violence_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UVXa7uMnOlkp"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "oDfDnlliPMd-",
    "outputId": "7582c7ed-2ba2-4164-e043-d0c8d31ba19f"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "import download\n",
    "from random import shuffle\n",
    "from keras.applications import VGG16\n",
    "from keras import backend as K\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Input\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense, Activation\n",
    "import sys\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "numpy.set_printoptions(threshold=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kFU6Qg8mPXvv",
    "outputId": "04a2d803-e425-4335-94e8-71e91d05d6b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.4.3'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qnafWmS7P3CG"
   },
   "outputs": [],
   "source": [
    "def print_progress(count, max_count):\n",
    "    # Percentage completion.\n",
    "    pct_complete = count / max_count\n",
    "\n",
    "    # Status-message. Note the \\r which means the line should\n",
    "    # overwrite itself.\n",
    "    msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n",
    "\n",
    "    # Print it.\n",
    "    sys.stdout.write(msg)\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j1V5cxltP5U-"
   },
   "outputs": [],
   "source": [
    "def download_data(in_dir, url):\n",
    "    \n",
    "    if not os.path.exists(in_dir):\n",
    "        os.makedirs(in_dir)\n",
    "    \n",
    "    download.maybe_download_and_extract(url,in_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RiRKgwBgP-NY"
   },
   "outputs": [],
   "source": [
    "in_dir = \"data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YX0Qk202QCvw"
   },
   "outputs": [],
   "source": [
    "url_hockey = \"http://visilab.etsii.uclm.es/personas/oscar/FightDetection/HockeyFights.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "urX5VyFzRF-o",
    "outputId": "ec619e67-5d10-413f-d510-aaeadfbf3116"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Download progress: 100.0%\n",
      "Download finished. Extracting files.\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "download_data(in_dir,url_hockey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SXTNEj6SRLZZ"
   },
   "outputs": [],
   "source": [
    "# Frame size  \n",
    "img_size = 224\n",
    "\n",
    "img_size_touple = (img_size, img_size)\n",
    "\n",
    "# Number of channels (RGB)\n",
    "num_channels = 3\n",
    "\n",
    "# Flat frame size\n",
    "img_size_flat = img_size * img_size * num_channels\n",
    "\n",
    "# Number of classes for classification (Violence-No Violence)\n",
    "num_classes = 2\n",
    "\n",
    "# Number of files to train\n",
    "_num_files_train = 1\n",
    "\n",
    "# Number of frames per video\n",
    "_images_per_file = 20\n",
    "\n",
    "# Number of frames per training set\n",
    "_num_images_train = _num_files_train * _images_per_file\n",
    "\n",
    "# Video extension\n",
    "video_exts = \".avi\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eu9c4a-3RVkO"
   },
   "outputs": [],
   "source": [
    "def get_frames(current_dir, file_name):\n",
    "    \n",
    "    in_file = os.path.join(current_dir, file_name)\n",
    "    \n",
    "    images = []\n",
    "    \n",
    "    vidcap = cv2.VideoCapture(in_file)\n",
    "    \n",
    "    success,image = vidcap.read()\n",
    "        \n",
    "    count = 0\n",
    "\n",
    "    while count<_images_per_file:\n",
    "                \n",
    "        RGB_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    \n",
    "        res = cv2.resize(RGB_img, dsize=(img_size, img_size),\n",
    "                                 interpolation=cv2.INTER_CUBIC)\n",
    "    \n",
    "        images.append(res)\n",
    "    \n",
    "        success,image = vidcap.read()\n",
    "    \n",
    "        count += 1\n",
    "        \n",
    "    resul = np.array(images)\n",
    "    \n",
    "    resul = (resul / 255.).astype(np.float16)\n",
    "        \n",
    "    return resul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qiv5NIJjRbIA"
   },
   "outputs": [],
   "source": [
    "def label_video_names(in_dir):\n",
    "    \n",
    "    # list containing video names\n",
    "    names = []\n",
    "    # list containin video labels [1, 0] if it has violence and [0, 1] if not\n",
    "    labels = []\n",
    "    \n",
    "    \n",
    "    for current_dir, dir_names,file_names in os.walk(in_dir):\n",
    "        \n",
    "        for file_name in file_names:\n",
    "            \n",
    "            if file_name[0:2] == 'fi':\n",
    "                labels.append([1,0])\n",
    "                names.append(file_name)\n",
    "            elif file_name[0:2] == 'no':\n",
    "                labels.append([0,1])\n",
    "                names.append(file_name)\n",
    "                     \n",
    "            \n",
    "    c = list(zip(names,labels))\n",
    "    # Suffle the data (names and labels)\n",
    "    shuffle(c)\n",
    "    \n",
    "    names, labels = zip(*c)\n",
    "            \n",
    "    return names, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dIsaAgcyRfIx"
   },
   "outputs": [],
   "source": [
    "# First get the names and labels of the whole videos\n",
    "names, labels = label_video_names(in_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xUfZO-0BRj0f",
    "outputId": "e2b913dc-c39f-4d5f-a0d6-da1273692fbc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'no206_xvid.avi'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EqBi8z6rRoMW"
   },
   "outputs": [],
   "source": [
    "frames = get_frames(in_dir, names[12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9ihSA_ogRsNU"
   },
   "outputs": [],
   "source": [
    "visible_frame = (frames*255).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "PM1kNhaHRvSv",
    "outputId": "e2a6d324-cb62-42d2-dd8f-1cf1c464d168"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e8b16e9bb0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(visible_frame[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "_gVWtYPvR8n2",
    "outputId": "f9858ffa-3981-4b85-9c4d-62057bb8972c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e8b25c41c0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8laPuEL14wCuvvMLNa9d46v3vo4+BG2ELUWmalqimTmzbNokitpFjjKxWK0QM4NvHKXjvGcfRNDvjWDb8qlsxhhHvjc12zrHdbokx0ratnZJMBCc/d7VapfYUcYP1ow90OILzjM7mWSI00aGjiXjRO4IoPnrTNKka0dAWJ3AiARciq8Zz4/gGg284uXkCqtZvL7RRQVasGm9g9MqurZyjkZa26zgeB272xvWM4zhb3zWnmwFDW12JaPiGM9KA+4QIZAFZdq3kZkVBJRprr0rTNfim4f3veg96a4O71XN0eIG1a7l0/SZE5WTc0ruGm2w4WXlEOzZ+hMbTawRafvD7f5DXnn2BV557nrVr+MC3fhsf66/xi5/8JF48nTqkHzkZAnSNTYx39ASaVcd3/NbvQF98HfnS11g7eCNuOPEweNiuDbUOCl3X4nAcbwJHXctJf4KXloPVihAjQQLeecYExDk87aqhaz3DMNJ4j8MRghmPaAL8jCkxQ6OrV69y6dIlXnzxRW7evMZq3YLzNL6hlY4w9MYVOWGIkXe99108/eTj/N3/93/Htz/6OK+/8CLXT75Od/GIDzz6JNdPTrggju/97b+d/+qnfhJddWw1EodYOIuu68wwKAS89zz55JMcHR3x0Y9+zN7ZOcZxpHEe7zxhDIkLgtY3rA7NKCqEQOsbunXLdrtNqk6fjHoiqrBadWy3W7z3dF1H3/eF+HgR/PGGyweHdN2ao63SSsdD73sXzjd88qMfR0OgxyO+42h1wKbfsI0waMApHPoV//Q/+bv55X/w97j8xi261nP1kYd55tpLfHLzOmG1pnEeF5Uj33FZHZdubThYrThsGy46z0VVojo2AY7XLW9cXvMbt27wa888g/d+Amb3lBiTRirtCdW3kzhwipfDDEQRw6l8Yyh6BN77nnfz4guv8Olf/ijrYeDRS1e41B3wzs3IxaZlCD03trd4YzzmtWbkeBROJLKNcL0f+b7f9n187fPP8NG//w9oR+XqwQW+/du+k9V6zXOvvQLbkVUAGSKvvf46lx55kHd90wfY9j3Xrt9Etw51juefe4Gbv/Zx1iivhBMe+cC7+fD3fy/Xxi3Pv/gCP/2Rn+Nf/Vf/t9y6cYv/6q/9f/j3/8K/z0c/9lE+/clP8t6nnuLHfuzHcF2LivLP/XP/HJ/7zGf5oe//Pp598XmefPIJfv3Xf51v+ZZv4YXnXmSzucXjTzzOdrvl85//Ar/4C79k2gPxvPOd7+Sd73wnn/rYZ3j++edYHXp+9J//5xli5Mrlq/zX//lf5frxa0jrGPqR/92Hv5V/+D//fdbR4Y63dJsN733kEZ558QWO21e5fOUq1157jYevXuHjH/8MJ2FARDhaXeKP/fE/Stu2fP3Zr/MTP/4TbLa9WdL5locfeYhf+7VfRRB+6Id+iPe///2sVyu+8IUv8P73vZ/tdsvVq1f59Kc/zaOPPspP/MRf54knHuf3/b7fw7Nff5aHH3mIL3/5Kzz99NMcn9ziK898hZ/6nz7Cv/t/+Hf4hV/4BX7hF36Rf+Pf+Nf5zGc+w2OPPcbHPv5xfvgH/zHe+NqX0X7k7/30z3J5CwfdAY8+8QQXrxyxfe06rWsYnce18PiT78KJ8tlnv8bNmzdZuwZRePqd7+J//Opfxb96Cx8DN7/6LO6hi3z2s58gAI0TDpqWi23LY80hq0E5WK1Zrdccth0aR24OkddOel4ce1449Bx967fwjqee4qvPPDMB2n6y7cicwLTwg2lsQpx00PdYzkQEROQZ4AZmHT6q6neJyFXgvwXejUUW+sOq+vrZuklaxDJjfWo5s8hKSdWHCEdHRxxvtnz2s19iMyiXD49QhaPVAQ/FgUvHG9y252TTcy30dL7nZTX5cNuDBMeHPvjN/Jf/1/+cdx2tueA6DjcNzevHbK4ENgcrjl+/Rvz6qybXb0Yee/Ip/onf+3u5fuMaP/43/weidxwPZlhzYauswki/bvjmd72X3/FDv50XTq7z3Csv8pnf+ALf+z3fxcULl/jJv/nTfPu3fzvveedTXDpY8/TTH+An/r//HUdHl4go3/td38dhd8C73vVOPvqxT/CH/+k/zJVLV/mWD32IL37xGbbbY5544jGOT25xfHKSYEPDDk5OTnjjjWtstz1hC7EVvuM7vpPLV65w5YEr/PX/9id55dVXYBtx6njo6lV+9tOf43c9+gEebR0v9gOP+pYbo3KhH+DadR574DK/8cmP883f/C384i/9Ojh45IFDfuAHfoCHHnqIZ778DB/5Wz/DzTdeIrjIrRvHHF04MRXrqHzoQ9+K88LLL7/Mww8/zHd+53fykY98hN/yoQ/Rti3vec97+Km/+bf5x37gB3js0cf45V/5Jf6pP/T7eeDKA7z73e/mxRdf5Kmn3sHXv/4c3/Xd38FT73oHn/r0J/n+7/9+fvZnf5bf9tt+G4888ggf/rZv429+/rN837d/Jx/7Oz+Pe/F1Lh6tOLg1sm5HrvSetmtZrdasji7hbvQ8fOUK3/GH/gi//sXP8qlf+VVW2wF37RYHJyNP3NzSjIFbr15ne3LCOw8u8ewrr3Pg4cCNHLRbLovyjs3A493IJb/lYttyctByPA4cuJYDGvqbPV/51Of4wA98Fy8891wxpqptKfJ6NwtF0sZPm+F/AeLAP66qr1Tf/wzwM6r6F0Xkz6Tv/9aZnqBKHEaE1t7dKAJAkRezrbh3Du+UYRi5cOmQT/zyJ3nfh97Dh55+Px9853v4/Mc/zbseeZwPPnGZn/sP/u882sBFL6xGR4yOUSJEGCMcXRS+8PWvsV57mq5DgzPrw6AMXQcXDjl6/AluvHaMbgPri5fZOOGjH/0km/6EzXbg0uOP0tNAs8YdHdFJ5KBTXr92k2eeeZ5tC1cefZg/8kf/EK/eep1BIj/ywz/CZ37jM/zPf//n+ON/7F/m53/+H3D1iQf5ge/9QR5/7AkuXTpiO5xwHI45unjAq6++zEsvPc+Vq5cJ9BxeXPP6jdfN7FfgytUHivXbet2hYUBDAG/y/tUrD3D56hU+9bFPcXzr2FaFgj/wiCiH0nLy/KtcfPABDqUjPv8qv+e938Ib25HPvv4S2xB4/ktf4amnnuQXf+nXERGGMPDRT3wc7xwPPfQwFy5dhBdfgkbYDj1jHLn0wCVCCGzDlosHF7n8wGU8QugHLhwcsj3ZMG57Ll24wO/8XT/Mow8/wvHJMRcuXuDmzVtsNhu896xWK7761a/yoz/6R/jc5z7Hu971Ln7wB3+Qa9eucfnyZfq+Z7PZcOPmDdzRIbfCyNHVK6xuCUftoZ2oY8SLt/F9+n187bmXuPHqNU5evcb3/FO/l+//g/8kf+n//Oe49qWvELc9EkdcY74XvnW88tJzPP1t7+PrL75OCxwCD/RwRSIHLtLpSNeu0Icu8ME//gf5+Cd/jaPLj/PBSw/zlKz4q//zz/DpT3+KpmkYx7GIThNWkuyaVBFcsgVJnMFvJidwSvkDwA+nz/8l8HOclQjAhDon2Y+MIsesO8+aJXPg0BDRUdme9Dx05SG+7bd+Ox9639M8/9yLPPPKS3z/7/o+boSRp1YHuN5x2HpOcLyOMqhyInB04YAbr73K1Scv04wNXhvwB7w0bhnjAd41uA7k8AC9dY22bVgfHfCxT3yU6zevsb76AF23JqrSHR1y+MSjHBLQNiAHDS++/jJyeMStmy9xcNTy2c9+FsHz/g+8l5de+iovvvAcX/7SFwnjyLd927fywNWL/NYPfyuvv/EK12+8wcFBx/f/wPfyta9+hddef5Vr11/n9Wuvc+PGTQ7WF1ivDzhcX+Rd73wPm5MTVIWD9QEHBwc8+c7ApcsPcHih5ROf+AQHR7axnnjqCS49ctH00J2Ymeum5/ja67gLl7h09ACfefUFLpxsiNdO+PKXnuWRb3qSdrPl4fe9g/e+7z048RxdOOK1V1/joQcf5Ctf/jJHRwc8/fT7AXjwwSscrNc88vAjNG3DKy+/zKMPP8xjDz3Mb3zuc3z5mWd4+umnWa/XfPmZZ3jllVf4ju/8Dr7+9ed46NGH+J2/43dx7Y3rDP3Ixz76cW7euMlDDz1E07Q888xXeOP1a7zn3e/j689+nd/+Q7+dEAJf/vIzXLxwgaeeehfPP/8CzWrF4SMP0khL6BpGIlcevMKHvu87eW3V8caXvsLVK1dgO/Cx3/gNHvv2b2V18TK+PWBzskWbjmsHW1y3YhtGxvVl9GhNt4K1g6PGcaTCuhFcBDSyGbYcHB7h3/d+PnDlEp/73Fc4vHqFuI289wNP83f/zk/z4NUrxSFIFdo2g7mCiNkvZMvDmGwUfrPtBBT46RQj8P+hqn8ZeFQtOxGq+ryIPLLvRhH5E8CfuJuHZAAoDBlNnlSFTTZPVfMCg+Q9pg4dlDAGdIj87Z/+O3z9g8/z5Zde4tatW/zExz/O125c57dc7bjUwGoIjDFyEqFxMPRwNMCVS0e89uhlOtZmq+0PeP6C4+atDdvjnv7GDaTzdAcHaBh4+ZUXuPjEw1x84iFC27Hd9gxjQFYdxw9dYJTA6EZujcc896u/iO8uc+lqw8HBlpONcv164MLBCzh3jSsXLvHT/9Pf5ujgkA89/R624SZ/66f+R2IcuHCh5SM//dOMY+DK5Yv0Q88//KWfYdW1vP7ahnHTAQ0a4cLhFVbdITEoly5e4ru/+3t48KGrPPf8s2w2J1y+9ABu1XL56hX+6L/yv0EcrA8PuHl8zKhb2ggHTccwBp577Q0+cfMG7uUX+FD3CI+0HWOIXD64wCMfeponPvh+Tm5teeiBh4yj2m5Yr1v+2J/8l/DOsd30rFdrPv3pz/D8s8/ju5bnv/48Lz/7PGDz/AX5PM55U4k5z5ef+Qpt29J2LZ//whfNPt+5ohkS13Dj2tf47Kc/T9TIM+OzBoaiDGNPPwzgPF/76v9EE0bC8QmdOLaHnhbHUQcXNXDpkYfp3v0Ir1+/zurxR3nsne/ioQcfRZ+8ysdffJ4b6tkEz83jkVed5+UjQaND/AEn4wEPX30AxdESWYtj3USkMQvFIcCoig/wt37+V/jKhSO++NzLfOuDT/DMM1/hi7/xBZpmTQiYxqZp6PuBYRiLerBpGrwXojfTZY3KGIqf9j2XsxKBH1DV59JG/4iIfPZub0wE4y8D3F2g0cnCrTaoyPJSNpqQ5JWlqvT9CQdHHc9+7Vm+8pWv8NVnvsr1N97giUcf5md+4n/gnR5cDBx6T+cbLujAg6Y8JjgIN445uXGTT378eY6SN5dvDzh6z3vQRx/Brzua9RX85Uu8+/0HPNqt+eKLX8eLpw/KoD14x2tvvM4LH/84r3zpCzROGaISQtKDR+FD3/ko3/f9H+ArX/0yH/v1F3nowQOi9MRxxfWbN/ARfs/v+266i5H//sd/mfVF4ff9vu/m537u1zi8CD/8w9/J4aWGV7/2LL69wmc+9yVe+Not0x+LIM4sIVF45JFH+Md/5Ef48G/9dsYw8PFPfIzHn3gc17RcWB9yK94CjXzrN38LNMIv/sovcuGJh9lcC3x+uMb13jicTYycaORgteLVTc/66mWuPPEEz3/009y8foOnHn2S977v/fzCP/x5Dg7XHB0dcevmLVSVb/rmD/IPf/4X+fhHP4Ukpx5zH6Q4ill/J4cxSZaXkhT6YppPUxpRmZR7LEumAySbVCfrmlEhRLyaTUBUOFqvaR+8xHsfepyTvucLz3yV5rFH+aZv/1YuXrhIaBr6NjK89hJXHniAa87zyrDlYy99nVeuXyM7iXkf+F992/sMPHQB9QZOrwaB1rGNkTBuufn6q/z9j/wsz12+wKuv3eDWazd4/oUX+erXnuW9T7+L1197rWhSsnrQe29i03aLcyBiDmGCY3PSz9zr76WciQio6nPp70si8hPA9wAvisjjiQt4HHjpTD2cP6/8zZxANnYphjMa0THiRNhuNnzgm97LJ379M7zw/Es897UX8QJf+dIXWB2seO/hBd4YIke+5eLhRcJWaGKgiYGDbuRGHwnquHDxiFdefg1QmnbktTdex12+RLh1E208Rxcu8O53vIcHo4OV543+hBeOr5taqRkZjm9xfOsGr75xLRvpTsQ7gpNHODg44IknHuKrX3mJD37w3Vx5qOPkWPn681/jtVdfY7Ve8dRTD3Hjlb/DMJi6T9yWd77jcR5++CoXLh7StObNeLBec+P1F40YJh8Zs+OFdbfi5o2bvPHGdcYQefW11/ilX/kVLl2+RL/dcmtzzMmtW1x84CLveOcTrLuO93zbB7nxyS/y7AsvEB088egV/HHkpeNb9F7YjCPd1cusDw74+b/7d3nHE+/g7//cz/Haa6/z6c9+hm/55g/yD/7+P+D6tes89tjjPPHEE9y6eYtbN4+T1iebWeeJNgNJjclsWfL8p7okAbkYRVFVqIzE/ISfSTows5+liNGKQZU3bh1z41LP8y+/xFd/6Sby8EMcHl0mOuHV6zdYPXiRw+6Am19+geHGDa7FnuuDcm07mpGVd/yWD76Xr7/yGtquUR8ZvLBROHbKde+4Noz0qjz/7Nf49PPK1zrh1jbwyY9/jrZreeKdj9O2DSGMZt+RbB+yyrBYC6b1P44BEWXox7O6Dtw7ERCRI8CpJSM9An4X8OeAnwT+ReAvpr9//WxdtFPg8OCQ5iDSNm1ii3wBTlxiDY19BAQab771jz/2BBePLvGpT32GfrvFOWXVdRz5Na0/4PUxsj5YMTSezbDFhZ4DjcR+xPUDL62P+L2//3fwax/7jFFicVx98Co0nie6Faw6Hn7wQdYSeO7rXzP7hGHgim85Es+67Xh43eKfepKDJnsHjgSN5gU2KhePrnDzutJ1D/LEE+/gHU++m0uXD3j+xdf4lm96iK8880UuHD7EjdeV9z39JOD4+rOv8e6n3sN7nno/XXOZza2B0Hc0TcejjzzCu9874sShombNKEoc4cLRJdbrNd16DeJ597vezZWrl3n/0x/gxeef57lXXmCzPeHBh6/iRbjYtbzj6af42NeeJfZHuBZee/V1ttFzUz3Hh553fPB9dI9c5pFLD/B93/7tHB0ecXThAuujQ77pgx/k6MIFvuu7v4e+7+m6DhVhfbTiHe96vDhbAcVHQ4q9vRTcJxMyyd6TLnOFQHJeCn3ANQ052IyYx07iFsxm3zuT0RVlEDhwLZeuXmFsHOFoBQjHL7/Ka6++Tug8wxjxN17jlVHh1sDFgxX9oefJ9zzG+vohznsefvQRvu/7vpuf+chP8+g7n+JCo6w1QhCOZWQrPaqOMTpubQau4hh0pNfAdgysDw947MnHuXl8zOXLDxSDoa7rksiTTbwVSbEbxtE4hXEbuPnGzZ1oVG9qfy2dFd7Exnwv8BPpawP8VVX9CyLyIPBjwFPAV4F/RlVfu0Nbt+3ExYsX+QN/4Pfz2FNX8L4pG75pmkIt87WY7MC9b2gSobh16xYvvPACm+MT62zXIuq4GhouhJFDLxx4h2sE1YHBOfqobIPyatvyyLvezcuvv8LJyQbVyKULR/SN46uvvEIU5fDwAD/2hFsbcwgRCBnRRXjw0mUYIv1wkvwItowhEIIQe+WgwxD9N25w49otHrl6mRvH17hxY8Oqa3nllZd4+KGHOdne5NobWyLK5QcuM/SRg4MD1uuGEHtu3rxO27XcujlwchwTq6o0beKSgoFL73vv+3jssce5cfM647jh6MIRV65e5fj6LV4/vk6z8mxvbdHBAMHXX/g6Jy++whtfe4ErDi7RMW43iAqvxshjH/oA4yNXeO83fTM3rl1Hg/LoY4/z+nYgeMfYb3n4oYcBYbPZoFH5/Gc/y8svv2wbHorPgKSNTgLA8soopsjOmVt1xfk5acwEOgaLJ6FGBJrk0hw1bR6gbS3gTAyBUZW1eI7Wh0hUjm/ewjce9Q1DVHoCGtQsS8WhAVrX8OSjV/nqc1/gRow0XccjjzwMvuG5Lz/DpehZA02M6DBwqCM0AcUT8QxROJaW17YbxjgQNCBtQ9t2XLt2g7brCNEiE3nfJE7ArC2dc7SrDhGXiIDyyosv86XPfpF+e1fRhfbmHbgvko9cuXKFP/mn/gRPvf+xEpYpm5eu12tgEhHMi3Cyrgbz3gvjQBgTOyXQx0gbHF6URmAlDSrmHedbU8GMAXrtgJYoyhCSDwEjvnNsxpAs3QRHyCsYxSLVtGLWe+a57tBGU3gpq6vRIYNDNLDtN2yH0dRV/ZagA0MfCEE5Pjnm+PgWYxhw0trCcZ7txuoMw8A4bjk5uUU/9HjXEqP5wWcry2xmG0MsjkW+8eWzOkejjo2OvONdT/Kzf+vneOKBCxxuR9YxcNF5Nq9f44kHLnIwAETeOL7FcdNw7JRh3XLQHPDl4xOefMdjXLhyhdfHkY0GGpfDotkGj0GJ44Dadjbb+8TCFbfndNp7cclLMXldiLnniHMQzaXWuxZp0m9JhQbQeZ+eMf3WtC1OgRgJMbJSV2xLQlo5nbRmw+BAh0AIg7kOiwGtGre4RgkpgEgIwaxLQ8D35hbsRBiGQBN6/EFDGCOuafDNinGE7fbEiFZjnoQuCv0YaNqJ0wWKz0d2HPJd8keJgkZ49eWX+dJnvkS/7e9mq+0lAveFxaCkzXXr5k0yi5hlptoRZRzHFAOvivOHsO0HM4eNRgRijJzoiAZliIFV27GiI9DTo6waaLxFy2ndEXELdN60DxJpPaw6IxRNNAW8OGDVJHdjh1c7DbxOqstxGFHn6Bpni7FtkaahAdarNTHa5vauxXvAKeNg/gTjuCFoZBh6I3SqjNvAOILQoBo42ZxwcnLMdnvC8ckN+n7LOEaGcSTEIfnCj8QwgkaGGEHNr2Dbj/QhshkHHAaUXlkd8Vjjcbdu8cBqzeYw8o4rD9O/fp2jg0PCEHjw8gWOQ2C9PmB7a+DjL9+keYdw8+ZNeufZDBu882a2rCk0nGIuwiRjGDGS7cSVE99APpc202CgbOL0JId5S4BY8JHY54guYqBZWjMhmojgkmzdjyHFejDQsE/OYtE7RjHyE4MFoYkCjMqowdzAGRm2I0hAWkc86dExMA6jxQ7oB1rXIZ3HNQ0hQBsEBkccRjqNNLl972i6Ftcmv48BfOtZrTrW64PkeDU5U9m8Rfq4Ne7HOZz3VQzHey/3BRGIMbI5OWEYOrJ2ACiIaXaMCSHQDwPbcSAm9UkMkX4YiMNAjMG89kQ40R4GO0lC0zCS7P3jyHaw+G9N23C4CsQAMRjB8aIMvbKWFd43BDAOQSMu2GkhROIY2BY3UIEoHG970EjjPeIbmm6N4FmJQ6VHxJx+giidNERGcA2SXI+9eNpVik/QNIQxIHQ0fmWRZlQJOhBjzxg2jMNIHJUQlRhGhnFk6EcLLhoD22EkxpEhBq6fbNHtyHbsuXzxEk89+QSb165xs1e642OQFTrCjeu36I9PIChd06JDYN00hGsnxKCsWmjWHddOTuhdCqehFpTETtHsBWcuuEji6jBDL1W1gJ/JMCZEc7cWlxBOcbjGE4axzHsGWlVjCuCS4gE0Dtc4wx2czQs44y6EFEQma5nEYIYQ8NFiUbbe4VrB0SDDQOwjXSM41zIw0iLmtu5A+pERB22D61asVgcQha4PxANPe6Cs2hbvOyLQjyccHR0gwDCMZqyEM9uOw0PatjGsNHlRGk4gDHHLGEdzXxbHzRs3diJGvdlyXxABFLOiijFF1GFGCLK3VYyRYRgIwSLlZq++sd8w9uMUwFKF1cr884NGREekcQzRgl1q0s2uDtcQe7wjBdwA8Z5OPF4DLibgDVBtCEGTI4uxu2EckiiixFHZbk+I42Begc7TrNaoeFrv8R6aztG2DY6Gg7hO4cgHY+HDgLoG35rbqQJhVJzrCek0Tecizkc7UVhBdDjahHdEYsjecMKoRhSJMA4RiY5eAn2/gZtbPv+xT7LdbNgcgKwd4+qIF/oNh1ePeC4GXHdEP/Y0Dg66Q9qjIz78be+jPVwj/cA2BE42xwAMvREhTV6NORKwiEVUNrWXEYcR873PIdXa5A1IDBbBGIc4XzAEHSPqzA0bCkNAjk+gMZq6UMD1AfWKtBZcRCWH8wKnFg4uRzh2koKqKgwhQMxWfAKj0K5XjIOz90Jp+hENgSaOHDSCbiGGAcYUIi4KYxxxKMfbLTgjSLEfGcNIHCLbTc/m+MTEgfQikkQi7z24YK7UKDmm1VnL/UEEBNqm4WC9Jr/00lYgs/9Nk8O3KsHZKTE2ztxkXXL5HZUQRtokc4YYUIn4tmHlOmg6vPccXDigi8Kqa4iZ6jcNK1oaF4nRoXGk6zqcXzGmCULErA5PjhnGgXFQhn5gGLZosIXim5ZWI3jPmADMBos03EpEerE+i4FYpNM06Ah4fHBocDivQF/CYzeNBayIY2Kpo8UN9FhkYmKKd4DLYf9pnOfCekXjGzYSiGHN+z7wfh6+chVxys3+Bk0CyMIYrF7Y2kYbA2vfcHF1BN2K7uoD3DreGHISIscnN3FiYbSGYWQcAxqMqA9jT99v2W5O2PYDfT8kYm5ms2ZoF/HO0/fmMixR0dHiR8eY4jSqFPWgpgMD7/DZt8QJpEjSsR8tchDJv9/bvU6wsHEhENWhKoRgRGUMI2hI2MqkndyOA+M4MA6jxSGMgTCODIxsULR3OAlsb0U26vC+ZVCliZHjOHByvGHdthAiGgMaYXNywg2yObzgfUvTtMlgqCHqAClStHOOa69fK6rDey33BREQEdYHa44uXDZgUGMxkMiBFoxlChysPc51gJ0EUWG4dMHY0BRyfBwiG+9YO4v2aiyhJwZh7VpWKHhlcAOEwIF3jKMFN4/eM4yYTKmOhgxeKcdiMQS8A68NzdaizKRofIzaMCT/+BhGGHt8insogAaPjqCdJyr4FJ3OeF2zFTfduWccBR1bJDicN1BIJcX8DMmSTJ15mxFgsDwElvfA4ZwFMnEiNIyMcpNGGm6Fns41rC82XLz8JNHBxg00Gmi1owkdoxNO6FFRmqgc+Bai0DcNa+l4SFwy+CHhMK4EfTW7nQwUjvT9Mf2wMUyi703+DSMhjAwaGfuBzfGG7XbDZnPCpu9Lvc1my0hAe7XTHbPl345mPRrGAdRi+MW4QSUig6KNQ4YUFdjbZmoEWucYUhCRVhrTIGDRhGIKozaqcY9DYv91HNFhRJxHRnCNRWnuh9FiQjozSBqGAE5ootCph65NkZhMQapeLMBpUgeK5HiaKcJTsBwSIY7JiMixXq8Y+4Gzgvv3CRGArhvp5EWL9wxkixBxRYdEbCOtjzQ5vBamD9aVnXmS2M0YldCMSaYWk9HtXMA88o2t94zQmEbAkfwXBCDa3kpKaOc8rk1YgEIOU3PUAupMPaRCCEIILTFaUE9NVivOtUXe9eJxrM0qjiz2tImNblEsLr4qqB8Tx2iLXSKErU/Ez0QfI5g5gQXGUYixyKJCRBiAAdA+MMTI6AZjOTVY6EERghjSPrJhHBVpTesgjSN6x9AHYjym98dI29kmHnsa8Vg0dKWRFu9M26LiCUC3PqCTQy6KS7bxzmI4IjRtS+gHxm1PCD0hBoaQ8yFYroKQiB5xJAymAYoK/dDz2iuvsNluGIaRGDW9T0jBv4z1DzFY/oTRTnGJhkOMelwA6DDa33EYyZuy73siObQ9Zplk4BExpMQlGOcXh1VycbcQZUOyfPQIOtoaiiGa81CcPAhBCOOY4p5bnM2cE0GIsEiocq/lviACqsownOAlyfopBFaMhnpXNRlywMzq2jgOiI5VPQF3zBh9AZ0swm+OvhOTAZvivcWvy5MimfMsGSqMR1TvilGKqsNJh/drhKYAir4dcas14kysURU0GbCo5NwEig9CcMl4RpLogwf1WEKRWKUYMKMjYlaLJqJDJgLZ0szaiqrG9mqKApi0F2M0VapGRRtNgJ2dSiVWorOYjGOI+Ogt8YXznAwjMSghHrN1A9dvRcYwoIw06mgl0voGJy1OOpA10qyQdkVU08KISNL3m4YlRhg2WyT5g2Qk3LUNh0dHJsc7MTUn4IMFkgnBxJWS6SfJ95nwhRBM/o5mmjsMA9tt/tczDCObYcPJ9pgxjMbq95EwRosCFSMxbNmOPdev3UAj9NuebT8w9D39OBBGCN7EMh0D/ckUAMWSxVgenMZZcPmsNrXI2BFNfbfMR5qMqaJxNEbJaRvPMBxwcnL89uAEjPW7zo2bnycEbxRRY6LiZj5pRYg6oJpThFmJmk1Op+KcRQ3K91maqBTCu6iplMZfNJY2EYV0YKffjb1GFHE2sU5so6IOoUXEotXaqa44vwJ3gEjKkuMcTbtKoaRB1eHby3gFUtI+wYG2mMviFIxSU6osiSbDWuciUdJpl1RyGhVRnwiUpjFRs9XHEcWiCI3jUPIMZNt1ZMQib0oiLpg2XSQBqbY5kEiItwjDwGZ7k2E4QRjxRCSONK6hcQ3iOsS1SLNG/CEiRijbZkXTdMVF1jgccwIbtoGgYmHLXEA10DQWUch1piqTQEkj5tUiMDVNY0Qb22Btu8a7yIFrk9ixzRYJFF/9xFmEEVApXnpj3xe1okVAFja3toxj4ObxLU5OjjnZnDAOI9t+S05yMmw3DFvot1tAGYLZ+g+jRTzqt705uUVTZ1oCFgtZnrkQMwxy+HzwhZEwGn5w/fXryXHu3st9QQTA2LY3Xj9BaYuu2TbvqqoliFslp5RJf9r41k7Sij9o2xVjwhVyaGvIxhn2gKhK64/Kb9lp07lIxBZFRq0tNp9tdhVThcWYNlt5boRxBG6gCYUWgaZpi9OTKmybVy3QqU598q4D6UqYcSMGDd41NDSUnIe+ATGcxCcig3Om5iJ7V2FEA5cWugkaqglCIBKC5SZQTRZ3CnhLrxXHBM6amIvzYqCjOlQaWjkA16RouAMSB04G26BKT5SeoLcQuUHrVxbfXzyN7+i6Fc41RvycCWfjGFFpODg8tCEMI+OYrALVm6p2GOh70+q00YJvhrQ5RIS27ThYrREX6DojXlGtfuYILJa/0vgVElvCEBmTZmEcB3zjDUBsHBqUg4sHqMKFBy7gvAHMYQhEDXTdCu88Og6cHJ+YctIlZ6AYS9/6bZ+SvNg8972JPfkMG8dAP9jGj2HkZLvlZLtl6Hs2m4Fnv/osn/7Ep9lut/e8t+4PIiDQth1XrjwO0uLEF2OJ1q/SgsYmXqJF+c3JOyHlAcj8c97IHRlTnUVxSWx5Xhy+IjK2SSNIQHWbotWqyYIaIOUFLOJE0fOGFB/e9PKaVHPm3YZdC6GwiptNbyd6HCsNiAdt03eXAKUGEYeX1ogCDsSjrkm6cY8TQ6VFGpx4k7vJ18yAx9oATe2i0DaJm0nsZ4yWcCNISr3lWsaQUHZpiOqIoSWIIocpEjMBkYhjTMYuSogGkyrQNCta11rkZRViyKbWxk3FlNvQOYg4mibbiNipGRXCQKKtFv03Jw8FZXPSF62RRXJuaDtommRHkFKxjeNoGIEYt9Y1B0jsGPvAEKKpkuNY3JfzRrZ0Y74YLzVNw3bY0jYNijKEQOMc3arBN60ZCDUW1BW1bE6aF0o0TiQbQ3mX7D5KtGRBdEB8a+DkaAlqP/upz/DMF5/5/38i4EQ4OLjElYe+OZm95ngCjsabAZEVCwGuMi0WFIYYFnK86fvXLhYRIIe2FsnBTMXUgDqFd85EwDsl0tuCVRLnISgmilg23cQ9qOKxDY6MyNhX8fQjzisxbonaJzEGxlGJ0cxqRewkijFgPGpEdQQ1HwSNSo+Wd0vLxYxqkjttTB0VSZZ7AhLFCJw4fONp2hZxTeKgBC+SErg0ODG22klDlwgKsbHAC2lcxXnIZDV2xNiYvO9ts2WYNqQI0EE9Km0iOoZ52N5NenHMpDkOG8bRrCQNwG3wXcsYRqYMPI7GKyqTl10MEe8CGgONx+wIZDRiF1LKuW1ImZGTaXEKKb71I63vGMdIH0akdTgPfrVOB4YS1KHqE9A64hyoBMQr0kDURJRGx6YPllpOHaNGfNcQxhFLUWrPFE2ZlzSpQEXMpFgsLJwr6uyUnSmJjyVJwhnKfUEERIRufYi2Fy0BhwlxZjAjTWJv00DkLLmkOmqZaLTAqvbXYvWn1B1iajOVUGy2k51fShRaQ7C2mB0mV0raBKZ1NlNP8ZaeyghVMuoQMz5RHyBYnmJLk6ZAMCCOkMAtS0BClkg1WuhxjYg3QyQDqAZTH8WcaswIC3FMKrmcO8/yKhi7a9yFqTEGEpqJbwRkGiefY9nhLIKSE5y3E000iRgiSfYRvO/w3sbBUmi3honk1xe7P9vpOxqQFajHMACPaz3jaGi9OIfzDeoCIqNF50HRONoTGnuuiVyYcVHIYKj5dIzDwDD0BhIm7tCSgiVRSMx/YhhGA0VTFqlsxOQboRGHpXsyHVHMfZWUGUiTzUIyHRQRRjP/KEZjm75Hh2iGRt4hx1VKuJiC4ajQdSbGOG/cFhn7wRAZNIUWwxcrws3m5O0BDFp+gY7oVrZQMzUUh7i2IgKTjA9MrJZq3rs2sEjKQZ+ShqcUQbZYUoTX8uxd2+wQAkJTchxEhRBS0BPJCS7S8yVt8mSyio+28QvNijiHcQTBtBpt2yDOxIHpmQOOiPiQxA9N4sOIBlMVhTAkLqFHQ0w6+kggWD0NBXCyRRgKgclcTC3OhGAJUAaaCgCVQty8M/2/qOBdZyezWDRh79q0yR04ixbiXQs+1aNF5BDn1sZpONN+NN7sd8DkcxWQlYkHgjfSqWKZe9ShQQjOoaPQ+DaJAoHWdzSNZ+gb+mHLOAzmP3Fi4ov5MaQMTUl0kkwsnICDRkwbEZtkRtx0eG+ckaqyjb3ZmbgssgE4gsIwBDvZgzmLhWhZrLJNi6rZtQCMw0AcA6v1Kom5zlLlJYJkOFUgDuZbQjCjLXHKZvs2IQLGmq9A1rahXUi6UzuRNOlRKaz7zu3F2SSrCRwkls5OP01AoMtRNlMJMRSiYrebnrjUySi+77CwNkmVWHqSCUEuseT7A1KSj2wElPHORIgStygI6knytaIpx55Gs6JzDbZBfUCc6Y+NK0iagGQODbbp8y+OgOhozknRHKyCbksUIhKrrfTJAakHHVC2RLYpUbM9ow+9WfNpwLkBkRTsIhPdxFEY82CnqmE7bcIpUt0kD7vyb00jByArA1DpiMnYCW2w1HTewDQyaGqnqKpH9BA4JAbra7+1sEMazS+fFJNiGCLZMLPxnlUy+olEs4MIyT0cS1uuKG23jGxlqjx726R1cA6RFW2zIoQx2WoEIoOpbKOCX4FTgjSE5IPS5BM/GVrFaAlh6Mec+AhQbp4c2/UzlPuECCRDCYOrKYEX82ZLC02LfGVbIKcii0nWm4xv6iJFxeicpiQdU9Ewt882fa9NtlXIASDbdC1vvIzA1xwKiSBMRCCzeVqIk8ykj4kjqcKqa9JoaGM+AdYRIJB9Saar2RMtJpHDTiEnHicRSRiGSELMZSDEYMBUwLJcyEDUnjCOhHEg6jbl9Es0VSertqgjQU+IcZusKlMvVOlDnr+YxmFEdSiEGTFPQuPSSOJWg/crnGuTWGLaj7ZbGWvvjItw5dQ0bUH2WJTsl984WhyrFTiXYk8EGONICcGEh5gA1mjp7YOquVlHs4BEXEmdHoMnJEvIQPZutffw4q0eijRpzNOMOAOSaL0kGEIKAcxzZ6KiTx6RGG6FJ/QjcYTgImD2F6ccfXddzhJZ6Juw/AK5vBf4PwIPAP8K8HK6/m+r6t+61+eUoj7lgkuLW2trOBJ7bC6gDlctPsnpWkB8MTOGjCKDEQLJ78W0STOBmBMBa7m21zZZM3UUIMmM2bsrqarSKR0y0Ul9tMy/9tGcQ0IhZN5lgFMThgESk61AY7YGY26fkmidTJAy4YtFLEmihPoEPFqGY1DEKU6CWQNoyn2nCvQoPXQejR4kEMaN2bKHkFypRyJbQhgY9BbKFlHbSISQOA0TSYgjqiORnhBO0nUmLkVJiKsR8NBXEYfSCMuJS+MsybjGp00joL6w/JLsBRyNcQ4e0IbGrYwDSxoV7zu8dDganHQ42iSTg3MrWt+As3TyZtHYoO4AVTFXbbFnDoOZazdtZ3DwsKVrjNuLMR8iwbCPaMhQ0GhGJkjSbEHrTPMQg4GbEFHnGVxDDI5hHBACB6uD5Aty7+WeiYCqfg74MICY4Px1LNLQvwz8J6r6H52pZ4siojQZ6U5smZeccjumfaJpM0o5AUFL7rrZRhdT69XBS4EdmurdEUv01aauYsHENkpexCDmwppAs7zDLS6GuQUDaMUBiEwEzlhMk8cL0ckGISKoT8x8YgmTLISKm+zySQSy4JoxgXDZXkKJyYkIkhNOercMZAKoOFRXmIxuC12kR7oDs3HvTLUX1UxovTeNRmZ6lK0FXMmJD9SiOpv6bySGwZxniIkYJI8/tc9j6BnHk4SCk/AKwy5isouMYTTjr5ANuBRlWzAhmy9j0Q3Jl/KemcPy3uEdlnMQb8i/iMVCoEUSluGbrEFpaOJB6lNyHKMz92jX0fg1LU2ycDXivlq35uDmKSy8EzGOS20di2uNG8rrtNKYBBxjY8Q1xMgwKofrk8l0/h7LWyUO/AjwRVX9yi67ffYiIslQw1Q182QMzDa3fXYTiwkLmTzdhzO2vv5J6g9pc7JngFV3qIWImxw7NYGW2TAnLziX1V9zjKG0mdtyYCf2ss+aZQpUDcGv+6GqFd6gKQ5fdV9eXFq/lxROanqOFEJrT0vGV4qZHIsAvjxbHGgUO8ycPbbQHmkQRtApoafXSSdPspmISSwxtaYkIpsk8NhjYUFJgJqZ/UZM3agEc9lNat4QB1T7qt3EFWkkxqHkajTLyAHVyIB5CVrsgwDiC6dpGpLWxAEknbwtXlfmgYoiXoh4VDsa3+F8i5PWYkA0HucbxrGKc+B9Spdn5CmMaS1EXwBBU9k6isoXT7caaRxEFbaDcnDo8jTfc3mriMA/C/y16vufFpE/CvwK8G/qW5CGzDmHb7piZHM6sbGFRo5iS/03l2jyZMVpawLDbHFW2gUZd+6fpYTT3H5j/Ec+3WMyRcbQC9Vs5DMRmNl7VO8ji00GmBypKdFKaiKf3sWvXMTEFElm07YLEzYxcTu2SafGI2qyMeamnKPy5H5JVV9RNAjIulwTMf7CFUAum9Yqzq3JoGtWqWZhJdvN53duRArA5iT7NgQ8I6IjIZ3sjZ/emgQ+aiIwENB4gmNrJuQxe94HhrBF+x4jcwGNG8Z4g3HsEw6aiIgOqIwQBsawRdVwEFCcGlMS1BKWqETGMNJvenORRvDZJwBH4zwH6xGRxmJU+DWeBieOQBZj2kKwbU0aKOhljZdVEWnUdzi3YZQBpWEMHuUFMoG813JmIiAiHfD7gT+bLv2nwJ/HpufPA38J+GN77rvr5CMAM0Ofmfy+qFaJ8PsJxVzGt3vibPNnx5NicDTv9057tYpm/rtUe0mq307v/2llwiIoJ3e+22TbzHFkln96kq/byADprJeKRTDJnMXkfLPsu0ZwzZyoFFAz+z8A6nI25AmlgMSFFE5uQtftOaBuMu9Wp6DmVakKDRFCEocy8ZZEIKt+qAQzw8lih5gtSKcBYsJJUFBTqeaDZfoXzCIxmr2BEhn7jQGf2TpUI/1wi6g9XnokmlchYcSLhURTzJT4WDbEMNJ4OJER57bktO/2/pnDM/VoQipwCdg2GwsH3hHdAYJZFo7R8cq1VxZOdG++vBWcwO8Bfk1VXwTIfwFE5D8D/sa+m/TNJB9J4rJ3zk6Dqf07dm6fNoBanscmol7w+e9S/3ra85YqxDrgyZ36c5qOd/+z7OyNhRNYvNYdRENJBgw5Evey3VSJ5aOnjZq5i8Tf7IhEiYPQpJkpxMQ2nYirnq2njlGRkJyp22RymSSHGs8Vza3aOJ9McJAp3oQR6ZgYq5hcsk0ccQkYLQ9NH0McJ4KiZrZsQT+Sw1hyiuqHW4TYE2LPOIz024g0Fup8GHrjwIYtsTsuokgMGxCzWUBIqs0EUqsmjCQWNa1pXAJq/scMRGI0X42gjhs3t5zRf+gtIQI/SiUKSEo8kr7+QeCTb8EzyAvwTht/eYKd1lb9U82W16fbmyEyp23+07mE6dpphGE/AauEnLznSOvX1c/dJTA1+LnbsvEME/ejps1gIgKFy9ImiQikU1Zn9WLufwLZ5vJTxRXsefccUSeKTABjea9syOUKPmH9MiKR1aWan51pBSbyCELrk2dlvffV8BOXRDUZc5wGUx0651PEI/NgJJkfr1YmWsQQGIbA+miEpgMdzSMTs8Vomw7vJIUuF0IciHGL0rPtj0Fjsr40i8ghjmzDFh17pN8Q4wAaCEROUnAYEfPlWK++hpOsiLu3ctbU5IfA7wT+ZHX5PxSRD2Nj/8zitzM8y526KYusm8KM1df2sbSZE7gdsTjtWTX7WqP79X37n3n35XbvqeW4zwZJ+fTLdUrvZp9Luxn3qNo1TcqeYJVL8QhS8otdoLX6RvI6SJs3Md8V8ChJjl+KA/V71rjEnKjaWphRMg2FJpgthaYDA7J5tqipezWpfCWnOFMsMIiIufDGiHjzZygiiycB0kKMYsQBn05nIfqIxhH8uuj1pTGHI48RMS/ZmashuWIhEjkYLVCJqwDdSEyxHUBi0oZE88IcwsgYNubrovD6y0c49xXgrkKO7y1nTUN2DDy4uPYvnKXNvUUMVKsXX73hrcqdOYQJlNslEHW5WxY9t1nHeLvdvfu4gjdj8llvJHNXhiLkp421e8aXu+1eJyWoyLJ/87pSRIP9fa++z+R6SXkWd8cqYwa3i41biIJRnEoTlN8tO3rVh4IroOx0s84+WaYj22LZ9MOlsHBZo2QaXSnaTJfWXcZJpn5RwtWp5I3eAuYKbG02CeU3jENxFrswOrIthIjD+2SZmE3Ek9l548CLGQLFFLEov3+Im+Ru7Ll08fUERN57uW8sBg0ImhZeZr/zorkbeb2+twaz9gVq3IcRQHIaKe6d8/bqa5lA3K1YsSyncRyqWm+/dC3hpnuetUPgksHV/h4Vfn8mepVxl2SZWRKB7AqjMdZzBCKny/7TM6d3qfutLPtgbH/tJ5LvleT0M1lyAmLuuYYPmLVk5kBqDEArHEMw46uY7yWFRJe0aZ2ANKh4xqRCzElUAdrk1Sou2Rj4DtHjEq5NktefZ5W0Tybf5wxJiIWJUxWCOtBoJuXqkzDUoLrGEth40Mvcnqzeudw3RMClDR9iKHbl+07RWi6/m1M2O9PUZUkk6mun1a0xgWV/yjucwvruFtlDBGzjmWHUAhsgcQZVe/PNU/VZJkFifo2qXjZfneSGRGMmLkBz5MX5O+STdxqr7Arr9r7/aaXW8lD6ZqelAYBaXc/ElkS/0jWSerQABVWcCY0FU8itzAitZoJmBCRmA6/01lGrg6PUc5h9g08EBSQ2FE1G6o/Z/9hFXzgcoQgvxZszcWMpInJWveb2jHwod17lty/3DRFQTZGFmYNv9cQBKUTURASWv+cTw+y8dwmA1ZnaDiHM6i5BxPp5S05g2eZ0Wt2puEVb2QYfY0FLrTQ2kMJ07Ixaeo/d96tNTRVsU5S9ld+l1hoU2lMY7YkIVZs71ZuLDBWwyGSEVDHre/t+OxpeiJvUmx5mj53dn3GA3FdHiX+Q9JoTIZjCzZXRzVyDKmNUQ+jL75pezoh1NvBx6ZmSwMhCmKp/bgZu1IRAJk6l0PGKe4mAs7pnLfcNEciZh6NE9p30sTIi2sdKZ/zAuQlfyPfPiUE+vaz+OI47p1hNBPLz9snYLunKJcmU2Vowhw2za/Z3KrrnWrWyVaefdPrViaRwZbWYUkaPsoCk5gOmPk+yuEBRyS2LrUaLcuS4HRuaF72VbP0n1SnGdO3U+zPJ2N+PHfKX3k8rojMRyomCFR2CTOOgYn4OmURJMmQqG9CZpiJp8lANRB2MQKctTbQ4EAXUFIc4S4JbAFFn5ssiyZRalm/opndB0ziDSsrb6CxJTEJBuJN2/W7KfUEERMxs2Ls5J7AECvex5NkSUH0+xau5T6UGGLP1XYw62+R1mznwSAaMFK300ntKOl2mfrtqgy4ncrn5SZxL2shMbkL1Vo5oCWRSj1uukS87MvGq2rcayR5r957cliDVYZRXNTv1yhafsdeTtsJwhfxe0zgs37t2ud4FcaUEdAFzJsuHqpZOCrXSQ0mxJGVXHLLfTASQ9G5SmW5LdkKzI7iIGco0Lokcl/5nQ6BMX7X0VRJWoiQHi7QohdodXipuRw05mMYwsTxvAQ24P4gAJNrsJxnUssqkxVEflGlaDIhZbs68EBO9T5vSDDVynRydxjbcahbIFDQqTTOx5KpJB1258GqZVKsgefIyO1xZP07uxnY3Yqh1ZvQm8cNXHMQui++yhFjx7JreV3VaXOX/asPPUfWpO0tyNOMfEsS+T9zKlSZRYt6nskHLYbxcyRPXk98nE2rvbZPVLhKzW2QxOJVjlLH7Sb5X0kaXHH7BALjs+emmBlV1gg90UtNaoJNMmlO/xILT5ExFtWWWpBcWkogXExGYnUxS1JmSWl8qhjMRdslhbEHS3nS5f4iASLF/VUjReatTXJK1dibEoinwiA2ZI6mEJLnI68QxzLkKO/VdXtxhwTkYL2jEI02PgZZ2rGVLOZUZ7jTDGbxO12KZ1GojJXdSp0vMwlVEYX4PYDrsYgMxXxgl/FjZrDZohe0uMqim2HfzfueRr92KrIXJYzPbIbAgvrHSIlhwkUkc0wKwpXFTLe9g/XIzEhFjKPEUsgZBE7HIz9do+SFtD4U8AOkxLrnmMpNGioAxDUM1bhCrqECW/9LPxkcFvGoSWy2+AZLC4Wt2e7Y2HZq4gUlc1BK8RimLWMvyRTR5p0g2pLKLFl/h7UIEnMloM9nckdDVwqCWhQGT3TpgySYLL6WUo3tRT5KsWNbMUg2mmiRsmZKAqoXdEi14ObWzcQabMi+dA4kAOBJYWD0zd0ezEcteFn/3+7SxlaW8rlpjEbPXMd4pn7oqqav7FlZyeJlG2NZqDZbmgKe19WLaYppUcNmQyBx2prPUgNM5Z6HlRJxKdlSaXauIaRbT9hVJA1VzMMsx3mfslevk0GA5YcpE3M0EqNkZt3oudHa5Go003DVVmtbI1PNkej1j2M5GAOA+IgIOs7rSync6i2EzGarIhfMiGgvrZtF7JtVfkWMFXIwlyKM1XzPC9lSXWtFq45YEI6oFAIzkeawWnCZWsCy+9JTFQgRNTjjzk7+2iKw1IPY5L5QMQlK9Y43QV8/I7LJW7tnpzfei8wqq2ckng1aV/FCt49yuK32ZT4ykiMi5f5M6VsjZnW3DTxvTVRxcPQ55lGN+/yLqTB0Xk7VKB5eiTG5vSSDy55KQpYhTWmxGjP1P66oAg5MX6WwAyfqU/Gvu72L+ixiQF7kkYFLTmLBYM/dW7gsiIGJ4gG3epRw7q3g6Wpost2o/epiGvSws5yeOoto89ckjZaVLOVVqTCD/lqMDTYQiT6aSjWhqtn2p219aSS7LPL4dSa01nVx77mC+0DIhyRzP5H04R/enYvhJ3kjZOaimxLDY6TubYP7dpf1Sb+6M/WghyHlM8hmwYwSlU/CQsqV0AtImMHXORSytTk/jDCYCkPtacQyQohoZIRPvCgEAtwPeFZyGSRW7UypMafmeMQVDQcSedcZyXxABsOCP9TFzewu00+rJHX6fl32/TZOWN4GWvT1Hxk9vg4pQZFEkL/JdpL3ue97omV01z/zawWfSariq3f1tZjFlWpT73pNZfYvklPuzh82t2gVTuwnz09vp/J0z91ITtUwAxdnpWjwF99DEohWqpC4Wz7Q5Mk7gdoS1JgT7Sm08BlMAFSeWL9E7t6hTc3m5zdqvI+/33bHe30E7QIqdR0Y1z1DuCyIgaYD3LejdyXJF9q1Ve3MEfM6z3m7Sc6lPDOeyvj/fbzLhUpVY93PZxxr8muTgiZOcTsNJZLDvzOrW12IkxQucE4HJJmK++GKKmpPbmI/vLhHIi9n7vGz2E5qZiJVM3guWozI7GWucIo9TJnQ5n5+45LiTe7Vg2V0B0pRMnyZ1ps7avh2mUhuc5fGp7UmWqmnnjDt1YolBvHNTctvS6Pw5tXh4u7IrHlrxkoDtzPXorsn7my33BRGA5fk9lf2brlIl7kx+TQxOp/rLE2EXoLM2CqK7PO1cbQuQnlzk9zmrmlniub3C8h2XRGvJeWR9dE08dhdRzcraYnelPzkMVxFnanl7hj3MRardvkKNGVhfExGIy03lS5vTeBsBMK5mPus1x1Df45y38OdZo5LnLXsLsjunsOujMX8HisXo7vslIiCSvAM9jfNoyjc4G/sQq3E0wpvtWjLnZofXfEPvnUPNqsX8im8TTMAQn3TS3oFtmjbHaQ2Vmqn+6ezhvsUyP4GEJXGpT8G9cms5kQvfWvqyz7Y+18jmBDPikRaPaJZV3akLdtpcszecNic5G9Ik6izvn95912tyrnqc2sf7YqAUzdeXrPeuxQQbssoKTgFJYeKckmMdNAVSm3M2tu9tQ9YOXjnOayZotXXo8oCoT/2ag5rAv5xubjIjcOJovEWx9t7bKS2ymMt6UMxceTqo8pjvd2Rbrk9TUY5MofHfLkQglRr13VcKnjpjN3etCKfPS1HBWlmePPs+2/fTCc5Epec+DFP1Jfq+h2gUXpIMDsMSZqvoSHbuK69xSp9Pe94EHLqdMau5gHr8cl7IdDhN15m4N2qRJJLSpmVHmWy/YETQIkObMZb32b3XAo0472lELHOPKiXRS/UaxgxV6l/MiCqfvE7mqtulyDipAecOY7NDQLUQNu9TSHOXbQF2xQu/h3OZg5D1gbAfxynXVGeCWrYpOUu5IxEQkb8C/D7gJVX9LenaVSznwLuxwCF/WFMwURH5s8Afx6TBf11Vf+qMfaxUJfMB2sEEEku9NOGd2LPMiqfKC3kxP21JBJan3Vx1NA9Qku+ZTqP93oezTqe/u0RgOhmKWfTsHibRAqF27XVag2yTyGJ9zSfQREQm/wc7lep3mcYBIFa/z52xCv3RdI3J9do2FsQxMvaWZ0CjWrTeanzatqFpWtv0AnhLmJpRv1g2beZWbKQkcRGWa8DMiF02wc2npghjtCg++1Std8INpL7PuYIDzMWTaXZiChOWYx9kGwNrLlTjLWS7g9O0E0sOJt9puSbPVu6GE/h/Av834P9VXfszwM+o6l8UkT+Tvv9bIvItWOThDwFPAH9bRJ7WpbBzj0VjfdTtsqJJL0TeePm3uaxZ7i7tTCq21IwoIdigt20LUFi15UTlBbJPnr8dCJUXTsBMljMrW9fLPmb5mqsIV/49k8iY3E8L7iTVM3XS1ceos+g68/FYnin7uIhyvpNPcFDEaXK9dWnzW7+yam0cAzEo4zCy3W4s+Ub6rT51m6ahbVvati0iQtNYajhLRe9pfJPmZSLAbWup15MzfgnmUY9n5gT0lLmp5zVbBmZMICetyf9yklaf0phNQzith5giHfdDX4LgxBjxnkIUjUB4RBxtW6todSd2RdQIaurH4ozG2csdiYCq/l0Reffi8h8Afjh9/i+BnwP+rXT9v1HVLfBlEfkC8D3APzx7VxPYs3jr2ebTmkWfdMsGvoAlm6gqV23YoGeNwnSt7y1s0xJQyp/zCZrzyEtm2d3yZJcUqWYuA6MVn5Plw7yBZwx2thqfeAHna2/ISNB0T/XYWneuAiVQDkzWfaV2nYJNCCEu3kGnE6sab0hst5cUyJMiWVgkozTOKSORhTgPjONQNllZ6IPVsdTj0xjnU99EAkeTOIQ6toQl85zcpJcbpHAQafPVUabrtZI3Xz3PpS8iSPJo9T5laUr5H0QFfGpLJ9FgDJM4komIaUDyerN/RuymGcmcQxl9NfPlEFPWZoSu604VSe+23Csm8KimYKKq+ryIPJKuPwn8QlXv2XTtLSi7rNqEcFe1qgG2QawBvVw3T761W9tw2z9XWNoa5JHZJq7YZMDPbA+munlTquoiyq9tfldZsE3vNgeVVCNVIuP8orP3VnRnMWjiAOwAL7t1UtFJrkchFLXqsqRpz2woyRCmOoGLzJyoQnRGPMgRhXCW3qxRsrlWl8bK7eGm8gk4DJY2vW27KrgHjCHQh4CoGZA1TYMT4VZrNvur9ZpWPL6pT+3KPq9wCIr3ibA4CxE3jpZizJKbxBlmUMQlERrn7Vndqji1RZnWY8alPHbCx6adEZodDivNZ50H0whE7vU0n9EHfMoa7Z2n6dpvPCbwJsu+/uzlWORN5R2wjZRj1FVt7Gu3nMi7m2LiBKa/k0w+tZH17XV7u1xAaRedZYbNhGiSo9MJOsuanE6ePSM2GRBNLGyO1T+Jijr75zHnktrmJ29SJ84AOZmfblOHgSxKpMzKJkZkTiH5HUjiNOLEXi/H0JGNp7IlokNcTJs2o/u2KdqmmY2tbcSxkoEza56JcXrdlJsgovTjADhcdJYe3o0EF+jUCIT4OpDLRGyX4chKxqgCuy02qpomwDtH27bGCXRt8ZGQeiyiztdfrlO3txRHFrsk/7xjxOUbS3kelcY1Rjh+kziBFyWFFheRx4GX0vVngXdW9d4BPLevAX0zeQcA9qLZe2rJBLDUJ7hWAE2mJAW99ZOuetlOZgv3IbZ12ZcUcgIGE3dAzeILiPkh7Hv5up/1fUviNREae0CNTJRNmpyCBKkCZ8yeNltIWsm28zq7z871RSpgLIk5IgZcmeFOlvsnPX5TEYEsd7dtW8nZwsnJphCG7HNhXnxuJzKUqunQo1NCUJrW2qcm4pXjj4l9WoiVmf6GxAFlhytNCLxxFE3T0HWdYRZ7THZF8lgbca59OBYV65FNcNZ8/aUOz+ZAxEBfJTmtnTUHGfdOBH4S+BeBv5j+/vXq+l8Vkf8YAwY/APzSWTspkmSxhby7v67MFtZpiO+ybv5+Ox3ysh37mNjhhcPOvnssNN6CF9/Dwu/cW8neS45kRpzcFOgrxikCkyQQMOYNuEN1ZLZYC9hXOR3NpC41MWuK+FyrQKOF8E4bUkNKjJo+xxCJY5J102bPJ7+rNpmp3TxN0zKOOc2WEZG+72fgHRio1jSN3edTVt/03cahYfniWRycgEnB+ySqOC3vBmoObN7RNIYDuKaZrcUcAs65LF5Z8S5rNnbneLqQ1vieiFj1fBeQ0Jni83bq8jdT7kZF+NcwEPAhEXkW+D9hm//HROSPA18F/pnU8U+JyI8Bn8aiQv5rb5Vm4DST3D39nW2OGoyxsn9Clvfsa7ew127PhqnURKcSHrXFUt+rlYi47z5VLWHCl2JJ3bfUi3IiL0OeZWLgFifT9P5LE+DMGk+bZWoshdHWOcdkz3YQg232YOm3Y9QZ0p5z/sU4bei80buuI4SQCEFL1tDYd1c4gUnrMKbNazYGIoI4ZdV4XOMrLkDInoq1NsI5Lf2PaibRGTSc3IYtQ3SXtBYGBsqsLSMceSwC4n2S9V3lem1lycGIpHiEi1N9Wc85V8S9ahXtrIc3W+5GO/Cjp/z0I6fU/wvAXzhLp/YVYc9i3FfvlBN83hLMtQN1CKjTB3UXY7B2dPoy40D292Eh48mC4dtDCCRV3PfmS84k7ulntqMvQOVuK7PnxhhTdKMEFVaUStMxN0M3KuJZq/xCsRGgnNoZ51BVhmFgGIbZYs+EwQJzNHjfsFqt8L4hRxnOJ35+nqnYJuLrGqFzUvAWO0D8jEDnZ4YwhZPL9iV2emeVnhGZVdux6roClmYfgfpwKKPpF3EvFmO1A2ZDsWqs6+3Mo0iJqKhkduM3Txz4R1qE5EDEnV/5NFXenDXP//I9EEJG6g39Pq2d3ZIMZtCduvsJ0Lye7m17vqhq9nLWmky/210TJ1D3YxZFaYcITZaLXirX4CQ2OLewThCZnHSEAt6FlKxzDJNu23vPOI5st4Mh7qqGaDdNOc27riv9yH0cx5Fbt46JUVmt1uXEdy5bFdpG7NKmNC4mzDkgcQuz7nmp802EEAljZByMu3BNmtNo7tVtK3RdW0QLJ4I6Z2AsZutRnJh2Z2lnvG3OqrVRiXt1vX191zjZViD71tibL/cFESiagbM2MxvU+vMkp1sVv/ee08SEiabsbubdZ033FUCtqjNt4Hk/k8V61e7c7VhP6R9Q4vRp1aV6MRa0PFeQyS69BtSmvqcelefFlHXIHGpw0DrHOBpSP8pomXrETllLCBLLRsxYQH5WNsU1GXuS9b3PIpRxEarKwcHaehBNzLBNigGTZUxh6a04iQSY6JIM0ZzzUyCT5LLrfZvEhAnHqf2a8uGxT6Q8jQjNx595LLo99YDEqWQ7A2fc2tuGCHC7k/jN3bO/nd0Tf5/C4vZ9mHMWU/3TCAM75H+aUJn9lXQUz4HA27S7t3uSEPv9J9O+a/PTaApIYnYYmWPKm8c2QQiBOAbGxOaj0DYtKAzDwKYfGIdIiCN90seLSEHc84Zfr9d43xWDHPs3bd5cr207LJ1XSyZa4hTv3ExTksduxxEs1uHo82meCAoT5+G937FrKHOz0JzcLRdZ6u2Zl70lEbfy+TZtv5ly3xCBt6rcbtDuldDU22+f/D+dprsbVZFJx5zZelncm2Ty2xMAWQgbixqze/fXikLNE5HfTJIIoElMKBZ5OuEA82SwFivQHjk52axWK/p+YOhHtv2WMeEE2Tgo/52AQGjbQw4ODhKxSOpBhYM2AYfOHIp8ytLTOCMWzmUsI2/Mpe2IgJr7bsYFCtCHpjyPKbGqCNnluSaEeeRtOu58ku/7LYOKy2p7T/jysInLeVsRgdN87e9U9lHkOxOCzOvNN/Qdx7uS3ZeTNM3pnpN3enhpI2MFWUZP59OuvJ+4DYH9sUFz17R+p9u/yASeTRve+hJ3hmUC/HT2L7u85ph4GQPo+wHTDjasV0LbtjNZPhsMiQjjGAtQaf231GHeObquw2mEABqUMQTjGro2iRwgMTvp7MZdzO8XwqTxyXOftR7OCb5paRIoubTVyJOXtyXUROfO4zxtdN05H/az+ZpCJDo03CX3cBfl/iACWabTOQAHu4DK7kJfAGwZjLvtA2Xxd98di36UGvMVsozBuY+YTJzCdHs+XGpm3AC5OXGZ1IO63J8z4HB2Au4pWdNRq/yUOWGRqt70RrpDCIZhMCehYST7Pdq1LScnG2JQmrbFNb5strmoY33PG3MYso9BwCN0R0eF83Aue/JZl7LqzjsIGlOw12UchTRiezgrVUo0Y+8b2qZNYcOWka0mJKcEaJ5Jenk17K6bjN+U9hSzq9iZE93lHmUebPZtgwkUbzQdq0HNbNQSMJwW6D75V6QyrV2OX8XaS/qOZhccyoKef7ISZyugOr3L6tBF3jn7NOu9y3ZmuxMbp4ZTDd3zAnXJANUk5y5eFqo2Js2IlTo6jv11xOgYBxMMQhiSCs2ARLOeUyAQBpCmpfEtgiv+9pnlb3xDIy1jMOIxDqOpNn2y9U82/bUFoarS9xs8ky9HblcJiG9YHx6WnAZjjPimrXI3ZJVgJlTjRFiSzC9p84tosTvwjViyGSc2mUmjoKpTJqp6SFls8CV1YPo+s6Rkfm/mpmIWT6pZ9YkAhjEQxnBmQnBfEAHYpx3Yf6LVYNaummU6DbX+ur+h6e+CStvmno7ZzLrvtjH/m7UBU9tYuC0ogMCp01ma18XfO94ApxKMfL1mc+dWkzXI5R1I64ubtb1TXdcsCdfdmtiafYAml2IRWK06xNnv6+4AkGJItOl7QhwLii+J2I1jKIZITdvSJZWgqrlER6LFIOg6mrSRJ0u/uThWQLz89oVDqki6Bksr7iUFODFiGWMwwp4IVfaWXGqPagKgWh0Wi406EwGr31XVPC2Tq7GixOqaIOC9vWNM7sVnLPcFEcgqkbuRZ+/c2OLvnepRgWWFa5eySBE78U+3DZgalCRXaiIECtXhq7nlvZ2brNJy+7sWlEvdc+6z7pE5pw2hZH1XifArc0KQ254YYMpJletMMrDDeY+oQ1QJEhiGbbEIFIHNyQkH3ZamMXDPeY9vPKEfC4tvtFdRDcQITePp2mwXkF8vofrJ1Dj3xf5RfQeLoZjEgpSWzfrrC8hnv095BJybiFHBilwOgXb7BTS389Cda6fdU9S1GRA2HeZkzJTHJaoRgXvEyupyXxABK3e3+SeCMX2vy70wTqepe/Ye/uXiFFCUhDATtRCAbHQyTaLfbawqE3A1qeqWbP7dAkXzhePJBKDGA5ZEzU41KX3P41wTjezt58UCsUQfGYYtIbT0yUXXgL9bXNNrk6GPCLgsCmRNQlO0D/lZxZZAYsIB7HtWLYYUg8DyAc5Nu0UaTGMxMoZQ3jmbItu/SUtQcggsuMmSFYrdFbm0HFyOX/1335zUHKyrx18ws3Svs+A2uLuf89uV+4IIZNT4zXIB9zpAu2LEbnuTNDA/MWs1Utn81fUshuTrtzOCqtnxfSBWzjtwuzIBeVPZJ1plTcRSx10v7GyQUy/o+WcLPZaJQUbds0NQnsftZsvY94xjMJwgjUzTeMBCiw2DPT9v8GwdmMAanJPKiMjPxrmevyWYF4JFOLJxmOwOsgYi2yXUBGAJwMUYS7q5+VifvuGX9y+JQW1uHZMIUGZHxEyRs60ClPRsTdOclTe+P4gAWCLKfeV2LNbub272p95k+5DW04jItMgMcKrrFpXdnnvVFxu2SZOw2Nv7wMzlxpzan9j85aLf0R7sie8/l+dlVif/Xv8LITKOsWxmA+v6xUYynbo1PSbgbdrEIQTapoF4SPaVb7qWbrUic0/5nfOpl7kDEWGMPUJC7lM8QqsfkpZByyZW1aJu3EesDP/IBk+GO5RAJQsiUI/XbMLYJZz7Su7DEjfYV2qihJgY6VJkPa+WiCcSIQqr2pLxHst9QwROOzH3UdTl9WkykyJtQonOYI6cF+yd75/URdavZIIynbCp3tKarb6/JlalBwoitRihTIk7JmxhORZ7ergj8ixR6oJWLwJeigh93xNjTEY+K5xrkhGQ0LaHNI2bxWrs2rYExChpuxwpu7Obxfcbx7EQASQisS1WfDZesRDCfVGmamBQdR6Upp5DSb4GvpGZKJDftXZ5RiRlwJ7mK4RATXj3cVS51G3lMV4S+ckHgslZiXTshICqmUfHf0SBRv8XUd4MtbsdlTVcaE7h78T631X/ivrulN9rTkOLYJB+oxAl6399ku+eNPu5g9QLWbS7QEJP43jqZy7LdPJTNmm9eGcqNswBaBxN/WbyfDv32tNI0FCeKWqy9qjGXZhfgHEvmQMIMRRZv2mSGa/bv8mW12bEbDliYsChc46mzdaGsqP+W4pvUhGBGofKY7Ivh0A9/st/dVTmWdgXAVHjblRIvgxNwih9ApvPVu4LInAaYr6v7Nsc04af/ta/7XveztBWXydNxYSX+6pS3HNPfpbmPsKMKCSvF6ao8hPLvyQES5a/fne7Pj18Qu1rp6h9kZHn7S3l2Sk6bjNbtEMS3vOi77cj2+1o5sGjcQhdl1n3zGaDRC3OQe2qS7H6tHgMml+AA/G2JdSCh9iGNfXdvJ8lm0C6PoVQt402JVO1WH5THErnoGk9bdcWw6N6LJbhyUUEz66YUP+ticgSwa8JypLLijGakZNRwYQhJeBV1YyXcnBSMrF6m4gDbwUomE9Jh+xwAzOWmV0MfnYS7+vL7PfFvUn2V5dQX7WAGzMwK7WxlPH3cyt5sy9P9LIFyrNzD+aJT5dEsrpfd7GAaXHb6Zw36tzxxlxyYwrJ5bzF/TNRYWC7zci/TzETSXJ9a5kMfMY4kjagcTRdl/qmZBbZuclZaRL9TjeYyUQ/xkD2MpxmhmIL4L3HuynF+JLzWs5B7cG3D8Ctry37O83DLuFVNQ1S1CpyUp5Xtee6okI8Kw9g5V6Tj/xfgP810ANfBP5lVX1DLDT5Z4DPpdt/QVX/1Jl7KWmh7tPLnFKWcmvNXluTaeOdIjmITGqa/H36DKesudxdaugh32knWjqXc3CK5TOSnFf3Nd89Jy+nlJmosY9wpM1cGQrlfmUCMLHuIW12rf5NTj81/mKLPYXgOjwoGMAwDIxjzziOyfx3JIQxWfZFi0EwDnjxZby997RdU2IZwORSbOz6bii3fOKXkU4Rea2vKWZAyHkrMqdFEmfcLK/AvihWO0j/Hi5vX6lB532Yz1LsKPylVLkLEmFAFRczvygULdMZacG9Jh/5CPBnVXUUkf8A+LNY3gGAL6rqh8/Wrd1yty+ralltgEQx7fSq/K4AxVe2w1n/rdVGyyz4bZ6089zZhlZ21Ug5F182GtrTvpBNeOcnhbU/r5vVRbleRr6tbVcs9SzqDhURNW7EkpAoqGX9rU/9abOHxFpDiEoYq6AWkJKESLmnaYT1+oD1epV88JVxjGy328QVjIRxgMQye/Gos3wCeY7btqFbdxCzh6LJwYYF5PHIVoFZb27Rg4wYBAwwFEJQwqiMg+WVmMTBKgeAzxzG/MAo47wISUY6qV21uZecQB3jMc9fvVbmHFa17qolkwlSMRtWcDlPIyyX4D2Xe0o+oqo/XX39BeCffmu6c2ofCDGaaqQKvrCP+iqBKTVYJFSOb4XlBlbtejaIzk0Za8AiDVnz86xA1m56frljYlFmfVrK8FXdvZhuwgpkaZNenVp58bgkQmg2QNp5jzlrOuUayGx3yl60EA8yJ2C29YolAk0gFDpb3PUJCtC0Du9WdN2K1WqdiAC0rdK2HcMw2rtglnlTDIKRMYxlQ7WtWRDGGAghn6ZTCLicsdg2cmbLtWAdIhZ+3O434yALE5ZdnCccwfmmxCas39+0PnNusj7NQYoNf72h6/ku+QndPEO1cSgj4gQtsRknwpb3uUPAOSbXMCWWoKtn5wByeSswgT+G5SXM5T0i8uvAdeDfVdW/t+8meVN5B+bUctHOol6Wlae62fAiU1NFd+wOlsDYtLGLAm/fW1S/Z+JwB51xev7pdTSFtSKphupnVfiB7nIqS3m3fM8vnk59yaBW5jsXRClhUvN2JacDs2L2ASa6jONgtvUOGg+qjhiFprGoQSTRou8HwhjTyZsWdRwTqKhFExBjtExEMSBC0gSUF6k2TP3yVThPNSIegjKOyjAEC0He1IRerb9NDlBazYDWOMCc+FYPLMBi8aAUFnkwjVg5JzMuJFuKZlBvAoAnwHmadmfvpoqSxRnjRnLYuLPSgjMRARH5d7Cowv91uvQ88JSqvioi3wn89yLyIVW9vrxX30zegcSyLZN/7lN33VW/T2HF95fp1NgVBC0W/rSZKhvz+mm3IwrLnoltyXjaM0uvdAId7thqukfTdpf5r1oRg1x8yYaTNkNildu2LdxA3lzb7UCMA6qCxhHvN+TIvpkbyPKtiHnl+cY28jiasVHXdckoRhhSSPIYR1arFVmHX5/IMyBXhGz+nJ2QhlEJAUIQxrGxvmkDbsy5QJKTkE9+ApkwqjmHSSaWGUdZsPsacDrFSQghMKJsNxsjZGrEPGtPzIrS3qN1LSCsupW9s0zRiyQROxM7XMlGaZCABRqvFZ1nJwFnIAIi8i9igOGPaBoZtRyE2/T5V0Xki8DTwK+cpZNCQmMX1/fryu+unKZmu30vZi3MfzqjfGYLfN763fTsbh5dt5VVTTmC8KSnBs0OKQrg8K7sAEKY8gLYYm5YrZTtNnMELcOwpQ+BzfGWcci5Bqd0Xt571us1Bwcr2s5OXwsgWlv0RTRmuXkK77W06do5CCSWk1lV07PN+8550Di3iHTO2PnsMzFLUV4P3GKOSj9jZBST1oNGAkoYR7bbLTl2onE/k0WlzbGj8S1OHCGGksuwazuaGHE+G7UZp4KIpaDLbThHYqKQOd2+53JPREBEfjcGBP52VT2urj8MvKaqQUTeiyUf+dLZu5lZ6DlAlqn0rN5dnvA1AdkHBtVPnv+tShYhlDueyLdDm5d19oGAe+um51uS0dNvqH+J0RZs/fiQsgWHMJlnT4k9KAvaTqsmiQAmmxoxWCfZfp2AOMMUNpuTVE9SXWG9XrM+WNE0YCbFbvIv8NltL9vwT8lD7J3n77FU3dU69zEMhNHex0kDKROwMAFuzvuiIlyuAU3YxXJupnwOlX1/jf2ImBiniiSWvyYCQNrcamnhnAOnRAJjVHwKzZbZfZLnYH6eTXt+73lo83st95p85M8CK+Aj6cWyKvCHgD8nIiMG2/4pVX3trJ1UMmNsA3K7jZ6Y8rM+0tqSiTHfSRZXnvbWltliPK35RHicu5soSfOSWDYmLsDUZ9vtUDY72Mbfbrez5JwxqhkBDQObzYZxjDRNW4yAvGtpmi7lCfB03ZqjI0vY4RyF5W1bj/MxqQtDERGQ7J4stG2T/rWp53MMYJlijsLRpEAl4wApHLpKqAiIs9O0BC/15bda3Vg2InMCLoBKZdbLpOaL0fAOcVLk9aUl4dRUdpO2/I0BUOeRtOnFgURJrsuJyKF4TfN3d5LgXZV7TT7yX5xS98eBHz9rp3YbPt3qanlNb3sm7rZ7WuVJ/jz99qXG4K2alGXZJ7rsQA+LulO96ZTKJ+EEamli2ye9f20jULeRrQP7vmcYQvo3cnKySZoUofFNCs/tqw3fcnBwwHrdWVxA72gGR9tOhj9N41P2nfyeSU3YdcWqrhZdTDMxRT7KXn2G/IdCCARXRAQbhsY2cIpfYF6JRnwsfF0+2ZmyOS/HUgRRpXGOsMAlvPdm8pu5EXElj+Iw9GRXZS2g7uRz0LYdTSOEhCu5NMG7c68JjjbCF8PbKJ6AJFR9uSl39PNn2In72pkchHZFhiwK6GJD1mzqaURkb1u3kfDzexYrPdlj2lzVnd1HkrUThhkLeh4Zx5Ds/CfgK8f0tw3aJoTbE4LSddA0cHBwlE4jYRhywlBL6pkJaD75vHfVBgbFnukbCxVeg2H2zNpBSKk3w3Sizt89xmnzD8OIVphqjk9p9hKuiDXiy1FfxjL3MXtp78OdBEnA6TRm3ns0KhcuTOx/TK7SANvtJtlcTH2tDcbatin+EKet4XzVeY+OybX+NwsT+EddRMx9Mi86u5ZP6sqVt+yhu2OSc6192y7/JpXJj8zUhNOpUbNmp3EodyJOS2Bwb38rVDzqFPb6buqTWH9FiuWf6dGnvH6ZA8gqLxGL+JsXrOnwu2S378tJ23UhWQaOZA3C0dEhbdvhfDJ1dZZbwDfCOPaE0JuI0NiY5pBg5jPQFjY9xzCY6uzndvphYEwWjhbLUPG+ng/DTiypaDJfdtW8WUPlhM4i097nQUr+ujAGEk12Gul7o6zXa1SVw8PDdD2m+IxzbYOJF1N72Vej7oMBg/O1ZNzM2SjBfUEEShFzACFveDviyudpQu7cVExNJLHLdLz5MXsJ7A5zSM2+Lp95d6DjmyunboLMjVQlL5qJCGQuIM5Y/5KpeFGmRTnZv3fdKhEBU8fFQNl0lkxPk4ZhTHEDJnuHbtXSrVpiGFCNiHMLJyCdbPi9xznPlBR0wdnMxtN+m1R1ZilosQoycGYE1ja/m8BAJyCxGOdMk1ix3lJ/r564By8oAGF1rNRBUXOJsdmZH/vrZkTBiPX0jqbZMUISU99itv84Q7lviIAtHKlW+3QSS/Wvpp53bDPvDOcKwD+BcomyFABYWMYOmMwb5oDAHLW+NyKQ6dvyVeY6ckOwl0tgadSS/9jmCMmOfgIBbVyVnD+6zqtX5/7Lfvc5OWgQM/ZRzLTXjHGSz6eo6eyDcSxRA+PQp1wEStP6mRedxRxokqxvmoNyKi/ee5rjiV+LSuJaAJXUR59EDBIRa2cuyBlonqDkJBZITdz3zF1hLup5TmOf19Riviarwbk36GRXYuurxr4m4H9yRgoxmu1AaiMUjuHey31BBDKQ4pwroaFyWaqJ7rbUrNgc4JsSURiTUSSxPbIh2LTLrL17718FNM2+7WNJ7+595wCnVqLAZLzjHPT9WMY2Yw95PZre3dyDVU3N1zQWJ2AYh+T66+m6ju12S9t1+MYhjuRx2BshKKbBE2YwOSL5ku/PWOZJJTwHhYWs27fgockxSPM6sTqZUKnGAkCamOEqYm43ZAJgNH9OefdyAnndVN/L/OSDpGBYNeGaA4lLR6j5IZNNo+tnZBWutR81FuvSs5T7ggiIpIQQKYJtRkbNdiAtiJyH2pTmFC8rhDGMFUyQT6pTwJcZe4ehyWkuQph007bA7BQyMpD0u4tzWQtClX/fX2JZUlP0hHRIIZhVmf0cSxKSWNfXFJZaFS+SdMoOcZ4Y2Sv/m5ee1W18h7oJ2TaPv4FxHEoswL7fAjCOG1arLm2qhqOjFgva2XLp0mFa4HX24YEYR2K0wB1taxs+z63FEGyZDHbm42SbZRk8VlJfAkM/lkAkFNUcCN60FU2OR+jp2gX2kp3MYm3QA8wIwcR9TjYcc1k915NIsejLTIQXv2gj+xvUYsfkZzDFbhAsCauSw7Z5mUKwxajs6jDefLkviAAkyimO6BSXCMA+WTEbhakoomKecrEwXLP2lu0vOYLl90WPqutJvjy13KXclp+ne2oXrr5exJKIhFZcS7pZk7NwivsHkMNm1W6zGo2gte08Dt84jrRtP8sRuNn4YiQUo3kGej/StsZ6Z92+gXsrnJNkZ9AwDENa1K74AeTn5WCh08loYzqfg3ou7O84mmgzhlAsICVTTszWXpxPBMknLmAxF+W+PSjQzrVaBJm4FNVM7K1EtRBkmtap901i27XgHLUjViY89vo5ypMv7zJxAWYxmHEwOxx+k30H/lEVG4CUjWUJeKVSTug8+In90ljH6pk3eq+A3VsF9J1WTo0VcYrwl8WlHJdIY0xbQMnq78wB1JxAvreWUfNpnE18c65AMBAwuwRnbUCNLfR9z8nJCW1rnoQG/E3JSp3ziNOUuCOW59QEYB/Iutx8kMUInYKbVMjuHKsTmqYtWY12Wi7i0t0J1ku1b/5cexMqWoKCAIw6cWBTSrUpEnMWU+ooQdmRdEIvMtFwaDnV9vqivulyXxAB0IT6xrLgYff0zjJm/j0swkLlz2AKrilPTQ4zNWc5d628prpvJer/pss+wJCJO9JEMAN2Eocx0A/bshDz5rUbjTyOVZpwmN4xL+ycVdgsAc0qMC/oUKnm7HRMgUL8nPPwjaLjNB85FHlG7Mu7KECYbbZ9HEGMAY2mpcjcTi0qGIfj6DpX+iJisQas7HMTn0DByYGrVgVmQrWPM6zMgzWaiBEjfb+dEcxaHKuDqWaiWNKhN+Z8RVGDYxxnzAlQLA/jWct9QQRUsUAhOh/8moIvZbOlamlqa0JrJ/Zx9/7pGexFe+s+3A0xKGLDGemGxkgUkuyrufHSR8XY0TEkM98h0G97xjCUTbvdbgubDxauqj7dYZJNc9+994UILJNz1LkFcp6Aeqzy4m6bppxoLqkI27ahnHez+Vye/vsGg2pN5LDp5tlpHICvjHBqMeB0sXBe5vzjPo5hihkgMxDTRUfj7Xvfj+Wds81FJrKZ+Gbz6Dx+maNw3pn2NRGeggGkoSnJW85Q7gsiIIbxcfPWLUSl5F9rW7NLr1n7HJ4ZJQVsEAPR4gSjxRiQrgM3n+Dlv/19kZ36kBfiabJ/vmf/oo4xglsGEtn7dGqrySyHuiz6pJMoREvVHYKy3fT0/ZaQdPclmGUxCrJ3GIah/FvGDgSSdqAvjkSr1aqcWJkzyMlAMoHIBMWnzdh4TzAKVox2ckSfieuaTtz5WCn55DajJjulcxQpsXQ8aR6yy/KUP6BgBHNUcDa6cw5zf526ntWd18sbuA5ZLuKLCGCRmMeZWJv7tJPiPYBXTRGy8uHlJxkkaQhOExPvttwXRABsgN64fr3kim/aFqIjpkArmZ2K40AMJhM7Z/7YojHZARgwJWo22r5yI81lhwgsWNIlx7AEEOdEYV7MSmx+f94wfWWrf9usRKlPmvXNGQ/QSNBJQ2BtC77xyCjEcZJH+75ns9kkQmD253Xg0Nz/WkSqF2peyNk3oP49v1MN9vkmuQ2HEVC6rk1qxolg1M/NbZRFzzxM93bbM/Rj6aM9e+qn9x1NkwHHhVZGZmf7bN528xbscg7z33fB5eUYqqZYCdW6yMRgyVXWIlgMZhdjeR2tD0pIOI+JxV6W6dLvrdw3RCAjsBn+stNgShM9hrHkicvXxOX47xM63iTEpWZ7a4eXemOKGNdR46/zhbckBLuiQrqr9GGp0cgOJHW8wNtNbO1DsWRWMohk19OJ6xyNbxjdFBo8y+99b5spVE4o+zidvDkynpDbyUZEdTRdizPY0LSNpSBvPQSH6lx8WBKX3THLfZjaLoQg7o5zjq3ovIUnW3VN8l6cZu923N2SmJ+mzdmnRZolJqmesyQI+7jN+j1mxNDbBItAzMZdMeCbbF8RUJksIs9S7gsioJrcKLumyEA5sWxELZFFDkppglLZXOZQMjnEOHHEEEqAzBqgyl5rNREQN8msFmZ68km3zuU/c3FgH9u5bwEVNdPdyHa6qxqdnpCi4OS/AjCavXzjcMP0XkscpQY682/l/RecTY0V5PpZ/Ze5iaZp6GILq4i4Fu+7ZCBEMdyZ+sisffs8gYA7kYVTduE5EfCEYWMEve1onNJ4l9SeizG6/QjPRnT+Nz9rV4TIYxhC2OlvXnd1dGaYaxNysQAraY4AHPg6tHk2EorBOAWyKPC2EAdskLuuQ3Bl0daBL1I1Rk1646yeGQa22y1KAl1SUsd+HFLsezvNJpfOtrDBzjkkRqL4tPEt8qt3FnQzu3sKQgqCS/ZSq0+G2ZssOIFcJ+uWrY3bAFeJNZwRE7Fe5EAYtnmL3gOLvpvBs2nR5uAbU+irKWpuze3U/a6v53YKkJU4G+csak6MmV3Nm0FmYkIdVdnu368FmPV3mDZc1gTkZByZyE/9qzU+skN4bgf6nVZvSYCXImLRTFWq0xjHmUZGVUvqthmHU4GorvHTWBW1oWUhGjQiXhDnGGI4s5rwXvMO/HvAvwK8nKr926r6t9Jvfxb441hQkX9dVX/qjH00TiCa0wmRnUU4r5upZoKSkquYzvS45e2mTZgXWZXAciIwaWGlRme4gBO8OJri9pqCZzQ+ncr5nysgzr5N7pbx528nEtSotaTDQPKHfH+cOANsYXVta2Gx2mEmkw7DQPGWlIn1zyJO3mxZ/s+LM6Pa2esvYwGGcHtclSMgm+1mriu3eTvmZxnqO7viZoCzpDaHomfPeQtzSLI7MVf1WE8b0Z16X+Yu8+f6es1l1Z+HYTqU8po9Pj6eqQzLWhMjlF2yyGx8k8KgZdPnya7CpQApZy33mncA4D9R1f+oviAi3wL8s8CHgCeAvy0iT6tq4C0oGiOVYVahovVkeCcWHtt7My7yvtBxOyls044aZ8BgDXyld7FnVqmI8gk5DAPDOEx1dW6KnA1t1gfredooBTQWdliVklpaF0L+rvxaOlG+q1bX00/Tpkheec62d+M8oYms2pbOe9bdiqHv2fQ9J9vNLBZeHquaa/De0XYNGrVsNjN06ZJjjm3stmlou7YkEM0qL5+y/WbzXSMueR6niEJajTVpXPIpOo7BgGCtT27ro3e+RDnKzzSbgFPWUh6znV/2my7vJ8pZzZxZcuO6anAToG1dUZvmDX94eFjWbyYG2+3W7quyKg/jYBGHJJtiZzP5OGXROvUt767cU96B25Q/APw3agFHvywiXwC+B/iH995FAE2moaFQaRFB0iAZW5n1sIBOJrKqmiLWJpBLzPx4lU72PIRL8CkXMSP0ejxskpESXlpRfMIi8nNCCIzDmGz+BTA5DgHJ2gtxiG8qe3HbGBaEI+/3ClyU5WLcBSEz222VBZyn9V1ZfOM4sl6vywnUDz2bzQmbzYZ+6JNrMUldOKYQYPasEAZ80+Jax8HhAQJp05k/QN6Eq7ZLeMoUOSifzrZ583tl/CTr2ScuZGm4VIxtYiRiyUmNmbB2uq6h7TxN65FGyUlJ6rHJn5d2dvUaOF3NW9UvYlvmUmIhyEtuwP7muZnW0Gq1mnGbRW2rc3AR1RSPIYGAQsK5Am3jZwZv91rOggn8aRH5o1gk4X9TVV8HnsSSkeTybLq2U+TN5B0QwTWezJFbySBZTMizTaA5xcxzy+/8Y5r4umQW+TQUOZd9HEgN/tUnaE7jpTrfwHnyh8oWP59sTeNLO7nNzEJnQDMDTmU0FidVCXAZrG+1Jdpqtarusci82+2WfuwJ0ezxN5vtjDuIMdoG1Bwr0BVMQTEC3Q89XdcxDFu888UkNsZ9thcTIJnxgHocl3J23iTTpkvYTDoQnHfp3yT715qOeanEkBk3lTmLmmjMx3YJitb16r7Pnpaam5zOpmIi1qQ9MmzI2opjSNjH1Gejnw4Rs7sQ+c0LKvKfAn8+9f7PA38JS0Kyrzd7CZW+mbwDUE7wvJnyabFjvSbmMLJUby0ePtvEuW4NcOWyjxPI9es8clm0WD43s7GTrDmdbgCbzaYARRPGYYuxBupqDUad+ivLwHWZERBHsjgz0pet6uz3/B7Grq5ZF4eYiUWfuCTjxibVpnELPRk3KGKVMiM6df/qd8y4hfUJtHB6k6YiYxbZiKnGgeo5tDnP955uDnz7DZOxlklYWEoBtbr0TqV+h2lOzOch/17PVVlbmTC5DP4n2FccgRSWLCazZB3Z6eSbLPdEBFT1xepF/zPgb6SvzwLvrKq+A3junntXP5OKXldsVO0RlxfiUr21t7E9pSYK9WZfOvRMEXCn9mRxf607n4jKbn/qOiGM5ZTLpTbYqReg956+t5N3X5tlPCoX3Hz/UkNB8tQ14xOTzUX8bBzsudlFKZ205GQfOhv/cZwW5jxk+JxzysRmwjdIYtC0eTKxqX0bcv/z99r+IIt8BWdZliRnTQDyvZ2iS05g+dv8kXPHtxp3qsW4mrjV91HFFfBtmyIQkw6z3yTfARF5XFWfT1//IPDJ9Pkngb8qIv8xBgx+APilM/dSSXYAgRxGOvVjtuFlLi/c7gXmun6YtbnzV+pbZWdTSupjXWqRYQ5iZXZ1bmteXjQ9Y0ncsmiRuYOsxbiTCKPRvPZmHIzzZpMuAo4SJjtzKxkcW242YCdTcx1wM89FkzQHuc7y5J7GwgKXTNeNG6hLtk6s1WlLS8bybG5D6Kg33X5c5W7L7QjAnerXxKAuGXCuNQXjODL2w6RRUmV98SLeW/DXVdsZBvaNNhaS/XkHflhEPoyN4DPAn0wv+CkR+THg01h6sn/tLdEMpF02jqMh/xUrvsuC77P+Wr7T/hNgSZnBFn1cVK0ntPzdsyZ2T4TM6teEzDZD0wgi03TUokBWveX2Mprctm05KfedPjHW2YDjrO0sWqxWq2RZN8XCM9l11+Iti1mqyV12jCiWNQeduJYsu+cxqOdjWvxZzq8durI8PtWtXW9rfALmIkdxCEtcWVTdu/l8Dlibr+2Zp33ltENijgvIjPgv33956Oxzcsv1iutxHM0wKDFs8dYtcJbN2SUV91kdit/SvAOp/l8A/sJZOnVaWbL8+Vr+W8CiPYDZvk2y73r9LPubg2BM9y2JANyZFlt7y+g4VibjmThD0Zf9nOTfSeTJ7OW+k6moM1PYa+998ROo4wFkIpB1+TmW4HwcEtqmkB10xBsGE2OE5LkXNaXKWoxVTVAmdj8Db5OxVhrlcm0Z/6Aer9ouIJ/wNbe0b273jdOEF0x/dMLqdu7Zd5Iv33HO9Uwlr9/8+9LUOBPE2luzOHThGKJhCs6nRLsVIHmv5f6wGEwHbeMbZAHALSva4N6+ObOruzvqmRfrncrdnCb76tSbbJ/sXC+CJVZQE4vT5FBr18SCMI7l5JtsFSYDKSARjOR/b1lDCwFwwwjO4WRiySe1X3bfbUxEUE2+7/s3xNTdZB8fs4+9L1xI7dRUE95MKOuNkt56h4NZjvXS4rGMm0DtmV0IAfNNf6d5vtOhk3/bxyGVgyxpdmbRlkRABRcMPPXOs+o62xO37dGdy31BBEQsXPQES92+7j5Zf3/dOw9fzBToLtq7c1meCnPDlHqh501WxJLKSaUWC/adePU9TetB2p3FWTasb419jvWJHVEcVOa5WSbVOHeHrTmSbBHYti1N21imH+dmrz0t+inclupcTAApXEp+55obyDLz0gtRM6q+p9SbbikiZJ+C2dyehrHc5fzvwySW/TntWs0pzLlCIcQBxTgwC8bCWRmB+4MIKCzyvt+h7p0mqsIEbksI0qI6m8S1t9HUjwqp1iTHLmb0diDUaWxpvg+SiW+TfR/M3lzN9rmYH5v35XTy2ck/bdR8soYxEMaRccyqQVukIcRi0mtRh1ccHBzgUy7BxnkQitFLVg3md8iaAshGM3EWtag2qMnvW2+OqZ38r4xC+Z4J1l5xIAOyi7HdW2cfF3HKPfW1uyEetTq7Vn9XncD5jrJKKvHlLOW+IAIZdLrbupo/nFLuVhx4azf/bosGTIVM5Qx/IOW2L52VpBlJC1ym8OGJ7Umn2byU8XJiKbVMDigEUEkyZbbDFkCljF06G8mofc5W7L0jhLwJMpfmLC5BjEQNxBjY9lvaBP4NSfRwYmbbMREBJ65sTpFJozCOoSQ+XbLR+W+9AW1sEi6RN37msNJYFWJaKQdm7HgagtKqTtfSNBTuYGdj5x0p1bV75B73YUAFV8ncU5ohp0tfmHsr9wcRyNZldxYGMvY+3blgAUVM7x/VQjUtF9ksVl2VmTa3NetVbrOutKfe1L6b92PG3STjIkkRZaMFCSFqtRjzgtX01Pw959pbnlLJqnIWTnueKbd2I5a0QVVBxykSUWbNxR5G3/ds+77Et5tZM6ZUX1nrUYN+WU0Uo/U3qxuzoY+JGSERgb5gFZntz8+pfT1QJepoc5XfW0nm2lrNXTpRF1Mzmw8mTlKwJqNQRhvVnY05iQ2ZK4sTUS7Pujdd/hKEtnaN/8/ZlsZhmFi4eyz3BRHIC2yi1bJDqeu6y4laymeTmmvegCWJ1rIglvfu6Rl5Sy7p8d67dFpQS8a1/J7UiPUJlRfYnP3M77bnmeUQrN6zUrvNuSohBkWJSIpKBJNmYbIMHAohMiKwxYlwdHRknoRdN0vvJTJlHa54fzM9jjFxIkbGhhAhKv3Qs91sGcZQAqQ2jXlkNr4hxORVl0yEx7G3nRoojjVO3BT+XSdymUuNqSzFifn3dL9SjMV2RMeZSDPNUw0jnXZs3QtmABQibcTm7OpBuE+IAJJt3y0JZimZxa3KaSq8uhT34QXoV1jI1LhxkvsnSAsFyTbdmoBL2Zn5GYcobv+0VRslnyaZfZ9et2Y/0/fEis6tGqWCt5P3olhos6lPExcT02KnAlXz6ZtVbtn0t2mamfdbdiVu2xbnFdQbZ5JY8mxmXE5REWLarC6LJUldOYwjQxgZghGfqJE4BiSaUdIqsfyMirrIGBxNcASErsQ3TFGiXHKzjZoMmubWhvVhsbREXM5/2dAi1ZjPxc7MBdSTbs3vDwG2fF7Nqc3mZ8l5kEPnCdKac9bbAhjkNgDY/uq3r7sP1Em/7Nx/O9XQjsqrsCaT3F7vbRWQ0/pWPzM3c5v32McFnV40Leb97zJjr8v7yMzuf6mlmLo9xRvIrrtGCJMFYPUehUCrcR7KrnWfc6b+wmegLD9XLKFMilCkag5NOlro+GHs7T1SaPEW005kn5NsZNYmkaLWFiwDqABF/EzSRXqPKd/5flB24rk0Edaljcm+z/U4Ltn/3fmy0GLe+Wo8OVO5P4gAZ37P3fYWlPdu6s+/T3t0OXn19pzdpxDlTsjGBPac1rddefS2Ha/UX3sMWKr+1HI21bXbIeJzonB6f5ZGMUBJJhNVCUU8MwJSMu0kud43yS4giQKVVAhYSPp+HApB62MyuElqj4L1pBtyzIGMj8ziS/rKJJmk4hRFF2LEknPIf+eb02JcZmJTj+tyTc29KneJQWp9EmdyX94WnMBdQIKz2nehutn3m8guRT6triZWO3+uH1nLiDX5SszBHQja7WXF0/pzx7IjotT9WizqauG5vBnrhS8W1CINGJAjP8UkDkwQ7b4+FzY8A4fJTTkkgpBHTSXFf3AOn4KFZEIgCXnLIt0EeIplsNesrTCxJabIymPfE0IsxCJbSeZ/TWtBUk0952dEIpuqL4mhyCJSsGohNjGOmC9GVs2eRkR22z0NiHbF9uLuD7HblfuECLw5PqA+dW43SLuUWAshWD73Tqzcbtv5/qWceTeT9lbzPQmJ3wGysmegbdgZIqITR+LqcdTqpMt1E9DnixgQp/2vU3t1mdqQqY2FeXA+6Zyb0pXZiW0N18E8cuSmxEigqMVGSCDj2Pf2eZjMkAWLQzBFSWotSlLrE7bQzALT1FaKkzhRBw3JCD4VfpM5kV3Lx9PG5fYc4IJLlDd7RO6W+4QITDLWm9kgtzO0OeUpnHZ63Q4byHUWTy9/y4RLfsY/upKfbcGBdm0tYjpR0hE1+206sRWpI/1UHEPZUM7QfhJiLREgotHtXfg50AroXh+BSZ0ps40HSeXJtLHI75bWSEzZi8ZhZBhTBqYEPo69ZVk2Y5ycrbhNyVk6QhxpY1sA0FxCGICMk7SJ2EjRgNS4ytI6UWTCNnSh6algx5kqOoPPKrGIczNfAa0J9dnW1H1BBJS88PZv0lndavBvJ/efRhx0sRfeHBHZbav6xj5txv775gTntD7cjnVcPDm92FS3jBMkAGw/AajbL5s4/V/EXjHGPERB8qJM2ZZkyTUUwkFK2jmZIceqnpRgMdlhKruKZ9uG9NzyWooTkgrS2hjGgTBGxjBaRqYYU4q2odguKAY2jsFUmzpoZkEAEj6giXDkefGI6MxvIo+ZqhaikMWHiYtpStLRLD7kd3Z5c6cSy7hlkSeNax30kvnHey33BREAkmHNmwPx7ka+X+qGzchlf927ezbsihGJjatk6KkIp83k7USQO9WfPeE2GIlkdvVOBplVE5Nr9SlnkEw4A9Vc5L85qGjUxLJrJFBpL3K/hJSFJ3/OJCvhEKkHTnPkKfslaACy/D5F4dsBQFNZake09DdrA/LJkAifhkLLs19D/tf3/dRuPoBEZn4VbYrB2BRNBeA8TvxiRJPIQ024J1xh4o7PVu4bIlAtqzd31yky1v7r+wb1dPl/wg8m2V9L0JP5ZJZ/s0fK7N759fkpv2+DLzmB2xGK5atOarIsEtx+NdUB4EqPZ/L7fBMVg6VTZOCcLi3EWJ16i/azSOBzDMnEQdRclczn0lB5M8fWxCaIYHH6xeF1snPIG3Em508vlEiATr2SRFRSzIN93KamdxIRcu6rYRxxfY8/OSn4wyz/grcsUV3bzQhSYfWryXN5bBMJDHE4Mzdwr3kH/lvgm1KVB4A3VPXDYlGJPwN8Lv32C6r6p87WxdKPuxAGrEwLKlPv3VFaeukZGwcibqHK0d1FRg5UmRZ0sl2vzYL3cQN3JbuVHXRnDcfSn2IHZKpaWUKSmWOJOYlNxQLvaysH6yhd1DkfYLYBWlWYKud6szaVlLpbzew/YycydUVIEZmpCd4EZOaxkoIP1Kpam9+oMXkzCl494i3keZ7/tm2nsPBOEGcpwbOlY6zmngQC5mhINTeZ8zCUMZSJ5c/xLUxrYNyPqDPuK4wwCM71bDebiQgkwiKVaOG9JzKpfJ3LhkJnowL3lHdAVf9I/iwifwm4VtX/oqp++Ey9WhSBN5FkYTrhqIx2ppYq9nzBblsQpHkgh6xfnqrW3ye5bTr9Z1tt9veuxQqtn7Q7xXnr+9zeHpFnxl+oqawmq8LqHep9u2yjWuQRnQhBqpYTCGluoeKec0VdtJW6a2UEHRQJgospIirG8nqXc0jsIUxZizMD0/K5q4VoiCQthRO8+KQNANcYsbaEql2J7gzgnFkdOgGRyWGnqCALMcyHQfqW8QI3RXW0AyW5eztrx7gfaGiMtU/cRUzBVmtjsuzoVhtuiTRJMlHapjGLzG+0dkBvk3dAbAT+MPBPnKkXd1Fs0qfkounqTj3VyX+92iN7SoquWbbLFOt/Nybesi81V5AX7j7WPosAtlMMzHJzZVwtayfcI2/yHN0ntz/tLyM6oSxIqU5QWzhlw6gau28DmDVWU5/T9+kdT8dR8uFbRIP6FfL+zNzJso5KAQJDTEDbaBFzs4xL6qZzgkOK6zGIxeNXaFyaFwQn6fSPkZQhkqhZrk7cGZZsZvIuDJAi9vrG4RtoWgdprI3dtvO2zG5eF5lLk7xBQ3lHdYo0wqrpykvbvREdIVZJSSxoSMxPK+Mda4vLaTogKiGOjKFHaJP5fAppHwLzNffmy1kxgR8EXlTVz1fX3iMivw5cB/5dVf17Z3wGMEWl3T1pFzu8Mu0sl6pFlsu0wSyzy7Tx57rq28vZp8czLARidjJSIfF2IeqUn27mACD11CaxZtmnWR1bqXMCMG1uLR05hT0vIsj0W1EJZruLzI3p7v3LePxFnacWFktVicHEjxjV9PWjxSDIPheatp5z3mLqV2nGZn1OlCiGWMS3WDg28wQVl9SFIdkSqOBkOtUT/cPEuGksY0yhvKs5mqvzcoAViJlc52krvlKFNKPkGApTePqduAZljia7h8KFISnDlRCCYrk4rd8aI9ttX7QW91rOSgR+FPhr1ffngadU9VUR+U7gvxeRD6nq9eWN8iaSjxSKqmZzni/mU2/R8pyS3kVZAjt7+7AgDKcGqNjDUpdrzriNclou5LlaZzxds3c/TRjKbHzGNOprLNbZclSWqq2aOO17l4kVnn5b1qm5KM0ycOG0UkbhQHJvjot3zpsgbTwxZNxCiEsxSgLQkDgIJ4ArvvY5WL9WLtAxKqKCk6TyU0p/UNtU4iY39ImJSe+YEraKJGtE65o9825O4ZyEKo1h0ABi94NxSWCmz9NQa/ktEwIENAbQgMbA6BybzQl6R9XO7cs9EwGx0Lh/CPjO0nFLP7ZNn39VRL4IPI1lKZoVfTPJR9Qi27J818yn7XTuzv2vkd1ZZuM99W5/fRd4PM2gKC+wmsrP+jtjyfOpl0xQHfNKahxsiQmS+FZbMMrED2W7+WxgcxrhyiLAMtzXRCT2LbbT0P/0pXBEOV9fTDr7pRvs7HOemyjgjEOa8Is8ikmGjhCDgmgSOSLECC7ZI2TX5YBlpPZQJ4EpKst0qJiYLmiUaWkVOpUJq4k0yF0eN7PE2RPhnV3TaT1kcSc9FLS8elkTMUYzhR7Hs0oDZ+IEfgfwWVV9Nl8QkYeB11Q1iMh7sbwDXzpbF+0dQ1o4NcuszEMypU68ubZ1HrfvtNRe+9VxZWUuulDJ1LV4p3NxwwCjxDbqdFYXBiFO/LzZ3lQLApKWKNdRU+DXdCU/h1r1OQ8kMr3jZI8PFvM+G8OU052JC8p1lpt/x4ApR/0h4wEjMYb0b+5UZHhAZXmXTkCDA4wYZl5gImh2OGQO0UQONdk/qT40BFA324B5/mIEFyPqplO3rLFqarNJuejcIeuuylKCZdr4e4vOP89tLuzHEOPpHqlvstxT3gFV/S+w7MN/bVH9h4A/JyIjRv/+lKq+9lZ0VBwQFicuMLNyUYVGuDvyPC95EYcQ9rrW7scJ9i+EfLKY7ezS9mAhduQ/ZXHlWrV8blZwOyJIuiGf9HteyqolAlLeKHENCEg0EEwkJzK14hUkJv4/n5SNL3KzS+1rnI+/cdqu6P6lysOYRaEcRbgOoFKMZpjwmoljsj6Me0LMaVID9kNf4Q4RJBS8ICTwTKIwDsEiJqfgJxbA1pl2AnNXXs5Z6UeOyAQW8anWmy7q1mV5UM1PeqqNvpsQ1ziUSZ7I2FgGgbMh1VnKveYdQFX/pT3Xfhz48bN1abfkc2EYhoIY1yaX5cRKp6JWcilMYbsz+2/f92etzffle+tsN7M+Jfm5tkibnfD/v/bOJkaO4grA3+vumZ11vJEhyMgxVjARh5BLQAhFIuIYfi4mh0i+RD4g5UIkkJKDCReOSaRwTCQiIlkRCkKCCI5BCInkAgFkfhzLwQQEBscW8cHg9e5Md78cXlV3zzC7O7bX6Rn6fZbVPdW93a+6ul9Vvap6b/JjD2cJk45HtG6NTFiL69GLRrN1ypNpDlNOypqE68XuQdF4Dqi5MpPKKGjy5Hm0etcvW3wmZeg6aTje/FAnDYnWbC2CLaBkNMobCmB85aVN3OnZR9kot5jvpqGy2eqI1yuCW7LKSCYFeT6sXJYJCf0Q/DNJbS5AmqWklf/Fut8/GXauKqnSnm90tVboeBj7jUZVJsPdN/NQP0whDrtMHmuWr93D2kNZtZjqyliIGYNFWXL+/Odcl++goK6p4wcdF5+UZUkpAo3YBMBYMAeAosgRsXiCZTl+PbCHHlsE8dpQR/qF8SZsXTiNY5V1fNzKXjmqaLQmaoUUJxxRXbvJpCGv2YVoroAcuydNw6edP3nd8d8JqvmYH8IkSYK9TWujYzx74gOo5Apz/bWoo+kUhXUFmsOusbthH3tKNM/Uyrqs+8KhDKysbU5HfK5WZmH2YWlzPobDIaurqzZjL8noSWoLckRIM5sotLZki4V62VIV6FXC0mUw410iwexfhlmGaYaWeW3YazyD5jY+v2Z5TCUqgUarSBsvQaxQYnpRhEVFic1G3MhuNSsLoQTKouDcuXOsnKGqpbLU3EkpGlxTjSiLkuWVFZZ37iQNLrG+FKwybItirXrgMTLwYDCwsi5r638z3Nlkl8Dei9AlCIeiMrJ172NVXb24ZqzP11hthlQut2J/18bDE6J7rKofTd11SSQJzj2jKbB5+Tj1lMq1eG0EjLYWyLJe+J1Xyi5eL0YwivH+xp9BGOPOy+Cr0P4laVr5C4jG16o1Ua29rz+SuqtjUZIBiqHNC0lDzAlFg6djrAukQOj6m+FY0aJkdXWVsrSRgeHauhkiyyHrsQzr8UGyLLEWSJKEpcMpg+VlBsuD4MfAJukkWq8DyMvcZhWWYcVksNwnRANPo9gJ3pXjv8quM/7hlkCqcTJSc4witDiJuje22mxhlE7GyLsMFkIJFGXJfz/7jNWL/2E0suZfv2fzrxGrHQZLAwbLA0ZlyYW1NXoh1l4WfM+tra1RFAU7duxgZWWFfm/Z3gWxedullvT7fQTzi28veEKvl9XNfOpgG2jU2EWtEKqhvzDTrfK4G2rHTKgckdhppKFPWxQ5SXQ+USmh8DKo2rr3JKEoC/KReeGNIcPKwu4fHXDGmlcISiPWZEkMpCqNIbWocILn4NSiHFuNW4RmeoYWISS2WvTk2I1QrOaVzF7u2imIKbs8z9HEDHTroR9tw3H1BKgkSUNsxDhdl3AvCX3i+kUvVasaOBFhNByRhLJL07LyVgxZ9RySRjehLEvy4J6rumZRMCwKkjBvYZSPWFtfqxyNpKlF+4lz+63LWdj7MqX2n6SpUMdahWMjBEIanSlVZRIJvhoBkZR+v6wMv0u9/qb3noWFUAKrq6v87e+vsHv3IEyOKEmTtKoxCE20XpaRDJZIlvpkwX1UlqZcuHABBXYsL7N792527drFDd+80daMa8lwfYhISdrvQVE3Q6MLKlvmag++1CL4CgyLToiWcyvoGOvPgowmJKlF7Q1nVDVjbOImYh+2JGHCTInNcJPMxo21tkuIWASaOM8+y+rmc1EUlcONoszrmp84B8DkULU+v2pRRSM2F1qxK2QvpwUXUbK+RS/Kkn51n6KwwCP26CXIBZlCIZCXBaPcvBOXI1vCm+c5w+F6kLsXWmVWq0WFtjRYIknMmWnW71s05bKs5shHe0Re2oeeBQX9xRdfkF+8SJKkrA1HXLx4kaIYMhrlVcsjz+tyKssy+CIU0ugZWZKq/12ExU1pUE5JalOBE0npL/UZLFkr4eu7VmyVY/URftlIZ7ZbHTPMNu1Z8SwJtXvsm9VKQKouooq1gIrculU7BgM++uRjRqP8sr6r6u5X2p/YDracJ3DVbhy2zZaXTjk+Dd3k+KzXmJXmveJ+c9tkWtpXickymzwWviVnKm+o6u2TiQvRErhqTL5IW/3e6u8v95xZ0Cn70679VVYAsHn+dIvjzlQuLzSK4zhfGVwJOE7HcSXgOB3HlYDjdBxXAo7TcVwJOE7HcSXgOB3HlYDjdBxXAo7TcbZUAiKyT0ReFpHjInJMRB4K6deKyIsi8l7YXtP4m0dE5KSInBCRu69mBhzHuTJmaQnkwM9V9TvA94EHReQW4DDwkqreDLwUfhOOHQS+C9wD/E5s9YrjOHPIlkpAVU+r6pth/3MswtBe4ABwJJx2BLg/7B8AnlbVdVX9ADgJ3LHNcjuOs01ckk1ALAjJrcCrwPWqehpMUQC7w2l7gY8bf3YqpDmOM4fMvIpQRHZi/gMfVtXzmzgymHbgS2u75BLiDjiOc/WYqSUg5pDvWeApVX0uJJ8RkT3h+B7gbEg/Bexr/PkNwKeT11TVJ1T19mnrmx3H+f8xy+iAAE8Cx1X18cahF4BDYf8Q8Hwj/aCILInIfiz2wGvbJ7LjONvJLN2BO4GfAO+IyNGQ9kvgV8AzIvIA8BHwYwBVPSYizwD/xEYWHlTVjUP8OI7TKt12L+Y43WKqezGfMeg4HceVgON0HFcCjtNxXAk4TsdxJeA4HceVgON0HFcCjtNxXAk4TsdxJeA4HceVgON0HFcCjtNxXAk4TsdxJeA4HceVgON0HFcCjtNxXAk4TsdxJeA4HceVgON0nJldjl9lPgMuhO2ich2LLT8sfh4WXX64unn41rTEufAxCCAiry+y+/FFlx8WPw+LLj+0kwfvDjhOx3El4DgdZ56UwBNtC3CFLLr8sPh5WHT5oYU8zI1NwHGcdpinloDjOC3QuhIQkXtE5ISInBSRw23LMysi8qGIvCMiR0Xk9ZB2rYi8KCLvhe01bcsZEZE/ishZEXm3kbahvCLySCiTEyJydztSj7NBHh4TkU9CORwVkfsax+YqDyKyT0ReFpHjInJMRB4K6e2Wg6q29h9IgfeBm4A+8BZwS5syXYLsHwLXTaT9Bjgc9g8Dv25bzoZsdwG3Ae9uJS9wSyiLJWB/KKN0TvPwGPCLKefOXR6APcBtYX8F+FeQs9VyaLslcAdwUlX/rapD4GngQMsyXQkHgCNh/whwf3uijKOqrwDnJpI3kvcA8LSqrqvqB8BJrKxaZYM8bMTc5UFVT6vqm2H/c+A4sJeWy6FtJbAX+Ljx+1RIWwQU+KuIvCEiPw1p16vqabACB3a3Jt1sbCTvopXLz0Tk7dBdiE3puc6DiNwI3Aq8Ssvl0LYSkClpizJccaeq3gbcCzwoIne1LdA2skjl8nvg28D3gNPAb0P63OZBRHYCzwIPq+r5zU6dkrbteWhbCZwC9jV+3wB82pIsl4Sqfhq2Z4G/YM20MyKyByBsz7Yn4UxsJO/ClIuqnlHVQlVL4A/UzeW5zIOI9DAF8JSqPheSWy2HtpXAP4CbRWS/iPSBg8ALLcu0JSLyNRFZifvAD4F3MdkPhdMOAc+3I+HMbCTvC8BBEVkSkf3AzcBrLci3JfHjCfwIKweYwzyIiABPAsdV9fHGoXbLYQ4svvdhVtL3gUfblmdGmW/CrLZvAcei3MA3gJeA98L22rZlbcj8Z6y5PMJqmAc2kxd4NJTJCeDetuXfJA9/At4B3g4fzZ55zQPwA6w5/zZwNPy/r+1y8BmDjtNx2u4OOI7TMq4EHKfjuBJwnI7jSsBxOo4rAcfpOK4EHKfjuBJwnI7jSsBxOs7/AAEd8AQBPxDXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(visible_frame[15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0o6FA6slSA1l"
   },
   "source": [
    "The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for classification. If include_top=True then the whole VGG16 model is downloaded which is about 528 MB. If include_top=False then only the convolutional part of the VGG16 model is downloaded which is just 57 MB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "id": "MjRN6oE4SC81",
    "outputId": "1a9fbd16-9741-406d-b4fd-e3a20cd34a0d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n",
      "553467904/553467096 [==============================] - 93s 0us/step\n"
     ]
    }
   ],
   "source": [
    "image_model = VGG16(include_top=True, weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 918
    },
    "colab_type": "code",
    "id": "ud7OU0t7SQPi",
    "outputId": "20718d3a-c1d3-4a06-d6f5-de3c465311df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 1000)              4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 138,357,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "image_model.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-NxnGLlDSTwr"
   },
   "source": [
    "We can observe the shape of the tensors expected as input by the pre-trained VGG16 model. In this case it is images of shape 224 x 224 x 3. Note that we have defined the frame size as 224x224x3. The video frame will be the input of the VGG16 net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9a0k89M5SUjP",
    "outputId": "50971481-7dff-49de-b4ad-a3e69c2853ac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = image_model.layers[0].output_shape[1:3]\n",
    "input_shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qFUDqTiBSa0H"
   },
   "source": [
    "The following chart shows how the data flows when using the VGG16 model for Transfer Learning. First we input and process 20 video frames in batch with the VGG16 model. Just prior to the final classification layer of the VGG16 model, we save the so-called Transfer Values to a cache-file.\n",
    "\n",
    "The reason for using a cache-file is that it takes a long time to process an image with the VGG16 model. If each image is processed more than once then we can save a lot of time by caching the transfer-values.\n",
    "\n",
    "When all the videos have been processed through the VGG16 model and the resulting transfer-values saved to a cache file, then we can use those transfer-values as the input to LSTM neural network. We will then train the second neural network using the classes from the violence dataset (Violence, No-Violence), so the network learns how to classify images based on the transfer-values from the VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "4YWFA-2tSdfB",
    "outputId": "55dcb0c7-2b8a-4420-e25e-381152197a81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The input of the VGG16 net have dimensions: (224, 224)\n",
      "The output of the selecter layer of VGG16 net have dimensions:  4096\n"
     ]
    }
   ],
   "source": [
    "# We will use the output of the layer prior to the final\n",
    "# classification-layer which is named fc2. This is a fully-connected (or dense) layer.\n",
    "transfer_layer = image_model.get_layer('fc2')\n",
    "\n",
    "image_model_transfer = Model(inputs=image_model.input,\n",
    "                             outputs=transfer_layer.output)\n",
    "\n",
    "transfer_values_size = K.int_shape(transfer_layer.output)[1]\n",
    "\n",
    "\n",
    "print(\"The input of the VGG16 net have dimensions:\",K.int_shape(image_model.input)[1:3])\n",
    "\n",
    "print(\"The output of the selecter layer of VGG16 net have dimensions: \", transfer_values_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-AdxYAtiSlLF"
   },
   "outputs": [],
   "source": [
    "def get_transfer_values(current_dir, file_name):\n",
    "    \n",
    "    # Pre-allocate input-batch-array for images.\n",
    "    shape = (_images_per_file,) + img_size_touple + (3,)\n",
    "    \n",
    "    image_batch = np.zeros(shape=shape, dtype=np.float16)\n",
    "    \n",
    "    image_batch = get_frames(current_dir, file_name)\n",
    "      \n",
    "    # Pre-allocate output-array for transfer-values.\n",
    "    # Note that we use 16-bit floating-points to save memory.\n",
    "    shape = (_images_per_file, transfer_values_size)\n",
    "    transfer_values = np.zeros(shape=shape, dtype=np.float16)\n",
    "\n",
    "    transfer_values = \\\n",
    "            image_model_transfer.predict(image_batch)\n",
    "            \n",
    "    return transfer_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D2BvaY3eSpSH"
   },
   "outputs": [],
   "source": [
    "def proces_transfer(vid_names, in_dir, labels):\n",
    "    \n",
    "    count = 0\n",
    "    \n",
    "    tam = len(vid_names)\n",
    "    \n",
    "    # Pre-allocate input-batch-array for images.\n",
    "    shape = (_images_per_file,) + img_size_touple + (3,)\n",
    "    \n",
    "    while count<tam:\n",
    "        \n",
    "        video_name = vid_names[count]\n",
    "        \n",
    "        image_batch = np.zeros(shape=shape, dtype=np.float16)\n",
    "    \n",
    "        image_batch = get_frames(in_dir, video_name)\n",
    "        \n",
    "         # Note that we use 16-bit floating-points to save memory.\n",
    "        shape = (_images_per_file, transfer_values_size)\n",
    "        transfer_values = np.zeros(shape=shape, dtype=np.float16)\n",
    "        \n",
    "        transfer_values = \\\n",
    "            image_model_transfer.predict(image_batch)\n",
    "         \n",
    "        labels1 = labels[count]\n",
    "        \n",
    "        aux = np.ones([20,2])\n",
    "        \n",
    "        labelss = labels1*aux\n",
    "        \n",
    "        yield transfer_values, labelss\n",
    "        \n",
    "        count+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I-Cnv0fRSswF"
   },
   "source": [
    "We are going to define functions to get the transfer values from VGG16 with defined number of files. Then save the transfer values files used from training in one file and the ones uses for testing in another one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tvU53ypSSvL0"
   },
   "outputs": [],
   "source": [
    "def make_files(n_files):\n",
    "    \n",
    "    gen = proces_transfer(names_training, in_dir, labels_training)\n",
    "\n",
    "    numer = 1\n",
    "\n",
    "    # Read the first chunk to get the column dtypes\n",
    "    chunk = next(gen)\n",
    "\n",
    "    row_count = chunk[0].shape[0]\n",
    "    row_count2 = chunk[1].shape[0]\n",
    "    \n",
    "    with h5py.File('prueba.h5', 'w') as f:\n",
    "    \n",
    "        # Initialize a resizable dataset to hold the output\n",
    "        maxshape = (None,) + chunk[0].shape[1:]\n",
    "        maxshape2 = (None,) + chunk[1].shape[1:]\n",
    "    \n",
    "    \n",
    "        dset = f.create_dataset('data', shape=chunk[0].shape, maxshape=maxshape,\n",
    "                                chunks=chunk[0].shape, dtype=chunk[0].dtype)\n",
    "    \n",
    "        dset2 = f.create_dataset('labels', shape=chunk[1].shape, maxshape=maxshape2,\n",
    "                                 chunks=chunk[1].shape, dtype=chunk[1].dtype)\n",
    "    \n",
    "         # Write the first chunk of rows\n",
    "        dset[:] = chunk[0]\n",
    "        dset2[:] = chunk[1]\n",
    "\n",
    "        for chunk in gen:\n",
    "            \n",
    "            if numer == n_files:\n",
    "            \n",
    "                break\n",
    "\n",
    "            # Resize the dataset to accommodate the next chunk of rows\n",
    "            dset.resize(row_count + chunk[0].shape[0], axis=0)\n",
    "            dset2.resize(row_count2 + chunk[1].shape[0], axis=0)\n",
    "\n",
    "            # Write the next chunk\n",
    "            dset[row_count:] = chunk[0]\n",
    "            dset2[row_count:] = chunk[1]\n",
    "\n",
    "            # Increment the row count\n",
    "            row_count += chunk[0].shape[0]\n",
    "            row_count2 += chunk[1].shape[0]\n",
    "            \n",
    "            print_progress(numer, n_files)\n",
    "        \n",
    "            numer += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8nK8uFExS0nX"
   },
   "outputs": [],
   "source": [
    "def make_files_test(n_files):\n",
    "    \n",
    "    gen = proces_transfer(names_test, in_dir, labels_test)\n",
    "\n",
    "    numer = 1\n",
    "\n",
    "    # Read the first chunk to get the column dtypes\n",
    "    chunk = next(gen)\n",
    "\n",
    "    row_count = chunk[0].shape[0]\n",
    "    row_count2 = chunk[1].shape[0]\n",
    "    \n",
    "    with h5py.File('pruebavalidation.h5', 'w') as f:\n",
    "    \n",
    "        # Initialize a resizable dataset to hold the output\n",
    "        maxshape = (None,) + chunk[0].shape[1:]\n",
    "        maxshape2 = (None,) + chunk[1].shape[1:]\n",
    "    \n",
    "    \n",
    "        dset = f.create_dataset('data', shape=chunk[0].shape, maxshape=maxshape,\n",
    "                                chunks=chunk[0].shape, dtype=chunk[0].dtype)\n",
    "    \n",
    "        dset2 = f.create_dataset('labels', shape=chunk[1].shape, maxshape=maxshape2,\n",
    "                                 chunks=chunk[1].shape, dtype=chunk[1].dtype)\n",
    "    \n",
    "         # Write the first chunk of rows\n",
    "        dset[:] = chunk[0]\n",
    "        dset2[:] = chunk[1]\n",
    "\n",
    "        for chunk in gen:\n",
    "            \n",
    "            if numer == n_files:\n",
    "            \n",
    "                break\n",
    "\n",
    "            # Resize the dataset to accommodate the next chunk of rows\n",
    "            dset.resize(row_count + chunk[0].shape[0], axis=0)\n",
    "            dset2.resize(row_count2 + chunk[1].shape[0], axis=0)\n",
    "\n",
    "            # Write the next chunk\n",
    "            dset[row_count:] = chunk[0]\n",
    "            dset2[row_count:] = chunk[1]\n",
    "\n",
    "            # Increment the row count\n",
    "            row_count += chunk[0].shape[0]\n",
    "            row_count2 += chunk[1].shape[0]\n",
    "            \n",
    "            print_progress(numer, n_files)\n",
    "        \n",
    "            numer += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R9axnZ8dS64T"
   },
   "source": [
    "We are going to split the dataset into training set and testing. The training set is used to train the model and the test set to check the model accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wjne3svdS9Y3"
   },
   "outputs": [],
   "source": [
    "training_set = int(len(names)*0.8)\n",
    "test_set = int(len(names)*0.2)\n",
    "\n",
    "names_training = names[0:training_set]\n",
    "names_test = names[training_set:]\n",
    "\n",
    "labels_training = labels[0:training_set]\n",
    "labels_test = labels[training_set:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xmiJi6G7TBnI"
   },
   "source": [
    "Then we are going to process all video frames through VGG16 and save the transfer values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "pyO9WP-6TER4",
    "outputId": "94209006-bfb9-470f-d418-cd2acfbdb12d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Progress: 99.9%"
     ]
    }
   ],
   "source": [
    "make_files(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0ThoefXUXPrS",
    "outputId": "9f4cdc1e-4bfb-4ab6-81c1-05abadc1239f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Progress: 99.5%"
     ]
    }
   ],
   "source": [
    "make_files_test(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0s2imcRixMAr"
   },
   "source": [
    "### Load the cached transfer values into memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RnK1JL-izzeS"
   },
   "source": [
    "In order to load the saved transfer values into RAM memory we are going to use this two functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ez0blP2z0CsF"
   },
   "outputs": [],
   "source": [
    "def process_alldata_training():\n",
    "    \n",
    "    joint_transfer=[]\n",
    "    frames_num=20\n",
    "    count = 0\n",
    "    \n",
    "    with h5py.File('prueba.h5', 'r') as f:\n",
    "            \n",
    "        X_batch = f['data'][:]\n",
    "        y_batch = f['labels'][:]\n",
    "\n",
    "    for i in range(int(len(X_batch)/frames_num)):\n",
    "        inc = count+frames_num\n",
    "        joint_transfer.append([X_batch[count:inc],y_batch[count]])\n",
    "        count =inc\n",
    "        \n",
    "    data =[]\n",
    "    target=[]\n",
    "    \n",
    "    for i in joint_transfer:\n",
    "        data.append(i[0])\n",
    "        target.append(np.array(i[1]))\n",
    "        \n",
    "    return data, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dne2XaQ90MGk"
   },
   "outputs": [],
   "source": [
    "def process_alldata_test():\n",
    "    \n",
    "    joint_transfer=[]\n",
    "    frames_num=20\n",
    "    count = 0\n",
    "    \n",
    "    with h5py.File('pruebavalidation.h5', 'r') as f:\n",
    "            \n",
    "        X_batch = f['data'][:]\n",
    "        y_batch = f['labels'][:]\n",
    "\n",
    "    for i in range(int(len(X_batch)/frames_num)):\n",
    "        inc = count+frames_num\n",
    "        joint_transfer.append([X_batch[count:inc],y_batch[count]])\n",
    "        count =inc\n",
    "        \n",
    "    data =[]\n",
    "    target=[]\n",
    "    \n",
    "    for i in joint_transfer:\n",
    "        data.append(i[0])\n",
    "        target.append(np.array(i[1]))\n",
    "        \n",
    "    return data, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "phyhoYc67VW8"
   },
   "outputs": [],
   "source": [
    "data, target = process_alldata_training()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dJpXHvEg7Xhc"
   },
   "outputs": [],
   "source": [
    "data_test, target_test = process_alldata_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HAgSUeVu58N_"
   },
   "source": [
    "The first input dimension of LSTM neurons is the temporal dimension, in our case it is 20. The second is the size of the features vector (transfer values).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XWABZ91b6f7l"
   },
   "outputs": [],
   "source": [
    "chunk_size = 4096\n",
    "n_chunks = 20\n",
    "rnn_size = 512\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(rnn_size, input_shape=(n_chunks, chunk_size)))\n",
    "model.add(Dense(1024))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(50))\n",
    "model.add(Activation('sigmoid'))\n",
    "model.add(Dense(2))\n",
    "model.add(Activation('softmax'))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 512)               9439232   \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 50)                51250     \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 102       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 10,015,896\n",
      "Trainable params: 10,015,896\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bVonOPYW7F_b"
   },
   "source": [
    "## Model training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 6834
    },
    "colab_type": "code",
    "id": "iRZlW4ZV_ygS",
    "outputId": "c223a55f-8ccd-4e5a-f5d4-dc6a1daf2d0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "2/2 - 2s - loss: 0.3291 - accuracy: 0.5067 - val_loss: 0.2966 - val_accuracy: 0.4600\n",
      "Epoch 2/200\n",
      "2/2 - 2s - loss: 0.2694 - accuracy: 0.5200 - val_loss: 0.2663 - val_accuracy: 0.5400\n",
      "Epoch 3/200\n",
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      "Epoch 21/200\n",
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      "Epoch 22/200\n",
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      "Epoch 24/200\n",
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      "Epoch 26/200\n",
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      "Epoch 31/200\n",
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      "Epoch 32/200\n",
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      "Epoch 33/200\n",
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      "Epoch 34/200\n",
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      "Epoch 35/200\n",
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      "Epoch 36/200\n",
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      "Epoch 37/200\n",
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      "Epoch 38/200\n",
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      "Epoch 39/200\n",
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      "Epoch 40/200\n",
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      "Epoch 41/200\n",
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      "Epoch 42/200\n",
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      "Epoch 43/200\n",
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      "Epoch 44/200\n",
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      "Epoch 45/200\n",
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      "Epoch 46/200\n",
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      "Epoch 49/200\n",
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      "Epoch 50/200\n",
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      "Epoch 51/200\n",
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      "Epoch 52/200\n",
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      "Epoch 53/200\n",
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      "Epoch 54/200\n",
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      "Epoch 55/200\n",
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      "Epoch 56/200\n",
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      "Epoch 57/200\n",
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      "Epoch 58/200\n",
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      "Epoch 59/200\n",
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      "Epoch 60/200\n",
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      "Epoch 61/200\n",
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      "Epoch 62/200\n",
      "2/2 - 2s - loss: 0.0626 - accuracy: 0.9280 - val_loss: 0.1143 - val_accuracy: 0.8600\n",
      "Epoch 63/200\n",
      "2/2 - 2s - loss: 0.0680 - accuracy: 0.9160 - val_loss: 0.0877 - val_accuracy: 0.9200\n",
      "Epoch 64/200\n",
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      "Epoch 65/200\n",
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      "Epoch 66/200\n",
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      "Epoch 67/200\n",
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      "Epoch 68/200\n",
      "2/2 - 2s - loss: 0.0791 - accuracy: 0.9000 - val_loss: 0.0865 - val_accuracy: 0.9200\n",
      "Epoch 69/200\n",
      "2/2 - 2s - loss: 0.0596 - accuracy: 0.9267 - val_loss: 0.0939 - val_accuracy: 0.9000\n",
      "Epoch 70/200\n",
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      "Epoch 71/200\n",
      "2/2 - 2s - loss: 0.0669 - accuracy: 0.9160 - val_loss: 0.0929 - val_accuracy: 0.9000\n",
      "Epoch 72/200\n",
      "2/2 - 2s - loss: 0.0636 - accuracy: 0.9267 - val_loss: 0.0870 - val_accuracy: 0.9200\n",
      "Epoch 73/200\n",
      "2/2 - 2s - loss: 0.0602 - accuracy: 0.9307 - val_loss: 0.1203 - val_accuracy: 0.8600\n",
      "Epoch 74/200\n",
      "2/2 - 2s - loss: 0.0616 - accuracy: 0.9280 - val_loss: 0.0982 - val_accuracy: 0.8800\n",
      "Epoch 75/200\n",
      "2/2 - 2s - loss: 0.0609 - accuracy: 0.9307 - val_loss: 0.1019 - val_accuracy: 0.8600\n",
      "Epoch 76/200\n",
      "2/2 - 2s - loss: 0.0636 - accuracy: 0.9200 - val_loss: 0.1042 - val_accuracy: 0.8600\n",
      "Epoch 77/200\n",
      "2/2 - 2s - loss: 0.0583 - accuracy: 0.9307 - val_loss: 0.0936 - val_accuracy: 0.9000\n",
      "Epoch 78/200\n",
      "2/2 - 2s - loss: 0.0552 - accuracy: 0.9373 - val_loss: 0.1001 - val_accuracy: 0.8600\n",
      "Epoch 79/200\n",
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      "Epoch 80/200\n",
      "2/2 - 2s - loss: 0.0514 - accuracy: 0.9427 - val_loss: 0.0890 - val_accuracy: 0.9200\n",
      "Epoch 81/200\n",
      "2/2 - 2s - loss: 0.0537 - accuracy: 0.9360 - val_loss: 0.0908 - val_accuracy: 0.9000\n",
      "Epoch 82/200\n",
      "2/2 - 2s - loss: 0.0508 - accuracy: 0.9427 - val_loss: 0.0866 - val_accuracy: 0.9200\n",
      "Epoch 83/200\n",
      "2/2 - 2s - loss: 0.0499 - accuracy: 0.9440 - val_loss: 0.0866 - val_accuracy: 0.9200\n",
      "Epoch 84/200\n",
      "2/2 - 2s - loss: 0.0505 - accuracy: 0.9427 - val_loss: 0.0860 - val_accuracy: 0.9200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/200\n",
      "2/2 - 2s - loss: 0.0494 - accuracy: 0.9440 - val_loss: 0.0866 - val_accuracy: 0.9200\n",
      "Epoch 86/200\n",
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      "Epoch 87/200\n",
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      "Epoch 88/200\n",
      "2/2 - 2s - loss: 0.0500 - accuracy: 0.9413 - val_loss: 0.0863 - val_accuracy: 0.9200\n",
      "Epoch 89/200\n",
      "2/2 - 2s - loss: 0.0512 - accuracy: 0.9440 - val_loss: 0.0907 - val_accuracy: 0.9000\n",
      "Epoch 90/200\n",
      "2/2 - 2s - loss: 0.0485 - accuracy: 0.9440 - val_loss: 0.0951 - val_accuracy: 0.8800\n",
      "Epoch 91/200\n",
      "2/2 - 2s - loss: 0.0501 - accuracy: 0.9440 - val_loss: 0.0869 - val_accuracy: 0.9200\n",
      "Epoch 92/200\n",
      "2/2 - 2s - loss: 0.0463 - accuracy: 0.9520 - val_loss: 0.0937 - val_accuracy: 0.8800\n",
      "Epoch 93/200\n",
      "2/2 - 2s - loss: 0.0465 - accuracy: 0.9507 - val_loss: 0.0883 - val_accuracy: 0.9200\n",
      "Epoch 94/200\n",
      "2/2 - 2s - loss: 0.0479 - accuracy: 0.9480 - val_loss: 0.0958 - val_accuracy: 0.8800\n",
      "Epoch 95/200\n",
      "2/2 - 2s - loss: 0.0472 - accuracy: 0.9493 - val_loss: 0.0865 - val_accuracy: 0.9200\n",
      "Epoch 96/200\n",
      "2/2 - 2s - loss: 0.0466 - accuracy: 0.9493 - val_loss: 0.0944 - val_accuracy: 0.8800\n",
      "Epoch 97/200\n",
      "2/2 - 2s - loss: 0.0469 - accuracy: 0.9480 - val_loss: 0.0865 - val_accuracy: 0.9200\n",
      "Epoch 98/200\n",
      "2/2 - 2s - loss: 0.0450 - accuracy: 0.9520 - val_loss: 0.0865 - val_accuracy: 0.9200\n",
      "Epoch 99/200\n",
      "2/2 - 2s - loss: 0.0444 - accuracy: 0.9507 - val_loss: 0.0978 - val_accuracy: 0.8800\n",
      "Epoch 100/200\n",
      "2/2 - 2s - loss: 0.0472 - accuracy: 0.9493 - val_loss: 0.0873 - val_accuracy: 0.9200\n",
      "Epoch 101/200\n",
      "2/2 - 2s - loss: 0.0441 - accuracy: 0.9520 - val_loss: 0.0872 - val_accuracy: 0.9200\n",
      "Epoch 102/200\n",
      "2/2 - 2s - loss: 0.0437 - accuracy: 0.9533 - val_loss: 0.0892 - val_accuracy: 0.9000\n",
      "Epoch 103/200\n",
      "2/2 - 2s - loss: 0.0441 - accuracy: 0.9533 - val_loss: 0.0873 - val_accuracy: 0.9200\n",
      "Epoch 104/200\n",
      "2/2 - 2s - loss: 0.0444 - accuracy: 0.9560 - val_loss: 0.0968 - val_accuracy: 0.8800\n",
      "Epoch 105/200\n",
      "2/2 - 2s - loss: 0.0452 - accuracy: 0.9493 - val_loss: 0.0880 - val_accuracy: 0.9200\n",
      "Epoch 106/200\n",
      "2/2 - 2s - loss: 0.0445 - accuracy: 0.9560 - val_loss: 0.0938 - val_accuracy: 0.8800\n",
      "Epoch 107/200\n",
      "2/2 - 2s - loss: 0.0435 - accuracy: 0.9533 - val_loss: 0.0873 - val_accuracy: 0.9200\n",
      "Epoch 108/200\n",
      "2/2 - 2s - loss: 0.0432 - accuracy: 0.9547 - val_loss: 0.0976 - val_accuracy: 0.8800\n",
      "Epoch 109/200\n",
      "2/2 - 2s - loss: 0.0447 - accuracy: 0.9507 - val_loss: 0.0892 - val_accuracy: 0.9200\n",
      "Epoch 110/200\n",
      "2/2 - 2s - loss: 0.0453 - accuracy: 0.9520 - val_loss: 0.0898 - val_accuracy: 0.9000\n",
      "Epoch 111/200\n",
      "2/2 - 2s - loss: 0.0418 - accuracy: 0.9573 - val_loss: 0.0879 - val_accuracy: 0.9200\n",
      "Epoch 112/200\n",
      "2/2 - 2s - loss: 0.0419 - accuracy: 0.9547 - val_loss: 0.0894 - val_accuracy: 0.9000\n",
      "Epoch 113/200\n",
      "2/2 - 2s - loss: 0.0417 - accuracy: 0.9560 - val_loss: 0.0909 - val_accuracy: 0.9000\n",
      "Epoch 114/200\n",
      "2/2 - 2s - loss: 0.0417 - accuracy: 0.9560 - val_loss: 0.0881 - val_accuracy: 0.9200\n",
      "Epoch 115/200\n",
      "2/2 - 2s - loss: 0.0415 - accuracy: 0.9573 - val_loss: 0.0890 - val_accuracy: 0.9000\n",
      "Epoch 116/200\n",
      "2/2 - 2s - loss: 0.0411 - accuracy: 0.9560 - val_loss: 0.0887 - val_accuracy: 0.9200\n",
      "Epoch 117/200\n",
      "2/2 - 2s - loss: 0.0419 - accuracy: 0.9547 - val_loss: 0.0931 - val_accuracy: 0.9000\n",
      "Epoch 118/200\n",
      "2/2 - 2s - loss: 0.0435 - accuracy: 0.9547 - val_loss: 0.1193 - val_accuracy: 0.8400\n",
      "Epoch 119/200\n",
      "2/2 - 2s - loss: 0.0488 - accuracy: 0.9427 - val_loss: 0.1113 - val_accuracy: 0.8600\n",
      "Epoch 120/200\n",
      "2/2 - 2s - loss: 0.0603 - accuracy: 0.9213 - val_loss: 0.0981 - val_accuracy: 0.8800\n",
      "Epoch 121/200\n",
      "2/2 - 2s - loss: 0.0471 - accuracy: 0.9453 - val_loss: 0.0886 - val_accuracy: 0.9200\n",
      "Epoch 122/200\n",
      "2/2 - 2s - loss: 0.0459 - accuracy: 0.9533 - val_loss: 0.0937 - val_accuracy: 0.9000\n",
      "Epoch 123/200\n",
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      "Epoch 124/200\n",
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      "Epoch 125/200\n",
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      "Epoch 126/200\n",
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      "Epoch 127/200\n",
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      "Epoch 128/200\n",
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      "Epoch 129/200\n",
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      "Epoch 130/200\n",
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      "Epoch 131/200\n",
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      "Epoch 132/200\n",
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      "Epoch 133/200\n",
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      "Epoch 134/200\n",
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      "Epoch 135/200\n",
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      "Epoch 136/200\n",
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      "Epoch 137/200\n",
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      "Epoch 138/200\n",
      "2/2 - 2s - loss: 0.0404 - accuracy: 0.9560 - val_loss: 0.0878 - val_accuracy: 0.9200\n",
      "Epoch 139/200\n",
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      "Epoch 140/200\n",
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      "Epoch 141/200\n",
      "2/2 - 2s - loss: 0.0381 - accuracy: 0.9613 - val_loss: 0.0883 - val_accuracy: 0.9200\n",
      "Epoch 142/200\n",
      "2/2 - 2s - loss: 0.0377 - accuracy: 0.9600 - val_loss: 0.0905 - val_accuracy: 0.9000\n",
      "Epoch 143/200\n",
      "2/2 - 2s - loss: 0.0377 - accuracy: 0.9587 - val_loss: 0.0904 - val_accuracy: 0.9000\n",
      "Epoch 144/200\n",
      "2/2 - 2s - loss: 0.0395 - accuracy: 0.9587 - val_loss: 0.0905 - val_accuracy: 0.9000\n",
      "Epoch 145/200\n",
      "2/2 - 2s - loss: 0.0369 - accuracy: 0.9627 - val_loss: 0.0970 - val_accuracy: 0.8800\n",
      "Epoch 146/200\n",
      "2/2 - 2s - loss: 0.0458 - accuracy: 0.9507 - val_loss: 0.0897 - val_accuracy: 0.9000\n",
      "Epoch 147/200\n",
      "2/2 - 2s - loss: 0.0388 - accuracy: 0.9627 - val_loss: 0.0988 - val_accuracy: 0.8800\n",
      "Epoch 148/200\n",
      "2/2 - 2s - loss: 0.0413 - accuracy: 0.9547 - val_loss: 0.0985 - val_accuracy: 0.8800\n",
      "Epoch 149/200\n",
      "2/2 - 2s - loss: 0.0414 - accuracy: 0.9573 - val_loss: 0.1001 - val_accuracy: 0.8800\n",
      "Epoch 150/200\n",
      "2/2 - 2s - loss: 0.0388 - accuracy: 0.9587 - val_loss: 0.1014 - val_accuracy: 0.8800\n",
      "Epoch 151/200\n",
      "2/2 - 2s - loss: 0.0429 - accuracy: 0.9520 - val_loss: 0.0978 - val_accuracy: 0.8800\n",
      "Epoch 152/200\n",
      "2/2 - 2s - loss: 0.0403 - accuracy: 0.9547 - val_loss: 0.0891 - val_accuracy: 0.9000\n",
      "Epoch 153/200\n",
      "2/2 - 2s - loss: 0.0364 - accuracy: 0.9627 - val_loss: 0.0913 - val_accuracy: 0.9000\n",
      "Epoch 154/200\n",
      "2/2 - 2s - loss: 0.0363 - accuracy: 0.9613 - val_loss: 0.0939 - val_accuracy: 0.8800\n",
      "Epoch 155/200\n",
      "2/2 - 2s - loss: 0.0404 - accuracy: 0.9560 - val_loss: 0.0911 - val_accuracy: 0.9000\n",
      "Epoch 156/200\n",
      "2/2 - 2s - loss: 0.0383 - accuracy: 0.9600 - val_loss: 0.0945 - val_accuracy: 0.9000\n",
      "Epoch 157/200\n",
      "2/2 - 2s - loss: 0.0374 - accuracy: 0.9587 - val_loss: 0.0893 - val_accuracy: 0.9000\n",
      "Epoch 158/200\n",
      "2/2 - 2s - loss: 0.0357 - accuracy: 0.9653 - val_loss: 0.0897 - val_accuracy: 0.9200\n",
      "Epoch 159/200\n",
      "2/2 - 2s - loss: 0.0355 - accuracy: 0.9653 - val_loss: 0.1004 - val_accuracy: 0.8800\n",
      "Epoch 160/200\n",
      "2/2 - 2s - loss: 0.0372 - accuracy: 0.9613 - val_loss: 0.0960 - val_accuracy: 0.8800\n",
      "Epoch 161/200\n",
      "2/2 - 2s - loss: 0.0390 - accuracy: 0.9640 - val_loss: 0.0937 - val_accuracy: 0.9000\n",
      "Epoch 162/200\n",
      "2/2 - 2s - loss: 0.0381 - accuracy: 0.9600 - val_loss: 0.0954 - val_accuracy: 0.9000\n",
      "Epoch 163/200\n",
      "2/2 - 2s - loss: 0.0393 - accuracy: 0.9587 - val_loss: 0.1032 - val_accuracy: 0.8800\n",
      "Epoch 164/200\n",
      "2/2 - 2s - loss: 0.0405 - accuracy: 0.9587 - val_loss: 0.1179 - val_accuracy: 0.8600\n",
      "Epoch 165/200\n",
      "2/2 - 2s - loss: 0.0412 - accuracy: 0.9520 - val_loss: 0.1373 - val_accuracy: 0.8200\n",
      "Epoch 166/200\n",
      "2/2 - 2s - loss: 0.0596 - accuracy: 0.9253 - val_loss: 0.1121 - val_accuracy: 0.8800\n",
      "Epoch 167/200\n",
      "2/2 - 2s - loss: 0.0409 - accuracy: 0.9547 - val_loss: 0.0986 - val_accuracy: 0.8800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 168/200\n",
      "2/2 - 2s - loss: 0.0370 - accuracy: 0.9613 - val_loss: 0.1141 - val_accuracy: 0.8600\n",
      "Epoch 169/200\n",
      "2/2 - 2s - loss: 0.0410 - accuracy: 0.9560 - val_loss: 0.1038 - val_accuracy: 0.8800\n",
      "Epoch 170/200\n",
      "2/2 - 2s - loss: 0.0404 - accuracy: 0.9560 - val_loss: 0.1049 - val_accuracy: 0.8800\n",
      "Epoch 171/200\n",
      "2/2 - 2s - loss: 0.0373 - accuracy: 0.9587 - val_loss: 0.0962 - val_accuracy: 0.8800\n",
      "Epoch 172/200\n",
      "2/2 - 2s - loss: 0.0368 - accuracy: 0.9627 - val_loss: 0.1079 - val_accuracy: 0.8800\n",
      "Epoch 173/200\n",
      "2/2 - 2s - loss: 0.0383 - accuracy: 0.9600 - val_loss: 0.0929 - val_accuracy: 0.8800\n",
      "Epoch 174/200\n",
      "2/2 - 2s - loss: 0.0367 - accuracy: 0.9627 - val_loss: 0.1274 - val_accuracy: 0.8400\n",
      "Epoch 175/200\n",
      "2/2 - 2s - loss: 0.0487 - accuracy: 0.9360 - val_loss: 0.1088 - val_accuracy: 0.8800\n",
      "Epoch 176/200\n",
      "2/2 - 2s - loss: 0.0438 - accuracy: 0.9493 - val_loss: 0.1037 - val_accuracy: 0.8800\n",
      "Epoch 177/200\n",
      "2/2 - 2s - loss: 0.0369 - accuracy: 0.9587 - val_loss: 0.0909 - val_accuracy: 0.9000\n",
      "Epoch 178/200\n",
      "2/2 - 2s - loss: 0.0372 - accuracy: 0.9587 - val_loss: 0.0889 - val_accuracy: 0.9000\n",
      "Epoch 179/200\n",
      "2/2 - 2s - loss: 0.0340 - accuracy: 0.9653 - val_loss: 0.0890 - val_accuracy: 0.9000\n",
      "Epoch 180/200\n",
      "2/2 - 2s - loss: 0.0339 - accuracy: 0.9653 - val_loss: 0.0897 - val_accuracy: 0.9000\n",
      "Epoch 181/200\n",
      "2/2 - 2s - loss: 0.0342 - accuracy: 0.9653 - val_loss: 0.0928 - val_accuracy: 0.9000\n",
      "Epoch 182/200\n",
      "2/2 - 2s - loss: 0.0341 - accuracy: 0.9653 - val_loss: 0.0914 - val_accuracy: 0.9000\n",
      "Epoch 183/200\n",
      "2/2 - 2s - loss: 0.0358 - accuracy: 0.9653 - val_loss: 0.1204 - val_accuracy: 0.8600\n",
      "Epoch 184/200\n",
      "2/2 - 2s - loss: 0.0414 - accuracy: 0.9520 - val_loss: 0.1325 - val_accuracy: 0.8400\n",
      "Epoch 185/200\n",
      "2/2 - 2s - loss: 0.0547 - accuracy: 0.9320 - val_loss: 0.1298 - val_accuracy: 0.8600\n",
      "Epoch 186/200\n",
      "2/2 - 2s - loss: 0.0505 - accuracy: 0.9320 - val_loss: 0.0902 - val_accuracy: 0.9000\n",
      "Epoch 187/200\n",
      "2/2 - 2s - loss: 0.0352 - accuracy: 0.9653 - val_loss: 0.0970 - val_accuracy: 0.9000\n",
      "Epoch 188/200\n",
      "2/2 - 2s - loss: 0.0343 - accuracy: 0.9653 - val_loss: 0.0885 - val_accuracy: 0.9000\n",
      "Epoch 189/200\n",
      "2/2 - 2s - loss: 0.0334 - accuracy: 0.9680 - val_loss: 0.1021 - val_accuracy: 0.8800\n",
      "Epoch 190/200\n",
      "2/2 - 2s - loss: 0.0351 - accuracy: 0.9640 - val_loss: 0.0903 - val_accuracy: 0.9000\n",
      "Epoch 191/200\n",
      "2/2 - 2s - loss: 0.0344 - accuracy: 0.9640 - val_loss: 0.1053 - val_accuracy: 0.8800\n",
      "Epoch 192/200\n",
      "2/2 - 2s - loss: 0.0364 - accuracy: 0.9600 - val_loss: 0.0892 - val_accuracy: 0.9000\n",
      "Epoch 193/200\n",
      "2/2 - 2s - loss: 0.0338 - accuracy: 0.9653 - val_loss: 0.0965 - val_accuracy: 0.9000\n",
      "Epoch 194/200\n",
      "2/2 - 2s - loss: 0.0333 - accuracy: 0.9667 - val_loss: 0.0882 - val_accuracy: 0.9000\n",
      "Epoch 195/200\n",
      "2/2 - 2s - loss: 0.0334 - accuracy: 0.9667 - val_loss: 0.1008 - val_accuracy: 0.8800\n",
      "Epoch 196/200\n",
      "2/2 - 2s - loss: 0.0344 - accuracy: 0.9640 - val_loss: 0.0879 - val_accuracy: 0.9200\n",
      "Epoch 197/200\n",
      "2/2 - 2s - loss: 0.0326 - accuracy: 0.9680 - val_loss: 0.0915 - val_accuracy: 0.9000\n",
      "Epoch 198/200\n",
      "2/2 - 2s - loss: 0.0325 - accuracy: 0.9680 - val_loss: 0.0886 - val_accuracy: 0.9000\n",
      "Epoch 199/200\n",
      "2/2 - 2s - loss: 0.0323 - accuracy: 0.9680 - val_loss: 0.0888 - val_accuracy: 0.9000\n",
      "Epoch 200/200\n",
      "2/2 - 2s - loss: 0.0326 - accuracy: 0.9680 - val_loss: 0.0888 - val_accuracy: 0.9200\n"
     ]
    }
   ],
   "source": [
    "epoch = 200\n",
    "batchS = 500\n",
    "history = model.fit(np.array(data[0:750]), np.array(target[0:750]), epochs=epoch,\n",
    "                    validation_data=(np.array(data[750:]), np.array(target[750:])), \n",
    "                    batch_size=batchS, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BVJCgz3bA4A3"
   },
   "source": [
    "## Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "VDXFFy6zBG3X",
    "outputId": "6ac389f9-6400-401d-dfe7-c3e90ce8a0e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 1s 190ms/step - loss: 0.0491 - accuracy: 0.9450\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(np.array(data_test), np.array(target_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "wBV2t2Q6BOt9",
    "outputId": "1dadcb49-b517-4cdc-9624-cb76c529b457"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.04911315068602562\n",
      "accuracy 0.9449999928474426\n"
     ]
    }
   ],
   "source": [
    "for name, value in zip(model.metrics_names, result):\n",
    "    print(name, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 571
    },
    "colab_type": "code",
    "id": "zO6YSbxgBZ3f",
    "outputId": "f8a99f0c-2cf2-4228-f6ac-9083c39443fb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.savefig('destination_path.eps', format='eps', dpi=1000)\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.savefig('destination_path1.eps', format='eps', dpi=1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def infer(curr_dir,file_name):\n",
    "    tr = get_transfer_values(curr_dir,file_name)\n",
    "    tr = tr[np.newaxis,...]\n",
    "    pred = model.predict(np.array(tr))\n",
    "    print(pred[0])\n",
    "    res = np.argmax(pred)\n",
    "    if res == 0:\n",
    "        print(\"Violent Video with confidence: \"+str(round(pred[0][res]*100,2))+\" %\")\n",
    "    else:\n",
    "        print(\"Non-Violent Video with confidence: \"+str(round(pred[0][res]*100,2))+\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.01168198 0.988318  ]\n",
      "Non-Violent Video with confidence: 98.83 %\n"
     ]
    }
   ],
   "source": [
    "infer(in_dir,\"no12_xvid.avi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "WARNING:tensorflow:From C:\\anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "INFO:tensorflow:Assets written to: model/vlstm_92\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save('model/vlstm_92')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('model/vlstm_92.h5')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "ViolenceDetection22",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}