amazon_NLP_(1).ipynb

{
  "cells": [
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      "cell_type": "raw",
      "id": "f2b3e74d",
      "metadata": {
        "id": "f2b3e74d"
      },
      "source": [
        "Business Problem. My task is to build models which can identify the sentiment (from very satisfying to very unsatisfied) of each of these non-rated interactions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "e81d6269",
      "metadata": {
        "id": "e81d6269"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import nltk\n",
        "from nltk import word_tokenize\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from collections import Counter\n",
        "from wordcloud import WordCloud\n",
        "import re"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0HzU3HJmfigk",
      "metadata": {
        "id": "0HzU3HJmfigk"
      },
      "source": [
        "# Exploratory Data Analysis:EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "V1-lhxdi3Bs8",
      "metadata": {
        "id": "V1-lhxdi3Bs8"
      },
      "outputs": [],
      "source": [
        "#Get our data\n",
        "Amazon_data=pd.read_csv('dataset_18000.csv')\n",
        "df=Amazon_data.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "1d3IlTqrG_p9",
      "metadata": {
        "id": "1d3IlTqrG_p9"
      },
      "outputs": [],
      "source": [
        "def get_len(dataframe):\n",
        "  return len(dataframe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "DcR81gnsED8M",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DcR81gnsED8M",
        "outputId": "3d69fa84-bf72-420f-b687-9fac7326a169"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "We have 18000 data\n"
          ]
        }
      ],
      "source": [
        "print(\"We have\",get_len(df),\"data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "13ae05a2",
      "metadata": {
        "id": "13ae05a2"
      },
      "outputs": [],
      "source": [
        "def null_values():\n",
        "  return df.isnull().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "RB7SIOasKXSN",
      "metadata": {
        "colab": {
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        },
        "id": "RB7SIOasKXSN",
        "outputId": "44b14bf6-23c7-460c-99b8-47b155add73f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#if True it means we have null value\n",
        "null_values()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "G995WvNnCm53",
      "metadata": {
        "id": "G995WvNnCm53"
      },
      "outputs": [],
      "source": [
        "def remove_null_rows(df):\n",
        "  df=df.dropna()\n",
        "  print(\"Number of rows after removing NUll data:\",get_len(df))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "bD1Tsk3lQv4V",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bD1Tsk3lQv4V",
        "outputId": "9bedf99a-4825-4130-e90c-14070fb59719"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows after removing NUll data: 17999\n"
          ]
        }
      ],
      "source": [
        "remove_null_rows(df)"
      ]
    },
    {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>B001E4KFG0</td>\n",
              "      <td>A3SGXH7AUHU8GW</td>\n",
              "      <td>delmartian</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1303862400</td>\n",
              "      <td>Good Quality Dog Food</td>\n",
              "      <td>I have bought several of the Vitality canned d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>B00813GRG4</td>\n",
              "      <td>A1D87F6ZCVE5NK</td>\n",
              "      <td>dll pa</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1346976000</td>\n",
              "      <td>Not as Advertised</td>\n",
              "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>B000LQOCH0</td>\n",
              "      <td>ABXLMWJIXXAIN</td>\n",
              "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1219017600</td>\n",
              "      <td>\"Delight\" says it all</td>\n",
              "      <td>This is a confection that has been around a fe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>B000UA0QIQ</td>\n",
              "      <td>A395BORC6FGVXV</td>\n",
              "      <td>Karl</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1307923200</td>\n",
              "      <td>Cough Medicine</td>\n",
              "      <td>If you are looking for the secret ingredient i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>B006K2ZZ7K</td>\n",
              "      <td>A1UQRSCLF8GW1T</td>\n",
              "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1350777600</td>\n",
              "      <td>Great taffy</td>\n",
              "      <td>Great taffy at a great price.  There was a wid...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Id   ProductId          UserId                      ProfileName   \n",
              "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian  \\\n",
              "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
              "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
              "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
              "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time   \n",
              "0                     1                       1      5  1303862400  \\\n",
              "1                     0                       0      1  1346976000   \n",
              "2                     1                       1      4  1219017600   \n",
              "3                     3                       3      2  1307923200   \n",
              "4                     0                       0      5  1350777600   \n",
              "\n",
              "                 Summary                                               Text  \n",
              "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
              "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
              "2  \"Delight\" says it all  This is a confection that has been around a fe...  \n",
              "3         Cough Medicine  If you are looking for the secret ingredient i...  \n",
              "4            Great taffy  Great taffy at a great price.  There was a wid...  "
            ]
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          "execution_count": 13,
          "metadata": {},
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      "source": [
        "df.head()"
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      "execution_count": 6,
      "id": "d45rmiwwNc8U",
      "metadata": {
        "id": "d45rmiwwNc8U"
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      "outputs": [],
      "source": [
        "#Remove useless column for our sentiment analysis\n",
        "cl=['ProductId','UserId','ProfileName','HelpfulnessNumerator','HelpfulnessDenominator','Time',]\n",
        "def new_df(df,cl):\n",
        "  return df.drop(columns=cl)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "9f30cc07",
      "metadata": {
        "id": "9f30cc07"
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      "outputs": [],
      "source": [
        "df=new_df(df,cl)"
      ]
    },
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            "text/plain": [
              "   Id  Score                Summary  \\\n",
              "0   1      5  Good Quality Dog Food   \n",
              "1   2      1      Not as Advertised   \n",
              "2   3      4  \"Delight\" says it all   \n",
              "3   4      2         Cough Medicine   \n",
              "4   5      5            Great taffy   \n",
              "\n",
              "                                                Text  \n",
              "0  I have bought several of the Vitality canned d...  \n",
              "1  Product arrived labeled as Jumbo Salted Peanut...  \n",
              "2  This is a confection that has been around a fe...  \n",
              "3  If you are looking for the secret ingredient i...  \n",
              "4  Great taffy at a great price.  There was a wid...  "
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              "      <td>Good Quality Dog Food</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Not as Advertised</td>\n",
              "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Cough Medicine</td>\n",
              "      <td>If you are looking for the secret ingredient i...</td>\n",
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              "      <td>Great taffy</td>\n",
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          "metadata": {},
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      "source": [
        "df.head()"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32f8356e",
      "metadata": {
        "id": "32f8356e"
      },
      "outputs": [],
      "source": [
        "def wordinreview(dataframe):\n",
        "    dic={}\n",
        "    for index,rows in dataframe.iterrows():\n",
        "        dic[index]=len(rows['Text'].split(\" \"))\n",
        "    return pd.Series(dic)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "117c7c02",
      "metadata": {
        "id": "117c7c02"
      },
      "outputs": [],
      "source": [
        "## Getting the number of words by splitting them by a space\n",
        "def hist_lenght_word(df):\n",
        "  words_per_review=wordinreview(df)\n",
        "  words_per_review.hist(bins = 100)\n",
        "  plt.xlabel('Review Length (words)')\n",
        "  plt.ylabel('Frequency')\n",
        "  plt.show()"
      ]
    },
    {
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        "id": "Je3MfV_UsgHQ",
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CBUpPT9edd96pt956S3feeaf8/H7JV9HR0Vq4cKEaNmzozbkCAABcFR4Fojlz5ujhhx/W4MGDVadOnfP2CQ8P/83vKgMAACgLPApEe/fu/c0+AQEBGjRokCfDAwAAlCqP7iFasGCBli5dWqJ96dKlWrRo0RVPCgAAoDR5FIgmT56s2rVrl2gPDw/XSy+9dMWTAgAAKE0eBaKMjAxFR0eXaG/QoIEyMjKueFIAAAClyaNAFB4erh07dpRo3759u2rVqnXFkwIAAChNHgWi++67T0888YS++OILFRYWqrCwUGvXrtXw4cN17733enuOAAAAV5VHT5lNmjRJBw8eVPfu3VW58i9DFBUVaeDAgdxDBAAAyh2PAlFAQIDee+89TZo0Sdu3b1dQUJBatmypBg0aeHt+AAAAV51HgajY9ddfr+uvv95bcwEAAPAJjwJRYWGhFi5cqDVr1ujIkSMqKipyW7927VqvTA4AAKA0eBSIhg8froULFyo+Pl4tWrSQw+Hw9rwAAABKjUeB6N1339X777+vO++809vzAQAAKHUePXYfEBCgxo0be3suAAAAPuFRIHryySc1a9YsGWO8PR8AAIBS59Els40bN+qLL77QZ599phtvvFH+/v5u6z/66COvTA4AAKA0eBSIwsLC1LdvX2/PBQAAwCc8CkQLFizw9jwAAAB8xqN7iCTp7NmzWr16td58802dPHlSknT48GHl5uZ6bXIAAAClwaMzRIcOHVKvXr2UkZGh/Px89ejRQyEhIXr55ZeVn5+vuXPnenueAAAAV41HZ4iGDx+u9u3b6+eff1ZQUJCrvW/fvlqzZo3XJgcAAFAaPDpDtGHDBm3atEkBAQFu7Q0bNtR///tfr0wMAACgtHh0hqioqEiFhYUl2n/88UeFhIRc8aQAAABKk0eBqGfPnnrllVdcyw6HQ7m5uXruuef4Og8AAFDueHTJbPr06YqLi1Pz5s2Vl5en+++/X3v37lXt2rX1zjvveHuOAAAAV5VHgahevXravn273n33Xe3YsUO5ublKSEjQgAED3G6yBgAAKA88CkSSVLlyZT3wwAPenAsAAIBPeBSI3nrrrYuuHzhwoEeTAQAA8AWPAtHw4cPdlp1Op06fPq2AgAAFBwcTiAAAQLni0VNmP//8s9srNzdX6enp6ty5MzdVAwCAcsfj7zL7tSZNmmjKlCklzh4BAACUdV4LRNIvN1ofPnzYm0MCAABcdR7dQ/Svf/3LbdkYo8zMTL3++uu69dZbvTIxAACA0uJRILr77rvdlh0Oh6655hrdcccdmj59ujfmhd/Q8OkVJdoOTon3wUwAACj/PApERUVF3p4HAACAz3j1HiIAAIDyyKMzRKNGjbrkvjNmzPBkFwAAAKXGo0CUmpqq1NRUOZ1ONW3aVJL0/fffq1KlSmrbtq2rn8Ph8M4sAQAAriKPAtFdd92lkJAQLVq0SDVq1JD0y4c1PvTQQ+rSpYuefPJJr04SAADgavLoHqLp06dr8uTJrjAkSTVq1NALL7zAU2YAAKDc8SgQ5eTk6OjRoyXajx49qpMnT17xpAAAAEqTR4Gob9++euihh/TRRx/pxx9/1I8//qgPP/xQCQkJ6tevn7fnCAAAcFV5dA/R3Llz9dRTT+n++++X0+n8ZaDKlZWQkKBp06Z5dYIAAABXm0eBKDg4WG+88YamTZum/fv3S5IaNWqkqlWrenVyAAAApeGKPpgxMzNTmZmZatKkiapWrSpjjLfmBQAAUGo8CkQ//fSTunfvruuvv1533nmnMjMzJUkJCQk8cg8AAModjwLRyJEj5e/vr4yMDAUHB7va77nnHn3++edemxwAAEBp8OgeolWrVmnlypWqV6+eW3uTJk106NAhr0wMAACgtHh0hujUqVNuZ4aKHTt2TIGBgVc8KQAAgNLkUSDq0qWL3nrrLdeyw+FQUVGRpk6dqttvv91rkwMAACgNHl0ymzp1qrp3766vv/5aBQUFGjNmjHbv3q1jx47pyy+/9PYcAQAAriqPzhC1aNFC33//vTp37qw+ffro1KlT6tevn1JTU9WoUSNvzxEAAOCquuwzRE6nU7169dLcuXP1zDPPXI05AQAAlKrLPkPk7++vHTt2XI25AAAA+IRHl8weeOABzZs374p3PnnyZN18880KCQlReHi47r77bqWnp7v1ycvLU2JiomrVqqVq1aqpf//+ys7OduuTkZGh+Ph4BQcHKzw8XKNHj9bZs2fd+iQnJ6tt27YKDAxU48aNtXDhwiuePwAAqBg8uqn67Nmzmj9/vlavXq127dqV+A6zGTNmXNI469atU2Jiom6++WadPXtWf/nLX9SzZ0/t2bPHNebIkSO1YsUKLV26VNWrV9ewYcPUr18/183bhYWFio+PV2RkpDZt2qTMzEwNHDhQ/v7+eumllyRJBw4cUHx8vIYMGaLFixdrzZo1euSRR1SnTh3FxcV58hYAAIAK5LIC0X/+8x81bNhQu3btUtu2bSVJ33//vVsfh8NxyeP9+lOtFy5cqPDwcG3btk1du3bViRMnNG/ePC1ZskR33HGHJGnBggVq1qyZvvrqK3Xq1EmrVq3Snj17tHr1akVERKh169aaNGmSxo4dqwkTJiggIEBz585VdHS0pk+fLklq1qyZNm7cqJkzZxKIAADA5QWiJk2aKDMzU1988YWkX76q49VXX1VERIRXJnPixAlJUs2aNSVJ27Ztk9PpVGxsrKvPDTfcoPr16yslJUWdOnVSSkqKWrZs6TaHuLg4DR06VLt371abNm2UkpLiNkZxnxEjRpx3Hvn5+crPz3ct5+TkSPrlhnKn0+mVWovHOd94gZU8+5Jcb82tNFysfhtQP/Wf+6dtbK7f5tql0q//cvZzWYHo199m/9lnn+nUqVOXM8QFFRUVacSIEbr11lvVokULSVJWVpYCAgIUFhbm1jciIkJZWVmuPr8OZMXLv9UnJydHZ86cUVBQkNu6yZMn6/nnny8xx1WrVp33E7qvRFJSUom2qR08G+vTTz+9wtmUvvPVbxPqp36b2Vy/zbVLpVf/6dOnL7mvR/cQFft1QLoSiYmJ2rVrlzZu3Oi1MT01btw4jRo1yrWck5OjqKgo9ezZU6GhoV7Zh9PpVFJSknr06CF/f3+3dS0mrPRozF0Tys/lv4vVbwPqp37qt7N+m2uXSr/+4is8l+KyApHD4Shxj9Dl3DN0IcOGDdPy5cu1fv16ty+MjYyMVEFBgY4fP+52lig7O1uRkZGuPlu2bHEbr/gptHP7/PrJtOzsbIWGhpY4OyRJgYGB5/1ONn9/f68fwPONmV/o2XtaHv/juhrvaXlC/dRP/XbWb3PtUunVfzn7uOxLZoMHD3aFhby8PA0ZMqTEU2YfffTRJY/3+OOP6+OPP1ZycrKio6Pd1rdr107+/v5as2aN+vfvL0lKT09XRkaGYmJiJEkxMTF68cUXdeTIEYWHh0v65VRcaGiomjdv7urz68tJSUlJrjEAAIDdLisQDRo0yG35gQceuKKdJyYmasmSJfrkk08UEhLiuuenevXqCgoKUvXq1ZWQkKBRo0apZs2aCg0N1eOPP66YmBh16tRJktSzZ081b95cDz74oKZOnaqsrCw9++yzSkxMdAW3IUOG6PXXX9eYMWP08MMPa+3atXr//fe1YsWKK5o/AACoGC4rEC1YsMCrO58zZ44kqVu3biX2M3jwYEnSzJkz5efnp/79+ys/P19xcXF64403XH0rVaqk5cuXa+jQoYqJiVHVqlU1aNAgTZw40dUnOjpaK1as0MiRIzVr1izVq1dP//znP3nkHgAASLrCm6qv1KXclF2lShXNnj1bs2fPvmCfBg0a/OYTVt26dVNqauplzxEAAFR8Hn11BwAAQEVCIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6lX09AUgNn15xVcY5OCXeK+MCAFDRcYYIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYL3Kvp4Arp6GT68o0XZwSrwPZgIAQNnGGSIAAGA9AhEAALAegQgAAFjPp4Fo/fr1uuuuu1S3bl05HA4tW7bMbb0xRuPHj1edOnUUFBSk2NhY7d27163PsWPHNGDAAIWGhiosLEwJCQnKzc1167Njxw516dJFVapUUVRUlKZOnXq1SwMAAOWITwPRqVOndNNNN2n27NnnXT916lS9+uqrmjt3rjZv3qyqVasqLi5OeXl5rj4DBgzQ7t27lZSUpOXLl2v9+vV67LHHXOtzcnLUs2dPNWjQQNu2bdO0adM0YcIE/f3vf7/q9QEAgPLBp0+Z9e7dW7179z7vOmOMXnnlFT377LPq06ePJOmtt95SRESEli1bpnvvvVfffvutPv/8c23dulXt27eXJL322mu688479be//U1169bV4sWLVVBQoPnz5ysgIEA33nij0tLSNGPGDLfgBAAA7FVmH7s/cOCAsrKyFBsb62qrXr26OnbsqJSUFN17771KSUlRWFiYKwxJUmxsrPz8/LR582b17dtXKSkp6tq1qwICAlx94uLi9PLLL+vnn39WjRo1Suw7Pz9f+fn5ruWcnBxJktPplNPp9Ep9xeM4nU4FVjJeGfNy9utr59ZvI+qn/nP/tI3N9dtcu1T69V/OfspsIMrKypIkRUREuLVHRES41mVlZSk8PNxtfeXKlVWzZk23PtHR0SXGKF53vkA0efJkPf/88yXaV61apeDgYA8rOr+kpCRN7eDVIS/q008/Lb2dXYKkpCRfT8GnqJ/6bWZz/TbXLpVe/adPn77kvmU2EPnSuHHjNGrUKNdyTk6OoqKi1LNnT4WGhnplH06nU0lJSerRo4favLjWK2Neil0T4kptXxdzbv3+/v6+nk6po37qp34767e5dqn06y++wnMpymwgioyMlCRlZ2erTp06rvbs7Gy1bt3a1efIkSNu2509e1bHjh1zbR8ZGans7Gy3PsXLxX1+LTAwUIGBgSXa/f39vX4A/f39lV/o8OqYv7W/suRqvKflCfVTP/XbWb/NtUulV//l7KPMfg5RdHS0IiMjtWbNGldbTk6ONm/erJiYGElSTEyMjh8/rm3btrn6rF27VkVFRerYsaOrz/r1692uIyYlJalp06bnvVwGAADs49NAlJubq7S0NKWlpUn65UbqtLQ0ZWRkyOFwaMSIEXrhhRf0r3/9Szt37tTAgQNVt25d3X333ZKkZs2aqVevXnr00Ue1ZcsWffnllxo2bJjuvfde1a1bV5J0//33KyAgQAkJCdq9e7fee+89zZo1y+2SGAAAsJtPL5l9/fXXuv32213LxSFl0KBBWrhwocaMGaNTp07pscce0/Hjx9W5c2d9/vnnqlKlimubxYsXa9iwYerevbv8/PzUv39/vfrqq6711atX16pVq5SYmKh27dqpdu3aGj9+PI/cAwAAF58Gom7dusmYCz9y7nA4NHHiRE2cOPGCfWrWrKklS5ZcdD+tWrXShg0bPJ5nRdLw6RUl2g5OiffBTAAAKDvK7D1EAAAApYVABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxX2dcTgO81fHqF2/LBKfE+mgkAAL7BGSIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYr7KvJ4Cyp+HTK0q0HZwS74OZAABQOjhDBAAArEcgAgAA1uOSGS7Jry+jcQkNAFCRcIYIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6PHYPj/Bp1gCAioQzRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArMdTZvAavgAWAFBecYYIAABYjzNEuGr4rCIAQHnBGSIAAGA9AhEAALAegQgAAFiPe4hQqorvKwqsZDS1g48nAwDA/48zRAAAwHqcIYJPtZiwUvmFDrc2nkQDAJQ2AhHKnPM9rv9rhCYAgDdxyQwAAFjPqkA0e/ZsNWzYUFWqVFHHjh21ZcsWX08JHmr49Aq3FwAAV8KaS2bvvfeeRo0apblz56pjx4565ZVXFBcXp/T0dIWHh/t6ergK+G41AMClsiYQzZgxQ48++qgeeughSdLcuXO1YsUKzZ8/X08//bSPZ4crdSlniTw9k0SQAoCKz4pAVFBQoG3btmncuHGuNj8/P8XGxiolJaVE//z8fOXn57uWT5w4IUk6duyYnE6nV+bkdDp1+vRp/fTTT6p89pRXxixPKhcZnT5dpMpOPxUWOX57Ax9q/NT7Xh8z0M/o2TZFav3MR8q/wvo3j+teoq3j5DUebfdb43iyzfm2O/f339/f/zfHvByezLm0Xc36ywOb67e5dqn06z958qQkyRjz252NBf773/8aSWbTpk1u7aNHjzYdOnQo0f+5554zknjx4sWLFy9eFeD1ww8//GZWsOIM0eUaN26cRo0a5VouKirSsWPHVKtWLTkc3jmbkZOTo6ioKP3www8KDQ31ypjlCfVTP/VTv43121y7VPr1G2N08uRJ1a1b9zf7WhGIateurUqVKik7O9utPTs7W5GRkSX6BwYGKjAw0K0tLCzsqswtNDTUyv8oilE/9VM/9dvI5tql0q2/evXql9TPisfuAwIC1K5dO61Z83/3FhQVFWnNmjWKiYnx4cwAAEBZYMUZIkkaNWqUBg0apPbt26tDhw565ZVXdOrUKddTZwAAwF7WBKJ77rlHR48e1fjx45WVlaXWrVvr888/V0REhE/mExgYqOeee67EpTlbUD/1Uz/121i/zbVLZbt+hzGX8iwaAABAxWXFPUQAAAAXQyACAADWIxABAADrEYgAAID1CEQ+Mnv2bDVs2FBVqlRRx44dtWXLFl9P6YpNnjxZN998s0JCQhQeHq67775b6enpbn26desmh8Ph9hoyZIhbn4yMDMXHxys4OFjh4eEaPXq0zp49W5qleGTChAklarvhhhtc6/Py8pSYmKhatWqpWrVq6t+/f4kPCy2vtUtSw4YNS9TvcDiUmJgoqeId+/Xr1+uuu+5S3bp15XA4tGzZMrf1xhiNHz9ederUUVBQkGJjY7V37163PseOHdOAAQMUGhqqsLAwJSQkKDc3163Pjh071KVLF1WpUkVRUVGaOnXq1S7tklysfqfTqbFjx6ply5aqWrWq6tatq4EDB+rw4cNuY5zvd2bKlClufcpi/b917AcPHlyirl69ern1qajHXtJ5/x5wOByaNm2aq0+ZPPZe+bIwXJZ3333XBAQEmPnz55vdu3ebRx991ISFhZns7GxfT+2KxMXFmQULFphdu3aZtLQ0c+edd5r69eub3NxcV5/bbrvNPProoyYzM9P1OnHihGv92bNnTYsWLUxsbKxJTU01n376qaldu7YZN26cL0q6LM8995y58cYb3Wo7evSoa/2QIUNMVFSUWbNmjfn6669Np06dzC233OJaX55rN8aYI0eOuNWelJRkJJkvvvjCGFPxjv2nn35qnnnmGfPRRx8ZSebjjz92Wz9lyhRTvXp1s2zZMrN9+3bzu9/9zkRHR5szZ864+vTq1cvcdNNN5quvvjIbNmwwjRs3Nvfdd59r/YkTJ0xERIQZMGCA2bVrl3nnnXdMUFCQefPNN0urzAu6WP3Hjx83sbGx5r333jPfffedSUlJMR06dDDt2rVzG6NBgwZm4sSJbr8T5/59UVbr/61jP2jQINOrVy+3uo4dO+bWp6Iee2OMW92ZmZlm/vz5xuFwmP3797v6lMVjTyDygQ4dOpjExETXcmFhoalbt66ZPHmyD2flfUeOHDGSzLp161xtt912mxk+fPgFt/n000+Nn5+fycrKcrXNmTPHhIaGmvz8/Ks53Sv23HPPmZtuuum8644fP278/f3N0qVLXW3ffvutkWRSUlKMMeW79vMZPny4adSokSkqKjLGVOxj/+t/FIqKikxkZKSZNm2aq+348eMmMDDQvPPOO8YYY/bs2WMkma1bt7r6fPbZZ8bhcJj//ve/xhhj3njjDVOjRg23+seOHWuaNm16lSu6POf7R/HXtmzZYiSZQ4cOudoaNGhgZs6cecFtykP9FwpEffr0ueA2th37Pn36mDvuuMOtrSweey6ZlbKCggJt27ZNsbGxrjY/Pz/FxsYqJSXFhzPzvhMnTkiSatas6da+ePFi1a5dWy1atNC4ceN0+vRp17qUlBS1bNnS7QMz4+LilJOTo927d5fOxK/A3r17VbduXV133XUaMGCAMjIyJEnbtm2T0+l0O+433HCD6tev7zru5b32cxUUFOjtt9/Www8/7PaFyBX52J/rwIEDysrKcjve1atXV8eOHd2Od1hYmNq3b+/qExsbKz8/P23evNnVp2vXrgoICHD1iYuLU3p6un7++edSqsY7Tpw4IYfDUeJ7IadMmaJatWqpTZs2mjZtmtsl0vJcf3JyssLDw9W0aVMNHTpUP/30k2udTcc+OztbK1asUEJCQol1Ze3YW/NJ1WXF//t//0+FhYUlPiE7IiJC3333nY9m5X1FRUUaMWKEbr31VrVo0cLVfv/996tBgwaqW7euduzYobFjxyo9PV0fffSRJCkrK+u8703xurKsY8eOWrhwoZo2barMzEw9//zz6tKli3bt2qWsrCwFBASU+McgIiLCVVd5rv3Xli1bpuPHj2vw4MGutop87H+teL7nq+fc4x0eHu62vnLlyqpZs6Zbn+jo6BJjFK+rUaPGVZm/t+Xl5Wns2LG677773L7Q84knnlDbtm1Vs2ZNbdq0SePGjVNmZqZmzJghqfzW36tXL/Xr10/R0dHav3+//vKXv6h3795KSUlRpUqVrDr2ixYtUkhIiPr16+fWXhaPPYEIV0ViYqJ27dqljRs3urU/9thjrp9btmypOnXqqHv37tq/f78aNWpU2tP0qt69e7t+btWqlTp27KgGDRro/fffV1BQkA9nVvrmzZun3r17q27duq62inzscWFOp1N//OMfZYzRnDlz3NaNGjXK9XOrVq0UEBCgP/3pT5o8eXKZ/GqHS3Xvvfe6fm7ZsqVatWqlRo0aKTk5Wd27d/fhzErf/PnzNWDAAFWpUsWtvSweey6ZlbLatWurUqVKJZ4uys7OVmRkpI9m5V3Dhg3T8uXL9cUXX6hevXoX7duxY0dJ0r59+yRJkZGR531viteVJ2FhYbr++uu1b98+RUZGqqCgQMePH3frc+5xryi1Hzp0SKtXr9Yjjzxy0X4V+dgXz/di/51HRkbqyJEjbuvPnj2rY8eOVZjfieIwdOjQISUlJbmdHTqfjh076uzZszp48KCk8l9/seuuu061a9d2+12v6MdekjZs2KD09PTf/LtAKhvHnkBUygICAtSuXTutWbPG1VZUVKQ1a9YoJibGhzO7csYYDRs2TB9//LHWrl1b4nTn+aSlpUmS6tSpI0mKiYnRzp073f6yKP6LtHnz5ldl3ldLbm6u9u/frzp16qhdu3by9/d3O+7p6enKyMhwHfeKUvuCBQsUHh6u+Pj4i/aryMc+OjpakZGRbsc7JydHmzdvdjvex48f17Zt21x91q5dq6KiIldYjImJ0fr16+V0Ol19kpKS1LRp0zJ/yaQ4DO3du1erV69WrVq1fnObtLQ0+fn5uS4nlef6z/Xjjz/qp59+cvtdr8jHvti8efPUrl073XTTTb/Zt0wc+6t2uzYu6N133zWBgYFm4cKFZs+ePeaxxx4zYWFhbk/XlEdDhw411atXN8nJyW6PUp4+fdoYY8y+ffvMxIkTzddff20OHDhgPvnkE3PdddeZrl27usYofvS6Z8+eJi0tzXz++efmmmuuKbOPXp/rySefNMnJyebAgQPmyy+/NLGxsaZ27drmyJEjxphfHruvX7++Wbt2rfn6669NTEyMiYmJcW1fnmsvVlhYaOrXr2/Gjh3r1l4Rj/3JkydNamqqSU1NNZLMjBkzTGpqquspqilTppiwsDDzySefmB07dpg+ffqc97H7Nm3amM2bN5uNGzeaJk2auD16ffz4cRMREWEefPBBs2vXLvPuu++a4ODgMvHo9cXqLygoML/73e9MvXr1TFpamtvfB8VPDW3atMnMnDnTpKWlmf3795u3337bXHPNNWbgwIGufZTV+i9W+8mTJ81TTz1lUlJSzIEDB8zq1atN27ZtTZMmTUxeXp5rjIp67IudOHHCBAcHmzlz5pTYvqweewKRj7z22mumfv36JiAgwHTo0MF89dVXvp7SFZN03teCBQuMMcZkZGSYrl27mpo1a5rAwEDTuHFjM3r0aLfPojHGmIMHD5revXuboKAgU7t2bfPkk08ap9Ppg4ouzz333GPq1KljAgICzLXXXmvuueces2/fPtf6M2fOmD//+c+mRo0aJjg42PTt29dkZma6jVFeay+2cuVKI8mkp6e7tVfEY//FF1+c9/d90KBBxphfHr3/61//aiIiIkxgYKDp3r17ifflp59+Mvfdd5+pVq2aCQ0NNQ899JA5efKkW5/t27ebzp07m8DAQHPttdeaKVOmlFaJF3Wx+g8cOHDBvw+KP5dq27ZtpmPHjqZ69eqmSpUqplmzZuall15yCw3GlM36L1b76dOnTc+ePc0111xj/P39TYMGDcyjjz5a4n94K+qxL/bmm2+aoKAgc/z48RLbl9Vj7zDGmKtz7gkAAKB84B4iAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAl83hcGjZsmW+nkaZ0K1bN40YMeKytysoKFDjxo21adMm70/qMl1ODffee6+mT59+dScE+ACBCKhABg8eLIfDIYfDIX9/f0VHR2vMmDHKy8vz6n4yMzPVu3dvr455MZ6GDm9KTk6Ww+HQ8ePHvTLe3LlzFR0drVtuucUr45WWZ599Vi+++KJOnDjh66kAXkUgAiqYXr16KTMzU//5z380c+ZMvfnmm3ruuee8uo/IyEgFBgZ6dUybGGP0+uuvKyEhoVT3efbs2Ssep0WLFmrUqJHefvttL8wKKDsIREAFExgYqMjISEVFRenuu+9WbGyskpKSXOuLioo0efJkRUdHKygoSDfddJM++OAD17p69eppzpw5bmOmpqbKz89Phw4dklTyktkPP/ygP/7xjwoLC1PNmjXVp08fHTx4UJK0a9cu+fn56ejRo5KkY8eOyc/PT/fee69r+xdeeEGdO3f2uOaNGzeqS5cuCgoKUlRUlJ544gmdOnXKtb5hw4Z66aWX9PDDDyskJET169fX3//+d7cxNm3apNatW6tKlSpq3769li1bJofDobS0NB08eFC33367JKlGjRpyOBwaPHiw23s6ZswY1axZU5GRkZowYcJF57tt2zbt379f8fHxrrbf//73GjZsmGt5xIgRcjgc+u677yT9comtatWqWr16tSQpPz9fTzzxhMLDw1WlShV17txZW7dudW1ffEbrs88+U7t27RQYGKiNGzfq1KlTGjhwoKpVq6Y6deqc9/LXG2+8oSZNmqhKlSqKiIjQ73//e7f1d911l959992L1giUNwQioALbtWuXNm3apICAAFfb5MmT9dZbb2nu3LnavXu3Ro4cqQceeEDr1q2Tn5+f7rvvPi1ZssRtnMWLF+vWW29VgwYNSuzD6XQqLi5OISEh2rBhg7788ktVq1ZNvXr1UkFBgW688UbVqlVL69atkyRt2LDBbVmS1q1bp27dunlU4/79+9WrVy/1799fO3bs0HvvvaeNGze6hQtJmj59utq3b6/U1FT9+c9/1tChQ5Weni5JysnJ0V133aWWLVvqm2++0aRJkzR27FjXtlFRUfrwww8lSenp6crMzNSsWbNc6xctWqSqVatq8+bNmjp1qiZOnOgWQn9tw4YNuv766xUSEuJqu+2225ScnOz2ntSuXdvVtnXrVjmdTtcltjFjxujDDz/UokWL9M0336hx48aKi4vTsWPH3Pb19NNPa8qUKfr222/VqlUrjR49WuvWrdMnn3yiVatWKTk5Wd98842r/9dff60nnnhCEydOVHp6uj7//HN17drVbcwOHTpoy5Ytys/Pv2CNQLljAFQYgwYNMpUqVTJVq1Y1gYGBRpLx8/MzH3zwgTHGmLy8PBMcHGw2bdrktl1CQoK57777jDHGpKamGofDYQ4dOmSMMaawsNBce+21Zs6cOa7+kszHH39sjDHmf//3f03Tpk1NUVGRa31+fr4JCgoyK1euNMYY069fP5OYmGiMMWbEiBFm9OjRpkaNGubbb781BQUFJjg42KxateqCdd12221m+PDh512XkJBgHnvsMbe2DRs2GD8/P3PmzBljjDENGjQwDzzwgGt9UVGRCQ8Pd9U0Z84cU6tWLVd/Y4z5xz/+YSSZ1NRUY4wxX3zxhZFkfv755xJz69y5s1vbzTffbMaOHXvBeoYPH27uuOMOt7YdO3YYh8Nhjhw5Yo4dO2YCAgLMpEmTzD333GOMMeaFF14wt9xyizHGmNzcXOPv728WL17s2r6goMDUrVvXTJ061W2+y5Ytc/U5efKkCQgIMO+//76r7aeffjJBQUGu9/fDDz80oaGhJicn54Lz3759u5FkDh48eME+QHlT2ZdhDID33X777ZozZ45OnTqlmTNnqnLlyurfv78kad++fTp9+rR69Ojhtk1BQYHatGkjSWrdurWaNWumJUuW6Omnn9a6det05MgR/eEPfzjv/rZv3659+/a5ne2QpLy8PO3fv1/SL2c/ii9RrVu3Ti+99JK+//57JScn69ixY3I6nbr11ls9qnf79u3asWOHFi9e7GozxqioqEgHDhxQs2bNJEmtWrVyrXc4HIqMjNSRI0ck/XLWp1WrVqpSpYqrT4cOHS55DueOLUl16tRxjX0+Z86ccduX9Mu9OTVr1tS6desUEBCgNm3a6H/+5380e/ZsSe5n0fbv31/iPfP391eHDh307bffuo3bvn1718/79+9XQUGBOnbs6GqrWbOmmjZt6lru0aOHGjRooOuuu069evVSr1691LdvXwUHB7v6BAUFSZJOnz590fcFKE8IREAFU7VqVTVu3FiSNH/+fN10002aN2+eEhISlJubK0lasWKFrr32Wrftzr1JesCAAa5AtGTJEvXq1Uu1atU67/5yc3PVrl07t0BS7JprrpH0f0+J7d27V3v27FHnzp313XffKTk5WT///LPat2/v9g/u5cjNzdWf/vQnPfHEEyXW1a9f3/Wzv7+/2zqHw6GioiKP9vlrlzt27dq1tXPnzhLbdO3aVcnJyQoMDFS3bt3UqlUr5efnuy59PvXUU5c9t6pVq15W/5CQEH3zzTdKTk7WqlWrNH78eE2YMEFbt25VWFiYJLkuyxUfX6Ai4B4ioALz8/PTX/7yFz377LM6c+aMmjdvrsDAQGVkZKhx48Zur6ioKNd2999/v3bt2qVt27bpgw8+0IABAy64j7Zt22rv3r0KDw8vMWb16tUlSS1btlSNGjX0wgsvqHXr1qpWrZq6deumdevWKTk52eP7h4r3v2fPnhL7bty4sdu9UxfTtGlT7dy50+2emHNvUJbkGquwsNDjuRZr06aNvvvuOxlj3NqL7yMqfk/8/PzUtWtXTZs2Tfn5+a4zQo0aNVJAQIC+/PJL17ZOp1Nbt25V8+bNL7jfRo0ayd/fX5s3b3a1/fzzz/r+++/d+lWuXFmxsbGaOnWqduzYoYMHD2rt2rWu9bt27VK9evVUu3btK3ofgLKEQARUcH/4wx9UqVIlzZ49WyEhIXrqqac0cuRILVq0SPv379c333yj1157TYsWLXJt07BhQ91yyy1KSEhQYWGhfve7311w/AEDBqh27drq06ePNmzYoAMHDig5OVlPPPGEfvzxR0n/d/Zj8eLFrvBTfPZjzZo1uu22236zjqNHjyotLc3tlZ2drbFjx2rTpk0aNmyY0tLStHfvXn3yySclbqq+mPvvv19FRUV67LHH9O2332rlypX629/+5pq7JDVo0EAOh0PLly/X0aNHXWfbPHH77bcrNzdXu3fvdmvv1q2b9uzZo927d7ueuuvWrZsWL16s9u3bu872VK1aVUOHDtXo0aP1+eefa8+ePXr00Ud1+vTpiz7KX61aNSUkJGj06NFau3atdu3apcGDB8vP7//+KVi+fLleffVVpaWl6dChQ3rrrbdUVFTkdlltw4YN6tmzp8f1A2URgQio4CpXrqxhw4Zp6tSpOnXqlCZNmqS//vWvmjx5spo1a6ZevXppxYoVio6OdttuwIAB2r59u/r27eu6Z+R8goODtX79etWvX1/9+vVTs2bNlJCQoLy8PIWGhrr63XbbbSosLHQFouKzHw6H45LuH1qyZInatGnj9vrHP/6hVq1aad26dfr+++/VpUsXtWnTRuPHj1fdunUv+T0KDQ3Vv//9b6Wlpal169Z65plnNH78eEly3etz7bXX6vnnn9fTTz+tiIiIywpcv1arVi317du3xGXGli1bKiwszHUWTfolEJ37vhWbMmWK+vfvrwcffFBt27bVvn37tHLlStWoUeOi+542bZq6dOmiu+66S7GxsercubPatWvnWh8WFqaPPvpId9xxh5o1a6a5c+fqnXfe0Y033ijpl3vDli1bpkcffdTj+oGyyGF+fc4WAKDFixfroYce0okTJy4aCD21Y8cO9ejRQ/v373eFn/Jgzpw5+vjjj7Vq1SpfTwXwKm6qBgBJb731lq677jpde+212r59u8aOHas//vGPVyUMSb9cMnz55Zd14MABtWzZ8qrs42rw9/fXa6+95utpAF7HGSIAkDR16lS98cYbysrKUp06dXT33XfrxRdf9PjpNwDlC4EIAABYj5uqAQCA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADr/X8KCyCVuJav3wAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "hist_lenght_word(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "99951e01",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99951e01",
        "outputId": "84c2a6db-8028-4ef2-830c-347d8bcbba78"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average words: 78.07866666666666\n",
            "max num words : 1707\n",
            "Skewness: 4.297628076912972\n"
          ]
        }
      ],
      "source": [
        "#in average we have  word per review\n",
        "def Print_info_word():\n",
        "    print('Average words:', wordinreview(df).mean())\n",
        "    print('max num words :', wordinreview(df).max())\n",
        "    print('Skewness:', wordinreview(df).skew())\n",
        "Print_info_word()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "771cf8e2",
      "metadata": {
        "id": "771cf8e2"
      },
      "source": [
        "We can see that the number of words per user review is highly positive skewed(4.3) with mean of 79.5, which means that on average, user reviews have 79 words.We can have very long review , until 1772 words ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9fb19e6e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "9fb19e6e",
        "outputId": "95c84532-61b8-41af-fa58-1aeb65bf71e1"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#distribution rating\n",
        "rating_percent=100*df.Score.value_counts()/len(df)\n",
        "rating_percent.plot.bar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "b5b85ebc",
      "metadata": {
        "id": "b5b85ebc"
      },
      "source": [
        "We can see that the distribution is quite skewed, with a giant number of 5s and very few 3s, 2s, and 1s."
      ]
    },
    {
      "cell_type": "raw",
      "id": "dc5c8310",
      "metadata": {
        "id": "dc5c8310"
      },
      "source": [
        "vizualisation with WorldCLoud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c13a6a99",
      "metadata": {
        "id": "c13a6a99"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "from nltk.corpus import stopwords\n",
        "from wordcloud import ImageColorGenerator,STOPWORDS,WordCloud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8333b5f",
      "metadata": {
        "id": "e8333b5f",
        "outputId": "1d93d365-794c-49f1-cfd2-26694afb4d02"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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nUBVVTW1dnCQnzdNlJWgPR2kLRUn3OHFY+7owhRAIAdogFgshBKoQp2wslkYtUj+BUHx9fktzOSjLSudPr20g2+Mmz+9hdnE+Vq17KrVqKpqict7Ucu59czOZ7qTfvyTDj8tq4efPryWSSGCYJhdMn8B5k8v5/Zr1hGJxthyr4so5U0gYBusOHae6NcD6wyeI6ToTcjPRVJWjDc1UtrSlkIbxuZk8/s5uyrLSOXvSWK6YPZX/W7cF3TAIRuOMzc4Y8rpH7ehUYMhhT1UUNEUhYRgYA9gqeh6jvy0EkOZwkOl0MiEzs58tklaCv21+h1cPH+Enq87Fa7fx5w2biCR0atsDhOIJgvEYqqLwy4vOpywjucrbW9/Aj195nbiuoyoKccPgtsULOGtcGU/s3ktNe4Bvnn0GdcEQH37wUT44ewYfnT+HQ43N/ODlNfzj+qtPKRlwu21868sXsn1XBWve2Mcvfvc8C+eN5bZbVqBpKnX17XzjB48xaUIeSxaMw+txIIBjJ7rZsMtp5ewzJ/HEs++weP44nntpByuWTcRximMFst0uwvE464+cYHl5KekuZ5cLAZKD5SNbdjEmM53SjDTao8lI1yVjS/jTGxvYXlXDed5yNh+vJGEYXDt3WlfA4KXTJ/H7V9cPuy/5jnzCRphjoWPk2fOoiFQQM2Pk2HOGfQwhBDP9M3mk4hEOBg4y3T+dfYF9GNJgum86QgiiepS2RBsbmjewq21X175tibauyUA5xROszTqb1vZf4fN8FiEEVutUYi3bcNrPBlQUxYfNOofW9l/hchxANypJ6EcQItUsLoTA7bqB2obLEcKFw7Zs0PP2F3T2brsUpJRdq5vignQ+esMSxo7JQlUVGhoDPPj4ZtZvOsw//7WR73wlaYUxDJP2QPLdmjwhr6uPiiJYedYU7nvkbZqaQ1TVtDKuLLvf8yqKF2E6kFIHDHoTqf5gSJPnazZxKFDNnw4912Wtm+gpwjqC+JH8kgyEEKRlenB5HLi9yfgWi1Uj1mEVEUKgagq1Fc20NAaJRuO0NAUwDZNEPLkga20OUVfVwjUfO6MraFJVFeafOZHVT2zpOt+ODUcwzWRcwYbX9nW1uzx2juyrIRqO4/LYh+y3lJKn3t7NQ69vw2mzYpgmkViCz1++nEWTS0gYJr974k0OVTfSFo7yh6fWYe+Y7L99wzmUZCcXDZv2V/D4W7uoaWpn2pg8vnfjecO+d+8GpJSYic1AvN/fVUXhiyuXsbuqjkgiwbjsDGwWjc+fu6T79/OWoiqCD8yfzq7KKiLxCOdPXYRVVcn3e/naBWdwuL4Jq6bhtllJczr4zNmLONrQzIeWzGZsVjqGKfHYbNy0ZCYCHbumY9Mk3754BSea2lhVmMvZPVb8H106h+2Vtdi1ZOzHhNxMbl2xkGONLYzJGl5c0KjIgMdq44dnn4NpDuyMUITAZbUQiMX51Vtv0hAOowpBgdfLhMxMxvjTyHa5cFosSXOKadIWi1LR1sb+xkYONDcRisexaxq3L13GijFl+GypATECgW6Y/N/mrTy5ey8/WXUuRf4km2uLxHh+3wF+vOpclo4pJhiL86nHnuLvm7fxvZVnEdMNvvX8aqbl5XDbkgVYFJUX9h/kuy+uYVJOFmXpaTy7Zz8S2F1Xj9tq5c2jx/nIvNnsrqvDY7ehvguDo8NhZcHcMubOKmXztuN858dPcNWlcyjIS2PrjhMEglE+98lzcHascl56dXfqPRGCC8+bzrMv3seuvZXsP1TLbR8765QP5GdOKOOGupn8ae0G/v72OywvL+WymZMZk5HWsXqB480tNAXD3PrAk137GabErlloj8QAON7cittmxefoHoCcVgu+EWQSlLvLmZ02m7uO3EWBo4CwHuaC3AtGnDHg1bxM9k5mbeNapvqmsrZhLdN803Br7pTtFmcsptxTntKmCe2UEwEAq2Uifu8XcDouSJ5Hzcfv/TI264yO+6yRmfZbguFHSehHsWhj8HpuJRJ5EaXXysailaEoaTjsKxDC3d/punAk0MT/HdgEQIHLxycmLjrl19YbUkJLawhVEXzptnOZMbXbr5qf66e4MIObP/s3tu2qIBbXsdssycG7YxzqHXDosFsYNyabxqYjVFQ3D0AGFLLTf4dhNmOaAUyzDcNswTRb0I1aguF/kSQIqVCFwtVFywgbMa4qXIoiFATg0Gwj+tY643iEEF3ZPl1ErOM/jbVt/PXXL3DsYB1urwOHy0ooEEs5TiQUw9AN0jI9Ke2+tFRSWF/TimGY/OveN1KyNUzDJL8kY9iZBwnD5C/Pb+BD58zh/LkTMU2T6uYAhZnJd86iKtx8/nzqW4N89FcP8fnLl1HYERyY4enu07KpY5g9roCfPLiGtlBkWOc+1ZAyitSPYxqVSP0QeuTJjvYQicAvoZeVRxNW5pZ+CiG6Y4Qm5yffLQFMzs/E1Pcio68wybsLaQYQURtxfSya7SzyfHPI949JOWZhmo/CNF/H/Y8jzMNMz9qMmdiJNOuQUifW4iBXK6CgaCGqbQmI9K53zW23saRXkGBxhp/iDP+w78OoyIDDYuGqyVOG3K4+FOSTTz9NQzhMltPJp+Yt4NyxY8lyubAofYNspEx6hILxOLvr67hj40beqjjB/769nnHpGWTk5qZsb7do/N/mrTyzdz+/uPB8JmZnphyz2O9j1cRyVEXBZ7czr6iQ/fWNHa6DZg42NvG7yy4k250cGK+cNpm/bNjMmoNHOHPsGOqDIaKJBOuPneCyqZN44J0dxHSDd6pqmFtYcMon2O27kn7LnCwvCEFzSwirVcNmTT4mj9tOImFworKZ7EwPBw7VsW3HCfy+1PSTgjw/k8bn8bcH3qK4MIPiwvST6peUEr2XS8hhsfDZFYu4bMZk1h46ytM79vH87gP87pqLkhYCkRzgphXkcusZC/ocs6gjC0QVSkf8Q69zjrCPLfEWVuasZKJ3Ig7Vgd/iH/HkLITgjKwz+OPhP1ITreFQ8BCfHvfprufsUB1kWDMI6SEmuCekuJs69z/VUBQfPs9tPfpow+f5eEqfNa0Qn+ezKW0e9429jiTR9QpMoxmP87oh+/pC5T7+eXgrANPT894TMqDrBq3tEbxeB5PG56X0UQhBmt9JWWkmO3ZXEg7HsfeKKu99SUIICvP8ADQ2BfucL2EYaIqC1TIx5e/OCdk0mwlFnurKyuh9bE2oXFO0HLdmT5nEpZQjeBcG305KyV9/8yLbNxzh67++juJxOVhtGrs2H+U7n/i/ru2sVg2hKETCqSQhHksNjHR57NhsGt/87fV9LABCgNM9tFUAkou9DI+TrYeqmFGWz5jcdCYXZ6fEN2X73ZhSghBkel3kpnn6HMdq0bBaNGwWrc8Y817BjG8h2voZMAOkWARkmEToz313EE4sro+C6BswLGWMROhe9NA9SLNTH0ABJMTWoIf/iWa/BKv3awjF309vEsTbvo4eew3M5uR+iI5/SYuVHv4ninUBNv8vEerJZ050YlRkIJpIUNcWoiTTD0BTIARCkOHuZnyGafKLN99kW20NTouFX648n6XFJYN+JEIkPa5em42FhUWMz8jkY089wbbaWr7xyss8eNU1+OzdL+u6o8fZUlnNjXNmMqEXEQDI8bhTgvCsqtoVdFjTHsCmqaQ7ux+oRVXJdrs42NjE1TOmoqkKdcEQ26pruHL6FB7duZuaQIC99Q1cOuXU52fX1rXz2DNbUTr8ZEIIPnXzmaSnJcnKnBklLJxXxq//8BIulw2f18GHrlvM25uOpBxHURQuu3AW3/zR43z1s+ePKF/boiYn52iiexAJxxPUB0KUZnTHA0gpURWFMZlplGb4uXDqBD5wz0M8tWMvk/OyEcDEnCwONzQzJT8HWw+fWs/Vx7jsDAKxGA3BEEVpyVVFIBqjdQQCGrrUqY5UU+goxBFKPk+X5mKseywuNRkDEjWiBPUgIT2EiUlLvIX6aD1WxYrP0p16Ns49Dqfm5IXaF3BrbsrcZV3nUYXKytyVPFb5GLn2XMrcZUgpaYg14NJcTPK+fzn7A31XUkriiV1IGaAt8Cfs9qVYLBMGPZYpJa/XHH43ujk0ZNK83amd0RsWi4qUdFkDkjkTA1NHlys5XkQifeONHt2zh1Xl5V1jyrMH9rO0pIRMp6vjfg49Ub9Wv4PzcmdhU61IJE9UrueywsWnLHJEmpL9OyoYP62QybO7A1uP7K9J2c6X4cKX5mTHxiMsWzmta7t921MDI6fOKeW+uE71iSbmLhufSmgliGGOFaoi+Nb153DXc2/z9XufY0xuOpcvnsqiyaWD+vf/HSHUAiyuW7r+NiLPYOq7QbiwuD4FvYJBBRYQfUmTlCaJ4F0kgr8DJIp1KZrtTISahTTbMGJrMWKvo0ceRBLF5vsFQvQ24WtJd5WMoVjmoFhnoWhjEcKBadRiRF/ATLyDGX+TePvPsPl/c8pif0ZFBo41tLLuwDFuPjOZ1lfdGmBXZR3XLZrRtc2RlhaePbAfgPPHlbNkCCLQG0II0h0OPrNgEbc8+Tj7Gxt55sB+rp82vWubqK7zlRVLuXPdBuYXF7KktDjlHMlJtf9zOiwWDFOmBMUBxHQDl9WKVVUpTfOzq7aO9miMkjQ/E7OyeKeqmuZQhDHpaf0e92Rw7orJzJtdSjCUZPc+rwOftzvg0eGw8NXPraKhoR0pISPDjdWismje2D7HslhUPG47i+aPHdF9z/N5cNmsPLh5Bzctmo2Ukid37CUQTV1xrNl/BK/DRr7Pi6oIKlvaiSYS5HSktggh+NDCWXzi/ie447W3uWj6ROyaRlskyqGGJi6cOhGrpjK3pAC/w84fX3+bTy5P5s0/+s4uYonhpXpJKamN1pJuTedg8CDHwseQUhLQA6Rb0/nMuM9gU23satvFaw2vkTAT+Cw+3m5+m22t2/Bb/dw85mbUDlOgVbGyNHMpG5s3sjxrORbRvfoUQrAkYwm6qbO2cS2r65MR+3bFzkX5Fw37Hr/XCIbuJ57Yi6YVk+b9BkIM/tm3xSMcaBta9exUQ9MUPB47NXVt1De2U5CX+o3FEwYVVS2YUtLYHCAj3UUkEifWsfrtz20Z73iPNC01Z781GuVAUyOz8vKSAbPAttpaZufnj6jPbzXuYWXeHCDpttzacogL8+djU09NjI5QBPnFGRw7WMuhPdW4PXYO7qnilSfeSTGF2B1WVlw8i5ce28y0uWMYMz6XquNNrH1hBz1JzfhpRcxdOp67fvoMocA5FJRkkEgYVB1rRLOorLho5vD6JQTlBZn85KZVHKxu5KUt+/n+/S/z1avP5Lw5g5PNfzcoWilW961df8f0A5j6boRwYnF/HCGG57I0EztJhO4EDDTXLVjdnwfh6iJmmvMaEoHfkQj9ESPyFIb9QjT7OfR8PkIoWD1fQDpvQLFMBuFOIWwWx5VEW27GTGzHiK0G2Qri5Cy/nRgVGTBMk4Rhdq3wEoZBNJHKvNccPUKsw+xzQfn4UQXaCSGYlZuL02olFI/z1P59KWRgcWkx186YRiiW4LsvvsIfr7yE8syMYU1+YzPSURTB9upalo1J+lpqA0Eq29pYULwoee6CPNYeOUau14PTYmFBSRFrDh7BabWQ5ji1OeWQXBFlpLvJSO/fnyuEwGbVKCxIffh+X9Ii0ylkEo3pPPzEZs5cOgGfd2T9zHA5+cyZi/jHhnfYcKwCi6oyPjuTC6ZOSMkT3ltbz7M792NRVVRFENMNFpcVc/nMbvfRtIJcvnfR2dy9bjMv7T2E0vFRFKf7uGBqcsBIdzr4zoVn8ZtX1nHrA09it1iYUZjL8vJSXNahA1/iZpy/HPkLZ+eczcL0hShCQSKpj9bzk30/oTZaS4mrhKm+qYx19yVNilBQeiQJCSFYmbOSZZnLcGmuPu+SpmiclX0W89PnE9SDycA8zY1LdfU+9L8N0nzfRhJHEW6G88nvbqkjkIgNud2phhCCaZMLOF7RxB//+jqfvOkMsjI9KEIQCsd4+sXtVNe2goRf/P5FbrhqAQeP1BOOJE27NXVtKSZ6KSXHK5LBtZkZqd/Ua8eOsrm6ioZwCLumgQSPzUaOa/BYit5wqjYqww0UOrMIJMLETR11GCs1gSC3MB2nOznRON02cgvTutxruUXpOFzJ+IMPfe487vjBE/ziKw9id1hx+5zc8tVV/O13L3cFCwoh+MAnzqS9JcRff/0iNrsFX7qLGz9zLvffsRrNktzOYlX57A+u4P47VnPfH1ZjdIj5OFw2LvvQkmFft9kRq2G1qEwtzWVScTbH6lp4fceRFDKgdFh7h0vu/1MhpSQRugtkBKEWY3V/DqF0v0vJd9KBxf1JEpFHwGwkEfobqu2cPu4tRRsDWmpMQecxpJKJ5vwQ8bYvgQwhjTqE8j6SgdLMNO5as4H739qG02ph7b6j3HrOwpRtdtTVdv1/vrevr2i4cFqtuC1JMnC8tZW4YWBVuyclVVH48NxZVLa18fXnXuZPV15ClnvogTnb7eKmubP52Zq1VM2ZidNi4fFde5iZn8eC4mT+9ayCfO7fup0Pzp6JEIIZeTn8z6tvsKCkKEUI6N8FLa1h/vCXNTS3hAiFY3zlMytH7McWQnDD/BmcMX4MjcEQTquFMRnphGJxEj3y/G9ZMo8Lp06gNRLFNCV+p4PCNG/Ks1GEYOXkcuaXFlLV2k5cN/A6bOT5PF3bCSE4e8JYpublUN3WjstqZUxmOi3hyLDucdyM0xRvIs+e1yX6I5HEzBgCgVVJEgq7aseuDs8falNtXWqDA90jj8WDxzL69/q9QjI1ywUMj6xIKXmt5tC726kBIITgsgtmsmbtPt5Yf5CDh+soKkjHYlGpbwhw9EQjAFdeMpsXXtnNj3/9HKZpIgT4vE6ef2UXZywZj6fD711Z3cKuvVUoiqCsJCvlXJdOnETCMFlUVIS/w01g1zS0AdwTA/X3yqKl/O3oKzhVKxEjzvl5c4dFBhRV8LVfXYfTlXw/5ywpZ9KskqRLTxF863c3dPnvx07K4/t/uonG2laEUMjK8+Hy2Bk7KR+vv/u5ur0OPvO9y6mraiYe08nK8+H2Opg8q6QrkFAIQXqWh0996xKa6toJtIbRLCr+DDfetP6FmfpDIBzl14+uZWppLmkeJ/UtAQ5WNfKpi1JjS3xOO4VZPu55cRPnzi4nltBZPrUMr8uObphsOlBBMBKjqrGNaELnpS0HcNotzBtfhM2icaK+lSM1TVQ3t2NKydqdRzha20xhpp/ygkwM02TzgUraw1GO1TXT1B7mxS37cdqszC0vxGF7jxRXZRAjnsyAUm1nwEABusKHoo3DjDcmXREyDGL4CwkhRJIsJE/abzzLaDEqMuC0Wbj94jNZf/A4kbjOLWfOozw3Ne2vPda9sjAGyToYCqaUXX7+qK6jmyZWVWXFuDImZiU/cKum8uUzl/L7N99ma1U1540fx5zCfELx1PSQaXk5Xf5BIQQfXziXfK+H1w4fRTdN5hcXcu2MaV3+7UnZWZxdPpZzxidXlEV+P6smjmdZ2chcHu8VHA4rC+eVoXSssLIyRzdZqYpCSbqfknR/V1tvIQy7RWNM5tCMVAhBustJumvggUYIQa7PQ66vu7+d7oah4NSczPTP5MGKB5nqnYpVsdIcb+ZQ8BCLMhaRbe8/new0+ochJW/VHX3fzj+2NJtPfuQM7v7Hm9TWt1Nb3971m9Wqcf5ZU7jlxmVMn1LIP/+1kUAwysypRUyakMev7niJ7/z0KRbMHQMSXlm7l/ZAlDHFmRT1CKLtjE26eMIEVKVbrMUchYb/JG8RX5hwGS3xIG7NQYatr0Jif0imE3a/4zaHFVsPHYSeWQFCCLx+J95eKoq9MweEEFhtGkW9sibSs/qOA5qmklOQRk7B6NyddquFwkwfG/dXEEvouOxWbrtkcR8XgdWi8v0bV/Lw2u08s2EPHqedRZOSlljdMHlz11EisQTFHYJEb+89jqYpTCvNw2bROFrbxBu7jmJKkwvmT+JITTPH61qYXpZPeUEmpilZt/sYwWiM7DQ32X43G/aeQFUVJhVlv2dkQBq1YLZ2/BFCj/xrkI1Dyf+aQaQMIvoh6skYjjDSbEz+k0GQMZAxTP3dIeujIgNCCArSvFw1fxqQ1NRvDkVSAgjdlu4Xe1d9HZOzskY1gdYGA13EwqqqqB3M/bYlC1KCdDw2G187a3nX31dOT5qrOzMU4qbO0rElmFIS0uPYVA2rqnLFtMlcMmUiUiaD53r2MdPl5Merzu06jxGKsbBGRWtvQB9TSuWBGkomF46aGCT7JokZBgnTQCJRhYJVSYpWCEYWoe6wWzhvxeBZHlJKDCmJmzoJ0wQkmlCwqhraMGRUTxWklMRNg7ihY3Zct22wPgzQLVWofKjkQ+xo20FlpJKwESbbns389PmUe8r71CD4T0Hne5swk+9GJyFWhIJFKGiKijpITMxoUR8NcizYckqPORIoiuDildMZW5rFa+sOcLyikXjCIDvTw7JF5SyYU4bNqrFsYTmzphYTjSfwe50kdIPnV+/inZ0neGdntzKbw2HllhuXYrX0fQ/qQkHu2boVvcPipQiFryxZ2mUpGA7aEiGeq96EieS6kjPZ3nqEmf6yk3ouSferRBJDygRgItA6gs0s/xYLEZtF45ZVC9BNE9NMBhOrSt/3UQjBlJIcvvvBc7vU+DoDmm0WlS9deUY/RzeABIbZxpIpGSyZko0QNgQaXQNBx380VeHzl/evmfFe3iZpNtEV7R95HCKPD7Z1x39NeqetSinBbEaPPokefRmpH0PKduh4D5L/3p0yACMiA1LKpAyjECksuj4Q5O2DFVw5f2pX25TsbF48nGQw923fzvnjylMyAYYDwzT5+7ZtXbLHBZ6kGVoiea3mMDGj2w+1IKuEdHsP5iwljdEQmxsr2NhwgqOBZtriUQxpYlc1suxupqTlsjC7hMn+HGxq30IdnSuIzmv/50+fxJfpYduaEyy9bB7//NmTfO1vt3W9dHWRAFsbK7v2d2pWluf2HRgM0+REqIVNDRW801RFZaiVYCKGISVWRcVvdVDk9jPZn8uUtBxK3Ok4tdEPAkm1tCh7W+p4p6mK/W31NESChPVkgRSbqpFpc1Huy2JOZiFT0/NIszpO+aAjpaQlHuGdxio2NBznUHsjrbEIujSxKSqZdjeT/NkszC5lanouDrX7mgeTd3VqThakL2AB3SmMnfsdbm/sEwyX4/AwO3N0UrwJ0+DV6kMpUtgCwRl5Y3FoJ7cKkVLSnohyuL2JXS217G+tpzbSTns8RtzsCIRTVFyalXSbkyKXn3G+TMZ5Myl0+fBY7COKzZFSkjANAokYjdEQlaE23qw7kvJdAbTFo7xQuW+Ao/SFQHBm3ljso7wfiqIwdVIBUybmE08YSFNisagoPSYbIQQejx0PyTFF0xS++cUL+NuDb7FnXw0J3SA328vlF85m8QBBtI/s3s3yklLGZ2R09Bvcw4hT6YSUkvuPv0qxM5u3GvcgEDxasY6pvhIsQwRp9n+8BAn9GNH4JmLxrclUUDOAxEAIK6qShdUyEYdtEVbrdBQxPCtE8tgmkeirmDLc1aYIBw77WaOORhdCoCmCSOJ1EolQj3YbTvtZXYGqnQqBai8XTKe6YJL86CT0o0RjG4nFt5IwKpFmAImJELYe174wee14U47x/qP7m1G0KQi1f4G8VKgIUt2R0qgi1vYFzPhmQCC0MajaTIRagFD8COFAGnXJ+IRTjBG9scFonPvWvcMlcybzlzUbsXQErwSjMaYWpqq9nVVWxu83vE3CNNnTUM93Xn2FryxeSoF36BdYSkkwHufBXTt5YGd3TfuV48oRQMI0+c7mF6iPBrp++9WCS7m4JLkqDutxnji2i/sObeFIoLFjBdwXL1Tuw2uxMT+7hI9PWMiszMIBB1PTlNRXNHLjt6/gji/8HUVViEXiSNMEJXkfdjXX8Ln13Yww2+HhlQs+hU3tlhOuibTz1/0beb5yH3XhAOYgLE9B4LPamZNZyO8XX4FVHdkAI6WkLR7lmYo9PHZ0BwfbGggbA0s6v1i1H7uqMdabyZWl07mkZAr+U0QKonqC5yr28veDm9nfVt+nzkAnXqraz937NzAns5BbJi5kYVYJqqKQZhs8EHKgPr5SdZBf7nw1pW1l4cRRk4GInuBrm54h2CPAThMqL1/wSQq0vvKlw4GUkppwO89W7OX5ir0cDjQRSsSGxf9VIfBY7BS7/czLKubGcXMpcvsHPdfOlpouknSovYmqUCsN0RCBRCwlLqQTx4MtKe/1ULAoKq9c8ClyT5IcdQbMDnfbvBwfX/n0StraI+iGiddtx27vS6JNmSzr67XZyHG5kmNSx28jDXSui7by0bLz2Nx8kORq3hyxu0FKk3hiH+3BewhH12CY9QykghiOPk9b8C6slqn43DfhdJyP0k++e18IAuEHCEVe6G4RDgpzXsGijb6ct2HWUd90K6bs1nFw2JbgtJ8zrP2llMT1fbQH/tJx7Q0MfO3P0RZ0YrNMxev+KE7HSpR+UvzeF/SIEdBcH0ZzXDrMHbvJp5Qm8cBPMOMbASsWz5fQHJd3BAh2EysjvvH9JwMOq8a508oJRmOUZKWxcFxS776+LUhVS3vKtuMzMjmrrIwXDx1CAk/v38/u+npWlY9nYWERBV4PHqutS+TDME0ieoLGUJgd9XU8d2A/m6uru1L/8jwerpg8uevDNqSZInG8rbmKi0um0BwL88OtL/Fcxd6uQjaDoT0RY3XVAbY2VvKZKcu4buwsLErfVaiiCHJLs3nlgbdorm3lubvXUDwhP6XKl4SUPrXFI4T1BDY1WUhkW3M139n8PHta64Z1v02SK+mQnkDrp0+DQUrJ9uZqfr59DVsaKwaUg+6NqKGzu6WWfa11PF+xl9tnnMXMjJMTWGqJRfjZ9ld46viuAUlAT4T0OGtrj7CtqYpPTFrMR8fPJ9M+umh9E9nn2k+2wJVumr2OOfrjxQydZ07s4c/71nO4vWnElUANKWmNR2htjrCnpY5LiqdQhH/Q7b+64RkOBxpHNGkN9/0BEOZgFPfdQ1KASR0wG6cT6ysqePnwIdpjMV4+fJhJWZmoQkEVgs8uXDQiC+YM/xj+efw16qIt3H/8VYqcWSOSI5YyQSD8CC3tv8QwaobeAZAyTCy+kYbm7bicr5Lu+yaqkj3oNyqEwOO8jlDkOTrNzFIGCUWexue+dVTft5SSUOQFTNmW0u5xXTssa4OUOsHwozS3/2LYtSGkDBONbyTWvB2360rSvd9EUdLed9eJUHMBGxDD1PcD1pH3yWzBiL0GgGpbgsV1S79pwNLsK6B1KjAiMqCpKuNyMghGY1wye1JXjEBBmhd3L/lYTVH4xrIzONrSwoGmZHrPkZYW7ti4gXu3bsFnt+Ox2ZJayggSpkEoHqc1GiUYj6cMVB6rle+ccSZZzoGD0Ha31BJMxPjGpmd5uepAv9sIBva2NMfC/GzbahKmwUfGz+ujXieE4ANfvZhXH36bogn5SFNy7VcuHvSBxwyd9kQUv9XOO01VfG7941SH2wfcfiCsLJwwYvPvmupDfHvL89RFAkPv0A8MKdnUWMGt6x7lB3NXcU5++ag+uEAixjc3P8tLlfsHnSD6ezbtiRi/2fk6UT3BtPRTp7T174JgIsYvd7zGQ0feGRZJGgrlviwm+IcOmIwY8VEFy70fkFJimJJoNE68Q4ffatWw2y39+qiHgwmZmaTZ7ejSTJYb7vxBJOOShqsgKITg4oKFbG4+gEO1kW3zsThz+FX/pNRpC/yJlsCvkbK3yJaCIjwoihtQkTKKKdtTtpPECIYfQTeqyE7/A6qSO+i57baFaGo+ulHV1RYM/Quf+2P0XKEOHwbB0MOpvVYycdhXDLmnlAZtwbtpaf9FPxHxGoriRRFOQCSv3WzrKM3dsT8xAqEHMIxGstL/F1WMzirXP9SOcwzfPy+UTBStHFPfhRFdA+4vjihLAOiIDUjeC8Uys38iICVG/K0RHXe4GFUAoctmxaoZtIQidOp/TSnsOwgVer38/oKL+N5ra9hYWdm1uojoOpFgkNrg0AynyOfj9qXLOHfsuEFf9GOBZn6983VW9yACmTYXU9NzmejLJtvhwaKotMWj7G+rY0tjZZ+JOWYa/Gbn65R5MjgzL9XPKKWkYl8N596wFIvNgqopVB+uY/vavUxbMgFvRt+IXUNKmqIhFARf3fB0yvmcmoV8p48Cp480mwNNUQnrceojAarC7TRGQyRMA4uisjS3rM+xB4KUkg31x/napmdojoVTfhNArtPL9PQ8JviyybC5ECK5cj/U3sjO5mpOhFpT40GiQb628Rl+u+gyluaMGdHga0iT/921tl8ioAmFCf5s5mQWUuJOw6poBBIxjgWa2NFcw+EO944uTe7at56Li4eWv/5PQkRP8IOtL/H4sZ39uooUBOl2JwVOH9kONx6LLZlvrydojAapCwdoiIaI9HD7XFM2E22IFZkQsCi7lKZoqN/f66NBdrXUprR5LDbmZQ6v6iUks1HsI3Rp9YaUkubWEBu2HGXzO8eorm3r0hNwOqzk5/qYM7OEBbPLyEjvqwcxGDKdTjKdTh7bs4cSvw+31UZ5RgZ7Gxr4+ZtvMCkri6unTB30GO+0HGaar5T6aCuLMiaxOHPyiK8vGH6UlvZfIemp6aBht83H7bgcm3UaipIGKEgZRTcqiURfJRh5CsPofkbR2Fs0NH+JnIw/M1jNCSGcuBwX0xb8U1dbXD9EPLEXm3XGgPsNhIR+mFhiT0qby34uivAPul/SovAULe0/TyE3Qrhw2s/G5bgAizYORfGSJANhEvpxwtEXCIWf7WGJkISjL9Pc9mMy/T8ZUkxruEiu8gEzgDRqEP3k/PeFiub6CPG2ryCNoyRCf8Hi/hT9WQiSmQIBEBZ61jdIyhtrQBxpVPYhpVJKpH4gGaD4LmBUdy8ST/CLZ9YS0/Wuzl40ayKLy1MLJQghGJeezh0XXsxT+/by+N69HGpuIpQY2G8NSZ9jvsfDWWVl3DBtOmPShjYDNUZD3HdoM5LkRHNpyVQ+Mn4+Y7wZ2JTunHbZkap4ItjKvQc28q+j21JiCiJGgh9ve5lZGQX4e/qpJfz9R4+SXZRJTnEm13zpQu799sNMmj+OnW/s4xO/uKHffh0PtnDnnnUcDTYDyYH1/MKJXFIylXJvJl6rPekqIRmUGTUSNEVD7G2r59XqQ9SE2yh0DZ/11kYCfGPzc32IgFuz8sFxc7iqbAaFLn9K1H5nhkFdJMDzFXu5e//bNPSYLFrjEb656TnuW3EDxe7hpSJJKdlYf4L7D23tM9Vl2l18bspyzi+amIxJoPvZSCTNsQiv1RziD7vfpCLUStw0ePTYjv5O8x8JU0r+vG99v0RAQTA1PY8rS6cxP7uEXIcHh2bpKIolMKVJzDBoi0c4HmxhS2MFb9YdoyrUyvmFE4f8ThQEP5y7asCCNC9W7ufzbz+R0lbqTueOJVeO4AoFFkUZdc0G05Rs2HKEe+57kyPHGvpNTd57oIZX39zPmOJMPnLDEpbMHzci2W2AfY0NbK2pxpAmN86YycO7d3JB+Xge37uXs8aUkT6It+CZqg1M8hbxwPHX+OLEK1BHKECc0I/S1PbDFCIghJ0075fxuG5EEZ4+981qGYfDthy38xqaWr9FNP5212+R2Gu0Bu4kzfvlAU30QgjcrmtoC94DdI7BOoHwI1gt00f0nJJk5jFSq/upeFzXD3kc3ThBU+v3UoiAquSSmfbjjmqcfSdQi1aO074Cl+NiGlq+1MOtIAmEHsRlX9kRDHny7gLVupQEdwJx4u0/xOL+PELNAkyQEaQZRrFMTbnPQgg0x0UY0RcxYi+RCN6BqR9Fc1yMohaQtO5EkGYtZmInRuwtrN5voVpndx9DyeiwLuxGj76Eaj8X1TIHhAUpg5jxrcSDvwVpkKx3cHLuzt4YFRk42tBChsfJjUtmdd383nnonRBC4LfbuXHGTC6ZOIkDjY3sbqjnUHMzjeEQkUQCKZPmOb/dTrHfz+SsLCZnZZPrdveJQB0Inf56geDjExfx6SlLuwL3evdHEypl3gy+Petc0m0O/rjnrZRB+WigmQePvMMnJi7quj5TSjxpbm781uW89I83aKhqxtANrvzcKn5xy58wEv2bef+8dz2H2pNiKaXuNL47eyWLckr7TaFThMCt2HBbbBS70zgnfzxt8ciQq71OGNLkt7te53iv1DCPxcaP517A+YUT+72fyXsiKHD5+OiEBczMKOALbz+RYsmoCrfx022v8L+LL+83pqI3EqbJb3a9TsxMjUz3Wuz8asElLOnHypDM3hBk2l1cWTqdyf4cblv3KCdCrcO6/v8UbG+u5u79b/chAnZV45YJC/lQ+VzSbc5+BzZFJNNOXRYr+S4fC7NLuGn8fKrDbWQNI65CCIFlkMyM/t+PJEHvJGxH9tXw9pq9hIJRvGkuVEXgz3BzzmXJga2lMcCbL+2m6lgjmbk+lp43hdzC9GEN1FJK3nz7IL/4/QsEgjGsFpX8PC9ZmZ5kVUIJwXCMhsYAdQ3tHD7WwE9/8xxf/vRKVixNFo9qDIfx22yoHfFIumlyoq2VNLsDv92ekpHwhUWLaQyH2VhVSVs0RnlGJjNzc2kKh0m3D3yf8p0Z3HvkJfa2V/CPo690HdOp2riiaMmgwkNSmrS2/xLTbOrRKvB7voDP/UnEIM9HCAWrZTLZ6XdQ03g9CX1/51FpC/4Ft/MyrJbxA+5v1cqxWaYSS7zT1RaKPEe692uDWhX6XANRguGnUtos2lhs1mmD7ydNWtp/0xEk2XlNDrLSf43DduaA70iyXcNhO4Ms/8+pbbqZbiKSoCXwKxz2ZYzO3ZEKxTob1XYORuxljNgrGPEtCMVHkgxEQThxZL0E9GaLNmy+nxBr1zCiL2NEn8CIPpd0FwglmR4oIySJmELfapgaFs+XiLV+DmQbsdbPIdQihLAjzVakUYtQMrH5f0287etIs5ZTiVGRAb/TjmGayYGlI4BuKFbeSQrmFxYyr6AgOXl3BmJJiaIIVKEMWk9gOJiVWcCtk5f0SwR6w6ZqfGrSErY0VvJ2/fGU3/55aCs3jpuDy2Lr6D/YnFaqD9fRVN3C6//aQKg9QigQQQ4iqnSgPZnWluvw8LtFlzMlbXC/Xic6J+iMEQTO7W+t5+kTqWY7geDzU5ezqmjSsOIOFCGYk1nID+eu4tY3H02ZzNdUH2RD/QmW5g5tNtvRXM22pqo+7bdNXtIvEegNIQST/Dl8Z/ZKPvnmI8MKBv1PgG6a/Hbn64T1VOuYJhS+Mn0FHxw3d8QqeG6LjfG+VDddWzzSp+5Gf7CpGm7L8MtF19e08pMvPsC5l81mwvQinv7negKtET76xZUABNoi/PRLD+L1O5kyp5TjB+r48ecf4Ju/vZ68oqFFqhoaA/zurlcIBGNMGp/HDVctYNL4XNxue1cBHF03CQSj7Nlfzf3/2sD+Q3X84S9rmDopn+xMLy8ePEhNMIDbauOG6dPZUFnJi4cOIoHvrzirS+Z6dl4ev9/wNjFDT9bNiEZ568QJdjfUs6yklJ7pYr3xgeIz2NdeweFgDeWegq5vy6okY6AGg64fIxR9IaXNZpmBz33LoESgE0IIVDWPDP93qW38UFc/pQzSFvgTmWm/GuT7UvG4riPW2k0GDKOWaGwDTsfZQ567E7H4dnTjREqbx3kVQ03GunGCUOTplDa38yoctjOGPS467Ctw2BYT6Qi2S/ZnB7HEHuzWmcO9hIHPgRWb72ckQmXosVeQRiPSbAY0hOJG0cYB/S+qpJKJzfdLDNuL6NEnMfVDSDMAUk+6BZRCFG0Mqu0MFG1in/1V2wps/t+RCN2D1A8ijRokKkLxotpXYXF/HEWbgmo/HzOxDSGGrxo5FEZFBiyaytZj1VQ0vZK0CAi4aOZEFvVyEwyEzvx9RVU5lfpQAvjUpMUj8lc6NAufm7KcTQ33p0SZV4fb2NpUxbIOf70Qgks/dS7rntzM9GUT8WV5ySnJ5C9ff4DSyYWo/YiadEIVgq/NOHvYRGA0kFLyt4Ob+uSIT07L4dqyWSMKQBRCsCy3jJVFE3jq+O6udl2a/HnfehbllAyx8pE8cnR7nwj0QpePa8fOGvY96OzH7MxCNjacGHqH/wDsa61jQ8PxPu0Xl0zhhnFzRkQEBsP3N7/cpa8gSJIQCR3uhqQVTRWCS0qn8InJwy9PvHvLcSwWlatvOQNFEThdNu744VPMWZqsgLfmqXcItkX4/p0fxuGyousGP/3igzxy9+t85nuXDfrspZQ889JOGpuDlBRl8IOvX0pWhrvPPpqmYrdbWJ4xngnluXz5O49QUdXC86t38aFrFxGIxyjxJ11hLx8+zP7GBj41bz5vVZzgSEsL03KSadBnl40lz+3BoqqU+P2EE3Ge3LePqdk55LrdySIwA8Cl2ZmTXk5bIsSizInDLpctpSQYeaJP0JzP88lU//EQEELgsC3tWOVv62oPRV8g3fwWqto/8RJCJFPy2n6EKTstf5JA+J/DNrNLKQmEHqCnmVoIBy7npUM+32D4SaTs6cK04HPfPCKtAyFUPK5rUsgAGIQjz50SMoAQCDUDi+d2NNctYDYiZQyEFSG8CCUNBpi5hBAgXKiOy1Edq0hE62iuqyAr34sQdlB8Hfv3n20ghIJqOwvVuhBp1iYzB4SW1BhQsoGkHo7V+7UOgvE+k4E0l4P/uW5VSpvHPvzVxbuFTLuLBVnFI55wZ2YUMMaT3mXOh6Tb4YWKfV1Bc0IIxs0spXRyYbdxV8KsFVNw+wcPYJqSlsvKwgnvavpLIBFjTXVfmcqbyueNKphLFQofHb+A506kpmhubqygKtQ2aOxAzNBZ14+k7UXFU3BrIzPjaYrCNWUz/r8gA1JKHj++s4/uhcdi43NTlg3L/TJcXD12Om2xpE/2WLCFR4/s4LLSqZR4ks/tcHsTL5zYx6Kc4RH4TjhdNmLRBOFgFKfLTnNDALvDSuervXX9IabMLsHhSg52FovGghUTeeTutUmlOnWwyQLWb0qWTr7m0rn9EoGeEEKQk+Xlqovn8Js/rWb9piN86NpF2DWNMf40FCF4bM8eWqNRhBBkOJ2Ee8QraYrC9Nzcrr8bwyGunzY9meEkBMYQCR5SSualTxjSEpAKk1DkxZQWRXhx2JaNYnzQcDsvJ9a2rfvoZgux+FacjoHz/FUlC4f9jJQVeiT6JobZgKYOnY1iyjbC0TUpbXbrfDR1KO0Ok3Dk+ZQWi1aCRRt+gHQnbJYZJKP+ux9SJLZ+2Jkgw4EQSjJWQM0aeuM++wrAwZG98Nx9J/jir/uPKRtwX+FCKH0Lq3Vv4xhQlXW0GBUZUIRg+4lajjcmfdOmlJw5qYwJeSO/aacSU9JycY5wsgGwKArLc8tSyAAkJz6zYwUlpeStp7ew8YXtRAIRFE2lsDyXG74++GoH4OoxM0/pQN8f9rbW0dIraNClWVnWjwLicDHBn02xO40jgW7fZszQebP2KNePG5gMVIXbqY+kZooI4NyC8aPqy7ysYqyKekrS795P6NJkbc2RPu1n5I2l0OUfcv/eQX+D3ctFOaVd+3z6zcf58owzOa8wtX6912LjpcoDTM8Yftne6fPHkFuYzk+/9CA5BWmcOFTPjZ85p0tvIx5NYHOkrnrsdiuJuD5g0GIndMOgqTn53kwaP3x32qQJybTTpuYgumFy7thx3LlxIyBZUFjI8dY27tu+nYZwiG8uP4NER8nipAJjNzF78dAhLho/gTzP8Gt63H3kBW4dd9GwSxabsp2EnkqULZZxKIp/2OfshBACu20xvRNzw7HXByUDQgg8rhsIRZ7p2s+U7YSjq/E4rxvyvkeiazHN1LHS47phyNW9abYR1w+ntFktUxjNNKSo6UlfuuwOdNaNSiCOntCoPtZAS307qkUlM9dPVn5aspppZQu5RekoqkJbc5BIMEZOUTKepamuDZvditvnQEpJQ3ULtSeasFgtFI3LxtWjnLyUknhMp+JQHaG2MOk5PvJLM1E7hPj0hEGgNcQbz26jsbaNqiPJGAmbw0pmnn/E1/teYFRkoLK5jRd3HKA0Kw2fw87Rhmai8eHXnz8ZDPaizkjPH9VkI4RgYXYJ9x7YmNJeE24nkIjhtzmQpuSlv63lhm9cxvP/9zrnXL+Et57aMuSxNaGwKKf0XbUKAGyoP9Enar/EnUaGffRmJItQmJ1ZkEIGANbWHua6Qcz9B9sb+gj7uC02xnhGV2oz2+4m3eakdpSaCf8uaIqGqAq19Wm/uHjKkO+HlJKdDXW8evwIGQ4n10+ZMayFgQQOtTeS7+yr/Jnj9LC2ZmRFiaw2CxaryqKzJlM0NpusXB/Z+f6uY5eW53LicD2mmYwDklJyZH8NOQVpqOoQpmDZPaWJEWQG9BygkZDv8fCtM87AlBKblhT8OtbaigCyXS7eqjhBOJGgJhBge11tl4n/UFMTF44fOPiuP0SMOLo0sA3T4WkYdcmiMz1g1SYw2mWephYghDNlUozHdw25QrZb56GpRSl+/0DoQTzOa+nMs+8PUpodLoJuJC0NywfYoxu6UZPSzyQEkdjaIfft248Yve+ZaYbQ9Qj3/fINtqzdh8vrIB5NkIjrfOPOm/BluPnhLXfzw/s+SUaOj3/86nl2rj/Eb576Ag6XjTu++Qirrl/M3BWTePXxLTx+92s4XDbisQQOp41P//QaCsYkF7zBtgh/+u6jVB6ux+60EmgNs/j86XzgM+ehWVS2rTvAc/etY9/WYxiGye9ufwiA8ulFfOzbl434et8LjIoMtIWjLBhXRGlmGgnDZOmEUtYdOM6Mkv6FYXTTpKKtjX2NDZxoa6MtGh2V6MnkrGwumThxwN/HeoejB90/SjzpKIiUCO+InqApFupKMdSsGqVTizB1gzFTi3jyzpeGNH16rXZyHCOrkT5SdErM9sZ4X9YITZipEEIwM6OAfx1NTes72NaIISXaQGSgVy0ASLpwRhKo1hMWRSXX4fmPJwMVodY+2RVWRWVKWu4AeyTRSaB/v3k9N8+YS47LlVwLdtQKESIZKNoZ7Q90rXwVIZiSlssdu9fx6alLybK7kEB1qI17921ked7ITLSJuM6xg3VMmF6MZtWoq2ohrz6dCdMKUTWVlVfN5buf+huvPbudiTOKqKlo5vXndnDLV1YNSXg0TSXN56S5JcShI/WMKc4cFknafygZVZ2e5kbVFJojEe7ZuoVgR9VSAdw6fwE57uR3OCsvH1NK/rF9G7fOW9BVj+Dh3bsY6aQ80VPInQefYYK3EIHArlo5O2fGgDEEulFPbyEbTRv9YkFRnCjCg5GyQq4l6c8fLCvBgdt5Ka2B33e1xeM7SOhHsVrGDbifYdQQjacumpKSyN4h+6ob1fS+9lDkmT6ug+EiVZ8BQKeloYWn//YG3777ZsZPLyYR16mtaCYz34+qJQWlao41kpbpYd/WY+gJndoTTRSOzebwrkpyizOoPtrAPT9+klt/dBUzFpcTiyT4wzcf4S8/eJzv3HMLQggevuNl6qta+NqdH8brd3F4dyU/+vi9TJ0/lplLxzNlXhljJuZz53f+RTya4PO/uA4AyzDltd8PjKpn+Wle3jlew5jsdH721Gt4HLY+GgOQ/FCrAu3ctWkTa44eoSEcHlaE80C4ZMLEQcnAaCVrAfxWB1ZVJdojAM9E0hwLM5bkSmXeyukgJYXlefzvZ/5KTknmkFkUXot9WJkNJ4vqcN8V5xhPxklbJPqLDWiOhYkaCdxK/5N7RbC1T1uW3Y1yEsQk610mVO8FKvpJkfRZ7aTZBrfehBMJHt63k33NjayrPM7kzGzG+NPZWF3JGxXH0KXJyjHlzMzJ40R7G7sb6zje1kYoEee22Qv4wvTlfHvTC9z25mPYVQ0pk+6eGRl53Dh+zrD7L6Xk4O4qsnL9HNpdxbEDtSQSOhWHG7jukys457LZlJbncOs3L+HJ+9/i+Yc3IoTgipuWsvCsSUMeXwiYM7OEw8caePjJzcybNQa/b+DaGFJKmlpCPPrUVgAWzStDAM8c2E+h18fsvDzoIEo9KxE6LclV/NVTppJmt3elU54/rhyfbfiEVQhBgTMjxUUwVKCu2VnmtgdUZXQWsyQ0hOJISTmXMoxERwxKBgRu51W0Be7qUvaTxAhFnsCifanfe54UC3quVwBgMjthOOOMaTb306ojB8naGBkkNruG2+dg05o9ZHSY7idkFHcR5bIpBRzaVUl+aRaGbjBr2QT2bzuO2+fENCWZeX6e+dsb2J02Csfm0NKYXIDMXFLO33/5HOFAFIvNwmtPbuXSjyxHTxg0N7Tjz/Tgz/Tw1os7mLl0PA6XDYfLhs1uBQkZuadSIfHdwahmqXS3k6sXTMNh0bjlzHm0hCPMKS3oY5qqbG/ntmefZld9/SBHO3UYTbxAJ+yqhlXRUsgAJCu2deKc65eiWlQuu+08GqqacXmHNsHbNe2kJsHhwJAmgXhvlgzZp2AC9Vv7RjhHjQRhPTHgSr8lHu7TdjIFj4QQ+Prpx38a6nrFUQD4rA6sQ8ST2DSN88aU88yh/VxcPpEMR/K9y3K6uGz8ZOpCQe7Y8jZ/XnUZjZEQ9+/ezreXrEBTFKyqSqHLxx+WXs7+1gbqIgEEggKXl3Jf1oiCSxNxgz98/0lu/vL5zF6SlKc2peSBP73K68/v4JzLZiOEYMGKiUybN4ZgIILDacMzyITeE0IILl45g+de3snBw/X84JdPc8NVCykvy8LpsHVZ4AxDEgzHOHColvsefpujJxrJSHNx4bnTkpHWqkp5egbjMzMH/fLsmpayTi3vqF44EsxPn9DlKvBoDiSDm+eljPdpS5YmHi1ER2nfHudAB2kOaeSwaGVYrTOIxTd1tQXDT+DzfBrRJ4ceOgWKesJqGY/NMjx1UFP2HRdONVw+B1/89Q08cudqfvTxexk3tZALb1zK5LljEIpg6vyx7Hz7EEXjcsgryWTGkvGse347uUUZZBekYXdaOXGwltamAL/9Src7JBHXScvyoCcMdN2kvSXEy//ayLoXuq2mhvGfnf48KjJQ0xrgaH0zOT43Y3MyMKXJ397YiiIE1y+egdNmRTdNfrz29RQioAiRrElgtY4qhSrbNfjKXz2JVbAqlH771JMcPHvPGlZ++AxsDivSlDz+hxf48HevGvS47zYRgGSaWNzsy66dJ1k1DpJ56KnhSQOfrxNRve9vJ1ve1zHMAK1/ZwTivfXnk89oqDdEUxTy3R5sqkaeO1ngS0rJ0bYW9jTWY5gmrbHuY0/KyGZCeqqJ3aVZmZKeQ7mRiUuzDlvMqyeklETCMeIxHT1hgIC25hAHd1VRPLY7Cl0Igctjx+UZeUW5ooI0bv7gUv7419fZuv0Ee/fXUJCfRk6WF7crKToUCEWprW+nuqaVaCyBy2njMx8/m6zMZOBfic/Pz954g5l5uV2Bux+dPZusXuPH37e9wyUTJ1HoHdrEPRD2Byr5V8WbAHxt0jX8q+JNri1ePuBE3K/e/EmvjFMnIYECwxoLVbyu62noQQYS+lHi8Z3YbfP6bB1PHCSeSC1l7XZew0Bpdr0h+snNt9sWYVFLh7X/0CewoAg7MxaXM2FGMYd2VbL6Xxv5ya1/5ft//TjjphVRPr2I5+57i51vH2LG4nLGTing/t+8wIHtJ5gwqwQhBDanjfySTG7/w4dS3awCPGkuwoEoqqZw1SfPZvrCVJeK3XnyokedOJnMCEM3UNS+wnaDYVRkoD0S5e7XNjE2J4NxORlMKcgmGI2Rn+bl9X1HWTVjAoebm3n1aHfkdInPz0dmzWZeQQF+uz05cY/wOocyt78bpVd6Bjym5fh4+JfPMOPMybzwf69x+W0r34Uzjg79XfvJxAt0H6P/sw0W8tGv1v5JuitOhuj9u6A/4aTR3peoofOHLW/z+3MvIqInWFfZHQhmVdWUQSBhGjx9fA9PH9tNSyzCb5dcSok7jaeP72F6Rh6lwwzstNo0rvvUWTz6f2+w+smtIAThYJTMHB9XfmTpqK6jN4QQXHL+TCwWlfsf2UBtfRuHjtRz6Ehf66IiBGNKMrn5g0tZMr+7dklZWhq3L1uWsm1nXEBPmFISTSRGPehKKTsm/zP4+7HVqEJhV9sxLjcXD5hdkNTb79UPs6+Lb/gwMHtpFghhH9RF0L2dwGk/B0Xx93BfmARCD2Kzzk25J521FLpljJO1BNyOwYu19UR/1+52XIHHdf2w9h8OTDNZntrusjFt4TjGzyzhtvN+zp4tRxk3rYjc4gwioSi7Nhzmk9+/gsxcP4ZusGP9IS744GIAZi+bwBvPvIOUsivTQEpJIq6jqgpOj53SCfns3XyUFZfNQbMkYxFMw+xDwjSLSnvL6N6x2mMN+LO8ONzDJ9VSSoJtYZ76yxqu/fwqtAGUgfvDqMhAXDe4esE0zp06jh88voaCNC/j87KYP7aQB9dvZ9WMCaw9fqwrbSfL5eLOiy5mYubQAUEng5OJRzA7ahb0hlVViYZjmIbJnLOnEWqLcM83H+Rzf/go+eNyTqa7pwyKEFj6CVjqLUA0Guj9lKNVhBjUstOffHLiJNMC35+iuH1hjrjIcDds/ZS27a05MBj8dnuXpcmiKBR5fDxzaB9RXSfH5e5oV3FbetZIlzx7fC9/2LWO8wrH8/jRnV3Wrp3NNexrreerM1cM6/xCCM6/ah5zl46nuSEASDx+J1m5fizWU5c6q2kKF503nZlTi3jj7YNs21lBfUOAaCw5ETkcVvJzfMydVcqSBeP66BHkejzk9kgPPNLcTDiRwGFJnaBn5eXxq/XrWFhYhKYk1U8vmTCxS6FwODClJM3qQkEQ0qMY0hyU4KlKNr1TAXW9Ytjn642k3n2q+0lRMhju0K4oGThtZxGMPNbVFo6+jCnbUyoBShntSEXsht22EFUdfjVRVe07XupG9YgEh4ZCxcFqHr/7NSbOKsXhtnPiYA2RUIwJM5MxbS6PA6fbTn1VC/mlWVhsGrnFGex/5zhjJiWtvLPPmMjkuWX84tN/Z9nFs7A7rNSeaMLmsHDDF1ahKIIPf/VCfvPlf3Lntx6hfEYx0VCMw7ur+MBnz6OwrNtKNn3ROP7ywyd45m9v4vY5cPuczDtreEWtdm84zN5Nh/nYD68ZlsVBSkl9RRN33v4Ah3ec4JrPrRpyn54YFRnI83l4aP0OKpvaCMcTPLdtPwXpXpxWC2mupG93V31d1/bXTZ32rhMBgLDe1x83XMRNnXg/KiNui43H//Ai1YeT1yOEwGKz8PgfXiSzIG1IN8F7AVUoSdnkXtH2LfHepUFHjkCibyyCRVEHNdt7+oklCCZiJ2X2iuiDF7d6r6CbZr+kcThIKXzVgWAi1qVlMRS+teTMrglNFQo/WH42NcEAOS43iY53d3x6JoWe1BXY/Qe38pUZZ7KyaAKvVSfzvEVHlkFPhcnhQFEE2fl+svP9I9pvpBBCUFSQznVXzOfKi+cQDseJx3UQYLNquJw2NC3VDBpJJIgZBpqi0B7rfm/XHj/O4qIiMnqVQPfZ7Zxb1m3mHY0U+gX58/jz4Rc4EW7gdwee5Mzs6WiDSAprah5COFKC8OL6gVF/G7rRgNkrXc+qDSxW0xtCCDzuGwhGnqDT3WCYDURjb+FydE8m8cTujjz+rj3xuj44ook8KUpkoad1IZ7YfUqFgtKyvfgzPWxduw9dN/FnuPnir2+gfHqy6qZQBOd9YCGtjUGc7mSdivOuXUjBmCwyc/0A2OwWvvjL61jz+GZ2bzxCIqGTmefvmsSFEExfNI6v33ETax7fzIbVu7HZLZRNLiAtM1WjYtmFM2lrDrLtrQMoisLs5ROGfS2mafL839disVn46HeuwGofeMyVUnJw23H+94v/4ND2E2QVjjwodVRkIMvr4sPLZ1PbFuTahdMJxRNsO1bN1mNVfGhZR7GSSLcPc2lJybtOBACaYv2XZR0OAolYH1EbAaRbnUy7eQV6vC9RULRTx2hPBgLIcbj7iCad6FWwaDToL53PY7ENGgOQ7egr2tIUG33wkJSyTxXG9wshPY4+SitHvrOvmbQ1FiEySGZGJ4QQ5Lu9KX/7bHZ8tlQTol3TsGvdn7UE2hNRSj1pfVw+MUMfdhGs9wtCCGxWDdswUrIONjdxoLEJU5qsO1GBrUMA5nhLK4uL+pZgnpqdw9Ts0Vv3hBDMSRtHkTOLhmgrfquLfMfgGTyK4seilhDX93a1JRIHkTKMECPPhoolttO74I3dtmhE463NOgtNK0HvIYYUCD2E035+l4k8GHmSnrEJqpqDw7ZkRH1V1Ww0NRvd6K5ZEkvsQsooI5FiHgy+dDc33X4Rhm4ipURVFYTSTfKEEFz0oaXJD6PjFi29YAZLV83o+lsIgSfNxSUfWc6FNy5NHkdTUzwAQgjGzyymfEYxhm4gFJE8V6/7bnNYueJjK7j0I8sBgTqCOSMzLw1VU3n6L2uw2i3cePslWGx9vwPTMNnw0g7uvP0BGiqbcfudXP/lC9EGkcjvD6MaCeK6QYbbyRkTx+B3OShI83LBzAl8cdUysjzJF7pzpSMA7wjSdU4GRwP9pa4MDzXh9j4rPquikWF34c3wkJbro+pIHb5sL+l5fnxZHir2Vw9wtPcWQggm+vsOagfbGk5a5GlPS9/KWPlO36AR8GXevlHZDZHgSSkI1v2baAw0RIJ9ai4MF8XuvhNyQI+lVIc81RDAjIx8Hj+6i7Z4FIlENw2qQm386+gOzsgf/iry3x2Ts7K5eMIELIrKl5cs4XsrzuJ7K87i2mnT3rXFSGOsneerN/Fi7Raerd7E8VD9EN+cgqNXQSDDbCSWGHmJbilNQuEne7VacdgWj+g4AjtuxxUpbdHY211VFSUxwpGXU353OS5kJFUOk+exYe9FIAyjllh884iOM+R5hECzqFisWr9BdEKIPgSh59/9HmeA3xVFYLFqaJo64DuWPI6GZhl4m/4wfekEPvT1S1E0hUfveImHfvsceiLV9ZuI6zx9z2v88tZ7aahsJqcog6/+6WZWfnDpiN/5UVkGjjQ0s/VoFTcsmdXVltTv796m02cngVB89Ob7kWBnc82oTU7bm/pO7Ol2J35rcuUlTcmjv32OyQ98tuv3x37/AlMWjUcMIjr0XmFhdgn37N+Q0nY40ERYT+CyjC7C1ZSSLY2VfdpnZRQMeo8n+PrKUjfHwjRGQxS4Rp5vGzV0asOjIwP9FVSKG8ao35M9rXVDbzQAClw+3BZbiuvFlJK36o5S7n133GhCCD49ZQlfWv80N7/+MNWhdn689RVaYhGK3X4uKRleWtipRCJhsO9Q7UkT1d5I8zkpKkhnZXk5dk3tEv5ZVlLSpS1wKiGl5G9HX2Z6WhmLMifRFG/n7iMv8v2pHxzQVSCEwOO8gvbAX3qI5pi0Be/Gbp3PcKoWdiKhHyESW5fSZrNOQ9OKR3QdnX1qC/yhq0+mbCMSewu38xISif29XAQaHue1I35fkzLI13UEInYuDExaAr/HbpuPEO9/fZt/J2gWlcs/dS6mKbnvZ0/x4G+ex2qzcOWnz0PVVELtEe7/n6d5+i+vkojrlM8s4bO/uZFx00denwdGSQZcNiuBaBzDTA2W6dmBhYWFPLRrJwBbamqYmZv3rrsK9rTWdZQjHdmHb0rJ67WH+7RP8men1BQwDJN4NIHdpZCI6/26Dt4vTE/Px63ZCOrdE01TNMye1lrmZY1scOhEYzTIvl6TnwBW5JcPut8YTwYuzUqoRwxH3DTY2lQ5KjJQEWrtV7tgOOgvfmG07iQpJW/0854MF16LnQm+bDY3pgaMPXFsF9ePm4N1BBPBSFDk9nPHsit4vfowB9oaulQJz8wfmxJsCKe89km/aA9E+MYPHyOROLXfz1nLJ/LVz5zP4eZmYrrOpKwsnBZLn5TCU4m4NFieNRWbYsEknzV124cMdrVo43HYzyQc7S5YFI6sJhx9Baf93OEJ+MgoLe0/6yXvq+Bzf5zBlAcHgqaVYLPOJhpf39UWDD+By3FxR+Bg97OyWiZhtQws/jYY7NbZ2K1zica7Fy7R2Dragvfgc3+839TLoZAkleaIiNR/CjSLypW3nYeUkvt//jT/+PlTWOwWllw0iz9/6xHWPbMVJMxfOZ3bfnE92YXpo55nR0UG3DYr245X87OnXsPdUa3wjEljmFnSXfBkSVEx6Q4HzZEIj+zaxTVTpr7r7oK6SIBdLbXMzerrHxwMNeE2djT3lfM9J7+7sItQBPPOm86fvno/RRPyqDxQy+yzp45IQ/3dRLrNyYLsYl6pPtjVJpHcf2grczKLRpzCJqXkmRN7CPYKysxxeJiePngEcbrNwXhfFu80VaW0P3Z0BxcWTRp2udfOfrxctX/UpvlcZ9/4hcpQGyE9PmJ55MZYqF9LyXAhgIuLJ/chA3taa3mj5ghn5Y9716wD2Q43V5VNx0R2ZST0d67+3D8RPdHTxXrSkEA0liCR6BuIKQSoioJumP0UZgIQfdoVReDzOPB6kn5nh6bxzP79PLZnD9NyclhUVEShz4c6igDBoZBnT+PX+x+n2JlFfbSVlniAR068gUVRuaxgEdZ+Am2FUEnzfoVobH2PMsJxGlu/Rnaaq8Pn3/83IqVEyhAtgd8SiryQ8pvdOg+n47xRXqOCx3V9ChmIxTdjyjZCvVwEHtcHGOXUAVhI932DmsbregRRmrS0/w9SxvC6bkJR/ENeQ/IdiKPrVURi6xDCicd15Sj7NDS6ZL4l9MwEEe/CO9UbmkXlqk93EIJfPMP//fBxXvjHGxzfW41qUTn/xqXc9K3LcfucJ9WXUT1Ru0Xjo2fMTWnL9acOuhlOJ5+YO4+fv/kGh5qb+Pmbb/DVJUvx2mzv2s0zpeSe/RuYmVEwbFEjU0r+fnBLn0wEl2ZleV63P1UIwQU3r2DfxsPUVzQxYXYZExe8O4P3aKAIwU3j5/NazeGU2IfVVQfY3lzNzBEWcaqPBvlrr8JNAJeVTsM1hNKjQHBJydQ+ZGBDwwl2tdQyLW34VqJAItanNsJIUOJOQxUihUy0xMJsbawcUUVHKSUPHHonRZFypBBCsLJwIr/dtTYl08OQkp9tf4VJadnkOfoWFDpZmFKys7mGFyv2Ux8J8qXpZ5Dr9LCtsYpidxrpPYpZ9SeN3BANEkzE8FpHLiLUH9wuG1/59EpMsy/B0zSF1rYIf/3nOmJxnfFlOUwcn0t+rh+n04oiBJFogtr6NvYeqGHfgVrcbhu3f3YVUyYmFyPjMjL46tKlVLW389T+fXz++eeYnZ/PqvLxzMnP7yLGqaTCJJk4aoA0kCQwzCZ6K3hIGcUwmkBYEGjMTiuhLZGDQKHElc08koWOVKEMSnqtlsn4vV+kue1H0CE6ZBg11DV/Aq/rw7idF6GphQhhJ0nDDEyznVhiF+3BewlHX6Xnal1RMsnw/wjB6BZcSc2Bs1CUjK5YAcNsJBJ9nYR+pMd2HlyOC05KTdRmnYvf8zla2n/RdQ1SRmlp/xXh6Cu4nZdjt85FVbKT1y9E8pnIKIbZhmFUE0vsIRbfTCyxG8Ooxee5dVT9GQhSSmJxndr6do5XNlFR1UJTc5BINN713gpF8KkPn4HfN/picMOFZtG4+jMrkabkgV8+y/G91Siqwo23X8IVt547aKbBsM8xmp2cNiszivM43thKwjAoyvDhsae+hEIIbpwxk5pAgH/u3MFDu3ZyrLWFD06fwYycXNIcDiyqOqIVq2DwqoUAr9Yc4rmKPVxUPGXIY0sp2dxQwYOHt/b57az8ceT0ioo3DUl6rh9/VjKqu6m6hZySdz9lcriYl1XEkpxS1tZ2f7wRI8F3t7zAn5ZcRV4/let6Q0pJSI/z43de7hPYlml38cFxc4Y8hhCCVYUT+d9ek17M0PnxO6u5a+nV/abZ9YZumvx53/qTyorIc3jJcXhSrkUCd+xZx5zMomHFU0gp2dBwgr8e2DDktkMh0+7iA2Nn8ce9b6W0Hwk08ZUNT/OTeRdS7Bp6ZdRfH2Omjk3R+ojFbKqv4GsbnqXY7WdbUzW3TFpALh4ePbqTIpefj09e2LV9vtOLTVGJ9Qj2bItHWVd3lPMLJ56Sd91us3Deiv5jFdoDEb7y3X9hGCZf+OQ5LF88Hrer7wJCSkkkkuCtTYf49Z2r+ee/NvCTb10OJFMMt9XWsPbYcYLxOB+dPYdCr5f7d+wgy+Wk1J/WIaLzMAn9KKYZwJRBpAxhmiGkDGPKKFKGOqrjdSMcXUtNw9VJYR/FQY5wkqs6UYQLRXEjhAe7bT5O+xmD3gMhBD73TehGJe3Bv9I5KZpmE62B3xAI/QNNK0VTswALpgyi61XoxnFkL5EhRfjI9P8Uq2Xo6peDQVHScNrPIRh+qPMu0xa8i56pgA7bkg6thNFDCAW/5xOYZgttwbuhS4HRIBbfQiy+FUV4UdR0FOEiaQ1KdDyXIKYZBN6dODQpJYFQjPWbDrP6jb0cPFJPe3sEvR+pYUUR3HTtYvwDeD6llLS0hon3CPqz2yz4vKOTZtcsGtd89nysdgv3/fxpEjGdiXPL+s0wGA1GLTr0+5feIhiNoyqCSELnC+cvJcvb7ZuLJhIcbW1lTn4+606c4GBzE+srKthSXU2u202h10uGw4ndog1bKW9mbh4fmDZt0G0SpsH3t75E3DS4sGgSdtXS70BiSMnGhuN8Y9NzfUzhTs3KJyctTiETUkoe/J+nqDveSN2JRrzpbnJLs/jYT68bVt/fC2hC4fYZZ7OzuSZlEt7dUstn1z/Ot2ady9S0vAHNpaaUnAi28Judr/N8RarsqCIEn568lNx+0gb7Q6bdxfXjZnPHntQApy2NFXxz83N8Y+Y5/ZbVheS9bk9Eue/QFu7d39c6MRLYVI2z8su571BquektjRX8bPsrfGHaGaQNUDdBSknU0FlTfZCfbFtNez+aCyOFEIKPTljA6qqDHGxPre74dv1xPv7Gw3xi4iKW540l3eYckAB3ViwMJGJUhFrZ3FDB1qZKfrngkj5KnX/a8xY3jp/DdeNmcfmLf+3qx/T0PFZXHUzZNsPuosSdzoEefZPAr3a+Rrk3i7HeoYtfda64R0NonnlpB/sO1nLVJXNYdc60AQuBCSFwOq2cvXwSBw7V8dATm3lhzW6uuGgWGyor2VRVxdllZUzOzsahJQlSeyxGoIf+QFvwHuKJnSPsYzAlLbA/eM0PD0kGktdgI937DRThpi345x5mc4lhNmDEG/rU5esNTS0kw/8DnPaVJ03UhBB4XdcTDD9CZxphLP5Ozy1GrC0w8LlspPluR1WzaA3c0auIkcSUbZj68JUZT4XaqmlKtu2u4K8PrGP3vup+CcBI8fRL23l2dfc7VlqUwU++cTlaR9prpz5AJDi0xVFRFWwOK7PPnEw4EOWh3zzP3d99hJu/d1Wf70SzakyaN3bIQnop+wx7yx441tiC02rhcyuXIBTB2wdPsGbPIa5dOKNrm83V1XzuhecIxmIpKmtxw+BEWxsn2kYuwRnV9QHJgFVRcWlWWuIRWuMRvrP5BV6o2McFRZOY4M/GZ7WjCYWooVMZauXlqgM8c2I3rb3MvgL4cPlcxvtS2a9pSo7sOMEX//QxHvjFk1z31Uv5+w8f5ZQ6U08SQggm+LL42syz+c7mF1LK5b7TVMXH3niYcwvGc1Z+OSXuNFwWKwJBRE9QE27nrfqjPHdiLyf6qa53UdFkrimbOewBRwjBR8bP5+WqAxxoS51YXqjcx+H2Rq4YM535WcVk2l1oQiFuGjTFwuxsruHZE3t4p6kSQ8pkelx6PtuaR57KKYTgg+Pm8PixnSkBjRJ48PA77Gut54ox05ieno/f6kAVgrhp0BKLsK+tnpcq9/N2/bEu1b4chyep9BXtW3RouEizOvjR3FV8at2/+ugnHGpv5Bubn2WsN5O5mUVMScslx+FJ6jrIZJGo1niEqlAbh9obOdTeSEWolfZ4FL/N0ac0uASqQu0syS3tKErU/fwsitolVtQJTShcPmYaP9++JqX9aKCZT7z5CB8sn8O8zCLSbM4u90vc1AnrCdrjUZpjYQKJGFeNmTFgieuBICW8+sZ+AFYsnTCsgUwIwdKF5Tz0xGZWv76HKy6axdScHMamp6MIQUskQgvJuiaLiop6iTu9/6qWiuIgzftFbNbptAZ+Tyy+i54r8QH3E14c9rNI834Wi3ZqLDYAVusMLFoZCf1Qn980NR+7bWE/e40OirDjc38Cu3UurYE/Eo291SOGYlhHQFUysVnn4HScf1J9MQyTZ17ewV/ue4P2wOhdgT0hhGD+7DH89cG3ulwLjc1Bjlc2M7a0O+Pqj197kCO7hlahFEKgWpRkmmJHOeZD20/wvet/T+9JKD3Hx5/f/gHKCEomj4oMKEIQ0w0ShoEiFSKJBBY1NfAooidoiZy8At5w4bHY+O7slXxj07ME9TgxU+fVmkOsrT2Mx2LDpdnQFIWYodMaj/SpTtiJZbllfawCkHwQVocVu8tKW2OAuhMNNNW0JFPU/l3YAMl+XlYyjeZomN/tXptync2xMA8d2cZjx3bitdiTRXKEIKonaE9E+70nAjgzbxzfmX3eiEsx+60OfjQnOen1Fh062N7IL7avwW2x4bXY0ZQkGQgmYkm1wh7bLs8dy+enLefK1f/XZ7IbDsZ6M7lp/Dzu3PNWipiwiWRrUyXbmqrwWm24LTbUDlISSsQIJuIpkeEO1cJ3Z5/HS5X7eeL4rhH3oxNCCOZkFvLTeRfyzc3P0RhNzW5ImCb7WuvZ11qPQGBROopoyWSFSl2aww6oFECxx8+G+hMp+g9hPc7LlQeYmZmfur0QXDVmBg8f2dZHt+NYsJkfv7Mar9WGx2LvIAMmccMgZurEDIOEqZNuc3J56TS0EcqY6LpBY3MQIcA9Aj12lzPpomxoDKDrJnvq63n2QJJURHSd+mCQ3194UZ+sAodtKZo6ukybwWC1TB3R9kJYcNrPx26dTyS2llDkReKJvRhmU4ebQiJQEYo7OSFb5+JynI/VOhOB9ZS6KQVWfJ5PEY680uc3h20BQpxa/7gQKjbrPLLTpxFP7CQcfYVofDO6XokpAx1VHiWgIIQVRbhR1Wys2jhstnnYrXOwaKXA6AsESSl5dvVO/nDPq8Tip6qcchJjS7PIy/FRVdMKgK6bvLpuP2U93MuGbvTRDxgIiTjQYS8SAlRNSRYN6wVdH3mmzqjIQEmmH01V+NETr6IoApum8pmVqUIXXpuNqdkn51vqjSLfwGlpUUNncU4p352zkh+/s5rWDjO5ISWt8WgfC0BvCGBpbhk/m39RvwFyQsBVn1uFoqqsuHYRT9+1muVXLvi3ySboCU1R+OiE+Xitdn6z6/V+JhuDpliIpiFskFZF5bLSaXxl+op+SxkPBSEEszML+dn8i/jW5uf7CAdJkgGC/UkeQ/KZLM4p5afzLsRlseK12Lue60igCMEnJi2mKtTGUyd29yEUJkO/I27Nypenr+CcgvHUR4InRQYgeW/Ozi/Ht/gKfrJtNTuba/uteiCRxE1j1IJNQgg+NXkxX9/wLDubamiMhrj/wFaqw23URYJ8e865ffZJ6yBxn3v7iT7vjkTSFo8OGkg52vW2EAJVEUgJVdUtlAwjTUpKSUV1krQkBWZgcVER8woKgCSxunvL5n6lxtN93zyJ3g6GkZvRhRCoagYux2W4HBdhmC2YZhOmGURioggbiuJHVdIRwn1KTPUD9cPj/AAe59X9/Dp0FTxT6sSNAEKoWJVkP6WUxM0AIDrakhkhugxjyAQ2xYuiOLDb5mOzzkvGBpgtGGYbUoaRGAgsCMWJKjwoiq+DlIysKl9/kFKya181f/y/1/slAg67hYw0N36fA4tFZceeqhGVKrZoKnOml3SRAYD1mw/zkesWd1mpPvrdKwi2ndqFs9VuGZHaIYySDFhUlU+evYC6tiC6aeKwaNS3hfA7uyeM2Xn5/OOKq6hqaceiKmR7R6ZWNdB5B0LM0IkaOpeVTCPH4eE3O19nR3PNsHTk/VYHV46ZzicnLR7QfyyEYNysUppqWsnMT+e62y8lp/jfJ3iwNzRF5ZqymUz0Z/PHPet4q/4Y4WHq+2tCYYI/m4+On8/5RZNGVPO+N4QQrMgbx51LruRXO15jU2PFsIoWeS12LiudyqcnLyXd5kSSDG4bDRmAZHbI9+ecT4knnX8e2krDMM38ihBM8GXzuSnLOKugHFUoTE47NQWqhBDMzSzirqXX8PCRbTx+bCcVoZZRpVF6LTbmZhb1K7I0O7OA/1l0MY8d2cmktGyOB1uYlJbNt2afS46j73cphGBBdgn/u+hyfrF9DTtbakad2jkSaJpCUUE6DU1BHnxsI1Mm5g8abCWlpLUtwsOPJxXsigvSUVWFmK4T6SijLaVEN03aYzEKeuybPOapCbw6lUj2y4KmZoN6ahdTw++DwmgIjZQmO5r/hi6jmDLBJP/VuLV8jgfX0BDdhSkNClwLKHAupjG2h6PtLyGEituSz0TfFQihdKTquVAUFxqFIzr/oepGpITygsxh7xOPG9xx76uEwqkLksx0N+csn8TieWMp6Mhkicd1rv/UPX22HQydroKnXtze1VZZ3UJ7IEpaRyrgzOWThn28dxOj/hosqkphenKlvvNELat3HWJ83tKU332qyr5QA06bBZ/91KQlDQRDmoT0OIoQLM4uZdKyHFZXHeCFin0caG+gLR4hYRhJg5MQODQLuQ4v87OKubR0KtPS8tAUBSljSL0K1JIUEQspJWseeItNL+3A7rQSDceZt3I6Z1+3pGuwyrA5WZ5bltKvYnfaqK5HShOM46DmMlrdbkUIZqTn89tFl7OlsYIXKvfxTmMVdZEAYSOB0RHLoQoFu6aRYXMx0Z/NOfnjWZI7hgzbyeWtdkJ09OOOJVfyes1hnq3Yw97WOlpiEeKmjpTJvtpVCzkON3Myi7isdCozMwq6RZ+k5PyiiWTau0295d6+SoeDwW2xcdvkJZxbMJ7nK/ayvu4YlaE2gokYCWkgZYc7SFHxWe2M9WZyTsF4zisYT5a9uzJeqTudFXnjuoimIpQRu1B63ptsh5tbJy/h8tJprK8/xpu1RzjQ1kBjNERYT6DL7px7RYiuQlFpNgfF7jRmZRSyMLuY8b5sLP2k1HYGC05LzyNm6AjAqmqDZtsIIZifVcyfl13D6uqDvFy5n4PtjbTGI8QNvcu60tkfp2Yl3eag0OVndmbhqGoeCCG4ZNUMtu2qYMeeKr77s6e44uLZjC/LxuOxdwVd6bpJIBjl4OE6/vX0Fnbvr0ZRBJecPwMhBBuqKnlmf9JNICW4rFZK/f4R9+c0RgYTndrIVuZm3obbko9VcZMww+xt/RcLsr+IbkbZ3PgH8pzz2d50L1PSrseuprG+/qeUus/CoY28uE5P/PPVd0j3OEdEBjZvP8beg6mS65PK8/jSp85l3JjsPnEroxkOy0oyUVWly6IQiSaoqW0l7T1ISRwJRjyCSSk51tDCtuPVgODMyWVIJIFolOe27cM0JWdOLsNtt7Gvup66tgCzxxR07bv+4Ak0VaGyqY3544ooSDs1edUSukyBQgjSbU6uHjODS4qnUBcNUhcJ0BaPYpgmDs1Cpt1FrsOD3+ZIXU3pR5GttyIyHgPh7z6+KXnziU18/s6bcfmchNrC/ObWezjr2sVdcsTTM/L509JU89qor0xGkC2fQPh+BtbZg28qDSDeL2kQHcRnSc4YFuWU0hqLUB8N0hQNdQXUOVQL6TYnWQ436TYnmjh581t//fBa7VxUPJnzCifQEA1SFw7QGo+QME3sqka63Umew0u6zdmngpwQgk9MXMQtE7oDmIZT6a83VKEwyZ/DRF82H5uwkIZoiMZoiPZEFN00sSgKPquDbIebLLsbu6r1uRfpNid3LEkVOBmsVsNwoAhBgcvHlaXTuaxkKm3xKE2xEC2xSJKsmCZCJLMj3JoNn9VOms2J12LDogyueW5KSWWolcpgG3ovS1muw8N4f/+kSghBht3FNWNmcGnJVJqiQeojIdo6Ym6EALtqwWNJ9sdvdeAZRn8Gw5IF41i5Ygovvrqbbbsq2L2vmox0F2l+Jw570n0XiSZoaQ3R1BIikTBQFMH5Z01h0bykLsiCwiKmZucQSSRQFIVMp7NfknQapxYKFmZlfIyjgZeRmExL+xCGTBA1WjgefA2QZNonYUqdoF5DTXgTQqik2cafkvOHYwkmFw8v2wmS89FTL25P0ZvIynDzrS9eSGHeyNN7B0K634XLYaW9R8ZARXULkyfkD7LXe48Rk4HGQIj/eWYtl82djBCiK0py54lazpxUxu7Keh7duIsPL5+D12HnYG0jDquFwnQfEvjdC+u4duF0PA4bP3/qNX79wYuwaKdKRrK3YpnArlkocadRMuwVegKMvmIjQhFkFWXQ1pj0e7fWt+PLcNPeHERRFTxpLlShoKqnatCRYDaBHIZpP7EVEjvAdfOAmwghUEkO7hn2UyvP2vNjGo4GgU3VKHT5KXT5R3QeTVFPmWFXCIHHasdjtfdbWGmofVUp2L+zsl/hnKIxmfjTh+cWq65ooqm+b90Fq01j/JTBa0AMB1JK3qg5wg+2vJzsd6/jnV80cUAy0AkhBHZVo8Dlp2CEz2yksFo0PvPxs8jO8vLcyztobA5SW99ObX3fKHMhIDPDzYXnTuPay+dh6ajSFtN17t6yhbpgMhhxZm4e102fPuLshtMYGSQmHksh09M/wt7WR6iJbKHItRSnlsU474VYFTe6jKIJGz5LCUXuZXgtxehmGLvqP+nznzGtjLf2HOOyxVPQBnEpdyIcibO7V7G5D1w2/5QSAUiW3PZ47ClkoLqu9ZQd/1RhxGPrjhO1zBlTwLnTyrtuWFVLG1OLclk2cQyFGX7ueXUTAPlpXkoyUydhi6pw3vTxuGwW/rVhJy2hCNm+k48nOKUQABJpNAFRUNIBOxarxr3fegh3mov25gCqEuQvt/8Sly+Lj//iU2iW5O2U0gQZBuHsCvSRUna02bvcD8m2VpAhEG66HkeviF1pBkG2AXZQ0lKPSQIZfQGkAWaHH1xoJJXL3hs01bTwxB9e5MZvX4nNMfqo3v8khIMxfvCFfxKL9BU/+cL3r+CM8wfXw+jEc49s4pmH+ooZ5RSkcecjn0Y9BUWw/rRnPecWjuf68llYeum3D1aK+v2Cy2njpusWcdbyiWzZdpw9+6tpaAwQiSaJsd1uISvTw5QJecyZUUJxYTpKj5X/swcOUOzz8aGZM0kYBndu2siJtlbK0k7ODH0ag8OUcfa0PgRSYqKTY5+BJhxMTfsge1oeRAiFNOtYxnkvYlbGxzjQ/hQAdtXP1LQbTzona9qYXF7csp+fP/wqs8YVpBACr9PGggmpBXzqGgIEgt3+f5tN48wl40+5VVRRBG5XqihfS+vwaq1IKdHjOif213B8XzWtjYFhBTA6XDZWfXj5iBanIyYDihBdvuaeK0K3PakSpgjRb1R0J0wpMTtMleYwKsdJKTnW2ko4Ecdnt1PoHXmhm5FDQOQxZOx1MEOgZiE8X+OGr1+GaZiAjgw/hIy+DhgIxYISz0ZqVyKEBmYLsvWzCP8vQO0IW5JBZOttCO93QRubJAyRh5DR5wEbEE1aAZRMhPdbHeQASOxAhv6SJANSRzguQzpvTBKC2OokEYi9CcKBNE4ke29bCq6Pvgf3KYn2xgDP3bOG626/9L+GDFhtGitWTaelKUgoEKW5McCR/Unf40iijSdOL6KtJUSwPUooGOXg7ioi4Tix6PCCPYeCJJlSetmYqaOOX3k/oCgKpUUZlBSmc9kFM4nH9S4RGE1VsPZTVrZTiEkRgrHp6WS5XJhSUuzzDVvptKK1je3VtQgBK8aOwWn973ifTwVUYWdm+i2YMo6q2FBIpi7nO+eT45iBKXU0JRkQ6rOOYU7mrRhmHE2xIUYRsNgb6/YcpykQprq5nZ3HalPejbLcdBZMSE0jbWgKpMxhmelu0v3vTlErmzWVdEeH8X1LKWmua+OvP3iMjS/vJNga7ph/hkZ6ro+VH1z67pKBGSV5PLZpF49t2o2qCOaVFSFIpgR1QlWS6STvHKvmYG0jTpuVTI+L6cW5xHWDxzbuxuuw4Xc6SHMNHRz37TWvsLu+jlXl4/nJOX1ToU45ZBSZ2I3wfD35Z+BXyPbv40y7G1Ah+hxSvw+R+W3QxoF+ENn+Y1DTkLZzgQQktoPsmX6ld7R1MELjODLwa4T/l6BNgcQmZNvXEWl/ASUDOmRQZeRxhPd2UIsgthYZ/C3CdiZoJaBNQDgzkfph0EoRzg8nj62MfAWUtFIkU8cGK74hpUSast/63/9NsNktfOKrF2CaEkM32b+rkq985O4RH2fJ2ZNZfNYkTEOi6wZf+9hf2bdjaAGS4UIA09Lz2NZYxXhf1ogLVr3fSKbcCRzDIJk76up4fM8ewokEj+7ZzfiMTOKGzvHWVq6dOjxLzaaKSr7+3MuoiuDFj930npOBzuyHcDROKBInmtCRUqKqCnaLBafdgsNqQVOHF9djmjJlcda5YBtJf3qn4vaO50n5DStCJAWypOiWkNc6LJWmKWkLR2kPR4knDDRV4HYIvE5l2Nc0EC5ZMJmVs/uPP1D7iRkJ9FL983udI1LsOxkM5zr1uMH/fuEfbHgxWZtFs6i4vC6UYUzw3mG6KXtixGQgzeXgG5euYHdlHYqi4HXacNutXDQ7mR6R7XVx3eKZQHLlv3h8Scf/JxmNy2ZlenEuzcEwt19yRgqJ6A8SaAiFaIvFCCdOzWppOBCuWxCWyckeuD+DbL4RjFpQ85Hhv4H9fLAly41KdQzYtyBD9yJs5wzvBPoxEBawLkAIO1IsJVl61IIQli5NdOG4DKzLk+dRsiF4BxjHQCtBaMVAMVLxgJKFsM5KOUV7U4Dn732VKz9/Iaqm8OSdL+HL9HDmNYsIByI8fddqrvzcBQgBm17czuaXdmDoBtOWTmTp5fO7VvmRYJSn/vgS53/kTNY8+BaHtx/Dl+nlA1+9FE9aXyYdi8R59u5XyBuTzcILZ/9/SRo6r0lVBaqqYBtloZBO4qUoyY/9VA9GQgjOKxzPD7euZn9rA+N8mSkBs2XedOZnn3rhnfcDpX4/10ztR/BHJDMKhgMpk+MWJ69EOyIYhsnx+hY27DvBOwerqGhspT2UnDBNKVEVBZtVw+e0k5fhYWJRNtPL8ikvyMTnsg/4jT3w6jvsOdFdhvy8OeM5Y/rYfrftD82BML997I0uQuCwWvjKNWdis/Q/ddzx1DpqW5IxMLPGFnDV8ukA6IbJrqM1vLB5PzuP1tAcCBPXDdSOOaQsN4MVs8axdMoY3I7RCSk5bBYctuF/h72rXw41F50MIr3ciS7X0AWl9m89wqbVST2T6UvGc/EtK8gbk41mGToeQtXUruyb4WLEZEAIQV6al7w0b0q7z5lkfk6blckFyfzYuWWpeaKdboHJhTnYB3iZesOUksgw8+NPHSygdOaSC1A7rsOsBzUTjAqE88NdL6wQAiwzkNGnGXYBDbUAZDzpBtAmQGIboPbNLbZM7v4whCX5Tw7vHIqSJADnffgM7E4bj/3vc/izfJxx9UKqD9Xx2kNvcfUXLuSZP7/CM39ezcoPn4HVbuGpP73M0V0n+OgPP4CiKsTCMR759TNUHarF6XEwdkYptUfr+02ziUXi/PU7D3Ns1wm+eNfHh3cvTuNdxcb6E+Q43OxormFnr1LdZxeU/39DBnx2+7uewnyqIaWksrGNf76ylTXbDtH4/9g77/A4yqvt/57ZXtV7b5bce+/G2MZgei+hhNSX9Dc9Ib2S8qaHACGQAAFCB4MbNu6927Is2eq9b68z3x8rrbXWqlomkC/3dXFhzc7MzszOzHOec+5z3zYng0k61NPN6ZpmthypwKDVcMX0Qr537+oB1z9cUcfWo+fCfxekJYwoGHB6/Ly9/0w4GLAYdHzhpiUMNObuPFlJeX0bAA3tNm5aPBmnx8dj6/fx6u6T2KP06Ld1Oznf2MG24+eYUZjB529aQnFm0ogDAkVRsLm8HCqvo6G9mwUTc8lPTaDN5gy1upsi741e5crwuUbh/4wFfL4AHV2R4l1pKUOXuw9uOYUclEnLTeJrj3+cuOSxdzTti/dVdUMAH1k8A80I6hgBWcYTGFuJyOFBifJv0ee/i6cOQ5gUKEHo29alLgDDLSjdXwd1MSguhOUrIKVetOHoCV5Gq4HYZCvN1a3oDFpS85LpbO7G2e3i3PFq8qfk4HF6+ecjr/E/v76XhdfNBmDiwmK+svpHXPXRFWQUhI7H6/GTOS6Nmz9/NZJKQpbliBtTkgR+X4B//PBl6s428L9PfJKEtLjwOrKshKSbBWGyl6IoBPxBnHYPgUAQjVaN0aRDrRm6NU1RFLxuPy6nF1mW0ek1GM36fnXkwbZXZAW324fH7UeRZdQaFQajDq2ufzvhBxGyLIcHjsHO+4tTlyEroXXlHr5P7/rDtfr+L8YeiqKwt7SaR17YRlXzyJ053T4/k/PSLsORjQ3q27rpdnp45MVtvHOg7KKuI3rKkhcQCMrsL6vly4++wU8+upaJuakjeg67nR6+8be3aWi30elwEW81kZ+awPYT5ymvb+MrtyyL2F9igpmewwBCvgFujx/jGPOeGltsdNkixdLysofWQmisCnm6zFk1+bIHAvB+BwNCsGLi8KNSgIAcxBtFSvTywgfBWhSp5wcLVhLKEKQA2hAB0HcQ9NeEpTUV30FQ5YY+RxVav4cfoCgKBOvolzUInkeYHgTtIpDMIEbb0iIBgZ7Btk9vviQonJbL+RM1oMCkhcWU7q2goaKZ0n0VTFpUTGtdO85uFxPmXWDRZpdkYDDpOX+8JhwMCGD26mnhepV00SAiqSSef+R1dr9xkF9ueTgiEADYs7WUHRtPUjQxnZs+sgifN8CebaVsW3+chpp2fL4Aer2G5PQ4Fl05kZXropsiKYpCU30nW9cf4+i+83S02ZGDMkaTnrxxKSxfO5Ups/IGTKUpikJ7i52Th6s4svccNedbsHe7CQZltDo18YkWJs3IYdnaqaRnDS2H++/E7ndL2bnpFEISPPC5VSSlRp9tGHs6Bk4freb150KdCzfft4jC8R+sPuf/n9AbCHzryXfodEQOFAathqzkWHKSY0mwmtBqVLg8flq6HNS1ddHUacfl8WMx6lg+rfADe492Odz89pWd4UAgKdbEjMJMSrKSibMYCARlapo72Xumhor6tnD2ob7dxnf/vpE/f+5mEmOGT+h7Y99pVJLgD/9zA796eXt4eWF6Is9uPdIjKnZh/ZREKwajFpcr9F622T2Un29m6sSssbkA9IjV7TgTQSrW6zTkZQ3dztwrdX/xu/RyYdTBgKKEaClKD3v3ch2sLxjs56x2+aFGcT4aGqgRKPbfgG5BuHQgTB9H6f4muKegaEogUAbeLQjr9wABUgyos1GcfwXj/SEBIdeThIKEXigQbEbx7UXInT3bJaPoloA0fAWt0OEWgW8P+I+gCC0IC0KdgxCCSQuLOb23HLfdw+r7lyFJEqf3llN1soZ1H19JMBAMEUD76FhLUuhvv/dCeUYIMahvttPmpq0+pBF/5N2TrLx7ccTn1eea2fb2cVoau7jm1rk8+ZuNrH/xAD5fAJUqRDiVZYXK8maSUqysXDet33coisKh3RX8+WdvUVfdhgA0PazyprpOKkob2LX5NNfcNpe7P7UcbZRcpq3LxcMPPU31uRaCgVB2Q6NVIUkSfl+A6ooWjuw9x7tvHeNrP72VwgnpH9iXbWpGHDs3nyLgD5KTn8RtDy4dlPj56jN72L7hJGarnk98Ze2YHIPN48EflIk1hOrWDd022l1uks0mUi0XdOgb7Q5aHY5QR1BsTL+MhN3rxRsIIglBnGHgGrjL58fl9yOAOKNhUDKcoii4/QHaXS663SFRKa1ahVWnI85owKjpb28+0D6aHQ5sHg+SEMQaDKSYTWhUoxdXauyw8cNnNkcEAipJYvHkPG5dOpXirGQsBl1EHdsflLG7PNS0dLH/TA1ef4AE6wdLxa4vAkGZ1/acRFFgyZR8Hrp2IbmpcaikC0RBRVG4x+7imS1H+MeWQ+GOkfONHTzx9j6+ctvyYV/j45WNXDWrhIzEGDR93mcmnQaPzx/qNOmTvbWY9RTmJnH8dH34WF54/RATizNQj1DXfyC0ttl5Y+OxiGWFeUnExQ79u2WPC2V9mmva+k30LgdGFQwoisLp1laePXEMh8/Hstw8ri0uiWBs1tts7KipvuQDtHu9728wIIygX43QLUVx/BEUB6iyEJYvXdAH0C5AWL6K4n4R3C+EtAMs/wu6pT0vPw3C+l0Uxx9R7D8EYUYYbkBBBcIYyhT4j4BkAaRQS6AShMBm8LyFiHsUhAo0U0IZgzAk0EwGKTbykE0PoASbUew/AbQIw7pQtwFQOC2Xt5/cis/jJ3dCJpIkePm3b+O0uUjJTSLgD6DWqmg834I1wYIQgu42O44uJxmFF5csBoZWp+F//u8+SveV88cvPk1ydiJTlozvdwN3tjt46amdvPXiAUqmZDJncTEp6bHIskJtZSsnDlaxeNWkftspikL5qXp+8a1/0dXuJCsviTU3ziS/OA2VWqK5oYvNrx3m+MFK/vXUToxmHbd9dEm//ZgtemLiTCSlxDBxeg4TZ+SQmhGHRqumo83OtvXH2ffeGeqr2/nzI+v52eMPjJiI834hryiFcRMzOH20hk2vH+GGexYOSGTs7nRxZE+odjxzQRGx8WPTQvXItp0crmvgDzeu43hjM7/ZsZtOt5sEk4nPL57PVSXjePvMWX6zYw+tTidmrZYbJ0/koYVzI7xG/rBrH++dryTBaOTJ224c0Ifk5ROneObIMfRqNU/dfjNWfX8ilqIodHu8rD9TxoYz5VR1duHw+sJEPLNOS7rVwsLcbD4xf07UUklvkLHtXCVPHzrK2dY2XH4/EgKLTsfktBQenDuLKWkpI35Jy7LCn97YQ2PHBbEptUriwavmcvfKGRi00YMUrVpFgtVEgtXEtIJ0AkH5Axuo9kJRYEJ2Ct+9Z1VUoqMQggSriU+tm4/D7eVfO46HP3tzXyl3r5xJRuLw2slTYs2cb+qI6H5QFIVjlY2kxlv7EQQlSXDVFZPDwQCETIQ2bjvFmhWTLonMqygKTpeP3zz+Lu2dkXyBdaumDut3m7t6Ks/98i0ObjmFvdM5qg6BkWBUwYCsKHz73c0cbQr1VW86d47ihEQm9HEpPN3awjc3bxqTg3xfXcdVuYiYHwF6hG55qMVPiqFv7V4IFYp+bajFT3GBMIAwRRAKFc0sROwfegSF9KGAQHcFCB0QRLH9AGH8CBiuJ5TmVyBwBqXjLpBtIMUjYn8f2jYMNSLmVz376AMpLdSiKNtCQYS4cNMk5yTR0dSNJc5ETKKF7JIMKk/WEpcSgynGAAhW3LGIv333RT7y8M1odWpe//MmCqflkjdp+MQyIQk0WjVz106nubqN3332Sb7z4hfJLIqs+7U2dvPi33Zw832LuOneRZjMuohZgtvlQxvFg9vvD/LoI+vpaneSmZvId397F+nZCRHbzl1SzA+/+BzHD1by4pM7WLZmCqmZkb31KrWK//nGOlRqieTUmB6nuwv7mLNoHD/40nMc3l1B2Yk6Wpu6Scv8YIrVSCqJdbfP5fTRGhpqOjh5uIqZC4r6racoCgd3nsVucyMkwVU3zx6zQaTBZudsWztbys/xxP7DYWJwVUcnP9i0jXijge9t3IpOrcKo0dBkd/CXvQeYk53Bwtyc8H6a7A4q2jqwmb2DPu/tLjcVbR0YNOpwh9LF51rb1c2339nCvppagoqCWpIwa7Vo1Sp8gSBNNjv13TaMGs2AmQVJCDadreB3O/fiDQRIMBlJNBqxe3002GzU22wcbWjkTzddy6TUkRlX1bZ2seVIecSyq+eO5/7Vs4etxiqEGEPl1ssHIQSfXDd/0I4HCLn7ffzqeWw5Wk6nPZQtcXp8bDhYxv2rh3e/Xjd/Il9+4i0kIWjssHO6uomG9m5e3X2K/715adRtlswr4u8v7qGhqRsI+V78rsfKePXyiRj0Q2ePLoaiKNQ1dPKXv+9g577I3zkrPY7F84qGtc/cCRmsvnsR6/+2nT997Z/c963rSepx8rwcQeCogoGALFNvuxDVegIBarq7I4IBeJ8H8TFCaPbfo30grIOsJ0CYgOgzrNDnRqBPOqhHWVBR/KEgQnGFgg2hCv3buzukMSAZ++w/2ndG+S50oOovK2u06Jkwr4iUnCQklURMooXiWfnkjM8M31T3fvcWXnjkdZ58+HkURSE9P4UvPvrxcFlApVFTMrdwQEEhvUlHyewCJJWEJElc87Er6GjsZMPftnH/929F1eelFQgEmTAtmzs+trRfGl8IgXGAlpuyE3WUHgv139/5ieURgUDvtpYYA7d/bCnHD1XitHvYsekkt9y/uN++MnOjl2GEEBhMOtbePJvDuysI+IO0NHZ9YIMBIQRzFheTkGylvcXGm8/vZ/q8wn4zGkVW2PDqIQDSMuMZP2XsaqK9eHTvQe6bPZ3rJ46nrLWNz776Fu0uF99Yv4nZWRn877JFBGSZh15+g/Mdnbx+6gwLcrL7zxQv8TgcPh9feWsDh+oaUAnBsoI8rp80nrz4OHRqNS6fj6qOLnZX17C2ZNyAwUBAlvntzj2kmM08tHAeU9JT0avVdHs8vH7qDE/sP0ST3cGv3tvF47dc349DMxAURWHDwTI8fexyrUY9n7h6/odicB8pkmJMzCzKHNbglWA1snRyAa/uvmAPvvVoBfetmh21c+liFKYn8q07VvLs1sPYXV52nqoiKcbE/6xbwOJJeVGPwWzS8dE7F/GT37wdLlE4XV5+98S77NpfwVVXTGLCuDTiYkICVtEGtd4Sp8vto6G5i90HzrFh66lwgNELtVriY3cvxmQcHkFRDspc++Byas82svWlfZw9WsX0pePJn5SFNW5wvQGNTsPMFROGfV/CJVgYL8rJ5o2yMmRFIdlkYnzSwPrmJo0G7Shd3WRFodsbKQ4hCcHV2ROw+S8sF4SsiC8H7A4PtbUdTBgzwpUaYf4fFOeT4H0v1C4oOwA/wvptYOzao4QQfO4PH0XqqdNJKon/feyTETNic4yR+753K44uJ7KsYI41oVJHfv7dF7+IboCbODU3mW//8/PoewZytVbNR75zM35vIOoNu/aW2VHr+QNBURR2bT6FLCvoDVpmzCuI+mALIcgvTkWn0+D1+Dmy7xw337doxFF0clps+N9jpQR4uWA061i+dgr/+ttOju47R2tTFynpkdmQpoZOyk7UAbDi6qloB+F+jBYalcS9s6Zj0mpJtVqYlJrCwbp6mh1OHrtlAXnxcSiKwpXjCnl07wHKWtqi7udSJhCKovDisZMcqgvpzd85YypfWrqwHzdgUmoKa8cPbo4j92QUfn3dWsYnX2hzS7GY+dzi+ZS1trHtXCWH6hrocHtINA2vdq8osP3E+YhlCyfmkhL3AZNkHyMUZyWhj5LpiwYhBMunRQYDVc2d2N3efm2BA20/szCDybmp2Fye0LvMoMOoG3h2L4Rg+cJiTpTW89o7R8PdOYGAzP4jVRw+UUNivJn01FgS4kx4+wRxiqLw8luHEULQ2m6noamL+qYubHZPlO8JlQcWzR0+4fPw1tM8+s3n6WzpBgXqK5qpr2jueY/3drVFR0JqDE8c/BGS9jIHA5IQfHvpcq7ML8Tp9zEhKZnsmIHrOp+fv4CFWaPrZe5wu7n/1ZfxyxdSgioh8Y1p/cV9Llf1zNNjOTlWwYAQAkV/HUIzu6fLIBDKQqgzQYwtc1QIgb5PP60QIjxo94WkkrAmRHf8EpLAYB74YZRUUr/P1Rp12Kvh4n3lF4+8HersqVBdT5IEm984MqDMps8XDLcwdbTaB+z4lGUFh81NW4uNzjY7TrsHnzeAzxegsbbjwoof8PSWEII1N87k9ef24nb52PrWcW578AJXQlEUtm84ic8bCEkorx1evXKkyImLxagJBXiSEIxLSuBgXT2JJiPZcbHhY81PCAUq3R5PKIU/hsfil2WeP3oCgMwYK59bPD+q2JAQ/Q2bomFN8biIQKAXGpWKq8ePY9u5Slx+Py0Ox7CDAZfXR21rV8Sy5dOiB7f/CSjOTB7RueWnJaCWJAI973uX10drt2NYwYA/GERCoNOoSerjdyMrCsFgMIK42BdqtYpPfGQJbo+fTe+djjAfCwTkAY2yFAWef+3gkMclBCxfVMLH71k8Iv6RvctJaw8pezDydtTvHAXfYdRThFi9njVF/euT0TAhKZmSQTIHg6HL40ajUkUEA0KIYQ/8fn+Qg4crkYSgpq6DZYuLSUy0UFnZyrGTdZhNOhYtHIfb5aO6po1pU0NBy45dZ5kzKx+73cP+g+dJSb5QMggEguzbf57mFhuFBckUj0vl2PFaZs7IZePmk6xYPoHS0gbGFaWi1anxB4LotZG960JIocFfndnvmN8vyIoSEgFRFHRaNXrt6DUNhgtJEugNI/seRVHo7iHhuJxeHv/VhmFtFwwEQ/LKfe4WRVForO1gwyuHOLS7gpamLjwuH8GAHO6Q+aAHABcjPTuByTNzObS7gk2vH+aGexaEiYQBf5Ctb4XYzOOnZpF2EYdirJBkMkXc3xZdKOCMNxgi7IP1PW2OQVnupwB3qWi2O6jtCqVmrygqwKobWuVtMKwsGniQTrFcGGw8/uHroHQ63Dg9F1qMJSEoSB9h99CHCOkJA5daoyHGpEev0+Bw98ixKyFRosJhXKNXd58iLd7Cool5EcubO+08u/UIX7xxyYDbmow6vvCJlSQnWHh5/WGcrrERINJp1Vx95WQevGtRP5GjoTBtcQnf+cf/jOp7NVp1RIfYcPC+6AzERGH8DhcaSYVWpRq1FLHfH+Dxv27nztvnUdAj5dhtc/P0M7u54bqZlJU38urrh7l6zRT++eJ+Jk/KxOX28db6YyyYV4jRpMVo0PLejjJmzQzdZNt3nqW2tp2ZM3J54aUDPHDvYta/c5y83ERef/MoE8an8/aG4+TlJfH713ZRUd/GTUumsGr22DtiXQrsTg+f/e2r+ANBrlkwgTtXzrjs3ykYni53XygKYYMOvVHL1Nl5DCcPlJRijVhPURTOnKjjl996ibqqNtQaFdn5SeQWppCcHovFakBn0NLRauPZR7eN6Bj/nZAkiWvvmMfhPedoqOng1JFqZswvBOD82SZqq0Ip+atumj0sXfPR4OIZeG8t3qC5mBcS+v9AYcClPB2NNnt40jAj49JaQgWQETvwQNaXazCYMdvF6E1f90Kr7q+M958EywgHQK1ahV6rDgcDEBITGg6qWzpRR5kRy7LC9pPn+fz1iwd1ATUatNx/50KmTMzkuZf3c7KsAZ9vdIJ3kiQoyE3ijhvmsGR+EdphKu72RWJ6HInplyd4j4bLHgyohMCivYRgQKVCNwxvaojUmu77IjAYtCxfWhImUxw+Wk1nl5MTJ2txuXzUN3Ry+y1zSEm2UlPbTk1NB/PnFaJSSZiMOnJyEjl89EKb5Nb3SklNieHosRqCQZnGpm70eg2nzzSyfGkJpWcaCQZlutwe/rXtGEFZweXxsWr24HXK9xsBWeFsXSs+f5C5F8llfpAQyiaEBpuYWCNf//ltw3LjChEkL/zt9fj53Q9eo66qDYNRy8e/vJb5y8djiTGESXdCCEqP136oggGAqXPyScuMo6G2gzef38e0uQUIAZtfP4IclIlNMEXtNBgrDKRkKImRBX+Xkiuwey8MIPHGS+MPCSGG/d4ZCbwXDS5qlYT2P5A42ItoBkGDQZL6q2J6h8i8+PwBfIEg/kAQrz8YEUgoQHlDW+g4hnEbqlUSc6bnMrE4naMna9m66wynzzbS1uHA5w0Men+qVRJWi4GCvCSWLRjHgtmFxMcaP1ATwMFw2YIBSQi0KhV6tRqTZvTpZ5UQ4dTiUFAU+MemQ5gNOm5ccsGlTKuNFAfRqFUkJ1lZtHAcAsLys6tWTmTL1lIaGrv4n0+sGPBH1Os0TJ6USU52AosWjiMp0UJVVSv7D5zntpvn8Mzze0lJjsGg1WDQaXG4vSTHRa/H/xfDQ3pOApXlzdi6XHg9fmKiGCQNhZrzrVSeDZm2XLFuGqtvmBm1l9jt7K+f/kGHTq9h1fUz+dvvNnFk3znaW2yYLXr2bC0FYP6y8Zitl3EG+j6974Jy/3bCXvSdrQ+23nDxfrzEFT7oValLO7q+WZBhfZvSP9My1O+wp7SaF3ce52xdK7s1at4+eObC/oC2bgc3LJg0bLdGIQRmk46FcwqYNzOP9k4ndQ2d1NR30NjcTWe3C4/XjywraNQqzCYdyYkWsjPjyc1MIDU5Bt2HRNK8Ly5bMDA1NY1H110bcqW6BPMQSYgwMWkodNpd/OWNvUzISekTDAg0F0nTFhaESC2Hj1SjUklkZ8WTlhrLuKJU/v7sHuLjTCQkhGqCZWebOH6ilpZWG7v3VjBrRi7rrp7GOxtP4HB4cTg8XLV6CiXFaRw4VEl6eixdXS4Wzi8kMcbEDx9cQ3VzJyumF476Gvz/DiEE85aWsGvzadwuH0f3nWPJ6skjftjamrvD2aMps/KiBgKKonDy8KWLZb3fEEKw4pqpvPDX7bicXvZuO0NWfhLtLTZUKonVN8780L2coqHT7R7ws7g+2YC+rc8fJFzMywkEZXz+ABgujd9wueAPXhq3w+0bWXk3KMv4/JEic8YhOo9mF2eRGGPmiQ37MOt1TC/MCH8mgORY87DbG/tCCIFarSIlyUpKkpUZU0J8st5Wwt51hCRGVf68HFAUhb3vHMPvDbBo3YwRlQUvWzCQaDSyNDdv6BWHgeV5eeTExjI1dXBFvNPVzbi9kTefTqfmo/dFEkf0eg0PfeoKzpaHRJPy8kLkRo1Gxcc/uhSj8YKFpk6nprAgmYL8ZFQ97XiTJ2USG2OkvrGTnOwEDAYtJcVpfPJjy1GpJD72wFJSU6xIkmDxlHz6d7r/FyPFnCXFJKZYaWu28cyft1IyJYvktOheDr3iRXqDNmLAN/SpX3a02ftJfCqKQnNDF5teO3x5T+YyISklhpkLi9ix8STbN54gOz8ZRYHs/CQKRtHB8X6it9YblOXQizZK5lxRFCraOvp/0IMMqxWDRoPb72dnZRW3TJn4gXhB94XVpEclCYI9g4kvEKTT4SbBOjaKkGMNh2twEaih0NY9svKjxxeI0GAAiLcM3qlh1GmZmJPCqhnF+INB1s2dMOLjHA76isp9UP29FFnhie++hMfpYf7aaR+MYGDMIARfmL9gKE/AUAvVsfP9lqtUEuOKIoMIIQRWqyFMCOy7vCA/UjgpJzuBnOyEkK51n5shOzuB7OwLZhNqtTb8PYUFF9kQXyIURQnZPxOqgY9GEQsIn8MH7QU5HFhjjdz7mSv57fdfpeZ8Kz/4wnPcdO9CiiZkYDTpkGUFt8tLa1M3Z07UUXqshm/+4vYw1wBCg6LRpMPl9LL+xQNMnZ1PenYCkiTwevxUlDbyt99txNbloldTPxoURcHnDRAMyAQCQQL+IJ1tF2aitk4n7S021BoVao0KlUpCq1X3ezAD/iB+f5BgIEggEMTvC+LveREGAzKtTd3o9JrQftQSKrUKzSCOjkISXHv7XHZtPkVlWRNN9SEnvCuvmzEsD/R/J1IsoTKa3euj1ekkK7Z/q3Kj3c6Z1tYB9xFvNDA5NYX9tXXsrKymoq2dwsSED9T9Hmc2YDbowqQ4RVEoq20ZFlt+dLhIgGqEI3t9e/fQKw2CyqaOEenqt3Y78fQhi6skQdIwzYoWTszFH3i/fWw+WAgGZTxO74jLM/AhCAYEA5NQfP4A3U4Pbd1O6lq72X2yCoAOu4u395b2W1+rUbNsWkFU8lldSxcnzjcSZzUyd3woHdTS6WDv6WpKq1twerwY9VryUuOZWphOSXb//llFUdh5ohJHFM/u9KQYphYMX6dAURTabS4On63jVGUTHXY3GrVEVlIsM4ozGZ+dgnaIF7yiKLR0OdhzqprSqmZcXj/xViMzijKYVZwVIne9X8XeS4QQguVrp9DV7uC5x7ZRUdrAL7/1EjHxJgzGUDDgcflwOjx4PX4sVgPKRQ9EfKKFq2+dw0tP7aTmfCvf+vRT5BSkoNGq6Gx3UFfZhpAEn/zq1fz9j1tCOgVR0NXh5Bff/Bcupxevx4/PG8DVh2fw7F+28cbz+9Dq1Oh0GnR6DXd8fBnT5kY6dv7rqZ0c2lWO1+MP7ccXoK1Htay91c7XHvwrWp0abc8+UjPj+NL3b0QMwogunpJFTkEyleXNOOweDEZtVK+HDxrmZGXw2N4D+IJB/nbgMF9ethidWhUOyjrdHn6xbScO78AtX5Ik8fF5szhS34Dd6+Nr6zfx3VUrKElORN3TY64oCkFFocvtocXhiKojcDlh0GnITYnj2PnG8LLNh8u5as74Yde0R4KLyYl21/CY+RB6fxypqB96xUFQVtsS8oQY5rmdrGqKCFhizYZhZ00sPaWWwcoaH/Tn4FIR9AfxefyotSMP/j/wwcBgeOzNfWw5VE6n3YXT4w+ThiobO3j4r/170WMtBhZOyo0aDBwur+cHT21iXFYSc755J/tKa/j1C+9R2RhpfCGA+ZNy+e1nr496TH98ZTcV9f3V1dbOGz/sYECWFbYfO8ejb+zlfEN7WCazFxajjuXTC/mfGxaSYI3OVlUUhX2lNfzqhfeobIg8hxfePcqc8dn8zw0L0apVQ7J1xwJGk57EFCtqtapHPWvkUKtV3PiRhRSUpPH6c3s5e7IeW5ebzjZHSEhGLWGy6CmakM785eP7Ke0JSXDnJ5aj1qh4982jdLQ5OLrvHKKnWyFvXAq3fXQpsxYVceJgJccPVkZV6wsEgtRVtUXYkkqSIDHlQita7wBvJ1TjdkRRJWtt6qaxLjLtHdPHQCjQkzHoDTRkWR4yZavRqLjq5tn88SdvAjBldh4JySPr9f53YGZmBkVJCZxtbee5I8dpdjhYkJONQaOhwWZj27lKTjW1sDA3m11VNVH3IYCFeTncM3MaTx06wvHGJj724qvMzsqgKDEBvUaNzeOltqubivZ2YvV6/nHnLe/reQpgxbSiiGBgf1ktVU0d5KWOvWX2xa6G5xrbhz1T9/oD7DsT/VoPF9UtnTR3OoalNyDLCpsOlUUsm5CdMmwFw0BQZsfJ85yoaur3zkyNs3DHsmnDPu73E4qihH4TBIwi89sXfn8Av+//w2DAoNOQn54AJKCgsPdUNR5fgFizgelFGf3WN19UQ+6L3lR8h93F0Yp6Hv7rO/gDQaYXZZCaYEFRoKnDRlVjJ3PGD6ymeM/qmdS0dNHtcNNpd7P1SAWBEZBwFEVh86Gz/Ojvm3G4faQnWplRlEFKvAW318/pqmZOVjbx+q5TtHY5+OknrsZ8EflIURTO1rby8BPv0G5zodeqmVOSTUFGAv5AkNKaFvaV1tBpd0UECZcTV6ybxpwl4xAIYuNM4esRLbvSi2ifSZJg+rwCJs7Iobm+i5aGTpwOD0KSsFj1JCRbSUyOwWDSRn2otHo1y66dxqrrZlBX3YbT7kGjVZOYbCUjJwGTJWSo8vEvr8Xl9BCf2L8LJC7BzE8fe2BY/eXt7Q70ei0ZWfEoikJ9QydpqbGoVBJ3f3IFN927cFjXD0LB0FBOakIIklJjev4Na2+efUnua5cbvb+3Savhh2uu5Btvb+RcWwcbyirYWFaBEAJZUTBptdw7ezpXFY9jd/XAA5RakvjCkgXEG408c/gYTXY775SV805ZpGGMJAQLcwd+ji/XFRNCsHJmEY+9vS/cAuf2+vn1S9v56YNXY9IPT7d+uCjOjBR7O13dTJfTQ5x58NZLRVF47/h56tv6K++NBB5fgFd2nuDT1y4YdJBTFIUztS0cqWiIWL52bn/n04Gwp7SaHz23han56ZTVtZCbEo+CwunqFj5z3fCfs/cbNWcbefXPmzGa9dz2hbVhd8LqMw0c2npqRPvyOLz4PAEMo1C3/lAHA/eunhV+IQeDCjc//BQN7Tby0+P56Sei+bWLfjaWF6PL4eYHT28iLcHK529eTEl2MrqeyNTrC1Df1k281RT1BhVCsHbe+PALzuH2sfd0dUjlb5hobLfxi39uw+H2MWd8Nl+7awWZSTHhFKLD7eO5LUd47I297D1VzfPvHuWBtXMijicoK/z2pR2021xoNSq+dtcKVs0qDpcVXF4/r+88yf/9a0e/CPpS4HH7CPQwgdUaVUS93mI1oNKp2Xe2hldOlBGUZTITYrhyehFmvQ5FUWi1OXnv5Hka2m1kJFhZNrmAhB7y0Kaj5RSmJbDxyFkm56aRFmfh7dJyZhRksGRhyAUsEJQ5fK6Ot3acQyCYXZTJtPz0iDKTzxdg05ZTfPS+JaRlDWxAFBtvGtDmV61WDbptX+zaf56iwhSMptA5btx8kttunovJpCMu0Uwcw39qy842YY41YhzAMApCBKKNPQTIxJQYJs8aHolXkRWcTk+4k8xo0g2LfHTLlEnMy8lifHLkoLM4PxeLXkeaJTKYKk5K5CvLF2PWalEJwY4DFUwoTCMx3sy09FQev+UGNp89x9GGRrrcHtQqiZy4WFYU5jM7KwN/UOZry5cgCYF+gC4jvUbDx+bN4spxBeyuquFEUzNtThdBWcag0ZBqMTMpLYU5Wf3VPyenpfCV5YuRgJhBuqCyY2P5yvIQNThjECn2aEiNs3D9gon8Y8sFouru01X89J/v8tB1C0mONQ85cCqExHiCQZnEQWrqk/PT0Kgk/D3PeafDzd83HeLT1y5APcDv2zsw//aVHWOiEvn8e0eZNS6TOSX9jal6v6/T7uYXL26LyFKmJ1hZMCF32N+z5Wg51y+YxCfWzuMHz23mxgWTKclK5sUdx2jsGF1QoygKwaCM2+PH4/XjDwTpNcsUApITLZdscf7yHzax4R87AUjPT+Hq+0MOi2WHK/nLN1+4pH2PBKMKBhRF4URzM1a9jjSzBa1qYFLT5UTfdL9ADofzAoF6lIIhPn8Qp9vHbz5zPVnJkWx1o15LUebQssq924x0RqYoCv989yjtNhdmg5av37Wi3zFYjDruXT2Ldw+XU17XxkvvHeeOK6Zj7DOjqG7q4GBZyJhm+bRC1s4bHzEgmvRablk+lZ0nqth7emza6BRF4c8/W8+5M6HIfuHKidz+4AXbUK8/wI9f3EJpbQszCzPRqlRsO3GOJZPyMeuh3e7iy399i3iLkfGZSewurebtQ2U8cv/VxJkN/HXzAXKT44gzG/nus5uYVZhJnNnA9/65iX999R5Mei3/2n2cl3efZNGEXAJBme//czMfWz2Xq2eVIITA6w1w5kwjs/sQR9va7QT8MrV1HRiNoa4QSRK0tNqprmlDCIHRoGXC+HTqGzoxGnVUnGvGajFQPC6VYFDhbHkjDoeXcUWpxMQYQqSws024XD7sDk9Yevd0aQMTJ2Si6yk9KIqC3e6h7GwTeoOG8T3ffb6yFVlW6Oh0UjwuldgYIzW1HTzzzz2sWDae+DgTkyZmRHUka27o5Oi+EJF22VVTMAzTIa21pZsffemfyEEZIQTfeOQ2UqO4Nfa2VUlSiIi6urgwvLxv+nlWZjoz0tP6PQN58XE8MGtGePtDx2tIT44hsWc2lG618JFZ07hLmUpAlpEAtUoVDoY1KhUPzJk55PlIQpCfEE9efByyohCQZRQlREhTSdKArWDjkhIZlzQ0mS/NauFjc2cNuV40CCG4f/VsDpTVUlYXIkQqCry1r5Sy2launT+BmeMySYo1o9eE+tWDQRm3z0+nw011cyfHzjVw5Fw96+ZN4M4VAyuHZibGMjE3laPnLsy4n333MELA9QsnkRxrDgcF/kCQDruLXaeqeGrjQRo77GjVKgJBedQZRJUkcLh9fOtv73D/6jksm1pAgtUY/k6PL0BZbSuPvrUn4hglIXhgzRzMgwS+F8Ph9jKvJBuNWkWMUU+nI5QVvXL6OD7zp1f55NXzh8VdUBQFny9AZU0bR0/WcvpsI02tNhwOL/5AgF4qkkoS/P7Hd5CcdGlluJSsBDQ6ddhV9mJodGoMA7i5XgxZVnB0uUZ1HKMKBmRF4dvvbqHN7WJycgq/WL0G80VypG6/n3Z36KDi9IaohiEfVFw1t6TfIPx+wBcI8u7hUDpzSkH6gMeg1aiYPzGX8ro2Wroc1DR3UZJzoYNhX2lNeMZ/3aJJUQmYapWKq+ePH7NgwOX0smdradhDoGhCZJlm/9ladpyq5LHP3ExhWiIC8AaC6Hqi6hd2HkdWFH50z2oMWg1un59P/fEVXth5nE+smYuiKMwoyOC6uRPZcqycheNzuGJaERuOnKXd7sIflHlsw35+8pE1zC4KWfSmx1t59J29XDmtCJ1GjYJCZ5eLt945xi9+chsAh49Us3N3OYsWFLFh8wmuvWY6xUVp/PHRd1m9ciLvbDrJ7Jl5jC9JZ/2GEwT8QXJzErHbPRSPS2XD5hO0tNhJTrawcctJvvyFtZw8VcfmraeZPjWbo8drmNpjGezx+Hn6mV1MKEnDYjHg9wf58+NbmTghg6ambs6fb2XNqkn8/Fdvs3L5BNRqiS1bT/P1L19De7ud5pZuurtdqFRSVFa43xfg2b9sw+30YrLoWXvL8HzgAU4drg47GyLA5+3PI3E4vby28RhOt5fEeAvXr5rK8TN1tLY7qGnoQAjBbdfMxKDXsn7rSRqau1CrVNy0djoxFgMt7Xbe3noKl9tLXlYiq5deaAGzOzy89e5J1q2cjMmoQy36K9GNBr2mRCNVwrvciDUb+N69q/nq429R3dwZXl7R0MavX96O1agn3mIMlTaFhC8QwOH2YXN5cLh9YX7UqpmDs+dVkuCT18zn8396Ldyy5wsE+dvGA7xzoIy8tHjizAZkWaHT4aK2pYumTjvBnoDvgTVzeG33qVHNrFWS4PZl03j+vWO021z86qX3+OfWI2QnxxFrNiArMk0dds41tGN3R2ZPr5heyNUjKBEA5KXGc7qmmdUziynJSmbDoTKm5qdT2dwxbBGqQCDI0ZO1vPzWEY6frsPmGJhwKfVpEY0GRVE4eKya5tYL1y45wcLs6bkR53X9J1dSODUbjU7DpHn9VUIXrZvJLZ9dPazjd3S5+OYtvxnWuhdjVMGAw+ejsqsThy/E7NVGmYUfbGjgG5s3AfCtpUtZXXj5pFDHGvMn5f5bMh1tXU5ae2SBk+PMlNUO3EbVG1krCtS3dYeDAUVROFkZ0k/QalTkpw+czh6XmYQQI283ioaacy3YBpE03nOmmnEZSRSlJYavrV5zYYa850w1C8fnYtCG7EaNOi0LJ+Sy49R5PrFmLgAZ8TFoVCoMWg2ZibFoVFIoBRoIUtXcgc3lYW9ZDUfOh2YYTZ12mjvtONxedBo1ep2GuXPyefPtY+HjkmWFqZOzuPKKiaFZf0ULeTlJuFxepk7J5mxFM5kZcUiSwO8PMGt6LnNm5/ccN6x/+zhrVk9Gp9VQX99Fa5udLdtOc8O1MygqTKHiXAsQGpRmTM/hny/uCzMN6uo7qa3rYMa0XBISzGzdXsrqHub/ddfOQCUJtm0PlVRmTM8lMcHCogXjwoJY7a022pptaDQqHHYP2zecYMubRxECrr1jHqkZw9M1VxSFve+dGXK9U+WNtHU6uOmq6bi9foSA6roOTp5t4P5b5vPmlpMcOF7N0rlFFOQkMW1CJpt3nmH3wXOsWTaRP/9jO4tnF1KUnxJBvnS6fDz54h7mTc8btATynwQhBEUZiTzy8Wv45YvvcfBsbXhgUZRQCWC4mvxDfc+scZl88pr5/PnNPeGAQFGgscM24CCvVknctHgK9145i+PnG0YVDMiKwtXzJmDUa3l60yG8/gB1bd3UtQ3crigJwaLJeXz19hXoRqjnf8W0IjYfCU2m5o/P4enNB/nU717C7vZx7bwJg3ZrKIqC3enl6Rf28Nam42NmVFRT18FvH98S/jspwcJTv7s/wrTIaNEzZ9WUAfeRXZxG/qSsYX2f0+YetUX5qLbqcLvDxkHpFkuEK1kvPAE/9fbQDTRak6F/F4YSubhcaLc5wzP6d/ad4d3DFQOu6++j0uXyRN64vQGFQavBNMjL1WLSoZKkMeENHNxVPmhQYfd4MQ9CjvL6A5j0kaQ/k16L23dBD7yvyUhf7ocCuHx+JBGK1H09vcbxFiP3rZwd5nwMhNg4Y4/amERQljGZdKQkW3nhpf1oNCqKewR7BIKYmAvdG7IsEwzKWC16dFoNH7l7AXFxRvy+IJqe79TrB/5ufyCIXqfBbNJhNum47+5FCEKiWBq1qiftToQibN9LfGTvOf7007dQqSR83gAetw8hQgJNN9+3aNgBrd8XoPTo0KzxcXnJ7Dl0nlc2HGXJnAvB/ZypuaSnxFJSmEJbhxOfP8iZiiYcLi9NrTb0Og1+f5CGpm4WzymMrLEKeOHNQ2SmxTFras5/XOuX3eXF6fGRGt8//SuEoCAtgZ997Go2Hizj9T2nOdfY3k84Ldp2FoOOgvQEpuQPLSYlSRJ3rphBSqyFpzYd5Fxj+4D9+CpJkJUUy+3Lp3Pd/InotGom5KSyp7Q6/N3DhaKEyJEPXjWXnOQ4nt50kPNNHVHfN0JAcqyF6+ZP5M4rpofbBEeCovRECtND2i9xZgM/f/AaDlfUE2c2MG8AvkIvum1ufvb7Dew+UDEmkyMIXavF84p49O/b8XhCv2lLm51jp+pYMLtgiK0vID5l+JwUtUY1Yrvj8Laj2cjm9YZrSInGD48Rw3AxFMnwcsHdR3lLo1ahG0RHQKdRYe6pB1882PkDof2oVNKg6VG1ShqT3mZZVji4q3zQdfKS41l/8Az+oBzVmCU7KZaKxrZw3VlRFCoa2shMjOnD7B74WFNiLUiS4NZFU0iPj17D6+p2UVXdhsvp5WxFM9k9NfFerQXR+58IrTthQgYWsx6n04uhxxK47yFIkmD+vEKam20UF6dht3vQ6zTMmZPPxk0nmD+vkNKyRqZOycbrDVBd04bT5aWiopmcnEQyM+LQ6zXIioJepw7LZg90lgnxZo4crSYrK55xhamYLQZi4kx43D40WjUZOQnMXVrCdXfOwzjMGiNAY20HbS1Dy/daTDo+dc8Squva+e2TW/n5N24E6HPcociloqqF0opG/ufeZWzbcxa3x4+kklCpBN12D/GxRoJBOcT5UWDl4hJ2HzzPyTP1TC7J+I96n2w+dJZOh5sHrpoT9XMhBFajnpsWT+HKmcWU17dyqqqJc40ddNiceHqCYa1ahcWgIyXOQkF6AuMyk8hOjh1294FaJbFq1jhmF2dx7HwDR881UNXYTn1tG9ZEC0aDjvR4K9MK0plRlElK3AUS49Vzx5ORGHqmNCrVkPomfeH0+NCoVVw1p4Q5JdkcKq/jcHkd9W3duH0BNCqJpFgzk/NSmVOcTXZybFQuzHCw4XAZNS1djMtIIisplqQYE+vmTkAlDS605vH6+dWjm9m1f+DJFzCqLGpivJmivGROlF7Qa9iyo5T5s/KHvM+t8WYKp2aTljs0T60XKrUKnV6LfxTt4qMKBvzyhagyWongvxgd+g6Sd105g9Wzi4e1XfxFvcSanv0Eg/KgSlSyPBLz1YFh63JSc75l0HVWTivk2feO8OTmA1w5rQiVJNHcZackMxmrUc+ti6by7Wc2sLu0moK0BM43tbO7tJqH71g5rGPISYplWl46v31jF/evnIVJp6XL6abb5WHRhBBh0Nbtpqmpm9WrJlFV1UZyooWS4rQwETUnO5H4eDPnK1tJSY5BrZbo7HTy58e28q2vrWPhgqIIspAQgttunsPe/ecor2gmNcWKEIKli4oRCGprO7j79vmkpcaGCEnVbVx5xUSaW+1YrQYK8pP51MeXc/BQFcGecoVareKG62aEMgJCcMN1M8MZkbvumMfuvRXU1XUwrjCVmQsK+fkTD+Dx+EOzRasBs9UwYuLq0f3nwxbRg+FEWQOHT9agVqnITItDo1Gh12nC9qzanr/jYow4XD42vHeaxpZucjMTUEmCG9ZM47HndpCcaCXGYuDGNdMwGbXkZiYwcVw6f3lmB1np8cTFXJ7MnMvrp6Gtm4Ke2WNtaxeyrJCTEiqnVDZ2kBJvwaDT0Nbl7Ck9ebEadYzLTCLGpA93rZxvbCcnJY6K+jaaOuzEmg2Mz0nGqAsNzk6Pj/ZuJxsPniUzKYbS6pBBVrzVSEoU0zIhBDEmPbPGZTGzKDNEegzK4Vq3JCTUKik8URlNwCSEIN5qZPm0QpZNLaDuXBPfvv7n/GTjt4lNsqJRq6ISK3NS4sLXaKTozQIIIUiMMbFq5jiunDkOfyBIUJaRhECjVvVTRj3b1UqywUysbvjuk5mJMZTXt/HWgVLsLi8qSZBoNZGflsDk3NSo/gSKovDaO8d4b/fZfvszm3QU5CZRkJtMekroffCnv73Xz3lyMAgBc2fmRQQDx0/X4/cH0Q6RsZy5fCIT5xViGIHFtaSSyJ2YgdvhZaS3yKiCAa10YdCyebwjkpv8LwZGgtUY1i13un2jJjH2thp5fAGcHh+GAYw+nB5fRO12tCg/3YDbOXiNLSsxlu/eeSVPvXuI906eRxICnUbNj+5Zg9WoZ2ZhBp9YM4/HN+4HEXpIH7hyNvOKQ73gGQkxGLRqEKF9heqJguykWLRqFWqVxMO3r+SP6/fww+dDNTqVJLFkUl44GLhYQhogro/7YWpqDKnEsH1nGbGxRsYVptLU1B0eXKdO7l+30+s1LFtSErFMq1WzckV/ffTVKyf1W5aeFse110S+aJcuDu1PQMS+U5JjuOHaC0x6SasmcQQpxGhQFIX928uGXhEYX5hKrDXULZGaFINaJbFgVn44+zS5JINgUMZs0vHZ+5fjdvtITrIS7OlQWLGghEnFGTicHuJjQ9f95rUzMJt0aNQqPnXPUvSjTHEOB+3dTr7y6Js888270GnU/ODpTXj8AZ78yu3IisLXH1/PTz62lgSrkW88sR5FAZNBS3OHHatRzy8+dQ0xJgNOj48v/PF1lk8roKy2Fb1WTU1zJ1MLMvj2PSvRqFW8s/8MO05UcrKqibq2bmpbuwC4YnoRtyybOuhxvh+kRyEEBBXaq1vRqVUjrs9fyvcKGPL7yrvb0EiqEQUDU/LSmZybhtvnp6HdxqnqZg6creWf246yNzWOmUX9W0nbO50889K+iBZKlUpiybwibl43k4LcpHBW0OX28fgzO0cYDAimTYx8b7R3OGjrdJCeEjvothqdesQpfyHgS394AEWWUY2w5XFUd0CMXo9KCIKKQnlHO+6AH6Pm30/86R02FfhQBijJcWYSY0w0dzo4WFZHUFZQj1CtTwjBxNxUNh44i9cfoKqxY8A+5IuVCUcDRVHY/97Qg4kQgoXjc5men0GHwwUKWI16rD1EGpUkccO8iSyblI/d7cVi0BFnNoRLBg/fvhKjTosAfnj3akwGLSpJ8LP7rsZi0CGEIDXOwsO3r6Td7sLrD2DWa4k1GcLHGe2YomH2zDycTi/7D5zHaNTysQeWRl1vLDCS47occDm9VJQ2DL0iIevuvKzItjtLn1lLX/JfapR2K0kSoeV9PusNCgASBrGlHovrlGA14vD4aLc5MRt0tNtdBIIyXQ53D5veTVKsGaNOwxdvWUpGohW9VkNDu40Hfv48h87Ws2J6YU9fvIvKxg5+/OBVWAx6DpfX8aU/vc79a2aRl5bAVXPHs3LmOD75638xf0Iu960OtSGOJMUe7Rq4bG7snQ70Jj3WBEtEFsjj8iIAjU5DV6sNv9ePNcGC3qQLP0c+t4+uVht6k45o9sSyLNPdasfn8WGJN2Mw68PXWVEUHF1OTDFGfB4/3a02NFo1MUnWIQeeVreDHY1VSAKWpRcQo9Wzo7ESjaSiyt7BlIQ0JsSlYPN52dFYiSfoD086ZUXhYEst520d5FnjmZ2cSaWtE0kI8qzxBGSZ9xrOkSli2X2qivON7XQ63KhVEmnxVj51zXzGZ6dEvZ4btp6iq/tCK54kBLdfP5t7b50/ZjbEGamxaLVqfD1BRCAoU9/QNWQwMBoIIbCOwt4dRhkMJBmNxBkMtLlcNNrtPHboEB+ZOg2rTjdgDXosxCtg4BeAECKcrrS7PCPSw/6gQKdRc8XMcTy7+TDlda3sPlnF4il5A57zQCp+cydko1aFiIGv7jzJ9HEZ/WYZQVlm/b7+/g3DgaIoyEEFt9tLV7uTw3vP9VvH4/HR3hKdgWwkdCwBp4+OPhkFlUoiLt7Uj8AphAgP6hBqy+pFXyU1IQRqlUSS1YTD5qa1vpszDdW0NnbR1eHE7faBoqDRqjFZ9MQnWkhOiyUlI5aEJCvanoffYNCyZtXkiP2OBRRFweP209bcTVNdJ82NXXS22XE5vQQDoRq63qglJs5IfJKVpBQriSkxxMQZ0WjHzh9dURT8viBOh4eTh6uwRelL7upwYrKMjEFuMuvRD1PXYDjH6PX4aW7ooq6qjaa6Dro6nHi9flQqCYvVQHJaLBm5iaRnxWONMSKGKI8YdBrSE6xUN3di0mvJSIhBIVQeEAKSYk2Y9FokIRiXmUSn3UV7twtvIIjFoOvnwHfj4skkxYQ6O6bkp6FRq2mzuchLS8Co06Bo1agkCZ1GhXUEqd6Brsee1w/y6u/fxu8LIIAF183muoeuCpNVNz21DafNjaQS7H3zEF6Xj2nLJ/HgT+8CoLmqlT998W90tXRjtBiYsnRCxD3ldft48Zevc3jTcRRFQWfUccfXb2DKktB6fq+fH97+az7ynVt55bfraalpJRiQ+erTnyG7pL/iay/8cpCfHtnKqsxxOPw+fnH0Pb4/ZxW/PLadG/ImkWOJ5UeH3uWvK25Fq1KRYbbyRtVp4nQGMswxHG6t55XKk1yTO4G3qktRUIjTGvhb2SF+PHcNVfYO3qk9S357HP987ygLJ+SyZlYxk3JTSYu3olVH18GRZYXN2yPfgdMmZXHvbfPRD2GbPBKYzXosJh3tfTIKtY2dzJ6eO2bfMRYYVTBg0GiYn5XFG2VlKMAf9u9jy/lzFCcmEqsLRZLVXV3h9V8vO0PpIG5jw8XE5GSuKxkf9TMhID89gcrGDmqaOzlUVsfM4sxwcKIooQFQ3WNDfLnQO0ArhKLsXsiKEq6RhY43uoLhXVdOZ8vhcpo77PzkmS34/EuZMz47zLSXFQWfP0CH3c2ZmlAdcuXMcRH7yUtLYHpRBgfO1PLu4QrmTczhypnjwrMSjy/ApoNn2XWicljn4/cFcdrddLQ5aKrvpOZcC9XnWnqIZ7aohj57t52h7Hjd8C5aD1Iz4/jBH+8dca0LQA7KtDbbKD1Ww6HdFVSUNtDW3B0eaKNBCIFWp8YaaySnMJn5y8czb2kJ8UmWMb1HFEWhqa6T3VtL2b+9jNrKVuzd7hDJZ4AYWaWS0Bk0xMSZyMxJZOKMHKbMyiO3MBlDz0xvOJBlBY/bh63LRVtzNw017VRVtFBb2UpzQxftLbb+pCgFfvGtl8LEwOHirk8uZ8U100a0zcVQFIXONgd7tpWyc9MpqsqbsXe7CET5DSVJYDBqSc2MZ9bCIpavnUp2QdKABLTerNnp6mZUksSUgjSEEBypqMeg0zAhJwVByHb3z2/sobalC5NBi1atosvp7jehSYm7oB4pCYEkGJVb3HBQU1rP7x56nPt+cDuTF0+gtbaN//vUX4hJtLLyniUIIeho7mLz09u58iNL+Z/fPBA26hJCIMsyf/7fpwgGgnzxsU8SDMj89RvPhrkiiqLw9hNb2P3qAT7zhweJSbSw69X9/OKBP/K7vT8hNsmKLCucP1rF37//Itc/tIb0glQ6mrpIykwY7NBp97g43t5ISWwyQUXmZEcTfllGI0ncmD8Ji0bHX88cwBPwE6szMD0xg+NtjeFH453aMm7Mn8zs5CwsGh0vVBzju7NX0eF10eZx8mZ1KTfmTaJgfALFmcmU1raw/WQlGw6VYTXqyUuNZ3pBBjMKI8mpXd0u6houaDwIAXfdPHdMAwEIcbgsZj3tnReCyeYBJkr/TowqGBBC8PGZs3mvqgqb10tAljnZ0sLJlugksveqqnivqupSjhOAa4tLBgkGBDcunsz2Y+fx+oN86/G3WTQ5j6Q4Mz5/kG6nG0kIvn73FZclYyArCm/sOkVzpwOnO9ROZHN5w21CR8rr+e6TGzHqNZj0WswGHSXZySyYlBuxn5Q4C9+8ZyXfe3IDLZ0OHv7rBgoyEshKjkWvUePy+mnvdtLYbqO128mty6f2CwZUkuBzNy/ms795lQ67i5/+YwubD5ZTmJFAIChTXtfG0Yp6phakU9HQTodtYMUqr8fPz7/+L+qqWulqd+JyeqK+mC+Gy+HF5Ri+DDMw5MwuGny+AOWn6tm2/jgHd5fT0tg14OB/MXpnn61N3bQ2dXN4dwVvPr+fOz++jAVXTIhqaDVSuF0+3nn5IK8/t5emus5hZ8iCQTl8DRtrOziw8ywms5784lRuvm8Rc5eWDL0T4Oj+czzzp3dpa7Zh63Lh8fj7uTlGQ0tj17D23xeOQQRahgO/L8CuLad54a/bqSpvHnJglWUFp8PLuTONnDvTyOY3jnDVTbO57s75mK36qAHT9MJ01u8/g98f5L41swF4fP0+Ykx6lk4J6Uc8/tY+Tpxv5CcPriU5zoxKkrjzR8/029f7Wc7Z8swO0vJTuPKepUgqibT8ZK56YAWv/v5trrhrcdjJUlJL3PbV69Fd1FJs73ByZMsJfvTm18mZkIWiKFz30FUc2XoSCAXTb/55I6vvW05ST5fN3Ktn8uyPXqZsfwVzrw6pHCqKwoLrZjP36pkIIcge378OfzEEAotGx7yUUHvfsvQC1EJCLVRopAudKAP92lpJhV8OhiYmchC1FOqSujZ3Am9Wl3K8rZFPTpiHTqVm8aQ8phek09hh53RtM4fL63hxx3EOldfxx4dujNhvc6stov5vMRuYMG7oVs2RQggwXpQxszvcQ24nB2W8Hj96Y3SflbHGqFkjE5KS+P7yK/j5rh002IduS3o/MKskiwevnsuzmw/TbnPx2q5Ik4fslLgBZ2JCiB5G6+i+W5YV/rp+Pw1RjD0kIWjpdPDOvkhhlzVzS/oFA0IIFkzM4ZFPrePRN/Zw/Fwjp6uaOV3VHLGeRiWREmeJKo8shKAkO5nvPbCaX7+4narGDnYcP8+O4yGZWq1GxYKJuXzlzuV8568b6LK7BzzvgD/Ikb0VuMdIhGOscWhXOb/81ktRXQFHCllWqDzbxC8ffpmOVjvr7pg76jYnALvNzR9/8ibb3zkxJkRNp8PDiUNVXH1r9Da1aGiq6+TUkUtznns/4HR4+PsftrD+Xweiqh8OB+0tdp55dCtnjtfymW9dS3J6fwJuUWYSdW/uRVYgLy0eRYHmDjsddhcfu3oeAGfrWynJTiYvLTQo1rV29ysRDBcatQq7+wLJerRcpurTtWQUpYX9IoQQ5E3O5sVfvoHfFwgP/kmZCWj1/We2ji4Hfm+ApOzE8PbJ2RcEwLxuH+0NnWz+x3b2vHkwtJECBose90UDV+7ErBGdQ6LeyMT4VA601BKvN6KWVIyLTYpQmFT3SERHw7W5E3iidD9dXg/vNZzjjqLpCGBJWj73b32epekF6FRqth47x1sHSsNiTbEmPTnJcXzxhgIKM/pLTPedqQMkxZsxXCbRK81FnAr/MCYsteVN/PGrzzJp/jhu+ORKzLGXV/9m1MGAEIJ1xcVMSE5iQ0UFhxoaaHE68AaDoEC310ObKzTjTDaZsGhHLiJxMVLMg5u6qFUS9181m3kTczhwpobali58/iBGvZbUeAvjs5MHbLuaUZTB9x9YDQKSo7T+DAVJEnzhliVDCob0RXpidCa4EIIpBWk88ql1nK5q5sT5Rupbu/H6A+i1GlLizRRlJDIuK4mUKGImvfuYPzGH/PQb2HOqijPVLbi8fuKtRmaOy2BWcRYGnYZPXbeAhrZu8tIHTvUJIQa3cRsopB/hO280L8nC8elDfo9Wp0an16DVaZBESEnQ7fLh9UT/rTwuH0/+dhPZBclMmzt0P3A0BPxBHvvF22xdfyzq9RECdHoNOoMWjUaFQBAIBMPWxwMFD/FJFmYtHL6ap+gVTxgIg02+R/r7jdLrz+X08ocfvcHW9ccHJArqDRr0Ri1qtYpgQMbj9uF2+fqtr/RoXvzkqy/wrV/e3q/bIjnWjMPtI85iIMakByWkk9Dt8ISFgRZMzOWl7cd5YdsxFGDP6SriLMNntffFgok5vLzjBEkxZtRqiXEZicwuGdgtcSAYzHo8zsjOLY/TG9K07/NOkwboqVepVSAg4LvQFh4MBOm9ASRJQqVRcd1Da5iyJLITJu4iottIA2SVJPHV6cs41FqHK+CnODYJAXx+yuIwSfChSQsxqS8MxDJK+H4qjk3iYxPmcs7Wzr0lsyiJDamtmjRaLBod1+SEZIv1WjUrphaSmxJHcqyFGKMuPAhHuyaei97VRqP2srlVXvw8a9RDX8NjO85wbEcZ50/Wcd3HV1ymI7uAS+onEUJQGJ9Awex4fMEg7oA/7I71dvlZvrttKwCfnj2Hq4rGDbarYUGnHvpwVSqJSXmpTMxNQVFAQRm0Tt+LzORYMpNjR31skhAsm1446u0vhhACk17L7JIsZhVnhs+ltzVnOAOUEILUeAsLC7NYMTk/1IN+UT/v1MJ0phamD7gPnV7DJ796NYGLFMuqz7WQlhmHSiXx5G824XJGlgQmzcxlxdWDt1BdDLNFP+LMTGKKlSWrJ7P+xQPhZVqdmtTMeApL0iienElmbiKx8Wb0xhA5zOcL0Nlmp/R4LTs3neJ8WVO/QcXj9vHX/9vAL5/6GNpR1BAP7i5ny5tH+w22eqOWaXPymbWoiOz8ZKyxxhBxkVCQ4rB7aG3qpvJsE+Wn6qk+10JnmyP8MlmyehImy/DJaBOn5/LZb1834Ofny5p48/l9/Zbf/akVxCeNLCieOD1nROtDSAv+qd9tihoI6A1aJs/KZd7SEnKLUrDGGlFrQsGAvdtFZXkze7eWcuxAZb/A7szxWn77g9f5xiO3Rbhn6rVq7r5yBrHm0LOAgLtXzsDm9GLUhaSw71wxHZNey+nqZqxGPQ+smUOXw01cD7FVp1Fzz5UzI7p01GoVd14xg/SESB2KO6+YgUmvo6y2Ba1GRUnWBQ+RgaAoCqXdDRjUWvLMoczfnLUzeOrh57F3OLDEmwkGgux+7QAT5o1DPUS/OkBsUgyxSVaObz9NekGIWX9q15lwyUhn0FIyp5DSveWsvGdpmJTocXrRjQEp1KzRsTQ9UnVvVvKFEsOMpBABcV9zDa6An4MtdSxKDbUECyEYF5vEuNgLWdA6RzcHWmpJNVrJtsQCIQnikQTuF5cBLxffQ1GUfh4HscPQ0jh3ohaA3PEZWEbZITASjElzqRACnVodMVgXxF+YaVp0OpJMl/9kLj6m0H3x4eooiIZLPZddW0vJzk9i5ryRBysarZpV1/d3RfvFd19h7tISEpIsPPPo1n7BQHZeEqtunEGHz06izookBo+EvUE/XnnkstVCCK6/az5b3zpGTLyJmQuKmL+8hPziNGLiTAPOlHIKkpk6J581N8zixb/t4LVn9vSL3s+daeTMiTqmDNMGuBeyrPDK07v6cRdi4kx89uHrmL2oaPDugKmwZNUkfN4AbS02yk7UsXdbKWUn61lz46wRvfCy85PIzh9YwWzP1tL+wYAIfX92wdAD16VAURR2bjzFWy8c6BcI5BQk88DnVzFtbkG4y+NilEzJ4oprpnFg51n+8sjb/XgOB3ac5a0XD3DjPQvC2/cO0H2xbv7EiL+Nei23L5+GooQyKxd/t0Gn4VPXLohYplWr+Pg18/odo0mv5Y4VoX0xRIKtF86Aly8ceo6Z8bn8ePrNAMxfN4v3XtjNzz7yO6Yum0hdeSMVRyv55rOfH9b9oDNqufmL63j2Ry/RUtOKIiuUH64MBxJCEtz/wzv42Ud+x88/8juyJ2Ti6HLSUt3GV596CEM4MzLMkxglBIIur5v7S2ZTEDNwttInB1AJwf9OW4JKXCidjAQxF2V7um2hFlPVCNu5h4LT5aPjopJEVsbQFuiuntJnWl7SB5szMBQSjcYecdLLD0VRqK/vpKamnZgYI8XFqaP2mHa5vOzaeXZQ2UmDUcuiReNG/QOdOllHfX3noOuMn5BBVlbkDaMoCnabm1NHa1CpJCbPyMFg1NHZ7sDj8VNX1UYwKDO7J4188kg1TocXe89NrigKDbUdnDvbREKShfGTMpFUEmWn6omJNXL2dAMZ2fEUFKfhdvk4cbiKQEBm8vQcLDEGfN4ARw+eRwiBJ0qK9mI0uNt5+MTf+f2sT2NWD55mXd94gEMd5fxg8kf6faYoCq6gF4MqeutqZm4iX/rhTRSUpJGcFjts4p8QgrhEM/d99krs3S42vXYk4nNZVti56RSTZ47MuMppd1MepXf/jo8tZcGK4TmxCSHQ6TVkZCeQnhXP4lWTaG3qJiU9dtjH8UFHd6eTJ3+7sV/WKTs/iW/96g4ycxMHvVa912jhFROISzDz3c/+A3v3hfq2oii8+NftLFszmYTkkdnMXgjALx0j3dc5RwvN7m68wQvBscGs53+f+DQ7X9lH1cla0gtSueWL68gsvpDVm3XlVHInRDe0EUKw7lOrSMyI5/SesySkx/HZPzzI/rePoDeHMk35U3L4zktfZu+bB2muasUUY+SqB69A26MFotaouee7t5A6iDzumlnFzOgj7pMxQCl0IMxJyQKGNuXJtyaQbx28i2EoJCdZwyJvAG2dDrq6XSTED16OHinKzjXjdF2YLAkBBTlDSwzHpYTu2WBAfl90cy5bMBCr16NRqfAFB7fYHAs0NXbx9a+9QEtLNzqdhk98cgVr104d1cXr6nTxyCPrB00ZpaXFsmBB0agjyHfeOc7bbx8fdJ3Pf2F1v2AgGJR57P82MmladmhgPlDJg59bRcWZRja+foS5S0LyxYqicHT/eXZsOc2sBUUcP1TF+ClZtLfa+cdftrFw+Xi2vn0Ct8vHrPmFPP+3HeQVppCVl4jD7kFRFJ59/D1S0mNRqyUe/81GPvfNdax/+SBdHU5yCpKpOje4/DCAXwnS5OkclrBRgTktHOFfDAX4v7JX+ey467Bo+gcVkiSxaOXE/hsOE1qtmrs/dQW7t5zGeVEHROmxkZPvujtd/QiXKpXE3KUlo7onhRBoNCrSs4aeTXxYoCgK6188QHNDV8RynV7DZx++bshAoC+EEEyYls2tDyzhiV9viPisq8PJxtcOc/uDSz8UImSKorCrpTzqJMoSb2bNAxdqxxefz8SFg3eYaLQaFt80j8U3zQtvv+6TqyL2l1GYyo2fuzrqd6g1Kq779JpBv+O+1bMH/fyDhKQEM3GxJto6HEDIYnzPofNcvXLymN0rQVnm1fVHIiaXsTFGMtNih9x2+tLxvPHYu9SUNVyWjMXFuGzBgEmrxaBWvy/BwJGjNTT09IsGAl42bjjB2rUjq1f3QkgCk0mH1+snEJCRZblfluBSBZR0eg0GQ8jJbUD/gCiLOtsddHU4WXXtdBRZ4aufegqXMzR4Z2QncMXaCzaYO7ac5uqbZlM0Pi2sMHf0wPlwdsFo1rH3vTJmzS/E7wuwfM1kMnMSURQFt8vHvh1lXHf7PGRZoex0PS6Xjz3vneFrP76Z2DgTW98ZPJiJOBVFocXThV8OkqizopUupH0DchCb30WmIZEcU3K/7fxKkGZPJ/s7ymj32fDLgZAIkcY0pi/35LQYSqZmc+giw6X2Fjt+X3BEtqCBQLD/PSIYsWfAfzLcLh9vv3Sw3/KFV0xgwrSRuxcKIVh9w0z+9dROujsiU7Jb3jjKzfcuCtfBo0FRFLxygHJbE2dtzXT4HGglNVmmeCbEZJCitw56TIqi0OV3UeVoo9rZTpvHjgJYNQayTfGMs6YSr41+z/Z+t83vpsVjY1tzqOuoy+/mSEd1v/XjtSZyzP3Z8cPBcLNSF8Ph96ISEgb1wPyZD0Ow1Rc6rZoZU7LZuO10eNk/XznAojmFw6rpDwVFUThwpIo9h85HLJ89LXdYXQvTFpdQODWHqtJ6Sg+cY+Lcwst6jS9bMKBXqymMT6DJYceoGVsRh6FwKS/dxEQLP/3Zbbhc3lCK3eHBZnPzrxf309Exuvaii3HrrXNZvmw8TpcXh8OL3e6msrKVN14/Muh2itLn3MQFMSUAa2yke2TIFS70d69UaDAgE59oJj0rnvSseGLjQzwOSZIw9KQCe9uf1OrQTFSSBJ/+ytqQu56shL9/JD34L9Tu4HR3Na6gl1R9HJ8bdz3xuhA5rdHTwZ8r3qLT5yBFH8fDE++8cA6KzOPn3qHMVke3z8mvzryCWpIwq/V8a+IdaMXY3VdCCCZNz+kXDHjcPvy+wIiCAaNJh1qlikh/BwMyxw9WsjJKu9v/jzh1pJrW5u6IZZIkWHfHvFE/v5YYA5Om57Bry+mI5Y21HTTWdQ7InVAUhQp7C388u4UD7ZU4/J5wLK4RKjKMcdyZN58bsmeglfrfB56gn7+d28nWplJqXR24Aj56LcAEoJM05JgT+XjRUq5IndDv99/Zcpa/V+6myd1Nh9eJIxCqFR/pqObT+57u931rMibznSnXj+ziXCJ+ePBdCmMSeHDC8NtaB0JvpnAwtVpZUfqRnccaQgiuWzOVLTvOhPlCNfUd/Pbxd/nCJ1ZiHoG418VQFIVTZQ388k+bIuzm1WqJG9ZOH9Z+9SYdn//Nvfz0Y4/x2y/8nc/9+h7GzchDrYmuqHipuGzBgEoIfnf11QSCMnGG0bXlDBfTpmWTnh5LS4sNo1HHumv7E96Gi77+9b1QFIV3t5wes2AgOdlK8kU1zDOlDUMGA/EJZkwWPds3n8LnDZCZk4DJrIMoNcn5S0t4+9XDzF9STNmpOiZMyWTq7DwO7C7H5fASCMokJIYG5ItrmgajjknTc6itaiM1PRavN4BKLTFzXgFvv3yIvKKUnvTu0DekJ+jDHfTy5fE34w36+VnpizxdtZnPjbseIQRp+ni+UHwjr9Tt5nBH5ECsEhI3ZC6g1tVK6fEaPl10NRa1IeTgJsb+1k3JiO23TJblEWsExMabiE8y09IYOdg98+et5I1LpaAk7f/rgEBRFHZsPNkv+xWfZCF/XOqo9yuEYMrsvH7BQDAoU36qLmowoCgK5fZmvnTon9Q420nUmZmVMp5kvQVX0EdpdwPn7K08cno93X4XDxYu6UeGVVBYX3+MOlcnqXor0+KySTXEIAlBrbODY501lNka+d7xV8k0xlMSE/l+cQZ9KAqk6GNI0cdwvKsWT9CPVWOgyNJfUz/T+P6Xi87bOjCNkf/Mu/UVxGj1zE6Ozg0IKDK/OrqDL05djOYyu+KOL0pjzvRc9hy8MHvfsqMUm93DfbfPZ1xBSsjNcZjPq6IodNvcbNtdxtMv7A2XIHqxYFYBJYXDu8d9Hj8mq567v7KOR7/5PN+7+49MXVxM0dQczHGDZ0b1Ri1Lb5gd1qUYDi5bMCCEINU88n790SAtLZYf/+Q2aqrbSEyyUFiY8h/5slWpJT72+VUc2XcejUbFAw9diSRJ5BUmk5IaSdSZu3gcKpVEd5eLez8ZahNLSLJwzyeWc+ZEHWqNCrM1FKRdd9scTOYL7WqSJLj30ys4vO887W12svNCL9Hrbp/Hvh1l+HxBPvXlq8KZhcEgCcENmQtINySgKAo3Zi3kLxVv8+midWiFGrWkIlFnxRqFCyCEIM0Qj1f2hwOHGO3l60qJZik6moqQRqtm/vLxvPbs3ojlTfWdfO/zz3DDXQtYsnoyCcljK3v8YYEclDl9tD8XI7cwZUQZmGjIzI2ePi8/3cAV66b3W+6R/fzwxBvUONsZb03jO1Ovp8CSjEaokFFo9zr43ZlNvF53lCcqtjMvsYApcZGDmF7S8OniKwjIQabH55CoM6PpySB4g342NZ7i+ydew+b38EL1fr49+dqI331l6gSWpYTq/bKicPuOP1LtbGdafDY/nX5Lv2MeaEb9fkBWFDxBPyohoZX6D5KKouANBpABvUodcayKEsqXvFBxnKuyi5mZFCIa9m2VVhSFRqeN1ypP85kpC1EpUr91Qsch4wkEUEn9j6P3e0T4eAOoo6wHoQznp+5bRtm55jDjX1Fg/5FKzpQ3Mm1yFnOn55Gfk4ROr+5X0nW4vHR0OnG4vDS32DheWseeg+c5V9XabxKREG/iEx9ZMuys6sEtJ3n0G8/jdnpx2d0EAzI7XjvEjtcODUlIjU+NZdG1Mz8YwcD7CSEEWVnx/Qh3/3mQsViqWboqGZXqQnSZlBIDF00g1GoV83oIhX2Rk59MTn5kbX763IJ+65nMehZfESk+YjBqWbZ6csQy3xAiSxpJjVUTqr8JIUg3xOMIuPEG/VFTru8n+gnWjFHrixCC6+9ewHvvnKDromxSa2M3j//qHTa8coglqyez8IoJZOQmoB7B7OPDDofdQ9tFJQIIdREEg/KFjMFwxJIuWsdoii5u1jRA987ulgqOd9aikVR8Y/I6SqwXsjYqBMl6K58fv5odLWfp9Ll4pnIPk2MzI34rIQSr0yaF/90XBrWWqzOn8q+agxzrrOFEV3+/DrWkQk2vQ58cFttRIdCr3t8S62DwBgP8+tgOjrU1olOpub1oKsszCsIDvsvv47mKY+xsrCIoy4yPT+aBktkkG0IB/Oa6CvY117CnqYZun4c9TaGA8Ib8icxPzaHb5+H5imPsa66l3evi2/s2oBISerWab8xcgV6lRlEUahxdPHnmIOe629Gq1FyZWciN+ZPQqkLvkwaXjafPHObu4un85dR+ztnaMao1fGf2SrLMsRHnJIQgJzOeL3xiJT/97TsRrH+bw8P2PeXs2FuB0aDBaNDi7qNnIcsKP/zVW8iygsPlweHwDmhvbDRo+fzHV5KZHjf86+324ehxVLxYXnooGAZ4DgbDf0Qw8P8NFC/dHR9Fb7wdk+Xz/+6jGRaCikxAvlAz8wT9qIVqwM6BsYKiKPi8ARx2N92dLmydTuzdbpwOL16PD583gM/rx+cN4PUG8Hv91FW3j9n3p2bE8eCX1vD7H76Bxx3ZWSDLCtXnWvj7H7fw5gv7mDwzl8VXTmLCtGziEs2XJIH8YUBHmz2q+uOhPRU0XdRdMFI4B5Cl7u2SuXgG+UbdURQUck2JTIzJiBqQxWtNjLOmsq/tPIc7qvHJAXQXDdKDBXIqIVFkSeZYZw3OgBcZBdWHUP9kS10FtxVO5f7xszjQUsdX96zn7ytvZ3xcMrKi8OvjOznYUscD42ejU6l45uwRvrpnPY8uuxGtpEItSRTFJKISggJrAtOTQm2RyYYLrXzxOiPZ5lg0ksSs5MzwjL7XT6bb5+GzO15nfFwy95fMos3j4ncnduENBvlI8QyEEHR5PTxXcZRaRxdTE9OZn5pNlb0Tszr6gCqEYPHcIgKfDvK7x9+l4yIXT0VRcLp8OKNIslfWtA153axmPQ99dDmL5xaNKOCfsXwiP37pC8Nevy/UGtWI2+v/Gwx8qKCgyDZQRmYA9O+EXw5ysruaJUmTUFDY01ZKtik5YsYzVHeGWqhCXQ5BL1blAss3WorS1u3iXGkjJw5WcfZ0Pc31ndi6XHg9fgL+YNTukMsBIQTL105FkiT+9rtNtAwwyHW2Odi+4SS73y0lLSueWQuKWHTlRApK0tDpNf+R2YKudmfU36C6ooXqiqFbVkeDoL9/V1NAkSmzNQJgVGvZ1Vreb51e9JLebD43zoCvXzAAofsvoMjY/R4cAQ+eoB+/HMQvB+nyu3tXIpzDHkN0+9x0+12kG2JRicvjzJpmsvLQ5AWoJYlFqbnsbKzkn+XH+O7slbS4HTxffownr7iFaYmhQT7HEse165+ivKuNSQmpLM8owC8H+c2JXcxJyeL6vMh24BitnpsLJhOr1fN61WluyJuE/iLV2XdqztLt8/CtWStCnQ0KtLgdPHnmIHcUTUPbwzGw+7ysyh7HdbkTwqTowSBJghWLSkhOtPDo09s5eabhkhUJhYD8nCQ+de9SZk3LHTExNjbRQmzi+1Nqh0sIBurrOmhtCxkUWa0G8vOjq5W53T7Oll2wozQatRQVpUa9Wf3+AKWnG0JMUkkwfnxGPxvVhoZOWoawf0xLiyXlIk3y/+LfA6vGwJsN+zljq8Ud9LGz9RT/W3JTOBW6tfkYda42DndW0Ozt4m+Vm7BoDFydNgeDOpTqStBZyTIm8X9nX2OCNVSvvTNnOWoRujcURaGjzcG29cfY9vZxas63Dug78H5CpZJYvnYKBSVpvPjkDvZsLR1w5hrwB6k930rt+VY2vnqYCdOyWXXDDGbMK8RoHj2r+YMIu21ox7axRrTXujfox9YzSJ/orOOLh54bcPtgjx15UJEJKJGBhaIodPvdbG8uY3tLGVWONrr9bnxygKAsIyPjDY7OfGm4OO9o4etHXmROQj5Xpk9icmwmMRrDmN43JbFJ4Rm6WpKYFJ/K6c6Qgdq57nacAR9Plh4Mp+t9wSBBWabO2c2khNETQ/tiT3M1zoCPh/dvCi+rd3bT5nHiDvrDwYBGUjE3+YKh0nBbKieVZPCjr9/A5u2lrN9ygqra9ohugOFAkgSpyVZWLhnPtaunkZRg/lA8v6MOBvYfOM9jf9kGQHFxKr/69V1RT/j06Xoe/vZL4ZlASoqVvzz20ahe6fX1nXzjGy8iywoxMQb++uTH+q339vpjvBSlP7kv7r9/MbfcOnd0J/ahgITfdwK/bweK4kGtmYZWtxAhLtSJFMVLwF9KwH8cOdiEEAZUmglodQsQwtBnPQVZbsLv3U4wUIMQBoTUE0gJNXrDjRH7HQkSdVa+VHwTRZYMdrSeQCOp+NbE25kSmxe+VyQhYdYYWJI8qc/ZRabJ9ZKG706+mx2tp3D43WQaE8N1SlmWObiznKd+v5nzZ5uGZc8LF2RmhSSQJIEkSciygn+Amt9oIYQgOz+Jzz18Hauvn8H6fx3g0O4KbF0D20Y7HR4O7DzL0f3nGD81mxvvWcjMBYWD9sl/mOD7AARqEBrYgz0vpkS9mSzj0Ip2akkK2+72os7VyXePvxrWBEgzxJBvTiJBZ8ak1mFQadjXdp4zPVmIywFZUWh0d/Na3RHeaThBoSWFZaklLE0uJtecFJU8N1JoJClicNVIKvw9QZI3GEAtqZiamI5edeE+nZuSRUnc2MlauwN+4nVGpideUF6cnpjO9XkTI75X6jm+kUIIQYzVwI1XT2fF4hJOnWngwNEqzp5rprXdjtPlxecPhrIGPWUnlVpCr9MQF2MkLzuR2dNymTE1m9SkmA+Vtsio3y6FhSl4e8hjtbUdeL0B9BdZZyqKwpHD1Xj6PPyNjV10dDiiztwrK1tx99RX4xOS+u2vd59+f2DQFE5gDOxiP8gI+I/h825GSIkoihO343EMpo9gtHwZ0TNbDvhPYOt8CEmKRZKSkeUugo4/oDfeisn6XURPzV6Wm7F1fBQAjXYmgUApPs+7SKp0dPqVYBj9tYzRmFiWEhJCui17aWihiHS3W5o8OdqmERBCkGVM4o7effQsk2WFja8e5rFfvjPgjFujVROfaCY5PZbktFgSkq3ExJkwmnQh10C9Bq1OjVan5tCucl74645Rn+9gx6/Rqpk0M5eSqVlUnW3mvQ0n2PdeGQ217f08DHrh9wU5fqCSsyfrWHbVFO759BXEJ/0HdCAMcPgp6bEYzcM3YRoJMrL7D/RqSYVGUuEOwtzEAr4xad2Q+xEQUeIKyEF+cvJNDrZXYlRpeahkJctTxhOvM6GRVPQa8/7gxOuXNRjINMZxVfpkDndU0+qxc6q7nlPd9TxXuZepcdmsSp/ErIRcEnWWUXcj1Di6I6yYq+wdZJpC7/EMcwwCWJqez7jYgQWRRM9/g6bthehJ5fRfpzg2iWp7F7cXTr2sbYdCCOJijCycU8CC2QW43D66bS66bG4cDg8ebwBZUVCrJUwGHTFWA3GxRqxmPSrV5SnTXG6MOhjIyIhHp1Pj9Qaw2dx0djpJiyKxePx4ZAuR3x+k8nxr1GCg9PQFPfeSi3r9e7Fq1WQKC1Ox293YbB5sNhdNzd3s3HF2tKfyoYPfd5SYhKdRayagKH5cjt/hcjyGznAtak2oA0CtHo819jeoNEUIYUJRvDht38fj+idG82cRqtAD63W9SDBQSVzSO0iqbMBLd8cDKHIXJuvDwNg8cGPxcFxM/jq2/xyPPvI2bmd/DkVsvIl5y0qYv3w8OYUpxMSZ0OnUCGngtGFDTcclH+NgCMkKqymamEHB+HSuv2sBJw5VsnPzKU4dqaGr3RF1O4/bzzsvH6LmfCtf/emtH3p/Ar0+OpHrrk+tYO7S/h0wYwF1lIFDr9KQpLNg87updXagV2lGPFA2ubvZ3xbqUb8+awZ35M7rtw9FUXAGLi/PJ1lv5QfTbqLe1cnetnNsbSrldHcDHT4nW5tL2dFSRpYpgUVJRaxIm0CxNRWjSjui5/JEexO7mqopjk3iTGcLR9sa+cWCqxFCkGeJZ1ZyJj8/so0vTl1CnE6Pw++j2t7JsowC1D2kWElIJOiN7GuuZWFqLgpg1mgjNAwS9UZcQT/H2hspiEkgIMskG8xIQnB93kSerzjOE2cOcFV2MWpJot3jwhXwMy9l5NbQQ6FXg8Vs0mE26chIG343wHCgKAq2DkdUTsulQKgkYhNHNnEYdTBgtRpITLJQX9dJMChTV9fRLxhwuXzU1IQY2iUlaZSVNaIocOxYDXPnFfR7uZ892xT+e/KUrKgnkp2TSHZOYngbgJqadnbtLL9kmeAPCzTamag103tuVD0G0/24HU/g9WwMBwNCMqHW9qqFBRBCh1a3Eo/reWTFhkToGgYCZUhSApKqt11Kj1ozCY/rhZ7tPpipaY/bz19+ET0QmDAtm098eS2F49NRDcM3/N8BSRIkplhZdtUUFq2cSF1VG7vePc2ODSeprWyNmvk6fbSG337/VR7+v7vQRcmafVhgjYsu9RrwBYmJff/cTSUE85IKOOdoodzeTKO7iwzjyF727V4H/h4OwZzE/KjBhF8OctbW1G95dFxQ3QsqwzeoEUKgESpyzYnkmBK4NnM65+wtbG8pY0dLGeftrVQ6Qv+9UnuI8THprEybyPykQjKGQTpM0BtZkVnAE6X7sft8dPvc3Fo4haXp+UCohPCTeWv46eFtfGn3m6iFhIxCljmGJT3rhM4OHpq0gF8c3c4n3nsZtSTx2ckLWZyeF16nJDaJNVnj+Pb+jVg1euJ0Bv6w5Dq0KjX51nh+Om8Nfzy5hzcqS0PlPgTX5k0IBwMaSSLVOPoMyPuN33z+79SU9Tc2uxTEJlr56atfHJa9dS9G/aZXqQT5ecnU14X6d8vPNjF7dn7EOvX1nTh6fJxXXjmJuroOHA4vJ0/277X1+QJhfwFJEhQMQEjsiwt15w/Hjz5WUKnzIx5cSYpHkmIIBi5kRxTFi8+7C793B8FgPSg+ZLkNUEC5kJZWqyfg82whGKhEpc5DUVwEfEdRqbKBD+6Ac2z/OarKm/stz8pL4ms/u5Wk1JgRZyNk+f0vL/WWEPLGpZJblMJVN85i15bTvPHPfdRWtvZb//Cec+zYeJIr1k37UKYiAeITLUiS6Bfw1FcP3aY1lhBCcFP2LF6pOYQz4OW3ZzbxrcnrMKv1UTtV7AEPGkmFQXVhFmtQaxEIFBQa3V39Bm9ZkVnfcJxq5/DOTQAxPQJcta4O/HIwTMgbyXkZ1Vomx2UyKTaDO/Pmcaqrni1Np9nfVkmju4sD7ZUcaq8i2WANkQ7TJjIlLmtA0uF3Zq8kTmfg9sKp1Dm6Mag1ZFtiw3V5IQSZphh+ufAa6h3dOP0+TBotqUYLmj6tskIIVmYVMSkhlTaPE51K3a/3X6/W8JN5a6h2dOELBonV6SO+Z3lGATOSMmhw2ggqCvE6A8nGC+2JuZY4nr7iNmK0l6fkNNZorm2jrqL/u2y0EJJADsojdgwedTAghGDS5Ex27CgD4ExZY78H4dTJuh5PcMHkyVmkpcVSXt5MXV0HLpcPUx9hhM5OF909Agu9WYf/YgCIi1OeApBQlBD5TVFknPZf4XE9i06/Cq1uOZIURyBQRsB/NGJLvfFWvJ4N2Do/ilozBVluQ5abMcf8FPhgzqoVReG9DSf7t6cJuOfTV4wqEACwd73/LPe+EEKQkGxl3e1zmbukmL/+ZiPbN5yIOE9FUXj9ub0sv3rqZXcxu1yISzRjMuv7dRVUnOn/DrncyDMn8UDhYv50disbG07S5XNxbeZ0cswJaIQKj+ynxWPndFc9B9ur+PLEqyIUCDOMcaQarDS6u3m2ci9FlhTyLckIBJ0+J1uaTvP0+V0k6Cy0eAbvgurFwuQijnbWcN7RyqPl27gqYwpGlQa/HMQZ8GLW6Mk2Dc++VwhBnNbEouRxzE8qpNndzdHOGna2lHOss4Z6Vyev1x1hQ8MJCizJLE8dz8rUCeSaEyNkl1ONofexTqUmVhddXl4IgV6lpiBm8GOThCDdZCXdNLCttF6toTg2upeEEIJYnWHA49Cq1GRbYgc9hg8SFq2bSfH0vH7LhSQwmHQc2nqaylN1GMx6MvKTSc5KwBxrRKVWEfAF6G6z01DVSlNVaPJw6+fWMPvKye+vzkBJyQVGZ011W4TNoqIoHD5cBYDJpCU1NYZx49IoL2/GbvfQ1NRFQcEF2by6unYCPUSqzMz4qOTB/yIEOdgQ8dJUFCeKYkelyuz5uwuP8yl0hnWYY37SQyoUKO7+LG4hxaBS56IoLjTa+UiSFbVmCpIqepnmgwBFUTh/pj8Zy2wxMGN+waiOW1EUas5fnh73kUIIQUpGHJ/59nW0NHZReqw24vPqcy3YOp3EvY89yGMJo1FHZl5iv/OqKm/G5fBisrx/MzpJCD6Sv4iAIvNM5R72tp3jQA8ZUBKCgCLjDfoJKDIqIYUNiMLnotLyqXEr+MnJN6l1dfD5g8+SrLciCUGnz0WXz8WCpEJuyp7FFw4+O+TxCCG4MWsmGxtOUm5v5omK7bxScxCtSkNADuKV/dyaM5fPlKwc+bkiiNeZyTMn0ejuptrZRoOrC4WQa+Lp7gZOdzfwr+oDrMuczl1584gbwGnxvxg73P7FtVF7XxUU3nl6B288sY3JC8Zx91fXkTcxE6NZj6SWQhkpRSEQCGLrcHDo3VP87QevcHDLKdbetzTMjxouLikYyMiIQ6/X4PH4aW934HR6sfbo3fv9QSp6Uh8pKTEYjTomT8nirbeOoigKZ0obI4KBsj4v95Lx6fwXA8PvO4Ait4KUBCj4PFtQFDda/XIAFMWPoviQpFRAFZpZKi687lf77UsOtuB1v4E1/s9owhwDFSATygwM54bqv87FL82xRDAg44jSqx6XaB41Gz0QCHL2VP2lHtqYwmTWcdO9i/jhFyP7370eP10dYxQMDPDzXk72jZAEc5eW9AsGurucnDhUxdylxe/rAKRTqflY4TLmJOTzRt1RTnXV0+V3EVRkDEJLpjGOPHMSC5OK+hkHCSG4JmMaJrWOF6sPUONsp9vvRiMgRRfgztxl3JIzF0lIzEnMx6I2RFxzRVFQFCdCaBEiVH5I0Jn55czb+fv53RzurMbmC2kW6CQ1KQYruSO0L5YVhTavnX1t59nQcIITnXV0+UNZWItaz9S4LOYmFrC//TzHO2tp9th4ouI9jnZU88PpN5FmiL2k6/t+QVEUvL4AOq36QxPAhI8zyuG21nXwtx++gjnGyFf+/FESM+L6nZdAoFVJJKbFserOhUiSxK8eepK///Q1Pv+be98fAiGE0vlJSRZqaztwu320tNjCwUBbm52OHsemgoIUhAi1I/bWCo8erWbt1VPDbSqlpRcIFJMnZX5ofsx/ByRVBrbOh9DqliIrNjyu59HqlqLRzg59LsWj0U7H43oOIVkQwoDPuwNFcXNxd4CQTKhU6Ti6voakSiZ0V2pQayZhtDyESjV4YCaEiGq84Xb6LlvKV1EuKML1RchdbHT7rChtpLH28nYTjBRCCLLykkKdVhedbmCM2McaTZRXgMJlF21atHIizz66FZ+3j66DAi8/vevfoqmgliRmxOcwLT4bm8+NPeAhIMtoJRVmjR6zWjcgyU4lSVyROoGFSUV0+Jx4gwEk2nF3f5yitI+hVhlRFIX/m3UnIMLthr1o6/wqet0crOZ7gR69fHMiX598DV0+F3a/BwUFnaTBotFhVA+t+6Eoodl+hb2ZzY2neK+5jBpnOwFFRkKQaYxjcXIxa9InUxyTil7ScGvuHCrszbxcc4g3645ysKOKn51czy9n3o5qGBLZIQJ3gGCwBTnYgCx3ouBnNKGlTrsASTUyrxlZVvj57zeQEG9m+qQsCnKTSEqwfKh6/fti79vHsHU4WX3XwqiBwMUQQjBvzVSMVgO73jjCg9+7BUvc8Am5l/TESZKgoCCF2toOFAWqKlspLAxFzmfPNoXT/hMnhTS/U1KsmM16bDY3Z882EQwqqNWCYFAOdx2oVBJ5wyAP/n8JoUZnvBGD8T78vt14PRtB8aI33orR9CDQ+5JQY4n9BS7Ho/g8G0Ho0GoXoDPegsv+G4QUqtUpiheX/VdIqlQ0usVhMSJF7sDjehFF7sAS96fBddfVEkaTjotV/etr2nv4ImN/GVQqEZVN77B7CAZk1FEErQaD3xfgub9sG7FV8fsBW5erXyAg9dQSxwIWa/S6a11lK+MmZozJd0RDelY80+cWsG97WcTyk0eq2Lr+OFdeNzzP97GEEAIVgjidiTjdyLoahBAY1FoyevTvAwEftbY66OHxhEh90X8zrXYKGnV/szCVkEjQmUnQmaNsFR2yotDutXOgvZK3609wrLOG7h6VRb1Kw+SYLNakT2ZR8jjSDDERHiF6lYZJsZkUW9PIMSXw69IN7Go9S4O7iyzT4AOzosj4fYdxOp/G5zuALLejKF5Gm2NKTHwZ7QiDgS6bix37yvF6A7zy1mHSU2P57Y9vJ9YavXtFURTaOhxhIqsQgsR48wcmeCjrKbNnjMCF12DWYTTraWvopL2p6/0LBnpJhNu2lQJQeqaBlVdOCvEFDlWF1+nVDDAYtGRmxnP6dD2trTa6u10kJJhx2D209/RYx8QYSEwY/s3//xe0mK3fBwQqdQF6462hzgChD4sIQeiaS6pczDE/AsXTQzgMvYjMMT+gNycV8J/C7fw7sYmvotZM6cNBUFAUF17PBiDAYF0FQghSM+P6Md9rz7fS3NBJWubYO0lKKonk1Fia6iKd6Drb7DTVd5CZG514FA3BoMybL+zn4K6BdelHiu5OJ2aLAUklLmlAk2WZrW8d67fcZNETN0bPSEKyBY1W3U958b0NJ1m2dsplM02SVBJ3fGIZR/efj8hCyEGFx3/1DgnJFmbMKxxx3bMvFDnkVWGJMUZ9wSuKgqzYkWUbQqiRRAxC6Ps8B0GCcjsofiRVPILILgNFkZHlLmTFhUqKRYiB6+uhZ8qNgh9JWHtEs9woeLAYb0VI/YOy8ExbbgcUVFICEN2vIiAHKbM1saXpNNuazlDtbCOgyAggSWdhbmIBazOmMCUuC7N6cHlrjaTi5pzZPHV+F+1eB9XOtkGDAUWRcbuex2b7MbI8VmZfIw8i6ho68fZkmnz+IC63D+Mgbn9BWeE7P3+D9s7Q2GPUa/nDz+4cdJv3E8FAKPvX3ti/S2UgeF0+PC5fKHs6wsnNJefi+ooDnato6bnpFUpLQ/VXi0VPWo9toxCCiZMyOH26Hq83QHV1GwkJZpqau8MqhVlZCZfsaf6fitDN0PeG0A9Y8w2tqwJxcWTY92XmIsQNCPb8rRAyQ+og4D+JpEpjKNEhIQTT5uZz4CLRJ4/bxz/+9C6f/fZ1Y94TL4Rg6px8jh+sjFju9wd58W87+cw3rx0yO6AoCl6Pn7dePMDf/7B5xA/OYHjyNxsBWLhyIkUT0rHGGns0IYY/sPn9Qd575zhb3jza77PxU7MwmscmMxAbbyYlPZa6qsjWtyN7Kzi4q5zZi8Zdthn6uIkZrL15Nq/8Y3fEcluXi59//V/c9cnlLF87BbN1eBr7ve8eh81DzbkW9u8o48yJOn74x3v7vVMUxY/d+RwO1yso+AAFrbqExLhHABVBuZsu26/x+o4CodJbnPVLaDW9xjc+uh2P43JvAhSE0BNj+QQG3bKobYlBuYX2rm9i0C3BYroHAKf7LZzuNwkGW7Ca78NiujViu6DcQmf3z/EHzgEKanU28davo1b3L92V25v59L6nw1wAjVBRbE3lyrSJLE8dT44pEfUIDIyMKi3xWhPtXseQvgp+/2G6u7+DolwsmqUm9P4Yxf0zClfTqtrIQCQjLRbNYIx6RaGlzUZLj8eO0aAdVNm2V8fm/cpY5RSHfue97xzj1s9fRXzK4F1SiqKw680jOLpc6AxaYpMG7taIhkseddMz4jAYNLjdfpqauvD7g3g8fpqaugHIzk7AaLwQaU2dmsOLL+wH4MSJWmbMyKWyz6xywoToNqL/xdhDrZmEWjMJe9cX0eqWIIQFWe4g4D+GLHdiiX2E4TzI85aW8NTvNkfWf4Gt64+j0ai4/u4FpGXGo9Wqw7sLeSIoBPxBfN4AbpcXEMNW11t05USef+K9ft+55Y2jxCWYufb2ecQmmEOiJH0yHijgcnopP13P68/tZf/2swR6InCTRY/L4b1k8ar2VjsHdpxl6/rjpGcnMGFaNpNn5pJdkEx8ohmDUYdao4qYrSqygj8QxGnzUH2uhXffOsaOjSf61e7VahU33L1wzJ4RlVpi4coJPP/49ojlPm+A//vOK3zkoZXMXVKMJdYYwQ2RZQU5KOP3B/F6/LgcHhKSrehHMKuSJIk7P7mcijMNnDhYFfFZd6eTRx9Zz9b1x1myaiLjp2WTmGxFb9SGjkMJZXV8vgAuh5fONjt11e2cPVlH+ekGGmracTm9xMab+pFZFUXB4X6dTtuviI/5OjrtVGTFjSzbCLXoKnTZfoXXd4yE2O8hhBmH62VaOz9LWtLLSFhxuF7G5vwHSbE/R63OwO3dS1vnV0hN/CdaTW/Kv0c8SG6mteOLaDT5mI03h387k2EVet0smts/RlBuu+gYg7R3fQshDCTG/RSATtsvaev6FikJjyEuai92BXx0+V3EaAxMj89hbcYUZifkjbobQCEkegRg0QxMylUUGbvt130CAYFaMxGDfg1qTUkoWzKKYECtLhrxNnUNkZnCjNSh6+wjQbfbQ3O3g+K04WceLwXz107juV+/RXNNOz//5BPc/ZV15E/KxGDSR2TMgoEg3W0O9r59lKd+/BqKojB+TgExIyQYX3IwYDbrSU62Ul3dTnd3SLe5tdUe9hi4WEkwPz8JjUaF3x/s0SFQKO8jHjNpUualHtJ/MUwIEYM1/jG87vUE/KdAaUVIFnSGG9HqlqFSD69NLy0rnvnLx/PeOycilstBmXdePsT+HWfJG5dKSlosOr2GQDCI1+3H6fDgsHlw2Nw47G4mz8zjf39407COPSs3kQUrJrDt7eMRywP+IC88sZ29284wZVYeuYUpmCz6kGhMt4u6qjbOnqqnqrwZdx9/crPVwOe/ez1//tlbtDUPrx98KHg9firPNlF5tol3XjqI2aonLsFCfJKF2HgTJosetVpFMCjjcnjobHfQ0thNa1MXnihtoAArrpnK5Fm5Y3J8EAqUrrppNm//62A/86SONge//+HrvJqbRHZBErEJoXqq3xfE7fTitHuw29w4bG6cDg8P//ouiieP7Pk1W/R86fs38uOvPM/Zk5HdHMGATOmxGs4cr8Fo1hMTZ8Js1aPVqlGUENfD7fLhdHhwObx4Pf5hBnIKNsdfMRtvwmy8td89Lst27K6XSI7/Pdqe8lms5dPYXc/h8e7FqL+SbsffsBrvQK8LBWZqVTYO5ws4XC8SH/O1nj1JBOUuOrp/iFZTTJz1K0h9ygGSZEWSrAjRf7ANBBtwebaSlvgskhQbulbGG2np+Ayy3IlKFdlRYNHo+Uj+QlalTWKcNQWtNDpGvTvgQ6/SoKCwJKWYKXFZJOutISfZKPuT5TZ8vn3hv/WGdcTEfA9JSn7fJ3UdXc6Iv0dbbg7KMsfrmmi3u0iJMTMpI4U2h4v1x8/QYnMy3+5kSlYqFr2Opm47pY2tJJiMTMpMQSVJnGtpx6LXcbqhhTiTgSmZqQRkmWO1jTg8PiZnphJvGjrblV2cxrUfXc7Lf9zEse1nqDhaTXZxGun5KcQmmlFp1HhdXtoauqgpa6ChsoVgQCYm0cL937phxNyHSw4GekmE1dXt+HwB2tsclJc3hUlP06blRKwfH28OlQaauqmpCdlDVvekKNVqiZzckbXN/BejR6gTIB2D6aOE5gIX2glH8iBLksR9n7mSshN1NNV39vu8o9VOR6t9yP0UFA9fv11SSdz/uSupKG3ol+KWZYWq8maqyptDmQFJgp5MRDRYrAYe+tY6FqyYwNa3jtHWfHrYxzFcBIMy3Z0uujtdVI1CbUwIwdylxTz4xTUjFhMZCinpsdz9yRU8+ov1/UyTAgGZqormYR3zaAiYvZoK33jkdn7/ozc4vLu83++kKOC0ewY0oxo5/AQC9egts6Pe50G5G0V2oFHnhj8XwohaSsHnL8OgX0ow2IBWM77P5yo0mgJ8/tKIfXV0fx+v/wSJcT+JCASGQjDYiKJ4aO/+IaKnVKfgQ5KsKEr/61BgSeYL41dFCAWNBn8p28PE2FRSjVY+V3IlL1cd5y9n9jInKYfrsif1u16BQFVPuTFUSomJ+S4qVUq0XV8SFEWhurad02caCQSCrF01GUkSNLfYSE6yolJJuC/Koo229n+8rolXDp1ifmE2ZY2tTMxIwen10dhlxx+Ucfp8yIpCl8vDI2/vYMX4fI5UN1DfZeOqyeP418GTCKA4LQlvIMiUTHjxwAlsbi8pMWZ+8tY2fnTjKnTROnn6QFJJ3P3Va1GpVax/ajv2TielB85TeuB81PWFJMidkMGD37uZcTNyRxyMXTI7qFddsBeNjV2c7unX1unU5OVFplTUaomCno6Dri4XHR0OWlpCJYW4OBMJ/yUPvu8I1bMlhFD3/H/kEX1qZhz/+6ObycgZnjLaWCA5LZav/OQWcosGfvn0EmkGCgTSsxP43x/fzOKel8usReMu+bhMY+y8Z7LoueGeBXzpBzdiiRn+gNIXfv9ZZDm6bbIQgqtumcVtH136b/E8ECJUHvrqT2/hzk8sJy5xbN4BKpVEWlZ8lBlt6F6XFWfU7YTQgJB62PC9UFDwIgkjAhVCaJGVC1oXiqKgyG6E6MNcV/wY9Msw6BbQ3vVdZDm6EVX0YwjpDiTEPExi3CMkxj1CUtxvSEt8IepgqxLSJQcC/4+98w6zq6r6/2efcvu90/tkMslMeu8JSSCE3kFAVFRERdDX3gsoKhZUFEWxK4qKUqT3GlIIAUJ675PJTKaX2+85Z//+OHfuzJ16ZzIJ+r6/7/PkgTn3nH32aXuvvdZ3fZeUku2tdezvbOIf+9+iIRLk+drdfGLKMl6p20vc6pvOalndQl26PhtFOTmGwLrX93HLdx7mnw++zp/vXYNhWJim5Ad3PMWRo/2nBA8W/x8MXoeDSDyBIhTOnlaNACrzc5heXsyM8iLOmz6RbI+bt48cY2pZIRfNmsx1S+fy8FvbAYgbJssnjuPS2VM4d1o1lpQ8snEHOR43AsHh5jYaO/t/93rD5XXywa9fxrfv+xSX33Q2k+ePp7A8l6w8H/4cL9kFAcqqiph/1nRu/N41fPdfn2beymkjGsNHhak3cVJxKhf6yJFmDiSV3AqLssjOTk/rEEIwe1YFa9fsIZEwObC/kfZ2+6OqqMhDH2Za2P/HfwaEEEybU8Gtv3g///rjq2xYvZuO1v4nn/6gqgpuz/AseSEEE6aWcuvPr+Vff3yVdS/tpL116I9MCMjKsasaXvWh5ZSNzUt9PNPmVKBqSmqFPBJP53WfOpsx4wt4fdUuag83j4iHoCiCnDwfMxeM44KrFjB1dkWaR0DKKKbZhGW1AgJdnwIIDGM/mlYNgGHsQ9OqMM1aOjp/gcdzBapSiK5PS8s+AVtv4H0fW8G4iUU89Je1HNhVRzw+OHGsJxxO7YSKQgkh8AfcvO/GFSw5cwrPPvwWb6zeQ0N924AlnvuDrqvk5PuZMLWUJSunMHtRVT9kUg2nYy6h8CN4XRekUmohDjhQlVx0rYpw9CV0bQL2fT2EYdbjci4ANFyORYSjz+J1nw9oWLKdWGIT2f5P9bgoBZ/7UvyeK6lv/gitHT8iN+sW29gYAppWiSICGGYNXsdckoV/k1ohJ49gneP0cN2Ehbx4bA87246TsExK3AHynF6iZgKn2peI2d3nsSclNJBImPz+ntW8792LmTyphC/f/EDyfAp+n4tdu+sYf0wfkQABAABJREFUNzYfdy9Dtql5aG9kf5hQlMf/nLWENXsO8d3HXuIHV52HQ7Ovu+dXbFoWjuQ3qaoKhtU9ZmR7emSlWDaxtcDvxalrfOac08j1ZW7Uq5rKlAXjmTx/PJHOCJ1tYSKhKJYp0R0qnoAbf44Xh7P/TJNMMSpvVWlJDm63g3A4zr59x2losGOukyeV9Bu3mDqtLGU8bNp8OMUvmDbt/4sN/TdDCEF5ZT6f+dZlHNrbwKbX97Nz8xEa6toJdUZJJOzJRdUUnE4dX8BNXoGfssp8xk0oZsK04StPdrmZ/+frl3DRuxfy5tq97Nx0hIb6NsLBGKZhIRSBw6HhC7gpKstmyswKZi0cR8X4QlQt3RNSUp7Lp26+lERS1EdTlWGR4rraeN/HVnDpexZTe7iJA3vqObT3OPW1rbS3hoiEYsRjBqZpIS2JUASapuLyOMjO9VI2No/JM8YwcXo5JWNy0bS+3hrDrKO19Yu43ReSMPbgcpyG230ebe3fJj/vr4Ckrf1W8vP+TCKxi4Sxi0RiF5bakjQc+k7cmq6y7OxpzFownp2bj/D2+v0c3Huc1uYg0Ugcy7Ts+Liu4vY4CGR7KSzJYmx1EeOqixhbdeL6IIqiUDW5hBu/fCFXfnApu7cdZfumI9QcaKClKUg0HE8RPlVNxenS8Qdc5BVmMbaqgKrJpYytLiS/MICmq/2OJ0IIcgKf43jzjTS03ITTMRtLhrGsdvKzbwc0crO+TnPbNzGtZlQll1DkGbzui3Ho9qorO/BZGlpuoqntG+haNdHYGjR1DF73Rf1cUz4FOT/lePP1aNo4At7rgATB8MOYVjumWUckthaBA00rxeO6AEVkkZP1ZVo77iAaexNVLcAw65Legu/0OUfYiPHssW1MzSql0lfQZ9LOFLNyy/jepueJmgmKXH58upMnarbTEA3iUvsaMV18BiBVG2W0EQzFCIVirDh9MtFoImWgCyHw+Zx0Jovh5eemE+b2HDiOZclhx873HG+isTNEdVEeL+86gGFJHEC+z8PTW/dQkZvNpOJ8Zo0p4aktu5lYlM/Wo8dZOaWvVgSAqgjOnFLFoeZWppYW0hGJ49SGX3xKCPBmefBm9a+bcKIYFWPA63NSWBjg0KEmduyoTT2cuQPELcrKcvB4nIRCMdat7Y4RThtE5KSLfW5ZtnvINEwShoVhmDQ2dtLTZuvsiNDQ0IGuq2iaiqYpqKqCoggURRnw5ehu3/5nGJbNdk8YqckBbOna4/XtOF16qn1NU1JtK0r/Mffe12AYJoZhM7J7W7Ft7eEBr0FVR+bKHwzhRIKtTfUcDwdxaRqVgRyqsnNToiSmtNjb2sy+tmbcms7M/GLy3Z40pn5NZzs7Wmyv0NTyQq6asgzDsDjS1EJbMMzRtnaiRoIl5WPZ3Hocp0vnjMpxOJKxs5FeU9cEVT2llKrJJSQSNsEtGomnjAHdoeFyO3C59UHvn+7QOP9d80fUj9598me5mTxzTIpU15U5EY8bJOI9jIFk/x1Ou4+6Q0vLgugX0kIIDz7vhzHNY7S2fRm3+zxkD9d31/+73ecQDP8dr+dKVLV4yH4Hsj0sPH0SC5dPIhZLEAnHiUcTmGbyXuoqDqeO06WjO9Rhp01mAlVVKCrLobA0m+XnTiceM4hG4sSiiZQxoKkqulPD6dJxOjWUYXwXujaZ4rx7CEWfwzCOIIQHj+tsuvgybucZFOT+nHDkOQyzjizfh/C4zqOrpLeuVVOY+ztCkScwjMN4XGfhdV+EEHaIQ1ECScKgrSmga1UU5NxJLLE1qcpnYVrtIBNJ48D29tgZDcn3x3MNDq2KcHQVptmAppbhdp3e7/Xs6qjnu1sfJUv38OtF1zE5q6Tf/QaDEIKrx83mSLCVLIcLv+4iaiZ4+uhOrp+wEIfS12uraWOxpxEDw9h/UlRHnQ4NRRG0tAbxuLtTaqPRBAcPNXHGskkAjB+bzjfbs7+Bw0ebqRyTN6w+eZ0OttTUI6XkC+cvx50cn+aMLaUpGOZAYwvVhXkU+L18/MxFbDpSx7iCHJZPsOe7C2ZMojir2zARQnD9snms2XuIA40tlOWMrJDaycaoGAOKIqiuLuLQoaaUeJCiCCZN7v+F9HpdlJbmsHdvPXV1bYDt3qsYOzB5MBpN8IufP0dHR4RoNE40ahCLJYjHDSKRRJpK2xNPbOLVV3fjcGj2AJscMNwuB2eunMoZZ0zu9xz/fugNtmypSbafIBYziCUH7+Yek3Vzc5AvfvEf6A4Np0PD6dRwOnVcbgeFhX7+53/O6beiXGdnlLvueo5QKEY0kiAWS54jyYruiQfu38AzT2/B0bN9l47LrXPhhbNYuLB/K3QkSFgmt6x7nkMdbZT6/HTGYsQskz+ecwUe3YGUkn/s2sK/dm+hwp9NMBEjGI9zxxkXMi4rByklr9Ye4sdvrqbUazOPjwbX8c3FK1lSMobNwQb+vW8HuS43G+qPslfppLaznU2NdfzEdwFLS8cO3ckMIYTtBXA4NLKGob51MtH14esODd2hMVq96hKqEcKNlNFeCXQWkpGv1IQQIEgaUO+cCEvXvXO69FHlMwgh0PXxZGk39jmX/f8KLsdcnPqcAX4XOPQqdO3TPVq1kOZ+pLQQaj7Z/pvS9nc55+Fyzktty/bfyGAQQsXlXIQzVTNkYANxW+tRTCmRSMo9OYO2Oxja4xE2t9QSTWoLuFSdqytnDyhHrKrlaFoVhrGbRGIblnkMVRtd5UqPx8GK5ZP48c+eYdlpE4jFDF5du4cNbx1EUQQzp9uctQnju+XuAaKxBD+5+zk+d+PZVI7Jy3gRVZ6TxdULZvTZ7tQ0Lp6VPndMLC5gYnE6L25eZd/rdzt0zpk2/HTJwdAz9DgaxsWoGANdJMIXXtie2pad7aWoKGuA/WHq1FL27q1PbcvN9ZKTM7D7Ix43ePXVXSlxosEQSrqV+kPF2LwBjYHNW46wLgMlOsuSHB8g/Sw/38/HP342aj/Uh2g0wapXdmXEug4GowSD/bOnp08vH1VjoC0W5ZlDe7nvwmuYmldIwjJpiUZwa/bgWxNs5+cb13H3WZcwp6CUmGny+VVP8bONa/n5iosIGwlufe0lPj5zIZdVT0FK+PXm17nt9Zd59NL3Y0rJsWAHvz37Mn644VXWHTvMgxe/j2+99iJraw+PqjFwqiClRWP4RfLcy1GVdMJgR2wHuhLArZ/cNNlEYg/xxCYSie04HPMQSZGXePwtLNmeWmUCqEoO8fgmNK0KTavqwxn4v4pM9N4z/V2a9SSaPwgk0HyfQvV+cDS6mNFAfyxiZ/GUe3LxZlC7oD9IKblrx2rKvdnkJ+WYXWq3Nkj/0PH5Pk5b2xeQsoOOzh+TnfWDfhUVRwohBNddu5SHH9vIi6/sxONxcN8Dr1NdVcjXv3gR3iTXqLwkm7LibGp66A1s3VnLl779IDOnljOxqojcbC9akttiWpJorHs+MU2Ll9fuxnWSRe8URWH5ompbd2UYkFISSRjsa2hmX0MzTcEQpmUxtaSIMyaNS9uvq3aLMgyv3ahd9bRpZUydWkaXu358VRHOAW6qEILFS6rTjIEJE0sGTZnSVIXJk0uJx0+sgEph4cCqTGPG5DF1amYsz4GQne0dkHSmaypTp5aesAZ+Xu7oZlz4dSdT8wr56ca1vHfSTOYVlVHq9adeotfrjgLQFAnzUo2d1pLndvPq0UMY0uJgeyvHgh0oQvBKja0K6NI09re10Ba3DZpirx+PplMRyKIhEsStaRR5vLTGRitd7NRCYrG39Wdkueagkm4MNIVXIYTKuOyPndQ+aNpY4vG3Qcbx+z8OqGQFvkI0thZNKyfg+wRdCpJ+3yeJRJ/AtBrRtPEntV//V2HF3wLLHtOk7FtV82RCTYoQ+UZoCHShMx7lA7Pm98sP6A9CCNyey4jF1xEJP0gk/BAg8Pn+B02rBPrnbQwXHreD9717EZdeOJtwJIamqWQF3GmrfV1Xuez82fzyTy+nHdvcGuLltbt5ee3uVOy9Cz0zDmJxgzvufm5EgonDgdOhMW/mx4ZlDEgp2XjkGL9dtcHWK4jFUxP+u+fPSDMGGjtD/Oz5tSiK4Mvnn06WO7PsplEzBsZW5vPD26/pbrgf0lNPzJs3jmnTyjPe3+N18t3bruxTtGW4GCxb4boPLsM4wYnazt3vf9WVle3h+99/9wmXhx2uRdkfYlFb9Ccrx4tTVfnFiov5154t3L3ZFhD5wNTZvKt6KopQaAjbkqSP7d+VOt6SksUlFQC0xiKY0uLZw3tTHAOJZMWYcajJZ+pQ7UFBEQKHqqXizCeq9jfaiITjRMJxsnM9Q+vy99N3KSUWCSzrxIzKTKCILHze64Hu1aPDMR+HY37aNgBdr0JLurT/E+OV/+2QUmJFX3zHzj8hkEzXToSRjHw+qwrkc+e2VUzPKUERAqeqsaKkOq2gUU/Y369OIPANkAaRyL+JhP9FPLYah2MJDsd8VG0MQvjorZo4GDRtEoriS50jYZjomorf78Lv757cTNNCCFI8rQvPnsHLa3ezffexftu1JasHPq+VVCk9mTCt4RV4l1Lyyu6D3PLI8zSH7AwtNTmW9le9NeB28ebhWo62trNsQiUXTM8sXXrUjAFFUfD2U0lNSklHKJo2yWb7bIuuv/17wzQt2oLdVramKgS8rowHNCklsYRJU1uQhpYgbZ1hosmUKZdDJ8vnIjfLQ16WF5/bgUOMvBa2lJJILMHBYy0cb+6gPRTFsiQel4OCHB8l+QGyfe53vCqWlJInH3yDZx7ZyPfv/iD5hQFKfH4+M+c03j9lNs8e2sd317/M/MIyKrNyKPH6yXK6uOOMC9B7kIgEoAmFXJcHh6Jy65KVFLjTvRZ6PxPqf+pUJKXkgb+s4bVXdnP7b68jkN03bCWlpC74GAmrhYTVQW3H/ahKNwvAklHqgo8xMfeLJ7WviuLH6VzY510d7N39/0bAyUQMK7HxHTv7ovzx+DUXR0ItNEQ7KHb3H6IdDEIIJmQVUBNspTGaLN6j6QxuXcTo6LgdI7ELwziU2mqax4hEHiIS+Td2cSV9sEb6IC//nzgccwE7tfCnv3yOz/7PObic6R6Lhx/fyJRJpUybYmcieT0Ovv6ZC/jhXc+wbVftCS8e/xPQ0BniO4+/SHMoTGm2n/OmTWRaaSHPbd/Hczv6hrWdmsr8yjKOtrbz4s59nD9tQkbf/impCPTDv7zI/hpbJU5RBD/7/BWU5GdWRKGxLchn73g45c6pKs/j+/9z8ZDHSSnpDMdY9dY+XnhjDweONtMRihJPmFhSIkiSiDQFr9uerCeNLWLJjLHMmlhGbsAzLIOjIxTlpTf28tzruzlY20wwHMMwLSR2aonLqVOc52fJjEouWT6dsSUj1802TYv6g8cpqx4+Y9juL6x+YQc1h5oxDYvOeIz6UJBirw+v7mBBcRmWlEQMOySzpLSCxBuv8tj+XZxfOQFNUWiPRTGkRWUgh/FZOYzLyuGvOzbx0enzcWkaoUSctliUybmnRsd7NGCZkrUv7eRYTQuWNbCHSFXctEb3YcoIzZF1KD3yxoVQKfVdQb57xUntq6IU4PW876Se4/8jc0jjCJj1Q+94klDsyuLD1afzq90v8Ns9L/OV6Rdl7OrviXPL0vlUh4Mtg45TUsYIh/6WlsXSaw8gjpTxAX4f6LDu7C3Tstiy7Wi/xcTq6tttjZOkMSCEoLw0h+9+9TKefH4LL67ezbH6tjRuwH8TpJQ8vHE79R1BSrMD3H3tpUwoykcRgl31jf0eI4RgbkUpj7y9gz31TRmXkj8lxkBdYzuH6myVKEUIDLOvktVAMAyLQ8daUu6Q3sIS/UFKyYHaZu7428u8vae23xi9tHfEjFtE4wbN7WF2HWrgqbXbOX/JFG7+yLkZ9U9KyY6Dx/nFP1exZe8xzH5UrwxTEgzH2BeOsa+miRc27OFjVyzh/CVT+oQUju6pIyvfj38QXkAsFOUPX/kb33roSxn1sTc628Mc2tetHNYUCfH5VU8RcDhxqCot0QgXj5/E+Gy7bGmxx8d3l57DLze9xkP7tqMJQdQ0uGbiTCoDObhUjR8uP4/bXn+Fj73wCC5VI2oazC0s5ZbFZ+JQVby6/dycqmavNrDJSV3//5+A5qZOjtX0r2bWBSEEhZ5zKPCsJJTYz/SC23vVXRcowjFsgp7tbjWBhF2WGgWEzkAxV3vbKK/0ZZf7MgEyYbcvNOxh4sTTB7uqYtrtGz3a1+0rGUXPRXf4yUhei2S0ryftfPHX6Kr++U5ACMH7xy0hYRn87eBrxC2TayoXUuHNxakMLUYjEOiKiiUteo6Wf9/3Fl+YceYQugW2hPnowg4hRqIJopEEliUJRxJp7nXDsKita2VsRbrqqRCC3Gwv779qMRedM5NDR5o5VNNEfUMHwR5e6kTC5JV1e1Lzg6YqrFg6KUUwPFnQNRXHMMT1Xtlt87Q+sHg2E4vyM3pvy7LtxXZbOErCMnEqQ0/1/ytrBTe2BvnG3U9yoLa7pKWqCLJ8bvweJ7quYpqSUDRGZyhGNNb9kiUMiwXTKjI6j5SSt3fX8q3fPs3xlu7UQ11TyPZ78HvsuuGhSIyWjjDxpFZBXVMHt//lRYLhOFefPTsVNqg/1MCDP32cGadPoWRcIZPmV4OAg9uO0N7YQWFFAeUTS5ASwkmd9mBriJb6VsZMLiMWibNv4wGEojBh7ngcvQynLp2DXVuPEg7FUFSFeNygODfAj087j+PhIIZlkef2MCmvW7hECME5FVXMKijmYHsrkWicfLeH6rzuVNAp2QX8YPZZbK2tI2IkKM/PYVp5MVJKTi+rZHaB7cU4v3IiZ5TbZJerJk7HtCxi0QTtbWFikTiqpuIPuPH6XRmHUyxLEgnHCHZEiccSCMVWM/QHXMmc/cHbkVJimZJtGw8Rjxk4nBrxuEm8n9WEXW1QAalS7n8PupqNOkLmtF1FMYhMbMOKv4k09iKtFpBxEBpCyUXoUxBqJYMNtkLJQnEuHfQ8Mr4GadnvqFCLUZIu2FQ/rFasxBvI2AakeRgpO7EnTC+oZSj6NIQ+A6GNp7/COoNfp4k0jyDjb2ElNoNZm1xJCoTwI7QKhD4PxTEHlOITCtMhQ3b/EzuQiV1IsxYpO2zjQ2jJ6ylG0SYjHLMQWjXgHJYX0EYMrA6k1YA0jmBGHkrfL7ELM/LUkO0JrRJFnzrMK+2L1niIVfW7sJBk626eqN3EqoZdlLqz8etulCEm61w9m49OWMaTNdtpjnWv8ne2DV6TQgg3WdnfTxpcowdVq8SyJL/90yoOHW6ipSXIN297JG1MSCRMOjujfPJj/WcjdRkFudle5iQl8+3HZz/DSDTBG5sO0ZEcSx0Ojc/deHYqO2GkkMnzDDx8iYztXtOyaEhKF88dW5rxe9pV98CwrH55Bf3hf50xIKXkr0+9kWYITBxbwHvOmcuUcUVk+VxoqoplWYSjCRpag+w50sBbO4+y82A9Ejht5riMbnptYzvf+cMzKUNAUQQLplZw+RkzmFBRgM/jRADhWIKDtc08umorazYdsFNa4gZ3P7iGytJcFk6rQAhB/aEGDu+ooaiyACRMmDueeDTBm89uJivfz4M/fYIv/ukTuDw216KjJci9376f069eQrkluffbD5BbnE00FOXtl7Zy7TeutI2RYJQtbx7iyIFGDu1vYHeyOpxlWtz5ncdwuPq+BtfecAbT53R/ZEIIijw+AsLBj77xEPGYwee+dRmuAo19O+t49J+vs2vbUUKdUVsZLOBmbFUBN33hfPIKA+S47Akz3+1JPSe/1Fn94nZWPbedY0eaiUYSqJpCTp6POQvHc/4VcykdkzuggFM4GGPHlho2rN7D3p3HaGkKEo8ZKIrA7XFQOiaXpWdN5Yxzp+H29OWndLZH2PLWIY4caODQ/kZ2bqkB7EHmx7f8ux8ZW/joZ86halIJQghKfJcN+Y4MBClNZGwNRui3yMQWkH2lmyVA9Cm6MgIGgtBn4RjEGAAwOn6ENGwCqHAuQ8/5U5LAaWDFXsQM/hqZ2IUtydurD4CFAko2evZPEc7+hW/6XqMEsxYzfA9m9JmkGz3dSycBYgD3IrSxKO6rUT3XgAgMY4K2jQ0r+iJW7AVkYi/I9j7nSr8eASILxbEQ1fcx0GcP6c2xSYKPIeObsIwDtlFjNYMMQS9NByv6GFb0iSH7rno/NCrGwMFgE7dueTStXHNnIsruRGahiy9PvYgybxZNsRAfmrAwZTz8etfaQY8TwoHHk1ml0eFCSsn7rl7E3v3H+eldz3HWiilp5GlNU5hUXUxZBmXPuwtKQZdh7XLq+LzOlDHQtd+QxOEhcLStnT+ue4uvnHM6riGKEQ0Hw6k90R6xr8mpa2gZXs//OmMgnjB55c19qb9zAx5++D+XUFbYV/UpNwvKi7KZM6mMd505k7qmDuqbO/H3M3H0hmFa3PXPVznW2KUYBtecPYcbrliC1+1IO1cOUJofYO7kcn7z0Dr+9dxGJBCJJfjFP1fxh1vei9upM3vFdJ6vWsXSyxdSMdkWrlBUhfnnzqL1eDu6U6P5WAtl1SWYhslvPn8PZ117OtOXTqatoYO3nt/M+77+LhJxgwd+8hjXfPlydIfG4QON/PDrD9nKbb2q9+3cWtPvmuHiqxb0e92mYbFpw0HCoRiN9e0c3Hucn33nUZobOtF01dboTpg0N3bSWN+O+pX+X8S2lhC/+N4TvL56N6Zh4XLrOJw6sViC5oYO9u44xtqXdvKZmy9h5vz+lSwfuW89//rzGmJJiVKny24jEbdobQ5y9HAzb63fz7a3D/OZb1yC3isLY/f2Wn5080MYCQuQqRCPtCTbNx3p974EO+yPTEqLY8F/YwyQNZDrXozfManf36Q0McP3Ynb+DGRv/XQ7NJAKGdh3vd92erQ4xO9dbSTbMeuxde5NzNAfMYN39WuMpMMCK5j0UgwN2xvxOkbHrUhjTwZHGEhjP2bnj7Di69AD30WqmcmTS2MPidaPgXmMzKngEmQbVuw5rMRGtKzbUJznDHE+C6PzTjAPZ9Z+JmEDeWLZS13wqg5m5oxc12JioAiP5uCjE5cw1tfNZ7p4zLQBMwlONoQQFBUGKMj38+iTm7jg3BkjrkTYHxRFkB3wcKy+fdTaBGgNR3h82y6+cNayE25LVRQK/F7q2jvZc7yRaaVDl4aWUrJu3xEAxuRk/d81BkKRWFr2wcSKgn4NgZ6w1cQ0xpbkMrYkd8D9emLHgXpWb+ouJTlrQhk3XbUUt7P/GLgQAo/LwcfetYT12w5x6Jgdm953tInXtx1ixbz+1anWP/4WW1bvYOEFc9B0DZmcsNqbOqiYXEZjTRNIMOL2ilgoCk6Xg+tuvQYlyUcor8jjK9/vtt7XvrSTFx7fhKIIPvW1i8nK7auJN2kQaegubNpwkMcf2IDTpfP+G1cwYWopbreDjvYwu7YepaMtQiC7b9vxWIJffO8J1r28E7fXwYXvnc+i5RPxZ7mJRRNs23SEf9/7GrVHmvnJtx7mR7+/npKydFU1IQRzFlXx5INvMm9xFXMWV1FZVYgv4MJImOzbVcf996yh7mgrLz6xmeVnTWXR6emT8/iJxXzl+1el/n7+sbdZ9/IuNF3ls7dcitff1x1eWd2lvy9piawnbnZ7oCwZJ2LUIoRKwDmt33smpbRX4h0/ArpWJAKhTUVxX4DQJiUVBcPIxE6s6NNIYzdpk5xajqJVgVqCUMegaMNbWUqrFYhjhR+0DZIub4DwgJKLEH5AtV3uVkvSYLEQ2jhQh64f0WUIJNo+A1ZPkpOOUEtBLUcoAZAm0mpCmofA6uJqWMjYqyTaPoue8xtQhyagCrVrEuxpCDjsa1GLEEoeCBfIGNKsQ5qHk6v5rlM2YbTfgiNvCmhjGAxCLUb243HAbCDp4kjumAVKBiTpHtr+J4LqQBG/XnTdiI/viilX+HJoiYUxkgS+qdnFqfTgdwpCwKduXIlzFFKq09sV5I2SSqmU9mJioNCmlBIz6a5XewgBDaQi2HP76RMq2XK0nr+/vpmVk6vIcg+cTSelZG9DM09t3Q3AudOqM76G/3XGQG+d9I5kel9/8sAjhZSSB1/clCKiCAEfumQhrgxeVq/LwSXLpnHX/auTbcGTa3Zwxtxq++UszWH72l3Eo3HGTa/g2IF6Csbk48v20tnSXQI1tzibG370AX7zhb9QWJHPjOVTKBlfhKoqZBcGMOJmipwYyPZw2opulvDRQ3ZmB0Iwd3EVRRm42frD/fesoXysbWiUVaTrfy87ayqxaKLPxyGlZM2LO1m/aheKKrjx8+dz7mVz0oiUk2eUU1lVyLc/fx8Nde3884+v8tlbLu3zAUyaVsoPfvNBSspz0XsVpZkwtZQx4wr42k1/IZEwefmZrSxcPjFtn9x8X9p92f62bU0rimDB0mqyBxV3Upiafxs9JyCJRdSoY1fzd9EGmghkuJchAIrrQrTALaAUpA8IzrNQPVdjtH8LK/Z89/7aZLTsO+0JbiREOBnEiq3D6PwxEAclH9V9KYrzLIRaAYoHUEBGkVYjMrEVK/oyQp/OUCELAKxjJNq/mmYICG0SqvdjKM5FoORiDz3SPodZgxm+Hyv8L7omVJl4G6Pj+2jZP6arHsCAED5Uz7WYnT9FqGMRztNQnMtQtAn2uYTLvh4s28BJ7MAI3p0k/XX1uREzdA9q4OZB7qeCnn0nvUMCAInWzyB7pBaqnvehejPI9hCjVK5ZKCNWHuyClJLHj2zjlbp9Kb6QT3fy1ZlnoQ1DI2C0IYSgYkz/pdHjccOulzGIYN1gKCrILKttMHTGYvzzrS3sqGugOOBndnlJGifgWHsHT2zbxZ6GZiwpqS7I45q5M8jzeuiMxbh79et8ZMl8Cny2YSKl5Pnd+2noDHLt/FlcMXca972xhZ3HGvjqQ89yw/IFVBXmdXsykcQMg85ojI2Hj3HXS+toDUcoyw5w4YxJp16B8D8FPo+TgmwvRxts18/emkaeWLOdC06bgq6NjhpWJJZgw44jqb+zfW5mTSjLqG0hBPOmpK8+dh48Tjxh4nRoXPY/5/Pyv9ax6/V9VE4dwzkfXMHL961h/+ZDvPdrV5Bfnofu0jnv+pW4fS4+9N33sPnlbai6yo13XMdrj7/J0T3HmLQwc4twpIjHDT7xlQv7GAJd19mfpr1lWjz899ewLEn1lBLOvnhWn4wKkTRSqieXsnNLDete3sXHPnden5W6qqmMHd9/pTwhBFNnjaG4PIeag03UHm7ud7+RQgiB2g+RzqtXkeOaT2PoRfyOvmIfVnwD0jzYvUEpRAt8E9HPClgIAUoRWtatxJveSq2erdhqpFmLoo9Q61xGMNpvAdmJ0KahZX3XJgf2GfCzEGoRUpuG4n4XSCMDF6WJ0XkHmN3fh9Bno2f/3PYI9D5e6AhlKiLwDUy1DLPzdrpi/Vb0SWT8SoRzcHerEALVfTVCKUJxLgGlkAGNJOEAx2no2ZNItN6ATGxO/WTGnkPly0D/k6oQAtSiATrR611XAgh1dDX6TwWer93NzbPPxa/b98B+z4cfJrBXtgaW1YJlNWFZHUgZRwgNIXwoSh6KkocQmWvG9IfnXtxOaUk2c2ePTNK8ZADJ/EwhpeTnL6/jjSO1fGjRHIKxOH9Y92baPvUdQWrbOlg6fiwSyT3rN3KsvYPvXnQ2bl1nY80xCv27uH7RXIQQmJbFr1e/zhWzbI9fSZafr15wBt9+7EVW7TnIW4drqcjLpiPJC1i/v4ZP3/cEx9o6ONLSRtwwCbicfOOiM8nxZE5u/l9nDGiqwvmnTeEPj6wH7OyAO/72Mm/sqOGKFTOYMq4I9wnWfT7W2E5bR3coorwoG88wiqjkZXnRVCXlWWgLRugMx3A6NPJKc7nys3YZVCEE2QUBLv/UBam/u7DyvfYAmVOYxYprbPJY4Zh8Lv34eSO+ruGifGweE6dmZgR1oaUpmEprXLhsYr8kPbCr1k2aXsbOLTV0toc5XtfGeP/gFfd6Q1EEWdkeaiCt6uRoYCDlRIlB1KjHpfXfVyv2Ej29CYpzJSgDF+iy5dWKURzLsKKPJTfGsGIvjtwYQIJVB0oJWs6dCHV8BmJFThBDrzylsRsr8nSPg31oWd/r3xBIO4eO6v0gVvQFZOKN5FYDI/g7dMdpQ6dqKrko7sszNsilkofm+yyJ1g+Teh5mA5hNMMqFdv4bEDbiJCyTIrffLles2emeQoqMnEFdsDOWWojFVhGNPoOR2IVpNdseIGkln6MDRclC0ypxOk/H5ToPVRvX5xl3fWODPdPDNc0EAiOvg3CinoG2SJRHt+7kZ++6iKXj7Sy0uGny69UbUvvMLithVlkxihDJBFfBb9a8jiUlmqLwvvmz+P26N7l2/iycmsb+phaOtXdw3uRusaALp0/Eqanc9eJr7G9sYcex7tTwmtZ2alrtxa8iBNUFuXz2nGWcMSkzInwX/tcZA0II3n3OHNZvPcy2/XUAROMGz63fxdpNB5hWVcxZCyayYGoFxfkBtAGkgwfDkfrWtHSNts4Iv3pgTcbHR2OJtMnEMCzC0Tgk69kNR1WuN06lytyYcQUowwy/1B1tJZ5UgNy38xh/uuuFAffdu9OWFJXSJhz2Bykl4VCM43VtNBxrp60lRCQcIx6zq1oerxtdclA3LA603U3CSm8/bjbTGn2TWYV39dtXmdidtk1xLBg6B1wIFMf8HsYAyPjbJ1wuVvN/ekhDYDiQUmKG76Nn7FxxnYvQJmc2SeNA9V6L0fZGaptMbLTDDQOtyLuOHeY1CCHAMRdEIJl5AJBAWs0I/u8ZAw8d2szWljpCRow7tr5ModuHQODXnXxt1tkZhQmkNInFXqaz404SiS30F06xh70IptmOaR4hFltNKHQPHu8H8XqvQ1Hs0r+GYfGXf6xlwdxxTJlUwv3/fqNf4aBNW44wY9rIiZP5ub6UZ3Ik+gINnUHihsnkHvn/s8rSxeDCiThr9h9mR30DoXiCmtZ2EpYtSCeE4MwJ4/npS2vZXFvPgooyHt26i/kVZRT4u/kMiqJw9pRqZpYXs2r3IdbtP0xNazuhWBwpwe3QKMsOcFrVWFZOrqI4yzfsb+J/nTEAkOV1cdvHL+Su+1ez+u39qfz+UDTOhu1HeHNHDYW5fuZPKefcxZOZUV2Kx5W5t6CuOZ0BXnO8jb8++cYAew8NKeUJ10R4J+D1Zp6f3YXWHryHDWv2smHN0FUiwc5i6I1wKMZLT23hpae3cPRQE6FgDNMw6crjVRRxwkWhBoNhhTCsYNo2Xcllav5tZDln9n9Qr+wBoWZGWLXj7D2asVoH2DFDKPkorgtG2XiMYcXSi8So7isyP4cQKPpc7IyK5MAvw1iJHahDGAMjgvAglByk2cOgO8UFhkYLJ1rO9vKxM7lwzNSUNlMXFDILE0hpEAr9ic6OO5IaFZlCYppH6ey4nURiK9nZP0RR7LLoR2tbqRpXiGGaPPToW0yaUNyH+9UzLXAkGDsmj1u/eAlgeyOHW7HQlBKEvSLvQk/2vmlZfPeZl6lp7eDK2dMoDvjYXtfAvqbusGXA5eT8qRO4763NTC8p4rlde/nm+WemtQnJ7IqAn6vnT+eKuVMJxxNEEwYgcWgaXocDPcMyzf3hf6UxIISgJD/Atz56Hms2H+TBFzexfX89sYRtqVpSUt/cwRNrdvDChj3MqC7lqrNmsWRmJU59aJGaztB/Z6W9UccI3rmekqJzF1eRP0gVyZ7IL0rfLxKO8/PbHmP18zswLYuSslzmLa6mYnwB2Xle3G4HDqfOH3/xfDdhclShMDH3y/RNZbMHgoHfofQVVsYyrbLXOydOTLlR0WeB8J9QG70hzWNg9hSpcSG0zIqkdHcsx85qkN0TtDR2AmeOSh/T0aVI2BP/nWL2dZF2Hql5i0JXgCsr5mc8IbxUv4PjkQ5WFk+lyB3giSPbuWDMlJQB8MzRnZxdOgltkPaklITD99PR/gO6vUIaqlaBrk1C08ahKPkI4USSwLJaMY0jJBK7McwDyXfbJBp5gnZ0snPuQNMcfO0LF6JpCrG4QcDv4pavXILDkf79/Op3L43gbnXD63FyxmnDfEd7IN/rQRGC2vYOcr0epJQcauk21IOxOM/v2s/d776URZW2B2NfY7rKqRCCd8+ZwYf+9iCv7juIlJL5FQN7p4QQ6KpKllsla/QqRf/nGwMj/TSFsOsBnLVgAounj2XTnlqeWbeTjbuP0tweShWwiMYN3thxhC37jnHG3Cr+5+rlFOf5B/2YeseLS/MDjC/rn+2aYWeHxTn4b4avR3zv3MvmcMa5/afg9UbvtJsXn9zMqme3ISWsvHAm131iJQVFWShqetrOv/68enQvoEd/pISY2Uhr9HUiiaMoiouAYxrZrtmIgUhoahnS2NF9LYndSOfZQ75v9oTYs52xJ7SqF/q0UQ8pSeMgabn1QseKv96XXDdoI13SwT02mf1XoBu0mS55Zxm1NRRkNGl4dUktJ0DGT3mp4ZOFukgbv937CtOzy7iyYn7Gxz1Zu4UX6rbjUnUuKZ/DK3X7OLtsIlLY8kXPHt3F6cVVaMrAz9A0D9LZ8X26DAFNq8bn+zhO5xkoagGg9fl+wcSy2knENxIM/pZ4/DVAEok8itO1Arf7ypTAkKapvOvSebhcep/spNKSbJzDXM2PJvK8Hk4bV8GdL6/jE8sXETUM/r1peyqbwKGpZHtcvH64hlyvm5rWdp7cvqvPOmpcXg7VBXncvfp1zp8yEbfeVz1WAnHDIGaYmIPUTukJXVXxuzLLMvmPNwYM02R4BR/TIYTA53GydNY4Fk0fS83xNl7beoiX3tjDnsMNxJIhhFjc4Ln1uznW2MGPP3MpeVkD55/6eokSzZ9awZc/eNaI+wi2hPH/BZSU5aCqCqZpsW9nHSvOmz6iSenFJzcjJXj9Lm743Lnk5vdd5UppyxSfLAQTe9jReAsSA13JwSJObcf95HmWMzH3Syi9JkEhBIprRVqaoBV9FtX3UWAQiV8Zxoqmu9+VDFUAB4JQh0fGzASyRwaBvaETo+3zI2ipV6w5wwnbliPutEmM8Y3IxHbbkLDakpN+ArsATtc/q6/H5f8YCp32d7Oj/RgtkQRbW4/x/U3PI4RdHtelaoPq2kspCXbejZXMdNG0qeTm/hZVG5iLYm/XUNU8FNfZOJzzaWv7GtHIY4BJsPOXuN0X0/VN6JrKpRfN7retC86ZgTbCtMLRgKoo3Hzemfxi1TrufGUdeV4PHzltPg9v3mEXqNM0vnn+mdy7YRNbj9VT4PPyxZXLeHjLjjSDQFUUrpo9na899hx3vOvCtHtnScnmmjqe3b6XPcebaA9HSWRY32dJVQVfu3BFRvv+xxsDwXBsVMpQCmHnoo4vy2NcaS6XLJ/G27uO8sCLm3hr59GUpbVtfx1/eux1vvj+Mwd8mXtXXGxqC6JrI4/VnGqkrGspT2pMvT8UlmRTXJ5D7eFm1r+6m/ffuAL3MLXALUvS2mwTCnNyvWT3I5oEduZCwzDUxbrIkF21CgaDlBZ7W35MvucMygPvQRNeJBaRxGG2Nn6F1ugb5Ln7SgQrzrNB+SlYdsxQGjsxQ39G9X4YcPSzgophhn6Xno6oltspdCeE0VNyS8Fq6WdjXxLZ8DHUs5BJg+lpzPD9tuyyDA56zP+HjSyH7alrjYf4xMSz2N/ZxHuq5qIKW5C4xBPoE7vuCSk7iUafTf6lk5V926CGQG/YujA5ZGV9h3hsA5ZVj2HsI5HYhcMxO3mOgbMKfL7h1ck4GSgO+PjuRWcTiidwahpOTWVx5Rgcqp3Kfkb1OBaOHUPCNPE4dDRFYVZ5Sdp9lVLS0BliWkkh4/Jy0rav2n2Qrz/8LG3hHtokGd7fcQUZcpI4ZcZAb+GZzI/s0gsYTQghCHhdnD63inlTx3DfMxv542PrUzK9z63fxY3vOo2At/8XbWxJLkrScgY7u8AwrRELX5xq5BbYqwHLkhw+0EBJ+cjLKQ8XukPlkqsX8Ns7nqH2cBMP3buOd1+/rI9oENgfQjxmkEiY+HpoDChC4PHZ3plQMEo0ksDjTffWJOIGD9yzhkgo89KpXfwFI2Fy9HAzOfkDM3JNGSEY38+0gttxqt0hIp9jMoXes2mN9G8MoOSjem/E7Pwhdj69hdn5c2RiN4r7YoRaYcdWZczW2488ihV9ju4J0YHm/7zNgj8hnITnLXt7YQSI0QhqDmy42EWWjmN0fBsr+hLdEs5d0EHxI0QAhNcWIBJOhHCBcGDFXmVoKeb/nZBSEjHt+2VJSY7Tw8enLKPMY+feC4YmIxrG4R5egWocjsz5Cj2hKAW43OcTDt0DWMTjb6aMAcuSPPvCNpYvnYj/P2Dy7w0hBFoyht8Fp6al/e5x6NjE2PTfDcuiORSmKRjinxu38IWVy9IUHyXwu1c30BaOUhTwce60CVTm5eBx6Bl9wSXZmfOCTokx0LNcoyVlMo1uaEgp2bir5mR1yw4huJ1cd/EC1mw+wM6DNvmpIxjleHPngMZAWUEWuVkemtrs1Wl9cydH6lupKh8kX/w/CJOmltmV+WIGf737JfwBN6VjcgFBIm4QDsUoKs3uM8GOBoQQnHf5XNav2s2mNw7yzz++St3RFlacP4OikmxUTSEeM2htDnJw73He3nCA01ZM5sIre8RBBcxdNJ79u+poawlx/5/XcPHVC/D6nRiGRWN9O0899BbPPLIRf8BNZ0dmbuZpsytQNQXTsPjTL57nxi9dQFGJPTDG4wbhYIzSMbm43A4EKkKoGFYHDqVnMSVJ3GzGM4CsrRAC1ft+pLEfK/Igtrs6nixs8wwofuzJLw5WJ2mFg4QH1fcJFNfF/5leqN46BGopetZPOGHDQx2EjyNDJNq+goz35IYIhFqJ4joT4ViEUMcglOykEqEOQsEmckrijeeC+X/PGLANgTgbmmxJ9UKXPWn4NAd/3L0eQ5p8bPJpvNFwmEUFA/NTTLOWLkNV16cxLFGCHhBC4HQsShoDYCS6s4yCoRi//fMqFswb9x9pDJwI2iJRvvLoswRjMVZOHM+ZE9K9KpZlcbzD9nJ97YIzOGfahIy9AsPFKTEGesff99Y0Mrly6FShcDTB69sOn6xupaBrKtXl+SljQGJzCAaC26mzbPZ4HnllK2AXLbr/+bf5ynVnZ1x2951ESXkO51wym6ceeosDe47zzU//3Y65C4jHDKKROLf+7L1MnZVZKefhwu1x8IVvX86dtz3O26/v54UnNvPq89vx+lwoqsBImETDcWIx+xnMXVSVdrwQgkvfs4h1r+yi9nAz/7pnNa++sJ3sHC/xuEFjfTsd7RHOOGcaVZNL+MOdz2XUr8rqQk4/ZxqvPLOVnVuPcvMn/0ZOnhdk9325/fcfYlx1EYpwUuBZwa6m26jI+iAutRBLJmiLbaQlsp6xxR8a8DxCuNACt2Bq4zBDv4VUmmA8FT5IhxOhT7blfF3nDC3P+w5BKL1UFKVEOOYiTjDzYSBIKTEj9yPjPTU+BIr7SjTfp0EtHVSsSMrRFaI6laiLtHGgs1vueV+nPXZ1JqKsa9w30GEpBI0oz9dtZ1d7HQJYlF+FlJLf736Nsb4cXjy2F4Hg7/veZE5eeUqeuDd6EjAVJfvESK09JLxlj9oRUko0VcU9BMlaSsneAw3k5njJyfLYtVpOgdEspURaEjGC8+W4XXz7wrNQhKA44ENX040pVVGYWJRPXXsnLj0zb8BIcdJHFSEEU8cX8eIb3ZXLnlq7k/OXTBnUrS6l5InV2znePJycVTBNa9gvgSUlRxvaUn8riiAwiAUqhODdZ8/mmdd2pYQwnl63k6WzxrN8Tubxsq7iFuopemm7oKgKH/nMOWTleHn1+e20NHZyvK4NIQQOp0Yg24PT1b9rVlEE5WPziETi/ZL2MoEQgoLiLL72g6t46aktrHp2G7VHmgmHYkgpUVUFj99FZXUW0+eOZfEZfav/FRQFuPlH7+Zvv32FHVtqaDreQWN9O7pDo6AowBXXLuaSdy+ipbGD5x57m+JehY76g6apfOLLF5JX4GfdK7toaw5x/Jh9X5xOjaxcb4rhLISgKvtT7Gv9BXtabkdKAxA41Xwm530Dj1Y5xE1wIbQqul2HOkKfic2AN5IyvdkIrQrhWITimAvixAbbkw3R+5plux27F0Pf+5EhgRX6Bz05BUKfixb4FkLJpACNlSQU/vdhTcNefrT9qdTfqZBlqJnPvvmPIY83pYWZrJg4NauUhfnjATgaauOTU5ez9vhBQGIh0wTWekMR3ffZslpOSAjL6sE5EUp3zQa/z8W0KaVs2XaUxQurBlxwxeMGt/7kcVRFYXJ1EfNmjeXMpZNPerbBkZ21PHr3s9x0xwdxDFCobiCoisLY3OxB9hB8/txlHGvv4OcvrkVXFSYW5+N1OFCVZKHpQW63QGQsrHdKlhiLplfy6wfXpoR1Nu2p5bFV27j0jOl9DAIpJQnT4tW39vG7h9cNO4/gta2H2H+0iSUzKxlTmI1rEOlhKSUJw+L513exbX933e/ivACFgxapgfFl+VyxYgb3PWsXJ4nGDb73p+f4xNXLWLlgAj53/4I89jlNmttDbD9wnM17a/nse84Y1UJKmcDrc/GBm1Zw0VXzaaxvJxyKo2oKPr+L7FwvOXn9X7/L7eA7v7gWKcHpGvnrI4TAH3Bz6TULOfviWTQe76C9NYSRMHG6dALZHnLzfHh8rn4/fiEE4yYU8ZXvX8nx2jZamjuxTInX76KwOIusHC+KInC78/jR7z6UquA4FALZHj786XO4/L2LaTzeTiScQNMUfAF38r50D366msWkvK8RM+qJW60owoFLLUFThk5NtaLPYbR/FWQH4ET1fwnVc3VyDwtQkil5jmSa0n+uEdAF27hJhjgAZBhp7Ec4Mk91GxasJqSZHkZUvR+ydQoygYz+1xINF+WP5/qqZWxqPcLBYCPNMXslLQHDGtrAEULg11zMyhnD56eej0e1jf9FBWP53a51HAt38Pvdr1HuzcY1gFcAQNXKsN9NSSKxHZswOnxPkM0PWpv6W9fSFwCnLarid/e8ysbNRygs8NPz81qysJryshwamjupO96OaVocPtrM2jf2c9qCqgGNAcuSvLJuN9GovaDTNJUzl00aNvcrEozScLgRmWG633AgBEwozOO2y8/lm48+z6fue5ySLD95Pg8ehz5kyGD2mBJuOH1hRuc6JcbA+NJcZk8s482d9odrmhY//+cqdh1uYOWCCRTl+tFUhUgsQc3xNla9tY9X395PJJagtCCLlvYQ0UHc9j1R39zB3Q+s4R/PvEVVeT7Tq0uYWFFAUa4fn9tpp7VZFqFInJrjrazZdJA1mw6QMOwPSAh49zmzceqD3xpFEXz0ssXsP9rEhu12SlVrZ4Qf/fUlnli9nUXTxzK+PB9/Mg0xFjdo7QhT09DGvpomDtQ209DSSX62l8+858TSxEYKRVHILwxkLPxjHyMGNBRGAiEEXp8L7whigfaKXadifAEV4/svdauoyrD7q6oKBcVZFBQPXsRESoklIyAUHIq9+jWsDgyrA13NRlMGOK/VjNHxnaQhAIrn3aje6+hbKOi/DEoRQhuXLLkMIDEjjyH0eSfFoyGtNtIJgwpCz0z6GEAaB0bRGOh9zpMrXlThzePjE1cSNRMcj3bwfN12frn7Bco9Odw0ceWgxwrAoWiUuLOp8hfgVruzWK4eN5s1xw/i0RwUuf28r2reoG2pagWKkodlNWEY+4nF1uF0nj6s5y2lxDRriEa7Um41HM5Fqd9N0+L1Nw+Sm+PlwMEGDhxsSLvdkyYUU16Ww5GjLWnZUfm5PryD8J4sy+LX96yiocn+Dr0eJ6ctqELX1DTXv7QkRsJA07W0UEDXPtVzxvH1v38GxwDeVCklRtywwx292rD7ITHiCVRNRelHQXB/Ywu3PfEy+xtaMCyL/Y0t7G/sL3OnL1Ql85T1U2IMqKrCp645nc/+9GFaO2yyTjRu8OiqrTyzbidetwNFEcQTJuFoPOVByPG7+eZHz+Unf3uZfTWZq8hJ7In5zZ01vLmzBlWxZSaduoaqKliWRTRuEIklUhkEYE8uZy+cyBUrZmb0Mvs8Tr750fP4wT0v8NqWQ1jJVf/mvcfYvPcYmqqkdK8ty8Iw5YAFbv4//rsgpaQ1uoH9rXeRsNr6/D4++xMU+y7s91grvh6sbk+U6r70v98QAEBFcb8Ls/MHqS1W5AnwfhiGCpuMCP19o5mG6CzM8L/oqpB4wj0RrvTp3zoZqpe9zylwaw4qffmcWzqdX+95iWyHh4vLZo3I+JJSsqmllp1t9VhSciTYyv0HNnHdhIUDKhAK4cPlOodw+D7AoL39ZnJzfo2WoaiVXdionva2r2Il+TK6PhNN6666qusqX/38BQOaV10r+UM16Xyb8pKcIVfOUspUdlvPuSAeifOHr/2Ds65dzrN/eYWmoy3kleZw5WcvonxiCUIIwp0R/nzzP4mF4zi9Tm768QfQepSxl1LSerydZ//8Mrvf3I+RMMkryeH9N19JwZg8pJTUH2zgsV8/R+2+ejx+N2ddu4x558xESU7iUkp++vwattbWo6sqc8aUUFWYR8CVmRT8hKLMxfBOiTEghGByZSHfuuE8fvr3VzhS3y3XGEsYKZng1P7Y6Xufu3YFcyaVU1Wen7ExkBvw4PM4CfYQm+nyBIQiA2cx5ATcXLJ8OtddvACXIzM3lxCCghwf377xAv71/Ns8+spWGlq7VxqGaQ1ac8DncTK9quSEV022gSGxB7YucXEFRlLrfhTR3a8ut6WCrXYOnMJ+9X9/7AFkpPdHYrKn5XaynDMp9J6D6PUpefTKgY/tpagnjX1Ifc6gZLf/BtilhK/ADP2529iRbSQ6bkXP/tmwOA9dJXDtd7j/YUooWaTVMcBCGvtAGz9k2zL+WlrhpxOGml4sx4pvRErjlJE9C5x+/PqJM+0fOLiJyypmEHDYbWlCGXRCtT17HycSeRop2zCN/bS0fBiv98O4XGejqKV2GmdyLOr+FuOYZiPx2GuEQn8gkdiWbNGJP/BFeoYaujyA0GU8yKQ+Qfr3W9crDb2kKGvE37eRMHnhb6+ya8M+zrv+TBacN5vHf/McP//E77n92ZtRNRWHy8GyKxaxfd1uHv3VM3zsh9emtRENx7j9ul+SiBmc88HTcXmdHN5+NGUwBNvC/OADdzFx3ngu/OhZHNtXx503/Z6v3vspZiybDNhz1+56myh6w/L5fGjpPLxOx0kJGp4yWrIQgiUzKrnzC1fw9NqdvLb1EHVNHURicUxLogiB26lTkh/gtFnjuGjpVEryAwghWDZrPI3JSbayZHARhWWzx3Pn56/gta2H2La/jvrmDjpDMWIJA9O0UlOBrql43Q6K8wLMmVTOmfOrmTCmILWSH851BbwuPnLpYs5eMJFXNu5jw/Yj1Da0E4zEMAwTie2ucTpUsrxuSguzmFldyoKpY6guzx9RqoiUFliNWMYerMQuW/3Nak3WDNdBybHTq/SpKNpEUHJPaLKR0rJldHsQroQ21k7ZStvPRFr1yMQOrMQOpHkUKYMgLXtQUPJRtPEo+hSENgHE8KtrZdZfM3l/difvz2Gw2pMDtG6XvNUqEdqUHvdnOKTTKHGzmfHZn8SpDS+lVGjptdeNjh+hGvsR+pzkBNe7H8JOi1OyEUreSbtnowIlD83/GYz2m+kyAmVsNYnWz6L5Pwf6VKB/Hk+XyBJmI1Zia1Kd8RMIvS+BtOtcqCXQQ/nQDN2D4jgNIbz9GpxSJrBia+z+jaK+gOJYhBW+t/s8ie1Y0WeTxaBOvpHnVnXG+woyKio0GKoDBTx8eAsFLjvE5dMdTMwqHHSM0rQqAoEv095+K/Ykf5SOjtsIBn+Hpo1H1SpQlVw79VQmsKw2TPMohnEA0zxGtzCVis9/E07n8j7vh2VJdu2pY826vRxv7MDtcjBrRjlLF0/AkxQt6+xVtCgnO0PuyACwLMmKa07johtsddmcwiy+esH3CHdE8Of60B0as8+chhDw6K+e6eO52PLKDvZvOsTdb/yQgjF5PYwhG+ufeJNQW5jrb3sPDpeDuWfPYOvqXTz6q2eYvnSSrViaJBgea+tk6YTKjKWFR4JTmqMkhKC8MJuPXr6Y95w3l+ef2UxDYwfzl09EU1UCPhcF2V58nnQXyFkLJ3L63CpqDzWxftUuHvjzGsor8zlt5ZQ+53DoGkVeD+NUFx/81CWEonE6QzFC0TjxhJFi77udDgI+Fzl+N+5eJEPDMHn+sbdZfPokcjJkzCuKYFxZHpWlubznnLm0doZp64wQSZYrdugaXreDLJ8bv9eJQ+srspMJ7El5N0bkQazYKqRRCwwiqSrcCHUsqutcVPe7kqI2I5lIYsRbP47sUbJXD3wNzfPeZL8k0jyIEf47VvTF5Oq3b8lRABMBwoeiTUL1XI3quhCEd1QmuNT9CT9g3x+zlp4lddP7AQhP8v6cl7w/5Rn1QxEOdCXL1hmQecPqu+JYhNAmII1kLrVsxQz9Aftz1PrxdCc9GcKHUMtQnMtR3ZfCCdYnOBkQQqC4r0BJbMUK/5Mub4yMrybRug3FsRDhWGjn/gsPYCFlGKxmpHEIy9iDNPYnCx4lksqMA8GB6r4UM/jL1BYZfx2j/cuong/Z2Q3CZffBCiLNA5iRJ209B9mZ/DbGII09A54hUyiORaDk9wgPJDDab0E19tjS0V1VJ2UiWSuhHWk2I/RpKAMZO8PELTMutavonQCOBFu5ZvwcspPKhEN5BiApquO9FksGCXb+wjb+sbCseuLxeoivG/K8Qrjxej+K3/+ZPiEzKSVrXtvL7/+8ijFj8ijI8xGJJLjvgQ1seOsgX/rM+TgcWqo6bRd0/cRDb1OXTEx9Y74cL5ZpJSuj9nMNvf7e+fpeyiaUpAwB+zq799q2ZhedrUHu+MhvUtsO7zyKr0cqvgC+cO5yvvrvZ7jv9U0UB3zk+7xoqpKxdyDTMeIdSVgWQuD3ODm26zitzUHm33j2oPtrqoKmKvj9LgJ+Ny8+sZmC4kC/xgDAgd11PHbfelZcMJP8bB/52cMjkMWiCf7917WMGVeQsTHQBSEEbpeO25VFacHgBLThQlodGKF7MMJ/B6shw4MiSGMXRnA3ZuRRNN9NybKyw7QwpURaQXqW4LXibyHd7wEkVvRpEp0/6qtP339jIDuxEm9itW/CjD6PHvgWqGUjn9ykRMogRugvGOF7h3F/wkhjJ0ZwV/L+fBLVfUm/ufFSWgTje7CSRk6+ZwW7mr/LmMD7cWoF9BwO3FoZjoHKE4sstKzbMdo+Z3ssUjDsfwON5zKItOoxE29hRh5GC3wNxXn2f1x4QQgHmv+rGIAVfoCUUWi1YkWfheiz2G5gW/inu1bA8CYyIQSq54NY0Wfs8ABgv4vPYMXWIJRCUHwgDaTsALOJbsPZher/EkLJxWj77AleMaDkono/1kNZEpBtmMG77LCJ8GC/HwbIuP2PBFrW92AUjAEhBOP9hSfczhhvNg8e3IxPdyIAr+7g01NPRxmC0yKEjs93E7o2kc7gXSTiW0kTzBoQGro+FZ/vE7jcF/T73SUSJn/662o++qHTWbLIJvhZlqSxqZOv3/oQ23ceY86sij4pdF1ZAiOFAHTHyKdI07AGzWIyDYuCMfmsfN+yNEvCl9Xt0RBCkOVxce2i2fx21Qau//NDjC/IId/nxet0oCnKoBHXCYX5XDq7/3myN065MTAUgW6wutxFpTlc+t7FHNrXQEdbqPehKcxdUs34ScUDWoa9+9D7PB6vk+/c9QHyB2CTn2jt8OG20SW5Gm+/BSv2EiMjPUmkeZhE+7ewEjvR/V9Ky+UdCazELsDCjDxKouNbI2RmG1ixF0i0teDI+S2ow1dxtO9PE/GOW7CizzPy+3OQRPs3kOYBNN+n+hhMEoNdzd8hnhIJEhhWJ7uab0MVrrQPuir7kxT7LuqnrzGs2FqsyL/tCWqkMA9jtH0FPfd3oI9MAvZkQig+tMDNWNpkjNAfwKwhfbJPVg4cEAooRTCUXoCSh5b1E4z2L6ev8GUQaQbTiih2H1OA5v8civsqMOtI5x2MDF3Kkpi1mOF/kuaNksH/ihRGIQTvq5rHoWALdeF2sh0eJmTlZxx6EELD6ToHh3MBsdg6otEXMBI7MK0mpBXBfhgKQrhR1Dx0bRJO19k4nctQlIG9a8FQjFA4zqL543Eks7xUVVBUGGDG9DHsP9jAnFkV+Hzp3+vRurYT0j04UUyYN45n//IK7Y0dZBUE0sIEQgimLJ7I9nW7mXfOTNzJbCq7zkb6nPCLF9fx7Pa9xJMeiSMtbRn34Zyp1f95xoCUktbmIGte2E5rU5ApM8f0WQgkEiabNxxgx+Yj6LrG3MVVTJhWlrGqXzgU46UnNyMtidvrYOVFs/tYTdFInM0bDrB35zGMhEVxeQ7Lz5mWSm3r6h/A0rOn9hHWCQWjvLF6D4f2HSeQ7WHR6ZMorbBf5FAwyqpntrLyolm43HYcq/F4O2+v389ZF8+2MxlMi+2bjrBt42GikTj5RQFmLxzPmHH9p8YBYLUQb/s8Vvy1fn4USX5AKULJx87zjiHNJttFLttJv9FxzPDfQIbRA99GKCOPq0nzGFb8NRId3+k12KkIpQChltr16VFBhpBm3YBueyuxkUTn7ehZPxw+s162EW/7Ilb81X5+VEEpQFFLkn3RbG+J1Zi8P71FraIYwd8AAs33mTQCmEBjasH3MlKuc6p9n6e02jE6fmgT12QEEKBWoOizbR5Bv/nxVvLeNSCN/cjETlKrW9mO0XE7et59DJnbrRSA0oNgNSo1AwaHEC4Uz7U4nMsxo08mw0eHwApie0G63suuMIgboeQhtAkozuUI52kIdeyA7dvnEKDPQM/5PWb4L5jRF8Gsx16V9mzfCWo+imMRqueDyRLOClItRujTkWaS8Dhcj1mv61UDX0U45mKG70Mmdiffr54EaYEtiexBqAW2wfMfAiklm1tqefzIdnKdHjoSUSZnFfGB6sx1IroKD7lcF+JynY+UnVhWG9IKIkkAGoriRRHZScXBocOlToeGEHC8sYOK8tzUpJowTI7WtjB7hi39XVqUnXbcjt3HSCTMlFDYaKPhSBONNc3s2XiQRMxg86s78GV5GTutHI/fzdyzZlBUkc8dN/yGc69bgdPt4Nj+ehZfNI/icYUsu2IhT/3hBe761J9YfuUihBAc3XOMyQurmb50cuo8lXk5LBk/MjXYaaWZv1+nzBiIRRL8+BsPYZkWM+eP48UnN3No73HKx9krQcuSPPDn1ax/ZRfzTqumrSXIj77xIDd9+ULmL52QsXUXjyXYtfUou7YeZcUFM1GU7oklHje4+4dPsnvrUWbMq0R3aLz28k4WLp+YMgYsU9Lc1MnD966jakpJmjEQjyW467uP0dzYyawF4zi0r4HnH9vEl753JeMnFtPRFuZ3P3maRWdMThkDtYeauOeuF1hxwUxUVeHt1w/wqx88zmlnTsHldrD1rUNoujqgMSBlnETHd/o1BIRajuq+BtW10p54hRubsW/Z7m+zFjP6Amb4AaTVk8FuYUb+jVAK0fyfH3lam2wj3valpMEBIBD6LDTPe1EcC2zjRDhJuUetDqzEVozQn5PXk24NmpHH0DzvRzhmZd4FmSDR8f1+DIHB+mKCFcIyD2FGHseMPJzK+U/2BCP4OxRtMoqru5yoEApefVzyvMPzDkmZwOi4DSvy7+R1Kyiea9C8N9kkuEEGxRQDWwaxoi9gtH+DLoNKJrYgzSNJwZ+BoWf/0I5Xd0EZPOUoYcVJWAmcqhNVaFjSpNPoIGzYRp9X8+HTAgiGrtYZIY+gdjmG+3QcNOMTQXTZbnMFwOYOKDlECSDUYtx6OT0rOMatGIZloAiBU3GnnS9mRjGliVMtRfV/DdX7YdtoMmuRViegIJQAQi2xDQu1GNB6tKGh5/wq6bYH1BNztQvhRHFdjOJciTQPI43DtjiSjIJQEcIPSj5CLUyGMXJO6HyjjYcPb+Ebs84l1+khZhl8acNjvHv8bFzq8ISE7PurIkQ2Si+S8XDh8Tg48/TJ/PCOpzj/7OkU5PuJRBNseOsgwWCMebNtg7F6XPqzq61v4+W1uznnjKnDlolXdZVFF83F28Nl7/a5WHzxPPRkZsOWV3fw1vNbSMQNZq2YyvP3voqmqbz3q5fjmVyGN8vD1//xGR791TM88dsXkFKSW5TF0sttEaBAno9v/OOzPHr3Mzz6y2eQUlIwJo9FF85N68vHTl+ANbwIWgrDuexRMQYi0QRGsr6yIgQet6PPALHlzYMc3FPPL//1CfIK/ERCcT7z/t+mfm+sa+PRf7zGbb++jurJJUgp8Xid3Pvrl5i7pDojhT6P18m7PrCU9a/sYteWo31+3/rmIV57eSc//N31jJtYjBC25ryjh0LV6edNZ+HpE3ny/g19PBebNhxg61uH+PnfbySvMIBpWvz6h0/yl1++wK132mkl9hzRfaDs3gjA9rcPU1CUxQc+cRYOp4ZpWAOGTmzt9Ucwo0/2+U1xLEIPfAehDWAoCSdCyUFoU1Fd55No/yZW4o0eO1gYoT+hOBehOPqydzOD7JEvr6J63ofu+zT06/LTQXWjKIU4HPNJdHzHnoTTEMcI/w1dz0znQUqJGX0KM/JIr19UVM+1yb70lyWgg+qy66nrs1Cdy4m3f7VXbnicROcPcToWD1AoR1LTeR+lvivQeq3oWyKvo6s5+B0Tu/eOv40VeZSud0M45qH5v5GRZ8buvwARQHFfhog8iIyvT/5qIBN7YBBjQAiRNDgyx+vNL7OhZRXnFV9JqXssLzY8xt7ObYRN2xjwqD4m+KdzVuGlZOt9n7eUkpDZyfrml9ja/gZt8RZMaeBQnBS5yjkt7yymZs1BTXpeElacew/+hLgV46PjvoRHc6baebruAfYHd+BWvXxk/JdwCEfqt3/X3kN9tIZrKz5JoavErkeglva5nqgZ4kBwMy3xDShCochZyXjf7OS9KR7WvRkKQgibEKtMTWZPZIaQ0c629jVMz1qGVxtdvlGmcCgazbEwbk2nMxEDJJlT1U4OhBBcd+1S/v3oWzzzwjZC4Ri6pjKxuohvfPli/MmKptXjCnA59ZREvGVJfvGHl2hs7mTZomrycnw4HFqKEGmYZp8KuoZhkkiYKLrKZ393I6qmkkgSEwMFAT7/h5tQdXvbsqsWs/Rdi+iNnsfkl+dx/ffeSzwSt4nkLgdqUtRICEFpVRE3/vgDxMK2MepIau70hKaeOBEyE4yKMXDPw+t5Y4tNhCrOD3Db5y7p457fvukIpRV55BXYUq1ur4MJU0uJJR/cvl11xGMG61/eyRuv2rG/uqOtHDvSQjyWwO058ZSKja/to7K6iPGTilODl3OI4hfpx+9nbHUheYV2/EfTVJaePY3bv3o/sQzJKkvOnMzq57fz4288yOnnzWDm/HFk5QwwIcg2jODP6R34FNokHNk/A6V4yElTCAW0Seg5dxJvub4XczpKouN2nHnzB3BRZw7VfTl64OvJnOLB+iNAZKMHbsaKv227jXvAjK1BJwpk4MaWHRidPyXdDdvVl68N2Re7PxqKcyV64Fsk2j6X1pY0j2KE/4Hm+2TfyQ6Lmva/UeQ9D430e9cWexspzZQxYBstj6S1rXquRSjDd9ULoaLoUzBTxgBgZaZGNhy0JVqoiRzg7bb1vHD8EWojhylwFlPsKidshGiI1bK26TnqIke4YfxXcKnp19JptPG3w79iX3A7TsVFgbMEp+qiM9HO3uBWDoR2srLwUs4uuhxVqGhCJ2wEqYkcpCXehEez+SwWJtva36QpXo+CQlu82Z70AUMm2N25hagZxqsNzH+xpMmTx37LgdBmSly2BkFHopnxvtmjft9OBO2JRp6u+x0VninviDEghOC6CQv4w27bayeBKytnoSunZjIaCFJK3C6da69ZzGUXzyESiaNpKgG/C7WHYl9ejpcZU8p4Y9Oh1LGdwSi//9tqHnxiI4X5fgI+Vyp9XEpo7+xOL43FEtz2syeHnV4+XOi6ylc/dQG+pDqioigpzsBowZKScDyBpghcemZz3KgYA8eOt7PrgF01KzyAsE80HE+beIUQuNyOlDEQDsZQVIVEwkxJShaXZXPF+5egjpJlFApGT6gsbzgUS7n/u+B26yTi5oDiQrKXf6d6SinfuvN9rHpmK//+61r+fe86bvjC+UydNSZtwunyCvQWqAGHzbzPwBDoghAClBL0wDeJt1xPT7KUNHZixp5HdV06YqKNUErQ/V/JaPLtPigHzftBm2/QE1Yj0qxDZCAcY0ae6Ju9oBSj+788rL4IIVBd52E6FmL1SoMyI/ejeT+SMpa6vTgy9Z90z47EsDoRvUq5ysTuHn8JhDaRfnIIh4StmNZLmvsE4txDYVPba7hVD++puImJ/uk4FCdxK8bmttd5uPYvHAjtYlv7m8zPXZ46xpQmj9Tey97gNoqcZbyr/HrK3ZVoik7UjLCt/U0eO/Z3nj/+MAXOEuZkLwGgwlNFTeQA9dEayj2VAHQm2mlLNFPmrqQ2cojayMGUMRA0OggaHRQ4i3GrAxMNg0YbOzte46yiDzA/93wEYGH9x5Euu/HOqZROziriljnn0R6P4NEc5Djc7/h9SiRMHn58I7NnVlBZkYfP23/tD0VRuPbKRWzeXpOWZigltLSGaGkdmHQOYFqSDW8fGu3u94HDoaXk708W2sIRPn3fE8ytKOXz5y7L6JhTlpNUXJ5DS2MnVnLSlFLSdLw97XdFEVz5waVc98mzU//e97EV6I7RMQbKK/OpO9qScuEMF5XVRRw/1oZlytQ11BxsIjvXi8ulp9w7ptF9jfW1rX3aKa/M5303ruDWX1zL2KpC/vrLF/o5m4ER/lefrYpjkZ2rPcwPVAiRPLa3W0tihv5K/7TrzKB6P2DnWA+3P84z6WuPmkizb4inLwyMcN/qbJrnPTZZbpgQQkfzXNtnuzRrsYwdadsOd9zDnpbbiVst7G/9ObtbfpD6t7P529QHnyDH1Yt0JXsbySMd8E1kYlN639UxI2wrg7NJg/OLr2ZO9hJ8WgCH4sSnBViSt5JKzwQAtne8lXbMschhtrRvQBUaV4/5KBN80/BoPhyKk4CezZK8lZxRcCGmNHi2/gESMo4QgnE+mzR1OLwvZWDVRg5jyASLclegoLI3uD31W2OsDlMalLkrEf0YVnbtCJOQ0Y4h4xS7KlN7acLRZ9+4FSVotBE1Q31Cd5a0MKx4n+2mNDCsRGq7KQ1MaSKlJGaGCRptxMxIv6FAKSVRM0TIaMOwTiybASBhmClF11jCIJ4whiV/LqVk9fEDaEJBIPjD7tdYVb9/0DZs49RESiP535EbMpbVhmEcxjAOI2U3yVgI2L7zGLf96Alu/f5jPPP8NuqTBYl6n2/WtHKuumTeSV/d/6cjGIvz9pFjHGruO/8MhFNGIJy/dAL3/X4VrzyzlZnzx3H0UBP7d9czeaYt41k9pZTSijz++qsXueSaRThcGm0tIRIxg1kLxyOlpLM9QiyaIBKKEY0kUiVrs3I8CCEIB6OEw3FaW4KYpkljfXuqAp6mqSxdOZWH713H/X98ldPPm46qqTQdb2f8pBL8ATeGYdLRFibUGcWyJC1NnTQ3dOD2OvF4nSw9ayqP/uM1nn/sbeYuqaK9NcSj963nvHfNQ9UU/FkefH4Xrzy9hRUXzqSjNcwLj72ddh82vrafQLab7FwfRsJEiP5DFdI4bIuv9ILmee+ICX9CaGie9xJPq/8OVmIb0jyG0EbCWHWiui4e0erBJlAF+ri5pTm0RoA0j3SL9qSgo7pH1hfALhOMk/RsB4kVW43ao/JelnMGphUCaZGwOlB6DFyK0KjK+TS57iXpjav5PaIEEhl/A6llXlQHkpNb9NlkRkESIoDQJ2TcxnDhVj3Mzl7cp5+KUKnwVrM/tJO2eHrp2k1t6zGlQamrgkrvxD7HCqGwKG8FLzU8RmOsnmORI1R6J1DmHotAoTZyKHW9e4PbUIXKBP90fFrANhSQCAQ14QMAjPf2fx83tj7H4dB22hL2+/Ti8b/hUu1wwsqi91HqtvXvTZlgS9sqtrS9QsQMoisuJgcWsSD3AhyK7WGqjx7k5eN/57KyT+HTu0l/b7Q8zfHIQS4p+yQCwbqmRxAIfFoOm9peJGKGcCke5uSczczsFSjJND3TSvBG6zNsa1uNKRPkOcuYHFh8Qs/q5/etoqa+LfW3pip862PnD1qOvTcePLiJBfkV/GH3aywsHMsDB9/mtKJxOAYYc6SM0N5+M8gooOD2XJ1UEBz+ZBwK/Z1w6B4AcnL/gCNJJNY0la9/8SIO1zSzfsN+nnhmMw8+8ibTppZx+tKJzJhalpIrVlWF699zGl6Pg4eeeJuWQVLQ/zcjEk8MWnq6P5wyY6BsbD7Xf/ocHvn7azzz77fILfBz6XsX0dJop3a53Dpf/O67uPful/jZrY+AAE1TWHnR7JQxcO/dL9FQ10ZrcxDLktz1vccIZHn41M2X4nTpPPfo22zacIBQZxR/wM3dP3gCh1Pno587j5IxuRSX5/C5b1/Bg/esYcPqPQhF4HBofOl7V+IPuKk/2sqffv4c8ZhBfmGAx//5Oi88von5Sydw8bsXUliSxWe+eRn3/2k1Lzz+NpYlmXdaNZe91x4s3R4H13/mXB75+2tsWL0Hl9vB2ZfO4ZWnt6Y4FHt21LJh1W5UTUFKu+Tu9Z8+p89gZsU30DsWjnCjnGA5WMWxwHZ5p0mxxrHiG1BGYAwIbSximOS07oOdCOFD0ivmnUFOthV7nd654UItOrFVspJrs7t7FBECbG6D7NJDF+S45pPtnEtL9HUm530DRxrB0Cb79XyetldmOWZsVWqbEfodumMuaFOHHDjt3ON2rOjTGJ130PO9UFzngzh5jPQsPRfPAC54p2KHJyx6umQlR8K2AFCZuxJlAOdjlp6DX8uiNdFETXg/ld4JZOt5eFQvzbEGDJlAExoHQ7vxqgFyHQUUukqpCR8gYobwqD4Oh/ciEIzx9B9SytILqfQqdBotHAnvpNI7nVyH/a76tOxUf99ofoZVjf9iYe6FlLqraY3Xs675UToTzZxb/GEUoRAxO9gXfJuETE+LbYzWcDjc7Tmqjx7kUHAruY4S5uaei0v1sq3tVZ6q+y3FrnEUu8chpWRL+ypeOv43FuReSIVnCg2xw7zScF8GT2RgbN1Xx66Dx1N/65qayk3PFKpQ2N/ZRHMsxLllk3ixdjeGZeIYkDeQIBp5HCntSTcae5mA/0t4vO9D9PK+DAVpdWCatck/uj1pQghcLp1JE4qZWF3EFZfOZc++47zy6i5+eMdTfPlzF7Bw3rjU/k6nzrVXLmLxvPG8vGY3b2+v4XhjB+GwXQQv5U2Q9HHXj4Zq4VBw6OpJp2SG48P3NJ0yY0BRBOddMY9Fp08iEomTm+9DVVXiSc6AEILyyny+eNuVtDZ3YiRMvH4XgWRqhxCC93/8zFSYoQtCiFQe6dmXzuGM86b3Obc/u7uNeadVM3V2BW0tIZASX8CNP8smPxWX5fCpb1zS53hHcuUuhGDO4iomTi+jvSWM06WTk+9LuaSEEJx18SzmLakmFIqmPAVLz5qKlqysdfV1yzj3UpsEoybL6/b2DEgpseJv0BtCKe2WNR0plFyEUoI0070OVnw90n3lsFfVijYFGOkHJOj/FRx8ALPvz/o+2+2c9OHXUu+GhlACyF7GgM3b6KpqkTob47JuQFP8GXlqVPdFmKHfdSsjmsdItNyI6rkKxbkMoRTbqaFCBWkBcTs1zqrHim/Gir2CTGwjzQBSK/olN44mXIqbgbkN/W8PGbaBH9BzBuybgopL9UACWhN2tTmn4iLPWURt5BBBowOn4qIxVkeZexy6cDDOO5F9we00xuoZ4xlPfbQWp+Imz9F/OmC1fw4ADdEjvNzwDyYFFjLGMzltn6gVYk3Tg8zOXskZhe9BEbaR7lb9PH7sbublnEeBa3gGZtQKcUHpDZS4quxxzT2RvXs3ciC0mWL3OCxM1jU9SoVnKiuLrkUVGhPlfEJGB02xTEJkJw+XVEznb/ve5EMT7PS3HKcHh5L5NCGtVtrbb8Uw9uMPfAEhRl4sqE/bUhKPGxxv6GDX7jpqalsJBNx43H2NDkVRmDC+iOpxhYTCcVrbw3R0RIhE7Vo4IIlEE3zvZ08Ri9vGtdOpcfNnL8LlOrnToiIUvP0Q4oPRGEdbO8jzecj3eVL3rTMaI2EOz6g73jF8katTqkCoKILcgnQRn55pfUIIHE6NotK+Kx0hBFk5g6uR+fwu8A/NZvck3f69oenqkPLDQgh8fjc+f/9McCEEOfk+cvK72c09+63pKnmFgUHPAWCZB/q2rY3hxGkeCkKr6GsM9HG5ZwahZ64BkTGG9G5JrH5CKFKGMSP/PLFzW30/Iltr3cAWdLIhhCDfc3r/veuhMpaCUogWuBmj/evdng+rLilX+ydQshDCi21YmXbMVIaS/ekr0iTU8WjZt4NaNrLrzBBCDK0j0M9RQBcffTDIHnvbqPBUcSS8j6bYcRyKk4gZpso3BSEE4732RH4otJsCZzHtiVbynUW2UTFCNMdqCRrtTM06LeXCF0JQ5Z+DAA6Htw/bGPBruRS5KlP3za36cSpuwoatZRExg7TG61mQe34qtVIIhQn+eaxvHsVKisOEEIIzS6o5ragSZ9IA+Oy0FagZP38dW/0zTij0JwzjIFnZ30VVK09ojJBSUn+8nY2bj7Bm3V6O1bdRXprDhefNYPaMCgoGGbOFEPi8Tpu532teiccNvF5nyhhQFYV5s8amWP6nEpaUfOeJl3h1zyHKcgL88bp3ke2x55ifv7CO1w/WDKu9UCwTKeh0vCO1Cf4/hoIBVnOfrUItPeGJVwjRbx62fT6D4a6shTLCEMEJwUD2c39k4m0S7ZtOsO3+JjCD4RAs22IbkdIk170wtU0IgeK6EA0VM/izJB+ky10ZAjOUGaVQyUFxno3quwlxgoPsyYJfC1AHtCfSuQQ9YUqTiGmHqrJ1m3zaNeGvaXqWumhNihRY7bXlVEtcY9CFzv7gLib4pxO3Yoxxj+uXPJgpImYQAbjV9NREh3CiKg46E0OlbfZ9ag7FjdLHWyZSxlHCimJKA28yVNEF11DSy6cAQog0gSGvnrmrX9Mm4HKfSyj4O6QME4u9REtzLVnZ38fhWDQiHgFAPG7y3dufACSLF1Tx4Q8so6Iiz3a3n8D7r2l2cbyhsgxOBUzLYuPhY7RHonREozR0hlLGwLH2DvY19B3vRhujbgwoqgIi6dJJmByubeFgbTMtbSGEEORlexk3Jo+Kkhz0DCr3xRMmR+u7GZHF+YF+3UK9EYnGqWvsVpUrL85O6VqDHSuqqetuNzfbS/YAq/3+IKWksSVIMGyv2hQhqCjNQVFGg8VqIGWkz1YxSmplop9Qg7TCjMwYOLH6BiOCjCflfPv98WSccFh7d8S2oQhnmjEA9upPcZ2P4piJGXkaK/YS0jhoKzjKBF0V/rp4B7ZEb7J0sVqB4liI4lqB0CaTrqL3nwMhBON9k9kT3EZt5BAWJmo/w0x7ooWg0ZGM+XfHe0vdFSgoHIscJmHFUiJFAD49QJaeR320huNRO3QzPuk1GCmcigcJxMz098mQCUxp4NYGXnVKKYlZ/XynQ5xTFQ4UoRIz0ycho0/GyX8XhHDh938eTaumo/27WNZxDGM3rS03Egh8HbfnSvorRDQUVFVww4dOZ/y4AgJ+16i990JATpaHQzUnf6IdCpqi8MElc3hm+14q87Ipz+mrM3FaVQVFgczG2/r2IK8dyKRoXI8+DGvvDKBr9mS4c/9x7n3kdTbtqqUzZLPzAVRFEPC5mTttDB+4fCETxhYM+nAbmjv59HcfSDEjv/OZi5k/fWii2+6DDXz9p7bLTQjB3bdew9jS7kkwkTD51i+epDnJNl2xcAJf+ujZGb9olpT84LfPsTtJ2Bk/Jp87v3HV6ORqSgv61b8fJWGKfnPwTfucw/7O3glBkmR1v1MMKSXNkbW4tRI8+jhao29gyb4u/I7YNrJd8/ptw1a8K0P1fgTV816k1QBmne3pkCGQhs0bEG47U0DJRSgFoOYCozcQnkzMyl7Mi8cfoyFax77gTib6pqf125IW65tfwpAJ8p3FlLq76w9k63l4NB+NsToiZog8RyHe5ISsoFLuqWRHx9scDO1GICh3V55QX/OdZbhVH/uCGyl1V9Ole380shtTGoxx26EJXbiQSMJGBzkOW7HQkAmORw8N+5we1Ydfy+VAaAtzcuyqk1JKasK7TuhahouRpAEO/f6puN1XoGljaW/7GonENiyriba2r2MY+/H5P42iDK8SrKapzJlVYXOFkvH+nlAUMaLvQghBft7w+nKyIITg/UvmcOW86Tg0Fa2fReXHVyxibkVmYcH1B4+888aA06GzYfMhvv+b52hq7Rt/NS1Ja0eYF1/bzbY9x/jaTeeyYMbAddkty6K1I5ySjcxUrMEwTNo6bKtdQB/iodulM29aBfc/vRGAl9fv4YZ3LyUnK7P447Hj7by9o1vcYsmccajD1L8ePkZr1TtQVb///InGxgD3QQSScfdRhpJDV02DQ+2/J9e9mMqsG9jZdGvS9ZluEMXNpgGNgVRXU5K140AbN+i+/20odJayKO9M1jQ9y4M1f+Rd5R+iwlOFJnSiVpgtbW/watMzqELlvKIr0Xuwzh2Kk3xHEY2xOuJWnDnZi1NhACEE1b5pbGpbz+7OLbhVDzmO4WtK9IRb9bM47xI2tDxFQM+j2DWO9kQTLxz/KxP98yl2288mz1mKVw2wqvFfLMu/CkUo7GhfR8QMptIPM4UqdOblnsPqxod4q/U5ytwTaInXs7X9VU72NyilJBYz2LXzGLt2H6OpKZgSeRsMhQUB3vPezFIfhRDo+lxycv9ER/utRKPPAFGCwV9jGAcIZH0bdRjlyqWUHK1t5fGnNtHUkj6nCCF497sWMGnCyCSlizPgb50qKELgdfbv9RZAltuVcY0Fv3P4vIdRNwbaO8L88HfP09QapDDPz+wpZVSW5eFy6jS2dPL2jqPsOdSAZUmON3fyvV8/yy9uvpqxZSfIkh8mhBBcetYMHnlhM/GESXswyqoNe7ns7KF18aWUvPja7pQh4HbpnLV40uit2oQGwtF3zhutMqhWPzEyodsr0v8KaPTnkdA816B63n8SzqdgkwcF0wt+hKZ0ZaeozCy8E63XSudIx70noQ//PVCEwoXF7yZotLOlbQP3HLqTAmcRDsVFyOikKXYcVWisLLyMOTlL+qRhVniqORS2Ca0T/DPSfh/rqUYgaIzVMcY9PpntMDg0oVPkqkTvR6lRCMFp+ZcjUFjf/DimZSCEYKxnGmcWvS9F8POoAS4ovYE1jQ/xSO0v0IROmWciK4vez8727iJiWXoBVi+vnhCCAmc5Pi0n9fei3IsJGu2sa3oERaj4tVzOKnw/qxr/haacPAJbS0uIX971HG+8cYBIJPP0s+rqooyNAbCvUdPKyc75GcHO8YRCf0DKKNHo05jmUbKyf4Cuz8lozLQsyR2/eBYpoaqqkFfX7GblGVPYu/84pmmRlzvyUGVJ0TtTA2I4ENiaEQMZCv3B49CHbVaOujHQJXqxZPY4PvmBMxhTkoOadONIKWnrjPDPJ97iH4+/gWnZcfe7//Eq3//8padcNWpsWS4zJpby1nabqfnoi1u46Mzp6Nrgk2LCMHluTbfwy5wp5RSOqrtJS+bfN6ZtlWmFdEaO/toRwseJpeWdQgiH7UbvU37YGpFWwnDg0uySoFKa5LgW4tbHoPaaZNxaOeLUiXueFOQ48hnrqaY4Ga/vD1l67oD7uDUv14y5kSrvFDa2raMl3ogp23AqTiYHZrE490ymBroLFfXE5MCspIaAwlhPddpv+c4iqn1TiVsxpgTmZHQt2Y5CPlj53QGzDnTFyfKCK5mTc1Zype/Gr+ek9U0IwbTAMsZ5ZxI2OtAUHb9m60tMCSxKeS/OKHg3Vi/Pm4LKVWO+hKZ0f18O1c25xdezOO8SDCuOT8/BpXgp90w+oeyIwWCaFr/65fO8+qotje3zOcnJ8abSngdDWdnI+EqK4scf+JLNI+j4HpbVSCKxlZbmjxLI+iZu98WIft6BnggGo9TUtvDrOz+A2+Vgx65j3PSRFYTDMW79wWO0tYfJzxuZQXCi47ZlSYQYOnxiGCZvvnmQjvYI8xeMIzfXh5SSbduOcqy2lQkTixk/vv8U2fcunMXKyVXkejN/L1wOHXWY/LWTkk1QnB/gazedS162t4/VnxPw8JGrl3CkrpVVG2zrf/2mQxyoaWJC5YmVDx0uVEXhyvPnsHF7DRLYd6SJvYcamFo9OEN+76FGjiTJhwK47OyZwy6ROTgUhFqCNA+mbZVm7YDs7EwhpexX7leohZxCdeoThI5Q8ux4ew9YxpETvj+ZQ2FS3lcR/RhQBZ4VyAFDMe8MhntfFuauYF7O0kGNmvk5y5idvWjAfVyqm6X55zI/dzlBowNDGjgVFz4tgCb0Afsz0T+DcV67doOzlwveqbj58LgvAhJ1kDZ6QhEqXm1wd7AQCn49F78+sIdSCIFXy+pTREjr8Q44+5nIhRB4+iEiqkIlx5Feb76//UYLx461sm6dPeYuWz6Ra65ZTFFRVkaLsBNZqAmh4/ZchaqNpb3tGxjGDiyrnva2L2IaB/D6bkQMUijNtCSKohBIErzjMYNEwsDnczFlYglbtx+leoCJdCgUFgSoHGMbdW5X34qBg8GyJM88s4ULLpg55L779zfw0ks7OOec6SmSeWdnlL/cs5prrlk8qNjRsgmVGfepCzkeN18+//R+iYgD4aQYAxefOb2PIdATDl3jQ+9axNq39mOYlr3SXruL6iHIhCcDC2eMpaggQH1jB6Zp8cgLW5hSNXARICklT63aniJEFuT5mDttdLXhhRAo+vQ+RXOkcQSIY0vmjhSJvsV9AKFN+a9hDIBA0SZgGjvTttrpeianJmNW0hh+BadaiEsrQVeyUJIeApc2uiVxM0E4cRC31jfVUEqJYXUQjG8jx710yHaktBPgzAREYpAwElgyjqYqOB06bqee8vRpip42EfYHO1XNM6zVripUFMWNYVp0hmPE4gamZaEqCi6njtvpTPXhZKGLXBdLGERj3Tr/mqbiduq4HHpGK8ITOb8EorEEkVgCwzBRFAWXU8PjdPRLmhuqJ3v3HieRMCko8PP5z19AVob8qNGAEAoOx0Jy8/5Ee/s3iUVfQMownZ0/S/IIbh7wWK/Xia6rHG/oYEx5Lj6vk1Vr9jB9ahl79h/n9KKJAx47FMaU5vDz266xc3iEwN2fNLyU7NhRy759x3E4NJYvn4Smqaxbt5fHHt2IogjKy3OZOrWMtWt2s3SZLcG9ds0eFi+pZs+eel552R6rWltDuFw6tbWtvPbaXsKROM0tQcZXje5C2OPQ+cCSzDxnXRj1UVMIWDpv/JAfyfgx+ZQUZqXS+97Yehgpl3OqydJul84Fp0/lzw/Zinar39zPx98XISfQ/4cSjsRZ/ea+1N9nLp6ExzU82c1MoDiWQOh3aduk1ZisITBywpk0jyHNxj7bVccSTvnNHyHsIkdLMaPpAi3SrEWa9QhtYNf26EHSEllPKHEAsNCUAB59LH7HZDz6OLz6OLRTmDd+uPUOiv3vIdu1NPXt2cVyatnf/G3c+vhBjQEpJa2dEbbsPcZbO2rsdOCOMLG4gSUluqbgczspLchibGluqib8YDhn8SQqijN3L0spicYN9h5p5M0dR9h58DgNLZ2EIrZqnKYq+DxOygqymDWpjPlTxlBRnDOq4UUpJe3BKNv31/Hmzhr21TTR0h4iGrPvg0NXyfa5GVuay/wpY5g9qYyCHN+oquyFowm27D3Gus0H2XukkdbOMPGEiaoI/F4XFcU5LJo+lgXTKsjvsega6j60JMl3kyaVEAgMv3z2iUIIgaqOISfnF3R23kkoeA8QJRL5N4Z5BFUp6vc4h65y+cVzANslf/W75vOr372Mpio4nBoL5lWOuE+qqpCTPfh3Go0m+POfXuX9H1hKOBxPedn8PheqKigvyyEn24OUkof+/SaLl0xA0wQPP/IWc+eNw+d1kpNrP6eyUvt9dTo1Cgr8eDwOystycDrfecmfUe+By6lTnD80Q1NTFSaMLUgZA/WNHURjiYw0BEYTQgguPGMa/3ziLSKxBO2dEVa/sY9LVs7o9wN/e+dRmpMiFZqmcNEZ007K6kBxzASRZeegp2BgRV9CeD88onNKKbFiL9Nb0x/hQXEMz4p8p6E4FtO3qFAMM/oMwvuRU+BhUpiU93VMK0TcbCaUOER7bDNHO+8nnDjIxNyvUOLrK219shBwzmNf881MzP8RAecCAILxbexrvgVFuCj2v2fAY6PxBM+v380Dz2/iQG3zoBk72w/UD/hbb0wcW5CxMWCYFm/trOGfz25ky55jBCN9Uza7sONAPS9s2E1+to8V86p597lzGFOUfcLhs0gswfOv7+bhl7aw/2hTWhnc3ti0p5YnV2+nvCibS06fziWnTyPgPbHUT0tKtuw9xp8eXc/m3bVE4/2nz+44UM/z63dRWZrHe8+fy3lLJuPQNXzuwT2GruSq1+nKLLxyMmDX9wgQCHwVTaums+MHWFYzifibJAZIUxZCcNXl8+iqDbJ0cTVFBQGaW0OMG5s/qALhaEDXVSrHFfDmmwdZsGA8Ho8TVVWYOKkEn9/FtOnlCCHsrIy0aub2HxVj86k52oLH7WDadHuhkp/vZ+LEErKzdjN9xsmrOjocjLox4HU7+nW19IZIivR0IRyNEwzHTrkxAFBSkMW8GRWsedOWuH3khS1ceMa0PsQaKSVPvLwt9bwnjC2ksjyPgWBZFm3NIXLy01cOUkoioRiRcHxgaWKRg+pcjhl9Im2zEfkXque9MEiMbWBEMcJ95XoVfd6Iyv6+kxBqGYpjTp8aBWb4XjTPVSCyT3ofIsZRQvEDdMZ3EkocwLA6cSg5BLzT8TsmD93AKKIk8H5MGWFP09eZlP8jElYrB1puw+eYQVXuzehq/883GI7x8/tW8fTanSe9xvpACEVi/PmxDfz7pc2EIpkJ70gJja1BHnhhE+u3HuJ/rlnOGXOrRiT6JaXkWGMHP79vFWs3HcDIINUObAPm0LEWfvWv1azdfIDPX3sm1WPyRzTRWpbk6bU7uOufr9LaOZCgVjdMS7L/aBO33/Miuw838Imrl5EzxGp/3LgChICjNS2YpvWOlvkVwoHH8x40rTLJI9hNT5VPCbS1h8lKXlPPe6ooChNHmEo4Eqiqwg03nMn+/cd55JG30HWVqVPLECI13/fot0RKiWlaRKMnXpb6VOIk6AxoGbMYs3u44k1TEo29MzdPUQRXnTebdW8dwJKSvYcb2Xu4kSlV6S9cS1uYt7Z3x9svWTl9UG0B07C44+sPcsUHlzLntGpU1RYXOXqoiT//9FlWXjKbZef2LawESZea93rM6LP0XMlLYx9m5CFUz/uHNehIKTHDD/dT9ldB835oxFKh7xSEUNG8HyEe30BP3QRpHiHReRd64KsjUjvLFBKTbQ1fJGF1ku9eTpH3fPyOSTjVfBRx6l2witApz7oBsNjd9EVMK0ih7woqsj+FKvrn7yQMk1/881Uef3Vb2qCmKgpjS3KYUFFAQY4PRRG0doQ5UNvM/pqmflesLoeG1+0g4HWRm+WhIMdPWcHQ5KVQJMbt97zI8+t391ty1enQUjyFhGERiSX6GC01x9v47u+fpeO9Z3DJ6dOHRQKTUnKkvpVbfv0Uuw/1LZ0thMDt1HAmOQLxhNmj2I0NS0o27jzKV3/xGN++6UKmDcI5GqgPL72xh5/c+xLhfiYQTVXwuBzomoJh2vegy2uRMEweemEzSMjyDf7eVVUVUVlZwIEDDWzadJi5c0dDzlqgKDlY0jFsMSGbR7DE5hG03UIs9gpd33IsZvCdHz7Od26+HKdD4x8PrOe9Vy1KFaU7lQiFYjz33NZU5kXX++V0aiDhxRe3M6Y8j4mTiikvy+WJx99GURUShpXicSiKsNV5e0AIUDPI5GgNR4glDLI9blx6ZtcfNwzC8QROTcPtyGwcHH05YkXJOPSspd0cmfaBnSiG29KsyeWMKc3hcK1tNT/6whYmjy/qEX+FNRv3EwrbKxe/18ny+dWDfkyqprJ45RR+98MnWXnpbC56z2K2vHGAv//qRabMrmDmgsFj/4o+E8V1Flb0mbQrS3T+DKFNRHEszOhjllJiJd4kEbyDPupdjnkozqGJZf+JUJzLURyL+xAtzfC9CDUPzXs9I1Hts8ljcaRxwK4HofSd1AQK5YH30R7bTNSspy74CK1qMX7nVHyOCXj1KrSTIYCU6qOJmVaG2kaJ//1IaVLXeV8yNCAxZRBF6ig9mPlSSla/fYAnVm9PMwSyfC5uuOI0zlwwgRy/OzXwSQnBSIy3dx3l7vvXcKguXbP/qrNnc3HSVe5xOXDo6pC8AsMw+dX9a3hu/a60PihCMKGigDMXTGBaVTG5AQ+aqhJLGDS0dLJx51FWbdxHbUN3CC0UiXPnP14hy+fijHmDf5c90RaM8J3fPdPHEHDoKnMnl3PGvGqqyvPxe10IAZFogprjbby2+SDrthykPRhNHXO0oZ1v/fZpfvaFK4YVtjhS38pP//ZyH0PA7dRZPmc8K+ZPoLwoG6eukTBMmtqCbNpdy0tv7OVIfSuWlDz88haKhkiTczo1Pvmpc/jB9x/jxz9+ius/tJy588aRleVGVYcat0W/hEkhvOTm/QWkhchA86FPq0KgqpXk5P6Szs67MBI20S4ed3O0tgVVFRimybMvbOfqy+e/I8aAy6VTVVVEMBjl8svnUl1tcxscDo1Pffocampa8HgdCCG44WNnsmvXMbKyPEyfVp4qxDd5cimJXkZ0To6Xd797YZ/z9cadz69l/YEjfPuys1k8PrPU6Y1H6rj96VUsn1DJ585ZmtG7OOp31jStPq6TgRDrcXMEopdxcGKwhmlYOHSVS1fO4K57VwHw6hv7uOm9y1LeC8uyeOqV7an9F86sJHcINq6iCC66ZhHVU0v5/e1P8caru2lpCvL+/zmL08+fgT7Eiy2Ehu7/MrH4xu7StwCylXjbZ9EDX0N1ncNAE549qUUxoy9hdHwfrF5FV4QP3f81elbj+2+CEA70wDeItXyg17UlMDrvRCZ2oXk/AvokwDlohghYIENI8xhWYhNm9CWsxNs4c//SvzEgFEp9V1Dqu4y41Uoovp+26Ebqgo8TSuxjYu5XKfFdfFKuGyBq1LC76Uv0Nu4EKkLoWDLCzoZPoCZJjHmecxmT9bHUfoZpcc9jr6epz2mqwpc+uJKzFk3qM5ELAQGvi9PnVlFSEODTtz+U5s5eu+kAH7l8ccZkWiklL76xh0df2Zo2Xjh0lfddMI9rzplDTsDT55lNGlvI0lnjeddZM/n9v19L8yiEowl+cu/LTKgooKwwe8g+mJbFbx9cx7b96TyIvCwPn3rP6ayYV43L2Te+PmVcEWctmMC2/fX8/B+vsCMpSQ5w9HgbP/7Li/z4c5fhymBFZpoWdz+whub2dMMux+/mS9edxfI5VehaeuXICRUFLJ5RyWUrZvDbh9by7Gu7MEwrzTjqD/G4QSgYY/LkUtas2cNPf/o0xSXZlJZk4/e7UdSBJ4yioiw+9KHlfbYLoaLrU4a8zsFgcwGyCQS+TpcXNBwBy1rH9p3HKCoIYJoWTc1BXKG+YaSsgAun8+R5ATVNZdYsexLuyjKxpAQJ5eW5lJfnpp5PdraHxYur+7SR00/FXafTNjKGQmMwxJGWdiIDcEj6g0NT2VXfiKIIPndOZou9UTcGorEERobxqOYe1aI0TcmIa5ApOkPRoXfqASEEZ582mT8/tJ5gOEZbZ4TVb+7n4jNtXfWjx9tSdQiEEBkpFdr72qWVnS6d5uMdICVZOd4+LqMBj1crcQS+Rbz9S9BzJWjVk2j7CqbzEVTXhSj6FLugDToSA6xWrMRuzOhTWPG1IHvfDw3d91mEPusdIxOdOARCm4IeuIVE+zfS7w8JzOjjmLE1KI7ZKI75KFqVTcoUOnYthgjSarMzLIz9WMZ+pFkDVisDSzbbkNKiMfwSHfEdhBOHSJhtADjVPLJdcwg4pp6siwZAER6ykkTB/uB3pOc+u7XKtL+P1LWyryZdfGrO5HLOXDBx0BW9EIIJYwp411mz+OMj3XyNQ3UtbNpdy2mzMst06QjF+M2D69Li84oQXH/pIj548QI0dWD3qaIIxhTl8NXrzwbg2de6Nf0bW4P85sG13HrTBUOGK3ccqOfJNdvTtnlcDm654XwWDyKRbqvrqcyaWMr3PnkxX/jZIxw42l3s5o0dNTy7bheXnjF9yG9rz5EG1m5K1xPRVIUvfGAlKxcMXB5cCEFpwf9j773j47jK/f/3mZntRVr1LlmSJffee6qTOL2QAEkIIRAul1DvpV+4cGmXDpdeAgkJJCG92el2bMdO3HuRJUtW73X7zpzfH6u20kpayQ5wv7/7eb0C1uzs7JnZ2TnPeZ7P8/kk8bkPXEqPN8hbh87G3W842tp6+eY3nyYUipYYIhGDutoO6moncmaMKhDGCwYuJIRQGVAWtdskl26YxQ9/9hJmk0Z7Rx//8V9PI+KUgD5+78UsWVj0rozJMCQ9vX4am7upa+ykqbWHri7fYEnbbNZwu2xkprnIyUomJyuJlGRHf6blH/dcdVstCKC9z0dYNzAnUI644MGA1x8lAlomWPVKKaluGLoJHXYLTvtYbFjBwAooUXON2sauhPYbjjSPg9WLi3lpezRV9exrR7hq/WwUBV57a0h+ODcziTnTJ7bulVKyfcsR/vrrN5i3rJjPfOsm3tl2it9+9wXWXTWPG+9ag30C7+yo9e1GTEYb4d7/HjHhBTCCb2AE3wThQiguoiqCYaTR26/QF48UpqE57kF13PG/jiswEkIIVOs1IP2Ee74zWpVQdvZfozcArT8QUADZbwY14BY4WUjafNvQ1CTS7RfjMBVhUbMwqW4EE7O1u4MB9rU0cHF+ccz24+0t7G9pYHFGDjNTx+49NqvpFHn+fQrjjuJwRQO6EXveV6yamVB2TgjBpcvKopkFY+B3Cdv2n2HlvInr0FJKXt51gobW2JXs/LJc3nfl4nEDgeGwW8188r3rOXS6gab2IYfSrfvOUFnbRlnh2NfPMCQPPLdnVMfAbRsXsnycQGA4hBBkp7n5t9sv5pM/eHKQyyCl5MHn93Dp8nIc4xCipZQ8+frhURyIVfOncdE4gcBwOGxm7rttHQdO1uGfgHOlaSolJZlxuRkTIT9/bKL0uwEhBHffuYYNa8tp7+jjRz9/hXvuWhs3A1BcdGHJz1JKwmGdk2ea2PrWKQ4eraOppRufPzRmxlkIgdWikeJxUF6SxfqV01k0r/CCuixOBpqigBCEIjq6NEjEUO6CBwPBUISa+nZSJ+jdDIV1KmuG+t3zs5LjBhCapqII0Pu/g96+iVf8UkoOn6qf3MCJfqE3Xr6AV3eeRDckp6tbqG3sJDcrmdd3nxrc7/I1MycMdgAiYZ1n/7KLmz+0jnVXzsVk0rjqPcsonpHNH364meLyLFZdOjuBcamo9veDcEYDAmMk0UkH2YXsX52OfzA3mvNf0BwfRIj/neWBkRBCRbXdilDSCPd8F6lXjbFnJOoKeEGgMCPtP2IUCEd2jAzfPvLvjoCfZytPcFHetJj3Ztqd9IWCHGhtjAkGRr5/5DFjEW977AOpona01kRZQeIP1cxUF3armV7fUAtgZW1ictm6bvD01iMx2xQh+OB1y7EkSJAaQEqSnVsvX8hP/7ptcFsorPPk64f5/F2XjPkgbu7oZc/xmphtyS4bt1y6MCENhQEIIVgwI5fFM/PZfaR6cHt9Sxd7j59j/eLRKeMBBIIRdh2ujtmmCNEfECUepBflpLBoZt6oDMNIpKe7+O5/35rwcWPGdUGs2ScHs1ljRnk2kYhOaoqDxQsKsY+5YLwwkFJScbaFvz75Dm/vP0ufd+wW15Hv8wfC1Dd2Ud/YxZu7KygrzuA91y1hzbLSvyvXQUrJuY4upJSYVAU1wQXfuzLCl3eeZOGs/HEjoqOnG2gd5kC1alFx3P0cNjNms4a/n1xz5HQDl68Z38O8taOPYxWNUxr7jOJMSgvTOXW2hXBEZ+s7FaxfNp1zDVE9BLNJY+MEnz8AVVX4zDdvJrtgqKYkFMGM+fl8+cfvw+9L3L88OuHdgKKVEu77n/7U/8QtSEMwo5gXoTk/jmJe0Z+S+38HQigolksxp5QR8f4JPfACGKMnvMSgIdQ8FMt6hJoz5l5PnTmNXTNzpqudVTkFLMrIoScUZFfjOXpCQZq8vdwxcyFus4VXz1VS0dVOntPNVdPKAegJBXng+AFCus4tZXPwWG2k2uzku5PpDg4Fvb2hIM9WnqQr6GdpVh5LM3N5pvIE3aEg+U43pzvb2ZCfT7r1OF2BXehxjKiSrMvJdF4/+HdnT2yNWlXEOJm50TBpGhazFhMM9PqCSDmxdlVDa09MVhAgK9XF/LKcSa+ihBBcsryM3zz5Vkw30s6DVQRC67GNUUt+52gNgWBsYLh8TuGE7XnxoCoK12+YGxMMSGDzzuOsW1Qy5jnVNncOWqgPIN3jZOa0ievIw6EIwaXLyicMBhRFwem8QDbof0eoqsLdd6591yfUSERn8+tH+eNf3xrljjiVYx0/3ch3frqZyzfM4p7b15LstiV0f/tCsd0qA6U0fzhMb2D84MSQkoauHn75RrSEl5+SjCnBwPJdubqv7zrN9ZfOp3xaRtyT9/qD/OHxtwbTVTaLiYvHcP1z2i1kpbk521+T2763krtuXEGaJ74xRSis86cno3X/qUBTFW64bD7f/e0rAOzYV4nFrA1+IXOmZ5OboNOVoirkFKZiGJLuzj68PQE0k0pyqrP/v8mNTQgBprmYk3+KEXobPfAsRugA0mjt5wQMT/sqIKwIJR3FNBfVugnFsipaThhxnQ1DjkoZD78eQggQCopWipQ9sTuIsQ1CBlau4/0AhDYt6tI4HEriqnUxxxICoRVicn8ZzX4beuAVjNAOjMhZMLqJXxJQ+42P3Ag1G8U0G8W8EkOZh1CziBgKGENpXFVRBhn2D584xF2zF7E2r4j/3vMmv7vsBnpDQX52YBdfXraBsuQ0rJrGvpYG3qw/yx0zF/JSdQUvnj3F/PRs6nq7WZSRw/GOFn59+B2+sHTdqGslpeT3R/aS53KzNreI7+/dzo83XMVfTx3mvgUr+P7eHXxy4Up+c+gP3FL8BDZTAVocwmPE6Bp1rWI+ByaVPh7opx51zATm8iNnGkb18s8ry510VmAA6ckOCrI8nK4Zypi1dXmpb+miNH90tkNKyVuHR0+c403cE2Hu9BysZi2m7fLomUaC4ciYRMKT1S2jrnl5UcaUrsOs4iwURUyaOP2/AUIIViyNv1i8UAhHdB56fDcPP/72uGJTk0UwFOG5lw/T1NLDlz51JaljzFvD8Z0Xt3G6eSjLVt0WXYj+z2tv8cBb+8d9b8QwaO7uo93ri2a6F02ceR7AuyJH3OcL8o2fv8h9d25gXnkONosZIaJCGU2tPdz/+C4OnRxK41++ZgY5GfEnWEURXLyijD88HrUJbe3o43u/e5V/ff86cjKTBtNp4YhOU2sPj790kOffOIqqKgn5dI8ev2Ddsun85pEddPb4OdfQyWtvDZUIrrs0vjLhWPD7gjz1p5289dpx/N4giqqQW5jKbfduoHze5JWnog9cO4plA4plLdJoQUZqkXo9GJ1IwtH0v5KCULIRWj5CSSfqhBh/3Icr6nnoxb2jtrudNr5096X9NVwL5pTfghxxTcfoLW7u6OVvrxzE7bDwnssWYo27QhOYk3/QX7sffszz00wXQkOYyhFaGcgPIo3mfqnilmg5RYYBBSHsUdKlkoZQMxBKGggnui757v2v0OPdN+rYd2xayvyyXABMqsq6vGkkmS0kW6y0+vowKSo5DhercgoGr/f2+mquLCpnRko6ZlXlR/t2Mj89m9LkVOamZZLvSuKjrz0T91wk8EZtFcuz86np6SJiGLT6fdg1EwvSc0iz2ZmXnsUDRxtJd1xFkeff4uocjMwEZYx4KBmGpKPbR14CLHyIynJ7R7TCeVy2hPwtjpwZnbWbSlZgAEIIygvTY4IBQ0pO17TGDQYMQ8YQ/iC6uo63b6JIdtlI8zipa+4a3NbR66e9yztmZ8Pw8Q5g5rTMKV2H1GQHNotpEqJNkkhEp7PTR0+Pn0hEx2oxUVg0NdGkkceGCPH5SonCjATCxoUXwtIUNSZ1bhiSJ1/Yz5//tptIZPScYTFrpKU4yUh3k5biwO20DcoHh0IRer1BOjq9tLT10Nrehy/Od7DnYDXf/8XLfO2zV2ObQFgvyWal2x+guaePQHgouKxu70r4HM2aylVzy7lyblnC3+cFDwbWL51O5blWqus7+MqPn6OsKIP8bA82i4mObi/HK5tpHNYCU5yfxoduWTWmUIgQgmsvmccLW4/R1BZdle7YV0lFdQszSrJI9zjQDUlzey+VNa00t/eiqgrvv2Ypj7ywd0pRXpLTykUrynjy5UP0+YKc7O8i8LjtLJ8/LeGLK6Xkkd9s5ejeam74wGrSMpMIBcMc2HWGH37pCb5z/92kTdFPOzoGLZrGHieVnQh6fUGOVzUTCkcIR3SCYR0pJekeB0Y/9yQahCS+Yn/i1UP8+YU9CCGYXZLN4pmjA5/oMZPPa+zjIXp8B0IpBi3xlYWUOpV1bTS29RKORK9JuP8hceXqoS6BiGHgD4dxmy34IxHMqoaUErMaG3h5LDY6A36klPQEg7jN0XR8XziIBHyRMBY1/k9RAG6LlSuKppPlcHFr+TwybI5+3gAx9e1U+2WY1MS+o3llOfz1pdhVxp5jNcwtzU7o/j5Z3TJKJGxW8cRiOwMiPyMxGQ+DkRBCUJybNmp7ZV18DkMgFBlVJrFaNFLG8CNJBKoiyEiJDQZ03aC5oy9uMBBVPRzdCjjV62AzazjtloSCgQE73c2bD1NZ2Yy3L4CuS4qLM/jxT94/uF8oFGH3rjMYUrJ8ecm4k5iUEin7CIXeJhjchR6pRkofk1d8icKd9A1Oee388vSrU3r/ePhgyTqWpkafB1JKDh+v4/6/7BwVCKSnOlm7Yjqrl5ZSmJ+C22nDZFJHzVVRwqFBny9AfWMXew5Us/WtU5yr74hpm921t5JHn93LB96zctzfyScvXcXtKxZQ1drB/nMNPLrnMG19PubnZ5PunKCdXQjSXQ5WFBewurQQmynxDr0LHgxcf9k8HDYL3/71S5yta+fQyfqYLMBwlBVl8OV/2Tgh2TDN4+AL917Gd37zMs1tUbZ4c3svze0j/eyjSmjvuWoRd16/jJd3HKepbfQ+EyHaOjif514/SjiiD6be1i4pwTUB+384Av4wW184xH/95i7yi4ccGRetmk5LQxd73jzNlbeM3R7298KSWQX88ou34A+G8fqD/PbJXRw6PXkC5nAM3OuKYEr2zj19AV595xTLZheSl5k8pTEEgmFe2nWSGUWZlE/CHlvTFL573zX0+UP4AqFB2dd4+POJg7gtFtJtdjLtDlp8Xmxa7A/wqmllfG/vdjqCPo62tXDXrChJLaQb/OnYfiq7O7hlelSJ8o3aKvY01eGPREiz2rm0sJS7Zy/i8YpjlCan4AuH+fDcpdg1EwL6/19g08z0hY7jsa1PaDKfX5aLy26Jqfk/v/0YN1w0j5Skici/Ef66JTZroiqCi5ZMn/BzISr0MxITqedNhLQ4fdzxng8QvS+CI3q2rWZTQqTgsSCEiBtMjOQEDEd3HDJ0smtqAYmiKtgT6LUPhyP8+cGdPPnkHvz+2GBu5IpWCMGDD+7g3Ll2/uM/rmPtuvgS29EsQwU93V8jGNxNrF/I1CCNbjpDkt1tZybeeZK4Nm/R4L9DoQi//NPWQU4aRCfUtSun88HbVlGYnzphi6oQAotFwWJxkupxMndmLldfPo/Hn9vHU5sPEuq/16SEx57Zy6VrZ5KXM3bQZ1JVspJcZCW5WFlSQENXD08dOM49a5awvnz81l0Bg+OdbIbnggQDqR5HfzeAibKiTJJcVr73uev525YDvLW/irZOL+GwDiKavkhPdbJ2SSk3b1xARuroGvZICCFYOreQ73/uBh7bvJ8Dx2vp6PIRiuggJYoadVQrLUznhsvms3pxMZqqMG9GHqYzjdG+4HH8ouOhOC+VmSVZg10JqiLGNC8aC5FQBEM3SM2IPUdFFWTmeuhqPz+SyoWCzWKiKGfIx/3J1w+f9zFvumQ+AKlJDmYXT15HfN/JWr73p9f4r3/dNOVgoLKuje/+6VU+etPqSQUDQgiyh0npOsYwgDGrKteVzKA3HOJ9M+ahKSppNgf3LVwRs1+m3ckXlq6jpqeLi/NLyHO6Cek6P95wFe1+H8uz85nhia5sc51ubiiNZh/MSvSe3ZBfTHFSCs2+PtJsDsyqymcXr8GiaXx60WrcFgufWHQTLX3/iarYcVsWoohYopimJGPRhohpKW47G1fO4PHXDg1ua2jt4XsPvMan338RGSmjnfiklHj9IR56cS97jsfaYC8oz0voGhtSjpqIhRDnNRED2C2jV63eQPxVciiij+LImDR1XGnxhMYQR3DJO47hUjAcR9Z5iu51gii5eTxIKdm8+TCPPLIbwzAoKEhl+vQsIrrOtq0nR+2vaQqLl0zj7NlWtm49yZq18XldhtFBZ+e/EgkfG/XapM9D2FCUTITiJNlkZ3HK6MlPACZFRQLvtFdiSIldNZNmceE227AoGhLw6yE6g17agn1EpE6JM4OrchcwL3koS7nnYA2nzjTHHP+KS2bziQ9djM1mnlLJRAhBZrqbe+9cR3qqi18/sG2QI9PnDfLkC/u5756LE25fnZ+fzVMHjqMqCqYE226nggsSDNx900ref81SFEWQ1N9XmZuZzH13rOe9mxZT39JNd090NZDstpOblURasnNSK0YhBKWF6Xzuw5fR1tFHU1sPXT1+dMPAbjWTkeoiJ8Mdoxj2mQ9eRLBfYMMzSe9uRREkD2MWF+amUjaJCQXA5rCQnOpk+5ajrN80D7PFhGFIWuo7OfDWGT78+asmdbz/TchIcfHRm6PKV1ORA95xoCqaYJwiH0pKye4j1RiGnOohJoRJUchxunGZh4IFs6pS5I6N+oUQZNidZNiH6vQWTSNbc5HtiOVclHlGp7sBipI8FCUNHXda/78Htpl4kZDeztnO76EJJ2IEKTPDcT3TUj4XM6YPXLOMXUeqY5Tr3th7hpqmTi5bXs7skmySXTYUIfD6g1TWtfPaO6c5eKouhqjmcdv55HvXJ94ON5J4SEK8w3ERr3tqrNbLeNvFBRhE3IlyHFJmvHFMpq0xkc8fDq83yMMPv4WuG2zatIA77lxNSoqTY8fq4wYDQgjmzy/g8b+9Q0VFE4YhUdXRAaLP++CIQMCCppWgqtkIYSISqSASqQTMWCyrAAVJCENvRddr+8sJYDavwum6D5NWiqJmMStZ8LMld8Q9F78e4htHnkYTClflLeDq3IXk2VOwq+ZBPkBY6vSE/BzrruOBqu20Bnspc2eRbUseHPuzLx2M+R6mFaTxsbs2XJAWRk1TuXHTQk5UNPLa9qHru23XaT58x1psCap1zshKP+/fRyK4IMGAe4x2FVVRyExzk5mApXGi0FSFrHQ3WekTH9N9HqnHzm4fB4/XDf591YbZaNrkem1VTeGuT13Or7/9PG+8cIiUdBfBQJj66jbmLCliznn4cP9vwFSJSBHd4OCpuol3HAdSMqqH+0Lj04tWjyoJjIeRWgEXEhnOG/DYLhrzdaspd9S2dI+TL3/ocr726820dg5lqarq2vlN3VtYTNH2QSGiXTqBUHiU1HhasoMv3HUpZYXpCa90RrmBIglPgew7HPG4QWOtlDVN7R9rbPuWkZg2yzhjiGfgNPb9EU+lNVHHxHiYKOytPNNMW2sv6RluPvyRi3C5os/t8b61rH5OU3ePn2AwHGeSDOPzPT74l6rm4U76KmbzShTFCSj09f6M3t4foihuPCm/RggLSAND9hEOH8Pb91uCwW2Ew8eRRieKGiWTqoBNGz1hSil5qHon21tOcmvhCj418wrMyujv2ga4TTZy7R5mJeXyod2/59tHn+XBVR8l1eLE6wtx/FQsmfW9Ny7DdQHbLzVN5c5bVvLm7opodhxo6/BSW99JWQJSxAAl6Sl88aoNlGa8u8JPf3/Xh/8FkFLy5t4z9PRLGtutJi5ZkTgrcwBCCBatns6Xfvw+9rx5iuaGTlIz3Fx+w2IWriqd0JsgHtr8e2kP7AckqdYlpNkWT/oY7wZ8gRBvH60ZNVkIASvmFI0rNS37U8c93gBtXV5O1bTQ1F/vPVbVGPehWZqXRkG2J+YYoYhOb/8xaho6BglkVXVtvL5npFsj5KS7mVE0uZ7uofMSLMyYmLhpGJL61i6OnmniXFMHwVCEZLed8oJ0ZhVn4bSP7ZkwGSRZJ889EUKwaEYe373vGn71tx0cPF0fMxkFw5G4qWyIeggsKMvlIzetYk5JYqRDiE48rhETipTjp9MTQVcc21+PK/5iwGrWMJs0IvpQGSEYihJFzZMsJw5AShmXA5A0zsQSr6wQj4me0OcTPyAajnPnoh0UZdOzcDoTW/larCYURRAJ63GZ9rrehK4PBO4mkpK/j8US2yIr+tuPh7p4LCBAxYairMdsXkhX1xcI+J+hq+tLpGr5mEyLxryngkaYp87txaSovG/aqriBwHAIIcizp7A+YwZP1O5ha/NxbipYRnNrT4xsvc1mYvnCiRU0J4uCvBQKclOorI7qnkgpqahqTjgYcFot3LFy4QUdUzz8XzAQB6GwzlMvD9VSl88vIn0CR7CxIISgqCyTwumZ6BEdRVFQVDHlG86kuDGrHs50PYCEKQUDUkqafdtJt69AvUAqhB3dPr76yxdHOU+qquCR73yAXGvymO81pOS/fv8yp6qb6ez1x1jEPvrSAR57+eCo9/zre9bw/uwlMdt+8vA29p2opbPHhzcQGmwtfXn3KV59+/SoY1y3YQ4z7ppaMJAI/IEwj768n6e3HqG5ozcmtW41a8yclsVHb1nN/OlTb6tLBIFIA2G9HZdl7qjXot0eWdx5zTLO/OrFwUnVbFLRVGUwOFAVBatZIyXJQXlhBhuWlLJ4Zj6OSdZVBzT1R7YXNrf3Mndssb4JMZzFP4D8MZj5NosJt8OCbxinwB8M0+cLjisfPB4kxGRXIBr4pI/RVy6EIDUOUbOlc2o8Il03JgyoBoIFuz3x7ywUiiClRNOUuEG5rjcwYC6kmcqwWEY75InBVuFI/3/DXhMCIZJISvpPQsF3MIxGerq/QWra3xjLQK096KU12IvbZCPNkthzWQhBqSv6W9/bXs1NBctobeuNWbykp7hwT0F0aiIoiqCkKH0wGAA4Vz+xH8TfG/8XDIyAlJItbx7nzLnoqtKkqbz36iVTruWFQxH276xg747TdLb1YbZolM7KZe3GOaRlJU16EkiylJFkKaO+b8u4+40n+KPLAMc6fsxa6wOo6oUJBpJdNj56y2q6ev109wWoONfK8aomJCKhmr3LbqE0P1ov9wbCvHM0KhU7Y1ommSmjH6jxSIV2q4ni3BTITSEU0dl1qBpDSqblpFCYPXpimD4J+d3JIhSO8LNHtvH01iMIIZhRlMn0/DQsZhNN7T0crmjgwKk6vvzz5/nOfdck3NI3FXT5d9EXOhw3GJBSsm3fGb7/4OuDgUBBlod7b1pFusc5qHdvMWm4HVY8SXbcDiuqMvWAdlZxVoy5EMCJs81csmzy2beBczh9brTa5Fjfr6Yq5Gd6BrNPEE3P17V0TWgDPBb8gXCMoipErW8zUsY+3rRhpN0BnKltRUo56evgC4QnFFpLS4uOpampC8OQE3K2pJScPt2ElJDscWCNk90zjCG9BpNpNvHqLKJf90LKCFLGH6OiZGC330Bf3y8JhQ4QCVdgMscXzAkZEQxpEDF0IoYO6sSlOiklvZFoFsCnR8cw0szO7bKeF2djLAghRi0mBzh0/0z4pwoGvBE/73QcZ6lnJk7T+QnPTAQp5agfhD8YZvfBan791+2Dk+m6paXMLJk8G37gM558YAcvPvoOZXPySMtMIhgIse3Fw7y5+TBf+8WdeNImVqSaDAwZoc2/h7ZAVEQo3baMVOtiFKEhpU5bYC8dgUP4I41U9zyGIixoio0C1/UoI5UAJwGn3cL7rohmKaSMtqkdr2qa4F1RKELwb3dezEANt6ahk/d/5UGkhNsuX8gly8vivGf0KuVjt6wZrJt29vi58d/+QCisc9mKcu68enQa/d2afKWUvLTrJM9sPYIiBPfetIrrNszFZbciBIQjBsermvjqr1+kub2XHzz4Or/58q2Tcu2UUkYFptAQQkFKg7HYlhGjo//10cc4cbaZb9//ymCKOyvVxXfuu5qSvPMXnxkL88tyUYSIIdcdOFk3pUkQor/bkb4IVrNGcW78GqsQgqWzC0Z1ROw9do5FM/KmNIZzTZ2DZcUB5GUkj9uKHE92+FhlU0KSziPR3N4zoVHR9OmZmM0aFRXNVFU2U1I6vsBRX1+Qp5+KPkcWLyqKGzwMn9wV4Y57PDGoUhpGGn2gjj5vIQQW6wb6+n4JRAiF9owZDLhNViyKib5IkHfaq7goc2J5+IARZltzNAAdIBCOymC8i9m5kZiKWdS7jX+qYOBU7zm+f/Jh/nP2PSxLfXctYKWU/OSBN0CCxaIRDEaorm/n2JmmQTGVzDQXH33vmoTsmOMh4A/xwl/f5hNfv4EFK0pQNQUpoafLy7c+9Rd2v3HiguoMSCk52/MoNT1Pku24GICj7T+gyHULRe5bkBh0B0/hjzQjpU7I6EEVFiTjP0QSxaD/wiS1BYQQaMNYysMZy4qiJORiJ4SIeZ+qKoh+alSix7hQCIQi/Om5d9ANyfrFxbzvisUxpDmzKWp/+6HrVvDt+1/h9LkW9p2sZc2CxIWRdNlHRdsXSXdcQ5pjI7Xdv8Ifjm/Q5A2dwmUZXXOUEn7zxFsxte4PXLP8XQ0EILoizkh1DYqIQbQNtKGtJ2EFxOE4VtlER09sP39xXhop43QQrV4wjd8+GWuhvHXfGT543fIJW/RGQkrJq2+fGvWAXz1/2rgrzdKCdCwmLYaXUVXfTkePl7TkxBcJUkr2Hq8dxdcZiaysJJYvL2H79lP89/de4KMfvZgZM3KGjVsiJYRCYWprO3j4oZ1UVDRht5u59tr4NXwhhoIdQ/bGDegUZSADYhDR69BMJXHHpyiZDDjUhiOjy3oDSDY7mJGUw8HOGn544kVMisrilGnY1Fi3UCklBpLWQA8PVO3gRHc9CoINmTOBqFPucPT2BaYUiE0EKeUor4OxSPf/SPxTBQMljlzuKb6WMlfBu/5ZUsKuA2dpGNZWNRypyQ6++JHLx5RJTgR6xAAhmLmwYFDnQAhI8jgon5dP3wVOFYWMTk53/oFFGV8nw7YGgBTrfA60fo0c52WYlWRKku6gJ1RBfd9mypLvwTSGnPD/Yeo4XdNCfUsXADdcNG8Uex769dbnFmHSVMIRnTf3V04qGJDSIGL0osvoQ6bTvx1DBjCpo1sTDRnf6bPXF+BIRcOwMfGu8xcgKu96+YpyHnx+z+C2QCjCk68d4r7bRvszjIeIbvCXLftGTYRXr5097kRclJ1CeWEGx4Zlr6obO9h7vDYhG+bh6Oz189JbJ2K2mU0ql6+cMe5x0pIcFOelcuLsUJ+7LxDi1bdPc+vlCxMeQ0Q3RpVd4kFRFD5y70XU1LRRVdnCf37tSaZNSx9M/7e19vGTn2yhqambqsoWOju9aJrC7bevprAofsurogyVYvRIddx9VDWDaPlAJxR6ZxTBcAhDX6I0euK83v+ZCD46/WI+s+9hGv1dfPHAo8xOzmNOUh459mRsqhlDSrrCPip7WzjYWcM5bxsSWJNexiJPERBVGBRiqNO1tb2PXm+ApDGIp1OFYUgqq2Olp/NzR5eI/tGYdDBgSAMpJYpQMJAE9RCaomES6qgvWO9PXapCxZCSkBFCAhYlqpo2sL8hDQwpcWg2rs1ZO6HlopSSoBHGkAaaomISo3X3pZToUidkRMbcx+WwYDap0dWBjIoBuexW5pRlc8f1y5g9hTqulBJDl4DEYjVRNieXAzvPsGRtGZpZRRqSjtZezhyr58Ofu7A6A33hGiRhUqwLBsedYl2AIcP0hc6Sals06j1/z9TY/19wuKIBKaPZkYGSQDwEQmFMmkI4olPT2DGpNLmmuJmV8SuUYSuzQs9nSLauGLVvfc8fCYRHt2oGQ7HdAlLCkcpGpuWmTkk1MlEIIbjhonk8/cZheoZZxD699QgXLy1jdsnEssbR8Ure2FMxyC8ZQIbHOSH/QFUVbt+0hC///IXBlbFhSH752A5mTsvEk6A0cUQ3+OMzu2kdoTS4bHZhjJBXPCiK4Op1s2OCAYC/btnHJcvKxiQfDoeUkp0Hq6g4N9rnIB6ys5P56lev55e/eo3Dh2o5dmxIabS728cLzx8c/NvjsXPLe5Zz441Lx7yWmpYHWIAgkUglUvoQIpYYqSjpKEoyhtFOwL8Zl+sT/e+JhR45y2BAIMYumQkhWJJazOdnX83PT71Ca7CXPe1V7Gmv6tes6J9XhgUXqlBYmVbKl+dch6lfzCsjzYXdZsHbz7Xw+oIcOHyO9aumxl0ZC7UNnZyrGyIMCgHlUyw9v5uYdDDwTvtx9nedYlP2ap6s20qtrwWXycalmUtZlTYvZiJ/tXkPzYEOrs1Zy1P12zjefRaJZKa7iA9O2zT4pe3vPMWLjbsG6/R3F19Nvn10XUlKSY2viVea3uFMXx0hI4JDszLTXcSt+Zei9X/JuqGzu+MYrzfvozPUg0OzsTptHhdlLMbSTzZRFMFXP34VzW099HqD6IaBw2YmJyOJvCwPZtPo4CYRhEM6v/ivZ/D7QgjA2xvgp197iuIZ2biT7YRDEerOtqJqKuYE5EMnA8MIIlARw75WBQ2Bij7G6vD/cOExoL1vGJL//M3mse8jKfH1y6COtNOdCEIIVDE0YTnNs7FqeShxukNMSipBMVpe2mGz4LRbYtryfvHYduqbu1g8Kx+P2x5XSEgQnUxtFhNOuwWrxTRp4lV2mpvbNi7id0/uGnxke/0h/uv3L/H1e6+kvCi+4+kADMNg95EafvTQGzGp/gExpfFa+gb2W7OgmCWz8nnn2BB3oKK2le/+6VX+/c6LSU1yjDkGKSWhsM7fXj3AU28ciXnNbjXzkRtXJiRje+mych547h1ahqWRm/p5JF+8+zKSnNZxx3CyuoWf/GXbqC6e8T6zaFo6X/vaDezZU8WutyqoqWmnry+ArhuYTCopKU5mz85lw0WzKC3NQBnnPBQlHVXLRY9UYRgdRMInMFtiu3yEsGMyzSEY3EYkchqf9xHsjtsZbp5lGD683gcG/9a00cqDMZ8rBJtyF1LmzuaZ2n3saa+iNdhLQI8uEgHMioZLszLNmcHGnLlcmjUHpzbUyut0WCmdls6hY0OB8sNPvs3ShUWjSghTRSRi8NDju2PaPtNSnBTm/T+QGWgItPFK0ztU9NYx013IuvT5HOyq4Een/ooiFFalDkn2Vnsb2dN+nDpfC7o0mJ9cSk/ER9iIDAYCADm2dFakzqaqr4Gn6rdxS/7Foz5XSklFXy3fOv4nVKGyJGUmSSYHTYEOGv1tgw8jKSUvNu7izzVbWJU2l3nJJdT5Wvl91bM0Bdq5s+gqFBHNSkzLS2Va3oUVchAKpGclEex/yGfkJFM6K7YfPb84mloLjiGXOlXYtCwMGSKot6MpedHP0NsxZBCbNhSJDj1c/vlILP8vYIDIJUS0z3y8iTK5PyWZEadjYjKYlvI5BPGDy2TrCuzm0X17dquJDYtLeXrr0GTW0xfgwRf28MjL+7GaTfElekW01dBmMZGa7GBGUSZrFhQztzS7X6QoMfGh2zYuYt+JOvadqB3cXt3Qwed++iy3blzIukUlpHucmDWtP50b1aNoaOvhpV0neer1w6NIe6vmF3H1utkJjcGkqXzm9ou473tPxLQFbt17hpb2Xt535RIWlOeS7LL1c1CiAZ43EKKqrp0nXjvIG3srBk2sIDpJfeDqpUwvTEytNMlp5a5rlvODB1+P4Rxs23cGrz/EB65Zxoxpmdit0YBLyqhDa1uXl52Hqnj4xb2DXRFWi2mUeVQ8CCFwOq1s2DCTtWtn4PMF8ftDg8GAw2GZhBSvhtWyAW+kCjDw+R/HZF48QmdAYLPfTDC4DTDo6fkWul6LxXoJinChG234fX8jGHxz4CpiMa+c8JMVISh3Z/Nvs66iM+SjJdBNZ8hLQA+jCAWnZiXd4iLD6sY6gk8A0QXhlRfPiQkGTlc284e/7OAjd6wbw2k1cei6wXMvH+L1HbElnIvXzIjbmfGPxpQ4Az49yPr0BVybuw4BXJ61nH8/9HP+UvMyy1Nmow2L+Or9bVyUuZhb8i/G1M9WD8tIzBeTY0sjx5bGyZ4anqrfFvczDSS/r3oWKSVfn/th8mzRCVUiCRnhQXZ5V7iXP9ds5qrsVdxRdAUK0XKGXbPwVP02rsxeSab13YvKNE3l/f96ybtybCkNgnoHugyiyyBhvQtfuAFVsWJWPNhNeaTalnCq8zdMT74bgDNdf8JjnY/DNKTHbVLcgKDNv4dkyywkOnZtaizq/8NoDBjGmE0a3//UdbgdE5OFzpfgOLxcMBJWUx5W8kZtF0LwkRtX0dDazd7jtTGTUSisJ+T4WdvcxcFT9Tyz7Qgr5hRx782rKMpOSeheslvNfPlDl/H5nz1HxbDWwOaOXn7+yHYeffkAhdkpZHicWMwa/kCYpvYeaho7aO/2jTrezGmZfP4Dl2JJkAAohKAoJ4Uv3n0ZX//N5hgi5fGzzXz9t1vITU8iP8tDSpIdVRH0eoPUt3ZR09g5yiFQCMGmtbN47xWLE86UCCG4eu1s3jlWw9a9Q6Y8Ethz/BzHqqJlm/xMDw6bmWAoQltXHzWNnTHaFbNLslg0I48/vzDainy8z9Y0gdttm3J//cBE7/U+CEQIBd8GAkT1/4ZgtV6GppX0lxL66Ov7JV7vnwAzUgb63xOFyTQbk3l0G+xYUIRCqsVJqmXyAfXaFdN54LFdNDZHuWNSwlMvHMDrDXHHe1aQk5k86ZKZlJKOTi9PvniAx57dGyPWlOSyceOmsQWV/pGYUjCgCoXlqUMEHZtqYXXaPP567hV6wl5SLENSwSZF5cqslZiVoUjIPE49aCx0hXo52VPDTXkXkWcbkj4VCKzq0IPwRE8NPWEfpc48znmHanG5tnQCeoiqvobBYCAcirB7yyFa6jspnZfPvJVlRMIRtj+3n46WHtJzPKy5eiGKqnBy/1lO7jmLoimsvWYRiqpw+mANSy+Jtr9sf3Y/Sy+djS2BB/9Uocsgxzp+TNjoAWnQFTzBobbvoirpLM34MkKozE/7Mic6/oeDrV8HIbBrWSxI+4+Y0oFVTac0+S4quu9HFTZsWiYL078Rs8//Yeooyolmm8JhHVVRxu01v3CQ/aTCnn6lt1goig1tBFlUSklEN1i3uJQT1c30eqeuAugPhHljbwVV9W18++OJtSYOCBB961838e0/vMKh0/WDuSpDyjGdSUcfBxbPzOeLd18W12BpojGsmlfE1z5yBd9/8HUah3U4hCM61Y0dVDdOLBBj0lSu2zCHj92ydtKmSxazxufuvIQ+X5C9x2tjXvMFwhyrbOJY5dhtusW5qfzHPRtp7/aOGwzoukE4rGOxJJa9SRQm02ws1ovR1Hxs9luIxwcQwoU76Rt0dvwLUkavcdSTIDaoU5QU3Elfj3uMdwNOh4UPvW813/nZlkGRMt2QbH79KIeO1XLRmnJWLC4hP8eD02Hpl7IefZxIxMDrC9LY0s3egzW8vuMkVTVtMRLkiiK44z0ryMq4cPL8FxJTevprQsWuDU16QgiyrCmEjQhe3U8KQydrVc04tPOfIHsiXoJGmGLn+GznBn8rEslvq56J4S/ohoFDsxE0hqL5bU/vpa2xiwVry/nb/7xMeo6HtGwPziQ7eaVZPPrTLRTNyCG3JJMHvvMs7//sJgzdQFEEFquJZ3//BvPXlBEORnj5r2+x8sr5o8YT7QePVQ0XiEnXWKWUNAb6mJP6bzHb24M9bG7cz9IM0R8YZbAg/asE9OgDzKKmoGAakbZTKEu+m0LXdegyjElxIM5HlP2CY2isU+3HHX51DeP8dO8ni4UzclEUgWFIdh2upignsZXy+SCsd1LT9TN6Avsw4gi7pDs2Uej5JNAvwOIL8vQbh3l++zHqWroHH4RCRCc2TVXjatZLJLoRDSL0ODr6NY2dfPePr/I/n78ZWwJpViEEBVkevn3f1fz5hT28uON4XFnfsZCSZOeatXN435WLx62vTzSGVfOn8YNPX8dvn3iL3UdrRjkrjvfewiwPt29awsaVMybdljiAlCQ73/iXq/jV33bwyu5TBBL4fFVVWDa7gE+9bwOF2R7sVvNgd0o8tLb28NOfvMSateUsWlRERoYb5TzEowYghAmP5ycI4YrhAcTuI7BY1pPs+RG9Pd/vNy4afo5mTKZZuNyfw2xePqkxRcniBl1hH10hH0E9jCoUHJoVj8WOQx1b8lsIwUVrZnDoWB3Pv3I4piulobmbh594h6dePEhGmousDDdpKS7cLutgCSEUjtDTG6C9o4+m1h5aWnvGDKovXTeT665Y8E+ZFYApBgMSOUjSGECUBxBPDEZw/p5koBDtGw8b46cuNaGiCoV/Lb0Jj3n0iizLOsQR2PHcfrIK09j3+nH0iE7D2VY8GUn0dftorG4jFAzT0+klX1WYv6qMnc8fYP6achxJdjSTyswl0zixpwpfb4Cll8we0yb5T2dfot43JIqyPmM+6zLmDXZFtAd7cZlsuLRoas2nB+kKebGqJlL6z6E77OX3lVv4aOkmrKoZj9lJyIiAMLgoc8XgDeaNBNAUla6wCYdmxdZfR/ZGAnSF+gY7K1IsLqza5FwY/16wWjRUJSqFW9vUOSUxGpOmYjKpBMMR6pq7pixoMxWU5KVRXpjBibPNPPbKAdYvLiEnPb7a5MDqXFOVKY9PSklt929o824mw3kDZjVj1C/Obh7yom/v9vKt37/M7iM1g8GW3Wpi6exCVswpJDczGZvFFHcFJGX0AdjrDVLX0sWBk3XsPV4b05VwtLKRXYfPcvHS0WJR8TAgzfvxW9dx2fJyNu88wd4TtTS39xIIhWNknFVVwW41kZ2WxLLZBWxcOYOS/LQJyXqJjKEkL42v/8tV7D12ji1vneBYVRMdPT5CYX2Yome0pOOyWyjMSWH9ohIuWjKdzASs2BO5Bp/7wCWsX1zKs9uOcqKqia6+AJGIPriYUBUFp91McV4aV62eycVLywYloT1uG/Om59DdFyWERoO6oesSCRvs2VPF3r1nychws2hREevWzaB8RjYu19QCqQEoSnIC56hgtV6J2byQUPBtwpFTSMOHongwmWZhMi9GUSYXOIeMCHvaq9hcf4gTPQ10hXyEDR1FCGyqmWxbEivSpnNlzjzy7PGPbdJU/uWuDXh9Id7YeWqUm6TPH6K6tp3q2vZR700EQgg2rCrjEx++5Lytut9NTGlkYUOnJdiFxxzNAETJfXXYNStJ2mi97QsBj9mFQ7NyqKuCizLGrrkUO3MxpEQgmO7MH/fGsjmtzF9TTkFZNhtuWIInM4kdz+2ntaGTS25ZTs3paA+2EHDLfRtpqGrhhQfexDAMVm9ayMU3L+cvP3qRSFjn7v+4YczP2ttxmhM9Q4zlQkcm65iHXw/yu8qXcGpW2oLd3F50CTm2FJ6u20VAD9Hgb+fG/NVMd+XwatMBTvTU8lLjPrJtKWzMXkxP2Mvmhr30Rnx8qvwGAJ6p301HqBe7ZqHa28xnym/Aopj5RcXzFDky2dF6lKtylnJFv66/ISW6bmD0r/Yi+lCt2DAk3X1+bBYTqqqgKgqqIkatJqSU6IaBrkf/P6IbQ/VUGSWl9ToCaKqKqgqU/uOMdb08LjsZKU4aWnt4Yccx1iwspig7BUUR6LpBMKxjNWvjEnxsVhMFWR6OVzWx8+BZDpyqY2ZRJqqqoBuSUDiCpio4bCNNc6IrX6P/fCK6HiNb6vUH6fEG0IZdD3XERG7SVD72njV8/qfP0tjWw5d+/jx3X7ciSgSzmJBE++o7urycrGnhcEUDX7jrkimvKkHS6d9OftK95LjvQozTmhsK63zvgdd5a5ijY4rbzhc+eCkr5hVh1hLvopFScstlC3hm6xF+8vC2wcBCStiy8wQXLZk+qQe7pirMLslm5rRMOnv81Ld00djWQ1efn4huYDFppCTZyU5zk52WhHsCcuZkIYTAZjGxZmExK+YV0drZR11LFy3tvfT6gkgZDZrSkp3kpLvJTHVjt44mpp0PTJpKRIOvfmQjHd0+apu6aOnsxR8MY9JUUpMc5GUmk5eRHPPZHb0+zjZ38N1PXDMYPAkBLvtQVtbuMDN3bj5nzjTT1NTNiy8e4rXXjjFtWjqrVpexYkUJ+fmpmKbYSZUIouJg2djs12M9TxfP7pCPn596hRcbDuHXR5Oxe8J+mgPdHOw8xwv1B/nEjMu5KHNW3HvG6bDwbx+7nPRUJ8++dAh/4MIIsdmsJq67YgF3vmclznHUKP8ZMOXMwFN12/hoyfXYVCt1/hZ2th1mQXIZDm1yRBQpJRGpo0uDQP8XGtBDBPQQqlDQ+vULXJqd1WnzeLP1IIs85SzwlGESav/KuptiZw6KUChz5VPizOXB6s2kWpIGMwHeiJ/GQBtzk0oGsxdX37Wel/+6k47mbrw9fq66cy12t43Og9UceauCrtZeFEUQCoR57v6tJKW60HUDW3/bUmZBKpH+iTMtK3nS13FPRwUgWZM+mwOdlbzYuId7ijdyRfZi+iJ+9ndWsqfjNLPcBVyTu4KtLUe4vejiwfGnmt1cmbOE31ZuHjxmd9jLdFcOl2ct4o9Vr1DV10SaJYmIjHBz/hoq+xqY6R4Sddp77ByPv3YQfyBMIBQhEAxT3xol03T1+vnk95/EatGwmk3YLCYKsz3cd9u6mPNo6ejjJ3/Zii8Qwh+MEAyFBwlehpR87debcdjMWC0mbGYNt9PG5++6JK5rG0QFW95z2UL+55E3aWjt4TM/fIppuamYNJVAMEyvL8hHblzF+sVjO9soQvD+Kxfzn7/ZQlefny/87DlK8tKwmDWCoQh9viDXrJ/Dey5bGPO+6GT5Gh3dPvyh8ODnDeD3T+3midcOYTGbsFk0bBYT/3LLGgqzh0ipQgiWzIymb3/+2HZOVrfwlV++QEaKC5fdgpQSrz9Ed1+APl+QJJdtQvW4RGA1FY4bCECUlLb9QOWwscLHb1vLukUlk34gCyGwmk1cv2Eef3vlILXDzIIq69owDBmjCpkoFEUhNdlBarKDeWWjrZffbQghMGkqOelJ5KRPXXRsqnhsxyEWFudQkOWhIMsz+L3Es8Ae2NbU1ctrh86wuHQ0UXQAHo+Db37rFiorm3lrZwV79lRRX9/JyZONnDzZyJNP7GH27DzWr5/BvPn5pKY6x20rPF+cT8AR1MN899hzvNx4BIEg355KuTuLXHsKDs1CxNBpD/ZR1dfC6d4man3tfP3wU1gXmFiVHj9IdTos3HvnOubPyeeRp97hREXToO3wZGEyqcwqy+a9Nyxj2cKiuMJjU0UiVujjedOMhSkFAzbVQtgI89Wjv8Op2aj1teA2Obhr2lWxtWnEhAJCfj3IT04/Sm/ER3eoD1Uo/K7qGZLNLmyqhfum34LHHE3B3TVtE93hPn5e8Tjp1mSsihlvJIBFNfGjBZ9EEWBRzHym/DZ+evoxvnb0d6RZkgHoDXtxm5x8f/7HGSCHzlpWTHK6i6Zzbbg9DmwOC8svm0Nyv6HHx75zKzaHFZNFY/6aGfR09HHl7WsomhV9QAkhyMhNYfaKUsQURFqaA530Rvzs7zyDLg2mu3Lojfj4beVmZrjz6QgNyXsqQoBg6N/9ny/ilGAK7BkoQsFlshE2IqRbkugOeXmidgcWxUS6ZegB19rZx5GKWAc5i0kbZGR39sQSfLxx2iH9wVCU/DViQkvpF27p8wVjTFSsFi2uHeoAhBDcePF8vIEQz795jLYub3/7WVS22GaZ2FxJCMGGJdP5RI+Px145QHN7LwdORXvtVSW6AhyZDoQov+BwRcMo05eBcwlH9Jie8IHzGwlFEVyzbg75Wcn8dct+jlY20tTeQ32LAUSlkx1WM+VFGaxbVBK3nz9xCFLtF9Pm3UKydSWKsI1ZknjuzaMxafd0j5OLl56fyIrZpFKUkxITDHj9IcK6MWUp7/9fQ8LOE9W0dPWxqCSXeUXZeIMhtuw7TY8/QFGGhw1zSkDAm0erqG7pxDlGYD0cQggcDgvz5hUwZ04+77l1OceO1bNj+ykOH66lra2XnTtPs3v3GXJyklm6tJg1a8spLc2clNPhuw0pJS81HuGVxqOYFY27StZxbd4i0i0uVBHN0kW5WuDXQxzvrudnJ1/mWHcdPzjxIn/2fBSnKT6PTdNUVi8tYf6sPPYdrmHrW6c5WdFER2cfwVBkzKBdCDCbNVI9TmZOz2LD6nIWzS3A6bgwFuWx0Onp/Ql2+42YtPiqpX3eP2IylWG1rEn4qFPLDEjJR0qu53RvLed8TaxOm8eSlJlkWDwx+12SuYS5SSWDYkBxB6BorEtfEKMWNQBBlIA4AI/Jxedn3MGxnrOc9TYQ0sMkm11Md+YPBh1CCIoduXx9zoc50l1Jna8FgSDTmkKZKz8mOBFCkFucQW5xbO189rLR2tnT58dKJPd0etnz6hGaa9t572euHPtijYMZ7nxqfa1ckrmAiNRxaFaaAl0E9TAXZcxjc+NevP1OW4LoDd4W7MamWnCb7AT0ED3hqG5DT9iHcwyiplmJmtnk2dNY6CmJ8f9eu6iEWZNQwzLHiXBz0pP4xRdvSfgYihA47OM/vCxmjbuvXc4VK2dS3dhBV68fIcDtsJLhcVKQPXF7qKYq3HzpAtYtKuFsfTsd3T4kUYfEdI+TgjgWtxaziR98+rpJERezU+OzgxVFsLA8j9nF2dS3dFHX0k2PN4AAXA4rmSkustJcuB2Tr9eG9Q5avS8MfZZw0BXYyYnW+3BblqApDoZzdeym6bjMy6ioiXX3K8xOwXoB6pgjVz7RQPUfA0OPPk3+mQOR8cYYiugEQhEWleTyixd28cMPXQ1AbqqbWfYMfvzMduYVZdPrD/LM28f48MblvLR/bC3/eFAUQUqKkzVryli5spSmpm4O7K9mx47TVFQ0UVfXQW1tB1u2HKa0NJONG+ey8Yp5496n0eA6jJReDMMLhCCOQVYiULVcBtwOR0KXBn+t3oWB5NaiFdxTsmEUZ2Tg/nNoFpakTONbC27h7l2/pcbbxt6Os4P+BPEghMDltLJ+ZRmrl5XS0emlrrGTuoZOmlt76O7xRwmmMhoAJLltZKa7yc/xkJfjIdXjGFU6vLDQ6fM+hMW8fMxgwOd/Bpu87O8QDBDNDqxNnw+MZtAPoMSZS4lz/DSfWdFYkz72MYZDCIFds7I0ZSZLU8b/Mj1mF+vSFyR03KlAUQQOt50PffUGLFP0QJ/tLqDO18ZjtdtRhcLVOcvIt6eTY0vhidqd5NhTybJGJyxVKFyTu4LHa3dQ6sxhY/ZiDnVVcaSrGrfJwXP1u7kudyUlzmzc/Y6PefY0UixuzvlasSomjnZX440EybWlckvBWiA6uSbSBz8ezCaNaTkXVrwJounivMzkQbtiKSURw8AbDtMZ8tPk7xt0nrSoKnaTCZvJhFkZqnkqQpCV6iZrjAkbIGLoMQpuWenjt/4oQmCaQBeg0ddDhtWJqihYzBrFeWkU58XXd58Kwnon9T0PxGxThB1/uBp/uJqRpN0Mx9XY1CWjMjtO2/nXMaWUMaZDEHWx1LQ4E10gTDAQwpX87nCLAJ77y268vQHe97HR4mX/LHjxsXfobOvjjvsuHfWapiqsn1NMqstORrKDps5e0twOGjp6qGxqxxcK4wuGOFHbworyQmblZxIK67x8YHIBAQxoDajk5aWQm+vhssvnUlfXwcGDNezZU0VVZQuHD9fi94fYeMW8uMeQUqLrdQQCWwgFdxHRa5FGL1KGgKkFAympf8JsXhD3ta6Qj+q+VsyKxi0FyxNSesy3p7AuYwZP1+1jd9uZcYOB4e8zaSqZ6W4y090snlcYzThIBvvDorL6Q/tPFdFAauAZNJpPFS+LOfYxpjaOf15q4z85nEl2VmyM/+NIFKqicmX2EiJSHyqpCLin5Ap0aaAJdeimE4KNWYsIy/mD2Y2lKWUs9kwfOp5QuCxr4WDpYEXqDASC+8++zMbsxcxNLqKqr4kXGt45r3FPFQMKcomq1A1ANwwqOzvYUVvD3sYGaru76QkGCOoRDBmdnC2qistiIdPhpNSTwrzMLC4uKsZmmri97bHjR3mp8syE+w1gdX4BH1k0vtvkD4+8zpcXXI7H8u5YcVtNBczLeijh/VVhQ0oxqhwx0u1vspBScrahg6r6WKZ1cW5qXKLW3q0naKnr4Pp7NpzX546HU0dq6Wzrm3jHfyAqjtXTcC4+O92QkkB/d4Y/FMGkqTy8dT+5aUlctmA6u07WIGXUprmxM1pK9IfOn/AWbf/TmDYtnaQkGykpTl55+Qh7954dMz0upSQUepvurs/1twteIFXTOFoZA+iNBAgZEZLNjoSFhoSIqhUCNPm7pjwsIQYm/wu36o9EGvAHXiIcOQWA2TQXu+06FCV6blIaBIPb8QdeBmHCbr0ipjwspSQcPorP/xRSBrBaL57S+CYdDEylR/7/MDaEEIPKjMO3DZAEh3/pQgjMw/ZVhMJIqsLw/QeOcXHGfDY37uVodzUSeG/Bhgt7EgnCMCS//sNW7rv3koSIZVJKugIBfrN/D0+fOk6L1zvho+ZYawuvV1fhMlt47Y4PJhQMVHZ28Oa56sROAkizTzzBd4cD/T35Bmf72pnmTEUIQXVvO93hAKWuNJym6Kq8yd9Do6+HPEcy6VYnveEgISNCg68Hl8lCkXN0S5QiTFi0yZmdSCnJSHHGSO+eqW2jrqWL/MzRJZNEjtfV6+cnD28d1Zd/8QijoHAwQld7L1uf3osn3c2501ERnZRMNyaLifamLrILo5mTpnPtWO1mPOluImGd5rp2cgrTQUBfl4+GfjGXnKJ0XMn2f5pa9oWCw2Lm6d3H8DhtmFSFHI+LFJed2tYutuuScL/WyfxpOTz79nGeeOsIZxrbsZmnLnErpaSnx8+pU43s3HGagwfP0dzcHRXO0hTS0+MLZ0nppbvr80Qiw4Nplai1sXlCQuuYGEeYThXRNnNdGv1meIlhoONAG0ML4R+FcOQYodA+zOYFSBmip/en6HojbtdnEUIQDO2io/PT2O03o6oZ9PT+AkMOZeJ0/RztHR/BYlmN2TQHr+/x/sBsdNZpPEw6GFjkKSfJ5IhR/fs//HOjyJHJh0uuICINzIqKwtR+oFJK/IEwmqrQ5w3idtvQVAUpJT5fiEAwjMtpHWxNMgxJb18AwzBw9QvCVFVHH+SGIfH5gjjGIdj0hIJ89tUtbK2umvR6oyg5mRTrhbUinSx0KXm8+iD+SJhp01N5o7GCt1tqyLK7eLTqAF9fdCW13i7+eHo35UmZPHRmL1+Yfyknu1t44uwhlqTns7+tjg+Vr2COJ3vcz+oO7MOm5WOOox3hC1chULFqBaxZUByjZuf1h/j+g6/zxQ9eRlaCvfIDJj3Hq5r43VO7YrwFAPIyk1k1f1rMtgM7TvHKY29zaOdpXMl26quiLns3fPgicosz+PZH/8iPnvk0SPjmR/7A9HkFfPJ7t1FX1cLPv/go//23T3D2WD2//cZTwIDrPdzzleuZPm90C7GiCCpPNHBg1xkMXTJvWTFlc/MGpWUN3WDb5sMsWVNGa1M3B3adIRSMUDYnl0Wro2zzcChCxbF6Ko7V09vlIynFwdylxRSWZgyS1Ha+coyyOXk0N3RyfH8NQghmLSpk1sKCQSa+lJLqimb276wgEtGZv6x43Ov86evWoqkKDR09XLV4BiZN5cZVczlxrhmzSWP1rEJSnHbMmsrnbtpAfXs362YXE9Ynx3yXUhIMRqiubmX37jO8vbuSmpo2gv2mWckeO3Nm57Fu/QwWLCiMe4xI+MSwQEDFat2I1XYFqpqPIhwwxYlXVeN/HkCy2Y5Ds9AXCVDV28Kc5Iml1HVp8E57FQDT3f9cjoFWy0VYLRsYmo4lPv9zuF2fjQqE9f4Kq3U9Se7PE/VtWEpz69WD7/f6HkFRkvAkfwswY7Ntoqllw6THMelgoNCRhdNw09LpJS91dNtNXyBIdWsnDouZgrTk8xYD+T+cPwayDxfCGuO3f9yGx+OgtzeAy2nljttW0NXt56+Pv42iKPh8Qe679xI0TeXpFw5QXdOGqilcun4mM8qiE5ohJW9uP4nXF+TqK+LzRQwp+fmet3mjumr0+QA2kwm7yYSmKFF7bF0nEIkQjESQwEVFxQlnsJbl5NETDNIdDNAbDNEXCuELh/BHIvSFgvSGJm8opSB44uwhwlLn3vJVKAgeqHiHG4vmkWy281LdSWq9nfzt7EHyHR4KnR5OdDWzu7UGh2amwOnh9pIlTHen82zN0QmDgZquH5Pj+gBp2mWjXmv1vkAo0sz0tG+yac1s/vbKQTqGdYm8faSGz/zwKTatnc2iGXmke5zYLKahidOQhCM63kCItk4vFeda2H2khkOn62PaLiFK/Pz4rWtxjuDRzF81nRmLivj63b9jzvISbro3Ws+32s3oEYOejj662/sIBSMYuqTi0Dl03eDs8Xo86W70iM7/fOFRFqwp48Z7L0YIwfMPbudnn3+EHzz5KawjCKlN9Z387nsvkpnroau9j2cf3sUnv3EDS9ZGMxa6bvCHH2yhrbmHPW+eIjnFSSgYpqO1l0Wro6W31qZufvWt50jPTiY5xcGB3ZU8fv92vvnbu8gvzkBKeOjnr5E3LY2u9j7ypqXT3tLDkw/s4LPfvpml68qBaEng25/+K9n5KWTmeti/swKfN4g1DtdICEFhRjRLkzvs+Wo1aSwsGc2/yklxk5OSuLyt7NcVGU4YPHWqkd7eKFHZYjExY0YOq1dPZ/mKUvLzU8bVHdD1BgZKA2bzUpI9P0OM0c1yoeDUrMzz5LOztYJfnH6Fby94Dx7z2A6TujR4tfEo+zvOYhIq6zNmxN3vHwUpgwRDbxMOH8cw+ghHjsGgimiEcOQUdvuNg+qOmlaK0i8tHi297sdsXoQQZkCgKGmo6tgtpmNhSpyBY3XNbDtRxZLiPASwtDSfFIeNsG7w1b+9QmVztBb2/tULuWXF3Nh0oREZVC/UFG2w/q1Lg0Z/O42BDgSQYfGQY0sd1YkgpaQj1Eudr5WAEcKl2cm1peE2TT5dKKXEQA6q83WHvfj1IIY00BQVu2rFbXKQbHZgVy+swMlkoEuDiBGbhhUITMrkau9SSgJGiI5gL52hXrx6EJBRRUOTi1SLC7s6PrO9+lw7V142l8L8VL767adpbe8j1ePgpusWEwnr/ORXr9Le0YcQgl3vVPLVz12DyaQOsqYVRbB1+ylq6zt4/y0rxvysht4eHjl2eNT2ackebpk5h0XZOaTabJhUFd0wCEQidAT81HZ3c6y1hStLEhe8uby4lMuKS9ENg7BhENZ1gnoEfyTCtppqvrbttYSOMxxhQ6cl0Nt/fwsk4A2HCOoRukN+bi1eSIrFQWfQR7LZRlfIz7L0AmYlZ1HT14HbFM2YuE1WesMTy/NKGWEsspYqrAT1aPtoRoqTf711Ld974LWY1H5VfTs/f/RNnDYLHrcdp92CuV9RMxzR8QfDeH0hen0B/MFw3Bqy3Wri3ptWx9UssNjMWGxmNE3BYjXh9gwRCKVZkp7jobG6jY6WHhauLePA9tN0t/dxYt9ZZi8rprGmjXMVTXzldx8iqd/hcdPta3jy169zrqKJshHdPp1tfXzlJ++ncHoG4VCEn/zHUzzw01dYuLJ0UClUSsnWFw7xxR+9l6w8D9KQhEJDJmqZOcl8+afvJzXdhaqp9HR5+cTNv2DX6yfI7+9AklJy+kgd//3Ah8nM9RAKhvnqvQ/wwiNvs2RtGUh46BevkZGTzFd+9n5sdgtNdR189n2/Jq/fvXQkwoZOW6CPbPv5axz0hPxRdrzJSjgc4e23q9i+/SSHD0VbCQcIuNnZySxaVMTadeXMKM/GmaAioaIOEYc10/R3PRCAKEfogyXr2ddRzZ72Kv5t/1+4pXA5s9y5uM22Qa6VXw/R6Ovi9ebjPF27j5Chc2XOfKa7/nkyA1JG6Oz+EpFIDXbbJszmIqT0o0cGnBQlyAhCDJf/V4mZumWo//XhJeXJk9qnFAwYUvLC/pMcr2tBSsnTe4/zkzuvpqGzh/1nG/jdh2+ksauHbz+9lSsXlOMaxlj+Y9UWTvVGT/S9hRezJKUMvx7kLzWv83LjXrrC0Xqm2+RgZeosPli8cVBWOGLovNq8n8fObaMx0I5u6JgVE7n2NG7OX8fFmQsn1DWA6OTaEujiaPdZ9ndWUNXXSHuwB58eJCJ16O/t14SGQ7OQbklmTlIR6zPmU+bKG7dV8kIjpId5qOY1jnVXD25ThOCSzEVszFoy9huHQUpJc6CT7a1H2NV+nDpfK32RABFDBySqomJXLWTbUlmaUs6GjPnk2zPiBj+qqpCR7sZqNZHqcdLbG6C5pYdtO06Rk52Mzxe1Qu3pC5CbnYzLNXQT67pBnzfIK28c5+or5mGxjH37vVR5hr4RK/J8dxK/3XQdJZ6xJUtlnpw0n3agDUlR1WiXwDCeQZ5raqYiqlD4lxlrePrcEf5atY87SpewJqsYQ0pmJGXSFfLjNlm4OKeMwx0NlCdl0BsOkmlzUtPXwdut51iXXcqWuhOsyCiKf65SEjbakNJAyhARo5tgpDlmH0MG6QrswqSmDZ7rlatmEgpH+N2Tu2IyBFJCry84arU/ERQhKMlP40PXr2DdwpIpZQPLFxZSeayOmlONLL98Li31ndScaqTyWD0X3bCE7vY+NJOGM2mIr2F3WrHYzHT0O84NR05BCoXTM/t9RMxccu0C/usTD9Pd6SV1mFHMiotnkluYOng/mYa1WSqqgifVSWtTN309fsLBCFa7ma72WHLi/BUlZOVFxYGsNjMlM7M5uq8agEAgxKnDtdx532U4+sXKsvNTKJ2dS8AfP+PUEfTyi5Nb+eai6yZ9HUdiZ0slqqJwec4sWlt7+dY3nyHUHwS6XFZmzMhh3bpyFk7Rq8BkmoeqFaNHqgiHDiKlHyHeHdLscCzwFPCR0ov4TcXrHOw8x9GuOjxmB8lmOxbFhC4N+iJBOkJ9eCPR+3lxyjQ+PfOKmPtzKuI8FxKG0Ybf/xzpaX/DbFoEQDg8nH+hoagZhMOnkNYr+8uv3Ug5RPzVtELCkTPDJNfDGHpsC3EimFIw0BcIsm7mNL50/UVICV98ZAtnmtuJGAZJdislWamUZKaSZN9NQ2cP5bahCLiir4H9nRUAlDpzWegp5f6qLTxZuz1Ga6At2M1zDbtoD3Xz1dl3YFZMvNDwNr868yxBY4hpGtZ1TvfW8cOTf0NKyWVZi8eeKKSkoq+eFxve5p32k7QEu8YmoEgIEcGnB2gNdnO8p4bNjXu4Inspd067DOcklRangpAe5o9nX+JvtdsGx6kguCJ7GWvT5yZ0A4eNCK807eORc29Q52sbYZnUv4+uE9BDdIR6OdZdzYsNb3Nz/jquyV0Zo/MAEInoVFW3UlqcQUtbD55kO89tPsTKZSXMLM9m244oIzYt1UVtfQetbb1Y+r0GrFYTVqvGv39iI7++fxu52R5KpqWPOg8pJdtqzo4a50cWLRk3EIB/bH/7cFyRNxOHycydpUt5ovoQvkiYD5ev5PnaY7xUf5Ism4vFaflcklOGISWv1J/CabIwIzm64ixxp/JmYyWZNhcb8+KnNSVhKju+iT9UhT9ylpqun1DX/buYfQxCGDLIzPT/GdymqgrXb5hHeWEGj7y0n73Ha+ns9U1KBVGIaPtgcW4aly4r46Kl00lLHjtVO/6xBLOWFrPzxUM0nG3lvZ+6gtaV0zm48zTd7b3kFKXT3tSFHtEJ+kPY+mVdQ8EwoWAkJkAYgMNlY/hQklOcRMI6fl/sBJyRnTymQFNtVSsP/PRlujt9uNw2zFaNrg7vqOuUnhXrO6GoyuA+oUCEoD9MWuZQACKEIDnFQVN9/GDAkJKesJ+T3U0E9TDlSVlYVRPdIT9n+9qiUuvuDOyaGW8kiD8Spi3YR1CPMDs5B1UIarwddIZ8dIf9OLXo9TJ0iRBQUpLBihWlrFo9naKi9PNyMRTCRXLy9+jq/DTh8DF6ur+Gw3kPqpo/uFp9NyZaRSjcPm01aVYXD1TuoNrbSmuwl9bgaJdLj9nB5dlz+VDpelLNsd0Hum7w7EuHmDszj8L8FEyTkONOBF5fkObWoTEpiiA/xzOkLyEsCGEjHD6GqqQTiZzFH3hx2BEEDvst9Pb9HotlOaqSgtf3eH8msL/d3n4z7R0fxR94AZNWTiC4A92Y2GlzJKYUDHiDIWbkpJPUr3tdmJ5Ma48Xl81Cv1AeCEi2W/EGx663nu6t43BXJc/UvYWBREGMcvjb1XaCba2Hme7M5fdVLw4GAgoKxrC0aNAI8/uqzaxImzXYZx8PrzUf4Jn6t8Z8Xem/eQ05ciTQG/HxeO2b9IR9fHbGzZiUd68zM34goHBt7ko+UroJWwIEzqAe5v6qzTxVv5OwMdoFLUoklKMEn1qCXfym8nnO+Vr42PRrYz5L01QOH6vjrbfPsHZlGZ5kB5dsmMlrW09w8nQjC+YVYLGYSE11svGSOTz02G5UVeGSdTOZUZ5F+fQsUlOc3H3HGra8epSigtRRgjW6lFR3d8VsMykKGwqn/a9hjt9YFG07FULw3uJFg/++ddqQ/PHAuVyZN3PUtlx7MneULonZNhICE8WeL+ILV1DZ/g3c1sU4zLGBg4IZp2UOTvOc2O2KYFZxFv/x4Ss419TBodMNHKtspL61m+6+AIFgGL3f7VFVFEyait1qxuO2kZOeRFlBOmWFGRRkeQaNchKB1W6hrakLPWJEJ2sR1ZOYNiOHh3+0GVVTSclwM2NREf/zhUex2i04k+2YrSZSs5LY+eIhNr53BQjB268exeYwU1g+Ou0bHLHq9vmCKIqC2Ty6cycepIRff/s5IhGDz3zrJjxpLlRV4TPv+9WofcdTH9VMKpqm4u0byrZIKQlOoH1f2dvKrpYq2oN9vNNWzT3T17CvvYY6XxedQR9vNJ3ikzMvprK3lT9Xvs3MpCwMKSlPyuRsXwe/OvUmS1OL2NZ8mqvz5gLgTrLxla9cx8xZuSRfoC4MIQRm80pSUh+gt/cn+HyPEgi8gmYqQ1OLUNR0FOGcNJHQZrsOVc0cdx9NUdmUs4DlqSXs66jmYGcNjb4u/HoIVVFIMTuZ4c5mWVoJJc6MuBnd2oZOfn7/GzgdVubNzOXitTNYuqAIl/PCWNG3tvfy2a89NvhbUhSF//7KjZSXRu9ZRSST5P4yXt8j+PwvoCqpJLn+DZ//WaBfNdL+PnS9nu6ebyOEA5vlYuy2awZ5AxbzCtyuT9Db9xsEFszm+TgdH5zw+o3ElGaz8ux0vvX0G5RkpWJIya7T56jv6MGsqQRCEbzBEGZNpb3Ph2Mc6dgaXzN/qNqCpihclbWKOUnT6Ar38WTtDhoDUd6BRPJU7Q4yrB76In4K7Blckb2UTKuH0711PFP3FoF+W+LWYBf7OyvYkBGflCaE4MrspTxTt3MwqHBqVnJt6RQ7syiwZ5JqcWNSNPyRINXeZvZ3nqaqr3FwwpRIXmnex8q0maxLH1+Ra6qIFwioQuGGvDV8qPjKUav1eNClwYPVL/N47faYoCnDkswCTynlrjyS+6PkjlAvJ7rPcaDrDJ2h3sH3v9DwNjbVwr2lVw9TeITrNy3AYjFh7SeZzZ2Vy/SSDBRFibrvKdH208sumsW6VdGVr7XfBe/eu9ajKILc7GTuvG1lXAW2YCRCbzA2Ve2yWEi1vfvpxwuFGFnuMf491rYsmxtLAnwQIQQWLQuzmkmSdRke2zrSHBsnNUazSaU0P52SvDSuv2guwX5/imBYj1oUi4FgQMFs0rCYtejqaYxzmQgX3biE+7/1LD/67MMoiuCq969m5pJppGYl4/eGKF9YiGZSySlKo7O1h/mrylAUgdVu5qNfv4n7v/0sh3dVoCgK1acauOcr18fNDDTWdtDZ3ocn1QkS9m4/TWqmm6SUxMSODN2g9mwbV968lOz8qNple0sPrU2jSxLjwWo3k1OYyt7tp1h/1TwURRDwhzl7qonktLF75FMtTu4sXUFX0McX9z/FPdPXsCi1AI/ZTlvQy8NVbwPRwFkVCneVrhrMiG2pP851+fNZlzmd3sgQ38TttrFqdWJOkpODjq43II0+QGIYLYSCLYTYMeUjmk0LE5rMhBCkW91szJ7L5dlzB31uFASaog4u7sbC2/vPEokYdHX7eHN3Bbv2VvHtL13P8sXxlf0mi5ysZKxWE/WNXYPbXnrjGGUlmf2aBQKH/TZstitBhlCUJMCMxbJ6cH9FsZPk/gqG0QEoKIoHKQODvAAhNJyOe7DbbgYiKEoKUXvoyf0+pxQMzM7PZE15ET/dvBMBXLN4JtOz0thdcY5pi1L44l+3DPa85sfpOBhAZ6iX7lAfHy+7nmtzV6GKaJtauSuPfz/428EJ+3RfPWf66sm0evivuR8k3x5NLa/PmI9A8Mi5NwaPubvtOOvHmaQL7BmsTJtFna+V1elzWJYygzx7Ok7N2q8mFSvm0Bnu44naN3n03NbBidmQBo+d28aa9LnEd32fOkJ6mPvPbuHx2jcHP08TKrcUrOcDRZdjUSfuCZBS8lbbMf527s3BQEBBcFnWYt5XeAm59tSoJXT/uUop0XMNqryN/PrMcxzojNasJJKn63awyFPK8tTo6jUnKzm6ShzGhBZCYIujjS6EwGqNHe/wldnIVdoAIoYxyqraqmpo/+SdKVJKzvkaybVlDmqkd4a6afC3Ms2Rh30MueiRmJU8uYheCEGW6z2oytT4DQPHUIXAbjWPaSB1IbB20wLSspKpq2rBYjWRVRgloFlsJj79w/fhSY+2N9pdNv7tJ7eTmjmUgl+0fgaZ+amcOliDlJLbPnE5+dOz4v7WHS4rv/jGMyzfMJP2lh5efmIft330Iszj8FSGQ9UUZszPZ9uWw+QUpqLrBjtePjpY908UiiK46e61/OIbz/Dgz14hvzidI3vOokwgleyx2KNGbYqKQTQ4/87hLazIKMalWWLksjOtrhh+T184gMsUJQAmmSYuZ45Ut5ssKdnnfYienm8j5T9O6GmgPGgWiU9pUkre2R9bjrRYNMpKLxzB0KSpLJlfGBMMvL3/LB/TjcGMqBAKqojV+RDCMeJvFVVNH/a3fcTrCqo6XKJ98lP7lIIBk6ry8Y0ree+q+SAg1WlHEYL1M6fhD0d4fPcRWnu93H3RkgmFMLJtqVyZvSzGW2B2UhGz3IUc6IpOSoaMTmm3FVw0GAhAdLV8RfbSmBV0VV9j3M8ZgCIUPjb9OjShkGxyTlh/TjG7uGvaRhr87WxtOTT4WmVfA12hPlItU38Aj0S8QMAkVN5beAnvL7okxlNgPHj1AL+rfIGwHCoNXJa1hE+V3xg3qxAlS6pMd+bypVnv498P/oZqb5SIFpY695/dwiLPdEyKxr0fXI9tivLLiWK4MOfwMf5vKBE8XPMsaRYP2bYMrsxax4PVT5Nq8bC/8xi3F16b0DlM5TxdlsQkvf/RUDWVOctLmLM81v9DCMH8VUNqmooiWLCmfNQ+eSUZ5JWM1lIYjtmLilh/1Xz6evy89epxdF3nzk9cyqU3DPGJhCJYc/kcsgvie1wIIfjI5zfxxP3bef25gzhcVjbeuIRwWCcSHqjXRgmIxeWxbZ/TZ+di6Q+ChRCsuWw2Rn8wca6yhaVry1h/5TxOH60b9bljISINznk7+FDSas70tsbl/gxgWfo0Xm44jkMzs6e9mg2ZsdkAKSWRiEFtbTsVp5toau4mFIqQmurkhhuWjNpPCMbU2jeMZnp6vjcsEFBQlAxULRdF8SCY2rNCUSYngiWlJGRE8OkhwoaOgsCqmrBp5jGzA6GwTnVtrArkjNIskt0Xjg8mhGD5omk8s2Vo7mhq7aGjy0tG2oWbOy4Eplz0VoQgze0YjCoHIlWLpnLH2mhdNJGH2gJPCRYlNmBQhMKS1LLBYADArJhYlTZr1DEzLMk4NCs94SgrujPcR0Tqo1T9hmO4a18iMCkatxVcxJstRwZX2kEjTGOgY0rBQDwFrKAe5o8jAwFF4wNFl/Gegg0J8xOklLzZcpha3xCbNM2SxEdKNk1YXhBCkGp284FpG/nG0QcHHzeVvQ2c6D3H/OQSHP/kntz/SEgkilC4InstO1r30RbqxBvxc9/0O/hpxYMT3pejjtfvxRDUdUK6jiENhBCYFAWzqmFWp56u/9+GgWsR0COEdB0pQVMUrJqGRR1YYfVzMN6zbJA8uO7KuSCjtfvh10lVFe794qZxr116VhIf+cJVRMI6iqoMlbSGzcMf+ORlgCAYiRCIRAgbBnM2lLLwknLChoFJUVA1lQ2b5rNm45zBsQAsWBnfgttpsnB5ziwALKrG1XlzsSga95av5c3mCopdadw2LSqHnWV1szw9VuBpbUYpXSEfb7VUcW3+fDKsQ+qBUkpaWnr48593sntXBd3d/kEXy5LSzJhgIBAI8+MfbyEYCPMvH7uErDg27eHwMaTs6v/Lgst1H1bbtahqJlEVwqnem4n9Tgwpqe5r5Y3m4xzorKEl0ENQj6D2t1MWOdJZk1HG8rRSXFpsu2RPr5/uHn/M8VYsHl8QaiooLkxHUxUievS5Hg7r1DV2/b8RDBhS8nbFOV49eoZefzAmRrWZNb50/cVYTYkdeoarIO7FL3LEpmpSzS5SzKMvnlk14VBtg8FAoD8yvNDkvkJHJm6TfbD1ERisr08WzhGpu3iBgEUxcXfxFdyYt3ZSrYwGkucbdsdsuzxrMR5z4hreS1PKcJscdIe9g8fc2nyIeUkX5odiSNnfy6/jD4fpC4foCQbo9Ado9/uo7ekepabmC4d48uSxQYnl8bAiN48899/fh14QNdLSDYOwjLC34wg+PTDoPTHeag4GWgV1Gnp7OdXexpGWZio7O2j1efGGwtFaqBBYVI0kq4Vcl5vZ6ZkszMqixJOKRU2cCe0NhXip6kxMutmqaVxRMv28yzFSSl6vrqIzMFSvFsCa/EIynYndhwPH6QuHON7ayp6GOo61ttDs7cMXDmNIiVlVSbHZmO5JZVluHouyckiz2wfFkgBMYzyHxss0+cIhtlRWMOBdZdM0rigdplsh+tPj4TCHmpvYWVvDibZW2vw+ApEIihDYNBPpDjvTPaksyMpmZlo62U5XTFvbWF+Vy2RlUz/pz6qauL5gAQDrM8tY37/KHxhLtj1plB6BWdW4sSD+gqytrZdvfP1pTp5s6L8+KiaTGFQdHA6LxURHRx8H9tcwc1Yut966PE7nz9B3bLGuw+n6JOLvJPcb1MM8VvM2D1e/RUugJ+4+R7rq2NJwmPmeAj45YyOzknIHz6Gj00s4EvucmT0j54KP05Nsx+GwxAQedQ2dLJpbMM67/v6Y0oxZ3drJlx55iYVFOWR5XDGxn8WkjXmTx0OWLX46yKXF1kTSLclxNQQUREwdXZc6upycLGciMCsaTs0aEwyE9KmZgwxvS4wXCFgVMx8p3cS1uStRJ/nD6gj2UDmsVCIQCbchDsCuWsmzpQ0GAwBHu8+ehx9WLJ48eYznK07RFQjQEwzSFwrhj4QJRXTChh53yuwMBPjCay8nNIL/uWLTPyQYALgkYyWvtrxFjjWDFHMSq9MW8cezT+Axu8fMCkgpafF5eav2HC9VVXCkuZlWn5ewMbHuuuAIyVYbS3Jy+OD8RSzLyUuoz18Rgp++/Ra1PUMPUbOqUp6axvSU83Og7AoE+NxrL9HpH5ooHGYTW973gYTeL6UkqEd47WwVDx05xJGWZrzhsbuStp+r4aEjhyj2eLh55hxunDmLZMvkbaEH0BkI8MXXXhm8/h6rlQ2F03CYo5k1Q0r2NNTzy71vs7exHl947OfAK1RiUlRyXS4euO4mCpKSpzQmmFwGKH5K3+D3v9vKyZMNuFxWLt84l4ULC2lt6eWnP31p1P6KIlixvJQD+2t4e/cZbr11+ah9TNoMEFaQAVQlDaYodT5Z6IbBb8+8wZ+rdhCRBi7NSp49hXSrG5sa1RnoCvlo8HfRHOhmb8dZPnfgEX646H2Uu7MRQtA1IitgMkUdCi80LGaNJJctJhhobo0fvPwjMaVg4GB1A+U56Xz3fVdMaOU6EUZO+oMDG7EaTjaPXd8fTp6J2ku+GxCjVqVT/RxHP5EsbET409mXYgIBm2rhY6XXcmXOsoQElEaiyttIaJgOg001k2ObvHVuujUZemoG/24L9hAywgl1MkyEIy3NbKupnvT75LD/nXi/vz+EEMxylzLNkQf91qZSSrrDfXjM7jH72R87fpT7D+6jqquTSAIBQMz7gc6An1eqKnmnvo6PL13BXfMXTbi6t2oaV00v49f79gxuC+oRnjl1gs+uWH1eGaA3z1XT4Y990C7OziHbGd/sZjiklDR5+/jeW9vZfOY0wQT19kOGzsn2Nr69cxuvnD3Df6zdwKy0jKmdh4xmwwYyOX3hEH3hEA6zGd0wePT4UX6wawedAf8EB4oibOj0hUOk/IO7YVpaetm+/RSapvDpz1zJ2rVlKIrCkcO1Y76nrCyaoW1o6CQc1keRflWtCKfjQ/T1/YZgcCeG3oCi5ryrpSspJbvbzvDQ2Z1IJFfmzOPWwhUUOtKwaWZUFCTRLFtnyMvutjP8oXIbjf4u/vv48/x62QexqCa8I8S1rGYthhh9oSCEwDmivDqyPPHPgCkTCD2OqBTs+XzpAsacXEYe1TEOEzvOIzbhMQxIEvv1IN5IAG8kgF+PusaFh/0XMiJ4IxPLwiYCu2pBlwZP1G7n8WHkR4dq5b6y67ksa3FC6fB4ONPbMGKLYHPD25POMDQFOmP+DuihCxYM/L8KKSW72g+yt+PI4O8i2eTi9sLrxv2d+CNhTnfEt7MFUIXApKpoQsEg6sMQL2joDgb5wa4dpNntXFc2c0Jy7A3ls7j/wH5Cwzo3nq84xceXLseqTc3JwugPbmI+C3jv7HkTynlLKant6ebTL29mf9PI+zgKTVH6uRKCiBHlUgz/tRtS8nZ9HR998Vl+cOkVLMuZ2MRmIoR1nU6/n3S7g0eOHeGbO7YSiIxOqw98Srynz5LsXBwJOGi+m6g43UQwGGH69CzWrCkbNFIaD8keB0KAzxciFIrE0WlQcbk/i6Jm4e27n46OD2F3vB+TaQ6KkkpUntjEZDMGQjjGLDdIJH+sepOwoXNFzjy+Mvd6rIpp1PesKSo2zcwN+UsodmZw394HOdJZy5GuWpakFhOJxP6GFFWJKTFdSJhMsecy0uVzJLz+EHpEx2G3xG2/fjcwpWBgaUkef33rIPvO1lOWnRa7ChECmykxRSuBSNhO0nQBJYCllOjSoN7fxpGusxzpPkutr4WuUB8+PUjYiKBLI7o66BcfGggaLgSsqplXm/bxp7MvERmmgLghc/55BQIADf62mL99eoBfVz4/5eMNwGBydqHjYUPhNJKtYwd3gYjOHw/ui0mTu8wWPjB/4SjL5ngoO88091QhkbzRspu7pt2IVYmuBCbK7gghuLZsJr/a+w4tvmhZRlMUshxOylLTmJuRSbEnhTSbHaumEZEG7T4fh5qbeO1sJZWdHTF3ZVDX+f5bO1hXMI0U2/is6GJPCrMzMjjQNFRWqu3p5lBzE8tz86d0Dep7ezgwYiJPdzhYlTdxfbQrGODfXt0yKhBQhWBmWjobiqYxJz2TFJsNRSj0hYJUdXayo7aGd+rr6BtWSqjr6eEzr2zm91ffwIzUtPMKCCTQ6vPS6vPy3bfeHAwEbJpGaUoqs9MzKEry4LZYkEjafD4qOzs41d5GbXc3vkh4Ul4ZMZ99AeVyW/pT0wUFqQlPegNdBFJKpDH6+SdlhGDwbUBH1fIIBbfT3XUUIZwoihsh7P1EQo3JFBmTPd/HZJoZ97XusJ+T3Q1oQuHukvXYEiBGz/cUsDJtOq82HWVHy2mWpBajjZhkdd0YJFNeaAyQBwcw0fXfvb+K13ee4iPvW0Nh3t/neTalYKDHH6DXH+STDzxHfmpSDFnQbjbzwzs2JeytnehNfqFEZqWUVHubeKJuO2+3n6Qj2HPBJvlEIBAc7jrLn86+FCOrDLCj9Sgbs5YyJ6loyj/+nohv4p3+wdhQOI0NhdPGfL07GOShIwdjggG3xcInl61E/SdmzgsEHnMSVsWC2+Ts3zYxPFYr75k1h78cO8zynDw2lkxnYVY2mQ5ndBUc55w3lkznznkL+Ok7u3j8+LGYe7ihr5eXqyq4bfa8cT9XFYJbZ82NCQYMKfnb8aNTWlFLKdl85jT+EavmjcXTcZrHf2DrhsGPd7/Fnob6mO0us5mPL13BTTNmk2IbbYKzrqCI986Zyzv19Xz3rTc50TbURdPQ28uXXn+ZB667Gbfl/LpgDjQ18vSpE/SFQihCsCI3n3sWLmZhVjZuizWmqyO62JB0BQKcam/l9bNVrMibfHAlpeTxHz5H/oxcVly9+LzGD0Or00gkcU5Vd7cPw5BYLKYY34ahMfro6vzXfkGcARhI2YOuT70uHhUwGmNMIT9+PUySyUbuGJyzkRgICF5tOso5XzQLN7IzKhiK4PMFL3ipQEpJX19sVtnR77IppcTrC9Lnjdq5O+1Rg7INK8s4cLR2MIPQ5w1is5kIhaLfncWs4guEsVlMdPX4ieg6KUkONE2hpy+Ay2FFUQSRfpMxl2NifYwpBQMOi5n3rV4Q9zWTpv7TisMYUvJm62F+XfEszcGuUa/bVAtuzY7LZMOuWbEqJsyKCYsa/f9tLYfw6ZMzcRkJieT+qs1xj9Md9vL9k4/ynXn3kGNLnVJAoI9IHysI7Nr5twOaFFNMQBZdsRhIDAQakjBi0CTZwJARFGFC9AtJRc88eiMLNAbUBAZSgVLq0dEmoLr3zwyLYubnZx4i3ZKCAJJMLt5bcPW47xFCcNf8RWwsmU5ZaiomZeLymyIEOS43X113EQ29veyorYl5/cWK09w6a3ziqBCCS6YVk2Sx0h0celi9UXOW7mCAZOvk+q0jhsFTJ4/HbNMUhVtmzZnwfA40NfLY8SMx26yqxjc2XMq1ZTPGLDEIIbBqJtYWFFKQdA0fffFZTrUPZccONjfxwOEDfHzJaCb8ZPD7A3vp7Q8E7pi7gE8vX4XbYol7zKhuhyDNbifNXsjKvIIpLWWkhGNvnUJKeUGCgfx+JcWqqpa49f/Rny/Z9VbURyYn1zPG/rL/t3uhOwjGuW/P88gD2ZZUjwPBUFknHNapb+oiLXVibstk4POHaO/0xmzL7G8rDIYiPPTUOyhCUN/UxSfuvohUjxNViS1ZbN56jNWLi3nznWi7/brlpezef5ZZ07N5bWf0HlFVhXtvX8ujz+7lig2zKchNYd+Rc7R3ebnqolg58niYUjCQm5LEe8cIBv5ZIaVkf2cFPzz5N/oiQ+QNTajMcheyOn02M9wFpFuScGhWTP32ygNKfYaUHOg8c97BADB4jAJ7BmvT5/JE7ZsE+rMEtb5WfnDyMb4+9wO4TYlJpw7HyJbKLFsKX5l9+3n/gAQipgtConO263fo0o/dVEBvqII8143YtHyqu/+ELr0owkJx0kcI6m00e18iIr1EjF7yXbeiKQ6ava9R4H4/IKns+jVFSXehicmf8z8Trsm5KEbsKVGuRqrdTqp98gQzu2bik8tW8lbduZg2wYrOdkKGjkUd/yeearOzrrCI506fHNzW4fezo/YcV08vH+edo3GqvY0znbEGKWUpqcxIHZ/AqhsGv9j79iiy4K2z53LN9PKErMOFEBQmJfNfGy7lzmcej6npP3BoP7fOmkuGY+r3Vm+/g+am0jI+v3ottklwKuKNX/YznYUiBv+NGCldDZ/9/b9gvkAr1dLSTFJSHNTXd7J58yE2bVqIpsVfuEkpOX68nhdeOAjAJZfMjtslJoSdZM+PgAvbwaVpY8sBu802rKqJvkiARn8Xxa7xRaggej6HOqMBc4Ejej+mpjixWEwEgkMZ2l17q5g36/x5JsNRWd02iqxYmN+vvGnWuPGKhfiDYZ54cT+VNW2keka332amuThb2051XTuGIcnP9pCZ7mZafio3XDEfry/E9371MpGwztL5RWzZdpwPv3c1L207zt23rR51vHi4oM34hpQ8vecYVy+aiVn7+/SaJoqgEeaXFc/GBAI21cw9xZu4PHsxDnWiVqQLd7NbVTOXZy3htoINZFg9ODQrf6jaPFiTP9hVyS8rnuXT5TcnJD88HCP1BAwpme7MfRdslw3aA7soT/k8FR0/YlryPbT6tlKUdDdZjo2owsrpzh/hDVcDkmbfqyzI+DG9oVOc6/kr5amfo83/Jjmu69ANL32hClTx7jtBvpsQQpBhja3vvdm6h0zLu1fzE0IwOyODdLudZu/Q6qMnGMQfDk8YDAghuG32XF6oOBUTTDx27AhXlZYlNBFD9GH75Mnjo4iNN8+cM2Gm8FxPN7vqzsVsS7JYuHfx0knZIQshWJydw8VFxbx45vTg9na/n+crTvLB+YvO6yGfYrXx+dXrJhUIjETFvip8vX5O762kvaGTmz59Ndse30VjVTM3fWoTOSVR9v6u5/ZSezJaMpm1qpw5q4cMqMKhCC/98Q2K5uQze1U5Qggi4Qgv/2kruWXZzFs3WpwNwOm0cvMty/j977by29+8QU11G2vXldPZEb1v9IhOS0sPnR1e9u07y7PPHqC7209RURqXXBL/mEKYorr6f0e4TTamuzI53FXLH6ve5MtzrsM6znNSSsmhrnPsajuDgmBdRjTIdTutZGcmcfbcUCbp9R0nuf3mFaPY/1OFISXPv3I4xunSpKkU9QcDtQ2dPPLsXqZPy6C3LzhmCae4II3nXzuC3WomEtE5crKeay+fz4tvHKOto4+sdDfhiI4hJbPLsvnL0+9Q39RFKBQhJyOxdskLms83DIOHdhwgNIma1N8Lx7qrqfY2xWy7teAirs9bhVMbXYscCSm5YAS6jVlL+Pj068m2paIKhZvz17Epe3nM6v2Vpn08eu6NSX/mSLGm3rAP/wXIZsSDpriwaTmYtXQsaiYRw09I76DR+yKN3hcI6Z0YMhp8ucwzsKiZOEzFhI0uFMyk2dfT7n+LFt/rZDs3Ic6DODkVSClH6bJPFQ3+FoJ6iHpfM2f76gb/29NxZEKxofOFVdXIGtG2F9Z1ggn+DhdmZpM/Qpdhb2MDjb2Ji2r5wmFeqqyI2eYwmWPFesbAa2crR2UF1uQXkuVIXKBoAIoQ3D53/qhM2NOnTsQEO1PB1WXl5CTQHjkeju48yS8+eT8BX5ADrx/h+x/8BR0NndSerOfP3/jb4P2omVTCwTBP/OQF9r18KOYYmqbi7fbyo3t+RXtDJ1JK3nhkJw9/6wmS08fW1xBCcP11i7nyyvmEwzrPPLOfr3z5cX7+81cAqK/v5DOffpjPf/4R/vjHN2lr6yUrK4nP/ttVOC+Qi994iP4ew0QiNRjG2PeeguD2aatRhcKWhsN86+gzHOmqpSfsHyR/Rwwdvx6iwdfJU7V7+fLBv+GNBFmUUsTc5Ch/Q1GiUsHD0dTSw9ObD5z3vTJwPqfPNPPmrtMx2/NykklLid7bR041kJedzMWryjH1Z2kGeAShsI7XHyIQDJOW4uRkZTNlxRkUF6ZzqqqZVI+D13ee4vJ1M5k/K2/w3jGZVJYvmsaDT7zNxatnJBwAJ5wZ8AZDnGpoZV5BNv5QmHPtXaP2CUd0fMGpCfG829jXWRHzULarFq7JWZEwcz9ghPDrF6a1MMXsiumOMCkaHy7dRFOgk3c6oulaA8nDNa+TY0vlkszEVzRlrryYOphPD1Lna2VmUuEFGXss+nXehz16G70vYlOzyHJeRV+ocnC7ItQRKVBBln0jJ9q/iUGYuenffRfGNzaklGxp2saatCW4TJOfdEZif+cxlqfM58Gap0mzDJGaOkLd533sROAwxaaSBxgdicCqaVxbNoP/2TOkXOmPhNlSWcHdCxK79/Y1NdDYF/sAX5GXT+YEE7qUkq01Z0dtv6K0bMqr+DkZmSRbbTE6ABUd7bT5fJNSQBwOAVxTlviDdSK870s34k518dj3n+G/nvsC+14+xB//46+D5YKlVyxkycYF7Hp+3+ixKILr77uSY2+d4nef+zO3fv56Hvjqo3zoO++nYGbuuGO0WE187F8vpbg4neeeO0B9fSfd3dHrFA7rNPW7MjqdFubPL+TOD6yhpGSKeg0JIDqBRdD1ekKhvQQCrxIO7ceT8mvM5kVx3yOEYH3mTK7PX8xT5/byQv1BtreciooOWVzYVHNUdCjso8HXSVOgG10aFDpS+cLsawafvUIILls/k8ef3xfTZvjQ428zrSCNlUtLEs6MxTuv5tZefvCrl/GPsKu+ZO3MwXbBJfMK+MvTe3j8xf3MnJ6NJ9lBKKzz7CuHkYZk174q2ju9XLK6nLJpGcwpzyEc0Wlq6cZsUrn+ivk89+oRUpIdrFs+HVWJlrXXL5/Ok5sPct9dGy58MHCivoUvPfISD37sPVQ2t/PlR1/CaY1NpRhS0ht4d1ah54tGf2wfd4rZTVKCEr0Adb42vJF379wcqpXPzriFLx3+/aCCYMgI8/OKZ8iwepibNC2hL7XQkUmyyUlnv1JitN3tEDPc8WWfpw6BRU1DoGBWU1GECbOahNs8h4a+pwka7WiKHUVYUIQZkxIlLwmhYVajNTuzmoqmuNAUO5qY3ENaSklYhhEogwHdcEMSKSURqSORmERsq2v0vRE2N77BIs9s7Jo9ygafwO50PFyZvR4FwUx3CdfkXDy4/YHqJ6d0vMkgKq17fu+/rnwmv92/J2aF/uTJY9w5b8GEwmJSSh47diQm9IhqC8yd8GEa0nXOdMTyDFQhmJ0+cR14LDhNZoqSk+lsGgoGApEIZzo7phwM2E1mSj3xTY0mC09mMoqqkJzuJindjdlqwu62EQ5GkFIm1DllsVn415/czec3/hffvO3HLN+0iPW3rEzo/rVaTVx3/WLWrivn1Kkmzpxppq21l3BYx2YzkZubwsyZORSXZGCxJNYmPhlEA4Awut5AKLSnPwA4gK43MliOnUBF1qSofHrGFXjMDp44t4fOkJfj3fVx9zUrGstTS/jUzI1Mc6bHnE9xYTqL5hbwzoHqwW0+f4jv/mwL97x/DZetn4nNZp7UNdB1g+OnG/n5H97gdGVzzGtul5UrLp49eLyMVBcfv2sDUkq0QY8NuHnTosEVnejnknzk/WtR+0mF97xvDUIILl5VzpqlpYNtkooiCATDnKvvYOWiaZMqdyQcDMzOy+THd1xNmsvBqYZWFhbl8rHLV8TsE47ofPahFxL+8L8nRqaDVaEk3K4opWRL4zvvarpXCEG6JYnPz7yNLx36A22haFtOd9jL9088ynfnfzihDgO7amFl2ixebHxncNurTfu4OX8dGdbkCzdeNKZ7PoWmuChO+jCq4iDPdTOqcGA3RXvKTYq7v1tAUOi+HQCLmk5J8r8MdhgIoZLjHF+UZyS6Qj280PgaLYFogKcIBbNiZlP2RRQ4cgkZIba27OZQ1wkMaVDqKmJj1jqcWtRYa2vrLk70nKEl2M6fq5/CqlowKRp3Ft2ETZ1aOnTAfntT9oYYbYFrcy6ZUltsVJJXj3o2BAJ0Bfz0BkP4ImGCkQhBXSeoRwYNcmq6uqY07gEUJSUzLzMrprXvdEc7pzvaJ5yYBwiHw5HldCakVdAbCtIdiM242U0m0s5TrW9asiemZRKgoqON1flT04NPtlpxmM0xz5GpTpLqgGmSEOclKJOWl0L+jBz2bD7AR75/x4S2yMMhhCA11cWqVS5Wriztr2nLwXN69wKAekLBPQSCAwFAE1PlY9k1C/dOv5iLMmfxRvNxjnTW0hbsJWhEUIWC22Sl2JnBuowZLE0rxqGO7v5QVYUPvX8NR07W4/cPreC7e/389PevsW3Xaa68ZA6zZ+SQmuzAZNZGtZFCNADo8waprm3n9R0neWPnqbgqg7dcs5j0Yd0KQghMcfh18bYN10XQ1KHvyTKsy0NKyfOvHeFcfQe33zi5DpqEgwGb2cTs/KjPusNqZvG0XKZnxbKEI7qBw2K+QIoAFxZpI5wKu8J9+PXguMqG0M+q7anhlabR6boLDSEEpc5cPjPjFr557KHBroM6fxvfO/ko35h7F0kTdBgIIbgpfx2vNx8kYEQZ0J3hPn515ln+fcat2NTJRblRsSVjFCteCIFZTQbApEavrSKiN7lVyxx1HKWf4KMIDbOaTCDSRF3v31CFFae5bNT+443n4Zqn8OsBbsi7go5QF7+oeIDbCq4l1ZKClJJn6l/hnY5DXJ97OWbFzKvN26n3NfKx0g+gCoUUczLlrhK2t+5hfvJM3CZX1Dt+Eo6CY8Gn+3EJJxEZodbXSLY1PeHrLaWkJxTkeGsLu+pqOdzcRF1vD50BP4FwhLARJQgZ8sKHpaqicOusuTHBQMQwePLkcWaljX0OA2n+4a2JAFeWliWkuNcTDMYoIAI4zGYs2tS/CyFE3Np+bc/USzYOkxkpvVR1/w4pJQVJd2NWL0ymYCqQUvLm47s5e/gc1993Ffd/+a+ULy7BE8dZcCIMZZberQCgLhoABF4lHD6ArjcTPwBQUdQMzKZ5KGp2nNfjvEMozEzKYYY7m6ARwRcJRi2MhYJtAgvjAZSXZHH7TSv4w192xIgORSIGew/VcOBoLempTvJyPORkRuv9dpsZoQhCoQhd3T6aWnuoa+ikobkbvz++j8aCOfncfM3iCx5ojcTVl0QzciNVDyfClH5x8wuymZM/+oGvKoKPXbYC8yQH8ffAnORpPFG3ffDvnrCX7a1H2Ji1ZMwvx5CSEz01/PeJRy5IS2EiEEKwPHUmHy7ZxC8qniHSny473FXFLyqe4bPlt0zYYVDkyOLq3BU8Xvvm4LZtLYdRhcoHp20k25Y6bvp2II3eGujmcFcVPREftxZsuCDnNwCTkkyWYxM2LRtlEpOwLg2O9ZzmQ9Nuo8RZSLEsINuWiSIUHJqNvoiXLY1buW/6XcxPjjKgC+25fO7Qt6nzN1LkyGN+8izag50oQmF+8qxRHQDng7/UPM+Him/mpaYdnPM1YFdt3FV0w7gPACkl3nCIzWcqePT4EU60tY5rfvNu4aKiaXhG1Nq3VJ7msytWYTfFb2+LJz+sKQo3zZid0ENvwIFwOKyqdt7iUklxFC67AlPn/GiKQJd9nOu+H4lOjuvmdy0YkFKiR3T0iIE0JJFwhFAgjKoqKP0ks9qT9fzhiw/zgW/cytqbVlB7sp7ffv4hPvO7j8YVB/p7YSgAqO0PAF4jFD6AMWYAYEJVszGbF2KxXozZvBhVzQUm104phMCqmsbtKhgLiiJ4z3VLaGnr5bmXD41SIdR1g6aWHppaeoCa+AeZAKXT0vn8xze+K94HwyGEwGqZWrfL1LwJNBVTHJEJIQQXz4nv0f2PxvzkYpKG2fJK4DdnojK9y1Nn4NSiEqdSSoJGmKZAB2+2HOb5ht10hHpRhUKyyUl76N13m1KE4OqcFTT62/lb7ZuD5YnXmvaTY0vljqLLxpW5VYTgzqLLONNbz8Guyv7zlbzWvJ/j3dWsSZ/DAk8pGZbkQa+BsKHjjfhpC/VQ3dfEyd5azvY10hbsYU367At+jqpixWkeu5d4LChCkG5J5XTfWcrdxfRFfHSHe8nob9/rDHUTMIIUOoZ6hVMtyTg1O+d89RQ58i7oeQyHRNIb6SNi6BzuOslny+/mZxV/RpfGmLLbUkpqurv4r+1befNc9ZhGRVHrYhWLqmFWVTRVwaSo/1975x0m11Xe/8+5dfrM9r7SrnpZdcmS1WVJ2LhibGw6xo5xqCEhJoFfAgQwISEkhAABDDHE4IYbNtgGFyTZkiVLsoptlVVdrba32Z1+2++P2TbaXUm7Wtkmns/z7PPs3Lnl3Jk757znvO/7fdHk9N/xrs4LNiByXG7WTqzikUHCQY09PexsbGBV5cRhj6nv7mZvc2aWzoz8AqbmnZ+BNVzUtiSNPXajj+HSGUdbBOpioKgymivdWcuK1K8hIMkSeq8qXWdzmJ9+8X+J9yRoq29n2xM7OV3bRE5xiDu+8xFs2+End97L/HWzWXvzchRV4ZPfu4UvX/ktNj+0jXW9/uSRiKQO0RnfRlngA0jiwgentAGQwjJPkUztIJl4jlRqD7bdwvAGgIaiVKJpi9Bd61DVuchyMTA0tsdy7LRL9yLPpnVN4ZO3rMHv03n4yd1Dgv7GihCCBXMq+es71lNaHHpbi6aNyRhwHIdIMsWx5g5iycwlEVmWWFhVNqr84DeDkOrjhopV/PzY0/2Da5cR4bsHH6LcU0ipOw+3rJGyDdqS3TTE2wkbERzSgWnXlS2n3FPA9w5f/IAwSBfZ+Fj1u2hKdLK5dR+Qjg6/7+TzlLry2FB89uUmn+LmizNv5p/fuI+9Xcf6tzcmOnjo1GYeq38Jt6KjSemOybQtUrZB0jbGLYXyYiAQfKDyWr57+G7qY41YjsWK/EXMDJ6/q+Fiti2kBvjVyd+yJG9OrwCUc9ZVmIaeHj799JO83toy5L2Q7mJ6fgFzi4qZmpdPsc9HUHfhUtIGgSpJKL1/H3n8Yfa3NA9zhVG0v1dzYHAankNac2BlxYQhz5vjODx55BBJK1N++MYZs8+74uZwg7Zl96Z8XkDHaVhDn+HxrG8yVpZds5jZK9Oa+7NXzKB4Yjoeo3rOBP76J3cgJIE34Obdt63vHWSv7D9WUWVkRcZOGtz8xWuZMLMCpVcKvnRSMV/5zRfOmSrrOA6ne+6nOfoEJf4bxmwM9BkApnmKVGr7GQbA2fsPj/f9+P2fQ5IKOD0IwJgAACYXSURBVJvqaJcR49/e+D2L8qq4qmygEqfjOMSsFI7j4FH0MUf8n4nbpfLx9y9n1rRSfvXwdg4eaRpSzGg0FOb7uXrjHK5793wCvrGX1H6zGJMxEEmk+OKvn+JYSwf6GS4Br65x9+034NHfXsZA2pe+klOxVp5t2o3d+8AajsXxaCPHo43DHqdLKteVL+djVe+iJdGFLKQ3bbB0yzqfm3o9rckuDnSnA7RStskPah+nyJXDnFD1iA+YEIIiPYd/mPVh7jn+DM81v5qhN2A4FoZx7joGAoHnHHEVbyZCCDpSYSZ4yvjIxOvRJK3f5w+Qq4Vwyy5ORk8TUgO9+3cRMaNUuEszzgNgnyNqebRtu6ny3ZyKNTLVX4XjwNWl60YMIDRti69veWGIIaBKMldPncaHa+YxJTcPjzq0IttgHMcZU7nr4ZhTWMzEYIhjXQNVK188dZL2eJz8MxQSDdvm8UMHMrb5NZ2Nkyafd8fnUVWkXoXPPhKmieU4FyRwO1x54ZyzFMd6s8gvyyW/LO1eCBUECBWkBWF8IS/TFqdXVXWPTs3K4Yv09L0/e0Xm+0IIqmrOHRzpYNGZ2D6mtg8YAHWDDIC9IxoAQvhQlGo0fRnJxPOYZlqHQpEre1cCzk5dtI3fN+xlf9cpriqb37/dcCz+7tUHiFspvrvwgwTU8RMrUxSZSxdPomZGGa/sOcELLx3i0JFmOruipIyz9xWSlC5VXFGaw9KFk1izfCoVpbkXrRLieDMmY+CVY/XUtXfxnQ+9mzxfZgchhMB1Fp+VKuR+yVyZkSP6BQJNUvoDpc7W2alC6T+nJo1cHatvcJ3oLeLJhpdpSnRijzCw65JKla+YGytWs7KgBlVSKHblUOTKoTWZDkQ6X40CVVIyZIJH03HnaL50hsG+n/VfN26n+N7hR7hrzq0Uu0f2WwohyNMDfHbqe1hZUMMTDdt4PXyCcCp61hz0dCSul4neIpbmzWRVQc15t/di4zgOcSvO6XgTTzY8jywkPIqbhTk1VHsr8chu3l2yjvvqHidhJ9AljT80bWF2cDoVnoGgJI/sJkcN8IfmLSzImY1pm8wOTr8gpUbHceg2IrhlF6diaePSK4/cUe1tbua5E8cytklC8JklS7l9/qJRBdGNV0ihJstcN20m393+Uv+2cDLJn04e54YZme6iw+1tHDtDfnh5RSWFnvOX/e1b6Rjs4oikUsQNA+0cKY0j4TgODT1D3XkVwZEFeUZHOhE1vYwdxbA6cLBQpCCqFDov8SzHsTDsMKadbqci+XuPPdc9OziOjWl3Y9g9OJhIaCiSD0XyAcPXtUgP5BZx4wRx4xSS0LDsGGf2lZLQkIR6xrEmpnmMVGo7icRzGKm92HYrQw0AgZBCqMpUdH0Fmr4CVZ2KEEHaU/uBWkbD6VjaIC10BTL6TMdxOBA+TdRMYtrjL3AnhCDgd7NuxXRWLp1CW3uEU6c7qDvdQVNLN13dcZLJdKyLqsh4PTr5eT4qSnOoLM+jtCiIzzt83Yq3M2MyBlq7IyyoKmVWedGob/ijVRu5rjytlSwQ5I6Q61/myefrNbf0vy50DQi5OI6DZdlIQiAkwaemXkusVwNAFhIeeeTcSo+ic1PlWlYXzGVvx1Fqo6dpS4VJWgaKJOFTPJS785keqGCyt4yA4ukf9FVJ4UszP0DUTAciTfKV9qvYOTZI8vC+zk9MuipDBrnMfXat9sEIIaj0FPKNmlv6jYHB750PqqSwOHca80KTqI+3UdtTz7FII63JMHEriYODJqn4FQ/F7hwqPUVM8BRS5M7BJQ3NPjhysJGGuvb+NixeMQXXMIExJ440U3estXc/WLhsCh7fhcl8dpsR9nYdoCY4Hb/ixcGhJdHGvx+6m2/OuZOg6ueq0svwKR42tWzHxqbaW8kVJWsyMiJ0SeP2SR/kD02b+O3pP5KjBZkZnMKFFlx5pWM/nakwDtCW7CSo+rlj0s1D9nMch8cODZXvXVBcMmpDACBpnr0++vkihOCaqdP54a7tGfr+vznwGu+ZNqPf/ec4Dk/WHsIanGoHvH/WnFH1CT5NI9/toc4YeLbjpkFrLDpsEOD54ADHB61s9DE5Z3wCRdMTGJvGyCM0Rh4mYZ7GwUaTcsjzrKUi8BFUKXfEQTlmHud09310JXaQ6q32p0ohclxLKAt8EI8yvKZIOtC0ltM99xFOvophdeJgIVDR5BBedQoFng3ke9ZnHN+d3EdH/CWixhEiqVocUliOxf7WzyDOEKGtDN5KgWd9xjbLPEl7200jGAAykpSHqs5E11ej6ctQlCqE8F3wYNhjpPtZr/LWLLELIdBUhdLiEKXFIZb0qhXadl9p+/Q+Um82xp/b4H8mYzIGppUU8NSeQ8RTBm7t7EuYQ44NnF8pT5/iZkne9IxtjuNw7FATz/9+L61NYTRNoXpqMZe/dxGeoE48muSRe7dSfUMpOXlpIyOZNHjkly9x2VXzKCwJpQO2apt59ok9dHVECeZ4mOjygoCr3reE/PwArY3dbHnyNf50+DVM06Z8Qh4br1tAYUmIWcGJADz35B66Jkep727jhaf2EY+lqFk4kStvXDxEaW/mBar/CSGo8pVQ5Tu/dJuRzqHJKtW+Eqp6JYtt7P7OXEIgCSkjh3Ykjh5sZMsfX6O5IUzT6Q5++uhnKS4bzhho4dkn9tDWHKb+RBs/fOhTVPoKxnwPADs79tKR6uKrsz+P1NuRxa0En9n9j3SkugiqfjRJZUPRStYULsNx0obOmfckhKAmOI0ZgclYjoUilHFZar+ubKAjTVhJfnb8NzgMLyRzZh48wI0za0Y9I05aFh3xocviY6U8GGB+cQnb6k/1b9vb3ERDT0//7NqwLf5w7EjGcWX+AItKSxkNiiQxs6CQukFpf7bjsK+5icm5Yxu8e1JJToYzDWeXolA9TqJBIKjvvpeGyMNocj66Uoxlx4iZJ4iEf0xP8jVmFf47qsjUhHcch+7kq7zR9nfEzTo0OS3jDQ4Js5H6nntpj29hVsF38GtDK05GjIPsb/k0CfN077GFCKFg2t3EzVP0pA5gOTHyzxjM22Iv0Bz9HeBgO32uQpuU1T7kubTsoRkXjpPAtgfHo0goShWqOh9dX46qzUdRKoDxHbS13tXUqJlIizG9xYNt3/VleaAdtmXT0dhJLJLA5dHJL83J0HtIxpIkYikCeRduHF1sztsYqO8I88rReiBdZSyWNLjz10+xbEolHn1gIFBliXfNnXpO1bKx0N0V45tfuJ8Fyyaz8NLJhDtjHHqtng3Xpv1J8ViK++/ezKXrZvYbA0bS5MGfb2Hu4moKS0L0hOPcdecDLF09nXVXzmHXtqM89sttfPxzG/G4XEhC4tSxVo680cCMuRXIiswzj+3i9T11fPOHH0HuFYP401P7cHt1jKTJjLkVWKZNIjZ8funbjf6HGhl5DM/n+qvnsebyGjY98xr/8bXHRtxv1YZZLFsznR1bDnPXnQ8wHivZLtlF2OjhRLSeoOonaaXY1bkfl6yTpw2sHgkh0MTZU2yEEGkX0zjW62pPdWHY6Rl10k4RNnqGvW3TtukcZgCfkZ8/6k6jvjtMxzA+8rEiC4mbZ83JMAYSpskfjx/pL/ZT29FBXbgr47grp0wbdREfIQTvqp7M02fUNfj90Vqumz5zTMFhe5ua6D5D92BSTu6QmIex4mDSEPkNE4K3U+y7DlUKYjtJOuIvcrj963QkXuRU+B6qQp/J+C4Nu4MDbV8ibtZR4ruBysDH0JRCcCBhneZo53fpiG/mYNv/Y0HJr1DEgFaC4zic6PoRCbOePPdqJufciSYXIoSEZcdJWA10JXYQ1OcPeX4qAh+j1H8TAN3JvbzW+llk4WVu0U+QReZnokrnU9RGIEQQWalElkuRpBCjTQU8H0o8IQCO9rRQH+ug3DP8astbheM4PPpfT/PCA1txeXRUl8qX/vcz+HMG3GTP/GITu57bz1cf/OvxlnEAwEga2LaDPg4pi+dvDLSHeejlfb2vBIos094T48lXD2bco1fXuGz25ItiDIQ7o7S39nDl+xYzcXJa58A0LZRRVEg8caSZrvYoN9+2Gq/fxdRZ5fzh0V1Mn1OOz59elpy/tJp5l1Qj9+b0lk/M52uf+xXRSJJAaODHU/t6A//2i9v6DQ/bzrRe+yJ7x/MBPt9zDqeUdua2kfZxHAfbTtfHHu46siwhyxK66+yPjyRL6LKE7hp7lbczWZhTw7FIHfeefBQJgQP4FA+fnPwR/MpbX/74+eaXaU919b/eWLwCaYReYDgjYbQxC47j8PjhA+OeNreqciJ5bg/t8YEg0ycOH+Kjc+YjAU8fOZzhIlAlmeunD1/Z7lysqJxASHfRNWgA315/ivruMJXB0KjOZdk29+7fM+SzvXrKtAvWLhhMSF/IhODtg/zrPoq81xAzTnAi/EMaIg9SGbylf0BPxzE8RMw8jl+bzZTcL6FIA32JIvmZlvsVdjRcS8Q4RHtsE0W+qwZd0SFqpNOEi7zX4NUmDzrWh64UENDmDNtWVQ6ikl7RiZtpA08IgS4X9sYZnAOhIoQXx+mrimlhGLsxjN1EhRtZ7ksTXIOqzhk2TXAsTPEX4VdcdBkxvrD716wumkGB7sdyHJJWuiDRk6dfxaOMzvW4JG8Sld4LdxnFuuM88K+/5fZvf5D5a2djJA28wcwYoSkLqtA9+kUxBAC2PLoD3aWx/LrFF3yu8zYGFlaX8ePbrj/nfkKAS704oheFJSHmLq7iX7/0MCs2zGLp6mlMGGURDU1XsR2HWDSJx6cTiyawbQd1UJtt2+HIwUZO1DbTHY7R1tSNZdlYZ6SZTJ1dRs6g5R9JcjDtFIqUttKiVienonuYEVzHeNGaPEbKjlPumZ2xPa3Fn0SVBvysD//yJYpKc1i5YRaO4/CHx1/l2OEmPnTHWvwBN9FIkl/99wu89yOXkl8UxDQsdm6tZfumQ3SH4xQWB1n9rtlMm12OeJtExLpknQ9OuI6IGSNlGyhCxqt4UMTwgVNvNu8t39gfzCcQyCO0S5YkfNpQa74p0sOM/PNzpTiOw4lwF/e9tu/cO4+SoK5zWVV1hqDQwfZWmqIRCj3eIS6C2YWFY16Gz3N7uGrqNO7dv7d/W8RI8f1XXuZb6zaeswRyH47jsP10PZvrTpxxLy6umTZjXJ+PIt9VQwLthBAU+67lZPgnpKw2osZRgvq8dNuwaI6mdU1KfNcPKdcthMCllOHVJtGd3Etr7DkKvVcOarPApZQSM47QFH2MoD4Xl1KWEax4sZ5/RZlIbt4vSCb+RDL5IqZ5BMfprX3ixDHNQ5jmIWKxB5HlElRtLrq+Bk1b2O8+GAu5mo8bJ1zCL45tpranmdqeoamz/3HwmVGf9655N16QMWDbNrHuOE3HW4lHEpRNLsbBwe13I/U+q0bKJNzWTUFFHiXVQwX6LNOiuz1CqDBAd3sP4dYe3H4XeSUDbgbLtOho6iLWE0d3aYQKg+iedAyXZVrEuuO8cP9W5q2dRVtDOvYkkOtDc41tleC8R21VllHdcu+HkZZEVc7QwnYcB8sen6jm4dBdKn971w28/KeDbHp6P88+sYelq6fx0U+vH3H2eWbabdWUImoWTORfvvwbZsyp4OjBRpaumU5FdboDti2bX/zgOXZtrWX+JZMoLsshJ39469nt0TJm1GGjmebEEab4lyMQJKwe6mJ7mRZYAziI3pzadPCJTW9pnL6Wpl8LgePYGf/b2L17CrpSjcSsMKXuGUA6eCVdXtlgZ/vDXJL//v5agscON7N35wlWrJ+J4zj89v7t1B1tYcX6mdQsmEhDXTtPPbKL9318Jbbt8NA9L/LbB7YzZ1EVRaUhjh5qZNMz+/n8V9/DouXnny52sZGERGAcKg2ON0KI85Y0FqSXrms7Mgto/a72EKsnVJ1zedxxHJqjUb70/B9pH8d4gf72CcFNM2t4+MDr/SsACdPk5fpTzC8uGRKg976Zs8c88xZCcPv8xfz+yOGM2IfHDx1gXlEJN82qOadB4DgOxzo7+MdNzw0ph/z+2TVjKod8Nrzq8OJqulyEIgUw7A5iqQFjIO3XT7tZ42YdDZEHhj3estP3n+jdtw8hBBMCt9Kd3ENHfAt7mj9OgWcDBZ4NeLVpyOLcZdjHihAaun4pmrYMnxPGMA72GgZbMM3afsMgrTxYhxWvIxH/HZKU3xtYuArbHhrQee7rCm6dtJqA6uaZhn00J8JpSW6c/qJxXkUfde2PkQTAzpe2+g5++ve/prMljJEy+enf34fuVpk0dyK33fV+AJpPtvKzL99HR1OYgoo8vnzvZzLO0dkc5lsf+S9u+sLVPPpfzxDtjiFJEl9/7Av4c3yYhsm933iEvZsPICsSRtKkdFIRX/jpJ5AVmS2P7GDzI9vZ/+JBmk62srO3zPVHv3Ij05eMTfhvTFP41+ub2XmsnlvWLMrYnjItvvPkZv7mqlUXZXVACIE/4Gb91fNYuWEWr+0+ybe++CCLV0xl/tJJCCkd1WkNEhyJ9MQxUgNR0ZquUDmpgM62CFVTilmwbDLTZpej9aZDdnVGefKB7dx51w0sW5vO5X1jbx33373prG1rS55kd8ejRK1O2pMnmRVMB/FEjHa2t91H0o5QE7qCXK2ChvgbHIvsQCCYHlxDnjaBHe0PsSD3WlThYlfHY8wObUAgeLXzCWzHJGK2MyOwrvdax9nadi+WY7Ao9wYkIbGn8wkOdm/CwaHMM5MJ3gXMnj+Bh37xIpZlE+1J0NkWYfqcCl5/tY6aBRM5driJvEI//qCbuqMtPPDzLdz2+Y1cfv1CZFkiFknylc/9ip/9xzPMXVLV/xm90xkPc1cIwYbhfOVHatlQPZn1VZOGFe5yHAfDtnm1qYF/2bqF3cMEIY4Xs3pn+4MNlmePH6U7mcQY5JYI6jrrqyZd0GBUHgjwV0su5Wubn+83Pgzb5psvbqI5GuEDs+dQ6PUNMZIcxyFummyrr+PbW7dw9IxUx2l5+fzF/MXjPFAK5BGW1yWhIkseDLuDlD3wuVl2pD9471T3Pee8gs1QBbyQawmzCv6dk+Ef053cS133z2iIPIhfq6HEdx35nnXI4xDFPxLpGgYhdH0pmnYJPucODOMQyeQmkonNvYZBXxlrG9tuIZlsIZnM7DstuwXbDiNE4JxtdSsaH65azrXlC2hPRohbKRKWwed33UvSNvn2/JsJjlJnoMxzYYGkOcUhbrvr/XQ0h7lz4ze49Zs3k1+akzEjL6kq5HP/dSuP/+gPvPr8a0POYZkWta8e57lfv8Qt//Q+Avl+ulrCeAJp11HjsRae+MmzfOOxOymakE+kK0pHU7h/1WDBZbOZvmQyX77m22z88CpWvfcSAEKFY0+fHVPv3hGJceD0UMU0IQSbDx7n9suW4LoIM7eOth56wnHyCwPIskRRaQhFkTHN9EzA7dHwB9y8suUwpeU5mKbNM4/syjAObMth6/MHuPw9CykpzwEhaGsOU1Kei9IroOQ46T8jZRGNJPjdQ6+cs21BrZBSzwxiZpiZwctwy0G6jAYiZgfTg2voSNazq+MR1hR9gm1tv2Jt0V9iOkm2tPwP15T/AyejrzI350pUoD62j2mBVTTE30CV3EwPrObx+n+iwFVFXXQvEbOdJXnvo7bnJQ73bGFO6AqqvItpiL1BTehyNCn945gys5Su9giR7gT1J9rw+HSWrpnG7m1HuenjKzmw7xTVU4uRZYkXn3sDIWD2ggl0tkf672v2ggk8dM+LdLT2UFyWM8Ld/3nSHo/RGY+TtEzihkncNIgZBlHDIJpK0dNbNOhMXm9p5j93bMOv6Xg1Da+q4lFV3KqKW1FxKQouWaEsEDjrjHbdxCpKfX4aIj392+KmwZ3PPcMNM2ZxWdUkSnx+dFnGtG3CyQRHOjp47sQxXqw7QTiZHlz8ms7G6sk8fPD1cf18tN44gG9vHajpsa+libZYplhVX3zBhSCE4KZZNRxoa+X+1/f1G1xx0+AHO7fz9NFaVlZOYE5hMfkeD7KQiBgpjnV28OKpk+xqbBgix5zv8XDX2g1vgdjQUHNxsA5Emf+D6PLZXUF9Zb4HI4RErms5QX0unYkdNEd/R1fiFToT2+hK7CDHdQlT876CWxnvcuVDGTAMLkHTluDzfQLTOEwiuYlkcjOmcRjH6dN6yPw8YtH/JZXaia6vQNdXoarTESI4oj6DEIKg5iGopZ8x07YIqG7akxGmB0rI1d/cVUJVUyiaUICspF2A+aU5FE3I/D5lRSZUGMQXGjmOyTIsrv3URqYsSKeSFg86h+7RUBSZAztqySsJUT6lhMrpZf3vB/L8+HN9KKpCINc35PpjYVTGgGXbJA2LpGli9mYUDKalu4ekYV60B7Ghrp0ffut3eAMuVFWmuyvGvCVVzJqfTt3TXSo33rKSR+/dyqvbjyKEoHxCHmUT8vstqp7uGMXlOTz1yE42PbMfx0mnH166dgYf/fRlBHO8bLx2Pvd8/488+8SrJBMm0+eUk18YYPCzqru1DNeEJnlwyX4sx8SvDvyQC1xVhNRSFKGxP/w0MbMLVXKRq6U18g07Qcoa6Fz78lcBPHKI+thrtCaO4VPyUHsH+UrPXAJqIYWuydRFX0UWCl4lB1nSMq5dXJb2P7U0drF/1wkmTi5i5txKHr13G8mkybFDTVx21VyEEJw40kwqafLNv30g4/uLRZJIkiD+Z5IpMRp+svsVHj34BinLxrQtTDudatlXGXAkDne0c3j7ViC93C8JgSxJyEKgSDKqLOFRVR654QMUeEfuDIK6i79ZtoK/f+4PGZX7upNJfr5nN/e9to+A7kLrNQZiRopIKpURuOdTNb6yai3zikvGPZBQCMGVU6bxnzu2Ee/VHGiKRGiJRvv3kYTg5lFqC4yEJsv8/YpVGLbFowff6L9P23Go7WintqMdWQhUWUaQzsgwRrjfIq+Pb63bwPzikovQHzlYdmTYd2zH6F/qV6UBv7QseZGEiu0kKfReTo5ryZiunHZF+cl3ryPPvYq4cZLm6O853XM/HYmXONT+VeYW/RQxjhky59MmIYJo+mJUbRE+3+2YxmGSyc0kk5swjEODDANwnBhGahdGahfRyN0oyiQ0fTkufQ2KOgNJyqXPTTocspAIaR7ak8N/B38uyIpMQfnwZekLyvL43A9u5bEfPsOz926hZsV0rvnLjZROGr22z/kyqiemrq2L7z31EnXtXbT1RPnkzx8b9K5DRyTO7IpiQp7xk4cczLSaCu686wbaW3uwLItQro/K6oJ+wRshBFfffAlzFlfR1tyNP+imemoxjac6KCoL4dgOP/rn31Nemc9nvnw1iiLjOA6HXjvNv/3jI7znQ5cSyvVy219fztGDjUR6EhQUBaioLmDF+ln4/AP39bFPr+9fSehDIGE5qYwofVlkRtVqkhvDTmA7JhYmNhaKpCEJCdNOogqdmJX2r+XqFUQ620nZcVYX3obUK4jTF7jUG63Q+7/AdqyMbAOvX6ewOMjJoy3s33WCZWtnUFaZRzJh0HCynZbGMNNmDxTu8fp0bvnshmFrrBeVhMb2pb2N6UokaI2dW5L5bDiA5ThY/X7q9KDZk0yeU7a6T+CnoaebH+7cQdzMNK7jpkncHLnDK/b5+NLy1Vw5ZRqGZVHk9XF6GOW9CyGtHVDGlrqTAEMMpfJAWpNgvPBrOl9bvY6KQJD/2bt7SKVBy3GwziKwJAnB/OISvrR89UUyBNJEUrUE9KHR+0mrpVdVUOBVBwpxqVIQXS4mbp6kK7GDkH5hrgshBAIVrzaZKvXT+LUZ7G/9LF2JnaSsVlzKOb6TixTalTYMAmj6IlRtIV7fX2Cah0kmtvQaBgdxnAENCMeJYhj7MIx9xKL3IMtV6PoyfP7PIp9l9SRf93O0Z+iq3Z8bI8rJS4JlVy9k7uqZHN59jCf++4/c9aHv851n/wG37+KsdI3KGCjLDfKJ9Zfw9J5DbDtSx8Y5UzLeD7pdLJ1aiXyRIs9VVWbilCImThkandmHLEtUTy2meuqA9nXf/qZhcWD/KW77/Lso7BvcHPD62pGE6NeQ1l0qM+dl6nwPPh+k0w3PJF+fwBvhZzHsJLOC65GQcUnp1CKBhEcO4paDTPQu4qW2X2I7JjODl6EIncn+S3m5/de45RBuOYAkJCJmOwmrh9Px1zkdf50FOdehSjqak34YZKGiSemZp0v2owqdbW2/psJTQ4U3PVubNL2EQ6/VU3+yjZlzK/AFXBQUBdm78ziWZVFakdt/fztfqmXqzDLyi84n1/jiIgspIyBtPNPC+pCEuCjnBc67UJciSdyxcAnVObn8eNcrHGxvJWWdXWI1pLtYXlHJXy66hJn5Bb2CUjLzi0to6nU5yGKkhMbRIQnB+2fNYeupumHfv3bqDFyjVEs8Fx5V41OLLmFZeSX37N3NtvpTdCXiZx2/dFmmOieX66fP5D3TZ5LruoCAOpF+3pxBx58pPd4cfZJi3zUZGQWO49AS/R0OJpqUj1ebNOiUCkXed3Mi/CMaI49Q4rsBXR46y0sb8wPBxEO2CzEkYE4ICb8+C4GMg4XjjFxxTxbpNDybFJYdO7/UwjGSNgz8aNpCVHUBXt+tmOYRksktJBN/6jUMuvr3T2cmvIFpvoHbfe2IxoAQgo0lNVR48s5Zzv2ton9C6AwEsY9GOCkZT4Hj4Am4mbdmFjmFQT636itEuqIZxoCqK4TbeoZNEx8to/oVa4rMjLJCdEWmIxrnA8vnjemibxWyIrF83Qzuv3sT9cdbcXl0Whq72LP9KO++YTG+wIWtaATVEtYW/SWGHccth/AosDjvRgA8SojlBR9FCMGC3GvpNloQSPjVdGc+J3Q5YaMFRago4hp02cem5rtZW/QJPHKIY5EdHI++wqzg+v56CgV6NTla2o8kC431JZ8lZnbiVdIDvBCC2fMn8Ph9L2NZNiXladGOaTXlbH3+ALn5/v57XrlxNg//71Ye+PlmPnTHWrx+F46dTsHs6owyoTpdXc1xHHDSM0TLSj+Apmml5aF7jal+DYP+/dLttYbZbzg8qsq3L9uYseTdV9BmPLl5Vg2Xlp+7uMtYkIQ4bzldRZK4YtIUlpaVs7OhgW2n6zjW2UFXIoFh2yiShFfVKPb5mFVQyOLScqbn5aPJA2mLQgg+vXgpG6snD1xfH58ZxKrKiXzvXVcO6zq5pKziosy+ZUliUUkpcwqLqO1o55WG0+xtbqIx0kPUSK++uRSFPLeHybl5LCotY05hEXluzwW3J8fl5jsbrsjoYIO6K2MQDid2Uhf+GSW+61GkAA4GnYnt1HX/AoAS//XIg0SDhBCUBT5IS/RpYuZx3mj9WyaGPoVXm4SEjoOJaXcTM07QldjOxNAnM0SHLCfCia7/Jtd9KR61GkUK9A/+htVBXff/4GDgUsqHjTfoQ1dKkYUXy4lyuuc+KgIfRRIuwMZ2kkjCjSyN/8xzwDCYj6rOw+v9eK9h8GLaMDAP4NhdDCxZnH3p4orSuVxROnfc2zlahCSGra75pwe3UX+4kX1bDtB6qp17vvIQvhwPV/3F+v7BPH3s8Oc9+MoRfvPd31E5vQxFkzn4ylHmrp7ZX9iqj0uvXsjvf/48kXAU23K4/GNrKJ86tpU64Zyr5uUwpEyL0x1hqgrHS97zzSMeS/HKi4c5cqABI2USzPEyvaacmfMnvK2i5R3HYVvbvQghoUteWpMnWJhzHfmuiaM6z5GDjfzVh3/CtNllfOfntyKE4E9P7+c7/+8RVmyYyRfvuqF/8H7m0d388kfPk5vvo7gsByNl0docprK6gL/7Vtqo6Wjr4d7/foFYNElDXTu1bzSwYOlkgrlegjkePtab5tndFeMXP3iOWCRJ4+kODu6rZ/4l1eTk+fAHPXzsM5fh9lxYnYL/q9iOQ8qySFkmlp0ugazKMros9+qgvz1SPN9M0kWB0mIzhmXj4KBIErqsoEoXv949QMJsZFv9elQ5l0LPu2iIPIhLKUeXi7CcKNFULZYTI6Qvoabw+6hyaMg9dKf2crDty0SNI8jCi64UIws3tpPCtMMYdicChWXlz6PJAwG7KauTrfVrAQddLkST85GFG8tJkrQaSZgN/cJFmfoEnNEGm8Md3+B0z68RKPi0qahSqPf6PUzK/QJ57pUX8VM8sz0OjhPFNI+SSr5EIvkCpnGA3Nx70PRF5z7BW0gimmTzwy+z8vpLhizdb3tyF10tmS47SRKsvnEpLq+LeE+CLY9uZ9V7l+LyDu0HI51Rdjy9h6YTLdi2Q9nkYhaur8Gfm5ktkogm2fbkLuprG/EG3Ky9aTk5RWPLKBiTMfB/gfFYVrnYmHaKLqMR2zHxKrl45NCo2xqPpXjm0V2UT8xn4aVprYCOth5eeGo/M+ZUMHPuQK0I27Y5crCRnS/W0tzQhaopVFTlM/+SSVT26jB0d8V49ok9w1bJU1WFK65fiKopRHsSPPP47mHrq8uyzBXXLxxXZcIsWS42SauVfc13ENTnMSnnb2mIPERT5FESZiMONqoUJM+9ignBv+iVCh76W3Uch4RZT0PkQdrjL5KyWrAdA4GMIvnxqBPIc6+h1P++DBeE7aQ40fVjOhNbSZpNWE68t0iRjCIF8GszKPXfRI5r6YhR+X2Ydg8nw3fTFnuWlNWOg40kNFQpxLS8rxJyvTWDcNowiGGax1DkMiT5z2+y+efMO9YYyDIy6R/lwOrX29VYypLlzaSvdLAkNGTJk1b9tLtIWq04WKhSDrpcwLnLEPedK0zK6sB2EgihokgBVCmEJIYvfztQMrkLy4niOCZCKChSEE3ORXD+ReMcxyZld2BY7TiOhSS5UPtLMI+/lHyWtz9ZYyBLlixZsmR5h3PhNVuzZMmSJUuWLH/WZI2BLFmyZMmS5R1O1hjIkiVLlixZ3uFkjYEsWbJkyZLlHU7WGMiSJUuWLFne4WSNgSxZsmTJkuUdTtYYyJIlS5YsWd7hZI2BLFmyZMmS5R1O1hjIkiVLlixZ3uH8f84JWLDZDr9VAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Combine all the text into a single string\n",
        "def Print_Word_Cloud():\n",
        "    text = ' '.join(df['Text'])\n",
        "\n",
        "\n",
        "\n",
        "    wordcloud = WordCloud(max_font_size=100, # Maximum font size for the largest word\n",
        "                          max_words=100, # The maximum number of words\n",
        "                          background_color=\"white\", # Background color for the word cloud image\n",
        "                          scale = 10, # Scaling between computation and drawing\n",
        "                          width=800, # Width of the canvas\n",
        "                          height=400 # Height of the canvas\n",
        "                         ).generate(text)\n",
        "\n",
        "    plt.figure()\n",
        "    plt.imshow(wordcloud,\n",
        "               interpolation=\"bilinear\") # to make the displayed image appear more smoothly\n",
        "    plt.axis(\"off\")\n",
        "    plt.show()\n",
        "\n",
        "Print_Word_Cloud()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "8ed05530",
      "metadata": {
        "id": "8ed05530"
      },
      "source": [
        "Br its for the space its not relevant.taste, love , amazon (the name of the website) love and great (thats why we have more 5 rating"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4a26c2ad",
      "metadata": {
        "id": "4a26c2ad"
      },
      "source": [
        "### Check for pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "25758a03",
      "metadata": {
        "id": "25758a03"
      },
      "outputs": [],
      "source": [
        "#remove links and tags\n",
        "def Clean_reviews(text):\n",
        "    # Remove links\n",
        "    text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text)\n",
        "\n",
        "    # Remove tags\n",
        "    text = re.sub(r'<.*?>', '', text)\n",
        "\n",
        "    return text\n",
        "\n",
        "# Apply the function to the 'Text' column\n",
        "df['Text'] = df['Text'].apply(Clean_reviews)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "985695b1",
      "metadata": {
        "id": "985695b1",
        "outputId": "786aec17-ef40-48f6-e6d1-0d13e831fad9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average words: 78.07866666666666\n",
            "max num words : 1707\n",
            "Skewness: 4.297628076912972\n"
          ]
        }
      ],
      "source": [
        "Print_Word_Cloud()\n",
        "Print_info_word()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "53b36731",
      "metadata": {
        "id": "53b36731",
        "outputId": "298c1145-e438-4689-bf23-e032b1a91493"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting emoji\n",
            "  Downloading emoji-2.5.0.tar.gz (355 kB)\n",
            "     ---------------------------------------- 0.0/355.8 kB ? eta -:--:--\n",
            "     - -------------------------------------- 10.2/355.8 kB ? eta -:--:--\n",
            "     -------- ---------------------------- 81.9/355.8 kB 907.3 kB/s eta 0:00:01\n",
            "     --------- --------------------------- 92.2/355.8 kB 744.7 kB/s eta 0:00:01\n",
            "     ------------ ----------------------- 122.9/355.8 kB 798.9 kB/s eta 0:00:01\n",
            "     -------------- --------------------- 143.4/355.8 kB 708.1 kB/s eta 0:00:01\n",
            "     ----------------- ------------------ 174.1/355.8 kB 615.9 kB/s eta 0:00:01\n",
            "     ------------------- ---------------- 194.6/355.8 kB 560.2 kB/s eta 0:00:01\n",
            "     ------------------- ---------------- 194.6/355.8 kB 560.2 kB/s eta 0:00:01\n",
            "     ------------------- ---------------- 194.6/355.8 kB 560.2 kB/s eta 0:00:01\n",
            "     ------------------- ---------------- 194.6/355.8 kB 560.2 kB/s eta 0:00:01\n",
            "     ------------------------- ---------- 256.0/355.8 kB 524.0 kB/s eta 0:00:01\n",
            "     ------------------------- ---------- 256.0/355.8 kB 524.0 kB/s eta 0:00:01\n",
            "     --------------------------- -------- 276.5/355.8 kB 459.8 kB/s eta 0:00:01\n",
            "     ----------------------------- ------ 286.7/355.8 kB 453.2 kB/s eta 0:00:01\n",
            "     --------------------------------- -- 327.7/355.8 kB 461.4 kB/s eta 0:00:01\n",
            "     --------------------------------- -- 327.7/355.8 kB 461.4 kB/s eta 0:00:01\n",
            "     -----------------------------------  348.2/355.8 kB 432.1 kB/s eta 0:00:01\n",
            "     ------------------------------------ 355.8/355.8 kB 433.6 kB/s eta 0:00:00\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Building wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py): started\n",
            "  Building wheel for emoji (setup.py): finished with status 'done'\n",
            "  Created wheel for emoji: filename=emoji-2.5.0-py2.py3-none-any.whl size=351237 sha256=c56cc2261bcf4a4a5c68af3ed384ff0340738a9b3392a5a8d614cc30abd51685\n",
            "  Stored in directory: c:\\users\\user\\appdata\\local\\pip\\cache\\wheels\\87\\14\\a9\\00a5678ff1642b8c7db714ca358d55144b54e37500de0b0909\n",
            "Successfully built emoji\n",
            "Installing collected packages: emoji\n",
            "Successfully installed emoji-2.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install emoji"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "214ee633",
      "metadata": {
        "id": "214ee633",
        "outputId": "7485c0ff-f175-42c4-d32b-cad6272c5b53"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index: 528\n",
            "Text: I once loved these chips and they were the only chips i would buy.  I discovered them when I was in England back in 2000 and quickly became a fan.  About a year ago I picked up a bag that was on sale at my local supermarket.  I was finding it odd that they were on sale so much but took advantage of it.  After opening the bag I found the chips were not even close to the Kettle chips I was used to.  They were all uniform whitish yellow in color, flavor was way off, the lovely extra crisp brown chips were gone completely and I was very disappointed.  So I e-mailed Kettle with the following:  \"What Happened? I bought this bag of chips the other day and they have a very different taste; It tastes \"cheaper\". I don't know how else to put it... I also noticed that the chips seemed to be less cooked as I did not see any of the browner colored chips that have the most flavor. I am hoping that nothing has changed with the ingredients or process used to produce this product. Did I just get a weird batch? I have been buying only Kettle Chips for about 10 years now so I am wondering.\"  I did receive a reply: \"Thanks for letting us know about your experience with your Kettle Brand® Chips. It sounds like you may have received a bag that should not have slipped by our inspections, and we apologize.Our potatoes do vary seasonally which could account for a variation in color-but our chips should still taste great!Thanks for providing us with the \"best before\" code from the bag.  This really helps when passing on your comments to our staff.We are sending some coupons to replace your purchase with any of our Kettle Brand® flavors or nut butters, believing you will have a great experience with your next purchase! In case you buy this product again, we suggest avoiding the same best before date (if you still have it) in the rare instance another bag from the same case slipped by our inspections at the same time.Thanks again for letting us know about this and expect the coupons to arrive within 3 weeks.  Please let me know if I can be of any more help.\"  Well the next bag was good.  Since that time I have picked up 3 bags of chips all were like the \"bad\" bag so I am pretty sure that they have changed there product to reduce cost.  I did some research and found out that Kettle was bought out by a private equity company.  So the same cookie cutter management processes are probably being employed at Kettle.  Acquire company with high quality brand recognition, sell product to mass merchants, lower quality/cost of product to increase margin and finally sell company just before the customer base starts to realize that the brand is no longer a quality brand.  increased profits make the company more valuable on paper so they make a nice profit for the investors. Customer and employees lose out of course as does an unsuspecting buyer of the exploited brand.  Stay away from Kettle chips they are no longer the same...\n",
            "\n",
            "Index: 571\n",
            "Text: With Kettle Chips, you really have to be careful.  Some of their flavors are nauseating.  With that said, they DO make fantastic plain chips.  Thick cuts of potato, fried to a dark golden brown.  They are crunchy and lightly salted with sea salt.  I can't recommend these chips enough.  You won't regret it.Some people say they are burnt but they aren't.  From their website: \"Take a quick look and you'll see an immediate difference: Kettle Brand® Potato Chips are a beautiful tawny gold. During cooking, the natural sugars of our select potatoes caramelize, creating chips from light gold to a deep amber.  The results are flavors as deep and rich as the colors, and an artisanal display in every bag\"Some have also said that these chips are oily, which is true.  But they explain that on their website: \"At Kettle Foods we exclusively use expeller pressed high monounsaturated sunflower and/or safflower oil to make Kettle Brand®  products. Both of these oils are naturally free of trans fatty acids and are not hydrogenated in any way. We have taken the extra step of sending our products to independent third party labs to test for the presence of trans fats and results indicate we are \"trans fat free\" meaning none were detected. You will see zero trans fats listed on our packaging.\"  That oiliness is natural and happens when quality oil is being used.You will not find chips with such a natural potato flavor.  I'm also a fan of their unsalted potato chips.  For those of us watching our salt intake, the unsalted chips is the best you can get.  Period.\n",
            "\n",
            "Index: 1497\n",
            "Text: MY QUESTION FOLLOWED BY THEIR REPLY - VERY GOOD CUSTOMER SERVICE!!Hey guys,So we are trying to feed a new born as much breast milk as we can, however there is not enough to meet his nutritional needs.So a formula is in order as a supplement. With that, we are interested in your toddler formula, and it appears that since this meets the FDA Infant Formula Guidelines, that it should be safe, (and opinions on the Internet seem to feel the same way). However, our doctor says that it should not be used because of the higher amounts of calcium and phosphorous. He was also concerned about using the DHA/ARA supplement with concerns about the egg white process and that the proteins in egg whites are not good for an infant. Any thoughts on this? Thanks!THEIR ANSWERDear Dave,Thank you for contacting Nature's One®.  Firstly, you are correct in that our Baby's Only Organic® formulas meet the nutritional requirements of the FDA's Infant Formula Act. No dilution is needed for babies under 12 months of age. Please follow label mixing instructions.Regarding the calcium and phosphorous content in Baby's Only Organic® formulas, calcium is an essential nutrient needed for bone health. The American Academy of Pediatrics Expert Panel for Nutrient Levels in Infant Formula has provided recommendations on the amount of calcium with the minimum per 100 Calories being 50 milligrams and the maximum being 140 milligrams per 100 Calories. Phosphorus is another important mineral for bone health. The Expert Panel has also recommended that the calcium to phosphorus ratio in an infant formula be no less than 1.1 or more than 2.0. Baby's Only Organic® falls within these ranges with our calcium at 135 milligrams per 100 Calories and a calcium to phosphorus ratio of 1.5.Reference: Committee on Nutrition, Pediatric Nutrition Handbook, The American Academy of Pediatrics, 2009, pages 1245-1246.The balance between calcium and phosphorus is most important for bone development. The calcium to phosphorus ratio in both Baby's Only Organic® formula and Enfamil NextStep is 1:5. Thus, Baby's Only Organic® formulas provide an appropriate ratio of calcium and phosphorus for toddlers and an appropriate overall quantity of calcium and phosphorus.Regarding our Baby's Only Essentials® DHA/ARA supplement, The DHA utilized in our Baby's Only Essentials® DHA & ARA supplement is sourced from egg yolk phospholipids. You are probably aware that most often an egg allergy is caused by the egg white rather than the yolk. Additionally, it is the protein component of foods that usually elicit an allergic reaction. Because we use the phospholipids (not protein) from the egg yolk, there is only a very minute chance that an allergic response will ensue.If your child has a history of allergies, we highly recommend that you consult your child's healthcare provider.DHA is also found naturally in other foods, including oily fish like trout, sardines, tuna and salmon and the yolk of eggs. Dietary ARA is available from animal meats, dairy foods, eggs, peanuts and nori seaweed. As your child increases the variety of foods in her diet, you can include some of these foods in her diet. Other natural sources of DHA would include fish oil or fish oil supplements. Unfortunately, it is difficult to insure that these are free from mercury or other environmental toxins. But, also remember that these fatty acids can be formed from dietary linoleic and linolenic acids (nuts, seeds, grains legumes, vegetables and their cold-pressed oils). By including good sources of high quality fats in the diet, your child will easily be able to meet her specific needsMost egg allergies are a result of ingestion of egg white; egg yolk allergies are rare.Proteins are the component of foods that cause allergic reactions. Egg allergy is usually due to the proteins in the egg white. These protein allergens are: ovomucoid, ovalbumin, ovotransferrin; lysozyme, and ovomucin.However, there can be a rare situation of egg yolk allergy. The literature to date usually associates egg yolk allergy to a reaction triggered by inhaled bird allergen, referred to as Bird-egg syndrome. This is a very rare syndrome.Egg allergy can also be seasonal. Oak pollen, short and western ragweed and the goosefoot family of weeds may cross react with eggs when these pollens are in season.The estimated point prevalence of egg allergy in children is 1.6% by 2.5 years of age. (1) Again, egg white is usually the cause of an egg allergy. Most children outgrow an allergy to eggs by 5 years of age. (1) Eggesbo M, Botten G, Halvorsen R, Magnus P, \"The prevalence of allergy to egg: a population-based study in young children,\" Allergy 56; 5:403I hope this information is helpful to you. Thank you for your interest in Baby's Only Organic® formulas. Please let me know if you have additional questions.Sincerely,LoriNature's One Inc.[...][...]Toll Free [...]\n",
            "\n",
            "Index: 3535\n",
            "Text: It wasn't a bad purchase at just $9.99 through Warehouse Deals fulfilled by Amazon and shipped for free, but took 2 stars off because it wasn't a freshness issue but Cracker Jacks® just don't taste the same like when I was a kid back in the 1980's.\n",
            "\n",
            "Index: 5442\n",
            "Text: Here is the response I got to my emailed question about BPA in the lids of their glass jars:While all of our Earth's Best Foods were and are safe; Earth's Best Organic® jarred baby food was converted to packaging that does not use BPA in October, 2011.That satisfies my concerns. They include a 1-800 number if people have further questions: please feel free to contact us at 1-800-434-4246, Monday through Friday from 7AM - 5PM Mountain Time.I don't know why they don't advertise this on the packaging, website, etc. It would reassure a lot of parents!\n",
            "\n",
            "Index: 5798\n",
            "Text: Would have rated no star if available.  Beefeaters® Chicken Liver Freeze Dried Treats for Dogs, are a product of China, and irradiated \"for your protection\" according to the company's hold message.  Called Amazon.com customer service, and requested a full refund with pre-paid returned shipping.  Too bad none of this is in the product description.  Amazon CSR was very helpful, and very cooperative, postage paid shipping label received via email immediately.  Thank you, Amazon.com!\n",
            "\n",
            "Index: 6762\n",
            "Text: I was offered the opportunity to taste this product because of Vine. I would not normally purchase this type of product, so my review and opinion will be biased in that regard.I'm not a big soda drinker, I'd just rather drink water or coffee. I will occasionally drink a diet soft drink, usually Diet Pepsi or Diet Coke.Did you know that a single 12oz can of Coke Classic® has 140 calories and 39 grams of sugar? This 8oz can of Switch has 140 calories and 34grams of sugar. And this is healthy? 4 ounces less is size, same caloric content and more sugar per ounce that Classic Coke. OK, so it's not \"added\" sugar, but still, that's a lot of sugar. 100% Orange juice, 8 oz, has 120 calories and 28 grams of sugar.The can says it is \"Orange Tangerine\" but in fact the contents are #1: Water #2: Concentrates of Apple, Grape, Tangerine, Orange, and Acerola juices. Then they throw in some \"gum acacia\", \"natural flavors and colors\" and some \"ascorbic acid(vitamin C)\". And as a little surprise bonus, they have added \"soy\". Soy?I asked my wife to taste this product with me. I poured the drink into two glasses and we both took a sip. Her reaction was swift and strong... she hated it. I agreed with her. It was for me, sickly sweet and left a sugary aftertaste in my mouth. Not only that, but the taste was just nasty. It didn't taste at all like orange or tangerine to me. It just tasted terribly sweet with an undefined cirtus something taste. The carbonation did not add anything positive to the experience. Neither of us finished drinking our 4oz portion. It went down the sink.I would not purchase this product in the future and if I could have given it 0-stars, I would have. It's that bad.\n",
            "\n",
            "Index: 10004\n",
            "Text: We breastfeed our month old boy, but since his bilirubin level was still high, doctors recommended to switch to formula for 48 hours. We started with Similac Advance (Shield Plus) which we still had from hospital. First 24 hours were wonderful in terms of pooping, then we decided to switch to Similac Organic for known reasons, but during the first 32-Ounce Bottle I began to notice that his stool was getting harder, and eventually he stopped pooing (actually he produced two stone-ball like poops) and had a hard time for another 24 hours. Breast milk and Similac advance tested similarly sweet, but Similac Organic was much much less sweet. I made a research on the internet and I found the following that may help you to understand the cause of constipation: ( />Constipation means infrequent, hard stools. It does not mean grunting or straining, although your baby may certainly exhibit these behaviors when having a bowel movement. So the first question to ask is, \"Is my child really constipated?\"Next, consider why true constipation - excessively hard, dry stools - arises. The normal function of the colon is to remove water from the stool. Otherwise we would rapidly dehydrate and die. That is why severe diarrhea is dangerous - the stool is moving through the colon and being expelled from the body so fast, there is no time to properly reabsorb the stool water back into the bloodstream.Conversely, if the fecal material remains in the colon too long, the water removal process may extract too much of the fecal water and leave the stool mass excessively dry and hard. It is just that simple.So constipation arises when feces remain in the colon too long because:  * the colon is naturally sluggish (has decreased \"motility,\" we say)  * a low residue diet produces insufficient stimulation to the colon to trigger its normal propulsive activity and clear out the feces  * a combination of the above two factors  * there is obstruction of the lower GI tract  o functional neurologic - the lower rectum and anus cannot properly relax to allow evacuation - Hirschprung disease  o functional anatomic - anal-rectal positioning doesn't allow easy evacuation  o mechanical - a tumor physically blocks stool passageBreast fed babies are essentially never truly constipated. This is because breast milk is such a low residue diet. Almost all of the milk is absorbed by the baby and used. The stools consist of a small amount of unabsorbed protein curds and a large amount of bowel secretions, chiefly mucus. And of course a lot of noisy gas. But these infrequent stools do not indicate constipation - just infrequent stools.Later, when breast feeding infants start solids, they technically can become constipated, but it is unusual in my experience. It is not until the child is pretty much on solids and especially when weaned to whole cow milk that constipation is much of an issue. By then we are really discussing toddlers.Breast fed infants do tend to save up after the first few months and poop less often. This worries some parents. Despite reassurances to the contrary, some folks just have to see a poop every day from their little darling to be happy and fulfilled. Go figure. They forget that stool can stay in the colon for a very, very long time and it will not hurt anything.When the stool hoarding breast feeder does finally get ready to go, another problem comes to the fore: grunting and straining. When the baby's rectum forms in utero, it is in a state of permanent contraction for nigh onto nine months. The baby does not pass the meconium (fetal stool) in utero until perhaps right at the end of gestation right before, during or after delivery. Until the baby is several months old, and has passed and been stretched by quite a few bowel movements, the rectum and particularly the anus may be quite tight and resist easy dilation to allow the stool to pass. Because the baby has weak, flabby abdominal musculature (that is why his little tummy is so poochy), he must push against this tight anal spincter with his diaphragm. Just as Mom pushed during delivery, and with seemingly equal discomfort. Hence the grunting and red-faced straining.  The other major problem of both breast or bottle fed newborns which creates this sort of straining is dyschezia, or uncoordinated stooling.The cure for the problem of a tight anus is pretty simple: a gentle rectal exam with a well-lubricated pinky finger by the doctor (which should be done on all babies with stooling troubles anyway to rule out a physical obstruction) will be both diagnostic and curative. If one such gentle dilation doesn't do the trick, I have Mom bring the baby back in a week and repeat the procedure. The anal spincter is stretched, and pretty soon, no more terrible straining.TopBottle fed infants are \"a whole 'nother story.\" Infant formulas are not nearly as well digested as breast milk, and leave quite a bit more residue in the colon. (The exception to this rule in my mind is the new Similac® that really does seem to leave the stools about the same consistency as breast milk.) So true constipation in bottle fed infants is much more common; my feeling is that soy formulas are even worse than cow milk based formulas in this regard.The young infant's colon is simply not designed for all the residue and stool bulk. The residue builds up, is dried too much, and becomes hard, pebbly stool. It is naturally difficult and uncomfortable for the baby to pass.The addition of cereal to the diet only compounds whatever problems already exist. Cereal + milk = concrete. Cereal in the formula bottle seems to be especially bad, probably because parents don't realise just how much cereal the baby is getting.TopTreatment of constipation mainly involves common sense diet modifications and sometimes medication:  * Again, as with breast fed babies, a search for the cause of the constipation and gentle rectal exam can work wonders.  * I use a lot of Karo® corn syrup for bottle feeders with constipation. It is my first choice recomendation; it almost always works if used properly. Unfortunately, many doctors and nurses don't use it properly, get poor results, and don't recommend it as often as they should. I start with two measuring teaspoons in every formula bottle, regardless of size, and adjust the dose up or down depending on how things are going. Usually, the baby can be weaned off the Karo pretty soon, but may need to stay on it periodically for the first few months of life. It is very important to put the corn syrup in the formula and not give it in between in water. The excess corn syrup does not get digested very well - the baby is already digesting all the sugar he can from the formula. The excess sugar passes down to the colon and produces a sugar diarrhea similar to sorbitol (as below, prunes, apple juice).  Before scolding me about \"the risk of botulism\" from corn syrup, please see:  Clinical Pediatric Emergency Medicine, Volume 4,Number 3,September 2003: \"Pediatric neurologic potpourri: cases to remember\"  \"In the past, C. botulinum was also found in corn syrup, but this is no longer a risk factor for infant botulism because of a recent change in the processing formula.\"  For the complete source of statement, see: Olsen SJ, Swerdlow DL. Risk of infant botulism from corn syrup. In reply. Pediatr Infect Dis J 2000;19:584-5.  * Certain fruits and juices - for example prunes, apple juice, apricots - are rich in sorbitol, a nondigestible sugar that passes through the body to the colon and causes water to be retained or drawn into the stool mass. They can be increased in the diet to loosen the stool and relieve or prevent constipation.  * Lactulose is a non-absorbable sugar product that is processed by colon bacteria to produce an inflow of water into the stool mass. We use it a lot - it seems to be a favorite of our local pediatric gastroenterologists. The dose is adjusted up or down according to respose. Side effects are usually not serious but can include cramping or diarrhea - obviously, stop the medicine and call your doctor if these occur.  * Glycerine suppositories are helpful to relieve your child if there is some hard stool that just won't come out easily. The suppository melts in the heat of the rectum and lubricates the stool. Glycerine also has a mild effect of attracting water into the stool, which softens it. The act of insertion also serves to stimulate the rectum and anus to initiate the defecation reflex. (Glycerine is completely safe - it is a food, found in candy, not a drug.)  * Fiber containing foods are helpful for older children and adults, but are not really of much use in the treatment of young infant constipation, for dietary reasons: babies just don't eat a lot of fiber. (I believe ISoyalac®, which was a high fiber soy formula, has been discontinued. It was never very available any place I practiced, anyway.)TopMyths and misconceptions concerning infant constipation:  * Iron in the formula causes constipation. No way. I can understand lay people thinking this to be the case because when adults take iron preparations (in large doses) they may become constipated. But the tiny amount present in infant formula is not nearly enough to cause any problems.  The irritating thing to me is to hear doctors and nurses who should know better repeating this nonsense and switching the infants to the less healthy low- or no-iron formulas.  * Increasing water in the diet is commonly recommended; it makes very little sense and demonstrates a failure to reflect on the basic physiologic cause of the overdry stool. The problem is too little water retention in the stool, not too little water in the body! Unless there is increased fiber or some chemical substance such as a sugar (Karo®, lactulose) to retain the water in the stool, excess water will simply wind up in the diaper as urine.Top\n",
            "\n",
            "Index: 10540\n",
            "Text: This is a great product for those who need gluten, dairy & casein free foods.Of course it doesn't taste like REAL macaroni and cheese! Geez - do you really think shelled hemp nuts and other things are going to give you a creamy cheesey texture or taste? If you're looking for something to replicate annies GF mac and cheese (which DOES contain dairy) then you will search in vain. You cannot get real cheese flavor from non-cheese products.However, if you're realistic and are aware that non cheese stuff won't actually taste like cheese yet you want something similar to mac & cheese then this is a great option! We really like this mix. It has a unique taste. We tend to alternate this stuff with Road's End Organics GF Cheddar Penne & Chreese®  The Road's end is closer to cheese but again, those who want a real cheese flavor/consistency will be sorely disappointed. However, for those of us who cannot have real cheese this stuff is great.We mix in veggies with ours (you know the ones that are little squares of carrots, peas, corn, etc.) and I use hemp milk when mixing for a bit more substance and flavor. Also if you add a bit of trocomare  it makes a huge difference in flavor (actually trocomare makes a huge difference in the flavor of everything! We love it and use it on just about everything.So - for those who expect a real cheese flavor - don't even bother and stick with real cheese or change your expectations.For those who realize it won't taste like real cheese but who want something different and unique (and gluten, dairy, casein free) - buy this, it's great!!!\n",
            "\n",
            "Index: 12240\n",
            "Text: My puppy loves these vitality treats. After reading the other two reviews I became concerned and quickly e-mailed the company with my concerns. Here is what they replied to me which immediately relieved my worries. They also sent me documentation of their checks on the products and pictures of the non-caged chickens.Hello Karen,Thank You for contacting us here at DOGSWELL®.The DOGSWELL® manufacturing facilities meet the highest and most strict sanitary conditions. All of our plants have HACCP programs, have received high scores by independent Third Party auditors, have strict raw ingredient standards and continuously check our products during processing and when they are finished to ensure they meet our high quality standards. Our wet food for dogs, our dry food for dogs and our biscuits are manufactured in the U.S. and our treats are manufactured in China. We regularly visit our manufacturing facilities to ensure the quality and safety of our products. We are always looking to bring the manufacturing over to the United States, we have just not found a supplier who will add all of the vitamins and nutrients that we want at a price the consumer is willing to pay, once we find one, we will definitely bring it back over to the US.We don't determine our source country for ingredients based solely on the country of origin; we find the best quality ingredient combined with the highest quality supplier and testing specifications and select them. You can be confident that DOGSWELL® products are natural, high-quality and provide the best for your pet.Please know we certify that our products are safe and clean with regular testing and ensure that our products meet high quality standards. We test product from each batch that is produced and have never had our results come back positive for ANY harmful ingredients such as cyanuric acid, melamine, e.coli and salmonella. Moreover, please note that no DOGSWELL® product contains milk powder.We regularly test our products for any harmful contaminants and to ensure that they meet our quality standards. Along with an in-depth knowledge of our supply chain, our regular tests assure our customers that we are providing their pets with only the very best ingredients. Tests are conducted at independent, third-party, U.S. laboratories that follow FDA and APPMA-approved methodology. Please see attached for an example of test results for our treats; I have attached one of our VITALITY(tm) products that were tested recently.Our material suppliers are certified clean and clear of any contaminants. If raw ingredients do not meet their specifications and pass safety tests, then the ingredient is rejected immediately and never enters our processing facilities. Moreover, we strive to use products from suppliers that treat their animals humanely. Please see attached for an image of our chickens in their barn. I do not know of a reliable site that describes the process.Please know that we honestly believe that pets are family too and we feed our products to our own pets regularly. Again, we test product from each batch that is produced and have never had our results come back positive for ANY harmful ingredients.Please don't hesitate to contact me with ANY questions.Thank You,JeffJeff HaluckMarketing Assistant\n",
            "\n",
            "Index: 13972\n",
            "Text: Hamburger Helper is a quick fix dinner for our sons (ages 8 and 7) on nights when school activities and/or swimming or judo lessons push our family schedule into the night.Chili Macaroni is one of the five Mexican flavors Hamburger Helper offers. Cheesy Enchilada, Cheesy Nacho, Crunchy Taco and Double Cheese Quesadilla are the others. Each package makes five one-cup servings. Our sons like the Old El Paso® seasoning;  and  are too bland for their tastes. This flavor has just the right amount of spice and seasonings to appeal to the under-10-year-old crowd. The box suggests adding a 4.5 oz. can of Old El Paso chopped green chiles five minutes before the end of the simmer time.A prepared serving (one cup) has 270 calories, 570 mg of sodium and 0.5 g of fat; it also provides a good carb load - 22 grams - and 3 g of protein.Adding the cost of the ground hamburger and vegetables as a side dish, we are able to feed two boys for less than $5 and still have enough leftovers to prepare them a weekend lunch.In summary, while this isn't Haute cuisine,  is an inexpensive option when pressed for time. For price and convenience, I give the product a five-star rating. For taste, I give it three stars. Again, though, I'm not the target audience, so I'm settling on a four-star rating. The slogan on the box sums it up well, \"One pound. One pan. One tasty meal.\"\n",
            "\n",
            "Number of rows with emojis: 11\n"
          ]
        }
      ],
      "source": [
        "# Function to check if emojis exist in text\n",
        "def has_emoji(text):\n",
        "    for character in text:\n",
        "        if emoji.is_emoji(character):\n",
        "            return True\n",
        "    return False\n",
        "\n",
        "# Find rows with emojis\n",
        "rows_with_emoji = df[df['Text'].apply(has_emoji)]\n",
        "\n",
        "# Print rows and their indices\n",
        "for index, row in rows_with_emoji.iterrows():\n",
        "    print(\"Index:\", index)\n",
        "    print(\"Text:\", row['Text'])\n",
        "    print()\n",
        "\n",
        "# Count the number of rows with emojis\n",
        "num_rows_with_emoji = len(rows_with_emoji)\n",
        "\n",
        "print(\"Number of rows with emojis:\", num_rows_with_emoji)"
      ]
    },
    {
      "cell_type": "raw",
      "id": "6a01fc5d",
      "metadata": {
        "id": "6a01fc5d"
      },
      "source": [
        "The \"emoji\" here are the mini logo beside the name of some manifacturies so we leave them as it is ."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade scikit-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxxNPSKH78Yx",
        "outputId": "d4b84ed7-3075-458e-8520-8e45eae1ba90"
      },
      "id": "kxxNPSKH78Yx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate the text and score columns\n",
        "text = df['Text']\n",
        "score = df['Score']\n",
        "\n",
        "# Define the range of n-grams\n",
        "# Define the range of n-grams you want to consider\n",
        "ngram_range = (2, 3)  # Consider bi-grams and tri-grams\n",
        "\n",
        "# Create CountVectorizer with n-gram range and exclude stopwords\n",
        "vectorizer = CountVectorizer(ngram_range=ngram_range, stop_words='english', min_df=1)\n",
        "\n",
        "# Fit the vectorizer on the text data\n",
        "X = vectorizer.fit_transform(text)\n",
        "\n",
        "# Get the n-gram features\n",
        "ngram_features = vectorizer.get_feature_names_out()\n",
        "\n",
        "# Calculate the n-gram frequencies\n",
        "ngram_counts = X.sum(axis=0)\n",
        "\n",
        "# Create a DataFrame to store the n-gram frequencies\n",
        "ngram_df = pd.DataFrame(ngram_counts, columns=ngram_features)\n",
        "ngram_df = ngram_df.transpose()\n",
        "ngram_df.columns = ['Frequency']\n",
        "\n",
        "# Sort the n-grams by frequency in descending order\n",
        "ngram_df = ngram_df.sort_values('Frequency', ascending=False)\n",
        "\n",
        "# Print the top n-grams and their frequencies\n",
        "num_top_ngrams = 10  # Number of top n-grams to display\n",
        "print(ngram_df.head(num_top_ngrams))"
      ],
      "metadata": {
        "id": "6JOSdKgw8tB3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05cf411a-1e4d-4fe6-ac02-4befe2a841c8"
      },
      "id": "6JOSdKgw8tB3",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  Frequency\n",
            "gluten free             780\n",
            "ve tried                531\n",
            "highly recommend        483\n",
            "cup coffee              475\n",
            "taste like              459\n",
            "grocery store           420\n",
            "just right              344\n",
            "peanut butter           338\n",
            "tastes like             338\n",
            "really good             324\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Those are the most  frequented bi-gram, and we see we have few positive sentence.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "8JNo7f8-AN_g"
      },
      "id": "8JNo7f8-AN_g"
    },
    {
      "cell_type": "markdown",
      "id": "e44eb6a4",
      "metadata": {
        "id": "e44eb6a4"
      },
      "source": [
        "# VADER Sentiment Scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c2a7c81",
      "metadata": {
        "id": "1c2a7c81"
      },
      "outputs": [],
      "source": [
        "from nltk.sentiment import SentimentIntensityAnalyzer\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I didn't touch punctuations and case for now as VADER SENTIMENT ANALYSIS SCORES are affected by factors like punctuations,capitalization,preceeding-trigrams,degree modifiers,conjunctions etc"
      ],
      "metadata": {
        "id": "qJvpjkuXXW2y"
      },
      "id": "qJvpjkuXXW2y"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "vRMta22yE1X7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRMta22yE1X7",
        "outputId": "fd9f1c38-ca20-4c99-dabc-b384cd84fd77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "nltk.download('vader_lexicon')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a5bf1f43",
      "metadata": {
        "id": "a5bf1f43"
      },
      "outputs": [],
      "source": [
        "analyser_object=SentimentIntensityAnalyzer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "425e3d2d",
      "metadata": {
        "id": "425e3d2d"
      },
      "outputs": [],
      "source": [
        "def vader_scores():\n",
        "    dic= {}\n",
        "    for i, row in tqdm(df.iterrows(), total=len(df)):\n",
        "        review=row['Text']\n",
        "        myid=row['Id']\n",
        "        dic[myid]=analyser_object.polarity_scores(review)\n",
        "    return dic"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vader retuns 4 values:\n",
        "\n",
        "\n",
        "*   a neutrality score\n",
        "\n",
        "\n",
        "\n",
        "*   a positivity score\n",
        "\n",
        "\n",
        "*   a negativity score\n",
        "*   an overall score that summarizes the previous scores\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "u0HaJEUVZ0mq"
      },
      "id": "u0HaJEUVZ0mq"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0dc8626d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "6e7b4488f25042eab38b8c6f6a7d3778",
            "b42cf7565a2f44af8f09fa23a69c70cc",
            "e27891ed4aa24ad3838e791e95077b72",
            "f7565bcd95ea401a9524a8db2fa3592a",
            "cad3cc718735452989f09b266f636c48",
            "d728374f7d984122bd0955d9030b74fb",
            "4dc2beefd09446ceb1a13bc597d0a435",
            "2add84b70c0a42a5aba5ebef9cbd6a14",
            "65d5b83324014ff69e02dcbd5c460d9e",
            "37f02af4c5394d32968dae16df1100e8",
            "cac53defb3e2444686775e2561125b1e"
          ]
        },
        "id": "0dc8626d",
        "outputId": "a0c3ff2c-0022-4b67-d0cc-4b683c819b32"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/18000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e7b4488f25042eab38b8c6f6a7d3778"
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          },
          "metadata": {}
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        {
          "output_type": "execute_result",
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              " 567: {'neg': 0.177, 'neu': 0.823, 'pos': 0.0, 'compound': -0.9679},\n",
              " 568: {'neg': 0.114, 'neu': 0.735, 'pos': 0.151, 'compound': 0.6749},\n",
              " 569: {'neg': 0.026, 'neu': 0.897, 'pos': 0.077, 'compound': 0.3291},\n",
              " 570: {'neg': 0.0, 'neu': 0.803, 'pos': 0.197, 'compound': 0.8422},\n",
              " 571: {'neg': 0.0, 'neu': 0.751, 'pos': 0.249, 'compound': 0.9118},\n",
              " 572: {'neg': 0.01, 'neu': 0.817, 'pos': 0.172, 'compound': 0.9945},\n",
              " 573: {'neg': 0.018, 'neu': 0.818, 'pos': 0.164, 'compound': 0.9915},\n",
              " 574: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0},\n",
              " 575: {'neg': 0.009, 'neu': 0.845, 'pos': 0.147, 'compound': 0.9874},\n",
              " 576: {'neg': 0.0, 'neu': 0.601, 'pos': 0.399, 'compound': 0.8834},\n",
              " 577: {'neg': 0.019, 'neu': 0.835, 'pos': 0.146, 'compound': 0.8291},\n",
              " 578: {'neg': 0.0, 'neu': 0.781, 'pos': 0.219, 'compound': 0.9749},\n",
              " 579: {'neg': 0.0, 'neu': 0.536, 'pos': 0.464, 'compound': 0.9307},\n",
              " 580: {'neg': 0.0, 'neu': 0.802, 'pos': 0.198, 'compound': 0.6369},\n",
              " 581: {'neg': 0.0, 'neu': 0.857, 'pos': 0.143, 'compound': 0.6124},\n",
              " 582: {'neg': 0.043, 'neu': 0.649, 'pos': 0.308, 'compound': 0.9254},\n",
              " 583: {'neg': 0.0, 'neu': 0.907, 'pos': 0.093, 'compound': 0.4019},\n",
              " 584: {'neg': 0.091, 'neu': 0.909, 'pos': 0.0, 'compound': -0.2732},\n",
              " 585: {'neg': 0.081, 'neu': 0.785, 'pos': 0.134, 'compound': 0.25},\n",
              " 586: {'neg': 0.083, 'neu': 0.721, 'pos': 0.196, 'compound': 0.8132},\n",
              " 587: {'neg': 0.0, 'neu': 0.779, 'pos': 0.221, 'compound': 0.9323},\n",
              " 588: {'neg': 0.116, 'neu': 0.75, 'pos': 0.135, 'compound': 0.2169},\n",
              " 589: {'neg': 0.0, 'neu': 0.81, 'pos': 0.19, 'compound': 0.9042},\n",
              " 590: {'neg': 0.016, 'neu': 0.861, 'pos': 0.123, 'compound': 0.846},\n",
              " 591: {'neg': 0.0, 'neu': 0.826, 'pos': 0.174, 'compound': 0.6369},\n",
              " 592: {'neg': 0.0, 'neu': 0.889, 'pos': 0.111, 'compound': 0.9446},\n",
              " 593: {'neg': 0.056, 'neu': 0.726, 'pos': 0.217, 'compound': 0.9739},\n",
              " 594: {'neg': 0.042, 'neu': 0.857, 'pos': 0.101, 'compound': 0.8112},\n",
              " 595: {'neg': 0.031, 'neu': 0.831, 'pos': 0.138, 'compound': 0.8932},\n",
              " 596: {'neg': 0.022, 'neu': 0.845, 'pos': 0.133, 'compound': 0.9206},\n",
              " 597: {'neg': 0.0, 'neu': 0.797, 'pos': 0.203, 'compound': 0.8567},\n",
              " 598: {'neg': 0.063, 'neu': 0.859, 'pos': 0.078, 'compound': 0.4101},\n",
              " 599: {'neg': 0.146, 'neu': 0.571, 'pos': 0.283, 'compound': 0.8802},\n",
              " 600: {'neg': 0.051, 'neu': 0.693, 'pos': 0.257, 'compound': 0.9842},\n",
              " 601: {'neg': 0.186, 'neu': 0.665, 'pos': 0.149, 'compound': -0.4503},\n",
              " 602: {'neg': 0.05, 'neu': 0.639, 'pos': 0.311, 'compound': 0.9834},\n",
              " 603: {'neg': 0.007, 'neu': 0.827, 'pos': 0.166, 'compound': 0.9938},\n",
              " 604: {'neg': 0.009, 'neu': 0.845, 'pos': 0.147, 'compound': 0.9874},\n",
              " 605: {'neg': 0.0, 'neu': 0.83, 'pos': 0.17, 'compound': 0.8481},\n",
              " 606: {'neg': 0.0, 'neu': 0.837, 'pos': 0.163, 'compound': 0.8715},\n",
              " 607: {'neg': 0.085, 'neu': 0.682, 'pos': 0.233, 'compound': 0.5705},\n",
              " 608: {'neg': 0.014, 'neu': 0.733, 'pos': 0.253, 'compound': 0.9779},\n",
              " 609: {'neg': 0.061, 'neu': 0.875, 'pos': 0.063, 'compound': 0.0384},\n",
              " 610: {'neg': 0.02, 'neu': 0.834, 'pos': 0.146, 'compound': 0.9668},\n",
              " 611: {'neg': 0.088, 'neu': 0.713, 'pos': 0.2, 'compound': 0.6222},\n",
              " 612: {'neg': 0.052, 'neu': 0.758, 'pos': 0.191, 'compound': 0.8139},\n",
              " 613: {'neg': 0.147, 'neu': 0.786, 'pos': 0.067, 'compound': -0.6341},\n",
              " 614: {'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'compound': 0.9585},\n",
              " 615: {'neg': 0.039, 'neu': 0.801, 'pos': 0.159, 'compound': 0.9169},\n",
              " 616: {'neg': 0.024, 'neu': 0.706, 'pos': 0.27, 'compound': 0.982},\n",
              " 617: {'neg': 0.0, 'neu': 0.7, 'pos': 0.3, 'compound': 0.8901},\n",
              " 618: {'neg': 0.0, 'neu': 0.694, 'pos': 0.306, 'compound': 0.8979},\n",
              " 619: {'neg': 0.0, 'neu': 0.875, 'pos': 0.125, 'compound': 0.5067},\n",
              " 620: {'neg': 0.0, 'neu': 0.599, 'pos': 0.401, 'compound': 0.9586},\n",
              " 621: {'neg': 0.075, 'neu': 0.817, 'pos': 0.108, 'compound': 0.8004},\n",
              " 622: {'neg': 0.157, 'neu': 0.788, 'pos': 0.055, 'compound': -0.8088},\n",
              " 623: {'neg': 0.039, 'neu': 0.627, 'pos': 0.334, 'compound': 0.969},\n",
              " 624: {'neg': 0.077, 'neu': 0.707, 'pos': 0.217, 'compound': 0.983},\n",
              " 625: {'neg': 0.0, 'neu': 0.867, 'pos': 0.133, 'compound': 0.9697},\n",
              " 626: {'neg': 0.028, 'neu': 0.697, 'pos': 0.275, 'compound': 0.9745},\n",
              " 627: {'neg': 0.078, 'neu': 0.792, 'pos': 0.13, 'compound': 0.5574},\n",
              " 628: {'neg': 0.061, 'neu': 0.674, 'pos': 0.265, 'compound': 0.8678},\n",
              " 629: {'neg': 0.053, 'neu': 0.947, 'pos': 0.0, 'compound': -0.3919},\n",
              " 630: {'neg': 0.13, 'neu': 0.587, 'pos': 0.283, 'compound': 0.9169},\n",
              " 631: {'neg': 0.029, 'neu': 0.918, 'pos': 0.053, 'compound': 0.4166},\n",
              " 632: {'neg': 0.0, 'neu': 0.69, 'pos': 0.31, 'compound': 0.933},\n",
              " 633: {'neg': 0.01, 'neu': 0.866, 'pos': 0.124, 'compound': 0.9167},\n",
              " 634: {'neg': 0.042, 'neu': 0.775, 'pos': 0.183, 'compound': 0.9739},\n",
              " 635: {'neg': 0.213, 'neu': 0.608, 'pos': 0.179, 'compound': -0.0997},\n",
              " 636: {'neg': 0.187, 'neu': 0.728, 'pos': 0.086, 'compound': -0.9194},\n",
              " 637: {'neg': 0.05, 'neu': 0.856, 'pos': 0.093, 'compound': 0.5597},\n",
              " 638: {'neg': 0.095, 'neu': 0.589, 'pos': 0.316, 'compound': 0.7074},\n",
              " 639: {'neg': 0.034, 'neu': 0.722, 'pos': 0.244, 'compound': 0.9375},\n",
              " 640: {'neg': 0.03, 'neu': 0.734, 'pos': 0.236, 'compound': 0.9554},\n",
              " 641: {'neg': 0.0, 'neu': 0.936, 'pos': 0.064, 'compound': 0.0772},\n",
              " 642: {'neg': 0.0, 'neu': 0.799, 'pos': 0.201, 'compound': 0.9545},\n",
              " 643: {'neg': 0.031, 'neu': 0.793, 'pos': 0.176, 'compound': 0.8316},\n",
              " 644: {'neg': 0.02, 'neu': 0.685, 'pos': 0.295, 'compound': 0.9814},\n",
              " 645: {'neg': 0.076, 'neu': 0.585, 'pos': 0.339, 'compound': 0.8595},\n",
              " 646: {'neg': 0.0, 'neu': 0.694, 'pos': 0.306, 'compound': 0.9151},\n",
              " 647: {'neg': 0.021, 'neu': 0.83, 'pos': 0.149, 'compound': 0.9249},\n",
              " 648: {'neg': 0.096, 'neu': 0.7, 'pos': 0.204, 'compound': 0.5719},\n",
              " 649: {'neg': 0.0, 'neu': 0.89, 'pos': 0.11, 'compound': 0.3818},\n",
              " 650: {'neg': 0.093, 'neu': 0.77, 'pos': 0.137, 'compound': 0.9356},\n",
              " 651: {'neg': 0.0, 'neu': 0.814, 'pos': 0.186, 'compound': 0.8395},\n",
              " 652: {'neg': 0.043, 'neu': 0.867, 'pos': 0.09, 'compound': 0.8845},\n",
              " 653: {'neg': 0.029, 'neu': 0.9, 'pos': 0.071, 'compound': 0.9377},\n",
              " 654: {'neg': 0.038, 'neu': 0.89, 'pos': 0.072, 'compound': 0.7588},\n",
              " 655: {'neg': 0.0, 'neu': 0.909, 'pos': 0.091, 'compound': 0.4404},\n",
              " 656: {'neg': 0.0, 'neu': 0.614, 'pos': 0.386, 'compound': 0.8932},\n",
              " 657: {'neg': 0.0, 'neu': 0.654, 'pos': 0.346, 'compound': 0.9022},\n",
              " 658: {'neg': 0.0, 'neu': 0.811, 'pos': 0.189, 'compound': 0.6808},\n",
              " 659: {'neg': 0.046, 'neu': 0.637, 'pos': 0.316, 'compound': 0.906},\n",
              " 660: {'neg': 0.02, 'neu': 0.777, 'pos': 0.203, 'compound': 0.9601},\n",
              " 661: {'neg': 0.228, 'neu': 0.545, 'pos': 0.228, 'compound': 0.25},\n",
              " 662: {'neg': 0.0, 'neu': 0.788, 'pos': 0.212, 'compound': 0.8439},\n",
              " 663: {'neg': 0.106, 'neu': 0.7, 'pos': 0.193, 'compound': 0.624},\n",
              " 664: {'neg': 0.018, 'neu': 0.882, 'pos': 0.099, 'compound': 0.9621},\n",
              " 665: {'neg': 0.0, 'neu': 0.774, 'pos': 0.226, 'compound': 0.9757},\n",
              " 666: {'neg': 0.0, 'neu': 0.697, 'pos': 0.303, 'compound': 0.9476},\n",
              " 667: {'neg': 0.0, 'neu': 0.816, 'pos': 0.184, 'compound': 0.7263},\n",
              " 668: {'neg': 0.05, 'neu': 0.69, 'pos': 0.26, 'compound': 0.8689},\n",
              " 669: {'neg': 0.0, 'neu': 0.744, 'pos': 0.256, 'compound': 0.8225},\n",
              " 670: {'neg': 0.077, 'neu': 0.634, 'pos': 0.289, 'compound': 0.7906},\n",
              " 671: {'neg': 0.0, 'neu': 0.604, 'pos': 0.396, 'compound': 0.8957},\n",
              " 672: {'neg': 0.046, 'neu': 0.854, 'pos': 0.1, 'compound': 0.8074},\n",
              " 673: {'neg': 0.065, 'neu': 0.88, 'pos': 0.055, 'compound': -0.1263},\n",
              " 674: {'neg': 0.062, 'neu': 0.708, 'pos': 0.231, 'compound': 0.9313},\n",
              " 675: {'neg': 0.0, 'neu': 0.797, 'pos': 0.203, 'compound': 0.9098},\n",
              " 676: {'neg': 0.095, 'neu': 0.773, 'pos': 0.132, 'compound': 0.6588},\n",
              " 677: {'neg': 0.044, 'neu': 0.72, 'pos': 0.237, 'compound': 0.9566},\n",
              " 678: {'neg': 0.049, 'neu': 0.685, 'pos': 0.266, 'compound': 0.749},\n",
              " 679: {'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'compound': 0.8896},\n",
              " 680: {'neg': 0.126, 'neu': 0.686, 'pos': 0.188, 'compound': 0.879},\n",
              " 681: {'neg': 0.052, 'neu': 0.829, 'pos': 0.119, 'compound': 0.491},\n",
              " 682: {'neg': 0.121, 'neu': 0.879, 'pos': 0.0, 'compound': -0.509},\n",
              " 683: {'neg': 0.06, 'neu': 0.753, 'pos': 0.187, 'compound': 0.6369},\n",
              " 684: {'neg': 0.184, 'neu': 0.816, 'pos': 0.0, 'compound': -0.5423},\n",
              " 685: {'neg': 0.141, 'neu': 0.411, 'pos': 0.449, 'compound': 0.9354},\n",
              " 686: {'neg': 0.0, 'neu': 0.56, 'pos': 0.44, 'compound': 0.9318},\n",
              " 687: {'neg': 0.015, 'neu': 0.844, 'pos': 0.141, 'compound': 0.9605},\n",
              " 688: {'neg': 0.0, 'neu': 0.833, 'pos': 0.167, 'compound': 0.7183},\n",
              " 689: {'neg': 0.051, 'neu': 0.743, 'pos': 0.206, 'compound': 0.9694},\n",
              " 690: {'neg': 0.041, 'neu': 0.716, 'pos': 0.243, 'compound': 0.8878},\n",
              " 691: {'neg': 0.045, 'neu': 0.817, 'pos': 0.138, 'compound': 0.8189},\n",
              " 692: {'neg': 0.035, 'neu': 0.826, 'pos': 0.139, 'compound': 0.7845},\n",
              " 693: {'neg': 0.0, 'neu': 0.916, 'pos': 0.084, 'compound': 0.8356},\n",
              " 694: {'neg': 0.0, 'neu': 0.86, 'pos': 0.14, 'compound': 0.7345},\n",
              " 695: {'neg': 0.0, 'neu': 0.705, 'pos': 0.295, 'compound': 0.8718},\n",
              " 696: {'neg': 0.0, 'neu': 0.734, 'pos': 0.266, 'compound': 0.9118},\n",
              " 697: {'neg': 0.037, 'neu': 0.89, 'pos': 0.073, 'compound': 0.3585},\n",
              " 698: {'neg': 0.052, 'neu': 0.759, 'pos': 0.189, 'compound': 0.9103},\n",
              " 699: {'neg': 0.076, 'neu': 0.698, 'pos': 0.227, 'compound': 0.911},\n",
              " 700: {'neg': 0.052, 'neu': 0.687, 'pos': 0.261, 'compound': 0.905},\n",
              " 701: {'neg': 0.055, 'neu': 0.731, 'pos': 0.214, 'compound': 0.7414},\n",
              " 702: {'neg': 0.067, 'neu': 0.672, 'pos': 0.261, 'compound': 0.7467},\n",
              " 703: {'neg': 0.0, 'neu': 0.623, 'pos': 0.377, 'compound': 0.9603},\n",
              " 704: {'neg': 0.0, 'neu': 0.552, 'pos': 0.448, 'compound': 0.9393},\n",
              " 705: {'neg': 0.059, 'neu': 0.536, 'pos': 0.405, 'compound': 0.9312},\n",
              " 706: {'neg': 0.091, 'neu': 0.563, 'pos': 0.345, 'compound': 0.7824},\n",
              " 707: {'neg': 0.07, 'neu': 0.648, 'pos': 0.282, 'compound': 0.8176},\n",
              " 708: {'neg': 0.0, 'neu': 0.635, 'pos': 0.365, 'compound': 0.7845},\n",
              " 709: {'neg': 0.0, 'neu': 0.717, 'pos': 0.283, 'compound': 0.8172},\n",
              " 710: {'neg': 0.028, 'neu': 0.865, 'pos': 0.107, 'compound': 0.9035},\n",
              " 711: {'neg': 0.0, 'neu': 0.88, 'pos': 0.12, 'compound': 0.9131},\n",
              " 712: {'neg': 0.029, 'neu': 0.911, 'pos': 0.06, 'compound': 0.3116},\n",
              " 713: {'neg': 0.048, 'neu': 0.773, 'pos': 0.179, 'compound': 0.9605},\n",
              " 714: {'neg': 0.0, 'neu': 0.85, 'pos': 0.15, 'compound': 0.8725},\n",
              " 715: {'neg': 0.0, 'neu': 0.76, 'pos': 0.24, 'compound': 0.9001},\n",
              " 716: {'neg': 0.088, 'neu': 0.669, 'pos': 0.243, 'compound': 0.9403},\n",
              " 717: {'neg': 0.09, 'neu': 0.887, 'pos': 0.023, 'compound': -0.6607},\n",
              " 718: {'neg': 0.075, 'neu': 0.858, 'pos': 0.066, 'compound': -0.1045},\n",
              " 719: {'neg': 0.087, 'neu': 0.774, 'pos': 0.139, 'compound': 0.6675},\n",
              " 720: {'neg': 0.102, 'neu': 0.824, 'pos': 0.073, 'compound': -0.2617},\n",
              " 721: {'neg': 0.024, 'neu': 0.75, 'pos': 0.226, 'compound': 0.9318},\n",
              " 722: {'neg': 0.029, 'neu': 0.714, 'pos': 0.256, 'compound': 0.9449},\n",
              " 723: {'neg': 0.073, 'neu': 0.757, 'pos': 0.17, 'compound': 0.8395},\n",
              " 724: {'neg': 0.091, 'neu': 0.779, 'pos': 0.13, 'compound': 0.224},\n",
              " 725: {'neg': 0.0, 'neu': 0.738, 'pos': 0.262, 'compound': 0.6901},\n",
              " 726: {'neg': 0.028, 'neu': 0.891, 'pos': 0.081, 'compound': 0.7227},\n",
              " 727: {'neg': 0.04, 'neu': 0.577, 'pos': 0.383, 'compound': 0.9168},\n",
              " 728: {'neg': 0.0, 'neu': 0.864, 'pos': 0.136, 'compound': 0.3987},\n",
              " 729: {'neg': 0.0, 'neu': 0.617, 'pos': 0.383, 'compound': 0.9825},\n",
              " 730: {'neg': 0.0, 'neu': 0.915, 'pos': 0.085, 'compound': 0.6697},\n",
              " 731: {'neg': 0.1, 'neu': 0.709, 'pos': 0.191, 'compound': 0.6886},\n",
              " 732: {'neg': 0.0, 'neu': 0.805, 'pos': 0.195, 'compound': 0.9022},\n",
              " 733: {'neg': 0.109, 'neu': 0.785, 'pos': 0.106, 'compound': 0.2249},\n",
              " 734: {'neg': 0.0, 'neu': 0.442, 'pos': 0.558, 'compound': 0.9764},\n",
              " 735: {'neg': 0.054, 'neu': 0.828, 'pos': 0.118, 'compound': 0.5632},\n",
              " 736: {'neg': 0.022, 'neu': 0.88, 'pos': 0.098, 'compound': 0.9257},\n",
              " 737: {'neg': 0.176, 'neu': 0.685, 'pos': 0.139, 'compound': -0.4376},\n",
              " 738: {'neg': 0.083, 'neu': 0.822, 'pos': 0.095, 'compound': -0.409},\n",
              " 739: {'neg': 0.135, 'neu': 0.758, 'pos': 0.108, 'compound': -0.3625},\n",
              " 740: {'neg': 0.247, 'neu': 0.562, 'pos': 0.191, 'compound': -0.7067},\n",
              " 741: {'neg': 0.154, 'neu': 0.754, 'pos': 0.091, 'compound': -0.9047},\n",
              " 742: {'neg': 0.126, 'neu': 0.821, 'pos': 0.053, 'compound': -0.9321},\n",
              " 743: {'neg': 0.0, 'neu': 0.834, 'pos': 0.166, 'compound': 0.8745},\n",
              " 744: {'neg': 0.039, 'neu': 0.722, 'pos': 0.239, 'compound': 0.9113},\n",
              " 745: {'neg': 0.049, 'neu': 0.86, 'pos': 0.091, 'compound': 0.8617},\n",
              " 746: {'neg': 0.016, 'neu': 0.858, 'pos': 0.126, 'compound': 0.9909},\n",
              " 747: {'neg': 0.092, 'neu': 0.838, 'pos': 0.07, 'compound': -0.2047},\n",
              " 748: {'neg': 0.012, 'neu': 0.875, 'pos': 0.113, 'compound': 0.9834},\n",
              " 749: {'neg': 0.22, 'neu': 0.78, 'pos': 0.0, 'compound': -0.7351},\n",
              " 750: {'neg': 0.152, 'neu': 0.664, 'pos': 0.185, 'compound': 0.1779},\n",
              " 751: {'neg': 0.354, 'neu': 0.566, 'pos': 0.079, 'compound': -0.8067},\n",
              " 752: {'neg': 0.0, 'neu': 0.823, 'pos': 0.177, 'compound': 0.9916},\n",
              " 753: {'neg': 0.0, 'neu': 0.786, 'pos': 0.214, 'compound': 0.9807},\n",
              " 754: {'neg': 0.023, 'neu': 0.796, 'pos': 0.18, 'compound': 0.9527},\n",
              " 755: {'neg': 0.0, 'neu': 0.598, 'pos': 0.402, 'compound': 0.902},\n",
              " 756: {'neg': 0.0, 'neu': 0.794, 'pos': 0.206, 'compound': 0.9097},\n",
              " 757: {'neg': 0.0, 'neu': 0.69, 'pos': 0.31, 'compound': 0.8934},\n",
              " 758: {'neg': 0.0, 'neu': 0.699, 'pos': 0.301, 'compound': 0.7841},\n",
              " 759: {'neg': 0.0, 'neu': 0.842, 'pos': 0.158, 'compound': 0.7768},\n",
              " 760: {'neg': 0.216, 'neu': 0.727, 'pos': 0.056, 'compound': -0.8049},\n",
              " 761: {'neg': 0.038, 'neu': 0.789, 'pos': 0.173, 'compound': 0.9582},\n",
              " 762: {'neg': 0.14, 'neu': 0.663, 'pos': 0.197, 'compound': 0.617},\n",
              " 763: {'neg': 0.0, 'neu': 0.789, 'pos': 0.211, 'compound': 0.9001},\n",
              " 764: {'neg': 0.023, 'neu': 0.807, 'pos': 0.169, 'compound': 0.9011},\n",
              " 765: {'neg': 0.0, 'neu': 0.749, 'pos': 0.251, 'compound': 0.9688},\n",
              " 766: {'neg': 0.0, 'neu': 0.815, 'pos': 0.185, 'compound': 0.9601},\n",
              " 767: {'neg': 0.033, 'neu': 0.851, 'pos': 0.116, 'compound': 0.966},\n",
              " 768: {'neg': 0.0, 'neu': 0.63, 'pos': 0.37, 'compound': 0.875},\n",
              " 769: {'neg': 0.166, 'neu': 0.75, 'pos': 0.083, 'compound': -0.4574},\n",
              " 770: {'neg': 0.278, 'neu': 0.722, 'pos': 0.0, 'compound': -0.7112},\n",
              " 771: {'neg': 0.035, 'neu': 0.797, 'pos': 0.168, 'compound': 0.9861},\n",
              " 772: {'neg': 0.0, 'neu': 0.777, 'pos': 0.223, 'compound': 0.7755},\n",
              " 773: {'neg': 0.0, 'neu': 0.717, 'pos': 0.283, 'compound': 0.9595},\n",
              " 774: {'neg': 0.0, 'neu': 0.677, 'pos': 0.323, 'compound': 0.9531},\n",
              " 775: {'neg': 0.365, 'neu': 0.635, 'pos': 0.0, 'compound': -0.8481},\n",
              " 776: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0},\n",
              " 777: {'neg': 0.075, 'neu': 0.76, 'pos': 0.166, 'compound': 0.785},\n",
              " 778: {'neg': 0.0, 'neu': 0.782, 'pos': 0.218, 'compound': 0.8481},\n",
              " 779: {'neg': 0.047, 'neu': 0.815, 'pos': 0.139, 'compound': 0.9021},\n",
              " 780: {'neg': 0.0, 'neu': 0.786, 'pos': 0.214, 'compound': 0.7783},\n",
              " 781: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0},\n",
              " 782: {'neg': 0.17, 'neu': 0.779, 'pos': 0.051, 'compound': -0.7785},\n",
              " 783: {'neg': 0.041, 'neu': 0.867, 'pos': 0.092, 'compound': 0.9161},\n",
              " 784: {'neg': 0.029, 'neu': 0.744, 'pos': 0.227, 'compound': 0.962},\n",
              " 785: {'neg': 0.139, 'neu': 0.783, 'pos': 0.078, 'compound': -0.2351},\n",
              " 786: {'neg': 0.107, 'neu': 0.837, 'pos': 0.056, 'compound': -0.9315},\n",
              " 787: {'neg': 0.0, 'neu': 0.735, 'pos': 0.265, 'compound': 0.9598},\n",
              " 788: {'neg': 0.075, 'neu': 0.694, 'pos': 0.231, 'compound': 0.8345},\n",
              " 789: {'neg': 0.119, 'neu': 0.881, 'pos': 0.0, 'compound': -0.4767},\n",
              " 790: {'neg': 0.078, 'neu': 0.77, 'pos': 0.151, 'compound': 0.769},\n",
              " 791: {'neg': 0.037, 'neu': 0.782, 'pos': 0.181, 'compound': 0.8807},\n",
              " 792: {'neg': 0.027, 'neu': 0.851, 'pos': 0.122, 'compound': 0.8841},\n",
              " 793: {'neg': 0.0, 'neu': 0.856, 'pos': 0.144, 'compound': 0.6369},\n",
              " 794: {'neg': 0.022, 'neu': 0.747, 'pos': 0.23, 'compound': 0.8879},\n",
              " 795: {'neg': 0.0, 'neu': 0.882, 'pos': 0.118, 'compound': 0.9091},\n",
              " 796: {'neg': 0.0, 'neu': 0.879, 'pos': 0.121, 'compound': 0.4404},\n",
              " 797: {'neg': 0.0, 'neu': 0.849, 'pos': 0.151, 'compound': 0.5848},\n",
              " 798: {'neg': 0.042, 'neu': 0.799, 'pos': 0.159, 'compound': 0.9371},\n",
              " 799: {'neg': 0.064, 'neu': 0.716, 'pos': 0.22, 'compound': 0.785},\n",
              " 800: {'neg': 0.0, 'neu': 0.526, 'pos': 0.474, 'compound': 0.9577},\n",
              " 801: {'neg': 0.0, 'neu': 0.711, 'pos': 0.289, 'compound': 0.899},\n",
              " 802: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0},\n",
              " 803: {'neg': 0.143, 'neu': 0.717, 'pos': 0.14, 'compound': 0.2278},\n",
              " 804: {'neg': 0.034, 'neu': 0.833, 'pos': 0.134, 'compound': 0.8289},\n",
              " 805: {'neg': 0.0, 'neu': 0.79, 'pos': 0.21, 'compound': 0.8422},\n",
              " 806: {'neg': 0.0, 'neu': 0.703, 'pos': 0.297, 'compound': 0.9366},\n",
              " 807: {'neg': 0.0, 'neu': 0.697, 'pos': 0.303, 'compound': 0.9298},\n",
              " 808: {'neg': 0.0, 'neu': 0.875, 'pos': 0.125, 'compound': 0.6124},\n",
              " 809: {'neg': 0.0, 'neu': 0.731, 'pos': 0.269, 'compound': 0.7088},\n",
              " 810: {'neg': 0.0, 'neu': 0.79, 'pos': 0.21, 'compound': 0.8271},\n",
              " 811: {'neg': 0.025, 'neu': 0.735, 'pos': 0.24, 'compound': 0.9505},\n",
              " 812: {'neg': 0.0, 'neu': 0.798, 'pos': 0.202, 'compound': 0.5859},\n",
              " 813: {'neg': 0.0, 'neu': 0.832, 'pos': 0.168, 'compound': 0.7537},\n",
              " 814: {'neg': 0.043, 'neu': 0.925, 'pos': 0.033, 'compound': -0.1502},\n",
              " 815: {'neg': 0.018, 'neu': 0.917, 'pos': 0.065, 'compound': 0.5859},\n",
              " 816: {'neg': 0.0, 'neu': 0.721, 'pos': 0.279, 'compound': 0.8655},\n",
              " 817: {'neg': 0.124, 'neu': 0.705, 'pos': 0.171, 'compound': 0.8427},\n",
              " 818: {'neg': 0.092, 'neu': 0.717, 'pos': 0.191, 'compound': 0.4404},\n",
              " 819: {'neg': 0.071, 'neu': 0.802, 'pos': 0.126, 'compound': 0.7569},\n",
              " 820: {'neg': 0.0, 'neu': 0.932, 'pos': 0.068, 'compound': 0.7712},\n",
              " 821: {'neg': 0.045, 'neu': 0.782, 'pos': 0.173, 'compound': 0.8791},\n",
              " 822: {'neg': 0.0, 'neu': 0.782, 'pos': 0.218, 'compound': 0.9198},\n",
              " 823: {'neg': 0.064, 'neu': 0.842, 'pos': 0.094, 'compound': 0.4005},\n",
              " 824: {'neg': 0.05, 'neu': 0.86, 'pos': 0.09, 'compound': 0.8774},\n",
              " 825: {'neg': 0.06, 'neu': 0.378, 'pos': 0.562, 'compound': 0.9634},\n",
              " 826: {'neg': 0.0, 'neu': 0.645, 'pos': 0.355, 'compound': 0.968},\n",
              " 827: {'neg': 0.119, 'neu': 0.628, 'pos': 0.253, 'compound': 0.7906},\n",
              " 828: {'neg': 0.0, 'neu': 0.895, 'pos': 0.105, 'compound': 0.7088},\n",
              " 829: {'neg': 0.0, 'neu': 0.947, 'pos': 0.053, 'compound': 0.2382},\n",
              " 830: {'neg': 0.0, 'neu': 0.779, 'pos': 0.221, 'compound': 0.945},\n",
              " 831: {'neg': 0.0, 'neu': 0.684, 'pos': 0.316, 'compound': 0.8633},\n",
              " 832: {'neg': 0.085, 'neu': 0.864, 'pos': 0.051, 'compound': -0.2755},\n",
              " 833: {'neg': 0.025, 'neu': 0.881, 'pos': 0.094, 'compound': 0.7184},\n",
              " 834: {'neg': 0.072, 'neu': 0.804, 'pos': 0.124, 'compound': 0.8118},\n",
              " 835: {'neg': 0.0, 'neu': 0.805, 'pos': 0.195, 'compound': 0.9074},\n",
              " 836: {'neg': 0.04, 'neu': 0.765, 'pos': 0.195, 'compound': 0.836},\n",
              " 837: {'neg': 0.197, 'neu': 0.769, 'pos': 0.033, 'compound': -0.8353},\n",
              " 838: {'neg': 0.0, 'neu': 0.811, 'pos': 0.189, 'compound': 0.7845},\n",
              " 839: {'neg': 0.0, 'neu': 0.791, 'pos': 0.209, 'compound': 0.9622},\n",
              " 840: {'neg': 0.079, 'neu': 0.684, 'pos': 0.237, 'compound': 0.9398},\n",
              " 841: {'neg': 0.038, 'neu': 0.722, 'pos': 0.24, 'compound': 0.9648},\n",
              " 842: {'neg': 0.0, 'neu': 0.736, 'pos': 0.264, 'compound': 0.7845},\n",
              " 843: {'neg': 0.0, 'neu': 0.777, 'pos': 0.223, 'compound': 0.6494},\n",
              " 844: {'neg': 0.023, 'neu': 0.904, 'pos': 0.073, 'compound': 0.6242},\n",
              " 845: {'neg': 0.0, 'neu': 0.785, 'pos': 0.215, 'compound': 0.9419},\n",
              " 846: {'neg': 0.0, 'neu': 0.667, 'pos': 0.333, 'compound': 0.8999},\n",
              " 847: {'neg': 0.053, 'neu': 0.791, 'pos': 0.156, 'compound': 0.6486},\n",
              " 848: {'neg': 0.0, 'neu': 0.879, 'pos': 0.121, 'compound': 0.4404},\n",
              " 849: {'neg': 0.0, 'neu': 0.844, 'pos': 0.156, 'compound': 0.8588},\n",
              " 850: {'neg': 0.0, 'neu': 0.734, 'pos': 0.266, 'compound': 0.8264},\n",
              " 851: {'neg': 0.049, 'neu': 0.734, 'pos': 0.218, 'compound': 0.9834},\n",
              " 852: {'neg': 0.028, 'neu': 0.852, 'pos': 0.12, 'compound': 0.9403},\n",
              " 853: {'neg': 0.0, 'neu': 0.846, 'pos': 0.154, 'compound': 0.8442},\n",
              " 854: {'neg': 0.0, 'neu': 0.613, 'pos': 0.387, 'compound': 0.8922},\n",
              " 855: {'neg': 0.0, 'neu': 0.764, 'pos': 0.236, 'compound': 0.8591},\n",
              " 856: {'neg': 0.0, 'neu': 0.78, 'pos': 0.22, 'compound': 0.8412},\n",
              " 857: {'neg': 0.051, 'neu': 0.818, 'pos': 0.132, 'compound': 0.7738},\n",
              " 858: {'neg': 0.0, 'neu': 0.75, 'pos': 0.25, 'compound': 0.8748},\n",
              " 859: {'neg': 0.0, 'neu': 0.603, 'pos': 0.397, 'compound': 0.9413},\n",
              " 860: {'neg': 0.011, 'neu': 0.801, 'pos': 0.187, 'compound': 0.9964},\n",
              " 861: {'neg': 0.033, 'neu': 0.839, 'pos': 0.128, 'compound': 0.6486},\n",
              " 862: {'neg': 0.035, 'neu': 0.551, 'pos': 0.415, 'compound': 0.978},\n",
              " 863: {'neg': 0.08, 'neu': 0.812, 'pos': 0.108, 'compound': 0.8955},\n",
              " 864: {'neg': 0.0, 'neu': 0.543, 'pos': 0.457, 'compound': 0.9118},\n",
              " 865: {'neg': 0.0, 'neu': 0.86, 'pos': 0.14, 'compound': 0.4754},\n",
              " 866: {'neg': 0.042, 'neu': 0.797, 'pos': 0.161, 'compound': 0.9943},\n",
              " 867: {'neg': 0.071, 'neu': 0.673, 'pos': 0.256, 'compound': 0.9795},\n",
              " 868: {'neg': 0.078, 'neu': 0.851, 'pos': 0.071, 'compound': -0.101},\n",
              " 869: {'neg': 0.09, 'neu': 0.586, 'pos': 0.324, 'compound': 0.8315},\n",
              " 870: {'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'compound': 0.7174},\n",
              " 871: {'neg': 0.128, 'neu': 0.717, 'pos': 0.155, 'compound': 0.1759},\n",
              " 872: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0},\n",
              " 873: {'neg': 0.072, 'neu': 0.882, 'pos': 0.046, 'compound': -0.7391},\n",
              " 874: {'neg': 0.0, 'neu': 0.697, 'pos': 0.303, 'compound': 0.9794},\n",
              " 875: {'neg': 0.155, 'neu': 0.658, 'pos': 0.187, 'compound': 0.296},\n",
              " 876: {'neg': 0.056, 'neu': 0.809, 'pos': 0.135, 'compound': 0.9227},\n",
              " 877: {'neg': 0.0, 'neu': 0.672, 'pos': 0.328, 'compound': 0.8516},\n",
              " 878: {'neg': 0.152, 'neu': 0.637, 'pos': 0.212, 'compound': 0.832},\n",
              " 879: {'neg': 0.023, 'neu': 0.831, 'pos': 0.145, 'compound': 0.9339},\n",
              " 880: {'neg': 0.073, 'neu': 0.868, 'pos': 0.059, 'compound': 0.0867},\n",
              " 881: {'neg': 0.012, 'neu': 0.866, 'pos': 0.122, 'compound': 0.9383},\n",
              " 882: {'neg': 0.043, 'neu': 0.824, 'pos': 0.133, 'compound': 0.9547},\n",
              " 883: {'neg': 0.07, 'neu': 0.568, 'pos': 0.362, 'compound': 0.8955},\n",
              " 884: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0},\n",
              " 885: {'neg': 0.0, 'neu': 0.644, 'pos': 0.356, 'compound': 0.8915},\n",
              " 886: {'neg': 0.02, 'neu': 0.686, 'pos': 0.294, 'compound': 0.969},\n",
              " 887: {'neg': 0.029, 'neu': 0.785, 'pos': 0.186, 'compound': 0.9429},\n",
              " 888: {'neg': 0.0, 'neu': 0.689, 'pos': 0.311, 'compound': 0.9695},\n",
              " 889: {'neg': 0.0, 'neu': 0.815, 'pos': 0.185, 'compound': 0.7717},\n",
              " 890: {'neg': 0.0, 'neu': 0.833, 'pos': 0.167, 'compound': 0.802},\n",
              " 891: {'neg': 0.0, 'neu': 0.747, 'pos': 0.253, 'compound': 0.6982},\n",
              " 892: {'neg': 0.129, 'neu': 0.773, 'pos': 0.097, 'compound': -0.7771},\n",
              " 893: {'neg': 0.0, 'neu': 0.883, 'pos': 0.117, 'compound': 0.8349},\n",
              " 894: {'neg': 0.0, 'neu': 0.786, 'pos': 0.214, 'compound': 0.9098},\n",
              " 895: {'neg': 0.0, 'neu': 0.777, 'pos': 0.223, 'compound': 0.9498},\n",
              " 896: {'neg': 0.009, 'neu': 0.775, 'pos': 0.215, 'compound': 0.9956},\n",
              " 897: {'neg': 0.11, 'neu': 0.712, 'pos': 0.178, 'compound': 0.6361},\n",
              " 898: {'neg': 0.0, 'neu': 0.563, 'pos': 0.437, 'compound': 0.9747},\n",
              " 899: {'neg': 0.089, 'neu': 0.895, 'pos': 0.017, 'compound': -0.7795},\n",
              " 900: {'neg': 0.013, 'neu': 0.783, 'pos': 0.205, 'compound': 0.9963},\n",
              " 901: {'neg': 0.012, 'neu': 0.741, 'pos': 0.247, 'compound': 0.9946},\n",
              " 902: {'neg': 0.0, 'neu': 0.836, 'pos': 0.164, 'compound': 0.9738},\n",
              " 903: {'neg': 0.045, 'neu': 0.918, 'pos': 0.037, 'compound': -0.126},\n",
              " 904: {'neg': 0.0, 'neu': 0.78, 'pos': 0.22, 'compound': 0.9891},\n",
              " 905: {'neg': 0.0, 'neu': 0.86, 'pos': 0.14, 'compound': 0.7066},\n",
              " 906: {'neg': 0.0, 'neu': 0.725, 'pos': 0.275, 'compound': 0.802},\n",
              " 907: {'neg': 0.085, 'neu': 0.714, 'pos': 0.201, 'compound': 0.913},\n",
              " 908: {'neg': 0.018, 'neu': 0.805, 'pos': 0.177, 'compound': 0.9832},\n",
              " 909: {'neg': 0.0, 'neu': 0.601, 'pos': 0.399, 'compound': 0.9856},\n",
              " 910: {'neg': 0.162, 'neu': 0.646, 'pos': 0.192, 'compound': 0.5719},\n",
              " 911: {'neg': 0.145, 'neu': 0.772, 'pos': 0.083, 'compound': -0.4588},\n",
              " 912: {'neg': 0.031, 'neu': 0.808, 'pos': 0.161, 'compound': 0.9702},\n",
              " 913: {'neg': 0.062, 'neu': 0.705, 'pos': 0.233, 'compound': 0.886},\n",
              " 914: {'neg': 0.0, 'neu': 0.8, 'pos': 0.2, 'compound': 0.6696},\n",
              " 915: {'neg': 0.0, 'neu': 0.925, 'pos': 0.075, 'compound': 0.2732},\n",
              " 916: {'neg': 0.11, 'neu': 0.638, 'pos': 0.252, 'compound': 0.8374},\n",
              " 917: {'neg': 0.094, 'neu': 0.655, 'pos': 0.252, 'compound': 0.989},\n",
              " 918: {'neg': 0.0, 'neu': 0.682, 'pos': 0.318, 'compound': 0.882},\n",
              " 919: {'neg': 0.0, 'neu': 0.742, 'pos': 0.258, 'compound': 0.8885},\n",
              " 920: {'neg': 0.0, 'neu': 0.607, 'pos': 0.393, 'compound': 0.9001},\n",
              " 921: {'neg': 0.091, 'neu': 0.769, 'pos': 0.139, 'compound': 0.7094},\n",
              " 922: {'neg': 0.0, 'neu': 0.716, 'pos': 0.284, 'compound': 0.8537},\n",
              " 923: {'neg': 0.088, 'neu': 0.67, 'pos': 0.242, 'compound': 0.9054},\n",
              " 924: {'neg': 0.06, 'neu': 0.814, 'pos': 0.126, 'compound': 0.5899},\n",
              " 925: {'neg': 0.066, 'neu': 0.631, 'pos': 0.303, 'compound': 0.836},\n",
              " 926: {'neg': 0.103, 'neu': 0.805, 'pos': 0.092, 'compound': -0.1531},\n",
              " 927: {'neg': 0.065, 'neu': 0.852, 'pos': 0.083, 'compound': -0.031},\n",
              " 928: {'neg': 0.036, 'neu': 0.794, 'pos': 0.171, 'compound': 0.766},\n",
              " 929: {'neg': 0.0, 'neu': 0.796, 'pos': 0.204, 'compound': 0.9633},\n",
              " 930: {'neg': 0.037, 'neu': 0.797, 'pos': 0.167, 'compound': 0.9609},\n",
              " 931: {'neg': 0.0, 'neu': 0.668, 'pos': 0.332, 'compound': 0.9175},\n",
              " 932: {'neg': 0.023, 'neu': 0.698, 'pos': 0.279, 'compound': 0.9945},\n",
              " 933: {'neg': 0.0, 'neu': 0.858, 'pos': 0.142, 'compound': 0.7537},\n",
              " 934: {'neg': 0.041, 'neu': 0.786, 'pos': 0.172, 'compound': 0.9235},\n",
              " 935: {'neg': 0.034, 'neu': 0.534, 'pos': 0.432, 'compound': 0.9666},\n",
              " 936: {'neg': 0.047, 'neu': 0.833, 'pos': 0.121, 'compound': 0.8633},\n",
              " 937: {'neg': 0.0, 'neu': 0.777, 'pos': 0.223, 'compound': 0.8225},\n",
              " 938: {'neg': 0.0, 'neu': 0.642, 'pos': 0.358, 'compound': 0.9055},\n",
              " 939: {'neg': 0.056, 'neu': 0.776, 'pos': 0.168, 'compound': 0.7449},\n",
              " 940: {'neg': 0.0, 'neu': 0.648, 'pos': 0.352, 'compound': 0.9732},\n",
              " 941: {'neg': 0.344, 'neu': 0.62, 'pos': 0.037, 'compound': -0.9419},\n",
              " 942: {'neg': 0.124, 'neu': 0.698, 'pos': 0.177, 'compound': 0.8038},\n",
              " 943: {'neg': 0.0, 'neu': 0.625, 'pos': 0.375, 'compound': 0.9781},\n",
              " 944: {'neg': 0.018, 'neu': 0.922, 'pos': 0.06, 'compound': 0.7089},\n",
              " 945: {'neg': 0.0, 'neu': 0.667, 'pos': 0.333, 'compound': 0.8591},\n",
              " 946: {'neg': 0.0, 'neu': 0.759, 'pos': 0.241, 'compound': 0.872},\n",
              " 947: {'neg': 0.092, 'neu': 0.774, 'pos': 0.135, 'compound': 0.685},\n",
              " 948: {'neg': 0.0, 'neu': 0.814, 'pos': 0.186, 'compound': 0.9195},\n",
              " 949: {'neg': 0.062, 'neu': 0.397, 'pos': 0.541, 'compound': 0.9509},\n",
              " 950: {'neg': 0.158, 'neu': 0.658, 'pos': 0.184, 'compound': 0.1012},\n",
              " 951: {'neg': 0.0, 'neu': 0.602, 'pos': 0.398, 'compound': 0.9109},\n",
              " 952: {'neg': 0.0, 'neu': 0.647, 'pos': 0.353, 'compound': 0.9808},\n",
              " 953: {'neg': 0.0, 'neu': 0.642, 'pos': 0.358, 'compound': 0.9668},\n",
              " 954: {'neg': 0.0, 'neu': 0.807, 'pos': 0.193, 'compound': 0.9416},\n",
              " 955: {'neg': 0.023, 'neu': 0.829, 'pos': 0.148, 'compound': 0.8623},\n",
              " 956: {'neg': 0.0, 'neu': 0.85, 'pos': 0.15, 'compound': 0.5719},\n",
              " 957: {'neg': 0.041, 'neu': 0.663, 'pos': 0.296, 'compound': 0.9103},\n",
              " 958: {'neg': 0.103, 'neu': 0.758, 'pos': 0.139, 'compound': 0.4274},\n",
              " 959: {'neg': 0.113, 'neu': 0.887, 'pos': 0.0, 'compound': -0.6249},\n",
              " 960: {'neg': 0.066, 'neu': 0.913, 'pos': 0.02, 'compound': -0.7129},\n",
              " 961: {'neg': 0.0, 'neu': 0.72, 'pos': 0.28, 'compound': 0.7906},\n",
              " 962: {'neg': 0.058, 'neu': 0.942, 'pos': 0.0, 'compound': -0.3875},\n",
              " 963: {'neg': 0.153, 'neu': 0.677, 'pos': 0.169, 'compound': 0.2263},\n",
              " 964: {'neg': 0.022, 'neu': 0.928, 'pos': 0.05, 'compound': 0.5267},\n",
              " 965: {'neg': 0.039, 'neu': 0.784, 'pos': 0.177, 'compound': 0.8705},\n",
              " 966: {'neg': 0.0, 'neu': 0.853, 'pos': 0.147, 'compound': 0.8625},\n",
              " 967: {'neg': 0.0, 'neu': 0.932, 'pos': 0.068, 'compound': 0.6463},\n",
              " 968: {'neg': 0.162, 'neu': 0.796, 'pos': 0.042, 'compound': -0.943},\n",
              " 969: {'neg': 0.0, 'neu': 0.736, 'pos': 0.264, 'compound': 0.8011},\n",
              " 970: {'neg': 0.0, 'neu': 0.859, 'pos': 0.141, 'compound': 0.9651},\n",
              " 971: {'neg': 0.04, 'neu': 0.766, 'pos': 0.194, 'compound': 0.7529},\n",
              " 972: {'neg': 0.0, 'neu': 0.666, 'pos': 0.334, 'compound': 0.9003},\n",
              " 973: {'neg': 0.122, 'neu': 0.649, 'pos': 0.229, 'compound': 0.8013},\n",
              " 974: {'neg': 0.026, 'neu': 0.806, 'pos': 0.168, 'compound': 0.9875},\n",
              " 975: {'neg': 0.0, 'neu': 0.741, 'pos': 0.259, 'compound': 0.9829},\n",
              " 976: {'neg': 0.0, 'neu': 0.846, 'pos': 0.154, 'compound': 0.6369},\n",
              " 977: {'neg': 0.056, 'neu': 0.876, 'pos': 0.069, 'compound': -0.0676},\n",
              " 978: {'neg': 0.036, 'neu': 0.836, 'pos': 0.129, 'compound': 0.9554},\n",
              " 979: {'neg': 0.089, 'neu': 0.791, 'pos': 0.12, 'compound': 0.5264},\n",
              " 980: {'neg': 0.0, 'neu': 0.661, 'pos': 0.339, 'compound': 0.9233},\n",
              " 981: {'neg': 0.0, 'neu': 0.484, 'pos': 0.516, 'compound': 0.9595},\n",
              " 982: {'neg': 0.0, 'neu': 0.767, 'pos': 0.233, 'compound': 0.9552},\n",
              " 983: {'neg': 0.0, 'neu': 0.883, 'pos': 0.117, 'compound': 0.6249},\n",
              " 984: {'neg': 0.0, 'neu': 0.781, 'pos': 0.219, 'compound': 0.9663},\n",
              " 985: {'neg': 0.0, 'neu': 0.846, 'pos': 0.154, 'compound': 0.7181},\n",
              " 986: {'neg': 0.022, 'neu': 0.805, 'pos': 0.172, 'compound': 0.9401},\n",
              " 987: {'neg': 0.059, 'neu': 0.778, 'pos': 0.163, 'compound': 0.5984},\n",
              " 988: {'neg': 0.0, 'neu': 0.805, 'pos': 0.195, 'compound': 0.902},\n",
              " 989: {'neg': 0.0, 'neu': 0.785, 'pos': 0.215, 'compound': 0.9586},\n",
              " 990: {'neg': 0.0, 'neu': 0.674, 'pos': 0.326, 'compound': 0.9631},\n",
              " 991: {'neg': 0.032, 'neu': 0.667, 'pos': 0.301, 'compound': 0.973},\n",
              " 992: {'neg': 0.0, 'neu': 0.633, 'pos': 0.367, 'compound': 0.9749},\n",
              " 993: {'neg': 0.0, 'neu': 0.662, 'pos': 0.338, 'compound': 0.9636},\n",
              " 994: {'neg': 0.0, 'neu': 0.886, 'pos': 0.114, 'compound': 0.8858},\n",
              " 995: {'neg': 0.0, 'neu': 0.828, 'pos': 0.172, 'compound': 0.7552},\n",
              " 996: {'neg': 0.026, 'neu': 0.721, 'pos': 0.253, 'compound': 0.9788},\n",
              " 997: {'neg': 0.0, 'neu': 0.786, 'pos': 0.214, 'compound': 0.9309},\n",
              " 998: {'neg': 0.0, 'neu': 0.673, 'pos': 0.327, 'compound': 0.9634},\n",
              " 999: {'neg': 0.063, 'neu': 0.874, 'pos': 0.062, 'compound': -0.0129},\n",
              " 1000: {'neg': 0.027, 'neu': 0.939, 'pos': 0.034, 'compound': -0.1027},\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "vader_scores()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "90dd473d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "90dd473d",
        "outputId": "35cd6af5-e9bd-4fcc-9b6d-aa5dff294256"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as \"Jumbo\".    Vader score :  {'neg': 0.138, 'neu': 0.862, 'pos': 0.0, 'compound': -0.5664}\n"
          ]
        }
      ],
      "source": [
        "#See on an example\n",
        "print(df.Text[1],\"   Vader score : \",analyser_object.polarity_scores(df.Text[1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If compound is superior to 0.05 its a positive review in general and if its inferior to -0.05 its negative in general ."
      ],
      "metadata": {
        "id": "VSQOMvUFXzsb"
      },
      "id": "VSQOMvUFXzsb"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "74dc3203",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "45b907c9cb694e71af99af5cd57a4719",
            "d0722dcc989549868167d91b1c10eac8",
            "73398898117b434e9b3d7765fab45ce6",
            "2d102091b92c4f0fa506c19821ba8e9c",
            "56c5747e3b5843cc9d3ff9d399736f28",
            "8bc65f1d2275469c90faf05517600b85",
            "3bccb780e47a4e54b085996ae398c916",
            "2c61e876878f42aab103d93d01c3648b",
            "3f16bf364ba74055a1b3759005443e3d",
            "da9fc0a9878f4fe991c70a4afcdcf6dd",
            "aac3707bd31746da9f328917ce1fff9d"
          ]
        },
        "id": "74dc3203",
        "outputId": "fa5f3126-8483-4bee-cea1-781066f3cb43"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/18000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45b907c9cb694e71af99af5cd57a4719"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "vaders_df=pd.DataFrame(vader_scores()).T\n",
        "vaders_df=vaders_df.reset_index().rename(columns={'index': 'Id'})\n",
        "vaders_df=vaders_df.merge(df,how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4c23e380",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "4c23e380",
        "outputId": "3145a467-9219-4047-851c-629af94c4c0b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Id    neg    neu    pos  compound  Score  \\\n",
              "0          1  0.000  0.695  0.305    0.9441      5   \n",
              "1          2  0.138  0.862  0.000   -0.5664      1   \n",
              "2          3  0.091  0.754  0.155    0.8265      4   \n",
              "3          4  0.000  1.000  0.000    0.0000      2   \n",
              "4          5  0.000  0.552  0.448    0.9468      5   \n",
              "...      ...    ...    ...    ...       ...    ...   \n",
              "17995  17996  0.000  0.847  0.153    0.7145      4   \n",
              "17996  17997  0.061  0.847  0.092    0.6757      3   \n",
              "17997  17998  0.000  0.850  0.150    0.9085      5   \n",
              "17998  17999  0.000  0.680  0.320    0.9899      5   \n",
              "17999  18000  0.000  0.814  0.186    0.9746      5   \n",
              "\n",
              "                                        Summary  \\\n",
              "0                         Good Quality Dog Food   \n",
              "1                             Not as Advertised   \n",
              "2                         \"Delight\" says it all   \n",
              "3                                Cough Medicine   \n",
              "4                                   Great taffy   \n",
              "...                                         ...   \n",
              "17995        peanut butter without all the fat?   \n",
              "17996                  Good..but price is AWFUL   \n",
              "17997  Great stuff but much cheaper @ netrition   \n",
              "17998                 a healthier peanut butter   \n",
              "17999                                       PB2   \n",
              "\n",
              "                                                    Text  \n",
              "0      I have bought several of the Vitality canned d...  \n",
              "1      Product arrived labeled as Jumbo Salted Peanut...  \n",
              "2      This is a confection that has been around a fe...  \n",
              "3      If you are looking for the secret ingredient i...  \n",
              "4      Great taffy at a great price.  There was a wid...  \n",
              "...                                                  ...  \n",
              "17995  I don't think this tastes more than OK when yo...  \n",
              "17996  It's a relatively smaller jar that you get..ab...  \n",
              "17997  I love this peanut butter. It has 25% of the c...  \n",
              "17998  I have been using Bell Plantation for 3 years ...  \n",
              "17999  I love this product.  If you love PB but never...  \n",
              "\n",
              "[18000 rows x 8 columns]"
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              "      <td>0.754</td>\n",
              "      <td>0.155</td>\n",
              "      <td>0.8265</td>\n",
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              "      <td>\"Delight\" says it all</td>\n",
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              "      <td>0.448</td>\n",
              "      <td>0.9468</td>\n",
              "      <td>5</td>\n",
              "      <td>Great taffy</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.847</td>\n",
              "      <td>0.153</td>\n",
              "      <td>0.7145</td>\n",
              "      <td>4</td>\n",
              "      <td>peanut butter without all the fat?</td>\n",
              "      <td>I don't think this tastes more than OK when yo...</td>\n",
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              "      <th>17996</th>\n",
              "      <td>17997</td>\n",
              "      <td>0.061</td>\n",
              "      <td>0.847</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.6757</td>\n",
              "      <td>3</td>\n",
              "      <td>Good..but price is AWFUL</td>\n",
              "      <td>It's a relatively smaller jar that you get..ab...</td>\n",
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              "      <td>0.850</td>\n",
              "      <td>0.150</td>\n",
              "      <td>0.9085</td>\n",
              "      <td>5</td>\n",
              "      <td>Great stuff but much cheaper @ netrition</td>\n",
              "      <td>I love this peanut butter. It has 25% of the c...</td>\n",
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              "      <td>5</td>\n",
              "      <td>a healthier peanut butter</td>\n",
              "      <td>I have been using Bell Plantation for 3 years ...</td>\n",
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              "      <td>0.186</td>\n",
              "      <td>0.9746</td>\n",
              "      <td>5</td>\n",
              "      <td>PB2</td>\n",
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              "<p>18000 rows × 8 columns</p>\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "vaders_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7931cc72",
      "metadata": {
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          "height": 308
        },
        "id": "7931cc72",
        "outputId": "26cc5d40-fe33-4959-bfcf-09e519fbeb14"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "#Plot for vader sentiment\n",
        "fig, axs = plt.subplots(1, 4, figsize=(15, 4))\n",
        "sns.barplot(data=vaders_df, x='Score', y='pos', ax=axs[0])\n",
        "sns.barplot(data=vaders_df, x='Score', y='neu', ax=axs[1])\n",
        "sns.barplot(data=vaders_df, x='Score', y='neg', ax=axs[2])\n",
        "sns.barplot(data=vaders_df,x='Score',y='compound',ax=axs[3])\n",
        "axs[0].set_title('Positive')\n",
        "axs[1].set_title('Neutral')\n",
        "axs[2].set_title('Negative')\n",
        "axs[3].set_title('Compound')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the sentiment distribution\n",
        "sentiment_distribution = vaders_df['compound'].value_counts()\n",
        "plt.bar(sentiment_distribution.index, sentiment_distribution.values)\n",
        "plt.xlabel('Sentiment Score')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Sentiment Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7PhJkd4ZmXxg",
        "outputId": "c5602c95-ff4f-484d-ad95-87eb141619d7"
      },
      "id": "7PhJkd4ZmXxg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can observe that most of the reviews tend to be neutral or being positive ( here we dont know the degree of the positivity)"
      ],
      "metadata": {
        "id": "S4z0VjmCq-rL"
      },
      "id": "S4z0VjmCq-rL"
    },
    {
      "cell_type": "code",
      "source": [
        "# 2.5 Check the correlation between sentiment score (compound) and rating\n",
        "import numpy as np\n",
        "import scipy.stats as stats\n",
        "print(\"The correlation coefficient between sentiment score (compound) and rating is {0[0]: .4f} with a p-value of {0[1]: .4f}.\".format(stats.pearsonr(vaders_df['Score'], vaders_df[\"compound\"])))\n",
        "vaders_df.groupby(\"Score\").mean()[\"compound\"].plot(kind=\"bar\", figsize=(10, 6))\n",
        "plt.title(\"Avg. Sentiment Score (Compound) per Rating\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "sPwAwWELV0CA",
        "outputId": "f376c99c-e6ba-425e-88f7-c56087d54615"
      },
      "id": "sPwAwWELV0CA",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The correlation coefficient between sentiment score (compound) and rating is  0.4936 with a p-value of  0.0000.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-e4799bc9c186>:5: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  vaders_df.groupby(\"Score\").mean()[\"compound\"].plot(kind=\"bar\", figsize=(10, 6))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " There is a moderate positive correlation between the sentiment score and the rating. Higher sentiment scores tend to be associated with higher ratings, and this relationship is statistically significant.The VADER model proves to be a good powerful tool for sentiment classification.But with pretrained model we will have more accurate classification."
      ],
      "metadata": {
        "id": "lNWWiHj9d_0r"
      },
      "id": "lNWWiHj9d_0r"
    },
    {
      "cell_type": "markdown",
      "id": "4WoZ6Bw8Jkzu",
      "metadata": {
        "id": "4WoZ6Bw8Jkzu"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "# **Le modele DistilBert**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "Tx4XsGfhTptc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tx4XsGfhTptc",
        "outputId": "855ccfb2-e815-4545-e12b-66e1bd497618"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m76.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m30.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m83.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m84.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.15.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "qL4g1Tz_U-nT",
      "metadata": {
        "id": "qL4g1Tz_U-nT"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification, AdamW\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "from tqdm import tqdm\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from nltk.corpus import stopwords\n",
        "import pickle\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "YwF2qGakO_rh",
      "metadata": {
        "id": "YwF2qGakO_rh"
      },
      "outputs": [],
      "source": [
        "#torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "FZhuJGB1V3J4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZhuJGB1V3J4",
        "outputId": "0b885184-e0d7-4abe-d95f-e151a014b38e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ca65549d",
      "metadata": {
        "id": "ca65549d"
      },
      "source": [
        "### splitting ,dataset,dataloader tensors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "IhFB9NNAVnJn",
      "metadata": {
        "id": "IhFB9NNAVnJn"
      },
      "outputs": [],
      "source": [
        "X= df['Text']\n",
        "y=df['Score']\n",
        "# Split the dataset into train, validation, and test sets\n",
        "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
        "train_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)\n",
        "\n",
        "X_train = train_data['Text'].values\n",
        "y_train = train_data['Score'].values\n",
        "\n",
        "X_val = val_data['Text'].values\n",
        "y_val = val_data['Score'].values\n",
        "\n",
        "X_test = test_data['Text'].values\n",
        "y_test = test_data['Score'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "FaapdQGMi5gj",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FaapdQGMi5gj",
        "outputId": "2b7cc85e-56e2-4647-ca15-5071bc970c68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    7294\n",
              "4    1626\n",
              "1    1022\n",
              "3     955\n",
              "2     623\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "pd.DataFrame(y_train).value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "RwIII8brVv9B",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
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            "2a8cb914210541a08edee855b788e62e",
            "9b6f87be1622407a802f30c85c665d62",
            "4425a92624884284a92550a32ac9762e",
            "eb19510960e0467297106e7732c0e2bd",
            "4e6bcf1329dd4d86be06756066a43beb",
            "5bf332524ce8422ab48e94985d45e31f",
            "038e89e9c5404cfdb7b4d45d1bcb0eec",
            "3c5b7abe68734874918f6330c69572d1",
            "6a42bdc4f092496fa270a92fa0f7399b",
            "c633c69a43ad4fdba80fab14c8bd7782",
            "166c2756f63f4958a7434411f37b4ff3"
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        },
        "id": "RwIII8brVv9B",
        "outputId": "9a157c18-3aae-4e57-e3c0-d9df2533db16"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f99b1a342306484dbd6112ba4e2db8a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7498832b18074dc58b669842636f0a0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b81ad7b35a0c4eff9e9f2dac78dcdcd6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4538cecefdb04d63a34f32bafc525197"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a8cb914210541a08edee855b788e62e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# Initialize the tokenizer and DistilBERT\n",
        "stop_words = set(stopwords.words('english'))\n",
        "num_classes = len(df['Score'].unique())\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased', do_lower_case=True, stop_words=stop_words)\n",
        "#model architecture\n",
        "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=num_classes)\n",
        "#print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "cLZBCZJMVyrJ",
      "metadata": {
        "id": "cLZBCZJMVyrJ"
      },
      "outputs": [],
      "source": [
        "# Tokenization\n",
        "train_encodings = tokenizer.batch_encode_plus(X_train.tolist(), padding=True, truncation=True, max_length=512, return_tensors='pt')\n",
        "val_encodings = tokenizer.batch_encode_plus(X_val.tolist(), padding=True, truncation=True, max_length=512, return_tensors='pt')\n",
        "test_encodings = tokenizer.batch_encode_plus(X_test.tolist(), padding=True, truncation=True, max_length=512, return_tensors='pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "Xov5Q4cgWGFc",
      "metadata": {
        "id": "Xov5Q4cgWGFc"
      },
      "outputs": [],
      "source": [
        "# Conversion in  pytorch tensors\n",
        "#Dataset\n",
        "#for tensors rating goes from 0 to 4\n",
        "train_dataset = TensorDataset(train_encodings['input_ids'], train_encodings['attention_mask'], torch.tensor(y_train - 1))\n",
        "val_dataset = TensorDataset(val_encodings['input_ids'], val_encodings['attention_mask'], torch.tensor((y_val - 1)))\n",
        "test_dataset = TensorDataset(test_encodings['input_ids'], test_encodings['attention_mask'], torch.tensor((y_test - 1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "ZXAKwKISWJNZ",
      "metadata": {
        "id": "ZXAKwKISWJNZ"
      },
      "outputs": [],
      "source": [
        "#Optimization in  mini-batch\n",
        "#Dataloaders\n",
        "batch_size = 16\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "SH9RGnQ88S51",
      "metadata": {
        "id": "SH9RGnQ88S51"
      },
      "source": [
        "The reason such a few epochs\n",
        "were applied in our model analysis was that we utilized the fine-tuning feature of the model; the model was welltrained, and only a few epochs were necessary for tuning.\n",
        "The number of epochs can be increased, however, it will give rise to overfitting problems as well as take more time for the model to train and with google collab its not fast."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00fb0a59",
      "metadata": {
        "id": "00fb0a59"
      },
      "source": [
        "### Fine-tuning and optimization of the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "WvwMCZ1jNOoc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvwMCZ1jNOoc",
        "outputId": "871fc211-13b3-4bfe-f803-bd0093a82d2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "Epochs:   0%|          | 0/2 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1 - Training F1-score: 0.6974, Accuracy: 0.6974\n",
            "Époque 1 - Perte d'entraînement : 0.8194295319004191\n",
            "Epoch 1 - Validation Loss: 0.7465\n",
            "Accuracy: 0.7219\n",
            "F1-score: 0.7219\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpochs:  50%|█████     | 1/2 [09:02<09:02, 542.70s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2 - Training F1-score: 0.7683, Accuracy: 0.7683\n",
            "Époque 2 - Perte d'entraînement : 0.6074371334165335\n",
            "Epoch 2 - Validation Loss: 0.7034\n",
            "Accuracy: 0.7493\n",
            "F1-score: 0.7493\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 2/2 [18:05<00:00, 542.62s/it]\n"
          ]
        }
      ],
      "source": [
        "#regularization with dropout\n",
        "# optimizer and loss function\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "#torch.autograd.set_detect_anomaly(True)\n",
        "model.dropout = nn.Dropout(0.5)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "# loop\n",
        "train_f1_scores = []\n",
        "train_accuracies = []\n",
        "val_f1_scores = []\n",
        "val_accuracies = []\n",
        "train_losses = []\n",
        "val_losses = []\n",
        "\n",
        "scheduler = ReduceLROnPlateau(optimizer, mode='max', factor=0.1, patience=2, verbose=True)\n",
        "best_val_f1 = 0.0\n",
        "early_stopping_counter = 0\n",
        "\n",
        "\n",
        "for epoch in tqdm(range(2), desc=\"Epochs\"):\n",
        "    model.train()\n",
        "    total_loss = 0\n",
        "    train_predictions = []\n",
        "    train_labels = []\n",
        "\n",
        "    #for batch in tqdm(train_loader, desc=\"Batches\", leave=False):\n",
        "    for batch in train_loader:\n",
        "        input_ids, attention_mask, labels = batch\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "\n",
        "        outputs = model(\n",
        "            input_ids = input_ids.to(device),\n",
        "            attention_mask = attention_mask.to(device),\n",
        "            labels = labels.to(device)\n",
        "        )\n",
        "\n",
        "        # loss function\n",
        "        loss = outputs.loss\n",
        "        total_loss += loss.item()\n",
        "        # backward and update weigh\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "\n",
        "        train_predictions.extend(torch.argmax(outputs.logits, dim=1).tolist())\n",
        "        train_labels.extend(labels.tolist())\n",
        "\n",
        "    # Calculate metrics for the training set\n",
        "\n",
        "    train_f1 = f1_score(train_labels, train_predictions, average='micro')\n",
        "    train_accuracy = accuracy_score(train_labels, train_predictions)\n",
        "    train_f1_scores.append(train_f1)\n",
        "    train_accuracies.append(train_accuracy)\n",
        "    print(f\"Epoch {epoch+1} - Training F1-score: {train_f1:.4f}, Accuracy: {train_accuracy:.4f}\")\n",
        "    avg_loss = total_loss / len(train_loader)\n",
        "    train_losses.append(avg_loss)\n",
        "    print(f\"Époque {epoch+1} - Perte d'entraînement : {avg_loss}\")\n",
        "\n",
        "\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "      predicted_labels = []\n",
        "      true_labels = []\n",
        "      val_loss = 0\n",
        "\n",
        "      for batch in val_loader:\n",
        "          input_ids, attention_mask, labels = batch\n",
        "          outputs = model(\n",
        "              input_ids = input_ids.to(device),\n",
        "              attention_mask = attention_mask.to(device),\n",
        "              labels=labels.to(device)\n",
        "          )\n",
        "\n",
        "          predicted_labels.extend(torch.argmax(outputs.logits, dim=1).tolist())\n",
        "          true_labels.extend(labels.tolist())\n",
        "          val_loss += outputs.loss.item()\n",
        "\n",
        "      avg_val_loss = val_loss / len(val_loader)\n",
        "      val_losses.append(avg_val_loss)\n",
        "    print(f\"Epoch {epoch+1} - Validation Loss: {avg_val_loss:.4f}\")\n",
        "\n",
        "    #Micro-average  when there are class imbalances\n",
        "    accuracy = accuracy_score(true_labels, predicted_labels)\n",
        "    f1 = f1_score(true_labels, predicted_labels, average='micro')\n",
        "    val_f1_scores.append(f1)\n",
        "    val_accuracies.append(accuracy)\n",
        "\n",
        "\n",
        "    print(f\"Accuracy: {accuracy:.4f}\")\n",
        "    print(f\"F1-score: {f1:.4f}\")\n",
        "\n",
        "    # Check if validation F1-score has improved\n",
        "    if f1 > best_val_f1:\n",
        "        best_val_f1 = f1\n",
        "        early_stopping_counter = 0\n",
        "        torch.save(model.state_dict(), 'best_model.pt')  # Save the best model\n",
        "    else:\n",
        "        early_stopping_counter += 1\n",
        "        if early_stopping_counter >= 3:  # Stop early if validation F1-score doesn't improve for a certain number of consecutive epochs\n",
        "            print(\"Early stopping triggered.\")\n",
        "            break\n",
        "\n",
        "    # Adjust the learning rate based on validation F1-score\n",
        "    scheduler.step(f1)\n"
      ]
    },
    {
      "cell_type": "raw",
      "id": "4744f675",
      "metadata": {
        "id": "4744f675"
      },
      "source": [
        "We try different numbers for the hyperparameters and epochs and those ones left seems to get the best result and avoiding overfitting.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "izG0Ok4r-fpx",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izG0Ok4r-fpx",
        "outputId": "c2bfb333-e4ea-4a6d-b120-12cf84e18374"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=5)\n",
        "model.load_state_dict(torch.load('best_model.pt'))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5inknFUhxXEU",
      "metadata": {
        "id": "5inknFUhxXEU"
      },
      "source": [
        "### Final evaluation  on the  unseen data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "3409804b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3409804b",
        "outputId": "8b97d7e3-b7d1-4210-8524-69c0d6fd7c90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 231   19   23    4   32]\n",
            " [  76   32   80    8   18]\n",
            " [  31   14  147   66   55]\n",
            " [  10    1   57  163  312]\n",
            " [  19    2   28   91 2081]]\n",
            "Accuracy: 0.7493\n",
            "Precision: 0.7372\n",
            "Recall: 0.7372\n",
            "F1-score: 0.7372\n"
          ]
        }
      ],
      "source": [
        "# Evaluation on the test set\n",
        "model.to(device)\n",
        "model.eval()\n",
        "test_predictions = []\n",
        "test_labels = []\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch in test_loader:\n",
        "        input_ids, attention_mask, labels = batch\n",
        "        outputs = model(input_ids=input_ids.to(device), attention_mask=attention_mask.to(device),labels = labels.to(device))\n",
        "        logits = outputs.logits\n",
        "        predictions = torch.argmax(logits, dim=1)\n",
        "\n",
        "        test_predictions.extend(predictions.tolist())\n",
        "        test_labels.extend(labels.tolist())\n",
        "    accuracy_score(test_labels, test_predictions)\n",
        "    precision = precision_score(test_labels, test_predictions, average='micro')\n",
        "    recall = recall_score(test_labels, test_predictions, average='micro')\n",
        "    f1 = f1_score(test_labels, test_predictions, average='micro')\n",
        "\n",
        "\n",
        "# Confusion matrix\n",
        "confusion_mat = confusion_matrix(test_labels, test_predictions)\n",
        "classification_rep = classification_report(test_labels, test_predictions)\n",
        "\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_mat)\n",
        "#print(\"Classification Report:\")\n",
        "#print(classification_rep)\n",
        "\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1-score: {f1:.4f}\")"
      ]
    },
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      "cell_type": "markdown",
      "id": "Z2ypoCnT9rKk",
      "metadata": {
        "id": "Z2ypoCnT9rKk"
      },
      "source": [
        "Our DISTILBERT model had an accuracy of 0.7493 and a F1-score of 0.7372 in predicting the testing group’s score based\n",
        "on the text. These results are very impressive given that the reviews were subjective and written by individuals."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f8d98c69",
      "metadata": {
        "id": "f8d98c69"
      },
      "source": [
        "###  Prediction on unseen review"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "6e3f20ba",
      "metadata": {
        "id": "6e3f20ba"
      },
      "outputs": [],
      "source": [
        "# Save the trained model\n",
        "\n",
        "model.eval()\n",
        "\n",
        "# Function to predict the rating of a new text input\n",
        "def predict_rating(text):\n",
        "    encoding = tokenizer.encode_plus(text, padding=True, truncation=True, max_length=128, return_tensors='pt')\n",
        "    input_ids = encoding['input_ids']\n",
        "    attention_mask = encoding['attention_mask']\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model(input_ids=input_ids.to(device), attention_mask=attention_mask.to(device))\n",
        "        logits = outputs.logits\n",
        "        predicted_rating = torch.argmax(logits, dim=1)\n",
        "\n",
        "    return predicted_rating.item()"
      ]
    },
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      "source": [],
      "metadata": {
        "id": "Z5naWfpTesl7"
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    },
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      "cell_type": "code",
      "execution_count": 25,
      "id": "tTwmXWG1V1HM",
      "metadata": {
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        },
        "id": "tTwmXWG1V1HM",
        "outputId": "ee4fe359-b506-457c-d632-0fe22321b0e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Really positive review\n"
          ]
        }
      ],
      "source": [
        "# Example usage of the predict_rating function\n",
        "new_text = \"Great price on a great gluten-free product!,We have paid over $5 a package in health food stores for this mix.  Buying via Amazon is both cheaper and easier thanks to the direct to the door shipping!  The mix works great in our bread machine and comes out perfect every time!  Our kids love it and usually eat half a loaf as soon as its done!\"\n",
        "\n",
        "predicted_rating = predict_rating(new_text)\n",
        "if predicted_rating ==4:\n",
        "  print( \"Really positive review\")\n",
        "if predicted_rating ==3:\n",
        "  print( \"positive review\")\n",
        "if predicted_rating ==2:\n",
        "  print( \"neutral review\")\n",
        "if predicted_rating ==1:\n",
        "  print( \"bad review \")\n",
        "if predicted_rating ==0:\n",
        "  print( \"Really bad review\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c8f77b1",
      "metadata": {
        "id": "9c8f77b1"
      },
      "source": [
        "### Pickle our model for futur use"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "Iy2RIYNnXJsi",
      "metadata": {
        "id": "Iy2RIYNnXJsi"
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      "outputs": [],
      "source": [
        "# Pickle the model\n",
        "with open('best_model.pkl', 'wb') as f:\n",
        "    pickle.dump(model, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "Eks4gBDlDYUt",
      "metadata": {
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      "source": [
        "# Load the pickled model\n",
        "with open('best_model.pkl', 'rb') as f:\n",
        "   model = pickle.load(f)"
      ]
    }
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