Demo2_Clustering_IMDB.ipynb

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      "source": [
        "# Aula 2 - Aprendizado Não Supervisionado - Clustering - Verificando tendências de mercado via Clustering ($K$-Means Clustering em detalhes)\n",
        "\n",
        "Prof. Dr. Ahirton Lopes\n",
        "\n",
        "Ref. https://www.kaggle.com/datasets/carolzhangdc/imdb-5000-movie-dataset"
      ],
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      "source": [
        "No mundo atual, onde os dados são considerados o novo petróleo, a capacidade de extrair informações valiosas de conjuntos de dados é uma vantagem competitiva crucial para as empresas. Uma das maneiras pelas quais as empresas podem obter insights valiosos é através do uso de técnicas de clustering, especialmente quando aplicadas a conjuntos de dados ricos, como o IMDB 5000. O IMDB 5000 é uma base de dados composta por informações sobre filmes, incluindo detalhes sobre elenco, diretores, avaliações dos usuários, orçamento de produção e receita de bilheteria. Ao utilizar essa base de dados em conjunto com técnicas de clustering, as empresas podem descobrir padrões e tendências que não seriam óbvios à primeira vista.\n",
        "\n",
        "Uma aplicação inicial do clustering na base de dados IMDB 5000 poderia ser a segmentação de filmes com base em gênero, orçamento de produção e receita de bilheteria. Isso permitiria às empresas identificar quais gêneros de filmes tendem a ter maior retorno sobre o investimento e quais são mais populares entre diferentes grupos demográficos. Por exemplo, pode-se descobrir que filmes de ação com orçamento moderado têm um desempenho excepcionalmente bom entre os espectadores jovens, enquanto os filmes de comédia romântica atraem um público mais maduro."
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      "source": [
        "## Lendo os dados"
      ],
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      "source": [
        "# Baixando nossa base de dados direto do repositório da disciplina (https://github.com/ahirtonlopes/Unsupervised_Learning) para o Google Colab\n",
        "\n",
        "!curl -O https://raw.githubusercontent.com/ahirtonlopes/Unsupervised_Learning/main/IMDB_movie_dataset.csv"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100 15228  100 15228    0     0  71557      0 --:--:-- --:--:-- --:--:-- 71492\n"
          ]
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      ]
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    {
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 103,
      "outputs": []
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      "source": [
        "dataset = pd.read_csv('IMDB_movie_dataset.csv',encoding='utf-8')\n",
        "dataset.head()"
      ],
      "execution_count": 104,
      "outputs": [
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              "    color      director_name  duration        gross  \\\n",
              "0   Color    Martin Scorsese       240  116866727.0   \n",
              "1   Color        Shane Black       195  408992272.0   \n",
              "2  color   Quentin Tarantino       187   54116191.0   \n",
              "3   Color   Kenneth Lonergan       186      46495.0   \n",
              "4   Color      Peter Jackson       186  258355354.0   \n",
              "\n",
              "                                 genres                          movie_title  \\\n",
              "0          Biography|Comedy|Crime|Drama              The Wolf of Wall Street   \n",
              "1               Action|Adventure|Sci-Fi                           Iron Man 3   \n",
              "2  Crime|Drama|Mystery|Thriller|Western                    The Hateful Eight   \n",
              "3                                 Drama                             Margaret   \n",
              "4                     Adventure|Fantasy  The Hobbit: The Desolation of Smaug   \n",
              "\n",
              "   title_year language country       budget  imdb_score  \\\n",
              "0        2013  English     USA  100000000.0         8.2   \n",
              "1        2013  English     USA  200000000.0         7.2   \n",
              "2        2015  English     USA   44000000.0         7.9   \n",
              "3        2011  English     usa   14000000.0         6.5   \n",
              "4        2013  English     USA  225000000.0         7.9   \n",
              "\n",
              "                                              actors  movie_facebook_likes  \n",
              "0  Leonardo DiCaprio,Matthew McConaughey,Jon Favreau                138000  \n",
              "1          Robert Downey Jr.,Jon Favreau,Don Cheadle                 95000  \n",
              "2          Craig Stark,Jennifer Jason Leigh,Zoë Bell                114000  \n",
              "3        Matt Damon,Kieran Culkin,John Gallagher Jr.                     0  \n",
              "4              Aidan Turner,Adam Brown,James Nesbitt                 83000  "
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              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 99,\n  \"fields\": [\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Color\",\n          \"color \",\n          \" Black and White\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"director_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"David Ayer\",\n          \"Tony Gilroy\",\n          \"Martin Scorsese\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": -50,\n        \"max\": 650,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          138,\n          135,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 139950256.81859657,\n        \"min\": 41229.0,\n        \"max\": 623279547.0,\n        \"num_unique_values\": 85,\n        \"samples\": [\n          52474616.0,\n          116866727.0,\n          132550960.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 56,\n        \"samples\": [\n          \"Biography|Comedy|Crime|Drama\",\n          \"Drama|Romance\",\n          \"Drama|Horror|Thriller\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 91,\n        \"samples\": [\n          \"The Help\",\n          \"The Revenant\",\n          \"Avengers: Age of Ultron\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 255,\n        \"min\": 202,\n        \"max\": 2016,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2016,\n          2015,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"English\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Kyrgyzstan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77031692.48433195,\n        \"min\": 17350.0,\n        \"max\": 250000000.0,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          100000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imdb_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9255135824433918,\n        \"min\": -7.5,\n        \"max\": 8.8,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 88,\n        \"samples\": [\n          \"Liam Neeson,Charlize Theron,Seth MacFarlane\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_facebook_likes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58108,\n        \"min\": 0,\n        \"max\": 349000,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          72000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Estratégias de limpeza de dados\n",
        "\n",
        "Como você pode ver em nosso exemplo, podem haver diferentes tipos de erros em um conjunto de dados:\n",
        "\n",
        "Outliers: outliers são os números em uma coluna que estão fora do intervalo. Na verdade, um outlier está fora da maioria dos outros valores em um conjunto de dados (células vermelhas na figura).\n",
        "\n",
        "Duplicatas: um conjunto de dados pode conter linhas ou registros repetitivos (linhas roxas na figura).\n",
        "\n",
        "Valores ausentes: podemos perder dados como resultado de erro humano ou falta de informações (células laranja na figura).\n",
        "\n",
        "Caracteres inválidos ou valores NULL: alguns valores podem conter caracteres inválidos, como valores ₮ ou NULL.\n",
        "\n",
        "Às vezes, dados nulos podem ser especificados com valores diferentes, como N/A ou NA ou NAN (células claras ou escuras na figura).\n",
        "\n",
        "Nominal: um atributo pode ser categórico como uma cor que pode conter um conjunto de valores restritos (por exemplo, azul, vermelho, marrom, ...)\n",
        "\n",
        "Texto: um atributo pode ser um texto livre ou uma string.\n",
        "Numérico: um atributo pode ser numérico (por exemplo, moeda, pontuações).\n",
        "\n",
        "Algumas das estratégias possíveis:\n",
        "\n",
        "1. Padronizar valores;\n",
        "2. Normalizar valores;\n",
        "3. Validação de dados."
      ],
      "metadata": {
        "id": "omOJPjh_XDe1"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VBPYFzozoKM"
      },
      "source": [
        "# Dropando colunas desnecessárias para nosso tipo de análise como cores e linguagem (que podem ser importantes em outros contextos)\n",
        "\n",
        "dataset.drop(['color','language'], axis= 1, inplace= True)\n"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8CA8eHi0AQ0"
      },
      "source": [
        "# Agora, preenchemos os valores vazios de duas colunas numéricas: renda bruta (gross) e orçamento (budget) com 0's.\n",
        "\n",
        "dataset['gross'].fillna(0, inplace=True)\n",
        "dataset['budget'].fillna(0, inplace=True)"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhYDByxy0cyf",
        "outputId": "6d01258a-d341-4239-a0c2-41311cd96c0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        }
      },
      "source": [
        "# Em seguida, unificamos os valores de outras colunas, como país, nome do diretor ou título do filme.\n",
        "\n",
        "# Colocando em maiúsculas todos os valores de países\n",
        "\n",
        "dataset['country'] = dataset['country'].str.upper()\n",
        "dataset['country'] = np.where(dataset['country']=='UNITED STATES','USA', dataset['country'])\n",
        "dataset.head()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       director_name  duration        gross  \\\n",
              "0    Martin Scorsese       240  116866727.0   \n",
              "1        Shane Black       195  408992272.0   \n",
              "2  Quentin Tarantino       187   54116191.0   \n",
              "3   Kenneth Lonergan       186      46495.0   \n",
              "4      Peter Jackson       186  258355354.0   \n",
              "\n",
              "                                 genres                          movie_title  \\\n",
              "0          Biography|Comedy|Crime|Drama              The Wolf of Wall Street   \n",
              "1               Action|Adventure|Sci-Fi                           Iron Man 3   \n",
              "2  Crime|Drama|Mystery|Thriller|Western                    The Hateful Eight   \n",
              "3                                 Drama                             Margaret   \n",
              "4                     Adventure|Fantasy  The Hobbit: The Desolation of Smaug   \n",
              "\n",
              "   title_year country       budget  imdb_score  \\\n",
              "0        2013     USA  100000000.0         8.2   \n",
              "1        2013     USA  200000000.0         7.2   \n",
              "2        2015     USA   44000000.0         7.9   \n",
              "3        2011     USA   14000000.0         6.5   \n",
              "4        2013     USA  225000000.0         7.9   \n",
              "\n",
              "                                              actors  movie_facebook_likes  \n",
              "0  Leonardo DiCaprio,Matthew McConaughey,Jon Favreau                138000  \n",
              "1          Robert Downey Jr.,Jon Favreau,Don Cheadle                 95000  \n",
              "2          Craig Stark,Jennifer Jason Leigh,Zoë Bell                114000  \n",
              "3        Matt Damon,Kieran Culkin,John Gallagher Jr.                     0  \n",
              "4              Aidan Turner,Adam Brown,James Nesbitt                 83000  "
            ],
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 99,\n  \"fields\": [\n    {\n      \"column\": \"director_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"David Ayer\",\n          \"Tony Gilroy\",\n          \"Martin Scorsese\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": -50,\n        \"max\": 650,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          138,\n          135,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140610076.67797837,\n        \"min\": 0.0,\n        \"max\": 623279547.0,\n        \"num_unique_values\": 86,\n        \"samples\": [\n          42615685.0,\n          116866727.0,\n          1206135.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 56,\n        \"samples\": [\n          \"Biography|Comedy|Crime|Drama\",\n          \"Drama|Romance\",\n          \"Drama|Horror|Thriller\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 91,\n        \"samples\": [\n          \"The Help\",\n          \"The Revenant\",\n          \"Avengers: Age of Ultron\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 255,\n        \"min\": 202,\n        \"max\": 2016,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2016,\n          2015,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"KYRGYZSTAN\",\n          \"UK\",\n          \"FRANCE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78245289.90016283,\n        \"min\": 0.0,\n        \"max\": 250000000.0,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          100000000.0,\n          250000000.0,\n          25000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imdb_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9255135824433918,\n        \"min\": -7.5,\n        \"max\": 8.8,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          3.0,\n          7.5,\n          7.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 88,\n        \"samples\": [\n          \"Liam Neeson,Charlize Theron,Seth MacFarlane\",\n          \"Leonardo DiCaprio,Matthew McConaughey,Jon Favreau\",\n          \"Christian Bale,Mar\\u00eda Valverde,Ben Mendelsohn\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_facebook_likes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58108,\n        \"min\": 0,\n        \"max\": 349000,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          72000,\n          56000,\n          83000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54EvQMMj1SZV"
      },
      "source": [
        "# Checando por entradas truncadas de dados\n",
        "\n",
        "dataset['director_name'] = np.where(dataset['director_name']=='N/A','', dataset['director_name'])\n",
        "dataset['director_name'] = np.where(dataset['director_name']=='NaN','', dataset['director_name'])\n",
        "dataset['director_name'] = np.where(dataset['director_name']=='Null','', dataset['director_name'])\n",
        "\n",
        "dataset['movie_title'] = dataset['movie_title'].str.replace('Â', '')\n"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Como mencionado, uma das soluções para lidar com os outliers é preencher as linhas correspondentes com os números apropriados.\n",
        "\n",
        "Por exemplo, a duração do filme em nosso conjunto de dados não pode ser inferior a 10 ou superior a 300 minutos.\n",
        "\n",
        "Para esses casos, preenchemos as células com valores zero."
      ],
      "metadata": {
        "id": "iDG_0KrLVcRa"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYhURGMY3l96",
        "outputId": "dc2109cd-93da-4e63-e8b7-5c3e71af8d2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "source": [
        "# De modo a normalizar os dados, dividimos os atores em três atributos diferentes e eliminamos a coluna real da seguinte forma:\n",
        "\n",
        "actor_list = dataset['actors'].str.split(',',n=2 , expand = True)\n",
        "\n",
        "dataset[\"actor1\"]= actor_list[0]\n",
        "dataset[\"actor2\"]= actor_list[1]\n",
        "dataset[\"actor3\"]= actor_list[2]\n",
        "dataset.drop(columns=['actors'], inplace=True)\n",
        "dataset.head()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       director_name  duration        gross  \\\n",
              "0    Martin Scorsese       240  116866727.0   \n",
              "1        Shane Black       195  408992272.0   \n",
              "2  Quentin Tarantino       187   54116191.0   \n",
              "3   Kenneth Lonergan       186      46495.0   \n",
              "4      Peter Jackson       186  258355354.0   \n",
              "\n",
              "                                 genres                          movie_title  \\\n",
              "0          Biography|Comedy|Crime|Drama              The Wolf of Wall Street   \n",
              "1               Action|Adventure|Sci-Fi                           Iron Man 3   \n",
              "2  Crime|Drama|Mystery|Thriller|Western                    The Hateful Eight   \n",
              "3                                 Drama                             Margaret   \n",
              "4                     Adventure|Fantasy  The Hobbit: The Desolation of Smaug   \n",
              "\n",
              "   title_year country       budget  imdb_score  movie_facebook_likes  \\\n",
              "0        2013     USA  100000000.0         8.2                138000   \n",
              "1        2013     USA  200000000.0         7.2                 95000   \n",
              "2        2015     USA   44000000.0         7.9                114000   \n",
              "3        2011     USA   14000000.0         6.5                     0   \n",
              "4        2013     USA  225000000.0         7.9                 83000   \n",
              "\n",
              "              actor1                actor2              actor3  \n",
              "0  Leonardo DiCaprio   Matthew McConaughey         Jon Favreau  \n",
              "1  Robert Downey Jr.           Jon Favreau         Don Cheadle  \n",
              "2        Craig Stark  Jennifer Jason Leigh            Zoë Bell  \n",
              "3         Matt Damon         Kieran Culkin  John Gallagher Jr.  \n",
              "4       Aidan Turner            Adam Brown       James Nesbitt  "
            ],
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              "      <td>John Gallagher Jr.</td>\n",
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              "      <td>Adventure|Fantasy</td>\n",
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              "      <td>7.9</td>\n",
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              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 99,\n  \"fields\": [\n    {\n      \"column\": \"director_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"David Ayer\",\n          \"Tony Gilroy\",\n          \"Martin Scorsese\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": -50,\n        \"max\": 650,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          138,\n          135,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140610076.67797837,\n        \"min\": 0.0,\n        \"max\": 623279547.0,\n        \"num_unique_values\": 86,\n        \"samples\": [\n          42615685.0,\n          116866727.0,\n          1206135.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 56,\n        \"samples\": [\n          \"Biography|Comedy|Crime|Drama\",\n          \"Drama|Romance\",\n          \"Drama|Horror|Thriller\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 91,\n        \"samples\": [\n          \"The Help\",\n          \"The Revenant\",\n          \"Avengers: Age of Ultron\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 255,\n        \"min\": 202,\n        \"max\": 2016,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2016,\n          2015,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"KYRGYZSTAN\",\n          \"UK\",\n          \"FRANCE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78245289.90016283,\n        \"min\": 0.0,\n        \"max\": 250000000.0,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          100000000.0,\n          250000000.0,\n          25000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imdb_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9255135824433918,\n        \"min\": -7.5,\n        \"max\": 8.8,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          3.0,\n          7.5,\n          7.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_facebook_likes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58108,\n        \"min\": 0,\n        \"max\": 349000,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          72000,\n          56000,\n          83000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actor1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 61,\n        \"samples\": [\n          \"Leonardo DiCaprio\",\n          \"Henry Cavill\",\n          \"Tika Sumpter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actor2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 84,\n        \"samples\": [\n          \"Chris Evans\",\n          \"Matthew McConaughey\",\n          \"Theo James\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actor3\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 85,\n        \"samples\": [\n          \"Mirlan Abdulayev\",\n          \"Jon Favreau\",\n          \"Michael Nardone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rt5vWpx42NqH"
      },
      "source": [
        "dataset['duration'] = np.where(dataset['duration']<=10,0, dataset['duration'])\n",
        "dataset['duration'] = np.where(dataset['duration']>300,0, dataset['duration'])\n",
        "dataset['imdb_score'] = np.where(dataset['imdb_score']<=0,0, dataset['imdb_score'])\n",
        "dataset['title_year'] = np.where(dataset['title_year']<2010,0, dataset['title_year'])"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xu4POW2V3_bb"
      },
      "source": [
        "# E, finalmente, definimos uma nova métrica chamada GOB que mostra o orçamento bruto acima do orçamento para cada filme:\n",
        "\n",
        "# Adicione uma nova métrica GOB (Bruto acima do orçamento, Gross Over Budget)\n",
        "\n",
        "dataset['GOB'] = dataset.apply(lambda row: row['gross']/row['budget'] if row['budget']!=0 else 0, axis=1)\n",
        "top_GOB=dataset.sort_values('GOB',ascending=False).head(15)\n"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1-OtXJT5Wgo"
      },
      "source": [
        "# Como temos os nomes completos dos diretores, adiciono outra coluna para encurtar seus nomes para melhor visualizá-los em um gráfico.\n",
        "# Pegamos a segunda parte do nome, que pode ser o sobrenome deles.\n",
        "\n",
        "top_GOB['director_familyName'] = dataset[\"director_name\"].str.split(\" \", n = 2, expand = True) [1]\n"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ln2VsA0h89En"
      },
      "source": [
        "# Nova etapa: Visualização -  a próxima etapa é então visualizá-los em um gráfico de barras simples, como segue:\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPSEAL988t6w",
        "outputId": "13608d7c-5d41-4a1e-98a8-2af35b80656c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        }
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(7, 5))\n",
        "\n",
        "# Desenhando um gráfico de barras\n",
        "ax = sns.barplot(x='director_familyName', y='GOB', data=top_GOB)\n",
        "\n",
        "# Girando o nome dos diretores em 45 graus\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
        "\n",
        "# Título do nosso gráfico\n",
        "fig.suptitle('Principais diretores de cinema com maior GOB', fontsize=12)\n",
        "\n",
        "# Definindo o nome e o tamanho da fonte do rótulo dos eixos x e y\n",
        "ax.set_xlabel('Nome do Diretor',fontsize=20)\n",
        "ax.set_ylabel('Bruto Acima do Orçamento (GOB)',fontsize=20)\n",
        "\n",
        "# Definindo o tamanho do tick do eixo\n",
        "ax.tick_params(axis='x', labelsize=15)\n",
        "ax.tick_params(axis='y', labelsize=15)\n",
        "\n",
        "# Mostra o gráfico\n",
        "plt.show();"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-114-e1fcd6c93983>:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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P70P27NkTbAyveR927twJU1NTfPnllzrrv/76a4gIdu3apbO8Tp06OqWgmjy3bt0a9vb2SZ5LUvr06aP+39TUFOXKlYOIoHfv3upyJycnFClSROeYpqamalvO2NhYBAUFITo6GuXKlUvy/YmJicHevXvRokULFCxYUF3u7u6OTp064ejRowgJCdHZp2/fvnq13w0JCVFLyLWNHj0aOXLkUF+dOnVS1719+1bn/UuIZn3cfP3www/Yt28f9u3bh3Xr1qFjx44YPXo0Zs+e/dG8UvpidSrpZd68efDy8oKZmRlcXV1RpEgRNfjRMDMzS1aVYNzATBPQaYIizYO2RIkSKcpzoUKF4n3henl5AfjQnsnNzQ2xsbGYPXs25s+fj4CAAJ12S9pVxXFZWlril19+wddffw1XV1dUqlQJTZo0Qbdu3eDm5pbofvfv3wcAFC5cON66jwWO2goUKKDz961btyAiCR4XgF6dS+7fv4/ChQvH+1yLFSumk/fE8nD79m2ICMaOHYuxY8cmmMaLFy+QO3duTJgwAc2bN4eXlxdKlCiBBg0aoGvXrihVqtRH8xi3igr48N5p0wSHmrabcTk4OCSZzp07d9C6deskt0mMlZWVTnALfLi2tYP9W7du4c2bN8iZM2eCx3jx4oXO33HvFc0PgLx58ya4XDutoKAgjB8/Hn/99Ve847558ybJc7lz5w6KFCmSZNuo+/fvI1euXPECiMSuG0POJSkJHdfKyipe1bejo2O89oDLly/HjBkzcP36dbx//15dHvca1xYYGIjw8PB41x7w4dxjY2Px8OFDfPbZZ3odT5u9vX2CbRYHDRqEJk2aAIhf1Wpvb4+3b98meVzN+rifVcmSJVG3bl3173bt2uHNmzf47rvv0KlTp3jXM2UcDOJILxUqVFB7pyZGu1RLH4n9IpU4jaFT0+TJkzF27Fj06tULEydOhLOzM0xMTDB06NCPDosydOhQNG3aFP/88w/27NmDsWPHYsqUKThw4ADKlCmTqvmO2wYsNjYWiqJg165dCb6vCf2qT408AMCIESNQv379BPcpVKgQAMDX1xd37tzBli1bsHfvXvzxxx+YOXMmFi5cqFOiklKavKxcuTLBoDo1G2zrU9ISGxuLnDlzJjoWV9wvzcSOqc891K5dOxw/fhwjR45E6dKlYWdnh9jYWDRo0CBVh/5JjCHnktzj6nPMVatWoUePHmjRogVGjhyJnDlzwtTUFFOmTNHpvGEMCbXdTEjRokVx4cIFPH78WKe2w8vLS/0hGrenbLFixXD+/HlERkYmOtzSpUuXYG5unuiPPW116tTB9u3bcerUKTRu3FivfFPaM9qTLDAwEHfv3sWzZ88QFhYGc3NzODk5IV++fChUqFCqDwFBnx5NFcWVK1dStL+mZEi7NO7mzZsAoFbnbNy4EbVq1YrXgPf169dJNl7X8PT0xNdff42vv/4at27dQunSpTFjxgysWrUqwe09PDwAIMFqxBs3buh1XonlQ0RQoEAB9SGfmMSqAz08PHDp0iXExsbqBOPXr19X1ydF83mZm5vr/KpPjLOzM3r27ImePXsiNDQUvr6+GDduXJJBnIeHh17vnaZ3Xs6cOfXKS1yenp4pvu70Pf6///6LqlWr6v3FnhLBwcHYv38/xo8fjx9++EFdrm81tqenJ06ePIn3798nWprr4eGBf//9N151nr7XTXrbuHEjChYsiE2bNuncGz/++GOS++XIkQM2NjYJ3rfXr1+HiYlJvNJFfTVp0gR//fUXVq9ejW+++Ubvffz8/LBhw4YEO0Tcu3cPR44cQd26dfW65qKjowF86ORDGVeK28SFhYVhzZo16Nq1K/Llywc3NzdUqVIFrVq1UgdfbdCgAYoXLw57e3tUrVoV48ePx6VLl4yZf/qE5ciRA76+vli6dCkePHigs06fX+dPnjzRGbojJCQEK1asQOnSpdXSGVNT03jH2rBhg9p2KzHh4eGIiIjQWebp6Ql7e3tERkYmup+7uztKly6N5cuX61Rl7du3Tx2iIyVatWoFU1NTjB8/Pt75iIhO1YytrW2C1WiNGjXCs2fPsG7dOnVZdHQ05syZAzs7O9SoUSPJPOTMmRM1a9bE77//jqdPn8ZbHxgYqP4/blWRnZ0dChUqlOR7p8njiRMncOrUKZ3jxi3Rql+/PhwcHDB58mSd6rGE8pKQ1q1b4+LFiwkO/WKMkuJ27dohJiYGEydOjLcuOjoar1+/NjgN4H8lUXHzrO+MHa1bt8bLly8xd+7ceOs0x2zUqBFiYmLibTNz5kwoiqIzDEZGlNB7dPLkSfj5+X10v88//xxbtmzRGQrl+fPnWLNmDapVq/bRavvEtGvXDsWLF8fEiRPjDVeiEfcz7d+/P3LmzImRI0fGa0cYERGBnj17QkR0gvmkbN++HQDg7e2dgjOgtJLskrjz589jzpw52LBhA8LDwwF8/KEWEREBPz8/nDhxAhMmTMBnn32GwYMHo2vXrh8dhoKytt9++w3VqlWDj48P+vXrhwIFCuDevXvYsWMHLly4kOS+Xl5e6N27N06fPg1XV1csXboUz58/x7Jly9RtmjRpggkTJqBnz56oUqUKLl++jNWrV+s0VE7IzZs3UadOHfVha2Zmhs2bN+P58+fo0KFDkvtOmTIFjRs3RrVq1dCrVy8EBQVhzpw5+Oyzz1L8q9fT0xOTJk3CqFGjcO/ePbRo0QL29vYICAjA5s2b0a9fP4wYMQIAULZsWaxbtw7Dhw9H+fLlYWdnh6ZNm6Jfv374/fff0aNHD5w9exb58+fHxo0bcezYMcyaNeujjaaBD20nq1WrhpIlS6Jv374oWLAgnj9/Dj8/Pzx69Egdf6948eKoWbMmypYtC2dnZ5w5cwYbN27EkCFDkjz+N998g5UrV6JBgwb46quv1CFGNKWIGg4ODliwYAG6du0KHx8fdOjQATly5MCDBw+wY8cOVK1aNcHARGPkyJHYuHEj2rZti169eqFs2bIICgrC1q1bsXDhQoO/2GrUqIH+/ftjypQpuHDhAj7//HOYm5vj1q1b2LBhA2bPnq0z+GpKOTg4wNfXF1OnTsX79++RO3du7N27Vx3f8WO6deuGFStWYPjw4Th16hSqV6+OsLAw/Pvvvxg0aBCaN2+Opk2bolatWhg9ejTu3bsHb29v7N27F1u2bMHQoUPjjVmW0TRp0gSbNm1Cy5Yt0bhxYwQEBGDhwoUoXrz4R+/HSZMmYd++fahWrRoGDRoEMzMz/P7774iMjMTUqVNTnCdzc3Ns3rxZHbKpVatW6niHjx8/xtatW/HgwQOdak4XFxds3LgRjRs3ho+PT7wZG27fvo3Zs2fHG+gX+DDsi+ZHqeY6P3z4MDp06ICiRYum+DwoDejbjfXcuXPSuHFjMTExERMTE1EURRRFEXd3d2nevLlMmDBBFi5cKOvXr5d9+/bJ1q1b5c8//5SZM2fKwIEDpVy5cmJhYaHuZ2JiIjly5JBp06bpDEdAGYuma/zp06eT3K579+5ia2ub6LqEhhhJaHgOxOnCLyJy5coVadmypTg5OYmVlZUUKVJExo4dGy+PcYcYady4sezZs0dKlSollpaWUrRoUdmwYYPOsSMiIuTrr78Wd3d3sba2lqpVq4qfn5/UqFFDatSoES/PmuEkXr58KYMHD5aiRYuKra2tODo6SsWKFWX9+vVJvk8af//9txQrVkwsLS2lePHismnTpnjvU0Lvh2Y4hcDAwESPW61aNbG1tRVbW1spWrSoDB48WG7cuKFuExoaKp06dRInJ6d4w5o8f/5cevbsKdmzZxcLCwspWbKkzhAa2u9FYsOr3LlzR7p16yZubm5ibm4uuXPnliZNmsjGjRvVbSZNmiQVKlQQJycnsba2lqJFi8pPP/0kUVFRH33vLl26JDVq1BArKyvJnTu3TJw4UZYsWZLgcBQHDx6U+vXri6Ojo1hZWYmnp6f06NFDzpw589F0Xr16JUOGDJHcuXOLhYWF5MmTR7p37y4vX77UeR/iDjGS0H2g+dziWrRokZQtW1asra3F3t5eSpYsKd988408efJE3UZzLccFQAYPHqyzLKHP5tGjR+r94+joKG3btpUnT54keK8lJDw8XEaPHi0FChQQc3NzcXNzkzZt2ugMq/H27VsZNmyY5MqVS8zNzaVw4cIybdo0naGAkpNnkf8NfxH3no0rsXsisc+iRo0a8tlnn6l/x8bGyuTJk8XDw0MsLS2lTJkysn37dr3uR5EP343169cXOzs7sbGxkVq1aukMByOi/3M0rtevX8uECROkTJkyYmdnJxYWFpI3b15p06aNzlBM2gICAqRv376SL18+MTc3l+zZs0uzZs3kyJEj8bZNaIgRCwuLZN2PlL4UkY/XDfTs2RMrV65UG8H6+Pigc+fOaN26dbweQUmJiorCf//9h9WrV2Pz5s0ICQmBoijw8PDAihUrdEaeJzJE/vz5UaJECbVKgIiI6FOjV5u45cuXw8zMDH379sX169dx5swZDBs2LFkBHPBhKo+6deti2bJleP78OVasWIEiRYrg3r17eo/KTURERER6tokbNGgQvv322xT3tEmIpaUlunTpgs6dO2PDhg3x5hUkIiIiosTpVZ1KlNmwOpWIiD51DOKIiIiIMiHOnUpERESUCTGIIyIiIsqEUhzEhYaGJjhBr8bSpUtRrVo1ODo6wtbWFkWLFsWIESPiTcBMRERERMmX7DZxS5cuxbRp09Q5KK2trdG8eXNMnDgRBQsWhIigQ4cO2LhxI4D/zeagmZMue/bs2Lt3L6fyICIiIjJAsoK40aNH4+effwagO9WWoijIkycPTp48iSVLlmDs2LEAABMTE+TIkQPR0dE6pXaenp64fPkyrKysjHUeRERERFmK3tWp586dUwM44MP8i23atEH58uWhKAoePXqE3377DfPmzQMAfP/99wgMDMTTp08RGBiIgIAAtGvXDgBw9+7deBNWExEREZH+9C6JGzBgABYtWgR7e3ts3rwZtWvXVtcdOnQILVq0QFRUFCIjI9GnTx/8/vvv8Y4hIqhWrRpOnDiBxo0bY+vWrcY7EyIiIqIsRO+SuGPHjkFRFAwdOlQngAOAmjVr4quvvkJERAQAoF+/fgkeQ1EU9O3bFyKCixcvGpBtIiIioqxN7yDuwYMHAD4EbAnRXl6sWLFEj1O8eHEAwMuXL/VNmoiIiIji0DuICw0NBQDkyJEjwfXZs2dX/29jY5PocWxtbQFALbUjIiIiouTTO4jTNJ0zMUl4l8SWExEREZHxMfIiIiIiyoQYxBERERFlQmbJ3WHLli04c+ZMvOWPHz9W/79ixYpE99fejoiIiIhSRu9x4kxMTNSpswwlIlAUBTExMUY5HhEREVFWk6ySuGROs0pEREREqUTvIG7ZsmWpmQ8iIiIiSga9q1OJiIiIKONg71QiIiKiTIhBHBEREVEmlOwhRhISFhaGy5cv49WrVwgJCYGDgwNcXFxQokQJ2NnZGSMJIiIiItKS4iDu3bt3WLp0KZYtW4ZLly4lOFyIqakpvL290bNnT/Ts2RPW1tYGZZaIiIiIPkhRx4bdu3ejZ8+eePHiBYCkhx7RjC3n6uqKZcuWoX79+inMKhERERFpJDuImzNnDoYNGwYRUYM3Ly8v+Pj4wNXVFXZ2dggNDcXz589x7tw53Lx583+JKQpmz56NIUOGGPcsiIiIiLKYZAVxW7ZsQatWrSAiMDExQb9+/fDVV1+hSJEiie5z8+ZNzJ49G4sWLUJMTAxMTEywadMmNGvWzCgnQERERJQV6R3ERUREoEiRInj48CGcnJywZcsWVK9eXe+Ejh07hqZNm+L169fIly8fbty4AUtLyxRnnIiIiCgr03uIkbVr1+Lhw4dQFAWbNm1KVgAHAFWrVsWmTZugKAoePnyINWvWJDuzRERERPSB3kHc9u3bAQAtWrRAzZo1U5RYzZo10aJFC4iIejwiIiIiSj69g7iLFy9CURS0adPGoAQ1+1+4cMGg4xARERFlZXoHcc+fPwcAeHp6GpSgZn/N8CRERERElHzJHuw3BcPKGXV/ADh06BBq1ar10e3Gjx+PH374Qa9jxsbG4smTJ7C3t1fHtiMiIiJKDSKCt2/fIleuXDAxSdksqHoHca6urggICMCdO3dQsWLFFCUGAHfu3AEA5MyZM8XHcHNzQ/fu3RNcFxMTg1WrVgFAsjpfPHnyBHnz5k1xnoiIiIiS6+HDh8iTJ0+K9tU7iCtdujTu3r2LjRs3olOnTilKDAA2btyoHi+lihYtij///DPBdbt27cKqVauQN2/eZHXAsLe3B/DhzXRwcEhx3oiIiIg+JiQkBHnz5lXjj5TQO4hr0qQJNm3ahC1btuDQoUMp6qF66NAh/PPPP1AUBU2bNk32/vrQlMJ17tw5WdWimm0dHBwYxBEREVGaMKQJl96D/UZGRqJIkSJ48OABnJycsH37dlSpUkXvhPz8/NC4cWO8fv0aefPmxc2bN40+2G9YWBhcXV0RFhaGq1evonjx4nrvGxISAkdHR7x584ZBHBEREaUqY8Qdereks7S0xOzZs6EoCt68eYMaNWrgiy++wK1bt5Lc7/bt2/jyyy9Ro0YNvH79GiYmJpg9e3aqzNawadMmhIWFoUyZMskK4IiIiIgym2T1Tm3evDlmzpyJoUOHIjY2FvPnz8f8+fPh5eUFHx8fuLm5wc7ODqGhoXj27BnOnz+PGzduAPhfr9Rp06ahRYsWRj8R4H9VqV27dv3otpGRkYiMjFT/DgkJSZU8EREREaUGvatTte3YsQO9evVCYGDgh4MkUZ+rOXz27NmxZMmSVGsL9/TpU7V36aNHj+Dm5pbk9uPGjcP48ePjLWd1KhEREaW2NK1O1da4cWPcvXsXM2fORJkyZaAoCkQk3ktRFJQuXRqzZs3C3bt3Uy2AAz7M7RoTE4N69ep9NIADgFGjRuHNmzfq6+HDh6mWNyIiIiJjS1FJXFwhISG4cuUKXr58ibdv38Le3h7Zs2dHiRIl0qxUy8fHB+fPn8fq1atTNAQKOzYQERFRWjFG3GGUIC69Xbt2DcWLF4ednR2eP38OGxubZB+DQRwRERGllXSrTs1oVq5cCQBo1apVigI4IiIioswm0wdxIoI1a9YA0K9XKhEREdGnIFlDjAQFBeHff/8FABQqVAg+Pj4662/duoX69esneQxbW1scPnwYzs7Oycxqwo4cOYL79+8jd+7cqF27tlGOSURERJTRJSuIGzduHObNmwdra2ucO3cu3vqoqCjcu3dP7a2aEEVR8OOPP2LOnDkpy3EcmrHhOnXqBBOTTF+wSERERKQXvTs2vHnzBm5uboiKisKkSZMwatSoeNtcvXoVJUuWhKIoyJcvX7z1kZGRePbsGaytrfH06VODOxBERkbC3d0dwcHBuHjxIkqVKpXiY7FjAxEREaUVY8QdepfEbd68GZGRkXBycsIXX3zx0e0DAgLiLQsPD0fBggURGBiI9evXo0+fPsnLbRyWlpYICgoy6BhEREREmZHe9Y8HDhwAADRp0gR2dnYpSszGxgYdO3aEiOC///5L0TGIiIiIKBlB3IULF6AoCurUqWNQglWqVAEAnD9/3qDjEBEREWVlelenPnnyBADg4eFhUIKatnLPnz836DhppezIFemdBb2dndYtvbNAREREaUTvIC4kJAQAkC1btkS38fDwwObNm5M8jrW1NYAPHSWIiIiIKGX0DuLs7Ozw5s0bvH79OsltmjdvnuRxNMGbra2tvkkTERERURx6t4nLnj07AODu3bsGJajZX3M8IiIiIko+vYO40qVLQ0TUGRtSat++fQAAb29vg45DRERElJXpHcRpeqX+/fffePDgQYoSe/DgATZu3AhFUVC3bt0UHYOIiIiIkhHEdejQAU5OTnj//j06deqEyMjIZCUUFRWFLl26ICoqCo6OjujYsWOyM0tEREREH+gdxDk6OuLbb7+FiMDPzw++vr7w9/fXa9/r16/D19cXR48ehaIoGDlyJKe2IiIiIjKA3r1TAeCbb76Bn58ftm7dijNnzqBUqVKoVasWGjRoAB8fH+TIkQO2trYICwtDYGAgzp8/j927d+PAgQPQTNHauHHjBOddJSIiIiL9JSuIUxQFf/31F/r164dVq1ZBRHDgwAF1Sq7EaAK4Tp06YfHixSnPLREREREBSEZ1qoaVlRVWrFiB9evXo0yZMhCRj75Kly6NdevWYdWqVepgv0RERESUcskqidPWpk0btGnTBpcvX8bhw4dx6dIlvHz5Em/fvoW9vT1cXFzg7e2NGjVqoGTJksbMMxEREVGWl+IgTqNkyZIM0oiIiIjSWLKrU4mIiIgo/TGIIyIiIsqE9AriNm3alKqZePLkCU6cOJGqaRARERF9SvQK4tq0aYPSpUtj48aNRk384cOHGDRoEDw9PbF3716jHpuIiIjoU6ZXxwZPT09cunQJ7du3R758+dCpUyd06tQJn332WbITDAsLw+bNm7FmzRr8+++/iI6OhpmZGTw9PZN9LCIiIqKsSq8gzt/fH7NmzcLUqVNx//59/Pzzz/j5559RuHBhVKpUCeXLl0eZMmWQM2dOZMuWDdmyZcO7d+8QFBSE4OBg3Lx5E6dPn8apU6dw6tQpREREqAMAt2rVCpMnT4aXl1eqnigRERHRp0QRTTSlh9DQUMyfPx/z5s3Dw4cPPxxAUfROTJOUpaUlWrVqha+++goVKlRIZpZTR0hICBwdHfHmzRudeV3LjlyRjrlKnrPTuqV3FoiIiEgPicUdyZGs3ql2dnb45ptvEBAQgF27dqFnz57w8PDQa9YGS0tL1KhRA7/++iseP36M1atXZ5gAjoiIiCizSdFgvyYmJqhfvz7q168PAHj8+DGOHz+OR48eITAwEEFBQbCyskKOHDmQI0cOlCxZEuXKlYO5ublRM09ERESUVRk8YwMA5M6dG23btjXGoYiIiIhIDxzsl4iIiCgTMkpJHGU+manDBsBOG0RERHGxJI6IiIgoE2IQR0RERJQJMYgjIiIiyoQYxBERERFlQgziiIiIiDIhBnFEREREmRCDOCIiIqJMiEEcERERUSbEII6IiIgoEzLqjA3Pnz/HoUOHcOXKFQQFBQEAnJ2dUaJECdSsWROurq7GTI6IiIgoyzJKEPf06VMMHz4cmzZtQnR0dMIJmZmhdevWmDFjBtzd3Y2RLAAgMDAQv/zyC7Zt24YHDx7A2toa+fPnR506dTBt2jSjpUNERESUkRhcnXrx4kWUKlUK69evx/v37yEiCb7ev3+PdevWwdvbG5cvXzZG3nH27FkUK1YMM2bMgLm5OZo3b45KlSohKCgIM2fONEoaRERERBmRQSVxYWFhaNy4MV69egUAqFu3Lvr27YuKFSvCzc0NAPDs2TOcOnUKf/zxB/bu3YuXL1+icePGuH79OmxsbFKcdmBgIBo0aIB3795hy5YtaNasmc76U6dOpfzEiIiIiDI4g0ri5s6diydPnsDExASLFy/G3r170bZtW+TLlw8WFhawsLBAvnz50KZNG+zevRt//PEHFEXB48ePMW/ePIMy/uOPP+Lly5eYNm1avAAOACpUqGDQ8YmIiIgyMoOCuC1btkBRFPTo0QO9e/f+6Pa9evVCz549ISLYvHlzitN99+4dVq1aBVtbW/Ts2TPFxyEiIiLKrAyqTr158yYAoEOHDnrv07FjRyxdulTdNyXOnDmDt2/folq1arC2tsauXbuwb98+REREwMvLC+3atUOuXLlSfHwiIiKijM6gIC40NBTAh2FE9JUtWzYAH9rTpZS/vz8AIGfOnGjRogW2bNmis/7777/HkiVL0LFjx0SPERkZicjISPXvkJCQFOeHiIiIKK0ZVJ2aI0cOAMC1a9f03uf69esAgOzZs6c43eDgYADA1q1bsXv3bsybNw8vXrzAvXv3MGLECLx79w7du3fHhQsXEj3GlClT4OjoqL7y5s2b4vwQERERpTWDgrhKlSpBRPDrr78mOj6ctujoaPz6669QFAWVKlVKcbqxsbHq8SZMmIBBgwYhR44c8PDwwLRp09C2bVu8f/8+yXHiRo0ahTdv3qivhw8fpjg/RERERGnNoCCuW7duAIALFy6gcePGePLkSaLbPnnyBE2bNsW5c+cAAD169EhxunZ2dur/E+rYoFl2+PDhRI9haWkJBwcHnRcRERFRZmFQm7imTZuiRYsW+Oeff/Dvv/+iYMGC+Pzzz1GxYkXkzJkTiqLg+fPnOHnyJPbt24eoqCgAQMuWLdG4ceMUp+vh4QEAsLGxUat0teXPnx8A8OLFixSnQURERJSRGTzt1tq1a9GtWzds2LABUVFR2LFjB3bs2BFvOxEBALRt2xYrVqwwKM0yZcoA+DDUSGRkJCwtLXXWa+Zt1S6xIyIiIvqUGDztlqWlJdatW4dt27ahYcOGsLa2jjfllrW1NRo2bIjt27dj3bp18YKu5MqXLx+8vb0hIglWmWqWaYI9IiIiok+NwSVxGo0bN0bjxo0RExODu3fvqqVhzs7OKFiwIExNTY2VFADgm2++QefOnTFixAjs2bMH7u7uAD60z5sxYwYAYMCAAUZNk4iIiCijMFoQp2FqaorChQsb+7DxdOrUCXv37sXy5ctRvHhxVKlSBe/evcPx48cRGRmJvn37om3btqmeDyIiIqL0YFB1aq9evdC7d288ffpU730CAwPV/Qy1bNkyLFq0CJ6enjh06BBOnToFHx8f/Pnnn1i0aJHBxyciIiLKqBTR9DhIARMTEyiKgsuXL6N48eJ67XPnzh0ULlwYiqIgJiYmpUkbXUhICBwdHfHmzRud4UbKjjSsE0ZaOjutm97bZqbzApJ3bkRERBldYnFHchjcsYGIiIiI0l6aB3EREREAYHAPVSIiIqKsLM2DuGPHjgEAXF1d0zppIiIiok9GsnqnTpgwIcHl8+fPR86cOZPcNzIyEnfu3MHWrVuhKAqqVq2anKSJiIiISEuygrhx48ZBURSdZSKCBQsW6H0MEYGVlRVGjhyZnKSJ9JaZOm2wwwYREaVUsqtTtWdiUBQFiqLEm6EhoZelpSXy58+Pzp07w8/PD97e3qlxPkRERERZQrJK4mJjY3X+1gwxcuXKFb2HGCEiIiIiwxk0Y0O+fPmgKAosLCyMlR8iIiIi0oNBQdy9e/eMlA0iIiIiSg4O9ktERESUCTGIIyIiIsqEjBLEXbt2DcOGDUO5cuXg7OwMc3NzmJqaJvkyMzOoJpeIiIgoSzM4kvr1118xatQoREdHQ0SMkSciIiIi+giDgrjdu3djxIgRAABFUVCpUiWULVsWzs7OMDFhTS0RERFRajEoiJs1axYAIFu2bNi6dSun0iIiIiJKIwYVl505cwaKouCHH35gAEdERESUhgwK4sLDwwEA1apVM0pmiIiIiEg/BgVxuXPnBgBERUUZJTNEREREpB+DgrimTZsCAI4dO2aUzBARERGRfgwK4kaMGAFnZ2fMmDEDz549M1aeiIiIiOgjDAricuXKhS1btiAmJgZVqlTBzp07jZUvIiIiIkqCQUOM1K5dGwDg7OyMmzdvomnTpnByckLhwoVhY2OT5L6KomD//v2GJE9ERESUZRkUxB06dAiKoqh/iwiCg4Nx6tSpRPdRFAUiorMfERERESWPQUGcr68vgzEiIiKidGBwSRwRERERpT1OcEpERESUCTGIIyIiIsqEDKpOTcijR4/w7NkzhIeHo3z58rC2tjZ2EkRERERZnlFK4t6+fYuxY8cib9688PDwQMWKFVGrVi0EBATobPfXX3+hXbt26Nu3rzGSJSIiIsqyDC6Ju3XrFho1aoS7d+9CRNTlCfVarVSpErp06QIRQffu3VGtWjVDkyciIiLKkgwqiYuIiEDjxo1x584d2NjY4JtvvsH27dsT3T5//vyoVasWAGDr1q2GJE1ERESUpRlUErdgwQLcvn0btra2OHLkCEqXLv3RfRo2bIj9+/fDz8/PkKSJiIiIsjSDSuI2bdoERVHw1Vdf6RXAAYC3tzeAD9WwRERERJQyBgVx165dAwB8/vnneu/j4uICAHj9+rUhSRMRERFlaQYFcaGhoQAAOzs7vfeJjIwEAJibmxuSNBEREVGWZlAQpylVu3fvnt77XL16FQDg5uZmSNJEREREWZpBQZyPjw8A4L///tN7nxUrVkBRFFSuXNmQpImIiIiyNIOCuDZt2kBEsGjRIjx48OCj28+aNUsN+Dp27GhI0qhZsyYURUn0tXv3boOOT0RERJSRGTTESNeuXTFz5kxcunQJNWvWxLx589CgQQN1vaIoEBGcOXMGs2bNwl9//QVFUVC9enU0bNjQ4MwDQOvWrRNsk5c7d26jHJ+IiIgoIzIoiDMxMcHWrVtRrVo13Lt3D02aNIGNjY06W0PNmjXx9u1btTODiMDT0xPr1683POf/b/r06cifP7/RjkdERESUGRg8d2q+fPlw4cIFdOzYESYmJggLC4OIQEQQGBiIiIgIdTqudu3a4dSpU8iZM6fBGSciIiLKygyeOxUAnJ2dsXr1akyePBk7duzAmTNn8OLFC8TExMDFxQVlypRB06ZN4eXlZYzkiIiIiLI8owRxGh4eHhg0aJAxD/lRS5YswatXr2BiYgIvLy+0aNEC+fLlS9M8EBEREaU1owZx6WHSpEk6f48YMQJjx47F2LFjk9wvMjJSbasHACEhIamSPyIiIqLUYHCbuPTi6+uLlStX4s6dOwgPD8eNGzfw008/wczMDD/88ANmz56d5P5TpkyBo6Oj+sqbN28a5ZyIiIjIcIpoeh0YKDY2Fv7+/rh79y7evn2LmJiYj+7TrVs3YyStY+/evahfvz6cnJzw5MkTWFtbJ7hdQiVxefPmxZs3b+Dg4KAuLztyhdHzmFrOTtP//cxM5wV8uueWnPMiIqJPR0hICBwdHePFHclhcHVqeHg4Jk2ahD/++AOvXr3Sez9FUVIliPv8889Rrlw5nDlzBidPnkTNmjUT3M7S0hKWlpZGT5+IiIgoLRgUxIWGhqJWrVo4d+4cjFSgZxSFCxfGmTNn8PTp0/TOChEREVGqMCiImzRpEs6ePQsAqFSpEvr16wdvb284OTnBxCT9mtsFBwcDAGxtbdMtD0RERESpyaAgbuPGjVAUBY0aNcKWLVvSNXDTCAwMxJEjRwAAPj4+6ZwbIiIiotRhUNT1+PFjAMCXX36ZpgHc8ePH8c8//8TrPHHv3j20bNkSYWFhaNasGfLkyZNmeSIiIiJKSwaVxOXMmROPHj1C9uzZjZUfvdy8eRM9e/aEm5sbfHx84OTkhPv37+Ps2bOIiIjAZ599hsWLF6dpnoiIiIjSkkHFZxUqVAAA3LhxwyiZ0VfFihUxcOBA5MqVC6dPn8b69etx5coVlC5dGjNmzMDp06c5PysRERF90gwqiRs2bBg2bdqEuXPnon379lAUxVj5SlKxYsUwf/78NEmLiIiIKCMyqCSuSpUq+OWXX3D8+HF06NABr1+/NlK2iIiIiCgpBg/2O2LECHh6eqJv377Imzcv6tWrBy8vL9jY2Hx03x9++MHQ5ImIiIiyJIODuBcvXmDz5s148+YNYmNjsWXLFr33ZRBHRERElDIGBXGvXr2Cr68vbt26laFmbCAiIiL61BnUJm7y5Mm4efMmRARt2rTBgQMH8OrVK8TExCA2NvajLyIiIiJKGYNK4rZu3QpFUdClSxcsX77cWHkiIiIioo8wyowNvXr1MkpmiIiIiEg/BgVxmpka7O3tjZIZIiIiItKPQUFc9erVAQBXrlwxSmaIiIiISD8GBXFff/01zM3NMX36dERERBgrT0RERET0EQYFcT4+Pvjjjz9w8+ZNfP7557h586ax8kVERERESTCod6qmQ0Px4sVx9OhRFC9eHKVKldJrxgZFUbBkyRJDkiciIiLKsgwK4v7880910ntFURAbG4uLFy/i4sWLSe4nIgziiIiIiAxgUBCXL18+NYgjIiIiorRjUBB37949I2WDiIiIiJLDoI4NRERERJQ+GMQRERERZUIM4oiIiIgyIYPaxCUkJiYGwcHBePfuHUQkyW3z5ctn7OSJiIiIsgSjBHEvX77EnDlz8M8//8Df3x+xsbEf3UdRFERHRxsjeSIiIqIsx+Ag7vjx42jVqhUCAwM/WvJGRERERMZhUBD36tUrNG/eHK9evYKdnR369OkDJycnjBs3Doqi4I8//kBQUBDOnDmDrVu3IiIiAlWrVkXv3r2NlX8iIiKiLMmgIG7u3Ll49eoVLC0t4efnh88++wxXr17FuHHjAAA9e/ZUt3369Ck6deqE//77D5UrV8Yvv/xiUMaJiIiIsjKDeqfu2rULiqKgV69e+Oyzz5Lc1t3dHTt37oSnpyemT5+OAwcOGJI0ERERUZZmUBB3+/ZtAEDdunXVZdrTcMXExOhsb21tjWHDhkFEsHDhQkOSJiIiIsrSDAriQkJCAAAeHh7qMisrK/X/b9++jbdPuXLlAAAnT540JGkiIiKiLM2gIM7Ozg4AdIYKcXZ2Vv+f0NyqERERAIAXL14YkjQRERFRlmZQEFeoUCEAwIMHD9RlTk5OcHNzAwAcPHgw3j5Hjx4FANja2hqSNBEREVGWZlAQV7FiRQDA6dOndZY3aNAAIoKpU6fi1q1b6vITJ05g2rRpUBQF5cuXNyRpIiIioizNoCCufv36EBFs2rRJZ/nw4cNhZmaGFy9e4LPPPkP58uVRvHhxVK9eHa9fvwYAfPXVV4YkTURERJSlGRzEdevWDZUqVUJAQIC6vESJEliwYAFMTU0RHR2Ns2fP4vr162pv1XHjxqFBgwaG5ZyIiIgoCzNosF9zc3P8+eefCa7r3bs3qlWrhj///BNXr15FdHQ0ChcujK5du6o9VImIiIgoZQyeOzUpRYoUwZQpU1IzCSIiIqIsyaDqVCIiIiJKHwziiIiIiDIhg4K4y5cvo2DBgihcuDAeP3780e0fP36MQoUKwdPTEzdv3jQkaSIiIqIszaAgbtWqVbh37x4KFSqE3Llzf3T73Llzw8vLC/fu3cPKlSsNSZqIiIgoSzMoiDt8+DAURUGzZs303qd58+YQERw4cABBQUEYN24cKlSogHLlyuH7779Xp+VKrlevXiFnzpxQFEWdSYKIiIjoU2VQ71RNlWipUqX03qdEiRIAPszeUKRIEbx69Updd/78ebx79w4zZ85Mdl6+/vprvHz5Mtn7EREREWVGBpXEhYaGAgDs7Oz03kd721q1auHMmTMIDQ3F4MGDISL4+++/k52P/fv3Y/ny5ejbt2+y9yUiIiLKjAwK4rJlywYAePbsmd77aLa1srLC+vXr4ePjAxsbG/To0QMAEBgYmKw8vHv3Dv3790fx4sUxYsSIZO1LRERElFkZFMQVLlwYALB7926999m1axcA4LPPPtNZHhISAgBwdXVNVh7Gjx+Pu3fvYuHChTA3N0/WvkRERESZlcFzp4oIFi1ahGvXrn10+6tXr2Lx4sVQFCXe3KkBAQGoUaMGunfvrnf6ly5dwowZM9CzZ09Ur1492fknIiIiyqwMCuIGDhwIW1tbREREoHbt2ti+fXui227duhV169bFu3fvYG1tjcGDB+us79WrFw4ePIjx48frlXZsbCz69OkDJycnTJ06Ndl5j4yMREhIiM6LiIiIKLMwqHdq9uzZsXDhQnTt2hUvXrxA8+bNUbBgQVSrVg3u7u4AgKdPn+LIkSMICAiAiEBRFCxYsCDZ1aZxzZkzB6dPn8ayZcvg4uKS7P2nTJmid8BIRERElNEYFMQBQOfOnREbG4uBAwciPDwcd+7cwd27d3W2EREAgK2tLRYsWIAuXboYlOaDBw8wZswY1KhRQ+0QkVyjRo3C8OHD1b9DQkKQN29eg/JFRERElFaMMndq165dcfv2bXz33XcoWbIkgA+Bm6bkrVSpUhg9ejRu375tcAAHAIMHD0ZUVBQWLlyY4mNYWlrCwcFB50VERESUWRhcEqfh5uaGyZMnY/LkyYiOjkZQUBAAwNnZGWZmRksGALB9+3Y4OTlhwIABOss1sz08fvwYNWvWBAD89ddfcHNzM2r6REREROnNoOiqYMGCAIDhw4djyJAh/zuomRly5sxpWM4+4vXr1zh8+HCC6yIiItR1KZ3Gi4iIiCgjM6g69dGjR7h//z5Kly5tpOzoR1NVG/cVEBAAAPD09FSX5c+fP03zRkRERJQWDAriNNWU1tbWRskMEREREenHoCCuYsWKAD4M4ktEREREacfgwX5FBDNnzsT79++NlSciIiIi+giDgrjatWtj1KhRuHjxIpo0aYKHDx8aK18pkj9/fogIbt++na75ICIiIkptBvVOnTBhAiwtLVGyZEns27cPBQsWRNWqVVGqVClky5YNpqamSe7/ww8/GJI8ERERUZZlUBA3btw4KIoCAFAUBTExMThy5AiOHDmi1/4M4oiIiIhSxuBReDVTaiX2NxEREREZn0FBXGxsrLHyQURERETJYJS5U4mIiIgobTGIIyIiIsqEGMQRERERZUIGtYl79eoV1q5diyNHjuDixYt49eoVQkJC4ODgABcXF3h7e8PX1xcdOnSAi4uLsfJMRERElOWlKIgLCwvDd999h6VLlyIiIkJdrumZ+urVKwQFBeHWrVvYuHEjvvnmG/Tu3RtTpkyBra2tcXJORERElIUluzr1xo0bKFOmDObPn493795BRCAiUBQFjo6OyJ07NxwdHaEoirru3bt3mDdvHnx8fHDjxo3UOA8iIiKiLCVZQdzjx49Rr1493LlzByICV1dXfPfddzh+/DhCQ0MRHByMhw8fIjg4GKGhoTh+/Di+++47uLm5QURw69Yt1K9fH48fP06t8yEiIiLKEpIVxPXv3x+PHj2CiKBPnz64efMmJk+ejEqVKsHKykpnWysrK1SqVAmTJ0/GzZs30a9fPwDAw4cP0b9/f+OdAREREVEWpHcQd/ToUezcuROKouDrr7/GokWLYG9vr9e+dnZ2WLhwIUaMGAERwa5du3D06NEUZ5qIiIgoq9M7iNuwYQMAoEiRIvj5559TlNjkyZNRtGhRAMD69etTdAwiIiIiSkYQd+jQISiKgl69esHU1DRFiZmZmaF3794QERw+fDhFxyAiIiKiZARxT548AQCUK1fOoAQ1+2uOR0RERETJp3cQ9/btWwCAo6OjQQk6ODjoHI+IiIiIkk/vIC579uwAgOfPnxuUoGZ/zuBARERElHJ6B3H58+cHABw4cMCgBDX7a45HRERERMmndxBXv359iAiWLVuGly9fpiixly9fYunSpVAUBfXr10/RMYiIiIgoGUFc586dYW5ujqCgIHTs2FFnzlR9REZGolOnTggKCoKZmRm6dOmS7MwSERER0Qd6B3EFCxbEoEGDICI4cOAAqlatirNnz+q177lz51C1alXs378fiqJg4MCBKFiwYIozTURERJTVmSVn419++QUXL17EoUOHcOHCBVSoUAGVK1dG06ZN4ePjAzc3N9jZ2SE0NBTPnj3D+fPnsXXrVvj5+QEARAQ1atTA1KlTU+VkiIiIiLKKZAVxFhYW2Lp1K3r16oWNGzcCAPz8/NQgLTEiAgBo1aoVli5dCgsLixRml4iIiIiAZFSnatjZ2WH9+vX466+/UKZMGYjIR1+lS5fG2rVrsXHjRnWcOCIiIiJKuWSVxGlr164d2rVrh8uXL+O///7DxYsX8fLlS7x9+xb29vbInj07SpUqBV9fX5QqVcqYeSYiIiLK8lIcxGmULFkSJUuWNEZeiIiIiEhPya5OJSIiIqL0xyCOiIiIKBNiEEdERESUCRncJo6I0kbZkSvSOwt6OzutW3pngYjok8eSOCIiIqJMiEEcERERUSbEII6IiIgoE2IQR0RERJQJMYgjIiIiyoQYxBERERFlQkYfYuT58+e4cuUKgoKCAADOzs4oUaIEXF1djZrOr7/+iqNHj+Ly5ct48eIFIiIi4Obmhho1amDkyJGcCoyIiIg+aUYJ4kQEixYtwty5c+Hv75/gNsWLF8cXX3yBvn37QlEUg9OcPHkywsLCUKpUKTVgu3r1KlauXIm//voLmzZtQpMmTQxOh4iIiCgjMjiICw4ORrNmzXD8+HEAHwK6hPj7+2PgwIFYuXIltm3bBicnJ4PS3bJlC8qWLQsrKyud5fPnz8fgwYPRp08fPHr0CGZmHM+YiIiIPj0GRTgigubNm+PYsWMAABcXF7Rr1w4VK1aEm5sbAODZs2c4deoU1q9fj5cvX+L48eNo3rw5Dh8+bFDGq1atmuDyQYMG4ddff8WdO3fg7++PUqVKGZQOERERUUZkUBC3Zs0aHD16FIqioFOnTpg/fz7s7e3jbdetWzf8/PPPGDx4MFauXImjR49i7dq16NixoyHJJ8rc3BwAYGFhkSrHJyIiIkpvBvVOXbNmDQCgRo0aWLlyZYIBnIadnR2WL1+OGjVqQESwatUqQ5JO1MqVK3Hjxg0ULlwYhQsXTpU0iIiIiNKbQSVx586dg6IoGDJkiN77fPHFFzh8+DDOnz9vSNKqadOm4erVqwgLC8O1a9dw9epV5MqVC2vXroWpqWmi+0VGRiIyMlL9OyQkxCj5IaLkKTtyRXpnQW9np3VL7ywQEakMCuI0w4gUKFBA730022r2NdSePXuwf/9+9W8PDw+sWLECZcuWTXK/KVOmYPz48UbJAxEREVFaM6g61dHREQDw5MkTvfd5+vQpAMDBwcGQpFX//vsvRATBwcH477//ULhwYdSoUQM//fRTkvuNGjUKb968UV8PHz40Sn6IiIiI0oJBQVyJEiUAAMuWLdN7H822mn2NxcnJCdWrV8fOnTtRtmxZjB07FqdPn050e0tLSzg4OOi8iIiIiDILg4K4Nm3aQESwefNmjBs3LtEx4jQmTpyIv//+G4qioG3btoYknShzc3O0b98eIoJt27alShpERERE6c2gNnF9+/bFnDlzcOPGDUycOBGbNm1Cjx49ULFiReTMmROKouD58+c4efIkli9fjitXrgAAihYtir59+xrlBBKSPXt2AEBgYGCqpUFERESUngwK4szNzbFr1y7UqVMHAQEBuHr1KkaOHJno9iKCggULYteuXak6k4JmIGFPT89US4OIiIgoPRlUnQoA+fPnx6VLl/D111/D0dERIpLgy9HRESNGjMCFCxeQL18+g9I8duwYdu/ejdjYWJ3l79+/x5w5c7By5UpYW1ujffv2BqVDRERElFEZpTjM1tYW06ZNw08//YSzZ8/iypUr6hAizs7OKFGiBMqWLWu0GRRu3bqFnj17Inv27ChbtixcXFzw8uVLXL58GU+fPoWVlRX+/PNP5M2b1yjpEREREWU0Rq3TtLCwQOXKlVG5cmVjHjaeGjVq4Pvvv8fhw4dx6dIlvHz5EhYWFsifPz/atGmDL7/8EoUKFUrVPBARERGlp9RrmJaKChQo8NFx4IiI0lNmmokCSN5sFJnp3DjLBn3KDG4TR0RERERpT6+SuAkTJqRK4j/88EOqHJeIiIjoU6dXEDdu3DgoimL0xBnEEREREaWM3m3iPjYbg6IoRtmGiIiIiD5OryAu7nhs2u7du4f27dvj9OnTaNiwIXr16oUKFSrA1dUVAPD8+XOcPn0aS5Yswa5du1C+fHmsW7cO+fPnN8oJEBERGQM7bFBmY1Dv1Ddv3qBevXq4d+8eVqxYgS5dusTbJm/evMibNy9atWqF1atXo3v37qhXrx7OnDkDR0dHQ5InIiIiyrIMCuJmzpyJO3fuYMCAAQkGcHF17twZR48exe+//44ZM2akWocJIiIi+oAljJ8ug4YY+fvvv6EoCtq2bav3Pu3atQMAbNq0yZCkiYiIiLI0g4K4e/fuAUCyqkU1296/f9+QpImIiIiyNIOCOHNzcwDA5cuX9d5Hs61mXyIiIiJKPoOCOG9vb4gIfvnlF4SHh390+/DwcPzyyy9QFAWlSpUyJGkiIiKiLM2gIK5v374AgBs3bqBmzZq4cOFCottevHgRtWrVwvXr1wEA/fr1MyRpIiIioizNoN6pnTp1wj///IONGzfi7NmzKFu2LEqWLIny5csjZ86cUBRFHSdOu8q1VatW6NSpk8GZJyIiIsqqDAriAGDt2rXIlSsX5s6di9jYWFy6dCnBNnIiAkVRMGTIEPz666+GJktERESUpRlUnQoApqammDVrFi5cuIBBgwbBy8sLwIegTfMqVKgQBg4ciPPnz+O3336DmZnBsSMRERFRlma0aKpEiRKYO3cuACAqKgrBwcEAACcnJ1haWhorGSIiIiKCEYM4bRYWFurcqURERERkfAZXpxIRERFR2mMQR0RERJQJMYgjIiIiyoQYxBERERFlQhzrg4iIiDKdsiNXpHcWkuXstG5GPyZL4oiIiIgyIQZxRERERJkQgzgiIiKiTIhBHBEREVEmxCCOiIiIKBNiEEdERESUCRkliIuKisKyZcvQvHlz5M+fH3Z2djA1NU3yZWbG0U2IiIiIUsrgSOrmzZto0aIFbty4ARExRp6IiIiI6CMMCuLCwsLQsGFDBAQEwMTEBM2bN0eOHDmwePFiKIqCMWPGICgoCGfOnMHJkyehKAoqV66MevXqGSv/RERERFmSQUHcwoULERAQAFNTU+zZswe1a9fG1atXsXjxYgDA+PHj1W3Pnz+Prl274sSJE+jQoQOGDBliWM6JiIiIsjCD2sRt27YNiqKgXbt2qF27dpLblilTBgcPHkTOnDkxfPhwnD171pCkiYiIiLI0g4I4f39/AEDLli0TXB8bG6vzd44cOTB8+HBER0dj7ty5hiRNRERElKUZFMS9fv0aAODh4aEus7S0VP8fFhYWb5+qVasCAA4fPmxI0kRERERZmkFBnI2NDQBAURR1mZOTk/r/Bw8eJLrvs2fPDEmaiIiIKEszKIgrUKAAAODJkyfqsuzZs8PZ2RkAcOzYsXj7aNrCWVhYGJI0ERERUZZmUBBXrlw5AMCZM2d0ltepUwcigmnTpiEoKEhdfvfuXfz8889QFAWlS5dOcbrh4eH4559/0Lt3bxQpUgRWVlawtbWFt7c3JkyYgNDQ0BQfm4iIiCgzMCiIq1evHkQEW7du1Vn+5ZdfAvgQtHl5eaFt27Zo1KgRSpcurZba9evXL8XprlmzBi1btsTSpUthamqKZs2aoXr16ggICMCPP/6I8uXL48WLFyk/MSIiIqIMzqAgrkmTJvD19YW9vT3u3LmjLq9atSp++OEHiAiCgoKwadMm7NmzRy0h69mzJzp16pTidM3NzdGvXz/4+/vD398f69evx+7du3Hjxg2UKVMG169fx9ChQw05NSIiIqIMzaDBfm1sbHDo0KEE140bNw7Vq1fHH3/8gatXryI6OhqFCxdGt27d0Lp1a0OSRffu3dG9e/d4y93d3TFv3jxUqVIFmzZtQlRUFNveERER0ScpVWehr1OnDurUqZOaScTj7e0NAIiMjMSrV6/g7u6epukTERERpQWDqlMzort37wL4UOWq6SVLRERE9KlJ1ZK49DB79mwAQIMGDXQGHo4rMjISkZGR6t8hISGpnjciIiIiYzF6EBcSEoK3b98iJibmo9vmy5fPqGnv3LkTS5Ysgbm5OSZOnJjktlOmTMH48eONmj4RERFRWjFKELd3714sWLAAR44cQXBwsF77KIqC6OhoYyQPALh+/Tq6dOmijk+naRuXmFGjRmH48OHq3yEhIcibN6/R8kNERESUmgwO4gYMGIDFixcDAETE4AylxOPHj9GgQQMEBwdj+PDh+Oqrrz66j6WlZZLVrUREREQZmUFB3O+//45FixYBAOzt7dGyZUt4e3vDyckJJiZp02ciKCgIn3/+Oe7fv4+ePXti+vTpaZIuERERUXoyKIjTBHDFihXDgQMH4OrqapRM6Ss0NBQNGzaEv78/WrVqhcWLF0NRlDTNAxEREVF6MKi47Pr161AUBePGjUvzAC4yMhLNmzfHqVOnUL9+faxduxampqZpmgciIiKi9GJQEGdnZwcAKFy4sFEyo6+YmBh07NgRBw4cQPXq1bFp0ybOzEBERERZikHVqcWKFcORI0fw9OlTlC5d2khZ+ri5c+di8+bNAIDs2bNj0KBBCW43ffp0ZM+ePc3yRURERJRWDAri+vXrh//++w9r165Fw4YNjZWnj9IexkQTzCVk3LhxDOKIiIjok2RQdWqnTp3QunVrrF69GnPnzjVWnj5q3LhxEJGPvvLnz59meSIiIiJKSwaPE7d69WqMGDECQ4cOxV9//YV27drBy8sLNjY2H93X19fX0OSJiIiIsiSDgzhzc3N4e3sjW7Zs8PPzg5+fn177GXvGBiIiIqKsxKAgLjo6Gh07dsSmTZsApN+MDURERERZjUFB3MKFC/H3338DADw8PNC9e/c0n7GBiIiIKCsyKIj7448/AACVKlXC/v37YW1tbZRMEREREVHSDCouu337NhRFwahRoxjAEREREaUhg4I4W1tbAEDevHmNkhkiIiIi0o9BQZy3tzcA4P79+0bJDBERERHpx6AgbsCAARARLFmyxFj5ISIiIiI9GBTEtWrVCgMGDMD27dsxYsQIxMTEGCtfRERERJQEg3qnrlixApUqVcL58+cxc+ZMbNy4Ea1atdJ7xoZu3boZkjwRERFRlmVQENejRw8oiqL+/fDhQ8yePVuvfRVFYRBHRERElEIGT7vFWRqIiIiI0p5BQVxAQICx8kFEREREyWBQEOfh4WGsfBARERFRMnCCUyIiIqJMiEEcERERUSbEII6IiIgoE9KrTVzt2rUBfBgWZP/+/fGWp0TcYxERERGR/vQK4g4dOgQAOmPCaZYripKsYUY028c9FhERERHpT68gztfXN8GgK7HlRERERJS6klUSp+9yIiIiIkpd7NhARERElAkxiCMiIiLKhBjEEREREWVCBgVxly9fRsGCBVG4cGE8fvz4o9s/fvwYhQoVgqenJ27evGlI0kRERERZmkFB3KpVq3Dv3j0UKlQIuXPn/uj2uXPnhpeXF+7du4dVq1YZkjQRERFRlmZQEHf48GEoioJmzZrpvU/z5s0hIhzol4iIiMgABgVxmirRUqVK6b1PiRIlAAA3btwwJGkiIiKiLM2gIC40NBQAYGdnp/c+mm1DQkIMSZqIiIgoSzMoiMuWLRsA4NmzZ3rvo9nW3t7ekKSJiIiIsjSDgrjChQsDAHbv3q33Prt27QIAeHp6GpI0ERERUZZmUBBXv359iAgWLVqEa9eufXT7q1evYvHixVAUBQ0aNDAkaSIiIqIszaAgbuDAgbC1tUVERARq166N7du3J7rt1q1bUbduXbx79w7W1tYYPHiwIUkTERERZWlmhuycPXt2LFy4EF27dsWLFy/QvHlzFCxYENWqVYO7uzsA4OnTpzhy5AgCAgIgIlAUBQsWLICrq6tRToCIiIgoKzIoiAOAzp07IzY2FgMHDkR4eDju3LmDu3fv6mwjIgAAW1tbLFiwAF26dDE0WSIiIqIszShzp3bt2hW3b9/Gd999h5IlSwL4ELhpSt5KlSqF0aNH4/bt2wzgiIiIiIzA4JI4DTc3N0yePBmTJ09GdHQ0goKCAADOzs4wMzNaMqqzZ89i3759OHXqFE6dOqXO3aop9SMiIiL6lBk/ugJgZmaGnDlzJro+MDAQa9aswVdffZXiNCZOnIgtW7akeH8iIiKizMwo1an6iIqKwoYNG9C0aVPkyZMHX3/9tUHHq1y5MsaOHYutW7fi6dOnsLS0NFJOiYiIiDK+VCmJ03bs2DGsWLECGzZswJs3bwBAbStniG+//dYY2SMiIiLKlFIliAsICMCKFSuwatUqtaeqpq2ahYUF6tati9atW6dG0kRERERZgtGCuDdv3mDdunVYuXIljh8/DuB/gZuVlRXq16+PNm3aoGnTpnBwcDBWskRERERZkkFBXExMDHbt2oUVK1Zg+/btiIyMBPAheDM1NUVMTAwURcGyZcvQvn17o2SYiIiIiFIYxJ07dw4rVqzAX3/9hcDAQAD/K3UrXbo0unTpgk6dOiFXrlwAYHD7t9QQGRmpBp0AEBISko65ISIiIkoevYO4J0+eYNWqVVi5ciX8/f0B/C9wy5s3Lzp16oSuXbuiePHiqZNTI5syZQrGjx+f3tkgIiIiShG9g7h8+fKpszAAgKOjI9q0aYMuXbqgRo0aqZbB1DJq1CgMHz5c/TskJAR58+ZNxxwRERER6U/vIC42NhaKosDe3h7Tp09Ht27dMvXYbJaWlpk6/0RERJS1JXuw39DQUHzxxRfo2LEjNm3ahKioqNTIFxERERElQe8gbtmyZahZsyaAD7MvbNmyBW3btoWbmxv69euHw4cPp1YeiYiIiCgOvYO47t27Y//+/bh//z4mTZqEIkWKQETw+vVrLFmyBLVr14aHhwdGjRqldnwgIiIiotSR7OrUPHny4Pvvv4e/vz9OnjyJQYMGwdnZGSKChw8fYurUqShZsiTKlCmTGvlV7dixA5UqVVJfmmpd7WU7duxI1TwQERERpReDBvstX748ypcvj1mzZmHHjh1YsWIFduzYgaioKFy8eFEdH27KlCl48uQJWrVqhXz58hkl44GBgTh58mS85drLNGPYEREREX1qkl0SlxAzMzM0b94cf//9N54+fYo5c+agQoUK6pAkly5dwtdff40CBQqgQoUKmDp1Ku7cuWNQmj169FCPn9irR48exjg9IiIiogzHKEGctmzZsmHw4ME4ceIErl+/jlGjRiFv3rxqYHX27FmMGjUKRYoUMXbSRERERFmG0YM4bV5eXvjpp59w7949HDhwAD169ICdnZ3OoMFERERElHypGsRpq1mzJpYuXYpnz55h5cqVqFevXlolTURERPTJSbMgTsPa2hqdO3fG7t270zppIiIiok9GmgdxRERERGQ4BnFEREREmRCDOCIiIqJMiEEcERERUSbEII6IiIgoE2IQR0RERJQJMYgjIiIiyoQYxBERERFlQgziiIiIiDIhM2Me7P379zh37hyuXLmCoKAgAICzszNKlCgBHx8fmJubGzM5IiIioizLKEFceHg4Jk6ciMWLFyM4ODjBbbJly4Z+/fphzJgxsLGxMUayRERERFmWwdWpDx48QOnSpTF16lQEBQVBRBJ8BQUF4ZdffkGZMmXw6NEjY+SdiIiIKMsyqCTu/fv3aNiwIW7fvg0AKFq0KHr27ImKFSvCzc0NAPDs2TOcOnUKf/75J/z9/XHr1i00bNgQ58+fh5mZUWtziYiIiLIMg0ri/vjjD1y7dg2KomD06NG4fPkyRo4cCV9fX3h5ecHLywu+vr4YMWIELl26hDFjxgAA/P398ccffxjlBIiIiIiyIoOCuA0bNkBRFLRo0QITJ06Eqalp4gmZmGDChAlo2bIlRAQbNmwwJGkiIiKiLM2gIO7KlSsAgF69eum9T+/evQEAly9fNiRpIiIioizNoCDuzZs3AIBcuXLpvY+7uzsAICQkxJCkiYiIiLI0g4I4Z2dnAEBAQIDe+2i21exLRERERMlnUBDn4+MDEcG8efP03mf+/PlQFAVlypQxJGkiIiKiLM2gIK5jx44AgEOHDqFXr14ICwtLdNvw8HD06dMHBw4cAAB06tTJkKSJiIiIsjSDBmrr3LkzFi5ciOPHj2P58uXYuXMn2rVrh4oVKyJnzpxQFAXPnz/HyZMnsX79egQGBgIAqlatis6dOxvlBIiIiIiyIoOCOEVRsG3bNjRu3BgnTpzAixcvMG/evASrV0UEAFC5cmVs2bLFkGSJiIiIsjyDp93Kli0bjh49ijlz5qBYsWKJTrtVrFgxzJ07F0eOHEG2bNmMkXciIiKiLMso816ZmJhg8ODBGDx4MJ4+fYorV64gKCgIwIdeqCVKlFCHFiEiIiIiwxl98lJ3d3cGbERERESpzKAgbsKECQCAQYMGIXv27HrtExwcjDlz5gAAfvjhB0OSJyIiIsqyDArixo0bB0VR0KZNG72DuKCgIHU/BnFEREREKWNwxwYiIiIiSntpHsS9f/8eAGBubp7WSRMRERF9MtI8iLtw4QIAIEeOHGmdNBEREdEnI1lt4lasWJHg8i1btuDMmTNJ7hsZGYk7d+5g6dKlUBQF5cuXT07SRERERKQlWUFcjx49oCiKzjIRwZgxY/Q+hojAxMQEX331VXKSJiIiIiItya5O1Z6FIaFlSb3Mzc1RtWpVbN26FTVq1DDqiRARERFlJckqiQsICFD/LyIoWLAgFEXBnj17ULhw4UT3UxQFVlZWcHFxgampacpzG8e7d+8wZcoU/PXXX3jw4AGcnZ3RoEEDTJw4Eblz5zZaOkREREQZTbKCOA8PjwSX58qVK9F1qSUiIgK1a9fGiRMn4O7ujubNm+PevXtYtmwZtm/fjhMnTqBgwYJpmiciIiKitGJQ79TY2FjExMSgePHixsqP3iZNmoQTJ06gcuXKuHnzJtatW4eTJ09ixowZCAwMRK9evdI8T0RERERpJVMO9hsVFYW5c+cCAObNmwc7Ozt13fDhw1GqVCkcPnwYZ8+eTa8sEhEREaWqTBnEHTt2DG/evIGnpyfKlCkTb32bNm0AANu2bUvrrBERERGlCYPmTq1du3aK91UUBfv370/RvhcvXgQA+Pj4JLhes/zSpUspyxwRERFRBmdQEHfo0CEoiqIz3EhcCY0rl9Dy5Hjw4AEAIE+ePAmu1yy/f/9+itMgIiIiysgMCuJ8fX0/GoyFhYXh9u3beP36NRRFgZeXF9zd3Q1JFqGhoQAAGxubBNfb2toCAN6+fZvoMSIjIxEZGan+/ebNGwBASEiIznYxke8Mymtaipv3pGSm8wI+3XPjefG8MopP9dx4XjyvjCLuuWn+Tqog7KMkjezYsUM8PT0lR44ccvToUYOO1bdvXwEgo0ePTnD9rVu3BIAULlw40WP8+OOPAoAvvvjiiy+++OIr3V4PHz5McTxkUElccjRq1Ag+Pj7w8fFBy5Ytcf78+RQPyKvpjRoeHp7g+rCwMACAvb19oscYNWoUhg8frv4dGxuLoKAguLi4GFTVq4+QkBDkzZsXDx8+hIODQ6qmlZZ4XpnPp3puPK/M5VM9L+DTPTeel+FEBG/fvkWuXLlSfIw0C+IAwM3NDcOGDcO3336LqVOnYvbs2Sk6Tr58+QAAjx49SnC9ZnlSAxBbWlrC0tJSZ5mTk1OK8pNSDg4On9TFr8Hzynw+1XPjeWUun+p5AZ/uufG8DOPo6GjQ/mk+xEi1atUAADt27EjxMby9vQEA586dS3C9ZnmpUqVSnAYRERFRRpbmQZyFhQUA4MmTJyk+RtWqVeHo6Ig7d+7gwoUL8dZv3LgRANC0adMUp0FERESUkaV5EHf06FEAifcs1YeFhQWGDBkCABg8eLDaBg4Afv31V1y6dAk1atRA2bJlDctsKrG0tMSPP/4Yrzo3s+N5ZT6f6rnxvDKXT/W8gE/33HheGYMiYkjf1uTx8/NDkyZN8Pr1a9SvXx87d+5M8bEiIiJQs2ZNnDx5Eu7u7qhevTru37+PkydPIkeOHDhx4gQKFixoxNwTERERZRwGBXETJkz46DaxsbEIDg7GmTNncPLkScTGxkJRFOzevRv16tVLadIAgHfv3mHKlClYs2YNHj58CGdnZzRo0AATJ05MdCBgIiIiok+BQUGciYlJsobjEBGYmZlh6tSpGDp0aEqTJSIiIsryDB5i5GMxoKIosLe3R4ECBVCjRg3069cPxYsXNzRZIiIioiwtTdvEEREREZFxpHnvVCIiIiIynEHVqZqODRUrVkT9+vWNkiFKWkxMDExNTdM7G0RERKlGRFJ9CsxPgVE6NmzevBnNmjUzZr4oAbGxsTAx+VB4umbNGvj4+KBo0aLpnKu0Fx0dDTMzM533I6PIiHmi1McvHEpLn/r1pl1YERoaqs6Xnlml5veCQUd1cXEB8L+5TCl1aS6CUaNGoUuXLvj111/x7t27dM5V2oqJiYGZmRnCwsLQrFkzbNiwIb2zpNK+UV+9eqUuo09bTEwMFEWBiCAiIgIAP3dKPdHR0VAUBe/fv0dQUFB6Z8foREQN4CZOnIjJkyfj8ePH6ZyrlIuJiVG/FwIDA41+fIOCuEKFCgEAnj17ZpTMUMK0vxAOHDiApUuXolmzZhg8eDCsra3TMWdpz9TUFJGRkWjcuDF27tyJI0eOICYmJr2zBeB/QXafPn3g6+uLsLAwlsol4lPpTxUdHQ1TU1NERETgl19+QefOnXH37l1+7hlEYsF0Zr3+YmNjYWZmhtDQUDRq1Ajfffed+oPxU6EpYZw2bRp+/PFHXL58GdHR0emcq5TRDki//fZbVK5cGTdv3jRqGgY9adq3bw8Rwfr1642VH4pDRNQvhJiYGISHhyMsLAyjR4+Gt7d3Oucu7WjfxLt378alS5cwduxY/PzzzxmqjaCI4OTJk3jw4AGuXr0KgKUyAOIF2pn1S1SbdqlwixYtMGbMGJw/fx7Xrl3L9OeXUX4YGSI6OhomJiaIiorCxYsXsW/fPty/f18tOc2M96XmfDp37oz9+/cjZ86cmb6qUUP7mouIiMCaNWtQt25dTJo0CR4eHumYs5TTBKQLFizA9OnTkTdvXoSHhxs3DUPaxEVFRaFixYq4fPky/vjjD/To0cOIWSNtffr0wZ49e1CpUiVER0dj8+bN6Z2lNKNp/xEaGoobN24gICAAw4YNg7+/P+zt7TNMOzRNPpctW4bevXvjyy+/xKxZs9I7W+lO04YxIiICmzdvxqVLlxAcHIxy5cqhfPnymfLHiOazDg8PR61atXDp0iX07NkT06dP15kXOqNcm8mh3R5p+/btePDgAfLkyYP8+fOjVKlSADJ+myzNOYSFhaFly5bw8/NDWFgYChQogFq1amHevHmwtLTMNB3FNPcQADx9+hQlS5ZEt27dMGnSJNjY2GT4zyM5NmzYABsbG3Tv3h2rVq1CgwYN0jtLyRb3umrQoAFev36NpUuXGn+cXDHA/fv35cyZM+Lt7S0mJiZSr149WbZsmZw9e1bu3r0r9+/fT/JF+qtTp44oiiKOjo5SunRpCQwMlJiYmPTOVpp5//69FCtWTBRFkapVq0qTJk3SO0uJunbtmri6uoqdnZ2cPHkyvbOTrqKjo0VEJDQ0VGrUqCGKoui8PDw8ZOrUqemcS/3Evd/ev38vPXv2FBMTExk7dqyEhYXF2yc2NjatsmcU2ufYuXNnnc8qb9688ueff6rrM/q5hYeHS9WqVdVnRosWLSRPnjyiKIpUq1ZNwsPDReR/12hG9/btWxk5cqTMnTtX8ubNK2/evBGR+NdlZrZ48WJRFEWaNm0qbm5ucvHiRYmNjc205zhx4kTx8/OTmjVryqJFi0TE+J+XQUGcoihiYmIiJiYmOv/X52Vqamqsc/ikaT8o27RpI4qiiK2trRw7dkxEPq0b+GN++uknsbCwEBMTEylatKjcuXMn3fLysS+wadOmiaIo8vvvv+u1/acsPDxcqlSpIqamptK7d2+5c+eO/Pfff/LXX3+JpaWlKIoiEyZMSO9sJurWrVsJLr937554eHiIj49PvADuyJEj8tNPP0mzZs1kyZIliR4jo+rXr58oiiLVq1eXsWPHSq9evdRgbvbs2ep2Ge261g7ItmzZItmyZZOxY8eqy54/f64GdhUqVMhUgVzv3r1FURQpX7685MmTR+7fvy/v379P72wZ1YULF3R+7P3zzz8ikvGuM338888/oiiK2NjYiKOjoyxZsiRV0jE4iEvpy8TExFjn8MmLjIxU/9+uXTtRFEVy584t169fF5FPP5DTvoFnzJghpqam6fplov1+R0VF6SzX5OP48eNibW0t+fLlk4cPH6ZZ3jISzXsxadIkURRFvvzySwkNDdXZplixYpI7d2759ttv5d27d+mRzSSNGjVKcufOLbt27Yq37vjx46IoinTq1EldFh4eLgMGDJDs2bOrzzpTU1Pp06ePPHr0KC2znizawUBwcLAUK1ZMunTpIi9evFCXr1y5UszNzUVRFJk1a5a6PKN8wWryER4eLmfOnJFhw4ZJkSJF1HtUc30FBwdLgwYNMl0g9+DBAzXAMTc3/2R/yF+5ckWaNWsmiqJIqVKl5Nq1a+mdpRTT/BgyMTGRESNGSExMjNE/L4MG+122bJmxanUJibefsbCwUP+/bt06KIqC9evXo3379tiwYQMKFy6cKdveJEbitO/QdKc3NzfH8OHDYWpqimHDhmHo0KHInTs3WrdurQ7xkNrtQkSro8l3332HZ8+eoUGDBujQoYPO+1+5cmW0bt0aq1evxrFjx9C+fftM0/7GWDSfxfHjx5E7d26MHz8etra2AD60GalWrRquX7+O77//HiNHjoSVlRUiIyNhaWmZntlWvXv3Di9evMCTJ08QHBwcb72Liwvs7Oywdu1aFCxYEDY2Nli5ciWuX7+OUqVK4Y8//sCzZ8+wdOlSrF69Gh07dkTu3LnT4Uw+TtPeatWqVTAxMcHt27exdu1a5MiRQ22P1aVLF1hbW6NLly4YNmwYAOCrr75Ks3vvYzTPiVq1auH169coWbIkPv/8c5ibmyMyMhJWVlaIiYmBk5MT1q5di44dO2LPnj2oWbMmDh06BGtr6wx7j4oI8ubNizVr1qB79+7Yv38/Bg4ciJ07dyJ37tyZ7vmf1PVSvHhxTJo0Ce/fv8fu3bsxceJETJo0CQUKFEjjXKac5vvq999/BwAsXrwYixcvRqtWrVC5cmXjXmdGDQkpxbR/Be7fv19mzpwpAwcOlLFjx8rBgwclKChIZ3tN1aq3t7fcvHlTRD6NX2Sa9yE6Olpev34tFy9elDdv3sjbt291tps9e7Za0rFp0yZ1eWqWCmh/Rrt27RJPT081Dw0aNJD58+fL8+fP1W3+++8/sbS0lEaNGqVanjKy2NhYefPmjWTPnl2qVq2q89lWrlxZFEWRMWPGSEhIiIh8uH4PHjwox48fT89s6wgMDJT//vtPRD6U8Pj5+emsX7dunU4NQ6lSpWTSpEk69+v48eNFURSZPn16muY9ucaOHSuKokj79u3Fx8dH53PRvq82bNggVlZWGbJE7sWLFzJ48GDJli2bKIoi5cqVi1flqLkOtUvkqlSpkmCbxvTwsef448eP1TbSLVu2lMePH+u1X0ah/Rx98+aN3L17N15zg+joaLl8+bLUrVtXFEWR7t27y927d9M6q3pJ7H3Xvh8GDhwoiqKIi4uLWrJorJJfBnEZgPZFMHbsWLG2ttb5YnBycpJGjRrFq5bTDuQ0N0FmuZETonnYhoWFycCBA8Xb21ssLCykcOHC0qpVK7X6QCMtAzntY44bN05GjhwpBw4ckP3790vNmjUlZ86cakP9adOmyYkTJ0REpFKlSqIoimzYsMHoecrooqOjJTQ0VPLnzy+5cuWS4OBgEZEEAzgRkYiICClWrJiMGjUqQwQE2iIjI6VUqVJSsGBB2bdvn846Pz8/mT17tixYsEACAwN1qthFRLp27SrZsmWTU6dOpWWWk2379u1Sq1ateO2RNBIL5DJax5RHjx7J999/L25ubmJvby/Lli2L95loB3KNGzcWRVGkTp066ZFdHZpnYFRUlPj7+8vu3btl9+7dEhwcrNOs5vHjx+Lr6yuKokjr1q0zTSCnHbjMmjVLKleuLGZmZmJmZiYVKlSQ3377TW12oAnkNAFrRgzktM/H399f/vvvP1m3bp1cu3YtXtORQYMGiaIokj17dqMGcgziMhDNL/aaNWvKhg0bZP/+/TJ69GgpXbq0KIoiXl5e8Xr1agK5YsWKZeq2A5qHT2hoqPj4+KilGi1btlQDIUVRZM2aNTr7/fbbb4l+6aSGGTNmiKIo0qhRI7l9+7aIiAQFBcnNmzelX79+aumciYmJjBkzRvr37y+Kosh3332X6nlLT5qHUWxsbLyH1xdffCGmpqYye/ZsqVChghrAaXrXaYwYMUIURZGVK1emWb6Toh20BAcHS//+/cXExEQqVqwoe/bsSXQ/7ZKfRYsWib29vdStW1devXqVqvk1hj179qhBTZs2bdRSfg3t9+Tvv/9W772AgIB0Cbw1acbtwfjgwQMZNWqU2NnZSdmyZWXfvn3xvjA1fwcFBUmbNm3kwoULaZfxBGj35G7durU4Ozur72+xYsVk5MiR8vLlS3X7zBbIaV8f3377rSiKIu7u7tK7d29p2LCh+uxs0aKFTsnclStX1ECuV69e6dqhTZv2+zxlyhQpWLCg2mbUxcVFGjRoIAEBATr7pEYgl+pBXFBQkLx48SLD/bLOaA4dOiROTk5Srlw5uXLliro8JiZGnjx5ohYrlytXTp49e6azb/PmzUVRFFm1alVaZ9uooqKipHXr1qIoiowaNUrny1DT2zN37txy9uxZnRtIO5Dbvn27UfOkfYPFxsZKpUqVpFatWuoDP+51ffv2bVm0aJGUKFFCLanQNETO6CUxKaX5nN69eydTp06VNm3ayOHDh9X1//77r+TOnVvMzMzExMREvvvuu3iB3qJFi8TV1VXq1Kmj05g+vWjOKTIyUr0GXr58Kd98840oiiIVK1aUvXv3qtsn9MU5efJkcXNzk1y5csULhtLTx77kd+/eLbVr1xZTU1MZMWKE3Lt3T2e99jW/fv162blzZ6rkMylxv/hCQ0PjnZcmkLO2tpby5csnGcil9/eTJu9hYWFStmxZtYnGggULZNiwYeoP2xo1aug02Xjy5Ina2aFdu3aZohPVokWL1B/C586dE5EP99mzZ88kT5484uLiIuPHj9fp6HTlyhWpX7++GrDG/QGYnkaNGqUWskycOFEmTJigBp0uLi5qrYzG4MGDRVEUcXV1latXrxqcfoqCuPfv38vly5flzJkzCT5w3717J2PHjpXcuXOrQ4rY2tpK69atdQKUrEbTCyohS5YsEVNTU50hKTQvEZGQkBD1V9fYsWMlJiZGp4pgx44dqZv5VKQ5x//++08cHBykSZMm8b7kq1atKtmyZZNhw4ap1XLaD95ffvlFbGxsUu36Wr58ufz999+SM2fOBEv84n6BPHz4UI4fPy6NGjVSx7f7+eefRSTj94JLDs25vH37VmrXri2KokiRIkVkw4YNavXP+/fv5fvvvxcXFxextbWV+fPnq59vbGys/PDDD+Ls7Czu7u4ZKth5+/atlCxZUvbv368uSyqQE/nw7FuzZo2ULVtWTE1NpUSJEkZ5UBuL9rX38uVLOXfunNy9e1dev36ts92ePXvE19dXzMzM5Ouvv44XyMW93rWfValNE2CHh4fL+PHjpVmzZuLh4SF169aV6dOny8uXL9X8aQdyFSpU0Ank0jtwi+v9+/fSp08f9Ues9jPwyZMnYmNjI+7u7rJgwQKd91u7jVy3bt0y7PMlNjZWIiIipG7duuLs7Cxnz57VWa+piWratKmcPn063r6XL1+W8uXLy8yZM9Mw10lbt26dmJiYSJ06deKV5FasWFEURZGePXtKaGioTqHEl19+qdbYBAUFGXQtJiuIi42NlbFjx4qjo6POmG9Vq1ZVSxkiIyPF19dXZ/w47WFFbGxs5N9//01xhjOrgwcPSu/evcXf3z/B9UOHDhVFUdTBNLU/cM0Dad++feLo6CjVqlVTA7jEGu1mRpo2btpBUkxMjFqdql0F9+bNm3glknE7fxjL6tWrRVEUqV+/vri5ucnRo0dFJOH3Ou7N+O7dO9m3b5/kz59fihcvnmQgn9loD+lQqVIlsbS0lC+//FKnE4rm2o2MjJRvvvlGXFxcxNraWkqUKCGNGjWSzz77TBRFkUKFCqV7sKNdNSfyvyofzY8GjaQCudDQUFm4cKHkzp1bhgwZkqEGNde+XkeOHClFixYVRVHEzMxMPD09Zfny5fLgwQN1m71796qBXEIlculB+0eD5rng4uIirq6u6vdMw4YNZdu2beqzMW4g9++//2bI8dWePn0qBQsWlMqVK+v8QI+KipJq1aqJiYmJfP/99wn+iH348KE0btxYLl++nNbZTpb79++LqampdOvWTWe5JoBr0qSJnD9/Xl3+6NEjnWemdpOE9AzCNWn36dNHzM3N43XGmjJlitrxJLGOWj179pTJkycbnJdkBXHdu3dPMDhTFEWcnZ3l1q1bMmzYMHWZi4uLVKpUScqVKyc2NjbqcldX13i//D5lISEh6rg3/fr1U8d3E/nfl9ysWbPUxpsacS/SR48eiZeXl1hbW+scIzOKW00p8r8Lf+vWrSLy4eGVWCP4s2fPSocOHeTBgwepfjPfunVLWrZsqV6/f/zxh177aT7b9+/fS/v27T/JDg6xsbEyevRoURRFRo4cmeCYW9rvw8aNG6V79+5ib28vNjY2UqFCBfn222/TLdhZsWJFvA4zGgMHDhRbW9t4PaNF4gdy2m3koqKi5P79+/FKk9OT9j2iabLw2WefyciRI6Vt27aSJ08esbCwkP79+8ulS5fUbfft26cGct9++228Nj7pISIiQmrXri2WlpYyYsQIefnypTx9+lS2bt0qtWrVElNTUylbtqxs2bJFvfYePXoko0aNEgcHBylcuLAcPHgwfU8iAfv27RNFUWTSpEnqspiYmASfgeHh4XLu3DmdACcz/IC/f/++GtxoaAK4xo0b65RmhYSEyJAhQxIs9EnvUlRNz/sCBQrIZ599JtHR0WqeEjuf8+fPJxr3GNKOUe8g7sCBA2ppmrW1tbRp00ZGjhwp7dq1ExsbGzExMZFBgwZJtmzZxMLCQhYvXqzzRoeHh8vIkSPVY2h3Tc8Kjhw5ogYCvXv3jheEXblyRWxtbcXe3l7++usvdXnc7v116tSR7Nmzq41YM4O4DxfNgyc0NFTOnDmjLl+yZIkoiiJDhw5VR/lPKIATEalfv754enqm2QCqd+7cke7du6s9UOO2c0iM5he15v7J6MNMJJdmaqN8+fKpD6iEHkhxlz1//lxt25NeXz6a4UFatGihU32jqQbu0qWLODg4yMuXLxPM48eqVjOiMWPGqM8g7ZLs48ePi5OTk+TKlUv+/PNPiYiIUNft27dPrSofNmxYmgzKnFT17Pr169Vqqrg/Gq5evSp9+vQRMzMzqVevns5z8vHjx/Lll1+Ku7t7huvlKPK/waMHDx6sLtOuhdB+Bt68eVMaNGggBw4cSI+sflRCz4DY2Fh5/fq1uLm5SZkyZeTdu3fy448/JhjwiPzv+0D7+zCj8fHxkWLFiql/J3U+vXr1kuHDh+sEfCKGB6R6B3Fdu3YVRVHEzc0tXpXgtWvXxN3dXW24/O233yZ6HM24PPXq1Ut5rjMR7Q/Iz89PmjZtmmggp5lWqnLlympplPYxDh48KHZ2dtKwYcMESwcysmPHjsnIkSPVv4ODg6VIkSJSoEABtQonMDBQ8uXLJ66urlKoUCFRFEV+/PHHeNVZU6dOFSsrKxk6dKjOl42htD8rzUNI+2F0584ddT7Jxo0by8WLF/U+tmbWgunTp6f7r0hjunLlitjY2Ej16tVFJOkHknYglFBJbFq7ePGidO3aVczMzKRly5bxOp40bdpU3N3dJSwsLNFfyppAztTUVLy8vDLsl6rIh+YGJUuWlMKFC8cr+ezUqZPapkoT4Gh/Lrt27ZIqVaqkWdvbp0+fikjCAb6m5FfzecWtGvX395eGDRvGK9US+dC2LCN0nEnInTt3xM7OTnx9fcXf318N4EaPHh3vR2ynTp3EysoqXruyjCDusBua5icamqZDmhLGxo0b61ShinxoH+3l5SVlypSRGzdupEW2kyUmJkbCw8OlXr16oiiKbNu2LdESOBGRNWvWiKIoMmPGDKPnRe8gzsvLS0xMTOSXX35JcP3UqVPVUrakGpevXbtWFEWRXLlyJT+3mZT2F8CxY8cSDeSuXr0q3bt3F1NTUylZsqRMnTpV/aV56NAh9ddw3GE2MjLNkBOaoGzMmDEiIlKyZEmxtLSUiRMn6gRiCxcuFBcXF7XIPe4Dd/78+ZIrVy4pVaqUUdvoxP2Sjhska77Q7t69q0591qJFC70CuX/++UdcXV3Fzc0tQ1RHGdOjR4/Ezc1NSpQokeiE3JqH+qFDhxKtukwv/v7+0q1bN1EURVq1aqVTIle3bl3x9PRMcD/tcwwMDJRBgwaJo6Njhv58z5w5ozaa16b5YdKnTx81uIuMjNQZzkJE1L9TO+jWDMOgqdbV7ogQExMjffv2FUVRZMWKFYkeY+/evaIoivj4+EhoaGiGqWpMrIQqOjpaYmJi1EFhXV1dxdTUVMaMGRPvWTRnzhxxcHCQTp06xQvu0pv2+zxz5kwpXLiweHh46IzjefToUSlQoIDao/PkyZM6xzh06JDUqlVLLC0tZfXq1WmW94RoPq/ESs527twppqam4ubmprbpi9su8ejRo+Lt7S2enp46NU/GoncQZ2dnJyYmJuro5XEdOXJEDeKSKh3RPEisrKySn9tMKKGHx/Hjx3UCOe3x3S5cuCCDBw8WCwsLtcF30aJF1fFnfv31V3XbzFSis3//fnUcoOzZs4ulpaVMnTpVfUBpbpaHDx/Kt99+K46OjuLh4SFjxoyRM2fOyOHDh6Vnz55iaWkpOXLkSLSDSEpof0arVq2S7t27S/78+aVdu3by66+/qr/0NXkMCAhQA7nmzZt/NJC7fPmyeHt7J6vkLjOIjY2VV69eqb+otR+4mmtT+0urbt26Urt27QzXHvbq1as6gZymIXK1atWkaNGiet1nr1+/1hn6ISM6evSoKIoi/fv3V5d16dJFfQ5pd2p48OCBVKlSJV1GE9BM9O7s7KzeM9r3qGaIii+++EJE4j8Ho6OjJTw8XIoWLSouLi4SGBiYdplPgCZ/2gP5nj17Vk6fPh2vWnffvn1SsmRJMTExkSpVqsTr7PPrr7+Km5ubFCpUKMNVCWt/DppmBkWLFpW1a9fGCzbXrVun/ljv37+/rF27Vg4ePCgzZsyQXLlyiaIoOr1Q0+O7Tvuau3//vvj7+4u/v7/O9fTy5UsZPHiwmJubi5OTk8ybN0/nGPv27ZMaNWqImZmZLFu2LFXyqXcQ97FStsuXL6vbJOXKlSt6bfep+eWXX3SqIuJWrWoHJS9evJDt27dLpUqVxMvLS3LmzClt2rSRjRs3qttk1AEdE6LJ6969e8XCwkJMTU2latWq6nrtkchFPtww06dPV3/daAJYCwsLqVGjhlE7dWi/j5rxfiwsLCRv3rzi5OSkjmekyWNCgVyrVq3iVQdoaB4+GXFyd319rBRj4cKFoiiK5MiRQ+ca135vp06dKiYmJjJ8+PB4n3dGcOXKFTWQa9q0qRw7dkzKli0rpUqVki1btsiBAwfkyJEjcujQIfnvv//kxIkTcvr0admwYUOGrO5JyNOnTyVXrlxSs2ZNERHp0aOHWgKnHcCJiPzwww+iKEqatvPT/qL++uuvRVEUcXR0VAM5TRDk5+cnrq6uYmZmJrt379bZV7tq1dPTU8qVK5dW2Y9H+z3VtI0NDQ2Vpk2bipOTk5iZmYmzs7MsXLhQnjx5om67fPly8fLyUq/F8ePHy9y5c9XvC3d393TvyZ2UefPmJfoDV/uZsHXrVqlSpYpYWlrqdJIsXLiwLF26NMF90op2mtOmTZNixYqpnbHKly8v48aNU9efO3dOunXrJubm5lKgQAEZNmyY/PbbbzJs2DBxdHRM9cKXZAdxiV08+gZnWTGI27VrlyhK/OlpkgrkRD4M/BgZGSnBwcEJDjmSmURHR0u/fv3UB7OmN6P2em2xsbHy4MEDmTx5snzzzTfy7bffyp49e+JV8RjLxIkTRVEUqV27thw5ckREPgRqmmL/SpUqJRjIdezYUd0vqV/8manUVJv2QL67du2SBQsWyOnTp+MNKvrFF1+oXzDr1q3T+Txnz54trq6uUqRIkQw1TIWI6AzloF0i1759e51hK5J6ZYRz0kjsOouOjpa3b9+qzxtNkDBgwAB15hGN9evXi6urqzRs2DDeMD6pTfuz0Yx04ODgEK+NkWaeV3Nz8wQDTc0A4P369ZPIyMg0v/+++OILyZ8/v85cu6GhoerYYT4+PtKgQQMxMTERU1NT+eKLL3R+nG7btk06dOigE+C4ublJq1at4s0zmpE8efJEKlWqJC4uLolWHWp/Fnfu3JGDBw/K6NGj5fvvv5dt27bFG+w+PX3//ffqd1aDBg3E29tbHZ2jdevWavMJf39/mTJlitjb26ufl42NjVSuXFmnhiI1zodBXBo4ffq0ZMuWTfLlyxevQXFSnR0SavydWYOB2NhYuXDhgmzdulUOHjwo+fLlE0VR5Msvv1S30ZxvalzoCXVa0Ni5c6fkzJlT6tatq5aohYWFye+//y7W1tbqjVm9evV4gdzdu3elUaNGOr+0PhXaU6HVrFlTTE1N1Qda3bp1dWZlePjwoToSuea9atWqldrDOKOUHmiC0oiICFmyZIn8+uuvOj+e/P391SpGe3t7adu2rfzxxx8yY8YMmTx5ssycOVOmT58uP/30k/zyyy9GrdY3lPbz4t27d3Lv3j21g4DG1atX1RLmUqVKxWtzumHDBilRooS4uLioP2bSUtwfc7169VIDOe2hT0T+13bOzMxMvv/+e9m8ebM8evRIfvzxR3WmjPSYoik6Olr9cVehQgW1HejChQvFyclJxo0bpzYj2bRpk9SoUUNMTEykf//+Ok1rQkJC5NKlS7J582ZZvXq13L17N0PNVJCQc+fOibW1tfTq1cvgY6V3Faqfn59aC6b5EREUFCQHDhyQggULqp0YtMcnvXnzpmzfvl0WL14sp0+f1hk9IbUCUgZxaUQzErdmjDHtEoC4gVxmqZ5JrpiYGPUmOXr0qOTNmzdeIKfdnlJTCmDMAFbzJa7Jx7t372TQoEFibW2tjh0VFRUl06dPF1tbW/Hy8pLr169L+fLlRVEUqVatWrxATru9R2YNshMTERGhTuvToEEDGTx4sDo6vJubm1qdJfLhPZ0zZ45a9aAoiuTPn186deoUr7QnPWjPTfn555+LoihSsGBBOXr0qM51d/nyZbVdVuvWrTP8AKoiul8+Y8aMUa9XW1tbadq0qWzZskX9stm9e7c4ODiIoijSsWNH2bRpk2zbtk2GDx8ujo6OYmZmJtu2bUvzc9Dcm2FhYTJs2DBp3LixOvOFoiji5OQU77P4/vvv1ZJ97VehQoXSdXagyMhINcgsW7asXLt2TSZMmCA+Pj7x2owfOXJEGjVqpAZymXkM0K1bt4qiKNKlSxcRid9zWOR/z0jt0vyM8NzUzsPz58/lxIkTYmlpqdMZS/PMP3/+vJQrV04URZG+ffsm69jGluwgbvDgwTJ+/Ph4L82vcBMTkwTXJ7RdVqD50M+cOSPZsmWT2rVrx1sn8r9AzsLCQrp3754hSi1SSrs32fv37yUkJERtE6Y97p2fn1+CgZyIyJ9//inlypWL1z09JR48eCArV64UX19f+eyzzyQsLExdFxMTI/3795exY8eqf69YsUJcXFykYMGCaluVs2fPqtVrVatWTbBdV0Z4EBmD9oP3xIkT4ubmJmPGjFHPLyoqSr777jtRFEWyZcumE8iJfOixeu3aNTlw4IA8ffpU5/1OL5q8h4WFSfny5cXCwkIGDRqU6HiLV69eVYdVatGihU4PuvSu4olLOz+agXw9PT2lR48eUqlSJXF2dpbs2bPLN998o5a8nTlzRp0tQ/tVvnx5dXDVtJxKS/vzKVu2rDg6OkqtWrVk+fLlMmDAAHXKQScnp3glcv/++69MmzZNWrRoIT169JA5c+bEa+OXHiIjI6V///6iKB+G0yhXrpx89dVXIvLhHtN+b48ePaoTyGWk6eeS49y5c2JjYyOlS5dWR1bQvj7jdn7IiDUYms+sdevWUqlSJXV53MKEc+fOiZ2dnSiKkq7TXiY7iDPG61MM4uJWA8R90AcHB0vVqlVFUXQHL9S+qE+cOKGOo7dv377UzXAq0Z7XcNSoUeLr6yuFChUSX19f2bNnT7wG/tqB3JAhQyQoKEjmzJkj+fLlExsbG53qhZTw8/OT6tWri6J8GKS3RIkSCTaG1lRzh4aGiq+vr7i7u6tVZZGRkfL06VPx9PSUHDlyiKJ8GO0+o32ZG4P2l+nevXtlxowZkj17drUaR/s6HzdunNqLUHvGgoz6vkRHR6s/IseMGZPgl4w27eFH2rZtm+j0ORnFiBEjRFEU6dWrlxrEREZGyp49e8Tc3FyKFSsma9euVWsBnj17JocOHZJZs2bJzJkzxc/PT63+ScsATiM6OlqtPh07dqwa/EdHR0tUVJT07Nkz0RI5kfgdpDKCyMhIdUgUExOTeDPyJBbIDRo0yOBnX2pJaLgN7edGmTJlRFEU+eabb9RrLe4At5s3bxZF+TAGXkb73DRtMW1tbcXd3V291hJqkjNhwgRRFEV+++23dMmrSDKDOGO+PrUgTmPdunXy6tUrnQtT84Hv3r1bzM3NZcCAATr7aF8cR44c0RnoNzPRntdQ04BXM6abtbW1WFpayk8//RSvIbifn5/a0FpTzePq6mpwNdb+/fvFwcFBnJ2dZcSIEfLq1auPzpm4ceNGUZQPgwyL6H4xfP755/LVV19JpUqV1PWfovfv36tjErZp00batm0rIgkPgJxYIJcRPX/+XIoXLy4lSpRQv1wSG7dL4+rVq2rw0LVr1wzby/jhw4dSoEAB8fb2jjeLiaZ9Vvfu3dV7LyMG2m/fvpUyZcpI/vz51fvu/fv3OnnVVHNrl8hp7umM0G44ofdV02RD890X9wd63EBO07Rm2LBhOs1uMoK47bQTaqO3f/9+yZkzp2TLlk0mTZoU7z3x8/OTqlWriouLi+zfvz/V85wSmo4ziqLIokWL1OVxh07SjHvbqVMnneVpSe8g7tChQ0Z/fWo0PRwLFiwoQ4YMiVcVePv2bSldurSYmJjo9FoSSfjBkxEftInRngy9du3aYmFhIV999ZVafTNv3jxxcHAQa2tr+e677+J18Lh48aIMGjRIGjVqJN26dTO4DZWfn5/Y29uLp6enrFy5Umed9oPo1atXOoNpzp8/XxRF0elCLvIhADcxMYnXE+5TqUKN6+eff1Yb77q5ucUrFUioRM7V1TVd2lLpSzMArGby7aTGs9QO9i9evCgDBw7MUJ0Y4tq/f78oiiJz5szRWa7ppKE9kG9UVJTOROIZQUxMjPj7+4u5ubl4eXnJ27dvE53hQ9Mm08HBQR3CIiM8KzV5fP/+vVy9elXnGRcREaH24C5RokSSz/+DBw9Ku3btMtz1pv0e//bbb9KkSRPJly+ffPHFFzoFD8HBwTJ79mxxdnZWS7EPHTokFy9elBUrVoi3t7coiiJz585Nj9NIknbQrHmuaVeXakpPNZ/XsmXLRFEUmTZtWrrkVyQZQRx93KVLl2Tw4MFqg0fNr1/tCdM1Y2qNHj1aRDLHpMWJiRvAxMTEyJgxY8TW1laGDh2qBkf37t1T2xc5Ozurk2nHLZHTlHJoT+qcEg8fPhRfX19xcHDQCeDiPugvXLggAwYMkMWLF6tDl/j5+Ym1tbV8/vnnahf5I0eOSJ06dcTd3V1nztRPMYDTPqdZs2ZJkSJFxNLSUn7++ed4E7prX7uaHzAFChSQ0NDQDPnenD59WiwsLKRVq1bqsoSuYZEPHbC0A9eMVuUT186dO3VKkEV0AzjtNmL+/v7St2/fNJt3WF9RUVFSvnx5cXV1VYduSCiQGzt2rNosR1GUdO3AoKHdKaNv376SL18+adu2rc4QLZGRkTJgwAC1s0NSgZyhz8DUpD2eppmZmVrj8tNPP6nbPH/+XJYvX642ldF+OTk56QRw6T0OnEji38OaQM7CwkI2b96ss87Pz0/KlCkjJiYm6dr8iUGckWhXNb1580bmzp0rdevWVS/yatWqyW+//SZHjhyRChUqiIeHR4b7NayvQ4cOJThe2+PHj6VYsWJSrlw59SF0+/ZtNYDr37+/HDp0SIoUKSJWVlYyZswYnWmKDA1oNQ/BzZs3i6mpqQwZMkRdF/em9ff3VwfrdXFxkfnz50twcLC8efNGneg+X758UrFiRbG2thZFUWT27NkG5S8jSqp9i8iHX9y5c+cWJycnWblyZbzSK+3PbOrUqRl6Vorr16+LjY2NWFhYqI33ReJXkbx//16qV68u48ePz3DBW9zmAJq8X7p0SUxMTKRPnz4iojuVVtxG/sOHDxdFUXSmGEtvMTEx8u7dO7XqV9O7UeR/15jm3z179oiXl5c0a9ZMFEVJ996c2kPx/F975x0WxfW+/efs0kF6UQQVLNh7ARXQKHaT2NDExB6/aoyiUbFiYuwaS9TYa2JHExU0SFNEE8VeUBJLkp+xK6IIoiz3+wfvnMxsATSRXcz5XNde4s6Z2TNzptzznKc0bNgQFhYWCAoKwunTp3VefOTBDtpCzhh+iEVB3qfo6GjY2dmhQ4cO+OWXX3DmzBl88803cHJy4mXC5Ny6dQuTJ0/GgAED0L59e8yfP18xQ2UMASe/Z+3btw9TpkxB165d0bdvX2zbtk0nD59U1J4xhn79+mHy5MmYNm0a/Pz8oFKpsGjRouLeBQVCxL0GBZ148pvs06dPcezYMfTq1Yu/kTg4OPByIzNnzjTJi7YgpBq5s2fP5ikLpH3466+/8Omnn/L8Yffu3eOZ1+V5g6QC1tbW1pgwYcK/Xm+yR48eUKlU3JKmLQ4vXLjABVxQUBA8PT3h5uaGZcuWITc3Fzdu3MDIkSNRtmxZWFpaom7duoo6jSVtzAwhfzg+efIEZ8+exYMHD3SE2pIlS1C6dOkiCTljU5iPmxRV26dPH0UEuHyfZs2aBcYYZs2aZVL7Jt8Pqfi7VHfzwYMHaN68ORhjvHD6wIEDda6trVu3wtXVFV26dHljibMLorDjmZaWBnd3dzDGMH78eP69/L46YMAANGjQAACMXlJL4vnz52jXrh3Mzc0RERGhI97kaAs57dqhpoT29TRr1iyUKVNG8bKWl5eHmJgYuLq68qAUOYbul8a2wE2cOJFXA5I+FhYWqF27tk6ORKm4vWRJbN++PUJDQ994It+iIERcEZBOwtzcXMVNKDk5GRs3bsT8+fOxY8cOnfbSoD579gw3b97E2LFjefJTxvITBZYkXr58ic2bN/Mozblz5yoSHQL5U5lSVNmxY8fg4uKC999/X9EmLS0Nbm5uqFWrFhhjiIiIKDTg4FUICgqCg4OD3nx7WVlZ6NWrl+JmI0Vgurq6YunSpXj+/Dlyc3Px8OFD/PHHH4qEqKbge/NvIJ/+GTRoEKpXr86nRYKDg3HixAmFr+DSpUsLFHKmgLRPL1++xG+//YaYmBicO3dOYYk6fPgw/P39oVKp0K9fP0XCYgBYvnw5PDw8ULNmTZ2qFKaC5Pi+Z88exfebN2/m95Z33nlHx+lcSuTr7u6uyH1VXMirf2zduhXh4eEYN24cvv76azx69Ij7I+3cuRNOTk7c8nHv3j1+vq1YsQJeXl7o3r27USoxaCP9/o4dO7iDu3T/K+heIRdyFStWNCmrqD5GjBiBQYMGYfTo0QgLCwOgaxVOSEjgQi4iIoJ/L8+raezxkpgxYwYYY2jbti2io6Nx+fJlLFmyBJ06dQJj+bXdpbyhElIpujJlymD//v2KZcZ8LggRVwgxMTF4//33dR5a06ZN45nPpU9QUBAuX75c4IDevXsXO3bsQO3atcEYw5YtW970LvyrZGdnY/fu3ahSpQqcnZ31CjkJaWpEOuGli/n333+HjY0NIiIi0L59+3/dgTcwMBCOjo58mkV7PBITExEeHs7//+TJE8ybNw9ubm5wd3fH1q1b36pqGdrIp3/q16/PLQIDBgzg6Vjc3NywdOlSRVF3uZDbvHmzSUVqyhP5fvjhh9zybWNjg5o1a+Lrr7/mbXft2oXGjRtDpVLB2dkZ/fv3x/jx49GhQwcwxuDu7m5yTuVypk+fzu85P/74o2LZnDlz+LK5c+fip59+wi+//IJJkybByckJZmZmiIqKKvY+yyPXW7ZsqeMnVadOHWzcuBHp6ekA8qP8pXQ+Pj4+CAgIQEBAAB8fY0+hajN27FgwxriFqigW3OfPn6N3795Qq9UmV8xezo0bN3hRektLS3Tu3NngM04u5LSDw0yFlJQUlC5dGjVq1NDxp5Qnafbw8MCpU6cUy6UyXM7OzjxVlbHFqRBxBRAbGwu1Wg1fX19FjqjJkyeDMYZatWph5cqViI+PR48ePfjDMCkpSeck1/5/VFSUYpqxJAmE7OxsREZGonLlyjpCTr4fUu2/pKQkxfqTJk1C6dKl9U7d/RMknxIp4am2fwagOw6SsMzOzuYOuwMHDjSpabQ3wYsXL9C9e3ceZCM/LkuXLuUP1uTkZMWxWLp0KRdI27dvN0bXdZBbvCVRGhwcjAkTJmDQoEE8Iac8tU9iYiJGjhypmE7x8PBAhw4dTLZiivzaWrx4sUEht2LFCpQpU0YhklQqFWrXrs2jq41xv8nKykJAQADUajUGDBiAkydP4vDhw5g2bRo8PT1RtmxZLF++nL8cpKamomPHjqhatSoYy6/+0bp1a5MScNK517FjRzDG+L3O0PF98eKFYhpbykFp6iQmJqJRo0YwMzND48aNCxyDhIQElC5dGowxjBkzphh7WTR2794NlUqlCMTQFmJSWqFBgwbh+fPniqhVySLn4uKi8K01FkLEGUAScH5+foqolLVr18LW1hYdOnTgdTaBfPOs5ABfs2ZNJCUl6RUCkg9LVlYW6tWrBwcHh2IvMv26yPfn2bNniIyM1GuRky4Gyf+oS5cu3LKxZMkSeHl5ITg4WFGu6t8kOjoaVlZWaNiwIfcdMoTcmXjSpEmwtrY2Ss3I4ubkyZMoVaoU2rdvr+O/07hxY9jb22PUqFF4/PgxAOVDad68efDz8zOph+nLly95QMqECRMUAQnx8fFQqVSwsbHBrl27FOudPn0aUVFRWLduHS5fvswtQaZAYWmHFi1aZFDInT59Gtu3b8fnn3+OcePG4cCBA7yOqLEc6KXEqKNGjVJYcdPT01GxYkWUKlUKs2fPVkyTZmZm4uHDhzhy5Ahu3bplUuMD/D1Gku/v6tWr9baTxu3WrVvo37+/SZShKwyNRqM43xISElC3bl3+QlSQC0x8fDzUarXRnf71sXDhQu7XDSjTikj7e/bsWZQrVw4+Pj5cdMuff5KQY4zpTLsWN0LE6SEuLg4qlQpVq1ZV3PT/7//+D4GBgahYsSIXBzk5OZgzZw5sbGzg6+vLQ8jr16+vV8jJb57BwcFwcXExWPrHlJD249mzZ/zhXZBFDsgvFixVoHB2duZv1B4eHm80G/njx4/5m/HQoUMNplKQj8X+/fvh4OCAtm3blog341dF+6G9du1aHb8qjUbDneInT57MfaoeP36sYzE1NIVuLK5cuQIvLy8EBQUp0jPk5uYiMDAQNjY2CA8PN7l+G0J+39D2bZM/WKUHEmPMpJOE5+XlISQkBF5eXooxePnyJZo2bQq1Wo1JkyZxkabtV2yKMxXyPkn+iD4+PopABemlXaJPnz5wcnIyyWCGovh1HTp0iLsChYWFFSjkTNWnVErQK6+DrX1+ZWZmokWLFmCMKdJKycdSEu7GfpkVIk6LxMREqFQquLu7KyxweXl5uHDhAqpVq4Zvv/0WQP4NaPny5bC3t4evry9/0El+RQ0bNsSRI0f0Xhxr166FpaUlGjZsWGIeLM+ePUPlypUVqTYkIWfIInf9+nX07dsXjo6OqFGjBrp3714sdQFTU1O5c/To0aMVPic5OTmKMTl27Bj8/f1hZ2enY9F4G5A7/EssWLAAjDFs3boVQP4xkXyOJk+erLCSHjp0CK1atVIEOpga27dvB2MM8+fP598ZEqVPnz4tEUXtgfxUIVOmTFEE1wDKB640lnIhl5eXZ1JBOLdv30bZsmXRtGlThaO7vnPu+fPniI6ONkmfy4LEpFSirV27dnpnAFasWAFHR0d06NDB5CyKcnESExODRYsWoXfv3liyZAkSEhIUbQ8dOsSD0sLCwgp1PTH2eag9dg8fPoSfnx8YY1i4cKGieovcSi25A2k/r+T7q31dGgMh4mRIFjjG8mtjahe1ff78Ofbu3csvwJMnT6JatWooV66cIgJOygwv+dNplxbJyMjA4MGDwRgzyUL32qVepJN61apVYIwpHDoBXYvcnDlzdHLgXblyBenp6cUqBE6dOgVLS0swxtC7d2+91QT27dvHHyTyBJSm+Ob/Okhj9PTpU1SuXBnTpk0DkF+BgjGGsWPHAgCPmtYWcADw3nvvwcHBwaSdr6OioqBSqTBnzhwA+YJVLuDk+3Tt2jW0bNlSp6KKsdCXqw/If7lwd3eHvb095s2bV6CQk6wC8qlVUzqHHz9+jEqVKqFq1aoA8vtmaHzy8vL4dL4pII/kHj16NLp06YJWrVph06ZNihQuKSkpaNu2LRhjqFq1KjZv3oxLly7h3r17mDhxIlxdXVGmTBmTK24vP48mT54MGxsbRTJlKdpUns7l8OHDryTkipOiJPLdsGEDHB0dUalSJaxatYq/MEjXzJEjR+Dq6oqmTZvqTcVjShZiIeL+P7GxsVCpVPD09ESDBg3AGEPTpk11fGjkgxYeHg7GGCIjIwH8LX5SUlLg6OiIDz/8EIwxnbJPQH6aDe2KBabArFmzsHbtWh4mL2fZsmVgjOlNT6BPyMktjMY62c+cOQMfHx8wlp+XrkePHpg9ezamTZvGE6La2NgoChgb+83x3+bFixc8LcUHH3yAvLw83LhxgzsfV6xYEYzlZ/vXtgrPnz8fNjY2+Oyzz0zKMqJNYmIiD2i4fv06F6WTJk3SEaXdunWDq6urSUegAvnnoRTJbmdnh7lz5+oIOekBlZSUBHt7e/7Sop1d3phI1g2p7umKFSsQHBwMxhgmTpyoMz6jR4+GmZkZtxIbE7lvXqNGjcBYfmF06X7SuXNnnD59GkD+WJw4cYLfVxhjMDc352NStWpVk3xpl5ByoTVv3hx79+7F8ePH8d133/Hp01GjRinOP7mQGz58+L+aJup10U7kGxERgdatW2PkyJFYunQptwLfunUL48ePh52dHTw9PTFs2DD8/vvvePToEQ4ePMinUjdu3GisXSkyQsThbwucn58ffvrpJ8UbVUBAgM60KpA//dSyZUvY29vzC1N6+G/atAkeHh44e/asIqrV1ElOTgZjDN7e3ti6datOvqN58+ZBrVYrfATkaAu5+fPnm0RViuvXryMsLEwnJYydnR06d+6ssNC9LQJOfkNNTU1F2bJlMX78eEUQw86dO/mx6Nixo86+f/vtt/D09ESNGjVM4oVD39jIv3vvvfegVqvh6ekJtVqNL774ggdmSCxcuBB2dnbo169fgQlZi4O0tDTs27cP/fr1Q9++fTFu3DisXLlS0WeNRoPt27ejZs2asLOzw5w5cxQPUmmc/+///g81a9bk1VG0c8gVB4VZY3788UeFuJk1a5aOgFu5ciXc3d3Rpk0bk0nkq9Fo0K9fP9jZ2WH48OFITU3Fd999xytGBAQE8Dxv0jT2mjVr0KdPH/j7+yM0NBQLFiwwuTJnco4cOQJHR0c0aNAAZ8+eVSzr168fnybWvg8kJSVxX2ftnIvFjXYiX6lakvwTEBCA8+fPAwBu3ryJGTNm8EhuFxcXeHh4QK1WQ6VSYcGCBXx7pmBxM8R/XsRFR0eDMYbq1asrrG5JSUkICQkxKORyc3PRpk0bMMawfPlyfgOT/KsaNGigeEiUBHHw+PFjLFy4EJ6enihfvjy2bNmisMiNHz8e5ubmBSamlISclDx28eLFJrHveXl5uHbtGvbs2YPly5fj+++/x6+//qqwPJlCP/9NMjMzsWzZMqxatQq2trY8gEZeqHv+/Pk8BcXUqVORnJyMhIQE9OvXD5aWlnBzczMJi5UkVnJycnD16lWcOHFC58YaFxfHi2sHBwfr9Pubb75BmTJlULVqVaNPDW/fvh21atWCWq3WedBUr14d69ev5w9MjUaDbdu2cSE3d+5c3Lp1S7E9KZO+RqPhy4rzwSNP5Ltt2zbMnDkTe/bs0cnDJVXDYIxh5cqVAP72UZ0+fTpcXFzg5eVl9ClH+UtQTk4OqlatikGDBilebO/du8cjov39/fXeF7VdU0wVKa2QtviXrHPvvfeewUj/+Ph4fP/998XRzSIh1XFu06YNDh48iOPHj2PLli383lCtWjXuSpGRkYHz58+jZ8+eCAwMhK+vLz755BOdgC9T5j8v4hITE+Hq6ordu3fz76SbX3JyskEhB+Q/NFxdXVGhQgUMGzYMc+bMQaVKlcAYw9q1a4tzN/4x0j4/efIEixYtgru7Oxdykh/bqFGjYG1tbTAaR9pGVlYWtmzZggYNGhg9cqcwTMm34d9Eo9Fwq0xAQABq167NA2+0C21v3LgRpUqVUggJc3NztGzZ0iTGT57It0OHDnwaOCgoCOvWreOWqczMTKxcuRLVqlWDtbU1OnXqhE2bNmH9+vXo3r07VCoVPDw8jD6lJfmWenp6Ytq0adi3bx8OHDiAZcuWoXHjxmAsv7TP4MGDuQiSW+RsbW0xduxYnuJo+/bt8PPzQ7NmzfDo0aNiOafj4uK4ZUmePFoq/cUYg5mZGXx8fHSEgTxZcYsWLdCpUyc+Lefr62vU8ZEfs6dPn6Jjx47YuXMn6tSpw/slF2bZ2dncUuXv769IDpubm2vyicKla6tnz55gjCnSZkkCrmPHjgrr3PHjxxETE6N3e8UpePQd0xMnTsDNzQ116tTReYHIyMjg+Vxr166t19Irj2wHTF/AAULEAVDW39POoVSQkHv48CG++eYbeHh48JuSk5MTli1bptheSUGfkKtQoQKvDzdmzBjY2dkVKcdRTk7OG8sDJ9CP9g1nz549CA4OhoWFBRhjekvDSVy5cgWbNm1CREQEZs2ahaNHj5rEVLhEdnY2Fwh169ZFnTp1YGtrC3t7e4SFhXFB8fTpU/z444/8mpU+zs7O6NSpk9EtPFIOQ39/f72JQp8+fYopU6bAx8cH5ubm6N27N0/Ho9FoEBkZiSZNmnDfLOlvtVrNA47eNFI07JdffsnT8eTk5CAkJAQqlQrdunXDzJkzebQmY0xRYxLIT/PQqVMnuLq6ws7ODnXq1MHnn39ulGn7tWvXIi4uTueakKzULi4uUKlUPFGyNllZWQohJ/nIlSSketZSvWmp6Lu2gAOAsLAwVK5c2Wj5TeXuBtpjJrmISD7O0j1Rsqw+evQIrVu3BmMMH330kc76xsqh+E8QIk6G/CEoH8ijR48qhJzcavfs2TNcunQJERER2LJli8IHriSoeG30CTlvb29s374d3bp1Q5kyZTBy5EgsWbIEixYtwsKFC7FixQqsXLkS3377Lb788kuTcqr+ryCPFJa/TR84cICXOerVqxf++OMPnXVLwk1r3759KF26NCIiIqDRaPDs2TPs2bMH9erVg0qlwrBhw7iQk/Zn165d2LhxIxYvXoyzZ8/q+McVJ3l5eXjw4AE6deoECwsLhR+mZA2R/s3Ozsbq1avh5+cHa2trTJgwgQvqvLw8pKSkYMSIEbCzs0OFChXQsGHDYq3EEB0djVq1asHOzg5ffvkl7ty5g2vXrqFs2bIIDw9X3PfklSW0hVx2djbu3LmDq1ev8nrFxc2ePXu4M7/2FHVOTg5GjRoFFxcXqNVqzJkzx+D0qFzIVa1aVUf4mDqSdbhHjx4YM2YMGGM6Ce2B/ChwKysrfPLJJ0YJdEpISEDLli0VGR/kwktKwiuV/JKfU9J5ef36dbi7u6NatWolJtF+QQgRVwBFFXKFrVvSkE52Sci5ubmhUqVKKFeunI7/jr6PthlbUDzk5OSgfv366Nmzp+L7mJgYHq05evRogw7jhlJdGAPt34+IiIC3t7fCR1OKBpSsUcOGDTPpxNlpaWlwcXFBaGgo/06fJQDIH8tFixbBwcEBXl5eOg9TID9g5c6dOwofuDc5bsOGDePFz/fv34969erB2toa06dPx6JFi+Do6Mh9TOUVMySBoC3kjJ2W4t69e3jw4AEGDx6MhQsXAtBflm/EiBFQq9UoU6aM3sh8iezsbHTr1g0qlUqResQUKIpBQcpvKvmTaVutf/75ZzRr1gzu7u6IjY19U101yNOnT3mpwNatWysCKaT927dvH1QqFT744AOdZUC+Re7Jkyc8rdTPP/9cfDvwhhAirhAKmlqVJ4YtiVY3oPAbqSTkfH19wVh+uZWoqCgcPnwY8fHxSEhIQExMDGJiYhAbG6vX0iMoHn7//Xf4+/vDzMxMx2clNjaW5+UaM2aMyUT+6UOa+tBoNMjIyEBWVha+/vprjB49GoDSJykvLw8nT55UCDlJ1JjaNblp0yaeqgGAwZQM0j0nOzub+yq99957RUo4+6Y4f/48GGMoX748j07/8ccfUb9+fdjY2ODdd99FixYtFP2TH//Vq1dzgSB3gjfWy8Lo0aPRqFEj3L9/n/v8ZmZmYvDgwUhISFD068WLFwgLC+P7X1hgl7ZFz9jI7/GJiYnYtm0b5s2bhwMHDijuAwkJCTydSI8ePRSBeXv37uXX2IoVK4q1/3JOnDjBr4kWLVroRMSmpqbyLATLly/n38v9E4H8SHZXV1deiq4kI0RcEdAn5MzNzdGwYUOTKQL+OkgPkaysLCxbtgyjRo3CkiVLdGqHZmRkYOHChShTpgx8fX2xc+dOxZu2wHTYuHEjGGOoVasWfvvtN8WykiDk5EEMn3zyCZo0aYKWLVvC19cXtWrV0lsSTVvIffbZZyaZzmH9+vVgjOF///tfoW0lAXT16lW4uLigTp06Ro10fPz4MYYMGQK1Wo2hQ4fy73fv3s2Pu0ql0rFUGRJyxgz8kgRZr169FEnaZ8+eDcYYQkJCkJycrCPkRo0aVSQhZ0rI9yEiIgLW1taKiOhKlSohKioKz549w8uXL/HDDz/wKE4vLy+8++67CAoKAmMMlpaWilqoxgpiOHXqFLfI6RNy0nXm7OyMdevW6WwrKSkJpUqVQmBgIO7cuWP0WYd/ihBxRUQ7k7qUDFDuLF6SkEeUSVNt0qdcuXK8OLBERkYGn1otV66cImpVUPwU9ECXkqpKPjzyN3G5kAsPD8fdu3eLo7uvxLNnz3iUpqurK0+u6urqio0bN+rdd0nINWvWjItUY0/XaXPs2DHY2NggJCSkSFY1aepHKhF05coVoz5wEhMT4eLiojMtGhkZyf0uBw8erFMzU/6wl2r2mpubIyMjo9j3RxJwPXv21Im8TktLw8iRI2Fubo533nnnrRFyADBz5kyevmbWrFmYOHEiF2eOjo6YP38+njx5wlMx9enTBxUrVoSjoyPKly+Pfv36mVQ+zVOnTqFbt25cyB06dIgvy87O5oEZkn/cpUuX8OLFC8TExOCdd97RsQiXZISIewXkF/ShQ4dKvAP/ixcv0L59ex4Ft2bNGowdOxZWVlZgjGH8+PGK9k+ePMHixYvh4eGBSpUqYd26dXorOwjeLPLgk6FDhyI5OVmR7+7ixYuoWrUqKlasyC0NcuETGxvLIz2nTJli9BsyoHwoTJs2DS4uLpg4cSJu376Na9eu4aOPPoKFhQUqVaqE+Ph4vQItLy8Pv/zyC1q3bm0Sue20uXz5Mk8sWpQavdI4N2/eHDY2Nrhx40axj5W2yPrmm2/AGIOfn58incYPP/yAxo0bw8rKCl9++aXOlKK83xs3bjRKGhFJwIWGhioEnHwfr127huHDh8PMzKxQIVexYkWT9amSvyQ8fvwYNWvWRLt27RT+ynl5eZg6dSpcXFxQqlQprFq1SrGNO3fu4LfffsP9+/cV94/iOAcNpWaRf3/y5EkeAd2mTRtFjddHjx7x6GLGGOzt7XlqopKUyLcoCBH3iugbcFN4CBYVuR/On3/+iQoVKmDMmDE8hxiQ/8ZtbW3NrTVynjx5giVLlsDMzAy1a9fmRcUFxcvLly+5EKtatSqGDBnCH4zPnz/nOZ66du3K15ELn+joaISEhBg9Zxrwd7+kc3DYsGEICgpSvCCkp6djwoQJsLa2RuXKlQsUcvJz2dT44osvuEXk+PHjBtvJ7zM1a9aEn59fcXSPI/dtlZKbA/ljFBoaCrVajalTpyrGaM+ePWjQoEGRhFxxI0VchoaG6gh8bd/E3377rVAhJ21Pnn/RFImMjMT9+/fh7e3NZ43y8vIUomzu3LmwsLCAm5ubjguGRHELHXlVEu3fll/3koVRGiu5RQ7IT0Tcp08fNG7cGDVr1sTAgQOxd+9evrwkPbsNIUTcf5CnT59i7ty52Lx5M0qVKsUj+jQaDb9gUlJSDAq5jIwMrFixAmlpacXed0E+GRkZGDZsGOzt7WFnZwcPDw+4urpi7dq1yMzMRGZmJurVqwfGGNavX8/Xk98QTcmKmpWVBVdXVzRu3BjNmjXjTsm5ubn8RpuRkYFJkyYVKuRMBX0PiD///BPt27fnObjkOcUKCghwd3fHgAED8PHHH2PGjBkFRkn+UyZPnox69erp1I2U+rVr1y64u7vD09MTFy5cULTRFnL6fBiLm08//RSMMZ4MGfh7X+Tnz+TJk7mj+9WrVwsUcjk5OZg4caJJvATdu3cP8fHxOjn2Fi5cyPMqent78xxw+s6zAQMGgDGGgQMH4uXLl0a1Tu3fv19nyl6fZW779u1gjKFGjRr8hVZ7ahXIF915eXnIzs5W7PPbIOAAIeLeag4cOKBTXkij0XCfqcDAQNSsWZM/zLUv7hMnThgUciXdBP02EBsbC19fX7Rr1w4RERE8crpt27ZITEzEuXPn4OzsjLZt23LfN7lQNyUyMzP5VKOVlRWPRJUesvqEXJUqVZCQkGCSQk7ep6SkJB4spNFosHfvXjRs2BCMMbRs2ZI/dLTHZc+ePahSpYpOCp8PPvjgjaWw2LJli+K3Bg8ejHPnzumkaZAe+s2bN9epQSsXctOnTzd6kElMTAzfn6ioKAD5x1oenCVVN5k+fTofu8KmVk2BM2fOIDQ0lAdqyFPsXLp0iZc/ZIzxKUT5Pkj7mpqaCnt7e7Rq1ap4d0APSUlJvM9yn3O5xVQ6Txs2bIi4uDikpqbyagzawQ7FPRVc3AgR95ayYsUKMMYQFhamE4AQFxeHkJAQWFtbQ6VS6c15p0/IDR8+vFj6LlAi3bz05QGbOnUqzMzMsH79ety9exdff/01bGxsYGVlhdDQUHTo0AGMMXz99dfG6HqRkB4kGRkZqFu3Lp9uTE9PVyzXFnL29vZwdXU1euFtbeQCLiwsjKc8kKxuubm52LlzJw/CkBLJRkdH4969e7h8+TK++uoreHl5QaVSITIyEqmpqbh48SISExPfaCWUnJwc+Pv7Q61Wo0uXLrys4IwZMxTTho8fP+alsubPn68zJblnzx4etTpnzhyjC225MNDOKNC7d28uWLWtWXIh16ZNGyQmJhZjrwvm0KFD8Pb2hpmZGXr16oW9e/fyslHS8b58+TJq1qwJxvLrCUvuL9ql2R48eAAXFxf4+vri2bNnRherR44c0RFyUp/kAk5eRSMlJUURtZqUlGSUvhc3QsS9heTl5SE6OhoNGjTA4sWL9bZJTExEmzZteE4geai9hHShp6Sk8AvKFKMZ/wtkZmbi3XffxcqVKxUPmpycHAQFBcHLy4tnHz969Cj69OkDZ2dnPm5qtdrkxA7w9zkmWUWePHmC+vXrc2dlSbBoC7knT54gLCwMZcqUKVIZuOJC/qYvRc+1atUKCQkJCvGVm5uLn3/+GYMHD1ZYvqToT0nIGqpR+SaQjnFkZCQcHR3RtWtXnDt3jk/LN27cGKdPn+aVL7Zu3YpSpUqhSZMmPDGs3OqxY8cOtGjRgpcNMzZyISfVc/3www/BGMOgQYN4VK32y9K1a9cwcuRIMMbw7rvv6tTXNAaHDx+GpaUlqlSpYjBvm3QuXr58mQvuLl266BXUsbGxYIyhe/fuJlN6Sp/w/v777xUWOG2k9CNqtRrNmjVTVHZ4WxEi7i0lNzeX35QyMzOxcOFCnSmYw4cP89QAw4YN0+u/It0ITp8+bTI34/8iS5YsgVqthqWlJVq0aKGIxNqxYwfMzc3xwQcf8FI4d+/eRUxMDE8n4urqapS6lNpoP0D0pal58uQJt8h17tyZt9En5OQO0KbE+PHjeU446QXJUMRdZGQkJkyYgIYNG6JFixbo3LkzNmzYwIXRm36oam/7zp07CA0Nhbm5Ofbv34/s7GxMmjQJnp6ecHJyQnh4OC5fvoyXL1/ivffe4xZ/CblVztTSEMmFQXBwMBdw0rVh6Dj/+uuvGDdunElEPf/666+oUqUKHBwcsHXrVv69/NrSdo1JTU3lQq5Tp044ceIEHjx4AED5HDC1vKfy8ZKEtLYFTvv6OH36ND8vd+3aZYxuFytCxL2FyE9ojUbDw7DDw8N1LG5JSUn8ZlaYkBMYj6ysLBw/fhwfffQRv6mNHz8e58+fR15eHj744ANYWVnppK7Izc3FF198YRJBKPLk0nPnzkWvXr3QpEkTDBkyBN99953i4f/kyRNuAerUqZOOkDO2pcCQIAOAv/76C1WqVIGfn5+iJJahbUiYwnUm9en06dOws7NDUFAQHj16hJycHBw9epT7Hfn6+uLAgQM4fPgwzM3NwRjDpk2b+HZMYV8MIRcGISEh3HpY2JSvMZMtA3+PzZo1a3T8lLX7rtFokJWVhezsbH7tXLp0iQs5Hx8fVK9eHb169YK1tTUcHBx46TH5b5kC8vGqUqWKoi6toVKBx48fR3R0dLH201gIEfeWo9FocODAATRv3hxWVlYYO3bsawk5gfHQvqGuXLkSDRo0gEqlQuXKlbFy5Uo8ePAAXl5eeO+993g7U0p9IK/EIFkHHRwc4ObmBpVKBcYY3n//fYWzeWFCzpjInfW1xychIYHn4AOK9vDXaDQKgVocImj48OHo3r07bt++rSh1JvVj2bJlOjm1gPyi9pUrVwZjDOPGjeNWj86dO+vU2zRVEhMT9QY7mJJ4MYT0Ui6lqNGe3o2NjUXfvn1RqVIl1K5dG61atcK5c+cA5FvkJB85KysrTJs2DWvWrFFYtkxRgMvHS0oRoi9Iq6SnAHsdhIj7D6DRaJCQkICAgABYWloWKuQ+++wzky4k/jYjFygajUYxHSW/QZ0/fx5z5szhQSft27dHx44dTTqI4fnz52jVqhXMzMwQFhaGx48f46+//sJff/3FS/28++67CiEhF3JBQUE6kZDGYPXq1bCxscH+/fv5d/Kx2bt3L59KNYTU3ljTwSdPnuQPxYYNG2LJkiVcmErH/o8//kBISAisrKx0ktqeOHECo0aN4lY4SYgbs5zWq3L48GG9UZCmLORevnyJrl27KqJNJS5evIivvvpK4WNpYWEBxhjc3NyQnJwMALhw4QJq1KjBI50l5Ol8TBH5eMmnfU15vIoDIeJKMPry/RiiqEKuVatWYMw0yxa97cinG2fOnInOnTsjICAAH374Ic6ePatXwJw5cwbt2rXjEZBmZmYoV64cjh07Vtzd50gBFtpTjlLOs/79++v4SjVq1Aienp4YNmyYjsXt6dOnqFChAhhjegNwipOtW7fyB4mXl5eObw4AnD17Fmq1GoGBgXj48KFimfbfQUFBRivdd/HiRbRt2xa2trawsrJCUFCQTimp77//HhYWFnj//fd1LPQajQZJSUl8io4xVuL8Zg2lszBlYbBmzRqoVCo0bdoUGzZsQHZ2NhYvXsxTDDHG0LdvXyxYsACbN29G69atwRiDt7c3T+IsD3bo0aMH37Z2lLGpURLH600jRFwJ5vz58/zvopzERRFy8fHx6NSpk0kksfwvIRcsAQEBYIyhVKlScHd3B2P59WwXLlzIo4PlWfRv376NTZs2ITAwEIwxeHh4GC031/Dhw+Hq6qr39z/55BPY2dkpAmw0Gg1PRTF58mQe+ZiZmak4p58+faqT87C4yc3Nxbx583igiGThkEfJaTQa3L17l6cP+eabbxTbkE+trlq1CowxfPXVV0Z7Ybp79y5WrlzJ6ydbWlpi9uzZiiTE/fv3h0qlwpYtWwAoU94AwI0bNzB9+nSD2f5NHbkwiIyMNHZ3CuXOnTsICQmBmZkZGGP8Bcfc3By1atXCypUrFe1zcnL4+bht2zb+fVpaGhdyH374YXHvxmtT0sbrTSNEXAll0KBBr/U2UhQhZwoh9P8lpHHLyspCcHAwzM3N8emnn+Lu3bvIycnB/PnzYWVlBW9vb0ybNo1PwWn78Dx79gxjxoxR1EcsTj7//HN+c5UqLsj7GBgYiDJlynBn/5cvX3LBOnnyZEUKjri4OKxevVohVk2BlJQUuLm5oVmzZjyFiKurq066AykVgvxYyNmzZw8qV64MX19fxcuYMcjNzcWjR48wbNgwWFhYQK1Wo0mTJvj2228BAPfv30f9+vXh5+fHraSS9V/735KKXBgUpa6tsblx4wb69u0Lxhisra3h7OyMmTNn4pdffuFtNBoN9zGVnhezZs0C8PdLY1paGk/p079//+LfkddEPl5vSyH710WIuBJKXFzca5uVtYXc+PHjTSL9xH+Z3NxcjB07Fra2thgzZgyvovHnn3/yXFa2trYoVaoUpk2bprDIAcZ/iErh/+XLl1c49QN/961Lly5gjPGpXkMCDgDq16+PLl268JQppkRERASsrKwQGxvLfZC0LXJAfgCAfHrrm2++wZEjRzBlyhR4eHhArVbjwIEDRtoL/WzdupX7XDGWnwD3+PHj+Prrr2FnZ4ePP/7Y2F18Y8THx4MxplNKzFTJyclBamoqzp8/rzPVrf2CN2DAANja2ipyRUrX5aVLl9CsWTNF1GdJ4NChQ2CMYd68ecbuilERIq4Eoy+rNVA0IZebm4uEhARecy4iIsLk/SHeZq5du4ayZcsiODiYW0J/++03Xg5o0KBB+Omnn1ChQgWULl0a06ZN475nxiYsLAyMMfTu3ZvXa5T8bORWtE2bNvF2UrCCfApVIiIigjtuG1ucypGuqxMnTsDJyQnNmzfHw4cPuQVSn5Bbs2YNypcvD7VarXA4r1ixIhdwpuDPIz/Ot2/fxrp162BrawvGGPz9/TFixAjUrl0b5cuXN7lcYv8mplRP+FWRj6H8nDp48CAsLS1Rv359nSlvaR1jp095XUQAnhBxJZ5XdfTUnpqKi4tD69atS5xD8tvGzZs30bNnT55h/M6dO1wcDRo0CED+jbZ///7cR2769OlGF3JSH0NDQ5Gamopr166BMYZq1arpFNK+ffs22rVrx8/Xzz77TGfqftWqVfD09ERAQIBJ36D79u0LlUrFRdvQoUMNCrkzZ85gx44dGDlyJMLCwhAZGYkrV64AMK20Ftr9SElJ4ZU/rKys4OTkBMYYunbtqiO8BaaDfByPHj3KX9TliYELWqckYkove8WNEHFvAUUVcnIBt3nzZh5yLnzgjE9eXh5u3rzJLQFxcXFwcHBQRI4BQHJyMqytreHl5QXGGObOnWu0G5hcwEmiJCMjAz4+PvDx8VHkfJOIjIxEo0aNoFKpMGDAAJw5cwbZ2dl4+PAhxo4dy3PHSdszFtHR0UhOTlb4HwJ/WyyuXr0KFxcXdOvWja8zfPhwg0KupHL37l3s37+fW04lB3pTrZTxX0b7PrB9+3Y+bqaayFfwzxEi7i2hMCEnF3CTJ0/mEUlCwL1ZXtdnrXPnzmCM8YLbkiA6deoUnJ2dMXfuXDRv3txoFlR9Ak6j0eDRo0eoXr06GGM4c+YMby9/cGzfvh1BQUE8JYqfnx/c3NzAGEOtWrWMXtpISoXi7u6OoKAgHDx4EOnp6Xy5RqPB48ePERoaquMIP2LECINRqyWZ7Oxs/O9//0OzZs2MFjgjKJy8vDxs2LCBn5seHh5YvXo1X17Sz0OBLkLEvUUYEnJyf4epU6eCMQY7OzuRRqQYeNU0MBIdOnSAs7OzTuTw2LFjUa5cOdy+fdto/juSxUku4IC/XxR69uwJxpgihxqgfID8+uuvWLp0Kfz9/VG7dm107twZixYtMvoU6q1bt/g15OHhAT8/P6jVaoSEhGDVqlUA/t6Pw4cPw8zMDMOGDePr5+XlKYTc21CAW16zVi5mBabHnTt30Lp1azg5OaFnz56IjY3ly4SAezsRIu4tw5CQA/4WcE5OTkLAFQP/JA1M7969wRjDwIEDeVqH5cuXw9PTE+3atTOaBfX+/fuoXLkyunbtanDKU7LSSSkqDDlcA9DxmzMFpChFyW9v5cqVsLOzA2MMLVu2xOLFi3H//n0AQL9+/WBlZaVI7QD8bZGTolhLOkIAlBxu3ryJU6dO8QL3gJhCfZsRIu4tRC7k9uzZA+DviD8h4IqPf5IG5q+//oKvry8YY6hduzbP5VS6dGmj+4vdvn1bb0oaab/mzJkDxhiGDBmi+B5QioGbN28qxKgpPWik9AWMMcTHx+PevXuIiIjgKVQqVqyI7777DuHh4ShVqhSGDBmCrKwsxf4NHjwYjLG3QsQJBALTRIi4txS5kOvVq5cQcEbiddLASNNXly5dQqNGjWBjY4OyZcuiffv2SEtLe+N9/qfExMTAwsJCJ6eYXOCsWLEC/v7+OlOupoS8VqNUJzUjIwMTJ05EnTp1oFaruf9fxYoV9UYKS9ebKQlUgUDw9sAAgARvJUeOHKHg4GAiInJ1daVDhw5R9erVjdyr/x7ycdi+fTv16NGDiIgAEGNMp71GoyG1Wk1ERC9evKDr16+TjY0NOTk5UalSpYqv46/J0aNHKTAwkOrUqUNJSUlkZ2dHAEilUhER0YYNG2jy5Ml069YtunLlClWpUsXIPTaMfOy2bt1KPXv2pLy8PHr48CFt3ryZtm7dSikpKeTg4EBXr14lFxcXvdsxNNYCgUDwTxAi7i0nNjaW2rZtSxcvXhQCzogUVcjJBdzmzZvJ29ubgoKCir/D/4C7d+9S/fr1ycLCgk6cOEFubm582YYNG2jixImUnZ1NycnJVKNGDSP2tGjIx27btm0UGhrKl12/fp2uXr1KderUIQ8PD8rLy+NiVSAQCN404m7zlhMSEkKZmZlCwBmZwMBAOnz4MBER9ezZk3bu3ElERIwxkt6j5AJuypQp9PHHH9Pq1aspKyvLOJ1+TVxcXMjFxYUePXpEt2/f5t9LAi4rK4uOHDlSIgQckXLsevXqRbt27eLLfH19qU2bNuTh4UEajUYIOIFAUKyIO85/ABsbG2N3QUAFC7mXL19yAffFF1/QjBkzyNbWliZMmFCixk+j0ZCZmRm5uLjQ06dP6datW0REtH79ei7gkpOTqWbNmkbu6ashH7sePXoohJwkwqXxEwgEguJCiDiBoBgxJOTMzc2JKF/ATZs2jRwdHen48eMlzoIqCZlGjRoREVF6ejrt2LGjRAs4CUNCTm5NFQgEguJEiDiBoJjRFnJ79+4lIqKpU6dyAZecnFziBBzR31YpycF/1qxZNGLECO4DV1IFnIS2kNu2bRsRkQhaEAgERkEENggERkLuMN+zZ0/avn17iRZwRH8HasTExNC7775LL1++JAcHBzpy5EiJF3BykpOTecDJrVu3yMPDQwg5gUBQ7AgRJxAYkbc1DUxaWhpVq1aNVCoVXbhwgapVq2bsLv3rHD58mO7fv0/du3c3dlcEAsF/FCHiBAIj87amgYmLiyNvb2/y8/MzdlfeKNItVFjiBAJBcSNEnEBgAmRlZZWoKFSBQCAQGB8h4gQCgUAgEAhKICI6VSAQCAQCgaAEIkScQCAQCAQCQQlEiDiBQCAQCASCEogQcQKBQCAQCAQlECHiBAKBQCAQCEogQsQJBAKBQCAQlECEiBMIBAItWrRoQYwxatGihbG78q/DGCPGGH3xxRfG7opAIPiHCBEnEBiZQ4cO8QcrY4x69uxZ6Dr9+vXj7QVvJ9rnhfQxMzMjZ2dn8vHxoaCgIBo1ahTt2rWLXrx4YewuCwSCYkaIOIHAxNi5cydduHDB2N0QmCgajYbS09Pp999/pyNHjtCiRYuoe/fu5OXlRdOnT6fc3Fxjd7HIbNiwgYvT33//3djdEQhKHGbG7oBAIFACgKZOnUq7d+82dlcEJsLQoUNp2LBh/P+ZmZmUnp5O58+fp/j4eIqLi6P79+/TlClTaN++fRQVFUVubm56tyWK9AgEbw/CEicQmBCurq5ERPTDDz/QmTNnjNwbgang7u5ONWvW5B9/f39q3749hYeH08GDB+nixYtUr149IiI6ceIEdenSRUyvCgT/AYSIEwhMiBEjRpClpSUREUVERBi5N4KSQvXq1eno0aNcyB09epSWLVtm5F4JBII3jRBxAoEJ4e3tTYMHDyYioqioKDpx4sQ/2t79+/dp8uTJVK9ePXJ0dCQrKyuqUKECffzxx5ScnFzguhUqVCDGGPXr14+IiE6fPk29e/cmb29vsra2pkqVKtHo0aPpwYMHivWOHTtGPXr0oHLlypGVlRVVrFiRwsPD6enTp4X2V6PR0MaNG6lTp07k6elJlpaW5OLiQs2bN6cFCxZQdnb2ax8LOb/88gv16NGDSpcuTVZWVuTj40ODBw+mtLS0V9rOvn37uD+a1NeAgACaPXs2ZWZm/it9LSrW1tb03Xff8WCX+fPn08uXL3XaFRSdqu2jlpOTQ4sWLSJ/f39ydXU1uF5iYiL17duXfH19ycbGhuzt7alWrVo0duxYunXrlk57KWijf//+/DsfHx+dII5Dhw7prJuZmUmzZ8+mgIAAcnZ2JktLS/Ly8qLu3btTVFRUgcdIO+r4t99+o+HDh1PlypXJxsZG+OYJSh4QCARGJTExEUQEIsL69etx69YtWFtbg4jQpk0bvev07duXr2OImJgY2Nvb83b6Pp9++ik0Go3e9cuXLw8iQt++fbFp0yZYWFjo3UaVKlVw+/ZtAMC8efPAGNPbrn79+nj69KnB/v7xxx+oU6dOgf2tVKkS0tLSXuHo6rJgwQKoVCq927e1tUV0dDSCg4NBRAgODta7jezsbHTp0qXAvnp6euLMmTOv3U/5eTF16tQir9emTRu+3tGjR3WWF7TN9evX8+UpKSmoW7euzn7J18vOzkavXr0KPA62trbYu3evwX0r6JOYmKhY7/Tp0/D09Cxwna5duyI7O1vvsZGP648//ghbW1ud9W/cuFHkYy0QGBsh4gQCI6Mt4gBg9OjR/LsjR47orFOYiDtz5gwXXebm5hg1ahQSExNx4sQJrFy5Ej4+Pnz9cePG6d2GJOLq1q0LCwsLVK9eHevWrUNKSgoSEhLw0Ucf8W307t0bu3btAhHB398fmzdvxsmTJ/HTTz+hQ4cOvF14eLje33rw4AG8vb1BRLC0tMTw4cOxc+dOpKSkIDExERMmTICNjQ2ICL6+vnj8+PFrHevdu3fzvjg4OGDmzJk4duwYjh07hunTp8Pe3h6Ojo6oXLlygSIuNDSUb6dOnTrYtGkTUlJSEBMTg/79+3Mh6+zsjJs3b75WX19XxM2dO5evN3v2bJ3lRRVxtWvXBmMMffr0QXR0NE6dOoUffvgB+/fvBwDk5eWhY8eOvH3nzp3x3Xff4ejRo/j555+xePFilCtXDkQECwsLpKSk8N/JzMzEhQsXMH36dL5+TEwMLly4oPhkZmbydW7evAknJycQERhj6N+/P2JiYnDy5Els2rRJ8QLQs2dPvcdGEnE+Pj6ws7ODm5sbZs+ejaNHj+KXX37BkiVLcP/+/SIfa4HA2AgRJxAYGX0i7u7du9xK0LJlS511ChNxjRo1AhFBrVYjJiZGZ/mjR49QvXp1EBFUKhUuXryo00YScUSEpk2b4tmzZzptunfvzn/H2dkZ3bp1Q25urqJNbm4u/P39QURwcXHBy5cvdbbz4YcfgohQvnx5XL9+Xe8+nT59mh+TiRMn6m1TEDk5OdyK4+DggNTUVJ02Fy5cUFgv9Ym4qKgovrxVq1bIycnRabNq1SreJjQ09JX7Cry+iIuLi+PrDRgwQGd5UUUcEWHNmjUGf0faR3Nzcxw4cEBvm0ePHqFGjRogIjRr1qzA3yvMAiada4b69fz5c7Rs2ZK3kcSmHEnESZbSP/74o8DfFAhMHSHiBAIjo0/EAUB4eDj/PiEhQbFOQSLu+PHjfNmQIUMM/m5ycjJvN2zYMJ3lkohjjOkVPACQkJDAt2FjY4OHDx/qbbdu3Tre7ty5c4plN27cgFqtBhFh3759BvsLAOPGjeMP4Fdlx44dvA/z58832G7OnDkFirj27dtz8fLnn38a3E7r1q1BRDAzM8OtW7deub+vK+LOnDnD1+vSpYvO8qKKuHfeecfgb+Tl5aFixYogInz++ecF9mf//v18m7/++qvB3ytIxP3111/8HGnXrp3Bdjdu3ICZmRmICB06dNBZLhdxmzZtKrDfAkFJQAQ2CAQmytixY6lUqVJERDRlypQirxcXF8f/HjhwoMF2zZo1o2rVqumso03t2rV5O23q1KnD/w4JCSFnZ+dC212/fl2xLDo6mjQaDdnY2FD79u0N9oOIKCgoiIiIbt26RX/++WeBbbWR9pExRn379jXYrn///gYrYeTm5tLhw4eJiKhNmzbk7e1tcDuffPIJX0efg/6bws7Ojv9dlGASQ/Tu3dvgstTUVLp27RoREXXv3r3A7UhjRkT0888/v1ZfDh06RBqNhogKPqcrVKhAISEhOutoY2FhQT169HitvggEpoQQcQKBieLi4kJhYWFElJ8yIiYmpkjrXbx4kYjyH1R169YtsG2TJk2IKD9Kz1BesSpVqhhc39HR8ZXbaQuLkydPEhFRVlYWmZmZ6S01JX06derE17tz547B39OHVAXDx8eH5+PTh5ubG1WoUEHvsuvXr1NWVhYR/X3sDCFfLo1JcSA/vvb29q+9ndq1axtcJo0ZEVFAQECBYyYXla86ZhLy41fU456VlaXzwiBRuXJlsrKyeq2+CASmhBBxAoEJM3r0aC6Apk6dWqR1Hj16REREzs7OZGZWcFGW0qVLExERAEpPT9fbxsbGxuD6KpXqldtpW0fu3btXYB8NIYmpoiIdF3d390Lbenh4FLiNomxHOrba671p5ClfDFlGi4KTk5PBZcU1ZhL/9nEvaN8EgpKEKLslEJgwjo6ONHr0aIqIiKDjx49TVFSUwhpVEIamBE0NSdS5urpSYmJikdfz8fF5rd/7t46LqR5feaUPPz+/196OWq02uEwuxPft22fQcqlNUQR0Yfwbx72gfRMIShJCxAkEJk5YWBgtXryYHj58SFOnTi1UxEnWl4cPH1Jubm6B1jhpeosxZjTrhIuLCxHlTwNWq1btjT1gpf27e/duoW0NtZFbtgrbjnzq8J9YxF6V2NhY/nfz5s3fyG9IY0aU/6JRs2bNN/I7EtrHvSBfRGMdd4HAGIjpVIHAxClVqhSNHTuWiPKrJvzwww8FtpceqC9evKCzZ88W2FaqCFG5cmWysLD45519DaRSUTk5OQpfq3+bWrVqERHRjRs36OHDhwbb3b9/32DWfqkiARHR8ePHC/w9ebWNNy1yJC5evEjx8fFElF/9o2HDhm/kd6QxI8r313xdimpVkx+/oh53Gxsb8vX1fe2+CQQlASHiBIISwPDhw/lU1NSpUwmAwbatW7fmf69bt85gu59//plSU1N11iluOnfuzB/mixYtemO/I+0jANq0aZPBdhs2bDB4fM3MzCg4OJiI8i1eN2/eNLidNWvW8HWkMk9vkuzsbOrTpw/v+5gxYwr1iXxd6tevT15eXkREtGrVKnr+/PlrbUceXJCTk2OwXYsWLbiFtqBz+s8//+SWSPk6AsHbihBxAkEJwNbWlsLDw4koP8py//79Bts2btyYW2BWr17NLTNyMjIy6H//+x8R5QcdDB069A30umj4+fnxdA/btm2jBQsWFNj+xo0btHXr1lf+nffff5/KlClDRERfffWV3jqpqampNGPGjAK38+mnnxJRvqVz4MCBeuuTrlu3jg4ePEhERF27duW/+6ZITU2l5s2bc3+44ODgNzqmKpWKJk6cSET5Ebt9+vQpUIQ9efKEli5dqvO9/LhIKUv04enpSV26dCEiogMHDtDGjRt12rx48YIGDBjAx2P48OFF2xmBoAQjfOIEghLC0KFDaf78+XT79m2dovParF69mpo0aUIvXrygDh060GeffUadO3cmW1tbOnPmDM2ePZunXxgzZkyxTfcZYvny5XTy5Em6fv06ff7557Rnzx7q06cP1ahRgywtLenhw4d07tw5+umnnyghIYG6dOlCH3zwwSv9hoWFBS1ZsoS6d+9O6enp5O/vT+Hh4dSiRQsCQIcOHaI5c+YQEVGlSpXo6tWrerfTsWNH6tGjB+3cuZMOHjxI/v7+NHr0aKpatSqlp6fTtm3buLXI2dm5UFFaFO7du6dIs/Hs2TNKT0+n8+fPU3x8PMXGxnILnL+/P0VGRpK5ufk//t2CGDJkCMXGxtIPP/xAO3fupNOnT9P//vc/aty4MTk4ONCTJ0/oypUrdOjQIdq7dy9ZWVnpCKt69eqRlZUVPX/+nKZMmULm5uZUvnx5Hs1ctmxZsra2JiKihQsXUnx8PKWnp9OAAQMoOTmZevbsSU5OTnTlyhWaP38+dx8IDQ0tNOegQPBWYMREwwKBAIYrNuhjyZIlirJIBV3CMTExihJS+j6ffvopNBqN3vWlig19+/YtsE9UQAUAiRs3bhS6j7dv30ZgYGCB/ZU+/fv3L7BPBTFv3jxe21T7Y2Njg6ioKEWhdH1kZ2ejS5cuBfbR09MTZ86cee1+FrVIvPRxc3PDjBkz9JY1k1PQeL1KGSwAePHiBYYOHWrweMo/Pj4+erchVeHQ90lMTFS0PX36NC+dZujTtWtXZGdn6/2twsZVIChpiOlUgaAE8cknnxQYmSenTZs2dPXqVZo4cSLVrVuX7O3tydLSksqVK0e9e/emI0eO0NKlSxU53IxJ6dKlKSkpiaKioqh37948iMDc3Jzc3NyoadOm9Pnnn9Phw4cL9IsqjDFjxlBycjJ17dqV3N3dydLSksqXL08DBgygkydPUseOHQvdhpWVFe3evZv27t1LXbt2JU9PT7KwsCAnJydq0qQJzZo1i9LS0gpNtvw6qFQqcnBwoHLlylFgYCCFhYXRrl276ObNmzRx4sQ35genD3Nzc/r222/p3Llz9Nlnn1GtWrXIwcGB1Go1OTg4UN26dWngwIEUGRlJly9f1ruN2bNn0+rVqykwMJCcnZ0L9GOrV68epaWl0axZs6hJkybk6OhIFhYW5OnpSV27dqW9e/fSrl27RCJfwX8GBhTgIS0QCAQCgUAgMElM4xVcIBAIBAKBQPBKCBEnEAgEAoFAUAIRIk4gEAgEAoGgBCJEnEAgEAgEAkEJRIg4gUAgEAgEghKIEHECgUAgEAgEJRAh4gQCgUAgEAhKIELECQQCgUAgEJRAhIgTCAQCgUAgKIEIEScQCAQCgUBQAhEiTiAQCAQCgaAEIkScQCAQCAQCQQlEiDiBQCAQCASCEogQcQKBQCAQCAQlECHiBAKBQCAQCEog/w/RC4zpt14cQgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iguyEZ9z9wER",
        "outputId": "073c61e0-5c07-4ee3-c0cc-a044a8a44163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        }
      },
      "source": [
        "# Vamos fazer uma análise de tendências baseada em Clustering?\n",
        "\n",
        "# Qual ano teve o maior GOB?\n",
        "\n",
        "# O próximo relatório mostra os anos com altas pontuações brutas acima do orçamento para todos os filmes.\n",
        "# Para fazer isso, devemos agrupar os registros com base em title_year.\n",
        "# A tabela dinâmica em Python é usada para agrupar colunas e agregá-las com base em colunas numéricas, conforme mostrado no código a seguir.\n",
        "\n",
        "# Dados de GOB com base no ano do filme\n",
        "\n",
        "dataset_pivot=top_GOB.pivot_table(index=['title_year'], values=['GOB'], aggfunc=np.mean ,margins=True)\n",
        "\n",
        "# A coluna do ano no conjunto de dados inclui alguns valores ausentes, iremos ignorá-los.\n",
        "\n",
        "# Algumas células estão vazias na coluna do ano\n",
        "\n",
        "# dataset_pivot = dataset_pivot.loc[dataset_pivot['title_year'] > 0]\n",
        "\n",
        "# Não precisamos do último registro que inclui a métrica de resumo\n",
        "\n",
        "dataset_pivot = dataset_pivot[:-1]\n",
        "\n",
        "# Como o índice mudou após a criação de uma tabela dinâmica, redefinimos o índice\n",
        "\n",
        "dataset_pivot.reset_index(inplace=True)\n",
        "\n",
        "# Algumas células estão vazias na coluna do ano\n",
        "\n",
        "# dataset_pivot =dataset_pivot.loc[dataset_pivot['title_year']>0]\n",
        "\n",
        "fig,ax = plt.subplots(figsize=(9, 8))\n",
        "ax=sns.barplot(x=\"title_year\", y=\"GOB\", data=dataset_pivot)\n",
        "fig.suptitle('GOB por meio dos anos', fontsize=12)\n",
        "plt.xlabel('Ano do filme')\n",
        "plt.show()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Podemos agrupar os filmes com base em suas pontuações GOB/IMDB?\n",
        "\n",
        "Diante do que fizemos até agora, podemos escolher um algoritmo de clustering para encontrar grupos de dados. A escolha do algoritmo depende de vários fatores, incluindo a natureza dos dados, tamanho dos dados, número e tipos de recursos em nosso conjunto de dados, etc.\n",
        "\n",
        "No exemplo, utilizaremos novamente o algoritmo K-Means, um dos algoritmos de cluster mais populares, que funciona iterativamente para atribuir cada ponto de dados a um dos 'K' grupos com base no\n",
        "recursos que são fornecidos.\n",
        "\n",
        "Em nosso conjunto de dados, procuramos uma relação entre uma pontuação GOB e IMDB para ver se podemos ou não agrupar filmes com base nessas duas métricas. Primeiro, precisamos especificar o número de clusters que procuramos.\n",
        "\n",
        "No exemplo, iremos escolher três. Se você visualizar seus dados em um gráfico de dispersão, poderá deduzir o número de grupos que poderá obter do algoritmo, mas se não, não se preocupe. Você pode alterar o número de clusters posteriormente e executar o algoritmo novamente para ver se obtém um resultado melhor ou não!\n",
        "\n",
        "Para tanto, acabei usando um gráfico de dispersão simples em Python para visualizar essas duas métricas (a pontuação GOB e IMDB) em um gráfico:"
      ],
      "metadata": {
        "id": "bHX8p52KhFeM"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYc8aw2z-yI0",
        "outputId": "fc57f7c1-cd16-4a60-a83a-17a671d0cec1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "plt.scatter(x=dataset['imdb_score'], y=dataset['GOB'])\n",
        "plt.show()"
      ],
      "execution_count": 116,
      "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",
      "metadata": {
        "id": "AlSF-mOZAU9p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f692d8d2-b79f-4a03-a889-05bd3506c027"
      },
      "source": [
        "# Agora, vamos gerar clustering com três grupos.\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Excluindo os valores ausentes e selecionando apenas a pontuação GOB e IMDB\n",
        "\n",
        "selected_dataset=dataset.loc[\n",
        "(dataset['GOB']>0) & dataset['imdb_score']>0][['imdb_score','GOB']]\n",
        "\n",
        "# Agrupando o conjunto de dados usando o algoritmo K-Means\n",
        "\n",
        "cls = KMeans(n_clusters=2)\n",
        "\n",
        "# Ajustar o modelo ao algoritmo\n",
        "\n",
        "cls.fit(selected_dataset)\n",
        "\n",
        "# Trazendo os centróides e o rótulo de cada grupo\n",
        "\n",
        "centroids=cls.cluster_centers_\n",
        "labels = cls.labels_\n",
        "\n",
        "# centroids\n",
        "# red = selected_dataset[labels == 1]\n",
        "# red\n",
        "# green = selected_dataset[labels == 0]\n",
        "# green"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHO6oCk2AjkQ",
        "outputId": "5b64813f-4818-4517-f13e-024e4d01b3b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "# Como próximo passo, vamos colorir os pontos com base em seus grupos e especificar o centro de cada grupo com um marcador (x).\n",
        "\n",
        "# Temos três clusters, uma cor para cada cluster\n",
        "\n",
        "colors = [\"g.\",\"r.\",\"b.\",\"y.\",\"p\"]\n",
        "\n",
        "# Criando uma matriz de cada conjunto de dados para percorrer os pontos\n",
        "\n",
        "dataset_array = np.array(selected_dataset)\n",
        "\n",
        "# plotando o ponto em um gráfico de dispersão\n",
        "\n",
        "for i in range(len(dataset_array)):\n",
        "    plt.plot(dataset_array[i][0], dataset_array[i][1], colors [labels[i]], markersize =25);\n",
        "    plt.scatter(centroids[:,0], centroids[:,1], marker='x', s=150);"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vemos então três clusters na figura: o primeiro cluster (azul) são os filmes com pontuação GOB baixa (não venderam bem) e pontuação baixa no IMDB (não foram bem classificados pelos usuários do IMDB).\n",
        "\n",
        "O segundo grupo inclui os filmes com uma pontuação IMDB muito boa, mas não uma pontuação GOB muito alta. O terceiro cluster inclui poucos filmes com pontuações IMDB entre 7 e 8 e GOB muito alto.\n",
        "\n",
        "Lembrando que esse é um subconjunto de filmes, você pode ter uma interpretação melhor aplicando o algoritmo em todos os filmes."
      ],
      "metadata": {
        "id": "INcw3eJriMEQ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGi03VEwBCgf"
      },
      "source": [
        "# E você, será que consegue descobrir alguma relação entre a pontuação do IMDB e as curtidas de filmes no Facebook? O que mais podemos descobrir a partir desse dataset, não somente a partir de clustering?"
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para encontrar uma relação entre uma pontuação do IMDB e curtidas no Facebook podemos, por exemplo, usar um algoritmo de regressão.\n",
        "\n",
        "Uma regressão linear simples pode ser usada para modelar a relação entre uma única variável independente de entrada (no nosso caso, curtidas no Facebook) e uma variável de saída (por exemplo, pontuação do IMDB) usando um modelo linear, ou seja, uma linha.\n",
        "\n",
        "Este é apenas um exemplo. Na realidade, podemos não encontrar uma relação linear entre duas variáveis num conjunto de dados."
      ],
      "metadata": {
        "id": "RttfkV4siTNh"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UM2-xQPYuUd"
      },
      "source": [
        "## RESPOSTA\n",
        "\n",
        "\n",
        "## RESPOSTA"
      ],
      "execution_count": 120,
      "outputs": []
    }
  ]
}