Association.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CK4wiLeNnv6N",
        "outputId": "f7da6ed1-ef7d-4e13-f639-e0a4ac939f34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "# company 데이터 다운로드\n",
        "company = pd.read_excel('/content/drive/Shareddrives/데이터마이닝 2023-2/datamining_dataset_final.xlsx')\n",
        "df = pd.read_excel('/content/drive/MyDrive/check.xlsx')\n",
        "\n",
        "# SQLite 데이터베이스 연결\n",
        "conn = sqlite3.connect(':memory:')  # 메모리에 데이터베이스 생성\n",
        "\n",
        "# 데이터프레임을 SQLite 테이블로 저장\n",
        "company.to_sql('company', conn, index=False)\n",
        "df.to_sql('cluster', conn, index=False)\n",
        "\n",
        "# SQL 쿼리 작성\n",
        "sql_query = \"\"\"\n",
        "  SELECT DISTINCT c.company, cl1.'check' AS cluster1, cl2.'check' AS cluster2, cl3.'check' AS cluster3, cl4.'check' AS cluster4, cl5.'check' AS cluster5\n",
        "  FROM company c\n",
        "  LEFT JOIN cluster cl1 ON c.colors1 = cl1.Colors\n",
        "  LEFT JOIN cluster cl2 ON c.colors2 = cl2.Colors\n",
        "  LEFT JOIN cluster cl3 ON c.colors3 = cl3.Colors\n",
        "  LEFT JOIN cluster cl4 ON c.colors4 = cl4.Colors\n",
        "  LEFT JOIN cluster cl5 ON c.colors5 = cl5.Colors;\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "# SQL 쿼리 실행\n",
        "result= pd.read_sql_query(sql_query, conn)\n",
        "\n",
        "# 클러스터 번호를 이름으로 매핑하는 딕셔너리 생성\n",
        "cluster_mapping = {\n",
        "    0: 'Green',\n",
        "    1: 'Red',\n",
        "    2: 'White',\n",
        "    3: 'Skyblue',\n",
        "    4: 'Black',\n",
        "    5 : 'Orange',\n",
        "    6: 'Gray',\n",
        "    7: 'Blue',\n",
        "}\n",
        "\n",
        "# 각 클러스터 열에 대해 매핑 적용\n",
        "for i in range(1, 6):  # 클러스터 열은 cluster1부터 cluster5까지 있음\n",
        "    column_name = f'cluster{i}'\n",
        "    result[column_name] = result[column_name].map(cluster_mapping)\n",
        "\n",
        "# 연결 종료\n",
        "conn.close()\n",
        "\n",
        "# 결과 확인\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ty_PVlkVn21O",
        "outputId": "215116b0-38a5-4dce-f0fa-a3c0884b2458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                        company cluster1 cluster2 cluster3  \\\n",
            "0                            Berkshire Hathaway     Blue      NaN      NaN   \n",
            "1                                          ICBC    Black      Red      NaN   \n",
            "2      Saudi Arabian Oil Company (Saudi Aramco)    White  Skyblue    Green   \n",
            "3                                JPMorgan Chase     Blue    White      NaN   \n",
            "4                       China Construction Bank     Blue    Black      NaN   \n",
            "...                                         ...      ...      ...      ...   \n",
            "1994                                     Aperam    Black     Gray   Orange   \n",
            "1995  Shenzhen Feima International Supply Chain    Green      Red      NaN   \n",
            "1996                                       NMDC     Blue      NaN      NaN   \n",
            "1997                 Sichuan Changhong Electric      Red      NaN      NaN   \n",
            "1998                         Satellite Chemical     Blue      Red      NaN   \n",
            "\n",
            "     cluster4 cluster5  \n",
            "0         NaN      NaN  \n",
            "1         NaN      NaN  \n",
            "2       Green      NaN  \n",
            "3         NaN      NaN  \n",
            "4         NaN      NaN  \n",
            "...       ...      ...  \n",
            "1994      Red      NaN  \n",
            "1995      NaN      NaN  \n",
            "1996      NaN      NaN  \n",
            "1997      NaN      NaN  \n",
            "1998      NaN      NaN  \n",
            "\n",
            "[1999 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_df = pd.DataFrame(result)\n",
        "\n",
        "# 새로운 데이터 구조로 변환\n",
        "new_data = {'Company': [], 'Colors': []}\n",
        "\n",
        "for _, row in result_df.iterrows():\n",
        "    company = row['company']\n",
        "    clusters = [row[f'cluster{i}'] for i in range(1, 6) if pd.notna(row[f'cluster{i}'])]\n",
        "    new_data['Company'].append(company)\n",
        "    new_data['Colors'].append(clusters)\n",
        "\n",
        "# 새로운 데이터프레임 생성\n",
        "new_df = pd.DataFrame(new_data)\n",
        "\n",
        "classification = pd.read_excel('/content/drive/MyDrive/Forbes_2000_top_company.xlsx')\n",
        "\n",
        "new_df['Industry'] = classification['Industry']\n",
        "\n",
        "# 결과 출력\n",
        "print(new_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_X34rl80n8xb",
        "outputId": "d931cc77-67b2-4dff-e778-f422b13acbd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                        Company  \\\n",
            "0                            Berkshire Hathaway   \n",
            "1                                          ICBC   \n",
            "2      Saudi Arabian Oil Company (Saudi Aramco)   \n",
            "3                                JPMorgan Chase   \n",
            "4                       China Construction Bank   \n",
            "...                                         ...   \n",
            "1994                                     Aperam   \n",
            "1995  Shenzhen Feima International Supply Chain   \n",
            "1996                                       NMDC   \n",
            "1997                 Sichuan Changhong Electric   \n",
            "1998                         Satellite Chemical   \n",
            "\n",
            "                              Colors                      Industry  \n",
            "0                             [Blue]        Diversified Financials  \n",
            "1                       [Black, Red]                       Banking  \n",
            "2     [White, Skyblue, Green, Green]          Oil & Gas Operations  \n",
            "3                      [Blue, White]        Diversified Financials  \n",
            "4                      [Blue, Black]                       Banking  \n",
            "...                              ...                           ...  \n",
            "1994      [Black, Gray, Orange, Red]                     Materials  \n",
            "1995                    [Green, Red]  Business Services & Supplies  \n",
            "1996                          [Blue]                     Materials  \n",
            "1997                           [Red]             Consumer Durables  \n",
            "1998                     [Blue, Red]                     Chemicals  \n",
            "\n",
            "[1999 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "# 데이터프레임을 transactions로 변환\n",
        "transactions = new_df['Colors'].explode().groupby(new_df['Company']).apply(list).tolist()\n",
        "\n",
        "# TransactionEncoder를 사용하여 트랜잭션 데이터를 one-hot 인코딩\n",
        "te = TransactionEncoder()\n",
        "te_ary = te.fit(transactions).transform(transactions)\n",
        "df_encoded = pd.DataFrame(te_ary, columns=te.columns_)\n",
        "\n",
        "# Apriori 알고리즘을 사용하여 빈발 아이템셋 찾기\n",
        "frequent_itemsets = apriori(df_encoded, min_support=0.1, use_colnames=True)\n",
        "\n",
        "# 연관 규칙 생성\n",
        "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.4)\n",
        "\n",
        "# 결과 출력\n",
        "print(rules)"
      ],
      "metadata": {
        "id": "33KvHErXn_KW",
        "outputId": "517f852d-861f-437a-abcf-782c07b311d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  antecedents consequents  antecedent support  consequent support  support  \\\n",
            "0     (Black)       (Red)            0.336168            0.384192  0.13957   \n",
            "\n",
            "   confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0    0.415179  1.080654  0.010417    1.052985       0.112429  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "# Assuming new_df is the original DataFrame with 'Industry' column\n",
        "# and 'Colors' column containing the color information\n",
        "\n",
        "# 선택하려는 분야 리스트\n",
        "selected_industries = ['Banking', 'Construction', 'Diversified Financials', 'Insurance', 'Materials', 'Oil & Gas Operations']\n",
        "\n",
        "# 필터링된 DataFrame 생성\n",
        "filtered_df = new_df[new_df['Industry'].isin(selected_industries)].copy()\n",
        "\n",
        "# 분야에 따라 그룹화하여 회사 목록을 수집\n",
        "grouped_companies = filtered_df.groupby('Industry').apply(lambda x: {'Company': x['Company'].tolist(), 'Colors': x['Colors'].tolist()}).reset_index()\n",
        "\n",
        "# 각 그룹을 따로 저장하고 연관 규칙 생성\n",
        "for index, group in grouped_companies.iterrows():\n",
        "    field = group['Industry']\n",
        "    companies = group[0]['Company']\n",
        "    colors = group[0]['Colors']\n",
        "    group_df = pd.DataFrame({'Company': companies, 'Colors': colors})\n",
        "\n",
        "    # 데이터프레임을 transactions로 변환\n",
        "    transactions = group_df['Colors'].explode().groupby(group_df['Company']).apply(list).tolist()\n",
        "\n",
        "    # TransactionEncoder를 사용하여 트랜잭션 데이터를 one-hot 인코딩\n",
        "    te = TransactionEncoder()\n",
        "    te_ary = te.fit(transactions).transform(transactions)\n",
        "    df_encoded = pd.DataFrame(te_ary, columns=te.columns_)\n",
        "\n",
        "    # Apriori 알고리즘을 사용하여 빈발 아이템셋 찾기\n",
        "    frequent_itemsets = apriori(df_encoded, min_support=0.1, use_colnames=True)\n",
        "\n",
        "    # 연관 규칙 생성\n",
        "    rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.4)\n",
        "\n",
        "    # 결과 출력\n",
        "    print(f'{field}_association_rules : ')\n",
        "    print(rules)\n",
        "    print('\\n\\n')"
      ],
      "metadata": {
        "id": "ijDimmQaoAek",
        "outputId": "ab29726b-0787-4169-9ac0-964877b0bc59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Banking_association_rules : \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0    (Orange)     (Black)            0.280277            0.370242  0.114187   \n",
            "1       (Red)     (Black)            0.439446            0.370242  0.183391   \n",
            "2     (Black)       (Red)            0.370242            0.439446  0.183391   \n",
            "\n",
            "   confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0    0.407407  1.100381  0.010417    1.062716       0.126748  \n",
            "1    0.417323  1.127162  0.020689    1.080801       0.201258  \n",
            "2    0.495327  1.127162  0.020689    1.110727       0.179142  \n",
            "\n",
            "\n",
            "\n",
            "Construction_association_rules : \n",
            "  antecedents consequents  antecedent support  consequent support  support  \\\n",
            "0       (Red)     (Black)            0.472441            0.377953  0.23622   \n",
            "1     (Black)       (Red)            0.377953            0.472441  0.23622   \n",
            "\n",
            "   confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0       0.500  1.322917   0.05766    1.244094       0.462687  \n",
            "1       0.625  1.322917   0.05766    1.406824       0.392405  \n",
            "\n",
            "\n",
            "\n",
            "Diversified Financials_association_rules : \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (Red)     (Black)             0.30137            0.369863  0.123288   \n",
            "\n",
            "   confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0    0.409091  1.106061  0.011822    1.066386       0.137255  \n",
            "\n",
            "\n",
            "\n",
            "Insurance_association_rules : \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (Red)     (Black)            0.342857            0.323810  0.142857   \n",
            "1     (Black)       (Red)            0.323810            0.342857  0.142857   \n",
            "\n",
            "   confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0    0.416667  1.286765  0.031837    1.159184       0.339130  \n",
            "1    0.441176  1.286765  0.031837    1.175940       0.329577  \n",
            "\n",
            "\n",
            "\n",
            "Materials_association_rules : \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (Red)     (Black)            0.299145            0.350427  0.136752   \n",
            "\n",
            "   confidence     lift  leverage  conviction  zhangs_metric  \n",
            "0    0.457143  1.30453  0.031923    1.196581       0.333079  \n",
            "\n",
            "\n",
            "\n",
            "Oil & Gas Operations_association_rules : \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0     (Black)       (Red)            0.320755            0.481132  0.169811   \n",
            "1      (Blue)       (Red)            0.367925            0.481132  0.150943   \n",
            "2   (Skyblue)      (Blue)            0.283019            0.367925  0.122642   \n",
            "\n",
            "   confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0    0.529412  1.100346  0.015486    1.102594       0.134259  \n",
            "1    0.410256  0.852690 -0.026077    0.879820      -0.214652  \n",
            "2    0.433333  1.177778  0.018512    1.115427       0.210526  \n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define a function to parse the color strings into RGB tuples\n",
        "def parse_color(color_str):\n",
        "    # Strip the brackets and split the string by space, then create a tuple of floats\n",
        "    return tuple(map(float, color_str.strip('[]').split()))\n",
        "\n",
        "# Load your data\n",
        "df = pd.read_excel('/content/drive/Shareddrives/데이터마이닝 2023-2/김수현/8_clustering_data.xlsx', engine='openpyxl')\n",
        "# Convert the color columns\n",
        "for col in ['colors1', 'colors2', 'colors3', 'colors4', 'colors5']:\n",
        "    df[col] = df[col].dropna().apply(parse_color)\n",
        "\n",
        "# Convert the lists in the 'color' column to tuples\n",
        "df_melted['color'] = df_melted['color'].apply(lambda x: tuple(x) if isinstance(x, list) else x)\n",
        "\n",
        "# Now calculate the color frequency for each industry\n",
        "color_counts = df_melted.groupby(['Industry', 'color']).size().reset_index(name='count')\n",
        "\n",
        "# Then find the top 3 colors for each industry\n",
        "top_colors = color_counts.groupby('Industry').apply(lambda x: x.nlargest(3, 'count')).reset_index(drop=True)\n",
        "\n",
        "\n",
        "# Select industries of interest\n",
        "selected_industries = ['Banking', 'Construction', 'Diversified Financials', 'Insurance', 'Materials', 'Oil & Gas Operations']\n",
        "\n",
        "# Filter the top colors for the selected industries\n",
        "top_colors_filtered = top_colors[top_colors['Industry'].isin(selected_industries)]\n",
        "\n",
        "# Create a palette that maps color tuples to their corresponding RGB values divided by 255\n",
        "palette = {color: tuple(component/255 for component in color) for color in top_colors['color'].unique()}\n",
        "\n",
        "# Plotting the bar graph with black borders\n",
        "plt.figure(figsize=(15, 10))\n",
        "for _, row in top_colors_filtered.iterrows():\n",
        "    plt.bar(row['Industry'], row['count'], color=palette[row['color']], edgecolor='black')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Top 3 Colors Frequency in Selected Industries')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 985
        },
        "id": "VgkcEjujA8su",
        "outputId": "2436740c-4336-48f3-8d56-4f19acc81261"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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+zrvvvptVVlllrnJtcY6nJDVzmn3dN4+RJXEsL+73bjLv7895WdD3si4yAcD8KMIA4Hu2++6751e/+lXuvPPOHHroofPdbvXVV89zzz2XWbNmpWnTpt9jwm83r6s1duzYMc8991zuvffe3HPPPbnnnnty7bXXZv/998+f//znJfKY1dXV2XrrrXPiiSfO8z4/+MEPFvpxKioq5rpAQJK5Rv5VV1enY8eOtUZmfd03f9H/+nxeXzevx/ou+++/f37729/mjjvuyN57750bb7wxO+64Y9q2bbvQ+6rLfG3atMnWW2+drbfeOk2aNMmf//znPPnkk9lss81SXV2d3r1756KLLprnfbt16zbf/S6J93lej5F8ecXZ+c231adPn8V+nG8zv/dgUS3osVwX37sLegXXBX0vl/TnCQDFpggDoHB69OiR+++/P1OnTq01Cuq1116rWf91b7zxxlz7eP3119OyZcv5jmz4Np9//nmSZPLkyd+63U477ZTHH388f//737P33nt/67ZfZR4zZkxWXnnlmuWzZs3K2LFjv3W01cK+HvPTtGnT7LTTTtlpp51SXV2dww8/PP/7v/+b00477TsvDLAoevXqlc8+++xbn1vyZf6RI0fms88+qzWSZsyYMXNt265du1qnon3lm6PievXqlfvvvz8bb7zxApcA36VXr1556aWXvnO7tddeO+utt17+8pe/ZMUVV8y4ceNy2WWX1UmGutKvX7/8+c9/zocffpjky+f2/PPPZ6uttvrO0xS/aUHf5169euXee+/NxIkTv3VU2Lwev0OHDmndunXmzJmzQMfTSy+9lFKpVGtf8zqe6tqSOJaT7/7eXdj3bH4W9L1ckEwAsKjMEQZA4Wy//faZM2dOLr/88lrLL7744lRUVGS77bartfzxxx+vmR8oSd57773ceeed2Wabbb51FEdVVdU8R2T88Y9/TPJlWfBtDjvssHTp0iXHHXdcXn/99bnWT5gwIWeffXaSL+eOatq0aS699NJaj/mnP/0pkydPzg477DDfx1nY12NePvnkk1q3Kysra0bQzJw58zvvvyj23HPPPP7447n33nvnWjdp0qTMnj07yZfPb/bs2bnyyitr1s+ZM2ee5VGvXr3y2muv5eOPP65Z9vzzz+fRRx+d67HnzJmTX//613PtY/bs2Zk0adJCP5/ddtstzz//fG6//fa51n3zONpvv/1y3333ZdiwYVl++eUX6D2qa9OnT8/jjz8+z3VfzSv31Sl7e+65Z/773//mD3/4w1zbfv755986Z96Cvs+77bZbSqVShg4dOtd2X3/9WrVqNdf706hRo+y22275+9//Ps8y8uvHw/bbb58PPvggt956a82y6dOnz/eUyrq0JI7lBfnebdWqVZIs0nH9dQv6Xpbj8wSA4jAiDIDC2WmnnbLFFlvkV7/6Vd55552ss846ue+++3LnnXdmyJAh6dWrV63t11577QwYMCBHHXVUmjVrliuuuCJJ5vkL99f93//9X6666qoMHDgwK6+8cqZOnZp77703I0aMyE477VQzUfj8tGvXLrfffnu23377rLvuutl3331rJu0fPXp0brrppmy44YZJvhzRcsopp2To0KHZdttts/POO2fMmDG54oor8sMf/vBbJ1Jf2NdjXn7xi19k4sSJ2XLLLbPiiivm3XffzWWXXZZ11123Zq6xunbCCSfkrrvuyo477pgDDjggffv2zbRp0/Liiy/m1ltvzTvvvJP27dtnp512ysYbb5yTTz4577zzTtZcc83cdttt8xyRd+CBB+aiiy7KgAEDctBBB2XChAm56qqrstZaa9WaOH6zzTbLoYcemnPPPTfPPfdcttlmmzRp0iRvvPFGbrnlllxyySW1JlNf0Odz6623Zo899siBBx6Yvn37ZuLEibnrrrty1VVXZZ111qnZ9mc/+1lOPPHE3H777fnlL3+ZJk2aLPoLuYimT5+ejTbaKD/+8Y+z7bbbplu3bpk0aVLuuOOO/Pvf/87AgQOz3nrrJfmyuLv55ptz2GGH5YEHHsjGG2+cOXPm5LXXXsvNN9+ce++9d77F8IK+z1tssUX222+/XHrppXnjjTey7bbbprq6Ov/+97+zxRZb5IgjjkiS9O3bN/fff38uuuiidO3aNT179swGG2yQ8847Lw888EA22GCDHHzwwVlzzTUzceLEjB49Ovfff38mTpyY5MsLUFx++eXZf//988wzz6RLly654YYb0rJlyyX+mi+JY3lBvnfXXXfdNGrUKOeff34mT56cZs2aZcstt5zvHHnzs6DvZTk+TwAokHJcqhIAvk+DBw8uffO/vKlTp5aOOeaYUteuXUtNmjQprbrqqqXf/va3perq6lrbJSkNHjy49H//93+lVVddtdSsWbPSeuutV3rggQe+83Gfeuqp0h577FHq3r17qVmzZqVWrVqV1l9//dJFF11U+uKLLxY4/wcffFA65phjSj/4wQ9KzZs3L7Vs2bLUt2/f0m9+85vS5MmTa217+eWXl1ZfffVSkyZNSp06dSr98pe/LH366ae1thk0aFCpR48ei/V6fNOtt95a2mabbUodO3YsNW3atNS9e/fSoYceWvrwww+/8/nNb59f6dGjR2mHHXaY57qpU6eWTjnllNIqq6xSatq0aal9+/aljTbaqHThhReWZs2aVbPdJ598Utpvv/1Kbdq0KbVt27a03377lZ599tlSktK1115ba5//93//V1p55ZVLTZs2La277rqle++9d56vWalUKl199dWlvn37llq0aFFq3bp1qXfv3qUTTzyx9MEHH3xn/s0226y02Wab1Vr2ySeflI444ojSCiusUGratGlpxRVXLA0aNKhUVVU11/233377UpLSY489Nt/X7puSlM4444ya22eccUYpSenjjz+utd21115bSlIaO3bsfPf1xRdflP7whz+UBg4cWOrRo0epWbNmpZYtW5bWW2+90m9/+9vSzJkza20/a9as0vnnn19aa621Ss2aNSu1a9eu1Ldv39LQoUNrHcc9evQoDRo0qNZ9F/R9nj17dum3v/1tafXVVy81bdq01KFDh9J2221XeuaZZ2q2ee2110o/+clPSi1atCglqfVYH330UWnw4MGlbt26lZo0aVLq3LlzaauttipdffXVtfK8++67pZ133rnUsmXLUvv27UtHH310afjw4aUk3/nZ8NVr+9RTT9UsGzRoUKlVq1ZzbfvV+/N1dX0sL+j37h/+8IfSyiuvXGrUqFGt5/lt35+L+l4uzucJAHyXilJpEWZpBYCCqKioyODBg+c6bZCG75133knPnj1z7bXX5oADDih3nIW266675sUXX8ybb75Z7igAAA2GOcIAABqYDz/8MP/85z+z3377lTsKAECDYo4wAIAGYuzYsXn00Ufzxz/+MU2aNMmhhx5a7kgAAA2KEWEAAA3EQw89lP322y9jx47Nn//853Tu3LnckQAAGhRzhAEAAABQCEaEAQAAAFAIDXKOsOrq6nzwwQdp3bp1Kioqyh0HAAAAgDIqlUqZOnVqunbtmsrK+Y/7apBF2AcffJBu3bqVOwYAAAAA9ch7772XFVdccb7rG2QR1rp16yRfPrk2bdqUOQ0AAAAA5TRlypR069atpjOanwZZhH11OmSbNm0UYQAAAAAkyXdOoWWyfAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISx0Efbwww9np512SteuXVNRUZE77rijZt0XX3yRk046Kb17906rVq3StWvX7L///vnggw9q7WPixInZZ5990qZNmyy77LI56KCD8tlnny32kwEAAACA+VnoImzatGlZZ5118vvf/36uddOnT8/o0aNz2mmnZfTo0bntttsyZsyY7LzzzrW222efffLyyy9nxIgR+cc//pGHH344hxxyyKI/CwAAAAD4DhWlUqm0yHeuqMjtt9+egQMHznebp556Kj/60Y/y7rvvpnv37nn11Vez5ppr5qmnnkq/fv2SJMOHD8/222+f999/P127dv3Ox50yZUratm2byZMnp02bNosaHwAAAIClwIJ2RUt8jrDJkyenoqIiyy67bJLk8ccfz7LLLltTgiVJ//79U1lZmSeffHKe+5g5c2amTJlS6wsAAAAAFsYSLcJmzJiRk046KXvvvXdNGzd+/Ph07Nix1naNGzfOcsstl/Hjx89zP+eee27atm1b89WtW7clGRsAAACApdASK8K++OKL7LnnnimVSrnyyisXa1+nnHJKJk+eXPP13nvv1VFKAAAAAIqi8ZLY6Vcl2LvvvptRo0bVOjezc+fOmTBhQq3tZ8+enYkTJ6Zz587z3F+zZs3SrFmzJREVAAAAgIKo8xFhX5Vgb7zxRu6///4sv/zytdZvuOGGmTRpUp555pmaZaNGjUp1dXU22GCDuo4DAAAAAEkWYUTYZ599ljfffLPm9tixY/Pcc89lueWWS5cuXbL77rtn9OjR+cc//pE5c+bUzPu13HLLpWnTplljjTWy7bbb5uCDD85VV12VL774IkcccUT22muvBbpiJAAAAAAsiopSqVRamDs8+OCD2WKLLeZaPmjQoJx55pnp2bPnPO/3wAMPZPPNN0+STJw4MUcccUTuvvvuVFZWZrfddsull16aZZZZZoEyLOglMQEAAABY+i1oV7TQRVh9oAgDAAAA4CsL2hUtsatGAgAAAEB9oggDAAAAoBAUYQAAAAAUgiIMAAAAgEJoXO4A/P/GjRuXqqqqcseggWvfvn26d+9e7hgAAABQ7yjC6olx48ZljdVWy/QZM8odhQauZfPmeXXMGGUYAAAAfIMirJ6oqqrK9BkzcmG7LunVuGm549BAvTV7Vo7/9MNUVVUpwgAAAOAbFGH1TK/GTbNW0+bljgEAAACw1DFZPgAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCI3LHYDa3vpiZrkj0IA5fgAAAGD+FGH1xIcffpikIsdPGl/uKDR4Ff/veAIAAAC+ThFWT0yaNClJKS1X2yOVLTuUOw4NVPX0jzN9zC3/73gCAAAAvk4RVs9UtuyQxsusUO4YNFCzyx0AAAAA6jGT5QMAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACF0LjcAahtzvSPyx2BBszxAwAAAPOnCKsnqqqqkiSfj7mlzElYGnx1PAEAAAD/P0VYPTF16tQkSYsWLVNZ6YxVFk11dXU+/3x6zfEEAAAA/P8WunF5+OGHs9NOO6Vr166pqKjIHXfcUWt9qVTK6aefni5duqRFixbp379/3njjjVrbTJw4Mfvss0/atGmTZZddNgcddFA+++yzxXoiS4vKyso0atTYl69F+lKiAgAAwPwt9G/N06ZNyzrrrJPf//7381x/wQUX5NJLL81VV12VJ598Mq1atcqAAQMyY8aMmm322WefvPzyyxkxYkT+8Y9/5OGHH84hhxyy6M8CAAAAAL7DQp8aud1222W77bab57pSqZRhw4bl1FNPzS677JIkuf7669OpU6fccccd2WuvvfLqq69m+PDheeqpp9KvX78kyWWXXZbtt98+F154Ybp27boYTwcAAAAA5q1Oz6MaO3Zsxo8fn/79+9csa9u2bTbYYIM8/vjjSZLHH388yy67bE0JliT9+/dPZWVlnnzyyXnud+bMmZkyZUqtLwAAAABYGHVahI0fPz5J0qlTp1rLO3XqVLNu/Pjx6dixY631jRs3znLLLVezzTede+65adu2bc1Xt27d6jI2AAAAAAXQIGbWPuWUUzJ58uSar/fee6/ckQAAAABoYOq0COvcuXOS5KOPPqq1/KOPPqpZ17lz50yYMKHW+tmzZ2fixIk123xTs2bN0qZNm1pfAAAAALAw6rQI69mzZzp37pyRI0fWLJsyZUqefPLJbLjhhkmSDTfcMJMmTcozzzxTs82oUaNSXV2dDTbYoC7jAAAAAECNhb5q5GeffZY333yz5vbYsWPz3HPPZbnllkv37t0zZMiQnH322Vl11VXTs2fPnHbaaenatWsGDhyYJFljjTWy7bbb5uCDD85VV12VL774IkcccUT22msvV4wEAAAAYIlZ6CLs6aefzhZbbFFz+9hjj02SDBo0KNddd11OPPHETJs2LYccckgmTZqUTTbZJMOHD0/z5s1r7vOXv/wlRxxxRLbaaqtUVlZmt912y6WXXloHTwcAAAAA5m2hi7DNN988pVJpvusrKipy1lln5ayzzprvNsstt1xuvPHGhX1oAAAAAFhkDeKqkQAAAACwuBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFEKdF2Fz5szJaaedlp49e6ZFixbp1atXfv3rX6dUKtVsUyqVcvrpp6dLly5p0aJF+vfvnzfeeKOuowAAAABAjTovws4///xceeWVufzyy/Pqq6/m/PPPzwUXXJDLLrusZpsLLrggl156aa666qo8+eSTadWqVQYMGJAZM2bUdRwAAAAASJI0rusdPvbYY9lll12yww47JElWWmml3HTTTfnPf/6T5MvRYMOGDcupp56aXXbZJUly/fXXp1OnTrnjjjuy1157zbXPmTNnZubMmTW3p0yZUtexAQAAAFjK1fmIsI022igjR47M66+/niR5/vnn88gjj2S77bZLkowdOzbjx49P//79a+7Ttm3bbLDBBnn88cfnuc9zzz03bdu2rfnq1q1bXccGAAAAYClX5yPCTj755EyZMiWrr756GjVqlDlz5uQ3v/lN9tlnnyTJ+PHjkySdOnWqdb9OnTrVrPumU045Jccee2zN7SlTpijDAAAAAFgodV6E3XzzzfnLX/6SG2+8MWuttVaee+65DBkyJF27ds2gQYMWaZ/NmjVLs2bN6jgpAAAAAEVS50XYCSeckJNPPrlmrq/evXvn3XffzbnnnptBgwalc+fOSZKPPvooXbp0qbnfRx99lHXXXbeu4wAAAABAkiUwR9j06dNTWVl7t40aNUp1dXWSpGfPnuncuXNGjhxZs37KlCl58skns+GGG9Z1HAAAAABIsgRGhO200075zW9+k+7du2ettdbKs88+m4suuigHHnhgkqSioiJDhgzJ2WefnVVXXTU9e/bMaaedlq5du2bgwIF1HQcAAAAAkiyBIuyyyy7LaaedlsMPPzwTJkxI165dc+ihh+b000+v2ebEE0/MtGnTcsghh2TSpEnZZJNNMnz48DRv3ryu4wAAAABAkiVQhLVu3TrDhg3LsGHD5rtNRUVFzjrrrJx11ll1/fAAAAAAME91PkcYAAAAANRHijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABRC43IHAICGZty4camqqip3DBq49u3bp3v37uWOAQBQKIowAFgI48aNyxqrrZbpM2aUOwoNXMvmzfPqmDHKMACA75EiDAAWQlVVVabPmJEL23VJr8ZNyx2HBuqt2bNy/KcfpqqqShEGAPA9UoQBwCLo1bhp1mravNwxAACAhWCyfAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQGpc7AAAAALDgxo0bl6qqqnLHoIFr3759unfvXu4Y3ztFGAAAADQQ48aNyxqrrZbpM2aUOwoNXMvmzfPqmDGFK8MUYQAAANBAVFVVZfqMGbmwXZf0aty03HFooN6aPSvHf/phqqqqFGEAAABA/darcdOs1bR5uWNAg2OyfAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBCWSBH23//+N/vuu2+WX375tGjRIr17987TTz9ds75UKuX0009Ply5d0qJFi/Tv3z9vvPHGkogCAAAAAEmWQBH26aefZuONN06TJk1yzz335JVXXsnvfve7tGvXrmabCy64IJdeemmuuuqqPPnkk2nVqlUGDBiQGTNm1HUcAAAAAEiSNK7rHZ5//vnp1q1brr322pplPXv2rPl3qVTKsGHDcuqpp2aXXXZJklx//fXp1KlT7rjjjuy11151HQkAAAAA6r4Iu+uuuzJgwIDsscceeeihh7LCCivk8MMPz8EHH5wkGTt2bMaPH5/+/fvX3Kdt27bZYIMN8vjjj8+zCJs5c2ZmzpxZc3vKlCl1HRtYQsaNG5eqqqpyx6CBa9++fbp3717uGAAAQANX50XY22+/nSuvvDLHHnts/ud//idPPfVUjjrqqDRt2jSDBg3K+PHjkySdOnWqdb9OnTrVrPumc889N0OHDq3rqMASNm7cuKy22uqZMePzckehgWvevEXGjHlNGQYAACyWOi/Cqqur069fv5xzzjlJkvXWWy8vvfRSrrrqqgwaNGiR9nnKKafk2GOPrbk9ZcqUdOvWrU7yAktOVVVVZsz4PC1X2yOVLTuUOw4NVPX0jzN9zC2pqqpShAEAAIulzouwLl26ZM0116y1bI011sjf//73JEnnzp2TJB999FG6dOlSs81HH32Uddddd577bNasWZo1a1bXUYHvSWXLDmm8zArljkEDNbvcAQAAgKVGnV81cuONN86YMWNqLXv99dfTo0ePJF9OnN+5c+eMHDmyZv2UKVPy5JNPZsMNN6zrOAAAAACQZAmMCDvmmGOy0UYb5Zxzzsmee+6Z//znP7n66qtz9dVXJ0kqKioyZMiQnH322Vl11VXTs2fPnHbaaenatWsGDhxY13EAAAAAIMkSKMJ++MMf5vbbb88pp5ySs846Kz179sywYcOyzz771Gxz4oknZtq0aTnkkEMyadKkbLLJJhk+fHiaN29e13GAemDO9I/LHYEGzPEDAADUlTovwpJkxx13zI477jjf9RUVFTnrrLNy1llnLYmHB+qJDz/8MJVJPh9zS7mj0MBV5svjCQAAYHEskSIMIEkmTZqU6iQXtuuSXo2bljsODdRbs2fl+E8/zKRJk8odBQAAaOAUYcAS16tx06zV1KnPAAAAlFedXzUSAAAAAOojRRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIjcsdAAAAYEkZN25cqqqqyh2DBq59+/bp3r17uWMAdUARBgAALJXGjRuXNVZbLdNnzCh3FBq4ls2b59UxY5RhsBRQhAEAAEulqqqqTJ8xIxe265JejZuWOw4N1FuzZ+X4Tz9MVVWVIgyWAoowAABgqdarcdOs1bR5uWMAUA+YLB8AAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAohMblDgAAALAkvfXFzHJHoAFz/MDSRREGAAAslT788MMkyfGTxpc5CUuDr44noGFThAEAAEulSZMmJUlatGiZykqzwrBoqqur8/nn02uOJ6BhU4QBAABLtcrKyjRq5FcfFtXscgcA6pA/iwAAAABQCIowAAAAAArB+GAAWASuIMXiqK/Hz7hx41JVVVXuGDRg7du3T/fu3csdAwDmSxEGAAvhyytGVbgCGXWgol5dgWzcuHFZbbXVMmPGjHJHoQFr3rx5xowZowwDoN5ShAHAQvjyilGltFxtj1S27FDuODRQ1dM/zvQxt9SrK5BVVVVlxowZrq7HIvvqynpVVVWKMADqLUUYACyCypYd0niZFcodgwaqPl9/zNX1WHT1+cgGgC/5cx8AAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCaFzuAMDS760vZpY7Ag2Y4wcAAKgrijBgiamqqkqSHD9pfJmTsDT46ngCAABYVIowYImZOnVqkqRFi5aprHQmNoumuro6n38+veZ4AgAAWFSKMGCJq6ysTKNGPm5YVLPLHQAAAFhKGKIBAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJoXO4AANAQzZn+cbkj0IDV5+Nnzpw55Y5AA+XYge/XW1/MLHcEGrAiHz+KMABYCFVVVUmSz8fcUuYkLA2+Op7qgw8//DCVSWbM+LzcUWjAKvPlsQQsOR9++GFSUZnjJ40vdxQauorKQn5mK8IAYCFMnTo1SdKiRctUVpphgEVTXV2dzz+fXnM81QeTJk1KdZIL23VJr8ZNyx2HBuit2bNy/KcfZtKkSeWOAku1SZMmJaXqtNjrrDTq2LPccWig5kwYm8//enohP7MVYQCwCCorK9Ookf9GWVSzyx1gvno1bpq1mjYvdwwAvkOjjj3TaMXVyx0DGhx/ygYAAACgEBRhAAAAABSCczoAAACggZkz4Z1yR6ABK/LxowgDAACABuLLKw5X5PO/nlbuKDR4FfXqCtbfF0UYAAAANBBfXnG4lJar7ZHKlh3KHYcGqnr6x5k+5pZ6dQXr74siDAAAABqYypYd0niZFcodgwaq/l6/eskzWT4AAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKYYkXYeedd14qKioyZMiQmmUzZszI4MGDs/zyy2eZZZbJbrvtlo8++mhJRwEAAACgwJZoEfbUU0/lf//3f9OnT59ay4855pjcfffdueWWW/LQQw/lgw8+yE9/+tMlGQUAAACAgltiRdhnn32WffbZJ3/4wx/Srl27muWTJ0/On/70p1x00UXZcsst07dv31x77bV57LHH8sQTTyypOAAAAAAUXOMltePBgwdnhx12SP/+/XP22WfXLH/mmWfyxRdfpH///jXLVl999XTv3j2PP/54fvzjH8+1r5kzZ2bmzJk1t6dMmbKkYgMAFNpbX8z87o1gHhw7ADQES6QI++tf/5rRo0fnqaeemmvd+PHj07Rp0yy77LK1lnfq1Cnjx4+f5/7OPffcDB06dElEBQAgSVVVVZKKHD9p3j+PwYKp+H/HEgDUT3VehL333ns5+uijM2LEiDRv3rxO9nnKKafk2GOPrbk9ZcqUdOvWrU72DQBAMnXq1CSltFxtj1S27FDuODRA1dM/zvQxt/y/YwkA6qc6L8KeeeaZTJgwIeuvv37Nsjlz5uThhx/O5ZdfnnvvvTezZs3KpEmTao0K++ijj9K5c+d57rNZs2Zp1qxZXUcFAOAbKlt2SONlVih3DBqg2eUOAAALoM6LsK222iovvvhirWU///nPs/rqq+ekk05Kt27d0qRJk4wcOTK77bZbkmTMmDEZN25cNtxww7qOAwAAAABJlkAR1rp166y99tq1lrVq1SrLL798zfKDDjooxx57bJZbbrm0adMmRx55ZDbccMN5TpQPAAAAAHVhiV018ttcfPHFqayszG677ZaZM2dmwIABueKKK8oRBQAAAICC+F6KsAcffLDW7ebNm+f3v/99fv/7338fDw8AAAAAqSx3AAAAAAD4PijCAAAAACgERRgAAAAAhaAIAwAAAKAQynLVSAAAgO/LnDlzyh2BBszxA0sXRRgAALBUqqqqSmVFMmPG5+WOQgNXWfHl8QQ0fIowAABgqTR16tRUl5LLj1g+q6zQpNxxaKDe/O8XOeLyTzJ16tRyRwHqgCIMAABYqq2yQpP0WblpuWMAUA+YLB8AAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCHVehJ177rn54Q9/mNatW6djx44ZOHBgxowZU2ubGTNmZPDgwVl++eWzzDLLZLfddstHH31U11EAAAAAoEadF2EPPfRQBg8enCeeeCIjRozIF198kW222SbTpk2r2eaYY47J3XffnVtuuSUPPfRQPvjgg/z0pz+t6ygAAAAAUKNxXe9w+PDhtW5fd9116dixY5555pn85Cc/yeTJk/OnP/0pN954Y7bccsskybXXXps11lgjTzzxRH784x/Ptc+ZM2dm5syZNbenTJlS17EBAAAAWMot8TnCJk+enCRZbrnlkiTPPPNMvvjii/Tv379mm9VXXz3du3fP448/Ps99nHvuuWnbtm3NV7du3ZZ0bAAAAACWMku0CKuurs6QIUOy8cYbZ+21106SjB8/Pk2bNs2yyy5ba9tOnTpl/Pjx89zPKaecksmTJ9d8vffee0syNgAAAABLoTo/NfLrBg8enJdeeimPPPLIYu2nWbNmadasWR2lAgAAAKCIltiIsCOOOCL/+Mc/8sADD2TFFVesWd65c+fMmjUrkyZNqrX9Rx99lM6dOy+pOAAAAAAUXJ0XYaVSKUcccURuv/32jBo1Kj179qy1vm/fvmnSpElGjhxZs2zMmDEZN25cNtxww7qOAwAAAABJlsCpkYMHD86NN96YO++8M61bt66Z96tt27Zp0aJF2rZtm4MOOijHHntslltuubRp0yZHHnlkNtxww3leMRIAAAAA6kKdF2FXXnllkmTzzTevtfzaa6/NAQcckCS5+OKLU1lZmd122y0zZ87MgAEDcsUVV9R1FAAAAACoUedFWKlU+s5tmjdvnt///vf5/e9/X9cPDwAAAADztMQmywcAAACA+kQRBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKQREGAAAAQCEowgAAAAAoBEUYAAAAAIWgCAMAAACgEBRhAAAAABSCIgwAAACAQlCEAQAAAFAIijAAAAAACkERBgAAAEAhKMIAAAAAKARFGAAAAACFoAgDAAAAoBAUYQAAAAAUgiIMAAAAgEJQhAEAAABQCIowAAAAAApBEQYAAABAISjCAAAAACgERRgAAAAAhaAIAwAAAKAQFGEAAAAAFIIiDAAAAIBCUIQBAAAAUAiKMAAAAAAKoaxF2O9///ustNJKad68eTbYYIP85z//KWccAAAAAJZiZSvC/va3v+XYY4/NGWeckdGjR2edddbJgAEDMmHChHJFAgAAAGAp1rhcD3zRRRfl4IMPzs9//vMkyVVXXZV//vOfueaaa3LyySfX2nbmzJmZOXNmze3JkycnSaZMmfL9BV7CZsyYkSSZM2dOSqVSmdPQUFVXVyf58niqD98fjmvqguOapVF9O66Trx3bUz9Iac6sMqehIaqeXpWkfh7XL46dmWkzqsuchobq7Q+/SFJ/jm2f19SF+viZvbi+eh7f9TN6RakMP8XPmjUrLVu2zK233pqBAwfWLB80aFAmTZqUO++8s9b2Z555ZoYOHfo9pwQAAACgIXnvvfey4oorznd9WUaEVVVVZc6cOenUqVOt5Z06dcprr7021/annHJKjj322Jrb1dXVmThxYpZffvlUVFQs8bzUD1OmTEm3bt3y3nvvpU2bNuWOA3XCcc3SyHHN0shxzdLKsc3SyHFdTKVSKVOnTk3Xrl2/dbuynRq5MJo1a5ZmzZrVWrbsssuWJwxl16ZNGx9mLHUc1yyNHNcsjRzXLK0c2yyNHNfF07Zt2+/cpiyT5bdv3z6NGjXKRx99VGv5Rx99lM6dO5cjEgAAAABLubIUYU2bNk3fvn0zcuTImmXV1dUZOXJkNtxww3JEAgAAAGApV7ZTI4899tgMGjQo/fr1y49+9KMMGzYs06ZNq7mKJHxTs2bNcsYZZ8x1miw0ZI5rlkaOa5ZGjmuWVo5tlkaOa75NWa4a+ZXLL788v/3tbzN+/Pisu+66ufTSS7PBBhuUKw4AAAAAS7GyFmEAAAAA8H0pyxxhAAAAAPB9U4QBAAAAUAiKMAAAAAAKQREGAAAAQCEowgCAOjVnzpw899xz+fTTT8sdBQAogNGjR+fFF1+suX3nnXdm4MCB+Z//+Z/MmjWrjMmojxRhAGVQXV2d119/PY888kgefvjhWl/Q0AwZMiR/+tOfknxZgm222WZZf/31061btzz44IPlDQcALPUOPfTQvP7660mSt99+O3vttVdatmyZW265JSeeeGKZ01HfVJRKpVK5QwAUyRNPPJGf/exneffdd/PNj+CKiorMmTOnTMlg0ay44oq544470q9fv9xxxx0ZPHhwHnjggdxwww0ZNWpUHn300XJHhMU2a9asjB07Nr169Urjxo3LHQfq1Jw5c/Liiy+mR48eadeuXbnjwEJr27ZtRo8enV69euX888/PqFGjcu+99+bRRx/NXnvtlffee6/cEalH/C9OvXbXXXfNc3lFRUWaN2+eVVZZJT179vyeU8HiOeyww9KvX7/885//TJcuXVJRUVHuSLBYqqqq0rlz5yTJv/71r+yxxx75wQ9+kAMPPDCXXHJJmdPB4pk+fXqOPPLI/PnPf06SvP7661l55ZVz5JFHZoUVVsjJJ59c5oSw8IYMGZLevXvnoIMOqhnJ+9hjj6Vly5b5xz/+kc0337zcEWGhlEqlVFdXJ0nuv//+7LjjjkmSbt26paqqqpzRqIcUYdRrAwcOTEVFxTxHzZRKpVRUVGSTTTbJHXfc4a9XNBhvvPFGbr311qyyyirljgJ1olOnTnnllVfSpUuXDB8+PFdeeWWSLwuERo0alTkdLJ5TTjklzz//fB588MFsu+22Ncv79++fM888UxFGg3Trrbdm3333TZLcfffdGTt2bF577bXccMMN+dWvfmUkLw1Ov379cvbZZ6d///556KGHan4WGTt2bDp16lTmdNQ35gijXhsxYkR++MMfZsSIEZk8eXImT56cESNGZIMNNsg//vGPPPzww/nkk09y/PHHlzsqLLANNtggb775ZrljQJ35+c9/nj333DNrr712Kioq0r9//yTJk08+mdVXX73M6WDx3HHHHbn88suzySab1BrBu9Zaa+Wtt94qYzJYdN82kvfrE45DQzFs2LCMHj06RxxxRH71q1/V/MH51ltvzUYbbVTmdNQ3RoRRrx199NG5+uqra314bbXVVmnevHkOOeSQvPzyyxk2bFgOPPDAMqaEhXPkkUfmuOOOy/jx49O7d+80adKk1vo+ffqUKRksmjPPPDNrr7123nvvveyxxx5p1qxZkqRRo0ZGy9Dgffzxx+nYseNcy6dNm+bUdhosI3lZ2vTp02eeJe5vf/tbxzRzUYRRr7311ltp06bNXMvbtGmTt99+O0my6qqrOu+bBmW33XZLkloF7tdP9zVZPg3R7rvvPteyQYMGlSEJ1K2v5nQ88sgjk6Sm/PrjH/+YDTfcsJzRYJF9NZL3q7lKjeRlaTFr1qxMmDChZr6wr3Tv3r1MiaiPFGHUa3379s0JJ5yQ66+/Ph06dEjy5V9mTzzxxPzwhz9M8uV8S926dStnTFgoY8eOLXcEWGyXXnrpAm971FFHLcEksGSdc8452W677fLKK69k9uzZueSSS/LKK6/ksccey0MPPVTueLBIjORlafP666/noIMOymOPPVZruT80My8VpW/OQg71yJgxY7LLLrtk7NixNWXXe++9l5VXXjl33nlnfvCDH+SOO+7I1KlTs99++5U5LUBxLOgVeysqKmpG8EJD9dZbb+W8887L888/n88++yzrr79+TjrppPTu3bvc0QBIsvHGG6dx48Y5+eST53lV9nXWWadMyaiPFGHUe9XV1bnvvvvy+uuvJ0lWW221bL311qmsdK0HGq633norw4YNy6uvvpokWXPNNXP00UenV69eZU4GACyNjORladaqVas888wzTu1lgSjCAL5n9957b3beeeesu+662XjjjZMkjz76aJ5//vncfffd2XrrrcucEICv/Otf/0qjRo0yYMCAWsvvvffeVFdXZ7vttitTMlg4RvKyNPvhD3+Yiy++OJtsskm5o9AAKMKo90aOHJmRI0fOc9LDa665pkypYNGtt956GTBgQM4777xay08++eTcd999GT16dJmSwaJ7//33c9ddd2XcuHGZNWtWrXUXXXRRmVLB4uvTp0/OO++8bL/99rWWDx8+PCeddFKef/75MiUD4CujRo3KqaeemnPOOWeeV2Wf1wXYKC5FGPXa0KFDc9ZZZ6Vfv37zPNf79ttvL1MyWHTNmzfPiy++mFVXXbXW8tdffz19+vTJjBkzypQMFs3IkSOz8847Z+WVV85rr72WtddeO++8805KpVLWX3/9jBo1qtwRYZG1aNEir776alZaaaVay995552stdZamTZtWnmCAVDjq2lzvvn7osnymRdXjaReu+qqq3LdddeZCJ+lSocOHfLcc8/NVYQ999xz6dixY5lSwaI75ZRTcvzxx2fo0KFp3bp1/v73v6djx47ZZ599su2225Y7HiyWtm3b5u23356rCHvzzTfTqlWr8oSCOmAkL0uTBx54oNwRaEAUYdRrs2bNykYbbVTuGFCnDj744BxyyCF5++23a47vRx99NOeff36OPfbYMqeDhffqq6/mpptuSpI0btw4n3/+eZZZZpmcddZZ2WWXXfLLX/6yzAlh0e2yyy4ZMmRIbr/99poLmrz55ps57rjjsvPOO5c5HSya7xrJCw3NZpttVu4INCCKMOq1X/ziF7nxxhtz2mmnlTsK1JnTTjstrVu3zu9+97uccsopSZKuXbvmzDPPdJUmGqRWrVrVjCbo0qVL3nrrray11lpJkqqqqnJGg8V2wQUXZNttt83qq6+eFVdcMcmXI2k23XTTXHjhhWVOB4vGSF6WRpMmTcqf/vSnmquyr7XWWjnwwAPTtm3bMiejvjFHGPXa0Ucfneuvvz59+vRJnz595pr00LBtGrqpU6cmSVq3bl3mJLDoBg4cmB122CEHH3xwjj/++Nx555054IADctttt6Vdu3a5//77yx0RFkupVMqIESPy/PPPp0WLFunTp09+8pOflDsWLLLWrVvnueeeS69evdKuXbs88sgjWWuttfL8889nl112yTvvvFPuiLBQnn766QwYMCAtWrTIj370oyTJU089lc8//zz33XefkY7UogijXttiiy3mu66iosIEzAD1wNtvv53PPvssffr0ybRp03Lcccflsccey6qrrpqLLrooPXr0KHdEAL6mc+fOeeCBB7LGGmtkzTXXzHnnnZedd945zz//fDbeeON89tln5Y4IC2XTTTfNKquskj/84Q9p3PjLE99mz56dX/ziF3n77bfz8MMPlzkh9YkiDOB7sP7662fkyJFp165d1ltvvbmuaPN1o0eP/h6TAfBdRo4cmZEjR2bChAmprq6ute6aa64pUypYdEbysrRp0aJFnn322ay++uq1lr/yyivp169fpk+fXqZk1EfmCAP4Huyyyy5p1qxZzb+/rQgDoP4YOnRozjrrrPTr1y9dunTx+c1S4aKLLqoZ9TV06NB89tln+dvf/lYzkhcamjZt2mTcuHFzFWHvvfeeKUiYixFh1Ds//elPc91116VNmzb56U9/+q3b3nbbbd9TKgC+brnllsvrr7+e9u3bp127dt9aDkycOPF7TAZ1q0uXLrnggguy3377lTsKAPNx1FFH5fbbb8+FF15Y66rsJ5xwQnbbbbcMGzasvAGpV4wIo95p27ZtzS9UrvDB0mjllVfOU089leWXX77W8kmTJmX99dfP22+/XaZksOAuvvjimr+wXnzxxUbJsNSaNWtWzS9VANRPF154YSoqKrL//vtn9uzZSZImTZrkl7/8Zc4777wyp6O+MSIM4HtWWVmZ8ePHp2PHjrWWf/TRR+nWrVtmzZpVpmQAfNNJJ52UZZZZJqeddlq5o8BiMZKXIpg+fXreeuutJEmvXr3SsmXLMieiPjIijHrtpptuyt577z3PdSeccEJ++9vffs+JYNHdddddNf++9957a414nDNnTkaOHJmePXuWIxosln/9619p1KhRBgwYUGv5fffdlzlz5mS77bYrUzJYfDNmzMjVV1+d+++/P3369EmTJk1qrTefEg3F10fyOk2MpVXLli3Tu3fvcsegnjMijHpt2WWXzU033TTXL1HHHHNM/vrXv+bDDz8sUzJYeJWVlUmSioqKfPOjt0mTJllppZXyu9/9LjvuuGM54sEi69OnT84777xsv/32tZYPHz48J510Up5//vkyJYPFt8UWW8x3XUVFRUaNGvU9poHFN3v27Nx4440ZMGBAOnXqVO44sMjMLc2iMiKMeu0vf/lL9t577/zjH//IJptskiQ58sgjc9ttt+WBBx4oczpYONXV1UmSnj175qmnnkr79u3LnAjqxhtvvJE111xzruWrr7563nzzzTIkgrrj5w2WNo0bN85hhx2WV199tdxRYLF8fW7pNm3amK+UBaYIo17bYYcdcsUVV2TnnXfOiBEj8qc//Sl33nlnHnjggfzgBz8odzxYJGPHji13BKhTbdu2zdtvv52VVlqp1vI333wzrVq1Kk8oAObrRz/6UZ599tn06NGj3FFgkV177bU1/77uuuvKF4QGRxFGvfezn/0skyZNysYbb5wOHTrkoYceyiqrrFLuWLDIjjrqqKyyyio56qijai2//PLL8+abb5q3gwZnl112yZAhQ3L77benV69eSb4swY477rjsvPPOZU4Hi+/pp5/OzTffnHHjxs11QROn29AQHX744TnuuOPy/vvvp2/fvnP90aJPnz5lSgaLZsstt8xtt92WZZddttbyKVOmZODAgU5jpxZzhFHvHHvssfNcfsstt2T99dev+SUrMUEtDdMKK6yQu+66K3379q21fPTo0dl5553z/vvvlykZLJrJkydn2223zdNPP50VV1wxSfL+++9n0003necPpdCQ/PWvf83++++fAQMG5L777ss222yT119/PR999FF23XXXWiMSoKH4at7Sr/tqDtOKiorMmTOnDKlg0c3vquwTJkzICiuskC+++KJMyaiPjAij3nn22WfnuXyVVVbJlClTatY7B5yG6pNPPql1xcivtGnTJlVVVWVIBIunbdu2eeyxxzJixIg8//zzadGiRfr06ZOf/OQn5Y4Gi+2cc87JxRdfnMGDB6d169a55JJL0rNnzxx66KHp0qVLuePBIjFNA0uLF154oebfr7zySsaPH19ze86cORk+fHhWWGGFckSjHjMiDOB7tvbaa+ewww7LEUccUWv5ZZddliuvvDKvvPJKmZIB8E2tWrXKyy+/nJVWWinLL798HnzwwfTu3TuvvvpqttxyS1ewBiijysrKmgES86o2WrRokcsuuywHHnjg9x2NesyIMIDv2bHHHpsjjjgiH3/8cbbccsskyciRI/O73/3O/GA0WCNHjszIkSMzYcKEmiukfuWaa64pUypYfO3atcvUqVOTfHlq+0svvZTevXtn0qRJmT59epnTwaK74YYbctVVV2Xs2LF5/PHH06NHjwwbNiw9e/bMLrvsUu54sEDGjh2bUqmUlVdeOf/5z3/SoUOHmnVNmzZNx44d06hRozImpD5ShFGvTZs2Leedd958f7l6++23y5QMFt2BBx6YmTNn5je/+U1+/etfJ0lWWmmlXHnlldl///3LnA4W3tChQ3PWWWelX79+6dKli1PXWar85Cc/yYgRI9K7d+/sscceOfroozNq1KiMGDEiW221VbnjwSK58sorc/rpp2fIkCH5zW9+UzMn2LLLLpthw4Ypwmgwvrry6Td/T4Rv49RI6rW99947Dz30UPbbb795/nJ19NFHlykZ1I2PP/44LVq0yDLLLFPuKLDIunTpkgsuuCD77bdfuaNAnZs4cWJmzJiRrl27prq6OhdccEEee+yxrLrqqjn11FPTrl27ckeEhbbmmmvmnHPOycCBA9O6des8//zzWXnllfPSSy9l8803N2cpDdYrr7wyzyv8uoo1X2dEGPXaPffck3/+85/ZeOONyx0FloivD9+GhmrWrFnZaKONyh0D6tzs2bPzj3/8IwMGDEjy5Vw0J598cplTweIbO3Zs1ltvvbmWN2vWLNOmTStDIlg8b7/9dnbddde8+OKLNVdATf7/C6y5EipfN/d1c6EeadeuXZZbbrlyx4A61bNnz6y88srz/YKG5he/+EVuvPHGcseAOte4ceMcdthhmTFjRrmjQJ3q2bNnnnvuubmWDx8+PGusscb3HwgW09FHH52ePXtmwoQJadmyZV5++eU8/PDD6devXx588MFyx6OeMSKMeu3Xv/51Tj/99Pz5z39Oy5Ytyx0H6sSQIUNq3f7iiy/y7LPPZvjw4TnhhBPKEwoWw4wZM3L11Vfn/vvvT58+fdKkSZNa6y+66KIyJYPF96Mf/SjPPfdczTw0sDQ49thjM3jw4MyYMSOlUin/+c9/ctNNN+Xcc8/NH//4x3LHg4X2+OOPZ9SoUWnfvn0qKytTWVmZTTbZJOeee26OOuqoPPvss+WOSD2iCKNe+93vfpe33nornTp1ykorrTTXL1ejR48uUzJYdPOb2+73v/99nn766e85DSy+F154Ieuuu26S5KWXXqq1zsT5NHSHH354jj322Lz33nvp27dvWrVqVWt9nz59ypQMFt0vfvGLtGjRIqeeemqmT5+en/3sZ+natWsuueSS7LXXXuWOBwttzpw5ad26dZKkffv2+eCDD7LaaqulR48eGTNmTJnTUd+YLJ96bejQod+6/owzzvieksCS9/bbb2fdddfNlClTyh0FgP+nsnLumUS+mn+moqLCvDM0eNOnT89nn32Wjh07ljsKLLJNN900xx13XAYOHJif/exn+fTTT3Pqqafm6quvzjPPPDPXH+ooNiPCqNcUXRTJrbfeak48gHpm7Nix5Y4AdW7LLbfMbbfdlmWXXTYtW7asmYJkypQpGThwYEaNGlXmhLBwTj311JoLPZx11lnZcccds+mmm2b55ZfP3/72tzKno74xIgzge7beeuvVOl2sVCpl/Pjx+fjjj3PFFVfkkEMOKWM6WDRPP/10br755nlesvy2224rUyoA5qWysjLjx4+faxTYhAkTssIKK+SLL74oUzKoOxMnTky7du1M08BcjAijXpszZ04uvvji+f5yNXHixDIlg0U3cODAWrcrKyvToUOHbL755ll99dXLEwoWw1//+tfsv//+GTBgQO67775ss802ef311/PRRx9l1113LXc8WCzXX3/9t67ff//9v6cksPheeOGFmn+/8sorGT9+fM3tOXPmZPjw4VlhhRXKEQ0W2RdffJEWLVrkueeey9prr12z3JkWzI8ijHpt6NCh+eMf/5jjjjsup556an71q1/lnXfeyR133JHTTz+93PFgoc2ePTs9e/bMgAED0qlTp3LHgTpxzjnn5OKLL87gwYPTunXrXHLJJenZs2cOPfTQdOnSpdzxYLF88wInX3zxRaZPn56mTZumZcuWijAalHXXXTcVFRWpqKjIlltuOdf6Fi1a5LLLLitDMlh0TZo0Sffu3c3ZyAJzaiT1Wq9evXLppZdmhx12SOvWrfPcc8/VLHviiSdy4403ljsiLLSWLVvm1VdfTY8ePcodBepEq1at8vLLL2ellVbK8ssvnwcffDC9e/fOq6++mi233DIffvhhuSNCnXrjjTfyy1/+MieccEIGDBhQ7jiwwN59992USqWsvPLK+c9//pMOHTrUrGvatGk6duyYRo0alTEhLJo//elPue2223LDDTcYCcZ3MiKMem38+PHp3bt3kmSZZZbJ5MmTkyQ77rhjTjvttHJGg0X2ox/9KM8++6wijKVGu3btMnXq1CTJCiuskJdeeim9e/fOpEmTMn369DKng7q36qqr5rzzzsu+++6b1157rdxxYIF99bNHdXV1mZNA3br88svz5ptvpmvXrunRo0datWpVa/3o0aPLlIz6SBFGvbbiiivmww8/TPfu3dOrV6/cd999WX/99fPUU0+lWbNm5Y4Hi+Twww/Pcccdl/fffz99+/ad6z/qPn36lCkZLJqf/OQnGTFiRHr37p099tgjRx99dEaNGpURI0Zkq622Knc8WCIaN26cDz74oNwxYLG88sor85yHd+eddy5TIlg035yDF76NUyOp104++eS0adMm//M//5O//e1v2XfffbPSSitl3LhxOeaYY3LeeeeVOyIstMrKyrmWVVRUpFQqpaKiwvwGNDgTJ07MjBkz0rVr11RXV+eCCy7IY489llVXXTWnnnpq2rVrV+6IsMjuuuuuWrdLpVI+/PDDXH755enWrVvuueeeMiWDRff2229n1113zYsvvljzM0iSmqvr+VkEWJopwmhQnnjiiZpfrnbaaadyx4FF8u67737reqdMAtQf3/zjRUVFRTp06JAtt9wyv/vd71wQggZpp512SqNGjfLHP/4xPXv2zH/+85988sknOe6443LhhRdm0003LXdEWGiTJk3KrbfemrfeeisnnHBClltuuYwePTqdOnVyNVRqcWok9donn3yS5ZdfPkny3nvv5V//+lc+//zz9OvXr8zJYNG9++672WijjdK4ce2P4NmzZ+exxx5ThNEgVVdX580338yECRPmmnvmJz/5SZlSweIzlxJLo8cffzyjRo1K+/btU1lZmcrKymyyySY599xzc9RRR+XZZ58td0RYKC+88EL69++ftm3b5p133snBBx+c5ZZbLrfddlvGjRuX66+/vtwRqUcUYdRLL774Ynbaaae89957WXXVVfPXv/412267baZNm5bKyspcfPHFufXWW50LToO0xRZb5MMPP0zHjh1rLZ88eXK22GILpyPQ4DzxxBP52c9+VnM1sq9zui9Lmzlz5uTFF19Mjx49nPZLgzVnzpy0bt06SdK+fft88MEHWW211dKjR4+MGTOmzOlg4R177LE54IADcsEFF9Qc20my/fbb52c/+1kZk1EfzT1RDdQDJ554Ynr37p2HH344m2++eXbcccfssMMOmTx5cj799NMceuih5gejwfpqLrBv+uSTT+aaOB8agsMOOyz9+vXLSy+9lIkTJ+bTTz+t+Zo4cWK548FiGTJkSP70pz8l+bI8+MlPfpL1118/3bp1y4MPPljecLCI1l577Tz//PNJkg022CAXXHBBHn300Zx11llZeeWVy5wOFt5TTz2VQw89dK7lK6ywQsaPH1+GRNRnRoRRLz311FMZNWpU+vTpk3XWWSdXX311Dj/88Jp5Oo488sj8+Mc/LnNKWDg//elPk3w5QuaAAw6odeXTOXPm5IUXXshGG21UrniwyN54443ceuutWWWVVcodBercrbfemn333TdJcvfdd+edd97Ja6+9lhtuuCG/+tWv8uijj5Y5ISy8U089NdOmTUuSDB06NDvttFM23XTTLL/88vnrX/9a5nSw8Jo1a5YpU6bMtfz1119Phw4dypCI+kwRRr00ceLEdO7cOUmyzDLLpFWrVrVOP2jXrl2mTp1arniwSNq2bZvkyxFhrVu3TosWLWrWNW3aND/+8Y9z8MEHlyseLLINNtggb775piKMpVJVVVXNzyT/+te/sscee+QHP/hBDjzwwFxyySVlTgeLZsCAATX/XnXVVfPaa69l4sSJadeu3TxHrUN9t/POO+ess87KzTffnOTLPzyPGzcuJ510Unbbbbcyp6O+UYRRb33zP2H/KdPQXXvttUmSlVZaKccff7zTIFlqHHnkkTnuuOMyfvz49O7dO02aNKm1vk+fPmVKBouvU6dOeeWVV9KlS5cMHz48V155ZZJk+vTpadSoUZnTwcI58MADF2i7a665Zgkngbr1u9/9Lrvvvns6duyYzz//PJtttlnGjx+fDTfcML/5zW/KHY96pqL0zVltoR6orKzMdtttV3Pq2N13350tt9yypjiYOXNmhg8fbgJmGqTPP/88pVIpLVu2TPLlVSRvv/32rLnmmtlmm23KnA4W3lenrX9dRUVFzXx4PqtpyM4888wMGzYsXbp0yfTp0/P666+nWbNmueaaa/KHP/whjz/+eLkjwgKrrKxMjx49st566811cZOvu/3227/HVFB3Hnnkkbzwwgv57LPPsv7666d///7ljkQ9pAijXvr5z3++QNt9NcIGGpJtttkmP/3pT3PYYYdl0qRJWW211dK0adNUVVXloosuyi9/+ctyR4SF8u67737r+h49enxPSWDJuPXWW/Pee+9ljz32yIorrpgk+fOf/5xll102u+yyS5nTwYIbPHhwbrrppvTo0SM///nPs++++2a55ZYrdyyA75UiDOB71r59+zz00ENZa6218sc//jGXXXZZnn322fz973/P6aefnldffbXcEQGApdTMmTNz22235Zprrsljjz2WHXbYIQcddFC22WYbU5HQoI0cOTIXX3xxzc/Sa6yxRoYMGWJUGHNRhAF8z1q2bJnXXnst3bt3z5577pm11lorZ5xxRt57772sttpqmT59erkjwne66667st1226VJkya56667vnXbnXfe+XtKBUvGyJEjM3LkyEyYMCHV1dW11plLiYbs3XffzXXXXZfrr78+s2fPzssvv5xlllmm3LFgoV1xxRU5+uijs/vuu2fDDTdMkjzxxBO59dZbc/HFF2fw4MFlTkh9YrJ8gO/ZKquskjvuuCO77rpr7r333hxzzDFJkgkTJqRNmzZlTgcLZuDAgRk/fnw6duyYgQMHznc7c4TR0A0dOjRnnXVW+vXrly5duhgxw1KlsrKyZk5Hn9U0ZOecc04uvvjiHHHEETXLjjrqqGy88cY555xzFGHUYkQYwPfs1ltvzc9+9rPMmTMnW221Ve67774kybnnnpuHH34499xzT5kTAvCVLl265IILLsh+++1X7ihQJ75+auQjjzySHXfcMT//+c+z7bbbzvPiJ9AQLLPMMnnuueeyyiqr1Fr+xhtvZL311stnn31WpmTURz7pAL5nu+++e8aNG5enn346w4cPr1m+1VZb5eKLLy5jMlg43bt3zyeffFJz+/LLL8+UKVPKmAjq3qxZs7LRRhuVOwbUicMPPzxdunTJeeedlx133DHvvfdebrnllmy//fZKMBq0nXfeeZ5XO73zzjuz4447liER9ZkRYQDAIqmsrKw5PTJJ2rRpk+eeey4rr7xymZNB3TnppJOyzDLL5LTTTit3FFhslZWV6d69e9Zbb71vPc33tttu+x5TweI7++yzc+GFF+b/a+/eY6q87ziOfw4ICEHxKBepY4LhUhUVFltnWbt6QbSueGmjUqp1jTMqttL0Mp1Y2+qk1awupnbeUsFZqdukjqRCVLTWu0FaFSuKXBRUUCu4KRYjsD+asjEvFdDzO3Der4SE8/yeP95/nnzP8/x+UVFRjfYI27t3r15//fVG24+8+uqrpjJhJxiEAYCNXb9+Xe+///5dN14uKioyVAY0zf8Pwjp06KAjR44wCEObMmvWLK1bt059+/ZV37595eLi0mj9ww8/NFQGNN3kyZPva5+7tWvX2qAGeHCCgoLu6z6LxcJ3bbBZPgDY2pQpU7Rr1y5NnDiRjZcBwM4dPXpUERERkqS8vDyzMUALpaSkmE4AHori4mLTCWhFGIQBgI1lZmbqiy++UFRUlOkUoMXWrFkjT09PSdKtW7eUkpIib2/vRvfwCgJas507d5pOAADcp8uXL0vSbd9FgP/Fq5EAYGNBQUHasmWLevbsaToFaJHAwMCffKKRVxDQWo0dO/Yn77FYLNq0aZMNagAAd1NVVaW5c+dq48aNqqyslCRZrVZNmDBBCxcuVKdOncwGwu7wRBgA2NiCBQv09ttvKzU1VR4eHqZzgGYrKSkxnQA8NF5eXqYTAAA/4cqVKxo4cKDOnTun+Pj4hh+av/32W6WkpCg7O1v79u2T1Wo1XAp7whNhAGBjkZGRKiwsVH19vQIDA2/beDk3N9dQGQAAANB6JCYmKjs7W9u3b5efn1+jtfLycg0bNkxDhgzR0qVLDRXCHvFEGADY2OjRo00nAAAAAK3e5s2btXLlytuGYJLUtWtXLV68WNOmTWMQhkZ4IgwAAAAAALQ6bm5uKiws1M9+9rM7rpeVlSk4OFjff/+9jctgz3giDAAMOXz4sE6cOCFJ6t27tyIjIw0XAQAAAK2Ht7e3SkpK7joIKy4uVufOnW1cBXvHE2EAYGMXL17UhAkT9OWXXzacYlNVVaVBgwbps88+k4+Pj9lAAAAAoBV4+eWXVVhYqG3btsnV1bXRWk1NjWJiYtSjRw998sknhgphjxiEAYCNjR8/XkVFRVq3bl2jk21eeuklBQcHKy0tzXAh8NP+9a9/3fe9HTt2fIglAADAUZWVlal///5yc3NTQkKCHn30UdXX1+vEiRP6+OOPVVNTo5ycHAUEBJhOhR1hEAYANubl5aXt27frsccea3T90KFDGjZsmKqqqsyEAU3g5OQki8VyX/fW1tY+5BoAAOCoiouLNWPGDG3dulU/jjcsFouio6P10UcfKTg42HAh7A17hAGAjdXV1cnFxeW26y4uLqqrqzNQBDTdzp07G/4vKSnR7NmzNXnyZA0cOFCStH//fqWmpio5OdlUIgAAcABBQUHKzMxUZWWlCgoKJEnBwcHsDYa74okwALCxUaNGqaqqSmlpaXrkkUckSefOnVN8fLysVqs+//xzw4VA0wwZMkRTpkxRXFxco+sbNmzQqlWr9OWXX5oJAwAAAP4PgzAAsLHS0lLFxsbq+PHjDfsVlJaWKjw8XBkZGXc99QawVx4eHjpy5IhCQkIaXT916pQiIiJUXV1tqAwAAABojFcjAcDGAgIClJubq+3btys/P1+S1LNnTw0dOtRwGdA8AQEBWr16tRYvXtzo+po1a9icFgAAAHaFJ8IAwEZ27NihmTNn6sCBA7edonf16lU98cQTWrFihZ588klDhUDzbNmyRc8995yCg4M1YMAAST8c/lBQUKBNmzbpmWeeMVwIAAAA/IBBGADYSGxsrAYNGqTXXnvtjuvLli3Tzp072SMMrVJpaan+8pe/NHrKcdq0aTwRBgAAALvCIAwAbKR79+7KyspSz54977ien5+vYcOG6ezZszYuAwAAAFqv1NRUeXt7a+TIkZKkt956S6tWrVKvXr2Ulpam7t27Gy6EPXEyHQAAjqKiokIuLi53XW/Xrp0uXbpkwyLgwdm9e7defPFFPfHEEzp37pwk6a9//av27NljuAwAALR1ixYtkru7uyRp//79Wr58uRYvXixvb++7vo0Bx8UgDABspFu3bsrLy7vr+tGjR+Xv72/DIuDB2LRpk2JiYuTu7q7c3FzV1NRI+mHvu0WLFhmuAwAAbV1paamCg4MlSZs3b9Zzzz2nqVOnKjk5Wbt37zZcB3vDIAwAbOSZZ57RvHnz9P3339+2duPGDc2fP1+/+c1vDJQBLbNw4UKtWLFCq1evbvTUY1RUlHJzcw2WAQAAR+Dp6anvvvtOkrR161ZFR0dLktq3b68bN26YTIMdamc6AAAcRVJSktLT0xUaGqqZM2cqLCxM0g97gy1fvly1tbWaO3eu4Uqg6U6ePKmnnnrqtuteXl6qqqqyfRAAAHAo0dHRmjJliiIjI3Xq1KmGE6uPHz+uwMBAs3GwOwzCAMBG/Pz8tG/fPk2fPl1z5szRj2eVWCwWxcTEaPny5fLz8zNcCTRd165ddfr06du+aO7Zs0c9evQwEwUAABzG8uXLlZSUpNLSUm3atEldunSRJB0+fFhxcXGG62BvODUSAAyorKzU6dOnVV9fr5CQEFmtVtNJQLMlJydr/fr1+uSTTxQdHa0tW7bozJkzeu211zRv3jy98sorphMBAAAASQzCAABAC9XX12vRokVKTk5WdXW1JMnNzU1vvPGGFixYYLgOAAA4iurqap09e1Y3b95sdL1v376GimCPGIQBAIAH4ubNmzp9+rSuXbumXr16ydPT03QSAABwAJcuXdLkyZOVlZV1x/Xa2lobF8GecWokAAB4IFxdXdWrVy89/vjjDMEAAIDNJCYm6urVqzp48KDc3d2VlZWl1NRUhYSEKCMjw3Qe7Ayb5QMAgCYbO3asUlJS1LFjR40dO/ae96anp9uoCgAAOKIdO3bon//8p/r37y8nJyd1795d0dHR6tixo5KTkzVy5EjTibAjDMIAAECTeXl5yWKxSJI6duzY8D8AAICtXb9+Xb6+vpIkq9WqS5cuKTQ0VH369FFubq7hOtgbBmEAAKDJxowZo/bt20uSUlJSzMYAAACHFhYWppMnTyowMFD9+vXTypUrFRgYqBUrVsjf3990HuwMm+UDAIAmc3Z2Vnl5uXx8fOTs7KwLFy40/BILAABgS+vXr9etW7c0efJkHT58WMOHD9eVK1fk6uqqlJQUjR8/3nQi7AiDMAAA0GRdu3bV6tWr9eyzz8rJyUkVFRXy8fExnQUAAKDq6mrl5+fr5z//uby9vU3nwM4wCAMAAE32zjvv6L333ruvvcE4shwAAAD2gkEYAABolvz8fJ0+fVqxsbFau3atOnXqdMf7Ro0aZdswAADgEKqqqpSWlqbp06dLkuLj43Xjxo2GdWdnZ61evfqu31HgmBiEAQCAFnn33Xf15ptvysPDw3QKAABwIEuWLNE333yjTz/9VJLUoUMHxcTEqEOHDpKk/fv3a8KECXrnnXcMVsLeMAgDAAAAAACtzoABA/THP/5RQ4cOlfTDIOzIkSPq0aOHJOnzzz/Xe++9p6+//tpkJuxMO9MBAACg9fnFL36h7OxsWa1WRUZG3nOvsNzcXBuWAQAAR1FUVKSwsLCGz2FhYXJ1dW343K9fPxUUFJhIgx1jEAYAAJps1KhRcnNzkySNHj3abAwAAHBI169f19WrVxUQECBJysnJuW29rq7ORBrsGIMwAADQZPPnz7/j/wAAALbSo0cP5ebmKjw8/I7rOTk5CgoKsnEV7J2T6QAAANC6lZaWqqysrOHzoUOHlJiYqFWrVhmsAgAAbd2YMWOUlJSkioqK29bKy8s1f/58jRkzxkAZ7Bmb5QMAgBZ58sknNXXqVE2cOFHl5eUKDQ1VeHi4CgoK9Morr+jtt982nQgAANqgf//73xowYIDKyso0ceJEhYaGSpJOnjyp9evXq1u3bjp06FDDKZKAxCAMAAC0kNVq1YEDBxQWFqZly5Zp48aN2rt3r7Zu3app06apqKjIdCIAAGijKisrNWfOHP3tb39TVVWVJKlTp04aN26cFi1apM6dO5sNhN1hEAYAAFrE09NTeXl5CgwMVGxsrKKiovT73/9eZ8+eVVhYmG7cuGE6EQAAtHH19fW6dOmSJMnHx+eeJ1rDsbFHGAAAaJHevXtrxYoV2r17t7Zt26bhw4dLks6fP68uXboYrgMAAI7AYrHI19dXvr6+DMFwTwzCAABAi3zwwQdauXKlnn76acXFxalfv36SpIyMDD3++OOG6wAAAID/4tVIAADQbPX19SotLZXVatWtW7dktVob1kpKSuTh4SFfX1+DhQAAAMB/MQgDAADNVldXp/bt2+v48eMKCQkxnQMAAADcE69GAgCAZnNyclJISIi+++470ykAAAC3uXLliukE2BkGYQAAoEXef/99vfnmm8rLyzOdAgAAHFifPn2UkJCgw4cPS5JOnTqlX/7yl4arYG94NRIAALSI1WpVdXW1bt26JVdXV7m7uzda55dYAABgCx9++KGOHTum9PR0DR48WLt379Zjjz2mzMxM02mwIwzCAABAi6Smpt5z/aWXXrJRCQAAcCR1dXWSftiq4X9t3LhRcXFx8vT0VElJiTp37mwiD3aKQRgAAAAAAGh1xo0bp0GDBmn69OkN1w4dOqShQ4fqrbfe0jfffKPu3bvrT3/6k8FK2Bv2CAMAAC1WWFiopKQkxcXF6eLFi5KkzMxMHT9+3HAZAABoq3bt2qWnn3664fOJEyc0cuRILViwQElJSZo1a5b+8Y9/mAuEXWIQBgAAWmTXrl3q06ePDh48qPT0dF27dk2SdOTIEc2fP99wHQAAaKuuX78uZ2dnSdKZM2c0YsQIffDBB5o1a5Ykyd/fX5cvXzaZCDvEIAwAALTI7NmztXDhQm3btk2urq4N1wcPHqwDBw4YLAMAAG1ZRESEEhMTtWbNGv3617/WjBkz9PLLLzesZ2VlKTg42GAh7FE70wEAAKB1O3bsmDZs2HDbdV9fX36FBQAAD82f//xnjR8/XosXL9bzzz+vJUuWyMvLSxEREfrqq6/07rvvaunSpaYzYWcYhAEAgBbp1KmTLly4oKCgoEbXv/76a3Xr1s1QFQAAaOv69++vwsLChs99+vTRnDlzVF5eLnd3d82aNUu/+93vDBbCHnFqJAAAaJE33nhDBw8e1N///neFhoYqNzdXFRUVmjRpkiZNmsQ+YQAAwGbq6+t18eJFWa3WRls2AD9iEAYAAFrk5s2bSkhIUEpKimpra9WuXTvV1tbqhRdeUEpKSsMmtgAAAIBpDMIAAMADcfbsWeXl5enatWuKjIxUSEiI6SQAAACgEQZhAACgRfbs2aNf/epXpjMAAACAn8QgDAAAtIirq6u6deumuLg4vfjii+rVq5fpJAAAAOCOnEwHAACA1u38+fN6/fXXtWvXLoWHhysiIkJLlixRWVmZ6TQAAACgEZ4IAwAAD0xxcbE2bNigtLQ05efn66mnntKOHTtMZwEAgDYmIyPjvu+NjY19iCVobRiEAQCAB6q2tlaZmZmaN2+ejh49qtraWtNJAACgjXFyur8X3CwWC99F0AivRgIAgAdi7969mjFjhvz9/fXCCy8oPDxcX3zxheksAADQBtXV1d3XH0Mw/D+eCAMAAC0yZ84cffbZZzp//ryio6MVHx+vUaNGycPDw3QaAAAA0AiDMAAA0CJRUVGKj4/XuHHj5O3tbToHAAA4gGXLlmnq1Klq3769li1bds97X331VRtVoTVgEAYAAAAAAFqVoKAg5eTkqEuXLgoKCrrrfRaLRUVFRTYsg71jEAYAAJosIyNDI0aMkIuLy0+e2sRJTQAAALAXDMIAAECTOTk5qby8XL6+vvc8tYmTmgAAgK1cvnxZktiqAffEqZEAAKDJ6urq5Ovr2/A/JzUBAAATqqqqlJCQIG9vb/n5+cnPz0/e3t6aOXOmqqqqTOfBDvFEGAAAAAAAaHWuXLmigQMH6ty5c4qPj1fPnj0lSd9++602bNiggIAA7du3T1ar1XAp7AmDMAAA0Gx1dXVKSUlRenq6SkpKZLFYFBQUpOeff14TJ06UxWIxnQgAANqoxMREZWdna/v27fLz82u0Vl5ermHDhmnIkCFaunSpoULYIwZhAACgWerr6/Xss89qy5Yt6tevnx599FHV19frxIkTOnbsmGJjY7V582bTmQAAoI0KDAzUypUrFRMTc8f1rKwsTZs2TSUlJbYNg11rZzoAAAC0TikpKfrqq6+UnZ2tQYMGNVrbsWOHRo8erXXr1mnSpEmGCgEAQFt24cIF9e7d+67r4eHhKi8vt2ERWgM2ywcAAM2SlpamP/zhD7cNwSRp8ODBmj17tj799FMDZQAAwBF4e3vf82mv4uJide7c2XZBaBUYhAEAgGY5evSohg8fftf1ESNG6MiRIzYsAgAAjiQmJkZz587VzZs3b1urqanRvHnz7vldBY6JPcIAAECzuLq66syZM/L397/j+vnz5xUUFKSamhoblwEAAEdQVlam/v37y83NTQkJCY32K/34449VU1OjnJwcBQQEmE6FHWEQBgAAmsXZ2Vnl5eXy8fG543pFRYUeeeQR1dbW2rgMAAA4iuLiYs2YMUNbt27Vj+MNi8Wi6OhoffTRRwoODjZcCHvDIAwAADSLk5OTRowYITc3tzuu19TUKCsri0EYAAB46CorK1VQUCBJCg4OZm8w3BWDMAAA0Cy//e1v7+u+tWvXPuQSAAAA4P4wCAMAAAAAAIBD4NRIAAAAAAAAOAQGYQAAAAAAAHAIDMIAAAAAAADgEBiEAQAAAAAAwCEwCAMAAAAAAIBDYBAGAAAAAAAAh8AgDAAAAAAAAA6BQRgAAAAAAAAcwn8Atz9MqvE4g+MAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}