{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "KAl36Uxl39lO"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Load the Dataset\n",
        "# Replace the file path with the actual path to your downloaded CSV file\n",
        "data = pd.read_csv('/content/tcc_ceds_music.csv')  # Metadata about songs and user interactions\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "l5dar23O4AN_",
        "outputId": "c422b158-07c8-4ae1-e425-8fc2aec21c51"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0           artist_name            track_name  release_date genre  \\\n",
              "0           0                mukesh  mohabbat bhi jhoothi          1950   pop   \n",
              "1           4         frankie laine             i believe          1950   pop   \n",
              "2           6           johnnie ray                   cry          1950   pop   \n",
              "3          10           pérez prado              patricia          1950   pop   \n",
              "4          12  giorgos papadopoulos    apopse eida oneiro          1950   pop   \n",
              "\n",
              "                                              lyrics  len    dating  violence  \\\n",
              "0  hold time feel break feel untrue convince spea...   95  0.000598  0.063746   \n",
              "1  believe drop rain fall grow believe darkest ni...   51  0.035537  0.096777   \n",
              "2  sweetheart send letter goodbye secret feel bet...   24  0.002770  0.002770   \n",
              "3  kiss lips want stroll charm mambo chacha merin...   54  0.048249  0.001548   \n",
              "4  till darling till matter know till dream live ...   48  0.001350  0.001350   \n",
              "\n",
              "   world/life  ...   sadness  feelings  danceability  loudness  acousticness  \\\n",
              "0    0.000598  ...  0.380299  0.117175      0.357739  0.454119      0.997992   \n",
              "1    0.443435  ...  0.001284  0.001284      0.331745  0.647540      0.954819   \n",
              "2    0.002770  ...  0.002770  0.225422      0.456298  0.585288      0.840361   \n",
              "3    0.001548  ...  0.225889  0.001548      0.686992  0.744404      0.083935   \n",
              "4    0.417772  ...  0.068800  0.001350      0.291671  0.646489      0.975904   \n",
              "\n",
              "   instrumentalness   valence    energy       topic  age  \n",
              "0          0.901822  0.339448  0.137110     sadness  1.0  \n",
              "1          0.000002  0.325021  0.263240  world/life  1.0  \n",
              "2          0.000000  0.351814  0.139112       music  1.0  \n",
              "3          0.199393  0.775350  0.743736    romantic  1.0  \n",
              "4          0.000246  0.597073  0.394375    romantic  1.0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-27958fa8-f7ab-4e3c-99df-8de953f12d2e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>track_name</th>\n",
              "      <th>release_date</th>\n",
              "      <th>genre</th>\n",
              "      <th>lyrics</th>\n",
              "      <th>len</th>\n",
              "      <th>dating</th>\n",
              "      <th>violence</th>\n",
              "      <th>world/life</th>\n",
              "      <th>...</th>\n",
              "      <th>sadness</th>\n",
              "      <th>feelings</th>\n",
              "      <th>danceability</th>\n",
              "      <th>loudness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>valence</th>\n",
              "      <th>energy</th>\n",
              "      <th>topic</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>mukesh</td>\n",
              "      <td>mohabbat bhi jhoothi</td>\n",
              "      <td>1950</td>\n",
              "      <td>pop</td>\n",
              "      <td>hold time feel break feel untrue convince spea...</td>\n",
              "      <td>95</td>\n",
              "      <td>0.000598</td>\n",
              "      <td>0.063746</td>\n",
              "      <td>0.000598</td>\n",
              "      <td>...</td>\n",
              "      <td>0.380299</td>\n",
              "      <td>0.117175</td>\n",
              "      <td>0.357739</td>\n",
              "      <td>0.454119</td>\n",
              "      <td>0.997992</td>\n",
              "      <td>0.901822</td>\n",
              "      <td>0.339448</td>\n",
              "      <td>0.137110</td>\n",
              "      <td>sadness</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>frankie laine</td>\n",
              "      <td>i believe</td>\n",
              "      <td>1950</td>\n",
              "      <td>pop</td>\n",
              "      <td>believe drop rain fall grow believe darkest ni...</td>\n",
              "      <td>51</td>\n",
              "      <td>0.035537</td>\n",
              "      <td>0.096777</td>\n",
              "      <td>0.443435</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001284</td>\n",
              "      <td>0.001284</td>\n",
              "      <td>0.331745</td>\n",
              "      <td>0.647540</td>\n",
              "      <td>0.954819</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.325021</td>\n",
              "      <td>0.263240</td>\n",
              "      <td>world/life</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>johnnie ray</td>\n",
              "      <td>cry</td>\n",
              "      <td>1950</td>\n",
              "      <td>pop</td>\n",
              "      <td>sweetheart send letter goodbye secret feel bet...</td>\n",
              "      <td>24</td>\n",
              "      <td>0.002770</td>\n",
              "      <td>0.002770</td>\n",
              "      <td>0.002770</td>\n",
              "      <td>...</td>\n",
              "      <td>0.002770</td>\n",
              "      <td>0.225422</td>\n",
              "      <td>0.456298</td>\n",
              "      <td>0.585288</td>\n",
              "      <td>0.840361</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.351814</td>\n",
              "      <td>0.139112</td>\n",
              "      <td>music</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>pérez prado</td>\n",
              "      <td>patricia</td>\n",
              "      <td>1950</td>\n",
              "      <td>pop</td>\n",
              "      <td>kiss lips want stroll charm mambo chacha merin...</td>\n",
              "      <td>54</td>\n",
              "      <td>0.048249</td>\n",
              "      <td>0.001548</td>\n",
              "      <td>0.001548</td>\n",
              "      <td>...</td>\n",
              "      <td>0.225889</td>\n",
              "      <td>0.001548</td>\n",
              "      <td>0.686992</td>\n",
              "      <td>0.744404</td>\n",
              "      <td>0.083935</td>\n",
              "      <td>0.199393</td>\n",
              "      <td>0.775350</td>\n",
              "      <td>0.743736</td>\n",
              "      <td>romantic</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12</td>\n",
              "      <td>giorgos papadopoulos</td>\n",
              "      <td>apopse eida oneiro</td>\n",
              "      <td>1950</td>\n",
              "      <td>pop</td>\n",
              "      <td>till darling till matter know till dream live ...</td>\n",
              "      <td>48</td>\n",
              "      <td>0.001350</td>\n",
              "      <td>0.001350</td>\n",
              "      <td>0.417772</td>\n",
              "      <td>...</td>\n",
              "      <td>0.068800</td>\n",
              "      <td>0.001350</td>\n",
              "      <td>0.291671</td>\n",
              "      <td>0.646489</td>\n",
              "      <td>0.975904</td>\n",
              "      <td>0.000246</td>\n",
              "      <td>0.597073</td>\n",
              "      <td>0.394375</td>\n",
              "      <td>romantic</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27958fa8-f7ab-4e3c-99df-8de953f12d2e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-27958fa8-f7ab-4e3c-99df-8de953f12d2e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-27958fa8-f7ab-4e3c-99df-8de953f12d2e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-23e33ff9-9fbf-4bd5-a643-7ee0ab169a5d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-23e33ff9-9fbf-4bd5-a643-7ee0ab169a5d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-23e33ff9-9fbf-4bd5-a643-7ee0ab169a5d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Exploratory Data Analysis (EDA)\n",
        "# Distribution of Songs by Genre\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(y='genre', data=data, order=data['genre'].value_counts().index[:10])\n",
        "plt.title('Top 10 Genres')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Genre')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "D_x6vIOE4LX6",
        "outputId": "c8eee8f6-8938-434f-adda-ed438a31b546"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Top Artists by Song Count\n",
        "top_artists = data.groupby('artist_name').size().sort_values(ascending=False).head(10)\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=top_artists.values, y=top_artists.index, palette='viridis')\n",
        "plt.title('Top 10 Artists by Number of Songs')\n",
        "plt.xlabel('Number of Songs')\n",
        "plt.ylabel('Artist Name')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "xM44W4_X4NbM",
        "outputId": "5a8cdb59-af8a-44ba-ff24-a1338727dff0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-9ca4b1a2bd56>:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=top_artists.values, y=top_artists.index, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Preprocessing the Data\n",
        "# Combine song metadata into a single feature for similarity computation\n",
        "data['combined_features'] = (\n",
        "    data['genre'].fillna('') + ' ' +\n",
        "    data['artist_name'].fillna('') + ' ' +\n",
        "    data['track_name'].fillna('')\n",
        ")\n"
      ],
      "metadata": {
        "id": "oOFBNTu14Rm7"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf = TfidfVectorizer(stop_words='english')\n",
        "tfidf_matrix = tfidf.fit_transform(data['combined_features'])"
      ],
      "metadata": {
        "id": "WvjIWelo4YzU"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)\n",
        "\n",
        "# Step 5: Build the Recommendation Function\n",
        "def get_recommendations(song_title, data, cosine_sim, top_n=10):\n",
        "    # Get the index of the song that matches the title\n",
        "    idx = data[data['track_name'] == song_title].index\n",
        "    if len(idx) == 0:\n",
        "        print(\"Song not found in the dataset.\")\n",
        "        return\n",
        "\n",
        "    idx = idx[0]\n",
        "\n",
        "    # Get similarity scores for all songs\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "\n",
        "    # Sort songs based on similarity scores\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Get top N most similar songs\n",
        "    sim_scores = sim_scores[1:top_n+1]  # Exclude the song itself\n",
        "    song_indices = [i[0] for i in sim_scores]\n",
        "\n",
        "    # Return recommended songs\n",
        "    recommendations = data.iloc[song_indices]\n",
        "    return recommendations"
      ],
      "metadata": {
        "id": "TWFqjRbP4rm-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "recommended_songs = get_recommendations('cry', data, cosine_sim, top_n=10)\n",
        "print(recommended_songs[['track_name', 'artist_name', 'genre']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUBx8CET47c2",
        "outputId": "6657cc67-0163-47cf-dcf5-10d2921d70eb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          track_name     artist_name  genre\n",
            "56                         here am i     johnnie ray    pop\n",
            "12887             keep on loving you  johnnie taylor  blues\n",
            "15294              too many memories  johnnie taylor  blues\n",
            "14542  there's nothing i wouldn't do  johnnie taylor  blues\n",
            "14679                         lately  johnnie taylor  blues\n",
            "13887                     steal away  johnnie taylor  blues\n",
            "14701             don't make me late  johnnie taylor  blues\n",
            "12881           baby, we've got love  johnnie taylor  blues\n",
            "13247  i got to love somebody's baby  johnnie taylor  blues\n",
            "12882            i need lots of love  johnnie taylor  blues\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(y='track_name', x='artist_name', data=recommended_songs, palette='coolwarm')\n",
        "plt.title('Recommended Songs Similar to \"Cry\"')\n",
        "plt.xlabel('Artist Name')\n",
        "plt.ylabel('Song Name')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "xzaR-4IC5L3G",
        "outputId": "2d88547c-4475-447f-c020-7bbf3f55e0cf"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-c385a2c003d4>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(y='track_name', x='artist_name', data=recommended_songs, palette='coolwarm')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/8AAAIjCAYAAABViau2AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAesNJREFUeJzs3Xl8Def////nkci+CSHWhCBi33eSKrWLqlJLSS3dPvZa2yK2WkpLdeFNhRLVllItRS1BQ9VWSlNLNKWa1p6IkJDM7w8/5+s0oQlJo9PH/XY7t3fmmmuuec2cvKPPM9fMsRiGYQgAAAAAAJhWvrwuAAAAAAAA5C7CPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAMC1/f3+FhYXl2HhxcXGyWCxavHhxjo1pZrlxvqKiomSxWBQVFWVtCwsLk7+/f47tAwDMiPAPAEAOWrx4sSwWi/Vlb2+v4sWLKywsTGfPns3r8vAPiouL03PPPaeAgAA5OTnJ19dXTZs21fjx4/O6tBzx5ZdfKjg4WIULF5aLi4vKlCmjLl26aMOGDXldWq56//33c+XDH39/f4WHh0uSQkJCMv3QKjExURMmTFC1atXk5uYmZ2dnVa5cWaNGjdLvv//+j9cD4N/FPq8LAADAjCZOnKjSpUvrxo0b+u6777R48WJ9++23OnLkiJycnPK6POSykydPqk6dOnJ2dlafPn3k7++v+Ph4HThwQNOnT9eECRPyusSHMnPmTI0YMULBwcEaM2aMXFxcdPLkSW3evFkrVqxQq1atJEl+fn66fv268ufPn2P7btq0qa5fvy4HB4ccGzM73n//fRUqVOgfD8OnTp1S8+bNdfr0aT399NN6/vnn5eDgoMOHD+vDDz/U6tWrdfz48X+0JgD/LoR/AAByQevWrVW7dm1JUr9+/VSoUCFNnz5da9euVZcuXfK4OuS2t99+W0lJSfrhhx/k5+dns+7cuXN5VFXOuHXrliZNmqQWLVpo06ZNGdbffXwWiyXHP+zKly9frn6Alp6ertTU1EfqQ7pbt26pU6dO+vPPPxUVFaXGjRvbrJ8yZYqmT59+3zGSk5Pl4uKSm2UCeMQx7R8AgH9AkyZNJEmxsbE27T///LM6d+4sb29vOTk5qXbt2lq7dm2G7a9cuaKhQ4fK399fjo6OKlGihHr16qULFy5Y+5w7d059+/ZVkSJF5OTkpGrVqmnJkiU249y5B3vmzJl67733VKZMGbm4uOiJJ57QmTNnZBiGJk2apBIlSsjZ2VmhoaG6dOmSzRj+/v5q166doqKiVLt2bTk7O6tKlSrWe7A///xzValSRU5OTqpVq5YOHjyY4Xiyctx3bqGIjo7WsGHD5OPjI1dXVz355JM6f/68TV/DMDR58mSVKFFCLi4ueuyxx3T06NFM34srV65oyJAhKlmypBwdHVW2bFlNnz5d6enpGfqFhYXJ09NTXl5e6t27t65cuZLpmH8VGxurEiVKZAj+klS4cOEMbe+//74qVaokR0dHFStWTP/3f/+XYV8hISGqXLmyfvrpJz322GNycXFR8eLFNWPGjAzj/frrr+rQoYNcXV1VuHBhDR06VBs3bsxwr/yJEyf01FNPydfXV05OTipRooSeeeYZJSQk3PPYLly4oMTERDVq1CjT9XcfX2b3/IeFhcnNzU2nT59Wu3bt5ObmpuLFi+u9996TJP34449q1qyZXF1d5efnp+XLl9uMn9k9/5mZOXOmGjZsqIIFC8rZ2Vm1atXSypUrM/SzWCwaMGCAIiMjre/BvW5d8Pf319GjR7V9+3brrT0hISHW9adOndLTTz8tb29vubi4qH79+lq3bt1968yKVatW6dChQ3rttdcyBH9J8vDw0JQpU6zLd35X9u/fr6ZNm8rFxUWvvvqqevfurUKFCunmzZsZxnjiiScUGBj40LUCeHRx5R8AgH9AXFycJKlAgQLWtqNHj6pRo0YqXry4Ro8eLVdXV3366afq2LGjVq1apSeffFKSlJSUpCZNmigmJkZ9+vRRzZo1deHCBa1du1a//fabChUqpOvXryskJEQnT57UgAEDVLp0aX322WcKCwvTlStXNHjwYJt6IiMjlZqaqoEDB+rSpUuaMWOGunTpombNmikqKkqjRo3SyZMnNXfuXA0fPlyLFi2y2f7kyZPq3r27XnjhBfXs2VMzZ85U+/btNW/ePL366qt6+eWXJUlTp05Vly5ddOzYMeXLly9bx33HwIEDVaBAAY0fP15xcXGaPXu2BgwYoE8++cTaZ9y4cZo8ebLatGmjNm3a6MCBA3riiSeUmppqM1ZycrKCg4N19uxZvfDCCypVqpR27dqlMWPGKD4+XrNnz5Z0+8OE0NBQffvtt3rxxRcVFBSk1atXq3fv3ll6v/38/LR582Zt3bpVzZo1u2/f8PBwTZgwQc2bN9dLL72kY8eO6YMPPtDevXsVHR1tM2X+8uXLatWqlTp16qQuXbpo5cqVGjVqlKpUqaLWrVtLkq5du6ZmzZopPj5egwcPlq+vr5YvX65t27bZ7Dc1NVUtW7ZUSkqKBg4cKF9fX509e1ZfffWVrly5Ik9Pz0zrLVy4sJydnfXll19q4MCB8vb2ztI5uVtaWppat26tpk2basaMGYqMjNSAAQPk6uqq1157TT169FCnTp00b9489erVSw0aNFDp0qWztY85c+aoQ4cO6tGjh1JTU7VixQo9/fTT+uqrr9S2bVubvlu3btWnn36qAQMGqFChQvd8eODs2bM1cOBAubm56bXXXpMkFSlSRJL0559/qmHDhkpOTtagQYNUsGBBLVmyRB06dNDKlSsz/F5nx50Pxp599tksb3Px4kW1bt1azzzzjHr27KkiRYrI1dVVH330kTZu3Kh27dpZ+/7xxx/aunWraZ5HAeAeDAAAkGMiIiIMScbmzZuN8+fPG2fOnDFWrlxp+Pj4GI6OjsaZM2esfR9//HGjSpUqxo0bN6xt6enpRsOGDY1y5cpZ28aNG2dIMj7//PMM+0tPTzcMwzBmz55tSDKWLVtmXZeammo0aNDAcHNzMxITEw3DMIxffvnFkGT4+PgYV65csfYdM2aMIcmoVq2acfPmTWt7t27dDAcHB5sa/fz8DEnGrl27rG0bN240JBnOzs7Gr7/+am2fP3++IcnYtm1bto/7zrls3ry59TgNwzCGDh1q2NnZWes/d+6c4eDgYLRt29am36uvvmpIMnr37m1tmzRpkuHq6mocP37c5jyOHj3asLOzM06fPm0YhmGsWbPGkGTMmDHD2ufWrVtGkyZNDElGRESEcT9HjhwxnJ2dDUlG9erVjcGDBxtr1qwxrl27ZtPvTu1PPPGEkZaWZm1/9913DUnGokWLrG3BwcGGJOOjjz6ytqWkpBi+vr7GU089ZW2bNWuWIclYs2aNte369etGhQoVbN6LgwcPGpKMzz777L7Hkpk7v5Ourq5G69atjSlTphj79+/P0O/O79vd56t3796GJOONN96wtl2+fNlwdnY2LBaLsWLFCmv7zz//bEgyxo8fb23btm1bht+p3r17G35+fjb7Tk5OtllOTU01KleubDRr1symXZKRL18+4+jRo1k69kqVKhnBwcEZ2ocMGWJIMnbu3Gltu3r1qlG6dGnD39/f5v3Nrho1ahienp5Z7n/nd2XevHk27WlpaUaJEiWMrl272rS/9dZbhsViMU6dOvXANQJ49DHtHwCAXNC8eXP5+PioZMmS6ty5s1xdXbV27VqVKFFCknTp0iVt3bpVXbp00dWrV3XhwgVduHBBFy9eVMuWLXXixAnrtwOsWrVK1apVy/TKocVikSStX79evr6+6tatm3Vd/vz5NWjQICUlJWn79u022z399NM2V3br1asnSerZs6fs7e1t2lNTUzN8U0HFihXVoEGDDNs3a9ZMpUqVytB+6tSpbB/3Hc8//7z1OKXbt1CkpaXp119/lSRt3rzZOovh7n5DhgzJcL4+++wzNWnSRAUKFLDu+8KFC2revLnS0tK0Y8cO6/m0t7fXSy+9ZN3Wzs5OAwcOzDBmZipVqqQffvhBPXv2VFxcnObMmaOOHTuqSJEiWrBggbXfndqHDBlinRkhSf3795eHh0eGKeNubm7q2bOnddnBwUF169a1nl9J2rBhg4oXL64OHTpY25ycnNS/f3+bse68/xs3blRycnKWjuuOCRMmaPny5apRo4Y2btyo1157TbVq1VLNmjUVExOTpTH69etn/dnLy0uBgYFydXW1eSZGYGCgvLy8bI4vq5ydna0/X758WQkJCWrSpIkOHDiQoW9wcLAqVqyY7X3cbf369apbt67NtHw3Nzc9//zziouL008//fTAYycmJsrd3T1b2zg6Ouq5556zacuXL5969OihtWvX6urVq9b2yMhINWzYMNuzKwD8uxD+AQDIBe+9956++eYbrVy5Um3atNGFCxfk6OhoXX/y5EkZhqGxY8fKx8fH5nVn6u2dB6fFxsaqcuXK993fr7/+qnLlytkESEkKCgqyrr/b3QFd+n9BsGTJkpm2X758OUe2z85x32tfd26duDPmnWMrV66cTT8fHx+b2yyk2/e4b9iwIcO+mzdvbrPvX3/9VUWLFpWbm5vN9tm5J7p8+fJaunSpLly4oMOHD+uNN96Qvb29nn/+eW3evNmm9r+O6+DgoDJlymR430qUKGHzAced83H3+/Prr78qICAgQ7+yZcvaLJcuXVrDhg3TwoULVahQIbVs2VLvvffefe/3v1u3bt20c+dOXb58WZs2bVL37t118OBBtW/fXjdu3Ljvtk5OTvLx8bFp8/T0zPT4PD09M/z+ZcVXX32l+vXry8nJSd7e3vLx8dEHH3yQ6fHlROj99ddfM/39uNf/B7PDw8PDJqxnRfHixTP9RoRevXrp+vXrWr16tSTp2LFj2r9/f7ZuKQDw78Q9/wAA5IK6detan/bfsWNHNW7cWN27d9exY8fk5uZmfbjc8OHD1bJly0zH+GtYy0l2dnbZajcMI0e2f5DjzmpNWZGenq4WLVpo5MiRma4vX758tsf8O3Z2dqpSpYqqVKmiBg0a6LHHHlNkZKT1A4fsjpWZBzkXkjRr1iyFhYXpiy++0KZNmzRo0CBNnTpV3333nXWWyt/x8PBQixYt1KJFC+XPn19LlizRnj17FBwcnO3jyKnj27lzpzp06KCmTZvq/fffV9GiRZU/f35FRERkeICgZDtL4FFUoUIFHTx4UGfOnMnwAdu93OuYKlasqFq1amnZsmXq1auXli1bJgcHB76FBPgPIPwDAJDL7OzsNHXqVD322GN69913NXr0aJUpU0bS7an5fxcCAwICdOTIkfv28fPz0+HDh5Wenm5z9f/nn3+2rn8UZOe4s+rOsZ04ccI6viSdP38+wxXjgIAAJSUl/e2+/fz8tGXLFiUlJdlc/T927NhD1XrnA6H4+Hib2o8dO2ZTe2pqqn755ZcHOkd+fn766aefZBiGzVX0kydPZtr/zgcTr7/+unbt2qVGjRpp3rx5mjx5crb3Xbt2bS1ZssR6fHll1apVcnJy0saNG21m3ERERDz02H+dmXCHn59fpr8fOfH/wfbt2+vjjz/WsmXLNGbMmAce545evXpp2LBhio+P1/Lly9W2bdsMs2QAmA/T/gEA+AeEhISobt26mj17tm7cuKHChQsrJCRE8+fPzzQo3f1Vdk899ZQOHTpknaZ7tztXRNu0aaM//vjD5gn4t27d0ty5c+Xm5nbfq7D/pOwcd1Y1b95c+fPn19y5c22uEN95cv/dunTpot27d2vjxo0Z1l25ckW3bt2SdPt83rp1Sx988IF1fVpamubOnZulmnbu3Jnp16mtX79e0v+b5t+8eXM5ODjonXfesan9ww8/VEJCQoan0mdFy5YtdfbsWZuvTrxx44bNswak2/eR3zneO6pUqaJ8+fIpJSXlnuMnJydr9+7dma77+uuvJWXv9ojcYGdnJ4vForS0NGtbXFyc1qxZ89Bju7q6ZvqVj23atNH3339vc26uXbum//3vf/L393+oZwp07txZVapU0ZQpUzI991evXrV++0BWdOvWTRaLRYMHD9apU6dsniMBwLy48g8AwD9kxIgRevrpp7V48WK9+OKLeu+999S4cWNVqVJF/fv3V5kyZfTnn39q9+7d+u2333To0CHrditXrtTTTz+tPn36qFatWrp06ZLWrl2refPmqVq1anr++ec1f/58hYWFaf/+/fL399fKlSsVHR2t2bNnZ/thYbkpq8edVT4+Pho+fLimTp2qdu3aqU2bNjp48KC+/vprFSpUyKbviBEjtHbtWrVr105hYWGqVauWrl27ph9//FErV65UXFycChUqpPbt26tRo0YaPXq04uLiVLFiRX3++edZvh9++vTp2r9/vzp16qSqVatKkg4cOKCPPvpI3t7e1ocR+vj4aMyYMZowYYJatWqlDh066NixY3r//fdVp06dBwplL7zwgt59911169ZNgwcPVtGiRRUZGSknJydJ/+/K9datWzVgwAA9/fTTKl++vG7duqWlS5fKzs5OTz311D3HT05OVsOGDVW/fn21atVKJUuW1JUrV7RmzRrt3LlTHTt2VI0aNbJdd05q27at3nrrLbVq1Urdu3fXuXPn9N5776ls2bI6fPjwQ41dq1YtffDBB5o8ebLKli2rwoULq1mzZho9erQ+/vhjtW7dWoMGDZK3t7eWLFmiX375RatWrcrwPI7syJ8/vz7//HM1b95cTZs2VZcuXdSoUSPlz59fR48e1fLly1WgQAFNmTIlS+P5+PioVatW+uyzz+Tl5fVAHzIB+Pch/AMA8A/p1KmTAgICNHPmTPXv318VK1bUvn37NGHCBC1evFgXL15U4cKFVaNGDY0bN866nZubm3bu3Knx48dr9erVWrJkiQoXLqzHH3/cel+2s7OzoqKiNHr0aC1ZskSJiYkKDAxURESEwsLC8uiIM5fV486OyZMny8nJSfPmzdO2bdtUr149bdq0KUOocXFx0fbt2/XGG2/os88+00cffSQPDw+VL19eEyZMsD6gMF++fFq7dq2GDBmiZcuWyWKxqEOHDpo1a1aWgu2rr76q5cuXa/v27YqMjFRycrKKFi2qZ555RmPHjrV5wFx4eLh8fHz07rvvaujQofL29tbzzz+vN954Q/nz58/2uXBzc9PWrVs1cOBAzZkzR25uburVq5caNmyop556yvohQLVq1dSyZUt9+eWXOnv2rFxcXFStWjV9/fXXql+//j3H9/Ly0oIFC7Ru3TpFRETojz/+kJ2dnQIDA/Xmm29q0KBB2a45pzVr1kwffvihpk2bpiFDhqh06dKaPn264uLiHjr8jxs3Tr/++qtmzJihq1evKjg4WM2aNVORIkW0a9cujRo1SnPnztWNGzdUtWpVffnllzkSrsuWLasffvhBb7/9tlavXq01a9YoPT1dZcuWVb9+/bJ93nv16qWvvvpKXbp0sbk1AoB5WYwHfUIMAAAA/jVmz56toUOH6rffflPx4sXzuhzksS+++EIdO3bUjh071KRJk7wuB8A/gPAPAABgMtevX7d52vuNGzdUo0YNpaWl6fjx43lYGR4V7dq1U0xMjE6ePHnPhxgCMBem/QMAAJhMp06dVKpUKVWvXl0JCQlatmyZfv75Z0VGRuZ1achjK1as0OHDh7Vu3TrNmTOH4A/8h3DlH4+UkJAQVa9ePdMnNJtZWFiY9WFJAAA8rNmzZ2vhwoWKi4tTWlqaKlasqJEjR6pr1655XRrymMVikZubm7p27ap58+bJ3p5rgcB/BeEfj5T/avhPSEiQYRjy8vLK61IAAAAAmBAf9cH0UlNT5eDgkNdl3Nedp0sDAAAAQG548C8cBXJJenq6Ro4cKW9vb/n6+io8PNxm/ZUrV9SvXz/5+PjIw8NDzZo1s/lO6PDwcFWvXl0LFy5U6dKlrV9p9HfbZWbUqFEqX768XFxcVKZMGY0dO1Y3b97MsK9FixapVKlScnNz08svv6y0tDTNmDFDvr6+Kly48N9+725YWJg6duyYvRMFAAAAAFnElX88cpYsWaJhw4Zpz5492r17t8LCwtSoUSO1aNFCkvT000/L2dlZX3/9tTw9PTV//nw9/vjjOn78uLy9vSVJJ0+e1KpVq/T555/Lzs4uy9v9lbu7uxYvXqxixYrpxx9/VP/+/eXu7q6RI0da+8TGxurrr7/Whg0bFBsbq86dO+vUqVMqX768tm/frl27dqlPnz5q3ry56tWrl+3zkZKSopSUFOtyenq6Ll26pIIFC/KQHgAAAOA/zDAMXb16VcWKFVO+fH9zbd8AHiHBwcFG48aNbdrq1KljjBo1yjAMw9i5c6fh4eFh3Lhxw6ZPQECAMX/+fMMwDGP8+PFG/vz5jXPnzlnXZ2W7rHjzzTeNWrVqWZfHjx9vuLi4GImJida2li1bGv7+/kZaWpq1LTAw0Jg6deo9x+3du7cRGhqa6brx48cbknjx4sWLFy9evHjx4sUr09eZM2f+Nstw5R+PnKpVq9osFy1aVOfOnZMkHTp0SElJSSpYsKBNn+vXrys2Nta67OfnJx8fH+tyVrf7q08++UTvvPOOYmNjlZSUpFu3bsnDw8Omj7+/v9zd3a3LRYoUkZ2dnc0nb0WKFLEeQ3aNGTNGw4YNsy4nJCSoVKlSOnPmTIZaAAB4VFyKWpnXJQBAjvIO6ZzXJWSQmJiokiVL2uSReyH845GTP39+m2WLxaL09HRJUlJSkooWLaqoqKgM2939pHxXV1ebdVnd7m67d+9Wjx49NGHCBLVs2VKenp5asWKFZs2a9bf13u8YssvR0VGOjo4Z2j08PAj/AIBH1k1Xl7wuAQBy1KP8395ZuR2Y8I9/lZo1a+qPP/6Qvb29/P39c3W7Xbt2yc/PT6+99pq17ddff81mxQAAAACQ93jaP/5VmjdvrgYNGqhjx47atGmT4uLitGvXLr322mvat29fjm5Xrlw5nT59WitWrFBsbKzeeecdrV69OrcODQAAAAByDeEf/yoWi0Xr169X06ZN9dxzz6l8+fJ65pln9Ouvv6pIkSI5ul2HDh00dOhQDRgwQNWrV9euXbs0duzY3Do0AAAAAMg1FsMwjLwuAkDWJSYmytPTUwkJCY/0fUcAgP+2i1tW5HUJAJCjCj7+TF6XkEF2sgFX/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhP9HREhIiIYMGZLXZfwjoqKiZLFYdOXKlRwbMywsTB07dsyx8QAAAADATOzzugAgJ8yZM0eGYeR1GQAAAADwSCL8wxQ8PT3zugQAAAAAeGQx7f8RtW7dOnl6eioyMlKSdObMGXXp0kVeXl7y9vZWaGio4uLibLZZuHChgoKC5OTkpAoVKuj999+3rouLi5PFYtGKFSvUsGFDOTk5qXLlytq+fft967h8+bJ69eqlAgUKyMXFRa1bt9aJEyes6xcvXiwvLy9t3LhRQUFBcnNzU6tWrRQfH5+t4121apUqVaokR0dH+fv7a9asWdZ1r776qurVq5dhm2rVqmnixImSMk77DwkJ0aBBgzRy5Eh5e3vL19dX4eHhNtv//PPPaty4sZycnFSxYkVt3rxZFotFa9asybTGjz76SAULFlRKSopNe8eOHfXss89alz/44AMFBATIwcFBgYGBWrp0qXXdnffhhx9+sLZduXJFFotFUVFRf3OWAAAAAODBEP4fQcuXL1e3bt0UGRmpHj166ObNm2rZsqXc3d21c+dORUdHW0N2amqqJCkyMlLjxo3TlClTFBMTozfeeENjx47VkiVLbMYeMWKEXnnlFR08eFANGjRQ+/btdfHixXvWEhYWpn379mnt2rXavXu3DMNQmzZtdPPmTWuf5ORkzZw5U0uXLtWOHTt0+vRpDR8+PMvHu3//fnXp0kXPPPOMfvzxR4WHh2vs2LFavHixJKlHjx76/vvvFRsba93m6NGjOnz4sLp3737PcZcsWSJXV1ft2bNHM2bM0MSJE/XNN99IktLS0tSxY0e5uLhoz549+t///qfXXnvtvnU+/fTTSktL09q1a61t586d07p169SnTx9J0urVqzV48GC98sorOnLkiF544QU999xz2rZtW5bPx1+lpKQoMTHR5gUAAAAA2UH4f8S89957evnll/Xll1+qXbt2kqRPPvlE6enpWrhwoapUqaKgoCBFRETo9OnT1qvF48eP16xZs9SpUyeVLl1anTp10tChQzV//nyb8QcMGKCnnnpKQUFB+uCDD+Tp6akPP/ww01pOnDihtWvXauHChWrSpImqVaumyMhInT171ubq+M2bNzVv3jzVrl1bNWvW1IABA7Rly5YsH/Nbb72lxx9/XGPHjlX58uUVFhamAQMG6M0335QkVapUSdWqVdPy5cut20RGRqpevXoqW7bsPcetWrWqxo8fr3LlyqlXr16qXbu2ta5vvvlGsbGx+uijj1StWjU1btxYU6ZMuW+dzs7O6t69uyIiIqxty5YtU6lSpRQSEiJJmjlzpsLCwvTyyy+rfPnyGjZsmDp16qSZM2dm+Xz81dSpU+Xp6Wl9lSxZ8oHHAgAAAPDfRPh/hKxcuVJDhw7VN998o+DgYGv7oUOHdPLkSbm7u8vNzU1ubm7y9vbWjRs3FBsbq2vXrik2NlZ9+/a1rndzc9PkyZNtrpZLUoMGDaw/29vbq3bt2oqJicm0npiYGNnb29tMuS9YsKACAwNttnFxcVFAQIB1uWjRojp37lyWjzsmJkaNGjWyaWvUqJFOnDihtLQ0Sbev/t8J/4Zh6OOPP1aPHj3uO27VqlVtlu+u69ixYypZsqR8fX2t6+vWrfu3tfbv31+bNm3S2bNnJd2+7SEsLEwWi+W+x3Kvc5wVY8aMUUJCgvV15syZBx4LAAAAwH8TD/x7hNSoUUMHDhzQokWLVLt2bWugTEpKUq1ataz3/9/Nx8dHSUlJkqQFCxZkuDfezs4u1+vOnz+/zbLFYsnxJ+9369ZNo0aN0oEDB3T9+nWdOXNGXbt2zXZd6enpD1VHjRo1VK1aNX300Ud64okndPToUa1bty7L2+fLd/vztrvPz923UGTG0dFRjo6OD1YwAAAAAIgr/4+UgIAAbdu2TV988YUGDhxoba9Zs6ZOnDihwoULq2zZsjYvT09PFSlSRMWKFdOpU6cyrC9durTNPr777jvrz7du3dL+/fsVFBSUaT1BQUG6deuW9uzZY227ePGijh07pooVK+bYcQcFBSk6OtqmLTo6WuXLl7d+eFGiRAkFBwcrMjJSkZGRatGihQoXLvzA+wwMDNSZM2f0559/Wtv27t2bpW379eunxYsXKyIiQs2bN7eZhn+vY7lzvnx8fCTJ5oGIdz/8DwAAAAByA1f+HzHly5fXtm3bFBISInt7e82ePVs9evTQm2++qdDQUE2cOFElSpTQr7/+qs8//1wjR45UiRIlNGHCBA0aNEienp5q1aqVUlJStG/fPl2+fFnDhg2zjv/ee++pXLlyCgoK0ttvv63Lly9bH1b3V+XKlVNoaKj69++v+fPny93dXaNHj1bx4sUVGhqaY8f8yiuvqE6dOpo0aZK6du2q3bt3691337X5tgLp9tT/8ePHKzU1VW+//fZD7bNFixYKCAhQ7969NWPGDF29elWvv/66JFlnXNxL9+7dNXz4cC1YsEAfffSRzboRI0aoS5cuqlGjhpo3b64vv/xSn3/+uTZv3izp9nMD6tevr2nTpql06dI6d+6cdb8AAAAAkFu48v8ICgwM1NatW/Xxxx/rlVdekYuLi3bs2KFSpUqpU6dOCgoKUt++fXXjxg15eHhIun01euHChYqIiFCVKlUUHBysxYsXZ7jyP23aNE2bNk3VqlXTt99+q7Vr16pQoUL3rCUiIkK1atVSu3bt1KBBAxmGofXr12eYUv8watasqU8//VQrVqxQ5cqVNW7cOE2cOFFhYWE2/Tp37qyLFy8qOTnZ5mv9HoSdnZ3WrFmjpKQk1alTR/369bM+7d/Jyem+23p6euqpp56Sm5tbhjo6duyoOXPmaObMmapUqZLmz5+viIgI6wMBJWnRokW6deuWatWqpSFDhmjy5MkPdSwAAAAA8HcsRk7fnI1HUlxcnEqXLq2DBw+qevXqeV3OIyk6OlqNGzfWyZMnbR5gmJnHH39clSpV0jvvvPMPVff/JCYmytPTUwkJCdYPfwAAeNRc3LIir0sAgBxV8PFn8rqEDLKTDZj2j/+s1atXy83NTeXKldPJkyc1ePBgNWrU6L7B//Lly4qKilJUVFSG2xIAAAAA4FFF+Md/1tWrVzVq1CidPn1ahQoVUvPmzTVr1qz7blOjRg1dvnxZ06dPV2Bg4D9UKQAAAAA8HKb9A/8yTPsHAPwbMO0fgNn826f988A/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj+QA8LCwtSxY8e8LgMAAAAAMmWf1wX8V4SEhKh69eqaPXt2XpeCXDBnzhwZhpHXZQAAAABApgj/wENIS0uTxWKRp6dnXpcCAAAAAPfEtP9/QFhYmLZv3645c+bIYrHIYrEoLi5OkrR9+3bVrVtXjo6OKlq0qEaPHq1bt25Zt01JSdGgQYNUuHBhOTk5qXHjxtq7d+999+fv76/JkyerV69ecnNzk5+fn9auXavz588rNDRUbm5uqlq1qvbt22fd5uLFi+rWrZuKFy8uFxcXValSRR9//LHNuCEhIRo0aJBGjhwpb29v+fr6Kjw83Lq+T58+ateunc02N2/eVOHChfXhhx9mWuvixYvl5eWlr776SoGBgXJxcVHnzp2VnJysJUuWyN/fXwUKFNCgQYOUlpZmc16GDx+u4sWLy9XVVfXq1VNUVNRDj3v58mX16tVLBQoUkIuLi1q3bq0TJ05kGHft2rWqWLGiHB0ddfr06QzT/tPT0zV16lSVLl1azs7OqlatmlauXGmznx49esjHx0fOzs4qV66cIiIi7vu+AgAAAMCDIvz/A+bMmaMGDRqof//+io+PV3x8vEqWLKmzZ8+qTZs2qlOnjg4dOqQPPvhAH374oSZPnmzdduTIkVq1apWWLFmiAwcOqGzZsmrZsqUuXbp0332+/fbbatSokQ4ePKi2bdvq2WefVa9evdSzZ08dOHBAAQEB6tWrl3Wq+o0bN1SrVi2tW7dOR44c0fPPP69nn31W33//vc24S5Yskaurq/bs2aMZM2Zo4sSJ+uabbyRJ/fr104YNGxQfH2/t/9VXXyk5OVldu3a9Z63Jycl65513tGLFCm3YsEFRUVF68skntX79eq1fv15Lly7V/PnzbcLzgAEDtHv3bq1YsUKHDx/W008/rVatWtkE9QcZNywsTPv27dPatWu1e/duGYahNm3a6ObNmzbjTp8+XQsXLtTRo0dVuHDhDMc0depUffTRR5o3b56OHj2qoUOHqmfPntq+fbskaezYsfrpp5/09ddfKyYmRh988IEKFSqU6flJSUlRYmKizQsAAAAAssNicKPyPyKze/5fe+01rVq1SjExMbJYLJKk999/X6NGjVJCQoKuX7+uAgUKaPHixerevbuk21fS/f39NWTIEI0YMSLTffn7+6tJkyZaunSpJOmPP/5Q0aJFNXbsWE2cOFGS9N1336lBgwaKj4+Xr69vpuO0a9dOFSpU0MyZM63HkJaWpp07d1r71K1bV82aNdO0adMkSZUqVVLv3r01cuRISVKHDh1UsGDBe17VXrx4sZ577jmdPHlSAQEBkqQXX3xRS5cu1Z9//ik3NzdJUqtWreTv76958+bp9OnTKlOmjE6fPq1ixYpZx2revLnq1q2rN95444HGPXHihMqXL6/o6Gg1bNhQ0u0ZESVLltSSJUv09NNPW8f94YcfVK1aNeu+w8LCdOXKFa1Zs0YpKSny9vbW5s2b1aBBA2uffv36KTk5WcuXL1eHDh1UqFAhLVq0KNPzcrfw8HBNmDAhQ3tCQoI8PDz+dnsAAPLCxS0r8roEAMhRBR9/Jq9LyCAxMVGenp5Zygbc85+HYmJi1KBBA2vwl6RGjRopKSlJv/32m65cuaKbN2+qUaNG1vX58+dX3bp1FRMTc9+xq1atav25SJEikqQqVapkaDt37px8fX2VlpamN954Q59++qnOnj2r1NRUpaSkyMXF5Z7jSlLRokV17tw563K/fv30v//9TyNHjtSff/6pr7/+Wlu3br1vrS4uLtaAfqc2f39/a0C/03ZnPz/++KPS0tJUvnx5m3FSUlJUsGDBBx43JiZG9vb2qlevnnV9wYIFFRgYaHO+HRwcMpyHu508eVLJyclq0aKFTXtqaqpq1KghSXrppZf01FNP6cCBA3riiSfUsWNH6wcOfzVmzBgNGzbMupyYmKiSJUvec/8AAAAA8FeEf5PKnz+/9ec7Hy5k1paeni5JevPNNzVnzhzNnj1bVapUkaurq4YMGaLU1NR7jntnnDtjSFKvXr00evRo7d69W7t27VLp0qXVpEmTLNd6Z8z77ScpKUl2dnbav3+/7OzsbPrdHeyzO25WOTs723xg81dJSUmSpHXr1ql48eI26xwdHSVJrVu31q+//qr169frm2++0eOPP67/+7//s86y+Os2d7YDAAAAgAdB+P+HODg42DxYTpKCgoK0atUqGYZhDZPR0dFyd3dXiRIlVLBgQTk4OCg6Olp+fn6Sbk/737t3r4YMGZKj9UVHRys0NFQ9e/aUdPtDgePHj6tixYrZGqdgwYLq2LGjIiIitHv3bj333HM5Wqck1ahRQ2lpaTp37tzffrCQHUFBQbp165b27NljM+3/2LFj2ToPdz8IMDg4+J79fHx81Lt3b/Xu3VtNmjTRiBEjMg3/AAAAAPCwCP//EH9/f+3Zs0dxcXFyc3OTt7e3Xn75Zc2ePVsDBw7UgAEDdOzYMY0fP17Dhg1Tvnz55OrqqpdeekkjRoyQt7e3SpUqpRkzZig5OVl9+/bN0frKlSunlStXateuXSpQoIDeeust/fnnn9kO/9Ltqf/t2rVTWlqaevfunaN1SlL58uXVo0cP9erVS7NmzVKNGjV0/vx5bdmyRVWrVlXbtm0faNxy5copNDRU/fv31/z58+Xu7q7Ro0erePHiCg0NzfI47u7uGj58uIYOHar09HQ1btxYCQkJio6OloeHh3r37q1x48apVq1aqlSpklJSUvTVV18pKCjogeoGAAAAgL9D+P+HDB8+XL1791bFihV1/fp1/fLLL/L399f69es1YsQIVatWTd7e3urbt69ef/1163bTpk1Tenq6nn32WV29elW1a9fWxo0bVaBAgRyt7/XXX9epU6fUsmVLubi46Pnnn1fHjh2VkJCQ7bGaN2+uokWLqlKlSjYP5MtJERERmjx5sl555RWdPXtWhQoVUv369TN81eCDjDt48GC1a9dOqampatq0qdavX5/hdoG/M2nSJPn4+Gjq1Kk6deqUvLy8VLNmTb366quSbs8EGTNmjOLi4uTs7KwmTZpoxQoejAQAAAAgd/C0f+S4pKQkFS9eXBEREerUqVNel2M62XmiJwAAeYWn/QMwG572D/z/0tPTdeHCBc2aNUteXl7q0KFDXpcEAAAAABDhHzno9OnTKl26tEqUKKHFixfL3p5fLwAAAAB4FJDOkGP8/f3FXSQAAAAA8OjJl9cFAAAAAACA3EX4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5B4o/F+5ckULFy7UmDFjdOnSJUnSgQMHdPbs2RwtDgAAAAAAPLxsh//Dhw+rfPnymj59umbOnKkrV65Ikj7//HONGTPmoQuKioqSxWKxjovb/P39NXv27Huuj4uLk8Vi0Q8//JCrdTyK78/ixYvl5eV13z7h4eGqXr36P1LPX/1T7w0AAAAA3Eu2w/+wYcMUFhamEydOyMnJydrepk0b7dixI1tjhYSEaMiQIdktIVf5+/srKioqz/aflSCbmZIlSyo+Pl6VK1fO+aLu0rBhQ8XHx8vT0zNX9/MoCAkJ0eLFi63LFotFa9asybN6AAAAAOBBZTv87927Vy+88EKG9uLFi+uPP/7IkaJyw82bN/O6hFxlZ2cnX19f2dvb5+p+HBwc5OvrK4vFkqv7AQAAAADknGyHf0dHRyUmJmZoP378uHx8fLI8TlhYmLZv3645c+bIYrHIYrEoLi7Oun7//v2qXbu2XFxc1LBhQx07dsxm+y+++EI1a9aUk5OTypQpowkTJujWrVvW9RaLRR988IE6dOggV1dXTZkyJUvb3S01NVUDBgxQ0aJF5eTkJD8/P02dOvW+x9SxY0fNnDlTRYsWVcGCBfV///d/Nh88XL58Wb169VKBAgXk4uKi1q1b68SJE5JuT6l/7rnnlJCQYD0n4eHh1m2Tk5PVp08fubu7q1SpUvrf//5nXffXqeV3pudv2bLlvudx8uTJKly4sNzd3dWvXz+NHj36vtPj/27a//Dhw9WuXTvr8uzZs2WxWLRhwwZrW9myZbVw4UJJUnp6uiZOnKgSJUrI0dFR1atXt+mb2f5++OGHDL8vfzVt2jQVKVJE7u7u6tu3r27cuGGzPivv1d38/f0lSU8++aQsFot1OTPff/+9atSoIScnJ9WuXVsHDx7M0Gf79u2qW7euHB0dVbRoUY0ePfqev4cAAAAA8LCyHf47dOigiRMnWkOSxWLR6dOnNWrUKD311FNZHmfOnDlq0KCB+vfvr/j4eMXHx6tkyZLW9a+99ppmzZqlffv2yd7eXn369LGu27lzp3r16qXBgwfrp59+0vz587V48WJrwL8jPDxcTz75pH788Uf16dMny9vd8c4772jt2rX69NNPdezYMUVGRt439EnStm3bFBsbq23btmnJkiVavHixzdTxsLAw7du3T2vXrtXu3btlGIbatGmjmzdvqmHDhpo9e7Y8PDys52T48OHWbWfNmmUNky+//LJeeumlDGH+r+53HiMjIzVlyhRNnz5d+/fvV6lSpfTBBx/cd7y/ExwcrG+//VZpaWmSbofcQoUKWW+lOHv2rGJjYxUSEiLp9u/BrFmzNHPmTB0+fFgtW7ZUhw4drB+IPIhPP/1U4eHheuONN7Rv3z4VLVpU77//foZ+f/de3W3v3r2SpIiICMXHx1uX/yopKUnt2rVTxYoVtX//foWHh9u8h3fOQZs2bVSnTh0dOnRIH3zwgT788ENNnjw50zFTUlKUmJho8wIAAACA7Mj2HPFZs2apc+fOKly4sK5fv67g4GD98ccfatCgwT1DdGY8PT3l4OAgFxcX+fr6Zlg/ZcoUBQcHS5JGjx6ttm3b6saNG3JyctKECRM0evRo9e7dW5JUpkwZTZo0SSNHjtT48eOtY3Tv3l3PPfecdblPnz5/u93dV5NPnz6tcuXKqXHjxrJYLPLz8/vb4ypQoIDeffdd2dnZqUKFCmrbtq22bNmi/v3768SJE1q7dq2io6PVsGFDSbcDeMmSJbVmzRo9/fTT8vT0lMViyfSctGnTRi+//LIkadSoUXr77be1bds2BQYG3rOe+53HuXPnqm/fvtZzNG7cOG3atElJSUl/e5z30qRJE129elUHDx5UrVq1tGPHDo0YMcJ6r3xUVJSKFy+usmXLSpJmzpypUaNG6ZlnnpEkTZ8+Xdu2bdPs2bP13nvvPVANs2fPVt++fdW3b19Jt2c3bN68OcPV//u9V3dqvePOrBYvL69M35s7li9frvT0dH344YdycnJSpUqV9Ntvv+mll16y9nn//fdVsmRJvfvuu7JYLKpQoYJ+//13jRo1SuPGjVO+fLafyU2dOlUTJkx4oHMBAAAAANIDXPn39PTUN998oy+//FLvvPOOBgwYoPXr12v79u1ydXXNscKqVq1q/blo0aKSpHPnzkmSDh06pIkTJ8rNzc36ujODIDk52bpd7dq1bcbM6nZ3hIWF6YcfflBgYKAGDRqkTZs2/W3dlSpVkp2dnU3td+qOiYmRvb296tWrZ11fsGBBBQYGKiYmJlvn5M4HBHfGzso2fz2Px44dU926dW36/3U5u7y8vFStWjVFRUXpxx9/lIODg55//nkdPHhQSUlJ2r59u/XDiMTERP3+++9q1KiRzRiNGjXK0vm4l5iYGJtzLEkNGjTI0O9+79XD7Ltq1ao2D8P8675jYmLUoEEDm+cmNGrUSElJSfrtt98yjDlmzBglJCRYX2fOnHmoGgEAAAD89zzw0+EaN26sxo0b52QtNvLnz2/9+U5ISk9Pl3R7avWECRPUqVOnDNvdHbr++mFEVre7o2bNmvrll1/09ddfa/PmzerSpYuaN2+ulStXZqnuO7XfqfthPcjY9zuPuSUkJERRUVFydHRUcHCwvL29FRQUpG+//Vbbt2/XK6+8kuWx7lwFNwzD2pZTD2/MzfcqJzk6OsrR0TGvywAAAADwL/ZA4X/v3r3atm2bzp07lyEsvfXWW1kex8HBwXpveHbUrFlTx44ds04dz83tPDw81LVrV3Xt2lWdO3dWq1atdOnSJXl7e2e3bAUFBenWrVvas2ePddr/xYsXdezYMVWsWFHSg5+TBxEYGKi9e/eqV69e1rZ73cueHcHBwVq0aJHs7e3VqlUrSbc/EPj44491/Phx6/3+Hh4eKlasmKKjo62zASQpOjraOgPhznT7+Ph4FShQQJKsDzW8l6CgIO3Zs8fmuL777ruHPq78+fP/7XsTFBSkpUuXWm+tyGzfQUFBWrVqlQzDsH4gEx0dLXd3d5UoUeKh6wQAAACAv8p2+H/jjTf0+uuvKzAwUEWKFLGZupzdr3/z9/fXnj17FBcXJzc3tywH6nHjxqldu3YqVaqUOnfurHz58unQoUM6cuTIPR+a9iDbvfXWWypatKhq1KihfPny6bPPPpOvr6+8vLyydZx3lCtXTqGhoerfv7/mz58vd3d3jR49WsWLF1doaKj1nCQlJWnLli2qVq2aXFxc5OLi8kD7+zsDBw5U//79Vbt2bTVs2FCffPKJDh8+rDJlyjzUuE2bNtXVq1f11Vdfadq0aZJuh//OnTuraNGiKl++vLXviBEjNH78eAUEBKh69eqKiIjQDz/8oMjISEm3vxmgZMmSCg8P15QpU3T8+HHNmjXrvvsfPHiwwsLCVLt2bTVq1EiRkZE6evToQx+Xv7+/tmzZokaNGsnR0dH6YcTdunfvrtdee039+/fXmDFjFBcXp5kzZ9r0efnllzV79mwNHDhQAwYM0LFjxzR+/HgNGzYsw/3+AAAAAJATsp005syZo0WLFikmJkZRUVHatm2b9bV169ZsjTV8+HDZ2dmpYsWK8vHx0enTp7O0XcuWLfXVV19p06ZNqlOnjurXr6+33377bx/Il93t3N3dNWPGDNWuXVt16tRRXFyc1q9f/1ABLSIiQrVq1VK7du3UoEEDGYah9evXW6egN2zYUC+++KK6du0qHx8fzZgx44H39Xd69OihMWPGaPjw4dZbHMLCwjK9BSI7ChQooCpVqsjHx0cVKlSQdPsDgfT0dJsr/JI0aNAgDRs2TK+88oqqVKmiDRs2aO3atSpXrpyk21fbP/74Y/3888+qWrWqpk+fft8PeCSpa9euGjt2rEaOHKlatWrp119/tXng3oOaNWuWvvnmG5UsWVI1atTItI+bm5u+/PJL/fjjj6pRo4Zee+01TZ8+3aZP8eLFtX79en3//feqVq2aXnzxRfXt21evv/76Q9cIAAAAAJmxGHffTJ0FRYsW1Y4dO6zhDObSokUL+fr6aunSpXldCu4hMTFRnp6eSkhIkIeHR16XAwBApi5uWZHXJQBAjir4+DN5XUIG2ckG2b6EPXTo0Af+CjY8WpKTk/XWW2/p6NGj+vnnnzV+/Hht3rzZ+lWIAAAAAABzyPY9/8OHD1fbtm0VEBCgihUrZnhi+ueff55jxSF3WSwWrV+/XlOmTNGNGzcUGBioVatWqXnz5nldGgAAAAAgB2U7/A8aNEjbtm3TY489poIFC2b7IX94dDg7O2vz5s15XQYAAAAAIJdlO/wvWbJEq1atUtu2bXOjHgAAAAAAkMOyfc+/t7e3AgICcqMWAAAAAACQC7Id/sPDwzV+/HglJyfnRj0AAAAAACCHZXva/zvvvKPY2FgVKVJE/v7+GR74d+DAgRwrDgAAAAAAPLxsh/+OHTvmQhkAAAAAACC3ZDv8jx8/PjfqAAAAAAAAuSTb9/wDAAAAAIB/l2xf+U9LS9Pbb7+tTz/9VKdPn1ZqaqrN+kuXLuVYcQAAAAAA4OFl+8r/hAkT9NZbb6lr165KSEjQsGHD1KlTJ+XLl0/h4eG5UCIAAAAAAHgY2Q7/kZGRWrBggV555RXZ29urW7duWrhwocaNG6fvvvsuN2oEAAAAAAAPIdvh/48//lCVKlUkSW5ubkpISJAktWvXTuvWrcvZ6gAAAAAgE4Zh6NqNFOvLMIy8Lgl4pGX7nv8SJUooPj5epUqVUkBAgDZt2qSaNWtq7969cnR0zI0aAQAAAMBGckqqnnvrI+tyxLBecnUijwD3ku0r/08++aS2bNkiSRo4cKDGjh2rcuXKqVevXurTp0+OFwgAAAAAAB5Otq/8T5s2zfpz165dVapUKe3evVvlypVT+/btc7Q4AAAAAADw8LId/v+qQYMGatCgQU7UAgAAAAAAckGWw/+OHTuy1K9p06YPXAwAAAAAAMh5WQ7/ISEh91xnsVis/3vr1q2HLgoAAAAAAOScLIf/y5cvZ9qenJysOXPm6J133lGZMmVyrDAAAAAAAJAzshz+PT09bZbT09O1aNEiTZgwQfny5dN7772n3r1753iBAAAAAADg4TzQA/8+//xzvfrqqzp//rzGjBmjgQMHytGR79QEAAAAAOBRlC87nbdv36769evr2WefVadOnXTq1CkNHz6c4A8AAAAAwCMsy1f+27Rpo82bN6tPnz5as2aNfH19c7MuAAAAAACQQ7Ic/jds2CB7e3t98skn+vTTT+/Z79KlSzlSGAAAAAAAyBlZDv8RERG5WQcAAAAAAMglWQ7/PMkfAAAAAIB/p2w98A8AAAAAAPz7EP4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmFyWn/Z/x7BhwzJtt1gscnJyUtmyZRUaGipvb++HLg4AAAAAADy8bIf/gwcP6sCBA0pLS1NgYKAk6fjx47Kzs1OFChX0/vvv65VXXtG3336rihUr5njBAAAAAAAge7I97T80NFTNmzfX77//rv3792v//v367bff1KJFC3Xr1k1nz55V06ZNNXTo0NyoFwAAAAAAZFO2w/+bb76pSZMmycPDw9rm6emp8PBwzZgxQy4uLho3bpz279+fo4UCAAAAAIAHk+3wn5CQoHPnzmVoP3/+vBITEyVJXl5eSk1NffjqAAAAAADAQ3ugaf99+vTR6tWr9dtvv+m3337T6tWr1bdvX3Xs2FGS9P3336t8+fI5XSsAAAAAAHgA2X7g3/z58zV06FA988wzunXr1u1B7O3Vu3dvvf3225KkChUqaOHChTlbKQAAAAAAeCDZDv9ubm5asGCB3n77bZ06dUqSVKZMGbm5uVn7VK9ePccKBAAAAAAADyfb4f8ONzc3Va1aNSdrAQAAAAAAuSDb4f/atWuaNm2atmzZonPnzik9Pd1m/Z3ZAAAAAAAA4NGQ7fDfr18/bd++Xc8++6yKFi0qi8WSG3UBAAAAAIAcku3w//XXX2vdunVq1KhRbtQDAAAAAAByWLa/6q9AgQLy9vbOjVoAAAAAAEAuyHb4nzRpksaNG6fk5OTcqAcAAAAAAOSwbE/7nzVrlmJjY1WkSBH5+/srf/78NusPHDiQY8UBAAAAAICHl+3w37Fjx1woAwAAAAAA5JZsh//x48fnRh0AAAAAACCXZDv837F//37FxMRIkipVqqQaNWrkWFEAAAAAACDnZDv8nzt3Ts8884yioqLk5eUlSbpy5Yoee+wxrVixQj4+PjldIwAAAAAAeAjZftr/wIEDdfXqVR09elSXLl3SpUuXdOTIESUmJmrQoEG5USMAAAAAAHgI2b7yv2HDBm3evFlBQUHWtooVK+q9997TE088kaPFAQAAAACAh5ftK//p6ekZvt5PkvLnz6/09PQcKQr4J4SEhGjIkCG5uo+oqChZLBZduXIlV/cDAAAAAPeT7fDfrFkzDR48WL///ru17ezZsxo6dKgef/zxHC0OeFQQ4gEAAAD8m2U7/L/77rtKTEyUv7+/AgICFBAQoNKlSysxMVFz587NjRoBAAAAAMBDyHb4L1mypA4cOKB169ZpyJAhGjJkiNavX68DBw6oRIkSuVEjkOuWLl2q2rVry93dXb6+vurevbvOnTsnSYqLi9Njjz0mSSpQoIAsFovCwsIk3b4NZurUqSpdurScnZ1VrVo1rVy5MtN9XLt2TR4eHhnWr1mzRq6urrp69WruHSAAAACA/7RsP/BPkiwWi1q0aKEWLVrkdD1Anrh586YmTZqkwMBAnTt3TsOGDVNYWJjWr1+vkiVLatWqVXrqqad07NgxeXh4yNnZWZI0depULVu2TPPmzVO5cuW0Y8cO9ezZUz4+PgoODrbZh6urq5555hlFRESoc+fO1vY7y+7u7pnWlpKSopSUFOtyYmJiLpwBAAAAAGaW5fC/e/duXbx4Ue3atbO2ffTRRxo/fryuXbumjh07au7cuXJ0dMyVQoHc1KdPH+vPZcqU0TvvvKM6deooKSlJbm5u8vb2liQVLlxYXl5ekm6H8jfeeEObN29WgwYNrNt+++23mj9/fobwL0n9+vVTw4YNFR8fr6JFi+rcuXNav369Nm/efM/apk6dqgkTJuTg0QIAAAD4r8nytP+JEyfq6NGj1uUff/xRffv2VfPmzTV69Gh9+eWXmjp1aq4UCeS2/fv3q3379ipVqpTc3d2twf306dP33ObkyZNKTk5WixYt5ObmZn199NFHio2NzXSbunXrqlKlSlqyZIkkadmyZfLz81PTpk3vuZ8xY8YoISHB+jpz5sxDHCkAAACA/6IsX/n/4YcfNGnSJOvyihUrVK9ePS1YsEDS7WcBjB8/XuHh4TleJJCbrl27ppYtW6ply5aKjIyUj4+PTp8+rZYtWyo1NfWe2yUlJUmS1q1bp+LFi9usu98MmH79+um9997T6NGjFRERoeeee04Wi+We/R0dHZlRAwAAAOChZDn8X758WUWKFLEub9++Xa1bt7Yu16lThyuS+Ff6+eefdfHiRU2bNk0lS5aUJO3bt8+mj4ODgyQpLS3N2laxYkU5Ojrq9OnTmU7xv5eePXtq5MiReuedd/TTTz+pd+/eOXAUAAAAAHBvWZ72X6RIEf3yyy+SpNTUVB04cED169e3rr969ary58+f8xUCuaxUqVJycHDQ3LlzderUKa1du9Zmlosk+fn5yWKx6KuvvtL58+eVlJQkd3d3DR8+XEOHDtWSJUsUGxurAwcOaO7cudZp/ZkpUKCAOnXqpBEjRuiJJ57gWzIAAAAA5Losh/82bdpo9OjR2rlzp8aMGSMXFxc1adLEuv7w4cMKCAjIlSKB3OTj46PFixfrs88+U8WKFTVt2jTNnDnTpk/x4sU1YcIEjR49WkWKFNGAAQMkSZMmTdLYsWM1depUBQUFqVWrVlq3bp1Kly5933327dtXqampNg8aBAAAAIDcYjEMw8hKxwsXLqhTp0769ttv5ebmpiVLlujJJ5+0rn/88cdVv359TZkyJdeKBcxi6dKlGjp0qH7//XfrLQVZlZiYKE9PTyUkJMjDwyOXKgQA4OFc3LIir0uAyV27kaLn3vrIuhwxrJdcnXhOEnJPwcefyesSMshONsjyPf+FChXSjh07lJCQIDc3N9nZ2dms/+yzz+Tm5vZgFQP/EcnJyYqPj9e0adP0wgsvZDv4AwAAAMCDyPK0/zs8PT0zBH9J8vb2JsgAf2PGjBmqUKGCfH19NWbMmLwuBwAAAMB/RLbDP4AHFx4erps3b2rLli3MlAEAAADwjyH8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf5hehaLRWvWrMnrMgAAAAAgzxD+8UgICwtTx44d87oMAAAAADAlwj8AAAAAACZH+Mc/ZuXKlapSpYqcnZ1VsGBBNW/eXNeuXVN4eLiWLFmiL774QhaLRRaLRVFRUZKkM2fOqEuXLvLy8pK3t7dCQ0MVFxdnHXPv3r1q0aKFChUqJE9PTwUHB+vAgQPZqmvDhg1q3LixvLy8VLBgQbVr106xsbHW9Z07d9aAAQOsy0OGDJHFYtHPP/8sSUpNTZWrq6s2b96cpfGaNWtmM54knT9/Xg4ODtqyZUu2agcAAACArCD84x8RHx+vbt26qU+fPoqJiVFUVJQ6deokwzA0fPhwdenSRa1atVJ8fLzi4+PVsGFD3bx5Uy1btpS7u7t27typ6Ohoubm5qVWrVkpNTZUkXb16Vb1799a3336r7777TuXKlVObNm109erVLNd27do1DRs2TPv27dOWLVuUL18+Pfnkk0pPT5ckBQcHWz+MkKTt27erUKFC1ra9e/fq5s2batiwYZbG69evn5YvX66UlBTrmMuWLVPx4sXVrFmzDPWlpKQoMTHR5gUAAAAA2WExDMPI6yJgfgcOHFCtWrUUFxcnPz+/DOvDwsJ05coVmwfzLVu2TJMnT1ZMTIwsFouk21fZvby8tGbNGj3xxBMZxklPT5eXl5eWL1+udu3aSbr9wL/Vq1dn+ZkCFy5ckI+Pj3788UdVrlxZP/74o6pVq6Y///xT9vb28vX11dixY3XkyBGtWLFCU6ZM0fr16xUdHZ2l8W7cuKFixYpp3rx56tKliySpWrVq6tSpk8aPH59h+/DwcE2YMCFDe0JCgjw8PLJ0TAAAAGaTlJSk0NBQ6/IXX3whNze3PKwI+OclJibK09MzS9mAK//4R1SrVk2PP/64qlSpoqeffloLFizQ5cuX77vNoUOHdPLkSbm7u8vNzU1ubm7y9vbWjRs3rNPo//zzT/Xv31/lypWTp6enPDw8lJSUpNOnT2e5thMnTqhbt24qU6aMPDw85O/vL0nWMSpXrixvb29t375dO3fuVI0aNdSuXTtt375d0u2ZACEhIVkez8nJSc8++6wWLVok6fYHI0eOHFFYWFim9Y0ZM0YJCQnW15kzZ7J8bAAAAAAgSfZ5XQD+G+zs7PTNN99o165d2rRpk+bOnavXXntNe/bsUenSpTPdJikpSbVq1VJkZGSGdT4+PpKk3r176+LFi5ozZ478/Pzk6OioBg0aWG8LyIr27dvLz89PCxYsULFixZSenq7KlStbx7BYLGratKmioqLk6OiokJAQVa1aVSkpKTpy5Ih27dql4cOHZ3k86fbU/+rVq+u3335TRESEmjVrlumMCElydHSUo6Njlo8HAAAAAP6KK//4x1gsFjVq1EgTJkzQwYMH5eDgoNWrV0uSHBwclJaWZtO/Zs2aOnHihAoXLqyyZcvavDw9PSVJ0dHRGjRokNq0aaNKlSrJ0dFRFy5cyHJNFy9e1LFjx/T666/r8ccfV1BQUKYzEu7c9x8VFaWQkBDly5dPTZs21ZtvvqmUlBQ1atQoW+NVqVJFtWvX1oIFC7R8+XL16dMnyzUDAAAAQHYR/vGP2LNnj9544w3t27dPp0+f1ueff67z588rKChIkuTv76/Dhw/r2LFjunDhgm7evKkePXqoUKFCCg0N1c6dO/XLL78oKipKgwYN0m+//SZJKleunJYuXaqYmBjt2bNHPXr0kLOzc5brKlCggAoWLKj//e9/OnnypLZu3aphw4Zl6BcSEqKffvpJR48eVePGja1tkZGRql27tlxdXbM1nnT76v+0adNkGIaefPLJbJ1PAAAAAMgOwj/+ER4eHtqxY4fatGmj8uXL6/XXX9esWbPUunVrSVL//v0VGBio2rVry8fHR9HR0XJxcdGOHTtUqlQpderUSUFBQerbt69u3LhhfZjFhx9+qMuXL6tmzZp69tlnNWjQIBUuXDjLdeXLl08rVqzQ/v37VblyZQ0dOlRvvvlmhn5VqlSRl5eXqlevbn2QTEhIiNLS0mzu98/qeJLUrVs32dvbq1u3bnJycspyzQAAAACQXTztH8gjcXFxCggI0N69e1WzZs0sb5edJ3oCAACYFU/7B7KXDXjgH/APu3nzpi5evKjXX39d9evXz1bwBwAAAIAHwbR/4B8WHR2tokWLau/evZo3b15elwMAAADgP4Ar/8A/LCQkRNxtAwAAAOCfxJV/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAAAAACZH+AcAAAAAwOQI/wAAAAAAmBzhHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wv8jJCQkREOGDMnrMnLVv/kY/f39NXv27LwuAwAAAACyjfBvcuHh4QoLC7Mu/5vD97/N4sWL5eXllddlAAAAAADhHwAAAAAAsyP855Fr166pV69ecnNzU9GiRTVr1qwMfS5fvqxevXqpQIECcnFxUevWrXXixAnr+jtXljdu3KigoCC5ubmpVatWio+Pz3SfYWFh2r59u+bMmSOLxSKLxaK4uLhM+/r7+2vy5MnWGv38/LR27VqdP39eoaGhcnNzU9WqVbVv3z7rNhcvXlS3bt1UvHhxubi4qEqVKvr444/vex7WrVsnT09PRUZGSpLOnDmjLl26yMvLS97e3goNDb1njZIUFRUli8WijRs3qkaNGnJ2dlazZs107tw5ff311woKCpKHh4e6d++u5ORk63bp6emaOnWqSpcuLWdnZ1WrVk0rV668b61/9dZbb6lKlSpydXVVyZIl9fLLLyspKcla13PPPaeEhATruQ4PD5ckpaSkaPjw4SpevLhcXV1Vr149RUVFZWvfAAAAAJAdhP88MmLECG3fvl1ffPGFNm3apKioKB04cMCmT1hYmPbt26e1a9dq9+7dMgxDbdq00c2bN619kpOTNXPmTC1dulQ7duzQ6dOnNXz48Ez3OWfOHDVo0ED9+/dXfHy84uPjVbJkyXvW+Pbbb6tRo0Y6ePCg2rZtq2effVa9evVSz549deDAAQUEBKhXr14yDEOSdOPGDdWqVUvr1q3TkSNH9Pzzz+vZZ5/V999/n+n4y5cvV7du3RQZGakePXro5s2batmypdzd3bVz505FR0dbP9BITU297/kMDw/Xu+++q127dlk/QJg9e7aWL1+udevWadOmTZo7d661/9SpU/XRRx9p3rx5Onr0qIYOHaqePXtq+/bt993P3fLly6d33nlHR48e1ZIlS7R161aNHDlSktSwYUPNnj1bHh4e1nN9530ZMGCAdu/erRUrVujw4cN6+umn1apVK5sPdu6WkpKixMREmxcAAAAAZIuBf9zVq1cNBwcH49NPP7W2Xbx40XB2djYGDx5sGIZhHD9+3JBkREdHW/tcuHDBcHZ2tm4XERFhSDJOnjxp7fPee+8ZRYoUuee+g4ODrfu4Hz8/P6Nnz57W5fj4eEOSMXbsWGvb7t27DUlGfHz8Pcdp27at8corr2TY/7vvvmt4enoaUVFR1nVLly41AgMDjfT0dGtbSkqK4ezsbGzcuDHT8bdt22ZIMjZv3mxtmzp1qiHJiI2Ntba98MILRsuWLQ3DMIwbN24YLi4uxq5du2zG6tu3r9GtW7f7npO33377nus/++wzo2DBgtbliIgIw9PT06bPr7/+atjZ2Rlnz561aX/88ceNMWPGZDru+PHjDUkZXgkJCfesBQAAwOyuXr1qNGvWzPq6evVqXpcE/OMSEhKynA3s8+Yjh/+22NhYpaamql69etY2b29vBQYGWpdjYmJkb29v06dgwYIKDAxUTEyMtc3FxUUBAQHW5aJFi+rcuXM5UmfVqlWtPxcpUkSSVKVKlQxt586dk6+vr9LS0vTGG2/o008/1dmzZ5WamqqUlBS5uLjYjLty5UqdO3dO0dHRqlOnjrX90KFDOnnypNzd3W3637hxQ7Gxsdmq1cXFRWXKlLFpuzMD4eTJk0pOTlaLFi1sxkhNTVWNGjXuu5+7bd68WVOnTtXPP/+sxMRE3bp1Szdu3FBycnKGY77jxx9/VFpamsqXL2/TnpKSooIFC2a6zZgxYzRs2DDrcmJi4n1nbAAAAADAXxH+/+Xy589vs2yxWKzT8HNybIvFcs+29PR0SdKbb76pOXPmaPbs2dZ74YcMGZJhyn6NGjV04MABLVq0SLVr17aOk5SUpFq1alnv/7+bj49PtmrN7LzcqfPOffnr1q1T8eLFbfo5Ojredz93xMXFqV27dnrppZc0ZcoUeXt769tvv1Xfvn2Vmpp6z/CflJQkOzs77d+/X3Z2djbr3NzcMt3G0dExy3UBAAAAQGYI/3kgICBA+fPn1549e1SqVClJtx/ud/z4cQUHB0uSgoKCdOvWLe3Zs0cNGzaUdPuBeseOHVPFihUfeN8ODg5KS0t7+IPIRHR0tEJDQ9WzZ09Jtz8UOH78eIZ6AwICNGvWLIWEhMjOzk7vvvuuJKlmzZr65JNPVLhwYXl4eORKjZJUsWJFOTo66vTp09bznV379+9Xenq6Zs2apXz5bj8649NPP7Xpk9m5rlGjhtLS0nTu3Dk1adLkwQ4AAAAAALKJB/7lATc3N/Xt21cjRozQ1q1bdeTIEYWFhVlDpCSVK1dOoaGh6t+/v7799lsdOnRIPXv2VPHixRUaGvrA+/b399eePXsUFxenCxcuWK+G54Ry5crpm2++0a5duxQTE6MXXnhBf/75Z6Z9y5cvr23btmnVqlUaMmSIJKlHjx4qVKiQQkNDtXPnTv3yyy+KiorSoEGD9Ntvv+VYne7u7ho+fLiGDh2qJUuWKDY2VgcOHNDcuXO1ZMmSLI1RtmxZ3bx5U3PnztWpU6e0dOlSzZs3z6aPv7+/kpKStGXLFl24cEHJyckqX768evTooV69eunzzz/XL7/8ou+//15Tp07VunXrcuwYAQAAAOBuhP888uabb6pJkyZq3769mjdvrsaNG6tWrVo2fSIiIlSrVi21a9dODRo0kGEYWr9+fYYp7dkxfPhw2dnZqWLFivLx8dHp06cf9lCsXn/9ddWsWVMtW7ZUSEiIfH191bFjx3v2DwwM1NatW/Xxxx/rlVdekYuLi3bs2KFSpUqpU6dOCgoKUt++fXXjxo0cnwkwadIkjR07VlOnTlVQUJBatWqldevWqXTp0lnavlq1anrrrbc0ffp0Va5cWZGRkZo6dapNn4YNG+rFF19U165d5ePjoxkzZki6/b726tVLr7zyigIDA9WxY0ft3bvXOgsEAAAAAHKaxcipG8QB/CMSExPl6emphISEXL09AgAA4FGWlJRkMyP2iy++uOczlACzyk424Mo/AAAAAAAmR/gHAAAAAMDkCP8AAAAAAJgc4R8AAAAAAJMj/AMAAAAAYHKEfwAAAAAATI7wDwAAAACAyRH+AQAAAAAwOcI/AAAAAAAmR/gHAAAAAMDkCP8AAAAAAJgc4R8AAAAAAJMj/AMAAAAAYHKEfwAAAAAATI7wDwAAAACAyRH+AQAAAAAwOcI/AAAAAAAmR/gHAAAAAMDkCP8AAAAAAJgc4R8AAAAAAJMj/AMAAAAAYHKEfwAAAAAATI7wDwAAAACAyRH+AQAAAAAwOcI/AAAAAAAmR/gHAAAAAMDkCP8AAAAAAJgc4R8AAAAAAJMj/AMAAAAAYHKEfwAAAAAATI7wDwAAAACAyRH+AQAAAAAwOcI/AAAAAAAmR/gHAAAAAMDkCP8AAAAAAJgc4R8AAAAAAJMj/AMAAAAAYHKEfwAAAAAATI7wDwAAAACAydnndQEAAAAAkF2urq764osvbJYB3BvhHwAAAMC/jsVikZubW16XAfxrMO0fAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCPwAAAAAAJkf4BwAAAADA5Aj/AAAAAACYHOEfAAAAAACTI/wDAAAAAGByhH8AAAAAAEyO8A8AAAAAgMkR/gEAAAAAMDnCfy4ICQnRkCFDHmqMqKgoWSwWXblyJUdqwoPhfQAAAABgBoR/5KjFixcrJCQkr8v4W3FxcbJYLPrhhx/yuhQAAAAAyHWEfwAAAAAATI7wn0tu3bqlAQMGyNPTU4UKFdLYsWNlGIZ1/dKlS1W7dm25u7vL19dX3bt317lz5zKMEx0drapVq8rJyUn169fXkSNHJEnXrl2Th4eHVq5cadN/zZo1cnV11dWrV/+2RsMw5OPjYzNG9erVVbRoUevyt99+K0dHRyUnJ0uSrly5on79+snHx0ceHh5q1qyZDh06lOn4mzZtkpOTU4Yp84MHD1azZs1s9tGkSRM5OzurZMmSGjRokK5du3bf2idPnqzChQvL3d1d/fr10+jRo1W9enXr+vT0dE2cOFElSpSQo6Ojqlevrg0bNljXly5dWpJUo0YNWSyWbM1WWLVqlSpVqiRHR0f5+/tr1qxZ1nWvvvqq6tWrl2GbatWqaeLEidblhQsXKigoSE5OTqpQoYLef//9LO8fAAAAALLLPq8LMKslS5aob9+++v7777Vv3z49//zzKlWqlPr37y9JunnzpiZNmqTAwECdO3dOw4YNU1hYmNavX28zzogRIzRnzhz5+vrq1VdfVfv27XX8+HG5urrqmWeeUUREhDp37mztf2fZ3d39b2u0WCxq2rSpoqKi1LlzZ12+fFkxMTFydnbWzz//rAoVKmj79u2qU6eOXFxcJElPP/20nJ2d9fXXX8vT01Pz58/X448/ruPHj8vb29tm/Mcff1xeXl5atWqV+vbtK0lKS0vTJ598oilTpkiSYmNj1apVK02ePFmLFi3S+fPnNWDAAA0YMEARERGZ1h0ZGakpU6bo/fffV6NGjbRixQrNmjXLGuglac6cOZo1a5bmz5+vGjVqaNGiRerQoYOOHj2qcuXK6fvvv1fdunW1efNmVapUSQ4ODn97viRp//796tKli8LDw9W1a1ft2rVLL7/8sgoWLKiwsDD16NFDU6dOVWxsrAICAiRJR48e1eHDh7Vq1Spr/ePGjdO7776rGjVq6ODBg+rfv79cXV3Vu3fvDPtMSUlRSkqKdTkhIUGSlJiYmKWaAQAAAJjTnUxw94XmezKQ44KDg42goCAjPT3d2jZq1CgjKCjontvs3bvXkGRcvXrVMAzD2LZtmyHJWLFihbXPxYsXDWdnZ+OTTz4xDMMw9uzZY9jZ2Rm///67YRiG8eeffxr29vZGVFRUlmt95513jEqVKhmGYRhr1qwx6tWrZ4SGhhoffPCBYRiG0bx5c+PVV181DMMwdu7caXh4eBg3btywGSMgIMCYP39+puMPHjzYaNasmXV548aNhqOjo3H58mXDMAyjb9++xvPPP2+zzc6dO418+fIZ169fz3TMevXqGf/3f/9n09aoUSOjWrVq1uVixYoZU6ZMselTp04d4+WXXzYMwzB++eUXQ5Jx8ODBTPdxx5334U693bt3N1q0aGHTZ8SIEUbFihWty9WqVTMmTpxoXR4zZoxRr14963JAQICxfPlymzEmTZpkNGjQINMaxo8fb0jixYsXL168ePHixYsXr0xfZ86cuW+uMQzD4Mp/Lqlfv74sFot1uUGDBpo1a5bS0tJkZ2en/fv3Kzw8XIcOHdLly5eVnp4uSTp9+rQqVqxos90d3t7eCgwMVExMjCSpbt26qlSpkpYsWaLRo0dr2bJl8vPzU9OmTbNcZ3BwsAYPHqzz589r+/btCgkJka+vr6KiotS3b1/t2rVLI0eOlCQdOnRISUlJKliwoM0Y169fV2xsbKbj9+jRQ/Xr19fvv/+uYsWKKTIyUm3btpWXl5d1zMOHDysyMtK6jWEYSk9P1y+//KKgoKAMYx47dkwvv/yyTVvdunW1detWSbc//fr999/VqFEjmz6NGjW65y0KWRUTE6PQ0NAM486ePdv63vbo0UOLFi2y3urx8ccfa9iwYZJu364RGxurvn37WmeBSLdvE/H09Mx0n2PGjLFuL92+peHSpUsqWLCgze8Y8F+SmJiokiVL6syZM/Lw8MjrcgAAeYR/D/BfZxiGrl69qmLFiv1tX8J/Hrh27Zpatmypli1bKjIyUj4+Pjp9+rRatmyp1NTUbI3Vr18/vffeexo9erQiIiL03HPPZSsQVqlSRd7e3tq+fbu2b9+uKVOmyNfXV9OnT9fevXt18+ZNNWzYUJKUlJSkokWLKioqKsM4d8L8X9WpU0cBAQFasWKFXnrpJa1evVqLFy+2rk9KStILL7ygQYMGZdi2VKlSWT6OR0m3bt00atQoHThwQNevX9eZM2fUtWtXSbePV5IWLFiQ4dkAdnZ2mY7n6OgoR0dHm7Z7nW/gv8bDw4P/2AMA8O8B/tPudRHxrwj/uWTPnj02y999953KlSsnOzs7/fzzz7p48aKmTZumkiVLSpL27duX6TjfffedNQRfvnxZx48ft7ka3rNnT40cOVLvvPOOfvrpp0zvGb8fi8WiJk2a6IsvvtDRo0fVuHFjubi4KCUlRfPnz1ft2rXl6uoqSapZs6b++OMP2dvby9/fP8v76NGjhyIjI1WiRAnly5dPbdu2ta6rWbOmfvrpJ5UtWzbL4wUGBmrv3r3q1auXtW3v3r3Wnz08PFSsWDFFR0crODjY2h4dHa26detKkvUe/7S0tCzvV5KCgoIUHR1t0xYdHa3y5ctbw3uJEiUUHBysyMhIXb9+XS1atFDhwoUlSUWKFFGxYsV06tQp9ejRI1v7BgAAAIAH9rc3BiDbgoODDTc3N2Po0KHGzz//bCxfvtxwdXU15s2bZxiGYZw7d85wcHAwRowYYcTGxhpffPGFUb58eUP6f/eg37nXvFKlSsbmzZuNH3/80ejQoYNRqlQpIyUlxWZ/3bt3NxwcHIxWrVo9UL2zZ8827OzsbO5LDw0NNezs7IzRo0db29LT043GjRsb1apVMzZu3Gj88ssvRnR0tPHqq68ae/fuvef4J06cMCQZVatWNfr27Wuz7tChQ4azs7Pxf//3f8bBgweN48ePG2vWrMlwT//dli1bZjg7OxuLFy82jh8/bkyaNMnw8PAwqlevbu3z9ttvGx4eHsaKFSuMn3/+2Rg1apSRP39+4/jx44ZhGMbNmzcNZ2dnY/LkycYff/xhXLlyJdN9/fWe//379xv58uUzJk6caBw7dsxYvHix4ezsbERERNhst2DBAqNYsWJGoUKFjKVLl2ZY5+zsbMyZM8c4duyYcfjwYWPRokXGrFmz7nnMAGwlJCQYkoyEhIS8LgUAkIf49wDIOsJ/LggODjZefvll48UXXzQ8PDyMAgUKGK+++qrNAwCXL19u+Pv7G46OjkaDBg2MtWvXZhr+v/zyS6NSpUqGg4ODUbduXePQoUMZ9rdlyxZDkvHpp59mWkvv3r3vW+/BgwcNScaoUaOsbW+//bYhydiwYYNN38TERGPgwIFGsWLFjPz58xslS5Y0evToYZw+ffq++6hbt64hydi6dWuGdd9//73RokULw83NzXB1dTWqVq2a4WF9fzVx4kSjUKFChpubm9GnTx9j0KBBRv369a3r09LSjPDwcKN48eJG/vz5jWrVqhlff/21zRgLFiwwSpYsaeTLl88IDg7OdD9/Df+GYRgrV640KlasaOTPn98oVaqU8eabb2bY7vLly4ajo6Ph4uJifYjj3SIjI43q1asbDg4ORoECBYymTZsan3/++X2PGcD/c+PGDWP8+PEZHkAKAPhv4d8DIOsshpGV7wTAo2zp0qUaOnSofv/99wxfWefn56cJEyYoLCwsb4r7h7Ro0UK+vr5aunRpXpcCAAAAAI8c7vn/F0tOTlZ8fLymTZumF154IUPwP3r0qDw9PW3ujTeD5ORkzZs3Ty1btpSdnZ0+/vhjbd68Wd98801elwYAAAAAjySu/P+LhYeHa8qUKWratKm++OILubm55XVJ/4jr16+rffv2OnjwoG7cuKHAwEC9/vrr6tSpU16XBgAAAACPJMI/AAAAAAAmly+vCwAAPPrCwsLUsWPHLPWNioqSxWLRlStXcrUm6fbXla5ZsybX9wMAZsff+ezz9/fX7Nmz87oMIMu45x8A8LfmzJmjR3GiWHx8vAoUKJDXZQDAv55Z/85HRUXpscce0+XLl+Xl5ZVzhQH/QoR/AMDf8vT0zOsSMuXr65uj4xmGobS0NNnb888jgP+W/8rf+UdJampqhgd2A7mJaf8AgL9193TQlJQUDRo0SIULF5aTk5MaN26svXv3Zthm//79ql27tlxcXNSwYUMdO3bMui48PFzVq1fX0qVL5e/vL09PTz3zzDO6evWqtU9ISIgGDRqkkSNHytvbW76+vgoPD7fZx1+ng545c0ZdunSRl5eXvL29FRoaqri4uHse152pq19//bVq1aolR0dHffvtt4qNjVVoaKiKFCkiNzc31alTR5s3b7ZuN3HiRFWuXDnDeNWrV9fYsWP/5mwCwKPHjH/n4+Li9Nhjj0mSChQoIIvFYv366w0bNqhx48by8vJSwYIF1a5dO8XGxlq3bdasmQYMGGAz3vnz5+Xg4KAtW7Zkur/Tp08rNDRUbm5u8vDwUJcuXfTnn39mOCcLFy5U6dKl5eTklOk4QG4h/AMAsmXkyJFatWqVlixZogMHDqhs2bJq2bKlLl26ZNPvtdde06xZs7Rv3z7Z29urT58+NutjY2O1Zs0affXVV/rqq6+0fft2TZs2zabPkiVL5Orqqj179mjGjBmaOHHiPb/W8+bNm2rZsqXc3d21c+dORUdHy83NTa1atVJqaup9j2n06NGaNm2aYmJiVLVqVSUlJalNmzbasmWLDh48qFatWql9+/Y6ffq0JKlPnz6KiYmx+Y/hgwcP6vDhw3ruueeyfC4B4FFklr/zJUuW1KpVqyRJx44dU3x8vObMmSNJunbtmoYNG6Z9+/Zpy5Ytypcvn5588kmlp6dLkvr166fly5crJSXFOt6yZctUvHhxNWvWLMO+0tPTFRoaqkuXLmn79u365ptvdOrUKXXt2tWm38mTJ7Vq1Sp9/vnn+uGHHzI9TiDXGAAA/I3evXsboaGhRlJSkpE/f34jMjLSui41NdUoVqyYMWPGDMMwDGPbtm2GJGPz5s3WPuvWrTMkGdevXzcMwzDGjx9vuLi4GImJidY+I0aMMOrVq2ddDg4ONho3bmxTR506dYxRo0ZZlyUZq1evNgzDMJYuXWoEBgYa6enp1vUpKSmGs7OzsXHjxkyP606ta9as+dtzUKlSJWPu3LnW5datWxsvvfSSdXngwIFGSEjI344DAI8is/+dv3z58n2P//z584Yk48cffzQMwzCuX79uFChQwPjkk0+sfapWrWqEh4dbl/38/Iy3337bMAzD2LRpk2FnZ2ecPn3auv7o0aOGJOP777+3npP8+fMb586du28tQG7hyj8AIMtiY2N18+ZNNWrUyNqWP39+1a1bVzExMTZ9q1atav25aNGikqRz585Z2/z9/eXu7m7T5+71fx3jXn3uOHTokE6ePCl3d3e5ubnJzc1N3t7eunHjhs1UzszUrl3bZjkpKUnDhw9XUFCQvLy85ObmppiYGOuVf0nq37+/Pv74Y924cUOpqalavnx5hqteAPBvY9a/83914sQJdevWTWXKlJGHh4f8/f0lyfp33snJSc8++6wWLVokSTpw4ICOHDlivW3gr2JiYlSyZEmVLFnS2laxYkV5eXnZnDc/Pz/5+Phkq1Ygp/BEIwBArsifP7/1Z4vFIknW6ZR/XX+nz93rs9rnjqSkJNWqVUuRkZEZ1v3df2i5urraLA8fPlzffPONZs6cqbJly8rZ2VmdO3e2mVbavn17OTo6avXq1XJwcNDNmzfVuXPn++4HAMzk3/R3/q/at28vPz8/LViwQMWKFVN6eroqV65s83e+X79+ql69un777TdFRESoWbNm8vPzy9Z+/uqv/94A/yTCPwAgywICAuTg4KDo6GjrfwDdvHlTe/fu1ZAhQ/K0tpo1a+qTTz5R4cKF5eHh8VBjRUdHKywsTE8++aSk2//B+dcHStnb26t3796KiIiQg4ODnnnmGTk7Oz/UfgEgr5nt7/ydp+mnpaVZ2y5evKhjx45pwYIFatKkiSTp22+/zbBtlSpVVLt2bS1YsEDLly/Xu+++e8/9BAUF6cyZMzpz5oz16v9PP/2kK1euqGLFilk+RiA3Me0fAJBlrq6ueumllzRixAht2LBBP/30k/r376/k5GT17ds3T2vr0aOHChUqpNDQUO3cuVO//PKLoqKiNGjQIP3222/ZGqtcuXLWhzEdOnRI3bt3z/RKVL9+/bR161Zt2LCBKf8ATMFsf+f9/PxksVj01Vdf6fz580pKSlKBAgVUsGBB/e9//9PJkye1detWDRs2LNPt+/Xrp2nTpskwDOsHwplp3ry5qlSpoh49eujAgQP6/vvv1atXLwUHB2e4tQzIK4R/AEC2TJs2TU899ZSeffZZ1axZUydPntTGjRtVoECBPK3LxcVFO3bsUKlSpdSpUycFBQWpb9++unHjRrZnArz11lsqUKCAGjZsqPbt26tly5aqWbNmhn7lypVTw4YNVaFCBdWrVy+nDgUA8pSZ/s4XL15cEyZM0OjRo1WkSBENGDBA+fLl04oVK7R//35VrlxZQ4cO1Ztvvpnp9t26dZO9vb26det236/ms1gs+uKLL1SgQAE1bdpUzZs3V5kyZfTJJ5/kyLEDOcFiGIaR10UAAB5t3bp1k52dnZYtW5bXpTxSDMNQuXLl9PLLL9/zqhEA/Bvwdz5zcXFxCggI0N69ezP9EBj4N+HKPwDgnm7duqWffvpJu3fvVqVKlfK6nEfK+fPn9e677+qPP/7Qc889l9flAMAD4e985m7evKk//vhDr7/+uurXr0/whynwwD8AwD0dOXJEDRs21GOPPaYXX3wxr8t5pBQuXFiFChXS//73vzyfCgsAD4q/85mLjo7WY489pvLly2vlypV5XQ6QI5j2DwAAAACAyTHtHwAAAAAAkyP8AwAAAABgcoR/AAAAAABMjvAPAAAAAIDJEf4BAAAAADA5wj8AAMA/xGKxaM2aNXldBgDgP4jwDwAAcB+7d++WnZ2d2rZtm+VtwsPDVb169Qzt8fHxat26dZbGyOoHBRaLRU5OTvr1119t2jt27KiwsLAs7QsAYH6EfwAAgPv48MMPNXDgQO3YsUO///77ffsahqFbt27dc72vr68cHR1zukRZLBaNGzcux8cFAJgH4R8AAOAekpKS9Mknn+ill15S27ZttXjxYpv1UVFRslgs+vrrr1WrVi05Ojpq2bJlmjBhgg4dOiSLxSKLxWLd7u6r+ampqRowYICKFi0qJycn+fn5aerUqZIkf39/SdKTTz4pi8ViXb6XAQMGaNmyZTpy5Mg9+2zYsEGNGzeWl5eXChYsqHbt2ik2Nta6Pi4uThaLRZ9++qmaNGkiZ2dn1alTR8ePH9fevXtVu3Ztubm5qXXr1jp//rzN2AsXLlRQUJCcnJxUoUIFvf/++39/cgEA/yjCPwAAwD18+umnqlChggIDA9WzZ08tWrRIhmFk6Dd69GhNmzZNMTExatGihV555RVVqlRJ8fHxio+PV9euXTNs884772jt2rX69NNPdezYMUVGRlpD/t69eyVJERERio+Pty7fS6NGjdSuXTuNHj36nn2uXbumYcOGad++fdqyZYvy5cunJ598Uunp6Tb9xo8fr9dff10HDhyQvb29unfvrpEjR2rOnDnauXOnTp48aTPLIDIyUuPGjdOUKVMUExOjN954Q2PHjtWSJUvuWzMA4J9ln9cFAAAAPKo+/PBD9ezZU5LUqlUrJSQkaPv27QoJCbHpN3HiRLVo0cK67ObmJnt7e/n6+t5z7NOnT6tcuXJq3LixLBaL/Pz8rOt8fHwkSV5eXvcd425Tp05V1apVtXPnTjVp0iTD+qeeespmedGiRfLx8dFPP/2kypUrW9uHDx+uli1bSpIGDx6sbt26acuWLWrUqJEkqW/fvjYzIMaPH69Zs2apU6dOkqTSpUvrp59+0vz589W7d+8s1Q4AyH1c+QcAAMjEsWPH9P3336tbt26SJHt7e3Xt2lUffvhhhr61a9fO9vhhYWH64YcfFBgYqEGDBmnTpk0PVW/FihXVq1eve179P3HihLp166YyZf6/9u4YJLUwDOP4k2EOpaYRpiTVDZo0MIrGCCRcgqDIodGGhghqidqEpsKmpk6HosWgxqihxkaDaGsowzEol4YKqzvEPVzL2617u1w4/H8gHD6/857PzYdz3vN9k8fjsZ4yKBQKZfM6Ozut40AgIEmKRqNlY1dXV5JeniY4Pz9XKpVSXV2d9VlYWChrKQAA/H/c+QcAAKjANE2VSiWFQiFr7Pn5WS6XSysrK/J6vdZ4bW3tp+t3dXUpn89rf39fh4eHGh0dVTwe187Ozh+vOZ1Oq6Ojo+IuAYODg2ppaZFhGAqFQnp6elIkEtHDw0PZPKfTaR1XVVVVHPvRKnB7eytJMgxDvb29ZXWqq6v/+HcAAL4e4R8AAOCVUqmkzc1NZTIZDQwMlH03NDSkbDariYmJX55fU1Ojx8fH317H4/EomUwqmUxqZGREiURCNzc38vv9cjqdH6rxs3A4rMnJSc3Pz6u9vd0av76+1tnZmQzDsFoCjo6OPlW7kkAgoFAopIuLC42Njf11PQDAv0P4BwAAeGV3d1fFYlGpVKrsDr/00jtvmua74b+1tVX5fF4nJydqbm6W2+1+s8Xf8vKygsGgYrGYHA6Htre31dTUpPr6eqvGj157l8sln8/3obXPzc3JMAzl83nrRYM+n08NDQ1aXV1VMBhUoVB49+WAn5FOpzU1NSWv16tEIqH7+3vlcjkVi0XNzMx8yTUAAH+Pnn8AAIBXTNNUPB5/E/yll/Cfy+V0enr6y/OHh4eVSCTU39+vxsZGZbPZN3PcbrcWFxfV3d2tnp4eXV5eam9vTw7Hy9+zTCajg4MDhcNhxWKxD6/d7/drdnZWd3d31pjD4dDW1paOj48ViUQ0PT2tpaWlD9d8z/j4uNbW1rS+vq5oNKq+vj5tbGyora3tS+oDAL5G1XOl/WoAAAAAAIBtcOcfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGzuO7SqpAuWnlPBAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}