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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install datasets"
      ],
      "metadata": {
        "id": "QfoiWUNwcoS-"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "YJXHkF3UcZhK"
      },
      "outputs": [],
      "source": [
        "\n",
        "from datasets import load_dataset, Dataset\n",
        "from tqdm import tqdm\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Carrega modelo e datasets"
      ],
      "metadata": {
        "id": "sfVA55FSbbM2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# login(token=\"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\")\n",
        "dataset = load_dataset(\"azmina/ementas_anotadas_inteiroteor\")\n",
        "\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484,
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        "outputId": "ab9b48f8-d871-43b4-ee32-0d6633dbd0f9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/751 [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "train-00000-of-00001.parquet:   0%|          | 0.00/3.49M [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "val-00000-of-00001.parquet:   0%|          | 0.00/396k [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test-00000-of-00001.parquet:   0%|          | 0.00/475k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1119 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating val split:   0%|          | 0/112 [00:00<?, ? examples/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "Generating test split:   0%|          | 0/168 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'text', 'label_tema', 'label_desfavoravel', 'proponente_mulher', 'partido', 'urlInteiroTeor', 'textoInteiroTeor'],\n",
              "        num_rows: 1119\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['id', 'text', 'label_tema', 'label_desfavoravel', 'proponente_mulher', 'partido', 'urlInteiroTeor', 'textoInteiroTeor'],\n",
              "        num_rows: 112\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'text', 'label_tema', 'label_desfavoravel', 'proponente_mulher', 'partido', 'urlInteiroTeor', 'textoInteiroTeor'],\n",
              "        num_rows: 168\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = dataset['train']\n",
        "test_dataset = dataset['test']\n",
        "train_dataset = train_dataset.filter(lambda example: example['textoInteiroTeor'] is not None and example['textoInteiroTeor'].strip() != \"\")\n",
        "test_dataset = test_dataset.filter(lambda example: example['textoInteiroTeor'] is not None and example['textoInteiroTeor'].strip() != \"\")"
      ],
      "metadata": {
        "id": "TdwdPJsWcit1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "outputId": "43f55d95-808a-4bac-fce8-b47aecb1f84b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/1119 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e42c5b8a79154e81b732f2ca50490106"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/168 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "01b20266f5d447e6aac8edac5a798a62"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_labels = train_dataset.unique('label_desfavoravel')\n",
        "label2id = {label: idx for idx, label in enumerate(unique_labels)}\n",
        "id2label = {idx: label for label, idx in label2id.items()}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fd380d5b9f814770bae9f5373a9d8338",
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        },
        "id": "daQ6Cz2Gc4sM",
        "outputId": "eda2e4b0-38d6-4065-e9d9-3a77865116da"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Flattening the indices:   0%|          | 0/992 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd380d5b9f814770bae9f5373a9d8338"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"neuralmind/bert-base-portuguese-cased\")\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"azmina/ia-feminista-bert-posicao\")\n"
      ],
      "metadata": {
        "id": "yovQYJ3bcxP-"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Avalia resultados"
      ],
      "metadata": {
        "id": "_gfkDfrybZv9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKJuIkdOdnp3",
        "outputId": "8ffaf395-feb9-43c2-f511-c3835f8304f3"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(29794, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.07, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSdpaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.07, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_sentence(string):\n",
        "    tokens = tokenizer(string, truncation=True, padding='max_length', max_length=512)\n",
        "    input_ids = torch.tensor(tokens[\"input_ids\"]).unsqueeze(0).to(device)\n",
        "    attention_mask = torch.tensor(tokens[\"attention_mask\"]).unsqueeze(0).to(device)\n",
        "    outputs = model(input_ids, attention_mask=attention_mask)\n",
        "    logits = outputs.logits\n",
        "\n",
        "    label_id = torch.argmax(logits).item()\n",
        "    label = id2label[label_id]\n",
        "\n",
        "    probabilities = F.softmax(logits, dim=1)\n",
        "\n",
        "    probabilities = probabilities.squeeze().tolist()\n",
        "\n",
        "    return label, probabilities\n",
        "\n",
        "test = test_dataset.to_pandas()\n",
        "tqdm.pandas()\n",
        "test['pred_bert'], test['prob_bert'] = zip(*test['textoInteiroTeor'].progress_apply(predict_sentence))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1HY70Kxcz7N",
        "outputId": "f87065a8-cbd6-4082-c433-d91d71176a70"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 147/147 [00:05<00:00, 24.90it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['pred_bert'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "ncNp9cjCjWOY",
        "outputId": "be8b5551-3315-437a-c6d5-440354ff5eb9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pred_bert\n",
              "1    83\n",
              "0    64\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pred_bert</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(classification_report(test['label_desfavoravel'], test['pred_bert']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kU-qYeijZZ2",
        "outputId": "8c9a917e-f151-48a3-fc42-3f2a8ad10078"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.53      0.67       114\n",
            "           1       0.35      0.88      0.50        33\n",
            "\n",
            "    accuracy                           0.61       147\n",
            "   macro avg       0.64      0.70      0.59       147\n",
            "weighted avg       0.81      0.61      0.64       147\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "y_true = test['label_desfavoravel']\n",
        "y_pred_proba = test['prob_bert'].apply(lambda x: x[1])\n",
        "\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred_proba)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.plot(fpr[np.argmax(tpr - fpr)], tpr[np.argmax(tpr - fpr)], 'ro', label='Best Threshold')\n",
        "print(f\"Best threshold: {thresholds[np.argmax(tpr - fpr)]}\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "Mgu5aukTcXFU",
        "outputId": "40c98721-be5f-45d8-e095-7313cebd3e24"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best threshold: 0.7084465622901917\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JSUmwsLBA69atsXbtWkRGRmZo//LlS+X/d+vWDdeuXcO+ffsytEv/67xHjx54+vQp1q9fn6FNYmKi8u6nrNSsWRN169bFzp07sXPnTlhZWakUJZqamujWrRv27t2b6T/U/81XXc7Ozmjfvj02btyIQ4cOZdg+efJkhIaG4scff8zwl/r+/ftVxvhcvHgRFy5cUH4JqfM5Z0dTUzNDD83KlSshl8tVYulzDn1cIH2ORo0aoUyZMli/fj1SU1OV8W3btikvn2XHxsYGgwYNwt9//42VK1dm2K5QKLB48WI8efJErbzSe4w+vky4cePGPD9Ghw4dYGRkhHnz5iEpKUll23/3NTAwyPRS5ef+fmRGLpdneC0LCwuUL18eycnJn8zpY2XLlkWrVq2wYcMGPH78WGVbXvUOUtHGHiEq8n744Qd0794dvr6+GDJkCFatWoUWLVqgbt26GDRoEOzt7fH8+XMEBgbiyZMnuHbtmnK/PXv2oHv37vjuu+/g6OiI169f4+DBg1izZg3q1auHvn37YteuXRgyZAhOnjyJ5s2bQy6X4+7du9i1axf8/f2Vl+qy4uHhgWnTpkFXVxcDBgzIMPnhL7/8gpMnT6JJkyYYNGgQatWqhdevX+PKlSs4fvw4Xr9+nevPZvPmzWjXrh06d+4MT09PtGzZEsnJyfDz80NAQAA8PDzwww8/ZNivSpUqaNGiBYYOHYrk5GQsW7YMZcqUwY8//qhsk9PPOTtff/01tmzZAhMTE9SqVQuBgYE4fvw4ypQpo9Kufv360NTUxPz58xETEwMdHR20bdsWFhYWuf5stLW1MWPGDIwYMQJt27ZFjx49EB4eDl9fX1SuXDlHPQ6LFy/GgwcPMHLkSPj5+eHrr7+GmZkZHj9+jN27d+Pu3bsqPYA50aFDB2hra6NTp04YPHgw4uPjsX79elhYWGRadH7OMYyNjbF06VIMHDgQjRs3hqenJ8zMzHDt2jW8e/cOmzZtAgA4Ojpi586dGDt2LBo3bgxDQ0N06tQpT34/PhYXF4cKFSrg22+/Rb169WBoaIjjx4/j0qVLKj2HWeWUmRUrVqBFixZo2LAhvv/+e9jZ2SE8PByHDx9GcHCwWvlRMSTJvWpEaspqQkUhhJDL5aJy5cqicuXKytuzHzx4IPr16ycsLS2FlpaWsLa2Fl9//bXYs2ePyr6vXr0SPj4+wtraWjkZnJeXl8qt7CkpKWL+/Pmidu3aQkdHR5iZmQlHR0cxc+ZMERMTo2z38e3z6e7du6ec9O3s2bOZvr/nz5+L4cOHCxsbG6GlpSUsLS1Fu3btxLp165Rt0m8L3717t1qfXVxcnJgxY4aoXbu20NPTE0ZGRqJ58+bC19c3w+3D/51QcfHixcLGxkbo6OiIli1bimvXrmU4dk4+5+zO3Zs3b0T//v2Fubm5MDQ0FK6uruLu3buZfpbr168X9vb2QlNTM0cTKn78OWU10d6KFStEpUqVhI6OjnBychLnzp0Tjo6Ows3NLQefrhCpqani999/Fy1bthQmJiZCS0tLVKpUSfTv31/l1vr02+f/O1nnfz+f/04iefDgQeHg4CB0dXWFra2tmD9/vtiwYUOGdukTKmYmp8dIb+vs7Cz09PSEsbGxcHJyEn/++adye3x8vPD09BSmpqYZJlTM6e8H/n9CxczgP7fPJycnix9++EHUq1dPGBkZCQMDA1GvXr0Mk0FmlVNW5/nmzZuiS5cuwtTUVOjq6orq1auLqVOnZpoPlSwyIdg3SERpwsPDYWdnh4ULF2L8+PFSpyMJhUKBsmXLomvXrple8iGi4oVjhIioxEpKSsowTmTz5s14/fo1WrduLU1SRFSgOEaIiEqsf//9F2PGjEH37t1RpkwZXLlyBX/88Qfq1KmD7t27S50eERUAFkJEVGLZ2trCxsYGK1aswOvXr1G6dGn069cPv/zyi6Sr2hNRweEYISIiIiqxOEaIiIiISiwWQkRERFRilbgxQgqFAs+ePYORkRFXHiYiIioihBCIi4tD+fLlM0xM+zlKXCH07NmzTy6USURERIVTREQEKlSokGfHK3GFkJGREYC0D9LY2FjibIiIiCgnYmNjYWNjo/wezyslrhBKvxxmbGzMQoiIiKiIyethLRwsTURERCUWCyEiIiIqsVgIERERUYnFQoiIiIhKLBZCREREVGKxECIiIqISi4UQERERlVgshIiIiKjEYiFEREREJRYLISIiIiqxJC2ETp8+jU6dOqF8+fKQyWTYv3//J/cJCAhAw4YNoaOjgypVqsDX1zff8yQiIqLiSdJCKCEhAfXq1cOqVaty1P7hw4f46quv0KZNGwQHB2P06NEYOHAg/P398zlTIiIiKo4kXXT1yy+/xJdffpnj9mvWrIGdnR0WL14MAKhZsybOnj2LpUuXwtXVNb/SJCIiomKqSI0RCgwMRPv27VVirq6uCAwMlCgjIiIiym8KhcCtWy/y5diS9gipKyoqCuXKlVOJlStXDrGxsUhMTISenl6GfZKTk5GcnKx8Hhsbm+95EhFRCRWyGzg/DUiJkzqTYiMyRg/9N7ngVGjpfDl+kSqEcmPevHmYOXOm1GkQEVFJcH4a8Pqu1FkUGwduVsfA3d8gOsEAQFK+vEaRKoQsLS3x/Plzldjz589hbGycaW8QAEycOBFjx45VPo+NjYWNjU2+5klERCVUek+QTAMwsJI2lyLuZZwuev/5LRKStQAAFkaJeJEPHW1FqhBq1qwZjhw5ohL7559/0KxZsyz30dHRgY6OTn6nRkRE9IGBFTD4idRZFGllASwzvYJBg/6Cu3sNLFniAnv75Xn+OpIWQvHx8bh//77y+cOHDxEcHIzSpUujYsWKmDhxIp4+fYrNmzcDAIYMGYJff/0VP/74I7777jv873//w65du3D48GGp3gIRERHlAblcgdRUBXR0PpQmAwY0gI2NMTp0qIy4uPwZdyXpXWOXL19GgwYN0KBBAwDA2LFj0aBBA0ybNg0AEBkZicePHyvb29nZ4fDhw/jnn39Qr149LF68GL///jtvnSciIirCIiJi0L79Fowf/7dKXCaTwdW1CmQyWb69tkwIIfLt6IVQbGwsTExMEBMTA2NjY6nTISKi4mRtBSD+KWBozUtjObRr1y0MHnwIb9+mDYY+fNgTHTtWzdAuv76/i9QYISIiIioeYmOTMXLkUWzadE0Zs7ExhpGRdoHmwUKIiIiIClRgYAT69NmHsLA3ypiHR22sXv0VzMwyvws8v7AQIiIiogKRmqrAnDmnMWvWacjlaSNzjIy0sWpVR/Tp45CvY4GywkKIiIgKj6I+M3NCpNQZFFqvXr1Dp05/IjDww9gpZ2cbbN3aBXZ2ZpLlxUKIiIgKj+IyM7O2kdQZFDqmprooVSrtZnVNTRmmTXPBpEktlTGpsBAiIqLCozjMzKxtBDSfJXUWhY6mpga2bOmCrl13YdWqjmjatILUKQFgIURERIURZ2Yu8k6dCoeenhacnKyVsUqVTHH58iBJxgJlRdr+KCIiIipWUlLkmDjxONq02YRevfYiLi5ZZXthKoIAFkJERESUR0JCotGs2R/45ZdzEAIIC3uD1asvS51WtnhpjIiIiD6LEALr11/B6NHHkJiYCgDQ0tLAnDltMW6cs8TZZY+FEBEREeXay5cJGDToLxw4EKKMVa9eBtu3d0PDhoV/wDsLISIiIsoVf//78PY+gKioeGVsyBBHLF7sCn19LQkzyzkWQkRERKS258/j4e6+E0lJaZfCzM31sWHDN+jUqbrEmamHg6WJiIhIbeXKGeKXX9oBAFxdK+PGjaFFrggC2CNEREQ5UVBLX3CJikJLoRCQyxXQ0tJUxkaMaIIKFYzRpUtNaGgUrtvic4qFEBERfVpBL33BJSoKlcjIOHh7H0D9+uUwf/4XyriGhgzdutWSMLPPx0KIiIg+rSCXvuASFYXKgQN3MWDAQbx6lYh//nkAV9cqaNvWTuq08gwLISIiyjkufVFiJCSkYNy4v7F2bZAyVq6coYQZ5Q8WQkRERKQiKOgZPD39EBr6Shnr3Lk6fv/9G5ib60uYWd5jIUREREQAALlcgUWLzmPKlJNITVUAAPT1tbBsmSsGDmxY6NYJywsshIiIiAjR0e/QvftuBASEK2OOjlbYvr0bqlUrI11i+YzzCBERERFMTHQQH58CAJDJgIkTW+D8+QHFuggCWAgRERERAC0tTWzb1hU1a5rj5EkvzJ3bDtramp/esYjjpTEiIqISKDAwAvr6WqhXz1IZq1atDG7eHFZkJ0fMDfYIERERlSCpqQrMnBmAli03olevvXj37r3K9pJUBAHsESIiKv7yYnkMLn1RLISFvUGfPn4IDEybC+rOnWj89tsljB/vLHFm0mEhRERU3OXl8hhc+qJIEkJgy5br8PE5gri4tAHRmpoyTJ/ugtGjm0qcnbRYCBERFXd5tTwGl74okt68ScSQIYexa9ctZaxyZTNs3doVTZtWkDCzwoGFEBFRScHlMUqcgIBw9O27D0+exCpj/fvXx/LlbjAy0pEws8KDhRAREVExFBkZB1fXrUhJkQMAzMx0sXbt1+jevbbEmRUuvGuMiIioGLKyMsL06S4AgDZtbHH9+lAWQZlgjxAREVExIISAQiGgqfmhj+Onn5rDxsYYvXs7lLjb4nOKPUJERERF3MuXCejSZSdmzz6tEtfU1EDfvvVYBGWDPUJERERFmL//fXh7H0BUVDwOHQpFhw6V0ayZjdRpFRkshIiIiIqgpKRUTJx4HMuWXVDGzMz0lPMEUc6wECIiaeTFbMeUM5wVuti5ceM5evf2w40bL5QxV9fK8PV1h6WloYSZFT0shIhIGnk52zHlDGeFLvIUCoGVKy/gp5+OIzk57bZ4HR1NLFjwBXx8nDgWKBdYCBGRNPJqtmPKGc4KXeS9evUOvXv7wd//gTJWt64Ftm/vhjp1LCTMrGhjIURE0uJsx0Q5YmCgjadPP1xKHjOmKebObQddXX6Vfw7ePk9ERFQE6OqWwvbtXWFnZwp//z5YssSVRVAe4CdIRERUCAUFPYOBgTZq1DBXxurWLYfQ0BEoVYr9GHmFnyQREVEhIpcrMH/+WTRt+gd69dqL5ORUle0sgvIWP00iIqJCIiIiBu3abcaECSeQmqpAcHAUfvvtktRpFWu8NEZERFQI7Np1C4MHH8Lbt0kAAJkMmDChBYYPd5I4s+KNhRAREZGEYmOTMXLkUWzadE0Zs7ExxpYtXeDiYitdYiUECyEiIiKJBAZGoE+ffQgLe6OMeXjUxurVX8HMTE/CzEoOFkJElL38WgqDyz5QCff0aSxat96ElJS0GaKNjLSxalVH9OnjAJmMM0QXFBZCRJS9/F4Kg8s+UAllbW2M8eObYe7cs3B2tsHWrV1gZ2cmdVolDgshIspefi6FwWUfqAQRQgCASm/PjBmtUbGiCQYMaMjb4iXCQoiIcoZLYRDl2ps3iRgy5DAaNy6P8eOdlXEtLU0MHtxIwsyIhRAREVE+CggIR9+++/DkSSz27buDdu3s0KABFxouLNgPR0RElA9SUuSYMOE42rbdhCdPYgEAhobaiIqKlzgz+i/2CBEREeWxkJBoeHr64cqVD3dHtmlji82bu6BCBWMJM6OPsRAiIiLKI0IIrFsXhDFj/JGYmLZGmJaWBubMaYtx45yhocHb4gsbFkJERER54PXrRPTvfwAHD4YoY9Wrl8H27d3QsCHHBBVWLISIiIjygI6OJu7ejVY+Hzq0ERYt6gB9fS0Js6JP4WBpIiKiPGBgoI1t27qifHkjHDzYE7/99hWLoCKAPUJEJUlulsvgUhhEmbpx4zkMDLRhb/9hNuhGjcojLGwkdHT49VpU8EwRlSSfs1wGl8IgAgAoFAIrV17ATz8dR4MGVjhzpr/KrNAsgooWni2ikiS3y2VwKQwiAEBkZBy8vQ/g778fAAD+/fcJVq++hBEjmkicGeWW5IXQqlWrsHDhQkRFRaFevXpYuXIlnJycsmy/bNkyrF69Go8fP4a5uTm+/fZbzJs3D7q6ugWYNVERx+UyiNR24MBdDBhwEK9eJSpjY8Y0xaBBjhJmRZ9L0kJo586dGDt2LNasWYMmTZpg2bJlcHV1RUhICCwsLDK03759OyZMmIANGzbA2dkZoaGh8Pb2hkwmw5IlSyR4B0REVNwlJKRg3Li/sXZtkDJmZWUIX193dOhQWcLMKC9IetfYkiVLMGjQIPTv3x+1atXCmjVroK+vjw0bNmTa/vz582jevDk8PT1ha2uLDh06oFevXrh48WIBZ05ERCVBUNAzNGy4TqUIcnevgevXh7IIKiYkK4RSUlIQFBSE9u3bf0hGQwPt27dHYGBgpvs4OzsjKChIWfiEhYXhyJEj6NixY5avk5ycjNjYWJUHERHRp0RExMDZeQNCQ18BAPT1tbB+fSf4+fWAubm+xNlRXpGsEIqOjoZcLke5cuVU4uXKlUNUVFSm+3h6euLnn39GixYtoKWlhcqVK6N169aYNGlSlq8zb948mJiYKB82NjZ5+j6IiKh4srExwbBhjQAAjo5WuHp1MAYObAiZjMtkFCdFakLFgIAAzJ07F7/99huuXLkCPz8/HD58GLNmZX03y8SJExETE6N8REREFGDGRERUlAghVJ7Pm9ceS5Z0wPnzA1CtWhmJsqL8JNlgaXNzc2hqauL58+cq8efPn8PS0jLTfaZOnYq+ffti4MCBAIC6desiISEB33//PSZPngwNjYx1nY6ODnR0dPL+DRARUbERG5uMkSOPwsnJGsOGNVbGdXVLYcyYZhJmRvlNskJIW1sbjo6OOHHiBNzd3QEACoUCJ06cgI+PT6b7vHv3LkOxo6mpCSBjFU8kmdzM3lxQOEs0UQaBgRHo3dsPDx++xc6dt9CmjS1q1iwrdVpUQCS9fX7s2LHw8vJCo0aN4OTkhGXLliEhIQH9+/cHAPTr1w/W1taYN28eAKBTp05YsmQJGjRogCZNmuD+/fuYOnUqOnXqpCyIiCT3ObM3FxTOEk2E1FQFZs8+jdmzT0MuT/tjWktLAw8evGEhVIJIWgh5eHjg5cuXmDZtGqKiolC/fn0cO3ZMOYD68ePHKj1AU6ZMgUwmw5QpU/D06VOULVsWnTp1wpw5c6R6C0QZ5Xb25oLCWaKJEBb2Bn36+CEw8MPEos7ONti6tQvs7Myy2ZOKG5koYdeUYmNjYWJigpiYGBgbG0udDhVHaysA8U8BQ2vO3kxUyAghsHnzNfj4HEV8fAoAQFNThmnTXDBpUkuVNcOocMmv72/Jl9ggIiIqCG/fJmHw4EPYteuWMmZvb4Zt27qiadMKEmZGUmIhREREJYJMBly48KGX1tu7PlascIOREe8sLsnYB0hERCWCiYkutmzpAnNzfeza9S02buzMIojYI0RERMVTSEg0DAy0UaHCh/EkLVtWQnj4KBgYaEuYGRUm7BEiIqJiRQiBtWsvo0GDtejXbx8UCtV7glgE0X+xECIiomLj5csEuLvvxJAhh5GYmIqTJ8Oxbl3Qp3ekEouXxoiIqFjw978Pb+8DiIqKV8aGDHFEv371JMyKCjsWQiStwrwcRW5xGQuiApWUlIqJE49j2bILypi5uT42bPgGnTpVlzAzKgpYCJG0isJyFLnFZSyI8t2NG8/Ru7cfbtx4oYy5ulaGr687LC0NJcyMigoWQiStwr4cRW5xGQuifPfo0Vs0brweyclyAICOjiYWLPgCPj5O0NCQSZwdFRUshKhwMLDichREpJZKlUzRr189rF9/BXXrWmD79m6oU8dC6rSoiGEhRERERdbSpa6oVMkE48Y5Q1eXX2mkPt4+T0REhV5CQgqGDDkEX99glbiBgTYmT27FIohyjT85RERUqAUFPUPv3n4ICXmFbdtuoGXLiqhcubTUaVExwR4hIiIqlORyBebPP4umTf9ASMgrAIBCIXDz5otP7EmUc+wRIiKiQiciIgZ9++7DqVOPlDFHRyts394N1aqVkTAzKm5YCBERUaGya9ctDB58CG/fJgEAZDJgwoQWmDGjNbS1NSXOjoobFkJERFQoxMUlY8SIo9i06ZoyZmNjjC1busDFxVa6xKhYYyFEBevjJTW4HAUR/b/kZDn+/vuB8rmHR22sXv0VzMz0JMyKijsOlqaClb6kRvzTtIdQpMW5HAVRiWduro9Nm9xhbKyDzZvd8eef3VgEUb5jjxAVrMyW1OByFEQlUljYGxgYaKFcuQ9rgn3xRWU8ejQapqa6EmZGJQkLIZIGl9QgKrGEENi8+Rp8fI6iVatKOHSoF2SyD2uDsQiigsRLY0REVGDevElEz5574e19APHxKThy5B42bgyWOi0qwdgjREREBSIgIBx9++7Dkyexypi3d310715LwqyopGMhRIBcDpw5A0RGAlZWQMuWgCbn6iCivJGSIse0aSexYME5CJEWMzPTxdq1X6N799rSJkclHguhks7PDxg1Cnjyn/E6FSoAy5cDXbtKlxcRFQt370ajd28/XLnyYaqMNm1ssXlzF1SoYCxhZkRpOEaoJPPzA779VrUIAoCnT9Pifn7S5EVExUJY2Bs0bLhWWQRpaWlgwYL2OH68H4sgKjRYCJVUcnlaT1B6P/V/pcdGj05rR0SUC/b2ZujatSYAoHr1Mvj334H44Yfm0NCQfWJPooLDS2Ml1ZkzGXuC/ksIICIC+MESqK6Td6/LmaSJSpRVqzqiUiUTTJ7cCvr6WlKnQ5TBZxVCSUlJ0NXlfA9FUmQOC5IX0YB1Prw+Z5ImKlaSklIxceJxODvbqAyANjHRxZw57STMjCh7ahdCCoUCc+bMwZo1a/D8+XOEhobC3t4eU6dOha2tLQYMGJAfeVJes7LKWTsLc8AwD3uEAM4kTVTM3LjxHL17++HGjRfw9b2Gpk0rwMbGROq0iHJE7UJo9uzZ2LRpExYsWIBBgwYp43Xq1MGyZctYCBUVLVum3R329Gnm44RksrTtCx/yVnoiypRCIbBy5QX89NNxJCenjSdMTHyPy5efsRCiIkPtwdKbN2/GunXr0Lt3b2j+5wuyXr16uHv3bp4mR/lIUzPtFnkgrej5r/Tny5axCCKiTEVGxqFjx20YPdpfWQTVrWuBy5e/R5cuNSXOjijn1C6Enj59iipVqmSIKxQKvH//Pk+SogLStSuwZw9g/dEgoAoV0uKcR4iIMnHgwF04OKyBv/8DZWzMmKa4eHEQ6tSxkDAzIvWpfWmsVq1aOHPmDCpVqqQS37NnDxo0aJBniVEB6doV6Nw57e6wF9FpY4J4OYyIMpGQkIJx4/7G2rVBypiVlSF8fd3RoUNlCTMjyj21C6Fp06bBy8sLT58+hUKhgJ+fH0JCQrB582YcOnQoP3Kk/KapmXaLvDXSBkazCCKiTMTGJmPv3jvK5+7uNbB+fSeYm+tLmBXR51H70ljnzp3x119/4fjx4zAwMMC0adNw584d/PXXX/jiiy/yI0ciIioErKyM8PvvnaCvr4X16zvBz68HiyAq8mRCZHbLUPEVGxsLExMTxMTEwNiYU7wrra0AxD8FDK2BwdlMtEhEJUZERAwMDLRRurSeSvzFiwRYWBhIlBWVVPn1/a12j5C9vT1evXqVIf727VvY29vnSVJERCStXbtuwcFhDQYPPoSP/15mEUTFidqFUHh4OOSZrD+VnJyMp0+f5klSREQkjdjYZHh774eHxx68fZuEPXtuY/v2G1KnRZRvcjxY+uDBg8r/9/f3h4nJh8my5HI5Tpw4AVtb2zxNjoiICk5gYAR69/bDw4dvlTEPj9ro2LGqdEkR5bMcF0Lu7u4AAJlMBi8vL5VtWlpasLW1xeLFi/M0OSIiyn+pqQrMmXMas2adhlyedhnMyEgbq1Z1RJ8+DpB9POkqUTGS40JIoVAAAOzs7HDp0iWYm5vnW1JERFQwwsLeoE8fPwQGfrhJwtnZBlu3doGdnZmEmREVDLXnEXr48GF+5EFERAXs/v3XaNhwLeLiUgAAmpoyTJvmgkmTWqJUKbWHkBIVSWoXQgCQkJCAU6dO4fHjx0hJSVHZNnLkyDxJjIiI8lflymZo184e+/ffhb29GbZt64qmTStInRZRgVK7ELp69So6duyId+/eISEhAaVLl0Z0dDT09fVhYWHBQoiIqIiQyWRYv74TKlUywaxZbWBkpCN1SkQFTu2+zzFjxqBTp0548+YN9PT08O+//+LRo0dwdHTEokWL8iNHIiL6TCkpckyYcByHD4eqxM3N9bFsmRuLICqx1C6EgoODMW7cOGhoaEBTUxPJycmwsbHBggULMGnSpPzIkYiIPkNISDSaNfsD8+efw3ffHcTz5/FSp0RUaKhdCGlpaUFDI203CwsLPH78GABgYmKCiIiIvM2OiIhyTQiBtWsvo0GDtbhyJRIA8OZNIs6d47/VROnUHiPUoEEDXLp0CVWrVoWLiwumTZuG6OhobNmyBXXq1MmPHImISE0vXyZg4MC/cPBgiDJWvXoZbN/eDQ0bWkmYGVHhonaP0Ny5c2FllfZLNGfOHJiZmWHo0KF4+fIl1q5dm+cJEhGRevz978PBYY1KETR0aCNcuTKYRRDRR9TuEWrUqJHy/y0sLHDs2LE8TYiIiHInKSkVEycex7JlF5Qxc3N9bNjwDTp1qi5hZkSFV57NmHXlyhV8/fXXeXU4IiJS04sXCdi4MVj53M2tCm7cGMoiiCgbahVC/v7+GD9+PCZNmoSwsDAAwN27d+Hu7o7GjRsrl+EgIqKCV7GiCVav/go6OppYscINR454wtLSUOq0iAq1HF8a++OPPzBo0CCULl0ab968we+//44lS5ZgxIgR8PDwwM2bN1GzZs38zJWIiP4jMjIOBgbaMDb+MAdQr1510aJFRdjYmEiYGVHRkeMeoeXLl2P+/PmIjo7Grl27EB0djd9++w03btzAmjVrWAQRERWgAwfuwsFhDUaOPJphG4sgopzLcSH04MEDdO/eHQDQtWtXlCpVCgsXLkSFClyXhoiooCQkpGDIkENwd9+J6Oh32LTpGvbuvS11WkRFVo4vjSUmJkJfXx9A2vo0Ojo6ytvoiYgo/wUFPYOnpx9CQ18pY+7uNeDiYitdUkRFnFq3z//+++8wNEwbeJeamgpfX1+Ym5urtOGiq0REeUsuV2DRovOYMuUkUlPTbkrR19fC8uVuGDCgAWQymcQZEhVdMiGEyElDW1vbT/6yyWQy5d1kObVq1SosXLgQUVFRqFevHlauXAknJ6cs2799+xaTJ0+Gn58fXr9+jUqVKmHZsmXo2LFjjl4vNjYWJiYmiImJgbGxsVq5FmtrKwDxTwFDa2DwE6mzIaL/FxERg7599+HUqUfKmKOjFbZv74Zq1cpImBlRwcqv7+8c9wiFh4fn2Yum27lzJ8aOHYs1a9agSZMmWLZsGVxdXRESEgILC4sM7VNSUvDFF1/AwsICe/bsgbW1NR49egRTU9M8z42ISGqhoa/QpMnvePs2CQAgkwETJrTAjBmtoa2tKXF2RMWD2jNL56UlS5Zg0KBB6N+/PwBgzZo1OHz4MDZs2IAJEyZkaL9hwwa8fv0a58+fh5aWFoC0nioiouKoSpXSaNLEGv7+D2BjY4wtW7pwPBBRHsuzmaXVlZKSgqCgILRv3/5DMhoaaN++PQIDAzPd5+DBg2jWrBmGDx+OcuXKoU6dOpg7dy7kcnlBpU1EVGA0NGTYuLEzvv++Ia5dG8IiiCgfSNYjFB0dDblcjnLlyqnEy5Urh7t372a6T1hYGP73v/+hd+/eOHLkCO7fv49hw4bh/fv3mD59eqb7JCcnIzk5Wfk8NjY2794EEVEeSU1VYM6c02jZshLatrVTxq2sjLB2bScJMyMq3iS9NKYuhUIBCwsLrFu3DpqamnB0dMTTp0+xcOHCLAuhefPmYebMmQWcKRFRzoWFvUGfPn4IDHwCa2sjXL8+FKVL60mdFlGJINmlMXNzc2hqauL58+cq8efPn8PS0jLTfaysrFCtWjVoan4YJFizZk1ERUUhJSUl030mTpyImJgY5SMiIiLv3gQR0WcQQmDz5muoX38NAgPT7taMiorHyZMPJc6MqOTIVSH04MEDTJkyBb169cKLFy8AAEePHsWtW7dyfAxtbW04OjrixIkTyphCocCJEyfQrFmzTPdp3rw57t+/r7K4a2hoKKysrKCtrZ3pPjo6OjA2NlZ5EBFJ7c2bRPTsuRdeXvsRF5f2h5y9vRnOnv0O3brVkjg7opJD7ULo1KlTqFu3Li5cuAA/Pz/Ex8cDAK5du5bl5amsjB07FuvXr8emTZtw584dDB06FAkJCcq7yPr164eJEycq2w8dOhSvX7/GqFGjEBoaisOHD2Pu3LkYPny4um+DiEgyAQHhcHBYg127Pvzx6O1dH8HBg9G0KZctIipIao8RmjBhAmbPno2xY8fCyMhIGW/bti1+/fVXtY7l4eGBly9fYtq0aYiKikL9+vVx7Ngx5QDqx48fQ0PjQ61mY2MDf39/jBkzBg4ODrC2tsaoUaPw008/qfs2iIgKXEqKHNOnn8T8+eeQPpWtqaku1q37Gt2715Y2OaISKsczS6czNDTEjRs3YGdnByMjI1y7dg329vYIDw9HjRo1kJSUlF+55gnOLJ0FzixNlO/Cwt7AwWE1EhLeAwBat7bF5s3uXC2eKAfy6/tb7UtjpqamiIyMzBC/evUqrK2t8yQpIqLiyN7eDMuXu0FLSwMLFrTHiRP9WAQRSUztS2M9e/bETz/9hN27d0Mmk0GhUODcuXMYP348+vXrlx85UnZCdgPnpwEpcZ93nISMxS0RfZ7o6HfQ19eCvr6WMvbddw3g4mKLKlVKS5gZEaVTuxBKH5xsY2MDuVyOWrVqQS6Xw9PTE1OmTMmPHCk756cBrzOfgDJXtI0+3YaIPsnf/z68vQ+ga9caWLXqK2VcJpOxCCIqRNQeI5Tu8ePHuHnzJuLj49GgQQNUrVo1r3PLF8VujFD62B6ZBmBg9XnH0jYCms8Cqn2bN7kRlUBJSamYOPE4li27oIwdOtQLX31VTcKsiIo+yVefT3f27Fm0aNECFStWRMWKFfMsEfpMBlYc5EwksRs3nqN3bz/cuPFCGXNzqwJHx/ISZkVE2VF7sHTbtm1hZ2eHSZMm4fbt2/mRExFRkaJQCCxf/i8aN16vLIJ0dDSxYoUbjhzxhKWlocQZElFW1C6Enj17hnHjxuHUqVOoU6cO6tevj4ULF+LJE/ZGEFHJExkZh44dt2H0aH8kJ8sBAHXrWuDy5e8xYkQTyGQyiTMkouyoXQiZm5vDx8cH586dw4MHD9C9e3ds2rQJtra2aNu2bX7kSERUKIWERMPBYQ38/R8oY2PGNMXFi4NQp46FhJkRUU591qKrdnZ2mDBhAn755RfUrVsXp06dyqu8iIgKvSpVSqNWrbIAACsrQ/j798GSJa7Q1VV7+CURSSTXhdC5c+cwbNgwWFlZwdPTE3Xq1MHhw4fzMjciokJNU1MDW7Z0Qd++Drh+fSg6dKgsdUpEpCa1/2yZOHEiduzYgWfPnuGLL77A8uXL0blzZ+jr6+dHfkREhYJcrsCiRefRsmUlODvbKOMVK5pg8+YuEmZGRJ9D7ULo9OnT+OGHH9CjRw+Ym5vnR05ERIVKREQM+vbdh1OnHsHOzhTBwUNgbKwjdVpElAfULoTOnTuXH3kQERVKu3bdwuDBh/D2bdqC0uHhb/H33w/w7be1JM6MiPJCjgqhgwcP4ssvv4SWlhYOHjyYbdtvvvkmTxIjIpJSbGwyRo48ik2briljNjbG2LKlC1xcbKVLjIjyVI4KIXd3d0RFRcHCwgLu7u5ZtpPJZJDL5XmVGxGRJAIDI9Cnzz6Ehb1Rxjw8amP16q9gZqYnYWZElNdyVAgpFIpM/5+IqDhJTVVgzpzTmDXrNOTytGUYjYy0sWpVR/Tp48DJEYmKIbVvn9+8eTOSk5MzxFNSUrB58+Y8SYqISAoPHrzGvHlnlUWQs7MNrl0bgr5967EIIiqm1C6E+vfvj5iYmAzxuLg49O/fP0+SIiKSQvXq5liw4Atoasowc2ZrnDrlDTs7M6nTIqJ8pPZdY0KITP8yevLkCUxMTPIkKSKigvDmTSL09bWgo/Phn8IRI5zQtq0dl8ggKiFyXAg1aNAAMpkMMpkM7dq1Q6lSH3aVy+V4+PAh3Nzc8iVJIqK8FhAQjr5996Fnz9pYuLCDMi6TyVgEEZUgOS6E0u8WCw4OhqurKwwNDZXbtLW1YWtri27duuV5gkREeSklRY7p009i/vxzEAJYtCgQbm5V0K6dvdSpEZEEclwITZ8+HQBga2sLDw8P6Orq5ltSRET5ISQkGp6efrhyJVIZa9PGFtWrc5Z8opJK7TFCXl5e+ZEH5VTIbuD8NCAlLu15QmT27YkIQgisWxeEMWP8kZiYCgDQ0tLAnDltMW6cMzQ0eEcYUUmVo0KodOnSCA0Nhbm5OczMzLK9jfT169d5lhxl4vw04PXdjHFto4LPhagIePkyAQMH/oWDB0OUserVy2D79m5o2NBKwsyIqDDIUSG0dOlSGBkZKf+f82lIKL0nSKYBGPz/P+LaRkDzWdLlRFRIhYREo3XrTYiKilfGhg5thEWLOkBfX0vCzIiosMhRIfTfy2He3t75lQupw8AKGPxE6iyICjV7ezPY2BgjKioe5ub62LDhG3TqVF3qtIioEFF7QsUrV67gxo0byucHDhyAu7s7Jk2ahJSUlDxNjojoc2hpaWLbtq7o2rUmbtwYyiKIiDJQuxAaPHgwQkNDAQBhYWHw8PCAvr4+du/ejR9//DHPEyQiygmFQmDFigu4elX1BoKqVctg794esLQ0zGJPIirJ1C6EQkNDUb9+fQDA7t274eLigu3bt8PX1xd79+7N6/yIiD4pMjIOHTtuw6hRx+Dp6Yd3795LnRIRFRFqF0JCCOUK9MePH0fHjh0BADY2NoiOjs7b7IiIPuHAgbtwcFgDf/8HAIC7d6Nx9Og9ibMioqJC7XmEGjVqhNmzZ6N9+/Y4deoUVq9eDQB4+PAhypUrl+cJEhFlJiEhBePG/Y21a4OUMSsrQ/j6uqNDh8oSZkZERYnahdCyZcvQu3dv7N+/H5MnT0aVKlUAAHv27IGzs3OeJ0hE9LGgoGfw9PRDaOgrZczdvQbWr+8Ec3N9CTMjoqJG7ULIwcFB5a6xdAsXLoSmpmaeJEVElBm5XIGFC89j6tSTSE1Nu0Svr6+FZctcMXBgQ85xRkRqU7sQShcUFIQ7d+4AAGrVqoWGDRvmWVIlxsfLZeQEl9SgEuzu3WiVIsjR0Qrbt3dDtWplJM6MiIoqtQuhFy9ewMPDA6dOnYKpqSkA4O3bt2jTpg127NiBsmXL5nWOxVdWy2XkBJfUoBKodm0LzJrVBpMmncCECS0wY0ZraGuzJ5qIck/tQmjEiBGIj4/HrVu3ULNmTQDA7du34eXlhZEjR+LPP//M8ySLrcyWy8gJLqlBJURcXDL09LRQqtSHG1x/+MEZ7dvbo1Gj8hJmRkTFhdqF0LFjx3D8+HFlEQSkXRpbtWoVOnTokKfJlRhcLoMog8DACPTpsw99+zpgxozWyrimpgaLICLKM2rPI6RQKKCllXGxQi0tLeX8QkREuZWaqsDMmQFo2XIjwsLeYNas0zh/PkLqtIiomFK7EGrbti1GjRqFZ8+eKWNPnz7FmDFj0K5duzxNjohKlrCwN2jVaiNmzDgFuVwAAJo2rQArKy6PQUT5Q+1C6Ndff0VsbCxsbW1RuXJlVK5cGXZ2doiNjcXKlSvzI0ciKuaEENi8+Rrq11+DwMC0y8SamjLMnNkap055w87OTNoEiajYUnuMkI2NDa5cuYITJ04ob5+vWbMm2rdvn+fJEVHx9+ZNIoYOPYydO28pY/b2Zti2rSuaNq0gYWZEVBKoVQjt3LkTBw8eREpKCtq1a4cRI0bkV15EVAKEhETjiy+2ICIiVhnz9q6PFSvcYGSkI2FmRFRS5LgQWr16NYYPH46qVatCT08Pfn5+ePDgARYuXJif+RFRMVapkilMTXURERELMzNdrF37Nbp3ry11WkRUguR4jNCvv/6K6dOnIyQkBMHBwdi0aRN+++23/MyNiIo5Xd1S2L69Gzp2rIrr14eyCCKiApfjQigsLAxeXl7K556enkhNTUVkJJd8IKJPE0Jg3bog3L79UiVep44FDh/2RIUKxhJlRkQlWY4LoeTkZBgYGHzYUUMD2traSExMzJfEiKj4ePkyAe7uOzF48CF4eu5FcnKq1CkREQFQc7D01KlToa+vr3yekpKCOXPmwMTERBlbsmRJ3mVHREWev/99eHsfQFRUPADg2rXnOHQoFN261ZI4MyIiNQqhVq1aISQkRCXm7OyMsLAw5XOZTJZ3mRFRkZaUlIoJE45j+fILypi5uT42bPgGnTpVlzAzIqIPclwIBQQE5GMaRFSc3LjxHJ6efrh584Uy5upaGb6+7rC05CzRRFR4qD2hIhFRVhQKgZUrL+Cnn44jOVkOANDR0cSCBV/Ax8cJGhrsNSaiwoWFEBHlmRs3nmPs2L+hUKStE1a3rgW2b++GOnUsJM6MiChzaq81RkSUlXr1LDFpUgsAwJgxTXHx4iAWQURUqLFHiIhy7d2799DVLaVyyWvaNBd06FAZLVtWkjAzIqKcYY8QEeVKUNAzNGiwFosXn1eJa2lpsggioiIjV4XQmTNn0KdPHzRr1gxPnz4FAGzZsgVnz57N0+SKnZDdwMaawNoKaY8EzspNRY9crsD8+WfRtOkfCA19hcmT/4crV/izTERFk9qF0N69e+Hq6go9PT1cvXoVycnJAICYmBjMnTs3zxMsVs5PA17fBeKfpj2EIi2ubSRtXkQ5FBERg3btNmPChBNITU37+XVwKAdDQ22JMyMiyh21C6HZs2djzZo1WL9+PbS0tJTx5s2b48qVK3maXLGTEpf2X5kGYGid9ihdA2g+S9q8iHJg165bcHBYg1OnHgEAZDJg4sQWOH9+AKpVKyNxdkREuaP2YOmQkBC0atUqQ9zExARv377Ni5yKPwMrYPATqbMgypHY2GSMHHkUmzZdU8ZsbIyxZUsXuLjYSpcYEVEeULsQsrS0xP3792Fra6sSP3v2LOzt7fMqLyIqBEJCotGx43aEhb1Rxjw8amPNmq9haqorYWZERHlD7UtjgwYNwqhRo3DhwgXIZDI8e/YM27Ztw/jx4zF06ND8yJGIJFKhgjFKlUr7Z8LISBubN7vjzz+7sQgiomJD7UJowoQJ8PT0RLt27RAfH49WrVph4MCBGDx4MEaMGJGrJFatWgVbW1vo6uqiSZMmuHjxYo7227FjB2QyGdzd3XP1ukSUPQMDbWzf3hWtW9vi2rUh6Nu3HhdXJqJiRSaEELnZMSUlBffv30d8fDxq1aoFQ8PcLaS4c+dO9OvXD2vWrEGTJk2wbNky7N69GyEhIbCwyHpG2vDwcLRo0QL29vYoXbo09u/fn6PXi42NhYmJCWJiYmBsbJyrnHNtbYW0u8UMrTlGiAodIQS2bLmO5s1tULly6QzbWAARkZTy6/s71xMqamtro1atWnBycsp1EQQAS5YswaBBg9C/f3/UqlULa9asgb6+PjZs2JDlPnK5HL1798bMmTM5LokoD7x5k4iePffCy2s/evf2w/v3cpXtLIKIqLhSe7B0mzZtsv1H8X//+1+Oj5WSkoKgoCBMnDhRGdPQ0ED79u0RGBiY5X4///wzLCwsMGDAAJw5cybb10hOTlbOdQSkVZRE9EFAQDj69t2HJ0/SfjcuXHiKQ4dC0aVLTYkzIyLKf2oXQvXr11d5/v79ewQHB+PmzZvw8vJS61jR0dGQy+UoV66cSrxcuXK4e/dupvucPXsWf/zxB4KDg3P0GvPmzcPMmTPVyouoJEhJkWPatJNYsOAc0i+Qm5npYt26TiyCiKjEULsQWrp0aabxGTNmID4+/rMTyk5cXBz69u2L9evXw9zcPEf7TJw4EWPHjlU+j42NhY2NTX6l+EHI7rSZpNMnUQS4pAYVGiEh0fD09FNZGqNNG1ts3twFFSoU8Ng5IiIJ5dnq83369IGTkxMWLVqU433Mzc2hqamJ58+fq8SfP38OS0vLDO0fPHiA8PBwdOrUSRlTKNKm+S9VqhRCQkJQuXJllX10dHSgo6OjzlvJG+nLaWSGS2qQRIQQWLcuCGPG+CMxMRUAoKWlgTlz2mLcOGeVVeSJiEqCPCuEAgMDoaur3twi2tracHR0xIkTJ5S3wCsUCpw4cQI+Pj4Z2teoUQM3btxQiU2ZMgVxcXFYvnx5wfT05NR/l9MwsPoQ1zbikhokmatXozBkyGHl8+rVy2D79m5o2NAqm72IiIovtQuhrl27qjwXQiAyMhKXL1/G1KlT1U5g7Nix8PLyQqNGjeDk5IRly5YhISEB/fv3BwD069cP1tbWmDdvHnR1dVGnTh2V/U1NTQEgQ7zQ4HIaVIg0bGiFsWObYsmSfzF0aCMsWtQB+vpan96RiKiYUrsQMjExUXmuoaGB6tWr4+eff0aHDh3UTsDDwwMvX77EtGnTEBUVhfr16+PYsWPKAdSPHz+Ghkau7/InKtGSk1Ohra2pcqfn3Lnt4OZWBV98UTmbPYmISga1JlSUy+U4d+4c6tatCzMzs/zMK98U2ISKnDyRJHbjxnN4evph6NBGGDassdTpEBF9lkIxoaKmpiY6dOjAVeaJCjGFQmD58n/RuPF63Lz5AuPG/Y3bt19KnRYRUaGk9qWxOnXqICwsDHZ2dvmRDxF9hsjIOPTvfwD+/g+UsapVS2ezBxFRyab24JvZs2dj/PjxOHToECIjIxEbG6vyICJpHDhwFw4Oa1SKoDFjmuLixUGoVaushJkRERVeOe4R+vnnnzFu3Dh07NgRAPDNN9+oDMBMX5RRLpdndQgiygcJCSkYN+5vrF0bpIxZWRnC19cdHTpwQDQRUXZyXAjNnDkTQ4YMwcmTJ/MzHyJSQ2joK3Tq9CdCQ18pY+7uNbB+fSeYm+tLmBkRUdGQ40Io/eYyFxeXfEumSPt4SQ0up0EFoFw5A6SkpPXC6utrYflyNwwY0ICrxRMR5ZBag6X5j2s2slpSg8tpUD4yMdHF1q1dMG7c39i8uQuqVSsjdUpEREWKWoVQtWrVPlkMvX79+rMSKrIyW1KDy2lQHtu9+xaaNq0AG5sPE5s2b14RgYED+IcKEVEuqFUIzZw5M8PM0vQRLqlB+SA2NhkjRx7Fpk3X0Lq1LY4f7wtNzQ83fbIIIiLKHbUKoZ49e8LCwiK/ciGiTAQGRqBPn30IC3sDAAgICMehQ6Ho3LmGxJkRERV9OZ5HiH9xEhWs1FQFZs4MQMuWG5VFkJGRNjZvdsc331SXODsiouJB7bvGiCj/hYW9QZ8+fggM/HCZ1dnZBlu3doGdXdFc54+IqDDKcSGkUCjyMw8iQtofHFu2XIePzxHExaUAADQ1ZZg2zQWTJrVEqVJqTwZPRETZUHutMSLKP5cvP4OX137lc3t7M2zb1hVNm1aQLikiomKMf14SFSKNG1tj8GBHAIC3d30EBw9mEURElI/YI0Qkoffv5ShVSkPlZoTFizugY8eqHBBNRFQA2CNEJJGQkGg0bfoHNm26phI3MNBmEUREVEBYCBEVMCEE1q69jAYN1uLKlUiMGHEU9++X0BnZiYgkxktjRAXo5csEDBz4Fw4eDFHGrK2NkJj4XsKsiIhKLhZCRAXE3/8+vL0PICoqXhkbMsQRixe7Ql9fS8LMiIhKLhZCRPksKSkVEycex7JlF5Qxc3N9bNjwDTp14lggIiIpsRAiykf3779G1647cePGC2XMza0KNm7sDEtLQwkzIyIigIUQUb4yM9PFq1eJAAAdHU0sXPgFfHycuHYfEVEhwbvGiPJRmTL68PXtjHr1yuHy5e8xYkQTFkFERIUIe4SI8tBff4WgcWNrlcteX3xRGUFBdtDU5N8dRESFDf9lJsoDCQkpGDLkEL75Zge+++4AhBAq21kEEREVTuwRyq2Q3cD5aUBKXNrzhEhp8yHJBAU9g6enH0JDXwEAjh69j0OHQnlHGBFREcBCKLfOTwNe380Y1zYq+FxIEnK5AosWnceUKSeRmqoAAOjra2H5cjd8/XU1ibMjIqKcYCGUW+k9QTINwMAq7f+1jYDms6TLiQpMREQM+vbdh1OnHiljjo5W2L69G6pVKyNhZkREpA4WQp/LwAoY/ETqLKgA7dx5E0OGHMbbt0kAAJkMmDChBWbMaA1tbU2JsyMiInWwECJSw7//PkHPnnuVz21sjLFlSxe4uNhKlxQREeUab2UhUkPTphXQt68DAMDDozauXRvCIoiIqAhjjxBRNhQKAQ0N1QkQf/21I776qip69KjNyRGJiIo49ggRZSEs7A1atNiAXbtuqcSNjXXg4VGHRRARUTHAHiGijwghsGXLdfj4HEFcXAru3DmEZs0qwMbGROrUiIgoj7FHiOg/3rxJRM+ee+HltR9xcSkAgNKl9ZQLpxIRUfHCHiGi/xcQEI6+fffhyZNYZczbuz5WrHCDkZGOhJkREVF+YSGUEx8vpwFwSY1iJCVFjmnTTmLBgnNIXyLM1FQX69Z9je7da0ubHBER5SsWQjmR1XIaAJfUKOLCwt6ge/fduHLlQ2HburUtNm9255ggIqISgIVQTmS2nAbAJTWKAT29Unj8OAYAoKWlgTlz2mLcOOcMt8wTEVHxxEJIHVxOo9ixsjLCH398g59+Oo5t27qiYUOrT+9ERETFBgshKlGOHw9DgwaWKFNGXxn75pvq+PLLKtDS4jphREQlDW+fpxIhKSkVY8YcwxdfbMHgwYcg0kdF/z8WQUREJRMLISr2btx4Dien9Vi27AIAYO/eOzh27L7EWRERUWHAQoiKLYVCYPnyf9G48XrcuPECAKCjo4kVK9zg5lZF4uyIiKgw4BghKpYiI+PQv/8B+Ps/UMbq1rXA9u3dUKeOhYSZERFRYcJCiIqdgwdDMGDAQURHv1PGxoxpirlz20FXlz/yRET0Ab8VMvPxTNKcRbrIOHfuMTp33qF8bmlpiE2b3NGhQ2UJsyIiosKKY4Qykz6TdPzTtIdQpMU5i3Sh5+xsgy5dagAAOneujhs3hrIIIiKiLLFHKDOZzSTNWaQLJSEEZLIPs0DLZDKsX98J33xTHV5e9VS2ERERfYyFUHY4k3ShFhERg3799mPcuGb4+utqyniZMvrw9q4vXWJERFRksBCiImnXrlsYPPgQ3r5Nwq1bL3D9+lBYWhpKnRYRERUxHCNERUpsbDK8vffDw2MP3r5NAgDo6pbCs2dxEmdGRERFEXuEqMgIDIxA795+ePjwrTLm4VEbq1d/BTMzPekSIyKiIouFEBV6qakKzJ59GrNnn4ZcnrZGmJGRNlat6og+fRw4IJqIiHKNhRAVauHhb+HpuReBgR8GrTs722Dr1i6wszOTMDMiIioOOEaICjUNDRlu334JANDUlGHmzNY4dcqbRRAREeUJFkJUqFWsaII1a76Gvb0Zzp79DtOmuaBUKf7YEhFR3uA3ChUqZ848QmxsskqsZ886uHVrGJo2rSBRVkREVFwVikJo1apVsLW1ha6uLpo0aYKLFy9m2Xb9+vVo2bIlzMzMYGZmhvbt22fbnoqGlBQ5Jkw4DhcXX4wYcTTDdi6WSkRE+UHyQmjnzp0YO3Yspk+fjitXrqBevXpwdXXFixcvMm0fEBCAXr164eTJkwgMDISNjQ06dOiAp0+fFnDmlFdCQqLRrNkfmD//HIQANm++hr//fiB1WkREVALIhBBCygSaNGmCxo0b49dffwUAKBQK2NjYYMSIEZgwYcIn95fL5TAzM8Ovv/6Kfv36fbJ9bGwsTExMEBMTA2Nj48wbra2QttiqoTWX2MhHQgisWxeEMWP8kZiYCgDQ0tLAnDltMW6cMzQ0eFs8ERGlydH3dy5Ier0hJSUFQUFBmDhxojKmoaGB9u3bIzAwMEfHePfuHd6/f4/SpUtnuj05ORnJyR/GnMTGxn5e0pQnXr5MwMCBf+HgwRBlrHr1Mti+vRsaNrSSMDMiIipJJL00Fh0dDblcjnLlyqnEy5Urh6ioqBwd46effkL58uXRvn37TLfPmzcPJiYmyoeNjc1n502fx9//Phwc1qgUQUOHNsKVK4NZBBERUYGSfIzQ5/jll1+wY8cO7Nu3D7q6upm2mThxImJiYpSPiIiIAs6S/uvMmUdwc9uGqKh4AIC5uT4OHuyJ3377Cvr6WhJnR0REJY2kl8bMzc2hqamJ58+fq8SfP38OS0vLbPddtGgRfvnlFxw/fhwODg5ZttPR0YGOjk6e5Eufr0WLinBzq4Jjx+7Dza0KNm7szFXjiYhIMpL2CGlra8PR0REnTpxQxhQKBU6cOIFmzZplud+CBQswa9YsHDt2DI0aNSqIVCmPyGQybNzYGb/91hFHjniyCCIiIklJfmls7NixWL9+PTZt2oQ7d+5g6NChSEhIQP/+/QEA/fr1UxlMPX/+fEydOhUbNmyAra0toqKiEBUVhfj4eKneAmUhKioeX321HSdOhKnELS0NMXRoYy6WSkREkpN8ljoPDw+8fPkS06ZNQ1RUFOrXr49jx44pB1A/fvwYGhof6rXVq1cjJSUF3377rcpxpk+fjhkzZhRk6pSNgwdDMGDAQURHv8O1a1G4dm0IypTRlzotIiIiFZIXQgDg4+MDHx+fTLcFBASoPA8PD8//hCjXEhJSMG7c31i7NkgZUygEwsPfshAiIqJCp1AUQlQ8BAU9Q+/efggJeaWMubvXwPr1nWBuziKIiIgKHxZC9NnkcgUWLTqPKVNOIjVVAQDQ19fC8uVuGDCgAccCERFRocVCiD7Lkyex6Nt3HwICwpUxR0crbN/eDdWqlZEuMSIiohyQ/K4xKtoSE9/j0qW0BW9lMmDixBY4f34AiyAiIioSWAjRZ6latQxWrPgSNjbGOHnSC3PntoO2tqbUaREREeUICyFSy8WLT/Hu3XuVWP/+9XH79nC4uNhKkxQREVEusRCiHElNVWDmzAA4O/+B8eP/Vtkmk8lgaKgtUWZERES5x0KIPiks7A1atdqIGTNOQS4XWL36Mk6efCh1WkRERJ+Nd41RloQQ2LLlOnx8jiAuLgUAoKkpw7RpLmjZspLE2REREX0+FkKUqTdvEjF06GHs3HlLGbO3N8O2bV3RtGkFCTMjIiLKOyyEKINTp8LRt+8+RETEKmPe3vWxYoUbjIx0JMyMSDpyuRzv37//dEMiyjVtbW2V9UULAgshUnHqVDjatNkEIdKem5npYu3ar9G9e21pEyOSiBACUVFRePv2rdSpEBV7GhoasLOzg7Z2wd2Aw0KIVLRoURGtWlXCqVOP0KaNLTZv7oIKFYylTotIMulFkIWFBfT19blkDFE+USgUePbsGSIjI1GxYsUC+11jIUQqNDU1sGVLF+zefRujRzeFhgb/0aeSSy6XK4ugMmU4WzpRfitbtiyePXuG1NRUaGlpFchr8vb5EuzlywR067YL5849Vonb2Jhg7NhmLIKoxEsfE6Svry9xJkQlQ/olMblcXmCvyR6hEsrf/z68vQ8gKioeV65E4tq1ITA25kBooszwchhRwZDid409QiVMUlIqRo8+Bje3bYiKigcAxMenIDT0lcSZERERFTwWQiXIjRvP0bjxeixffkEZc3Orghs3hqJRo/ISZkZEVHiEhITA0tIScXFxUqdS7DRt2hR79+6VOg0VLIRKAIVCYPnyf9G48XrcvPkCAKCjo4kVK9xw5IgnLC0NJc6QiPKat7c3ZDIZZDIZtLS0YGdnhx9//BFJSUkZ2h46dAguLi4wMjKCvr4+GjduDF9f30yPu3fvXrRu3RomJiYwNDSEg4MDfv75Z7x+/Tqf31HBmThxIkaMGAEjIyOpU8k3q1atgq2tLXR1ddGkSRNcvHjxk/ssW7YM1atXh56eHmxsbDBmzBiVn6fTp0+jU6dOKF++PGQyGfbv35/hGFOmTMGECROgUCjy8u18FhZCxVxkZBw6dtyG0aP9kZycNvisbl0LXL78PUaMaMKxD0TFmJubGyIjIxEWFoalS5di7dq1mD59ukqblStXonPnzmjevDkuXLiA69evo2fPnhgyZAjGjx+v0nby5Mnw8PBA48aNcfToUdy8eROLFy/GtWvXsGXLlgJ7XykpKfl27MePH+PQoUPw9vb+rOPkZ46fa+fOnRg7diymT5+OK1euoF69enB1dcWLFy+y3Gf79u2YMGECpk+fjjt37uCPP/7Azp07MWnSJGWbhIQE1KtXD6tWrcryOF9++SXi4uJw9OjRPH1Pn0WUMDExMQKAiImJybrRGmshFiHtv0XczZvPhY7OLAHMEMAMMWbMMZGY+F7qtIiKhMTERHH79m2RmJgodSpq8/LyEp07d1aJde3aVTRo0ED5/PHjx0JLS0uMHTs2w/4rVqwQAMS///4rhBDiwoULAoBYtmxZpq/35s2bLHOJiIgQPXv2FGZmZkJfX184Ojoqj5tZnqNGjRIuLi7K5y4uLmL48OFi1KhRokyZMqJ169aiV69eokePHir7paSkiDJlyohNmzYJIYSQy+Vi7ty5wtbWVujq6goHBwexe/fuLPMUQoiFCxeKRo0aqcSio6NFz549Rfny5YWenp6oU6eO2L59u0qbzHIUQogbN24INzc3YWBgICwsLESfPn3Ey5cvlfsdPXpUNG/eXJiYmIjSpUuLr776Sty/fz/bHD+Xk5OTGD58uPK5XC4X5cuXF/Pmzctyn+HDh4u2bduqxMaOHSuaN2+eaXsAYt++fZlu69+/v+jTp0+m27L7ncvR93cu8K6xYq52bQssXPgF5s49i02b3NGhQ2WpUyIq+rY2AhKiCvY1DSyBPpdzvfvNmzdx/vx5VKr0YcHkPXv24P379xl6fgBg8ODBmDRpEv788080adIE27Ztg6GhIYYNG5bp8U1NTTONx8fHw8XFBdbW1jh48CAsLS1x5coVtS+NbNq0CUOHDsW5c+cAAPfv30f37t0RHx8PQ8O0y/v+/v549+4dunTpAgCYN28etm7dijVr1qBq1ao4ffo0+vTpg7Jly8LFxSXT1zlz5gwaNWqkEktKSoKjoyN++uknGBsb4/Dhw+jbty8qV64MJyenLHN8+/Yt2rZti4EDB2Lp0qVITEzETz/9hB49euB///sfgLRelLFjx8LBwQHx8fGYNm0aunTpguDg4CyXmpg7dy7mzp2b7ed1+/ZtVKxYMUM8JSUFQUFBmDhxojKmoaGB9u3bIzAwMMvjOTs7Y+vWrbh48SKcnJwQFhaGI0eOoG/fvtnmkRknJyf88ssvau+XX1gIFTPXrkWhRg1z6Oh8OLU+Pk7o08cBZmZ6EmZGVIwkRAHxT6XO4pMOHToEQ0NDpKamIjk5GRoaGvj111+V20NDQ2FiYgIrK6sM+2pra8Pe3h6hoaEAgHv37sHe3l7tSe62b9+Oly9f4tKlSyhdujQAoEqVKmq/l6pVq2LBggXK55UrV4aBgQH27dun/DLevn07vvnmGxgZGSE5ORlz587F8ePH0axZMwCAvb09zp49i7Vr12ZZCD169ChDIWRtba1SLI4YMQL+/v7YtWuXSiH0cY6zZ89GgwYNVIqWDRs2wMbGBqGhoahWrRq6deum8lobNmxA2bJlcfv2bdSpUyfTHIcMGYIePXpk+3mVL5/5DTDR0dGQy+UoV66cSrxcuXK4e/dulsfz9PREdHQ0WrRoASEEUlNTMWTIEJVLYzlVvnx5REREQKFQFPi6YplhIVRMyOUKLFp0HlOmnMSoUU2waFEH5TaZTMYiiCgvGVgWidds06YNVq9ejYSEBCxduhSlSpXK8MWbUyJ9AUI1BQcHo0GDBsoiKLccHR1VnpcqVQo9evTAtm3b0LdvXyQkJODAgQPYsWMHgLQeo3fv3uGLL75Q2S8lJQUNGjTI8nUSExOhq6urEpPL5Zg7dy527dqFp0+fIiUlBcnJyRkm2vw4x2vXruHkyZPKHqv/evDgAapVq4Z79+5h2rRpuHDhAqKjo5U9ZY8fP86yECpduvRnf57qCggIwNy5c/Hbb7+hSZMmuH//PkaNGoVZs2Zh6tSpah1LT08PCoUCycnJ0NOT/ruJhVAxEBERg7599+HUqUcAgMWLA+HuXgMtWmTsFiWiPPAZl6gKkoGBgbL3ZcOGDahXrx7++OMPDBgwAABQrVo1xMTE4NmzZxl6EFJSUvDgwQO0adNG2fbs2bN4//69Wr1Cn/qi09DQyFBkpc/o/fF7+Vjv3r3h4uKCFy9e4J9//oGenh7c3NwApF2SA4DDhw/D2tpaZT8dnawnjzU3N8ebN29UYgsXLsTy5cuxbNky1K1bFwYGBhg9enSGAdEf5xgfH49OnTph/vz5GV4nvReuU6dOqFSpEtavX4/y5ctDoVCgTp062Q62/pxLY+bm5tDU1MTz589V4s+fP4elZdbF9tSpU9G3b18MHDgQAFC3bl0kJCTg+++/x+TJk9Xq2Xn9+jUMDAwKRREE8K6xIm/XrltwcFijLIJkMmDixBZwcrL+xJ5EVJJoaGhg0qRJmDJlChITEwEA3bp1g5aWFhYvXpyh/Zo1a5CQkIBevXoBSLs0Eh8fj99++y3T4799+zbTuIODA4KDg7O8vb5s2bKIjIxUiQUHB+foPTk7O8PGxgY7d+7Etm3b0L17d2WRVqtWLejo6ODx48eoUqWKysPGxibLYzZo0AC3b99WiZ07dw6dO3dGnz59UK9ePZVLhtlp2LAhbt26BVtb2ww5GBgY4NWrVwgJCcGUKVPQrl071KxZM0MRlpkhQ4YgODg420dWl8a0tbXh6OiIEydOKGMKhQInTpxQXkLMzLt37zIUO5qamgDU7y28efNmtr1yBY09QkVUbGwyRo48ik2briljNjbG2LKlC1xcbKVLjIgKre7du+OHH37AqlWrMH78eFSsWBELFizAuHHjoKuri759+0JLSwsHDhzApEmTMG7cODRp0gQA0KRJE/z4448YN24cnj59ii5duqB8+fK4f/8+1qxZgxYtWmDUqFEZXrNXr16YO3cu3N3dMW/ePFhZWeHq1asoX748mjVrhrZt22LhwoXYvHkzmjVrhq1bt6r1Renp6Yk1a9YgNDQUJ0+eVMaNjIwwfvx4jBkzBgqFAi1atEBMTAzOnTsHY2NjeHl5ZXo8V1dXDBw4EHK5XPlFX7VqVezZswfnz5+HmZkZlixZgufPn6NWrVrZ5jZ8+HCsX78evXr1wo8//ojSpUvj/v372LFjB37//XeYmZmhTJkyWLduHaysrPD48WNMmDDhk+/5cy+NjR07Fl5eXmjUqBGcnJywbNkyJCQkoH///so2/fr1g7W1NebNmwcgredqyZIlaNCggfLS2NSpU9GpUyfl5xQfH4/79+8rj/Hw4UMEBwejdOnSKr1TZ86cQYcOH4ZvSC5P70ErAorD7fPnzz8W9vbLlbfEAzOEh8du8fr1O6lTIypWitvt80IIMW/ePFG2bFkRHx+vjB04cEC0bNlSGBgYCF1dXeHo6Cg2bNiQ6XF37twpWrVqJYyMjISBgYFwcHAQP//8c7a3z4eHh4tu3boJY2Njoa+vLxo1aiQuXLig3D5t2jRRrlw5YWJiIsaMGSN8fHwy3D4/atSoTI99+/ZtAUBUqlRJKBQKlW0KhUIsW7ZMVK9eXWhpaYmyZcsKV1dXcerUqSxzff/+vShfvrw4duyYMvbq1SvRuXNnYWhoKCwsLMSUKVNEv379VD7frHIMDQ0VXbp0EaampkJPT0/UqFFDjB49WpnrP//8I2rWrCl0dHSEg4ODCAgIyPbW87yycuVKUbFiRaGtrS2cnJyU0xn89/14eXkpn79//17MmDFDVK5cWejq6gobGxsxbNgwlfN+8uRJASDD47/HefLkidDS0hIRERGZ5iXF7fMyIXI5Aq6Iio2NhYmJCWJiYmBsbJx5o7UV0u4IMbQGBj8p2AQ/ISAgHO3bb4ZcnnbajIy0sWpVR/Tp48DJEYnyWFJSEh4+fAg7O7sMA2ip+Fq1ahUOHjwIf39/qVMpdn766Se8efMG69aty3R7dr9zOfr+zgVeGitimje3gaNjeVy8+BTOzjbYurUL7OzMpE6LiKjYGDx4MN6+fYu4uLhivcyGFCwsLDB27Fip01DBQqiI0dLSxLZtXbFz50389FMLlCrF8e5ERHmpVKlSmDx5stRpFEvjxo2TOoUM+C1aiL15k4jevf0QFPRMJV6lSmlMntyKRRAREdFnYo9QIRUQEI6+fffhyZNYBAU9w5Urg6Gvr96MrkRERJQ9dikUMikpckyYcBxt227CkyexAIAXLxJw61bWqwITERFR7rBHqBAJCYmGp6cfrlz5MLlYmza22Ly5CypUyLsR8kRERJSGhVAhIITAunVBGDPGH4mJqQAALS0NzJnTFuPGOUNDg7fFExER5QcWQhJ7+TIBAwf+hYMHQ5Sx6tXLYPv2bmjYMOOK0ERERJR3OEZIYhERsThy5J7y+dChjXDlymAWQURUYoSHh0Mmk+V4jbG8EhAQAJlMluU6aTklk8mwf//+LLdL9f4oZ1gISaxhQyvMnt0G5ub6OHiwJ3777SveHUZUHMnlQEAA8Oefaf+Vy/P15by9vSGTyZSPMmXKwM3NDdevX8+z15gxYwbq16+fbRtbW1uVPD5+eHt751k+RLnBQqiA3b0bjffvVf8BHD/eGbduDUOnTtUlyoqI8pWfH2BrC7RpA3h6pv3X1jYtno/c3NwQGRmJyMhInDhxAqVKlcLXX3+dr6/5sUuXLilz2Lt3LwAgJCREGVu+fHmujiuXy6FQKPIyVSqhWAgVEIVCYPnyf1G//hrMnn1aZZumpgYsLAwkyoyI8pWfH/Dtt8CTj9YtfPo0LZ6PxZCOjg4sLS1haWmJ+vXrY8KECYiIiMDLly+VbSIiItCjRw+YmpqidOnS6Ny5M8LDw5XbAwIC4OTkBAMDA5iamqJ58+Z49OgRfH19MXPmTFy7dk3Zu+Pr65shh7JlyypzSF8x3cLCQhkzMTFRtg0LC0ObNm2gr6+PevXqITAwULnN19cXpqamOHjwIGrVqgUdHR08fvwYycnJGD9+PKytrWFgYIAmTZogICBAud+jR4/QqVMnmJmZwcDAALVr18aRI0dUcgwKCkKjRo2gr68PZ2dnhISEqGxfvXo1KleuDG1tbVSvXh1btmzJ9nO/ePEiGjRoAF1dXTRq1AhXr17Ntj1Ji4VQAYiMjEPHjtswerQ/kpPlmD37DC5efCp1WkSU3+RyYNQoILO1rdNjo0fn+2UyAIiPj8fWrVtRpUoVlClTBgDw/v17uLq6wsjICGfOnMG5c+dgaGgINzc3pKSkIDU1Fe7u7nBxccH169cRGBiI77//HjKZDB4eHhg3bhxq166t7N3x8PD4rBwnT56M8ePHIzg4GNWqVUOvXr2Qmpqq3P7u3TvMnz8fv//+O27dugULCwv4+PggMDAQO3bswPXr19G9e3e4ubnh3r20sZfDhw9HcnIyTp8+jRs3bmD+/PkwNDTM8LqLFy/G5cuXUapUKXz33XfKbfv27cOoUaMwbtw43Lx5E4MHD0b//v1x8uTJLD/nr7/+GrVq1UJQUBBmzJiB8ePHf9bnQvksT9eyLwJiYmIEABETE5N1ozXWQixC2n8/0/79d4S5+QIBzFA+Ro8+KhIT33/2sYkofyUmJorbt2+LxMTE3B3g5Ekh0kqe7B8nT+Zl2kIIIby8vISmpqYwMDAQBgYGAoCwsrISQUFByjZbtmwR1atXFwqFQhlLTk4Wenp6wt/fX7x69UoAEAEBAZm+xvTp00W9evVynNPJkycFAPHmzRuV+MOHDwUA8fvvvytjt27dEgDEnTt3hBBCbNy4UQAQwcHByjaPHj0Smpqa4unTpyrHa9eunZg4caIQQoi6deuKGTNmZJvP8ePHlbHDhw8LAMpz7uzsLAYNGqSyX/fu3UXHjh2VzwGIffv2CSGEWLt2rShTpozKz8zq1asFAHH16tXsPh4S2f/O5ej7OxfYI5RPEhJSMGTIIbi770R09DsAgKWlIfz9+2DpUjfo6nLmAqJiLzLy023UaaemNm3aIDg4GMHBwbh48SJcXV3x5Zdf4tGjRwCAa9eu4f79+zAyMoKhoSEMDQ1RunRpJCUl4cGDByhdujS8vb3h6uqKTp06Yfny5YjMp1wBwMHBQfn/VlZpd86+ePFhVn1tbW2VNjdu3IBcLke1atWU+RsaGuLUqVN48OABAGDkyJGYPXs2mjdvjunTp2c6WDy7171z5w6aN2+u0r558+a4c+dOpu/hzp07cHBwgK6urjLWrFmznH0AJAl+G+eDoKBn8PT0Q2joK2Wsc+fq+P33b2Buri9hZkRUoKxyOA1GTtupycDAAFWqVFE+//3332FiYoL169dj9uzZiI+Ph6OjI7Zt25Zh37JlywIANm7ciJEjR+LYsWPYuXMnpkyZgn/++QdNmzbN83y1tD7cMSuTpU0k+98B0Xp6eso4kHYZSlNTE0FBQdDU1FQ5Vvrlr4EDB8LV1RWHDx/G33//jXnz5mHx4sUYMWJEjl+Xijf2COWx//3vIZo2/UNZBOnra2Hduq+xb58HiyCikqZlS6BCBUCWxezwMhlgY5PWrgDIZDJoaGggMTERANCwYUPcu3cPFhYWqFKlisrjv4OYGzRogIkTJ+L8+fOoU6cOtm/fDiCth0ZeAOObstKgQQPI5XK8ePEiQ/6WlpbKdjY2NhgyZAj8/Pwwbtw4rF+/PsevUbNmTZw7d04ldu7cOdSqVSvL9tevX0dSUpIy9u+//6r5zqggsRDKY82b26BWrbS/pBwdrXD16mAMGuSo8lcMEZUQmppA+u3hH/8bkP582bK0dvkgOTkZUVFRiIqKwp07dzBixAjEx8ejU6dOAIDevXvD3NwcnTt3xpkzZ/Dw4UMEBARg5MiRePLkCR4+fIiJEyciMDAQjx49wt9//4179+6hZs2aANLmCHr48CGCg4MRHR2N5OTkfHkfWalWrRp69+6Nfv36wc/PDw8fPsTFixcxb948HD58GAAwevRo+Pv74+HDh7hy5QpOnjypzD8nfvjhB/j6+mL16tW4d+8elixZAj8/vywHQHt6ekImk2HQoEG4ffs2jhw5gkWLFuXJ+6X8wUIoj+nolML27V0xeXJLnD8/ANWqlZE6JSKSUteuwJ49gLW1arxChbR416759tLHjh2DlZUVrKys0KRJE1y6dAm7d+9G69atAQD6+vo4ffo0KlasiK5du6JmzZoYMGAAkpKSYGxsDH19fdy9exfdunVDtWrV8P3332P48OEYPHgwAKBbt25wc3NDmzZtULZsWfz555/59l6ysnHjRvTr1w/jxo1D9erV4e7ujkuXLqFixYoA0uYbGj58OGrWrAk3NzdUq1YNv/32W46P7+7ujuXLl2PRokWoXbs21q5di40bNyo/w48ZGhrir7/+wo0bN9CgQQNMnjwZ8+fPz4u3SvlEJkRm93UWX7GxsTAxMUFMTAyMjbNY0X1tBSD+KWBoDQx+knkbALGxyRg3zh+jRzdF7doW+ZQxEUklKSkJDx8+hJ2dncrg11yRy4EzZ9IGRltZpV0Oy6eeIKKiKrvfuRx9f+cCB0vnUmBgBPr02YewsDe4ePEZLl4cCB0dfpxElAVNTSCLXgQikg4vjakpNVWBmTMD0LLlRoSFvQEAPHz4BtevP5c4MyIiIlIXuzDUEBb2Bn36+CEw8MPlMmdnG2zd2gV2dmYSZkZERES5wUIIAEJ2A+enASlxac8TVCcME0Jgy5br8PE5gri4FACApqYM06a5YNKklihVih1rRERERRELISCtCHp9N2Nc2whv3iRi6NDD2LnzljJsb2+Gbdu6omnTCgWYJBEREeU1FkLAh54gmQZg8P8zvGobAc1n4c6daOzefVvZ1Nu7PlascIORkY4EiRKRFErYzbVEkpHid42F0H8ZWGW4Xd65GjB5ckusXHkR69Z9je7da0uUHBEVtPSlF969ewc9PT2JsyEq/lJS0oefFNzUEiyEPvLw4RtUrGgCTc0P436mTm2FwYMdYW2dd/MWEFHhp6mpCVNTU+UCnPr6+pwlniifKBQKvHz5Evr6+ihVquDKExZC/08IYN3pGhgz5jdMn+6Cn35qodympaXJIoiohEpfs+q/q6ATUf7Q0NBAxYoVC/QPDhZCAF7G6WKgb08cvFUDQCqmTDmJDh0qo0GD/FkRmoiKDplMBisrK1hYWOD9+/dSp0NUrGlra0NDo2DvxC4UhdCqVauwcOFCREVFoV69eli5ciWcnJyybL97925MnToV4eHhqFq1KubPn4+OHTvm6rX9/e/De1Y3RMV+WBl+4MAGqF7dPFfHI6LiSVNTs0DHLRBRwZB8ApydO3di7NixmD59Oq5cuYJ69erB1dU1y27o8+fPo1evXhgwYACuXr0Kd3d3uLu74+bNm2q9blJSKkaPPgY3t23KIsjcMBEHD/bE6tVfQ19f67PfGxERERVuki+62qRJEzRu3Bi//vorgLTBUjY2NhgxYgQmTJiQob2HhwcSEhJw6NAhZaxp06aoX78+1qxZ88nXS1+0rWbNxbhzJ04Zd6t+Dxu/uwDLH+/lwbsiIiKivJRfi65K2iOUkpKCoKAgtG/fXhnT0NBA+/btERgYmOk+gYGBKu0BwNXVNcv2Wblz5yUAQEdHEys8zuPIwG2wNElU8x0QERFRUSbpGKHo6GjI5XKUK1dOJV6uXDncvZvJTM8AoqKiMm0fFRWVafvk5GQkJycrn8fExKRvQS2rN/ij72nUMrmLuGQApRRAbGyu3w8RERHlj9j//37O6wtZhWKwdH6aN28eZs6cmcmWpbgdCTRb8N9YJDDGpIAyIyIiInW9evUKJiZ5910taSFkbm4OTU1NPH/+XCX+/Plz5dwdH7O0tFSr/cSJEzF27Fjl87dv36JSpUp4/Phxnn6QpL7Y2FjY2NggIiIiT6/3Uu7wfBQePBeFB89F4RETE4OKFSuidOnSeXpcSQshbW1tODo64sSJE3B3dweQNlj6xIkT8PHxyXSfZs2a4cSJExg9erQy9s8//6BZs2aZttfR0YGOTsZ1wUxMTPhDXUgYGxvzXBQiPB+FB89F4cFzUXjk9TxDkl8aGzt2LLy8vNCoUSM4OTlh2bJlSEhIQP/+/QEA/fr1g7W1NebNmwcAGDVqFFxcXLB48WJ89dVX2LFjBy5fvox169ZJ+TaIiIioCJK8EPLw8MDLly8xbdo0REVFoX79+jh27JhyQPTjx49Vqj9nZ2ds374dU6ZMwaRJk1C1alXs378fderUkeotEBERUREleSEEAD4+PlleCgsICMgQ6969O7p3756r19LR0cH06dMzvVxGBYvnonDh+Sg8eC4KD56LwiO/zoXkEyoSERERSUXyJTaIiIiIpMJCiIiIiEosFkJERERUYrEQIiIiohKrWBZCq1atgq2tLXR1ddGkSRNcvHgx2/a7d+9GjRo1oKuri7p16+LIkSMFlGnxp865WL9+PVq2bAkzMzOYmZmhffv2nzx3pB51fzfS7dixAzKZTDnxKX0+dc/F27dvMXz4cFhZWUFHRwfVqlXjv1V5RN1zsWzZMlSvXh16enqwsbHBmDFjkJSUVEDZFl+nT59Gp06dUL58echkMuzfv/+T+wQEBKBhw4bQ0dFBlSpV4Ovrq/4Li2Jmx44dQltbW2zYsEHcunVLDBo0SJiamornz59n2v7cuXNCU1NTLFiwQNy+fVtMmTJFaGlpiRs3bhRw5sWPuufC09NTrFq1Sly9elXcuXNHeHt7CxMTE/HkyZMCzrx4Uvd8pHv48KGwtrYWLVu2FJ07dy6YZIs5dc9FcnKyaNSokejYsaM4e/asePjwoQgICBDBwcEFnHnxo+652LZtm9DR0RHbtm0TDx8+FP7+/sLKykqMGTOmgDMvfo4cOSImT54s/Pz8BACxb9++bNuHhYUJfX19MXbsWHH79m2xcuVKoampKY4dO6bW6xa7QsjJyUkMHz5c+Vwul4vy5cuLefPmZdq+R48e4quvvlKJNWnSRAwePDhf8ywJ1D0XH0tNTRVGRkZi06ZN+ZViiZKb85GamiqcnZ3F77//Lry8vFgI5RF1z8Xq1auFvb29SElJKagUSwx1z8Xw4cNF27ZtVWJjx44VzZs3z9c8S5qcFEI//vijqF27tkrMw8NDuLq6qvVaxerSWEpKCoKCgtC+fXtlTENDA+3bt0dgYGCm+wQGBqq0BwBXV9cs21PO5OZcfOzdu3d4//59ni+wVxLl9nz8/PPPsLCwwIABAwoizRIhN+fi4MGDaNasGYYPH45y5cqhTp06mDt3LuRyeUGlXSzl5lw4OzsjKChIefksLCwMR44cQceOHQskZ/ogr76/C8XM0nklOjoacrlcuTxHunLlyuHu3buZ7hMVFZVp+6ioqHzLsyTIzbn42E8//YTy5ctn+EEn9eXmfJw9exZ//PEHgoODCyDDkiM35yIsLAz/+9//0Lt3bxw5cgT379/HsGHD8P79e0yfPr0g0i6WcnMuPD09ER0djRYtWkAIgdTUVAwZMgSTJk0qiJTpP7L6/o6NjUViYiL09PRydJxi1SNExccvv/yCHTt2YN++fdDV1ZU6nRInLi4Offv2xfr162Fubi51OiWeQqGAhYUF1q1bB0dHR3h4eGDy5MlYs2aN1KmVOAEBAZg7dy5+++03XLlyBX5+fjh8+DBmzZoldWqUS8WqR8jc3Byampp4/vy5Svz58+ewtLTMdB9LS0u12lPO5OZcpFu0aBF++eUXHD9+HA4ODvmZZomh7vl48OABwsPD0alTJ2VMoVAAAEqVKoWQkBBUrlw5f5MupnLzu2FlZQUtLS1oamoqYzVr1kRUVBRSUlKgra2drzkXV7k5F1OnTkXfvn0xcOBAAEDdunWRkJCA77//HpMnT1ZZJJzyV1bf38bGxjnuDQKKWY+QtrY2HB0dceLECWVMoVDgxIkTaNasWab7NGvWTKU9APzzzz9Ztqecyc25AIAFCxZg1qxZOHbsGBo1alQQqZYI6p6PGjVq4MaNGwgODlY+vvnmG7Rp0wbBwcGwsbEpyPSLldz8bjRv3hz3799XFqMAEBoaCisrKxZBnyE35+Ldu3cZip30AlVw6c4ClWff3+qN4y78duzYIXR0dISvr6+4ffu2+P7774WpqamIiooSQgjRt29fMWHCBGX7c+fOiVKlSolFixaJO3fuiOnTp/P2+Tyi7rn45ZdfhLa2ttizZ4+IjIxUPuLi4qR6C8WKuufjY7xrLO+oey4eP34sjIyMhI+PjwgJCRGHDh0SFhYWYvbs2VK9hWJD3XMxffp0YWRkJP78808RFhYm/v77b1G5cmXRo0cPqd5CsREXFyeuXr0qrl69KgCIJUuWiKtXr4pHjx4JIYSYMGGC6Nu3r7J9+u3zP/zwg7hz545YtWoVb59Pt3LlSlGxYkWhra0tnJycxL///qvc5uLiIry8vFTa79q1S1SrVk1oa2uL2rVri8OHDxdwxsWXOueiUqVKAkCGx/Tp0ws+8WJK3d+N/2IhlLfUPRfnz58XTZo0ETo6OsLe3l7MmTNHpKamFnDWxZM65+L9+/dixowZonLlykJXV1fY2NiIYcOGiTdv3hR84sXMyZMnM/0OSP/8vby8hIuLS4Z96tevL7S1tYW9vb3YuHGj2q8rE4J9eURERFQyFasxQkRERETqYCFEREREJRYLISIiIiqxWAgRERFRicVCiIiIiEosFkJERERUYrEQIiIiohKLhRARqfD19YWpqanUaeSaTCbD/v37s23j7e0Nd3f3AsmHiAo3FkJExZC3tzdkMlmGx/3796VODb6+vsp8NDQ0UKFCBfTv3x8vXrzIk+NHRkbiyy+/BACEh4dDJpMhODhYpc3y5cvh6+ubJ6+XlRkzZijfp6amJmxsbPD999/j9evXah2HRRtR/ipWq88T0Qdubm7YuHGjSqxs2bISZaPK2NgYISEhUCgUuHbtGvr3749nz57B39//s4+d1arh/2ViYvLZr5MTtWvXxvHjxyGXy3Hnzh189913iImJwc6dOwvk9Yno09gjRFRM6ejowNLSUuWhqamJJUuWoG7dujAwMICNjQ2GDRuG+Pj4LI9z7do1tGnTBkZGRjA2NoajoyMuX76s3H727Fm0bNkSenp6sLGxwciRI5GQkJBtbjKZDJaWlihfvjy+/PJLjBw5EsePH0diYiIUCgV+/vlnVKhQATo6Oqhfvz6OHTum3DclJQU+Pj6wsrKCrq4uKlWqhHnz5qkcO/3SmJ2dHQCgQYMGkMlkaN26NQDVXpZ169ahfPnyKiu7A0Dnzp3x3XffKZ8fOHAADRs2hK6uLuzt7TFz5kykpqZm+z5LlSoFS0tLWFtbo3379ujevTv++ecf5Xa5XI4BAwbAzs4Oenp6qF69OpYvX67cPmPGDGzatAkHDhxQ9i4FBAQAACIiItCjRw+YmpqidOnS6Ny5M8LDw7PNh4gyYiFEVMJoaGhgxYoVuHXrFjZt2oT//e9/+PHHH7Ns37t3b1SoUAGXLl1CUFAQJkyYAC0tLQDAgwcP4Obmhm7duuH69evYuXMnzp49Cx8fH7Vy0tPTg0KhQGpqKpYvX47Fixdj0aJFuH79OlxdXfHNN9/g3r17AIAVK1bg4MGD2LVrF0JCQrBt2zbY2tpmetyLFy8CAI4fP47IyEj4+fllaNO9e3e8evUKJ0+eVMZev36NY8eOoXfv3gCAM2fOoF+/fhg1ahRu376NtWvXwtfXF3PmzMnxewwPD4e/vz+0tbWVMYVCgQoVKmD37t24ffs2pk2bhkmTJmHXrl0AgPHjx6NHjx5wc3NDZGQkIiMj4ezsjPfv38PV1RVGRkY4c+YMzp07B0NDQ7i5uSElJSXHORERUCxXnycq6by8vISmpqYwMDBQPr799ttM2+7evVuUKVNG+Xzjxo3CxMRE+dzIyEj4+vpmuu+AAQPE999/rxI7c+aM0NDQEImJiZnu8/HxQ0NDRbVq1USjRo2EEEKUL19ezJkzR2Wfxo0bi2HDhgkhhBgxYoRo27atUCgUmR4fgNi3b58QQoiHDx8KAOLq1asqbby8vETnzp2Vzzt37iy+++475fO1a9eK8uXLC7lcLoQQol27dmLu3Lkqx9iyZYuwsrLKNAchhJg+fbrQ0NAQBgYGQldXV7mS9pIlS7LcRwghhg8fLrp165ZlrumvXb16dZXPIDk5Wejp6Ql/f/9sj09EqjhGiKiYatOmDVavXq18bmBgACCtd2TevHm4e/cuYmNjkZqaiqSkJLx79w76+voZjjN27FgMHDgQW7ZsUV7eqVy5MoC0y2bXr1/Htm3blO2FEFAoFHj48CFq1qyZaW4xMTEwNDSEQqFAUlISWrRogd9//x2xsbF49uwZmjdvrtK+efPmuHbtGoC0y1pffPEFqlevDjc3N3z99dfo0KHDZ31WvXv3xqBBg/Dbb79BR0cH27ZtQ8+ePaGhoaF8n+fOnVPpAZLL5dl+bgBQvXp1HDx4EElJSdi6dSuCg4MxYsQIlTarVq3Chg0b8PjxYyQmJiIlJQX169fPNt9r167h/v37MDIyUoknJSXhwYMHufgEiEouFkJExZSBgQGqVKmiEgsPD8fXX3+NoUOHYs6cOShdujTOnj2LAQMGICUlJdMv9BkzZsDT0xOHDx/G0aNHMX36dOzYsQNdunRBfHw8Bg8ejJEjR2bYr2LFilnmZmRkhCtXrkBDQwNWVlbQ09MDAMTGxn7yfTVs2BAPHz7E0aNHcfz4cfTo0QPt27fHnj17PrlvVjp16gQhBA4fPozGjRvjzJkzWLp0qXJ7fHw8Zs6cia5du2bYV1dXN8vjamtrK8/BL7/8gq+++gozZ87ErFmzAAA7duzA+PHjsXjxYjRr1gxGRkZYuHAhLly4kG2+8fHxcHR0VClA0xWWAfFERQULIaISJCgoCAqFAosXL1b2dqSPR8lOtWrVUK1aNYwZMwa9evXCxo0b0aVLFzRs2BC3b9/OUHB9ioaGRqb7GBsbo3z58jh37hxcXFyU8XPnzsHJyUmlnYeHBzw8PPDtt9/Czc0Nr1+/RunSpVWOlz4eRy6XZ5uPrq4uunbtim3btuH+/fuoXr06GjZsqNzesGFDhISEqP0+PzZlyhS0bdsWQ4cOVb5PZ2dnDBs2TNnm4x4dbW3tDPk3bNgQO3fuhIWFBYyNjT8rJ6KSjoOliUqQKlWq4P3791i5ciXCwsKwZcsWrFmzJsv2iYmJ8PHxQUBAAB49eoRz587h0qVLykteP/30E86fPw8fHx8EBwfj3r17OHDggNqDpf/rhx9+wPz587Fz506EhIRgwoQJCA4OxqhRowAAS5YswZ9//om7d+8iNDQUu3fvhqWlZaaTQFpYWEBPTw/Hjh3D8+fPERMTk+Xr9u7dG4cPH8aGDRuUg6TTTZs2DZs3b8bMmTNx69Yt3LlzBzt27MCUKVPUem/NmjWDg4MD5s6dCwCoWrUqLl++DH9/f4SGhmLq1Km4dOmSyj62tra4fv06QkJCEB0djffv36N3794wNzdH586dcebMGTx8+BABAQEYOXIknjx5olZORCWe1IOUiCjvZTbANt2SJUuElZWV0NPTE66urmLz5s0CgHjz5o0QQnUwc3JysujZs6ewsbER2traonz58sLHx0dlIPTFixfFF198IQwNDYWBgYFwcHDIMNj5vz4eLP0xuVwuZsyYIaytrYWWlpaoV6+eOHr0qHL7unXrRP369YWBgYEwNjYW7dq1E1euXFFux38GSwshxPr164WNjY3Q0NAQLi4uWX4+crlcWFlZCQDiwYMHGfI6duyYcHZ2Fnp6esLY2Fg4OTmJdevWZfk+pk+fLurVq5ch/ueffwodHR3x+PFjkZSUJLy9vYWJiYkwNTUVQ4cOFRMmTFDZ78WLF8rPF4A4efKkEEKIyMhI0a9fP2Fubi50dHSEvb29GDRokIiJickyJyLKSCaEENKWYkRERETS4KUxIiIiKrFYCBEREVGJxUKIiIiISiwWQkRERFRisRAiIiKiEouFEBEREZVYLISIiIioxGIhRERERCUWCyEiIiIqsVgIERERUYnFQoiIiIhKLBZCREREVGL9HwCeJUX85JeWAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import f1_score, recall_score\n",
        "\n",
        "thresholds = np.linspace(0, 1, 101)\n",
        "\n",
        "recall_values = []\n",
        "f1_weighted_values = []\n",
        "\n",
        "for threshold in thresholds:\n",
        "    y_pred_binary = [1 if prob >= threshold else 0 for prob in y_pred_proba]\n",
        "\n",
        "    recall = recall_score(y_true, y_pred_binary, pos_label=1)\n",
        "    recall_values.append(recall)\n",
        "\n",
        "    f1_weighted = f1_score(y_true, y_pred_binary, average='weighted')\n",
        "    f1_weighted_values.append(f1_weighted)\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(thresholds, recall_values, label='Recall')\n",
        "plt.plot(thresholds, f1_weighted_values, label='Weighted F1-score')\n",
        "plt.xlabel('Threshold')\n",
        "plt.ylabel('Score')\n",
        "plt.title('Recall and Weighted F1-score vs. Threshold')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Rd7Jmtg9eOon",
        "outputId": "22ab46ff-5c60-4497-86da-eda8e4a31821"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_recall_vs_threshold(fpr, tpr, thresholds):\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(thresholds, tpr, label=\"Recall (TPR)\", color=\"darkorange\")\n",
        "\n",
        "    best_threshold_index = np.argmax(tpr - fpr)\n",
        "    best_threshold = thresholds[best_threshold_index]\n",
        "    best_recall = tpr[best_threshold_index]\n",
        "    plt.plot(best_threshold, best_recall, 'ro', markersize=8, label=f'Best Threshold ({best_threshold:.2f})')\n",
        "\n",
        "    plt.xlabel(\"Threshold\")\n",
        "    plt.ylabel(\"Recall (TPR)\")\n",
        "    plt.title(\"Recall vs. Threshold\")\n",
        "    plt.grid(True)\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "plot_recall_vs_threshold(fpr, tpr, thresholds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "HUGpGDIfegkZ",
        "outputId": "9cd33adb-93dd-4e66-9e87-93177260cedf"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Avalia como limiar alterado"
      ],
      "metadata": {
        "id": "zNYKa1wneDtM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_threshold = thresholds[np.argmax(tpr - fpr)]\n",
        "\n",
        "test['pred_bert_best_threshold'] = test['prob_bert'].apply(lambda x: 1 if x[1] >= best_threshold else 0)\n",
        "\n",
        "print(classification_report(test['label_desfavoravel'], test['pred_bert_best_threshold']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8kue2QRIeFbN",
        "outputId": "34e644d5-d203-4ad2-c8d3-968ff04f616c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.86      0.88       114\n",
            "           1       0.59      0.70      0.64        33\n",
            "\n",
            "    accuracy                           0.82       147\n",
            "   macro avg       0.75      0.78      0.76       147\n",
            "weighted avg       0.84      0.82      0.83       147\n",
            "\n"
          ]
        }
      ]
    }
  ]
}