Authorship_Attribution_CNN.ipynb

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        "<a href=\"https://colab.research.google.com/github/saadhashmi91/Meeshkan-TwitterAuthorhipAttributionChallenge/blob/master/Authorship_Attribution_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "import pandas as pd\n",
        "\n",
        "#Upload training_set nnd test_set from url\n",
        "train_set_url='https://raw.githubusercontent.com/SaadHashmiVeon/TwitterAuthorAttribution/master/train_set.csv'\n",
        "test_set_url = 'https://raw.githubusercontent.com/SaadHashmiVeon/TwitterAuthorAttribution/master/test_set.csv'\n",
        "\n",
        "df = pd.read_csv(train_set_url,lineterminator='\\n')\n",
        "test_df=pd.read_csv(test_set_url,lineterminator='\\n')\n",
        "df.head(5)\n",
        "test_df.head(5)"
      ],
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              "      <th></th>\n",
              "      <th>author</th>\n",
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              "      <td>west coast we on! #JokersWild https://t.co/8KG...</td>\n",
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              "   author  day  ...  has_media                                              tweet\n",
              "0     NaN    4  ...      False  Just got this photo... seems like everything i...\n",
              "1     NaN    8  ...      False  In Arizona, a group of @OFA fellows have becom...\n",
              "2     NaN   16  ...      False  \"We shouldn't have to choose between rewarding...\n",
              "3     NaN   31  ...      False  west coast we on! #JokersWild https://t.co/8KG...\n",
              "4     NaN    6  ...      False  ICYMI: Read about the historic #ParisAgreement...\n",
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        "id": "DmVrugLYM20d",
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      "source": [
        "# Twitter Authorship Attribution\n",
        "\n",
        "> ## Introduction\n",
        "The goal of the assigned coding challenge is to implement a Machine Learning or Deep Learning algorithm for predicting correct Twitter Author names for a  given  test-set containing tweets without author name information and an accompanying training-set having a larger number of example tweets with author name information.\n",
        "\n",
        "> To this end, I have chosen to implement a Deep Learning Alogrithm to complete the task. The following code is an implementation of a Deep Learning Algorithm for small text classification.\n",
        "\n",
        "> ## Algorithm Description\n",
        "The algorithm defines a basic Deep Learning architecture for NLP: the 1D CNN. The original papers for this model are [[1](https://arxiv.org/abs/1408.5882),[2](https://arxiv.org/abs/1412.1058)]. Though the orginal algorithms are targeted at the task of binary-class text classification, the algorithms can be adapted to the task of Authorship Attribution which is a general case known as multi-class text classification.\n",
        "\n",
        ">## Multi-Class Classification\n",
        "One-of-many classification. Each sample can belong to ONE of C\n",
        "classes. The CNN will have C output neurons that can be gathered in a vector s (Scores). The target (ground truth) vector t will be a one-hot vector with a positive class and C−1 negative classes. This task is treated as a single classification problem of samples in one of C classes.\n",
        "\n",
        ">>### Loss\n",
        "The loss that the model will learn to minimize is the log loss, also known as the cross entropy. In a multi-class classification setting with 1-C classes, the log loss is a Softmax activation plus a Cross-Entropy loss. If we use this loss, we will train a CNN to output a probability over the C classes for each input.\n",
        "![alt text](https://drive.google.com/uc?export=view&id=1pXJQaq3WkMGauHvXtlDrTM5AWczrderI)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        ">## Algorithm Implementation\n",
        "The algorithm is implemented using Python 3.6, Keras 2.2.5 with TensorFlow as backend.\n",
        "\n",
        ">## Run Instructions:\n",
        "In order to run the complete training,validation and prediction pipeline, open **Runtime Tab** in this notebook and select **Run All** or just press **Ctrl + F9**. At the end of the pipeline the test results will can be downloaded as ***test_results.csv*** by the prompt dialog. ***Note***: Kindly re-run the ***last cell*** in case there is a network error to download the ***test_results.csv***.\n",
        "\n",
        "\n"
      ]
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          "base_uri": "https://localhost:8080/",
          "height": 748
        }
      },
      "source": [
        "#Concatenate Year,Month,Day into Date:\n",
        "df['date_time'] = pd.to_datetime(df[['year', 'month','day','hour']])\n",
        "df['date'] = df['date_time'].dt.date\n",
        "test_df['date_time'] = pd.to_datetime(test_df[['year', 'month','day','hour']])\n",
        "test_df['date'] = test_df['date_time'].dt.date\n",
        "df.head(10)\n",
        "test_df.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
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              "      <td>2014-12-08 20:00:00</td>\n",
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              "      <td>106</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>\"We shouldn't have to choose between rewarding...</td>\n",
              "      <td>2015-04-16 18:00:00</td>\n",
              "      <td>2015-04-16</td>\n",
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              "      <td>31</td>\n",
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              "      <td>True</td>\n",
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              "      <td>west coast we on! #JokersWild https://t.co/8KG...</td>\n",
              "      <td>2019-01-31 06:00:00</td>\n",
              "      <td>2019-01-31</td>\n",
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              "      <td>False</td>\n",
              "      <td>ICYMI: Read about the historic #ParisAgreement...</td>\n",
              "      <td>2016-10-06 18:00:00</td>\n",
              "      <td>2016-10-06</td>\n",
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              "      <td>My new BE KIND Box is available now. And one l...</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>A comprehensive overview and break-down of 14 ...</td>\n",
              "      <td>2019-01-23 17:00:00</td>\n",
              "      <td>2019-01-23</td>\n",
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              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>21</td>\n",
              "      <td>Mon</td>\n",
              "      <td>119</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>en</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>RT @PlanetKhloeK: Don’t miss part two of the B...</td>\n",
              "      <td>2019-04-29 01:00:00</td>\n",
              "      <td>2019-04-29</td>\n",
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              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>I love him so much I can’t stand it. @drewbree...</td>\n",
              "      <td>2018-09-23 20:00:00</td>\n",
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              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>You asked: Say hello to Ireland 😘 http://t.co/...</td>\n",
              "      <td>2014-08-29 15:00:00</td>\n",
              "      <td>2014-08-29</td>\n",
              "    </tr>\n",
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              "   author  day  ...           date_time        date\n",
              "0     NaN    4  ... 2011-11-04 13:00:00  2011-11-04\n",
              "1     NaN    8  ... 2014-12-08 20:00:00  2014-12-08\n",
              "2     NaN   16  ... 2015-04-16 18:00:00  2015-04-16\n",
              "3     NaN   31  ... 2019-01-31 06:00:00  2019-01-31\n",
              "4     NaN    6  ... 2016-10-06 18:00:00  2016-10-06\n",
              "5     NaN    5  ... 2019-06-05 22:00:00  2019-06-05\n",
              "6     NaN   23  ... 2019-01-23 17:00:00  2019-01-23\n",
              "7     NaN   29  ... 2019-04-29 01:00:00  2019-04-29\n",
              "8     NaN   23  ... 2018-09-23 20:00:00  2018-09-23\n",
              "9     NaN   29  ... 2014-08-29 15:00:00  2014-08-29\n",
              "\n",
              "[10 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKeYuJlnGxzS",
        "colab_type": "code",
        "outputId": "c97bdd50-aec9-44e1-ee41-06ac0d15c312",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        }
      },
      "source": [
        "#Create a windowed aggregation by Author and Date to find number of tweets per hour for each day.\n",
        "\n",
        "# Create variable with TRUE if nationality is USA\n",
        "kim = df['author'] == \"Kim Kardashian West\"\n",
        "kate= df['author'] == \"KATY PERRY\"\n",
        "elen= df['author'] == 'Ellen DeGeneres'\n",
        "# Create variable with TRUE if age is greater than 50\n",
        "source = df['source'] == 0\n",
        "year= df['year']==2019\n",
        "month= df['month']==5\n",
        "df_kim=df[ kim & source & year ]\n",
        "df_kate=df[kate & source & year]\n",
        "df_elen=df[elen & source & year]\n",
        "#df_elen.sort_values(['author','date']).head(10)\n",
        "\n",
        "df_kim.sort_values(['author','date_time']).groupby([df['date_time'].dt.hour,'day_of_week'])[['lang']].count().unstack().plot()\n",
        "\n",
        "df_kate.sort_values(['author','date_time']).groupby([df['date_time'].dt.hour,'day_of_week'])[['lang']].count().unstack().plot()\n",
        "\n",
        "\n",
        "df_elen.sort_values(['author','date_time']).groupby([df['date_time'].dt.hour,'day_of_week'])[['lang']].count().unstack().plot()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61dd631080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JNg/gRGVtlVvy94u+Uv40qUalMfUOrtCVDE6OrV07inKLUSV/68bPP/8sX3vt\ntRqdW28lf6WUl4ELQojOJZuGAkeBNcC0km3TgNU1ukB2Us0XMJVSJQgUpUloKiV/pZT1VpGytrNl\nZgPLSmbKnAHuR5fq+VkI8SBwDqj+I741xZCbapi0DKjgrihNQFMo+TthQvXDYU3VKrhLKaOBwHJ2\nDa1Nu+SlAbLmq1NLlQb30mmViqIotwjjXKFqiDnuADYugLi62lVRFOUWYZzBPaeWdWVKmZqBjbNK\nyyiKcssxzuBeWqa3tiP30jZUcFcU5RZjnME9RwV3RVGuaqiSvwB33XUXq1at0r/u3Lkzb775pv71\n3Xffza+//lrl9qZPn05ERAQAEydO5OTJk4brbBlGGtyTwNQCrBxr35YK7orS6DVUyV+AkJAQfbXJ\n1NRUbG1t2bVrl37/rl27CA4OrlHbjz76KO+9955B+nk94wzu2cm6aZBC1L4tFdwVpdFryJK/wcHB\n+nZ37tzJHXfcQXJyMlJKzp49i7W1NS1atECj0fDMM88QFBSEn58fX3/9NaCb2z5r1iw6d+7MsGHD\nSEq6OnsvNDSUjRs3GrzuDRjrwzpyDLCAqZStm678QHEBmFlWfryiKDd1Ze1pCi/lGLRNi5a2ON3R\n/qb7G7rkb69evThy5AiFhYXs3LmTgQMHcubMGY4dO8aBAwf0o/ZFixbh6OjIvn37KCgoICQkhOHD\nh3PgwAFOnDjB0aNHSUxMpFu3bvo5+yYmJnTo0IGDBw/Sq1evGvz1bs5IR+61eDD29UqLj6npkIrS\nKFVW8nfGjBn4+voyfvx4jh49qt9XWvLXxMREX/L3+PHjN5T8rYylpSXdu3dn//797N69mz59+tCv\nXz927tzJzp07CQkJAeDvv//mu+++w9/fnz59+pCamsrJkyfZunUr9957L6amprRs2ZIhQ4Zc035d\nlQA20pF7CrQw0DLgsqtUHVsZpk1FuUVVNMKuK8ZQ8jckJIStW7eSlZVFs2bN6Nu3L59//jkHDhzQ\nly+QUvLZZ58xYsSIa879448/Kmy7rkoAG9/IXUrDlPstpUoQKEqjZgwlf4ODg/n666/p0aMHAH5+\nfuzevZvz58/rZ+6MGDGCr776iqKiIkBX2jcnJ4cBAwawYsUKNBoNCQkJbNq06Zq266oEsPEF97x0\nXZne2taVKaWCu6I0eg1d8jc4OJgzZ87Qr18/QPegjubNmxMYGIiJiS6MPvTQQ3Tr1o2ePXvi4+PD\nww8/THFxMXfddRcdO3akW7duTJ06Vd8GQGJiov6GrMHdrFxkfX5dU/I36biuTO/Bn2tUFvMG+Vm6\n9rZ/bJj2FOUWoEr+1o8PPzK00jgAACAASURBVPxQLly4sErHVrfkr/Hl3EtH2IZKy1jYgpm1Kh6m\nKI1Y2ZK/tZ3r/s0337B06VIKCwsJCAi4puRvfXNycmLKlCl10rbxBXd90TADpWWEKHncnpotoyiN\nWVMo+Xu9+++/v87aNr6cu37kbqDgDmohk6LUgFTPHjYaNfm3ML7gnp0EwgSsnQ3XpgruilItVlZW\npKamqgBvBKSUpKamXjPNsyqMLy2TkwQ2rmBiwPcdW1dIOGi49hSlifP09CQ+Pp7kZDUoMgZWVlZ4\nenpW6xwjDO4phk3JgC5/n5Osm0NviHo1itLEmZub61dxKo2TcaZlDFHqtyxbN9AWQ/4Vw7arKIpi\npIwvuOcYsK5MKf2zVNVHTEVRbg3GFdylLCn3a+CRu51apaooyq3FuIJ7YQ4U59VNWgZUcFcU5ZZh\nXMHdUA/Gvp4K7oqi3GKMK7jrH4xt4OBu4wIIFdwVRbllGFdw14/cDZyWMTHVBXgV3BVFuUXUOrgL\nIUyFEAeEEOtKXnsLIfYIIU4JIVYIISyq3Fhp8DV0zr20TVU8TFGUW4QhRu5PAMfKvH4X+EhK2QFI\nBx6sckvZdRjcVfEwRVFuIbUK7kIIT2AUsLDktQCGABElhywFwqvcYE4SWDcDU/PadKt8qr6Moii3\nkNqO3D8GngW0Ja9dgCtSytKHFcYDVX9waXaS4W+mllLBXVGUW0iNg7sQYjSQJKWMquH5M4UQkUKI\nSH1xopxkw0+DLGXrBgWZUFT+g3YVRVGaktqM3EOAMUKIOGA5unTMJ4CTEKK0IJkncLG8k6WUC6SU\ngVLKQDe3MvPQbV1r0aUKlObxc1XeXVGUpq/GwV1K+YKU0lNK6QVMBP6VUt4HbALGlRw2DVhd5Uaz\nk+s2LQMqNaMoyi2hLua5PwfMFUKcQpeDX1Sls4ryoSDD8HPcS6niYYqi3EIMUs9dSrkZ2Fzy8xmg\nd7Ubyamj1amlVPEwRVFuIcazQrUuFzCVbVcFd0VRbgHGF9zraraMhS2Y26jgrijKLcF4gntpaYC6\nGrmXtq2Cu6IotwDjCe51Ve63LBXcFUW5RRhPcM9OBgt7MLeuu2vYuqnZMoqi3BKMJ7jX5QKmUnZq\n5K4oyq3BiIJ7HTwY+3q2broVqlpt5ccqiqI0YsYT3OviwdjXs3UDbTHkX6nb6yiKojQw4wnu9TVy\nB5WaURSlyTOS4C4hN61+Ru6ggruiKE2ecQR3bTEg6y+4q8ftKYrSxBlHcNeUPNujrtMype2rx+0p\nitLEGUdw15YE97oqGlbKuhkIE5WWURSlyTOS4F6k+17XI3cTU7BxUcFdUZQmzziCe2lapq4XMYHu\n04EK7oqiNHHGEdy1xWBqCZYOdX8tW1cV3BVFafKMJLgX6VIyQtT9tVTxMEVRbgHGEdw1xXU/DbKU\nKh6mKMotwDiCu7a47m+mlrJzg8IsKMqrn+spiqI0ACMJ7kX1czMVyqxSVXPdFaUqtp1MZsHW0xQW\nq4J7jYlBHpBda9riup/jXkof3JPAqXX9XFNRGqnsgmKeXB5Nak4hq6Mv8clEfzo0t2/obilVYBwj\ndynrLy1jq1apKkpVLdp2ltScQv4zvBMJGfmM+nQ73+2KQ0pp8GtpsgrJO5LClXVnODdvL8de3kFG\nYo7Br3OrMI6RO9TjDdWS9I+aMaMoFUrNLuCbbWcY0d2dWUM6MiGoNc9GHOK/q2P451gS74/zo7mD\nVY3allJSnJJHYVwmBXGZFJ7LpDil5D6YmQk5xVocgL0fHaDXEwE4e9hWue3EzHwcrc2xMjetVp+0\nGi3Z6QXYu1gh6mPmXh27BYO7Kh6mKFXxxabT5BYW88yIzgA0t7diyfQgfth9jjd/P8aIj7fyzlg/\nwnxaVNqW1GgpupRDQVyGLpjHZaLN0a1MN7Exw8LLEdveLbDwciA+KY9NC2MY2doWr8xCVn2wnzvm\n+OPWpvJ00PnUXEZ8vBUPRys+nuiPn6dTlX7XvKxC/lxwhEsnr+DgakXHIHc6BbXAuWXV31SMjfEE\n9/pKy1jYgIWdSssoSgXi03P5Yfc5xvXyvCbHLoRgSj8v+rV34ckV0TzyQxT3BLbmlTu6YWd5bTgp\nzi3g9PdbME8zwzzbHKHRbTd1tsKqczMsvRyx8HLAzM36mpHy4YhT2Dtb0eLuDqR9e5Q25oJVH+5n\n1OM9aNnx5sFaSsmra45gIiCvSMPYL3fy5LCOPDKwPWamN89Ap8Rn88eXh8jNKiTwdi8S4zLZ/+c5\notafw8XTjk5B7nQMcsfeuWafUhpKjYO7EKI18B3gDkhggZTyEyGEM7AC8ALigAlSyvRKG6yvG6qg\nVqkqSiU+2nASBDw5rFO5+zs0t+fXR0P4eGMsX205za4zqXx0jz+92jYDIDcujYsL9mKrtSWtIIGU\n/Isk58eTUhBPcXwR9pfcsI91w8HVDXuXku+ubmiKbYk/kUzw2C5Yd3bGoo09XdMLSLYwZc2n0YTN\n9MHLt/yZdX/FJLLpRDIvj+rK+F6teXn1Eeb/HcumE8l8NMGfNi42N5xz+kASG789hqWVKXc93RN3\nL90q+dzMQk5FJRK7N5Fdv51m12+n8ejgSKfeLWjf0w1rOwsD/aXrjqjpjREhhAfgIaXcL4SwB6KA\ncGA6kCalnCeEeB5oJqV8rqK2Aluaycj4QjCpp/u7C4eBhS1MXV0/11OURuTE5SzCPtnKjNB2vHh7\n10qP33s2jadWRJOQkcfswR2YojUld/NF8otzKAoyxXtkX7JSkslMTdZ9T9F9z0pJJis1mewr6bpJ\nFWVY2zvQzKMV7Vv1omVcK8wHtWDL/iukxmcz7IFudAx0v+b4nIJibvtwCw7W5qyb3V8/Ul8dfZGX\nfzuCVkpeG9Odcb08EUIgtZLI9XHsXXsWd28HRj7ii62jZbm/X0ZyHif3JRK79zLpl3MxMRG07u5M\npyB3vPxcsbBquASIECJKShlY7j5D3fUWQqwGPi/5GiSlTCh5A9gspexc0bmB7d1k5Ol6HEn/NAmu\nnINHd9TfNRXFAIo0RSDA3MS8zq7x0NJ97DmTxtZnB9PMtmoj1Mz8Ij5ceZi+R9LxERaczzlOs7Gd\n6Dp4YKXnaoqLyE5LJSU+gfVf7sS5pcTNE1Liz5N4OpZBbvdgY+bAjuLfKSxypSDPjX5jQwkc1UOf\nznln/TG+3nKGiEf6EejlfE378em5PP3zQfacTWOkTwtev70bUStPcfpAMl36tmDgfZ0xq8LNVykl\nqRezid2byMl9iWSnF2BmYYK3nysde7egTXdnTCtI/9SFioK7Qd5yhBBeQACwB3CXUiaU7LqMLm1T\nsWZtDdGNqrN1hYuR9XtNRaklKSUzN8zk9JXT3Nv1XiZ1mYSjpaNBrxEZl8bGY0k8M6JzlQO7lBLT\nmDQeOplHEbAj+XcWObRluo03XaSsdOaJqZk5js1bcDq6AGHWleEze+PqaQdAcWEhl7cfhb+z6Gjf\nk+hzGynK2c/W7/9id4Q9bf18sWjZnjV785jQ1++GwA7g2cyGH2f05ZttZ1iw/gT/23MF52JByLgO\n9BjausozY4QQuHra4+ppT7/w9iScziB2XyKno5I4GZmEZ5dmjJ7VA1Mz45hhXuvgLoSwA34BnpRS\nZpb9Q0kppRCi3I8GQoiZwEyANm3a1LYb1WPrpruhqtXoarwrSiOw7/I+IhMj6eDUgS+jv2TJkSWM\n6zSOqd2m0sK28hkrlZFS8u6fx3Gzt+T+EK8qnaPJKeLKqlPkHU4hTXOZvanrCXrkcZofKubF3w7z\n7/FE5o/vgZNNxW8UWq3kyJaLtOzopA/sAGYWFrQa3IPk2EO0T/Ml+KvppCTE88+3/3D59HHOHzlO\nwZ6djAPM1q1j5YnOtGpuQ7ve/XH3H6AP3KYmgjEtXTAptCNXU8xKmwKSc7PoUqyt9pRJAGEiaNnR\niZYdnQi9pyNHt11i6/JYtvx4gsFTuhjFVMpapWWEEObAOuAvKeWHJdtOUN20TGCgjIysx5H04Qj4\n5UHoPhbu/EI3g0ZRjNyMv2dw6sop/rz7T85lnmPJkSWsP7seIQSj243mfp/7aefYrsbt/3MskQeX\nRvJGuA9T+lb+aTr/ZDppK2PRZhdyLHsPsbkHuPuF12jRviNarWTJzjjeXX+cHq0d+eGhPlia3TyI\nnolOZv3/DhM204f2PW+cXJF/Kp2UhUdwurM9dv1aotVKtvx4gqPbL3HYNJ3efQRtrxzh4sHdJGcL\nQODkYEmXIaPpOvA2Ek4Lti6PxcHVmqEzuvO//ef5dmccHZvb8fFEf7q3rP0noD1rzhD5Rxx9w9vR\nK8yr1u1VRUVpmRp/fhC6t6ZFwLHSwF5iDTCt5OdpQJ3ctSzSFPHevvfYk7Cn+if73A3D/g9ifoMl\nYZARb/gOKooBxaTEsDthN1O7TcXS1JJOzTrxTug7/D72d8Z3Gs+fZ/8kfFU4T256kkPJh6rdvkYr\nee/PE3i52DAxqOKyHLJIy5W1p0lZdAStiYbNKSs4U3iIia+9Q4v2HQEwMRE82N+bDyb0YF9cOs//\ncrjCVa2HNsVj18wS7x7lz4SxbO+EhZcDmZsuIIu0mJgIAu5qxyE7Lb6aZniezWBQ6sdM7XqKx2eP\nYXiQEw7FiexeFcGSpx7hr69ewcYuhrCZXrTwtOe1Md1Z+kBvruQVEf7FDr7echqNtnb3H3vf4U3H\nIHd2rzrDqaiGX0dTm9ky/YFtwGGgtKLQi+jy7j8DbYBz6KZCplXUVnVH7lJKXt7xMmtOr8HazJrv\nRn5HF+cu1f8lYv+CiAfB3AruWQZt+lS/DUWpB09teoo9l/ewYdwGbM1vXFiTlp/Gj8d+5KfjP5FZ\nmElQiyAe9HmQ4JbBVUoR/Lo/nrk/H+SzewO4o0fLmx5XeCmbtBUnKE7MRdvJnDWbP8LKyZ7xL7+J\nY/PyU0Of/3uS+X/H8tSwTjwxrOMN+1MvZbP89b2VjnjzT18h5ZvDON3RDruQVrz6634sohYyvFjL\n3owJeHmkMOLpEZjZ6Ubh+Sf2sO5/+7iYnoeljCInPxuEoHVXH7r0H0jHPiHkCUte/PUwf8Zcpm87\nZz6Y4E8rJ+tK/143U1ykYfVH0SRfyCJ8bgAtvA17T+R69TJbpjaqG9y/OfQNnx74lPu63sfGcxuR\nSH4a9RPNbWowVz75BPw0Ea5cgNEfQc8p1W9DUerQmStnuHP1ncz0m8nsgNkVHptTlENEbATfxXxH\nUl4SXZy78KDPgwyTlpitmKabvNCmH7Tpq/vu4EFBsYahH2zB0dqctbP6Y2Jy45uB1Eqyt18k4684\nTGzMyPfTsmb5ezTzaMXdL72BXbOSG5l5V+DCXriwG87vhoSDSA8/Igr78dbZTvzfxFDu9G91Tdub\nfzzB8Z0JTJsXXOH8cSklyQsOUZyST/7Ay8i/n6O9SQJ0HM5hpxfZujadVp2duP1RP7JS8/njq0Pk\nXClgcP9UOp9/jvQrORy3vY1jiZakJ17GxNSMFr7d0HZ2YwMXiUyNRohCnu35NlN69q/+P1SJvKxC\nIt6NpKhAw7jnAnFwrfmbRWWaVHD/8+yfPLP1GUa1G8U7/d8hNj2Wqeun0tahLd+GfYuNeQ3y53np\nsPJ+OLMJ+jwKw98EU+NZvKvc2l7a/hIbzm3gr7v/oplVsyqdU6gp5Pczv7P4yGLiMuNoXaxhepE5\nd1m3wTw+EopydQc6teWUtS+Lzrtzd/g4Anv1vWG9SXFaPukRsRScycCqmwvJLS+zftHHuLfrwNjH\nZmKdehjO79IF86SjgAQTM/Dwhxa+cG4npJygGFO2af1oM2ga7UPGg6Ud+TlFLH1hBx0C3Rk6tfI5\n9fnRx0hZnoKj2dekm0XiOu4DbHxGAXBiz2X+WXoMZw8bMlPyMbcyZeQjvrrRc94Vije/Q2z0t+y3\nseeIrQ9ZcXm4nxfYFphRbCrJbmvDPpcU4pvlMbntPJ4bOrDGN0bTL+fwy3tR2DhacvezvbC0rpt4\n0mSCe3RSNA/+9SA+rj58M/wbLEx17/Jb47cy+9/ZDPAcwMeDPsa0JjNgNMWw4RXY/SW0GwTjloDN\njdOqFKU+Xcq+xKhfRzGxy0Se613hWsByabOT2PTdMBZaaDhiboKvqy/vBL9B29wMOL+b4ridXDmx\nDVeu6E6wbgat+0KbvhTZ9iHrhCO5h1IRZqY4jfYi9uw6/v11DW2aW3Bnm1gsci7ozrOwh9a9r34q\naNXr6kQFKSHxCPn7V5Cx7yfcZQpaM2tMuowiuvAedmw1Y8KLQRXXjinIhu0fws7PSMx7nQxte06O\n7cCo3tfeQD57MJm/vonBpZUtgx7qyJniE+xP2s+BxAMcTD5IbrHuTc2juJgAaUlAp7tpY9GLrIOn\nOLlnBwU5OezrlsvhVgUMsHuNj+4eUqPZNADxx9NY++lBWnV2YtSsHnUyB75JBPcLWReY/MdkbM1t\nWXb7shtGMD8e+5F39r7D1G5TeSbomZp35sAPsO4pcPSEe5eDW4UTfRSlTr29521Wxq5k/dj11Z/u\nWJQP342BhIPk3L6UdUuWsdX2IimOxYz2H8eoHnez9NAVPtl8lj+meNKtMAbO76Lg5GWy0vqSr+2D\nIB9bp4PYNT9E5MGj7LjcgvZ2KYzukoGZV9+rwdy9e5WmFcclZ/H6l4sJN9vJ7SZ7+Sn+LWzNMhk7\n7Dj4jte1V/aTg5S62W0bXoGsBPK6jWfukZG8VNgcx9He2Pf3LHOoJPvIITb/+CXRNhf4tfVFioUW\ngaBjs44ENA+gZ/OeBLj54xG/H/56Aa6ch65jYPibFNu1ZN3H8zgduYfdPbI56qbFu+hZFk8eVuPq\nl8d2XuLf747TPbQlAyd1NvgUyUYf3DMLM5nyxxRS8lL44fYf8Hb0Lve4d/a8w4/Hf+SVvq8wofOE\nmnfo/B5YMVn3KL67F0LnsJq3pSg1lJKXQtgvYdzufTuvh7xevZOlhF8egiMR5A//gp8++Zk0qSn3\nIfTFFra0atUST4dOtCpqh3WeDdJcYto6Dftm+7BL383WExAZb01XH29GPPoUpi7eNX6g/d6zaUxe\nuIfhjtZ0PaNluO92Ol75SpcqcmgFPmN1gR7gj2d1+XsPf7j9fZ7aacHvhxLY2LIFZlcK8Hg2iKKL\nF8hY9zvpa1ejOXdBf51cdwe0946h030zcbQvp+psUR7s/Fz3iUBqIeRJioMe5beP3ud8zCG29bzC\nqWbm2KTO5pv7BtOjddUqTF5v12+n2f/XOYLv7kDAbYZd09Oog3uRtohHNz5KVGIUC25bQFCLoJu2\no9FqmLNpDjsu7uCLoV8Q0iqk5p3KiIflkyDhEAx7FUKerPF/zIpSE5/s/4RFhxexJnwNXo5e1Tt5\n09uw5V0Kg19g+ZI9pGgKGdjVH6vVv6Px9iZychBrj/+OXY41463voEteR2y09uQWZ3I8Yy9nsg6h\nkSUleU3N0GqK8R8xiiHTH0YYoAbUbwfi2bnoGJ6m5jz+QShm2nw4sR4Or4RTG3VPZwOwcdX9/+c/\nmV1n07n3m93MHtKBR1wsSV95AU3yVnJ3/ABCEOtlwdZuktsmv0RwshOp3ywk//BhTF1dcZ42lWYT\nJ2JqX07qJyMeNvwXjvwC9h4UdR1HxNZ0Es5f4N9eKVxwdCD//AzeG9v3hpvBVSG1kr8WxnD6QBIj\nH/alnb/hyps32uAupeT/dv0fv5z8hTdD3uTODndW2lZOUQ7T1k8jPjue70d+T8dmN069qrLCXFgz\nS/eP7jsexnwG5nV351tRSmUVZjE8YjjBLYP5YNAHV7fnFyEBB6sKasscXA6/PUyxz738vCqFhMI8\nBoYMIfCJp8neupULj8/CpKsf33a4g/GmNjQvciLdPofWw7vjENCSgvzcMgW+kshMScbJ3QO/YWEG\nSyukJeTw0//tYZtVEaF3tufxwR2u7sxNg6OrdN+DHgJrJwqLtdz9/p90P72fh4tOkb93L9bBT2La\nrA0XPbbxqs1fFLs68ungT+nu2h3QxY/cPXtI/WYhOTt2YGJnR7N778V56hTM3MoJsHE7dKP405vI\nL4aVl3qTnGfO30FJpDm1IOnkNB4J7cYzIzpjWs6MoooUF2pY9dEBUuOzues/PWneVld9slijJe3s\nBZp3qFkJlkYb3JccWcKHUR8yw3cGc3rOqXJ7l3MuM+n3SZiZmPHjqB9xta7Fw7el1P2D//MGtPTX\nzYd3rP67t6JUx8LDC/lk/yf8PPpnurroZpGcSspmyqI9XMktYmLv1swIbUfL6+dkx+2A7+5E06oP\nv26153x+NsH+ven3wn8B0OYWkfr9VvKO52Ni6YD0sGKb10HmpX9KW8e2zAudpw+OdWnLTyc4uuMS\np3o78MvRy3w+KYDRfjfOr9fm55O9eTPRS1bgdHgf5loNFm3b4jB6NFY9h5CxNpUFzSM43SWFjwd9\njJtN+aPivJgYUhcuJOvPvxDm5jiOvQuXBx7AorzSJzkpEPMbuVERrNhZxJViS9b1ScLO0ZELsdPw\n79yNjyf6V/wGW47czEIi5kWi0WjxmdqJXdv30WbZV3ROOk1c76EM+2Ie5vZ2lTdURqMM7hvPbWTu\n5rmM8BrBuwPexURU76NgTGoM9/95P+0d27M4bDHWZlf/J0jMzGfHqRTC/VuVO6e3XCfWIyMe4kxR\nfxwHT8Y1ZDiYlV8i1KAKsuDEn7obvG371f31lAaXV5xH2C9hdHXpyv+G/Q+AIxczmLp4LyZCENLB\nhd8P6Wrz3enfikcGtqOjuz2knoaFQ9FaubD6UBfOZGcQ2MmHgW/MQxZpyNhwnpzdCchCDbF5KXhG\nLsa6Rxs8P/mYPSlRvLT9JdLy0njU/1Ee9HmwZrPOShRoCohJieFY2jGCWgTRqdnVuvAFecV8+/wO\nOgS40X9yZyYv3MPB+AyWz+xLzza6iRIFp06R+s1CsjZsQJubS5qVA2f9grn7mQex8ulOXnEeL21/\niSHbutG52Ju2L4RgaV35p+rCuDhSFy8h47ffkBoNDmFhuMx4CKuu5U/DzD57kOVvv0FWbi6r+iTQ\n3TybcQnu7LUZwsSpj+HVsuo3uU8nZ7N26znYeBnTgiuE7nmTXBtLTrXrQLeYgxTaNaPjxx/i1rfc\nWF2uRhfcj6Qc4f4/76eTcycWDV+ElVnN7lT/e/5fntz0JMPaDmP+wPmYCBOSsvKZ8L9dxKXmEu7f\nkvfH98C8ClOUcjML2bR4H3HHCzATedze/DNa9+oIPuPAq79hC5AVF+jyjodX6gJ7cR6YWsCkFdB+\niOGuoxil0plf34Z9Sy/3Xuw9m8aD3+7DwdqcHx7qg7erLfHpuSzcdpYV+y6QV6QhvLMV89KfxqIw\nkz/O9ufElTT8PNtx2wefos0vJmXpUQrjMrDu4cYHVzJYd/kK69smkvnuOziMGkXL994lszibN3a/\nwV9xfxHQPIC3+7+Np71n5R0GruRfITo5Wj/tMCY1hiJtkX7/AM8BPOjzID3de3LwnwtsX3mS8S8E\n0rytA2k5hdz15Q6y84tZ9XgITgd2cemZZ8HEBPuwESy06MiKQjf+/s9gWjlZczH7InP+ncOpK6d4\nvfWL9PqrJY63e2M/oGp9BShKSiL9u+9I/2k52pwcbPv3x2XGDGx6B92QespISmT5q8+Sm59FROBZ\nepsVMT/hAkWYk9VmKG79JkOH23Qr3a9zOSOftQcvsebgJWIupDHq3G7uSjjLsS5TsdTsIrcohqKC\nAjAxwTU9j64JiThNnErH/zyBMK/8k4HRB3fntl3l+djD2FmacSn7EpN+n4SVmRXLbl+Gi7VLrdpe\nGrOU+ZHzecDnAaZ3eZyJC3ZzIT2XsT1b8cPu8wzr2pzPJ/WscC5r3OEU/v3uGIV5GoJub8PJnWdJ\nT9UywuUz2pltAbsWuno1vuOgZUDNbrxqNRC3HY5EwNHVkJ8BNi664mZdRsHfL0PaGZj8qxrBN2FF\n2iJG/TqKFrYt+G7kd2w6kcSjP0TRysmaHx7qg4fjtaPTtJxCftgeS/Cuh/CVJ/nuxBAyZR5dXD0Y\n+dnXyDwNKYuPUJSQg/OETsQ4mTH+f7t4Nqwzjw3qQMqCb0j+8EOcxo+jxeu6GTnrzqzj7T1vI5G8\n0PsFxrQfw3XVXrmYfZEDSQf0wfx0xmkAzEzM6O7SXTflsHkAHZw68MfZP1h2bBnpBekEuAUQum06\nzZo5MO7ZqzHpdHI2Y7/YweRT/zI6cg1Wvr54fv4ZW1Lhoe8ieX5kFx4Z2J7Iy5HM3TyXYlnM/AHz\nCW4VTPKiwxRdyqHFc0GYWFRvkKXJzCR9+QrSli5Fk5qKla8vjuF34hAWhpnL1diTevECK157ngIK\nWdHrJP09gwg5lENowTZcRSbS0gHRbQz4judK8z6sP5rM6uiL7DmbhpQQzmUm74vA5mIcGUEBHDA3\nIy8nDScPH4bPvI8TO7dy6N8NyOJiWmRk09bSiaBPPsLSu/yZgaWMPrhbenSUYS8t4YvJ3Zi95UES\ncxL5/vbvae/UvtZtSyl5c/eb/Bz7M67593HxQg8WTuxOb29nVh5O5b+rj9DH25mF04JueAZkUaGG\nnRGnOLL1Ii6tbLntge44eFhSnCv5/YtDJJ3LZMiQHLoUL4eTf4OmEJzb626++o4D10pu5koJlw7o\n5vEe+QWyL+ue79pltK6NdgPBtOTdOzsJlozUfZ+2RvcmojQ5q0+t5uUdL/PF0C/ITOvAk8uj6eJh\nz9L7e+NiV04aUEpY9Sgc/IlfLoQRl50DBZZsHDiH2X28CNiVjCa9AJfJXbHs5MSEr3dzPi2XLc8M\nxrokECZ99DGpX3+N87RpNH/+OYQQXMq+xIvbXyQqMYrb2t7G1G5TiUmN4UDSAQ4kHiApT1cYy97c\nnh7Ne+iDuY+rT7mftPOK8/jt5G+s37KNvtHjOdzjT0YNDyXMOwxzE3O0eXkcnvMfLLb9y+Gu/bjz\nh8/Rmlty20dbsDY3HsW/RAAAIABJREFU5fc5oaw+8wtv79Z9mvhsyGf6GUQF5zJJ/uogjiO9sR9Y\n9dF7Wdr8fDJWrSJ92Y8UnDwJpqbYBgfjOHoUdkOHYWpnS1LcGX5+/QUKzSXLe8Yy2nc8F08OJffE\nJp5wO0CPnO2YF+eQKJ1Yp+lHpP1Qenh1ZfCWFWj/3Uh+61bE+nYm/uI5nFt60rz9KOKO2NJ/Qkd6\nDGlNzpV0Nv/6C0f/XouJ1OCSnU9Q2Gi6Pj4Hk5vMUDL64N7Jp4ck/C3s236PxvIEXw37in4tDTc6\nzcrP57Yfp5FtcpzHu86j7xUN/y75GtfWbSl0acsvF81xaNuRrx+9Tf+AgqRzmWxYfJQribk495Fc\n7BrNgdT9HE09SjundjzT4zkSfzMn/ng6ofd0xK+vHRxbq0ulnN0GSN3cXN/xunm7DmVuFqWc1AX0\nwysh7bQu5dJxuG703yns5iWIM+Jh8UgozIb7/4DmlS/XVhoPrdQSvjocCxMLxrp/wEurjhDU1pmF\n0wNvfvNuy/uw6U12pIWxOzGHNtb2yEfe4ret53kqTeCE4FhvN+xyBcd3JnDapJget7Vm6pirC2qk\nlCS+/Q7p33+P62OP4TZHV79Go9Xwbcy3fB79OcUlUxM9bD2uLgZy143Mq3M/bPWnB0g4l8rf/b/i\nZGYsHrYePNQ8nF4fbaDw+Aku3/Mg9+d15t4+bXC2teCLTaf54aFAtqQsZPmJ5fRv1Z/3BryHvcW1\nUxp1o/dsWjzXu9qj9+vln4gl8/ffyVy3jqJLlxCWltgNGYzj6NFkejTnl3n/R6GdCcsDTjCl5wNo\nUsP49N9TWFLIWLsj/9/enYdHVd4LHP++s2YmyUx2skA29oSQEDbZRKiARVGoYKFVUSztbe1t663e\n9l611dZWW2+ttk9rtSKglWq1uKFssgpiWELYkgAJJGSd7MkkM5n1vX/MsMq+mBDfz/PwzOTMnDkn\nh5PfvOc97/v7MT9sB/2attFcaKCx2IJHq6Ny4miKG+rRGYyMnfMtcqbdhtBoWfXSPo7ubWDE9FRG\nTE9Fq9XQ1NzKC7/7KxElW/FrIVKrZ8yC7zFw8hQ0Z3T/dvvgPmLECHnDb25hU+37mNrm8s63Hjpr\nMdvL4fL6WPjaLraUVpA+dDEOXxN/yf49ncUVVBUXUn2oGE+nEwCnIZyUzH44vOG4yvvgNoewtu/r\nVFsPo9foyYrJYpxmJNtrt5Mnd/P15OmMKZxN9X47o2akMWJ6auAPpq06kE5439uBljki0C+ffAMc\nXgs1BYFlaRMCwX/wjMC074vRdCQQ4AEWrISoy8/frXQNr9tN2Z58kgZnYgo7GaQ+Kf+EhzY+xLTY\nh3lncww3DYzlxW8PP9HC/oJgXYIdTTey2SZJ1JuY8/fXkK1+6hftw+328Q98hNR5MEqoMEMvlyDE\nB/HpFnKnpZCaFYPQCKTfT81jj9O6fDlxjzxC9AMLTmympLmEktYSsmOySQhLuOzfu7m2g2VP5DFq\nRhrDp6fwaeWnrPrgeW5fVIzRJyh5aAbT5v0vL2+s4S8bShECbs224Ixcwvba7dw/5H5+POzHZ73R\ne7L1nkr4xPOnLL5YUkqcuwtoW7GCtpUr8TU3o7FYcE4Yw8aaMtzRBt7MLuYHo3/EMMuduL1+RqZG\n4lj3CXVPP427ppbGjBj2G004/HqGJHgYf+tUQkfNOzHizmV3s2tpEXv2NxKTamHK/RlE9DLj90te\nWLWf+sV/prenHKdRj9Uayei59zB4wiR0wf74bh/c04akybBHwrgt+Vus/HQ4Bq2G1x8YzcD48+Sa\nuAhen58Hl+Wz+oCN3985lPGDtcz7aB4mnYk3pr+B1WilqL6QHfs3sn/3DvxlTSQ1mdD4OgHw6zUY\nkmNJHpxFdtYErEfCcG4PXI62h7v40LSBLZYCprV9B3nISvbNfRh3Z7/Tb8g0lAT60fe9DY0lge6U\nrDmQOev01vylqCsOdNEYwgIB3np5l6LKl8/tdPDe739NReE+NFodqTm5DB43kfTcUdyz7j4qWpqo\n3v8jbhuaxHN35WA4V8m2Y3mwdAYFtiGsqzMRpzXwzZdfQzZLGhYfQArYJQWVNQ7Ck8P4zOJjVWUT\nf507jD4tfnavPYa9sZPIhFBypyXTf2QvNEiqHn4Y+8pVxD/xSyLnzr2qv/vmtw5xYHMV858eh9li\noOWdd6h58lf446J4c0Ea73l3YtKZmN1/DqWluew4VkVM+jIaO+t5YuwTzOg747yfX//qfjxVduL/\nexQa49WtsCY9Hjo+/5y2FSuwr/2EWi3sSovHoId1GceYM+vn3KkfRe1vf4tj2+c4BvajMDWRutoq\nEtLTmTwqnvj6tVCdDwhkynic1vtoKUrCb/fiD9OT3+zG5vEzfk5/MsYnIoRg5b4ann95JQ8Uvkmr\nWdJmNhIaEcmI22Yx9OZbMJpDu3dwj0tMkve9eD/P3P4rSuo6uPuVPNw+P0vuH0XOZU759fslD7+9\nh+W7q/jljAzuHxe4MbG3fi8LVi/AarBi99hxep0gYbR9KkMPTsXjg93WauaPjQVbGVXFhfjrXNwQ\nOwOLIZpKUYJ1QCLR7njcR9sAOBRSTrWUuOqSSB2TwKS7B31xiKWU0Nly8S30II+rk10fvY/RbA5c\nyh3/4qgugKUzICwO7l8ZeFS6Nae9jeVP/xLb0VIm3v0A7c2NFG/dRHtTIxqDnsMxzRwMyWHU8Pk8\n9Y2cc0+UaToKr3yNwrIYVtXHEqXRMe9vi5GNGhqWHsAlYXOTC02EkfGz+5M+LBYhBO0u74n7Sn6f\nn5JddeSvLqexqoOwSCM5NyczeFQMtocfon3zZhJ/9wzW22+/Kr+7Ozj8MS0nhpvvGYjtd7+n+fXX\nCR07lqQ/PofWauVQ86HTqkvphI5wQzgvTHqBrNisC27DdayN+r/uwXJLKpabrk7r/Wz8TiftGzey\nf/m/2NZaR0y7kz5NNfRqE3gs4RwbP4rDFUcJjYhkwrfuI2PCpJOzehtLcW9ZScsuC253GnrNYULj\ny2lvuQGvI4wWrY+dzX5iB4Uzaf5QzFYjhdVtfH/xNqblvcv4mh0c7dOLBr0GY2go/7n4X907uCfH\nDZA/u/Nv9M2JJfeWFDrDdNy9KI/Gdhd/nz+CsX0vbRKSlJLH3tvPG3nHeHjqAH44+fQbm+uPrWfR\n/kVkRmeSHZ6LZ2M0VXvtJPaPIP22ZBa+U4DL62fpfSNJP9JO29oypBFq46s5WPY5tiOHiemTwqQ5\nC4l0xVC/swxDncSPpNEjaYvTM/b7I9BbLq7A8Ll+h4PbPmXzPxZjb6wHYNC4iUz9jx+hNwRvrB37\nHF6fFeiamf9ht8li2eHyYtRp0H3JleC7M3tTA//+zS9osdUw46Gf03d4oDCM9PspK9zPc0t/QUK1\nG6NXQ0h4OANvmMCg8RNJGjD49On+zmZYNJVD+x181JhCuNAy708vI206mt8sxu6T5HX6yZyawrCp\nyegv0P8spaR8fyP5q8upKWklJFTPkAnxRL37f3h3biXp+T9imTLlin//vRsq+PStw8x6cCDePz5O\nx2fbAjdwH3kYoTt9IENVexVLDyylpr2Gx8c8fkl1GhoW78ddYQ+MnDFe+7Tde1d+wNolL2ORHuya\nNtyaSLRSQ+jogcy4+z/pE5N64r2+djdta8rp2FGLxqzHOkaL2fc+4vAaZHM17a6bafPOwy+NlLp8\nlLvtTOz9Nqm92+k0J/JBmYaKgw5u3nMAh99L5agc7npxcfcO7plhFvmHO/6X8rAcvMJAr3AHaX39\n/L20nr0+E0/eP5EpWRc3K1RKyTMri3lp8xH+Y2JffnbLuTOxVRQ2sW5pIc52D6NvTydnSjIajeBY\no4OfvJzHd9sEQ6QW09AYImf2Q2PWI6WkZPs2Nr6+iLZ6GwNuGM/Euxdg8JvYtX4Tofu0xPjD8CPR\nDbAQMSKJkEFRl3STp67sCBuWvExl0X5iU9OZfN93qSouZMubr9ErvT93PPIo4VHBL7zSDbDsrkDe\n7HvfB+OVdWVdiaYON0s+K2PpZ2X8euYQbj9PRZ+vkhZbLe889SiOtjZm/ffj9MkceuK1To+P+cve\nppDfMCb8Xv47+UaKt26idGceXreL8JhYBo2byKDMdGKbtyH2/JOyYjvvNQzEjIZvPvtnmnf70OTV\n0OL1Y+sbyei7+mOJvvQ0GTWlreSvLqdsbwM6g4Y+bXtI2Pdv+j//W8ImXH7xCumXLHsyD73GS+7W\nX+OtqSH+iSeIuPMbl/2Z5+KusFP3lwLQissakqwxajFlRmPKjsWYZkVcxCTH3as+ZP3ilwAw9Itn\nZ2Yb+Z4iBIKR8SO5NWU6E2w5uDfYkG4/YWMTsXwtGc2pOd6lBEcTvupyWjc14ygx4JZ+9jn8RFh3\nMtb6GpqOCo7qdRQII+ZtoaQd1pJxsLh7B/fspCS54o6ZOGobKPckUx4zBrcxgnB7OSnH1hJVvwcZ\nHY0lpQ/6hAT0iQnoEhLQJyRgHjYMbcTJrps/rTvMc2sPce+YFJ68PfOsgb2j1cXu1cfYs76CyIRQ\nptyfcSKXtJQSR34dze+X4PD4eF64mPXtLKZknj4TzeN2sfPD5Wx/7x0ARt0xmxG3fwO7r4N/v/Yh\n/YuSSTBCqNAhDBq0A6Oo7m3GmWg+50nncbRT+clybNs3oDOF0vvmbxCSPRR7fQm9w/SYPRq2vPQy\nhpAQbv/poyQOCJYWLP4I3roHUsbCt9/+0vPfHJ9Q8+aOY3R6/EzN6MWPvtafIUnXtsTY9aDhWBnv\n/PYX+Dwe7vyfJ4nvd3KmZrvLy8KlOylwP0eYtZJNcz85UWzG3emk9NPVFK17n7KyOqQURBs6SI00\nsrdagx7B1//raao3OEm2u2nRCCK+NYjeQ64g1UZQY3U7u9cc4/D2WqTPR3xDPjlzhpE0MRtDr9hL\nThxWfqCRFX/eQ2bpP0l0FtP7T3/CnHvthvJ2bK/F0+i8rHV9LS46ixqRbj9aiwFTdizmnDj0iaHn\nzatzcNunGExm0nKGA1DWWsbKoyspLdjPzKM3kupK5Gh0Ld6vWbhh6I2nzZg/G9exNpreL8FX1UGT\n188uYWd5/6WUGYsBMHiNzNin48kX8rp3cD9zhqqn1U7h+lL2ftZEmx3wd+Bv2M0IfxGxHY14amvB\nE5z9ptcTNn48lttuZXlIOk+uPcKdub15dvZQNBqBlJIWm4OaklZqSlqoLm2lrT7wHz90Um/GzOqL\nLtiq9js8NL9bgnNfA4ZUC9oZ6Sx4by/7q1r5vzlDmTXsizcu2xrq2PSPxRza9imW2Dgm3vMA/UaO\n4cPVOzj2QRtGczOGyEZGtw8mHC278fI8nZTiB+FGo29Bo2sms7WEUTWVGHx+ipIEu/vZ0WibmZXn\nYUaexOCDg0mwY6AZszMeQ6fAOTmZ6JGZJIQmEF93mISNzxKffCOmeW+C7vK7hC7WwVo7L20q5f09\n1Qhg5rDAVPh+cV139dCd1JQcZPnTT6DV65n96K+J6XMyOVSLw838xTs4UF+MKe15fpD9A76f8/1A\nmbriFcEhtZtB+nFEZVPYPoQDhTYaPC6MPsmAm36KKNEwOESLO85Myg+y0YZc3W6ItkYnu1ccpmhr\nDT6NHq3XidVeTpTfRozZQUycDlNirxMNLn1CArqERLRhJ2u8Sil592craKz3Mrl1Gcl/+RP6hMsf\ncfNl8Lt9dBY14Sioo/NQM/gkulgT5uxYTDlx6C+ibJ63qZPWj47gPNCI36phS0YRizr/SV1nHWad\nmcnJk5meNp0bEm9ArwmMfGl1tbKnfg/5tnx21+3mQP0BJjQP4zu2O7H4zJR7/FQO7WTszAwqGkw8\nuGw3e3457foK7sf5/ZIju+vZtaqMhop22oUkbGgkD9yTidbRiufYMezr1tP28cd4bTacWgNlg0cx\n9Jvfoi08ldqjdmpKW+lsD3wRmML1JPSNIL6vlT6DI4npfTIIdR5upuntQ/g7PFimpBB+Y2+ERpxo\nXeUfquaZwRomeGwYUpIJnzoVoQ18KXh9fj7bkkf+W4vxNVZjC+vDJ9axWEQ0Mx16OozNrBr8IrPF\ndG45loPJa2BNVB6vRi/H3OZl9IEoouwGaqI7KRoqMcTFMqbIz6QPSwlrcWAbm8M+Yxh9C/aR1txK\np17L5wMTcWgNHExu5fPMFuQpjYpIdMRH9Sc+NJ6E0ATmDJhDv8h+XC27ypt4cWMpnxTVYTZomTcq\nmQfGp5FgDcFT1Y6joB5zbhyGxEtLgtSTHNu/l/ee/TVmq5XZjz5FRK+TV362tk7uWZRHWaODMaNX\nUdS2jbVZD2EtXHFyMlxkGjJrDnZ3BpXvbqV5bymu8F64huZS6e1DP2Giv1GLISuG2LmDENprl466\nw9ZMycoCbMc6qGsQtLpDAIGQPsLtFVhbS4loLcXaWorB047GYgkE/IQE2tx6NhpmMlB3iMnPzkdz\nEflfuhO/w4NjfwPOgnpcR1tBgr53GObsOMzZMWgtp08s87t92DdVYt9UiRAQPqkP4RN6I/QafH4f\nu2y7+Pjox6wpX4PdbSfSGMmohFGUtpRS0lICgE7oyIjJODExLNuSBRtbcXxeg1dKqkMNDPleFq06\nQUpMNx8tc6FiHVJKygobeef1QswtXqROMPzmZLIn90Gr1/DemhLyPipgoA/82kj8wW/CMK2DhL5W\neo9KJ7F/JNY40xcuraTHT+uqo7RvrUYXayJq7iAMSWF4Gxpw5Ofj3JVP+65ddB4oRCP9J9bzJSRx\ncOIdfJiQw46qDjrcPoT0M957mCzbNrReF6kTppA7YRafLC7FpXGyJusVwi165tTcTG5lPzzSw76G\njVTqyhg5dy65E6bhO1xK7VNP4dy5i5CMDHo99ijm3FwAtpU28tLSNaQUbGVK7R5qwyTlsRHE6Y0M\n/PpN1OUmUVO6gpqj66mJSqY2NIrqjhr+PPnPjEoYdUX/R1JKNhys428bj7C9rIlIs577xqZx75gU\nwhxenHvqcRTU421wglYQObMfoSMvsXJQD1GyM48Vzz9DRK8EZj/21Mni0UHrimz89K1d/GFCDY8d\neZXZzaHc3qilXZeO3TqSdl06rXVe7E2deDSnz/YUAsb0MhHb6SV0TAIRM/peVL/w1dTZ4aH2SCu1\npYGrYdvRNny+QBwJN7qJ1jYT4azAUl9MmehHRfRo7n1mHGERl5cjqrvwtroC5/meejxV7SDAmG7F\nnBOHKTOazpIWWj86iq/VhSk7Fuv0NHTWsycXdPvcbK3aysdHPybflk//qP6nzfI9W7eN29ZB9RvF\n6Ooc2P0S3bgkBszsd30H9+P8fsmvXy+gcWcDAzxatFqBP3hSSSA2OZyk9HAi3NWYdq/Fs2EVsrMT\nXUIC1lunY7ntNowDT95gdVe30/TWQbw2ByGZoWgNR3AW5OPMz8ddXg6AMBoxZWVhzM3ljY4IFjeH\nkd1YyuxD6xnUXEGbyULpxBmE3zWH3IxkkiJMOO1tbH3rH+z9ZBUhYWFkT7uL4rxIhNBw64OZHN21\nhqKP1pNtmUhcSDK6eBOWyfG0vruIlrf+hdZqJfahnxBx550nrg6O8/r8LNt+jD+sPkhS7RHmOnbQ\n0GnD7PIwst5OwqTJWPu0Y65eghi9EPn134MQl52H2+vzs2JvDX/bVEpxrZ1EawgLb0xnzsA4ZFEz\njoJTTvS04Ik+JBqN+dLSofYUhZ9uYNVf/xgoHv3zJzCFW06+KCVU7qR83QY27UyhzReJkKd3pei0\nkpDORsI6GrAaBJH9UohIScYoNOjcPmSrC1+9k/DJfbBMSbnqZdsuh8/jp77CTnVJS6D7s7QFV4f3\nxOv9R/Zi6gPXPo3wl8lT58Cxpx5nQR3exk4QBFr1CaFEzOiLMf3a3G+SUtK0vZbm90sJ8Uv6/O7G\n7h3ch+fkys/Xbrmo90oJr249woefVTAxJJTqDhe6mBD+Z14WlrDT+5ml00nH55/TvmEjHfn54PVi\nSEkmbNIktBGDcBZ6kb5OXPuX4TmSB4A2IgLT8OGYc3MxD88lJCMDYQh8rt8veXXrUZo63IxMjSSz\nroTO15bQsWVLsBDAXKLuvfdEIYC6siOsX/wSVcUHiO6dhjZkCJ0t22lrsDFg9DhuvHsB+hpofqcQ\n6dHhqdpByAA/cT9aiNZ6/pOjqcPNc2sPsizvGAN9NqZVf4zG52ZYZQMx9c3oLCFY4huw3D6TkAUv\nXPJNsHaXl+X5lby8+QiVzU76x4Xx4JhUJgk97r0NJy9Rk8Iw58RiHhqL9hytlK+KgtUfse7VF+mT\nOZSZjzyGwRScZV1XHOhD3/c2tJRT582kQCxkt6aFvtZERkZlQEUVsqYBjdaMMEchxBnTzA1atJFG\ndBFGTJkxhI7qvldF0i9ptjmoKWmhoaKd7K/1IaLX1Zlx3t1IKfFUtuM80IA2KoTQEfFfypWUz+Xj\n8NIDDP5edvcO7kMTBsmP5//9S9+up3o3vvr1mLMHYxqei3n4cAxpaZfcGuosLKTxlVdoW7UaodNh\nnTWL6AX3Y0hJCYxX/2wzm/7xKu1NjUT3Tmby/d8jeUg2HXnbsf3mN7hKywi9aQGaiFyERkP4TX0C\nff76CwfkA9WtPPlBIUWHy7mzeQ3hjnpGjxhLytEqOjauR/okGnMIphEjMecOD3xhZWWhCTn9Ermu\nrZOd5c3sKGtiZ1kzhTVt+PyS0b2t/Fd6PH3rXSdvLsWYMOfEYsqORR/bM/9oL4WUku3vvc2WN1+j\n74jR3Pbjn6Fz1AaSwe37N9j2gdBA+k2QNQeHdzxN75Sfsr4P6WxBaN0YUuMxDuiDLtKINiIEXYQR\nrdWICNF2i1a60r10+/QDuRk5csuyNZe8XllDB3GWEMyXmCjI29yMr7GG0AmDMfTqdcnbPRd3efnJ\nQgBeL5ZbphH9ne8QkpGBp7MT29ESEgcMxmezYXv2WewrV6FPTCTu5z8jfMoUfC0uWj8+inNfoBUQ\ncWsaIRnRF/yjllLy4d4afvfBXrKOfEx/xxHSxkzk1m/Pp/ONZ3A0huIsKMB1OHDDBr0OMWAwtpRB\nFESksFrEUdihwQD00WkZF2NhWISZIX4N5rJ2pNuHxmLAPDQWc04s+qSwaxJoKgv3U19RhiUmlvDo\nWCwxcRhDzz8E7UoVNRaxvXb7Za0b+8keNB2d2GpaaKhrwxoRQm9rJ8JeHZhsBIEZyZbEQFroYHEX\nnd9EW3kjAw82omlrIPSGYUQvfODEvRVFuVhdEtyFELcALwBa4BUp5TPneu/F9rlfL7z19TS99jrN\n//wn/vZ2QseNI3rhQkw52TQuWkTj318BIPq7C4lesOALrejOkhZaPizFa3Ng7B9BxIy+6OMu3EJ2\nuL28uKGEHe/9i5FN2xFxKdz3iycIi4xmX2ULhQVlOHYcILyiimSnk3g0aE0RaExRiLBYNPrQ0z5P\nhOgwD425pAkdV2L9kpfYvfLD05bpQ0yBYB8TiyU6+Bhz8jEsKhqt7vL7998oeoNntp/z1DyvP//V\nS114LBXRFlIaWsmoauBij5BPgG7aJFJ/8BNCBgy48AqKchZfenAXgQ7DQ8AUoBLYAcyTUhae7f09\nLbgf57PbaX7zTZqWvoavoQGN2Yzf4SD867fQ65FH0Ceeewan9Ek68mpoXVOOdPswpFi42MyqnR4/\nFdV16O11CKEDYxxRQofhjNAj9Rq0JgkeO76WGjyVJfhaapCOJjQm0CdGwmUG9Kh77yV88qRLWkf6\n/TjaWmlrqDulQHPwsTHw6GxrPX0lIQiLiCQ8JpasydPImjz1krbp9rlx+9yXtA6Az+1i/ePf4Ui1\nh5zICkakehGDbw0UVokZcMHZkfoQMyaz5bzvUZQLOV9wv1bJF0YBJVLKI8EdeBO4AzhrcO+ptOHh\nxCxcSNS999L67ns4tucR8c25hI6+8LBEoRWEjU3ENDQG+/oK3NXtSN/FbdeoEfTr3Yvm1lDsddU4\nIrRoe0eS0NuKNT70RF/umf240u/HVVKCMz8fx85deGpq4CK3+QWnDBu9WEKjITQiktCISBL6DTzr\nezyuTuyNjYGg33j6l8DlMGgNGLSXPuHLrzWhBW7MjWXkvMeg9yi4xJvWinItXauW+2zgFinld4I/\n3wOMllL+8Gzv76kt9+5A+v2XPFJGuTjq2Cpd7Xwt9y47M4UQ3xVC7BRC7Kyvv7xWl3JhKvhcO+rY\nKt3ZtTo7q4BTEyr3Di47QUr5spRyhJRyRGxwXLiiKIpydVyr4L4D6C+ESBNCGIC5wAfXaFuKoijK\nGa7JDVUppVcI8UNgNYGhkK9KKQ9ci20piqIoX3TNSpVIKT8GPr5Wn68oiqKcm7ojpCiK0gOp4K4o\nitIDqeCuKIrSA3WLxGFCCDtwsKv3o5uLARq6eie6OXWMLkwdo/O73o5PipTyrGPJr9kN1Ut08Fyz\nrJQAIcROdYzOTx2jC1PH6Px60vFR3TKKoig9kAruiqIoPVB3Ce4vd/UOXAfUMbowdYwuTB2j8+sx\nx6db3FBVFEVRrq7u0nJXFEVRriIV3BVFUXqgLg/uQohbhBAHhRAlQoifd/X+dEdCiDIhxD4hRIEQ\nQlU1AYQQrwoh6oQQ+09ZFiWEWCuEOBx8jOzKfexK5zg+TwghqoLnUYEQYnpX7mNXE0L0EUJsEEIU\nCiEOCCF+HFzeI86jLg3uwVqrfwG+DmQA84QQGV25T93YJCllTk8Zg3sVLAFuOWPZz4F1Usr+wLrg\nz19VS/ji8QH4Y/A8ygkm9/sq8wI/lVJmADcADwbjT484j7q65X6i1qqU0g0cr7WqKOclpdwMNJ2x\n+A5gafD5UmDml7pT3cg5jo9yCilljZQyP/jcDhQBSfSQ86irg3sSUHHKz5XBZcrpJLBGCLFLCPHd\nrt6ZbqyXlLIm+LwW6NWVO9NN/VAIsTfYbXNddjdcC0KIVGAYkEcPOY+6OrgrF2e8lDKXQPfVg0KI\nG7t6h7o7GRiYVHm4AAADfklEQVTjq8b5nu5FoC+QA9QAf+ja3ekehBBhwL+Bn0gp20597Xo+j7o6\nuF+w1qoCUsqq4GMd8C6B7izli2xCiASA4GNdF+9PtyKltEkpfVJKP/B31HmEEEJPILC/IaVcHlzc\nI86jrg7uqtbqBQghQoUQ4cefA1OB/edf6yvrA2B+8Pl84P0u3Jdu53jACprFV/w8EkIIYBFQJKV8\n7pSXesR51OUzVIPDsZ7nZK3V33TpDnUzQoh0Aq11CGTxXKaOEQgh/gncRCBFqw34JfAe8C8gGSgH\n7pJSfiVvKp7j+NxEoEtGAmXA907pW/7KEUKMBz4F9gH+4OL/JdDvft2fR10e3BVFUZSrr6u7ZRRF\nUZRrQAV3RVGUHkgFd0VRlB5IBXdFUZQeSAV3RVGUHkgFd0VRlB5IBXelRwmmtX34PK/PvNzMo2eu\nK4T4lRDi5sv5LEW51lRwV75qZhJIL33F60opfyGl/OSq7JWiXGUquCvXPSHEo0KIQ0KILcDA4LKF\nQogdQog9Qoh/CyHMQoixwO3As8FiFX2D/1YFM25+KoQYdI5tnG3dJUKI2cHXy4QQTx8vqCKEyBVC\nrBZClAoh/uOUz3kkuF97hRBPXvODo3xlqeCuXNeEEMMJ5CTKAaYDI4MvLZdSjpRSZhPI0/2AlPIz\nAnlDHgkWqyglUO3+P6WUw4GHgb+ebTvnWPdMx6SUOQSmtC8BZhMoAvFkcF+nAv0JJOzKAYarDJ/K\ntaLr6h1QlCs0AXhXSukAEEIcTzw3RAjxFBABhAGrz1wxmOp1LPB2IIcUAMYr2Jfj294HhAULQNiF\nEC4hRASBpG9Tgd3B94URCPabr2CbinJWKrgrPdUSYKaUco8Q4j4CSbPOpAFagq3tq8EVfPSf8vz4\nzzpAAE9LKV+6SttTlHNS3TLK9W4zMFMIYQqmRp4RXB4O1ATzdX/7lPfbg68RLMxwVAgxBwIpYIUQ\n2efZ1ol1L9NqYEHwigEhRJIQIu4KPk9RzkkFd+W6FqyB+RawB1hJoEYAwOMEUrduBYpPWeVN4BEh\nxG4hRF8Cgf8BIcQe4ADnr+F75rqXuq9rgGXANiHEPuAdruzLQlHOSaX8VRRF6YFUy11RFKUHUjdU\nFeUMQohHgTlnLH5bVcBSrieqW0ZRFKUHUt0yiqIoPZAK7oqiKD2QCu6Koig9kAruiqIoPdD/A0MP\nykrqnhhzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RX1/Pww8/zLfffouPjw9r1qzh2Wef5YMPPgAgJiaG3bt3m3xDshVdGnBJkj4H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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hk4FkTjds2EBxcTFarZZjx455r3kkSAGvBGl4eHgvmVOPAQ0WR44c4dChQ944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\niRX+vQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BS7pHf0cVLw-",
        "colab_type": "code",
        "outputId": "085f093b-4b3c-4088-83c2-509d5581ba74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "# Text Preprocessing + Vocabulary Building + Author Indexing\n",
        "from keras.preprocessing import text\n",
        "from keras.preprocessing.text import Tokenizer, one_hot\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils.np_utils import to_categorical\n",
        "import numpy as np\n",
        "\n",
        "TEXT_LIMIT=280\n",
        "\n",
        "def createTokenizer(lines):\n",
        "    tokenizer = Tokenizer()\n",
        "    tokenizer.fit_on_texts(lines)\n",
        "    return tokenizer\n",
        "\n",
        "def create_tokenizer_char(lines):\n",
        "    tokenizer = Tokenizer(char_level=True)\n",
        "    tokenizer.fit_on_texts(lines)\n",
        "    return tokenizer\n",
        "\n",
        "def fill_in_missing_words_with_zeros(embeddings_index, word_index, EMBEDDING_DIM):\n",
        "    embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))\n",
        "    for word, i in word_index.items():\n",
        "        embedding_vector = embeddings_index.get(word)\n",
        "        if embedding_vector is not None:\n",
        "            # words not found in embedding index will be all-zeros.\n",
        "            embedding_matrix[i] = embedding_vector\n",
        "    return embedding_matrix\n",
        "\n",
        "# encode a list of lines\n",
        "def encode_text(tokenizer, lines, length):\n",
        "    # integer encode\n",
        "    encoded = tokenizer.texts_to_sequences(lines)\n",
        "    # pad encoded sequences\n",
        "    padded = pad_sequences(encoded, maxlen=length)\n",
        "    return padded\n",
        "\n",
        "#Preprocessing Logic:\n",
        "\n",
        "#Get all distinct authors\n",
        "authors=df['author'].unique().tolist()\n",
        "\n",
        "print(\"Distinct number of authors: \" + str(len(authors)))\n",
        "\n",
        "#Assign unique numeric label to each author\n",
        "labels_dictionary=dict(enumerate(sorted(authors),1))\n",
        "\n",
        "#Prepend date as string and source as string to each example. Concatenate test and train data into single list\n",
        "df['joined'] = df['date'].map(str) + ' ' + df['hour'].map(str) + ' ' + df['source'].map(str) + ' ' + df['tweet']\n",
        "test_df['joined'] =  test_df['date'].map(str) + ' ' + test_df['hour'].map(str) + ' ' + test_df['source'].map(str) + ' ' + test_df['tweet']\n",
        "train_list = df['joined'].tolist()\n",
        "test_list = test_df['joined'].tolist()\n",
        "merged_lst =  train_list + test_list\n",
        "\n",
        "\n",
        "#Create parallel lists of example and label\n",
        "tuples_list= [(list(labels_dictionary.keys())[list(labels_dictionary.values()).index(row['author'])], row['joined']) for index, row in df.iterrows()]\n",
        "y_label,x_text=list(zip(*tuples_list))\n",
        "\n",
        "#Create tokenizer\n",
        "tokenizer = createTokenizer(merged_lst)\n",
        "\n",
        "#Calculate vocabulary size\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "print('Vocabulary size: %d' % vocab_size)\n",
        " \n",
        "#Encode the list of text by converting to sequence and PAD the sequences, where TEXT_LIMIT=280\n",
        "x = encode_text(tokenizer,x_text,TEXT_LIMIT)\n",
        "print('Shape of data tensor:', x.shape)\n",
        "\n",
        "\n",
        "#For Evaluation Phase\n",
        "x_eval=encode_text(tokenizer,test_list,TEXT_LIMIT)\n",
        "print('Shape of test data tensor:', x_eval.shape)\n",
        "\n",
        "\n",
        "#Convert labels to categorical vectors\n",
        "y = to_categorical(y_label, num_classes=None)\n",
        "\n",
        "\n",
        "# Randomly shuffle data\n",
        "shuffle_indices = np.arange(x.shape[0])\n",
        "np.random.shuffle(shuffle_indices)\n",
        "x_shuffled = x[shuffle_indices]\n",
        "y_shuffled = y[shuffle_indices]\n",
        "\n",
        "# split the data into a training set and a validation set\n",
        "dev_sample_index = -1 * int(0.10 * float(len(y)))  # validation set 10 percent\n",
        "\n",
        "x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]\n",
        "y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]\n",
        "y_label_val = y_label[dev_sample_index:]\n",
        "\n",
        "del x, x_shuffled, y_shuffled\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "print(\"Train/Dev split: {:d}/{:d}\".format(len(y_train), len(y_dev)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Distinct number of authors: 10\n",
            "Vocabulary size: 58759\n",
            "Shape of data tensor: (23164, 280)\n",
            "Shape of test data tensor: (5791, 280)\n",
            "Train/Dev split: 20848/2316\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-p7AeqAht1A",
        "colab_type": "text"
      },
      "source": [
        " The following figure depicts a toy example of the 1D-CNN architecture for text classification with\b an input document of size s = 7, word vectors of dimensionality d = 5, 3 branches corresponding to filters of sizes h = 2, 3, 4 , n f = 2 filters per branch, 1-max global pooling, and 2 categories. Adapted from [[1](https://arxiv.org/abs/1412.1058)].\n",
        "\n",
        "![](https://drive.google.com/uc?export=view&id=1Wk1uUeBs3FOiocPqLUqOR6vL7VdQaqIm)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ev4xv36hN8L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Define a 1-Dimensional CNN python class for Text Classification\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Embedding, Dropout, Conv1D, GlobalMaxPooling1D, Concatenate, Dense\n",
        "\n",
        "\n",
        "class TextCNN(object):\n",
        "    \"\"\"\n",
        "    A CNN for text classification.\n",
        "    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.\n",
        "    \"\"\"\n",
        "    #Adds a convolution and a pooling layer\n",
        "    def cnn_branch(self,idx,my_input):\n",
        "      x = Conv1D(self.nb_filters, self.filter_sizes[idx], activation=\"relu\",name=\"conv-\"+str(idx))(my_input)\n",
        "      x = GlobalMaxPooling1D()(x) # Global Maxpooling over the outputs\n",
        "      return Dropout(self.dropout_rate)(x)\n",
        "\n",
        "    def __init__(\n",
        "      self, num_classes, word_index,MAX_SEQUENCE_LENGTH,\n",
        "      embedding_size):\n",
        "\n",
        "        # Member Variables  \n",
        "        self.dropout_rate = 0.3 #amount of dropout regularization\n",
        "        self.nb_branches = 2\n",
        "        self.nb_filters = 50 #number of filters per branch\n",
        "        self.filter_sizes= [3,4] \n",
        "        self.optimizer = 'adam'\n",
        "\n",
        "        #Define an empty  model usinf Keras Functional API\n",
        "        print('Build model...')\n",
        "        doc_ints = Input(shape=(None,))\n",
        "\n",
        "        #Add an Embedding layer\n",
        "        #an efficient embedding layer which maps our vocab indices into embedding_size dimensions\n",
        "        doc_wv = Embedding(input_dim = len(word_index) + 1, output_dim=embedding_size, input_length=MAX_SEQUENCE_LENGTH)(doc_ints)\n",
        "\n",
        "        #Add Dropout Layer\n",
        "        doc_wv_dr = Dropout(self.dropout_rate)(doc_wv)\n",
        "\n",
        "        branch_outputs = []\n",
        "        \n",
        "        for idx in range(self.nb_branches):\n",
        "          x=self.cnn_branch(idx,doc_wv_dr)\n",
        "          branch_outputs.append(x)\n",
        "        \n",
        "        concat = Concatenate()(branch_outputs) # branch output combination\n",
        "\n",
        "        preds = Dense(num_classes, activation=\"softmax\",name='dense-1')(concat)\n",
        "\n",
        "        model = Model(doc_ints,preds)\n",
        "\n",
        "        model.compile(loss='categorical_crossentropy',\n",
        "                  optimizer=self.optimizer,\n",
        "                  metrics=['acc'])\n",
        "        \n",
        "        print('model compiled')\n",
        "        model.summary()\n",
        "        self.model=model\n",
        "        print('total number of model parameters:',model.count_params())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvMINUFwNLJG",
        "colab_type": "code",
        "outputId": "86dc8afc-6ef9-49d0-da96-bd6026093552",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        }
      },
      "source": [
        "# Test Define TextCNN model for Visualization\n",
        "word_index = tokenizer.word_index\n",
        "num_classes= len(authors) + 1 # 10 as counted before\n",
        "model_ins = TextCNN(num_classes = num_classes, word_index=word_index,MAX_SEQUENCE_LENGTH=TEXT_LIMIT,embedding_size = 30)\n",
        "\n",
        "# Restrict label max value to num_classes in y_label_val (Validation Set Labels) (For Visualization purpose only)\n",
        "# y_label_val=[ label % num_classes + 1 for idx,label in enumerate(y_label_val)  ]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Build model...\n",
            "model compiled\n",
            "Model: \"model_9\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_9 (InputLayer)            (None, None)         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_9 (Embedding)         (None, 280, 30)      1762770     input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_25 (Dropout)            (None, 280, 30)      0           embedding_9[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv-0 (Conv1D)                 (None, 278, 50)      4550        dropout_25[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv-1 (Conv1D)                 (None, 277, 50)      6050        dropout_25[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "global_max_pooling1d_17 (Global (None, 50)           0           conv-0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "global_max_pooling1d_18 (Global (None, 50)           0           conv-1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_26 (Dropout)            (None, 50)           0           global_max_pooling1d_17[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_27 (Dropout)            (None, 50)           0           global_max_pooling1d_18[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_9 (Concatenate)     (None, 100)          0           dropout_26[0][0]                 \n",
            "                                                                 dropout_27[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dense-1 (Dense)                 (None, 11)           1111        concatenate_9[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 1,774,481\n",
            "Trainable params: 1,774,481\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "total number of model parameters: 1774481\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTCBlsziJZKu",
        "colab_type": "code",
        "outputId": "dfba2629-d2ad-4432-a105-396cce7306b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "# = = = = = visualizing doc embeddings (before training) = = = = = #\n",
        "\n",
        "from keras import backend as K\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "import numpy as np\n",
        "\n",
        "def visualize_doc_embeddings(my_doc_embs,my_colors,my_labels,my_name):\n",
        "    my_pca = PCA(n_components=10)\n",
        "    my_tsne = TSNE(n_components=2,perplexity=10) #https://lvdmaaten.github.io/tsne/\n",
        "    doc_embs_pca = my_pca.fit_transform(my_doc_embs) \n",
        "    doc_embs_tsne = my_tsne.fit_transform(doc_embs_pca)\n",
        "\n",
        "    #print(doc_embs_tsne.shape)\n",
        "    \n",
        "    fig, ax = plt.subplots()\n",
        "    \n",
        "    for label in list(set(my_labels)):\n",
        "        idxs = [idx for idx,elt in enumerate(my_labels) if elt==label]\n",
        "        ax.scatter(doc_embs_tsne[idxs,0], \n",
        "                   doc_embs_tsne[idxs,1], \n",
        "                   c = my_colors[label-1],\n",
        "                   label=str(label),\n",
        "                   alpha=0.7,\n",
        "                   s=40)\n",
        "    \n",
        "    ax.legend(scatterpoints=1)\n",
        "    fig.suptitle('t-SNE visualization doc embeddings',fontsize=15)\n",
        "    fig.set_size_inches(11,7)\n",
        "    fig.savefig(my_name + '.pdf')\n",
        "\n",
        "### create a K.function 'get_doc_embedding()' that returns the inner representation of the model before the prediction layer ###\n",
        "### this function should take as input the input of the model and return the output of the penultimate layer ###\n",
        "\n",
        "model=model_ins.model\n",
        "get_doc_embedding = K.function([model.layers[0].input],[model.get_layer('dense-1').input])\n",
        "\n",
        "n_plot = 1000\n",
        "labels_plt = y_label_val[:n_plot]\n",
        "print(type(labels_plt))\n",
        "\n",
        "doc_embs = get_doc_embedding([np.array(x_dev[:n_plot]),0])[0]\n",
        "\n",
        "print('plotting embeddings of first',n_plot,'documents')\n",
        "visualize_doc_embeddings(doc_embs,['blue','red','green','yellow','orange','black','brown','purple','pink','silver'],(labels_plt),'before')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'tuple'>\n",
            "plotting embeddings of first 1000 documents\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SCCGEEEIURAJIIYQQQghREAkghRBCCCFEQSSAFEIIIYQQBZEAUgghhBBCFEQCSCGEEEII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3d5Ial0nBpc+SqqEGoIt6UFkQweAVAKNxZjOBKhRSk6OiSdnVC9vp1gEfz2yW7Gj45S9cRprvyv\nLSSuvx7D759x23W/zsYdG7N6IKORKEbCYPgtw7jaZNnNNE1GRkb50pfC7N3bgLJsAu4Ed9wd5FP3\n696Am5yZxofAPgNlG7PfXJrgjnqZytlGtc3C1N61FD7NpmbgEFrTZMm6rLGMkeMjjJ4YpbS5FNd2\niYSOs+mGA+jBIMhynEScjn/4a7pe+Dtsvaowqu18YDYRaMbxJ6TJuhaor/Xx+8eaePRnJ1hT14gQ\nfqy4RdHhIn7b/G2+v/37swrNXNcFAS7JWzfpLcsuPMHM/H0gU33K4YkwJ4dP4jN8XDR8EYFAYH6V\ngXngui69r+xmYDiA67sHgUu1e4j6ow8jYN6VgwJn5/wJIHM2Catpi+5CueOOOwiHwxiGwVe/+lXK\nz6ZMLVBgMdSvRQKBgdPgqOxpDJkPS2bpBgYGcBwHIcSM2bu8kFQ6poPHJJrPR9N1ZZxyHETqos11\nUYkEwbo6EILx8QRdXT6qqkA3NX60bhf7mnZSkggRDcCjv98wp+xZSoxzqPsQalQhpCBUE2KkbgQz\nw3zbsiyGhnSe2F3BxviLVPcfAkfx2l9I/v7nG/nMY5cjcwTD+GsgfBxUDGTG9qhkxs+38B6wpN4I\ny8oOIt3IBBJFoGgy6Ctr8UyeR46NMH5qHCOgsem9h7j6tkh6ezt+eIrOn4xSVSox116GlbCXbIEu\nsMTMRQSacfwp5XLk2TGO7R/nYsfPH1U18nx4iIO6ScvQRVwhrqC5tRndp+cUYNkxm1cff5Xqt1Yj\ntIwStuPy6rdeZdN9m5JtJ/O7WEr1KfvX+Ol9M1kZkAJb2lT3VzOwaWBJJsH0hnroG3HRpUISQyHp\nE1eBHxoWUTkoMDPnTwAJs49AWiTPPvvsol+jQIE5c7ZpDOmHeWrrurq6eU9xWRBJpSPKyg4ilcXF\nVyuGZQWxgUEvzSYEwbo6AjU1KNdldNTIyr5J6fC+DzzMDTd0oZSNEDqwA69MNnP2TGqSzbs2c9XO\nq5gITxCsDGI9bE1TOg8MDOG6bWyM/5La3gPYZhGuoSFth969B3jhf4/wro3Tg2G0gPc5o30QqJ/M\nTMaHvNm7i1iE/H7PyrOzk6weyPEJPxddYiJdRUrbKISgpL4Es8Tkw499mNKKM+gvfg2MagBilkPX\nT0JUlfkwNRuUhWn6C6PaVgGp3sBgZYb6PiUC1TWvFUsp79/gnQsg6/g78mw0rTSWukuj38ftrOPe\n37mJX3Yb+Ev9OQRYr3DVzmp0fw0TYSurn1gGkucNjUWNNUz1KQMoV2FIr5XGlS7CFgQJMmKP5HUS\njFKKgf4+dPcMUiSHIaDQiTIgNlLnPodcROWgQG7OrwByjotugQKrhtQ0hrM+TM5ZMLMoUkrHqWXf\n+BB6axstRVdwqqIXCeimiQvpknplpczKvr3//R3cdFMnw8NVWJaJrlt4ZsXglc9mR/fr6QUu5evW\n3d3N6Ogouq5z551t/OA7bVT/pssLHpP90ULXiNtFHPz2ca69zEQ3pgfD+OuhZRv0POGVrYXuBY/r\nF+8fl7Kg6+6GM2MuW985yI73naZEbOBM/zg9vx7nzV+NY8c9c/FNbZtY07oGnGBW8B4etbAdF1MH\nXJH+DNPEE+QYi1ngnDCjh+knrkKeRQTqJBLER0bx1f8e/OYbHHvRTQePOBay/CL8xU0M/exFXOcd\nU4RmLpp5AjU6wET4UUobHIKV25G6HyduoemOt/8IbUHTrDJJ9SlraEgh0z3aQglc4TLBRN4nwSQS\nCVw0JC6eN3CyzQeFgyAhSvEtonJQIDfnVwCZYo6LboECFyJOPL64udGpIOpY97Tgql54J+6BgQHs\nKSV1ISazb3V1MW64oYvh4SqiUZPmZtC0hZsVT1U6e4uTn33fGQNH4RqTi6lSgKYhBUyUbaN0/BvT\ngmFa27yeqYvbF+UDmXtbYdcuuPdexfiRU6yJjXgenqIMx4HWt2kkYjav7x/O9s+cErxXlpnoGlhW\nHLOsBZLfvWVZ6QXacRw6Ojro6urCtu1Cj2QGmceBMIxlyeDP5GFq6PD2a4pzikBdO8GbTzzJkX/6\nAa5tIzSN+svfjnL2I6UDrgZX3wYXX4UUgiIrQdkLYL1mo6X3++M41gmkHiBYWQ7E0c1ONr5vMwe6\niygqi6PpAseoJxKpTJavFxYeZPYp19fUc6L3BD7dh2Eb9NX3MTg8SFtbW14vZAzDQEgNFWhERnuS\nF1MSBQgVx2i5tVC+XgLOzwCyQIEC01COw5Hduzm2d6+3EOk6626+mfXbtiHnE0xIzQuuWndOC64E\nzFpST2XfnnoqjFI2luUFjynl6GLNiv1+f1ZZ7I67g7z2FxJpOwhdS9lM0lTvoJuS4NWfhJNazmAY\n8D5XnsteruvS29vLwMAA7vgEISGoFgHqfUWUtZSj7FLe0bqGd/x/l6AHp1wIZwTvftdmx++sofNf\nE1RV1nlmK5bF0NBQeoF+4IEHppX2L/QeyazjwHHQrriC4ObN+CsrEUlhmnfBs5DhEzFmEp3M5GFa\nVFXEK92v8rarr0PmEIFGw2He+M4ejNLStMl/z/4wtqrFLKlFXv42eEurZ9CvFLgutb/bRK86hf26\njWaCY/USGSpiU1s4aQ5uwpEoV2/uhpEPcehfa1ARkLKPTR/xTV60LJDU8w92HcSNuQyGB3mz8k1O\nrTlF2462vE+CSQsKTzmeBCIaQuFgixJqKwTy4o/n9f0KeAjXnWoWeu645ppr3P3792fdd/jwYTZs\n2HCOtig/nA+focDq5/XHH889beb227nkve9d1nF1sVgMIbag68XJzGMKC28G9D7yYQrvOPD321+k\nd+8B4loRaBr1NQ6VgQib7ts0OdFlkRNn5kMoFPKU81JDRiZQuNgu1PqDNPiLvQclbLi8deZKSnJ7\nHb2cjoceobu7O51h3L59O+3t7SQSCbZs2UJxcXHWGEXLsohEIuzbt++CLGdnHQeXXQYXXYQaHydY\nU0OgtjbdcjG//jwHb050F2Dj5Way+3nHQmPs3rqbouqiac+ODEbY0XkTgdj4ZBlbKVwrweGnfkjf\nsTemjRk909ODWVaG/847EbY3YCAlWgvU1DLWO8aRvzmCE4si9dfYuP0MV7cPeklOx4GnXwDdAXkt\nthVgYlQnWDKBLsdhy768HAfL6QOZdWGmHAQO1TW11Dc0L/BioACAEOIl13WvyfW3QgayQIELACce\n59jevengEUDqOr7xcY598Yu0/uM/ohmGV2Nub/fqrEuIt4jswOt5TM3cXZhZ8WxoGnym+2pe+Aoc\n7DrkNdabko3bN2VnWZYg05gLpRQDAwPouoaMngTbROJlTQaiFnW+INLl7NZjye3VYFrpPrVA9/X1\nYdt2VvAIuXskLxSyjgOfz0t9x+NITWOiv59gbe2sc9hnpoPp+3J2P28uD1Mg3XNovKUOTg9niUCt\nEj8nXn0ZX0VF1rtJw8BXUUHdTTdxGlCWheu6BGtrKUq6HhTVFrHtB9twzkwQrNyK7g+Snm0dt8B1\nkiVzE910KV2bAAyI24uyqspE9+sU1xWTSCSoK53P9zl/ll1QWKAQQBZYxSxR1igWI7fp9ComPjKC\na9tZWQx6epChEK6mEa+sJKiU16AIXpPekpMqYy3MrHiuSE3yrk9v5tr2q6YrX5eZ9PSgaAgmekBW\nA+VIbBxlkRg/gc9XP2/rsamle/DGYuq67hlKT8lA5lvEsFrIOg58vrTdVKbtlObzzXOSUwwv85gK\nHkneZvfz5vIwdaxJoZQeMKaJQPVEAqFpqERiWgZS6jqXbtvGv/7sZ/QND2M5Dhw7RqNt09jYiBCC\nQHEAWVoE3EVWgOsDhAOqfkrJfPFWVSmyMoKuixBike0Bc2PZBIUFFj8LWwjRJIT4NyHEr4QQvxRC\nfDp5/xohxE+EEG8kbyvO9lorlZ07d1JdXc0VV1yRvu/06dPceOONXHzxxdx4440MDw+fwy08X4kB\noeRtBsoh8asHiP9oC+qni5tTnInjwAMPwJYtsHWrd/vAA979sViMUCg0OVd2leErL0foOiqRAEDZ\nNrGeHmy/H1FR4U2tSY1E6e72ouglR8PLzuwD9iRvdzGbhc98UEoRj8fTM8FTqu35BI92zGYsNJa3\n2dOGYSBwURO9yUb/MWAUhYZAw4ifhpqyvFiP+f1+duzYwdDQEJZlAZM9ktu3b78gy9dZx0E8nhU8\nIoTX2jHvSU5hvLL11HYDM3l/OH3P1e1Xs6ltE1bEIjIYwYpY2UIpmBSBSonm87Hu5puJDw9PHrvJ\n1pN1N9/Mw48+yo9+9COCpaX4AwF0XU+3SFRXV2dk4NrxLs4iwCBoMVh3M8QDXtAIkwKyddvzckHe\n29tLX18fUsp0NrCvr4/e3t5Fv3aBlUE+LsNt4E9c1z0ghCgBXhJC/AS4F3jadd3PCyE+B3wO+LM8\nvN9ZUUrlNYV97733cv/99/Pxj0824n7+859ny5YtfO5zn+Pzn/88n//85/nCF76w6PcqALP1EzmO\nxnNf76B2vJPTE1U4rklzo0WT0znjtIG5Wph0dEz353voIYef/rSDkydXt4o1tRAd3bOHgfFxTvb2\nUrt5M7XvehearnPIMKg5fJj6gwcRo6NeCnYJypu5j835mxXPRj4yHzParSQneSxUyS6lpLrCT99o\nEToJJArFGWwcankNaf0Y1rx70jh6keSyN2pry7+IYbWQeRz4KiqQx49n9UBm2k7Nfe2oxDtHWWQH\nkVby/slsXi4P07Nd0Kzftg3AE/2MjyM0jdY77qDpttvouukmSkpKKCsrY8OGDfiS2dOnnnqK66+/\nPvOTM22yzHoD6JhZQDYHZjq3KqXo7+tHOAKhe/uylHKB7QEFVip5F9EIIb4PfCX53w2u654SQtQB\nz7iue+lsz12siGYpU+bHjh3jlltu4dVXXwXg0ksv5ZlnnqGuro5Tp05xww038Nprr+V87rKKaJZR\nDLB0PMBMvXEdD+zkusgWEhTjYqZVteuaLZrrIpPN30rhxOI89Mg3ePSb3zxr8BeLeRnH4uLsCSFH\njjzAiROdvPOdVfj9ZjqD8/GPf5yPfOQjq8pXTzkOD99/P6effZb666+n8tprGR0d5XQ8TnNxMXVr\n1lD78ss0PPcc7NuX1/r9cpaz0iKVHOMdJ8u8M6tlAV584EVefuRlShpLwAEn6pUar/r9t1Ne+ZtF\nKdldO0rvM3/BgLYJV2gIFNUcpl7tR9j5EzBkkmuhX7Sd0yolS4WtFNrll+dBhT3zOWsunqZzYerv\nFQqF2Lp1K9XVnsm4pmkEAgGi0Sh9fX3s2bNnbj2u814zYjjOAB0d36Wra/e0c6tA8GLni4wERrDH\nbc8Qv7GE8uZyEF4bx+WXX14oM68Slk1EI4RYB1wF/AdQ47ruqeSf+oCaGZ7zSeCTAM3NzYt6/1TK\nPHPh6OvrA8h7s3h/fz91dXUA1NbW0t/fn9fXnzfK8eb6HuvyRl0J3fOMW98+3VtsRTNzP5HjdLNv\n7/v57Zs8+xfwqj2mCSdCJo3Vo8jYEIyYMHCawb4+rgtUEH/Pe3n6V68Qi8dntDDJNaPYcWL093eh\naZPbYhgGExMT/OVf/iXf/OY3MU1z1WQkrUSCrx88SEVTEzve9S76x8ZQ8TimEIQiERpMk4FLL6Wu\npQWZ56B4uY7NSZGKns5wZGc+qpHya8ymlk1EExw/cpx1f7QOoQlc5RI9HIX98OI/PsO6t+4nUFWe\nVrIf3bMHgEvuumtO2yj0AA3N66k7+o8kfI0Y0kGqaF4m3cxEZo9k3uycVilS07jkrrto3bo1jz6Q\nS9/Pq/l8BGsml9GpPa6O4zA+Pj7/Htc5C8gmK0MdHSE6O4eoqmrCNFuxrET63LqZzbzyyCus+6N1\n6AEd13YZ6xkDoLSpdJ7tAQVWMnnLIQshivEamf7Ydd2xzL+5XpozZ6rTdd0HXde9xnXda9auXXjf\nz9kWjlQf1FIghDj3NgFHOjyDYb0YfNXe7dFO7/5Vxcz9RI5jY2vgoqMJK/0XKUETFglHh2GB6h0i\nEocjx4+RUIoPXrWZ979tE6Zppse8Te1lzJxRnMKywjiOjaaZ6cDy+PHj9Pb2IqWkrKyM4uJiOjs7\nQB926gAAIABJREFU6ehY+d9zOBzGtm1KystBSpRpQiCAxDt+bMvCra0lcc89eX3f5Tw20yKVKUGA\nlBLXdUkkvo6XKSoGqpO3nXgLo0fPb3oouboE13ZRUYWbcAluDFJ8dRGx8DAysGZSyZ5Uwx7buxcn\nHp/7hq5vR7beg8/uR8b7wI7kbdLN2TiyezdH9+xBDwTwrVmDHghwdM8ejuzePfmgWAxCoWXqhT03\npAIyzedLCy8WXlZd2n7eXCx/j6unNI/FAnR1jVFV5cM0e4Hj6XPrt775LV557BWC5UGiv44iAxKh\nCzRT48ypMySsxJTezAKrmbz8ikIIA++o6XJd9zvJu/uTpWuStwP5eK+ZOPvCkcjr+9XU1HDqlJdg\nPXXqVLqMcE5wYl7mMTVNA7xbX5XX3+KspkUgs58oEwtN0xk508C+Izso8w2lg0iBRUVwCFp2cOrg\nGX7xssb+lxKcOeMSmXAZnYhww4a3YWgapmli2zbhcDjr1VMzioeGMoPIShxHp6bGStqyKUKhUDoQ\nMk1z1qB0pZHKWIyOjnrjxTQNgkFUWRmypAT9uusQtbUYeV54lvPYNAwDIcS0oNQTRrgYhpfdduI6\nE/02TlxnUi0bQynFaGwUJ+Lg2slrXgUqqghcGkCYAl9xdlAgDQPXcYiPjMx9Q1Nm7Fv2wXv2eLeX\n7FryakFOO6fMIHhiYmYlWYFZ8WJuP7FYA/myoTob7e3ttLW1EYlEGBwcJBKJLFGP62RlyKvWuJim\njnehHwIUpmkiYgIrZqGZGhMHJ5g4NIEwBFqRhjQkFcEK6uvrZ36X8/+65bwiHypsgXcJf9h13S9l\n/OkHQCqVcQ/w/cW+12zMvnDkP2V+22238cgjjwDwyCOPcPvtt+f19edFPOyVreWUrJ00vfvj4dzP\nWyFkK5xT/oBDTAaRXj+Rpm3nzjv9PPbv7fzw9TYCeoQScxDdjdBX1Ma3//1ehoZckBK/30QIQTTq\nciai0KSkJBCctbzT3g5tbRCJwOAgxGJ+br55B4GAd4VvWRaO42DbNg0NDemS9UxB6blgNrV4KmPR\n39/PoUOHCAQCgFfabmhqQrluXrMDKRW0pmnLdmymJlLYtp1+v1QPZHW1H9worz8e5em2Uzz76X6e\nbjvF649HUU4CCHvBrICS+hIcy8FVXhDp2i6u61KzwQI3O+BVSasVX3n5/Dc4VT5cpn7lnHZOZATB\nX/mKpyQrLobqau+2s9NTmBXIyWzuDfNlqnPA2UiN8Ny3bx979uxh37597Nq1awnaaSYrQ5WVJrou\nsKzUzGkX8M6Prt/F9Js4lgMuTLwyQfiJMENPDnHqG6doaW3JWa3L53dYYPnIRw/ku4G7gUNCiP9M\n3vfnwOeB3UKINuA4sC0P7zUj6VFGMzTPL2ZRvOuuu3jmmWcYGhqisbGRv/qrv+Jzn/sc27Zto7Oz\nk5aWFnZnln+WG1+l1/OorOwgMo+eXkvB1Dm9Pk3jrg99iLb7/wAzCFP7iZz4vdz9oX6wy+nevYsn\nD+xkTXGYD9xeycfu8vM/Pqi4+hMCQ1c4SuL3NxCNnsCx/NhKER4dzRrzNpXUjOKdOyd9IA2jnY4O\nT8VqJc16GxoaaJmcvbcifPXONvM4JaK4J1mefvzxx4nFYlx11VVcdNFF1NTUUFNTM2t2YK7kEsyY\npkksFsv7sZmL1GcYGBjAyZrHvYY3vqU4umcMX4WONAQq4XJ0zxigc8ldlekL0dLGUgDOhM7gJlyE\nLihZW0LLzqs59t3vTJ/mc8cdq0KIkmljM9VXUAiB73vfm7QhgGxrp507zx9j1DySy71hvnaqixWZ\n5fIBzS+TlSG/32THjgY6O3uoqtIxTYFlkT63XsmV2V6X0QyvyxkU5zN9h1oiRvtHzjNT3vOIRQeQ\nrus+hzcCNxdbFvv682HmhWNxi+Ljjz+e8/6nn356Ua+bNzS/J5g52jlZxk55ei1RU34+6OjooLOz\nk7WVlWyIxWg+c4bIo4/yrR//mHfcdx/rt/0YqY2gnHKO7P4Bx/Z+Ete2Wa/rPNh+M6XXb6OqugG/\n3yt7TMQkP/31Gm7aOMRoFIKBFnRNUGSe4fv//jwjY6NzKu/4/ZkONlrWpI89e/bwve99D6UUmqZN\nmz28LORwOk99l1NnHiulkFJOCyz/5V/+hZGRESoqKtA0La9TG3IJZqLRKIFAIJ3FzdexmYuZJlJ4\n5dsyfBX9SEMDBNJw8VXYHNtbSetWgeabvBAtbSqlrKkMO26jhKKuvo662nehSTHNUiVltbLSmWZj\nkxkEv+99aEePZivJwPv3Elo7rWZiMejqWnzMvZwC0IWRPTmqvb0FsOnuPsHo6Fp0PZo+t4pkOHCo\n+xBq1LPAmuZ1mUGu79BvONwz0cE7/7IL9U0baeqwYwf2PW1MjMTP6TCAApOcV7/ABT3KKNV8n+Hp\n5TTdQ7zkQ/ji8RWXHYnFYnR1dVFVVcWGaJS3nDlDXNNwpKR3aIgjTz4JeMrWI7unz3A+/v09tOrg\nTypfUyKYf9rvCbFu2HAazadI+Br50Usl/N4fb+azjVULDvJSlhO/9Vu/xcUXX8zg4CAHDhzgpZde\nWj5fPcfxLtW7ujzJuO6dVGP33JP+LlMTR1K9mV/+8pcpLS2luro6K7CE6Ur0fDCTYMYwDCzLYuPG\njTiOsyzH5tSJFF75tgpp+PH6thKAizSacMfLiI+MEMzIwqazQYagrrounQ2aquBdacfW2ZjJV3D9\nbbfBY4956Z/MINKyvH3tApxcMzsxhofDSFmJaWafV+YTc5/dOWCleCZOKs01zWbXrkp27vwU4fCH\nqayszjq3zsfrMpcDxvuPd3BTbydDoop4uYlPWLz01/s49HfDqKqaab6sBc4N51UAmeKCHGWUaspv\n3YmaGODIP/2UY4/+GNf++Yq06Ugpgv26zrrxceKahisEUggs14WiIo7t3cu6m2+etem/detWNJ8v\nLYLp7BR8/8Vq9r1ahV+zefOkzt33SFrXL2w7U95rp2Mx+oeG0HWdxsZGamtrufjii/nMZz6TVc5e\nUmao84RHRnLOPNY0jYGBAZqamqYFlt3d3ezcuTM7oF7MDMfkcxMlJTMKZhzHwXGcc3ZseuVbA5Wo\nQxoKOAmASoQQmoWvvASY24XoVEuV1UQuG5t0EOwdRNn72NCQ1xxcKCEmcXDiXyU+8jhV5Q5f/arB\nM8/s4Omn21HKO7/OJ+aeTWQ2v5GKS810M3KvdJ770anJT2cj0wHDNMFwYtwQ6mJY9wZFmCa81NPA\ngdFKimQf2mUtODYc6DwAeMFqgXPDqgjd8212vpws+7Zrfo788/Mc/c73Z7fpOMekFMEyHkcAbrLP\nRymFkBJ/MIjrOJzp6Zm96T9D+Zopguntk/T0m9x9j2QhyUHlOLz++OM83dbGs5/9LG+88ALW0BAy\nuZ26rhMMBjl9+vSSWkSlmaVWVvnP/4wuZdrKI0UkEgGgqKgo6/5pop/FdLBPea5x662Ivj7UlOcu\nlZhtPkyOhXsDlTgBmKiEj/iwZN3NQ2i+b2Q9fvHWLiubTBubNFOVZJGI9+8LdHLNVLzzwv083fYg\nz346yr99wib6HLzvvQ+xZYsnNErF3Nu3zy3mXm4B6OJJTY7KzwXFVAeMEiuMcGyitklDA7iu4FBo\nDUU+B014kyM0U6OoqohD3YfyNma0wPxZ8WdGv99POBxelUGk67qEw+FlnVRyVpuO+XjVLSEpRXDo\n9Gkc10W4LkopLMvyen4cB6FplDQ3Z81wTpFL+ZoSwezbB3v2eLe7dnn3z5csr7zaWoSuEz11isip\nU+nHLJVFVE5y1XkATBO/Uuy49dZpfnBjY2NpRXIm00Q/qczmQpS3U54r/X6qf/Qj7L6+HCroc+//\ntn7bbbTeEcGOGsSHXeyoovWOUtZvqyFl5XNBk8+D6DzkyO5uju55Dj1g4ltjoAckp/91gjM/C3D9\n9d2MjsbmHXPP7hxw7o+ZheLE40z0989pzcm8bnlzrJKEq7Ou3qKlBSYsHeUKNGxvZrnmFU41U0PZ\nionwxFJ/lAIzsOJL2I2NjZw8eZLBwcFzvSkLwu/309jYmMdXnH0E26w2HePj6T6vlUCqb/CVhx9m\nQzyOLSXNzc001dWlla1maenMTf8zKF+zRTDzZ1oQHo+D6yJNk4n+foK1tYhko/uyZQim1nlSJGtl\n7Z/+NJSXZ808/sQnPoFSiq9//etZ4pos0c9iVAAzPLf+jTfgqacY2LEDJ2myv1SCmfkitREuuUuj\ndWsN8RGFr1yi+VIL9BjesbUSRAvnmMUeROch3nnhB/gqSIqwQBoCX4XG6M+jbPp9g29/O0xFRcO8\nq/1LJQA9Fyxk0lG2A4af6id3YHzra1BaSlAWIWNRHOsMmk+HX/wCGhpw6hqRuiRYGVzmT1ggxYoP\nIA3D4KKLLjrXm7ECmBwjNdMINjiLTcdCveqWiJSHWfSeezj02GMM/+xn4LrYsViWsnXGpv8lUr5O\nC8KVguPH4S1vgfFx77s0jCWzocnJZJNnzv40ragoSy2emnnsOA5SyqzAMkv0M0tm86wqgORzVSBA\nIhDAiEaRjoMwDBp+/nPqPvMZEtXVK0zM5tmRaD6bYI33mZXSSCQ0DMOPlAWhyGLJNWPbjtlzElSs\nZLzzgkQaEkh5IHpBpDuuSIxp1NUtbP85nwSgR7q7Ofrkk/iqqpClpfMa9+ldtzjwRwq2jcHpI+jR\nBBvPnObAC9dSVKSj4eAcP0Fk1GHT596/aven8wGxkkrD11xzjbt///5zvRkrlAdIWSh47v+eubY3\nbzVbTfv649NVy6mM3Vzn9Z4Lci08U/8ePX0aWVyMv6hoyU6wTjzO021t6IHAZBApBG5LC7S0UNra\nitS0efm05WfDkirs7u5JFfb27V795ywlxpQPZCqwzPiD179YXDw9sxmJeCXMGdIpbjRK71/8BQOb\nNuFqGkIpqg8fpn7/fsT4+KzPPbd4x5LrrqW39xoGBi7DdRVCVFFdfeXy/qbnEbkyT80f+CBjw628\n+vgvUbaaUT27GgLMyfPCENII4Z2HJSrhYEcttnR+Es33X871Zp47HAfnq1/l6YceQk+JghobobkZ\nZdvY0ShbOjvn4FqQsdYlBOz/Oapc8NL/uIFDP7kY5Qokio1rQlx98GGUZq74fWc1I4R4yXXda3L9\nrfBtrwomx0hNzog2mRzBtpPMcvasNh2h0Io1ZZ1N2eq6Ln1DQ97sZMcBx6GmtpaG5ua8L/Y5vfIs\ni/gvfsFbmppYt3HjuckQaBrqU58i8bGPYYyOIteunfPvOKPR8Fkym7O9fu/p0/TddBP68eNITUPp\nOn1XXAGjozRs2LAi9zEPL/va0/MGvb2X4fdPYBj1KFW/wrz3VhepvuHMC9cX/vYZxkZ6WHNJo2cq\nbTlZ6lnlKF7qeIlDXYdmDTDzwYwXUXMk+7zQgDT6UAmL+DC03vEeNN8f5nV7F8bsLU5LSkcH8W98\nA1dK78JbKejpAUC2tMyxhWrKWmfHwNaQEzqb/99nuSpmMxHxETRtZHiAl/7heQ7tPbHk+06B3BQC\nyFXB5BipbEy8SS3ZfVvTbDpKStC+8Q246aYs/8C5ZK5WCr29vZw6dYpEOEy0vx+EYOzkSQb/8z+5\n8pZb8m5PNFvZ/FxYIeWcVCFlfrJlqXJ2d7dXttb1s6oA0t51tbVI24ZQCJlIoCcSDHzgA9TdeOOK\nVeg5Djz4oCAYfDux2Dium0qUSHRdrDDvvdVBLvEewmC4txyhhZG61weeqZ69audVvPzwy9lTS6YE\nmHnZtrNMaZoP2eeFMoTm0nrHrazftp3MVqLlZ0qLk/JD4mNg3Asyo0fbiXmjbX2V+R0wkeyH9lVV\nIYaGUKkMpGlCKISqrZ1jC9WUtc40QQjPsrXYRS+PU6okWBYvDq3jwBNHKaouXrJ9p8DsFALIVcHk\nGKnsINJK3p+77yad0XvggcXP2jqHpIKVRDhM9NQpb4ESAuJxhi2LN3bv5tI8l+Zn9co7ByzppIpc\nMxzPkqFJe9fpOrS0QFMTWBbSND3vOqVYCc51uejo6ODJJ5/kvvvuw3EclFL0JDMlLS0tK8x7b3WQ\nS7yXiElcBFKASthoPi/A0kwNNaoYC41xqOtQOnhM/S0zwMxHSXKmKU0wfzP9lXZemKQD6AR3LfS+\nAwY2gqtAPAfVl0PtGjj6IBzrAtf2Rtyu2+ENoJB5CHyT/dCaz8e64mKOnjmDT9O8c5VlER8aovWj\nH53DdzVlrdMkNDZA73FIaHDGW7/sgdMc4rfTwSMszb5TYHYKl9irgtQYqSG8AwsmeyC3M2up4mwq\n29jKty1JJBIoxyHa3z8ZPCaRhsHxf/u3JbMnyumVt8ycbVJF3nwoU8rbOZT3pnnXSQl+PwpWqHed\nR2oCkt/vT5s3SykxTZNQKIRt20u//bGY10qyCo69uZIp3kth+BUCF9cFaUwu5o7lIHVvP1a2SgcA\nKfJpz5I58SqXmX5sgb/BSjgvTJJR9u19B/RtAmmBEQd5EvoG4OB3vVG3ejH4qr3bo51wZA5WXXMh\nwyVifWkprSUl2EoRTySwhaD1Ix+Zo+gxx1rXUgcXl8O/rIHe0xCJMHHnx1FV1Uu67xQ4O4UActXQ\njieYiQCDyds2JsdLzcBsKlvb9v6+wjEMw6s7CpEVPKb+352YyDIUP9+YbVLFsvlQTmG1etelJiBp\nmsbhw4cJBALpIBK8gGPJtn8xhu0rnEmT9uHJINJNUFE/hPRVopJWpI7lEBmKsHH7RkobSpG6xLGy\nP38qwMyHPUvq9546pWmamf6qJln2VQEYeBvoEZAKECBt0GwYccFXCzL5PUgTfFXe6FsnDxcyGW7g\nMpHgkrIytlRV8R4p2XLffVxy993zaP2ZstaJCVj7Obj/YNqbNPjZP0Dq2pLuOwXOTiHHu2qYPkZq\nTk3SZ/EPXA3zbaWU1NTWMnbypOfJCF7w6PfjvvEGAlaUPVG+ycz2ZQY253pSxWr0rktNQLIsi4MH\nDwKwYcMGhBBomkZ9ff3Sbf8MoyiBVdFKcjZy9Q2/478mVdjf+iVq1BM6bGrblBY6bNyxcVoPZGQo\nwqa2TQsvQSoFCRsMPev3zgwip5np55GzuUnkn2TZN2GgHEHctjFNiSZdQEz+J8uAM5NPkya4o15P\nZDAPorEpvdSarhO8774FTDGaXOuUGiSRKMMwipF+CQ3eVC0dlmbfKTAvCjY+FwK5eiBTKttVsnC5\nrst//uAHDE9MpPus3N/8htiLL9K6deuKtifKB6FQaFoPZMqH8lwrhpVSq8q77oEHHqCz82tUVRZj\n6gaOaxKLW9x55538wR/8wdK8aYZdkqPrxJXCJyWabZ/VLmm1MR8fyLQKuztDhb19/kraWCxGeGiI\nakdgnD7jTSwRAqrX8H++/yQPTemBTJnpz7cHcjYWYqCdLxznKzz04Jd4Z/C/E4lFcVybxkadlub1\nCNUEwwfBfcRrNUlvsAV2BLbsy7+gZo691DORUzQ4xTYtX/tOgdkp2Phc6CxAZbvSEEJw5S238Mbu\n3Rx/6inciQkE0Lp168y9NXk4ka0UFpLtW5RtyTzUmqmZ0asDh/ZP2NB/gu4nhxl1QNcMtn/kdj55\nX9vSvW04jEokOBKNcmx8nFRuaF1xMesTCeRshu2rjFx2XLpfp7ShdNpjpSbZvGszV+28akFefpkK\n69uueScfuu7dBEpLaGpuRrgu9A3RfttWXJjZTD9P5LIxmquB9mLxkts28Ruf5oNXfYDhyCg9PSAR\nNNcpKBfQ1+eVraXpBY/xIWhty0PwOMU6aBFTjFKnbNvuZWhodtHgYvedAounkIG8kDhPAqqzlohS\nhttdXavWtmgm5pLtW6htScyOEY4MUN37XYwTu5dGrXnOeQBe/2s4OkpMmITPKCpLLPxuBbT+N7hk\niTLysRiv/9ZvcdS28ek6UgiU6xK3bVp1nUuee25VH5PnCi+b3EldTS1/s6ONSDxGNBajubmJdS3r\nvHK2cuHKS4hZ1qJ8IGcj5/ABvAlgczfQXhixWIwtW7ZQXFyM32fw/re9nRs2XIUQAith89Yb3o3R\nWJNUYXdnHNfbF3lc55iOltgGAx+GyurZ9+cpa1HmKRsUn/zkK1RWSpqbZbrtXSmFUoorr7xyVVQ6\nzhcKGcgCHufJfNtcGY6sEtnDD563vWZzyfbN17bEUQ4dL3XQdaiL20SIDxlDBIqbaCpvRaiEp9aE\npQuulo0YOI/BsXHw+fDLOA3+GOCC6oVjfwut94BWlPd3doTgWFkZvv5+r5wpBNJ18dk2xyoraRXi\nnLoIrkYyFdaVZWVoUuLiphX1TU3NaFKCY0PCntlMf1Hb4MVBQWe6jRF4LhFzM9BeOJlCIeXCj155\nhX2vvkpJIMibJ3p4/H3vpkHTveO3dWcefSCT1kFUgWvAiaMQ/Qv43pfhBw25L9pnuLh/ULXT+XWN\nqipYsyaBYbj09HhBYkuL91QpZcFia4VRCOMLrGqUo3jxgRd5dMuj7N66m0d/5xu8+Lc/RVWuXtui\nmYkBoeTtDI9YgG1Jx0sddB7opMIMcKtvjDHh49hYL8dHj+dfrXlOCUM8Bi4g40A0eb/0esPcPoj/\nw5K8c3xkBLeqCtnS4i2i8Tg4jjehY+3apXcRcGIwEUr/hk48zkR/f5b9lVKKeDyeP1uoJSYzcDoT\nncBRCk1KpBS4ysWyLC8DKQQY+c2VOA488L/j3PY7/dz54Ti3f7Sc/kEdZWU7IqhEYo4G2gsnUyiU\nIuE49J0Oo5J/T6P5PcFMXsrWGRNjjh+HY70w5oNbx6Ai4F20d0yxCUoJyYqLoboaiotRX+sk8qWO\n9PV+NGrgugK/XxEKeT8hnHvRYIHpFDKQBVY1L3W8lK3EOzPOgf4GKCpi87rByQeaptf/uSp7zXKU\nitiBZ3eRnbeazbZkdHSUcDiclYWJ2TG6DnVRVVRFpXTQcHGFgalJQmMhmkqb0fKt1jxnVILPD8IF\nFQWZUqi6eAaWJvj+Gfgs+R4D5ysvRxgGqr4emTRdxzRRjoOIRpcuwFCO5/WXNJBWrsaR167k2C/G\ncB0Hoeu03HwzRe95D4ODgzMKFlYiWYGTafLM4YPctHETw5FxhBSYugG2A7VV2eKRRaIch6/fv5vI\ns3u5WbdB6hwxbuaZwQ9wg/1dGi+Z7IGMDw/TescdS6rG9vv97NixY1rVISUUyne53iNjYoyj4GTI\nO8e6EjTLa4dMJC/ad+70ql8zeBLHy6p435Fufn7ZThL4cRzJ4cPVbNzYx8iIjmVJTHNSNFgoX68c\nCr/EBYxSirGxOCdOqCVLzNkxm7HQGHbMXpLXnjbJoihAkZHg0MkKbCdj8VtFtkXTSZWKioHq5G1n\n8v5MYlRWxtB1mZWNgJltS8ITYSzbRlkmo8rEQaChkMIrB1qO5TXcC90re61q/KDdDeuCEFde0Ijr\npTjiOqxrAk3hLY75Jcsn0XE803XHIT48zLqbb166AONIR5aB9JH/EBzd+yw6YXxr1qAHAhx/7TWO\nHz6MlDLdW9vX10dvb+/SbFOeSAVOQ0NDWJbFjw8e4Icvv4guBK3N67xLq9oqqF+b1/f9dfduxp/b\nA2YAy1hDQga4ZGIPxcWC5yN3kIhEiQ8PY0ej6fGnS017ezttbW1EIhEGBweJRCJLIhSaJGNijGV5\nqncpvePHEd7EmKlewzN4EptFJgY2/sjkcXfwYD0vv1yL368QIoFSasVbhF2IFDKQFyCu6xIK9XLw\n4ADhsIvjCA4erGb9+nra20VedCZpi4WuQ0s26H4iPDF9koXU0JrqUb/pYyLiUlpKtm3RKhIqeH2d\nwwQrH0f3J0tFkLytArrxfEENJjOUFjff3M8TT5ykunoDpumbMRvhOPDkY5W89oaOSFhowmTPpQ3c\nVdnDsKsjEJiCvKk1l98fLxftsD4O/Dc4ZnvlbOGD1otgfR0wwUyjQRfLbPPVlwQn5mUek8pbJ6E4\ntj+Kr8REWn3grkP6fBiXX068v5+S6moge8rRYmeCL7VuLxUgpRTWu0//G76Weu67/kPg9+U18wje\nPvybH+xlggoMLWknJgziWgVvif2QJ4xO/p//tZXKYHI/NwzPk1KI7G3J8xejaRq7du1i586dSyYU\nyiY1MaYTzDXJIQ82lNnwVLM3dnDqRfsMnsSabbGmWufNsUpK/d6f4nHBT37SQEtLHRs3ztEiLMP/\nM9+/e4HcFALIC5De3l5efbWPEyd0NE2i64prr+3j+eeho6MhLzqTaaXlJRh0H6wMpidZZAaRTl0j\nckIRTLwMg9aqsy3KDr5jSH0TG3ec4er2wQzBpAmM4mXLvo/jPERHR5Surn5s22FsLMbYWJSqqlYM\nw8iZjejogEcf9tNw+Q56azsRVhV//6sm3Eti3Fp5iqaitWhO1Ase1y/8uzuX/njT0UB+Bi6R0Pog\nxCvAV+qV3TgF3Ee+y9cpln2OcjzsKW6T00fiEYXrgNQ1ULaXWQ4WIaTEdRxUIpHengULFpKBkVNe\nSccj/iU3QljuwCk+MoImbVxp4DgKsJDSBGEg1DjF2ghr62rw+6qhdxAGTmd5UlKzBh58cMkcIpZC\nKDQzyXOC1g3ry+DUIPywCZ5uyX3RnppWk8OTuOYzbdwt/dOc5j75SYmmnWX/c93c33X92uzJZQXy\nTiGAvMDw/LQG6OnxgkcpQSlJLKZz3XUDPPRQHTt3ykVdFOcsLS/BoHvdr+eeRhCeYNOf3IS+809W\npW1RdvBt4liKA53eVfzmXam+Tgvv8C0CuujoiNLZ2UtVlYlpGpSX6wwMxLj99g/y2c/+2bRFNbMd\nyRhux9AUkeIvU+QO8L1fwdozVXzyt9vg1s+AuThV8oz+eLbNJe997zn6fe4HzYBgF3AYLxBfA+zG\ny+hO7y/NF7lcBJYEX6XXeqAskCa+IonQQNmOl82RJsTjuEqBpmXbz8xXsDBFXTswpDNq7aAqH3Uu\nAAAgAElEQVR0Qzu6T1tyI4TlCpx85eVIXSPo/w19A6cQwusZDfjqcGUlt3+03NuVQ4PQNwS65tnk\nKOX9+2c/PY8cIjKmo60dgO9/F3Y/AfbQzBftM3gSy/Z2dmleu+S8T9m9M3zXAA3VefqsBXJRyPNe\nYCQSCRIJF8eR2UMJlETTXEwzsejx2DlLyyzNoPur269mU9smrIhFZDCCFbHSo9LStkWrKHicHnxr\naGY9RVURDnVXYMcEXvA4BGwHIsRiFl1d/VRUGDiOi+O4mKZOdbXGD3+4N+f7ZLYjCTQ+tV/yg65S\nvvAvm/jid9/DvX2XonV+Fx56ZFGfx4nHObZ3bzp4BJC6jm98nGNf/CLOhz98jmZCpxa/bTjxIib6\nr8GJX87M/aWrEM3veXjGh0BZaIZk3TUB4mcslFkLQqLicRK//CW+mhpSjsALmmmeoa51qqo5Hi7m\nI6Od3HrK+x7PFyMEzefjRGkR7vgJigISIXxIV8OwThKuLaX9D31eADNwOhnQJL8/mVT6Wy7U1J5n\nDhF+0Jqh/dPeRKXkvGp27ZqeVdU07/4ZHjfvU/ZM37WuefcnJdyrzWFgtVDIQF5gGIaBYQg0TaGU\nzDjmFI4jsCxj0TqTGUvLSzDo/nybRpA7+G5BM0GNDjARHqW0wQHa8LJkCQYGFKFQlLExO13BaWjw\n0dJiJHvYw9OyM5ntSEVajBtCXUS1aioiJo4Cs0jzeucyVZQLID6Swx+vpwcZCuFqGvHKSoJKnZMs\njHIiHNn9GMf2Grj2CEIfYd3NxazfVonUUv2lq+fiIyep1oNj3eCOsv46Dcrfw7EXx3CHhxGaRsul\nl1K0YQODg4Pzmmme9l4tkugZ6lorBglhMuar4oZQN/uadpLQ/KvbCAEAh1jsK3zjlW9xVXkxrdEi\nyn0mjuvjaKCMV6IHSSRiaEImRSXeMaxcl4SrMBIWUggoK4PxjJnUq/+LmWSuXsP58iRO2FnfdZqk\n/6drJegND806ErHAwlm9K22BBSGlpLa2mubmPo4f1wGvB9Lvt3n++Vq2bVtc+RpmKS0v4aD7mUal\nrTZyB98Cx2pC6msJVv4xUMNkYKPx3e++haGhl/H5NAzDG/vV0xPFtouprDSnKa8hux2psjiM5tok\nMLEsaG4GTZKXhc1XXo7QdVQi4QWRSsHJkyjDQAA+Kb1Itmrxwep8ObL7MY7uieGr8CMNgUq4HN3j\nLeyX3GXjlbWXZ0FfMrGJ1LIMpKWvkkt+109rDkFTTWUlkXCYospKjEBgxpecJpBTCTYO1nP1FTFS\nu40QkMCk2B2lxApzOtCwuo0QAOggHH6IhCM4Vu3Qo0bwKZu4bEDJBhKDg97FWl0dCIHrOPQmogxY\nUe/CDqgu8lM/NoaQ54tDxDnG0L2dTSmQElcp71yj6Qgh6B0apK+/Pz0S0bZtenp6sG2blqRD+aJG\nvl7gFALIC5D6+npcF5QaIBx2cBzBL35Ry4YN9XnTmVzdfjWAN+h+1FNhp0vLBWbk7MF3S9bjY7EY\nu3f30tRUTW/vEFIqpBToup8TJ8b51KfunPGkmPqtn/xmJTFbR7gWzc1mevJDPha2lH1NugfScbxy\nkpS0lpR4k0Jg2bMwXmn9Z/gqJNLwJlNLQ+Cr0Di29wytW4Noy2BbtGxTN1MG0ql/ZvRhzlfk9MJX\nXuLAQwcoqS0i4NdwYhYHDjfB0SE2rx9Gk9DYAL3HLRKazhmzcrUaIWTgGWdXVtag6yEsS2GakqjU\ngV4sq27SJktKqF5Db88J+uwYupTe6Eql6CsNwFsuouH117NEJGf7Ys6TKbT5J/ldu6cGiYQHifYP\ngBCYwSATOgy43u8ihOD48eOcPHkSIQSvv/464+PjuK7L448/Pq+RrwUmKQSQFyBCCBobG6ivr2N8\nPMHoqMFHP7r4zGMm51tpeTmZT/AdHhyiIlhEddU70PUjhEInsW0QQrJ2bRkf/vCHZ3yfVDvSzp1+\nol/aQdkTncjqKs9Qe44L28TEBIODg6xdu5ZgMHdrQpZ9TSKBEILWYJD1pRkZ42XOwnildYU0moAe\nPFW7F0y64wniI7cTrFn6lTrVOpjSVLjRGD/4P2G0RCXtn16uTOwMIifgkrvuSj/OceD/fsXmjb88\nREIUIUIaDQ3Q0mJS1FTFoRM2VzUMoAcMWuosikaHeMRoo/e0f1mNEOYyL37+eMbZfn+AHTsa6Ozs\nSQrWZNImq4+2tj9IX6yp2koGTh5DFwLpAsJF+n1oxQH6PvhBag4fRh8cPKtDxLJdYKxm6tcysP9F\njAkLM1iEi0vf0dd481cHKd2xg5LaWo4fP05PTw+maab9Tjs6OhgZGWHDhg1zGvlaYDqFFf0CRkpJ\naamP0iWs/J4vpeV8kDWve5Zgek7Bd9K6onZglC98/JMgBD/99SF+9J/7iSXH00WjUaqrz65C9PvB\n/2ftUM40deRMC5tt2zzxxBNYloWUXtncNE3uvPNOdD17W6fZ13z722iPPAKJxLyyMPlksrRehzTA\nGxGZQCUEQqvBV37/kr5/LAahEDz2mBc8+g2H9x/r4IZQF8KxSfylToIdGPcvbaSQU+RkGPgqKji2\ndy+tW7emS9wdHdD90ATvFIr/n713j46rvM/9P++79+y5SLJ1Getu2ViKucUQbEhCyKWtawohHFpD\n3GKHEGxAxU3Cas75pac9q+u31jnrnNOe/nJWaalbAUoCxA4Y3DQBhxLoSU+TkguWHGPAxMggy5Ys\nSxpbI2ukmT17v/v3x54ZzYxmdB9JNvOs5TXWjDSzZ2bv/T77+/0+zyN8GkpBT4/7PGub16KiDmPh\nt1gxOozQdVb95118+d5W7h5enMqZ4zj09fUVaN5twji7tdUt0e/b10s4HEfXHXbtuj/DJituWTge\nHRnwg3JwBMRiMcyxKKq0lCN/8RfU+v3Ur1uHmGJcIPsCY9mItu3oAmZqz3NTTJMjz30Xo6QUo7QU\nKxZDKRvD6yV27hy+qipOnz6dIo/J81V/f38qYAEyI1937txZbGfPAEUCWUQRBcZcTdWnJN8J6wpN\n1ympWElfbx9bPnwdAN9//WezjzGbKEfOqFf23HPPYdt26h+Abds899xz3J1Wtcp4iWTb9I/+yCWo\nMySrhUBma70e6VmNio8ROx+h+c670LwLJ/RKh21G+U57iG9/t4rhCz66umDtWnjIaePmU+2EjSC2\nx0BFTZwn2l1HoQIyhZwiJ1wS6YyOEhseJlBTk7J9KqsJQK9EKBspNQzDJcL1tQrZUEfgxe9DJJza\nf3xAw/xcoGaMvr4++vv7U/NuSZIALIDFz4RxtqYF2b17LTt31hAKnaWq6n58voczftvj8SASbWsp\nJbFolFjiwk5Kie7x0B+NwrlzebdteDjKN78ZoqKiCsNwj8V00fYijgtPICsaE6G7Sv+W1slClkVC\nch9Gk5hjoynbKqlpWMePE01cQCTJo9/vp7Ozk3iiG2KaZuo8mS/ytYjcKNr4FFFEgZH0dTRKDUqq\nSzBKDTrbO+lo65jbE2ZZV6xpWkN9Qz3nLoxwU8uVxGOxuceYzcBHY2xsDNM0c8YlmqbJ2Ng0Nk3T\nWHksFlq2baP5zjuxxseJnQ9jjSua77yrMMkwyobjexh4ZjMbI1t59Pc2s+vTezA8Nv3dUT7+3l6X\nPEoDpcDWDLTawtu7pIucMjY3HkdoWiqjO2n75PHrnGvYgG5GEiQSsG1GzkRY/dkNWL7SJbHOUkox\nMDCQIo9AijScPXt2gexbWnHdDyLAID5flIaGh/D5JlerpZRUV1djWRa2bWccK8lKWDLtJ3vbbNtm\nz549bNmymXfe2Upn52a6u/fgOHbi7zMTAhcVWdGY6KXuz11LZ3vlLS9HaBoq/B4M/QzOHYKhn6HC\n76GO/5q6ujp8Ph8+nw+Px8PRo0d58803AbdqbaSl4uSLfC0iN4oVyDlgeHiYrq4uWlpaKE+cYIso\nIhcKYqqeZV0hhGDtmrWsXt2EPR7lhz94Ad+KsoV+KykMDg6mkkqyIaVkcHAwpXCcEgtl5TFHLGoy\nTFcbqqudk71urKAHk1ta2jn/OfjOP96BbVqoMgNHMaGE9xlwobDCokkip8QMZOz8eZrvvDP1eaTb\nPg2ucWdxK3uP4pgKFZP8JLKR7/zTJvSDsOP3Y9xz1zCBqsWLrIzH4ziOg5QSx3HcdrFp4jgOjuNw\n6tQpmpqa5tnKTjPOJoTb1s5PlJM2SP39/alKpGEYqWpXvrSftrY22tvbqawMYhgGQpj09Lh967Vr\ndy+daDsrGtN9E4b7c/c+V+m/BO1szetl7UdXcOLgETemU9dQlk1s8DTNt13GZS0t/PMrr/Dcc8/h\n8/nQNA3LsigtLUUIgWVZqRnIWXduPuAoEshZwDRN7rnnHl566aXUCeHWW2/l6aefzriKKWL5o1Cq\nxmxLiKSvo8ev4/VLYuMKZTuuqXrYNVWf9YxolnVFEhruyZTSwvYMV61albeio5Ri1apVBX39hUbB\nk2ESC2+MIHHbwKuD7RiEY0G2btzHD3+1HestHWfcxNENmppwlfCLxBRmktGdmUInGVx7A30119H9\n9hjRsgCXr9ep8disOf9dIo8d5Hvfs6htWLzIyvSWsWmaxGIxhBApwjg4OIiu6wvUlvQxE3snIQQN\nDQ3U1NRw5MiRjOoo5E77iUaj7N27l2DQJY8NDdDTY6DrQXp791FTs5Pz531Lo2bPisZMQRrghN3H\nA0twQWhHabn8CAyvpPvQOE7URkhovmklLZe/AXaUBx98EMdx2LdvX0px/Sd/8icAPPPMM4TDYXRd\nn3vn5gOKIoGcBe655x5eeOEF/H4/uq5jWRYvvPAC99xzD88+++xSb14ReZBOFj2ewqgabdumra2N\nvXv3ZlhC7PriTq66KUjLxkqkJnAUdL89zPHX+pFSzM1UPWFd4cZ3JX5WCiwbaoMZpLIQCAQCGIaR\nszWnaVpeNfYHFomF1/AZGbzfdgx0Geay5gg/Hd3BVwLteOqCbuVxEYVFM63EZqfQSalj+lawvsVt\nqzaH97Nu7ABRo4LT5z3Ur8ut5i7Ie0i0jM+cOZMij+C2KL2GgXQczvb3U1dXt4DK7JlB13Vqa2sn\nzWdalkVtbW3G9oRCoVRFDEhZavX2GphmmEhkkNbWau67z8OiT6BlRWMCWKZg7LxDoMRAXwTbq5yI\nhZDCZv1nqmn+hCIWUXhLJJpHQmwQYiG0QEPezPT777+/6AM5RxQJ5AwxPDzMSy+9lCKP4J4Y/H4/\nL730EsPDw8V29jJDLguMxkZ4911YtWphVY3JtlOycpC0hLi6YhXX37yWcydHEJpExmOsW+dh/LiF\nv+Nf0b9pTsFeo+RtldUnqnwD58C23IpkbXDi/gLj85//PM899xyapqUWRE3T+PznP78or39RIbHw\napg0Nhr09Lj7nkczids67/dW8amvteKTuOzswtIJi6aqxGbrrKJR2L4dvF6QKsba0YPEtAoQHhwL\nLCe3mrtQqK+vTxlFg2vcLaJRLrz/PiiF8Pv59eHDXH733ciSRVL2pG0bwMDAwJRpP1VVVei6jmma\nifa1K7Kqq4tz5ZWf4AtfGECIQTo7RSIQYhETVZLRmCfaUXqQjh82cPSVClTcQpZWs6Hn6LTCwIIg\njdhqHoNAeeL1lenen0Zsc2WmL1aO+qWIghNIIcQtwCO4HbYnHMf5i0K/ZiHQ1dWFUmqSRYmu68Ri\nMbq6urj++uuXaOuWIZRyZ/U8esErYvmQbYERjcLBg7B6de4o2rmqGrPbTu7zGtTV1FIRd/BVl1Jm\nwciJQVTMRHkk193aSMnoaB72auNmMe8FLNzDdAfuEH+CaAoBDdVQF1ySz1nXde6+++4Z+UB+4JG2\n8K5ZHQQMzp4xKdGHePHYLu75ko8HWwFt5ir4pURydDUanZiLXCmHEVg4woNSIKR7bEmZqeYuJIQQ\nrF69mnPnzgEQGxhg7MwZV2FuWTijo3Q/+ijaP/wD63fuXFQzxWQ7u66ubkqPSp/Px44dOyZdjF5+\n+XpuueVmzp7VOX1aIoSirKyf116DbdsaFk9/lojG7Pjbf6Pzn8spWWmi1TXAiiYOP3kYgBt237BI\nG5NA2vGVms9UppsB37xryW2GLmUUlEAKITTg74AtwGngdSHEDxzHebuQr1sItLS0pKKQ0kmkZVlI\nKWlpaSmmBUDKn5CBc6SCmasr3cqYEHn9w2b82c2QmCZtR5LkMQlNg7Nn4bLLJtaO+YagZLedkqha\nuRIpBKZtUb56BStOv43t19E0gfQHoHwlKCvFXqP4CIWguroNj6cdcMkGmECCaJJVJpUSvEs3fxsI\nBGYmmPmgI7Hwiu59rK0Ns7pWZ6RiF19ubcWXbgO4xMIicL0hZyIqSp+LlFXlOOhgxzHjHpqaEpMV\nWWruQiPVyu7rY2xw0CWPiVlS8ctf4tV1um2b5ieecC/FFtlMUUqZIZjJheQM3r59+wiHw/h8Pm69\n9VYsq5ZTp2SCmEuiUR0hBnjssToeemiRLh6lhtX0IEc7DUrWOpR9NIj/qhKEJqhygpzsPMlHxj+C\nx++Z/rkWElmZ7wjdJY8txXnGQqLQFciPAl2O47wHIIR4BrgDuOgIZHl5ObfeeuukGcjx8XFuv/12\n9u0rL6YFQMqf0LWY0VzC1z8EjoKxf5zkH2Zf1krb49r0n910xDSr3Zu0HUnndIbhPqdKKF2T/r3z\n1Spkt52SCIXDKMfB0N1FTDoKqQMyMQg3PgaGgTMc5qn/HeKxgw1IGeXv/m4v5eVBVq92W1guiQwC\n+3AVoB/Uq5OLGFmZ1Jq3ioplVhnJjjR0hIfaT27hqi/9PkZJbtIzMRfp5U1u48PxA1Q3VLCmKbea\nezFQX1+PeeECI7ruDj3HYvDLX8IvfoEUAkcIYpWVBJbMTHFqaJqWMa9XVlbG8eNdHDqkJchj8jcl\nXq/Nc8/Fue8+76K9DVcYCGUfqyZwTQChCWSJRAhB1ZYq3n7rba7ZdM3itdZh0vG1HAzOPwgo9GVL\nA3Aq7efTzES+tkzx9NNPc/vttxOPx4lEIsTjcW6//XZuuulp2tuhtBRqqiwq9RG+9bhF29JZYy0N\nsvwJAfdW16DnGJx4cpJ/2E+fbEt9dtXV7m17O5M/uyQxlSJRfRTuz31ngT3AZmBr4nYPVVV2qr2W\nhJRQU+MSyySSWoXt2+e+jiTbTkNDQylRiWmanDnbz3mPQFMKdE+C6Arwl8CxN1BAzDDoG/bxD89V\nUVoK69aF8HgsursNTp5MfxUDt529FOZvRSwYkpnUS7m42VEY63Vv05CMNNS8fob6Gjj2f1bz6p+/\nxeMb/5rX97yOsicr79MtPf/ff9rGlq/fSV1wnNjweazx8Ulq7sWAEIKmyy7Deukl1CuvwKOPws9/\nDo6DchwEuFXAJTNTnBmSs3mlpaXYtkAIldF0kVLhOIKREc+ivo1AVQDNp+G/wo/QBFqpBg44loOK\nK0bGRzh9+vTibVA6lsPx9QHCkotohBAPAg8CNDU1LfHWTA3DMHj22WczfCB9vnI2b4ZglaKhr4PK\n3qMIR7Eeyb99YwNfuncTgZIPiF97lj9hCoLEVWETyMSiJQ1sT5Dy4X3UVe9E6FMkLeQlpsDAMah7\nEmQ5yXavFf0WZshm+7aH+OZTekYMmN8Pt93mpmeMji6cViG77ZS0hPjk538Pzp5zt3/NWug/g3P4\ndfo8MHDXXTi2ou+WIJ85EeLtt+u5cKEKx9Hx+016ew1Wr05WYk3cNzyDMukymD8tYhliihQRO26l\nIg3P/HoF/cf86D6F7hXELwzR+bhrep9vvs3ng8YmDe65m8u3LYKv5jTQvF7W3nILJ55/Hq9lIYVA\nCUHMtmkuK0NLtjvyth2mELAtMqSU1NZWU1bWTzSqAxIpFX6/xeHDtYBcVE9I3adz1Y6rOO+cRy/R\ncZQDylW8634dIQQDAwM0NDQsuuK9iMVFoQlkL7A67efGxH0pOI7zGPAYwPXXX+8UeHsWBOXl5SnB\nTG+veyHbcKaDYE8nllGCIzWEsqnv7+TfH4Etf7bIQ8VLhTz+hFixhH9YZtqFaRloIkxVWYhz4xOF\n6UkzifmIqQTsEMSbwBtF2dDR1sDRvR9GWe/h07/N9pZr+d7pTYRtia7D/fe7ZDEeX9h51ey2U0VF\nBZqmIaScELtcvgae/DZ9ZoT+DZvQx8awg3X0DdRz3XX96DocOdLAv/7rDm6+uZ2hoSCmaeD3m8AQ\nbgrGFBs7bZt/OWBpF+aZzvddkkimiKQLDU64s7WxlXe6cXDCw8C7PnSfShxuEiFsfOWeGRvfF9xX\nc4ZI+Vs+8QTO2bMIj4fmsjJafL4pLJJmIGBbAjQ11fPaayDEAF6vjeMIDh+u5dVX65ekC7/pvk28\n9uprWFg4tgMCdL+esW8kDdKtqMVYaIxAVWD2oQlFLGsU+tt8HfiQEOIyXOL4B8D2Ar/moqKqCgxp\nUXH6aIo8AthoKE8JJ188ivW1OaSNXIzI50+oJKhOYDzDhNbQTWxHJ3ShCpH28UyaScxHTFUMhAUe\nl5h2tK2isz1ISdBC6CZmxKbkeAf/60uw5q4bMsiiphVGq+A4DsPDw/T396eMjKurq6mvr0cE/KjW\nVgYOH0a3baTXC0jUWYhEBFdeOcCbb9bx8sutWBZ85jP78HrDuB/mLtxFbArkmz8Fl8QuEnILopZ2\nYc6e7xP64plcLwtMkyLi/eQOhK4TG7VxVNq1mlIIAZ6Al3goOjfj+yVCyt/yjjuIPfoo3u9/H800\n3eMib9uhDVewNgMBWz4UQE0phGDbtgYee6yO556LMzLi+kAmxeSLDd2js27DOt5/733AvYB2cFLR\ngFJKNKnx+p7XObr3KMpSSF2yYceGpbH6KaIgKCircRzHEkJ8GXgZd5X4puM4bxXyNRcbPh9su32M\nrjcU6BqSCZFGU5O7iF9MJ915I5c/YV01lK2F9/4lo/qhxYcYLt/FmQFfRpt5UnEgr3G2hNpOkONY\nUS9H91YSqLK40Kcz0usH5zyOA6//zWtcf/+1GL7CqZWTRuJdXV1s2rSJSCRCfX09jY2N9Pf3A9DQ\n0OBGrgmBTCh4JK43ZU+PxOez8fvjnDvn5cknd+P17uTaa2dYrZuyzX/OrYDOoJ2klJpkMzLTCkIu\n380JQVSOhVk9DuFh8H+t4CWU5HxfelzfiQMHsCxY+Vt3X/rOCdOkiGiMsva223h3/z8iqELZIIVC\nxeME6upQFkhdzs34foYoVKVKCwQIfP3r8NWvTkPsorgXOMl9FGYlYJv6AJj/+9DgoYck993nXRZu\nHw0NDYTDYc6dO4dy3FSdJHmsrq7m8OOH6WzvTMW42qZNZ3snsARWP0UUBAUvizmO80Pgh4V+naXE\ngw8H+P/aJf0hG0toCOFm2TbW2cTHCnvSBfJa4ywJ8vkTqlZ3FjLLZuGTv93KLnMi3SLvTGJO4+xq\nqF8L/AtjoXqUJRg9oxPuMdAMAyHd+ZxIf4RfPPILPvVnnyrY225ra+PJJ5/kK1/5CvF4HCllytB4\n9erVDAwMUFdXlxG5liRo7uivIhQSnDrl2l/s2gX33z+zyDRgija/dD+vuDWl3Y/jOPT19TEwMIDj\nOAghWLVqFX0/6OPNvW/OqIKQ7buZNGnXtCitrWkLs+PAyT7oPwW+/wl//AJ8/p6C2RbYsVhqvk8m\nYuOk7mFgtIK3/uog//i3WxEe76XtnJAjRQTIMFtOtnxDp3/C2RMr8PhsSurq8AVriAxF2LhrY0E6\nKcpWdLR1FL5SNa1FUgi3Op78fBRuBTIpYOvFJZB5LujyHQCwoHZBy8DpCXCroldddRWnT59mYGAA\nmLBRqq6s5uW9L6fII4BmaJQES2Y8ClHE8kfxG1wAeEt0bv6PG+h4vBNtZQn+Eg0su6AnXWDKofhJ\nRGKxke1PmMdmQSMz3SLvVXVe42yXaQaqnkHIOOFTJQnyOEHapSE5/uJxbvzajQX5LpJG4o2NjWia\nhmmaSCkxDIPe3l5Wr16N4zipmaDq6uqMWDPHUdTVWWzYUMtv/7acW2Uhb5tfufd7pn7ffX19k6LW\n3nvzPfqO9WGUGtNWEHL5biYFUS+9FOKBByxkkricPEkqjsUvoM4oyEKbRGx4GMeyUuQR4GQP9PR6\nWKGN0lg1TFjVFHITlh4zMFuWuJGDl93xu/zyb3/GO99/D0dBfCzOxl0b2dS6qSCb1tHWweEnD1PW\nWAY22OMLV6maUVUzdQFeApoOxIAzuITRSfzzAF/AJZU5xi+mOgCWqV3QQiBp3p7sriQ7FyO9IyhL\npchjEpqhocIfsK7cJYwigVwgJE+uR/cdJRpyr6ILedK1YzFinY/gHXwGrXTVpKF41i/TVTBps5CF\nGV9VTzLO1oDd6L6dfOj2V+l/4wiax33cUQ62abOyaSWOcgp20koaidu2naosJm/j8TixWAyPx4Mn\nQWCmijWbs9ZlHvnYSikGBgZS5BFAIBjtGaXyY5WEToXAnrqCkMt3E9yfz52rIh7X8XpNsHU43ZvI\n8gNsAdESCPoKttB6y8sRuo6Kx932tYLTp8HnieOgEZPlGPoc1vqLLTlghmbLnoCfm/7kt/jYw58u\nuPghPh7nZNdJ1n5lLUITOMph/Ng4HGJelaoZVTVzXoA3QstrIMNAUmR1AYgAw0ALOecipzoA5pNS\ncJEg2yA9UBVA6hLbtDNIpG3aBR+FKGLxUCSQCwSpSW7YfQPX7byuoCfdlBjgxRdwzr2J0DTW3jBO\ny6c8boUnMRRP886lb2dnobBqPB8ff/hmDj9xguhoFE+pB2vUYmXTSkrrSomPxQt20koaiY+Pj3Ps\n2DE2bNjA+Ph4KrUoKaRJkbMZxprNGnPMx47H4ziOk7ENtmnj2I5rEuyXqFHXAzBfBaGqaiLWLn0N\ndTULPqTcAbSDWea2sD3AShN+1ARxDQytYAut5vWy9rbbUjOQpu1BqDg+eZ73yu5E80DjMVMAACAA\nSURBVDqUl/Vy4UIV4bBv2k2wIjHGHnmMwAv70ZV58SQHzNJsWffpBa8S9bzXQ9mmMpy4g2M6ICGw\nwT1OB18dnPNFX0dbx/TzdzlV6b8GxmG9htu2BndSOQCcBdaRcy5yqgNgPikFFyl0n86GHRsmfQcF\n78oVsagofosLjEKfdFNigBUBZCkopXHiZxcAWP+ZlamheGKhnJW+pUDuasAVbGpdjdRWsVB2Lp6A\nhxv++w2Eo2Gkx01GGHtrjAu/OMc1OzYU7KSVnl976NAhAK644orUHGF9fX2q6piOmcSazQpzzMfO\nNZepGZpbEbId1PiEgXS+CkJ6rF0uQZTHk6hyGd+BSsvtCv6oCV5OxCAWeKFNWbocPIgTH8UQGl0l\nW7l8W4gHfmszmmZhWTovvbSDqqrcyvDUfvyNH6H6B5DGR9nQeJ5Ndb3Ii6n/nacLsFhIXkj6KnyE\no2HsiI2UEiEFKFDjCv8VfrSfajO+6Eu/OAU4uvfo1PN3HiuPKr0cuk9A8ydAs3HdA36Fe8VjJv75\ncElkGHdusmH6A+BiqFAvMNK7cipc+K5cEYuPIoG8iJAhBtA1uCCQuoO3RKP70CjNnyhD06zUUPxy\nQWY1QGKb3XS2vwmc4obdfSyUnUtfXx/GhwxK+ksY7R2lpNzDit+o4JrfqOayYCn0DhTMEzHdSPzV\nV1/lpz/9Kdu3b2fLli2p1vWiYZb52MnB94y5TBxKm0rpO9iHPW7PqIIwEWuXSxDljhqg7YRD/xv+\n4TlYUQ2GWJSFNmXpstU1uX7/2XJqI+1s+Z1vMTISJB43UMrk3nvbE5swmQh2tHXQ+XgHJaF+tIDE\nxqGzJwjADfVc0rNuC4HsC0lPpYfLvnoZZXVljJwacS9apMCxHBzhcNX2q6a96Mt1cfqh2z6EbdlT\nz99VXMijSi9xL25iUQisAOzEpIKZmFRI/n4OY/8cB4D1xZ2Mfe5uAlHrA1d1W6yuXBFLB+E4y8e7\n+/rrr3eSFZwiJmPs7Fl+8vDDeCsr3TsiJyHSA5pBbNThU7sqCfjPu3NNy2QG0opaPLX5qZQQA7qB\nHmzTixnR+eKrb6L7BnF9Due+zUopjhw5gpTSJUDjMYiaOFKghMO1JZVIW7kt3aQnYgHU69FolFAo\nRFVVFb75EolFVNdPqcLel6bC3j69Onba0cCk3cm+fRN2J9u3L2oL2LajDAxspqenlHjczRxvaIA1\na0yEiID6EcT1VBU3tR97bLQjh8FwK8e2Epi25IsfP45+bgAOHJh9C/5STw5KvL9D3zxCxxNpLU3b\npmpbFSVVJQghuNB7AUc5CF1Q1lDGjVtuRJ9G/PX6ntcntUlHB0aJjcSoaqmaNH9nRky++OoX3Qrk\nv2x2I1WzVelWF2xegc0q2trOsHdvF5YVQ9e97NjRQmtrHZoWIu85KxpFDQzS8b1THN3/TtEDsYiL\nGkKIDsdxrs/1WPFy4CJCthiAEjf6UV04jUDh9Y7nHIpfSoyFxtLUeDaustFAMwQqLBgLBVjRkDlP\nlMuLMB25Hs+e4xPxOGgSIcBWDnEBXl1z5wNrKuC9xwuiXk/m184LGcP9pmt/tPYL0PLlgqnr881l\nNu5uZOPOjbOqIEwriEoGKE8rvS8cNC1EXZ1FdbWRGlvTNMDxQu8GGHgXHCOV5jPmeN39uNwPQqbU\n7pp0UJZgLOKwYrYt+IsiOWgeSHt/ylasDoxhb6nn/bdHcRzXfDr6ThR5jaTmyhpWrl6JFbNQQlFX\nXzctebSiVs5WdWl1KbGRGKMDo5RWl+apnutTqNL/GDRJ255v0N7eTzDoxTAqMM0x2tvfA8bYvfs/\nktfY3+ej48V+Op86WvRALOKSRpFAXkTIFgNIjwdl1BOTPpq3bUG75UvLTjiTqcYzcW0wPNim234P\nVCV918I4zhB9fSKjCpZKcREiZ5Us+XjGHB/CbUNJUI7jutgI6S7KtgXvfhu6c0e6LYvKbVcbnHgC\nvOMgz7qE8sR/AV6D9fsoZHJLrrnMgs31LqmhXRWgo2kmfn9aBarvWujfCLo3I82nJFjh7scWaA0N\nKRsiGw2JIjDSDw/cRxQfod6ZcOIo9PVAvwm6Z/rkoOXk9TpTpCUj2ZYgHrVpuc79HLuOjAAQfTtK\nPBqnqrnKnVv2COqq63LODGcj8+J0ApqhEQgGuPyOy+n6YVf++bspVOnRaJy9e58mGFyNYZQAEsNQ\nBIMR9u0z2blzJz5f7uMwH7FdFA/EZPm/vARE5OLaX4q46FAkkBcZMsQAo6MITaP5zrvc+wvZ/pvj\nApapxvMlZiAdIkMeNu4aQvc5JOeJ+vps+vuHMrwI01NccnkVpj+eMccnQCkHC0WtN4BMeiTiwKnv\n5I10W3L1eipybhxkH2CA9IDXgu6D0PwoaA8v3fYlt/FiIzOT4MOdvU1Lx1EKBq4GvWyi0ptI89HO\nhbnmCxvcFmxdIxpgn+ojEtfYWNuL3HUff69a+c7m6UJIEpGO6lkY2A26CbIWWJM7OWgeXq8LOk4x\nW2QlI2mGQDkQjVisuXIF7715AWU72DGb8UPjXPM/rsHRnFk5EkxlFaN5ND7xtU/wia99In/1fApV\neijUj2UpDKMs/Q8wjDLC4UFCoVDeTsNUxLZgHoipsZDvwBV9cMU5t5pd0wCXLRNv4CIuORQJ5EWG\nbDGAt7wcbSGVvNlYALPyTDVeHVI/y8Zd/WxqPY9LHodQ6n4GBs5leBFKKdF1nYGBAWpqaiZ5FaY/\nXldXl+mvqElE3KLW66feWzLhiVipw2AUZNYJfLmo12Mht20tz+JWZpOxhDo4NsS+A4FWFkq5Piss\nZ+P6OSHZgtwH6jxEVoKqAT3r+0+k+Wy8dwN23OLo3jdw/FVoV61i4+fWsOnhm/iHJwMzDCFJRDrG\n17ntchkHTiUeWzs5OSin1UxatTzHwGkyVnPv3r0pK6kdO3bQ2tqKtlg2Q1nJSFIKVjSWEe4JY/g0\nvH7J6Dkz1VY2ArOPGZ2pVcy0ZC2HKj1pzWWaJkaaLY9pmui6TtUUowpL4oGYTMH5xBisPw9RD5w8\nD44f1DLqrhRxSaFIIC9SaF4vgZqawr/QdAvYDJCpxrtAoGofuu+7uD5rOrCLePxeHOedSdUHKSW2\nbTM+Pj7JqzDj8dFx7As2NVU17hyfaeIZHIaB89hjMaRHR9YGoaYM3p060m1J4a1yZx6V7VYek1DK\nnb3zClLWIYuNKfaFaNPui8pT24UGdis8EYcfPg0jo/ClQaiy3XzJ5ByiUjjAie//E+FfvkhDk41l\nG7TccTOXf+G3MOPaDENI0rKWPXEQCpSW2A97gSZ3wiOZHJSqRueqlu+FV+Owdz9YFlEpCd1+O1UP\nP8w3n3yS9vZ2gsEghmFgmibtCTa7e7FshnIkI61sWokAxkMRzp+6gIOYt61Loaxi0q250j/HoaEh\ndu3aNWVFd9E9EJMpONUV0PI+xBIXnoaE3rPQsGl5dFeKuORQJJBF5EfGAuYHAiDH5tzudWfpKoA/\nwlUwhkjmyno8apIXIbiCGSEEfr8/9+O2ItIfYf8f78eO2im143X3X0fHC6d4+9k38RiSuKm46vc/\n7Kogp4l0W1JoPlcwc+K/uG1rqbuLcMyE5nrQDDKsQxYLeciM4w3S//N9bL9/J2Mx38w9tZeL8rit\nDdqfctleqQHHjsKHPwL0wJo1qcr1wOn36TrwPN6KCowVHlQ8TvcL30MzJCWfvnuGISRpWcvShuo3\nEvOWEbcSqWJg6RPJQWOhPFYzBvT3wbNPYJfU0XbmDHtPn8Z64w3k448zICXNzc2pyplhGASDQfbt\n28f27duJRCKFb2vnSEYSjsPKuhJKNzRy529vXBBbl/laxeQW7EWBEK2t9wKuNVc4HEbXdXbt2pWy\n7JoKi+qBmEzBKQOEA06yayHd+y1AWEvfXSnikkORQBaRH7GQ2zaVNwLX4LZTFcg3wHl5nickH+lV\ntFxehEopLMuitrYWXddzPh7uD9P3Uh8enwffCl9K7Xjy/54k1BWiJFiCMDQ0maaC/MOZRbotGVq+\nDLzmzjw6tlt5bK6HFj+wnYn2tbvQJUn47JDrb6d4vlhuMnPylMGFwTB1VSEuWA1TtG4TWE7K41z5\nxW8fcW+v3AC1daBpqOBKjvz1M3jKKojHvHiEQno8eCsq6D54kJs+uxVd984ghMQV7rhjGwbUu/sj\nAx8G2+Puh+nJQd4q977sarkVhYEQBJppO32G9p4egoaBoeuMhEL02zaBQIC1a9em/sTj8dDb28uW\nLVtSox8Fb2vnSUbS6lexYoG/69kKvXIL8lZRX/8DhNgLWGiazu7dO9i582VCoeFZke5F9UBMpuBc\nABzhVradhFOAEO4up5ZBd6WISw5FAllEfnirQPs0cB0whttfk+7P2tiCn5CmyojO9TgODPxoAOtd\nK0Pt6K/08+5L79Lw0Yb8KshZRLotOqTmqq2bH3VnHr0iUXncjju3lxBi4C507mE8UzP2XH97d+Kx\n9LGCrOfLQWZsBf19JqU+nahy94Xcrds0pClzp1UeFwrJ6ufg0OT8YseBt34FP/0x7N0HqxsZ6z/L\nmbdLONdXhZOYJKj+UJT6q8CxRxHRYXbsqJk6hCQlPNoG2lNAEIQBDT+Dupcgfj94rsisxsaB8ttg\n6DnwVU9Uy8f64Z1KosJgb2+vSx4Tf1ciJYYQnDp1iqamplRV7cSJEwwODtLS0oLP51uctvYck5EW\nA7kFeW8Cx2hoKMWdP3Yzr12zgLl9RosRB5mRgtNVA+v7IKqDaUFTPcTPLY/uShGXHIoEsohJSKk3\nKyrwBW6FaE9iGF4ANqhhCNzqLoALiOkyorMfHx8Yp/PfOylZ5QN73F1ghUt4HDXZIH+SCnKJI92m\nhuaqrQOtTK4KPgI8AdQm7nMXOhfTLXQJEUdSeYwJ/C9c36OrSF84M55P803yzTOjJuX+If7PyV3E\n1cTiNLl1m0CWMhfIrTzOgWytiB2L5RGRTVFFza5+Ogpu+gy8/QakpwWZpntbUw1S8tbz3Qy+X4HH\nbyO9EmVD/zE/jm1TfZmGt7w8fwrPAzYczxYetUDLabeNjQ7yXvDuJCWYSipq9+4FOw7XhuGmEagO\nunOxLffDO88S0iJYjpMijyiFJiWN9fW89/77RCIRysrKGB8f59SpU6xevTpVQUtva7uWNAVuZ88i\nGanQUErlEOSBrvcwMPAx6upOIaVNzszr5YrkDvjdve7BckUI1lRBbeWE0K2IIhYYRQJZRArZ6s3a\nyiravvr/UFNejxjvxSUZwiVdRs2EUnSBMV1GdPJxrQqkdRa7vx9NtwHpbpusd3N1s99fIVWQBUN6\nq98GHgX+HJfM9yYeW8PMFro0EQfJ700HRhLPl6xe5lk4s3zzDE3nxWO7+JeTrehZ/Cunp3aWMjeF\nbOVxGtK5lGWBR7P50rX7WT1yEGwLoeusve02WrbdidSeYMqqbK7q5823ua97/K2cpUMravHWs8co\na6wkOtgPyoPUJLphcfbXBpsevDVFYHN6ox+fLDyyjr3H2NntBG7Yiu6rmfx9JRW1yXJmdwUcHoD7\n7oAHv+aS+e0eqh5/3G2IK+V+m6YJTU3U1dUxNj6OaZoMDg5i2zbBYJDm5uaMlzEMg3A4PKUlzaWI\n7NABFyZS2ti2Rjzux+sdTdyflXm9XJFtzl/0gSxiEVAkkJcicmbJTT8v19bWxuNPPM7KipWU+8vd\nFuXZs5h2kKamGyfalw6gHLcltYTQT7ez4eMddL7STMlKG023scOnGI/afOjWKwh1hZCaLLwKctHQ\nhlt5FLjfoQJ6Eo+tZfqFLk3EkYKZeL7k//2J/+dYOLN88zRvFYETPgYOTdG6TUcOZS4wMauVY3/K\n5lJrz+3nwk8O0N9YwZp1rpjlxIEDwL+x/u5fkllZTaui5qt+1tbCrbfD0c5cAd4pT7+ypgakLhgf\nOItj2wgB3vIq6n7zsxnbm+GNniU8UjZ0vNjA0Vc+jLJ6kFWvsGHHtZnxdrnmMg0DKqph7w/hvq+5\nfLi11XWy/MY3aO/vJ+jxYDQ14TSuRsZifP0//Sfuve8+QqEQJSUl3Hbbb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gAAIABJREFU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1LI5edx0NjrNIZbSK45KSkydPIoRIddb6+yEQkJSUSJ566npmZ0VqvHxxghIOkuRAzs6a/OY3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\njbUXePgMX0Iwkw9qNK7rbbzySoi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rjYHWFVpx8FhNycY0JeFwJjuBW+XrCV3J1WzevSoB\n1EKpl3kuKXOMRSwLLwQ50fdKWRWzsTh7bVlW/doE6vRBVLNYyNvVCQcypy9awF4/yrRGZKerL1eK\n+lSz3w2pxCTD/+UBwutb8guWQCDA66+/zujoKN994gkCjkNDWxvTMa3mwb5aOBc9x4nJE5w4ewLp\nSYQmiEQibNy4Ma++sFKLVh8fn9rxA8gayPWtNXc04zkeL35fme287bc70QQIzeO81kC4t5zuem0c\nHDy4ILjICWbf/MkbmHrjDaKpFF52WrdmPbaLEEAtrl95QTdl1ammxF5J9H1hP6diIDsAUa5HrKZt\nqfWDKMlU5toVKvlNAwV2dXGYOwnpKLgvgHzuorQM1I8C7cs6W2ReMugW8TkLx5FF2W6AYCBA78wM\nz+7ZQ0DX0QyD9XfcRUC/D9vWV0XXcuS7I5yMnVTqBAEtL7bvOR5doa4C9QUfH58LiV/CLmJx3+TC\nvjXP80gcTXB28izC0LAaDd4+cDPv+vPfWnFTd8XyZtrBPnKM933wFNFbtmMkk4ieHuT69TiuS1dX\n1yXbB5dKwZ3vT9EdipOSITJS7d96lbmWQ750lxugyGXLVjCJvJxm/+r7ORVVZf4qbct9b2XHt/53\nxJoq6o0VMpV7pWToK18pmwnNT4NPRpVUiz0Fc8dAWEAzeD+D1PfV0NIqCrdfydR6vCyHSi0Tm06d\nYsvZs1x/660YwWBe/miy/R6+8ML9demBLLcdm09tpmuqC8d08ISH53joaZ17/voe3vnZd9bhHfv4\n+JTDL2EviYvSdxtmMUu/xlAjwhC4totu6rRuaqVlQwuZZAY34/KOR95Zl4nA2fgs0pFFwSOAfuI4\nXjzGye03E8hk1DTksWMIwNiwYfmWdquNdJl7eZC/+9Awlungegb/Mt7HD48MYJp6fezbatqcEgu3\nQICb7v0DOjZcrSSYNG3Zep6LZY4rNfvnRN+rZbE2gSW3Zd8hONfDLVtLRNPL1RvLZCpTX/oSw2fP\nEt6yZYHf9MjIiJoGN00l0aILmDuelUzygCRo2yB4SPXZbd59mZezLywXchK5XMuEk07Tm0jQun49\nRnZiXQQCBNeto/fsfvofupuRx4N11bWMx+M4jsPpzacxDIO2yTY0R8PTPI62H2XDRzYs/SQ+Pj6r\nwiUWaVwscuLAzUAH0Ah8Afh80W/l+taSsSSurfT6PNcjfT7NjQ/cWLdsQGGgmke6uBNTGKHGrGxQ\n1n7ONGFyEoFyLslkMhWf96IxNsja2BBzmWZOzXaQTDfzvquHuGPzYP3s2xYhlUoxOTmZd8TJWbgZ\nDQ0E29rYuHUblu2RnDmZzT5qKns2NVPT6+RaHFTABjCHbpK3WnRSTt3ek5SSdDqNrOB+VLwtaiGi\nmzpNXWsYPRXBmSs5Tko/iAqWR/GWFpxoFNMoPtZN08RxHOLx+LxdHRlU4JgLwiUgQKzN6o7G67Er\nrmgKj93cgsBsNmnqaMJsNjk0dIiDgwdX5bUHBgbo7+8nmUwyMzODPH+ezvZ2Nl51VdHviUAAT7p8\n4mNnOHAAnnxSJbL37Fl5b3MoFMIwDOyMzcymGV6//XWO7DjCK9tf4UTkBO0dvpi4j8/Fws9AFokD\nB4BxYBI37ZI+858ItjrowT8hl4mstW9tOZQdsEjOkcwE2NZyEiHfhhTzQaTMZEifPYve2Lg8S7vV\nxE3B+DCaFSYQNDl5EjTN5KwR5rbOEb7+0908+ElrVcrXZXv1PvYxNj//fN7CTQidzqvfgmPb2Cej\nNHV1K2F1A5VFq0HPU2WOXXRzAmX9rgIm3YwgE+G6NPtX60dcMYttmchQO7Mnf0ZLT3NlIdEKlkeh\npibl8ZNMYq5Zk7+/SDQ+Z1dHABU85oJIofaJTFwU4fYLQrZfVK5bR0bXl3bUKYebwp2NMvi17zL8\n2OM4joMpTH4v+nv0bu4tXhAs8IGvH6Wi7K3NzTz7mc8gCx1sKLaA1IOLVBJKlA+qacUolwlN6sm6\naMz6+PisDD+ALBIHHke6xxh7fC3j+y08x0Mzvsymu8bYct/nEbqO0AW37LmFbbu3rWof0oJAVWhs\n75xkR/sZThw+zPTWreizsxw/f56pTIamY8c4ePAgzz///KppL1ZN4YUiHQfP4eiESTIJDQ2QTkPG\nNXGdBLfcOMnAgMVqDCMMDg4umFr+1le/Sv/atfRedx0ARjCI0DQcPCWUnMmofkghlEtGxqlaz1Nl\njqO49iS6GUAFUBLXnkAYsi7N/jk/4pylnJSS6elpgKKydmm7RQ7XdhE9XTTetwueeKyyj14FyyPL\ndenr6GAokSAcDFaeBs/Z1TWsL+iBbFI9kOnpCy7cvupk+0W9kRGmduwgun07XkcHWmcnHZ2d1Q24\n5SW2hhl8apKhH8YId23AbN8M5zSi01FEo2DTpk35P9FNHZlY3UnkwpaJIvHxQKA6C8iC94Xn4Eqd\nwec2MHzgeOUhrAKWOzzm4+OzuvgBZN43OQVMMvb4Wo48aRFcJxEBD5kxOfLkT4ERrr3/4/m/qrVv\nrVbKBqpfVtOyPdlBo5euuoq4adLY2Mjhw4d59dVXeeaZZwCWb223EkouFGgG9N6H6xlMTymbQyGg\nsRGElqanfYZP//4DaJqkUt/pcnBSDqcnT/PYNx7LB4+gyqwiHGZmepqedBojGMRJp5Geh4aaZs9n\nVqRUWbRA9aeIYTls7XuZQ0O9NIVddNPDtXWSMZPt/aMYluqvXe4QhJSyaj/ipWSCjD23wEB/ZR3I\nRSyPBh55ZIHf9IILet6uTgPPyE5h/wTk8yp43LK6F38pZXWe2vUi2y869b73Mb1tmxpw+81vkK7L\ndHZQccm+1bFBODJESlvH8L+cJdwaxLSnYNZAa7oKwzSYmphiQ+8G9Io+8KtLkbXo+fPVWUBm31fO\n/Wnwu2MM7f8F4e6NmB1bsG2boawKwMLvrRS6HmfPnt3F9pQB1ELkspeF8vG5fPGnsAElDvwF3PQx\nftzfidEgEQEJNACNyEwKZ+46dg59owq7s1WkwIImZdvcceQI4Wuvxe3owM32wq1IWHqlvL636EKR\nswlM6luYeHWMpBPG9Ux0zaa34zDeZo839Ldx000mDQ3VCzIvmJ7OUjhkkJpN8drYa5zbdI6ZjTPz\nbXhA1+QkH9ywgeaODkQgQPemLXRddS16SzPNPREVPDoudIVr9DSfRLp3c3DwekZH2pCOhjA8tu46\nxY6BV4EnODg4vewhiHQ6zS9/+UsCerZPs4BMJsP1119f5COd3x8jBa+3q4ahi9zx9u1HoSkFSQvu\nfSCvFzk7O8vMzAzt7e00NlYIYDKzkIyB1QxybtUv+NWW+OtKKgU7dyLXruWlvj5EJqPaS6QE10Xe\neisSuOmmmyoHs24KfrwTjGYm4y53/8eDdKwzlb2V50L4HbQf7aT1jVauu+U6mtc0X1Q3lornYGng\nXvC+ECYp22Xn556juUHD1D0IvwM0UeZ7q8Jgo3wYxvYVL1I39V3GslA+Ppc2/hT2kgwAGdJn/hOe\n4yECHip4bAAkIqDjnddInzkzL7h7MSjwt42/8gqpP/xDDBqw6wAAIABJREFU7HBYXaiymKZJIpEg\nHo9fWEmfbK9jPngEdRsMY9nHefpXD/K+tzxBQE8gEWQ2GkwGtuBmzGyF1ET1oY6gNPYWBhkLpqcN\ng0133cWW++5D6HrR1LHZaiJ/IwkdVX12M5vUQIxt2/yqoYFH7r2X4z/8Id758xw99yKtb3kLHe2d\nqmStaSp47Km1QT+E0A1u2TPJtt0xZuMGjSEHw0oDBj/bO8GhoVGawha6Ca6tLzmhncfzCERPo82m\nkB4IoYFpIk2DdDqNnu23K2TF7RY5ycMBD1wPdA/0bJA2OZkP0k6dOrUwSCuXjc5d6FeRiiV+zyHS\nZqxOAJvtF82sXYuX600G1QaRySAcB1fXyWQyRQF+EdlWD4RJaK2LoWvYGYkZEOp+aTPZPcns7Cxv\ns99Gcia5Kv3X1VIkPs4igXurh5Z9XwDxhI3jepiBgFpgSht0q8z3Vm6wsdDxZwjGnoEjY8WL1CNZ\nDVNfFsrH54LiB5CAulJ+lmCrg2Z8GZkxEQEDNQRhIzMRND1AsLX1Im9nFssidMMNGNn+s1Jru/ww\nw4Wk4AJYhDDRNZe2m+7lU18Z4KpInMbWFH/6vl3MzQTp7S2c1DRR7h5xYGHwm5ueLuy/OvLkkwBc\n/ZGPLpg67tnQw8TRCdYdX0dsQ4y0O0csNk1//8O87eMf5y333VdnHUgLVYYfwrDCtEQ8clZ3Tmo3\no8OHaQrH0c0pQKKbgqZwD6MjLy89BDE1g4ieosO0mLbnMDwN59x5JmNRzmXsRftfl99ukb2I62H1\nL3sRn5pax/T0tYv3YZaULS/Ehb58iV/DsE8Q/fUrdNtfVInbemessv2igUQCTcr5AbfckJthoMHi\nA27BkAqypY1lmvS9L8LQ/mOE1xqYuobtQCwe48N/+mE+sfsTq64DWSuVA/cwkez7UsGxmQ2OHUxd\ny39fFH9vFQ425r5PTHDXwfjTELxtwSLVl4Xy8bnw+DI+BejBP2HTXb9D+rSDzKQAF5mJkD7dzKa7\n7rq45esSctOJsVgM27YB8sMMu3btuvDl64ILYBHSBs3ggf4QH/+ExbGZCK8fiZDJGGzaZLNxY+Ev\n26g1zcLg102nGd+/Px88wrwG3fj+/ZybOrNg6njjxo1s2LgB6UgS00dIJp+nv/8cAwPfAvaiBw0a\nOzvnP1ch1MDMinrmBlBl+CQwk73tZza+C+lMZye0dSAI6OjmBNKZZjY+W/kppVQT4YZOj9VMV7CR\n2XSK83aKUGsrr7/2Gq+++ipDQ0MMDg6uYNsLKX8Rl7KTaDSDYYiyfZhSykWz0YyPqMdXgUwmg+d5\nxWXi5DHE3DE8LUAm2KtKqUeGVIBbL7L9ouLkSTpGR3EaGpAAto2MRHCkpKOjY/FeTN1SgW06BtJm\n4Pc30n9nD8nZNDNza0nOzuX7THMLgksleFy0N3fmFHLjA/n3ZZk6fTs7iZ2eww505svXxd9bhYON\nBaRRJf3S3ShMXxbKx+cicGl8A10y6Gy57/PACOP7/wHvvIamB9h8z12LN4lfJC6p6cTcBbBMDySb\n+9FNK1d9Jx636OjoIxAoLVHleiAXBr/pM2fwSuRDIKtBd/48up5eMHWsaRobejbQvsblT75h0Bm5\nFctqIF8OA5bqt1zGjqCc1V1j6DzCmMG1g+hmrhdP4NpBhDFDY2iR4CKnqyh0NKDLbOSNsZcxTZN1\nVgPjvx4jEAgUi3mveAFR/iKeyazF8zSEyPWlZd+JELiuq8q0buVsNF5CXehXwUYzEAigacrqTgih\ngo3Z40hhoeERYK4okHUiDzJ7RtYnk5c953oeewxSKaLbtuFedRVaZydd2SnschS5RObK++Mj6J7D\nnrtC7H743xJv/Qih9o5LVrKmbOBOwTHR+xBBzVOLBy/BwO+1QeuNjBw4TmJmpsz3Vm6w0abo+AsC\nmsjLiebJLlKvSFkoH59LGD+ALEHoOtfe/3E2331f2SbxS4lSnbalrO1WnYILIF5W669k4taycjpx\nuftGUGVrAxU8lg9+g62taIaBzGTKatA1dYUrTB2fZ3v/KBs3d1FUDlui33LlWBSW4Q0rwda+SQ4N\nXUVT2MlOaGskYwG29/8Gw0qghOzLkNNVzJZEbdsGKTE1lf05N6eyl/Xsf02lmojHXUKhuWzQnd2U\nQAJN85DSKErUSinRNE2VaY2CbHRhELnEhb7SYEa1CCHo6OiYL6V6NhINhya6GEWghPmlZ3LwqRZG\n/89vIGWgPo4u2f5kbfduIvE43UvoQJZ3idR5+BP9OM2/T7DBRW/pxtIturL7xdW0S/K7aEHgniV/\nTAQt1baweTek4+jBEHvutNj9l5V0IOdbQYoWmPpp2HTnwh7I7CLVL1/7+FxY/ACyAqVN4mUpEca9\nWFRrbbfqCL3oQrH4fimfqauEHgwuqUFXXuR9MzsGvoJyGCpk8X7L+hNix8AU0MDoSAcyoSa0t/dP\ns2PgBOXK9nmEmNdVNFSgGNANWqxGfvTKi2RcFRgt1f9alO2qsKuLxddjGMav6OvbwMDAZnQ9gxAx\nOjoCTE9LDEPm+90cx6GrqysbQCyejS49JpYajqqFXKYvGo3iSg1Ns+jyXqRHezX/OwefWsehH7bT\ndFUjetCsym6yarIrJIFKmFWi1CUyk3b5+eDjND+5n852tQ82fuAD4HkcffrpFe+X1WRB4F72mEB9\n7gWZ58W/tyosMLc8DOxbdJHq4+NzYfBlfJbDYhOmvpTEqlEUaLgumq6XvaAW6yw6wE5Udq+wpGqj\n+hMPsDoZyHLsBYZwUu3MxhtpDM1iWDNUI12E5ylrxegp8DxOTE/z1af/gZ/8+lWMQKBIzLtUS698\ntgseegjOnCkOKPfu3Vsgvh7Ato8Qi03Q39/Onj0RYBee92mmpk4uLpWTP0dGCs6RXWXPkdcfe6zi\nwuDa++9f1p7Oy8mMfxnxxr58IOukMnz9c9dihrqgpXd+CM0BO2nz4IEHV723MKv6Q3PzvEb7lsRj\nXHX2SWa9ddz6jgC4Gc6+8QYesPbqq+u2X1aL1ZNPSlF2gXmJLN59fK50FpPx8QPI5VBB75DN/b6U\nxFKseNJ5OaVOFbiV77e8kJ9XTttuhHltu13UJJ6e3X+u0Bj80pcYGRnJu3ns2rWr7BT23r3F2a50\nGg4fVv8PhwsDyhTvf/9OmpubSyb7UySTCQ4c+Ecsq7VgU2bJZGYIBNoRooIO5BIXejed5sf9/RgN\nDQtaE5y5OXYODRV/xrUePyWB7Nl4E9/6r+8h5gkmJ6eQnkRogkgkQrgxzMe+87FVNQgAmJyEu++G\njmxSXMg0/+ZEPxnRQNoOsONmsExJ9Be/QAPCb3+7stdkkf2yFHU476p7mQss4O7j47Oq+DqQ9WSp\nCVNfSqI8JRk0NE2VZXvas77J1VPYXlBdMFlbv2W9Kb6oVl+2L0t2UlyHqvpfUymVecwFjwAnTigH\nQyHgrW9VWcmhIThzJq58l0v8r03TIpE4RzyeJBJpJRcICzFMMFgg8lwuEC4pW5ay1HBUXnt1ucdP\nSVtFo7eW6H/4b0zOHCcQDBAQAaSUTBydQLbXx25yKUpdIoPyDBoOrhdAE+o+N5PJa9/n7TXL7Zel\ncOZgYgJOZwCxovOuGoQQlbUufXx8rij8ALJWFtE7XM0J08ueqZlsD5+uLupSqp+hRrcXRW19c7X1\nW9aLxct69TlGlup/zWpc54NH11UZsGBQ3W/bEAiocupTT4UQwqhCW7SCyDNQa0Z3qeGovPbqEsfP\nkvaQ2UDWSaV42XuZXnpxcfHw0NExMRllFAcHo55fi2UysKUukcJoxfUMHDvD+o0BlSAMBPBQBkr5\n/SIEUgg001xakzaXeT02Dvrt4CWhsQsaeld03vn4+Pjk8APIWgkub8L0TU2BjmG+fCaEOvqip6A7\nvKCsttTAx2Ki4pX7w4ono1ebiuLKVOGLXCcKs10NDRLDyGAYATxP7e8TJ2BqSiX1HMfiPe/p4/jx\nIdra1uWf4/Tp0/T392cznClwvwH2GjCNbMJx+VPt1QxHLXb8eCfjHHzqKC8/WsEesqR0G4/HGQ+P\n09TURNtkG5qj4Wke8Y1xxhvH6+fgtESfdE6xZmQEEk6QX+l38a6WJ1nfvQ4IIF0Xs6kJD/CkRLvm\nGrzeXpCS5nXrmI7FFu8vHBuEI18D689BS6ts4+yEeqyht+J55+Pj41MtfgBZK0voHfrl6zIU6BgW\nIQS4jno8qILxSgMfWftl9TtLiIpvvvvuiy53sqi4cjRKd3d3cY/YKvWoqWyXx+HDU9x2WxRd97jj\nDo1//ucOfvrTHiYmtHx20vPg6NGH0fVneOGFp/OyLHfeeScPP/yw+nAe/Vu4+RU4lZUWWh+BjRtB\nW/5Ue05jdXz/frzz59F0nc333DOvvbrI8ZOcOsvhJ0Yxm828dFN+ovrDGxeUvENtbRgBg8l1k8Q2\nxDBsA8d0SLtpjGQdHZzGBnF/tY+000ZwTRBdd4qceApcSYnHoa31Piaeyu6DpNoH1/X3g+cxMT6O\ntnEjXipFoLWdhnDH4guRfJtNL2g6Sjgx6/oyOwmNvWqfFJx3Pj4+PrXiB5DLoQq9wyueajRhcpTo\nGOaRUt0fmD8MS+VNbFv9DOqCCzX0zdWBJUujFVhSXDnni1zH3tBKfOhDU1x99TQTEwaOI3BdyZ13\nTqNp8OMfqwDEtqG3F2x7HxMTY9x66235gH1sbIx9+/ap4vQ3noB9BjQakAGOHVO/tKmHSi5CS6G0\nV+9n8913l+9nrXD8SEdyPpoETUfT1b7STZ2mcBOZsUm8qUY00ygqeVvAgw88wHef+DZSN8g0eEUT\n7PXQUZV2krFHv8z4IQ9PnkLTYdPNzWx5VwhR0ic9r4tafh9IKUm++CJTxyWTMRN5TqAdh/XrNTSt\nzEIECtpsMsyrbmeDSDxw0irgLzjvajmdfXx8fMAPIJdHTXqHVxjVpAhLyesYRsGQIILqeua40JUr\no6VIpeI88USIcNjKZ8Vyk8IjIypbY1k19M2tAOlKDg4eZHS4Qml0CZYUV85td517Qxe8DymZmYnS\n3W3Q3S2yPY+C8XGD3/mdKP/0T904jqC3F9avT/H888PoehhdN/OBRDgcZuTRR9nteVgtHfCMA+8/\nBgkTMOHkBPQaID7FSnpLK2qvluhgIgSeK0lOneEX359g8tAJNE2jJdLC2o1rCTQYXH1TK46nESht\nmTg+zadu/V0+vOk6ojNRfnDoBb538Pm6OjiNPfYNjjyXIrjGQhga0vE48r/OAXDt7c6ifdKl+yCT\nyXAyqnFsUp0TgWxV/tgxga4XLERgPgpsbcq22cyBeBnYjpKscgEDpIDuNhA2rhtn374QX/+6VfXp\n7OPj4wN+ALkylpgwvewpJ8FSTYpw4RNBz7eBcYhuB9cALQRd10FPG0pmZxghHP7u7wyefbaPH/5w\nACnVFcw01dRwPK6yNVX1za2Qg4MHF7ja1CI2XZW48jJ6Q2ulNBOaCwqvukpw7pzLbbdlkDKIEDA3\nF8d1HXTdpHAQ2zRNEqkUcc8jEg7DD7MG5u+ehGYPUi4kPgrrVjED39OubqOnwHU4P32eF79/nJd+\nNIluqOMkcSwBQMe16xBCoAdLvt7sDNgZhBmga32E9q4u3nrNtTzyyCMENtbnPHbTacb/8ScEmwXC\nUGMwwtAINumM//wcm29uRK+hT9p1A0SjGpY1798nBFiWJBrVcN1A+UXdJ9dD5NcQfE79mXcDeDpY\n7eq46vk2MMzMjMNttxmk0338+McDpFL60qezj4+PDwtt6X3ehDgph7OTZ3FSjrpDukrr8sc74V/v\nVrev74W55EJNmMIUYSpV4RUGQRuCyEtw0zfh+m/BTX8Bke+A9kXUBG8zut7B3Fwz/+Z3vsJ7b9uL\n5qrtsW11TSxsT9ty331svucenLk50qdP48zNFffNrXB/jA6P5oNHmC+Njo6Mzu+nJejp6aGrqysv\n4yOlpKura94XOd/bl9X48zzS0kVq2nyP2gopzIQWIwmFNKLRAE7+ZUK4rkFnp10Ut9q2jWFZhCxL\nfRhSg6c3wf9xO/znG+GRrdDwOarWslwOmqYysjddi7NlI9/5m1c4NjZHS2Qtrq2ceHRTJ3E8walj\nCRpCDQgKNW49SNvqebKpNd3QMRsbCJw6p4L5OpA+cwbPlYg1G8C1Ual2EIaH52RIt324pmrF6dOC\nQ4c6aGpyECL7XELS1OTw4osdnD4t5hd1zc3qXAwEYO9rMHkNOOch/QPIfB66E3Drb2XPuyFct5mx\nsQ4ymWY+8IEh7rhjsLrT2cfHxwc/A/mmpmKZ9j0vIMa/XDwkdGRI2ZYUasLkKE0RFpEChslLvggX\ngi7QCjwKePnHNODMjyI898QNCN7grZNfZbrjJn7ZsIP+h0VRb9aSfXMrYDY+i3RkPnjMoZs6MiGZ\njc9WJTataRqRSITu7u7y4srZ3j7PdZnKzBG151QbJNARCNJj6GisrD9tsUzojTd28dBDQk0CJ8Aw\nLO66q49f/3oI2w5jmmZxfyAUZ5+TLhw9D/39F65xTghmzzlkUi5mi8XajWsBODt5FjzVF/nWj7yN\nphs3wMlT+ZI3jlRBuRUs7i0tM8i1EvLtFWa3Wp3PTgIZpKuhNXYSfPsf1fR8oRAcPNhDQwNs3RpF\nCBcpNV58sYuDB3sINWWFPkMhNU4/Oanep+fB/30OXnwBjFRBFWH+fLRtM/urJolEmHe/e4QDB9QU\nfcXT2cfHxyeLH0C+iSlbpt13EA7/hFs+UkYo/fT3ICjmFZBzlEsR5omjXFdKL84m6mKWCyDh4GA7\nR78bpqnVwRM2zgmP9olD7LoLBgbKl42r8iyvkcZQI8IQ2CkbFxfTNNGF2j/CEDWLTVcUV8729k0d\nm2DaSWEIgchmC6dlBu/ENE89Famp3bQcRf7QroumaflMaOEkcCgEgcAAg4MwMjJCIpHAMIyF/YHz\nEacKHuvUO1gtuc/HtV10U6d1UystvS3YSRtpS97xuXcggjpoIl/yRgPMQNHgCFB2kGslFLdX9CAa\nN+DMnWf29Fm23PtR9Ibajh3Lgl27NIaGIrz8cjdr12ZIJAKcPClU3J7MCn2eOKEGmkxTHVdSwvQ0\nfH4Q/uqvCp5x/nw0zfnZJDDR9QRr1sSZno5UPp19fHx8svgB5JuUimXaNp3Rf+pi2wfnMMyCEqAw\nAQn3fwj2PVncAxmLLZKFCqEOM5uFXtQWKoC0cVJBRofbaGp30E0Xz9O44e0N6IA7OYqX2Qb6hTlc\ntYDG6fWnmdw/SVpPo+kaPZ09hBpCbH94e129kmdbQ0yNjxNAQ3iA5iGsIIZp8PLLUb72tW5aW0X1\n7abl3s8SmdD5SWAAfXGHm9KI8yKM7BqWwda+rUWLH8/xsM/ZbO8v+HwiHarfLyePdCKmBnE0bT7I\nKhrkqg95WaLvfY/pEyeIxmL8Stf51Re+wK5z58raTS7GvGak4NSpYHHcngmpbZ+YmA8ec5gmfO97\n8LnPFXxO8+ejrpusX6/izoYGG9c1iMdDi5/OPj4+Pln8Hsg3KRXLtFYD0hXMni7xSM8Jpfd/Vl1d\nkkmYmVG3i2ahLJTNXQwVNMK8F/UDwMeBGLNxD+lo6KYL2GhahAZLx7R0pCM5PTnL5GSZvqxUivIP\nLJ/BwUFGfj1CbEOMBr0BSwaZnjjB+WsS7BjYUZfXcF3lUX3PvQ6/esPghcNrOJ5owlvTDJYJCOJx\nj/XrM7W1my5CLhNajUdxzuGmrKxNLuK8iBHGjoEdbO/fjp20Sc4ksZMqeFzw+WStHxFCDeJ0hUFm\n+0ulp37ODejUiVx7xdhttzF05gw/7uri5Pr1NK1Zw9DQEIODgzU9X04z8sABePJJdbtnTzYLbVnw\nwQ9CJjP/B1Kq1cb69er/8XjBs82fj04qwzojwPquDC0tMb73vV2cOWNdjKSyj4/PZYifgXyTUloG\nzOFmQDSFabR+CrJtoVC62bSMLNTSXtSNoW8iDBvXluhmL6AmfZ20y8kZwd0PNGLLgjLuwy76vhrl\nhKoglUoxPDxMuD3MafMUZ656CcM+xSxJDk06fCYTxNL/iJUOjOTmHjo7AwihYRiS3xwVuJ7S5U6n\nJa6bnbItYNF208ueFNVaTQpdcMueW9i2e1v1Op25QZzCrORSwfQyG1BTqRTD3/oWze3tGNkVgGma\nShJpZITdu3fXrDlZnCku4LOfhX374PRp9bOmKVHP7m6YnV1Qi5bupzg4OMfo8ATS0RCGx9v+4H3c\n/wef4o//2M88+vj4VIefgXyTkisDJmPJ/BSra7skY0m27n4vxnWfBCcJ6Rl1WyqUXlMWKudFfQB4\nMnu7J3u/esyw/pGtfb9PMnY9rr0B0HBtl6O/SnLQ3kpji0FHhxo0HRqCn368YPK08IEaszulxONx\nHMfJekEfxdPHyTSkCDQEcBxBPL4P5QWNkjmanVS3NZBKzQ+zG0hef7mF5qYMliWZnATHkWiaw8sv\ndzA3V3yKLtpuetnioqScdgJ3Z2/3Zu9fHMMyaIm01NZWUJiVrLhJ2RTxzp1w993qdu9edX8VFB9H\n85imieM4xIuygiukqQn+7M9g0ya4/nq46Sbo7FSB765dC87Rg4O/4NBQM2bzNpo6tmI2b+OlrzYz\n/b1f1C14lFIyNzfH3NxcGQUAHx+fKwE/A/kmJlfuGx0ZRSbUFHa+DKjfVrtQejndyCIW86K22DGw\nE2jNbw9CMGpsJ7llR1EZt3tdinVPD+PeFkZfTHF8GYRCIQzDwLZTmOYkqm9TYNsSwxCEQp0gh2Es\nA+OPl/U5Xop4HJyMJDJ1kLbJUWafl5y8o5XO93aSbmjCtjXWr+9iy5YennmmhnbTy5ZBlJRTdlIf\nO/szqIXGxdik5eidzjN/HNlFQaRt2xhGHS0Tczz8MDzzDDz99Lxjz513qvsLKN/7DE1hndGRUbbt\n3lZdMF7hXPc8j6mpKY4fP45tq5YV0zSJRCJEIpHK3t0+Pj6XHX4A+SZmyTJgtULp0oWxQeW/u4yA\nqtL2nE418qVdBh0lA8wh4gjpYGPSUPhAHeq7lmXR19fH0NAXCIddTDOAbUtiMZv+/m4sKwivH4Ej\n+yDYUyxzBMqhaAlCIdgUO0godgg32IQndH7zoxTHf3iY2KabuOdfb6GxURQMT6z+0HNOq7JowGbJ\nBUE9KJF5guxtGNxHYfoOCF3gfsvCFHHJAiX1zUeJf/QOQusiWEaFbUqlsOJx+u67j6Gvf51wuIwk\n0iLvZ1lV8337YGwMbrtt/r6xMXV/QcC7YomqJc71qakpjh07huM4+ePItm0mJibyw1w+Pj5XBisO\nIDVN+9+Az6DqTfs9z/vz7P1/iWp0c4E/9jzvhyt9LZ/VIVcGXDZjgyqAKtWNhKoCqkrbY6ZU0FSq\nGhQnhBQGZulkd53qu0qyJsPIyH8gkUhhGA79/R4DA9PgnoDxNAR/a6HMUYnPccX3h8ON2iiTNBFA\nRwAuOqlMI1ef+SWm2AGI/PDEag495zJG0WgUz/PQNI2O9nZ6kk+hHV3ZgqA6ysg8eR4cnYLkOPy7\ne+F044X114vHF+idungMRqYYXjOO88S9GFYjfVv7GNgxgJ7bJyWOMAO6Dlu2MHL8OAnXLS+JVMBy\nXEKB8gEvlM3IV+x9rlaiapFzXW75t3mpKCFEPtuY838/efJkee9uHx+fy5IVBZCapr0H+DBwk+d5\naU3TOrL3vw34A+B6oAc4oGnatZ7nVddA5HP54KZUNiJYRjeyyoCqCCnzAw6WJejrW1hJjJ22OH1n\nH/pY6QP1qe/qus6ePZ/NBm5/Qyh0bj5jNDsHngRxEtg0/0fCBC+xqM9xjtn4LB1hiWjUsz2Pau5h\nw0adcOPCLFDF4Yk6MDU1tUBkfProK5A8TMRorsuCYHHKyDwdPQpTR6HFAKsDOlLw1S+px1bZXy+V\ngngqRJcw0AtWLoPtRxkKHSWc0jFberBxGDqk9smeW7LbVFL21m2bPWNj7H7wQeL33rtQEqmEZVfN\nywS8QNmMfDkJpFzvc5EEUjmWONcz6x/AzfaIFpaqNU3D87x8lrusJqqPj89lx0qXgn8I/FfP89IA\nnudFs/d/GPim53lpz/N+A4wBt67wtXwuRdJxlaUSJRcvYar701UOC3geTEbhpdfhl0fgpdeRE9N8\n4qEUDz8sF6gG/dY3BmqUE6ody3qYSMTCsgxUlsyF4EbQGkFOkLOpA+ZljqrwOW4MNaIbgg09Lre/\nA3bcDLe/Azb0uOiB2oXKl4uUkmg0mg8eAYQGRvoY0cBtSJFtEChcENQ4MLQ0JTJProTpCQh78FwD\n/PbP4D0vw/3j8NL/q+w0V4GimZldFv/fyT5mDsfw0jYpzWV43XHCSTB7NoAuMHWTcFOYkdERUk6q\nfBawSYe3NGP9/beILBE8LlI1X1q2KRSaT9UXUiEjX7UEUilLnOsBmcjrW3revAxY7v9CCAKBYlUB\nHx+fy5eVlrCvBX5b07S/RjUz/ZnneT9DTUo8V/B7x6k8PeFzORMMqcBJ2sUXliUCqgU9d1MzSuTZ\n0PE0wVTqPNHxON6kwe23m3zgAx3oeg/hsJZNMF6A+i5ngHbgreQzZLoOmwQceQOCSRBrimWOdAs3\nnV7UXrE0C9Rg1ZAFqiOZTAbP84pLitJG4OJqOhkaCHJe3V9DhrV2CmSe3FmwXPjJGkglIWOCJ0CT\ncN00jP43uPWvFn225VCa/fvx2gEyh+ETR0aQ62dxpIO5YZPSWMpi6iYJmSA+GydyjvksoPDgjqPw\n7knQPUg5MPe3YP07Ksk/1ZBEXIhlUT5VXz4jvywJJFjyXBcN7XR0iAU9kFJKDMOgs7PTL1/7+FxB\nLPmtoWnaAaCrzEP/Pvv3bcDtwC3A45qmXV3LBmia9mng0wC9vb21/KnPpYBuqf640r6ogoCqkIo9\nd9FzaIYOQgWP0/YchiYQrkQKQSw2TVcXWFbJVXSLGIn2AAAd/0lEQVQ167v58qoLheM6W7qBWRi3\nwZtRF9XN/cirHmbssccY378fz3HQDINNd93FlvvuQ5Q0sS06AX+BCAQCaFnrxPyFXZhIdDTPJaDN\nzf9yDRnW2snJPO0GOQl/tgvueh3cbPAI6iNwAspO0/1cXYd6ymX/jKDOv7xtDz86t5vvfGkS4ycP\nYFstmAWlWdu1MYRBqDEEZgq6XHDn4I6T8P5jkDAhA2gerH0C5f9evhZdmESs2iW0kGVMXNXc+1zF\nuZ6zzSw3hZ17zMfH58pgyQDS87z3VnpM07Q/BL7jqRrFC5qmSdQ45SSwoeBX12fvK/f8XwS+CHDz\nzTd75X7H5xInpw85PqKyVNmAqkg3MkvZnrsT0+BqRJpakJ5HNBc8ahpID4GGYRhEo9EL3ISfK6+W\nSMyIOFz7pwtkjsYeeyzrgbwOEQggMxmOPPkkANfef3/RMy87C1RHhBB0dHQUfx4eOMFeupL7Ecbc\nkguC+mKBtRnu+TAk/xpcQzXZ5JxVensBWfcs6KLZP2mRbNpM300fZ+jQEOGmMKZuYrs2sWSMh7d/\nEsv4MhjDMBiDk4ehXcKJZhU85rZbdKCE9HdTTiS9xiTiQi7ExBUsea4X2mam02mAqt2PfHx8Li9W\nesX6H8B7gH/WNO1a1BU2BjwFjGia9reoIZprgBdW+Fo+lypCV8MVS+hGlu25EwLDDBA9l6Tbdclo\nqh1SCE3ZZGuA0BBouK57EZrwF3HR0fV8IOOm04zv358PHgFEIEBw3TrG9+9n8913Vyxnr2gCfoXk\nskK56VlN0+jaeAM9yTfg6ItLLghWhf7PwjeHYCoOtga6Bld1Q08nOKm6Z0Gryf4NdKv3PjI6QkIm\nMIRB//Z+BnZI8guMzhvA/RWse4PUaYhPBAldtx5r40bUgZxATZ2XD37rIttkmhBuVy47q0GV57oQ\ngoaGhjJP4OPjc6Ww0m+ZLwNf1jTtFVST2EPZbOQvNU17HHgVNX3wGX8C+03AErqRZXvuyMp8GHq+\nJ1LTQEpPTXgF1RVdSommaRehCb+gvLqIzV76zBk8x8kHjzlEIIB3/jzpM2do7Oxc9a1NpVLE4/El\nJ35zFGaMinUg98CWGoXk64XZBLf/KYx9EfQkBGMgT0D6OGy+C/T6HgPVZf909tyyh93bdhOfjRNq\nDKESxjvJZ6c1cLuuYfC/TzI8YuM4QQzjBH19goGBbnTdQB0/5VlREtHzVB9x9JT6v6ZBR5vy+c6W\n3ZfpylhhY6vUiPXx8bliWVEA6XmeDTxQ4bG/Bv56Jc/vc2VRtueObHAYNAmE2xGxM3ToQaYzKYxg\nEGGZSClxHIeurq6LWApbzEUHgq2taIaBzGSKgkiZyaDpOsHW1lXdOtd1GRwcZHh4GMdxaLQsPtH3\nAA988hPoVQTdQoiFmd2LGSRsGQCegfH9kDZA02FzD2z5Ncq5pr5yPtVm/yzDItKS2yeTlGpYDg4e\nZ+jLEA57mKaObQuGho4CZ9iz5y9ZyuMbltnWWzCEhtBV2X86BoDb1bE8fUkfHx+fRdAK5RYuNjff\nfLP385///GJvhs8qMjk5uaAHMhccRiIRkBLPzjAVmyE6MzM/aNPRQU9PT1ZfLsVi2cCLxetleiDT\np0+z+Z57FvRA1pu9e/cyNDRER3s7H7z5dn73rVuRUhIOh+m+6fqiTNTlQQrYCW4D7lmP9JxOMBRA\nDzpAEuWnXv/PPpWC+IwktNbBajYW98vObSPNgEkq5bJz53M0N2uYZgoIAh627ZFMhjhw4GUsq6nu\n24yUSv5KaMXbKyVIj8HnruWL+0TZ7Ooqy2r6+Phc5miadtDzvJvLPeZbGfpcUMr23HV1zU9oCoFm\nBYmsX093T09JWdUFBkF+CzJBCKRBfAzVp3jxUylb7rsPQE1hnz+Pputsvuee/P2rRSqVYnh4mHA4\nzAdvvp33b91OYi5JxnGZnTxOZ3u7ageIdKzqdqwEJ+WUDBPFkW6Gscd1xvefnzfEuauZLfdlsDNx\n4vFIfWdFPA8rPkMkdgpmypeBiykesorHXRzHzfZRXg30AsoLO5GIE4+fIRJZhQAy42Qbh0vOASGQ\nGYfvP+UQDptl9SVXYBvv4+PzJscPIH0uKJV77hayoKzqDcLUGET/GDwdNBc6noWeQdAufipF6DrX\n3n8/m+++e1EdyHoTj8dxHIemhgbefd2NJOaSuFIihIadzpD2JA3RU9AdXiKjdgEoacSTruTg4EFG\nh0eRjpIz2tq3lR0D1zP2uOTIk2cJrjMQAQ2Z8Tjy5FkSZw3+/JshZmfrXI5dpAxcOfieH7IKhWwM\nw8O2I5hmbnDGwrZtDMMgtEKLzYoEDLKNwwsykBlX49RZg9a24j+pg228j4/Pmxw/gPS5KJTtuVuU\nFEyNw/TvgjELQoIU6meehUiKS6WcrQeDF2RgJkcoFMIwDCw9gC4ErlQOOVJ6aELDDAaVw0vGyQ8l\nrQYLs4gFVDB6Pih3cOgrvyiy1Ts0dAjpOCReWEtw3UlEQAc0RMBDa3IYfSyEZWo0dyy0+6t1iCiP\nlGoAJatFCqhbA3V/meB7XjC+Hz24G8uK09f3bYaGvk44nME0TWzbJhaL0d/fX9v21IIQKlM6HVPb\nK4R6P46L6AojEcvXl/Tx8fGpgB9A+lweyBhEd8wHj6BujVl1f3cMxPqLu40XCcuy6Ovr4xtf+xqO\n66ILQcZxsW2b3t4Nqrivaasm7VI5i7gDoWeDrjJGz86XvsLo2dM0bdmIbqr0oW7qNIWbGB1+mUhv\nO2aLhRpWyYAnmDq5kXOJNazpOsMcnfly7PCwSyYzyOOPqyEiwzDo6+tjYGAgb6+3KIuUgXGdouBb\nui5jjz++UDD+3nsZ+MSn0TWTbwwPk0gkMAyD/v5+BqrQ4lngzlQLPe3qNnpKba+mQVeYQE/7yvQl\nfXx8fCrgB5A+lweZtapsLTKo0mAW4YIbUI8HL83hmgtBLkB5+tDz/P6O20lkkvT2bmDj+l5wXOha\nQflaZrOXgfJDJQcHD+ZtGQuziAC37LmlotHzbEsXciyG/taNRc+nmzqep+G4QWRmDSKwAbBJpU2m\nJ10aA3OkxfxUu2nC1NQg+/YN0dMTzmf+hrKpyT3VTIqUloFz4uVGYEHwPfb448XDUo6D/dobpP/5\nORrCYQZufw+7P/AhorpHKBxeMvNY1p2paGisCjRNldm7wws+q7roS/r4+PiU4AeQPpcHgSbQQiCP\nZbNEGuCBdEFrg8CjwKMoWRUDNdxwaQzXXAh0XWfPnj2k5uaYe2OCa1IOupZVZe8Kz2eoaiGvLThD\nfoqlo71oqMRJOYwOj+aDRyjIIo6Msm33NowKVi+NTRoCiZucQ1/TnL/ftV1002DLh9/P+D98Nxuo\nWRhahkZOc6ThHqSYb3+Ym0sRjw+zZYsKHkHZ54XDYUZGRti9e/fS5eNcGfjEDEyfgOPHQROwZg1o\nDtx4jdq2MoLx699yA11XXUMyehKrswMNCMQSRLrCVaX4yrozTU8DKGWCWhBiQZvChTKp8fHxeXPh\nB5A+lwdCQMd1MC3AOKYykdIAZxN0nQSxjyK7QbKNcXXWC7zUsRoasK6/dsmsYVVMnYTp17L721H7\nezprJxjpAmA2Pot0ZD54zKGbOjIhmY3P0lLB6sVw02ztOMmhxHU0Bd189jIZS7K9fztveWA7uimK\nptqbf+se/tfz9xGy58uxJ0/GaWtzsKziwElNPyeIx+PVBWI97fCTZyDlQEuLckJ6+RAceBpOz8Ce\nPQsE44XQ6di4mUw6jXRdZCajBqcW6Z0spJI7k67rTE9P09nZiWHU52t6VW3jC6mrYrmPj8+lih9A\n+lw+9HQCAqLt4GZAC0DXWuj598wHj2RvwzipbzIb/yiNoXUX3Gf6olMmE1UTUkL0cDZ41AEThAfG\nOEQldHeAEDSGGhGGyGYN54NI13YRhnocy6ho9bLjkQ+B2MHoyCgyofont/dvz/dPlk61YwSxB4vL\nsQ8/HOLxxw1s285nIIEap59TkJ6EL/4tNLTB2rUwN6uGf0KhvOZNqWC8EQwiNA1HKkmqvIh8md7J\ncpS6M3meRzqdxrZtpJS89NJLeZmrqsvZF5j84FJrK9bXvuYrlvv4vEl4k11VfS5ryvZ5nQAyFLqB\nSBcODkYYHV6DdJ5AGNbCoQ6fxckkwYsXtAugboUOblw9HlyDYRls7du6oAcyl0XMB+4VGvHEwAC3\n6Drbdm+rOMFdOtW+sBxrEQj0MTQ0RDgcrnH6OastyjCIWfi7MXh2E/xwI8js+y7QvNEjETbddVe+\nB9IRaaR08VyXhs4OtFy2UcqqBpdK3ZnS6TTpdBrI+sQbxvLL2atMqfuREYvRZ9sMXHedysKWjsj7\n+PhcUfgBpM/lR1F2LYQ6jG1yQeTBwXYODYVoCqfQzRZcm+KhDp+lCSSUzqbU56feQf2suepx1gCw\nY2AHQNksYp4lGvEMy6Al0lL15pWWY3NDRCMjIzVOPw+SEwJHb4W538D7jqqHnt6kbks0bwoF4zPn\nzzP1+mGuvvWdBNuzfaZZCZ1qBpeEEHR0dDA9PY2u69i2nX/MNM18EBmNRunu7r6IVp4LGRwcnA/a\nDQP7tdcYkhJOnGDPpk2+YrmPzxWOb2XocwWwl1wQ4KSCfH3nNZjNc+jmBmAToEqqdtLmwQMPXvnl\nbDcF6TgEQ8rPelmkYPI/wvS7inU3nUboehYi/4XSSfdFdSAvELXpQBZbEQIwPg5TR6FFh//4Dki6\nFX3/8jqQa9eix8+qnkevGgebYnJT2NPT08zOziKEwDTNou3PZDJcf/31NWqnrh6pVIqdO3fS3Nys\n2gZSKfj5z7ENg6TrcuD227FyZeuZGXjySV+x3MfnMsS3MvS5wpl3A5mNp7JDHRuAeXmYoqGOGjJd\nlxXShbFBGB9Gug4Z10Bc3UfgrQML9Q2XxIKeTcAzEH0nuIaaRu56Bnq2UE4mqdYs4mpgWVbFUu9C\nncU4amq/oEdxY/aYSY5DZhqSjRU1b4pK6xGrrIRONWiaRlc4zBrg1ydOYJToQEop0TSNQK6/8hIg\n535kFvojahomkPA84rZNpKHBVyz38bmC8QNInysAHTVtvZvG0EmE8SNc20I357M/RUMdVypjg3hH\nhpiIhjl23ETXbNrGhpj+n/Bbn9xT+xyDNgCRQej+fIH3+H3MB+yXCItNnEuJZ2eYis0QnZkp0Vls\nQ9OK2x/QNNjUA+4a+O/fgFCk+tLrMgaXSkXJ9a1babj1Vprb2xG6jpQSx3Ho6uq6pMrXOfej/OCS\nEBCJYB89iqHrhHzFch+fK55L5xvJx2fFWBjWRrb23UQylsS1XYD8UMfWXVuv3PK1m4LxYSaiYcaP\nmRgG6AGTs5kwLadG2DeYWsaTZgNz8QMIflndsofV1NZMpVJMTk6SSlWxvZ4Hk1F46XX45RF1OxlV\n9xc8NvWLV5j+zVFExs1nH6enp5maOoXSC42hgkiytzHQH4DI5lUPfHKi5EZDA8G2NsTRoySffZZk\nNEomk0FKmZ/CLsVJOZydPIuTclZ1G8uRcz+KxWL5vk27u5tYayu72tqwTp2CZNJXLPfxuYK5Qq+m\nPm9mqhrquNxIpWAmpuRlmpsWZtrScaTrcOy4SS4hBOBhYgUTPP33cR7aHVlmPGQBq9u/tmCitxor\nwqmZrP+zrkr0Uqqfc0zHkLog6qYxNIGwM6BpCMssGEz5VHZfjQAJ1FdiPxciy1pOlFwYBsbkJOeH\nh3n75z+P1dS0IPNYlXXkBaDs4NJf/AUDDz0EZ874OpA+Plc4fgDpc8UhdMEte25ZVBrmssF1lY/0\n2DjcuENNM4dCcON1EOmcH9IIhsi4BrpmI8R8GVXXbDzP4NT5EPH4pTvHUDTRW40VoZRqaMXQ56Nl\nIbIC3nElAm7oZPCUxbXQ1H22DZaabnZdl0xGEgyq9gfluQ0qWF593cJSUfIcIhDAO38eZmcRa9YU\nPZZKpfjXv/1XjjxxhOaO5vLWkReInPvR7t27Fw4uNTVdsO3w8fG5OPglbJ8rltxQx2UbPIIKHg+P\nwa3v+v/bu//guso6j+Pv7703NxcoEEyppWlLYdqRZWXVbkV22VldUReLWmdnVTZlgTbQOGUZRGdU\nZGZndmZn9ufg4mgdGXBVJqU4sDvUCgutIIxCq0GlUpHS1W3SlNr8sBHant7e3O/+8Txpb9u0eJMm\n57T385rJ5JznnPQ+zZOT+83z4/sAVTiQQG8PvPhy6IEblS+Ru3gpbzpzAIvDsXkrc27zABv+t52q\nlTK7jiFJErruv5/pZ59NMe66UrsV4ZjD2QcrxMjwyPJcDkaqh/a0brJc2OLa/dDul1T9qIUpI8DX\ngeuBdsLK7FWxfPLUJiWvVqskSUK1WqV68CCWz4fE6dHIyAirVq3i/X/xftb90zq2/HoLvTt7cfcj\nto5Mazi7ra3t91j1LiKnEwWQIlmVJPDgg/CuKyHZF4MiC72QvT1huLZ6OEdj0yWd7JrWQcH3cnax\nnzMKe3l0awf3P9dJe3tGRxNHRhi86y4qL75I8YUX4LnnQiodd4rFIpVKhcHBwWO/rqkQel9r/v9A\nOM/nQiBZrZIzY0bxDCpepVp1HNh/IKFcLjNjxow4PDyaC3IaMCN+vi+WT558czNzFy9mx9atbHr2\nWbq7u9n07LPs2LqVuYsXh2Tc0WgPbUuxhWKhSK4pR09PD9u3h5yV+WKeaiVkGRARmQqncNeMyGlu\ncBCKzSFgLNcESjmDAwfh4MEjt8rL5fmzZSu592vLeeyRQYZeb6VqJZYtz/A6hnvuofWJ9Zx5xhmU\n3cNa6J4eAMqzZh1/K8JcLuRa3DUQfovFgPFQAm84dG1W81lQdfr2vkb/8G/pH97D5s2bmT9/Pp2d\nN5DPdzHWVphhXuRyxkpZdLI8OTRE9549XJbPkzdjxJ3v79nD60NDXBLvSZKErq4upk+fjuUNNydP\nnmKxSF9fH3PmzoEKp3+WARHJFAWQIlnV2grlA2Ee5GiABFD1kFuvqemYrfLyeehcWeKG5W1jbfiS\nHe7QsxPObKW04lZWJwnfeOJRnnlqPYVikXJvLwOFAh0333z8odFZceeX3UNh32mzEDyOlsdr5lVy\ng8M8+fh3eb7v/zhYqbB//36efvppYA8rVx6VCxLi+TAhV+TkTBxNkoSuBx5g2syZDBYKNFerHMjl\nSCoVtq5Zw/KbbqJUKh2Rc9FxhtqGmN4zHYpQ8Qr79+6n+lr1yK0jRUQmmX7biGRVqQSf+ARs+iFc\n+e4wjF0ZCQHlhfNOuFXe0Vv9Zc7O/vCRJDCSZ6bluOmaj9KUy/HQY9+hMDJCx8c+duKtCMfcG73m\n+xGvJa/vpf32T1IslQ4lvj48x3Idy5fnKJVqckECIZ1PgbBV5uSoDQyrwP5Y92KxyPDwMIODfbS1\nlWhtPeuInIv9F4a5r+ftOI9ipYiV7dTPMiAipxzNgRTJss5O+IP58KMfAjloLsGcufDWtxzZ03Yq\nGV1BXSqBV6HqmFc5f+Qgty9ewsMLF7LhsstY+elPHz+FT63RBN5jBdO5HIOv/Y59SXJ415QozLGs\nMjj4YcbMBUk7kzl8XZuMu1a5fIBCoZ/W1uuAv6JUuoalS2czMNAf7jXom9XHM/Oe4eI7LubGJ2/k\nnSvfOaUpfERE1AMpkmX5fNiD+Y3yQJ5KRldQNxVgdhv09Iat8KiSzxltDlx33Ukbez9m15SoXC7H\nOZa3AS1MdS7I0WTcR6cvGhj4JR0dTql0DqFXtExn5yvAAlav7juUc3HZzctOnCdTRGQSmbunXYdD\nFi1a5N3d3WlXQ0QmU7Uado3JWRiG7tkOfX1ALgSN+wZhxQrq33vx+FatWjVGoDZAR0dHTZ7JhDDn\nsZXJ7HmsNZpAffXq1TGBeo729l10ds4nn2+uubMM7CVJ1jE4uPfInIsiIpPEzJ5390VjXlMAKSJT\nrm93zS4yuTC3M0nCsPyFJ3/y5rGBWoH29vbM9OAlSRKTcSeUSu2EdEJH6wceZrJ3BRIRGaUAUkSy\nxT0sotk9FI7NQlqeWecf3l1nEhwO1LLag5cQEplP49hFPXuBDUxV76iIyIkCSM2BFJGp90YrqCfJ\n6K4p2VUClhISmY/mphxd1NOBgkcRyQoFkCKSntEV1FJjdPHO1C7qERGphwJIEZFMyQMrCbvgTO2i\nHhGR35cCSBGRTCqhBTMiklWncDI5EREREUmDAkgRERERqYsCSBERERGpiwJIEREREamLAkgRERER\nqcuEAkgze7uZbTSzn5lZt5ldHsvNzL5kZtvMbLOZLTw51RURERGRtE20B/JfgX9w97cDfx/PAT4I\nLIgfK4CvTvB1RERERCQjJhpAOnBOPD4X2BmPlwDf8mAj0GJmF0zwtUREREQkAyaaSPxTwONm9u+E\nYPRPY3kb0Ftz345Y9uoEX09EREREUvaGAaSZbQBmjnHpTuAq4HZ3f9jMPg7cB7yvngqY2QrCMDdz\n586t50tFREREJAXm7uP/YrNhoMXd3cwMGHb3c8zsa8D33f2BeN/LwHvc/YQ9kIsWLfLu7u5x10dE\nRERETg4ze97dF411baJzIHcC747H7wVeicdrgevjauwrCIGlhq9FRERETgMTnQN5M3C3mRWAhDgU\nDTwKLAa2AfuAZRN8HRERERHJiAkFkO7+A+CPxyh34JaJ/NsiIiIikk3aiUZERERE6qIAUkRERETq\nogBSREREROoyoTQ+J5uZ9QPb067HaWo6MJB2JeS41D7ZpvbJNrVPtql9su1E7XOhu58/1oVMBZAy\necys+3i5nCR9ap9sU/tkm9on29Q+2Tbe9tEQtoiIiIjURQGkiIiIiNRFAWTjuCftCsgJqX2yTe2T\nbWqfbFP7ZNu42kdzIEVERESkLuqBFBEREZG6KIBsEGb2GTNzM5sez83MvmRm28xss5ktTLuOjcbM\n/s3Mfhm///9tZi011+6IbfOymf1lmvVsZGZ2dWyDbWb2+bTr0+jMbI6ZPWVmvzCzLWZ2Wyx/k5mt\nN7NX4ufz0q5rIzOzvJn91MzWxfOLzGxTfI4eNLNi2nVsVGbWYmYPxfeel8zsT8b7/CiAbABmNgf4\nANBTU/xBYEH8WAF8NYWqNbr1wFvd/Y+ArcAdAGZ2KXAt8IfA1cAqM8unVssGFb/nXyE8K5cCfxPb\nRtJTAT7j7pcCVwC3xDb5PPA9d18AfC+eS3puA16qOf8X4IvuPh/4LdCRSq0E4G7gf9z9EuBthHYa\n1/OjALIxfBH4LFA74XUJ8C0PNgItZnZBKrVrUO7+hLtX4ulGYHY8XgKscfcD7v5rYBtweRp1bHCX\nA9vc/VfuXgbWENpGUuLur7r7T+Lxa4Q3vzZCu3wz3vZN4KPp1FDMbDZwDXBvPDfgvcBD8Ra1T0rM\n7Fzgz4H7ANy97O57GOfzowDyNGdmS4A+d3/hqEttQG/N+Y5YJulYDjwWj9U22aB2yDAzmwe8A9gE\nvNndX42XdgFvTqlaAv9B6LCoxvNWYE/NH8t6jtJzEdAP/GecYnCvmZ3FOJ+fwiRVUqaQmW0AZo5x\n6U7gC4Tha0nBidrG3R+J99xJGJrrmsq6iZyqzGwa8DDwKXf/XejkCtzdzUzpRVJgZh8Cdrv782b2\nnrTrI8coAAuBW919k5ndzVHD1fU8PwogTwPu/r6xys3sMsJfHC/EX7CzgZ+Y2eVAHzCn5vbZsUxO\nouO1zSgzuxH4EHCVH86ppbbJBrVDBplZEyF47HL3/4rFvzGzC9z91TgVZ3d6NWxoVwIfMbPFQAk4\nhzDnrsXMCrEXUs9RenYAO9x9Uzx/iBBAjuv50RD2aczdf+7uM9x9nrvPI/zwLHT3XcBa4Pq4GvsK\nYLimC1umgJldTRjq+Yi776u5tBa41syazewiwkKnH6VRxwb3Y2BBXEFaJCxsWptynRpanE93H/CS\nu99Vc2ktcEM8vgF4ZKrrJuDud7j77Ph+cy3wpLsvBZ4C/jrepvZJSXzv7zWzt8Siq4BfMM7nRz2Q\njetRYDFhgcY+YFm61WlIXwaagfWxh3iju3/S3beY2bcJD3YFuMXdR1KsZ0Ny94qZ/R3wOJAHvu7u\nW1KuVqO7Evhb4Odm9rNY9gXgn4Fvm1kHsB34eEr1k7F9DlhjZv8I/JS4iENScSvQFf8o/hXhvT/H\nOJ4f7UQjIiIiInXRELaIiIiI1EUBpIiIiIjURQGkiIiIiNRFAaSIiIiI1EUBpIiIiIjURQGkiIiI\niNRFAaSIiIiI1EUBpIiIiIjU5f8B441dLI1/Te0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 792x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZ66P3wvu11U",
        "colab_type": "code",
        "outputId": "7413707e-f017-4027-c52a-f00d384056ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        }
      },
      "source": [
        "# = = = = = Training Phase= = = = = #\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "\n",
        "batch_size = 64\n",
        "nb_epochs = 10\n",
        "\n",
        "history = model.fit(np.array(x_train),\n",
        "          y_train,\n",
        "          batch_size = batch_size,\n",
        "          epochs = nb_epochs,\n",
        "          validation_data = (np.array(x_dev), y_dev))\n",
        "\n",
        "#Display curves of loss and accuracy during Training\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20848, 280)\n",
            "(20848, 11)\n",
            "Train on 20848 samples, validate on 2316 samples\n",
            "Epoch 1/10\n",
            "20848/20848 [==============================] - 6s 308us/step - loss: 1.6652 - acc: 0.4247 - val_loss: 0.8964 - val_acc: 0.6973\n",
            "Epoch 2/10\n",
            "20848/20848 [==============================] - 4s 191us/step - loss: 0.8007 - acc: 0.7472 - val_loss: 0.5086 - val_acc: 0.8402\n",
            "Epoch 3/10\n",
            "20848/20848 [==============================] - 4s 189us/step - loss: 0.4800 - acc: 0.8561 - val_loss: 0.3557 - val_acc: 0.8895\n",
            "Epoch 4/10\n",
            "20848/20848 [==============================] - 4s 190us/step - loss: 0.3022 - acc: 0.9146 - val_loss: 0.2785 - val_acc: 0.9154\n",
            "Epoch 5/10\n",
            "20848/20848 [==============================] - 4s 190us/step - loss: 0.1902 - acc: 0.9494 - val_loss: 0.2472 - val_acc: 0.9279\n",
            "Epoch 6/10\n",
            "20848/20848 [==============================] - 4s 191us/step - loss: 0.1245 - acc: 0.9671 - val_loss: 0.2331 - val_acc: 0.9301\n",
            "Epoch 7/10\n",
            "20848/20848 [==============================] - 4s 191us/step - loss: 0.0904 - acc: 0.9767 - val_loss: 0.2252 - val_acc: 0.9292\n",
            "Epoch 8/10\n",
            "20848/20848 [==============================] - 4s 190us/step - loss: 0.0633 - acc: 0.9844 - val_loss: 0.2242 - val_acc: 0.9352\n",
            "Epoch 9/10\n",
            "20848/20848 [==============================] - 4s 190us/step - loss: 0.0516 - acc: 0.9872 - val_loss: 0.2265 - val_acc: 0.9322\n",
            "Epoch 10/10\n",
            "20848/20848 [==============================] - 4s 190us/step - loss: 0.0397 - acc: 0.9901 - val_loss: 0.2278 - val_acc: 0.9296\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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++ugKPzt79mz69+9Pnz59ePPNN1m8eDFbtmxh06ZNnHPOOUC4Eah169bMmjWL\nK664glatWgFw0EEHVX9H1JACXUQSmjgx9JnHa906zK8LBxxwwN73y5Yt44EHHuCNN95g4cKFDB06\ntMLrslu0aLH3fUZGBiUlJRWue//996+yTUWKi4sZM2YMM2bMYOHChVxxxRUN9i5bBbqIJDRqVDgB\n2r07mIWvqTohWpUvvviCAw88kLZt27JhwwZee+21lG9j4MCBTJ8+HYAPP/ywwr8AduzYQbNmzejU\nqRNffvklzz//PAAdOnSgc+fOvPTSS0C4Yau4uJghQ4YwdepUduzYAcDnn3+e8roTSebWfxFpwkaN\nqp8AL69fv3707t2bnj170r17dwYOHJjybYwdO5ZLLrmE3r177321a9euTJuOHTty6aWX0rt3bw47\n7DAGDNg38kl+fj4//vGPGT9+PC1atOD555/n7LPPprCwkJycHJo3b84555zDXXfdlfLaK6LLFkWa\nmOpctpjuSkpKKCkpoWXLlixbtozvf//7LFu2jP32axjHutW9bLFhVC0iEoHt27czePBgSkpKcHce\nfvjhBhPmNdF4KxcRqaX27dszf/78qMtIGZ0UFRFJEwp0EZE0oUAXEUkTCnQRkTShQBeRejVo0KBv\n3SQ0efJkrrnmmko/16ZNGwDWr1/PiBEjKmxz+umnU9Xl0JMnT6Y4bsSxs846i61btyZTeoOnQBeR\nejVy5EimTZtWZt60adMYOXJkUp8//PDDee6552q8/fKB/vLLL9O+ffsar68h0WWLIk3YDTdABaPF\n1krfvhAbtbZCI0aM4LbbbmPXrl20aNGCVatWsX79ek455RS2b9/O8OHD2bJlC9988w133303w4cP\nL/P5VatWcfbZZ7No0SJ27NjB5ZdfTmFhIT179tx7uz3ANddcw7x589ixYwcjRozgF7/4Bb/73e9Y\nv349gwYNolOnTsyZM4esrCwKCgro1KkT999//97RGkePHs0NN9zAqlWrOPPMMzn55JN5++236dKl\nCy+++OLewbdKvfTSS9x9993s2rWLjh07kp+fzyGHHML27dsZO3YsBQUFmBl33HEH5513Hq+++iq3\n3noru3fvplOnTsyePbvW+16BLiL16qCDDqJ///688sorDB8+nGnTpnH++edjZrRs2ZIZM2bQtm1b\nNm3axIknnsiwYcMSPl/zoYceonXr1ixdupSFCxfSr1+/vcsmTpzIQQcdxO7duxk8eDALFy7kuuuu\n4/7772fOnDl06tSpzLrmz5/P448/znvvvYe7M2DAAE477TQ6dOjAsmXLePbZZ3nkkUc4//zzef75\n57n44ovLfP7kk0/m3XffxbNJhVEAAAb1SURBVMx49NFHuffee/nNb37DXXfdRbt27fjwww8B2LJl\nCxs3buSqq65i7ty5ZGdnp2y8FwW6SBNW2ZF0XSrtdikN9MceewwIY5bfeuutzJ07l2bNmrFu3To+\n/fRTDj300ArXM3fuXK677joAjjvuOI477ri9y6ZPn05eXh4lJSVs2LCBJUuWlFle3ltvvcUPf/jD\nvSM+nnvuufztb39j2LBhZGdn07dvXyDxEL1FRUVccMEFbNiwgV27dpGdnQ3ArFmzynQxdejQgZde\neolTTz11b5tUDbHbqPrQU/1sQxGJxvDhw5k9ezb/+Mc/KC4u5oQTTgDCYFcbN25k/vz5LFiwgEMO\nOaRGQ9WuXLmS++67j9mzZ7Nw4UJ+8IMf1GrI29KhdyHx8Ltjx45lzJgxfPjhhzz88MORDLHbaAK9\n9NmGq1eDe/iam6tQF2mM2rRpw6BBg7jiiivKnAzdtm0bBx98MM2bN2fOnDmsXr260vWceuqpPPPM\nMwAsWrSIhQsXAmHo3QMOOIB27drx6aef8sorr+z9zIEHHsiXX375rXWdcsopvPDCCxQXF/PVV18x\nY8YMTjnllKS/p23bttGlSxcAnnzyyb3zhwwZwpQpU/ZOb9myhRNPPJG5c+eycuVKIHVD7DaaQK/P\nZxuKSN0bOXIkhYWFZQJ91KhRFBQUcOyxx/LUU0/Rs2fPStdxzTXXsH37dnr16sXtt9++90i/T58+\nHH/88fTs2ZOLLrqozNC7ubm5DB06lEGDBpVZV79+/bjsssvo378/AwYMYPTo0Rx//PFJfz8TJkzg\n3//93znhhBPK9M/fdtttbNmyhWOOOYY+ffowZ84cOnfuTF5eHueeey59+vThggsuSHo7lWk0w+c2\naxaOzMszgz17UliYSJrT8LmNR3WHz200R+j1/WxDEZHGptEEen0/21BEpLFpNIEe5bMNRdJNVF2t\nkrya/Iwa1XXoUT3bUCSdtGzZks2bN9OxY8eEN+xItNydzZs307Jly2p9LqlAN7OhwANABvCou/8q\nQbvzgOeA77q7Hhgq0gBlZmZSVFTExo0boy5FKtGyZUsyMzOr9ZkqA93MMoApwBCgCJhnZjPdfUm5\ndgcC1wPvVasCEalXzZs333uHoqSXZPrQ+wPL3X2Fu+8CpgHDK2h3F3APUP+3R4mISFKB3gVYGzdd\nFJu3l5n1A7q6+59TWJuIiFRDra9yMbNmwP3AjUm0zTWzAjMrUP+diEhqJXNSdB3QNW46Mzav1IHA\nMcBfY2fMDwVmmtmw8idG3T0PyAMws41mVvlADQ1fJ2BT1EU0INof+2hflKX9UVZt9kf3RAuqvPXf\nzPYDPgYGE4J8HnCRuy9O0P6vwE1N4SoXMytIdAtuU6T9sY/2RVnaH2XV1f6ossvF3UuAMcBrwFJg\nursvNrM7zWxYqgsSEZGaSeo6dHd/GXi53LzbE7Q9vfZliYhIdTWaW/8bqLyoC2hgtD/20b4oS/uj\nrDrZH5ENnysiIqmlI3QRkTShQBcRSRMK9Bows65mNsfMlpjZYjO7PuqaomZmGWb2gZn9KepaomZm\n7c3sOTP7p5ktNbOToq4pSmY2Lvb/ZJGZPWtm1RtCsBEzs6lm9pmZLYqbd5CZvW5my2JfO6Rqewr0\nmikBbnT33sCJwLVm1jvimqJ2PeGyVgkjk77q7j2BPjTh/WJmXYDrgBx3P4YwYuuF0VZVr54Ahpab\ndzMw2917ALNj0ymhQK8Bd9/g7v+Ivf+S8B+2S+WfSl9mlgn8AHg06lqiZmbtgFOBxwDcfZe7b422\nqsjtB7SK3aTYGlgfcT31xt3nAp+Xmz0ceDL2/kng/6Vqewr0WjKzLOB4mvawwZOBnwF6XDdkAxuB\nx2NdUI+a2QFRFxUVd18H3AesATYA29z9L9FWFblD3H1D7P0nwCGpWrECvRbMrA3wPHCDu38RdT1R\nMLOzgc/cfX7UtTQQ+wH9gIfc/XjgK1L4J3VjE+sfHk74RXc4cICZXRxtVQ2Hh+vGU3btuAK9hsys\nOSHM8939f6OuJ0IDgWFmtoowVv73zOzpaEuKVBFQ5O6lf7E9Rwj4puoMYKW7b3T3b4D/Bf414pqi\n9qmZHQYQ+/pZqlasQK8BC8NKPgYsdff7o64nSu5+i7tnunsW4WTXG+7eZI/A3P0TYK2ZHRWbNRhY\nUslH0t0a4EQzax37fzOYJnySOGYmcGns/aXAi6lasQK9ZgYCPyIcjS6Ivc6KuihpMMYC+Wa2EOgL\nTIq4nsjE/lJ5DvgH8CEhc5rMMABm9izwDnCUmRWZ2ZXAr4AhZraM8BdMhc9ortH2dOu/iEh60BG6\niEiaUKCLiKQJBbqISJpQoIuIpAkFuohImlCgi4ikCQW6iEia+P9ScIP2xo/0hQAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hzZqa73/cOJg1C7p1C8fq1i0sjxtX832LSMOkybnqyK5d8F//FW6a8eWX4QKl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\n6CIiaUIJXUQkTSihi4ikCSV0EZE08f8BgcyfJjSYrWcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOq4-9pv28Uf",
        "colab_type": "code",
        "outputId": "9db68930-14a4-4993-c98a-fac3a2964f60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "#Visualization of Document Embeddings after Training\n",
        "doc_embs = get_doc_embedding([np.array(x_dev[:n_plot]),0])[0]\n",
        "\n",
        "print('plotting embeddings of first',n_plot,'documents')\n",
        "visualize_doc_embeddings(doc_embs,['blue','red','green','yellow','orange','black','brown','purple','pink','silver'],(labels_plt),'after')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "plotting embeddings of first 1000 documents\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HeP7I14xtbW2lo6MDgP7+fn77299y/PHHj6q8B6MBpFJKjYJlDHl33MHk66uw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\nDxAxrp4PdzwHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 792x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mr0jjgL4E2Oj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36b6ZgqrA6PR",
        "colab_type": "code",
        "outputId": "539c0ce7-4576-4859-ecc9-e1175ffaae1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "# = = = = = predictive text regions for the first branch = = = = = #\n",
        "\n",
        "from numpy import linalg as LA\n",
        "\n",
        "### define a K.function 'get_region_embedding' that returns the feature maps of the first branch ###\n",
        "\n",
        "get_region_embedding = K.function([model.layers[0].input],[model.get_layer('conv-0').output])\n",
        "\n",
        "test_tweet = '2015-04-16 18 2 We shouldn\\'t have to choose between rewarding work and raising a family.\" —President Obama'\n",
        "test_tweet_seq = tokenizer.texts_to_sequences([test_tweet])\n",
        "\n",
        "\n",
        "\n",
        "tokens = test_tweet.split()\n",
        "\n",
        "# extract regions (sliding window over text)\n",
        "filter_sizes = [3,4]\n",
        "regions = []\n",
        "regions.append(' '.join(tokens[:filter_sizes[0]]))\n",
        "for i in range(filter_sizes[0], len(tokens)):\n",
        "    regions.append(' '.join(tokens[(i-filter_sizes[0]+1):(i+1)]))\n",
        "\n",
        "test_tweet_seq = np.array(test_tweet_seq)\n",
        "\n",
        "reg_emb = get_region_embedding([test_tweet_seq,0])[0][0,:,:]\n",
        "\n",
        "### compute the norms of the region embeddings ###\n",
        "norms = LA.norm(reg_emb,axis=(1))\n",
        "\n",
        "zipped = [list(zip(regions,norms)) for idx in np.argsort(-norms).tolist()]\n",
        "\n",
        "print(zipped)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)], [('2015-04-16 18 2', 1.648366), ('18 2 We', 0.32052934), (\"2 We shouldn't\", 0.99044394), (\"We shouldn't have\", 0.67258716), (\"shouldn't have to\", 2.1346104), ('have to choose', 0.9120297), ('to choose between', 0.84900314), ('choose between rewarding', 0.22648914), ('between rewarding work', 0.24620531), ('rewarding work and', 0.27532214), ('work and raising', 0.48528758), ('and raising a', 0.32942832), ('raising a family.\"', 0.20915858), ('a family.\" —President', 0.22390583), ('family.\" —President Obama', 0.21084253)]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Ps_PZHF6skk",
        "colab_type": "code",
        "outputId": "e391413e-d2e6-455c-b44c-d11fba0ef553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "  # save the model\n",
        "import os\n",
        "import time\n",
        "import datetime\n",
        "\n",
        "# Output directory for models\n",
        "timestamp = str(int(time.time()))\n",
        "out_dir = os.path.abspath(os.path.join(os.path.curdir, \"Twitter_Authorship_Attr/runs\", timestamp))\n",
        "print(\"Writing to {}\\n\".format(out_dir))\n",
        "\n",
        "# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it\n",
        "\n",
        "checkpoint_dir = os.path.abspath(os.path.join(out_dir, \"checkpoints\"))\n",
        "checkpoint_prefix = os.path.join(checkpoint_dir, \"model/\")\n",
        "if not os.path.exists(checkpoint_prefix):\n",
        "   os.makedirs(checkpoint_prefix)\n",
        "\n",
        "# serialize model to JSON\n",
        "model_json = model.to_json()\n",
        "with open(checkpoint_prefix + 'Twitter_Authorship_Attr_CNN_word_model.json', \"w\") as json_file:\n",
        "  json_file.write(model_json)\n",
        "  # serialize weights to HDF5\n",
        "  model.save_weights(checkpoint_prefix + 'Twitter_Authorship_Attr' + '_CNN_word_model.h5')\n",
        "  print(\"Saved model to disk\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing to /content/Twitter_Authorship_Attr/runs/1574095665\n",
            "\n",
            "Saved model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFVt7Em-_qSn",
        "colab_type": "code",
        "outputId": "f9e29ce6-b8b2-4487-c329-27fa67fe9db5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "ls -R Twitter_Authorship_Attr"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Twitter_Authorship_Attr:\n",
            "\u001b[0m\u001b[01;34mruns\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs:\n",
            "\u001b[01;34m1574055064\u001b[0m/  \u001b[01;34m1574072695\u001b[0m/  \u001b[01;34m1574075137\u001b[0m/  \u001b[01;34m1574077896\u001b[0m/  \u001b[01;34m1574091847\u001b[0m/\n",
            "\u001b[01;34m1574072563\u001b[0m/  \u001b[01;34m1574072790\u001b[0m/  \u001b[01;34m1574075582\u001b[0m/  \u001b[01;34m1574091264\u001b[0m/  \u001b[01;34m1574095665\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574055064:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574055064/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "modelTwitter_Authorship_Attr_CNN_word_model.h5\n",
            "modelTwitter_Authorship_Attr_CNN_wordmodel.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574055064/checkpoints/model:\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072563:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072563/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "modelTwitter_Authorship_Attr_CNN_word_model.h5\n",
            "modelTwitter_Authorship_Attr_CNN_wordmodel.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072563/checkpoints/model:\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072695:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072695/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "modelTwitter_Authorship_Attr_CNN_word_model.h5\n",
            "modelTwitter_Authorship_Attr_CNN_word_model.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072695/checkpoints/model:\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072790:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072790/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574072790/checkpoints/model:\n",
            "Twitter_Authorship_Attr_CNN_word_model.h5\n",
            "Twitter_Authorship_Attr_CNN_word_model.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574075137:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574075137/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574075137/checkpoints/model:\n",
            "Twitter_Authorship_Attr_CNN_word_model.h5\n",
            "Twitter_Authorship_Attr_CNN_word_model.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574075582:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574075582/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574075582/checkpoints/model:\n",
            "Twitter_Authorship_Attr_CNN_word_model.h5\n",
            "Twitter_Authorship_Attr_CNN_word_model.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574077896:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574077896/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574077896/checkpoints/model:\n",
            "Twitter_Authorship_Attr_CNN_word_model.h5\n",
            "Twitter_Authorship_Attr_CNN_word_model.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574091264:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574091264/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574091264/checkpoints/model:\n",
            "Twitter_Authorship_Attr_CNN_word_model.h5\n",
            "Twitter_Authorship_Attr_CNN_word_model.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574091847:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574091847/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574091847/checkpoints/model:\n",
            "Twitter_Authorship_Attr_CNN_word_model.h5\n",
            "Twitter_Authorship_Attr_CNN_word_model.json\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574095665:\n",
            "\u001b[01;34mcheckpoints\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574095665/checkpoints:\n",
            "\u001b[01;34mmodel\u001b[0m/\n",
            "\n",
            "Twitter_Authorship_Attr/runs/1574095665/checkpoints/model:\n",
            "Twitter_Authorship_Attr_CNN_word_model.h5\n",
            "Twitter_Authorship_Attr_CNN_word_model.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRzqnqpA6qKU",
        "colab_type": "code",
        "outputId": "420038c6-ad9c-4bf8-d959-c464c86dee50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "source": [
        "# Model Prediction Phase\n",
        "from keras.models import model_from_json\n",
        "from google.colab import files\n",
        "\n",
        "\n",
        "#Print Test Set Sample\n",
        "#print(x_eval[:5])\n",
        "\n",
        "# load json and create model\n",
        "json_file = open(checkpoint_prefix +'Twitter_Authorship_Attr_CNN_word_model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(checkpoint_prefix +'Twitter_Authorship_Attr_CNN_word_model.h5')\n",
        "print(\"Loaded model from disk\")\n",
        "\n",
        "#Recompile loaded model\n",
        "loaded_model.compile(loss='categorical_crossentropy',\n",
        "                  optimizer='adam',\n",
        "                  metrics=['acc'])\n",
        "\n",
        "#Evaluate model on test set\n",
        "out = loaded_model.predict(np.array(x_eval), batch_size=64, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)\n",
        "\n",
        "#print(out.shape)\n",
        "\n",
        "labels= np.argmax(out, axis=-1)\n",
        "\n",
        "\n",
        "author_names=[labels_dictionary[label] for idx,label in enumerate(list(labels))]\n",
        "\n",
        "test_df['author']=author_names\n",
        "\n",
        "#Save Test Output as csv\n",
        "test_df.to_csv('test_results.csv')\n",
        "\n",
        "files.download(\"test_results.csv\")\n",
        "\n",
        "test_df[['author','joined']].head(10)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model from disk\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>author</th>\n",
              "      <th>joined</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>2011-11-04 13 26 Just got this photo... seems ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Barack Obama</td>\n",
              "      <td>2014-12-08 20 2 In Arizona, a group of @OFA fe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Barack Obama</td>\n",
              "      <td>2015-04-16 18 2 \"We shouldn't have to choose b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Snoop Dogg</td>\n",
              "      <td>2019-01-31 6 2 west coast we on! #JokersWild h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barack Obama</td>\n",
              "      <td>2016-10-06 18 2 ICYMI: Read about the historic...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Ellen DeGeneres</td>\n",
              "      <td>2019-06-05 22 4 My new BE KIND Box is availabl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Sebastian Ruder</td>\n",
              "      <td>2019-01-23 17 7 A comprehensive overview and b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kim Kardashian West</td>\n",
              "      <td>2019-04-29 1 0 RT @PlanetKhloeK: Don’t miss pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Kim Kardashian West</td>\n",
              "      <td>2018-09-23 20 0 I love him so much I can’t sta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>2014-08-29 15 25 You asked: Say hello to Irela...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                author                                             joined\n",
              "0    Cristiano Ronaldo  2011-11-04 13 26 Just got this photo... seems ...\n",
              "1         Barack Obama  2014-12-08 20 2 In Arizona, a group of @OFA fe...\n",
              "2         Barack Obama  2015-04-16 18 2 \"We shouldn't have to choose b...\n",
              "3           Snoop Dogg  2019-01-31 6 2 west coast we on! #JokersWild h...\n",
              "4         Barack Obama  2016-10-06 18 2 ICYMI: Read about the historic...\n",
              "5      Ellen DeGeneres  2019-06-05 22 4 My new BE KIND Box is availabl...\n",
              "6      Sebastian Ruder  2019-01-23 17 7 A comprehensive overview and b...\n",
              "7  Kim Kardashian West  2019-04-29 1 0 RT @PlanetKhloeK: Don’t miss pa...\n",
              "8  Kim Kardashian West  2018-09-23 20 0 I love him so much I can’t sta...\n",
              "9    Cristiano Ronaldo  2014-08-29 15 25 You asked: Say hello to Irela..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        }
      ]
    }
  ]
}