Speech_Emotion_Detection.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "02wVzRtCOqtj"
      },
      "outputs": [],
      "source": [
        "import librosa\n",
        "from librosa.display import *\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from matplotlib.pyplot import specgram\n",
        "import pandas as pd\n",
        "import glob\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import IPython.display as ipd  # To play sound in the notebook\n",
        "import os\n",
        "import sys\n",
        "import warnings\n",
        "# ignore warnings\n",
        "if not sys.warnoptions:\n",
        "    warnings.simplefilter(\"ignore\")\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CREMA = \"/content/Crema\""
      ],
      "metadata": {
        "id": "0NtiWlwitHaU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dir_list = os.listdir(CREMA)\n",
        "dir_list.sort()\n",
        "print(dir_list[0:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hw3fidsTthSD",
        "outputId": "1d6beeee-35d5-4418-9e54-85da9b7a22e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['1001_DFA_ANG_XX.wav', '1001_DFA_DIS_XX.wav', '1001_DFA_FEA_XX.wav', '1001_DFA_HAP_XX.wav', '1001_DFA_NEU_XX.wav', '1001_DFA_SAD_XX.wav', '1001_IEO_ANG_HI.wav', '1001_IEO_ANG_LO.wav', '1001_IEO_ANG_MD.wav', '1001_IEO_DIS_HI.wav']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender = []\n",
        "emotion = []\n",
        "path = []\n",
        "female = [1002,1003,1004,1006,1007,1008,1009,1010,1012,1013,1018,1020]\n",
        "\n",
        "for i in dir_list:\n",
        "    part = i.split('_')\n",
        "    if int(part[0]) in female:\n",
        "        temp = 'female'\n",
        "    else:\n",
        "        temp = 'male'\n",
        "    gender.append(temp)\n",
        "    if part[2] == 'SAD' and temp == 'male':\n",
        "        emotion.append('male_sad')\n",
        "    elif part[2] == 'ANG' and temp == 'male':\n",
        "        emotion.append('male_angry')\n",
        "    elif part[2] == 'DIS' and temp == 'male':\n",
        "        emotion.append('male_disgust')\n",
        "    elif part[2] == 'FEA' and temp == 'male':\n",
        "        emotion.append('male_fear')\n",
        "    elif part[2] == 'HAP' and temp == 'male':\n",
        "        emotion.append('male_happy')\n",
        "    elif part[2] == 'NEU' and temp == 'male':\n",
        "        emotion.append('male_neutral')\n",
        "    elif part[2] == 'SAD' and temp == 'female':\n",
        "        emotion.append('female_sad')\n",
        "    elif part[2] == 'ANG' and temp == 'female':\n",
        "        emotion.append('female_angry')\n",
        "    elif part[2] == 'DIS' and temp == 'female':\n",
        "        emotion.append('female_disgust')\n",
        "    elif part[2] == 'FEA' and temp == 'female':\n",
        "        emotion.append('female_fear')\n",
        "    elif part[2] == 'HAP' and temp == 'female':\n",
        "        emotion.append('female_happy')\n",
        "    elif part[2] == 'NEU' and temp == 'female':\n",
        "        emotion.append('female_neutral')\n",
        "    else:\n",
        "        emotion.append('Unknown')\n",
        "    path.append(CREMA + i)\n",
        "\n",
        "CREMA_df = pd.DataFrame(emotion, columns = ['labels'])\n",
        "CREMA_df['source'] = 'CREMA'\n",
        "CREMA_df = pd.concat([CREMA_df,pd.DataFrame(path, columns = ['path'])],axis=1)\n",
        "CREMA_df.labels.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXZk1JMWtmnh",
        "outputId": "4daa2236-d47e-424b-dad1-ed79f35e34fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "female_angry      166\n",
              "female_disgust    166\n",
              "female_fear       166\n",
              "female_happy      166\n",
              "female_sad        166\n",
              "female_neutral    141\n",
              "male_angry        111\n",
              "male_disgust      111\n",
              "male_fear         111\n",
              "male_happy        111\n",
              "male_sad          111\n",
              "male_neutral       95\n",
              "Name: labels, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fname = '/content/Crema/1012_IEO_HAP_HI.wav'\n",
        "data, sampling_rate = librosa.load(fname)\n",
        "plt.figure(figsize=(15, 5))\n",
        "librosa.display.waveshow(data, sr=sampling_rate)\n",
        "\n",
        "# Lets play the audio\n",
        "ipd.Audio(fname)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "o203mYMmucyL",
        "outputId": "f0bc5be8-e42d-4fe2-9025-6874e305fd58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
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type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A fearful track\n",
        "fname = '/content/Crema/1012_IEO_FEA_HI.wav'\n",
        "data, sampling_rate = librosa.load(fname)\n",
        "plt.figure(figsize=(15, 5))\n",
        "librosa.display.waveshow(data, sr=sampling_rate)\n",
        "\n",
        "# Lets play the audio\n",
        "ipd.Audio(fname)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "jrX6krDOwAA8",
        "outputId": "d0141357-f23e-4f79-cec9-bac618453e7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([CREMA_df], axis = 0)\n",
        "print(df.labels.value_counts())\n",
        "df.head()\n",
        "df.to_csv(\"/content/Data_path.csv\",index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCZo8o3iwQT5",
        "outputId": "b2f34981-13c3-4df7-e314-d55c7c33ef9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "female_angry      166\n",
            "female_disgust    166\n",
            "female_fear       166\n",
            "female_happy      166\n",
            "female_sad        166\n",
            "female_neutral    141\n",
            "male_angry        111\n",
            "male_disgust      111\n",
            "male_fear         111\n",
            "male_happy        111\n",
            "male_sad          111\n",
            "male_neutral       95\n",
            "Name: labels, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install resampy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ATYOwQO0Scn",
        "outputId": "0f2228a9-af0f-4f97-baf4-82cda5df15b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting resampy\n",
            "  Downloading resampy-0.4.2-py3-none-any.whl (3.1 MB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/3.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/3.1 MB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/3.1 MB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m34.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m27.0 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 resampy) (1.23.5)\n",
            "Requirement already satisfied: numba>=0.53 in /usr/local/lib/python3.10/dist-packages (from resampy) (0.56.4)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.53->resampy) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba>=0.53->resampy) (67.7.2)\n",
            "Installing collected packages: resampy\n",
            "Successfully installed resampy-0.4.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing required libraries\n",
        "# Keras\n",
        "import keras\n",
        "from keras import regularizers\n",
        "from keras.preprocessing import sequence\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential, Model, model_from_json\n",
        "from keras.layers import Dense, Embedding, LSTM\n",
        "from keras.layers import Input, Flatten, Dropout, Activation, BatchNormalization\n",
        "from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "# sklearn\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Other\n",
        "import librosa\n",
        "import librosa.display\n",
        "import json\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from matplotlib.pyplot import specgram\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import glob\n",
        "import os\n",
        "import pickle\n",
        "import IPython.display as ipd  # To play sound in the notebook"
      ],
      "metadata": {
        "id": "-8zY2mINwlRZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lets pick up the meta-data that we got from our first part of the Kernel\n",
        "ref = pd.read_csv(\"/content/Data_path.csv\")\n",
        "ref.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oc3MHg13x7sl",
        "outputId": "42049e09-8d49-4ef3-aeed-6cb8d5608990"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         labels source                                path\n",
              "0    male_angry  CREMA  /content/Crema/1001_DFA_ANG_XX.wav\n",
              "1  male_disgust  CREMA  /content/Crema/1001_DFA_DIS_XX.wav\n",
              "2     male_fear  CREMA  /content/Crema/1001_DFA_FEA_XX.wav\n",
              "3    male_happy  CREMA  /content/Crema/1001_DFA_HAP_XX.wav\n",
              "4  male_neutral  CREMA  /content/Crema/1001_DFA_NEU_XX.wav"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>labels</th>\n",
              "      <th>source</th>\n",
              "      <th>path</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>male_angry</td>\n",
              "      <td>CREMA</td>\n",
              "      <td>/content/Crema/1001_DFA_ANG_XX.wav</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>male_disgust</td>\n",
              "      <td>CREMA</td>\n",
              "      <td>/content/Crema/1001_DFA_DIS_XX.wav</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>male_fear</td>\n",
              "      <td>CREMA</td>\n",
              "      <td>/content/Crema/1001_DFA_FEA_XX.wav</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>male_happy</td>\n",
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              "      <td>/content/Crema/1001_DFA_HAP_XX.wav</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "        const element = document.querySelector('#df-d5eae807-6f1f-4a33-a754-12538991f4de');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "\n",
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              "    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",
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              "  }\n",
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              "\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",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6742239e-f50d-4667-95eb-d873ba653766 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Note this takes a couple of minutes (~10 mins) as we're iterating over 4 datasets\n",
        "df = pd.DataFrame(columns=['features'])\n",
        "\n",
        "# loop feature extraction over the entire dataset\n",
        "counter=0\n",
        "\n",
        "for index,path in enumerate(ref.path):\n",
        "    X, sample_rate = librosa.load(path)\n",
        "    sample_rate = np.array(sample_rate)\n",
        "\n",
        "    # mean as the feature. Could do min and max etc as well.\n",
        "    mfccs = np.mean(librosa.feature.mfcc(y=X,\n",
        "                                        sr=sample_rate,\n",
        "                                        n_mfcc=13),\n",
        "                    axis=0)\n",
        "    df.loc[counter] = [mfccs]\n",
        "    counter=counter+1\n",
        "\n",
        "# Check a few records to make sure its processed successfully\n",
        "print(len(df))\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "yVsS9fwFyFu1",
        "outputId": "075a2290-9bdc-4256-9f44-af5f9b4192c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1621\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            features\n",
              "0  [-47.588882, -29.184845, -23.81415, -23.018381...\n",
              "1  [-25.360691, -23.582808, -21.892937, -19.54212...\n",
              "2  [-23.172516, -23.247349, -24.189016, -23.83220...\n",
              "3  [-21.348787, -19.612019, -21.584219, -25.19538...\n",
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              "      <th></th>\n",
              "      <th>features</th>\n",
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              "      <th>0</th>\n",
              "      <td>[-47.588882, -29.184845, -23.81415, -23.018381...</td>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7656a434-2467-4f62-9d8b-f0f91ca4904b')\"\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-7656a434-2467-4f62-9d8b-f0f91ca4904b 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-7656a434-2467-4f62-9d8b-f0f91ca4904b');\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-6a2e2e58-4b8b-4c72-9f22-d489d577bc31\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6a2e2e58-4b8b-4c72-9f22-d489d577bc31')\"\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",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --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-6a2e2e58-4b8b-4c72-9f22-d489d577bc31 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([ref,pd.DataFrame(df['features'].values.tolist())],axis=1)\n",
        "df[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "oqG_q-Um0uvH",
        "outputId": "1a491dda-027f-4086-88c9-59e885a380c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         labels source                                path          0  \\\n",
              "0    male_angry  CREMA  /content/Crema/1001_DFA_ANG_XX.wav -47.588882   \n",
              "1  male_disgust  CREMA  /content/Crema/1001_DFA_DIS_XX.wav -25.360691   \n",
              "2     male_fear  CREMA  /content/Crema/1001_DFA_FEA_XX.wav -23.172516   \n",
              "3    male_happy  CREMA  /content/Crema/1001_DFA_HAP_XX.wav -21.348787   \n",
              "4  male_neutral  CREMA  /content/Crema/1001_DFA_NEU_XX.wav -23.650738   \n",
              "\n",
              "           1          2          3          4          5          6  ...  206  \\\n",
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              "\n",
              "[5 rows x 219 columns]"
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              "      <td>NaN</td>\n",
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              "      <td>CREMA</td>\n",
              "      <td>/content/Crema/1001_DFA_HAP_XX.wav</td>\n",
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              "      <td>/content/Crema/1001_DFA_NEU_XX.wav</td>\n",
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              "      <td>-21.733772</td>\n",
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              "</table>\n",
              "<p>5 rows × 219 columns</p>\n",
              "</div>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "      async function convertToInteractive(key) {\n",
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              "          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",
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              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-005ad9e7-5376-440d-95d7-69318cce82eb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-005ad9e7-5376-440d-95d7-69318cce82eb')\"\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-005ad9e7-5376-440d-95d7-69318cce82eb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.fillna(0)\n",
        "print(df.shape)\n",
        "df[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "hv60ynvb0_Fe",
        "outputId": "cbdaba9e-9843-481c-ce6d-d4d5eed1f84f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1621, 219)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         labels source                                path          0  \\\n",
              "0    male_angry  CREMA  /content/Crema/1001_DFA_ANG_XX.wav -47.588882   \n",
              "1  male_disgust  CREMA  /content/Crema/1001_DFA_DIS_XX.wav -25.360691   \n",
              "2     male_fear  CREMA  /content/Crema/1001_DFA_FEA_XX.wav -23.172516   \n",
              "3    male_happy  CREMA  /content/Crema/1001_DFA_HAP_XX.wav -21.348787   \n",
              "4  male_neutral  CREMA  /content/Crema/1001_DFA_NEU_XX.wav -23.650738   \n",
              "\n",
              "           1          2          3          4          5          6  ...  206  \\\n",
              "0 -29.184845 -23.814150 -23.018381 -23.318026 -21.501926 -21.099583  ...  0.0   \n",
              "1 -23.582808 -21.892937 -19.542120 -19.785917 -21.775402 -21.783445  ...  0.0   \n",
              "2 -23.247349 -24.189016 -23.832201 -22.605062 -22.817146 -21.990183  ...  0.0   \n",
              "3 -19.612019 -21.584219 -25.195385 -23.193407 -21.178469 -21.584627  ...  0.0   \n",
              "4 -21.146597 -20.340878 -20.604824 -21.733772 -21.209166 -21.185301  ...  0.0   \n",
              "\n",
              "   207  208  209  210  211  212  213  214  215  \n",
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              "\n",
              "[5 rows x 219 columns]"
            ],
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              "\n",
              "  <div id=\"df-c4c144c3-0aab-4b1a-b373-de93574c7566\" 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",
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              "\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>labels</th>\n",
              "      <th>source</th>\n",
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              "      <th>0</th>\n",
              "      <td>male_angry</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>male_disgust</td>\n",
              "      <td>CREMA</td>\n",
              "      <td>/content/Crema/1001_DFA_DIS_XX.wav</td>\n",
              "      <td>-25.360691</td>\n",
              "      <td>-23.582808</td>\n",
              "      <td>-21.892937</td>\n",
              "      <td>-19.542120</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>male_fear</td>\n",
              "      <td>CREMA</td>\n",
              "      <td>/content/Crema/1001_DFA_FEA_XX.wav</td>\n",
              "      <td>-23.172516</td>\n",
              "      <td>-23.247349</td>\n",
              "      <td>-24.189016</td>\n",
              "      <td>-23.832201</td>\n",
              "      <td>-22.605062</td>\n",
              "      <td>-22.817146</td>\n",
              "      <td>-21.990183</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>male_happy</td>\n",
              "      <td>CREMA</td>\n",
              "      <td>/content/Crema/1001_DFA_HAP_XX.wav</td>\n",
              "      <td>-21.348787</td>\n",
              "      <td>-19.612019</td>\n",
              "      <td>-21.584219</td>\n",
              "      <td>-25.195385</td>\n",
              "      <td>-23.193407</td>\n",
              "      <td>-21.178469</td>\n",
              "      <td>-21.584627</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male_neutral</td>\n",
              "      <td>CREMA</td>\n",
              "      <td>/content/Crema/1001_DFA_NEU_XX.wav</td>\n",
              "      <td>-23.650738</td>\n",
              "      <td>-21.146597</td>\n",
              "      <td>-20.340878</td>\n",
              "      <td>-20.604824</td>\n",
              "      <td>-21.733772</td>\n",
              "      <td>-21.209166</td>\n",
              "      <td>-21.185301</td>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 219 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-c4c144c3-0aab-4b1a-b373-de93574c7566')\"\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-c4c144c3-0aab-4b1a-b373-de93574c7566 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-c4c144c3-0aab-4b1a-b373-de93574c7566');\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-2a80caf2-596f-4cd1-97f5-f132d2575ce8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2a80caf2-596f-4cd1-97f5-f132d2575ce8')\"\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",
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          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split between train and test\n",
        "X_train, X_test, y_train, y_test = train_test_split(df.drop(['path','labels','source'],axis=1)\n",
        "                                                    , df.labels\n",
        "                                                    , test_size=0.25\n",
        "                                                    , shuffle=True\n",
        "                                                    , random_state=42\n",
        "                                                   )\n",
        "\n",
        "# Lets see how the data present itself before normalisation\n",
        "X_train[150:160]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
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        "id": "30Zf3_Up0_tr",
        "outputId": "1b6df603-edd8-4b1a-dcd5-97163eae7ee1"
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      "execution_count": null,
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              "        document.querySelector('#df-ed1a27cd-44a7-4f2a-a6e1-2caf858bf2c8 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-ed1a27cd-44a7-4f2a-a6e1-2caf858bf2c8');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a6e06446-a4e4-46bc-943f-599f148e0d69')\"\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",
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              "    </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",
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              "    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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-a6e06446-a4e4-46bc-943f-599f148e0d69 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lts do data normalization\n",
        "mean = np.mean(X_train, axis=0)\n",
        "std = np.std(X_train, axis=0)\n",
        "\n",
        "X_train = (X_train - mean)/std\n",
        "X_test = (X_test - mean)/std\n",
        "\n",
        "# Check the dataset now\n",
        "X_train[150:160]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "EcwN8yyM1PRj",
        "outputId": "fcc4386e-fa4b-4063-a5f4-756c6ae4b66f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0         1         2         3         4         5         6    \\\n",
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              "\n",
              "           7         8         9    ...       206       207       208  \\\n",
              "282  -0.430369 -0.934106 -1.829221  ...  0.049648  0.049583  0.049476   \n",
              "291  -1.357442 -0.656872 -0.734379  ...  0.049648  0.049583  0.049476   \n",
              "802  -0.123346 -0.371199 -0.179549  ...  0.049648  0.049583  0.049476   \n",
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              "129  -1.195871 -0.816752 -0.503771  ...  0.049648  0.049583  0.049476   \n",
              "323  -0.329676 -1.242412 -0.947879  ...  0.049648  0.049583  0.049476   \n",
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              "1569 -0.579083  0.034156  0.246454  ...  0.049648  0.049583  0.049476   \n",
              "1434  0.635137  0.980674  1.103640  ...  0.049648  0.049583  0.049476   \n",
              "\n",
              "           209       210       211       212       213       214       215  \n",
              "282   0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "291   0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "802   0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "576   0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "129   0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "323   0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "781   0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "1004  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "1569  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "1434  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  0.028701  \n",
              "\n",
              "[10 rows x 216 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
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              "      <th>282</th>\n",
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              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>-0.518536</td>\n",
              "      <td>-1.616093</td>\n",
              "      <td>-1.496362</td>\n",
              "      <td>-1.420181</td>\n",
              "      <td>-1.497707</td>\n",
              "      <td>-1.698604</td>\n",
              "      <td>-2.233810</td>\n",
              "      <td>-1.357442</td>\n",
              "      <td>-0.656872</td>\n",
              "      <td>-0.734379</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
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              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
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              "    <tr>\n",
              "      <th>802</th>\n",
              "      <td>0.099702</td>\n",
              "      <td>-0.319972</td>\n",
              "      <td>0.183076</td>\n",
              "      <td>1.052697</td>\n",
              "      <td>1.210505</td>\n",
              "      <td>0.550853</td>\n",
              "      <td>0.160365</td>\n",
              "      <td>-0.123346</td>\n",
              "      <td>-0.371199</td>\n",
              "      <td>-0.179549</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049648</td>\n",
              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>0.455763</td>\n",
              "      <td>1.097847</td>\n",
              "      <td>1.328756</td>\n",
              "      <td>0.094837</td>\n",
              "      <td>-0.616693</td>\n",
              "      <td>0.260859</td>\n",
              "      <td>-0.095833</td>\n",
              "      <td>0.243825</td>\n",
              "      <td>0.234672</td>\n",
              "      <td>-0.094355</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049648</td>\n",
              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>0.203383</td>\n",
              "      <td>-1.189670</td>\n",
              "      <td>-1.684968</td>\n",
              "      <td>-1.875791</td>\n",
              "      <td>-1.513855</td>\n",
              "      <td>-1.735604</td>\n",
              "      <td>-1.858067</td>\n",
              "      <td>-1.195871</td>\n",
              "      <td>-0.816752</td>\n",
              "      <td>-0.503771</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049648</td>\n",
              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>323</th>\n",
              "      <td>-0.089727</td>\n",
              "      <td>-0.770103</td>\n",
              "      <td>-1.804763</td>\n",
              "      <td>-1.988724</td>\n",
              "      <td>-1.404669</td>\n",
              "      <td>-0.946108</td>\n",
              "      <td>-0.772373</td>\n",
              "      <td>-0.329676</td>\n",
              "      <td>-1.242412</td>\n",
              "      <td>-0.947879</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049648</td>\n",
              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>781</th>\n",
              "      <td>0.171305</td>\n",
              "      <td>0.215766</td>\n",
              "      <td>0.214855</td>\n",
              "      <td>-0.055543</td>\n",
              "      <td>-0.148838</td>\n",
              "      <td>0.003476</td>\n",
              "      <td>-0.486913</td>\n",
              "      <td>-0.132238</td>\n",
              "      <td>-0.446355</td>\n",
              "      <td>-0.339938</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049648</td>\n",
              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1004</th>\n",
              "      <td>0.243737</td>\n",
              "      <td>0.559974</td>\n",
              "      <td>0.824750</td>\n",
              "      <td>0.622140</td>\n",
              "      <td>-0.037267</td>\n",
              "      <td>0.563338</td>\n",
              "      <td>1.501228</td>\n",
              "      <td>1.874903</td>\n",
              "      <td>1.145298</td>\n",
              "      <td>0.290915</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049648</td>\n",
              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1569</th>\n",
              "      <td>0.539198</td>\n",
              "      <td>0.786347</td>\n",
              "      <td>1.345553</td>\n",
              "      <td>0.471836</td>\n",
              "      <td>0.849287</td>\n",
              "      <td>0.637967</td>\n",
              "      <td>-0.077773</td>\n",
              "      <td>-0.579083</td>\n",
              "      <td>0.034156</td>\n",
              "      <td>0.246454</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049648</td>\n",
              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1434</th>\n",
              "      <td>0.329540</td>\n",
              "      <td>0.504265</td>\n",
              "      <td>0.221599</td>\n",
              "      <td>0.374780</td>\n",
              "      <td>0.264848</td>\n",
              "      <td>0.281415</td>\n",
              "      <td>0.463369</td>\n",
              "      <td>0.635137</td>\n",
              "      <td>0.980674</td>\n",
              "      <td>1.103640</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049648</td>\n",
              "      <td>0.049583</td>\n",
              "      <td>0.049476</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "      <td>0.028701</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 216 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-0b4aabe0-eef2-4777-a15b-3804651106c2')\"\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",
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              "  </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-0b4aabe0-eef2-4777-a15b-3804651106c2 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-0b4aabe0-eef2-4777-a15b-3804651106c2');\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-97c4aeea-adf4-4567-994d-dbe9eaf1dfd5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-97c4aeea-adf4-4567-994d-dbe9eaf1dfd5')\"\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-97c4aeea-adf4-4567-994d-dbe9eaf1dfd5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets few preparation steps to get it into the correct format for Keras\n",
        "X_train = np.array(X_train)\n",
        "y_train = np.array(y_train)\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "# one hot encode the target\n",
        "lb = LabelEncoder()\n",
        "y_train = to_categorical(lb.fit_transform(y_train))\n",
        "y_test = to_categorical(lb.fit_transform(y_test))\n",
        "\n",
        "print(X_train.shape)\n",
        "print(lb.classes_)\n",
        "#print(y_train[0:10])\n",
        "#print(y_test[0:10])\n",
        "\n",
        "# Pickel the lb object for future use\n",
        "filename = 'labels'\n",
        "outfile = open(filename,'wb')\n",
        "pickle.dump(lb,outfile)\n",
        "outfile.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gbfZMMX81U8o",
        "outputId": "15aab4ae-6835-4cb9-e452-47846a04fcb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1215, 216)\n",
            "['female_angry' 'female_disgust' 'female_fear' 'female_happy'\n",
            " 'female_neutral' 'female_sad' 'male_angry' 'male_disgust' 'male_fear'\n",
            " 'male_happy' 'male_neutral' 'male_sad']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.expand_dims(X_train, axis=2)\n",
        "X_test = np.expand_dims(X_test, axis=2)\n",
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zLiO3gt1ZqD",
        "outputId": "dd92b9ef-0578-4d7a-fa5a-e2edc6932aad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1215, 216, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)"
      ],
      "metadata": {
        "id": "RfyRs__f5f36"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# New model\n",
        "model = Sequential()\n",
        "model.add(Conv1D(256, 8, padding='same',input_shape=(X_train.shape[1],1)))  # X_train.shape[1] = No. of Columns\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv1D(256, 8, padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(MaxPooling1D(pool_size=(8)))\n",
        "model.add(Conv1D(128, 8, padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv1D(128, 8, padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv1D(128, 8, padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv1D(128, 8, padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(MaxPooling1D(pool_size=(8)))\n",
        "model.add(Conv1D(64, 8, padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv1D(64, 8, padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(12, activation='linear')) # Target class number\n",
        "# opt = keras.optimizers.SGD(lr=0.0001, momentum=0.0, decay=0.0, nesterov=False)\n",
        "opt = keras.optimizers.Adam(lr=0.0001)\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1yU9DMPe1fVs",
        "outputId": "5f20c5a5-9602-4ecb-ff42-6f29d7746457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_32 (Conv1D)          (None, 216, 256)          2304      \n",
            "                                                                 \n",
            " activation_34 (Activation)  (None, 216, 256)          0         \n",
            "                                                                 \n",
            " conv1d_33 (Conv1D)          (None, 216, 256)          524544    \n",
            "                                                                 \n",
            " batch_normalization_8 (Bat  (None, 216, 256)          1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_35 (Activation)  (None, 216, 256)          0         \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 216, 256)          0         \n",
            "                                                                 \n",
            " max_pooling1d_8 (MaxPoolin  (None, 27, 256)           0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " conv1d_34 (Conv1D)          (None, 27, 128)           262272    \n",
            "                                                                 \n",
            " activation_36 (Activation)  (None, 27, 128)           0         \n",
            "                                                                 \n",
            " conv1d_35 (Conv1D)          (None, 27, 128)           131200    \n",
            "                                                                 \n",
            " activation_37 (Activation)  (None, 27, 128)           0         \n",
            "                                                                 \n",
            " conv1d_36 (Conv1D)          (None, 27, 128)           131200    \n",
            "                                                                 \n",
            " activation_38 (Activation)  (None, 27, 128)           0         \n",
            "                                                                 \n",
            " conv1d_37 (Conv1D)          (None, 27, 128)           131200    \n",
            "                                                                 \n",
            " batch_normalization_9 (Bat  (None, 27, 128)           512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_39 (Activation)  (None, 27, 128)           0         \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 27, 128)           0         \n",
            "                                                                 \n",
            " max_pooling1d_9 (MaxPoolin  (None, 3, 128)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " conv1d_38 (Conv1D)          (None, 3, 64)             65600     \n",
            "                                                                 \n",
            " activation_40 (Activation)  (None, 3, 64)             0         \n",
            "                                                                 \n",
            " conv1d_39 (Conv1D)          (None, 3, 64)             32832     \n",
            "                                                                 \n",
            " activation_41 (Activation)  (None, 3, 64)             0         \n",
            "                                                                 \n",
            " flatten_4 (Flatten)         (None, 192)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 12)                2316      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1285004 (4.90 MB)\n",
            "Trainable params: 1284236 (4.90 MB)\n",
            "Non-trainable params: 768 (3.00 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of x_test:\", X_test.shape)\n",
        "print(\"Shape of x_train:\", X_train.shape)\n",
        "print(\"Shape of y_test:\", y_test.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSOQmjTt281v",
        "outputId": "0d986194-8d3b-425a-e4ee-e0d08991f3da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of x_test: (406, 216, 1)\n",
            "Shape of x_train: (1215, 216, 1)\n",
            "Shape of y_test: (406, 12)\n",
            "Shape of y_train: (1215, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=['accuracy'])\n",
        "model_history=model.fit(X_train, y_train, batch_size=16, epochs=100, validation_data=(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HGvUKeNy1l9P",
        "outputId": "869521a3-78d6-4712-d8e9-782c646580f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "76/76 [==============================] - 33s 404ms/step - loss: 8.3177 - accuracy: 0.1070 - val_loss: 7.4636 - val_accuracy: 0.0887\n",
            "Epoch 2/100\n",
            "76/76 [==============================] - 33s 437ms/step - loss: 8.3177 - accuracy: 0.1070 - val_loss: 7.4636 - val_accuracy: 0.0887\n",
            "Epoch 3/100\n",
            "76/76 [==============================] - 30s 399ms/step - loss: 8.3177 - accuracy: 0.1070 - val_loss: 7.4636 - val_accuracy: 0.0887\n",
            "Epoch 4/100\n",
            "76/76 [==============================] - 31s 402ms/step - loss: 8.3177 - accuracy: 0.1070 - val_loss: 7.4636 - val_accuracy: 0.0887\n",
            "Epoch 5/100\n",
            "76/76 [==============================] - 33s 436ms/step - loss: 7.8826 - accuracy: 0.0897 - val_loss: 7.4636 - val_accuracy: 0.0788\n",
            "Epoch 6/100\n",
            "76/76 [==============================] - 30s 402ms/step - loss: 8.3177 - accuracy: 0.0840 - val_loss: 7.2254 - val_accuracy: 0.0837\n",
            "Epoch 7/100\n",
            "76/76 [==============================] - 34s 436ms/step - loss: 8.2779 - accuracy: 0.0889 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 8/100\n",
            "76/76 [==============================] - 30s 397ms/step - loss: 8.3708 - accuracy: 0.0922 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 9/100\n",
            "76/76 [==============================] - 30s 396ms/step - loss: 8.3443 - accuracy: 0.0864 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 10/100\n",
            "76/76 [==============================] - 32s 425ms/step - loss: 8.2912 - accuracy: 0.0905 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 11/100\n",
            "76/76 [==============================] - 30s 392ms/step - loss: 8.3443 - accuracy: 0.0840 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 12/100\n",
            "76/76 [==============================] - 30s 398ms/step - loss: 8.3310 - accuracy: 0.0905 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 13/100\n",
            "76/76 [==============================] - 33s 435ms/step - loss: 8.3443 - accuracy: 0.0864 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 14/100\n",
            "76/76 [==============================] - 30s 397ms/step - loss: 8.3177 - accuracy: 0.0848 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 15/100\n",
            "76/76 [==============================] - 31s 409ms/step - loss: 8.3708 - accuracy: 0.0864 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 16/100\n",
            "76/76 [==============================] - 32s 419ms/step - loss: 8.3177 - accuracy: 0.0815 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 17/100\n",
            "76/76 [==============================] - 30s 396ms/step - loss: 8.2381 - accuracy: 0.0889 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 18/100\n",
            "76/76 [==============================] - 30s 397ms/step - loss: 8.3708 - accuracy: 0.0864 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 19/100\n",
            "76/76 [==============================] - 34s 444ms/step - loss: 8.3045 - accuracy: 0.0922 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 20/100\n",
            "76/76 [==============================] - 30s 395ms/step - loss: 8.2912 - accuracy: 0.0840 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 21/100\n",
            "76/76 [==============================] - 30s 398ms/step - loss: 8.3310 - accuracy: 0.0881 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 22/100\n",
            "76/76 [==============================] - 31s 408ms/step - loss: 8.3310 - accuracy: 0.0856 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 23/100\n",
            "76/76 [==============================] - 30s 396ms/step - loss: 8.3045 - accuracy: 0.0831 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 24/100\n",
            "76/76 [==============================] - 30s 390ms/step - loss: 8.3177 - accuracy: 0.0864 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 25/100\n",
            "76/76 [==============================] - 31s 408ms/step - loss: 8.2779 - accuracy: 0.0864 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 26/100\n",
            "76/76 [==============================] - 30s 391ms/step - loss: 8.3443 - accuracy: 0.0872 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 27/100\n",
            "76/76 [==============================] - 30s 397ms/step - loss: 8.3177 - accuracy: 0.0848 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 28/100\n",
            "76/76 [==============================] - 30s 391ms/step - loss: 8.2249 - accuracy: 0.0864 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 29/100\n",
            "76/76 [==============================] - 31s 408ms/step - loss: 8.2116 - accuracy: 0.0897 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 30/100\n",
            "76/76 [==============================] - 30s 394ms/step - loss: 8.2249 - accuracy: 0.0840 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 31/100\n",
            "76/76 [==============================] - 30s 396ms/step - loss: 8.3575 - accuracy: 0.0905 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 32/100\n",
            "76/76 [==============================] - 33s 431ms/step - loss: 8.3045 - accuracy: 0.0872 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 33/100\n",
            "76/76 [==============================] - 30s 390ms/step - loss: 8.2647 - accuracy: 0.0831 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 34/100\n",
            "76/76 [==============================] - 30s 397ms/step - loss: 8.2381 - accuracy: 0.0922 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 35/100\n",
            "76/76 [==============================] - 31s 412ms/step - loss: 8.2647 - accuracy: 0.0922 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 36/100\n",
            "76/76 [==============================] - 30s 393ms/step - loss: 8.2514 - accuracy: 0.0831 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 37/100\n",
            "76/76 [==============================] - 30s 395ms/step - loss: 8.3443 - accuracy: 0.0905 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 38/100\n",
            "76/76 [==============================] - 30s 397ms/step - loss: 8.2779 - accuracy: 0.0872 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 39/100\n",
            "76/76 [==============================] - 31s 403ms/step - loss: 8.2779 - accuracy: 0.0831 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 40/100\n",
            "76/76 [==============================] - 30s 399ms/step - loss: 8.2779 - accuracy: 0.0881 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 41/100\n",
            "76/76 [==============================] - 30s 401ms/step - loss: 8.3177 - accuracy: 0.0864 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 42/100\n",
            "76/76 [==============================] - 31s 403ms/step - loss: 8.3443 - accuracy: 0.0881 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 43/100\n",
            "76/76 [==============================] - 30s 396ms/step - loss: 8.2779 - accuracy: 0.0815 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 44/100\n",
            "76/76 [==============================] - 29s 386ms/step - loss: 8.2514 - accuracy: 0.0922 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 45/100\n",
            "76/76 [==============================] - 33s 440ms/step - loss: 8.3310 - accuracy: 0.0864 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 46/100\n",
            "76/76 [==============================] - 30s 393ms/step - loss: 8.2912 - accuracy: 0.0905 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 47/100\n",
            "76/76 [==============================] - 30s 389ms/step - loss: 8.2779 - accuracy: 0.0922 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 48/100\n",
            "76/76 [==============================] - 33s 436ms/step - loss: 8.3177 - accuracy: 0.0889 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 49/100\n",
            "76/76 [==============================] - 29s 382ms/step - loss: 8.3575 - accuracy: 0.0831 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 50/100\n",
            "76/76 [==============================] - 30s 393ms/step - loss: 8.3045 - accuracy: 0.0848 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 51/100\n",
            "76/76 [==============================] - 31s 408ms/step - loss: 8.3177 - accuracy: 0.0889 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 52/100\n",
            "76/76 [==============================] - 30s 399ms/step - loss: 8.3443 - accuracy: 0.0807 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 53/100\n",
            "76/76 [==============================] - 30s 394ms/step - loss: 8.3310 - accuracy: 0.0831 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 54/100\n",
            "76/76 [==============================] - 33s 435ms/step - loss: 8.3177 - accuracy: 0.0881 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 55/100\n",
            "76/76 [==============================] - 30s 389ms/step - loss: 8.2779 - accuracy: 0.0922 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 56/100\n",
            "76/76 [==============================] - 30s 399ms/step - loss: 8.3443 - accuracy: 0.0889 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 57/100\n",
            "76/76 [==============================] - 33s 433ms/step - loss: 8.3177 - accuracy: 0.0905 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 58/100\n",
            "76/76 [==============================] - 30s 390ms/step - loss: 8.2912 - accuracy: 0.0889 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 59/100\n",
            "76/76 [==============================] - 30s 392ms/step - loss: 8.2779 - accuracy: 0.0881 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 60/100\n",
            "76/76 [==============================] - 32s 428ms/step - loss: 8.3973 - accuracy: 0.0881 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 61/100\n",
            "76/76 [==============================] - 30s 391ms/step - loss: 8.3045 - accuracy: 0.0831 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 62/100\n",
            "76/76 [==============================] - 30s 395ms/step - loss: 8.3310 - accuracy: 0.0872 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 63/100\n",
            "76/76 [==============================] - 31s 406ms/step - loss: 8.3310 - accuracy: 0.0815 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 64/100\n",
            "76/76 [==============================] - 29s 382ms/step - loss: 8.2779 - accuracy: 0.0897 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 65/100\n",
            "76/76 [==============================] - 30s 392ms/step - loss: 8.3310 - accuracy: 0.0914 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 66/100\n",
            "76/76 [==============================] - 30s 396ms/step - loss: 8.2779 - accuracy: 0.0897 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 67/100\n",
            "76/76 [==============================] - 33s 437ms/step - loss: 8.2912 - accuracy: 0.0848 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 68/100\n",
            "76/76 [==============================] - 30s 391ms/step - loss: 8.2912 - accuracy: 0.0897 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 69/100\n",
            "76/76 [==============================] - 30s 393ms/step - loss: 8.3310 - accuracy: 0.0856 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 70/100\n",
            "76/76 [==============================] - 30s 388ms/step - loss: 8.2647 - accuracy: 0.0856 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 71/100\n",
            "76/76 [==============================] - 30s 398ms/step - loss: 8.3443 - accuracy: 0.0872 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 72/100\n",
            "76/76 [==============================] - 30s 394ms/step - loss: 8.1983 - accuracy: 0.0889 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 73/100\n",
            "76/76 [==============================] - 33s 436ms/step - loss: 8.3310 - accuracy: 0.0889 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 74/100\n",
            "76/76 [==============================] - 30s 397ms/step - loss: 8.3177 - accuracy: 0.0914 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 75/100\n",
            "76/76 [==============================] - 30s 391ms/step - loss: 8.2912 - accuracy: 0.0889 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 76/100\n",
            "76/76 [==============================] - 31s 404ms/step - loss: 8.2647 - accuracy: 0.0807 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 77/100\n",
            "76/76 [==============================] - 30s 392ms/step - loss: 8.2912 - accuracy: 0.0840 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 78/100\n",
            "76/76 [==============================] - 30s 399ms/step - loss: 8.2912 - accuracy: 0.0831 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 79/100\n",
            "76/76 [==============================] - 33s 436ms/step - loss: 8.3575 - accuracy: 0.0881 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 80/100\n",
            "76/76 [==============================] - 31s 403ms/step - loss: 8.3177 - accuracy: 0.0922 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 81/100\n",
            "76/76 [==============================] - 30s 401ms/step - loss: 8.2912 - accuracy: 0.0889 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 82/100\n",
            "76/76 [==============================] - 31s 408ms/step - loss: 8.2647 - accuracy: 0.0872 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 83/100\n",
            "76/76 [==============================] - 29s 387ms/step - loss: 8.2381 - accuracy: 0.0922 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 84/100\n",
            "76/76 [==============================] - 30s 399ms/step - loss: 8.3177 - accuracy: 0.0848 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 85/100\n",
            "76/76 [==============================] - 33s 437ms/step - loss: 8.2779 - accuracy: 0.0864 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 86/100\n",
            "76/76 [==============================] - 30s 396ms/step - loss: 8.2249 - accuracy: 0.0831 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 87/100\n",
            "76/76 [==============================] - 30s 392ms/step - loss: 8.3177 - accuracy: 0.0840 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 88/100\n",
            "76/76 [==============================] - 33s 432ms/step - loss: 8.2779 - accuracy: 0.0889 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 89/100\n",
            "76/76 [==============================] - 30s 400ms/step - loss: 8.3177 - accuracy: 0.0872 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 90/100\n",
            "76/76 [==============================] - 30s 399ms/step - loss: 8.3575 - accuracy: 0.0889 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 91/100\n",
            "76/76 [==============================] - 32s 413ms/step - loss: 8.2912 - accuracy: 0.0905 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 92/100\n",
            "76/76 [==============================] - 30s 398ms/step - loss: 8.2647 - accuracy: 0.0864 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 93/100\n",
            "76/76 [==============================] - 30s 397ms/step - loss: 8.2912 - accuracy: 0.0864 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 94/100\n",
            "76/76 [==============================] - 31s 406ms/step - loss: 8.3310 - accuracy: 0.0872 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 95/100\n",
            "76/76 [==============================] - 30s 390ms/step - loss: 8.2381 - accuracy: 0.0848 - val_loss: 7.2254 - val_accuracy: 0.0862\n",
            "Epoch 96/100\n",
            "76/76 [==============================] - 30s 402ms/step - loss: 8.2779 - accuracy: 0.0831 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 97/100\n",
            "76/76 [==============================] - 33s 432ms/step - loss: 8.2912 - accuracy: 0.0872 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 98/100\n",
            "76/76 [==============================] - 30s 393ms/step - loss: 8.3045 - accuracy: 0.0897 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 99/100\n",
            "76/76 [==============================] - 30s 401ms/step - loss: 8.2779 - accuracy: 0.0848 - val_loss: 7.2651 - val_accuracy: 0.0862\n",
            "Epoch 100/100\n",
            "76/76 [==============================] - 31s 411ms/step - loss: 8.3310 - accuracy: 0.0856 - val_loss: 7.2651 - val_accuracy: 0.0862\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "FiGgfj7GF5zU",
        "outputId": "1710615d-cd71-4101-a684-51e9558b07d9"
      },
      "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": "code",
      "source": [
        "# Save model and weights\n",
        "model_name = 'Emotion_Model.h5'\n",
        "save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
        "\n",
        "if not os.path.isdir(save_dir):\n",
        "    os.makedirs(save_dir)\n",
        "model_path = os.path.join(save_dir, model_name)\n",
        "model.save(model_path)\n",
        "print('Save model and weights at %s ' % model_path)\n",
        "\n",
        "# Save the model to disk\n",
        "model_json = model.to_json()\n",
        "with open(\"model_json.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khRZxb2dGELp",
        "outputId": "76203226-3b37-4db1-e952-06f0451eebd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save model and weights at /content/saved_models/Emotion_Model.h5 \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loading json and model architecture\n",
        "json_file = open('model_json.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(\"saved_models/Emotion_Model.h5\")\n",
        "print(\"Loaded model from disk\")\n",
        "\n",
        "# Keras optimiser\n",
        "opt = keras.optimizers.Adam(lr=0.00001)\n",
        "loaded_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
        "score = loaded_model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4w9ONhjGHbd",
        "outputId": "5fade1a9-a48c-4d24-8715-8c3f430c8b8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded model from disk\n",
            "accuracy: 8.62%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = loaded_model.predict(X_test,\n",
        "                         batch_size=16,\n",
        "                         verbose=1)\n",
        "\n",
        "preds=preds.argmax(axis=1)\n",
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0cXGTePGZ-_",
        "outputId": "c3cdc495-b959-4a5b-cbb2-b74cf756e367"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26/26 [==============================] - 2s 79ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 8, 8, 7, 8, 7, 8, 7, 8, 8, 8, 8, 8, 7, 8, 5, 8, 8, 8, 5, 7, 7,\n",
              "       5, 8, 7, 5, 8, 8, 7, 5, 8, 7, 7, 8, 7, 7, 8, 8, 8, 5, 8, 8, 7, 8,\n",
              "       8, 8, 7, 8, 8, 5, 8, 8, 8, 8, 8, 5, 8, 8, 8, 8, 8, 7, 8, 8, 5, 8,\n",
              "       8, 8, 8, 5, 7, 8, 8, 7, 8, 8, 5, 8, 8, 5, 8, 8, 8, 8, 8, 8, 5, 8,\n",
              "       8, 8, 8, 8, 7, 8, 8, 8, 7, 7, 7, 8, 5, 8, 8, 7, 8, 8, 8, 8, 8, 8,\n",
              "       7, 8, 8, 8, 7, 7, 8, 8, 8, 7, 8, 8, 7, 8, 7, 8, 8, 8, 5, 8, 5, 8,\n",
              "       7, 8, 8, 7, 7, 7, 5, 8, 8, 8, 8, 7, 8, 5, 7, 5, 7, 8, 7, 8, 7, 7,\n",
              "       7, 8, 7, 7, 8, 8, 7, 8, 8, 8, 8, 8, 8, 7, 8, 8, 8, 8, 8, 8, 8, 8,\n",
              "       8, 8, 5, 5, 8, 5, 8, 8, 7, 8, 5, 8, 8, 8, 7, 8, 8, 8, 7, 8, 7, 8,\n",
              "       8, 8, 8, 7, 7, 8, 8, 8, 8, 8, 7, 8, 7, 8, 8, 5, 7, 8, 8, 7, 8, 5,\n",
              "       8, 7, 8, 8, 7, 8, 8, 8, 5, 5, 7, 8, 5, 8, 7, 7, 8, 8, 8, 8, 7, 7,\n",
              "       7, 8, 7, 8, 8, 5, 7, 8, 8, 8, 5, 8, 8, 7, 7, 7, 7, 5, 7, 8, 8, 7,\n",
              "       8, 8, 8, 7, 8, 8, 7, 8, 8, 8, 7, 8, 5, 8, 8, 8, 8, 8, 8, 8, 5, 8,\n",
              "       5, 8, 7, 7, 8, 5, 8, 8, 8, 8, 7, 7, 7, 5, 5, 7, 8, 8, 7, 5, 8, 5,\n",
              "       5, 8, 8, 7, 7, 8, 8, 8, 8, 8, 8, 7, 5, 8, 8, 7, 8, 8, 8, 7, 5, 5,\n",
              "       8, 5, 8, 8, 8, 7, 8, 8, 8, 8, 8, 8, 8, 8, 7, 5, 8, 8, 8, 8, 8, 7,\n",
              "       8, 7, 8, 5, 8, 8, 8, 7, 8, 8, 7, 7, 8, 8, 8, 8, 7, 7, 8, 8, 7, 5,\n",
              "       8, 8, 7, 5, 8, 5, 8, 8, 7, 8, 5, 8, 5, 5, 8, 7, 8, 8, 7, 8, 8, 5,\n",
              "       7, 7, 8, 8, 8, 7, 8, 5, 5, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predictions\n",
        "preds = preds.astype(int).flatten()\n",
        "preds = (lb.inverse_transform((preds)))\n",
        "preds = pd.DataFrame({'predictedvalues': preds})\n",
        "\n",
        "# Actual labels\n",
        "actual=y_test.argmax(axis=1)\n",
        "actual = actual.astype(int).flatten()\n",
        "actual = (lb.inverse_transform((actual)))\n",
        "actual = pd.DataFrame({'actualvalues': actual})\n",
        "\n",
        "# Lets combined both of them into a single dataframe\n",
        "finaldf = actual.join(preds)\n",
        "finaldf[170:180]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "ew4yyCaRGdhA",
        "outputId": "760fcfa7-89f2-405a-eec8-b9174d154988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       actualvalues predictedvalues\n",
              "170       male_fear       male_fear\n",
              "171    male_neutral       male_fear\n",
              "172  female_neutral       male_fear\n",
              "173       male_fear       male_fear\n",
              "174        male_sad       male_fear\n",
              "175      male_angry       male_fear\n",
              "176    male_neutral       male_fear\n",
              "177    female_happy       male_fear\n",
              "178      male_angry      female_sad\n",
              "179  female_disgust      female_sad"
            ],
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4177fa2b-9fca-4e56-aa18-1d84b4bf0590');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "        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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Write out the predictions to disk\n",
        "finaldf.to_csv('Predictions.csv', index=False)\n",
        "finaldf.groupby('predictedvalues').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "NtPNxoFlGf4c",
        "outputId": "e4d839cc-e976-43a3-a0a5-a64da5118d2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 actualvalues\n",
              "predictedvalues              \n",
              "female_sad                 56\n",
              "male_disgust              102\n",
              "male_fear                 248"
            ],
            "text/html": [
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              "                                                    [key], {});\n",
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              "\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",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "    0% {\n",
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              "    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-5ff6e9cf-1578-4d80-b21d-c88f2daa2093 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the confusion matrix heat map plot\n",
        "def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):\n",
        "    \"\"\"Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    confusion_matrix: numpy.ndarray\n",
        "        The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix.\n",
        "        Similarly constructed ndarrays can also be used.\n",
        "    class_names: list\n",
        "        An ordered list of class names, in the order they index the given confusion matrix.\n",
        "    figsize: tuple\n",
        "        A 2-long tuple, the first value determining the horizontal size of the ouputted figure,\n",
        "        the second determining the vertical size. Defaults to (10,7).\n",
        "    fontsize: int\n",
        "        Font size for axes labels. Defaults to 14.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    matplotlib.figure.Figure\n",
        "        The resulting confusion matrix figure\n",
        "    \"\"\"\n",
        "    df_cm = pd.DataFrame(\n",
        "        confusion_matrix, index=class_names, columns=class_names,\n",
        "    )\n",
        "    fig = plt.figure(figsize=figsize)\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "# Gender recode function\n",
        "def gender(row):\n",
        "    if row == 'female_disgust' or 'female_fear' or 'female_happy' or 'female_sad' or 'female_surprise' or 'female_neutral':\n",
        "        return 'female'\n",
        "    elif row == 'male_angry' or 'male_fear' or 'male_happy' or 'male_sad' or 'male_surprise' or 'male_neutral' or 'male_disgust':\n",
        "        return 'male'"
      ],
      "metadata": {
        "id": "bH56XsSKGkuw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the predictions file\n",
        "finaldf = pd.read_csv(\"Predictions.csv\")\n",
        "classes = finaldf.actualvalues.unique()\n",
        "classes.sort()\n",
        "\n",
        "# Confusion matrix\n",
        "c = confusion_matrix(finaldf.actualvalues, finaldf.predictedvalues)\n",
        "print(accuracy_score(finaldf.actualvalues, finaldf.predictedvalues))\n",
        "print_confusion_matrix(c, class_names = classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "Z82pHgGFGqfN",
        "outputId": "a101c4be-be10-4d29-be00-f6f9af1946a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.08620689655172414\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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0CqnO18bYUgLb5m6wbV783z9LV0fYNneDRR17GFtK0Gz+e6jRphEs6jnAoVNzeIfPxNPk+3gQHa/nyEsnWJpD0qwBJM0aAABM69aGpFkDmDg7oujJczw9ewm1PhkLy/YtYVrXCdLBPSAd1A2Pf9Puj9C6Up2/a6URUz5iyoUqR1S3p7Zo0QLr16/H5MmT0aRJE/Tr1w8NGzbE48ePkZSUhJiYGAQEBODrr7/W+thBQUEICgqCp6cnhgwZAplMhiNHjkAul8PDw0OxWcyrSCQS7N27F3379kXXrl3RrVs3tGzZEoIgIDU1FSdPnoS9vX2pm/m8yqRJk7BlyxYMHjwYw4YNg729Pc6ePYsLFy6gf//+OHz4cEXTBgDUqFEDq1evxgcffIC2bdtixIgRkEqliIyMhEQigYuLi9o1iGXp06cP3NzckJqaitq1a2PAgAGVilVX9uz5CY4OdgiZPwu1azsiPv4v9Pd7H+npGerfbIDElI+YcgGYjyG7EX8TwRM+x/hPAjFq2n9w9/Y9bAj5GscPROk7tAqpztemRusGeGvffMWfm38+GgBwe3cMLv13M2ybuaLesC4wtbVC7v1HeBB9CdeX7UFRfsXujNE1i5aN4b5jqeLPtecV/4Ca9eNRpM1Zg3+mLYfT7DGos3oWjGvYoOBOOtJXfY9HOyL1FbJGqvN3rTRiykdMuWgFN8IRjwkTJqB169aK2bqIiAhIpVK4urpixowZGDNmjE7GnTp1KkxNTbF27Vps3LgRNWrUQP/+/REaGoqhQ4eWux9vb2/Ex8djxYoViIyMxKlTpyCRSFCnTh0MHDgQI0eO1Dg2T09P/Pbbb5g3bx727dsHY2NjvPXWWzh16hR++umnSheNQPHnXrNmTSxZsgTh4eGQSqV45513sGzZMri5uaFhw4rtsmVkZIRRo0Zh0aJFCAgIeOVOrPq2fkM41m8I13cYWiOmfMSUC8B8DFncsTjEHYvTdxhaU12vzcPT1xBRu+z/XsaNXFrmMUP0LO4yrjbsX+bxwoxHSPtvWNUFpAPV9btWFjHlI6ZcqOIEuVxe/faWpmojISEBjRs3xrBhw7B7d8VuZfDz80NkZCRu3ryJRo0aVTgWE7PSN+MhoteHj1MLfYegVdH3K7/EwFDst+ui7xC0qqGV5s9UNmStbv+p7xDoNSHLv6PvEMqUd71ym0eWl6Rp1yoZRxOiWtNI+vPo0SOVR1w8f/4cM2bMAAAMHDiwQv1evXoVkZGR6NmzZ6UKRiIiIiIiqhjDvdePqpWYmBiMGzcOvXr1gqurKzIyMnD8+HGkpKSgW7duGD58uEb97dixAzdu3MD3338PAAgODtZF2ERERERE5cM1jVTdhIWFISsrS+15AQEBcHd313k8zZs3R8+ePXHq1CkcOHAAANCoUSMsXLgQs2bN0ngjnG+//RYnT56Em5sbNm/ejLfeeksHURMRERERkTosGqupsLAwpKamqj3Px8enSorGxo0bY9euXVrrLzo6Wmt9ERERERFVWhFnGqmaSUlJ0XcIRERERET0GmDRSEREREREpM5rvKaRu6cSERERERFRmTjTSEREREREpM5rvKaRM41ERERERERUJs40EhERERERqSGXF+o7BL3hTCMRERERERGViTONRERERERE6nD3VCIiIiIiIiJVnGkkIiIiIiJSh7unEhEREREREaniTCMREREREZE6r/GaRhaNRET02thoJ67/4De+r+8ItGex0V19h6BVUXPb6DsE7Zryp74jICI94u2pREREREREVCbONBIREREREalTVKjvCPSGM41ERERERERUJs40EhERERERqfMab4TDmUYiIiIiIiIqE2caiYiIiIiI1CniTCMRERERERGRCs40EhERERERqcM1jURERERERESqONNIRERERESkDtc0EhEREREREaniTCMREREREZE6nGnUn4KCAoSEhKBx48aQSCQQBAEHDhzQd1jlFh4eDkEQEB4eru9QIAgCfHx8lNoCAgIgCAJSUlL0EhMREREREVVvei8aV61ahQULFsDFxQWzZs1CcHAwmjZtqu+wyMAYUnFORERERK8fubywSl6GSO9F46FDh2BtbY0jR45g8eLFCAkJYdGoRaGhobh27Rrq1Kmj71CIiIiIiEiLNmzYgFatWsHW1ha2trbo2LEjfv75Z8Xx3NxcTJ06Ffb29rC2tsbgwYNx//59jcfRe9GYlpYGe3t7mJmZ6TsUUXJ2dkbTpk1hamqq71CIiIiIiKqvoqKqeWmgbt26WLp0Kc6fP48//vgD3bp1g7+/P/766y8AwIwZMxAREYE9e/YgJiYGaWlpePfddzVOXW9FY0hICARBQHJyMlJTUyEIAgRBgLu7u+KcEydOYMCAAXBwcIBEIkHjxo0xb948PHv2TKmv6OhoCIKAkJAQnD59Gr6+vrCxsYGjoyOmTJmC58+fAwAOHz6Mjh07wsrKCk5OTpgzZw5kMplSX9nZ2Vi2bBm6du0KFxcXmJmZwcXFBaNHj0ZiYqJGOSYnJ2P8+PFwdXWFRCKBs7MzAgICkJqaWrEP7f/btGkTWrRoAXNzc9SrVw9z5sxBbm5uqeeWtabxxx9/RNeuXVGrVi2Ym5vDxcUFPXr0wI8//qjSxzfffIPmzZurjFfaGkp3d3ela/giHx8fCIKg1Jabm4tVq1bBw8MDUqkUVlZWcHd3x7BhwxAfH6/IITAwEAAQGBio+K683Je+TZ40Bgk3z+JJTiJOx0bA26u1vkOqFDHlI6ZcAOZjKMzbtITTl5+j3pFdqB9/BJa+bykdN7KrAYfPZ6PekV1wOxsBp/VLYOJave76qK7X5mXjZwbgbFq00mvXie/1HVa5bD6bgPe2xeKtsF/gu+4Ipu//AymZTxTH72Q/Q+sVh0t9/Xbjrh4j14xYvmslxJSPmHIRowEDBqBfv35o3Lgx3njjDSxevBjW1tY4e/YssrOzsXnzZqxevRrdunVD27ZtsWXLFpw+fRpnz57VaBy97Z5aUmyEhYUBAKZPnw4AqFGjBoDiqdapU6eiRo0aGDBgAGrVqoU//vgDixcvRlRUFKKiolRmJ+Pi4rBs2TL07t0bEydORFRUFDZs2ICcnBwMGDAAAQEB8Pf3R8eOHXH48GGsWLEC1tbWmD9/vqKPa9euYf78+fD19cWgQYNgZWWF69evY8eOHTh8+DAuXLgANzc3tfnFxcWhd+/eePr0Kfz8/NC4cWOkpKRg+/bt+Pnnn3HmzBk0aNBA489t4cKFmD9/PpycnDBhwgSYmppi9+7duHbtWrn72LBhA6ZMmQJnZ2cMGjQI9vb2uHfvHn7//Xfs378fgwcPVpw7f/58LFy4UGm8H374AdevX9c49tKMGTMGP/zwA1q1aoXAwEBIJBLcvn0bUVFROHfuHDw8PDBw4EBkZWXh4MGD8Pf3R+vWrbUytjYNHfoOVq4IxpSpn+D3cxfxUdB4RB7ejjdbdMGDBw/1HZ7GxJSPmHIBmI8hESzMkX8jCY8P/AqnNSEqx53CFgAyGe5Pnw/5k2ewHT0Yzt8swz/vjof8eek/9BmS6nxtSpN4PRlBw2cq/lxYaJjrhl52/nYmhnu6oXntGigskmPtyeuYvOd37AvsAgszE9S2scDRyd2V3vPjpdvY+nsiOtV31FPUmhHbd01M+YgpF62QV83uqXl5ecjLy1Nqk0gkkEgkr3xfYWEh9uzZg6dPn6Jjx444f/48CgoK0KNHD8U5TZs2haurK86cOYMOHTqUOyZBLpfLNUtDu0pmpV6cCbt69So8PDzQvHlzHDt2DPb29opjS5cuxdy5c7Fy5UrMnFn8l390dDR8fX0BAAcOHIC/vz+A4p1Zvby8cPnyZdjb2yMyMhLe3t4AgMePH6NRo0aQyWS4d++e4vbN7OxsFBYWws7OTinOqKgo9OjRA2PHjsXGjRsV7eHh4QgMDMSWLVsQEBCgGPeNN97Aw4cPERMTA09PT8X5sbGx8PHxQd++fREREaHRZ5WQkIBmzZrByckJFy5cQK1atQAAOTk5aNeuHW7cuIGuXbsiOjpa8Z6AgABs3boVycnJis+6bdu2uHLlCm7fvq3oo8TDhw8Vn/fNmzfx5ptvonbt2krjPX78GB06dMDVq1dVxivtepbw8fFBTEwMSr5y2dnZqFmzJtq0aYO4uDgYGxsrzi0sLMTjx48VPyKU9jlrysRMd7/wn46NwLk/4jFt+jwAxTvZpiSdw7r1W7B8xTqdjasrYspHTLkAzKey/m72ptb7BID68Udwf3ownkWdBgCYuNVBvZ/C8c+741GQ+P/vLhEEuB7fjcwvt+DJ/p9f0Vv5Nb52VSv9lKaqr42XQ2Ot91li/MwAdOnTCaN7jtfZGC+L+ryNTvrNfJaHbuuOYvOIDmhbz77Uc4ZvPYlmTrYI6eOhtXFtpuzWWl8v499rhksfucjy7+ikX214HrWpSsZZFvMPFixYoNQWHByMkJCQUs+/fPkyOnbsiNzcXFhbW2PHjh3o168fduzYgcDAQJUCtF27dvD19cWyZcvKHZPe1zSW5ptvvoFMJsPatWuVCkYAmDNnDhwdHbFz506V9/n6+ioKRgAwNTXFkCFDIJfLMWDAAEXBCAA2Njbw8/NDZmYm/vnnH0W7VCpVKRhL+m7evDmOHj2qNv5Dhw4hJSUFs2fPVioYAaBTp07w9/dHZGQkcnJy1Pb1oh07dkAmk+Hjjz9WKvZsbW0xb948jfoyNTUtdZ3ji5/3zp07UVhYiJkzZyqNZ2Njo/F4pREEAXK5HObm5jAyUv4qGhsbKwpGQ2dqaoo2bVrh2PGTija5XI5jx2PRoUNbPUZWMWLKR0y5AMynOhH+/9+v8rz8fxvlcsjzC2Du2UJPUZWfGK9Nvfp1EHFhL348swMLvvoUTnVqqX+TAXqSV7ysRmpe+l4QV+9l40Z6Dga2rFeVYVWY2L5rYspHTLlUN3PnzkV2drbSa+7cuWWe36RJE/z555+Ii4vD5MmTMWbMGFy9qt0fFfV2e+qrlNxj++uvv+LYsWMqx01NTUu9PbK02xadnZ3VHktLS0P9+vUV7dHR0QgLC0NcXBwyMjKU1j2WZ8Oekvhv3LhR6i8C9+7dQ1FREW7evAkvLy+1/ZUoWePXuXNnlWOltZVlxIgRmDNnDlq0aIH33nsPvr6+6NSpE2xtbUsdr1OnTip9vP322+Ueryy2trbo168fIiMj0aZNGwwdOhQ+Pj7w9vauVhv3ODjYwcTEBOn3M5Ta09MfoGmThnqKquLElI+YcgGYT3VSkHIbsrT7qPnRODxcGIai57mQjhoMk9q1YOyo+sOkoRHbtfnrwlUsnL4UtxJvw76WPcbNHIOv93+J//gG4tnT5/oOr9yK5HKsOH4VrevURCNHm1LP2X/5FhrYW6N1HcP/ngHi+66JKR8x5aI1Gm5SU1HluRX1RWZmZmjUqBGA4jsKz507hy+++ALDhw9Hfn4+srKylCZj7t+/j9q1a2sUk0EWjZmZmQCAxYsXa/S+l4seADAxMVF7rKCgQNG2Z88eDB8+HNbW1ujduzfc3d1haWmpeEZgeTaxKYl/+/btrzzv6dOnavt6UXZ2NgCo3FIKAE5OTuXuZ9asWbC3t8eGDRuwatUqrFy5EiYmJujfvz/WrFmjKKBLZkIrO96r7NmzB0uWLMGOHTvw6aefAii+VoGBgViyZAksLS0r1G9p94LL5XKD2zyHiERKVoj7Hy+AQ8hMuMXuh1xWiOdxF/Ds5O8A/xqqcmeiflf874RrSfjr4jUc+H0Xur/ji4idkXqMTDOhR64gIeMxwt/rWOrx3IJC/HwtDR901N2tvkRk+IqKipCXl4e2bdvC1NQUx44dU+xZcuPGDdy6dQsdO5b+90hZDLJoLCnwcnJyYGNT+i9puhISEgJzc3OcP38ejRsr/6W7a9eucvVREn9ERAT8/Py0FptUKgUApKenq2zGo8nzVgRBwNixYzF27Fg8fPgQJ0+exM6dO/HDDz/g77//xqVLl2BsbKzIQ5PxjIyMkJ+fX+qxkqL3RZaWlli0aBEWLVqE5ORkREVF4euvv8YXX3yB58+f45tvvil3Xi8KDQ1VuRdcMLKGYKz640FlZWRkQiaToZaTg1J7rVqOuHf/gdbH0zUx5SOmXADmU93kX/sbacMnQbC2hGBqiqJH2XD+35fI/+tvfYemltivzZOcJ7iV9A/qulef3WxDj17BiaR0fDeiI5xsLEo95+jNu8gtKIRf8+qTl9i+a2LKR0y5aE0VbYSjiblz56Jv375wdXXF48ePsWPHDkRHR+PXX3+FVCrFuHHj8PHHH8POzg62trYICgpCx44dNdoEBzDQNY3t27cHAI23gtWGxMRENGvWTKVgvHv3LpKSksrVR0n8Z86c0WpsHh7FC9pPnjypcqy0tvKwt7fHwIEDsXv3bnTr1g1Xr15FQkKC0ninTp1Sed/p06dL7a9mzZpIT09XeZTJ06dP8fffr/6HUv369TF27FjExMTA2toaP/30k+JYySY55d3trrR7wQUj3fwAUVBQgAsXLqGb77+38QqCgG6+nXD27HmdjKlLYspHTLkAzKe6kj95hqJH2TBxrQPJm2/gWXTpf38aErFfGwtLC9Rxc8HDdMPf/VEulyP06BUc//sevh3eAXVqlH0Hzv7Lt+HTyAl2luW/rU3fxPZdE1M+YspFzNLT0zF69Gg0adIE3bt3x7lz5/Drr7+iZ8+eAIA1a9bAz88PgwcPRpcuXVC7dm3s27dP43EMsmicMmUKTExMEBQUhFu3bqkcz8rKwsWLF3UytpubGxISEpRm0nJzczF58mSl21hfxd/fH66urli9ejVOnDihcrygoACxsbEax/bee+/B2NgYq1evRnp6uqI9JycHixYtKnc/0dHReHnT3IKCAsVttebm5gCK1z4aGRlh1apVyMj49372p0+flnnrsLe3NwoKCpRuzZXL5Zg7d67K7bgPHjzAlStXVPp49OgR8vLyFHEAUGxOdPv27XLlKJFIYGtrq/TS5a2pa77YiPHj3sOoUUPRtGkjrPtqKaysLBC+VXe7zemSmPIRUy4A8zEkgoU5zJo0hNn/X9tjUqc2zJo0hHHt4sccWPbsAnOvVjCpUxuWPh1R++uleBZ1Gs/PVI9/bFXna/OyoPmT4dnBA851a6OlV3Ms+24hioqK8Nt+1X0TDM2So1dw+OodhPp5wsrUGBlPcpHxJBe5Bco/ot569BQXbmdiUKvqsQHOi8T0XQPElY+YctGKoqKqeWlg8+bNSElJQV5eHtLT03H06FFFwQgU/7t+3bp1yMzMxNOnT7Fv3z6N1zMCBnp7aosWLbB+/XpMnjwZTZo0Qb9+/dCwYUM8fvwYSUlJiImJQUBAAL7++mutjx0UFISgoCB4enpiyJAhkMlkOHLkCORyOTw8PBSbw7yKRCLB3r170bdvX3Tt2hXdunVDy5YtIQgCUlNTcfLkSdjb22v8rMNGjRph/vz5CA4ORqtWrTBs2DCYmJjgxx9/RKtWrXDjxo1y9TNw4EDY2tqiQ4cOcHNzQ0FBAY4cOYKrV69iyJAhiltRmzRpgk8++QRLlixBy5YtFePt27cPLVu2xJUrV1R2Pf3www+xZcsWjB8/HkeOHIGjoyNOnjyJrKwslc/vzp078PT0hIeHB1q1aoU6derg4cOHOHjwIAoKCjBr1izFuR07doSFhQXCwsLw6NEjODoW/6NMG7u4asOePT/B0cEOIfNnoXZtR8TH/4X+fu8jPT1D/ZsNkJjyEVMuAPMxJJLmb8B58yrFn+1nTwYAPD74GzLmr4CJox2ksybC2L4mCh9k4vGhI8j65tVr3Q1Jdb42L6vl7IjP138GaU1bZD3MRvy5yxjvNwVZmarLJgzNnj+Lfzwfv0v57qsFfVvBv8W/BeKBy7fhZGOOju7V49mMLxLTdw0QVz5iyoUqxyCf01ji3Llzitm6Bw8eQCqVwtXVFb169cKYMWPQtGlTAP8+p7G055e86vl+ISEhWLBgAaKiouDj4wOgeFbs22+/xdq1a5GYmIgaNWqgf//+CA0NxdChQ5WeM6iu/zt37mDFihWIjIzErVu3IJFIUKdOHbz99tsYOXIkunXrVqHPbNOmTVizZg0SEhJQq1YtjBgxAp9//jksLS3L9ZzGDRs24JdffkF8fDzu378PKysrNGzYEIGBgRg3bpzKzqUbNmxQfB4l402bNg316tWDv78/Dhw4oHR+VFQU5s6diz///BPW1tbo168fVq5ciWHDhil9fllZWfjiiy9w/Phx/P3333j48CEcHBzQqlUrTJs2DX369FHqNzIyEiEhIbhy5QqeP3+uuF7lpcvnNBJR9aCr5zTqiy6f01jVdPmcRn3Q1XMa9UWXz2kkepFBP6fx16+qZByL3h9WyTia0HvRSNVTydT3nDlzNHowqD6xaCQiFo2Gi0WjYWPRSFWFRaNhFo0GeXsqGY4HDx7Azs5OsRENUDxDWPKA0YEDB+opMiIiIiKiKlRFz2k0RCwa6ZW2b9+OlStXolu3bnBxccHdu3fxyy+/ID09HQEBARo/44WIiIiIiKoXFo16FhYWhqysLLXnBQQEKNYkVqW33noLbdu2xdGjR5GZmQljY2M0a9YMn332GaZMmVLl8RARERER6QVnGklfwsLCkJqaqvY8Hx8fvRSN7dq1w8GDB6t8XCIiIiIiMgwsGvWstF1jiYiIiIjIwMhf35lGI/WnEBERERER0euKM41ERERERETqvMZrGjnTSERERERERGXiTCMREREREZE6XNNIREREREREpIpFIxEREREREZWJt6cSERERERGpw41wiIiIiIiIiFRxppGIiIiIiEgdboRDREREREREpIozjUREREREROq8xmsaWTQSEdFrw//+E32HQEREVO2waCQiIiIiIlLnNZ5p5JpGIiIiIiIiKhNnGomIiIiIiNSRy/Udgd5wppGIiIiIiIjKxJlGIiIiIiIidbimkYiIiIiIiEgVZxqJiIiIiIjU4UwjERERERERkSrONBIREREREakj50wjERERERERkQrONBIREREREanDNY1EREREREREql7rorGgoAAhISFo3LgxJBIJBEHAgQMH9B1WuYWHh0MQBISHh1fZmF9++SWaN28OS0tLCIKAsLCwKhubiIiIiEhv5PKqeRmg1/r21FWrVmHBggXo0qULhg0bBlNTUzRt2lTfYRmsXbt2Ydq0afD09MS0adMgkUjQoUMHfYdFREREREQ69FoXjYcOHYK1tTWOHDkCMzMzfYdj8A4dOqT4vy4uLnqOhoiIiIiIqsJrXTSmpaXB3t6eBWM5paWlAQALRiIiIiJ6/XAjnNdLSEgIBEFAcnIyUlNTIQgCBEGAu7u74pwTJ05gwIABcHBwgEQiQePGjTFv3jw8e/ZMqa/o6GgIgoCQkBCcPn0avr6+sLGxgaOjI6ZMmYLnz58DAA4fPoyOHTvCysoKTk5OmDNnDmQymVJf2dnZWLZsGbp27QoXFxeYmZnBxcUFo0ePRmJiokY5JicnY/z48XB1dYVEIoGzszMCAgKQmpqq8edVsnYyKioKABSflyAISuddunQJI0aMgLOzM8zMzODm5oagoCA8fPhQpc/vvvsO/v7+cHd3h7m5Oezs7NC7d2/FGC96+TPu1asXatSooTK+vk2eNAYJN8/iSU4iTsdGwNurtb5DqhQx5SOmXADmUx2M/XAULt07gzmfT9d3KJUilmszfmYAzqZFK712nfhe32GVy+azCXhvWyzeCvsFvuuOYPr+P5CS+URx/E72M7RecbjU12837uoxcs2I5btWQkz5iCkXqrjXsmj08fFBcHAwpFIppFIpgoODERwcjOnTpwMANmzYAB8fH5w6dQr9+/fHRx99hLp162Lx4sXo2bMn8vPzVfqMi4tD9+7dIZVKMXHiRLi6umLDhg2YMGECdu/ejSFDhsDNzQ0TJ05EjRo1sGLFCixZskSpj2vXrmH+/PmwsLDAoEGDMH36dHh5eWHHjh1o165duQu+uLg4eHp6YuvWrWjbti2mTZuGzp07Y/v27WjXrh2SkpI0+rxat26N4OBguLm5AYDi8woODlac89NPP6Fdu3b46aef4OPjg+nTp6Nly5b46quv0LFjRzx69Eipz6lTp+L+/fvo0aMHZsyYAT8/P5w5cwY9evTAwYMHS43j9OnT8PHxgSAI+OCDDzB8+HCN8tCloUPfwcoVwVi4aDW82/dB/KWriDy8HY6O9voOrULElI+YcgGYT3XQvHUzDB09EDf++lvfoVSK2K5N4vVk9PN4V/GaODBI3yGVy/nbmRju6Ybv338bXw9tD1lRESbv+R3P84t/eK5tY4Gjk7srvSa//QYsTY3Rqb6jnqMvH7F918SUj5hy0Yqioqp5GSBBLjfQLXqqQMnMYkpKiqLt6tWr8PDwQPPmzXHs2DHY2//7/xRLly7F3LlzsXLlSsycORNA8SyYr68vAODAgQPw9/cHULwzq5eXFy5fvgx7e3tERkbC29sbAPD48WM0atQIMpkM9+7dg6mpKYDimcbCwkLY2dkpxRkVFYUePXpg7Nix2Lhxo6I9PDwcgYGB2LJlCwICAhTjvvHGG3j48CFiYmLg6empOD82NhY+Pj7o27cvIiIiNP68fHx8EBMTg5e/Mg8fPkSDBg1gY2ODU6dOKYpLoHjznJEjR+LDDz/E2rVrFe3JycmoX7++Uj93796Fl5cXrKyscPPmTUX7i5/xd999h8DAQI1jBwATszoVel95nI6NwLk/4jFt+jwAxbOxKUnnsG79FixfsU5n4+qKmPIRUy4A86msN+1ctd7niywsLbD7SDgWf7ISH8wIwI0rf2P5/DCdjXc185bO+q7qa+Pl0FjrfZYYPzMAXfp0wuie43U2xsuiPm+jk34zn+Wh27qj2DyiA9rWK/0f7sO3nkQzJ1uE9PHQ2rg2U3Zrra+X8e81w6WPXGT5d3TSrzY83zyrSsaxGLeySsbRxGs50/gq33zzDWQyGdauXatUMALAnDlz4OjoiJ07d6q8z9fXV1EwAoCpqSmGDBkCuVyOAQMGKApGALCxsYGfnx8yMzPxzz//KNqlUqlKwVjSd/PmzXH06FG18R86dAgpKSmYPXu2UsEIAJ06dYK/vz8iIyORk5Ojtq/y+v7775GTk4PQ0FClghEARowYgTZt2mDXrl1K7S8XjADg7OyMwYMH4++//y51VrVNmzYVLhh1ydTUFG3atMKx4ycVbXK5HMeOx6JDh7Z6jKxixJSPmHIBmE918OnSWTh59DTiTp7TdyiVIsZrU69+HURc2Isfz+zAgq8+hVOdWvoOqUKe5BXPMErNS9+P4eq9bNxIz8HAlvWqMqwKE9t3TUz5iCkXrZEXVc3LAL3WG+GU5uzZswCAX3/9FceOHVM5bmpqiuvXr6u0t27dWqXN2dlZ7bG0tDSlAio6OhphYWGIi4tDRkaG0rrH8mzYUxL/jRs3EBISonL83r17KCoqws2bN+Hl5aW2v/IoGTMuLq7UtZe5ubnIyMhARkYGHBwcAABJSUkIDQ3F8ePHcefOHeTl5Sm9Jy0tTaUAfbHwNiQODnYwMTFB+v0Mpfb09Ado2qShnqKqODHlI6ZcAOZj6Pr490Czlk0wss9YfYdSaWK7Nn9duIqF05fiVuJt2Neyx7iZY/D1/i/xH99APHv6XN/hlVuRXI4Vx6+idZ2aaORoU+o5+y/fQgN7a7Suo/ojtCES23dNTPmIKReqPBaNL8nMzAQALF68WKP32draqrSZmJioPVZQUKBo27NnD4YPHw5ra2v07t0b7u7usLS0hCAICA8PL9eaxpL4t2/f/srznj59qrav8ioZc926V9+m8PTpUzg4OCAhIQHt2rVDTk4OfH19MWDAANja2sLIyAjR0dGIiYlRKSIBwMnJqdwx5eXlqfQhl8sNbvMcIhIHJ5da+O+iGfhg2EfIz1Nd9076dSbqd8X/TriWhL8uXsOB33eh+zu+iNgZqcfINBN65AoSMh4j/L2OpR7PLSjEz9fS8EFH3d3qS/Q6kxe9tqv6WDS+rKTAy8nJgY1N6b/i6UpISAjMzc1x/vx5NG6s/Bf+y7d3lqUk/oiICPj5+Wk9xleNefnyZbRo0ULt+WvWrMGjR4+wbds2vP/++0rHJk2ahJiYmFLfp0nBFxoaigULFii/38gagrFqAV9ZGRmZkMlkqOXkoNReq5Yj7t1/oPXxdE1M+YgpF4D5GLI3WzWFvaMddh8JV7SZmJigbYfWGDF2MLxcu6LIQDc3KI2Yrk1pnuQ8wa2kf1DXXXdr3bUt9OgVnEhKx3cjOsLJxqLUc47evIvcgkL4Na8+eYntuyamfMSUC1Ue1zS+pH379gD+veWyKiUmJqJZs2YqBePdu3fLveNpSfxnzpzRenzaGrPkFtYX14ACxTOBp06d0kpMc+fORXZ2ttJLMNLNjwAFBQW4cOESuvl2UrQJgoBuvp1w9ux5nYypS2LKR0y5AMzHkMWd/APv+vwHw3qMUbyu/HkVh3/8FcN6jKlWBSMgrmtTGgtLC9Rxc8HDdNVHQhkauVyO0KNXcPzve/h2eAfUqWFZ5rn7L9+GTyMn2FlKqjDCyhHbd01M+YgpF615jXdPZdH4kilTpsDExARBQUG4dUt1V7qsrCxcvHhRJ2O7ubkhISEB9+/fV7Tl5uZi8uTJSrexvoq/vz9cXV2xevVqnDhxQuV4QUEBYmNjtRYzAAQGBsLGxgaffvop/vrrL5Xjz549UyrCS9YqvhzH0qVLceXKFa3EJJFIYGtrq/TS5a2pa77YiPHj3sOoUUPRtGkjrPtqKaysLBC+VXe7zemSmPIRUy4A8zFUz54+Q8L1JKXX82e5yH6Ug4Trmj3myFCI5doAQND8yfDs4AHnurXR0qs5ln23EEVFRfhtv+reBYZmydErOHz1DkL9PGFlaoyMJ7nIeJKL3IJCpfNuPXqKC7czMahV9dgA50Vi+q4B4spHTLlQ5fD21Je0aNEC69evx+TJk9GkSRP069cPDRs2xOPHj5GUlISYmBgEBATg66+/1vrYQUFBCAoKgqenJ4YMGQKZTIYjR45ALpfDw8MD8fHxavuQSCTYu3cv+vbti65du6Jbt25o2bIlBEFAamoqTp48CXt7+1I386mokh1lhw4dCg8PD/Tp0wdNmzZFXl4eUlJSEBMTg7feegu//PILgOJbULds2YLBgwdj2LBhsLe3x9mzZ3HhwgX0798fhw8f1lpsVWXPnp/g6GCHkPmzULu2I+Lj/0J/v/eRnp6h/s0GSEz5iCkXgPlQ1RHTtanl7IjP138GaU1bZD3MRvy5yxjvNwVZmdn6Dk2tPX8W/4A9fpfyHVAL+raCf4t/C8QDl2/DycYcHd2rx7MZXySm7xogrnzElItWGOjOplWBRWMpJkyYgNatWytm6yIiIiCVSuHq6ooZM2ZgzJgxOhl36tSpMDU1xdq1a7Fx40bUqFED/fv3R2hoKIYOHVrufry9vREfH48VK1YgMjISp06dgkQiQZ06dTBw4ECMHDlS67H3798fFy9exIoVK3D06FEcOXIEVlZWqFu3LgIDA5XWLnp6euK3337DvHnzsG/fPhgbG+Ott97CqVOn8NNPP1XLohEA1m8Ix/oN4foOQ2vElI+YcgGYT3Ux7t2p+g6h0sRybT6b/Lm+Q6iwP2f3L9d5H3Vpio+6NNVxNLojlu9aCTHlI6ZcqOIE+ctPaicSKROz6rMxABHpxpt2rvoOQauuZqouo6iuvBzEteNn1Odt9B2CVtlM4e2IVDVk+Xf0HUKZnq37sErGsZz6VZWMowmuaSQiIiIiIqIy8fZUIiIiIiIidQx0Z9OqwKLxNRYWFoasrCy15wUEBMDd3V3n8RARERERkeFh0fgaCwsLQ2pqqtrzfHx8WDQSERER0euNM430OkpJSdF3CEREREREZOC4EQ4RERERERGViTONRERERERE6rzGTyrkTCMRERERERGViTONRERERERE6rzGG+FwppGIiIiIiIjKxJlGIiIiIiIidYq4ppGIiIiIiIhIBWcaiYiIiIiI1JFzTSMRERERERGRCs40EhERERERqcM1jURERERERESqONNIRESvjaSce/oOgcrgZGKt7xC0ymTgh/oOQbum7NZ3BER6J+dzGomIiIiIiIhUcaaRiIiIiIhIHa5pJCIiIiIiIlLFopGIiIiIiEgdeVHVvDQQGhoKb29v2NjYoFatWhg4cCBu3LihdI6Pjw8EQVB6TZo0SaNxWDQSERERERFVQzExMZg6dSrOnj2LI0eOoKCgAL169cLTp0+VzpswYQLu3r2reC1fvlyjcbimkYiIiIiISB0DXNP4yy+/KP05PDwctWrVwvnz59GlSxdFu6WlJWrXrl3hcTjTSEREREREZCDy8vKQk5Oj9MrLyyvXe7OzswEAdnZ2Su3bt2+Hg4MDWrRogblz5+LZs2caxcSikYiIiIiISJ2ioip5hYaGQiqVKr1CQ0PLEV4Rpk+fjrfffhstWrRQtL/33nv43//+h6ioKMydOxfbtm3D+++/r1HqvD2ViIiIiIjIQMydOxcff/yxUptEIlH7vqlTp+LKlSuIjY1Vav/ggw8U/7tly5ZwdnZG9+7dkZiYiIYNG5YrJhaNREREREREBkIikZSrSHzRhx9+iEOHDuHEiROoW7fuK89t3749ACAhIYFFIxERERERkdYY4EY4crkcQUFB2L9/P6Kjo1G/fn217/nzzz8BAM7OzuUeh0UjERERERFRNTR16lTs2LEDBw8ehI2NDe7duwcAkEqlsLCwQGJiInbs2IF+/frB3t4ely5dwowZM9ClSxe0atWq3ONUm41wCgoKEBISgsaNG0MikUAQBBw4cEDfYZVbeHg4BEFAeHi4zsdKSUmBIAgICAjQ+VhERERERK8FeVHVvDSwYcMGZGdnw8fHB87OzorX7t27AQBmZmY4evQoevXqhaZNm2LmzJkYPHgwIiIiNBqn2sw0rlq1CgsWLECXLl0wbNgwmJqaomnTpvoOi4iIiIiISC/k8lffMluvXj3ExMRUepxqUzQeOnQI1tbWOHLkCMzMzPQdDhERERERvU4McE1jVak2t6empaXB3t6eBSMREREREVEVMviiMSQkBIIgIDk5GampqRAEAYIgwN3dXXHOiRMnMGDAADg4OEAikaBx48aYN28enj17ptRXdHQ0BEFASEgITp8+DV9fX9jY2MDR0RFTpkzB8+fPAQCHDx9Gx44dYWVlBScnJ8yZMwcymUypr+zsbCxbtgxdu3aFi4sLzMzM4OLigtGjRyMxMVGjHJOTkzF+/Hi4urpCIpHA2dkZAQEBSE1NrdiH9oKEhAQMGjQINWvWhJWVFXr06IH4+HiV86KiojB27Fg0adIE1tbWsLa2hpeXF7799ttS+xUEAT4+Pvjnn38wcuRIODg4wNLSEm+//TaOHj2qcn5AQAAEQUBSUhKWL1+Oxo0bw9zcHPXr18fnn3+OgoICxblHjx6FIAiYMmVKqWMnJibCyMgIvXv3ruCnohuTJ41Bws2zeJKTiNOxEfD2aq3vkCpFTPmIKReA+Riqt99uhx/2bsLfiWfx5Fky/Ab01HdIlSaWa/Ptqc04eOuQymviwkn6Dk2tXfsPYdDoyWjf81207/ku/vPBDJw8c07lPLlcjkkzP0OLt/vi2InTeoi0csTyXSshpnzElEtlyYuKquRliAy+aPTx8UFwcDCkUimkUimCg4MRHByM6dOnAyhe/Onj44NTp06hf//++Oijj1C3bl0sXrwYPXv2RH5+vkqfcXFx6N69O6RSKSZOnAhXV1ds2LABEyZMwO7duzFkyBC4ublh4sSJqFGjBlasWIElS5Yo9XHt2jXMnz8fFhYWGDRoEKZPnw4vLy/s2LED7dq1K3fBFxcXB09PT2zduhVt27bFtGnT0LlzZ2zfvh3t2rVDUlJShT+7lJQUdOjQAZmZmRg7dix69uyJY8eOwdfXF/fv31c6d9myZThx4gS8vb3x4Ycf4v3330dGRgYmTpyImTNnltr/o0eP8Pbbb+Pvv//G+PHjMXLkSMTHx6NPnz5lblI0ffp0LFu2DD169EBQUBAkEgmCg4MxcuRIxTndu3dHw4YNsWPHDpXCHwA2bdoEuVyOCRMmVPiz0bahQ9/ByhXBWLhoNbzb90H8pauIPLwdjo72+g6tQsSUj5hyAZiPIbO0ssCVy9fw8Yz5+g5FK8R0bWYNmIExbd9XvOa/9ykA4NThU3qOTL3ajg6YMSkQP3y3Frs3f4l2bT0Q9MnnSEhS/nfGtt0HIOgpxsoS03cNEFc+YsqFKkeQq1s9aSBKZhZTUlIUbVevXoWHhweaN2+OY8eOwd7+3y/w0qVLMXfuXKxcuVJR9ERHR8PX1xcAcODAAfj7+wMo3pnVy8sLly9fhr29PSIjI+Ht7Q0AePz4MRo1agSZTIZ79+7B1NQUQPFMY2FhIezs7JTijIqKQo8ePTB27Fhs3LhR0R4eHo7AwEBs2bJFsatpQUEB3njjDTx8+BAxMTHw9PRUnB8bGwsfHx/07dtX492NUlJSFM9oWbp0Kf773/8qjn322WdYtGgRQkND8cknnyjak5OTVZ7rIpPJ0K9fPxw/fhxJSUlwdXVVHBOE4v80vffee/jf//6n+POlS5fg7e0NqVSK1NRUWFhYACieady6dSscHR1x4cIFxUNH8/Pz0bNnT5w4cQJ79+7F4MGDAQDLly/Hf//7X4SHh2PMmDFKMbm6uqKwsBD//POP4nqUh4lZnXKfq6nTsRE490c8pk2fB6D480lJOod167dg+Yp1OhtXV8SUj5hyAZhPZZmbVM0ShyfPkjFi+Ac4FHFEp+PkylR/GNWWqr42/Wt7qj9JS8YFT4B3d29M6vKBzsbYe+FLnfX9Vp+hmDl1PAYPKL7j5vrNREydE4zdm7+Ezzv/wRehn6F7l7e0OqaFS2et9vci/r1muPSRiyz/jk761YYn/323SsaxXravSsbRhMHPNL7KN998A5lMhrVr1yoVjAAwZ84cODo6YufOnSrv8/X1VRSMAGBqaoohQ4ZALpdjwIABioIRAGxsbODn54fMzEz8888/inapVKpSMJb03bx581Jv0XzZoUOHkJKSgtmzZysVjADQqVMn+Pv7IzIyEjk5OWr7Kk39+vUxe/ZspbZx48YBAM6dO6dy7stMTEwwadIkFBYWIioqSuW4sbExlixZoigYAaBVq1YYNWoUHjx4gMjISJX3TJs2TVEwAsXbAC9evBgAlB5HEhgYCDMzM2zatEnp/YcPH8bdu3cxZswYjQpGXTI1NUWbNq1w7PhJRZtcLsex47Ho0KGtHiOrGDHlI6ZcAOZDVUfM18bE1AQ+g3xwdLduC3pdKCwsROTRaDzPzUXrFsU7yD/PzcWcBcvw6cypcLBX/XeJoRPbd01M+YgpF6q8arN7amnOnj0LAPj1119x7NgxleOmpqa4fv26Snvr1q1V2pydndUeS0tLUyquoqOjERYWhri4OGRkZCiteyzPhj0l8d+4cQMhISEqx+/du4eioiLcvHkTXl5eavt7WevWrWFkpPy7QEnBlpWVpdT++PFjrFy5EgcOHEBiYiKePn2qdDwtLU2lf1dXV7i5uam0d+7cGZs3b8bFixcVM4cvHntZx44dYWJigosXLyraHB0d8e6772LXrl24fv264vEqJUXk+PHjy0obAJCXl4e8vDylNrlcrlTgaouDgx1MTEyQfj9DqT09/QGaNmmo9fF0TUz5iCkXgPlQ1RHztWnfuwOsbK1xfK/qvxsM1c3EZPxn4sfIz8+HpYUFvljyGRrWL/7v7/Ivv0XrFm+iW+eOeo6yYsT2XRNTPmLKRWte491Tq3XRmJmZCQCKmarysrW1VWkzMTFRe+zFzVr27NmD4cOHw9raGr1794a7uzssLS0hCALCw8PLtaaxJP7t27e/8ryXC7jyelUuhYWFirb8/Hz4+PjgwoUL8PT0xKhRo2Bvbw8TExOkpKRg69atKgUYADg5OZU6bkl7dnZ2ud5jbGwMe3t7lfMnTpyIXbt2YdOmTVi5ciXS0tLw888/o2vXrnjjjTdekTkQGhqKBQsWKLUJRtYQjFU/EyIien30HN4L56PPI/N+pr5DKbf6rnXxY/g6PH7yFL9FxeLTxasQ/tVy3PrnLuLOx2Pvlq/0HSIRiVy1LhpLiqKcnBzY2NhU6dghISEwNzfH+fPn0bhxY6Vju3btKlcfJfFHRETAz89P6zGW18GDB3HhwgWMGzdO5XbQXbt2YevWraW+7+XNdF5ul0qlpR5r0qSJUlthYSEePnyoUlD6+PigadOm+P7777FkyRJs2bIFhYWF5doAZ+7cufj444+V2mraN1X7vorIyMiETCZDLScHpfZatRxx7/4DnYypS2LKR0y5AMyHqo5Yr41jHUe06uSBpR8sUX+yATE1NYVrXRcAQPOmjfHX9Zv4356DkJiZ4fadu+jYZ4jS+TM+XYw2Hs0R/tVyfYSrEbF918SUj5hy0Rq5Ye5sWhWq9ZrG9u3bA/j3Ns+qlJiYiGbNmqkUjHfv3i33jqcl8Z85c0br8Wmi5BEhL67zLHHy5EmVthK3bt0qdUa15D0vr9Msq78zZ85AJpOVev4HH3yABw8e4MCBA/juu+9Qs2ZNlVteSyORSGBra6v00sWtqUDxDPSFC5fQzbeTok0QBHTz7YSzZ8/rZExdElM+YsoFYD5UdcR6bboP64nsh9n447jqIyuqk6IiOfLzCzB+1DDs+3499oavU7wAYM5HH2DR/32sphfDILbvmpjyEVMuVHnVumicMmUKTExMEBQUhFu3bqkcz8rKUlonp01ubm5ISEhQmm3Lzc3F5MmTlW5jfRV/f3+4urpi9erVOHHihMrxgoICxMbGai3mspSsS3x5rJiYGKUdYF9WWFiI//u//8OLG/BeunQJ27Ztg6OjI/r166fyni+++EJpQ6H8/Hx8+mnx1uclu8q+aMyYMTA3N8eMGTOQlJSEUaNGwdzcXKP8qsKaLzZi/Lj3MGrUUDRt2gjrvloKKysLhG/dre/QKkRM+YgpF4D5GDIrK0u0bNUMLVs1AwC4udVDy1bNUPf/zxBVN2K6NkDxP3a7D+2BqL3HUFRYfWYL1mzYgj/+vIw7d+/jZmIy1mzYgnMXL6F/L1842NuhcQN3pRcAODs5oq5Lbf0GrgGxfdfElI+YctGKInnVvAxQtb49tUWLFli/fj0mT56MJk2aoF+/fmjYsCEeP36MpKQkxMTEICAgAF9//bXWxw4KCkJQUBA8PT0xZMgQyGQyHDlyBHK5HB4eHoiPj1fbh0Qiwd69e9G3b1907doV3bp1Q8uWLSEIAlJTU3Hy5EnY29uXupmPNg0YMADu7u5Yvnw5rly5ghYtWuDGjRs4dOgQBg0ahL1795b6vlatWiE2Nhbe3t7o0aMHHjx4gN27d0Mmk+Hbb79VPG7jRR06dICHhweGDx8OKysrRERE4MaNG3j33XdLnUG0s7PD0KFDsW3bNgAwqGczvmjPnp/g6GCHkPmzULu2I+Lj/0J/v/eRnp6h/s0GSEz5iCkXgPkYsjZtWuLnX/9dnrBs+WcAgP9t24tJE2eX9TaDJaZrAwAenVqjVt1a1W7X1MysLPzfwpV48DATNlZWeKNRfXyzehHeatdG36Fpjdi+a2LKR0y5UOVU66IRKC4iWrdurZiti4iIgFQqhaurK2bMmKH0jD9tmjp1KkxNTbF27Vps3LgRNWrUQP/+/REaGoqhQ4eWux9vb2/Ex8djxYoViIyMxKlTpyCRSFCnTh0MHDhQ6aH3umJtbY3jx49j9uzZOHHiBKKjo9G8eXNs374dTk5OZRaNNWvWxOHDhzFr1ixs3LgRz549g6enJxYsWICePXuW+p6wsDDs2bMHmzZtwq1bt+Ds7IyQkBDMnTu3zPjGjBmDbdu2oUOHDmjRooVWctaF9RvCsX5DuL7D0Box5SOmXADmY6hOnoyDtaXq44uqM7FcGwD48+RF+Lvqb/+Ailo4d4ZG51859bOOItEtMX3XAHHlI6ZcKktuoLOAVUGQv3hvIVE5CYKArl27Ijo6ulznBwQEYOvWrUhOToa7u7tGY61cuRKzZ8/G5s2bMXbsWM2D/f9MzOpU+L1EJA7mJuofh1Sd5Mry9R2C1vSvrbquvTrbe+FLfYegVRYuqo/MItIFWf4dfYdQpsfTB1TJODZhEVUyjiaq9ZpGEr/c3Fx89dVXqFmzJkaMGKHvcIiIiIiIXjvV/vZUEqfY2FjExMTg119/RWpqKkJDQ2FpaanvsIiIiIjodfUa357KorGaCAsLQ1ZWltrzAgICNL790xAdPXoUCxYsgIODA2bMmIFZs2bpOyQiIiIiotcS1zRWE+7u7qU+E/FlUVFR8PHx0X1A1RDXNBIR1zQaLq5pNGxc00hVxaDXNH6o+jg5XbD5KrJKxtEEZxqriZSUFH2HQEREREREryEWjUREREREROq8xmsauXsqERERERERlYkzjUREREREROpwppGIiIiIiIhIFWcaiYiIiIiI1HidHzrBmUYiIiIiIiIqE2caiYiIiIiI1OGaRiIiIiIiIiJVnGkkIiIiIiJShzONRERERERERKo400hERERERKSG/DWeaWTRSEREr40GtrX1HYJWXc28pe8QtCYuJ1HfIWhVwfeh+g6BiEhrWDQSERERERGp8xrPNHJNIxEREREREZWJM41ERERERETqFOk7AP3hTCMRERERERGViUUjERERERERlYm3pxIREREREanxOj9ygzONREREREREVCbONBIREREREanDmUYiIiIiIiIiVZxpJCIiIiIiUoeP3CAiIiIiIiJSxZlGIiIiIiIiNbh7qp4VFBQgJCQEjRs3hkQigSAIOHDggL7DKrfw8HAIgoDw8HB9h/JacHd3h7u7u77DICIiIiJ6LRjETOOqVauwYMECdOnSBcOGDYOpqSmaNm2q77ConAICArB161YkJyezmCMiIiIicXqN1zQaRNF46NAhWFtb48iRIzAzM9N3OERERERERPT/GUTRmJaWBnt7exaMRERERERkkLimUU9CQkIgCAKSk5ORmpoKQRAgCILSLY4nTpzAgAED4ODgAIlEgsaNG2PevHl49uyZUl/R0dEQBAEhISE4ffo0fH19YWNjA0dHR0yZMgXPnz8HABw+fBgdO3aElZUVnJycMGfOHMhkMqW+srOzsWzZMnTt2hUuLi4wMzODi4sLRo8ejcTERI1yTE5Oxvjx4+Hq6gqJRAJnZ2cEBAQgNTW1Qp9ZSkoKBEFAQEAAEhISMGjQINSsWRNWVlbo0aMH4uPjS31feno6ZsyYgUaNGkEikcDBwQGDBw/GlStXVM4VBAE+Pj6l9vPyekJ3d3ds3boVAFC/fn3FNXzx/SV/vnPnDkaPHo3atWvDyMgI0dHRAICoqCiMHTsWTZo0gbW1NaytreHl5YVvv/22Qp+RvkyeNAYJN8/iSU4iTsdGwNurtb5DqhQx5SOmXADmUx2M/XAULt07gzmfT9d3KJUilmsTNGMCfjn+AxJu/4Erf8diy/a1aNjIXd9hlYuJd19I3vsUFh+uhcWkVTB7ZwqEmk4q5xk5N4BkyExYBH0Fi6lfQjJsNmBiqoeIK0Ys37USYspHTLlQxem1aPTx8UFwcDCkUimkUimCg4MRHByM6dOnAwA2bNgAHx8fnDp1Cv3798dHH32EunXrYvHixejZsyfy8/NV+oyLi0P37t0hlUoxceJEuLq6YsOGDZgwYQJ2796NIUOGwM3NDRMnTkSNGjWwYsUKLFmyRKmPa9euYf78+bCwsMCgQYMwffp0eHl5YceOHWjXrl25C764uDh4enpi69ataNu2LaZNm4bOnTtj+/btaNeuHZKSkir82aWkpKBDhw7IzMzE2LFj0bNnTxw7dgy+vr64f/++0rmJiYlo27YtwsLC0LBhQwQFBaFfv3745Zdf0KFDB8TFxVU4junTp8PDwwMAMG3aNMU1DAgIUDrv4cOH6NixIy5duoQRI0bggw8+gK2tLQBg2bJlOHHiBLy9vfHhhx/i/fffR0ZGBiZOnIiZM2dWOLaqNHToO1i5IhgLF62Gd/s+iL90FZGHt8PR0V7foVWImPIRUy4A86kOmrduhqGjB+LGX3/rO5RKEdO16fi2N7Zs2oH+PUdg2KBxMDUxxe79m2FpaaHv0NQyrvcGZH9GIXdnKHL3roFgZAzJ4BmAyb93Zxk5N4Dk3WkoTP0LuTuWIHfHYsj+jALk1WNWREzfNUBc+YgpF60oqqKXARLkcv3/jVIyc5WSkqJou3r1Kjw8PNC8eXMcO3YM9vb/fjmXLl2KuXPnYuXKlYqiIjo6Gr6+vgCAAwcOwN/fH0DxzqxeXl64fPky7O3tERkZCW9vbwDA48eP0ahRI8hkMty7dw+mpsW/yGVnZ6OwsBB2dnZKcUZFRaFHjx4YO3YsNm7cqGgPDw9HYGAgtmzZoiiWCgoK8MYbb+Dhw4eIiYmBp6en4vzY2Fj4+Pigb9++iIiI0OizSklJQf369RWfw3//+1/Fsc8++wyLFi1CaGgoPvnkE0X722+/jbi4OBw+fBi9e/dWtN+8eRNeXl5wd3fHpUuXFO2CIKBr166KmcAXlXat1G2EIwgCACAwMBAbN26EsbGx0vHk5GRFTiVkMhn69euH48ePIykpCa6urq+MoTxMzOpodL4mTsdG4Nwf8Zg2fR6A4pxTks5h3fotWL5inc7G1RUx5SOmXADmU1lv2rmqP6kSLCwtsPtIOBZ/shIfzAjAjSt/Y/n8MJ2NdzXzls76rupr42Bpq/U+y2JvXxN/JZ7GwH6jcPb0HzoZI2l+J530CwtrWE5eg9zdy1F0p/iHCcnIuShKvYqC0wd1MyYA208iddY3/14zXPrIRZZ/Ryf9akOmf9cqGcfuYEyVjKMJg3jkRmm++eYbyGQyrF27VqlgBIA5c+bA0dERO3fuVHmfr6+vomAEAFNTUwwZMgRyuRwDBgxQFIwAYGNjAz8/P2RmZuKff/5RtEulUpWCsaTv5s2b4+jRo2rjP3ToEFJSUjB79mylghEAOnXqBH9/f0RGRiInJ0dtX6WpX78+Zs+erdQ2btw4AMC5c+cUbRcvXsTp06cxZswYpYIRAN544w1MmDABly9fLvU2VW0yMzPD8uXLVQpGACoFIwCYmJhg0qRJKCwsRFRUlE5jqyxTU1O0adMKx46fVLTJ5XIcOx6LDh3a6jGyihFTPmLKBWA+1cGnS2fh5NHTiDt5Tv3JBkyM1+ZFNrY2AICsR9l6jkRzgqR4dlSe+7S4wcIGxs4NIH/2GJIR/4XFxFWQDJsFI5dGeoyy/MT2XRNTPmLKRVvkRVXzMkQGsRFOac6ePQsA+PXXX3Hs2DGV46amprh+/bpKe+vWrVXanJ2d1R5LS0tTKl6io6MRFhaGuLg4ZGRkKK17LM+GPSXx37hxAyEhISrH7927h6KiIsVsn6Zat24NIyPlmr9u3boAgKysLJU47t+/X2ocJZ/h9evX0aJFC43jKK/69evDwcGh1GOPHz/GypUrceDAASQmJuLp06dKx9PS0jQeLy8vD3l5eUptcrlcMeupTQ4OdjAxMUH6/Qyl9vT0B2japKHWx9M1MeUjplwA5mPo+vj3QLOWTTCyz1h9h1JpYrs2LxIEAQtD5yLuzHlcv1bdbiEWYOYzAoV3/ob8YfF/G41qOAIATDsOQP6JPShKvw2TNztCMuRj5H4fAnlWuh7jVU9s3zUx5SOmXKjyDLZozMzMBAAsXrxYo/eVrJN7kYmJidpjBQUFirY9e/Zg+PDhsLa2Ru/eveHu7g5LS0sIgoDw8PByrWksiX/79u2vPO/lAqm8XpVLYWGhShyHDx/G4cOHtR5HeTk5qS7aB4D8/Hz4+PjgwoUL8PT0xKhRo2Bvbw8TExOkpKRg69atKsVfeYSGhmLBggVKbYKRNQTjqrv9iYheH04utfDfRTPwwbCPkJ+nut6eDMfSlfPR9M3GeKfPf/QdisZMu78Hwd4FebuXv9Ba/GOo7NIJFP51GgBQEHMbxq7NYNLibRTE7tdDpEQiZaCzgFXBYIvGkqIoJycHNjY2VTp2SEgIzM3Ncf78eTRu3Fjp2K5du8rVR0n8ERER8PPz03qM5VUSx9q1a/Hhhx+W6z2CIKjsKFsiOzsbUqlU4zjKmuE7ePAgLly4gHHjxmHTpk1Kx3bt2qXYmVVTc+fOxccff6zUVtO+aYX6UicjIxMymQy1nJRnUmvVcsS9+w90MqYuiSkfMeUCMB9D9marprB3tMPuI+GKNhMTE7Tt0Bojxg6Gl2tXFBVVn39tiOnavGjJ8nno0bsrBvUfhbtp99W/wYCYdhsJ4watkLd7BeRPHina5U+Lb7EtylS+K6co8y4EG8PfrERs3zUx5SOmXKjyDHZNY/v27QH8e3tlVUpMTESzZs1UCsa7d++We8fTkvjPnDmj9fg0UZE4atasiTt3VBchp6SkKN36WqJkneKLM5zlVfIIkxfXoZY4efKkSlt5SSQS2NraKr10cWsqUDxLfeHCJXTz/XfTA0EQ0M23E86ePa+TMXVJTPmIKReA+RiyuJN/4F2f/2BYjzGK15U/r+Lwj79iWI8x1apgBMR1bUosWT4Pff16YMg7gbiVargbbZTGtNtIGDfyRN6eVZDnKN8qKM/JQNGTRzCqWVup3aimE+Q5D6syzAoR23dNTPmIKReqPIMtGqdMmQITExMEBQXh1i3V3eGysrJw8eJFnYzt5uaGhIQEpUdX5ObmYvLkyUq3sb6Kv78/XF1dsXr1apw4cULleEFBAWJjY7UWc1natWuH9u3bY+fOndi9e7fK8aKiIsTEKO/Q5O3tjZSUFKX2/Px8lZm7EiWbBt2+fVvj+Nzc3ABA5bOIiYlR2qHW0K35YiPGj3sPo0YNRdOmjbDuq6WwsrJA+FbVz7w6EFM+YsoFYD6G6tnTZ0i4nqT0ev4sF9mPcpBwveKPV9InsVwboPiW1MHDB2DKhNl48uQpHGs5wLGWA8zNJfoOTS3Tbu/BpGkH5Edugjw/F7C0LX698AxG2blfYeLZDcaN20Co4QjTt/wh2NWG7Iru/52hDWL6rgHiykdMuWgDN8IxQC1atMD69esxefJkNGnSBP369UPDhg3x+PFjJCUlISYmBgEBAfj666+1PnZQUBCCgoLg6emJIUOGQCaT4ciRI5DL5fDw8EB8fLzaPiQSCfbu3Yu+ffuia9eu6NatG1q2bAlBEJCamoqTJ0/C3t6+1M18tG3nzp3w9fXFiBEjEBYWhjZt2sDCwgK3bt3CmTNn8ODBA+Tm5irO//jjj/Hbb7+hX79+GDlyJCwtLXHkyBHUqFFDsXHQi7p164aVK1figw8+wODBg2FlZQU3NzeMGjVKbWwDBgyAu7s7li9fjitXrqBFixa4ceMGDh06hEGDBmHv3r1a/Sx0Zc+en+DoYIeQ+bNQu7Yj4uP/Qn+/95GenqH+zQZITPmIKReA+VDVEdO1CRg/EgCw//D3Su3TpszF7h0H9BBR+Zm2Ln6cmPkw5R3T837ZgsKrxWsYZRePASamMPUZDsHcCkUPbiNv7xrIs6vHLYRi+q4B4spHTLlQ5Rhs0QgAEyZMQOvWrRWzdREREZBKpXB1dcWMGTMwZswYnYw7depUmJqaYu3atdi4cSNq1KiB/v37IzQ0FEOHDi13P97e3oiPj8eKFSsQGRmJU6dOQSKRoE6dOhg4cCBGjhypk/hfVr9+fVy8eBGrV6/GgQMHsGXLFhgbG8PZ2RldunTBkCFDlM7v1asXfvjhB3z++efYtm0b7OzsMHToUCxZsqTUHVb79u2L5cuXY+PGjVi1ahUKCgrQtWvXchWN1tbWOH78OGbPno0TJ04gOjoazZs3x/bt2+Hk5FRtikYAWL8hHOs3hOs7DK0RUz5iygVgPtXFuHen6juEShPLtaldo5m+Q6iwZ6snlOs82blfIDv3i46j0R2xfNdKiCkfMeVSaQY6C1gVBLlcLtd3EERVwcSsjr5DICI9e9POVd8haNXVTNXlG9WVg6W4drdOmt9J/UnViO0nkfoOgV4TsnzDXXOc0btrlYzj8GuM+pOqmEHPNBIRERERERkCQ11vWBUMdiMcIiIiIiIi0j/ONBqAsLCwUh9l8bKAgAC4u7vrPB4iIiIiIlL2Os80smg0AGFhYUhNTVV7no+PD4tGIiIiIiKqUiwaDUBKSoq+QyAiIiIiold4nWcauaaRiIiIiIiIysSZRiIiIiIiInXkgr4j0BvONBIREREREVGZONNIRERERESkBtc0EhERERERUbUSGhoKb29v2NjYoFatWhg4cCBu3LihdE5ubi6mTp0Ke3t7WFtbY/Dgwbh//75G47BoJCIiIiIiUkNeJFTJSxMxMTGYOnUqzp49iyNHjqCgoAC9evXC06dPFefMmDEDERER2LNnD2JiYpCWloZ3331Xo3F4eyoREREREVE19Msvvyj9OTw8HLVq1cL58+fRpUsXZGdnY/PmzdixYwe6desGANiyZQuaNWuGs2fPokOHDuUah0UjERERERGRGlW1pjEvLw95eXlKbRKJBBKJRO17s7OzAQB2dnYAgPPnz6OgoAA9evRQnNO0aVO4urrizJkz5S4aeXsqERERERGRgQgNDYVUKlV6hYaGqn1fUVERpk+fjrfffhstWrQAANy7dw9mZmaoUaOG0rlOTk64d+9euWPiTCMREREREZEa8ip6TuPcuXPx8ccfK7WVZ5Zx6tSpuHLlCmJjY7UeE4tGIiIiIiIiA1HeW1Ff9OGHH+LQoUM4ceIE6tatq2ivXbs28vPzkZWVpTTbeP/+fdSuXbvc/bNoJCKi10Yvc3d9h6BVV3FL3yFojbulk75D0CrZDfFcGyIyXHK5HEFBQdi/fz+io6NRv359peNt27aFqakpjh07hsGDBwMAbty4gVu3bqFjx47lHodFIxERERERkRpVtRGOJqZOnYodO3bg4MGDsLGxUaxTlEqlsLCwgFQqxbhx4/Dxxx/Dzs4Otra2CAoKQseOHcu9CQ7AopGIiIiIiKha2rBhAwDAx8dHqX3Lli0ICAgAAKxZswZGRkYYPHgw8vLy0Lt3b6xfv16jcVg0EhERERERqSEvqpqNcDQhl8vVnmNubo5169Zh3bp1FR6Hj9wgIiIiIiKiMnGmkYiIiIiISI1yTOqJFmcaiYiIiIiIqEycaSQiIiIiIlLDENc0VhXONBIREREREVGZONNIRERERESkBmcaiYiIiIiIiErBmUYiIiIiIiI1uHsqERERERERUSnKNdP4008/lbvDd955p8LBGIKCggIsXrwY27dvx61bt5Cfn4/9+/dj4MCB+g6tXMLDwxEYGIgtW7YgICBA3+HohCAI6Nq1K6Kjo/UdChERERG9Jl7nNY3lKhrLWzAJgoDCwsLKxKN3q1atwoIFC9ClSxcMGzYMpqamaNq0qb7DIiIiIiIi0otyFY1FRUW6jsNgHDp0CNbW1jhy5AjMzMz0HQ4RERERERkAufz1nWms1JrG3NxcbcVhMNLS0mBvb8+CkYiIiIiICBUoGgsLC7Fw4ULUqVMH1tbWSEpKAgB89tln2Lx5s9YDrCohISEQBAHJyclITU2FIAgQBAHu7u6Kc06cOIEBAwbAwcEBEokEjRs3xrx58/Ds2TOlvqKjoyEIAkJCQnD69Gn4+vrCxsYGjo6OmDJlCp4/fw4AOHz4MDp27AgrKys4OTlhzpw5kMlkSn1lZ2dj2bJl6Nq1K1xcXGBmZgYXFxeMHj0aiYmJGuWYnJyM8ePHw9XVFRKJBM7OzggICEBqamrFPjQAUVFR6Nu3L1xcXCCRSODk5ITOnTvj22+/VTpv//79GDlyJBo1agRLS0tIpVJ07twZP/74Y5l9b9q0CS1atIC5uTnq1auHOXPmGPQPFZMnjUHCzbN4kpOI07ER8PZqre+QKkVM+YgpF4D5GIoG7Zpi7KZZ+CxuPVam7ETzXl4q5/SeMQTzf1+P0Otb8cH//g8O7rX1EGnFVddr87LxMwNwNi1a6bXrxPf6DqtczPoMh9XcL2HzxX5Yr9gNi8nBMHKqq3SO+X8+gvWiLbBZ+xOsV+6GxeQQGDnV01PEFSOW71oJMeUjplwqS15UNS9DpHHRuHjxYoSHh2P58uVKs3EtWrTApk2btBpcVfLx8UFwcDCkUimkUimCg4MRHByM6dOnAwA2bNgAHx8fnDp1Cv3798dHH32EunXrYvHixejZsyfy8/NV+oyLi0P37t0hlUoxceJEuLq6YsOGDZgwYQJ2796NIUOGwM3NDRMnTkSNGjWwYsUKLFmyRKmPa9euYf78+bCwsMCgQYMwffp0eHl5YceOHWjXrl25C764uDh4enpi69ataNu2LaZNm4bOnTtj+/btaNeunaL418Thw4fRvXt3xMXFoXfv3pg5cybeeecd5OXlYdu2bUrnzp07F3/99Rc6deqEadOmYejQobhx4waGDBmCtWvXqvS9cOFCTJgwARkZGZgwYQKGDh2K3bt3Y+jQoRrHWRWGDn0HK1cEY+Gi1fBu3wfxl64i8vB2ODra6zu0ChFTPmLKBWA+hsTMUoK0a7ewf/53pR73nTQAnQL74MdPN+PLgZ8h/3keJnz/CUwkplUcacVU52tTmsTryejn8a7iNXFgkL5DKheTN1ohPzoCT5dOx7Mv5gLGxrCctgQwkyjOKbz1N55vXYUnIRPw7ItPAQGwnL4EEKrHJvli+66JKR8x5UKVI8jlmj1xpFGjRvjmm2/QvXt32NjYID4+Hg0aNMD169fRsWNHPHr0SFexVomSmcWUlBRF29WrV+Hh4YHmzZvj2LFjsLf/9/9Rli5dirlz52LlypWYOXMmgOKZRl9fXwDAgQMH4O/vD6B4Z1YvLy9cvnwZ9vb2iIyMhLe3NwDg8ePHaNSoEWQyGe7duwdT0+J/VGRnZ6OwsBB2dnZKcUZFRaFHjx4YO3YsNm7cqGgvbffUgoICvPHGG3j48CFiYmLg6empOD82NhY+Pj7o27cvIiIiNPqsBg8ejH379uHPP/+Eh4eH0rGHDx8qfU5JSUlo0KCB0jlPnjzBW2+9hVu3biEtLQ2WlpYAgISEBDRr1gxOTk64cOECatWqBQDIyclBu3btcOPGjQrtnmpiVkej8zVxOjYC5/6Ix7Tp8wAUbwqVknQO69ZvwfIV63Q2rq6IKR8x5QIwn8qa7tJF630CwMqUndjywSr89dsfirb5v69HzMbDiNl4GABgbmOB4D++xu5ZX+PPiDNaGTcs7YRW+ilNVV8bL4fGWu+zxPiZAejSpxNG9xyvszFe9tsAifqTKkCwlsJm1Q94unImCv++Uuo5RnXqw3r+13j8aQDkGXe1Mq7dltLH0gb+vWa49JGLLP+OTvrVhoQ3e1fJOI2u/lol42hC45+g7ty5g0aNGqm0FxUVoaCgQCtBGZpvvvkGMpkMa9euVSqEAGDOnDlwdHTEzp07Vd7n6+urKBgBwNTUFEOGDIFcLseAAQMUBSMA2NjYwM/PD5mZmfjnn38U7VKpVKVgLOm7efPmOHr0qNr4Dx06hJSUFMyePVupYASATp06wd/fH5GRkcjJyVHbV2ksLCxU2l7+nF4uGAHA2toaAQEByM7Oxrlz5xTtO3bsgEwmw8cff6woGAHA1tYW8+bNq1CMumRqaoo2bVrh2PGTija5XI5jx2PRoUNbPUZWMWLKR0y5AMynOrGrVwu2tWri71P//kM79/Fz3PozEW5tdFccaYsYr029+nUQcWEvfjyzAwu++hROdWqpf5MhsrACAMifPi79uJkEpm/1QtGDu5A/elCFgVWM2L5rYspHTLloS5FcqJKXISrX7qkvevPNN3Hy5Em4ubkpte/du1elIBGLs2fPAgB+/fVXHDt2TOW4qakprl+/rtLeunVrlTZnZ2e1x9LS0lC/fn1Fe3R0NMLCwhAXF4eMjAyldY/l2bCnJP4bN24gJCRE5fi9e/dQVFSEmzdvwstLdU1OWUaMGIF9+/ahQ4cOeO+999C9e3d07twZDg4OKuemp6dj6dKl+Pnnn5GamqpY11kiLS1N8b/j4+MBAJ07d1bpp7S20uTl5SEvL0+pTS6XQxC0//+IDg52MDExQfr9DKX29PQHaNqkodbH0zUx5SOmXADmU53YOEoBAI8fZCu1P3mQDRvHGnqISDNiuzZ/XbiKhdOX4lbibdjXsse4mWPw9f4v8R/fQDx7+lx9B4ZCEGA+bBJkCVdQlKa8PMW0qx/M3x0PwdwChfdu42nYXKBQVkZHhkNs3zUx5SOmXKjyNC4a58+fjzFjxuDOnTsoKirCvn37cOPGDXz//fc4dOiQLmLUu8zMTADF6zk1YWtrq9JmYmKi9tiLM7Z79uzB8OHDYW1tjd69e8Pd3R2WlpYQBAHh4eHlWtNYEv/27dtfed7Tp0/V9vWioUOH4sCBA1i9ejW+/vprrFu3DoIgwNfXF6tWrVIUxpmZmfD29satW7fw9ttvo0ePHqhRowaMjY3x559/4uDBg0oFXnZ28T+yXpxlLOHk5FSu2EJDQ7FgwQKlNsHIGoKx6udORETidibqd8X/TriWhL8uXsOB33eh+zu+iNgZqcfINGM+8kMYu7jh6YqZKscK4o6j8NoFCFI7mPUcAssPPsXT5TMAmTjvAiPSh9f5kRsaF43+/v6IiIjA559/DisrK8yfPx9t2rRBREQEevbsqYsY9a6kwMvJyYGNjU2Vjh0SEgJzc3OcP38ejRsr39K0a9eucvVREn9ERAT8/Py0Gp+/vz/8/f3x+PFjnDp1Cvv27cPmzZvRp08fXL9+HTVq1MDmzZtx69YtLFy4UOX20qVLl+LgwYNKbVJp8S/06enpKjPa9+/fL1dcc+fOxccff6zUVtO+qabplUtGRiZkMhlqOSnPsNaq5Yh79w3/1qCXiSkfMeUCMJ/qpGSG0cZRiscPshTt1o5SpF1N0U9QGhDztQGAJzlPcCvpH9R1191ad20zHzEVJi3b4+nKmZBnZaiekPsMRbnPgPQ0PE+6Dps1P8LE823IzkVXeayaENt3TUz5iCkXqrwKbavVuXNnHDlyBOnp6Xj27BliY2PRq1cvbcdmMNq3bw/g39s8q1JiYiKaNWumUjDevXu33DuelsR/5ox2Nl4ojY2NDfr06YNvv/0WAQEBuH//PuLi4gBA8WiQF9d3ljh58qRKW8mmOqUdK62tNBKJBLa2tkovXdyaChTPDF+4cAndfDsp2gRBQDffTjh79rxOxtQlMeUjplwA5lOdZN5OR076IzR+q4WiTWJtAdfWDZF64W89RlY+Yr42AGBhaYE6bi54mP5Q36GUi/mIqTBp/RaerZkD+cNy/HgqCIAACCaGv1Ov2L5rYspHTLloi7xIqJKXIarwXsx//PEHtm3bhm3btuH8eXF/caZMmQITExMEBQXh1q1bKsezsrJw8eJFnYzt5uaGhIQEpRm23NxcTJ48udwbD/n7+8PV1RWrV6/GiROqO+0VFBQgNjZW49hOnDiBwsJClfb09HQAgLm5uSIHACpj7NixA5GRqrcFvffeezA2Nsbq1asVfQHFM72LFi3SOM6qsOaLjRg/7j2MGjUUTZs2wrqvlsLKygLhW3frO7QKEVM+YsoFYD6GxMxSApc33eDyZvHfcXb1HOHyphtquBRvBHbyu5/RPWgg3uzRFrWb1MPI1ZORc/8Rrryww6ohq87X5mVB8yfDs4MHnOvWRkuv5lj23UIUFRXht/2q+xQYGvORH8K0fTc837wU8tznEGxrQrCtCZgW72kgONSGWZ/hMHJtBKGmI4wbvAmLDz6FPD8fsiu/q+ndMIjpuwaIKx8x5UKVo/Htqf/88w9GjhyJU6dOoUaNGgCKi6a33noLu3btQt26dV/dQTXUokULrF+/HpMnT0aTJk3Qr18/NGzYEI8fP0ZSUhJiYmIQEBCAr7/+WutjBwUFISgoCJ6enhgyZAhkMhmOHDkCuVwODw8PxaYxryKRSLB371707dsXXbt2Rbdu3dCyZUsIgoDU1FScPHkS9vb2pW7m8yofffQR0tLS0KlTJ7i7u0MQBMTGxuL3339Hhw4d0KlT8S9To0aNwrJlyxAUFISoqCi4ubkhPj4ex44dw7vvvot9+/Yp9duoUSPMnz8fwcHBaNWqFYYNGwYTExP8+OOPaNWqFW7cuKFRnFVhz56f4Ohgh5D5s1C7tiPi4/9Cf7/3kZ5eyi1E1YCY8hFTLgDzMST1WjXA5F3zFX/2/2w0AODc3hjsnvU1or6OgJmFBENCx8PC1hLJ525g45ilkOVVjzVm1fnavKyWsyM+X/8ZpDVtkfUwG/HnLmO83xRkZWarf7OemfkMAABYzVqp1P48fCUKzhwBCvJh3KgFzLoPgmBpDXlOFgr/voxny2dA/tjw8wPE9V0DxJWPmHLRBs0eVCguGj+nsU+fPsjKysLWrVvRpEkTAMW7cgYGBsLW1ha//PKLTgKtKqU9p7HEuXPnFLN1Dx48gFQqhaurK3r16oUxY8agadPiNXMlz2kMDg5W2a20tOcolggJCcGCBQsQFRUFHx8fAMU7fn777bdYu3YtEhMTUaNGDfTv3x+hoaEYOnQoYmJi8OIlfFX/d+7cwYoVKxAZGYlbt25BIpGgTp06ePvttzFy5Eh069ZNo89q9+7d2LdvH86fP4+7d+/C1NQU7u7ueO+99zBlyhRYW1srzo2Pj8ecOXNw7tw5yGQytGnTBp999hlu375dZrybNm3CmjVrkJCQgFq1amHEiBH4/PPPYWlpaXDPaSSi6kFXz2nUF10+p7Gq6fI5jfqgq+c06osun9NI9CJDfk7jtcb9qmScZn8b3gZdGheNFhYWOH36tMrjNc6fP4/OnTvj2bNnWg2QSFtYNBIRi0bDxaLRsLFopKpiyEXj1Yb9q2ScNxMPV8k4mtB4TWO9evVKXUtXWFgIFxcXrQRFREREREREhkHjonHFihUICgrCH3/8u5D/jz/+wLRp07By5cpXvJOIiIiIiKh6KpILVfIyROXaCKdmzZpKjyt4+vQp2rdvr3gYvUwmg4mJCcaOHYuBAwfqJFCqGmFhYcjKylJ7XkBAgGL9JxERERERiVe5isawsDAdh0GGIiwsDKmpqWrP8/HxYdFIRERERK8NuYHOAlaFchWNY8aM0XUcZCBK2zWWiIiIiIheXxo/p/FFubm5yM/PV2qztbWtVEBERERERESG5nV+TqPGG+E8ffoUH374IWrVqgUrKyvUrFlT6UVERERERETioXHROGfOHBw/fhwbNmyARCLBpk2bsGDBAri4uOD777/XRYxERERERER6xd1TNRAREYHvv/8ePj4+CAwMROfOndGoUSO4ublh+/bt+M9//qOLOImIiIiIiEgPNJ5pzMzMRIMGDQAUr1/MzMwEAHTq1AknTpzQbnREREREREQGQC4XquRliDQuGhs0aIDk5GQAQNOmTfHDDz8AKJ6BrFGjhlaDIyIiIiIiIv3SuGgMDAxEfHw8AOCTTz7BunXrYG5ujhkzZmD27NlaD5CIiIiIiIj0R+M1jTNmzFD87x49euD69es4f/48GjVqhFatWmk1OCIiIiIiIkPwOj9yo1LPaQQANzc3uLm5aSMWIiIiIiIiMjDlKhq//PLLcnf40UcfVTgYIiIiIiIiQ2Soj8OoCuUqGtesWVOuzgRBYNFIREREREQkIuUqGkt2SyUiIqrOvk4/q+8QqAy/dBbXL/ijfjbVdwhEpGWG+jiMqqDx7qlERERERET0+qj0RjhERERERERi9zqvaeRMIxEREREREZWJM41ERERERERqvMaPaeRMIxEREREREZWtQkXjyZMn8f7776Njx464c+cOAGDbtm2IjY3VanBERERERESGoEguVMnLEGlcNP7444/o3bs3LCwscPHiReTl5QEAsrOzsWTJEq0HSERERERERPqjcdG4aNEifP3119i4cSNMTf99BtHbb7+NCxcuaDU4IiIiIiIiQyCXC1XyMkQaF403btxAly5dVNqlUimysrK0ERMREREREREZCI2Lxtq1ayMhIUGlPTY2Fg0aNNBKUERERERERIakqIpehkjjonHChAmYNm0a4uLiIAgC0tLSsH37dsyaNQuTJ0/WRYxERERERESkJxo/p/GTTz5BUVERunfvjmfPnqFLly6QSCSYNWsWgoKCdBEjERERERGRXslhmOsNq4LGM42CIODTTz9FZmYmrly5grNnz+LBgwdYuHChLuITtfDwcAiCgPDwcH2HQkREREREVCqNZxpLmJmZ4c0339RmLERERERERGRgNC4afX19IQhlT80eP368UgEREREREREZmiK5viPQH42LxtatWyv9uaCgAH/++SeuXLmCMWPGaCsuIiIiIiIiMgAar2lcs2aN0uurr75CbGwspk+fDlNTU13EqHPR0dEQBAEhISE4ffo0fH19YWNjA0dHR0yZMgXPnz8HABw+fBgdO3aElZUVnJycMGfOHMhkMkU/2dnZWLZsGbp27QoXFxeYmZnBxcUFo0ePRmJiokYxJScnY/z48XB1dYVEIoGzszMCAgKQmppa4Ty/++47+Pv7w93dHebm5rCzs0Pv3r0RFRX1ys/kjz/+QM+ePWFjYwOpVIpBgwYhJSWl1DH27dsHLy8vWFhYwMnJCRMmTMCjR4/g7u4Od3d3pXMDAgIgCAKSkpKwatUqvPnmm5BIJAgICMC8efMgCAJ++OGHMnMRBAGhoaEV/jy0bfKkMUi4eRZPchJxOjYC3l6t9R1SpYgpHzHlAjAfQ/X22+3ww95N+DvxLJ48S4bfgJ76DqnSquu1kQx8D9ZLNkAafhi23+6D1ayFMHKupzguWNnAIjAINmu2QrrtF9iu2wWLgCDAwkqPUZfft6c24+CtQyqviQsn6Tu0Cquu37WyiCkfMeVSWUUQquRliDQuGsvy/vvv47vvvtNWd3oRFxeH7t27QyqVYuLEiXB1dcWGDRswYcIE7N69G0OGDIGbmxsmTpyIGjVqYMWKFViyZIni/deuXcP8+fNhYWGBQYMGYfr06fDy8sKOHTvQrl27chd8cXFx8PT0xNatW9G2bVtMmzYNnTt3xvbt29GuXTskJSVVKL+pU6fi/v376NGjB2bMmAE/Pz+cOXMGPXr0wMGDB0t9z7lz59ClSxeYmZlh4sSJ8PLywoEDB9CjRw/k5uYqnfvdd99h8ODB+PvvvzF69GiMGTMGZ86cQc+ePVFQUFBmXEFBQViyZAm8vLwwffp0tGzZEhMmTICRkRE2bdpU6ns2btwIExMTBAYGVuiz0LahQ9/ByhXBWLhoNbzb90H8pauIPLwdjo72+g6tQsSUj5hyAZiPIbO0ssCVy9fw8Yz5+g5FK6rztTFp5oH8Xw/g8bypeLJ4NmBsAutPlwMScwCAYGcPoaYDnm/7Go9njcWz9ctg4uENy0mz9Rx5+cwaMANj2r6veM1/71MAwKnDp/QcWcVU5+9aacSUj5hyocoR5HK5Vu7O3bZtG/773/8iLS1NG91VqejoaPj6+gIADhw4AH9/fwDFt956eXnh8uXLsLe3R2RkJLy9vQEAjx8/RqNGjSCTyXDv3j2YmpoiOzsbhYWFsLOzU+o/KioKPXr0wNixY7Fx40ZFe3h4OAIDA7FlyxYEBAQoxnzjjTfw8OFDxMTEwNPTU3F+bGwsfHx80LdvX0RERGicZ3JyMurXr6/UdvfuXXh5ecHKygo3b94s9TPZtWsXhg8frjg2evRobNu2DTt37sSIESMAAFlZWahXrx7kcjkuXryIxo0bAwBkMhl69+6N48ePw83NTWmGMiAgAFu3bkXdunVx6tQpuLq6KsXWr18//PLLL0hKSlKapfzrr7/QokULDBw4EPv37y93/iZmdcp9rqZOx0bg3B/xmDZ9HoDiXYZTks5h3fotWL5inc7G1RUx5SOmXADmU1nmJmZa77M0T54lY8TwD3Ao4ohOx8mV5eus76q+NhmD3tB6nyUEGymkmw7gccg0FF67VOo5ph26wvLD/0P26L5AUeUfrz3mTNXNWo4LngDv7t6Y1OUDnY1x+N5FnfXNv9cMlz5ykeXf0Um/2nDMabj6k7Sg+/3dVTKOJjSeaXz33XeVXoMGDUKHDh0QGBiIiRMn6iLGKuPr66soGAHA1NQUQ4YMgVwux4ABAxQFIwDY2NjAz88PmZmZ+OeffwAAUqlUpWAs6bd58+Y4evSo2hgOHTqElJQUzJ49W6lgBIBOnTrB398fkZGRyMnJ0Ti/lwtGAHB2dlbMDpY2E9qlSxelghEAxo4dC6B4FrLEwYMH8eTJE4wbN05RMAKAiYkJFi1a9Mq4Zs+erVIwAsCkSZMgl8uxefNmpfaS2ccJEya8st+qYmpqijZtWuHY8ZOKNrlcjmPHY9GhQ1s9RlYxYspHTLkAzIeqjtiujWBZXMDJn5T9307B0gry58+0UjBWJRNTE/gM8sHR3br9gUJXxPZdE1M+YsqFKk/jjXCkUqnSn42MjNCkSRN8/vnn6NWrl9YC04eXN/kBiosqdcfS0tIUBVl0dDTCwsIQFxeHjIwMpTWPZmbqf+E+e/YsAODGjRsICQlROX7v3j0UFRXh5s2b8PLyUtvfi5KSkhAaGorjx4/jzp07yMvLUzqelpYGNzc3pba2bVX/Uqhbty6A4tnFEvHx8QCKC9uXtW/fHiYmZX/V2rVrV2p7//79UadOHWzZsgUhISEwNjZGfn4+tm3bhnr16qFPnz5l9pmXl6eSn1wuf+XOvxXl4GAHExMTpN/PUGpPT3+Apk0aan08XRNTPmLKBWA+VHVEdW0EARZjPoTs+mUU3U4p/RQbW5i/Owr5Rw9VbWxa0L53B1jZWuP43mP6DqVCRPVdg7jyEVMu2lK9flLSLo2KxsLCQgQGBqJly5aoWbOmrmLSG1tbW5W2kmLnVcdK1uvt2bMHw4cPh7W1NXr37g13d3dYWlpCEASEh4eXa01jZmYmAGD79u2vPO/p06dq+3pRQkIC2rVrh5ycHPj6+mLAgAGwtbWFkZERoqOjERMTo1JkAa/Ou7CwUNFWMvNZq1YtlfONjIzg4OBQZmxOTk6lthsbG2P8+PFYsGABfv75Z/j5+WH//v14+PAhPvzwQxgZlT1RHhoaigULFii1CUbWEIxV8yEiIvGyGDsNxvXq43FwUBknWMLqv0tR+E8qcveGV2ls2tBzeC+cjz6PzPuZ+g6FiERMo6LR2NgYvXr1wrVr10RZNFZWSEgIzM3Ncf78eaVbNIHidYHlUVKkRUREwM/PT2uxrVmzBo8ePcK2bdvw/vvvKx2bNGkSYmJiKtV/Sdzp6ekqx4qKipCRkYE6dUpfU/iq2b/x48dj0aJF2LhxI/z8/LBp0yYYGRkpbpEty9y5c/Hxxx8rtdW0b6oujQrJyMiETCZDLSflwrhWLUfcu/9AJ2PqkpjyEVMuAPOhqiOWa2MR+BFM23TEk5BpkGdmqJ5gbgHrucsgz32Gp6s+A174MbQ6cKzjiFadPLD0gyXqTzZQYvmulRBTPmLKRVvkBrqzaVXQeE1jixYtKrx7p9glJiaiWbNmKgXj3bt3y/2ZtW/fHgBw5swZrccGQGnNJlB8y+apU5Xfbc3DwwMASu3r999/V7pNVxN169ZF//79ERkZidOnT+PYsWPo3bt3qWsgXySRSGBra6v00sWtqUDxTPOFC5fQzfffW3MFQUA33044e/a8TsbUJTHlI6ZcAOZDVUcM18Yi8COYtuuEJws/RtGDe6WcYAnrT1cAMhmeLv8UeMUu34aq+7CeyH6YjT+On1N/soESw3ftRWLKR0y5UOVpXDQuWrQIs2bNwqFDh3D37l3k5OQovV5nbm5uSEhIwP379xVtubm5mDx58isfOfEif39/uLq6YvXq1Thx4oTK8YKCAsTGxlYoNgAq7126dCmuXLmicX8v8/f3h7W1NTZv3qz0TEqZTIbPPvusUn1PnDgRMpkMQ4cOhVwuN5gNcF605ouNGD/uPYwaNRRNmzbCuq+WwsrKAuFbDW/3q/IQUz5iygVgPobMysoSLVs1Q8tWzQAAbm710LJVM9St66LnyCqmOl8bi3HTYda5J55+uRjy588gSGtCkNYETP//3gL/v2AUJOZ49s0KCBaW/54jaO1pZDolCAK6D+2BqL3HUFRYvVdaVefvWmnElI+YctGGoip6GaJy3576+eefY+bMmejXrx8A4J133lGauSnZZKSwmt3aoU1BQUEICgqCp6cnhgwZAplMhiNHjkAul8PDw0OxWcyrSCQS7N27F3379kXXrl3RrVs3tGzZEoIgIDU1FSdPnoS9vT2uX7+uUWyTJk3Cli1bMHjwYAwbNgz29vY4e/YsLly4gP79++Pw4cMVTRsAUKNGDaxevRoffPAB2rZtixEjRkAqlSIyMhISiQQuLi6vXIP4Kn369IGbmxtSU1NRu3ZtDBgwoFKx6sKePT/B0cEOIfNnoXZtR8TH/4X+fu8jPb2U26GqATHlI6ZcAOZjyNq0aYmff/13KcKy5cU/mP1v215Mmlg9nv/3oup8bSS9iu+qsQkJU2p/tn4p8mN+hUn9xjBp/CYAwPZL5T0Ecj4cgaIH92HoPDq1Rq26tartrqkvqs7ftdKIKR8x5UKVU+6iccGCBZg0aRKioqJ0GU+1NnXqVJiammLt2rXYuHEjatSogf79+yM0NBRDhw4tdz/e3t6Ij4/HihUrEBkZiVOnTkEikaBOnToYOHAgRo4cqXFsnp6e+O233zBv3jzs27cPxsbGeOutt3Dq1Cn89NNPlS4ageJHYNSsWRNLlixBeHg4pFIp3nnnHSxbtgxubm5o2LBiO20ZGRlh1KhRWLRoEQICAl65E6s+rd8QjvUbwvUdhtaIKR8x5QIwH0N18mQcrC1VH21UnVXXa5M13PeVx2VX49WeY+j+PHkR/q7a2/tA36rrd60sYspHTLlUlqHOAlYFQS6Xy8tzopGREe7du1fq7phEr5KQkIDGjRtj2LBh2L27Yrcz+Pn5ITIyEjdv3kSjRo0q1IeJWekb8RDR68PcRP2jj6qTXFm+vkPQmoxBb+g7BK0ac8ZK3yFo1eF7F/UdAr0mZPl39B1CmSKdRlTJOP3ul28DzRInTpzAihUrcP78edy9exf79+/HwIEDFccDAgKwdetWpff07t0bv/zyS7nH0Oh+QV1tJELi8OjRI5XHdjx//hwzZswAAKUvryauXr2KyMhI9OzZs8IFIxERERFRZcghVMlLU0+fPoWHhwfWrVtX5jl9+vTB3bt3Fa+dO3dqNIZG9/m98cYbagvHkucM0usnJiYG48aNQ69eveDq6oqMjAwcP34cKSkp6NatG4YPH65Rfzt27MCNGzfw/fffAwCCg4N1ETYRERERkcHIy8tTmYiRSCSQSCSlnt+3b1/07dv3lX1KJBLUrl27wjFpVDQuWLAAUqm0woORdoWFhSErK0vteQEBAXB3d9d5PM2bN0fPnj1x6tQpHDhwAADQqFEjLFy4ELNmzdJ4I5xvv/0WJ0+ehJubGzZv3oy33npLB1ETEREREalXVEU3XYaGhmLBggVKbcHBwQgJCalwn9HR0ahVqxZq1qyJbt26YdGiRbC3ty/3+7mmsRpzd3dHamqq2vOioqLg4+Oj+4AMHNc0EhHXNBourmk0bFzTSFXFkNc0RtTWfDPKiuiVGq7RTOOLBEFQWdO4a9cuWFpaon79+khMTMT//d//wdraGmfOnIGxsXG5Yir3TCPXMxqelJQUfYdARERERPRaKKrAesOKKG+BWF4jRvy7gU/Lli3RqlUrNGzYENHR0ejevXu5+ij3/YLlnJAkIiIiIiIiA9WgQQM4ODggISGh3O8p90xjUdHr/GQSIiIiIiKi6u+ff/7Bw4cP4ezsXO73GOZT0omIiIiIiAyIod53+eTJE6VZw+TkZPz555+ws7ODnZ0dFixYgMGDB6N27dpITEzEnDlz0KhRI/Tu3bvcY7BoJCIiIiIiqqb++OMP+Pr6Kv788ccfAwDGjBmDDRs24NKlS9i6dSuysrLg4uKCXr16YeHChRqtm2TRSEREREREpIahLtbz8fF55f4zv/76a6XH0OzBeURERERERPRa4UwjERERERGRGkWv8SMIOdNIREREREREZeJMIxERERERkRqGuntqVeBMIxEREREREZWJM41ERERERERqGOruqVWBRSMREb02GtjW1ncIWnU185a+Q9CaNsey9B2CVh2rI65/Yh2+p+8IiEifxPU3GhERERERkQ4Uvb6bp3JNIxEREREREZWNM41ERERERERqFOH1nWrkTCMRERERERGViTONREREREREavA5jURERERERESl4EwjERERERGRGtw9lYiIiIiIiKgULBqJiIiIiIioTLw9lYiIiIiISI0ifQegR5xpJCIiIiIiojJxppGIiIiIiEgNPnKDKiw8PByCICA8PFzfoUAQBPj4+Ci1BQQEQBAEpKSk6CUmIiIiIiKq3lg0UrVgSMU5EREREb1+ioSqeRkiFo0iFxoaimvXrqFOnTr6DoWIiIiIiKohrmkUOWdnZzg7O+s7DCIiIiKiao27p4pAdHQ0BEFASEgITp8+DV9fX9jY2MDR0RFTpkzB8+fPAQCHDx9Gx44dYWVlBScnJ8yZMwcymUzRT3Z2NpYtW4auXbvCxcUFZmZmcHFxwejRo5GYmKhRTMnJyRg/fjxcXV0hkUjg7OyMgIAApKamVirXTZs2oUWLFjA3N0e9evUwZ84c5ObmlnpuWWsaf/zxR3Tt2hW1atWCubk5XFxc0KNHD/z4448qfXzzzTdo3ry5ynilraF0d3eHu7t7qbH4+PhAEJTn3HNzc7Fq1Sp4eHhAKpXCysoK7u7uGDZsGOLj4xU5BAYGAgACAwMhCILiZUgmTxqDhJtn8SQnEadjI+Dt1VrfIVWKmPIRUy4A86kOxn44CpfuncGcz6frO5RKEcu1+U/gUETG7EZ88knEJ5/E3p+3omv3t/UdVrmZt2kJpy8/R70ju1A//ggsfd9SOm5kVwMOn89GvSO74HY2Ak7rl8DEtXrdYSSW71oJMeUjplyo4kRTNJaIi4tD9+7dIZVKMXHiRLi6umLDhg2YMGECdu/ejSFDhsDNzQ0TJ05EjRo1sGLFCixZskTx/mvXrmH+/PmwsLDAoEGDMH36dHh5eWHHjh1o165duQu+uLg4eHp6YuvWrWjbti2mTZuGzp07Y/v27WjXrh2SkpL+H3v3HRbF2fUB+De0pYPSBBRQUbGLgmKLoBi72HvBFtTYFRMrWGLXEI0aO8bYojHGnlhAJSq2iB0VAQs2QAFRkHK+P/h23l12EVBgl/Xc78X1xtnZmefszM7Omad9Unzz5s3DiBEjEB8fjxEjRqBnz57YvXs3evbsWeBtrF27Fj169MD9+/fRtWtXTJo0CW3btsXz58/x559/yq07e/ZsjBw5EgkJCeL+fv/9d/Tq1euTyp/b4MGDMWXKFAA5CeGYMWPQpEkTnD17FpcuXQIAdOnSBT4+PgAAHx8fBAQEiH/qomfPzli2NADz5q+Ae6O2iLh+G0cOb4eVlYWqi/ZJNCkeTYoF4HhKg5r1qqPnoC6IvHVf1UX5LJp0bJ7FvcCSeavg06o/unj3x/mzF7Fu24+oUq2SqotWIIKBPj5EPkTCwlVKX7cJmgPd8uXwYsJsxPUehcxnL2C7bjEEA/0SLumn0aRzDdCseDQplqKQXUJ/6kggIo0YPTY0NBReXl4AgP3794tJRkZGBtzc3HDjxg1YWFjgyJEjcHd3BwCkpKTA2dkZmZmZeP78OXR1dZGUlISsrCyULVtWbvshISHw9vbG0KFDsWHDBnF5cHAwhgwZgi1btsDX11fcZ9WqVZGQkIDTp0/D1dVVXD8sLAyenp5o164dDh48WKgYHzx4gOrVq8PGxgZXr16FtbU1ACA5ORkNGzZEZGQkWrRogdDQUPE9vr6+2Lp1K6Kjo8UawAYNGuDmzZt4/PixuA2phIQEWFjkXAju3buHGjVqoFy5cnL7S0lJgYeHB27fvq2wP+k+lI3W6unpidOnT0N6yiUlJaFMmTKoX78+wsPDoa2tLa6blZWFlJQUmJub5/k5F5aOXvE9dT0XdhCXLkdg/ISZAHJGso15eAmr12zBkqWri22/xUWT4tGkWACO53PVKOtQ5NuUZWBogN3Hg/HD98vwzURfRN68jyWzg4ptf7cTHxXbtkv62DiYWue/UhG6ej8UiwKD8Pv2/cWy/ZP2lsWy3YoRx/FiQgDehZwDAOg42qPCgWA86TYcGVH//2BbEOBwajcSV27B2z+PFsl+q9y5XSTbUYava+pLFbFkfnhaLNstCuvKDyiR/fg9+a1E9lMYGlfT6OXlJSaMAKCrq4sePXqAiNCpUycxYQQAExMTdOzYEYmJiXjy5AkAwMzMTCFhlG63Zs2aOHHiRL5lOHToEGJiYuDv7y+XMAJAs2bN4OPjgyNHjiA5OblQse3YsQOZmZmYNGmSXLJnamqKmTNnFmpburq60NXVVVguTRgBYOfOncjKysLkyZPl9mdiYlLo/SkjCAKICPr6+tDSkj8VtbW1xYRR3enq6qJ+/To4eeqsuIyIcPJUGDw8GqiwZJ9Gk+LRpFgAjqc0mLFoCs6eOIfws5dUXZTPoonHRkpLSwsdu7aBgaEBrl66rurifDbh/3/LKf3D/xYSgT5kQN+1lopKVXCadq5pUjyaFEtRIaFk/tSRxg2EU69ePYVl0oFgPvZaXFwcKlasCCCn1jIoKAjh4eGIj4+X6/Oop6eXbxkuXLgAAIiMjERgYKDC68+fP0d2djbu3bsHNze3fLcnJe3j17x5c4XXlC3LS58+fTB16lTUqlUL/fr1g5eXF5o1awZTU1Ol+2vWrJnCNpo2/fy+IKampmjfvj2OHDmC+vXro2fPnvD09IS7u7vShLYw0tPTkZ6eLreMiIqlH6SlZVno6Ojg5Yt4ueUvX76CS7XKRb6/4qZJ8WhSLADHo+7a+nijeu1q6Nt2qKqL8tk07dgAQLXqzth7dCsk+np4l/oeowZPxoN7n9ZVRJ1kxDxGZtwLlBk3DAnzgpD9Pg1mA7tDp5w1tK0UH4KrG0071zQpHk2KhX0+jUsacyc+AKCjo5PvaxkZGQCAPXv2oHfv3jA2NkabNm3g5OQEQ0NDcY7AgvRpTExMBABs3779o+ulpqbmuy1ZSUlJAKDQpBQAbGxsCrydKVOmwMLCAmvXrsXy5cuxbNky6OjooEOHDvjxxx/F5FlaE/q5+/uYPXv2YMGCBdixYwdmzJgBIOc4DRkyBAsWLIChoeEnbXfhwoWYM2eO3DJByxiCtuI5wBhjn8vGzhrfzZ+Ib3qNwwfZGh+mNh4+iEFHrz4wMTVGu07eWPrzXPTtPLz0J46ZWXgxaQ4sAyfDMexPUGYW3odfxbuzFwE1rbFgrLRS1/6GJUHjksbPFRgYCH19fVy5cgVVqlSRe23Xrl0F2oY0OT148CA6duxYZGUzMzMDALx8+RKOjo5yr7148aLA2xEEAUOHDsXQoUORkJCAs2fPYufOnfj9999x//59XL9+Hdra2mIchdmflpYWPnxQfsMkTXplGRoaYv78+Zg/fz6io6MREhKCX375BT/99BPev3+PdevWFTguWdOmTcOkSZPklpWxcPmkbeUnPj4RmZmZsLaR779ibW2F5y9eFcs+i5MmxaNJsQAcjzqrUccFFlZlsft4sLhMR0cHDTzqoc/Q7nBzaIHs7NJzu6FJx0YqIyMTsdGPAQA3I+6gjmtN+Pr1xczJP6i4ZJ/vw537iOs9EoKxIQRdXWS/ToLtbyvxoRQMxqRp55omxaNJsbDPp3F9Gj9XVFQUqlevrpAwPnv2rMAjnjZq1AgAcP78+SItW926dQEAZ8+eVXhN2bKCsLCwQJcuXbB79260bNkSt2/fxoMHD+T29++//yq879y5c0q3V6ZMGbx8+VKuSS+QU6t6//7Hf7wqVqyIoUOH4vTp0zA2NsaBAwfE16SD5GRlZRUoLolEAlNTU7m/4pqiIyMjA1evXkdLr/814xUEAS29muHChSvFss/ipEnxaFIsAMejzsLPXkY3z/7o5T1Y/Lt57TYO//E3enkPLlUJI6BZxyYvgpZQoC4npQm9fYfs10nQcbCHpEZVvAtV/lutTjTtXNOkeDQplqLyJY+eykljLo6Ojnjw4IFcTVpaWhpGjRolNmHNj4+PDxwcHLBixQqcOXNG4fWMjAyEhYUVumz9+vWDtrY2VqxYgZcvX4rLk5OTMX/+/AJvJzQ0FLkHzc3IyBCb1err5wzR3adPH2hpaWH58uWIj/9fe/bU1FT88IPyJ7Pu7u7IyMiQa5pLRJg2bZpCc9xXr17h5s2bCtt4/fo10tPTxXIAEAcnevz4cYHjLEk//rQBw4f1w8CBPeHi4ozVPy+CkZEBgrfuVnXRPokmxaNJsQAcj7p6l/oOD+4+lPt7/y4NSa+T8eBu6Wz+qCnHBgD8Z46Fe+P6sK9gi2rVneE/cyw8mrrhwN4jqi5agQgG+tCrVhl6/9+PTMe+HPSqVYZ2OSsAgGHrr6DvVgc69uVg6NkY5X5ZhHch5/D+fOm4sdekcw3QrHg0KRb2ebh5ai5jx47F2LFj4erqih49eiAzMxPHjx8HEaFu3bri4DAfI5FIsHfvXrRr1w4tWrRAy5YtUbt2bQiCgNjYWJw9exYWFha4e/duocrm7OyM2bNnIyAgAHXq1EGvXr2go6ODP/74A3Xq1EFkZGSBttOlSxeYmprCw8MDjo6OyMjIwPHjx3H79m1xHksAqFatGr7//nssWLAAtWvXFve3b98+1K5dGzdv3lQY9XTMmDHYsmULhg8fjuPHj8PKygpnz57FmzdvFD6/p0+fwtXVFXXr1kWdOnVgb2+PhIQE/PXXX8jIyBDnbwSAxo0bw8DAAEFBQXj9+jWsrHJ+KItiFNeisGfPAVhZlkXg7CkoV84KERG30KHjALx8GZ//m9WQJsWjSbEAHA8rOZp0bCwsy2L56nmwsrFESvJbRN6+D9+eoxF2OlzVRSsQSc2qsN20XPy3hf8oAEDKX/8gfvZS6FiVhdkUP2hblEHWq0SkHDqON+s+Pq6COtGkcw3QrHg0KZaioBHzFH4ijZunMSAgQGHE0o/N8RcYGIg5c+YgJCQEnp6eICKsX78eq1atQlRUFMzNzdGhQwcsXLgQPXv2lJtnML9tP336FEuXLsWRI0fw6NEjSCQS2Nvbo2nTpujbty9atmz5SbFu3LgRP/74Ix48eABra2v06dMHc+fOhaGhYYHmaVy7di2OHTuGiIgIvHjxAkZGRqhcuTKGDBmCYcOGKYxcunbtWvHzkO5v/PjxqFChAnx8fLB//3659UNCQjBt2jRcu3YNxsbGaN++PZYtW4ZevXrJfX5v3rzBTz/9hFOnTuH+/ftISEiApaUl6tSpg/Hjx6Nt27Zy2z1y5AgCAwNx8+ZNvH//HgAUakw/pjjnaWSMlQ7FPU9jSSvOeRpLWknP01jcimueRlUpznkaGZOlzvM0rqpQMvM0jn2sfvM0akzSyErWiRMn0Lp1a0ydOhWLFy9WdXEKhJNGxhgnjeqLk0b1xkkjKynqnDT+5FAySeP4R+qXNHKfRvZRr169Uhh85s2bN5g2bRqAnKaujDHGGGOMMc3FfRrZR23fvh3Lli1Dy5YtYWdnh2fPnuHYsWN4+fIlfH190bhxY1UXkTHGGGOMMVaMOGlUsaCgILx58ybf9Xx9fcU+iSWpSZMmaNCgAU6cOIHExERoa2ujevXqmDVrFkaPHl3i5WGMMcYYY0wV1HU6jJLASaOKBQUFITY2Nt/1PD09VZI0NmzYEH/99VeJ75cxxhhjjDGmHjhpVLGYmBhVF4ExxhhjjDGWjy+5ppEHwmGMMcYYY4wxlieuaWSMMcYYY4yxfHzJ8xRyTSNjjDHGGGOMsTxxTSNjjDHGGGOM5SNbUHUJVIdrGhljjDHGGGOM5YlrGhljjDHGGGMsHzx6KmOMMcYYY4wxpgTXNDLGGGOMMcZYPnj0VMYYY4wxxhhjTAmuaWSMMcYYY4yxfGR/wXWNXNPIGGOMMcYYYyxPXNPIGGPsi/E2M03VRWBfiDIumaouQtG6o+oCMKZ6PHoqY4wxxhhjjDGmBNc0MsYYY4wxxlg+vtwejVzTyBhjjDHGGGPsIzhpZIwxxhhjjDGWJ26eyhhjjDHGGGP54IFwGGOMMcYYY4wxJbimkTHGGGOMMcbykS2ougSqwzWNjDHGGGOMMcbyxDWNjDHGGGOMMZaP7C940g2uaWSMMcYYY4wxlieuaWSMMcYYY4yxfHy59Yxc08gYY4wxxhhj7CM4aSzlgoODIQgCgoODS2yfK1euRM2aNWFoaAhBEBAUFFRi+2aMMcYYY0wVskvoTx1x81RWKLt27cL48ePh6uqK8ePHQyKRwMPDQ9XFYowxxhhjjBUTThpZoRw6dEj8fzs7OxWXhjHGGGOMsZLBo6cyVkBxcXEAwAkjY4wxxhhjXwhOGotIaGgoBEFAYGAgzp07By8vL5iYmMDKygqjR4/G+/fvAQCHDx9G48aNYWRkBBsbG0ydOhWZmZnidpKSkrB48WK0aNECdnZ20NPTg52dHQYNGoSoqKhClSk6OhrDhw+Hg4MDJBIJbG1t4evri9jY2ELHJ+07GRISAgAQBEH8k3X9+nX06dMHtra20NPTg6OjI8aOHYuEhASFbW7evBk+Pj5wcnKCvr4+ypYtizZt2oj7kJX78/36669hbm6usH9VGjVyMB7cu4C3yVE4F3YQ7m71VF2kz6JJ8WhSLADHo676D+mJI6d3IyL6LCKiz2Lv0a1o0aqpqov1WfjYqJ6kSz8YL1gLs+DDMF2/D0ZT5kHLtoL4umBkAoMhY2Hy41aYbTsG09W7YOA7FjAwUmGpC09TzjUpTYpHk2L5XFRCf4V15swZdOrUCXZ2dhAEAfv375cvNxFmz54NW1tbGBgYwNvbG/fv3y/UPjhpLGLh4eFo1aoVzMzM4OfnBwcHB6xduxYjRozA7t270aNHDzg6OsLPzw/m5uZYunQpFixYIL7/zp07mD17NgwMDNC1a1dMmDABbm5u2LFjBxo2bFjghC88PByurq7YunUrGjRogPHjx6N58+bYvn07GjZsiIcPHxYqrnr16iEgIACOjo4AgICAAPFP6sCBA2jYsCEOHDgAT09PTJgwAbVr18bPP/+Mxo0b4/Xr13Lb/Pbbb/HixQt4e3tj4sSJ6NixI86fPw9vb2/89ddfSstx7tw5eHp6QhAEfPPNN+jdu3eh4iguPXt2xrKlAZg3fwXcG7VFxPXbOHJ4O6ysLFRdtE+iSfFoUiwAx6POnsW9wJJ5q+DTqj+6ePfH+bMXsW7bj6hSrZKqi/ZJ+NioB53qdfHh7/1Imfkt3v7gD2jrwHjGEkCiDwAQylpAKGOJ99t+QcqUoXi3ZjF06rrDcKS/iktecJp0rgGaFY8mxaLJUlNTUbduXaxevVrp60uWLMHKlSvxyy+/IDw8HEZGRmjTpg3S0tIKvA+BiL7cxrlFKDQ0FF5eXgCA/fv3w8fHBwCQkZEBNzc33LhxAxYWFjhy5Ajc3d0BACkpKXB2dkZmZiaeP38OXV1dJCUlISsrC2XLlpXbfkhICLy9vTF06FBs2LBBXB4cHIwhQ4Zgy5Yt8PX1FfdZtWpVJCQk4PTp03B1dRXXDwsLg6enJ9q1a4eDBw8WOk5PT0+cPn0auU+bhIQEVKpUCSYmJvj333/F5BLIGTynb9++GDNmDFatWiUuj46ORsWKFeW28+zZM7i5ucHIyAj37t1T+vlu3rwZQ4YMKXTZdfTsC/2egjoXdhCXLkdg/ISZAHJqYmMeXsLqNVuwZKnyL7A606R4NCkWgOP5XA6m1kW+zY+5ej8UiwKD8Pv2/cWy/UfJL4tluwAfm8/efivzYtmuYGIGs437kRI4Hll3ritdR9ejBQzHTEfSoHZAdtGMxWj55738V/pEfF1TX6qIJfPD02LZblGY4tS3RPazLGbnJ79XEAT8+eef6NKlC4CcWkY7OztMnjwZU6ZMAZDTstHGxgbBwcHo06dPgbbLNY1FzMvLS0wYAUBXVxc9evQAEaFTp05iwggAJiYm6NixIxITE/HkyRMAgJmZmULCKN1uzZo1ceLEiXzLcOjQIcTExMDf318uYQSAZs2awcfHB0eOHEFycvKnhqng119/RXJyMhYuXCiXMAJAnz59UL9+fezatUtuee6EEQBsbW3RvXt33L9/X2mtav369T8pYSxOurq6qF+/Dk6eOisuIyKcPBUGD48GKizZp9GkeDQpFoDjKU20tLTQsWsbGBga4Ool5Tf26oyPjfoSDHOandLbvH/DBUMj0Pt3RZYwFidNO9c0KR5NiqW0SU9PR3Jystxfenr6J20rOjoaz58/h7e3t7jMzMwMjRo1wvnz5wu8HR49tYjVq1dPYZmtrW2+r8XFxYlJVGhoKIKCghAeHo74+Hi5Po96enr5luHChQsAgMjISAQGBiq8/vz5c2RnZ+PevXtwc3PLd3sFId1neHi40r6XaWlpiI+PR3x8PCwtLQEADx8+xMKFC3Hq1Ck8ffpU4csQFxenkIDKJt0fk56errA9IiqWPpCWlmWho6ODly/i5Za/fPkKLtUqF/n+ipsmxaNJsQAcT2lQrboz9h7dCom+Ht6lvseowZPx4F7hugOoAz42akoQYDB4DDLv3kD24xjlq5iYQr/bQHw4cahky/aJNO1c06R4NCmWolJSo6cuXLgQc+bMkVsWEBCg9L4+P8+fPwcA2NjYyC23sbERXysIThqLmKmpqcIyHR2dfF/LyMgAAOzZswe9e/eGsbEx2rRpAycnJxgaGkIQBAQHBxeoT2NiYiIAYPv27R9dLzU1Nd9tFZR0n3m1pZbdp6WlJR48eICGDRsiOTkZXl5e6NSpE0xNTaGlpYXQ0FCcPn1a6ROV3Cd8XpR92QQtYwjaiseAMcaKysMHMejo1QcmpsZo18kbS3+ei76dh5e+5EQDacKxMRg6HtoVKiIlYGweKxjC6LtFyHoSi7S9wSVaNsZY0Zk2bRomTZokt0wikaioNDk4aVQzgYGB0NfXx5UrV1ClShW513I378yLNDk9ePAgOnbsWORl/Ng+b9y4gVq1auW7/o8//ojXr19j27ZtGDBggNxrI0eOxOnTp5W+r6A1hcq+bGUsXAr03sKKj09EZmYmrG0s5ZZbW1vh+YtXxbLP4qRJ8WhSLADHUxpkZGQiNvoxAOBmxB3Uca0JX7++mDn5BxWXrHD42KgfgyHjoFu/Md4GjgclxiuuoG8A42mLQWnvkLp8FpCVVfKF/ASadq5pUjyaFEtRKamBYCQSSZElieXKlQMAvHjxQmzhKP23slaQeeE+jWomKioK1atXV0gYnz17VuARTxs1agQAhWqn/LkKu09pE1bZ/p9AThPSf//997PLI5FIYGpqKvdXXNNzZGRk4OrV62jp1UxcJggCWno1w4ULV4pln8VJk+LRpFgAjqc0ErSEAnUrUDd8bNSLwZBx0G3YDG/nTUL2KyXNyQwMYTxjKZCZidQlM4D/b71UGmjauaZJ8WhSLF+yihUroly5cjh58qS4LDk5GeHh4WjcuHGBt8NJo5pxdHTEgwcP8OLFC3FZWloaRo0aJTZhzY+Pjw8cHBywYsUKnDlzRuH1jIwMhIWFFVmZAWDIkCEwMTHBjBkzcOvWLYXX3717J/Z7BCD2VcxdjkWLFuHmzZtFWraS8ONPGzB8WD8MHNgTLi7OWP3zIhgZGSB4625VF+2TaFI8mhQLwPGoM/+ZY+HeuD7sK9iiWnVn+M8cC4+mbjiw94iqi/ZJ+NioB4NhE6DXvDVSV/4Aev8OglkZCGZlAN3/T3j/P2EUJPp4t24pBAPD/60jlI7bPE061wDNikeTYtFkb9++xbVr13Dt2jUAOYPfXLt2DY8ePYIgCJgwYQLmz5+PAwcO4MaNGxg0aBDs7OzEEVYLgpunqpmxY8di7NixcHV1RY8ePZCZmYnjx4+DiFC3bl1ERETkuw2JRIK9e/eiXbt2aNGiBVq2bInatWtDEATExsbi7NmzsLCwwN27d4us3FZWVti5cyd69uyJunXrom3btnBxcUF6ejpiYmJw+vRpNGnSBMeOHQOQ0wR1y5Yt6N69O3r16gULCwtcuHABV69eRYcOHXD48OEiK1tJ2LPnAKwsyyJw9hSUK2eFiIhb6NBxAF6+VNKEqBTQpHg0KRaA41FnFpZlsXz1PFjZWCIl+S0ib9+Hb8/RCDsdruqifRI+NupB8nVOixyTwCC55e/WLMKH039Dp2IV6FSpAQAwXSk/lkHymD7IfvUC6k6TzjVAs+LRpFiKgrqOR3z58mVxajoAYhetwYMHIzg4GFOnTkVqaiq++eYbvHnzBs2aNcOxY8egr69f4H3wPI1FRDqPoLKRjZTNpSgVGBiIOXPmICQkBJ6eniAirF+/HqtWrUJUVBTMzc3RoUMHLFy4ED179lSYI/Fj23769CmWLl2KI0eO4NGjR5BIJLC3t0fTpk3Rt29ftGzZstBx5jVPo1RkZCSWLl2KEydO4NmzZzAyMkL58uXh5eWFAQMGyI1+GhoaipkzZ+L69evQ1tZGkyZNMG/ePBw4cEDuM8nv8y2o4pynkTFWOpT0XIDFrTjnaSxpmnZsimueRlUpznkaGZOlzvM0jncq2JyGn+unmIKNY1KSOGlkXwxOGhljmpaYcNKovjhpZOzTqHPSOM6pd4nsZ2WM+jX/LR2N3RljjDHGGGOMqQT3aWSMMcYYY4yxfKhrn8aSwEnjFy4oKAhv3rzJdz1fX184OTkVe3kYY4wxxhhj6oWTxi9cUFAQYmNj813P09OTk0bGGGOMMfbFysaXOxQMJ41fuJiYGFUXgTHGGGOMMabGOGlkjDHGGGOMsXx8ufWMPHoqY4wxxhhjjLGP4JpGxhhjjDHGGMvHl9ynkWsaGWOMMcYYY4zliWsaGWOMMcYYYywfX/I8jVzTyBhjjDHGGGMsT1zTyBhjjDHGGGP5IO7TyBhjjDHGGGOMKeKaRsYYY4wxxhjLB/dpZIwxxhhjjDHGlOCkkTHGGGOMMcZYnrh5KmOMsS/Gu8w0VReB5WGztrOqi1Ckjp7VU3URitg9VReAMZXjgXAYY4wxxhhjjDEluKaRMcYYY4wxxvLBA+EwxhhjjDHGGGNKcE0jY4wxxhhjjOUjm7hPI2OMMcYYY4wxpoBrGhljjDHGGGMsH19uPSPXNDLGGGOMMcYY+wiuaWSMMcYYY4yxfGR/wXWNXNPIGGOMMcYYYyxPXNPIGGOMMcYYY/kgrmlkjDHGGGOMMcYUcU0jY4wxxhhjjOUjW9UFUCGuaSwhwcHBEAQBwcHBxb6vmJgYCIIAX1/fYt8XY4wxxhhjTLNxTSNjjDHGGGOM5YNHT2WMMcYYY4wxxpTgmkbGGGOMMcYYywePnvoFCQ0NhSAICAwMxLlz5+Dl5QUTExNYWVlh9OjReP/+PQDg8OHDaNy4MYyMjGBjY4OpU6ciMzNT3E5SUhIWL16MFi1awM7ODnp6erCzs8OgQYMQFRVVqDJFR0dj+PDhcHBwgEQiga2tLXx9fREbG/vZ8T548ABdu3ZFmTJlYGRkBG9vb0RERCisFxISgqFDh6JatWowNjaGsbEx3NzcsH79eqXbFQQBnp6eePLkCfr27QtLS0sYGhqiadOmOHHihML6vr6+EAQBDx8+xJIlS1ClShXo6+ujYsWKmDt3LjIyMsR1T5w4AUEQMHr0aKX7joqKgpaWFtq0afOJn0rxGDVyMB7cu4C3yVE4F3YQ7m71VF2kz6JJ8WhSLADHo67GThyBY6d+x4PHl3Hzfhi2bF+Fys5Oqi7WZymtx8bMozpqbfsOjSPWwfPFHli2c1dYx7CKPWr9+h2a3d+K5tHbUP/YQkjsLVVQ2vxZNXLBV1snw+fqz+gbtx32bRvkua7boqHoG7cd1Ya3LcESfr7Seq7lRZPi0aRY2Kf74pJGqfDwcLRq1QpmZmbw8/ODg4MD1q5dixEjRmD37t3o0aMHHB0d4efnB3NzcyxduhQLFiwQ33/nzh3Mnj0bBgYG6Nq1KyZMmAA3Nzfs2LEDDRs2LHDCFx4eDldXV2zduhUNGjTA+PHj0bx5c2zfvh0NGzbEw4cPPznGmJgYeHh4IDExEUOHDkXr1q1x8uRJeHl54cWLF3LrLl68GGfOnIG7uzvGjBmDAQMGID4+Hn5+fpg8ebLS7b9+/RpNmzbF/fv3MXz4cPTt2xcRERFo27Yt9u/fr/Q9EyZMwOLFi+Ht7Y2xY8dCIpEgICAAffv2Fddp1aoVKleujB07duDdu3cK29i4cSOICCNGjPjkz6ao9ezZGcuWBmDe/BVwb9QWEddv48jh7bCyslB10T6JJsWjSbEAHI86a9zUHVs27kCH1n3Qq+sw6OroYvefm2BoaKDqon2S0nxstA0lSL0Vi/vfb1L6ur6jDVwPzMO7+09xrWsALnlOQeyPfyA7/UMJl7RgdAwleH3rEa5MD/7oeuXbusGygTPePUssmYIVkdJ8rimjSfFoUixFIbuE/tSRQERfVD1raGgovLy8AAD79++Hj48PACAjIwNubm64ceMGLCwscOTIEbi75zyZTElJgbOzMzIzM/H8+XPo6uoiKSkJWVlZKFu2rNz2Q0JC4O3tjaFDh2LDhg3i8uDgYAwZMgRbtmwRRzXNyMhA1apVkZCQgNOnT8PV1VVcPywsDJ6enmjXrh0OHjxYqBhjYmJQsWJFAMCiRYvw3Xffia/NmjUL8+fPx8KFC/H999+Ly6Ojo8X3SGVmZqJ9+/Y4deoUHj58CAcHB/E1QRAAAP369cNvv/0m/vv69etwd3eHmZkZYmNjYWCQc7Pk6+uLrVu3wsrKClevXkX58uUBAB8+fEDr1q1x5swZ7N27F927dwcALFmyBN999x2Cg4MxePBguTI5ODggKysLT548ga6uboE/Fx09+wKvW1jnwg7i0uUIjJ8wE0DO5xPz8BJWr9mCJUtXF9t+i4smxaNJsQAcz+eyNDQt8m3mxcKiDG5FnUOX9gNx4dzlYtlH/LvkYtkuUPLH5kSZJkW+TQDwfLEHN32XIP7oJXFZjXUTkJ2RhbtjVhXLPgHgmbZesWy3b9x2nBm6Ak+PXZFbblCuDL4+NBch/RahxTZ/3NtwDJEbjxXZfgfGhxbZtnLj65r6UkUsmR+eFst2i0I3x84lsp99sQdKZD+F8cXWNHp5eYkJIwDo6uqiR48eICJ06tRJTBgBwMTEBB07dkRiYiKePHkCADAzM1NIGKXbrVmzptImmrkdOnQIMTEx8Pf3l0sYAaBZs2bw8fHBkSNHkJz8aTcFFStWhL+/v9yyYcOGAQAuXbqksG5uOjo6GDlyJLKyshASEqLwura2NhYsWCAmjABQp04dDBw4EK9evcKRI0cU3jN+/HgxYQQAPT09/PDDDwAgNx3JkCFDoKenh40bN8q9//Dhw3j27BkGDx5cqISxOOnq6qJ+/To4eeqsuIyIcPJUGDw88m5CpK40KR5NigXgeEobE1MTAMCb10kqLknhafSxEQSU9a6P91FxqLNrBprc2oj6RxcobcJaaggCGq8chTtrDyH5nvrecCujaeeaJsWjSbGwz/fFDoRTr149hWW2trb5vhYXFycmWKGhoQgKCkJ4eDji4+Pl+jzq6eX/hPHChQsAgMjISAQGBiq8/vz5c2RnZ+PevXtwc3PLd3u51atXD1pa8s8FpAnbmzdv5JanpKRg2bJl2L9/P6KiopCamir3elxcnML2HRwc4OjoqLC8efPm2LRpE/777z+x5lD2tdwaN24MHR0d/Pfff+IyKysrdOvWDbt27cLdu3fh4uICAGISOXz48LzCBgCkp6cjPT1dbhkRySW4RcXSsix0dHTw8kW83PKXL1/BpVrlIt9fcdOkeDQpFoDjKU0EQcC8hdMQfv4K7t65r+riFJomHxs9SzPoGBvAYVwXRC/ahYfztqNsy3qouXkKrnWbg6Tzt1VdxEKr8W0nZGdl496mv1VdlELTtHNNk+LRpFiKyhfWQFPOF5s0mpoqNlHS0dHJ9zXpgC179uxB7969YWxsjDZt2sDJyQmGhoYQBAHBwcEF6tOYmJjT52D79u0fXS93AldQH4sjKytLXPbhwwd4enri6tWrcHV1xcCBA2FhYQEdHR3ExMRg69atCgkYANjY2Cjdr3R5UpLi03Vl79HW1oaFhYXC+n5+fti1axc2btyIZcuWIS4uDkePHkWLFi1QtWrVj0QOLFy4EHPmzJFbJmgZQ9AuuaZpjLEv16Jls+FSowo6t+2v6qKw3LRyHh7GH7uMJ+sOAwDe3oqBqXs12A1uXeqSxjK1nVB1eBv83WaGqovCGNNgX2zS+LkCAwOhr6+PK1euoEqVKnKv7dq1q0DbkCZ1Bw8eRMeOHYu8jAX1119/4erVqxg2bJhCc9Bdu3Zh69atSt+XezCd3MvNzMyUvlatWjW5ZVlZWUhISFBIKD09PeHi4oJff/0VCxYswJYtW5CVlVWgAXCmTZuGSZMmyS0rY+GS7/s+RXx8IjIzM2FtIz/qnrW1FZ6/eFUs+yxOmhSPJsUCcDylxYIlM+HdpgW6dhiIZ3HKr5PqTlOPDQBkJKYgOyMT7+49llv+7t4TmDUqnt+J4mTdyAX6lqbofGmluExLRxv1Avqj6oi2ONhoguoKVwCadq5pUjyaFEtRyeYpN1hhRUVFoXr16goJ47Nnzwo84mmjRo0AAOfPny/y8hWGdIoQ2T6eUmfPnlVYJvXo0SOlNarS9+Tup5nX9s6fP4/MzEyl63/zzTd49eoV9u/fj82bN6NMmTIKTV6VkUgkMDU1lfsrjqapQE7t89Wr19HSq5m4TBAEtPRqhgsXrnzknepJk+LRpFgAjqc0WLBkJtp19EaPzkPwKLZ09S2TpYnHRooyMpFyLQoGleUHRzOobIe0J/F5vEt9Rf8RhqOtpuFY6+ni37tnibi79hBC+y1WdfHypWnnmibFo0mxsM/HSeMncnR0xIMHD+Rq29LS0jBq1Ci5OQc/xsfHBw4ODlixYgXOnDmj8HpGRgbCwsKKrMx5kfZLzL2v06dPy40Am1tWVhamT58u1777+vXr2LZtG6ysrNC+fXuF9/z000/iYEJATtPYGTNymtRIR5WVNXjwYOjr62PixIl4+PAhBg4cCH19/ULFVxJ+/GkDhg/rh4EDe8LFxRmrf14EIyMDBG/dreqifRJNikeTYgE4HnW2aNlsdO/dCaNH+OPt21RYWVvCytoS+voSVRftk5TmY6NtqA/jmk4wrukEANB3sIZxTSdxHsbHqw/A2qcJbAe0goFTOdgPbQvLrxsgbot69gnUMZTAvKYjzGvm/F4bV7CCeU1HGNpb4MPrt0iKfCL3l52ZhbSXSUiJeqbikhdMaT7XlNGkeDQplqLwJU+5wc1TP9HYsWMxduxYuLq6okePHsjMzMTx48dBRKhbty4iIiLy3YZEIsHevXvRrl07tGjRAi1btkTt2rUhCAJiY2Nx9uxZWFhY4O7du8UaS6dOneDk5IQlS5bg5s2bqFWrFiIjI3Ho0CF07doVe/fuVfq+OnXqICwsDO7u7vD29sarV6+we/duZGZmYv369eJ0G7I8PDxQt25d9O7dG0ZGRjh48CAiIyPRrVs3pTWIZcuWRc+ePbFt2zYAUKu5GWXt2XMAVpZlETh7CsqVs0JExC106DgAL1+WvqfWgGbFo0mxAByPOvMdnjPf7J+Hf5VbPn70NOzesV8FJfo8pfnYmNSrhHp//q9fu/NcXwDA812huDt+NeKPXsS9qevhMK4rnOcPxfuoONwctgxJF4v39/ZTla1bCa3+mCn+u/6cgQCAh7vPIHziOlUVq8iU5nNNGU2KR5NiYZ+Hk8ZP9O2330JXVxerVq3Chg0bYG5ujg4dOmDhwoXo2bNngbfj7u6OiIgILF26FEeOHMG///4LiUQCe3t7dOnSRW7S++JibGyMU6dOwd/fH2fOnEFoaChq1qyJ7du3w8bGJs+ksUyZMjh8+DCmTJmCDRs24N27d3B1dcWcOXPQunVrpe8JCgrCnj17sHHjRjx69Ai2trYIDAzEtGnT8izf4MGDsW3bNnh4eKBWrVpFEnNxWLM2GGvWBqu6GEVGk+LRpFgAjkddlTOvruoiFLnSemzenLuNUJuP/xY/3xmC5zsVp5NSRy/P38FOu4IPqqTu/RiVKa3nWl40KR5NiuVz0Rfcp1GgL3nsWPbJBEFAixYtEBoaWqD1fX19sXXrVkRHR8PJyalQ+1q2bBn8/f2xadMmDB06tPCF/X86evb5r8QY02iWhpo1gnL8u0+bx1cdnSjTRNVFKFLPtPOfeqs0GRgfquoisC9E5gf17Q/e0aFDiezn0KPDJbKfwuCaRqbW0tLS8PPPP6NMmTLo06ePqovDGGOMMca+UF/y6KmcNDK1FBYWhtOnT+Pvv/9GbGwsFi5cCENDQ1UXizHGGGOMsS8OJ42lRFBQEN68eZPver6+voVu/qmOTpw4gTlz5sDS0hITJ07ElClTVF0kxhhjjDH2BfuSe/Vxn8ZSwsnJSemciLmFhITA09Oz+AtUCnGfRsYY92lUX9ynUb1xn0ZWUtS5T2O7Cu1KZD9HHx8tkf0UBtc0lhIxMTGqLgJjjDHGGGNfLHWdQ7EkaKm6AIwxxhhjjDHG1BfXNDLGGGOMMcZYPr7keRq5ppExxhhjjDHGWJ64ppExxhhjjDHG8vElz9PINY2MMcYYY4wxxvLESSNjjDHGGGOMsTxx81TGGGOMMcYYy8eXPL091zQyxhhjjDHGGMsT1zQyxhhjjDHGWD54IBzGGGOMMcYYY0wJrmlkjDH2xYh/l6zqIrA8XJFIVF2EInVPK13VRWCMFTHimkbGGGOMMcYYY0wR1zQyxhhjjDHGWD6yefRUxhhjjDHGGGNMEdc0MsYYY4wxxlg+vtx6Rq5pZIwxxhhjjDH2EVzTyBhjjDHGGGP54HkaGWOMMcYYY4yVKoGBgRAEQe7PxcWlyPfDNY2MMcYYY4wxlg91rWmsWbMmTpw4If5bR6foUzxOGhljjDHGGGOslNLR0UG5cuWKdx/FunXGGGOMMcYY0wBUQvM0pqenIz09XW6ZRCKBRCJRuv79+/dhZ2cHfX19NG7cGAsXLoSDg0ORlon7NDLGGGOMMcaYmli4cCHMzMzk/hYuXKh03UaNGiE4OBjHjh3D2rVrER0djebNmyMlJaVIyyRQSaXMjKmYjp69qovAGGMsD4vLeam6CEXqnlZ6/iuVIpvizqm6COwLkfnhqaqLkKeGdi1KZD9no/8pVE2jrDdv3sDR0RErVqzAsGHDiqxM3DyVMcYYY4wxxtREQRNEZczNzVG1alU8ePCgSMvEzVOLQHBwMARBQHBwsKqL8kVwcnKCk5OTqovBGGOMMca+IFRC//scb9++RVRUFGxtbYso6hycNLLP5uvrC0EQEBMTo+qiMMYYY4wx9sWYMmUKTp8+jZiYGJw7dw5du3aFtrY2+vbtW6T74eapjDHGGGOMMVYKPXnyBH379kVCQgKsrKzQrFkzXLhwAVZWVkW6H04aGWOMMcYYYywf6jh+6K5du0pkPxrVPDU0NBSCICAwMBDnzp2Dl5cXTExMYGVlhdGjR+P9+/cAgMOHD6Nx48YwMjKCjY0Npk6diszMTHE7SUlJWLx4MVq0aAE7Ozvo6enBzs4OgwYNQlRUVKHKFB0djeHDh8PBwQESiQS2trbw9fVFbGzsJ8UYExMDQRDg6+uLBw8eoGvXrihTpgyMjIzg7e2NiIgIpe97+fIlJk6cCGdnZ0gkElhaWqJ79+64efOmwrqCIMDT01PpdnL3J3RycsLWrVsBABUrVoQgCArvl/776dOnGDRoEMqVKwctLS2EhoYCAEJCQjB06FBUq1YNxsbGMDY2hpubG9avX/9Jn5GqjBo5GA/uXcDb5CicCzsId7d6qi7SZ9GkeDQpFoDjUWeaFAtQeuOxb1gNXTZPgt+lVZj86Dc4f91AfE1LRxvNp/XGoH8WYtzdjfC7tAptf/SDkY256gpcSBIjffSa7YsFYWuw6u52TP1jPhzrVFZ1sT5LaT3X8qJJ8WhSLOzTaVTSKBUeHo5WrVrBzMwMfn5+cHBwwNq1azFixAjs3r0bPXr0gKOjI/z8/GBubo6lS5diwYIF4vvv3LmD2bNnw8DAAF27dsWECRPg5uaGHTt2oGHDhgVO+MLDw+Hq6oqtW7eiQYMGGD9+PJo3b47t27ejYcOGePjw4SfHGBMTAw8PDyQmJmLo0KFo3bo1Tp48CS8vL7x48UJu3aioKDRo0ABBQUGoXLkyxo4di/bt2+PYsWPw8PBAeHj4J5djwoQJqFu3LgBg/PjxCAgIQEBAAHx9feXWS0hIQOPGjXH9+nX06dMH33zzDUxNTQEAixcvxpkzZ+Du7o4xY8ZgwIABiI+Ph5+fHyZPnvzJZStJPXt2xrKlAZg3fwXcG7VFxPXbOHJ4O6ysLFRdtE+iSfFoUiwAx6PONCkWoHTHo2sowavbj3By5laF13QM9GBTywkXVu7HtvazcOCbIJStZIsumyapoKSfZtDiUajerA62TFqFuW0m4/bZCEz8bTbMbcqqumifpDSfa8poUjyaFEtRyAaVyJ860qh5GkNDQ+HllTPP0/79++Hj4wMAyMjIgJubG27cuAELCwscOXIE7u7uAICUlBQ4OzsjMzMTz58/h66uLpKSkpCVlYWyZeUvviEhIfD29sbQoUOxYcMGcXlwcDCGDBmCLVu2iMlSRkYGqlatioSEBJw+fRqurq7i+mFhYfD09ES7du1w8ODBQsUYExODihUrAgAWLVqE7777Tnxt1qxZmD9/PhYuXIjvv/9eXN60aVOEh4fj8OHDaNOmjbj83r17cHNzg5OTE65fvy4uFwQBLVq0EGsCZUlrGWUHvfH19cXWrVsRHR2tdFRTQRAAAEOGDMGGDRugra0t93p0dLQYk1RmZibat2+PU6dO4eHDh3BwcPhoGQqiOOdpPBd2EJcuR2D8hJkAcmKOeXgJq9dswZKlq4ttv8VFk+LRpFgAjkedaVIsQMnHU1zzNE5+9Bv+Gv4jHvxzJc91bOpUwoBDc7HeYzxS4hKKZL/FNU+jrkQPP936FWtGLMHNkKvi8ukHF+NW6H/4a3nxNFUrznka+bujvlQRizrP01jftlmJ7Ofqs7AS2U9haGRNo5eXl5gwAoCuri569OgBIkKnTp3EhBEATExM0LFjRyQmJuLJkycAADMzM4WEUbrdmjVr4sSJE/mW4dChQ4iJiYG/v79cwggAzZo1g4+PD44cOYLk5ORPirFixYrw9/eXWyadwPPSpUvisv/++w/nzp3D4MGD5RJGAKhatSpGjBiBGzduKG2mWpT09PSwZMkShYQRgELCCAA6OjoYOXIksrKyEBISUqxl+1y6urqoX78OTp46Ky4jIpw8FQYPjwYfead60qR4NCkWgONRZ5oUC6B58eRHYmoAys5GevI7VRclX1o6WtDW0UZm+ge55RlpH1DZ3UVFpfp0mnauaVI8mhRLUSGiEvlTRxo5EE69evUUlknnKvnYa3FxcWICExoaiqCgIISHhyM+Pl6uz6Oenl6+Zbhw4QIAIDIyEoGBgQqvP3/+HNnZ2WJtX2HVq1cPWlryOX/58uUBAG/evFEox4sXL5SW4+7du+L/16pVq9DlKKiKFSvC0tJS6WspKSlYtmwZ9u/fj6ioKKSmpsq9HhcXV+j9paenIz1d/ikvEYm1nkXJ0rIsdHR08PJFvNzyly9fwaVa6etjoknxaFIsAMejzjQpFkDz4vkYbYkuvprWB3f/Oo8Pb9+rujj5Sk9NQ9SVSLQf1wPPHjxFcnwSGnZuikr1q+JlzHNVF6/QNO1c06R4NCkW9vk0MmmU9pWTpaOjk+9rGRkZAIA9e/agd+/eMDY2Rps2beDk5ARDQ0MIgoDg4OAC9WlMTEwEAGzfvv2j6+VOkArqY3FkZWUplOPw4cM4fPhwkZejoGxsbJQu//DhAzw9PXH16lW4urpi4MCBsLCwgI6ODmJiYrB161aF5K8gFi5ciDlz5sgtE7SMIWgrfm6MMca+TFo62ui0ZiwAASdmBKu6OAW2eeIqDF46GksurkdWZhYe3YzGpQNhcKhdSdVFY0yjqWt/w5KgkUnj5woMDIS+vj6uXLmCKlWqyL1W0GFtpUndwYMH0bFjxyIvY0FJy7Fq1SqMGTOmQO8RBEGuZlVWUlISzMzMCl2OvGr4/vrrL1y9ehXDhg3Dxo0b5V7btWuXODJrYU2bNg2TJskPalDGonia7cTHJyIzMxPWNvI1qdbWVnj+4lWx7LM4aVI8mhQLwPGoM02KBdC8eJTR0tFGxzVjYWJvgT19FpaKWkap+EcvsLx3APQMJNA3NkDyqzcY8fNExD96qeqiFZqmnWuaFI8mxcI+n0b2afxcUVFRqF69ukLC+OzZswKPeNqoUSMAwPnz54u8fIXxKeUoU6YMnj5V7IQcExMj1/RVStpPUbaGs6CkU5jI9kGVOnv2rMKygpJIJDA1NZX7K46mqUBODfXVq9fR0ut/naMFQUBLr2a4cCHvgRfUlSbFo0mxAByPOtOkWADNiyc3acJYpqIN9vZbhLQ3b1VdpE/y4X06kl+9gaGpEWp8VRcRxy/l/yY1o2nnmibFo0mxFBUqof+pI04alXB0dMSDBw/kpq5IS0vDqFGjxCas+fHx8YGDgwNWrFiBM2fOKLyekZGBsLDiHxmpYcOGaNSoEXbu3Indu3crvJ6dnY3Tp0/LLXN3d0dMTIzc8g8fPijU3ElJBw16/Phxocvn6OgIAAqfxenTp+VGqFV3P/60AcOH9cPAgT3h4uKM1T8vgpGRAYK3Kn7mpYEmxaNJsQAcjzrTpFiA0h2PrqEEVjUcYFUjZ+Rt0wpWsKrhABM7i5wmqb+MQ7k6FXFk3FoI2lowtDKDoZUZtHQVB2tTRzW+qouaLerBorw1qjerg0m7AvE86in+3aPeA8flpTSfa8poUjyaFAv7PNw8VYmxY8di7NixcHV1RY8ePZCZmYnjx4+DiFC3bl1ERETkuw2JRIK9e/eiXbt2aNGiBVq2bInatWtDEATExsbi7NmzsLCwEAeiKU47d+6El5cX+vTpg6CgINSvXx8GBgZ49OgRzp8/j1evXiEtLU1cf9KkSfjnn3/Qvn179O3bF4aGhjh+/DjMzc3FQYNktWzZEsuWLcM333yD7t27w8jICI6Ojhg4cGC+ZevUqROcnJywZMkS3Lx5E7Vq1UJkZCQOHTqErl27Yu/evUX6WRSXPXsOwMqyLAJnT0G5claIiLiFDh0H4OXL+PzfrIY0KR5NigXgeNSZJsUClO54bOpUQu/fZ4j/9goYAAC4uecMzv+4D85f54z8OOjvBXLv293rBzy5cKfkCvqJDEwM0XVqP5iXs8C7pLe4ejQc+5ftRHZm4Vv8qIPSfK4po0nxaFIsRSFbTUc2LQkaOU9jQECAwkihyuZSlAoMDMScOXMQEhICT09PEBHWr1+PVatWISoqCubm5ujQoQMWLlyInj174vTp03LD4X5s20+fPsXSpUtx5MgRPHr0CBKJBPb29mjatCn69u2Lli1bFipG6TyNgwcPRnBwsMLrec2x+Pr1a6xYsUIcoVRbWxu2trZwd3dHjx490LVrV7n19+7di7lz5yIyMhJly5ZFz549sWDBAnGE1dxzJC5duhQbNmxATEwMMjIy5MrwsXkfgZx5Gv39/XHmzBmkpqaiZs2amDx5MmxsbJQeT3Wcp5ExxtjnKa55GlWluOZpVJXinKeRMVnqPE9jLRuPEtnPzRcXSmQ/haFRSSNjH8NJI2OMqS9OGtUbJ42spKhz0ljTplGJ7OfWi/AS2U9hcJ9GxhhjjDHGGGN54j6NjDHGGGOMMZaPL7lPIyeNaiAoKEjpVBa5+fr6iv35GGOMMcYYY6wkcNKoBoKCghAbG5vvep6enpw0MsYYY4wxpgLqOodiSeCkUQ0UdhRQxhhjjDHGGCspPBAOY4wxxhhjjLE8cU0jY4wxxhhjjOXjSx4Ih2saGWOMMcYYY4zliWsaGWOMMcYYYywfX/JAOFzTyBhjjDHGGGMsT1zTyBhjjDHGGGP54D6NjDHGGGOMMcaYElzTyBhjjDHGGGP54D6NjDHGGGOMMcaYElzTyBhjjDHGGGP5IMpWdRFUhpNGxhhjXwxLQ1NVF6FIxb9LVnURiswZvFF1EYrU1kapqi5Ckdr0p6pLwBhTJU4aGWOMMcYYYywf2dynkTHGGGOMMcYYU8Q1jYwxxhhjjDGWD+J5GhljjDHGGGOMMUVc08gYY4wxxhhj+eA+jYwxxhhjjDHGmBJc08gYY4wxxhhj+eA+jYwxxhhjjDHGmBJc08gYY4wxxhhj+cjmmkbGGGOMMcYYY0wRJ42MMcYYY4wxxvLEzVMZY4wxxhhjLB/EU24wxhhjjDHGGGOKOGnUMMHBwRAEAcHBwaouSrERBAGenp6qLgZjjDHGGPuCEFGJ/KkjThoZY4wxxhhjjOWJ+zQyxhhjjDHGWD6yuU8jY4wxxhhjjDGmiJPGYhIaGgpBEBAYGIhz587By8sLJiYmsLKywujRo/H+/XsAwOHDh9G4cWMYGRnBxsYGU6dORWZmpridpKQkLF68GC1atICdnR309PRgZ2eHQYMGISoqqlBlio6OxvDhw+Hg4ACJRAJbW1v4+voiNjb2k+MMCQlBu3btYGdnB4lEAhsbGzRv3hzr16+XW+/PP/9E37594ezsDENDQ5iZmaF58+b4448/8tz2xo0bUatWLejr66NChQqYOnUq0tLSPrmsxW3UyMF4cO8C3iZH4VzYQbi71VN1kT6LJsWjSbEAHI+6GjtxBI6d+h0PHl/Gzfth2LJ9FSo7O6m6WJ9FU47N+n834a9HhxT+/OaNVHXR8iXp0g/GC9bCLPgwTNfvg9GUedCyrSC+LhiZwGDIWJj8uBVm247BdPUuGPiOBQyMVFjqwtOUc01Kk+LRpFg+F/dpZMUmPDwcrVq1gpmZGfz8/ODg4IC1a9dixIgR2L17N3r06AFHR0f4+fnB3NwcS5cuxYIFC8T337lzB7Nnz4aBgQG6du2KCRMmwM3NDTt27EDDhg0LnPCFh4fD1dUVW7duRYMGDTB+/Hg0b94c27dvR8OGDfHw4cNCx3b48GG0atUK4eHhaNOmDSZPnozOnTsjPT0d27Ztk1t32rRpuHXrFpo1a4bx48ejZ8+eiIyMRI8ePbBq1SqFbc+bNw8jRoxAfHw8RowYgZ49e2L37t3o2bNnoctZEnr27IxlSwMwb/4KuDdqi4jrt3Hk8HZYWVmoumifRJPi0aRYAI5HnTVu6o4tG3egQ+s+6NV1GHR1dLH7z00wNDRQddE+iSYdmymdJmJwgwHi3+x+MwAA/x7+V8Uly59O9br48Pd+pMz8Fm9/8Ae0dWA8Ywkg0QcACGUtIJSxxPttvyBlylC8W7MYOnXdYTjSX8UlLzhNOtcAzYpHk2Jhn0cgdU1nS7nQ0FB4eXkBAPbv3w8fHx8AQEZGBtzc3HDjxg1YWFjgyJEjcHd3BwCkpKTA2dkZmZmZeP78OXR1dZGUlISsrCyULVtWbvshISHw9vbG0KFDsWHDBnF5cHAwhgwZgi1btsDX11fcZ9WqVZGQkIDTp0/D1dVVXD8sLAyenp5o164dDh48WKgYu3fvjn379uHatWuoW7eu3GsJCQmwsPjfBeXhw4eoVKmS3Dpv375FkyZN8OjRI8TFxcHQ0BAA8ODBA1SvXh02Nja4evUqrK2tAQDJyclo2LAhIiMj0aJFC4SGhhaqvDp69oVavzDOhR3EpcsRGD9hJoCcEV5jHl7C6jVbsGTp6mLbb3HRpHg0KRaA4/lcloamRb7NvFhYlMGtqHPo0n4gLpy7XCz7iH+XXCzbBUr+2HQo55r/SkVkWMAIuLdyx8ivvim2fWxtnFos2xVMzGC2cT9SAscj6851pevoerSA4ZjpSBrUDsjOLpL9Wv55r0i2owxf19SXKmLJ/PC0WLZbFMqaVCmR/SSm3C+R/RQG1zQWMy8vLzFhBABdXV306NEDRIROnTqJCSMAmJiYoGPHjkhMTMSTJ08AAGZmZgoJo3S7NWvWxIkTJ/Itw6FDhxATEwN/f3+5hBEAmjVrBh8fHxw5cgTJyZ9282FgoPgUXTZhBKCQMAKAsbExfH19kZSUhEuXLonLd+zYgczMTEyaNElMGAHA1NQUM2fO/KQyFiddXV3Ur18HJ0+dFZcREU6eCoOHRwMVluzTaFI8mhQLwPGUNiamJgCAN6+TVFySwtPkY6OjqwPPrp44sfu4qovySQTDnGan9Dbv32zB0Aj0/l2RJYzFSdPONU2KR5NiYZ+PR08tZvXq1VNYZmtrm+9rcXFxqFixIoCcWsugoCCEh4cjPj5ers+jnp5evmW4cOECACAyMhKBgYEKrz9//hzZ2dm4d+8e3Nzc8t2eVJ8+fbBv3z54eHigX79+aNWqFZo3bw5LS0uFdV++fIlFixbh6NGjiI2NFft0SsXFxYn/HRERAQBo3ry5wnaULVMmPT0d6enpcsuICIIgFOj9hWFpWRY6Ojp4+SJebvnLl6/gUq1yke+vuGlSPJoUC8DxlCaCIGDewmkIP38Fd++o3xPj/GjysWnUxgNGpsY4tfekqotSeIIAg8FjkHn3BrIfxyhfxcQU+t0G4sOJQyVbtk+kaeeaJsWjSbEUlS+5gSYnjcXM1FSxKZSOjk6+r2VkZAAA9uzZg969e8PY2Bht2rSBk5MTDA0NIQgCgoODC9SnMTExEQCwffv2j66Xmlq4pjQ9e/bE/v37sWLFCvzyyy9YvXo1BEGAl5cXli9fLibFiYmJcHd3x6NHj9C0aVN4e3vD3Nwc2trauHbtGv766y+5BC8pKeepvGwto5SNjU2ByrZw4ULMmTNHbpmgZQxBu+SapjHGvlyLls2GS40q6Ny2v6qLwnJp3ftrXAm9gsQXiaouSqEZDB0P7QoVkRIwNo8VDGH03SJkPYlF2t7gEi0bY0yzcdKo5gIDA6Gvr48rV66gShX5dtS7du0q0DakyenBgwfRsWPHIi2fj48PfHx8kJKSgn///Rf79u3Dpk2b0LZtW9y9exfm5ubYtGkTHj16hHnz5ik0L120aBH++usvuWVmZmYAcmonHR0d5V578eJFgco1bdo0TJo0SW5ZGQuXwoZXIPHxicjMzIS1jXwNq7W1FZ6/eFUs+yxOmhSPJsUCcDylxYIlM+HdpgW6dhiIZ3EFu2apG009Nlb2VqjTrC4WfbMg/5XVjMGQcdCt3xhvA8eDEuMVV9A3gPG0xaC0d0hdPgvIyir5Qn4CTTvXNCkeTYqlqPA8jUxtRUVFoXr16goJ47Nnzwo84mmjRo0AAOfPny/y8kmZmJigbdu2WL9+PXx9ffHixQuEh4cDgDg1iGzfTqmzZ88qLJMOqqPsNWXLlJFIJDA1NZX7K46mqUBOrfDVq9fR0quZuEwQBLT0aoYLF64Uyz6LkybFo0mxABxPabBgyUy06+iNHp2H4FGs+g7mkB9NPDYA0KpXayQlJOHyqUv5r6xGDIaMg27DZng7bxKyXz1XsoIhjGcsBTIzkbpkBvD/rZVKA0071zQpHk2KhX0+ThrVnKOjIx48eCBXw5aWloZRo0aJTVjz4+PjAwcHB6xYsQJnzpxReD0jIwNhYWGFLtuZM2eQpeRJ5suXLwEA+vr6YgwAFPaxY8cOHDlyROH9/fr1g7a2NlasWCFuC8gZPXX+/PmFLmdJ+PGnDRg+rB8GDuwJFxdnrP55EYyMDBC8dbeqi/ZJNCkeTYoF4HjU2aJls9G9dyeMHuGPt29TYWVtCStrS+jrS1RdtE+iSccGyLnZbdXTGyF7TyI7S/0HiJEyGDYBes1bI3XlD6D37yCYlYFgVgbQ/f8xDf4/YRQk+ni3bikEA8P/rSOUjts8TTvXNCkeTYqlKHzJ8zRy81Q1N3bsWIwdOxaurq7o0aMHMjMzcfz4cRAR6tatKw4a8zESiQR79+5Fu3bt0KJFC7Rs2RK1a9eGIAiIjY3F2bNnYWFhgbt37xaqbOPGjUNcXByaNWsGJycnCIKAsLAwXLx4ER4eHmjWLOfJ1MCBA7F48WKMHTsWISEhcHR0REREBE6ePIlu3bph3759ctt1dnbG7NmzERAQgDp16qBXr17Q0dHBH3/8gTp16iAyMrJQ5SwJe/YcgJVlWQTOnoJy5awQEXELHToOwMuXSpoQlQKaFI8mxQJwPOrMd3hfAMCfh3+VWz5+9DTs3rFfBSX6PJp0bACgbrN6sC5vXepGTZV8ndNKxyQwSG75uzWL8OH039CpWAU6VWoAAExXyo9dkDymD7JfqX8TaU071zQpHk2KhX0enqexmEjnaQwICFAYsVTZXIpSgYGBmDNnDkJCQuDp6Qkiwvr167Fq1SpERUXB3NwcHTp0wMKFC9GzZ0+cPn1a7onEx7b99OlTLF26FEeOHMGjR48gkUhgb2+Ppk2bom/fvmjZsmWhYty9ezf27duHK1eu4NmzZ9DV1YWTkxP69euH0aNHw9jYWFw3IiICU6dOxaVLl5CZmYn69etj1qxZePz4cZ7l3bhxI3788Uc8ePAA1tbW6NOnD+bOnQtDQ0O1m6eRMVY6lOQ8jSWhOOdpLGklOU9jSSiueRpVpTjnaWRMljrP02hsWLFE9vP2XXSJ7KcwOGlkXwxOGhljnDSqL04a1RsnjaykcNKonklj6WjszhhjjDHGGGNMJbhPI2OMMcYYY4zlg77gKTc4aWRygoKC8ObNm3zX8/X1hZOTU7GXhzHGGGOMMaZanDQyOUFBQYiNjc13PU9PT04aGWOMMcbYFyP7Cx4KhpNGJicmJkbVRWCMMcYYY4ypEU4aGWOMMcYYYywfX/KkEzx6KmOMMcYYY4yxPHFNI2OMMcYYY4zl40sePZVrGhljjDHGGGOM5YlrGhljjDHGGGMsH9ynkTHGGGOMMcYYU4KTRsYYY4wxxhjLBxGVyN+nWL16NZycnKCvr49GjRrh4sWLRRo7J42MMcYYY4wxVkrt3r0bkyZNQkBAAK5evYq6deuiTZs2ePnyZZHtg5NGxhhjjDHGGMsHldBfYa1YsQIjRozAkCFDUKNGDfzyyy8wNDTE5s2bPzVUBZw0MsYYY4wxxpiaSE9PR3Jystxfenq60nU/fPiAK1euwNvbW1ympaUFb29vnD9/vugKRYyxIpOWlkYBAQGUlpam6qJ8Nk2KhYjjUWeaFAsRx6PONCkWIo5HnWlSLESaF4+6CwgIUKiADAgIULru06dPCQCdO3dObrm/vz81bNiwyMokEH3BY8cyVsSSk5NhZmaGpKQkmJqaqro4n0WTYgE4HnWmSbEAHI8606RYAI5HnWlSLIDmxaPu0tPTFWoWJRIJJBKJwrpxcXGwt7fHuXPn0LhxY3H51KlTcfr0aYSHhxdJmXieRsYYY4wxxhhTE3kliMpYWlpCW1sbL168kFv+4sULlCtXrsjKxH0aGWOMMcYYY6wU0tPTQ4MGDXDy5ElxWXZ2Nk6ePClX8/i5uKaRMcYYY4wxxkqpSZMmYfDgwXBzc0PDhg0RFBSE1NRUDBkypMj2wUkjY0VIIpEgICCgwE0K1JkmxQJwPOpMk2IBOB51pkmxAByPOtOkWADNi0fT9O7dG69evcLs2bPx/Plz1KtXD8eOHYONjU2R7YMHwmGMMcYYY4wxlifu08gYY4wxxhhjLE+cNDLGGGOMMcYYyxMnjYwxxhhjjDHG8sRJI2OMMcYYY4yxPHHSyBhjjDHGGGMsT5w0MsYYKxLZ2dmqLkKR4wHGGWOaeG1jrLA4aWRMw8n+2GnCDXBWVhaAnFg0IR7ZGEprPBcvXsTNmzehpaWlETdXZ86cwdq1awEAgiCU2uPCGPs8t27dQkZGhkZc2y5duoTTp0+ruhisFOOkkbFcZH8Y3r59q8KSFJ6yHzUtrZyv+du3b0v9DXB2dja0tbUBAHFxcRoRjyAI4r9l/7u0iIqKgoeHB9q2bYtbt26V6psrIkJSUhJatWqFb7/9FuvWrQPAiaO60oQHLrI07QFfaXft2jXUrl0bPj4+pTpxJCI8evQIjRo1wqBBg3DmzBlVF4mVUpw0MpaLNMmaPXs2goODAfyvdkvdScs+b948XLp0SVzu6+uL2rVri4ljaSWNb/To0ahQoQLu37+vEfFMnjwZc+bMUXFpPk3lypUxcuRIxMXFoWfPnqW6xlEQBJiZmeHvv/+Gvr4+Jk+erFE1jrLXsdIWS+7zSdkDl9IWk6zs7GzxeiCNrTTHU1p+Mz+mQoUKcHd3x7Fjx9CvX79SmzgKggAHBwfMmjULjx8/xujRo7nGkX0aYowpiIqKIn19ffrqq69UXZRCW79+PQmCQH379qWnT5/SuHHjSEtLi/z8/Oj58+eqLt4nyc7OFv97x44dZGlpSX369KHbt2+rsFRFIy4ujgRBoA4dOqi6KIWWmZkp/vekSZNIEASqXr063bhxg4iIsrKyVFW0TyYtc2hoKOno6JCBgQGtXr1afF32XCytFi9eTEePHiWi0hfPr7/+SjExMeK/Bw4cSL1791ZhiYrW8OHDqXbt2pSRkaHqohSJgIAA2rNnj6qLUWjSa1tCQgK1bNmSBEGgHj160IcPH4iodF3bZMv6ww8/kCAIVLNmTQoJCVFdoVipxEkjY0qkpqZSt27dSBAE2rdvn6qLUygPHjygadOmka6uLlWtWpUEQaCpU6dqRMKYmJhIc+fOpaZNm1JsbKwKS1W0hg8fToIg0F9//aXqohSapiWOsjfr165dIx0dHbKysqKffvpJXF7aEi1Zly5dIkEQaNKkSaouSqHNmDGDBEGg+fPnU0ZGBk2cOJEEQaBvvvmGEhMTVV28TyJ7Lq1du5bMzMyoY8eOFBUVpcJSFY3Lly+TIAjk7+9PRKXvWiC9tsXHx5f6xDE9PV3875UrV5IgCFS3bl06ceKECkvFShtOGhnLRfojfv78edLR0aHRo0eruESFl5KSQnXr1iVBEMjFxYWOHz8uvlaafuhkfffdd9SxY0eqU6cOTZw4kYjkE5bS7ODBgyQIAg0dOpQyMzNL3THSlMRRNo7Dhw/T8ePHqU2bNiQIAtna2tLPP/8svl5aE8dHjx5RjRo1yMrKiq5evarq4hRKWFgYde7cmfT19alRo0YkCAJNmzaNnj59quqifRLZ70VaWhqNHTuWOnTooDEPxN69e0dfffUVWVlZldokWBMSR9nr2uXLl+nmzZvUoEED0tHRoXr16nGNIyswThrZFy33BV96I5idnU1v3ryhr7/+mnR0dOjChQuqKN4n++eff6h8+fJUv3590tbWpj59+tD169dVXaxP9vbtW+rbty8JgkC6urpi0lia5D7Xcv+7a9euZGpqSvfv3yei0peUyNbQlcbEUfbznjx5MllbW5ODgwP16tWLqlatStra2mRiYqIRTVXXrFlDgiDQL7/8QkTqf2xkRUVFUfny5UlbW5uaNm1KV65cEV8rrcdj1qxZNG7cOLK3t6clS5YQUemNJbf58+eTIAi0aNEiIipd55pUaU4cZc+jqVOnkr29PZUvX54aN25MlpaWJAgC1apVi0JDQ1VYSlZacNLIvliyF9OEhATxv2V/ADZu3EiCINC8efMUXlMnucuVnp5Op0+fpsuXL9O0adNIW1ubevToId7AEynW0qlrbFLPnj2jyZMnkyAIVK5cOTpz5oyqi1RgsufakydP5F6THoe1a9eSIAg0btw48WZEXeV1rsg2gSqNiSMR0apVq8Qm3ZGRkUSUUzu3ZcsW0tHRISMjo1KROOYuV3Z2trgsJiaGnJ2dqUqVKhQfH6+K4hWatOy//fYbGRsbk6OjI+nq6tL8+fMpLi5OxaX7dM+ePaNq1aqRnp4elS1bljZv3kxEpasVhbLvgHRZSkoKVa9enRo0aCDGpK7fGaK8yya9JpfGxJEopx+z9Lp28+ZNIiK6cuUKjR07VuzjyIkjyw8njeyLJHuBnzZtGtWoUYNmzZpF8fHxcj/W7969Izc3N3J0dJRLLNWJbHn//PNPWr58udwAMY8ePaIpU6aQtrY29ezZUy5xzMrKopCQELW+cZT9EY+Li6MJEyaIP9iysZQG/v7+5OTkRD/88APFxsbK1c6lpaVRjRo1qFatWpSSkkJE6nlzJXu+RUZG0sWLF+n48eNKy1qaEsfs7Gx6+/YttWvXjiwsLMSEUdbff/9NOjo6ZGZmRqtWrZJ7rzqR/Yzfv3+vdPnIkSNJEATatWuXwmvqJHe5oqOjaffu3XT06FHq1KkT6enp0ezZs+nZs2fiOqXtgVhERAS1a9eOBEGgJk2a0OPHj1VdpAKT/WyTk5OJ6H/fh4yMDEpPTxev2dKabXUle948e/aMoqOj6c6dOwrrlbbE8fnz51S/fn1ydnYWW7LICgwMJEEQqHbt2nTq1CkVlJCVFpw0si+O7A/DsWPHaPLkyVShQgUSBIHs7e1p7Nix9O+//4rrzJ07lwRBoOXLl8s9rVcHsj9SM2fOJHNzcypbtiyFhobKJSSyiWOPHj3EpqrHjh0jGxsbcnd3p6ysLJXHpqy5sHSZtGzPnj2jUaNGkSAINGDAgFKTOCYmJtL48eOpTJkyJAgCOTg40ODBg+n27dviIB7Lly8nQRBo8eLFKi6tcrLHZ8GCBeTs7Ew6OjokCAI1btyY/vzzTzHhlZJNHKVPuNX15iolJYVq165NderUEZflPv+CgoJIS0uL7O3t5RJHdSH7HZ4+fTr16dNHTAxlPXz4kCwtLaljx44lWbxCkb1WHz9+nPbu3Sv38O7y5cvUvn170tPTo4CAALnEMTs7m/7777+SLO4ny87OpmvXrpG3tzcJgkDTp0+nV69eqbpYhTJ9+nRq27Yt/fPPP2LyKHXjxg2SSCTUvXt3FZUuf7Ln2ooVK8jNzY3KlClDpqam9O2339Lly5fl1i9NieOTJ0/I2tpa7vPP/Xs/dOhQEgSBXF1dOXFkeeKkkX2xJk2aRI0aNaLQ0FB68+YNrVixgr7++msSBIF0dHSob9++tG/fPoqPjydbW1u1u7nK3VdBR0eHvvnmG7n+l7I/YDExMeTv70+6urrUuHFj6tu3L1WqVIns7e3p3r17JVp2ZWR/tH///XeaOHEidevWjSZMmEAPHz6UW/f58+elKnGUPVaRkZG0Y8cOatCgAQmCQEZGRtSpUyc6cuQIXb58mczNzalNmzaUkJCg8iRelmxZpkyZIiaKy5Yto0WLFpGjoyO5uLjQqlWrFG4apc2Kra2t6e7duyVd9AJLSUkhd3d30tPTo/Pnzyu8np2dTefOnSMDAwOSSCQkCAJt3bpVBSVVTvY79M8//1CnTp1IEAQSBIFat25NK1euFB9QpKWlUffu3UkQBDpw4ICqipwn2VgCAwPJwsKCHBwc6OLFi3IPxK5cuSImjrNnz6YXL14QEdGRI0fI0tKS/Pz8SrzsyuSXTGRnZ1NERAQ1a9aMDA0Nad68efTy5csSKt3niY2NpaZNm5IgCKSlpUWenp60fPlySk1NpbS0NCL6X1KijqN1yh4b6bWqRo0aNHr0aOrfvz9JJBL6+uuvFb4nsolj69at1bZbQXR0NFlYWFDlypUVfkuJcs697du3kyAIpKenR3Z2dkqvf4xx0si+GLnn+tPX16exY8cqJEw7d+6kESNGkJ6eHgmCQA0bNiRbW1sSBIG2b99e0sXO16+//koSiYS+/fZbio6OlnstPT1d7ocsNjaWli5dSjo6OmRsbEwNGzYU5zxT5bxgsj/a/v7+pKenR3p6emRjY0NaWlpkbW1NBw4coHfv3onrySaOgwcPVqtahdzN45T1T3r//j0dOHCAhg8fTrq6uiQIAnXs2FEcnEAdb66IiH7++WcyMTGh8ePHi82g4+PjqWLFiiQIAtnZ2dFPP/2kkDj6+fmRlZWV2vY/k14fVq9eTVpaWjR58mS5a4b0+5GdnU0eHh40ffp0cnR0pEePHqmkvLnlrmHs2LEj3b17l8LDw2nkyJHk6OhIgiBQxYoVacGCBRQZGUnXrl0T+znl3oYqyZbD39+fdHR0aPDgwXJ9rmSvGZcuXaIOHTqQnp4eDR48mCZPnkzVq1cnGxsbtXsg9vfff9PSpUtp2rRptG7dOrn1srOz6fr169S0adNSlzgSER09epSmT59OJiYmYj+5yZMn0+PHj2nPnj3i9ChpaWlqWSO3bNkyMjQ0pPHjx4utIh4/fkw2NjYkCAI1atSIDh48KPeehIQEcnV1pTJlyqh17bCfnx9JJBIKDg6WWy69rj179ozc3Nyoa9euZGhoqLbXaaZanDSyL0Lum6FJkyaRq6ur3EiVuX/Ebty4Qd9//71YI6SlpUVDhgxRuq4qDRkyhKysrMS+F9JY161bRz4+PtSmTRuaM2eO3Huio6MpIiKCkpKSiEh9Bl0ICAggbW1t8vPzo4sXLxIR0S+//EKCIJCNjQ3t2LFDro/W8+fPacyYMSQIAo0dO1YtJsSW/Sy3bdtG3377LXl7e9N3331H+/fvV1rGs2fP0uLFi8nW1la84Wrfvj29efOmJIuer6ioKGratCl5e3uLCWNSUhJVq1aNbG1tacaMGVSpUiWytbWloKAg8fySev36NRGp9nzL67sr/d5cuXKFGjZsSIIg0JIlSyg1NVVcJyMjg3755RcqV64cJScni9tSl+8P0f++L2PGjBFrFdLT0ykxMZFmzJhBzZo1I0EQSCKR0ODBg8nc3JyMjIwoIiJCxSVX9Msvv5Cenh6NHz9e4YEYkfx1/cqVKzRw4EBxhOVatWqJ71GXB2Lff/89GRoairW/giCQp6cn3bp1S26QGNnE8YcffhBrT9VBQX777ty5Q0FBQeTu7k6CIJClpSWNGTOGjIyMyNHRUZwiRV0eUhARXb9+nerWrUtdunQRW0OkpKRQtWrVyM7Ojnx9fcWpXnLPp5uYmCgeI1XdG+S1X+m5v3//frKxsaEyZcooPJDMyMigBQsWkIuLCxGReM1Tp+saUw+cNLIvysSJE6lz587k4+NDCxcuJCLlF1vZjvxpaWkUFBREX331FUkkErW6uXr79i25ublR+fLlxWTqyJEj1LlzZxIEgczNzcUa0xkzZijdhrokwAcPHiQnJycaOnSomMx/+PCBXFxcyMrKiiwtLals2bK0c+dOuRrHuLg4mjZtmtrNbSZt5iSRSEhfX19s9tyvXz9xlFFp0y2pJ0+e0NatW8nT05OsrKxUPv1G7puGy5cvU5UqVejw4cNElHNz4erqStbW1rRt2zZKSkqidevWiX0YV65cqZA4qvJGUTae8+fP07Zt22j16tV07tw5ufX+/PNPseZ02LBh9Pvvv1N8fDytXLmSateuTU2aNFGbhD7397dXr17k7e0t1wxNtl9mZmYmbd++nbp27Sr2r9XS0qIFCxYo3Z4qSAclat++PVWqVElh8I7ffvuNxo0bRyNGjFBoMnj8+HE6ceKE2PdRlTe+suf6jBkzxFYRZ86coffv39PUqVNJEARq1qwZXbx4Ue44Xb9+nVq0aEGCIFBQUJBaJFiyn+X+/ftp0aJFNGzYMNq2bZvc1CeyNm/eTP369RObPgqCoFCLrwq5z4sDBw6Qvb29mFC9ffuW6tSpQ9bW1vTbb79RXFyc2D/b29tbaZNuVX13ZGO5ffs2nThxgg4dOqQweN8PP/xAurq6ZGZmRsuXLxeP2ZYtW6hu3brUpk0bSk1NVbuxG5j64KSRfTFevXol9lkUBIGGDBmS7xNo2R+BTZs2qbQpV14jAo4ePZoEQaDmzZuTl5cXGRkZkbW1Na1Zs4bu3btH9+/fJ0NDQ2rUqJHajgCbnp5Ow4YNo/Lly4sTjkuHai9XrhwFBweLtQ62tra0e/dupaNCqssN4tatW8nExIRGjx5NN2/epOjoaDp06BBVqVJFof+LtMzSGLKysuiPP/4Qa09VRfbc//XXX8VmftJBoj58+ECjRo0iQ0ND+umnn8QE+ObNm2RoaEjGxsYkCAJt2rSp5AuvhGw8s2bNInNzc7kaH9kpNohyHmK0bt1afF1LS0ts3imtwVKHBEvK39+f5s2bR15eXuK0DbLly329Sk5Optu3b5Ofnx85OjpS9erV5WpVVe3Fixfk4OBATZs2FZedOXOG+vTpI9YmSo/Nvn37lG5DXY7Prl27yM7Ojvz8/OjBgwdElHM8XFxcyNzcnLS0tMjNzY0uXrwoV+N45coV6tKli1o0gc7dZFj2OyEIAlWoUIGWL18urpP7gdiJEydozpw5ZGdnR/Xr16e3b9+WWNlzkz0vjh8/TklJSZSamirWIKanp9PAgQPFeVml12rpdVlPT48qVqyoFl0IZGOZO3cu2dvbi8ekXLlytH37drma6qVLl4q/Q4IgkJWVlTgwm7SrCieMLC+cNDKNpSyBuHXrFvXv35+MjIzIw8ND6fDTuclelCtWrEjNmjVTaXIye/ZsuZHcsrKyqF+/fqSvr0/VqlWjvn37KgzZXqVKFfL29lbr5iY7duwQJ7Z+//49tW7dmiwsLGjjxo3iOtJh6W1sbGjjxo1y8wKqUu4f2fHjx1OjRo0UBh2Ij48XB4wYOXJknje1qampZGdnR15eXiqPcfr06eLk3LIPWV6/fk21a9emFi1ayK2fkJBATk5OtGjRIvr666/VYvoA2ePz3XffkZaWFnXr1o2OHTtG4eHh1LdvXxIEgfz8/MSRhYlyBo86evQojRw5ksaNG0fLli2j58+fE5F6Nd2KjIwUR4CWjvScF+lnIT33UlNTxRov2e9aSVLWB/jdu3fk4eEh9oPr1auX2Npg/vz5dOXKFdq6dSsJgkD9+/en9+/fq+XNbnJyMnXp0oVcXV3p2rVr4jIXFxcqV64crV27VhxY6quvvlKocZR+59TlfFu1ahUZGBjQ4MGDKSwsjI4fP04LFy4Uz71Zs2aJ6+Yuc1pamjhf4JYtW0q45IomTpxIgiDQr7/+Krf83r175OjoSF26dJFbHhUVRdWqVaPJkydT1apV5UbrVYXcibyWlha1atWKtm7dShs2bKD69euTpaUlLVu2TGwSTER08eJFWrhwIXl7e1PXrl3J399ffF1dzjOmnjhpZBpP2jdO6tatW9S7d28SBIH69eun8EQ0Ly9fvqSaNWuSh4eHymrsQkNDSRAEMjY2Vmgme+PGDYqPj1dIMjZv3kxmZmY0a9YstXnyLpVXM5jffvuNjIyMaOrUqXLxBAYGUr169ah8+fLk4uKiVjUjRDk3IT4+PtStWzdx2gzpj7D05u/58+fk5ORE9vb2cgmKrLdv31K9evXI3d1dHO2ypMjeNNy7d4+cnJzIz89PrhZOWgsi/Q7JLl+xYgWVL1+eIiMjxW2py43I1q1bxf5V0prTtLQ0qlatmlhzNWjQIIXReHN/b1Qdj7Lv8YEDB8jT05O0tLRowIABBTpvpNt59OiR+CBDlX755Re568G9e/eoatWq4oOiDh060P379+U+fxMTExo0aJAqiltgo0ePpmXLlhFRzty/X331FVlaWoo1wsnJyeTk5CTO03ju3Dm1SYBzn2vdu3enjh07irVSUidOnBCbO39sPkbp4EvDhg0jopKt1ZI9b06fPk12dnY0fPhwhYfHBw8eJEEQaPbs2XLLZ82aRRUrVqQnT56ILV1UfS0gyvnemJub07hx48T+mG/evKHKlSuTIAhkYmJCixYtUhjcJnffRXWIhak3ThqZxpH9EZo9ezYJgkBr1qyRW+fOnTvUq1cvsY9JfoljamqqOMDEpEmTiqXcBbVo0SLS09MjU1NT8ck1kfIf3+DgYKpWrRrVqlVL5U9FifJvLiZ9XToZdO6b97Zt25KPjw8dO3ZM7UZ3y938uU+fPgoJvPRHWfpkXnrTKOv9+/e0fv16EgSB/P39S6Tsyhw/fpyOHTtGFStWFEcSlJWRkUEeHh5UoUIFOnLkCD179oy2bNlC1apVoxYtWqi0+Zkyr169ok6dOpGHh4f4vUlKShJrfJYvXy7WOH7zzTdy555sk0FVky3DkydP5F47ePCgOPjIx2obc3v8+DFZW1tTt27d5Jp9l6Rdu3aJ/ftkY3z9+jUdP36cbt++LVe27Oxs+vnnn8nAwEAt58skUn4TvmLFCjI0NKTAwEC5eIYMGSIOwPT111+r3fQN/v7+NH/+fGrVqpU4AmdmZqbcfH/Hjh0Tp+LJfX2WrvP69WsqX748denSRWXfp7t374rdBZSNrhsdHU3W1tbiIEUfPnygHTt2ULVq1ahbt24FftBcEh49ekReXl7UsmVL8Tr95s0b8bo2a9YsatCgAZmYmNDSpUvl7gPU7SEyU3+cNDKNkvtHeuXKlWRpaUmmpqa0YcMGudcKkzhK+9z16dNHXKbKPo2LFy8mbW1tucRR9vUHDx7QyJEjyd7enpydncU+WKp8kii7771799K0adOoTZs2FBgYSEeOHJFbd+bMmaSlpUX//POPuGznzp1UoUIFWrlypdJtqgNp82djY2NydXVVSLak58yhQ4dIEARaunSpwjbevHlD33//PQ0cOFDhfSVl0aJFJAgCubu7U61atSgjI0Pus5YOqLJlyxayt7cnQ0NDsrGxIT09PapUqZJa9o1JSkqiJk2aiPMqpqamkoeHB1lZWYnN086dOycOWjRixIg8a4JVRfbznDhxIpmamlJ4eLjcOocPHyZXV1cSBIFWr16d7zbfvn1LK1euJEEQaN68eUVe5oJ69eoV9e/fX+yfnd+5s337dnJxcaEGDRqoxbQUH7sBl42ld+/eZGFhodBs29XVlQYOHEjr169XiybdsnI3f5YOmiRLGuPgwYNJV1dX6Tx/b9++peXLl5MgCDR37txiL7cy0v6Ybm5u1LJlSyIihWvb27dvafr06eL1rHbt2mRsbExOTk7igGvqcm27e/culS9fnv744w8iyrmuubm5iQP4vH//noKDg8V5cpctW6YWD5BZ6cRJI9MYsj/aCxYsoBYtWpC7uzuZmZmJnfZzD8ohmzgOGzbso0/ZZZtCFvcTurwGvZHd76JFi8TEUdpUNSMjgxISEmjQoEEkCAL16tVLLfoqyJZ7ypQpZGRkRCYmJuTo6CiOqCd7I7J//34yNTUlc3NzWrZsGY0aNYocHR3J2dlZrm+GquS+YZD9982bN8Xmz3369FE6yuasWbNIEATatWuX0u3JDlxQHOda7v3lHhDq8ePH4k1i+fLlxfnHcp9Db9++pb///pt69+5NrVu3pm+//VbtzjdZb968EWtGZs2aRYaGhrRw4UK577a3tzdVr15dnLZCHaZxIVL8PIcPH04GBgZUtWpVuT7ORDkjKBc0cXzx4gV5enpSjx49xGWqeiCWkJBAAwYMEPv3yY5iLfXs2TPy9/en8uXLk5OTk/iAQpW1JrLH5p9//qHVq1fTpEmTaMeOHWK/ZmksnTp1Upiv9Ndff6Xy5cvLjcipbg/EpM2ftbW1qVu3bmLfXilpeZcuXZrnnMZPnjyhfv36Ubdu3cRlRX2u5d5e7s9xx44dYvLbrFkzcXnu8+fRo0e0evVqcnFxodq1a1Pv3r3Fmn1VHZu8znHpdFuZmZk0btw4MjIyohUrVojXtfj4eKpcubI43/TPP/+sNkkvK104aWQa5/vvvyddXV3q168f/f3337R3717xJl1bW1tpjaO0WdqECROUblP2AluSF9vFixdTWFiY3H5zJ47SqTWkzekyMzMpLi6OTp8+LU6wXtI/cnkN3vLDDz+QlpaW3DyMhw4dEvstrVixQlx3xYoVVK1aNfG4ubm5iTeI6lJjmpqaSvHx8Qr9x27evCk+jOjatSudOXNGvPH9/fffqXLlylSzZk2Kj4//6L6K41yT3WbuJnArV64Uh2F//vy5+Pn37dtXYbRXZWVTh1Fsc/db2rt3r9L1WrVqRc7OzpSSkiIu+/DhA1WoUIEmTJhAc+fOVZtpXGS/8z/++CP5+vpSkyZNxBFqK1eurDDlgWziuHbt2o9uPyoqSum+ikPuc0P6vZDuNz4+XmnimJ6eTs+ePRPnmezUqZNYI6cuDyi+//57KlOmDGlpaYkPw+zs7ORG2ZTWtDVr1owOHTpEs2bNImdnZ6pevbpCIqYKH0u6Dhw4IDeHqTKjR48mQ0NDOnPmjNLXpQkOUdGfa7m3J/uwYefOneJ3XdrSI3czbmXlSUtLo4yMDPE3TVXnmux+r169qvCgiCgnXldXV2rYsKFcLImJiWRpaUkzZsyg/v37q8VovKx04qSRaZR///2XJBIJde/eXeGGb/fu3WLTs9w1jjdv3qSRI0cqdO5XpX/++YcEQSBHR0cxwVKWOI4bN05MHKVNVVWV5BIRhYSE0MKFCxUGC4qKiqKKFStS27ZtxX4kmZmZdPjwYbK2tqaKFSuKNVpSDx8+pOPHj9OlS5fUbmL45cuXk6enJ9na2lLlypVp8uTJFB4eLt6oyCaO1tbW5O7uTg0bNhQH8VH1tA0tWrSgTp06iefHt99+S1paWvTLL7+I82C+ePFCHJ59ypQpSkdyVKcn1rLlmj9/PlWoUIEcHR3lmsplZ2dTSkoKlS9fnurWrStXE7x582ZydHQUpxXJvU1VmzJlChkYGFC7du1oy5YttGDBAnFO1sqVKyutcZT2cZT2Q/uYkjyWW7ZsUXioJZs4SpuqtmzZUi5xDA8Pp71794rHraSPT16fkbT/vK+vL504cYISExPphx9+EBP7v//+m4hyalOHDRtGJiYmYuJSq1YttasxzcrKorS0NIUHgAcPHqT69euTIAgUGBgo1yfwzz//JHt7e3J3dxev11Ifa51R1Fq1akWLFi0S/z1mzBgyNDSkPXv2iJ/v0aNHSRAEMjAwEJusE8l//urS5y9315Rq1apRjRo15Abwyc7OpqioKPFBpayff/6ZKlasSLdv3+ZBb9hn4aSRaZSdO3eSIAi0Z88eIsq5MMpeHLdt20aCkDPJ+vr16+XeK70hVlVzNGU/UDNnzhTnhsudOErjevjwITk4OJCRkREJgkC3bt0quULncurUKRIEgbp166YwSId05Nfdu3cTEYmDCzg6OpKTk5OYMKalpYkJVW7q8iMu7Rfj4OBAX3/9NVlaWpIgCFS7dm1at26dWCsnbapqZmZG5ubmFBQURAcOHBD7YKnqh/vRo0fUsmVLccRM6VyfkydPFo+b7GivlSpVUkgc1eVYSMnehE6ePJl0dXWpW7duYk197vUmT55MgiDQd999R2FhYbR8+XKqUqUK1a5dWy3nMz18+DAJQs78srkfbkmnDlBW4/jnn39SmzZt1KbWlCgnYRQEgdq1ayfW/uROHF++fElNmjRRSBxlj3NJn4Pnz5+nnTt3KtTQX758mWxsbKhXr17iPIxEOc3szczMyNbWVq5VwZs3b+j8+fO0du1aOnDgQJ7Nv0uS7Gf5yy+/UL9+/cjDw4PatGkj18yWSD5xrFWrFvn6+pKXlxdVrlyZKlasqNIEODw8XGyGuW3bNnFQtSlTpohN56XnkHSU1LJly+aZOKqa7Pk+adIkkkgk1LZtW6VzRGZnZ1OHDh3IyMiIdu/eTQ8ePKD169dT1apVqVGjRuJDGsY+FSeNTKNI5+2SHSyFSP7C269fPxIEgUxNTeXmJVPlD0Xu0QJlSZ9gV6xYURz0QjoQCVFOP5EKFSpQ9+7dqXz58iobRCE6OpqqVKlCTZo0oZCQELnXsrKy6K+//iJBEMRBb3bt2kUODg7k5OQkN5BFQkICtWnThi5cuFCSxf8o2Zu5U6dOkYWFBU2ZMkWchuLGjRs0f/58srCwIHt7ewoODhbfc/PmTXFC8tGjR6vNvGtRUVFi31fpqMC5B0gobYkjUc48coaGhjRhwgS5Zpe5hYeH09dffy03QXn16tXVosZHmWXLlpEgCHTq1Ckiyimf7AOuUaNGiYnjpUuX5N4r7autqgdiuWuVkpOTxTlXO3TokGfi+M8//4g1ctWrV1dprfaJEyfEuTxzNyvfvXs3CYIgDtyVkZFB27dvFx+ISddPTU1VuL5Lqcv5Jn2YYmpqKn7ntbS0qEuXLmJtKVFOwtW4cWPS1tam8uXL06xZs2jNmjXiNUSV17cTJ06IzWgFQaDvv/9e7iGm7FRP0sSxTJkycrXx6nI8pJYtW0b6+vo0YcKEj84vvXPnTqpcuTLp6OiQoaEhaWtrk7Ozs1oOTsZKH04amUa5cOECCYJAbdq0Uaitkv6IzZ49m2xtbcnMzIwsLCzo6NGjKiipcqNHj6YmTZoo3OzKJo65k6lFixaRu7s7ZWRkiNMcqOIH+/jx4ySRSGjhwoXissWLF4tNHf/9918SBIGmT59Ou3fvVpowEuUM8GFjYyM3nYi6uHv3Lh08eJBcXFwUjlFycjL99ttvZG5uTk2aNJFL3m/fvi02VR04cKDaDNkufYAiCAL17NlTXC57Y6EscfTz81N50qtMUlISffXVV1SrVi26ffu23Gu///47BQQE0Pjx48Vk/969e7R7926aMGECrV27VuxTpo6xBQQEkCAIFBoaSkSKLQ7S09PFG2VlTVVVRfbmW/a8evv2LXXs2JEEQaD27dvLJY7S95w/f57s7e2pefPmZGRkpLI+f3fu3KEKFSpQixYtFB6IZWdni/0UpdeEnTt3Kr2+xcTE0KBBg9RquiDZ47N7924yMzOjSZMmiSMHHzx4kAYOHCjOISl9aEGU08dRWhssHYGYSHXfH9nzq3379uIDofnz5ytdN3fiaG1tnW//X1V4+vQp1axZk5o1a6aQMP7zzz+0adMm2rRpk/hbGxISQtOnT6euXbvSjBkzxPNNHa9rrHThpJGVOsqeAMqOLjpo0CDS0dGhhQsXKm2OMXjwYJo4cSL98ccfJAgCDRgwgFJTU1XyZDH3RdzLy0sc5CGvxNHc3Jz27dtHkZGRtGHDBqpWrRq1atVK/MFQ1ZPECxcukEQioQEDBhDR/5rMBQUFUXZ2Nr1//55at25Nenp6ZGVlRRUrVlRIGLds2UJOTk40cOBAuREt1YG0SWrDhg2pRYsWRERyc5QR5dSSSptD5a7tlh2p19fXVyXz4eXuzzJq1CgaMmSIePM+ePBgpeWSfjeePXtGJiYmZGlpmWeNiSo9evSIDA0NqXfv3kSUk/CeP39evOmV3kTWqlVLoRmnlLreWG3atIkEQaBvv/1W4bOXNpfs1asX2drakpaWFjVs2PCjNRIlbeLEiTRy5Ei5zzd34pj7ej19+nSxCat0sClVXKeDg4NJT09PrmXK77//Lv73hg0bxOaQe/bsyfOBWM+ePaly5cpyTT1VSfba9ebNG9q8eTO5uroqPHB9+vQpTZs2TbxGyB6nQ4cOUd26dZVe80qS9Lz48OEDvX37lpo0aUJdunShmjVriiOGKntP7mmQatasqXa/PdeuXSNBEGjWrFlElHNdu379Ovn5+YkP/QRBoO7du+c5/Yy6XtdY6cJJIytVZC98165do7CwMIqMjJSruTl69CjVqVOHdHR0aObMmXJzre3fv5+cnZ1p2rRpRETUsmVLsrS0VMloYrKxbNiwgaZNmyZO1aCtrU0dOnRQ+PFesGCBOLCCtrY2CYJAFSpUUIumJ8+ePRNrrmrUqEGCINDMmTPlkt+tW7eSg4ODOOCKrM2bN5OzszO5uLgo9D1RB7///ruYeDRs2DDPJppnz54V+57JPs0myqmplCaO69atK9Hyy5IdGObDhw8UExOjdM7S3CPwEeXUOKrj8SHK6QfXuHFjkkgkFBQUREOHDiU7OzsqW7YszZw5kw4ePEgzZswgQciZYkfdKEuIpOfZ69evxXkl9+/fr7SZc+fOnSkwMJAmTZpEgiDQnDlz5GruSlLufRoaGpKuri5Nnjw5z8RR2sri1atX9Ntvv1HVqlWpZ8+eCs1WS5q0+an0miXtA/znn38SUU5SValSJbKzsyN7e3uFB2LZ2dm0ceNGqlChAn377bdq09JAasyYMVSpUiVydXUVH/plZ2fLfd4PHjygDh06kCAIdPbsWbn3F2aKl+Igex2S9t1NSUmhlJQUOnXqFNWrV0+hbNLYsrOzxe/SsWPH1GowPKm7d+9SuXLlqGbNmnTy5Eny9/cnZ2dnMjExoW+//ZbWrl1L3bt3J0EQaNWqVaouLtNgnDSyUkP2h2HWrFlkbm4ujn42cuRIuREPd+/eTe7u7qSjo0POzs7k5+dH3bt3J0tLS7kkq3///qSrq6vSppCTJ08mExMT8vDwoGXLltHAgQOpQYMGYn+f3InjoUOHgAD1dQAATi9JREFUaObMmeTj40NTp05V+dxRRP87NvHx8VShQgXS0dGh2rVri0+kZW+SlixZQtbW1mRgYEDe3t40fvx48vLyojJlylClSpXEeNXpyWjup9GCIMiNzpeVlSWWNzk5mQwMDKh///5Kt3Xjxg2V/rBLmznmboYl24R28ODBYs01Uc5Noa+vr9wDGHU6PrI2bdpEzs7OJAgCGRsbU8uWLenevXviKJAxMTFkZGREQ4YMUXFJ5cl+no8fP6bIyEj68OGDeO59+PCBfvnlFypbtiw5ODjQ9u3b5Zpr7tu3jypUqECbN2+mzMxMcnJyooYNG6oksZeN5ffff6dt27ZRt27dSFtbm0xMTGjKlCkKiaP03CtTpgzZ2dmJfeXUYXqAiIgIceAXb29vsW+v9FqVlpZGM2bMIFNTU9LR0VFowrpt2zZydnamWrVqiU0F1emBy8iRI0kQBDIzMxObqeduRUFEtH79ehKE/82pK3sMjx49KiaOx48fL7nCy5gyZQqZmpoqdOE4fPhwnkntsWPHaNasWWLXDiLV9f3NS0ZGBk2aNIkMDAzEgfzc3d3p2rVrYrlPnjxJgiCID8QZKw6cNLJSR3rT26xZMxo9erTYpLN58+ZyP1Znz56l77//ngwNDcWbEU9PT/GH/tGjR+Ti4kJ16tRR2YTxu3btIkEQaNSoUWK50tPT6enTp+Tj45Nn4kgk/6OuLjfwa9asEUcVFQSBvvnmG/E12cTxjz/+IF9fX5JIJKSrq0vVq1enMWPGqEXfC2WjNGZmZor/lg7VrqurqzDnZ1ZWFq1bt44EQaAffvgh332pouZk586dZGJiQgYGBgojCMs2oR00aBBFRkbS9u3bqUaNGmRnZ6dWfbFykz1e169fp3/++YeuXLki19w2KyuLgoKCyMTERDx26nDzLnsezJ07l5ycnMRmtCtWrBBrT1JTU+mHH34gGxsbMjMzozZt2tDatWtp1KhRVKFCBapUqZKYZDVu3JjMzc1LPOnKPdqjhYUFVatWjcaNG0fNmjUjCwsLEgSB/P395b7n79+/p6CgIOrcuTM1aNCABg4cqBbzMEqdPn2azMzMSEtLi9q1ayfXHJIopyZ4yJAhJAgCubi40OjRo2ndunXUvXt3sra2JgcHB7WYZ1aW7LGaPn26+EBM9ndUtiYuOjpabEGibBv79u2jGTNmlEDJlRs/fjwJgkBVq1ZVOv2MNHFctWoVxcfH059//knVq1dXOt2TupA9z44dO0YbN26kv//+Wy7JJSKaN28emZmZ0V9//aWKYrIvBCeNTO3J/sC+e/eOmjdvTsOGDRNvhhISEuiHH34gQRDIw8ND4SlnTEwMXbt2je7du0dJSUlERBQXFyf+SM6ePbvkgslFOlKd9Acu99Pd9u3bi4mjtJmnuo3qJpWZmUkHDx6kTZs20alTp6h169YkCAINHTpUXCd3s6wnT55QTEwMpaenq8X8UblrP968eSM3j5+UdOAEaT+TO3fuUGJiIv38889Uq1YtcnR0VOsEa//+/WRubk56enoKiePdu3epf//+pKWlRRKJhPT19cnBwUHtzz+ij5ctOzubduzYQS4uLlS/fn21vEmUXpPq1KlDbdu2JScnJ9LW1qbhw4eL/RPfvXtHO3bsEK8N0ibT9evXF/vKxcbGkr29PXl5ecnVGJckaT+/SZMmidNQvH79ms6cOSM+VMqdOEq9e/dObUYZll6P582bJ9bGCYJA27dvF9eRJo5JSUm0ePFiuZE7bW1tacCAAWrVIkT2N0Z2HsZZs2aRIAhUrVo1sQm7bDP8pUuXkiAItGXLFqXblVWS14ncrZDyGkX46NGj4rFxcnIiMzMzsrOzU/trW37l2rVrF1WtWpWaNGkidiNgrDhw0shKjeDgYNqwYQMZGxsrnaNo8eLFYuKo7HWpixcv0ujRo8nAwID69OkjLi/JGgfpvgYMGECCIIijOeauObx58yY5OjqSRCKhDh06qEXfxY/JzMwUb1Jv374tzgUomzjmnuNMXcjezK1du5batm0rNinbvHmzQo2NdN48ac2qnZ0dubi4UOPGjdWiiW3ufed+IPHnn3/mmThGR0fT2rVrqXfv3jR27FixJl7VN/CfKiMjg6ZOnSpOgaAu02rIfp7Pnj0jFxcXGjVqlFjDdvfuXerSpYvYZFiaOErLfebMGTp69CidO3dOnFvy6dOnNGXKFIUm1CVFWjPVp08fMjAwkJs3Vnr+PXz4kMqXL0+CIMj1cVS3ZoFSKSkptGrVKlqyZAmtX79eTHo3b94sriO9rkmPzeXLlyk8PJwSExPFh2XqMg8jkfx1WPa177//ngRBIHt7ezpw4ICYhGzZsoWqV69Ozs7O9OLFi5IpdB5yxyI7BRXR/+Y3VjaKcFhYGI0fP54aNmxI/fr1U6va7E8xZ84cqlixIjk6OootElR9XWOai5NGViocP35cTAjr1Kkjjh6Y+yZDNnFUNlfggwcPqF69emRoaEgjR46Ue6045ZXkSUdEXbVqlcKPVnZ2NqWnp5OHh4c4V9mgQYPyHB1NnUjjvXPnjtLEUd1+oGWPv7T2197entq2bUuurq6kra1Nfn5+dPXqVbn3HTt2TBzRdtiwYZSSkiImzeoSo+woocoSR1NTU9LW1qZNmzYpfb/05lJd4imsmzdvUv369cnIyIjatm0rJv/qFM/Ro0fp119/pbJlyyrc5CYnJ4uDXAwePPijc09GRESQv78/SSQS6tu3r7i8pB8yffjwgb766iuysbFRGDBK+rkfOHCAjIyMyNzcnCZNmqTywW7y8/btW7Gps7T/aO5aN9lm7Lmp8kGf7Lm+bds2Gjx4MDVv3px69+5NFy9eVBi1VjpSqiAIVLduXapWrRpVqFCB6tatq/IHYrKfY+45ifNKHHOPlJyVlSV3PNXpWlBQ58+fJ1dXVzI0NKQWLVqICWNpjIWVHpw0slIhMTGRZs+eTTo6OgpPeHPfZEgTx6pVq8oNjkOU0yfozJkz4gTzyt5f1GS3HxsbKzcxdEREBJUvX56qV69Od+/eFZfLXvi9vb1p9erV1K1bN9LT06M9e/aUSLk/V2lLHIlyRqeVSCQ0duxYunnzJhHlJB3GxsYkkUioX79+CoMmHThwQEwct23bJi5Xh+MjnSYk96TVsjde0pEhtbS05Goc1bXW51Ns3ryZduzYIT5sUqdzb+vWrWIT9K+++oqIFEeuTElJkUscpTfusi0Tbt68Sfb29mRgYCA3MmxJPxCTll1aQ3ro0CGl74mOjiZ7e3vS1dUlXV1dWrBggdq2oJDK/cBFWeKobjHIHv8pU6aQlpYWmZqakouLC5mZmZGVlRUtXryYnj17Jvc+aTNPJycnGjNmDMXExIi/Xerw/Rk7dixVqlSJLl68KLdctmzSUYRlm6pmZWXlOXdoafL+/Xvy9/enNWvWiK0M1OG4MM3GSSMrNRITE2nRokUkCAI1atRIbtjv3DdGAQEBZGNjk+9k0MV9QyV7EV+5ciV99dVX5OvrK/aTS01NFWu2GjRoQFeuXBHniMrOzqa9e/eSra0tHThwgCIjI0kikVCXLl2KtcxFSVni2KNHDxWXSrnz589T1apVqV+/fnTv3j0iyqldqFatGtna2ooDLvXr10+hxlHax7FMmTK0detWcbmqb0j27NlDgiCQlZWVXLlyJ47SKQTKlClDP/74owpKWjyU3RyqQzIv6969e9S5c2exZic8PFzperKJY9euXRWaSz979oy2bt0q19euJB+IvXr1Su7ff/31F+nr61O3bt0oJSVFXC77MKJFixa0bNkycnR0pAoVKoixq/p78zGyMZaWxJGIKDAwkHR1dWnkyJHiKMgnTpwgLS0tsrW1pYCAAIXfS+lDJ3d3d/Ga97Ha1JI0atQosVXRxxJH6XW7WrVqCg+RSyvZ6ULUvYaeaRZOGplayevCJ/2RSkxMpDlz5pAg5EwGLTvfXO73SpvcqOpimvsJr5GRETVo0EB88i6NKSEhgXx9fUkQBKpUqRKNGTOGDh48SN9//z1VrlyZqlWrJvYhcXJyopo1a9KbN2/U4oe7IKTlvHv3Lrm6upKBgYH4ZFRdZGdn07p168jW1pbOnDlDRDkJY82aNcna2pq2bdtGt27dog4dOpCenh75+vrmmTiWLVuW1qxZo4ow5Eg/d9ly5U4cpTfwCxcuJEdHRxIEgRwdHRVG5lOF/Aa10SQPHjwQ+zcPGDAgz8nfU1JSyNvbm6ytrZV+h2Q/l5J8ILZ+/Xrq3r07zZkzR9xvTEwMderUiQRBoBEjRtCLFy/kBrv69ddfycbGhm7cuEFHjhwhQRAoICCgWMtcVPKqcVSH770yx48fJ0dHRxo6dKjcgEouLi5kbW1NlStXJiMjIwoICFCocZw6daqYOOYeWEYVZD97af9Ld3d3hcRROsDPsGHDxD6oTZo0kZvGRhXUsfkyYwXFSSNTG7I3Iffu3aOQkBA6c+aMwiiUr1+/FqfdaNeu3UcTR3W4EM+dO5d0dXVp/Pjxck1QZcXHx9OcOXOoVq1aYo2DdMh9aR+mqKgoMjU1pe7du6tFXIUhLe/9+/fFBFjdnoxGRESIzUvT0tKoW7duZG5uTuvWrRPPzfnz55MgCGRoaEjt2rWj27dvE5HiPI5+fn4lWvb8Psu//vpLaeIo1a9fP/ruu+8oLCxMLebFk70WPHz4kC5cuECXL18Wa4ALS/b7oqrvTn77ffDgAXXr1o20tLRo0qRJYh+l3N6+fSuO/KoOD8SmTp1KJiYmVKNGDYXh/i9fvkyNGzcmQRCoTZs2tHLlSrp37x4tXryYatSoQa6urpSQkECxsbGkp6dH3t7elJ6ernbXBmVkj6e0VrVMmTL09u1btbo+Z2Rk0Ny5c6l8+fLib2VKSgrVqFGDbGxsaOvWrXTixAlycXEhGxubjyaODRs2LPHaOmXnguwy2dpQaeIoe/3o2LEj/fDDD7Rq1SqlU1eVJNlyJSYm0v379+nly5dis/nCnjfqdJ6xLwMnjUwtyP4IzJs3TxxZTxAEsrGxoT/++ENuugbZxLF9+/YKk/mWtLxuci5fvkw2NjbUtWtXhQEsIiIi6Pz582LfuQ8fPlBCQgJt27aNNmzYQPv27RMHvYmLi6MJEyaIg+aURiVZC1LQckj/nXvU2rNnz5KVlRX5+vrKNacLCwujunXrUpcuXcjW1lZu2gbpNv77779ijkCe7I3ItWvX6ODBg/THH3/Q6dOn5dbbv3+/2PdSWiOSnZ1Ne/bsIScnJ5o3b57SbZa03NcC6ZyF0uaz8+fPF5twF4Ts8Q4NDaV//vlHYeqX4ib7eT5//pxu3bqlNAGOiooiHx8f0tbWpokTJ8oljrnPW3VIrKT9zMeMGSNex4jky3bp0iUaOHAgmZqayj0Qc3Z2FqfiuHXrlrid0kT2mBw7dkycVkPd7Nq1Sxzo6v3799SuXTsqW7Ysbdy4UYyhf//+JAgC2dnZ0ZQpUxSaG3/33XclPkWV7PcmNjaWLl68SP/995/Cgy1p4li/fn25pHbXrl1kb29PO3bsEJepqq+27Ge5ZMkSqlu3LgmCQEZGRlS3bl2FqcLyI3vuXbx4kW7dusVJJCt2nDQylZO90EmHivf29qbg4GD65ZdfqEaNGmRgYEBr1qyRG+VNmjjq6elR48aNFUZIKymhoaHUs2dPhRHoiHImsRcEgfbt20dEOYnh48eP6bvvvhPn+zIwMKCNGzfmuf07d+7QtGnTSE9Pj/r37y8uL4kfCE1pIph75EbpstzJh3S9devWkSAI9Ouvv8q97ufnR7Vq1aK4uDhxzk/Z/iXKtlWcZPcxc+ZMsrKykrsx79+/P127dk28UTp06BDp6uqKD1vatm1LZcqUIUdHR3FaDXUhrd3o0KEDbdq0iTZu3EhNmzYV+5Xmrg1RRvaYHD9+nJydnaly5cpK594sLrLn3JIlS8TWBCYmJtSyZUu6d++eXDk/ljiqk5MnT5K5uTkNHDhQoQbn0aNHdO/ePXF0yoSEBLp27RrNnDmTZs+eTWvWrBGP39OnT2nEiBEKAzaVFrm/5+o0GIls2aSjIO/du5dMTU1p3Lhx4vEhyhlV1c3NjapWrUpOTk7i9U02nl27dpVQyT/+8MjMzIx+/PFHueRRmtSamprSd999R4MHDyY7OztydnbOd2yDkjR58mTS0tKiZs2a0fz582nMmDFka2tLhoaGtGTJkgJ1DZC9Xvzzzz9UqVIl+vrrr1U2Jyv7cnDSyNTG2rVryczMjMaNGyfOW/ju3TuqVKkSCYJAEomEVq5cqZA4Tp48mcqXL6+SqSjS0tJo8ODBJAgCLVu2TFwuvahv376dBEGgcePG0Zs3b2jFihVUv3590tPTo44dO5Kfn584V17uGqqMjAy6ePEiVapUiQwMDMjX11d8rSQSEk1oIihbEyg7L9mPP/5I7du3J2dnZ5o9e7ZCrZy0j9WoUaPEbezevZuqVq1KI0eOFD8bdUmcpUPk9+7dmw4dOkRnzpwhPz8/0tXVJXd3dzp//rxY1tOnT5O3tzeZmpqSqakpNW/eXJy3UF1ueP/44w8yMTGhb775Rq6GfsWKFSQIAlWoUCHfm6vcN1aurq5kbm5eog+XZMsgvVl0c3OjOXPmkJ+fHxkbG1OtWrXo1KlTcuenNHHU19enESNGqKwGKzQ0lJYuXar0tVWrVpEgCOJ3JzMzk16/fk1z5swhJycnkkgkVLdu3Y/2g3vw4AHNmjWLdHV1adCgQcUSQ140rW9ZQb+7gYGBJAgChYWFyS338fGhli1bik0miRTnDZYqyVpuaS1iu3btKDg4mFatWkWenp6kq6tLgwcPFu8ViIiWL18u3i8IgkD16tVTmzlZiXJGSjYwMKDRo0eLtexERNOnTydBEKh27dr5toLIfV1r0KABmZqaquyhOfuycNLI1EJMTAw1adKE2rVrRzdu3CAiojdv3pCLiwuVK1eOpk6dSjVr1iR9fX1as2aN+BSUiCgpKUmh1qckRUZG0qpVq8RJkGVv/qKjo6lOnTrilAbSUdxCQkLE9efOnUuCINDOnTsVtn3lyhWaP3++3FQIJV2DVVqbCIaEhFD16tUVno5Lb0IsLS3JysqKtLS0qFq1avT777+L6zx+/Ji6detG2tra1LJlS/L29qayZcuSo6OjWvT5k/X3339T2bJlaeDAgXI3Ihs2bCAdHR2qUKGC+KBFehxev35N0dHRdP/+fTH5KumEUfZ7kpu/vz8ZGxuLo2mmp6fTrl27yMnJiZydncVEXnYbeT2UkCaMJiYmCtOllJSgoCAyMjKi8ePHi/1gnz9/LjbDd3Z2phMnTsg1nXv48CG1aNGCHBwcxD5PJenNmzfk4eFBgiDQ/v37FV6X9u+VTgG0adMmsSa4cePG5O3tLfbLzt0KIyMjg86fPy8OwDJgwADxtZJ+IFZa+5bJPjSRPW/Wr19Pfn5+1LlzZ9qxY4fC9CwLFiwgQRDo559/Fr8/u3fvJmdnZ5ozZ45ajTK8d+9eMjExoREjRshd25YvXy7+lub+HYqKiqKwsDA6f/68SqbY+di50L9/f7KzsxOvQ2lpabR3715ycHCgqlWrite1vEZEzeu6FhERUdRhMKYUJ41MLTx8+JAMDAzExCk1NZUaNGhA1tbWtH37dsrKyqL169eLg5CsXr1a4UakJH+4pYO55N735MmTydvbW26I+bt379L3339PEyZMoNWrV8slvEREI0eOJBsbmzwv/LLJVUn/kJfmJoLS+e9q165Nf/zxBxHlJJJly5al0aNH061btyg2NlasMbGxsZHr+3L+/HkaM2YM6enpkb29vdpODL9s2TLS0dGh0NBQIspJpHbu3EmOjo5UuXJl8UZEOpogkeoHjAoNDaWAgAClTWIzMjKoWbNmVKNGDSLKKevOnTvJwcGBnJyc5FoUhIWF0d69e+Xer24J461bt6hBgwbUpUsXMWF88+YNVa9encqVK0dDhw4lMzMzqlq1Kh0/flzuOMXGxorHTxWJycGDB2natGniAy5Zx48fJ0NDQ/EBjHTk3QMHDohl7tmzJwmCIJ6bubc9bNgwWr58ubispB+Ilda+ZSdOnKDOnTsr1C5Ju3dIJBISBEFs0SKdYoMop8917dq1ycrKivz8/Kh///5Urlw5cnJyUrs+mRMnTiQzMzNxgJv09HT6/fffydHRUe7h0cf6KZbkb+a5c+dox44dSltBJCcnk729PXXu3JmI/nedVnZdO3v2rMKgeep2XWNfJk4amdqQbSL37bffkrGxMf3444/ik8TExERycXERpwb4WD/A4nTy5Elx1DmpzMxMSkxMJB8fH7GZoGxSK/1Ry30z8fvvv5OTkxO1b99eaZ9IVdKEJoLbtm0jLS0tql69Oh09epR27NhBtWvXFoedl5JOcC99SCErJiaGEhMTxfNQnRJGIqK+ffuSpaWl+O8dO3bkmWDJ3qCrSkpKCnXo0IEEQaA5c+bIjY4snT+yffv2ZG1tTREREeKT+NzxEBF99dVX9NVXXynUpBKpz41VSEgICYJAR48eJaKcGqJ69eqRtbU17dq1ixISEmjs2LFirdyJEycUamFL8sY393daer5PmTJFYUqMP/74g3r06EHdu3enwMBAhWlAunfvTs7OzgojYBPlHCvZB2gl/UCsNPct+/bbb8X5OqXn9o4dO8RaubNnz1JYWJg4jUuzZs3E9T58+EB79uwR5801MTGh5s2bi31n1eH6lp2dTRkZGdSgQQOqX78+EeX8huaVZElHWlel+Ph4qlKlCpmYmNCuXbsUakDfv39PVatWJQ8Pj/9r787DqqrWP4C/64CADAqKiBqIMmtIqDihppVDg+E1r5XmUJoWWQ6IGs6KmQqW5FRmZCke08B+N9TC/GkOlY8mlqUomrd7U+/NIa8oHqbv7w+fs377TKhd5ey9z/t5np4nz94c9uGss89613rXenHu3Lka72sJCQkYPHiwvA+o8b7GXBMHjUx1ysrK0KZNG3Tq1Mlilu3ixYvw9/fHa6+9hqeeesppaYKffPIJhBBo1qyZTUrpqVOn8Pzzz8si9uYZR3sdoiVLliA8PBxhYWHytdT2bIIrpAiuXbsWBoMB999/P7p164ZBgwYBuHndyvfF/L5aB472CsSryYQJE+Dm5oaCggLk5eU57Ig8+uijTlv7a+3AgQNITk5GnTp1MHPmTJugYsWKFRBCYNCgQWjevDnCwsIs1qcCQHZ2NgIDA7Fo0SKbz9f27dvRtm1b1XSszOvHysvLMXz4cNSrVw/Lli2TwcXhw4cREBCAevXqwcfHx2kd4C+++AJdu3a1GNSprq5GSUkJIiIibNZuA/8fZFq/B0ajEU2aNMEzzzxzyyCqtj9XWl9bVllZiddeew1CCPTr1w8lJSXIzs5Gly5dbHbpHjt2LIQQSEpKkrVlKysrYTKZ8PXXX6O4uFhmfaghYFR6/PHH0axZM/z000/YsmWLw3tbu3bt0L59+ztaMnG33bhxA5s2bUJsbKzcsdX6ekaMGAFfX1+kpqbK+5r1a1m4cCECAgKwevVqm8/FF198oar7GnM9HDQy1Tl9+rRMf1RaunQpIiIicObMGTlz56wvuc2bN8sAwzpwPH36NIYNG2YTOAI3Oxo7d+5E+/btERAQgPbt2zttExJXShH88MMP5ZrSxx9/XD6uLLcBWA4I5OTkOOFK7aupU719+3YIIdCxY0eEhIQgLCzMZsZn5cqVCA4ORnp6utO2nAcsX8ehQ4fw+OOP2w0ci4uL0aNHD7lTovXmSxs2bEBERAQ6dOhg0+m6evUqkpOT4enp6fS1PtaB1Pnz59G8eXM89thjFp/3Y8eOwd/fHykpKUhMTHTaTrbmGc8HH3zQ5rO6Y8cOmZq+aNEi+bi9trl8+XKZhm5OeaztwFDva8sqKytlQNivXz888MADGDdunDym/JybA8wuXbrIwNH6dahhDaO1+fPnQwiBv/71rwgNDUWLFi1sPu9vvfUWgoKC7A4e1TaTyYQtW7YgMjLSbuC4a9cutGjRQi6H+Mc//mHx8+b7Wvfu3W3u4b/99hs6d+4Mb29vp9/XmOvioJGpzvXr1/HQQw/Bzc0N+fn5OHfuHN5//33ExMSgc+fOTk3jrK6ulh0Jo9Eob/7movBmNQWORUVFSExMxPTp0+UXYG0HjK6WIgjc3MnWzc3NZmt/8+s1Mw8IxMfHO3Xk2uxWZUJKS0sxZswYGAwGeHl52cxSrVu3DpGRkUhISFDV1vNAzYHjunXr5E7DM2fORF5eHoqLizFx4kQ0a9YMISEhMqXOurN45MgRHDt2rFZfy+3Yt28fhBCYPXu2xePz58+XpVzMs3LOGhAz14NNSkqy+cx+9dVXcnMcZeAI3Lze3bt3o2/fvggMDERcXJzTBsRcZW2ZeSmHea1/WlqaxTUq/+7mwLF79+417mhbm27VLs6ePYvu3bvLDdh++ukni+MbNmxAZGQkOnXqpIoMCuDmAISjwLG0tBRz587Ffffdh9DQUHzyyScoKirC77//jtmzZyM0NBShoaEO72tr1661WJ/KWG3joJGp0kcffYTGjRvLL0MhBMLDw2UnxFlpgtZfcu+//768tjsJHK9cuSI3vHDW6KirpQgCNwNHg8GAqKgoWTsTsA0c//a3v8m25kzKv2lNZUL27NmDgQMHwmAw4Mknn0RWVhYKCwsxatQoBAUFITQ0VFVbzytZB47KzTg2b96MPn36WNSe9Pb2xsMPP+xwDZbaXp/SuXPn0LJlS3kvKy0txfr16xEVFYXevXvLunnOuL8p/47mQESZ0mhWU+D4wQcfoGnTphg/frwcoKjtgNHV1pZVVlZiwoQJckMi83IC5XEz83nJyck1Lk2oDcrPqaMdXysqKmA0GpGYmAhfX19MnToVBQUFOHr0KFJTU9GkSZMaB4+cxV7gaP7+/+OPP7Bw4UKEh4fLzYo8PT3h5eWFzp072x1oUeOyCOaaOGhkqqK8ORYUFOD1119H//79MW3aNJmy5awReOtd93r16oU+ffrA3d1dpjQqd98ELAPHZ555xmbnVGdwtRRBa+Y1jrGxsXJXVcA2VRWoeVe+2uSoTIiynMiBAwcwYcIE1KlTRwZYgYGBSE5OVuWur8rPk3U7VKZtXbx4EQUFBVi6dCmWLFmCb775RrVrsG6lsrISU6dOle9Ny5Yt4eXlhbCwMNnxVcuA2JgxY+QmKncSOJ45c0YGv854f1xxbVlFRQVSU1MhhMBjjz1mc03K92HGjBk4ffp0bV+iQ452fDW/BpPJhPz8fFnCxfyfn59fjYNHzmJuK/YCR3M2TllZGU6cOIF58+YhJSUF48ePR35+vkxJVctrYcwaB41MdWpaP6KGm+mUKVPg6emJgQMHIj8/HytWrMDLL78sU1XtrXE0b44zevRoJ121Y66WIghYBo7KGUe1jOgq/5Y7duyosUzIunXrLH62qKgIBQUFWLduHU6cOCFHuJ352bmdGYCaZhz/7HOqibltmUwmZGdn44knnkD79u3xwgsvyNeqhgGxZcuWYcyYMXjqqadkfVnl7ptmysDR3q68zvwsueLaMus1jjUFjoDzBsSUbe1WO76aByuqqqpw/fp1rF+/HkuWLMEbb7yBr7/+2umDR7e6B9U04/hnn5MxZ+KgkbE7sHPnTtSpUweDBg2SwRJws4P0wQcfQAiB4OBgmxnHkydPYty4capIebTHlVIEzcyBY1xcnM37pRanT5/Ghg0b7rhMiDVnduCVbaOwsBDvvPMO0tPTkZ+fbzPzXtMAhh5Y78RbVlYm/z5qGBCbNGkSfHx80KNHD6xYsQIpKSno2bNnjWscu3btCiEEVq5c6aSrts8V15bdKnB0NuV96D//+Q+WLVt2yx1fDx8+XONzOuu7Rvl5PXjwIPLy8mA0GnHw4EGL82pqh2oZpGTsdnHQyO66mm7iWr9JmgPDzz//HIBtR2/58uUQQiA0NBQfffSRxTHzuWpJeQRcM0VQ6eOPP5a7Rd7r2mp3aurUqQgODkbXrl1vq0yIcoZbLZ8z5bW+/vrrqFevnhx4cHd3R48ePWzSAs3t0MPDA3PmzFFdwXG9ys/PhxACo0aNkoNbFRUVuHTpEl588UWHnfht27ahX79+qhwQc8W1ZcrAsX///jZBjBpMnDgRXbt2RXx8/G3t+GoOftXy91fe12bOnImgoCB5X2vYsCEmTJhgcb6yHd53330wGo23VQeUMbXhoJHdVcov2NOnT+Pbb7/FwYMHbdbD3S5Hdf9qm/l3Z2VlQQiBNWvW2D1++fJlPPTQQzAYDIiMjLQ5z5k4RdC+TZs2Wcwaq8XKlStlR0TrZUKmTZsmy+hs27YNZWVlGDp0KIQQiIqKwr/+9S+Lnz106BCSk5NlXUC1tDNH9yC1dGb/G3PnzoUQAt988w0A28/2iBEjZOBoXYvQXNNQTQNirry2rLKyEuPGjYMQAiNGjFDd+2L+7Pv5+Wlux1flZ928Pjk5ORlGoxEHDhxAYmIihBAYMmSIxc+ZTCZ89tlnaNWqFTw8PJCfn1/LV87Yf4+DRnbXKDsZ8+bNQ1hYmOz0BgQEICMj445KGChvzrt27cKXX355y4LL99quXbsghMBzzz0nZ9rMzNf76quvIiQkBEIItGnTRhUzWJwieGtq7CDm5uZqskyI0vr169GkSROMHj3aYvCoVatWCAgIgBACkZGRNjOO3377LYYNGyY38XE2Zfu4dOkSTp48iX//+9+4fPkygDsPHNUSaJqvw9xB//bbby2Om+/rp06dQnR0NDw8PPDggw/iwIEDtX6tjvDaMluVlZWYPn26KmeAKysr5QY4WtrxVWn16tUIDAxESkqKxdKBqKgo+Pj4OAwcN27ciK5du3IGBdMkDhrZXTd58mQ5O7JmzRq8//77sij04MGDce7cuVs+h7JDVVhYKAtFWwdqte3y5ct47LHHYDAY8N5779k95y9/+Qvmz5+PXbt2qeILW28pgnpOf7ZHa2VClK5cuYInn3wScXFxcg3Y1atXERMTg+DgYOTk5MjdhWNiYmxmHM2dRGcH9NY7J8fHx0MIAR8fH8THx6OwsPCOnk/5vh04cAA//fST09vusmXLIITAvHnzHHbOH374YQQGBspOvBpmsHht2a2p4X2ypuUdX8+fP4/u3bujZ8+eOHr0KICbazSjo6MRHByMzMxMtGvXDkIIDB8+3OJnTSaTU3cXZuy/wUEju6s+/fRT+Pn5YfTo0RaL25csWQIhBEJCQm6Zy2+vFpa/v79NSpSzbNq0CcHBwRBC4O2337aYCdmyZQvCw8ORkZEhH3PmF7beUgT1mv58K1osEwLcvJZJkybh3XffBQBcv34dSUlJCAwMtEjdjo6Olu1QzTPbqampcjfRjIwMjB07Fk2aNIG3tzcWLVp0W+uUrO9vLVu2RO/evZ2ekXDq1CnExsaiadOmFrONys99r1698O677+LNN99URSdeb2vL9Jz+bI9Wdny19scff6B3797yHnbt2jV06NABjRo1kksEjh8/LssfDR482IlXy9jdw0Eju2M1pYikpaXB19dXppuYTCYYjUaEhYUhIiJCFodXPoejjrtaiiebKa9tzZo1aN68Odzc3JCYmIjx48dj8ODBCAoKQvPmzWVNSbXQQ4qgK6Q/10TtZUKUlNdkTn+urq5GZmYmPD09kZGRAZPJJM/p06cPYmJi5MYXanxNa9euRd26dZGSkoKSkhL5eHp6OoQQiIuLu2X7sb6/tWvXDvXq1VPFgFhlZSXmz58PIQRatGiBr776yiKzIy8vD02bNrXYaVgtA2J6WFum1/TnW1H7jq/2Bkurq6vl4FZVVRXS09Ph7e2NzMxMeQ8oLS1FQkKCvK+lpKTU6nUzdi9w0MjuyK5duzBr1iy7QVFFRQW6du2KVq1aAbh5M92wYQNCQ0Ntiifv3bsXmzdvtvh5NQeMZsovkIKCAowePVoWJa5fvz66detmd9c9Z9JLiqCZntOfb0WtZUIczUJbd1wHDBiAoKAgm/VkHTt2RFpaGhYsWIBffvnlXl3mLdXU0R4yZAiaNm0q70c3btzA5s2bERoaiqioKDkgZv6c1FRvVnl/U0OtP2UNyYkTJ8LDwwNBQUF49tln8cEHH2DcuHFo3rw5IiMjcf78eSdfrSU9rC1zhfTnmqh1x1fld96ZM2dw6dIlGcSbj12/fl32e6yzBWJiYpCamoqhQ4eqYmaesf8WB43stl29ehWPP/44hBCYM2eORRqZeX3VY489hqCgIBw5ckR2qKwDRgDo3r07unfvLnex00LAaGbdGSwuLsa+ffvw448/ytejlgAL0FeKoCukP9+K2sqEKNt6fn4+3nvvPXz00UcyiDIrKytD3759ERAQIMu5VFVVIScnB4GBgdi+fbs81xkzWPv370dubq7d9vOf//wHzZo1w5NPPgng5kCKowGxPXv24Pjx4xY/r4X7m/m+ZjKZkJ2dje7du8uZfIPBgISEBBnQqyFVHdDf2jI9pz/fitp2fLXObImMjERUVBSSkpLw9ddfy2MXL15EeHg4EhMT5WMVFRVYvnw5GjVqhOLiYovHGdMyDhrZHTlw4ACSk5Md7qy5YsUKCCEwaNAgNG/eHGFhYTadx+zsbAQGBmLRokU2nY/t27ejbdu2qutQ3Qm1jOhqNUXQVdOf74Qay4SkpaXJIEMIgbCwMOzevdtuGmGvXr2wd+9ezJs3D1FRUWjdurXNDHdtunDhAiIjI+Hn5wej0WiT5lxWVoaoqCh06tQJ586dq3FALCEhAYMHD5ZtUEttznw/rq6uxo0bN7B9+3Zs3rwZX331lc0MixroaW2Z3tOfb4cad3ydPn06hBCIiIhAly5dIISAl5cXVq1ahWvXrsFkMqFfv34QQmDSpEk4f/48srOzERsbi/bt28vyLYzpAQeN7LYov4xqKslQXFyMHj16yHRN6w1KNmzYgIiICHTo0MGms3X16lUkJyfD09NTFSlbWqOHFEFXT3++U2rpwK9Zswa+vr54+umnsWXLFowbNw7BwcEICAjA+vXr5YwOAPTv398iuGzVqpXTZ7Bu3LiBTZs2ITY21mZnTbMRI0bA19cXqampckDM+h62cOFCBAQEYPXq1Tafuy+++EITA2I1DRY5c4ZRD2vLXDX9+c9Qw6zc2bNnkZCQgFdffVXeozZu3IiOHTvCw8MDWVlZAIBffvnFYo29Och09n2NsbuNg0b2p9QUOK5btw5t27aFh4cHZs6ciby8PBQXF2PixIlo1qwZQkJC5CyJ9c30yJEjOHbs2D2/fr2VbdBDiqCe05/11t7MzNeekpKCPn36WHSSNm7ciNatW6N+/fpYv369RWqd0WjEsmXL8PHHH9t0hp3FZDI5LMkA3BzQaNGiBYQQaNy4sfz8mJkHxLp3724zu/Dbb7+hc+fO8Pb21mwn3pn0sLZMr+nPetrx1V5/xN3dHXv37rV4vLCwEF27dkWdOnWwePFiADcDzDfeeAOpqanIzMyUa3+dfV9j7G7ioJH9adaBo3JDgc2bN6NPnz4WI2/e3t54+OGHZcBofTOtrdE4PZdt0HKKIKDP9Ge9tTd7naBhw4ZhyZIlAGCRlpmXl4f777/fbuCopJaR+Jpq+ZWWlmLu3Lm47777EBoaik8++QRFRUX4/fffMXv2bISGhiI0NNThgNjatWvlRlT3kp468YA+1pbpNf1ZTzu+Kl/L0aNHceLECWzduhXJycnyceWyhx07dtgEjmbm18EBI9MbDhrZHVN+iVsHjsrR94sXL6KgoABLly7FkiVL8M0338jdKZ11M9Vz2QYtpwjqNf1Zb+1N+XrWrVuHWbNmYcKECYiPj7foOCk/38rAMTc316IdqpG9wNGcyv3HH39g4cKFCA8PhxACHh4e8PT0hJeXFzp37mx35+Ta7AjrqRNvTctry/SY/qynHV+Vv2fWrFkIDg5GgwYNEBgYCCEEPvvsM3lc+RkzB47e3t7IyMiQg2Jq+twwdjdx0MhqdDsBRE0zjn/2Oe81PZVt0FOKoJnW05+t6am9AbYz2kIIPPLIIxZpc9aB4wMPPAAhhEWNSbUx/43tBY7mVOeysjKcOHEC8+bNQ0pKCsaPH4/8/HwZlKhhQEzrnXhrelhbptf0Zz3t+Dp79mwIIZCYmIhnnnkGsbGxMBgMGDBgAH7++Wd5nvIzvnPnTrRu3RphYWE2+wQwpjccNDKHlDfGwsJCvPPOO0hPT0d+fr7cjdOspk6+2uihbIOeUwTNtJr+bE0P7U35t9u8eTP8/Pzw/PPPY9++fVixYgV69+4NNzc3TJw40aKzq3wPcnNz8dBDD9l0hp3lVu2hphnHP/uctUEPnXi9ri3TQ/qz9e/U8o6vyjZRVlaGXr16YdSoUTJj4LvvvsOoUaNgMBjwwgsvWGS2KH927969sp3xLCPTMw4amV3KL6TXX38d9erVk51zd3d39OjRwyZ1xtzJ9/DwwJw5c5xaNFnPZRv0niKoxfRnPbc35e8/f/48Fi1ahC5dulhsKHLo0CE89dRTcHNzQ1paGn799Vd5TPlemIMOZ3fglb//4MGDyMvLg9FotCkqXlMnX42dQ6134gH9ry3TWvqzK+z4unLlSmRnZ8PHxwfbtm2zOHby5EmMGTPmloEjoI5BI8buJQ4amQ3ljXzatGkyhW7btm0oKyvD0KFDZRF4641TDh06hOTkZAghkJmZ6ZSbqKuUbdBDiqAe0p9dpb1NmTIFSUlJ6NKlC9LS0gDAos7nDz/8gIEDB8rAURngq6kzpbyWmTNnIigoSH5+GjZsiAkTJlicr+zk33fffTAajbc1W3ev6LkTr/e1ZVpLf9brjq9K+/fvhxACrVu3RmxsrJwxVA5MlJSUOAwcGXMlHDQyh9avX48mTZpg9OjRFjfJVq1aISAgAEIIREZG2sw4fvvttxg2bJjFbENtcZWyDXpIEdRD+rOe25vSjRs3kJqaKoOrF198UR5Tvo/KwHHq1KmqKtINWP5NzbsIJycnw2g04sCBA0hMTIQQAkOGDLH4OZPJhM8++wytWrWCh4cH8vPza/nKb3KFTjygj7VlWk9/1uuOr9bKy8uxbNky+Pv7y8FuM2X7MgeOnp6eGDhwoNNKtzDmTBw0MruuXLmCJ598EnFxcXKdxNWrVxETE4Pg4GDk5ORg2LBhEEIgJibGZsbR/OXgjLQgPZZt0FuKoNbTn5X02N7suXz5MhYuXAghBIKDg/HFF1/IY9aB49NPPw0hBDIyMlQ1y2i2evVqBAYGIiUlBSdPnpSPR0VFwcfHx2HguHHjRnTt2tUpbU/PnXi9rS3TQ/qzHnd8dcRkMmH16tXw9vZGTEwMCgoK5DHle3nq1Ck8++yzCAkJwaVLl5xxqYw5FQeNzK6KigpMmjQJ7777LoCbgUZSUhICAwOxZs0aeV50dLRMVXX27I9eyzYo6SFFUOvpz2Z6bW/m12Wv0/r7778jIyMDQgj07t0b+/fvl8eUnavvv/8eo0ePlpt2qMn58+fRvXt39OzZE0ePHgVwc5YuOjoawcHByMzMRLt27SCEwPDhwy1+1mQyyfXAtT3o4gqdeD2sLdN6+rOSXnd8tae8vBwrVqyAl5cXOnXq5HBQ7MyZMzJgVMt3KmO1hYNGZkHZiTCnCFZXVyMzMxOenp7IyMiwCFL69OmDmJgYCCHQpUsXp4+OKumtbINeUgTNtJj+XBM9tDdlOzp79iyOHz+OkpISiw7fhQsXZPrgo48+6jBwNBdQV9MmJMDNzUZ69+4tB7+uXbuGDh06oFGjRsjJyQEAHD9+HHXq1JEDGmqh5068HtaWaT392R697fhak/Lycixfvhyenp41Bo6A82eCGXMGDhpdnKORMusb4oABAxAUFGSz5qJjx45IS0vDggULZC0sNdFL2QYzvaQIajn9uSZabm/K37Vw4ULEx8ejTp068Pb2Rrdu3bBq1Sp5/OLFiw4DRzW1NXvXUl1dLQP6qqoqpKenw9vbG5mZmXJn0dLSUiQkJMgBsZSUlFq97protROvp7VlWkx/ronWdnz9b1gHjl9++aWzL4kx1eCg0YUpb+L5+fl477338NFHH9mstyorK0Pfvn0REBAgR66rqqqQk5ODwMBAbN++XZ5rnl1wNi2WbVDSe4qgFtOfa6LF9qacoVK2M/Nsdrdu3ZCeno5p06bJjvzYsWPleVeuXJGB4xNPPIHdu3fX6vXfir2UssuXL1scu379utzd1roGYUxMDFJTUzF06FDVBSZ67cTrYW2ZVtOfHdHajq93gzlw9PX1RVRUlOrubYw5CweNzKZ0Q1hYGHbv3m031aZXr17Yu3cv5s2bh6ioKLRu3dpmFqi26aFsg5KeUwT1kP6sh/a2detWjBgxwmZAYd26dfDy8sIrr7xiESjNnz8fQggEBgZarLe6fPky5s6dCyEEnnvuOYv3zpmUf8958+YhMjISUVFRSEpKwtdffy2PXbx4EeHh4UhMTJSPVVRUYPny5WjUqBGKi4stHlcDvXfitb62TMvpz1rf8fVuKi8vx+LFixEeHi5TpRlzdRw0urg1a9bA19cXTz/9NLZs2YJx48YhODgYAQEBWL9+vUUR+P79+1sEl61atZIpqc76YtBD2QYlvaUI6i39WQ/tbffu3RBCICkpyWZN2MiRIxEYGCg3QTGZTMjNzUWLFi0QGRmJCxcuAIBFgfiLFy8iKytLlTPa06dPhxACERER6NKlC4QQ8PLywqpVq3Dt2jWYTCb069cPQghMmjQJ58+fR3Z2NmJjY9G+fXubdX+1zVU78VpZW6an9Gc97Ph6t1VUVMhBMi18bhi71zhodFHmm3tKSgr69OljEfxt3LgRrVu3Rv369bF+/XqLmQWj0Yhly5bh448/tikUXdv0ULZBzymCekt/1kN7O3v2LDp06IA2bdpg586dFseuXr2K1q1bIykpCcDN989oNNot23D48GHs2LFD/tvcdtU0g3X27FkkJCTg1Vdflfe3jRs3omPHjvDw8EBWVhYA4JdffkFYWJjFgFhERISqBsRcsROv9rVlekp/1tOOr/eClj9HjN1NHDS6EHsdumHDhmHJkiUAYFGzKy8vD/fff7/dwFHJWR0qPZRt0HuKoJnW058BfbQ34OYmIo0bN8bIkSPlYxMnTkRubi6qq6sRHx+Ptm3b4urVqw4Lw1dWVqJjx44YM2aMxYyjs9nbddbd3R179+61eLywsBBdu3ZFnTp1sHjxYgA3A8w33ngDqampyMzMlOloahgQc+VOvFrXlukp/VmPO74yxu4NDhpdhPJLbt26dZg1axYmTJiA+Ph42XECLDtJysAxNzfXIlVVLbRatsFVUgS1nv5sTavtzezkyZOIjo5GkyZNcP78eRnQT5s2DcDNOqAGgwGpqakIDw9HWFiYTRCclZUFf39/rFq1SjUj8Mr71tGjR3HixAls3boVycnJ8nFl6YYdO3bYBI5mzp415U68JTWvLdN6+rOS3nZ8ZYzdfRw0uhjrWR8hBB555BEcP35cnmMdOD7wwAMQQiAvL88Zl+yQVss2uEKKoB7Sn61ptb1ZW7x4sZyxEkJg9uzZckfNbdu2ITAwUNb5s75Wo9GIli1bIikpSQ5eOJsyyJo1axaCg4PRoEED+To+++wzeVz5esyBo7e3NzIyMmQ7VEsgzJ34/6fGtWVaT39W0tuOr4yxe4ODRp1TfiFt3rwZfn5+eP7557Fv3z6sWLECvXv3hpubGyZOnGhRGkB588/NzcVDDz1kUyja2bRatkGvKYJ6Sn+2R6vtzUz5t4yLi4O7uzsaNGiAwsJCi/PefPNNubNjXl4eDh48iAsXLmDGjBkICwtDSEiInDFV0/tjXtubmJiIZ555BrGxsTAYDBgwYAB+/vlneZ6yne7cuROtW7dGWFjYLTeQqU3cibdPTZveaDn92ZqWd3xljNUeDhp1TPkFe/78eSxatAhdunSxWCd36NAhPPXUU3Bzc0NaWppF+pzyC828iF8NX3JaL9ugxxRBvaY/A9pvb9Y2bNgANzc3JCQkQAiB2NhYHDlyxOKct99+G82aNbOYHXF3d0dSUpJMgXb2vUD5+8vKytCrVy+MGjVKzpp+9913GDVqFAwGA1544QWLdGLlz+7du1d24tXyXnEnXl30lP6spx1fGWO1i4NGFzBlyhQkJSWhS5cuSEtLAwCLTu4PP/yAgQMHysBROaOohpkEvZVtAPSXImimh/RnPbY3pd9++w15eXm4dOkS0tPTIYRAdHS0TLk1+/7775GTk4Px48dj+vTp2LZtm83ukGqwcuVKZGdnw8fHB9u2bbM4dvLkSYwZM+aWgSPgvHsdd+LVTU/pz3ra8ZUxVvs4aNS5GzduyPINQgi8+OKL8pjyC0QZOE6dOlUGMM6m57INekgR1Fv6s97amyPKa5o0aZLDwNEeNQwkme3fvx9CCLRu3RqxsbFyxlA561NSUuIwcHQ27sRrh9bTn/W04ytjzDk4aHQBly9fxsKFCyGEQHBwsMNCyT/88AOefvppCCGQkZGhqs6hHso2KOkhRVCv6c+A/tqbPcrPt73AsbKyUjXpmo6Ul5dj2bJlsoZpZmamPKZsS+bA0dPTEwMHDlRFgMWdeHXTa/qznnZ8ZYzVLg4adcT8hWTvi+n3339HRkYGhBDo3bs39u/fL48pv+C+//57jB49WlWlG/RWtgHQV4qg1tOfremxvTliL3CMiYnBjz/+CEAdndxbMZlMWL16Nby9vRETE4OCggJ5TPkZOXXqFJ599lmEhITg0qVLzrhUu7gTr25aT39W0tOOr4yx2sdBo04ov6DOnj2L48ePo6SkxKJDceHCBZli8+ijjzoMHM2j1c4OSvRYtkFJDymCWk9/VtJ7e3NE2ZbMM6yBgYE4duyYE6/qzpSXl2PFihXw8vJCp06dHGZTmFNAAXV8hrgTr25aT3/W846vjLHax0GjDii/GBYuXIj4+HjUqVMH3t7e6NatG1atWiWPX7x40WHgqIaOh97LNljTQ4qgltOfXa29OaK85pdeeglBQUE4d+6cE6/ozpWXl2P58uXw9PSsMXAEnDeDyp14bdFy+rOednxljKkDB40adO3aNfn/ys6PecanW7duSE9Px7Rp0+SX3dixY+V5V65ckYHjE088gd27d9fq9Tui57INNdFKiqDe0p9dtb05ovx7/PHHHwC010m0Dhy//PJLZ1+SxJ14bdJi+rOednxljKkHB40as3XrVowYMcKm071u3Tp4eXnhlVdesRjlnD9/vkw3U86MXL58GXPnzoUQAs8995zFGjRn00PZhjul9hRBPaY/m7lie3NE2TnUakfRHDj6+voiKipKFYNi3InXNq2mP2t9x1fGmLpw0Kghu3fvhhACSUlJNusmRo4cicDAQBQVFQG4OTqam5uLFi1aIDIyUtbzM9f4Am6mqmZlZTl91kdvZRv+LLWmCOop/Rng9uYKysvLsXjxYoSHh8s0TjXgTrx2aSH9Wa87vjLG1IGDRo04e/YsOnTogDZt2mDnzp0Wx65evYrWrVsjKSkJwM2bv9FoRGhoKMLCwvDvf/9bnnv48GHs2LFD/tvZaU56LtvwZ6ghRVCv6c8AtzdXUlFRIWfmnDVgwZ14fVFz+rOSnnZ8ZYypBweNGlFSUoLGjRtj5MiR8rGJEyciNzcX1dXViI+PR9u2bXH16lVs2LDBbsBYWVmJjh07YsyYMRYzjmqgt7IN/w1npgi6QvozwO3NlaghyOJOvH6oMf1ZSes7vjLG1IuDRo04efIkoqOj0aRJE5w/f16uw5o2bRqAm51gg8GA1NRUhIeHIywszKbAeFZWFvz9/bFq1SpVdKTM9FS2Qcv0mv5sjdsbq03cidcftaY/A9re8ZUxpm4cNGrI4sWLIYRAw4YNIYTA7NmzZUd227ZtclOFxo0b24xQG41GtGzZEklJSbKDryZaLtugB3pNf3aE2xurLdyJ1yc1pD87osUdXxlj6sdBowYov5Di4uLg7u6OBg0aoLCw0OK8N998E3Xq1JG7Oh48eBAXLlzAjBkzEBYWhpCQELk+y1lfcnor26AXek1/5vbG1IA78fqlpqwdJa3u+MoYUy8OGjVkw4YNcHNzQ0JCAoQQiI2NxZEjRyzOefvtt9GsWTOL8gHu7u5ISkqSnV5nzfrouWyD1ukx/ZnbG1MT7sSz2qaFHV8ZY9rBQaOG/Pbbb8jLy8OlS5eQnp4OIQSio6Pxww8/WJz3/fffIycnB+PHj8f06dOxbds2XL58GYDzOr16K9ugR3pKf+b2xtSIO/Gstmllx1fGmPpx0Kgx5hkPAJg0aZLDwNGe2uwA67lsg97oIf2Z2xvTCu7Es9qm9h1fGWPawEGjBik75PYCx8rKSqeOUrtK2Qa90Wr6M7c3pjXciWe1Tc07vjLGtIGDRo2yFzjGxMTgxx9/BOC81CZXKdugR1pMf+b2xrSKO/Gstql5x1fGmPoJACCmSdXV1WQwGIiIaPLkyZSZmUkNGzakPXv2UExMTK1fz7lz56h///5048YNevvtt6lnz57yWGlpKXXq1In8/f1p7969VFVVRZs3b6bJkyeTwWCgAwcOUKNGjYiIqKioiC5evEgPP/wwEREBICEEVVVVkZubW62/LldSWVlJ7u7uRESUlpZGWVlZFBUVRZs2baK4uLgaf1bZHmsDtzemdZWVlWQymcjHx6fWPz/MdZnvcYwxdif4G0rDDAYDVVdXExHRokWLaMyYMWQwGMjf398p13P9+nX6+9//TomJibIDn5qaShs2bCAfHx9yd3ensrIyKi0tpU2bNtntwFdVVdFLL71EmzZtIpPJREQkv9y4A3/vubu7yza1ePFiSk1NpRMnTtBf//pX+vHHH4no5ntkb6yptju83N6Y1rm7u5OPjw8B4ICR1RoOGBljfwZ/S2mcMnBcuXIlnThxgoKDg6mqqqrWrwUA+fv709atW+lf//oXTZ48md566y366aefSAhBffv2paKiIpo9ezZNnz6dDAYDfffdd7IDT0S0dOlSKi4upoSEBPLw8Kj118As25QycBw0aBAdPXpUNcEUtzemF9yJZ4wxpnacnqoTynQTZ6aeZGZm0uTJk6lBgwZ06dIlmjVrFo0YMYKaN29O27dvp6FDh9LFixcpKCiIfvvtN4sAZOPGjZSenk5NmjShzz77jBo2bOiU18BuUlv6sz3c3hhjjDHG7j0OGtldoQww2rRpQ8eOHaN69erRxo0b6ZFHHpHnLVy4kGbMmEGVlZX06aefUmhoKIWFhdHSpUvp448/pqqqKtq3bx+FhITwGh8VUL4HL7/8MuXl5dGRI0coODhYNdfF7Y0xxhhj7N7ioJHdVUajkZ577jlq06YNFRUVUUxMDBmNRmrTpo08Z+nSpbR48WI6e/asfMzNzY06duxIubm5FBoaypuQqIgymLpy5QrVr19fNe8PtzfGGGOMsXuPg0Z2V509e5a+++476tGjB2VmZtKCBQvs7r55+PBhOnLkCB05coR8fX0pKSlJ7nbJHXj1UUv6szVub4wxxhhj9x4Hjeyu01LZBqZ93N4YY4wxxu4tDhrZPaHsjNvryFdVVZHBYFDNjBXTNm5vjDHGGGP3DgeN7J6x15GPjo6mTZs20f3336+qNEemfdzeGGOMMcbuDQ4a2T2lhbINTD+4vTHGGGOM3X3uzr4Apm/mQvEGg4EWLVpEV69epby8PPL393f2pTEd4vbGGGOMMXb38UwjqxVqLtvA9IfbG2OMMcbY3cNBI6s1ai3bwPSJ2xtjjDHG2N3BQSNjjDHGGGOMMYe4QBljjDHGGGOMMYc4aGSMMcYYY4wx5hAHjYwxxhhjjDHGHOKgkTHGGGOMMcaYQxw0MsYYY4wxxhhziINGxhhjjDHGGGMOcdDIGGOMMcYYY8whDhoZY4wxxhhjjDnEQSNjjDF2j4wYMYL69+8v/92jRw8aP358rV/Hrl27SAhBf/zxh8NzhBC0ZcuW237O2bNn0wMPPPBfXdeZM2dICEFFRUX/1fMwxhi7tzhoZIwx5lJGjBhBQggSQpCHhwdFRETQ3LlzqbKy8p7/7ry8PJo3b95tnXs7gR5jjDFWG9ydfQGMMcZYbevbty/l5OSQyWSirVu30iuvvEJ16tSh119/3ebc8vJy8vDwuCu/t0GDBnfleRhjjLHaxDONjDHGXI6npycFBwdT8+bN6eWXX6ZHHnmE/ud//oeI/j+ldP78+dS0aVOKjo4mIqJ//OMfNGjQIPL396cGDRpQcnIynTlzRj5nVVUVTZw4kfz9/alhw4Y0efJkAmDxe63TU00mE02ZMoVCQkLI09OTIiIiaM2aNXTmzBnq2bMnEREFBASQEIJGjBhBRETV1dW0YMECatGiBdWtW5fi4+Np8+bNFr9n69atFBUVRXXr1qWePXtaXOftmjJlCkVFRZG3tze1bNmSZsyYQRUVFTbnvfvuuxQSEkLe3t40aNAgunLlisXx999/n2JjY8nLy4tiYmJoxYoVd3wtjDHGnIuDRsYYYy6vbt26VF5eLv/91VdfUXFxMRUWFtLnn39OFRUV1KdPH/Lz86M9e/bQvn37yNfXl/r27St/Lisriz788EP64IMPaO/evXTp0iXKz8+v8fcOGzaMNmzYQNnZ2XTs2DF69913ydfXl0JCQujTTz8lIqLi4mI6d+4cLV26lIiIFixYQB999BGtWrWKfvrpJ5owYQI999xztHv3biK6GdwOGDCA+vXrR0VFRTRq1CiaOnXqHf9N/Pz86MMPP6Sff/6Zli5dSqtXr6a33nrL4pySkhL65JNP6G9/+xtt376dDh8+TCkpKfL4+vXraebMmTR//nw6duwYvfHGGzRjxgxau3btHV8PY4wxJwJjjDHmQoYPH47k5GQAQHV1NQoLC+Hp6YlJkybJ440bN4bJZJI/8/HHHyM6OhrV1dXyMZPJhLp16+KLL74AADRp0gSLFi2SxysqKnDffffJ3wUADz74IMaNGwcAKC4uBhGhsLDQ7nX+7//+L4gIly9flo/duHED3t7e2L9/v8W5I0eOxLPPPgsAeP3119GqVSuL41OmTLF5LmtEhPz8fIfHFy9ejHbt2sl/z5o1C25ubvjnP/8pH9u2bRsMBgPOnTsHAAgPD0dubq7F88ybNw+dO3cGAPzyyy8gIhw+fNjh72WMMeZ8vKaRMcaYy/n888/J19eXKioqqLq6mgYPHkyzZ8+Wx+Pi4izWMR45coRKSkrIz8/P4nlu3LhBp06doitXrtC5c+eoY8eO8pi7uzu1b9/eJkXVrKioiNzc3OjBBx+87esuKSmh69evU69evSweLy8vp4SEBCIiOnbsmMV1EBF17tz5tn+H2caNGyk7O5tOnTpFpaWlVFlZSfXq1bM4JzQ0lJo1a2bxe6qrq6m4uJj8/Pzo1KlTNHLkSHrxxRflOZWVlVS/fv07vh7GGGPOw0EjY4wxl9OzZ09auXIleXh4UNOmTcnd3fLr0MfHx+LfpaWl1K5dO1q/fr3NczVq1OhPXUPdunXv+GdKS0uJiKigoMAiWCO6uU7zbvnmm29oyJAhNGfOHOrTpw/Vr1+fjEYjZWVl3fG1rl692iaIdXNzu2vXyhhj7N7joJExxpjL8fHxoYiIiNs+v23btrRx40YKCgqymW0za9KkCX333XfUvXt3Iro5o3bo0CFq27at3fPj4uKourqadu/eTY888ojNcfNMZ1VVlXysVatW5OnpSb/++qvDGcrY2Fi5qY/Zt99+e+sXqbB//35q3rw5TZs2TT7297//3ea8X3/9lc6ePUtNmzaVv8dgMFB0dDQ1btyYmjZtSqdPn6YhQ4bc0e9njDGmLrwRDmOMMXYLQ4YMocDAQEpOTqY9e/bQL7/8Qrt27aLXXnuN/vnPfxIR0bhx4+jNN9+kLVu20PHjxyklJaXGGothYWE0fPhweuGFF2jLli3yOT/55BMiImrevDkJIejzzz+n33//nUpLS8nPz48mTZpEEyZMoLVr19KpU6fo+++/p3feeUduLvPSSy/RyZMnKS0tjYqLiyk3N5c+/PDDO3q9kZGR9Ouvv5LRaKRTp05Rdna23U19vLy8aPjw4XTkyBHas2cPvfbaazRo0CAKDg4mIqI5c+bQggULKDs7m06cOEE//vgj5eTk0JIlS+7oehhjjDkXB42MMcbYLXh7e9PXX39NoaGhNGDAAIqNjaWRI0fSjRs35MxjamoqDR06lIYPH06dO3cmPz8/+stf/lLj865cuZIGDhxIKSkpFBMTQy+++CJdu3aNiIiaNWtGc+bMoalTp1Ljxo1p7NixREQ0b948mjFjBi1YsIBiY2Opb9++VFBQQC1atCCim+sMP/30U9qyZQvFx8fTqlWr6I033rij1/vkk0/ShAkTaOzYsfTAAw/Q/v37acaMGTbnRURE0IABA+ixxx6j3r17U5s2bSxKaowaNYref/99ysnJobi4OHrwwQfpww8/lNfKGGNMGwQcrdBnjDHGGGOMMebyeKaRMcYYY4wxxphDHDQyxhhjjDHGGHOIg0bGGGOMMcYYYw5x0MgYY4wxxhhjzCEOGhljjDHGGGOMOcRBI2OMMcYYY4wxhzhoZIwxxhhjjDHmEAeNjDHGGGOMMcYc4qCRMcYYY4wxxphDHDQyxhhjjDHGGHOIg0bGGGOMMcYYYw79Hyzi4b+A6//uAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification report\n",
        "classes = finaldf.actualvalues.unique()\n",
        "classes.sort()\n",
        "print(classification_report(finaldf.actualvalues, finaldf.predictedvalues, target_names=classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V07fwuubGxhY",
        "outputId": "8e3b6472-32d3-407c-ebef-63b6ac2dbe28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                precision    recall  f1-score   support\n",
            "\n",
            "  female_angry       0.00      0.00      0.00        39\n",
            "female_disgust       0.00      0.00      0.00        51\n",
            "   female_fear       0.00      0.00      0.00        36\n",
            "  female_happy       0.00      0.00      0.00        42\n",
            "female_neutral       0.00      0.00      0.00        32\n",
            "    female_sad       0.18      0.26      0.21        38\n",
            "    male_angry       0.00      0.00      0.00        30\n",
            "  male_disgust       0.03      0.12      0.05        26\n",
            "     male_fear       0.09      0.79      0.16        28\n",
            "    male_happy       0.00      0.00      0.00        32\n",
            "  male_neutral       0.00      0.00      0.00        21\n",
            "      male_sad       0.00      0.00      0.00        31\n",
            "\n",
            "      accuracy                           0.09       406\n",
            "     macro avg       0.02      0.10      0.03       406\n",
            "  weighted avg       0.02      0.09      0.03       406\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modidf = finaldf\n",
        "modidf['actualvalues'] = finaldf.actualvalues.replace({'female_angry':'female'\n",
        "                                       , 'female_disgust':'female'\n",
        "                                       , 'female_fear':'female'\n",
        "                                       , 'female_happy':'female'\n",
        "                                       , 'female_sad':'female'\n",
        "                                       , 'female_surprise':'female'\n",
        "                                       , 'female_neutral':'female'\n",
        "                                       , 'male_angry':'male'\n",
        "                                       , 'male_fear':'male'\n",
        "                                       , 'male_happy':'male'\n",
        "                                       , 'male_sad':'male'\n",
        "                                       , 'male_surprise':'male'\n",
        "                                       , 'male_neutral':'male'\n",
        "                                       , 'male_disgust':'male'\n",
        "                                      })\n",
        "\n",
        "modidf['predictedvalues'] = finaldf.predictedvalues.replace({'female_angry':'female'\n",
        "                                       , 'female_disgust':'female'\n",
        "                                       , 'female_fear':'female'\n",
        "                                       , 'female_happy':'female'\n",
        "                                       , 'female_sad':'female'\n",
        "                                       , 'female_surprise':'female'\n",
        "                                       , 'female_neutral':'female'\n",
        "                                       , 'male_angry':'male'\n",
        "                                       , 'male_fear':'male'\n",
        "                                       , 'male_happy':'male'\n",
        "                                       , 'male_sad':'male'\n",
        "                                       , 'male_surprise':'male'\n",
        "                                       , 'male_neutral':'male'\n",
        "                                       , 'male_disgust':'male'\n",
        "                                      })\n",
        "\n",
        "classes = modidf.actualvalues.unique()\n",
        "classes.sort()\n",
        "\n",
        "# Confusion matrix\n",
        "c = confusion_matrix(modidf.actualvalues, modidf.predictedvalues)\n",
        "print(accuracy_score(modidf.actualvalues, modidf.predictedvalues))\n",
        "print_confusion_matrix(c, class_names = classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "qMvhNtMyG1xH",
        "outputId": "bcd86eee-0f54-410f-9196-56aa10312857"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.49261083743842365\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification report\n",
        "classes = modidf.actualvalues.unique()\n",
        "classes.sort()\n",
        "print(classification_report(modidf.actualvalues, modidf.predictedvalues, target_names=classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuyhqrcWG306",
        "outputId": "21aabcc9-5e32-49e8-fb6a-fea8396e2e96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      female       0.79      0.18      0.30       238\n",
            "        male       0.45      0.93      0.60       168\n",
            "\n",
            "    accuracy                           0.49       406\n",
            "   macro avg       0.62      0.56      0.45       406\n",
            "weighted avg       0.65      0.49      0.42       406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modidf = pd.read_csv(\"Predictions.csv\")\n",
        "modidf['actualvalues'] = modidf.actualvalues.replace({'female_angry':'angry'\n",
        "                                       , 'female_disgust':'disgust'\n",
        "                                       , 'female_fear':'fear'\n",
        "                                       , 'female_happy':'happy'\n",
        "                                       , 'female_sad':'sad'\n",
        "                                       , 'female_surprise':'surprise'\n",
        "                                       , 'female_neutral':'neutral'\n",
        "                                       , 'male_angry':'angry'\n",
        "                                       , 'male_fear':'fear'\n",
        "                                       , 'male_happy':'happy'\n",
        "                                       , 'male_sad':'sad'\n",
        "                                       , 'male_surprise':'surprise'\n",
        "                                       , 'male_neutral':'neutral'\n",
        "                                       , 'male_disgust':'disgust'\n",
        "                                      })\n",
        "\n",
        "modidf['predictedvalues'] = modidf.predictedvalues.replace({'female_angry':'angry'\n",
        "                                       , 'female_disgust':'disgust'\n",
        "                                       , 'female_fear':'fear'\n",
        "                                       , 'female_happy':'happy'\n",
        "                                       , 'female_sad':'sad'\n",
        "                                       , 'female_surprise':'surprise'\n",
        "                                       , 'female_neutral':'neutral'\n",
        "                                       , 'male_angry':'angry'\n",
        "                                       , 'male_fear':'fear'\n",
        "                                       , 'male_happy':'happy'\n",
        "                                       , 'male_sad':'sad'\n",
        "                                       , 'male_surprise':'surprise'\n",
        "                                       , 'male_neutral':'neutral'\n",
        "                                       , 'male_disgust':'disgust'\n",
        "                                      })\n",
        "\n",
        "classes = modidf.actualvalues.unique()\n",
        "classes.sort()\n",
        "\n",
        "# Confusion matrix\n",
        "c = confusion_matrix(modidf.actualvalues, modidf.predictedvalues)\n",
        "print(accuracy_score(modidf.actualvalues, modidf.predictedvalues))\n",
        "print_confusion_matrix(c, class_names = classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "M1vW_e94G-h5",
        "outputId": "fd453ccd-198e-48cf-bedd-0f01b513ae64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.16995073891625614\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification report\n",
        "classes = modidf.actualvalues.unique()\n",
        "classes.sort()\n",
        "print(classification_report(modidf.actualvalues, modidf.predictedvalues, target_names=classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMw3pDstHCL6",
        "outputId": "2180fd95-0ad1-4f06-c8f1-68f3a31083b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       angry       0.00      0.00      0.00        69\n",
            "     disgust       0.08      0.10      0.09        77\n",
            "        fear       0.20      0.77      0.31        64\n",
            "       happy       0.00      0.00      0.00        74\n",
            "     neutral       0.00      0.00      0.00        53\n",
            "         sad       0.21      0.17      0.19        69\n",
            "\n",
            "    accuracy                           0.17       406\n",
            "   macro avg       0.08      0.17      0.10       406\n",
            "weighted avg       0.08      0.17      0.10       406\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data, sampling_rate = librosa.load('/content/Crema/1006_DFA_HAP_XX.wav')\n",
        "ipd.Audio('/content/Crema/1006_DFA_HAP_XX.wav')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "id": "wl2AZPHEHJ1-",
        "outputId": "ca1d1219-e9f9-4b11-bc65-f32fdaba07e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "librosa.display.waveshow(data, sr=sampling_rate)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "Glk4VUdYHVxb",
        "outputId": "089737ca-8a62-4a30-ebbb-53b623628eab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<librosa.display.AdaptiveWaveplot at 0x7df97bca5c60>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    }
  ]
}