Tweet_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tweet Classification",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/manashpratim/Tweet-Classification/blob/master/Tweet_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QzGirNa_di7b",
        "colab_type": "code",
        "outputId": "3c331fad-d13b-4bfc-c86f-2b31ba49ce58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "#Downloading the Datasets\n",
        "!wget --no-check-certificate \\\n",
        "      \"https://datahack-prod.s3.amazonaws.com/train_file/train_E6oV3lV.csv\"\\\n",
        "      -O \"/tmp/train_tweets.csv\"\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "      \"https://datahack-prod.s3.amazonaws.com/test_file/test_tweets_anuFYb8.csv\"\\\n",
        "      -O \"/tmp/test_tweets.csv\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-07-10 02:04:27--  https://datahack-prod.s3.amazonaws.com/train_file/train_E6oV3lV.csv\n",
            "Resolving datahack-prod.s3.amazonaws.com (datahack-prod.s3.amazonaws.com)... 52.219.66.32\n",
            "Connecting to datahack-prod.s3.amazonaws.com (datahack-prod.s3.amazonaws.com)|52.219.66.32|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3103165 (3.0M) [text/csv]\n",
            "Saving to: ‘/tmp/train_tweets.csv’\n",
            "\n",
            "/tmp/train_tweets.c 100%[===================>]   2.96M  1.71MB/s    in 1.7s    \n",
            "\n",
            "2019-07-10 02:04:30 (1.71 MB/s) - ‘/tmp/train_tweets.csv’ saved [3103165/3103165]\n",
            "\n",
            "--2019-07-10 02:04:36--  https://datahack-prod.s3.amazonaws.com/test_file/test_tweets_anuFYb8.csv\n",
            "Resolving datahack-prod.s3.amazonaws.com (datahack-prod.s3.amazonaws.com)... 52.219.66.0\n",
            "Connecting to datahack-prod.s3.amazonaws.com (datahack-prod.s3.amazonaws.com)|52.219.66.0|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1635543 (1.6M) [text/csv]\n",
            "Saving to: ‘/tmp/test_tweets.csv’\n",
            "\n",
            "/tmp/test_tweets.cs 100%[===================>]   1.56M   299KB/s    in 6.9s    \n",
            "\n",
            "2019-07-10 02:04:44 (231 KB/s) - ‘/tmp/test_tweets.csv’ saved [1635543/1635543]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jaONxCuud6mb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "df1=pd.read_csv('/tmp/train_tweets.csv')\n",
        "df2=pd.read_csv('/tmp/test_tweets.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwVt4sNoeXXD",
        "colab_type": "code",
        "outputId": "83283bd4-e43a-41a5-d61a-29518068cc9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df1.head(5)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>@user when a father is dysfunctional and is s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>bihday your majesty</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>factsguide: society now    #motivation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  label                                              tweet\n",
              "0   1      0   @user when a father is dysfunctional and is s...\n",
              "1   2      0  @user @user thanks for #lyft credit i can't us...\n",
              "2   3      0                                bihday your majesty\n",
              "3   4      0  #model   i love u take with u all the time in ...\n",
              "4   5      0             factsguide: society now    #motivation"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEWMXosli8Lp",
        "colab_type": "code",
        "outputId": "bad91719-85bd-4c4d-f188-ed3f40233ab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.bar([0,1], df1['label'].value_counts(), width=0.5, bottom=None, align='center', data=df1)\n",
        "plt.title('Labels Distribution')\n",
        "plt.xlabel('Labels')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(np.arange(0, 1, step=2))\n",
        "print('Labels Distribution:\\n',df1['label'].value_counts())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels Distribution:\n",
            " 0    29720\n",
            "1     2242\n",
            "Name: label, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEWCAYAAACufwpNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGQxJREFUeJzt3Xu0XnV95/H3hwAVinKRSDEBgpKx\njW1FjIDLG8URAo4NdqkDrRKVIa4lTHXVWsFxChVZlZkqFW9LrBkBtYhXGI3FQKkMHbnEwgBBGTIB\nSsItEhARCgLf+WP/Ao+Hc3IeYD85nOT9WutZz97f/dt7//Zh8Xyy76kqJEnqwxZT3QFJ0qbDUJEk\n9cZQkST1xlCRJPXGUJEk9cZQkST1xlDRZiHJPyX5Txt73iGWvSLJAT0t60+S/GBgvJLs1cey2/Lu\nS/KCvpanTZOhomklyU1J/v1U92MySea0H/X72ueOJN9N8vrBdlX14qr6pyGXteWG2lXVV6rqoB66\nP26QVtV2VbWqj+Vr02WoSKO1Q1VtB7wEWAZ8O8k7+l7JZIEjbSyGijYJSXZsewJrk9zdhmePafbC\nJJcnuTfJuUl2Gph//yT/O8k9Sf7PRIekkuyV5IdJfp7kZ0m+Nkz/qur2qvokcCJwSpIt2vIe2/NK\nsm+S5a1/dyT5RJv94vZ9T9vreUWSdyT55ySnJrkLOLHVLhmz6kOTrGp9/e8D6z0xyZcHtuuxvaEk\nJwOvBj7d1vfp1uaxw2lJtk9yZvt735zkwwPLfkeSS5L8TftvcWOSQ4b5O2n6M1S0qdgC+B/AHsDu\nwAPAp8e0ORJ4F7Ar8DBwGkCSWcD3gI8COwF/Dnwzycxx1nMS8ANgR2A28Kkn2c9vAc8DXjTOtE8C\nn6yq5wAvBM5p9de07x3aIagftfH9gFXALsDJE6zvTcB8YB9gId32b1BV/RfgfwHHtvUdO06zTwHb\nAy8AXkv3t33nwPT9gOuBnYH/BnwxSSZbt6Y/Q0WbhKq6q6q+WVX3V9Uv6H5kXzum2VlVdW1V/RL4\nr8Bbk8wA3gYsraqlVfVoVS0DlgOHjrOqX9EF1/Or6t+qauyewWRubd87jTPtV8BeSXauqvuq6tLJ\nllVVn6qqh6vqgQnanFJV66rqX4G/BY54kv19gvY3Oxw4vqp+UVU3AR8H3j7Q7Oaq+kJVPQKcQRfk\nuzzddeuZz1DRJiHJtkk+3w7F3Et3yGiH9gO43i0DwzcDW9H9S3oP4C3t0Nc9Se4BXkX3QzjWXwAB\nLm9Xbk36L/8xZrXvdeNMOwr4d8BPk1yR5D9MsqxbJpk+ts3NwPOHmGcyO9P97W4es+xZA+O3rx+o\nqvvb4HY9rFvPcJ7c06bi/XSHlParqtuT7A1cSRcA6+02MLw73Z7Bz+h+eM+qqqMnW0lV3Q4cDZDk\nVcAFSS6uqpVD9vNNwJ10h4bGLvsG4Ih2buKPgG8keS4w0aPEh3nE+G7Aija8O4/vKf0S2Hag3W89\niWX/jMf32K4bWPaaIfqjTZx7KpqOtkryrIHPlsCz6c6j3NNOwJ8wznxvSzIvybbAR4BvtMMzXwbe\nmOTgJDPaMg8Y50Q/Sd4yUL+b7sf30ck6nGSXJMe2fh1fVU+YJ8nbksxs0+5p5UeBte37qdwj8oF2\nEcNuwHuB9RcWXAW8JsnuSbYHjh8z3x0Tra/9zc4BTk7y7CR7AH9G93fUZs5Q0XS0lC5A1n9OpDtf\nsA3dv6IvBf5hnPnOAr5Ed2jmWcCfAlTVLXQnsT9E9wN+C/ABxv//4+XAZUnuA84D3jvJvRv3JPkl\ncA3dOZq3VNWSCdouAFa0ZX8SOLyqHmiHj04G/rkdntt/A+sb61zgx3Qh8j3gi22bl9EFzNVt+nfH\nzPdJ4M3t6q3Txlnuf6bb21kFXAJ8FZhou7QZiS/pkiT1xT0VSVJvDBVJUm8MFUlSbwwVSVJvNrv7\nVHbeeeeaM2fOVHdDkqaVH//4xz+rqvEeXfRrNrtQmTNnDsuXL5/qbkjStJLk5slbefhLktQjQ0WS\n1BtDRZLUG0NFktSbkYVKeyjf5e0teiuS/FWr75nksiQrk3wtydat/httfGWbPmdgWce3+vVJDh6o\nL2i1lUmOG9W2SJKGM8o9lQeBA6vqJcDewIL2ILxTgFOrai+6p7we1dofBdzd6qe2diSZR/dCoBfT\nPXDvs+1JsjOAzwCHAPPoHhk+b4TbI0maxMhCpTr3tdGt2qeAA4FvtPoZwGFteGEbp01/XXv96ELg\n7Kp6sKpuBFYC+7bPyqpaVVUPAWe3tpKkKTLScyptj+IqupcSLQP+H3BPVT3cmqzm8bfFzaK9pa5N\n/znw3MH6mHkmqo/Xj8VJlidZvnbt2j42TZI0jpGGSlU9UlV7A7Pp9ix+e5Tr20A/Tq+q+VU1f+bM\nSW8IlSQ9RRvljvqquifJRcAr6N4bvmXbG5nN468gXUP36tPV7U1+2wN3DdTXG5xnovpIzDnue6Nc\nvJ6Cmz72hqnugqQBo7z6a2aSHdrwNsDrgZ8AFwFvbs0W0b2ZDrq36C1qw28G/rG6N4idBxzerg7b\nE5gLXA5cAcxtV5NtTXcy/7xRbY8kaXKj3FPZFTijXaW1BXBOVX03yXXA2Uk+ClxJe71p+z4ryUpg\nHV1IUFUrkpwDXAc8DBzT3pFNe+f3+cAMYElVrRjh9kiSJjGyUKmqq4GXjlNfRXd+ZWz934C3TLCs\nk+ne0T22vpTufeWSpGcA76iXJPXGUJEk9cZQkST1xlCRJPXGUJEk9cZQkST1xlCRJPXGUJEk9cZQ\nkST1xlCRJPXGUJEk9cZQkST1xlCRJPXGUJEk9cZQkST1xlCRJPXGUJEk9cZQkST1xlCRJPXGUJEk\n9cZQkST1xlCRJPXGUJEk9cZQkST1xlCRJPVmZKGSZLckFyW5LsmKJO9t9ROTrElyVfscOjDP8UlW\nJrk+ycED9QWttjLJcQP1PZNc1upfS7L1qLZHkjS5Ue6pPAy8v6rmAfsDxySZ16adWlV7t89SgDbt\ncODFwALgs0lmJJkBfAY4BJgHHDGwnFPasvYC7gaOGuH2SJImMbJQqarbqupf2vAvgJ8AszYwy0Lg\n7Kp6sKpuBFYC+7bPyqpaVVUPAWcDC5MEOBD4Rpv/DOCw0WyNJGkYG+WcSpI5wEuBy1rp2CRXJ1mS\nZMdWmwXcMjDb6labqP5c4J6qenhMXZI0RUYeKkm2A74JvK+q7gU+B7wQ2Bu4Dfj4RujD4iTLkyxf\nu3btqFcnSZutkYZKkq3oAuUrVfUtgKq6o6oeqapHgS/QHd4CWAPsNjD77FabqH4XsEOSLcfUn6Cq\nTq+q+VU1f+bMmf1snCTpCUZ59VeALwI/qapPDNR3HWj2JuDaNnwecHiS30iyJzAXuBy4ApjbrvTa\nmu5k/nlVVcBFwJvb/IuAc0e1PZKkyW05eZOn7JXA24FrklzVah+iu3prb6CAm4B3A1TViiTnANfR\nXTl2TFU9ApDkWOB8YAawpKpWtOV9EDg7yUeBK+lCTJI0RUYWKlV1CZBxJi3dwDwnAyePU1863nxV\ntYrHD59JkqaYd9RLknpjqEiSemOoSJJ6Y6hIknpjqEiSemOoSJJ6Y6hIknpjqEiSemOoSJJ6Y6hI\nknpjqEiSemOoSJJ6Y6hIknpjqEiSemOoSJJ6Y6hIknpjqEiSemOoSJJ6Y6hIknpjqEiSemOoSJJ6\nY6hIknpjqEiSemOoSJJ6Y6hIknpjqEiSejOyUEmyW5KLklyXZEWS97b6TkmWJbmhfe/Y6klyWpKV\nSa5Oss/Asha19jckWTRQf1mSa9o8pyXJqLZHkjS5Ue6pPAy8v6rmAfsDxySZBxwHXFhVc4EL2zjA\nIcDc9lkMfA66EAJOAPYD9gVOWB9Erc3RA/MtGOH2SJImMbJQqarbqupf2vAvgJ8As4CFwBmt2RnA\nYW14IXBmdS4FdkiyK3AwsKyq1lXV3cAyYEGb9pyqurSqCjhzYFmSpCmwUc6pJJkDvBS4DNilqm5r\nk24HdmnDs4BbBmZb3Wobqq8epz7e+hcnWZ5k+dq1a5/WtkiSJjbyUEmyHfBN4H1Vde/gtLaHUaPu\nQ1WdXlXzq2r+zJkzR706SdpsjTRUkmxFFyhfqapvtfId7dAV7fvOVl8D7DYw++xW21B99jh1SdIU\nGeXVXwG+CPykqj4xMOk8YP0VXIuAcwfqR7arwPYHft4Ok50PHJRkx3aC/iDg/Dbt3iT7t3UdObAs\nSdIU2HKEy34l8HbgmiRXtdqHgI8B5yQ5CrgZeGubthQ4FFgJ3A+8E6Cq1iU5CbiitftIVa1rw+8B\nvgRsA3y/fSRJU2RkoVJVlwAT3TfyunHaF3DMBMtaAiwZp74c+N2n0U1JUo+8o16S1JuhQiXJ7426\nI5Kk6W/YPZXPJrk8yXuSbD/SHkmSpq2hQqWqXg38Cd2lvT9O8tUkrx9pzyRJ087Q51Sq6gbgw8AH\ngdcCpyX5aZI/GlXnJEnTy7DnVH4/yal0z+86EHhjVf1OGz51hP2TJE0jw15S/Cng74APVdUD64tV\ndWuSD4+kZ5KkaWfYUHkD8EBVPQKQZAvgWVV1f1WdNbLeSZKmlWHPqVxAd9f6etu2miRJjxk2VJ5V\nVfetH2nD246mS5Kk6WrYUPnlmNf7vgx4YAPtJUmboWHPqbwP+HqSW+me5/VbwH8cWa8kSdPSUKFS\nVVck+W3gRa10fVX9anTdkiRNR0/mKcUvB+a0efZJQlWdOZJeSZKmpaFCJclZwAuBq4BHWrkAQ0WS\n9Jhh91TmA/PaO08kSRrXsFd/XUt3cl6SpAkNu6eyM3BdksuBB9cXq+oPR9IrSdK0NGyonDjKTkiS\nNg3DXlL8wyR7AHOr6oIk2wIzRts1SdJ0M+yj748GvgF8vpVmAd8ZVackSdPTsCfqjwFeCdwLj72w\n63mj6pQkaXoaNlQerKqH1o8k2ZLuPhVJkh4zbKj8MMmHgG3au+m/DvzP0XVLkjQdDRsqxwFrgWuA\ndwNL6d5XL0nSY4a9+utR4AvtI0nSuIa9+uvGJKvGfiaZZ0mSO5NcO1A7McmaJFe1z6ED045PsjLJ\n9UkOHqgvaLWVSY4bqO+Z5LJW/1qSrZ/cpkuS+jbs4a/5dE8pfjnwauA04MuTzPMlYME49VOrau/2\nWQqQZB5wOPDiNs9nk8xIMgP4DHAIMA84orUFOKUtay/gbuCoIbdFkjQiQ4VKVd018FlTVX8LvGGS\neS4G1g3Zj4XA2VX1YFXdCKwE9m2flVW1ql19djawMEmAA+nunQE4AzhsyHVJkkZk2Eff7zMwugXd\nnsuTeRfLoGOTHAksB95fVXfT3Ux56UCb1a0GcMuY+n7Ac4F7qurhcdqP1//FwGKA3Xff/Sl2W5I0\nmWGD4eMDww8DNwFvfQrr+xxwEt09Lie15b7rKSznSamq04HTAebPn+/9NZI0IsNe/fUHfaysqu5Y\nP5zkC8B32+gaYLeBprNbjQnqdwE7JNmy7a0MtpckTZFhD3/92YamV9UnhlzOrlV1Wxt9E917WgDO\nA76a5BPA84G5wOVAgLlJ9qQLjcOBP66qSnIR8Ga68yyLgHOH6YMkaXSezJsfX0734w/wRrof/Rsm\nmiHJ3wMHADsnWQ2cAByQZG+6w1830d1ISVWtSHIOcB3d4bVjquqRtpxjgfPpnoq8pKpWtFV8EDg7\nyUeBK4EvDrktkqQRGTZUZgP7VNUvoLvfBPheVb1tohmq6ohxyhP+8FfVycDJ49SX0t3BP7a+iu7q\nMEnSM8Sw96nsAjw0MP5Qq0mS9Jhh91TOBC5P8u02fhjdvSGSJD1m2Ku/Tk7yfbq76QHeWVVXjq5b\nkqTpaNjDXwDbAvdW1SeB1e2KLEmSHjPsAyVPoLva6vhW2orJn/0lSdrMDLun8ibgD4FfAlTVrcCz\nR9UpSdL0NGyoPFRVRXuFcJLfHF2XJEnT1bChck6Sz9M9GuVo4AJ8YZckaYxhr/76m/Zu+nuBFwF/\nWVXLRtozSdK0M2motBdlXdAeKmmQSJImNOnhr/YMrkeTbL8R+iNJmsaGvaP+PuCaJMtoV4ABVNWf\njqRXkqRpadhQ+Vb7SJI0oQ2GSpLdq+pfq8rnfEmSJjXZOZXvrB9I8s0R90WSNM1NFioZGH7BKDsi\nSZr+JguVmmBYkqQnmOxE/UuS3Eu3x7JNG6aNV1U9Z6S9kyRNKxsMlaqasbE6Ikma/p7M+1QkSdog\nQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktSbkYVKkiVJ7kxy7UBtpyTLktzQvnds9SQ5LcnK\nJFcn2WdgnkWt/Q1JFg3UX5bkmjbPaUmCJGlKjXJP5UvAgjG144ALq2oucGEbBzgEmNs+i4HPQRdC\nwAnAfsC+wAnrg6i1OXpgvrHrkiRtZCMLlaq6GFg3prwQWP8Y/TOAwwbqZ1bnUmCHJLsCBwPLqmpd\nVd1N9zrjBW3ac6rq0qoq4MyBZUmSpsjGPqeyS1Xd1oZvB3Zpw7OAWwbarW61DdVXj1MfV5LFSZYn\nWb527dqntwWSpAlN2Yn6toexUZ58XFWnV9X8qpo/c+bMjbFKSdosbexQuaMduqJ939nqa4DdBtrN\nbrUN1WePU5ckTaGNHSrnAeuv4FoEnDtQP7JdBbY/8PN2mOx84KAkO7YT9AcB57dp9ybZv131deTA\nsiRJU2Sy96k8ZUn+HjgA2DnJarqruD4GnJPkKOBm4K2t+VLgUGAlcD/wToCqWpfkJOCK1u4jVbX+\n5P976K4w2wb4fvtIkqbQyEKlqo6YYNLrxmlbwDETLGcJsGSc+nLgd59OHyVJ/fKOeklSbwwVSVJv\nDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwV\nSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlS\nbwwVSVJvpiRUktyU5JokVyVZ3mo7JVmW5Ib2vWOrJ8lpSVYmuTrJPgPLWdTa35Bk0VRsiyTpcVO5\np/IHVbV3Vc1v48cBF1bVXODCNg5wCDC3fRYDn4MuhIATgP2AfYET1geRJGlqPJMOfy0EzmjDZwCH\nDdTPrM6lwA5JdgUOBpZV1bqquhtYBizY2J2WJD1uqkKlgB8k+XGSxa22S1Xd1oZvB3Zpw7OAWwbm\nXd1qE9WfIMniJMuTLF+7dm1f2yBJGmPLKVrvq6pqTZLnAcuS/HRwYlVVkuprZVV1OnA6wPz583tb\nriTp103JnkpVrWnfdwLfpjsnckc7rEX7vrM1XwPsNjD77FabqC5JmiIbPVSS/GaSZ68fBg4CrgXO\nA9ZfwbUIOLcNnwcc2a4C2x/4eTtMdj5wUJId2wn6g1pNkjRFpuLw1y7At5OsX/9Xq+ofklwBnJPk\nKOBm4K2t/VLgUGAlcD/wToCqWpfkJOCK1u4jVbVu422GJGmsjR4qVbUKeMk49buA141TL+CYCZa1\nBFjSdx8lSU/NM+mSYknSNGeoSJJ6Y6hIknpjqEiSemOoSJJ6Y6hIknpjqEiSemOoSJJ6Y6hIknpj\nqEiSemOoSJJ6Y6hIknpjqEiSejNVb36UtAmbc9z3proLGuOmj71ho6zHPRVJUm8MFUlSbwwVSVJv\nDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvpn2oJFmQ5PokK5Mc\nN9X9kaTN2bQOlSQzgM8AhwDzgCOSzJvaXknS5mtahwqwL7CyqlZV1UPA2cDCKe6TJG22pvv7VGYB\ntwyMrwb2G9soyWJgcRu9L8n1G6Fvz3Q7Az+b6k48XTllqnsgTQ89/L+yxzCNpnuoDKWqTgdOn+p+\nPJMkWV5V86e6H5I2LdP98NcaYLeB8dmtJkmaAtM9VK4A5ibZM8nWwOHAeVPcJ0nabE3rw19V9XCS\nY4HzgRnAkqpaMcXdmi48HCipd6mqqe6DJGkTMd0Pf0mSnkEMFUlSbwyVzYyPtZE0Sp5T2Yy0x9r8\nX+D1dDeKXgEcUVXXTWnHJG0y3FPZvPhYG0kjZahsXsZ7rM2sKeqLpE2QoSJJ6o2hsnnxsTaSRspQ\n2bz4WBtJIzWtH9OiJ8fH2kgaNS8pliT1xsNfkqTeGCqSpN4YKpKk3hgqkqTeGCqSpN4YKlKPktz3\nJNqemOTPR7V8aSoYKpKk3hgq0ogleWOSy5JcmeSCJLsMTH5Jkh8luSHJ0QPzfCDJFUmuTvJX4yxz\n1yQXJ7kqybVJXr1RNkaahKEijd4lwP5V9VK61w38xcC03wcOBF4B/GWS5yc5CJhL96qCvYGXJXnN\nmGX+MXB+Ve0NvAS4asTbIA3Fx7RIozcb+FqSXYGtgRsHpp1bVQ8ADyS5iC5IXgUcBFzZ2mxHFzIX\nD8x3BbAkyVbAd6rKUNEzgnsq0uh9Cvh0Vf0e8G7gWQPTxj4nqYAAf11Ve7fPXlX1xV9rVHUx8Bq6\np0x/KcmRo+u+NDxDRRq97Xn8FQOLxkxbmORZSZ4LHEC3B3I+8K4k2wEkmZXkeYMzJdkDuKOqvgD8\nHbDPCPsvDc3DX1K/tk2yemD8E8CJwNeT3A38I7DnwPSrgYuAnYGTqupW4NYkvwP8KAnAfcDbgDsH\n5jsA+ECSX7Xp7qnoGcGnFEuSeuPhL0lSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSb/4/\nIkVMQeZ5a1QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_A3QNfaAPUb",
        "colab_type": "text"
      },
      "source": [
        "# **Clearly this is an imbalanced dataset.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvNhObZlAYLM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "outputId": "41df5d1b-992c-4d33-8aa6-4ef4cb585c0f"
      },
      "source": [
        "#Functions for Preprocessing the Dataset\n",
        "import nltk\n",
        "from nltk import word_tokenize\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('punkt')\n",
        "stop_words = set(stopwords.words('english'))\n",
        "from textblob import Word\n",
        "nltk.download('wordnet')\n",
        "\n",
        "def mystopwords(text):\n",
        "    return ' '.join([w for w in word_tokenize(text) if not w in stop_words])\n",
        "\n",
        "def lemmatize(text):\n",
        "    return ' '.join([Word(word).lemmatize() for word in text.split()])\n",
        "\n",
        "import re\n",
        "def clean_text(string):\n",
        "  clean=re.sub('@[a-zA-Z0-9_]+', '', string) #removing usernames from tweets\n",
        "  clean=re.sub('[\\n]',' ',clean) #remove newline character\n",
        "  clean=re.sub('[^a-zA-Z\\']',' ',clean) #remove non alphabetic characters\n",
        "  #clean=mystopwords(clean) #remove stopwords\n",
        "  #clean=lemmatize(clean)\n",
        "  clean=' '.join(clean.split()) #removing unnecessary spaces\n",
        "  return clean"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2y_qchZaB9_d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Cleaning the train data and separating the texts and labels\n",
        "tweets=[]\n",
        "labels=[]\n",
        "for i in range(len(df1)):\n",
        "  labels.append(df1['label'][i])\n",
        "  tweets.append(clean_text(df1['tweet'][i]))\n",
        "  \n",
        "#Cleaning the test data \n",
        "test_tweets=[]\n",
        "for i in range(len(df2)):\n",
        "  test_tweets.append(clean_text(df2['tweet'][i]))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCb2AJspF0vj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "4ab5d6b6-d02e-46b8-8885-09aff034110c"
      },
      "source": [
        "#Example of a tweet before and after cleaning\n",
        "print('Tweet before cleaning: ',df1['tweet'][1])\n",
        "print('Tweet after cleaning: ',tweets[1])"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tweet before cleaning:  @user @user thanks for #lyft credit i can't use cause they don't offer wheelchair vans in pdx.    #disapointed #getthanked\n",
            "Tweet after cleaning:  thanks for lyft credit i can't use cause they don't offer wheelchair vans in pdx disapointed getthanked\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RToLFGBuHzS4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "3c5c6baa-8832-4b52-f8ba-2119feb993d7"
      },
      "source": [
        "#Word Cloud of Top 100 Words in Hate Tweets \n",
        "from wordcloud import WordCloud, STOPWORDS  \n",
        "hate=[]\n",
        "for i in range(len(tweets)): #getting the hate tweets\n",
        "  if labels[i]==1:\n",
        "    hate.append(tweets[i])\n",
        "h=' '.join(hate)  #transforming the list of hate tweets into a single sentence\n",
        "h=mystopwords(h)\n",
        "stopwords = set(STOPWORDS)\n",
        "wordcloud = WordCloud(width = 800, height = 800, \n",
        "                background_color ='black', \n",
        "                stopwords = stopwords, \n",
        "                min_font_size = 20,max_words=100).generate(h)\n",
        "# plot the WordCloud image                        \n",
        "plt.figure(figsize = (6, 6), facecolor = None) \n",
        "plt.imshow(wordcloud) \n",
        "plt.axis(\"off\") \n",
        "plt.tight_layout(pad = 0) \n",
        "plt.title('Top 100 Words in the Hate Tweets')"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 100 Words in the Hate Tweets')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAccAAAHECAYAAABMaOEcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXd8W9XZgJ+jYVvee6/sPSAJScgk\nZLHDCpsChUJpoZQyWr5Cy2gpsxRoC4Wy96a0kJAQyE7I3jvxdmzHe8iyxv3+kOxY1rqStezch19+\nSGe+V5Lve8857xCSJKGgoKCgoKBwElWoBVBQUFBQUAg3FOWooKCgoKDQA0U5KigoKCgo9EBRjgoK\nCgoKCj1QlKOCgoKCgkIPFOWooKCgoKDQA0U5Kij4GSHEcSHEdC/7zBVC7PCjDB8IIX7vr/EUFE41\nFOWo0IUQoqXbP4sQQt/t/TV+nusaIcR62xxLnNRPEkJsF0K0CSF+FEKM7lanEkL8VQhRL4Q4IYR4\nzM08xUKIi7q9P1sIITkpqxNChOzvQZKk5ZIkjfOlrxDiNiHEcn/L1G38DUKIa3uULRRCHJbZX3Zb\nJ31/2u03qLf9Ljvfn/BlTB9kWCSE2B2MuRTCB0U5KnQhSVJs5z+gBLigW9m7fp6uFngGeLZnhRBC\nB3wJ/AtIAj4GPhdCaGxN7gDmASOB04ErhBA3uJhnFTCz2/uZwH4nZWskSbJ4cwHd5FEIEJIk/bvb\nb/Ji4Gi332RqqOVT6L8oylFBNkIInRDi70KISiFEmRDiKSGE1la3UAhxWAjxsG0VdkwIcbmrsSRJ\nWiJJ0idApZPqeUC7JEn/kCTJgFWJxgGdW5U/AZ6UJKlSkqQS4DngBhdT9VSOM4AnnJStsl2H2nYN\nJUKIKiHEa0KIOFvdcCGESQhxixCiFPjaVv5TW/saIcS9PT6zaUKIbUKIJtt26+POhOy5urK1/bUQ\nYrcQolEI8a4QIsJJv9Ns1z/btpo63q06VQixVAjRLIRYK4Qo6NZvtBBihW31vU8IscjF5ycLIcSt\nQoj9trkOCyFuspWnAJ8DA7ut+FJsn/ODQoijttX/u0KIRB/m/bUQ4u1u76uFEK/YXquEEA1CiELb\n+/FCiJW2a94jhDivW78Y22+7zPb7/qsQQiuEyAbeB0Z0kz9GCDFbCLHD9r1WCiEe7s3npxB+KMpR\nwRseBsYCY4AJwGzgvm71hUAEkAncArwphBjgwzyjgK7zN9uKbret3KHe9noUzlkFnCaEiLUp8jHA\ne0Bet7IptnYAtwKLsSrMIUA69qtbNTAZGAZc1E05XQHkYv0Muq9oXgT+LElSvG28L+R9BABcBpwN\nDLbNeXXPBpIkbQPuAn6wraYyu1VfDfwOSMb6EPIwgBAiHlgG/Nsm6/XAa0KIwV7I1pNK4BwgHrgN\n+LsQYpQkSbU4rvhqgXuA+VgfeHIBI/BXH+ZdCcyyXddIoIWTDz7jgUZJkoqEEMnAt1i/j1Ssv8+3\nuz0wvIh1l2Kk7d944G5JkiqAq4B93eRvBf4J/N72vQ4H/uuD7AphjKIcFbzhGuAPkiSdkCSpCngM\nuK5bvQl4WJKkDkmSlgPLsd7gvSUWaOxR1gjE2ZRZRI/6RqwrSwckSToAnACmAROBnZIkdQAbupWZ\nga3drvEpSZKKJUlqAv4PuEYIIboN+5AkSW2SJOmBy4FPJUlab1vlPoD935URGCqESJEkqVmSpI1e\nfA5/lSSpSpKkGqyr1PFe9AX4SJKkrZIkGbE+EHT2vxjYLUnSu5IkmSVJ2gR8BVzqZqyXbauwBiFE\nA/BJ90pJkv4jSdIxycpyrErLnVHSbcBvJUmqkCSpHavivqLH5yyH7UC8EGIgVqX4CYAQIhOr0lxp\na3c5sFGSpI9t17wO6wPCIts2/nXAnZIkNUmSVA88CVzpZl4jMEwIkSRJUqPtM1ToRyjKUUEWtptW\nJlDcrbgYyOn2vsZ2o+ten+3DdC1YVyDdiQeabTf6jh718UCzm/E6t1ZnAqttZWu6la2TJMlkK8/G\n8Rp1WFdfABbbaoJu7Us730iS1Ii94v4J1tX2QSHERiHEAjdy9qT7Fmkb1ocGb3DVvwCY2UPZXQpk\nuRnrVkmSEjv/0eOhRwhxobAaTtXZxpuD/Qq6e1sB5AFfd5t/G9b7UYo3F2jbVViDdRdjJlZl2Lma\n7K4cC4D5Pa75PNs152HdETjcre4DrLsGrrgG647DYSHEOiHEHG/kVgh/FOWoIAvJmr7lONabTCf5\nQHm396lCiKge9d0ViVz2AF2Wm8JqRTraVu5Qb3u9B9d0KscZnFSOq7uVrerWtgLHa9QDdbb3PdPY\nVGK9uXbKmgAkdL6XJGmfJElXYL3RPg985uzssJd4m1qnFPi2u7KzbRfe5cvkQogYrEZTjwLpNuW5\nAuhcBdrJZ/stlQNzesgQJUmSLxaoK7Eqx2lYFeVK4Cys322nciwF/uPkmn8LlGHdPcjrVpcgSVLn\ng5/D5ytJ0i5Jki7D+r2+itVgTLmf9iOUL1PBG94H/mAzqEjHuuX4Trd6LfCgECLC9iQ9D/jU2UA2\ng4woQAOohBBR4qT15zJAJ6wuCpHAr4FWrDc+gLeAe4UQmUKIPKxnbm+4kXsVMAnrud0GW9lWrOeU\n07FXju8D9wgh8oXVEOcx4D3JdW63j4BLhBCTbbI+BnRZvQohrrdtqZqxriglvFdmnqjCeoaqldn+\nC6znsFfYjE4ihBBThBBDfZxfh/W7rwYsQogLsSqr7vKlCyG6r3xfAv5i+/4QQqQLIS7wcf6VwCLg\nhG3l/gPW89Z2SZI6jZw+BqYJIS4WQmhs13ymEGKQJEltwLvA34QQycJKgRDi7G7yZ9q2X7HJ+xMh\nRLLte23CqlwV+hGKclTwhoeAvVhXaduBtVjPZjopwnrueBx4DbhRkqSjLsa6BeuK7K9Ylageq1EE\ntrO8i7CeSzVgPftZ1G3r83ngO2CfTY6PJUl6w43cu7Aq12M2Ywps27PbgUjgx25t/wl8BqwDjmBd\nMd7tamCbQcxvsJ51lWF1gem++jkfOCCEaAYeBxbb5vYnS7B+9tVCiDJPjW1naguAG7GufCuwKnW5\nyrXneCewGth8hdVFZxE2S14bO4D/AMW2bctkrL+b5cAK22ezDqtbji9sxbpKXWWTpxyrou566LHJ\nuBC4HauyqwD+iPXhDFt5I9bt3QasBjaFtrofsa6Ey23yxwCXYN0qbwZ+D1zhrSuQQngjXD8QKyjI\nRwixEHhRkqTeWDwqKCgohAXKylFBQUFBQaEHinJUUFBQUFDogbKtqqCgoKCg0ANl5aigoKCgoNAD\nRTkqKCgoKCj0ICyyCgghlL3dMCZKE8fsgbfLarvk4BMBlubUoSBxAnmJ44nWJmEwtVDVcpA6fQnV\nLT5lf1JQOKWQJMnbUIR2hIVyVFBQOElW3EjGZdn7w+u0CRQmTaIwaRIA3x1+DqPFEArxFBROCZRt\nVQWFMCImItlBMTrj7ME+RXpTUFCQiaIcFRTCiOmFN8tuOzxNiXWtoBAoFOUYABLV6cyKuyLUYij0\nQQTyj0k6t1gV+gcLc+8MtQgK3VCUY4CwxiNWUJCPWnbccAUFhUCjKMcA0GCuZlXLJ54bKih0Q6ft\nmcJSQUEhVPQLa9VkTRbDIicRp06mxVzPDv0PtFqs+WZPj57LwfYtjNRNRYWaDa1fcVr02cSrUljZ\n8pHdODGqRE6PPptIEU2Z8SD72+2Tti+Iv5GlTa8Tr05hvG4OaqGhuGMPRw07AYhWxTMj9mQy9aVN\nr7uUd2TUVHSqOFrM9RwxbKfaVCLrehT6L0ZLu+dGCmHN2dk/o9l4goSIDCraDpCpG4xWFcWSsucB\n69apRTLTaqonTpvKpprPqTWUOoxzRtqlWCQzm098AcCwhGkMiJtAk7GGeG0a22u/5rhecekJJP1i\n5Tghej5lxoOsbfmCWnMl02MvQdNti2pC9Dz2t28kQZ3KlJjzKes4iAUzZ8Sc29VmQfyNTI+9mEOG\nrWxuW0qmtpD58Tc4zJWpLWS87iz2tW/gqGEH5q4sStBmaWJp0+vs1K906NfJ1JgLmRS9kOPGY2xr\nW069uQqtiOyqHxAxxuP1KPRPDKbWoM2lUUWQHT0saPOdSuxrWMWy8n+SFzOa7yr+RXnrXtKirPmz\nl5Q9z7flf2dt1Xusq/qACakXOvQ/I+0SjJb2LsUoEAyIm8CSsudZV/U+S8qeZ3zKuQ79FPxLv1g5\nLmt6s+v1gfYfKYwYRX7EiK4VXYekp8lcy1HDTgZGjqXGVIpRb2ByzHkADIk8HbNk5PuWD7qU3Q/N\nH7Ig/kYS1Gk0mmu6xh8RNZXvm9/3IJHzmAZqoSFencKyprew2HKjnjCV27U51rGLYx273F6PAqwt\nGsC0wmNd7+ddGMtPf53IlWeVddVXV5qIjlERqRPMHlIEwPR50TzxSgYlR41kZGvQaGDm4KKucVYe\nLuR4mQljh0RWrpar55ZRVWEiHGloL/fcyAkTUy8iNTIfgIq2AwAszLmDJeUv+E223pIZN5zxWRc5\nlNfpS/mx9L0QSCSfZuMJu/dGSzsalfUBOEKlY3TS2SRGZqFVRTkYYI1PORch1Gyr/V9XWZbtIUYx\n2Aku/UI5qlAzLGoSKZpsIkU0ABoiuuqbLfUAGKQ29JYWAEzd8s0OiByDQMXcuOscxk7V5Ngpx4Pt\nm32WM087HKBLMbrC0/UowBfvNXPXH1J47uFaAB58NpXLZlgV4yer87jjqkq2rrduU/7joyxuuz+J\nl56oZ82yNjulurZogN24Go3g6rnlmE2hCdpU1XKAjFh5K7qNJe/6NEdqZD5Lyl9gYc4dPvUPBsNS\nZ4daBL8TodIxJ/sWvi3/OxbJ3PW+Ow2GSoYnziBak0CbyXqUIhC0GOtYU/VOKMQ+ZenzyjFFk83E\n6AUcaN/E2pbPkZBYEH+jXRtLtwTdkpNVnUBFpfEohw3bHOqMksHte2+IEFEe28yPvwGBcHs9CvDU\nAydYWzSgSzmqNYLqSusKLytPwwvvZ9m1T0hS89IT1oekK29O4MKr4kjPVAMQESnoMJz8XYRKMQJs\nq/iChUPvl9Huc6e/ZTm0m5sdysItib1OmxBqEfxOhm4QraYGLDZL9vzYsQ5tilq2UdK6k/k5v2Bp\n2QtISFS07WdM8rxgi+uScTFzyIwYyNL6V+3KFyTdbFcmEMxP+qlD/579wpU+rxwnRi+g2lRCUcdu\nn8dottSTpMmkTd/kR8kcqTdXMYAxLutVqBGIXl/PqUJjnZnCIVrOvTSOrz6wv+F3Xx12Z/meAr7+\npIWrzz65/RpuLDn4BOOzFpEZ57iCtEgmfjj6TzrMbT6PH6WOcygTInzMD7zx9exLlLbuZlTSHM5I\nu5QYTSJlrXudtrNIZtZUvcuC3DtYUvY8EhIHGtewMPdOGjuqiNUmoxbaLiOfcGVC7ELaLa2sbDx5\nDBWligmhRN7R55WjSTISozppAh+j8v6Jc0vrUmbHXcms2MUOFqz+pMZktUobETWVfe3ru8pVqLFg\n7tpu7e31nCrcvKiCf3yUTVqm2k4ZVpaZuPzGeD5+3fFhRxej4tk/1AZTTJ/YXvkFVAZm7D0N33dt\nqS7I+SUCwfa6JYGZzAcGJk8NtQg+813Fv7pedyqv/Y1rHMo6OdS03mldi7HW7v2x5q0ca97qd3kD\nSZ2pkhRtDjpVbNdxVrsleEZnvaXPK8c1LZ8yO+7Krq1Hk9RBpdH5qsEVBknPTv0PjIqa5rCF6cod\nwxlTYi4gShVDpNABMDfuOtqlVra3raDF0gBYDX1mx11BfsTwrn515uNsav0GAL2lhRhVYq+u51Sh\nosREWqYaS48dwcuml7L66ABu+lUS9bVmCgZpuWJ2GWVFRjat0bPqcCFb17czaYaOokNG54P3Y0pb\nd1Paupu0qELrTkV7eP2+BqWcGWoRgoo2Mx3j8epQi+F3jrZvRyVUzEy4EoASw172ta0LsVTyEZIU\n+mxRSsqq8CacU1Z9tDKXV//awLdftAR1XoXA4e7MtS9Yq3pL9h/uo+LhJ0MthmycnTlGiCjOSrzW\n5XlijDqRM+MuRiXUQTtz7G3KqvA5aFBQ8IGcAq2iGP3A0PjwWK1FasLvTCrvmUe7Xhe88ATx888C\nIPG8+cRMPA2EQDd6BDl/vJ/8vz1O6k3XdrXXpCSR9rOfEDt9CvnPPkbWA78GlfW2G5GXQ/b//QZt\nehoFf3+q61+4Y5D0DmUDdePd9mk1N7CswboL11fOlBXlqNAnefXLbNYWDeCJ353w3FjBIzonRjqh\nYGgYunC0rN2IbsxI6xshiJ89HYCYyRNp3bIdAN2IoZQ/8hQlv/odqmgdmXef3GmJHjcaIQQld/+e\n1o1bKHjBurvSUVpOxZ+eAaD4F/d2/Qt3Duk3ATAp7lySNJmMiD6TTO1Ah3YLkm5mZPR0UrQ5pGnz\nmR5/GeDcYyAc6fNnjgqnJjdfVBFqEfoUk1IXua2P16YHSRL35MSPDrUIDjSv2UDi+QvR795H69Yd\nxEywrpI0KUlgO5aq+/jLrvYnXnuHvKcesR9jtdXwpum7VSRd4jlfZzhjlkysbvyYCXHzmRC7kFLD\nPn5ofI8FSfbp1jY0f8lw3RRyIoYgYaGy4yhrmvpOzGlFOSoonALo1PFU6A+4rI/WKFbRrjBVnyDm\ntDG0759Ey5oNRA4otKvXjR5B6o3XgNlM69YdWPT9P0Zum6WR1Y0f25X1PEtsNNWwsfmrYIrlVxTl\nqKBwClBrKOVw00aX9Tp16DOCJOvyQi2Ca1QqYqdN5vhTL9D8w2oicrLR7zsIQPrPb8Kib6f0vj8A\noM3OJMF2LqnQd1HOHBUUTgH2NHzvtt5ZZohgMzRtdqhFcIlFryey0BqPtnnVOpKvvJiW1d18lSNt\n4R2FIOu3d3k1tmQ2Ezt9it9kDSRCJRj10wksXncr6gi11/0TB6f4XaZAjAmKclRQUAAq2vaHWgQS\no7JDLYJLWtZuxNxoDSohGU1EDiykbYc1ilXJXb+j4X/LyH/ucTLuuo2SO3/r1dgldz1A/Ozp5P/t\ncdJ+9hO/y+5PJIvEnn9v8bn/vDcu9dwoDMYExc9RQQbh7Oeo4DspkdZtzHBYNYJ7/8ZO+qOfY19k\n8bpb+XT2q5g7TiZRUEdqmProXJKGp6KNieDAezu6FGna+CwmP3QW0ZknraL3vbGVXf+yWr4iYM4/\nLyJpWCp739jKvjdPxrnWxkQw+8ULiC9M5MSO46x/cDkdzQaPY/bWz1E5c1QIOVGaONJjh5Csyyc2\nMoUoTRxqVQQWixGzZMJobqe1o46G9grq9aXU68tCLXKf5rTkc8nQDbIrq9IfYVvd1yGSCHITHINw\nK7gnSZdLavQAEnXZ6LSJaFVRqFVaJCx0mPR0mNtoNlRT21ZMnb448PlCBWx7bi2tFdY4xxcvu5Ga\n7cep3lJOzfZK/nvJeyxedysfnfmyQ9fFa2/ly3PfxNDQzuwXLyAmM47NT6zqGueTma9gMVmIy0+g\no9ma/MHTmL3llFGO2fEjGZvp2YTa3yufeUPuRi0zUXEgVl2ensYb9OVsKA1uKhyVUDMm81yy4ka6\nbadWRaAmggh1NDERyaTHDrar7zDr2V+znIom5wGcQ8XI9HnkJ54e8Hl8/b1k6AY55G4MdvqqxKhs\nsuNHkxM/CrVKfjq2ZF2erBVmbzjReozN5YGLsewreQnjGZ42B7XK0/1EjU6rRaeNJyEq0+HBo6b1\nCDsqv8Jk8T3DkDPM7aYuxQiw/+3tpE/IpnqL+7yjGRNzOLHzOIYGq5XvD3d8xeK1t3YpR4CcmYWU\nrjhKc0mjX2V2xymjHCua9spSjjptPHqj/7JzyFWMACnRhdS2Fflt7pToQo9tShod03QFiiRdLpPz\nrvHbeBFqHWMzL2Bs5gU0GapYV/yG38buz3TmCfRU5g9UQkNW3Ahy4keTHJ0fkDn6M1GaOKYV3IRW\n7TndnVzSYgYxd7DVaOjgiVUcrVvvoYc8Bpw/nEkPzKLpWD1l3x8laUQaDQc8B+lIHp1BTGYcM54+\np6uscl1J1+uPznyZmc+dx9TH5nHsq/1senylX+T1xCmjHOWSHTeKI376sXhLYdJEvyrH3ATX6bE6\nqWja47f5XKESGuYN/nVA0yLFR2awcOj9VLccZmvFpwGbpz/gzKfRH36OkZpYCpMmkRM/mgh1dK/H\nO5WJUEdz1sBfBDyV2NDUmQxNncmhE6t6fd+b9MAsu+3NyX+YI6tf/b4aTAuNrL7nG5dtVt31PwDO\nef8KChYMoXjpoV7JKgdFOfYgO95/ytHTtmFP0mIGeW7kBZlxI/w6niec5RgsSJrIiLSzgyZDeuxg\nFg69n6WHngq7BL7hglkyOt1G7V7Wc9tVDtlxIxmQdEavZFOAKfnXBd1yd0jqTIakzuS7I3/DaPY9\niEFkYhSGhnY00VoKFgxh3xuOabaiM2JpqzoZD/n4xlLi8hNJHJxCw+FaEJA3ZxCl3x0BIHVcJid2\nHLc2FmBsc8yk03NMf3BKKcfatiKPW40xEf7zmRmYPNlvY/lCsAP8tpvsEw5Pyr2SlOiCoMrQyYIh\n97LiyAu9SgrcX1l5/M1Qi6DgBLUqgnmDfx1SGc4e9Cv2VC2ltHG70/oLvrwWXZo1OPylP1jDxXWu\nFj+f/zrzXr+U6PRYylcd44sFbzDsKvvzzi8WvMGCty9HGxvBxodXUL66qGuM6U8sIHNqPo2Ha9n4\n6Em/3KGLxzDrufOwGC3seulHKmx9PI3ZW04p5VjSsF3WOZy/iIv0Pl6lWmgwS6YASOOIP7dwAdqN\nJ5XjGXlXhzziyZxBdygrSCd0WByzKiiElsSobKbkXxdqMQAYlbGAtNhBbC13PJ746iLXxnvGlg6+\nvvx9u7IuVw0bHc0GvlrkfIw19y91Wr7u/5a5ldfdmL3hlAoCUNXiOrZkuJCfOMEv40RHJHlsU9Lg\nX2OcdpPVkGlI6syQK8ZOFgwJ/ywHoeDsrJ+xMOcOFubcwdzsW0MtzilNeszgsFGMnaTHDPar8Vxf\n5JRaOcolNWYgJ1qP9mqM+MgMn/oVJk3kWL3rGJhyyY33bIxT1XKw1/N0p93UTKQmlkHJU30ew2Qx\nIEmSX63zJuVeyaayD/w2nif2Vi9jb7X1aVenTSAmIoXYiGRiIlKI0Vr/H8q8hQtz7mBX/XLK2/YB\nkBM9goU5d/h0zqjQO5J0uZye458ILxISRrMelVCjUUX2erwkXS6jM85hd5VrQ5n+jKIcnRAXmdZr\n5Zioy/GpX6QmlihNfNcqzFd82dLtLSaLgVEZC7zq09JxgvLG3dS2HaO544TdFmhCVCZRmjhy4sc6\n+Dh6Q6jOPQH0xkb0xkaH35NGFUlMRDKxESlWpRmRbP2nTQ64hSLQpRg7X49JmtvrMSXbf77gzfl4\noPMBSgRvG/70nMt87mswtVLauI3atmIa2yuxSCej1QihIiEqixRdPvmJpxOpifVpjtyEsYpyPFUw\nmFo9PrVnx43iWF3vVm+9McYpTJrI/poVvZrf35avchiZPl9Wu5rWI2yr+Nzuj9kZje3HaeQ4VS0n\nzbYn5Fzm07XlJ55OSYOj5VyoMFkMNLZX0theaVc+OGU6g1OmBXTuQPk5FtVvoqh+k+eGThidcY6s\nKDn9KXzc+KyL0Hq5wmvtqGN9yVseHfglyUKDvpwGfXmX9X2EOobphTd57WYzd/BdLD/8nFd9+gOn\n1Jkj4NIKqztxkWm9nidK4zoF0IETP7jtG4zoKr1dmfpCa0cdSw4+wZbyTzwqRldsKf+EpQef9Hr1\nMDJ9nk/z9UcC5eeoIJ9kXR6ZccNlt+8w61l68ElWF73ic2SbDnMrK468wIoj3m2fa1SRQbknhRun\noHIMXkQYV3halaqE96lgvKWkwfNDgj/ZUv4Jq4te8ctYEhJLDz6J0cubhD/ddPoyFW37WZhzB5PT\nLmNy2mUszLmD4pYdoRbrlOKMvKtltz14YiUrjjzvt+3kDnMbSw4+4eB65Y5T8eHylFOOAQ++S2jP\nuAASorI8tgnmQ8KqYy9T03rE7+N+5+VWz6TcxX6XoS+ys34Z31W+Qr2hnDpDGcsrX2Zf4yrPHRX8\ngjeWqVvKP+Zo3YaAyPHD0X945Qd8ZsENAZEjXDnllKNceqPgBiS5Pm9s0LsPwttJeuwQn+fPkWGp\n2psoGN6wufxj2owNARt/xZEXZbd1t9V9qmG0tHOwaT2HmjZgsnSEWpxTBrXQyo5+c7h2DTW9NAz0\nhDdbrPGRGUExFgsXTp0r7Yac87bs+FE+j58aM8BlXVnTTgAqm91nkihMmuTz/L2R3Z80tlf22urX\nEx3mVq+efoMdNSgcUQk187NvtwsXNzZJnjGVQu+YOeBnstoZLQYO164NsDRW1pe8JbvtxJzLAyhJ\neBFW1qpj73y212PsfP5uj21KGrYxNHWW2zZZcSPYddz/+e3Km3Z3yeAu9mpvnOg1HlIAtXbU+jy2\nN3jzR9cb1hW/ITsZc2bccCqb93lu2I+Zn307O+qWMi75pNtNdvQwdtZ/G0Kp+j8qoZbtUrHi8N8C\nLM1JelpMuyOYEcZCTb9aOZo75G0VyrFYVQnfnhs8uYl0+vGFMmGvvyPjOOPgieCklQHHmK7uCJdV\ndSgxSyYq9fYBIHy1HlaQzwSZq64GfXnAfTl7sqX8Y9ltTxUFGVYrR1ervtw5i0kePYXSb9+jfv9m\nuzohVAy+4i506bnseekBWfME8rzN3Xmjt2jVUV7LGqHWeWxT1hh4y8RAGRG4wmBqkfVUHmpjqXBA\n5eTcKBgW0qc6cn97G0vfDbAkjnhztjk6YyErj70UQGnCg7BfOWpjE0kePYX9bzzmoBjBuhI79MGz\nmDvaGf3zv/h3bh9CMBW4iY3a0mGf+NPT03pB4kSv58+J9+xIHejA5nX60oCO74zOs1xP+Loj0L8Q\nFMaeZnslGJ+8ELPkmAZIwX94EzEr2KvGrnllBujXaU8Nn9iwV475C61mzx1NdW7bHV/3P1Ra92dt\n3WnrqPfYJsuHLTh31lxljfY38HIPN/SCJB+UY8Jor/v4m52VXwV9zqpm/8aJ7c8sLX+RLJ3VGnpB\nzi+J1iSwrKL/rwRCidzoUcEZrNtYAAAgAElEQVQywnFGccOWkM0djoT9Y3R0Rr6sdhajd+boJY3b\nGJ7mPlN1dvwov4Yc66kcSxq2kZdwmsv2vqxcYyNS3dY3th/3ekxv8eYM0F80d9QEfc6+zPqaj0It\nwilFvMxYx0f9lGjdFyqb98m2kk+LGRQQ3+VwIuxXju21FQAItXs9njFloVfjlso4d/M2G3dewji3\n9T3DPjUbgn9DD7Tzf7hvCSmcJCUyj5TI8EgtpmAllIZR3lityomD29cJ+5Vj6bL3GXrNfYz5xZPs\nf+vPdDSccGhTcO4NRMQlgST/xmwOgOOzP41xOkmJLqC2rdhv45U37vLbWM7ouTpWCD9OSz6XDJ19\n8PYq/RG21fnfdUnBmhuxv5Gkyw21CAEn7JVje+1xWsuPEJMziOHXu7dG3S3TWjVQuEsw7Gqr0WQx\nuM29VpA0UbZylBPIONAru+OnuA9hXyBDN8ghd2P3gAAK/kWu+1Brh3u7inDC28wefZGw31YFOPLp\n3yn6779d1utrytn5/N1YjN4Fom6Rsa2ZGOVbXsaeuFpRefI59OapMyc+9MY4dW0loRZBwQOBSlml\n4JyMuGGy2jW0ywstqRAcwn7l2EnT0T2yot94Q0njdo/R5rPjR8n60XraZnClHEsbtzEweYrH8eXg\nKc9hMFwsQnXmqCAfJWVVcJEbsjAnfoysuMgKwaHPKMdAUNa4U4ZyHMneas9htTydN7qK56o3eo7z\nqhYav/gmlgYhMo5C+GOWjE63UbuX9dx2VVA41TillaNFhsJxdx7YnfTYwB265yeezrH6H3s9zqke\nU1TBysrjb4ZaBAWFsCfslaMQqj5vpu8pc3e7qZkoTZzL+oKkiR6Vo5wcjgoKAB0WfahFUFAIe8Le\nICf9jHmMvfNZkkf5300CoEmGU7zaQ8gxrYd4pp6Mborr3UemcKc4O1HOKhQUFBT8R9grx6Rh1lil\ndXs2BmT8EhkZOrLiXaeWAhiQdIbb+iIPq74SP4Rt8mQu3t+jWSgohCNKLN++S9grR22ca99Bf1De\n5Nkp3l3eRYBCDzFQO8zut7HkGNt42jb1lMMxGGmqFBQU7NGqo0ItgoKPhL1ybC0P7IpHznmmp1Qz\nwXg69KSAPaGsHBUUgo9cNw6F8CPs1/zF37zJqFv/RFzhCJqL+p61pdw8aTWtR9z6KWbFjWSHi2wX\nytOpgkJ44k0qsFAE7FdwTdgrR7NBz/H1XzPgwlvY+8qDmPStfp+jQV/uVb617nhy4ThWL++stKh+\ns0cnfld42vZVUFAIDd4kK19X/LrHIxiF4BH2ylETHUf7iUoaj+xi5C2PAmCor3aZourQB896PUdJ\n4zaPyjFKE+/UkT8/4XS3/eSGU6ttK5LVzhlZcSPc1iv+jQoK4U+kJlZRjmFE2CvHkTc/7FAWmSQv\nN5pcKpv2MjbzfLdtsuJGOF0FpsYM8Kss7mUYSWXzXodyT6HrSmVY5CooKISWaG2SyzR2T66dQnyK\n1ejutuGrginWKUvYK8dDH/w14HPIiQeaHjtY9hZpJ96eIXjO0DHBqXL0hBIMXEEh/EnS5VLVctBp\n3X3TNgDw0v6ZPo39l5WT+e2swLjD9VfCXjnqqwMfLFsOvuQv8zbkW3HDFgYln+my3tvkywoKCn2H\nQOVITMlRDPZ8IeyVY7CoayshOTrfqz6ezvpKGrZ6NV5R/Wa3ytEZnmK/Klky+jcXfn0Dy2/8hAXv\nXcl/L3qbqX+aT0RCJMtv+ASArGkFjLvzTKIzYilfVcTGh5YBEJMVx7g7z+T4hlLG3jGV1vImlt/4\nCZJF+b34G72xEZ3Wc9YTX0NAXnLPAOb9NI+GKgOR0WoidGp+OWY1ALc+P5KYRA0xCVpufd5quPfq\n3fswm5Tv2RNh7+cYLEoavXeSz00Y67be25iwRhmH8ULYf2UZsUPdti9v9BzkQKFvM+za01jxs8+4\n8JsbWHPP12h0WuLyrDfjjEm5LL3qAz6b/QoRcRHM/ueirn7ZMweAgC/m/pvibw5w6epbQ3UJ/Zry\npt0BG1uoYP7Nefx8xCp+N3sjd5+xjqIdTSy622oL8fKde/nP34ppbTTy8p17efnOvYpilImiHG0c\nb97v0ey6p2JKiS4MoETOyY6zDxPnyZWk1Aelr9C32P3SRhqP1CFUAovRTMWaYhKGpAKw/bm1XavB\njQ8tJ3Vspl3fo19Yz7APfuA836hC7ymu3yS7rVZmFqBOzjjfapz40v6ZXf8GT0xg3JwUr8ZRcETZ\nVu1Gk+G4W4Wn0ybQ1lEva6zWjlqfZDBLRtRC67I+ISrTLuRdjNZ9eD1X1m8K/Qdjaw+3JklCqKyR\nWSITdUz43SxSRmUQkaCcPYUCo4esPN1JiSnkePMB2e11cRoMbWbK9tv7f9dWyPevVHBOn1CO6ogo\nRt7yKEKt9th25/N3+zxPScN2t8oxNXoAJTKVo6/5F0satrpNnJwTP4a91cu63sdGprkdzyKZfZJD\noe8Tmajjgv/9hM9m/wuL0dL1XiF8GZk+3yvleGxHM5oIFU9drbhr+Zs+sa066rY/y1KMDYd69wOp\nanH/o0yNPunT6ClkW1mjb9tURfWb3darVa5XlQoK3cmeWUhzaSMWo/Xse9Cl7jO3hJb+fQ7mykWj\nJxHqaK/GLd7djFojmHxRBp2nPtlDYkhIP5mIoGx/C4kZ3m3XKvQB5Zg+aS4AbVUl7Hz+bnY+fzcW\nkxF9dVnX+/r91pRPJd+8FVBZUmJOBiDPiR8dkDkMphaON+/3y1gN7eV+GUehb3LsP/uIy0tg1osX\nct6X13VttYYjJovziFc98WSdHa7sPP4/2W2Hpc12KOs8T+z5GqxBAYZNTuDRb8/gr5vO5Nzb8mmp\nOxnTtb3VzKdPHuXFndN5/PvA5MXtj4T9tmryCGuuxMMfPtdVZjboUUedfMIq/fZd4geOYvTPH2f3\nP38XMFm6nwVmu1GO3lqp9qSofjOZccNd1mtUkZgsBo8R/5U0Vf2f/5z7RtfrT6a9BMDOF9c7lHWy\n5xWrcUhrZbNDXc/3wURuDNLYiOQASxIYzDKVP8CApMkcqPnBrsxTVJy3HnC/Ml32WhnLXiuTLUM4\nMfbZK0kYm8vquU8Hdd6wXzlq4x0NTkxtzUT0yPNY/t1HqLS9f6qUm9opPjLDZZ0vbiHd8bTiy7Yl\nX072kEqrssn7aDoKCqGg1Vgnq11fTh68p2qJ7LaTcq8MoCR9i8j0uJDMG/bK0VkWDkN9NQj7VZP+\nRIVf5vPHautYXWDDNHVm4Uj14EqiBABQ6Cs06P3z9xvOlDbukN02JbqAmD66SvY3kWmKcnRK02FH\nw5bGQ50/spMKMjbHt3RPPfG0cpSTvNQfedkMJtepuTrDTKUEMei5gkIgcZbxxhW+pnYLB/bXfCe7\n7YzCWxx8q09FhDo0n0HY71FUrvsfKeOmkzXtfCrX/heAxiNWhTn2zqc5/PELqCN15My5PCjy+Jr3\n0VuKGzYzNHWW2zbxka6zk/QmBZaCQjhzes6lLD34ZKjF8Imi+s0MTztbdvsFQ+5l2aFnMEumAEol\nj0hNLAZTi099Zyy/x8/SBJ6wfyyxGA0gSTQcst+SaDyyCxAMvvxOBlx4CwC1O9cGXJ4kXZ7bmKr+\nyoBR3LClV/0VYxyFvoZci1WBICN2WIClCRwrjrzoVft5Q34T0tXy0NRZLBx6P2fkXhUyGUJB2K8c\nAXa+8BuHsuL/vU7y6KlkT78QyWKmdPkHNB31TwzDqpYDLv/4knW5xLsJEOyr839PzBaj23qNKsJt\nvVy/KgWFcGFf9XLGZJ4rq+1p2YtYceQFOsxtAZbK/3SYWylv2kVO/BjZfSbkXEaHWc/3R14Iii1B\nYdJEr1a4clh3wd8w693f15wRqlVnn1COrqjbvZ663es9N/SSkobtLpVjki7PrSO+XGvX3pKbMC4o\n8ygoBIvypl2ylSPAnEF3YLQY+O7wc54bhxm7jn9NeswQj8FEuhOh1rFg6H2YLAZ+LPuApvbjfpMn\nI3YoA5On+JwZRA6+KEYAc7sRdVTwg5/0aeUYKNyd1wUzQs2J1qOkxgx0Wjc8bU7Q5FBQCBYtHbXE\nRsgPmq1VRbJw6P3U6UvZWv4pJi/imHYSpYkjUZdNYlQuydF5bCx91+POjT/47sjfWDj0fq/7aVSR\nnJl/MgxgZfM+jjcfoN3URFP7cacrS5XQEBORRGxEKsnR+STr8vuMNayhuono/OAHUleUox/pkJFy\nyhuK6je7VI7uqGo55Fc5FOQhEERp44jSxKPTxhOlibd/rY33OuuCO+TcWI1mPXpTM+2mJtqNtv/b\nXutNTbSbmmUHrRgcM5HDre7DG/aWNUWv+qQwknV5zB18V9d7SbJgtLQjSRJqlcbLyDrBiyS05OAT\nPl1vd7LiRnjMLRtqeuPA33KgCnWksnJ0IGv6hQBUrvmP23ZxBSOIiE+idte6YIjllCI/nTd2cqLt\nmE/9ShVjnKAyf8g9qITn2L+hQKvWoVXr3Fo2d7Lk4BNu66PUsf4Syy1ljTt6fWwghMrrOKWhYsnB\nJ1gw9D5ZbmKnIgee+Dok84a9ckw7fTbgWTnGZBeSPmme35RjZfPeLmd7uRR7CBoeLHxVqgqnNgsz\nfu6xze6mHwIux+6qJWTHjw7bB45AsPTgk8wacBs6bUKoRVGwEfbKUS7tdVV+Ha+0YbvXyjEQvkgm\nS4dHy1QFBX+xpOqfLuvGxAfvnPvbQ0/3eruxr7Hy2EsUJE5gRPrcUIuiQB/wcwwVdfrSUIsAQEkv\n/R0VFOTSYXF/Zt5u8c0B3FeWHHzCJwObvkxxwxaWHnzSqwTJwaLZUB1qEYJKv1k5dp5NhorK5sAE\n+S6q38zA5Kmy21c07QmIHAr9nxU1b7itP9Ti3zN1OSw//BxDU2cxMHlK0OcOFRIS3x1+jmhtEjMK\nbw55CLl91ct7HZTEFYN+OYfsRafLahvsrBxhqRxH/exRWWVWhF36Kn8iIck+JD9WF5gbh7dOzr3N\nCKKgEG4cPLGSgydWMjnvmq64wqcCbcZ6lh56Co0qkil51xAbmRa0uY/WbeDgiZUBnWPY784j/WzP\nVraG6ibKPw3+DlpYKsfm4gMkDrN/mlBHxXjst+vv9/lVjoqm3bKjWDQZ/Hvm6SsNeiXBsULvWZjx\nc7fnj6FgY+m7gDXR+Mj0eaj9eBZf3rSb4vrNYfN33B2TxcCa4tcAiItMY0TaXJKj8/06R2XzXo7W\nbaDZUOPXcd3RqRg3Lv4nHXXWRAszlt/TtULUxEUx9fNfUrvucEiUo5Ck0Kc1EkK4FGLsnc8CsPeV\nB53WSxKY211nsPCGsTeNZ/ytJ5XyNz/9LzW73e+zX/X9dWijtbw1+TWHutzpecx5Zp7TunDk+o03\n2b2Xc/2hoqescj7j6zfexL4P9rDpr/5LKebu++/LTEtZzNraj0Ithiwi1Doy44YTH5lJfFQGkeoY\nNOooVKgwWgyYzO10WPS0GE7QZKiiyVBFo76iX6R0Uwk1qTEDSdblEqmJJSYiBZ0mHrUqAiFUmC1G\nzJYODOZW2jrqaTXW0aCvoE5fEpRAB+6Ysfweqr7dw8Env7Er6759qomNZOoXd7Dhsn9gbPBuF02S\npF75xoTlyrE7TUf3ED9wlNO8jv5m52vb2fnadsDx5uuKHa9uY9C5gwMpVtDovMEv/uYqopJ1IZbG\nPb7Iamwzsvd9z2ey12+8Sbay60/ff3diNX0jegpYg2+cqoH2LZKZ6pZDVPfRwB91G486FgrofG4x\ntVgNk4b99lx2//aT4AlGH1CO9fs3ET9wVKjFcMned3ez913/BDxXCCzvn/W2xzbDL/fOfae/fv8/\n1LzFtJTF7G9eh0Uyd5XXGytDKJVCfyMqI96hLHF8Pg3b7LMbJYzLC5ZIXYS9K0fj4Z3sfP7uUIuh\ncIpw2s8nhFqEsGBaymIiVdGMS5jLaYkLuv4pKPiTrAvHO5QNvtPRz9PSHvwt4LBfOYYr3px5xecn\nsOjjS+3K3pn2BhaTfUzLzIlZzP/7OXZlkkXinWlvIFnsz0eu33gT/73+S/Jm5DPultO6ylsqW/hs\n0cmzonkvLCDrDMcEze/OfBOzwexQ7k86z1w/vegjLv1ysV2ds+sf+9PxjP+ZvSFWR0sHH5z9js8y\nFM4byMzHZne9X/PHlRz9xjFzSs/vs/v73W/tZOvfN7tt7+77H3LRUKY+MN2hvOdnMPFXZzDy6tEO\n7T5c8B6GhnaX4wcCT24dCgq9xWI0E5VpHxGoYVsJiaflM/hXcyn/ZAtD71sIQNW3wXdRU5Sjj3Te\nDBf881wyTs9023bRx5fy/llvY2wzIlSC69bfyLVrb7C7ocZmxTL/7+dw8LP9bHlxE8ZW65PSNat/\nwnXrb3R68x1z4zjSRqd33WS1MVricu23KY5vPc7e9/dQvq4MgIjYCK787lqu/uF63p76eq8+A7lc\n+uVi3pv9Nia96+sfdtkIxv/sdDb8ZR0HP98PwMirRzPhjklenQH2pGjZUYqWWc813J0jd47f2cbT\nfHK//2GXjWDyvVM5saeGb27+L5JFQqgEE345yU4xaqO1jLx6NKt+/z1Fy6zh/4RKMPGuM4KuGBUU\ngsHu+z4m+2L7h+Fd937EjOX3kHXBeLIuOLmqPPrP74MtXvhvq3ZHl55LwXk3MvKWRxjziycZdu39\npE/0b0LOQPDD/d9hbLMqO8ki8eUVnwLWFWUnF396GQAbnljXpRgBPpxnM18/09G/q+CsQj45/4Ou\nm6yx1UjdgVq7Nrte39GlGMG6Ejv6zRGEKnhBjlfcsxyT3v31T753Ko3HGroUI8De93bzzrQ3ACic\nNyBo8vqTyfdOxdhm5Oubvupa/UsWic3P2/vFxuXGAVC68uRZi2SR2PSs/yxre8vgmImhFkGhH9G4\nq4x9jzjGzF497xma91WCZDXYCbbzfyd9YuWoiohk9G2PO5RHJmeQeeZ5ZJ55Ho2Hd1D89ZshkM4z\nJT8U271vLGoEYPD5Q9j6D+t2nVCrOLbUcbvP1G6N1zrwnMF2Sg6g8scKn+RpLm/yqZ+vlK22P1x3\ndv0A215y9GXqVCjjbj69a0XV19jw+FqPbeoO1gHWnYLydWV8f99yLEZ5qaSCRbCycvQHhnz8CB3l\nJyi+6/lQi9Ir5pz1ZwB+3PQCLS3eGWNNO/O3AKxd9xfvJpUktt/xrnd9AkCfUI6dilGSLFRtWEJL\n2WEsxg4ik9JIHTeTmOwBJAweR87sSyj/4bMQSyuf6Az7wAYDFgxiwIJBTttGJTlmDG+r8ezekn9W\nIbP/Ep6JkTuvv3MV21ze7LJtfF5cUGTyJxqd9c+r/nC9rPZvTX6NkdeMZuKdZ3DtmhtAgs8u/oiW\nyuDENJ2XfjMSsLz6VZcZOoKRlaM/0FFWQ+0H34VajJASGeloidqXCHvlmHnmeQDU7lpH+ff2fi7t\nJypoPLQDEIy98xlSxk7vU8qxtcr+pnfgk31sfGq938ZPGpzM7L/MYd+He9n07IaucleGH8Gm8/o7\nV4dxufHUH6pz2rapNLirXX9g0ltX/QkFCTQclacgO11DIhMiufizy7nki8V8cPY7dLR0BFJUAJZV\nv9r1uky/z0ERjok/K+Ay9BeKf/1CqEVQ6CVhrxyTR54B4KAY7ZGo2bKCtAnhuUIaeM4gOwvJxIFJ\nABz+8mBXmWSRGHrJcL8qxzE3WBPGdleMALkz/Bt6yhNyrh/g9NsnUPJ9kV1Z56py1xs7AitkAJny\n2zMp7nFdnjA0Gvjg7He4fuNNjLx6NNv/tTUwwrmgvsNxC00f5KwcvpL78E1YWtupePI9khbNIOnC\naUjtHdT/dz0NX5/8+xJqFWk3nkvstDEIjRr9rqNUPvMhktm5FXfaDecQN30sqFUYjlZQ+/5y2g/b\nh2sc8vEjXa+b1+3m+F+dRxkSahWZv15MzPghSBYLbdsPU/P615jqrbsnKVedjSYxjpaNe8m8ezEV\nf34H/d4i8p/+BUKrpvhXfXu7FqzRcErf20jRa6tDLYpTwt4gR7LIO3fR15R5bhQipv9xFpHxkYD1\nZn/h+xcD9tuIny36CKESnPPq+aSPy+gq10RpmHT3ZJ/mrdp+HDipjACGLBpGfF5wtzvkXP/K360g\nPj+BYZedDEQ84oqRXLv2BgCn7heBoGKj9WbnbBvbF7a9tIXIxCjmvbjQrnzA/IF278fcOI7syfYu\nN4POHwLAsW+dRBEJMOXtBxzKDrdsCrocvhI5KJtBbz5A4rlTaFm/G0t7B5ok+zPTwe89RNzs8TR9\nt4XGpT8SM2k4gz/4g8NYmtQEhnz8CInnTUW/r4i27YfRjSggepxjZKQj1/+J0t+97FY2oVEz+IM/\nEjUoh7ovVtP43RZiJgwl4/aL7drFTh5J0sUzMBypIPfhmyh84S5af9xLRHYqhc//yodPJfzIvWJS\nqEVwSdivHGt3rSVz6rke2yUMGterec597QJSR9lHvT/n3+d3vV7ys/9RvcMalHj6w7MYuND+bLC7\nm0BPN4AvFn/KFcuusSt7+8w37N63VrXyxeJPWfTRpSz813kO8vlitXjgk32cdtvpXcoIoK26lffP\nepurvr/Orq031+8tcq6/eEURG59az+R7pzL53pMpukx6E+/NfsujrK4+/57uG9P/OIvpf5wFwNpH\nV3Pkv/Zht5bfuZTrN97E4iVXd5Vt+utG9n1w0s/Km+9/1+s7kCwSp98+0UGW7kpPpVYx93lHJ/t9\nH+yhsajBoTzQaFWRYZlTUC6a5HgqHn+H1q0HXbY5dMUf7d6feOdbhnz8CAnzz6Dx25PWxAP++Rss\nbe0c+cmfPc5r0RscVpM9SVhg3Q079vNnTs795hKHdqqYKMoe/DdgXZHq9xZR+9H3mGqbSL/tIo+y\nhJL4eHkRbaqWhG90qbBfOTYcsG4naXTuLeXiB45CMpuCIZLXNBU3sunZDRga2mmtamXZHUuQzI4r\n4qbiRpbdsYQDn+7H0GTApDdRvq6Mr67+3Oe5v7zyc+oP12ExWqjeXsWXV3zW5VYSLORe/4FP9rH9\n5a0YGtppr29n+8tb+fzSj4MqK1gfBJrLm7EYLdQfqqNmV++Cr+9+cyeb/rqR1uMtSBaJxqJGfnzG\nfqt7x6vb2Pvu7q427XV6dr62vVdB0uNSC4lLKfCpb3bUUKfls1OvY07aDYyIcwxqEG64U4zu0CQ6\nZgCq+9x/W3+mE1ZrbYR37lTth6y7Y51br+HM6NFXe24EGBvdJ9gOJWGflQMg9+zFJI+awrEvX6G5\neJ9dnUobwdCr7yUiIYVdL9yDJIWX+fupTF/LStJfUKm1TL70cSxmE43Vh9i/+t/Epw0kMWs4JTu/\nljXGgozbunKZLqt+BbNkYnLSIor1u2g1NTAuYS6HWjZRZQj+lq8nch++Cd3IQg5d/pDHtrFnjibh\n7AlE5KShjtUhIrXUf7GaE+8uA0CTlsiAf9wta6zuDPn4EbdnjoPffRARocVYVU/5w69jrLHfHUi5\n6mySL5nVNe+Qjx+h7MF/o99fjG7UAHL/eKPXMsmh03XDXzQ1lbJ5i/PUZzOW30PNygPsf/Qrv87Z\nSb/KytGZnsoVAy66xX39xbdy9LPwykGnoBBsxsy9k01fPISpo43hM34KQFPNUYZOvU62cjSYW/nh\nxNsIVExLuZw1tR+SFJHFxvovAFhT+yEzU68OS+UolyEfPwKSRM3r31DzxjeY6poY9MYDdm008Z7z\nyPrC4WseJSIrhdw/3ULhP6yxo49c/ycsetdb2XLtL8IFSbK4VIwAplYDabOGBUw59paw31b1htjc\nIaEWQUEh5ERGJ2HqcJL7zottvJoOa+AGCQsxmkSnbXTqvuvHlnXvVQAcWvwHGr7ZQEdpNZZWxzB9\nhhLrObs6wf9KsqOylqM3/YVDlz+ExdDBoLf+z+9zeMuK7x9gxfcPsHnLP2ho8D7ohiRJtBsa2bP3\nQ77/4fdu266/yOruMuapxW7bhYqwWjkq2TcUFHrPsW1fMGLmz9i36l9dZaef9wCHN34ge4w83Uga\njVWkRxbSbm5hesoVAESodHRY9GhEBC0m5z6pfQG1kxWhNtMxh6VktNoxFDzzS47e/ETA5Dnxxjek\n3xo+RjZNTWVs3fYKM6b/H1ptjE8RcjwROzSDw88tY/Bd85ix/B5Mze20Vza6bL/tds8p5/xJWClH\nhf5F2ZpS5bwxBNQUbSYiKp6pi60xKacufpriHV/RcHy/h54nWV79b05LXECjsYatDUtQCw1myWQX\nOWdFTXAC1weCmle/Iv/pX5D7yE9p+O86Ys4YQfwsx/RJAMV3vUDBc3dYzxHX7AQgevRATHVNlNz/\nkl1bTVIcmjTrSluTGEtkYSamE42YW04anuQ8dAO64fk0Lt+MqbqBqGH5xE4ZSdvu8NuirqzcSn7+\njICMfdo/7C3mNXFRxMb5x4XKHyjKUUGhH1K+fwXl+1f43N8kdbCp/uRZkFmyrqCWVPWPM31DcRWl\nv3uZrHuuIuOXl9Cyfg+HLn+I7PscrSw7yms4dPlDpN96EXFTRyKZLbQfLqf6X/ZBswue/SUReeld\n73UjC8l/6nYAKp/5kJYNVnegyiffI/3WC4mbMQ51dCSGshrKH3mDtl3hpxyPH98WMOW47XbfU9EF\ngz5hrSqXvHlXUbrsfX8MpaCgoKAAzJj+e7Zue4XWVt/8nENFb61V+5VBTtKI8I22oKAQajq3WRUU\nvGH1msf6nGL0B8q2ahgTp8sgI2E46QnDiIvK8NzBDQ2tZVQ17qeyYTcGY/g7EQeCkbnnkpcywW2b\nupYidpV8QbufP6PYqDTGF1xGTFSq23ZN+koOVCyjrqXYbTtnqNR9689Zq9YxKGMGOcnj0agjZfcz\nmvSU1W3jaPVaTGYlEbRCYOg326oqbSSjf/64LIvXgpeecGnWXnzrfb0VxSdiIlMZmjWH9IRhQZuz\ntvkoe8u/oc0QXlaHMyzio9MAACAASURBVEfciS4iwWO7pTselTXe1KE3E6/L8lqOHw+/SX1rieeG\nbkiOLWDSoOt96ltet53dpfJ9wOSsDNd/dI/LugXjHpQ1T11LMZuOvOW5oRN0EYlMGnQtuogkz429\n4EjVKg4fX+nXMRWCjzZBh6m1A8nkPPi7N/SrIAAp46aTM+sSSpa8TcPBbQCMuMkxELAzhEreDnHG\nb27FcLiI40+H3rCgIPUMhmXPQ4jQ7G6nxA1kxvBfANYV05aj72GRev+jDBd0EYnMHHGHz/3PGPwT\n2o1NrNz7N5/6zxr5K6K0vvsC5iSPJyd5PKv3/13WA0zpnm8p2/Oty3p/basmxxYQqY3FYJSfpSMz\ncRTjCi7xy/zOGJQxk0EZM9F3NLB6/9+VSFl9BQFTPrkdbUJ0V9HeP3xB7drDAIx5ejEqrZodvwq+\nLUlYKcfMKecAkHPWZV3KURvreQXhDZEDCyn55QOeGwaInOTxjMo9L2QK0RXJsYXMG/sAJrOBH/Y+\nh9kS+PyBvSVel0WT3rnvVUrcQCYOvMZpnTdEaeNZMO5BDlZ+x7HqdbL7yV2FyWHG8F+wv+Jbimvc\nx1kt37vcbX2H3rUPmbdkJY6mqGaDx3bREUnMGPFLv83rCV1EIvPH/h+NbRVsOPTvoM2rACAA+ZuA\nQiWY/u1v3LY5/vVOhj9wPjED02g9WtNL+bwjrJTjnpd/T8roKdTudsxpuO+1R5z0OIk2Jp7BV9zl\ncY7W9ZvRjRqGfrd8n6/eolZpmTToehKis4M2p69o1JHMHXM/Da2l7C79ilZDbahFcklW0minyjEu\nKt0virE7Q7POxmBsoaJ+p8e2/lSMnQzPnk+HqY3K+l0u23haLW35St42tByykjwrx8mDbyAxRl52\nBn+TEJ3NgnEPsunI29S1FIVEhv5AfHwup43/KVu3vkJzS4XTNrk5Uxg69EK7MovFzMpVf3D7m5z6\nhXVX59CzSzn+tfV3PWO5/bZ/zYr9DH/gfPKunsz+x/7bm0vxmrBSjiA5VYwWUwfGFvdpe4wt8p6K\na9/9jPwX/4x+517atu+BHvEKWzdtly+uBxKis5k85KauAM59icSYPKYPv53jDXvZUfxpqMVxSlbi\naA5ULHMoP3PYrQGZb0z+RdQ0HcToxghk/jj3IbN6w9j8RQzJnM2qffKyzGsiY4hOyKSt8TgmQ6tf\nZfF0hhuIBwRfmDToOmpbjrH5SHj71IUrY0Zfi1odycSJtzsNB5eaOtJBMQKoVGrOmv0YK753vUun\njo6g5XB1l2J0R+JpwU3QDmGnHJ1jqJeznJa3nM9/8U+AhG7sCHRjRzjU+0M5psQOYOKga3s9TjiQ\nmTiSOF0Ga/b/I9SiOBCpdUxjNm9sYLfM54y+16Uh0NShNwf8QUgXkUhGwnCqGl3vfKjUGiZf+hdA\nwtBaT2SMNSzaxk9/iyXAad3UKi1zx/w2oHN4S0rsAOaMvpcVu58KtSh9jshI65n5ocP/c1o/dszJ\n+9y+/Z+i19eRkjKMgvyZAIwaeQV79n7ocvzyT7fIkkMTE/zIOWGvHHe9eA/+tKgt+UXgzxtrW7wP\n2BvOxESmMHPEHbJXLKEiL2UCKqEO+DyFaVMcthTjo7N8soj1hfGFl7u11B234F62/u/PGFpPGvFE\nxiQzbuG9bPvf4wGTKxwVYydadRTzxj7Asp3+TcnUG0SEFnVsHKa68LIWd0ZZmeOOXkHBrK7XGzY+\nS1vbCQAaGo5hNhsYOGAeGRnj3CpHtU4ra/62suB/RuFlFeIEyWIBmcoxnAKXt3XUh1oEv6KLSGRk\n7nmhFsMtI3PPDco8w7LnOZRNHXJzUObuJDfldJd1KpXaTjECGFrrUKn8+yzc8ww9XBVjJyqhZu6Y\n34VaDBLOPosBf3uawqceJ+8PJx/WB/ztadQJ/jVADCSDBi4ArGfdnYqxk6Ki72WNMeDmmW7r02ZZ\nXdvKPvzRBwl7R9grx0CQ/dDdFLz8JAUvP9lVVvDyk0QNH+y3OdYdeMlzoz5GXsrpxES6d2IPNunx\n1oz1zhRWIMlKHN31enjOgqDODTDKzYPK9qVPM2bur8C2xSuEinELfsOOpf6NkNP9MwjkWas/Uas0\nXe5LoUCblkryhedR+8nnHPuVvfFJR0UlqZcHzt3Fn2i1J7OabNvum1Vw3fojqKMjGPnIIqf1urxk\nhj94AQDVy/b6NEdvCPttVX+TcP5cUAmKb73PTjkCJF12HpWP+ebT1hOzxYTZYkStkrdt4C2thlpa\nDbUYTXpM5naEUKNVRxIdmUy8LitgriLTh/9ctvN9MMhKGk1100EK06a4bddhaqOu5Rjtxuauc7ve\nMLbgYiobdgNWf1U5NOkradZXY7IYiIvKIDm2oFcyCKFyag14xsWPATB1sf0Z26RF9t+bu4AAcshK\nGs3+im+ZMuSnvTprtUgmGlrLMJhaMZr0gESkNpYobTwJ0Tm9ktEZ0ZHJDM2aw8FK3wOz+0r6zTfQ\n8uNmmlavdajT79tP/IzpQZfJFyacftLozZe8jwB7Hvyc6UvvJuXMwXZWqiMftleWW29+w6fxe8up\npxznz6bkV84t6bTZmX6da+2Bl3rlhA5Q1biPopoNNLSW+dQ/OjKZ4dnzSYv3XyLoAelneuXzF0gy\nEkaQk+w81RDAqn0voO9wbukshOCsUb9Bq9b5PL+nFWtDaykbD7/hsj5CE8NZo3w7DhiRs5C9ZV87\nlPdW6cklQhNDUkye1y5KR6vWcKRqldcBJwamT2dI1lle9XHFgPRpFNVsoMPkJCl0ANGmpNL0/SoX\ntQLJZAyqPHIRQtjZfkRHW3eQqqo9uza5Y82CZxly93wyzx3rUGcxmvnxypcwNuqd9Aw8/Uo5jr3z\nWY/njqYTdWizMzCWH3eoMxwu8qs8rm7KrjBZOjhYsZwTzUfR++nMss1Qx9Zj1iS3WYmjGVtwca/H\nHJp1dtgoRyFUjM67wKG8qnE/24s+dttXkiRW7H6ayUNuItGHFcqA9KluV6w/7H3OYxzbDlMrS3c8\nyryxD3htTJSXMsGpcgQYNGkx0QmZDjsIO5c959Ucnjhj8A2y2uk7Gliz/x+9isB0tHoNR6vXEBWR\nwKwRd/o8TidnjfpN0HdB2nbvJv6smTRvcDxDi589w+mKMpRIkhkh1Ew787esWWs15po+7eS57Z49\n8hNou+LQs99y6FlrZCdVhAZLR2AtquXSr5SjHI4/8xJ5z/6Rug++AECTnkr6bddjqqml+gX/J+bd\nfPRdtw7pHaZWdpZ8Tm1z4C1cKxt2U9mwm9MHXEVafO/OV5Ni8nsddzRQVDcd9KgYu7Px0Gs+Kaeh\nWXNd1i3b+WevFMGynX/2m2/gGRc/xuFNH3Js6+d4E7EkUKzY/TRGs/+e/ts7Glm641EGpE9jSOZZ\nCBdxkuUwPHs++ytch9zzN9VvvMOAvz1N9q/voOb9jwDQpqeRcctNCLWaus//42GE4PLjjy8wefJd\nRETEMecse0vf6mrX/olpaaN8mi9cFCOcggY5ltY2yu59lMRFCwHIefQ+2o8UUf77J5CM/t/SqG12\nnsB0X/kSlu54lO/3PBsUxdidrcfe5//ZO+/wpsr2j39PdpomHene0EEHtOy9RBDcouKerzh/ioqK\nKA4UByDii6+vuPd6nSgOcLD3LqW0paV7rzTNHifn90do2jQnzToZLflcV64reead0/Tc53mee5ys\n+dGjMcYOv5EhaZiFoigcr7JvOm6Pv4tWMybD3rJ33Voh1bYfdrkPl2O7JVx+8Gtw+WKYSANMpNHq\n5UtUunZsLVzFqGLsS1XrXvx58iW7IQSdITV6EoMSOUfN8mfBT01B0lNPAACSVjwJbnQUqp/wvyVt\nf1TqVjQ12foiGgwanCq2H+80K9O8m0OSgblN7AxDZuXICRE73ZbsVqDu4ee8KI01pQ1bkZ04H2q9\nDPvPvA8jqfPZ3PZo6ioGi8XByGTb6BbOwGHxGJaIGXacfsOtfhRlglovQ4iH2SJUug4ota1u9S1p\n2IKUKNdykiZFjkZVq7UPmliajMScuRg+bqGNj/CB73yTdaZTWY3DZz/3yVz7z3yAqVn3QCx0L63b\nxIzbcajiU4also9Jo0HVI0/4bD5PKSn9AaVlPyE5eRo4HAEaGg5Bpxs4IllP8IDjJz5wao7kmycj\ndm4u+HFhMCp16C6qR+2X+6E669t4qn0JKOWYv2S9v0XwCjXth1DT7ns/HUc0dBYiJWqC287rYkEs\nFNrASYJKUSboje6HSTtw5kPMGemZMYuvIwnFhefZKMfIpAKfGeXQoTMofaYYe9h35j1cOOpJtx7a\nIkS+D0022KAoE2prdzvdfqCwcX3JW7UQkVPSrcp4ESGImpmFqJlmN619l20AqfX9CvS821bt777R\nQ8qbLyFq8U0+lsb/7D/j3JMdHSnRrq1yvI2nMWA93f5jYvuwodO18IV0DzayxmJMuW4dJlz1IsZc\n8pTVyxe4u3r3lH+K1rjdlwlDNWfgJSaA4AXmrouvyXh4nkUxdp2oRcnKn3Hs7k9Q+PBXqPlsnyX4\ny9RfH/aLfAG1cuzBnUg3bEEI8u55ye05KYpCSH6u2/0HM+XN25EZ57qJfGJEAYrrfBspfyAGijfq\nLCpdB0R8qVt93Tnr7E9N++EBXVOcGqPwV9QU+ufvcrTyK7/M28Oxqm8wdtgNLveLDx+JkzU/eUEi\na6JvvQksHg91LwZOGDt/EX95AQBg97x1NnZj3cWNqP1sHzIemYf4ywqQdtcMVH/o/MqVCQJu5ego\n+4Y9SK1n/kosAR8mtX/8afxNZcset/oFWk5KJqhsdd+UXqaq83h+hcbWxchdxFFpEEs9CzTgKu2K\nsz6drz9t3eVuu4tIxcMYlsYWbpQUymPHvT7PYKH+28MDGlRX/NucdSfuklE+kqiXgFs56mTuGTM4\nIvq+2xAyxhzuim5r1aRUoXndRq/MPRgwmvQBa2TjDF0MKCYAaJIVYZSbRkqBAovNxaRrXoWJNELe\nWo7S3R9CEj0c4fHZqD1J7xfJBEcqv/Ta2K6wt3SjWwmWR6cuwj+n6I9dmEJ17AT4yUlenWMwoTzj\nnM0CRfreJSmglKOngcP1CvuO823vfAbAfLZYu2RwxIH0JWUNfyIv+TJ/i+E2DZ2FjIzjKGGwPRoH\nSELsa0bNXYLDm56DUa9G9oy7AADdbZXImnKrV5WjPbclX+Nu0H8Om8+wJLa0ffU/DNuwDiw+Hyad\n/63WvUmPX+RAxjmRU9LRtsPxcUjjT8cYk8tZAko5ekrpx46jXSgPOJc/7HyjQVbolnJks3ggTXov\nSOQaTJw3ekJ9h+//eXvo/zfgh0TAqKc5ZvDAWd4Rncpqr43tDiUNfyAn8WKX+0WIkhnZHrdH+Py5\nUJ8qRuralwEAlNHW97T6scDObsIUVe/vwrC7Z+LMa3+AMtI/lBZsMPtT13190JeiARhiytEZOr/y\n/qH7YMTdFROPI4RG73/l6C1Hc2fxZ7QgAVcCla43ZVDV8U3ImXkPSna9Zykbe+nTqDjoeagvexTX\n0yfD9Re17UfcUo7ZifM9suB2hHiyOeiAsXNopbRzRPIN1sEWKJMJRoUG8pP1mL5lKYwqHdp3lEHX\n2g22kAdJfhIkueaYvXsW+MfF77xTjkGYhUV4J+vI+YyJIl0KZcdmW/8N2qqPImPiDRg550HwRREY\nffGT4Isi0dXsvdW1Whf4CXudQSxkNvlAf+peeNmr4wcqaYtnDFjPEfERd6lt8HEAmLLpITR8dwQ1\nn/o27mxQOQLm7SYnEyoHsYbLEQBD7OjEVeXENHqjCgKuxOn2PHZIvxIK+799HGGxmQiNTIVKVoeu\n5jJmhRwEdKkbXA4o70nqrSC9cDjMnd+yBVyk3DolqBy9jWT+bPBTk9D23hcAei1XjbIuNL2wHiaN\n1p/iDTo4LO8bMfga3bmcj/5Cb1S7pBztGZLIW8ohbylnSiy7uGsAwzQEi4XRi9fh+Htmw77ypm2Y\nkH6r6+PYyZM5lEkfPh8mikRV1d9W5aGh7kXPEgojrT7vnstsom1fcN4pR/HMyWh+zeyyEfPAHVBs\n34vOb35G6rtrEbNkMZrXvOVnCYP4G71R5Vfl6KqBE4uw/jdOGbUAtUVbbNpFJuahs6HYI9nocCdg\nui9w10goMXK0Xw2sfM3kSY8iJCQaABAVlY3Dh3vvgRMneJaPdjBz3ilHdpgEZJc5aK6wIBc195oD\nMZu0WvDTgv5HgxGtoZvR8Vx1Imf6vM1kcm3+/sEYEnPm0irHjEk34dCPKzySjQ5n3WhSZl6HyKyJ\nIFhmeWUVR1G9zewbGTd2HmJHXwh1Wx3KN//Xqt9AdRyhGNnXPAaTXouS719j4NuYo+WcT8qx7yrZ\nn8cJgcZ5pxzVR08iYtHlMClVIOW9N1WWQGD1OcjgwdWk0o4gTa4FOVbpOhid30S5llrKmXMyXkg4\nSIN3jgyMpONxk2csAqnX4cQHjyM0IQOps26wKMb0BXeDNGhR+NFycEXhGHPPesvW6EB1qRfcjMjM\ncTj5ydMg9Vrk325r7GKijDYra0dEhvo2qpC/OXhoA1JTZwEUUFO7k7ZN4clPoNE4v30uFEaiIP92\nRuRLWjQeIcOicWbtH4yM5yznnXJs//gbpLz1Mgg2GzUPWAdiVuw64CepgniCVj9w+hxvEyiWmqLw\nBOTPexQAMOU62zOew5uYSabsDuL4dFT9bQ7EoWysAE/ceyYlScmxKDyDqgvarhaExqdD2XR2wLrI\nzHHQydtA6s3KufzXt5F9zWNW8zbJipEYWeCLr8g48zjX4y+j5/F6naGmhl4p9tDZWeHSOaxOx9xC\nI+aikRANiwoqR19Q+6Dt1lLP9mqQwYfWoPTr/J6kyWISVVcj9n/3BMZetgLHfg0sl4HKPz9CzqIn\n0XDgF8SNnYea7fZDzanb6iGKTYWyyTZOa/86TUejpU4rs41L26mqGbTK0VeK0RlcNVAiGcxZy492\nPlcvk5yXyjHI0MJA+lc56Y2eBb1nmoqD9jO0+wttVytIgw6tRTvRWjTwKiUkOhkdZfQRUfrXCSJ7\n/RL5kiib9jJljZsSBwkUOKH+sYgPKsdzhE4ZB0FOFto/CrwbS38IggWJMB5iQQxCBdEQ8MIg5IWD\nxxGBzxENyWwZA6E3+jc6jp4MLOXY3ea7GKc6g8KpdoKIWOiVMgAE+qdhkFUcQ9qFt6H6n8/AFYVD\nEB4DZWOFU3URGWPB4vJhMuiQefkDNvO6ex7NZQu9FnWp/3YpAQJzOddZyuKIVMSwEhFLJNusHsMI\nKSay54KEEQRYYIHl1RXmjp3PMTLO9K1LQZEm7L3k35aySd/cx8jY3iKoHM8hLMhDyJiRAaUcw0MS\nkSgdg7iwHHDYAn+LE7D4O7arq9alQwlnjZFC44aDIxAhfcFdIDhciBMyUfjRcpiMelRv+wKxBXNQ\ncNdaqNtqcfy93nNDR3UNB35G7vVPw6TX4NQXL2D03cz404kEUYxleulPE1WDPPYkFJPmFfBI9mQ0\nmKos9c1UDZrJGszjXG/TV051WCnDSex5SGPloNpU4hVZTSbXjMP6cuxYb/hCgs0CwbZ+aOdFhbo9\nti84L5QjRxoBiiRBdnWDm0AfHooba7sl40t4nBBkxV/ocaLb8xEj6V/lSLmZP3Ao4OyWcvKMRRbD\nGgCIHX0hovOmo6VwGwCgpXCb5X1/BqozqBU49cXzls995/AEET/Sa8rxFHkA8zjXoxhm5RhHpOAv\n0vnVXyJrOJKJTAgJETjgopNyLu2Tr+mSV1ven3ryO9ogZMcf+AIG2cDHIhO/vpdhyZzjvFCOsUvN\nF7dhxWokPM/MPw8TCLhijBl2PSRC96JQBDHjquvDUJvfn7h03moJ00ggfsLFOPXFSi9J5TmuRChy\nBwoUQggxCBAwwXljl1mcK6GiunGA3AoAKGBP85aIjCI7Sn/2qzzjRHJv80/G55wXyrFhxWqrz3SW\nqbGP3A1BTqZP5BkWMwVZ8XN9Mtf5gLuZ34N4jrOWusVfrULudcvBk0ihaa/H6W9egVHjXyvjgeBx\nRF4d/wS5B2NZM0GBwglyt9P9eBBgF7nZ8jmaSEQN5d90bd5G167wi8XqeaEc+9L91y7acqPM+75y\nWfFzMCxmcDzpDS6CQeP9h3PXXq+U4fT/XvWyLMzB9fIZfzvVCCFhzlTRQVmvnkawxkBAmIPJj2HP\nhJZSo9xUCCMMaKJqMJezCHKqA2GEFOWmQnDB86qs3mLPfOdSUelau4PKMX6a55nom/b+OmC97Hv6\n+o5Pv0XHp996PD8dEmE8pmQt9srYQRDMqBKEcVgs798a7VmZlpmO2+1zijyAU/BdsBKCIEB56f+L\nIp3bTi582D9GkgGlHKPHzfF4DEfK0ddMyVocPFMMEmSQEYwxambsmHsRFpaCxsbDKC1zPlH8nAte\nAQA0Nh5Cadkmb4nnVQJKOTbvs80mbiKN0MvakHaFeeWlbqqGqqkKpFYNbmg4wrPGgC0IQdeZE6jd\n8pnbc4eMzgM/Y5jdlaU7zC/wX7guVzBRRugNKgh4Yf4WJQhDZE25BZFJ+TY+r/u/fZzhmYZm/kPT\neZayyh5hYSkAAGGI1KV+NTU7kZo6CwkJE4PKkQlaj/xDW56/ZD3UzTWo+HaDTV3Djh9AsFgY9eA6\nKOvPoPOUe1sOosnjEDJmJCPKkSBYuCif+ewHdJAmPWSqOsjVjVBqW6HStkOtl7kcPBsYPMo8yMBk\nT78TLDYPRzY9D5MbvwNXGKorrPPZApmO8nLbhctA1NbtNgczH8QElHKko2erlU4x9kCZTJCVHEbS\nnOtolSMr1LHlGUca4b6Q/fCWYmzqOoXa9iNe878KMjQIj8vGge+f9Mlc9hItD3aMDMYGHQoolU0u\ntTcYmIsalb/+BoTlJ/k8YXLAK8eogulOtVNUlyAiZwJtXfLrz9OWe4OLCp5hbCwjqUVhzY9oV9gG\nYA4SxB5N5bshikiEStbg9bmGauSmQIuXez7DjwkGHqeFYDm3bSOMGThRsaOsG7GP3gNBdobTctEx\nfvjNTuXWGwiKotDcdQonawfnPn0Q/zH6YvNqkTKRSBgxG6AoaJTtVm1O/LGG0TkFXP/cuLyNszFj\ng3ifYFYOO3QWH0TMhLlIvOBaNGz/3m67gSxdVQftm0b3YOz0LGEuly2EVDzcozGOVn4VXCUGcZuK\ng1/5fE5vR5LxF2p9YOToDBQkkmR0dzt/nBMSwlw4zv4xWX1FwCvH5v2/I2bCXEhHTYV05BS0HNwK\nRd0ZmPQ68MKjEJU/HaHJ5sg2HUX7aMdwJpi4p36Os/MedbsvRVH48+RLbvcPEgQAlJ2+P4sO4Uc6\nbjQIUQVIAutAYczou7Bz10qn248dc7fV5xl/M20l7X0GRW6jUxuXm98QBGInL0DGoiXIuvkJpF16\np0Uxdp8tGnBl6U14HJHbVnsmigwqxiCMMvla261TvigSU67zrUFDIMDnupf5Qav3fsSswUB1zXYA\nAJvNQ1amc0FaUpKng8czb4U2Nh3xmmzeJuBXjgBgMuhx8s2lEEjjETNxHkQJw8HmC6DvlqGr9Ihd\nFxBfMSnzTrf7/nXyFQYlCRIEKNn1PiYvWosD35nP2RNGzEZqwWXY/90TfpbM90SIUrw6ftx1t4FU\nK9H264+Imn85wiZOA6mQo+HzD2DoaLNqyxaFIvG2e8CLjYeq7DSavvnEHN2JIJD54usof9acFCHy\ngosgnbPA8lmcPxYxVy7C2VVPefW70FFZ+RfSUi8AACQlTUVi4mQUFX2B9g7reK4sFgdZWVcgIX68\nVXlp6Y+W9/su3wBS47prkb9WnYNCOfag7WhC7R/uO/p7ixCee24gB8o/ZFiSIEEAeWsFiv7egCmL\nXoNK3gQ2l+8F5//BQYQo1etzSPLHITQ3H+qzZ9D4+fuQzpmPtEeeQsWLy0EZzOnURCNykXDLYnT8\n9Ru0f/4K6YUXmxXi848DJuuAAxHTZoMiSRBsNiiShGTsRMgP0R8Z+YKdu57HrJkvADD7cOfn3+ZU\nvz17rR/83VGMAEBqDWALuG719YRBsa0ayIj4rkWO6MFEkZCrGxmWJkgQMypZA47/vhqi8AQc/23w\nBPxmmrjwXK/PwRIKoamqQMsPX0FTfRb1H70NAIiae4mlTcIti1H733Xo3PUP1GfPoO49s9920h33\nWdpwI8zntyy+AN1HDyJs4lQAQEh6ll+VI0kasG3709DpnNtqNhq12L5jBfR6ZrKu6Fq7GRnHVQbV\nyjEQGR7rnB9mfw5XfMqwJEHOd+ydKfYt98YKMlyUhC5VPePjMgGPE+JyH7UbxjjtW20ja7HF1pa8\nKf9ne+2Fw8zuY+rKcohHj0fn9j8BAF0H9yDh1sXo2m9OZ2WQdbgsE9Ps3Wc+y46LHY3YuNEQh8aD\nxeJBb1CiS1aJurq9UKlbaft64sCvLGsBm+/7lWPAK8eQ+DQkXXAtard+CW2Ha1EafEF8+Ci3+nWp\nve+g7Qp8jnuGC0ECB39tnSZE5AescnSHpq5TLvchlfZXNwTLvEHXs1qkQ35oH6RzL4ahvRWq0mLo\nW5vBDQ9MS+DmlhNobjnhs/nK1vzus7n6EvDKMW7KJRBEJcCgkPlbFFoIwnWn/0CM25gYWeBvEYIM\nUhIjC3C63j83sIEQ8sLd6lffcYxROahzZ4qGjnaQavrk0MriQsTfcDsk4yZbVo8AwI9LgFHhn23F\n852AP3MMiU0GAJB6rZ8lYY5WeZm/RbAhJWqiv0UI4mW85crBIgLzGTsjbrZb/bReiI5DqpQYtmyl\nTTlbaL3tG5KeBU1NJQDzVqrZGGcv4/IEcUxg/qr7QOp1YHGHVnDjQIyC464/WJAggDk7h4ki/S2G\nFQkR7h15eIPK1c9h2LKVyFy13qq85s3VIDX0cVy79u1C9KULUflqMFuOPwh45dh2dBsSZl7F2Hip\n7661G2c1bMEFudBdnQAAIABJREFUCF94MZT7jngULccROgMzVlxM4Y7RQpDAY8p167D/28f94uyf\nlTAXpQ1bfT4v07R1l7vUvvnbz9D8ra17WY+PYl+q1q4ccKz+fboO7EbXgd0uyROEOQJ+W1VWchgA\nIE7LYWxMbnwMIm++GvxhvQ7CogmjEb7wYgBA6NTx9roygtGk9+r4rhItyfK3CEEYxqhX27y8Saxk\nhFfHdxWJMM6tfoG4qxNocDgCl16DlYBfOZI6DZr3/45hV9yN0+8/C6OG/kDbFRJWPg71kULEPfl/\naHvnM6hPFCPypoUAzNk7QqeOh/TWa9HxuXfC0bGIwHomGZl8ub9FCMIAfa1VD296zqbemytKAS/M\na2O7w9hhN7jVr7b9MMOSDH44HAFmTF8Bws0Qmdu2P82wRL4h4JUjJ0QMbXsT5GeLkHv3KgCATtYK\nk4F+9VX+zXra8r5YtlXf/xLJ61dCfaIYrBChpV657wgiFl3uNeXI5wROmh93LfqCBC72XDqUHbVe\nnTcpcgzqOx1nwPEF/CGaSsvXEAQLM2fYPmg5C1OBAPxBwCvH3MUv2JTxI2LcHo9UWP+xCD6Pth1L\n4D0joHBRslu+VN5gZs5D/hYhiI8o+udNr46fl3xZQChHd1eNZ1t2MSzJ4GfypN5zUIoicebMZsjl\ntTBRJCZPehQyWSXOVm4FlxuC6KgcJCT0Wr1v274CAOUHqZkh4JVj+TdvMDpex8ffIPXdtdAUnoaw\nIBeKbXuQ+u5aAIBJY3YXEeaNgPZMJaPz9iUhYiRKGv7w2vjOkhp03wjCMHHhuWjuOu23+QkQiJZk\nutW3onknw9IMfoRCcyAChaIBh4/816aeIAhLnseOjjKUlm2CgB+GqVOfxJwLXh60W6rAIFCOmlZm\nc9Rpis+gceU6iGdPQ9PLG6CvbUDn/34BALDFIouirH3QuT+qiSJdTlfFYfv/kJrD5iM7cb6/xQji\nBeIyp0OvkaOzvggcXggmXPUiAODAd8tAUSYHvT2jIPUavypHd/Oq6o3eNVga7NApRgDg8WxdwLQ6\nOUpLf0R29tWYOOEhHDr8H2+L5xUCyzLERxiaWtH59U/Q11qHcCMVKtTcuww19y4DZXAuik19h3vb\nSAI/n4lcOJLenSXI4CchayY664sAAOOvXIkjP6/E0c2rkHfBAz6Zf3au+4m/PSE1ehJ4HJFbffeW\nvcOwNIGFdNFCr4wrENCHuOvJ4xgaGu+VeX3BeakcmaSm/aBb/aZm3cuwJM4zvyDoVDyUYXPP7UwQ\nBAiCgEGnhF4jh1jq/fRNgDmgREHq1T6ZqwchLwzZCRe51ZeiTNAbPbeCD2TEM6Z5ZVwWyz0L1sFA\nwG+reoPUd9YAdmKi2gsQYA93IvgDAJcjhEgQBZW23a3+7sBmcTF31HKfzRfEPzSUbkf2jLsglqai\neHvPioiAVu3eb9Ud4sLzQBBsnKj+zutzcdlCzMxZ4nb/3aVvOdWOYLEQvfgOCLOzYJR1Qb71bygP\n9Wa6DxmZC+mN1wGg0PbxF9BWmH0mk1auQOPq15H80vNo/s9GCHOzIZk1A7VPPQdQFOIefgAmtQac\nyAg0rlmPlNUvQnnwMDp/2mwZO+qWGyAaNwa66ho0v7nRnCQZQNqb61C95HHE3ncXeKkpoAxGtLz1\nDgyt5kTLUTddD9GEsZa2ACD/eztkv/zm9vU6Xxg0ypHF40OaNxkc0cD+VE17fhmwPvaxe6GrqEbz\nuo2MyWYkdeCwXbdunT7ifmwtXMWYHAORJB2LvKRLfTJXEP/SWLodJqMOzeV70d1mvkFHp41D5ZEf\nfCpHbFg2ZuU+jJ2n7Wej8JQocTrGDb/J7f56oxoavRN5CgkCqf9ei84fNqH1vY/AjY4CIei1HQi/\nZD4ks2egdvlzIAAkr1kF2S+/Q7HbHBc15p47Uff8KqS8+iJaNn4AuXE7Yu/5F1re/RCC9OGoWboc\nSStXIPWNNah59EmkvbnOohzTNryGjv/9gPYvvoEwOwtpG15D9ZJed520Da+h7pkXQSoU4A9PQ+Iz\nT1rq27/6Hzp/2ISU11626uMqEkmyxfCmB5PJCBaLg/j4cWhqOur22IHKoFCOWTc9DkFUglNtHSlH\n/vA0p41tnOVE9XcYn36LW33nFzyLv4teBWnyTqYODouHWXmPgsOid1kJMjRprrBOjttWfcROS+8i\n4Eowv+BZFNVuQqOsiLFxI0TJmJhxh8fj7Ch27BcNANG33wxNcQm6d+4BABjaend8ROPHInzBPFQ/\nsgwwmUABqFv+HFLfWGNRjp3f/QSTymz0oykphb6+HpKZvblgKaMRir37wRJZn5lyIsIBgoBi3wFz\n39IzAABhdpblfcd3P4JUmIOl6yqrAQAEl+O03cRAaDQdEAqlyM1ZhAMHra9VWdkm5ORci5zsa6DR\ndKKrq8pSN3XK4LdpCHjlyAuTWhSjrPQIlHVnkDzvRujlnWg7th2ixOEIzxoL2elDqPv7G4fjqfYf\ngTBvBDSnShmTsUNZ5bjRAMwd9RQqmnfgbAtzcRQFXAkmZ/4r6AwdJCAYlXIVRqVchYrmnR75E4aL\nkjAx/XYQDESZqmjeCcpJPzz+8GGQb/2bvi7t3FmuqdcSmCKtg7CTqn5nmiYKYFkf7VBGI0z92vHT\nhwMUhcRnnrSUGVrbQPB6H3Z7FGJfCBYbFDxXjieLPsekiY8gJCTKpq6p+Rhycq4FAIwdczdtf5nM\nvkvcjL/NK1lSY0DTr4Vo2nwC2sYuj2VmioBXjgkzrgQAlHy4EgaVOa9ZwsyFoEgjOor2oaNoH2q3\nfIn8Ja9D3VqHjpMDp3fp+PJHpLz1CjQnT0N9otjqBw0AqsPuJfEsadiCnMQFbvUFzOl1MuJmo67j\nKJpkpyBTuR7NRMCVIDfpErf8vDT6Luwq+U/QWCeI06j1MjTJipAeO9PpPhlxs5ARNwsA0NZdgbqO\no+hUVoE0GWjbc9h8xIZlIyN2FqMh6oykziUlra+tgzBnBBR799vU6c5WAjOngWCxLLkbCTYzhiq6\nqmqAINDw0hr7jSjvOdqrVK2oqPgdtXV7aOv37H0V06c9RVtnMhlx/MQHdsdu21mG6JlZYAu5SFo0\nHkmLemNa6ztVaPrlBJo2F8Ig94+bTcArR1HCcACwKEbAHG+VI5L0aUWho2gfEmdf41A5prz1MgAK\nwvwcCPNtg5m7qxxr2w97pBx7SJaOQ7J0nOWzRi9Hl7oOSm0bjKQOAAUuWwguWwhJSDzCQ5I8foo2\nUSR2lZh9kbo1TZAIB6/5dRDfweeIUdG8EzFh2RALXI9aFS3JQLQkwwuSOeafU2tdat/28edIfWMN\nxFMmQbH/IDjhYQjJH4XuXXugOl4Idng4kle/iNrl5lBryatfZMToxdjRCcpkQvRtN6Hts6/AEoVA\nMmsGun53PgOKSacDALAEApi0WhBsts3KdiDsKUYA0OsV2Lb9aWSPWIjY2AIQBAGdToGKs3+gra14\nwHFLV21G3/070fBoxM4fiZi5OeBFipB6xzSk3tFrZbt7rm+zzQS8ciQ4tiLquzsQmmS9OuosPgDp\nqKkOx6v9P+9FbNhe/DouyHuM0TGFvDAIvRzU+a+Tr1jeVzTvdDv8VpDzCzbL/L+5r+xdzMt/2uVg\nGP5i26nXXO5DkSRqli5HzD3/QuR1V8PY0YmOb3sNnLq374ShsRHJq54DQKHto8+hKWHm6KbmkWWI\nuPIypLz2MkwqFbp3uZ78uPX9j5H04jOAyYS2z7+GpriEEdl6KC37CaVlP3k0hqqyDZUbt6Ny43aw\nQ3iIWzASUbOzIcl1zt6EaQJeOWraGiCKH2ZVpqo/a6McmTiD8BS9UY0zTX8jK36uv0Vxmv7Wsq7m\nswsSBDA/YA2GLfm9Ze/AQGrd6ksZjWh5+z279ZqyctStWGlTXr/yZcv7HotRUqVC3YoXrMrkf22z\nadeD7OdfIfv5V5ux6SxQ6crURcWoXfaMXdn9TdT0TCReOx6SkYk2dZ37z6L+e98blAW8cmw/sQui\n+GEQSOOh7WgylxXuRuzkBYgaPRPtJ8znBklzr/doHukd1yN0yjiX/Rz7U9W6H5GhwxAlTvdoHG9j\nokirFWOQIJ6ytXAVLip4BgTofYj9zd6yd6HUtvlbjCDnmL7lURAc690GfacKDT8cReOmYzDpvGPB\n7ywBrxzl5YXAxcDwq+/H6ffN+/mkTgMASJh5FRJmXsXIPJwI5rYuj1Z+hYkZdyBClMzYmEyi1suw\nu8S+47Nc3YiwEP9sZQQZ3PxZ+BLmjHwcXLbQcWMfsr349WD81ACD4LBh0hlQ+PDXUFa0+lscGwJe\nOQJA6ScvQd9tHd3j1ManMPL+V63Kiv77BG3/pNUrQBmNaHhmjSWwuLc5VPEJhsdOR2bcBT6Zz1mc\nMaWvaN7hkWN1kPObbafWYVTKVUiIGOVvUWCijPjr5KuOGwahZcKEB1Ffv98rTv6Gbg24EiHGvHOb\npUxV1Y76bw+h9S//Ba/vYVAox/6KEQBMBh1OvrmUprUtTa9Y57FreNr2nyX6gTvAS2LWSrOyZQ8a\nOgsxO/cRRsd1BwoU/ilaY9dkvi/tirM+kCjIUKaodhPKm7ZhVu7DfpPBU5/KIIA4NAE52dcgIX48\njh57l9GxD1zdm+lDOi0DyTdMgjgnHiOevAQjnrwEAGAykGj88Sjqvz3ic5cO/1ux+ACyWwGyW2H5\nbOyQ2b5avRPjVGdQYGvhKtS0uRegnAlO1v6EPwtfckoxBgniCiG8CLt1WkM3thauQlkjvfO8t+jW\nNGNr4SprxUgABd+bLclZfC7G/t5rtd7znmCzkLXuNoz5+Ukk/muOpT7/m0fBi5Zg9E9PgC0SIPOV\nm5Dz1l2W+rCJGUh77HKM3rQMIz99EKIRCdZjE0D2hjsxetMyxCyc5K2v7VVOl3g3Rm7H3gqceOhL\n7J67DrvnrsOBa95G9Qe7oW2QIen6iZj8g28yyvRlUKwcmcSewY1y72EYGpu9Nm9p458obfwTmfFz\nMDzGOxHy+0JRJhyp/BKdymq3+nep6hEuSmJWqCBDDiE/Amq9bMA21W37Ud22H8nSscj1YnzfVnkZ\njld/S19JAewQc/zjmKsmQHGyxlJFqsx+gGM2L0fx4nega+xE0r3zkPfRAyj+19sAgNhFU1D26KfI\n/9+jOLFwLXI33gN+YiR0DZ0QjxmGzh3FqH59MzhiIfL/9yiOXdJr7DZm81M4frl5t2rsb09DcbwK\nmurAO2MbCI3G+0HrQzNiEDMvD9GzRoAXZZsn0tecd8rRHppTpYyGlLNHedM2lDdtMycbTrgIiZGj\nGRtbpetAedM2tMg9/x4HKz5mQCL32FXypuNGXuRo5Vd+nf9YleMwiIGCkOu8IVtdxzHUdRwDQbCQ\nnXARUqImeDQ3RVGQqxtQXP+rS1aoMVdPQs3rmxExIwcGmRLtfxxHzJUToCppgK7RrATq3/0LMVf2\nytf4yQ6Qah0IFgHKQEJ+8AxChsdC19CJ+nf/srQzKszGggSXDcpgdrQ/88Rn6IlSJ9tRjIiZOYNO\nOXqDUa9dh/AxKbR1hm4Nmn89iabNJ6BrU9C28TYBrxzzl5iD3To6X0yedyMiciY4fQ7pb4ykDqfq\nNuNUnTnyPkEQiJZkISwkEQKuGGJh3LlIOAKwWByYTEaQJj20hm5o9HJ0a5ogVzeiU1kFyovho4IE\nGQgux3WrVIoyoaRhC0oatgAACBCIkmRCGpoGsTAOIn4kOGwB2CwuSJMBeqMaal0n5Brz771D4V4s\nY8XJGhAcNjhiIeSHKpC17jZo69rR/L99SLhlJlRlDTZ9iHPxT0m1rt93gCU2asEPj4Mt5EF+qAKa\narOSJgjCErVVfabJ0s+kM4JgnxenWQ7pUYwmnQFNvxWhafMJaOp8l1bNEQGvHJ1F3VqHiBznnkTj\nVzwMXoq1s2nDs2vdOnccudQ2qv+p9a4raIqi0CovQ6u8zOW+9oidcRmiJ8xx3LAPBqUcZe+9wJgM\n7jAv4T781ThwZvbc8FkwUSRK5fZDWw02IvOnImHutTbl7vyefAUTUXEoUGjrPoO27jMMSGSf9t+P\nQzJ2GNQV5uOT0NwkCNOiUbvhd6hK6hF5oa11LWUa+MEzYnYeYKJw7NJXLTFO466bwrzwfkKj6YRQ\nGIlhw+aiqorZs2Nfh4NzlSHzCGNUOpGTDUDkjVeBLRGj5t5lllfL6+8gcdUyRN7IjM9kEM9wNlNC\nkCCuINt9GjFXT0LLj2bjOMpospxDtv12DKLsRPATIgEASffOg6FD6XBMfUsXWEKexQgn6pIxXpLe\nP+w/YFZgw9LmgLCTIH6oMmSUozCWfu+6P6HTJ6F++ctWZdozlaAMRoROH5yWZEONvxuZNRkPEgQA\nQAHi/FTIdpgDYrdvOQ5jV2+KqGOXvorURy/FmJ+fBEWaUHSr47NvVUkDmr7chdRHLkXBd4/BKNeg\nY2uh176CP9i+wxx27oLZLyMy0vWMP4OVIbGtGpqUgZjxFzrVVn2kENzYaBiarQ/ECS4HqoPHvSGe\n3+g4ugPqhirwpbHgR8aYXxGxYAv8E71kuHg8InkJONLRm5B6QeJD2NJgzgiSGJKNCF4CkkR5lrIe\n2AQH8xLuh9LQiRBOGFo0Z6GjVFbjaEkl2AQXbIKDPxvfPtePi3kJ90Fp7ISQLYbRpMf25o988G2D\nBCJ9rUjr3t6Kurf7ZLegKJx54nObPidveMOmf8MH/1jKmr/ei+ave4OBd+0ttWnfQ80GzzN1+BKh\nMBJhYalobDyEhISJGF1wp8tjbNvuvWQP3iQglWOPEY6jsv50nLLNtdaf9o+/Qcp/X4Vix17oKqrB\nFoci7Ir5kH23GYbmNoSMzrO0VZ8YOOVKoGNUK6GoLIaikv57ZN6+DHxpnM/kqVQcQVbiQ5bPPJbQ\nagu1QV2KBnUpkkR5Nn1nx92Jk7I/0ag2n8nOT/g/1KjMT+iz4m7Hofaf0KmrBwBMir4GWZKpONO9\nD3MT7sXuls+hMpqTqE6OvhbDQseiSnnMa98zSJChwpTJtkHM3SH1tqmgANR+ts9SVrDhRpfHMSq0\naNteitZ/mM0qQkdAKseTby6FICoB8dMugzg122F7o0aFuj+/hKLGsQtDT/g4ydyZwNzeJK0Riy63\naetpEPIgthhMOoRypVAaOjBOejmKZH857gSAyxJYFCMANGp63wvZEkyMWmjT/kz3PhAgLIoRAMrk\nezE+6sqgcgwSxIek3GZOJ9hXOUrybDNwOEPk5HSMeOpSrxv0BKRyBABteyOqfn4P2Xc+C544gjEX\njaDC8y9HO37BeOkV2NH8McJ4sWhsc886t7/RTv9tWPsQ515BggRxBFNbouqaDos1b19KXvwF+k4V\nTQ962EIeMh+ZB36sBFN+ehD7F9pPoOApAasce5CVHEbsxIsYHZOXlgz+8FSbdCkA0P3nTkbnCmJN\nl74ZAnYoQrlSqPus6BxhMOkQL8xEk8acbzJemIU6VREAQEN2IzW0ADVKW0MICiaIOBFQGc1RXEaE\nTUOlwve54YIEOZ85ehd9UJHOA5Uw6V1LTXXo5vcw9Zcl4IgFTIhml8BXjqVHED1mNmPjxT31EHjx\nsVAdKQRFkoyNG8R52nW1GC+9AgfarOM1ZkgmQcgWAwBGRlwIrVEBmb4JHbo67Gj+CPMS7sdwwwSE\nciNQrTxhyRu4s/lTzE98EBniidCbNBBxIrCr5TOojXL81fgOLkp4wGKQQ1JGnFUc9vl3DhIkiC2u\nKsYeqt7biYxH5jEsjTUBrxz1Xe04tXE5Y+MRLBbqHnsBlCEYhNtfHGn/mba8otvsf1Yks3U2Jinj\ngFunWxvot1dMFOnClmuQIEEGA6TO+/fvIePn6CwdX/yAqLtct5IKEiRIkCCBAUfk3S1VYBCsHJmG\nGxeDkDEj7SY9DhrsBAkyOOFnpCD8movAT08BKEBTXI7Oz34G2dXt9Bhxz/0feEmx6Pr5H3T/1mt/\nEP3gzRCOyYXm+Gm0vfWlU2OFTh8H8fxp4CXGwdghg/rwKXT98KfLxzm8lARE3HQp+OkpMGl10JVW\novOrX0HKnP9egUj1B7vd7iseEcugJPQMKuUojElCzIR5ECUMA5sngF7eAVnpEbQe+cdx53NE3nQV\napc8A0qn96KkQYIE6U/8i0vAS02A9vRZtKx536pOete1CJ05HgAg/2Ubun7406o+Yc3j4MZFQbnz\nMDo++sGqji0JRdJ/nrGZL2RcHkLG5YHSG1B797O0MqV+uhoAUHP7cst7AIi47mKEFGSj+ZV3rcpD\nJoxC6qerUXO7/aMe4ZhcxDxym1UZJ0YKyaWzILl0FvRV9WhaSX8MEHbZbIQvWgDKaETtXc8g5cOX\nQHB6b9NsHhchE/MRMjEfxnYZGh5bY1eOQKfuG/dz3MbMzWVQEnoGhXJk8fgYed+rNuX8yFjETb0U\ncVMvhbyiEDW/f+pwrLpHnkfSq0+j89tfzj1RWpsX6yprmRLbBvGwHITnjENI4nBwRBKYdBpo2hrR\nXV6IzsJ9jgcYogii4iHJzIcoOQMCaRxYAiEo0ghSowapVUHb1ghFZQnkZ074RT6uOALCuGSI07IR\nkjAMHJEYbL4QRo0KRrUCus5WqGrLoaw9A32Xd5JmDwUU2w5AeufVEOSm29T1KEYAEF80zUY5cuOi\nLGP0hSUWWSlGzalyqPYeA8HlQHLRdHCTYkHwuA4VWtT9N8LY0QXZl5shvfNqsMQi8EcMQ8R1FwMA\nOj/7GexwMcKuMAfyD509Ecodh2zGEU0bi6h7rrN8lv+6HbqKOnCiIxBxzXwQAh54w5KQ+PqTAyo2\ngsNB8rsvgOBwQBmN6PrxLxgaWyHITIPk0lkAAE5UBGIeuR2t/3Z83xtq7J73utfnGBTKsUcxUpQJ\nLQe2QFlfAZNBD35ENKIKZkKUMAxhGQVInH01Gnb8OOBYCc8thUmjRfjl9O4hjS8we9EJFgtZi58F\nN9Q27x1bKEJoSiZCUzKRcKE5G0PF5+ugbWtkVIZAg2CxkDDvekTk2c+iQnB4YIl54IrDIYhORHju\nBCTD/DTeXX4StZs/8aqMksx8pFx2OzBAsGVOSCg4IaEQRMUjLKvAqk5ZU4b6P76CUe2fXHQAwAuT\nIuuuFbR1pe+uhFHl22051e6jkN55tcN2LAHfbp2+ujetVOiMcZAuXmT53F/5KXceBsHjIuX9VQCA\npA0rUP+wdVzlHkSTCyz91UeLLatFyaWzUHvvc6C05p0m0bSx4EjDEXb5BbbKkSAsilFzsgytr1u7\nLyj+3Iuwqy5E+MJ54ERFQFiQDU2h/cAlLAEfXT/8Cfkv2yxlmuMlkH37h0U+4Zgcu/2DeEbAK8e4\nqebM4R1F+9Cw/XurOm17I+TlhQAI5C95HdL86Q6VI9PKbyCkY2cifrZrmT4ybjWHazr71b+hafbe\nKtYfiIfnIfWquzweR5KZj5FL16Np+0/oOO7+uYW9sVMuv8PjcUJTRyD7vhf8lm6KFxGNrDufoq0r\n+e/TIHVaH0sEu2dtPduGmhMlEI6mudnbeUDpqxgbHqe3IaD0BnR88B2kixeBHS4GwefRHqmoD520\nL7e2t71y52GEX21Wbv2Jefxflvf9FWMP8k3/IHyh2QUhZukdA65mKZ3eSjH2pe3NzxG95FYAACHg\nWckYhBkC3lo1MnciANgoRmsotB2l/xHRQbDZiFv2f0h9dy1S3n4V4pmTPZTSloS5i1xWjH1Jv+kR\nhOeMY1Ai/8ILi2REMfYl/oKFSJi7yHFDJxlx93OMKMYeSJ2GsbFcgR8ZY1cxFr+5zC+KsT+81ATL\ne8mC6QDMikd1wBzIQZg/wlIvmpQPAFZKrX8AD2Ob/SS5yt1HLe+j77+Bto3mVLnVZ7LTnALPpLL+\nGw40j3CkOWNFz3ewh+5M9YD1PbRt/MZunfpob7xkXqL3jVO8AYtnf20WPiYFqXdMAz9a7EOJrAl4\n5UiZTE6107TVO9WOYLOR8varMDS3ovU/H6Hzy58Qce1lSHh+qdXBtyeEpo5AZD59wlOTQQ99VwdI\nrdrhOEkX3wy+dHD+8PujlzuX4ZsymWDoloHUOBdSKjJ/CrgS26d4V8l7dB244nCPx+lL1bdvMzqe\nM/Clcci8g341UvzG46CM7jldM4WuvAYAIJram/dQcok5xrH62GnLmaJkwQxLvWiG+TxS0WcbM3RO\n7wOtK9aowjH0hhzGVuvfp+mcIjY0WmfvoQyOr58j5aivbXI4BgBojp92qh0rxD9Zdjxl8vcPYMbf\ntoHNJ3//AEa9dh1SbpmCiV/fi5GrbROA+4KA31btKNqLuCmXOGwXll7gsA0AxD52H1o3fADN6d6s\n48q9h5D67lrEPnYvmtf8121Ze0i75l6rzy17f0fbQftZtFMX3g3xMPqzg8zbnwzoTPCuUP3je0i7\n+h6rsvYj29Gy9w9Q5MA3nfhZV0I6bhZt3YjFz+DU+sfcliv3odUgCPvPiaqGSigqitB+dBf6G3D1\nIEpKR+z0SxGSkGYp07Y10Lb1FoLoBMu2fH8C5Tek2H4Q/MxUiKaNhexrc/omlijEUq8rqwIACPIy\nLGU9KzLFP71Zd3gpvSvPHoU7ECaV2moem3oN/Wra5KRVOzss1PK+v6XqQNjb5j0fYIfwbMoiJw0H\nNzwEFGlCxYa/kPHIRYgYnwa2gAtS69vALQGvHFsP/424KZcg7fK7UL35Q9o2LC4fYZkFaNr7q8Px\neKmJVoqxB0qnBy8lyWN5++PMTanmp/chiIpHxm1P0NanXPkv1P48+HMQKqvNxgfyshOo++1z2FM0\ndDTt/Bkt+7Yg9yFbq+XeYOLOj9dD4rzrwOLa/pMCgKq2HFXfb3RqHFX9WVR+Y06Oy+LyETHKt4mz\nhTFJSL+F/rcWKIoRAFT7jiPqnuvAFotc7mts6bC850T2GriRCse7DGS3akDlCJOd3w5NsGw66M4g\nneI8j4HrEIhnAAAgAElEQVTf/HuR1ee8l80GW3vmm1MUNv9RhBl/PY70B+fgzLqtNv29SUApx/4J\niymKAmU0oHbLF0hZcAvyl6yHsr4C6uYaUEYDeJJIhI8YC4LNgbKu3KlzR9XBY4i8aSE6v/rJqpzg\n86DYdcBOL/dw5aakbW9C/R9fIunim23qJOkjmRTLr3hyozYZdKj+/h2kXXufTZ10zHS3jHMiRtGf\nN9f9/gXkpe6ltTIZdOg4tsutvu4gjEtB+k2P2FZQFE694f6K2iv0UzbCAvPZompfb6Jxk0YHlpA/\noKGJsV1mec+WhNK26YszbTzB1EfO5hf/C93ZOq/ON1SQF/YaHRJs8+6NqqqPO9S5n0vkZFv3H28T\nUMqxxzJ1IEKTMhCalGFbnpyJ/CXrHaa26vjse6S+swbiWVNgbO8EWxwKgs9D+wdfQXWYOT+6zhN7\nXO7TVXIUSQtuorXOi554IdoOOR/sYKiirLVd9QNAeO54l5Vj+s30v5X2ozvdVoy+JiQ+DcNvXGJT\nTpFGFG8I/GhP4gvMK+zuP3ofJhR/7UXYFXMgnj0JmpPmlGZkt9Kqn66yDqGzzK5A/Kw0h/OwRN49\nlzM0tFje8zPTgsrRSfoaVmWvuAwAcGrZtzbtuGG+P1cNKOXoisWpJ9Tc9yT4w1IgzM8BKe+GYsd+\nx51cpHHbwC4l9qj6fiOGLXrApjx2+qVB5XgOTUs9hLHWW+D9PzuDvT7NO+kDo/uFAbb1RInDMez6\nB23KTXodTr9Fb60aCGhPV0CQmwF+RorFQKavkUr3lj1m5ThnEnBuNaH4x3pXR7nzsMVn0pVVoabQ\nvfyhriC5bDa6tzDrYjRUibtkFFq2ngIARM3MAgDoZbbGioZu31tYB5RydObMkCl0VbXQVQWeH6Gq\nrgIdJ/ZAOnq6v0UJWLorTtIoNtcOb0ISh9GWV//wjptSeQeDkj7npSg5g/YhyqhWovSd57wtlkco\n/jkAQW4GBLm2O0CA2XgGADixURaXDmW/yDj9Hxq48dEwNLXRjtezwgSA9o1fuyu2Q/TVDeClJYIt\nFoHgsEEZgynxBoJU6yHJS8TI1dcifHQKANg9V1Sc9n1glIB35WAaguv95wFVXYVH/Vv3/UFbzha6\nbsQwFGEiRFvczCtoy5U19Nu2/sKgkNuUhaZm0SpGfXdnwCtGAFAfMa8UBDnmc6SBXBsEI8wPMf23\nVQGgedXbwDlXr4TV9GerBI8L6b+uAWC2SLVnlcoETc/3pkZL+fDlAaMrERwOeGmJXpNlMLDvSvP1\nihifBoLDgkGuRssWawOd2Plme4vGTb4/5giolaMvSHnrFdrMG6nvrIGxvRMNz3geyFdebj/ahjOQ\nWnrncUnGKMiKmDUaGowwkaQ6JD7Vdlw/+wDS0X/lGBKfhrRrbA2StO1NqPjsNV+JxQg9MVb7njf2\noC05a1aeAygYXUUtahY/g9SPXgFgDiKuPV0B5e6jIDhsiOdNtXL5qLtvJbNfgIaWV99D7FNmd6XU\nT14FpdVDdeAESKUanAgJhAXZYIWarWbbN35tFQ7vvIOisHvuOvBjJDDI1TDpbP//QlKlqP/uCGRH\nHbvrMM2QUo7OGOTYgyJJcKSeO5MDgKaxmpFx+iNKzggqRy/SVXrUcSMfY1D0Ksesxc+AJ4m0aaNu\nqELl/wZvQue+lqo9dG/dY1lZGlsG2CkgTai9a4V5pQbQbtdSJInau2yzdngDbWklGh5bg8TXnwRg\nDu0WOnsibdvgtqsZXav9IA5V7+20W+dthpRyHIiQ8QXgRplvLGELLrCq40RFguBwoNzPzM1R56XM\nDIKoeK+M61cIAhG54xGeMx6ilAz40/Er0LZUgV7lmHbtfbSKUdvaMCgV40AxRQFzgG1HbXqgjCRq\nbl8OXnK8Oe9hZhookoS2uAKyL36BsdN2a9qRHI3L6WMwqw8XOZTL2C5Dze3LwY2PRvi18yHITQfB\n54GUdUNbXAHF3/vsbiXLf90B+a87BhzfkexBmOG8UY7qoychucgcYUU0dbxVnUmtQcPyV2CU0Rs/\nuIpJ751zDY7If3EGGYUgkHrlXRAP935ONlfQdTT7WwQbDEo54mZejtCULNp6QUwi+NK4gJTd1+jr\nmtCy5gN/i2HB0NSGtv984W8xgrhJwClHYUwSNK3OxUntC1ckGbgBRaF76w6EFOShea3nIeL8AZsn\n8LcIHpNx6+MQRCc4bugH/BUofCBCk9IR6cByOfP2ZSjesMxhCL4gQYI4T0Apx9zFL4ATIoZB0YWS\nj18EYD5HZJK29wfvk5wpAA1GnIXgcJC3hD6tEB3mAO1tIHVaGNUKkGrlueTCSgjjkhGRR3+O45GM\nrMAz3nakGHvIe3htQIWJCxJksBNQylF+tgjSUVPRXVXsuLGbkDL75w9MQRAsUJRz2URcwaQPvJWN\nswykGI0qBeq3fAVljXMO2lJiOuAF5cgOEQNOZg/xB6RWg+Zdv0AgjaMNwp67ZDVOvxk8hwoShAkC\nSjk2bP/ebt5GR1aobJ4Aefe94t7EBOF0gGFn4IZFMuKL1x9dR4vjRgFI9r0v0JZTpBGn31zulQcJ\ndxBGJ0DT5HuTcWeo/vE9S+B2wGy5LIix9pNjcXhIv2Upzn7B7G5LkCDnI4G3j0RDX3N2e5BOGsFI\n5s9G9D23WD6nvrsWqe+sQeLqp8ESMnOmFxKXwsg4/dG0Dj6fKILFojUkokwm8zmZG4qRYLMdN3ID\nScYor4zrKafWL7VSjABQ8cXrMBltg3ILY5IQO91xjOIgQYIMTECtHO2hk7U6buQk4pmT0fyaOQ1R\nzAN3QLF9Lzq/+Rmp765FzJLFaF7zlsdzhA7LQZcXAld3nz3F+JjeJmYafS7OkrdXuD0ml8algQlC\n07K9Mq63OP3mcoxcartKjJ54IRRVp6FuqHJpvK2FqxiRq0em0o3PwaixjWzDxNiBfr4qjE1G+s2P\nWpUpKk+jZpPz1rTOXMfYGZchesIctOz57byLvRw98UJzzOnD29Cym/nQowG/cjz55lJUbnIu3qW8\nwnFkGnaYBGSX+dxRWJCLzm/MQaZNWi34aczkcwwbMcZxowEIzx1PWx6oW34DEWHnu5j0OrfHFKfn\nud23h8GSdcMRxf+mT248/PqHwPKzdTNFBaaTuzAmCZH5U7w6h6alDqfWL8Wp9UvR8Of/PBrLF9dR\nOnam1+cYbAS8cnSFmt8/cdhGffQkIhZdjrCL54CU90ZmYAkEIBXMPOV6YvVIsNiIn30VI3IEAhxH\nLjZuQOcM7ypN2zfRlidceI3HY/sSymTCmXPRYfqT+6CbZ/Ae0qMU7IVB9DepV9+N2On0OxregNTa\nZplwBl9ex6F0z2GKIaUcnaH9428gnjUZ4VdchPonrW8qTCY7dtfBPXXh3WALbDOWq+rPeirSkMCd\n1FR02NumiiyYxsj4vkQv70D9H1/S1uU9ss7H0gQ+nJAhEkyDIaJoLJ+DnIfKEQBqH1yBmvuXW1mo\n1ty7DPJf/2ZsjtSrFrvVLzSVPhJK7S8feyKO33DGmMoV7CUodoe2g/R/79yHVjM2h6/oKjlKu1VM\nsFjI+pf757tDDbZgaGa2oUzuW3zHTLuYQUl8CIMeBnQMCoMcZ4kaPRPtJ2wj/PuLtGvvR/X3G51u\nb98XkHJ7a8bfyEuPIWrCHJtytkDo8nZR9r0rGZLKTMve3xE9aa5NOYvLQ879q1Cy8VlG5/M2db9/\nAWF8Gnhh1tvOvHApEuffgIat39jtm/foOhAEC+Ufv4rMO3sTJZ9avxQJc69FZP5UcwFF4dQbtumh\n6AyDnDGaiZ9ztcPcpfbGyfm/l8HmW2eIp0gSxW8us7lx9pePLRDZlLUf3YHmnb84lNmbuHsdSZ0G\nIAjkLVlrbc1NUTj936dtzvhzH1oNFpfn1twpl90OSVYBrRwlbz8LUquiretrTBWeMw5JF9/s1Hx0\nv5GmnT/DoFLQzsMUQ0o5Jsy8KqCUY2hKJjJvX4byTx1Hhsl96FUQHPo/R9kHLzEtms9o2beFVjnm\nPPCy0xaHBJuD3IdWeyWCTfmna5F5u20KM7bQfPNs2vYjOk7scWFEAtKxMxA/+0oAhM+tKs98+BLy\nHllnc60i8iZCcbYY3RVFdnqaybzzKTRs/QbRk+aBFy5FwtxFiMyfgvotXyF26sXgSiIQmT8FnSf3\nW/Xr+Z6uRELqGduoUeLMh6/ApNciIm8CEuffaDUmHSMffR0gCGha69F5fA9YPD7iZl8Jgs3GyEdf\nt+l75lxaK7YgBOk3PQJSq8HZr96wahMIZ6TuXEcACM8Zh8R518Gk16J5588wGfSImTofXHEEch98\n1eZ6VHz2miUdWNa/ngbQe40c0aMYu0qOQllTBhaXh7iZV5gfKh9YhY7ju9G0/Se7/SPyJiJx/g0w\nKrshP3MCBIeLiJGT0H54m03bvoq4/ch2aFrqEZZVgPhZV3r9qGlIKcdAgTKZLDcnvjQOI5euh6a5\nFi17foOytgKA+alWEJOI+AsWQpQ43O5Y6qZqGLpljMnG4vLAFYeDK44Aq99TN2B2JA9Ny4ZB0QWT\nXnsu2a772xcUaTQ/xdPk5RtI+RAsNuJmXo6wnLHgCEOt6toO/YPoiRe6LVNfdB3NaD+yHVHjL6Ct\nj59zNeLnXG1u29kCxdliaDtaYDLowBFJwBVJIErJQEh8GiPyMEHxvx+nXQWkXHEnSt9dCaPKfoqg\ns1+uh6alHrLiQxi5dD0i86fg9FtPwaTXoev0EXPZ6Ok2yrEHV3Ji9liMlm7sTdAsKz4MbpgUMZMv\nQszUBWjdt4V+HopCcb8VbMfx3ZbvzRFJrL5nT1CO3oThJq8E6mAKV3OLipLSoWmpx9kve//usuJD\nSLn8Dkgy85Fx6+Oo+Lz3/Fkv77AZw9nrUfzvx222cTsL9yF3yWqwODxIx8wYUDkmzr8Bp//zFEyG\n3tVs49/f0bbtUYyn1j+GnvuQvOw4WDyB1w3OhoxyZHG4/hYBAKCqq0DVd2/b3JyEcSlIu/Z+l8ai\njEZUfv2myzL0bJG5A1sgRNrV9zjVtu3g32jZ+7vDdiVvP4Oc/6O3qOyrfJxBUVWClj2/MaYcAaB5\n12YQbA6kY2YM2I4fGQt+ZCxj83qT0/9ZTnt2mn3vSqsbTX80LbZB//tvyfGl3r0GnSf2ImbyRZCk\nj7SrHCvs7MZ0nT6M8NwJkKSPROfJfd4UM6AgdVpUfr3Bprx28ycYuXQ9o8H+7Z1vtuz53Smr15a9\nv1spRnv07DipGirR//dq0mtBGY12d9uYIKCUY9oV7hmxAIGjHFv2/AYAKH7jceQ96r6loMmgw+n/\nPOW44SCA1GlQ89P7SF14t0fjyIoOoOGvbwGYnx499SftS9P2nyAvPYbhNz7M2Jj+xGTQ4+wX65F+\ni+3W5MilttuOrkAQLBAcjsurG2fpWd0NdM5uLzCIeaej7wrx/EBedhyUyb9+paSG/qyxP/YM4frT\n4yPdcXw3bX1X2XFE5E1wTjg3CCjlKEnzT36/UQ9PR9KFGXbr/7jiE6fH0rTUAQAoyoSm7T8h/oKF\nbsnkzooxkFFUlYDUqNy+aZmMBotiBABV3VlGlSMAqJtq0H50B6LGzWZ0XH+haa23u6UdEp8GdVO1\nz2VyBlGy+X+x538piGN8bbDHFgghHT0DoqR0cMOkYPOFYAuYDTrB5ptd2owK+mQRJMPRl/oTUMrR\nX6QvymdsrL5bDh3Hd6Pj+B5k3bXCxoLQTm+Uf7IWus7BGWDcESUbn0VY1mgkX3abS/3qfvsM8rIT\nVmWdJ/chYe61TIoHAGje+Quad/6C2GkXI3rSPLfHafjrW8iKmPObdZdTbzxGe/44/MYllrNEf6Gs\nLkVoWjYybn0MFZ+/DgAQxqci4dw2e/OuzX6TbbBBGQ0+mYfF5SP3oVetykitBkZ1N0itGrxwKXNz\nnVO29rZgvf1AEJDK0VEGDjrYghDk3eN7q07H21MUznxolis0JRPhIydBlDgcnFAJTDoNtK2NkJ8p\nZPR8pPgN+pBigYD8zAnI158AwWJDOnYmJBmjIIiKB4vLA6lVQ9/dCVVdBTqLDkAvaxtwLG9agrbs\n/QMte/8AAIgSh0MQmwRRUjqEsclgC0JAsNgwapQgNUpoWuqhrC2HsqrEo4TJnSf3eeWcLFDjkFb/\n+B6GLXoAouSMfgqcwun/PO03uQYjXKcevj2nRzE27diEjmPWngGRBVORcCFzD6wGuQx8aSx44VHQ\ntjfZ1HPF4YzNRUfAKUeD0j2ncXefIiJHxrnVzx2UteVQ1pa73X/kf+6AMFkKloCLE7dvhK6Ffrsh\n6faZSLxxKg4ucN+ZXZgshb5dAVJjm/mBCSgTifYj29F+ZLtXxu+PMFkKTZ2thZ4zqBoqoWqotLkZ\nuAOLywbBYXvtug42RMkZMKq6UfruSt9MaLHrsN1qHsyEjxiLpm0/wmTwze+K7n9hIKt7d+gqOYLY\n6ZcicvR0Whckpo9V+hNwEXKYzMDhDHn3ezcAMZOceugTHL7qdYft6j/d5ZFiBID89+9G2Hhmf+z+\nJP99z4yBmCL1gXlD6rp6Quy5yCxl77/oszl7HNTpQjQOZggOBxm3PWFTnnrlXQDorZB76DGk6Tnr\ndVMChGWP9aC/LT1ZRkJTMm1nY7G8/jcMqJWjO9upfdErXPcHlBbEezTnUESYEuVvERglkL5PzMWj\nIT9W7W8xvEbfTCCcUAmMSvs+ld2VpxE9aZ5N/FeTXgtFVSnqt3wJivSeBWb2vSstEXH40lioG6uh\nqCphdA6CxbIYobE4XLA4XJicOB905ToC5l2p0JRM5C1Zi+bdm0HqtIidtgBccQQAWPk/9qfujy+R\ndvU9GLboAbQd+gea5lpwQ8MgiE1Gw9avaftk3PaEOdsIZULEqMmIzJ8KWfEhRORNdPjdXKG7ogiS\njFEYuXQ92g9vg6alHuLhOQjPnQB52QmEjRjN6Hx9CSjl6CmlHzOTj26wwpOGInfdLeDHm/fi6VaP\nOatvgGR0mlXZ8Vv+C327AiweBxN+6T2vzFzR67PU+O0B1H20w/J54m/LQLB7Nx7Ort2M9m3Fljm0\nDTJEXzwaBKt3++rQZWtBGZ2PATlpy3KYDCSq39qK4Y/2ZlHo+73ir52ElMW9DvwmA4nDl78GADbf\nZ9KW5VZj5K6/FfJjVWj4Yg8INgsTf1uG6re2ouXX42CL+Bj/w6OWuQaax5lr0nfuga7rYKTH0bw/\n2festPpMd/ZJ6jQ2IeBYPAHCRoxG2IjRqPjsNdrzJk8o3rAMeQ+vBUckQdIlvYnPG//+nrE56Iyg\nRCmZyF2yxvK5/7mdJ9ex+vuNIAgWch9abW0hT1E4/dbALmHK6lJ0ntyPyPwpNv7D/ZVj8YYnkPfw\naxBExSP9pkcs5Y3//IDOwr2MK8faXz62hC/sG2mrx+DNm8oRFEX5/QXzSYDPX8kLRlBX7XnA4ctf\n8tl7TdqynOLHhtmtl87KoSZtWW63r6PxJfkp1KQty6nIGdm09RN+eZwa9tB8y2eORGgelyAoAFTO\n6huoSVuWU7wYSW+fTY9RBR/e4/L3nPT/7J13WFNnG8bv7DDD3ku2IijuiXvPVqu2aqdatdphravV\n1vFZba1ttXW0tdZa66xbESeiooKCiAoyZMneOwGSfH9EAjEJGZyTBDm/6+K6yDnveBJC7rzv+4wL\nK8S+a15XeJ9tbSb3fLof+UTc5Y95Cp/Py/1tRwRKbbKf0E3s+v5gcdBvc8UAxA6Tuos7fT9T+nxV\nzaPqNWn+nJS9ru3px2XMTHHnJVvFtr2HK7xv5Ogu7rxkq7jzkq16t5X6aZs/rdUlgztz1CUBC9vO\neSORuLzTusKmdDYTadvDpI8bKiQemvYTZM8c6gqatoJyDt8G11k7j7qkdccVXvddO0VuizL9lzBw\nXdSbp+jKI6lNjq/3Qs7BSBi5SVzRbYYFoiBUEj5CZzNVzqPua0IhwaJjdwDKA8LbYmHv1jBvfx9s\nfDxG7odCf7xS26qawrV6tQ7l1eH+tJ/R7d/FcH6zH2ozixG/cI9GW51MM8lZSPNtwkZYlk0B/vxc\nWa9jIZ/4OCwTbweltqiDWCSW/s5xtJDxIDXxdUDRtSfS58vr5qF0HnVfEwoKRczY0hX1fCHW972M\nkA888eBsDhYd64/VXRSnzqPQDe1WHOms9rlobqioRdT47wAaDR2/nYFeZ5chetIPEAnUEy9htSQg\nV6U3rFD7+nJqIxaj9Hay0pWlNggKysG2NpWO3/h8SyOTlM6j9mtCIQfXxlHuTJHGZKLTQklS6YLb\nYYq6vVJ0GmaPNcGS58k1YyI/uRJrgsMwd19v/P7OXT1b135pt+Lo9y55OfnaBGIxElYcRO8LK+D4\nRm9k/9NUGUNUL/EQZBiz5bu9WG1Z9PZG2d0U3diqhJwjd+A0XfXWeOPzUUR1ch7Mu7ijKiEbAJB/\n6j5sRzXVqmt8vpb9FBehbt5G3ddE0eva3kja+y1831upMPygkarMpHYhjuV5fNAZNIiEYgSNdsSp\ndY8hFonhGkhukDtFy7TP5RMA39lt/CyI3kIQs5J7HHse/L+dAV73DjByt4HL25IqFPmn78u0axQK\nz0/HwtTfCSa+jjLhECmbT8Nv7VS4LxgO864esJ/YHcEHPmrlE9KcrL3XAQA9T34Oq4H+sOzjDc8l\nY+HzlWw+28bn4/r+YBi5WsOyT1PcVEHoA3gtHYfc49EAgPwzMbAb21XaB5A8X1XzqPuaVKfmK31d\n2xN1pYV4tHUJ8q6fgqAkH2KhEEJBLWrzMpF3/RQebV2C9GO79G2mTji3KQFjl3UEANCZNKy5MwIb\nHo7Gtd/0++WzvUMTv1QxWy9GSDz6dMrkmwvVbntywA4SLVEP60Ed4b1yksJ7jdt5ys7EHn/6N6oS\ncwAa0OGTMbDq7weGMRtViTlIWvuf1HmkOUau1nCa1gfWQwJQX1aN1C1nUfGgyUmCaW4E32+mwtTf\nCYKcUuQcvo3CS5IsFh03zQDbxhxxc36Ttnd4rSfcPxym0dZj4/NR1cf5zX5weK0n6GwmSu+mIO2n\nULkMNEau1vD/33QwLYxR+eg5ElcdAiDJWNPzzBcyc/S+sALPtp5H4cWHGs3T0mvSHK/Pxyl9XSko\nuox1RGlOLTIfaJctjEKCWCxuVRqkdimOxo7mGHl0luqGLzAEcaSgoKCgUJ/WimO7PHNsryEcFBQU\nhsnyq0PAs2/KilOez8fmodf0aBFFuxRH5yFe+jaBgoKCAgAw8+dueHa3GEdXNm3jT9vUBbO2dcM/\nH8fo0bL2Tbt1yKGgoKAwBPwG2soIIwAcWREH34G2erKIAmiHK0e73m76NoGiGTbBzvCaFgTHgR00\n7luVVYbsqyl49t8jCEp0WwmdQn94Tg1E0CcDW6w6VZtfhXvfXEJxPAF5WWlAj69HwGW4fHWI5gj5\n9Xiy+y5Sjz5ssd3LhG5JhE8/GyRHFkmvefW2RtiPT7Uyl0hYphz4f9ATHhM7gcFRXy6qcyqQ8m8s\n0k4+JtE6cnnlHXIc+nvAbbQfnAZ7tZkSbsXxubix4IRe5lbXi7c1Tkquo/3QbdVQmaTkRJF5LhFx\nP94gJSNPczTxdgYMy6nLOsgRA3e8prrhC6JWhyHnWmqr5lT1ej3cGoFnxx+12MZ9QicELx+s8dy3\nl55F/p1MjfsBwPCDb8HUVfN4w+IHObix6KRabWd83xVBYyXVgcQisfT/QlDdABaXIW33VZBsxhyy\n/lcZbAb6/TwR1oHEVSxKPRyH+O23CBtPHSiHnBdwrY3hOtoPbmP8YeZhqW9zKBQQ9NlAeE4JJHUO\nt3H+cBvnDwDIi0zHnWXnSZ2Pghjcxvq3KI6jjr8NIztTrcbuu2U8qp+X49KMA2r3MfOwxLB/3tRq\nPgCw7uqEyTcX4tKMA6h+rrgoeSOXtifh0vYkreciCpYpB6NPvg0Gl0X42F7Tu8BreheUJxXh2vtH\nCB+fDNqkONp2d4HbGD84D/cBnUkdmxo6NsHOGLBdcYwmmTj089D5nIZEyNjNuH15HerrqvVtikos\n/O2U3ptwZZ5GW3qKMHHhYcKVeTgz7DeVbR0HdkDvb4lJ+j3i0ExELjmDgqgspW2KM2tg42ECRz8z\n0Biyi52H51u/LezQzx15kS3H0g7e8wYs/Mg/4+T52mDyzYV4diweD3+6Qfp8raFNiaOmW1kU5MFg\nMyCsU12Idui+6TD3staBRfLUVwr0Mi8FcYw8NrvVwtgIg8PE6BPv4MJr+5S2sexoR5gwNtJv6wRc\nnLofNXmVCu8v/q8/2MZMJFzLlxRbagYR4ug6xl+pOHIsjDDm7HutnkNTPKcGosPrnXF68C6ZAgCG\nRJsSRwrDwbKzA4pisltsM/nGQr2e88ZtjVDdiMJg8ZkZDGMHM0LH5NqawPP1zgq3cGkMOgb9PpXQ\n+RoZeWy20rO/mrJ67HzzNhrqyEnW7zzYC9EKrtv3cUPfLeNJmVMdaHQaJkUsUGvrWR9Q4kihFVYB\n9krFsfFNr2+eX0rWtwkyMBhs9BvxDWh0OgS15eAYWUDYIMCti2ukbQaO+RYAIBYJQWewcPPClxCJ\nGgAAjm694R0wWen9Tt1mw8ahM/g1JeAaa1c7U5/Y93ZD/t0XjjM0IGABOck6gpaEKBTHidc+JGW+\nRnqtH4Wo1fKJ1A98Gotv7o0EnSH/TXJVQGjrJ1bwBdV1tB+6fzWs9WMTwIhDM3HjoxMojiPAs5hA\nKHGk0Aqrzg4KrxuKMBoqNDoDEaErgBde4iFjN0vvBfdfjLTEUDxPk6x47Zy6ov/IdbhxYRUAwKfz\n63iWcE7pfRuHzog4v1w6XvOx2wJuY/2l4jj6+DukztVn01jcWdHkrOX3TndSvKeb46Qk+cjK8CH4\nZeot5CUp3nYlGtvuzgYjjI0M/PU1hL9/FGVJhfo2RQoljhRaoUwcDUUYVYUF6JWXwqcYDDaEwjqY\n8US/ys0AACAASURBVFxgxnOBZ8dxcl2s7CRVGzw7jlN439Km5Ri8toDTEC/ga4DBZYFrS26RaIcB\nHjKPO87tTep8jQQs6IvHO2/LXPtx/A2EfOCJsJ+eQlDVQMq8Zh6WqEwvBcuUg/4/6945Th0G//kG\nzo3+A/VVdaob6wBKHCm0gs3jyl0je1tKEx7/otuYKqJovqpsDo1Gg1gskqwSDSA2mQwaV24jDr7V\nYru8yAxUZ5eDbc6BiTNP6Rc1VbiO8kVWWBJ6fDNCZVshvx55kRmoLaiCsb2Z1nHTPjOD5cRx0bH+\nAICgMfJxhRv6X9Z8EgW4jfHH4523Me7CB1r1F9ULUfwwF/ziGtSV80Fn0sG1MQHP2wbGjsSdC4+7\nMMdgYoLblDgm/qnoWFkzrLs4wra7i87n1YTa/CqdzkcEIbungM5iqG7YDLFQhIxziXh+KRkl8bkQ\nNSh3SDDzsITjgA5wH98RJi48lWOr40lraFSVZ6Nj17eQECsfj1ec/wQ0Gl3p/dIiwzpf1RarAHuF\nq8bcG2m4u1L5+Ztdbzf0+0Ez55Juq4YiKyypxcw3d5afR96tdIX36Cw6JlyeBxpDw3AyGmS8UokS\nwJZwG+sP+z7qZwcruJuJ++uvQFAmX85OGWYdrND3u3GtFsthB97ElZkHWzUGEbQ7cfSd1c3gxbGt\nYd/bDVYB9mq1rXhWgrsrQ1GdrZl3WmV6KSrTS5H0T1MiZscQTwQvHyy3ii1PLnq5e5sg5tY29B+1\nHiFjN0MsEoJGZ6AgJxaJDyS1JxNiD8A36A2l90sKEhAydjNqa4phZGwFodAwtqc0IWT3FLlrl6Yf\nUPl+KbibiTPDf8OEy/PUnovGoKP7asVnb3UVApwfu6fF/qJ6EU4N2oWJ1+aDzlJfIP3e7oGn++7J\nXJu3vw88usknLyHEIQcAx9IIHEsjle3urb2ktSNbZVoJLr6xHwBg28MF/X+aqNU4Zu6WsO7qhOIH\nOVr1J4o2JY4UhklfNb6xP7+cjHvfXCJ03tyIZ8iNeAZAItC9No4Gg8PEg++vEzoPUQiFdTIOMwDk\nHt8KW620f2HuQxTmKs/b+ejeX62yzxC5Muug2l+khPwG3Pz4FAZsU/9MzXWUn9w1Ub1IpTA25/TQ\nXRi0ewosO6n3BdFrehcZcZyxpSvq+UKs73sZIR944sHZHCw61h+ru1xoYRRiKYjKQuSSM4SNV3jv\nOU4O2IERR2bBxMlc4/4Df5ms9+1VKr0MhdbQWQyVZxhCfj1ODtxBuDC+TP7dTJwZ9htODtiB0if5\npM5FoRuKYrNRmV6qWZ+Y7FZvqZ8eukuzDmLg+rz/1G7ONufIPO40zB5750ajtqIeXDMm8pMrsSY4\nDHP36cZJKPbba4QKY3MuTfsHWWHapcbTd91dShwptGbitQ/BMuUovZ9++gnODP9dLusHRctwHJz0\nbYJBcHPxKa36XZ97TOs5008/0fr9qm2ml/I8vjTGMWh0UwJy10DNE55rStyW68g4l0DqHPfXX0b+\n7ZbT1ynC561gEqxRH0ocKUgh7ocIPPguXN9mtEmc35sPGrN9n3hUZZVp3bcitVjrvq15zybuUd83\noXly73ObEjB2mSRUh86kYc2dEdjwcDSu/ZaitS3qUBKfp7OSUre/OAdRveYrerISQagDJY4UhCMW\niZF2woDjDA0cOocLcQM58W5theeXyRUGMih5nKd2WzO3plXh0xuFOLdZsno7/EUcuGZM0Bk03Nib\nRriNzYnddI3U8V8mfpvm4VXu4zuSYIl6UOJIQTinQnbq24Q2TXl0JBimxOYUbWsk/hnVqv4ZZzXf\nKnz2X3yr5iy891ztttbBTVvnYpFYuiWbeL0AqwJCsSogFA0CcnKtApJixJUZmp3ntpa0E4803npW\nFE+tKyhxpCCUlioeUKhH4bmTcH5nHiz6DICReweZn3ZDK8+pU4/EadznkQ6L8Vo2K9HF5Oj+Y/jy\nm//qfE4ACJ2wV+M+XZcNJt4QNaDEkYIwCqKzwC80/NqBho7nirVgmpnDavAIOL75rsxPe4CIEkYV\nz0o07tNSEgqisfBvqp24LmaUwjbLrwwhZe6GmnqIhbp7rs2pK+drPLfHxE4kWdMy7fvUn4JQIj8j\nxx28vfFs09f6NkGv5N4g96xNITr2qFYn9s/Eik3K3JGf6/f/9Pq8/zB4zxt6tUEdKHGkIITUw5pv\nY1FQKEJZujZS59Qi1EAR9VV1YJmqFjUag45uk50x8mNfAMCKq7KrRHN7LkK3JBJi08uUxKvvOEQG\nZU81r7zRcW4vJPzeunNoTaHEkYIQ4nV4XtMe8P56M2gsFmoz0vD89+2wHjEOgtznqHr06n8JUVVE\nmwxyrhHjHVudXQ4LP1vVDQHEnMxGzMlsbHg4GoeWPpC5l/u0EoJq4j2Whfx6wsfUhvKUIvC8bdRu\n7/NWsM7FkTpzpGg1glL1kxNTqMZh6kwUX7uI5K+WSK8VXzoH62Fj9GiV7qjJI6auoSZniEUE5fGs\n0yBRdyMPQ3ORHlMq80OGMAJAwh+6FRhlxP+s2ZdpTYsaEAEljhSt5v568qsKtCdMA7uiNOKK3HWW\npZUerGm7CEpq1G5bk0uMIAvK+Rr3ObJcd7sBaSef6GyuliiK1f3ugKZQ4kjRagqisvRtwitFTcpT\ncF1kywuZde2ByocxSnpQKKKhWvdVSbTJAqNLDGVbVRucBnnqdD7qzJFCr3BYZujmPxvGXGtkF8Yg\nMf2c9F5I8OfgsM1xJXoDegfMhTHXCln50UjKDJMZw82hDzydB0MoqkNc0iFUVOu31E1rydn/B3w2\nbIWIXwvQ6fBe+z1oDIbMNiuFavRR01PUBuuI6gt+UTW4NvK1O5XhOLADcq4/I9EiWShxpGgVrcmB\naW7ijN6d5+Fewl6UVWYhyHsqRvT+BpfufiPTbkj3FbgV9zNoNDoGdP0MLKYRHj87CQDo3+VjMBkc\n3I7fATbLBH06z0dc8iEUlJCbTJlskr9aAo6jM4zcOoD/PAP8bGp1rin6iOUTG3CSfSLiR4kk88JT\n+M7qpnZ7hwG6TYJBbasShNfStfD7Ziv8vtlK2JjWISPgMPlNwsYjg6wLT7Xu27vzPDxKPY7SinSI\nxULEJR8GQIONhWxl9ivR68Gvq0CtoAyJ6efgZNuUrd+Ya43rMd9DUFeByupcxKccQxefGVrbZAg0\nZsIR5Gaj7O5NShgpCKH4Ya6+TZAh/06mRu3VCZEhEmrlSBCpWySB20SKY1ugOK51/3D5xbIJyqtr\ni2BvFYCiMsXVyAV1FdLfeabOAIARvde2ygZDw+ntuaBzuKhJTULOP3sgrm+750QUhkNFivbVSsig\nxMDE+mUocaRoFaWJBa3qz2GboVZQ1uyxKQT16nkO1tVLUtVduvtqZZRJXb8KAGDRdyC8v94MACi+\nfB4l4ZRXMIX2VDwzLHE0tG3el6HEUQe4vrsQxu5e4Odmg+vkgvQd30FQkKfyXnPoLDZ8vtyEpP8t\nN6iVhJCvfTxWcXkKgv1mIfLhLy+u0MBkcPHsebha/RtF1ZhrhRp+Uy5NGo0OsVg/uSOJpOz2DZTd\nvgEAsBo8Aj4btr7yTjmteT9RtIyuq3C0dShxJBmWlTWM3b3wdO3n0mt+32zF02+WKL1XeOkMSm69\nqLUmFDYJ4/ovIBa+Ot5wMYn7Mbj7Cplt0YT0sxCJ1X+OEbFbEBK8VOZaXvEjxKccJcxOfWIzajws\nBw4FxGLkHdmvb3NIRyhof+LI5NBJLU/VSF2Z5jGYhgaNTtPZipMSR5Kx6jsE5TF3NbpnPWiUVBzr\nK8vh8+UmPF23FBC1/dXQy4Tf36T0XkTsD3LXCkoTZbZRBXWVr9y2qlmXbnCY8hZAp6M8+vYrv1ps\nTnsUx3Uxo7AqIFTu+vIrQ7B5GHEFiQVaZO8xNDgWRuBrkNyhNVDiSDpigKbhvWbXrPoNQdGV8/Be\nuhYp360mwT4KQ8NqyEikbVmPhopyfZuic/RVSskQIboqR50W2XsMDaYJG9CROFKhHCRTfOMKeMG9\nNbpXcqPJ8aLySRyKb1xGXVEBXN6aQ5qdhoy5h6XcNRNHM0y9NV9lX3XatKY9GWT8tKldCiNg+E4a\nRNJtsrO0GseKq0NkfjY+HoNL25L0bKHhwTTRXTgHtXIkCJsho8HkST7EHSZOQ315GYqvX0RDRRmq\nkp7A7+sfwM99Dq6TKzJ++xEAlN7j58jHtWX+uR0+K78FL7gXymMNI3mwrui7aTTCZhzUqu+x/rsI\ntkY3tOeqHO0FfVTlaOuwTFg6m4sSRzVx7WKByV93xvbXbyq8X3TtAgAg76T8h3j2wT1Kx/3oqxys\nClB8vzjikszj5G9XqmvuK0Gvr4fBwtsaZq48mRWdVPDEQIeJHdHl436ozq7A5feOSVceI/a9AZ63\ntWx7SFacXT7uh7w7WQha3FeuX3OYXCYmX5mDE8P+0KkXZWNVjtKIK3CZuxiApCqH+ycrKHF8BWms\nykE6NOi8qDPR0Bi62+yktlUpDJaotVdwcfYRABKBa/yRQpN4r50cvgcZoU8x5caH0luX3jmqdNXo\nFNIBoEFhv0YYL4Tx+ODfdR5eQFXlaF/oqioHXYfCQha6TCbf9l8tHdKezkPaCs9elOBJOvSQsH4M\nNgOvXZmD/0J+00uVBbKrcjC41IaRoWHhZIS5+3pj+nddAADmdhwwmEo9+bSCwdXdliRZ1OtQHKn/\nEg1Ze38knj8qR4ceVvgq6AJEQolgbnw8BqXZtagpq4NzAA9fdg6VJiH27GmFOX/1RkZsKSwcjZAY\nXoBT6x/Ljf3NvZE4/MUDJFxrXdYZitbx2rW5EDWI9OY5SXZVjlfhQ/JVwsHXDB+fGIAbf6bBLdgC\nAGDEY2PsFx1x6IsHKnqrD8uMjfoqAWHj6QNKHA0Up47m0nikoLGO+PjkAPw0QZLBpHmc0qTVARjw\nXgfc+DMNADDnr94K45ia8+XNYTixJp4SRgPgWP9d6PnVEIw8MB0XZx7Wiw1kVuVgGlHiaEi8u6uH\n9Iv2h//0AQDkJ1fCd6AtofNwrYwJK+qsL+ordSfu1LaqBhRnVEt/f3g+F3aeptLHli5G+GBPL6yO\nHI5e093ANZV8ANHU2Bn59MxA3PwrDXHnDTsRr74QNYjgOamTTueM3nANDA4TQYv66nTe5pBVlYNj\nZUToeBStg8GmS3egGqHRaagtJzZNpJGdqepGBo4uz/+plaMmKFE6KxdjLA0bJF0djljs2xTIr4Y6\nhm19itm/dMf1P3RXyLMtcWLI7xjx9xvo+ll/5EZm4vaqMJV9mnu3Nv6uaVhH6NQDmHLjQ+REpKHo\noXy+W9Kg0+GzbovCW0RsrZq6WrR6DAriOLwsDl+EDcaW0eEAALYRA1/eHIZdM+8QOo+pm3y8MIVy\nKHHUAGs3Y+nvXcc7oSC1CgAQMNIeT67mS+91f80Z909mA1DPiSfhWgG2vXYTGx6OxldBFwi2uu0j\nFolxcdYRmWvVuZVyYtf8sTIh1LTffwN3a2Vza3BbuARFoadQeus6KeObuvJIGZdCO1Iii3D512T8\n79EYABLfg0NLHyA3sUJFT80wfxHaRKEelDhqQMK1AqyLGYWsh2Xo0NNKKmS39qVjzOf+ePOHrvDs\nbY07BzNBZzStGP9eeB8bH4/Bs+gS8Ow4KEqvwb6F92TGzkuqxI2/0vDxiQHY+eZt1PNfnQTjjdBA\nw0Dv+TBmSVYuNXWluJG6G+K2HnxFMCwra9KEEQB4PjakjU2hHbGnsxF7OpvUOSz9iT3DbC0WfoZl\nz8tQ4qgmWXFl2L/ovsJ7IqG4RYebxOsFSu83vx629SnCtj5tnaEGzKiOK5BeEoXkwggANPjahmBU\nxxW4kPCtvk0zKIpCT4PXqx/KoyJJGZ9lyiFlXArtsXAywhvfBqEin4/Dy+JgbsdBdUkdhA3EfXE0\ndjQnbCwisO3uolF7XYfSUeJIoTMaRAIk5jcFtyfkX4YTL1CPFhkmxr7+MO0YCLuJU+XutacKHe0F\nXYVyGBouI300al8YTaxjmioocaTQGTQaQ+4ag06FFbxM0flTKDp/St9mUOgIXYVyGBo8b82293Nv\nppNjiBIocaTQGYn5lxHkNAHpJVGggQ5Pm354VhQJE3aTo0B1XbEeLTQM6ktLSBvbspMdaWNTaIeu\nQjkAwMTJHNU5xDr66Irnl5J1Oh8ljhQ6w99uGBh0Fpx4naXX7M184W07UPqYOn8kFws/ShwNjayH\nZXAJ5OF5fFOZsq7jnfAsmvgvipad7NusOOo6uw8ljhQ643rKr/o2od3jO7ubvk2geIlGb3Z+ZT3o\nDDrWx40Gg0lTmVVLG3zeCsbzy7pdgSnCZbhm5436oN2Jo1hMhQ3oizphrb5NIAxRvQh0VttLMNVW\ns6Sw2CZqtauvq1bdyABZFRAKR39zuAdbIuthGbIfk1PsmudrGGE8QZ8NVN2oGYJS3X92tDtx1GXJ\nEwp5hvl+BhaDCwBoENXh8tMf9GyRdpQnF8Kyk73a7S38bFH2tJBEi1TDsWy7aeP6Dlujsk1VRTZi\nbm3TgTXEIlk1NiDqSCbuHslE6XNyhYDOpEPUoJ+k+o2weVyN2if8ofsC7+1OHOt0mLiWQpbRHVci\nPucsssvjAQDOvECM7riyTZ4zliZqJo7eM7ri3tpLqhuSyIBfJut1/tYQEbpc+nvImM0yjwHAyb0f\naqraZtL+xu1Tj+6WGDzXC0GjHcExZaKqpA4bB8rX9Wwt3dcMR/Sai4SPqy5OQ7w07pN+Sr6KEdm0\nvX2hVsIvbJvbLq8CQlG9VBgBILs8Hg0iw1jJM9l0MDmKfxSRdzNNo/FdRuj/jMXM/dXIrcmvlffm\nzcmIREC32XqwhjjMbDiw8zIFx5QJYYMYj8LIKUTgPNSblHHVpdf6UXqdX13a3cqxNCFfdSMKUjDE\nNHFrY8eAoeLs8KvO5+SuFUTpNiC5tfTa0DY+kNSBa2Qld83euQdqa8gLgSGTxtSSN/Y+w+5ZxCYb\nV4b7uI7IOJegk7maY+qmedJ7fWypAu1QHEX1mu+1u4/viIyzun8jvWow6Ww4mndCbsUTAICjeQCY\ndLbe7Bky3wcMFh1Hl8ci7lwO1sWNxfcjrqK2rA6zf+0JljEDv80kLoVb72/H4O5K4j0QVUGj0+A0\nWPOtLEOlKP8RQsZsfvFIjMYSODcurNSbTa3h6+4X0f9tD0z7tguMeCzkPKnAjb3PSC1hF7xyiF7E\ncfi/b2nc5+lf91Q3IoF2J47a0GXpIEocCSAsYROG+y9FF+dJACTp5PR53th7hjsSw/MRdy4HAFBb\nUQ8TSzYqC/jYO/cuvrw1EoGjnRB/IUdhf009Vh0HdiDEbk2ZcGWeXuYliycx+wEAltY+oDPZqK7M\nBb+NrhoBoJ4vRPhvqQj/LRUMFh0+/Www4L0OmP59V1LCORrp98N4RH5+lrTxX6bTvN4a96lM09/f\nlRJHNaAz6WBwWRDyic9Y0Z4QQ4xLid/r2wwpRjwWCp9VSR+X59bC3tsUeU8lQdJXdyRh9FJ/peKY\nuDda43/4EUdm4dK0f7Q3WkP6/zQRdJZ82r5XgdJi/cfrEcHXd0eAYyr5KH4WVYyoI1nY895dkB11\nZtfbDQ4DPJCng7RsbB4Xvm9317jf9Xn/kWCNerQ7hxwASD/9ROM+Y8+/T4IlFPokI7YUHYc6SB8n\n3ypEyJwmZwUzWy5MLJVXsEj6W3GVlpYwcTKH94yuGvfThu6rh8G2h2aVD9oC/l1m6NsEQlnX5xJW\nBYRiVUAo/ngvCg9Dc0kXxkb6bBpLeuwjnUXH2HOaf37yi2vQUKu/BYlBieNn18fB3NEYX9ydCK4Z\nC2/9NgAfHBkq02ba9r5YcX8SJm7sIb3GczLG1B/7IHiqB5benoA5R4aCRqe9PLyU+J9vamwbg81A\nzzbiZWWo9HKfKXdtpP8yPVgiIWxrImw8moLLr/6aBHsfM0zd1BVDFvggZI4XYk617HijTRmdzov6\nwX1cR437acLIo7PgOsqP1Dn0hZ1TsL5NIJTGIscvM/Nn3WQzGvLnNLiO8iVlbDaPi4nX5mvVN+z1\nvwm2RjMMShwBoO97Ptg3KxyfRYzD4UWRYBsxYeUuyeqxKu413NydiE3dT6EwpQLLoiZK+/kOdQSN\nRsOWvmcQfyYTK2OVx3QJBQ1a2eY8xAt9vhurVV8KxfAbKvU2d/ajMnwb0hR7KGwQ4+6hDHQd74xh\nH/miQSDC6XWPWhzj7irtzoSCVw7BwB2vadW3Jex7u2HyzYUGV7uPQnP8QnRXlaP76uEYdfxtQsf0\nmRms1YoRAIoe5EAs1G+iAoM7cwz/+TEE1Q2g0WkQ1omQdD0X9n48sLgM8CvrkfOoFABw+88kDPkk\nQKZvzFFJ7Nnd/SkYtrTlOoHJB2LhM1Pzb6AO/Tww+eZCXJl5EJUZpRr3b4+YciT/5Aw6S/o7ALAZ\nxjBmae7aTSTVJbJxlmc2PMKZDS0LYnNac15jHeSIyTcX4vHO20g+EKv1OADg9UYQAj8Z0Kox2grF\nBU9gYuaA6so8fZtCGr3ecEVxZo1O5zSyM8XkmwtR/DAXkUvOaudjQZN8Qeu7ZXyrbLm56GSr+hOB\nwYmjoPqlVZ1YDBqdBpdga9AZNLy9L0R66/kD7bPWP955WytxbGTYgTcBALk30pD8TwxKHquOn2SZ\nsmHqZglzTyvwvG1g4W8LCz87GY9HsUiMUyE7tbbLELE0coav3WCwGEYY4DlH5t7DnDN6soo4Ij87\ng34/TtC6f8CCvghY0BdCfj2S9sci/dRjCMqUpxBjcFmw7eaEDlMCYd/bTeP5ToXsxPjL88Bgt01H\nHWu7TrC266Tw3suZcwwZO09TzNou2Trd+Fh2a1XYIMbX3cIImyvvVjp4PjZq5da1DnLEhMtzAQCl\nCQVIPhCLgruZSs//zNwt4TGpE7ymdSHE1guv7SNknNZicOKojJz4UjDZdPz9TgRhY2q7emyO48AO\nenPRbytklT1AVtkD9HKfiaiMA/o2R4qZLQfLrw1X2U5REoDmFERngV9SA66VcavsYXBZ6Di3FzrO\n7dWqcVoi9ttrEIvEeB72FO4TFAuModOWBLAlCp5VYeu4CHxycgB+nqy5H4Qm1JXzEfb635h8c6FG\n/Sw72uk0gUTKoTiDyWLWZsQx93Ep6Ew6Oo93w6OzmQCAwR8HIHyb9jn3Wrt6pNCMpIJwfZsgw8en\nBqGmtA7fhlxqtXfghYl/afzBo2vKEgukgd+Z59uuOL5qHF35kPQ5eN4Sj9TTg3dhYrh2DjJkU5Fa\njEe/3NK3GVIMziGnJTZ2OQGPnjZYHj0JH54cjvzEslaPeXrIbgIso1CHstpsfZsgQ/LNQqTfLyHM\nbf78uD+JGYgE6sr5CJ9zTPq4OJ687Cu6gMHkwN17OLw6ToCNQ8v+BYZOTgL5xYfNvSQp90QNIoN8\nn/ILq3H1ncP6NkMGg1o5/jioaftqY5cTAIArP8g6R5z9OgZnv46RuVaeUyNt/3J/VYjqhbgy86D0\nDJGCPNhME9Q1SLZMaDQ6vKz7IaWI3O2kljiyLBaDP/TGN/fHIOFaHqqK66BIKc9tUi8utq6cj2vv\nHcGQvdOINrVVCEpqEDrxL32bQRgd/MbC1XMQKsuzIGwQwMGlJzoFz2rT261r748Ei8tARmwpds+6\ng5Gf+iI3oQLxYcQ4HdEYTeugunI+zo/dg7HnPyBk7NZSlVmGy2/9q28z5GhTK0eyqMwoxdV3DqMm\nT39hBe2BoT4fS38f5b8cPCMnjO6ov3yYPd9ww/DFfmBy6Agc7YS+Mz3Qd1YHuR9NKE8uMighKrz/\n3KDsIQJXz0GICF2O2Mhf8DDqd9y6tAYJDw4goNs7+jZNK6Zt6oKrO1NkUsVd/CkJwxeTE3sIAHUV\nApwcuIO08dUl42yCQQojYGArR31SkVqMi1P3o+uywfCYSJ3FkEGDSFJL08WiC/IrnyL2+XGM8P9C\nb/aMWdYJSREF+HthNKHjCkpqcHLADow5+x44FvorMHzz41MoilG+lV0QlQW7Xq46tIgYFOVRLcx9\n2GYz5wSOccSRLnFy1y2dSX7viIGTA3ag/88TYdtd95mUDD0cjlo5vsSD78JxZvhveq+U/SoT4DgG\nD7IlcUx1DVUqWpPH7rduwcGPvGD50PF79RKvVRSTjZMDdrQojACQeT5RRxYRC9dYvmSVjUMgSgqe\n6sGa1pN8qxAugTyZa8ETnaUJ8cnm1ienETp+r1bZnrQhN+IZTg7YYdDCCFArR4UI+Q04PXgXjOxN\nMezvGWCa6K6sklCPuQTJhknnYJD3QohE9RCLJV8+jPSYBGDkp/4ws+Vgw6NxLbZTFcrREkUPcnBy\nwA7Y9XZD3+/Gypz9EE3+nUzcXqp+lYXsqyno8c0I0uwhi/zsGISM2YyK0nQ0NAhgbuEGJsuozZ45\n/r3wPjY+HgN+ZT3oDDrWx40Gg0kjtSLHywjKanEqZCfMPCwxZO90jarNqEtuxDPcXXWB8HHJgibW\nVYbbloyg0fRvhAo8JnZCpw/7gM3jEjeoWBKcm376CfIi04kb14CxMfVEUdUz6WMf20FILryuF1tC\n5qhX4zDij1RC5/WcGoiAhf0ICcIvTylC7LfXUPa0kADL2g4sljFcOoSAweSgvDQNhbnkh0OQjaO/\nOdyDLZH1sAzZj8tVttc0dOjkAM3OGP0/6AX/93qobtgC1TkViN14FUUPdLMKbo5YLFaeYFsNKHHU\nElM3C9j1cAHPzxYWPrZgW3DBMuWAacyCqF6IhtoGCEpqUFtYhaqMMlSklaDkUR4qUrXP6kPxasLz\ntoFVZ3tYdrIHz9sGLHMO2GYcMI3ZEArqUVcuQNXzMpQlFqImrxL5dzJRk0u++7+hEthrDnIyIlGc\nr3l1nVcJssWxOXQmHY6DPGHb3QU8b2uYOPPANGaDRgcaautRk1OJimfFKIzJRlVGqVoZw8iGqzBy\nbAAAIABJREFUEkcKCgJgsumNBeXlaBBQ58+K6DZnq8o2MX8sIXxeY1N7OLr2goNLDzCYkp2cmqoC\n5GREIifzNuHzkc17v/fE7QMZSAwv0KifLsWxLdJacaTOHCnaNWtjx4Ch4nylNWeO7ZmGWnKcrWqq\n8pGacAapCWdhbGoHZ4/+sHMKhnfA5DYpjh16WGHvXGI9pg0Jfx8WEpOV+1LwczqA65SmQ4vUgxJH\ninbLkPk+YLDoOLo8FnHncrAubiy+H3EVtWV1mP1rT7CMGfhtZqS+zWyz8MvJ2VobOPpb0Gh0lJem\no7IsExnJl5D86Lja/Wl0OuwDh8hcy4u7QrSZahN1OBOm1hxUFQv0ZgOZHN1rj8ABz5XeN0RhBKhQ\nDop2TO8Z7kgMz5e6zNdW1MPEko2GOhH2zr0LWw9TBI520rOVbRd+mWbbhGrz4ihILGqASNQAoVAz\nD2+HriPg1HOczI8+ObspAe/u7oG+b7nDo5ulzE9bpmtnNu5fdYaPJwv8nA7Sn0aiLjvLXQOAnMfu\ncHVmoiTVAxbmdIQeccDdS84ybY7vs0dlhgf2biev5qVBrxz9gzjYc1r2wynyag2+eF/9b6S30iUv\n/LhumSgrESpss2C5FWYtkMQZ9fcwzG8xZDF7gQXmL7fEPzvLsXOzfHC1Pmn82/35Uxn2/ER8TJQR\nj4XCZ01bf+W5tbD3NkXeU4mzy9UdSRi91B/xF3TvafcqwC8jZ+V4I2yV5BcaDSamDvD0GwN7lx6g\n05lqhXM4dDWs8JVVEUMBAEPmy3tPbwy5qmtzCOPBozp0H5qtdNu013BJHO7L4ggASz/iIWR8DrIf\nu8PKKx0x4RKRTX5WD35OBwwcl4PX38nHF4ssUPrMA5ae6YTbb9DimPhQIBWr8dPNsHKzjdZjCYXk\n+/xMf98ch/9sv16EZCEGOX+7jNhSdBzqgLCtkmD45FuFCJnjLV1JmtlyYWLJIWXu9gBZK0dTnjMc\nnHvA3rmb1CGntrpI7fNGGt2wNszasgCSxepvS1FRKQKDAQjqxDgbVoOgADaMjWgoqxAhOlayBf39\nL2VYv4qcFbZBi2NzKkoVr/pUocuV4MdrrClxJBCy/3ZhWxOx4FB/6eOrvyZh0FxvTN3UFcUZ1QiZ\n44WoIxmk2tCWaaitAtNIefFcfjk54ujpNxY5GbeR8uSUxn1ZJvpLOkGhPhWVsh7iYjHAoAN9e3HB\nZADXTjlK792O5pNiQ5sRR0Nnxhye6kYUBkX2ozJ8G3JJ+ljYIMbdQxnoPcMdgCSE4/S6R8q6t3v4\n5fkwbUEc6yrJ2aZ/GPW71n2de40n0BIKXRMdIwCHTcOQSeSXXKPEkSB6DtRfgmkK7akuqZN5fGbD\nIzA5dJhac3BmPSWMLdHAN4yK7Zpg5uSjbxPaJTSawmpwGhMbLwCL1arwRbV5JcWx0ZGjOZps0Tm6\nMPHNNjv4BbLxLLEO//uiCKmJdXLtXDxYGP26KcZMMYWDM1PjuYeOM8Eb75nDN4CDOoEYcdF8bN9Q\nguwM5d53jeM3jjn9fXO89aEFTExpSIyvw67vSvAoRrFL+LyllpgwwwwN9WIc21eBA7vKUVdHznme\nqRkdS9ZZo88gI5iY05Gb1YDw0Grs3VYGAV9+zkbHIEU8vMfHgqlN3xSZTBqup3jg8J5ybFvftDr5\n7g979B9ujMexAsx7rcmJZvZCC8xfZqn2e+DE6rafikwXkOaNSiIsIzN9m9Du+OWPCtRmd0B0jACu\nzkx4BGcCAFYvtYSrsySF4q4fbPA8pwEbfmi5gL1IBKzaUAJ+TgfceyCAvw8LpiZ0UsJBXklxjL5R\nC3dvFuwcNX96KzbZYMKMpn8gv0AO/r7gjPj7fMyfIruU33feGVxjzb/FOLowceymbKkgrhENA0cY\nY+AIYwDAqMAMVFW2nJnlZSEO7sPFFxtt8M5o2WoMXGMarjzxkLm2cIUVFq6w0sjzV12+22OP/sOM\nZa65dmBh9kILzF5ooVCkstLr8SROABs7BqxtGWAwlb+uDQ0ScR39upmMOPYfLpkzIFjWiWbcG8q3\n/ppjZM6CWAzwK1/d5O9EQpY3KlnQGK/kx53Bs3RNMZaukU+buX6LxAN93mdFMtedAprO+RtFb8W6\npv/zrTvKsXWH6tyzreWVfLd8OruperailVxLTJhhJhP2webQcO2pBwK7czFkrAmunW/aShrWKV1u\nHnVWJ7nPG1BaLMQnM/NkVqTWdgycjnIDAJyNccNgn3QlIwBXEjyQnlyPmSOagmtNzeiwc5L/kzYK\n48kDlfj+y6Y34m8nnPD9n/Yq7dUEOgPoP8wYIiEw0Ev2tbC2ZWDNT4rjksJDqxEeKrtNp+pvx7OU\n9zr87+8KTHlbtgyVaweW0jGcO1vIOOU0Z8e0m8h5Qv4/YVulra0cHbuN0rcJFG0Iw/JpNgA+fzdP\nJh6yTiDG1AFZAIANO+wIm2d890y5rdriAiEuHJfE3anaV6+tEckIIwBUVYrw7KnsmJ5+knJbIhFk\nhBGAzNYjUbh6SIQo+lat3L3iQskXAiJ4eE/WQ83CSrI98+PXkm+oNvayFS8eRMl7tJnacLDgUH8I\nqhuw/6NobOgThg19L+LAJ/dRV9OAhUcGwNSaCuVQBlneqGThEDRU3yZQtCFeyZVja7gTLv+hnvu8\nQWfz37tVi9Gvq94GbH4G1xLvLZa4rv+pJIj+UYwAnbsRJwAZqZItyd4hRli4wgo7NpHjsXjyQCWC\nenDBYtNQXyeWJnFoPPSf/gEPv24skW7PHv9bPsTmvd97g19Zjw19L8pcT7iSh3W98vDV7ZF4f09v\nbJscQcpzaIRlwoOlZ1fw3ALAMbMGy8gMYrEIDYIa1BY9R2VuCoqToiCsI8dlXVtE9W0s3RlNN44c\nFK8GlDjqETMeHVPfNUdwby6cXFkwNafD1Fy9xXxWmnrnYkE9JUHS0TflRR8AHkbzCRVHAJgx5DkO\nXXPBzPk8zJzPQ3mJEB/PzENKgrxTk7ZcPFmFNT/aYsREE5w/VoWp75pD1OyIdsrb5vh1YwlGTjIB\nAFw5K+9ZaelshNsH0pXOcfdQBvrO9FDLHkUVKoqTopERcVBhe5YJD/6TPgPL2FzhfRoANpMNtokF\neO6d4dJnsvRe/sOryI5Sv6ixPjAE4TR19IK1Tw9YenUHXcV5ozoVRlRBRgWStoKRkTX69PoM165/\n1WK7nt0/QlV1PhISjxE2t6NDN+TmxRA2XiOUOOoBIxM6Lj92l7lWWS5CcaEQFWVCOLsrPyPTlEax\nralW7JVaUUZ8OaastHr090hD/+HG+HaXHXhWDOwLleRGnNI/C3nZrV+JN64QR04yxfljVWCxaDh5\noBIAEHGxBiEjJc45Y6YoX4VHHcmE/2B7XPr5qcL7ASMcceeg9kkArLy7yYkjjU5H0OwNYLC0L5pt\nHzQU9kFDkRd7CTn3dVctXhN06axDZ3Fg5d0d1j49YGLnobN5KTQn+v6vhI/p2WEkJY6vCo3C+PO6\nYhx5KaPOa7PMsXSDNWFz5T1vgIcPCy7uTLnzSACwc2p9NXpl3LpcgxDvdADAD/sc0GeQEf675YqB\nnmkyq7zW0Dy+9MBuiRv4oT/KpeLYvZ/y+NMLWxLQ500PjPzUHxd/SpS5N3F1Z1g4GeHij4lKequG\nRpd9bU3sPeA34WOtx3sZh+ARsO00AHH7vyRsTKLQ1XmkpVcwOgyZrZO5KFpGJGqAm+sAODp0B5tt\nhti4Paiqajr+GTp4IwAg9VkYMjKvy/T18hwFV5f+oNObJOlquCSHrpNTL3h7jkZdXRXux+5Efb1k\nF6xTx2mwtwsCjUaXjh0esQYiETHHYJQ4vgTXmAZ+jW5qL78sjADQpRexW5wXjldh/nJLTHnHHBEX\na+TuD5+gXphDa/n8nTwsWWeNKW+bY9YCC/z9a8vxTOoQdaMWvQYaoc8giQDmZEr+KeJeON906ytZ\nnd263PS8NzySr8AQMscLIXPkkz4DwOq7o7C+d1irbTVz8oHP2AWtHudlGBwjdH13Mx78pTrhti5p\na56sFK2HwWCjrDwDmVk3QaezMDhkLcIjvoZIJDkCuhq+CgGdZsj183AfCp65G8Ij1oDBYGPQwG+k\nwtjRfyrMTJ0QcXOddMzrN9ZCKBTgScIR5OREIaDTDNy6vYnw50OJ40vsv+CCN0KyZK5t/l0S7vA0\nXvk5SnmJEDwrBrr15SLmtnaOEzQaMGIisWK1f2cZ5i+3RI/+8isoBpMGcwtiHZbZHBrqBIq/XDQ6\nxyirjqIpJ/ZXoNdAI4xWsnXa+Foe/6fpS8jxr+IImVtduDw71NdWkiKMjdCZLHiP+RApobtJm0NT\nKHFsn1RUSD47RaJ65ObFwNdnPBKfnmixj5WlF3JyJcWehULZ3S1Hh25SoRSJ6vE06SSCu7yPezE7\nSbBeljYhjgwmDbwXrvocLg0cLk1hlpWXMTFt+uC3sWegKF/1h7KTGxPXnnpgx6YSVFWIMPdzS9i/\niB18f4Ly0Id1nxXih30O2H7QEft3liHhgQA2Dkz4B3Lwv6WFCvvsD3PGxmVFEImAiTPMMHmmGc4d\nrcS4N4jN4tF4BncrvQMO7CpH4iMB+g0xxpgpprhythrDxpsQNte8pZZ4cy4PyU/qcPFkFbIzG2Bj\nz8D0982lZ6mnD1YSMlfjSnjoWBO50I7SYiFGTpKIY3MP5JiTyouukoFtwEDYdlIcRwkAVbmpyLkf\niqq8Z3L3jK2d4dLvdZjaq47VNXf2A4PFhbBetx6tonoB6Cz53Q6dnTmKRBAL1dtGaykJgLpjULSE\n7GdyVVUO7O26qOz16MlBDOi3CiYm9rCx9pcKrCLKK7Lg5ztZ6X0iMWhxVBQE3r2fEa4mekgfb1tX\nLFMJ43877TB4jPyH/am7bjKPFQXr34+sxaez83DliQc+/brp3E8kAkZ0Tm/R1jvXa3Hy30pMfssM\nsxfIZv5/WRwH+6QjPNkDnn5s/HGqqV7lD6uLcXx/BYyM6BhKoGCtnJePL/5ng8kzzTBzflOC9M0r\ni3D6YCWGjdcsUUJLPH5RSsanExs+naxk7lVVijD2ReooImEwafh3t2yw/uE9FZi/TP1SNmZ2XPgP\nsoNYDCTdKEBFPjEio0wYc2PCkBvT8nZtTXE2ks5sBwB0mrIMXEuHFtt3fnM14v7W7fnjg30rdTrf\ny5SmxaE0Tb3dgJY8UmP3LiPKpHaMbKiMmakzqqpVxzXX1Uliu1OfhSH1Wcv/EzxzN1RU6uYLrkGL\nozYli75coNl2zsjhXKz/2AhAHTYfKIdICAzxS9d4XgD4flURvl9VpLJdfb24xee2elEBVi9SfM+b\nXgd7t2zFN18wcjgX/+6TiLuNs6Tt918WSZMAfPCuCQQCMU4flKy8iCoNFX/fAYHd83RaJkzZXPt3\nlGH/DtXnmlYuxlhyYYjCe9tfi0B+MjGr3OY82LsMIg1XKk/++w4dhsyGpVew0jYMNpX8nkK/WFp6\nobQ0FXQ6Ew4OwQiP+FplHwaDg4YGPmg0OsRiWU+9rOeR6NXzY0RFbwOdzoSf7yRE3FwvvV9ZlQsO\nR3E4VGsxaHH8aL4p/thbDYGSMywiuHiZDxvnbBRlO5M2h65R9Zz2/NXGqimQ9Oen0YAlF4Yg50k5\ndr91C8IXOVsZLDo+PNAPi0+EYHXQeYhFxBkQ++dSiLV01U27th+5sWHoNHWF0jY2/n1QlHhHW/Mo\nKLQmIzMcpiYO8PUeDw7HHFH3tkudcYI6z4KNTScAgL1dELw8RyE7JwpPk07C0qID6HQmunWdCxqN\nDnNzVySlnMXz55FITjkLR4duCBnwNerqKnEzciMaGpqOSYRCAVJSQzE4ZB3q6qsReXszYc/HYNPH\nnTpmg7Wrech+5oSibGesWt707cDJkYH4+w54EOUAqxf5NXnmdIwZxUVGkhM83Jm4ccUOX62Q9Hlz\nmjG6dmEj7KwtnsY7ws5O/fCFH7+3QE66EzauU12vkcul4Wm8o8y15gK17HNzPI13xIMoBzg6MGTa\nDB0ssT30tC2GD5WNgQs9LbG7eZ+WxmuJomxnFGU7493Z8tu2e3ZbISfNCRfP2cLGRvatoex1GDaE\ni+epTti5zVKpI05rSU8hJxH49C3dIKhqwI5pN6XCCADCehF2TLsJfmU93tzajbD5ks79qrUwNsIv\nK4CoQfnr4dRD3huXgoJsamuLkfrsIrKe38Ld6J8RcXO9TBjHw0f/4Gr4Kpmfp0knAQBBgW8jPGIN\n7sfuxr2YnQiPWA1f76a6m7l5MYi4uRZ3orZKt2Cbk5l1A+ERawgVRsCAxXHS1CKkpDbA2TMHNs7Z\n2Li56VwxLlqyfde1Vx6SHjWJ0batlnD3zcG9SHsMHFaATxc3ObZcOmeLUeML4ReYiyexLZ/dNHL1\ngh3OnOPDySMH5RVi/LSl5TMsPl8Ma6uml9Taio7klKbts+Mna+AXmIue/fIRf1/WBl8fJtx9c3D0\nvxoc2t903vk41gFfrimHX2Autn4ne5bZ0njKsHHOxtfr5ZNp7/3NCtt+rYRThxyMGl+IoqKmD3Fl\nrwOLScO/+6zh4pWDRZ+Vwt1du40IYxP5t+FP/zQ9n1+/JScFnf9ge9w9rDzIP+pwJnwHEpNPVyRs\nQFVuKiFjpV78Q+k9Jpe4s2oKCl3BZDY5dfXsvgjPn0fq0RoJBr2tqogvPjPD6rVNH+7JKQ3w92Mh\nN1eIK1dlnSiEzZxTf9tTJdPH1paOwsKWv8UHBbJwNVwy5uYtFSjKdsanSxXnKG1k1+9VGDuai/MX\n+PjrdyvMeLupVEtKqkQo6xvkV1i7fpfY9+e+aiz/vGmVbG/HQMwDiXvzrPeKkZPWtBJtaTxNOX22\nFkf/tUH/Iflyr4uy12HxQlOs2yj5WwiF2ttx6aVsQTJ2HaxEVQXxWXwAoCSrGm5dlH/hce9miZIs\nYragE/77jpBxAKAyJ5mwsShU4zdrBZhGJuCX5MPEyRNP/liDhlrJ/6vb6Ldh7tEJ/JI8cK3sISgt\nRPJhieMPjUZH4KItqMnPlLtH0cSNW/9Dt67zYGLigFp+CVKfhaGwUP+FxtucODo7M2FrQ4eTo2Qb\n8dIVPmpqJB+e/BbSOZZXNH1wFxeLYGPNUCmOALBuTdM24o7d8kv6l/nqm3JkpTjB1TsHfftwkJEh\nETA2i4acdCds+7USD+Nb3iZUtvPWXOw1GU8dTpyuRdhlPs6ftEVAJxZsXWSdfhS9DjY2dMTGNc1d\nUqKdiK1ZVIAJ083g6ceGhRUdWWkN+GdXGUL/U/16t4Zjq+Kw8MgAOPqbIzdRNiGDcwAP7t2s8POE\n60p6a4agQrWjFoVhwrG0w8Pt8nlT6UwWeJ6dEb+jydM1aPFW0FlsiOrr0GnOOhkhbX6Poon6+mpE\n3duubzPkMGhx5PPFYLMBQTPRO3a8BvPnmmLNOtmtQZ6KhN0jhnLw3Q+S33v3YiMhUT1BeXkedTAy\nosHGho7LzVay67/h4ZPPS3HgkMRD9I9dGg+LTv5NOVeJGO9lamrEGDyyADPeMMani83w0/YmT01F\nr8PVcAFmvWmMo/9JbLDX4Cy3OVfOVitMDE42OU/KkXq7CB8dG6jwfty5HBSmtV6gqwu0z89KFsbu\n3nAcPRVsKxsIivJRfCcc5fHRMm1sB46CRbf+oLPZqE5LwvNje9HcO6rjqq1I3b0JdcWyHuIdV21F\nSfQN5F9qOfi7rVD86DaCFm9FfvRF5N+5IL1uFdAHNAYTQYtlV4OmLj6oSHsMBtcYneasU3iPwvAx\naHH8en05Hsc44p+D1Yi8U4ez52txM1KA7T9aSpxrkhrw+iQjuHiprksoqAP+2WsNa2s60tKbzgFH\nDufC+UV+0VEjuWAwgROnJN5Q3fvloyjbGfsPVMPdnYmcXCEWfdrytioArFxdjtBTthg0oulDY8fu\nSty5YQ9XVyY+XaR+kP9780rwPNUJx07UYmA/tlrjNX9Ob88yQWZmA8IjJN8wXp9shP59ORAIxKio\nFOHEqVpJbN8jR9yPqUNurhBvzzKBq3fTa6rsdbh8lY+Df1tj9y+WCApU/wuHIbF37l2Y2XIw7ftg\ndOghOetNiSzEoc9jwa8k5vnkP7xGyDhEYdmtHxxGT0Ve6FEIivJh4uUPpwlvyogjL7AnzDsFI//S\nCYjqBHAcNx0dV/2AhI1NK6iazFR4vPMJkrY2xVbS6JIvqa+KMAJA9rWjyL52FI79xyNo8VbE71gm\nSRpAo6E8JQ4ZofuU9lW04qRoG9DEYt3kEW3RCBqNVCPenGYMRwcGtm4jPmaNggJoOcCcjFJGrZnP\n7c35YFvZIOXXDRrN2XHVVhlxpNHp8F+xReaay5R3YerVEYnfGVauV4CYv5FDv3EQ1QtQEH0ZdCYb\nnRdsUiqAAfP+h7Qzv6MmN10La1Uz+eZCjdqfHLCDFDsMFbFY3KoCnga9ciQSqs4pBYWE58f+hN8X\nm9Bx5RZknzqAiiexCtuZePjAqs8QcGwdwDSSpOKjMZkQN0h2XsQiEcQiERzHvIHc0KMAADO/IGT8\nQ3xZIn0StHgraguzAbEYRnYueLj9cwCAqKEOxY8iEbT4B/CLJGELXBsnqVg+/u2rF/dy5O5RGD7t\nRhwpKCgkiOrrkLBxCSy79YPz5NlwnjwbhdfPo+jWZWkb/2XfAXQaMg/uRk1GCgDJyvFlMv/dCfdZ\nHyE39Cg4NpIE/TWZxISsGAotCVr2tWPIvqascK9Yad+R062waKPE83yiV7xWdrW3laCuaRfiePCI\nfKkmCor2TmlMJEpjIsGxsYfnvOUy4khjMmW2S5XRKIRcBxe4THkPVcmUswnFq4HBJgGgoKAgB2M3\n2dqV9eWKncwYRsbS312nz1U6Xum9m3B+7W2weJZ4/t9eYoyk0Csm5gy8NtdG32bolXaxcqSgoGjC\nfdZHctfKHkbJPOYX5MD3syaHncKICzD16qhwvLyLx6Vbrq1Nj0dhGLy73AGjZljhxO/tNz633Yjj\n7D8HwL2H4m9CJ5bdw+MLmpVB+eqh8ppiG4JOajQWUTTalHQtF0c+uasXG/QFz8kYiy+MBADEncrE\nmdUxavcl+r1h6KizXZr2xxa5a0U3L7bYJ+fMQa1tojAsRs2wUt3oFafdbKtW5NYSWl2hprSFdDwU\nOqf7Gx7S37tMclPeUAFEvzfaG2xrSf7Zl5MIUFC0ZdrNyvHUl/dx6sv70sddJrlhwnrtKy5sHRQq\n87illSQF+dw/koZ+H/gCACL3JGnUl+j3RnvB1LsTGFwjOE2cqXJVSaEaS1smFqx3Rpd+phAJxUh9\nXIt/fshHYqx6DoU0OjD5AxsMmmgJJw826uvEyEkT4OrxUoT+q37y/h5D1E9SoopuIWaYvsgOXp2N\nUC8QIfpqJXavzUF1hVB1Zz3TbsSR4tWmPLdWb9vZ7RX7EZPBMuMh/+IJlNy7oW9z2jR/3vSHjSNL\n5lpQX1N8d0wilJN9lSfi3nbOBx7+XLnrXGPAL9gYfsHGWLDeGf9szceRXxUXgz+VEqgwFvx0aqDC\n9mf+KsLv63MV3gMAJw8Odl3xlbnG5jAweLIFBk+2QHlxA2b3SlDa3xCgxJGCgkIrUndu1LcJrwTb\nzvlIhTH0QDGextbCxYuDqQtsAQB0Bg1HHwXgjc6Kw2ScPZvKPYmEYlw/XY7EmGrQmTSMn20tvT9r\niT1unitHTrr8kRCRSVLcfLj45YKP9PHdyxWIvloJUx4D0xbawtiMAZ41EyeTOrco+vqGEkcKCgoK\nPeLhz0XSgxosnSKbPOHvLXn4515HmFsywTGiw8iEjtpqeW/gxWOSUVstRGlhg9y9c38Xw9Wbg1/D\nJKu4n854Y1qgvMg2T0TQc6gZVv/uIXddHWYtsce0jyRn0IJakZygH/+tEAPHW+CLn11BZ9CwaKMz\nflmVrWgovdNuHHIoKCgoDJWXhbGR9/snSn+f9L5ij+qcdIFCYWwkK0UA4Ytaq1xjcj/yG4URgNKV\n7o2zZVJ7Rk43XK/YNrFytPMxR/fpHeDZzw48R2PUlAiQn1yB+NOZeHT+1XGz7zbVA0ET3WBiw2nV\n82yeS957oD36z/GFYycLCKobkPuoFBG7niLnkerqIs0x4rERMt8P3oMcYG5vhLqaBpRkViP5eh7u\n7k9Bfa36B+xGPDaGLQmAT4gDjCzYKEmvQsyxdEQdeLXSjlFQqENLwlYnaPpnbr59qilpiXx4dzbS\nur86MFnq783eD69Er+HmqhvqEYMVRwaLjnf/DoFjgIXcPVNbLkxtufDqZ4fJm3rg5u9PEb7dsA93\nlRE43hWTNnZXeE/b51lVxIexBRtLIsbKXGdyGPAOcYB3iAMA4H9dT6kMYWByGFgRNQF46X1vxGPD\nOZAN50BLDF4kCQ4/tOgOUiLylI5FZ9KxInoC6AzZwWy8zDByeSBGLg9E3MlMnFmjOkYxZIE/Qhb4\nK7wXczQd59c/UDkGBYUhsGuNetuK9q5s1Y2UkPSghnRxfGNh06rxcVTL9VnP/F0sFcceg81wL9zw\nKiYZ7LaqsF4kJ4yVhXxkRBehOF22AO2AuX4IHO+qS/MIw+eFUDWntc+zIq9WRhjLcmqQHlWEuhrZ\nb6hfPpikcqwV0bLCWJxehbQ7hSh6Jv9mbkkYOaYsrIqZKCOMZdk1yLxfLLPq7DLZDYvDRqm0q6ZE\ngAaB4buDUxADg2uEjl8qLznl8/HXOrSGWO5crlCrnSYrs5dp3MYkk7GzmrZIo662/JxymzkFeXSU\n97Q1BAx25QgAaXcLkRNfimvbnii8/9p3PRAw2gUAMGljd8SfzdKleYRwfFk03Hva4K/ZESh9rvjb\nlqbPc8jHnQAA17Y9wa0/5GP+msdkBk/xQOx/6QrHmb69j/T3Qx/dRsqNfIXtnDo3rR6V8UXkOOnv\nYZvjEa1gC/XjS6Ngbm8EnqMRJqzr1uIK8t7hNNw7nCZ9bGrDxadXR7doA0XbRcivVXrM3Fc2AAAY\nMklEQVSPxTPccyt1EBOQcc/ChokBY3mYu9oJND0teXhWTXLy3gpHvLfCUa1+1vYs1Y30gEGL44G5\nt1q8f2LZPalotGV+HBLa4n1tnufTq7kKhREAfhp2AZ9ekQjJiC86KxRHY0sOfAY1rWqVCSMA5Dwq\nxb/zI5XeH/FFU6zUg+MZCoURALaNCJMKd5fJbmptr1LoHq+FX4JtaY368hLQ6AwwTc2RsFFS47Dj\nl1shFjagrrgAHDsnZB7Yier05BbHY/Gs4PbWh2Bb2YKfmwWuoyvK4+8h5/S/LfYzdveGVY8BYBgZ\ng2FkDJcp7wIAsk/sh1jUfnYVfgv3g4OCLdeS/HrkZdWhvFgIN18OnDtof2bZHjFocVSHpPA8+A5+\n8SFOA/CKZgHT9Hke/VR5btWqQr70d7ax4rdAQx1xHy69ZzdVgTj7jeLCuo0cWnQHM36RrFjHfxOs\nsj2FbrHqFQIag4Gn36+EqE4+Xi7hf015W7kOLvD4f3vnHR9Fmcbx32xLstn0bEjfhEDohCaCig1P\nOeUoCiKimCMfFaQcVUroEmooAgoqnlg5sAAKyuFF6SAEMNRQQyrp2WTTts79MWRbdje7yYZs8Pl+\nPvNhZuedt+yG+c087/M+T/wUpK98t8F6Rf5Sk2s7Ja6rJ47G5wGgOvMmqjNvQhwRjbDhY5Hz/XYH\nR9P6WbUrxkQY54y6jSup9S1QU9eE31dx3DArB7/94JjTn6vhsnOO9lJ4o1y/7y5xzddzZ3C/x6mq\n0uDYx9f0x/POD4W7V9Pa1WkbfnIxnrfs8aIM0hjnhbIimk7AIwOR/8t3FoURAPhiCcJHJqD91KWI\nHjcVDL91PH9HPTmmpbvQKDr15tKK1Vbr8Eb/qxaFEQC8fJv/d7h12WD67txHbKNk66DVi6PuPkw0\nuwKOjLPopn0T/A1xaPNV/Rsqj89g5vEXkPjnUHR8JtTuOkI6G5yqrv9uPdyUNbq+0DodrR5UGB7P\naloqvliC2GlLkfvDdtzYsBDXNyy+v51rgLJb1s30/u16txohr8PYQeeLNfkoK7S+JKTHYxK762Ub\neUv99sMi/f7fXm7d88BAKzCreviKMDSpN9oNaNPSXWlWegyXYdC87hC48ZtcV0VhbcOF7GRZ3B6M\n3T4Akb0CAAAMj8GIdX0BADlppfj8jaM2l4MEGr35lWXbdu+2RFB7114L9Vej5NQhhPz9Jdz6aDVY\njdrknFeHrlCVFoHVciZ5/z6P3pc+6TRq8NwbXqaQdew7+MVYDyjf85+rkfZFIrQq684/roRxyDe1\nyraiCUX2e7qWFhh+Vx9/AcpLrYuuMScOGKxbDAMEhQlRmKu2cYVr49LiOPP4CzZNeayOhVatc4qg\ntBRtHwnCq1sfsVnG0XFqnbzE4Yt4Lqj06A/7I+Yxw0NKeJw/Ev8ciuzzJfj8DcuBp/lCg3FC5UCg\nAP31olZv3HigKDmRAv+HBqDj7FVQlRaBEQgg9PbD1aTpkJ8/hZDnX4bs9YkQ+UkhT7s/OUVr7+aA\nJxQh5p15gE6HW1tXWiynVdeCM4VYF4q4sUlQVZbhzqGvUZl/20opBu6+QRAHhsMzSAZJcAxEXn5I\n+3xek8fiCMaCGD87GMd+LreY7eKH9K4O1Xv7iuHhesE2GWa+aH9wjl0fFOqj5Gw70hGv9bmKijLL\n4sowwKvT2uDrddad/VoSlxXHualD9DdGnZbFZ2MO4+4Veb1yz87uhr5jYup93hqQxniZCGPa3nsL\n4C08BLrCOHe8cxIAF3Vn1KZ+YHjcTSaiZwDmnRuC5b1+rHeNosDwH00S6LhDgMKJb8GEc7jx/mKr\n58ydZooO2/bEBgB1eWm968yPG8Le8pd2JqHrqPk2y4gkfogdPMmh9luK4nw1AoOF8PTmY8f5ztix\nsRBH98nh5cvHoNEBeGo4N63xydI8vLnQ/ukQrYYFX8AgNk6Mr1I74ev1hVBW6yANFaJtFw8c+7kc\nR/fVvx9/ta4A+74owRd/cEu7vkrl/k39XYHifDX8pAJ07y+Bhyd3b8+9rSRxdITAGC+DMGp0Fm+6\ndQg9XHIIdvH27oH6/U9fOWRR/OtwpXHePFqApB574R3sgSkHuQX7PAEPvV+OxtldGSZl75wxzENE\n9wuCo2SeLmq4EEHYiUpRCvmdi/CNspyKqbUx7tF07L7WFXwB96A6ekoQRk8x/X+W+OptXPyjyiFx\nHN7hkj5dlbefABOWml579rD1iDbyYg1GdL6EXRe76IN+WMsRqax1XZ8R17njGvFogiEP2HfTT9ss\nG9mz9U/8ArApjIBrjrMivwYr+vyIualDAABPT+1STxy1KoPzhn+kZ4N1GptRa+QqXNz/4MTOJVyD\n2//7DLGDJ0MSHN1ifTi4sxQHd9qXgLihzBjDO1xC9/4S9B3ohQGDfeHtz0dehgq/fluKPduK7a7H\nUrv9n/XGyHeCENneDTXVOuRnqnBkXzmO/GT7fqVScjkovXz5iJ8dgr4DveDtJ4C8WIMrqVX433dl\nNgXWFXBJcfQKMoQTKsm07cQREP3XcPV31XEai5/Qw/KcaFlOFfzCOWH0DvZARb51hwfjqDy756Q2\nGPuVIBrD9X2b4N+ud6tdwmHOhZOVuHCyEtuWOe4RbouTBytw8mDjvd8Vci02zW2dD7gu6e2Q86fh\niar3y1FWyyXseLL5O+MCtMQ4/SM99XOKtghsaxBta7FVtw5L0e/XmWEttimToG1/g0no9gnLWcsJ\nwhmU3jyLc9umIz8tpeHCNtDUVKI8y3KIy9ZCSkasfttz3vm+DWu/CceuU22RkhHbcGEXwSXfHI9s\nScdjb3UAAPQdE4M7p4tN1sj5hHjg7d0DIRILUFlUC4nUjsC1DOAd5AHvEA94B3ug7aOGm3C7xzkP\nzNpKNSrya1CRXwNlpW0XZJGnAD7BHvAOEcMn2NSNvNeIKJTfq8dWXfLcaviGcYtlp/42CBsGHjBx\nxmnUOJ3Ek5M6o/OgMABcHNMz39xGSYbBDCJw4+O5ud3R80WZ/rPvZ52xWJdWpcOxj6/pf9P5F4bh\nx8SzuPATFyOW4TEYvKQn4oZG6q9pKKQewMVT9Q7mfk9pO8OSjzYdvNH1hQhU5Fdzv0FBLXQaGwEs\nnfC3cW6bYw4kTeV+t/cgk3dmP/LO7AcAeIW2h1/bHhAHRsDNKwB8kTt0GhU0qhqoFGWoLslBdVE2\nyjMv3fN+bf28GM857QyMthxu0hnMeJV7eyRxbCI6LYuLP2Wj2z+4BeAvv/+wxXLFtxTYOjzFJJC2\nJRo6321whMVsF7vfTcXlA6YmAd9QMSYdeNZmfc8v7GFXXZv/flDfN0mgO+anWe6nveNsLvqMikaf\nUbbnZ3ZOPmViYjXn0OarcPcR6esZktQbQ5Isp+raNuoQqkosR2ABgAk/PoOAKOuLmsO6+yOse/05\n2mXd99T7zJl/G44SGD8G4p7doa1QIG/pKrBq56wJC3zjVXg+xK3ny56zCLpKx9eXtgb43l4InjEZ\nBe9vgabUOaHKFHk3oMizHQv2QaNNuBC11U6Ifv6A4ZLiCAB7E8/i9slCq7kOf1p4Dml7su5zr5zP\nsu57MPHnv+nn5MxpqXHumZuK9JQ8DFvRGzyBdev73cty/HvMYbvmBg8kpeHo1nRM3P83izFdba2X\nfNCQJoyFW7QMWVPngBGJnCaMAFD8+Tco/vwbyDYnO61OS0jfjEfRJ9ubtQ17qAs60Fr48lA0QmWm\n67df7HUL5WXcOFIyYqFSshC5MTh/ohpxD4vB4wP52WqMeTxDX+bQPgWeHGw0rXFFibdfyLS7H5/8\nIkNopBACEQOBgMH+y+0AAAveysO549UAADd3Bj9fbW9y3YiHbqGs2PCdL9kaiseek9gs0yphWbbF\nN3DGxBbdojYms1EbVls9H5G0iCuzMdmpbbb0uP/qW7e3Vjq9zuC+g1i/2F42y8g2rmbFveKadWyy\nzcksT+LZrPW39O/XGjc3D8bkeMuPkeyO4231xykZsSwAls9n2JSMWFYgYEw+r9sf+aaf/njS4iA2\nJSOW7dzT3eH+vDk7kN1/uZ3FcykZsewzw7z1x4NGepv0o9/TnibHlsqY13e/vuem6pLLvjm6GtmJ\nSwAAURub92m8tdA5fhGubF/S0t1wAmyz1KrTWH4TDJqQADdZJMDjQTrudWDc6wCAzEkz9WV4YjGC\np06AIDAA5b/+jvJffq1Xj+8/BsH7qcehLipBwfrN0NVaN0M3hsCxoyHuGQdteTkUx0+h4tffAQB8\nXx9Ix70OURiXq8/47dR4DMKQYASNHwee2APynw5AccSQfi5s8VxUX7gMcY9uyF2YhJDZ08DqtMhf\ns1FfZ+akmVwfevdA5fFTKN2126R/xu3mLU+GOs/UGSxy/QrkLV2FNtMmgufhjtId36Pq3J8mZYLG\nj4NH544Az2AZMR5Dc6GsMf2b+3lnOaYuqx8eU3svUL/GSlzlbz8xmJI3Ly5Ej/4eGJ8oxZQRzslr\ny7+3RvF/ewzeqge+rcCs1YZUdgs2heCH7abLOszLtFZIHI1gGxtx9y+GyMv11lw2losfz3V6nfmn\nD1g9V7jlUwDczb3o4+2ovnDJ5Dzf2wvhyxchb/laqO/mIzRxJjx7dkfe8rX6MuErF0OdexdZ0+fB\nrW0UIpKTkPWv2U4zL0auXwHF0ZPImjYHfF8f+L80RH9OKy9H/rrN+jFYEhOPrp0QND4BOfPfg7ZC\ngbAF78LzoV7IX7sJACAIDEDl8ZNwi4lCxJplyJm3BBErF0Mki4Aqk7uxR76/CneXr0Xxl/9B+LIF\ncIuOwt1V6/VtZE6aCUGAP8KWWA7ZxgiFaDP1HeQuXgFhkBSh82dBmZkFTQnnCS/btAYlO75D4dZ/\nw/OhXggcOxqZk2c55ftriAnzpRiR4AeNmsWtq0p9tJimkn1Lhbh+zsuGERHDmX5tOdG4i3l4Md5X\n79TzIEHiaISuthZRG5OhzMqGW2QE8jdtRe2Nmw1eF7UxGaxGA3VhEUShIcjf/BFqr3OT+sI2QQhL\nfBea0lIwfD743t6486/6/wmDp0wAq9Gi4MOPG2xP3K0Lgt78J9SFRRD4+QJ8PjKncjnzQufMAE8k\ngiAwALU3bkIQGAhWrUbuslWI2piMytOp8OjUEYxQAIYvQOaMOQ59R1GD4sF3F4PvJkbUoHgAQOZB\nLrmsd3QXRD+fAKW8EEKJHxgeHxe2cDfPDq/MAk8ggsgnAJW5NyHyDgCrVSP965WIm7gOpeln4C3r\nBIYvAI8vwIWthhyAcRO5BLpKeSHcA0Jxa+8WVObYdprokvAeakvuQhwUgbIb5+HTthsE7p5I+4Dz\n8vTr0AeS0Lbw79xP/5k97dkaBwCE9H8BfrG9UXzxOArPOb5EIHThbJR+uxvqPM47O2/ZGtO5Qx4P\nfIkEOZs+AgAob9+B8lYGAhNeR9HH2x1uzxIMnw9dFefEo5WXo+jTLx26Pmh8Au6uWAutnAtEnbtk\nZb35T3VBEeR7f0bQ+HFg1WpUnUuDW7RML46Kw8egLuCW8uQkLm3U/GnuouVcW/kFUOflw2vAIyjb\ns+/eIBlUnuBiv1adOYfAN151uP7GIBQxGJHgZ+IZOmaSP8bNCGxy3VHt3ZB9S9XkeurIyeCsH7a8\nWFVKFr/sLMfGRQ/esisSRyP4np64M4W7mTNCAWRrV+qPbWFcRhQRhpBpk5E5nROdsMR3kTUrETql\ndbNX8OTx0FXXoHDbdrv6WX3xskmbxqZeUWgI7kyZibD5s1F5+iwq/zhjcl6VlY3ir/4DAIhIWgzP\nPj1RlWp/QuE7B7bDMyQasufG4s4B0/5WZFw2EZq4iev0++4BIUj7YDo6jpmLsvQzKE0/Y3K+pjAb\n2Sk7AABdxi2FX2wvlF3nUgwZ1+khDUf7l6aYiKc18o7tQU1xHuImrkPaB9MR8fQr8JJ1giLzKsqu\npaLsWir8O/erd52t9hoax92T+yHyCmiwb9bgubvDf+Rw+I8cbvG8RwfOacJcLLSVlY1u05zMf81G\n+LIF8P3H31Fz8TIKP/rM4TpUufUXo/O9JNAqDP3U1SqhKeMElNVqTVJGKW9lNKLn1tEqFGBEpk4w\nHl07oebSVbh3aG/lKucjtvCW+M9pjRPG+OkB2L6uBAAwZWkQItuJ8M5Q5znvadScJW3CfCm2LDOE\ncRSKGH3Q84Vv52Hl9rB64mhcprVC4mgFVm1fmhYA4EskCBg9Em5RMvA9xSZzGABsCqN03FgwfIHd\nwliH91NPwOuRh8H39QHAiblxn7VyOZSZ9f+jKI6d1O9XHDoCr359HRLHhpD2eAIBnftDKLnXL74A\nrNbQL3WlHNWF9ftVcvmEfr8o7TD8Oz2sF0eBhwQRT42COFgGvrsYDGOfGaqmOM/kWKusBl/U8FpR\nS+3ZOw5nkLtkJTRFxRbPae8ty2jWuTGWRU7iUgCA/8hhVs2nNmEYmCcG1FZV230536t5I0JlTprJ\nPWCwLGqv3bgvc40AUF6mhVrFIiUjFhoNC4GAQfzAO9ieEuVwXe27uJuYPG9eVuLaBeeuvXy23Q38\n90Z7jEjwM/m87m3yzOEq/PBZmUXTa12Zfk97YuzUAASFcA8n36fGoOiuBr/sKsfeL22HoWtJSByb\nCF8iQcTyxcicxs351B3bizLjDjyHD4EgIACakhK7rolck4TKP1KRm7QagJmTkHEiWktzqHyeoQxj\nf443e+j21gqUpp9B+jecidH4jYpldUb7FvrF8ADUlTH0S+AhQZdxS3Fh6yywWq3+uLloqL0Gx9FE\nFMdOIujNN0zmGI1RZecC4ObUnLn8wxql3+6B1xOPWRQ7a6hychEYPwbFn30FABD437uxWkmSbAmf\nwc/pH+Tc2zs/YkvwtInIX7sJygz7lz44i0Ed6k8JGJsu7dkHgMSEXKf055NVxfhklZWHMS2LZ9ra\nDg7wwdIifLDUeoKAU79V4dRvrW+tLYmjFRih9TySxoi7d4G60JDg1WtA/dyMtm5kFb8fgeLIccjW\nr+LmIu24AfHc3FD63e4Gy1nC+8nHUf7rb/f2B6D0+70O16HTqMEX1U8uyxO6IffID43qlzTuCf0c\nnTTuceQe5Rbs+0R3hVJu+H4DuzVvAt373Z45pf/5HmGL59Yzmxq/2eQlJSNy/QqT83nvrdbP0QW8\nNkovSG0mvgVNSSmqTp+t5/xjjbq2dbW14Lm7Q1Mmt/h3qausgmxzMnS1SjA8BlnTOeeYuyvXI3ID\nN8/ImUv5yF2wzM5vgKPi4G/c9WoNGKEAuUsMORo9OneE5JG+EEg5c6Q0/jWoi4pRc+ESKv9Itav+\nqrN/InjGZO6AZVF78zYK3t/iUB+JBxsSRyOUdzI5h5zMLLjJIpG/eav+nO/zz+lvOAGjR0JbJof8\nwK9QnPgDAa+MRPCUCRBKpVCcMs0ikrdmA2RrV0BdVAxGIIDAz7fePCar1SJ3RTKi3l9j1xxnzbUb\nkG1YjdrrN+HRMRbqfPvzoblFRiB80TzwxGIwQgGqzjpuUq0p4pLLdnyNSy5b96aoyL6O7hOSUZl7\nE14Rsagttb9f4jYR6PT6fPDdxeDxBZDf4EyqJVdOIfyplxEzfCLcfKQoveqcBLrBfQdBKOE87CKe\nGgVVpRwFZ/7bpPZ82naDJDQGniFRcPcPgkAsgSLzKhTZ9Z+8bZnxchevsHoOANR3821eX/LVTrv7\nbAl7TYzZcxZZPZc11bqjV139qqxs5L23CgD3UGCMprjEaj9qrqSj5kq6zb6ZX1twz4EJ4Jaj+I8c\nhtwFy6AprwAj4MP/pWGIXJuErBmJNusl/kK0dAAAVwkC8FfYXDXoQNzEdS3eB9pcZ5NtTmbF3bs2\nW/3ShLGs/4ihJp+5d4xlIzc4PyBEc233czF9a90oCABBEIQDlHy9ExHJSfB8+CFo5XIIg9sADIPs\nWfNbumt205xBwgkOEkeCIFyK5vYc1dUq75t3KtF6YVwhKgzDMC3fCYIgCOKBgWXZJrnju2SyY4Ig\nCIJoSUgcCYIgCMIMEkeCIAiCMMMl5hwJgiAIwpWgN0eCIAiCMIPEkSAIgiDMIHEkCIIgCDNIHAmC\nIAjCDBJHgiAIgjCDxJEgCIIgzCBxJAiCIAgzSBwJgiAIwgwSR4IgCIIwg8SRIAiCIMwgcSQIgiAI\nM0gcCYIgCMIMEkeCIAiCMIPEkSAIgiDMIHEkCIIgCDNIHAmCIAjCDBJHgiAIgjCDxJEgCIIgzCBx\nJAiCIAgzSBwJgiAIwgwSR4IgCIIwg8SRIAiCIMwgcSQIgiAIM/4P5D2Y2Ne//7IAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlgCqbUKFxWc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Suffling the data and splitting the data into train and test sets (90:10)\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_tweets, val_tweets, train_labels, val_labels= train_test_split(tweets, labels, test_size=0.1, shuffle= True,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXueAak9LreW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "d424af5e-ea21-4241-8f9d-68dd550db496"
      },
      "source": [
        "#Data Summary\n",
        "print('Length of the Training Set: ',len(df1))\n",
        "print('No. of Training Examples: ',len(train_tweets))\n",
        "print('No. of Validation Examples: ',len(val_tweets))\n",
        "print('No. of Test Examples: ',len(test_tweets))"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Length of the Training Set:  31962\n",
            "No. of Training Examples:  28765\n",
            "No. of Validation Examples:  3197\n",
            "No. of Test Examples:  17197\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMk1YFicNM-F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f16269aa-94c6-43d1-d132-b08aa7e76b21"
      },
      "source": [
        "#Tokenization and Padding\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "\n",
        "#embedding_dim = 200\n",
        "max_length = 40\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "vocab_len=49999\n",
        "\n",
        "tokenizer = Tokenizer(num_words=vocab_len+1,oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(tweets)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "vocab_size=len(word_index)\n",
        "print('Size of Vocabulary: ',vocab_size)\n",
        "\n",
        "train_sequences = tokenizer.texts_to_sequences(train_tweets)\n",
        "train_padded = pad_sequences(train_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "val_sequences = tokenizer.texts_to_sequences(val_tweets)\n",
        "val_padded = pad_sequences(val_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "test_sequences = tokenizer.texts_to_sequences(test_tweets)\n",
        "test_padded = pad_sequences(test_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "train_labels=np.expand_dims(train_labels, axis=1)\n",
        "val_labels=np.expand_dims(val_labels, axis=1)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size of Vocabulary:  38363\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHUjkz-7Pnim",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "outputId": "f54c3b72-0d71-4738-976a-cd954a5ceb3a"
      },
      "source": [
        "#Dimension of Data after Tokenizing and Padding\n",
        "print('Dimension of Training Data: ',train_padded.shape)\n",
        "print('Dimension of Validation Data: ',val_padded.shape)\n",
        "print('Dimension of Training Labels: ',train_labels.shape)\n",
        "print('Dimension of Validation Labels: ',val_labels.shape)\n",
        "print('Dimension of Test Data: ',test_padded.shape)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimension of Training Data:  (28765, 40)\n",
            "Dimension of Validation Data:  (3197, 40)\n",
            "Dimension of Training Labels:  (28765, 1)\n",
            "Dimension of Validation Labels:  (3197, 1)\n",
            "Dimension of Test Data:  (17197, 40)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBXkH7brCq7_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "e51c426c-33c9-4c91-af46-c9e59b0ed03a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jl5Fxg6NPvSo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Unzipping the fasttext pretrained model from Google Drive\n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "#local_zip='/content/drive/My Drive/glove.twitter.27B.zip'\n",
        "#local_zip='/content/drive/My Drive/crawl-300d-2M.vec.zip'\n",
        "#local_zip='/content/drive/My Drive/glove.6B.zip'\n",
        "local_zip='/content/drive/My Drive/wiki-news-300d-1M.vec.zip'\n",
        "zip_ref=zipfile.ZipFile(local_zip,'r')\n",
        "zip_ref.extractall('/content/drive/My Drive/wiki-news-300d-1M.vec')\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2vP2f3yDFog",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loading the pretrained vectors\n",
        "embeddings_index1 = {}\n",
        "embeddings_index2 = {}\n",
        "embeddings_index3 = {}\n",
        "embeddings_index4 = {}\n",
        "\n",
        "import numpy as np\n",
        "with open('/content/drive/My Drive/glove.twitter.27B/glove.twitter.27B.200d.txt') as f:\n",
        "    for line in f:\n",
        "        values = line.split()\n",
        "        word = values[0]\n",
        "        coefs = np.asarray(values[1:], dtype='float32')\n",
        "        embeddings_index1[word] = coefs\n",
        "\n",
        "with open('/content/drive/My Drive/crawl-300d-2M.vec/crawl-300d-2M.vec') as f:\n",
        "    for line in f:\n",
        "        values = line.split()\n",
        "        word = values[0]\n",
        "        coefs = np.asarray(values[1:], dtype='float32')\n",
        "        embeddings_index2[word] = coefs\n",
        "\n",
        "with open('/content/drive/My Drive/glove.6B/glove.6B.300d.txt') as f:\n",
        "    for line in f:\n",
        "        values = line.split()\n",
        "        word = values[0]\n",
        "        coefs = np.asarray(values[1:], dtype='float32')\n",
        "        embeddings_index3[word] = coefs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8zBk9yXEt_O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "216dd322-0337-4312-b77f-acd55f9d0641"
      },
      "source": [
        "#Creating an embedding matrix that contains the vectors of the words in the dataset\n",
        "embedding_dim1=200\n",
        "embedding_dim=300\n",
        "\n",
        "embeddings_matrix1 = np.zeros((vocab_size, embedding_dim1))\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index1.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix1[i] = embedding_vector\n",
        "        \n",
        "        \n",
        "embeddings_matrix2 = np.zeros((vocab_size, embedding_dim))\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index2.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix2[i] = embedding_vector\n",
        "\n",
        "embeddings_matrix3 = np.zeros((vocab_size+1, embedding_dim))\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index3.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix3[i] = embedding_vector\n",
        "        "
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'embeddings_matrix3 = np.zeros((vocab_size+1, embedding_dim))\\nfor word, i in word_index.items():\\n    embedding_vector = embeddings_index3.get(word)\\n    if embedding_vector is not None:\\n        embeddings_matrix3[i] = embedding_vector\\n        \\nembeddings_matrix4 = np.zeros((vocab_size, embedding_dim))\\nfor word, i in word_index.items():\\n    embedding_vector = embeddings_index4.get(word)\\n    if embedding_vector is not None:\\n        embeddings_matrix4[i] = embedding_vector'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZW2FjxqEz1U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "61154a5a-8a17-4ef4-efe9-4f644cbf7954"
      },
      "source": [
        "print('Shape of the Embeddings Matrix (GloVe Twitter): ',embeddings_matrix1.shape)\n",
        "print('Shape of the Embeddings Matrix (fastText Crawl): ',embeddings_matrix2.shape)\n",
        "print('Shape of the Embeddings Matrix (GloVe Wiki): ',embeddings_matrix3.shape)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the Embeddings Matrix (GloVe Twitter):  (38363, 200)\n",
            "Shape of the Embeddings Matrix (fastText Crawl):  (38363, 300)\n",
            "Shape of the Embeddings Matrix (GloVe Wiki):  (38364, 300)\n",
            "Shape of the Embeddings Matrix (fastText WikiNews):  (38363, 300)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEOo3S1O3AmD",
        "colab_type": "text"
      },
      "source": [
        "## **Building the model using GloVe (Twitter) pretrained embeddings**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sTn_3JvGo3g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model1_lstm = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim1, input_length=max_length, weights = [embeddings_matrix1], trainable = False),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.CuDNNLSTM(128,return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.CuDNNLSTM(128)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCi4LZFxHXJ4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "8ece55bb-badb-46e3-a5e5-5e15b7f6d29b"
      },
      "source": [
        "reduce =tf. keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, mode='auto')\n",
        "#early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1e-4, patience=10, mode='auto')\n",
        "model1_lstm.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "num_epochs = 10\n",
        "history1=model1_lstm.fit(train_padded, \n",
        "                    train_labels, \n",
        "                    epochs=num_epochs, \n",
        "                    batch_size=256, \n",
        "                    validation_data=(val_padded,val_labels),\n",
        "                    callbacks=[reduce],\n",
        "                    verbose=1)"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 28765 samples, validate on 3197 samples\n",
            "Epoch 1/10\n",
            "28765/28765 [==============================] - 11s 397us/sample - loss: 0.2180 - acc: 0.9298 - val_loss: 0.1349 - val_acc: 0.9484\n",
            "Epoch 2/10\n",
            "28765/28765 [==============================] - 8s 272us/sample - loss: 0.1538 - acc: 0.9465 - val_loss: 0.1281 - val_acc: 0.9525\n",
            "Epoch 3/10\n",
            "28765/28765 [==============================] - 8s 273us/sample - loss: 0.1407 - acc: 0.9495 - val_loss: 0.1331 - val_acc: 0.9528\n",
            "Epoch 4/10\n",
            "28765/28765 [==============================] - 8s 274us/sample - loss: 0.1319 - acc: 0.9541 - val_loss: 0.1158 - val_acc: 0.9543\n",
            "Epoch 5/10\n",
            "28765/28765 [==============================] - 8s 274us/sample - loss: 0.1237 - acc: 0.9559 - val_loss: 0.1103 - val_acc: 0.9562\n",
            "Epoch 6/10\n",
            "28765/28765 [==============================] - 8s 274us/sample - loss: 0.1123 - acc: 0.9595 - val_loss: 0.1092 - val_acc: 0.9578\n",
            "Epoch 7/10\n",
            "28765/28765 [==============================] - 8s 274us/sample - loss: 0.1079 - acc: 0.9621 - val_loss: 0.1079 - val_acc: 0.9581\n",
            "Epoch 8/10\n",
            "28765/28765 [==============================] - 8s 273us/sample - loss: 0.0985 - acc: 0.9639 - val_loss: 0.1050 - val_acc: 0.9603\n",
            "Epoch 9/10\n",
            "28765/28765 [==============================] - 8s 273us/sample - loss: 0.0937 - acc: 0.9667 - val_loss: 0.1102 - val_acc: 0.9556\n",
            "Epoch 10/10\n",
            "28765/28765 [==============================] - 8s 272us/sample - loss: 0.0854 - acc: 0.9687 - val_loss: 0.1029 - val_acc: 0.9647\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ykivcg33QCav",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 788
        },
        "outputId": "05cbb5db-d9d3-47ab-80d5-8f7cc57e80a2"
      },
      "source": [
        "#Determining the optimal threshold value to seggregate the classes\n",
        "pred1= model1_lstm.predict(val_padded, batch_size=256,verbose=1)\n",
        "from sklearn import metrics\n",
        "for thresh in np.arange(0.1, 0.501, 0.01):\n",
        "    thresh = np.round(thresh, 2)\n",
        "    print(\"F1 score at threshold {0} is {1}\".format(thresh, metrics.f1_score(val_labels, (pred1>thresh).astype(int))))"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3197/3197 [==============================] - 1s 319us/sample\n",
            "F1 score at threshold 0.1 is 0.6371681415929203\n",
            "F1 score at threshold 0.11 is 0.6484517304189436\n",
            "F1 score at threshold 0.12 is 0.6579925650557621\n",
            "F1 score at threshold 0.13 is 0.6629001883239172\n",
            "F1 score at threshold 0.14 is 0.6717557251908397\n",
            "F1 score at threshold 0.15 is 0.6705202312138728\n",
            "F1 score at threshold 0.16 is 0.6784313725490195\n",
            "F1 score at threshold 0.17 is 0.6798418972332015\n",
            "F1 score at threshold 0.18 is 0.68389662027833\n",
            "F1 score at threshold 0.19 is 0.6829268292682926\n",
            "F1 score at threshold 0.2 is 0.6748971193415637\n",
            "F1 score at threshold 0.21 is 0.6804979253112032\n",
            "F1 score at threshold 0.22 is 0.6919831223628692\n",
            "F1 score at threshold 0.23 is 0.6950959488272921\n",
            "F1 score at threshold 0.24 is 0.6995708154506439\n",
            "F1 score at threshold 0.25 is 0.7012987012987013\n",
            "F1 score at threshold 0.26 is 0.7002188183807438\n",
            "F1 score at threshold 0.27 is 0.70509977827051\n",
            "F1 score at threshold 0.28 is 0.701123595505618\n",
            "F1 score at threshold 0.29 is 0.7048054919908466\n",
            "F1 score at threshold 0.3 is 0.7080459770114943\n",
            "F1 score at threshold 0.31 is 0.7006960556844547\n",
            "F1 score at threshold 0.32 is 0.705607476635514\n",
            "F1 score at threshold 0.33 is 0.7058823529411764\n",
            "F1 score at threshold 0.34 is 0.7064439140811456\n",
            "F1 score at threshold 0.35 is 0.7070217917675543\n",
            "F1 score at threshold 0.36 is 0.7055961070559611\n",
            "F1 score at threshold 0.37 is 0.704156479217604\n",
            "F1 score at threshold 0.38 is 0.713216957605985\n",
            "F1 score at threshold 0.39 is 0.7204030226700252\n",
            "F1 score at threshold 0.4 is 0.7204030226700252\n",
            "F1 score at threshold 0.41 is 0.7139240506329114\n",
            "F1 score at threshold 0.42 is 0.7139240506329114\n",
            "F1 score at threshold 0.43 is 0.7106598984771575\n",
            "F1 score at threshold 0.44 is 0.712468193384224\n",
            "F1 score at threshold 0.45 is 0.7109974424552431\n",
            "F1 score at threshold 0.46 is 0.7061855670103093\n",
            "F1 score at threshold 0.47 is 0.7098445595854921\n",
            "F1 score at threshold 0.48 is 0.7031250000000001\n",
            "F1 score at threshold 0.49 is 0.6981627296587927\n",
            "F1 score at threshold 0.5 is 0.70026525198939\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tk_evjeHgqU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "f4f6d286-af9d-44d3-9ca2-171197badfa9"
      },
      "source": [
        "#From above, we can see that F1 score is highest at a threshold of 0.4\n",
        "pred_val_labels1=(pred1>0.4).astype(int)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(val_labels,pred_val_labels1))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(val_labels, pred_val_labels1))\n",
        "CM = confusion_matrix(val_labels, pred_val_labels1)\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CM ,  figsize=(5, 5))\n",
        "plt.show()\n",
        "  "
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98      2984\n",
            "           1       0.78      0.67      0.72       213\n",
            "\n",
            "    accuracy                           0.97      3197\n",
            "   macro avg       0.88      0.83      0.85      3197\n",
            "weighted avg       0.96      0.97      0.96      3197\n",
            "\n",
            "[[2943   41]\n",
            " [  70  143]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT0AAAFACAYAAADOJ6uCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpOG9T3W21zJ",
        "colab_type": "text"
      },
      "source": [
        "## **Building the model using fastText (Common Crawl) pretrained embeddings**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VB0lXTu4tm1g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2_lstm = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim2, input_length=max_length, weights = [embeddings_matrix2], trainable = False),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.CuDNNLSTM(128,return_sequences=True)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.CuDNNLSTM(128)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wzdOJKjwwwW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "d4de644a-adf9-41e8-e87e-752df49ce6db"
      },
      "source": [
        "reduce =tf. keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, mode='auto')\n",
        "#early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1e-4, patience=10, mode='auto')\n",
        "model2_lstm.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "num_epochs = 10\n",
        "history2=model2_lstm.fit(train_padded, \n",
        "                    train_labels, \n",
        "                    epochs=num_epochs, \n",
        "                    batch_size=256, \n",
        "                    validation_data=(val_padded,val_labels),\n",
        "                    callbacks=[reduce],\n",
        "                    verbose=1)"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 28765 samples, validate on 3197 samples\n",
            "Epoch 1/10\n",
            "28765/28765 [==============================] - 12s 423us/sample - loss: 0.2009 - acc: 0.9331 - val_loss: 0.1310 - val_acc: 0.9478\n",
            "Epoch 2/10\n",
            "28765/28765 [==============================] - 8s 284us/sample - loss: 0.1421 - acc: 0.9508 - val_loss: 0.1266 - val_acc: 0.9540\n",
            "Epoch 3/10\n",
            "28765/28765 [==============================] - 8s 286us/sample - loss: 0.1339 - acc: 0.9524 - val_loss: 0.1203 - val_acc: 0.9578\n",
            "Epoch 4/10\n",
            "28765/28765 [==============================] - 8s 284us/sample - loss: 0.1238 - acc: 0.9560 - val_loss: 0.1115 - val_acc: 0.9603\n",
            "Epoch 5/10\n",
            "28765/28765 [==============================] - 8s 287us/sample - loss: 0.1165 - acc: 0.9586 - val_loss: 0.1077 - val_acc: 0.9603\n",
            "Epoch 6/10\n",
            "28765/28765 [==============================] - 8s 284us/sample - loss: 0.1096 - acc: 0.9602 - val_loss: 0.1066 - val_acc: 0.9606\n",
            "Epoch 7/10\n",
            "28765/28765 [==============================] - 8s 285us/sample - loss: 0.1019 - acc: 0.9634 - val_loss: 0.1048 - val_acc: 0.9622\n",
            "Epoch 8/10\n",
            "28765/28765 [==============================] - 8s 285us/sample - loss: 0.0965 - acc: 0.9653 - val_loss: 0.1030 - val_acc: 0.9631\n",
            "Epoch 9/10\n",
            "28765/28765 [==============================] - 8s 285us/sample - loss: 0.0921 - acc: 0.9665 - val_loss: 0.0988 - val_acc: 0.9653\n",
            "Epoch 10/10\n",
            "28765/28765 [==============================] - 8s 287us/sample - loss: 0.0861 - acc: 0.9698 - val_loss: 0.1027 - val_acc: 0.9662\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7c9qjxewx0W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 788
        },
        "outputId": "1bdcf916-e9ae-4383-e1d8-3a8e3978601b"
      },
      "source": [
        "#Determining the optimal threshold value to seggregate the classes\n",
        "pred2= model2_lstm.predict(val_padded, batch_size=256,verbose=1)\n",
        "from sklearn import metrics\n",
        "for thresh in np.arange(0.1, 0.501, 0.01):\n",
        "    thresh = np.round(thresh, 2)\n",
        "    print(\"F1 score at threshold {0} is {1}\".format(thresh, metrics.f1_score(val_labels, (pred2>thresh).astype(int))))"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3197/3197 [==============================] - 1s 354us/sample\n",
            "F1 score at threshold 0.1 is 0.6679536679536681\n",
            "F1 score at threshold 0.11 is 0.6758349705304518\n",
            "F1 score at threshold 0.12 is 0.68\n",
            "F1 score at threshold 0.13 is 0.686868686868687\n",
            "F1 score at threshold 0.14 is 0.6995884773662552\n",
            "F1 score at threshold 0.15 is 0.7112970711297072\n",
            "F1 score at threshold 0.16 is 0.7091295116772823\n",
            "F1 score at threshold 0.17 is 0.7109207708779444\n",
            "F1 score at threshold 0.18 is 0.712742980561555\n",
            "F1 score at threshold 0.19 is 0.7076923076923077\n",
            "F1 score at threshold 0.2 is 0.7035398230088497\n",
            "F1 score at threshold 0.21 is 0.7085201793721975\n",
            "F1 score at threshold 0.22 is 0.7165532879818594\n",
            "F1 score at threshold 0.23 is 0.7123287671232877\n",
            "F1 score at threshold 0.24 is 0.7139588100686498\n",
            "F1 score at threshold 0.25 is 0.7146171693735499\n",
            "F1 score at threshold 0.26 is 0.7132867132867133\n",
            "F1 score at threshold 0.27 is 0.7058823529411764\n",
            "F1 score at threshold 0.28 is 0.7092198581560283\n",
            "F1 score at threshold 0.29 is 0.7064439140811456\n",
            "F1 score at threshold 0.3 is 0.7132530120481928\n",
            "F1 score at threshold 0.31 is 0.7087378640776699\n",
            "F1 score at threshold 0.32 is 0.7076167076167077\n",
            "F1 score at threshold 0.33 is 0.713216957605985\n",
            "F1 score at threshold 0.34 is 0.7167919799498748\n",
            "F1 score at threshold 0.35 is 0.7167919799498748\n",
            "F1 score at threshold 0.36 is 0.7153652392947103\n",
            "F1 score at threshold 0.37 is 0.72264631043257\n",
            "F1 score at threshold 0.38 is 0.7244897959183674\n",
            "F1 score at threshold 0.39 is 0.721227621483376\n",
            "F1 score at threshold 0.4 is 0.7216494845360826\n",
            "F1 score at threshold 0.41 is 0.7253886010362695\n",
            "F1 score at threshold 0.42 is 0.7253886010362695\n",
            "F1 score at threshold 0.43 is 0.7220779220779221\n",
            "F1 score at threshold 0.44 is 0.7206266318537858\n",
            "F1 score at threshold 0.45 is 0.7206266318537858\n",
            "F1 score at threshold 0.46 is 0.7206266318537858\n",
            "F1 score at threshold 0.47 is 0.7206266318537858\n",
            "F1 score at threshold 0.48 is 0.7139107611548556\n",
            "F1 score at threshold 0.49 is 0.7139107611548556\n",
            "F1 score at threshold 0.5 is 0.7142857142857142\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3agNf7SzwyJ6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "0d2fc6b4-adc0-46b6-dd0e-5caa1e9290ca"
      },
      "source": [
        "pred_val_labels2=(pred2>0.42).astype(int)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(val_labels,pred_val_labels2))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(val_labels, pred_val_labels2))\n",
        "CM = confusion_matrix(val_labels, pred_val_labels2)\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CM ,  figsize=(5, 5))\n",
        "plt.show()\n",
        "  "
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98      2984\n",
            "           1       0.81      0.66      0.73       213\n",
            "\n",
            "    accuracy                           0.97      3197\n",
            "   macro avg       0.89      0.82      0.85      3197\n",
            "weighted avg       0.96      0.97      0.97      3197\n",
            "\n",
            "[[2951   33]\n",
            " [  73  140]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT0AAAFACAYAAADOJ6uCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\novw/FKtYure/FngAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oiGhVEWUQAFi",
        "colab_type": "text"
      },
      "source": [
        "## **Building the model using GloVe(Wiki) pretrained embeddings**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D15Bpc6ALtbI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model3_lstm = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=max_length, weights = [embeddings_matrix3], trainable = False),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.CuDNNLSTM(128,return_sequences=True)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.CuDNNLSTM(128)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SyPH8ZqdLyJZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "1f3e6602-1ea0-4dd1-9251-f6db3b8bfe0a"
      },
      "source": [
        "reduce =tf. keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, mode='auto')\n",
        "#early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1e-4, patience=10, mode='auto')\n",
        "model3_lstm.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "num_epochs = 10\n",
        "history3=model3_lstm.fit(train_padded, \n",
        "                    train_labels, \n",
        "                    epochs=num_epochs, \n",
        "                    batch_size=256, \n",
        "                    validation_data=(val_padded,val_labels),\n",
        "                    callbacks=[reduce],\n",
        "                    verbose=1)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 28765 samples, validate on 3197 samples\n",
            "Epoch 1/10\n",
            "28765/28765 [==============================] - 12s 432us/sample - loss: 0.2159 - acc: 0.9323 - val_loss: 0.1414 - val_acc: 0.9462\n",
            "Epoch 2/10\n",
            "28765/28765 [==============================] - 8s 288us/sample - loss: 0.1523 - acc: 0.9463 - val_loss: 0.1294 - val_acc: 0.9537\n",
            "Epoch 3/10\n",
            "28765/28765 [==============================] - 8s 289us/sample - loss: 0.1406 - acc: 0.9515 - val_loss: 0.1241 - val_acc: 0.9546\n",
            "Epoch 4/10\n",
            "28765/28765 [==============================] - 8s 290us/sample - loss: 0.1275 - acc: 0.9548 - val_loss: 0.1266 - val_acc: 0.9565\n",
            "Epoch 5/10\n",
            "28765/28765 [==============================] - 8s 289us/sample - loss: 0.1202 - acc: 0.9587 - val_loss: 0.1140 - val_acc: 0.9593\n",
            "Epoch 6/10\n",
            "28765/28765 [==============================] - 8s 289us/sample - loss: 0.1099 - acc: 0.9610 - val_loss: 0.1217 - val_acc: 0.9615\n",
            "Epoch 7/10\n",
            "28765/28765 [==============================] - 8s 290us/sample - loss: 0.1050 - acc: 0.9639 - val_loss: 0.1114 - val_acc: 0.9618\n",
            "Epoch 8/10\n",
            "28765/28765 [==============================] - 8s 290us/sample - loss: 0.0933 - acc: 0.9661 - val_loss: 0.1117 - val_acc: 0.9637\n",
            "Epoch 9/10\n",
            "28765/28765 [==============================] - 8s 290us/sample - loss: 0.0865 - acc: 0.9701 - val_loss: 0.1111 - val_acc: 0.9650\n",
            "Epoch 10/10\n",
            "28765/28765 [==============================] - 8s 290us/sample - loss: 0.0774 - acc: 0.9736 - val_loss: 0.1054 - val_acc: 0.9656\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmGgQ-TJL5c4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 788
        },
        "outputId": "3cf76dfe-aa69-4afd-9b21-58af9293430f"
      },
      "source": [
        "#Determining the optimal threshold value to seggregate the classes\n",
        "pred3= model3_lstm.predict(val_padded, batch_size=256,verbose=1)\n",
        "from sklearn import metrics\n",
        "for thresh in np.arange(0.1, 0.501, 0.01):\n",
        "    thresh = np.round(thresh, 2)\n",
        "    print(\"F1 score at threshold {0} is {1}\".format(thresh, metrics.f1_score(val_labels, (pred3>thresh).astype(int))))"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3197/3197 [==============================] - 1s 320us/sample\n",
            "F1 score at threshold 0.1 is 0.5826513911620295\n",
            "F1 score at threshold 0.11 is 0.5906040268456376\n",
            "F1 score at threshold 0.12 is 0.5996592844974447\n",
            "F1 score at threshold 0.13 is 0.6151142355008787\n",
            "F1 score at threshold 0.14 is 0.6227758007117438\n",
            "F1 score at threshold 0.15 is 0.6327272727272726\n",
            "F1 score at threshold 0.16 is 0.6408839779005525\n",
            "F1 score at threshold 0.17 is 0.6641221374045801\n",
            "F1 score at threshold 0.18 is 0.6796875\n",
            "F1 score at threshold 0.19 is 0.6850393700787402\n",
            "F1 score at threshold 0.2 is 0.68937875751503\n",
            "F1 score at threshold 0.21 is 0.6829268292682926\n",
            "F1 score at threshold 0.22 is 0.6871165644171779\n",
            "F1 score at threshold 0.23 is 0.6861924686192469\n",
            "F1 score at threshold 0.24 is 0.690677966101695\n",
            "F1 score at threshold 0.25 is 0.6909871244635193\n",
            "F1 score at threshold 0.26 is 0.69281045751634\n",
            "F1 score at threshold 0.27 is 0.6916299559471365\n",
            "F1 score at threshold 0.28 is 0.6948775055679288\n",
            "F1 score at threshold 0.29 is 0.6919642857142857\n",
            "F1 score at threshold 0.3 is 0.6876404494382022\n",
            "F1 score at threshold 0.31 is 0.6938775510204082\n",
            "F1 score at threshold 0.32 is 0.6986301369863014\n",
            "F1 score at threshold 0.33 is 0.7020785219399538\n",
            "F1 score at threshold 0.34 is 0.7037037037037036\n",
            "F1 score at threshold 0.35 is 0.7069767441860465\n",
            "F1 score at threshold 0.36 is 0.7136150234741784\n",
            "F1 score at threshold 0.37 is 0.7105882352941176\n",
            "F1 score at threshold 0.38 is 0.719047619047619\n",
            "F1 score at threshold 0.39 is 0.7194244604316546\n",
            "F1 score at threshold 0.4 is 0.7228915662650602\n",
            "F1 score at threshold 0.41 is 0.7233009708737864\n",
            "F1 score at threshold 0.42 is 0.7286063569682152\n",
            "F1 score at threshold 0.43 is 0.7303921568627451\n",
            "F1 score at threshold 0.44 is 0.7321867321867321\n",
            "F1 score at threshold 0.45 is 0.7339901477832512\n",
            "F1 score at threshold 0.46 is 0.7295285359801489\n",
            "F1 score at threshold 0.47 is 0.7281795511221945\n",
            "F1 score at threshold 0.48 is 0.7254408060453401\n",
            "F1 score at threshold 0.49 is 0.7254408060453401\n",
            "F1 score at threshold 0.5 is 0.7208121827411168\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rW_kpFXL5vJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "6c0b988f-61e5-4902-aa26-6675df98a95c"
      },
      "source": [
        "pred_val_labels3=(pred3>0.45).astype(int)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(val_labels,pred_val_labels3))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(val_labels, pred_val_labels3))\n",
        "CM = confusion_matrix(val_labels, pred_val_labels3)\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CM ,  figsize=(5, 5))\n",
        "plt.show()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98      2984\n",
            "           1       0.77      0.70      0.73       213\n",
            "\n",
            "    accuracy                           0.97      3197\n",
            "   macro avg       0.88      0.84      0.86      3197\n",
            "weighted avg       0.96      0.97      0.97      3197\n",
            "\n",
            "[[2940   44]\n",
            " [  64  149]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT0AAAFACAYAAADOJ6uCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwN7epZR3XoE",
        "colab_type": "text"
      },
      "source": [
        "##**Ensemble Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bp2DVrRg0Zm-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "outputId": "868a32f3-2844-4157-caef-6180c568a8ae"
      },
      "source": [
        "pred1_l = model1_lstm.predict(val_padded, batch_size=256,verbose=1)\n",
        "pred2_l = model2_lstm.predict(val_padded, batch_size=256,verbose=1)\n",
        "pred3_1 = model3_lstm.predict(val_padded, batch_size=256,verbose=1)\n",
        "pred_val = 0.5*pred1_l + 0.25*pred2_l + 0.25*pred3_1\n",
        "\n",
        "for thresh in np.arange(0.1, 0.501, 0.01):\n",
        "    thresh = np.round(thresh, 2)\n",
        "    print(\"F1 score at threshold {0} is {1}\".format(thresh, metrics.f1_score(val_labels, (pred_val>thresh).astype(int))))"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3197/3197 [==============================] - 0s 121us/sample\n",
            "3197/3197 [==============================] - 0s 106us/sample\n",
            "3197/3197 [==============================] - 0s 90us/sample\n",
            "F1 score at threshold 0.1 is 0.6506849315068494\n",
            "F1 score at threshold 0.11 is 0.658493870402802\n",
            "F1 score at threshold 0.12 is 0.6583184257602862\n",
            "F1 score at threshold 0.13 is 0.6715328467153285\n",
            "F1 score at threshold 0.14 is 0.6728971962616822\n",
            "F1 score at threshold 0.15 is 0.6793893129770993\n",
            "F1 score at threshold 0.16 is 0.6820809248554914\n",
            "F1 score at threshold 0.17 is 0.683794466403162\n",
            "F1 score at threshold 0.18 is 0.6975806451612904\n",
            "F1 score at threshold 0.19 is 0.7063655030800821\n",
            "F1 score at threshold 0.2 is 0.7136929460580912\n",
            "F1 score at threshold 0.21 is 0.7200000000000001\n",
            "F1 score at threshold 0.22 is 0.7215189873417721\n",
            "F1 score at threshold 0.23 is 0.7237687366167024\n",
            "F1 score at threshold 0.24 is 0.7331887201735359\n",
            "F1 score at threshold 0.25 is 0.7352297592997812\n",
            "F1 score at threshold 0.26 is 0.7340659340659341\n",
            "F1 score at threshold 0.27 is 0.734966592427617\n",
            "F1 score at threshold 0.28 is 0.7449209932279909\n",
            "F1 score at threshold 0.29 is 0.749425287356322\n",
            "F1 score at threshold 0.3 is 0.7470997679814386\n",
            "F1 score at threshold 0.31 is 0.7505827505827507\n",
            "F1 score at threshold 0.32 is 0.7576470588235295\n",
            "F1 score at threshold 0.33 is 0.7600950118764845\n",
            "F1 score at threshold 0.34 is 0.7511961722488039\n",
            "F1 score at threshold 0.35 is 0.7536231884057972\n",
            "F1 score at threshold 0.36 is 0.7450980392156863\n",
            "F1 score at threshold 0.37 is 0.736318407960199\n",
            "F1 score at threshold 0.38 is 0.7368421052631579\n",
            "F1 score at threshold 0.39 is 0.7341772151898733\n",
            "F1 score at threshold 0.4 is 0.7328244274809161\n",
            "F1 score at threshold 0.41 is 0.7333333333333335\n",
            "F1 score at threshold 0.42 is 0.7268041237113402\n",
            "F1 score at threshold 0.43 is 0.7168831168831168\n",
            "F1 score at threshold 0.44 is 0.7154046997389034\n",
            "F1 score at threshold 0.45 is 0.7154046997389034\n",
            "F1 score at threshold 0.46 is 0.7154046997389034\n",
            "F1 score at threshold 0.47 is 0.7142857142857142\n",
            "F1 score at threshold 0.48 is 0.7142857142857142\n",
            "F1 score at threshold 0.49 is 0.7161803713527851\n",
            "F1 score at threshold 0.5 is 0.72\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJw2aBic1Pl6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "9065669a-c91f-42f0-ae59-eee22b5f3ffd"
      },
      "source": [
        "pred_val_labels=(pred_val>0.33).astype(int)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(val_labels,pred_val_labels))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(val_labels, pred_val_labels))\n",
        "CM = confusion_matrix(val_labels, pred_val_labels)\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CM ,  figsize=(5, 5))\n",
        "plt.show()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98      2984\n",
            "           1       0.77      0.75      0.76       213\n",
            "\n",
            "    accuracy                           0.97      3197\n",
            "   macro avg       0.88      0.87      0.87      3197\n",
            "weighted avg       0.97      0.97      0.97      3197\n",
            "\n",
            "[[2936   48]\n",
            " [  53  160]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT0AAAFACAYAAADOJ6uCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6fM6Mlbl4gUV",
        "colab_type": "text"
      },
      "source": [
        "##**Ensemble Model gives the best F1 Score**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_h5yFzjd1PdC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "99c436fd-e14c-4b50-e113-ee446600ce91"
      },
      "source": [
        "pred_test1 = model1_lstm.predict(test_padded, batch_size=256,verbose=1)\n",
        "pred_test2 = model2_lstm.predict(test_padded, batch_size=256,verbose=1)\n",
        "pred_test3 = model3_lstm.predict(test_padded, batch_size=256,verbose=1)\n",
        "pred_test = 0.5*pred_test1 + 0.25*pred_test2 + 0.25*pred_test3 \n",
        "pred_test_labels = (pred_test>0.33).astype(int)\n",
        "out_df = pd.DataFrame({'id':df2['id'].values})\n",
        "out_df['label'] = pred_test_labels\n",
        "out_df.to_csv('submission.csv', index=False)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "17197/17197 [==============================] - 2s 93us/sample\n",
            "17197/17197 [==============================] - 2s 89us/sample\n",
            "17197/17197 [==============================] - 2s 89us/sample\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}