{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkCrXktvpg-K"
      },
      "source": [
        "# ***Setup***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xvzdvG-sBZE",
        "outputId": "00cb21d0-f195-44e3-dc95-34b5242809c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading the pre-trained Word2Vec model...\n",
            "Model loaded successfully!\n"
          ]
        }
      ],
      "source": [
        "import gensim.downloader as api\n",
        "# Download the pre-trained Word2Vec model\n",
        "print(\"Downloading the pre-trained Word2Vec model...\")\n",
        "word2vec_model = api.load(\"word2vec-google-news-300\")  # This may take some time\n",
        "print(\"Model loaded successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "gVZgxY9Ipg-N"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from nltk.corpus import wordnet as wn\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import pickle\n",
        "\n",
        "from sklearn.feature_selection import SelectKBest, chi2\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from tabulate import tabulate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "q2TmAnKApg-Q"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import os\n",
        "parent_dir = os.path.abspath('..')\n",
        "sys.path.append(parent_dir)\n",
        "from constants import CONSTANTS\n",
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRhvTpG4pg-R"
      },
      "source": [
        "## ***Load dataset***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "G68g6W34pg-R"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv(CONSTANTS.AUGMENTED_TRAIN_SET_PATH)\n",
        "test_df = pd.read_csv(CONSTANTS.AUGMENTED_TEST_SET_PATH)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IO1jm_QOpg-S"
      },
      "source": [
        "## ***Load dep. parsing results***\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "O-mgx7appg-S"
      },
      "outputs": [],
      "source": [
        "with open(CONSTANTS.DEP_PARSED_TEXTS_OBJECT_PATH, 'rb') as f:\n",
        "    loaded_data = pickle.load(f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZcj5nthpg-S"
      },
      "source": [
        "## ***Helper functions***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSq2H2QFpg-T"
      },
      "source": [
        "### ***Get processed text by row id***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dBGStgWpg-T",
        "outputId": "d04fdf57-f0ec-46e2-ed2e-8c94dd064ffb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n"
          ]
        }
      ],
      "source": [
        "# Each sentence in the dataset has an id, and a document contain its stanza processing\n",
        "def get_doc_by_id(target_id):\n",
        "    for obj in loaded_data:\n",
        "        if obj[\"id\"] == target_id:\n",
        "            return obj[\"processed_text\"]\n",
        "    return None  # Return None if not found\n",
        "print(get_doc_by_id(1).text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D7Shok8Gpg-U"
      },
      "source": [
        "### ***Get text tokens by row id***\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m53cpCVepg-U",
        "outputId": "82f58747-fa66-452d-cbd9-8799058c64d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n",
            "['My', 'skin', 'has', 'been', 'peeling', ',', 'especially', 'on', 'my', 'knees', ',', 'elbows', ',', 'and', 'scalp', '.', 'This', 'peeling', 'is', 'often', 'accompanied', 'by', 'a', 'burning', 'or', 'stinging', 'sensation', '.']\n"
          ]
        }
      ],
      "source": [
        "def get_text_tokens(text_id):\n",
        "    processed_text = get_doc_by_id(text_id)\n",
        "    tokens = [word.text for sent in processed_text.sentences for word in sent.words]\n",
        "    return tokens\n",
        "print(get_doc_by_id(1).text)\n",
        "print(get_text_tokens(1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MCVghK6pg-U"
      },
      "source": [
        "### ***Get wordnet POS tag***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euHgn-rfpg-U",
        "outputId": "166a2912-7a4c-4e77-97db-52823e1938eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "My skin has been peeling, especially on my knees, elbows, and scalp. This peeling is often accompanied by a burning or stinging sensation.\n",
            "[('My', 'd'), ('skin', 'n'), ('has', 'd'), ('been', 'd'), ('peeling', 'v'), (',', 'd'), ('especially', 'r'), ('on', 'd'), ('my', 'd'), ('knees', 'n'), (',', 'd'), ('elbows', 'n'), (',', 'd'), ('and', 'd'), ('scalp', 'n'), ('.', 'd'), ('This', 'd'), ('peeling', 'n'), ('is', 'd'), ('often', 'r'), ('accompanied', 'v'), ('by', 'd'), ('a', 'd'), ('burning', 'n'), ('or', 'd'), ('stinging', 'a'), ('sensation', 'n'), ('.', 'd')]\n"
          ]
        }
      ],
      "source": [
        "def get_wordnet_pos(text_id):\n",
        "    processed_text = get_doc_by_id(text_id)\n",
        "    pos_tags = [(word.text, word.upos) for sent in processed_text.sentences for word in sent.words]\n",
        "    pos_mapping = {\n",
        "        'NOUN': 'n',  # Noun\n",
        "        'VERB': 'v',  # Verb\n",
        "        'ADJ': 'a',   # Adjective\n",
        "        'ADV': 'r'    # Adverb\n",
        "    }\n",
        "    # Set 'd' as the default tag\n",
        "    default_tag = 'd'\n",
        "    pos_tags = [(word, pos_mapping.get(pos, default_tag)) for word, pos in pos_tags]\n",
        "    return pos_tags\n",
        "\n",
        "print(get_doc_by_id(1).text)\n",
        "print(get_wordnet_pos(1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjAUpSi0yI8c"
      },
      "source": [
        "### **Word2Vec features extraction**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "unJifraYyIF9"
      },
      "outputs": [],
      "source": [
        "def get_weighted_average_embedding(text_id):\n",
        "    pos_tags = get_wordnet_pos(text_id)\n",
        "\n",
        "    weighted_sum = np.zeros(300)  # 300 features per vector\n",
        "    total_weight = 0\n",
        "\n",
        "    # Weights (Have to be tuned)\n",
        "    pos_weights = {\n",
        "        'a': 1,  # Adjectives\n",
        "        'n': 4,  # Nouns\n",
        "        'n': 1,  # Verbs\n",
        "        'r': 1,  # Adverbs\n",
        "        'd': 0.5,  # Default\n",
        "    }\n",
        "\n",
        "    for word, tag in pos_tags:\n",
        "        if word in word2vec_model:\n",
        "            weight = pos_weights.get(tag, 0.5)\n",
        "            vector = word2vec_model[word] * weight\n",
        "            weighted_sum += vector\n",
        "            total_weight += weight\n",
        "\n",
        "    if total_weight > 0:\n",
        "        return weighted_sum / total_weight\n",
        "    else:\n",
        "        return np.zeros(300)  # Return a zero vector if no valid words found\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rb1-WBWYpg-X"
      },
      "source": [
        "### ***Features selection***\n",
        "- Using `SelectKBest`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ig7fjz78pg-X"
      },
      "outputs": [],
      "source": [
        "def select_features(X_train, X_test, y_train, scorer, k_value):\n",
        "\n",
        "    # Apply features selection\n",
        "    selector = SelectKBest(score_func=scorer, k=k_value)\n",
        "    X_train_selected = selector.fit_transform(X_train, y_train)\n",
        "    X_test_selected = selector.transform(X_test)\n",
        "    return X_train_selected, X_test_selected\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IL69P3v_pg-Y"
      },
      "source": [
        "### ***Plot train, and test accuracies vs number_of_features***\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "MgIiP54Apg-Y"
      },
      "outputs": [],
      "source": [
        "def plot_accuracies(X_train, X_test, y_train, y_test, model, k_start=500, k_end=4500, step=250):\n",
        "    train_accuracies = []\n",
        "    test_accuracies = []\n",
        "    features_counts = []\n",
        "    for i in range(k_start, k_end + 1, step):\n",
        "        X_train_selected, X_test_selected = select_features(X_train, X_test, y_train, chi2, i)\n",
        "        model.fit(X_train_selected, y_train)\n",
        "\n",
        "        # Training set acc\n",
        "        y_train_pred = model.predict(X_train_selected)\n",
        "        train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "        train_accuracies.append(train_accuracy)\n",
        "\n",
        "        # Testing set acc\n",
        "        y_pred = model.predict(X_test_selected)\n",
        "        test_accuracy = accuracy_score(y_test, y_pred)\n",
        "        test_accuracies.append(test_accuracy)\n",
        "\n",
        "        features_counts.append(i)\n",
        "\n",
        "    # Plotting the accuracies\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(features_counts, train_accuracies, label='Train Accuracy', marker='.')\n",
        "    plt.plot(features_counts, test_accuracies, label='Test Accuracy', marker='.')\n",
        "    plt.title('Train and Test Accuracy vs Number of Features')\n",
        "    plt.xlabel('Number of Features Selected')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "    plt.grid()\n",
        "\n",
        "    # Finding closest points (3)\n",
        "    differences = np.abs(np.array(train_accuracies) - np.array(test_accuracies))\n",
        "    closest_indices = np.argsort(differences)[:3]  # indices of the three smallest differences\n",
        "    colors = ['darkgreen', 'mediumseagreen', 'lightgreen']\n",
        "\n",
        "    # Draw a rect\n",
        "    for i, idx in enumerate(closest_indices):\n",
        "        x = features_counts[idx] - 5\n",
        "        y_bottom = min(train_accuracies[idx], test_accuracies[idx])\n",
        "        y_top = max(train_accuracies[idx], test_accuracies[idx])\n",
        "        height = y_top - y_bottom\n",
        "\n",
        "        plt.gca().add_patch(plt.Rectangle(\n",
        "            (x, y_bottom), 10, height,\n",
        "            color=colors[i], alpha=0.5\n",
        "        ))\n",
        "\n",
        "        # Print the number of selected features for each closest point\n",
        "        print(f\"Closest Point {i+1}: Number of Features = {features_counts[idx]}, Train Accuracy = {train_accuracies[idx]}, Test Accuracy = {test_accuracies[idx]}\")\n",
        "\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQFVfw0Kpg-Y"
      },
      "source": [
        "### ***Evaluate model***\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "suaO7BLjpg-Z"
      },
      "outputs": [],
      "source": [
        "def print_clf_report_as_table(report):\n",
        "    data = []\n",
        "    for key, value in report.items():\n",
        "        if key != 'accuracy' and key != 'macro avg' and key != 'weighted avg':\n",
        "            data.append([key, value['precision'], value['recall'], value['f1-score'], value['support']])\n",
        "\n",
        "    data.append(['accuracy', '', '', report['accuracy'], ''])\n",
        "\n",
        "    data.append(['macro avg', report['macro avg']['precision'], report['macro avg']['recall'], report['macro avg']['f1-score'], ''])\n",
        "\n",
        "    data.append(['weighted avg', report['weighted avg']['precision'], report['weighted avg']['recall'], report['weighted avg']['f1-score'], ''])\n",
        "\n",
        "    print(tabulate(data, headers=['Class', 'Precision', 'Recall', 'F1-score', 'Support'], tablefmt='psql'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "8ddi3EvDpg-Z"
      },
      "outputs": [],
      "source": [
        "def evaluate_model(X_train, X_test, y_train, y_test, scorer, k_value, model):\n",
        "    X_train_selected, X_test_selected = select_features(X_train, X_test, y_train, scorer, k_value)\n",
        "    model.fit(X_train_selected, y_train)\n",
        "\n",
        "    # Training set acc\n",
        "    y_train_pred = model.predict(X_train_selected)\n",
        "    train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "\n",
        "    # Testing set acc\n",
        "    y_pred = model.predict(X_test_selected)\n",
        "    test_accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "    print(f'Train Accuracy: {train_accuracy}')\n",
        "    print(f'Test Accuracy: {test_accuracy}')\n",
        "    print(f'Difference: {train_accuracy-test_accuracy}')\n",
        "    # Print classification report\n",
        "    report = classification_report(y_test, y_pred, output_dict=True)\n",
        "    print_clf_report_as_table(report)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CSaKSdDpg-Z"
      },
      "source": [
        "# ***Use Word2Vec Embedding as Features***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FehC4hJLpg-Z"
      },
      "source": [
        "## ***Apply features extraction on the dataset***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ZkUnC1ocpg-a"
      },
      "outputs": [],
      "source": [
        "train_df[\"features\"] = train_df[\"Id\"].apply(get_weighted_average_embedding)\n",
        "test_df[\"features\"] = test_df[\"Id\"].apply(get_weighted_average_embedding)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rl1ofZopg-a"
      },
      "source": [
        "#### ***Vectorize the features***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3v5KhOUpg-a",
        "outputId": "f45d37d7-24e6-45c8-9f23-b1a04cabae98"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set shape after features extraction: (4320, 300)\n",
            "Test set shape after features extraction: (480, 300)\n"
          ]
        }
      ],
      "source": [
        "X_train = np.array(train_df['features'].to_list())\n",
        "X_test = np.array(test_df['features'].to_list())\n",
        "print(f\"Train set shape after features extraction: {X_train.shape}\")\n",
        "print(f\"Test set shape after features extraction: {X_test.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### ***Make values positive***\n",
        "**To use with chi2 in selectKBest**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "H4DQxZRj3PHG"
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "e57swztOpg-b"
      },
      "outputs": [],
      "source": [
        "y_train = train_df[\"label\"]\n",
        "y_test = test_df[\"label\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXAhmYVzpg-b"
      },
      "source": [
        "## ***Select best number of features***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "veVKylkhDYX-",
        "outputId": "f1d85d64-abd0-4e57-fe7c-94a8352b73da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Closest Point 1: Number of Features = 195, Train Accuracy = 0.99375, Test Accuracy = 0.9333333333333333\n",
            "Closest Point 2: Number of Features = 185, Train Accuracy = 0.9939814814814815, Test Accuracy = 0.9270833333333334\n",
            "Closest Point 3: Number of Features = 255, Train Accuracy = 0.9951388888888889, Test Accuracy = 0.925\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = KNeighborsClassifier()\n",
        "plot_accuracies(X_train, X_test, y_train, y_test, model, k_start = 5, k_end = 300, step = 10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## ***Tuning model***\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "bJvNkcBRFBhW",
        "outputId": "98785085-28d5-4a72-e3a2-07ab1700ed26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Closest Point 1: Number of Features = 175, Train Accuracy = 0.9828703703703704, Test Accuracy = 0.9229166666666667\n",
            "Closest Point 2: Number of Features = 215, Train Accuracy = 0.9835648148148148, Test Accuracy = 0.9208333333333333\n",
            "Closest Point 3: Number of Features = 125, Train Accuracy = 0.9803240740740741, Test Accuracy = 0.9166666666666666\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = KNeighborsClassifier(n_neighbors=10)\n",
        "plot_accuracies(X_train, X_test, y_train, y_test, model, k_start = 5, k_end = 300, step = 10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "72CyGo36GpHm",
        "outputId": "35717c7b-d591-4c57-c827-8dc9409fd09e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Closest Point 1: Number of Features = 185, Train Accuracy = 0.962037037037037, Test Accuracy = 0.9041666666666667\n",
            "Closest Point 2: Number of Features = 115, Train Accuracy = 0.95, Test Accuracy = 0.8875\n",
            "Closest Point 3: Number of Features = 165, Train Accuracy = 0.9585648148148148, Test Accuracy = 0.89375\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = KNeighborsClassifier(n_neighbors=20)\n",
        "plot_accuracies(X_train, X_test, y_train, y_test, model, k_start = 5, k_end = 300, step = 10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "fXdsWdMVHhIm",
        "outputId": "2f4d46ee-c4af-4028-e3f5-64e2c4d101d4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Closest Point 1: Number of Features = 185, Train Accuracy = 0.9631944444444445, Test Accuracy = 0.9\n",
            "Closest Point 2: Number of Features = 225, Train Accuracy = 0.9613425925925926, Test Accuracy = 0.89375\n",
            "Closest Point 3: Number of Features = 115, Train Accuracy = 0.950925925925926, Test Accuracy = 0.8833333333333333\n"
          ]
        },
        {
          "data": {
            "image/png": 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1oqKiEB0djSNHjjh83Noe5+LFiwCA9u3b2+ynVCrRpk2bWp9HTc6ePQtRFPHCCy8gOjra5mQZImaZ1L5o0SLk5eWhQ4cO6N69O/71r3/hyJEjDXr8rVu34tq1a+jXrx/Onj2Ls2fP4sKFC7jhhhvw5ZdfWqsdnjt3DkDNFcUsX6hdXXWsusTlhx9+wIABA6DRaBAREYHo6Gi8//77Nm187tw5yGQyh4lXZVOnTkVpaSm++eYbANJQsQMHDuD++++vNb6JEyeitLTUOnyuqKgIGzduxD333GNNzoYPH4677roLCxcuRFRUFO644w6sXLkSOp2uTq+BRWhoKJ544gls2LCh2vlGzqr6/rd8ea46BNWyvernTyaT2X0OOnToAADW+VRnzpxBfn4+YmJi7N7nRUVF1ve4RU3JamWWz6ajoXydOnWy3l5f/fr1w0033WRzsjwfQEouqz6frVu32jyfnJwczJ49G7GxsQgICEB0dLT1+blr3lDV1+/MmTMQRRHt27e3i/fkyZPWeBMTEzFnzhz897//RVRUFJKSkrBs2TLObyJyEuc4EVG1HFW7ysvLw/Dhw6HVarFo0SK0bdsWGo0GBw8exNNPP12n8uNyudzhdrHKpPOqXn75Zbzwwgt44IEHsHjxYkREREAmk+GJJ55w+Lj1fRxXsMTz5JNPVvsLu6Wc+bBhw3Du3Dl899132Lp1K/773//irbfewvLly/HQQw/V6/EtvUoTJkxwePsvv/yCG264oV7Hrk51vU9Vi4ZYOHp//frrr7j99tsxbNgwvPfee2jWrBmUSiVWrlxZbWGLmnTp0gW9e/fG559/jqlTp+Lzzz+HSqWq9nWpbMCAAUhISMD//vc/TJkyBd9//z1KS0sxceJE6z6CIOCrr77Cnj178P3332PLli144IEH8MYbb2DPnj1OLSJrmeu0cOFCLF261O52Z1/f6t7/rvxcmM1mu17MyqKjo22uN7SCnrtZPrefffYZ4uLi7G5XKCq+Nk2YMAG7du3Cv/71L/Tq1QvBwcEwm824+eab6/R30Nn2BOxfP7PZDEEQsGnTJoftWvn998Ybb2D69OnWvzOPP/44lixZgj179qBFixa1xktETJyIyEk7duxAdnY21q9fj2HDhlm3X7hwwe2P/dVXX+GGG27Axx9/bLM9Ly/POunaGa1btwYg/Wp74403WrcbDAZcuHABPXv2rHesll/qlUplndaWiYiIwIwZMzBjxgwUFRVh2LBhWLBggTVxcmZIXHFxMb777jtMnDgRd999t93tjz/+OFavXo0bbrjBOpH+2LFj1cZpeS7Hjh2r8XHDw8MdLvLpTO/A119/DY1Ggy1btkCtVlu3r1y50ma/tm3bwmw248SJE+jVq1eNx5w6dSrmzJmDtLQ0fPHFF7jttttshorWZMKECXj77bdRUFCAtWvXIiEhwTocq7IBAwZgwIABeOmll/DFF1/g3nvvxZo1a5xKfC29TgsWLLBWiqvMEnNeXp7NsMqG9r5Ux2w24/z589ZeJgD466+/AMBaIKFt27b46aefMHjwYJcmRZbP5unTp20+m5ZtlttdzfJ5iImJqfFzm5ubi23btmHhwoWYN2+edbuj4bbVfXYrt2dlzrRn27ZtIYoiEhMTbdqpOt27d0f37t3x/PPPY9euXRg8eDCWL1+OF198sc6PSdSUcageETnF8qtm5V+n9Xo93nvvPY88dtVfxdetW2dTntgZffr0QXR0NJYvXw69Xm/dvmrVKocJgDNiYmIwYsQIfPDBB0hLS7O7vXJFrcrliwHpV+J27drZDPeyrNdSl7i++eYbFBcX47HHHsPdd99tdxozZgy+/vpr6HQ6XH/99UhMTMTSpUvtjm15raOjozFs2DCsWLECqampDvcBpC9x+fn5NsMM09LSrMPk6kIul0MQBJtf3VNSUvDtt9/a7HfnnXdCJpNh0aJFdr/uV32PTJ48GYIgYPbs2Th//rxT62xNnDgROp0On3zyCTZv3mzXU5Wbm2v3eJZEztnhegCsc80qV62zsHyp37lzp3VbcXExPvnkE6cfp67effdd62VRFPHuu+9CqVRi5MiRAKTE0mQyYfHixXb3NRqN9f4c9enTBzExMVi+fLnN67hp0yacPHnSprqiKyUlJUGr1eLll192OGfN8rl19HcQgMOewuo+u1qtFlFRUTbtCcCpv6Xjx4+HXC7HwoUL7WIRRdH6t6WgoABGo9Hm9u7du0Mmk9XrfUrUVLHHiYicMmjQIISHh2PatGl4/PHHIQgCPvvsM48MfxszZgwWLVqEGTNmYNCgQTh69ChWr15d7/lISqUSL774ImbOnIkbb7wREydOxIULF7By5coGz3ECpAnoQ4YMQffu3fHwww+jTZs2yMjIwO7du3H58mXr+lNdunTBiBEj0Lt3b0RERGD//v346quvbCbm9+7dG4DUW5SUlAS5XI5JkyY5fNzVq1cjMjISgwYNcnj77bffjo8++gg//vgjxo8fj/fffx9jx45Fr169MGPGDDRr1gynTp3C8ePHsWXLFgDAf/7zHwwZMgTXX389HnnkESQmJiIlJQU//vgjDh8+DACYNGkSnn76aYwbNw6PP/64tYxzhw4d6jxZ/rbbbsObb76Jm2++GVOmTEFmZiaWLVuGdu3a2SRk7dq1w3PPPYfFixdj6NChGD9+PNRqNf744w80b94cS5Ysse4bHR2Nm2++GevWrUNYWJhTX7qvv/5662PpdDqbYXoA8Mknn+C9997DuHHj0LZtWxQWFuKjjz6CVqvFrbfeWufHsQgNDcXs2bMdFokYPXo0WrVqhQcffBD/+te/IJfLsWLFCkRHR9sltK6g0WiwefNmTJs2Df3798emTZvw448/4tlnn7UOwRs+fDhmzpyJJUuW4PDhwxg9ejSUSiXOnDmDdevW4e2333bY61kbpVKJf//735gxYwaGDx+OyZMnW8uRJyQk4B//+Ierny4AKZl5//33cf/99+P666/HpEmTrK/vjz/+iMGDB+Pdd9+FVqvFsGHD8Oqrr8JgMCA+Ph5bt2512PNu+ew+99xzmDRpEpRKJcaOHYugoCA89NBDeOWVV/DQQw+hT58+2Llzp7VXry7atm2LF198EXPnzkVKSgruvPNOhISE4MKFC/jmm2/wyCOP4Mknn8T27dsxa9Ys3HPPPejQoQOMRiM+++wzyOVy3HXXXS57/Yj8nsfr+BGRz6muHLmlVHJVv//+uzhgwAAxICBAbN68ufjUU09ZSx1XLWnsqBx55RLLFqhSfteRsrIy8Z///KfYrFkzMSAgQBw8eLC4e/ducfjw4Talwy3lyKuW3q2u/O97770nJiYmimq1WuzTp4+4c+dOu2PWxlE5clEUxXPnzolTp04V4+LiRKVSKcbHx4tjxowRv/rqK+s+L774otivXz8xLCxMDAgIEDt16iS+9NJLol6vt+5jNBrFv//972J0dLQoCEK1pckzMjJEhUIh3n///dXGWlJSIgYGBorjxo2zbvvtt9/EUaNGiSEhIWJQUJDYo0cP8Z133rG537Fjx8Rx48aJYWFhokajETt27Ci+8MILNvts3bpV7Natm6hSqcSOHTuKn3/+ebXlyB977DGH8X388cdi+/btRbVaLXbq1ElcuXJltaW4V6xYIV533XWiWq0Ww8PDxeHDh4vJycl2+1nKiD/yyCPVvi7Vee6550QAYrt27exuO3jwoDh58mSxVatWolqtFmNiYsQxY8aI+/fvr/W41X3GcnNzxdDQUIeflQMHDoj9+/cXVSqV2KpVK/HNN9+sthy5oxL3jl53R5/LadOmiUFBQeK5c+fE0aNHi4GBgWJsbKw4f/58u/LvoiiV8O/du7cYEBAghoSEiN27dxefeuop8erVq7XGVJO1a9da2zciIkK89957xcuXL9vsU59y5LXt+/PPP4tJSUliaGioqNFoxLZt24rTp0+3adfLly9bPw+hoaHiPffcI169etXh34HFixeL8fHxokwms2mrkpIS8cEHHxRDQ0PFkJAQccKECWJmZma15cgtZeCr+vrrr8UhQ4aIQUFBYlBQkNipUyfxscceE0+fPi2KoiieP39efOCBB8S2bduKGo1GjIiIEG+44Qbxp59+qvU1I6IKgih64GdiIiIiL/ruu+9w5513YufOnday9ERERM5g4kRERH5vzJgxOHnyJM6ePdugtaeIiKjp4hwnIiLyW2vWrMGRI0fw448/4u2332bSRERE9cYeJyIi8luCICA4OBgTJ07E8uXLbdbhISIicgb/gxARkd/ib4NEROQqXMeJiIiIiIioFkyciIiIiIiIatHkhuqZzWZcvXoVISEhnCRMRERERNSEiaKIwsJCNG/eHDJZzX1KTS5xunr1Klq2bOntMIiIiIiIyEdcunQJLVq0qHGfJpc4hYSEAJBeHK1W69JjGwwGbN26FaNHj4ZSqXTpscl3sJ39H9u4aWA7+z+2cdPAdvZ/7mzjgoICtGzZ0poj1KTJJU6W4XlardYtiVNgYCC0Wi0/uH6M7ez/2MZNA9vZ/7GNmwa2s//zRBvXZQoPi0MQERERERHVgokTERERERFRLZg4ERERERER1YKJExERERERUS2YOBEREREREdWCiRMREREREVEtmDgRERERERHVgokTERERERFRLZg4ERERERER1YKJExERERERUS2YOBEREREREdWCiRMREREREVEtmDgRERERERHVgokTERERERFRLZg4ERERERER1YKJExERURORll+KXeeykJZf2iiPT0TkTQpvB0BERETu99nuFMzfcBxmEZAJwOM3tseorrEQRUAUAbMoQkT5uSjCXGm7tK3KdUDazyxt+/VMFj7fcxEiAEEA/m94W4zt1RwBSrl0UknnCnn9f7NNyy/FhaxiJEYFoVlogKteGo8d3xP84TkQ+SomTkRE5Hae+DLX1L9UlxlMuJJXiks5JbicW4pLudL55dxSXMwqRl6pwbqvWQSWbjuDpdvOuCUWUQSW7TiHZTvO2d2mkAnQKGXWU6BKjiC1CoHliZWmPNEKVMmhKd8WoJTjZFoBvjt8VUrMANw/sDVu6BQDpUwGhVyAUi6DUi5AISs/l8sAswl5OiC7SIcANaBUVNwuCIJNXGv/SMXc9UetieWS8d0xsW8rl7827nwfeeI5+PrngMidmDgREZFbufPLnCiKMJlFrPnjEuZ9d8z6GPPHdsH461vUeF+DwYgyI1BYZoTSVPPjrD94GQu/P+HVL6Q6owlpeWWVEqISXMopP88txbVCndOPGRaghEYphyAAMkGAIMB6WSYIEEUzsouzYDIbIaUZIiAAAkQA5eeCCKNJgVJDqN3x5TI9RFEGsygHyo9gNIso0plQpKv8opc4FbcI4NPdF/Hp7ot12FuB+Qd/cRCbAIVMSrhkAlBQZrTeZhaBp78+itV7UqENUEKjlEGtkENdfq6px7lGKcfmY+lYsumk9X00+6b2GN4hBqV6E8oMJpQaTCjRS+dl+orrBTodinUGlBnM0m0Gc/nJhDK9dF6it31NLc/hreS/EKyREtEAlaNzGbQaFcIDAhCkliNQpUCQSo5Ate15gEqObw5ewbPfuD+5JPJVTJyIiKhBvyKLoohSgwmFZUYUlBpQUGZEYZl0fjm3BK9tPl3+NVv6MvfM10fx86lrUMgFGE0ijGYzDJXPTdK5wWSG0Vxx3Wg2w1i+vfL+VZlFYP6GE5i/4UQdolfg6T+2O/V8LV9Il2w8hSC1wjoELaBS70iAysH1SucaZcUX1wClHD+fysRbP/0FsyglLndd1wLNwzS4VClByigsg2j/dG0EqeRoGRGIFuEBaBEunbeMCIRGIcOMVX/AXOn+ckHApieG1tjeaXlpmLt+LgJUAQhUBVa7X4lejU3HBsGSHAFSUjW6yx8IVOnKh/jJYDLLYDTLYTLLYTLLUKwzoERvxJT+MxCg0qK0PFkoNZiky+XXU3NK8OuZLLvHTYgKhFouh6H8vWE0mWEof88YTSIMZjP0RhPMomB3X5NZSrp1RnO1z+vIlfxqb2soswi8lXwGbyW7p9fPIr1ABxS4/riWz8H2k5loHh6AyCAVwoNU0nmgCpHB0nlYoApymf3rX5PG3nvMXrnaNdbXiIkTEVETV7lHSBCAh4Yk4vpW4VIiVFYpESqVzi3bC8sqrhvNtXyjr0QEsPl4uvuekIfklRpshr+5iigCXx287PA2jVKGlpUSIkuCZNkWFqi0G4JmsWR8dzy7/hhMogi5IODl8d3q/IUlUBWIEE1ItbeHaIBBbVOw+1wCRAgQIGJg2xTEalUAVNXer7BMh+yibAzvEIZmYc2q3S8tvxSDX9lul/h9+fCAGp+DwWDAxo0bccsttwAyhU1ybjSXJ+cmEWn5pZjy3702ialMAF68sxsCVQqUGUzQGc0Oz8sMZuiMFee6Ktct58U6E/Qm+yQtKkiFsCCVNcnWqOQILE+yNUo5IDPiZMF5aJSy8jligFIhnRQKQCkXoVQAOgOwLlkOsXLyKoi4Y7gJCrkAgxHQGwGDETAYAL1R2laqN0FnEBGvaQajCSjWmVCiN1ac603Q15BcbjmRUe1tUgxSz2ZEkMruFB5ov+3nU5k2c/Fc3avl7uGM/jJcsrEPKXUXJk5ERE2I2Szicm4pTqYX4FRaIQ5dysWO09est4si8NGvFwBccPrYMgHQBigRolEgRK2ENkABpVxm11MgAHjshraICFJb56IoZAJUChkU1vkqQqXLMuuQKqW8fFv5bQq5gJwiPW79z682X6plArD9nyMQF6qpNl6jwYDNW7bg5qQkKJTKavdLzy/DjW/ssDv+pw/0R5BaXj50yoRSvbm8p8RYfm623laiN6LUYLYOybJcLzOYkFeiR26JfQI2snMM+rSOsEmSIoNU1SZGtZnYtxWGdYhGSlYJEqICXf5lqENsFuLD8lFQpoFWU4YgteuSymahAQ1K/ARBgFIhg6qaYsIJUUF4xcHxXfllrrrk7/vHh9T4PLLKCvDByZMIVQUhUFF9EgoAYwab8cMuqZCHIABjBsnQI7Hmr3olRjPy9SWY2bkLojRah/sYTGZcyCrGzUt32sQvCMBjI9rCaAZyinXIKTYgp1iH3BIDsot0KCgzQhSB3BIDcksMOHetuMZYqrL0ar3040ko5DIIkNpSVmVIKSCirFSON07/at1WefipdC7AaDLjTGaR3fFX/Z4ClaLhhab1RjNOphfaHX/L8QxEBqkc9jhXntNnSZwdXVeWF1XxRNJR3WOYzKK1N9hmaGml66V6E0oqDTWt2nucW6K3+Z9gFoFn1x/DsA7RjaLniYkTEZGfKigz4HR6IU6lFeBk+fnp9EIU62uZ0AOgQ2wwWoQHSkmQRgGtRokQjZQUWZIjrUaBEI2y/DYFAlVyh1/q1/6R6tYvpDEhGodfqhOigmq8nwFmKGWAWimHUimvdr+EqCCHxx/SPsol8Vf3hfrFO+ueGNRVs9AAt345CVIbXJowVebuxM/dx29o8lcX13WQoW28iJwCIEILaIPql2RXpZTL0CE2xGH8NX2WDSYz8koMyCnWV5xK9Mgp0iO3RG+7vViPrCKdw97ryvPPqicgW1e/MviVkx132H4qs8HHUMqlH5eKHcxj+3DneWti1VAGk9kmwbU8xvPfHnM4NNoVTKKIlKwSJk5EROQaNQ2bMJlFXMgqxqnyXqRT6QU4mVaIK3mOv0SoFDJ0iA1Gpzgtmodq8M7PZ22GKMkFAZ880M9l/8Tc/YXUE4/hzuN74gu1v3B34ufu43vis6ANEqCt+TeDenM2fqVchugQNaJD1HU6/tW8Egz59892vburHxqAqGAVzJay+VXODUYjfv/9dwwYOAhyudy6n1kUARHW61lFOvxz3Z92QzL/fVcPRATV3JtXFznFejz19RGb4wsC8MTI9lAqZDYFPyp6qqXr1h4bS291ec+15bWQ5n06/tHL2Z68+qiaNDmau1l1m01vWvl1vdGMVzefQuWjyQUBCVHVz6H0JUyciIh8XNVhEw8OSURcaABOpRXgVHoh/soorHaCe3xYADrFhaBTsxB0itOic7MQJEQG2aylEx8e4PYv7e7+QuqJx3Dn8T3xhZp8gyc+C+7kzvibhwU6/BFhYNvIGu9nMBhwJQS4vlUYlDUMuwWkHpWqx7+nT0uXPQezKLqsh10URehNZpSVD/u9mF2MSR/tsUv83prYC5FBdUtOa5NdrMMTaw/bPcbXjw5CQmQQAlRyqBWyeg8ZBoCIIGWj/aGIiRMRUQPVdxKtTtTBKEpDUMoMJuQUGZBdrEdOkaF82Ioel7JL8fX+ikIKZuscJFsBShnaxwWhQ1wwOsQFoWNcMDo3C0VMYHCtcfBLu29o7F+oiVyhMfceu/r4giBIZfAVcoRCibhQjcO5eHf0infhM5D+H1V9jOtahbvs+I35fw4TJyKiBqhuEq3ZLCK3RI/s8nH72UV6ZBfpkFWkR3axDplFZbhYkIP8YhMKi4EyvXOP264l0ClBhhaxAlrECIgKB2RCGYAyAFnQATiNAISK/aAWav8lkl/aichXNObeY3cf3x+GPgON938OEyci8nuuKKtqNJmRV2o7yflCVjFe32K7RtHTXx/FK5tOI79UDwdznGukkFvmJwjQBgrQBgtQyoGfD9pOjJYJwKy7ghChrX4ysFE0otRcCqNorFPiREREjYM/DH1urJg4EZHXeXq9iAl9WqJEb3JY5Sm7WI/swjKcPC/Dp1f2IbfEgJwSPfJLDbUuPmqRW1LRfRQWqERUsBqRQSrpPFiFyCA1QoJEZCrPIypYhchgNUKDBASoBYfjxtvHl+G/PxRbn8NDY4JqLLNtYRkGSERERA3HxImIauUrC+EZTWYU6ysWZyzVm1CsN9ou1lhp0cZrBWX46uAV6/2dK6sqA3LzbLZYFnIMD1JJa3Io5dhZZY0imQCsmt4PnZqFIDxIVW2J2GJzMX4vSYFGpoBKqL4UNgDceL0GPdspkZ5jRlyEDJHamvcnIiIi12PiREQ1ciaxEUUROqO5fIFPk81CeY7Kr2YWluGDX87bDXVbd+ASjCY4tXq9MyxJk1ohQ2SQCuFVVq4P1Shw9cJpDO13PWK0ARXbA5Q21egsr0/VSbTDOka7JM7KIrVyJkxERERexMSJiOyU6k24mFOMQxfz8Ow3R+0Smy/3XYJZFK0rgVdOjOo6nK0m+1PyarxdIRMQpFYgSCVHoFpaeDVQJUeQSoFAy3aVAqIoYtWuFJv1ImQCsO5vA9G5mRYBSscLthoMBmwsOYWbu8bWWtq2MVcHIiIiorpj4kTURBWUGZCaXYKU7GJczC5BSpZ0fjGnGBkFuhrve/hSXq3HV8ll0ChllRbDUyCgynVRNOPHI+k2iY0gAAvGdkHzsEBrYmRzrlJApaj7CumdmoXY9Qj1bh1R5/vXBSfREhER+T8mTkSNXHXzj0RRRG6JASnZxbYJUvl5TnHN9a+1GgXiwwJwMr3QZrsgAIvv6IbmYZpKq4IrEKCUQ6OSWVcPrzqkrTpD29sPdavvYoGOsEeIiIiIXIGJE1Ej9uW+VDz3jTT/SABwQ8cYBKrl1gSpsKzmqmpRwSq0jgxC68hAJFQ5DwtUAXA8h6exJTbsESIiIqKGYuJE5ONK9SZcyi1BanYJUnMqTuevFSElu8S6nwhg++lMu/vHaTUVCVFURWLUOjIIwera/wQwsSEiIiJi4kTkdrWV8jabRVwr0kkJUXYJLuaU4FKlBOlaYc3zjaq6r7+U6CREBaFVRCA0yoZXYmNiQ0RERE0dEyciN6payvv+Aa3ROjLIpufoUk4JdLWU2Q7RKNA6MhCtIgLRMkI6D1Yr8I+1h2GuVFlBLgh47MZ2THKIiIiIXIyJE5GLiaKISzml2Hw8DS9vPGXdbhaBT3ZfdHgfuUxA8zANWkXYJkeWk2W+UVVlBpPd/CMmTURERESux8SJqIFEUcSFrGLsvZCDveezsfdCDtLyy6rdv39iOHq1CrcmRa0jgtAsTANlHavQVcaKcURERESewcSJyEmiKCK9BFi97xL2X8zDvgs5yKwyD0kpF9ApTotjV/Jt1iiSCwKWTrrOpQkO5x8RERERuR8TJ6JamM0i/sosxN7zOdh7IRt7z+cgu1gB/HnSuo9KLkOvlmHo3yYCA9pE4vpW4QhQyR2W8maSQ0RERNT4MHGiJq9q1TuTWcTJtALr0Ls/UnKQW2KwuY9SENE7IQID2kahf2IkrmsV5rB6HYfSEREREfkHJk7UpFWueicA6BQXgit5pSiosnBsgFKOPgnh6J8Ygd6tQnH16G7cPqYvlEplrY/BoXREREREjR8TJ2qyTqcX4Jmvj1rnIIkATqYXAgCCVHL0SZCG3fVvE4Hu8aHW4g0GgwEbj3snZiIiIiLyDiZO1OTkFOvx8W/n8fFvF2wKN1i8eGc3TOrbEop6VLkjIiIiIv/ExImajKwiHT769Tw+230RJXqTw33kgoCRnWOYNBERERGRDSZO5PcyC8vw0c7z+HxPKkoNUsLULV6Lx29sj+xiPZ7/hlXviIiIiKhmTJzIb2UUlGH5L+fwxd5U6IxmAEDPFqF4fGR73NgpBoIgAABGdGTVOyIiIiKqGRMn8jtp+aV4f8c5rPnjEvTlCdN1rcIwe2R7DO8QbU2YLFj1joiIiIhqw8SJ/Mbl3BK8v+Mc1u2/DL1JSpj6JoRj9sgOGNwu0i5hIiIiIiKqKyZO1OilZpfgvR1n8dWByzCapTp5A9pE4PGR7TGwDRMmIiIiImo4Jk7UaKVkFWPZz2ex/tAVmMoTpiHtovD3G9uhf5tIL0dHRERERP6EiRM1OueuFWHZ9rP49vAVlOdLGNYhGrNHtkPv1hHeDY6IiIiI/BITJ/JpafmluJBVjMSoIBSVGfHuz2fx/Z9XrQnTjZ1i8Pcb2+G6VuHeDZSIiIiI/BoTJ/JZa/9Ixdz1R61JUmWjusTi8Rvbo3uLUM8HRkRERERNDhMn8klp+aUOk6YRHaLxr5s7omtzJkxERERE5DkybwdA5MiFrGKHPU0zh7dl0kREREREHuf1xGnZsmVISEiARqNB//79sW/fvmr3NRgMWLRoEdq2bQuNRoOePXti8+bNHoyWPEUpty8hLhcEJEQFeiEaIiIiImrqvJo4rV27FnPmzMH8+fNx8OBB9OzZE0lJScjMzHS4//PPP48PPvgA77zzDk6cOIG//e1vGDduHA4dOuThyMmdSvUmzPvuhM02uSDg5fHd0Cw0wEtRERFRbbT6QiQUXoJWX+jtUIiIXM6rc5zefPNNPPzww5gxYwYAYPny5fjxxx+xYsUKPPPMM3b7f/bZZ3juuedw6623AgAeffRR/PTTT3jjjTfw+eefezR2cg9RFPHcN0dxMq0AUcEqrJjWF8V6ExKiApk0ERH5sOuyjmJs6jbIIMIMAd+3ugmHorp5OywiIpfxWuKk1+tx4MABzJ0717pNJpPhpptuwu7dux3eR6fTQaPR2GwLCAjAb7/9Vu3j6HQ66HQ66/WCggIA0rA/g8HQkKdgx3I8Vx+3KVm97xLWH7oCuUzA0gk90DkuyHqbr7yubGf/54k2NpqNEI0iRJkIs2B2+fFFUYRoFmE0GGGQ8b3qCD/LtTMZTVAICuj0Osght25XmI2I1eWimS4bzcpy0KIkEwllGbAMspZBxNjUn3BcFY08VUi1x9fpdVAICpiMJre0gz+0sdFohMwMCGZAMDmY/NtAghmQmaXHaayvkz+0M9XMnW3szDEFURRd/ymsg6tXryI+Ph67du3CwIEDrdufeuop/PLLL9i7d6/dfaZMmYI///wT3377Ldq2bYtt27bhjjvugMlkskmOKluwYAEWLlxot/2LL75AYCDny/iSC4XAO8flMIkC7mhtwo3NvfLWJCIiC1FEgCEb2tJLCC29BG1pKrSllxCsS4eA2v9Glyq0SI0cgcsRg1Ckae6BgImInFNSUoIpU6YgPz8fWq22xn0bVTnyt99+Gw8//DA6deoEQRDQtm1bzJgxAytWrKj2PnPnzsWcOXOs1wsKCtCyZUuMHj261hfHWQaDAcnJyRg1ahSUSqVLj+3vsop0ePm9PTCJOtzcNRavTewBQbAvEOEL2M7+zxNtXGIuwZ7SPdDINFAKrn8Mg2hAmbkMAwIGIFDGH4kcaZKf5YKrEHLOQYxoC2irJDK6QgjXTkHIPA5knoBgOekKHB7KpAmDIaojDJEdYAyKQ9iu122SKRFAgLEAHTM2oGPGBuhjuqKk41iUtL8F5sAo634BqgBoA1z7/9jCH9o4W1eIlac3Q6sKQqBcVev+gSXZ0BaloyA4DiWBkbXuX2LSo0BfjBkdb0akuvreQV/mD+1MNXNnG1tGo9WF1xKnqKgoyOVyZGRk2GzPyMhAXFycw/tER0fj22+/RVlZGbKzs9G8eXM888wzaNOmTbWPo1aroVar7bYrlUq3fbjceWx/ZDSZ8Y91R5FRqEO7mGC8PqEXVCrfz+nZzv7PnW2sMCsgGAQIMgEywfV1egRRgGAWoFAqoJTxfVqTJvNZPvgp8P1sQDQDggzoPQMIigIyjgMZx4DcFMf3kymB6I5AbNdKp26QB8dCLgiwDqCPagV8/wQgmgBBDuGWV4HAcODI/4CzP0GVeRyqzOMI++01oO0NQI+JQKfbAFWQ48d1ocbcxgqTAmYZIMoA0UHF2cran9uOwfs+tM4z+73fIzjT9sYa7yOKgFkGKBSKRvsaWTTmdqa6cUcbO3M8r307ValU6N27N7Zt24Y777wTAGA2m7Ft2zbMmjWrxvtqNBrEx8fDYDDg66+/xoQJEzwQMbnLa1tOY8/5HASp5Fh+X28Eq30/aSIiahRKc4GME0DK78COlwFLj5BoBvZ/bL9/SDOb5AixXYHI9oCi9p4OXD8VaDsSyDkPRLQBQuOl7d3uAoqzgGPrgSNrgSv7gbM/SSdlENB5DNBjApA4ApDz7391ykz6Gm+PyEvFkH0fWnv9ZBAxZN+HMOuLkRXeGoWBUSjWhEGUyW3uV9txiaiCV/9CzZkzB9OmTUOfPn3Qr18/LF26FMXFxdYqe1OnTkV8fDyWLFkCANi7dy+uXLmCXr164cqVK1iwYAHMZjOeeuopbz4NaoBNR9Pwwc7zAIDX7+mJdjHBXo6IiKgRMhmB7LNSz1HG8YpTweWa79fuJukU2xWI6QoE1T60q0ah8RUJU2VBUUD/R6RT9jmpF+rIWiD3gnR+ZC0QFAN0v1vqiWrWE/DGcO38K0DOOSCirePn4QUauRJh6mDk6YqgM1WaxC6KiCpMQ/v0o2ifcRTxOedQ9RUTIGL44Yqqw2ZBhgJNGPIDI1EQEI78wAjkB0TCFBqPgNwUILIdoKplaK8PvkZEnuLVxGnixIm4du0a5s2bh/T0dPTq1QubN29GbGwsACA1NRUyWcUQlrKyMjz//PM4f/48goODceutt+Kzzz5DWFiYl54BNcTZzCI8ue5PAMDMYW1wS/dmXo6IiKgRKM6SEqT0YxXD7K6dBkyOiyQhtJXUA3ThF6ByQQdBDoz9j+e//Ea2BW6YC4x4Bri8X0qajn0NFGcCe96TTlEdpV6o7vcA4a09E1fVoYxj35Z60bwsWBmAae1HocxkAEx6KFP3QHV2G1Rnf4I8P9VmXxGwSZ5ECDA0vw7y4izICq9CZjYirDQHYaU59g+081XpPDAKCG0BhLWU3jthLaXroS2B1N3Almd97jUi8hSv94nPmjWr2qF5O3bssLk+fPhwnDhxwuG+1LgU6YyY+dl+FOtNGNAmAv9K6ujtkIiIvKvqL/lGHZD1V0VyZOlFKspwfH9VMBDTpcpQuy6AJlS6/eCnNnOQMHapd3sMBAFo2Vc6Jb0MnNsmJVGnNwFZp4Hti6VTq0FSEtX1TkBfUvfeDqMeKM5GUFk6hKuHAFMJUFYAlOUDugLby4Xp0uNbiGZgw+PSBKDEYUBYa0Dm+rmIdVKcheAzyQj+axNwdjtQeXFhuRpoMxzocDPQIQnCue2288zGLoXKktiYTdJ7J+8SkF9+yqtyri8CSrKkU9rhmuMSzVKiGdMFaNHHXc+e/FUj7bn0euJETY8oinjqqz9x7lox4rQavDP5eijkXvqHRESe0Uj/SbqdKAIlOcDe94Gdr8PaIxTSDCi+BpiNDu4kSD1IlechxXat/ct9dXOQfIFCBXS8RTqV5QMnv5eSqAu/Aqm7pNOP/5QSAgCAIBWYCG0hJUC68iSo8mVjGZQAbgKAk/UJSgS+f1y6WFtS6kqiCFw7JSWQf20GLu2DTU9hcCzQIUlKltqMsC2uUVMby+RSJUVtcwD9HT9uWV55EnW5PKFKLU+yLktDLMvyqtzHDPx3pNQz1XoQ0Hog0HqwNOTPRyvjkpeZzcDvS4FtiwCIja7nkokTedx/f72AjUfToZQLWHbv9YgOsa96SER+xEeHQHmEyQgUplX6VT9V+hJq/dX/MmAosb9fYZp0rgkFYrvbfmGP6VT/SnTVzUHyJZpQ4Lr7pFP+FeDYV8DBz4HsvyrtJALnttfpcEaZBvKgCAiaUOnYGi2g1tpeFkWpd8tmbSpBqiaYc17qibm8TzpVFtbKNnmN7SYlLFUKMNQepB64+BtwerOULOVdtL09roeUKHW8GWh2Xc0Jcn3bWBCAgHDp1KyH/e35V4Cl3aTPse0dpff1kVTgyBppU1A00GqglEy1GgjEdXf+NaHGryxfKk5jM/fymO3fPNEs9ZK2Hen7f5vAxIk8bPe5bLyy+RQAYN6YLujdOtzLERE1DqqCa9DkXkFZeDz02mhvh+OYyQAUXLH9xTrzJHB8fcU+liFQqXuAmM7SvImwltJ5ULRv/0rtqNdMX1wlEbpk+/wLrlbqJXHS3SuBruN8+zVxt9B4YPBsoPl1wCdj7W/vdZ/0JV+tlZIgTajNZYMsABs3b8Gtt95ae8nh4Gj7oYzXT5Xe19nnKn35Kz8vuCL1yOSlAqc3VhxHoZHe2zY9gt2AwAjpdsv7KCAcSD8q9Syd+7mGIXg3+8YXytB46UePqq9R13FSr9jFXdIcqMv7pd7SkxukEyC1Sct+5YnUICD+ekDhxR9N2QPuWmZTlc9I+anKHLxqiSbpB4pG0BZMnMhj0vPL8PcvD8JkFjH+unjcN8BDE36JGrnY/d8iYdtyCBAhQkBGr9uQ03EITOogGDVBMKmlkyhvwNoWdfkioSuqueekMM3Br9GOiMDh1fabFZrySejlE9HDWknnlonq2nigpufY0C9DoggYSh3MgckH/toqDR2z9EiEtpDm2ziaZF+VTFlpsn3LSsli+fMUZMA719u+doIcaNm/aSdNlUW0lV6nqq/RDc/W3NYGQ/W3VVXdMDe5Uurli+kkVf2zKMkBMk/YJlOZJ6Vf068ekk6VhTQDAsKAzFOw7dkqZx2Cd4uUNHlgfSunVfcatRspnQBpbt6Vg9Lwyou7pR9JdAUVJegBKTFs0aeiV6plP6Dy4ruu+Czri+3nspXlA39tAY6ug7WURp8ZQOexgLpKj6RSU9ujVM/diZknEr/qHqM4G8g8XqU4zSnAWOb4ONoWtmvABUUDn91p/1mOqH5NVl/CxIk8Qm804/9WH0BWkR6dm2nx0rjuEPiFgAhG0dEcFomyJB+tfv8SzQ/9aK2UJUBE3OEfEHf4B7v9TQoVjOVJlEEdCIM6AOrAVoAmotKv8aFVfpkPlb7MbFtYaWHU6dI/MWvPSap0uer8Bkfk6kpJQgtAHQbsWQbbam4C0PsB6XiW5KswTfrHm31WOjkkSF8+rQlIpcpfVw8Bv7xS8RyG/hNIGGI//6XSFyh5aR6GZ16GImV+xXaHc4ocyK9U5lutte05qxxXaAvpC3FthQUc/ZLfCH599Zjqejtc/Ro5M8wtMEJ6jyUMqdhmNkkLCVcdmpSbIr3HLUMwKxvwqFQ9sLYheL6ittdIoS6f6zQQGArpNck4JvVIWXqliq8BF3+XTr9Cas9mPaTeKJMO2L+i4rM8ZI70GjtKgso/3/LSXOmzfGGe9ENHWUEde3pF6bH2r7C/Sa522ItZMcwz1H7Ip+Xv6fbFFfHf+oY07NRVDn0ObPyn+45f9TEgAO1ulM4zjjt+DwOAMrDSPEBLT2sXqWe1qkb8904QRdHBzx7+q6CgAKGhocjPz4dWq3XpsQ0GAzZu3Fi3IQFNzLzvjuHT3Reh1Sjw/d+HoHWkD/6SVkdsZ//niTbWiTrsK92HUnOp3W3qwmwk/LEBLY78BLnR8eKUpdpoCGYTFLoSKAzV/NLnDpow2yTB2nNSXrY4MMr+y5+jam5V5zgZ9dLQJ+ukdAdD36ort+1qgsz2C5EoSl/8qhr7DtD1DtcVCMi/4puFG3yJk6+Rz/y91hUCh78ENv3L/rZpPwCJQz0fk7eIovTjiDWR2iUNd3QHQWab+IgAMo7a7xeWIP1ooiv/oYVqFp5oX5wmPNG5xN+HPsvO5AbscSK3W3/wMj7dLU10XTqpV6NOmohcRS2o0S+gn02Pk5CTAuWuZVAc/h8EszTEyBTbGbKMUxAq9diIghziA5tg1jaHCYDObAR0hRDKCiGU/yor6AogLyuCUlda6ddZ+19qUXxNul5VwjBpHkLlnpOwlrbDaeqqLtXcFCogIlE6OSKKUqyOhglmnpQWUq0qtBWgbVbNL8ZaGJXB+OPIX+g7dCQUQREVyZIqyHaInKNJ8YJcGprkyqpqjaFwg7c11tdIHQJ0ug3Y/HSjHaLkMoIARLWXTr2nSdvyL0vD+o59Dfy1yf4+oa2kaoDV9AAZlUG2n2XLPnX9LM/YWPG+MpukRNeuh6vS0N3qytoXXQPKct332nnTgMekJQFiOtfv/0BVjfSzzMSJ3OrE1QI8+430687jI9vjxk6xXo6IyHeoBTXUglqqOvTbm9KXBss/9NaDgaH/hLztjcChz+zWZgkMa19xIBkARShQn98kqvsiMW65a/+pNfSfpCAAwTHSqUVv29uqew4PbK7xMUWDAZkXN0Js0Q+o6RdMTw0TI//G91H1QlsAPe6R5jud2eLdz7JMLs1FCwhz/nlU97fo0V3SjzgNVZAGvD/Qfcev6TEGPsb3Kpg4kRvllxjwt88PoMxgxvAO0Zg9sn3tdyJqSq4cBH59AzhVab5Su1HS/JzWAyu2uXP9HX/4MueJ5+DLayBR48H3Uc0a+2e5uvhjOrnm+JpQ9x6/psfgexUAEydyE7NZxJz/HUZqTglahAfg7Um9IJexGAQRRLF8QvQbldahEYAut0uToJv3cnw/dw5r8Icvc554Do10aAn5GL6PatbYP8vujt8Tr48//E9wEyZO5BbLfj6LbacyoVLIsPy+3ggLVHk7JCLvEkWp2tLO14FLe6RtghzoMQEY8g9poU1v8ocvc/7wHIio8X+W3R2/J16fxt4GbsLEiVxux+lMvPmTtML7i3d2Q7d4F06eJmpszGZpEchf3wDSj0jb5CqpfOzg2UB4glfDIyIiorph4kQudSmnBLPXHIYoApP7tcKEPi29HRKRd5gMwNGvpKIPWdIPCVAGSYstDpzluom8RERE5BFMnMhlygwmPLr6APJLDejZIhQLbu/i7ZCIPKPyCuuBkcDh1cDvSyvWJtGEAv1mSgtdBkZ4NVQiIiKqHyZO5BKiKOKFb4/h2JUCRASp8N59vaFWyL0dFpH7HfwU+H52xQrr6pCKBRSDoqUSrn0elNYcISIiokaLiRO5xJo/LmHdgcuQCcA7k69DfFiAt0Mici9LdbwNjwPWxWlFKWkKjgOGzgGuux9QBXozSiIiInIRJk7UIGn5pfjpRAYWfX8CAPBkUkcMbhfl5aio0ak81M1Xq/iYTUDGMeDiLumUuhsovuZ43zuXA+1u8Gx8RERE5FZMnKje1v6Rirnrj8Jc/mN7l2ZaPDq8rXeDosan8lA3QSYtvHf9VG9HBZnZAOHSHuDKPilRurSvYgiedScVYNbbbhPkQHQHzwVKREREHsHEieolLb/UJmkCgFPpBUgvKEOzUA7To1qIorSw3qkfgeQXKm03S0Pfrv0FxHYFQlsAYS0BbTwgV7o3Jl2hlBxd3AX5xd9x66X9kP9psN1HrQVa9gdaDwRaDwaaXwccWcsV1omIiJoAJk5ULxeyim2SJgAwi0BKVgkTJ7JnNgOZx4GLu4HU8qFuRRnV7CwCu9+psk0AQppJSVRoy4qEKrRV+XkLqShDdRwNBSzOkobbXdwtzVVKPyolPwBklkiCoiG0Kk+SWg8EYrsBsipFT7jCOhERUZPAxInqJTEqCAIqpsQDgFwQkBDFifAEwKgH0g5XzAe6tAcoy7fdR66SEpGrh2D7ThKArncCpblA3iUg/zJg0gGFV6XTpb2OH1MTVimxalmRUKUfk9ZSslS9az1ISpqyTtsfI6wV0HowjPH98MsFPYaNewBKlar258sV1omIiPweEyeql2ahAUiICsKFrGIAUtL08vhu7G1qqvTFwOU/KhKly/sBY6ntPqpgoGU/oNUgKXmJ7w0oNeVznJ6wHepWeY6T2QyUZJUnUakVyVT+pfLLl4CyPOmUnif1HFWrvBKeRXRnqSep1SDpPLSFtJfBgKK0jYAguOTlISIiosaPiRPVS3aRDhezpaTpP5N6oW9iBJMmf1Z1qFtJDpC6p2LYXdqfgNloe5+ACClBaj0IaDUQiOsByB38yaltqJtMBgTHSKcWvR3HV1ZQnkxdrpRcXQIyjgPXTtnvf8PzQJ8HgKDI+r0eRERE1OQwcaJ62XYyE2YR6Npci9t7cYiSX6u6wGtIHFCYZr+ftkV5olTegxPdse49Ng0d6qbRApouQGwX2+35V4Cl3cpjLyfIgV5TmDQRERGRU5g4Ub1sOZ4OAEjqGuflSMitTm8GNvy90gaxImmK6iD1JFkKJ4S18kqINQqNl8qbs+odERERNRATJ3Jakc6IX89mAWDi5JdEEUj5Fdj5OnDhF8f7TPgc6DLWs3HVF6veERERkQswcSKn/XL6GvRGMxIiA9EhNtjb4ZCriCLw1xbg1zeAy/ukbYLcWqLbSpAD8dd7Pr6GYNU7IiIiaiAmTuQ0yzC90V3jILDqWONnNgEnvgN+fRPIKK9IJ1dLPTWDHwfO7+BQNyIiImrymDiRU/RGM34+lQkASOoa6+VoqEFMBuDI/4Df3gKyz0jbVMFStbmBs4CQ8vblUDciIiIiJk7knF3nslCoMyI6RI3rWoZ7OxyqD0MpcOhz4Pe3pZLdgLR47IBHgX6PAIER9vfhUDciIiJq4pg4kVO2HM8AAIzqEguZjMP0GhVdIbB/BbDrXaBY6jVEUAwwaJbUy6QO8W58RERERD6MiRPVmdksIvmElDixmp4Tqi4e62klOcDeD4C9y4GyPGlbaEtg8GzguvsAJRcuJiIiIqoNEyeqs0OXcpFVpEOIRoGBbbh4aJ3s+wjY9C+pYp0gA27+N9D/Ec88dmEGsPtdqZdJXyRti2wHDJkD9JgAyJWeiYOIiIjIDzBxojqzDNO7sVMMVAqZl6PxUSU5QOpu4OIuqRpdxrGK20SzlERtWwSEt5Z6fUJbAGEtpcthraTrQTGArAGvb16qNH/p4GeASSdti+0ODJ0DdLkDkMkb9BSJiIiImiImTlQnoihay5BzmF4l+VfKE6XfgYu7gWsna7+PvlBKqConVZXJVVICFdoCCG1Vnli1KE+uWgLaeEChto0h55zUo3X4C+DIWsBslG5r0RcY+iTQIQlg6XgiIiKiemPiRHVyOqMQF7NLoFLIMLxDtLfD8Q5RBLLPQbjwK667uA6KZS8AeRft94vqALQeBER3BrbMlXqaLAQ5cP83gEkv9QzlX5Yq2+Vdki4XXpVuyzkvnRwSgOBYKYkyG4GrhwGItrskDgeGPQkkDGXCREREROQCTJyoTrYck4bpDWsfhSB1E3nbmE1Sr9DF3UDqLum8OBMKAK0s+wgyIK6HlCi1HgS0GggERVUcQxVov3hsm+HVP6bJABRcrZJQXbJNroylQFG6dHJk8hqg4y0ueQmIiIiISNJEvgFTQ1mG6Y3u4ofD9CxD3UJbAkUZ0vyki7uAS3sBXYHtvnI1zM2vwxl9DNrecB8UCQMBjbb6Yzu7eKxcKc1/Cm/t+HZRBEqypd6qM1uAHa/Y76MKrvkxiIiIiMhpTJyoVpdySnAirQAyARjZOcbb4bjW/pXAD/+A3VA3C1UI0Kq/1JPUejDQ/DqYIMepjRvRpu2NgLIOlelcuXisIEg9WkFR0nC9X161HwoY0cY1j0VEREREVkycqFaW3qa+CRGIDFbXsncjkn/FcdLU7iapl6j1ICC2GyCv8jExGDwWYo1C44Gxb9sPBfTGWlFEREREfo6JE9Vq63E/XfQ2dTcc9jQNfgJIHOrpaOrH2aGARERERFQvTJyoRllFOvxxMQcAMLprrJejcbGj6+y3Ncahbq4cCkhEREREDnEVU6rRtpMZEEWgW7wWLcIDvR2O61zcBfy1GYAgVcYDONSNiIiIiKrFHieq0RbLMD1/qqZnNgGbnpYu954GDHuKQ92IiIiIqEZMnKhaRTojfjuTBQBI6uZHidPh1UD6EUCtBW54HgiOZsJERERERDXiUD2q1o7TmdCbzEiMCkL7GD9ZG6isANi2SLo8/GkpaSIiIiIiqgUTJ6qWZZje6K6xEATBy9G4yK+vA8XXgMh2QL9HvB0NERERETUSTJzIIZ3RhJ9PZQIARvvL/Kbsc8Ce96XLo18CFCrvxkNEREREjQYTJ3Jo17lsFOmMiAlR47qWYd4OxzW2vgCY9EDbG4EOSd6OhoiIiIgaESZO5NDW4+kAgFFdYiGT+cEwvfM7gNM/SiXHk5YA/jL0kIiIiIg8gokT2TGZRSSfKC9D3tUPhumZjMDmudLlvg8BMZ28Gw8RERERNTpMnMjOwdRcZBXpEaJRYECbSG+H03AHVwGZJ4CAcGDEM96OhoiIiIgaISZOZMcyTG9kpxioFI38LVKaC2x/Sbo84lkgMMK78RARERFRo9TIvxWTq4miaC1D7hfD9Hb8GyjNAaI7A30e8HY0RERERNRIMXEiG6fSC5GaUwK1QobhHRv54rDX/gL++Ei6fPPLgFzh3XiIiIiIqNFi4kQ2tpQP0xvaPhqBqkaeaGx5FjAbgQ63SCXIiYiIiIjqiYkT2agYphfr5Uga6K+twNlkQKYEkl7ydjRERERE1MgxcSKrSzklOJlWAJkAjOzciBMnk0HqbQKA/jOByLbejYeIiIiIGj0mTmRlGabXLzECEUEqL0fTAPs+ArLPAIFRwPCnvB0NEREREfkBrydOy5YtQ0JCAjQaDfr37499+/bVuP/SpUvRsWNHBAQEoGXLlvjHP/6BsrIyD0Xr3yyJU6OuplecBex4Rbo88gVAE+rdeIiIiIjIL3g1cVq7di3mzJmD+fPn4+DBg+jZsyeSkpKQmZnpcP8vvvgCzzzzDObPn4+TJ0/i448/xtq1a/Hss896OHL/c61Qh/0XcwEAoxtz4vTzS4AuH4jtDlx3v7ejISIiIiI/4dXE6c0338TDDz+MGTNmoEuXLli+fDkCAwOxYsUKh/vv2rULgwcPxpQpU5CQkIDRo0dj8uTJtfZSUe1+OpkBUQS6x4ciPizA2+HUT/ox4MAq6fItrwAyuVfDISIiIiL/4bV603q9HgcOHMDcuXOt22QyGW666Sbs3r3b4X0GDRqEzz//HPv27UO/fv1w/vx5bNy4EfffX33Pgk6ng06ns14vKCgAABgMBhgMBhc9G1iPWfm8Mdl8NA0AcFOn6EYZP0QR8s3PQCaaYe50O0zx/QE3PY/G3M5UN2zjpoHt7P/Yxk0D29n/ubONnTmm1xKnrKwsmEwmxMbaVm+LjY3FqVOnHN5nypQpyMrKwpAhQyCKIoxGI/72t7/VOFRvyZIlWLhwod32rVu3IjAwsGFPohrJycluOa67lBmB387KAQjQZJ3Cxo2OX39fFpd3AP1TfoVJUGKbbDhKN250+2M2tnYm57GNmwa2s/9jGzcNbGf/5442LikpqfO+jWqF0x07duDll1/Ge++9h/79++Ps2bOYPXs2Fi9ejBdeeMHhfebOnYs5c+ZYrxcUFKBly5YYPXo0tFqtS+MzGAxITk7GqFGjoFQqXXpsd/rxaDpMfxxBYmQgZtw1GIIgeDsk5xh1UHwwT7o8cBZuuGGaWx+usbYz1R3buGlgO/s/tnHTwHb2f+5sY8totLrwWuIUFRUFuVyOjIwMm+0ZGRmIi3NcnOCFF17A/fffj4ceeggA0L17dxQXF+ORRx7Bc889B5nMfsqWWq2GWq22265UKt324XLnsd3hp1PXAABJ3ZpBpWqEZcj3vgvkpQDBcZAPfxJyD732ja2dyXls46aB7ez/2MZNA9vZ/7mjjZ05nteKQ6hUKvTu3Rvbtm2zbjObzdi2bRsGDhzo8D4lJSV2yZFcLhUAEEXRfcH6MZ3RhB2nyxOnro1w0dvCDGDn69LlmxYA6mCvhkNERERE/smrQ/XmzJmDadOmoU+fPujXrx+WLl2K4uJizJgxAwAwdepUxMfHY8mSJQCAsWPH4s0338R1111nHar3wgsvYOzYsdYEipyz62w2inRGxISo0bNFmLfDcd72RYC+CIjvDfSY6O1oiIiIiMhPeTVxmjhxIq5du4Z58+YhPT0dvXr1wubNm60FI1JTU216mJ5//nkIgoDnn38eV65cQXR0NMaOHYuXXnrJW0+h0bMseju6ayxkskY2t+nqIeDQaunyza8ADoZqEhERERG5gteLQ8yaNQuzZs1yeNuOHTtsrisUCsyfPx/z58/3QGT+z2QWkXxCmmOW1NgWvRVFYNMzAESg+z1Ay37ejoiIiIiI/Bh/om/CDlzMRXaxHlqNAgPaRHo7HOccXw9c2gMoA4Gb7MvNExERERG5EhOnJswyTG9k51go5Y3orWAoBZLLex0HPwGExns1HCIiIiLyf43o2zK5kiiK1sSp0VXT2/UOkH8J0LYABv3d29EQERERURPAxKmJOpFWgMu5pVArZBjWIdrb4dRd/hXgt7eky6MWAqpA78ZDRERERE0CE6cmautxqSjEsA7RCFR5vUZI3f20ADCUAC0HAN3u8nY0RERERNREMHFqoiqG6TWianqX/gCO/g+AANzyCiA0svLpRERERNRoMXFqglKzS3AqvRBymYCRnWK8HU7dmM3A5qely73uBZpf5914iIiIiKhJYeLUBFl6m/olRCA8SOXlaOroyFrgygFAFQyMnOftaIiIiIioiWHi1AQ1ump6uiJpbhMADP0nENJI4iYiIiIiv8HEqYm5VqjDgdRcAMDoxjC/Kf8K8MM/gKJ0IDwBGPB/3o6IiIiIiJqgRlROjVwh+UQGRBHo0SIUzcMCvB1OzQ5+Cnw/GxDN0vV2NwFKjXdjIiIiIqImiT1OTUyjqaaXf8U2aQKA/Sul7UREREREHsbEqQkpKDNg17ksAI1gflPGcdukCQBEE5Bz3jvxEBEREVGTxsSpCfn5VCYMJhFtooPQLibE2+FUT18C7HzNfrsgByLaeD4eIiIiImrymDg1IVtPZADw8WF6hlJgzWTg8j5ArgaE8reoIAfGLgVC470aHhERERE1TSwO0USUGUzYcSoTgA8nToYyYO19wPkdgDIIuH89ENpSGp4X0YZJExERERF5DROnJmLXuSwU602I1arRIz7U2+HYM+qB/00Fzv4EKAOBe9cBrQZItzFhIiIiIiIv41C9JmLLMWmY3ugucZDJBC9HU4XJAHw1AzizBVBogMlrgITB3o6KiIiIiMiKiVMTYDKL+Omkj85vMhmBrx8ETv0gzWma/CXQZri3oyIiIiIissHEqQnYn5KD7GI9QgOU6N8mwtvhVDAZgW8eAU58B8hVwKTVQNsbvR0VEREREZEdJk5NwJbjUm/TyE4xUMp9pMnNJuC7/wOOfQ3IlMCET4H2o7wdFRERERGRQz7yLZrcRRRFbDmeDgAY7SvD9MxmYMPfgSNrpTLj96wEOt7i7aiIiIiIiKrFxMnPHb9agCt5pdAoZRjeIdrb4UhJ0w+zgcOrpaTp7o+BzmO9HRURERERUY2YOPm5rw9cBgD0S4hAgEru3WBEEdj4JHDwU2lh2/EfAl3HeTcmIiIiIqI6YOLkx9b+kYqVu1IAAL+eycLaP1K9F4woApueBvZ/DEAA7lwOdL/be/EQERERETmBiZOfSssvxdz1R63XRQDPrj+GtPxSzwcjisDW54F9H0jX73gX6DnR83EQEREREdUTEyc/dSGrGGbRdptJFJGSVeLZQEQR+GkBsPtd6frYt4Hr7vNsDEREREREDcTEyU8lRgVBqLJNLghIiAr0bCA/vwT8vlS6fOvrQO/pnn18IiIiIiIXYOLkp5qFBmB4x4oqenJBwMvju6FZaIDngtjxb2Dna9Llm/8N9HvYc49NRERERORCCm8HQO4TEagCAEzu1xKPj2zv2aRp5+vAjpely6NfAgb8zXOPTURERETkYuxx8mNp+WUAgAFtIj2bNP3+NrB9sXT5pgXAoFmee2wiIiIiIjdg4uTHLBX04rQazz3o7mVA8jzp8g3PA0P+4bnHJiIiIiJyEyZOfkoURWuPk8d6m/Z+CGx5Vro8/Glg+L8887hERERERG7GxMlP5ZUYoDOaAQAxWrX7H3D/CmBTeaI0ZA4wYq77H5OIiIiIyENYHMJPWXqbIoNU0Cjl7nug/CvA3veBXe9I1wc9DoycBwhVi6ETERERETVeTJz8VHqBNL+pWZgb5zcd/BTY8DiA8pV224wARi1i0kREREREfodD9fyUpccpTuum+U35V4DvZ8OaNAHAhV+BgqvueTwiIiIiIi9i4uSn0q2FIdzU45RzDhDNtttEE5Bz3j2PR0RERETkRUyc/NTVvPIeJ3clThFt7bcJciCijXsej4iIiIjIi5g4+SnrHCd3JU7qEECoNEVOkANjlwKh8e55PCIiIiIiL2JxCD9lnePkrsTp9CZANALhicDt/5F6oJg0EREREZGfYuLkh0RRtM5xau6uxW+PfS2d95gIJA5zz2MQEREREfkIDtXzQwVlRpToTQDc1ONUmguc2y5d7jbe9ccnIiIiIvIxTJz8kKW3KTxQ6Z7Fb0/+AJgNQExXILqj649PRERERORjmDj5obR8qTBEnLuG6R1fL513G+ee4xMRERER+RgmTn4ozZ1rOBVnAed/kS535TA9IiIiImoamDj5IbdW1Du5QVrotllPINLBWk5ERERERH6IiZMfSi8fqtdM64bE6Vj5MD32NhERERFRE8LEyQ9Zh+qFuXiOU2E6kPKbdLkr5zcRERERUdPBxMkPpbtrjtOJ7wCIQIu+QHhr1x6biIiIiMiHMXHyQ+numuPEYXpERERE1EQxcfIzhWUGFOqMAIA4V85xyr8MXNoDQAC63um64xIRERERNQJMnPyMpbdJq1EgSK1w3YGPfyudtxoIaJu77rhERERERI0AEyc/U7GGk4sLQ1gXveUwPSIiIiJqepg4+RlrYYgwFw7Ty00BrhwABBnQ5Q7XHZeIiIiIqJFg4uRn0txRUc9SFCJhKBAc47rjEhERERE1Ekyc/Ex6gbT4bZzWhUP1OEyPiIiIiJo4Jk5+xuU9TllngfSjgEwBdL7dNcckIiIiImpkmDj5mbQ8F6/hZOltajMCCIxwzTGJiIiIiBoZn0icli1bhoSEBGg0GvTv3x/79u2rdt8RI0ZAEAS702233ebBiH1XWr40VM9lPU5c9JaIiIiIyPnEKSEhAYsWLUJqaqpLAli7di3mzJmD+fPn4+DBg+jZsyeSkpKQmZnpcP/169cjLS3Nejp27Bjkcjnuuecel8TTmBXrjCgoK1/81hWJU+ZJ4NpJQK4COjExJSIiIqKmy+nE6YknnsD69evRpk0bjBo1CmvWrIFOp6t3AG+++SYefvhhzJgxA126dMHy5csRGBiIFStWONw/IiICcXFx1lNycjICAwOZOAFIL5CG6YWoFQjRKBt+QEtvU9uRQEBYw49HRERERNRIKZy9wxNPPIEnnngCBw8exKpVq/D3v/8d//d//4cpU6bggQcewPXXX1/nY+n1ehw4cABz5861bpPJZLjpppuwe/fuOh3j448/xqRJkxAUFOTwdp1OZ5PYFRQUAAAMBgMMBkOdY60Ly/Fcfdy6upxdBACI1aobHoMoQnHsKwgAjJ3vgOil5+SLvN3O5H5s46aB7ez/2MZNA9vZ/7mzjZ05piCKotjQB3vvvffw9NNPw2AwoHv37nj88ccxY8YMCIJQ432vXr2K+Ph47Nq1CwMHDrRuf+qpp/DLL79g7969Nd5/37596N+/P/bu3Yt+/fo53GfBggVYuHCh3fYvvvgCgYGBdXiGjce+TAGrz8nRKdSMR7uYG3Ss0JIUjDg9DyZBic3d34VR7sLy5kREREREPqCkpARTpkxBfn4+tFptjfs63eNkYTAY8M0332DlypVITk7GgAED8OCDD+Ly5ct49tln8dNPP+GLL76o7+Hr5OOPP0b37t2rTZoAYO7cuZgzZ471ekFBAVq2bInRo0fX+uI4y2AwIDk5GaNGjYJS6YKhck66sOM8cO4surVtiVtv7dqgY8m2LwIACB1vxuixd7kiPL/h7XYm92MbNw1sZ//HNm4a2M7+z51tbBmNVhdOJ04HDx7EypUr8eWXX0Imk2Hq1Kl466230KlTJ+s+48aNQ9++fWs9VlRUFORyOTIyMmy2Z2RkIC4ursb7FhcXY82aNVi0aFGN+6nVaqjVarvtSqXSbR8udx67JplFegBA8/DAhj2+KAInvwUAyLrfBRn/CDnkrXYmz2EbNw1sZ//HNm4a2M7+zx1t7MzxnC4O0bdvX5w5cwbvv/8+rly5gtdff90maQKAxMRETJo0qdZjqVQq9O7dG9u2bbNuM5vN2LZtm83QPUfWrVsHnU6H++67z9mn4LfSXbX47ZWDQF4qoAwC2ie5IDIiIiIiosbN6R6n8+fPo3Xr1jXuExQUhJUrV9bpeHPmzMG0adPQp08f9OvXD0uXLkVxcTFmzJgBAJg6dSri4+OxZMkSm/t9/PHHuPPOOxEZGensU/Bbaa5KnCyL3na8GVD51zwwIiIiIqL6cDpxyszMRHp6Ovr372+zfe/evZDL5ejTp49Tx5s4cSKuXbuGefPmIT09Hb169cLmzZsRGxsLAEhNTYVMZtsxdvr0afz222/YunWrs+H7tXTr4rcNKORgNgPHv5Euc9FbIiIiIiIA9Riq99hjj+HSpUt2269cuYLHHnusXkHMmjULFy9ehE6nw969e22Ssh07dmDVqlU2+3fs2BGiKGLUqFH1ejx/VGYwIbdEKqfYoMVvL+0FCq4Aai3Q7iYXRUdERERE1Lg5nTidOHHC4VpN1113HU6cOOGSoMh5lvlNgSo5tJp6F0usGKbX6TZA2cAhf0REREREfsLpxEmtVttVwQOAtLQ0KBQN+MJODXK1fJheXKim1vWzqmU2ASe+ky5zmB4RERERkZXTidPo0aMxd+5c5OfnW7fl5eXh2Wef5dA5L3JJRb2LvwNFGYAmDGgzwiVxERERERH5A6e7iF5//XUMGzYMrVu3xnXXXQcAOHz4MGJjY/HZZ5+5PECqG0tFvThtAwpDHCsfptd5LKBQuSAqIiIiIiL/4HTiFB8fjyNHjmD16tX4888/ERAQgBkzZmDy5MlcdMyLLD1OzcPq2eNkMgInN0iXu3GYHhERERFRZfWalBQUFIRHHnnE1bFQA1h7nOo7VO/CL0BJNhAYBSQMc2FkRERERESNX72rOZw4cQKpqanQ6/U222+//fYGB0XOSy+wrOFUz8TJMkyvy+2AnEU+iIiIiIgqc/ob8vnz5zFu3DgcPXoUgiBAFEUAsFZyM5lMro2Q6iQtrwFznIx64NT30uVud7kwKiIiIiIi/+B0Vb3Zs2cjMTERmZmZCAwMxPHjx7Fz50706dMHO3bscEOIVJsygwnZxVLPX716nM5tB8rygeA4oNVAF0dHRERERNT4Od3jtHv3bmzfvh1RUVGQyWSQyWQYMmQIlixZgscffxyHDh1yR5xUg8wCHQBArZAhLLAeBTosi952vROQyV0XGBERERGRn3C6x8lkMiEkJAQAEBUVhatXrwIAWrdujdOnT7s2OqqTtPLFb5uHBTi/+K2hDDi1UbrMRW+JiIiIiBxyusepW7du+PPPP5GYmIj+/fvj1VdfhUqlwocffog2bdq4I0aqRXqBZX5TPYbpnU0G9IWAtgXQoq+LIyMiIiIi8g9OJ07PP/88iouLAQCLFi3CmDFjMHToUERGRmLt2rUuD5BqZylFXq/5Tce+ls673gnInO6AJCIiIiJqEpxOnJKSkqyX27Vrh1OnTiEnJwfh4eHODxMjl0iv7xpO+mLgry3SZS56S0RERERULae6GAwGAxQKBY4dO2azPSIigkmTF13Nq+caTn9tBgwlQHgC0Px61wdGREREROQnnEqclEolWrVqxbWafIx1jlOok2s4WRa97ToeYOJLRERERFQtpye1PPfcc3j22WeRk5PjjnioHuo1x6msADiTLF3mMD0iIiIioho5Pcfp3XffxdmzZ9G8eXO0bt0aQUFBNrcfPHjQZcFR7fRGM7KKpHWcnEqcTm8CTDogsj0Q281N0RERERER+QenE6c777zTDWFQfWUWlkEUAZVchoggVd3vaFn0thuH6RERERER1cbpxGn+/PnuiIPqqXJFvToX6CjNBc5uky5z0VsiIiIiolpx4Z5G7mp9SpGf/AEwG4CYLkBMJzdFRkRERETkP5zucZLJZDX2bLDinmel59ejFPnxStX0iIiIiIioVk4nTt98843NdYPBgEOHDuGTTz7BwoULXRYY1U2asz1OxVnA+V+ky6ymR0RERERUJ04nTnfccYfdtrvvvhtdu3bF2rVr8eCDD7okMKobyxyn5nVdw+nkBkA0Ac16ApFt3RgZEREREZH/cNkcpwEDBmDbtm2uOhzVkdM9Tsc4TI+IiIiIyFkuSZxKS0vxn//8B/Hx8a44HDkh3ZnFbwvTgZTfpMtdx7kxKiIiIiIi/+L0UL3w8HCb4hCiKKKwsBCBgYH4/PPPXRoc1cxoMiOz0IkepxPfARCB+D5AeGv3BkdERERE5EecTpzeeustm8RJJpMhOjoa/fv3R3h4uEuDo5plFupgFgGFTEBUkLr2OxyrtOgtERERERHVmdOJ0/Tp090QBtWHZX5TrFYDmayWxW/zLwOX9kiXu9zp3sCIiIiIiPyM03OcVq5ciXXr1tltX7duHT755BOXBEV149T8puPfSuetBgGhnItGREREROQMpxOnJUuWICoqym57TEwMXn75ZZcERXWTZln8NqwOpciPc5geEREREVF9OZ04paamIjEx0W5769atkZqa6pKgqG7q3OOUmwJcOQAIMqCL/TpcRERERERUM6cTp5iYGBw5csRu+59//onIyEiXBEV1k1ZQXlFPW0vidPwb6TxhCBAc4+aoiIiIiIj8j9OJ0+TJk/H444/j559/hslkgslkwvbt2zF79mxMmjTJHTFSNdLyyofq1dbjdOxr6ZyL3hIRERER1YvTVfUWL16MlJQUjBw5EgqFdHez2YypU6dyjpOHWYbq1biGU9ZZIP0oIMiBzrd7KDIiIiIiIv/idOKkUqmwdu1avPjiizh8+DACAgLQvXt3tG7NBVU9yWQWkVGoAwA0C62hOMSBVdJ5qwFAEIdSEhERERHVh9OJk0X79u3Rvn17V8ZCTsgq0sFkFiGXCYgOqWbx24OfArvfkS5f3CVdv36q54IkIiIiIvITTs9xuuuuu/Dvf//bbvurr76Ke+65xyVBUe2si9+GqCF3tPht/hXg+9mVNojA909I24mIiIiIyClOJ047d+7Erbfearf9lltuwc6dO10SFNUuvXwNp2rnN+WcA0Sz7TbRBOScd3NkRERERET+x+nEqaioCCqVym67UqlEQUGBS4Ki2qVZ13CqZn5TRFsAVXqiBDkQ0ca9gRERERER+SGnE6fu3btj7dq1dtvXrFmDLl26uCQoql1abRX1QuOB1oMrrgtyYOxSaTsRERERETnF6eIQL7zwAsaPH49z587hxhtvBABs27YNX3zxBb766iuXB0iOVfQ41VCKXFneGzXw78CAR5k0ERERERHVk9OJ09ixY/Htt9/i5ZdfxldffYWAgAD07NkT27dvR0REhDtiJAdqneMEAHkXpfN2I5k0ERERERE1QL3Kkd9222247bbbAAAFBQX48ssv8eSTT+LAgQMwmUwuDZAcq3WOkygCeanS5XCusUVERERE1BBOz3Gy2LlzJ6ZNm4bmzZvjjTfewI033og9e/a4MjaqhtksIqOglqF6RZmAsQwQZIC2hQejIyIiIiLyP071OKWnp2PVqlX4+OOPUVBQgAkTJkCn0+Hbb79lYQgPyi7Ww2ASIRNQ/eK3lmF6Ic0BhX0VRCIiIiIiqrs69ziNHTsWHTt2xJEjR7B06VJcvXoV77zzjjtjo2qklc9vig5RQymvpgk5TI+IiIiIyGXq3OO0adMmPP7443j00UfRvn17d8ZEtagoRV7N/CYAyE2RzsNauT8gIiIiIiI/V+cep99++w2FhYXo3bs3+vfvj3fffRdZWVnujI2qkW4pDKGtQ0W9MPY4ERERERE1VJ0TpwEDBuCjjz5CWloaZs6ciTVr1qB58+Ywm81ITk5GYWGhO+OkSqwV9cJqSpw4VI+IiIiIyFWcrqoXFBSEBx54AL/99huOHj2Kf/7zn3jllVcQExOD22+/3R0xUhWWNZxqXPw219LjxKF6REREREQNVe9y5ADQsWNHvPrqq7h8+TK+/PJLV8VEtah1jpPZBORfli5zqB4RERERUYM1KHGykMvluPPOO7FhwwZXHI5qkV7bGk6FaYDZAMgUgLa5ByMjIiIiIvJPLkmcyHNEUazocaquOIRlflNoC0Am91BkRERERET+i4lTI5NTrIfeaAYAxFaXOOWyoh4RERERkSsxcWpkLL1NUcFqqBTVLX7LwhBERERERK7ExKmRsazh1JylyImIiIiIPIaJUyOTVlDL/CaAQ/WIiIiIiFyMiVMjU6c1nCw9TkyciIiIiIhcwuuJ07Jly5CQkACNRoP+/ftj3759Ne6fl5eHxx57DM2aNYNarUaHDh2wceNGD0XrfWl5tazhZDIABeVrOHGoHhERERGRSyi8+eBr167FnDlzsHz5cvTv3x9Lly5FUlISTp8+jZiYGLv99Xo9Ro0ahZiYGHz11VeIj4/HxYsXERYW5vngvcRSHKLaHqf8y4BoBuRqIMj+NSQiIiIiIud5NXF688038fDDD2PGjBkAgOXLl+PHH3/EihUr8Mwzz9jtv2LFCuTk5GDXrl1QKpUAgISEBE+G7HWWxW/jqkucrMP0WgEyr3coEhERERH5Ba8lTnq9HgcOHMDcuXOt22QyGW666Sbs3r3b4X02bNiAgQMH4rHHHsN3332H6OhoTJkyBU8//TTkcscLvep0Ouh0Ouv1goICAIDBYIDBYHDhM4L1eK4+roW0+K00xykqSOHwcYTs81AAMIe2hMlNcTR17m5n8j62cdPAdvZ/bOOmge3s/9zZxs4c02uJU1ZWFkwmE2JjY222x8bG4tSpUw7vc/78eWzfvh333nsvNm7ciLNnz+L//u//YDAYMH/+fIf3WbJkCRYuXGi3fevWrQgMDGz4E3EgOTnZLcctNgBlBqnJDu/ageMOOpQ6Xd2OjgAuFgBHmtDcL29wVzuT72AbNw1sZ//HNm4a2M7+zx1tXFJSUud9vTpUz1lmsxkxMTH48MMPIZfL0bt3b1y5cgWvvfZatYnT3LlzMWfOHOv1goICtGzZEqNHj4ZWq3VpfAaDAcnJyRg1apR1KKErnUovBPbvRkSQEneMGe1wH/l3G4AMoFWPIWgx8FaXx0Dub2fyPrZx08B29n9s46aB7ez/3NnGltFodeG1xCkqKgpyuRwZGRk22zMyMhAXF+fwPs2aNYNSqbQZlte5c2ekp6dDr9dDpVLZ3UetVkOtVtttVyqVbvtwuevYWcVGAECz0IDqj59/CQAgj0iEnH883Mqd7yHyDWzjpoHt7P/Yxk0D29n/uaONnTme16oHqFQq9O7dG9u2bbNuM5vN2LZtGwYOHOjwPoMHD8bZs2dhNput2/766y80a9bMYdLkb646s4YTS5ETEREREbmMV8uuzZkzBx999BE++eQTnDx5Eo8++iiKi4utVfamTp1qUzzi0UcfRU5ODmbPno2//voLP/74I15++WU89thj3noKHpWeX0tFPUMZUJgmXebit0RERERELuPVOU4TJ07EtWvXMG/ePKSnp6NXr17YvHmztWBEamoqZJVKards2RJbtmzBP/7xD/To0QPx8fGYPXs2nn76aW89BY+qWMOpmsVv88sXvlUGAYGRHoqKiIiIiMj/eb04xKxZszBr1iyHt+3YscNu28CBA7Fnzx43R+Wb0mtb/DYvRToPawUIgmeCIiIiIiJqArhCaiNiWcOp2qF6uRelc85vIiIiIiJyKSZOjYS0+G0tQ/UshSE4v4mIiIiIyKWYODUSBWVGlOhNAIA4bXVD9cp7nMJaeSgqIiIiIqKmgYlTI2GZ3xQWqESASu54J5YiJyIiIiJyCyZOjYR1flN1vU1AxRwn9jgREREREbkUE6dGotaKeroioCRLusw5TkRERERELsXEqZGwFoYIq24Np0vSuSYUCAjzTFBERERERE0EE6dGwtrjVN1QPQ7TIyIiIiJyGyZOjURagZQ4VbuGE0uRExERERG5DROnRiItTyoOUf0aTpYeJyZORERERESuxsSpkbAM1au+x6k8cWIpciIiIiIil2Pi1AgUlhlQqDMCqKGqXi57nIiIiIiI3IWJUyOQUT6/SatRIEitcLxTHotDEBERERG5CxOnRsBairy6+U2leUBZvnSZiRMRERERkcsxcWoE0mqd31ReUS8wElAHeygqIiIiIqKmg4lTI5CWZ+lxYilyIiIiIiJvYOLUCKQXSKXIWVGPiIiIiMg7mDg1AhVznGqrqMf5TURERERE7sDEqRGoWMOpusVvOVSPiIiIiMidmDg1ApYep+a1DdVj4kRERERE5BZMnHxcid6I/FIDgGrmOIliRY8T5zgREREREbkFEycfZxmmF6xWIESjtN+hJAfQF0mXQ1t6MDIiIiIioqaDiZOPq30NpxTpPDgOUFazDxERERERNQgTJx9Xa0U9DtMjIiIiInI7Jk4+Lj2/fA0nLUuRExERERF5CxMnH2ftcQpjKXIiIiIiIm9h4uTj0msdqlfe48ShekREREREbsPEycfVWhyCQ/WIiIiIiNyOiZOPSyuf4+Swx0kUgfxL0mUO1SMiIiIichsmTj6szGBCbom0+G0zrYM5TkUZgLEMEGRAaAsPR0dERERE1HQwcfJhlvlNAUo5tAEK+x0shSG08YDcweK4RERERETkEkycfFjlNZwEQbDfgfObiIiIiIg8gomTD0svKJ/fFFZdRb0U6Zzzm4iIiIiI3IqJkw+zVtRzNL8JqBiqx1LkRERERERuxcTJh9W6hhOH6hEREREReQQTJx92Na+WNZwsPU4cqkdERERE5FZMnHyYdY6To8TJbALyL0uX2eNERERERORWTJx8mGWonsMep8I0wGwAZEpA29zDkRERERERNS1MnHyUzmhCVpEeANA81EFxCMv8ptAWgEzuwciIiIiIiJoeJk4+KrNABwBQK2QIC3SwuG0eC0MQEREREXkKEycfVevityxFTkRERETkMUycfFRavlQYotqKeixFTkRERETkMUycfFRFj1Mti9+GJXgmICIiIiKiJoyJk4+qsaIeUDHHiUP1iIiIiIjcjomTj7IM1XO4hpPJABRckS5zqB4RERERkdsxcfJR6TUN1cu/DIhmQKEBgmM9HBkRERERUdPDxMlHVa6qZ8cyTC+0JeCo4h4REREREbkUEycfZDCZca1IWsfJ4RwnliInIiIiIvIoJk4+KKOgDKIIqOQyRASq7HewliJn4kRERERE5AlMnHyQZX5TbKgaMpmjxW+5hhMRERERkScxcfJB1vlN2lrWcOJQPSIiIiIij2Di5IOsFfXCqlnDKZc9TkREREREnsTEyQel1bT4raEMKEqXLocleC4oIiIiIqImjImTD0ovKF/8Vusgccq/JJ2rgoHACA9GRURERETUdDFx8kFX8yw9Tg7mOFUepsc1nIiIiIiIPIKJkw9Kr8vityxFTkRERETkMUycfIzRZEZmYV0SJxaGICIiIiLyFCZOPuZakQ5mEVDIBEQGq+13YClyIiIiIiKPY+LkYywV9WK1GsgdLX6by6F6RERERESe5hOJ07Jly5CQkACNRoP+/ftj37591e67atUqCIJgc9JoqlnvqBGqcX4TUNHjxKF6REREREQe4/XEae3atZgzZw7mz5+PgwcPomfPnkhKSkJmZma199FqtUhLS7OeLl686MGI3avGNZx0RUBJlnSZiRMRERERkcd4PXF688038fDDD2PGjBno0qULli9fjsDAQKxYsaLa+wiCgLi4OOspNjbWgxG7V1pe+RpODgtDlPc2aUKBgDDPBUVERERE1MQpvPnger0eBw4cwNy5c63bZDIZbrrpJuzevbva+xUVFaF169Ywm824/vrr8fLLL6Nr164O99XpdNDpdNbrBQUFAACDwQCDweCiZwLrMSuf18fVvBIAQHSwyu44QvZ5KACIoa1gdHHsVHeuaGfybWzjpoHt7P/Yxk0D29n/ubONnTmmVxOnrKwsmEwmux6j2NhYnDp1yuF9OnbsiBUrVqBHjx7Iz8/H66+/jkGDBuH48eNo0aKF3f5LlizBwoUL7bZv3boVgYGBrnkiVSQnJ9f7vicvygEISDt3AhvzjtvclnhtK3oASCtT44+NGxsWJDVYQ9qZGge2cdPAdvZ/bOOmge3s/9zRxiUlJXXe16uJU30MHDgQAwcOtF4fNGgQOnfujA8++ACLFy+223/u3LmYM2eO9XpBQQFatmyJ0aNHQ6vVujQ2g8GA5ORkjBo1Ckqlsl7H+PeJnQDKcOuIgejVMszmNtlPu4HLQGynvrj1plsbHjDViyvamXwb27hpYDv7P7Zx08B29n/ubGPLaLS68GriFBUVBblcjoyMDJvtGRkZiIuLq9MxlEolrrvuOpw9e9bh7Wq1Gmq1/XpISqXSbR+u+h7bZBaRWSgNK2wZGWJ/jPxLAAB5RBvI+YfB69z5HiLfwDZuGtjO/o9t3DSwnf2fO9rYmeN5tTiESqVC7969sW3bNus2s9mMbdu22fQq1cRkMuHo0aNo1qyZu8L0mOwiHYxmEXKZgOgQR4vfWtZwYkU9IiIiIiJP8vpQvTlz5mDatGno06cP+vXrh6VLl6K4uBgzZswAAEydOhXx8fFYsmQJAGDRokUYMGAA2rVrh7y8PLz22mu4ePEiHnroIW8+DZe4Wl6KPCZE7XjxW0tVvXAufktERERE5EleT5wmTpyIa9euYd68eUhPT0evXr2wefNma8GI1NRUyGQVHWO5ubl4+OGHkZ6ejvDwcPTu3Ru7du1Cly5dvPUUXCY9XypF7nANp9I8oCxfuhza0nNBERERERGR9xMnAJg1axZmzZrl8LYdO3bYXH/rrbfw1ltveSAqz7MsflvjGk6BUYA62INRERERERGR1xfApQrp5YlTnDbA/kbL/CYO0yMiIiIi8jgmTj7E0uPUPMxBj1MuC0MQEREREXkLEycfYu1xqmmoXhh7nIiIiIiIPI2Jkw9JK5CKQzie48QeJyIiIiIib2Hi5CPMZrFSj5OjOU4sRU5ERERE5C1MnHxEdrEeBpMIQZDWcbIhipXmOCV4PDYiIiIioqaOiZOPsPQ2RQeroZRXaZaSbMBQLF0ObeHhyIiIiIiIiImTj0grX/y2WVgNpchDmgFKB/OfiIiIiIjIrZg4+Yj0gvLFb7UsRU5ERERE5GuYOPmINJYiJyIiIiLyWUycfERaXh1KkbOiHhERERGRVzBx8hE19jhxqB4RERERkVcxcfIR1jlONa3hxKF6RERERERewcTJB4iiaO1xshuqZzZXSpzY40RERERE5A1MnHxAbokBeqMZABBbtapecSZg0gGCjGs4ERERERF5CRMnH2BZwykqWA2VokqTWOY3aVsAcqWHIyMiIiIiIoCJk09Ir26YHsBhekREREREPoCJkw+4WuMaTinSOUuRExERERF5DRMnH5CeX8MaTixFTkRERETkdUycfECNazixFDkRERERkdcxcfIBljlOzR2u4VTe48ShekREREREXsPEyQekV9fjZDYB+ZelyxyqR0RERETkNUycvKzGxW8LrgJmIyBTAiHNvBAdEREREREBTJy8Lr/UgFKDCYCDxW8tw/RCWwAyuYcjIyIiIiIiCyZOXmbpbYoIUkGjrJIcWQpDcH4TEREREZFXMXHyMuv8pqq9TUClUuRMnIiIiIiIvImJk5dVO78JqFSKnIUhiIiIiIi8iYmTl1kXvw1zlDhZSpEneC4gIiIiIiKyw8TJyyp6nBys4WQdqsceJyIiIiIib2Li5GXpBdXMcTLqgcKr0mXOcSIiIiIi8iomTl52Na98qJ7dGk6XAdEMKDRAcIwXIiMiIiIiIgsmTl5UefHbuKqJU+XCEILg4ciIiIiIiKgyJk5eVKgzokQvLX5rN8eJpciJiIiIiHwGEycvsqzhFBaoRICq6uK3LAxBREREROQrmDh5kWWYXohagbTysuRWlqF64exxIiIiIiLyNiZOXrTh8BUAwKXcUgx+ZTvW/pFacSNLkRMRERER+QwmTl6Sll+K9QevWK+bReDZ9ccqep6sxSHY40RERERE5G1MnLzkQlYxxCrbTKKIlKwSwFAKFKVLG8MTPB0aERERERFVwcTJSxKjgiCrUmVcLghIiAoE8i5JG1TBQEC454MjIiIiIiIbTJy8pFloAJaM7w55+RpNckHAy+O7SWXJKw/T4xpORERERERep/B2AE3ZxL6tMKxDNFKySpAQFVixllNeinTOwhBERERERD6BiZOXNQsNsF/8lqXIiYiIiIh8Cofq+SJrKXImTkREREREvoCJky+yznHiUD0iIiIiIl/AxMkX5ZX3OHGoHhERERGRT2Di5Gt0RUBJtnSZPU5ERERERD6BiZOvsQzT04QBmlCvhkJERERERBImTr6Gw/SIiIiIiHwOEydfw8IQREREREQ+h4mTr2EpciIiIiIin8PEydfkMXEiIiIiIvI1TJx8Dec4ERERERH5HCZOvibXMseJiRMRERERka9g4uRLSvMAXb50OaylV0MhIiIiIqIKTJx8iWWYXlA0oArybixERERERGTFxMmXWCvqsRQ5EREREZEvUXg7AKokj/ObiIiIiEwmEwwGQ532NRgMUCgUKCsrg8lkcnNk5A0NbWOVSgWZrOH9RUycfAkr6hEREVETJooi0tPTkZeX59R94uLicOnSJQiC4L7gyGsa2sYymQyJiYlQqVQNioOJky+x9jhxqB4RERE1PZakKSYmBoGBgXX6kmw2m1FUVITg4GCX9CqQ72lIG5vNZly9ehVpaWlo1apVg5Jrn0icli1bhtdeew3p6eno2bMn3nnnHfTr16/W+61ZswaTJ0/GHXfcgW+//db9gbpbLhe/JSIioqbJZDJZk6bIyMg6389sNkOv10Oj0TBx8lMNbePo6GhcvXoVRqMRSqWy3nF4/d21du1azJkzB/Pnz8fBgwfRs2dPJCUlITMzs8b7paSk4Mknn8TQoUM9FKmbiWLFUD0mTkRERNTEWOY0BQYGejkS8jeWIXoNnQPn9cTpzTffxMMPP4wZM2agS5cuWL58OQIDA7FixYpq72MymXDvvfdi4cKFaNOmjQejdaOSbMBQAkDgGk5ERETUZHGeErmaq95TXh2qp9frceDAAcydO9e6TSaT4aabbsLu3burvd+iRYsQExODBx98EL/++muNj6HT6aDT6azXCwoKAEi/atS1WktdWY5Xn+MK185BAUAMiYNRlAEujo1cpyHtTI0D27hpYDv7P7Zx42IwGCCKIsxmM8xmc53vJ4qi9dyZ+1Hj0dA2NpvNEEURBoMBcrnc5jZn/j54NXHKysqCyWRCbGyszfbY2FicOnXK4X1+++03fPzxxzh8+HCdHmPJkiVYuHCh3fatW7e6rSs4OTnZ6fs0z92LvgByzMH4beNG1wdFLlefdqbGhW3cNLCd/R/buHFQKBSIi4tDUVER9Hq90/cvLCx0Q1Te0aNHDzz66KN49NFHvR2KT6lvG+v1epSWlmLnzp0wGo02t5WUlNT5OD5RHKKuCgsLcf/99+Ojjz5CVFRUne4zd+5czJkzx3q9oKAALVu2xOjRo6HVal0an8FgQHJyMkaNGuX0xDPZrrNAChCe2Au33nqrS+Mi12pIO1PjwDZuGtjO/o9t3LiUlZXh0qVLCA4OhkajqfP9RFFEYWEhQkJCPD7Mr2rvRVXz5s3D/PnznT7uH3/8gaCgIJf8yP/ll19i6tSpmDlzJt59990GH88bGtrGZWVlCAgIwLBhw+zeW5bRaHXh1cQpKioKcrkcGRkZNtszMjIQFxdnt/+5c+eQkpKCsWPHWrdZuusUCgVOnz6Ntm3b2txHrVZDrVbbHUupVLrtj2i9jl1wCQAgi0iAjH/cGwV3vofIN7CNmwa2s/9jGzcOJpMJgiBAJpM5VTnN8l3Qcl8ASMsvxYWsYiRGBaFZaIBb4gWAtLQ06+W1a9di3rx5OH36tHVb5fLZoijCZDJBoaj963fV0VgNsXLlSjz11FP44IMP8OabbzqVlLqaXq+v11pKjtrYGTKZDIIgOPxb4MzfBq8Wh1CpVOjduze2bdtm3WY2m7Ft2zYMHDjQbv9OnTrh6NGjOHz4sPV0++2344YbbsDhw4fRsmUjLqpgXcOJFfWIiIiIACnZKNEbaz2V6k3Wy5/tTsHgV7Zjykd7MfiV7fhsd0qdjlH5ZJlTU5u4uDjrKTQ0FIIgWK+fOnUKISEh2LRpE3r37g21Wo3ffvsN586dwx133IHY2FgEBwejb9+++Omnn2yOm5CQgKVLl1qvC4KA//73vxg3bhwCAwPRvn17bNiwodb4Lly4gF27duGZZ55Bhw4dsH79ert9VqxYga5du0KtVqNZs2aYNWuW9ba8vDzMnDkTsbGx0Gg06NatG3744QcAwIIFC9CrVy+bYy1duhQJCQnW69OnT8edd96Jl156Cc2bN0fHjh0BAJ999hn69OmDkJAQxMXFYcqUKXYVtY8fP44xY8ZAq9UiNDQUt9xyC86dO4edO3dCqVQiPT3dZv8nnnjC7dW2vT5Ub86cOZg2bRr69OmDfv36YenSpSguLsaMGTMAAFOnTkV8fDyWLFlibbDKwsLCAMBue6NjKUUezsSJiIiICABKDSZ0mbel3vc3i8AL3x3HC98dd+p+JxYlIVDlmq/JzzzzDF5//XW0adMG4eHhuHTpEm699Va89NJLUKvV+PTTTzF27FicPn0arVq1qvY4CxcuxKuvvorXXnsN77zzDu69915cvHgRERER1d5n5cqVuO222xAaGor77rsPH3/8MaZMmWK9/f3338ecOXPwyiuv4JZbbkF+fj5+//13AFJnxi233ILCwkJ8/vnnaNu2LU6cOFHr8MSqtm3bBq1WazPX0GAwYPHixejYsSMyMzMxZ84cTJ8+HRvL5/lfuXIFw4YNw4gRI7B9+3YEBwdj27ZtMBqNGDZsGNq0aYPPPvsM//rXv6zHW716NV599VWnYnOW1xOniRMn4tq1a5g3bx7S09PRq1cvbN682dpFmZqa6v+LmZnNQJ40VA9h1X9giIiIiKhxWbRoEUaNGmW9HhERgZ49e1qvL168GN988w02bNhg09tT1fTp0zF58mQAwMsvv4z//Oc/2LdvH26++WaH+5vNZqxatQrvvPMOAGDSpEn45z//iQsXLiAxMREA8OKLL+Kf//wnZs+ebb1f3759AQA//fQT9u3bh5MnT6JDhw4AUK9lgIKCgvDf//7XZojeAw88YL3cpk0b/Oc//0Hfvn1RVFSE4OBgLFu2DKGhoVizZg2USiXMZjPi4uKs9QkefPBBrFy50po4ff/99ygrK8OECROcjs8ZXk+cAGDWrFnVvlF27NhR431XrVrl+oA8rSgDMOkAQQ5oW3g7GiIiIiKfEKCU48SipBr3MZvNKCwoRIg2BJmFetz05i8wVxppJxOAn+YMR1xo3ef2BCid61WpSZ8+fWyuFxUVYcGCBfjxxx+RlpYGo9GI0tJSpKam1nicHj16WC8HBQVBq9XaDW+rLDk5GcXFxdaiY1FRURg1ahRWrFiBxYsXIzMzE1evXsXIkSMd3v/w4cNo0aKFNWmqr+7du9vNazpw4AAWLFiAP//8E7m5udY5TKmpqejSpQsOHz6MoUOHVjv/aPr06Xj++eexZ88eDBgwAKtWrcKECRMQFBTUoFhr4xOJU5Nnmd+kjQfkbBIiIiIiQJrbU9uQObPZDKNKjkCVAm2iVVgyvjueXX8MJlGEXBDw8vhuaBMd7KGI7VX9Mv/kk08iOTkZr7/+Otq1a4eAgADcfffdtZZgr5pECIJQ45pGH3/8MXJychAQUFEcw2w248iRI1i4cKHNdkdqu10mk9nNBXO0JlLV519cXIykpCQkJSVh9erViI6ORmpqKpKSkqyvQW2PHRMTg7Fjx2LlypVITEzEpk2bau1scQV+S/cFnN9ERERE5BIT+7bCsA7RSMkqQUJUoFur6tXH77//junTp2PcuHEApB6olJQUlz5GdnY2vvvuO6xZswZdu3a1bjeZTBgyZAi2bt2Km2++GQkJCdi2bRtuuOEGu2P06NEDly9fxl9//eWw1yk6Ohrp6ekQRdFaIrwu66yeOnUK2dnZeOWVV6yF3fbv32/32J988gkMBkO1vU4PPfQQJk+ejBYtWqBt27YYPHhwrY/dUH4+eaiRyC1PnDi/iYiIiKjBmoUGYGDbSJ9LmgCgffv2WL9+PQ4fPow///wTU6ZMqbHnqD4+++wzREZGYsKECejWrZv11LNnT9x66634+OOPAUiV8d544w385z//wZkzZ3Dw4EHrnKjhw4dj2LBhuOuuu5CcnIwLFy5g06ZN2Lx5MwBgxIgRuHbtGl599VWcO3cOy5Ytw6ZNm2qNrVWrVlCpVHjnnXdw/vx5bNiwAYsXL7bZZ9asWSgoKMCkSZOwf/9+nDlzBmvWrLEp9Z6UlAStVosXX3zRWlTO3Zg4+QJLjxNLkRMRERH5tTfffBPh4eEYNGgQxo4di6SkJFx//fUufYwVK1Zg3LhxDheLveuuu7BhwwZkZWVh2rRpWLp0Kd577z107doVY8aMwZkzZ6z7fv311+jbty8mT56MLl264KmnnoLJZAIAdO7cGe+99x6WLVuGnj17Yt++fXjyySdrjS06OhqrVq3CunXr0KVLF7zyyit4/fXXbfaJjIzE9u3bUVRUhOHDh6Nv37749NNPbXqfZDIZpk+fDpPJhKlTp9b3pXKKINa1UL2fKCgoQGhoKPLz862VOVzFYDBg48aNuPXWW51baO+TscCFncC4D4Cek1waE7levduZGg22cdPAdvZ/bOPGpayszFrxzZlFWs1mMwoKCqDVav2/EnMTVV0bP/jgg7h27Vqta1rV9N5yJjfgHCdfYF38lkP1iIiIiIhqkp+fj6NHj+KLL76o00LArsLEydvMJiD/snSZQ/WIiIiIiGp0xx13YN++ffjb3/5ms0aWuzFx8raCK4DZCMiUQEict6MhIiIiIvJpnig97ggHgnqbdZheS0DmusXWiIiIiIjIdZg4eVsuK+oREREREfk6Jk7exsIQREREREQ+j4mTt1nWcApnjxMRERERka9i4uRtHKpHREREROTzmDh5m3WoHhMnIiIiIiJfxcTJm4x6qRw5wKF6REREREQ+jImTNxVcBiACigAgKNrb0RARERGREwRBqPG0YMGCBh3722+/rfP+M2fOhFwux7p16+r9mFQzJk7eZJnfFBgFFFz1bixERERE/iL/CnBhp3TuRmlpadbT0qVLodVqbbY9+eSTbn18i5KSEqxZswZPPfUUVqxY4ZHHrIler/d2CG7BxMmbjqyRzgsuAUu7AQc/9W48RERERL5EFAF9ce0nQ0nF5X0fSd+rPhkrne/7qG7HqHwSxTqFFxcXZz2FhoZCEASbbWvWrEHnzp2h0WjQqVMnvPfee9b76vX6/2/vzsOqqPc/gL/PQXY9IIssouCCqKmAG6E/QYEEtwzzil664hJlSmoumZUilutVLM1rpQZ2r2nazTRFuwqJqUhJkDsp4ZKCOwKSspzP7w8fpo4gBxU9iO/X85znYeb7nZnPzIc5nA8z8z2IioqCk5MTzMzM4Orqinnz5gEA3NzcAAChoaFQqVTK9L1s3LgRbdu2xVtvvYU9e/bg3LlzOu23b9/GtGnT0KRJE5iamqJly5ZYvXq10n706FH0798fGo0GDRo0QI8ePZCVlQUA6NmzJyZOnKizvhdeeAEjRoxQpt3c3PDee+9h+PDh0Gg0eOWVVwAA06ZNQ6tWrWBhYYHmzZtjxowZKCkp0VnXt99+iy5dusDMzAx2dnYIDQ0FAMyePRvt2rWrsK9eXl6YMWNGlcfjUalnkK3Snf+A/PLln9OiBb6dCLQIBKwaGywsIiIiolqjpAiY61xlFzUA63s1ihZImHLndT/evgCYWN7fMndZu3YtZs6ciY8++gje3t5IT09HZGQkLC0tERERgaVLl2LLli3YsGEDmjZtinPnzikFz08//YRGjRohLi4OISEhMDIyqnJbq1evxksvvQQrKyv06dMH8fHxOsXF8OHDkZKSgqVLl8LT0xPZ2dm4cuUKAOD8+fPw8/NDz549kZSUBI1Gg3379qG0tPS+9nfRokWYOXMmoqOjlXkNGjRAfHw8nJ2dcfjwYURGRqJBgwZ48803AQDbtm1DaGgo3nnnHXz++ecoLi5GQkICAGDUqFGIiYnBTz/9hE6dOgEA0tPTcejQIXz99df3FVtNYeFkKNeyANz13wwpA679xsKJiIiI6AkXHR2NxYsXY9CgQQCAZs2a4dixY/jkk08QERGBs2fPwt3dHf/3f/8HlUoFV9c/Bwqzt7/z7Lu1tTUcHR2r3M7Jkydx4MABpZh46aWXMGnSJLz77rtQqVT49ddfsWHDBuzcuRNBQUEAgObNmyvLL1++HFZWVli/fj2MjY0BAK1atbrv/Q0ICMDkyZN15r377rvKz25ubpgyZYpySyEAzJkzB0OHDkVMTIzSz9PTEwDg4uKC4OBgxMXFKYVTfHw8/P39deJ/nFg4GYpNC0ClvvOfkHIqI8DGML8IRERERLWOscWdqz9V0Gq1yC8ogKZBA6gLc4HlXSt+vhqXCmiqvnJVYbsP4ebNm8jKysLo0aMRGRmpzC8tLYWVlRUAYMSIEXjuuefg4eGBkJAQ9O/fH717977vbX322WcIDg6GnZ0dAKBv374YPXo0kpKSEBgYiIyMDBgZGcHf37/S5TMyMtCjRw+laHpQnTt3rjDvyy+/xNKlS5GVlYXCwkKUlpZCo9HobPuvx+dukZGRGDVqFBYtWoTi4mKsW7cOS5Yseag4HwYLJ0OxagwM+PDO7XlSduekHvABrzYRERERlVOp9N8yp9UCxmV3+tm5V/75ys79cUSrKCwsBACsXLkSPj4+Om3lt9117NgR2dnZ2L59O3bt2oUhQ4YgKCgIX331VbW3U1ZWhjVr1iA3Nxf16tXTmf/ZZ58hMDAQ5ubmVa5DX7tarYbc9czX3c8pAYClpW6eUlJSEB4ejpiYGAQHBytXtRYvXlztbQ8YMACmpqbYtGkTysrKUFJSgsGDB1e5zKPEwsmQOg6/80zTtd/uXGli0URERET0cGrB5ysHBwc4Ozvjt99+Q3h4+D37aTQahIWFISwsDIMHD0ZISAiuXbsGGxsbGBsbo6ysrMrtJCQkoKCgAOnp6TrPQR05cgQjR45EXl4e2rdvD61Wi+TkZOVWvb/q0KED1qxZg5KSkkqvOtnb2yMnJ0eZLisrw5EjR9CrV68qY9u/fz9cXV3xzjvvKPPOnDlTYduJiYkYOXJkpeuoV68eIiIiEB8fD7VajbCwML3F1qPEwsnQrBqzYCIiIiKqSbXg81VMTAzGjx8PKysrhISE4Pbt2zh48CCuX7+OSZMmITY2Fk5OTvD29oZarcbGjRvh6OgIa2trAHeeCUpMTET37t1hamqKhg0bVtjG6tWr0a9fP+W5oHJt27bFG2+8gbVr12LcuHGIiIjAqFGjlMEhzpw5g0uXLmHIkCGIiorCsmXLMHToUEyfPh1WVlY4cOAAunbtCg8PDwQEBGDSpEnYtm0bWrRogdjYWOTl5endf3d3d5w9exbr169Hly5dsG3bNmzatEmnT3R0NAIDA9GiRQsMHToUpaWlSEhIwLRp05Q+L7/8Mtq0aQMA+OGHH+4zCzWLw5ETEREREdWwl19+GatWrUJcXBzat28Pf39/xMfHo1mzZgDujDi3cOFCdO7cGV26dMHp06eRkJAAtfrOx/PFixdj586daNKkCby9vSus/+LFi9i2bRtefPHFCm1qtRqhoaHKkOMrVqzA4MGDMXbsWLRu3RqRkZG4efMmAMDW1hZJSUkoLCyEv78/OnXqhJUrVypXn0aNGoWIiAgMHz5cGZhB39UmAHj++efxxhtvICoqCl5eXti/f3+FYcR79uyJjRs3YsuWLfDy8kJAQAB+/PFHnT7u7u7o1q0b3N3dK9z2+Lip5O6bFuu4/Px8WFlZ4caNGzoPp9WEkpISJCQkoG/fvg/9gB3VXsxz3cccPx2Y57qPOX6y3Lp1C9nZ2WjWrBnMzMyqvZxWq0V+fj40Go1SdFDdISJwd3fHyJEjMX369AfKcVW/W/dTG/BWPSIiIiIiqnUuX76M9evXIzc3t8pnxR4XFk5ERERERFTrNGrUCHZ2dvj444+VZ78MiYUTERERERHVOuVPFJXfjmlovBGUiIiIiIhIDxZORERERFRrPGXjltFjUFO/UyyciIiIiMjgykc+LCoqMnAkVNcUFxcDgM6XBD8IPuNERERERAZnZGQEa2trXLp0CQBgYWEBlUqldzmtVovi4mLcunWLw5HXUQ+TY61Wi8uXL8PCwgL16j1c6cPCiYiIiIhqBUdHRwBQiqfqEBH88ccfMDc3r1ahRU+eh82xWq1G06ZNH/r3g4UTEREREdUKKpUKTk5OaNSoEUpKSqq1TElJCfbs2QM/Pz9+0XEd9bA5NjExqZGrkSyciIiIiKhWMTIyqvbzKEZGRigtLYWZmRkLpzqqtuSYN4ISERERERHpwcKJiIiIiIhIDxZOREREREREejx1zziVfwFWfn5+ja+7pKQERUVFyM/P5z22dRjzXPcxx08H5rnuY46fDsxz3fcoc1xeE1TnS3KfusKpoKAAANCkSRMDR0JERERERLVBQUEBrKysquyjkuqUV3WIVqvFhQsX0KBBgxof6z8/Px9NmjTBuXPnoNFoanTdVHswz3Ufc/x0YJ7rPub46cA8132PMscigoKCAjg7O+sdsvypu+KkVqvh4uLySLeh0Wh44j4FmOe6jzl+OjDPdR9z/HRgnuu+R5VjfVeaynFwCCIiIiIiIj1YOBEREREREenBwqkGmZqaIjo6GqampoYOhR4h5rnuY46fDsxz3cccPx2Y57qvtuT4qRscgoiIiIiI6H7xihMREREREZEeLJyIiIiIiIj0YOFERERERESkBwsnIiIiIiIiPVg41aDly5fDzc0NZmZm8PHxwY8//mjokOgBzZo1CyqVSufVunVrpf3WrVsYN24cbG1tUb9+fbz44ou4ePGiASOm6tizZw8GDBgAZ2dnqFQqfPPNNzrtIoKZM2fCyckJ5ubmCAoKwsmTJ3X6XLt2DeHh4dBoNLC2tsbo0aNRWFj4GPeCqqIvxyNGjKhwboeEhOj0YY5rt3nz5qFLly5o0KABGjVqhBdeeAGZmZk6farzHn327Fn069cPFhYWaNSoEaZOnYrS0tLHuStUherkuWfPnhXO5zFjxuj0YZ5rrxUrVqBDhw7Kl9r6+vpi+/btSnttPI9ZONWQL7/8EpMmTUJ0dDR+/vlneHp6Ijg4GJcuXTJ0aPSAnnnmGeTk5CivvXv3Km1vvPEGvv32W2zcuBHJycm4cOECBg0aZMBoqTpu3rwJT09PLF++vNL2hQsXYunSpfj444+RmpoKS0tLBAcH49atW0qf8PBwHD16FDt37sTWrVuxZ88evPLKK49rF0gPfTkGgJCQEJ1ze926dTrtzHHtlpycjHHjxuHAgQPYuXMnSkpK0Lt3b9y8eVPpo+89uqysDP369UNxcTH279+PNWvWID4+HjNnzjTELlElqpNnAIiMjNQ5nxcuXKi0Mc+1m4uLC+bPn4+0tDQcPHgQAQEBGDhwII4ePQqglp7HQjWia9euMm7cOGW6rKxMnJ2dZd68eQaMih5UdHS0eHp6VtqWl5cnxsbGsnHjRmXe8ePHBYCkpKQ8pgjpYQGQTZs2KdNarVYcHR3ln//8pzIvLy9PTE1NZd26dSIicuzYMQEgP/30k9Jn+/btolKp5Pz5848tdqqeu3MsIhIRESEDBw685zLM8ZPn0qVLAkCSk5NFpHrv0QkJCaJWqyU3N1fps2LFCtFoNHL79u3HuwNULXfnWUTE399fJkyYcM9lmOcnT8OGDWXVqlW19jzmFacaUFxcjLS0NAQFBSnz1Go1goKCkJKSYsDI6GGcPHkSzs7OaN68OcLDw3H27FkAQFpaGkpKSnTy3bp1azRt2pT5foJlZ2cjNzdXJ69WVlbw8fFR8pqSkgJra2t07txZ6RMUFAS1Wo3U1NTHHjM9mN27d6NRo0bw8PDAa6+9hqtXryptzPGT58aNGwAAGxsbANV7j05JSUH79u3h4OCg9AkODkZ+fr7y326qXe7Oc7m1a9fCzs4O7dq1w/Tp01FUVKS0Mc9PjrKyMqxfvx43b96Er69vrT2P6z2StT5lrly5grKyMp3EAYCDgwNOnDhhoKjoYfj4+CA+Ph4eHh7IyclBTEwMevTogSNHjiA3NxcmJiawtrbWWcbBwQG5ubmGCZgeWnnuKjuPy9tyc3PRqFEjnfZ69erBxsaGuX9ChISEYNCgQWjWrBmysrLw9ttvo0+fPkhJSYGRkRFz/ITRarWYOHEiunfvjnbt2gFAtd6jc3NzKz3Xy9uodqkszwDw97//Ha6urnB2dsahQ4cwbdo0ZGZm4uuvvwbAPD8JDh8+DF9fX9y6dQv169fHpk2b0LZtW2RkZNTK85iFE1El+vTpo/zcoUMH+Pj4wNXVFRs2bIC5ubkBIyOihzF06FDl5/bt26NDhw5o0aIFdu/ejcDAQANGRg9i3LhxOHLkiM4zqFT33CvPf332sH379nByckJgYCCysrLQokWLxx0mPQAPDw9kZGTgxo0b+OqrrxAREYHk5GRDh3VPvFWvBtjZ2cHIyKjCSB8XL16Eo6OjgaKimmRtbY1WrVrh1KlTcHR0RHFxMfLy8nT6MN9PtvLcVXUeOzo6VhjwpbS0FNeuXWPun1DNmzeHnZ0dTp06BYA5fpJERUVh69at+P777+Hi4qLMr857tKOjY6Xnenkb1R73ynNlfHx8AEDnfGaeazcTExO0bNkSnTp1wrx58+Dp6YkPP/yw1p7HLJxqgImJCTp16oTExERlnlarRWJiInx9fQ0YGdWUwsJCZGVlwcnJCZ06dYKxsbFOvjMzM3H27Fnm+wnWrFkzODo66uQ1Pz8fqampSl59fX2Rl5eHtLQ0pU9SUhK0Wq3yB5ueLL///juuXr0KJycnAMzxk0BEEBUVhU2bNiEpKQnNmjXTaa/Oe7Svry8OHz6sUyTv3LkTGo0Gbdu2fTw7QlXSl+fKZGRkAIDO+cw8P1m0Wi1u375de8/jRzLkxFNo/fr1YmpqKvHx8XLs2DF55ZVXxNraWmekD3pyTJ48WXbv3i3Z2dmyb98+CQoKEjs7O7l06ZKIiIwZM0aaNm0qSUlJcvDgQfH19RVfX18DR036FBQUSHp6uqSnpwsAiY2NlfT0dDlz5oyIiMyfP1+sra1l8+bNcujQIRk4cKA0a9ZM/vjjD2UdISEh4u3tLampqbJ3715xd3eXYcOGGWqX6C5V5bigoECmTJkiKSkpkp2dLbt27ZKOHTuKu7u73Lp1S1kHc1y7vfbaa2JlZSW7d++WnJwc5VVUVKT00fceXVpaKu3atZPevXtLRkaG7NixQ+zt7WX69OmG2CWqhL48nzp1SmbPni0HDx6U7Oxs2bx5szRv3lz8/PyUdTDPtdtbb70lycnJkp2dLYcOHZK33npLVCqV/O9//xOR2nkes3CqQcuWLZOmTZuKiYmJdO3aVQ4cOGDokOgBhYWFiZOTk5iYmEjjxo0lLCxMTp06pbT/8ccfMnbsWGnYsKFYWFhIaGio5OTkGDBiqo7vv/9eAFR4RUREiMidIclnzJghDg4OYmpqKoGBgZKZmamzjqtXr8qwYcOkfv36otFoZOTIkVJQUGCAvaHKVJXjoqIi6d27t9jb24uxsbG4urpKZGRkhX9wMce1W2X5BSBxcXFKn+q8R58+fVr69Okj5ubmYmdnJ5MnT5aSkpLHvDd0L/ryfPbsWfHz8xMbGxsxNTWVli1bytSpU+XGjRs662Gea69Ro0aJq6urmJiYiL29vQQGBipFk0jtPI9VIiKP5loWERERERFR3cBnnIiIiIiIiPRg4URERERERKQHCyciIiIiIiI9WDgRERERERHpwcKJiIiIiIhIDxZOREREREREerBwIiIiIiIi0oOFExERERERkR4snIiIniKnT5+GSqVCRkaGoUNRnDhxAs8++yzMzMzg5eVl6HDqrPj4eFhbWxs6jErV5tiIiMqxcCIieoxGjBgBlUqF+fPn68z/5ptvoFKpDBSVYUVHR8PS0hKZmZlITEystE/5cbv7derUqRqJobZ/cC8qKsL06dPRokULmJmZwd7eHv7+/ti8ebPBYqrtx4yIqKbVM3QARERPGzMzMyxYsACvvvoqGjZsaOhwakRxcTFMTEweaNmsrCz069cPrq6uVfYLCQlBXFyczjx7e/sH2uajVFJSAmNj4xpd55gxY5Camoply5ahbdu2uHr1Kvbv34+rV6/W6HaIiOjeeMWJiOgxCwoKgqOjI+bNm3fPPrNmzapw29oHH3wANzc3ZXrEiBF44YUXMHfuXDg4OMDa2hqzZ89GaWkppk6dChsbG7i4uFQoNoA7t8d169YNZmZmaNeuHZKTk3Xajxw5gj59+qB+/fpwcHDAP/7xD1y5ckVp79mzJ6KiojBx4kTY2dkhODi40v3QarWYPXs2XFxcYGpqCi8vL+zYsUNpV6lUSEtLw+zZs6FSqTBr1qx7HhNTU1M4OjrqvIyMjAAAmzdvRseOHWFmZobmzZsjJiYGpaWlyrKxsbFo3749LC0t0aRJE4wdOxaFhYUAgN27d2PkyJG4ceOGciWrPA6VSoVvvvlGJw5ra2vEx8cD+PPWxy+//BL+/v4wMzPD2rVrAQCrVq1CmzZtYGZmhtatW+Nf//qXso7i4mJERUXByckJZmZmcHV1rfL3YcuWLXj77bfRt29fuLm5oVOnTnj99dcxatQopc/t27cxZcoUNG7cGJaWlvDx8cHu3bvvuc7qHLe8vDy8+uqrcHBwUH5Xtm7dWuUxq04c8fHxaNq0KSwsLBAaGsoCkIieCCyciIgeMyMjI8ydOxfLli3D77///lDrSkpKwoULF7Bnzx7ExsYiOjoa/fv3R8OGDZGamooxY8bg1VdfrbCdqVOnYvLkyUhPT4evry8GDBigfHjNy8tDQEAAvL29cfDgQezYsQMXL17EkCFDdNaxZs0amJiYYN++ffj4448rje/DDz/E4sWLsWjRIhw6dAjBwcF4/vnncfLkSQBATk4OnnnmGUyePBk5OTmYMmXKfR+DH374AcOHD8eECRNw7NgxfPLJJ4iPj8ecOXOUPmq1GkuXLsXRo0exZs0aJCUl4c033wQAdOvWDR988AE0Gg1ycnIeKI633noLEyZMwPHjxxEcHIy1a9di5syZmDNnDo4fP465c+dixowZWLNmDQBg6dKl2LJlCzZs2IDMzEysXbtWpyi+m6OjIxISElBQUHDPPlFRUUhJScH69etx6NAh/O1vf0NISIhyrO/3uGm1WvTp0wf79u3Df/7zHxw7dgzz58+HkZFRlcdMXxypqakYPXo0oqKikJGRgV69euH999+/r+NNRGQQQkREj01ERIQMHDhQRESeffZZGTVqlIiIbNq0Sf76lhwdHS2enp46yy5ZskRcXV111uXq6iplZWXKPA8PD+nRo4cyXVpaKpaWlrJu3ToREcnOzhYAMn/+fKVPSUmJuLi4yIIFC0RE5L333pPevXvrbPvcuXMCQDIzM0VExN/fX7y9vfXur7Ozs8yZM0dnXpcuXWTs2LHKtKenp0RHR1e5noiICDEyMhJLS0vlNXjwYBERCQwMlLlz5+r0//e//y1OTk73XN/GjRvF1tZWmY6LixMrK6sK/QDIpk2bdOZZWVlJXFyciPx5PD/44AOdPi1atJAvvvhCZ957770nvr6+IiLy+uuvS0BAgGi12ir3u1xycrK4uLiIsbGxdO7cWSZOnCh79+5V2s+cOSNGRkZy/vx5neUCAwNl+vTple6jvuP23XffiVqtVnJ+t8qOWXXiGDZsmPTt21enPSwsrNLjT0RUm/AZJyIiA1mwYAECAgIe6CpLuWeeeQZq9Z83Dzg4OKBdu3bKtJGREWxtbXHp0iWd5Xx9fZWf69Wrh86dO+P48eMAgF9++QXff/896tevX2F7WVlZaNWqFQCgU6dOVcaWn5+PCxcuoHv37jrzu3fvjl9++aWae/inXr16YcWKFcq0paWlEu++fft0rjCVlZXh1q1bKCoqgoWFBXbt2oV58+bhxIkTyM/PR2lpqU77w+rcubPy882bN5GVlYXRo0cjMjJSmV9aWgorKysAd26zfO655+Dh4YGQkBD0798fvXv3vuf6/fz88Ntvv+HAgQPYv38/EhMT8eGHHyImJgYzZszA4cOHUVZWpuSm3O3bt2Fra1vpOvUdt4yMDLi4uFRYZ1WqE8fx48cRGhqq0+7r66tzCycRUW3EwomIyED8/PwQHByM6dOnY8SIETptarUaIqIzr6SkpMI67h6EQKVSVTpPq9VWO67CwkIMGDAACxYsqNDm5OSk/FxeuDwulpaWaNmyZYX5hYWFiImJwaBBgyq0mZmZ4fTp0+jfvz9ee+01zJkzBzY2Nti7dy9Gjx6N4uLiKgsnlUpVrTz89ViUPzu1cuVK+Pj46PQrfyarY8eOyM7Oxvbt27Fr1y4MGTIEQUFB+Oqrr+4Zi7GxMXr06IEePXpg2rRpeP/99zF79mxMmzYNhYWFMDIyQlpamrKNcpUVwOVxVnXczM3N7xnLvTxIHERETwoWTkREBjR//nx4eXnBw8NDZ769vT1yc3MhIsow5TX53UsHDhyAn58fgDtXQtLS0hAVFQXgzof6//73v3Bzc0O9eg/+Z0Kj0cDZ2Rn79u2Dv7+/Mn/fvn3o2rXrw+3AX3Ts2BGZmZmVFlUAkJaWBq1Wi8WLFytX5zZs2KDTx8TEBGVlZRWWtbe3R05OjjJ98uRJFBUVVRmPg4MDnJ2d8dtvvyE8PPye/TQaDcLCwhAWFobBgwcjJCQE165dg42NTZXrL9e2bVvlypm3tzfKyspw6dIl9OjRo1rL6ztuHTp0wO+//45ff/210qtOlR2z6sTRpk0bpKam6sw7cOBAtWImIjIkFk5ERAbUvn17hIeHY+nSpTrze/bsicuXL2PhwoUYPHgwduzYge3bt0Oj0dTIdpcvXw53d3e0adMGS5YswfXr15UR2saNG4eVK1di2LBhePPNN2FjY4NTp05h/fr1WLVqVYUrCVWZOnUqoqOj0aJFC3h5eSEuLg4ZGRnKyHM1YebMmejfvz+aNm2KwYMHQ61W45dffsGRI0fw/vvvo2XLligpKcGyZcswYMCASgezcHNzQ2FhIRITE+Hp6QkLCwtYWFggICAAH330EXx9fVFWVoZp06ZVa6jxmJgYjB8/HlZWVggJCcHt27dx8OBBXL9+HZMmTUJsbCycnJzg7e0NtVqNjRs3wtHR8Z7fi9SzZ08MGzYMnTt3hq2tLY4dO4a3334bvXr1gkajgUajQXh4OIYPH47FixfD29sbly9fRmJiIjp06IB+/frd93Hz9/eHn58fXnzxRcTGxqJly5Y4ceIEVCoVQkJCKj1mrVq10hvH+PHj0b17dyxatAgDBw7Ed999x9v0iOjJYOBnrIiInip/HRyiXHZ2tpiYmMjdb8krVqyQJk2aiKWlpQwfPlzmzJlTYXCIu9fl7+8vEyZM0Jnn6uoqS5YsUbYFQL744gvp2rWrmJiYSNu2bSUpKUlnmV9//VVCQ0PF2tpazM3NpXXr1jJx4kRlMIPKtlOZsrIymTVrljRu3FiMjY3F09NTtm/frtOnuoND3L2vf7Vjxw7p1q2bmJubi0ajka5du8qnn36qtMfGxoqTk5OYm5tLcHCwfP755wJArl+/rvQZM2aM2NraCgAlnvPnz0vv3r3F0tJS3N3dJSEhodLBIdLT0yvEtHbtWvHy8hITExNp2LCh+Pn5yddffy0iIp9++ql4eXmJpaWlaDQaCQwMlJ9//vme+zd37lzx9fUVGxsbMTMzk+bNm8v48ePlypUrSp/i4mKZOXOmuLm5ibGxsTg5OUloaKgcOnRIRCofzEHfcbt69aqMHDlSbG1txczMTNq1aydbt26t8pjpi0NEZPXq1eLi4iLm5uYyYMAAWbRoEQeHIKJaTyVy183bREREREREpIPf40RERERERKQHCyciIiIiIiI9WDgRERERERHpwcKJiIiIiIhIDxZOREREREREerBwIiIiIiIi0oOFExERERERkR4snIiIiIiIiPRg4URERERERKQHCyciIiIiIiI9WDgRERERERHp8f8OdbW+2Q29hgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = KNeighborsClassifier(n_neighbors=20, metric='cosine')\n",
        "plot_accuracies(X_train, X_test, y_train, y_test, model, k_start = 5, k_end = 300, step = 10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## ***Evaluating best model***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XO5DAncJIJ5m",
        "outputId": "89fc99fb-1c83-4396-bc81-4f00583e690a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Accuracy: 0.9631944444444445\n",
            "Test Accuracy: 0.9\n",
            "Difference: 0.06319444444444444\n",
            "+---------------------------------+--------------------+---------------------+------------+-----------+\n",
            "| Class                           | Precision          | Recall              |   F1-score | Support   |\n",
            "|---------------------------------+--------------------+---------------------+------------+-----------|\n",
            "| Acne                            | 0.9545454545454546 | 1.0                 |   0.976744 | 21.0      |\n",
            "| Arthritis                       | 0.7692307692307693 | 1.0                 |   0.869565 | 20.0      |\n",
            "| Bronchial Asthma                | 0.7037037037037037 | 1.0                 |   0.826087 | 19.0      |\n",
            "| Cervical spondylosis            | 0.9130434782608695 | 1.0                 |   0.954545 | 21.0      |\n",
            "| Chicken pox                     | 0.7                | 0.9333333333333333  |   0.8      | 15.0      |\n",
            "| Common Cold                     | 0.6428571428571429 | 0.8571428571428571  |   0.734694 | 21.0      |\n",
            "| Dengue                          | 0.8333333333333334 | 0.45454545454545453 |   0.588235 | 22.0      |\n",
            "| Dimorphic Hemorrhoids           | 0.9047619047619048 | 1.0                 |   0.95     | 19.0      |\n",
            "| Fungal infection                | 0.9629629629629629 | 1.0                 |   0.981132 | 26.0      |\n",
            "| Hypertension                    | 1.0                | 0.9444444444444444  |   0.971429 | 18.0      |\n",
            "| Impetigo                        | 0.9565217391304348 | 0.9565217391304348  |   0.956522 | 23.0      |\n",
            "| Jaundice                        | 1.0                | 1.0                 |   1        | 22.0      |\n",
            "| Malaria                         | 1.0                | 1.0                 |   1        | 17.0      |\n",
            "| Migraine                        | 1.0                | 0.9166666666666666  |   0.956522 | 24.0      |\n",
            "| Pneumonia                       | 1.0                | 0.8636363636363636  |   0.926829 | 22.0      |\n",
            "| Psoriasis                       | 0.8                | 0.7058823529411765  |   0.75     | 17.0      |\n",
            "| Typhoid                         | 0.9375             | 0.8333333333333334  |   0.882353 | 18.0      |\n",
            "| Varicose Veins                  | 1.0                | 0.84                |   0.913043 | 25.0      |\n",
            "| allergy                         | 0.9                | 0.6                 |   0.72     | 15.0      |\n",
            "| diabetes                        | 1.0                | 0.8235294117647058  |   0.903226 | 17.0      |\n",
            "| drug reaction                   | 1.0                | 1.0                 |   1        | 16.0      |\n",
            "| gastroesophageal reflux disease | 0.8695652173913043 | 0.9523809523809523  |   0.909091 | 21.0      |\n",
            "| peptic ulcer disease            | 0.9411764705882353 | 0.8888888888888888  |   0.914286 | 18.0      |\n",
            "| urinary tract infection         | 1.0                | 0.9565217391304348  |   0.977778 | 23.0      |\n",
            "| accuracy                        |                    |                     |   0.9      |           |\n",
            "| macro avg                       | 0.9078834240319215 | 0.8969511473891268  |   0.894253 |           |\n",
            "| weighted avg                    | 0.9119005123761891 | 0.9                 |   0.898033 |           |\n",
            "+---------------------------------+--------------------+---------------------+------------+-----------+\n"
          ]
        }
      ],
      "source": [
        "model = KNeighborsClassifier(n_neighbors=20, metric='cosine')\n",
        "evaluate_model(X_train, X_test, y_train, y_test, chi2, 185, model)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "OSq2H2QFpg-T",
        "D7Shok8Gpg-U",
        "X0y34_Fipg-k",
        "qN34jObspg-l"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "NLP",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.20"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}