diabetic_retinopathy_diagnosis(1).ipynb

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   "source": [
    "# Bayesian Deep Learning Benchmarks\n",
    "\n",
    "**Bayesian Deep Learning Benchmarks** (BDL Benchmarks or `bdlb` for short), is an open-source framework that aims to bridge the gap between the design of deep probabilistic machine learning models and their application to real-world problems, hosted on [GitHub](https://github.com/OATML/bdl-benchmarks).\n",
    "\n",
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/OATML/bdl-benchmarks/blob/alpha/notebooks/diabetic_retinopathy_diagnosis.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/OATML/bdl-benchmarks/blob/alpha/notebooks/diabetic_retinopathy_diagnosis.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": "both",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 58540,
     "status": "ok",
     "timestamp": 1566544799078,
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      "displayName": "Hugo Aguettaz",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 19.2.1, however version 19.2.2 is available.\r\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "#@title Dependencies & Prerequisites\n",
    "!pip3 install --user --quiet --upgrade git+https://github.com/OATML/bdl-benchmarks.git"
   ]
  },
  {
   "cell_type": "code",
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    "outputId": "df1faf0d-cf1f-41df-b582-4b621e31bad8"
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "GPU device not found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-0eb18c5b74da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpu_device_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"/device:GPU:0\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"GPU device not found\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m       \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SUCCESS: Found GPU: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpu_device_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: GPU device not found"
     ]
    }
   ],
   "source": [
    "#@title Imports\n",
    "\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import functools\n",
    "\n",
    "import numpy as np\n",
    "import scipy.stats\n",
    "import tensorflow as tf\n",
    "tfk = tf.keras\n",
    "tfkl = tfk.layers\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "if tf.test.gpu_device_name() != \"/device:GPU:0\":\n",
    "      raise OSError(\"GPU device not found\")\n",
    "else:\n",
    "      print(\"SUCCESS: Found GPU: {}\".format(tf.test.gpu_device_name()))\n",
    "\n",
    "import bdlb\n",
    "from bdlb.core.plotting import leaderboard\n",
    "from bdlb.core.constants import DIABETIC_RETINOPATHY_DIAGNOSIS_URL_MEDIUM\n",
    "from bdlb.core.constants import LEADERBOARD_DIR_URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mJBK_BFYRUMf"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "from torch.autograd import Variable\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torchvision import models\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.gpu_device_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5uL1LO_5fCQc"
   },
   "source": [
    "## [Diabetic Retinopathy Diagnosis Benchmark](https://github.com/OATML/bdl-benchmarks/tree/alpha/baselines/diabetic_retinopathy_diagnosis)\n",
    "\n",
    "The benchmark that we use in this example is built on the [Kaggle Diabetic Retinopathy (DR) Detection Challenge data](https://www.kaggle.com/c/diabetic-retinopathy-detection/overview).\n",
    "It consists of 35,126 training images and 53,576 test images. We hold-out 20% of the training data as a validation set.\n",
    "Each image is graded by a specialist on the following scale: 0 -- No DR, 1 -- Mild DR, 2 -- Moderate DR, 3 -- Severe DR and 4 -- Proliferative DR.\n",
    "We recast the 5-class classification task as binary classification which is easily applicable to any BDL classification algorithm by asking the user to classify whether each image has sight-threatening DR, which is defined as Moderate DR or greater (classes 2-4) following [_Leibig et al._](https://www.nature.com/articles/s41598-017-17876-z.pdf).\n",
    "The data are unbalanced, with  only 19.6% of the training set and 19.2% of the test set having a positive label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": "both",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 95362,
     "status": "ok",
     "timestamp": 1566544842730,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Building wheel for gdown (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1WAvS-pQsVLxUJiClmKLnVNQkoKmRt2I_\n",
      "To: /content/medium.zip\n",
      "685MB [00:11, 59.1MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1LQeAfqMQa4lot09qAuzWa3t6attmCeG-\n",
      "To: /content/leaderboard.zip\n",
      "100% 4.64k/4.64k [00:00<00:00, 5.93MB/s]\n"
     ]
    }
   ],
   "source": [
    "#@title Fetch Data (in TFRecords) & Leaderboard\n",
    "\n",
    "!pip3 install --quiet --upgrade gdown\n",
    "!gdown $DIABETIC_RETINOPATHY_DIAGNOSIS_URL_MEDIUM\n",
    "!mkdir -p data/diabetic_retinopathy_diagnosis\n",
    "!unzip -qq medium.zip\n",
    "!mv medium data/diabetic_retinopathy_diagnosis\n",
    "!rm -rf medium.zip\n",
    "\n",
    "!gdown $LEADERBOARD_DIR_URL\n",
    "!unzip -qq leaderboard.zip\n",
    "!rm -rf leaderboard.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0pIDXVJGfCQv"
   },
   "outputs": [],
   "source": [
    "#@title Hyperparameters { display-mode: \"form\" }\n",
    "BATCH_SIZE = 200 #@param {type:\"integer\"}\n",
    "NUM_EPOCHS = 10 #@param {type:\"slider\", min:1, max:100, step:1}\n",
    "DROPOUT_RATE = 0.1 #@param {type:\"slider\", min:0, max:1.0, step:0.05}\n",
    "NUM_MCDROPOUT_SAMPLES = 10 #@param {type:\"integer\"}\n",
    "\n",
    "LEVEL = \"medium\" #@param [\"medium\"]\n",
    "\n",
    "input_shape = {\"medium\": (256, 256, 3),\n",
    "               \"realworld\": (512, 512, 3)}[LEVEL]\n",
    "learning_rate = 1e-3\n",
    "l2_reg = 5e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "Vg8K088efCQ4"
   },
   "outputs": [],
   "source": [
    "#@title Helper Functions\n",
    "\n",
    "def visualize_tfdataset(tfdataset, num_samples):\n",
    "  \"\"\"Visualizes `num_samples` from the `tfdataset`.\"\"\"\n",
    "  \n",
    "  def to01(image):\n",
    "    \"\"\"Normalizes image to float [0, 1].\"\"\"\n",
    "    min_ = np.min(image)\n",
    "    max_ = np.max(image)\n",
    "    return (image - min_) / (max_ - min_)\n",
    "  \n",
    "  for X, y in tfdataset.take(1):\n",
    "    images = X.numpy()\n",
    "    labels = y.numpy()\n",
    "    B, _, _, _ = images.shape\n",
    "    num_samples = min(num_samples, B)\n",
    "    fig, axes = plt.subplots(figsize=(2.0*num_samples, 2.0), ncols=num_samples)\n",
    "    for i in range(num_samples):\n",
    "      ax = axes[i]\n",
    "      ax.imshow(to01(images[i]))\n",
    "      ax.axis(\"off\")\n",
    "      ax.set(title=\"healthy\" if labels[i] == 0 else \"diseased\")\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BBe1ZqSMrRFt"
   },
   "source": [
    "### Benchmark API\n",
    "\n",
    "The benchmarks can be easily loaded using `bdlb.load(benchmark, level, data_dir, batch_size)`.\n",
    "All the data management (i.e. download, prepare, preprocess, load, batch and prefetch) is abstracted away from the user and is **compatible with any deep learning framework**!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "both",
    "colab": {},
    "colab_type": "code",
    "id": "zBuL1yXjfCRI"
   },
   "outputs": [],
   "source": [
    "dtask = bdlb.load(\n",
    "    benchmark=\"diabetic_retinopathy_diagnosis\",\n",
    "    level=LEVEL,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    data_dir=\"data\",\n",
    "    download_and_prepare=False,\n",
    ")\n",
    "train_ds, validation_ds, test_ds = dtask.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7614,
     "status": "ok",
     "timestamp": 1566477176173,
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    "id": "i_euCQwUfCRV",
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   "outputs": [
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y5INMLj3K+h33oWpBmKTMrl7h0if+GB9F7D5xnp3LF+je8Xq25zOOnjzG1sk1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ZvT7F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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DHo3ZbRx7/FHmtu9jbt9e+pvrNOa345brJPEIpSJacwcBhWFIht11EAqtUpJx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\nleM4juM4zhFxgZXjOI7jOM4R+b9G2xebZDMA/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hOvl4h6asEGGAQaKThKaswbWeKykk1hqU0ot5NZ2lv3IMqT1NNcaaimo64/7v\n+D4+/qv/O8fvezN1kSPDgKg/YP70mFPn34axNdPdLZSUSARp1qWoCiY7Nzl+7wPoMGK6u8nSyjGk\n9Yx3bpIMVnHOEqcdqqrA2uagi/6trcnLQfhFK5EgDKlm++A8t59/G6Wd44Eg0MRJSKASiv1NQqFJ\nBkOKyRhtDJONy6zfdidONxAFWFMSh12CKOPS4w+xdOw2jLfsbl2hM1hB6wDrG+KsSz7ZJeutMt9c\nFJaYosDL115q6i1rtgshMOWMINJYUzDd33pheosoybCmYbx7g5XjJ5FKUBU5+fYm2nh8XpCECa7M\nsXnO/ug6s/kOG5ceZ7J/nb/2D/4Jadbhj3/lX9PrLBNFKUJYOkvL1PPZ4qL2go1nniDtDLj61BfJ\nuv0/lZPh/SFyXbwEhBCLsmprCMII7xry+WTRG8h5qroC4dFaEuqQUIc8X1sZd3qL/kFSIz2LyZWd\nxZmS3lIf/3x/FBnQmIN8LSVx5WKqmjhJ6QyGRElG1OmTZENuPPcsJ+48z+7Wsxy7+27qomA62qfK\na2ajCc5InPXoWDOb7LC/eY3ZaB+BJNARAMVsvOjOzcKoct6hgoj+6lGElItGeEovEj4PdJfycFya\ngoWe+WxClHYQfjHQxmmH3so6o+1N6rxEq4AkSfHWkU8nBEGGd4ow7uORbFx6DmMl8+kGyDlxT/Ou\nH/rP+NCP/WM++Zv/GzcuPIWtHWk2QGvJ0dvvZDraxpQV5XTKaHsTLwTWN+zduML66bu48tjD5KNd\nJGDLkmJ/l8nOFs45dBBgmwYhJFl/yM6156hmM1QQYJpF9dN0tEM+HZP1h1TFHB2FOGep8jnWO6SU\nbek/4LwD7xed68MYEDzyid+jLuY0VU7c6TA8epb/8Mv/knve8l5wFqQEpaibiqqpSAZL7N28SpSk\nJFmPKM4wZYUSkqy3RJikRGHCYOkIWIs1DWGaoaOYIIwZbW8AoMN44U1ueUkIIbBNs7h/GUtTzugM\nljh++91EaYLA4p1cFGw0JdiaJh+RDoaknRhfjZnvXuLE3ec4escZnJ+Sz3KcTbAmxvsO0/2Swepd\nYBYJ8J3BCiqKMN7iBDTWoLIO89mYNM7o9IZEaQfPwX3V2Vf7a3qBW+4Mc84u3PJC0FlaIulkKKUY\nrKzRFCW2Xpwg3krGO9cxJqeYjRiunyZNMzq9jDiISIKMfmcNSoswjjhJuO38W7hx5XFUBN//3/xT\n+murDLqnSdMBQmuKcsxo7wZCa776iY8w3rnJE5/+Q8696Z30hiuk3S7eu4Wr+ha+ITtrUTpg5eRp\nPJYwigh0QJXPicM+cRQSCknhvzjNAAAgAElEQVQnSXB1hQ4jam+YzUYIIVBeEKkIKTRCCLrDAaaZ\nUVcNZWloLBRFjvENzhuSThdpGqZbG4RxjAz6aNnnqS98HGTJyonjDNbWsY1hPsrBB1TzCltDUxrq\nwiBkgNCe1TMnuPDwp5htz7BlgjEe7zV5Psd7Rd04rPNYU+MFyCDA2gaUJB0M0cGBoXiIvBxJp0+Q\nJFT5DCUVaafHbH8HBGT9Y9x89gL1LEfh8KZB6RAlYkQTYHJLGPRJu2sIEdPNMiINdb2Lj6acevAu\n/ubP/Awf+fn/hV//H38SDKS9DsV8l9vuv5+dy4+BLYk6Gbs3rzIbT+kuHaUua7LVdU7c8yauP/MY\nrqpYWT1OJ+kceFUcUmuSTo9nv/oQWXdIPa+YbO4gCCjzmjDp0ztygr2dDdLBEloFFJP9xQPSwTkM\nINUhrUj4C6KDiPHOTXrLa0ilGK6fIO0tsbx2DG89dWV5+tGHOf3G95CXc5q6pjNYxmARccRtb34n\no60NoqxDZ2WNh//wt3ACUJKo318YYc4x394ky/psPfc0j3723/PMo59lPtrFmJqV47cRZ12kktRl\n/mp/Ja87nHPocOFNlwqO3nYn+zcu8cyXP0Wn26cuZ3T6Q6QISNOQSFmkcszme5TzRePcY3eexzaO\npLfM1o3LXHr8MW5cuMjmc9f44h/8PpPtfZTq4WyIN4al1WMI54mCmCBK8IHEWYMwFl/WhEITymDR\nJBpQSr9mjKtbbvTWYfRCl1+tFbaeY+uSvY0NtMzwjaKcVQgZcM/bP4xSIUGYsXfjJrqf4lPJvJlQ\n2wZjLcYKZtt7uMIgnMc7w2f/4Jco7D4f/Dv/mE//0i+ytHKWrH+ExhuWT59BhgECxb3v+B5Ov+FB\ncB6BI+4sen/cykbV8/konaUVpANRN/i6AevoZkO8MURSIV2DL2Y4U+O0oLQ1Qi88XaEOF3H3g4us\nqqcQStKlZWQUEXUGyCwBBVGS4ZuGIAgwVU7dlOTjms3L1zl+172s3XaCyfQqHkWZN1hbEWcdsn4H\nGdQYs0uYKeaTGmMbgshx79s/yMr6fWx87QYEMVZpos4KxgFSgwxwYmHkW7fovt6UBaauCOPk4Ht4\n/XssTVNRVwX5ZB+pNeqg51g1nRAmGUi5yKOSIdPdTeI4PcjLUmSdDraeYaop091rFJNtrMm58uXH\n2H7mMtPtPYpyxnNPf57+yTV+7Gd+g/f+4D/k0mNfYra3yfDYGlW5x/t/5O/gm5zucABKooIEbxUi\niJGdDCsh6S2RdgcI68j39w4m6V5UpD71+Y/znh/82wyOnAQf0h+epJjmdAfHULrDdH/M7s4GTgqa\npkYiCWVAlnSJss4iafswJc59CzRlzp1veR+dpRVMXXHhy5/h9L0PoghYWT/NxsWLnDr/HVROYJQk\nGywz2dtiNh2xt7NBYxrm4z3SwTL7uzc5/4HvI+z2EKEmHQwxtmEwWGPn0jOMblxl5eRZ7njH+7n3\n3d9D2h3QVOViP6qybW/zLbJ44PdIKVk5cpJyPuXombtZPXqa+f4uYZgsWlwYQzObkO9vkiQpcdZl\n8/IuSh7nkY89xJOff4ILX7nI2m3v4553vY+T5+9g7fZ1zn/gvQyO9ajtDvnsKptPPYpGsnr0NEEU\nE+gAW1ZUxRwlFJ2ki5lMiYKYXne4yJW2Bv8aCfHeEiP416tAHAKHlhAEkl7cJUk6iChALw2wWuCl\npBYNlbTIQDPd3aUsckygGOc5yJhyf8po4xrbVy7iigIvPenqKqPZLvHyKsfvejPlaE6YBWxtX+AL\nv/lRsuV1OkGX7uoaz934AqfveyNFuUOjpjRuTlPV+FLglceYxXxKi/2WHPZUOIEB4XBCU1aLqqFe\nv4s1cxyWpnEgNVlvQD29iXCeuqxpdEgpPHk+R0cxkgglFF5avKwIwhDhNTExkZNkoSYQJdqP6QQ1\ncZQglUCHntn+ZSIlieqM8cURNx+7TD8ekoYhWa9LI2uKco6ZG/CKqqgwRYm0AleWoB1Jp49rHGHo\niaKSs+fvYDaeIJWgqfeRyiClxZYl2sRIEYMD6xZT8CgBcZZgy9lBE1m3SB54rSUQfBOcdHhhkN6h\nhCAMFKapmI1GFPkcq8BqiceBa6imNzh67nbS5WWKyZgwDCirEbmdUcsaGSuCUKNNQ4ogGy6TVzVx\nbxkaiZmWPPfQ50hWJ7z1+9/H7pOb3H33+6j2tjl5/n6uP/ckx+88z/bFr3H67L2kaUJRT/EBdLpd\nxlu7JN1Vuusr+MijOglN7ajmAltHLK2cQKqQsiwQmaBsRqRZSNqJMM2EbKnLfH8T31TY2tBfPkoQ\nabRWSCyBEgjh8d6+7uY7e6m8+Pi8XbSRCeOE8fZN4rTDfHebXrbEsWPnuO3c/Tz1mT/ha597mFP3\nvZPpdItsaYCZz5lu3WCwtEYv6NCVMbEMyPMJDhjtbKK7ffamOzz+hT+mHE947OMf4cpTX2bt3D3o\nTkaQZpSzKc4Y5rN9qnKGo8Y2Jc40ZJ0ewi4qBV+8z4dYmm8Ri6BB0ODriiQOCbWk8SGIjL2rG/S6\nK6QrQ+hqCOY4OUFHMb7S+CpCzhMQ8PjDH+EtH/pu7nn3/azfscLu9GtYX+PqGhqHFppAaCIZcPTE\nOU7d8wCf+f1f4YnPf4qLX3qE/ctbHF+/l+PLJ1gbrhOlHRySurYk3WX6S30EDVp7BHaxvIo21q1h\nWPH1i76pc8I0xTnD7s4WxTwHIzF5vjBotKMyFbVpFnOJZR06vQGz3evgHHHaY2n5GEfP3oNOEjor\na9xx79u49uWHkEXNoLPM8aPnePSjv0Mn7HLfBz/AU1/+D3zkX/0sUilG29e4/fz7kVoTpikeu8gl\nspa6qvDWEmUZUkm+PqXL4b7kBbwwbUGUdgnTbNFb0YE3ftEWQQeUxZhsZZX5NMephMZ5wqxDmERI\n0+BNBVphhcdKqJoC65pFR+zG4qXAe4sA8uk+Hktta2bTGZ3hGVZO3cPe5mVG28+wsrbC9Scf4+aT\nz9DpHEF4CKIALyR1XVPXi2R3KTVKLLxdzhpA4GyNrSc4V2BNvQhtqQDTFDjToA/yBpq6xjmDxBMI\nAXUB1hBlGWEUvWp6vDI4vGvoDIYgHFoHZIMhxjSLzuRAknYBgwgE/fVj7Fy7inSaLF0BIXHGUMym\nBCpAAFppbFkQRxEmL6inc8zc8vgnP8Hnfv9X2bj0KPe86538+//zXxOFR9l45kmMLanLGavHz/D5\n3/0lhHEcPX0HveEKo8ke8dIR8qZi9dxdEIeky8toX9HtJASuYmmYUY2u4uopYdahLnOCIGL/5jXC\nKGE23uG2+x5chHC9p8rnCCkZ792gLmbEnQ6mLl5oFnqrVAd673CmJkm73PaGB/nKH/42F770aZ58\n+BPs3LjMQx/9Ve5+9wcI0pAk1Oi6xs3HFNMRveUjONMQpx2SpIuta9JOn9neFr3BCnUxp9tf4YEP\nfD83nn6U+9/1IYbHz6DjGCEVo92bDFbWMU2F8A6cIR/vo5VCK0nW6xPoEA68oy1/Pkkno7u0ggCC\nOODphz/OaPMKaW+JYjYDodFE2LxB6xQhQy58+XOM9zY496YHeOC7voe9za8xH92gmO9g6wrqhvnO\n1iLHsSqpixyhNaPdm+iswz3v/E7Ov/fDnHnDG7jtzjcwuX6R6eYms9EeUinS5SFeOMIsxQaKpL+E\nR4JXi+VVbAp4SxhW8HXDSkcJIlAYa9jevclkf0w9GtMPO0RaIQNPf+0kOsjwzuNNjRaWGEsWJ1Tj\nXZpyTuNqdCdle+8aW88+ycnTd3Pm7Hmm167yhd/7Zd77Q/8Vv/uz/5Sl+07zA//TPyO4UlBoyE6c\n4dnf+jg6DGiqgmZe4ayjrnMuPf45srRHJ+sShuGL5l16bbg3v134g9NQYlDSLpKBmwacQjpBlma4\nOgcceT6FKKOuG1SQUE0nUMxIlCAMJMbUC8+I0HgpMbZeGNY6wouA6TRnNq9IB8dYOnYKrwKme4b9\nHcNXPvUp4rUu5952liPnhgzXT1KNHTvPXMfPK7Kkg5QKezAh9CJHKkSFMUJqyjynzHPqpqGyBi+h\ns9Rj+9qFg3E1QIgAaw1GOYyoQYGUjkBCp9OnLnM6g2WcfW3kCrwc1k7ejm0OprAwDThPePAQ4a1l\nPtqlrufM5vuM9rdYPXUXN59+DjeHeloQqIgwSBBBiLGOqmnodnqcOn0H2gmuPfkoN569wPE73koY\nnKWpPPd+54Ncu3iBX//pf8FgeBtSS5LBElIKHvzgD/DMQx9DVjXSCnTQ5cpTj7Bz/TnS7hLjm5tc\nefQLdJb6BEqAqTjzhgewlDjhaMqGtLvC7sYNhutn8E4QJz1m4z3y+RQvIBsu472jO1xlMem0ZvXk\n2cUA7v0LRuVhR4cxnaUVqmLGpUceQng4e/87uOvt30n36HHW7ryX7b2nWT61QrV3BcZ7BFagw8Uk\n6UookqxH3ZTMJnuk/SFrR88QqIAbT36Vh37rF3BlyYk77mf5+JmDh5oFUqpFOxXrEN4zGB4hSzpE\nYYT04OsaqRQ6iLBN/U2OouV5itkE7zxhkhKEgvu/87u4/cF3MJuN2Ll2icnmHte/eoFiu+HmUxdx\nXtJbP8L6PXfh/BRT7VGXBUIGuMrTS1YQpSENM3zdYExDXs5osOheD4MlShL2rn8NaSfY+gZJRxPE\nPYq8RqgIY0qiXkRejPBBxLxqsEIBAcIHr+r3dWsYVn4x97lzlqX1E5TTCUprVk6eZbB2gjDKqOdz\nhPM4a9FKc/krn6M7WEV6RzWbcPzMnQyGq9iqXCSZu4b+kaMgPMunb6e2i/JxAs3K2Tt56vHP8X3/\n6KdIkxStG370f/63fOrXfoHRtWt0V9a5ceVrFJMxNBYcRFmXrD8A25Dv75FmHYQSOFMvJos91EgE\ncvF0aQ14i44zVJQhhcbWJWmS4U0DAoTSGOup65IoztBKo5BgG6QUBHEHqcNFIrsSeBz2oDGhEPJg\nbjfP1tVnKWc5MkgZ727yhvd8N9lyH5VC0FFkgwHLx89QTqfEYQ9hPMY0i/mwOj2aqkQHIVJKmrLG\nu4Ngs5AgFm3rhID+8jqmNuDkogP/QT6Vd4sn5jDMMFUD3pP2hiSdHs6/Pg0rZwy2WQxcQimCA89b\nnGZ4a3HG0l1eQ0mFVsGigaMQhGlK2F3M8zfZvIm0AokiSbrgJWHapaxKVo6fYbRznfHOde5+y/s4\nc8+DlPmM3vAEVVmxs3mRD/7of01neZ1yOuf2N7+T8d4mneEKjSm548F38/SXPsVka4NQhFCWnH/L\ne9l/9imOrp/m5G33sb2xQbZ0gktPPUZjPFUDBoW1BmcXU1ftXHuOMMkQUhGl2SJnM1rMjdYZrLB7\n4yphHFOXJfl0DEIgpHwhof1QcpCD473DmgYpFTtXn+PE3W8kn41Yv/0eymKGimImox16y0OE9Px/\n7L15sKbZXd/3Oec8+/O8+93v7X26Z7pnNJqRRprRgDYkQAQIezAQY4yLlGOn4qRcqUrKdqVigqn8\n4zhOAEOMwTFQhcAoYrMlCy1oXxjN0jM9vXff2333++7P/jzn5I+nZ8COy2AWaablb9Wtun+923nf\nc37n9/suXuDR6i5iqoosmZGMR8TjIYfbt1Cuh9bNHjw92mPj9CNk0zFPfvP3Ew8PObh7g5e/8NFG\nxX24y+Rw915R1Ywi66pRslVlSTqfUmRJE9bcvOBmXYTA3GeCkT8PKKshq1dlybEH30jUG7Bz/SWy\n+YgsHpPEI4QSDNZPEnW7PPy293Hs3OOce+Ib6C6vM1g7wdHedfJ4hKlLTj78BEq6mFqgdI1lWY0P\nlpRYjsPC+kmmh3vUVcloexNLgChystmIss4woiKJm+dWroXlBhRZwXx8RDodYyvJ8olz6LpsgtC/\niurPr4nCylIW1DU2ijpNEdrgeyGW0Zg6pRY1ldBkcYyvQl760K9z4bGnCMKAum5yv7J5zGhnC1NW\nJIeHCG2o04SFxXV2b12mrEuU7bC3dQ3j2tCJePn2RT79Uz+LGsXEpyT+3GP+3DWiR49x6tG34oVt\n/LCP4wZgDMtnHiCfjHCUwOQFlpAoS34NRN7Ie1QiDXWB1+mxt7eFDHpkdU2Vx1hoPEvhKgdJTbff\nJxkfNGuL1USQGIGqQRhJNkuQWuEJialiHNtgWQZLCdqdHu3eArZoM7qzTTLe5tF3vh23LUjmQyoc\ncm2w2hZ2ZEjjA6qsQMkmVkdaDnkagxBUuqIsc3RZoyuD7YYI6YByKHWF5YDju8xHYyzbf1VubquA\nMFjAstsUeYlwQqQTMh8fUubZ65ZgK5XCdjz8sM2dy8+jtW66b9oglWoOuSxBCokxBtcJ0XlFniZM\nRjtk5ZSlMyeY7t8hm86YD0co26XSGr+3wMUvfYzduzeIlpcpdUKS7KPrGUZNsD2Xw919eieW+KYf\n+xFe/swnuHvpJRCG6y98HqOgvbpO/9QZ9nZucHTrGoHvoNMxrdDBNjU6SWm1l5jt73HpEx+BWiGV\nQ5LEuIGH1iXzyQGWbTHe28L2XJLZhFI3B/j4cJf97VvousINWgAoy8ay3UaYcR9L/RvFdbOuncEy\n29df4sLT38jlZz/Nw9/w7RzuNc71zYXHgloijKIAEp0jQp+VEw9iSYXtuPTWT5AXKY4fksdztq9f\n4sXPfpgHH387uiwBaK9uYLc7TXjvYJnu0hpht7G/qMoCpCRLY4wQuFELr9UmS+N7r7dRXxut70VN\n3b9r86dBcW+PA8iTOZ//7V+ms7gKpUEXFdpoJpM9pKVxXUlWHSHcmvl0h7qeI02BJaBKUzw7ZPfm\nbbQWOJZDy3fJsgmz8RGz6RC0wVQax3awkQSWhVWD1GDZAWlpSI3B6/i0FrtceeaT1LVBygCpPTxd\n0mtFOKZC2hXIjLJM/5h3+BeHr41vUl1jCQvXcslnU6KgjWf7yLKgLlNqq8b4Lu3eOrsvXOSxJ98F\n6YTp3m1s1wOh0Fri+iFZPKZIp8T727hCUYxHhF4LT7mM79zm+KkL2CgGi+vMdrZ483f8TT79kQ9i\nTbb4vr/3C2zdusETb32C8f4Wk4MdTKmxbJfZaA/bdYlaHbywRZknTehwVaGs+50HcG8UaDRrZx9C\nWTZhd4nbN19G+iGO4yLqAp3GRG6EIw3F7JAwDBt/FS0xwqEucjwnwJIWUXsBqTWu8mhFIUpWhL6H\n59o4tmK6e4ern/k8g94ZzjzyMJgDqmKfIs1IYohTzXS+g4pyVs+d5O6Na9S1TThYbFyzlETadjNm\nxCCkwnJclGo8srIiaQa4EuqqwnYCdm9eJZlOmezeJTucMLyzzWx/iJEe+9vb1Ebg+iFlkRN1F76a\nC/KnhhCSsshwPB+/1aHMm/+rvMDUmrrIyOMYoSTCGCwtUVpgWw52aOP3fbTMWT9znmw6ZrS7STqb\nUlUVm1eeZ/GBC/ROnkH5LtKThP0WRs9RzpTDvevkmcPe3S8h3D3KNOUT7/8FWv0Fjp1/lM3LX8Z4\nFosPPkT/zDnchQHH3/wEqmUzTw8Il3q0FtpY+ZiF0CEyCXq8hUdCrxMiZA2iAko6SwtkyRBBjR0E\n6LrCCAg6PcJun4WNU2TxFMfzEKIprpRlv2bk4H8RsGyn4aC5PluXn+P8U+/Bi9o8/O5vo722gfA8\n1s4+QhbPSMdDti++SD7N8JfWsZeWKRXoIkMXJaaq2br8HFkaE3b72K7Hhae/Eb/TQ9o2QggWT51j\nZ+sq89mIa898GscPsR0PgFZvgbDXp6hyamHAtih1RYVG2HbjR1bk/xbn7X5emz8NxD0/wbDbR9k2\nT37bD9FdXCOdxQjjUdcatxXhBBZZvEdaHLCz8zxZdYClNEoaIq9F6PdJZwlVXtNq99F1Tn+pS14m\naMeiu3ac+fiIKk0QRYXvuERhC/ICT0VYbpekUFitAZZT4UQuJy68id3bm1x95hmmewesDpZouTZS\nZ7QHXaRtUO5XrwP5NVFYVVVFXedUZYGSAtdzyJIxeZ6ha0Pgteh3F7jy+Q9x7qmnKW2H1BgsJ0I4\nHkmVYXlOE7zse7TW1nH8gPnuXVxl43kBluOhjcbvDpoRQa0JfI9CjXnnd/4NPvpT/wdZtMv27k2e\n/+CnWTr9AFk9we6HZGmK57cRVYYxUGQltt+iRtNaXsRYr8/uxZ8UwqoxskbbFnlRUekKIQ3t7jJ5\nWjCdz0lrTWFJEpNhlIdUHsoIbF3gOSBkRa5yhKXxHQvKGCFrtCjJyhIcFzvysQsfJ+myffEavY02\n4aqDiRyG8xjcPlK6JNM5uhLYToeyUkgU/W6b3ctfwJUWvushyxpb2biuT5VnWI6FshVlNkNhkHVF\nlWfoSmKQCAXdxTVsy2Vx4wE6qwt0F7r4oYWipLPQZ/fmSwz3d8mSWROnInSjLnsd3aR10XBWsiKj\n1V/CDgKKNCEvKvI0w3Z93CiiNlBogbEUVZWjy5J0OCIdzalKTW3ldDdW8VtdZvuHJLuHMImpJ2OU\nATtoo4VD1Ftmce00gdVH1eC6FUpaUFn0+h0mL4748M/9cxYfeoh3/eBfR4QDdq4+z0q/Dbrg4uc+\nTvfYQwyOP8LRfM5RMmXh1BmClWVOPPl2tKmxpUWyv0OelcTzBCwPpE88y0kmM1xK+v0B7fYAU9So\nSlPOp2T5iGw+ohV1kbVuisv7NWUbSGcTlLSo8oyw06fIcz71G/+MOssIvYhO2OO3/uHf4eWP/g7b\nLz3LnWsvsX/nFp//jV+mniUM+sdI05REVaQehKtL+FGLIplz+9nPYPKY9ROnmR1uUZdzkqNtfAwL\nvUWOnX8CpVzKvFGZVUlOHWeEQRtbWvhuAICuNUhBnsUINK6lcB0X0Ajrfp8M/Mej4SV7pPGEyc4t\nXCDqdrA9Bz/qIEqo5jUnzz3NeO+AwfpZtPCYz2ZI6VDO52gN0nNBVkz2t0EbNi++jKkMpiioswzP\n8bGQ+F5EMR4TtXrEyZjMZNiOZLEVYnZ2ca0O0zTl5u4VeksLvOHxt3LizEM8/9yLJKbDPBdECycp\na41QX72x+/2t438FQhN2+ghdk08nNLN1Q9BdoCoLHCdkuH2LN7zzWzDU5FmMkg4YSVkXzZfCtlFG\n4/gBfqdHFs9Y3DhDOhsjlCJL58TzMXar1cQ3aAddFszHO7SsLr2l49y59Dn+63/0q/zKf/fX+KH3\n/gxe2CKejwm9FfLZEY4SeFGX8eEOrc4qUbdpq9dVieL+iTr5d1GVFZbn4N7jU8giwy5zcg2V1iwu\nH0cXGRiB0SV1nWBJia0cpgfbhIvr1BLwfGqtKeMYhcRI2XQMMKggQmqP3eee5c4Lz7J6+mGi412k\nqoknI6R0SMYTHNenmMxw/Bbzo33CqIMKbAYnQvY3b3O4uUlrcRElGi5JlgzxWx3yPENq8Yc34FoT\nBC2yeaMSU0oiLIHjdcnTGdIUSFsgC5AYlBQM1k4g7ZAin2KoccOAdJYgxeuHl2M7Ho7ncrR9i8Hq\niSYrT2sc129847IZ6LqxmHA9lGdj0kYtF7UHjSjAGOL5lLAzwC1h/dyb+P33/xwbZy9gBy3KqqSW\nJVmVIbKYxZMn2bt8jai9it8KSWdz7rz0HKfOPsHbf+QHmG/u8ot/5W/wV//pr3C838I/9Qg7uzv4\nUZvZtMWdK5dwlcvW5sssnDzN7s4WnY4mrWuSIkfagqi7hHB65I5DOptTOj5nHn4zhoqqOKSua+bz\nMY4XcfPZT3PmTW+j3znJ+O4m21cusnzqQYylmk7mfcqX9oM2Qgimwz1WTj7ItS9/msH6SV7+9Ie5\n+NHfpJjPeft3/ShISVkVKMsmHh8SdgZ8+aMfIB4ecOyhxzg8uEXQHdDuDrClw+7Vizzxvu+nqhs+\n6uLZh9G1pqpqAt0ocdPxiOHereaFlDWDteNsXXqOwbFThP1Fxvt3G3sFo4nHR0RhF9tYr8btgMZI\ngdD/qbh6BVWR47ciLKm4ffFLnL7wOFkyw+r0MKZGVxWT7S3yJMaxIxaPncP2IyQWskyRQtHtLHIw\nPEAnBUIalATbC7Esh8nuZvN40sJvdajyDNf1QQj2D/fxFpfJqxykwpgKLIdZVjCLY3r9ZULHZbK7\nhdvpc/LxdzI+vMPerZc5ceExessnmO5vI75KF5nXz1X4zwBl2WTxlCrPCTs94tEheZZS5GljXGjZ\nuH7IdHjAeH8bYaCYTrFtF6M1WmsqUzE+2EVoGO7ewQ1bjPbv4rU6GCkIuwOyLAZj8MI2Qkh0VaM0\npLMjTj36JMn+EYdbV0jLGVVScfrC2+gvHUdXmqqs0RjSeIplu+xvXadIYqSQr6uOxZ8OiiKrcL0I\nzwtxlMIxGkcJFIY0j8myBM8N8IRCZ1OkKJDUhN0Fai3RxiIeT5HCRTo+BkmazDHSIU9TjJCNQWXg\nMDh+grya43gheZISuAFKG0yWYEmFb7nkoyGO7aLLnKpIKXVFa3GV2XCIwCLqDLB9F8txqcoUYZri\nQEiFEALL95vQWWET+G0sZWF5LjVlY7GgFFKphgciBLbrUZVFQ7LPU4p0jmXfi8F5HalCjdH4UbuJ\nC7JthGw4LKYuEaIxf63KHIFp3LLHQ8J2D601dV1TVxV1WaKNBKmQjsX+9g0eeed/hvJ9br30HLs3\nrzddRSMoq5yj/U22b76I327RXzvF7Gifh59+L2k6RZs9HnrqrZTDgp/94e9GJ1OKOuPUI0/jRV3e\n8I5vQyOwHJ+F9dMk8RwnGjA8OmAyHlIZwTyOMQhMHiOqgsDzkHVFr7cARY50A2oDlhcymww5+dhT\n1HXF8O4diiSnv3acsi4QUiPM/asKFEKQzEZIqXjwre8mT2bsb14DXdHpLfC2b/9B0niE5TuUOidL\nJ9ieR1WmnH/y3UgpuPLFj7F2/Dxr6w/SChbYfPFZ/uDDH+D5T36YD/3i/87zn/wQfmuB9vIxkjSh\ns3EKr7fA+be9CyMNner8QBQAACAASURBVMVlju5e52jrOqff8GY0mnQ+oa7rhtBuIGz3qaoCZTsU\naYy0LLh3gf1P+EO4fogfthEGLGnd60BHDdm/qpgO9znxyBMsHj+D3+qgHIfx4R4AUiikAcdykMrC\nb3Wxba+JoNGaUleEnT5e0EI5Lu3+EnVd4fgBxhhSXRIXJWUNcZ6SG0hMiWx3GvPkJCPe26K92Mfu\nBRzduY7vBTz05nfgOC5h2KEqvno3mPv6xBZSonWNbSuidvfVg6q9uI7lhiTxFNvzeeGzHyLsDfBa\n7ebWYqAzaFK7HT+k1k32VNRbwvFbtHrLuFGXU49/PdPJmPbyGkeHd+msbGD5QaMwqWu8sIOlBf2V\nRW5ef55sbwJWxXf83Z9gdOMuxSRB1BBPR3SXNtCV4ODOTdbPPkKru0CZpYzu3mb9zIXGwZnXVh7S\nnx8kutK4UZcsnhHYLiESVczxQ4/R/i7z2Zh4dIQqa1zPx3LcV1VYh1s3ydOsiYmpCyylKOsSN2qR\npjOU5eN6bebjIcqxaC0vMTh9jDieoKRHNp1TzWdQlHi2TeiHBJ5PMZ4gJRRFjBYlRtYsHjvN7q2X\nSWZjLMtGWVZTPBQFAkGVppRVgQbqssB1FZhmFG3qkiKbE/T76HsbveuHAK/yPeqqahSDplEs/VEZ\n+esBQgjyJG5+d46HlE2EiJCNIhJTE7baWI5Nkcf4QYuqLCmyhGQ6oioybNfFaIGQLgbIqxhjG5Js\nytLqaepxzNbnPo0ZzpjeuEUxnPDI296BLqdc+exv0V/s4/qgxZDj5x9n7s/5Hz/8Yd76jm/hX/7M\nP2fh4Tcy2nkO6fsc7m3ywif/NQBhp0+rv0iSxbRXT3Cwt8Pa+TfTXT9NVQsoMnQ6R5ma6e4tsvE+\n8eiAqtA4UYfbl75Me3GF6eiQsipYPXaB8dEeNZrZ+AgpBfJ1VCT/x6IsMkBw7ol38tv/5MdZPHaG\n80+9h3d873/FsYfewHj/Dp2lFdJsxs1LX+T2y8+weelLxNMjov6Ac295O+vnHuZf/fRP8Ks//t/y\nzL/+db78bz7A27/vr/PYN34P3/M3f5JzF76Oj/zcP+IX/vaP8MKHf4tqkmMbF2nZuH6A5bgcP/84\nUW9ANp8CgjyZgzG4no/nh1DXtHqLBK1OMxEoC6RlvRoa/rUMrWuEVGhdk8QTXNvDUQ4PP/VeJoc7\nVNTs3ryMzgs2Tp5n59ZlVs89AraF7flMj3Ybrz7VxInt3brMdHxAnsYIA7po4rvmyZhKAraF5XlU\nuiHD79y+wjweNxmPlo1juxTxHGU5VMIwHe6w0F9lEHTpdLok6YSDw01WTp7AsqEuY8a7m1z54qfw\n/PYfvqevsH/cfV1YYUyjPtE1pi4wpm7GCBgcP6SzuMZ8OkRZNkWREY+H2J5PtLSKUZKiyMniGX4Q\nUczn7Fy5yNHdRvFT5Bm3L3+Z9Yce5ejgLtJxcIIQr9V+NZbEb3dZPnmWqxd/n6XzD5PsHNJZXeL0\nu57md//x32d1/QxC53QWFqjrgrLM6C2vMzy4S29pnXZngYXVExzevo7jNRyB+1JVpAv80EeLxhtq\nf3sT2+2w2l/F1TWDtWPUBhZOnKPV7ZHXGhyPsqqoyoIwCHCVoNfpYqNxlcAWBqkLPN+h211BZ5q7\nF59j/fRDxKO72C5UaGazCZbtYAuLqNUmy2YoW1KXGZ2FRSQGeU/Z4noubmCxcuosxmgO72yB8chn\nORgwdYXf6VKVJWWZYYfNaDJPplhCNuaWjo8om+JY3wuLxTTkd2U7eEGE5bgIqSjSGNv1X1eq0Lqq\nKIuMPJmTTMfUZdl05ooUoSuUkpRpDFWJ4zgYU6NsGykl3cXVZgMtChw7gtLgeB6u56BNyfq5N+CG\nLqtnz/Ho098I84zZrbsstE+SjeYU04S2v4yJNeUk5aG3vJvdbE5V5ezvfYGN73o3w60dhpc2idZP\nUesM5QhOP/4EQtTUZYwX+DhhhHIcUBDHI9J8RlEllKZGuD5WGJIVBWme4LW61BU4bod2f7U5xKXB\nDyMufeZ3WD19ttlvwogyL6nz+/Fi1CDs9Dn96JM88PjTtAfLLG6cpswztm68QGtljVIXXH32U1iW\nzbFzj7B26kGOX3gcZSmyeMruzUsIAe/50b/F49/83aT5hPf9tf+BskwZ7d5idLiFkYa3fdsP8D3/\nzd/nHd/+l3nxw7/B53/t/+bjv/hTfPJXf5Hf+xc/zWDjQbauXqYwgixLmkBsy2I+Onz1l1RkCUWW\nNIpNp4kRM5ivOSPXfxevKCOVsljcOE1VZNx+/gtkkxHKdoizGbblsHzsAW69+CWWT57jyx/7IF6r\nDWlBP+zjSofJwQ5e1KZWkvbCMkIqqjShu7hCUeaoKKLWFY4XMp+OmBzt0VlZZ2HjFLbtItIJy50e\n9nzKSq+Pnh/RtiRrUZtQGSxLgnIQIkAYmzQ+RJuCWufMx4ccP/84tuc3+6rlUJVf2W7kfXhK/yGM\nMUhlYdk2ZZbgOC5KWtiux3w6YT4ZYt9LO1eOg9dqURUF46Md5rMRs8kB3aV1dFHgOB4PvfU9LB07\nh9ZwtLPFfDLmYHuT0eEOlutRFhnxZHivG1FRVzXbN16mu7rG3u4tLjz1PpzQ49bNZ+kOVljcOIHj\nOViOYj7ZJ+oPcPyAO5eepSpylGXjB22UVCir4QO8XmX4/yHYSiFERaVzhO/QWTnBcP8AnSX4tkMe\nz+mtneJwb6tR4DkecZxSlDVSClzXphUGhEFAGEVIUeN5Dl4YYCuBznKqeUyr3SOdjprDPZ9RmRor\niijyAsvzqE2NNhVFUeC1WqRJE+rsuj6tVhdLCJRTURZzlJIYLdnf3EQpD8+PcBz/VZ+sV4K+taiw\ngwAn8BvvJgSmbHzJhBBNIKwQGF1Tl8Wrqiqj66Zt/hpLbf/jIJVCCEnQajrEdV3h+tGrHk6O66Fs\nC9t1sSyLqmzsTCzbpa5KHN+nKnOkUVjK4u6lZwjCRm7veAEZGYXKUW2f3VvXsb2QT33gl7j74lX2\nrt6hmllsPnOZ5z/yCeJhSuS5KNvi2qUXkZbmh3/yH/DlD/wG1bagtbAAluCx9/7nDDaOUeRz0niI\n5VjUZUZ/eZUinRO2OlRlRq3BjdqMR0d0Vo7hhF2yvCAZj5kfHRJ1FjjYvEF/aZ18PkGbnPHBXYoi\nQSqLqLXAV9MN+i8ao707eGGLn/+f/gprDzxMlsyYHO7SXz/B6GiXWtdNVl9dIZEE7S5Ga9wg4uYL\nn8d2fWajQ+7efIlwoU9ndZ3h4RZ+K2rUtTZUNgxne8zzCWWd8/R3/GWkVLRay3zbj/093vwN38en\nfvOX2Hj0KYaHe9i+j7zXLVWWjS5LFPe+i/eyKZv/fZRlv1pg6fsgq/PPAtttmgNSWeTzKclkDIDr\nNxe/PIlxvZDZ8IDH3/Od3L1xCaWh212Cqqa/epxnP/HbLJ46iwbKKqezuMrWy8+iLBs7jNCmxqAp\n84y8yEimIzQGz/PoBS2K/R16QYtQWrQcj5bjYCcTFvp9ppN94ukEiU0ULFCkM3RR4HnNqFEYGg+8\ne3hlWvWVwn1WWElANtlP0qClRjmKos6RjoXl2UhVM9q5jhQFtqWQaJaOnUY5PpU2jEb71FlJu7/C\njRf/gBtf/CyDwXHavQ3Gu1uk80OgxI1C8iImzWM6vSalHUoMGdtbLxKtrjCZH9E6cYrpPGdx/SyF\nU+JbC8xevAlWwi/9L/89+zuHVE6J3wtJDg8QSrD6wENMDu/Q6vTYvfYCjiXwPQ0mRqgaLRtpt7lP\nlq+W0N84AckUN8vIZ2PGjubO/pCD4YjClJTllGxyxGR7h7SYo8sJLdsmcF1ay4uMpnsUcUaVJtiW\nBJkQdDsEC8voWrD15S+xtnqaeXqIXGxj2Q6r/XVUYRCOTe0oirrAVhKpoDJQ1ZJ4lpInFfPxmNHe\nHZLhGKSgkprW8jKthT7ChvHogLws0HmFZWwsGZDNc6SyKKsMLSq0VVPbmtFkm6quEJaFG7bJ0wQj\nJEZCUc7RVBgUZVYQtiKkfG1v8lJZ6Hvmma2oQ+h3GKyexQm6KNsGUeL7PkJZzKYz8ryiLGsQgsAP\nSI72UUIhhGI+nYHlMx7ucbS/Q//YQ6RJhaU8qjglHSW4Xp/rL36RB97zLh77S9/PEz/6w0QnjtM9\ncQa1EnDmPU/z0DvfzTMf/CDTazvUhcXqqUcYXX+Jqy/9PksPP86v//jfYbo5JU3nXPrdX2bzUx9m\n7cQbWVy5gN8e0B4ssrxxkv7CCtPDXWzPR6oaaSncVhdju6RFRqvfxxYZ0iTUyYzI6+CVDtM726w/\n9ib8XhdqsKTD7HCEEB66aj4rqaxXEyFe7xmCxmgeevq9fOq3/h9OPv4UfrfHZHTA0ukH2b56kfHO\nHforxxmsn+Lozm0Uimw2JY3nZEnMA296B9s3LhF2B/SWNphPRmTxjH57iXzvgGoyIx2NmOxsN67u\nC4sMJ9uMkh3Ov/s9vOk934SROdFilye/9Xu5/MnfYtAb0Al7eK6P7TnYfkBJhQhtijxBAEGnTe1I\nhGNRp00Xq65KLOv+FQv9uzCixogaUAgDVTbBtkrGO7dxwxYPvvN9eL0+naVVyDLWTl7g6hc/ycLa\nCUI/4OjqCyx1uzhew51L8gkHO1t0l48zvLOJnRd4GrJijj3oNtmC8RxXSfLxEZ12C19JTBxzcPlZ\nOmGIoyukKZkNdzg6uIvX7TOdjEjzku1bm7hhG+VJRoc3mRzt0ep18SMbk885vPUyfsunvbiC6weY\ne0kZX0ncHyfzvwda1yghqcocL4yoq5IsnlFmTTvS8Vz6/UVUranmE9KDHShLBoM1BsvrlOmc3vIK\n62cfYXqwy+Gda7hRQNgdgFI4XsDpNzyJ4/pIx0I5iqjVIT7cpRf1YDxDzVKYJ/i46KMJR1vX+eQv\n/RPW1x7i+//Xn+Fg9y6rGw/iuT5lOifqLmIHIY4fsrBxhsM7N/CCFpbt4agAS/n8W0smXtsH7p8U\npjYIoXAcD0uApRROu0VrY426Lokcn3I8YmnjFG5/DQsbWzpUuqZQglmWEI/HlGmCH0U0IYOGqtSk\n85zLX/oYy8eO44celakRrovj9aCoCByPeDJkeLhLVlbEs4yizDG6RioHqTyk61EZg98a4NoB1j3B\ng65ylATLtogWV6mKgtH+NnWpsS2fuqgoawm4VBXYTgu0otPfwHH95mYlJbbbdE2REuk6WK6HvucY\nLYWE1/jtWQjRjBCEQN7rvmldU1f1q0kGxmjSeIa0XZCKLInRdUWezHH9kHQ2Zv/2DXSlUZZLf+M4\nlus0Lvd+iOf41EnMfP8AXdQsrJwgj8dc/sJHqIs5Jx95jO7aInbHplQJVsfl5JveSnwp5sWf/xD5\npRFdax1LCU5+/Ql+7Bf/T577qZ+m+ugLbL98g87SSe4+8xnSbMT0aI/ZcJ+zj389B3dv0O4vsfHA\nI3iOR5HELB47g9/to6mZjvfx2xFRt4ffahH1F7j0+d+jt3KS2WxCls5xfI/5aI+o36LVayNl09V7\nZexkeP2GVr3ism67PrOjfRzXo91bYHKwQ9juks0nJOMxrd4iWTInnU8BOLx7kzSNifoLBN0BZZ4S\ndQbMhwckw0Mcx8WPukyPdhFSIj0XUxZ0uz2kLkmnIzy/hRQWRZqxv3sNO/CQriSej3nkHd/CJ37t\nZ9m6/BxSNOMgN2pRVTV5lmPZbmMiWtcY4Gh3i6OtmwzWTuBH7Xt8sa896LrGshTdxRVOPPgYL33m\nw5RFju37jHbvMDvcZ/vWS5x98p24UQvLsTCiJi8z0mxKHE+oMfSPn6a1tILrthgsbbC4crzJW7Vs\n5gd79LpLhJZP5Pl4yiEK2oTtNr3BKtl8irRcdBBRRhHOwjJlpgnbKwzOvgWnf5rxNMb2HFZPnqW7\nsEIWz0jGQ+LhPk9+6w8QDw8ZHdyis7BKXZYN4f0riPuysBIGJI1juVKKKslBC2w3wPEjdFGjhEU2\nH6OrgunBNkopjjavIoQhTyZIC0694S0EnTZVGYMw2Pe4U9Q1re4Ch5vXyecz5ntb2BioahaXj7N3\n5SK3vvgpTp6+wHTzNnuXXmD/2kVspTh27g10149RORI7CAm9NsV4hOdGHG7fxvFDxgc7pPGUZD6h\nvbCM67cZ74+Yjyb39uJXAppf2wfunxRKCubjQzQSLa3G90QbyjLGtiU+gq7XpdVZ5e7eLWzpYFkB\nWV0T6wpsH8sK6K+sYUyJsiW6MoTtFXRl4UcBSyePkcYjWoMl6lKTzRJm+7tk8xFeGKLcAGH5lMYG\nZaEcC2krhKVAWmhlkd8jqOs0x6QZpqixlA1aoqsSoSSL66cosoTx4S61McRxTC0U0vIp5ykmq5sY\no3t4ZbQrpaRMEzwvIJs1h499j6CvX+PHbl2VcG+cZ4xg5/aVxpwxnWE7LkWWI6RF1OkjZTMKk0rh\n+o2T/Wzc8BwXj51EWrLxmJuNqYocy3JQQnJ48wqTu7fpr25gjKazsAxlRSvoMr55iytf+Cj5fITn\n+7hhRJpOyMhYeeMZnvr+/4LZ6Igrn/o0cqzJb+3w4gffz4kHLnD40g16zjEmccKN5z9J13EYLB0j\nm0852r79avzQweY1qiTD9QJuPvtZ8mSGdCxQMDs6YHiwTWf1GMpxOf+ObyEvUxw7oNVbpshjWoMO\ndTnBtutmXHqfcCXrqkRI2dgbvPRlHnrzuxBG0Ouv4LohxTym1V/Csmxs12v+XuGgdrrkeUqWTNFV\nSaszIGr1KJO4UVXXFUVVUpkabBvLVqTToybmq8hAG3ynzd71yxhpMRnusXvrKkWeMBkd8l1/+39j\n6/ILfPJf/jOEVkhh43oRtu3jhV0WNh5gsr/Py5/7OMl0xpk3fh11VVIVxf0dPfQfgFQKhGiaEEWG\nF7apq7JR6uqaxY3TXH3h0xgLbrz8B1hRRBzPiWcj8qwgzxLKumQ2OmiKLCXZ37zGwZ0bOJaDSTOi\noE06GiJrQ8vvoJDk8xnDnU2E55BnGcpuk0vJtMhJqpI8TpkdHDIvNNoKsLx2EwOmNSYvMLpGSBvL\n8wjaPcYHd/DDFmWRYTnOq/vOV+xz/Io+218wXrn5CZoYG0taWEJRxjG27ZPHCbUxeFEXJWy0Y5HJ\nmsUHL2Aci96J01SmJuz1MboCYZgebRP1BrQXFqmrkqOd241T+uEeAkHgR3jzlIEM2L/4AqZSbDz6\nNNHJB9jcuozTili98AirDz/K4NyD7Bzd5s72FWqlSOdzBisn8bwOdVWDZXO0s8XwYId4OmGwcYbW\nwirXn/8829cuc+6JdyOwwdxXy4asCzw/RLgeeaEpygxLVwjb0Bss4hQ1HSdg/+bLqCCimiXovKQG\nKtumqis8J2R6cIe6Soknh9hem2Qa84Xf+QCnHn0ztgdlOSHqdPGCNpYTYqoC13NxXBdleSAVdhDR\n7vYxVYkfRpR5TJ5lFFlOnmXkaYbvdxCVxJYOdW2o64qyKhCWxWx6hBtG+O0ONYYSSTyLicdzsnlG\nndVUcUFdG4o0p640ugYpXWw3IBkP8YNWI1YQgsporFcK+tcoXuGAKcui1oYTD7+FqkixXIsyT3Gd\nEFPrJgYI7kW7KOLpGOUEBN0+wrKQlgKTI0VFNh3RX1qj211gvL3J6QtvZvX4Weo8piwS6jxFaign\nU6xa4nkuRTpjeOc2XtBhcOwsKnJJ18d84e6vEftz3vrdP8LoxhFXf+ez7D9zhXjVYeW//EG2bt1A\nj3POP/Vufu/nf5JOZwHX8RnubRH2FiiLDGW5tAYrJMMh3d4SjmWRzia4QUgQDZDS5u6NS1Si4uBg\nE+HaKG2Tzue0FxZwowjbVZgyb7hi9xTJr3cYrfHCFkvHH2D76kWqLKVKEw42r7N77UWUEDiuRzIb\no5RFkaWNMGewzOUvfAyMpq4rxgc7zQMK6CysMNy+zZu+6XvJ0znLJx9k+ewFjNDYrkVVZmA0xSyh\nSgqy6Zx4PgMtONi8iTHgRh1GR/u8+4f+Fm/7zh/l9qVneebf/L9MD/Yp05znPv7bfPF338/CxgOc\nf/qbWTvzMOl8zu6tK41K8Ct8EL9WIKTEC1okszFSWYTtHq3+EkWe4noh48Md3vy+72Hz+pfpbaxx\n5dnP4rX7WH4HZTW2CkII4nhCZWrSfI4MfGzXZ3j3NnWSMt25S51n1FmGRDI/OqC3vE6rv0Rnab0R\nFWUp49kMt7+I64fU6Yze4hqTeMZoNKSzfBy0oc5yHNvG83vUtaEE5pMdOosLDLeuYjseXtT5iudA\n3l8n9B9BXRb4XojUYEsHP+wwPtjl6O4W4/1d9q5fIZtnOH4HKR2C1hKD5VM4dkg8mWK0pEhTpodH\nYBTZfE5VlbR6iySzMd2FVWxlk45GuGGLZDph8MAjlJZP6bbQXoCzuIK70MNqtRjPDqhtxfLZ8+ze\nusLhpRd55/f8GHt7dygdid3pMTh2ht7KcZTtkMyn2G7AdHhIVRU88KYnmBzeRkj9R0aA98ny6Rrq\nktIYkipvgomVIDk4IIvnWJFPkoxptSI6jkU+PWRxZY2W7yPzFIkhGvRxgjbGKJQV4AYRk4NdLMdj\n8dhp6qICo9Gmwglc4sk+bhg0nYO6JPBsXGUIXQgsh8gPycYjfCfAlBXt/grCcnCiFvF0gu14aG2o\njaaWDUcmzxIcPwQlEZaFQOB5PkG7jev7OL6LtCR+u9uMDffuvupjBU3oqeN46LKgSGKU49wbqb22\nLReUZd/zb2tRFjXxfEJVJkgFpiyRRiCkQFl2cyOm6cZJqajKEmnZaKMbqwXbxg98/DAgGx9x9+pz\nuK2IyewQ40icMMB1XeLJCJDYfkB1r7CNegOqtOLo5iauFbB64iHUsMOjZ7+XjeMXGI6v0H1yhcF3\nP8zxv/pe2qeWOfWNX8c7/sH/zI3PfZQzDz3N0z/2d7n4kd/k7GNP0109RpbF+GEzvsuqitqAqEHW\nFcurG9hKsrZ+jsNbN/HcoFGZ+Ta27zXxSsohmcfE4zGh38O2fLpLawStHsl09NVduD8P3OtufPRX\n/i+e+ObvI+ovIC2L/sox6rqkvbiKEIKw3WM+PsILW+RpjFSKMw8/iS1tfC+it7zB+GiXaGmV2XRI\nXdd89Jf+MZ4X8YXf/mUufuSD3Ln2MmUtWDz9MEe7dwj7XbJ8jLIlQatHMY955C3vxcKmKnMmB3sc\n7d7CiyIeeNNTPPHe76S3uEy73+eN7/xWLjz9HuoyocoTijxBWoLTjz6JF7WoqvvUxfWPQVU0AepN\n5E/BnasvUGQJftRm7/YV0tkEITR+FN3L9vOYHR1RpDnKCByh8N2IMOwgaTzr3F4P7Vr0N04yPdoj\naHUQlkVaZ0zzKQUVw+FeU4jNJpS6IhUVljF4WYZbV9iBxXR6B0fVhC2Ho82XCaIQ27JxnTZVVmI7\nzV5Q6QrHsWn1+o0Fj66R6ivrhX6fnMz/f1i22yizDOTxlDye0Vs5RndlA0tZXHjyG9i/conJrduM\nbt5m98WLyLTGrhQWTsMPqWC0t43RILDJ5jPagyW4p+YyWuMFIWqxz8wRuCvHmGYZZW4wmSH0OpRZ\nShqPkUJy8sFHCfwIu4b55l12Lr/ElYufo7YtSqkpqwLXD1GWg1QKy3G4+Kl/xca5Rwi6PlUVo3VO\nMwKU903nKoq6TYJ9UZJmKUaAEoowWiRPcgpT4/d7lNmMftTl2LkLJEe7eELQ9UKkqdCiaFQiwsGP\nekihuPIHHyfoRKTzOVk8p7u0gbIsdF1gaDpMAI7nocsYUSdYdUY2PcAS4CjF/HCPVtTH5BVB2KWW\njRt+lmdoXZPmGTUGpMSyHcoyx0DDE5ASqevGZLTMcAIfYQmMNFi2x8rJ89SlpkhSDrZuks9noDVS\nWThBSF0WGKOx/Nd2Z0PXzXhrNjxo7EscBzcMSGZD3DCkygqMbiTuVZEjpXp1hBT2B1RVhRCCskhA\naATNKDgZD+ksrBL1Fih1SVwk1HVOFk/B1Nieh+0GWG6AUDZlVWNJD1Eq7l58jtHWbZRrMZvcpdZH\nCDXDcgWYEF17OLZg//lP4Iicv/RP38/7f+YnGF6/zc7Vi+xce4mNc4/SW1rH8QLS+ZSjw7u4YZu6\nrHAtl2IywXdDFJJWa0B3aY1kMsQYjRAwPdhFSIXrtQEL1x8wOzzis7/5L6iqAjeIvtpL92eG4wWN\ns74XNAkEWUoaz7n0pY8T9hY53L6NF3XIkhg3jFCW3WSg1jWdzgK+G3Jw8wpuEDWhy2VO0BuwcupB\nhrtbSAOd7iKh36azuMYsnjWih+6A2pRE/R627yK0odUakI6HxEf7VGlOqz9AOYqtK8+wv3mloQjU\nOZP9O2hTIIRhPjkEqTGmBKM52LpOkaZfA6bM/37Ie/uYZdlYjkN/9Th1VaEsm/bCMg889jSmrvD8\nkDwe4QctqGuqNKPOShzpQFUhhaLTX8LzIuJ4SpzOKMqc7vI6QllUpqayBRkV7sIA6Tk4fojWNX67\nQ1YmRJ5Dy3ZhPsFyBeGggyxnVPER+ewQZTnouuZo+w5h1CNP5yAFxtQE7R69hVXi6ZjJ4d5XnKd6\nf317RNPNMWhqNPP5EVqB318ENKKuCMMWq2fPc/3SFzj95DvoHj/F4tnzLJw+RVHEOJYknhxx88Vn\neemzH6PbW8NSNmGnjeUFbL18ESUsdJkhdI0pUgoZUFgRszTD7wwwxtBd3EAJG9dzkFVBMt7n8ouf\nY5bFtE8/yO3da7z17d/KoLPMsCjZOthBRj5bWy8ReB697iI3Lv8BdrtDd7BKkZUoYSEqkFr+4ft9\nnUII8SqhMFzYQBKhhEcVKCLXoVXOaHs2ju+ANqTDEaJU5JZNVqTcvX0N7YSUeYUrQZqUyOkjfZtY\nzKA1IJ7nnH/71+NGNns7t5jphFJVZMkIdAYVyEwhU7BLG09F1FJh+W2O9vbIq4rW6gpaJgjmSJVS\niIraNlQ6RwuDtzCvwwAAIABJREFUFJoqS5DaUFUFMvApTY10XfLZGFGVVFmM5dpUpiQzGYXOMJTo\n/4+9947VLTvP+35rrd3310+959w6d+70GU4RydGQoig2SZYs21KkRJSMIHaMxDYQI3aCBAmMBA4S\nGEECpyBxigMntiM5jiPCVmUVJZIatuHMcHq5/Z57+td3XyV/7DM3UhDDUmwOZ0i/wMH58+Ds/e1v\nv+t9n+f36ILeoEeSxqxsnqKzsorWlvl4zOzwENs4JAGRn55ctXemzPkttETc6dEZ9bn16jMsDo/w\nvbSNK+rGVFWB9EKUH9DUJeKED8bJ0+rFMc4pPBFRLXLm0zGjM+eRnqJpGqwUaNdCcpXno62kbCxW\n+TirCYTAaxpiTxEYg6wajq68znK5T7zZozI1dalZ3pzBnoHbFV0vYS1doblxk/HBbWQ0ZHP9fv74\nX/5reFIgB31s3KXIM8J+zOr2JeL+kNpVlEYznR6jvA7GxaSrI6aza6gElPCZ7R9iwoaqmOGJACV7\nfOnXPsV4lvP+H/sprC5bECwSgUW+ww0Kv78cEitBK4uWDacuXSTtRNy6/Cqz6ZjOyil6a9tEgxWG\nZy8wW4wJOx2aPEcXOcYZyqYkmxySHe6xcuosg1PnGJw6hwwjbCeikJq7f+BD3PPBj3H+iQ/QWd8k\nHA1ZOXsXl5/+IvZ4Rn00pa41ldU4V2NVQzTsE/ZShhubLI73yBdz1rYuYPICGoNEUSxn4ATZYkJ/\ndROlHUnUwY8T4rSHF4QtkPl7xLH5TyuLwqKAGmtzOt0OylqsUGxcfIT++jneeOHriCigVAVFXtDx\nBohJhW0M8do6RD45Gjnoo6Rg2Bu2yJE0JbeO0jhUnGKNQtcW5yS9rYuozoDGOPA8snoKXR/tNSRN\nw9raacoyI8MyyedMJ3uoxRFBMUc6n6wUTMZzts5fwPM9grQLYUJWlBzv7TI/PCQMFfc8+l6EcIB5\n2+7n91Zj9fvKWU3TVCDBT0KcAD+MyKbHZLMxYdolm4+ZHe+hTc1ieYyVFqMsQTfl3vf/MD/woz/L\nfHzA7HAXU+eEaRcv7eL3elglCYdD0vUN+sM1pAVb5MRBwHTnTULfo6lbJlHgx/TWtqi1ZjY9pqgK\nnvzpX+SFp3+Vc+fuhazA1jUNhuHmWaKow/bFhzi6fY27H3uKgxtv4gwnIaPFuwgX+U8u59wdCJ85\nEaoGwiMIU4rljN7aaZT0kLLN35MqwItSZos5TkI0XOG157+Ko41OiMIQnecUiyleEmCdIAhjVCAp\nljO0tYTdFfLpgiqrCYMRMuigrQN8ivmMyf4u08MD9m9ebiGP/T4IKPMZfuhTlwssBqE8grCNO1JS\n4SkfZ9roozxfYHFY0+IElOfjB2EbmeFMOwLPl0gpwBmEAC8IUP7J9CzukfZW8f2Y5XSKqQ1SvrOF\ntG/lAQIkacp97/0QUZwggLrITqZ44k68zVuMHN/37pDnbaPx/RAVRCgvYvXcJWZHBxR5wWJyhJIB\ny+n4DhOrsZq4N6IuCjw/aKdcQpF0ekjpwGmiOMYTHrKRBGGP/tpp+iubeIFHmCTUi4yrz36N5XTC\n7Ze/xZN//Of52qd/ieO9azzzm79Mf/0UN175NuVizurWOXRRUJUZxlmCJCXpr1DkOS9+9XNsP/Ae\nVOijm4K6KlnZukA6WGG0cYb+cJ1vf/5XCTtdehuncLZurTUnn3/hQLyLjCiSlhMkT348FXLqzCVG\nm6dRfsDu5RdOgukls8ku1jU4Y0i6fZRonxdOYp+8KGIxOeTqC18njBIWR4d0kiFnzj9AdnSAsYZk\nfYON+x/h3MPvY7B+ivt/8GOgFE1TI5XH9GAXodotQl0t8cKI+XgfLwwJ4wRT16xt34UUit0rrzBY\nP4NSXstXqiqsNpTLRSu8rquWGXiisXpnHmW+AyUAYYnihKoqCOKEbDEhXyyYHh+QdEcYY9EGHIrF\n4SGDtTPUZdG6rJMO0vPIFjOEEEz3b7QH4sUU5ftEYYLTEKRdVNi62+MgRpYVqRcSGDDLnDhMSYMu\nnaTL/HgPgyMZjlB+TNJfI+506PR6hHGHxWJJ3FvhaOcqs6M9jDEEUYxpDGHcQ6kIKaCpMqx5e1e7\n37MhzLOjPbYu3IO1hrLI6XQHFPMZnudTLKYEYYTnhRCmJHGXo/0rbSaSkmgLMozZu/Um6xfvY7R1\nFl86jqf7BJ0utakxaIIkxUUR49uvkaR9hBRQT9g6s8XBjefojtbIFhmmsgzWz1EsJzTzI66//gp7\nHjzw3g/y2f/uP+Dcez9INEy4+sYzBMuYi5ce5TO/9N9z3wd/hMAPWeiqdWZYTbGcE6b97/bl/Wcu\naw3eifNmno8ZDjYJRcT88Db4XV67fYv1tW2aekIpG+IkxhiHlTWLIqOzcYrlYopAEQQpnu9TZjcZ\nhZvYuqGxBdJBLH0mx3tsX3yQejIjlClHO9eYVwbr3cJai+/g9F33UeZLEl8ShRG6zinyJRZHnK6Q\nHR/jhMD3PJxrsMYhpEQ0po1wCIJ29tBUOOHwVYTvh20zdZIJ+BYkU4Uxpm4ZV7WukboGIaGqsQ6E\nkgw2NllOjmjqjGx+COKd+6hKKWnqVpRtdMn04DZ7115j49w9d7K/BOIEgdI2E3Gnh2naZAMl2/+t\nLJbosiDu9InTHkW2QDiHH7TRNr4fYR1ILyBSIb5SHE+P6A0GOOch8BEyQAUNvi9QiaKoC+pshud5\nSOswuqS/tkGU9MnnAdX+FcYH1zj38FNk2YJoFNENhzz6kT9BiOL6t57m8R/6CMujI7Rp8FSIlJLl\n5AhrNALFyvkLTA92WOztE8Qd4s6QKs+JwxhnDJ/75f+CH/q5fwMtBUJaiuMdvCDEWAvvQpG0srb1\nJUuJNY43n3mGB578OHZ+yGznMpcefT9NmbGYHWOrnDBKWd+6gKsblJAc7F6j2x0yObiFqWvO3v84\n/ZVtbr/yPOMbV2Ayo/R9Tp+5xPO//Y+596mPkZc5x1deZ+P03UzzGemF83R7I2Y3rrKxeR5nQHo+\num6oywVK+Qhj0FWFbRrwA+bTQ1bOXjzJNGxTARwOY9tDkHMWKRV+GJPPp8i3GSr5Xa2T7rGqKnRT\nkeVzgt422nckvSHd3uO88HufZ9QERKFH0utC3dDpjyjLgjTtQWMxRYbne6T9FbIsI5IBoZ+iAsXe\nlVfx/QDlhxhh8W9fhbqim46gLtnonGJ+7YDj3ets33s/IvCI0h5No7G65WwZAXmxpDMaIMMYtAah\nCZIO5XKCtLaFNScdlnu38dK0lZnky7f1vfk9NbGSJxZm5yy9lXXkSV6RFY5KV+0awfe59foLNHXN\ncn+XJOm2qdpxh3i4QlHn+JFPU+d01zform7yylc/z3hvB1E0hFpQ7O4R4iHyisiPEL5EixLnGSwV\nlVngjzpo36FkQJgM+NZnf5Wd116il3Z58D3v58FH34dKJPX8kEurZ3jqBz/B9vm76XbW+Opn/yEP\n/+hPo4SiGB8xPbjJ4c3LJN0h25cewuFaEfDb7HT451lKeW2qvBA0tqLOxwS6pB93SfqrFCpmf+c2\nWV4ynY/RUrLIpzS6gtCnMZbe2ml2r75KrTXzxZi17XuIeytUukZGIU1dEwYpupoivYogMOxffoaV\nzRGnLp6mt32KdGOV9XvuJcuWLKZjorBLXVUUWUbd1AjlIZzA9yJCP6FZZAihcMqjqSpQPs6YdmLi\n+SfNuYfneQRhhLEG62yrmfMDbFURJ118L0F4IdpBY8FagZQeRldIJdB1gfQUcbfLqbvuJ4yT7/Yt\n+yeWkAo/CLFG01QlRjeMNs9SLuc0dYW1pp0mFBlGNxzceIPp4S5CQDGb4vsBYRQzO9pFBSFIyTKb\nttOKwRBon20pBAiPIs/xJJTzQ4arq1gLZV1iPcX+zhVUEFKXBVGnw8bpC3TTIb5SNNWSweoWuqpw\nztJoTRh3WTl1hv0bL9PfXOWHfu7P8uLnf4P+xhkOX38RPZ2j6wZbW7JsjnWWYjknCCLStN9OLmOH\ntQ2deMDW9n2kvSFHu9d5+Uuf49Wnv8CTP/lJymzC0c7rFPmEt95i79bQXy2h8cBIifE8JssZS6OZ\nTPeJkgRdlxxcew2na3qDEcpBtVzwxrNf5vnf/Q0Egjpbkq6sEfYHHN2+xitf+TRJ0uOexz5IcuoU\n3Qt3sXPzDZRQvPbF3+LgG1+nqxJuvvBNinJJsDJikc/or24RhQlxZ0i5zAjClKQ7AqFQXkjgBURp\nBwIfEQagBGWREXf67UHHb7lxQRQTxAl+GFHlCzz/+wcQ+lYT4ICw36Ooc3obW6gw5a5H3s8rX/0M\nk73rdIcbXH3+OfLZnGIxvcP78sII5fkMVzaxTYMfRkipCLwAkVdkezuU80NWT28TD7t014YMNjeo\ndYaMfOhEhBtraKUQYcy5R36QweZZjm/vIKVPU9aEQUKVZQS9IVGng6c0rpiSzXdJBgOKfNHy1KSH\n05rF5BC/20V5PmW2YLh5GiFVywZ8Gw4z31ONlbuT8yTx/JDl5Ij58QHLxYSyyBCeIl/OWTt9F0m3\nz+xoj6rMyGYTkm7/jhvL5DnF9AhdVySrq2w/8ARVVZGkfVxVcfvl51C1IcQj39ulP9ykN9omm4zJ\np1OCsEdZ5Ejfb/UjYcT9T32c0cY2rikZDoctpiGOKOqM9Qt3k65t0xMee28+z+rFu1kWczCGxfEB\np+97jI3z95LNjtvJB+Jd3VS9VW+tkPwgRCkfnCaOfXxP4AnADyFM2qkDtgVRGo1rLLoqieJWmI7v\nUTlL1BuR5zlOhRg8HD5VXhB0BjR1w+z4kI1zFwk6MZYSXWUEcYw1ps0c7A6Z7O2SLZZ4YY+4M6Cp\nanC2zaurS/wgwUqFjGKc57drLtkSi7EWIRWmrrFNS9aWYdj+9nxMU+P5IcVyjq5PmC/WtM5C3RDF\nHcIkoTkJLcYZpJLMDndOol/k224b/sOUrkuMaaepzlribp8gSpgd71EXGbPD26SDEWHcocjmbF18\nAJxjsr9DkvawWjM/2mfrrvuxrjUC1E2NA5qmFXl7yiOMUvLlHOWH+EFAb7iCNRbpB0g/YLEYs3bh\nHq6+/E2KfIE1Fk96oGuy8SGmLMlnh0SdlKYuWE6PcdqxPDwAbZgfHzM72ueFL/4Wy+WUajYljGI2\nLtxHU1RIz8NxwvqxlipbUNc5KgmYHx8Q+DEHV97gc3/nv2Z6cJuLTzzFwx/+Y0B72PPDkGxy2IqD\n/eBdy0rSCqwAKyxCQVXOuXn5OZqmxotSlosZp+5+iCjpUiyXGK157nd+lf7aFsJT3HjjebqjNebH\nBy1Xrq4Yrm9z6/VvY61l5cIl3nz1m/S3zzKfHnJw9XWqoyOuPfs0xWLKysYZ3vza7xCEMbPxHr21\nTZqqoqlrdF3z6te+iFJtqHJdtJFRy9kxFkvTNIRxwvRgp13BK4Wzts24rKoTI4bXyki+D0t57ZrW\nYBjv7nLjlWcZbpzi9L0P0Bn2OHv/I9RFxnJ6zGTvJmW2oMqXHNx4kyvf/irL2Zgbrz3Hq9/8Irou\nieKUwfoWRjeoIGQ23idMUlxVcfrex4nXTnF4vMvN3cu42EN1Y7TQjA92WDlzgf0bb+J5Pk5bivmC\nyd5t+sN16vkRnoJup3fyHJbEcRdnLDhIekOcECi/Ded+Kx1CCNEe6L/D9T3XWLV6nLYjFUq1JN3+\nEBWGCM8j6Q0AUMrn7EM/gPQD0pXVlhx8uEe3t4JoDD4CY2qcUBD6dNa30NIho4jHfvRn6AzXuPLc\n0xxefpXqYMrtZ57n2u9+hTc/83m2Vu+lq4Ykfp+mzhCe4HDnKsvxLjsvPc1odZUmy3Cl5uf/07/F\nsctojvZJZyV//C/8O4TDiMpmLI732dy+yHI6wZ5Md5TnIZRqNTzv8nrrXnlaIl2D1jMWy1vQzFiJ\nY1QQ4PVHuCimyOZ4KkA5QT2fg7YEXkxnuI71feh12dvfZTZdsihKiPpUteDo9gHHBzX7OyX718YY\nnVAuDcuixmmNbGrKyTEYQ5lllFVJXQh2r93k8MYO7bbc4HmGuBuD71E7S42hkZYahzYaiSDp9An8\n8A5kD8DhKIoluq6I4g62rvFVC0E1FsoixyoPP0rJ59OWfBxGFIs2m6tYzIi7g+/eTfpD1FsNchAn\npIMRxWJGlHbYPH8faX/E2pmLBFHCfLxPELWICy8I6fRX8HyfOE5JugOW0zFSKYzVbUC11WTTMbqq\naOoKpzV+3KPKc5oyZ7J/E5yhyJaoxCMdpezeeIUz9z1Kf/0su5cvc3jtMrODHQYrG+xfeZk47XD9\nha/zzGf+AW9843dY2zhPcTzl2reepZzX3HrtDS499kNcfeM5Hv3wT3LxkSe5/NIzNHXDcGO7Dac9\ndzfS85js3eKuR96H340xumYw2uLFL/4GH/n5f4tT9z+MiwXasxwe3KAsi3ZtHfUp82Wrz3ubLeD/\nvEo6ibKSQFsio4lNSSpqDm9d4dqbL1OUFUVWkWUZ5x/+QcZ7N7n3yY/gAsW5x57k9P2PcfWVb/H+\nn/gkw5VT+NLn5S9/hnMPPI4Rjld+5zcZdFeYH9zm3qc+Rnf7NA//5M+SrK0jhGB5+xbnzz1AvzNi\nfLDDvR/8UY52rqKkTxCnbF64jzJvo2kCP6SpSlQUU+sG6XmoICCIEjy/DecVUtJUBdZodF1jtCaM\n0n/6hfgeKknbLAvlYQVoHLvXLrO6fZF00OeFL/8K/Y0uyIz++jq9lXXWzlxkZes8QRST9kcEnS6n\n7n0YEQVsPfg4hG24dVkukGnIbHHI6tnzNFVON0oYX7/FZGefZGUTKxRZOSNOfPwAtLGUWc5g9QzK\ni1gcH+G0w1MJ2eE+K6NVfN0gjUXnC+LegKpYUi8XSKnIFhOk76HriqrIiTv9Oxust4PC/j3VWEkp\nTxALDiUV050bNFlGNx4gNJjGcHDrKsPtCxhridIUrKZZzsmOjxEamkWG8oOWCDw9ZveN59BVyWR2\nhPE8ZvmY0lW8/u0vkQ4HoDWLw306/RGP/fjPculDH2M+P+LGC9/klc//GmkwoiwlLhOcCU7zyT/z\n17j55c8xVzN6a+usrT7Ab/+9/4G/+a//FP1kwP4bz+Kj0LqB1GdWzykaQ2MbmqbGGI2ztk1if5eX\n5wf0VjZwiYfnPGohCKMhtYZFqChMTuBZ0liSeNAPPcKyZNhJGMYhxf4Vwk7M7q1b5GNN1Q2I+h26\nq+tkBztE4yUqkNQOltMJ4WiDybjE1glJWZNKQSeIiZRHmiQoX5AMUlQ/ZHT+LKPtLeI4oq4aZgtD\noYFqgtJzmtl+K4okwKgO86xkOcvRJ3E2YRy1X+5WosIOXtKhzOZ4CNIgwaEx5ZxuFCDrBUpYpBdQ\nL8q2aQwVUjiUn6C1JVDgnG7dLQJAIr6Lj+/vd9cIB1JIprs3aaqGuNMhnx+1U0g86mIOuqA3HKFU\nS6k3GBoMk/E+RZHTNA1B0gUHSvrQaJQTpN0+xllqUyM7MZ7S9IY98nyJlw7IsgIlA6omx3qKuL9B\nUTjmxxlhb4NmKfHlGpPbR1x69EcosjFb91/k/CMP8qFf+HPcuPYthnedY/s9D7F34wUWxQGTsOLK\n732Jr3/5HxOdHfHq1z7N1oOP40vF/OgQaxQq6mA9ycHNV6kP9lg9dR+f/T/+d37gZ/4Ci2yXqOvI\n51N23nwRPIGKvLbh1hrfT3FYtM5AaJyQ2Hewhu7/Xd7Jmc5IyGfHnN4+y1M//DE++id/Bpo5R/s3\nKbUlUl3e/MJvMThznmQwwuUV1XRM3B8S9wZ8/e//baZXr+FHHdJTp0nWt1iOD5nv73Fw5Qr3Pflx\nosE6qjvkW1//HN27LjK6+17KfMmLX/x19GJBpzeiLjJGp84yO9yhWEwYbW7RX1lFVzl+nICx9NIB\ngZMoC65uTpA5BWG3Der1vZAoiLHOtPly7g8K9L+XqyZHmgLPCfyVU0yDDrdvzVjs3ObZr32aCYb4\n/Pso5x6JFyA9H9XrcXS0g5kd0ccnRhEFAcc33mBl8xxVnpMtpxS2QPgxzoQMBmeR2sMsKqx2GNu+\nk5UWxDIg8gJmx7dBaeJejPUbCA1SNFy85wE2N7ZodE3dGA5v7OJKxeboHKfX7yJRsLl2ilj5yEbj\njKOuapb5gsYaol4fIzSGmrcj9ODd8zT/UUoI/CCgv7pJFHdYzscYa+itrBOEEdligtENi/0d/DAm\n6fYxzqAQVPmSIOmx2huxXM7wog4yCDFGU1cF0gswRrNx1/1Mbl0jGayyfv5evvH5T7H98BPsvPki\nT116iAc/+GN4wP7ta1y9/jKictx45Rm2Rwl3PfIk46HH8Pwj/JdP3MP22Xu49P6P0t/cZmXrDDu7\nt6nqBhTUdUFntI7ROcZqTFGDaPVVrvVafbev9j9bCUHVVDh8bByTz4+Jww7To12GYR+bLQmtwMfg\nSUekYowATEPa7ZMMBuy88AwrIkYOBEFjiKMhvgrZGKziBxHZ0S227nqQ2tTM9mbIyZh+4Eh6PaQA\nUxfgHFJJtAIvUOgqxzmHCkKqokaGCXWt6faHzMaHdDp9jM6pHYDBCklZFIBFCY+mKgn8CN04HBoC\nHy8IUcBscoDwI5K0ix96SOVRzHP8sIPTDulaUB9CYI1DV4Io6lBkGcIPMLyzTkRCSnw/QAqJM7Sr\nVtdQzHOssQw2B0x3dkgGa2hdI2SrJ3NI4t6QpizbSZVu2rWpkvhh3OII7IkeyVkao5FViQwTDILJ\n0QFBmGBNjS8U1WKOR8rezWtU+Zz10+eYzxeUO5fR9Zx40CVOA+xiwT0PvY9bO7c48/ATTA9us3Xp\nIdTuTZz0WLnwADcXv8lo4xyHhzucvfdxdq+/hnSWtTPnGe/sMGtKVrbPtkHDm+fxwyFnHngP3dVV\nmM+Z7t/GNTH9wRpaN3jKpzEV1hmawqCU1zLOvsv37v9XCXuCiYCV1W1Or2+w8/Wv4DoJ997/KJdf\nf53DW1eIPMlwNKB/4RL7r72E7yCMOzRViTUaL0o598iT1GguX32J2zdfZ7h5hnN3P8Qzn/k/WTt9\nF68/9xWkEvQGG0TdPkY3JGmfh374j9EZrHLP4+u88eyXKcslSXdAU1UoMSebTQiTDpPjXXorG+1h\nWymEVK0W9wQRki9mCNr3hTnJ58xmE5QfwPfAVuAPVwJQeMJnMTmkO9hAV8d0gpCzd93N1Z3XCNQa\n6cYl+mvbzJYzDnavs7G+RXk8BqUwUqKtptPv44cRuq4IghhrNH4QEYSKyc4N1jbPstQL6qpEm4Z0\nZcRyetwmNQSKsNNjNh6DEfidhMX0iLQzYnq4y/DUWZqmJhsfEa5uUdU1u7evI31JtLbC4vCQJO1h\n5jMGvRHj8R4yCBG+z/H+rTv/rRUW5b6zOqt30vfzP3NZa++sA8OkS5h00E2NH0aYqmSyexNdVRSz\nKRhLZ2WtXbEBaXeEs5aw26MsFhzt3iKMO9Rlzvxgl6Q/AOsIggRE6yYMRmvMqgUHx7e4/4c+gW5K\n7nrsSQ5uvcHXP/cPeOXZL3K4f4WHPvwROltDbu2/wuixR5jImtlUsyDlr/zWq/zkv/rv86E//+/y\n0re+gGcMgfCoZ3MCv/1g1kWGtYYo6dwRBr7bG6p2JOtQyiNSHUTYYSkEptsBU7EdpoyCPpENCFWE\ns5Avlzjfx0lBrQ2EMXgRa6fOcHj5ee6+cBdD30Pd3KGnaw4vP8NIB7zvBz/C+uaQwSClyI7Z27mK\nTVeoFjmBn6C8gKKusL6kaiqqvKBczNFlQT6fUDcVWjcIP2SxKOmvnSZUAV0/oCMNvm3aQGXRIh7E\nyeRUYIikQDmLzhaUxYKsKhBxhLUad0Irr8v8hLBeE8QRKIkxBnvy8Aur0U2FfIdqcpxzxJ1eq1O0\nhmI5by3rJyvr6f5t0v6IKl+iPL+1aEuJH0TURf7/fBY8v13LlGWbj6gUdZW3MM0wxjYNed4wmy8R\nKiSMU6wu8QSorEZlhs5gRG97k9MPPowuc1QvoHdhm3hlhWyeY+aGyZu3aI5LRut3c3T9JlHUo1hk\nbFx4qI1CWiw4PtxnfOUa9eGUl37v80zyJen5c1x++Wk2t7Ypj3fpj1ZI+uuYpsc//M//Cve/9z2U\n4ysUuzsEOsE3giRMUU7Q5DnWGPwwbOnsfviO1Mv9YcoBCIuycPvFZ6mOjnnxs7+Kv6x57/t/nKCu\nGayu4Y+GzEWDl6YEQUh3tNbe37pk+64HKVzNM9/4DPNizns+8lP017e49sozvPK1z5J0Ojz9qb/F\n8fXXmO1eo5wdUc4m5LMxu2++RDY+4oUv/QaL+XF7CFvfJur06AxWmE0OifsDwk6X7midqsioigxr\nLXV5oq2zlnw+badT1mKNoS4LrDUI2U5Vv38qxlqF1Q4vK+g3BY+/53E++tGf5NzKkEcffoD66HXG\nh1e5fuMGR7dvE6UDyqJAJV0qT2LTFBfFVMpjOhtTG6grjXOC8c51sskh0oNldojfi6lcRW1zxvu3\nEJ5H3OtjrWUxHRMmffygizWSqLdKXlQUSnK8mDObHKLSlGk9p+mGHOgpetjn5o0buCBGW03STTDF\nnJXVVTzpnaAe9J3p49sxgfyeaqyklCcCNUlT5kilKJZzxjvXSXtDRhtn8P2QMEqQQjI+3EFFEXVV\nMNm9gbGGRtdY4WGcAyS6qTFNRbWYtvZ57SgLzXyaUVlJMNqgqJbs3HiZla0zLThUOM48/Bjrl+7n\n7MNPsJzuMdxY5aGP/zjRuW388xfoP/phqtmcf/Sf/GX2ViTBYMS5B9/HZPcaVbbA91MkijTtEcUB\nQRRTlwUAVbE8eXm/e2+fc65lOxmNaxqss0zzJS5KEKZhszOgrnJMICmkIxeWOg2YuprK9yitYZEX\n7O9cxROOj//cnyHauUkhHStNF1Z9/NMJ+TPPopzBizVBYlg/fQY/8BmPj0mGa5iywQmPsq5YzuY4\n44jiFO/jBcF7AAAgAElEQVTkZOucu6PLmE2OKSpNs8wxWUmqAgJnUPpE6CoEXhCcaDUqvMBHYYmV\nj4fAD2OEpyjrAicEXpxQVjnatateJwXKFzR1g5+OAEXoB3Q6vT8gvnynlfL8VgcmBFIqrD5xBzYN\naX/YcqakR5j0sMbhBTFS+DRVdZIbKNu8QeewRp9kD0q0NiAD4nQAVuIaByrECg8nPJazMUGYIoXE\n5g2xl2CqJabJyefHJJ2U1PMIGsOFe56gzEuOZgu8zTPcPtzh6V/537j2/De4+tw32b92jXJZspzN\nUEHAuQ98mNSPuHD2AbYffAy1NgTf564nPsCtV7/FaOM0t15+niRZ5Qt/93/ip/7CX+Xg5ouUiwM8\nfJJoSNztYeoKrMPzfJJODyxIJYG3x530nShlwbOAkDRK8vf/9l/ngV/8c3zzm7/Njasv8chHfoLp\n7mXIl5w9/wDjG1fIp2MmuzeQvk82PcZTHmfvfZT+cI3XvvRp1gabvP7bv4mZzukNVkj6A5JOjyhJ\nOXfvI7imIp9PMVXVss+cY7h5msn+Lc7e/zgvf+nTVIs5tqkZnjpDVZWYkyZJSEkQp2TzCXGnR5h2\nEULgh9EfaOiDKCbtDdtkgLdB4PxOKVcYrAyoypywMVzauICXTZlfu8z26ohTfZ8PfPTDFMsxViV0\nN87ikCAV8XCNUjdkZUaDYj6f47wEK32yssSIkLDfJ+z3sZ6kMhWz2REEiv76FoPNbcpiCcrDOoUf\nDvC8HlVVIQiwpaO7cgrrhWSLOaudNcxkSsdP8FGsbpylLks6ow0cAul55Isj/NhHNzlJp0eZLegN\nV1sXrnM4+52fRL5738z/H9XUFUIqrDU0dUWUdsnnY7YvPUwQJUgpMScZUEII/KQDSpzYp0O8MER6\nfmu77/Q4PtihWM7wo7CNxSky4t6IIB60rrF4hTjtE/cH9AZrZJNjoijF1DW94dpJeGVJnWfosuTW\nK89jhcTr9Jg1M/6Xv/gz2GVGfO/d/Dd/+afxow5hr9+OvBvLwbXLVNmc6e6NFqAZtQ62pDdo1yZv\nx7L4O1lCtO6dQFEVCzzlc3j9MkoGlIspy2zGJJtQS0uNpjoRjVd1iQgSnPJb7kpdEIUehy9+m8Gp\ne7h9/Rr75ph7f/5f4pkv/AraNphAkoyG9Ff7WNOgs5zlZMwym1M7Q9Qb0tQl/f4mkR+3UQ3WEQQx\nQRS3MMLAo27Klr10ArxsBfge9uTUW+bL1r1H2ySEaUicRoRBQBx3W77OiYjyrSapjTFqXZE4gwgC\nqrJlWpm6ol7O8Pw2od3ad97ySEh5Er/jWM7G7YRYN0Rpp53kSUm5zGiqEiEUptHopgEh7wBCnW3D\neN8Cq77FEGsRDhVWa7C2bVSca6deXkDTNJRViUp6oDzq+YL19VP4zmKakvnB7TZWapHTWTlNjcOm\nEXmT8b5P/BT3PPEUZx94nI3z9/HGM9/ANI7J9ZdIOj6Xzt9DffNN1tdX2b38Aq9/9St4YUItHVv3\nP0rcW2V86zrD9VXK5QQ/iPCjFCsUVV1RNRW1aZCB1x7aihwpxZ31pn0bvuC/03X1lef4j37jdS58\n6BM88hM/iw0kFx9/kmo5wReC/Vde4PpzX6OpCrbufrDlkQVR6/IM21XpcLDB1z71d9g8fZH17QvU\nTU2jNQaBQ2Ct4/Q9j7XQUeURJR2UH3Cwc4XFbMzsaI+nfupPE3f66KZuo8a6PcoioywzqqrAOks6\nXEHbtmGqqxJd1zR1hfQUYZy24cGzMU1dMljf+u5e2Lex4jjFCUPQSbj98re48uXPEgUBL/zuFzi8\neQ3hKrbPbFPOj5HSR/kxcXfUviuPj0AbhJEU0ykSD+skGkFtDbWAysFsuSDLMqxVSBHS5BXLySHK\nU6ycOos1hny+IJstKIolQkGTjen2uriqwBOWwFOUk2NCPGStEZVGVg2cSDCk76FNgx91KbOCIsvb\nQ25TUSzn+EF057D8na53+Zv5D5ZUCmcNYZKim9alsrJ1juXkkGIxI5uOAVBKoeuasNMhiDtIKVs3\niDEt1bvTw/Njwjilv7KBcA6rK+aTQ/ZuXMPzUqQLieMhSdih2x3hrEFojStL6vkUISCbjcF5eE4i\nK8185xbldI5wklP+kH/zv/1lblx/Hjmds/3YU8zyOarXYTw5wDoYbmwTBhHd/gghBEc7V3HOYXRr\nCdbN90ZQaND1iDshvoZevIogQIsQEUR0eqtII/BliKclgVUIGVBUFUVVY1CM1jfJj3bxOyt8++/8\nA3jgPZitHuEnHufcJ/9lvCglU5baVZy9dB9p4KGaCicFFQYtHFo3xOmQwEl81dLSf78dXvoecX9A\n1E8IOylhmuKEJNeG0rg7xgnnWsaYlArhKWpbYKUl8D2ksVBrwiDC4phPDxFKYUyDH8bUTYUxJVW+\nxCnVZt+pAClonWS/j1b/TivnHEEUEyXd1oV1svKrivyEth7h+SG6bqNc3jo9WmNai/tJfqDRTavV\nQvLW1Mpqi240utYIV4HOsaZG+RFVYyHoUkUBhWjDkfvCI1U+y9kRKw8/QefiA+xNjrh18xpS+dSz\nCcIW6GYfQYnVBVZblJfwjU/9Mu+5eB/ve98HeOF//Ov0D/b5yCd+htOjdQ5ffY2961e59+N/gkxr\nuv1TXPvGV3j8Ex8GaahLxdHumMqCi30yU6I90M6imxpPelTzOQjRTpvfpavARlm0suAkq8NNPv03\n/mP+5r/yce6663H+r//s3+b680/jKQh7Xfx+j9MXH+bsPY+yc/llmrJAKY/Z/m0uf/urJKM17vvg\nx4mHIw4ObjKrFuAH4IcIP8IgybKMfLEgTnsnW4MGaw0PPPUJvCjCOEMxn5L2RyAFxhom+zttbpx1\ndIarVGWO57estSCMTw4z3smqeucOvTuIE9L+6CTk+/ujpCnxItCuxDYl26fv48WvfYmN93yE/pmH\nKPMMzIK1lTXy4yOqsrmDMwn8iEQleNoSygSFh240y+Uc0g6TasmsqRlnC2yYYl1AtajpJRvtRFpJ\ntNGUZcViOqcsCqqqxOuEpL0u1eQAWWaEzoKpCFdWMYGHwVGWyzajMk3wRInJW+q7lh42TKicQkkP\n3wvJJsc0RYEzFiX+Bcfqj1RCKpQfMD/YxTQ1/fUttHDg+y3TImzttcYavChCIZkd7eH1ujRSIJwg\nsiGBSsinEzylcDQgIewO8ANLld1gPn0TvBrnCyoCEAFNqYk6ayAk+fKA2e4N5ocH9LfOIEREkdc8\n8OEfpy72yHZfwiejM1xjmGzAYs7GTNPUFa9+44tEawkrFzYpplP6ozOIIEFIxfrpi8RxgtCmjcF4\nl0+spGyJ5P0qoqwzfK9mPVBEScwERxXE1JVBagj8EIUjRtDD4cuKsOfhRZbe2oBwGLLxxIfZfOq9\nDB69QHnuHEeqQ7iyRm5ytBCU5QLVdXTP9PG8GKclGImrNMpadD5FNxl1XZL2V1Cej8QipcNWObYu\nUPg4GTNeZIznM1AKz1eEgcBThiSNEdaAaRBSURYNRVGSVw15NsfzfVxTo5sCKbw2ZLpoUL7f2u9l\nQBwneKYhDdpGpDEGXbfrxrfDKvxHK4vRBUJalpN9qnKB8mKc9fEChReCF0ZY4UC2UMw25NUD3U5s\nrDMgRYvXiGKMMzgpaJo2sBlrcc5ihUNrhx90oWxQDQjr8LsdnFNkTc1evkA7H19GbL3/KbjrLJ37\n7qE76hMpi3CaYrGkO9hGeBGzPCNev8Dtq9fZffFL/OBHPsT6/ecp/JrHP/ln2RxtsPv0r/OxD/0E\nD0VnUAc1l3ePWdl+mOc+/2s88XO/QD2fYeuafH6MUwYZSZznMEWBQFJXBSLwqa3G73RprGn1ck4g\n3EmY+rsqUF0irIcVkksf/VMUUZe/9Pe+zRtvfJsf/fN/lbA7ojPcIuqskY62QQiy5QTPDyiOjxgN\nNwnihOVsn/1rr/CVX/mfEUowOrVNEPuYRqPwMEVFqCLCIMWLUqZHB3RXN/D8gCBJePXrv83K6fM4\nIbh9802ybMZ4bweMI056LI4O8MKA5fQYANvUSAflYoY1mrQ/Iu306Y7WSUfrDDZPYxuNQsDJZPj7\nIitQOaRV9LoxP/ynf5EXnv1HPPHkn2R2eJ2rr32Lw909lC+494cfY/fyMX5qyfN9DK1THSyhlHQ6\nCdVyShD4+FFMmeU0ZYUQPsqLMKamdhVBfwTxGlHaxfcUTbEgShP6aysYZ/CjmGa2xBY5q+srBJ4k\n9Doo4bOYHxB2E2qzJEpT6nLBZO8WlKCrmny+YLmY4JIIIp+6KRBK0F1ZRygfh6Sp/wVu4Y9Ub+k7\nRuun29OJs9R1icNicTS61bFYHEW+oMqWRGmHuirBUwgkuixwxgAO5Xs4164o/CAi6fUZrK4iFVT1\nEul7LOZjFvMpUW+NsLvK5OiIMO3geRG9wQqLo1s4KSiqAnyP0I8p92+xnN7kH/35T/Ijf/Hf45u/\n9Dd55MMfpydDFuMpdZ5hdcFw8zRlkbentKahypbM9m/fQUq8O8+7f7CElCjnYRB4wqKspsyXZLrG\nSMBpnK4xVYEKFFK2YdqeVOimxNryRBDscevVl4hOb7LsOjIRsLpMOH72OdRgiDECzwvxEp/e5iZF\nvsQLEsI4wfMChBAoL0D4Pg7IF/M2ALhuV7l+GCCFwBpLWZfUusZ5LfNFBR6+UgRBgLDmjp23LnKk\n9NHaonWLyhBSIoXC8wKitEtTVuimQdc1yg/w47Q1VJgGKVp9l1Q+UdJ5x06rhBA0dYGQbUanaSyC\nVrMipURXNbopAZCeQuvqRFelaYrsJGexXaNabMvR0c0dIOpbn3QHxJ0hdVEQxj2cbSNIrGshsyiJ\n6nTJi4L++jYiCKh1gR+FrJ3ahqbAWYMXJ2itWUzHqDDh8PYN9t58heXhTR7/6I9TxXBcTZGPPsRR\nNuPKlRfg6IBuErBcHMCi4Cv/+O9y4YkPcPjGSyymE5bT4xO3o8BTgmo5QwmJEpIgjMG2rlNjWw2Z\naQwgWrDsu64kjpbAHm+s8dG/+B9iY3jPJ36asNMl7vVBKCZ7tykWLQx3OTnGDyLipMOt157H80OS\nbp+1U2fo9IY422DqCqfNSUaoBGOwWrdrYCBMUjqDVfwoacXmRY4QksHmNulghbos6AxWmR/vE0YJ\nUdLFWksUp6gTaLSSiijpALB7+eU2WkpKhACLQwqJbiq84PuHvN5O9qDKMuJRlzOPvZfJwQ4f/9f+\nEg/88I9x6b0/ggolZx+4lyBKqLIxcdJFSoVTAhlFiMBHxT7pcEhd5ngnmZBRmJAEMZHvo5ygWIwJ\nez0m0yPS/oimKOiNVrB1gS4KBqvrVIs5wrXxU/PxAVbr1kRUlNTFAoEjSQdYXeGcJukNIYhIuqss\np8dEJ2kqAFW+vBMS7/kBzrXSje/4Jf2O/4W3sYxuUH7QZpL5Pka3zivd1CjPA+dIegOqIscLIkIv\nhMYQ+AFCCkpdkZmSIs+pioKmrDCNJogT5uMjisUCgd+OFJuKbLLPyuoqk8kRWVNz+bVXGJ17kCBd\nB6OghiROcNKgPcf1V16mI9eIqoTj3/h1PvinPslz/+t/xQd++hd4+Quf4nj/Brcu32C+O6WcZiAl\nx3vXCcIAP4wAWq2Y8k7WQu/e2/cWqE0pj7nL8WMfXztiGVJhKX2JCgX+/83em8bamqZnedc7fOOa\n156HM1TVqalr6K5uu51ujGk84AREIAQFESISiIQi8iP8iJThZ/4SRVEShQQSJQREg0lMQDHC7rYN\nbbvdc3V3dY1dp868573mb36H/PjW2W0kSDDQ5aoyj7SlkqpOSWftb33v8z7PfV93qAgiTdLrYG1L\nv7bOE3YGOOuwpj1cq9WSszdfg6MLxitJ55e+jv7Lv8DD732PVdQj6R2SLw2ryYJ0uMGqmFOZFY0p\nUZEGHaDSDoVtMGt4Y76YorQmjNvQYGctIgxanUYQoNMEtMSsV1imqdFhiI6itevLXbkFpVIopWnK\nAtNU7f9zbQEPgghnGkxdUa8nU1oHWLemtytNU5drTc4Hr7mSSrXTPaWIkoSmzpFa0tQ1zjmUjlpi\n/dq+7oy5srwLD5EO0cg2O1KINl9RSqRS7XPyW9y+RbFk96nnsTisb+OC6qxNT7BlRaQjvJIUdY5d\nrajPT/DLCaF0CAx1taApM7y3EGhMk9Pkc8Yb2xzefIbBzj7Z8Ski97yTXXJ6smB2L+c3/+Zf4+X/\n+M/RuTHk7Kv/kCAOmOczwtxhlSTqDoiSXpvOUDsiFTPe2ifUYfsThERRSriOSsmXP4i2+bCVR7bZ\nlsKAqJkevw4UHL/xDd78tV9EqTaRoKgzHt1/m7rMEUqh4pDaNHT641bPuLXP/OKEKO2R9kdXYGdr\nDVWe4Wx7oJbZgrrMKbMlFw/vkM0nXB7d4+DpF1E6IO2PGGztIaSiO9yg0x8hhGAw3qbKVu0zZUy7\navaOMl8xPXtEd7iJWkNCm7qmWM6vwtDrIv8d/ITf33LGgAerQi5mU27+2Of4pf/rL3H3wTv87f/h\nv6R0JUU9ZWNvE2MmCAvWeBrrQaVUzrOoS84mp1S2oqwKrDFI6/FFhV8tkXlBJ+6iZcj5o/sYGmZH\nx0gPrmxI4y69dEjQOPq9PmES44OQCrCRRnZj4mGftDde57RqVBAjhCbtDZBaUVYZg50DAh0iywZt\naS/LUiGkuAqKf/yc/TDrw3sy/5NqPcmxTbNuqtw6w8wipURpzeLi9AqxEMYpSiqkA289VVPipGAx\nO6M32qaqSjyCzmgbaw1x2m8Fj2VOPr0kDEPy+TnWGjrjXW6+8hOUBo7fu41WMVJIbFlgTM14/wa9\n8S73Xn8NUSv6129SVhmjjR3cOOH3/sk/j9/e4daP/gzFbMFouM/s/BHbN57C+/al4KylO9psw57h\nAzvB+Getx4elSEJMUxELjXMGIyVeamzd4F0b4VNlq7VwMkQFKavZjHy1RApNELSuyVCFRJOc/Od/\nEfFzv8rd777Ki3/mz7MsMuaXl4Q6wRvbavGkwNoKa2p0GLUhrEJQFgWmqVnOLlBB0DZTUraruPW0\n03iLUBLnWrYSgUIGwXqN5TDWYGwLIZRKI9YrzyBuUQxJt98KtFdzwnUGoA6jNkvQNCBF26ytHXLO\nt1E5bUP9QVsFAgiaqgQERT5HSEFd5Gv3jUTIVqv2mGkj1A8yu5RQ4FqtlV03l9651qklWkfXldh0\nrU06vfd9HB4dJ+t1bdugBWGErxtkGDKfXSCM5albL+HzHFcVrCYnBFHr2nTGouOE8eYei9Mjzm6/\nwRMvfILs4R3ssuD+177CdDJnsSzpMCTdOIDiEbdefoXLxX2CJCaKQmrt10G+DiRtWKzzYNswWNs0\n1HnG8uKU8/u3ObnzNk1VECadD+3FSHqQzhFYg8/m3HjiaVhdUE8vuf70S+3fX0oGu4ekgzFb6+Dj\nxjSUxZLF9IymKrh48F6LuElb4bhdxzYFQfvOfpwzaU3DanpB2htimoq0N1gzqwpm50csLk44fu9N\n/DqT8vHlM1/MwFps3QraVRAilSbq9uj0x8SdLvPzE+z64q2C8Cre5rGp4ndDaQHWGYqypHGeyeSY\neDDk+kufZPfpFzh79C5RElMsLyiXM7SKCcI+pnKgQorGUBQVSI3xrt0K2fbylHT7dMKANIxaU1Bn\nhHOOLL8k7vSQaLx1VPMl3pRIb4lUO+WtyhJrJWVZooIIoSVCKqp81QZoe4+pa8osRyqNiiKMqckX\nU0IV0OsMCaKIpiqo8gxTVwgh35fUkg/nN/ufVt5fTQ5Yx2xYawnjpA3bDGOS3gDb1KS9AavlFOss\nQZS0wZthhMVRN4aju+9imrqlt86nJL0N5hfnQHvgd7pd8skRobRs7x0ivaDOa+J0g6d/7Kdo6pLe\neAfvNAGKfneEimJ+8ef/Nxbesawt3/qlv8e1f/dP4ntDvvSdX+at176JTEM+/pmf5eh732S8f4Ns\nNcHbhsuju2zsXacuiytX0YeVgwP8Y4dlvrhENQ1p0scrhVEQE6IthCJqJxHxAK27NJVA6gQVdlBB\nSqe/SW8wQnjL5niXRw9vc7GYsHjxJruf/8vYP/ozJIMBgQRpGlxe4OqGIO3Q7Q3pdfsEKqCpCpQO\nWc2n61w+gV3HXZRrR4nw4I0hiVuLv6lKtFRgXZsLuZ6gqCBAaNWuOXV7Aw7CCGjXGWW2whUl/cEm\n0guE8wjTZs8Z3yI/smzeuuLWL6qtwyevDp4PWnnvyOYTlNZ0+kOCKLz6/Xrr8Jb11DjAWnMlVHfW\n4JVAhOtpV9whSbpEKkKumyoemwGUvvq7G2/xSmKEQ4QBzbqJs1kOdcPZ8V1UJ6E4OUUtcgZBysPv\nfIPBaAclY6SKyedLXO6ZH52S6h40jh/9I3+GzvUnqJ6/xRN/5E8yfPIF5i8+xUWW8fSf/nO8+d1f\n5/r1ZyiCBqRBNCVukBBFfaKox/z0HGvAGM/Dd9/knVd/jarIiDpdNg5usnntCTb2bpB0B6j11PnD\nWNI7tIXAeo6+9Zv8jf/03+Ov/YU/wa/+7/8NUdxhsL2HTjuc3nsHvGd28hBnLb2tPfp7h3glqMuS\n0fYBGwc3mRzfB1jDOwXG1FTFiuHWHjqIrphf1jSsZpM1AiVgcvKgjaZZM7JAEMYpk5MH7eFrDWlv\ngNYaHYZtQy/g0XtvIh6T15MOQZy0U9E1nsEas9YO/e4o5xzWgXGKu2+/QRB0GQ7HfOEv/1ccXH+S\nnb2b2MIyP56wvf8s1vorPqRr6vY9HUTU2Yo06WHWPDCEXMfJdDD5lHI2gdrj6xItLMv5BRenJ0xP\nTwGoVjMGgz6rsyO2tnfp9jexTrCcTZicvIeWEtcI0t42dWlIO5uE8QBvwOUF+XyK9Y7OaKt9XprW\nTey9QyrdXqDfp/pokteh5VmspoSdHlK2a0AhRTtazldMTx/SHY+xdUNdFww3dpkvW4Flb7yDAIRo\n8AKEUJT5jK3DZ8hml+xeP+T47lt0+j0kIb7O0XEP6aEolvR7AULBw9uvEW/sMLlzj3Sw4Mf/2J9l\n0cxZpRHFq+9w69/801AIvvj/fJ5Ruku4dZ23vvMFnnvqk7hywXx2ShxEZJfnDLcPmV+eooOQMi8Q\nQuC846PQG6cqJETTmIJaOUSYEC9tm11lIQwGSB9xcuddVosVvfGIeTHDKEVfOebnR2z2O4gkJrAJ\nvZefIfnZn2GCI9Eppp4h7QLtMrTvsDp+RBSlSOsRvqFpcmgsjSnZ2L2Brxpssw7d9Y4o7WJp3UXZ\nao4tSpTWBCokDAOca12aNHU7uRECD6jf8kXWYQQe6rIg7Y/a2/TjfxdE2KYmSjotYbwqGO9dp1wu\nYL0SK7NlO7H6AK6PnLUEWq/X7jlBGKO0xhqLDiKaugbag5E1ZkII1Ua5AEWZIYUkjXvUeY40BtFN\nWvo8IFj/GQQ6ivBC4AXIIKAxNYEO0XGKrRUCw94zz7G4fESsIs6/9z1mJw/YP3iGoqxpwoggGnP8\n9neo53OSbp9qkaODLgQxaE9JQaR62C+9yit/6j/ku9/8LjpNufbMJ3Grgpf2PkFVLFhlc57d2aUp\nLFHcOpGNM2wc3MApwWBjk/nFKWhFUeZIJfGqZe1ZY/iwrgKNcggJysP45jPEYUyadFHOMJ+e8eVf\n+OuEaYeT++/RTTeQCIL+gPnkhCjpkvSGFMsZRnre+PIvkfSGa1e2aU0OVqGDkLoqCeOEbHZJ0h3Q\n1BWPn6N2GjKgehxn8+gBnf6QbDFlY/8G+XJOGMVUqxU6jonSLovLU4p8weEzL+GbhqauUEph19IR\n4VQ7yQxCPPDhh2H8s5XDAhFCJ9R5TifsoYuGV37k92GmFxyfn/LJ3/+T/IO/9Tdx9S3i3pDy8ogA\nCKMU6SSxiEl6GmFdmzbgHF4prLE41dDb3KIqHatVTifuUmVLxGATKTVaty7szrCHA3rjEfnJCb62\n9PpbKKUplkcEWjFbXbQNsBBMzx4RJV1G4x3qxYJQaBpaM0zVlFC3/6x02PIorUEIfbXx+WHWh/9U\n/ieUF20XHnf6rfjRGup1jAJAb7TVEnonZ+soDUO+mKGkxNU1UrV6j3SwSae/xeL8jKaq2/9PmbOc\nnJHEXU5vv4n0knI+5ftf/2Xe/davEAUhs/Mj5tMTnvrUZ1vBZpLw1MufpqwzorTD+Z3vs/HTP4k9\numTiKpSKySdTgihm+/A6YdLh8OmP4xtHECUMd65T56v2YFqvRtqD5qNSEryiNIa8qjm/f4fZ0R3O\nT+6xWs44v/8eD954ld0bt3jipVcY7e1y8PQLJL0BSTLi4RtvsDg6J9nYorm4xD2asrH0HBaQNgXW\nrXD1AowhDlKWZycEYcRqdkmZt9wUZy0qUAjjSAcD4jRFq3AtfGzz8MrlDGEtzjYoIdbkb3NlJpBK\nk2WLK5KzMw2mMS1bzXpM3RAlPRaXZ+hOh7qqW02V90RpD+/btZlH4b0kTHtY52nMD1xyQsgP3Jc2\njBOE1JiqRsqAIIoRUrQrwbLVqgglW+ii923j6f16utceYl5AVeRX2qtiNSOIQnQY4qXHe4vWEu/b\nl6oKwnbl3zQI3XLQnG0oywznDflyzuXpfcrFjGq1AqnahnY2xTUVn/oDfwxna86P3qO3tU087MM6\nYMZPl0ROEdw9pbr9DpwdcfSrf586L5A64vL1NxgMdhlcv8Hy4X2y+ZTju29z8MxLDLZ2OXt0hyAO\nsa5BKMVqMW15efkK57iaYn5YJ1ZOthJSK6GscjyOusrojbZ56Sf/KO+98x2csGihyecXBElKECfk\n8ymz04fYpkZGIS/9vj+E0gFVlWOdBaVQWlOWK6QOyObn1MWKII7bZ19Csw7UFUIig/b7OTm6h5KK\nMs/QYdhOn5Vq8R+dLknaYzk5J4wTdq4/Q5Et26nxmrbeTqs85XJOEEVttNXvoomVdx4pIdAhNIL5\n2VLOQAsAACAASURBVIQoGbA6f0izmHD92U9SNwn5CtJ+lzKbo0OFkI58dg62QSuJyTPy+eTKkOG9\nxWKQKsRL2UbEhW0OY6IjkjCl0x9j8py6WFFlBU54lss5l8ePQGryoiDo9tk+fI66KOmOtijLDB8o\nZBzjvKXIFq3hQGpwjrDTXWM3zJXWNYo7LceyKd+Xqf8H7R39L1RCCDy+bazW+iqxHiELJclWc7rj\nLTqjMUW+YmO8h1qLZpNOhwBBL07pdENUCKvpRbuGCnukcZ/l5IzOoI9WCuktz338J5jce0ScpFx/\n9mluPnmAL87Z3Nrn2ksvM53e4fpTT/Ly536Kws2QqebRl36VvdEe04vbfP3v/3X8aER9PmXnpZcZ\nas3BtY/x5jd+DRH22d66BTbEKkna6ROmHRaT83Y95P2H1FH0g3LWMjs7wmjFZVmwLASmjtk8fJr+\ntUOGN59CdTscPv0CB088hWRJEC5wYk5dTun3R6yOL7ELiVtF1NWE3t4u/nTGV/6j/4DVr3weee+r\nRJdH6MWKXneL4iLHLGr6G7ukOzvo4QAfh5CESKmxRYHJluAtEkenNyAMNFpK4igklI4gEAjVHia2\nKmjyDOPb23bS7eGwuDUyQAcdNBG2MHgrqauGdGOPsmpwUuFQ1MZSlTVJd0R/uAdGMr+4xFpBbT15\nWbGYz4g6A6x160GHw/8OJs35xz9CUpU1xoEXAc6CW4tGvbNIrdChwnkDyiEFBEGEjhK80JjGrRtK\njRVQ+pqKhiDU1NUS6yuQDpTDmhKlJEpJaGqENfSHI6xwqMDjA0d/Z4diOSWMEtKdfTqbexw89wor\nW2ACS2/UI4o95w9fY/v5G/Se2MekkJWX1Iv7NNkJAxFxYWdc1Od8+2/+9/zMf/5fMJxcEqYJpClq\nd4ejN96gPs0Idg+wyiBjxXJ5xnx6jNKidTyWK7xtGIw3UUi8aY0MVZ5RrBYfWvI6XiOdRDlBqEJU\nYxkNxvTH1/gHn/9LbD3/CUabh1y7dpP9G9cpXYlZow4Orj2LCAL0oI+xhoNnX6a3sU08GCC0QuqA\nqNfHa0XaSYgCCLUiGY1J4pRO2m/XT3HIcnrKYGObOluihKLXHxHqdnWIECwuz5hPzjC2aQ1I5yf4\npmndmmHY6hu9JwwjXFO3brI16PbDOk385ymtIhQG6WrG157hG1/9GoMf/Rzp1ibb15/l4mLBb/76\nm8zzIaODhkhqtIoRaOL+mNo6lkVGE2jEWpsmrEELSNKU+XTB5ekFJghoQotIQ7QUUC4wk3MG3Q6x\nDqiamovZObkK8JvbZNkC2xR473n3ztssrUVIj+olzJaXlFisDlkspzSRwmlJkvbILs4xdU0YJUgV\n8LjNEQpkIJDqX2msfnvl21Biaw1n97/f5ut1ukRp50qwVpc51hh02IYpO9NcCeGCsMUaNFmOr2sC\nLaiXc7R0KOWoipZDpBTEaYcwTdg4vIHUAu9rVChYTo9ZzU/4pb/6F8FLvvmFv8tqMUPpEOUcg/0d\nGpcxireYzy/xJmf7+jVkXZIOx5SrBePtfap8xruv/hphGOJr02bHWUNvvIV3tv3yf4jF663wviVr\n4z298TZB2sUKR20y0I64rEiEIquWFAqWZYYpSnzcpbGOOFBsb23QhBWXasLej96ie6NL53DIj/3M\nH2f5y9+k+bkvk757yoaLwY559avfpiQh7oRkywXWSrxMCaMBEKCDDnmekS8XqCilzBZ4oSjyjOVs\nig6S1qVXVmuzhECrEMqKSGrSpIv2knjtdGuqjKpYIKVABQqpBHWxIkQQKIlWsrV2a8izKVQLklCQ\nJgpfL0jigDhWVHnWMqA+gHmBzlqEkNRFhl673mzTXN0MnXMEcUKU9DGNAdpplg5ClFREUYwUbYSQ\n8KxF/IZARyjROg7DMFmvGFtRu5SSME4ps9ZOXRf51cuszJZEUUzS6XF0702KJkOnEatsSthJCDsd\n0tGIqNMhDAZUC0+vt8e3fuHzBMqSPXqN1b3vcPjjP8L+x17hiz/3V/jG//23SfKKRtdUA4nuxHSW\nNSKKcU1Fp9OjnF0SSkW318c1Ldm7WM2ZHD8iX84QogUTx50eSbf/oZ1YSdc29w6N8Zpap7z++ut8\n/au/QRCNmL5xm2Basb1xnVjHXH/24+SLGclgxGxyQtofMtw5YHr8gNXknCpfUeVZO810DikhiiPi\n7pCkt0kyHGGbkspUpJublFVGVeTsXn+ay3u3ufnsKwRRRJWvKPMVpmmnwIM1PX05OScIo3blaGqq\nfAXe09QVVZFhjbkSrv9ur/HeDaaLU85XZ1TDXe6cTnjjze8xWy0ZXbuG9Rm4hlCFVNkSKVvjjopC\n8jKjMhVOCbQO0V4g84pscoqMQoI4wZqaxeUxTlpEEmBCQ21zkI4o0iRBAFS4JESsCl584pN0rGb3\nhU9QhxIrDKGX7HQ2GCVDEA4bBRhrQClWi0vCJCXtDa5gzXHauTJBCCHfl/SKj1RjdcW7cY7B1h5S\nSso8Q3ha4q/UV/RerVogY1XkZIsJSrcrDOccUkk2966jpaK/MSLppvQGfeJOl+XknG5vjDeGyfF9\nimJFZ7zVHijeEkSa+cURH/vsH2idCkGMV7oN520anv/MTzLYPcRO5+w/9xJbu9fYf+pZXL5qWVrW\n09/YZDDe4PL+2+SX5/i1AL8uC/LFDCFVu3r6sN541+W9X7vDQuq6oDYFIlR44ZDeoq1FO9eOdaXF\nSUljHMZ5gihBItpstm7I0q3I8jMW2RlyEJO7mn5/m+Xdh4zCMW5Z0RSOh/fvURiL8zWd/gZ17bCN\nQWhNni84ffQeYZwy2DygzBboKG2fjbRzlWknVUCYdFrX4tqlF8VpqwWyrp0kOo98jFnQISoIrtAL\npioIVYB0P9BXCa0wpsSUGTpQhIEmTlOErdFCXEXmfBCb6TBOsaZhuL1Ps7Zae+8J4gQdhJiqpC6y\nFj/hwTatk8c5D7RUcoRDCJBSEEYRQdRmeD52wyodoHSAt7YVtovWqSkQSNFm79mm1d4MNndZTi6o\ny4K406e/vYexhijtUOUZ2WJKUxWs5jOaqkYHEVKEnN+7B8ayOR7TcTWzxUM6nZTh4Q0++dP/Dmrp\nUTJA7G0w3Nzh4KVPMrt/l25vSJktiNMOSiuEdwRBhKkb0t6Y8d51wriLs7ZlWsGHetUkcCBcC1t0\ncHLyiO7ek5ytLsnKmnKVs7y8oHEVebHk/huvsn/rY1ekeWsttm7p6U1VXCFFgijG08ZIJWkfHcZI\nHRDEHbx1pL0hQZxirGmdtd5TrZZ4Y66CvZNunzDpkHQH4D3J+s845xhu76NUS1uv1jiFKO1SrBat\nZnUtFWkvfR+879kPq64AqB5O779Jd3sLYlgZR2Y9ee3ZeeJ5lPaEsUaJFm3U6Y9a+K2SVGVBZzAm\n7vbXGJTWHRu69vuoo4AqnyOdoz/eoDsa0d0aE6QRBIK418GaAucbBsNNVOPoXb/Bd177Eqrfozo6\noadiimxJGid0k946QcFjhacqC4yzVGXV9ue1IUn7Vz1A+zt+fNH7VxOr31YJIa+CYJs1IExKRVOW\nJEmXJO22tuwgolzMW8CZ94x2DpFStlbdJKE7HJGvLhjvH4BoqIoJcTdEBZLh9g7lakqSpAy2dnCi\nXePE6Yh8OaOpc2688Cm2rz/HanLJ7pPPMp2dYOqKkze+y3I2YXZxzqv/59/g9OQu2ewY72r8bMbJ\ng3cJgwBX5Tx6++t8+mf/bb7/zX+E1jGmrog73bX7xa/jez68NyxrDWqdmWdMg3UepEBKj25qEueI\n0xi8A2eQiBbYCZSrnDhOmZ8fkY42cVKzmC1YzDPi0S56vM1FPUdev845JTkB4eCAO2+9yuGzz7Ox\nv8Ps7D4Xj+6ymlxSFRllsUTHAbtPPUmRrZheHOMRZMslZZFT5wXWOpwVSBGCV4BCBwkIhbP2ChNQ\nZotWGBsEqKDl/ThXg7A4VxMmMWioygUSg9KCUCsUrblCByFNXWHrqn3hO4fSGh2Erc7uA1btBNVh\nTcNga49iNacuc5wxKK2J0i4qSPBeImWAcxKBwruWLRMnMYHSaKUItELiEd4RR202nHAOjGkT6hHg\nLBJBknawTQXOkXaHJGmPYjHn8tFdRtv71Kslcb/Hcn6GjjRNnRNGmvF4i3K+oNe/Rm+8gYwcnZ1N\nLi+nvPP1bzBI+4xzQzdV2JO7PPMjP8LUzXn983+L83ffY+djLxOkIQ/yB2z0t8mzjE5vi8HmAdl8\nzmqeUVcGpaPWaTqZIqVuA6NVcDUhfz94Oj+sEr49kIXWCK1ZzC7Yev4FNp+4weFzL5JeP+DhyR0q\n26CM5/Tdt7h8eJfZ+REXd7+PzTLObr+FsJ5AagKp8HWDK2tMmZHNzqjrmsvzU5qmoTcagXPMTh7S\nH26RL2Y0TcWLP/FvcHF8DyEkVZ5d8a5W8wk6jAjCFkSK92SzS5qqxHtPtF4DxmmH0c7BFbfqB/DI\nD+c08Z+nPAovFMpbZvfeQQuLEIZ3X/sqx3fe4dqt55nce4tQKzAV3tUtAFRopNZUZY6KIqIkxT1m\n14UxQgjipEuo2t9vLBWD3oBExRTTCZPjR+A0aW8ToSNU0KFqGqSBuLBcVlOWw4BJOWPDJ3QnJbIp\nyWYXZMtTkm4XLT2+KiGMMR6Mc5iqwTmoipLVfEJT5ZTr5lms82l/2PWRaqx+a8WdHkLIlnMjJKaq\nKJcLyuWCt77yy4x3r60hdbs0dRvS6L2nKUuKfInSijKfUtcrnvrkjxJ1Q3SoSHtDev0x3hmKxRS0\nIpvOmZ6cUuY5w80Dzu7dZnJ0hEBRFBPqJieMY977+m8QpWNG+0+TPzrmj/9nf5Hjt7+NtzXZyUNU\nEPDed75Gpz/i2R/5cXZvPMmLn/0ZZqePGGzt472nLrLf6Y/2X0q1DYPFmjaQV4ZhS+5uSmLn6Hko\nmozGN/SCDqkNofFUAuIgYXl5ikpCaiUQLqUfXWO+FCxKRVEososFO08+w/7HPsYyTjmuCoxccvDk\nFr2uYPPwKZLBkGTcpzMeIpRHhR7rctLRiHSwgdQRSW+EjlKCpEsYt+yhpioJotb5VhUFzjRkqxlV\nVVCbmrDbB6VaQOZ62uIeU9eVIu728FK2zCvT4JqaZrUijTsEaZ+mNui4h9AxTiicby3gbu2G+aDV\nb2XDlNmq/e6tLypuHcIcxgliTT9ucQvtfx9EbbNorcGu6ezGlGsoaDule5yL6V37WTZliUBQLpcE\nQURd5IRh3E5AjUGrgPnZUTuxSHu4psHXhk7ao9ffYHV2QqS7nN85YbV8RLyhiDe71KrH2999myDa\nYLf3BGp2yccOrnP8xq/w1M/+ATY//QeZvn4P8eCSshdiTMNkdYZQMV7HHN27w8mjB1yenVDXDUIo\nnHHEnS6msURpr+X7rDNNP4jojN9OCRxBoLjx3IsUs1N0PWcYh/TigGx1QToeIyvD6uF9+r0Rm9vX\n2Lv2DLv7T/Hw219jsLHNrVd+D2XeQjwfs8ycaSG5zkN/a59itQIcNstQDpo8Q0jJ9hPPcvuNr9O4\nZh363SMIY0Y7h0RJSpWviDq9dpUcp5imJh2MfxD87T1CKorV4gosbZuGME753TSxsuvNh/TQ6wzY\n37tONwoJfM2zL7zE5Tvf5XD/SWy2pNPrg1knJWRLisUMB5R1jihqAguBCqjrAuMNdVNSl0uEbUhV\nyCAZkAhFEifYZYXJG1aXE5rKkNc1hbEsvcV2YiIj2NA9xHyFT0J8FLC9cYDSknm1IqsWyNrSERGN\ntXgUtnE0VUNTNS0+qWlQOnzfSfof7m/2/0c9fhlb0yC1Jum2qzwhJXu3XmB+cULTGIpsSWc4JkzS\ndtceBiA8dZ2jBMRhxOntNzl78B5aKM7fexOhBDIMKfIlcg0zG24dMhjvUWULlpcXRFEPsQZe9rf2\nufO9b/Lxz/1RHnz/u+0B4Bx1mXH47EucP7zD1s1n6Q+3GG8dEIQx07NjvvflLzDaPeTiwW1W03Pi\nzoC6rH6nP9p/afVYJBpEEbau0EIRhQlBmNIUFQaHxSNE0L5l17oyITy2Kdi49iSmMWSzCafff4va\nKvLScnzvNkIqTu+9w1Of/Cy333yDi7NTRGAw5RwdBOSrDBQEYbw+sCVBGKKkxZRLwOFsRVks8KYm\nDAKiOEKsp0qzsyNm56eoMKZxhjDtEHXbF7sKAmSgsb4FYgZRQpikKK2pixxTV1RVjtIBpipRUhOG\nCf3hNmW2pLHtVMp6R1WstSf4D5wm5wcbBH8VQF0sZzRlQRDGrftRh5iqplgtmJ0+YnZ+xPT8Ad4Z\nsvklZZZd6asQChUorHnMvGq5YE1d4fAIpder+larpgJFEIZ0BgN6mzt0RxsIKVlNzwl0hLcWV9X4\nxlDlS5YXZ9TLRbtSBC4f3sU0FbOL+xhfE3ZHOJngTlbc/o1fptvtUZkVWzsH2DTm7/2d/4XT23co\nb9+nrnLG421G128QdvoUywUqiNm/+TEOn3mZ6ekR1jmqqqAs8ysKfxgmoCTwg9XTh6/k+qd1hQ23\ndukO+9STS0RZYYocUxe42tIfbrP/zEuU2YqmLrk8vsdw75Dh7jWkkLz11V8h7Q2wTUMQxxhnCMMY\nAWuXqVqv65ZYZ0l6Ay4f3QXvef03fonl5BxjG7auP9WCYc+PyRdTsvmE+dkRFw/fa9EOdcnujac5\nuf3GOmopaAPOlaLMVkyOHpAt55yvw5jVB1DL+MOr9pLjBZiqpFpMCKylWk4Zbhy2kG1XI2QbvG3q\nstVAdrvE3RFJnJImPZp8yWA4RgvQcUhtSrxwBELT6wxQOiCbXeCsYWPzgMFgk+2dA6q8IMtWBHFC\nd7CHVBGrKmPU32Knv4eZzTk5vovopSwmZyA1UbeLCgJ0moIQKCVQoWoNCVIQJh1wgrjTA/9bIMPv\nUy7nR7axsqahWC3aSYGSTCcnTC9PkGGAUAoZBuikS1GWlGVJXbc6l7LMySaXRAQEdYOuKkIZMtq/\nBbVFmIZGCybVnDpUSBlRm4LL87sgPYuLU5JOl7jfobMxIh1uoUTIxsEtGuVJ+jE3n3+e4dPPUmQT\n5pNL8B1mVU00Lxhs7fH61/4RRgXEmztUdc7Ln/uDvPutr1Flxfqh+Cj82sT6xi6Yn5+ghEB7h7SG\n2lgy3WIHamuYlXMWTRukbPKSxeUR450DqvkE6SyBltz61KdBKqLBiHBjzHl+zlQUvHv0fRbHC0Rh\n2H3yBscP7jE5L0ClSOFw5QpTliTxCIxD1jWhtmjVEMcxaadHHIcUk4eUkyOEsoy2dukOxvQ3NjCq\nJhj20FGIqXOUc4Q6Ju2PkWFMtpi0+iLnaKqypbXXFaYq1y8BhZcBBsXDO2+wvb9HqAWmWVHmUzqd\nPnW2+Mc+rw9CSd+ug4QHU1dr9o8nTCJU0N6At6/dYjmdYWpPEnUYbuww3D9gdHBIkAb0xn3izoDV\nbEY2m6NUiDWGpNPHCwiiGB3FqDgC3boGG1sSpAmNrQiTGKEcYaS4eHinjTiqKq7deokg0KT9EeVi\nhkQhg7jty63AhwGVLNl79pD+5j5be08z2NzkuU//XrSL+Lt/5X+ievll8jiB7ja/+Y9+g3fymmrQ\nx4UhHat5cnhAnV2yPLuP0h7nS5zJWU4eIVzFxs4uRkJlKqx3TM8eUK2mXDx8l2yxwgnZRsPQTn7E\n76DD85+n2mNKkvY3uP3qV9h74gWO3nodIzQl0BvuYuuGlatY2hI6EcGgjwsUk+kxwWjA+aM7LC9O\n8c7RG23RmIbueJOqKEk7A4JAEoWKxfkDrHVYKXjvzW+x+/SLJJ0Bw/Eu23tPEogQu4Z7PvfZnyYM\nYxSSrf0nGG3s4RvLve99g5N33wRjmR0/4Ojt11lNJuSzJcOd62w/+TxxZ8juU8+1TLT15ebxM/5R\nLu0swluMULggJB70iQOoqprKKsLuPmend9m6+SSTSZto0CjDrFohohSbF5AtGA56ZMd3GSqNNgXD\njSGDXofIK2xR4rTHxAI17DM5OYNqiTBLett9DDlxv0cSaaqzc8pVzbRa8nB6j6arGR88wf3vv4Uf\nbrLMVoyG1xhtHrLIZpSiIpEKZQ3p5hAXKXJbYoRHEBJGXQQh+ABQ658fbn0UTuh/anWH49Y5VBWU\nZUbTVNR5hikKjt5+DZvnSNc6CZ13ICUqjBj3tvBlTdTfINm/TiUs50f32XryRXRvm8n5BWItSjfS\nY7UkGA1ppGd0/QkaU+MF7a3He+6+9jXqbIXJc7RTfOF//a/5yT/7F7j/9rcRzmOKnDTt4dYTtroq\nWFyeEXd63H7tqywuT7j2sVdasboUV1b3j0qVdY6KYmQUYGjFiF62WpokiVuAnI7Agmsc3d7mVY7j\ngzdfJUo6FKsFSZogpSdKYvA1SSchm5+x+9STnN2/jbcRN174MYrlJVpDICWBEKRRjCtLXONQYZ9Q\nplBagiBBq5Ayy0GFdMZbeGfwriYMFVJ6FFAv5q0zzkOznpAqFZBPLwGuVnhR8tiharBN3cadrPlq\n1hiqMkcHAcZUxL3h2jYuP5CC9X9SSaVQQYgQYJqCMp8RRAFBHKxPKIEzLQuuNXYE1OWCpNvBumr9\nOYWY2uGadtVQZcvWqr92kQoRonWCkgG2sdR5Qb5YIrzD1SX94QZFtqCua4Jej7g3QAYBQdK6OZ01\nhEmnnVpoCU6gCaCq0VKwmh4T/t7P8KgxTJYB3/o7v8zW7/lDvP3tb5At5kSDIVbC3hPP0R2MGO0e\nthPJqJ1Mbh7c5PLRPbZu3CLtDeiPNgl0wGj7gLQ3YHx4k7jTbcnr74OI9oddzlp0GGGamo/9+L/O\n+PAmyWCEDiLCMEELTZNlNEWJ0gFaKJx1xGkPay1Jf8hqesH84gS8p85z4sGQR/ffRkYhm088Tdzt\nk6RdFILP/qE/xZ3vfpXB5i6jzT2mx/fRSpPPLnj361/izre/whtf+SJFuQIlsd4SdFL2n32ZPF9w\n8PwnuPnxH+PguZcZ7h0iA33lSnwsajbmw2ss+BcuIXDWU+clO9ducvTO99A6RDrP2Z3vc3D9WUb7\n19FxAlJQ5guipINOE8p8yXi0Qz2dMkhHqLxCFw3Z5AKkJO72r1AjXkAUdlicn9Hvb9AfbFHmcxpT\n0dnapLs5orIZPpAk2/tMsgXxzgHL5ZR0tMns8pjV5IJQKPqdIdp4VufH2LxAKslqdkZZtOzHMl+2\nDL33sT6yjZUUrS7FrnPbOqNN4v6QIIzYu/kse9eeYbx5wHBjl95oi+O7b7d7+jhBRAFZUyK3dlgZ\ni45H9HWX03feZXGxQPmQWHWJdUwSpqzmE+qyoMpW1NmKTtonEgGL4yPe/PIXSAdDvDcMhpvs7jzL\nr/4f/zM2cXzscz+LViGuaghUQNTtkmdznv/MT7cPuLM886M/gYpidp94jje++kWaurxqqvyH47z9\n/y0ZBtRNiXEtbd1Ij5GQpDHSG0xTgJQsp1N6g12UTgjiLvff+ja3PvnjONcQpTGSBlxJHCmkMHR7\nPeIk5JlPfYrOYIBtEsr5kv64z/L8PlQNcRgRIFHOIb3EiwSMQHnJ7OQRF4/uUeQZnfEWdVPjfI2p\nc0ydoQV4U9Ltd9vJqNa4lsDAan5JFKdtHpZU6CDi4uhOaysHOsONK3zC46gF7yBbrYh7Y/LVAqna\nqBdnP9httFhn+NVlQVMVpIN+m9FV5wSxQoceHbaXDOfseg2ety4+7VGBoNPvI5DMTk4RIkYIRRjF\nhHFKpzvArc0OUZgyOz1GqZhyuUKqEK0iAhmggEfvfKd1ciYJpW2wUmCFRwUh3Y0tAFYXZ212mPME\nKqJarugmfb7/m/+AF3/fT5Fs9PBO8aX/9i+x8Yf/BO+V07Xrr4fodZgtL7j/+jfJLy/J5lOqMkMo\nRdTpUOQrBtt7LCdnLC/PKWZTRGNJky5VVZAt59imJdC/3y/7H1YVyxmmKunv7PPrf/evMt6/weLi\nFCUkvq6JwoTBaAtf13SHG9AY7n33a9x65ffgvCcdjFFac+vjn+Xm85/ES8FLv/8PM9w95NUv/h2a\nsmB6/ABfG179ws+jhKRaLsBDr79B2hmgZcDNj32KjYMbPPWJz6LimOVySmVq8mKFimPSjS1m0zMm\nF0dX6QhlnmGtwTvHcHOPuihw63Pjd1+1Zpwqa7CN4NbLn8HkS7rdHqHWkBccf+8b1HWJjkO8K2nq\nOToKEEozmV4gen102sNbCNMBMknp719r34vWEqc9wjBBhhHGe7rjPVaXE6rFDJsX1GVG1AmhnGMX\nM0RRsDg9Q+gOeVFR5jkq7mI8hFEHYSV+WaKcZXtzn3oxxVUFgdRYU7a0danedwf9R7axeqzNkFIh\nECipidMuUimmpw8pyxXf/dIvMD0/4uL4Hjs3nkFLhTSeJhCI8Yj7d95hcTbF54YHX/tNitklnU6f\nwMP9b3yZcXcHURjGgy02elvsbF4jO3pEeXHB9N5tlmfH/Gv/1p8lSGMOnn+J13/9i+RnU3b3b/Ha\nV36Bulxx/PZrJPGAPF9Smry9/VlD0OmQLWZkiynHd96mrgs2Dp5YhwWD+4g0VQAmL/HG4tdxEs63\nE6s6y9E6pawrJoszeju7eNkGb57fu832tacxdYEONcaUnN17B1dW5JMLxpt71MsFOwdPURSn3Hz5\n07z6i19EOE2312EwHuJUxHQyZTm5IJ+dUy5nrBZTHtx7l+liju4MiQdbhL0NlvM5ddNQO4uMYpzU\nSB2idIRAEUftrr+qC5q6IghC4ii9CnEG6PRGdIcbJN0BzhjCOLmCvZbZks5wA49gMb1EqghrHWVZ\nto7JD3ApHbSuwKZBoMGHNFVFlPRoyqLVLBb5VQajkC2Hytp12K1w66imFcOdQ5x1VNmCMIxbgGOU\noqRGCkmxnBIlCabM6Y+3aLIVTZlR5lMQhuHuPmWxYj6ZtAyk0Zj+7j4iCilWy1arYw1BFHN5vr92\nwAAAIABJREFU8gglIyLd5dHt17n1mZ8gb+ZsByn5z/08n/jxz5FvOoInRxz0+8Sxore7xef+/f+E\nu1/5MqnqY0vP9OIYtKKqCqoqx0tBVeSEQYRyUK2WZJNLJqcP0XFMU1d479D6/RXU/jBKKkUQJczO\nj7n/+jf5kZ/+Y/zDz/+PDLb2CJIEtEIGmtXsEqmD1q0XRWzfeJo8XzDcOyTPFmTzCfff+AYP3nyV\n7PKc+fFDTJ5z8MTz9EdbKKmw1vDkJz7DwdMvsZpPOLn3NovJKa/92t9Hxwmd0SbFck62mDLc3GuD\nlbUmCKO1LMS2jCvA4HDCo+OYuNNbi91bJIjU+kOPsvnt1Q/W0Lax5Is5rhFMzh4S97rMJydoJemP\nx/QHY1ztqPOMQEI3CYnjlgFWKZhXS0rpOV+cc15MOfc5ORUGS1MWLaBVa/JqRW4bzs+O8c7T726w\ntbFNRwe4vIBKMu5sozLD4cYhUWMwiyXjrUNWkwuc8+RFG6zcVCW4BimhPxixMdhia7RNP+oQxR3K\n1fx9N/18ZBsr71zLB5KC3mAM1oKxTM6PQElG+9c5fOEV+jv7oBVBFFFMJ0RohHGUk0vG3T3MLOPu\na9/i8JVPs/X0LVQCloZP/NQf4Ttf+gUaYwBJXRVcntzn4PlPkGxu0ts/5ODFT3HvrVd5+sc+x+X5\nI7auP8kX/up/x+6N55id32d5eczejedRYYAINEWVYUyNlLIVDOJYTs7ZvvEUj959nc5o8x8TVX6w\nj9t/9gp1QJp028b3/2XvzWItvc4zvWetf/3jnvcZq4o1s8hikSIpUqImS5bUGjwkkWMbiNPpNGIk\nSIIknZvESecicC66b4I0MgEJggCdNhCn0R05jmWrbbUHWdZASyIlkeJQLBZrPPM5e97/vIZc/Idl\nQVDSdtsRyWK/QF1U3dQe1/7W933v8wpxPPYBT0UsF3MIFMnKKtZ3WGmIugOKNCXpDJuiRQAYwrCN\nryJwgtXNiygvYra/y84b3yNMYpwJCaMunpSNTddP6Jx4EPyA5eyIfDkmm4/pPXAWopi0yDFSITxF\nELWJukPwQrRU1MaACijLirpqgr+d0XhBE6GAseiyuPfDo+uSqN1Fel4T8yIEuiob5MQxf8cPItL5\nHCl96rIpqKzlbT8KfJNH1jC9FGVaIGWIH7aI232kbAovP4waSB8CIT3qqkDXGk/FWOuOIY8z/LBZ\nWC7mM8IwZnawQ5kuCKMEXWW0Ol2idsRyvEtvfZ3OoE9d5yS9Id2NjeYi5SmK5QLr3HF0SUZZpKio\nQZeUeUp7sIquambjI5bpDOcLnPIo744IZcDmk+/BlXOyq1dZP3keJWp2b7+ECnyuf/NrzLcOCYMu\ncavb/KDPGh5eVeT38BPKD+kN1uivbjDcPH2PoWW1vldwv9MljuOdprt36Q7WefRDn8ZYTdxfoX/y\nDJPRHn4rYff2NZL+ED9pIcOA5WxMni7YOPcQ+XLO/u3rWK1ZWTmJWSyZbt+hXC7Yfu3FZnzbapEu\nZxRlRmu4iheEFGXGBz/3N3ES8mxBVRUkSQfPCXypKNPlsYGiAOdI2j3KPCPPU5bzCbpufpjrsvyx\nO8feVhLHcU4G6qLGGEs8WCHsdBFKkWZz0uWYztoaR3ffwHNAVZEoD5c3yJPeyiY6ywhV2ETFOcN0\nOUaGMV4YHoddNzFCrcEqo+kh8cYG6XzevGfa0YpiRls3KIC8LIi7fYpyRjLsUmUTXF5QLhdYT1BW\nBfg+tanx2zF1XeBMhc1yQiTtsIXR1Y8FCPrDuj++2T9CngpBeuiyunfQWmMYnjhNWReUdUEYt6jL\nnP76SazRKKkYrJ3k6NXv46Ulh1dfZDDY4MzlJ5nVS9JiTFocoT2D9h1P/PQvIMKQsNOhvbHO/u4b\nLPIJqc5JbUkpDGm1wFnLqXMPMzhxlnZ/hZ/86/8ha6cvNnbVxRTPD6iqDBUnVGXB/HCPMEjoDNaI\n+ivIICLu9NB1RX1843p7/9T+xWRr3RC2PQ/P90FKsJZ0nmG0IGz1wJdYNNqWbF37Lg88/BgCg64y\nTFWSTqZEUauB0oUtgrjD3u3XiZIBrX6Pnevf48ozf43rzz1LmAwIkj5hd0BpQbV7dNY3WTt9gcHq\ngLJaoj0QYYDBkC7mVHXN9HAfe7zzpI2jrMuGY0XTTUHIhrhdlzgsSXeA1ZpsOWU62qdYzpnsbxFG\nLTguRmpdoXWBF/o4ASqKUFHcMLDevDW/zdyAP6w3HTfWWoy1GCvAeZgaqqrGWYvyo+bGqg3OOjxP\nEbf6COE3TlehUL7CWI02miAKCeIW6WxM3O6RtPpYY+iurFEXc5yp6W+us5wdoG1J/8w58jonGa6B\ncLS7PdqtHqYomRzsUJclvfUTaF2xcfoi3f4awo9YTpc4PFr9ASL0qZ3jpf/rC9iHL+INWuTLCefr\nAUfzEeXykLNXHufVb/1Tfuk//+/49hf+Ia9++Y+QBCg/pru2hgpV89ijFkJ5ZIsxRTqnKjOsMfTW\nTxy7Et072nn25vnTGDKa3aQrz3ySm997FuUHPPtbv4YX+CynRxhdMx8dMDhxmle/+UcU2QJdVVhd\n4/sB6XTElQ9/mvUzF5kc7pCOj9i7/gqd7pDB+ikuf+CTx8DYgjJfMj3YJp1PWDt9ERCk2RwjoCwz\nVBihTU2RLjBVRbs7oFjMWNk4Tae3Qrlc4Cv/3uP3gybsWwhBXd4/juu/sJxEuiYsPm4PSNMlQZKw\nnI9BGLpra6AkRZZz9tH3sdjbJvAEnudT5jOEkIRG0EsG2DTDdwLf84lURDobI1Acbd0E5xGEbfLF\nlPULl0mGmwjpszgaEfoJ5XjMmQevkOoSrz/AJRF5lTM5usPambNM97fpDtfwpMIi0ELiD4YslguE\n8lFe1HSx05QiXQBgj5E+P07dV4XVnzk4Guu2Q2HwKHWFl8TIMMBYi/QUAvClj6hq0oM9wqhF9+Rp\ndvZucOKRRxmeP0ew3iPqxfjO0BEew3DAmQcepSoKxuM9DI6rX/0Si4MtqnxKf2MDJ8ELAy488QxR\nt0OyOmTy8qvc/dqzPP2ZX2SR7lIHNWGwgh91mUz3iNptoqDFdOtu8xy0RRlJtUyZTY44HO/fW4BW\nKrj3POXb+/f2zy3hJFuvv8RiMcN6Cu0sddnsYyTtDsJUUOREokW6NWH1xFnqYoKUJb7nUEKhsxI/\nCDC2RAaSRTplOh0hky6b5x6lv7pC3K1JZwu++cU/pHQDgsCn3fGJkqQpboslSSiJ4gBhcwJPoKxG\nSoE2zVg5KDW+MyhZ4WGJ/ODe+6CEh++ahfgoTqirnGI+IwgTgqSFFyra/SHF4qjpbvkBUbuNVE3O\nnPOPl9+lIDoef9m6ftt3Nt5sszdsshxjllRVgTYaIaHSGdViSbVoMt0wBiV9XF1TpgUqCBGexYsU\ncbdLlWf4QQjKEiQJR9t3WCyWLMYpnhN4TuAqzfxoRLe/iTEexvrIqMfsYBecJklitl/8NrO9HaIg\nJghjKqdZ6IzdW68ihCDNaqS/wmTrDmcvnMPPLd5Ec+r9T/HIRz5BWs0Y33yN5PI5Bt0VwtYmUoNU\nhq39V7j00Y/y5Md+lv1XbjO9e5dWO8bzLNLZBmgbC6LVPi6WGGFAQjYdAxyzvN5+sNc/j948f4Tj\nXsSQdRYnYH/rBlL5/NQv/wrf+sKvI/E4e+kJ4riD0JZLT3yY0I/AWNbWT+PjIZzjzrUXGB1uc/nj\nP0W8vo5IIlS7ze1r3+PWq89T5lmTESg8er1VhDZQ11x5/8dJD3Ypl/MGVhoETaRKvwdRAM6h/IDF\n+JD5aL95rLVGaIsvFXGr0/DSnEX5fjO6v6+urf9sORq3nMBgdEpVZ1TWMNrbZ/eVF+hGPfbuvI4L\nFEW+RNdpw6ozPjIZUAUBNRoVACIlbkd0211i57EWD+hHA2IvpNNbJ4wavpjnRbjFnOJgh1Z7yGQ0\npqg1rZUhi/3bbIYebj5B1BpZS1Zb68gsJxh28COfXm9I3OlAKBkvJmg/Jq8MCJ9aAv0uM9PAYj3f\n/7HDeN/eJ/ZfUm9+QTxPkU3GOK0xZUk2OqTKMopsgfQDsA6rNXVdUFcV2699HwEk/TUWiykEilqA\n7LYZz/aprSYvMnZuvszjn/oct154jiQa0O5vNEnhniBLxxhTMn3pFfL5jCxf8vn/5lf42V/5O9x9\n6TnKZcpyNGK4cZbpwQF+1GG4eZao00VFEYvxASqISNpdoqiFeZvGmfxVyJiaMk/xlMKYulmA7vax\n1uAfH44gGe3dZu3MeaTysUJSGwteRJlnSD9sYn6UIl3Oj7Ojmi7QcjSj01tjOb3LYz/xEZQPr/7J\nV6COQQfUdYohQwZQWNlQgx1Nx0gIgiBsyNnHjBQA6TURK/ly1rj3aD5nQZwQRAl1mZNOR/TWTlKk\nC5LuAKP1ceRL3YyCjAVtm3zLvMBVVdNC1zUO1xz07zC6vtMGTygC3yfwfUxVNrmbQUi7v4o5Drut\nqzdHM22qPMXWpnktjpMRrLNYa6nqAhVGdFZWkb5ktL3DcjRHmBDfxlAI0p0jXF6x3NvBFBWmrEhn\nM+KVNQ63b7EcTxlt32b31jUGpx5g7dJD1Dpl0IqR5YzF/h1CmUBW88KXPs9TP/3zDatsd5uNs5fo\nDdcoxiPKLKNaLoiTLp3VAWeffIKinLBy6iQHd28TRBt4skdVapytMcfMORXEDX8NeY/wfT/q7msv\ncumpn2Dn+svMRweNI3s5I2y1Ub6PVM2ea1XkSCnRZUGdZeTTCVGYcOahJynnDSLn5MVHKdI5Jy9c\nobuyiQoCwjhB6waLE8TNfuJ8fICuKzzpIYylXM6p85yDG1eb2KO6oipywqTVfK+cRQVBQwVvdSiW\ni3u7WOV9/N78efUmldzoGl8pTFkShQn9wQk6/TXipAGtdobr5IsZALou8YSg0iVFltIJWrS0R89L\nELWh1W6zf+tVev0eHg5b5phyQVUumY32kLHP8PRZ5osxRni0h5sEwieUPlEQN/mPzrJy5jy+CqmL\nnHw5J5tPKJZzTN3kP8adHtrUTTy91k3qSlW+JWDl+7awstYglWqAZ4sFWItSPtVy0YT+9laI+8Nj\nguwSe3x79FsJ55/4MKY2zEb7oBRFXeHCkBuvfAfhebRbXXpRi0gopK25/N5PsPfa6yiVYK2js3aC\n6d4dDm+8xkc//K9y7bmv88Rnf4Hp3l0YJPi+z+GtG5jKYCsDSMosQ9c109EeUnkcbt2gWMzx/Qgh\nmnamQLwtydt/WXmeT5i00VVFlWe0+6voqkLrnHQ6BXyiZECc9CjSKbouQCqc54OfUGmNH4THAaw1\nWbFEC0jTJgB3vHvIaO+A/dsv0up6PPPZfxlpFM/97u8wP5zgSa9ZgJdQ+RFlljcgR+cI/JBiuUA4\nqPIM6zXL14Hy0Wmzp2OdRUiFQ+DHbeaTI2ajQ/qbDxxnIXpYq2n3V+7lDfph3BzoQiJqzebpi2Aa\niKTRDS3YHKMJ3qSavxNktcPVNUEQ4AmHKQqUaqzZQRKj4gghBWEco5TCEw5pHEr4uFow3rpN4Df7\nN1XVWPRPPHiZIAoRCpK1FZKNFby2oqjmWM9QZCNsscS3jjhKGjel3xzWp88+Rrq7Rywizp97jG7S\nB2sIlU9S14xvfYuf+hv/HqJWjN+4yQNnL7PIDukNB3hWki9S8tkUl6UkyZAqz4mSBG0Lgn4H1VHE\ng4CP/+t/i69+/h8jZR9rJHk6x3MSZxzZcoaVHhZ3/1h5f4Q81VwsFpNDPOXzib/+H/HCV36H6dEu\nFkdV5mTpHKGa4N7Z0T4IgR9EtNp9Jrt3qNMl84NdpKeoyoLx3l3WHjjfUNI9Dz+KmzFyu8cbLzx7\n7//udFbIxyN2r77IyfOX6a+cQLoGABy3u2TzCf31Jr3CHmevjnfv4vkKYwzO2HuL7e9mvTnWd84x\nP7rDez78WYrJnCgaUEymhH7E+GCbui5YOXMBaw3lfIbOcpyUOGsoD48Ia1C1QznB/HCL1Y1NQt+n\nLqbErYgoCNCm5sJTP0FRLhlPtwl7PeZ5gYz7hH5IK2pTTqf4KiAvUoq6JOn2ySYjbF1TLuY4a+h0\nh3RXN8nzJcaTZNkClIdz9p5j+cet+7awEjTMHOkptKkJWx0cIHyfwYnTZPNpw7cqc4JWqxmxSUme\npxRFjlAB7ZUTXH/+G7TaQzqdFTbPP0wS9Ri2hnScRz+I6bZb1EXKbLxP2OnSXX+AVmvIuYtPceLE\nJV74xh/wN/7uP+APf/1/4FP/9n/KG9/8MmGccPbhx7n7yrdxVpO021hbs5yPiLp9hOfx0NMfw2rd\ncHyK4t7zervRt/8qJKTE6sYtEkTxvdiPKIkRHmSzBQe3b5F0V1ChTxJH2LLEao/Z0ZjeygN4QhEn\nPWSUoK2kdJaV0w8RJG2yPMVvd1m/+ARFWVJVKU/91Kfprnf5zu//BqL06cRniKNVqmLZ7Hk500Aw\nEXT7q6ggoNUb4mTjMEVborhDOh03S5nCoygK6qpmcrDL8OQ5rIOqyHDOUaZLsvkE6TW3dnwPPA+h\nPKJOj2K5IGq1UUGI9BTLyWETMizEO4p3JFUAUmFchaFC26ppz+uSNJvhhyFYTZ0v8T2PMpsjfB8Z\nBFgncDi0aToQaEcYxBzduQbUxHFAuBIzfPAUhVjSOrOCaxmufPazrJ4+zfXvfIXDO3cIoz7peEo6\n2ae30ufUuYuY2Zij114m29mjHi+pjjKWe/s8+emf4uYbz7G3s83L3/hD4kGP8cENyAv2r73M6pmL\nBEmTd1amS8KghcTDWsXo8ACRtDjcuka62OF9P/NzvPrtL5EuU6RoEXgRnoowVlAWOc4JxI8BTvhW\nyQ/jptPuB8cXAs2Tn/wcYdKhzFMeuPxEA2dWqimawxB7zKybTQ7wlM9k9y4nLl7h4M7r3HnlOwRx\nwsHdG4RRQq0r/LhBaWT5nAtPfZjlcoIFpCeJO31OXrzCdH/7Xq6mMbpxaEbJcSJAiJCSbDEj6fap\n8ubfgHf0zttfpd68vDth6a6tMz7cB9FcVmaTXayy5FlKlaeMdm4zWD+FM5pY+AR4VE4zLqfsH94h\n6HaRSoFULGczHB5lUZCXBSfPPcp0+wZSWHqDAbPZiLoSLJcFmS7IsyVxu0vc7lJUGfPJAflkhPKD\nxozmHL4KyeczqjxlMRuRFUvCThddNRF2P/h8fpy6bwurH5TnBw20E/B8n6DVaRKxq4yo3QHn0Mfh\nnE40ER3zyRHTwx0uPPYM3/m9z5NEbW6+9DxOeRT5gp03XiWdj7h99Xnaq0NOXHqE7ZsvU+Qp6XjM\nta/+Md/73d/k0qc/x63vfYO9G1d58AMfo+u1kL6HLlI2z15CqcbVZq2h1sU9e36ezbHWNAvd8v//\n0Mi3UtZo7HF8ixDy3g3DmAqlFM5ZusNNimyG9AS2qoiiFrousdZhqxphGrJ5VZY4TyCCkM76JpOD\nXWQckpc5Mhhw+7XXsALS/DbnnnyU80++l+9/+Y8Quk1AFw+LM1Vzs7WmGWU5h6kqqiJrRhFC4Hke\ndZ4RxC3qsllYD6KEWy8/x8rJs5R5SpmnOGvwo/h4FNE4wYyzaGswWLTRLKYj6qrAaA1CNADNY9fc\nmwysd4ocEoMF4bCubkCCSLStQYrGgVUVmKoC6/CiAEONsZaqqlBxCyscnvBQ0kN5Hp4Ak2eYIkOU\nhuXOPq24j7Qei6MRFo8iW3DuPR9gerDDePsug41TlMsluVlAx2PlwkUMlu0br3KwvcNob5+bL73I\nS8//AaUwaC+kf/ZBzjzxFDiNSZcop/CihPHhXVQQcvLSo/gqYrK3i7ESz++yd+cu0g8psjGWKY9+\n7OPcffUFeiunsaUGbcGTaGOpigLPv3+dZ1WRkU7HPPmJz5F0++xcfxnlB9y5+l28IGQ+PsABdVU1\n2BglUUlC2Oke5/ZJVk6d4+o3/4jB5mke+dCnjl21zeffD6Jml8tatq59vzknHLT7K3z9t36NwYnT\n4HnNPpazeEFINp/i/cBI3QG6qohabXCg/IBsPqEqsvtyGvCXkXGa6WSfzUtX8OMOlanprK0RRC2Q\ngrDdYWXjNEEQEcVtvFITygBaMW61x9K3jMd7oFoUpaaoShw+YWcFP2qzODpo8AselOUUT0IYtXFC\nUCuoXE1Zl2SLKe3BGn6ryXj0PEUQxsRJB1OWtPsrzdJ9d4CXxGirQb610513RWFVFRlSBaSLCXVV\nMtnfQvo+TliW0zGmqlBSUSymxN0OBkFvdZMkapFOjnj60z/P1z7/9yFKKKzBKsnaQw8zvPggXq+L\nDSDqt6nrktHtG7z4xd/Ab/d55KM/g24r/u+/97c5f/kpnv+T3ybyFPl8hPQccafNfLxHEAQMNk8S\nxI2TqCwaIOb66QfJ0+Wxjf3+VVU1HbnFaIQpNVbXOCERno/0QzwVoXVF3B6QZ/PGtl/WBCIkittY\nHCpQmONuXl0VlMUCbTTj/W2SdpssnYNKEEGHnRtXMXZJZSZsXrrAlZ/8ab7521/g1otX6XZXaLfa\nJHEX5TU7VnWVAgY/DAiTmKrMkSpA+orRcRYZSPyoxbnHPgBCUZcV1jTuN1tXCCHx/QBfhYRBCyt0\n4/CUgiydIVRAOm0yzpLuoHEWvum2eweBNYTX8Kkqram1bkakAvygha1LpA9+HDRZcs42AbwIPKUa\n55E1+H6I7/m4smKxv4fOS4RzlNmSdrhCJHpkuzOqccnK8ALjO9tMR/sYai5/4BPc+v6zHN56jeFg\nBReG1L7CH65y+ukPs/nI0yQrmwi/zcMf+xnOvO+jdB+4yHe+/CUuf/yzqH5MGCRcf+5P+Imf+5sc\n7d1G14aDWzdZPXmKIpvR6Z9iOZlT5CVe0ObCez9Jb2MTKQusnvPUp36O1597lmw+pcyXTRC8p5pu\n3I/ZnfTjlB9EOGe58eKfshgd3AvcPnvlaQ7uXMfZZp+wrop7I5rx/l0sTRHkBQH722+wdvoiZbpg\nfrRPGLebVIJsiTMGnabMDne58sFPUaTzposyPeT8kx/iYPtGM/5RiqrIG5zJ8f5U1Gr/2eOM46ZL\n7ixCiqZTJQS6fhcT13+EVBg2QNXRHs/9/m9QLlKc8BAoku6Q2WTE9GiPdDwiDlr0gg7KKTpnz7I1\nukP37DmsNUTtLl7cIhmeoL1+EmMdRiik57Ocj+n11+h3VignY5wxDZ2dmkWeMR0fEkQx1WxK4sfE\nnT5CeqTLOUm3T6u3gqmbEXNZ5QjrAI/pwT5Gv3W/m++KwspTMTqv8EVE4rdIOj1UGKKShChJiP2A\nlh9AVZCOD7A4luMxpBmD9U3myzEP/sRnuPPa6wTBkHKe4SUJJRZEgOr3EYHP+rnHmN24y1C0Wfvg\nk+TrHvnuDuZwzJX3/TWsUixMxurKScJOH7/XxeAx3ztElxY/7lHXBotkNj5kuZyB8NH6/qYAq7CF\nFBGNccpDihCEh9aC5WyBCiVKOYwp8IKYZdl0h6QuUL7GBDXLekGGAaUIHCRRQJAoapMziFexswXT\n2RYbF89RLBcEYh1PtEAVxGcMp555L7v7e+SlQgmPSAVI36N2BVrWaGxTYFU1BIqsKsjyjLVTF4jj\nGHxJXWVU5RIVKKQnkbpGWQhUgACE89BpjnICpWs8U6OQ9AebVMslQdym0xti64qo1UXXNVa4dxRb\nwzqNMRbPi/FkgpIBOItyCt+Psc4gA4/eiQdQfou4NSAMYzwJejmlm3QhKzBFRdIe0OmuEvgxWZoR\n9lfJKSGWyFDgBw5dTdDFlPZgnbDVYZkecvnDHwGbke1v0Y8uoOoV6hSmB/tU+YJqOWLt7Aa2V2AC\nH2naDPxNBg9scji7S5EqBifPc+fmc+iyxBUh2y9ex4qMbDnFCQG2xvMywqhmuci59d2XaQcbqBz0\nYpvN85sc7h0QRG2SJCaKAnTVYCXuVxln0LrGj2KOdm9ztHsbFcVsXf0+dVoShj2k8BHGIwy72ErT\nbQ+xaQ51jS4KksEKvuchrcPzJNKTZLMRSdLBZSWtpE96cEA+m6JrTZh0kEoRtFqEnS4IgQQCP4Ba\n4ymfOs+brrJtHl9VNYgIz1doXSM8DwSoMHqrX8K3lVzlKIoUG0AQCAbddYpJzmr/LIqIVm8DG3bI\nFzkm1dRpTV1prj7/NSJ87HjM+soJDu5eZ7kYs5geMhntUtaNE3hWZhQG5nt7lEdTVgabCAoMFXla\nIOI20fAMOMX6yjpiPsOmGdILKZ1gkS6wuiROfPIiBSRx1MUWGvAR8q17P98VhVUj0eSTRSFVloG1\neLVDODDKY5LPKauKUEZI5VFUS2QUoOKE+fQAoQQf/MzPs/3in9KOOtRphudHHGzf5M6LL/LG9at4\neyNWh2v0f/IDVLs7DAen+M2/+6v8a7/6P1JHJUZVjHf2ObxxhzLPCJMu7f6Q0e5NZkc73H71OZJ2\np9m1iWLmkwOctfetG/BH6fDujXvjr6LOkb4C14yGdFUiRePay5Yzos4AkBRZhpTHtu+qIoiSe3sT\nD773I2hnqDBUVcHhzWtcuPw0d174Fq6Q5EcZ5WzMcCPhwfdc4drXv0o6nVHpCqVClEzAhtjKQ9gW\ngepjC4HONel0RBCFTfiyM+Bq0DlKuAbVECqckjjfY7mcUdQFXpKwXE6R0sdTYVM82Zqo3cHz5PFy\nrWY5Hb21b8Q/r47J6uUxFdnoulnG9+WxNb+58Wpdky0mBF6ANA5X1Y17JwwgUIhOQikMfq/LdLyP\nritMVaOLnL2bV3G2JghDwiAhiCKoDUr4YAx+1OLU5fdy+qkPk2djDrde5foLf8ydq9+iqlJWTp9h\nNt5l9cwVOEr4xt//n3nkgw8yO9qi1TvH6OAuqtslywqEJ8myOe2NNfxIESQBi9Ee2WwKRuG0QGcj\n8sVBQ+2OAoJeh3DQ4/x73ke2mOL7CXVRIjwPcZ8n+kqlMEZjdE1nsEo6G9FfP8mDT32yTfUrAAAg\nAElEQVSE5770j5qEizBESg8nBEWZNY68xRxbVoTCx/MDwnYHoTzGB9u0Bit4QYC2husvPMuph99D\nmacgYO/Wa83/66mmcD3mwzUdMfA8jyBOjkPQc4SUqCBoArmFRAUhxuhjg8k7Z5fxxyEhBCqIyecL\nLj75UepKU9cVb7z0VaxNqfIJvtQEbZ/R0Rv0NjYQUcjqmQu0uk3KxPRgG68d4rdChKiJhSCqapLl\nkqGLGcYD8jxlUs1ITQkOekmffm+Ndn+FrTsvMzFztuf7VO0YJyVVkRElCU5XzfSisgw7qyRexGz3\nLroqsKZBaLxVelcUVtLzsMJR65LlfEzc7kKlCZxE1zUVltIaTlx6nChuURYFfqdDjWU22sFPGstn\nIBynzzzIwRvXOLhzG2sg7HQ5+fDTPPr4J3n2N/8BaqWNHUZ0hidwdyfYmSHaWGOa7nJw+yWS9pDp\neIrvx6SzKbquOHXpCmEcc/LCI5i8yRyM+wNawzWMM297QORfpRwO6XksJodI1WRQecfGAk8qdFVR\nVnlz+zQarQ3Kjxs3XVUipQTTjBywjuneXQpdIIKgWYSMO9Tpkoee/ihvfPvruEqhlyVxYhmsh0Sq\nzWDzPLOjXQ7v3iKMu9jK4bQHRlFMKyY7BxzcucWJ85dBQpDEYAqUsHgCJAaJRgrQwlFj8NttvCgk\nK5Z01k7irAc0h0Q2nxLEMcZUx7yVZs/qHanjXZk3w3mb59NAj6oiw2hDkWXgHEU2abLB4qQhsvs+\nR4dbaOGoRJMXOR7vI32f1Y3T+MIDbWi3u0gLi8M98tkIaSyjrRvYymCMxQrBMl+yu3OL6fg2KpBs\nXjjPqYeu4CcBk6NtZBKSz2ue/bVf5yOf+kWcOCBqtbj5/HfpbmxSuhq/06HTWefat/+YlUsXSdMj\nynJ5HEkE+WKJUj66WHDm8uPUdUncHaKx5MWCbDFm541XyJdLdFU3ENn7BT73I/Qm3kYg6AzW2L/9\nOod33yBu9+gM1nj607/IV3/jf2W4eRpP+Q1aJo4bEnen35hFakM2m5JnS8o8pz1YxSHYvfUaQavF\niYcexW+18YIQFUa0ekPqomh2F+saaxselTGGVm+l2Y/UdePO9RQqCKjyHFM337UyT5HHHC75FrjH\n3s7yVDPK1XVNvpxz+/XvcfLyEzz8wY+hqwV1NqEqJniR5MLTz7B7cBsCH2Mds8MdJBIvDDnRWSdc\npqzEXWyxAKmxkUTkOUFt6Z18ABeH5DpvHH/zHLt1yMH3n+fU6QsEvo8IYyprmI+PSLp92u0Wzmm0\nrqmrisBJeu0hCg9cU+C/lQbcd8Un6c3FRSsg7g3IZhPCMGY6OmQ5mxKEMflyhrEa6QWURYrDEUUR\n1lTEcUy718fZkuVkn8X4gDDqUBYFYavDwc5tTg3P8NP/yX9JdHaDcjQizVJe/eJvc+LBh6hMQTqf\nEEddALrrJ6mKAnkcXBvGTYhvEKgmVidpMT3cJV1MjzsA96+T6IclpcfyeM9I+T5CNTdJXRU0JCCw\nTiCCiMVs1MAlhUB6ARIPXTa31tnBDlWWoouCxfgA4zQYiyc9nLEspyNOPnyZ3Rsv4fsDinSBkA4v\n8Ciygv7maZL+Kns3rjLaukGZTpju3CCd7BFGEacuXqEqMqqyuLdHZI9jlJTyEdK7x4SRQjZLs3WF\nNZrZ4R6mbpyP6XREZ2W9YcEoj7osjt2RyVv6PvxzS4h7HYM3n3++nGNtjZAN/8sZgeeH9NdPNPuD\nQmCdxY8iWr2Vhj2Up8jjJdVWd0hd5mAtvu9TFxlY3XwnTY3yFN3BJva40K51c+CWZY70A1q9TsMK\ns83SdGuwhrHw0pd/j40HznDq4UfonDhJt7cBeUnS62E8QV1rqsWc7uoGla3orm7cO6ylarqjcauL\nF/noumIxmrF3+wZWO+JWB2tqrnzoMxzevYXRBmtrnLl/d6zgz4or6+yxw9cSxgnz8QHdlQ3OPPIU\n0lOEcdLsP/l+M26CZiTnScK4gy5r2sMNpAzI5nNWTpxH+j5aV8yO9pq8yWOjizteaPdUk65hjSGb\nT9i69iLTgx0A0tkIT/koFeCHIa3+8DiGyjSfV8R96bj+y6j5zDYF1nI+p64No4M9ssWUKs+Iwpgk\n6SCkZHS4w3iyj9dKUEHAmYefBGuYjw+Y37zJajRkONhEm5pSGGaUFLrAOocpC1rtXlNstzocbd9i\nbeUBVgbrnDhxlljFdOIu1SxDKh9Tlox3bqI8nyJbYK2jLnPmowOguZwDyLewvHlXFFbQuEGcgGw5\nw+iavTeuEgz6OOlx+MbrqNqBkYynR/hRiOcJhHC0e316/TU86yAQhIMOF5/5SeL+Gp4fsXb6EhvR\ngC/+L7/KyB6x+vBF1Dzn5u9/je3rr/HJ/+CXmY13qKc53kxTmwzZDaiK5kMVRAkvfOUL9NbW0VVB\nqztg/+ZreH5w7FB8kyb/7pAxmvHuncYpKLhXWCo/BKOJwhbC87A0xRVONNlWtW7CU48P9lZngBSC\nKl3S6a9RLRcNzVxILIbClBix4PyT72f79R10HWLRxL2Q8f4uYWsVL4hYu3CeUw9eYPPsOuffc4VT\nF86ydmqTMFLHC7BgncS6gNpKZNKl0A6DT23B1hU6z7FVidW6gV/qGuk1y+rrZy4RRAmmzvCjkCCK\nmzgO/c5fpM0WU6wxxO0upm5ctzgP52TjeiwzSp2zdec1glabfDGn0xui84zAC1AIFJLAD5HCa7oP\ndUlvsEoUxyjpkSRtsvkI50kW2YKg3cM5QbZcEERtXF2DbSz9cWcVbQVBe4DVAbtXX+bChy5w5+C7\nVOEGr3/9Tzi1uorOlowOdpFexHe/+H/y+Ac/RRC3mB5NKYuK+fgQpSTKF1TlDOMJxqMRyBjhEuYH\nU4pZSnvQIwhj/KCNNa7Z/1HvjiPXUz6t/irDzdPcfvW7+EGErkuEkHzri/8Hz/72/06rv0Jdlmyc\nfQgR+thAUUuHU5Kw1QUkL379n9JbO4VDsFxMKeoSlSRk2aLpELa7+H5ENpvgHSMWhBC0ekO6ww26\nKxtIzyNu9/E8j/K4E9yMqqt31ZrFX1TCeQjnAc15e+mZT3L3+su0VzbBCgLVQhiJ1pZ0ueDEo09y\nsH+b5XzM4nAfU5Z0VzbpP/QIZdzm+usvQ9THiwYIGbJwNXWkCPwIv7R0/BbDB85h44C9xT62FXE0\nuU2QRKSTMVVa0lk/gRCCtRMnUMpDSh9twSQRpWcpbXNxEby1q6nvjm/5D8gh8aOE/sYpwm6PtTMX\nWNk8zcmzD3P7lee5c+0Fhqsn6XcGoA1SSCYH2wR+RLlcELQ7LLIJrzz/B2TpDD+MGF17kc/9V/91\nA0ybpzz65KexBwt+4e/8T4ynN6mLnGH/AbrxEM+TVNWiCWktm0Pg4uPPUC5mUGkkHv21U5TlD7Cr\n3kXffV/5RHEbP4jwVYj0wFp9D8nQ5M41RaeQPtrq45trEwRb13XTNfJCnAGtK0xZkERtdJ4hPUVZ\n5mRlirYZtV5y5pFn2L3xBtkyY+PieebzI268/Dx+3ALhoSKF0XOMnmDqlGxxSFmlVMejR2s1VVVQ\n1yVVWeMQx2yu4+VaIYiChDhq4aqK3mATZ6G39gAAxXJG1Oo1b7T0qfKGefTOl0TrmjBu4WwTwqzr\nEmuazp0MAyyCU5ceY+uNl+murOOrACqLXiyIwwhhzTGJ3uCcbiz2UpCnC6TyqaoKP/AJ2x1kGDEf\nHyJk86M+P9pFVzm6rKjTojnshGa6N+Y7X/o9PvPL/zGVmrOze4127zxFljEf7SAdnDj9EIuDQ9rd\nNWxdky1HeH6CED6e59Hq9hhubjI8eRptHZ21k8yODvDDhMCPcbVm58bV40SBJjIE7k8O3f+X6qog\n6fTZuvYi+7df59Slx7jy4c/w5Cc/x4t/9AWmezs4Kai1QUYRMmmI6vlyystf/10e+/Cn0LrACotU\nfjM2rivqPMMUJbpokgyCpENdleSLKdZagiDEjyLqWoPwyJdThOcjkBzcvtEUVcdj63+hf7Z0XXG4\ne5uoN+DFP/4nDNbPkS1ThBcQ+BHt/oBiOWPlxFmO7rwBQJbN0Ri2szF7oqDudlBBjFc74kxhpKAO\nBL6MacmEm89/HS9u0ztzFrPaJQ9gf7LH3sE2i8UcGUWN41pX1JXBOkFta0SckNcVy2yG9zYJ0n5X\nfKrezNWTtiEhF9qQWUs5nVAuJjgFtXRsnL3Ew49/hK0//Rp2vMCVFclgHS+KmY0PCEyMyTQrlx/k\n9HsfJ8sOMXnNlV/6N7lz80XM0QK9W/M7//1/S+eJB5nk24T+KtnoiHw6amzA0ww9yxGeIGm3KbM5\n3fUNkjBipb9GoQ15XjaLuK5hXL1rjmIHzhhMkZNPRjhT44zGHt9CrHXNzllpGjK6tERRgsBR6Zqy\nSDESnB/jy4jZ7i6tlTWELwmUIgljKqMRcUJdWXy1RrEsMO6ItTPnWE7m1GXJ2rmLTMcjikJQVQpo\nsA7p+IhKO2okFQ6nIAgCAl8iXQnWgHAoT6CwJO2QLF8ig2a52hUVrc6Qo707TPa3aHYrfaz2WIzm\nHO0ccLh1BysDPL/hPzWt1rfuLfmLSjiJcBJoxp/GOGpjqGoQQiE9gxc6KlNivAjlx5RZyulLjzLf\n30IXFXF7SBAnlFWGVY68mqFdjRcGeGFAXhSQJOS2QiuJavUwtqbd6TFYeYBqnpGNxjjRhHS3Wl2S\nVp8wVoSBY3LnEKFDlvUBSxNz7tJH2frK7/HA4++j//j70cs5oZa0W+usPfwYNRWBsDhjKRdTwiDA\nkx5VUTM7PMKXinxxiJUZrbUOKlb4YUxv/SxZtgRpENIcvzb3ryvwB2W1vhcrUqTzJuS4KllOj5Ce\nRxi3OXvhCUxWUGQ5R7t32b36CrPrN9l/7ft4zvKeD36Sw5uvoMsMIR2+lMjKQWlI4i7CClxlEAZA\nkXRW6W+cJgwjdJXixwnt9ZPkeU5reAJP+Oi8YuPUgzhr740A/4X+32Tu/QnjkEB5eM7w0Ps+wavP\nfR3Z6pNXOdIU+MWMThSRTQ7YPHsR6yrWH3yYzOUkw3Wm27dpRQmtJIayYPXkeWa7e+y8fJU7r7zM\nnVdf48H3foLx/hZVvsCMx+h5TifZRLqIznATJ2Axm+K3huSVJNeO2km0qanmS6SRYOUPPfa3Ru+K\nwuqHJRAN/LGuGogjcOOV58jqnLA/YGXzPEk8QFSSbH+EXWpC2SZ122TeHovJiEHvMt//rS9z+8vf\n5vN/6z/j63/vf+O7//iLHL56A61Tzr3/cSbT60wmR6TpklLX1DjwA1rDNZw1pLMxrdV1jsZ7HE4O\nWJZZw3RyrnFQwTvqR/WvQlJ6CCFJZ2Os0U22lx/gR81Ntj5OojdaY44Pb0/5+EGEVP49cnmWzjG2\nZu3MJaSnaPWGZIsZUqrmhhsnuDcDWieHTf7VYI3p/haUNVee+ijX/vRLpOM9hPIQYQsbNU41gmbX\nw1nTdCScpJMMCYRH5CcYbaiqnKrWOGuRsnE2GmsYH2wRJi3CVsjh1mvU5QwVSVTg0V1dQXpe41Ay\n9wdiQ0jvXpdG1xVh0sbqZg/GlM141JQldVUyPHGa6dEuuzdeQUiFlAFVXmFqCMIOSsWkswnGaPww\nPn6dTJM95wy+tIS+oDfs0hsM6HT7dNceJqsyVKuiXFoW213uvPwVfvbf/zcosxTfC3HGkBULnDOM\nt26QzibMDnep8jmdwSpCCIK4Q5E2f88Xx4VCWTA92KGcz/FVSKe3is4yFIJOfwXpKUxdveu6VD8s\nFUZcePKDaF3x4lf/CS8/+/sc3L2Odobu2gmwjouPf5CVk2c5eek9bF58hNrWLGdj1k5faPax/IAs\nnVPZAuNp8nqO8yzL7IiwHRMoWBxto5Si1obaKdLlAlNXzbgwjNm/fQ2p1L1x4L/QX0xNyLphOdnj\n0Y9+Botj67VrbJx7L0IMWC4KHCHloqDbPsErf/j7nH3gPUyuf4/zF87S63gU89v0Ntt8/1u/wekn\n38upJ58kXu2z8fBlvMjHZjM8XTDorhH6IaYuiTotaldR6orFcspiMaE1WANtiP0Yk2Z/Fkf0NmE+\nvvsKK3FMZHWuWZgUgrqusLKJ08iKJfQUSzemdHOuvfTH7G6/wuuvfoPRrdv0oxP0orM8+4/+Ieun\nLnD54x/iX/kv/jYf+3f/HS5/6mOsPXIO7XJcVVGnKVGnS3vjFEWdg/JZPXeJ/d1bIAVV2Yx8aieo\nfZ9pkWKMxgsCqjJvHi7vKIzRX1pvWp7rsmC6exelfKR4c6nbRwT+PUqyNaZxa0Zxk1BvDQgPoRQI\nyfq5yyymRyym48aeDeSLaYM00DUqCHHO4QfhvSLu3GPv5/Dma9TLKc988l+inB1y66XvUouQcHgK\noyRhu4NSIRKB7wfk8zmu1MRhnyovmu+2ijjcusVk5w6jO9fJlzOmo32ido+oP2CwvklnOATRhHYH\ncUCZze8tfr/Twpd/lO7FitQV7f4K1miK5QzP8zBVhYckDCKcMQhgfLhD0hlw5spTxN1Vbn7/O/h+\nwsrJC1gDujYYbYiSFkII8nROGCcNiNJZbFURBh6Lg7ssjrZYHh0StgVhqwNuk2vffpVXnv0SP/lL\n/xa1TgniNic2L7D3xmtsPPQIRpckSQs/btIYJjs3kNKh/Ajw6K1uUh0nNCwnIzyliJMOSbuD76kG\nGXEMG04no6aYPo5nejfLGsPBnTfwlM+5R9/HQ09/lLoq8OOEldMX2H79JY62bv4/7d3Zj6TXed/x\n71nerfbeprtnHw6HHJoitVC0oMWL7Niw4xgIEiBBECBwgFwG8E2A/AG6zlWuEiBALpwEBmIHMWzL\nsC0psC1b1E6Kuzicfeut1rfe7Sy5eGuGlCE5jt22hpzzGTQGmB709FR1vfW85zzn95B2emyef5Le\nxg7DrZN017cQKkLpmKYoSAdDZNTGJySdHhZLd7SB9YZyts/O2Qsk3T5lVeO8pK4Mpiwo8wXvfPvP\nyPprNE2Fe8wL3b8JHSWrSSAai+fGW98G73jyE5/nu1/+Em98/euUywq8ZG3nAntX3oFlzZWv/gmb\ng7P4maeZVWyfeY5X/uwr9HeeZDY/Iup36Gzvcji5w+G9qwhrSKOMw+tvE0lBp9vH2AoRR0zne6hO\nhleKuljS7Q6pZtNVXlm8ast4NK6bj8e69Ps8OP0hVifywCOl5MJHXlyNEpF4CS6yiI7kwic/hVIx\nMulwZvgkV77xEq996/f5xX/967zxnd8lO9vn/t23aSpDtjUki0ecOHcJl1fIWlGLimJyj96oXaWa\n7N+lynNY32zH6TgHUrfzCaMU4S3WWVQUP1ZN6+8RD49GN1WBRCCjCJRutxdsg1olPOvVTL+qyNEq\nxqymnOtRzHjvHv2tHTwSHUXoJMWZhjhJibKManqAbWrwDiEipGxT1heTA554/lPcePXrbLz4U5x/\n+nneefM7HNy6TdTp0Enb50UhSLpD5kf7ZL0BRzeuUtU1sttH5hqtoL9+gqFMSLMuzjT0N3eYTvbp\nZtkqoDCmWs5pqgIdJauCMX04uPmDztRVG3XiLMVy0Q61TjtEcUpTVtRFgUxSpJRtoRVFIBV1U7OY\n3eTM0x/l2qvfoCqWRHHCs5/9RaQGHUeYpiTt9KmrkjhOKJbzdqC1loy2d2nqkrWd85RmzGB9h5f+\n15eplkd89p//KjqKufPuNbYuPUWsUib3bjHc2SWOO2giDvavEesMU7cxAIf7t+msjajzKUII0k63\nLcSdI+sNmB7ts37iNOW83Uq2dYVaFe1Am0X3GJNat6OaqoI47SCUYrR9ivMf+SSTe7dBCA5uvcv1\nyREbpy/QP7HLvXffYOPkeY7u3SDt9PEO9q69zqmnnmN95xST+7fBC6QSFIsZo/Ud6qqmrkokAp1k\nSCm58fp36AxGnLr0XLuqIcTjdad6TNwq68t7Tz4fo+IMRzsW7sxzH8PkF7l/8y3wjo3ds/RG62zs\nnmX/2tu89md/inOWrBOzcfo8z3zmnzCd54y2hhhTkU8P6G2foRzv09s8we23vsv2uafIq5Ll/BCp\nY6pqwXB9Y5VPJtqfi6zb3iTjEasb8kdlLNFjV1i9nxNtQGhZ5PSHm6jG4U2FTFPSbJ1YbJEmPbLB\nCa68/k1+679+gU53yGhnHS8OiVLHzbdfIR1s0swP8c2Sqr5NlHaxjUFAe2y4u44S7ZiFtD9i+/xT\nJN2U+cE+UlS4woCQWOGQgjb2h/c+PmBtNscm1gnF+Iio011l0ETYyiK8WB2VbtCRWm0fCqyzJL0h\nMs4wxiFlhMNjmgqdpMwP99g8fQHTNKTdAXWRgzFtAbAawGqNoZFLNs9d4PqrX8M5x0+88HN8+8+/\nwtqpc5hqynDzNPNqyp2rrzI6dQbV67J28TJNY9l98qPcePVrKAFpJyPJ+hSzCfnRQTsXrddnfHiX\nbtxFqhgl2y1MZyxSJrgP0RMtlXqYI9Ru2zWkKsXVNbYs8ZUl7g9pmlWUho4wjSPqduiOhpim4MJH\nX6ApKwYbm1x//eucvvyxtuBWkroqiJKszT7TCSiFVwJURJL2uPPudcrFEW+99Bu88Av/knSQMl/e\nR+qEWhnWts/xyhd/i5MXn0X3u4xvXANj0UnGYLjF5M5V4iSjt7bB+GiPmDaIEtpTb8576qYk6Q+p\nbY3XitpUdAYDvPNUh4cIKdtRLo9xk7SQEmMNUkcUyzlSa47u3qA2FaPNXU49+SxVPufss5/kK//z\nP3F0/yZKR5x+8jk2d89x9523cNZw7ic+gVvWfPsPfqe98XCepNdjuLXLtbdeo1xMkTri7OXnEa5k\ndGKHXvenWS4mVGUJUj4c+C7EB//G5cdFxymmMczG+3T7I8rlHC1h5/w5ykWOSsAYQ9pRDJ44TROn\nbJ++SH844Prr3+DdV1/i4qd/CVuV3Pv+W1TlhLvNNXbPXODOrVtEgy0MDVEaYcoGbwwRniwb0TSO\nSGpGG9tMj/Ywsh2jJR6xC+djXVgJKUEIFJKD6++yd/UNsu6A5SxndnjIYLDFcH2XummYjY/4h//+\nC1TlIdKVjO+8S6ozins11dVDhtu7zA7uMlhfY5kvWOSHqFRTLywykW1OEpK777yKzjIas6Q32KJa\nlu3xex23W1nyByvuR+vH5e+Xs5b5eJ+d9RN479vZYDpFCIfwFqFUuxqwWhWRkQY8rqkZbu204Xau\nJNYKKWE5n7IYHyCTBCt8u80YJW2B5X2b2uwcR/t3iSLF+rkLKKG58vJfMFg/iVnWnHzqMm9//cvs\nPnGZs089T7a5zcH921QekJr59IB8PqXX73H/+jv0104QRwnd4Tq1a1c5ktXWUl3kxGkPGSWM965i\njSHKPqD5VT/Eg2V5gaRYLBhtncA1BXGSofsJuZkAgjhJqaqcxeF9BpunKJc5ebmg0x9gTUOUxBzt\n3Wbj7EW+/ce/zdlnXmD9xGkQUbs9HDmofTt3cVGwdfo0h3ducevdK5iF4xf+1b9juXitzSV7a87z\nP/8pTkvHa1/6Ih/7mV+FVHP1+nfx3tIbrjGeTIk7PcpFznJ6QFksEB7Sfp9iMW9nHyZJOzBcSaz3\nHO3dZnZ4n+1zl7AC5tNVURVOnv3A9IgoiqlXw8u1iliMD+h0+piqYDHe54nnP8VnLv4a9TKnLgua\n5YLnf+ZXmO7f5bU/+QOSbMDuhUsM1rf5zpf+N2tRQr0sOffcp8mnB/RH69h6Sb2cUc0nTCfThyeJ\na1O1rRZRjDchZf1vylmPR+GFo1qOqeoFm7sniZMeVdVmkqEtZTPD+IYTT53n/rU3Gey+wMb5U6AU\ns4MrCJtx5vLHyCcHFIsx9+/cZOP0GZSKqWnbBnqjE+2Q7PmCZHvQbu2Wc8p8hpNtXppAPozZeVQ8\n3oWVbdeELECcsP3sJ7l75XVKllz+/M+wtnuaqloQdTJUkqAHkqtvvM7a+g7DrTMs5zlW1vjYsr//\nCoPNEbY+RMcKhSYRGYVth0Mmgz5lvmC4vb2agzdqAw1t0/YE4dDiB48APs5FFYD3AiFTlrM53eE6\nUmZIoWC1UuWsBQRCaqypkZFASompCrRUWFeAM6A91lqy0YDGQYxYNYc3ONqwR+8MQihMUyB0RCM8\nhW6wZkm6u423A45uXOPGG99j89JlZsWU+qjEXbtF46CKE6S3VAcH9Hs9qnrB1sVL1FXdzjSTCuXa\nFbE4SdsBzzQ4YaiXJSKK2q3ND7z3tr3evyqgkwzXNO3oH1vjACcFRb1oIxQQdAYbNOUSb2virIs3\nHl87pFbEvRFWwSd/6V/wyh/+LmvDC1Cn+FTRkJMNO7haYGvN/Mhy/Y136YxOMvz4Nnvjd9mIMvKD\nAzYv7eIaQWxSmBXcvX+V2hRM92/R6Q2YzPfYPPs0Qg6IehssF4dUyxydDsiLJQiJiBK8q3DeUS8L\nynJBXS5Z2zmLjDJEkmEZI/GPzNbEj5NAvK+toS0y06TD8rA9IUhjiLMeWX9Ec/UtZndu0R2utb0z\nUq96tN7h537t17n55su8+ed/RLe/xjM/9fOIKKIzGHH9e99kuLXL/s2rKB2joxizbAd9OxymWrYr\n28hQVP0tWS9AgvQKHHj6LGYw9QcknQwlIrqdPqYskVYg8ppuMqJZeJpCoLUm1opSFxzufx83zzlx\n8jybW1tceeObbF54ir179xisbdE4T1M2jDZ3acqa2WSfJO0ilER6j1wlrT9qHuvC6v2cs0g0GyfP\nIdVZRrtn2gtqnJDFPVxj+MP/8h+4/JOfZ7R9pm1wbhqSeICMEqRT1MZQ1TNUJ6NxlsbWq7u1Nn1a\nCEmcZuTzGfbBaztceH8ksZo7Nzu8j3eOtNtfHTxo37Cl1A//XpRkOFeBa4MYdZphXYFQkqaaobIe\nvdE2iIRlPibupGDMw8MMKlIIKUnSjJgY62qEq8FYokiAthgzp7PxLI2v6G7sogty45UAABDASURB\nVLM50oBxjlpAp9MlP9hDRZo0GpFPJ20hFSXUjWm/dwG2KnGrBnVrCsplgVjNKvswH/8ul3O6nRTr\nBc4YTFO0g66rBaMTJ5kd3CfrD6EBTE3S67PIx5R5g0zalO7F0R5PvfgZOiPF29/6Pzzx8c9hTQfR\nO4OpKt75zh+ik1fZOHMSldb4+hBbG+qkRzzcYLEscOOCb/7Rb3LhhReYzvYpyxytOtRFQ9oZkc/G\nRMpRLKZtk3Wni0OCb08S59Nxe1FXGpxlY/sM0/17bVq+tUxuXW97UtSHoVD+uyOUwlpD3OkxH+9z\n56tvcOrSR7j26jfpDEYMt3a59fb36N18hzOXP87dq28yOnGSS5/4HFq3PZU4y/TgHh6PimOirIv3\njuViglDt9AMhJFLpx/505rHybYnsvcdLqOuKvJwwSLaxzlDcPmBj+yQYTyNq0lGPo713sEWDLUqk\nMwxOnsZrWKSOcXPI9N5t+oM+XRUxNY5msaCYTekORizrJQe3ryGiLk1d/j+/vR+3UFitJFmH5XwK\n3tM4y91332T77JMIZ1ns32dxtMfG9hmEl9x+61VwDiEjsv6IujFoGVE3E/obO9z+kz/lqc98jvz2\nBBHFWOswdY2QkqapsaYBES66fx3OtaGgi/EBSdZFRjHCO5DtmzNCUC0XxGmHJM0AsK5dCfTetsNb\nPTR1BSJFR7rtsSmKdtizjoiSNnTQOY/3AtM44mxIWR5gjUEq0JHjzOXLjO/ex6qGnhthmyVJFFM3\nC5wSeK/QkUQqhW3aOIgkiVFCgmS1SgXlsgahkEpRFquj3w8a1h+xXoFj4yxISb7IWT9xEiMq0u6A\nKM3AObSO6a9t0qxGEikp8aYGHHGiKcocaTRJNiLrd3AUPP2Zz3H/+i2O7uQk6S3y+QH99Q3Of/Q5\nmnqK9XPwoDsZ6dYm3bVN5N6MP/3v/5knP/IieTFBS0mW9dFxh+V0itIZdZkjZIzSEZ21daSMONjb\nI+l2AYjTFEU7aDrJuriqItIR3jrKfI5S0WPfsP7XIaREoikWE5wxbJ46z098+hcZbZ3kpd//H/TW\nNnnqhZ8mTjMme7eRUcRSSOKsy+23XuHssy8wHe8TdzqcOHeJfHLU9t6VqxVP5x6ONwlF1fF5/8Z2\n20yu8DjS3hp5PkNZTyeKyScTYinI6wKVZkgJvUGX4elzUBvu33iH4dlzHHlLPttncGKbtPRM7tyg\n21tDRhG+KjDec7h3i6jfB6ffazF4hBclHu/N//dpqqpN+06y9uRS1GF89xazw31AkXQGbO5eoJhO\n6I02KRYzdi8+06bLNgZrLd5Cucx54uOfW6Uwr0IehcR7gTUOaxxC6HZ5fPUr+KtJpdsiJJ8/fDGZ\nusI0Fda0BatzFtFY3LJArU74OefwxtDpraPjDs5DU5t2vqCK2mJGqXYa+upUSVXkSCmYHd6jXi7x\nVuGNxzYLrC1Z5lM2Tz7BfDKlM9hARgqtBUkkafIxUjqqZQ5StEOf02wVcGqQUoB39AbD9vtbnSx7\n6EN88Re0/RBORNx89/ssZjO0VrimJooTFkf7FLMJURQ9PO3TmIZ0uIaKE7LugFgnuKakyucsiylR\n1uHpF3+WejFn81Sfz/7jX+LJj13k4PoraC8QRYfRYJvdC0/j0UzvH3Dw1pt0+yOifheVJtRVQTmb\nUM3baQj59KjN17KGrD+imM+wq2wcKRVpt9fmpM2nKN3OCLSrE1MPmKb6UJzq/LvmvMMLqMoCh6PI\n53zt936D+ze/TzYY8fRPfp4inzKf7KPimHxyxHy8T7GYcvrpj3Lj9W+jo4isO2A+OWiDIqu2Sf3B\n1w7X1+Mn/HsfXrQfDo+zpg0BlhHWeopyiY9TGuvxAspiwdG9WzjvWOQTts5cIJ9OyHoD0v6Aqm4w\n0hH1+sTZgGJZUCxzpof77SlEx8M4mke5qIKwYvVDSf9evWmcZb6YUhULpFAMRps0ZUExn+Kcb/t7\nKouUMQJQIuLC5U9w9/rLZIMe+XyKUrpdin7fZvDjGaXwN2eamunhPdJunzjSSKfw3rUNxMK1Wy9C\n47xvZ+0hSNKIpi6xTY6OO+g4YTkvkZFExgnCGOp82Tazeo+OFU1ZtHMcXZuLJaVCKom0hiiWbJ89\nybsvf5WtM09hK4f0giQd4IxDaHCNQ6aKsinb1S/jUUrQWINdjEmzLlgL3lPmi1Ut9WhfJI6HAy9x\nQhP31pmO75MmpxDiQa+cJ4pTvG0HWQsdYfEY5zF1jfIeiaDT71JWS5LBFlXpuPryH/PpX/llFrNb\n5EfXkMD2zgZJImBZkyY9oqiPO5hy7syzXPmd32N4+RmmoqS5O0V7webO2XZsikxYzA9RqoNWGpx7\nOLNTJW0MRl1XJFJSF3mb02UNRlucs+3zKSRIiZfikTup9Ch68Ag9CJI11uDyBYPNHb7zld9p4xmE\noC7byI1yMSeOU8b3btIdrVPmc4xtb2z/ssfhVfXj8P73rraoAgQop1Crz9e2QMcxe4d32teDVyiV\nIToRe4cHmLKgEpr+9hOMx3eIdURlLEf5BGMs1EdtzqBQD4u4B//eB0FYsfoR2udPYq2hakpkJ0Mq\nxfjOLeb7+3QHW+TTCQ5BpNP2IoykGB9x8/XvooXm4MaV9sIgVdga+NsSgjjJyGdHWGNWfySBttdF\n6QjX1G1j6upuxq0yynSUAGAbt3oTVwgEWqfEWQ+QOGOxjcV7SZTGxFmMFAIdJ+jVsGesI+nERFFK\nUxUIr4mTLpgIrKRZWpqqxjQ1OorpDNYwZYGzFinbeYbCebCOqsgf+buu4/Tg7hYkUsXsPvEMs4N7\n6DihqcvVY6TQ8ep0LArTWKqqpqornFBESYdiMQOhgA7FomSyd4/pwRV8Y0mjEd3+kBOndqnz+2xu\nD7j68tf4/rf+gjNPPM8X/+MX+Mmf/6fEax1M7Oj0ttjYPY+QEmsbVCRBOLJOD4FgfnRA2utRLhek\nvR7eO5JstR2YdVBRhIoTdJK0SfwCnPc4CdaFBun/H75djgDafldTVyRZl7pc0tRlG9dhDFGSYlcJ\n/iBQUTsb7uFr6X2vqVDW/t34UaHVwoNyqxFyOsZ6i9MRAoWpDct5Tp7nTI4OKJqao+mUW1e/T743\nZnbjLvn+mNJYjFSIOMJ5izVtbJFcjaX7oAgrVj/E+3OjpEja6rMGkET9EfXqs9OjAwCqv/wFtCMv\nDTpbxxu7+iF8nIb+HRP/XuXfngZ01IsFU2vprZ9ARLod6OsMUihMN8Xhmd+9SX9tCyUURArp28fe\nS3AuRyrdXrxthbcGGcUY0+CkhAQUitpYaixaeChKVCelEeArw7nLz7K4f8C1736DzfMXOHHuAvPZ\nDVACbwXSgfCOerYg67fT2MvFHClTTONZLsbUDh4M5n3wf/0wc0K3d54YvHUc3VswPPUU871Dsk4H\nmWiMq/Eomkgi0hgUpErRlDlZL0FISKI+gpj6cI/y4DYv/oNfpiz38YuGXneNhc25c/MW3c2TFP0+\nG2tnKMZjvvnF3+Sz/+bfcv/+FbI0ZaATVE9TzqbUxmDrCmEcHZkhvMEaiLOIZpmjvCexAlFaYiWY\nTQ6Jsg62aYjSDsWyxNWgRIL07r1b+OCvJPz7HyXxA78rHePqmugvHQBw1oJS1KZG6DY0mNWbb3jE\n/3649z3QbVgy7fVVWOwPfE6hHqw0rYotqdtC2AF4i8NCBEQxinbVa/VJAJTWH8iXUyisgg8cU1fM\nj/bor28QJSmmLlFRTFO2cxa73SGR1LimRiJwSuG8QycpOk5w3q2CHg3eC5SOSGWvHRqLQMuIyleg\nJE1TkakE49oL+nI2QXTX2b5wCZkk5PmCa699l+VkgnMOiUBJTZKkqEjj6xnlMufU088TDwYc3L6G\ncYbH+qW3Wn2cH+3TG6yhU4Ux7cgiJSSZTsmMZD4+Wk0gMOAccdZluHORum748y/9Nz71j/4Z1pZs\n7FzC5XOO9m+QVzlJt89oeIrlfMz9u1foDtYRkeLGO9+jXKV/N6LCLg/RcUJZLOn0hkit8EbgKoOk\nndWplEai2gBZrUFKjGlQ3qGidqWqLpernjn7WK1CBkHwwz3GV/fgg8pZS5PPUVrS39gEIdutJCHb\n4/FxgpIaaIf9qjhGuAeJwTVJktBUBVEct02X3iKFaPu1fHsS0ZgaFWuUjMAK6mKJTFOqsgE3xmOw\ntmJ4YheAYjhvIyGyDnGSgbMoIfC2pFwumBzehfkhVbVsDzU85jtFD6IlaluzOJww2tjCe0M5myCE\nxinH9u5ZvBTMJvurLR/BYjxmsn9ANhyxmB8Bjvl4j06qKcs5zlq6SY96vkRYzXw5YfPiRWSSspjP\niJIu6BgVRTTLBR6BjFPm80lbcFVzBt0NoqSHkBHVctkOjJ4tsd4R93roJKNpGhCKqsixxj1M/w+C\nIAiFVfCBI4RAKk2Rz9FJTGcwbFcLIolvqnYbwTtkFGNdgRNgcYi6QMVxe0TeWuqyQEXt33Wm7fHw\nxoAUbYq71piqRssYoSSoiMVkyvDiBbSOsJ2EsphR1TX9QRdjKpyaUzYTpJLQOKgjom6HTG0ym4/b\n1Hcc8jFub3xQgAgpaeqKxjZMJntkSYZvGgSOMgPvNEpFqP6QtD9A6Yg3vvpVinzGpU//LFW1II5T\nFvt3yYYnSTs7yLwijnp4Ay9/6Yuc++SL1Bqa2bQNnDWepszxaYqQMcZ6GutIhhtUdUncW8M6EM5R\n1zXJYIQpa1xpiJIIKyWNtegoaecCGvswTy0IggBC83rwAZdPDinzOVlvgOp0kEmMixVOS6zwyCyD\nSGG8RaYpUZph6ppIx6RZtw0HjVNkmmAUNJEApdpBz/h2ADS0+VlSMjhxFucdZTNDKkE+n5B0htRC\nURlLg4I4wQlBLRw2lszyCfPpYduA6dqPoCWVotsbtoc/ViuIMk2YVQVVpMgbi+6t4RrNS7/72zS2\n5MlP/xQH42vUusIlHt0bcn9vn8IKxuMj0t4a9669TXdrHaIEYxVVXhHJBOkMuBLbFFivmE7G5PmS\nojFYoZnOJzR4lvWSjfNPs8gXOC0p6gqLoG4aRJRQ1xVN3TzsGQmCIHgg3GoFH2hCSuZH+wgh6O+c\nprE1cZw+DNo0ddn29HTabKQ462CqmjjNKIs5Wkd4376Ze1uDEtR1AVGEtQ3IttFdRQkizugOB7hm\nHx1pVNymtaMURV4hdRfrHL6xKBXjnKEqZm3opfoQh3/+bVhHY9qwVuss/e0dinmO0DFFviSSMQfX\nb1CNx6S6z2j7JMZXbF28yLKYMj3aI+v0MbVkWVRE6YA3Xvoy0/FdzjzzCawF5zUqShFCUuVTkl4X\noWPwmtGJk9RNzWx2QDoYkfSHRFmXppJ01jdo7BjjHCLS7Uk/Kx7GprQDfc1jPwswCIIfJEIibRAE\nQRAEwfEIt1pBEARBEATHJBRWQRAEQRAExyQUVkEQBEEQBMckFFZBEARBEATHJBRWQRAEQRAExyQU\nVkEQBEEQBMckFFZBEARBEATHJBRWQRAEQRAExyQUVkEQBEEQBMckFFZBEARBEATHJBRWQRAEQRAE\nxyQUVkEQBEEQBMckFFZBEARBEATHJBRWQRAEQRAExyQUVkEQBEEQBMckFFZBEARBEATHJBRWQRAE\nQRAExyQUVkEQBEEQBMckFFZBEARBEATHJBRWQRAEQRAExyQUVkEQBEEQBMckFFZBEARBEATHJBRW\nQRAEQRAEx+T/AvNkdE0Y75lzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Visualize Data\n",
    "visualize_tfdataset(ds_train, num_samples=5)\n",
    "visualize_tfdataset(ds_validation, num_samples=5)\n",
    "visualize_tfdataset(ds_test, num_samples=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 712,
     "status": "ok",
     "timestamp": 1566482930370,
     "user": {
      "displayName": "Hugo Aguettaz",
      "photoUrl": "",
      "userId": "05385056281991694353"
     },
     "user_tz": -120
    },
    "id": "-0tPUdROpLjr",
    "outputId": "68e8365c-91e1-4ca4-fba1-8561ae2686e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.61957869, 2.59067358])"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "from numpy.random import binomial\n",
    "\n",
    "y = binomial(1,0.195,1000)\n",
    "\n",
    "compute_class_weight('balanced', np.unique(y), y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "59yHdWEeSSjH"
   },
   "outputs": [],
   "source": [
    "def train_from_tf(model, train_ds, test_ds, batch_size, epochs):   \n",
    "    \n",
    "  \"\"\"\n",
    "  train model on train_loader dataset (with parameters batch_size and epochs) and display\n",
    "  the accuracy on test_loader dataset after each epoch\n",
    "  \"\"\"\n",
    "\n",
    "  optimizer = optim.Adam(model.parameters(), lr=1e-4, weight_decay=5e-5)\n",
    "\n",
    "  class_weights = torch.FloatTensor([1,7]).cuda()\n",
    "  criterion = nn.CrossEntropyLoss(weight=class_weights)\n",
    "\n",
    "  num_batch = int(35e3/batch_size)\n",
    "  \n",
    "  for e in range(epochs):    \n",
    "    for img, lbl in train_ds.take(num_batch):\n",
    "      x = torch.from_numpy(img.numpy()).float().transpose(1,3).transpose(2,3)\n",
    "      y = torch.from_numpy(lbl.numpy()).long()\n",
    "      optimizer.zero_grad()\n",
    "      x, y = to_var(x), to_var(y)\n",
    "      out = model(x,False)\n",
    "      loss = criterion(out, y)\n",
    "      loss.backward()\n",
    "      optimizer.step()\n",
    "\n",
    "    print('\\tat epoch {} of the training the f1 score on test set is {}'.format(e+1,test_f1_score_from_tf(model, test_ds, batch_size)))\n",
    "        \n",
    "def test_f1_score_from_tf(model, test_ds, batch_size):\n",
    "    \n",
    "    \"\"\"\n",
    "    return the accuracy of the model on the test dataset\n",
    "    \"\"\"\n",
    "    \n",
    "    from sklearn.metrics import f1_score\n",
    "    \n",
    "    nb_errors = 0\n",
    "    num_batch = int(10e3/batch_size)\n",
    "    \n",
    "    y_pred = np.array([])\n",
    "    y_true = np.array([])\n",
    "    \n",
    "    for img, lbl in test_ds.take(num_batch):\n",
    "      x = torch.from_numpy(img.numpy()).float().transpose(1,3).transpose(2,3)\n",
    "      y = torch.from_numpy(lbl.numpy()).long()\n",
    "      x, y = to_var(x), to_var(y)\n",
    "      out = model(x,False)\n",
    "      _, y_hat = out.data.max(1)\n",
    "      \n",
    "      y_true = np.concatenate((y_true, y.cpu().numpy()))\n",
    "      y_pred = np.concatenate((y_pred, y_hat.cpu().numpy()))                          \n",
    "            \n",
    "    return f1_score(y_true,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3380,
     "status": "ok",
     "timestamp": 1566480225633,
     "user": {
      "displayName": "Hugo Aguettaz",
      "photoUrl": "",
      "userId": "05385056281991694353"
     },
     "user_tz": -120
    },
    "id": "Dc-rW_xiYVT7",
    "outputId": "11718abe-6615-4cec-a3b2-40be400cd191"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class 0 : 0.8125, class 1 : 0.1875\n",
      "class 0 : 0.75, class 1 : 0.25\n",
      "class 0 : 0.8125, class 1 : 0.1875\n",
      "class 0 : 0.8359375, class 1 : 0.1640625\n",
      "class 0 : 0.8046875, class 1 : 0.1953125\n"
     ]
    }
   ],
   "source": [
    "for img, lbl in train_ds.take(5):\n",
    "  y = lbl.numpy()\n",
    "  print('class 0 : {}, class 1 : {}'.format(y[(y==0)].shape[0]/y.shape[0],y[(y==1)].shape[0]/y.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4858110,
     "status": "error",
     "timestamp": 1566490782584,
     "user": {
      "displayName": "Hugo Aguettaz",
      "photoUrl": "",
      "userId": "05385056281991694353"
     },
     "user_tz": -120
    },
    "id": "UBz2zuwNfCRq",
    "outputId": "7e25eeeb-2664-44fe-9439-285bb8da2c74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model VGG_MC contains 2657858 parameters\n",
      "\tat epoch 0 of the training the f1 score on test set is 0.3219716832721552\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.32219163717394156\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.32219163717394156\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.32219163717394156\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.32219163717394156\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.32219163717394156\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.32219163717394156\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.3224115334207078\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.32175167169267077\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.32263137203511993\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.3224115334207078\n",
      "\tat epoch 11 of the training the f1 score on test set is 0.3240312128025393\n",
      "\tat epoch 12 of the training the f1 score on test set is 0.3504175536269854\n",
      "\tat epoch 13 of the training the f1 score on test set is 0.34945054945054943\n",
      "\tat epoch 14 of the training the f1 score on test set is 0.35818930041152264\n",
      "\tat epoch 15 of the training the f1 score on test set is 0.3750946252838758\n",
      "\tat epoch 16 of the training the f1 score on test set is 0.37509266123054114\n",
      "\tat epoch 17 of the training the f1 score on test set is 0.3671848013816925\n",
      "\tat epoch 18 of the training the f1 score on test set is 0.3755747654956778\n",
      "\tat epoch 19 of the training the f1 score on test set is 0.37789203084832906\n",
      "\tat epoch 20 of the training the f1 score on test set is 0.3881642982285381\n",
      "\tat epoch 21 of the training the f1 score on test set is 0.38924558587479935\n",
      "\tat epoch 22 of the training the f1 score on test set is 0.3907948007671\n",
      "\tat epoch 23 of the training the f1 score on test set is 0.391519143680527\n",
      "\tat epoch 24 of the training the f1 score on test set is 0.3926535782140596\n",
      "\tat epoch 25 of the training the f1 score on test set is 0.3927966101694915\n",
      "\tat epoch 26 of the training the f1 score on test set is 0.3945849094166832\n",
      "\tat epoch 27 of the training the f1 score on test set is 0.39696788350289247\n",
      "\tat epoch 28 of the training the f1 score on test set is 0.394700415265968\n",
      "\tat epoch 29 of the training the f1 score on test set is 0.40155945419103317\n",
      "\tat epoch 30 of the training the f1 score on test set is 0.4007561436672967\n",
      "\tat epoch 31 of the training the f1 score on test set is 0.4020768431983385\n",
      "\tat epoch 32 of the training the f1 score on test set is 0.41355302854490605\n",
      "\tat epoch 33 of the training the f1 score on test set is 0.40382571732199796\n",
      "\tat epoch 34 of the training the f1 score on test set is 0.4066840277777778\n",
      "\tat epoch 35 of the training the f1 score on test set is 0.4093023255813953\n",
      "\tat epoch 36 of the training the f1 score on test set is 0.4187515180957007\n",
      "\tat epoch 37 of the training the f1 score on test set is 0.41150947704955465\n",
      "\tat epoch 38 of the training the f1 score on test set is 0.413512218495448\n",
      "\tat epoch 39 of the training the f1 score on test set is 0.41644498186215234\n",
      "\tat epoch 40 of the training the f1 score on test set is 0.41425818882466275\n",
      "\tat epoch 41 of the training the f1 score on test set is 0.423412204234122\n",
      "\tat epoch 42 of the training the f1 score on test set is 0.42047440699126093\n",
      "\tat epoch 43 of the training the f1 score on test set is 0.4298966339782666\n",
      "\tat epoch 44 of the training the f1 score on test set is 0.4211320754716982\n",
      "\tat epoch 45 of the training the f1 score on test set is 0.4202235772357723\n",
      "\tat epoch 46 of the training the f1 score on test set is 0.4158156585493585\n",
      "\tat epoch 47 of the training the f1 score on test set is 0.41720123544784987\n",
      "\tat epoch 48 of the training the f1 score on test set is 0.4216352201257862\n",
      "\tat epoch 49 of the training the f1 score on test set is 0.41705237515225335\n",
      "\tat epoch 50 of the training the f1 score on test set is 0.4215784215784215\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-9f185b0e960f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\tat epoch 0 of the training the f1 score on test set is {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_f1_score_from_tf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mtrain_from_tf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mF\"/content/gdrive/My Drive/models/{model.get_name()}_pretrain{str(epochs)}.pkl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol)\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \"\"\"\n\u001b[0;32m--> 224\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_with_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_with_file_like\u001b[0;34m(f, mode, body)\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[0mnew_fd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/gdrive/My Drive/models/VGG_MC_pretrain50.pkl'"
     ]
    }
   ],
   "source": [
    "model = VGG_MC()\n",
    "epochs = 50\n",
    "batch_size = 200\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    model.cuda()\n",
    "    \n",
    "print('model {} contains {} parameters'.format(model.get_name(), model.count_parameters()))\n",
    "print('\\tat epoch 0 of the training the f1 score on test set is {}'.format(test_f1_score_from_tf(model, test_ds, batch_size)))\n",
    "train_from_tf(model, train_ds, test_ds, batch_size, epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RqNTHuczIRbf"
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), F\"/content/drive/My Drive/models/{model.get_name()}_pretrain{str(epochs)}.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NyrMMuHO1xIo"
   },
   "outputs": [],
   "source": [
    "def prune_network(model, train_ds, test_ds, batch_size, epochs, mode = ['weights','local']):\n",
    "    \n",
    "    \"\"\"\n",
    "    prune the network with various pruning rates from 0% to 100%\n",
    "    pruning can be made by weights/filters and global/local, default is localy per weights\n",
    "    \n",
    "    \"\"\"\n",
    "        \n",
    "    pruning = np.linspace(0,95,20)\n",
    "\n",
    "    for p in pruning:\n",
    "        if os.path.isfile(\"drive/My Drive/models/{}_pretrain{}_pr{}.pkl\".format(model.get_name(),epochs,int(p))):\n",
    "            continue\n",
    "        if p == 0:\n",
    "            # Load the pretrained model\n",
    "            model.load_state_dict(torch.load(\"drive/My Drive/models/{}_pretrain{}.pkl\".format(model.get_name(),epochs)))\n",
    "        else:\n",
    "            model.load_state_dict(torch.load(\"drive/My Drive/models/{}_pretrain{}_pr{}.pkl\".format(model.get_name(),epochs,int(p-5))))\n",
    "\n",
    "        # prune the weights\n",
    "        masks = weight_prune(model, p, mode[1])\n",
    "        model.set_masks(masks, mode[0])\n",
    "\n",
    "        # Retraining\n",
    "        train_from_tf(model, train_ds, test_ds, batch_size, 10)\n",
    "\n",
    "        # Save the entire model on Google Drive\n",
    "        torch.save(model.state_dict(), \"drive/My Drive/models/{}_pretrain{}_pr{}.pkl\".format(model.get_name(),epochs,int(p)))\n",
    "        \n",
    "def weight_prune(model, pr, mode): \n",
    "    \n",
    "    \"\"\"\n",
    "    return a mask to prune weights whose absolute value is below a treshold computed to reach\n",
    "    the given pruning rate (pr)\n",
    "    \"\"\"\n",
    "    \n",
    "    masks = []\n",
    "    \n",
    "    if mode == 'local':\n",
    "        for name, p in model.named_parameters():\n",
    "            thresh = np.percentile(p.cpu().data.abs().numpy().flatten(), pr)\n",
    "            if 'weight' in name:\n",
    "                if len(p.data.size()) != 1:\n",
    "                    pruned_inds = p.data.abs() > thresh\n",
    "                    masks.append(pruned_inds.float())\n",
    "                    \n",
    "    if mode == 'global':\n",
    "        all_weights = []\n",
    "        for p in model.parameters():\n",
    "            if len(p.data.size()) != 1:\n",
    "                all_weights += list(p.cpu().data.abs().numpy().flatten())\n",
    "        thresh = np.percentile(np.array(all_weights), pr)\n",
    "\n",
    "        for name, p in model.named_parameters():\n",
    "            if 'weight' in name:\n",
    "                if len(p.data.size()) != 1:\n",
    "                    pruned_inds = p.data.abs() > thresh\n",
    "                    masks.append(pruned_inds.float())\n",
    "    \n",
    "    return masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 16226341,
     "status": "ok",
     "timestamp": 1566510313047,
     "user": {
      "displayName": "Hugo Aguettaz",
      "photoUrl": "",
      "userId": "05385056281991694353"
     },
     "user_tz": -120
    },
    "id": "g6C0LI_51RMU",
    "outputId": "2ebbb165-9a5e-433d-fb74-c9dae75f5d11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tat epoch 1 of the training the f1 score on test set is 0.4139065069297628\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.43916242218449353\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.43690409422321924\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.41851676901275386\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.42824014429270807\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4179807013414922\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.42228598756575797\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.40143369175627236\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4395296752519597\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.40913043478260863\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.41167501633631015\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.41754225673823664\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.40812797233030695\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4089962515618492\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.42144007361398667\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4001592673700976\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.41328577797592514\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.40707964601769914\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.40617525549032396\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.4134871339840285\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.4216923796166433\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.39857651245551606\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.4300811010076185\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.3976811594202898\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.42241169305724724\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.39512855209742886\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.3655559221379083\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.4101382488479263\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4212900315741993\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.3863385971355123\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.39392759828727136\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.4065708418891171\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.42485065710872155\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.39992041384799043\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.4045630474638419\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.3893672781729689\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.3857434564692779\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.4171263462043604\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.38868855570316946\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.44416\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.41491228070175434\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.4374038757305444\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.4102448887890362\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4223512336719884\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.4281422133199799\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4321734745335351\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.44172145845786015\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.42265587815326033\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4436183395291202\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.43232008313847753\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.43197193685793034\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.4166478172359194\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.4404294317884263\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4382111669753903\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.42553191489361697\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4371077762619373\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.42908054169636495\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.40969441517386723\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.42110288851754596\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.42100073946265715\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.43283582089552236\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.4297725024727992\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.44910868315123637\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4143585198315976\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.41408895913298716\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.42846589509972915\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.43478260869565216\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.4216141001855288\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4027317880794702\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.4311406750431141\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.42545198403381074\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.4235526966848095\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.43280423280423286\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4180500350713117\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.3932670237184392\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.3989729409441043\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.43037974683544306\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.42469950506716947\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.43307298680312417\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.4406047516198704\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.4007134363852556\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.426400759734093\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.4311199207135778\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.44231281641353587\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.43654555028962616\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.42716165413533824\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.4127806563039723\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.3962860529435006\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4392135142619773\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.44118431581755135\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.41946054953365264\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.41202110575820144\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.43327469213370196\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4148550724637681\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.42064398264443936\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.42309468822170904\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.41113028472821395\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.4179510426110608\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4337288563494067\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.43365359646844975\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.3910196445275959\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.41776814734561213\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.42418471792430373\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4197365483366823\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.4216444132115249\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4031514219830899\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.40998064099806414\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.4336504757135703\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.43497214821990793\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.44087510384934914\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.38720863032171066\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.39555555555555555\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.38245866283249463\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.40549102428722283\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.41783100465735196\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4037305699481865\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.4209783631232361\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.43913043478260866\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.43845315904139426\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.44357541899441344\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.40741548527808075\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.43794147325933397\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.4289624284647922\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4183719872669395\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.42040256175663315\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.45130920859076196\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.4438056912906007\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.4337782340862422\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4479229379891632\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.45440589499539447\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.3468024180719058\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.4185610499179751\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.417787913340935\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.42390804597701154\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.44759521823742004\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4416469971151324\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.4316682375117814\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.43060765191297823\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4127531236535976\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.42259225922592264\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.4131960692559663\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.42982248520710065\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.41621504443962715\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4118029941418963\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.4258872651356994\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4391768689762959\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.44166014095536416\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.44868035190615835\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4531015882529218\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.45140515222482436\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.3877511961722488\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.3994391025641026\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.40630252100840336\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.4161191414805081\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.41118279569892474\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.4149286498353457\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.4121343445287107\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.41014429383471795\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.421271676300578\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.4195481788842784\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.34841282011961355\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.37427499147048787\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.38354203935599285\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.39632042928325023\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.4110950113848064\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.41781411359724613\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.4165429480381761\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.42273534635879223\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.42237947810703236\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.423455684870188\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.3487096774193548\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.348319641523525\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.3710407239819004\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.3931623931623932\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.4023382382584156\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.41058201058201055\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.4151346729371526\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.41312169312169306\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.4233156028368794\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.4168096054888508\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.3224537947306331\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.3398579092358997\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.36324926301998034\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.3640881232400199\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.37199795605518654\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.37368692956776306\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.37708077799193973\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.38517707192405987\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.39407522900019487\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.38618113912231555\n",
      "\tat epoch 1 of the training the f1 score on test set is 0.3244269632442696\n",
      "\tat epoch 2 of the training the f1 score on test set is 0.33150289017341045\n",
      "\tat epoch 3 of the training the f1 score on test set is 0.3351664254703329\n",
      "\tat epoch 4 of the training the f1 score on test set is 0.35391470401018454\n",
      "\tat epoch 5 of the training the f1 score on test set is 0.3642679900744416\n",
      "\tat epoch 6 of the training the f1 score on test set is 0.36662788765165366\n",
      "\tat epoch 7 of the training the f1 score on test set is 0.3598515410682589\n",
      "\tat epoch 8 of the training the f1 score on test set is 0.35859226379201015\n",
      "\tat epoch 9 of the training the f1 score on test set is 0.37069696455824996\n",
      "\tat epoch 10 of the training the f1 score on test set is 0.36064526433477084\n"
     ]
    }
   ],
   "source": [
    "mode = ['weights','local']\n",
    "\n",
    "prune_network(model, train_ds, test_ds, batch_size, epochs, mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3345,
     "status": "error",
     "timestamp": 1566545630679,
     "user": {
      "displayName": "Hugo Aguettaz",
      "photoUrl": "",
      "userId": "05385056281991694353"
     },
     "user_tz": -120
    },
    "id": "XfxksLUAFC_l",
    "outputId": "001d544e-762a-4c1c-88c2-f08845642640"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model VGG_MC contains 2657858 parameters\n",
      "LOADING drive/My Drive/models/VGG_MC_pretrain50.pkl\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-1957a0230e74>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\tat epoch e of the training the f1 score on test set is {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_f1_score_from_tf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0mtrain_from_tf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0me_load\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mF\"drive/My Drive/models/{model.get_name()}_pretrain{str(epochs)}.pkl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-e0ad9d4f7eee>\u001b[0m in \u001b[0;36mtest_f1_score_from_tf\u001b[0;34m(model, test_ds, batch_size)\u001b[0m\n\u001b[1;32m     43\u001b[0m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m       \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlbl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m       \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_var\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_var\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m       \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m       \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-14-4f0aa127b375>\u001b[0m in \u001b[0;36mto_var\u001b[0;34m(x, requires_grad)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequires_grad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequires_grad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 150.00 MiB (GPU 0; 11.17 GiB total capacity; 10.58 GiB already allocated; 29.56 MiB free; 237.68 MiB cached)"
     ]
    }
   ],
   "source": [
    "model = VGG_MC()\n",
    "epochs = 100\n",
    "batch_size = 200\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    model.cuda()\n",
    "    \n",
    "print('model {} contains {} parameters'.format(model.get_name(), model.count_parameters()))\n",
    "\n",
    "for e in [99-i for i in range(epochs)]:\n",
    "  if os.path.isfile(\"drive/My Drive/models/{}_pretrain{}.pkl\".format(model.get_name(),e)):\n",
    "    model.load_state_dict(torch.load(\"drive/My Drive/models/{}_pretrain{}.pkl\".format(model.get_name(),e)))\n",
    "    print(\"LOADING drive/My Drive/models/{}_pretrain{}.pkl\".format(model.get_name(),e))\n",
    "    e_load = e\n",
    "    continue\n",
    "\n",
    "print('\\tat epoch e of the training the f1 score on test set is {}'.format(test_f1_score_from_tf(model, test_ds, batch_size)))\n",
    "train_from_tf(model, train_ds, test_ds, batch_size, epochs-e_load)\n",
    "torch.save(model.state_dict(), F\"drive/My Drive/models/{model.get_name()}_pretrain{str(epochs)}.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fiDsHjppeVo4"
   },
   "source": [
    "### Network pretraining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6--xOT40fCR3"
   },
   "outputs": [],
   "source": [
    "class MaskedConv2d(nn.Conv2d):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size, stride=1,\n",
    "                 padding=0, dilation=1, groups=1, bias=True):\n",
    "        super(MaskedConv2d, self).__init__(in_channels, out_channels, \n",
    "            kernel_size, stride, padding, dilation, groups, bias)\n",
    "        self.mask_flag = False\n",
    "    \n",
    "    def set_mask(self, mask):\n",
    "        self.mask = Variable(mask, requires_grad=False)\n",
    "        self.weight.data = self.weight.data*self.mask.data\n",
    "        self.mask_flag = True\n",
    "    \n",
    "    def get_mask(self):\n",
    "        print(self.mask_flag)\n",
    "        return self.mask\n",
    "    \n",
    "    def forward(self, x):\n",
    "        if self.mask_flag == True:\n",
    "            weight = self.weight*self.mask\n",
    "            return F.conv2d(x, weight, self.bias, self.stride,\n",
    "                        self.padding, self.dilation, self.groups)\n",
    "        else:\n",
    "            return F.conv2d(x, self.weight, self.bias, self.stride,\n",
    "                        self.padding, self.dilation, self.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LI-9es3TfCR-"
   },
   "outputs": [],
   "source": [
    "class MaskedLinear(nn.Linear):\n",
    "    def __init__(self, in_features, out_features, bias=True):\n",
    "        super(MaskedLinear, self).__init__(in_features, out_features, bias)\n",
    "        self.mask_flag = False\n",
    "    \n",
    "    def set_mask(self, mask):\n",
    "        self.mask = Variable(mask, requires_grad=False)\n",
    "        self.weight.data = self.weight.data*self.mask.data\n",
    "        self.mask_flag = True\n",
    "    \n",
    "    def get_mask(self):\n",
    "        print(self.mask_flag)\n",
    "        return self.mask\n",
    "    \n",
    "    def forward(self, x):\n",
    "        if self.mask_flag == True:\n",
    "            weight = self.weight*self.mask\n",
    "            return F.linear(x, weight, self.bias)\n",
    "        else:\n",
    "            return F.linear(x, self.weight, self.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3QLT_OHYfCSO"
   },
   "outputs": [],
   "source": [
    "def to_var(x, requires_grad=False):\n",
    "    \n",
    "    \"\"\"\n",
    "    return the input in Variable format after having applied cuda transformation if possible\n",
    "    \"\"\"\n",
    "    \n",
    "    if torch.cuda.is_available():\n",
    "        x = x.cuda()\n",
    "    return Variable(x, requires_grad=requires_grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "II6us-NZfCSX"
   },
   "outputs": [],
   "source": [
    "class VGG_MC(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(VGG_MC, self).__init__()\n",
    "        # Block 1\n",
    "        self.conv1 = MaskedConv2d(3, 32, kernel_size=3, padding=1, stride=2) # padding = 'same'\n",
    "        self.relu1 = nn.ReLU(inplace=True)\n",
    "        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "\n",
    "        # Block 2\n",
    "        self.conv2 = MaskedConv2d(32, 32, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu2 = nn.ReLU(inplace=True)\n",
    "        self.conv3 = MaskedConv2d(32, 32, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu3 = nn.ReLU(inplace=True)\n",
    "        self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "\n",
    "        # Block 3\n",
    "        self.conv4 = MaskedConv2d(32, 64, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu4 = nn.ReLU(inplace=True)\n",
    "        self.conv5 = MaskedConv2d(64, 64, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu5 = nn.ReLU(inplace=True)\n",
    "        self.maxpool5 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "\n",
    "        # Block 4\n",
    "        self.conv6 = MaskedConv2d(64, 128, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu6 = nn.ReLU(inplace=True)\n",
    "        self.conv7 = MaskedConv2d(128, 128, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu7 = nn.ReLU(inplace=True)\n",
    "        self.conv8 = MaskedConv2d(128, 128, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu8 = nn.ReLU(inplace=True)\n",
    "        self.conv9 = MaskedConv2d(128, 128, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu9 = nn.ReLU(inplace=True)\n",
    "        self.maxpool9 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "\n",
    "        # Block 5\n",
    "        self.conv10 = MaskedConv2d(128, 256, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu10 = nn.ReLU(inplace=True)\n",
    "        self.conv11 = MaskedConv2d(256, 256, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu11 = nn.ReLU(inplace=True)\n",
    "        self.conv12 = MaskedConv2d(256, 256, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu12 = nn.ReLU(inplace=True)\n",
    "        self.conv13 = MaskedConv2d(256, 256, kernel_size=3, padding=1, stride=1)\n",
    "        self.relu13 = nn.ReLU(inplace=True)\n",
    "        \n",
    "        # Global poolings\n",
    "        self.maxpool14 = nn.MaxPool2d(kernel_size=256)\n",
    "        self.avgpool14 = nn.AvgPool2d(kernel_size=256)\n",
    "        \n",
    "        # Fully connected\n",
    "        self.linear14 = MaskedLinear(512,2)\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x, training):\n",
    "        x = self.maxpool1(F.dropout(self.relu1(self.conv1(x)),0.1, training=training))\n",
    "        \n",
    "        x = F.dropout(self.relu2(self.conv2(x)),0.1, training=training)\n",
    "        x = self.maxpool3(F.dropout(self.relu3(self.conv3(x)),0.1, training=training))\n",
    "        \n",
    "        x = F.dropout(self.relu4(self.conv4(x)),0.1, training=training)\n",
    "        x = self.maxpool5(F.dropout(self.relu5(self.conv5(x)),0.1, training=training))\n",
    "        \n",
    "        x = F.dropout(self.relu6(self.conv6(x)),0.1, training=training)\n",
    "        x = F.dropout(self.relu7(self.conv7(x)),0.1, training=training)\n",
    "        x = F.dropout(self.relu8(self.conv8(x)),0.1, training=training)\n",
    "        x = self.maxpool9(F.dropout(self.relu9(self.conv9(x)),0.1, training=training))\n",
    "        \n",
    "        x = F.dropout(self.relu10(self.conv10(x)),0.1, training=training)\n",
    "        x = F.dropout(self.relu11(self.conv11(x)),0.1, training=training)\n",
    "        x = F.dropout(self.relu12(self.conv12(x)),0.1, training=training)\n",
    "        x = F.dropout(self.relu13(self.conv13(x)),0.1, training=training)\n",
    "        x = torch.cat((self.maxpool14(x).view(-1,256),self.avgpool14(x).view(-1,256)),dim=1)\n",
    "        \n",
    "        x = self.softmax(self.linear14(x))\n",
    "        return x\n",
    "\n",
    "    def set_masks(self, masks, mode):\n",
    "        # Should be a less manual way to set masks\n",
    "        # Leave it for the future\n",
    "        if mode == 'filters':\n",
    "            self.conv1.set_mask(masks[0])\n",
    "            self.conv2.set_mask(masks[1])\n",
    "            self.conv3.set_mask(masks[2])\n",
    "            self.conv4.set_mask(masks[3])\n",
    "            self.conv5.set_mask(masks[4])\n",
    "            self.conv6.set_mask(masks[5])\n",
    "            self.conv7.set_mask(masks[6])\n",
    "            self.conv8.set_mask(masks[7])\n",
    "            self.conv9.set_mask(masks[8])\n",
    "            self.conv10.set_mask(masks[9])\n",
    "            self.conv11.set_mask(masks[10])\n",
    "            self.conv12.set_mask(masks[11])\n",
    "            self.conv13.set_mask(masks[12])\n",
    "        if mode == 'weights':\n",
    "            self.conv1.set_mask(masks[0])\n",
    "            self.conv2.set_mask(masks[1])\n",
    "            self.conv3.set_mask(masks[2])\n",
    "            self.conv4.set_mask(masks[3])\n",
    "            self.conv5.set_mask(masks[4])\n",
    "            self.conv6.set_mask(masks[5])\n",
    "            self.conv7.set_mask(masks[6])\n",
    "            self.conv8.set_mask(masks[7])\n",
    "            self.conv9.set_mask(masks[8])\n",
    "            self.conv10.set_mask(masks[9])\n",
    "            self.conv11.set_mask(masks[10])\n",
    "            self.conv12.set_mask(masks[11])\n",
    "            self.conv13.set_mask(masks[12])\n",
    "            self.linear14.set_mask(masks[13])\n",
    "            \n",
    "    def get_name(self):\n",
    "        return 'VGG_MC'\n",
    "      \n",
    "    def count_parameters(self):\n",
    "        return sum(p.numel() for p in self.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pcprhAdbA75g"
   },
   "source": [
    "### Evaluate Model\n",
    "\n",
    "Machine learning researchers often evaluate their predictions directly on the whole test set.\n",
    "But, in fact, in real-world settings we have additional choices available, like asking for more information when we are uncertain.\n",
    "Because of the importance of accurate diagnosis, it would be unreasonable _not_ to ask for further scans of the most ambiguous cases.\n",
    "Moreover, in this dataset, many images feature camera artefacts that distort results.\n",
    "In these cases, it is critically important that a model is able to tell when the information provided to it is not sufficiently reliable to classify the patient.\n",
    "Just like real medical professionals, any diagnostic algorithm should be able to flag cases that require more investigation by medical experts.\n",
    "\n",
    "<p align=\"center\">\n",
    "<img src=\"http://www.cs.ox.ac.uk/people/angelos.filos/assets/bdl-benchmarks/diagnosis.png\" >\n",
    "</p>\n",
    "\n",
    "This task is illustrated in the figure above, where a threshold, τ, is used to flag cases as certain and uncertain, with uncertain cases referred to an expert. Alternatively, the uncertainty estimates could be used to come up with a priority list, which could be matched to the available resources of a hospital, rather than waste diagnostic resources on patients for whom the diagnosis is clear cut.\n",
    "\n",
    "In order to simulate this process of referring the uncertain cases to experts and relying on the model's predictions for cases it is certain of, we assess the techniques by their diagnostic accuracy and area under receiver-operating-characteristic (ROC) curve, as a function of the\n",
    "referral rate. We expect the models with well-calibrated uncertainty to refer their least confident predictions to experts,\n",
    "improving their performance as the number of referrals increases.\n",
    "\n",
    "The accuracy of the binary classifier is defined as the ratio of correctly classified data-points over the size of the population.\n",
    "The receiver-operating-characteristic (ROC) curve illustrates\n",
    "the diagnostic ability of a binary classifier system as its discrimination threshold is varied.\n",
    "It is created by plotting the true positive rate (a.k.a. sensitivity) against the false positive rate (a.k.a. 1 - sensitivity).\n",
    "The quality of such a ROC curve can be summarized by its area under the curve (AUC), which varies between 0.5 (chance level) and 1.0 (best possible value)."
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