Inter_annotation_agreement.ipynb

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        "import pandas as pd"
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        "df1= pd.read_csv('annotations_s.csv')\n",
        "df2= pd.read_csv('annotations_a.csv')"
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        "df1.describe()"
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              "                        ID          Page  State\n",
              "count                15628         15628  15628\n",
              "unique                  50          1098      5\n",
              "top     mdp.39015006801313  00000001.txt   Core\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eb23f44d-2d09-4a1f-b18c-d663af59343e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          50,\n          \"1098\",\n          \"15628\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Page\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1098,\n          \"50\",\n          \"15628\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          \"14063\",\n          \"15628\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ID_list_1= df1['ID'].value_counts().to_dict()\n",
        "ID_list_2= df2['ID'].value_counts().to_dict()"
      ],
      "metadata": {
        "id": "_1u03ukc_YeO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for key, value in ID_list_1.items():\n",
        "    if ID_list_1[key]== ID_list_2[key]:\n",
        "        continue\n",
        "    else:\n",
        "        print(key, value)\n",
        "        print(key, ID_list_2[key])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSr9A9WO0O1_",
        "outputId": "77cbd0ef-5576-4558-d03f-df0ac80bc8d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mdp.39015006801313 1103\n",
            "mdp.39015006801313 1098\n",
            "mdp.39015002758699 267\n",
            "mdp.39015002758699 268\n",
            "mdp.39015018916802 247\n",
            "mdp.39015018916802 248\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b1= df1[df1[\"ID\"]==\"mdp.39015006801313\"]\n",
        "b2= df2[df2[\"ID\"]==\"mdp.39015006801313\"]"
      ],
      "metadata": {
        "id": "ApHSczRT25Dz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p1=b1['Page'].value_counts().to_dict()\n",
        "p2=b2['Page'].value_counts().to_dict()"
      ],
      "metadata": {
        "id": "_iZCC-p16Vf7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Find all unique keys in both dictionaries\n",
        "all_keys = set(p1.keys()).union(set(p2.keys()))\n",
        "\n",
        "# Check for differences\n",
        "for key in all_keys:\n",
        "    if key not in p1:\n",
        "        print(f\"Key '{key}' is in p2 but not in p1. Value in p2: {p2[key]}\")\n",
        "    elif key not in p2:\n",
        "        print(f\"Key '{key}' is in p1 but not in p2. Value in p1: {p1[key]}\")\n",
        "    else:\n",
        "        if p1[key] != p2[key]:\n",
        "            print(f\"Key '{key}' has different values. Value in p1: {p1[key]}, Value in p2: {p2[key]}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWbi_qbR-DOm",
        "outputId": "2a3b2ef8-3258-48ef-ff11-7e08e665a1c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Key '00001075.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00000917.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00001011.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00001012.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00000945.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00000966.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00001010.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00001028.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00001025.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00001060.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00001057.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00000952.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00001009.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00000951.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00000923.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00000978.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00001046.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00000927.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00000939.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00000969.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00000953.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00000925.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00001065.txt' has different values. Value in p1: 2, Value in p2: 1\n",
            "Key '00001042.txt' is in p2 but not in p1. Value in p2: 1\n",
            "Key '00000926.txt' has different values. Value in p1: 2, Value in p2: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "difference= d[d !=0]\n",
        "display(difference)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "q0E-CLaMCWCK",
        "outputId": "ae04c734-ea83-4a1d-fd7c-2d9d43413319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ID\n",
              "mdp.39015002758699   -1\n",
              "mdp.39015006801313    5\n",
              "mdp.39015018916802   -1\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mdp.39015002758699</th>\n",
              "      <td>-1</td>\n",
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              "      <td>5</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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    {
      "cell_type": "code",
      "source": [
        "faulty_id = ['mdp.39015002758699', 'mdp.39015006801313','mdp.39015018916802', 'msu.31293001706088']\n",
        "df1= df1[df1['ID'].isin(faulty_id)==False]\n",
        "df1.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "y5ruzn-6DHgL",
        "outputId": "86a01e7e-b7c0-4ec7-f7e3-2f2939af45b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              ID          Page  State\n",
              "count                      13618         13618  13618\n",
              "unique                        46           610      4\n",
              "top     uc2.ark+=13960=t29882z7p  00000001.txt   Core\n",
              "freq                         610            46  12548"
            ],
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              "\n",
              "  <div id=\"df-fe08619c-8330-4887-813d-5461f4c82660\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>State</th>\n",
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13618</td>\n",
              "      <td>13618</td>\n",
              "      <td>13618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>46</td>\n",
              "      <td>610</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>uc2.ark+=13960=t29882z7p</td>\n",
              "      <td>00000001.txt</td>\n",
              "      <td>Core</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>610</td>\n",
              "      <td>46</td>\n",
              "      <td>12548</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe08619c-8330-4887-813d-5461f4c82660')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fe08619c-8330-4887-813d-5461f4c82660 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fe08619c-8330-4887-813d-5461f4c82660');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-621944c2-0884-4c71-ab72-3ecaed5b6eaf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-621944c2-0884-4c71-ab72-3ecaed5b6eaf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-621944c2-0884-4c71-ab72-3ecaed5b6eaf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          46,\n          \"610\",\n          \"13618\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Page\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          610,\n          \"46\",\n          \"13618\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          \"12548\",\n          \"13618\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sCm3nCBZ6hWj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2= df2[df2['ID'].isin(faulty_id)==False]\n",
        "df2.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "SNru5-hcEGPY",
        "outputId": "65a8e60d-4da8-4a13-b538-bf728d4fe4eb"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                              ID          Page  State\n",
              "count                      13618         13618  13618\n",
              "unique                        46           610      5\n",
              "top     uc2.ark+=13960=t29882z7p  00000001.txt   Core\n",
              "freq                         610            46  12329"
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-abf1ca84-26fa-4ce7-a4d7-210d0c5f8203 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          46,\n          \"610\",\n          \"13618\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Page\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          610,\n          \"46\",\n          \"13618\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          \"12329\",\n          \"13618\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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    {
      "cell_type": "code",
      "source": [
        "merged_df = pd.merge(df1, df2, on=['ID','Page'] ,suffixes=('_df1', '_df2'))"
      ],
      "metadata": {
        "id": "WjRi_vzsEVGG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(merged_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9mkl0X2eF973",
        "outputId": "2ca45369-61aa-4304-f1f6-8da706c983f2"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                       ID          Page State_df1 State_df2\n",
              "0      mdp.39015066052906  00000001.txt     Front     Front\n",
              "1      mdp.39015066052906  00000002.txt     Front     Front\n",
              "2      mdp.39015066052906  00000003.txt     Front     Front\n",
              "3      mdp.39015066052906  00000004.txt     Front     Front\n",
              "4      mdp.39015066052906  00000005.txt     Front     Front\n",
              "...                   ...           ...       ...       ...\n",
              "13613        uc1.$b703964  00000292.txt      Back      Back\n",
              "13614        uc1.$b703964  00000293.txt      Back      Back\n",
              "13615        uc1.$b703964  00000294.txt      Back      Back\n",
              "13616        uc1.$b703964  00000295.txt      Back      Back\n",
              "13617        uc1.$b703964  00000296.txt      Back      Back\n",
              "\n",
              "[13618 rows x 4 columns]"
            ],
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              "      <td>00000005.txt</td>\n",
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              "      <th>...</th>\n",
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              "      <td>Back</td>\n",
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              "    <tr>\n",
              "      <th>13614</th>\n",
              "      <td>uc1.$b703964</td>\n",
              "      <td>00000293.txt</td>\n",
              "      <td>Back</td>\n",
              "      <td>Back</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13615</th>\n",
              "      <td>uc1.$b703964</td>\n",
              "      <td>00000294.txt</td>\n",
              "      <td>Back</td>\n",
              "      <td>Back</td>\n",
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              "    <tr>\n",
              "      <th>13616</th>\n",
              "      <td>uc1.$b703964</td>\n",
              "      <td>00000295.txt</td>\n",
              "      <td>Back</td>\n",
              "      <td>Back</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13617</th>\n",
              "      <td>uc1.$b703964</td>\n",
              "      <td>00000296.txt</td>\n",
              "      <td>Back</td>\n",
              "      <td>Back</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13618 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46c049f4-f26f-41e2-b5d5-4897acbcb41b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-46c049f4-f26f-41e2-b5d5-4897acbcb41b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-46c049f4-f26f-41e2-b5d5-4897acbcb41b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-28406d08-b2f8-43b9-b486-a017cbb59932\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-28406d08-b2f8-43b9-b486-a017cbb59932')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-28406d08-b2f8-43b9-b486-a017cbb59932 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_4a37d085-0626-4e75-9a57-703e5733bba3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('merged_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_4a37d085-0626-4e75-9a57-703e5733bba3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('merged_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "merged_df",
              "summary": "{\n  \"name\": \"merged_df\",\n  \"rows\": 13618,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"mdp.39015012456565\",\n          \"mdp.39015011475111\",\n          \"wu.89062346192\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Page\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 610,\n        \"samples\": [\n          \"00000082.txt\",\n          \"00000219.txt\",\n          \"00000056.txt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_df1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Core\",\n          \"Unknown\",\n          \"Front\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_df2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Core\",\n          \"Core_Back\",\n          \"Back\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "counts = merged_df.groupby('State_df2').size()\n",
        "\n",
        "\n",
        "ax = counts.plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "ax.spines[['top', 'right']].set_visible(False)\n",
        "for i in ax.containers:\n",
        "    ax.bar_label(i, label_type='edge')\n",
        "\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "xap_2ZojIXCP",
        "outputId": "11729d6e-f5f9-4505-a90a-8213e4349db7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "counts = merged_df.groupby('State_df1').size()\n",
        "ax = counts.plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "ax.spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "\n",
        "for i in ax.containers:\n",
        "    ax.bar_label(i, label_type='edge')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "lv7Ut1EyIZ_e",
        "outputId": "b08464a7-7a6d-4bd2-9370-01cb66b46837"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aPyWlcVG_mNK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['State_df1'].replace({'Front': 1, 'Core': 2, 'Back':3 , 'Unknown':2}, inplace=True)\n",
        "merged_df['State_df2'].replace({'Front': 1, 'Core': 2, 'Back':3 ,'Unknown':4, 'Core_Front':2 ,'Core_Back':2 }, inplace=True)"
      ],
      "metadata": {
        "id": "d2chmdL8LgoR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-K4skJoLMdFS",
        "outputId": "6f1bfb0c-fde1-47c7-fcd1-4e8376862ebf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ID          Page  State_df1  State_df2\n",
              "0  mdp.39015066052906  00000001.txt          1          1\n",
              "1  mdp.39015066052906  00000002.txt          1          1\n",
              "2  mdp.39015066052906  00000003.txt          1          1\n",
              "3  mdp.39015066052906  00000004.txt          1          1\n",
              "4  mdp.39015066052906  00000005.txt          1          1"
            ],
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-30411ce2-89d7-4dc4-83bc-2446c14cac03 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "merged_df",
              "summary": "{\n  \"name\": \"merged_df\",\n  \"rows\": 13618,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"mdp.39015012456565\",\n          \"mdp.39015011475111\",\n          \"wu.89062346192\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Page\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 610,\n        \"samples\": [\n          \"00000082.txt\",\n          \"00000219.txt\",\n          \"00000056.txt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_df1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_df2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['State_df1'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "rUO1eeD4MnEH",
        "outputId": "91c8a6a9-f03f-4aea-8669-c409be1751b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State_df1\n",
              "2    12549\n",
              "1      620\n",
              "3      449\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>State_df1</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['State_df2'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "EQxnUqthMs7u",
        "outputId": "e861298b-1f2d-4866-8bb3-3863158b74cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State_df2\n",
              "2    12331\n",
              "1      656\n",
              "3      631\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "      <th>State_df2</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['State_diff']= merged_df['State_df1']-merged_df['State_df2']"
      ],
      "metadata": {
        "id": "ePBvegm5N2ZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['State_diff'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "-jEJDEx2OWAW",
        "outputId": "b944816f-3e63-41b6-ea55-e1b2c7efdc28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State_diff\n",
              " 0    13242\n",
              "-1      261\n",
              " 1      115\n",
              "Name: count, dtype: int64"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>State_diff</th>\n",
              "      <th></th>\n",
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              "      <th>-1</th>\n",
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              "    <tr>\n",
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              "      <td>115</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          },
          "metadata": {},
          "execution_count": 19
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['State_diff'].replace({-1:1} , inplace=True)"
      ],
      "metadata": {
        "id": "A3JyT6SqRRiX"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "merged_df['State_diff'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "rLbxa6PURioi",
        "outputId": "fbd149da-2cc4-4bbc-f23c-345595f2cf59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State_diff\n",
              "0    13242\n",
              "1      376\n",
              "Name: count, dtype: int64"
            ],
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              "<style scoped>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "state_diff_sum = merged_df.groupby('ID')['State_diff'].sum().sort_values(ascending=False).to_dict()\n",
        "display(state_diff_sum)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "0RDMZ2S7BsGG",
        "outputId": "fa348468-416c-4288-f6e5-3ad1a0c8a067"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'coo.31924013010594': 50,\n",
              " 'mdp.39015018888910': 44,\n",
              " 'mdp.39015012456565': 34,\n",
              " 'wu.89062346192': 32,\n",
              " 'mdp.39015019124075': 32,\n",
              " 'uc1.32106005854044': 27,\n",
              " 'wu.89062292206': 21,\n",
              " 'uc1.b4655689': 19,\n",
              " 'mdp.39015026429558': 19,\n",
              " 'wu.89069257962': 13,\n",
              " 'mdp.39015001575367': 11,\n",
              " 'mdp.39015065600481': 11,\n",
              " 'uc1.$b453271': 8,\n",
              " 'coo.31924105253953': 7,\n",
              " 'uc1.$b730193': 6,\n",
              " 'mdp.39015008530829': 5,\n",
              " 'uc1.c2947484': 5,\n",
              " 'mdp.35112100069899': 3,\n",
              " 'mdp.39015075340128': 3,\n",
              " 'uc1.$b56473': 3,\n",
              " 'mdp.39015009354625': 2,\n",
              " 'uc2.ark+=13960=t29882z7p': 2,\n",
              " 'uc1.$b703964': 2,\n",
              " 'uc1.b3435562': 2,\n",
              " 'uc1.$b285041': 2,\n",
              " 'mdp.39015063918554': 2,\n",
              " 'mdp.39015004151679': 2,\n",
              " 'uc2.ark+=13960=t7cr5p059': 1,\n",
              " 'uc1.b3796918': 1,\n",
              " 'wu.89042494500': 1,\n",
              " 'wu.89049799737': 1,\n",
              " 'inu.30000041692793': 1,\n",
              " 'uc2.ark+=13960=t8tb1209f': 1,\n",
              " 'uc1.$b799073': 1,\n",
              " 'uc1.b3725672': 1,\n",
              " 'uc1.$b404059': 1,\n",
              " 'uc1.b2806638': 0,\n",
              " 'uc1.b4951365': 0,\n",
              " 'uc1.32106000740495': 0,\n",
              " 'uc1.$b70833': 0,\n",
              " 'uc1.$b299111': 0,\n",
              " 'mdp.39015066052906': 0,\n",
              " 'mdp.39015041026801': 0,\n",
              " 'mdp.39015023647459': 0,\n",
              " 'coo1.ark+=13960=t99603t9g': 0,\n",
              " 'mdp.39015011475111': 0}"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ids= []\n",
        "for key, value in state_diff_sum.items():\n",
        "    if value<6 and value!=0:\n",
        "        ids.append(key)"
      ],
      "metadata": {
        "id": "Ly7cCKSEB6hv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df=  merged_df[merged_df['ID'].isin(ids)]\n",
        "new_df = new_df[new_df['State_diff'] != 0]\n",
        "new_df.drop(\"State_diff\" ,axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d7H-vJNKDFyY",
        "outputId": "f6ac1e0f-2b4b-4324-cff4-931bf8e8e88e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             ID          Page  State_df1  State_df2\n",
              "832          mdp.39015009354625  00000481.txt          2          3\n",
              "844          mdp.39015009354625  00000493.txt          3          2\n",
              "1554   uc2.ark+=13960=t7cr5p059  00000007.txt          1          2\n",
              "2569               uc1.c2947484  00000008.txt          2          1\n",
              "2576               uc1.c2947484  00000015.txt          1          2\n",
              "2653               uc1.c2947484  00000092.txt          3          2\n",
              "2657               uc1.c2947484  00000096.txt          3          2\n",
              "2662               uc1.c2947484  00000101.txt          3          2\n",
              "2980                uc1.$b56473  00000315.txt          3          2\n",
              "2981                uc1.$b56473  00000316.txt          3          2\n",
              "2982                uc1.$b56473  00000317.txt          3          2\n",
              "3326               uc1.$b799073  00000341.txt          3          2\n",
              "3342               uc1.$b404059  00000013.txt          2          1\n",
              "3528             wu.89042494500  00000007.txt          1          2\n",
              "5020         mdp.39015008530829  00000009.txt          2          1\n",
              "5021         mdp.39015008530829  00000010.txt          2          1\n",
              "5189         mdp.39015008530829  00000178.txt          3          2\n",
              "5190         mdp.39015008530829  00000180.txt          3          2\n",
              "5195         mdp.39015008530829  00000179.txt          3          2\n",
              "5210         mdp.35112100069899  00000016.txt          2          1\n",
              "5405         mdp.35112100069899  00000212.txt          3          2\n",
              "5406         mdp.35112100069899  00000213.txt          3          2\n",
              "5970   uc2.ark+=13960=t8tb1209f  00000551.txt          3          2\n",
              "5979               uc1.$b285041  00000004.txt          2          1\n",
              "5983               uc1.$b285041  00000008.txt          2          1\n",
              "6224         mdp.39015063918554  00000017.txt          1          2\n",
              "6225         mdp.39015063918554  00000018.txt          1          2\n",
              "6870   uc2.ark+=13960=t29882z7p  00000008.txt          2          1\n",
              "6878   uc2.ark+=13960=t29882z7p  00000016.txt          1          2\n",
              "9064         mdp.39015075340128  00000003.txt          2          1\n",
              "9065         mdp.39015075340128  00000004.txt          2          1\n",
              "9068         mdp.39015075340128  00000007.txt          2          1\n",
              "9720         mdp.39015004151679  00000001.txt          1          2\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c804c883-b4d2-4637-81c5-89132c6c4bcf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 42,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"mdp.39015009354625\",\n          \"uc1.b3435562\",\n          \"uc1.b3725672\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Page\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"00000208.txt\",\n          \"00000178.txt\",\n          \"00000017.txt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_df1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State_df2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['State_df1'].replace({'Front':1, 'Core':0, 'Back' :3} , inplace=True)\n",
        "new_df['State_df2'].replace({'Front':1, 'Core':0, 'Back' :3} , inplace=True)\n",
        "# new_df.drop(\"State_diff\" ,axis=1)\n",
        "new_df[\"Error_type\"]= new_df[\"State_df1\"]-new_df[\"State_df2\"]\n",
        "\n"
      ],
      "metadata": {
        "id": "yVOfF3ilKpsb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df[\"Error_type\"].replace({-3:3 , -1:1} , inplace=True)\n",
        "new_df[\"Error_type\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "ejNsX-PzPdql",
        "outputId": "58b34c6d-1e87-4473-96d1-0ea76371e389"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Error_type\n",
              "1    26\n",
              "3    16\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Error_type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the data\n",
        "error_types = ['Misclassification in Front', 'Misclassification in Back']\n",
        "error_counts = [26, 16]\n",
        "\n",
        "# Define colors for the pie chart\n",
        "colors = ['#ff9999','#66b3ff']\n",
        "\n",
        "# Create the pie chart\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(error_counts, labels=error_types, autopct='%1.1f%%', colors=colors, startangle=140, shadow=True)\n",
        "\n",
        "# Add a title\n",
        "plt.title('Distribution of Misclassification Errors')\n",
        "\n",
        "# Show the pie chart\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "Jy7bdaWiQQTh",
        "outputId": "5fad991a-1662-4855-9c24-42df2951ac1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "state_counts_df1 = df['State_df1'].value_counts()\n",
        "display(state_counts_df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "WyBTdCfxL2eC",
        "outputId": "e3802042-c241-412d-9c1b-543e62a82123"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "State_df1\n",
              "3    15\n",
              "1    14\n",
              "2    13\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State_df1</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "state_counts_df2 = df['State_df2'].value_counts()\n",
        "display(state_counts_df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "C-mSFKAiNDzb",
        "outputId": "dedd8c73-a4f1-4ee5-f0b2-82ce84a4eaa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "State_df2\n",
              "2    29\n",
              "1    12\n",
              "3     1\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State_df2</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k9MprTHVNIUf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "state_diff_sum = merged_df.groupby('ID')['State_diff'].sum().sort_values(ascending=False)\n",
        "\n",
        "plt.figure(figsize=(14, 8))\n",
        "\n",
        "ax = state_diff_sum.plot(kind='bar', color=sns.color_palette('Dark2'))\n",
        "\n",
        "ax.spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, label_type='edge')\n",
        "\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "\n",
        "plt.xlabel('ID')\n",
        "plt.ylabel(' Number of Pages')\n",
        "plt.title('Differences in annotation per volume')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "nu7K3u1BTKr0",
        "outputId": "66cba930-a7a1-43ab-aaa3-3c133336b28e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ],
            "image/png": 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wwNhsNrNu3TpjjDG//fab2bZtm/nwww/N6dOnnfoX9sWLF5sGDRqYEydO2JclJiaasmXLmg8//PCuj3+z7BybMX+9+bl8+bJ59NFHzalTp8zTTz9t/Pz8zOeff26MMfY+nGXChAmmZcuWJiUlxYSHh5sHHnjA9O/f3zRp0sQEBQWZ2bNnm+vXrztlfKNHjzZPPvmk/XZKSoqJiooyNpvN1K1b15w5c8a+/G59//33ply5cmbFihX2ZWFhYWbRokUmNTXVqW9us/ucnDFjhsmTJ4/9xVKLFi1M586dnV4nzYYNG0yZMmVM1apVTeHChc1HH32UZS8yJk2aZB544AHzxRdfmHr16pkmTZpkWWh74cIF065dOzNgwACHY7dt29asWLHCLF++3MTExDit3siRI03r1q0d/mA2ZMgQ07t3bzNy5EiHEMQZ49y+fbspU6aM+eyzz+zL/vWvfxmbzWY6d+5sjh49etc1btavXz+zceNG8+STT5r69eub2bNnG2OMU98gxcbGmrCwMIfH1qZNm4zNZjMFCxa0P1c660X2tm3bTKlSpRzux3LlypmvvvrKYbu7/Zldu3bNdO7c2XTp0sXhvyratGljFi5caNauXev0mQTz5883NpvN/hhLexO9Zs0a8+effzr18XbjsV5++WVToEABU79+fVOkSBETHh5uJk2a5LRaN9q3b59p27at+fnnn83TTz9tfH19zZYtW8ySJUvMJ5984pTAMe2PdkOGDLEv2717t8mTJ4/ZvHnzXR//Zh988IFp3Lix+eKLL8xjjz1mGjRoYKZNm5aloe24ceNMRESE6d+/v6lTp47DTFtniomJMZUqVTIrV660L3vjjTeMzWYztWrVcupM+p07d5qQkBCzevVq+/Fee+01Y7PZzKOPPmq+//77u65xo/nz55vGjRubhIQE+7JWrVqZefPmmXfeecccPHjQaTP3k5OTTf369c2MGTMclp89e9Y0adLEVK5c2WmvXy9fvmyaN29uBg0aZIz563Xs9evXTe/evU1ERIQ9HHCGxMRE8/TTT5tnn33WXLt2zf747dq1qxkzZowZOnSoWblypVP+kySjfT/99FPj6elpAgICTFRUlH25M55HUlNTs/V1QnY/b23fvt2EhYWZ2NhY+7IffvjBFC5c2Hh6epru3bs7td6kSZNMmzZt7GFfYmKiadasmVm9erVZsGCBU//jLjY21thsNvv77rSf5UMPPWR69OhhJk6caN/2bp+fT5w4YYoVK2Y6duxoX/bqq6+aLl26mCZNmpjZs2enm+STWdn9uuTKlSuma9euJjQ01ISGhpoGDRpk2R/+0x4/UVFR9v8yPXTokBk1apR54oknzLhx48zBgweNMc77nZqd76c2b95sgoKCzOeff27/r5F27doZHx8fM2jQIHP69Gmn11y+fLkpVKiQ2bJli0lJSTHHjx83w4cPN76+vub111+3b3e3z12bN282FStWNOHh4aZ8+fJm7NixlgltCWxzkOnTp5u2bdsaY/76N7OIiAgTEhJibDbbXf8CSQujWrRo4fDiKe3BMWXKFIfZos74hd+gQQNTpkwZY8xfgeb9999vKleubPLly2fKlCljxo4da86dO3dXddIcP37cTJs2zX47LXiuW7eumTVrllNqpDl16lS2ju1GvXv3NgsWLDDGGPPYY4+ZIkWKmGbNmpkaNWo49d9QPvjgA1OlShWzdetW07lzZ4cXif/6179MYGCg015sd+vWzbRq1Spd/eeff96UK1fO/phwhq+++spUqlTJIZQKDw83Xbp0MbVr1zZRUVFO+7ev7DwnV69ebWw2m8Ob2TVr1hibzWbWrl3r1FrG/PVG7J133jF9+vQxycnJpn///sbHx8dpLzJufBF09epVs3XrVvts4d9//93UrVvXHtpm9C/2d/P8FRcXZ95//32Hf2kcPXq0sdls9r9G169f3z6L5m7t3bvXbNiwwT7mTz75xB6eduzY0ZQoUcJEREQ4pZYxf10yoH79+ub8+fP2n9X27dtNxYoVTaNGjUy/fv3sb2rvVtrPoXXr1mbOnDnmwoULplu3bqZp06amRo0aplixYiY5OdkpL3p//vlnExQUZLZu3WpflpKSYh588EETGRlpChYs6DBj824tWbLE3H///Q4vBuvUqWP69u1rmjdvbt555x2HN6B34ubzd8qUKfbA2Zi/foaenp72Ny+FChWyj/tu78sjR46Y8uXLm/DwcGOMMSdPnjQNGzY05cuXN/nz5zcVKlQwixcvdsobvxvHGRMTY+rWrWsfx8GDB81zzz1n7r///nThUmZr3Pj9Tz/9ZEJDQ80ff/xhYmNjzcsvv2zy5ctnbDab/T877jbU+fjjj02HDh3ML7/8Yoz567ksOTnZhIWFmdWrV2f62Ldy9OhR+++c+Ph48+ijj5qGDRuaadOm2YOjG8fkjMfdxx9/bJ5//nlz6dIl88wzz5iGDRuat99+29SrV898++23Tgv3161bZ0JDQ01sbKx9LL/88oupVauWadq0qWndurXT3pitXr3a/q+9aW9od+7cacqXL29q1aplBg4c6NT/MnrjjTdMyZIl7aHsO++8Y/LkyWNatWplypUr5/CHIWfcnw0aNDBdu3a13047D06cOGFCQ0PN448/ftc10o576tSpdMsXLlxofHx8zPbt240xzhnTtWvXzCeffOIQpo8aNcp4enqaxx9/3NSrV8/UrFnTDB069K4CnrT7KikpyZw/f94eGm3evNksWrTIzJs3z5QvX9707dvXvk9mQtubH5vZ8TohNTXVJc9b//3vf80999xjduzYYV92+vRp06RJEzN27FhToUIFs2TJEqfVGzx4sMMlw9566y3j7e1t6tata4KDg0358uXtr/+cEaIGBwebtm3bmtjYWNOkSRPToEED07NnT9OxY0dTpkwZ06tXr7uqkWb37t2matWq5oknnjBr1qwxTZo0MS1btjS9evUyzzzzjLHZbCY6Otopj7fU1FQzderUbHtdsmHDBtO+fXuzYsUK89VXX5kaNWqY8PDwLAltU1NTzfXr103dunXNnDlzTEJCgilZsqR5/PHHTfPmzU3Tpk1NhQoVnDbhIKvfT91s5syZpnXr1iY5Odn+e2zFihUmKCjI1K5d24wdO/au7s+MXmOMHz/e1KxZ02G706dPm0GDBjnt8gjXrl0zM2fONN27dzcXLlwwI0eONKVKlbJMaEtgmwOk/TLftWuXadasmX1548aNTf78+c0DDzzgtBDp1VdfNcWLF7e/kbzxQRkdHW3KlCnjtBN7yZIlJiIiwtSsWdPcc8895pVXXjF79uwx165dM4MHDzaVKlUyixcvNsbc/Qu2W70JadKkicMTwQcffJDpN9E3Wrx4cbaOLe0Y0dHR5qmnnrKvK1eunPHw8DDvvvuuU2c9JScnmwoVKpiSJUuaevXqmYSEBIcXnSVKlDBz58694+Nm1OPkyZNNo0aN7L/4Dx8+bAoVKmRmzJhhvvjiC1OhQoW7uh7YjTNnV69ebcqXL2/ee+89c/DgQdOhQwcTEhJi5s6da4YOHWpat25tHn30URMfH5+pWseOHcvw3+az45xMu6xD2vly/vx507x5c/PMM8+Yq1evOv1fXHbv3m22bdtmv92vXz/7i4wbw5y0sd9u/bTzLCkpyf5HiMuXLzucf6dPn3YIbdN89913mR7PjW78A8VXX31lPD09zfLly82VK1fMqVOnTKtWrUyPHj3u+D7NaPvk5GT78lOnTpl7773XTJ482aF+0aJFzaZNmzI5GkeTJk0y99xzj8Nz/4wZM0ybNm3M0KFDTXBwsPnxxx9v2e+dSPsjxZtvvmn/F73U1FRTpkwZU6BAAYd/28tsrbT9Dh48aGrWrGmmT5/+/9j76rAqtvbtaxRFpASku6W7WzqVULDAxlYM7O4+dgdY2HHU47HrHLsAW0RFQqWRjvv7g2vWO8Pe6J7B83vf77rO/Y8ye+9Zs9as9axnPXE/ZP4dPnwYKioquHbtGlxdXVnP0Fbs2LEDKioqOHfuHPLz8xEZGQlNTU2sXr0aCQkJcHJywvTp0zlzjdPzvLa2FtnZ2eT/9PX79+9DTU0Na9euxefPn1FVVYWYmBhYWVn9Ml7zVatWwdfXF+Hh4VBTU8PkyZNx48YNfP36FdHR0dDS0vqlxpYlS5agd+/eiI6OZkUSvnv3Dv3790dERASvCEN6zOrq6oT+PigoiPBbJiUloWPHjpCUlCRGCa59a3koffbsGRYvXizwPQcHhzanhLc0AglzXBUWFiI2Nhaurq7Ytm0b+exXptq/fv0arq6uaGxsRHFxMYYPHw5ZWVkoKysTx8+vMAynpaVBTU2NxUeakpICBwcHrF+/Hnp6ekSH4DMnmb/ZuXMnJCUlWc+9c+dO9OrVCwsXLkTHjh1/aZZFTk4O1NXVoauri4iICHTs2BEXL14kh/Xg4OBfymmbkpICCwsLlkOZ7uv69ethY2PTZoe8sHfAvBYQEICAgADeBglh92euiczMTGhoaODMmTPk2rhx42BlZcWb/o3Jf+ru7g5ra2vY29sjKSmJRKV9+/YNW7ZsgYGBAW+jLVMHoiNaa2pq/jE9oWUE/uPHj//P5BbQHL2ora2NSZMmEV5qExMT9O3bF7W1tXBycsLMmTPb1C7wnzlz9+5dyMnJwcrKCnFxcejYsSMuXLhAAqPs7e15B4oIk3XZ2dnQ19cHRVGIiooic6W6uho7d+6Erq4u/vjjD569YuPWrVtwc3ODkpISQkNDSYYi0GxQ5RvEIczx+X+tlxw/fpyML9NoS+/tv5qGZ9iwYRg4cCCSkpIwdOhQos8+fPiQOEp+lePu8ePH/8h5igbTjjBz5kzo6emxPl+/fj3i4+ORkJAAdXV1FBUV8e2K0Pdw+vRpqKmp4eHDh6zrN27cgLi4ODp37oylS5fybpPGp0+f8OjRI/L3nDlziNGWGTkszIH9T+Nfg+3/h6ioqEBBQQHKy8tZwr24uBi2trYoLi5GfHw81NTUsGTJEgQGBqJ79+68o4OYkbQvXryAj48PoVsA/mO0vXz5MlRVVdsUhZSdnc1S9E6cOAEvLy8MGTIEFRUVrMXh7e3N2yP87t07bNiwAZMmTcL58+dZaedMA52rqys2bNgAoHnhUhTF2/h94sQJlpf3n+ob0KyQMYUO3Z/z589j6NChAID4+Hioq6sjMDAQioqKOHz4MC9Ki+zsbKSmpmL9+vV4+vQpuUdqaioMDQ1hYGDAihYuKiqCg4ODQOrvz8AsilJcXEwiVzIzMxESEgJDQ0M4ODhAQkKCpA/l5eVBQkKiTcpMywNBQkICzM3NERQUBBUVFcJ/BzQbszQ0NFjzSVQ8e/YMysrKSEpKwtevXwU+/yfmZH5+PiuNsiWWLl0KeXl5EuXyT2xOzHsylYza2lqUlJRgzZo1QsdDGOhNtKKiAlpaWnBxcRF4f/R3aKOtl5cX/vzzT6xevRqqqqq8xvLhw4eEY68lSktLyT3pvo4bNw4uLi6cvNBMQ9zr16/x+vVrocoebaSjce3aNejq6uLly5cit/UjlJaWQlNTE/7+/jh48CBWrFgBiqJIBJeJiQnmzZvH+97fvn0jXLg0Dh8+jICAAADNWQLKysoICAiAt7c3fvvtt1+WVjZw4EDY2NggJCQEI0eOBEVRSElJAdA8N5kO0V8BLy8v6OjowMvLCyoqKqx3N3nyZOjp6XHKjqHnSHl5OWxsbDB37lyB7+Tk5AhwYC9btgxWVla/tODZmjVrYG5ujpEjRwpE05qbm7cpTfX8+fMsY8PGjRtBURSUlZVJqiHzuxRFcXba0XOqvLwcERERWLNmjcB69fHxwalTp7Bq1SpISEhg+/btJOLj+PHjvNr79OkTLl26JHBIZcpJJycnsgcAzZxrV65c4dQe0Ny3NWvWCG2Dfp7CwkLExcXBzc0N27dvx+rVq0FRFK8aCR8+fBAYw+zsbOjq6pL9vFu3blBTU4ODg0Ob6SxOnDhBsr4aGxthbm4Oa2trLF26FPPmzQNFUTh69CgAwM3NDSNGjODdFlNvLSsrg42NDSwtLXHgwAGSYUHPCVdXV8yZMwcAtz2VKedaHrw/f/6MnTt3EudFY2MjGdOtW7fCxsaGlyM5JycHp0+fxrFjx0hm0devX9G3b194eHgQ+Ujj/Pnz0NHRERoZKwrevXuHpUuXYvDgwTh58mSrBtl169bBwMCA7K1c9gD6u6WlpcjJycHXr19ZBeEaGxvR2NhIgk/oOZuSkgIzM7M2ZaO9ffsWCgoKSEpKws2bN7Fo0SKYm5vD1NSUyP/CwkJitOU6J5l7gJaWFmRlZYXqeL9KT6DHMjMzEx4eHgL71a+UW3TfSktLMWbMGNaaO3v2LLS0tKChoQEVFRVEREQQGZqQkECKgvFZb9XV1WhsbCT3q6iowP3797FixQpMnDgRo0aNAgCyf86dOxceHh6cowzp9t6+fYu1a9ey9s3379+jb9++LAoloPmMIysriy1btnBqi4aw8bh58ybi4uIEHHN1dXXQ09MTaoz/EZhFvkpLS4XKhn9SLxEmGxoaGnDp0iVitKXPrYcOHWKdnduC3377DQ4ODnB0dGRRjgDNMllXV5dlEP8V+JXnKSaY74CuG9OnTx/89ddf2LBhAyiKImtZW1sbO3fu5PX8x48fx8CBA9G3b1+MHTuWOMfevn1LsliZMiozMxP9+vXD4sWLYW5uLmDQFRU/2j/mzp1LjLZfv35FeXk5kpOTee9xfPGvwfb/M9CeWTMzM7i4uKB///5kQpeXl8PR0ZFUW6SjnPbs2YOIiAiBQ/DPwDS8Mj0eO3fuhKurK+Li4lj3fP36NYyMjHjzc+3evRvOzs64fPkyyxBx6dIlVuQbvQkOHToUffr04dxORkYG5OXlERISAn9/fygoKCA0NJQVaUcLJ0dHR+zfvx9r166FhIQEb0G+Z88eUBSFoKAgVt8uX778S/sGNG/g8vLyCAoKEuCL+vvvv2FjY4Pg4GAoKyuTAhEhISHQ0dH5ofFOGNLT06GoqAgbGxsYGRlBTEwMU6ZMwatXr1BfX4/169dDTk4OTk5OePDgAZ4+fYoFCxZAV1eXk1GTqRTS6dCqqqq4desWgGZhnpaWhlWrVrGKv7x69QoODg68i4/99ttvMDMzQ21tLUv5+vr1K37//XdYW1uzlPdr167BysqKM49nVlYWVFVVkZyc3KrR/J+Yk927d8eDBw8E2mSS9FtZWWHUqFFtNtZeunQJc+bMwezZswUM6Ez5MmLECEhKSmLLli1wcHCAi4uLSIcx+h5lZWXQ1NSEoqIiLC0thR7o6HdZUFAAV1dXqKqqgqIoHDp0iHO/nj59ig4dOmDcuHEifb+xsREDBw7EhAkTRD5kMivqurq6wsrKCt26dYOxsTGuXLkicNhkYtOmTXB1deWtXGRnZ+PZs2d4//49mYPp6emwt7dHt27dYGRkRAwRdXV1cHR05OXpzsjIgJeXF4yNjWFvb88qeHf//n14e3ujX79+UFVVxbt371BWVoaQkBAEBgbyOkDfvHkTW7duxe7du3H58mVyffny5ejXrx969erF4vaeOnUqevfuzbkdGrm5uQIOSaB5fPfu3QtfX19UV1eTtXj8+HE4ODhwdlaUlZVBX18fkpKS0NDQYDkgWq5h+u9Zs2ahX79+vPllhc0RoDmqkRn1Qa+7iIgI3saxHTt2gKIoeHh4sK4fOnSIpGwys3yePXsGY2Njog9xQUVFBQwMDBAZGYkXL14IRH7MmzcPurq66NKlC+H+zsrKwogRIzhFITGLy6ipqWHevHkknbixsZG8E7r9wMBAwrU8ffp0dOjQgeU4/BnoaBl3d3cWPyLzWYD/vK+ioiL069cPSkpKoCgKaWlpIrdFg47MF5aBNWDAANy5cwdWVlbo3r07njx5gtGjR0NPT4+XTAbYOhcdqfv9+3eEhYXB2dkZ9vb2rOjJ0NBQFucmF2zZsgVmZmasyMsHDx4gODgYRkZGMDMzI3pJdXU1HBwcODu16PeSlZVF9DZhUT7z5s1D9+7dWb9NTExEWFgY52jUZ8+eQVVVFSYmJtDU1ISEhATWrl1LMkV69OgBd3d3Uk2+rKwMU6dOhYODAy+ZnJ6eDlVVVQQHB8PHxwft2rVj0TQx+1pTUwNtbW0MHz6cUxvMKFcLCwvY2tpCU1MTsbGxRF8WFm0OAGPGjEFMTAyvqF56zc2aNQv9+/dnfZaYmAiKoqCmpkZ0x+LiYmzfvh3y8vIYP368SG0w9wAtLS3Y2trCzMyMREIzo+Ra9o2PnkCP5dOnT9G1a1dQFEUo15iy8lfILWbf6ECTlnj16hXu3bvHMjQ2NDQgJiZGoLiaqH17+fIlYmJi4OPjA0dHR4Fz9MiRI1n0IECzUzkuLo5TAAxzD1BSUsKYMWPIGZz+rKSkhDX3mpqa8PXrV7i6uuL8+fOc+gc0B2ANHTqU7MeXL18mRukPHz4IzPMvX77A0dGROLm49Ku8vBzh4eFwcnKCpKQk1q9fz+oHE23RS+h5IkokaX19PTHaenh4kOADPs7PBw8e4OjRo7h48SJL5woLCwNFUYiJiWGdr//++284OTkhNzeXc1st22M6qekihjT4nqeY2LJlC4KDg8l8rqurw+nTp6GnpwddXV2oq6sTnaC0tBR6enq8aKhSUlIgKSmJ6dOnIykpCRYWFtDT0yNOikOHDsHIyAjx8fE4dOgQnjx5gsDAQCQkJODx48eQkZHhNDcBsPTrluPC/Hvu3LnQ0tLCrFmzYGdnBwsLi3+kMOqP8K/B9v8jvH37Fl27diXphRs3bkS3bt1gampKBMT8+fNhamoq4GXg6lk/cOAAKIoifLUAu/Lxli1b4OnpCVNTU5w6dQqnT59GcHAwL2EANHta6QgVYcpQS6FbVVUFHx8fEqUgKoqLi+Hm5sbavM+fPw8xMTGYmpoSGgIawcHBUFBQQOfOnXmnS2/duhUdOnRAQkIC5OTkfmo85Ns3Gnfu3IG4uDi8vb3Ru3dvYtQEmg1ULi4uMDMzEzD0cTXolJWVwd3dHZMnTyae4F27dsHQ0BBxcXFIT09HU1MTLl68CFtbWygqKsLQ0JC3Uf/ly5dQUFDAhAkTcPjwYcTFxUFfX/+H3tfk5GSYmJjw8mJu27YNHTt2ZB0YmXP77NmzMDMzw5cvX1BbW4va2loEBwcjLCyMs9Fj27Zt6NmzJ4BmJWLdunUYOnQoVq5cKeDl/hVzMjU1FRISEti0aZNQ2UA/f319PSZMmABbW9s2pe7s3LmTOEYcHBzg4OAgEPHG3PwGDhwIiqJY1Vx/JFeYBk1tbW0MGDAAlZWV0NLSYhXwENYeHWlFH965vLunT59CUlISU6ZMafU7LaMsZs2aBTU1Nc6RvNXV1bCxsUGfPn2QmZmJ+/fvIzY2FnJycti4caNAClJZWRl27NgBSUlJVpE8LkhJSYGJiQmhIFi4cCGLn7awsJAVPV9SUgJnZ2dSVVvUsXzx4gXk5OSQnJyMAwcOYNSoUfDz8yMKbn19PYyNjaGpqcmSHUVFRbwM0bt27SJOLWdnZ8jIyGDKlCmsOciUKzU1NXB3d//he/4R9u/fD1tbW+jo6EBeXp4camls374dtra25O+GhgYEBgYiJiZGpDFs6azo3bs37t+/D2NjYxIBJ0y5bGpqwt69eyErK8ubq7rlHFm0aNEPo4IrKirg5eUlEHEiCrZt24YOHTpg9uzZUFBQEKi+vHPnTlAUhaFDh+LcuXN49OgRgoODYWNjw1kvaWpqwsSJExEWFkauvXjxArdu3SJGx9u3b0NXV5dl+APAq+hSdnY2VFRUSJES5nMA7EwnLy8v7N+/HwsXLmzTPjBhwgQMHDgQKioqAkYkGvS4LVq0SEBOitq/rVu3omPHjq1Wyw4ICABFUejevTvZqz98+ICVK1fyOhT9TOeqrq5mGVe/f//Ou0Dd1q1b0b59e5ajmIkvX76w1kNxcTHc3d2xZ88eAKLNEfo7r169gri4ODp27Ej0t5bjc/v2baioqGDChAk4e/YsJkyYAAUFBc5O6+LiYlhbW2Pq1KkoKSlBXl4eli5dCgkJCYwbNw7l5eXIy8vDpEmToKWlBXl5eTg4OKBr16689Lu3b99CXV0ds2bNIkajmJgYoXowrRPMmDEDTk5OKCsr47TW3r9/D2VlZUyePBmvX79GamoqvL29ISsrSzK/mPerrq7GjBkz0LVr1zbzmA8fPhyhoaFoaGgga2vv3r0YMGAAunfvjvDwcKKTlZSUYM+ePSIZNennLSsrg46ODuLi4gA007b4+Pi0+ju+egLTWNupUydMmzYNYWFh8PPza/XZ+Mqtln1ryZ/MLBLHRGlpKdnfuFA90H3LyMiAnJwcRowYgaVLl8Lb2xsODg4sirAjR47A3NwcK1aswN27dzF58mQoKCjwmie5ubnQ1dVFUlIS6/qPZOCsWbNgaGjIWQ968eIFunTpggEDBmDRokWws7ODrq4uZsyYQc7gLdfUzJkzYWxsLHLwF/378vJyGBgYoHfv3jhy5AiWLVvGygwUZrDlo5cw23N1dSWBIT+iV2loaMDly5fRpUsX3s7InTt3Ql5eHpaWllBUVISFhQXLIO3v7w9paWkibz5+/IiQkBBeZ8XW2mNGWNMcujS4nqeYoPc3YRlDNTU1yMrKYlEFFBQUwMnJiTiwRcXnz59haWlJ9kWgeY7SdQ9ox92hQ4fQs2dPSEhIwNDQEHZ2dqirq0NdXR2sra0FItB/hLS0NPj6+v6wADVzHJOTk0FRFGxtbTmP46/Avwbb/4+waNEiDBgwgHVt/PjxoCgK2trahKKAWVmRz2S6desWDA0NERoaChUVFdbmwUzTu3HjBuLj4yEnJwcbGxsEBARwnsRNTU349u0bPD09sXHjRvL8x48fx8GDB1nGRqD5IEQLO1tbW84pJ7m5ubCyssK1a9fQ1NRENnpPT094enrC19eXlern7+/PK52SxubNm9GhQwcivFxdXREbGyuUk6etfaNRWVmJqKgo7Nu3D46OjoiOjia8qPX19bh37x5LAeTrJSovL4eJiYkAF+3p06dhZWWFgQMHsgT533//jczMTF4cx3V1dYiLi2OR61+/fh29evVCcXExSktLWYL27t27pKgan8PD9u3bIS4uTigsysvLUVpaiq9fv5K5XV9fD11dXRgYGCA0NBSenp68NkSgmeuIjqj29PSEh4cHQkJCYGdnBxsbG9YY+/n5tWlOfv78GXZ2dmSD//r1K27cuIHLly+zUk3oeZGbmwuKorB7925e7d24cQNdu3Ylns/79+/DwMBAQKGl0xCLi4vh7e0NJycnsgZEWQvfv3+HoqIiYmJiyLWVK1fCxMSk1bHav38/KIoi75mLESI3Nxft2rUj6XB1dXWYNWsWYmJiEBkZiXXr1rEMN4cOHcLAgQOhrKzMa07euHED7u7urPVD8+PKyMiQQnRNTU14/PgxZsyYAVVVVVbfuODQoUOQkZFBamoqXr16hXXr1kFSUpJVlItGTU0N3r59i4CAANja2nKSKbW1tRg4cCAr4vLSpUsICgrC58+f8ebNGwDNHF1MIzdfRen+/ftQUVEhzrmioiLMnz8fFEVh9OjRxLjS2NiIuro6XLlyBX5+frCwsOAlk/fv3w8pKSns3LkTN2/exPTp0yEhIUH6BTQrucrKyrC3t8eQIUPg4+PDkiWivLuysjIoKCggNjaWXIuOjoapqanQ7z99+hTJycmQk5PjzZcubI5ISUmxomppVFdXo6CggMg1rmO5bds2tG/fHidPnkRTUxMCAgIwYMAAcmCnsXv3blAUBYqiSKQT/TnXORMWFkYoAwYPHgxTU1MoKipCSkoKK1euBADOWSktQY/5hg0bCO9hY2MjVqxYgXHjxmHs2LECh3F/f38oKSmhU6dOvIy1dJtJSUkYMWIErl69CikpKSQkJABoXn/M+Xn48GHecnLHjh0QFxcn8v/bt2/IzMzEvXv3SBTx8+fPMXXqVKK7/ujQ9DP8SOdqed/a2lpkZGQgICAAVlZWnOfkzp070aFDB2LoKi0tRUlJCekXE9XV1Xj79i2pXcBV7/r69SspgBgZGQllZWXy7pnzurCwEL/99hs0NDTQrVs3uLu78ypu8+XLFxgbGwtkw+zZswcKCgpITk4G0Dz/c3JysGXLFpw6dUpo33+GmpoaTJ06FaNGjWKlgfft2xdxcXEIDg7G6tWrBeb6s2fP8OHDB87trV27Fj169GBdo41IHTt2ZEUrnjp1CpGRkdDW1uadPQj8Zw5Pnz4ddnZ2ePToEerr6/Hx40coKSlh/fr12LNnDwwNDVkGMS4yuaqqCjIyMiwd6K+//kKXLl2EOkvaqic8fvyYGGuB5rOjnJxcqwW+2iK3amtroaWlBScnJ3JtyZIlpJDT1KlTidO6sbEROTk5WLJkCaSlpXkZ4d6/f49u3bph+vTp5FpqaioGDBiA+vp6cob7+PEjJkyYQAJSbG1tefNTX7hwgRjXGxsbMXbsWPTs2RMODg44duwYyzn+999/Y9iwYZCTk+NMT1NdXY3o6GiByG0jIyNIS0tj1KhRLIf12bNnMXjwYMjLy3NeAzU1NQgJCUGvXr3IGigqKoKfnx+ysrIE5MWTJ0/apJdUV1fD29sbFEVBUlKSGON+dI+WtF5c9rdnz56ha9euSE1NRXV1NR48eIDp06dDTEyM5WwaNmwYbG1ticHP3t6e11nxR+0tXLiQfI+mduR7ngKaz8EdO3Yk67myspJVaIwJOusiMDAQDg4OnPe3Fy9eQFtbm5wLGxoaUF5ejpCQEFhaWkJdXZ1Ew5aVleHdu3d49eoVeU+TJ0+Grq6uyI6Lc+fOQVZWFmpqaoiOjmZl0wlzIBQVFcHDwwOOjo6cx/FX4V+D7f9HGD58uACH3r59+5CYmAgnJyd4enq2meulvr4ey5Ytw5AhQ/Ds2TNs27aN8C3RaGls/PTpE0pKSlhReVxQVFQES0tLfPjwAc+fP4eRkRGcnJygpaUFBQUFLFu2jHx37969sLW1hbu7OxF2XARDRkYGZGRkWF67N2/ewM7ODuvXr4empiaL0+3hw4e8FFAAOHPmDDp27IgTJ04AaBbK8+fPh5GREREqTEGdmprapr7R9ysrK4ORkRFev36Ny5cvE+oMBwcHBAcH8+pLSzQ1NaGgoADm5ubEk8ice8eOHYOcnBx27NjxS9qrqakhFV9pzJw5E126dCGH6CVLlhBjS35+PpKSkngZNTMzM0FRFEm1y8zMREBAAExNTSErK4vhw4cTrqXKykokJiZixIgRmD9/Pm9BvmPHDvTs2RObN2+Gv78/Obi+fPkSiYmJ8PX1JUp8W+Yk0KxgWltbo7y8HM+fP4epqSlsbW2Jx5bJPVRfX4+amhps2bKF9+a0du1agfQ1Nzc3jB07FmPGjGF5VBsaGrB27Vpoa2uTNSBqu2lpaQIRkPfu3YO0tHSr0Uzv379nRdRwUQxfvXoFGxsb2Nvbo6CgAP7+/nBxcUFiYiJCQkJgY2ODfv36kXVx7949jBgxgjff8P79+9G5c2dWqumbN28wbNgwDBgwADIyMmSO5Obm4uDBg6zCTlz69u7dO3h6egpEnPn4+JC9gHm/e/fuYciQIXBzc+Mluzw9PYnhG2hOP1JWVoauri50dXVZKdttxaFDh+Dt7c1KNT9//jy0tLTQoUMHTJo0iXy3qKgImzZtQlhYGK9+PX/+XGihFRsbG6xevRrAf/aAV69eITw8HL1798bkyZM5y5J9+/YhMjKSde3OnTvQ1dUlUbZ0fxsbG3Ht2jVMnjyZ7IX/xBxhYu/evbCwsGDxxYk6lidOnABFUawIsB07dkBMTIzIeOa90tLSQFEUFi9eTA7xXPfS2tpaREREYP/+/Thw4AAsLCzw999/IycnB4sWLYK8vDx5r78iyiI5OZnsOY6OjvD19UVQUBDs7e0hIyNDHCXfv39H9+7dIS0tzZvqh37eU6dOEUfJ77//DllZWVhYWLDokmjQex6XeZKfn4/27dsTI0RmZiYcHBxgamqKzp07w8rKiuw3v2IMuepcmZmZmDx5Mry9vTnPyfT0dEhJSaFXr14AmmWxv78/unXrhg4dOmDQoEGs7JgnT56gf//+vNoCmlNg+/Tpg0uXLuH58+fo1asXy2hbX1/PSncvKirCp0+fePHWNjQ04O3bt1BSUiKGb2bmG01L8qsKHQHNe8hff/1F/p4/fz46duyIUaNGYcyYMdDX10d8fLxAvQdR0PL7c+fOhZOTEytb5MyZM+jVqxf69+8PZ2dnQtn1/ft3rFy5khPlCPCfecZMI6bvZ2lpCV1dXdjZ2UFKSopF69CWjJisrCyyrwDN/c7Pz4e3tzepXcGc/3l5ebz1hKKiIjg5ObF0rs+fP8PJyYns5cy09JqamjbJrZqaGvTo0QPq6upIT09HQkICTE1NkZSUhD59+sDe3h52dnbkDFBeXo5r164R5yHX/e3YsWOIjIxkZedNmTIF6urqsLW1hYGBAcmUKSsrQ1ZWFp4/f96mYkv79u2Ds7Mz6uvr4e7ujoCAAMyYMQP9+/dHly5dsGzZMtTU1KCwsJA4HfhQ/TQ2NsLT05Oc3WinY2JiIvz9/eHk5ER0hsrKShw6dAj+/v683tv79+8xePBgVtZvWloaxMXFYWVlBWlpaQwYMAAvXrxAU1MTrl692ia9ZMeOHQgICMCFCxcwcuRIUhCOvldLPHr0CN26dSMODa7tXb16Faampiy9vKysDOvXr0e7du2wYsUKcj0nJwcXL17EgwcPWLy+XPCz9pgFqRsbG3mfp+7duweKoog+/OLFC0RFRcHa2hpqampYuHAhK+jm+vXriI6OhrOzM6f9jZYRBQUF0NXVxZIlS8hnR48ehYmJCd6+fQszMzMkJiYCYL/Ha9euoU+fPlBSUhLZmVBWVob+/ftj3Lhx+P333+Hu7o4ePXr80GibkpICTU1NzuP4K/GvwfZ/HEwFbNWqVfDw8MCff/6JhoYGZGVlQV5eHmvXrsWpU6dgYmLCiorgi7y8PNy8eRNA86a3ZcsWAaNtQ0OD0MXIR+nOz8+HsrIyTp06hfj4eCQlJZEohXXr1kFMTIyk2ALNB26+wg5o5oft0qULVq5cic2bN0NKSgpjxowB0GwEdHR05FV8qyUuXLhAIoSZZPlKSkqtpta2tW/0+A8ZMoSk6N2+fRuKioqQlpYWSJtoK6ZNmwYpKSnCu8U05icnJ8PAwIDFrykqhM2jfv36QV1dHbt27cKYMWMgISGBlJQUPH/+HKtXr4a0tDSLi5LvATArKwvjx4+HkpISITJPTEzEoUOHsHnzZnh5eSEkJKTVlCc+Ecvnzp2DsrIyvLy8WFHEQHPUQqdOnYRGNvLBmzdvoKSkhKtXryI0NBRJSUnIzc3FvXv3kJycDDU1tVb5sPjMyU2bNkFDQ4McXnv06AFVVVUMHToUCQkJoCiKRNcDzZE9zChmUcFcs8y5PWbMGBgZGf2UK4rPenj9+jUcHBxAURQiIiJYyv3GjRthYmLCmpNtqXb77t072NvbY9KkSSgoKMC3b99gbGyMAQMGoK6uDg4ODizlsC0GkCdPniAoKIhEZtH36t+/P+Lj44X+5v79+5zfG53hMGXKFDg4OGDmzJmYMGECJCQkcOTIEdy6dQsnTpyAjIwMq8hUW7B//34oKyuz1u/u3bsRFxeHrVu3onPnzqx3xuRP4zr/b9++DUdHRxK5Tt/H39+f7AE/mnc/kiU/+h39WWlpKRwdHVncu0ydgpbNXA8pQHN0FZc5UllZia1bt/Jyan38+JGktNJjUlVVBVdXVwwdOpSsK2Yftm3bBoqiMG/evJ9WrGf+jrluxowZAw0NDYwdO1aAwmH69OlQV1cXKKjGF/PmzYOVlRUOHjyI4OBglJWVob6+HlVVVejTpw9UVVVJGv+dO3dELg7UWt+AZuefiYkJObAPGDAAYmJi8PLyIt9pGQTAdZ4cPXoUkpKS6NevH0xMTDB27Fjcv38f169fx8SJE9G1a1dOaYw/wvnz5znrXG/fvuW132RnZyMpKQk2NjaYMGECjIyMMHbsWKSlpeH48eOwtrZGWFgYSyd//Pgxr7ZoMGmsMjIyBIy2dJo9X/21pa42ePBgaGpqkqwO5n0HDBgAHx8f1NTU8N5r3r17xwqQoJGTk4PIyEicO3eOlXrevn173sUDmTJgy5Yt0NbWxtmzZ5GXl4esrCx07doVq1atws2bN6Gpqcm7eA2zzefPnyM+Ph7e3t5ITEwkhtjq6mps3rwZ69atI1GEjY2NyMjIgIWFhciGsda4P1v+f/fu3WjXrp3QSGsu+mpLmghmlCz92fbt2yEmJia0Lb5yi1lwNTo6GhRFwdHRkbW2Ll26BEtLSyxfvvyHKfCtQdgcZjqtNmzYgPbt22PDhg24dOkSJk2ahE6dOvEymLaGU6dOQUlJCVeuXEHv3r1ZEbXLly+HhIQEmf8lJSW8nTHV1dWwtLTEsGHDyPX8/Hzo6+vjyJEjCAoKEqC2EDWTRNg45+TkENlx69YttGvXDkuXLsXTp09x69YtSEtLY8GCBQDAKpbIRy+5efMmtm3bhrq6OlRUVGDEiBE/NdrSHLB82rtz5w46dOiA27dvs65///4dS5YsgaqqaqucuHzOiqK0x6T/4HueunfvHnr37g0nJyesX78e3bp1w9ChQ7F+/XrMnTsXxsbGGD58OOvMc+nSJd72i8rKSkybNg22trbw9fXFmDFj0L59e5I5OHnyZERERAj8rqioCFOnTuVcLPHatWvEQHv79m24ubkJGG2ZoDNAgf+OsRb412D7P41Xr17h5MmTRNAVFBTAy8sLJiYmsLCwgKSkJImOqK6uRufOnTlXJhYFpaWlAkbb4uJirFmzhneFQ3rC08Jx8ODBiIuLg6urK0vYVFVVYfDgwejfv3+r1d5/BOZhg1lBfvz48dDT04ONjQ0pmAA0H8JaFmz4VaDbnzVrFuzt7VmpXC2VBVEFecvNhb7P6NGjiRF6yJAhkJeXh4WFBfr27StQiVNUFBUVISsri+XFKisrQ0BAADQ1NUl0H/0MO3bsYKV9iApmtWymEC4oKEBsbCzi4+NhYmLCMk4BgIWFRat8paKA+ZyfP3/GxIkTISYmhsTERJaAvnDhAhQVFQWKYYiK/Px8PH36FFeuXGHx6M2dOxcURcHGxoYVQVtYWAhHR0fePIUtUVxcjNDQUIwfPx7+/v4shfPTp08ICQnBrFmzALTN6EfPzfv376NHjx6QkpKCj48PFBUVWVGmU6ZMgZ2dHUs5/VnbLQsKMDm3Wq6J8+fPQ19fn0QKtYUovqWxqbGxEZmZmRg7diwxctPPVlVVBWlpaaEH0h+h5fPR96upqcHixYtha2uLLl26QEVFhaRQA4CLi0ub5n9L0DQqwH/WxqxZs1iRsAAEigeKMmeYkU1AsyEiOTkZ8fHxcHV1ZRnwKysr4eHhwYp8bQsePXoEDw8P9O3bF7t27cL69eshJiaGI0eOoKmpCU5OTqyobxp8HVx09BLwH4N9fHw85s6dy/pey/n/IzDnPXP9Cjuwnz9/HhISEryKkggDM7KDuRdwmSOirkEmb6swTJs2DYaGhiSqquX637VrFyiKwtKlS1udly2LlDDT/XJycuDk5ASKooieQPfz4sWLMDc351xciVlAhtlWeno6KWxDpzTT3339+jW0tLRYjgRRQPeNdrAz95umpiZkZWXBysoKAEgRy1mzZkFBQaFNBfaYVEhAc7Rahw4d0K9fP5bT6tOnT/Dw8ODNDU2jNZqlH+lcLcFnr3v//j2mTJkCJSUljBgxgqVDPHz4EJ07d2YFG4jalqjPkpmZiZiYGCgrKxMD4/Tp03npJk+fPoWPjw8+fvxI1lBmZiY8PDzg5eVF3im9982ePZtl2OeKZ8+eQV5eHnp6eqz1C4A1V+l3eOPGDVhaWuLTp08it0GP44sXL9ChQwcWr2RcXBxUVVVhYGAAGRkZVhaHuro6NmzYwLtvQPP5TVZWFsOGDcPw4cMRExMDMTExYpgS9qwzZ86Eubm5SLRh9LjU1dURPuGW96NRXFwMZ2dnjBs3jsW/ygX0/fLz84UW1aXvmZ+fD2dnZyQnJ7PkN5c2W8pkpiOhtrYWU6ZMwcaNG0naNw17e/tWHco/An2P9+/fC9WzKyoqsHDhQgEeXDU1NVYGKNf2hMHT0xNycnIwNjZGYWEh67vGxsa8uN8BCOxTFy5cQMeOHeHr64sRI0ZASkoKQ4YMAdAsu5SVlfH+/XtOcpG53xQWFuL79++s3zc1Ndc0OXfuHOt3/fv3h6enZ5sMYcz5xbxPSUmJ0EjbR48e8S74xURBQQGCgoIwcOBAgczH9+/fw9nZGb/99pvAM/JFfn4+goKCMGjQoJ+2xwSf/e3hw4fo168fOnfujDFjxrDGNTU1FZKSkiz9lktbN27cwNmzZwE0R3avWbMGubm5SElJQXh4OPr27cvKhJ4yZQqp89KyHT7j2vI3t27dgru7OyIiIojRtqioSEDn+hXvkC/+Ndj+j+Lp06egKIqEtzO9xPv27cPGjRuJcbaxsREvX77kzZ1z9+5d/PHHH9i7dy/Ky8tJW8xFV1JSgq1bt0JRUREjR46El5cXtLW1eQmBLVu2YMWKFaxD24EDB6CpqQmKoogwpxfGxIkTBVI9RcGLFy8QHx/fqqGrqKhIoDjKkCFDMHz4cFZ6GRe8fv0aGRkZP+QMu3PnDiQkJHDgwAEA/AXAq1evMGvWLMTHx2PHjh0sb3BaWhrhp1NVVUV2djYuX74MY2NjDBw4kHNUUEZGBuzt7WFmZkaKutBK1MOHD+Ht7Q1VVVXcu3ePeGLHjx8PDw8PTsWqmBV89fT0MH78eAFFo7a2Fu7u7qT4SU1NDWpqauDj4yN0oxIF9JxkbkjZ2dnYsWMHOQgx35O2tjaWL1/OuZ1nz55BS0sLnp6eEBcXh6+vLxYtWkQ+p0nNR44cib///hvFxcWYNm0a9PT0eHH/As3G5+zsbBYv2qpVqwjPY8vI3R9FUYqC48ePC2xy2dnZSE9Px8KFCwlPIo3FixfD09NT5OhTpuNl8uTJiI6ORr9+/QQqPDPh4+MjUFWeK168eEEqAo8bN06gmF9L59CXL1/g5ubGyVDGNBoxo87oeVlbW4uXL1/i6NGjRNmhr/fq1atNUahPnjzBtWvXBIoFMI1gkydPJsakpqYmREdHt3oAbQ3Pnz+HrKwsNm/ezLpOc5E6OjoKrOPg4GDO7dC4ceMGdu3ahSVLlhCDCk0foKmpiW7durE492xsbDhXcBeGlocP5jj269ePGNebmpoQGxvLoiIRBTU1NbCwsICvr+8PC6p8/PgRrq6uxCjWFofFoUOHMH78eIH97WdzhOkUFRVM3eLy5cssYxvdVklJCVRUVFg8g8zPgeaDRWvZEMwK1n379kVoaChcXFywZcsWohukpaXByMgIenp6rIiuDRs2wMnJCYWFhSLv4XR7jx49goWFBcvYXl1djYkTJ0JSUhLm5uase+bk5MDc3JyVMi5qW+Xl5QgLC4Orqyv09PSwdOlSvHv3jnyvd+/e8Pb2RqdOncieevLkSXTu3JmXPrl161b07t1bIDX477//ZtEq0PDz82OlhHPFgQMH0KdPH6EHRxq/SufKycnBy5cv8e3bN7KO3rx5g02bNrGcF/T9TU1NsXjxYk5t0O/t3bt3WLduHWbNmoUTJ06w9CDmGs7MzESvXr2grq5Oog+5pi4/ffoUHTt2xIwZM1jXm5qacOzYMbi5uQkYvEePHo3Q0FBOFdyZ7UlISKBv376QlpYWWlG85T2nTJkCHx8fltPhR6DH8cmTJ5CRkUGHDh0wcOBAlvHv1KlTOH78ONmjm5qa8PHjR9jY2PAObKAxffp0BAUFkb/LysqwefNmiImJYebMmazvPnv2DIMHD4asrKxIab302JSVlcHHxwd2dnaQlpbGmDFjiB4EsGXoxIkToaenx6t4LFMvt7S0xIIFC34Y3Tl27Fjo6Oi0WrxKlLbKy8vRp08f+Pn5wcvLCykpKeTd1dfXs+YBbRiOjY0VCOQQtb1nz55BTU0NiYmJQp2nLQNPPn78CCcnJ86OUOb6Xrx4MYYOHcriDz59+jQp3scMWKmqqoKbmxsvPt5Xr17BwMBAQK+5desWQkNDMWDAABa10aFDh9CtWzdO/OzM9xYdHQ07Ozu4urpi9+7drb7/pqbmOjL9+/cXkD0/Q2tFVIX9v7i4mBht//zzT2zcuBGKiookspYLPn36hOzsbJYTeufOnTAwMMCsWbMEHEohISHEEM4Hd+/exYkTJ0ikKdAcxf5PtHfhwgWBffT+/ftYunQp0VWYRtKuXbvyoj0sLCyEi4sLQkJCEBMTg86dOwvwMDMD9L5//w5PT0/Cld0WtJw3zHly8+ZNuLu7o2fPnjhy5Ag8PT1hb2//XzXSMvGvwfZ/EM+ePUPnzp0xdepU1vXWDlqNjY2YMWMGunXrJhDZ8DPs3LkTSkpK8PDwgKqqKnR0dLBlyxaiHDI3/PLycqxcuZKko/AhzN6+fTsoihIaCbxx40Z06tQJDg4O5GDy/ft3hIaGco6uev/+PXR0dEBRFLy8vFgHI2HjmJ2djUmTJkFWVpZ3FdgdO3ZAX18fysrK0NbWRkREBIsAm7nohw8fDhsbG0KizRW00SM6Ohpubm5wcnKChoYG4eN8+PAhKIqCiooKK43u8uXLyM7O5tTWq1ev0LVrV8yYMQN3797F5cuX0bFjR5an9+XLl4iNjYW4uDisra3h4eEBWVlZzmT4QPMBqGvXrpg8eXKrdAo9evSAv78/ampq8PXrVyxatAjq6uqcecaAH89JYXxUnz59gq2tLa8qmAYGBpg5cyYKCwuRlZWFmJgYdOjQgURDA8DSpUthYmICaWlp2NjYQENDg3exi9TUVDg5OUFZWRne3t6syMEFCxaAoihER0eTOV9ZWYmAgADexrFnz56Boii0a9eO5UWn5/7GjRsRFhZGjCHV1dUIDQ0VWcGg71NeXg4jIyP06NEDgwcPRs+ePaGjoyOQnkbLpqtXr0JJSYl36u3z589JteBRo0YhLi4OYmJi2LZtW6vOjzlz5sDQ0FDkiCC6b9+/f4eJiQkoimJRmLQWfVBbW4u9e/eiS5cunAw6TOzevRt6enowNDSEsrKyQIQkjfHjxxNjXEhICLS0tDhH0K9duxYdO3aEvLw865BA92/QoEEYMWIEHj9+jJKSEsyYMQMqKiq81ja9v3l6ekJKSgouLi7ks5KSEhQVFbEcIbm5uXB0dGy1aMrPcOPGDRavY2uKXlxcHDHYhoSEQF1dnfM4ZmZmQkpKCh4eHoiLiyM0RoBgZOqKFSvQoUOHNkWU7Nq1C+Li4ti8ebNAZg1TB/gVc4R5v2nTpoGiKGzdulXgOw0NDUhKSoKHh4fAIVtUJfv79+/o1q0bYmJisG7dOiIXe/XqReTuyZMnYWFhAXl5efTr1w+DBg1C586deVdVl5SUxLhx4wS+U1ZWhsTEREhLSyMsLAyFhYXIycnB/PnzYWJiIhBF9zNUVVXB1NQUkZGR2LJlC5YsWYIuXbogKiqKyOf4+HgoKiqyHECNjY28sqdoGgqajokJYXpXcXExPD09eUcz7tmzB+Li4ti+ffsP5yTQdp1r165dMDExgYKCAmxtbdG/f39i/BKmJ+Tl5cHOzo6TLKHnbEZGBhQUFBAQEIDQ0FC0b98eYWFhrIgj5ng+efIEysrKkJeX52xkT09Ph4SEBMtg0vL9X758Gb6+vujcuTMCAgIQEBAAKSkpXsXMnjx5AgkJCXL4HjZsGDw8PFp1SOfm5mLWrFno0qWLyIZo5lqTkJDAsmXLcObMGbRv3/6Hhv3q6mrMnj0b+vr6IhevaQ3x8fGswmb0u927dy8oiiL8101NTXj27Bnmzp3Lie6hpqYGlpaWiIyMxKlTp7B3716YmprC19eXZdyh91W6KGVL55aoePXqFeTl5TFx4sRWjbX0nPz27Ru0tbUFskhERWVlJdHv6IwNuohka3MgJSUFioqKAqnioiArKwvKysqYMmVKq3tVS/1r1qxZMDc35zRPWhqHAwMDSWFrmkO1trYW+/btg76+PjQ1NXHu3DlcvXoVc+fOhYaGBq/6FZs2bYKMjAwCAwNZhanoZ2q5V06cOBGBgYGcC2pWVFTA0NAQPXv2xMGDBxEREQFnZ2fWOa7lPrB3716oqKiw9JefgR7HiooKrFu3TiBjSxhKSkowZswYUBSF9u3bC818+Bl2794Na2traGtrw87ODrGxscSwuGLFCmhra2PChAnkHFJWVgY3NzeBMRcVu3btgrq6OmxsbCAhIYHw8HDy2ZIlS35pezRnrYGBAcvpA4C1F9Bz5fXr16SAu6hgzrOXL19CR0cH7dq1Y2XUMddZTU0NTpw4gZCQEN5Ff4HmQuizZ88m7QuzWdGf/fXXX3BxcQFFUbCysvol9Ji/Cv8abP/H8PLlS4iJiRFlvqmpCTt37sTkyZOxcOFCEu3HFPz9+/eHnJwcZ6POtWvXoKqqirNnzxLB7OvrCykpKSQnJxPOJ6Y319bWFjY2Nrx46OgiIbRhrK6ujkRH0tiyZQscHBwgLy8Pd3d32NrashaqKIewmpoazJo1C71798ajR4+go6MDZ2fnVlNH8/LysHDhQtjY2PA2jF25cgXS0tI4fPgw7ty5g7Nnz8LExAQmJib4+++/BZ775MmT6NSpE6dNikZDQwMGDBiAvn37kmtPnz7FsGHD0L59e5IOt3//ftJnvh6i8vJyxMbGYtSoUawoquTkZPj7+wt8//jx41izZg3WrFnDy8ACNHM19e/fH0BzX3ft2oXZs2cjJSWF3PP06dOwtLSEuLg4nJycoKOjw8s43NqcZAppZnpWSUkJwsPD4enpyTlS7cSJE6TQBT2OV69eRZcuXaCjo4OxY8eS77558wbXrl3D1atXeRtZ9u7dC0lJSezcuROnT59Gjx490Lt3b1bf5s2bBx0dHZiYmKBnz55wcXFp08aYl5cHBwcHwhPd0kB67tw56OjoYMCAAZg+fToCAgJgaWnJaX3X1tYiLCwMvXr1Ir979eoVnJ2dyUGl5X0+fvwIPT09Vpq/qKipqUFMTAzr/eTn58PExAQdOnQg1eJpXL9+HSNHjkSXLl04z8n6+nqMGDEC7u7uGDt2LGRkZFjKTEtFIysrCwMHDkSXLl14RV4AzWtWSkoKJ06cwIsXL7BhwwY4ODiwDNH0nFmwYAGGDBmCqKgoGBoa8iLg3759O3x8fLBq1SrIyMgIFK06efIkTExMoKKiAltbW+jo6PCSyxcuXICCggLOnz+P8vJyvHjxAqqqqkLlEk0tEBYWBltbW15RqHShK3d391aLF9DjNGzYMEybNg29e/fmPY719fWIiIjApk2b4OTkhKioKBL1TUer0/34+PEjzM3Nce/ePc79ApqNOoaGhuRQWVpaik+fPrEMDPSzt3WOtDT+ysvLw9fXlzhtW65t+qDBNDhywbJly+Dj48O6b3JyMtq1a4eYmBiSuVJYWIjZs2ejT58+SExMZBVFERXp6emQlZUlzvjGxkZ8/PgRb968IRkQZWVlWLZsGczMzNChQwdYWlrydtr9/vvvMDExYUVn3rlzB87OzggKCsLjx49RVVX1QyOfqP3bs2cP2rVrR6J0y8vL8e3bN6EGhvr6emRnZyMsLAwODg689pu3b9/CwsKCHLwLCwvx6tUrAZ2Kfv626FwXLlxA586dsXv3bty+fRsrV66EjY0NDA0NyWGWmQ5cVlaG0NBQeHh4cJYlxcXFcHJyIrREQHN0MkVRcHFxYRmAaZ1s3Lhx6NixI+fI2oKCAujr68PR0ZFcGzlyJDw8PKCmpoaRI0eSKLS6ujps3rwZkydPxuzZs3kVz8zKyoKMjAwrUurQoUOQlZUlhjbm+qe51I2MjDjvpenp6aAoihiia2tr4efnh759+6KqqkpgXmdkZBC9hc9aa3k/umCzsGyEefPmQVdXlxWxz3UN3Lp1CyYmJiz9MD09nVDL0QWx6DYrKysxePDgH2ZjCANNO5CcnIzBgwcDaJ7jR48exYYNG3Du3DlW1G5TUxNqa2sRFBSE8PBwASo7UXDgwAGYm5uzdJBLly5BWVkZffv2Zc29x48fY9asWZCUlOTtaN2+fTtxMtbX1+O3337DyJEjsWLFCgEn+L1795CUlMRLvwOa5ZaGhgZmzJhB9sY+ffqwjGwNDQ14/PgxIiIioKSkBCMjI1hYWPA+nx46dAjm5uYYPXo0HBwcWg3IePbsGcaNGwdpaWnOzpimpiZMmjSJZVT8+PEjXFxc8PHjR5SXl7Nk4Z07dzB//nxIS0vz0l0rKythYWFBMhJFyRqdN2+e0CxeUUDryYcOHcKtW7dw5MgRdO3aFd27dyeR0OvXr4erqys0NDTg6+sLOzs7mJub89rfzpw5A1lZWcKzfe7cOejr67McWxs3boSLi8svae/p06eksKOVlZWA0ZYGLUvCw8Ph6+vLaX+jn6uurg45OTkkc6NHjx6ssyJzD5g9ezaCgoJ4F2I/efIkKIqCvr4+5syZ80OjLf1s5ubmcHJy4l1E/J/Cvwbb/zGcOnUKFEVh3bp1+Pr1Kzw8PODq6gpbW1u4uLhAXFycCJuGhgZkZ2dj3rx5nIn4gWbjaHBwMOrq6siGe+TIEaioqMDBwQGrV69mGavWrl3LqvDMZRJfvnwZFEWR8PmXL19i+PDhsLOzQ8+ePVkpk0+ePMGhQ4cwY8YMbNq0ibQj6kKtqanB77//TjaBsrIyaGtrE6OtMCH95s0b3ny8QLNy5u7uzkrrrqyshJOTE4yMjIhAZ47Z+PHjeRkG6urq4O3tLRCB/e3bN4wePRqdOnXifTBviaKiIgQGBmL//v2s6xs3boSZmRmJcvqVKQN9+/YlEX50SoK7uzukpaURERFBFPt3795hw4YNOHbsmABHoij42ZxkUhVUVlZi48aN8PPzg7W1Na/NY8+ePTAyMmJF+Ny4cYPwc1pbW5OKtm3F7du3oampiUOHDpFrhw4dwqBBg5CZmcmKIr9w4QJWr16N4cOHY/HixZzXGxMVFRWws7PDiRMnkJiYCFlZWXJAplMMt23bhpCQEHh7e2PkyJGc23v8+DF8fHwEyOEjIyMJD52wQjuiplK2xPfv32FlZUVS+OnnHTp0KMLCwkBRFDFSVFVVYcWKFYiKiuJVUTc/Px/Dhg1DamoqPn/+jHnz5kFaWvqHRttdu3aRseBaNKG2thaDBw9mRVc9evQI/v7+OHfuHPbv3896LzNnziQ8y3yrpb548QIxMTHIy8vDtGnTIC0tjZSUFMycOZOkLN+7dw/79+9HWloaJ85CJhYvXoyYmBgyXoWFhXBycsLatWsxceJEPHz4kDgL379/j/DwcDg5OfFa2+np6aT4kIODA3x8fEi2A8Au9AU0c7a3ZRzpwiE2NjZ49OgRrl+/DmdnZ8THxyMuLg4ODg6syJmmpibOmRVMXLt2DXZ2dgCaD3Xe3t4wNDSEuro6QkNDWQbBXzFHgOaUa3l5eWRmZmLOnDlCueXp+b5mzRreSvXIkSPJYZ2OBNq2bRsCAgLQtWvXH/JCi7re6EOOrKwsDAwMyPXY2FjY29tDQUEBpqamJDK1trYW379/x5kzZ3D37l3e0X7nz5+HhoYGoUCg5/SDBw9gY2OD2NjYX1I07e7du6Aoiji13r17h549e8LIyAi6urro2bMnmSM1NTVIS0uDv78/HBwceB/E6OrelZWVyMjIgKOjI4yNjdGlSxe4uLgQYwrz/fDVudasWcPiz2tqasKTJ0/g7OwMPT09EuFVU1OD5cuXk2ADPn3Lzc2FjY0N/vrrL7LOa2pq4OzsDCsrK4SEhLAMfZmZmfDz8+NlzPn8+TP69OmD7t27Y+3atfDy8kJQUBAWLFiALVu2QFlZGSEhIT/k/uWCN2/eYPfu3QDY7yUgIAC+vr5C1/Cff/7JWXbV1tYiKSlJgIqFjlJsGYwCNGfZbd26lXPxmtYO/k+fPoWfnx8SEhKIk5DJ66+kpNSqUUQUPHjwAIqKisSgSD/Hq1evEB0djeDgYGLYpNul1zoffT08PJwY+jw9PWFtbQ1zc3MS+dqysObTp095GfWB5khGY2NjEtxAz4urV69CTk6OBDTV1dXhwIED6Nu3LzH48OlbYmIi2QO8vLzg4uKCiIgI6OnpwdPTE3v37gXQTImyaNEiODk58Your6urw5gxYzB8+HBWoFJcXByio6PRo0cPLFmyhOVUzszMRE5ODieO+5Z4+PAh4uPjUVJSgnHjxsHZ2Rnr1q3DoEGDcPLkSTQ1NaGurg579+5FZGQkLyocABg4cCDi4uLIXExJSUGXLl1gYmICBwcHDBo0iOyxJ06cgK+vL6/3RvM9+/n5YceOHZCUlMTQoUN/uJf98ccf6NChA3E6c9GVGxoaMHz4cJY8qampQc+ePUFRFOzt7VkUgampqZg0aRJLL+GinzQ1NWHy5MksqqCsrCx4eHggNTUVS5cuJefIZ8+etbk9APj69StcXV1x6NAhREdHw8LCgszx58+fo7GxEbW1tVi1ahV8fX1Z0aei7G+nT59GYmIiIiMjsXbtWvKbjIwMUsibabQVRm/BtU9v3ryBi4sLkpKSMGnSJOIIbc1oW19fj8DAQCgpKbVJd/2n8K/B9n8QdKEMFRUVREdH48OHD2hsbER+fj5GjhwJBQUFFgcZVx5ZJtdct27dWJ/99ttvSEhIQFxcHHR0dFipL9++feNdJe/NmzfQ0NBAdHQ0bty4AR0dHfTp0wfJyckYOnQotLW1f5gWLYpAqK6uJs/V0qtbXFwsEGnb1NTE4qFsC+bPnw89PT3yN224raurIxQBNFr2hc8BYuzYsXB2dhZIxcvJyUFMTAypMv0rwCwwQI9vWloanJ2dWd9rK4E7PQ5Tp07FuHHjcOTIEfj7+xND+tOnT+Ho6MiKLG4LRJmTzA3z5MmTmDVrFqcN8ePHj+T5nzx5go4dO2LmzJm4f/8+Hj9+DFlZWSxbtgz19fVQV1dnFcXgi8bGRly7dg2rVq1iKXn+/v7Q0tKCgoICLC0tSRSzMPDZpOrr61FXV4eePXviyZMnyMvLw/jx4yErK4uuXbuyirdVVlay5j2X9j5//ow9e/YQBYm+z6BBg0gEiDCDLV+nQk1NDSIiIjB48GBi9P3w4QMUFBTw559/YvDgwXB1dSVZCtXV1W1ae1lZWUSZz8vLw9y5cyEtLc1KG2ZW0qXB1VhL38fDwwNRUVHkWkREBIluVVdXh4ODA3meHTt2wNXVldMaaPlML1++hJ6eHnJzc1FSUoLly5dDXFwc7dq1483TLAxDhgyBqakpUbyioqKgoqKCvn37wsLCAlpaWiyewpcvX/KucPv06VOMHj0aHz58QEFBAezt7QWMtkwsXLgQhoaGIo2jsHdKX0tKSiL8j/fv34empiY6deqEOXPmkO/+CoVz7969sLGxQVNTE+zs7DB16lRcuHAB586dg6GhIdzc3Mh3d+7c2eY5cuLECWhqahI6n61bt8LHxwfAj/dLPn1duXIllJWVWUYpAwMDpKam4sSJE0I5QfnKkgMHDkBcXByTJ0+Gp6cnfH19cerUKRw7dgyjR48GRVEsSg0uEDYujx8/hoSEBIm2q6+vJ/Lw+vXraN++PalS3xY0NDQgMDAQnp6eWLlyJdTV1TFy5Ejs3LkTBw8ehL6+PlxdXcn3Hz58iJSUFN7rDWgu+mZhYYHv37/Dw8MD06ZNw7179/D8+XM4ODjAxMSEyOGW9+eqcyUnJ0NHR0fgemZmJuzt7eHv7090vlu3bmHatGkiz/+Wc4kukMWcB1lZWYQPUkpKSqCQZVv2mw8fPmDYsGFQUVFBQEAAy6H84sULdO7cmXPhTFFB93379u0wNDQkRna+xU6ZY8kMwKDvV1tbCy0tLVa2DBNc26W//+HDB2zatAlLly5lBTds3boVtra2SExMZNWYKCkpgZmZGSmCxAfv3r2DpqYm4Uhm7v+PHz9Gly5dfok+2dDQgLq6OsTGxmLp0qU4duwYAgICCP3ejRs3oKSkRApS0xG5okKYLL1y5Qrat2+PK1eukGeg70kHNdGf1dfXk3OQKDqQsM83bdqEPn36YP/+/fDz8yN6SFZWFmJjYxEaGoqKigo0NDQgPz+/TcbTzMxMVhr5okWL0L59ewwbNgxTpkyBtLQ0BgwYwCnr7GcoLS2Fra0t8vLyUFhYiKlTp0JFRQUURbEMzzU1NSLRC7QEHbgzcuRIuLu7Y8mSJdi4cSPat2+PRYsW4datW1i3bh2cnJxIfYLGxkaW44RLP8vLy7FhwwZCK3LlyhV07tz5h0bbO3fukIAYru01NjbCxcVFoP7GjBkzsGjRImhqahKDvzDwOeP37NkT7u7urL+VlZXh7+8PXV1d6OrqtkqPwbU9eg15eXnh06dPePDgAfr16wdLS0soKChgypQpZD6mpKSwAm1E2bt37doFGRkZTJo0CSEhIbC2tsaJEyfIO7h37x68vb0RERFBaKa6d+/OqhPDZx1UVlZi0qRJuH//PiorK5GcnAxHR8cfGm1fv379P2msBf412P7Pgo7Gaxlxd/fuXcjLy3OuFiwMmZmZkJGRwZAhQ3D//n1s3rwZFEWRlBk1NTWSbsac1Fw2Y+Z33717B21tbZKmRAvW79+/Y9OmTTA0NOQdGZqZmYnExETcvXtXQFjRi66oqIgYbR89eoSRI0cKrU7PB2/fvoW8vDwr1YRW4NPT06GiosI7XYd5L7pvx48fh42NDVatWiWgrKempkJVVZVX1CnQ7IUvKipCdXU1ayyZ7/LIkSMwNTUlf8+YMQODBg3iFK1D36/l89OH5O7du7O4XYHmjbldu3a8IsqZbdLCWpQ5KYzzTJQN8cWLF5CXl0dCQgJ5fydPniQUCPLy8kTJBZoLY3Hlam4N379/Zx1YoqKiYGBggLt37+LZs2fYt28ftLS0hHINiorWCP5HjhxJDnivXr2CoqIiOnToQIwGLRX6H23EP/se8/PJkyezFKp9+/b9EjkJNEdYOTk5wd3dHVOnToWkpCRGjBgBoNl5oa2tzbtafGug+5ufn485c+awIm3XrFmDJUuWiFyoTdh96fZ37NgBNTU1ks2ho6ODd+/eobi4GAUFBVBXV8fkyZMFfi+KMvPmzRsi95hrJiAggDh4oqOjISMjAykpKaKA80HLtf3y5UsoKipCR0cHjo6O0NDQYEXruri4IDg4uNX7/AzM+VhVVcUq6peTkyPUaMs8DIni/KTHrKqqCn/99ZeAkX7mzJmIjo4GAKxevRri4uIwNjZG3759OXGLtQamQUJbWxsjR45EYGAgi0v17du3UFRUZBWR4xoRUV5ezuLLe/PmDcsBeObMGejr67NSb3fv3s1pj2tNlrx69QphYWHo0qULYmNjoaqqCj8/PwDNCr+Ojg4vIyqzgCATBw8eJNQZ9IEVaHYq9+zZE1FRUZwLOdHzpLy8nESp05g1axY6depEnNN1dXXk2QIDA4lTsq2GgYaGBoSEhKB9+/aYMmUKSza9fPkScnJyrEyBls/OFSUlJVBTU0Pfvn0RFBTEikqrr6+HtrY2y3HRFvz1118wMzPDtm3bWPOooaEBR44cgaWlpdDitj/rG30vWt+iMXLkSEhKSmLFihXYunUrZGRkSPbIvHnz4OPjwwpQ4IKsrCwBvv4PHz5g5syZQjlyvby82lQ0p7S0FB8/fsS3b99IH1uOy/fv36GhodEqd/rP0JrMbqm/NjQ0YOrUqXBxcSFR63znPbMQl5qaGvz9/WFubg4tLS1WxgqdJu3t7Y0//vgDz549w9SpU6GmpiZS5PyP5tCGDRvQrl07sscysyr69++P8PBwXk5cQFBubd++HR06dEBgYCDhwaXvfeTIEUhKSnI+czAN6S1/SwdN0JHjtNxqbGyEnZ0dKwOOa3st19vVq1fRsWNHuLi4oF+/fqzf0PVA2hrcI+w9vHv3DtHR0Th//jz5/MKFCwKG1Lagrq4OZWVlsLOzI8FKvXr1gpSUFCwtLTkXaQNaX2+5ubmIjo5GTEwMXF1dMXHiRNbnnp6e6NOnD/dOCMGXL19Ye8zFixeFGm2FZWiJmhVD/9vY2Ij58+cjICCAUA4eOnQIFEXhr7/+wsGDB2FpadnmgAPmuF68eBGSkpIwMzODk5MTtLS08OHDB2JMNDY2xoABA3i3RZ+7mW3269eP7A3Xrl2DnJwcpKSkWuXqF2XvPnHiBLp27YoTJ06Qaw4ODsSxRffnzp07CAgIgKmpKYyNjWFsbMzrfAOw3y/zHsXFxUhOToaTkxNmzpxJvtey+Dzwv2esBf412P7X8fr1ayQnJxO+nPz8fLKAnj9/TgQPfS0jIwOmpqasQlKi4uHDhzh48CDOnz9PNsejR49CU1MTurq6UFVVJSnUJSUl0NHRIelLfHDmzBksXbqUlfrx7t07jBkzRkC5zcjIQPv27VmFikRFRkYGZGVlkZiYKCCc6QVJC4WSkhIYGBhAQkICnTp14jWOQHMUxYMHD8j9v3//jrlz58LW1lagOMrXr1+hp6cntBKuKKDfeUv+zTFjxsDY2BibNm1iGZ1fvHgBQ0NDXkbN58+fIygoCBYWFrC3t8eWLVuEFv46fPgwdHV1ATQfCNu1ayf0wNIamEUh7OzsBAq9JSUlkcIvTMXqxYsXsLW1bZMxGmAL43fv3mH06NG/dE4+efIEnTp1goqKCqytrVkKRFZWFh4/fsxqr7KyEp6enqwiU1xx/vz5VgsunDhxghVBlp2dDUVFRV7E+zTKysrQ0NBAxpJ+p5MmTUJycjIAwMrKCo6Ojhg0aBDk5eU5OS1oZaCiogITJ05Er169MGTIEJIeKaxQAq0M0oU9WhovRMG7d+8we/ZsjBw5EmvXriXXU1JSMGjQIFKciMbJkydhaWnJiXKBWTQhOTkZiYmJmDp1KjIyMoRSROTn52Pu3LmQl5dHREQEKIpi0V1wAT0X6fvn5+fjwoUL2LlzJyIjI5GSkgIAJE0uODgY48ePZ91DFIWXXj/i4uICxZJ69OiBtLQ0DB48GKqqqrh48SIWLVrEoijhirKyMtTX17PW9rdv33D16lVMmTKFGJ3pg+j06dMRERHB22DUMgKGfqf0XpObmwt7e3t4e3vj8uXLyM/PR1RUFHbu3El+J4qzoqysDGZmZhg9ejTJrKHbvnTpEsaNG4c1a9ZAQkIC165dw5UrV2BhYYHIyEihiqgoYPK9NzY2oqKiAuPGjYOBgQErpb+hoQEVFRVwcHAgkTM0RDUUHDlyhCjqbm5uQtOf//jjDygqKpJ7du/eHaampiIb14XJkqFDhxJ94eXLl1i5ciXGjx/PiurIzs6GsbEx59Rl5v7Ws2dPZGdns973lStXsG3bNgE6of79+yMwMJBTW3TfysrKoKGhgfj4eNbnnz9/Rt++fSElJSVgxA8PD+ddhCgzMxMXL17E48ePyTxrbGzEnDlzBIwb3759g56engBfNVe0dMqsXr0a+vr6UFRUJAfQmpoaNDY2IiwsjMUDywUt525JSQliYmLg7e0toA98+/YNMjIyJG2aa1/S09NhZmaGffv2Edn84cMHJCcnQ1NTE7a2tqziTRMnToS3tzePXjUfTOXk5KCnpycgZ0tLS1m6OtC8V/j5+QmsbVGRnp5OeBUtLS0RHh7OytgC/jN/N2zYAGNjY87p2PQ4vnv3DvPnz8fYsWOxZMkSgc9p0JlOfPVxJrKzs6Gnp4epU6eiqakJX758werVq+Hu7s5y4J06dQr9+/cHRVEwMzODoaGhSBQW9LOXl5djxIgRiImJQXx8PA4ePEjmyvjx4yEmJob9+/cLyBKuBnBhcotGUVER+vbti/bt25NAA2a0vo2NjdDiez9DVVUVbG1tMXjwYFYU8r179xAeHg4rKyuBc5qXlxdnQ+OP1hsArFq1ivBEM99dQUEBLwoEURyyTU1N5OxG7w9XrlyBpaUlr4zFnJwcHDt2DKdOnSL1bmiMGjUKV69eRUJCAtTU1HDp0iVMnjwZRkZGWL16tcht0Ou1srIS+/btw7Jly/D333+TMxl9XuzXr59A0blhw4ZhwoQJQgud/ay91sC8T0uj7Zo1axAZGcmL7pCWhfT7u3//PsLDw6GmpgZTU1OIiYkR+8j9+/fRvn17XpzGNGh7CW1grK2tRWZmJk6ePIkhQ4aQIt/0eXjQoEECe72o2L17N7S1tcnco+dqnz59sHTpUgDNZzdra2v07NkTNjY2rWaL/QgVFRWYOnUqFixYwHqP7u7uCAkJQffu3TFgwABi6H7+/DkOHjyItWvX8qZ2OHHiBNLS0gTmF32f0tJSYrSdPXs28vLyYGFhweJV/1/Fvwbb/yKeP38OGRkZBAcHo0ePHpCVlYWnpycOHjzYqpCaOnUq7OzsWJEZomDXrl1QU1ODlZUVFBQUMGHCBNLG9+/f8fbtW9YGkZeXB0dHR17GKqA5LVJGRgZr1qwhhyJ6ATGjjOhrNAcZV4FXUlICd3d3lifv06dPyM7OJu20TLsbOnQoFBQUeEdpXr9+HRRFwdfXF0+ePCF9ePPmDQYNGgR7e3siXOl2bW1tWZVbuSAxMREURUFRUVEg2nPQoEEwMzPDuHHj8ObNG3z79g3JyckwNDTkPEdevHhBOPvS0tLQv39/mJubsxRouq9Hjx6Fj48P5s+fD3FxcU6Gb6ZS2LFjR5agZDorBg4cCDExMaxcuRIfP35ETU0NZs+eDTMzM859A5ojISMjIxEUFITo6Gi8ePGCpRC37CPfOfnkyRNISkpi0aJFqKqqgoKCwg+r5RYXF2PmzJlQUVHhVQEWaE69oygKf//9N+t6y8MK3bcPHz7A3d2ddwTq/v374eXlBQcHB3h7e+POnTtE+b116xZ69+4NIyMjeHh4oKSkBB8/fkRcXBwCAgJEuj9TVhgbG8PPzw9JSUlQUVER4BKk1/fQoUMxevRo/PHHH2jXrh3hsOYSYfLs2TOoqKggNDQU4eHhEBMTY61lAAJe3xEjRsDf31+oY+NHqKiogJ6eHtzc3NCvXz+oq6vDzs4OS5YsIcpiS6Ott7c3KIoiHm+u0TNpaWno0aMHfH190b9/fwHjmLOzM+bNm0f+rq2thbu7O0m7FBV0JfCoqCjY2tqS6Et67KZMmQIJCQno6+uTg2tubi6WLl3Ki/fuwIED8PX1hZubG5ydnXHp0iXW4TE+Ph69evUifzc1NcHHx0cggl9UHD9+HIMHD0Z0dDTmzJkjcFCl31tubi4cHR3h4eEBAwMDdOvWjZMCWlVVRSqBC4vgfv78Oal6TEd+AM0GzpbFUkTFsWPHSCEPJp49ewYfHx9QFMXicmtqaoKHhwcvQ/vevXshLS2NZcuWYcOGDfDy8oKxsbHAGKWnp0NXVxd5eXkIDg6GiYkJMYyLmgIrTJYwC6QAgvJy8+bNMDAwYFFQ/QzM/a19+/asaDtmv5hrm37G+Ph4TJkyReQDLdOor6WlxapKz0RWVhb69++Pdu3aYdGiRdizZw82bNiATp068doDdu/eDS0tLRgZGaFTp04/PfDn5eXBxcWFFWXDBVevXiURtMx3lJWVhUGDBgmdr3yMOi3vD/znneXk5MDJyQmenp4sZ1lJSQlsbW1x+vRpzm3RkccTJkxgGYlofPnyRUC2DBkyhKSkcpX/+fn5JH3X09OTZbQUdq8ZM2ZAW1ubl16SlZUFJSUlJCcnE55FJycnKCsrk8xB5linp6ejQ4cOnJzWTCOckpIS6ZeZmRl69+4t8H26jxMnToSNjQ1vbmigef0uX75cgH7s3r17kJaWFnq+eP36NT58+MBJf62oqIChoSGCg4OxYsUKUoQtISGB6Fx0kcRRo0ZhyZIlWLlyJTp27MgpM+BHcovGlStX4O/vj44dO+Lw4cOk/dmzZ8PR0ZFzlhF9T4qiYGxsjLFjx7I4hC9evIjw8HDIyckhJSUFFy9exLZt29C5c2dexQN/tN4qKiowY8YMwsV9//59lJSUYNasWdDX1ycUEKKAHsvMzEzExsYK3T9a8trTSE5Ohq+vL+eaC+np6VBWVoadnR26du0KTU1NljNu5MiRoCgKmpqaxJibn5+PadOmceaILi8vh5GRERwcHKCpqQl9fX10796dnD/q6+vRv39/REVFobi4GMXFxUhNTYW0tDShshAFrTks0tLSBAyqNC5dugRZWVlYW1vzDmw4ceIE+vXrBx8fHwwcOJDI4Pfv3+PSpUs4cOAAy4B/7do1uLq68o6wFWYvYSIkJITlfGloaICXlxcvZ+u2bdvQrl07dO7cmWS80LrI0aNHMWrUKFhaWsLT0xPl5eW4c+cO/Pz8eBmHm5qakJmZyQoWCg0NhaamJjZv3owFCxbAzc0Nvr6+AhH9zOcSFfQ5uLXsMqa+NG3aNNjb20NeXh4mJia8o3n/L/Gvwfa/hNraWsTGxmLo0KHkWn5+Pvz9/eHi4oJdu3axBPnDhw8xZswYyMnJcfZAnz17Fl26dMHhw4dRW1uLzZs3Q05OTigVQE1NDXJzcxEYGAhHR0de0Ue3b9+GkpISCXmnedOY1emZ+P79O8LCwuDr68uZQ+rbt29wdHTEmzdvUFtbi169esHGxgba2tpwcnIih3+6H8uWLQNFUbyrbQLNBilTU1OoqqrC1tYWT548IYv91atX5DAYEBBAuOrMzMx4h9ivXbsWI0aMwNixYyElJSVglFuxYgU8PDxAURRsbW2hoqLCuX9FRUXw9fUVKLBiamoqkNYCNBssKIpC165dBTy5PwLTGCohIYHZs2eTzyoqKliKQ0FBAaZNm4YOHTpAT0+P9I2PF3Pfvn2QlJTE8uXLsXjxYoSGhkJGRgY7duwQGhXAd06+ePECYmJiZCOtqanBsGHD4Ovri+LiYgHlLD09HZMmTYKioiLvOUmnqrUWvdryYEdX+PTx8eHFFXfs2DGIi4tjxYoVWLp0KXr27AlxcXEsX74cX79+JUakgIAAlgKTm5vLqb2qqio4OzujV69eZO2cO3cOvr6+QisPT58+HVJSUhATE2PRL4h6qH3z5g20tbWJA6GyshIDBw5kReswcfv2bYwePRoyMjKcoi/o51mwYAF8fX3J3zU1NRg1ahQcHR0xdepUopDSY0YrI0xjLZcD+6FDhyAuLo65c+di5MiR8PT0hKysLNLS0oixOTk5GQEBAdi0aRNu3bqF4OBg2NracpJdT548gZSUFFnbERERsLW1ZX0nIyMDfn5+ArKDz35z8OBBSElJYf369UhNTcXAgQPRoUMHzJw5kxyUTp06BSUlJQwdOpQUlLK2tubFE7dv3z6Ii4tj4sSJCA8Ph729PdTU1AQyIOi+PH36FBRFwdnZmXMRoqNHj8LV1ZX8LiUlBTNmzMDChQvJ2O3YsYMY3X4F311KSgoMDQ3RpUsXASU9PT0dkZGRkJeXR1RUFBYtWgQfHx9YWVlxfnf379+HiYkJiegGmg9E2traAgUF8/LyoKysDFVVVRgYGHDmGOMqSx48eICFCxeiU6dOnLICWu5vzAjPurq6Vg01JSUlmD17NhQVFTkXPaqqqoKuri4iIiLItcOHD2Pt2rVYsmQJXr9+TdI6161bBzMzMxgZGcHe3h5Hjx5lPbcooGl9Dh48iLKyMsyZMwddu3YV6rBqaGhAaWkpQkND4erqymt9p6WlEd3m9evX5L70M79//x5JSUkQFxeHl5cXRowYAU9PT1haWrYprfG3335DfHw8IiMjSaZGfn4+goODYWNjg/DwcKxevRpeXl6wtLTk3LeGhgYkJCQQGp+mpibcvn0b+/fvR3p6uoD8f//+PZKTkyErKyuQkcQFMTExSElJwcCBA+Ho6Egig+mxBZrHvHfv3m3SS9auXStgNKWrtMvJyZE9kzlu8+fP5xxI8enTJ3Tr1o0U4a2qqkJaWhpsbGxaXUs7duyAvr5+m+nQzpw5w+KJbWxsRHl5ObS0tMh+IIxPX1Q0NTVh6tSpLCc1AAQFBYGiKISFhZF1l5aWhqCgIJibm6N79+4ktZlL+ndrcosZofjw4UMMGDAAFEXBysoKrq6uUFBQ4F2oqqioCH369MG2bdugqqqKkSNHst7bt2/fMG3aNCgqKsLAwADGxsa8eLdbW28HDhxAZmYm2VPWrVsHJSUlqKiowMLCAhoaGrzWwPv376Gjo0Oidn9WuO/Tp0+YPXs2ZGRkWBkuoqC0tBSWlpYYP348ampq8OLFC2zbtg0SEhKkVsXnz58xcOBAktlHv3OucqupqQlDhw5FUFAQMSqfPHkSMTExUFVVJdkoz549Q+fOnaGjowN7e3uoq6vzem/CHBbGxsZISEgQqGNBY9iwYaAoijjRuOxvu3fvhrS0NObMmYPExES4u7sjODhYKN1fXV0dKisrERAQgKCgIF76lyj2ko0bN8LCwgKzZs3CqVOnEBgYyNJfRcX27dvRvn17HDlyBH379mVx5AL/cZ74+Piwzm7Pnj3jzSvO/N2HDx/g6+vLcmBs3ryZ0D20BS3PwS2zceg5Ql9/8+YNZGVl4ezszDua9/8a/xps/4sIDg4mET70ZCosLETPnj3h6upKPIhv377FggULYG9vz8swMGLECJZhuKysDCEhIdizZw/S0tJYB80rV64gKioKjo6OvKv3bt++HZGRkQCaDVg9evSAk5MTjIyMsG/fPnJoqaqqwunTp+Hl5cWqOMiFQ/Dx48dQVFREQUEBEhMTERgYiCtXruDgwYPEM0sLgsbGRty9e5d35VIaL1++RJ8+ffD9+3dy8MnOzkZdXR1evnyJuro6XL16FaGhoYiLi2MVW+JzYDl79ix8fX2Rn5+PyMhISEtL482bN1i4cCHxoBcWFuLPP//EX3/9JTRS42d4+vQpoqOjSSojbYAeP3680LSq9PR0KCoqChRjEQXfvn2Dubk5iwOXjkyWk5NDeHg47ty5Q+bv3bt3cejQIaSlpfES6oWFhXBxcWFRVdAGAJp7kVmsri1zcuXKlQIRmVevXmUpD0yUlpbi3LlzvCNr9+zZA4qiSOXT9+/fY9OmTZgwYQJWrFjBiniorKzEH3/8gcDAQN7rrbGxEbGxsQJFO+bPnw9FRUUsXLgQQHNkAbN4CROitpeWloY+ffqwPM5r166FsbExvL29ycGTfndLliwBRVEk0pCLQbOxsRGjR49Gnz59WJ7W/v37IygoCFFRUZgyZQpZWw0NDfj999/h6urK+6AyYcIEuLu7s8ajsrISU6dOhZOTE7Zs2UKenz7UMA0sovatqakJtbW1CAoKYkXN1NXVYfTo0RAXFyfUGPfv30dsbCzk5eVhZWWF4OBgTvtATk4OKIoih2eg+YCnqqpKMgzo+7RMv+WD8vJy+Pr6CkT4mZmZQVFREdOnT0dpaSm+ffuGzZs3Q09PD+7u7ujXrx8vmVxRUQE3NzeWET8rKwtRUVGQk5Mj8pO+59evX2FnZwcLCwteSuGaNWsQEhICAOjduzfs7Ozg7e0NV1dXdOrUicU5+atw6NAhBAUF4dq1a5CWlsbAgQPJZ2VlZfjy5Qv27dsHV1dX9OjRA4MHD+alKxw4cADOzs6sg0F9fT0MDAxYVCZNTU3Izc1Ft27d4OnpyasgBFdZkpGRgd69e/OqYJ2fnw91dXVWodExY8bAw8MDhoaGSExMZGUz3bhxA3379oWysjIvw8CxY8egpqaGYcOGAQCGDx8OU1NT2NjYQEFBAba2ttixYwfp25cvX1BeXk6MMKLKkqam5mrtffr0YXGv0/P/7NmzuHbtGjmM1dXV4eDBg3BycoKdnR2vOXL37l3Y2Nigb9++CAkJgYuLC8sJTz93SUkJbty4gcjISMTHx2P8+PGc1zdTDs+YMQNdunTBgAEDEB0djXbt2iExMRGlpaUoKSnBjh07EBgYiJCQEMTHx/PqW1NTEzw9PUnUsYeHB5ydnSEuLg4bGxsMHDiQGCRo57W1tTVvAyr9bL6+vjh58iRycnIwePBgeHp6wsLCApaWlkQm379/H3FxcW0yDCcnJ8PW1pYl50+cOIEhQ4YgJCQE5ubmvFLomWhqasKuXbuIfszkfe/atesPi3r9igKXTN5t5hoyMjJiRYDyLSQINEfd0xzCtNFo/fr1CAwMhJOTE5KTk8n8q6ioQF1dHTGkcdETfia3hg4dyjLc/vHHH1i1ahW2bNnCKQOhJQoLC6Grq4ucnBwcPnwY6urqmDp1KkaNGoXIyEjy/NnZ2cjPzyc6GFeH9Y/Wm7W1NQYOHEii/F6+fImrV6/izz//5BWFXV1djVmzZiEqKgp///03zM3NYWdn1+r5JT09HTExMazCe1zw7ds3WFhYkPozNC5fvgwZGRnCU/4rDFJ1dXXw9/cXiMB+9uwZ+vTpA11dXVKL5v3791i0aBF27tzJMhRz0V1/5LAIDQ0l74y+544dO0BRFKnPwaW9W7duQVdXlxWVe/jwYZiZmREqF2Zk9Pnz5+Ho6MjrPEXjZ/aSiooKfPz4EVOnToWSkhLs7e3Rs2dPznsOzXdNO3IePXoESUlJAVq8v//+m8jGluMmSt9u3LiBTZs2Yfv27ayAjJZ1DejnPnLkCFxdXdvkPDt8+DAoiiIZI+/evcOCBQuQkJCAkSNHCqzh4uJieHh4sALp/teNtcC/Btv/CmhuwKioKJYgog0FhYWFMDc3J8VEAHBOo2Fi4MCB6NmzJ+ERDA8Ph7y8PDw9PWFnZwdra2si6JuamvDHH3+0qXrvkiVLEBcXh+LiYqirq2PUqFHYsmULRo0aBQUFBXK4zs3NxapVqzhXHATY3hIfHx+SlszkTvv06RO6d++OpKQk3jyFrcHOzg4FBQX4+vUr9PX14eDgACMjI9bhtiW4jCVTMN6/fx8ODg6ELzAhIQFiYmKQlpbmxXMkDPThqmX7c+bMESDhpzdIringNMrLyzF27FiS0uHm5oaQkBBs2LABZ86cgZmZGRwcHH4Z6f6HDx+gpqZG5jj9HmJjYxEQEABJSUmiJOXl5fGak/R4tSxsQ29Sffr0gZ+fX5sPKC0xbtw4UBSF27dv4+HDh9DV1UVQUBAsLS1hYWEBTU1Nksr18eNHzJ49G8OHD2/TJhUaGkoMtkzj5qJFiyAhIUEiQPlEkzBRUVGBGzdukLVLF6MbPXo0duzYgdDQUFhaWpIDbHp6OnmPXJV5oNmQwUzXWrp0KSiKwqhRozB//nzIy8sLpFDzqajLjPa3t7cnyhGTZzM2NhaOjo6s6D+6LT59q6urg4ODA3EmMOXhuHHjICkpSZSrL1++4O3bt3j16pWAkvUzfPr0iZWaDzTvZz4+PoRf+FdEgdKguTFpJZQer/79+yMsLAxSUlLE6AY0jy0zfZXr/C8tLRUwKALNB+nY2Fh07dqVyGTa0BgTE0PWCdf2UlNToa+vj7S0NLi7u+P169dobGxEQUEBxowZAwUFBVZk3K9ATk4OgoODUVtbiyNHjkBaWhqDBg2Cq6vrDzlIufaNdnrQoMfI0dGR0Jkw8fDhQ956SUVFBa5fvy6yLKF/A3Bfb8XFxQgJCUFAQAC2bt0KFxcXBAYGYt68eVi/fj0UFRXh4+NDnGmPHj3CunXrRH6PLZ/l+/fv2LZtG+zt7aGoqAg7OztkZGSgpqYGNTU1iImJgbW1NcvQwRcNDQ0IDg7GqFGjyFoLCwuDvLw8bGxsYGBggNDQUKSnp6O+vh5Xr17F3Llzee83p0+fRlRUFF6+fIkLFy4gMDCQZbT92f347G/Z2dmYOHEii1Lk5MmTUFBQEMg2YspRPm35+flh+fLlmD59OgIDA5GdnY2CggJs2LABzs7OmDFjBqvwHx8jY0uO7eXLlxOKpvz8fOjr60NSUlIgvZZviig9Jtu2bYODgwOOHTuG0tJSvHv3DgoKCli+fDlu3LiBbt26tYnzkcZff/3FcsbTPOaGhoZCneR8HIXM4lhA60YSmu9bTU2N1BOYPXs2KIpCTk4Op7VHF0mLjY2Fv78/631YWVlh4cKFmDhxIqytrcme1lqavSgQRW55eXnxoj1oDfRz9uvXjxi1//jjD8jKyqJjx46s6OVfoTf8bL1NmzaNdyQhE/X19UhLSyPRfgUFBT802jY0NODixYucqQlofPv2DVJSUkLpRM6cOQNxcXHe9TGEzfXBgwcjJCRE4Az44MEDBAcHIz4+npduLAw/clg4Oztj6tSprCCAbdu24cyZMwC4B21s3boVPXr0QFFREYuOQU1NTSiVQ15eHpYsWdKm89SP7CXy8vJEZ6+srERBQQHy8vI46+U5OTno3r07CfhobGzEt2/f4Ovri8GDBwPgX/yTiZ07d0JWVhaOjo7o1q0bOnbsiGXLlrHoPZjrq7q6GqGhoYiPj2/T+l6wYAEkJSWRkpKCa9euQVdXFz179kRAQAAJBKOdCPScmDhxIi/n/38T/xps/wugJ+a9e/fQrl07VnQQrUzcvHkTUlJSvKO3mFi3bh0xKnp4eEBDQ4N4zjMzM+Hr64spU6YILFi+Bpdt27ZBWVkZx48fx6BBg1j3WbRoEaSkpEikC9NDLcqiycnJYXHXNDU1Yfbs2TA0NIScnBzevHnDepbY2Fj07duXUz9+hPr6ejQ0NMDFxYV4poqKitCpUyeBoh58BFBrypCLiwvxbsfExEBSUhKdO3cmB0w+SgatALZMCWXea8qUKQgKCiJ/r1y5EtOnT0dDQ4PIbQr7XmlpKaZOnQotLS2B1Hl6g2xZ6Igr6PGvrKyEo6Mji+7h+PHjUFRUxMePHxEUFISIiAhWISgaoszJ58+fIzU1VWjKDA16TdDpfm1VCpkbIM1xrK6ujhkzZqC4uBiNjY148eIF3Nzc4O7uTt5xSUkJ71QoGhMmTICGhga5J/MgMXToUOjo6HBW1uhnaW0Mq6qqsHTpUoFCIVJSUqwiQTREXXutRQG/ffsWUVFRrMiYBw8ekKqwXNDau/78+TPk5OSQmJhIrtHzraioCGJiYrw5H4UhLi6OlSbMTLnq0aMH7O3thY6/KHO1tblEv4eTJ0+CoijOxZtEQffu3REcHEz+PnXqFLp06YLCwkIkJCTAzs5OqPGBr4Lo7++PoKAgASfNly9f4OHhgZiYGKFy40eyhGmIZL6XzMxM+Pn5ISwsjETa0njz5g2MjIx+6RwBmg8hzDTQEydOoEOHDpCTkyPOupZ9EXUsc3NzyaGjtd/T0eX09cTERBZ3+8/kVmvzlW7jR7KED+epsL5//foVvXr1QteuXREWFsba3z59+gRZWVlWlDbXKFBmX4DmPWvjxo0ICwsjUYVMvjaKotpUYJKJmTNnQkZGBhEREXB3d4eOjg7JtLl69SosLS0FuPFa/p8LmCnyZ8+eJUZbOm2ajvyl/98W0PzNGhoahJefHsfDhw+jffv2AtQnfNql9afRo0cjKCgIsbGx2LNnD/m8vr4ekydPhpubG2/DaWvrYOPGjfD19QXQzIerqKiI8PBweHl5sQxkXMEslgM0z82IiAgYGxtDX18fUlJSLJ5hOTk5znUdhPWJqb8yP7ezs2OlYHMtCtfynhkZGTA3NyeRgsKehaYAUVNTQ3p6OpYtW8ZyhorSTku8fPkSnTp1gpubG4YPHw5tbW1SdK6yshKSkpICkZVcQbctityiixLxuX9L0Otm+PDhRN9fu3YtxMXF0bVrVyQlJXGmiGHel8Y/vd6E9a8lBWBubi7Mzc1ZRZPr6+tx//79NhmM6L5OmjQJDg4OrMLDTU1NqKmpwYgRI9CrVy/U1NRwklW0zC4vLyc0Y0BzVGi3bt2QlpYm0M8NGzZAXV2dd4AZDS4OC+Z5g8ltzzUC+8GDB6yspfr6elRUVEBTU/On3Lt897ef2UskJSWF8tpyOUMyaU2YY7J3716IiYlxojVsDa9evYK6ujoOHjyIpqbmAoybN29G+/btMWbMGFYELU3bERwcDCsrK977N1M+zZ07F0pKSlBWVsbUqVOJrvr9+3dERUXB0NBQKE/u/y/GWuBfg+3/GVozii1fvhwdOnTAhg0bWJ//9ddfMDIy4pUC/uHDB7x//57lrTty5AjOnDmD6OhoVsEQoNkAGBcXx7kdGrdu3WI9Z2NjI1xcXCAhIQE3Nzdi5ASaBYeGhobAIVOUhZqdnQ2KomBtbc0yLNTU1CA2NhYURSE+Pp712fDhwzFx4kSBasxcIEwQJycnkwMQzeOkq6sLFxcXotBxxZMnT+Du7s4y0jc2NqK+vh4ODg64desWRo0aBVVVVVy6dInwSPERts+fP0d8fDwCAgIwcOBAEqHGbBcAFi5cSObGrFmzQFEUp8hX+j4PHjwgfGb0tdLSUqxYsQLnzp0T8BhGR0cjJiaGc7+Eoa6uDsuXL4eDgwNMTU0RHx+P9u3bk2rty5Ytg5OTk4CiJsp8ofkply1b9tPf2drasqLm+eLIkSOk4j2N8ePHw8PDAwUFBaz2N23aBG1tbQGaDK5rgakcfPnyBZaWlggODiZyjV5zd+7cgZqaGidHE7PKuYSERKtGE6Y3v76+Ht++fYOHhweJ6OWKJ0+ewMDAQKCiOY2WCufly5dhYWFBlG1RwCzsuHnzZkyZMgXHjh0jKTq///47xMXFMWnSJIG2LS0teReFa9k+0Lyn2NjYYNSoUUSppef84cOHoaurK1Qx/Blev36N5cuX/zA6pLCwEB4eHhgzZgwnZ48wXLhwgWXAO378OGxsbKCoqIiQkBCIiYmRAlgHDhxAt27dSBV7PmCmlwLNBffs7e2xYsUKAV6sFStWwMLCglPBEPoepaWlMDc3F3AITJs2DRRFQU1NjaWgVlVVwc7OTkB2c0HLvZvuY3BwMHF+mpmZwczMDNLS0iS1kg/27dsHZ2dnqKmpISQkpNU0YQcHByKbQ0JCoKSkxDnzpmUFa2bUM/05jbbIEmYUzufPn1FSUkLW1tevX5GUlISzZ88KfN/d3V2gSJaofauoqMDo0aMRGRmJQYMGEcN6ZWUlbt26xYogb2howPv372FiYsKrSA8TzDW7YcMGpKamwtvbWyDq2tPTk+WE4oqWc7Ilzp07R4y2b968QX19PXr37s2qMC8q/vrrLxZF1oMHD9C3b1907NiRVMWmAwS+f/8OfX19zoY/etwqKytRUVHBkskfP36EqqoqKIpi8YYCzY4nU1NTXrLr5cuXmDhxInr27ImFCxeyjA1PnjxBr1690K9fP6iqquLt27eE1iIgIIBXBGVLfZI2lNbW1uLMmTNITU0l66CxsRHZ2dlwcHDg5PykxzE7Oxv79u3D6tWrce/ePZa+zzTSmJqakueYM2cOKIoiMo0rPnz4QCLGmHUbWtvH7Ozs4OrqCnFxcZHOA0w9Yf/+/Vi8eDGuX79OqAaeP3+OXr16IT4+npWKTmdVcc2yYEYMtzS4/VNyqzWZDDTz88+ePRubNm2ChIQEbt68iWPHjkFGRgYjR44Uamj5Wd/KysqQm5uL8vJy0sd/Yr0JO+MwwdS1P3/+TCJt3759ixEjRsDNzY1zmy9fvsTatWtZc//GjRvw8vJCXFycwJybP38+bGxsWq0jIwxMY62pqanAWTM6OhoaGho4f/486zlevHgBLS0tznQq/y2Hxfnz58mzMt8V8//dunUjBdhpJzJzn20L6uvrf2ov4XvGycvLE5oJS/etrKwM3t7eGDZsGGdjfktkZmZCX19fwMFy6tQptGvXjqy3xsZGnDhxArGxsfDz8+NNvXn06FHExcWxAqwWLVqEiIgIfP78mdWX33//HV27duVdaP5/Bf8abP8PkJmZCScnJ1b0JY2vX79i7ty5pFBRVlYWvn79ipkzZ8LY2LjVCLDWkJKSAltbWygrK8PLy4sQWdMYPHgwFi1aRP6urKxE9+7dMWfOHF5927RpE7p27UoMNLTCdOTIEVhbW0NdXZ3Fb5SbmwtLS0tekVYvX76EgYEBbGxsYGtryxJE1dXVSEhIgLa2Nry8vLBy5UoMGjQIsrKyvBR5oDmaY+zYsfDz88OWLVtYgmHNmjXw9fWFtbU13NzcADQrW5KSkiy+YFHx9OlTdOzYEVOmTBH6+cSJE6GgoABNTU2SRpafn48hQ4Zw5uR9/vw55OTkMHbsWMycORMjRowARVFYvHgxUdbpzXPZsmUYMWIEFixYgE6dOnEyDjOrzkpKSrJ4T+nPqqurBVLUGhoaEB4eThwLXDeR69evY/Xq1Zg+fTqZZ+Xl5Th79iwSExMxduxY1lpcvXo1AgICOG8Y6enpkJCQIEWqhIHmfQWALVu2QFdXl3NBASZ27twJiqJAURSJAGI+Dw26L6mpqXBxceGdxnb8+HEMHz4cgYGBWL9+PZlrx48fh5WVFXr06MFSAjMyMmBgYCByH5nGWm1tbURFRQl8p2VKJ43du3fD0NCQ13g+ffoU4uLiSE5OFvisNW/vtGnT4OPjI7JyzTTmmJqawsfHBxYWFjAxMSEGqfr6euzbtw+dOnVCv379cO/ePeTm5mLPnj2Ql5fnnWHx+++/Y8yYMfD19SWREZWVlViyZAkcHBwwbtw4loPir7/+grGxMeHpEhUVFRWwsrKCtLQ0Fi1a9EP+bNoD3pbIi507d6JLly5YvHgxiaqora1FRkYGZs6ciUWLFrGMAHv27IG7uzunAx8TqampsLCwYKXulpeXY9iwYXB2dsaaNWtY43j27FlYWlqKnLrMnP+6uroIDAwU+r3x48ejc+fO6NGjB7KyslBeXo6UlBRoaGjwlict924mRo0ahZkzZ8La2hoeHh7Iz8/H2bNnQVFUq0X4foR9+/ahc+fO2LlzJ/bs2QNfX1+B9HLa+ePp6YnU1FTExsbC2NiYs0LfWgVr2sjPlMk0+MgSZiVwDw8PmJubQ0tLC6mpqayMgZb7W3V1NQICAoihk0tRoPLychgaGhLuYH9/f/Tv35/oQsLutWvXLpiYmPCKVMvIyGBFL7U0nIeGhrIir8rLy+Hu7o41a9Zwbgv48Zxk9u38+fMIDg6Gk5MTbG1toaioyDlSZuPGjUIL/Dx9+hRhYWGQlZVlOaeLi4uhra3N6u/PwIzOdHd3h6OjI8zMzDBnzhxyeLx79y5UVFRgYGDAogiZNGkS/P39f5i5IwyZmZkkc2Pw4MGIjo5G586dSURhcXExZGVlBTiT37x5w4tiqzV9cv78+UKNBbW1tZg1axb09fVF5gel3316ejqUlJTg5eUFZ2dntGvXDsOGDWMZqOrq6lBbWwsDAwNcuHABa9asgYSEhIC+JCpqa2uxePFiREZG4tatW+jVqxdkZGRaNdrS49uhQweRghtarm0/Pz9YWVnBwcEB9vb2ZD8TZmzbunUrZycy/bwvX75Er1694ObmBldXV/z999+syP1fIbdotCaT79y5A6C58BJFUejYsSPLOHXgwIGfRjYK61t6ejpsbGzQrVs3aGlpYdSoUWSO/Mr19qMzDiDcAJibmwsrKyuIi4tzPlM1NTWhoqICWlpaoCgKc+fOZekfaWlpcHR0RFRUFHE4NTY2Yty4cYiIiBC5b0y9RF1dHc7OztDX18fmzZtZ3/P394eOjg42b95MbBUbN24knMSi4v/aYUFjy5Yt6Ny5s0Ah75bo1q0byVwJDQ1Fly5deEVmMs+mdKBIfX39P2IvOXz4MLS1tXH8+PEfRo3Tma5MCig+SE9PR7t27ch6ZQZmpKamgqIoMobV1dW4efMm+ZxP8TT6HNyyQC3T3kPf//Tp07C3t/9lFJL/LfxrsP2H8eHDBxgbG0NcXBxqampCoxxKS0uRmpqKLl26QFNTE8bGxlBTU+OsYBw4cACSkpLYu3cvTp8+jbCwMMJPQiM5ORmSkpLYuXMnDhw4gPDwcN7VdLdu3QoxMTGhfHNVVVXYv38/9PX1oaGhgb179+Lw4cOIiIjgVS24qakJL168gJGREdmU7O3tyYIsKSlBfX099uzZg5iYGLi4uCA6Opo3D+ru3bvRtWtXDBo0CH379hWIorx37x5UVVURGhqKr1+/EiH3/ft3zn3LzMyEhIQEMZo3NTWhsLCQVYjqwIED8PT0FOD84tpWXV0d4uPjWYXEiouL4eTkJFAsCPhPZELnzp05KxbAfxSZlhGhwr5L/3/GjBlQV1fH27dvRW6Pxs6dO6GiogI/Pz/Y29ujffv2ApFczLleXV0Nb29vzlEDr1+/JhyINDZs2IARI0ZgypQprGIX9Bx99eoV9PT0eBWFA5rXW/v27bF+/Xr4+/sTvq3WvOZ0oamWld5FRUpKCjp16oRBgwYhISEBcnJyCAoKImT+hw4dgrm5OUxMTHDy5EmcPn0aISEhcHV15RRBWVFRAXV1ddZzvnz5EtevX0d+fr4AvcKLFy+wY8cOiIuLk2fhgufPn6NTp06YN28egOY5l5OTgydPnghVVt6/f48ZM2YIHOBFQWVlJaytrdGnTx+iEMXExAhwXV+/fh16enrQ0dGBjo4OVFVVWYUPuGDnzp2Ql5fHoEGDEBcXB4qiSHRAeXk55s2bBzs7O/j6+uLJkye4ffs2QkJC4OPjw4sGhy6SoK6ujjlz5ghEx9HKYmVlJVRVVQVkjKj4/fffIS0tzdpvWso/prG9uroafn5+vOZ/Y2Mjmpqa4OXlBYqiYGJiwnr3RUVFSEhIgKOjIwYPHoycnBxkZmYiKCgIoaGhnJTesrIy6Onpsfjsv337ho8fP7IctvPnz4eDgwPatWsHe3t7yMvLC917RcGP9m7gP/zNwcHB5BkaGxtx8+ZNzrpCXl4eXF1dsXv3bnJt5cqVmDBhAj5+/CgwXzw8PEBRFMzMzDhzjDU1/biCNX1Iot8PX1lC/z4zMxMKCgoYP348rl+/jvj4eKioqLCcuy0xc+ZMaGlpcS402dDQgL59+yIyMpKs0zVr1pB5Qxu8mXvv5s2bIS4uzisKe9++faAoCiNHjmRliDHn9pAhQ6CmpobLly/jjz/+QM+ePXlVrwZ+PCeF0fgcOHAAFEXB2dmZs1F/69at6NixY6sy9unTpwgJCSGUO5s2bUJYWBjMzc0561xv376FoqIixo0bh2vXrmH27NkwMjKCo6MjMZg+ffoUurq6MDc3h4WFBaKiotClSxfODruqqipERkaynCGvX7+GtrY2KIoihvSHDx+yIuD4HtK56pOZmZno168f5OXlORdP+/r1K6ytrVmcyCkpKUROXb16lfV9Dw8PGBkZQUJCgnfWG43z58+TaN1v376hV69ekJWVJTpxS+7YzZs3czIi1dfXIyoqCrGxsWStnT17Fl26dIGkpCQJMKDv/+7dOyxbtgzi4uKEl1IUMOVW165dMXz4cKxatQr+/v7Q0ND4oTOar9z6mUymz8Rz584lxpeW85HL/Pz06ROUlJQwbtw4XL9+HQsXLoSfnx8MDAyI4evJkyfQ0dFp03rje8YBmnlZFRQUeEf8DRs2DIMHDybZWUyH9J9//omIiAjIysrC3d0dfn5+kJWVFblv9LOWlpZCR0cHvXr1AgBMnjwZampqAg73wYMHw9raGl27doW/vz8kJSVZVCSitvd/5bBg/vZHOhDdZmVlJQwMDPDHH38gLi4ORkZGvKJChZ1NmQbM/fv3w8DAoM32EiYdJEVR0NbWxunTpwXGj5ahVVVV0NLSEsjyEwUtz7IJCQmwtrYmRlOm0TYyMhJDhgwReA6uZw76HLxv3z70798fvXv3FshcYaK2thbBwcHo3bv3L62f8d/AvwbbfxB1dXVYvXo1evTogYyMDPTq1QsKCgqtpqbl5ubijz/+wIULFzgbdb59+wYfHx8W+f7evXsxfPhwvHjxguV1iI+Ph6KiIqytrRETE8NL+Bw8eBAURREP5adPn3D69Gls3bqVbIqNjY3IzMxEbGwsdHV1YWtri7CwMN4h8A0NDejduzfev3+P69evk7Qjumo9837CUn1ExdWrV6GhocFS5nfs2AElJSXioamqqsKJEydYvCx8+NoKCwthaGgIa2trcm3QoEGwtbWFiooK3NzcyEbLJc22NdTU1MDBwQELFy4E8J9NMCkpiQh4Jrffpk2boKWlxStKuaCgADIyMoiNjQXQ/E4mTpxIDDy//fYbS/k7evQowsPDeVfLPnfuHBQVFXHkyBE0NjaitLQUiYmJpJIo05hTW1uLgwcPIjAwkFXBXVSB/tdff4GiKMycOROfP3+Gu7s73Nzc4O3tDT8/PxblAvBzjtafYePGjRAXFydFNMaPHw91dXVWISoa1dXVuHr1KgICAljOGFH7RjsMPDw8WB71hw8fIioqCs7OzkTJyczMRFRUFDQ1NWFhYYHAwEBO1VKbmpowd+5cVrri0KFDYW5uDnFxcWhoaCAxMZF8VlpaipEjR8LQ0JDQqnDZhEtLS+Hm5gZNTU1yLS4uDubm5pCUlISJiQmOHTtGFOBnz55h8ODBvKr3NjU1YePGjYiMjERBQQGLZqR///6Ij4/HypUryX1LSkpw+/ZtXL58WWiKlig4efIkunbtyjLQ0EZ2+r3U1NTg9OnTcHd3h7S0NExNTeHp6cm5yi39vaSkJFy7dg07duyAmpoaFi5ciOrqapaBjm532rRpvBwxQLPziFYqX7x4QSJiZs2axeIdq6mpwZEjRxAYGMgy+nEt+AI0HyLXr1+PXr16QUtLi+VErampwW+//QYbGxt06NABpqamcHJy4jSOjY2N6N69Ozp06ECujR07Ft7e3pCQkED37t3x22+/kc8+f/6M06dP4+LFi8SAzHWO/Gjvpg2aJSUlWL58eauRwlwMcoWFhdDR0SE0FQDg4+ODbt26oWvXrrCysmLRNPXo0QPdunXjVchDlArW9DtsqyzJz8+HnZ0dyzhWUFCA4OBgvHz5Evn5+aw9++zZs+jVqxeUlJR47W8VFRVwcnJiyeRt27bB1tYWvr6+8Pf3JwfA79+/IzExkcVxzKVvd+/eRbdu3dCvXz9IS0sjMTFRKFdoUVERwsLC0LFjR5ibmyM0NPSX65NMTka6H2VlZfD09GTt3aLOEzrahzZ0ffr0CcePH2cVqwGaI4Z69uwJiqIQFRWFAwcOkP1blL7R2WZTp04VqKMwdOhQUBQFU1NTYkwsKChASkoKJkyYgOXLl3POnAKa37ulpSW2bdvGes7BgwcjPDwc7dq1EygK2RZUV1dz0ifz8vKwdu1aXvrk69evYW1tjYyMDJI2XFRUBDMzM1haWqJPnz5EP29oaICVlRXExMTalNEkDE1NTSgoKCCRtrQ8qa6uxl9//YWqqirORoji4mJSoI2J0NBQmJmZQVFRkciM+vp6XLhwAXFxcbzW9tevX+Hm5iZQI8LAwIAUOGLi3LlzbZJbP5LJcXFx0NXV5Zw+/yOcOXMGzs7OrOjuu3fvIjY2FioqKkSe5Ofnt3m9cTnj0PJgzZo1oCiK11jS8youLg4bN27EuXPnSHYu0Hzm//79Oz5+/EiyrJYtW8a5b9XV1SSTg8bt27dhYGBAqACZEdj37t3D5s2bsWvXLhI1zWVO/l85LGgcOXIEFEXh/PnzAJppVlJTUzFv3jycPn2atW/X1tbC1NQUHTt2hImJCa9CVT86m9LO3fr6emRkZJA1wddeQo/Rrl27sGXLFiQmJkJWVlZorQOacjEyMpJzZnBqairs7e1ZlHKXL1+Gr68vevToQeYc/TwDBw4kxn++2LBhAzp16kT2sOXLl0NBQYEY7FsWNDt9+jQCAgJgYWHB+Xzzv4h/Dbb/MC5fvkwOz01NTYiJiRFqtG1rhb7v37/D2NiYxf/o5+cHPT09KCgowNzcnMUr9ubNG3z58kWAO1QUFBUVwcbGBlZWVsjKysK7d+9gY2MDS0tL4slPTk5mef1yc3NRWlrKqz0mvLy8kJKSAqDZgKSoqAiKosgmzLdAA42amhpMnToVgwYNIsp5U1MTHj9+DC0tLeTn57fp/sIwZswYuLm5Yc6cOXBwcEBQUBC2b9+OU6dOwdXVFRoaGsTI0VZh09DQgISEBPTu3Zuko2VnZ6NLly44e/YskpOTYW1tjaKiIgDN4ylq2lpLpKenIywsDHp6erh//z5CQ0Ph6emJiRMnom/fvrCxsUG/fv1YzzFq1Cheynx5eTni4+MxYcIE1vVt27bB2NhY6Lht27YNkZGRnDdg+l5//PEH2rVrBxkZGcTExBD+z+LiYsyZMwdSUlK/pAry8+fPQVEUS5kvKCiAgYGBAA8X0KycJiYmsoynXNdbVVUVDA0NWQURgWZDWe/evVkpxkAzN1hhYSGv9f3w4UP07t0bGhoaCAoKgouLC86dO4dPnz7ht99+g4uLC8aMGUMUufz8fBKZwLWwAACsWrUK3bt3x4ABA2BnZ4fQ0FAcOXIET58+RVRUFLS0tEg62bdv33Dz5k2RPfgt5fj9+/dx+/Zt8oxpaWkQExMjyoufnx/c3Nx4VwdmoqysDP3792dlAgCAo6MjQkJCYGNjg+nTp7NSrjIyMvDx40feqUlAc7QkffhbtWoVNDU1YWVlBTk5OWLwa1mgiyuamprg7++P5ORkfPnyBTo6OoiNjSXRHV5eXjh48CD5flpaGoYPH96m6r1As0JIR0d7eHhAT08Pjx8/Ru/evXHv3j00NjaisbERt27dQmZmJq9xPH/+PGRkZDBhwgQkJCTA0tISKSkp2L17N8aPHw9FRUWBVES+EGXvnjx5cpv3GWbEWW5uLoKCghAcHIxly5bBz88P+vr6uHfvHm7evIm1a9dCT0+PRFeVlJSIPI5cK1iHhISweO7bIkvu3r2LWbNmsSKEZ8+ejU6dOsHIyAjGxsbo27cvoRn5+++/kZCQwNs4UVNTg/DwcISEhODBgwdITU0lh3Wauqhjx46kGnJJSQmrMJeofaNpWoYNG4acnBxcvHgREhISAkZbJu7fv4/379/zmv+izMlp06axOAPPnj0LX19fTvtbU1MTqqqqYG5uDiMjI3z8+BEZGRmwsLCAvb09tLW10b59e9bB9eHDh+jbty/U1NSIg6S1MWgNQ4cOJdE9tH66fft29OzZk2TAtIVjm9m/0tJSBAQEIDk5mdAgvX//HsrKyjh69Cj69OmDiIgI1NbWtinaiGlEEEWfZPaPb7u3bt2CmJgYSz98/vw5unfvjpUrV7Ki5BsbG3H8+HHWXvcrwJQ3+fn5JNL27t27GDt2LAwNDVlBHKLcB2iOVLO0tMSmTZvIterqalhZWWH9+vXw8vLC2LFjWeuL1tFFXdv0d65cuQJHR0cyn+m1ExgYiNmzZws8Z1vlFvBjmUyvAb7BDC1x6NAhdOrUSUBnS09PR+/eveHm5vbL5kVGRganM05jYyP+/PNP3in89PtPSUkhuv/x48chJiaGbt26wdjYmHcGHxMvX75kBX/R8PX1haenJ/n7R3KXyzr/v3RYlJeXIyoqCvr6+rh58yays7NhYWEBOzs7mJmZoV27dhg0aBDL4Oji4gJbW1te+iSfs+nnz5/bbC/Ztm0bodiKjIyEgoICTp48CR8fHwHj7fv374lMEmUs6ezLHTt2CBSQTU1NhaenJ7y9vYkzq7KyEr6+vhg3bhznfgD/4T1nOlppmJmZISEhQeA3r1+/xuTJkxEeHs77HPy/hn8Ntv/HqK+vJ5G2TA6TS5cukQ1YVDC5HQsLCxEdHQ0fHx9MmTIFvr6+0NfXx+PHj/HkyRPs2bMH+vr6RCAyFyWfw9n169fh4+MDHx8fKCsrY9KkSXj37h2+f/+OQ4cOgaIoUimYT3sthQa94EaPHk2iB/r16wcFBQWYmprC1dVVKF8WH1y9epUVHQk0RwlpaGjgxYsX/0hY/cSJE6GsrIzQ0FCBqKbWBBJf7Nq1C87OzrC1tUViYiI6d+6MESNGAGg2QtKRxHwLUzGFYmZmJqKjo0FRFAIDA1nK7Pbt26GtrU2MYwB/x0VTUxN27twpkN5CcxyVl5cLvXdbHQgXL16EpaWlAD91RkYGFBQU2hTNwhz/lmloVVVV6N+/Pzw9PQUqh9fX1+PNmzdtMsKVlpaie/fuxBBHp4gDzelkRkZGSEpKEvqsP1rfrb3fzMxMxMTEwMjIiBgcaCxYsABqamoiHYRExbp162BqaoqAgAABXiNPT0/4+/tzviezSAOzIjs9HlVVVXBycsLq1avJeJ09exaqqqoCEQRcwPzNixcvSMV2AAgLC4OGhgYWL16M1atXk2g5Yb/lsg8weUBTU1PRvXt38pm9vT3ExMQwfPhwkblcf9YWAEyfPh0jR47EmjVrMGTIEBLh8fr1a0RGRqJXr15C5zofmUK3eeHCBYSHh5PrPj4+EBMTg729fasZD3za+/PPP9GhQwcBPra8vDwMGjQIYWFhqKio+CV7jyh7N9NwwActs1t+//13DBkyBOPGjUO3bt1IFA7QbHRRUVHBkSNHWL/52Xz8v65gLex5mJzPW7duBUVRSE1NRVZWFnbv3g1LS0tWpLmoDuXW3nNqaip8YKMVKQAA1ZRJREFUfHzg6OgIdXV1LFiwgHxWWFgICwsLVo0CvqApYmicP3+eGG2ZhhVhxst/Sp9sOSe57t30c7158wb29vaET3PixIn49OkTCgsLcfbsWXTq1Alz584lv3v69Cl69uwpEGH/ozaYsiEpKQl6enrIyspCfX09cnNzoaysjJ07d2Lbtm1QVVUVOPRyQUtD19KlS2FiYoLw8HBMnz4dnTt3JpRPe/bsgYaGhgDNEBc8f/4cQ4YMIeO/Z88ekfRJLmAWjmLOp7CwMGhqaiItLQ2HDh2CtLQ00VFovl4uhofWwCwE2VLOM+dbQUEBiSSWlpbG/fv3Rb53S7kVHx8PQ0NDLF26FGfOnIGhoSFCQ0MBNGflmJub8+qLsDnJzHagZeTAgQMFOP1bFif9GVqjKNqxY8c/IpOFccRmZGTAzs4Ov/32m8CZ8Ny5czA1NSU0ab8i2i4jI4PTGYcrCgoKBAzMR48ehb29Pfnby8sL7dq1401/9jPQ7/XWrVtQUVHhTcXUGvLy8v5xhwUTT548QXx8PGxsbNClSxckJycTo/qlS5cgJydHopYbGxtx+/ZtMgZ8zlOink2FBTbwnaMZGRnw8/Mjf8fExKB9+/YwMzNjBZ4xx04U3TU/Px+Ojo7EDlNQUIAHDx7g+PHjZG1fvHgRPXv2JPRdZmZmMDc355XtxgRzn2xsbERDQwNmz54NGxsb4qhgBgvwDUr8X8W/BttfjMLCQrx8+RLv3r1j8ZgxF0JdXR0x2l69ehWJiYkwNjbGly9fOLXVMj3r9u3bSEpKwrRp02BpacmqnJidnQ1NTU3s2rWLd99aRgHcuHEDDg4OGDp0qEDFxDFjxsDc3Bzfv3/nLHDevn2LiRMnIj4+HjNnzmR9lpKSgsmTJyMuLg4qKip48OABbt26BT09PXTv3r1NStqPnrOoqAhKSkosLqwtW7Zw5nMCmgXe5cuXBSL3Vq1ahePHjwvwtUVHRyMmJoZzO0Azh/KmTZuwdOlSVgG6EydOYOLEiUhISMCWLVvI9UuXLsHS0pKz8sQ8DK1evZqlYDx9+pSVtswcZxUVFaFRoqKCOSeFeeifP38OLS0tFhdkS64zUeZMVlYWFi1ahKSkJIGo05ycHFY0NtCcsmNlZSWQzskFraUx0W08e/YM7dq1Y1WtbtkXLmvv6dOnLO/8gQMH0K5dO6JoMI10W7duhby8PC8lm/Zwt1RC09PT8fvvv5PIfHr+nz9/HhoaGrwPtWVlZcjLy0NeXh5rfFJTU3HmzBkBw/a4cePg6+vLqY2fVdSlQR8gmLzGBgYGrGhlrmhtnjx8+BDDhg1jyajdu3ejQ4cOJBqcC2pqagQKkQDNayMsLAwA0L9/f6irqyMpKQm6urqYNGnSLyP6P3HiBNq3bw9dXV2BdM4LFy6AoihehZV+hK9fv8LBwYE4LMzMzCAvLw8tLa1fXnH29u3b2Lx5s4AcmzZtGoyNjX9ZBBIAXLt27R/Zu4FmWo4+ffqgZ8+erP27oaEBhYWF0NPTY2UYFRQUwM7OjtC9iIL/VgXrz58/t6qn3bt3T0DeW1hYYNiwYZzaovtGF9N79OgRaw19/foVeXl5sLa2ZqXwV1dXw83NDRs2bODU3s/AzCihjba1tbX48uULZs6c2SZ+UKY8FlWfZBp8RNX3mPO4qKgIeXl5sLKyYnGL05g8eTLs7OxYukV6ejp8fHxgYmLSanRqS5nOfDcODg5QVFSEq6srJCUlMXz4cPKZvLw8p7nPRGZmJtTV1XH27FnW9c2bN6Nfv34ICwvD2rVryfVDhw7BxsaGc5QwjWfPnkFGRgYURbEi8E6fPv3L9ElmoTYHBwf8/vvvZN/JzMxEXFwcFBUVYWhoyJIvCQkJLB5wrrh06RJrLz1+/Dg8PDygo6ODhIQElpGTWcW9f//+kJeXF0mmCJNbTCPvwIEDYWlpCRMTEyQkJJB5tnfvXpiamnJ+by3nZMsAAua6GDBgAAYNGkT+XrduHQ4ePMh5D2hNv/unZHJxcTHy8vJYjs6hQ4dCV1cX586dEzAQm5mZ8Yr0Y+qJzAAGAHj06NE/csbJzMyEnp4eMWQ2NDSgqakJz549Q0hICIBmR4W6ujqWL1+Ozp07Y9SoUbzpAIEfG/Dy8/Ph5ORE9rS2BhgwMWDAgH/EYdEaHj16hF69emH48OECwSBLly5F165dBa7zDSgS9j5EOZuKipcvXwrIhsrKSlhZWRHdwdraGkpKSpCTk8PZs2d5ZyO/fv0aurq6+Pr1K968eQNTU1NYW1uDoijY2dlh3759oGtInDp1CmvXrsWOHTt4Z7sxgz6Ejf+HDx8gKSmJ5cuXt3qP/9+5a2n8a7D9hUhPT4eBgQFMTU3RqVMnREVFsUL8mUK8vr4evXv3BkVRkJKS4qz0Hjt2DJGRkfD19cXo0aNZBqPKykp069aN8LMAzUqq4/9j76rjqtie+K4FiiIgIIgCKo10N9IlBip2F4qtT7EVuzsRu571bH0+A/Vho4DdjYGodN37/f3BZ8/vLheQXWBRHt9/lL17d+6cnTNnZs6cGVtb3o1sdu/eDX9/f6luhfHx8azAMDMxRowYwcpMKi2YLrAdOnQg9dOYnS6g4KgrTdPQ0dEh2Q75+fm4dOkSr+ApUDDhS+qyLBaL8fnzZ2hra5Pjj56entDT0+NszDAyYmZmhnr16mHw4MEsB7BwQIQpo8EcVeKieBISEqChoQFvb28YGxtDU1NTKgBe+Hnh4eFwcXEpsWlKYUh2ZlVQUMCoUaNIzVEGr1+/Zi0Q+fn5+PLlCxwdHXnVIAL+L5OS8lcY169fR9OmTYmid3d3h56eHudxVFNTQ1BQEFq3bo2GDRuyshSLwsSJE2FiYsJ5E4YBExAt7ii+SCRCdnY2unTpgvbt27OOz/DB5s2b0aRJExw4cIDlLI8fPx61a9eWalqzb98+2NnZ8cpqHzZsGGiahq6uLpET5rcXZUTMnTsXrq6u5IgnF9y7dw+Ojo4wMTFBgwYNMHfuXNY7KcqQ6t69O4YPH17qnfufddSVNDIKP2/16tWwsLCQarxUWpQkJ4zRBPx/jm7ZsgVubm6cg3+JiYnw9fWFg4MD3NzcsHTpUjKOL1++hL6+Pqm7zRy1jIyMhJGREcsgLS0uXbqE3bt3Y/369Xjx4gV5T5GRkaBpGsHBwawg1p07d+Do6Mg7qB8bG4s9e/Zg7dq1RM7y8vKQlJQEZ2dn3L17F3Z2dnB3d8fLly/h7e0NWVlZzvV4mawAoCDAVljei6oTOmHCBPTu3Zu3gX3x4kWsXr0aM2fORGJiInluea/dQIEeadCgAcaOHYvQ0FCYm5uTBiQikYgEGpcuXYoHDx4gKSkJgYGBcHBwKLUzJHQHawavX79GjRo1YGNjQzY8ivvN+fn5SE1NRdu2bUkmSmnAvJvU1FQ4OTnBysoKderUQbt27ViNLMViMYKCgrBkyRKig7du3Qp1dXWpEwqlQUxMDPbs2YPly5fjwYMHxTpXp0+fRr169dCnTx/Y2NjA0NCQlxMruW5I6sSKkEnJ50+YMAGenp7Iz8/Hhw8fiuxAP27cOPj7+0tdv3fvXrFyw7y3O3fuoEGDBqBpWirTeenSpVixYgUr0yo+Ph4GBga8m+NOmjSJNIUt6jRPYXty8ODB8Pf357X5c/fuXcjKymLYsGHo1q0bOnfuLPXuy8OeBAqCGYqKiggPDy+yXNDLly+lNgJ79OiBCRMmcM64E4vFSEhIgKysLIYPH453797h1q1bkJeXx7Rp0zB37lz06NEDWlpapFEpUDDHN27cCFlZ2VI1iC6t3kpOTpZaxyZOnIjg4GBkZWVx3qQoSiYl3xtzX9++fUnzuKlTp4KmaV6bkkXZdwy8vb2hpaVVZp0sGdR3dHSEkZERtLS0sGjRInKPj48PNDQ0WD0JxGIxAgICpJIuSkvv/v376NevH9zd3TFy5EhSng8oqIVdnj7O3bt3Ub9+fSgqKkJfX5+1gZSdnQ1PT08YGhqicePGRP527NgBFRUVzn5HYbuksF0sOad27NiB2rVrc2pCzaCozUjJkntMSajy2rBgcPbsWaxYsQIzZ87E5cuXiV588OAB67QP857nzJnDOjXGFYz9um7dOrx69arI7Nzy8E2BgiQMLS0tVhA2Ly8PqampJGZjZ2eH1q1bIzc3F127dgVN06zas1zw4sULWFpaIiEhAR4eHhg/fjyeP39OmjFaW1uzxlQSXG2FHTt2kL4iDIraUJgwYQKsrKzKpbTcr4zqgG05ISkpCU2bNsXo0aPx8OFD7Nu3D127dkXTpk1Zaf6MQsjPz8egQYNKvTMriejoaNSvXx/Tp0/HgAEDYG1tTY7wi0QiEpydNGkSbt++jVevXiEwMBC2tra8jOvU1FTY2tpCU1MTvXv3/ulEz8zMhJ+fH+du4E+fPkXz5s0xceJEAAVKfdSoUVK7k0uXLuW1WBSFHTt2QEdHB5s3by4x+PTx40fo6enh/v37CAoKYhUfL23Q9sWLF1BTU8OECRPw5csXbN++HXJycsVmyOXl5WHq1KlQU1PjHBh4+fIlWrRogQkTJkAkEuHjx49YsmQJnJ2dWeUOmH8vX76MQYMGceomKomPHz/C0NBQqlNqSbXZpk+fDl1dXV7BqpJkUvJ9XLt2DS1atEBaWhoCAgJgZGTEaff5zZs30NfXJ0fFsrKycPDgQejp6bGOnjO4fv06hgwZAkVFRd71a9evX48aNWoU27VccsHaunUr5OTkeDUwYLB//37Iyclh+/btUrKcm5uL8PBw1KxZE/PmzcPly5fx5s0b+Pj4ICAggFeQeP78+QgJCUHXrl2hqalZrPz/+PEDGzduRP369UkzGi54+PAhVFRUMG7cOJw7dw4LFy6EnJwcayNLEllZWZg8eTJUVFRK3aSB4b+0HXUL8yYnJ4fDhw9z5g0oWU4Kz2+gQJ8GBQWhX79+nN7b48eP0ahRIwwbNgyHDx/GoEGD0LJlS/j7+xMna9CgQbCyspJyXLmW+QEKjo81aNAAzs7OqFu3LszMzPDHH38QZ/WPP/4ATdMYPnw4zpw5g2fPniEwMBAeHh68MkI3b94MdXV1uLi4oGbNmvDx8WF93q1bN9A0DTc3N+JkpqamSjW6LAmFNzoTExPh7u4Oe3t7eHh44OjRo1IbEllZWdi6dSsaNmxIjnDy4U1FRQVBQUFQUFCAg4NDie+E79oNFASftLW1SUAqPT0dDg4OUps9c+fOhZKSEpo0aQIzMzM4ODiUurEG1/lW1g7WkkhMTESzZs3g7+8PNzc3ErQtTuamTZuGli1bssomlAZM46hOnTrhwYMH+Ouvv+Dt7U1K0DBjMHLkSBgZGcHBwQHdunWDgoICL96ioqLQsGFDhISEoEmTJrCyskJ4eDhxagsHbZlNc1tbW16NPLZt2wYvLy+Wc1WSPiqLTEo+9+rVq3BwcCjxNENGRgZ8fX0xfvz4Uv024P+83717F3Xr1sWiRYuwbNkyNGjQoEQHUiQSYfLkyWXa2N20aRP69OmDyMhI1KxZk7WWSAaP4uLiMGzYMDRs2JBXE65bt26hfv36pHHUwYMHWU5/edqTOTk5pKM488z4+HicPn2alZTB0Hr//j0mTJgABQWFMp2w2LJlCzQ1NTFhwgTMnDmTlSiSlJSEJUuWQFtbmxWki4mJKdX85msnvH37FqtXr0bdunWlsqhLQkkyWdjeZub3wIEDMWPGDCxatAh169bl7WP9zL4bNGgQzMzMyqyTHz58CCUlJURERODQoUOYOnUqzM3NWce9g4ODoaenh+7du2PZsmUICwtDw4YNeTUYe/DgAdlEmDx5Mvr374+6deti2rRpxX6Hr4/DvDfGh9fX18euXbsAFNjkaWlpCA0Nha2trZTNxaXcSWnsEsaHY2Tq8+fPMDIywoQJEzhlS5a0GSlpk5fXhgWDzZs3Q05ODkFBQVBTUyNra+GTfAyys7MREBCAYcOGcaLDoCj7dcKECSTYzNC7fv16mXxToODERL169UizucIYMmQIsV0l58WUKVNK/e4Klx1JS0uDpqYmunbtivbt27PWk7y8PBgaGvIeO0nk5+ejffv20NXVhZ+fH8uOLGxvnDx5EgoKCsX6dlUF1QHbckJsbCzMzMxYhtfTp08xceJEKCkpsWqiikQibNmypdhjsyXhwYMH0NXVJQ1WmGYsW7ZsAfD/xXfr1q1QUVFB48aN0apVK7i4uPDq3sugW7dupEN8p06disxqzMzMxJ07d9CmTRuYmZlx7k4fERGBTp06sXb/+/XrBycnJ/j7+6N79+7luoNy9uxZaGpqokWLFjA3N8f27duLzbRNSUmBpqYm1NTUoKOjw6uI9fz580kRcAb+/v44cuQITp48yTJsT58+jaCgIKirq3MOxuXn52PBggUICAhgZUteu3YNDRo0KHKDICYmBl5eXrwzPf7991/Y2NggMzMTeXl5GDp0KNzc3GBjY8PKSgAK6nYyzkNZmnKVRiYfPXqE5s2bw8TEBC1atODcpGT16tXw8PBgHY15+vQpVFRU8O+//7Luf/PmDRYsWABnZ2fe47hlyxbUqFGDLDxfvnzB06dPcfHiRdZRTMk5bGRkhPDwcM60xGIxyfRnava9fPkSa9euxcSJE7Fz505iZCxfvhxaWlpQVlaGoaEhbGxseHfdPHLkCNq1a4fbt2/Dw8MDWlpayMjIwMmTJ8lR6Tt37qBv377Q0NAgxjwXQ+379+9o27Ytqd3HoGPHjuTYpOTzTp48CU9PT2hoaHCeb6XpqCs5Rnfv3sWQIUPQvHlzUreTqxFaWjkBCvTy3bt30aZNG7Rq1YqTXs7Pz0d4eDhxnhkw3dMdHBzw8eNHfPjwgZXlW7iucmnx6NEjaGpqYvfu3cjNzSVNIG1sbNCtWzcij0xWTqNGjWBiYgJHR0de8rh7927Ur18fhw4dQmZmJp48eYLatWuzHP9169ZhyJAhxdbj/dl6mpycDCUlJdKs49OnT1BQUECvXr2wZMkS+Pv7Q09PD7NnzybB1Bs3bmDcuHFo2LAhL/kHCgJrDRs2JEGcr1+/QlZWtsgMzLKu3UBBfUAdHR3WJp2zszN8fHzg4eHB6hB8+vRp7N69G4cPH+ZcG07oDtYMnj9/Dl1dXaxcuRIeHh6sAL6k3Xf06FGMGTMGCgoKnHWJSCTCpEmT4Ovry3K8165dCyUlJakjmitXrsSAAQMwduxYsv5x4e3atWvQ0tIi2ZkikQj9+/cHTdMICQkhtpiks25jYwNzc3NeRxxPnjwJRUVF1K5dG05OTiWWZikPmWSwfv169O7dG127di0yAzMrKws3btxAUFAQTE1NOdOKj49HzZo1SZDvyZMnMDU1xZw5cyAWi6V0RFxcHIYMGQJ5efkybbbGxsYSORw0aBBq1aqFmJgYzJw5E9u3b4dYLEZ6ejoOHz4MJycnXpvxP378gK6uLqtmfXp6Ory9vdG7d+8is3XLYk/m5eXB0dGRBJPc3d1hZ2dH1hvJwP2rV68wevRoGBgY8BpHxgZisGPHDmhoaEBNTU2q7E5SUhJCQkJIfV6u4GonpKSkYMuWLWjRogUvO+FnMll4nRw6dCjvE5+SKM6+O3LkCLH3y6qTGR+jT58+5FpsbCx8fX3x6tUrll+9dOlShIaGwtTUFEFBQbzmQE5ODnr27MkqpfDhwwdYWVmBpmnW3ADK5uPcvHkTtWvXJqch8/Pz4eTkJFWm69WrV2Xyh/nYJQxGjhxJyj9wwc82IwuD74YFg9evX0NXV5fUkc/Pz8eGDRtgbW0Ne3t7VpAzPT0dsbGxCAoK4mwnMyjOfrW1tUXXrl1ZGcJl9U0zMjLg7+9PSgG8fv0a+/fvx+LFi3H58mXk5eXhwoULmDhxIrFdCz+7NLTS0tKkMpv/+usvKCgogKZp4gcza9ywYcM4l4EqDv3790dgYCBCQ0Ph4uIi1edA8t04OTkhNDS0XOj+qqgO2JYTYmNjUadOHakaZm/evMHYsWNhamrKShO/ffs2L2V78eJFNGvWjGXourq6wsXFBXZ2dvD09CTK9dq1azh69CjOnDnDu2A2MyHGjRuHPXv2IDY2FjY2NujRowdiYmIQGhpKDLZLly7BwcGBd3A4IyODteO3YMEC0rV6xYoV0NHRgYODQ7nUI8nOzsacOXPQr18/JCUlITQ0FEZGRsUGbd++fQuapnl3igQKmifZ2dmR984c73V0dISOjg6rJt3Tp08RERHBO2PgyJEjWLlyJflbJBIhNTUVmpqaxRpjZWnatnfvXlIE39vbGwEBAZg3bx6mT5+OunXrol+/fuTe1atXIyQkhHcNSC4yef36dVJbh0+Q/ebNm6xjVow86+npsY6oMnj//j3vjs/Pnj1Dy5YtyTi+fv0aDg4O0NXVJXKybds2VlMNsViMv/76i3dtpaysLJiYmODixYv49OkTNDQ0EBwcDAMDA5ibm8PNzY0YNU+fPkVcXByuXr1apoZmFy9ehLOzM4CCwGy7du3QsGFD0DTNOkK3a9cukg3F9Zjjs2fPEBAQQJo9ML9z2rRpRTYUy8zMxOzZs3llXpS2o64kDh48SOYhH964yMnly5fh6enJ6qrORV5CQ0NJzUUmELZkyRJ06NABLi4uGDVqFO/j+oVx5coVqKurs7KWMjIysHbtWlhZWSEsLIzQevXqFRITExEXF8dLHp8+fQoPDw/Wu/vx4wecnZ2xYsUKTJ8+ndQXLOuaExMTA21tbfj7++Pff/+V2mCZOHEiDA0NSXA2Li4O8+fPJzXNuMrImzdv0L59e9YaABQYtVOmTEF4eDh27dpFxvLixYu8127md8XGxqJ58+aYNm0akpKSEBQUBE1NTaxduxZLliyBvr4+qwmGJLjIo9AdrJnfl5aWhrZt2+L9+/c4ceIEPD09yUmDMWPGEKdm4cKF8Pf357W+paenY/r06Vi5ciVEIhHr6C9TN648eduxYwccHByQlpZGnnnp0iW0bNkS5ubm6Nu3L8tx/euvv+Du7s5rLU1OTkZYWBiGDx+OhIQEGBkZscpLFEZZ7MnCwagxY8aQUlqFy8eIxWJcunQJfn5+vGilp6cjKCiIlK1i0LVrV5iamhb5nevXr2Pq1KllroN97949tGrVCqmpqcjLy8OECRNQo0YN1K1bl5VRlZuby7vRWE5ODh48eCB1fdasWVBXVyfBgMJyx9eezMvLg5mZGaKjozF69Gj4+fnh0aNHuHfvHmbMmAFLS0tWXd74+HjeTc0KN8gFCtbmhg0bws7OTuoUFdMfhE9JCT52wrt378jpOi5rAB+ZHDlyJGiaLvJdc0FJ9l1Jz+ait/Lz8+Hp6cnyKyIjI6GgoAB9fX2oqKigR48erO+kp6cXWYO/tPTs7e1J8gkjN+PGjUP37t0hIyOD5cuXk/vXrFnD28eZM2cOCQwzOujChQtQVlbGoUOHAJRfTU6udklhO4/L7+C6GVnWDQugYN1UVVVlBc1zcnJw8uRJWFpawt/fn6zbt27dgq2tLTw8PHgnt5Vkv1pbWyMsLIw8+9q1a2XyTVNSUqCjo4P4+Hi8ffsWWlpa8Pb2hpKSEiwsLDBw4MAy1TIGgJ07d6J169awsbGBg4MDjhw5gpSUFIjFYixatAiysrLo1q0bPn36ROo6u7q6SpVe5ArmPa9YsQJLly7FkydP0K5dO3h6emL//v3o0KEDGWNGl1+7do23H/y7oDpgW054+/YtXF1dMX78eKnjLXfu3IGZmRmrGD9XMAJ89+5dGBgYIDw8HI8fP0ZAQAC0tLSwY8cObNy4EY6OjsU2FSiLMG/bto3sZp4/fx62trZo1KgRmjZtSpRCZmYmLl26VKZuigxev36Nnj17snbxXrx4AZqmeR8PLYx79+6xsiQ7deokFbSVxN9//80rWMu8uz179kBHRweurq7o0qULatSogcOHDyM3Nxf3799Hp06d0KtXL2JUlOV9SQYNJRc5XV1dVvmAooKOfJCQkABFRUXMnj0bAQEBrM2Ic+fOoU6dOuRID8Dt2E5xKI1MMosl3yB7cfVHjY2NWUfRDx06xLu+EoP09HSsWLEC9vb28Pf3R4sWLTBu3DhcuHABL168QHBwMKysrEgWamH54Csvnp6emDRpEgYNGoShQ4ciJycH2dnZOHHiBGxtbTFkyJAin823e2lubi6cnJzIEfCwsDDUqVMHKioqvGu5FvWbJAv4S3YrZpo1MOB7HLU4lNRRtzwMba5ykpWVhatXr3LWy8xv7d69O+zs7IhB/eHDBzRp0gTR0dGYMWMG9PT0ONcoLA7MCRImK5T5DdnZ2Zg/fz7Mzc2L1Vlc5TEtLQ2bN29myVxAQACUlZXRpUsXGBsbw9jYmHfJCkmIxWJcuXIFzZo1Q926dREcHIz8/HzWvOrYsSNsbGzI35LOJVe5yczMxKFDh6R4U1VVRXh4ODw9PWFmZkYCH9nZ2WVeuz9//ow//vgD2traaNOmDVRVVVmnOfbu3YumTZuWOSBQGBXRwbq48Q4MDCTPP3v2LJo0aYKaNWtKlWwp3FmeC+Lj46WaWL579w4tW7ZkBeEkA49c5YO5f/ny5TAwMGD93o0bN8Ld3R3Tpk2DkZER68itZK10rjKSk5ODvXv3Evvj3bt35LRGUUFbsViMixcvcpZJST0gmXW5ePFiKCkpYcqUKaxxBIBv375x1pGSYy7ZYIl5xvPnz9GoUSOpTRMGZdnkktQb7u7uZJ537twZDRo0QM2aNYmdXB5rjmTJA2Z8c3JyoKOjQ+qelgeYjNfu3bujR48eCA0NZenf79+/o2/fvujQoUOZ+Xrz5g3s7OwAFGREqqmpkezLPXv2QE1NDcOGDWMd+e3Xrx8CAgLKpQFkRestPjJZXM8ELiiNfcdnfSlcdmP+/Plo1qwZxowZg6FDh6JevXo4dOgQ7ty5g4sXL6JevXqsZAu+EIlEyMzMRGhoKAYOHEjKQL148QLKysrYtWsXRo0aBXd3d5ZfUx4+DoO3b9/CwsKCZKLytb0Lo7R2ia2tbZlplXYzUhKvX7/mtWHBIDk5GUZGRqwNHqBgPuzduxfm5uasZoLx8fFlsoF+Zr9aWFiQJJKy+qYikQhOTk7YuHEjQkNDMXLkSKSmpiI9PR2rV6+GjY0NFi9ezJkHBnv37kX9+vWxdOlSHD58GJ06dUKdOnUwevRofPjwAXl5eVi3bh3q1q0La2tr+Pn5wc3NDSYmJmWK/Uji+PHjJLM8Pj4eHTp0QMOGDdGkSRNWeVFJVOWgbXXAlie+f/+O9+/fs2oqLVq0CIqKili7dq2Usm7Tpg06dOhQZro/fvzArFmz0KpVK4SEhEBNTY21C3zixAlex+iLA6Nwjh49CgsLC3JdW1sb8vLyaNeuXZEFpkszaT58+IDz589j586dxOGXXIgK1/SLjY2Fqakpryy4olDUsd3OnTuToG1WVhZSU1OxaNEilvNcWmV09+5dqWMje/bswfr16zF48GD06tWL9dno0aNhY2PDS9kxY7ljx44iDQUmw7ZJkyaIjY0F8P/GAu/evePcyECy1ALz7IEDB8Lc3BwGBgbk/TPGjr29PRYuXMiZLwYfP36UCohylcnSjOvLly9x8OBBLFmyBE+ePCEyKFmKQCQSQVdXlxzRYcaxLMFGZlwzMjKwZs0aGBkZYcCAAayaTdnZ2dDW1pY6qscVjKPI7IhOnDgRPj4+cHZ2ZgXVgYJaRw4ODmXKvpZEXl4eMjMzoa+vj/j4eCxbtgx169bF0qVLERwcDCUlJalGFaXFnTt3sHDhQilnSlKnrFq1ChYWFmRMZ8+ejREjRvAOthdVAJ9BeXTULQ6pqalYvXo1ZznhY8wkJydDQ0MDLVq0gI+PD+Tk5DBgwAAABfLaoEEDqVpofPH9+3c4ODjA399fKpAuEolgZGQkVeaiLJDUCXv27IGJiQmr27S1tTW6du3K+bmFm48w/165cgUWFhYwMDAgJ2GYz5kNvcKZJnwhmV3x999/w9TUlDW3mIwFvgbvs2fPcOPGDTx79ozoyR8/fuDr1684dOgQLCwsWOvmyZMn0apVqzLpyYqeb8WV8mDeZ8+ePUnDml69ekFRURE2Njbw8fEp82ZTYZqSeuv+/fto0KABsTd37twJQ0NDXs3TJPHixQvIycmhS5cu2LlzJ1asWAGapnHixAkAQLNmzbB06dKf/tbSovAa/PbtW6mgbUpKCv7++28W/1yzvYGCddnIyAg7d+4k12bMmIFmzZph3rx5xZY4+VkwpDgZKfwbv3//jjZt2pBSIGXR/58/f8atW7fwzz//kDFk5pa7uzuOHDmCoUOHQl1dHTExMRg7dmyZkhvevn2Lc+fOYdeuXfjy5UuRzcXy8/Mxbtw42Nra8tr0LGmcr127BllZWdA0LRVcXL9+PczMzIqs2cgF169fh4ODA7S1tVGrVi2p47bbtm2DmpoazM3NMXDgQISFhUFFRYVXGa/K0lullcnyCnJUhH1XnJzcv38f8+fPR//+/eHh4SEVnA0ICGBl4HKlJ+njAMD27duhq6sLNzc3Ur+WKY9x5swZyMvL4+3bt7zeX15entQGTuF3wgTH+JZbA8pml/A9OSgJPpuRXPHy5UuyAZmVlYUePXrA3d29yMaAPj4+6Nixo9R1vvMhNTUVjo6OnO1XPj5/dnY2OnXqBF9fX3h6erJiDSKRCD179oSfnx93JlDAh4eHh1RzPmNjY6ipqSE8PJwkJj59+hTjx4/HyJEjMXPmTMJLeeiUmzdvwtjYmPyto6MDVVVVODk54ciRI2V+/u+G6oAtDyQmJsLNzQ16enowNzdnFR0fN24cZGVlsXTpUpYx3alTJ1Yjg9Li3LlzmDdvHiZNmkSO0DKBxLNnz0JPT4+VHXH58mUYGxvzzmJ58+YNEhIS8P79e1YQ48uXLwgJCcH3799hYmICT09P7NmzB46OjrxqVTFH4kxMTKCkpITmzZuTQuPF1Y6ZNGkSnJ2deTu0Dx48QExMDOLj48niUNTRqM6dO6NVq1ZYvXo17O3tYWVlxXlHMz4+HjRNkyYNhb/PlGOQRFhYGHr06MH52E5JYym5C/Xt2zeoq6sjMTER8+fPh5ycHK/GAg8ePICcnBxWr17Nun7y5EmYmJiApmmpJgK+vr7F7ur/DFu3bkVwcDCuXr2KvLw8Ihc/k0mutaoSEhKgqqoKDw8PGBgYQF1dHYMHD2ZlieXm5iI3Nxe6urqIiYnBggULeI8jUHRX+PT0dPz555+s0hWMfPr7+/OqV8tg2bJl8PHxYcn7+/fvYWhoCJqmpRq77Nu3D87OzlLG68/ws66z4eHhsLa2hry8PMlOu3HjBry9vVmZsaXF3bt3QdM0aQ5XHNasWUOyGKdNmwaapnnVmRSio64kLl++jHXr1mHdunVEt6emppa7nDx9+hQLFixA7969sWfPHpLd8O3bN0yaNAkzZszA1q1byf2xsbHQ09NjbVxywYMHD3DhwgXcv3+fGLj3799H/fr10bNnTyknYdiwYVJHHblg2bJlrFqqkvj06RNZSyUbsXTr1o0TjWfPnpEswkOHDiE6Opq1IXnlyhVoamrCy8uLtXbPnDkTrVq14u0YvXz5EgkJCUhKSiLzldmUyc/PJwEOhrcpU6agTZs2vBu16ejooEWLFjA1NYWjoyNLT8bGxsLR0ZHUy83IyECbNm3Qtm1bzg6tUPONGYenT59i3LhxGDBgAKvxEFDgvM6cORPdu3cnG+PHjh2DpaUl2rRpwytL5me8AQUypaqqih8/fmDPnj2gaZr0MeCCu3fv4siRI/j7779JgPTWrVswMjKCsbExdHR0WM09bG1ty7TReujQIVaWT1FO3Js3b2BoaAg7Ozvcvn0bdnZ26NSpU5kCnNOmTYOKigrOnTsnFUifPn06NDU1MX/+fN5H6UuSEUkcO3YMNE2TrCo+SEhIIBvhqqqqsLCwYMnJ1KlT0bBhQ2hqapJg4rdv3zBhwgRefkB8fDyaNm0KOzs7NG/eHEpKSpg/f36RGd0PHz6ErKyslC34MzDj+OrVK5w+fZpl8zKfHTt2DDIyMrC3t2edChs1ahQ6dOjAOUO5KHlavnw5aJqGhoYG0Y+Sv2XPnj1o1KgR1NXVsXjxYk4NBH8lvSWJ8pBJoew7STnZsGED5s2bh/3797PuYWoez58/n3U9MDCQ+F9cUZyPc+jQIYwcORI9e/bE2rVryfWTJ0/CzMyM18mK+/fvo3v37nBycsKwYcOwZ88e8pmkvnz16hVsbW1JE26u63Zl2SUA/81IPr0d3N3dcerUKeJbvXnzBpqamvD29kZCQgLrmZGRkQgICOCdESrpvzHv6vHjx2jQoEG526+PHj3CxYsXkZiYSDbInz9/DjU1tSI3ttauXQtfX19eZRG+ffsGQ0NDMtcY3di1a1cEBASgefPmJZ484xqsLS4bXSQSITg4GI8fP4aJiQk8PDxw6tQpklhXVN+aqozqgC1H3Lt3D4qKihg3bhz++usvTJw4EdbW1nj37h25JyIiAtra2vDx8UFYWBj69u0LeXl5zvVsoqKioKqqCi8vL1hZWaFFixbEgQYKDCt7e3ucO3cOOTk5SEtLQ5s2bRAQEMDLCdu2bRtatWoFDQ0NaGlpYdasWUQxME23aJqGq6srOb5w6NAhDBw4kBO9p0+fQl1dHZMmTcLr16/x/v17uLq6Frsb9PjxY0RERKBhw4a8dxejoqKgqakJIyMjGBoawszMTCorTFJpM011zMzMODezuXPnDurWrVtigH7VqlWoXbs29u7di5MnT2Ly5MlQUFCQqpn1M3AZS7FYDCsrKzg5OUFGRoZ3Y4HVq1eDpmnUrl1bagfu2LFjsLKyQuPGjbF06VKcOXMG48aNQ+PGjTl3ywYKZFJWVhZr1qyRUupfvnxBs2bNykUmP336BAsLC8yYMYMY7PPmzQNN0/Dw8JAaKwcHBxgaGpZpHA8fPoypU6eyNnZKqsX548cPODo6smplccGGDRtQq1YtVhYtY7y8fv0aWlpaaNq0KVauXImMjAx8+fIFAQEBnAJWpek6m5ubi+joaDRo0EDqKDETaOLaXKNevXqYOHFisfcw7zQ6OhodO3bE7NmzISMjU+Sue3EQsqOuJKKioqCmpgZbW1sYGhrCy8tLanNLEnzlJDExEWpqamjbti0cHBzQsmVLTJw4scTs6oiICFhbW/PaRIuKikKTJk3QrFkzNG3aFM7OzmSMz507h/r166Ndu3a4ceMGcnJySKZ+cQ0qfgZG/iUdIqB4vc5kGcyZM4cTnZiYGOzevRv9+vWDrq4uFi9ezDqeKnkM0czMDP3798eMGTMgIyPDKrPCBUwAVVdXF/r6+qTpS3H8paenw8fHp8Q5UxxOnDiBhg0bYs+ePXj37h1OnDgBS0tLqKiokDIcz58/h6mpKZycnODh4YHWrVtzXksro4N1QkIClJWV0alTJ3To0AG6urqs4AcTLNXU1GTpjmPHjnHKsOXCG1CwqWZra4vIyEjUqFGD6HAuenLz5s3Q1taGgYEBWrRogQEDBpBsqtTUVCQnJ7OygpKTk2FlZSV16qK0ePXqFWiahrGxMebNm0euS7575vdLbhoaGhqWqfbemzdvYG1tLRXYkXzm9OnTUbt2bdLsqTQorYxIHt3OyMhA27Zt0b17d14ZoY8fP4aKigoiIiJw7949xMbGwtLSktQUB4BTp07B19dXKvOTjw/w4cMHGBgYYObMmUT+hg8fDpqm0a9fP9bpNsYp79q1Kzw9PUvdwZ2559GjR6hbty4aNmyI48ePk/cj+QymSZ2NjQ08PDzQrVs3Xn4AMxZfvnzBw4cPic12+vRpzJ8/H15eXtDX1yfrqmQgZteuXTA2NpZqvFQcfkW9VV4yKaR9J8lbkyZN4OfnB21tbVhYWEjV5h07diw6duyIy5cv49OnT5g0aRLU1NR4n9Yqyccp6vcPHz4crq6unMsgPH78GAoKCujZsyf++OMPuLm5wdjYmJWFKRn8GjFiBFRUVHjZkkLbJUJvRu7YsQOysrLYtGmTlM365MkTNG7cGJ6envjzzz+RmZmJlJQUuLq6SjXULS2K8t+Y93Lx4kU0aNCg3OzXLVu2QE9PD1paWtDU1MTcuXNJpvLVq1ehoqICIyMjHD16FNnZ2UhLS4OPjw85CccHtra2CAwMJH8fPXoUKioqePfuHTp16sQqk1GWzdX9+/ejZ8+erNIzDDIzM9GqVSvQNI3WrVsTH//SpUuYNGlSlS5/UBSqA7YckJSUBBMTE1YWWkJCAjw8PHD//n2WwP3555+IiIiAm5sb+vbtW6QwloQLFy5ASUmJdFG+efMmTE1NWQr2y5cvcHR0hLW1NaytreHm5gZzc3Ne3bL37NkDeXl5bN68GQkJCZgyZQpatmzJMoxWr16N/v378z5KBhQo7kGDBqFfv34sBR4VFQULCwupZ9y/fx9DhgyBgYEBr+6eQMFxFQUFBezduxcpKSm4dOkSWrdujTp16pAi7gxdkUiE9PR0uLi4wN7ennN9mRcvXqBGjRqkOH1OTg6ioqIwYcIELFu2jFUzd8CAAZCXl4euri7s7Ow488d1LFNSUiAvL4/atWuX6VgNU/Q7OjoaNWrUkDJo4uPjSYdzU1NTWFlZ8TpG9urVK1haWpIOn58/f8alS5dw/vx5shu7YsUK9OvXr0wyCRTMY1NTU9y7d48sAl++fIGxsTHs7e0REhJCFubMzExoa2ujRo0anAPsDA4fPgyaptGwYUPMnj2blelT1HHcz58/w9/fH7a2trwWqY0bN0JGRoYc+0tPT0dKSgq+f/9OjPaXL1/Cx8cHenp6aNiwIaysrFj65GeLcmm6zuro6BDnXfKd8V3w37x5A5qmiYErFosxa9YsUg+6cNBy48aNoGka8vLynLJZKqOjLlDgUCooKODgwYPIzs7GoUOHoK+vzxo7Rsazs7N5y8mbN29gYGCAiIgI8i5Wr14NFRUVqXqPQMGmFNPlnM/c/vfffyEvL48dO3bg/fv3OHToEEJDQyEjI0OcvLi4OLRo0QIWFhbQ1dWFvb09jI2NeTkrGzZsgIyMDMn8z8rKQk5ODks/MP/Pzc3Ft2/fEBAQwLtEzeDBg6GiooKlS5eyNnMlaV25cgWWlpagaRpHjx7l1S0bKKjzJS8vj507d+Ldu3fYtWsXzM3NoaKiQpr2SdadZGTEysqKVy32cePGYdSoUazPRowYAZqm0aBBAyIP9+7dw8SJEzFgwADMnj2b01paGfPt6dOn0NbWJoGO9PR0DBgwANOnT2fdt3btWlbDQK7gw9v9+/dB0zRomia1LrnU9Nu5cycaNGiAPXv2ICUlBStWrIC2trZUvwWgQEaePHkCPz8/WFlZ8XaKXr9+DVVVVXTr1g1OTk7FBm2BAtvEysoKjo6OnG0uySQG4P8beIytJTlGko78xo0bOfNWWhmRxPjx49GiRQvOdb4zMjLQs2dPVnAWKMiMd3NzY10rrxriV65cgZWVFd6+fUsyWB8/fgwNDQ2YmZlhzJgxUqdtTpw4wXkz/tu3bwgKCkLfvn3Rpk0bKCkp4dixYyw7lpGRe/fuYdmyZejevTsmTZrEOWtYMvBnYmJCAgCMjQ4UnAhwc3ODvr4+a9OCyeAqbSDuV9ZbkuAjk5Vh3718+ZJsHItEInz69AlTp06Fp6cn650cPnwY7u7ukJeXh4WFBVq2bMnLLmFQko8jKf+3bt3CwIEDeScTzZgxA+3btyd/f/nyBWvWrEGzZs3Qu3dvcp2ZF48ePYKxsTHvUjhC2CVCb0aKxWJ8/PgRLi4u2LBhA4CCjadjx45hz549xK9++fIlPD09YWJiAmVlZVhZWcHU1LTU/o0kSuO/xcfHo2XLlmW2X7dv34769etj+/bteP/+PcaOHYvmzZuz5m58fDx0dXVhaGgILS0tODg48OaNwblz59CkSRM0adIEXl5eqFWrFhnf48ePQ19fv0j7gQuOHj2K2rVro0mTJhg8eDArqVHypMXAgQOL9EOAql2ztjCqA7Yc8PTpU0yePJllMEyfPp0cR9LV1YW7uzvrO4ULeJcWS5cuRbt27VjXbG1tMXbsWAwcOJDU7/jw4QPmzp2LMWPGYMmSJbwKWD99+rTIjCxzc3NWcPrHjx+snWe+i3BERAQiIyNZ1+Li4qCmpoakpCSW0Zaeno5bt27xWqCY3zdt2jSpnaYZM2agRo0aqFWrFjkaxNw/Y8YMqKiocO7cKBaLsXv3bsjJyZHMJW9vb5ibm8Pe3h4aGhpwdHTEmjVryHdu376N58+f8y7zwGUsRSIR1qxZw6rRyAfPnj2Dq6srvnz5gtmzZ4OmaURFRWH8+PFYtWoVue/r169ISUnhfJyewcuXL2FmZoYfP34gPj4e5ubmMDY2hoaGBpo2bYpz584BACtLgK9M/v3332jYsCFrQ+Tu3btwdHTE9OnToaGhgaNHj5LPjh07xnsc3717B29vb0yfPh0zZsyAhoYGZs6cWeTxTLFYjBUrVsDGxgZ2dna8upcmJCSwynMkJiYiICCALO59+/ZlHaWMj49HdHQ0Tp06xbkAf2m6zurp6ZHAcVkX29evX6Np06YIDAzEmzdv4OrqCkdHR/Tu3Rtt27aFtrY2aUwHFBhaKioqvI6KCtlRl7l30qRJLMM9Ly8PLi4umD9/PubOnUuOnOfl5WH58uW85EQkEiEqKgodO3bEmzdvWKU59PT0WJtMDM6fP48RI0bw3rDYt28fHB0dWfrp/fv3GDJkCGrVqoV//vkHQMEm6cGDBzF37lxs2rSJ1/p248YN0DRNjkwmJiaiU6dOsLCwgLGxMWbNmkU2gPLz87FmzRo4ODjA3t6e83wTi8X48OEDrKysMHr0aGzZsgUJCQlIS0vD0aNHsWzZMsTGxpJSBRcvXkSTJk1YmwdcHBWxWIzw8HCpAGpYWBgJoDIywuh/FxcX1thznYM9e/aEr68v63fOnTsXY8eORUBAACvj+mc1FUuC0PMtMjISoaGhrPVk4MCBcHV1Rfv27dG1a9di+eIKrry9evUK3t7epOEel2Dto0ePYG5uTpwtoCBA6uvri6ioKPz5559EH+bn5+PGjRsYMGAA7/VGEt27d8e5c+cwatQoWFtbk2ObkoGpnJwchIWFsZqFlnZ+b9u2DTRN46+//iLXXr9+DWNjY2zYsIFVpxEoSEwobOdymds/k5EePXoQJ1ZStxXVVO1nyMrKwujRo6WOuv7zzz/Q1dVFWloa7673xWHfvn1o0KABay5du3YNAQEBGDFiBBo2bEiST8rS+Oj58+cYP348kYOOHTtKBW0lG5wx4Dvvnjx5AlVVVUydOhW3bt3Cli1bICsrSwLNYrEYV69ehYuLC3R1dXHz5k1MnjwZzZo1I1m3pcWvprfKUyaFtO/y8/OxcOFCtGnTBt++fSPjEhcXh3r16kklQd27dw9//fUXjhw5UmQwkguK83EmTJiAFStWIC8vD1lZWTh//jwCAwN5J8D06dMHjo6OrGtpaWmIjo6Gjo6OVCZxVlYWr9IEQtkllbEZCRQk8xgbG+PFixdITEyEsbExzM3NoaWlhVq1amHjxo0ACnzSW7duYf369SQZDuBmT5bGf2Oe9/nzZxw6dIi3/RofHw9jY2NERUWRay9evED79u1x5MgRXLlyheiwjx8/4tixY1i8eDF27tzJidbRo0cxZcoUhISEYN++faRp+KtXrzBhwgTMnTsXV65cIfdv3rwZdnZ2Zept8unTJwQGBmLChAlYvXo1LC0tMWDAAKmT6CKRiKW3yrMXyO+G6oAtB+Tn57N2X9esWUOOBsbFxeHy5ctQUVEhSrYsgrVgwQIoKiqSI0jt2rVDkyZN0KtXL3Tp0gU0TZOjnYWNGq6L5M2bN9GlSxfifDMTvGfPnhgxYgRvHiTBdC8Eij4SFxcXB01NTdau6fPnz8tl92T48OFwdnZmNSNasmQJwsPD0aNHDxgaGrKUbUZGBu9Okd++fcO2bdugqqqKevXqoX379qT215s3b9C1a1e4uLhwrp0mCb5jCZTPbtTr16+hq6tLghzr1q1DjRo1ULt2bcJreRUcb9SoERITExEcHIyxY8fi5cuXuHnzJgYPHgwZGZky1QZNTU1lyYSDgwMsLCxw6NAhHDx4EPXr1ycbFm3bti2yOD0ffP36FWvWrCEL4Pz589G0adNig7ZJSUlYvnw5b5l8//49+vbtCyUlJURFRcHY2Bj9+vXDzp07MW/ePHh5ecHV1bXYOqRc3qWQXWcZ2WeyMGiaRocOHchObFZWFqKiotC8eXOcPHmSfI/v5oiQvDEYO3YsDA0NiXPVtm1bNG7cGP7+/vDy8gJN0+QY+sePH3nLyZEjR6Q66aanp6NJkybkFIIkxGIx70ZtQEHGn6ysrFRm/KdPn9C7d2/o6OgUuyFSWnlk5OP69esICgqCnp4eDhw4AGNjY5J9PXz4cNja2qJ3797EqX358iUWLlzIu3vv06dPoaSkhH/++QcxMTFYtGgRTExMYGpqCiMjI9SsWZPUCRWJRFLNNbmia9euCA0NZa0FkZGRGDx4MIKDgxEYGEhofPnyhRXI4nP8duHChbCzs8PWrVvx/v177N69G7Vr18bff/+NM2fOQF9fn6wDZbGBhJ5vX79+ZZW3WbhwIanrPXfuXBgbG7MaFpYFfHhjjgNydWZfv36NXbt2sTa9g4OD0ahRI1hbW8PJyQmqqqqkxENWVhZu3bpVYnmen4H5rpubG/78808kJyeTBlX169cnnZ9FIhHy8vIQExPDWSbXrVuH2rVro1mzZhgyZAgJfOXn5yMwMBCtWrUiuhEoCIwFBQWhe/fuvN9haWTE0tKSdQSdKyQbSEkGC5lnnT17Fjo6OsjPzyfXylJfUlJvpKWlwcDAAF5eXrh9+zbOnj0LOTk50qfD0dGR15HewuMgFovx9OlTFu2QkBAoKSmRY/XA/5tYlRVMvW4G79+/h6+vL+7evYuLFy8S2blz5w68vb2hoqICXV1d3LhxgzOtX1FvlVUmGQht3+3evZu10SQSifD582eoq6sTfVURAZySfJzCJcz4BK0Yud++fTusra3JSRgGKSkpiIiIgJOTE/EJysqnUHaJkJuRzP1MyYOrV6+id+/eGD9+PD59+oT3798jMjKyxJrNXH3V0vpvxT2XC734+Hhs3LiRZScHBQWhUaNGMDAwgKWlJezs7Fj9A7jS2rJlC+Tk5DB48GCEhoZCWVmZ9T4A9pqUkZEBHx8fqabpXJGfn4/du3eTmtZbtmwpNmjL0P6vozpgWwoUt5N84sQJKYPQ1dUVw4cPLzPN+Ph4+Pn5oWbNmnBzc4Oqqior+y8sLAxGRkb48eNHuQTHmGMPwP8n+aRJk6QydxingQuYIxGnTp1i7QJLjuvt27ehpaVFggDjxo2DmZkZ55pARWHTpk0wNzfHqlWrcP/+fVLg//Tp07hx4wZ0dHSKzLgrbQZBYUMoJSUFUVFR6NChA1mImc/u3LkDmqZ5NVYC+I+lqalpuYwlg6CgILKr3KVLFzRs2BA0TbOyh/mCGauMjAwEBASgf//+cHV1Ze1if/78Gb6+vpgwYQLnBR74/ziePn2ajAtzZKZJkybQ1tZmNSzo27cvOnfuXGbeGBRuTsAs+jNmzCCL/rdv36SCqFzmuiSNr1+/on///qBpGsOGDWPtWB45cgQtW7YkR9G5jGVldJ0tKuvr+fPn6Nq1q1TNtA8fPqBhw4ZYt24dZzqV2VGXmc9//fUXXFxcoKysDFdXV6irq+PJkyckYBoSEoKAgACpo418uqoXvpafnw8TExPSMR4oyMAqSzkV5tlPnz6FnZ0dIiIipMbq2rVrMDMzI00N+GZySQZ8ExIS0LZtW9A0jREjRrCCQqtXr4aGhkaRzef4rq1t27aFoqIiOnToAC0tLdjZ2WH37t0Qi8Xo3r07PD09y1SnE/j/WEZEREBfXx8nTpzAhw8fsG/fPsjKyuLixYv4888/oaWlVWQX99LylpqaiqysLHJSgulOrKuri6ZNm6J+/frYsmULgAI9U7duXV7d6Su7g7Uk3rx5g7Zt27IyQRMTEyEnJyc4b3yPHUrqf8lTLpMmTYKWlhbu3buHnJwckk02cuRIqWAp17nHyCQj27NmzSI1oN+8eQNlZWUoKSmRxjmFUVqZ3LhxI2rWrIkzZ85g06ZNqFevHiurLisrC9bW1jA1NSW1GJ2dndGqVatim9pyRXnLCFCgs+bPn08c9KKCbGfOnEHLli3J32PGjIG9vT3nJlxF0cvPz8e5c+dgbm6ORo0aoXHjxqxeDL6+vkV2OS8JjAx9/PixyN8oeY0J2h47dgxpaWmYOnUqJk+eXKZsXrFYjG7duiE4OJhcmzdvHurUqQNLS0vIyMggMDCQlNbIycnB7du3iz2CW9Lvr9Zb5cubpN6SDBoZGBiQkyMApOy+8kBF+DiFdc7du3dhYGCAAQMGSJ0iffjwIWia5q1LioJQdokQm5GFT2527doVfn5+sLW1lWpGFRoaik6dOiE3N7dM8RJGBgv70sX5b5KNGvlCMuYyYsQING7cGHfu3EFmZibOnz8PS0tLrFixgvX7SouHDx/CyMiIlZSxf/9+0DQNExMTkikPFNh9x48fh5+fH4yMjMpUboFBYXuDCdr279+fBG2Tk5Olyh79V1EdsC0Bb968IZmJpZkIOTk5CA4OJpOHjyBLKpNPnz7h5s2biIyMJLtUzOczZsyAl5dXmY3O4gqBAwVB4dDQUHK9c+fOxRraxeH+/ftQUFBAWFhYkcdUGHqJiYlo3Lgx0tLSMGnSJNSrV09q15ErJMcyLCwMhoaGUFdXh6KiIrZt2wagwLBXUFCQalBRWjx58gRTp07FsGHDsGLFCpK99+3bN9y9e5cYPMzxrri4OBgbG7OaN5QWQo9lUbLFKNgOHTpgw4YNGDx4MNTV1XH16lWsWLECNE3zCo4BBQHa9PR0lhKfNWsW1NTUUK9ePdJAgJmLHTp0wODBgznT+dk4vnjxQuq4WOfOnUmNMD5z7suXL/j+/btUoFaS13nz5qFp06aYNWsW7t69Cy8vL1IQnyvNP//8E8OHD2fJ2adPn7BhwwapupYikQiqqqpSGZY/Q2V0nb1//z5CQ0MRGBiI9u3b4/bt22SOffv2Tapky+fPn+Ho6MjKsP1VeQOKlpObN2/in3/+QXh4uNQRuSFDhrBqoHEBI1P37t3D48ePi8xss7W1JeUJJk6cCCUlJV7NA9++fYuPHz+y5tWkSZNgZGSEdevWSRnARkZGUrxywa5du0DTNMsQjYuLw4IFC0hNM8nav/Xq1SNrQlnAjGlqaipmzJiBRYsWkRM4UVFRuHv3LgYNGoSBAwfybkLHyIhkBoy/vz+aNGmC5s2bswKo379/R/369Un5GK7Yu3cvgoODoaenh9DQUDKP8vLycPXqVZw/f5612Xnjxg1YWlpyLhdTGfOtKJ0qaecxdJj7rly5glatWnFeuyuDt6L0P4P09HSpzHYPDw+pTCguYGSysBO9bt06dO3aFZmZmTAzM4O9vT0GDhwIR0dHTJ48mRetDRs2sOZ2dnY2zMzMMHjwYFZgIDs7G5MmTUJgYCC8vLwQFhZG5lxZNrQqQkaAgvdiYmICDQ0NTJ06lWyyFP4NV65cgba2NoCCzRo5OTlWwgVfeoXroicmJrL4yMvLQ7t27bBw4cIif1dJePbsGWRkZNC2bVtSxkHyHUj+PyQkBI0bN4a3tzdq1qzJuf8HA8nft3PnTtA0jR49eqBHjx6QlZXF0aNHkZycjJcvX5LGblxRrbcqnrfC19LS0qChoYHY2FgAwNSpU1GrVi1eJR6E9HEK+4rMRtyJEydQu3ZtDBkyhNUkjanrfeHCBc60CqMi7ZLK2Ixk1jfJ8dq+fTtMTExQs2ZNVqkwABg2bBin5smFcebMGRJQl5xrpfXfuIDxg4va3EpKSpKql2tiYsJ7LY2Li4ORkREePHiA3Nxc4jPZ29vDx8cHfn5+LFtu165drI1dvjZs4fVX8u/o6GhYWlpi4MCBiImJgYuLS7EN6f9rqA7YFoNHjx5BSUkJhoaGxCH5WdB2ypQp0NTULPZocXE4deoUaawESE+COXPmwMvLi0zg3NxcBAQE8O4AWJhecYZXeHg4qf/o7+8PbW1tTrtvWVlZCA4OxsCBAwmduLg4XLp0CQ8fPmTde+/ePRgZGWHQoEGoU6cO76PuhXmTVHrx8fG4efMmS8knJCTAwsKiyOyqn+H+/fuQl5dHSEgIbG1tYWFhAQ0NDdbxpMKYPHkyrK2tOS9UlTGWjFw8f/4cd+/exfv378l4LlmyBHXr1oW2tjYZu6ysLKxatYpXfdB9+/ahY8eOaNmyJcLCwlgZAkxDm7Zt25Ksh9zcXPj5+WH27Nmc6BQ3jjExMUUew0hKSsL48eNZ5Um4YuvWrXByckKzZs3g7e1NNnQYSC5WCxcuRNOmTaGkpAQDAwNeu92bN2+GjIwM1q9fL1Vioah6wq9evYKtrS3nnXyhu84+fvwYDRo0QL9+/RAeHg5fX1/Url0bCxcuLLbO3JQpU6Crq8u5ppnQvAHScrJ06VLW56NHj2YdQ8rJyYGfn5/UKYjSgJnbhw4dQrNmzbBs2TIpWcnKykLLli1x4sQJREZGom7duiXqtuIQHR0NU1NTNGnSBIaGhqS+NwD069cP+vr6mD9/PglApqWlwc7ODuvXr+dMCyhoClWzZk0oKipKOeFFZZo+ePAA5ubmRdbqLU/ExcVh9OjRqFevXpFlJkqDwjKyePFi8tk///yDv//+m6XHYmNjYWZmxitDYevWrahXrx6WLl2KadOmITQ0FN7e3kXWk8/JycGXL18QGBiI1q1bc870qIz5VtT6Jvm7CzsVERERcHFx4VxSRWjeitL/hTNfJZGSkgIvLy/emWMlrW8xMTGk2YqrqytpeterVy8MHDiQ80bkjx8/MGbMGFKzViwWIz8/HyNHjoSFhQUJEhV2diX/5tNor6JlBCgIoNrb28Pe3h42NjaYPHkyeX+SdC5fvgxzc3OMGjWqTPZdUfSKWydTUlIwceJEKCsr89IlMTExUFdXh5ubG0JCQsjGanHBj8aNG6NRo0acG/FKNo5kwGSo7dixAxMnTkRISAhGjhzJuq9///7w8fHhHICo1lvC8paXl4cvX75AWVkZ8fHxWLBgAWRlZX95H6c4X5EJLB49ehQKCgro2LEjNm/ejHv37mHcuHFo3Lgx7wZjpUVZ7JLKCOqX5N/MnTsXcnJyMDU1JYkFeXl5CAwMxLhx4zjTAoBLly6Bpmk0a9aM1HaVnGOS8llW/62wH8wkSxSmw+Djx49o3bo1adTGFefPn5eaP7t27YK9vT22bdsGJSWlYhMZuGYqF47NFOZH8u9t27bB3NwcsrKyMDExKXPWd1VBdcC2CCQlJcHDw4NE9u3t7UmNkKImzbVr1xAWFoZGjRpx7kx54MAB0DQNY2NjbN68mVyXpHPw4EFYWVmhU6dOJHhramrK62hXaegx/4+MjMSwYcPQtm1b6Orqcm4IIRKJ4OrqShS6h4cHbG1tUa9ePVhaWrKaZcXGxoKmaSgpKfEKnpbEW1GTPScnB+/fv0dQUBBcXFw4O5h5eXno2LEjunfvDqCA10ePHqFDhw5o2LChVEZTXFwcxowZA3l5eV5HioUayyVLlrACdwcPHoSysjI0NTVRt25dDB48GHfu3MGrV6/QqVMn3u9KEtu2bUP9+vUxb948REREwMfHB6Ghoayg9oQJE2BgYAAtLS10794dtra2rOONpQWXcXz8+DEmTZoEHR0d3nweOnQIsrKyWL9+PVavXo1x48ahTp06GDRoEKs2m2S9QDU1NV7dsoGCDYiWLVti586dAAoc3M+fPxdpYIrFYnz9+hXBwcFwcXHhdVRIiK6zDMaNG4fAwEDWtcjISCgrK2PmzJkshyQ2NhYDBw6EoqIi727BQvJWkpwwNdKWL18OY2NjjB8/Hhs2bICfnx/vdQAoCPDJyclh3bp1RQYyc3JyYGtrC3Nzc9StW5eXU3TixAnUq1cP27dvx/79+7Fy5Uo0aNAA/v7+JKNr1KhRsLCwgJGREQYMGAB7e3tecxv4/1HpmJgYbNy4EXXr1iVGdlFITU1FcHAwPD09S7UGSI4xl/G+ceMGoqKiIC8vj8GDB+P+/fucO7sXJyP9+vWTGqvc3Fx8+fIFQUFB8Pb25ry+Xb9+Hfr6+kSPAAVZJqqqqlJBe7FYjNjYWDg7O8Pc3Jyst1xpCjHfSrO+STbXAAqc0okTJ/LuBC4Ub0Dx+r/wpi7z3OTkZLRp0wZ2dna85ltJeisvLw85OTkwNDSEh4cH62jn169fWUecSwPJZoiSPAAFGfzy8vKYO3eu1GeFef4ZKktGgILg2j///IP58+fD1NQU06dPh1gsZjVVO3nyJGiahoKCAqnjWd70mHI0QMGG1tChQ6GlpcWb3tWrV6Gjo4MlS5bAxsaG1Q9A8qRAdnY2wsLCULt27SI30EuDV69eYdq0aRCJRNi/fz80NDRY2cOdOnUi5a4YmerYsSOGDRvGq/RCtd4SlrecnBxYW1vD3d0dsrKynDeRhfZxSvIV5eXlcfbsWQDAuXPnSHa5vr4+9PT0Sk27suwSoYP6xa1vkrpi7dq1sLS0RP369eHj4wMLCwve9iRQkOhlbW2NwMBAaGlpFZmQJ1l/na//VpwfXFwt4dTUVLRp0wZOTk6cfTdJGRkwYAAUFBQwfvx4DB8+HDRNY/v27QAKmuK1a9eOEx9FoTRxJ8nflZWVBW1tbdjb25c5m7cqoTpgWwQuXLgAb29vXLp0CadPn4a/vz8raCs5Ob59+4Y1a9awmnaVFvHx8bCxscGQIUPQu3dvODg4YNOmTeRzSTorV66En58fnJ2diTFc+J6y0is8ecaOHQuapmFqaso5WAsUGNba2trYvXs3Jk6cCF9fXzx+/BixsbGYO3cumjRpQrI68vPz0aNHD96GBRfexGIxbty4AXd3d1hYWPByMEUiEVq3bi2V4ZmVlYXu3btDUVGRKPa3b9+iX79+sLOz45wxwECosQwODoacnBzOnz+PFy9eoGnTpli5ciUePXqErVu3wtnZGYGBgUU6gHxw7do16OrqsgIDJ06cQP369aXG6vz585g+fToGDRqE6dOn85oDXMYRAG7dulWmBnEjRoxA7969yd85OTk4duwY5OXl0bdvX3JdLBaTzEItLS3ei9Tp06dhZWUFoKA0hpubGwwNDaGkpIR+/foRozsrKwtbt26Fp6cnK8jC5aioEF1nJTF06FC0bdsWAHsTZv78+WjQoAHZZf78+TOWLVuGtm3b8jpOWRm8lSQnzCkHoCDL1srKClZWVujevTuvLu7Mb+vXrx/69evH+kxSB+bk5MDc3Jxks/DB9OnTWc1egAK5VFFRgZeXF9m0OHXqFCZNmoTevXtjwoQJvOb2pk2bWEelk5KSYGlpiT/++IPVoAf4f0M6Hx8f1vpWEr2imiKVZs1ITk7G0qVLERQUhFOnTiExMRHbtm3j3KyktLokNzcXly9fhoeHB4s3LuvbwYMH0blzZ7x+/Zo1bhYWFsTRknxeUlISduzYwUtvCTnffra+ubi4IDg4mGRbx8XFYeTIkTAwMOC1dgutS7jo/507d8Lf3x9WVla89AhQskwypwHevn3L2nwtKjngZ5AMIhR2mJmxGzlyJFxcXJCUlFSmUmFCy4gkRo0aRTLBpkyZAhsbGxgbG6NGjRrkdMzHjx95r2186AHA33//zbseo1gsxpcvXxASEoLk5GRs2bIFtra26NmzJ0JDQzFv3jxSJuHr168YOHAgr2ZfDJYsWQI9PT20a9cOMjIy2Lp1q9Tn6urq5DTClClToKqqyjlrslpvCcsbUwLh27dvUFRUhKys7G/h4/zMV2zYsCEJcn7//h3v3r3D48ePS50RXdl2iZCJDSWtb7169SJj9vDhQ6xZswaTJk3C0qVLedmTDB49egRjY2McO3YM3t7e0NbWJtnCkvJeFv+Nix+clZWFLVu2ICAggLPvdubMGfJ+p06diuXLl+Pbt28YP348nJyc4OXlhePHj5PndenShVdZB0lwjTtlZGTAxsYGmpqa1cHaQqgO2BYDyeORJ0+eJEFbZieHWaiAAiXLNVsGKOhE2bNnT9y7dw/Pnj0rUpglAxOZmZmsrDyuQlwaepIKlAkS85k0zPgMGDAA/fv3R5s2bVjHLb58+YKhQ4eiS5cu5HhUWYqBc+Xty5cv+Ouvv8qkEEJDQ2Fvb0/+ZhRPSkoKPD094eHhQXh7/fp1kVlspYGQYykSidC7d28oKSkhKioKgwYNYn1++vRp2NvbY8SIEeR+vhCJRNi1axd69eqFd+/esd6Pubk52Xku6ThERctkWSAWi0mtVclrQME4ysjIYMaMGazvbN26ldfmCIOdO3fC1dUV+fn5MDIywh9//IGjR4/i2LFjaNSoEQl4AgVHXyQD31zpCdV1lsHs2bOhoqJC6p1KHnMdOXIklJWVSWDg27dvRZZ/KC2E5K00ciJZOzw5ORnfvn0j9/CRE7FYDEdHR0yYMAGAtL5gslKjo6N5lwIBCnbv3d3dyd+MbD969AiKiopS+kUSXPj6+vUrBgwYgKNHj7Ku9+/fH8bGxkU271m1ahWrAVlJ9Bg9l5aWBh8fH1ZDwtLowIyMDFaWl2SWYGlQGhmZOXMm+ezp06fYtm0b57nNyMGdO3dYp0REIhHy8/NhaGgoFQAp7hlcINR8K8365uDgQNa3lJQUXL16lXNJlcrgDeCm//fv34958+bx1v8/k8k6deqwZJIvzpw5g9GjRyM3NxfDhg2Dvr5+kUGFU6dOoXbt2qQbON+gbWXICDP2O3fuRKdOnch1ExMT1K1bF/369SNykZ+fz/IDKpoeVxQ37g4ODjh//jzEYjH27t0LVVVV0DRNAnFFlTPgC6bBqr+/P7EDmOfHx8ejV69eoGkaRkZGMDY25h1or9ZbwvOWnp6O+fPns0rblTe98vJxGPzMV3R3d+flc1SmXVIZiQ0/W9+CgoKK/S4fu0QkEuHHjx9o06YNPn/+jLt37yIwMBDa2trQ0tLCtGnTWO+Nj/9WWj9Y8vr69etZiQ2lofXx40eYmpqiVatWGDZsGOrUqcNKMszOzmbxkpGRAXd3d3Jqhe96yjU2AwDHjx8vkx9cVVEdsC0lTp06JZVpO2XKFM5HhAo7jpI1XB4+fEiEeePGjeR6UYGH0k4ePvQkG8BIHtX+GTIyMvD161eWwbV37140aNAANE2TRigM5s2bBysrK7K7zhVl5Y0B3+DmsWPHYG5ujtmzZ5PxYcZr586d0NXVxatXr3g9W+ixlIRIJELPnj1B0zQMDQ2l6gytW7cO9evXZx1x5AJJ2b169apU7dS8vDzo6elh3759vJ4vicqSSQbbtm1D48aNi2z6s2zZMujr6xfZoKe0i1RhegkJCZCVlcW4cePQrl07VmbTo0ePim2uxHcOCNV1FijQgzY2NvD09CQOK/Pv69evoaGhQXaHywMVyRsfOSmurAVf9OjRA1ZWVmQMGRl4/fo1IiMjy+TsMWCCiZLdZpkxO3z4MJSUlKSOcnKBJP+SeorRw+/evYOSkhIWLFhQ5HcYlEb+MzMz4e3tDXl5eTg4OGDWrFlS9CSxfPlyzJ8/v9jf+zPwkZGiguulnduSm86SDVzE4v93bjY3NydH2sRiMbp06SLVkZkvhNAlQOnWNzk5Od4brEWhonjjo/+Z446S4NuEi+/6xgWzZs2CiYkJbG1t0ahRoxKfxwRGCjf35IqKlpGi7BKg4P25uLgAAHr16oUmTZqgf//+sLOzw6hRowSnx6XXQnEBopycHIjFYrRt25Zk53fr1g0KCgowNjZGt27dymWDXJLW6NGj0a1bN1hbW2Ps2LFSdUDT0tLw77//4tatW2We59V6S3jeyhrEqWgfpzBK4yvyzWAX2i6RREUH9fmsbzt27ODFS2F6kv/39fUl/ujVq1ehoKCAOnXqkHIcheWxNPLJxw/ma7sy342Li4OSkhLq1q1LNsoK697s7GzcuXMHXl5eMDEx4Z0QIvlvaWIzRSU9Vgdr2ahBVYN68eIFtXz5cmrWrFnUiRMnqOzsbPJZfn4+RVEU5efnR40YMYJSVFSkBg4cSHXr1o2aM2cOJSMjw4kW8zyapimKoihFRUWKoihKJBJRBgYGVEREBKWnp0dt2bKFio6OptLS0ig3Nzdq+/btrOcw368Ieq6urlR0dDRFURRVo0YNCgBVq1atEukkJiZSbdq0oRwdHamAgABq4sSJFEVRVGhoKLV06VKKoigqKiqK+vfff8l3vnz5Qunp6ZWKj/Lkbdu2bazn1KxZ86e0Xr9+TW3dupXavHkzFRsbS1EURXl5eVGurq7UkSNHqFWrVlE5OTlUjRoFU8rAwIDKy8ujsrKyOPNVGWMpiRo1alDR0dFUeHg49fTpU+r8+fMUAPK5hYUF1bhxYyotLY3X85n3RlEUZW9vT/n5+VEURbFo0DRN5eXlkb+HDBlCnTt3jhOdypRJBtbW1pSVlRW1fv166vbt26zP7OzsqKSkJColJUXqOT+bb8XRMzY2poYOHUrt3buXun//PqWsrExRFEXl5uZS2tralImJCZWamir1nNLMAUkw72rHjh3UyJEjKQcHB2r+/PnUunXrqMzMTCohIYGSk5OjWrRoUWpdJYnv379LXWvQoAE1ceJE6vv371SXLl2onJwcqm7duhRFUVSdOnWoevXqcdbHRaGieaMofnLy48cPqefwoc/w17lzZ0osFlNjxoyhcnJyiAxs2LCB2rlzJ2eZYCDJm7W1NdWzZ09q2bJl1NmzZymKoqjatWtTFEVRRkZGVO3atYvkiw8tRv9T1P/XLUVFRSogIIC6dOkSlZGRQQGgaJpm6RqKKp3879mzh8rIyKB27NhB2dvbUydOnKAiIyMJPZFIRO4ViURUWloaFRERQb18+ZJc5/K++MjIt2/fpJ5TGt7+/vtvatq0aVReXh41dOhQytvbm8rMzCS/mfnd8vLylKysLEVRFOXv70/duHGDcnR0LDVPRUGI+SaJ0qxvampqvNc3SVQ0b3z0f1HzrbRzvbzWNy6YOnUq1axZM+rmzZtUUFAQ1bhxY4qiKKk5TFEUZWZmRjVt2pSSl5cvE82KlJHCdsmkSZPIZ3Xr1qW+f/9OeXl5Uf/88w/1zz//UFFRUZSjoyN1584dXjJSFnpFjXFREIvFVI0aNagXL15QS5YsoZYsWULt3buXoqiCtZmmacrFxYX68OED1atXL+rChQvUqVOnqAkTJlA3btygwsLCOPNVGPn5+YTW0qVLqV27dlHt27enzp8/Ty1fvpx6//49uTclJYVydHSkrKysKFVVVV70qvVW5fFWWvu4LPT4+jh8fUVmveUKoe0SSejo6FAuLi5Up06dqBUrVlCrV6+m6tWrR02cOJG6d+8e1aVLF2rt2rVUXl4eVaNGDUpBQYHT8/msb0X5Dnzo0TRNicViiqIoSkFBgUpPT6coiqIGDRpEtWjRgnJ2dqa6detGPXv2TEoeSyOffPzgsLAwYkczKM3azdi9NWvWpJSVlSltbW1qxIgR1I8fPyhZWVnWb/nw4QO1d+9eSlZWlrp9+zZVq1YtlgyVBnxiMy4uLlJxrrLO8yoHIaPDvyISExOhqKgIR0dH2NjYoEaNGujVqxcra0Qyyn/8+HEoKipCUVGR8zGaP//8E+3bt4e/vz8GDBiApKQkVuMFZjfi0aNH6Nu3L+zs7KCpqYkWLVrw2sUUit7z58+hpKSE8PBwbN26FcOHD4e+vj6sra3J7s2GDRugqakJU1NTBAcHo0uXLmUqhi/kWCYkJEBZWRmWlpbQ1dVFjRo1MHr0aHz+/BkZGRkkIyEsLAzp6en49OkTIiIiYGxszLkza2WMZXHIz89Hr169IC8vj3379iEpKQk5OTkYO3YstLW1OWVfMJB8b/3790dSUhLZIZTcPXR0dCTZkt7e3tDX1+e021bZMtm/f3+SDXD8+HFYWVmhS5cupNkZAHz48AGtWrViXeNLb8CAAeRo0+3bt+Hj4wOaplk7mPn5+bC3t2d16qwIlKXrLFDQUVdGRgZTp04l15i5nZOTg+3bt8PCwgImJib4999/ERsbi6lTp6JJkya/dEddBkLLSXHIycnBwoULYWtrC2NjY/Tv3x9BQUFQUFDg1ajt4sWLJFtXcq5eunQJbdq0gbOzM44cOUKup6amwtjYmHWNC0paAyRx+vRp0DQtlcHAFSkpKVi9ejWAgjJIo0aNgp2dXbEZLd+/f2c14OACoWXkZ1mMjG52cHDAhg0b0LlzZ1Yj0rKUMyoJ5THfikNFrG9cUFbehNb/laG3cnNzkZWVhUmTJmHkyJFSGZNFyR0zB8vjKHN5y0hxdomdnR2xSzp27Ag9PT2p03t8sv2EpJeYmAgFBQW4u7vDyMgIioqKCAwMJEfX165dC5qmoa2tTY5FZ2dnY8eOHUU28uECyRqVU6dOZTUhmj17NiwtLTFmzBg8fvwY06dPh5aWFueyNFxQrbf4QWjeypuekL4iAyHtEkkwNkFqaipmzJiBRYsWYc+ePYiLi0NUVBTu3r2LQYMGYeDAgbwyJStzfWPsSWZ92bFjB8LCwmBkZAQXFxd8/vwZcXFxsLKyYjVQ5EOrIv1gBpJr4tevX3Ht2jXY2NjAwsJC6jRKfn4+kpOTeZdcEzrO9V/Cfzpgm5GRAR8fH4wcOZJcO3/+PExNTREYGMhy8BiBGzFiBOrVq8e5g+nu3bshIyOD8ePHIzw8HAYGBtDU1MTevXuJ4SA5Ua9cuYI6derw7pInJL2tW7fC1dWVHCPPz8/H5cuXYWRkBBMTEzJ2Fy5cwPLly9G+fXv88ccfpLQEVwjJ248fP+Dg4ICxY8ciLy8P3759w/79+yEvL4/Q0FC8efMGmZmZmDt3LkxNTVG7dm2Ym5tDTU2NV3dRocfyZxCJROjVqxdq1KiB5s2bY8CAAWjevDkv3kr73sRiMezs7LBz5060b98eenp6nAMDv4JMamhokKPgR44cQevWrdGqVSvMmzcPe/bsgZeXF6ytrXk5lkXRa9q0KTk+ExcXh+DgYNA0jcGDByMyMhLe3t6wtLQs1RhWVtfZ9+/fw9raGiYmJpCVlcW0adPIZ8zvzs3NxfXr1xEUFIRGjRpBV1cXBgYGpS5PU1m8AcLLSXHfkawXGBMTg/DwcLRr1w4jR47k1Wxj7969oGkaFhYWJGgraXydP38e3bp1g4qKCkaPHo2lS5fCy8sLZmZmvIJ9XHQJUNAZ3N/fv8xHpQvXQR89ejRsbW1ZzhFTQ7O47/0MQssIg4CAANA0jd69e5NxkvzdeXl5sLa2Bk3TaNWqValrjFXmfPsZyrq+VRZvFa3/S0OvomWyqN85Z84cWFhYYOzYsaySLYXLxZSlVExhlKcNVJJdYmZmBpFIhC9fvpAa4sw9fCEUvczMTHh4eGDIkCEACoJIt27dQsuWLWFpaUnWlKlTp5KGYuX1jiSPmJuYmMDX11eKh4ULF8LCwgItWrRAkyZNcO3atZ8+t1pvSeN34K2y6AntK0pCCLuEC8oa1P8V1jdNTU3s2bMHmZmZOHbsGKmJLVnz9+HDh5zXNyH9YMl7Hz58yKoXfvHiRdjY2MDa2prYe3369GEFwbnKh9Bxrv8a/tMB2/z8fFhbW2PhwoUA/i9IN2/ehIODA9q1a8cyBK9evcopMMA8MyMjA56enizlCQAhISFo0aIFtm/fzqqZmZycDBcXF1b9EC41LYWkBxQY0U2aNJH6Hbdu3YKhoSH8/f1L/aySUBm8paenw9TUlHRvZGTkwoULUFdXR48ePSASiZCbm4sfP37g4MGDiImJYdUA5IKKHsvCi0tpFpv8/HyMHj0aNE3jwIEDnDMvSvvemCZSWVlZMDU1JU0h+BQf/1VkUktLC7t27QJQ0Al06tSpUFVVhbOzM4KCgjgvwD+jp62tTWT1zZs3iIqKgoWFBQICAtC7d+9S0ausrrMikQhbtmxBx44dERsbi02bNqFWrVpFBm0Z3LlzBy9evCi1TFYWb0LKybNnz7Bq1aoS+SvPjMgbN27A3NwcvXv3ho2NDWxsbMi4SDaGe/78OTZs2AAjIyN4e3sjNDS03OW/qDUAACZPnoyAgADOBijzu9LT01mOKfOcz58/Y/To0bCzs8Ps2bOxbNky0DSNW7ducaYltC5hwCWLsUePHnBzcyv1Wir0fBNyfasMXSKE/udCr6Jkkrk/MTERQ4cOZWVgzp49G9bW1ggPD8fNmzfh7e0NR0fHUj+7MmwgBiXZJfr6+ggODub13Mqmx2S3STr7QEHzTyajtzz6KxSGZLDWysoK3t7eROY2bdrEOily7do1nDx5khWcLg7VeksavwNvlUGPgZC+ohB2SWUE9X+19a158+ZkfTt16hQrWCuJ0shYZfjBkutow4YNWXTz8vJw4cIFcqrK2dkZmpqavGvWCh2b+S/iPxuwzc/Px/fv3+Hg4EA6Zefm5pKJd+3aNaiqqrKCBUBBN2o+tOzt7Umhb0mhDQ0NRbNmzZCQkECuJSUloWPHjry75AlFjxmruLg46OrqSh1FyMvLw759+2BiYoKYmBgAZd/RE3Is8/Pz8fHjR2hpaZEAiKSMnDt3DjRNY926dWXiCRBmLBkafAI6ubm5GDRoEO8GIlzeW3Z2Ntq2bYvWrVtzVuS/okw2adKElZH/48cPZGVl8T5y8jN6GhoarO6fhY+ZlESvMrvOAgWyefjwYQAF72Xjxo1SQVuxWMy74ytQebwJISdPnjyBsrIy1NTUMGfOHHK9OP4uXLjAiYeisHfvXvTp0weJiYmIiYmBpaVlsUFbAFKOe0Wub0U5paWd74yM3bt3D+3atcP27dtZTTOY5yUnJ2PcuHFQVlZGjRo1sGfPHk78cOGtInRJYRSXxfj06VMkJiaWuhGp0PNNyPWtMnVJRep/PvQqSiYTExPRoEEDjBs3jnUdABYvXgxbW1toamrCwcFBSscUh8qygbjYJZcuXeL8/F+BnpGREcmwBf6v99+/fw81NTWEhYWVmY4kCgdrJRtg9ejRA3p6evjw4QPn51brreLxK/NWGfQknyWUryiEXVJZiQ0M7V9pfVNXVyfrm5Dxi7L4wQwtoKBMh4qKCsaMGQOg4D0ySYdisRjPnz/H7NmzMW3aNFZpmYrkDSh7nOu/iP9cwLaws7hp0ybQNI3z588DKBAaRnDWrVsHFRUVfPz4kbcwMUrO398frVu3LvJ32NrawsvLq8jvc6UrBL3Cju/Hjx/RoUMH+Pr6knFkkJKSAmVlZaxcuZITHyXRreixLHxsdtasWWjQoAFRcvn5+eRZkydPhrm5Ob59+1amI8sVPZbM8yUDOrNnz5b6HYVR+DfwAZ/3du3atSKNhp/R+FVl0sPDo8jv8w26/4yep6dnkTRKQ0/orrMlZd7k5uZKZdrm5uZi3759ePToUalpMKisjrpCyElKSgqCgoLQrl07hIWFlVjHDAD++ecf0DTNcmy4QPI3MUf6xGIxzp07R4K2jEPH6MzChmBFyb+kLpHkm6uOvn//PhQUFDB06FCWk8CA+f2zZs0CTdM4evQouV5RvJWXLuGSxejp6Um6yUv+1p9BqPlWGetbZegSIfQ/H3rlLZNMLcixY8eS3zFt2jTWibdHjx7h+vXrpbYTKkNGhLZLhKBX3Dht374dGhoa2LJlC7nGBG3Xrl0LU1NTJCUllcsx7JKCtX379oWOjk6ZyoRV663/43fhrTLGUkhfURIVaZdU5mbkr7q+SdLjCyH8YAaF19HRo0eT32BmZoYePXoU+7yyJMMIFef6r+I/FbB9+PAhunXrxlLQWVlZ6Nu3L+rXr48rV66w7j948CBatWqFHz9+cKYVExPDUuZxcXFQU1PDqFGjyDVm1+nw4cPQ1tbGq1evONMRmt6jR48wZswY9O7dG5MmTSJG0cOHD2Fqago/Pz+cOHGC9Z3WrVuzjDiuEHIsExMTYWlpSbL8AODly5do164dTE1NSWCCUVArV66ElZUVL4Uj9FgKHdDh894KN58ojYFQ1WVSaHqbN2+Go6Mjjhw5Qo5VScqJ5IKen5+PyMhI0DTNq3FIUTq5sNGVk5NDgrZTp07FyJEjISsry6vBmJC8AcK+t/T0dAwZMgSHDx9GcnIyxowZU+IcT01NxdKlS6XqP5YVIpEI58+fJ0HbrKws5OTkYMiQIawddi4Qer4xR7wCAwMxdOhQqc8kcfjwYdA0TeqrcQ3WCs0bULFZjJIQcr4Jvb4JyVtVXm8YlORkmpiYICAggJW5W9R3fwYhZURou0QIesz4vH37Fv/88w/2799P5OTTp0/o27cvbG1tsXv3btb39u3bh5YtW/JurFTUOxeJRDA0NISvry8rWNuiRYsyr2nVeuv///+deBOSnpC+IgOh7BKhNyOr8vomlB/MQNK2K7yOWllZwc/PD6mpqeT+smygVYad8F/GfyZgm5eXh86dO0NOTg69e/cmx6GBAkOnW7duqFu3Lvbt24cvX74gPz8f48ePh5WVFedGJevWrYOioiKrflJGRgaWLFkCXV1d4hwxOHPmDAwMDPD+/XtevAlF7969e1BUVMSAAQPQo0cPBAQEQE5ODnv37gVQ0PjBwcEBjo6OGDlyJI4fP47hw4dDQUGBd1dKIceS2bUcMWKE1LGYCxcuICAgAC1atGDJztixY+Hu7s658H5ljKWQAZ1qmSw7b5VFT6iusyXp5MJGRG5uLjZs2ACapqGoqIibN29ypgcI21FXyPfGjFdaWhr5/6dPn4hDNnPmTHKv5K43H2MtJiYGq1evxvjx40kzmcIGZX5+Ps6fPw9ra2tYWVnB0dERjRs35rV7XxnjyPBgampKjjEWHitG52dlZeHy5cvkHi5jKvTcBio2i7EwhJxvQm9YCMVbVV9vgJ87mf7+/rwSJwpDKBkR2i4Rgh6j1+Lj46GhoQEDAwOoqKigcePGWLduHVJTU/HkyROEhobC3Nwcy5YtA1Aw5hEREbCzs2Md3S4NSgpUHDt2DJ6enmQjqbyCtUC13vpdeROKnpC+IiC8XSJkUL8qr2+VsZYCBeuokpISqwyClZUVfH19WcHasqCyePsv4z8TsAWAESNGwM7ODo6OjggJCcG5c+fIZykpKRgzZgxq1aoFPT09WFlZoVGjRpy7N65fvx41atQg3XMlkZSUhPnz50NDQwMhISG4desWbty4gcDAQHh5efE6JiEUvczMTAQGBrJ2Tl6+fIlmzZpBXl6eNBt4+vQppk+fjlatWsHExAT29vasCf0r8gYUZPF16NABAwcOBFCwwN2/fx8nT54kCu7evXvo0aMHatSoAVtbWzg7O6Nhw4ac+auMsRQyoFMtk7/f/C4MobrOFtbJhWuqStZU6tevH+Tl5cvskAnBm5DvjfldkkY0M24Mf4zBnZubi379+mH48OGcaDDYvHkzNDQ04ObmBkNDQ8jJyZGsWeZ3SI7TwYMHQdM0HBwceDUgqgz5//TpEx4+fIjs7Gw0btwYK1asAMAOVr558wZ79+6Vyhj7VWWEgRBZjIUhxHwTcn0r7jdWBG9Vdb0p6hhrQkICZGVliaNX3k6mUDIitF0iJL2vX7/C1NQUERERSEpKwo8fPzBixAi0bNkSo0ePxrdv3/DixQtMnDgR9erVQ/PmzWFpaQllZWXOvhQjT0+ePMGUKVMwYcIErFixQip4BQBdu3aFnp5euZ4WqdZbvxdvQtET0leUhFB2CSBcUL+qrm+VwRuDlJQU2NnZVWiwtrJ4+6/jPxGwZZTV+vXrsWrVKly/fh02NjYIDQ1FQkICZs6cSQQ5NjYWW7duxdatWznvFu3evRs0TePMmTMACo4NnTlzBuvXrydFqzMzM3Hq1CkYGxtDTU0Nenp6cHFxIc4sF2EWkt63b99gbGxMduwZQ6lLly5wc3NDrVq18O+//5Jn5ufn4+vXr5wb81QGb8yzbGxsSMDIw8MDVlZWoGkalpaWmDNnDnnu0aNHMWfOHCxbtgxPnjzhzJvQYylkQKdaJn/P+S1E11lJlKST7927h+nTp7Myqo4ePYpmzZrxyqwVmjch3xvz+2JjY7F//35W7TDmM2aOOzg4wMzMDLKysrh27Rpnvo4dO4ZGjRph//79yMzMhEgkQocOHUhn7sJjlZqaCgcHB7Rq1YpX0wSh5xtQ0DDJzs4OPXr0wPv37xEZGQlVVVWpjYRly5bBx8en2K7BvyJvQmUxVpYuEWJ9E5K3qrzeFIW9e/dKyWR5B2uFkBGh7RIh6b169QrNmzdnJbsAwMKFC2FkZIQpU6YgNzcXOTk5ePLkCVasWIHdu3fj+fPnvHi7f/8+5OXl4evrC3d3dzRs2BD29vY4d+4ceX9XrlxB8+bNWY3v+KBab/2evAlNT0hfkYFQdokkKjqoX5XXN6Fo3bp1C2fOnMHp06eRkpLCus48w8LCQqoMQllQ2XbCfxn/iYAtg1OnTsHHxwdAQXq2s7MzWrRoAZqm8fLlyzI9++PHj3B0dIShoSGSkpLw9u1bWFlZwdDQEGpqapCRkcGsWbPIzpdYLEZ8fDweP35MhJeLMys0vfT0dLi5uWHEiBFk0r169Qpqamo4d+4c2rZtC29vb2RnZ5d5MgrNG1Cwa+rg4IB9+/Zh2LBh8PPzQ0JCAl6/fo1Ro0bB1taWZCqUFUKOpZABnWqZ/D3ntxBdZ4tDSTr59evX5L63b9/yOkojNG9Cvjdm/h44cAANGzbErFmzSImCwve8f/8eurq6UFRU5FVH9tu3b+jcuTMiIiJY19esWQNLS8siv3Pw4EEEBgby6gJbGWsAg3Xr1sHY2BjDhw/Hnj170K9fP2hoaCA6OhqHDx/GokWLICsri4MHD/J6vlC8VUYWo9DzTcj1TUjeqvJ6c/jwYfTp0wddu3bFzJkzpTK6RSIRDAwMEBAQUK7BWiFkBBDWLhGa3uPHj6GtrY1jx44BKDh2zYAJIkkeA+cLsViMnJwctGvXjmQy5ubm4tOnT7C2toaFhQWOHTtG5mRZN5qq9dbvyVtl0BPSV5RERdslQgb1q/L6JhStqKgoKCoqwszMDDRNo3Xr1qQMDfOM9u3bl2uwtjLt8mr8RwK2TP2WmzdvwszMjFz38vKCjIwMvL29eStvSRw9ehTBwcFwcHCAsrIy/vjjD3JEZ/HixWjQoAG2bt1a5Hf5GFJC0JNUxJGRkbC3t4evry/mzp2L+vXrIywsDACwYcMGGBsbIzMzkzMfRUHIsczPz0dubi4CAgLQvn179OrVC3/99Rf5nDkCExgYyJ8hCD+WQgZ0GFTL5O81vxlUZNfZoiCUTgaE503I93b9+nU0atQIUVFRxTaFysnJwahRo1jlC/hg0aJF2LZtG+vav//+Cy0tLaSmppJggSSY8eNjpAkxjoyTUvi4f3R0NIyMjDB69GgcOHAAM2fOhLKyMpo3bw5ra2scOnSIxd+vyFtRqMgsRgZCzbfKWN+E1CVVcb3Zvn07ZGRkMHDgQHTr1g1KSkqwtrbG33//TXTEsWPH4OPjUy7Z3kLKSGXZdxVFrzh59ff3h7GxMflb8nh5QEBAuXRUZ+Dl5YWpU6cC+P8akpmZCVdXV5iZmZH6oeUR+K7WW78fb0LTE8pXFNouqYykjaq4vglF686dO1BRUcHu3buRmpqKx48fo2/fvjA3N8eECRPIfefPn+d9cqM4VJbtWo0qGrBNTU3Fhw8f8PXrV5ajmJubC39/f6SmpqJHjx7Q0NDA6tWr4eLiAl9fX8TGxvKiJ6lUjx8/Dnd3dwwePFjK4OzRowfMzMyQn59fphpEQtB7/PgxRo8ejQ4dOmDx4sWE7urVq9G+fXsEBARg0aJF5P4dO3bAzMyszMpBqLHMyMhAamoq67t3796FgoICaJrG2rVrWfdHRUWRjudcUVljCQgX0KmWyd9rfjMQi4XpOiu0ThaSNwZCvzeg4Aicm5sbSy8VNory8vLQqVMnckyKK0oysq5evYpmzZqxGnNeunSJFRz4lccRAJ4/f45Fixbh7du3rOtRUVFo3rw5wsLC8P79e6SmpiI5OZlk8PzKMiJ0FiMg/HwDhFvfhOStKq43YrEYKSkpsLe3x/Lly8n1b9++wc7ODhYWFjh+/Djv5nYloaJlRGi7RAh6jM7/+vUrXr16hRcvXpB3w5RFaN26NbmfeV8zZ87kHbCVlKn8/HyIRCJ4eHigY8eO5Drz/rKzs6GtrY3OnTvzolWYbrXe+v14E4qekL6iJIS0SwDhgvpVcX0TmtaRI0egq6vLsrvfv39PapRHRkby+v0lQej3Vg1pVLmA7b179+Di4gJjY2Noa2tjzpw5+PLlC4CCnVlbW1uoqamhSZMmuH37NoCCHX1vb28pxfgzSAqqpDBfuXKF1IyS/Cw8PBzt27fnzZtQ9BITE6GiooJ27dqha9euqFWrFsvIBgoWMUkMHDgQwcHBrN12LhByLBMTE+Hk5ARzc3MYGhpi0aJFZKd+3759UFRUhJubG6tAPEOvqCyyn9ESeiwB4QI61TL5+81vQNo5qsius0LqZKF5AyrnvTFBtuHDh8PBwYF8Ljm34+Li8OHDB150ADZfxY1HTEwMWrRoQf52dXWFg4MDL0NN6PnG4OrVq6hbty5mz54tVXZj8eLFqF+/Pvr161ekE1NaCMlbZWUxMr+/oueb5HMren0TkreqvN4ABbrexMQEmzZtAvD/4FtGRgacnZ1hbm6ON2/eACifTBwhZERou0QIeszYJCQkQE9PD8bGxqhZsyZ69uxJsgnPnj0LTU1NODk5ITk5mdjFAwYMQJs2bZCTk8NpTjM0P378yNrsu3DhAuTk5LBkyRJyjfn82LFj0NDQwOPHj3mf8mFQrbd+H96EpCekr1gYQtglgHBB/aq8vgnN2+XLl6GpqUmezbyDjx8/YsyYMXB0dCS+VFlRWXZ5NaRRpQK2Dx8+hIqKCkaNGoWzZ8/ijz/+gI6ODs6fP0/uiY6Ohpubm5Ti5rrbvWvXLnTs2BH3798n10rqopyVlQUfHx+pGoC/Gr2vX7/CwsICkyZNItfCw8NZHTYlcfXqVYwaNQry8vK8d0qFHMuXL19CSUkJQ4YMwYEDBxAWFgZra2t4e3vj7t27AIC//voLioqKsLS0hK+vL7p3746GDRty5q8yxlLIgE61TP4fv8v8loQQXWeF1MmSEKqjbmW8t8uXL5PsiuPHj6N27dpSzR/S09MxduxYHDt2jJfzVRRfReHKlSvQ0dFBeno6fH19YWhoWGyWC1d6FT2Okrhy5QqaNm2KmTNnspyjkydPonnz5jAwMPjpWBQHoXirzCxGoeabkOsbAyF4q+rrjUgkQm5uLiwsLNC3b19yndEVmZmZ0NDQQL9+/Xg9XxJCyYjQdomQ9D58+AANDQ2MGTMGd+7cwcGDB+Hr6wsLCwtSn/Pff/9Fq1atoK6uDk9PT7Rp0wb169dHfHw8J1qSAeKWLVtiwYIFZG79+PEDU6ZMQfPmzbFq1SrW986ePYuWLVvi3bt3nOhJolpv/X68CUVPSF+xOFSkXSJkUL8qr29C0UpJSSGy/erVK+jr62Po0KFSftK7d++gqqrKqmfLF5Vtl1eDjSoTsP3+/TsCAwMxZMgQ1nUPDw906dKF/P3t2zd8/fqV/M0oHC4L1PHjxyEvLw+aphEYGEh23Ip6TkZGBh49eoTAwECYmZnxcpKEpPf8+XOYmpqyurF3794d3t7ecHNzIwYcUDBxd+/eDUtLS7KA/cq8AQXHSdzd3VnXDh48CD8/Pzg4OJDdytevX2Pq1Kno27cvxo0bR2q0cIHQY8lAiIBOtUyWD2+VQQ8QpuuskDpZEkJ11K2M9wYUBLjV1dUxfPhwnD9/HgMGDICenh5Onz4NoGDcp06dCjU1Nbx48aJc+SqM27dvQ0tLC9bW1mjRogWvBmOVNY6FcfnyZeIcMeO2fv16LFu2jHdTUqF5EzqLERC+g7UQ6xsDIXiryuvNzZs3WRmfJ06cgLy8PMuZZDImN27cCAMDA3z69KnMRymFkBGh7RIh6f3zzz8wMTFhdR5PTExEeHg4DAwMWDUKFyxYgLFjx2LixIlS9UNLi0ePHkFRURHjxo2TCrC9ePECY8aMQePGjTF58mT8+PEDycnJmDJlClq1aiUVbCwtqvXW78mbUPSE9BVLQkXYJQyECOpX5fVNKFo7duxA//79ER8fT+y2I0eOgKZpREZGSp2eaNu2LUaMGMGJl8L4VezyavwfVSZg++TJE4SGhuKff/4BAOI4zpw5k1UDqaz4+vUrBgwYgAkTJuDOnTtQVlaGj49PsU7tqVOnYGJiAldXV/KbStqhqGx6T58+BU3TWLp0KZKSkjB79mzIyspi/PjxWLBgAdTV1aWOe0jWUeECoXkDgFWrVkFdXZ0cyWZw8uRJ+Pj4oGfPnqxaQAB/x1bIsZRERQd0qmWy/HirjDnAoKK7zgqlk4tCRfNWme8NALZu3QpDQ0OMGjUKW7ZswYgRI1CrVi2YmJjA0tISampqiIuLqxC+JI21CxcugKZp2Nvb8wrWVvY4FsaVK1dgYGAAKysrhISEoHbt2jh+/DivZwnNm5BZjIVR0fNNEhW9vhVGRfJWldeb6OhoqKqqYvPmzeQ7ycnJiIiIQIsWLbBy5UrW/Tt37oS5uXm5lOkQQkaEtu+EpHfp0iUoKiri6tWrrOtPnjzBwIED4ebmVm5HbgFg1KhR6N27N4ACPXb69GmsWLECt27dQlpaGtLT07F69Wo0bNgQzZo1g5GREdTU1Mr8G6r11u/JmxD0hPQVf4bytEsYCBHUr8rrm1C0oqOjISsri1WrVrE20ABgzZo1oGka48ePx7NnzwAUBE+trKwwe/bsUvFRmbxVgxuqTMAWACmCDfxfWNavX4+goCAA/1eqZTGaMjIycPDgQaLUXr169VNhPnHiBO9jiELSk6wJRNM0/Pz8ICMjQzpPAgXHlmiaJkGYskDosQQKjlEZGBjg6NGjUovrhg0b0LRpU3Kki/m8LPWxhBrLwqiogA5QLZOF8TvMb6G7zjIQQidXBm9Cy8nz58+l6pht3rwZxsbGGDFiBG7duoVLly5h/vz5iIqK4u2k8OFr3rx5hJ9fVf654MGDBxg/fjz69OlDHMBfWUaEzmKsLF0iiYpa34TmraquN/v27UPdunWxa9cuqeY7L1++xNixY6GsrIzx48fjyZMnePLkCQICAhAUFFRujUoq0gYS2i6pSHpFjffjx49hbGyMGTNmSGVv3bp1C+rq6oiKivrpc0pL38PDg2T3ubq6wsrKCurq6mjWrBl69uyJp0+fAigo1bB3714cP34cr1+/5kSnWm/9nrxVFj2hfMXSorzsEklUdFC/qq5vQtF6+PAhDA0NsXPnTgAFpxETExNx584dUqZi586dUFRUhKOjI1xdXeHs7AxjY+My2ca/ol1ejd88YJuVlYW8vDwiuAwkleuiRYtgbW1Nrs2dOxeDBw8uU0FwxjlilOXLly+hrKwMb29vPHnyBEBBAIJRqgz47jgITQ8oOAbx4MED2NnZEecuNzcXDx8+RKtWrVjHssoCoXiTlImAgAC0bNmyyNo/zZo1K9POVFEQYiyFCugwqJbJ329+C9F1trJ0stAddQHh3ltKSgrU1dUxadIkqeOimzZtQp06dTBs2DDODmxxYBx0rnzxNdIqQ5eUBpJO2K8qI5WVxSj0fBNyfROat6q23uTl5aFbt26YO3cugILxjIqKwsiRI3H27FkkJycjKysLmzdvhqqqKtTV1aGrqws7OzuyBvDJVBPaBmIglF1SEfSYcc7IyMDXr19Za/DixYtRo0YNbNu2TUqu27Rpg27dupUPQwD69OmDOXPmYO7cufDx8cGrV68AFATlnJ2dMX78+DI3cQKq9dbvypuQ9CrTVywN+NollRXUr2rrm5C04uLiiK90584dODo6QldXF1paWrCwsCAlMW7evIm1a9di+PDhrBrgZQ3aViRv1eCO3zZge+/ePbRv3x4ODg5o164dq4so8H+ltmbNGrRu3RoAMHXqVNA0zeroWBq8fv0aiYmJ+PjxIzleWHgn4cWLF1BWVoavry/+/fdfODs7IyQkhJeSE5pecXjy5AmUlZVx5coVcm3GjBkwNDREUlISr2cKyVtSUhKrAQJj8OXm5sLGxgZGRka4efMmkZXs7Gw4OTlhy5YtvHgrCRUxlgyECOhUy+TvP78ruuuskDq5MIToqFuZc+DChQvQ1taWaj4BAJaWlpCXl8cff/yB7OxszrQYHor6XlWSf6741XmrzCxGoTpYA8JvWFQ0b1V9vUlLS0PLli1x5MgRvH//Htra2vDx8YG+vj5atmyJbt26kYzJr1+/4sqVK7hx4wZZH/g4mULLSGFUpH1XUfQkm315eHhAX18fXl5e+OOPP8g9Y8eORZ06dbBu3TpWnfmgoCBMnDiR8+8uLE/Mb5g4cSJatGiB0NBQLF68mHXP3LlzoaGhQZpMlQXVeuv35K2i6f1KvuLPUJa1W4igflVe34Tm7a+//kLTpk2RnJwMNzc3jBs3Djdv3sSpU6fg5eUFdXV1fP78ucjvcg2c/i52+X8Zv2XA9uHDh1BSUsKYMWOwcuVKTJ8+HXXq1MGQIUPIQsUo1i1btqBLly6YMWMGZGRkpDqR/wybN2+Gnp4elJWVYWJigs6dO5M6IoUNzNevX0NVVRU0TcPAwIDXjrDQ9IqbaGKxGJmZmejZsyfU1NTQuXNndOjQAaqqqryDK0Ly9uDBAzRq1Ajt2rVjHYthnv/161c4ODhAS0sLU6ZMwY4dOzB27FgoKioSZ4IrhBzLwqjIgE61TP6+87swKqrrrJA6WWjegMp/b8D/m0/MmjWLOC0ZGRkYMmQI5s6dyyuj5NSpU9iwYUOJznBVkv+KhJC8VVYWoyQqcr4VRkWub0Whonj7r6w37dq1w/r16zF06FAMHz6c6Jft27fD3t6+2My0smTnVLSMCG3fCUHv+fPnUFJSQnh4OLZu3Yrhw4dDX18fNjY2ZBNoypQpkJWVRefOnREeHo7BgwdDXl6es/wz8vbjxw98//4dnz59Yn3u6uoKmqYxfPhwluzFxsbCzMys2MAEV1Trrd+Tt4qiVxm+YmWhooP6VXl9E5IWI7+fP3+Go6Mjhg0bBk9PT5aN//TpU1hZWWHp0qUAyrZ2VmW7vCrhtwvYikQiDB8+HAMHDiTXMjMzERQUBJqm0b17d9YO/apVq0DTNOTk5DgHBs6ePYt69eph69atuHnzJlauXAl7e3toamqSIzvMJBGJRBCLxbC2toaTkxOvlHQh6TEKgbm/uMl+//59LFiwAL6+vhg9ejTvLrBC8vbx40c4OzvDxcWFZHRILsSSjuqYMWPg4eEBXV1duLm58TKwhR7L4lARAZ1qmfw953dJKO+us0Lq5J+hIjrq/irvDSjgT1tbG+Hh4di9ezcmT54MIyMjXkfcDxw4AJqmoaWlhejoaKkyFpKoSvJfERCat8rIYiwKFdnBujha5bm+lYZeefH2X1hvGPkaPHgwNDU14ezsjG3btrHuGTt2LHR0dKRqo5YHKkJGhLZLhKS3detWuLq6kneRn5+Py5cvw9DQEGZmZuR97tu3D+Hh4XB2dkaPHj1Y2YilgWQ2r5WVFezt7aGoqIjRo0eTOon379+Hg4MDVFRUcODAAZLRO378eNjZ2ZVLhi2Dar1VvrSE5q286AntK/4KqKigflVe34SilZOTw5K5rKwshIWFQU1NDerq6qwyBbm5uXB0dMS0adNKxUNl81aNsuO3C9iKxWK0bt0aYWFhAP4vKJGRkRg4cCBkZWVZR3X+/PNP6Ojo4MGDB5xprVq1CoGBgaxr9+7dQ+vWrdGkSROyQ8x0Z+7RowcaN27Mq1t2ZdC7fv067O3tyc51cTVumGtlycgRkrdr166hY8eOSEhIQExMDJo3by61EEvuCqWlpSE5ObnEgMXPIORYloTyDOgA1TIJ/L7zuySUZ9dZIXVyaVDeHXV/pfcGFNSrcnFxId2y+XTKfvbsGZycnDB9+nT06NEDenp6iIqKKlYHVjX5L29UBm+VkcVYFCqig3VxKO/17WcoT96q6nrz/PlzPHnyBC9evGCVV3F3dwdN04iMjCRHLIGCjSIvL68KCdgCFSMjQtt3QtGbM2cOmjRpwromFotx69YtGBgYwM/Pj1xnnHXJd8kFz58/h5qaGsaPH4/r169j586dUFdXh6mpKamT+ebNG3h4eKBx48bQ09ODj48PlJSUcPfuXV40S0K13iofCM1bedKrDF/xV0BFBPWr6vomFK29e/eid+/ecHJywvjx43H+/HkABY3GAgMDQdM0wsPDyf35+fnw8vLC6tWrS81HZfFWjfLBbxewBYA//vgD/v7+SEhIAFBgCMjLy+PPP//EypUrYWBggHfv3pH7Cx+9KS2mT58OdXV1qeuPHz8mu3LMjkd+fj6uXbtWph0HoekdP34c5ubmcHV1xZcvX8hzGezYsYPzM4uDkLz9+PGDFcS4cOECWYglr5dnKr+QY/kzlEdAh0G1TP6+8/tnKM+us0Lp5NKiPHn71d4bAKSmpuLNmze8j4m+efMGM2fOJFkivXv3LjFom5OTU+XkvzwhJG+VncVYFCqig3VxKM/1rTQoL96q4nqzefNmtGzZEubm5qhfvz4GDhyIs2fPAgASExNha2sLVVVV7Nu3D69fv0ZaWhr8/PzQuXNnzvxwQXnLiND2XUXRY+SW0SFxcXHQ1dVFdHQ06768vDzs27cPJiYmiImJYX2HL+bMmYM2bdqwrkVGRqJWrVqwsbHB4cOHyfU9e/Zg8eLFWLlyZYUeO6/WW+UDoXkrL3qV4Sv+KijvoH5VXN+EorVt2zbUq1cPixcvxujRo9GpUyfIyMhg9+7dAAqCtp07d0aLFi1gZ2eHiIgIODk5wcTEpMy2cVW2y6safpuAraSxcurUKbi4uMDQ0BAdOnRA3bp1SXbXrVu3UL9+fdy7d48zjcLGzPXr19GqVSusWrWKJZgikQh//fUXTE1NcfXq1RJ/669ErzBEIhFOnToFJycnODo6sgzDGzdugKZpDBo0iNezfwXegP8rlIsXL0JbW5u1ezp79mwcOXKE1/OLoldRY8kHfAM6v8J7qyoyWdljWVqUtessUHE6uazgw9vv8t7KArFYjOTkZNa1Xr16SQVtv3//jszMTNZ9VU3++UBo3n61LMbiwFeXcEVZNyz4gCtvVX29OX78OJSUlLB79268efMGR48eRePGjWFkZIQDBw4AKGjm4+PjAw0NDaiqqsLKygrm5uYkAPK7yIjQ9l1502NkonBW1MePH9GhQwf4+vqSjC4GKSkpUFZWxsqVK8uDJQwfPhxBQUEAQOrjbtmyBb6+vvD19UVoaCipmyg0qvVW2SE0b+VFT2hf8VdCWYL6VXl9E5LWhw8f4OjoyNp0v3XrFmRkZEDTNDZs2ACgQN737NmDrl27olOnTggPD/9p2ZzK5q0a5YtfPmCblJREnA5JYbpy5QoWLFiA0aNHszo1Xrt2DWZmZnjz5g1nWmlpaRCJRMRBTU1NRffu3eHo6Ii//vqLtcP8/ft3KCkpkcnEB0LTA4A7d+6wHHCxWIwTJ07A2dkZTk5OpG5USkoKdu3axbsOl5C8vX37FgcPHsSmTZtw48YNFm+S/zJHXrp374727dujXr16ZSrAL9RYColqmSzA7zq/+YCrES+kTi4r+HY5/x3eG1ccOHAA169fl7oumT3CBG2jo6Px7Nkz+Pv7Y/LkybzoVdVxBITl7VfNYiwKVblbMFfeqvp6M2zYMAwZMgTA/52/kSNHQlFREQ4ODqxsrb///ht79uzBoUOHpDpP/6oQ2r6rKHrMu3nw4AF69eqFNm3aoE+fPiSD8PHjxzA1NYWfnx9OnDjB+m7r1q1Za3lZsGrVKtStWxc3b95ERkYGXr9+DQUFBezcuRPnzp1DrVq1pGraC6VPqvXWfwOV5Sv+6uAT1K/K65uQtF69egV1dXWcOnWKdb1Dhw4IDQ1FnTp1cO7cuWK/z3Udrcp2eVXHLx2wffjwIVRVVREQEECE62f1k0aNGgVra2t8+/aNE61du3YhODgYdnZ2CAkJwdGjRwEAycnJcHV1hYODA2sH5MePH7CyssLBgwe5MVVJ9ICCLB1PT0/4+fmRHW6gYMIfPHgQGhoa8PPzIzuWfBd6IXlLSEiApqYm3Nzc0KBBAzg7OyMqKkrqPoaXf/75BzRNQ0FBoUxF44UaSyFRLZO/9/wWAkLq5MpAVX1vGzZsAE3TUllUDCR3y/v06QM9PT1oaGjw7gJbVccREJa3Xz2LsRpFoyqvN4w8BQUFkUaTjKxNmTIFnTp1gru7O/r06SOVnc/gV8/OEdq+q2h6jx49gry8PPr27YvevXvDz88PtWvXJtmzDx8+hIODAxwdHTFy5EgcP34cw4cPh4KCAp49e8aJFuPwZ2ZmIiUlhSUDvXr1Ak3TsLCwQL169cgpHABo3rw5du3axYlWNapRWlSWr/g7gKs+qcrrmxC0JMf75cuXsLOzw+LFi8kJgz///BNKSkq4dOkSfH19MWzYMABlXzersl3+X8AvG7B99+4dHB0dSUfRkJAQUkejqB2FK1euoHfv3mjYsCFn5bpjxw7Uq1cPq1evxoIFCzB48GDUqFEDS5YsAVAgzG3atIGFhQW8vb0xZ84cuLi4wMTEhNcEEpoeULB7369fPyxYsADOzs7o2LEjyzAUiURwcnICTdPw8vIiDQZ+Zd6eP38OLS0tREREICMjA69evUKXLl3QqVOnIu/Pzs7GiBEjoKCgUObMWiHGUkhUy+TvPb+FgJA6uTJQVd/bxo0bUatWLRLgKwxmTjE8fPv2DXXr1oWjoyOvWlVVdRwB4Xmr6lmMVRH/lfVm7ty5qFevHmJiYpCUlIT9+/ejZs2aSEhIwKlTpyAjI4MnT57wfn5lQWj7Tgh6o0aNYjWWEYlEmD17Nmiaxty5cwEAL168wPTp09GqVSuYmJjA3t6e87rN6Kj79+8jODgYxsbGCAwMZDn8Bw4cwI4dO0iwACgIWrRq1QoXL17kRK8a1SgNKstXrIqoyutbZa2lY8aMgbGxMVxdXdGzZ0/QNE1ONkRERMDMzKzMtnFVtsv/K/hlA7a7d++Gt7c3Ll26hC1btsDW1pYVICgsQAkJCejUqRNpelNafPv2DR4eHqyU79evX6NJkyaoWbMmZs2aRe7bunUr2rdvj+DgYPTv359kFXARZqHpMViyZAns7Oxw7do17NixAxYWFujUqROr++zgwYMRHR3Nag7EBULylpubi6lTp6JTp04kxR8Azp49CwUFhSKPXz958gRNmzYt8lgwFwgxlkKiWiZ///ktBITSyZWBqvre9uzZA5qmicP87NkzLFu2DL169cLixYvJsVgmAJCSkgILCwvo6enxCtZW1XEEhOXtv5DFWBVRldebjx8/4uXLl+RoPgD07dsXNE1DX18fcnJy2Lp1KwDg8+fPUFVVxZUrV0rNy68Coe07Iej17t0b3bt3BwBWwHfx4sWoUaMG9u/fT2jl5+fj69evSE9P50SDscETExOhpKSEsLAwREVFoXXr1ggICCj2pEZubi4iIiKgp6eHDx8+8OKvGtUoDpXpK1Y1VOX1TShaJ0+exPDhw9GrVy8sX76cXF+/fj2GDRuGPn36sE7CRUZGom3btqXiobJ5q0bF4pcN2AIgO7BisRjR0dEkQMAYEoUF6GdHc4tCUlISGjdujD179gD4v9HRo0cPYoxKdi+VvAfgnsEiFD3GIJN05pycnODn5wegIPhiYWEBFxcXHD58GMOHD4eRkVGZDCYhxzInJweLFy/G5s2bWdcfPHiARo0a4fnz50V+r6gO6D9DZYylkKiWybLzVln0hIYQOrkyUFXf24oVK0DTNI4dO4b79+9DW1sb/v7+sLW1hZOTE5o2bcoyDrOzszF16lSp5jSlRVUdR6ByeKuqWYxVFVV1vdmxYwecnJygoqICLy8vVgOec+fO4fz586xMtNu3b8PExAQPHjzgxE9lQGi7pDLsoFmzZqFRo0akpILkex85ciTU1NTKxV599+4dDAwM8Mcff5Br586dQ2BgIN69e4fU1FRyXSQS4erVqwgPD4e8vDxp8FSNapQnhPQVqzqq6vomFK2oqCgoKiqiT58+CAkJgZqaGqFX+DlisRg5OTlwc3NDeHh4qfkoClXZLv8v4ZcO2EoiJyeHldXFGDsrVqzAp0+feD/3x48f8Pf3R3h4ODFY9u/fj3r16iE+Pp4U6c/NzWUJMMCvbpWQ9E6fPo0ePXrgzJkzAAp2VJo3b47FixcDKOjs7ubmBi0tLZiZmZXZYBJ6LCUNTOZ53759g56eHl69ekU+K49jVkKPpZColsnfc35XNipKJ1cGqvJ7W7BgAWiahqKiIqZMmULqZCUkJKBLly5wdXVlZc0x4GOkVeVxFIK3/0oWY1VFVVxvtm7divr162Pt2rU4duwYHB0dERoaWuS9eXl5SE5Ohr+/Pzm+/ztAaLukougVN97Pnj2Ds7MzOnXqhOTkZAD/z9iPi4tD06ZNcenSpTLxBBRkLU6YMIGVtThx4kQ0adIETZs2hZubG6nHCBSUTpg/f/5v0ZC3Gr8vhPQVqzKq4vomFK0DBw5ARUUFf/75J4AC+83V1VWq0SNQoJv//vtvtG7dGq1atWIFcfmgKtvl/yX8MgHbV69eYfPmzZg6dSrZqZfsXAgUZP8wAYKOHTti8ODBoGkaT58+5URLJBKxMsEWLVoEe3t76OnpISQkBDRNIzo6GgCwdOlStGzZkhz75QOh6QEFYzZw4EDQNA0lJSVMnz4dL168wJw5cxASEoJ79+6R+168eMG7IZCQvL19+xYHDhzA2rVr8fbtW/L7JRXK+/fvoaKigsePHwMApk6diqZNmyIpKYkXTYaGEGMpJKpl8vee30JASJ1cGaiq700kEkkFW1esWIHAwEC8evWKZZBt2LABioqKLKeFD72qOI6AsLxV5SzGqoyqvN5cvnwZzZo1YzWD2r17N4YMGYL79++zjujn5OTg5MmTCAoKgomJCQkI/upBW6Htu4qix4zz06dPERERga5du5KmYmKxGOvXr4e9vT169+5NMm2BguwrHR2dYhtSckFaWhoePXpE/o6MjETNmjURFRWFc+fOYf78+WjVqhUpwQBUZ29Vo/xRWb5iVURVXt+EopWeno7u3btj+vTprOuOjo7w9/dHQEAAxo4dS3R4SkoKtm/fjo4dO/I+7VaV7fL/KmpRvwASEhKo4OBgqlmzZtSXL1+oZcuWUVevXqVatWpFURRF0TRNicViSkZGhurWrRslEomo8PBwqm7dutTt27cpHR2dUtM6evQodfjwYerDhw+Uvb09NXPmTGrcuHGUvr4+dfPmTSozM5O6dOkS5ezsTFEURdWrV49q2bIlJSMjw4s3IekBoGiapiiqYMwGDBhApaenU61ataIOHz5Mffr0icrPz6cePHhA/f3335SxsTFF0zTVvHnzX563hIQEKiQkhFJQUKC+fPlCTZs2jbp79y6loaHBui87O5vKysqiZGVlqblz51ILFiygYmNjKTU1NU70hB5LIVEtk7/n/BYSQurkykBVfW+SfDk6OlLTp0+nKIqiRowYQQUFBVFaWloURVGUSCSiatasSTVu3JgyMDCg5OTkykyvKo0jRQnL27Zt26jw8HBq4cKFVLNmzah58+ZRu3fvpoKDgymKoigPDw9yb35+PvXjxw9qypQpVOPGjSl9ff3yYbganFGV1xuxWEx9//6dGjVqFBUQEECuR0dHU8+ePaP27t1LGRoaUqamptT69eupOnXqUFpaWlTbtm2pPn36ULVq1aLy8/OpWrV+CTeDBaHtkoqmJxaLqRo1alAJCQmUr68vZWNjQ8nKylJjxoyh8vLyqDFjxlADBw6kMjIyqAMHDlBt27al1q5dS9E0Te3fv5/Kycmh9PT0ePPE/F2/fn2WPtLR0aGOHz9O+fn5URRFURYWFtSKFSuoZ8+ekXt+Rfmoxu8LoX3FqoyqvL4JSUtOTo6KjIykcnJyyLWOHTtSL1++pAYPHkzJyclRM2bMoFJSUqjo6GhKUVGR6tKlC9WjRw+KpmnO62hVtsv/06jEYDEA4PHjx1BTU8OUKVPw/ft3ZGZmwtbWFuvXr5e6l9l9GDx4MOTk5MhOdGkRFRUFBQUFDB06FGFhYahduzbWrFlTJA2RSITc3Fy4u7ujX79+vHgTmh5QkImzadMm8szw8HD069cPqampWLt2LQYMGACapkHTNK5du8abjpC8PXz4EI0bN8akSZPw6dMnPH36FEZGRlI1VwDgy5cvsLS0ROfOnSEjI4Nbt27x4g8QbiyFRLVM/t7zWwgIqZMrA1X1vRXF17p168jnhY825eTkwM/PD507d+Z17KmqjiMgLG//hSzGqoj/wnqTnp7Okr82bdpAU1MTly9fRkJCApYuXYpWrVrh7NmzUt/91ZuUCG3fVTS9Z8+eQVNTExEREUSfDxs2jPW3WCzGqVOn4O/vjzp16sDAwAAtW7YkzSdLC0bOPn/+XOpGaCKRCCkpKfDx8WFl2FajGuWFyvIVqyKq8vomBK2SbOrLly8jJCQEz549I9eio6OhrKyMly9flqkMQVW2y//rqNSAbUZGBrp3745hw4axjLvg4GCMGTMGQ4YMweHDh1lHFI4dOwYNDQ3OBsahQ4egpqZG6ofk5eWhQ4cO2L17t5RhmZubi0OHDsHd3Z13/RCh6QEFBvLcuXNB0zR69uyJK1euQCwWw9LSknQB/PHjB8LDw6GhocH72LKQvKWlpcHLy4tV9woAfH19MXfuXMycORP//vsvcVzfvHkDmqYhJyeHO3fu8OIPEG4shUS1TJYfb5UxlkJASJ1cGaiq760kvgoH8zIyMhATEwNPT0+YmZlVy38hCMmbSCTCsWPHsGTJEtbRZy8vL2hra0NBQQEODg4YPHgw+ez+/fvYtGkToVV9pFh4VPX1pvDxYQZ//fUXXr58Sf5+8eIF5OTkWJsNvwOEtu8qml5+fj4mTJiA/v37s5qZ9erVC15eXvD29sbo0aNZG6q3b9/G06dP8fHjR060mPXkwYMH0NHRQf/+/YstqVNYhqZMmQI9PT1WjdtqVKM8UFm+YlVEVV7fhKL1s1IGhcsPrFu3Dm5ubsjKyioVH0WhKtvl1fgFatieOXOGVeg7MjIStWrVQkhICNq1awcZGRnMnDmT9R2u3Uxzc3PRp08fREREsK47OjrCyckJVlZW6NOnD+kWmZeXh6NHj6Jbt2686ocITa8w4uPj4ePjA0dHR4wcORKnTp1C27Zt8e+//5J7+NbhqgzeDh48yMrgYGTEy8sLHh4eoGmaNGJJTk7G0KFDSV2isqIix1JIVMtk1ZnfFQ0hdHJloKq+t9Lw1bt3b7Kbf/XqVQwdOpQ0GQCq5Z9BZfBWlbMYqyKq8npz584dTvWsnz59Cmdn59+28Z3Q9l1F0nv9+jViYmLI33PmzEGNGjUwYsQIzJs3D2pqaggICEB2dnZZ2cC7d+9gZ2cHfX192NjYYOTIkaxAfmFcv34dY8eOhYKCQnVwrBoVhsr0FasKqvL6JhStbdu2wcTEBF++fAHAttMkTzswyMrKQlBQEAYNGlQqPiqTt2pUHiotYFtUFD8+Ph7Ozs44ceIEEaD58+dDXl4enz9/LtOxv5SUFCQmJpK/Q0JCoKGhgaVLl2LXrl1QU1ND27Zti/wuHyEWml5hfPz4Edu3b4e5uTnk5OTQvHlzTJ48uczPBYTjrSgZuXTpEkxNTXH8+HGyQzVq1Cg0bdoUqampAP6/s1VeqMixFBLVMtm2yO/+jvO7IiC0Tq4MVMX3BnDn69mzZ+R9V8s/G0Kub1U5i7EqoyquN7t27YKcnBzCwsJKlTGZmZmJNm3awNPT87dbByQhtH1XEfQK65HXr1+jT58+OHXqFLl2584d0DSNy5cvl4kWABw9ehTe3t5ISEjAkiVLYGFhwQraSv6elJQUjBkzBl5eXkhISCgz7WpUozB+FV+xqqAqrm9C0Tp16hQUFRWhpKQEMzMz0uCxqM31nJwcxMfHIzAwECYmJmXOcq3Kdnk1foEMW0lkZGSQTC1GuPft2wczMzP8+PGD93MLC39cXByGDBmCFy9ekGunT59G7dq1cf/+/TKnhAtNryTk5uZi9OjRqF27NlRVVclCxReVzVtKSgrZIWKevWLFCtjY2CAnJ6dcaRVGeY+lkKjs9yaJ310mf6WxrGhUlE6uDFTV98aFr8I1hvnwWFXHERCGt/9aFmNVQ1Vcby5dugQDAwN4eHjA1ta2xIzJjIwMHDt2DP7+/jA1Na0ydZSFtu8qmp5kpq5IJEJsbCxMTU2J/VwWpKWl4fz58+TvxYsXk6CtpFwyMvHx40ckJyeXmW41qlFaVKav+DujKq5vQtH69OkTevTogeHDh+PkyZNwdnaGkZFRkUFbkUiEM2fOICQkBO7u7mQd5Xtqqirb5dUowC8VsAWkhW7kyJFo27atVL2PsqJwnZDdu3fD0dER379/L1c6lUUPYI/l2bNnOTmJXCAUb8UpmGHDhqFHjx7lctSrNLQrciyFRLVM/r70hIRQOrkyUFXfW7X8lx/Kk7f/ahZjVcfvPt927dqFTp064cOHD1i2bFmJGZOfPn3CyJEj0bdv3ypTR1lo+64i6RV15BYAIiIi4OLigq9fv5YbLUksWbIE5ubmLLlZsGBBlbCVq/F7oTJ9xaqI3319E5JWdHQ0KU0TGxsLJyenYoO2nz59wqVLl8i18l5Hq7Jd/l9ELeoXA03TFEVR1Ldv36hly5ZR27Zto65cuULVq1evXJ4PgKJpmpKRkSHXcnJyqD179lA6OjqUvLx8udCpLHqSoGma0Pfy8ir35wvNGyMbDNLS0qhFixZRe/fupS5dusT6HeWNih5LIVEtk78vvcpARevkykBVfW/V8l9+KG/eLl++TEVGRlJ2dnbU7du3qWXLllGjRo2itLW1WffRNE1lZmZS58+fp9auXUu9f/+eunXrFlWjRg1KLBZTNWrUKA/2qlEOqCrzLTg4mDI2NqbU1dWpUaNGUSKRiNq1axdFURQ1cuRIqnnz5hRFUVR+fj6lqqpKTZs2jVJUVKRomqZEIhFVq9Yv50pwgtD2XUXSY9Zr5t9nz55R27Zto1avXk1duXKFUlJS4v1skUhE1axZk3WN0UljxoyhKIqidu7cSVEURX39+pXavXs31bZtW970qlENPqhMX7Eqoaqsb0LQYp7Xt29fcs3Ozo5auHAhNWHCBMrd3Z2KiYmhlJWVqU+fPlGfPn2iTE1NKVVVVYqiCvRoea2jVdku/09DyOhwaREbG4t27dqhefPmiIuLqzA62dnZuHbt2v/au/PAmK7+f+DvySQRWiS2RGqJnZQiknpCbFWKprU+HhoRiWgSQlFiq35R2lJB1VJ7a6udqqqWihK7UDTUHtVYI4RIhMx8fn/kN7czoi3JbLl5v/5p3Lkz557ec+459zOfOVcCAwPNsn6IvZVnTeaq27NmEcXHx0uPHj2kQoUKFm0jasc2WXDLsyZrXZNtQa3nje3ffMxRt8Kexah2autvhozJwYMHy+XLl+XBgwfSt29fkwfkqaFvF1TPkiV1+vRp6dmzp9SqVSvfD/sytC+dTpfrs4yzxj777DNxdHSUEiVK8AFjZDG8V7QutY1vlijLeH9D+9Tr9RIfHy8BAQFSt25dSUxMlFdffVU6d+5svgr8AzXPywsbq6RqiAiAnG9cr1y5AhGBTqcDkPOtguFvA29vb3Tu3Bnbt29Hw4YNn7kcvV7/TNsMx3T06FFMnToVDx8+REJCAhwdHaHT6XJ9O2cv5VmTNet24cIFzJo1CwCULKInGbcREUHp0qXRpEkT7Ny587naiNqxTeZg//5n1rom24Jazxvbv/nYom5vv/02xowZo2QxBgUFYffu3ZgxYwYuXbqkfJZxFuOiRYuUsgp6FmNBUxj62z+VOXToUAQHB2P37t34+OOP0bx5c8TFxcHd3V3Z1x77dmFw/PhxVKpUCXv37jXZ/uS4XbNmTbz33nv46aef0KBBgzyXl52dDUdHR+j1evj7+2PBggUmr2u1WqXdXLlyBcWLF8e+ffvyVSbRk3ivaD5qHt+sXTdDm8vIyFC2OTg4KJmuTZs2xWeffYbixYujbt26yMjIwOrVq5/ps5+lHmqZl9O/sHRE2BDF37hxozRo0EA8PT2lQYMGMnz4cLl+/brJvhs3bszz2hrG37YdP35cTp48KcnJySb7PPnNQlpamvz666/Ke58ng8Xa5VmTNet29uxZKVOmjHh4eMikSZOeegzG4uLi/nWfwoptkv37WVjrmmwLaj1vbP/mY091YxajfSoM/c2QFXn+/HnZv3//U18TEZkwYYJoNBpp1KhRvh+MQuaRlpYmgYGBUq5cuVznTiTnumL8tPD8MM6s9fX1lVatWint4OzZsyb77tixQzQajRw5csQsZRMZ8F7RfNQ8vlm7boax8NixY1KjRg05d+6cyeuGMq5duyY1a9YUf3//PP9qyp7mrmR9VlkSYceOHVKkSBH59NNPZceOHTJ8+HAJCAiQTp06KQGCw4cPi0ajkZEjR+brBiUmJkbc3d3F09NTSpYsKZMnT5YrV64or6empkpUVJTJNpG8X9CtXZ41WbpuqampEhgYKJ06dZKoqChp3LixTJgw4W8/xzAZnDlzZj5qpX5sk+zf/8aa12RbUOt5Y/s3H2vV7Wn7G2+LjY0VHx8fiYiIkEaNGknVqlU5qbYTau1vhpvMEydOiLu7u0ybNi3XPnq9Xm7fvi0BAQHi5+fHpTlsyHj8Nb4R7969u7i5uZkEbVNTU6VcuXLSsmVLycrKyle5xsHaRo0aSevWrZVgbd++faVjx46SkZFh8h7Dw3WIzIX3ipah1vHNWmUZj6Ply5eXiIiIp+6XkZEhYWFhUqtWLeX6mZ9xVM3zcvp7Fg3Y6vV6yc7OlsjISAkPDzd5bcWKFeLv7y8ffvihMgFZs2aNnDp16rnKMG6Au3btEg8PD9m+fbscOXJEZsyYISVKlJChQ4dKSkqKiIhcuHBBSpUqJSEhIXmqk7XLsyZr1y09PV0iIyNl48aNkpKSIkOHDv3HgfjevXsybdq0524jasc2yf79rKxxTbYFtZ43tn/zsUXdmMVYsBSG/mZ8k1m2bFkZPHiwcixPrjn6zTffiIeHh1luMil/fvnlF+UG/MmgbalSpeTgwYPKvpcvX5bz58/nq7x/CtaGhYVJ5cqV5fTp08r+xms2EpkT7xXNQ83jm7XrZjyOlilTRoYMGaJsf+211+TXX3812X/Dhg1myaxV27ycnp1VMmyDg4OlQ4cOubYPGDBA/Pz8zFLGnDlzZPz48TJu3DiT7StWrBBnZ2dZunSpiOQ0/HPnzuX7Zsja5VmTNepmmNTdv39f+fvGjRsyZMgQady4sYwfP17Z9+HDh7neR7mxTbJ/PytrXJNtQa3nje3ffKxVN2YxFlxq7W9/d5Op0+nEz89PBg4cmCsLxzDnYpu0nYyMDGnatKmUKlVKWS7FcF5u3rwpjRs3lurVq8vevXvNWq5hGYTXX39dCdaGhoZK1apVGQwjq+C9ovmpdXyzVln/NI42btxY/P395f79+yKSux3ae93Iflk0YKvT6USv18sHH3wg9erVk3Pnzpk03rVr10rt2rXl1q1bz/W5Xbt2lREjRij/Tk5OlmbNmolGo5F3331XRESysrKUieeAAQOkUaNGkpmZafI5z9qYrV2eNdmiboY2YPwew83ArVu3lIF4woQJ8ujRIwkLC5OBAwfmrYIqxTbJ/p0Xlrom24Jazxvbv/nYqm7MYiw4Ckt/+6ebTF9fX2nTpo3cu3dP2d84cMvgh+2dO3dOWrZsKZUrV871U9fQ0FBxcHAQLy+vXO0kP5o3b26yZi2DtWRNvFfMPzWPb7YYSw2fdfz4cXF3dzcZRxs1aiRvvPGGyTiaV2qel1PemDVga7i4Xr9+XW7fvq18E5yeni7VqlWTli1byu+//65ccPv37y/NmjWT9PT0Zy4jOztbpk6dKk5OTjJx4kRl+759+6Rjx47i6uqqLIRvaKgfffSRtGjRIk+TTmuXZ022qJvhffv27ZO1a9eaPOTI8JphIPb395f69euLi4uLHDhwIE/lqRHbJPv3s7LGNdkW1Hre2P7Nx1Z1YxZjwVHY+pvhCwRL3WSSeRjO9e3bt03WhL18+bI0bdpUvLy8TIK2Q4cOlR9//DHXQ0Pza8eOHSbLIDBYS9bCe8X8U/P4ZsuxNCkpSUqVKiWDBg0SEfOPo7aeJ5B9MlvA1vjJ4w0bNpTq1atLtWrVlDVm/vzzT6levbq8/PLL0rRpU+ncubOUKFEiV7bJs3j8+LHMnTtXnJyc5KOPPlK2Hz58WFq1aiXu7u5y4sQJuXv3rmRkZMhrr70mnTt3znNDtnZ51mTNuhnes27dOilZsqRMmDDBZA0s432Sk5OlRo0a4ubmJidOnMhHDdWJbZL9+99Y85psC2o9b2z/5mPtujGLseApDP1Nr9fL3bt3pUqVKvLee++JCIO19m7Dhg3yn//8RypXrixDhw5V1kS8fPmyNG/eXNzd3SUmJkZ69eolZcuWlYsXL5qt7CfbWv/+/aVy5coM1pJV8F7RfNQ8vtlq7rpr1y5ZsGCBiPz1Rby5x1E1z8spb8yaYbt9+3YpUqSIfP7557JixQqZMWOGaLVaCQsLExGRBw8eyGeffSaDBg2SUaNG5boA/xvj1O4DBw7IoEGDRKPRSGxsrLLd0JhdXFykdu3a8u6770r9+vWVb4mfpzFbuzxrslXdDh48KKVLl5aFCxf+7RNss7KyZPDgwfLCCy9wAH4C2yT79/Ow9DXZFtR63tj+zceWdWMWY8FQGPtbQkKCiPz1BQLbpP0wPreHDx+WsmXLytixY2XSpElSuXJl6dSpk/zyyy8ikrOmbUREhDRr1kxat26d6wE3z1ue8Vz8aVn+mzZtkjNnzjx3GUR5xXvF/FHz+GYPY6nhMxo2bCitW7c22zhqL3Uj+2OWgK2hcURFRck777xj8lpcXJw4ODjIp59++tT35MWIESOkXr160rt3b6lRo4ZoNBqTp0UeOnRIunXrJiVKlJCff/5Z2Z7XnxtauzxrslbdDOd7+vTp0qJFC5N1Vp78eejjx4/lv//9rxw5ciQvVSoU2CbZv/+Jta/JtqDG8ybC9m9O1qwbsxgLpsLQ34yv7Xq9Xpo3b54r25tsY9WqVSZflJ4/f14+++yzXFlVjRo1ko4dO0pcXJyy/f79+/les3bq1KmydOlSefDggbLt0qVLMmfOnHx9LlFe8F7RvNQ8vlmqrCfb2d+tA3vgwAEZO3asRcZRNc/LKW/yFbA1XFgN6x22a9dOevbsqbxm+FZs0qRJ8sorr8itW7eUhp/X4MCWLVvkxRdflL1794per5erV6/KlClTxMHBwWStj/3790v79u3Fy8tLkpKSRCRviy9buzxrskbdDOfZcEEbOHCg+Pv7K68bXxiPHj0qV69ezXe91I5tkv3779jimmwLajtvBmz/5mOrujGLseBQU38zfkCPIcih0+ly3XyKiOzcuVN5ijXZzpUrVyQgIED++OMPERFJTU2Vl156SYoWLZrr4UkHDx4UHx8f6dq1q2zdutVsxxARESFt27aVVatWiUjOevdlypSR0NBQs5VB9G94r2h+ahrfrF3Ww4cP5aOPPlL2NWSvJiQkmPyiwRLzYDXPyynv8hywNVxct2/fLkOHDpXLly/L3LlzxcPDQw4fPmyyz5w5c6R+/fqSkZGR7wNetGiR1KtXzyS4cO/ePRkzZoxoNBqZOXOmsv3IkSPSoUMHKVGihFy6dKlAlGdN1qrbnj17JCoqSpKTk2XLli3i5OQkP/30k8k+6enp8v7778t3330ner2+QAWPrI1tMgf7tylbXZNtQU3nzRjbv/lYu27MYix41NbfHj16JFFRUbJ06VKTgOyaNWtk+vTpeTpmsizDGHzixAlJTU2V/fv3S6VKlSQgICDXevKHDx+WKlWqSFBQkElGbF4Yt8Hhw4dLYGCgzJ49Wzw9PWXQoEG88Ser472iealtfLNmWXv27JGmTZtKRESEEqxdvXq1lCpVSnbt2vXcx/s81Dwvp7zLV4bt+vXrpWjRojJhwgQ5cuSI/Pbbb/LWW29Jhw4dTH6i8P7770vLli2f+8blaZkB27Ztk2LFismhQ4dMtu/Zs0e0Wq1oNBpZuHChsv3AgQPStWtXOXfunN2VZ022rNvixYulfPnyMnDgQNm5c6eEh4dLzZo1Zdu2bSIicvfuXRk7dqx4eHiY9cEJasA2mYP9+9lY+ppsC2o9b2z/5mOtujGLseAqLP0tMjJS2rVrJ6tXrxYRke+//14cHR1l1qxZz/wZZF1paWlSr1496dmzp9y+fVv2798vFStWlD59+uRamzMhIcFs82TjoGxERIS4uLhI27ZtC8S8gNSH94p5p+bxzRZjaVZWlnz//ffy1ltvydChQ2Xt2rVSsmRJ+eKLL57p/c9KzfNyMq88B2zPnDkjVapUybXO0aZNm+Stt96S0qVLS4cOHeSNN97I05PHjRvxDz/8IKtWrZLExES5deuWtG/fXoKCgkw+8/fff5fQ0FD59ttvc63h8fDhQ7srz5rsoW5fffWV1KlTRwYPHixLliyRQYMGiaOjo9SrV098fHzEw8NDjh49mqfPVit7OG+Wwv5tfpa+JtuCWs8b27/5WLtuzGIseApDfzMuc/jw4fL222/LsGHD5MUXX5QlS5Y802eQ7Rw+fFh8fX0lLCxMUlNTJT4+Xgnanjx50iJlGgK2N2/elAoVKkiDBg2kTZs2snbt2nxn8BLlBe8Vn5+axzdbjKWGL+YzMzPl0KFD4ufnJ05OTsq9lbl+faDmeTmZn0ZEBHmwY8cODBgwAD/99BMqV64MvV4PBwcHAMDvv/+OhIQE/PTTT6hQoQKCg4NRu3btvBSDUaNG4YsvvoCnpyeSkpIwf/58ZGZmYu3atXBxcUFwcDAqVaqEiRMnwtHREZs3b4ZGo0F2djYcHR3tvjxrsmbdLl68CBcXF3h6eirbFi9ejGnTpqF169bo3bs3MjIysG/fPpQpUwavvfYaqlSpYu4qqwLbJPv3s7DWNdkW1Hre2P7Nx5p1i4qKQlJSEkJDQ9G9e3ds3boVHTt2xIwZMzBgwAAL1ZDyS+39zfh9nTt3xpYtWxAWFobZs2fbff8l4NixYwgLC4OPjw+mTp2KU6dOoXfv3mjYsCEmTJgAb2/vPH+2iECj0eDmzZsoXbo0tFotAODKlSto0KABevXqhc8//xwjRozAyZMn8c4776Br164oWrSouapHlAvvFc1HzeObNcsSEej1emi1WqxZswZ9+vRBjRo10KRJE8yaNQtardasc2I1z8vJjPIa6d24caNUrFhRWehYp9Mp3zrExcXlex04vV4vly5dkoCAANm3b5/cvn1bJk+eLI6OjjJ79mxZvHixhIWFiaOjo9SuXVsaN26srDPyPGvaWLs8a7JV3VJTU6V8+fIyevToXAvDL1iwQJydnWXAgAFy+fLl/FdShdgm2b/zwlLXZFtQ63lj+zcfW9SNWYwFi5r729N+Sml479atW6V48eLStm1beeONN5gxWYAcPXpUGjRoIGFhYXLnzh2Ji4uTunXrSnJy8nN/luEhowa7d++WevXqSUpKiojkZIqNHTtWBg8erLQ/EZH+/ftLly5duDQCWRTvFfNHzeObtcoy3ufJrNXVq1eLm5ubzJ49W3766Sd5++23JTIyMt8Ztmqel5Pl5Dlge/HiRSlatKiMHj0612vvvfeefPjhh7kmC//GeAJ6+/ZtOXv2rIwcOdKkc0ybNk20Wq1Mnz5d7t27J8nJyXLx4kXlvU92OHsqz5psXbe4uDjx8vKS8ePH55po+vj4SIkSJSQmJkYePnzIC48RW583S2L/tixLXJNtQa3nje3ffGxZN+P3derUSRwdHeXdd9+12/9XhVVh6W9nz541KWPz5s2i1Wrlq6++EhGRmJgYadeunXz99dfKustk344ePSq+vr7SvXt3uXv3bp4eDjp37lyZMWOGpKWlKdvWrVsn7du3N9nvxo0byt/G7ch4O5Gl8F4xb9Q8vlmrLOPPmzx5snz55ZcikhMMTUpKkqJFiypr1mZlZcnWrVulSZMmMnny5Geqhy3rRuqTr4eOLVq0SJycnGT48OFy8uRJOXXqlMTExIirq6ucPn06z587evRo8fPzk5IlS8orr7wiv//+u8nr06dPF0dHRxkxYoTJROZpGQf2WJ412bJue/bskQoVKsiECROUb08fPHggkZGR8vHHH3PR+H/ANsn+nReWuibbglrPG9u/+Vi6bsxiLPjU1N+eDNx9+eWX4u/vr2TdpKeny7Bhw2Tx4sUm+0VERMjgwYPzVB+yjUOHDknz5s1zZR4+q+7du4uXl5csWLBA7ty5IyIis2fPlrZt24rI32doFYTrPqkL7xXzTk3jm7XKio2NVdZ8NYyd9erVk61bt5rsd+bMGRH561r58OFD2b9/v1mukWqel5Nl5Ctgq9PpZM2aNeLm5iYVKlSQ6tWrS61atZ57QXDjBvjNN99I+fLlZebMmTJ48GApVqyYDBs2TPmZr8GkSZOkSZMmefrGzdrlWZO91W3Pnj3i5eUl0dHRsnLlShkzZox4e3ubfOtP9nfezIn923rMdU22BbWeN7Z/87FV3ZjFWHCotb/1799fpk2bZjJ3mjJlivTt29dkv7t37yp/G7dZe+/blFteriXG5zk8PFyqVasm8+bNE5GcIECrVq3MdnxE5sJ7xWej1vHNWmXt2LFDKlWqJD169FB+cXjv3j2pWLGibN++/W/f9+TnP2/gVM3zcrKOfAVsDZKTk2Xfvn2yf/9+uX79ep4/Z9euXdK/f3/5+uuvlW2zZ8+WChUqyIgRI3I1ZuN1QApCedZkT3U7fPiwNGvWTCpWrCje3t6SkJBg9jLUwp7Om7mxf1uPua7JtqDW88b2bz6WrBuzGNVBbf2ta9euUrNmTZk/f77cvn1bRERGjRolAwYM+MfPMb5RLAh9m/LPeOmjYcOGSeXKlWX58uUyZswY6devn6SkpEhSUpL8+eefkpKSIr/++quShUtkK7xXfHZqG9+sVVZGRoYsXLhQXn31VenevbtyraxVq5bs379fRHKun4YvO/V6vVnHTTXPy8myzBKwNYdr165JtWrV5MUXX5QZM2aYvDZr1iypUKGCjB49Wi5cuGDyWl4bsbXLsyZ7rNu9e/fkjz/+kJs3b1qsjILOHs+bubB/07NQ63lj+zcfS9aNWYzqoKb+Zhxw7du3r1StWlW+/PJL0el0MnDgQAkPD8/TMZO6ff3119KzZ08REQkNDRVvb2+pVauWaDQaadiwoRQvXlzKlSsn1apVEy8vL87NyS7wXvHfqWl8s2ZZhi/ds7KyZP78+UrQ9s6dO9K6dWvZt29frveY88GLap6Xk+XZTcBWROT48eNSs2ZNadOmjZw4ccLktTlz5ohWq5W5c+cW2PKsSc11UzM1nzf2b3oWaj1vbP/mY6m6MYtRPdTS35786eWwYcOkSpUqsmLFCgkJCZHo6GhJTEyUw4cPS0JCgpw+fVq2bdtm8qUCFS5JSUnSoEEDmT59urItOjpaypYtK0OGDJGTJ0/KmTNnJDExUS5fvix//PGH7Q6WiJ6bWsY3W5S1dOlSOXTokMybN0/8/PwkICBAnJ2dxcfHR/z8/KRhw4bi5+cnr7zyigwbNiy/1TGh5nk5WZZdBWxFRH799Vdp2LCh9OvXT3777TeT19avX2+SzVIQy7MmNddNzdR83ti/6Vmo9byx/ZuPOevGLEZ1Kuj9zTj4HxkZKX369BERkT59+ki1atWkQoUKotFopHHjxuLm5iaurq5StWpVad68Ob84KKSOHj0q0dHREhQUJJmZmcrDdURyrm01a9aUJUuWMKBPVMAV9PHNWmUZz+9mzpwpGo1GLly4IA8ePJAFCxZIs2bNpFy5cjJnzhxZsWKFzJs3TxYsWCDTpk2Tx48fm61OBmqel5Pl2F3AViRnwuHj4yP9+vWTxMTEXK+buzFbuzxrUnPd1EzN5439m56FWs8b27/5mKtuzGJUr4La34wDrgcOHBBfX1+Ji4tTtg0cOFBeeuklGTt2rFy7dk3S0tIkOTlZ7t+/r7RnBm0Ll4yMDAkNDZVy5cqJr6+vst04aPvuu+9KqVKl5KuvvuITx4kKuII6vtmirL1798qXX34pa9asUbZlZmbK/PnzpWnTphIaGvrUa6Il5shqnpeTZdhlwFYkpzH7+flJt27d5OLFi6orz5rUXDc1U/N5Y/+mZ6HW88b2bz75rRuzGNWvIPe31atXS9euXZUs78zMTOW1vn37Sq1atWThwoW5HhrFYFzhYXwdOnfunEREREjJkiVlypQpynbjB5ENGjRIzp8/b9VjJCLLKMjjm7XKOnr0qGg0GtFoNMoDvwxjZGZmpvIgstatWytr3VqamuflZH4OsFMNGzbErFmzULx4cVSuXFl15VmTmuumZmo+b+zf9CzUet7Y/s0nP3UTEWg0GgDAwYMHceTIEYSEhAAAlixZgg4dOkBE8MEHH2DTpk1ISkpCYmIijh8/jri4OGg0GoiI2etE5lVQ+9udO3ewfv167N69G0lJSQAAFxcXZGVlAQAWLlyI5s2bY8iQIfjll19M3uvgYLfTezITw7VHr9cr/61evTpiYmLQrVs3bNiwAbNnzwYAODs7K+3m888/R7Vq1Wxz0ERkVgV1fLNmWbVr18aiRYvg6uqK/fv3A8gZI3U6HVxcXNCrVy8EBQXBy8sLWq3WXIf/j9Q8Lyfz04id320Ybqj0er1VJqDWLs+a1Fw3NVPzeWP/pmeh1vPG9m8++anbmjVrsGbNGri5uWHBggV4+PAhXFxcAADh4eGIj4/H8OHD0bVrV7i6uirvU+P/RzWz9/5m/AWCwcWLFzFlyhRs2LABI0eOxNChQwEAjx49grOzMwDg008/xfDhw612o0m2Z2grO3fuxKZNm3Dr1i20aNEC3bp1Q5kyZXD27FlMmTIFiYmJCAkJQWRkpK0PmYgsyN7HN2uV9Xf7pKen45tvvkH//v0xZswYjBs3DgCg0+mg1Wrx+PFjODo6Wn2OrOZ5OZmP3QdsgadPYtVUnjWpuW5qpubzxv5Nz0Kt543t33zyUrc7d+4gMjIScXFxqF+/PrZv3w4AyMrKQpEiRQAA7777LlatWoVly5ahY8eOZj9ush577W/GN2uZmZkoWrSociN54cIFfPLJJ0hMTESvXr0wYMAAAKZtFPjrxpMKh40bNyI4OBi9e/fG3bt3kZycjLJly2L27Nlwd3fH2bNnERsbi927d+P9999HeHi4rQ+ZiCzIXsc3a5VlPI5u3rwZqampyMjIQP/+/QHkfNG5ZMkSDBgwAB9++CE+/PDDXO+zxRxZzfNyMo8CEbAlIiIiyi9mMZK9Mb5ZnDFjBvbs2YO0tDS0bt0a4eHhKFu2rJIxeerUKQQHByMqKsrGR022lJCQgB49emDEiBEIDw/Hn3/+ifr166NYsWLw9vbG0qVL4e7ujtOnT2Pu3LkYOnQovLy8bH3YREQWYTy3GzlyJL755ht4eHjgzp07cHNzw6pVq1ClShU8fvwYS5YswaBBgxAdHY2pU6fa+MiJ/h0DtkRERKR6zGIkezZq1CgsXLgQI0eOxI0bN7Bz505UqlQJc+bMgYeHB86ePYupU6ciLi4OkydPRpcuXWx9yGQjP/zwA5YvX44VK1YgKSkJr7/+Olq2bAl/f3+MGTMGTZo0wezZs1G+fHk8fvwYTk5Otj5kIiKLmz59Oj777DN89913aNSoEZYvX47evXujYcOGWLVqFWrUqIHHjx/jiy++wKZNm/DLL78wu5XsHgO2REREpGrMYiR7tmrVKowbNw7Lly+Hr68vtmzZgm7duqFSpUqoWrUqli5dinLlyuHUqVPYvHkzs70JZ86cQY0aNdC5c2e4urri66+/BgD4+PjgzJkzaNOmDdavXw8HBwcGJIhI9W7cuIHx48ejRYsW+N///odvv/0WvXv3xtixY7FixQo4OjpixYoVqFmzJnQ6nXJt5JIEZO+4ujERERGpmiFYO2rUKEyaNAlNmjSBj48P1q9fj4iICFy/fh01a9ZETEwM6tati2nTpmHDhg02PmoqLJydndGhQwf4+vpi8+bNCAkJQWxsLEaMGIGDBw8iIiICycnJ8Pb2xsiRI6HVaqHT6Wx92GQDhjybWrVq4caNGzh37hw6d+4MALh79y7q1KmDKVOmYPbs2dBqtQxEEFGh4O7ujsDAQLRo0QLHjh3D0KFD8fHHH2PYsGGIjo5GQkICXn/9dVy5ckW5NjJYSwUBM2yJiIhI9ZjFSPbs+vXrcHFxQfv27dGxY0eMHDkSd+/exX/+8x/cvn0bPXv2xMyZM3mDSYrbt2+jQ4cOePnllzF69GgsWbIEP/74I77//nu4u7vb+vCIiKziyXFx3rx5WLduHb755huUKVMGq1evxi+//AIRwaxZszi3owLF0dYHQERERGRpT2YxhoaGIjY2Fi4uLhg2bBgiIiIwa9YseHt7w9vbGwDXrCXr8fDwwMmTJ/HHH3+gRYsWAIDU1FQ0aNAA3bt3R6dOnQCAwVoVMyzd8ujRI+j1ejg5OSnXn6cF6t3c3NCzZ08sWrQIAQEBcHZ2xqZNmxisJaJC5WkPkz1z5gyKFSuGu3fvYuXKlfD19cXYsWMBcG5HBQszbImIiKhQYBYj2YO/a19JSUno1q0b6tevj7CwMHz00UcoUqQINm7cCAcHB5O1mEldDOf29OnTygMQ69atizfffBPdu3c32dc42PDo0SOcP38et27dQvXq1fHSSy/Z4vCJiCzKMG4+S7D16tWraNKkCR48eABXV1e4uLjg6NGjfAAjFUic9REREVGh4OHhgStXrjw1i3HevHmYMWMGAGYxknmlpaUhJSUFGRkZAKCsnfekihUr4p133kFCQgJ69OiBjIwMrFu3jsFalRMRODg4IDExEc2aNUPRokURGBiI1NRUzJ8/H2fOnDHZ3xCsSEhIgLOzM7y9vdGiRQsGa4lItc6dOwcg5/qn1+ufuo9hXPX09MShQ4cwcuRIxMTE4NixY3ByckJ2drbVjpfIXJhhS0RERKrDLEayBytXrsTSpUtx/PhxNG3aFF26dME777xjso9xxtDjx49x69YtpKamwtvbGw4ODsjOzoajI1cxU7ObN2+iU6dO8Pf3R2xsLICcAEVAQABiY2PRq1cvk/3XrVuH7t27Y926dejSpYstDpmIyCpWrFiB4OBgjB49GhMnTgSAXHM1w5wvIyMDjx49gqurq8lncBkEKqh4R0JEREQFHrMYyd4sW7YMERERaN++PcaOHQuNRoNly5bh2rVrJvtptVqkpKQgLi4OTk5O8PT0RN26dZU2yWCt+p07dw6enp743//+ByAnuFCjRg20bNkSV69eBQCTrDIfHx8MGTIEtWvXtsnxEhFZw549ezBu3Di0bdsW8+fPxwcffAAAyvhoLDMzE126dMHEiRNx584dk9cYrKWCijNAIiIiKtD+LovROMPWkF2h1WoxcOBA9OjRg1mMZDHHjh3Dp59+ii+++AJ9+vQBAAQEBMDf3x/Hjh1D+fLllX1FBKtWrcKgQYOwfft2tG7dWnmNXyAUDp6ennj77bfx6quvAvjrvGdnZ+P69esm2wCgatWqmDx5Mq9ZRKRajx49wu7du9GyZUsMGDAAe/bswf/93/8BACZOnAgHBwdlbqfRaFC0aFG88sor2Lt3b64MW6KCiqM8ERERFVjLli1D//79MXHiRLz99tuIi4vDsmXL0KpVK5OgmCGL8eTJk2jVqhU8PT3h6ekJAMxiJLNLTk5Go0aN0Lx5cwA5beyVV16Bn58f7t27p2xzcHCARqNBu3btMGXKFGVtZSocDD/jrVKlCqpUqQLA9Ke+zs7OJllksbGxKFasGKKionjNIiJVc3Z2Rq9evXD58mU0aNAAXl5e0Ol0mDBhAoCcoK1Wq4WIQK/XQ6vVYsqUKcp1lQ+QJTXgSE9EREQFErMYyV7Vrl0bwcHBqFq1KoC/2phOp0NqaqrJNgCoXr06hg0bBgDM9i4kDMGEffv24erVqwgICICHh4dJuyhdujReeOEFAMDo0aMxbdo0HD582FaHTERkNSKCypUro3LlygAAV1dXhISEQKPRYPz48QBygrapqalYtWoVunXrBnd3d+W9DNaSGnA2SERERAUSsxjJnhhnRlavXh3Vq1cHAJNsn6ysLGWdZQAICQlBmzZtTB4qxWCt+hnaxPr169G3b1+8//77qFu3Ljw8PEz2S09PR8mSJfHxxx9j+vTp2Lt3L+rVq2ejoyYisizjcdQ44GrY7ubmhuDgYADAhAkTkJmZiYSEBPzxxx+IiorK9T6igo4zQiIiIiqQmMVI9sL4JnPJkiVwdnbG66+/Dnd3d+XmUaPRoEyZMnBzcwMAtG3bFpcvX8aiRYtsdtxkGxqNBocOHUJERARiY2MRHBwMZ2fnp+73ySefoFixYtizZw8aNWpkg6MlIrI843H0/PnzSEtLQ506deDo6AhnZ2dlvdpSpUqhT58+SE9Px9ixY+Hn54czZ87AwcGBmbWkOvwNIBERERUYxus5Vq9eHW3atAGQk7Fm+O/TshiXL19u8jkM1pI5GW4yhw8fjlGjRiErKyvXE6yBnHaXnp6Ozp07IykpCb/99hscHR2h0+msfchkI4Zr1b59+1C3bl0EBQUpwdon20zFihVRoUIFHDhwAL6+vlY/ViIiaxARZRwdPXo03nzzTbRs2RJ+fn4YPHgwrl+/Dq1Wq4yVhl8oNGzYEHv37oWTkxOys7MZrCXVYcCWiIiICoQnsxhXrFiBGzduAMA/ZjEeOHAAPXr0sM1BU6GxbNkyrFixAlu2bEFYWJjJGspATlZ3WloahgwZgvPnzyMxMVG5ydRqtTY6arIWQ6A2PT0dAHDx4kU8evQILi4uAEyvb8eOHcP9+/fRq1cvxMfHo27durY5aCIiKzDM4aZNm4b58+dj2rRpOHjwIHr27IlTp04hKioKN2/eVIK28+fPh7OzMw4cOABHR0f+aopUiwFbIiIiKhCYxUj2xDirG8gJsjVt2tTkZ+uG13Q6HRwdHVGnTh28/vrrOHbsmBKs5U1m4aDRaBAfH48RI0bg6tWreOONN3DkyBFs374dwF/XtwcPHmD58uXYtWsXatasiUqVKtnysImILE5EkJmZiV27dmHw4MF488034e3tjQ8++AD9+vVDcnIyVq5cCQDQarUIDg7Gvn37OI6S6jFgS0RERAUGsxjJXhgygm7dugUAOHfunMkDxgx/63Q6xMfHIzMzEzExMdi2bRszggqpc+fOYdOmTfj0009RrFgxhISEIDo6Gj/++CMAIC0tDZMnT8bKlSvx8ssv2/hoiYisQ6PRoGjRosjKysKVK1dMXgsKCkKVKlWwefNmZZu7uzscHByg1+s5jpKqsXUTERGR3TIOgGk0mr/NYjQExgxZjEWKFMHWrVsZGCOLmjdvHvbu3YupU6eiR48e6NevH37++We0bt1a2efatWuYN28eAKBFixYAwJvMQio0NBQODg6YPHkytFotmjZtimLFiiEwMBB16tSBk5MTrl69iq1btyoPUyQiUhvjJWCMt1WtWhUHDhzApUuX4OXlpXwx6u/vjy1btuDhw4fKMjIAcn0GkdqwhRMREZHdYhYj2TMRwd69ezFx4kSULl0aPXv2RFRUFL7//nukp6cjKSkJkZGRuHDhAgICApT38Saz8Lh48SKuXr2q/DskJATDhg3D9u3bcezYMfTu3Rs7d+5EUFAQ+vfvj3379qFhw4Y2PGIiIssxDtYmJibi7NmzOHPmjPJlVlpaGkJDQ/Hbb78hPT0dmZmZ2LhxI8qXL28SrCUqDHgHQ0RERHaNWYxkD56WERQZGYmiRYti8uTJcHR0RIsWLeDq6opOnTqhfPnyKFasGFxdXREfHw+tVvvUzyD1unPnDgICAhAaGoro6GhlCZewsDDo9XoMGDAAOp0OMTExaNasmY2PlojIskREGQNHjx6NdevW4cGDB8jOzkZ4eDgmTZqE+Ph4tGrVCv/9738hInB1dUVGRgZ27NihfIbhy3witeNdDBEREdk14yzGDh06KFmM06dPR4sWLZCSkoLo6GjcunWLWYxkMYb2FB8fDy8vL1SoUAFATsakTqdDbGwsHj9+jMjISPTp0wenT59G8eLF0bZtW2i1WmZ7F0Jubm5YuXIlQkNDUaRIEYSHh8PT0xMAEB4ejrlz52LZsmV44YUXMGHCBDg7OzMQQUSqZbi+TZ06FfPnz8fatWuh0Whw6dIlREZG4saNG1i4cCGOHz+ONWvW4MaNGyhevDjCw8P5qykqlNjaiYiIyG4wi5Hs2ZUrV9C2bVvExMQgIiLCJGMyOzsb0dHR0Ov1GDJkCLp37668z7C+MhU+LVu2xLJly9CzZ09oNBqEh4ejfPnyyMjIwKuvvopu3bqhR48eKFKkiK0PlYjIIoyzYvV6Pfbu3YvIyEi0atUKQM510svLC61bt0b9+vUxcOBA9O7d2+QzOI5SYaQREbH1QRAREREZezKLEQAWL16M2NhYvPbaa4iMjIRer2cWI1nd7t27ERISgtDQUJOMyezsbNSpUwc3btzA+PHjMWTIEBsfKdmT+Ph4BAcHIzAwEE2aNEFiYiI2btyI/fv3o0SJErY+PCIiizD+Ej0lJQVlypTByy+/jDfffBNTpkyBiCA7OxtOTk4YMmQITpw4ge+++w5FihSBVqu18dET2RbvaIiIiMiuMIuR7Fnz5s2VjEkAStD21q1baN++PXx9fREUFGTjoyR7ExAQgLVr12Lo0KH49ttvUbx4cSxbtozBWiJSLeNg7bRp03D+/HmMGTMGQUFBWLhwIbp37w5fX19l7vbiiy/CwcEBxYoVs+VhE9kNZtgSERGR3WEWI9m7+Ph4hISEICAgAL6+vti6dSv0ej1+/PFHADlfIDA7iJ50//593L17Fy4uLihbtqytD4eIyOJGjBiBJUuW4PPPP0fTpk2RkpKCcePG4fHjx/joo4/g6+uLBw8eoEuXLvD09MSSJUtsfchEdoEBWyIiIrJL8fHx6NmzJ/r166cEba9du4ZPPvlEyWJkQIxs6ejRoxgzZgyuXLkCLy8vbNy4EU5OTnyKNREREYCff/4Z/fr1w7Jly9C0aVNl++bNm7Fo0SL8/PPPqFOnDrKysiAiOHr0KMdRov+PAVsiIiKyW8xiJHuXlZWFjIwMuLq6QqPRcB1lIiKi/2/JkiWIjY1FfHw8XF1dTZZJuHjxIs6ePYvDhw+jbNmyCA8Ph6OjI8dRov+PvYCIiIjslmHdxzFjxmDevHnw8vLC5s2bAeQ8dZjBWrK1IkWKoEiRIgBy1uvjTSYRERV2hgzZzMxM6HQ6ZbtGo1G+bE9ISICPjw/atWunvM7nERD9hRm2REREZPeYxUhERERUsJw+fRr16tXDBx98gHHjxinb09PTERQUhLZt22LAgAG2O0AiO8aALRERERUoxj+nIyIiIiL7NX/+fERHRyMqKgqBgYFwdnbGxx9/jOvXryMhIYFfwBP9DQZsiYiIiIiIiIjI7EQEmzdvxqBBg6DT6eDq6oqXXnoJW7ZsgZOTE59HQPQ3GLAlIiIiIiIiIiKLSUlJQVpaGvR6PapVqwYHBwcucUX0DxiwJSIiIiIiIiIiq+ESV0T/jAFbIiIiIiIiIiIiIjvBrzOIiIiIiIiIiIiI7AQDtkRERERERERERER2ggFbIiIiIiIiIiIiIjvBgC0RERERERERERGRnWDAloiIiIiIiIiIiMhOMGBLREREREREREREZCcYsCUiIiIiIiIiIiKyEwzYEhEREREREREREdkJBmyJiIiIiGyoT58+6NSpk/K3RqOBRqOBk5MT3N3d0aZNGyxevBh6vd62B0pEREREVsGALRERERGRHWnXrh2uXbuGpKQk/PDDD2jVqhXee+89BAYGIjs729aHR0REREQW5mjrAyAiIiIior8UKVIEHh4eAICXXnoJPj4++M9//oPWrVvjq6++Qnh4uI2PkIiIiIgsiRm2RERERER27rXXXkP9+vWxYcMGWx8KEREREVkYA7ZERERERAVA7dq1kZSUZOvDICIiIiILY8CWiIiIiKgAEBFoNBpbHwYRERERWRgDtkREREREBcDp06dRpUoVWx8GEREREVkYA7ZERERERHZu586dOHnyJLp27WrrQyEiIiIiC3O09QEQEREREdFfsrKycP36deh0Oty4cQPbtm3DJ598gsDAQPTu3dvWh0dEREREFsaALRERERGRHdm2bRvKly8PR0dHuLm5oX79+pg5cyZCQkLg4MAfyBERERGpnUZExNYHQURERERERERERERcw5aIiIiIiIiIiIjIbjBgS0RERERERERERGQnGLAlIiIiIiIiIiIishMM2BIRERERERERERHZCQZsiYiIiIiIiIiIiOwEA7ZEREREREREREREdoIBWyIiIiIiIiIiIiI7wYAtERERERERERERkZ1gwJaIiIiIiIiIiIjITjBgS0RERERERERERGQnGLAlIiIiIiIiIiIishMM2BIRERERERERERHZif8HmRVpaqX+jYQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import cohen_kappa_score\n",
        "kappa = cohen_kappa_score(merged_df['State_df1'], merged_df['State_df2'])\n",
        "print(\"Kappa score:\", kappa)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woo3sCb4JyL3",
        "outputId": "f393a8c1-6e19-4abf-95ea-a249d7a595fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kappa score: 0.829424355905021\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ids= ['mdp.39015002758699', 'mdp.39015006801313','mdp.39015018916802']"
      ],
      "metadata": {
        "id": "j8-acX8SUI74"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "faulty_df_s= df1[df1['ID'].isin(ids)]\n",
        "display(faulty_df_s)"
      ],
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    {
      "cell_type": "code",
      "source": [
        "faulty_df_s['ID'].value_counts()"
      ],
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        "colab": {
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        "id": "LDD11jmL8WBa",
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              "ID\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "faulty_df_a= df2[df2['ID'].isin(ids)]\n",
        "\n",
        "faulty_df_a['ID'].value_counts()"
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              "ID\n",
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              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mdp.39015006801313</th>\n",
              "      <td>1098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mdp.39015002758699</th>\n",
              "      <td>268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mdp.39015018916802</th>\n",
              "      <td>248</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ids= ['mdp.39015002758699', 'mdp.39015006801313','mdp.39015018916802']\n",
        "\n",
        "l=[]\n",
        "for id in ids:\n",
        "  ds= faulty_df_s[faulty_df_s['ID'] ==id ]\n",
        "  da= faulty_df_a[faulty_df_a['ID'] ==id ]\n",
        "  s= ds[\"Page\"].to_list()\n",
        "  a= da[\"Page\"].to_list()\n",
        "  d={}\n",
        "  d['ID']= id\n",
        "  d['missing in a']= list(set(s)-set(a))\n",
        "  d['missing in s']= list(set(a)-set(s))\n",
        "  print(d)\n",
        "  l.append(d)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCTk3M7fCjbz",
        "outputId": "8a6842c9-078f-40d6-ad85-ca0449209aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'ID': 'mdp.39015002758699', 'missing in a': [], 'missing in s': ['00000014.txt']}\n",
            "{'ID': 'mdp.39015006801313', 'missing in a': [], 'missing in s': ['00000952.txt', '00000951.txt', '00001042.txt', '00001011.txt', '00001046.txt', '00000939.txt', '00000953.txt', '00001012.txt', '00001009.txt', '00001010.txt']}\n",
            "{'ID': 'mdp.39015018916802', 'missing in a': [], 'missing in s': ['00000244.txt']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_W2YLAvHEJJb",
        "outputId": "067c825d-ab45-4432-e85d-955d78366eec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'ID': 'mdp.39015002758699',\n",
              "  'missing in a': [],\n",
              "  'missing in s': ['00000014.txt']},\n",
              " {'ID': 'mdp.39015006801313',\n",
              "  'missing in a': [],\n",
              "  'missing in s': ['00000952.txt',\n",
              "   '00000951.txt',\n",
              "   '00001042.txt',\n",
              "   '00001011.txt',\n",
              "   '00001046.txt',\n",
              "   '00000939.txt',\n",
              "   '00000953.txt',\n",
              "   '00001012.txt',\n",
              "   '00001009.txt',\n",
              "   '00001010.txt']},\n",
              " {'ID': 'mdp.39015018916802',\n",
              "  'missing in a': [],\n",
              "  'missing in s': ['00000244.txt']}]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  n= faulty_df_s[faulty_df_s['ID'] =='mdp.39015006801313' ]\n",
        "  p = n[\"Page\"].value_counts().unique()\n",
        "  print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHnYQHzaSU1J",
        "outputId": "ca43668b-2988-4184-9f4b-a4e331c08a68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n= faulty_df_a[faulty_df_a['ID'] =='mdp.39015006801313' ]\n",
        "p = n[\"Page\"].value_counts().unique()\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gddO2qzkb0bi",
        "outputId": "5aa91186-defb-45a9-e969-a8c37b07854d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Data for the first graph\n",
        "data_1 = {'Front': 656, 'Core': 12331, 'Back': 631}\n",
        "colors = ['#4CAF50', '#FF7043', '#29B6F6']  # Green, Orange, Blue colors\n",
        "\n",
        "# Data for the second graph\n",
        "data_2 = {'Front': 621, 'Core': 12548, 'Back': 449}\n",
        "\n",
        "# Function to create bar charts with labels inside the bars\n",
        "def create_bar_chart(data, title, xlabel, ylabel):\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    bars = plt.barh(list(data.keys()), list(data.values()), color=colors, edgecolor='black')\n",
        "\n",
        "    # Adding data labels inside the bars\n",
        "    for bar in bars:\n",
        "        width = bar.get_width()\n",
        "        plt.text(width - 200, bar.get_y() + bar.get_height()/2,\n",
        "                 f'{width}', ha='center', va='center', color='Black', fontsize=12, fontweight='bold')\n",
        "\n",
        "    plt.title(title, fontsize=16, fontweight='bold')\n",
        "    plt.xlabel(xlabel, fontsize=14)\n",
        "    plt.ylabel(ylabel, fontsize=14)\n",
        "    plt.grid(True, axis='x', linestyle='--', alpha=0.6)\n",
        "    plt.show()\n",
        "\n",
        "# Create the first plot\n",
        "create_bar_chart(data_1, \"Ana's Annotations\", 'Number of Pages', '')\n",
        "\n",
        "# Create the second plot\n",
        "create_bar_chart(data_2, \"Saksham's Annotations\", 'Number of Pages', '')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-J-NoIVzy0oI",
        "outputId": "5ea35079-5675-4396-8cab-fbe0a5d8377f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}