Sentimental_Analysis.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **SENTIMENTAL ANALYSIS OF IMDB DATASET PROJECT**"
      ],
      "metadata": {
        "id": "tA5v5_i0djIn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Main Libraries\n"
      ],
      "metadata": {
        "id": "wMAwL0n3dy03"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We start by importing the main libraires that we will use:\n",
        "\n",
        "the re module (for regular expression matching operations) the nltk toolkit (for natural language operations) the random module (for random number generation) the numpy library (for arrays operations) the pandas library (for data analysis) the scipy.stats module (for statistics) the seaborn library (for statistical data visualization) the matplotlib.pyplot interface (for MATLAB-like plots)"
      ],
      "metadata": {
        "id": "egTyfY51fmq9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import nltk\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#nltk.download('punkt')\n",
        "#nltk.download('stopwords')\n",
        "\n",
        "# Setting as large the xtick and ytick font sizes in graphs\n",
        "\n",
        "plt.rcParams['xtick.labelsize'] = 'large'\n",
        "plt.rcParams['ytick.labelsize'] = 'large'\n",
        "     "
      ],
      "metadata": {
        "id": "0a1LXiLKf1MP"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMDB dataset**\n",
        "We retrieve from Kaggle the csv file \"IMDB Dataset.csv\" consisting of 50'000 IMDB movies and TV shows reviews with their positive or negative sentiment classification."
      ],
      "metadata": {
        "id": "1FPWdJtRgJpu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "-cNksQokkrRi"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1Vqu7LwkvyX",
        "outputId": "cb89124e-5c40-4a37-a53f-b48a65a7d054"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "WsvVVsqwk4mv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMDB dataset**\n",
        "We retrieve from Kaggle the csv file \"IMDB Dataset.csv\" consisting of 50'000 IMDB movies and TV shows reviews with their positive or negative sentiment classification."
      ],
      "metadata": {
        "id": "1Rq66WIkk_a4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/IMDB Dataset.csv\")\n",
        "df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Dlg7Vtq8lJF_",
        "outputId": "cd9420cf-7ac9-470f-86ca-ea5c61ac1e36"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  review sentiment\n",
              "0      One of the other reviewers has mentioned that ...  positive\n",
              "1      A wonderful little production. <br /><br />The...  positive\n",
              "2      I thought this was a wonderful way to spend ti...  positive\n",
              "3      Basically there's a family where a little boy ...  negative\n",
              "4      Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
              "...                                                  ...       ...\n",
              "49995  I thought this movie did a down right good job...  positive\n",
              "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
              "49997  I am a Catholic taught in parochial elementary...  negative\n",
              "49998  I'm going to have to disagree with the previou...  negative\n",
              "49999  No one expects the Star Trek movies to be high...  negative\n",
              "\n",
              "[50000 rows x 2 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>One of the other reviewers has mentioned that ...</td>\n",
              "      <td>positive</td>\n",
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              "      <td>positive</td>\n",
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              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49995</th>\n",
              "      <td>I thought this movie did a down right good job...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>I am a Catholic taught in parochial elementary...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>I'm going to have to disagree with the previou...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>No one expects the Star Trek movies to be high...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
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              "<p>50000 rows × 2 columns</p>\n",
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      "cell_type": "code",
      "source": [
        "#We print the basic properties of the DataFrame. \n",
        "#In particular, we note that there are no null values in the DataFrame.\n",
        "print('\\033[1m' + 'df.shape:' + '\\033[0m', df.shape)\n",
        "print('\\033[1m' + 'df.columns:' + '\\033[0m', df.columns, '\\n')\n",
        "print('\\033[1m' + 'df.sentiment.value_counts():' + '\\033[0m')\n",
        "print(df.sentiment.value_counts(), '\\n')\n",
        "\n",
        "with sns.axes_style(\"darkgrid\"):\n",
        "    df['sentiment'].value_counts().plot.bar(color=['darkblue', 'r'], rot=0, fontsize='large')\n",
        "    plt.show()\n",
        "\n",
        "print('\\033[1m' + 'df.info:' + '\\033[0m')\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
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          "text": [
            "\u001b[1mdf.shape:\u001b[0m (50000, 2)\n",
            "\u001b[1mdf.columns:\u001b[0m Index(['review', 'sentiment'], dtype='object') \n",
            "\n",
            "\u001b[1mdf.sentiment.value_counts():\u001b[0m\n",
            "positive    25000\n",
            "negative    25000\n",
            "Name: sentiment, dtype: int64 \n",
            "\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mdf.info:\u001b[0m\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   review     50000 non-null  object\n",
            " 1   sentiment  50000 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 781.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To make the DataFrame suitable for analysis by converting its sentiment values to integers:\n",
        "\n",
        "positive → 1 negative → 0"
      ],
      "metadata": {
        "id": "5b78eSVZnC6f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.sentiment = [1 if s == 'positive' else 0 for s in df.sentiment]\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vmW7x9bFnGBH",
        "outputId": "d01d0dce-a348-448f-c5ec-0549fec4cbab"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  review  sentiment\n",
              "0      One of the other reviewers has mentioned that ...          1\n",
              "1      A wonderful little production. <br /><br />The...          1\n",
              "2      I thought this was a wonderful way to spend ti...          1\n",
              "3      Basically there's a family where a little boy ...          0\n",
              "4      Petter Mattei's \"Love in the Time of Money\" is...          1\n",
              "...                                                  ...        ...\n",
              "49995  I thought this movie did a down right good job...          1\n",
              "49996  Bad plot, bad dialogue, bad acting, idiotic di...          0\n",
              "49997  I am a Catholic taught in parochial elementary...          0\n",
              "49998  I'm going to have to disagree with the previou...          0\n",
              "49999  No one expects the Star Trek movies to be high...          0\n",
              "\n",
              "[50000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6858b6a7-c386-4f33-841a-f122e2ec2eac\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>One of the other reviewers has mentioned that ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49995</th>\n",
              "      <td>I thought this movie did a down right good job...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>I am a Catholic taught in parochial elementary...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>I'm going to have to disagree with the previou...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>No one expects the Star Trek movies to be high...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data preprocessing**\n",
        "\n",
        "First, we use regular expressions to make the following transformations to the reviews:\n",
        "\n",
        "remove punctuation marks remove HTML tags remove URL's remove characters which are not letters or digits remove successive whitespaces convert the text to lower case strip whitespaces from the beginning and the end of the reviews"
      ],
      "metadata": {
        "id": "3qWsGvFsnKKQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Storing in \"before_process\" a random example of review before preprocessing\n",
        "# Defining and applying the function \"process\" performing the transformations of the reviews\n",
        "# Storing in \"after_process\" the example of review after preprocessing\n",
        "idx = random.randint(0, len(df)-1)\n",
        "before_process = df.iloc[idx][0]\n",
        "\n",
        "def process(x):\n",
        "    x = re.sub('[,\\.!?:()\"]', '', x)\n",
        "    x = re.sub('<.*?>', ' ', x)\n",
        "    x = re.sub('http\\S+', ' ', x)\n",
        "    x = re.sub('[^a-zA-Z0-9]', ' ', x)\n",
        "    x = re.sub('\\s+', ' ', x)\n",
        "    return x.lower().strip()\n",
        "\n",
        "df['review'] = df['review'].apply(lambda x: process(x))\n",
        "after_process = df.iloc[idx][0]\n",
        "     "
      ],
      "metadata": {
        "id": "Ip8bj0QWnP0e"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we remove stopwords from the reviews using the word_tokenize() function from the nltk.tokenize package."
      ],
      "metadata": {
        "id": "hKoxnOmUnUBw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Storing in \"sw_set\" the set of English stopwords provided by nltk\n",
        "# Defining and applying the function \"sw_remove\" which remove stopwords from reviews\n",
        "# Storing in \"after_removal\" the example of review after removal of the stopwords\n",
        "\n",
        "idx = random.randint(0, len(df)-1)\n",
        "before_process = df.iloc[idx][0]\n",
        "     "
      ],
      "metadata": {
        "id": "0cASG5AYnXIf"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process(x):\n",
        "    x = re.sub('[,\\.!?:()\"]', '', x)\n",
        "    x = re.sub('<.*?>', ' ', x)\n",
        "    x = re.sub('http\\S+', ' ', x)\n",
        "    x = re.sub('[^a-zA-Z0-9]', ' ', x)\n",
        "    x = re.sub('\\s+', ' ', x)\n",
        "    return x.lower().strip()\n",
        "\n",
        "df['review'] = df['review'].apply(lambda x: process(x))\n",
        "after_process = df.iloc[idx][0]\n",
        "     "
      ],
      "metadata": {
        "id": "wa-l3wtJnbwj"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "nltk.download('stopwords')\n",
        "sw_set = set(nltk.corpus.stopwords.words('english'))\n",
        "def sw_remove(x):\n",
        "  words = nltk.tokenize.word_tokenize(x)\n",
        "  filtered_list = [word for word in words if word not in sw_set]\n",
        "  return ' '.join(filtered_list)\n",
        "  df['review'] = df['review'].apply(lambda x: sw_remove(x))\n",
        "  after_removal = sw_remove(after_process)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGxlJXNhneXA",
        "outputId": "5d2d72c0-f7e5-4cdf-d9d7-7b4e3cbf3b21"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\033[1m' + 'Review #%d before preprocessing:' % idx + '\\033[0m' + '\\n', before_process, '\\n')\n",
        "print('\\033[1m' + 'Review #%d after preprocessing:' % idx + '\\033[0m' + '\\n', after_process, '\\n')\n",
        "# print('\\033[1m' + 'Review #%d after preprocessing and stopwords removal:' % idx + '\\033[0m' + '\\n', after_removal)\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5tJ170Tn2rB",
        "outputId": "b47feb50-a0c6-499d-8938-206c113f82f5"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mReview #25657 before preprocessing:\u001b[0m\n",
            " ah yes the vs series mvc2 being the pinnacle it s been said before this is what you get when half of the crew fell asleep on the job unfortunately the gameplay half did don t get me wrong this is fun but you get tired of mashing buttons as for the plot summary ahahahahahaaaaa there is no plot beat that guy at the end and win eh who plays this by their self anyway \n",
            "\n",
            "\u001b[1mReview #25657 after preprocessing:\u001b[0m\n",
            " ah yes the vs series mvc2 being the pinnacle it s been said before this is what you get when half of the crew fell asleep on the job unfortunately the gameplay half did don t get me wrong this is fun but you get tired of mashing buttons as for the plot summary ahahahahahaaaaa there is no plot beat that guy at the end and win eh who plays this by their self anyway \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For illustration, we print the example of review before preprocessing, after preprocessing and after stopwords removal."
      ],
      "metadata": {
        "id": "N-Xzrji1oL_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_rev, test_rev, train_sent, test_sent = train_test_split(df['review'], df['sentiment'], test_size=0.1, random_state=42)\n",
        "\n",
        "print('\\033[1m' + 'train_rev.shape:' + '\\033[0m', train_rev.shape)\n",
        "print('\\033[1m' + 'test_rev.shape:' + '\\033[0m', test_rev.shape)\n",
        "print('\\033[1m' + 'train_sent.shape:' + '\\033[0m', train_sent.shape)\n",
        "print('\\033[1m' + 'test_sent.shape:' + '\\033[0m', test_sent.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evc4JrL1oQ3g",
        "outputId": "b93db0a1-48a0-496a-bb7b-f9555610bf75"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mtrain_rev.shape:\u001b[0m (45000,)\n",
            "\u001b[1mtest_rev.shape:\u001b[0m (5000,)\n",
            "\u001b[1mtrain_sent.shape:\u001b[0m (45000,)\n",
            "\u001b[1mtest_sent.shape:\u001b[0m (5000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data splitting and tokenization**\n",
        "\n",
        "We start by splitting our DataFrame into a training and test lists. We use the train_test_split() function from the sklearn.model_selection module which allow to perform the splitting randomly with respect to the index of the DataFrame."
      ],
      "metadata": {
        "id": "XuBE1rCmok0x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "\n",
        "dict_size = 35000\n",
        "tokenizer = Tokenizer(num_words=dict_size)\n",
        "tokenizer.fit_on_texts(df['review'])\n",
        "\n",
        "print('\\033[1m' + 'Dictionary size:' + '\\033[0m', dict_size)\n",
        "print('\\033[1m' + 'Length of the tokenizer index:' + '\\033[0m', len(tokenizer.word_index))\n",
        "print('\\033[1m' + 'Number of documents the tokenizer was trained on:' + '\\033[0m', tokenizer.document_count, '\\n')\n",
        "print('\\033[1m' + 'First 20 entries of the tokenizer index:' + '\\033[0m')\n",
        "print(*list(tokenizer.word_index.items())[:20])\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0DG0nJRoqV4",
        "outputId": "56719c97-79cf-451b-f140-a1f3c9a31d8c"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDictionary size:\u001b[0m 35000\n",
            "\u001b[1mLength of the tokenizer index:\u001b[0m 125949\n",
            "\u001b[1mNumber of documents the tokenizer was trained on:\u001b[0m 50000 \n",
            "\n",
            "\u001b[1mFirst 20 entries of the tokenizer index:\u001b[0m\n",
            "('the', 1) ('and', 2) ('a', 3) ('of', 4) ('to', 5) ('is', 6) ('it', 7) ('in', 8) ('i', 9) ('this', 10) ('that', 11) ('s', 12) ('was', 13) ('as', 14) ('for', 15) ('movie', 16) ('with', 17) ('but', 18) ('film', 19) ('you', 20)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use the texts_to_sequences() function of the Tokenizer class to convert the training reviews and test reviews to lists of sequences of integers (tokens) \"train_rev_tokens\" and \"test_rev_tokens\", and we store in the numpy array \"seq_lengths\" the lengths of the sequences included in \"train_rev_tokens\".\n",
        "\n"
      ],
      "metadata": {
        "id": "HtHiUznmpGUe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_rev_tokens = tokenizer.texts_to_sequences(train_rev)\n",
        "test_rev_tokens = tokenizer.texts_to_sequences(test_rev)\n",
        "seq_lengths =  np.array([len(sequence) for sequence in train_rev_tokens])\n",
        "     "
      ],
      "metadata": {
        "id": "80X7G1JfpJpx"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If the lengths of the sequences were normally distributed, then a given length could be considered small or large when outside the interval\n",
        "\n",
        "mean value of seq_lengths±2 standard deviations of seq_lengths,\n",
        "\n",
        "and lengths not belonging to this interval would only represent 5% of the elements of seq_lengths. Here, we follow this heuristics, and thus define an upper bound for the length of sequences accordingly.\n",
        "\n",
        "(note that we use only the training set to define this upper bound, in order to avoid any data leakage or look-ahead bias)"
      ],
      "metadata": {
        "id": "-GU-Fs68pM52"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Storing in \"upper_bound\" our chosen upper bound for the length of sequences\n",
        "# Computing the percentage of lengths smaller or equal than \"upper_bound\"\n",
        "\n",
        "upper_bound = int(np.mean(seq_lengths) + 2 * np.std(seq_lengths))\n",
        "percentage = stats.percentileofscore(seq_lengths, upper_bound)\n",
        "\n",
        "print('The value of upper_bound is %d and the percentage of sequences in \"train_rev_tokens\" \\\n",
        "of length smaller or equal than upper_bound is %.2f%%.' % (upper_bound, round(percentage, 2)))\n",
        "\n",
        "# Histogram plot of the lengths of the sequences in \"train_rev_tokens\"\n",
        "\n",
        "with sns.axes_style(\"darkgrid\"):\n",
        "\n",
        "    _, hist = plt.subplots(figsize=(10,6))\n",
        "    hist.hist(seq_lengths[seq_lengths < 2*upper_bound], color='darkblue', bins=40, rwidth=0.7)\n",
        "    hist.axvline(np.mean(seq_lengths), color='darkorange', linestyle='--', label='Mean value')\n",
        "    hist.axvline(upper_bound, color='r', linestyle='--', label='Upper bound')\n",
        "\n",
        "    plt.xlabel('Length of sequences in \"train_rev_tokens\"', size='large')\n",
        "    plt.ylabel('Number of samples', size='large')\n",
        "    plt.text(upper_bound, 0, 'test')\n",
        "    plt.legend(fontsize='large', facecolor='palegreen')\n",
        "    plt.xticks(rotation=30)\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "WD1bvVKVpQzl",
        "outputId": "5ad94c5b-3a53-4ca4-a240-1b21e7181251"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The value of upper_bound is 575 and the percentage of sequences in \"train_rev_tokens\" of length smaller or equal than upper_bound is 94.57%.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The value of upper_bound is 575 and the percentage of sequences in \"train_rev_tokens\" of length smaller or equal than upper_bound is 94.57%.\n"
      ],
      "metadata": {
        "id": "nV-SvoHqpT9Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the pad_sequences() function from keras.preprocessing.sequence module, we transform \"train_rev_tokens\" and \"test_rev_tokens\" into 2D numpy arrays of shape (number of sequences, upper_bound). Sequences of length smaller (resp. larger) than \"upper_bound\" are extended (resp. truncated) to get a length equal to \"upper_bound\"."
      ],
      "metadata": {
        "id": "jeZDTwNWpsKV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras_preprocessing.sequence import pad_sequences\n",
        "train_rev_pad = pad_sequences(train_rev_tokens, maxlen=upper_bound)\n",
        "test_rev_pad = pad_sequences(test_rev_tokens, maxlen=upper_bound)\n",
        "\n",
        "print('\\033[1m' + 'train_rev_pad.shape:' + '\\033[0m', train_rev_pad.shape)\n",
        "print('\\033[1m' + 'test_rev_pad.shape:' + '\\033[0m', test_rev_pad.shape, '\\n')\n",
        "\n",
        "# Printing an example of review after padding\n",
        "\n",
        "idx_pad = random.randint(0, len(train_rev_pad)-1)\n",
        "print('\\033[1m' + 'Review #%d after padding:' %idx_pad + '\\033[0m' + '\\n', train_rev_pad[idx_pad])\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKLDwgchpyNh",
        "outputId": "0bf45b5e-7f1d-44c8-f621-f2c1f471af66"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mtrain_rev_pad.shape:\u001b[0m (45000, 575)\n",
            "\u001b[1mtest_rev_pad.shape:\u001b[0m (5000, 575) \n",
            "\n",
            "\u001b[1mReview #39596 after padding:\u001b[0m\n",
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            "  1254     6     3    81    29    18    14     8   107   510    16   134\n",
            "     7    46    48  1873     2   783  3946   810  3452    21     3  3450\n",
            "     2   703  1884  1493    15    48    77    49   195  1531  3314]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LSTM Model**\n",
        "\n",
        "We start by importing some classes from Keras:\n",
        "\n",
        "the Sequential class from the keras.models API (to group a linear stack of layers into a model) the Embedding class from the keras.layers API (to turn positive integers (indexes) into dense vectors of fixed size) the LSTM class from the keras.layers API (to apply a long short-term memory layer to an input) the Dropout class from the keras.layers API (to apply dropout to an input) the Dense class from the keras.layers API (to apply a regular densely-connected NN layer to an input)"
      ],
      "metadata": {
        "id": "1lcuzrFJp6oX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, LSTM, Dropout, Dense\n",
        "     "
      ],
      "metadata": {
        "id": "Ma6u56czqHP-"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the \"imageio.v3\" library (for reading and writing images)\n",
        "# See https://imageio.readthedocs.io/en/stable/\n",
        "import imageio as iio\n",
        "im = iio.imread('imageio:chelsea.png')\n",
        "image = iio.imread(\"https://www.tensorflow.org/text/guide/images/embedding2.png\")\n",
        "plt.figure(figsize = (7, 7))\n",
        "plt.imshow(image)\n",
        "plt.axis('off');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "6zDcD6DgqJns",
        "outputId": "ee05762d-c58b-4e5c-db9f-4afa49febf4c"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In the LSTM model, we set the following parameters:**\n",
        "\n",
        "the output dimension of the Embedding layer (dimension of the vector space containing the word embeddings) is \"output_dim\" the number of units of the LSTM layer is \"units_lstm\" the dropout rate of the Dropout layer is \"r\" the activation function of the final Dense layer is sigmoid (this is a natural choice since the output of the model should be a number between 0, for negative reviews, and 1, for positive reviews)"
      ],
      "metadata": {
        "id": "Hh2sRWDIqOc1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output_dim = 14\n",
        "units_lstm = 16\n",
        "r = 0.8\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=dict_size, output_dim=output_dim, input_length=upper_bound))\n",
        "model.add(LSTM(units_lstm))\n",
        "model.add(Dropout(r))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "     "
      ],
      "metadata": {
        "id": "SB5ce3j7qUl8"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We give a summary of the model using the summary method of the model class of Keras. The \"None\" value stands for the (not yet defined) value of the batch size."
      ],
      "metadata": {
        "id": "xxHfsNVNrTI7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfaVTxE1rYAk",
        "outputId": "ad28edd9-830d-4401-c6bb-595896d67b2b"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 575, 14)           490000    \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 16)                1984      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 16)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 492,001\n",
            "Trainable params: 492,001\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We import the plot_model function from the keras.utils.vis_utils module to plot a schema of the model."
      ],
      "metadata": {
        "id": "HEdmJ2PLrd08"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "\n",
        "plot_model(model, show_shapes=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "IN2UNjlurheM",
        "outputId": "87c4bcae-ceb2-4834-dcfb-6579c71076d5"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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lr7zUcjd9bmr2BX1hezQ0NOCLL75AcXGx6oarr68vtmzZgi1btvS6S66tra3IyclBbW0twsPDzR1OpzZv3oxx48YhKioKwJ9PuZeVleHUqVO4fPmyQZfFycLEJBIJRCIRUlNTcfPmTSiVSpSVlWHv3r2IjY1FeHg4ysrKOn2V8d1/uu7Ev/zyi0HbY+xed+7cUb1I8KWXXlKVr1u3DvPnz0d4eHivelngiRMnkJ2djWPHjun8rIip7dy5E/n5+Th69KjqGbXc3FzViwTvfZtvj+nxM4QZyDfffEOPPfYYOTg4kJWVFTk6OlJgYCAlJyeTUqk0d3jdtm7dOrKxsVHdn8rKyjJrPOa+DGVp20MXxjr+v/jiC3rjjTcM3m5flZOTQ9u3b6eWlhaDttvVZSgBkfqjxJmZmViwYAG/w5/1evPnzwcAZGVlmTmS3oOP/76ti/7P4stQjDHGtOJkwRhjTCtOFowxxrTiZMEYY0wrThaMMca0su5sgim+O82YKfC+rD/eZuxenSYLU3+LljFD27VrFwB0+dI2pi4vLw+JiYl8/PdR7f3fkU6TxTPPPGO0gBgzhfbnK3hf1k9iYiJvsz6ss2TB9ywYY4xpxcmCMcaYVpwsGGOMacXJgjHGmFacLBhjjGl13yWL7777DiNHjoRQKIRAIIC7uzvi4uLMHZaa7OxseHt7q74j4eHhgYiICHOHxVi3LFu2TO27KB3ty8ePH8e6des09v3nnntOo+706dNhb28PKysrjBo1Sus3sM0tLi6uw+/DjB49Wq3elClTOv2WjJ2dnV7tHT58GAkJCRof1srJyVGbp1+/fgZbz/suWUycOBH//e9/MX36dADAxYsXsXHjRjNHpS4sLAy//vorfHx84OjoiPLycuzfv9/cYTHWbS4uLjh27BguXryI1NRUtWmbNm1CUlIS1q9fr7bvu7q6Yv/+/Rof6fnyyy+RlZWFWbNmoaioCP7+/qZcFbOYPHmyXvVDQkIgFosRHByMqqoqVXloaChKS0vxzTffYMaMGQaN8b5LFpaooaEBgYGB5g6DmYEp+t4S9i+JRKL6Up6tra2qPD4+HhkZGcjMzIS9vb3aPElJSRAKhYiMjOxVX9HryL59+0BEan+FhYVqdcRiMWpqajTqRUZG4vXXX9e7vZUrV2Ls2LGYMWMGWlpaAPz55H37l/KGDh1q0HXkZGECqampuHXrlrnDYGZgir631P3r8uXLeOutt/D2229DLBZrTA8MDER0dDRu3LiBNWvWmCFC0/r88881Eub169dRWFiIadOmdavNzZs3Iz8/v9MH6QypzySLlJQUyGQySKVS5Obm4qmnnoKDgwO8vLyQnp6uqpeUlASxWAw3NzcsW7YMAwYMgFgsRmBgIM6cOaOqFxUVBRsbG3h4eKjKXn31VchkMggEAty+fRsAEB0djZiYGJSUlEAgEMDX17db8X/77bfw8/ODo6MjxGIxxowZgy+++AIAsGTJEtU1Sh8fH5w/fx4A8OKLL0IqlcLR0RGHDx8G8OeH6GNjYyGXyyGRSPDAAw+oXu3wf//3f5BKpbC3t8etW7cQExMDT09PXLx4sVsx90ZEhJ07d2LkyJGwtbWFs7MzZs+ejV9++UVVpyd9b6r96/PPP4eDgwO2bdtm1O3VlaSkJBARQkJCOq0TFxeHYcOGYe/evTh+/HiX7enSN7oe50DXx4KpxMfHY+XKld2e39nZGY8++igSExON/3VDPb7B2qs88cQTBIAqKytVZRs2bCAA9NVXX1F1dTXdunWLgoKCSCaTUXNzs6peZGQkyWQy+vnnn6mxsZGKioooICCA7O3t6dq1a6p6ixYtInd3d7Xl7tixgwBQRUWFqiwsLIx8fHw0YvTx8SFHR0ed1icrK4s2b95Md+7coT/++IMmTpxIrq6uasuwsrKiGzduqM337LPP0uHDh1X/XrNmDdna2tLBgwepsrKS1q9fT0KhkM6ePau2jVauXEm7d++muXPn0n//+1+dYrQ03fkGd2xsLNnY2NC+ffuoqqqKCgoKyN/fn/r160fl5eWqej3pe1PsX5999hnZ29vTli1b9Fr/7hz/kZGR5OnpqVHu7e1Nfn5+Hc7j4+NDV65cISKi06dPk1AopMGDB1NdXR0RER07doxCQ0PV5tG1b3Q9zrUdC7raunUreXl5kZOTE4lEIho8eDCFhobS999/3+V8paWl5OfnR62trT1qb926dQSAzp8/r1a+cuVKtXOELrr6Bnef+WVxt8DAQDg4OKB///4IDw9HfX09rl27plbH2tpa9T8YPz8/pKSkoLa2FmlpaWaJed68edi0aROcnZ3h4uKCkJAQ/PHHH6ioqAAALF++HK2trWrx1dTU4OzZs6obXY2NjUhJScGcOXMQFhYGJycnbNy4ESKRSGO94uPjsWLFCmRnZ2PEiBGmW1EzamhowM6dOzF37lxERETA0dERY8aMwfvvv4/bt29jz549BluWsfevmTNnoqamBm+99ZZB2tNXfX09rly5Ah8fH611J02ahFWrVuG3337Dm2++2WGd7vRNV8e5PseCNi+88AIOHz6M69evo66uDunp6bh27RoeffRRFBUVdTpffHw8XnvtNQiF6qdhfdtrvzdx4cIFveLWV59MFnezsbEBACiVyi7rTZgwAVKpVO0nrzmJRCIAUA2dmzZtGoYNG4YPP/xQ9XM0IyMD4eHhsLKyAvDnyDCFQqE2BE8ikcDDw8Ni1sucioqKUFdXhwkTJqiVBwQEwMbGRu0ykaFZ2v7VU7du3QIRQSqV6lQ/Li4Ow4cPR3JyMk6dOqUxvad9c+9xbshjYeDAgXjwwQdhZ2cHGxsbTJw4EWlpaWhoaEBycnKH85SVleHw4cNYvHhxj9tr38Y3b97UK2599flkoQ9bW1vV/+RN7ciRI5gyZQr69+8PW1tbjdETAoEAy5Ytw6+//oqvvvoKAPCvf/0LL7/8sqpOfX09AGDjxo1qY7GvXr0KhUJhupWxUO1DEO8e897OyckJtbW1Rl2+OfcvQ2tsbAQAtZFRXRGLxUhLS4NAIMBLL72EhoYGtemG7htjHwtjxoyBlZUVLl261OH0hIQELF26tMMb//q2J5FIAPy1zY2Fk4WOlEolqqqq4OXlZZLlffPNN6rvMVy7dg1z5syBh4cHzpw5g+rqaiQkJGjMs3jxYojFYuzduxcXL16Eg4MDBg0apJrev39/AH9+54HuGZaXl5dnkvWyZE5OTgDQ4YnH2H1v6v3L2NpPYPc+NNaVSZMmYfXq1SguLsbWrVvVphm6b4x9LLS1taGtra3DZFleXo5PPvkEr7zyikHaa25uBvDXNjcWThY6OnHiBIgIEydOVJVZW1trvXzVXT/++CNkMhmAP69FKpVKvPLKK/D29oZYLO7wS2bOzs5YsGABcnJy8M4772Dp0qVq0wcOHAixWIz8/HyjxNzbjR49GnZ2dvjhhx/Uys+cOYPm5maMHz9eVWbovjf1/mVsbm5uEAgEej8/sXXrVowYMUI1oq+dPn2jC0MeC0888YRG2dmzZ0FEmDRpksa0hIQEREREwMXFxSDttW9jd3d3fUPXCyeLTrS1taGyshItLS0oKChAdHQ05HK52jVGX19f3LlzBzk5OVAqlaioqMDVq1c12nJxcUFZWRl+++031NbWdnkCUCqVuHnzJk6cOKFKFnK5HMCfr0xobGxEcXFxp9doly9fjqamJnz22WeYNWuW2jSxWIwXX3wR6enpSElJQU1NDVpbW1FaWorff/9d30103xGLxYiJicGhQ4ewf/9+1NTU4MKFC1i+fDkGDBiAyMhIVd2e9r2x969jx46ZdeisVCqFt7c3SktL9Zqv/XJU+322u8t17Rtdl6PtWAgPD4e7u7vW143cuHEDGRkZqKqqglKpRF5eHpYsWQK5XI7ly5er1b158yY+/PDDLr/eqE97AFTbeMyYMXptA73pMXSqV/juu+9o1KhRJBQKCQB5eHjQtm3bKDk5maRSKQGgoUOHUklJCe3Zs4ccHBwIAA0aNIguXbpERH8OBRSJROTp6UnW1tbk4OBAs2fPppKSErVl/fHHHzR16lQSi8U0ZMgQeu2112jt2rUEgHx9fVXDIM+dO0eDBg0iiURCkydPpvfee498fHwIQJd/hw4dUi3rjTfeIBcXF3JycqL58+fTu+++SwDIx8dHbbglEdGDDz5I69at63D7NDU10RtvvEFyuZysra2pf//+FBYWRkVFRZSQkEASiYQA0MCBA2nfvn2G7BqT687Q2ba2NtqxYwcNHTqURCIROTs705w5c+jixYtq9brb9+Xl5Ubfv8rLy+no0aNkb29PcXFxeq2/IYfORkVFkUgkIoVCoSo7dOiQat/v168frVixosM2165dqzF0Vpe+0ec47+pYICKaM2cOAaDY2Ngu1z8mJoZ8fHxIJpORtbU1eXl50dKlS6msrEyj7urVqykiIsJg7RERzZw5kzw9PamtrU2t3NBDZ++7ZGEIkZGR5OLiYu4wum3GjBn066+/mjsMs+tOsjAFS96/DJksiouLydrautf+p6O1tZWCgoIoNTXV3KF06vbt2yQWi+mdd97RmMbPWZiIPjfmzO3uy1oFBQUQi8UYMmSIGSNi2vSm/UsXDQ0N+OKLL1BcXKy64err64stW7Zgy5YtqKurM3OE+mltbUVOTg5qa2sRHh5u7nA6tXnzZowbNw5RUVEA/nzKvaysDKdOncLly5cNuixOFveBN954A8XFxbh06RJefPFFjZEkjBnbnTt3VC8SfOmll1Tl69atw/z58xEeHt6rXhZ44sQJZGdn49ixYzo/K2JqO3fuRH5+Po4ePap67io3N1f1IsF73+bbU5ws7rF+/XqkpaWhuroaQ4YMwcGDB80dklZSqRQjRozAY489hs2bN8PPz8/cIbFO9Mb9S5v3339fbejpva/b37ZtG6KiovCPf/zDTBHqLzg4GB9//LHau7ksSW5uLpqamnDixAk4OzurymfPnq3WF+3vEDMEAZH626cyMzOxYMEC47+UijEjmz9/PgAgKyvLzJH0Hnz8921d9H8W/7JgjDGmFScLxhhjWnGyYIwxphUnC8YYY1pZdzYhMzPTlHEwZnDtr0HgfVl37S/R423WN3X1EsVOR0MxxhjrmzoaDaWRLBjry3joKGMd4qGzjDHGtONkwRhjTCtOFowxxrTiZMEYY0wrThaMMca04mTBGGNMK04WjDHGtOJkwRhjTCtOFowxxrTiZMEYY0wrThaMMca04mTBGGNMK04WjDHGtOJkwRhjTCtOFowxxrTiZMEYY0wrThaMMca04mTBGGNMK04WjDHGtOJkwRhjTCtOFowxxrTiZMEYY0wrThaMMca04mTBGGNMK04WjDHGtOJkwRhjTCtOFowxxrTiZMEYY0wrThaMMca04mTBGGNMK04WjDHGtOJkwRhjTCtOFowxxrSyNncAjJlLaWkpXnjhBbS2tqrKKisrYW9vjylTpqjVHT58OP7f//t/Jo6QMcvByYL1WV5eXrh69SpKSko0pp08eVLt34888oipwmLMIvFlKNanPf/88xCJRFrrhYeHmyAaxiwXJwvWpy1atAgtLS1d1hk1ahT8/PxMFBFjlomTBevTfHx88MADD0AgEHQ4XSQS4YUXXjBxVIxZHk4WrM97/vnnYWVl1eG0lpYWzJ8/38QRMWZ5OFmwPm/hwoVoa2vTKBcKhZg4cSIGDx5s+qAYszCcLFifN2DAADz88MMQCtUPB6FQiOeff95MUTFmWThZMAbgueee0ygjIsydO9cM0TBmeThZMAZg3rx5avctrKys8Nhjj8HNzc2MUTFmOThZMAbA2dkZjz/+uCphEBEiIiLMHBVjloOTBWP/ExERobrRLRKJMHv2bDNHxJjl4GTB2P+EhITA1tYWADBr1izY2dmZOSLGLAcnC8b+RyaTqX5N8CUoxtQJiIgM2WBmZiYWLFhgyCYZY4zpwcCndQDIMo8KhacAACAASURBVNpbZw8cOGCsppkFy8vLQ2JiYq/t/9bWVhw4cADPPvusSZe7YMECREdHY9KkSSZdLru/tB9/xmC0ZPHMM88Yq2lm4RITE3t1/8+ZMwdisdiky1ywYAEmTZrUq7cbswzGShZ8z4Kxe5g6UTDWG3CyYIwxphUnC8YYY1pxsmCMMaYVJwvGGGNa3dfJIiAgAFZWVhg3bpzB216yZAns7e0hEAiQn5+vd72jR4/C0dERn376qcFj6662tjbs2rULgYGB5g7FIrfP/er48eNYt24dsrOz4e3tDYFAAIFA0OGbeKdPnw57e3tYWVlh1KhROHfunBki1l1cXJxqfe7+Gz16tFq9KVOmdFhPIBCoPcmvS3uHDx9GQkICWltbTbaepnBfJ4uzZ89i6tSpRml77969+OCDD7pdzwgPzfRIcXExHnnkEaxevRoKhcLc4Vjc9rlfbdq0CUlJSVi/fj3CwsLw66+/wsfHB66urti/fz+OHDmiVv/LL79EVlYWZs2ahaKiIvj7+5spctOZPHmyXvVDQkIgFosRHByMqqoqI0Vlevd1smjX2feVzWnmzJmorq7GrFmzzB0KfvrpJ7z55ptYvny5UX6FdYclbZ+GhgaL+LVlaPHx8cjIyEBmZibs7e3VpiUlJUEoFCIyMhLV1dVmitAw9u3bByJS+yssLFSrIxaLUVNTo1EvMjISr7/+ut7trVy5EmPHjsWMGTPQ0tJi9HU0hT6RLEQikVHa1TUJmSJZERGysrKwZ88evecdO3YssrOzsWjRItWL9NhfUlNTcevWLXOHYVCXL1/GW2+9hbfffrvD50oCAwMRHR2NGzduYM2aNWaI0LQ+//xzjYR5/fp1FBYWYtq0ad1qc/PmzcjPzzfaQ3KmZhHJorW1FbGxsZDL5ZBIJHjggQdUr4tITEyETCaDUCjE+PHj4e7uDpFIBJlMBn9/fwQFBWHgwIEQi8VwcnLS+F8A8OeBMWLECMhkMkgkEgQFBeHUqVM6xwD8eTLesWMHhg8fDltbWzg6OmLt2rUay9Kl3qlTpyCXyyEQCPDuu+8CAFJSUiCTySCVSpGbm4unnnoKDg4O8PLyQnp6ukas27dvx/DhwyGRSNCvXz8MGTIE27dvvy+eAO7J9klKSoJYLIabmxuWLVuGAQMGQCwWIzAwEGfOnFHVi4qKgo2NDTw8PFRlr776KmQyGQQCAW7fvg0AiI6ORkxMDEpKSiAQCODr6wvgz5OLg4MDtm3bZopNYnBJSUkgIoSEhHRaJy4uDsOGDcPevXtx/PjxLtsjIuzcuRMjR46Era0tnJ2dMXv2bPzyyy+qOvru410dj6YQHx+PlStXdnt+Z2dnPProo0hMTLw/LquSgR04cID0bXbNmjVka2tLBw8epMrKSlq/fj0JhUI6e/YsERFt2rSJANCZM2eovr6ebt++TU8++SQBoCNHjlBFRQXV19dTVFQUAaD8/HxV28HBweTt7U1XrlwhpVJJhYWF9NBDD5FYLKZLly7pHMOGDRtIIBDQP//5T6qsrCSFQkHJyckEgM6fP69qR9d6169fJwC0e/dutXkB0FdffUXV1dV069YtCgoKIplMRs3Nzap627ZtIysrK8rNzSWFQkE//vgjubu705QpU/Ta7h156KGHaOzYsd2evzv935GebJ/IyEiSyWT0888/U2NjIxUVFVFAQADZ29vTtWvXVPUWLVpE7u7uasvdsWMHAaCKigpVWVhYGPn4+KjV++yzz8je3p62bNnS43UlIgJABw4cMEhbuvD29iY/P78Op/n4+NCVK1eIiOj06dMkFApp8ODBVFdXR0REx44do9DQULV5YmNjycbGhvbt20dVVVVUUFBA/v7+1K9fPyovL1fV07UPtR2Putq6dSt5eXmRk5MTiUQiGjx4MIWGhtL333/f5XylpaXk5+dHra2tPWpv3bp1Gse+MRnq+OtAptmTRUNDA0mlUgoPD1eVKRQKsrW1pVdeeYWI/koWtbW1qjofffQRAaALFy6oyr7//nsCQBkZGaqy4OBgjZNfQUEBAaA1a9boFINCoSCpVEqPP/64Wjvp6elqO4Ku9Yi6Phk2NDSoytoTzeXLl1VlAQEB9Le//U1tGX//+99JKBRSU1MT9URvSBbatk9kZCQ5OjqqtXf27FkCQG+//baqrCfJwtBMmSzq6upIIBDQrFmzOpx+d7IgIoqJiSEAtGLFCiLSTBYKhYLs7OzUjh+iv47HuxOqLn2oyzlBV9euXaNz585RbW0tNTU1UV5eHj344IMkkUiosLCw0/lWrFhB7733Xo/b+/DDDwkA/etf/9Ir7u4yZrIw+2WoixcvQqFQqA09k0gk8PDwUPsJey8bGxsAULt51H5vQqlUdrnMMWPGwNHREQUFBTrFcPnyZSgUCgQHB3fZrq719NG+nnevU2Njo8bP2tbWVohEIrXvSPcFHW2fjkyYMAFSqbTLfaqvuHXrFogIUqlUp/pxcXEYPnw4kpOTNS7fAkBRURHq6uowYcIEtfKAgADY2NioXf7ryL192N1zQkcGDhyIBx98EHZ2drCxscHEiRORlpaGhoYGJCcndzhPWVkZDh8+jMWLF/e4vfZtfPPmTb3itkRmTxb19fUAgI0bN6qNW7569apRh3CKRCLVzqkthtLSUgBA//79u2xT13o9NWPGDPz444/Izc1FQ0MDfvjhB+Tk5ODpp5/uc8lCH7a2tqioqDB3GGbX2NgIADoPZhCLxUhLS4NAIMBLL72EhoYGtentw0M7+rKgk5MTamtr9YrP2OeEMWPGwMrKCpcuXepwekJCApYuXarzCyW7ak8ikQD4a5v3ZmZPFu0n1l27dmkMR8vLyzPKMltaWnDnzh3I5XKdYmjfaZqamrpsV9d6PbV582ZMmzYNixcvhoODA+bOnYtnnnlGp+c++iqlUomqqip4eXmZOxSzaz+B6fPQ2KRJk7B69WoUFxdj69atatOcnJwAoMOk0J1tbuxzQltbG9ra2jpMluXl5fjkk0/wyiuvGKS95uZmAH9t897M7MmifSRTV09BG9p//vMftLW1qR4o0hbD6NGjIRQKcfLkyS7b1bVeTxUVFaGkpAQVFRVQKpW4du0aUlJS4OzsbNTl9mYnTpwAEWHixImqMmtra62Xr+5Hbm5uEAgEej8/sXXrVowYMQLnz59XKx89ejTs7Ozwww8/qJWfOXMGzc3NGD9+vF7LMeQ54YknntAoO3v2LIioww9NJSQkICIiAi4uLgZpr30bu7u76xu6xTF7shCLxXjxxReRnp6OlJQU1NTUoLW1FaWlpfj9998Nsozm5mZUV1ejpaUF586dQ1RUFAYNGqS6Jqkthv79+yMsLAwHDx5EamoqampqUFBQoPFMg671emrFihWQy+Woq6szaLv3k7a2NlRWVqKlpQUFBQWIjo6GXC5Xuw7t6+uLO3fuICcnB0qlEhUVFbh69apGWy4uLigrK8Nvv/2G2tpaKJVKHDt2rNcOnZVKpfD29lZdNtVV++Woey91isVixMTE4NChQ9i/fz9qampw4cIFLF++HAMGDEBkZKTey9F2TggPD4e7u7vW143cuHEDGRkZqKqqglKpRF5eHpYsWQK5XI7ly5er1b158yY+/PBDrFq1yiDtAX9dmh4zZoxe28AiGfqWeXfuxjc1NdEbb7xBcrmcrK2tqX///hQWFkZFRUWUmJhIUqmUANDgwYPp22+/pfj4eHJ0dCQA5O7uTh9//DFlZGSQu7s7ASBnZ2dKT08nIqK0tDSaOnUqubm5kbW1Nbm6utLChQvp6tWrOsdARFRbW0tLliwhV1dXsrOzo8mTJ1NsbCwBIC8vL/rpp590rrd7927y8PAgACSVSikkJISSk5NV6zl06FAqKSmhPXv2kIODAwGgQYMGqYb6fv311+Tq6koAVH8ikYhGjhxJ2dnZevdZXl4ePfzwwzRgwABVex4eHhQYGEgnT57Uqy1DjMbo6faJjIwkkUhEnp6eZG1tTQ4ODjR79mwqKSlRW84ff/xBU6dOJbFYTEOGDKHXXnuN1q5dSwDI19dXNcz23LlzNGjQIJJIJDR58mQqLy+no0ePkr29PcXFxfVoXdvBxENno6KiSCQSkUKhUJUdOnSIfHx8CAD169dPNfrpXmvXrtUYOtvW1kY7duygoUOHkkgkImdnZ5ozZw5dvHhRVUefPtR2PM6ZM4cAUGxsbJfrGRMTQz4+PiSTycja2pq8vLxo6dKlVFZWplF39erVFBERYbD2iIhmzpxJnp6e1NbW1mW7hnJfD51l+ktOTqbo6Gi1sqamJlq1ahXZ2tqqnQBMzRL6PzIyklxcXMwag75MnSyKi4vJ2tqa9u3bZ7JlGlJraysFBQVRamqquUPp1O3bt0ksFtM777xjsmXe10NnmX7Ky8sRFRWFl19+Wa3cxsYGcrkcSqWyT16Hv9f99sZPQ/P19cWWLVuwZcuWXnc5s7W1FTk5OaitrUV4eLi5w+nU5s2bMW7cOERFRZk7FIPgZNHLSCQSiEQipKam4ubNm1AqlSgrK8PevXsRGxuL8PBwlJWVdfq65bv/LPlAY8a3bt06zJ8/H+Hh4b3qZYEnTpxAdnY2jh07pvOzIqa2c+dO5Ofn4+jRo0Z7N52pcbLoZRwdHfHll1+isLAQw4YNg0QigZ+fH9LS0hAfH4+PPvoII0aM0Bhy2NFfRkaGuVfH4NavX4+0tDRUV1djyJAhOHjwoLlDsmjbtm1DVFQU/vGPf5g7FJ0FBwfj448/VnuvlyXJzc1FU1MTTpw4cV+NULQ2dwBMf0FBQfj3v/9t7jAs0vbt27F9+3Zzh9GrTJ8+HdOnTzd3GPeN0NBQhIaGmjsMg+NfFowxxrTiZMEYY0wrThaMMca04mTBGGNMK6Pd4M7MzDRW08yCtb/ojftff8Z6cSbrO4y5DwmIDPu9v8zMTCxYsMCQTTLGGNODgU/rAJBltF8WRgiW9QLt/1ng/tePQCDAgQMH7otvqDPzMeZ/1vmeBWOMMa04WTDGGNOKkwVjjDGtOFkwxhjTipMFY4wxrThZMMYY08rik0V2dja8vb27/C7D4MGDDbKsgIAAWFlZYdy4cQZp725LliyBvb09BAJBlx+i76ze0aNH4ejoiE8//dTgsbHe7fjx41i3bp3GsfLcc89p1J0+fTrs7e1hZWWFUaNGaf2GtbnFxcV1eMyPHj1ard6UKVM6PT/Y2dnp3Z6+2trasGvXLgQGBupUv7GxESNGjMDGjRtVZYcPH0ZCQoLFfrjL4pNFWFgYfv31V/j4+MDR0VH1LYaWlhYoFArcvHnTYB9AOXv2LKZOnWqQtu61d+9efPDBB92ux88tsI5s2rQJSUlJWL9+vdqx4urqiv379+PIkSNq9b/88ktkZWVh1qxZKCoqgr+/v5kiN53Jkycbtf3i4mI88sgjWL16NRQKhU7zbNiwARcvXlQrCwkJgVgsRnBwMKqqqowRao9YfLLojJWVFSQSCdzc3DBs2DCDti0QCAzaniHMnDkT1dXVmDVrlrlDsXgNDQ06/w/PkpehTXx8PDIyMpCZmQl7e3u1aUlJSRAKhYiMjOxVX8HryL59+zQ+3FVYWKhWRywWo6amRqNeZGQkXn/9db3b09VPP/2EN998E8uXL9f5isTp06c7Xd7KlSsxduxYzJgxAy0tLd2KyVh6bbK4W05OjkHbM9ZnEHVNQqZIVkSErKws7Nmzx+jLMrXU1FTcunWr1y+jK5cvX8Zbb72Ft99+G2KxWGN6YGAgoqOjcePGDaxZs8YMEZrW559/rpEwr1+/jsLCQkybNs1oyx07diyys7OxaNEi2Nraaq3f0NCAtWvXIjExsdM6mzdvRn5+fpd1zOG+SBZ3S0xMhEwmg1AoxPjx4+Hu7g6RSASZTAZ/f38EBQVh4MCBEIvFcHJy0vhfB/DngThixAjIZDJIJBIEBQXh1KlTanVaW1sRGxsLuVwOiUSCBx54AAcOHFBNJyLs2LEDw4cPh62tLRwdHbF27VqNZelS79SpU5DL5RAIBHj33XcBACkpKZDJZJBKpcjNzcVTTz0FBwcHeHl5IT09XSPW7du3Y/jw4ZBIJOjXrx+GDBmC7du3W8TrJYgIO3fuxMiRI2FrawtnZ2fMnj0bv/zyi6pOVFQUbGxs1D6l+eqrr0Imk0EgEOD27dsAgOjoaMTExKCkpAQCgQC+vr5ISkqCWCyGm5sbli1bhgEDBkAsFiMwMBBnzpwxyDKAP09YDg4O2LZtm1G3F/DnLwciQkhISKd14uLiMGzYMOzduxfHjx/vsj1d+kDffa6r48MU4uPjsXLlSpMuU5sNGzbg1VdfRf/+/Tut4+zsjEcffRSJiYmWdfmZDOzAgQNkhGbJx8eHHB0d1cpWrlxJFy5c0Ki7adMmAkBnzpyh+vp6un37Nj355JMEgI4cOUIVFRVUX19PUVFRBIDy8/NV8wYHB5O3tzdduXKFlEolFRYW0kMPPURisZguXbqkqrdmzRqytbWlgwcPUmVlJa1fv56EQiGdPXuWiIg2bNhAAoGA/vnPf1JlZSUpFApKTk4mAHT+/HlVO7rWu379OgGg3bt3q80LgL766iuqrq6mW7duUVBQEMlkMmpublbV27ZtG1lZWVFubi4pFAr68ccfyd3dnaZMmdKDHulYd/o/NjaWbGxsaN++fVRVVUUFBQXk7+9P/fr1o/LyclW9RYsWkbu7u9q8O3bsIABUUVGhKgsLCyMfHx+1epGRkSSTyejnn3+mxsZGKioqooCAALK3t6dr164ZZBmfffYZ2dvb05YtW/RafyIiAHTgwAGd63t7e5Ofn1+H03x8fOjKlStERHT69GkSCoU0ePBgqqurIyKiY8eOUWhoqNo8uvaBrvuctuNDV1u3biUvLy9ycnIikUhEgwcPptDQUPr++++7nK+0tJT8/PyotbXVIO3p4qGHHqKxY8d2Ov3UqVMUEhJCREQVFRUEgDZs2NBh3XXr1mmcA3RhrPMvEWX2qmQBQOOvq2RRW1urKvvoo4806n///fcEgDIyMlRlwcHBGh1eUFBAAGjNmjVERNTQ0EBSqZTCw8NVdRQKBdna2tIrr7xCCoWCpFIpPf7442rtpKenq+0AutYj6jpZNDQ0qMraE83ly5dVZQEBAfS3v/1NbRl///vfSSgUUlNTk8b26wl9+1+hUJCdnZ3atiT6q2/uPvH2NFnc+5+Ns2fPEgB6++23DbKMntAnWdTV1ZFAIKBZs2Z1OP3uZEFEFBMTQwBoxYoVRKSZLPTpA132OW3Hhz6uXbtG586do9raWmpqaqK8vDx68MEHSSKRUGFhYafzrVixgt577z2DtaeLrpKFQqGgCRMmUGlpKRFpTxYffvghAaB//etfesVgzGTRqy5D3T0aioj0+olpY2MDAGo3jdrvTSiVyi7nHTNmDBwdHVFQUAAAuHjxIhQKhdpwO4lEAg8PD/zyyy+4fPkyFAoFgoODu2xX13r6aF/Pu9epsbFR4+dsa2srRCIRrKysDLbs7igqKkJdXR0mTJigVh4QEAAbGxu1y0SGNmHCBEilUrVLLb3BrVu3QEQ6jwKMi4vD8OHDkZycrHE5Feh5H9y7z2k7PvQxcOBAPPjgg7Czs4ONjQ0mTpyItLQ0NDQ0IDk5ucN5ysrKcPjwYSxevNgg7RnC+vXr8fe//x2enp461W/v25s3bxotJn31qmRxr8TExB6Pj9aVSCRSHQz19fUAgI0bN6qN1b569SoUCgVKS0sBoMvrkgB0rtdTM2bMwI8//ojc3Fw0NDTghx9+QE5ODp5++mmzJ4v2IYJ3j4Vv5+TkhNraWqMu39bWFhUVFUZdhqE1NjYCgE43VIE/RwqlpaVBIBDgpZdeQkNDg9p0Q/eBtuOjp8aMGQMrKytcunSpw+kJCQlYunRphzf+u9NeT506dQoXLlzAkiVLdJ5HIpEA+KuvLUGvTham0tLSgjt37kAulwP46+S+a9cujSF4eXl5qp20qampy3Z1rddTmzdvxrRp07B48WI4ODhg7ty5eOaZZ3R67sPYnJycAKDDE1JVVRW8vLyMtmylUmn0ZRhD+4lEn4e3Jk2ahNWrV6O4uBhbt25Vm2boPtB2fPRUW1sb2traOkyW5eXl+OSTT/DKK68YpD1DSE1NxVdffQWhUKhKnO3baNu2bRAIBPjhhx/U5mlubgbwV19bgvsiWfz+++948cUXjdb+f/7zH7S1takeYGofTdXZk9ijR4+GUCjEyZMnu2xX13o9VVRUhJKSElRUVECpVOLatWtISUmBs7OzUZeri9GjR8POzk7jYDlz5gyam5sxfvx4VZm1tbXWS4b6OHHiBIgIEydONNoyjMHNzQ0CgUDv5ye2bt2KESNG4Pz582rl+vSBLrQdH/p44oknNMrOnj0LIsKkSZM0piUkJCAiIgIuLi4Gac8Q0tLSNJJm+6/ZDRs2gIg0LgG29627u7tRYuqOXp0siAgNDQ3Izs6Gg4ODwdptbm5GdXU1WlpacO7cOURFRWHQoEGqa6BisRgvvvgi0tPTkZKSgpqaGrS2tqK0tBS///47+vfvj7CwMBw8eBCpqamoqalBQUGBxjMNutbrqRUrVkAul6Ours6g7RqCWCxGTEwMDh06hP3796OmpgYXLlzA8uXLMWDAAERGRqrq+vr64s6dO8jJyYFSqURFRQWuXr2q0aaLiwvKysrw22+/oba2VnXyb2trQ2VlJVpaWlBQUIDo6GjI5XK1a9s9WcaxY8dMMnRWKpXC29tbdRlTV+2Xo+699KhPH+i6nK6ODwAIDw+Hu7u71teN3LhxAxkZGaiqqoJSqUReXh6WLFkCuVyO5cuXq9W9efMmPvzwQ6xatarH7ekan7G09+2YMWPMsvwOGfqWuaHvxh86dKjTkVB3/23cuJGIiBITE0kqlRIAGjx4MH377bcUHx9Pjo6OBIDc3d3p448/poyMDHJ3dycA5OzsTOnp6URElJaWRlOnTiU3NzeytrYmV1dXWrhwIV29elUtrqamJnrjjTdILpeTtbU19e/fn8LCwqioqIiIiGpra2nJkiXk6upKdnZ2NHnyZIqNjSUA5OXlRT/99JPO9Xbv3k0eHh4EgKRSKYWEhFBycrJqPYcOHUolJSW0Z88ecnBwIAA0aNAg1VDfr7/+mlxdXdW2l0gkopEjR1J2drbB+oqoe/3f1tZGO3bsoKFDh5JIJCJnZ2eaM2cOXbx4Ua3eH3/8QVOnTiWxWExDhgyh1157jdauXUsAyNfXVzUE9ty5czRo0CCSSCQ0efJkKi8vp8jISBKJROTp6UnW1tbk4OBAs2fPppKSEoMt4+jRo2Rvb09xcXF6bzfoOXQ2KiqKRCIRKRQKVdndx0q/fv1Uo5/utXbtWo2hs7r0gT77nLbjY86cOQSAYmNju1zPmJgY8vHxIZlMRtbW1uTl5UVLly6lsrIyjbqrV6+miIgIg7Sna3x5eXn08MMP04ABA1THloeHBwUGBtLJkyc7nU/baKiZM2eSp6cntbW1dbn8e/HQWdYjycnJFB0drVbW1NREq1atIltbW7UTTk9Zav9HRkaSi4uLucPolL7Jori4mKytrWnfvn1GjMp4WltbKSgoiFJTU80dSofMGd/t27dJLBbTO++8o/e8PHSWdVt5eTmioqLw8ssvq5Xb2NhALpdDqVRa/DV6Q7HUt3l2h6+vL7Zs2YItW7ZY5OXFrrS2tiInJwe1tbUIDw83dzgazB3f5s2bMW7cOERFRZl82V3hZHGfk0gkEIlESE1Nxc2bN6FUKlFWVoa9e/ciNjYW4eHhBr3fw0xn3bp1mD9/PsLDw3vVywJPnDiB7OxsHDt2zGBvjDYkc8a3c+dO5Ofn4+jRo0Z7R113cbK4zzk6OuLLL79EYWEhhg0bBolEAj8/P6SlpSE+Ph4fffSRuUM0uvXr1yMtLQ3V1dUYMmQIDh48aO6QDGbbtm2IiorCP/7xD3OHorPg4GB8/PHHau/gsiTmii83NxdNTU04ceKERYxUvJe1uQNgxhcUFIR///vf5g7DbLZv347t27ebOwyjmT59OqZPn27uMFgPhYaGIjQ01NxhdIp/WTDGGNOKkwVjjDGtOFkwxhjTipMFY4wxrYx2g3v+/PnGappZsPbXFHD/62/Xrl3IysoydxisF9P3FTD6EBAZ9rt9eXl52LlzpyGbZMxkysvLcf78eTz11FPmDoWxbjPCfzqyDJ4sGOvNMjMzsWDBAsv69jFj5pfF9ywYY4xpxcmCMcaYVpwsGGOMacXJgjHGmFacLBhjjGnFyYIxxphWnCwYY4xpxcmCMcaYVpwsGGOMacXJgjHGmFacLBhjjGnFyYIxxphWnCwYY4xpxcmCMcaYVpwsGGOMacXJgjHGmFacLBhjjGnFyYIxxphWnCwYY4xpxcmCMcaYVpwsGGOMacXJgjHGmFacLBhjjGnFyYIxxphWnCwYY4xpxcmCMcaYVpwsGGOMacXJgjHGmFacLBhjjGnFyYIxxphWnCwYY4xpxcmCMcaYVtbmDoAxc1Eqlairq1Mrq6+vBwBUVlaqlQsEAjg5OZksNsYsDScL1mfduXMHnp6eaG1t1Zjm4uKi9u+pU6fi66+/NlVojFkcvgzF+ix3d3c88sgjEAq7PgwEAgEWLlxooqgYs0ycLFif9txzz2mtY2Vlhblz55ogGsYsFycL1qeFhYXB2rrzq7FWVlZ48skn4erqasKoGLM8nCxYn+bg4ICnnnqq04RBRIiIiDBxVIxZHk4WrM+LiIjo8CY3ANjY2ODpp582cUSMWR5OFqzPe/rppyGVSjXKRSIR5syZA5lMZoaoGLMsnCxYnycWizF37lyIRCK1cqVSiUWLFpkpKsYsCycLxgA8++yzUCqVamUODg54/PHHzRQRY5aFkwVjAB577DG1B/FEIhEWLlwIIH/K4QAAIABJREFUGxsbM0bFmOXgZMEYAGtrayxcuFB1KUqpVOLZZ581c1SMWQ5OFoz9z8KFC1WXotzd3TF58mQzR8SY5eBkwdj/BAYGwtPTEwDw/PPPa30NCGN9Sa9+kWBeXh6uX79u7jDYfSQgIAA3btyAq6srMjMzzR0Ou48EBgbCy8vL3GF0m4CIyNxBdNf8+fNx8OBBc4fBGGNaHThwAM8884y5w+iurF79ywIA5s2bh6ysLHOHYfEEAkFv31lN5uDBg5g3bx7mz58PALx/sR4TCATmDqHH+KIsY/eYN2+euUNgzOJwsmCMMaYVJwvGGGNacbJgjDGmFScLxhhjWnGyYIwxplWfShbvvPMO3NzcIBAI8P7775s7HJ20tbVh165dCAwMNHcoAICjR4/C0dERn376qblDsXjHjx/HunXrkJ2dDW9vbwgEAggEgg6/+z19+nTY29vDysoKo0aNwrlz58wQse7i4uJU63P33+jRo9XqTZkypcN6AoEAdnZ2erenL32Pn8bGRowYMQIbN25UlR0+fBgJCQmdfiCrr+hTyWLNmjU4ffq0ucPQWXFxMR555BGsXr0aCoXC3OEA+PMzo0y7TZs2ISkpCevXr0dYWBh+/fVX+Pj4wNXVFfv378eRI0fU6n/55ZfIysrCrFmzUFRUBH9/fzNFbjrGfvdWd46fDRs24OLFi2plISEhEIvFCA4ORlVVlTFC7RX6VLLojoaGBrP8r/6nn37Cm2++ieXLl2PcuHEmX35nZs6cierqasyaNcvcoZitb7SJj49HRkYGMjMzYW9vrzYtKSkJQqEQkZGRqK6uNlOEhrFv3z4QkdpfYWGhWh2xWIyamhqNepGRkXj99df1bk9X3Tl+Tp8+3enyVq5cibFjx2LGjBloaWnpVky9HScLLVJTU3Hr1i2TL3fs2LHIzs7GokWLYGtra/Ll9wbm6puuXL58GW+99RbefvttiMVijemBgYGIjo7GjRs3sGbNGjNEaFqff/65RsK8fv06CgsLMW3aNKMtV9/jp6GhAWvXrkViYmKndTZv3oz8/Pwu69zPOFkAOHnyJP72t79BKpXCwcEBY8aMQU1NDaKjoxETE4OSkhIIBAL4+voiMTERMpkMQqEQ48ePh7u7O0QiEWQyGfz9/REUFISBAwdCLBbDyclJ439PvdmpU6cgl8shEAjw7rvvAgBSUlIgk8kglUqRm5uLp556Cg4ODvDy8kJ6erpq3qSkJIjFYri5uWHZsmUYMGAAxGIxAgMDcebMGVW9qKgo2NjYwMPDQ1X26quvQiaTQSAQ4Pbt2wDQYd8Af56cHBwcsG3bNlNsEg1JSUkgIoSEhHRaJy4uDsOGDcPevXtx/PjxLtsjIuzcuRMjR46Era0tnJ2dMXv2bPzyyy+qOrr2AQC0trYiNjYWcrkcEokEDzzwAA4cONCzldZTfHw8Vq5cadJlarNhwwa8+uqr6N+/f6d1nJ2d8eijjyIxMbFvXo6lXmzevHk0b948veYpLi4mAPTee+8REVFdXR05ODhQQkICNTQ0UHl5Oc2dO5cqKiqIiCgsLIx8fHzU2ti0aRMBoDNnzlB9fT3dvn2bnnzySQJAR44coYqKCqqvr6eoqCgCQPn5+T1az4ceeojGjh3bozYA0IEDB3rUBhHR9evXCQDt3r1bVbZhwwYCQF999RVVV1fTrVu3KCgoiGQyGTU3N6vqRUZGkkwmo59//pkaGxupqKiIAgICyN7enq5du6aqt2jRInJ3d1db7o4dOwiAql+IOu6bzz77jOzt7WnLli09Xtfu7F/e3t7k5+fX4TQfHx+6cuUKERGdPn2ahEIhDR48mOrq6oiI6NixYxQaGqo2T2xsLNnY2NC+ffuoqqqKCgoKyN/fn/r160fl5eWqerr2wZo1a8jW1pYOHjxIlZWVtH79+v/P3p1GRXWlewP/F9REQRWDjC2CUhAJOL3EiVKjaft6V8INgmDASBKTlb5oxyCituJAFMFoayNNIvHa8XJvq1EmG00iWS7TjWnboc0NDgvjEGTQGAUMyCzT835IU52ykKqCooqC57eWHzxnn72fs88unqpz9jmHrKys6OLFiwbt59atW8nT05McHBxIJBLR6NGjaf78+fSPf/yj1+3u3r1LAQEB1NnZaZT69KHr83PmzBkKDQ0lIqLq6moCQBs2bOixbGJiIgGg4uJig2Iw1ufPjHKG/S+L8vJy1NfXIzAwEFKpFG5ubsjPz4ezs7PObQMCAiCTyTBixAgsWrQIAODl5QVnZ2fIZDLExMQAgMa3wKFMpVJBoVDAxcUF0dHRaGpqQmVlpUYZoVCo/pYcEBCAzMxMNDQ0ICsryygxhISEoL6+Hps2bTJKfYZoampCWVkZlEqlzrLBwcFYuXIlysvLsW7duh7LtLS0IC0tDQsWLEBMTAzs7e0xfvx47N27FzU1Ndi3b5/WNr0dg9bWVmRmZiI8PBwRERFwcHDAxo0bIRKJDO7/N954A8ePH8edO3fQ2NiIw4cPo7KyErNnz0ZJSclTt9u+fTveffddrXeF9LW+/mppaUF8fDwyMzP1Ku/n5wcAuHr16oDFNFgN+2Th4+MDV1dXxMTEYPPmzSgvL+9TPd3vav75xa+fv6JzuOnuD137PnnyZMhksiGRUKuqqkBEkMlkepVPSUnB2LFjsWfPHpw5c0ZrfUlJCRobGzF58mSN5VOmTIFYLNY4fdeTJ4/BjRs30NzcrDEd1cbGBu7u7gb3/6hRo/D//t//g52dHcRiMaZPn46srCy0tLRgz549PW5z7949HD9+HEuWLDFKfcawfv16/Od//qf6pVe6dB/bBw8eDFhMg9WwTxY2Njb4y1/+gpkzZyI1NRU+Pj6Ijo5GS0uLuUMbNiQSCaqrq80dRr+1trYCgN4TEqRSKbKysiAQCPDWW29pjbnuaZo/vx+hm4ODAxoaGgyKr6mpCQCwceNGjXsZKioqjDI1e/z48bC2tsbNmzd7XL9jxw78+te/7vHCf1/q668zZ87g6tWrePvtt/XexsbGBsC/jvVwMuyTBQAEBgbi008/xb1797B27VpkZ2dj165d5g5rWGhvb0ddXZ1Fv0GsW/cfEkNu3goODkZCQgJu3bqFrVu3aqxzcHAAgB6TQl/6rPvi7e7du7WmqJ47d86gunrS1dWFrq6uHpPl/fv38cknn+A3v/mNUeozhv379+PLL7+ElZWVOnF291FqaioEAgG+/vprjW3a2toA/OtYDyfDPlncu3cP165dA/DTh+n9999HUFCQehkbWEVFRSAiTJ8+Xb1MKBRa5Km77qcDGHr/xNatW+Hv74/i4mKN5ePGjYOdnZ3WH6wLFy6gra0Nzz33nEHtdM/Su3TpkkHb9eTf//3ftZZdvHgRRITg4GCtdTt27EBMTAycnJyMUp8xZGVlaSXN7l+4GzZsABFpnQLsPrZubm4DEtNgxsni3j0sXboU169fR1tbG4qLi1FRUaH+4+Xk5IR79+6hvLwcDQ0NFvlHbDDp6upCbW0tOjo6cOXKFcTHx8PLy0vjPLavry9+/PFHFBQUoL29HdXV1aioqNCqq6djU1hYaLapszKZDD4+Prh7965B23WfjrK2ttZavmrVKhw9ehQHDx5EfX09rl69imXLlsHDwwOxsbEGt/Pmm2/i8OHDyMzMRH19PTo7O3H37l388MMPAIDo6Gi4ubnpfNzI999/jyNHjqCurg7t7e04d+4c3n77bXh5eWHZsmUaZR88eID//u//xsqVK/tdn77xDZTuYzt+/HiztG9WZpmEZSSGTm38/e9/T25ubgSAbG1tacGCBVReXk4qlYocHR3J2tqafvGLX9CGDRuoo6ODiIi++eYb8vb2JhsbG5o5cyYlJiaSTCYjADR69Gj629/+Rtu3byd7e3sCQG5ubnTo0CE6cuSIui1HR0c6fPiwQft27tw5mjFjBnl4eBAAAkDu7u6kUqno9OnTBtVFZJypex988AG5u7sTAJLJZBQaGkp79uxR94efnx+VlpbSvn37SKFQEADy9vammzdvEtFPU2dFIhGNHDmShEIhKRQKCgsLo9LSUo12Hj58SC+88AJJpVIaM2YMvfvuu7RmzRoCQL6+vupptk8em/v379OJEydILpdTSkpKv/aVqG9TZ+Pi4kgkElFzc7N62dGjR0mpVBIAcnZ2puXLl/e47Zo1a7SmznZ1ddHOnTvJz8+PRCIROTo6Unh4ON24cUNdxpBj8PjxY1q7di15eXmRUCgkFxcXioiIoJKSEiIiCg8PJwCUlJTU636uWrWKlEol2draklAoJE9PT/r1r39N9+7d0yqbkJBAMTExRqlP3/j6+vnRNXU2JCSERo4cSV1dXb22/yRjfP7MLGdYJYvhbDAM1tjYWHJycjJrDIbo6308QqGQDhw4MEBRDazOzk6aNWsW7d+/39yh9Mic8dXU1JBUKqVdu3YZvO1g+Pz1E99nwUxrqD+509fXF8nJyUhOTkZjY6O5wzFIZ2cnCgoK0NDQgOjoaHOHo8Xc8W3evBmTJk1CXFycydseDDhZmMj169ef+qjmn/8bjB9SZpjExEQsXLgQ0dHRFvWwwKKiIuTn56OwsFDve0VMyZzxpaWl4dKlSzhx4oT6/qnhRmjuAIYLf3//4fk8mX9av349srKy0NbWhjFjxmDnzp2IjIw0d1gDJjU1FSdPnsT777+P7du3mzscvcydOxdz5841dxhPZa74jh07hsePH6OoqEhrEsJwwsmCmcS2bduwbds2c4dhUvPmzcO8efPMHQbrp/nz52P+/PnmDsPs+DQUY4wxnThZMMYY04mTBWOMMZ04WTDGGNPJ4i9wnz9/HgsXLjR3GBZh9+7dyM3NNXcYFuP8+fMAwOOLMfAvC8YYY3qw+F8W06dP52/LehAIBFi5ciVeeeUVc4diMbp/UfD4Yv0lEAjMHUK/8S8LxhhjOnGyYIwxphMnC8YYYzpxsmCMMaYTJwvGGGM6DatkkZ+fDx8fH/XjwN3d3RETE6Nzu8uXLyM6OhpjxoyBRCKBs7MzJk6ciJSUFHWZ6OhovR5BLhAI8Nlnn2nFsmnTpl5jSEtLg0AggJWVFfz9/fHVV1/1uz+YZTl16hQSExO1xs5rr72mVXbevHmQy+WwtrZGYGCg2V5Daqiuri7s3r0bKpXqqWXa29uxbds2+Pr6QiwWw8HBAePGjUN5eTkA4Pjx49ixY8eQf3eKyZn79Uv90dc35SmVSrK3t9er7JUrV0gmk9GKFSuorKyMWlpa6MaNG/Tb3/6W5s6dqy4XFRVFJ0+epLq6Ompvb6cffviBAFBoaCi1tbVRU1MTVVVV0a9//Wv69NNPNWLBP1/52NbW1mMMHR0d5O3tTQA02jQELP9NXSY3mN7EmJSURC+//DLV19erlymVShoxYgQBoM8++0xrm8LCQq3XtA5mN2/epBkzZhAAmjhx4lPLhYeH09ixY+n8+fPU3t5O9+7do9DQULp69aq6THp6Os2ePZtqa2tNEbpOQ+Dzx2/K02XXrl1wcHBAeno6Ro8eDalUimeeeQZbt26FjY2NupxAIMCMGTNgb28PoVCosVwkEkEmk8HFxQXPPfecVhvPPfcc7t+/j4KCgh5jyM/Px8iRI42/cybU0tLS67dFS2nDHLZv344jR44gJycHcrlcY11GRgasrKwQGxtrUS9aetLly5exbt06LFu2DJMmTXpquSNHjqCgoAC5ubmYNm0ahEIhPDw8cOzYMYwbN05dbsWKFZg4cSJeeukldHR0mGIXhjxOFjo8fPgQjx49wo8//qixXCwW49NPP1X///Dhw3q9vSs2Nhb/8R//obHsN7/5DQDgo48+6nGbtLQ0rFq1ytDQB5X9+/ejqqrK4tswte+++w6bNm3Cli1bIJVKtdarVCrEx8fj+++/x+rVq80QoXFMnDgR+fn5WLx4MSQSyVPLffTRRwgKCsL48eN11rl582ZcunQJ6enpxgx12OJkocOUKVPQ1NSEX/7yl/j73/8+IG388pe/xLPPPou//vWvuHHjhsa6v//972hubjb5S3SICGlpaXj22WchkUjg6OiIsLAwXL9+XV0mLi4OYrEY7u7u6mXvvPMObG1tIRAIUFNTAwCIj4/HqlWrUFpaCoFAAF9fX2RkZEAqlcLV1RVLly6Fh4cHpFIpVCoVLly4YJQ2AOCLL76AQqFAamrqgPbXQMnIyAARITQ09KllUlJS8Mwzz+Djjz/GqVOneq1Pn+OamZkJW1tbyGQyHDt2DC+++CIUCgU8PT1x+PBhjfo6OzuRlJQELy8v2NjYYMKECcjOzu7fTj9FW1sbzp8/3+svj59zdHTE7NmzkZ6ePqzfUmk05j0N1j+muGbR3NxMkydPJgAEgAICAmjHjh308OHDXrfrvmah65yxUqmksrIy+sMf/kAAKD4+XmN9eHg4ZWVlUUNDg0mvWSQlJZFYLKYDBw5QXV0dXblyhYKCgsjZ2Znu37+vLrd48WJyc3PT2Hbnzp0EgKqrq9XLIiIiSKlUapSLjY0lW1tbunbtGrW2tlJJSQlNmTKF5HI5VVZWGqWNzz77jORyOSUnJ+u9790GwzULHx8fCggI6HFd99ghIjp79ixZWVnR6NGjqbGxkYh6vmah73HdsGEDAaAvv/ySHj16RFVVVTRr1iyytbXVuLa2evVqkkgklJeXR7W1tbR+/XqysrKiixcv9nmfp02b1uM1i7KyMgJAkyZNojlz5pC7uztJJBLy9/enDz/8kLq6urS2SUxMJABUXFzc53iMwdDP3yDE1yx0sbGxwdmzZ/GHP/wB/v7+uHbtGtauXYtnn30Wp0+fNlo7b7zxBmxtbfG///u/aGlpAQDcvn0bFy9exKuvvmq0dvTR0tKCtLQ0LFiwADExMbC3t8f48eOxd+9e1NTUYN++fUZrSygUqr/lBgQEIDMzEw0NDcjKyjJK/SEhIaivr9c522wwampqQllZGZRKpc6ywcHBWLlyJcrLy7Fu3boey/TluKpUKigUCri4uCA6OhpNTU2orKwEALS2tiIzMxPh4eGIiIiAg4MDNm7cCJFIZLTj93ONjY0AABcXF6SmpqKkpAQPHjxAWFgYli9fjk8++URrGz8/PwDA1atXjR7PcMPJQg8ikQhxcXH49ttvcf78eYSFhaGqqgoLFy5EbW2tUdqwt7fHq6++itraWhw5cgTAT48U/81vfgOxWGyUNvRVUlKCxsZGTJ48WWP5lClTIBaLNU4TGdvkyZMhk8k0TosMV1VVVSAiva6FAT+djho7diz27NmDM2fOaK3v73HtHoft7e0AgBs3bqC5uVnjwrKNjQ3c3d0H5Ph1X8sIDAyESqWCk5MT7O3tsWXLFtjb2/eY7Lr77sGDB0aPZ7jhZGGgadOm4c9//jOWLVuG6upq/PWvfzVa3d0Xuvfu3Yu6ujrk5uZi6dKlRqtfX3V1dQAAOzs7rXUODg5oaGgY0PYlEgmqq6sHtA1L0NraCgC9XvD9OalUiqysLAgEArz11lvqX6jdjH1cm5qaAAAbN27UuI+ooqICzc3NBtWlDw8PDwBQX6fqJhaL4e3tjdLSUq1tumcsdvcl6ztOFk/46quvsHv3bvX/IyIiepx6130jlDE/FJMmTcL06dPxj3/8A7GxsVi4cCEcHR2NVr++HBwcAKDHPx51dXXw9PQcsLbb29sHvA1L0f2HzpCby4KDg5GQkIBbt25h69atGuuMfVxdXFwA/PQLmIg0/p07d86guvRhZ2cHPz8/XLt2TWtdR0cH7O3ttZa3tbUBgMY0d9Y3nCye8H//93+wtbVV///x48c9Ds7uWUsTJkwwavvdvy7y8vKwcuVKo9atr3HjxsHOzg5ff/21xvILFy6gra1N414RoVCoPi1hDEVFRSAiTJ8+fcDasBSurq4QCAQG3z+xdetW+Pv7o7i4WGO5IcdVH6NGjYJUKsWlS5cM2q4/oqKiUFxcjNu3b6uXNTc3o6KiosfptN195+bmZrIYhypOFv/U3t6OBw8eoKioSCNZAEB4eDhycnJQV1eHR48e4dixY1i3bh3mz59v9GTxyiuvwNnZGeHh4fDx8TFq3fqSSqVYtWoVjh49ioMHD6K+vh5Xr17FsmXL4OHhgdjYWHVZX19f/PjjjygoKEB7ezuqq6tRUVGhVaeTkxPu3buH8vJyNDQ0qP/4d3V1oba2Fh0dHbhy5Qri4+Ph5eWFJUuWGKWNwsJCi506K5PJ4OPjg7t37xq0XffpKGtra63l+h5Xfdt58803cfjwYWRmZqK+vh6dnZ24e/cufvjhBwA/PQbHzc3NaI8bSUhIgLe3N5YsWYLKyko8fPgQa9euRUtLS48X9rv7Tp/7MpgO5pyL1V+GTm08evSo+vEavf07evSoepuTJ09SVFQUKZVKkkgkJBaLaezYsbR582ZqbW3VaqO+vp6ef/55cnJyIgBkZWVFvr6+lJqa+tRYnJ2dafny5ep1v/3tb+ns2bPq/2/cuJHc3d3V9QUEBNDf/vY3Q7rK4Kl7XV1dtHPnTvLz8yORSESOjo4UHh5ON27c0Cj38OFDeuGFF0gqldKYMWPo3XffpTVr1hAA8vX1VU+B/eabb8jb25tsbGxo5syZdP/+fYqNjSWRSEQjR44koVBICoWCwsLCqLS01GhtnDhxguRyOaWkpBjUX0SDY+psXFwciUQiam5uVi/rbez83Jo1a7SmzupzXPfs2UMymYwAkJ+fH5WWltK+fftIoVAQAPL29qabN28SEdHjx49p7dq15OXlRUKhkFxcXCgiIoJKSkqI6Kep3wAoKSmp1/08d+4czZgxgzw8PNSfQ3d3d1KpVHT69GmNsnfu3KFFixaRo6MjSSQSmjp1KhUWFvZYb0hICI0cObLHabWmZOjnbxDKGVbJYjgbjIM1NjaWnJyczB3GUw2G8XXr1i0SCoV04MABs8bRV52dnTRr1izav3+/yduuqakhqVRKu3btMnnbTxqMnz8D8X0WzLz4yaC98/X1RXJyMpKTk9X3GViKzs5OFBQUoKGhAdHR0SZvf/PmzZg0aRLi4uJM3vZQxMmCsUEuMTERCxcuRHR0tEU9LLCoqAj5+fkoLCzU+14RY0lLS8OlS5dw4sQJiEQik7Y9VHGyYGaxfv16ZGVl4dGjRxgzZgzy8vLMHdKglpqairi4OLz//vvmDkVvc+fOxaFDhzSe62UKx44dw+PHj1FUVGSWqedDlVB3EcaMb9u2bdi2bZu5w7Ao8+bNM/kDJS3R/PnzMX/+fHOHMeTwLwvGGGM6cbJgjDGmEycLxhhjOnGyYIwxphMnC8YYYzpZ/GyovLw8CAQCc4dhEaKiohAVFWXuMCwOjy/GAAGR5b6c9ty5c7hz5465w2BDyLlz55Cenj5g75Fmw5dKpbLkR+/nWnSyYMzYcnJyEBUVBf5YMKYhl69ZMMYY04mTBWOMMZ04WTDGGNOJkwVjjDGdOFkwxhjTiZMFY4wxnThZMMYY04mTBWOMMZ04WTDGGNOJkwVjjDGdOFkwxhjTiZMFY4wxnThZMMYY04mTBWOMMZ04WTDGGNOJkwVjjDGdOFkwxhjTiZMFY4wxnThZMMYY04mTBWOMMZ04WTDGGNOJkwVjjDGdOFkwxhjTiZMFY4wxnThZMMYY04mTBWOMMZ04WTDGGNOJkwVjjDGdOFkwxhjTiZMFY4wxnThZMMYY04mTBWOMMZ2E5g6AMXOprq7Gn//8Z41lX3/9NQBg3759GsvlcjkWLVpkstgYG2wERETmDoIxc3j8+DFcXV3R2NgIa2trAED3x0EgEKjLtbe344033sD//M//mCNMxgaDXD4NxYYtiUSCyMhICIVCtLe3o729HR0dHejo6FD/v729HQDw6quvmjlaxsyLkwUb1l599VW0tbX1WsbBwQG//OUvTRQRY4MTJws2rL3wwgtwcXF56nqRSISYmBgIhXx5jw1vnCzYsGZlZYXFixdDJBL1uL69vZ0vbDMGThaMYdGiReprE0/6xS9+geDgYBNHxNjgw8mCDXtTp06Ft7e31nKxWIw33nhDY2YUY8MVJwvGALz22mtap6La2tr4FBRj/8TJgjEAixcv1joV5evri/Hjx5spIsYGF04WjAHw9/dHQECA+pSTSCTCm2++aeaoGBs8OFkw9k+vv/66+k7ujo4OPgXF2M9wsmDsnxYtWoTOzk4AQFBQEMaMGWPmiBgbPDhZMPZPXl5emDZtGgDgjTfeMHM0jA0uQ/K21LS0NJw7d87cYTAL9PjxYwgEApw8eRJfffWVucNhFighIWFI3pszJH9ZnDt3DufPnzd3GKwHeXl5uHv3rrnDeCpPT0+4ublBKpWaOxS18+fP83i2EHl5ebhz5465wxgQQ/KXBQBMnz4dubm55g6DPUEgEGDlypV45ZVXzB3KU3333Xfw9fU1dxhqCxcuBAAezxZgKN/AOSR/WTDWH4MpUTA2WHCyYIwxphMnC8YYYzpxsmCMMaYTJwvGGGM6cbJ4irfffhtyuRwCgQCXLl0ydziDQldXF3bv3g2VSmXuUHDixAnY29vj008/NXcog96pU6eQmJiI/Px8+Pj4QCAQQCAQ4LXXXtMqO2/ePMjlclhbWyMwMBDffPONGSI2nD5js729Hdu2bYOvry/EYjEcHBwwbtw4lJeXAwCOHz+OHTt2qO/iZ5o4WTzFxx9/jD/+8Y/mDmPQuHXrFp5//nkkJCSgubnZ3OGAiMwdgkV47733kJGRgfXr1yMiIgK3b9+GUqnEiBEjcPDgQXz++eca5U+ePInc3Fy8/PLLKCkpQVBQkJki15++YzMqKgp/+tOfcOjQITQ3N+Pbb7+FUqlEY2MjACA0NBRSqRRz585FXV2dqcK3GJwshomWlpY+/yK4fPky1q1bh2XLlmHSpElGjqxvQkJC8OjRI7z88svmDqUm8qWTAAAgAElEQVRffTuQtm/fjiNHjiAnJwdyuVxjXUZGBqysrBAbG4tHjx6ZKcL+03dsHjlyBAUFBcjNzcW0adMgFArh4eGBY8eOYdy4cepyK1aswMSJE/HSSy+ho6PDFLtgMThZ9GIo3WCzf/9+VFVV9WnbiRMnIj8/H4sXL4ZEIjFyZJavP307UL777jts2rQJW7Zs6fFudJVKhfj4eHz//fdYvXq1GSI0Dn3H5kcffYSgoCC93k+yefNmXLp0Cenp6cYM1eJxsvgnIsLOnTsxduxYSCQS2NvbY82aNRplfve730Emk0Eul6OqqgqrVq3CyJEjcePGDRAR0tLS8Oyzz0IikcDR0RFhYWG4fv26evuMjAxIpVK4urpi6dKl8PDwgFQqhUqlwoULF7Ti0VVfXFwcxGIx3N3d1cveeecd2NraQiAQoKamBgAQHx+PVatWobS0FAKBwOJvOjtz5gy8vLwgEAjw4YcfAgAyMzNha2sLmUyGY8eO4cUXX4RCoYCnpycOHz6s3lbfY9Dfvv3iiy+gUCiQmppqii7RkpGRASJCaGjoU8ukpKTgmWeewccff4xTp071Wp8+41HfYwAAnZ2dSEpKgpeXF2xsbDBhwgRkZ2f3b6efoq2tDefPn9f7V7GjoyNmz56N9PR0Pt35czQERUZGUmRkpEHbbNiwgQQCAf3+97+n2tpaam5upj179hAAKi4u1igHgFasWEEffPABLViwgL799ltKSkoisVhMBw4coLq6Orpy5QoFBQWRs7Mz3b9/X719bGws2dra0rVr16i1tZVKSkpoypQpJJfLqbKyUl1O3/oWL15Mbm5uGvuyc+dOAkDV1dXqZREREaRUKg3qk55MmzaNJk6c2OftAVB2dna/47hz5w4BoA8++EC9rPvYfPnll/To0SOqqqqiWbNmka2tLbW1tanL6XsM+tO3n332GcnlckpOTu73vvZlPPv4+FBAQECP65RKJZWVlRER0dmzZ8nKyopGjx5NjY2NRERUWFhI8+fP19hG3/Go7zFYvXo1SSQSysvLo9raWlq/fj1ZWVnRxYsXDdrPn3va2CwrKyMANGnSJJozZw65u7uTRCIhf39/+vDDD6mrq0trm8TERK3Pvj6MNb4HoRz+ZYGfzjnv3r0bv/rVr5CQkAAHBwfY2NjAycnpqdts374dy5cvR35+Pry9vZGWloYFCxYgJiYG9vb2GD9+PPbu3Yuamhrs27dPY1uhUKj+hhYQEIDMzEw0NDQgKytLHY8h9TFNKpUKCoUCLi4uiI6ORlNTEyorKzXK6DoG/RUSEoL6+nps2rTJKPUZoqmpCWVlZVAqlTrLBgcHY+XKlSgvL8e6det6LNOX8djbMWhtbUVmZibCw8MREREBBwcHbNy4ESKRyGj9/3PdF7BdXFyQmpqKkpISPHjwAGFhYVi+fDk++eQTrW38/PwAAFevXjV6PJaKkwV+Or/b3NyMuXPn9mn7kpISNDY2YvLkyRrLp0yZArFYrHWK6UmTJ0+GTCZT/6Tvb33sX8RiMQBovV/7SU8eA0tWVVUFIoJMJtOrfEpKCsaOHYs9e/bgzJkzWuv7Ox6fPAY3btxAc3OzxoVlGxsbuLu7D0j/d1/LCAwMhEqlgpOTE+zt7bFlyxbY29v3mOy6++7BgwdGj8dScbIA1I/MdnFx6dP23dPs7OzstNY5ODigoaFBZx0SiQTV1dVGq48Z7ufHwJK1trYCgN6TEaRSKbKysiAQCPDWW2+hpaVFY72xx2NTUxMAYOPGjep7PgQCASoqKgZkWraHhwcAqK8zdROLxfD29kZpaanWNjY2NgD+1ZeMkwUAqGeLPH78uE/bOzg4AECPH5q6ujp4enr2un17e7tGuf7Wxwz35DGwZN1/6Ay5uSw4OBgJCQm4desWtm7dqrHO2OOx+0vZ7t27QUQa/wbipWV2dnbw8/PDtWvXtNZ1dHTA3t5ea3lbWxuAf/Ul42QBABg3bhysrKxw+vTpPm9vZ2eHr7/+WmP5hQsX0NbWhueee67X7YuKikBEmD59usH1CYVCnadYmG5PHgPAcvvW1dUVAoHA4Psntm7dCn9/fxQXF2ss7+/4ftKoUaMglUpN+mSEqKgoFBcX4/bt2+plzc3NqKio6HE6bXffubm5mSzGwY6TBX76phMREYG8vDzs378f9fX1uHLlit4XkqVSKVatWoWjR4/i4MGDqK+vx9WrV7Fs2TJ4eHggNjZWo3xXVxdqa2vR0dGBK1euID4+Hl5eXliyZInB9fn6+uLHH39EQUEB2tvbUV1djYqKCq0YnZyccO/ePZSXl6OhocEi/wgak65jAPSvbwsLC802dVYmk8HHx8fgNxJ2n46ytrbWWm7I+NannTfffBOHDx9GZmYm6uvr0dnZibt37+KHH34AAERHR8PNzc1ojxtJSEiAt7c3lixZgsrKSjx8+BBr165FS0tLjxf2u/tOn/syhg0zTsUaMH2ZatjQ0EBvv/02jRgxguzs7GjmzJmUlJREAMjT05MuX75MO3bsIBsbGwJAo0aNogMHDqi37+rqop07d5Kfnx+JRCJydHSk8PBwunHjhkY7sbGxJBKJaOTIkSQUCkmhUFBYWBiVlpZqlNO3vocPH9ILL7xAUqmUxowZQ++++y6tWbOGAJCvr696Kug333xD3t7eZGNjQzNnztSY7qjLuXPnaMaMGeTh4UEACAC5u7uTSqWi06dPG9TPMMLUwg8++IDc3d0JAMlkMgoNDaU9e/aQTCYjAOTn50elpaW0b98+UigUBIC8vb3p5s2bRKT/MehP3544cYLkcjmlpKT0a1+J+jae4+LiSCQSUXNzs3rZ0aNHSalUEgBydnam5cuX97jtmjVrtKbO6jMeDTkGjx8/prVr15KXlxcJhUJycXGhiIgIKikpISKi8PBwAkBJSUm97qchY/POnTu0aNEicnR0JIlEQlOnTqXCwsIe6w0JCaGRI0f2OK22N8YY34NUDicLE4uNjSUnJydzh2E2g+HDZGnHoC/j+datWyQUCjW+0FiSzs5OmjVrFu3fv9/kbdfU1JBUKqVdu3YZvO1gGN8DhO+zMAd+qqX5DfVj4Ovri+TkZCQnJ6vvM7AUnZ2dKCgoQENDA6Kjo03e/ubNmzFp0iTExcWZvO3BjJPFMHT9+nWNKYtP+2eODyoznsTERCxcuBDR0dEW9bDAoqIi5Ofno7CwUO97RYwlLS0Nly5dwokTJyASiUza9mDHycKE1q9fj6ysLDx69AhjxoxBXl6eWeLw9/fXmrLY078jR46YJb6BNFiOgamkpqYiLi4O77//vrlD0dvcuXNx6NAhjedymcKxY8fw+PFjFBUVwdHR0aRtWwIB0dB7UtbChQsBALm5uWaOhD1JIBAgOzsbr7zyirlDsRg8ni3HEB7fufzLgjHGmE6cLBhjjOnEyYIxxphOnCwYY4zpxMmCMcaYTkJzBzBQ8vLyhtQ7tIeSqKgoREVFmTsMi8PjmZnTkE0W06dPx8qVK80dBntCVFQU4uPjERwcbO5QLMbu3bsBgMezBRjKX4KGbLLw9PQcinOdLV5UVBSCg4P52Big+/4K7rPBbygnC75mwRhjTCdOFowxxnTiZMEYY0wnThaMMcZ04mTBGGNMJ04WesjPz4ePj4/W+x7EYjFcXV0xZ84c7Ny5E7W1teYOlTEtp06dQmJiotY4fu2117TKzps3D3K5HNbW1ggMDDTaO7AHWldXF3bv3g2VSvXUMu3t7di2bRt8fX0hFovh4OCAcePGoby8HABw/Phx7NixY8i/GKuvOFnoISIiArdv34ZSqYS9vT2ICF1dXaiqqkJOTg7GjBmDtWvXIjAwEF9//bW5w2VM7b333kNGRgbWr1+vMY5HjBiBgwcP4vPPP9cof/LkSeTm5uLll19GSUkJgoKCzBS5/m7duoXnn38eCQkJaG5ufmq5qKgo/OlPf8KhQ4fQ3NyMb7/9FkqlUv0mwdDQUEilUsydOxd1dXWmCt9icLLoI4FAAAcHB8yZMwdZWVnIycnBgwcPEBISYlFvJXualpaWXr+lWSpT7Ndg6bvt27fjyJEjyMnJgVwu11iXkZEBKysrxMbGWvR4vXz5MtatW4dly5Zh0qRJTy135MgRFBQUIDc3F9OmTYNQKISHhweOHTuGcePGqcutWLECEydOxEsvvYSOjg5T7ILF4GRhJJGRkViyZAmqqqqwd+9ec4fTb/v370dVVZW5wzA6U+zXYOi77777Dps2bcKWLVsglUq11qtUKsTHx+P777/H6tWrzRChcUycOBH5+flYvHgxJBLJU8t99NFHCAoKwvjx43XWuXnzZly6dAnp6enGDNXicbIwoiVLlgAACgsLAQC/+93vIJPJIJfLUVVVhVWrVmHkyJG4ceMGiAhpaWl49tlnIZFI4OjoiLCwMFy/fl1dX0ZGBqRSKVxdXbF06VJ4eHhAKpVCpVLhwoULGm3rU19cXBzEYrHG6yrfeecd2NraQiAQoKamBgAQHx+PVatWobS0FAKBAL6+vgPVZToN9H7p28f97bsvvvgCCoUCqampA9pf3TIyMkBECA0NfWqZlJQUPPPMM/j4449x6tSpXuvT5zhkZmbC1tYWMpkMx44dw4svvgiFQgFPT08cPnxYo77Ozk4kJSXBy8sLNjY2mDBhArKzs/u300/R1taG8+fP9/rL4+ccHR0xe/ZspKenYwi+SLTvaAiKjIykyMhIo9erVCrJ3t7+qevr6+sJAI0aNUq9bMOGDQSAVqxYQR988AEtWLCAvv32W0pKSiKxWEwHDhyguro6unLlCgUFBZGzszPdv39fvX1sbCzZ2trStWvXqLW1lUpKSmjKlCkkl8upsrJSXU7f+hYvXkxubm4ace/cuZMAUHV1tXpZREQEKZXKfvVXTwBQdna23uVNsV/69nF/2vjss89ILpdTcnKy3vverS/j2cfHhwICAnpcp1QqqaysjIiIzp49S1ZWVjR69GhqbGwkIqLCwkKaP3++xjb6Hofu8f7ll1/So0ePqKqqimbNmkW2trbU1tamLrd69WqSSCSUl5dHtbW1tH79erKysqKLFy8atJ8/N23aNJo4caLW8rKyMgJAkyZNojlz5pC7uztJJBLy9/enDz/8kLq6urS2SUxMJABUXFxsUAyGjm8LksO/LIxILpdDIBCgoaFBa9327duxfPly5Ofnw9vbG2lpaViwYAFiYmJgb2+P8ePHY+/evaipqcG+ffs0thUKhepvdAEBAcjMzERDQwOysrIA/HSO3JD6LIUp90tXH/dXSEgI6uvrsWnTJqPU15umpiaUlZVBqVTqLBscHIyVK1eivLwc69at67FMX46DSqWCQqGAi4sLoqOj0dTUhMrKSgBAa2srMjMzER4ejoiICDg4OGDjxo0QiURG6++f676A7eLigtTUVJSUlODBgwcICwvD8uXL8cknn2ht4+fnBwC4evWq0eOxVJwsjKipqQlEBIVC0Wu5kpISNDY2YvLkyRrLp0yZArFYrHWK6UmTJ0+GTCZTnwLob32DlTn368k+tiRVVVUgIshkMr3Kp6SkYOzYsdizZw/OnDmjtb6/x0EsFgP4aeoqANy4cQPNzc0aF5ZtbGzg7u4+IP3dfS0jMDAQKpUKTk5OsLe3x5YtW2Bvb99jsuvuuwcPHhg9HkvFycKIbt68CQDw9/fvtVz3tDw7OzutdQ4ODj3+MnmSRCJBdXW10eobjMy9Xz/vY0vS2toKAL1e8P05qVSKrKwsCAQCvPXWW2hpadFYb+zj0NTUBADYuHGjxn1LFRUVvU597SsPDw8AUF9X6iYWi+Ht7Y3S0lKtbWxsbAD8qy8ZJwuj+uKLLwAAL774Yq/lHBwcAKDHD1ldXR08PT173b69vV2jXH/rG6zMuV9P9rEl6f5DZ8jNZcHBwUhISMCtW7ewdetWjXXGPg4uLi4AfnpPBxFp/Dt37pxBdenDzs4Ofn5+uHbtmta6jo4O2Nvbay1va2sD8K++ZJwsjOb+/fvYvXs3PD098dZbb/Vadty4cbCzs9O6ge/ChQtoa2vDc8891+v2RUVFICJMnz7d4PqEQqH6dMBgZ879erKPB6KNgeLq6gqBQGDw/RNbt26Fv78/iouLNZb3d7w+adSoUZBKpbh06ZJB2/VHVFQUiouLcfv2bfWy5uZmVFRU9Didtrvv3NzcTBbjYMfJwkBEhMbGRnR1dYGIUF1djezsbMyYMQPW1tYoKCjQec1CKpVi1apVOHr0KA4ePIj6+npcvXoVy5Ytg4eHB2JjYzXKd3V1oba2Fh0dHbhy5Qri4+Ph5eWlnqprSH2+vr748ccfUVBQgPb2dlRXV6OiokIrRicnJ9y7dw/l5eVoaGgwyx9JU+6Xrj7ubxuFhYUmmzork8ng4+ODu3fvGrRd9+koa2trreWGjFd92nnzzTdx+PBhZGZmor6+Hp2dnbh79y5++OEHAEB0dDTc3NyM9riRhIQEeHt7Y8mSJaisrMTDhw+xdu1atLS09Hhhv7vv9LkvY9gw1zysgWTsqbPHjx+nCRMmkEwmI7FYTFZWVgSABAIBOTg40NSpUyk5OZkePnyosd2OHTvIxsZGPZ32wIED6nVdXV20c+dO8vPzI5FIRI6OjhQeHk43btzQqCM2NpZEIhGNHDmShEIhKRQKCgsLo9LSUo1y+tb38OFDeuGFF0gqldKYMWPo3XffpTVr1hAA8vX1VU8V/eabb8jb25tsbGxo5syZGtMj+wMGTi00xX7p28f9aePEiRMkl8spJSXF4D7ry3iOi4sjkUhEzc3N6mVHjx4lpVJJAMjZ2ZmWL1/e47Zr1qzRmjqrz3HYs2cPyWQyAkB+fn5UWlpK+/btI4VCQQDI29ubbt68SUREjx8/prVr15KXlxcJhUJycXGhiIgIKikpISKi8PBwAkBJSUm97ue5c+doxowZ5OHhQQAIALm7u5NKpaLTp09rlL1z5w4tWrSIHB0dSSKR0NSpU6mwsLDHekNCQmjkyJE9TqvtjaHj24LkcLIY5GJjY8nJycncYRjNYPwwDfY+7st4vnXrFgmFQo0vKJaks7OTZs2aRfv37zd52zU1NSSVSmnXrl0GbzsYx7eR8H0WloCfgjnwhlof+/r6Ijk5GcnJyer7DCxFZ2cnCgoK0NDQgOjoaJO3v3nzZkyaNAlxcXEmb3sw42TB2BCVmJiIhQsXIjo62qIeFlhUVIT8/HwUFhbqfa+IsaSlpeHSpUs4ceIERCKRSdse7DhZDGLr169HVlYWHj16hDFjxiAvL8/cIQ05Q72PU1NTERcXh/fff9/coeht7ty5OHTokMZzuEzh2LFjePz4MYqKiuDo6GjSti2BgGjoPSlr4cKFAIDc3FwzR8KeJBAIkJ2djVdeecXcoVgMHs+WYwiP71z+ZcEYY0wnThaMMcZ04mTBGGNMJ04WjDHGdBKaO4CBcvfuXeTk5Jg7DNaDgXhY3FDW/egJHs/MnIbsbKihNgWSMWYZhupsqCGZLBjrq5ycHERFRfG7lxnTxFNnGWOM6cbJgjHGmE6cLBhjjOnEyYIxxphOnCwYY4zpxMmCMcaYTpwsGGOM6cTJgjHGmE6cLBhjjOnEyYIxxphOnCwYY4zpxMmCMcaYTpwsGGOM6cTJgjHGmE6cLBhjjOnEyYIxxphOnCwYY4zpxMmCMcaYTpwsGGOM6cTJgjHGmE6cLBhjjOnEyYIxxphOnCwYY4zpxMmCMcaYTpwsGGOM6cTJgjHGmE6cLBhjjOnEyYIxxphOnCwYY4zpxMmCMcaYTpwsGGOM6cTJgjHGmE6cLBhjjOkkNHcAjJnL3bt38cYbb6Czs1O9rLa2FnK5HHPmzNEoO3bsWPzXf/2XiSNkbPDgZMGGLU9PT1RUVKC0tFRr3enTpzX+//zzz5sqLMYGJT4NxYa1119/HSKRSGe56OhoE0TD2ODFyYINa4sXL0ZHR0evZQIDAxEQEGCiiBgbnDhZsGFNqVRiwoQJEAgEPa4XiUR44403TBwVY4MPJws27L3++uuwtrbucV1HRwcWLlxo4ogYG3w4WbBhb9GiRejq6tJabmVlhenTp2P06NGmD4qxQYaTBRv2PDw8MGPGDFhZaX4crKys8Prrr5spKsYGF04WjAF47bXXtJYRERYsWGCGaBgbfDhZMAYgMjJS47qFtbU1fvWrX8HV1dWMUTE2eHCyYAyAo6Mj/u3f/k2dMIgIMTExZo6KscGDkwVj/xQTE6O+0C0SiRAWFmbmiBgbPDhZMPZPoaGhkEgkAICXX34ZdnZ2Zo6IscGDkwVj/2Rra6v+NcGnoBjTJCAiMncQ5vC0O3YZY+xpIiMjkZuba+4wzCF3WD91Nj4+HsHBweYOY8jYvXs3AGDlypVmjqTvOjs7kZ2djVdffdUk7Z07dw7p6enIzs42SXus77rH93A1rJNFcHAwXnnlFXOHMWR0f+Oy9D4NDw+HVCo1WXvp6ekW32fDwTD9RaHG1ywYe4IpEwVjloKTBWOMMZ04WTDGGNOJkwVjjDGdOFkwxhjTiZNFH7399tuQy+UQCAS4dOmSucPpl66uLuzevRsqlcrcoQAATpw4AXt7e3z66afmDmXQO3XqFBITE5Gfnw8fHx8IBAIIBIIen6I7b948yOVyWFtbIzAwEN98840ZIjacPuOzvb0d27Ztg6+vL8RiMRwcHDBu3DiUl5cDAI4fP44dO3ags7PTRFEPPZws+ujjjz/GH//4R3OH0W+3bt3C888/j4SEBDQ3N5s7HAA/PcSP6fbee+8hIyMD69evR0REBG7fvg2lUokRI0bg4MGD+PzzzzXKnzx5Erm5uXj55ZdRUlKCoKAgM0WuP33HZ1RUFP70pz/h0KFDaG5uxrfffgulUonGxkYAPz3KRSqVYu7cuairqzNV+EPKsL7PYri7fPkykpOTsWzZMjQ1NQ2aP9IhISF49OiRucMAALS0tGDu3Lk4e/asuUPRsH37dhw5cgSXL1/WmuqbkZGB1157DbGxsSgpKYG9vb2ZouwffcfnkSNHUFBQgMuXL2P8+PEAfnqh1bFjxzTKrVixArdv38ZLL72Er776CkIh//kzBP+y6AdLf2TIxIkTkZ+fj8WLF6sfoMc07d+/H1VVVeYOQ8N3332HTZs2YcuWLT3eE6JSqRAfH4/vv/8eq1evNkOExqHv+Pzoo48QFBSkThS92bx5My5duoT09HRjhjoscLLQExFh586dGDt2LCQSCezt7bFmzRqtcp2dnUhKSoKXlxdsbGwwYcIE9aMcMjMzYWtrC5lMhmPHjuHFF1+EQqGAp6cnDh8+rFHP6dOnMXXqVMhkMigUCowfPx719fU627B0Z86cgZeXFwQCAT788EMA+vdbRkYGpFIpXF1dsXTpUnh4eEAqlUKlUuHChQvqcnFxcRCLxXB3d1cve+edd2BrawuBQICamhoAPz0OZtWqVSgtLYVAIICvry8A4IsvvoBCoUBqaqopukRLRkYGiAihoaFPLZOSkoJnnnkGH3/8MU6dOtVrfUSEtLQ0PPvss5BIJHB0dERYWBiuX7+uLmPI2DXl+Gxra8P58+cxadIkvco7Ojpi9uzZSE9PHzS/pC0GDVMAKDs7W+/yGzZsIIFAQL///e+ptraWmpubac+ePQSAiouL1eVWr15NEomE8vLyqLa2ltavX09WVlZ08eJFdT0A6Msvv6RHjx5RVVUVzZo1i2xtbamtrY2IiBobG0mhUNCOHTuopaWF7t+/TwsWLKDq6mq92uiLadOm0cSJE/u8PRFRZGQkRUZG9qsOIqI7d+4QAPrggw/Uy/TpNyKi2NhYsrW1pWvXrlFrayuVlJTQlClTSC6XU2Vlpbrc4sWLyc3NTaPdnTt3EgB1PxMRRUREkFKp1Cj32WefkVwup+Tk5H7va3Z2Nhn6MfTx8aGAgIAe1ymVSiorKyMiorNnz5KVlRWNHj2aGhsbiYiosLCQ5s+fr7FNUlISicViOnDgANXV1dGVK1coKCiInJ2d6f79++py+h4DU47PsrIyAkCTJk2iOXPmkLu7O0kkEvL396cPP/yQurq6tLZJTEzU+tzqw1jj20Ll8C8LPbS0tGD37t341a9+hYSEBDg4OMDGxgZOTk4a5VpbW5GZmYnw8HBERETAwcEBGzduhEgkQlZWlkZZlUoFhUIBFxcXREdHo6mpCZWVlQCA8vJy1NfXIzAwEFKpFG5ubsjPz4ezs7NBbQxFvfVbN6FQqP6WHBAQgMzMTDQ0NBitf0JCQlBfX49NmzYZpT5DNDU1oaysDEqlUmfZ4OBgrFy5EuXl5Vi3bl2PZVpaWpCWloYFCxYgJiYG9vb2GD9+PPbu3Yuamhrs27dPa5vejoGpx2f3BWwXFxekpqaipKQEDx48QFhYGJYvX45PPvlEaxs/Pz8AwNWrV40ez1DGyUIP3333HZqbmzF37txey924cQPNzc0YN26cepmNjQ3c3d01ftI/SSwWA/hp+h8A+Pj4wNXVFTExMdi8ebN6+l9/2hiKnuy3p5k8eTJkMtmQ6J+qqioQEWQymV7lU1JSMHbsWOzZswdnzpzRWl9SUoLGxkZMnjxZY/mUKVMgFos1Tt/15MljYOrx2X0tIzAwECqVCk5OTrC3t8eWLVtgb2/fY7Lr7rsHDx4YPZ6hjJOFHu7evQvgp28vvWlqagIAbNy4UT3fXSAQoKKiwqBpqTY2NvjLX/6CmTNnIjU1FT4+PoiOjkZLS4vR2hhuJBIJqqurzR1Gv7W2tgKA3hMSpFIpsrKyIBAI8NZbb6GlpUVjffc00p7eCujg4ICGhgaD4jP1+PTw8AAA9XWmbmKxGN7e3igtLdXaxsbGBsC/+pLph5OFHrpnnDx+/LjXct3JZPfu3SAijX/nzp0zqM3AwEB8+umnuHfvHtauXYvs7Gzs2rXLqG0MF+3t7airqzFVl1gAABA+SURBVIOnp6e5Q+m37j90htxcFhwcjISEBNy6dQtbt27VWOfg4AAAPSaFvvSZqcennZ0d/Pz8cO3aNa11HR0dPU4bbmtrA/CvvmT64WShh3HjxsHKygqnT5/utdyoUaMglUr7fUf3vXv31IPfxcUF77//PoKCgnDt2jWjtTGcFBUVgYgwffp09TKhUKjz9NVg5OrqCoFAYPB9KFu3boW/vz+Ki4s1lo8bNw52dnb4+uuvNZZfuHABbW1teO655wxqxxzjMyoqCsXFxbh9+7Z6WXNzMyoqKnqcTtvdd25ubiaLcSjgZKEHFxcXREREIC8vD/v370d9fT2uXLmidT5UKpXizTffxOHDh5GZmYn6+np0dnbi7t27+OGHH/Ru7969e1i6dCmuX7+OtrY2FBcXo6KiAtOnTzdaG0NZV1cXamtr0dHRgStXriA+Ph5eXl5YsmSJuoyvry9+/PFHFBQUoL29HdXV1aioqNCqy8nJCffu3UN5eTkaGhrQ3t6OwsJCs02dlclk8PHxUZ8a1Vf36Shra2ut5atWrcLRo0dx8OBB1NfX4+rVq1i2bBk8PDwQGxtrcDu6xmd0dDTc3NyM9riRhIQEeHt7Y8mSJaisrMTDhw+xdu1atLS09Hhhv7vv9Lkvg/2MOeZgDQYwcOpsQ0MDvf322zRixAiys7OjmTNnUlJSEgEgT09Punz5MhERPX78mNauXUteXl4kFArJxcWFIiIiqKSkhPbs2UMymYwAkJ+fH5WWltK+fftIoVAQAPL29qabN29SeXk5qVQqcnR0JGtra/rFL35BGzZsoI6ODp1tGOLcuXM0Y8YM8vDwIAAEgNzd3UmlUtHp06cNqovIOFMLP/jgA3J3dycAJJPJKDQ0VO9+I/pp6qxIJKKRI0eSUCgkhUJBYWFhVFpaqtHOw4cP6YUXXiCpVEpjxoyhd999l9asWUMAyNfXVz3N9ptvviFvb2+ysbGhmTNn0v379+nEiRMkl8spJSWlX/tK1Leps3FxcSQSiai5uVm97OjRo6RUKgkAOTs70/Lly3vcds2aNVpTZ7u6umjnzp3k5+dHIpGIHB0dKTw8nG7cuKEuY8gx0DU+w8PDCQAlJSX1up+GjM87d+7QokWLyNHRkSQSCU2dOpUKCwt7rDckJIRGjhzZ47Ta3gz3qbOcLJjRDIYPU2xsLDk5OZk1BkP0JVncunWLhEIhHThwYICiGlidnZ00a9Ys2r9/v8nbrqmpIalUSrt27TJ428Ewvs2I77NgQ89Qf7Kor68vkpOTkZycrL7PwFJ0dnaioKAADQ0NiI6ONnn7mzdvxqRJkxAXF2fyti0dJ4sh5Pr16xrTFZ/2zxwfUmZciYmJWLhwIaKjowfNQxf1UVRUhPz8fBQWFup9r4ixpKWl4dKlSzhx4gREIpFJ2x4KOFkMIf7+/lrTFXv6d+TIEXOHOiDWr1+PrKwsPHr0CGPGjEFeXp65QxpQqampiIuLw/vvv2/uUPQ2d+5cHDp0SOO5XKZw7NgxPH78GEVFRXB0dDRp20MFP6OXDRnbtm3Dtm3bzB2GSc2bNw/z5s0zdxiD3vz58zF//nxzh2HR+JcFY4wxnThZMMYY04mTBWOMMZ04WTDGGNNpWF/g5gfvGVf3YxRycnLMHInl6B6D3GeD3927d4fEwyj7SkA0PN8taOnvz2aMmV5kZCRyc3PNHYY55A7rXxbZ2dl45ZVXzB3GkLFw4UIAGK4fpj7JyclBVFQUvw/aAnSP7+GKr1kwxhjTiZMFY4wxnThZMMYY04mTBWOMMZ04WTDGGNOJkwVjjDGdOFkYQX5+Pnx8fLTeGyEWi+Hq6oo5c+Zg586dqK2tNXeobBg4deoUEhMTtcbla6+9plV23rx5kMvlsLa2RmBgoNHeiz3Qurq6sHv3bqhUKq11x48fx44dO4b8S7BMjZOFEUREROD27dtQKpWwt7cHEaGrqwtVVVXIycnBmDFjsHbtWgQGBuLrr782d7hsCHvvvfeQkZGB9evXa4zLESNG4ODBg/j88881yp88eRK5ubl4+eWXUVJSgqCgIDNFrr9bt27h+eefR0JCApqbm7XWh4aGQiqVYu7cuairqzNDhEMTJ4sBIhAI4ODggDlz5iArKws5OTl48OABQkJCLOrNZpampaWlx2+bltZGX2zfvh1HjhxBTk4O5HK5xrqMjAxYWVkhNjbWosff5cuXsW7dOixbtgyTJk16arkVK1Zg4sSJeOmll9DR0WHCCIcuThYmEhkZiSVLlqCqqgp79+41dzhD1v79+1FVVWXxbRjqu+++w6ZNm7BlyxZIpVKt9SqVCvHx8fj++++xevVqM0RoHBMnTkR+fj4WL14MiUTSa9nNmzfj0qVLSE9PN1F0QxsnCxNasmQJAKCwsFC9rLOzE0lJSfDy8oKNjQ0mTJiA7OxsAEBmZiZsbW0hk8lw7NgxvPjii1AoFPD09MThw4c16j59+jSmTp0KmUwGhUKB8ePHo76+Xmcb5kZESEtLw7PPPguJRAJHR0eEhYXh+vXr6jJxcXEQi8Uar+J85513YGtrC4FAgJqaGgBAfHw8Vq1ahdLSUggEAvj6+iIjIwNSqRSurq5YunQpPDw8IJVKoVKpcOHCBaO0AQBffPEFFAoFUlNTB7S/niYjIwNEhNDQ0KeWSUlJwTPPPIOPP/4Yp06d6rU+fY6LIePTHGPQ0dERs2fPRnp6Oj9OxRhomAJA2dnZRq1TqVSSvb39U9fX19cTABo1apR62erVq0kikVBeXh7V1tbS+vXrycrKii5evEhERBs2bCAA9OWXX9KjR4+oqqqKZs2aRba2ttTW1kZERI2NjaRQKGjHjh3U0tJC9+/fpwULFlB1dbVebRhLZGQkRUZGGrRNUlISicViOnDgANXV1dGVK1coKCiInJ2d6f79++pyixcvJjc3N41td+7cSQDU+0lEFBERQUqlUqNcbGws2dra0rVr16i1tZVKSkpoypQpJJfLqbKy0ihtfPbZZySXyyk5Odmg/c/OziZjfAx9fHwoICCgx3VKpZLKysqIiOjs2bNkZWVFo0ePpsbGRiIiKiwspPnz52tso+9x0Wd8Eg3MGJw2bRpNnDix1zKJiYkEgIqLi/vcTre+jO8hJId/WZiQXC6HQCBAQ0MDAKC1tRWZmZkIDw9HREQEHBwcsHHjRohEImRlZWlsq1KpoFAo4OLigujoaDQ1NaGyshIAUF5ejvr6egQGBkIqlcLNzQ35+flwdnY2qA1Ta2lpQVpaGhYsWICYmBjY29tj/Pjx2Lt3L2pqarBv3z6jtSUUCtXfkgMCApCZmYmGhgaj9UFISAjq6+uxadMmo9RniKamJpSVlUGpVOosGxwcjJUrV6K8vBzr1q3rsUxfjktv49OcY9DPzw8AcPXq1QFtZzjgZGFCTU1NICIoFAoAwI0bN9Dc3Ixx48apy9jY2MDd3V3j5/6TxGLx/2/v/kKaesM4gH/XzuzsYLJJamYprklCf4iyMLWLCITowiSEXXSTNyuoEYVIfwiZ/y6SdmUXQXhREWLJKtCuYkGQ3aQUiinCBrGsZX90ZG5uTxexxX7z19n0bMfc84HdnHP2Pu/e857zsHPe8x4AQDAYBACYTCbk5+fj5MmTaGlpgdvtjm673BjpMDo6Cr/fj4qKipjl+/fvR1ZWVsxlIqVVVFRAkiTV20AJnz59AhFBkqSEtm9ra8P27dvR3d2NFy9exK1f6X75b/9Usw9G2uTjx48pjZMJOFmk0cTEBACgvLwcwO/kAQBXr16NeT7D4/EsOSTw/+j1ejx79gw1NTVob2+HyWSCxWLB/Py8YjFSITKsMTs7O26dwWCI/gNLlfXr18Pn86U0Rjr8/PkTAGRv+EaIooienh5oNBo0NjZifn4+Zr3S+0XNPqjX6wH8aSO2fJws0ujp06cAgKNHjwIA8vLyAAAOhwNEFPNJ9i1+O3bswJMnT+D1etHc3Ize3l50dXUpGkNpBoMBAJY8+Xz79i2lbyULBoMpj5EukRNiMg+hHTx4EBcuXMDk5CRaW1tj1im9X9Tsg4FAAMCfNmLLx8kiTaanp+FwOLBlyxY0NjYCALZu3QpRFDEyMrKisr1eL8bGxgD8PjA7Ozuxd+9ejI2NKRYjFXbu3Ins7Oy4BxVfvXqFQCCAffv2RZcJghC9rKEEl8sFIkJlZWXKYqRLfn4+NBpN0s9PtLa2ory8HMPDwzHLk9kviVCzD0bapKCgIO2x1xpOFgojIvj9foTDYRARfD4fent7UV1dDa1WC6fTGb1nIYoiTp06hfv37+PmzZuYnZ1FKBTC+/fv8eHDh4Rjer1enD59GuPj4wgEAhgeHobH40FlZaViMVJBFEVcvHgR/f39uHv3LmZnZ/H27VucOXMGhYWFsFqt0W3NZjO+fPkCp9OJYDAIn88Hj8cTV2Zubi68Xi/cbjfm5uaiJ/9wOIyvX79icXERb968wfnz51FcXBwdzrzSGIODg6oNnZUkCSaTKfoO9ERFLkdptdq45Ynul0TjyPVBi8WCgoICxacbibTJrl27FC03I6V9ANYqAQWHzj5+/Jh2795NkiRRVlYWrVu3jgCQRqMhg8FABw4cILvdTjMzM3HfXVhYoObmZiouLiZBECgvL49OnDhBo6Oj1N3dTZIkEQAqKyujqakpunXrFuXk5BAAKikpoYmJCXK73VRVVUVGo5G0Wi1t3ryZrly5QouLi7IxlLScoYXhcJiuX79OZWVlpNPpyGg0Un19Pb179y5mu5mZGTp8+DCJokilpaV07tw5ampqIgBkNpujQ2Bfv35NJSUlpNfrqaamhqanp8lqtZJOp6OioiISBIFycnLo+PHjNDU1pViMgYEB2rBhA7W1tSX1+5UaOmuz2Uin09GPHz+iy/r7+2nbtm0EgDZu3Ehnz55d8rtNTU1xQ2cT2S+J9k8i+T5YX19PAOjatWt//Z0vX76k6upqKiwsJAAEgDZt2kRVVVX0/PnzuO2PHTtGRUVFFA6HE2vIv8j0obOcLJhiVuvBZLVaKTc3V+1qLEmpZDE5OUmCINCdO3cUqFX6hUIhOnToEN2+fVuxMj9//kyiKFJXV5ci5a3W/p0m/JwFywxrfQZSs9kMu90Ou90Ov9+vdnWSEgqF4HQ6MTc3B4vFoli5LS0t2LNnD2w2m2JlZjJOFoytEZcuXUJDQwMsFss/NVmgy+XCw4cPMTg4mPCzInJu3LiBkZERDAwMQKfTKVJmpuNkwda0y5cvo6enB9+/f0dpaSkePHigdpVSqr29HTabDZ2dnWpXJWFHjhzBvXv3YublWolHjx5hYWEBLpcLRqNRkTIZIKhdAcZSqaOjAx0dHWpXI61qa2tRW1urdjVUU1dXh7q6OrWrsebwPwvGGGOyOFkwxhiTxcmCMcaYLE4WjDHGZGX0DW6Hw4G+vj61q7FmDA0NAQAaGhpUrsm/IzIdBbfZ6jc0NBQzl1im0RBl5vsG+eBkjCUrMltvBurL2GTBGGMsYX18z4IxxpgsThaMMcZkcbJgjDEmi5MFY4wxWb8ANAHGuz873vkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We compile the model for training with the following parameters:\n",
        "\n",
        "adam as optimizer to use during training process (a combination of gradient descent with momentum and RMSP) binary cross-entropy (bce) between true labels and predicted labels as loss to minimise during training process accuracy as metric to display during training process (how often predicted labels equal true labels)"
      ],
      "metadata": {
        "id": "Mzubzf_PrmfG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.utils.vis_utils import plot_model\n",
        "\n",
        "plot_model(model, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "rGxxIPxWrq2t",
        "outputId": "a3cee965-e1d0-4036-b8c1-94d91d4a63d4"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model.compile(optimizer='adam', loss='bce', metrics='accuracy')"
      ],
      "metadata": {
        "id": "AvgtpUITrv1S"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We train the model with \"train_rev_pad\" as input array, \"train_sent\" as output array, validation split, batch size, number of epochs, and the option \"shuffle=True\" to shuffle the training data before each epoch. An epoch is a pass of the neural network over the entire training set and the batch size is the number of samples that are passed to the network at once. For each epoch, we thus have\n",
        "\n",
        "number of training steps=length of training set - length of validation setbatch size"
      ],
      "metadata": {
        "id": "lkll7pdjr0ml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "validation_split = 0.1\n",
        "batch_size = 384\n",
        "epochs = 3\n",
        "\n",
        "fitted = model.fit(train_rev_pad, train_sent, validation_split=validation_split,\n",
        "                   batch_size=batch_size, epochs=epochs, shuffle=True)\n",
        "     \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uu-k9D8Rr9Wj",
        "outputId": "ef439a6c-5c0a-472a-b2bb-7937fea150e6"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "106/106 [==============================] - 56s 503ms/step - loss: 0.6781 - accuracy: 0.6069 - val_loss: 0.6403 - val_accuracy: 0.7447\n",
            "Epoch 2/3\n",
            "106/106 [==============================] - 54s 508ms/step - loss: 0.5003 - accuracy: 0.8113 - val_loss: 0.3752 - val_accuracy: 0.8600\n",
            "Epoch 3/3\n",
            "106/106 [==============================] - 57s 536ms/step - loss: 0.3531 - accuracy: 0.8818 - val_loss: 0.2931 - val_accuracy: 0.8856\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Storing in \"ep_values\" the values of the epochs\n",
        "\n",
        "ep_values = range(1, epochs+1)\n",
        "\n",
        "# Plot of the training loss and validation loss (binary cross-entropy)\n",
        "\n",
        "with sns.axes_style(\"darkgrid\"):\n",
        "\n",
        "    _, (loss, acc) = plt.subplots(1, 2, figsize=(15, 6))\n",
        "    loss.plot(ep_values, fitted.history['loss'], color='darkblue', linestyle='dotted',\n",
        "              marker='o', label='Training loss (binary cross-entropy)')\n",
        "    loss.plot(ep_values, fitted.history['val_loss'], color='r', linestyle='dotted',\n",
        "              marker='o', label='Validation loss (binary cross-entropy)')\n",
        "    loss.set_xlabel('Epoch', size='large')\n",
        "    loss.legend(fontsize='large', facecolor='palegreen')\n",
        "\n",
        "    acc.plot(ep_values, fitted.history['accuracy'], color='darkblue', linestyle='dotted',\n",
        "             marker='o', label='Training accuracy')\n",
        "    acc.plot(ep_values, fitted.history['val_accuracy'], color='r', linestyle='dotted',\n",
        "             marker='o', label='Validation accuracy')\n",
        "    acc.set_xlabel('Epoch', size='large')\n",
        "    acc.legend(fontsize='large', facecolor='palegreen')\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "KDRMPHL2syq_",
        "outputId": "93172693-5f38-429f-c5f1-1aabbf0dea48"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Results**\n",
        "\n",
        "First, we evaluate the loss and accuracy of the trained model on the test set."
      ],
      "metadata": {
        "id": "bjrUjx9Ns6LD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result= model.evaluate(test_rev_pad, test_sent)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5fsh77AtCMr",
        "outputId": "b33d1b5b-2a7b-4e73-a259-57e461f01225"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 6s 37ms/step - loss: 0.2914 - accuracy: 0.8864\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "predictions = np.round(model.predict(test_rev_pad))\n",
        "cf_matrix = confusion_matrix(test_sent, predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NBD3ghIntKo_",
        "outputId": "3e34f38b-8053-4030-a95f-b8dafbd61b5f"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 6s 36ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Storing in \"legends\" the legends of each entry of the confusion matrix\n",
        "# Storing in \"percentages\" the percentages of each entry of the confusion matrix\n",
        "# Storing in \"labels\" the grouped values (legend + percentage) of each entry of the confusion matrix\n",
        "\n",
        "legends = ['True negatives', 'False positives', 'False negatives', 'True positives']\n",
        "percentages = [round(100*num, 2) for num in cf_matrix.flatten()/np.sum(cf_matrix)]\n",
        "\n",
        "labels = [f'{v1}\\n\\n{v2}%' for v1, v2 in zip(legends, percentages)]\n",
        "labels = np.asarray(labels).reshape(2, 2)\n",
        "\n",
        "# Heatmap plot of the confusion matrix\n",
        "\n",
        "plt.figure(figsize = (7, 7))\n",
        "\n",
        "cm = sns.heatmap(cf_matrix, annot=labels, fmt='', cmap='vlag', annot_kws={'fontsize': 'large'})\n",
        "cm.set_xlabel('Predicted sentiments', size='large')\n",
        "cm.set_ylabel('Actual sentiments', size='large')\n",
        "cm.xaxis.set_ticklabels(['Negative', 'Positive'])\n",
        "cm.yaxis.set_ticklabels(['Negative', 'Positive'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "2DHrJBn3tSG0",
        "outputId": "1841aedf-b6a9-4553-d281-c3e32b5de5c7"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ],
            "image/png": 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/7lOjF3LVwHa89q/92VxYylvfL2f0pCyaNkykR+v0imZAgBiP8b8p2dXGsSZnW7NnVk4hcbEe6qfEk1EvgeyNRRV5zsGaTcU0qZfAtEWbePTT+Qw+vh3/uTSFifM28thn8/9Sh5ivp6/huL6ZTF+8ieP7NuWrqVkANGuYSEZ6/HbP4jFjxuJNANz73h9cObAdH956EKs3FvHi2MX8NHfDLt9/b6BmyfBpDZwDXA08b2ZfAO8AXznnysIcS9iUFRURk5BQ8TmhQYOK/ZK8PMqKivj+6n9QtGH3/oMVrluHt7SUMeedX2UnluKNOSRlZFR8TmrcuGLfExtLv9tuY/p/HyV74kRceTn73zFs238O5ypfbgerfhhP5wsuYMPs2cTEx7F+1ix/XOvZMHs2v94+rMrzNi1YwOR77sFiYtjn5JPp9+9b+ebSQbvw5NHtoiNa06ZJMn9/fAobNpfQqXkqb998oO/vrtLf24bNJdz3ge+LQ+996vHM1fsyffEm1uQUM21RDtc8v2OnqOpkNtj2b7pp/URKy7xs2lLC+txi2jdP3f7Y+gkV7wDHTlvD2GlrSEmI4d/ndOHakztw59tztzu+5n9tMHZaNk9ftS+vf7eMHm3SK5ph1+QUsXpjEWfe/2uV561YX8iwN+dgBkf2asIDg3pyzLAfKSqJwI5fUTxDSVif3Dm33jn3rHPuMKAHMBO4D8gKZxzhlrdkMWlt2pDerh2euDg6X3jBtkznWD52LN2HDCa+Xj0AEhs1onHfvrt8n+KcHNZOm073wVcQm5QEZiQ3bUqjHj0AWD1hAvucegqJjRoRm5JCx7O39fi0uDhi4uIoyc3FlZfTpN9+NN53323X3rSJ+LQ0YpOTq73/2smTSWrShC4XX8SqHydU/GLNnjSJlBYtaHnUkVhMDBYTQ/2OHUlt1QqLjaXFEUcQm5yMKy+nrKAAF0RBKtskJ8ZSXFrO5sIy0pNjueL4fao99ujeTWhSz1cobS4swzlwzvHT3PW0bpzMwH5NifEYMR6jW6s02jap/u/7hP2ask9mCglxHq4c2I7vZq7F6+DbGWs5rGsG+3dsQIzHuOiI1pSWO2YtzaVN42T6dWhAXIxRXOaluNS7Q+0SICe/hHKvo0WjxGrvP39VPpu2lDLs3C78Om8D+UW+78dzludRUFzGJUe1ISHOg8egfdMUuvnfLw7cryn1U+JwDvILfS0s+icXefbk3JJNgEwgA9i0B+OodVtWrWb+O+9y8H334i0p4Y/XXqftiSdW5M995VU6XXA+/R99lPh66RSt38DS/33JumnTdvle0//7X7r9fRBHvvA8sUlJFGRns2CUr4fjsjFjSGnRgiOeeZrSggKWfD6aRj174rxeygsL+f2FF+j371vxxMWRPWkSa377reK6+StXsmr8eI555WXM42HcVVfvcG9vWRlZv/xCm+OP264zTHlhIb/ePowegwfTffBgzIzcJUuY8+JLALQ66ih6Xn01FuMhf+VKpj308C4/dzR7d/xy7r24B9/c2591uSW8/cNyjuxV9buybq3TueG0jqQmxbJxcwmPfjqfVRt8TYjXPj+D60/ryL9O7YiZsWD1Zh7/tPpehF9NyebO87vSJjOF6YtyeODDPwBYtq6A4W/P4aYzOtOkXgLzV23mhpdmUlbue992zUntaZuZQpm/wLv/gz92uHZxqZdXv1nKS0P7ERtjDH2h6hrl2KnZXHVie2597feKNK+Df704k+tP7chnww4hLtbDsrUFPP+V713uwV0acv2pHUmM95CdU8Ttb8ymuDQCa21E9/RbFs5vyWbWDTjfvyUBHwDvOucmBXuNz0/8m75jhUiTfvvR65pr+HbQ3/d0KHXWPZ1v2NMh1EnP/7MvX03J5rPfVu/pUCLO5MeODlmJNOO/D4fs92WfG2/eq0rKcDfI/gw0A64EWjnn/rUrBZvsHk98PE369cM8HhIbNaLTBReQ9csvezosEZGQC3ezZKZzbu+fJ2gvZWZ0vuhC+t16C+UlJayZPJk/33yr5hNFZO8Uxc2StV64mdnFzrmtfdMvqq5rqnNOKwPUsvLiYiZc/689HYZEgKue2fX3wRJ+Ws+tdp0PbC3cLq7mGIeWvRERCalo7lBS64Wbc+7EgP0ja/t+IiIi4Z5+a3o16VOqShcRkd1gntBte5lwdyjpUDnBfC/h2oU5DhGRiKdmyVpmZm/4d+MD9rdqC8wJRxwiIhIdwlXXXOTfAvcX4Vuk9G3g1DDFISISPcLcLGlm15jZFDMrNrPXAtIvNLP8gK3AzJyZ7efPH2FmpZWOaRdwfh8zm+o/b6qZ9akplrDU3JxzdwGY2UTn3Nhw3FNEJNrtgWbJ1cC9wPH4ZqECwDn3Nr6KjC8us0H4Fq8OHFPyvnPuosoXNLN44DPgceBZfJOAfGZmHXc2bjrcEyePNbN4M+tpZkea2VFbt52dZ2ZD/N8Gpoxdvjxc4YqIyC5wzn3snPsUqGmJk0uBN1xw8z8ega8i9rhzrtg59yRgwE7LjbB2KDGzw4BRQAKQDuQBacAKdtKpxDk3EhgJmltSRCRYdXEQt5m1AQYAl1XKOtnMNuJbJeZp59xz/vTuwKxKBeEsf/qY6u4T7t6SjwEPOeceM7Mc51xDMxsOFIQ5DhGRyBfCZkkzGwIMCUga6a947KpLgAnOuSUBaR/gq8CsAQ4EPjKzTc65d4FUILfSNXLxVYyqFe7CrRPwRKW0B4AlwCNhjkVERIIU2IK2my4B7q907cDVan8xsyeAs4B3gXx8LX2B0oHNO7tJuOusuWwLMsu/BE4DfCWziIiEkHk8IdtCEo/ZoUBz4MMaDnX43quBb6hYL9t+YuJe1DCELNyF28fA1um4XgG+B6ZS84OKiMiuMgvdFtTtLNbMEoEYIMbMEs0ssIXwUuAj59zmSuedamYNzOcAYCi+HpIAPwDlwFAzSzCza/zp43YWS1ibJZ1z1wfsP2Jmv+GrtWl4gIjI3m8YcGfA54uAu4AR/kLvHODMKs47D1+FJwFYCTzonHsdwDlXYmanAS/he431B3BaTcunhfud23accxP25P1FRCJZqJoTg+WcGwGMqCavCKhfTd75NVx3OrDfrsQS7qEAE/C1pVZWjK+0/tg5NzqcMYmIRKrq1s+MBuF+5/YDvrkkxwNv+X+2Aabg6wL6ipn9X5hjEhGRCBPuZsnjgOOdc39sTTCzt4HXnXMHmtnH+Lp+PhTmuOqklObNOeLZZ8j66WemPfIIjXr1oudVV5KUkYHzetkweza/P/c8RRt2PhlAox49OPShB5n/3nvMe+PNivTkpk3pcdWVZPToQXlpKSu++Ya5r7wKQPchg2l19DHkr1zBlPv/U3GPFkccToPOXZj9wgu19+Cyxx27byaDj9+HpvUT2bC5mLve/YMZizdtd0z7pilcd2pHurZMo35qPPv/67ugr5NZP4H/XNqT1o2T+WLSah7/fGHFOU8M6c3zXy3mjxU77ektwQhzs2RdEu7CrQuwuFLaMqAzgHNukpllhjmmOqvnP65m0/z5FZ/zly/n12F3ULxxI57YWLpccjG9/vlPJt19d7XXsJgYelx5JRvnzds+PTaWg++7lyVffMHU/zyA83pJbdECgPqdOlG/Qwe+vvBCulxyCR3POZvfn3ue2ORkOpx5Jj//3y2188BSJxzQqSHXntSe296YzZzleWSkJ1R5XFm549sZa/jw55X89/Leu3SdQUe35cvJWYyZls1bNx7A2Olr+GPFZo7t04TVG4pUsIWImiXD50fgVTPr4O8i2gF4EfgJwMx64pt6Jeo1HzCA0i1bWD9zZkVa8aZNFG/cWPHZeb2kNG+20+u0P+MM1k2fRv6Kldultz7mGIo2bGDxJ59SXlyMt7SUvKVLAUhumsnGuXPxlpWxbuZMkps2BaDrpZew8KOPKCssDNFTSl105Qn78NLXS5i9LA/nYF1uMetyi3c4btm6Aj7/LYvF2Vt2+TrNGyUyZUEOW4rKmbt8My0aJZGSEMOlR7flmS8XVXk9kV0R7sLtUv895+KbcmsOvvEQg/z5JcBOe81Eg9ikJLpcfBFzRr64Q15S48YM/OB9/vbpJ7Q/4wwWfvhRtddJatKY1scdy5/vvLtDXoMunSlYu5YD776L4999h0Me+A9pbdsAsHnZchp2744nPp7GvXuzedly6nXsQGrLlqz6YXzoHlTqHI9B11bpNEiN5+PbDuaLOw/l5jM6kRC3a78qarrOoqwtHNC5IamJsXRplcbi7C1cdWJ73v1xOflFZbXxaFGprg3iDqdwrwqw0Tl3HpAINAOSnHPnO+fW+/P/dM5NCWdMdVGXSy5m+divq3yXVrhuHV+dcy5jzjufeW+8Sf6KFdVep8eVVzHvzTcpLyraIS8xI4MWAwaw5PPP+friS1gzeTIH3DEci41l87JlZP38C/0f/S9JTRqz8MMP6XnlVfz+/Avsc8rJHPrQg/S9+SZiU1JC+tyy5zVMiycu1sNRvZsw+OmpXPjIJDq3TOOyY9uG9DqvfbeUfdvV54Vr+vLhzyuJjTE6NEtlwpz13HNRd164pi9nH9Yy9A8YbcK8nltdEvaIzawLcDtwh3POa2adzaxXuOOoq9LbtSOjTx8WffrpTo8rzc9nxXffccDwO6r8VpV5wAHEJiex+seqhxKWF5ewcc5c1k6ZiisrY9FHHxOfnkZaq1YALP70U8Zfcy1TH3iQ5gP6s2H2bMyMNiecwC//vo3NK1bQ8eyzd/+BpU4pLvUC8MGEFWzIKyF3Sylv/7CcQ7tmhPQ6eQVl3PbGbC58ZBLv/biCm8/ozCMf/8mlR7dlUXY+/3xuOmce0oK2TZJD+4ASNcI9zu1sfIvNfQRcAFyDb2bnB4BjwhlLXZXRsyfJmZkc+9prAMQmJWIeDwNaP8GPQ6/b7ljzeEho0IDY5GRK8/O3v06f3tTv2JHj3noLgLiUZJzXS1qbtky+5x7yli6hYbduNcaTUL8+bU44gZ9uvInMAw8gb+lSXHk5m+YvoN2pp4TmoaXO2FxYxpqcIrZbXOQvLDK1K9c5/eAWzF6Wy6LsLXRolsI741dQVu5YmJVPh+apLF2rRUP+qj2wWGmdEe7ekncDxzjnZprZuf60mcCOXa2i1LIxY1j1448Vn9ufcQbJmU2Y9fQzNDvkEPKWLWPL6tXEp6XRfchgNi1cuEPBBjDvzbdYOGrblJ09rhxC0YaNzH/X9/5t5bjvaX/66WT06cP6WbNod8oplOTlsblSM2f3wVfw59vvUF5cTEH2Gup37EhMYiIZvXpSkJ1dS38KsieNnpTFuf1b8eu8DZSVO84/ojU/zV1f5bHxsR7iYjwV+845Sstd0NdpkBrH2Ye25LInfG8jVm8ool+HBsxakkvXVum8/YMWJ94te2FzYqiEu3Brgm+ROdj2Pc7xl74bRqby4mLKi7f1TCsvKsRbUkpJXh6JjRrR7YrLSahfn7KCQjb8PovJ995bcWyva/4JwKynn6G8sJDygF6N5cUllBcXVRSEW1atYvoj/6X3Nf8kvn59chcuZNJdd+PKtr3Mz+jdi7iUVLJ//RWATfPns2byZI59/XXyV61kyn3brVohEeKlr5dQPyWOj247mJJSL9/MWMsr3ywls34CH9x6EOc8MJE1m4pp1iCRz4cfWnHezw8fyeqNhZx6zy87vU6g607pyEtfL6GwpBzwvYt7YFBPzjikBaMnZWlIgPxlFtwq3yG6mdnXwFvOuTfMbKN/sdKLgPOccycFcw2txC3hdE/nG/Z0CBJlJj92dMjaEhd++H7Ifl92OOvcvaqNM9w1t6HA12Z2OZBiZmPxDeA+NsxxiIhEPFOzZHg45+b5e0ueBHwBLAe+dM7t+NJIRETkLwpL4WZm31P9e7Urzcw5544ORywiIlFDvSVr3VvVpLfA11SpwSwiIiGmZsla5px7OfCzmTUC/g0MBt7HN0RAREQkJMI9iDsduBnf4O0vgL7OOc2SKiJSCzSIu5aZWRJwPXAjvgVLD3POzQnHvUVEopaaJWvdUnzzWD6Eb9XtzMrrtjnnxoUpFhERiXDhKtwK8fWWvLqafAe0C1MsIiJRYW9cqiZUwtWhpG047iMiIgJurnMAACAASURBVAG0EreIiEjkCPf0WyIiEiZqlhQRkYgTzUMBordYFxGRiKWam4hIpNI4NxERiTRqlhQREYkgqrmJiEQorQogIiKRJ4qHAkTvk4uISMRSzU1EJEJZFE+/pcJNRCRCRfMMJX/pyc3sSDM7PNTBiIiIhEJQhZuZjTezQ/37twDvAe+Y2W21GZyIiOwGs9Bte5lgmyV7ABP9+4OBI4HNwM/A/bUQl4iI7KZobpYMtnDzAM7M2gPmnJsLYGYNai0yERGRvyjYYv0n4GngEeATAH9Bt76W4hIRkd1kHk/ItqDuZ3aNmU0xs2Izey0gva2ZOTPLD9juCMhPMLNXzCzPzLLN7IZK1z3azOaZWYGZfW9mbWqKJdia2yDgRmAd8JA/rQvwRJDni4hIuIX/Xdlq4F7geCCpivz6zrmyKtJHAB2BNkBT4Hszm+ucG2NmGcDHwBXAaOAe4H3goJ0FEmzhdpRzbrvOI865L83srCDPFxGRMAv3Ozfn3McAZtYPaLkLp14KDHLO5QA5ZvYivkrVGOAMYI5zbpT/2iOA9WbWxTk3r7oLBvvkL1eTPjLI80VERJaZ2Uoze9VfI9vad6MZMDPguJlAd/9+98A859wWYFFAfpV2WnMzs3b+XY+Z7QME1nHbAUU1P4uIiOwJoZyhxMyGAEMCkkY654Kt4KwH9gdmAI2AZ4C38TVfpvqPyQ04PhdI8++n4nslRjX5VaqpWXIh4PAVaosq5WXjaycVEZE6KJTNkv6C7C+11jnn8oEp/o9rzOwaIMvM0oB8f3o62ypM6fiGm+HPT690ycD8Ku30yZ1zHudcDDDBvx+4Nd+FUltERGQr5//p8b9nywJ6B+T3Bub49+cE5plZCtA+IL9KQRXrzjlNtSUisrfxeEK3BcHMYs0sEYgBYsws0Z92oJl1NjOPmTUCngR+cM5tbYp8AxhmZg3MrAu+yUJe8+d9AvQwszP91x4OzNpZZxIIfvqtfczsHTOba2bLA7egnlhERMLOzEK2BWkYUAjcClzk3x+Gr4/GGHxNibOBYuD8gPPuxPfqaxkwHnjYOTcGwDm3DjgTuA/IAQ4EzqspkGCHArzjv/GNQEGQ54iISBRxzo2g+r4Y7+7kvGLgMv9WVf63+MZWBy3Ywq07cKhzzrsrFxcRkT1Hc0vW7EdgX2BqLcYiIiKhZCrcarIUGGNmn+AbAlDBOTc81EGJiIjsjmALtxTgCyAOaFV74YiISKiYZ+9bhy1UgircnHN/r+1AREQktPTOLQj+sQdnA5nOuWvMrDOQ4JybVWvRiYiI/AXBjnM7G5gAtAAu8SenAY/WUlwiIrKb9sA4tzoj2Jrb3cAxzrmZZnauP20m20+XIiIidUkUN0sG++RNgK3Njy7gp6v6cBERkT0n2MJtKnBxpbTzgEmhDUdERELFzBOybW8TbLPkUOBrM7scSDGzsUAn4Lhai0xERHaLhgLUwDk3z99b8iR8491WAF/41+gRERGpU4IeCuCcKwA+qMVYREQkhDTOrQZm1hrfkgT7sm1JcACcc51qIS4REdlde+G7slAJtuY2CpiHb5G4wtoLR0REZPcFW7h1AQ7WkjciInsPdSip2WjgcOD7WoxFRERCaG/swh8quzIU4BczWwSsCcxwzlW5cqqIiMieEmzh9ipQDvyB3rmJiOwV1FuyZkcBzZ1zm2szGBERCaEofucWbLE+C2hUm4GIiIiESrA1t3H4pt96lR3fub0S8qhERGS3qUNJzQ4DVrHjXJIOUOEmIlIHaShADZxzR9Z2ICIiIqFSbeFmZuacc/79auu2GtgtIlJHqVmySrlAun+/jB0XJjV/WkwtxCUiIrtJzZJV6x6wv09tByIiIhIqO2tuXBHw8Wzn3LLKG3Bm7YcoIiJ/RTSvxB1sxMOrSR8WqkBERCTEzEK37WV22lvSzI7y78aY2ZH43rNt1Q7QjCUiIlLn1DQU4GX/z0S2H8/mgGzg2toISkREdp/thTWuUNlp4eac2wfAzN5wzl0SnpBERCQkNHHyzgUWbJXHvGmcm4iI1DVBFetm1tfMfjWzLUCpfyvz/xQRkbpIHUpq9Dq+1bgvAwpqLxwREQmdva9QCpVgC7c2wO1bp+MSERGpy4J92/gJO64IICIidZmFcNvLBFtzSwQ+MbOf8A0BqKBelCIiddRe+K4sVIKtuc0FHgR+BhZV2kRERDCza8xsipkVm9lrAekHmdk3ZrbRzNaZ2SgzaxaQP8LMSs0sP2BrF5Dfx8ymmlmB/2efmmIJdijAXbv4jLXmhrY37ukQJIrc/2t1M8+J1Jaj93QAu2M1cC9wPJAUkN4AGAmMxdfT/mngVeCEgGPed85dVPmCZhYPfAY8DjwLXAl8ZmYdnXMl1QUS9Ag/MzvWzF42s9H+z/0CpucSEZE6J7wv3ZxzHzvnPgU2VEr/yjk3yjmX55wrwFe4HRrkQxyBryL2uHOu2Dn3pD+gnZY/wY5zuxZ4DlgADPAnF+IroUVEJMKZ2RB/k+PWbchuXG4AMKdS2sn+Zss5ZnZ1QHp3YFal3vqz2H5Zth0E26HkeuBo59xSM7vFnzYP6Bzk+SIishdzzo3E17S4W8ysF76VZk4NSP7Af+01wIHAR2a2yTn3LpCKb/HsQLlA2s7uE2yzZBqwdX23raVnHFBte6eIiOxZLoRbKJhZB+Ar4Drn3ISKOJ2b65xb7Zwrd879AjwBnOXPzgfSK10qnRpWpQm2cPsRuLVS2lDg+yDPFxGRKGZmbYBvgXucc2/WcLhj24u+OUAv236Jg17s2Ky5nWALt2uB081sKZBmZn8C5wA3BHm+iIhEODOLNbNEIAbfOqCJ/rQWwDjgaefc81Wcd6qZNTCfA/BVnj7zZ/8AlANDzSzBzK7xp4/bWSzBDgXIMrP9gf3xTcW1ApikFQFEROquPTBh4jDgzoDPFwF34auJtQNGmNmIrZnOuVT/7nn41gxNAFYCDzrnXvcfU2JmpwEvAQ8AfwCn7WwYAATfoQR/T5VJwCT/qtz9gfHBni8iIuHlQva2LMj7OTcCGFFNdrXjpZ1z59dw3enAfrsSS7BDAcab2aH+/VuA94B3zOy2XbmZiIhIOARbc+sBTPTvDwaOxNdT5Wfg/lqIS0REdlM0r+MSbOHmAZyZtQfMOTcXwMwa1FpkIiKyW1S41ewnfNOlNMO3/A3+gm59LcUlIiLylwVbuA0CbgTWAQ/507rgG2gnIiJ1kDeKq27BDgXYANxWKe3LWolIRERCwkVx4Rb0qgAiIiJ7i6DHuYmIyN4liituKtxERCJVNL9zU7OkiIhEnGprbmb2JkGsdOCcuySkEYmISEhEc4eSnTVLLgxbFCIiEnJerwq3HTjnqp3kUkREpC4LukOJmcUDnYEMti0ih3Nup2vqiIjInqFmyRqY2WHAKHxr7aQDeUAavnXd2tVadCIi8pept2TNHgMecs41BDb7f94DPFtrkYmIiPxFwTZLdmLHeSQfAJYAj4Q0IhERCQl1KKlZLr7myE1Alpl1AzYAqTs9S0RE9pgobpUMulnyY+BE//4rwPfAVODD2ghKRERkdwS7KsD1AfuPmNlv+GptY2srMBER2T3R3KHkL80t6ZybEOpAREQktPTOrQZmNoFqpuJyzg0IaUQiIiK7Kdia20uVPjcFLgfeCm04IiISKhrEXQPn3OuV08zsI+BV4O5QByUiIrsvmt+57c6SN6uAXqEKREREJFSCfed2WaWkZOAMYGLIIxIRkZCI5ppbsO/cLq70eQvwC75puUREpA5y6i25c865I2s7EBERkVAJtllyo3+y5Mrpa51zTUIfloiI7C41S9YsrnKCmcUBMaENR0REQiWKy7adF24Bg7cTzezHStkt8b13ExERqVNqqrm9hG/V7f2BlwPSHbAG0CrcIiJ1lKbfqsbWwdtmNtE5Ny88IYmISChE8zu3YAdx/8PMDglMMLNDzOzxWohJRERktwRbuJ0PTKmUNhW4ILThiIhIqDjnQrbtbYLtLenYsSCMqSJNRETqCDVL1mwCcK+ZeQD8P0f400VEROqUYAu364BjgCwzmwSsBo4FhtZWYCIisnu8XheyLRhmdo2ZTTGzYjN7rVLe0WY2z8wKzOx7M2sTkJdgZq+YWZ6ZZZvZDcGeW52gCjfn3EqgL3Aa8LD/5374VgYQEZE6yLnQbUFaDdwLvBKYaGYZwMfAHUBDfH043g84ZATQEWgDHAn8n5mdEOS5VQr6nZlzzuuc+9U5NwrfxMkPAiuDPV9ERCKbc+5j59ynwIZKWWcAc5xzo5xzRfgKs95m1sWffylwj3Muxzn3B/AiMCjIc6sUdOFmZo3N7DozmwbMAA7A11wpIiJ1kNe5kG27qTswc+sH59wWYBHQ3cwaAM0C8/373Ws6d2c3rGn6rTjgFHwl6PHAQuBdfFXHs51za4N4KBER2QNCOUOJmQ0BhgQkjXTOjQzy9FRgXaW0XCDNn7f1c+W8ms6tVk1DAdYAXuA14E7n3DQAM/tHDeeJiEgE8RdkwRZmleUD6ZXS0oHN/rytn4sq5dV0brVqapacBdQHDgT291cfRURkL1CHBnHPAXpv/WBmKUB7fO/ScoCswHz//pyazt3ZDXdauDnnjvBf5GvgJiDbzEYDKVSxDI6IiNQdXhe6LRhmFmtmifgm+Ygxs0QziwU+AXqY2Zn+/OHArIA5i98AhplZA39HkcH4WgwJ4twq1dihxDm3zDl3j3OuI3A0vhLWC8w0s4eCe2QREYkCw4BC4FbgIv/+MOfcOuBM4D4gB19r4HkB592Jr5PIMmA88LBzbgxAEOdWKdjpt/Df5CfgJzMbCpwOXLIr54uISPiEe05I59wIfF31q8r7Fqiy+75zrhi4zL/t0rnV2aXCLeBGRfh6Tb77V84XEZHap7klRUREIshfqrmJiEjdp5W4RUQk4kRxq6QKNxGRSKV3biIiIhFENTcRkQgV7qEAdYkKNxGRCBXNHUrULCkiIhFHNTcRkQgVxRU3FW4iIpEqmt+5qVlSREQijmpuIiIRKprHualwExGJUFFctqlZUkREIo9qbiIiESqax7mpcBMRiVDR/M5NzZIiIhJxVLiFydvX78s5hzTb02HUmq+GHcCBHevv6TBkL5Ocmcnp477GPNX/Kjp93NekNG8exqgih3Oh2/Y2apb8C36452Ay0uIpD2jPPvauiazNLdmDUYXPgxd3JXtTMY+NXlyRNvDeSXswouhy+rivK/ZjExMpLynFecsBmPrgwywf+82eCm2XFaxZwydHHVfx+Yhnn2LZmLEs+fyLirTAfNk10dwsGfbCzcyOBc4DmjjnTjazfkC6c25cuGPZHUOem8Uvf+bs6TAkCgX+sv/bJ6OYfP+DrJ08ZYfjLCYGV14eztBE6oywFm5mdi1wHfAScJY/uRB4EjgknLGEWnpSLI8M6kaftunExBhTF+Uy/N0/yd5UvMOxbRon8Z+LutC1ZRql5V5+/TOH616eA0C7zGSGn9OJHq3T2JhfwuOjl/C/aWurvOfb1+/L5IWbOLhzAzq3SGXGkjz+9coccraUAtCnbTq3ndWRDk2TWbWxiHtHLeC3BZsAaNkokYcu6Ua3VqnMXJrHkjUFpCXFcuNrcwF46ooe9Gtfj8T4GP5Ymc+d7/3JgqwtnHtoc045IBPnYNCRLfltwSaGPDeLH+45mNvemsfC7C2Mu+tgDr3tZ3ILygDo1jKV14b24ZBbf6bM6zjr4GZccWxrGqfHM2tpHre/8yerNxYBcPuZHTjlgKYkxHpYtbGI61+Zw4KsLaH9y4pQjfvuy4Ej7mDhqI/oeN45rJk0mbVTprLPKSfz/ZX/qDjunIk/8b+zziV/5So8cXH0vGoILY8+ipj4OFb98CMznniS8uIdWyHa/m0g7U49mZw/F9Bm4PEUrd/AtEceZe2UqQAkZjRiv1tuJqNXL0ry8vjzrbdZ/NloABp260rfm28krXUryouLWTb2a2Y+8TTJzZpy0icfMurQw+k++HIyeveiUfdu9Ll+KEu//Irp/32sIt74evU49KH/8MXJp+O8XgBaHD6A7oMv4+uLBoEZXS66kHannkxcWiprJ09l6kMPU5K3GU98PPvfdgtNDz4I83jIX7GSCTf9H8UbI/sLajRPvxXumtv1wNHOuaVmdos/bR7QOcxxhJzHAx/9msXQl2YT4zEeuLgrd57biatf+H2HY68/qR0T/tjIhY9PJy7GQ882aQAkxXt4fWgfHh+9hMufmUnn5im8NrQP81fnszC7oMr7nrJ/Uy57ZgZZOcW88s/eXHFMax7+bBGZ9eJ58R+9uen1ufw4dwOHdG7A04N7cvzdE9mYX8pjf+/O1MW5XPrkdHq1Teflf/Tmu9/XV1x3/JwN3PrmH5SWe7n5tA78d1A3TvnPZN7/eTV929XboVlyq7W5JUxfkscJ+zbh/Z9XA3Dy/pmMmb6OMq/jmF4ZXH18G4Y8P4ulawu58rg2PH5Zd855ZCr9uzZk/471OWbERDYXltG+aTJ5/gJSgpPYsCHx6el8edpZmMdodczROz2+1z+uIqVlC765ZBDesjIOunsE3S77O78/90KVxzfs1o2V437gs+P/RssjD+eQB+7jf2ecTUneZg6+5y5yFy9h9Mmnkd6mNQOefIz8latYO3Uaff51HQveH8WyMWOJTUoivV27Ha49+/mRZPTquUOz5FYb58ylvLCIJv36smaSr6ba+rhjWT72WwA6nn0WLQ7vz/dXX0Pxpk3se8P19L3pRiYOH0HbEwcSl5rKF6ecgbe0lPodO1JetOMXz0gTxSMBwt6hJA1Y4d/f+sceB+x1L6ueu7In0x7pz7RH+vPclT3ZtKWMsTPWUVTqZUtxOc+OWcoB1XSwKPN6adEwkcx6CZSUeZm6KBeAI3tmsHJDER9NzKLc65i7Mp+x09cxsG+TauP48Ncslq4tpLjUy/+mraVry1QATj2gKePnbGD8nA04Bz/Py2H28jwO796IZg0S6NkmjcdHL6a03DF1Ue52BdvW624pLqekzPHkl0vo1iqN1MSYoP5sRk/O5qR+mRWfT+qXyejJawA4v38Lnv96GYuyCyj3Op4bu5SuLVNp3jCR0nJHSkIs7TOTMYNF2QWsy9vr/mnsUc45Zr/4Mt7S0iprX5W1O+0UZjz+JCV5mykrKOSP196g1bHVF4jFOZuY/94HuPJyVnw7js3LltPskENIatKERr16MuuZZ/GWlLBpwUKWfP4FbU48wRdXWRmpLVsQX68eZYWFbJwz5y893/JvvqX1sccCEJucRLNDDmL5N753jO3POJXfnx9J4bp1eEtLmfPSK7Q86ghf82xZGfHp6aS2aonzesn580/KCqr+wiiRIdw1tx+BW4H7AtKGAt+HOY7ddvULv2/3zi0xzsPtZ3VkQLdG1Ev2/bGmJsXisR2/PT34ySL+dVI7Pvq/fuQVlPLydyv48NcsWjRMpHfbdKY90r/i2BiP8emk7GrjWJ+37dtnYUk5yQm+AqhFo0QG9m3MUT23XSs2xsPE+ZvIrJdAbkEZRaXeirysnCKaNUgEwGNw4yntGdi3CQ1T4ypeSjdMjSe/qLDGP5sx09cx/JxONE6PZ5/MZLxemLzQ1xzaomEiw87qyL/P6FBxvAGZ9eOZOD+HN8evZMR5nWjeMJGvZ6zjgY8Xkl+k90bBKs7ZhLckuC8ECQ3qE5uUxLGvvbwt0WynPRcL163b7nNB9hqSGmeQ1DiDkrw8ygoKA/KyadC1CwCT73+AHoOvYOD7b7NldRZzXn6VrJ9/2YUn81k+9huOevE5pj70CC2OOJycP+dTkO374pTctCmHPnh/RZMlgCsvJ7FhA5Z+NYakzCYcdM8I4lPTWDZ2LL8/NzLi30lqEHf4XAuMNrPBQJqZ/QlsBk4Kcxwhd/kxrWmXmcyZD09hfV4JXVumMvq2AzCzHfrRrs8r4fZ35gGwX/t6vDG0D5MXbiIrp5hJCzYx6KkZux1PVk4xn/62puI+gZo3TKReciyJcZ6KAm5rwQa+ps6je2dwyZPTWbmhiLSkWKb/dwBmvvya/rvkFZbx0x8b+dt+TWjfNIUvpq4JiKuIZ8cs5fPJa6o8940fVvLGDytpmBrHU1f04IpjWvP4F0t27eGj2vZ/O2WFRcQmJlR8TmzYsGK/eFMuZUVFjL3gYgrXbV9zr05S48bbfU5umsnqCT9RuG498enpxCYnVRRwyZmZFYVh/oqVTBw+AsxoecThHHL/PXx6/N+qCH/n/7ryli6lIHsNTQ8+iDbHHcvyr7f1DC1Ys5bJ9/2HDbN2fBUAMPflV5n78qskN2tK/0cfZvOy5SwZ/WVQz723iuJXbuFtlnTOZQH7A+cCFwCXAgc456qvmuwlUhJjKCr1kldQRr3kWK49cZ9qjx24b2Oa1vf9wskrKMM53zes739fzz5NkjntgKbEeoxYj9GzTRrtmybvcjyfTcrmqJ6N6N+1IR6D+FgPB3asT9P6CazeWMTs5ZsZetI+xMUY++6TzlE9M7Z7lpJSL5u2lJIU7+GmU7d/P7Ihr4RWGYmVb7mdz6es4fQDm3HCvk0qmiQB3pmwiquOb0PHZikApCbGMHBf3y/Mnm3S6N02nViPUVhSTnGpN6r/c4bCpgULSd9nH+p37IAnPp7uV1y2LdM5Fn82mj7XDSWhga8JPalxBpkHHlDt9RIa1KfjOWdjMTG0POpI0tu2IeuXXylcu5YNv8+m59VX4YmPp16H9uxzykksG+MbttD6hONIqF8fnKMkP99/f+8O1y/auJHUGsa0Lf/6GzqdezYZffqw4rttjT6LP/mUnlcNIbmpr0k8oX59mvc/DPB1tqnXvh3m8VC2ZQuurBwXxbWaaBDu3pLXA+86534DfgvnvWvba+NW8NjfuzP5ocNYm1vCy98t57g+jas8tmfbdG4/uxNpiTFs2FzKPaMWsGKDr7fgoKdmcNtZHbjtzA6YGfNW5XP/Rwt2OZ6snGKueuF3bjm9PY9d1h2v1zFzWR7D3/0TgBtencuDl3Rl8sP9mbU0jy+nriHG46uaffJbNv27NuSn+w8ld0sZj32xmAsHbLv2B7+s5qkrejDtkf78tmBTlZ1mvpu1nvsv7ELWxiLmrcqvSP9m5npSEmJ5/LLutGiYyOYiXy3vq+nrSE2M5fazOtKqUSLFZV4mzN3Ii98u3+Vnl23yV6xg7iuvcfhTj1NeXMys516g/RmnVeTPeuY5ul82iKNfGklC/XoUrlvHoo8/Zc1vVY9b3Dh3LqmtWnLq2C8p3pjDL/8eRkleHgAT7xjBfrfcxMmjP6V082bmvPhyxRCFZgcdSJ+h1xKTmEBB9hp+vWNEle8EF7w/igOGD6P9GaezbMwYpj/6xA7HLP/6W3pefSVZv06kJDe3In3++6PAjAFPPEZSRgbFOTms+PY7Vk/4icRGvp6cyU0aU1ZYyIpvv2PZmLG79We7N4jmcW4Wzq6iZvYpcBzwC/A28JFzLm9XrtHhH+Oi92+rFj1xeXcWZxfwxJdqAgx0/7ThezqEOqPt3wbuMKxAQu+ciT9ZqK713lfTQ/b78ryB+4YsrnAId7PkaUBz4D3gYiDLzD4yszPCGYf4mgBbZyRhBgO6NeSYXhl8M3NdzSeKiOwFwj5DiXNuE75B3C+ZWWv//igguH7mEhKN0+N5dkhP6qfEkZ1TzPB35zN3ZX7NJ4rIXiOaXyvukbklzeww4Hx8s5RsAO7cE3FEs3G/b2Dc77veFVui19Ivv2Lpl1/t6TBkF0TzO7dwdyh5GDgHX3/l94HjnXO73+9dREQkQLhrbinARc65CWG+r4hI1KlitEXUCGvh5pxTNysRkTBRs2QtMrMxzrkT/PsTqGaCC+fcgKrSRUREdlU4am5vBOy/FIb7iYgI0T39Vq0Xbs65dwI+zvPPTrIdM6t+vh8REflLorlZMtxL3nxTTfqYsEYhIiIhZWb5lbZyM3vKn9fWzFyl/DsCzk0ws1fMLM/Mss3sht2NJywdSszMg29lEzMz8+9v1R7QipQiIiEWzkHczrnUrftmlgpk45ugI1B951xVv+9HAB2BNkBT4Hszm+uc+8sVn3DV3MrwLUia7N8vDdjmAs/u7GQzG2JmU8xsSt7cHVfoFRGRHTnnQrbtojOBtUCww74uBe5xzuU45/4AXgQG7epNA4VrKMA++Gpr44HAXpEOWOec2+kKmM65kcBI0MTJIiJ7gUuBN9yOpeL/t3ffYVZV5x7Hvz96G4pIkSKIChYUUJGYWEgwasR2vV6NLWos2E2MsV2jxmiiscUYa2ISjYLlEWOLMRBjIXYJeK2gAtJ7Gzoz7/1j74HDwMAwDGdm9vl9nmc/c85eu7x7OJx31tprrzVJUpDcovppRMyR1AbYDhibs91Y4Bi2QF6SW0RMSl92y8f5zMysejuUSDoHOCdn1YNpxaP8dt2Ag4Azc1bPIZnLcwzQFriHZGaYQ4Gy5syFOdsvBIq2JN58POf2YESck75+pKLtIuIHWzsWM7NCUp2dJXNb0DbhVGBURKyZPysiioH307czJV1IMitMEVA2YntLYHnO68VbEm8+am65E4R9mYfzmZlZzfkBcPMmtilLu/UiYr6k6UAf1vao7wN8vCVB5OM5t1/lvP751j6fmZklSvM8542kbwKdKddLUtIAYAEwHmgD/BZ4NSLKmiIfAa6R9D7QATgbOGNLYsnrc26Svi1ph/R1R0kPS/qTpI75jMPMrBCURvUtlXQaMDwiyjcp9iB5nnkx8BGwgmTaszLXkbTsTSLpeHjrljwGAPmfFeBekhuIAHekP5eRtOMeledYzMysGkXEkArWDwOGbWS/FcAP06Va5Du5dY6IryU1IEly3Uief5uW5zjMzDKvgEffyntyWySpA9Ab+CQiiiU1AhrmOY467bEf9aPvDi1ZXZJ8cmcuXMEhXlfITAAAFX5JREFUP19vyE7OOnh7jv1GRzpt04T5xat47PWp/GHk1wDUryduP303DtxtG8ZMWMTFD31E8fISAM47tBsrVpXyx1cm5++irMa16NqFQx99mCn/epV3rv8F7fbqx8Df3cXq5cvXbDP6tjuY9LcNtxbtfeXltOvXl6KuXXjvpl+tN2t37yFn033w4TRo1pQF48Yz+tY7WDQh6W/W6+QT2eXUk1k+bz5v/+w6Fn75FQBt99yDXU4+kX9fcfVWuupsK+SxJfOd3O4G3gMaAT9K130L+CzPcdR5P39iHE++OX2j20jw04c/4bOpS9h+26b8+aK+TJ+/nBc/mMWhfdsRAf0vH8UdZ+zGCft34qGRk+nStgmD9tyWE24fnacrsdpir8suZd6n6/5XXDZnDi8cdWyl9l84/gsmj/wne15w3nplXQZ9hx2OGMwrQ85n6YwZ9B5yNgOuv4YRp51Jk7Zt2eHII3jx2OPpfvhh7HHeEEZddgWqX5++F1/IWz+7rlquzwpLXjuURMQtwMHAtyLi8XT1VOCsfMZRKH4/4ms+nlxMSWkwYdZSRn44m713bAVAl7ZNeGf8fEpKg7fHzWf7bZsCcO3xPfnl019QkudeVlazuh48iFWLi5n1/gdVPsYXTw9n1vsfULJy5XplLTptx5yxH7Jk2jSitJRJf/8HLbt3B6BZxw4sGDee1UuXMvO992neuRMAPU84nmlvjGLp9BlVjqnQRVTfUtfke1YAgK+AzpJOlHQg8FVE/F8NxFGnXXb0jrz76/154id7MWDn1pXap/9OrRk/fQkA46YvYb9ebWjUQHyjZxvGT1vCd/tsy7ziVYz+auEmjmRZ0qBZM3qfcxZj7rp7vbLGbdpw1N+e4/DhT9L3kouo36RJlc7x9YiRNO/SmRZdu6L69ek++DBmvJ00pRdPnkKrHXvQsEULOvTfh0VfTaRp+/Z0/e4gPn+swj4IVgmlEdW21DV5bZaUtAvwPNAUmAx0BZZLOjIdLNMq4dd//ZIvpi9hVUkpg/fuwAPn7clRv3yPr+dUPETnJYN3QIKn30qaMl/9aC79d2zN8Cv6M2bCQl74YCaPXNyP0+8ew6VH9WCfHVszfnoxNz41nlUlde+DbZXXe8jZTHj+BZbNnr3O+sUTJzHi1DNYNGkSzTt2ZN9r/5e+l1zEB7fcutnnWD5nLnPGfsjhTw2jdPVqls6axWsXXALAykWL+OTPjzDwnrtYPm8+H9xyK/0uvYQP77mPzgMPZMdj/4tVxcWMvvWO9WI0q0i+a273knT77xoR+0VEF+B+NjErgK1r7MRFLFlRwsrVwTPvzGD0lws5qHfbCrc/9aDOHDOgI2ff+yErV69NVLc++yVH3PQu1wz9nCGHdGPYqKns0a0le2xfxEl3jqZh/Xoc983t8nFJVkNa77wTHfrvw7hhT6xXtnzePBZNnAgRLJk+nbG/u48u3z6oSufZ7cwz2GbXXXj+yP/i6YMG8clDf+Kge+6ifuPGAEweMZIRp53JGz++jFY9elC6chULxo2jz0UXMOqyK5j8yr/oc/EFW3KpBakGnnOrNfKd3PoCd5QbKfo36XqromDdCfJyHbffdgw5pBs/uOs/zFiwYoPb9OzUnL16tOLxUdPo1ak5H32dPH/54aRF7NK5xQb3sWxot1c/mm/XkcHPPs2RLz5Lz5O+T+eBA/nuww9tYOsAVe0ro/XOOzF55Cssmz2bKClh4osv0aioiJY7dF9nu/qNG7HHeUMY89u7adG1K0tnzmL10qXM/+RTWu20Y5XOXchqcMqbGpfv3pLTSEaLfiVn3QH4ObdKK2ragL7dW/LO+AWUlAaD925P/51a84unxq237VH9O/CTo3pwyl3/YfLc5Rs4WuL6E3pyw5PjiYApc5dzykFdaFhf7Ltzaz6evEVjl1ot99Vfn2PyiH+ued/r5BNptl1HRv/6dtrt1Y8l06axdMZMmrZvz57nn8u0NyqenqtegwZQrx5S8rpeo0aUrloFEcz79DO6DPo2X48cyYr5C+h26CHUa9CA4ilT1znGrmeczsQX/8byOXMhoKjb9jTepg3t996LJVP9NWGVl+/kdhXwnKQXSIZZ6QYMBk7Jcxx1VsP64sdH9aBHh2aUlgZfzVzKeQ98yMRZy9hnx1Y8dEEf+lz6OgA/PrIHrVs0ZPjl+6zZ/9n3ZnLtsM/XvD9uv+0YN23JmiT28pjZHNK3He/++gDGTFjIE6P8hZJlJStWULJibY1+9bJllK5cyYoFC2jTqycDrr+WRi2LWLlwIVNfe53/u3/toPAH3Hkbc8aM5dOH/wLAgb+9k/Z79QNg2z33ZJ+rruBf51/E7NH/4bO/PEaTNm045JE/0aBpU4qnTOHNq65hVXHxmuMVdduejvv2559nJYNcLJ87l88eeZTDhv6F5fPn8/Y1fiRgc9XF5sTqonxUNyU1A64heXh7MfAp0ImkxvZkRKxf7aiAJyu1fPrl6GtrOgQrMMe/Paqiuwyb7eaH/11t35dXnvataosrH/JVc7sH2Ad4CTgcmB8R5+fp3GZmBam0tKYjqDn56lByGHBIRFwOfI+kKdLMzGyryFfNrXlETAeIiMmSWuXpvGZmBauQ77nlK7k1kPRt1vZYL/+eiHhlg3uamVmV1MUu/NUlX8ltFvDHnPdzy70PksnszMzMtlhekltEdM/HeczMbC03S5qZWebUxQGPq0tNzApgZma2VbnmZmaWUQVccXNyMzPLqkK+5+ZmSTMzyxzX3MzMMqqQO5Q4uZmZZVQB5zY3S5qZWfa45mZmllGF3KHEyc3MLKMKeWxJN0uamVnmuOZmZpZRbpY0M7PMKeTk5mZJMzPLHNfczMwyyg9xm5lZ5hRwbnOzpJmZZY9rbmZmGVVaWtMR1BwnNzOzjHJvSTMzswxxcjMzy6iIqLalMiS9Kmm5pOJ0+Tyn7CRJkyQtkfRXSdvklG0j6Zm0bJKkk7b02p3czMwyqjSqb9kMF0ZEi3TpBSBpd+AB4FSgA7AUuDdnn3uAlWnZycB96T5V5ntuZma2tZ0MPB8RrwNI+hnwqaQioBT4b6B3RBQDoyQ9R5IIr6zqCV1zMzPLqBqquf1K0hxJ/5Y0MF23OzC2bIOI+JKkptYzXVZHxLicY4xN96ky19zMzDKqOh/ilnQOcE7Oqgcj4sFym10BfEKSuL4PPC+pL9ACWFhu24VAEVACLKqgrMqc3MzMbJPSRFY+mZXf5p2ctw9LOhE4HCgGWpbbvCWwmKRZsqKyKnNyMzPLqFowtmQAAj4G+pStlNQDaAyMI0luDSTtHBHj0036pPtUmZObmVlG5fMhbkmtgQHAa8Bq4ATgQOASoCHwlqQDgNHADcDwiFic7jscuEHSWUBf4Gjgm1sSj5ObmZlVh4bAjcAuJPfRPgOOKesoIulc4DGgLTASOCNn3/OBPwKzgLnAeRHhmpuZma0vnzW3iJgN9N9I+VBgaAVl84BjqjMeJzczs4zy2JJmZmYZ4pqbmVlG1XxnyZrj5GZmllFuljQzM8sQ19zMzDKqFjzEXWOc3MzMMsrNkmZmZhnimpuZWUYVcs3Nyc3MLKMKObm5WdLMzDLHNTczs4wq4M6STm5mZlnlZkkzM7MMcc3NzCyjCrnm5uRmZpZRhZzc3CxpZmaZ45qbmVlGlZbWdAQ1x8nNzCyjCrlZ0snNzCyjCjm5+Z6bmZlljmtuZmYZVcg1Nyc3M7OMKing5OZmSTMzyxzX3MzMMsrNkmZmljmFnNzcLGlmZpnjmpuZWUYVcs3Nyc3MLKPcW9LMzCxDXHMzM8soN0uamVnmuFnSzMwsQ1xzMzPLKDdLmplZ5rhZ0szMLENcczMzy6hCrrk5uZmZZVRJqWo6hBrjZkkzM8sc19zMzDKqkJslFVHAV19AJJ0TEQ/WdBxWOPyZs5rkZsnCcU5NB2AFx585qzFObmZmljlObmZmljlOboXD9z4s3/yZsxrjDiVmZpY5rrmZmVnmOLnZGpJeknRaTcdhdZOkqyX9YSPlJ0v6Rz5jssLlZskaJmki0AzYISKWpOvOAk6JiIFb8bzXAztFxClb6xxW+6Wfvw5ACbAEeAm4MCKKt/C43YEJQMOIWL1lUZptPtfcaof6wCU1HYQVrCMjogWwF7APcE0Nx2O2xZzcaodbgcsktS5fIGkXSSMkzZP0uaTjc8raSnpe0iJJ70m6UdKonPK7JE1Oyz+QdEC6/jDgauAEScWSxqbrX5V0lqTGkhZI6p1zrHaSlklqn74/QtKYdLs3Je251X47lhcRMZWk5tZb0lGSPk7/fV+VtGvZdpKukDRV0uL0MzkoXX+9pEfTzV5Pfy5IP2P7STq97PMp6T5Jt+WeX9Kzki5NX3eS9LSk2ZImSLp4a1+/ZYuTW+3wPvAqcFnuSknNgRHAUKA98H3gXkm7pZvcQ9KU1BE4LV1yvQf0BbZJj/GUpCYR8Xfgl8ATEdEiIvrk7hQRK4DhwIk5q48HXouIWZL6AX8EhgBtgQeA5yQ1rvJvwGqcpK7A4cBiYBjwI6Ad8DfgeUmNJPUCLgT6R0QRcCgwcQOHOzD92Tr9jL1VrnwYyR9XSs/dBjgEeFxSPeB5YCzQGRgE/EjSodV2sZZ5Tm61x7XARZLa5aw7ApgYEX+KiNUR8R/gaeB/JNUH/hu4LiKWRsQnwMO5B4yIRyNibrrv7UBjoFcl4xlKkkzLnJSug2RYpQci4p2IKImIh4EVwDc275KtlvirpAXAKOA14BPgxYgYERGrgNuApsA3Se7NNQZ2k9QwIiZGxJdVOOcbQAAHpO+PA96KiGlAf6BdRNwQESsj4ivg96z7eTTbKCe3WiIiPgJeAK7MWd0NGJA2DS1Iv4BOJqmptSOZ1WFyzva5r5F0maRPJS1M920FbFvJkP4FNJM0IO0c0Bd4Jieun5SLqyvQqfJXbLXIMRHROiK6RcT5JP+Ok8oKI6KU5LPVOSK+IKnRXQ/MkvS4pM3+d4+kJ9vjrG0dOAl4LH3dDehU7vN1NUnHF7NKcXKrXa4DziZpioHkC+W19IunbGkREecBs4HVQJec/buWvUjvr11O0pzYJiJaAwuBstkLN9pNNiJKgCdJvnxOBF6IiMU5cd1ULq5mETGs6pdutcg0kgQDQNp02BWYChARQyNi/3SbAG7ZwDEq0w17GHCcpG7AAJJWCUg+XxPKfb6KIuLwKl+RFRwnt1ok/av4CaDs5vkLQE9Jp0pqmC79Je2aJp/hwPWSmknaBfhBzuGKSJLfbKCBpGuBljnlM4Hu6f2NigwFTiCpLQ7NWf974Ny0VidJzSUNllRU9au3WuRJYLCkQZIaAj8haXZ+U1IvSd9J768uB5YBpRs4xux0fY+KTpI2s88B/gC8HBEL0qJ3gcVpx5WmkupL6i2pf7VdoWWek1vtcwPQHCCtKR1Ccq9hGjCD5K/kso4bF5I0Nc4A/kLyl/CKtOxl4O/AOJImpuWs22z5VPpzrqTRGwokIt4h6bDSiaQXXdn690lqmL8D5gNfAKdX7XKttomIz4FTgLtJks+RJI8LrCT57N2crp9B0tHpqg0cYylwE/DvtGmxovuxQ4GDyfnjKf3D7QiSpvAJrE2Ararj+qww+CHuDJF0C9AxIjzKiJkVNNfc6rD0Gbg906bBfYEzWdvpw8ysYDWo6QBsixSRNEV2IrmHdjvwbI1GZGZWC7hZ0szMMsfNkmZmljlObmZmljlObmZmljlOblbjJP1Z0o3p6wMkfZ6n84aknfJxro3EcL+kn9VkDGZZ5ORmlSJpYjrlTbGkmWlCalHd54mINyJik4M7506fUldsKOaIODciflEDseROT2OWOU5utjk2OamlJD9eYmY1zsnNNlvupJawpnnvAknjgfHpugonM5XUT9LodLLLJ4AmOWUDJU3Jed9V0vB00sq5kn6nZOLM+4H90prkgnTbxpJuk/R1Wru8X1LTnGP9VNJ0SdMk/XBj15jWsr5KY5wg6eScsh+msy3Ml/RyOvBvWVlIOlfS+PTa70kfsq8o5twm2YGSpki6XNKsNNZjJB0uaZySCWuvzjlXPUlXSvoy/d08KWmbtKx7Gstp6e9jjqT/Tcsqmqy2wms2q3MiwouXTS4kE1IenL7uCnwM/CJ9HySTqm5DMu9XP2AWyUjv9UkmUZ1IMi5hI5KxLn8MNCSZx2sVcGN6rIHAlPR1fZIJK+8kGW+zCbB/WnY6MKpcjHcCz6VxFJFMePmrtOwwkgfde6fHGprGvdMGrrU5sAjolb7fDtg9fX00yViau5IMgnAN8GbOvkEy4HVrYHuSAYQP20jMfy537atJ5vZrSDJ+5+w01iJgd5KBindIt78EeJtkZojGJJPGDkvLuqex/D79N+lDMu7ormn59cCjlblmL17q4lLjAXipG0uanIqBBWlyuhdompYF8J2cbe8jTXw56z4HDiKZoXka6QACadmbFSS3/dIv9wYbiGedREEylc8SYMecdfuRTJ0CyczhN+eU9dxEcltAMhls03JlLwFn5ryvBywFuuX8LvbPKX8SuHJDMafryie3ZUD99H1RerwBOdt/QDL/GsCnwKCcsu1I/lBokJPcuuSUvwt8P329oeS2wWv24qUuLm6WtM2xzqSWEbEspyx3xoGNTWbaCZgaEblD40xiw7oCkyJidSViawc0Az7IOeff0/Wk582NsaJzEhFLSKb6OReYLulFJVMKlV3bXTnnmEeSWDvnHGJGzuulwOZ0vJkbyaj4kCQ6SGqc5KwrO1434JmcWD4lmSk7d1LPSsWyiWs2q3Oc3Ky65CarjU1mOh3oLEk5229fwTEnA9tX0Eml/Lhxc0i++HfPOWerSDrAkJ63a872FZ0zOXjEyxHxXZLa0GckzXtlMQ0pd21NI+LNjR2vgpi31GTge+ViaRLJPdHNjmUj12xW5zi52dawsclM3yK5r3SxkslXjwX2reA475IkpZvTYzSR9K20bCbQRVIjgIgoTc97p6T2AJI6Szo03f5J4HRJu0lqRjLr+QZJ6iDpaEnNSe5TFbN2Qs77gask7Z5u20rS/1Ty97JOzNXgfuCmsg4tktpJOnozYlkzWe0mrtmsznFys2oXG5nMNJIJL49N388jaQobXsFxSkgmytwJ+BqYkm4P8ApJp5YZkuak665Iz/W2pEXASKBXeqyXgN+k+32R/qxIPeBSknuD80juFZ6XHucZkgljH0/P8RHwvUr8WiqKeUvcRdKB5h+SFpN0LhlQyX3LT1Zb4TWb1UWeFcDMzDLHNTczM8scJzczM8scJzczM8scJzczM8scJzczM8scJzczM8scJzczM8scJzczM8scJzczM8uc/wdgDU3RPQptQQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we test the trained model on a randomly chosen review from the test set. We display the original review, the sentiment predicted by the model with its probability, and the actual (correct) sentiment"
      ],
      "metadata": {
        "id": "GxiS-2HptaY2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Storing in DataFrame \"df_original\" the original reviews and sentiments\n",
        "\n",
        "df_original = pd.read_csv('/content/drive/MyDrive/IMDB Dataset.csv')\n",
        "\n",
        "# Choosing randomly a review and its sentiment in the test data\n",
        "\n",
        "idx_test = random.randint(0, len(test_sent)-1)\n",
        "idx_original = test_rev.index[idx_test]\n",
        "(actual_rev, actual_sent) = df_original.iloc[idx_original]\n",
        "\n",
        "# Storing in \"prediction_sent\" the predicted sentiment of the chosen review\n",
        "# Storing in \"probability\" the probability of the predicted sentiment of the chosen review\n",
        "\n",
        "prediction = model.predict(test_rev_pad)[idx_test][0]\n",
        "prediction_sent = 'positive' if prediction >= 0.5 else 'negative'\n",
        "probability = round(prediction if prediction >= 0.5 else 1-prediction, 2)\n",
        "\n",
        "# Printing the original review, its predicted sentiment and probability, and original sentiment\n",
        "\n",
        "print('\\033[1m' + 'Review #%d:' % idx_original + '\\033[0m' + '\\n', actual_rev, '\\n')\n",
        "print('\\033[1m' + 'Predicted sentiment:' + '\\033[0m', prediction_sent, '(with probability %.2f)' % probability, '\\n')\n",
        "print('\\033[1m' + 'Actual sentiment:' + '\\033[0m', actual_sent)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPTMUOMQtgtE",
        "outputId": "df22b937-5bee-46dd-aaea-105ca43197a3"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 6s 37ms/step\n",
            "\u001b[1mReview #26106:\u001b[0m\n",
            " When you want to celebrate life and love, especially for precious little daughters, you have to shout it from the countryside. And what gorgeous countrysides! There are so many tears of joy even God joins in. See this movie.<br /><br /> \n",
            "\n",
            "\u001b[1mPredicted sentiment:\u001b[0m positive (with probability 0.90) \n",
            "\n",
            "\u001b[1mActual sentiment:\u001b[0m positive\n"
          ]
        }
      ]
    }
  ]
}