqdm(os.listdir(data_dir)):\n",
    "        frames_dir=os.path.join(data_dir,videoname)\n",
    "        X_global_features=[]\n",
    "        for filename in sorted(os.listdir(frames_dir)):\n",
    "            faces_dir=os.path.join(frames_dir,filename)\n",
    "            imgs=[]\n",
    "            global_features=[]\n",
    "            for img_name in sorted(os.listdir(faces_dir)):\n",
    "                img = Image.open(os.path.join(faces_dir,img_name))\n",
    "                img_tensor = test_transforms(img)\n",
    "                \n",
    "                if img.size:\n",
    "                    imgs.append(img_tensor)\n",
    "                    if len(imgs)>=32:        \n",
    "                        #global_features,feats,scores=feature_extractor_model.predict(inp)\n",
    "                        scores = feature_extractor_model(torch.stack(imgs, dim=0).to(device))\n",
    "                        scores=scores.data.cpu().numpy()\n",
    "                        #print(scores.shape)\n",
    "\n",
    "                        if len(global_features)==0:\n",
    "                            global_features=scores\n",
    "                        else:\n",
    "                            global_features=np.concatenate((global_features,scores),axis=0)\n",
    "\n",
    "                        imgs=[]\n",
    "\n",
    "\n",
    "            if len(imgs)>0:        \n",
    "                scores = feature_extractor_model(torch.stack(imgs, dim=0).to(device))\n",
    "                scores=scores.data.cpu().numpy()\n",
    "                #print(scores.shape)\n",
    "\n",
    "                if len(global_features)==0:\n",
    "                    global_features=scores\n",
    "                else:\n",
    "                    global_features=np.concatenate((global_features,scores),axis=0)\n",
    "                #print(videoname,filename,global_features.shape)\n",
    "                X_global_features.append(global_features)\n",
    "        \n",
    "        #print(videoname,len(X_global_features))\n",
    "        videoname2features[videoname]=X_global_features\n",
    "    return videoname2features\n",
    "\n",
    "video2Allfeatures_val=get_features_scores(os.path.join(DATA_DIR,'preprocessed/Val/mtcnn_aligned'))\n",
    "video2Allfeatures_train=get_features_scores(os.path.join(DATA_DIR,'preprocessed/Train/mtcnn_aligned'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enet2_8_pt_feat_vgaf_aligned.pickle\n"
     ]
    }
   ],
   "source": [
    "#model_name='mobilenet_v1_ft'\n",
    "#MODEL2EMOTIW_FEATURES=model_name+'_feat_vgaf.pickle' #'_feat_bgr_emotiw.pickle'\n",
    "MODEL2EMOTIW_FEATURES=model_name+'_feat_vgaf_aligned.pickle'\n",
    "#MODEL2EMOTIW_FEATURES=model_name+'_feat_emotiw_cropped.pickle'\n",
    "\n",
    "#MODEL2EMOTIW_FEATURES='enet0_vggface2_new1_pt_feat_vgaf_aligned.pickle'\n",
    "#MODEL2EMOTIW_FEATURES='enet0_vggface2_pt_feat_vgaf_aligned.pickle'\n",
    "print(MODEL2EMOTIW_FEATURES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(MODEL2EMOTIW_FEATURES, 'wb') as handle:\n",
    "    pickle.dump([video2Allfeatures_train,video2Allfeatures_val], handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2661 766\n"
     ]
    }
   ],
   "source": [
    "with open(MODEL2EMOTIW_FEATURES, 'rb') as handle:\n",
    "    video2Allfeatures_train,video2Allfeatures_val=pickle.load(handle)\n",
    "print(len(video2Allfeatures_train),len(video2Allfeatures_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2661, 5632) (2661,)\n",
      "(766, 5632) (766,)\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "def create_dataset(videoname2features,labelsfile):\n",
    "    x = []\n",
    "    y = []\n",
    "    has_faces=[]\n",
    "    ind=0\n",
    "    with open(labelsfile, mode='r') as csvfile:\n",
    "        labels_reader = csv.reader(csvfile, delimiter=' ')\n",
    "        for i,row in enumerate(labels_reader):\n",
    "            if i==0:\n",
    "                #print('first:',row)\n",
    "                continue\n",
    "            videoname,label=row[0],int(row[1])\n",
    "            X_global_features=videoname2features[videoname]\n",
    "            #print(videoname,label,len(X_global_features))\n",
    "            \n",
    "            total_features=[]\n",
    "            for cur_features in X_global_features:\n",
    "                #print(cur_features.shape)\n",
    "                mean_features = (np.mean(cur_features, axis=0))\n",
    "                std_features = (np.std(cur_features, axis=0))\n",
    "                max_features = (np.max(cur_features, axis=0))\n",
    "                min_features = (np.min(cur_features, axis=0))\n",
    "\n",
    "                # join several features together\n",
    "                #feature = np.concatenate((mean_features, std_features, min_features, max_features), axis=None)                    \n",
    "                #feature = np.concatenate((mean_features, std_features, min_features), axis=None)\n",
    "                #feature = np.concatenate((mean_features, min_features, max_features), axis=None)\n",
    "                feature = np.concatenate((mean_features,std_features), axis=None)\n",
    "                #feature=max_features\n",
    "                \n",
    "                total_features.append(feature)\n",
    "            \n",
    "            if len(total_features)>0:\n",
    "                total_features=np.array(total_features)\n",
    "                mean_features = (np.mean(total_features, axis=0))\n",
    "                std_features = (np.std(total_features, axis=0))\n",
    "                max_features = (np.max(total_features, axis=0))\n",
    "                min_features = (np.min(total_features, axis=0))\n",
    "\n",
    "                # join several features together\n",
    "                #feature = np.concatenate((mean_features, std_features, min_features, max_features), axis=None)                    \n",
    "                #feature = np.concatenate((mean_features, std_features, min_features), axis=None)\n",
    "                feature = np.concatenate((mean_features,std_features), axis=None)\n",
    "                #feature=max_features\n",
    "                x.append(feature)\n",
    "                has_faces.append(1)\n",
    "            else:\n",
    "                x.append(np.zeros_like(feature))\n",
    "                has_faces.append(0)\n",
    "            y.append(label-1)\n",
    "    x=np.array(x)\n",
    "    y=np.array(y)\n",
    "    has_faces=np.array(has_faces)\n",
    "    print(x.shape,y.shape)\n",
    "    return x,y,has_faces\n",
    "\n",
    "x_train, y_train, has_faces_train = create_dataset(video2Allfeatures_train,os.path.join(DATA_DIR,'Train_labels.txt'))\n",
    "x_test, y_test, has_faces_test = create_dataset(video2Allfeatures_val,os.path.join(DATA_DIR,'Val_labels.txt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_norm=preprocessing.normalize(x_train,norm='l2')\n",
    "x_test_norm=preprocessing.normalize(x_test,norm='l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7044534412955465\n",
      "Complete accuracy: 0.7023498694516971\n"
     ]
    }
   ],
   "source": [
    "#clf = svm.LinearSVC(C=0.01) #0.25 0.5\n",
    "clf = svm.SVC(kernel='rbf',C=1.9)\n",
    "#np.random.seed(1)\n",
    "#clf=RandomForestClassifier(n_estimators=1000,max_depth=7, n_jobs=-1)\n",
    "#clf=KNeighborsClassifier(n_neighbors=3,p=2)\n",
    "\n",
    "#import xgboost as xgb\n",
    "#clf = xgb.XGBClassifier(n_estimators=1000,use_label_encoder=False)\n",
    "\n",
    "if True:    \n",
    "    clf.fit(x_train_norm[has_faces_train==1], y_train[has_faces_train==1])\n",
    "    y_pred = clf.predict(x_test_norm)\n",
    "else:\n",
    "    clf.fit(x_train[has_faces_train==1], y_train[has_faces_train==1])\n",
    "    y_pred = clf.predict(x_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test[has_faces_test==1], y_pred[has_faces_test==1]))\n",
    "print(\"Complete accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  }
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