{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<center><h1>Speaker Classification with Deep Learning</h1></center>**\n",
    "**<center><h2>Speech Technology Assignment 2023-24</h2></center>**\n",
    "**<center><h3>Matthias Bartolo</h3></center>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>1. Introduction</h3>**\n",
    "\n",
    "<p>\n",
    "<b>Speaker identification (SID)</b> is the task of determining a speaker’s identity from a specific audio sample chosen from a pool of known speakers. With applications in forensics, security, and customization [1], SID may be expressed as a pattern recognition problem. The SID pipeline, according to [2], is dependent on two critical components: <i>feature extraction</i> and <i>feature classification</i>. These factors work together to classify an input speech segment as belonging to one of N known enrolled speakers.\n",
    "</p>\n",
    "<br>\n",
    "<p>\n",
    "[1] S. Sremath Tirumala and S. R. Shahamiri, “A review on deep learning approaches in speaker identification,” 11 2016, pp. 142–147.\n",
    "\n",
    "[2] A. Antony and R. Gopikakumari, “Speaker identification based on combination of mfcc and umrt based features,”Procedia Computer Science, vol. 143, pp. 250–257, 2018, 8th International Conference on Advances in Computing & Communications (ICACC-2018). [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050918320908\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>2. Package Installation</h3>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install librosa\n",
    "# !pip install tensorflow\n",
    "# !pip install keras\n",
    "# !pip install matplotlib\n",
    "# !pip install numpy\n",
    "# !pip install pandas\n",
    "# !pip install scikit-learn\n",
    "# !pip install seaborn\n",
    "# !pip install scipy\n",
    "# !pip install keras-tuner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>3. Package Imports</h3>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import librosa\n",
    "import librosa.display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import warnings\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score\n",
    "from keras_tuner.tuners import RandomSearch\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import json\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Declaring constants\n",
    "# Sample rate is the number of samples of audio carried per second, measured in Hz or kHz (one kHz being 1000 Hz).\n",
    "SAMPLE_RATE = 16000\n",
    "# The number of melodies to extract from each audio chunk\n",
    "N_MELS = 128\n",
    "# The number of mel-spectrogram frames to extract from each audio chunk\n",
    "MEL_SPEC_FRAME_SIZE = 1024\n",
    "# The number of speakers/classes in the dataset\n",
    "NUM_CLASSES = 285\n",
    "# Mel-spectrogram flag\n",
    "MEL_SPECTROGRAM = \"Mel Spectrogram\"\n",
    "# MFCC flag\n",
    "MFCC = \"MFCC\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>4. Loading and Filtering Dataset</h3>**\n",
    "\n",
    "**<h4>Function to get the list of speaker roots in the data path</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_speaker_roots_in_data_path(datapath=os.path.join(os.getcwd(), 'ABI-1 Corpus\\\\accents')):\n",
    "    \"\"\"Function to get the list of speaker roots in the data path.\n",
    "    \n",
    "    Args:\n",
    "        datapath (str): Path to the data folder.\n",
    "\n",
    "    Returns:\n",
    "        speaker_list (list): List of speaker roots in the data path.\n",
    "    \"\"\"\n",
    "    # Declaring the list of speakers\n",
    "    speaker_list = []\n",
    "\n",
    "    # Retrieving the list of accent subfolders\n",
    "    accent_subfolders = [f.path for f in os.scandir(datapath) if f.is_dir()]\n",
    "    \n",
    "    # Iterating through the accent subfolders\n",
    "    for accent in accent_subfolders:\n",
    "        # Iterating through the gender\n",
    "        for gender in ['female', 'male']:\n",
    "            # Retrieving the list of speaker folders\n",
    "            speaker_folders = os.listdir(os.path.join(accent, gender))\n",
    "            \n",
    "            # Iterating through the speaker folders\n",
    "            for speaker in speaker_folders:\n",
    "                # Checking if the speaker folder is not a hidden folder\n",
    "                if not speaker.startswith('.'):\n",
    "                    speaker_list.append(os.path.join(accent, gender, speaker))\n",
    "    \n",
    "    # Returning the list of speakers\n",
    "    return speaker_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to get the list of wav files in the data path</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_wav_files_in_path(datapath):\n",
    "    \"\"\"Function to get the list of wav files in the data path.\n",
    "\n",
    "    Args:\n",
    "        datapath (str): Path to the data folder.\n",
    "    \n",
    "    Returns:\n",
    "        wav_files (list): List of wav files in the data path.\n",
    "    \"\"\"\n",
    "    # Retrieving the list of files in the data path\n",
    "    files = os.listdir(datapath)\n",
    "\n",
    "    # Filtering the list of files to get only the wav files which are shortpassage files\n",
    "    wav_files = [f for f in files if f.endswith('.wav') and 'shortpassage' in f]\n",
    "\n",
    "    # Appending the path to the wav files\n",
    "    wav_files = [os.path.join(datapath, f) for f in wav_files]\n",
    "    \n",
    "    # Returning the list of wav files\n",
    "    return wav_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mNumber of speakers found: \u001b[0m285\n",
      "\u001b[1mNumber of shortpassage wav files found: \u001b[0m855\n"
     ]
    }
   ],
   "source": [
    "# Retrieving the list of speaker roots in the data path\n",
    "speaker_roots = get_speaker_roots_in_data_path()\n",
    "print('\\033[1m' + 'Number of speakers found: ' + '\\033[0m' + str(len(speaker_roots)))\n",
    "\n",
    "# Retrieving the list of wav files in the data path\n",
    "wav_files = []\n",
    "\n",
    "# Iterating through the speaker roots\n",
    "for speaker_root in speaker_roots:\n",
    "    # Retrieving the list of wav files in the speaker root\n",
    "    wav_files.extend(get_wav_files_in_path(speaker_root))\n",
    "\n",
    "print('\\033[1m' + 'Number of shortpassage wav files found: ' + '\\033[0m' + str(len(wav_files)))\n",
    "\n",
    "# Setting the number of classes\n",
    "NUM_CLASSES = len(speaker_roots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>5. Preprocessing Data, Chunking and Dataset Splitting</h3>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to display the spectrogram</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_spectrogram(spectrogram, sampling_rate=SAMPLE_RATE, y_axis='mel', title='Mel Spectrogram'):\n",
    "    \"\"\"Function to display the spectrogram.\n",
    "    \n",
    "    Args:\n",
    "        spectrogram (numpy.ndarray): Spectrogram to be displayed.\n",
    "        sampling_rate (int): Sampling rate of the audio (default is 16000).\n",
    "        y_axis (str): Type of y-axis to be displayed (default is linear).\n",
    "        title (str): Title of the plot (default is Mel Spectrogram).\n",
    "    \"\"\"\n",
    "    # Setting the figure size\n",
    "    plt.figure(figsize=(20, 8))\n",
    "\n",
    "    # Setting the title\n",
    "    plt.xlabel('Time')\n",
    "\n",
    "    # Setting the y-axis\n",
    "    plt.ylabel('Mel-Frequency')\n",
    "    \n",
    "    # Displaying the spectrogram\n",
    "    librosa.display.specshow(spectrogram,\n",
    "                            y_axis=y_axis,\n",
    "                            fmax=sampling_rate / 2,\n",
    "                            sr=sampling_rate,\n",
    "                            hop_length=int(MEL_SPEC_FRAME_SIZE / 2),\n",
    "                            x_axis='time')\n",
    "    \n",
    "    # Displaying the colorbar\n",
    "    plt.colorbar(format='%+2.0f dB')\n",
    "    \n",
    "    # Displaying the title\n",
    "    plt.title(title)\n",
    "    \n",
    "    # Displaying the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to chunk the audio file into specified-second segments</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chunk_audio(audio_path, chunk_size=3, plot=False, feature_extractor=MEL_SPECTROGRAM):\n",
    "    \"\"\"Function to chunk the audio file into specified-second segments.\n",
    "    \n",
    "    Args:\n",
    "        audio_path (str): Path to the audio file.\n",
    "        chunk_size (int): Duration of each audio chunk in seconds (default is 3 seconds).\n",
    "        plot (bool): Flag to plot the audio chunks (default is False).\n",
    "        feature_extractor (int): Flag to indicate the feature extractor to be used (default is MEL_SPECTROGRAM).\n",
    "    \n",
    "    Returns:\n",
    "        audio_chunks (list): List of audio chunks.\n",
    "    \"\"\"\n",
    "    # Reading the audio file, whilst ensuring the sampling rate is 16kHz\n",
    "    audio, sampling_rate = librosa.load(audio_path, sr=SAMPLE_RATE)\n",
    "\n",
    "    # Preprocessing the audio by normalizing the audio\n",
    "    audio /= np.max(np.abs(audio), axis=0)\n",
    "    \n",
    "    # Calculating the number of samples per chunk\n",
    "    samples_per_chunk = int(sampling_rate * chunk_size)\n",
    "    \n",
    "    # Calculating the number of chunks\n",
    "    num_chunks = int(np.floor(len(audio) / samples_per_chunk))\n",
    "    \n",
    "    # Initializing the list of audio chunks\n",
    "    audio_chunks = []\n",
    "    \n",
    "    # Iterating through the audio chunks\n",
    "    for i in range(num_chunks):\n",
    "        # Calculating the start and end sample\n",
    "        start_sample = i * samples_per_chunk\n",
    "        end_sample = (i + 1) * samples_per_chunk\n",
    "\n",
    "        # Calculating the audio chunk\n",
    "        audio_chunk = audio[start_sample:end_sample]\n",
    "\n",
    "        if feature_extractor == MFCC:\n",
    "            # Extracting the MFCCs using librosa\n",
    "            mfcc = librosa.feature.mfcc(y=audio_chunk, \n",
    "                                        sr=sampling_rate,\n",
    "                                        n_fft=MEL_SPEC_FRAME_SIZE,\n",
    "                                        hop_length=int(MEL_SPEC_FRAME_SIZE / 2),\n",
    "                                        n_mfcc=N_MELS)\n",
    "\n",
    "            # Setting the spectrogram to be the MFCCs\n",
    "            spectrogram = mfcc\n",
    "\n",
    "            # Plotting the mfcc\n",
    "            if plot:\n",
    "                display_spectrogram(spectrogram, sampling_rate=sampling_rate, title='MFCC of Audio Chunk ' + str(i + 1))\n",
    "\n",
    "        else:\n",
    "            # Extracting the mel spectrogram using librosa\n",
    "            mel_spectrogram = librosa.feature.melspectrogram(y=audio_chunk, \n",
    "                                                            sr=sampling_rate,\n",
    "                                                            center=True,\n",
    "                                                            n_fft=MEL_SPEC_FRAME_SIZE,\n",
    "                                                            hop_length=int(MEL_SPEC_FRAME_SIZE / 2),\n",
    "                                                            n_mels=N_MELS)\n",
    "\n",
    "            # Converting the raw amplitude results to decibels (log scale)\n",
    "            mel_spectrogram = librosa.power_to_db(mel_spectrogram, ref=1.0)\n",
    "\n",
    "            # Setting the spectrogram to be the mel spectrogram\n",
    "            spectrogram = mel_spectrogram\n",
    "\n",
    "            # Plotting the spectrogram\n",
    "            if plot:\n",
    "                display_spectrogram(spectrogram, sampling_rate=sampling_rate, title='Mel Spectrogram of Audio Chunk ' + str(i + 1))\n",
    "\n",
    "        # Appending the audio chunk to the list of audio chunks\n",
    "        audio_chunks.append(spectrogram)\n",
    "    \n",
    "    # Returning the list of audio chunks\n",
    "    return audio_chunks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to preprocess, chunk and split the data</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(speaker_roots, training_set_ratio, validation_set_ratio, testing_set_ratio, do_display=False, do_save=False, plot=False, feature_extractor=MEL_SPECTROGRAM):\n",
    "    \"\"\"\"Function to preprocess, chunk and split the data.\n",
    "\n",
    "    Args:\n",
    "        speaker_roots (list): List of speaker roots in the data path.\n",
    "        training_set_ratio (float): Ratio of the training set.\n",
    "        validation_set_ratio (float): Ratio of the validation set.\n",
    "        testing_set_ratio (float): Ratio of the testing set.\n",
    "        do_display (bool): Boolean to display the audio chunks (default is False).\n",
    "        do_save (bool): Boolean to save the audio chunks (default is False).\n",
    "        plot (bool): Boolean to plot the spectrogram (default is False).\n",
    "        feature_extractor (int): Flag to indicate the feature extractor to be used (default is MEL_SPECTROGRAM).\n",
    "\n",
    "    Returns:\n",
    "        training_set (list): List of training examples.\n",
    "        validation_set (list): List of validation examples.\n",
    "        testing_set (list): List of testing examples.\n",
    "    \"\"\"\n",
    "    # Error checking for the ratios\n",
    "    if training_set_ratio + validation_set_ratio + testing_set_ratio != 1:\n",
    "        raise ValueError('The sum of the ratios must be equal to 1.')\n",
    "    \n",
    "    # Creating dictionary to store the speak to utterances mapping\n",
    "    speaker_to_utterances = {}\n",
    "\n",
    "    # Retrieving the list of speakers through the speaker roots, and giving each speaker a unique ID, since one of the speakers has the same name\n",
    "    speakers ={speaker_root.split('\\\\')[-1]+str(unique_id): speaker_root for unique_id, speaker_root in enumerate(speaker_roots)}\n",
    "  \n",
    "    # Iterating through the speakers\n",
    "    for speaker, speaker_root in speakers.items():\n",
    "        if do_display:\n",
    "            # Printing the speaker being processed\n",
    "            print_message = '\\033[32m' + 'Executing Speaker: ' + '\\033[0m' + speaker + '\\t {} / {}'.format(speaker_roots.index(speaker_root) + 1, len(speaker_roots))\n",
    "            print(print_message)\n",
    "            print('-' * len(print_message))\n",
    "\n",
    "        # Retrieving the list of wav files in the speaker root\n",
    "        speaker_wav_files = get_wav_files_in_path(speaker_root)\n",
    "        \n",
    "        # Initializing the list of utterances\n",
    "        utterances = []\n",
    "        \n",
    "        # Iterating through the wav files\n",
    "        for wav_file in speaker_wav_files:\n",
    "            # Chunking the audio file into 3 seconds segments\n",
    "            utterances.extend(chunk_audio(wav_file, plot=plot, feature_extractor=feature_extractor))\n",
    "        \n",
    "        # Appending the list of utterances to the dictionary\n",
    "        speaker_to_utterances[speaker] = utterances\n",
    "\n",
    "    # Shuffling the utterances\n",
    "    for speaker, utterances in speaker_to_utterances.items():\n",
    "        random.shuffle(utterances)\n",
    "\n",
    "    # Splitting the utterances into training, validation and testing sets\n",
    "    training_set = []\n",
    "    validation_set = []\n",
    "    testing_set = []\n",
    "\n",
    "    # Iterating through the speakers\n",
    "    for speaker in speaker_to_utterances:\n",
    "        # Retrieving the list of utterances\n",
    "        utterances = speaker_to_utterances[speaker]\n",
    "\n",
    "        # Calculating the number of utterances for each set\n",
    "        num_training_utterances = int(len(utterances) * training_set_ratio)\n",
    "        num_validation_utterances = int(len(utterances) * validation_set_ratio)\n",
    "\n",
    "        # Appending the utterances to the relevant sets\n",
    "        training_set.extend([(utterance, speaker) for utterance in utterances[:num_training_utterances]])\n",
    "        validation_set.extend([(utterance, speaker) for utterance in utterances[num_training_utterances:num_training_utterances + num_validation_utterances]])\n",
    "        testing_set.extend([(utterance, speaker) for utterance in utterances[num_training_utterances + num_validation_utterances:]])\n",
    "\n",
    "    # Shuffling the relevant sets\n",
    "    random.shuffle(training_set)\n",
    "    random.shuffle(validation_set)\n",
    "    random.shuffle(testing_set)\n",
    "\n",
    "    # Displaying the number of utterances in each set\n",
    "    if do_display:\n",
    "        print('\\033[35m' + 'Percentage of utterances in each set:' + '\\033[0m')\n",
    "        print('\\033[35m' + 'Training Set: ' + '\\033[0m' + '{:.2%}'.format(len(training_set) / sum([len(utterances) for utterances in speaker_to_utterances.values()])))\n",
    "        print('\\033[35m' + 'Validation Set: ' + '\\033[0m' + '{:.2%}'.format(len(validation_set) / sum([len(utterances) for utterances in speaker_to_utterances.values()])))\n",
    "        print('\\033[35m' + 'Testing Set: ' + '\\033[0m' + '{:.2%}'.format(len(testing_set) / sum([len(utterances) for utterances in speaker_to_utterances.values()])))\n",
    "\n",
    "    # Saving the training, validation and testing sets in a pickle file\n",
    "    if do_save:\n",
    "        # Saving the file in a folder called 'filtered_data' with the feature extractor name\n",
    "        if not os.path.exists('filtered_data'):\n",
    "            os.makedirs('filtered_data')\n",
    "\n",
    "        # Saving the file in a folder called 'filtered_data' with the feature extractor name\n",
    "        if not os.path.exists(os.path.join('filtered_data', feature_extractor)):\n",
    "            os.makedirs(os.path.join('filtered_data', feature_extractor))\n",
    "\n",
    "        # Saving the training, validation and testing sets in a pickle file\n",
    "        with open(os.path.join('filtered_data', feature_extractor, 'training_set.pickle'), 'wb') as handle:\n",
    "            pickle.dump(training_set, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "        with open(os.path.join('filtered_data', feature_extractor, 'validation_set.pickle'), 'wb') as handle:\n",
    "            pickle.dump(validation_set, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "        \n",
    "        with open(os.path.join('filtered_data', feature_extractor, 'testing_set.pickle'), 'wb') as handle:\n",
    "            pickle.dump(testing_set, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "    # Returning the relevant sets\n",
    "    return training_set, validation_set, testing_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Preprocessing, chunking and splitting the data, based on the feature extraction method</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mExecuting Speaker: \u001b[0malw0010\t 1 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcxb0011\t 2 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjah0012\t 3 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjep0013\t 4 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mknb0014\t 5 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlcg0015\t 6 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlst0016\t 7 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmjd0017\t 8 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmpt0018\t 9 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrkk0019\t 10 / 285\n",
      "---------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0majh00110\t 11 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mapd00111\t 12 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mars00112\t 13 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0maxm00113\t 14 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mikp00114\t 15 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mixj00115\t 16 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxs00116\t 17 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmam00117\t 18 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mmrs00118\t 19 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mwxh00119\t 20 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mcxc00120\t 21 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mdmc00121\t 22 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mejr00122\t 23 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mexc00123\t 24 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjmc00324\t 25 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjrs00125\t 26 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mlms00226\t 27 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0msar00127\t 28 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mslm00128\t 29 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0macr00129\t 30 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mbxp00130\t 31 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mcgm00131\t 32 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mcxg00132\t 33 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mejd00133\t 34 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mgmc00134\t 35 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mkjr00135\t 36 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mmjm00136\t 37 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mpar00137\t 38 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mrpg00138\t 39 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0msxc00139\t 40 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mljl00146\t 47 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mrwj00157\t 58 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0msvj00166\t 67 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjmd00177\t 78 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjxp00178\t 79 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mljk00179\t 80 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mcpd001218\t 219 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdjd001219\t 220 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgwd001220\t 221 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mipd001221\t 222 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjas001222\t 223 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmtd001223\t 224 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpgd001224\t 225 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mrme001225\t 226 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwhc001226\t 227 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mccm001227\t 228 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mccs001228\t 229 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mdsf001229\t 230 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgxi001230\t 231 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mhas001231\t 232 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjmr001232\t 233 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlhg001233\t 234 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlml001234\t 235 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mppb001235\t 236 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msfm001236\t 237 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msmf001237\t 238 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mabl001238\t 239 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0majm002239\t 240 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcnb001240\t 241 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgdw001241\t 242 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mggg001242\t 243 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgsb001243\t 244 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxw001244\t 245 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mnwc001245\t 246 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpja001246\t 247 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrjm001247\t 248 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msxl001248\t 249 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgke001249\t 250 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmjo001250\t 251 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mnjb001251\t 252 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mnta001252\t 253 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msce001253\t 254 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwjh001254\t 255 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mais001255\t 256 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdme001256\t 257 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mhak001257\t 258 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjph001258\t 259 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkmp001259\t 260 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmga001260\t 261 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmjc001261\t 262 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mprz001262\t 263 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrkm001263\t 264 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrws001264\t 265 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mbmm001265\t 266 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0memg001266\t 267 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjmc001267\t 268 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmab001268\t 269 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmcw001269\t 270 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmmm001270\t 271 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0momg001271\t 272 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mroh001272\t 273 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msmc001273\t 274 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0myeq001274\t 275 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mapc001275\t 276 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mbjn001276\t 277 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcgd001277\t 278 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mfmv001278\t 279 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mfod001279\t 280 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgpb001280\t 281 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgpd001281\t 282 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgtc001282\t 283 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmar001283\t 284 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrmn001284\t 285 / 285\n",
      "------------------------------------------------\n",
      "\u001b[35mPercentage of utterances in each set:\u001b[0m\n",
      "\u001b[35mTraining Set: \u001b[0m59.19%\n",
      "\u001b[35mValidation Set: \u001b[0m19.13%\n",
      "\u001b[35mTesting Set: \u001b[0m21.68%\n"
     ]
    }
   ],
   "source": [
    "# Calling the preprocess_data function to preprocess, chunk and split the data, but using the mel spectrogram as the feature extractor\n",
    "training_set_mel, validation_set_mel, testing_set_mel = preprocess_data(speaker_roots, 0.6, 0.2, 0.2, do_display=True, do_save=True, plot=False, feature_extractor=MEL_SPECTROGRAM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mExecuting Speaker: \u001b[0malw0010\t 1 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcxb0011\t 2 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjah0012\t 3 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjep0013\t 4 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mknb0014\t 5 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlcg0015\t 6 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlst0016\t 7 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmjd0017\t 8 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmpt0018\t 9 / 285\n",
      "--------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrkk0019\t 10 / 285\n",
      "---------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0majh00110\t 11 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mapd00111\t 12 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mars00112\t 13 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0maxm00113\t 14 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mikp00114\t 15 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mixj00115\t 16 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxs00116\t 17 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmam00117\t 18 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmrs00118\t 19 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwxh00119\t 20 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcxc00120\t 21 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdmc00121\t 22 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mejr00122\t 23 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mexc00123\t 24 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjmc00324\t 25 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjrs00125\t 26 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlms00226\t 27 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msar00127\t 28 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mslm00128\t 29 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0macr00129\t 30 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mbxp00130\t 31 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mcgm00131\t 32 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcxg00132\t 33 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mejd00133\t 34 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mgmc00134\t 35 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkjr00135\t 36 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mmjm00136\t 37 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mpar00137\t 38 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrpg00138\t 39 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0msxc00139\t 40 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mald00140\t 41 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mcxb00241\t 42 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mdab00142\t 43 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mhjl00143\t 44 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjkb00144\t 45 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mljb00145\t 46 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mljl00146\t 47 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmja00147\t 48 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mnxp00148\t 49 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mrxt00149\t 50 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0masp00150\t 51 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mbaj00151\t 52 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mhtl00152\t 53 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mifp00153\t 54 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mkxk00154\t 55 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mmjc00255\t 56 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mspd00158\t 59 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mcet00159\t 60 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0medc00160\t 61 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mhxc00161\t 62 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjat00162\t 63 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjfj00163\t 64 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0msjf00164\t 65 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msjt00165\t 66 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msvj00166\t 67 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msxc00267\t 68 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mtlr00168\t 69 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwxr00369\t 70 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0myjs00170\t 71 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0majt00171\t 72 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0maxc00172\t 73 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mccw00173\t 74 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mdps00174\t 75 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdwc00175\t 76 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjlf00176\t 77 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjmd00177\t 78 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjxp00178\t 79 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mljk00179\t 80 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mrmn00281\t 82 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mjoh00187\t 88 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mses00193\t 94 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgty00194\t 95 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0miwd00195\t 96 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjmc00296\t 97 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mljg00197\t 98 / 285\n",
      "----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrac00198\t 99 / 285\n",
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      "-----------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msxm001100\t 101 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwht001101\t 102 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwnh001102\t 103 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwxr001103\t 104 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcsg001104\t 105 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mhxa001105\t 106 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjrg001106\t 107 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxb002107\t 108 / 285\n",
      "------------------------------------------------\n",
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      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0movs001109\t 110 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrlg001110\t 111 / 285\n",
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      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrxb001112\t 113 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mseg001113\t 114 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwsf001114\t 115 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0madl001115\t 116 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdrb001116\t 117 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdxa001117\t 118 / 285\n",
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      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjum001119\t 120 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mnam001120\t 121 / 285\n",
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      "\u001b[32mExecuting Speaker: \u001b[0mpad001121\t 122 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mped001122\t 123 / 285\n",
      "------------------------------------------------\n",
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      "------------------------------------------------\n",
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      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0macf001125\t 126 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcxf001126\t 127 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdrm001127\t 128 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgtb001128\t 129 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgxf001129\t 130 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxd001130\t 131 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlrg001131\t 132 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmjh001132\t 133 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmmg001133\t 134 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mzlt001134\t 135 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0masp002135\t 136 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0maxp001136\t 137 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdxc002137\t 138 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgxb001138\t 139 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgxi002139\t 140 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjrh001140\t 141 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxe001141\t 142 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmrm001142\t 143 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpdk001143\t 144 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrjr001144\t 145 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mtfg001145\t 146 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcxm001146\t 147 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0meah001147\t 148 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjrk001148\t 149 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxh001149\t 150 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkjd001150\t 151 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mklm001151\t 152 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkxe001152\t 153 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkxh001153\t 154 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlfm001154\t 155 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlms001155\t 156 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0maaj001156\t 157 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcts001157\t 158 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdgf001158\t 159 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdwl001159\t 160 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjco001160\t 161 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjcp001161\t 162 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmfl001162\t 163 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpam001163\t 164 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msjb001164\t 165 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwrq001165\t 166 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mext001166\t 167 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxh002167\t 168 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkjb001168\t 169 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlaw001169\t 170 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmlg001170\t 171 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmlg002171\t 172 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpah001172\t 173 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mscn001173\t 174 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msxp001174\t 175 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mtkb001175\t 176 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdxc001176\t 177 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mggc001177\t 178 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjon001178\t 179 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxb001179\t 180 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxm001180\t 181 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkrt001181\t 182 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkxr001182\t 183 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpxa001183\t 184 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mtxp001184\t 185 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mtxr001185\t 186 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0maaj002186\t 187 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0macl001187\t 188 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mbea001188\t 189 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mbej001189\t 190 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mehl001190\t 191 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlej001191\t 192 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlrc001192\t 193 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlxp001193\t 194 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0moxp001194\t 195 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpaj001195\t 196 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msxt001196\t 197 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcpr001197\t 198 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcse001198\t 199 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdro001199\t 200 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mexj001200\t 201 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mixr001201\t 202 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkmj001202\t 203 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlwe001203\t 204 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrwl001204\t 205 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwxr002205\t 206 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwxt001206\t 207 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0maxm001207\t 208 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcas001208\t 209 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcmd001209\t 210 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcnf001210\t 211 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mhmm001211\t 212 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmgm001212\t 213 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmmd001213\t 214 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msmd001214\t 215 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mtaq001215\t 216 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mtot001216\t 217 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0maor001217\t 218 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcpd001218\t 219 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdjd001219\t 220 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgwd001220\t 221 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mipd001221\t 222 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjas001222\t 223 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmtd001223\t 224 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpgd001224\t 225 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrme001225\t 226 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwhc001226\t 227 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mccm001227\t 228 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mccs001228\t 229 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdsf001229\t 230 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgxi001230\t 231 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mhas001231\t 232 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjmr001232\t 233 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlhg001233\t 234 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mlml001234\t 235 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mppb001235\t 236 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msfm001236\t 237 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msmf001237\t 238 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mabl001238\t 239 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0majm002239\t 240 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcnb001240\t 241 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgdw001241\t 242 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mggg001242\t 243 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgsb001243\t 244 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjxw001244\t 245 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mnwc001245\t 246 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mpja001246\t 247 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrjm001247\t 248 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msxl001248\t 249 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgke001249\t 250 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmjo001250\t 251 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mnjb001251\t 252 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mnta001252\t 253 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msce001253\t 254 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mwjh001254\t 255 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mais001255\t 256 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mdme001256\t 257 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mhak001257\t 258 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjph001258\t 259 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mkmp001259\t 260 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmga001260\t 261 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmjc001261\t 262 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mprz001262\t 263 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrkm001263\t 264 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrws001264\t 265 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mbmm001265\t 266 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0memg001266\t 267 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mjmc001267\t 268 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmab001268\t 269 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmcw001269\t 270 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmmm001270\t 271 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0momg001271\t 272 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mroh001272\t 273 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0msmc001273\t 274 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0myeq001274\t 275 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mapc001275\t 276 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mbjn001276\t 277 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mcgd001277\t 278 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mfmv001278\t 279 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mfod001279\t 280 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgpb001280\t 281 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgpd001281\t 282 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mgtc001282\t 283 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mmar001283\t 284 / 285\n",
      "------------------------------------------------\n",
      "\u001b[32mExecuting Speaker: \u001b[0mrmn001284\t 285 / 285\n",
      "------------------------------------------------\n",
      "\u001b[35mPercentage of utterances in each set:\u001b[0m\n",
      "\u001b[35mTraining Set: \u001b[0m59.19%\n",
      "\u001b[35mValidation Set: \u001b[0m19.13%\n",
      "\u001b[35mTesting Set: \u001b[0m21.68%\n"
     ]
    }
   ],
   "source": [
    "# Calling the preprocess_data function to preprocess, chunk and split the data, but using the MFCCs as the feature extractor\n",
    "training_set_mfcc, validation_set_mfcc, testing_set_mfcc = preprocess_data(speaker_roots, 0.6, 0.2, 0.2, do_display=True, do_save=True, plot=False, feature_extractor=MFCC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to load the filtered data</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_filtered_data(path):\n",
    "    \"\"\"Function to load the filtered data.\n",
    "\n",
    "    Args:\n",
    "        path (str): Path to the folder containing the filtered data.\n",
    "\n",
    "    Returns:\n",
    "        training_set (list): List of training examples.\n",
    "        validation_set (list): List of validation examples.\n",
    "        testing_set (list): List of testing examples.\n",
    "    \"\"\"\n",
    "    # Loading the training, validation and testing sets from the pickle files\n",
    "    with open(os.path.join(path, 'training_set.pickle'), 'rb') as handle:\n",
    "        training_set = pickle.load(handle)\n",
    "\n",
    "    with open(os.path.join(path, 'validation_set.pickle'), 'rb') as handle:\n",
    "        validation_set = pickle.load(handle)\n",
    "\n",
    "    with open(os.path.join(path, 'testing_set.pickle'), 'rb') as handle:\n",
    "        testing_set = pickle.load(handle)\n",
    "\n",
    "    # Extracting the number of labels in training set\n",
    "    num_train_labels = len(set([label for _, label in training_set]))\n",
    "\n",
    "    # Extracting the number of labels in validation set\n",
    "    num_val_labels = len(set([label for _, label in validation_set]))\n",
    "\n",
    "    # Extracting the number of labels in testing set\n",
    "    num_test_labels = len(set([label for _, label in testing_set]))\n",
    "\n",
    "    # Printing the number of labels in each set\n",
    "    print('\\033[35m' + 'Number of labels in each set:' + '\\033[0m')\n",
    "    print('\\033[35m' + 'Training Set: ' + '\\033[0m' + str(num_train_labels))\n",
    "    print('\\033[35m' + 'Validation Set: ' + '\\033[0m' + str(num_val_labels))\n",
    "    print('\\033[35m' + 'Testing Set: ' + '\\033[0m' + str(num_test_labels))\n",
    "\n",
    "    # Error checking for the number of labels\n",
    "    assert num_train_labels == num_val_labels == num_test_labels == NUM_CLASSES, 'The number of labels in each set must be equal to the number of classes.'\n",
    "    print('=' * 100)\n",
    "    print('\\033[32m' + 'The number of labels in each set is equal to the number of classes.' + '\\033[0m')\n",
    "\n",
    "    # Returning the relevant sets\n",
    "    return training_set, validation_set, testing_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Loading the filtered data</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[35mNumber of labels in each set:\u001b[0m\n",
      "\u001b[35mTraining Set: \u001b[0m285\n",
      "\u001b[35mValidation Set: \u001b[0m285\n",
      "\u001b[35mTesting Set: \u001b[0m285\n",
      "====================================================================================================\n",
      "\u001b[32mThe number of labels in each set is equal to the number of classes.\u001b[0m\n",
      "****************************************************************************************************\n",
      "\u001b[35mNumber of labels in each set:\u001b[0m\n",
      "\u001b[35mTraining Set: \u001b[0m285\n",
      "\u001b[35mValidation Set: \u001b[0m285\n",
      "\u001b[35mTesting Set: \u001b[0m285\n",
      "====================================================================================================\n",
      "\u001b[32mThe number of labels in each set is equal to the number of classes.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Loading the training, validation and testing sets from the pickle file for the mel spectrogram\n",
    "training_set_mel, validation_set_mel, testing_set_mel = load_filtered_data(os.path.join('filtered_data', MEL_SPECTROGRAM))\n",
    "\n",
    "print('*'*100)\n",
    "\n",
    "# Loading the training, validation and testing sets from the pickle file for the MFCCs\n",
    "training_set_mfcc, validation_set_mfcc, testing_set_mfcc = load_filtered_data(os.path.join('filtered_data', MFCC))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to encode the data, and convert them to numpy arrays</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_data(training_set, validation_set, testing_set):\n",
    "    \"\"\"Function to encode the data, and convert them to numpy arrays.\n",
    "\n",
    "    Args:\n",
    "        training_set (list): List of training examples.\n",
    "        validation_set (list): List of validation examples.\n",
    "        testing_set (list): List of testing examples.\n",
    "\n",
    "    Returns:\n",
    "        x_train (numpy.ndarray): Training set.\n",
    "        y_train_encoded (numpy.ndarray): Encoded training labels.\n",
    "        x_val (numpy.ndarray): Validation set.\n",
    "        y_val_encoded (numpy.ndarray): Encoded validation labels.\n",
    "        x_test (numpy.ndarray): Testing set.\n",
    "        y_test_encoded (numpy.ndarray): Encoded testing labels.\n",
    "    \"\"\"\n",
    "    # Preparing the training, validation and testing sets in the format required by the model, and converting them to numpy arrays\n",
    "    x_train = np.array([utterance for utterance, speaker in training_set])\n",
    "    y_train = np.array([speaker for utterance, speaker in training_set])\n",
    "\n",
    "    x_val = np.array([utterance for utterance, speaker in validation_set])\n",
    "    y_val = np.array([speaker for utterance, speaker in validation_set])\n",
    "\n",
    "    x_test = np.array([utterance for utterance, speaker in testing_set])\n",
    "    y_test = np.array([speaker for utterance, speaker in testing_set])\n",
    "\n",
    "    # Encoding the labels using LabelEncoder\n",
    "    label_encoder = LabelEncoder()\n",
    "\n",
    "    # Fitting and transforming labels for training data, validating data and testing data\n",
    "    y_train_encoded = label_encoder.fit_transform(y_train)\n",
    "    y_val_encoded = label_encoder.transform(y_val)\n",
    "    y_test_encoded = label_encoder.transform(y_test)\n",
    "\n",
    "    # One-hot encoding the transformed labels\n",
    "    # Converting encoded labels to one-hot encoding\n",
    "    y_train_encoded = to_categorical(y_train_encoded)\n",
    "    y_val_encoded = to_categorical(y_val_encoded)\n",
    "    y_test_encoded = to_categorical(y_test_encoded)\n",
    "\n",
    "    # Returning the relevant sets\n",
    "    return x_train, y_train_encoded, x_val, y_val_encoded, x_test, y_test_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Encoding the data, and converting them to numpy arrays</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the training, validation and testing sets in the format required by the model, and converting them to numpy arrays for the mel spectrogram\n",
    "x_train_mel, y_train_encoded_mel, x_val_mel, y_val_encoded_mel, x_test_mel, y_test_encoded_mel = encode_data(training_set_mel, validation_set_mel, testing_set_mel)\n",
    "\n",
    "# Preparing the training, validation and testing sets in the format required by the model, and converting them to numpy arrays for the MFCCs\n",
    "x_train_mfcc, y_train_encoded_mfcc, x_val_mfcc, y_val_encoded_mfcc, x_test_mfcc, y_test_encoded_mfcc = encode_data(training_set_mfcc, validation_set_mfcc, testing_set_mfcc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>6. Speaker Identification (SID) Model Design and Implementation</h3>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to plot the training and validation curves for the specified metric</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_curves(history, metric, title, colors=['blue', 'cyan', 'green', 'purple'], marker='o', do_save=False, save_path=None):\n",
    "    \"\"\"Function to plot the training and validation curves for the specified metric.\n",
    "    \n",
    "    Args:\n",
    "        history (keras.callbacks.History): History of the model.\n",
    "        metric (str): Metric to be plotted.\n",
    "        title (str): Title of the plot.\n",
    "        colors (list): List of colors for the training and validation curves (default is ['blue', 'cyan', 'green', 'purple']).\n",
    "        marker (str): Marker for the best validation score (default is 'o').\n",
    "        do_save (bool): Flag to save the plot (default is False).\n",
    "        save_path (str): Path to save the plot (default is None).\n",
    "    \"\"\"\n",
    "    # Retrieving the metric values\n",
    "    metric_train = history.history[metric]\n",
    "    metric_val = history.history['val_' + metric]\n",
    "    metric_loss = history.history['loss']\n",
    "    metric_val_loss = history.history['val_loss']\n",
    "\n",
    "    # Retrieving the number of epochs\n",
    "    epochs = range(len(metric_train))\n",
    "\n",
    "    # Plotting results\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    plt.plot(epochs, metric_train, label='Training ' + metric.title(), color=colors[0], marker=marker)\n",
    "    plt.plot(epochs, metric_val, label='Validation ' + metric.title(), color=colors[1], marker=marker)\n",
    "    plt.plot(epochs, metric_loss, label='Training ' + 'Loss', color=colors[2], marker=marker)\n",
    "    plt.plot(epochs, metric_val_loss, label='Validation ' + 'Loss', color=colors[3], marker=marker)\n",
    "    plt.axvline(np.argmax(metric_val), linestyle='--', color='red', label='Best Val '+metric.title()+':' + str(round(np.max(metric_val), 2)))\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss/'+metric.title())\n",
    "    plt.grid()\n",
    "    plt.legend(loc = 'upper right')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Saving the plot\n",
    "    if do_save:\n",
    "        # Saving the file in a folder called 'plots'\n",
    "        if not os.path.exists('plots'):\n",
    "            os.makedirs('plots')\n",
    "\n",
    "        # Saving the plot\n",
    "        plt.savefig(os.path.join('plots', save_path))\n",
    "\n",
    "    # Displaying the plot    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Defining Different Model Architectures</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resetting Keras Session\n",
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4> Model 1: Multi-Layer CNN followed by LSTM with Batch Normalization and Dropout</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 126, 92, 32)       320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 124, 90, 64)       18496     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 62, 45, 64)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 60, 43, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 30, 21, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 30, 1344)          0         \n",
      "                                                                 \n",
      " lstm (LSTM)                 (None, 30, 64)            360704    \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1920)              0         \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 1920)              7680      \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1920)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 285)               547485    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 971613 (3.71 MB)\n",
      "Trainable params: 967773 (3.69 MB)\n",
      "Non-trainable params: 3840 (15.00 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Defining the model architecture\n",
    "model1 = keras.Sequential([\n",
    "    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 94, 1)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Reshape((30, 21 * 64)),\n",
    "    keras.layers.LSTM(64, return_sequences=True),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])\n",
    "\n",
    "# Setting the optimizer\n",
    "optimizer1 = keras.optimizers.Adam(learning_rate=0.0005)\n",
    "\n",
    "# Early stopping callback\n",
    "early_stopping1 = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Compiling the model\n",
    "model1.compile(optimizer=optimizer1,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Printing the model summary\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4> Model 2: Increased Convolutional Layer Depth</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_3 (Conv2D)           (None, 126, 92, 64)       640       \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 124, 90, 128)      73856     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 62, 45, 128)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 60, 43, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPoolin  (None, 30, 21, 128)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " reshape_1 (Reshape)         (None, 30, 2688)          0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 30, 64)            704768    \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 1920)              0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 1920)              7680      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 1920)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 285)               547485    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1482013 (5.65 MB)\n",
      "Trainable params: 1478173 (5.64 MB)\n",
      "Non-trainable params: 3840 (15.00 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Defining the model architecture\n",
    "model2 = keras.Sequential([\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(128, 94, 1)),\n",
    "    keras.layers.Conv2D(128, (3, 3), activation='relu'),  # Increased depth\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Conv2D(128, (3, 3), activation='relu'),  # Maintaining the depth\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Reshape((30, 21 * 128)),\n",
    "    keras.layers.LSTM(64, return_sequences=True),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])\n",
    "\n",
    "# Setting the optimizer\n",
    "optimizer2 = keras.optimizers.Adam(learning_rate=0.0005)\n",
    "\n",
    "# Early stopping callback\n",
    "early_stopping2 = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Compiling the model\n",
    "model2.compile(optimizer=optimizer2,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Printing the model summary\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4> Model 3: Altered LSTM Configuration</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 126, 92, 32)       320       \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 124, 90, 64)       18496     \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 62, 45, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 60, 43, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 30, 21, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " reshape_2 (Reshape)         (None, 30, 1344)          0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 30, 128)           754176    \n",
      "                                                                 \n",
      " lstm_3 (LSTM)               (None, 30, 64)            49408     \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 1920)              0         \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 1920)              7680      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 1920)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 285)               547485    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1414493 (5.40 MB)\n",
      "Trainable params: 1410653 (5.38 MB)\n",
      "Non-trainable params: 3840 (15.00 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Defining the model architecture\n",
    "model3 = keras.Sequential([\n",
    "    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 94, 1)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Reshape((30, 21 * 64)),\n",
    "    keras.layers.LSTM(128, return_sequences=True),  # Increased LSTM units\n",
    "    keras.layers.LSTM(64, return_sequences=True),  # Additional LSTM layer\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])\n",
    "\n",
    "# Setting the optimizer\n",
    "optimizer3 = keras.optimizers.Adam(learning_rate=0.0005)\n",
    "\n",
    "# Early stopping callback\n",
    "early_stopping3 = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Compiling the model\n",
    "model3.compile(optimizer=optimizer3,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Printing the model summary\n",
    "model3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4> Model 4: Additional Dense Layer</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_9 (Conv2D)           (None, 126, 92, 32)       320       \n",
      "                                                                 \n",
      " conv2d_10 (Conv2D)          (None, 124, 90, 64)       18496     \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPoolin  (None, 62, 45, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_11 (Conv2D)          (None, 60, 43, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPoolin  (None, 30, 21, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " reshape_3 (Reshape)         (None, 30, 1344)          0         \n",
      "                                                                 \n",
      " lstm_4 (LSTM)               (None, 30, 64)            360704    \n",
      "                                                                 \n",
      " flatten_3 (Flatten)         (None, 1920)              0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 128)               245888    \n",
      "                                                                 \n",
      " batch_normalization_3 (Bat  (None, 128)               512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 285)               36765     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 699613 (2.67 MB)\n",
      "Trainable params: 699357 (2.67 MB)\n",
      "Non-trainable params: 256 (1.00 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Defining the model architecture\n",
    "model4 = keras.Sequential([\n",
    "    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 94, 1)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Reshape((30, 21 * 64)),\n",
    "    keras.layers.LSTM(64, return_sequences=True),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(128, activation='relu'),  # Additional dense layer\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])\n",
    "\n",
    "# Setting the optimizer\n",
    "optimizer4 = keras.optimizers.Adam(learning_rate=0.0005)\n",
    "\n",
    "# Early stopping callback\n",
    "early_stopping4 = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Compiling the model\n",
    "model4.compile(optimizer=optimizer4,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Printing the model summary\n",
    "model4.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4> Model 5: Variant Convolutional Layers</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_12 (Conv2D)          (None, 126, 92, 16)       160       \n",
      "                                                                 \n",
      " conv2d_13 (Conv2D)          (None, 124, 90, 32)       4640      \n",
      "                                                                 \n",
      " max_pooling2d_8 (MaxPoolin  (None, 62, 45, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_14 (Conv2D)          (None, 60, 43, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_9 (MaxPoolin  (None, 30, 21, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " reshape_4 (Reshape)         (None, 30, 1344)          0         \n",
      "                                                                 \n",
      " lstm_5 (LSTM)               (None, 30, 64)            360704    \n",
      "                                                                 \n",
      " flatten_4 (Flatten)         (None, 1920)              0         \n",
      "                                                                 \n",
      " batch_normalization_4 (Bat  (None, 1920)              7680      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 1920)              0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 285)               547485    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 939165 (3.58 MB)\n",
      "Trainable params: 935325 (3.57 MB)\n",
      "Non-trainable params: 3840 (15.00 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Defining the model architecture\n",
    "model5 = keras.Sequential([\n",
    "    keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(128, 94, 1)),  # Fewer filters\n",
    "    keras.layers.Conv2D(32, (3, 3), activation='relu'),  # Fewer filters\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Reshape((30, 21 * 64)),\n",
    "    keras.layers.LSTM(64, return_sequences=True),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])\n",
    "\n",
    "# Setting the optimizer\n",
    "optimizer5 = keras.optimizers.Adam(learning_rate=0.0005)\n",
    "\n",
    "# Early stopping callback\n",
    "early_stopping5 = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Compiling the model\n",
    "model5.compile(optimizer=optimizer5,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Printing the model summary\n",
    "model5.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4> Model 6: Adding Batch Normalisation to each Convolutional Layer</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_15 (Conv2D)          (None, 126, 92, 32)       320       \n",
      "                                                                 \n",
      " batch_normalization_5 (Bat  (None, 126, 92, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_16 (Conv2D)          (None, 124, 90, 64)       18496     \n",
      "                                                                 \n",
      " batch_normalization_6 (Bat  (None, 124, 90, 64)       256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " max_pooling2d_10 (MaxPooli  (None, 62, 45, 64)        0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " conv2d_17 (Conv2D)          (None, 60, 43, 64)        36928     \n",
      "                                                                 \n",
      " batch_normalization_7 (Bat  (None, 60, 43, 64)        256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " max_pooling2d_11 (MaxPooli  (None, 30, 21, 64)        0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " reshape_5 (Reshape)         (None, 30, 1344)          0         \n",
      "                                                                 \n",
      " lstm_6 (LSTM)               (None, 30, 64)            360704    \n",
      "                                                                 \n",
      " flatten_5 (Flatten)         (None, 1920)              0         \n",
      "                                                                 \n",
      " batch_normalization_8 (Bat  (None, 1920)              7680      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 1920)              0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 285)               547485    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 972253 (3.71 MB)\n",
      "Trainable params: 968093 (3.69 MB)\n",
      "Non-trainable params: 4160 (16.25 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Defining the model architecture\n",
    "model6 = keras.Sequential([\n",
    "    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 94, 1)),\n",
    "    keras.layers.BatchNormalization(),  # Batch Normalization after first Conv2D layer\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.BatchNormalization(),  # Batch Normalization after second Conv2D layer\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.BatchNormalization(),  # Batch Normalization after third Conv2D layer\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Reshape((30, 21 * 64)),\n",
    "    keras.layers.LSTM(64, return_sequences=True),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.BatchNormalization(),  # Batch Normalization before Dense layer\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])\n",
    "\n",
    "# Setting the optimizer\n",
    "optimizer6 = keras.optimizers.Adam(learning_rate=0.0005)\n",
    "\n",
    "# Early stopping callback\n",
    "early_stopping6 = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Compiling the model\n",
    "model6.compile(optimizer=optimizer6,\n",
    "               loss='categorical_crossentropy',\n",
    "               metrics=['accuracy'])\n",
    "\n",
    "# Printing the model summary\n",
    "model6.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Training Models</h4>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Training model 1 with the different feature extractors</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 75s 301ms/step - loss: 5.6885 - accuracy: 0.0067 - val_loss: 5.6375 - val_accuracy: 0.0102\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 78s 319ms/step - loss: 5.5729 - accuracy: 0.0135 - val_loss: 8.4927 - val_accuracy: 0.0114\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 80s 326ms/step - loss: 2.8346 - accuracy: 0.4195 - val_loss: 1.2562 - val_accuracy: 0.6789\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 81s 330ms/step - loss: 0.3386 - accuracy: 0.9264 - val_loss: 2.3539 - val_accuracy: 0.5503\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 81s 331ms/step - loss: 0.1180 - accuracy: 0.9795 - val_loss: 0.2107 - val_accuracy: 0.9528\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 82s 333ms/step - loss: 0.0631 - accuracy: 0.9907 - val_loss: 0.2012 - val_accuracy: 0.9513\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 80s 326ms/step - loss: 0.0406 - accuracy: 0.9948 - val_loss: 0.7826 - val_accuracy: 0.7976\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 81s 329ms/step - loss: 0.0256 - accuracy: 0.9977 - val_loss: 0.7180 - val_accuracy: 0.8086\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 81s 330ms/step - loss: 0.0167 - accuracy: 0.9977 - val_loss: 0.1621 - val_accuracy: 0.9572\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 82s 335ms/step - loss: 0.0106 - accuracy: 0.9995 - val_loss: 0.2254 - val_accuracy: 0.9414\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 79s 321ms/step - loss: 0.0087 - accuracy: 0.9994 - val_loss: 0.1555 - val_accuracy: 0.9587\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 84s 342ms/step - loss: 0.0088 - accuracy: 0.9992 - val_loss: 0.1722 - val_accuracy: 0.9560\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 85s 346ms/step - loss: 0.0084 - accuracy: 0.9994 - val_loss: 0.1659 - val_accuracy: 0.9611\n",
      "Epoch 14/100\n",
      "246/246 [==============================] - 85s 345ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.1398 - val_accuracy: 0.9662\n",
      "Epoch 15/100\n",
      "246/246 [==============================] - 82s 334ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9654\n",
      "Epoch 16/100\n",
      "246/246 [==============================] - 85s 346ms/step - loss: 0.0037 - accuracy: 0.9996 - val_loss: 0.1728 - val_accuracy: 0.9579\n",
      "Epoch 17/100\n",
      "246/246 [==============================] - 81s 329ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9674\n",
      "Epoch 18/100\n",
      "246/246 [==============================] - 83s 336ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.2486 - val_accuracy: 0.9383\n",
      "Epoch 19/100\n",
      "246/246 [==============================] - 84s 340ms/step - loss: 0.0138 - accuracy: 0.9981 - val_loss: 0.4621 - val_accuracy: 0.8762\n",
      "Epoch 20/100\n",
      "246/246 [==============================] - 83s 339ms/step - loss: 0.0159 - accuracy: 0.9972 - val_loss: 0.3865 - val_accuracy: 0.9006\n",
      "Epoch 21/100\n",
      "246/246 [==============================] - 82s 335ms/step - loss: 0.0306 - accuracy: 0.9930 - val_loss: 0.2950 - val_accuracy: 0.9265\n",
      "Epoch 22/100\n",
      "246/246 [==============================] - 83s 337ms/step - loss: 0.0101 - accuracy: 0.9981 - val_loss: 0.1861 - val_accuracy: 0.9536\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 1 on the mel spectrogram\n",
    "history = model1.fit(x_train_mel, y_train_encoded_mel, validation_data=(x_val_mel, y_val_encoded_mel), epochs=100, batch_size=32, callbacks=[early_stopping1])\n",
    "\n",
    "# Saving the model\n",
    "model1.save('model1_mel.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 1 (Mel Spectrogram)', do_save=True, save_path='model1_mel_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 83s 334ms/step - loss: 4.4263 - accuracy: 0.1221 - val_loss: 3.9637 - val_accuracy: 0.2410\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 97s 394ms/step - loss: 1.7115 - accuracy: 0.5856 - val_loss: 2.8182 - val_accuracy: 0.3447\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 98s 397ms/step - loss: 0.5442 - accuracy: 0.8896 - val_loss: 1.0557 - val_accuracy: 0.7178\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 92s 373ms/step - loss: 0.1664 - accuracy: 0.9795 - val_loss: 0.5609 - val_accuracy: 0.8546\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 93s 378ms/step - loss: 0.0740 - accuracy: 0.9926 - val_loss: 0.5364 - val_accuracy: 0.8679\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 94s 381ms/step - loss: 0.0389 - accuracy: 0.9981 - val_loss: 0.8356 - val_accuracy: 0.7744\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 94s 381ms/step - loss: 0.0245 - accuracy: 0.9994 - val_loss: 0.3514 - val_accuracy: 0.9147\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 92s 375ms/step - loss: 0.0167 - accuracy: 0.9997 - val_loss: 0.2814 - val_accuracy: 0.9340\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 95s 384ms/step - loss: 0.0122 - accuracy: 0.9999 - val_loss: 0.3762 - val_accuracy: 0.9072\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 96s 392ms/step - loss: 0.0109 - accuracy: 0.9994 - val_loss: 1.5307 - val_accuracy: 0.6187\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 96s 390ms/step - loss: 0.0077 - accuracy: 0.9999 - val_loss: 0.4524 - val_accuracy: 0.8829\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 100s 405ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.3106 - val_accuracy: 0.9237\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 98s 399ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.2911 - val_accuracy: 0.9347\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 1 on the MFCCs\n",
    "history = model1.fit(x_train_mfcc, y_train_encoded_mfcc, validation_data=(x_val_mfcc, y_val_encoded_mfcc), epochs=100, batch_size=32, callbacks=[early_stopping1])\n",
    "\n",
    "# Saving the model\n",
    "model1.save('model1_mfcc.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 1 (MFCCs)', do_save=True, save_path='model1_mfcc_accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Training model 2 with the different feature extractors</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 184s 746ms/step - loss: 5.6695 - accuracy: 0.0088 - val_loss: 5.6364 - val_accuracy: 0.0079\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 190s 774ms/step - loss: 5.6297 - accuracy: 0.0099 - val_loss: 5.7335 - val_accuracy: 0.0039\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 189s 770ms/step - loss: 5.6255 - accuracy: 0.0090 - val_loss: 5.6157 - val_accuracy: 0.0102\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 183s 743ms/step - loss: 5.6235 - accuracy: 0.0097 - val_loss: 5.6122 - val_accuracy: 0.0102\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 187s 762ms/step - loss: 5.6222 - accuracy: 0.0094 - val_loss: 5.6119 - val_accuracy: 0.0102\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 188s 764ms/step - loss: 5.6222 - accuracy: 0.0086 - val_loss: 5.6121 - val_accuracy: 0.0102\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 192s 782ms/step - loss: 5.6213 - accuracy: 0.0097 - val_loss: 5.6112 - val_accuracy: 0.0102\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 184s 747ms/step - loss: 5.6213 - accuracy: 0.0090 - val_loss: 5.6106 - val_accuracy: 0.0102\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 183s 746ms/step - loss: 5.6221 - accuracy: 0.0094 - val_loss: 8.5467 - val_accuracy: 0.0047\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 178s 725ms/step - loss: 5.6208 - accuracy: 0.0098 - val_loss: 5.6364 - val_accuracy: 0.0086\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 192s 779ms/step - loss: 5.6208 - accuracy: 0.0090 - val_loss: 5.6133 - val_accuracy: 0.0102\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 193s 783ms/step - loss: 5.6208 - accuracy: 0.0089 - val_loss: 5.6108 - val_accuracy: 0.0102\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 191s 778ms/step - loss: 5.6198 - accuracy: 0.0095 - val_loss: 5.6098 - val_accuracy: 0.0102\n",
      "Epoch 14/100\n",
      "246/246 [==============================] - 189s 767ms/step - loss: 5.6200 - accuracy: 0.0095 - val_loss: 5.6097 - val_accuracy: 0.0102\n",
      "Epoch 15/100\n",
      "246/246 [==============================] - 178s 724ms/step - loss: 5.6197 - accuracy: 0.0094 - val_loss: 5.6108 - val_accuracy: 0.0102\n",
      "Epoch 16/100\n",
      "246/246 [==============================] - 180s 733ms/step - loss: 5.6200 - accuracy: 0.0093 - val_loss: 5.6102 - val_accuracy: 0.0102\n",
      "Epoch 17/100\n",
      "246/246 [==============================] - 176s 716ms/step - loss: 5.6197 - accuracy: 0.0089 - val_loss: 5.6113 - val_accuracy: 0.0102\n",
      "Epoch 18/100\n",
      "246/246 [==============================] - 186s 757ms/step - loss: 5.6198 - accuracy: 0.0099 - val_loss: 5.6107 - val_accuracy: 0.0094\n",
      "Epoch 19/100\n",
      "246/246 [==============================] - 178s 723ms/step - loss: 5.6195 - accuracy: 0.0088 - val_loss: 5.6102 - val_accuracy: 0.0102\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 2 on the mel spectrogram\n",
    "history = model2.fit(x_train_mel, y_train_encoded_mel, validation_data=(x_val_mel, y_val_encoded_mel), epochs=100, batch_size=32, callbacks=[early_stopping2])\n",
    "\n",
    "# Saving the model\n",
    "model2.save('model2_mel.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 2 (Mel Spectrogram)', do_save=True, save_path='model2_mel_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 190s 770ms/step - loss: 4.4871 - accuracy: 0.1164 - val_loss: 4.5682 - val_accuracy: 0.0472\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 198s 806ms/step - loss: 1.5793 - accuracy: 0.6326 - val_loss: 2.0262 - val_accuracy: 0.5047\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 204s 829ms/step - loss: 0.3897 - accuracy: 0.9259 - val_loss: 0.8254 - val_accuracy: 0.7842\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 191s 777ms/step - loss: 0.1065 - accuracy: 0.9876 - val_loss: 0.4376 - val_accuracy: 0.8911\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 191s 777ms/step - loss: 0.0468 - accuracy: 0.9962 - val_loss: 0.3733 - val_accuracy: 0.9119\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 193s 785ms/step - loss: 0.0261 - accuracy: 0.9989 - val_loss: 0.3310 - val_accuracy: 0.9230\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 195s 794ms/step - loss: 0.0156 - accuracy: 0.9997 - val_loss: 0.3083 - val_accuracy: 0.9226\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 189s 770ms/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.2435 - val_accuracy: 0.9383\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 178s 726ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.2823 - val_accuracy: 0.9222\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 179s 727ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.2326 - val_accuracy: 0.9403\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 199s 807ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.2303 - val_accuracy: 0.9391\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 199s 811ms/step - loss: 0.0039 - accuracy: 0.9999 - val_loss: 0.7064 - val_accuracy: 0.8208\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 182s 738ms/step - loss: 0.0050 - accuracy: 0.9999 - val_loss: 0.2119 - val_accuracy: 0.9434\n",
      "Epoch 14/100\n",
      "246/246 [==============================] - 200s 813ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.9410\n",
      "Epoch 15/100\n",
      "246/246 [==============================] - 205s 832ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9410\n",
      "Epoch 16/100\n",
      "246/246 [==============================] - 196s 798ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9485\n",
      "Epoch 17/100\n",
      "246/246 [==============================] - 180s 732ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1820 - val_accuracy: 0.9497\n",
      "Epoch 18/100\n",
      "246/246 [==============================] - 181s 736ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9454\n",
      "Epoch 19/100\n",
      "246/246 [==============================] - 179s 727ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9442\n",
      "Epoch 20/100\n",
      "246/246 [==============================] - 187s 759ms/step - loss: 9.6627e-04 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.9520\n",
      "Epoch 21/100\n",
      "246/246 [==============================] - 187s 762ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.9204 - val_accuracy: 0.7685\n",
      "Epoch 22/100\n",
      "246/246 [==============================] - 179s 730ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9387\n",
      "Epoch 23/100\n",
      "246/246 [==============================] - 182s 740ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.5214 - val_accuracy: 0.6604\n",
      "Epoch 24/100\n",
      "246/246 [==============================] - 187s 762ms/step - loss: 0.1801 - accuracy: 0.9507 - val_loss: 2.1384 - val_accuracy: 0.5975\n",
      "Epoch 25/100\n",
      "246/246 [==============================] - 187s 759ms/step - loss: 0.0498 - accuracy: 0.9879 - val_loss: 0.5020 - val_accuracy: 0.8726\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 2 on t he MFCCs\n",
    "history = model2.fit(x_train_mfcc, y_train_encoded_mfcc, validation_data=(x_val_mfcc, y_val_encoded_mfcc), epochs=100, batch_size=32, callbacks=[early_stopping2])\n",
    "\n",
    "# Saving the model\n",
    "model2.save('model2_mfcc.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 2 (MFCCs)', do_save=True, save_path='model2_mfcc_accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Training model 3 with the different feature extractors</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 89s 356ms/step - loss: 5.0311 - accuracy: 0.0682 - val_loss: 4.8915 - val_accuracy: 0.0763\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 85s 347ms/step - loss: 1.0719 - accuracy: 0.7567 - val_loss: 0.8614 - val_accuracy: 0.7968\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 87s 354ms/step - loss: 0.2767 - accuracy: 0.9466 - val_loss: 0.3615 - val_accuracy: 0.9061\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 87s 353ms/step - loss: 0.1441 - accuracy: 0.9709 - val_loss: 1.0736 - val_accuracy: 0.7205\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 89s 362ms/step - loss: 0.0980 - accuracy: 0.9813 - val_loss: 0.4438 - val_accuracy: 0.8821\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 86s 350ms/step - loss: 0.0601 - accuracy: 0.9893 - val_loss: 0.3735 - val_accuracy: 0.9025\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 86s 349ms/step - loss: 0.0485 - accuracy: 0.9906 - val_loss: 0.2640 - val_accuracy: 0.9285\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 86s 349ms/step - loss: 0.0324 - accuracy: 0.9948 - val_loss: 0.2470 - val_accuracy: 0.9281\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 84s 343ms/step - loss: 0.0213 - accuracy: 0.9973 - val_loss: 1.4675 - val_accuracy: 0.6789\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 84s 343ms/step - loss: 0.0168 - accuracy: 0.9971 - val_loss: 0.2653 - val_accuracy: 0.9304\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 84s 343ms/step - loss: 0.0153 - accuracy: 0.9983 - val_loss: 0.5245 - val_accuracy: 0.8593\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 85s 344ms/step - loss: 0.0151 - accuracy: 0.9976 - val_loss: 1.0405 - val_accuracy: 0.7382\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 85s 344ms/step - loss: 0.0101 - accuracy: 0.9990 - val_loss: 0.3262 - val_accuracy: 0.9163\n"
     ]
    },
    {
     "data": {
      "image/png": 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JRKcOVLVeVKSoqEiwt7cX3nzzTXFfUFCQ0KlTJ51yv/zyiwBAmDNnTplraN+juLg4wcjISBg5cmSZ96T0+/jg+6/VsmVLnfdW+3d56qmnhJKSEp2y5dXTM2fOCACE7777Tty3bNkyAYCwe/fuCuMODw8XAAgHDx7UOd6xY0fB19e3zHnlxT1kyBCdfevWrRMACN9//724r6ioSOjZs6cgl8uFrKwsQRD++15QKBTC3bt3K71X6fuVlJQITk5OwqpVqwRBEISYmBgBgHDy5EnxvYuMjBTP69y5s9CkSRMhPT1d3BcdHS0YGRkJkydPFveNGDFCMDc31/m8x8TECFKpVOc7JDExUZBKpcI777yjE9+FCxcEY2Njnf0P1vHKmJmZCQDE78xPPvmk0nOKi4sFiUQizJ8/v8wx7XdqamqqsGDBAqF169bise7duwvPP/+8IAiaujlz5kzx2ObNmwUjIyPh1KlTOtfbuHGjAECIiIgQ9z1Yf8uTl5cntG3bVgAgtGzZUggODhY2bdok3Llzp0xZ7Xfs7NmzxX1qtVoYMmSIYGpqKqSmpgqCIAinTp0SAAhbtmzROV/7Handf//+fcHKykp48sknhfz8fJ2ypT+fQ4YMKfdvdfz4cQGA0KpVK53PX1FRkdCkSROhQ4cOOtf9+eefBQDCsmXLxH3e3t6Ci4uLkJ2dLe47ceKE+H5oPexzUdXveu01HB0dhfv374v7Fy9eLAAQOnXqJBQXF4v7AwMDBVNTU53/j2uZmpoKL7/8cpn9RFQz7F5ORCIzMzM8//zzZfZbWFiIj7Ozs5GWloY+ffogLy9Pp8tiRcaNGwdbW1vxubbV89q1a5WeO2DAAJ1ukx07doRCoRDPValUOHr0KEaMGIFmzZqJ5Vq3bo3BgwdXen1A9/Xl5uYiLS0NvXr1giAIOH/+fJnyL730ks7zPn366LyWAwcOwNjYWGz5BjRjqGfPnl2leADNOPzk5GT8+uuv4r6wsDCYmpriueeeE6+pHeunVquRkZGBkpISdOvWrdyu6Q9z9OhRsWWkdEtUeUtBmZmZieMZVSoV0tPTIZfL0bZt22rfV+vAgQOQSqWYM2eOzv758+dDEAQcPHhQZ39l9eJhDh48iPT0dAQGBor7AgMDER0drdOdfteuXZBIJFi+fHmZa2jfo71790KtVmPZsmVlxng+yljIF154ocyY+9L1tLi4GOnp6WjdujVsbGx03vddu3ahU6dOGDlyZIVxDxgwAM2aNdNZYurixYv4+++/K53roSIHDhyAk5OTzvtqYmKCOXPmICcnBydPntQpP3r06Cr3RtGSSqUYO3asODHYli1b4OrqKn6nlJaSkoKoqCgEBwfrtKJ37NgRAwcOxIEDBwBo6nB4eDhGjBiBFi1aiOWUSmWZLuu7d++GWq3G2LFjkZaWJm5OTk7w9PSs0tCOihw8eBAHDhzA2rVr0aJFC7GHw8NkZGRAEASd79fyBAUF4erVq4iMjBT/W1HX8p07d0KpVMLLy0vnNfbv3x8Aqv0aLSws8Mcff4i9SEJDQzFt2jQ4Oztj9uzZ5XZZLz2bunY4SVFREY4ePSrGaG1tjYEDB+rE6OPjA7lcLsZ45MgRZGdn44033igz0V51Pp9TpkzR+fz9+eefuHv3Ll555RWd6w4ZMgReXl7Yv38/AM1kkhcuXMDkyZN1Ws99fX3h7e1d7r3K+1xU97v+ueeeg7W1tfj8ySefBKD5/0rpuVOefPJJFBUVldsl3tbWFmlpaRW/KURULUy6iUjUvHnzcids+eeffzBy5EhYW1tDoVDA0dFR/Id5ZmZmpdct/Q9ZAOI/EMsbe1fZudrztefevXsX+fn5aN26dZly5e0rz/Xr18V/mGvHafv6+gIo+/q043origfQjL11dnYu00Wxbdu2VYoHAMaPHw+pVCqOOywoKMCePXswePBgnX9gf/vtt+jYsSPMzc1hb28PR0dH7N+/v0p/l9KSkpIAAJ6enjr7HR0dy/yDXq1W4+OPP4anpyfMzMzg4OAAR0dH/P3339W+b+n7N2vWDFZWVjr7tTPqa+PTqqxePMz3338Pd3d3mJmZ4erVq7h69So8PDwgk8l0ktD4+Hg0a9bsod2e4+PjYWRkVKuTigGAu7t7mX35+flYtmyZOOZd+77fv39f532Pj49Hhw4dHnp9IyMjTJgwAXv37kVeXh6A/2YA1/6oU11JSUnw9PQs8+NDRX/D8l5jVQQFBSEmJgbR0dEICwvD+PHjy02gtPcr73OnVCqRlpaG3NxcpKamIj8/v0zdL+/cuLg4CIIAT09PODo66myxsbG4e/dujV4TADz99NMYPHgwXnvtNezcuRMrV67Ehg0bqnSu8MB45wd16dIFXl5eCAsLw5YtW+Dk5CQm0Q+Ki4vDP//8U+b1tWnTBgBq9Bqtra3xwQcfIDExEYmJidi0aRPatm2LDRs2YNWqVTpljYyM0KpVK5192ntr5w6Ii4tDZmYmmjRpUibOnJwcMUbtePLKPg+VebCuPqxueXl5ice1/63O/58q+lxU57v+we9HbQLu6upa7v7yvjcFQeAkakS1iGO6iUhU+pd8rfv378PX1xcKhQJvvfUWPDw8YG5ujnPnzmHRokVQq9WVXreiWbIr+4fio55bFSqVCgMHDkRGRgYWLVoELy8vWFpa4ubNmwgODi7z+uprxu8mTZpg4MCB2LVrFz777DP89NNPyM7O1hlr+/333yM4OBgjRozA66+/jiZNmoiTYGn/sVkX3n33XSxduhRTp07FqlWrYGdnByMjI8ybN69K9aE21LReZGVl4aeffkJBQUG5SVZYWBjeeeedevvH5oMT8GmV91mcPXs2QkJCMG/ePPTs2RPW1taQSCQYP358jd73yZMnY82aNdi7dy8CAwMRFhaGoUOH6rSQ1aXyXmNVPPnkk/Dw8MC8efOQkJBQYYttXVCr1ZBIJDh48GC5dfDBH9pqysPDA126dMGWLVseuoa2nZ0dJBJJlX5sCgoKwueffw4rKyuMGzeuwtm31Wo1vL298dFHH5V7/MHErbpatmyJqVOnYuTIkWjVqhW2bNmCt99+u1rXUKvVaNKkic6PZKVVtwdFZWpaV2vrXtX9rq/o+7E635v379+Hg4NDNaMnooow6Saihzpx4gTS09Oxe/du9O3bV9yfkJCgx6j+06RJE5ibm+Pq1atljpW370EXLlzAlStX8O2332Ly5Mni/iNHjtQ4ppYtW+LYsWPIycnR+Ud4ddelnjBhAg4dOoSDBw8iLCwMCoUCw4YNE4//8MMPaNWqFXbv3q2TJJbXHboqMQOaFqTSrUypqall/kH/ww8/4Omnn8amTZt09j/4j7TqJK4tW7bE0aNHkZ2drdParR2+UFvrie/evRsFBQX4/PPPy/yD8vLly1iyZAkiIiLw1FNPwcPDA+Hh4cjIyKiwtdvDwwNqtRoxMTEPnbjO1ta2zEzURUVFSElJqXLsP/zwA6ZMmYK1a9eK+woKCspc18PDAxcvXqz0eh06dBATOxcXF1y/fh2ffvppleN5UMuWLfH3339DrVbrJHS1/TcENMMB3n77bSiVygrfd+39yvvcXbp0CQ4ODrC0tIS5uTksLCwQFxdXptyD53p4eEAQBLi7u4utr3UlPz+/0tnCjY2N4eHhUaXv46CgICxbtgwpKSnYvHlzheU8PDwQHR2NZ555pk5/fLK1tS23rqrValy7dk3n/dWu5a2d+M7DwwNHjx5F7969H5oQa4egXLx48aE9n6r7OkvXrQd7DFy+fFk8rv1vTf//pFWb3/VVcfPmTRQVFYm9VIjo0bF7ORE9lPaX8dK/hBcVFZW7tqw+SKVSDBgwAHv37sWtW7fE/VevXi0zDrii8wHd1ycIAtavX1/jmAICAlBSUoLPP/9c3KdSqaqd0IwYMQIymQz/+9//cPDgQYwaNUpn/GB5sf/xxx84c+ZMtWMeMGAATExM8Omnn+pcb926dWXKSqXSMi0jO3fuLDMuULu2dFWWPQoICIBKpSrTnfbjjz+GRCKp8vj8ynz//fdo1aoVXnrpJYwZM0ZnW7BgAeRyudh6Nnr0aAiCgJUrV5a5jvb1jxgxAkZGRnjrrbfKtDaXfo88PDx0xucDwJdffllhS3d5ynvfP/300zLXGD16NKKjo7Fnz54K49aaNGkSDh8+jHXr1sHe3v6R3ueAgADcvn0b27dvF/eVlJTg008/hVwuF4ds1Ibp06dj+fLlOj9APMjZ2RmdO3fGt99+q1MHL168iMOHDyMgIACA5n318/PD3r17cf36dbFcbGwswsPDda45atQoSKVSrFy5ssx7KQgC0tPTq/U6SkpKym2lPnv2LC5cuCDOkP8wPXv2LLOEXXk8PDywbt06rF69Gk888USF5caOHYubN2/iq6++KnMsPz+/SmPNS4uOji53bHBSUhJiYmLK7aJd+ntAEARs2LABJiYm4mzuY8eOhUqlKtM1HdC8p9q/96BBg2BlZYXVq1ejoKBAp1zpv5+lpWW1hsZ069YNTZo0wcaNG3V+GDl48CBiY2PFWeGbNWuGDh064LvvvkNOTo5Y7uTJk7hw4UKV71eb3/VV8ddffwGoeLUJIqo+tnQT0UP16tULtra2mDJlCubMmQOJRILNmzfXWvfu2rBixQocPnwYvXv3xssvvywmbx06dKh0nVEvLy94eHhgwYIFuHnzJhQKBXbt2lWl7poVGTZsGHr37o033ngDiYmJaNeuHXbv3l3t8c5yuRwjRowQx3U/uIzT0KFDsXv3bowcORJDhgxBQkICNm7ciHbt2un8A68qtOuNr169GkOHDkVAQADOnz+PgwcPlmkRHjp0KN566y08//zz6NWrFy5cuIAtW7aUGYfp4eEBGxsbbNy4EVZWVrC0tMSTTz5Z7pjFYcOG4emnn8abb76JxMREdOrUCYcPH8aPP/6IefPmVbgGcXXcunULx48fLzNZm5aZmRn8/Pywc+dOfPLJJ3j66acxadIkfPLJJ4iLi4O/vz/UajVOnTqFp59+GrNmzULr1q3x5ptvYtWqVejTpw9GjRoFMzMzREZGolmzZuJ619OnT8dLL72E0aNHY+DAgYiOjkZ4eHi1um8OHToUmzdvhrW1Ndq1a4czZ87g6NGjZZZIe/311/HDDz/gueeew9SpU+Hj44OMjAzs27cPGzduRKdOncSyQUFBWLhwIfbs2YOXX3650iX8HmbGjBn44osvEBwcjL/++gtubm744YcfEBERgXXr1pUZr/8oWrZsWe665w9as2YNBg8ejJ49e2LatGnikmHW1tY6569cuRKHDh1Cnz598Morr4g/FrRv3x5///23WM7DwwNvv/02Fi9ejMTERIwYMQJWVlZISEjAnj17MGPGDCxYsKDKryMnJweurq4YN24c2rdvD0tLS1y4cAEhISGwtrbG0qVLK73G8OHDsXnzZly5cqXS1vfy1mZ/0KRJk7Bjxw689NJLOH78OHr37g2VSoVLly5hx44dCA8Pr9KPAVpHjhzB8uXL8eyzz6JHjx7iOtzffPMNCgsLy/wdzc3NcejQIUyZMgVPPvkkDh48iP379+P//u//xG7jvr6+ePHFF7F69WpERUVh0KBBMDExQVxcHHbu3In169djzJgxUCgU+PjjjzF9+nR0794dQUFBsLW1RXR0NPLy8sQ15H18fLB9+3a89tpr6N69O+RyuU6vogeZmJjg/fffx/PPPw9fX18EBgaKS4a5ubnh1VdfFcu+++67GD58OHr37o3nn38e9+7dE///VNXv6dr8rq+KI0eOoEWLFlwujKg21c8k6URkSCpaMqx9+/bllo+IiBB69OghWFhYCM2aNRMWLlwoLjl0/PhxsVxFS4atWbOmzDXxwBJKFS0ZVnopG63ylqk5duyY0KVLF8HU1FTw8PAQvv76a2H+/PmCubl5Be/Cf2JiYoQBAwYIcrlccHBwEF544QVxCarSy11NmTJFsLS0LHN+ebGnp6cLkyZNEhQKhWBtbS1MmjRJOH/+fJWXDNPav3+/AEBwdnYud0mqd999V2jZsqVgZmYmdOnSRfj555/LXarowff7wSXDBEEQVCqVsHLlSsHZ2VmwsLAQ+vXrJ1y8eLHM+11QUCDMnz9fLNe7d2/hzJkzgq+vb5nlpn788UehXbt24vJt2tdeXozZ2dnCq6++KjRr1kwwMTERPD09hTVr1ugs7aN9LVWtF6WtXbtWACAcO3aswjKhoaECAOHHH38UBEGzVM+aNWsELy8vwdTUVHB0dBQGDx4s/PXXXzrnffPNN0KXLl0EMzMzwdbWVvD19RWOHDkiHlepVMKiRYsEBwcHQSaTCX5+fsLVq1crXDKs9LJXWvfu3ROef/55wcHBQZDL5YKfn59w6dKlcl93enq6MGvWLKF58+aCqamp4OLiIkyZMkVIS0src92AgAABgHD69OkK35cHlbdkmCAIwp07d8QYTU1NBW9v7zL1/WHfC9W9X2kVvXdHjx4VevfuLVhYWAgKhUIYNmyYEBMTU+b8kydPCj4+PoKpqanQqlUrYePGjeV+tgVBEHbt2iU89dRTgqWlpWBpaSl4eXkJM2fOFC5fviyWqcqSYYWFhcLcuXOFjh07CgqFQjAxMRFatmwpTJs2TeezWdk1HBwcxGXUtEovGfYw5X2eioqKhPfff19o3769WKd9fHyElStXCpmZmWK5qiwZdu3aNWHZsmVCjx49hCZNmgjGxsaCo6OjMGTIEOGXX37RKav9jo2PjxcGDRokyGQyoWnTpsLy5cvLfP8JgiB8+eWXgo+Pj2BhYSFYWVkJ3t7ewsKFC4Vbt27plNu3b5/Qq1cvsQ488cQTwtatW8XjOTk5QlBQkGBjY6OzlJd2ybCdO3eW+9q2b98ufu7t7OyECRMmCMnJyWXKbdu2TfDy8hLMzMyEDh06CPv27RNGjx4teHl5iWUe9rmo6nd9Rdeo6HWU95lRqVSCs7OzsGTJknJfMxHVjEQQDKi5ioioFo0YMQL//PNPuWM1iUhj5MiRuHDhQrXGmJJhWbVqFUJCQhAXF1dvkz3WheDgYPzwww910npraDp37gxHR8dHmj+kLuzduxdBQUGIj4+Hs7OzvsMhajQ4ppuIGoX8/Hyd53FxcThw4AD69eunn4CIGoCUlBTs378fkyZN0nco9AheffVV5OTkYNu2bfoOhR5QXFyMkpISnX0nTpxAdHS0Qf7/6f3338esWbOYcBPVMo7pJqJGoVWrVggODkarVq2QlJSEzz//HKampli4cKG+QyMyOAkJCYiIiMDXX38NExMTvPjii/oOiR6BXC5/pDXCqe7cvHkTAwYMwMSJE9GsWTNcunQJGzduhJOTE1566SV9h1dGXU3ORvS4Y9JNRI2Cv78/tm7ditu3b8PMzAw9e/bEu+++W+5azESPu5MnT+L5559HixYt8O2338LJyUnfIRE1Sra2tvDx8cHXX3+N1NRUWFpaYsiQIXjvvffKTIJIRI0Xx3QTERERERER1RGO6SYiIiIiIiKqI0y6iYiIiIiIiOpIgx7TrVarcevWLVhZWUEikeg7HCIiIiIiInpMCIKA7OxsNGvWDEZGFbdnN+ik+9atW3B1ddV3GERERERERPSYunHjBlxcXCo83qCTbisrKwCaF6lQKPQczcMVFxfj8OHDGDRoEExMTPQdDjUArDNUXawzVF2sM1RdrDMVyM0FPDw0j+PjAUtL/cZjQFhnqCYaSr3JysqCq6urmJdWpEEn3dou5QqFokEk3TKZDAqFwqArDhkO1hmqLtYZqi7WGaou1pkKSKVAfr7msULBpLsU1hmqiYZWbyob6syJ1IiIiIiIiIjqCJNuIiIiIiIiojrCpJuIiIiIiIiojjToMd1ERERERFR1KpUKxcXF9Xa/4uJiGBsbo6CgACqVqt7uSw2bodQbExMTSKXSR74Ok24iIiIiokZOEATcvn0b9+/fr/f7Ojk54caNG5VONkWkZUj1xsbGBk5OTo8UB5NuIiIiIqJHYWQE+Pr+99gAaRPuJk2aQCaT1Vsio1arkZOTA7lcDiMDfW/I8BhCvREEAXl5ebh79y4AwNnZucbXYtJNRERERPQoLCyAEyf0HUWFVCqVmHDb29vX673VajWKiopgbm7OpJuqzFDqjYWFBQDg7t27aNKkSY27mrPmExERERE1Ytox3DKZTM+REDU82s/No8yFwKSbiIiIiOgxoO+xsUQNUW18bph0ExERERE9itxcwNFRs+Xm6jsaIjIwTLqJiIiIiB5VWppmI4Pm5uaGdevWVbn8iRMnIJFI6n3Wd2pcmHQTEREREVGlVCrNfHFbt2r+W5fLJ0skkoduK1asqNF1IyMjMWPGjCqX79WrF1JSUmBtbV2j+9WEl5cXzMzMcPv27Xq7J9UtJt1ERERERPRQu3cDbm7A008DQUGa/7q5afbXhZSUFHFbt24dFAqFzr4FCxaIZQVBQElJSZWu6+joWK0J5UxNTR95jebq+O2335Cfn48xY8bg22+/rZd7PsyjTB5G/2HSTUREREREFdq9GxgzBkhO1t1/86Zmf10k3k5OTuJmbW0NiUQiPr906RKsrKxw8OBB+Pj4wMzMDL/99hvi4+MxfPhwNG3aFHK5HN27d8fRo0d1rvtg93KJRIKvv/4aI0eOhEwmg6enJ/bt2ycef7B7eWhoKGxsbBAeHg6lUgm5XA5/f3+kpKSI55SUlGDOnDmwsbGBvb09Fi1ahClTpmDEiBGVvu5NmzYhKCgIkyZNwjfffFPmeHJyMgIDA2FnZwdLS0t069YNf/zxh3j8p59+Qvfu3WFubg4HBweMHDlS57Xu3btX53o2NjYIDQ0FACQmJkIikWD79u3w9fWFubk5tmzZgvT0dAQGBqJ58+aQyWTw9vbG1q1bda6jVqvxwQcfoHXr1jAzM0OLFi3wzjvvAAD69++PWbNm6ZRPTU2Fqakpjh07Vul70hgw6SYiIiIieowIgma+t6psWVnAnDmac8q7DgDMnaspV5XrlXedmnrjjTfw3nvvITY2Fh07dkROTg4CAgJw7NgxnD9/Hv7+/hg2bBiuX7/+0OusXLkSY8eOxd9//42AgABMmDABGRkZFZbPy8vDhx9+iM2bN+PXX3/F9evXdVre33//fWzZsgUhISGIiIhAVlZWmWS3PNnZ2di5cycmTpyIgQMHIjMzE6dOnRKP5+TkwNfXFzdv3sS+ffsQHR2NhQsXQq1WAwD279+PkSNHIiAgAOfPn8exY8fwxBNPVHrfB73xxhuYO3cuYmNj4efnh4KCAvj4+GD//v24ePEiZsyYgUmTJuHs2bPiOYsXL8Z7772HpUuXIiYmBmFhYWjatCkAYPr06QgLC0NhYaFY/vvvv0fz5s3Rv3//asfXEBnrOwAiIiIiIqo/eXmAXF471xIETQt4xUOejQDYiM9ycgBLy9q591tvvYWBAweKz+3s7NCpUyfx+apVq7Bnzx7s27evTEtracHBwQgMDAQAvPvuu/jkk09w9uxZ+Pv7l1u+uLgYGzduhIeHBwBg1qxZeOutt8Tjn376KRYvXiy2Mm/YsAEHDhyo9PVs27YNnp6eaN++PQBg/Pjx2LRpE/r06QMACAsLQ2pqKiIjI2FnZwcAaN26tXj+O++8g/Hjx2PlypXivtLvR1XNmzcPo0aN0tlX+keF2bNnIzw8HDt27MATTzyB7OxsrF+/Hhs2bMCUKVMAAB4eHnjqqacAAKNGjcKsWbPw448/YuzYsQA0PQaCg4Mfm2Xs2NJNRERERPQojIyAbt00mxH/eV1funXrpvM8JycHCxYsgFKphI2NDeRyOWJjYytt6e7YsaP42NLSEgqFAnfv3q2wvEwmExNuAHB2dhbLZ2Zm4s6dOzotzFKpFD4+PpW+nm+++QYTJ04Un0+cOBE7d+5EdnY2ACAqKgpdunQRE+4HRUVF4Zlnnqn0PpV58H1VqVRYtWoVvL29YWdnB7lcjvDwcPF9jY2NRWFhYYX3Njc31+kuf+7cOVy8eBHBwcGPHGtDwZZuIiIiIqJHYWEBREbqO4oqk8k0Lc5V8euvQEBA5eUOHAD69i27X61WIysrCwqFAkZGRqjGHGaVsnygyXzBggU4cuQIPvzwQ7Ru3RoWFhYYM2YMioqKHnodExMTnecSiUTssl3V8sIj9puPiYnB77//jrNnz2LRokXifpVKhW3btuGFF16AhYXFQ69R2fHy4ixvorQH39c1a9Zg/fr1WLduHby9vWFpaYl58+aJ72tl9wU0Xcw7d+6M5ORkhISEoH///mjZsmWl5zUW/CmOiIiIiOgxIpFounhXZRs0CHBx0ZxT0bVcXTXlqnK9uuxNHBERgeDgYIwcORLe3t5wcnJCYmJi3d2wHNbW1mjatCkiS/0Io1KpcO7cuYeet2nTJvTt2xfR0dGIiooSt9deew2bNm0CoGmRj4qKqnC8eceOHR86MZmjo6POhG9xcXHIy8ur9DVFRERg+PDhmDhxIjp16oRWrVrhypUr4nFPT09YWFg89N7e3t7o1q0bvvrqK4SFhWHq1KmV3rcxYdJNRERERETlkkqB9es1jx9MmLXP163TlNM3T09P7N69G1FRUYiOjkZQUNBDW6zryuzZs7F69Wr8+OOPuHz5MubOnYt79+5VOH65uLgYmzdvRmBgIDp06KCzTZ8+HX/88Qf++ecfBAYGwsnJCSNGjEBERASuXbuGXbt24cyZMwCA5cuXY+vWrVi+fDliY2Nx4cIFvP/+++J9+vfvjw0bNuD8+fP4888/8dJLL5VptS+Pp6cnjhw5gtOnTyM2NhYvvvgi7ty5Ix43NzfHokWLsHDhQnz33XeIj4/H77//Lv5YoDV9+nS89957EARBZ1b1xwGT7nqgUqtwMukkfr33K04mnYRKrdJ3SERERERUW/LyNItWu7lpHjcyo0YBP/wANG+uu9/FRbP/gTm39Oajjz6Cra0tevXqhWHDhsHPzw9du3at9zgWLVqEwMBATJ48GT179oRcLoefnx/Mzc3LLb9v3z6kp6eXm4gqlUoolUps2rQJpqamOHz4MJo0aYKAgAB4e3vjvffeg/TfXzz69euHnTt3Yt++fejcuTP69++vM8P42rVr4erqij59+iAoKAgLFiyo0prlS5YsQdeuXeHn54d+/fqJiX9pS5cuxfz587Fs2TIolUqMGzeuzLj4wMBAGBsbIzAwsML3orGSCI86AEGPsrKyYG1tjczMTCgUCn2HU67dsbsx99BcJGf9t7Chi8IF6/3XY5TSQL6hyCAVFxfjwIEDCAgIqNKvkESsM1RdrDNUXawzFcjN/W868NqcnruWFBQUICEhAe7u7o+U7KhUwKlTQEoK4OwM9OlTeQv3g2O6H0dqtRpKpRJjx47FqlWr9B2O3iQmJsLDwwORkZGV/hhiSPXmYZ+fquajnEitDu2O3Y0xO8ZAgO7vGjezbmLMjjH4YewPTLyJiIiIqEGQSoF+/fQdheFLSkrC4cOH4evri8LCQmzYsAEJCQkICgrSd2h6UVxcjPT0dCxZsgQ9evTQS+8DfXs8f26qByq1CnMPzS2TcAMQ9807NI9dzYmIiIiIGhEjIyOEhoaie/fu6N27Ny5cuICjR49CqVTqOzS9iIiIgLOzMyIjI7Fx40Z9h6MXem3pXrFihc7i7QDQtm1bXLp0SU8R1Z5T10/pdCl/kAABN7Ju4NT1U+jn1q/+AiMiIiIiojrj6uqKiIgIfYdhMPr16/fIS6o1dHrvXt6+fXscPXpUfG5srPeQakVKdkrlhapRjoiIiIiIiBoevWe4xsbGcHJy0ncYtc7ZyrlWyxEREREREVHDo/ekOy4uDs2aNYO5uTl69uyJ1atXo0WLFuWWLSwsRGFhofg8KysLgGZwfnFxcb3EW1U9nHuguVVz3Mq+Ve64bgkkaK5ojh7OPQwudjIM2nrB+kFVxTpD1cU6Q9XFOlOBkhIY/ztet6SkBDCw96e4uBiCIECtVtf7utXabsXa+xNVhSHVG7VaDUEQUFxcLC7PplXV70K9Lhl28OBB5OTkoG3btkhJScHKlStx8+ZNXLx4EVZWVmXKlzcGHADCwsKqtMZcfTtz/wzeT3y/wuOL3Bahp03PeoyIiIiIiB432p6lrq6uMDU11Xc4RA1KUVERbty4gdu3b2t+VCslLy8PQUFBlS4ZZlDrdN+/fx8tW7bERx99hGnTppU5Xl5Lt6urK9LS0gx2ne49l/bgtSOv4Wb2TXGfi8IFawesxUivkXqMjAxdcXExjhw5goEDB3ItVKoS1hmqLtYZqi7WmYapoKAAN27cgJub2yOt010TgiAgOzsbVlZWkEgk9XpvargMqd4UFBQgMTERrq6u5a7T7eDg0LDW6baxsUGbNm1w9erVco+bmZnBzMyszH4TExOD/eIf6z0Wo9uPRsCWABy+dhiTvSfjmxHfQGokrfxkIhh2/SbDxDpD1cU6Q9XFOtOwqFQqSCQSGBkZwcioflcM1nYN1t6fqCoMqd4YGRlBIpGU+71X1e9Bg6r5OTk5iI+Ph7Nz45pcTGokRb+W/QAA+SX5TLiJiIiIGpO8PKB9e82Wl6fvaKiUfv36Yd68eeJzNzc3rFu37qHnSCQS7N2795HvXVvXoYZPr0n3ggULcPLkSSQmJuL06dMYOXIkpFIpAgMD9RlWnVA6aCbXuJTe8NcgJyIiIqJSBAGIidFshjNys9apAJwAsPXf/6rq8F7Dhg2Dv79/ucdOnToFiUSCv//+u9rXjYyMxIwZMx41PB0rVqxA586dy+xPSUnB4MGDa/VeFcnPz4ednR0cHBx0huOSYdBr0p2cnIzAwEC0bdsWY8eOhb29PX7//Xc4OjrqM6w6oU26r6RfQYm6pJLSRERERESGYzcANwBPAwj6979u/+6vC9OmTcORI0eQnJxc5lhISAi6deuGjh07Vvu6jo6O9TYBs5OTU7lDY+vCrl270L59e3h5eem9dV0QhDITjj3u9Jp0b9u2Dbdu3UJhYSGSk5Oxbds2eHh46DOkOtPSuiVMJaYoVBUi4V6CvsMhIiIiIqqS3QDGAHgw/b357/66SLyHDh0KR0dHhIaG6uzPycnBzp07MW3aNKSnpyMwMBDNmzeHTCaDt7c3tm7d+tDrPti9PC4uDn379oW5uTnatWuHI0eOlDln0aJFaNOmDWQyGVq1aoWlS5eKS0WFhoZi5cqViI6OhkQigUQiEWN+sHv5hQsX0L9/f1hYWMDe3h4zZsxATk6OeDw4OBgjRozAhx9+CGdnZ9jb22PmzJlVWpZq06ZNmDhxIiZOnIhNmzaVOf7PP/9g6NChUCgUsLKyQp8+fRAfHy8e/+abb9C+fXuYmZnB2dkZs2bNAgAkJiZCIpEgKipKLHv//n1IJBKcOHECAHDixAlIJBIcPHgQPj4+MDMzw2+//Yb4+HgMHz4cTZs2hVwuR/fu3XH06FGduAoLC7Fo0SK4urrCzMwMrVu3xqZNmyAIArp27Yq1a9fqlI+KioJEIqlwDjBDZVBjuhsrtUqN5FPJeOrSU3BLcMM/t//Rd0hERERE9JgSAORWccsCMOffc8q7DgDM/bdcVa5X1c73xsbGmDx5MkJDQ1F6saWdO3dCpVIhMDAQBQUF8PHxwf79+3Hx4kXMmDEDkyZNwtmzZ6t0D7VajVGjRsHU1BR//PEHNm7ciEWLFpUpZ2VlhdDQUMTExGD9+vX46quv8PHHHwMAxo0bh/nz56N9+/ZISUlBSkoKxo0bV+Yaubm58PPzg62tLSIjI7Fz504cPXpUTG61jh8/jvj4eBw/fhzffvstQkNDy/zw8KD4+HicOXMGY8eOxdixY3Hq1CkkJSWJx2/evIm+ffvCzMwMv/zyC/766y9MnTpVbI3+/PPPMXPmTMyYMQMXLlzAvn370Lp16yq9h6W98cYbeO+99xAbG4uOHTsiJycHAQEBOHbsGM6fPw9/f38MGzYM169fF8+ZPHkytm7dik8++QSxsbH44osvIJfLIZFIMGHChDKvPSQkBH379q1RfHolNGCZmZkCACEzM1PfoVQoZleM8JHLR8IKrPhvc1whxOyK0XdoZOCKioqEvXv3CkVFRfoOhRoI1hmqLtYZqi7WmQrk5AiCZjS35rGByc/PF2JiYoT8/HxBEAQhRxAE6GmrzrsTGxsrABCOHz8u7uvTp48wceLECs8ZMmSIMH/+fPG5r6+vMHfuXPF5y5YthY8//lgQBEEIDw8XjI2NhZs3b4rHDx48KAAQ9uzZU+E91qxZI/j4+IjPly9fLnTq1KlMudLX+fLLLwVbW1shp1T92L9/v2BkZCTcvn1bEARBmDJlitCyZUuhpKRELPPcc88J48aNqzAWQRCE//u//xNGjBghPh8+fLiwfPly8fnixYsFd3f3Cj+3zZo1E958881yjyUkJAgAhPPnz4v77t27p/N3OX78uABA2Lt370PjFARBaN++vfDpp58KgiAIly9fFgAIR44cKVNOpVIJMTExglQqFf744w9BEDTfPw4ODkJoaGil96lND35+SqtqPsqW7joUuzsWO8bsQFZyls5+IVXAjjE7ELs7Vk+REREREREZNi8vL/Tq1QvffPMNAODq1as4deoUpk2bBkCzFNqqVavg7e0NOzs7yOVyhIeH67SkPkxsbCxcXV3RrFkzcV/Pnj3LlNu+fTt69+4NJycnyOVyLFmypMr3KH2vTp06wdLSUtzXu3dvqNVqXL58WdzXvn17SKX/rXTk7OyMu3fvVnhdlUqFb7/9FhMnThT3TZw4EaGhoeKyW1FRUejTp0+5y1vdvXsXt27dwjPPPFOt11Oebt266TzPycnBggULoFQqYWNjA7lcjtjYWPG9i4qKglQqha+vb7nXc3Z2RkBAgPj3/+mnn1BYWIjnnnvukWOtb0y664hapcahuYfK7UMjgWaB90PzDkGtUtdzZERERERUqyQSoGVLzSaR6DuaSskA5FRxO1DFax6o4PwstRrJ9+8jS61Gzr/3ro5p06Zh165dyM7ORkhICDw8PMQkbc2aNVi/fj0WLVqE48ePIyoqCn5+figqKqrmXSp25swZTJgwAQEBAfj5559x/vx5vPnmm7V6j9IeTIwlEomYPJcnPDwcN2/exLhx42BsbAxjY2OMHz8eSUlJOHbsGADAwsKiwvMfdgyAuEa2UKqLf0VjzEv/oABoVqras2cP3n33XZw6dQpRUVHw9vYW37vK7g1o/v7btm1Dfn4+QkJCMG7cuHqbCK82MemuI9dPXS/Twq1DALJuZOH6qer9SkZEREREBkYmAxITNVsDSAgkACyruA0C4PLvORVdy/XfclW5XnV/khg7diyMjIwQFhaG7777DlOnToXk3x82IiIiMHz4cEycOBGdOnVCq1atcOXKlSpfW6lU4saNG0hJSRH3/f777zplTp8+jZYtW+LNN99Et27d4OnpqTNeGgBMTU2hUj18ATWlUono6Gjk5uaK+yIiImBkZIS2bdtWOeYHbdq0CePHj0dUVJTONn78eHFCtY4dO+LUqVPlJstWVlZwc3MTE/QHaVeVKv0elZ5U7WEiIiIQHByMkSNHwtvbG05OTkhMTBSPe3t7Q61W4+TJkxVeIyAgAJaWlvj8889x6NAhTJ06tUr3NjRMuutIdkp2rZYjIiIiIqpvUgDr/338YMKsfb7u33J1QS6XY9y4cVi8eDFSUlIQHBwsHvP09MSRI0dw+vRpxMbG4sUXX8SdO3eqfO0BAwagTZs2mDJlCqKjo3Hq1Cm8+eabOmU8PT1x/fp1bNu2DfHx8fjkk0+wZ88enTJubm5ISEhAVFQU0tLSyl0ne8KECTA3N8eUKVNw8eJFHD9+HLNnz8akSZPQtGnT6r0p/0pNTcVPP/2EKVOmoEOHDjrb5MmTsXfvXmRkZGDWrFnIysrC+PHj8eeffyIuLg6bN28Wu7WvWLECa9euxSeffIK4uDicO3cOn376KQBNa3SPHj3ECdJOnjyJJUuWVCk+T09P7N69G1FRUYiOjkZQUJBOq72bmxumTJmCqVOnYu/evUhISMCJEyewY8cOsYxUKkVwcDAWL14MT0/Pcrv/NwRMuuuIlbNVrZYjIiIiItKHUQB+AND8gf0u/+4fVcf3nzZtGu7duwc/Pz+d8ddLlixB165d4efnh379+sHJyQkjRoyo8nWNjIywZ88e5Ofn44knnsD06dPxzjvv6JR59tln8eqrr2LWrFno3LkzTp8+jaVLl+qUGT16NPz9/fH000/D0dGx3GXLZDIZwsPDkZGRge7du2PMmDF45plnsGHDhuq9GaV89913sLS0LHc89jPPPAMLCwt8//33sLe3xy+//IKcnBz4+vrCx8cHX331ldiVfcqUKVi3bh3+97//oX379hg6dCji4uLEa33zzTcoKSmBj48P5s2bh7fffrtK8X300UewtbVFr169MGzYMPj5+aFr1646ZT7//HOMGTMGr7zyCry8vPDCCy/o9AYANH//oqIiPP/889V9iwyGRCjdQb+BycrKgrW1NTIzM6FQKPQdjg61So31buuRdTOrwrURFK4KzE2YCyMpf/ugsoqLi3HgwAEEBASUO/EF0YNYZ6i6WGeoulhnKpCfD/Ttq3n8669AFcaq1qeCggIkJCTA3d0d5ubmNb6OCsApACkAnAH0QeUt3Gq1GllZWVAoFOL4YKLKlK43EREReOaZZ3Djxo0a9wp4FA/7/FQ1HzWu6yAfV0ZSI/iv98eOMTs0fW9KJd7Cv0/81/kz4SYiIiJq6NRq4M8//3vcSEkB9NN3EPTYKCwsRHJyMlasWIHnnntOLwl3bWHGV4eUo5QY+8NYKJrr/uqRpchC1MtRUI5S6ikyIiIiIiIiw7Vr1y64u7vj/v37+OCDD/QdziNh0l3HlKOUmJs4Fy69XQAALae0xLp563CixQk04J79REREREREdSYoKAjFxcX466+/0Lz5gzMKNCxMuuuBkdQILZ5qAQCwNraGRCpBZmEmbufc1nNkREREREREVJeYdNcTBy8HAMC9K/fgYesBAIhJjdFnSERERERERFTHmHTXE3svewBA+qV0tHNsB4BJNxERERERUWPHpLue2LfVJN15qXlQmmomUGPSTURERNRIODhoNiKiB3DJsHpiKjeFiYMJitOK0SqrFQAgNi1Wz1ERERER0SOztARSU/UdBREZKLZ01yNzF81i6vapmlZvtnQTERERERE1bky665GZixkAwDTZFACQmpeKtLw0fYZERERERPTYcHNzw7p166pc/sSJE5BIJLh//36dxUSNH5PuemTuqmnpzrySiZbWLQEAsansYk5ERETUoOXnA/36abb8fH1HU2dUahVOJJ7A1gtbcSLxBFRqVZ3dSyKRPHRbsWJFja4bGRmJGTNmVLl8r169kJKSAmtr6xrdr6qY3DduHNNdj7Tdy1NjU9FuYjskZSYhJjUGfVr20XNkRERERFRjajVw8uR/jxuh3bG7MffQXCRnJYv7XBQuWO+/HqOUo2r9fikpKeLj7du3Y9myZbh8+bK4Ty6Xi48FQYBKpYKxceWpjaOjY7XiMDU1hZOTU7XOIXoQW7rrkTbpzkzKRDsrLhtGRERERIZvd+xujNkxRifhBoCbWTcxZscY7I7dXev3dHJyEjdra2tIJBLx+aVLl2BlZYWDBw/Cx8cHZmZm+O233xAfH4/hw4ejadOmkMvl6N69O44ePapz3Qe7l0skEnz99dcYOXIkZDIZPD09sW/fPvH4gy3QoaGhsLGxQXh4OJRKJeRyOfz9/XV+JCgpKcGcOXNgY2MDe3t7LFq0CFOmTMGIESNq/H7cu3cPkydPhq2tLWQyGQYPHoy4uDjxeFJSEoYNGwZbW1tYWlqiffv2OHDggHjuhAkT4OjoCAsLC3h6eiIkJKTGsVD1MemuR8bWxrCwtwAAeGR7AABi0ph0ExEREVH9EQQBuUW5VdqyCrIw5+AcCBDKXufffXMPzkVWQVbF1yn+77EglL1OTb3xxht47733EBsbi44dOyInJwcBAQE4duwYzp8/D39/fwwbNgzXr19/6HVWrlyJsWPH4u+//0ZAQAAmTJiAjIyMCsvn5eXhww8/xObNm/Hrr7/i+vXrWLBggXj8/fffx5YtWxASEoKIiAhkZWVh7969j/Rag4OD8eeff2Lfvn04c+YMBEFAQEAAiouLAQAzZ85EYWEhfv31V1y4cAHvv/++2Btg6dKliImJwcGDBxEbG4vPP/8cDlzerl6xe3k9s/eyR3JEMpqmNwXAMd1EREREVL/yivMgXy2vvGAVCBCQnJ0M6/erNuY5Z3EOLE0ta+Xeb731FgYOHCg+t7OzQ6dOncTnq1atwp49e7Bv3z7MmjWrwusEBwcjMDAQAPDuu+/ik08+wdmzZ+Hv719u+eLiYmzcuBEeHppGtFmzZuGtt94Sj3/66adYvHgxRo4cCQDYsGGD2OpcE3Fxcdi3bx8iIiLQq1cvAMCWLVvg6uqKvXv34rnnnsP169cxevRoeHt7AwBatWolnn/9+nV06dIF3bp1A6Bp7af6xZbueubgpflVyfyWpqv5zeybyCzI1GdIREREREQNjjaJ1MrJycGCBQugVCphY2MDuVyO2NjYSlu6O3bsKD62tLSEQqHA3bt3Kywvk8nEhBsAnJ2dxfKZmZm4c+cOnnjiCfG4VCqFj49PtV5babGxsTA2NsaTTz4p7rO3t0fbtm0RG6tpwJszZw7efvtt9O7dG8uXL8fff/8tln355Zexbds2dO7cGQsXLsTp06drHAvVDFu665k26c6Jy4FzL2ek5KQgNi0WPVx66DkyIiIiInocyExkyFmcU6Wyvyb9ioCwgErLHQg6gL4t+5bZr1arkZWdBYWVAkZGRpCZyKodb0UsLXVbzBcsWIAjR47gww8/ROvWrWFhYYExY8agqKjoodcxMTHReS6RSKB+yIR45ZWvzW7zNTF9+nT4+flh//79OHz4MFavXo21a9di9uzZGDx4MJKSknDgwAEcOXIEzzzzDGbOnIkPP/xQrzE/TtjSXc/svewB/DuDuaNmMjV2MSciIiJq4GQyzdYASCQSWJpaVmkb5DEILgoXSCAp/1qQwFXhikEegyq+jsl/jyWS8q9TGyIiIhAcHIyRI0fC29sbTk5OSExMrLP7lcfa2hpNmzZFZGSkuE+lUuHcuXM1vqZSqURJSQn++OMPcV96ejouX76Mdu3aiftcXV3x0ksvYffu3Zg/fz6++uor8ZijoyOmTJmC77//HuvWrcOXX35Z43io+tjSXc+0Ld0ZcRloZ9sOxxKOcQZzIiIioobM0hLIzdV3FHVCaiTFev/1GLNjDCSQ6Eyopk3E1/mvg9RIqq8QRZ6enti9ezeGDRsGiUSCpUuXPrTFuq7Mnj0bq1evRuvWreHl5YVPP/0U9+7dq9IPDhcuXICVlZX4XCKRoFOnThg+fDheeOEFfPHFF7CyssIbb7yB5s2bY/jw4QCAefPmYfDgwWjTpg3u3buH48ePQ6lUAgCWLVsGHx8ftG/fHoWFhfj555/FY1Q/mHTXM4WrAiYyExTnFaNNYRsAnMGciIiIiAzXKOUo/DD2h3LX6V7nv65O1umuiY8++ghTp05Fr1694ODggEWLFiErK6ve41i0aBFu376NyZMnQyqVYsaMGfDz84NUWvkPE3376nbRl0qlKCkpQUhICObOnYuhQ4eiqKgIffv2xYEDB8Su7iqVCjNnzkRycjIUCgX8/f3x8ccfA9CsNb548WIkJibCwsICffr0wbZt22r/hVOFJIK+ByA8gqysLFhbWyMzMxMKhULf4TxUcXExDhw4gICAAIT0CEHKuRS039gez91+Dm42bkiYm6DvEMnAlK4zD44dIioP6wxVF+sMVRfrTMNUUFCAhIQEuLu7w9zcvMbXUalVOHX9FFKyU+Bs5Yw+LfpU2sKtVquRlZUFhUIzpvtxpFaroVQqMXbsWKxatUrf4TQIhlRvHvb5qWo+ypZuPXBQOiDlXArkKXJAAiTdT0JuUW6tLZ9ARERERPWooAAYPVrzeNcu4BESW0MmNZKin1s/fYdh8JKSknD48GH4+vqisLAQGzZsQEJCAoKCgvQdGunJ4/lzk545KDXjuvPj8+Egc4AAAZfTL+s5KiIiIiKqEZUKOHBAs6lU+o6G9MzIyAihoaHo3r07evfujQsXLuDo0aMcR/0YY0u3HjgqHQFoZjBX9lfi1PVTiEmNQVfnrnqOjIiIiIiIHoWrqysiIiL0HQYZELZ064G2pTvtUhra2XPZMCIiIiIiosaKSbce2LW2g5GxEYpzi+EleAHgDOZERERERESNEZNuPZCaSGHX2g4A0PxecwDgWt1ERERERESNEJNuPdF2MVfc1kwtH58Rj8KSQn2GRERERERERLWMSbeeaJPuomtFUJgpoBJUiMuI03NUREREREREVJuYdOuJdgbztEtpaOeomUyNXcyJiIiIGiBLS0AQNJulpb6jISIDw6RbT8QZzGPToHTQrNnHpJuIiIiIqPb069cP8+bNE5+7ublh3bp1Dz1HIpFg7969j3zv2roONXxMuvXEwUuTdOel5UFpokm6Y9O4bBgRERERGSa1So3EE4m4sPUCEk8kQq1S19m9hg0bBn9//3KPnTp1ChKJBH///Xe1rxsZGYkZM2Y8ang6VqxYgc6dO5fZn5KSgsGDB9fqvR4UGhoKGxubOr0HPTpjfQfwuDK1NIV1C2tkXs9Ei8wWANjSTURERNQgFRQAkyZpHm/eDJib6zeeOhC7OxaH5h5CVnKWuE/hooD/en8oRylr/X7Tpk3D6NGjkZycDBcXF51jISEh6NatGzp27Fjt6zo6OtZWiJVycnKqt3uRYWNLtx5pu5jb3rUFAFxOu4wSdYk+QyIiIiKi6lKpgB9+0Gwqlb6jqXWxu2OxY8wOnYQbALJuZmHHmB2I3V37vTWHDh0KR0dHhIaG6uzPycnBzp07MW3aNKSnpyMwMBDNmzeHTCaDt7c3tm7d+tDrPti9PC4uDn379oW5uTnatWuHI0eOlDln0aJFaNOmDWQyGVq1aoWlS5eiuLgYgKaleeXKlYiOjoZEIoFEIhFjfrB7+YULF9C/f39YWFjA3t4eM2bMQE5Ojng8ODgYI0aMwIcffghnZ2fY29tj5syZ4r1q4vr16xg+fDjkcjkUCgXGjh2LO3fuiMejo6Px9NNPw8rKCgqFAj4+Pvjzzz8BAElJSRg2bBhsbW1haWmJ9u3b48CBAzWO5XHGlm49clA6ID48HupENWSOMuQV5+HavWtoY99G36ERERERUSMlCAKK86qWyKlVahyccxAQyrsQAAlwcO5BuA9wh5G0bHueWq1GcW4xiqRFMDIygonMBBKJpNL7GhsbY/LkyQgNDcWbb74pnrNz506oVCoEBgYiJycHPj4+WLRoERQKBfbv349JkybBw8MDTzzxROWvTa3GqFGj0LRpU/zxxx/IzMzUGf+tZWVlhdDQUDRr1gwXLlzACy+8ACsrKyxcuBDjxo3DxYsXcejQIRw9ehQAYG1tXeYaubm58PPzQ8+ePREZGYm7d+9i+vTpmDVrls4PC8ePH4ezszOOHz+Oq1evYty4cejcuTNeeOGFSl9Pea9Pm3CfPHkSJSUlmDlzJsaNG4cTJ04AACZMmIAuXbrg888/h1QqRVRUFExMTAAAM2fORFFREX799VdYWloiJiYGcrm82nEQk2690s5gnn4pHV5KL5xLOYeY1Bgm3URERERUZ4rzirFavrp2LiYA2cnZeN/6/SoVX5yzGKaWplUqO3XqVKxZswYnT55Ev379AGi6lo8ePRrW1tawtrbGggULxPKzZ89GeHg4duzYUaWk++jRo7h06RLCw8PRrFkzAMC7775bZhz2kiVLxMdubm5YsGABtm3bhoULF8LCwgJyuRzGxsYP7U4eFhaGgoICfPfdd7D8d4b7DRs2YNiwYXj//ffRtGlTAICtrS02bNgAqVQKLy8vDBkyBMeOHatR0n3s2DFcuHABCQkJcHV1BQB89913aN++PSIjI9G9e3dcv34dr7/+Ory8vAAAnp6e4vnXr1/H6NGj4e3tDQBo1apVtWMgDXYv1yNt9/LU2FQuG0ZEREREVIqXlxd69eqFb775BgBw9epVnDp1CtOmTQMAqFQqrFq1Ct7e3rCzs4NcLkd4eDiuX79epevHxsbC1dVVTLgBoGfPnmXKbd++Hb1794aTkxPkcjmWLFlS5XuUvlenTp3EhBsAevfuDbVajcuXL4v72rdvD6lUKj53dnbG3bt3q3Wv0vd0dXUVE24AaNeuHWxsbBAbqxkS8Nprr2H69OkYMGAA3nvvPcTHx4tl58yZg7fffhu9e/fG8uXLazRxHWmwpVuPtC3dmUmZ8LLU/LrEpJuIiIiI6pKJzASLcxZXqWzSr0kICwirtFzQgSC07NuyzH61Wo3srGxYKazE7uXVMW3aNMyePRufffYZQkJC4OHhAV9fXwDAmjVrsH79eqxbtw7e3t6wtLTEvHnzUFRUVK17PMyZM2cwYcIErFy5En5+frC2tsa2bduwdu3aWrtHadqu3VoSiQRqdd3NEr9ixQoEBQVh//79OHjwIJYvX45t27Zh5MiRmD59Ovz8/LB//34cPnwYq1evxtq1azF79uw6i6exYku3HskcZJA5yAAA7tnuALhsGBERERHVLYlEAlNL0yptHoM8oHBRABUNw5YAClcFPAZ5VHgNE0sT8XFVxnOXNnbsWBgZGSEsLAzfffcdpk6dKl4jIiICw4cPx8SJE9GpUye0atUKV65cqfK1lUolbty4gZSUFHHf77//rlPm9OnTaNmyJd58801069YNnp6eSEpK0iljamoKVSUT6CmVSkRHRyM3N1fcFxERASMjI7Rt27bKMVeH9vXduHFD3BcTE4P79++jXbt24r42bdrg1VdfxeHDhzFq1CiEhISIx1xdXfHSSy9h9+7dmD9/Pr766qs6ibWxY9KtZ9ou5g6pmv/GpsZCLdTdr1lERERERFVlJDWC//p/18t+MF/+97n/Ov9yJ1GrDXK5HOPGjcPixYuRkpKC4OBg8ZinpyeOHDmC06dPIzY2Fi+++KLOzNyVGTBgANq0aYMpU6YgOjoap06dwptvvqlTxtPTE9evX8e2bdsQHx+PTz75BHv27NEp4+bmhoSEBERFRSEtLQ2FhYVl7jVhwgSYm5tjypQpuHjxIo4fP47Zs2dj0qRJ4njumlKpVIiKitLZYmNjMWDAAHh7e2PChAk4d+4czp49i8mTJ8PX1xfdunVDfn4+Zs2ahRMnTiApKQkRERGIjIyEUqlZAm7evHkIDw9HQkICzp07h+PHj4vHqHqYdOuZNuk2umEEU6kp8kvykXQ/qZKziIiIiMhgyGRATo5mk8n0HU2tU45SYuwPY6FortDZr3BRYOwPY+tkne7Spk2bhnv37sHPz09n/PWSJUvQtWtX+Pn5oV+/fnBycsKIESOqfF0jIyPs2bMH+fn5eOKJJzB9+nS88847OmWeffZZvPrqq5g1axY6d+6M06dPY+nSpTplRo8eDX9/fzz99NNwdHQsd9kymUyG8PBwZGRkoHv37hgzZgyeeeYZbNiwoXpvRjlycnLQpUsXnW3YsGGQSCT48ccfYWtri759+2LAgAFo1aoVtm/fDgCQSqVIT0/H5MmT0aZNG4wdOxaDBw/GypUrAWiS+ZkzZ0KpVMLf3x9t2rTB//73v0eO93EkEQShvAUAGoSsrCxYW1sjMzMTCoWi8hP0qLi4GAcOHEBAQIDOWI3f1/2O8FfDoRylxKoBq3Dx7kXsD9qPAM8APUZLhqCiOkNUEdYZqi7WGaou1pmGqaCgAAkJCXB3d4e5uXmNr6NWqXH91HVkp2TDytkKLfq0qLSFW61WIysrCwqFAkZGbO+jqjGkevOwz09V81HWfD3jDOZERERE1BAYSY3g1s8N3oHecOvnVmddyokaG35S9Ew7g3lGXAba2TDpJiIiImpwCguB4GDNVs54XiJ6vDHp1jOFqwImliZQl6jRKl+z4DyTbiIiIqIGpKQE+PZbzVZSou9oiMjAMOnWM4lEAgcvTRfzJulNAGiWDWvAQ+2JiIiIiIjoX0y6DYC2i7lpsimkEimyCrNwK/uWnqMiIiIiIiKiR8Wk2wBoJ1O7f/k+Wtu1BsAu5kRERERERI0Bk24DUHoGc6WjZp3D2LRYfYZEREREREREtYBJtwHQdi9Pu5SGdnacwZyIiIiIiKixYNJtAGw9bGFkbITi3GJ4qj0BMOkmIiIiIiJqDJh0GwCpiRR2re0AAE4ZTgCYdBMRERE1GDIZcPeuZpPJ9B0NGajg4GCMGDFC32GQHjDpNhDacd2yWzJIIEF6fjpSc1P1HBURERERVUoiARwdNZtEou9oGo3g4GBIJBJxs7e3h7+/P/7+++9au8eKFSvQuXPnh5aZPXs2lEpluceuX78OqVSKffv21VpMAODl5QUzMzPcvn27Vq/bkNy4cQNDhw6FTCZDkyZN8Prrr6OkpOSh55w7dw4DBw6EjY0N7O3tMWPGDOTk5IjH09PT4e/vj2bNmsHMzAyurq6YNWsWsrKy6vS1MOk2ENqkO/NKJtxs3ACwtZuIiIiIHm/+/v5ISUlBSkoKjh07BmNjYwwdOrReY5g2bRouXbqE06dPlzkWGhqKJk2aICAgoNbu99tvvyE/Px9jxozBt99+W2vXrani4uJ6v6dKpcK4ceNQVFSE06dP49tvv0VoaCiWLVtW4Tm3bt3CgAED0Lp1a/zxxx84dOgQ/vnnHwQHB4tljIyMMHz4cOzbtw9XrlxBaGgojh49ipdeeqlOXw+TbgMhTqYWm4Z2jpxMjYiIiKjBKCwEZs7UbIWF+o6mUTEzM4OTkxOcnJzQuXNnvPHGG7hx4wZSU//rEXrjxg2MHTsWNjY2sLOzw/Dhw5GYmCgeP3HiBJ544glYWlrCxsYGvXv3RlJSEkJDQ7Fy5UpER0eLremhoaFlYujcuTO6du2Kb775Rme/IAgIDQ3FlClTIJFIMG3aNLi7u8PCwgJt27bF+vXra/SaN23ahKCgIEyaNKnMPQEgOTkZgYGBsLOzg6WlJbp164Y//vhDPP7TTz+he/fuMDc3h4ODA0aOHCkek0gk2Lt3r871bGxsxNedmJgIiUSC7du3w9fXF+bm5tiyZQvS09MRGBiI5s2bQyaTwdvbG1u3btW5jlqtxgcffIDWrVvDzMwMLVq0wDvvvAMA6N+/P2bNmqVTPjU1Faampjh27FiZ13j48GFcvnwZmzdvRufOnTF48GCsWrUKn332GYqKisp9337++WeYmJjgs88+Q9u2bdG9e3ds3LgRu3btwtWrVwEAtra2ePnll9GtWze0bNkSzzzzDF555RWcOnWqgr9G7WDSbSC0Ld1psWlQOnDZMCIiIqIGo6QE+N//NFsl3V8NSm5uxVtBQdXL5udXrewjysnJwffff4/WrVvD3t4egKYV1s/PD1ZWVjh16hQiIiIgl8vh7++PoqIilJSUYMSIEfD19cXff/+NM2fOYMaMGZBIJBg3bhzmz5+P9u3bi63p48aNK/fe06ZNw44dO5Bb6nWcOHECCQkJmDp1KtRqNVxcXLBz507ExMRg2bJl+L//+z/s2LGjWq8xOzsbO3fuxMSJEzFw4EBkZmbqJIQ5OTnw9fXFzZs3sW/fPkRHR2PhwoVQq9UAgP3792PkyJEICAjA+fPncezYMTzxxBPVfavxxhtvYO7cuYiNjYWfnx8KCgrg4+OD/fv34+LFi5gxYwYmTZqEs2fPiucsXrwY7733HpYuXYqYmBiEhYWhadOmAIDp06cjLCwMhaV+lPr+++/RvHlz9O/fHytWrICbm5t47Pfff0e7du3E8wHAz88PWVlZ+Oeff8qNubCwEKampjAy+i/FtbCwAKDpPVCeW7duYffu3fD19a32e1QdxnV6daoyBy9N0p2Xloe2xm0BsKWbiIiIiOqQXF7xsYAAYP/+/543aQLk5ZVf1tcXOHHiv+dubkBaGgBNC5+Ndr8gVDvEn3/+GfJ/48zNzYWzszN+/vlnMbHavn071Go1vv76a0j+HU8fEhICGxsbnDhxAt26dUNmZiaGDh0KDw8PANAZny2Xy2FsbAwnJ6eHxhEUFIT58+dj586dYnflkJAQPPXUU2jTpg0AYOXKlWJ5d3d3nDlzBjt27MDYsWOr/Hq3bdsGT09PtG/fHgAwfvx4bNq0CX369AEAhIWFITU1FZGRkbCz00zE3Lp1a/H8d955B+PHj9eJpVOnTlW+v9a8efMwatQonX0LFiwQH8+ePRvh4eHYsWMHnnjiCWRnZ2P9+vXYsGEDpkyZAgDw8PDAU089BQAYNWoUZs2ahR9//FF8P0JDQ8Vx+w4ODuLfBwBu376NJk2a6Nxfm4BXNM69f//+eO2117BmzRrMnTsXubm5eOONNwAAKSkpOmUDAwPx448/Ij8/H8OGDcPXX39d7feoOtjSbSBMLU1h3cIaAND8XnMATLqJiIiI6PH29NNPIyoqClFRUTh79iz8/PwwePBgJCUlAQCio6Nx9epVWFlZQS6XQy6Xw87ODgUFBYiPj4ednR2Cg4Ph5+eHYcOGYf369WUSsKqwsbHBqFGjxO7eWVlZ2LVrF6ZNmyaW+eyzz+Dj4wNHR0fI5XJ8+eWXuH79erXu880332DixIni84kTJ2Lnzp3Izs4GAERFRaFLly5iwv2gqKgoPPPMM9V9eWV069ZN57lKpcKqVavg7e0NOzs7yOVyhIeHi68vNjYWhYWFFd7b3Nxcp7v8uXPncPHiRfEHjFmzZpXbzbw62rdvj2+//RZr166FTCaDk5MT3N3d0bRpU53WbwD4+OOPce7cOfz444+Ij4/Ha6+99kj3rgxbug2Ig9IBmdczYXXbCgCQkpOC+wX3YWNuo9/AiIiIiKjxKTWrcxlSqe7zu3crLvtAQoNS46nVajWysrKgUChq1NpnaWmp05L79ddfw9raGl999RXefvtt5OTkwMfHB1u2bClzrqOjZs6kkJAQzJkzB4cOHcL27duxZMkSHDlyBD169KhWLNOmTcMzzzyDq1ev4vjx45BKpXjuuecAaFqoFyxYgLVr16Jnz56wsrLCmjVrdMZaVyYmJga///47zp49i0WLFon7VSoVtm3bhhdeeEHsLl2Ryo5LJBIID/Q4KG+iNEtLS53na9aswfr167Fu3Tp4e3vD0tIS8+bNE8dXV3ZfQNPFvHPnzkhOTkZISAj69++Pli1bllvWyckJv//+u86+O3fuiMcqEhQUhKCgINy5cweWlpaQSCT46KOP0KpVqzLXd3JygpeXF+zs7NCnTx8sXboUzs7Olb6OmmBLtwHRjuvOuZqD5laa1u7YVI7rJiIiIqI6YGlZ8WZuXvWyDyZcFZWrBRKJBEZGRsj/dxx5165dERcXhyZNmqB169Y6m7W1tXhely5dsHjxYpw+fRodOnRAWFgYAMDU1BQqlapK93766afh7u6OkJAQhISEYPz48WJyGhERgV69euGVV15Bly5d0Lp1a8THx1frtW3atAl9+/ZFdHS02LofFRWF1157DZs2bQIAdOzYEVFRUcjIyCj3Gh07dnxoi7Gjo6NOS39cXBzyKho2UEpERASGDx+OiRMnolOnTmjVqhWuXLkiHvf09ISFhcVD7+3t7Y1u3brhq6++QlhYGKZOnVph2R49eiAmJgZ3S/3Yc+TIESgUCrRr167SeJs2bQq5XI7t27fD3NwcAwcOrLCsdjx8YR1Ogsik24BwBnMiIiIiov8UFhbi9u3buH37NmJjYzF79mzk5ORg2LBhAIAJEybAwcEBw4cPx6lTp5CQkIATJ05gzpw5SE5ORkJCAhYvXowzZ84gKSkJhw8fRlxcnDiu283NDQkJCYiKikJaWtpDEy+JRIKpU6fi888/x5kzZ3S6lnt6euLPP/9EeHg4rly5gqVLlyIyMrLKr7O4uBibN29GYGAgOnTooLNNnz4df/zxB/755x8EBgbCyckJI0aMQEREBK5du4Zdu3bhzJkzAIDly5dj69atWL58OWJjY3HhwgW8//774n369++PDRs24Pz58/jzzz/x0ksvwcTEpNL4PD09ceTIEZw+fRqxsbF48cUXxZZnQNN9fNGiRVi4cCG+++47xMfH4/fffxd/LNCaPn063nvvPQiCoDOr+oYNG3S6pg8aNAht27bF5MmTER0djfDwcCxZsgQzZ86EmZkZAODs2bPw8vLCzZs3da5z7tw5XLlyBZ999hlmzZqF1atXw8bGBgBw4MABhISE4OLFi0hMTMT+/fvx0ksvoXfv3joTudU2Jt0GpPQM5ky6iYiIiOhxd+jQITg7O8PZ2RlPPvkkIiMjsXPnTvTr1w8AIJPJ8Ouvv6JFixYYNWoUlEolpk2bhoKCAigUCshkMly6dAmjR49GmzZtMGPGDMycORMvvvgiAGD06NHw9/fH008/DUdHxzLLYD0oODgYmZmZaN++PZ588klx/4svvohRo0Zh3LhxePLJJ5Geno5XXnmlyq9z3759SE9P10lEtZRKJZRKJTZt2gRTU1McPnxYXBvc29sb7733HqT/Dgfo168fdu7ciX379qFz587o37+/zgzja9euhaurK/r06YOgoCAsWLAAMpms0viWLFmCrl27ws/PD/369RMT/9KWLl2K+fPnY9myZVAqlRg3bpxOSzWgmcDM2NgYgYGBMC/VmyItLU2nZ4BUKsW2bdsglUrRs2dPTJw4EZMnT8Zbb70llsnLy8Ply5d1usefPXsWAwcOhLe3N7788kt88cUXmDNnjnjcwsICX331FZ566ikolUq8+uqrePbZZ/Hzzz9X+h48ConwYKf+BiQrKwvW1tbIzMyEQqHQdzgPVVxcjAMHDiAgIKDCX5Py0vKwxnENAKDJySZ45fgrGNx6MA5MOFCfoZKBqEqdISqNdYaqi3WGqot1pgJqNaCdMKtFi7JjnPWsoKAACQkJcHd310l06oPOmG4De1+o/iUmJsLDwwORkZHo2rVrheUMqd487PNT1XyUE6kZEJmDDDIHGfLS8tAiqwUAtnQTERERGTwjI80yWURUruLiYqSnp2PJkiXo0aPHQxPuxog/NxkYbRdz2zu2AICkzCTkFD1kZkkiIiIiIiIDFhERAWdnZ0RGRmLjxo36DqfeMek2MNqkO/9aPhxlmonVLqVd0mdIRERERPQwRUXA669rtn+XUCKi//Tr1w+CIODy5cvw9vbWdzj1jkm3gSlvBnMuG0ZERERkwIqLgQ8/1GzlrHlMRI83Jt0GhjOYExEREVFdaMDzJxPpTW18bph0GxhtS3fG1Qx42XgBAGLSmHQTERERUc1oZ5rPy8vTcyREDY/2c/MoKzZw9nIDo3BVwMTSBMW5xXDPdQfA7uVEREREVHNSqRQ2NjbimskymQwSiaRe7q1Wq1FUVISCggK9L/1EDYch1BtBEJCXl4e7d+/CxsZGXAu9Jph0GxiJRAIHLwek/JUC+1R7AED8vXgUlBTA3Lh+11UkIiIiosbByckJAMTEu74IgoD8/HxYWFjUW6JPDZ8h1RsbGxvx81NTTLoNkKPSESl/paAkoQQ25ja4X3AfV9KvoGPTjvoOjYiIiIgaIIlEAmdnZzRp0gTF9TjZW3FxMX799Vf07dv3kbrn0uPFUOqNiYnJI7VwazHpNkDiZGqX0qB8WokzyWcQmxrLpJuIiIiIHolUKq2VJKI69yspKYG5uTmTbqqyxlZvOLDCAHEGcyIiIqIGxMICuHhRs1lY6DsaIjIwbOk2QOJa3ZfSoLRXAuAM5kREREQGy8gIaN9e31EQkYFiS7cBsvWwhZGxEYrziuFR4gGALd1EREREREQNEZNuAyQ1kcLO0w4A0CStCQAgLj0Oxar6m/SCiIiIiKqoqAhYsUKzFRXpOxoiMjBMug2Utou55LoEliaWKFYXI/5evJ6jIiIiIqIyiouBlSs1Wz3ODE5EDQOTbgOlM4O547/jutnFnIiIiIiIqEFh0m2gSs9grnTQJN2xqbH6DImIiIiIiIiqiUm3gRJnMC+9bBhnMCciIiIiImpQmHQbKPu29gCAvLQ8tDFuA4Ddy4mIiIiIiBoaJt0GytTSFNYtrQEATdOaAgAupV2CSq3SZ1hERERERERUDUy6DZi2i7lJsgnMpGYoKClAUmaSnqMiIiIiIiKiqmLSbcC0k6llXM5AW4e2ANjFnIiIiMjgmJsDZ89qNnNzfUdDRAaGSbcBKz2DuTiZGpNuIiIiIsMilQLdu2s2qVTf0RCRgWHSbcBKz2AuLhuWxmXDiIiIiIiIGgpjfQdAFdO2dGdez4SXzAsAW7qJiIiIDE5REbB+vebx3LmAqal+4yEig8KWbgMms5dB5igDALhkugDQJN2CIOgzLCIiIiIqrbgYWLhQsxUX6zsaIjIwTLoNnLaLucUtC0glUuQU5SA5K1nPUREREREREVFVGEzS/d5770EikWDevHn6DsWgaLuY37t8D572ngA4rpuIiIiIiKihMIikOzIyEl988QU6duyo71AMDmcwJyIiIiIiarj0nnTn5ORgwoQJ+Oqrr2Bra6vvcAxO6RnM2zkw6SYiIiIiImpI9J50z5w5E0OGDMGAAQP0HYpB0rZ0Z1zNgJeNZgZzdi8nIiIiIiJqGPS6ZNi2bdtw7tw5REZGVql8YWEhCgsLxedZWVkAgOLiYhQb+EyR2viqG6dFUwuYyk1RlFOEZpnNAAD/3P0HRUVFkEgktR4nGY6a1hl6fLHOUHWxzlB1sc5UoLgYJuLDYs5gXgrrDNVEQ6k3VY1PIuhp/akbN26gW7duOHLkiDiWu1+/fujcuTPWrVtX7jkrVqzAypUry+wPCwuDTCary3D16vKCy8i/mo/mC5tjhmwGBAgIbR8KGxMbfYdGRERERCoV7GM0w//S27UDpFI9B0RE9SEvLw9BQUHIzMyEQqGosJzeku69e/di5MiRkJb6UlKpVJBIJDAyMkJhYaHOMaD8lm5XV1ekpaU99EUaguLiYhw5cgQDBw6EiYlJ5SeUsi94Hy6GXYTvSl9Ms56Ga/ev4XDQYfRz61c3wZJBeJQ6Q48n1hmqLtYZqi7WGaou1hmqiYZSb7KysuDg4FBp0q237uXPPPMMLly4oLPv+eefh5eXFxYtWlQm4QYAMzMzmJmZldlvYmJi0H+M0moSa5P2TQAAGVcy0G5IO1y7fw1x9+Mw0GRgXYRIBqYh1W8yDKwzVF2sM1RdrDNUXawzVBOGXm+qGpvekm4rKyt06NBBZ5+lpSXs7e3L7H/c6SwbNqUdfr7yM2cwJyIiIjIUxcXAl19qHs+YARhwkkBE9U+vE6lR1YjLhl1Kg9JeCYDLhhEREREZjKIiYNYszePgYCbdRKTDoJLuEydO6DsEg2TrYQsjYyMU5xXDvdgdAJcNIyIiIiIiagj0vk43VU5qIoWdpx0AwPauLQDgds5tZORn6DMsIiIiIiIiqgST7gZC28U892ouXBWuAIDYVLZ2ExERERERGTIm3Q1E6cnUlI4c101ERERERNQQMOluIHRmMHdoB4DjuomIiIiIiAwdk+4GQtu9PDU2Fe0cNUk3W7qJiIiIiIgMm0HNXk4Vs29rDwDIT89Hd6PuAJh0ExERERkEMzPg55//e0xEVAqT7gbC1NIU1i2tkZmUCYc0TVfzG1k3kF2YDSszKz1HR0RERPQYMzYGhgzRdxREZKDYvbwB0XYxL7xWiKaWTQEAl9Iu6TMkIiIiIiIieggm3Q2IzmRqHNdNREREZBiKi4HQUM1WXKzvaIjIwDDpbkB0lg1z0CwbxhnMiYiIiPSsqAh4/nnNVlSk72iIyMAw6W5AOIM5ERERERFRw8KkuwHRtnRn3chCW1lbAEy6iYiIiIiIDBmT7gZEZi+DzFEGAGiaoZlI7dq9a8gvztdnWERERERERFQBJt0NjLaLuTpRDVtzWwgQcCX9ip6jIiIiIiIiovIw6W5gxMnULnEGcyIiIiIiIkPHpLuB4bJhREREREREDYexvgOg6ik9gzmXDSMiIiIyAGZmwI4d/z0mIiqFSXcDo23pvhd/D91sugFgSzcRERGRXhkbA889p+8oiMhAsXt5A6NwUcBUbgp1iRou2S4AgLiMOBSpivQcGRERERERET2ISXcDI5FI4OClae2W3pBCbipHiboEVzOu6jkyIiIiosdUSQmwc6dmKynRdzREZGCYdDdApWcwF8d1p3JcNxEREZFeFBYCY8dqtsJCfUdDRAaGSXcDxBnMiYiIiIiIGgYm3Q2QdgZznaQ7jUk3ERERERGRoWHS3QCV7l7uZecFgN3LiYiIiIiIDBGT7gbIzsMORiZGKM4rRsuilgCAS2mXoFKr9BwZERERERERlcakuwEyMjaCvac9AMDipgXMjc1RqCpEwv0EPUdGREREREREpTHpbqC0XczTL6fDy0HTxZyTqRERERERERkWJt0NVOkZzLlsGBEREZEemZoCISGazdRU39EQkYEx1ncAVDM6M5hP5AzmRERERHpjYgIEB+s7CiIyUGzpbqC0Ld2psalcq5uIiIiIiMhAsaW7gXJo6wBIgPz0fLjDHYCme7kgCJBIJHqOjoiIiOgxUlIChIdrHvv5Acb8JzYR/Yct3Q2UicwENi1tAACK2woYGxkjtzgXN7Ju6DcwIiIiosdNYSEwdKhmKyzUdzREZGCYdDdg2i7m967cQxv7NgDYxZyIiIiIiMiQMOluwErPYM5x3URERERERIaHSXcDVnoGcy4bRkREREREZHiYdDdg5c5gzmXDiIiIiIiIDAaT7gZM29KddSMLbWT/jekWBEGfYREREREREdG/mHQ3YBZ2FrBsYgkAsL1rCyOJEe4X3Med3Dt6joyIiIiIiIgArtPd4DkoHZB7NxdZcVloZdsKVzOuIiY1Bk5yJ32HRkRERPR4MDUFNmz47zERUSls6W7gOIM5ERERkZ6ZmAAzZ2o2ExN9R0NEBoZJdwPn4FUq6XZg0k1ERERERGRI2L28gdNOppYamwql47/LhqVx2TAiIiKieqNSAadOaR736QNIpfqNh4gMCpPuBk7bvTzjagaesH4CAFu6iYiIiOpVQQHw9NOaxzk5gKWlfuMhIoPC7uUNnMJFAVO5KQSVgKaZTQEAd3PvIj0vXc+REREREREREZPuBk4ikYjjunOv5qKFdQsA7GJORERERERkCJh0NwKcwZyIiIiIiMgwMeluBHSSbs5gTkREREREZDCYdDcC5c1gzqSbiIiIiIhI/5h0NwJiS/elNCjtuWwYERERERGRoeCSYY2AnYcdjEyMUJJfApd8FwBAclYysgqzoDBT6Dk6IiIiokbOxAT44IP/HhMRlcKW7kbAyNgI9p72AICihCI4y50BALGpbO0mIiIiqnOmpsDrr2s2U1N9R0NEBoZJdyNRejI17bhudjEnIiIiIiLSLybdjYQ26U6NTeUM5kRERET1SaUCIiM1m0ql72iIyMAw6W4ktDOYc61uIiIionpWUAA88YRmKyjQdzREZGCYdDcSpbuXezl4AWD3ciIiIiIiIn1j0t1IOLR1ACRAfkY+3OEOAEi4l4C84jw9R0ZERERERPT4YtLdSJjITGDT0gYAICQJsLewhwABl9Mu6zcwIiIiIiKixxiT7kZE28U8/VI6x3UTEREREREZACbdjUjpGcyVDlw2jIiIiIiISN+YdDcinMGciIiIiIjIsBjrOwCqPaVnMO/g2AEAk24iIiKiOmdiAixf/t9jIqJSmHQ3ItqW7qzkLHiYewAArmZcRZGqCKZSU32GRkRERNR4mZoCK1boOwoiMlDsXt6IWNhZwLKJJQDA7JYZrEytoBJUiEuP03NkREREREREjycm3Y2M2MX8Esd1ExEREdULtRr45x/NplbrOxoiMjBMuhuZ0uO6mXQTERER1YP8fKBDB82Wn6/vaIjIwDDpbmRKz2DOZcOIiIiIiIj0i0l3I1N6rW62dBMREREREekXk+5GRtvSnXE1A22t2wIALqdfRom6RJ9hERERERERPZaYdDcyVs2tYGplCkElwCrNChbGFihSFSHhXoK+QyMiIiIiInrsMOluZCQSCRy8NF3M0y+lw8vBCwC7mBMREREREekDk+5GqPRkahzXTUREREREpD/G+g6Aap/OsmG+/ybdaUy6iYiIiOqEiQmwYMF/j4mISmHS3QiVnsFcXDYslcuGEREREdUJU1NgzRp9R0FEBordyxshsXv5pTQo7f9bq1stqPUZFhERERER0WOHSXcjZNvKFlJTKUryS+CQ4wATIxPkFefheuZ1fYdGRERE1Pio1UBiomZTs5GDiHQx6W6EjIyNYOdpBwC4d/ke2ti3AcAu5kRERER1Ij8fcHfXbPn5+o6GiAwMk+5GijOYExERERER6R+T7kaq9GRqTLqJiIiIiIj0g0l3I6WzbJgjlw0jIiIiIiLSBybdjVTp7uVe9l4ANGO6BUHQZ1hERERERESPFSbdjZR9W3tAAuRn5MNF7QIjiREyCzORkpOi79CIiIiIiIgeG0y6GykTCxPYuNkAALListDarjUAjusmIiIiIiKqT0y6G7HSXcyVDkoAXDaMiIiIqNYZGwOvvKLZjI31HQ0RGRgm3Y0YZzAnIiIiqgdmZsBnn2k2MzN9R0NEBoZJdyPGGcyJiIiIiIj0i0l3I1a6ezlbuomIiIjqiCAAqamajSvFENEDmHQ3Yg5empburOQsuJu6AwDS8tKQmpuqz7CIiIiIGpe8PKBJE82Wl6fvaIjIwDDpbsQs7Cxg2cQSAJB3LQ9uNm4AgNg0TqZGRERERERUH5h0N3LljutmF3MiIiIiIqJ6waS7kSs9gzmXDSMiIiIiIqpfTLobuXInU+MM5kRERERERPWCSXcjx+7lRERERERE+sOku5HTtnRnxGegjaINAOBW9i1kFmTqMywiIiIiIqLHApPuRs6quRVMrUwhqASU3ChBM6tmADiDOREREVGtMTYGpkzRbMbG+o6GiAwMk+5GTiKRiOt1s4s5ERERUR0wMwNCQzWbmZm+oyEiA8Ok+zGg7WKeGpuKdg5MuomIiIiIiOoLk+7HQOnJ1JSO/y4bxu7lRERERLVDEIDcXM0mCPqOhogMDJPuxwBnMCciIiKqQ3l5gFyu2fLy9B0NERkYJt2PAXGt7stpUNprWroT7ycityhXn2ERERERERE1enpNuj///HN07NgRCoUCCoUCPXv2xMGDB/UZUqNk28oWUlMpSvJLYJxqDEeZJgm/nH5Zz5ERERERERE1bnpNul1cXPDee+/hr7/+wp9//on+/ftj+PDh+Oeff/QZVqNjZGwEO087ALrjutnFnIiIiIiIqG7pNekeNmwYAgIC4OnpiTZt2uCdd96BXC7H77//rs+wGiXOYE5ERERERFT/jPUdgJZKpcLOnTuRm5uLnj17llumsLAQhYWF4vOsrCwAQHFxMYqLi+slzprSxqevOG3b2AIA7v5zF217tgUAXLx70eDft8eZvusMNTysM1RdrDNUXawzFSguhon4sBjg+yNinaGaaCj1pqrxSQRBv+saXLhwAT179kRBQQHkcjnCwsIQEBBQbtkVK1Zg5cqVZfaHhYVBJpPVdagN2r1f7yHpoyRYelki7808LI9fjmZmzfA/5f/0HRoRERFRgyYtKMDQ8eMBAD9v2waVubmeIyKi+pCXl4egoCBkZmZCoVBUWE7vSXdRURGuX7+OzMxM/PDDD/j6669x8uRJtGvXrkzZ8lq6XV1dkZaW9tAXaQiKi4tx5MgRDBw4ECYmJpWfUMvuRN3Bpic2wdzWHOPixsF9gzuMJEbIfD0TZsZm9R4PVU7fdYYaHtYZqi7WGaou1pkKFBRAGhwMAFCFhgJMukWsM1QTDaXeZGVlwcHBodKkW+/dy01NTdG6dWsAgI+PDyIjI7F+/Xp88cUXZcqamZnBzKxsgmhiYmLQf4zS9BVr0w5NAQlQcK8ADiUOsDazRmZhJhKyEuDd1Lve46Gqa0j1mwwD6wxVF+sMVRfrzANMTIBduwBwPd6KsM5QTRh6valqbAb3vaBWq3Vas6l2mFiYwMbNBgCQdikN7Rw1PQli02L1GBUREREREVHjpteW7sWLF2Pw4MFo0aIFsrOzERYWhhMnTiA8PFyfYTVajkpH3E+4r1k2zFmJM8lnOIM5ERERERFRHdJr0n337l1MnjwZKSkpsLa2RseOHREeHo6BAwfqM6xGy0HpgLgDcZplwzpy2TAiIiKiWpGbC8jlmsc5OYClpX7jISKDoteke9OmTfq8/WPHQekAAEiL/a97OZNuIiIiIiKiulOjMd2+vr747rvvkJ+fX9vxUB1yVDoC0CTdSkclAOBK+hWUqEv0GRYREREREVGjVaOku0uXLliwYAGcnJzwwgsv4Pfff6/tuKgOaFu6s5Kz0NSoKWQmMhSrixGfEa/nyIiIiIiIiBqnGiXd69atw61btxASEoK7d++ib9++aNeuHT788EPcuXOntmOkWmJhawHLppoxRhmXM6B00LR2s4s5ERERERFR3ajxkmHGxsYYNWoUfvzxRyQnJyMoKAhLly6Fq6srRowYgV9++aU246RaUrqLOZcNIyIiIiIiqluPvE732bNnsXz5cqxduxZNmjTB4sWL4eDggKFDh2LBggW1ESPVIm0X89TYVLZ0ExERERER1bEazV5+9+5dbN68GSEhIYiLi8OwYcOwdetW+Pn5QSKRAACCg4Ph7++PDz/8sFYDpkejM4P5FM5gTkRERPTIpFIgIOC/x0REpdQo6XZxcYGHhwemTp2K4OBgODo6linTsWNHdO/e/ZEDpNpVunu5n6MfAOBS2iWoBTWMJI/c8YGIiIjo8WNuDuzfr+8oiMhA1SjpPnbsGPr06fPQMgqFAsePH69RUFR3tC3dGfEZaGHZAqZSU+SX5CPpfhLcbd31HB0REREREVHjUqOmTRcXF8TFxZXZHxcXh8TExEeNieqQVTMrmFqZQlAJyIzPRFv7tgDYxZyIiIiIiKgu1CjpDg4OxunTp8vs/+OPPxAcHPyoMVEdkkgk5c5gzqSbiIiIqIZycwFLS82Wm6vvaIjIwNQo6T5//jx69+5dZn+PHj0QFRX1qDFRHSs9gzmXDSMiIiKqBXl5mo2I6AE1SrolEgmys7PL7M/MzIRKpXrkoKhulZ7BnMuGERERERER1Z0aJd19+/bF6tWrdRJslUqF1atX46mnnqq14KhuVNS9XBAEfYZFRERERETU6NRo9vL3338fffv2Rdu2bcVZzE+dOoWsrCz88ssvtRog1T6xpftyGlrbtoZUIkV2UTZuZt+Ei8JFz9ERERERERE1HjVq6W7Xrh3+/vtvjB07Fnfv3kV2djYmT56MS5cuoUOHDrUdI9UyW3dbSE2lKMkvQV5yHlrbtQYAxKZyXDcREREREVFtqlFLNwA0a9YM7777bm3GQvXEyNgI9m3scffiXbGL+eX0y4hJjcFAj4H6Do+IiIiIiKjRqHHSDQB5eXm4fv06ioqKdPZ37NjxkYKiuuegdMDdi3c1M5h3aYc9l/ZwMjUiIiKimjAyAnx9/3tMRFRKjZLu1NRUPP/88zh48GC5xzmDueErPYN5u0FcNoyIiIioxiwsgBMn9B0FERmoGv0UN2/ePNy/fx9//PEHLCwscOjQIXz77bfw9PTEvn37ajtGqgOlZzDXLhv2T+o/nMGciIiIiIioFtWopfuXX37Bjz/+iG7dusHIyAgtW7bEwIEDoVAosHr1agwZMqS246Rapm3pTo1NRRv7NpBAgoz8DKTmpaKJZRM9R0dERERERNQ41KilOzc3F02aaBIzW1tbpKamAgC8vb1x7ty52ouO6ox9G3tAAhTcK4BwT4C7rTsAcFw3ERERUXXl5gKOjpotN1ff0RCRgalR0t22bVtcvnwZANCpUyd88cUXuHnzJjZu3AhnZ+daDZDqhomFCWzcbADodjHnsmFERERENZCWptmIiB5Qo6R77ty5SElJAQAsX74cBw8eRIsWLfDJJ59wGbEGRDuuOzU2Fe0cNZOpsaWbiIiIiIio9tRoTPfEiRPFxz4+PkhKSsKlS5fQokULODg41FpwVLcclA6IOxCnmcG8579JdxqTbiIiIiIiotpS7Zbu4uJieHh4IDb2v27IMpkMXbt2ZcLdwJReNozdy4mIiIiIiGpftZNuExMTFBQU1EUsVM9Kdy9XOmqS7pScFNzLv6fPsIiIiIiIiBqNGo3pnjlzJt5//32UlJTUdjxUj7Qt3dk3s2FWaAYXhQsAIDaNrd1ERERERES1oUZjuiMjI3Hs2DEcPnwY3t7esLS01Dm+e/fuWgmO6paFrQUsm1oi904u0i6loZ1jOyRnJSM2NRa9XHvpOzwiIiKihsHICOjW7b/HRESl1CjptrGxwejRo2s7FtIDR6Ujcu/karqYN1XicPxhzmBOREREVB0WFkBkpL6jICIDVaOkOyQkpLbjID1xUDog8USiZgbzDpzBnIiIiIiIqDax/8tjrvQM5lyrm4iIiIiIqHbVqKXb3d0dEomkwuPXrl2rcUBUv0rPYD7QYSAA4HrmdeQU5UBuKtdnaEREREQNQ14e0E7TeIGYGEAm0288RGRQapR0z5s3T+d5cXExzp8/j0OHDuH111+vjbionmhbuu/F34O11BpNLJvgbu5dXEq7hG7Nuuk5OiIiIqIGQBCApKT/HhMRlVKjpHvu3Lnl7v/ss8/w559/PlJAVL+smlnB1MoURdlFyIjLQDvHdribexcxqTFMuomIiIiIiB5RrY7pHjx4MHbt2lWbl6Q6JpFIdLqYt3PQdI2KTeVa3URERERERI+qVpPuH374AXZ2drV5SaoHpSdTUzoqAXAGcyIiIiIiotpQo+7lXbp00ZlITRAE3L59G6mpqfjf//5Xa8FR/dCZwXwSZzAnIiIiIiKqLTVKukeMGKHz3MjICI6OjujXrx+8vLxqIy6qR6W7l/d27A0AuHbvGgpKCmBubK7P0IiIiIiIiBq0GiXdy5cvr+04SI+0Ld3pl9PRxKIJbMxtcL/gPq6kX0HHph31HB0RERGRgZNI/lsy7CHL6hLR46lGY7oPHDiA8PDwMvvDw8Nx8ODBRw6K6petuy2kplKUFJQg83om2jmyizkRERFRlclkwD//aDau0U1ED6hR0v3GG29ApVKV2S8IAt54441HDorql5GxEezb2AP4d1y3A5NuIiIiIiKi2lCjpDsuLg7ttF1oSvHy8sLVq1cfOSiqf9ou5qmxqWJLd2walw0jIiIiIiJ6FDVKuq2trXHt2rUy+69evQpLS8tHDorqX7nLhrGlm4iIiKhyeXlA+/aaLS9P39EQkYGpUdI9fPhwzJs3D/Hx8eK+q1evYv78+Xj22WdrLTiqP9oZzNNi08SW7ivpV1CsKtZnWERERESGTxCAmBjNJgj6joaIDEyNku4PPvgAlpaW8PLygru7O9zd3aFUKmFvb48PP/ywtmOkelC6e7mLlQvkpnKUqEtwNYPDBYiIiIiIiGqqRkuGWVtb4/Tp0zhy5Aiio6NhYWGBjh07om/fvrUdH9UT+zb2gAQouFeAvNQ8eDl44c9bfyI2LVbsbk5ERERERETVU6OkGwAkEgkGDRqEQYMG1WY8pCcmFiawdbfFvWv3xC7mf976EzGpMRilHKXv8IiIiIiIiBqkGnUvnzNnDj755JMy+zds2IB58+Y9akykJzozmHPZMCIiIiIiokdWo6R7165d6N27d5n9vXr1wg8//PDIQZF+lJ7BnMuGERERERERPboadS9PT0+HtbV1mf0KhQJpaWmPHBTpR+kZzHs69gQAXEq7BJVaBamRVJ+hERERERkuiQRo2fK/x0REpdSopbt169Y4dOhQmf0HDx5Eq1atHjko0o/S3cvdbdxhJjVDQUkBEu8n6jcwIiIiIkMmkwGJiZpNJtN3NERkYGrU0v3aa69h1qxZSE1NRf/+/QEAx44dw9q1a7Fu3brajI/qkbalO/tmNkpySuDl4IXoO9GISY2Bh52HnqMjIiIiIiJqeGqUdE+dOhWFhYV45513sGrVKgCAm5sbPv/8c0yePLlWA6T6Y25jDrmTHDm3c5B2KQ1KRyWi70QjNi0Ww9oO03d4REREREREDU6NupcDwMsvv4zk5GTcuXMHWVlZuHbtGiZPnoyMjIzajI/qGWcwJyIiIqqm/Hyge3fNlp+v72iIyMDUOOnWcnR0hFwux+HDhzF27Fg0b968NuIiPSlvBnMm3UREREQPoVYDf/6p2dRqfUdDRAbmkZLupKQkLF++HG5ubnjuuedgZGSE7777rrZiIz0oPYN56WXDBEHQZ1hEREREREQNUrXHdBcVFWH37t34+uuvERERgQEDBiA5ORnnz5+Ht7d3XcRI9ah09/LWdq1hbGSMnKIcJGclw9XaVc/RERERERERNSzVaumePXs2mjVrhvXr12PkyJFITk7GTz/9BIlEAqmU6zg3BtqW7nvx9yApkcDTzhMAu5gTERER/T979x0fRZ0+cPwzu+kVSAiEQCD00DuCtNCbIEU6iA3vbHj+1PP0vLPreSenZ1dUei+K9B6K9CYl0gkhCZBAes/u/v6YlE1IZ7Ozmzzv12teWWZnZ58sk9l55vv9Pl8hhKiIciXdX3/9NU8//TRbt27l2WefxcfHp7LiEhrx8PfA2csZk9HE3Yt3ZVy3EEIIIYQQQtyHciXdCxcu5PDhw/j7+zNx4kTWr1+PwWCorNiEBhRFKdDFPNg3GFDHdQshhBBCCCGEKJ9yJd2TJ09m27ZtnD59mpYtW/Lss89St25djEYj585JS2hVUVQxNWnpFkIIIYQoga+vugghRCEVql4eFBTE22+/zbVr11i0aBHjxo1j2rRp1K9fnxdeeMHSMQorK27aMKlgLoQQQghRBHd3iIlRF3d3raMRQtiY+5oyTFEUhgwZwooVK4iKiuLll18mNDTUUrEJjZh3L2/u0xydoiMuPY7bKbc1jkwIIYQQQggh7Eu5ku7evXvzn//8hwsXLtzzXK1atXjxxRc5deqUxYIT2sjtXn7n/B2cdc4E1QgCpIu5EEIIIYQQQpRXuZLup556igMHDtC5c2eCg4P561//yv79+6XbcRVTI6gGemc92enZJIQnyLhuIYQQQoiSpKVBv37qkpamdTRCCBtTrqR7xowZrF69mtjYWD755BPi4+N55JFHqFu3Lo8//jg///wzaXKisXs6vQ6f5up0cDFhMXlJt1QwF0IIIYQogtEIoaHqYjRqHY0QwsZUaEy3s7Mzw4cP59tvvyUqKop169bh7+/Pm2++iY+PDyNHjmT//v2WjlVYkW/L/GJqudOGSUu3EEIIIYQQQpTPfRVSy9W9e3fef/99Tp8+zenTpxkwYADR0dGW2LXQiHkxNeleLoQQQgghhBAV41CRF0VERKAoCvXr1wfg8OHDLFmyhFatWjFr1iz+8pe/WDRIYX3mc3X39+0PwK2UW9xNu0st11pahiaEEEIIIYQQdqNCLd1Tpkxh165dANy8eZOBAwdy+PBh3njjDd555x2LBii0YT5Xt4eTB4HegQCExci4biGEEEIIIYQoqwol3WfOnKFbt24ArFixgrZt2/Lbb7+xePFi5s2bZ8n4hEZ8mvuAAunx6aTcSpFx3UIIIYQQQghRARVKurOysnB2dgZg+/btjBo1CoCWLVvKWO4qwtHVkZpBNQEZ1y2EEEIIUSo3N3URQohCKpR0t27dmm+++Ya9e/eybds2hg4dCkBUVBQ+Pj4WDVBox7yLuUwbJoQQQghRDHd3SElRF3d3raMRQtiYCiXd//rXv/j222/p168fkydPpn379gCsW7cur9u5sH/mFcyle7kQQgghhBBClF+Fqpf369eP2NhYEhMTqVmzZt76WbNm4SbdaqoM8wrmD9R+AICIxAgSMxLxcvbSMjQhhBBCCCGEsAsVaulOS0sjIyMjL+EODw/n008/5fz58/j5+Vk0QKEd8+7ltVxrUdejLgB/xP6hZVhCCCGEELYlPR1GjFCX9HStoxFC2JgKJd2jR49mwYIFAMTHx9O9e3c++eQTHn74Yb7++muLBii0k9vSnRSVRHpCev64bpk2TAghhBAin8EAGzeqi8GgdTRCCBtToaT7+PHj9O7dG4BVq1ZRp04dwsPDWbBgAf/73/8sGqDQjksNFzzqegAQ+0esjOsWQgghhBBCiHKqUNKdmpqKp6cnAFu3bmXs2LHodDoeeOABwsPDLRqg0FZRFczPxUrSLYQQQgghhBBlUaGku2nTpvz8889ERESwZcsWBg8eDMDt27fx8pICW1WJeQVz6V4uhBBCCCGEEOVToaT7H//4By+//DKNGjWiW7du9OjRA1BbvTt27GjRAIW2zCuY53YvvxJ3hbSsNC3DEkIIIYQQQgi7UKGke/z48Vy/fp2jR4+yZcuWvPUDBgzgv//9r8WCE9oz717u5+5HLddamDBx/s55jSMTQgghhBBCCNtXoaQboG7dunTs2JGoqChu3LgBQLdu3WjZsqXFghPay23pjrsShyHTkD+uW4qpCSGEEEIIIUSpKpR0G41G3nnnHby9vWnYsCENGzakRo0avPvuuxiNRkvHKDTk4e+Bs5czJqOJuxfv0spXxnULIYQQQhTg7g4mk7q4u2sdjRDCxjhU5EVvvPEGP/zwAx999BEPPvggAPv27eOtt94iPT2d999/36JBCu0oioJvsC+RhyKJCYshuEHOtGFSwVwIIYQQQgghSlWhpHv+/PnMnTuXUaNG5a1r164dAQEBPPPMM5J0VzG1g2sTeShSnTask3QvF0IIIYQQQoiyqlD38rt37xY5drtly5bcvXv3voMStqWoubov3b1EpiFTy7CEEEIIIWxDejo88oi6pKdrHY0QwsZUKOlu3749X3zxxT3rv/jiC9q1a3ffQQnbYj5Xd4BnAJ5OnmQbs7l095LGkQkhhBBC2ACDAVatUheDQetohBA2pkLdyz/++GNGjBjB9u3b8+boPnDgABEREWzcuNGiAQrt5VYwv3P+DiajieDawRyOPMy5mHN5Ld9CCCGEEEIIIe5VoZbuvn37cuHCBcaMGUN8fDzx8fGMHTuWs2fPsnDhQkvHKDRWI6gGemc92enZJIQnyLRhQgghhBBCCFFGFWrpBqhXr949BdNOnTrFDz/8wHfffXffgQnbodPr8Gnuw+3Tt4kJi8mfNixWpg0TQgghhBBCiJJUqKVbVD+5Xcxjw2IJrp0zbZi0dAshhBBCCCFEiSTpFmViXkwtt3v5+djzGIxSLEQIIYQQQgghiiNJtygT82nDGno3xNXBlQxDBlfjr2ocmRBCCCGEEELYrnKN6R47dmyJz8fHx99PLMKGmXcv1yk6Wvi24OTNk5yLOUfTWk01jk4IIYQQQkNubpCcnP9YCCHMlCvp9vb2LvX5GTNm3FdAwjb5NPdB0Smkx6eTciuFVrVb5SXdo1qM0jo8IYQQQgjtKAq4u2sdhRDCRpUr6f7pp58qKw5h4xxcHKgRVIO4y3EFKphLMTUhhBBCCCGEKJ6M6RZlZt7FPLeYmkwbJoQQQohqLyMDZs5Ul4wMraMRQtgYTZPuDz/8kK5du+Lp6Ymfnx8PP/ww58+f1zIkUQLzCua504aFxYRhNBm1DEsIIYQQQlvZ2TB/vrpkZ2sdjRDCxmiadIeGhvLss89y8OBBtm3bRlZWFoMHDyYlJUXLsEQxzCuYN6nZBEedIylZKUQkRGgcmRBCCCGEEELYpnKN6ba0zZs3F/j3vHnz8PPz49ixY/Tp00ejqERxzLuXO+odae7TnLMxZwmLDaNhjYYaRyeEEEIIIYQQtkfTpLuwhIQEAGrVqlXk8xkZGWSYjZNJTEwEICsri6ysrMoP8D7kxmfrcZbEu6lavT4pKomk2CRa+LTgbMxZTt88zYCGAzSOruqpCseMsC45ZkR5yTEjykuOmWJkZeGY9zAL5PPJI8eMqAh7OW7KGp9iMplMlRxLmRiNRkaNGkV8fDz79u0rcpu33nqLt99++571S5YswU3mRLSKM4+dITsum2YfN2Od5zqW31rOwFoDeS7wOa1DE0IIIYTQhD49nZGTJgGwftkyDC4uGkckhLCG1NRUpkyZQkJCAl5eXsVuZzNJ95///Gc2bdrEvn37qF+/fpHbFNXS3aBBA2JjY0v8JW1BVlYW27ZtY9CgQTg6Opb+Ahu1eMhiwneFM3LuSP7o8gfTfp7GAwEPsOfRPVqHVuVUlWNGWI8cM6K85JgR5SXHTDFSUnCsWROArLg4mbPbjBwzoiLs5bhJTEzE19e31KTbJrqXP/fcc6xfv549e/YUm3ADODs74+zsfM96R0dHm/7PMGdPsRbFr5Uf4bvCuXvhLu1GtgPgjzt/4ODggKIoGkdXNdn7MSOsT44ZUV5yzIjykmOmELPPwtHRscC/hUqOGVERtn7clDU2TauXm0wmnnvuOdauXcvOnTsJCgrSMhxRBuYVzJv7NEen6IhPj+dm8k2NIxNCCCGE0IibG9y+rS4y5FEIUYimSfezzz7LokWLWLJkCZ6enty8eZObN2+SlpamZViiBOYVzJ0dnGlSswkA52LOaRmWEEIIIYR2FAVq11YX6fknhChE06T766+/JiEhgX79+uHv75+3LF++XMuwRAlyW7rjrsSRnZ5Nq9qtAAiLDdMyLCGEEEIIIYSwSZqO6baRGm6iHDzqeuDs7UxGQgZ3Lt4h2DeYX87/Ii3dQgghhKi+MjLgpZfUx3PmQBE1iIQQ1ZemLd3C/iiKUqCLeW5LtyTdQgghhKi2srPhq6/UJTtb62iEEDZGkm5RbrldzGPCYqR7uRBCCCGEEEKUQJJuUW6+LfMrmLf0bQnA7ZTbxKbGahmWEEIIIYQQQtgcSbpFuZlPG+bu5E5D74YAhMVIa7cQQgghhBBCmJOkW5Rb3pju87EYDUYZ1y2EEEIIIYQQxZCkW5RbjaAa6J31GDIMxF+Ll3HdQgghhBBCCFEMSbpFuen0Onya+wBqF/Ng32BAWrqFEEIIIYQQojBJukWF5HYxN69gLkm3EEIIIaolV1e4elVdXF21jkYIYWMk6RYVYl5MLbi22tIdmRRJYkailmEJIYQQQlifTgeNGqmLTi6vhRAFyVlBVIh50l3DpQb+Hv6AVDAXQgghhBBCCHOSdIsKMe9ebjKZpIu5EEIIIaqvzEx45RV1yczUOhohhI2RpFtUiE9zHxSdQkZCBsk3kyXpFkIIIUT1lZUF//mPumRlaR2NEMLGSNItKsTBxYEaQTUAtYu5TBsmhBBCCCGEEPeSpFtUmHkXc5k2TAghhBBCCCHuJUm3qDDzYmq5Ld3X4q+RmpWqZVhCCCGEEEIIYTMk6RYVZp5013avja+bLyZMnI89r3FkQgghhBBCCGEbJOkWFZbbvTz2j1gA6WIuhBBCCCGEEIVI0i0qLLelOykqifSEdKlgLoQQQgghhBCFOGgdgLBfLt4uePh7kBydTOwf+eO6z8VK0i2EEEKIasTVFc6cyX8shBBmpKVb3Je8Lubm04bFyLRhQgghhKhGdDpo3VpddHJ5LYQoSM4K4r7kdjE3nzbs0t1LZGRnaBmWEEIIIYQQQtgESbrFfTGvYF7Psx5ezl4YTAYu3r2ocWRCCCGEEFaSmQlvvaUumZlaRyOEsDGSdIv7Yt69XFEU6WIuhBBCiOonKwvefltdsrK0jkYIYWMk6Rb3JbelO+5KHNnp2TJtmBBCCCGEEEKYkaRb3BePuh44eztjMpq4c/GOVDAXQgghhBBCCDOSdIv7oihKkRXMpaVbCCGEEEIIISTpFhZgXsE8N+m+cOcC2cZsLcMSQgghhBBCCM1J0i3um3kF80DvQNwc3cg0ZHIl7orGkQkhhBBCCCGEtiTpFvfNvHu5TtHR0rclIF3MhRBCCCGEEEKSbnHf8lq6z8diNBhl2jAhhBBCVC8uLnD4sLq4uGgdjRDCxjhoHYCwfzUa1UDvrMeQYSD+Wnz+tGFSwVwIIYQQ1YFeD127ah2FEMJGSUu3uG86vQ7fFvnjuqWCuRBCCCGEEEKoJOkWFlFUBfOwmDCMJqOWYQkhhBBCVL7MTPj3v9UlM1PraIQQNkaSbmER5hXMG9dsjJPeibTsNK4nXNc4MiGEEEKISpaVBa++qi5ZWVpHI4SwMZJ0C4swr2DuoHOguU9zQLqYCyGEEEIIIao3SbqFRZh3LzeZTDKuWwghhBBCCCGQpFtYiE9zHxSdQkZCBsk3k2nlK9OGCSGEEEIIIYQk3cIiHJwdqNm4JqB2MQ+uLdOGCSGEEEIIIYQk3cJiiqpgfi7mHCaTScuwhBBCCCGEEEIzknQLizGvYN6sVjP0ip7EjESik6M1jkwIIYQQQgghtCFJt7AY8wrmzg7ONK3VFJBiakIIIYSo4lxcYNcudXFx0ToaIYSNkaRbWIx593Igf1y3JN1CCCGEqMr0eujXT130eq2jEULYGEm6hcX4tlST7uToZNIT0vMqmEvSLYQQQgghhKiuJOkWFuPi7YJnPU9A7WKeW0wtLFamDRNCCCFEFZaVBV9+qS5ZWVpHI4SwMZJ0C4sy72Iu3cuFEEIIUS1kZsJzz6lLZqbW0QghbIwk3cKizCuYt/RtiYJCbGosMSkxGkcmhBBCCCGEENYnSbewKPMK5m6ObjSq0QiQLuZCCCGEEEKI6kmSbmFRhSuY547rli7mQgghhH0zGoyEh4YTtyeO8NBwjAaj1iEJIYRdcNA6AFG15FYwj78aT3Z6NsG+wWy4uEGSbiGEEMKOha0JY/PszSTeSAQgfE44XvW9GPrZUILHBmscnRBC2DZp6RYW5VHXA2dvZ0xGE3cu3JGWbiGEEMLOha0JY8X4FXkJd67EyERWjF9B2BoZQiaEECWRpFtYlKIoeeO6Y8JiZNowIYQQwo4ZDUY2z94MpiKezFm3+cXN0tVcCCFKIEm3sLjCFcwBopKiiE+P1zAqIYQQQpTX9b3X72nhLsAEiRGJXN973XpB2SJnZ1i/Xl2cnbWORghhYyTpFhZnnnR7u3gT4BkAQFiMtHYLIYQQ9iQpOsmi21VZDg4wYoS6OEjJJCFEQZJ0C4sz714OSBdzIYQQwk55+ntadDshhKiOJOkWFpfb0n3nwh2MBqMUUxNCCCHsVGDvQLzqexW/gQJeDbwI7B1ovaBsUVYWzJunLllZWkcjhLAxknQLi6vRqAZ6Zz2GDAPxV+MJ9lWnEpGkWwghhLAvOr2OwZ8MLn4DEwz9dCg6fTW/pMzMhMceU5fMTK2jEULYmGp+hhSVQafX4dtCbe02r2AuSbcQQghhf1JiUgBQdMo9zzl5OBHUP8jaIQkhhF2RpFtUCvNiarlJd3hCOCmZKVqGJYQQQohyyEzJZM+7ewAY+tlQpm6bSsOXGjJ5y2R8g33JTM5k/8f7NY5SCCFsmyTdolKYJ90+bj7UdlOLq/0R+4eWYQkhhBCiHA59doiUWynUCKpB51mdadi3ITX71CQoJIgBHw4A4OCnB0mKqubVy4UQogSSdItKUVwFc+liLoQQQtiHtLtpea3YIe+GoHfSF3i+xagWNOjZgOy0bELfCdUiRCGEsAuSdItKkdfS/UcsJpNJpg0TQggh7Mz+j/eTkZCBX1s/2k5ue8/ziqIw8F8DATg+9zix52OtHaIQQtgFSbpFpfBp7oOiU8hIyCD5ZrK0dAshhBB2JCkqiUOfHQKg//v9iyyiBhDYK5DmDzXHZDCx6++7rBmiEELYDUm6RaVwcHagZuOagDquW6YNE0IIIexH6LuhZKdnU79HfZqPbF7itgM+GAAKnFt1jsjDkVaK0MY4O8OKFeri7Kx1NEIIGyNJt6g0uV3MzacNuxx3mfTsdC3DEkIIIUQJ7l66y4m5JwAY+NFAFKXoVu5cfm38aD+jPQDbX9uOyWSq9BhtjoMDPPKIujg4aB2NEMLGSNItKo15BfO6HnWp4VIDo8nIxTsXNY5MCCGEEMXZ9Y9dGLONNB3alIZ9GpbpNSHvhKB31nNt1zUub71cyREKIYR9kaRbVJrcCuaxYbEoiiJdzIUQQggbd/PUTc4sPQNA/w/6l/l13oHedH22KwDb/7odk7GatXZnZ8PKleqSna11NEIIGyNJt6g05t3LQaYNE0IIIWzdzjd2AtB6Ymv8O/qX67W9X++Ns5czt07d4syyM5URnu3KyIAJE9QlI0PraIQQNkaSblFpfFuqSXdydDLpCekybZgQQghhw67vu87FDRdR9Aoh74aU+/VuPm48+NcHAdj5950YMg2WDlEIIeySJN2i0rh4u+BZzxNQu5hLS7cQQghhm0wmEzv+tgOAjo93xKeZT4X20312dzzqehB/NZ6j3x61ZIhCCGG3JOkWlcq8i3numO4Ldy6QZcjSMiwhhBBCmLm06RLX913HwcWBvv/oW+H9OLk70fct9fV73t1DRpJ0tRZCCEm6RaUyr2DewLsB7o7uZBmzuBwnlU2FEEIIW2AymtjxutrK3fW5rnjV97qv/XV8vCO1mtUiNSaVA3MOWCJEIYSwa5J0i0plXsFcp+gIrq22dofFyLhuIYQQwhacWX6GW6du4ezlTK/Xet33/vSOevq/r1Y+P/CfA6TcTrnvfQohhD2TpFtUqsIVzGXaMCGEEMJ2GLIM7HpzFwA9X+mJm4+bRfbbanwr6nWtR2ZyJnve22ORfQohhL2SpFtUqtyW7vir8WSnZ+cXU4uVpFsIIYTQ2okfTxB3OQ53P3ceePEBi+1XURQGfjQQgKPfHCXuSpzF9m2TnJzgp5/UxclJ62iEEDZGkm5RqdzruONSwwWT0cSdC3fypw2T7uVCCCGEprJSswh9OxSA3m/0xsnDssliUP8gmgxugjHLmNeaXmU5OsLMmeri6Kh1NEIIGyNJt6hUiqIU6GJuPle3wSjzdwohhBBaOfzFYZKjk/Fu6E3npztXynsM+GgAAKeXnCb6RHSlvIcQQtg6SbpFpTOvYB5UIwhnvTPp2emEJ4RrHJkQQghRPaXHp7Pvo30A9Hu7Hw7ODpXyPv4d/WkzuQ1A3jzgVVJ2NmzYoC7Z2VpHI4SwMZJ0i0pnXsFcr9PTwrcFIMXUhBBCCK3s//d+0uPSqd2qNu2mtavU9+r/Xn90jjoub7nM1V1XK/W9NJORASNHqkuGzE0uhChIkm5R6QpXMJdx3UIIIYR2km8mc+jTQwD0f78/On3lXg7WbFwzr/v69r9ux2QyVer7CSGErZGkW1S63JbuOxfuYDQY86cNkwrmQgghhNXteX8PWalZBHQPoMXoFlZ5zz5/74OjuyNRR6IIWy033YUQ1Ysk3aLSeTf0xsHFAUOGgfir8fnThkn3ciGEEMKq4q7GcezbYwAM+GAAiqJY5X096njQ8+WeAOx8YyeGLCmmKoSoPiTpFpVOp9fh08IHKFTBPCZMupgJIYQQVrT7n7sxZhlpPLAxQf2DrPrePf6vB2613bhz4Q4nfjxh1fcWQggtSdItrMK8mFrTWk3RK3qSMpOITIrUODIhhBCierh95ja/L/odgP4f9Lf6+zt7OtPn730ACH07lKzULKvHIIQQWpCkW1iF+bRhTnonmvk0A6SLuRBCCGEtO9/YCSYIHhdMQNcATWLo/HRnagTVIDk6mYOfHdQkBiGEsDZJuoVVSAVzIYQQQjsRByI4v+48ik4h5N0QzeJwcHbIe//9H+0n9U6qZrFYlJMTfPGFujg5aR2NEMLGSNItrMK8e7nJZKKVrxRTE0IIIazBZDKx4287AGg/s33ed7JW2k5uS532dchIzGDfh/s0jcViHB3h2WfVxdFR62iEEDZGkm5hFbWa1ULRKWQkZpAcnUxwbZk2TAghhLCGy1svEx4ajt5JT79/9tM6HBSdwsCPBgJw+IvDJFxP0DgiIYSoXJJ0C6twcHagZpOaQMEK5udizkkFcyGEEKKSmIwmdr6+E4Auz3TBO9Bb44hUTYY0oVG/RhgyDOz+526tw7l/BgPs3q0uBpkOTQhRkCTdwmrMu5i38GmBgsLdtLvEpMZoHJkQQghRNZ1bfY7o49E4eTjR+/XeWoeTR1EUBv5Lbe0+teAUt8/e1jii+5SeDiEh6pKernU0QggbI0m3sBrzYmqujq4E1VTnB5Vx3UIIIYTlGbON7Pr7LkCdI9u9trvGERUU0C2A4HHBBVrjhRCiKpKkW1iN+bRhAMG+6rjuBacWsPvabgxG6Y4lSmY0GLm2+xqnl57m2u5rGA1GrUMSQgibdXLeSe5cuIOrjys9XuqhdThF6v9+fxS9wvl157m+77rW4QghRKWQpFtYjXn38jVha9gTvgeAn07+RMj8EBp91og1YWu0DFHYsLA1YXzW6DPmh8xnzZQ1zA+Zz2eNPiNsjUw7J4QQhWWlZRH6digAvd/ojbOXs8YRFc23hS8dn+gIwPbXtkudFyFElSRJt7Aa35ZqS3fyzWSmzp9KUmZSgecjEyMZv2K8JN7iHmFrwlgxfgWJNxILrE+MTGTF+BWSeAshRCFHvjpC4o1EvBp40fXPXbUOp0T9/tkPB1cHIvZHcOHXC1qHI4QQFidJt7AaZy9nPOt5AuAb63vP8ybUu9svbn5RupqLPEaDkc2zN0NRjR856za/uFm6mgshRA7z+a/7/rMvDi4OGkdUMs96nnSf3R2AHa/vkPO5qBZkyFz1Ikm3sCp9kB6A2jG1i3zehImIxAj2Xt9rzbCEDbu+9/o9LdwFmCAxIpHre2UsoBBCAPz2yW+k3UnDp4UPHR7toHU4ZdLrr71wqelCzNkYfl/4u9bhCFGpZMhc9SNJt7CuQPVHUS3d5qKToq0QjLAHSdFJpW9Uju2EEKIqS7mdwoFPDgDQ/73+6Bzs41LPpYZL3pRmu/6xi+z0bI0jKidHR/j4Y3VxdNQ6GmHDZMhc9WQfZ2JRZeRWMC+upTuXv6e/NcIRdsDT39Oi2wkhRFW294O9ZKVk4d/Zn+BxwVqHUy7dnuuGVwMvEiMSOfzlYa3DKR8nJ3jlFXVxctI6GmGjZMhc9SVJt7CqB3o+AJTc0t3AqwG9A3tbKyRh4wJ7B+LkVfIFjLO3M4G9A60UkRBC2Kb48HiOfn0UgAEfDkBRFI0jKh8HFwf6vd0PgH0f7CM9Pl3TeISwNBkyV31pmnTv2bOHhx56iHr16qEoCj///LOW4QgrqNO6DgA142rimFV096snOz2JXqe3ZljChh3//jiZiZklbpORkEHoO6Ey1YwQwiIMBti9G5YuVX8a7KS2Z+hboRgyDTQKaUTjgY0tvn+DAUJDFfbsCSA0VKmUz6X9jPbUblWbtLtp7P94v+XfoJIYMg0c++YIW947wu4dBrs5ZiqbNY4ZeyJD5sqmKh43mibdKSkptG/fni+//FLLMIQVuddxx6WGCwoKwekFu725OboB8L9D/+Na/DUNohO25tzqc2x4ZgMArR5phVd9rwLPezXwot30dgDseWcPO9/YKYm3KMBekydrqIoXNZawZg00agQhITBlivqzUSN1vS2LORfDqQWnABjwgeVbuXM/l0GDHJgzpwuDBjlUyuei0+sY8OEAAA5+epCkKNtPPtasgeCgdDr/uRtD3uzGiIHpdnHMVDZrHTP2RIbMla6qHjeaziExbNgwhg0bpmUIwsoURcE32JcbB26wsNNC7va8S3RSNP6e/nTx70LIghCORh1l9LLR/Pb4b7g7uWsdstDItd3XWDNlDZig06xOjPxmJCajiet7r5MUnYSnvyeBvQPR6XX4d/Jny1+2sO/DfRgyDQz69yC761Z5v8wTKHd3hZAQ0FfzDiNr1sDs2XDjRv66+vXhs89g7Fjt4rIF+Z+NA9CFOXPkswH1cxk/Hgrfu4uMVNevWmW7n8+uN3dhMppoMboF9R+ob9F9W/tzaf5Qcxo82ICI/RHsfns3D337kOV2bmG5n42rHR4zlcme/5YqlQ5QlHs/mBwmwMnHq9oOmavKx42M6RZWl1tM7c75O/Rr1I/JbSfTr1E/PJw9WDtxLXXc6/D7rd957JfHpNWymrp56ibLRi/DkGmg5ZiWjPhqBIqiYELHNRpxhrZcoxGmnFPYAy8+wPAvhwNw4JMDbJ69uVodO1X1rvD9yP3iNk+4If+LWz4b+WwKMxjUGxFFnTpy1734om32log8EqlWPFag//v9LbpvLT4XRVEY+NFAAE78cILY87GW27kF2fMxU5nkc7mXyWTi6DdHWThwIZhMmCi6lhrA/qyuedc31UlVP240bekur4yMDDIyMvL+nZioFiLIysoiKytLq7DKJDc+W4/TGmo1rwXA7bO37/k86rjWYfnY5QxaPIiV51bSNrQtrz34mhZhaq66HjPxV+NZNHQRGYkZNOjdgFHzR2EwGli12shLL+mJjMxvwQ4IMDFnjoExY0x0eKoD6GHjnzdy+PPDZKVnMfTzoSi6qt3ivXatwqRJ+pwvpPzfNTLSxPjxsGyZ+vlUJwYDvPCCwz2fCahf3IpiYvZsGD4826K9AXIvCmz5p8EAzz1X8mfz3HMQHJyNTgdGo7qYTMU/Vv+tlLhNafso6+P89zNfr1RoP4XXXbsGN24Uf0CYTBARAePGGahXLz+WgotSzHrLLrnx5C7BR3ZQA7hVtx1TZvtiMhkt8D7q75KUBDduFH8ezf1cmjY14V5E57Sydjq6d7sG9PBsTr2kC/yt204ONXzkPvdn+e3K+tk0b27Cw8Pyf9Pm71PRn5VxnsnKguTk0j8Xb29T3uxq5p9vcY8tsZ013yv3sc6UTffYTTRPPgHAWVrxBy0ZyHa8yS+qloUeRwy0TjxA6zotyPD0ve//M/vYtmx/hLnHza5d2fTtayr9BVZS1ut0xWQjzUGKorB27VoefvjhYrd56623ePvtt+9Zv2TJEtzc3CoxOmFJCUcTuPreVVwCXWj5v5ZFbrP1zla+ivgKBYW/Bf2Nbt7drByl0EJWfBYX/3aRzOhMXBq60PT9pjh4OHDggD//+lfXnK3MT87q6euvfz1Cjx7q3O53d97l+ufXwQS1BtSiwTMNUPRVM/HOyoI//Wkwd+64UDiBUpmoWTOdd97ZDyg5yYm6GAwF/13S+vJsW/R6ctbrCvw7fx0Wfc+MDD2Jic6lfn4ODgZ0OY0J917MKmVcXzWPLWE/grjCoyzEgI7PeZ54amgdksX4cZs/8zUK8D1PEkmA1iEVy40UUvAAwJ1kUpHhcQI8SWQiK6hPJCZgOwPYz4OAgoKRhlzHgySS8SSausxgIQFEEY83P/AESVTfsd3Feemlo/TpE6l1GHlSU1OZMmUKCQkJeHl5FbudXSXdRbV0N2jQgNjY2BJ/SVuQlZXFtm3bGDRoEI6ORVftri7irsTxdcuv0TvpeSXhFXT6orvQvLD5Bb45/g2eTp7sm7mPYF/7mm/0fqjFnwxs23aGQYPa0K+fvsqPz81IymDJ4CVEH4vGu6E3M0Jn4FnPE4MBmjZ1IDISikosFcVEQACcO5eNyQTZ2XBu+Vl2PrcOk9FE0Og2dH1/JEZ0ZGeriarBANnZCtnZFFiysrhnnbqtun1Rz5u/Nn/bgvsv/LqC25k/r9yzTUnvaTRKwicqh5OTCScn0OnU1hqdrnyPc5eStzVVaN/m71Ge7QuuM92zPjIS1q4t/UQ7ZYqBRo0K/p6Vv5iKXA8mbr//E1nXovEY0AWfaUMs/t6nTyu8/nrpn8vHHxto377gJWVZrzBL2u7ynF+J3fY7Xu0b0uKDqWWq12GJ9y3LduafTUlJ94cf5n82ueFr/7NgPJbc99GjCo8/Xnpn2h9/zKZr1+KPmfI+tsTri9+XUuT6kvYXdyqC02+uJvNuCg4eLrR+62GuOzZl9uzi/57cSeFxfsSHu3g08aPLl9Nx9HQBzP/uK/7/Z41tKvr6335TmDCh9ONm2zbbaulOTEzE19e3aiXdhSUmJuLt7V3qL2kLsrKy2LhxI8OHD6/2SbfRYORDjw/JTs/m+YvPU6tprSK3yzJkMXDhQPaE76FZrWYcfuowNVxqWDdYDdh78SejEdLS1CU1VV2Kemy+LjXJgGHhEvTXrmBwceP6wMdJcPAhLQ2iouD06fLH0YqzjGc1OkycoTVrGIORKn7nohiuruqi16uLg0P+Y/PFEuttYd8nTsDTT5f+uSxdCj16WPZiVvsL6ZJ/hoZCSAil2rUL+vUrfbuqxGBQayNERhZ9Ia4o6rn46lXbKVIYtiaMFeNW4OjuyAuXX8CjjofF30PrzyXhegKfN/8cQ4aBqZun0nRIU8u/SQWZfzaupnuTbls8ZqxB62NGa7njtze/sBljthG/Nn5M/HkitZrUKtNn07JuHE+YfiT5ZjIN+zZk2uZpOLjY1YjgCrHX46as+aim/4PJyclcunQp799Xr17l5MmT1KpVi8DA6lm1rzrQ6XX4tPDh1qlbxITFFJt0O+odWfXIKrp834WLdy8yefVk1k9eX6Xn8K6sqo0mE2Rmli8Rrsjzqalg1hmlTBRMjOVn2nKFTByZnz6FyPU+5f8lCzlHa1bp9IwzrqQNZ3F2NLLNaxw6Rz0ODuDoqCZrxS0lPV/R5yy930OHoCz3KTdurF4JVKdO8O67pX9xP/KIbX1xW0Pv3urvHhkJJgXoDfgD0cBeUEzq8717axunFvR69ebm+PGAHuhF/mezDzDCp5/azjFjNBjZ+fedgFpMsjISbij4uShKwb+p3Js5lfm5eAd60/XZrhycc5Dtf91Ok0FNbKZWh/lnk6Vz5K0G/wQPyKrlCL9hc8eMtWh9zGgpOyObjc9u5MQP6vjtVo+0YvSPo3HycALK9tm890VNejaeyk99fiI8NJw109Ywfvn4YnuGVhVV/bjRtKV79+7dhBRxy/3RRx9l3rx5pb5eWrrt1+rJqzmz7AwD/zWQB199sMRtT948Sc8fepKWncarPV/lX4P+ZaUorSv3Dl/hisLmvL3hhRcgPb18iXBamtoCbW3OzuDmpi6urgV/urmBq4uJBue24B12CJNOh2HCZFzaNC3wmqtX4a23Sn+vX39VE8vchFSvV0/SFzZcYMXYFRgyDTR/qDmPrHwEB+eqccfYXu8KW0PuDSwo+ovbnqcduV9r1sC4xcCnQAOzJyKAF2H11Or72QC8ehDmBIKhXv46fRS8dB0+fkC7uAo7Oe8kvzz2C661XHnhygu4eLtU6vsV1QurQQP1Iriyj5fUO6n8r/H/yEjMYOzisbSd0rZy37Cc7OWYsTYtjxktJEYmsmLcCiIPRYICAz4cwIOvPljkkIiyfDZXd11l8dDFGDINdHmmC8O/GF6m4RX2bs0aeOEvEBlE3o3P+tfgszm2edyUNR+1me7lFSFJt/0KfSeU3f/cTYeZHRj90+hSt19+ZjmTVk8CYMnYJUxuO7myQ7S61avzkwR03NMChYWSZr3+3kS4uMdlXVfU866u5BWpKs6+f+1jx2s7ABizaAztpra7ZxtLJJaXtlxi+cPLyU7PpunQpkxYMwFH16rxdyjJZfGq2wVfWa0BxplQ6xCa/40aAQVWK1BdP541wHhy/pbMx0rm/HsVtvHZZGdk80XzL0i4nsDAjwfy4Csl37y2FINBrRy8adNJhg3rQEiIg9Vu6O39YC8739hJjaAaPPfHc+idbONOor0cM1rR8pixpuv7rrNi/ApSbqXgUsOFccvGlToUwmCAvXshOhr8/dUeRoU/m7Mrz7Jq4iowQb93+tH3zb6V+FvYhjXAbBOYTwxQ3wSf2eh3k110LxfVV+5c3TFhMWXafmKbiZy6dYoP933I4+sep7lPczrX61yZIVY6oxGOHoUNG9Tl2LGcJ8YAn3FvC9RsYC0MGgTt2lU8abaVez4nfjqRl3APnjO4yIQbLNPdqOmQpkzZMIWlDy3l0uZLLBu1jEm/TMLRzUY+jPswdqyaWBdVB6C6J5djx8LI0fDVabicCk3c4Jm2YCPX6lZjAjKBVCAReBbU5KBwg0lOAv404Ijaw7q4NpWS2lqs8ZrKeH8D8Cdy5kQotLFJUVe9CIwGzatDHP3mKAnXE/Cs50m356w3u4deD337mkhJiaRv3/ZWTZ66z+7O4c8PE381nqPfHqX7892t9+bFMKB+NZsAxWQk+FwYAGHBwZh0OpSc523hmNGKlseMNdwzfrutHxPXquO3S6PXlz70q/UjrUm5ncKm5zax+x+78ajrQeen7Pv6tyR5N7EKnYMjFXW9Pd/EkqRbaKJ2cG0AYsNiMZlMZeou827Iu5y6dYqNFzfy8PKHOfrUUep41KnsUC0qMRG2blWT7I0b4fbtQhuMQT2jFBaQs348vP6C/Y/PvbD+Ar8+9SsAPV/pSY+/9Chxe0sklkH9g5i6aSqLhy/myvYrLBmxhMm/Ts4bZ2XPxo6F0aOrR2tCeawBZuvhRof8dZ+g3tOypS/t3IQ4FUgp5nFJz5Vlu/J0lIkFRt3n71QVmVDvf+4F+mkYR0ZSBnvf3wtA33/2rTK9dkrj5O5E37f6suFPG9jz7h46zOyAs2fpUwNWplAg9yvJNS2Ns23aAOCenEyquzumnOfrAk2A+jlLA7PH9YF6qDe6hH0pPH679YTWjPpxFE7ulr2u6PZsN5Kjk9n7/l42/GkD7n7utBxd9JS79sz8JlZhuR1JXsR+b2JJ0i00UatZLRSdQkZiBsnRyXjWK30eQr1Oz5KxS+g+tzvn75xn/Mrx7JixAye9bSdNFy7A+vVqor1njzrVUy5PTxg8GEaOhAGDIQj1pEPhbtk6wAj6z6FnXauFXikiDkSwcsJKTAYT7We0Z+BHA8v0utzEsrSuWCVp2Kch07dOZ/GwxVzbfY1FQxcxdeNUnL20vXCzCD1qJlAD6IB9fiNZUN7d8kLrIynf3fJsKi8Rzl3MTgmVTk/OOaYUQUBx7TQljUkr7jlLvqay3j8B9fgozR9om3Qf/O9BUmNSqdW0Fh0e66BhJNbX8fGOHPjkAHcv3uXAJwfo91Y/TeK4DvwEfFHG7WNzlkPFPK+gJubmiXjhxDwAsO2rnerFfPy2olMY8OEAer7Ss9LGXIe8G0LyzWRO/HCC1ZNWM337dAIfrFpFp/eSfxOrKLZy47OiJOkWmnBwdqBmk5rcvXiXmLCYMiXdAN4u3vwy6Re6ze3Gvuv7mL1pNl+P/LqSoy2fzEw1uc5NtM0K9APQvDmMGKEm2r16gZOTehG8lFIuhnVgCFALovarrOArWcy5GJaMWEJ2WjZNhzXlobkPlasKbVm6YpWmQc8GTN82nUVDFhGxP4KFgxcybfM0XGpUbhGiyrQG9e7wDQcH6NKFOagXabbWomsJBiC9lCUFmEXxd8sBZgDLcrYvKUnOqqTfoyh6wB1wy1nK8rg827kB+4EyzBjGj9jveaaidlO2z+ZZYAvwODAM615Ipcam8tt/fgPUi3C9Y/W6u6Z31NP//f6smrCKA58coOszXXH3cy/9hRaQCfwKzEX9/y9PQaSvgTqoCUVEzk/zJQu1fEs0cKSE/dSh+KS8AWpiXgVuIdu8AuO3a7owftl4mgxuUqnvqSgKI78ZScrtFC78eoGlI5fy2L7H8GvtV6nvW9mSUM+9Wyi6o2dRoistmsolSbfQjG9LX+5evMvvC39Hp9cR2DuwTNMhtPBtwdJxSxm5ZCTfHPuG9nXb86cuf7JCxMW7eVPtLr5hg9p9PDk5/zlHR+jbV020h40Ap2ZwBjiMeqf8DGrLSXoZ32svao01e7vUSohIYNGQRaTHpRPQPYBHVj6i2QVjQLcAZuyYwcJBC4k8FMmCgQuYvnU6rrVcNYnnfliqRbescscHpwNplJ4Al2Wb8mxnqSQ4BVhZju113F/CW5btHCl53LEl9Ea9QI+k6KRByXm+Gs4YVupnA2pLYybwc87iDzyKmoA3q/QIYd9H+8hMyqRuh7q0ntDaCu9oe1qNb0W9rvWIOhLFnvf2MOx/wyr1/f4AfgDmA+ZVaEJQ/99fA6KKeW3u39NTFP+dbczZb24CXjgpz/13JnArZzlW5J5UtSk+Kc9tMbfmN50BCFUU9gQE4K4ohGB/1y+5TCYTR78+yubZ5R+/bQk6Bx3jl41n4aCFRPwWwaIhi3jityfwDvS2yvtbghE4gZpkb0VtSCrv97q/pYOyEqlebiVSvbygsDVh/DzzZzKTMvPWedX3YuhnQwkeG1ymfXy07yP+tuNvOOgc2DljJ70bWu8y0WhUC5/lFkE7erTg8351oN80aD4GXDrDJRc4C5xDvdgvSu7FXFn4AkNRW1mGAPc/q3XlSrubxk+9fyLmXAy+LX15bN9juPm4aR0Wt36/xYIBC0iNTaVO+zpM3zYd99rWaTWxBAPQiJK7Y9UCPsCyibKtcABcCi2uqH9jV8rw+hmoiVZZkmQnKj8htpbcGzVQMLnM/f3suVDN/SrLZ9MSNQlbgNplOFcf4Imc11fG2S0hIoHPm32OIcPAlI1TaDbMGmn+vWzheubqzqssGLAAnaOO5/54jpqNa1p0/7k35eai9g7JVRd4DDXZzq1LnXvMuKWkkOyhzpXunpxMmrv6XWKJvycT6rFWWmJe1vOzD8Un5bmLJY7hvF5YZuvstRdWdno2G57dwMkfTwKVN367LApcUwX78vi+x2260SAaNcHeCmyj4M0rUOsdDAYGAs/nbF/STeGr2NaNG5kyzMbYwpeUrQhbE8aK8Svu/YvKuaqZsGpCmRJvk8nE5NWTWX52ObXdanN01lECvStvfEtiImzbll8E7datnCfqAq2hwTDw7QfZLSDcHRKLuUJ3Qr1oaw20yfnZGghEPfGU1MriippoJJmt0wHdgOE5S0fuHRKupazUrLy7sp4BnjZ3V/b22dssGLCAlFsp1G5dmxk7ZuBRx0PrsEplAhaitrJpKTfRLZz8Fk6ES3q+Its4U3xXrd2UrZvwLqpfF+pcRV0MN0CdutveLoYtrayfTW534x+BzeQXq/MCJqMm4F2w3M2adU+t48TcEwT2DmRm6EzN5uu1leuZRUMXcXnLZdpOacvYxfd/1JpQW5DnAkvI/57Vo363Ppnzs6jzzhrgtZQULpgl3T7u7lb9ezIBdyk5KY9AvalaFjUpPTEv6ZuyuF5Y9nhzLzEykRVjVxB52Drjt8siISKBH3v+SOKNROr3qM+M7TNsZkaWdGAf+a3Zvxd63hPoj5poD0G99s1ljzeFJem2MbbyJaU1o8HIZ40+I/FGYtEbKGqL9+yrs8vU1Tw1K5VeP/bixM0TdPLvxN7H9uLmaLk2hosX1SR7/XoIPQfZzcnLlnXtwKEdZBYzHN0ByN3cPMFuSvHJQllONg8BB4CNwCbuPZn5obaADwcGoX5xasWYbWT5mOVcWH8BlxouPLb3Mfza2N74o9jzsSzov4CkqCR8W/oyY8eMMtcZsBYTcBE1ocxdyjquqRPqcWfpZNhWW39zewCU1oXa1u6WW5sB2JWdzaaTJxnWoQMhDg7V+vMwZ0AdyhON2pWxtCE9N1C7H/9IwV4WbVFbRaeh9lCqqNjzsXzV+itMBhOP7XtM0wJKtnI9E30imu86fQfArOOz8O9YsU6nd4HFqMm2+fdpE9QbJ4+iVhcvjSElBX1O0r0nOZkH3d1t7u/JBMRT9Lhy88S8uB55hdWg6KTcH7VHwK1iXmdP52Atxm+X1e2zt/mp10+kx6fTfGRzJq6diM7B+s0uJtRhGFtyllAK3txRgM6oCfZgoAclV+u3t5vCknTbGFv5ktLatd3XmB8yv9TtHt31KI36NSrTPsPjw+nyfRdiU2OZ3GYyi8curvDdx8xM2HQAlpyC3TFwuzb52XIxs5PpUL+cczfL/dmcilUaLe/J5gZq8r0JtduO2XBy9Kgnt+GoiXh7rJckmUwm1j2xjpM/ncTBxYHp26YT2Mt2K23evXSX+f3nkxiRSK2mtZixcwbeDbRrkS9Lku1A2SpfV8cWXXu8W64F+W6yLCPqBecPwGryu/s6oU5z8wRqF8ryJhorJ6zk3MpzNB/ZnMm/TrZUuBViS8fM6imrObP0DE2GNGHa5mllfl3u/9Nc1P+njJz1zsA41FbtvpSz11hmJrzxhvr4/ffVKql2yAQkUnxree5STNNJudjyd5PW47fL6vr+6ywcuJDs9Gw6PN6BUXNHWaUF/i6wg/zW7IhCz9cjvyV7IOW/6WhPN4XLmo9KITVhVUnRSaVvVI7tABrWaMiqR1YxcOFAlp5ZSoe6HXj1wVdLfw/UMda/JcDmCDiZDTF+YOqL+m1bhCAKdglvA7TAskVJxqJenJX1ZJNbpOUp1O6O+1AT8I2ov9++nOV11JPgUNQkfCBQmSnlzjd2cvKnkyg6hfHLx9t0wg1Qq2ktZobOZEH/Bdy9dJd5fefx6M5HqdGohlXe3wRcomCSXbg4jxPqTZR+OUsXIBgpilWUsaiJdVHjCT9FEm5ROXSoQxtCUKeTWoKagB9HHSO8EvUm6mM5S6My7DP6eDTnVp4DBfq/378ywrZb/d/rz7lV57i85TJXd14lqH9QidtHAfNQeyRcNlvfDvU7dArFT5VXKicn+Pe/K/pqm6GgXht4o17jFCeR4lvLz1ByrZFcTwADUGe57ID6/2ALg7tsafx2aQIfDGT88vEsH7Ockz+exKOuBwPeH2Dx98lGnfIuN8k+Qv6QGlBvWPUlP9Fuzf018uiBviYTKZGR9G3f3mYT7vKQpFtYlad/2brslnW7XH0b9eV/Q//HMxuf4bXtr9GuTjuGNh0KqNP+hKEWMjsDnDHByUyIzp1XI/fbxYz7HWiWBb1rQidn9eQRjPW+DCp6snFCHSfTH/g3EE5+Ar4D9YLjx5zFAXiQ/LHg93uCNHfws4Ps+3AfACO/G0mLUS0stOfKVTOoJjNDZzK//3ziLscxr+88ZuycUSl3tiuSZHfn3hs8n6G26CoU3aL7Kbbffa+y5N7AKk83YSEspQbwTM5yEjX5XoyamLwDvIuacDwOjEEdslGUHa/vAKDtlLbUaVdMl6tqqmbjmnR+ujNHvjjC9te28+ShJ+9p5ctG/Q6cm/Mzd2pOT9Qk+0nUrq+2OFTGlnkBrXKWwnZTtroaVyg4HENBHQrVodDij/X+f2xx/HZpWoxqwchvR/LrU7+y74N9eNT1oPvz3e97v9fIT7J3AAmFnm9NfpLdB+tWxbdHknQLqwrsHYhXfS8SIxOLrRamc9ThFVj+4QKPdfkTO26eZPXx73h41SQefOow4T7NuUKht1LIn8gyCjgLfjHQzR0ebgZjW0JNWy8HXkYNgT/lLOmoyUfuWPDzqN3rQoG/orYA5ibgA6j4DYYzy86w5cUtgNoq0+mJTvfxG1ifd6C32uI9YAF3zt/Ja/H2aX5/B4UJtWVlt9kSWWgbJ+AB8pPsByj9S0xadEumx3a7L4rqowPwOerN0J9RE/DtZktNYCpqy18Hs9dd232Ny1suo3PQ0e/tflaL1570+XsfTv50kqgjUYStDqPVeDUNvIR6g3keBYfm9EJNtMejzkxgMUYjXL+uPg4MBJ0tlTS1rrJMTVgX+C9wGvWm1Mmc7S/mLOZTOtbm3kS8OZZPYmx5/HZpOj3ZieSbyex6cxebZ2/Go45HuacVTCZ/zuwtqP8P5mqh1goakvOz/n1HXb1I0i2sSqfXMfSzoawYvwKTAorZ2diEeiI2ZhmZ23UuYxePpenQpvfsIwv1RHCG/Nbrs8AlRcEw/HOIOUtGxH52LhsNTx4CZy91fgKzF7hcgQF1YUxfGD4c/O110r9ycEE9SQ5C/aK7TP5Y8J2oCdt3OYsj6l3L3IJsLSnbXebL2y6zdsZaALo9341ef+tl2V/CSrwCvJi5W028Y87F5LV41w6uXeZ9VFaSXZTyDkkQQmjDBZiUs1wDfspZIlC7o3+BWvjwCWCyycSOv6mt3J2e6mRzY0lthUcdD3q+3JPQt0PZ/sZOjo9uwQ+OenabbVMbtSDaE6jfZ5UiLQ2Ccrq3JyeDu/1MP2lpekrvhfUF6nfXRLPnbgOnyE/CT6IW6IpBrVmzzWxbF9RChR24/+7pRY3fnvTzJItPRVfZer/Rm+SbyRz58ghrp6/FrbYbQSHFD7kwon7GW1GT7P0UnDM7ty7QkJylE9JT7H5I0i2sLmxsMMtXTWDo7M14m1UxT2zgxZ43ejNh7gnSjkaxePhi2vyzL7zZl3M6JS/BvkDBk4I5D6MTHt1Xcyu6C6bYP+DlqbD8F4jR0aQJjBwJI8ZBnz7g7FzMTqqJJsBzOUsaaov3xpzlMmpXoh3Ay6jjDnMT8BCKbh2IOhrFirErMGYZaT2hNUM/HWrT3bFK41HXg0d3P8rCgQu59fstNfHeMYM6bYvu3mlC7Sa3i+KTbEfuTbItVWu/Ko5/EqIqawS8DfwDtbX7B9RW8OM5y9e/XmD8wRvoXB3o9WYfrcK0Cx7/1wPjV0eIu3CHBT+e4NjTXVBQa5g8gTrrh+2Nxq3aKtILy4/8xoFcaajXfifNllOoFdaP5Cy5FKAZ97aK16X4hgN7Gr9dGkVRGPrZUFJupXBu1TmWjV7GzNCZBSr736TgnNm3C+0jiPwkO4TKrf1T3UjSLazKQM4JeGwwf4xuQcO91/GITiLZ35Pw3oGY9DouzOzAmBc30/ibY5x5K5SLByNZs2gMaT756Ykn6liSxmlgPA2RW+D4Qki+CMnUgXpr4bHe4LueAS/+gy/HvUfz5mDHOWClckW9OBkK/A+1J0FuN/TdqC0yX+csucUyciuiNwPuXrzD4uGLyUzOJGhAEA8veBhFZ/8ftnttd2bsnMHCQQu5eeIm80PmM33bdPw7+ucl2bvNlsKFYyozyRZCVA168i9yY4FFwA8GI31yxnKHzu7OPH9PHgNmIl06cyUAS1HHah/zdKbbm30Y/sJmBrwdykPT2/OYmyO2Xb6z6rNELyxXoGvOksuI2jhwstAShdowcwFYYba9H0V3T0+5kciKcfY1frs0Or2OMQvHkBqbyrXd11g8bDHBvz1BaOOabEW9YWHOg4JzZt/bv1RYiiTdwqr2kp+YmPQ6rhUxLViSswMLvh5J+x4NGPn0epptvsRLnb/DY9UE2nauB2fg6FrYuAGWHgHzSe9q11a7i48Y0YX4wLnM2jyNHVnvcyq7HS2UCdb4FauEZqg3R2aj3k3eRX5Btmvk3yV9EWgTncSYIYvQx6RSp5M/E9dMxMG56pxa3HzcmLFjBouGLibqcCTf91/A9a3T2NI1QJJsIYRF+aKeV0OWnObnszEYa7jw+6sPEgO8CfwT9cK4urbemlC7wM5FTapy5wJ2BBo93QXn/x6Eq/H0/+wggX+rjvM22J7K6IWlQ71OaQY8Yra+uO7pt8m/bsnVbG8448evxPl2CkpNFzouG0/7wU3svqCeCbjk4kDczxNJ6TsPTt1iy5BF/Lj/cVL81H6KhefMrm7nEa1UnStjYRcKzzNcnP8DnpnRHpf2dVg5dgXxV+JI7/4jczyHsTOhE+YdhTp2zOk2PgK6djWvXTKVCwkn+c+B//DYL4/RwqcF7eu2t+wvVA24AyNzFhPqF1huAn4oIZ1ewxajvxrP3SY1+d/GKazzcs4ryNZYs6jvnwm4Sk4rdk1X9m+dRr/hSwj8LYLaAxfC5qk49mhAd/KT7B5Iki2EuD+GTAO7/7kbgIGv9uSvNV1Zhdr9fA/5tThqA9NRE/CiKkhXJbeABaifwXmz9a1Qi6JNA2o76fn93RDWTlvL/o/203lWZ9x85IxcnZSpe7rJhPL1UfrP3ow+28ittn4s+3kScY1rVqh7ui2Io+Cc2dcBvF3w2DSVJ3r+iM+lu7wwYgmtdz3KYA8nyl6dRliSJN3Cqspar6xjJKxfDevX1+VgxCxG8DMtjefpk7Cemg43MAwZzoiHHRk+HOrVK34/Hw38iN9v/87Wy1sZvWw0R2cdxdfN1yK/S3WkoE6dFgy8kJ7N/IeXc+PULQx+7mzfMo27dTzyLgifR+2+lZuA9yG/aLwtMqG24puPyY4w38DbhVubp/LkyKXU3BPO04MXMWHTVJrb+PzjQgj7cuz7Y8RfjVen/XmhO07AjJzlImpF7vmoN7Hn5CwPoCbfE1GHX1UFBtQEYi6wDnXqL1BvBE9C/X0foGAy1HZyW37792/cOnWLfR/uY/B/BlszZGGDzLunZ6dns+GZDZz86SQAXhNak/njKHq4O5Wre3pH1ORcq9op2cBh8gugHebeObN7A0P8Pem5ZRr7H/wRjkZhGreCWr9OBiep+qKF6jufgdBEb8A9s4QNjKCLhGmBMHs2bNsGSVkuHGo0kZSeA0Cn0Db7JENu/MDYkLslJtwAep2eZeOW0bRWU8ITwnlk5SNkGYorwybKymgwsnb6Wm7svoaTpxN/3jyVM01qcRr4F2qrrwPqF9enqF2YagGjUMeFh2sTdgG5Ldk/oVa1bYTaMv8EsBA14XZEncv8DdSCI7c8nfl44xSC+gdhSM5k1ZBFXN11VYvwhRBVUGZKJnve3QNAnzf73FPMqRnwIWpL1q/Aw6jn2oPAU6g3th8H9lHsrJw27xpqcblGqDds16AmGd2B71FvNsxF7VlUuPVR0SkM/GggAIc/P0zC9cIzC4vqKvFGIvP6zuPkTyfV4+Tjgby4bBzvuzuxAbXw6S3UJPZfwGTUBgYd+d3TP0ad2z0Y9ebWA6hTsn6D+jeYUs6YDKg3+Jfm/DSUsG046uwy41F7uTyIWojxIGrC3Qr4C2qjx13Ua5aXgZ7NfZiyYQqO7o5c3nqZXx77BZPRXs8O9k1auoVV/WqAlNwbbLlzhOXKuU1nfF49yfXpp3YZHzECWrZUUJReXN0VwOpJq7l16hbfdf6OMQvG0GJUixLfs6ZrTX6Z9Avd53Zn97XdvLTlJT4f/rnlf7lqwmQysemFTZxbdQ69k55JP0/Kq4zZJmd5FbXIzXbyu6JHo14k/pqzn1bkV0TvhXXGFF1D/WLLbc2+Xuh5B7inu/g9ldrdnZi8fjLLH17O5a2XWTJ8CZN+mWQ3c3kKIWzXoc8OkXIrhRpBNej0ZKdit3Mgf9hP4a7XudOQtUBNwGegdo+1ZRnAL6jJ9HbybxjUIr8Lfdsy7qvJkCY06teIa7uvsfufuxn902iLx1skBwd45pn8x8JmhO8NZ+X4laTczpl/e/l4mgy69zvbD7WRwLx/RCpFV09PBQ7lLLkU1B5+Hbi3e3phayi6svtnqAXoUsifM3srBYdVANSk4JzZDYr8zVUB3QKYsHoCS0cu5fSS07jXdWfIJ0NKeIWoDHJWEFZzELVLGHrUb9UWFDxL3ECtILMWfvlVHaddWFBIELOOz2LlIyu5ceAGy0Yvo9ffehHyTgg6h+I7brSq3YrFYxczetlovjjyBR3qduCJTk9Y7HerTva8t4ejXx0FBcYsHENQ/6LngPQGxuUsJuB38qckOwCcy1k+Qa2eOZD8iujFVec1oBbji0Zt0elNyd27rlGwunjhFvYyJdlFcHR1ZNIvk1gxfgUXN1xk6ailTFwzkWbDm5Xh1UIIca+0u2ns/3g/ACHvhqAvYxfQOsArqK1av6Em3ytQL9L/CryOmpw/gXp+taULv7Oo8S4A7pitH4g6Vns06nzM5aEoCgP/NZC53edyasEpevxfD/za+Fkm4JI4O8OXX1b++4gyKzz/dp12dZi4dmK55t92A7rlLLkMFF09PRr17+48sNxs+zoUTMJvo17uFm5vjkS9ZmqTs4/Cc2Y/QP5MB50pX/f2pkOaMvqn0aydvpaDcw7i6e9Jz5d7lmMP4n5J93JhFX8AI4AMPbAB9Zu/EWqmMznnZxCwVt0+Kan4fXkFeDFz90y6z+4OwL4P97FoyCJSbpfcsWdUi1G8G/IuAH/e8Gd+i/itor9OtXXsu2Ps/sduAIb9bxitJ7Qu0+sUoD3wN9SkOQb1C+lR1C+jZNT5aWeh3odpB7yGOnd47pfOGtRDJgS1e1dIzr/XmL1POOpYx5k5zwUBj+WsC0e92OyJehG6FYhH7Yb5HupFXlkS7lwOLg5MXDORlmNaYsgwsOzhZfzxyx/l2IMQQuTb9699ZCRk4NfWj7aTy9qum09B7XL6I+rF//eoF+kG1FbkUUAg6nn4oqWCroBk1ES7B2py8V/UhDsA+DvqVIzbUMenlzfhzhXQLYDgccGYjCZ25Ey9JqqX7PRs1j2xjo3PbsSYbaT1xNY8/tvj5Uq4i6NHbdGeAHyA2pgQhToHdlHd0wt3W59N0cM/ctedQb32aQQ8jXqdcwf1euVN1BsAFRmV3W5aOwb9Wy0zt+2VbZxaWHgCMVGZJOkWlS4Kdf7nu0DLRNSzVDZqd/JQYFnOT7MqEP6lVFzTO+kZ+ulQxi0dh6O7I1d3XuXbTt8ScSCixNe90fsNxgWPI8uYxdjlY7mRWHjSJ1GcsLVhbPjzBgB6/7033Z7rVsorilcT9TCYh3p8HAXeIX+MnvnYcF/URHkc986DnXtXuD9qgt0INeEuKcneD7yP2h2rPEl2UfROesYvH0+rR1phzDKycvxKzq06d597FUJUN0lRSRz+32EA+r/fH0V3f7WSPVFbig+gtib/H+o40GjgI9SEoQ/qubK841ArwoTaDTd33PmTqL3fHIAxqPfiw4F3Uc/lltD//f4oeoULv17g+r7Cg4kqgckEMTHqYpIxs1pKvJHIT31+KjB+e9zScffUSLC0Oqhd018FlqD25ktCPfa/Bf5M2WcaWIh6A+ob1L8RbwvF2PPlnvT4vx4ArHt8HZc2X7LQnkVpJOkWlSoBtctwOGoBmK+ug5JW/PaKAg0aQO8yTq/ZZlIbnjr8FL4tfUmKTGJen3kc+vwQpmK+8BRFYd7D82jr15ZbKbcYu3ws6dnp5fytqp/wPeGsnrwak9FExyc7EvJOiMX2rUPtJvUmatfIGNQvq2moCXci6oVjUXL/l3ehdiV3QE3c/4Z6VzkOyybZRdE76hm3ZBxtp7TFmG1k1aRVnF56uhLeSQhRVYW+G0p2ejYNejag+cjmFt13K+A/qDctV6N+J+tQex3NRE2Cn0atgGzpVDEWtZhmO9RW97moLd3NUG+sRqC24g3H8pWgfVv40vGJjgBsf217sdcFFpOaCn5+6pKaWrnvJYoVvjec7zp/R9SRKFxqujB181QefOVBFEWbSb9yu6fPAr5C7c1RFnoqb5qyQR8Pou1U9ZplxbgVRB6OrKR3EuYk6RaVJgO1GMQp1Lt/34bD1MH5N4ALn/9y//3pp6Avx7dv7Va1efLwk2prY7aRzS9sZs3UNWQmF10m3cPJg18m/UIt11ociTrCrF9nVf6XsR279fstlo5aiiHDQIvRLRj59chK/fLyQe1+tRC1q9ZXZXzdx6hJ9m+o3b0Go44Vtwadg46HFzxMh5kdMBlMrJ22llMLpNuWEKJ0dy/d5cTcEwAM+HBApZ1fnVC/k3Nbld9DnbEhCbUqcnfU5PhT1GS5KAYgVFHYExBAqKIUWW3ZiFq2ZRJql/G/oHaXdUUt6haKOl71VSq/wFu/f/bDwdWBiP0RXPj1QiW/m9CSyWTi8JeHWdB/ASm3U6jTrg6zjs4qsmCalso6dW5Zt6sIRacw+sfRNBnchKzULJaMWMKdC3dKf6G4L5J0i0phRL2DvhM18Zl/G54IgehoaNMG5s2DgICCr6lfH1atgrFjy/9+zp7OjF8+niH/HYLOQceZpWeY230usX8UfekQVDOIlY+sRK/oWfj7Qj49+Gn537QaiL8Wz6Khi8hIyCCwVyDjlo4rsWCdpemBGmXctj7WS7KLotPrGPXDKDo91QmT0cTPM3/m+A/HNYxICGEPdv1jF8ZsI02HNqVhn4ZWec/6qFMhXkT9np6KOn76DGqSXA91CNBm8qcxyq2rMcjBgTldujDIwaFAXY0bqN3Dm6D2LFoOZAKdUG+eRqF2Z+9D5bXgFeZZz5MHXnwAgB2v78BoMJbyCmGPcsdvb3puk8XHb1tab9S/v+L+BhTU2jZl7PBZYXonPRNWT6Bel3qkxqayaMgikqJLKKgk7psk3aJSvII6VNsB+CkBXgqBq1ehSRPYuhUefRSuXYNdu2DJEvXn1asVS7hzKYrCAy8+wKO7HsXD34OYczF83/X7YsfY9g/qz5whcwB4edvLbLu8reJvXgWlxKSwaMgikqOT8Wvjx6R1k3B0dbR6HLZwV7isFJ3CyG9G0vXZrmCCX5/8lSNfH9E6LCGEjbp58iZnlp4BoP8H/a3+/jrUopSLUMd7f4U63CcLWIla8zQIeAR1fuDi6mp0Bhqizq99DXX86bPAceAY6ljWGpX5i5TgwVcfxKWmCzFnY/h94e8aRSEqi1bjtytKjzotGBQxz3zOz0+x/HCLojh5ODFlwxRqNa1F/LV4Fg9bTHqCDLmsLJJ0C4v7BJiT8/jrVPjXQDh3Tm3Z3r49v0iaXg/9+sHkyerP8nQpL0lgr0CePv40Dfs2JDM5k5WPrGTry1sxZN3bEe75bs/zWIfHMJqMTFw1kct3L1smCDuXmZyZ193IO9CbqZun4lrTVZNYbOWucFkpOoVhnw+j+4tqdf2Nz2zk4GcHNY5KCGGLdr6xE4DWE1vj31HbW4c1UJPjo6jTHz2PWvQyAlhFydWWj6P2cOuLOjQoGvgC6FiZAZeRSw0Xer+ufkPs+scustOzNY5IWIqtjd8uq7Gof1OFOnxSP2f9fbQ/lZu7nzvTtk7Do64Ht07dYvnDy+VvpJJI0i0sagnqXKEAH2TBouFw9Cj4+MC2bdCokXXi8KjrwYztM+j5ijoH4YFPDrBgwIJ7us4oisLXI77mgfoPEJcex+hlo0nKqN7dawyZBlaMW0HUkShcfVyZtmUaXgFemsVjS3eFy0pRFIbMGULPV9Xjb8uLW/jtPzJFnRAi3/V917m48SKKXiHkXcsVp7SE9sD/ULuEv1nG1ywAdqMWwdTmFm3xuj3XDa8GXiRGJHL4y8NahyPuk72M3y7JWNReIbtQr513AVexbsKdq2ZQTaZumoqTpxPXdl9jzbQ1MhSjEkjSLSxmB+o4boAXDLBvDISGgqcnbNkCwcHWjUfnoGPQx4OYsHoCTp5OXN97nW87fkv4nvAC2zk7OLN6wmr8Pfw5G3OWGT/PwGiqnicbk9HEL4//wuWtl3F0c2TKhin4tvTVOiybuitcVoqiMPCjgfR5sw+gzom594O9GkclhLAFJpOJ7a9tB6DjEx3xaeajcURFc0Gda7gsHCozkPvk4OJAv7f7AbD3/b2kx0sXWntVePx2m0ltbHb8dmn0qFOjTs75qWXjQd0OdZn0yyT0TnrCVoex6YVNUmTYwiTpFhZxEnUewSxgghFuz4CNG8DFBdavh86dtYsteGwws47Owq+NHym3Upjffz6//ee3AieTep71WDtxLU56J37+42feCX1Hu4A1YjKZ2PryVk4vPo3OQceE1ROo372+1mHlsaW7wmWlKAoh74TQ751+gNqVdPdbu+WLTIhq7uLGi0Tsj8DBxYG+/+irdTglsqe6GiVpP6M9tVvVJj0unf0f77f8Gzg4qAVrHn1UfSwsrvD47UH/HsTYJWNtdvy2vQkKCWLMojGgwNGvjrL3fWkosCRJusV9u4pabCUJCDGB92xYtkT9zlm9Gvr00ThAwKe5D08cfIK2U9tiMpjY9so2Vo5fSUZiRt423et357uR3wHwdujbrA1bq1W4mvjtP79x8L/q2ONRP46i6dCmGkd0L1u6K1wefd/sy4CPBgAQ+nYoO9/YKYm3ENWUyWjKG8vd9bmumg7fKQt7q6tRHJ1ex4AP1fPwwU8PkhRl4aFkzs7q1Czz5qmPhUWZj992reXK1M1T6flyT5sfv21vWj/SmmH/GwbArjd3cXyuzMJiKZJ0i/sSCwxBnU+5HdD+bfj+C3XO7UWLYPhwbeMz5+TuxJiFYxj+1XB0jjrC1oTxXZfvuH3mdt42j3Z4lNndZwMwfe10Tt86rVW4VnVqwSm2v6p2dRz070G0n95e44iqnl5/7cXgOYMB2PfhPra9sk0SbyGqoTPLz3Dr1C2cvZzp9VovrcMplT3W1ShO84ea0+DBBmSnZbP77d1ahyPKoKjx208dfcquxm/bm27PdaP3G+pttPVPr+f8uvMaR1Q1SNItKiwFGIk6z2cgMOIL+PRt9blvvoGJE7WLrTiKotD1z115bO9jeNX34u7Fu8ztPpfTS/KT6/8M/g/9g/qTkpXCw8sf5m7aXQ0jrnwXN17kl8d/AaDH//Wg58s9NY6o6urxlx4M+0K9g3zgkwNsnr1ZEm8hqhFDloFdb+4CoOcrPXHzcdM4orKxx7oaRcmttQFw4ocTxJ6PtdzOTSZISVEXOa9bRHZ6NuseL2L8dpD9jd+2NyHvhtDxiY6YjCZWTVzF9f3XtQ7J7knSLSokG5gEHAJqAY8tgw+fV5/7+GOYNUu72Mqifvf6zDo+i8aDGpOVmsWaqWvY+PxGDJkGHHQOrBi/gqAaQVyJu8LEVRPJNlbN6RNuHLzBykdWYjKYaDetHYM+HqR1SFVet2e7MfK7kaDA4c8Ps+GZDZiMcoEmRHVw4ocTxF2Ow93PnQdefEDrcMolt67GtuxsXjp6lG3Z2TZfV6Mogb0Caf5Qc0wGE7v+vstyO05NBQ8PdUlNtdx+q6mEiAR+6v0TJ+fJ+G0tKIrCyG9G0vyh5mSnZ7N05FJun71d+gtFsSTpFuVmAv4ErEetbPr8Vnhnivrc66/DK69oF1t5uNd2Z+qmqfT+u9qF5sgXR/ipz08kRCTg4+bDL5N+wd3Rne1XtvPqtlc1jtbyYsJiWDJiCVmpWTQd2pRRP45C0cnYKGvo/FRnRv84GhQ49s0x1j21TqbnEKKKy0rNIvSdUAB6v9EbJw/7Sx70QF+TiT6RkfQ1meyiS3lRBnwwABQ4t+ockYcjtQ5HFBK+J5zvu3xP1FEZv60lnYOO8cvGU79HfdLj01k8dDEJEQlah2W3JOkW5fYW8APqwfPyEXhvuNqT6pln4L33tI2tvHR6Hf3f7c/k9ZNxqeFC5KFIvuv0HVe2X6FtnbbMf3g+AP89+F/mn5yvcbSWk3gjkUVDFpF2N42AbgE8svIR9I72evlknzrM7MCYhWNQdAonfzzJLzN/wZgtibcQVdWhzw+RHJ2Md0NvOj+t4ZQeAr82fnR4tAMA2/+6XYb52AiTycThLw6zYICM37YVjm6OTFk/Bd9g3wLXjqL8JOkW5fItkDuZ1ovn4T99wGCAadPg88/VAmr2qPmI5sw6Nou6HeuSGpvKwsEL2fP+Hsa2HMs/+vwDgKfXP83hyMMaR3r/0uLSWDR0EYkRifi08GHKhil22eJSFbSb2o5xS8eh6BV+X/Q7a6evxZBl0DosIYSFpcens/9f6jRV/d7uh4OzTCmltX5v90PvrOfa7mtc3nJZ63Cqvbzx28/njN+eLOO3bYVrLVembZmGV30vYsNiWfrQUrJSs7QOy+5I0i3K7GfgmZzHT0bCd10gPR1GjYIffwSdnR9NNRvX5PH9j9Ph8Q5ggl1/38Wyh5fxavtXGd1iNBmGDMYsH0N0UrTWoVZYVloWSx9aSszZGDzreTJtyzTcfO2jkE9V1XpCax5Z+Qg6Rx1nlp1h9eTVGDIl8RaiKtn/7/2kx6VTu1Vt2k1rp3U4AvAO9Kbrs10B2P7adqmtoaF7xm//ZxBjF8v4bVvi3cCbqZun4lLDhYjfIlg1cZX0zisnO0+ThLXsR50b2QiMvwur20FyMoSEwPLl4OiocYAW4ujqyOgfRvPQ3IfQO+u58OsFfuj6A580/oRWtVsRlRTF2BVjycjOKH1nNsaYbWT1pNVE7I/A2duZqZunUqNhDa3DEkDwmGAmrpmI3klP2OowVoxfQXZG1SzeJ0R1k3wzmUOfHgKg//v90enl0stW9H69N85eztw6dYszy85oHU61VHj89rQt0+j5fzJ+2xb5tfZj8vrJOLg4cGH9BX59+lcZmlEOcuYXpQoDHgLSgQEpsK89xN2Fbt3gl1/AxUXjACtBpyc68cRvT1CjUQ3irsSxrO8y5hjmUMOlBgdvHOSZDc/Y1YnGZDKx/k/qXIt6Zz2Tf51MnbZ1tA5LmGk+sjmT1k1Sv8x+vcDyh5eTlSbdt4Swd3ve20NWahYB3QNoMbqF1uEIM24+bjz41wcB2Pn3ndLLyIruGb/dXh2/3XhgY61DEyUIfDCQ8cvH59WjyZ0CUZROkm5RokhgCBAHdMqEy93g5g1o3Ro2bgRPT40DrET+nfyZdWwWzYY3Izs9m4MvHOSDEx/gmO3Ijyd/5MsjX2odYpntenMXJ344gaJTGL9sPA17N9Q6JFGEpkOaqneRXR24tPkSy0Ytk3FTQtixuCtxHPvuGKBWzJbWO9vTfXZ3PPw9iL8az9Fvj1Z8R3o9jB+vLnopTJrLaDASHhpO3J44wkPDMRqMRY7ffuK3J2T8tp1oMaoFI78dCcDe9/dy+Av7r3dkDZJ0i2LFA8OACKCpAVL6w7Vz0LgxbN0KPj7axmcNrrVcmfzrZELeDQEFbi+/zZsr36RGXA1e3Pwiu67a/h2+Q58fYu/7ewEY8c0IWj7cUuOIREkaD2jM1E1TcXR35Mr2KywZsYTM5EytwxJCVMDuf+7GmGWk8aDGBPUP0jocUQQndyf6/rMvAHve3UNGUgWHj7m4wMqV6lIVuwBWQNiaMD5r9BmLBy0mfE44iwct5r8N/stXbb66Z/y2o1sVGadYTXR6spN6bQxsemETZ1ec1Tgi2ydJtyhSBjAGOA3UMYLbWDi/H+rVg+3b1Z/VhaJT6PP3PkzbPA1XH1eM5408/8PzBF0I4pGVj3A17qrWIRbrzPIzbJ69GYCQd0Po/JRMU2MPGvVtxPSt03HydOLa7mssGrqIjET7qyMgRHV26/Qtfl/8O5AzL7SwWR0f70itZrVIjUnlwCcHtA6nSghbo9YnSbyRWGB9cnQycZfjcPJwkvHbdq73G73VYoQmWDt9LVd32e71sC2QpFvcwwjMAHYDniZo8DT8vk5t2d62DYKq6c36JoOb8PTxp6nXtR76ZD1Tl0yl7ca2PLz0YVIyU7QO7x5Xtl9h7fS1YIKuz3al9xu9tQ5JlEODng2YsX0Gzt7OROyPYOHghaTHp2sdlhCijHb9fReYIHhcMPW6VKM71XZI76jPuzFy4JMDJN9K1jgi+2Y0GNUb/iWUvnHydKJRSCOrxSQsT1EUhn42lFbjW2HINLBs9DJunrypdVg2S5JuUYAJ+AuwAnA0QZs34ehcdez25s3QqpXGAWrMO9Cbx/Y+Rpc/d0ExKfQL7Ue7Oe14YuETNlVYLfp4NMvHLMeYZaTVI60Y+tlQuZNshwK6BfDozkdxreVK5KFIFgxcQNrdNK3DEkKUIuJABOfXnUfRKfR/r7/W4YgyCB4XTL2u9chMzmTPe3vKv4OUFFAUdUmxvRvx1nR97/V7WrgLS45O5vre61aKSFQWnV7HmIVjaNSvEZlJmSwetpi4q3Fah2WTJOkWBfwb+F/O425fwoH31aFJv/4KXbpoGZntcHB2YMRXI3h4wcPoXHQ0vdwU///z5/3v3tc6NADuXrrL4mGLyUzOpFFII8YsHCNT1Ngx/07+zNg5AzdfN6KPRTO//3xSY1O1DksIUQyTycSOv+0AoP3M9vi29NU4IlEWiqIw8KOBABz79hhxVyRxqKgbB2+Uabuk6KRKjkRYg4OLAxN/nkid9nVIvpnMosGLSLldvW88FUWuxEWehcBfcx4/uAr2Pw8ODrBqFfTtq2Vktqn99PbMOjwLfQM9NRJqkPFsBj++/aOmLd7JN5NZNEQ92dXtUJeJayfi4OygWTzCMuq2r8ujux/FvY47t07dYn7IfOn+KISNurz1MuGh4eid9PT7Zz+twxHlENQ/iCZDmmDMMspUSOWUmZLJiZ9OMPeBuXk3nUrj6V+Fp8CpZly8XZi6aSo1GtXg7qW7UgS2CJJ0CwC2Ao/nPO6+F/Y/ovaQWrAARozQMjLbVqdtHV4+/TIZPTJwMDgQ8VYE8yfN12Sap4zEDBYPX0zclThqBNVg6qapuHhLBdWqwq+1HzNDZ+JZz5PbZ24zv998kqKklUAIW2Iymtj5+k5AraXhHeitcUSivAZ8qI7tPr3kNNEnojWOxvbdOn2Ljc9tZE7AHNY9vo7IQ5EoegUH1xJu+Cvg1cCLwN6B1gtUVDpPf0+mbZmGm68bUUejWDFuBYZMg9Zh2QxJugXHgXFANtDhHBzKadX+6iuYPFnDwOyEi7cLb+15i8uPXMaoGAlfEc53D3zHnYt3rBZDdkY2y8cs5+aJm7jVdmP61ul41PWw2vsL6/Bt4cvM0Jl4NfAi9o9Y5vWdR0JEgtZhCSFynFt1jujj0Th5ONHrb720DkdUgH9Hf9pMbgNQ5hbb6iYrLYuT80/yQ88f+KbdNxz58ggZCRnUCKpB/w/689KNlxi7aCwoqIu5nH8P/XSoDH2rgnya+zBlwxQc3R25vPUyvzz+Cyaj7dQ80pIc7dXcZdS5uJOBljfgZAfABB99BH/6k6ah2RUnByc++ekTNj+zmWT3ZGJPx/J9l+/545c/Kv29jQajOlXDzqs4eTgxddNUajWtVenvK7RRq2ktZobOxLuhN3cv3WVe33nEX4vHaDASHhpO3J44wkPDMRqMWocqRLVizM7vktzj/3rgXttd44hERfV/rz86Rx2Xt1zm6k6ZBilXzLkYNs3exJx6c/hl5i/cOHADRa8QPDaYaVum8cKlF+j9t9541PUgeGwwE1ZNwCvAq8A+vOp7MWHVBILHBmv0W4jKFtAtgAmrJ6Bz0HF68Wm2vbpN65Bsggz2rMZuA0NzfjaMgz9aA1nw2mvw17+W/Fpxr9rutfn2798y2H0wDy19iMCIQJY/vJwH//qg+gXuYPl7XCaTic2zN3Nu5Tl0jjomrp1Ivc4yNU1VVzOoJo/teYz5/ecTdzmO77t+j85BR/JNdZx3+JxwvOp7MfSzoXJhI4SVnJx3kjsX7uDq40qPl3poHY64DzUb16Tz05058sURtr+2nScPPVltZwDJSsvi3KpzHP/uONf35Vcb927oTaenOtHx8Y7Fjs0OHhtMi9EtuLLrCvs27aPXsF40DmksLdzVQNMhTRn902jWTl/LgU8O4FHXg54v99Q6LE1J0l1NJQMjgUtAnVS40RZIVFu3P/hA29jsWYe6Hfh8+udMcZ7CoG2D6HGwB/v/tZ/Iw5GMWzoOjzqW7fK994O9HPnyCCgwZuEYGg9sbNH9C9vlHejNzNCZfN/1e5Kj7y2qlhiZyIrxK6RFQQgryErLYvdbuwHo/UZvnL2ctQ1I3Le+b/bl1LxTRB2JImx1GK3GlzJnql4Pw4fnP7ZzsX/EcvTbo5yaf4r0uHQAFL1C85HN6fx0Z5oMblKm5Fmn19Gwb0POppylYd+GknBXI+2mtSP5ZjLbXtnGtle24V7HnfbT22sdlmYk6a6GsoAJwBHAOwviuoMhEqZMgS+/VAuoiYqb2GYip26d4kP9h9xqeItxv47j2q5rfNfpOx5Z+QgNejawyPscn3ucXX9XuzIO/WwobSa2sch+hf0ocdy+CVBg84ubaTG6hVzoCFGJjnx1hKTIJLwaeNH1z121DkdYgLufOz3+rwehb4ey842dtBjdAr1jCcm0iwts2GC9ACtBdkY2YavDOPbtMcL3hOet92rgldeqXbi7uBAl6flyT5Kikzg45yDrHl+He213mg5tqnVYmpCku5oxAbOATYCzEbKGQOYZGDkS5s0DnVyXW8S7Ie/y+63f2cAGshtl8+TaJ4m/EM+8vvMY/Mlguj3f7b66qv3xyx+sf3o9AL1e70X357tbKnRhR67vvV5kK3ceEyRGJLJw8ELqda6HV30vPAM88arvhVd9LzzqekgyLsR9Sk9IZ9+H+wDo+8++OLjIpVVV0eP/enDkqyPcuXCHEz+eoMvTXbQOqVLcuXCHY98d4+S8k6TdSQNA0Sk0G9GMzk93punQpvJdISps8L8Hk3IrhdOLT7Ni/Aoe3fUoAV0DtA7L6uSboZp5E5gH6E3gNA2SdkG/frBiBTg6ahtbVaLX6Vk8djHd53bnzJ0zrJ29lmdCnyFsRRibZ28m4rcIRs0dhZOHU7n3Hb43nNWTVmMymujweAf6v9e/En4DYQ+Soss2Zdi1nde4tvPaPesVvYKnf34S7lk/53GAV/66ep7oney/q6QQleXAJwdIu5OGTwsfOjzaQetwhAU5ezrT580+bH5hM6Fvh9JuWjuc3Mv/vW2LDJkGwtaqrdrXdl3LW+8Z4EmnJzvR8YmOeDeQKe/E/VN0CqN/HE1qTCqXt15myfAlPL7/cXya+2gdmlVJ0l2NfAW8n/PY42VIWApdusC6deDqqmVkVZO3ize/TPqFbnO7ERoTSstpLZnZcybbXt7G2eVnufX7LSasnkDt4Npl3uet07dYNmoZ2enZNH+oOQ99+1C1Le4iKLZ4TWFd/twFBxcHEm8k5i1JUUmYDKa8f5fEvY57XhJeuLU8N0l3dJO7dqL6SbmdwoE5BwAqrWCm0FaXp7tw8L8Hib8az6HPDtH79d5Fb5iSAn5+6uPbt8HdNqvX3710N69VOzUmVV2pQLPhzeg8qzPNhjeT41hYnN5Jz4TVE5gfMp+oo1EsGrKIx397vMzXMVWBJN3VxBrguZzHNf8LcXOgVSvYtAk8q8/xbnUtfFuwdNxSRi4ZybfHv6XDiA48uvtRVk1YRWxYLHO7zWXUD6NoPaF1qfuKD49n8dDFpMen0+DBBoxfNl6+GKu5wN6BeNX3IjEyUR07UpiiTs8y7PNh93QNNBqMpNxOKZCIJ95IJOlGUv6/IxMxZBhIuZVCyq0Uoo9FFxuLay3X4pPynEWL4lJGg5Hre6+TFJ2Ep78ngb0DpZuksJi9H+wlKyUL/87+BI+TgoVVkd5JT8i7Iaydtpb9/9pP56c74+bjVvTGqanWDa6MDJkG/vjlD459e4yrO/KnQPOs50nHJzrS6clOeAdKq7aoXE4eTkzZMIUfH/yRu5fusnjYYmaGzsTF20Xr0KxCku5qYC8wBfWavOYKiHsJgoJg61bw9dU4uGpgeLPhfDjgQ17b8RrPb3qeVjNaMev4LFZPWs213ddYNXEVEQciGPTxoGKLtKTGprJoyCKSopKo3bo2k9dNlpZFgU6vY+hnQ1kxfgUoFEy8czpADP10aJFJpk6vw9PfE09/z2LHVplMJtLupN2TmOcl6JFJJEQkkJWSRdrdNNLupnHr91vFxuvk6VSg+3ped3azxbWWq8V6b4StUYdzmLfky1RqwlLiw+M5+vVRAAZ8OEB6HVVhbSe35bd//8atU7fY9+E+Bv9nsNYhlUnclTiOfX+Mkz+eJOV2irpSUadz6vx0Z5qPbC4374VVufu5M23LNH7o+QO3Tt1i+cPLmbpparWohVH1f8Nq7iwwCsgAaoZC3GTw94ft2yGg+tUw0MyrD77KyVsnWXZmGeNXjOforKNM3zadnX/fyf5/7efQp4eIOhLFIysewbOeJ0aDkfDQcOL2xHFJf4l97+zjzvk7eDXwYtrmabjWkvEAQhU8NpgJqyYUnVx+en/JpaIouPm64ebrRt0OdYvcxmQykZGYUWxSnvs4PS6dzKRMYsNiiQ2LLfY9HVwcSmwt96rvhbufO4qu5AQnbE2YejOiUA8AmUpNWEroW6EYMg00Cmkk0zVWcYpOYeBHA1k8bDGHPz9M9xe622zLsCHLwPl15zn+3XEub72ct96jrgcdHu9A56c6U6NRDe0CFNVezcY1mbZ5Gj/1+Ylru6+xdvpaxi0bV+V7oUnSXYVFAEOBeMD7DMQNhVo11BbuxnJ9YFWKovDDqB84H3ueEzdP8PCyh9n3+D4GfjSQ+g/U5+dHfyZifwTfdvyWrs925fj3x/MSqPA56rQdTh5OTNsyDa/6Ml2HKCh4bDAtRrfgyq4r7Nu0j17DetE4pLFVvsAURcHF2wUXbxf8WvsVu11mSmaBJLyoxDzldgrZ6dncvXSXu5fuFrsvnYMOz3pFFIDLrcru78GmFzYV3eVeplITFhBzLoZTC04BMOADaeWuDpoMaUKjkEZc23WN3f/czeifRmsdUgHx1+LzWrWTb+bPatFkcBO1Vfuh5iVPeSaEFdXtUJdJv0xi8dDFnFt1DvfZ7gz7fFiVPpdK0l1FxQHDgBuARwQk9AEPB3UMdxuZzlkTbo5u/DzpZ7p814UTN0/wxLonWDJ2CS0fbslTR59ixbgV3D59m93/3F3k6zOT1VbC8hReE9WHTq+jYd+GnE05S8O+DW0umXRyd8KnuU+J1UqzM7JJikoqNilPvJFIcnQyxmwjCdcTSLieULFgcqZSu773Oo36NarYPkS1tvPvOzEZTbR8uCX1H6ivdTjCChRFbe2e230upxacosf/9cCvTfE3Gq3BmG3kwvoLHPv2GJe2XMq70eju557Xql2zcU1NYxSiOEEhQYxZNIZVE1dx5MsjePh70OeNPlqHVWkk6a6C0oGHUbuWu96F5AfBORXWbYJu3bSNrboL9A5k1YRVDFgwgGVnltGxbkdeffBVfJr58Pj+x/mk7idkpWYV/WJpnRNVnIOzAzWDalIzqPiLRGO2keSbyXmF3ooqAJcQkYDJUFQzd0Gbnt9E0MAg/Fr74dfGj9qtamtS7E3Yl8jDkfyx9g9QIOS9EK3DEVYU0C2A4HHBhK0OY8frO5i8brImcSRcT+D43OOc+OEESVH5U0cGDQii89OdaTm6pUz1KOxC60dak3IrhU3Pb2LX33fhUceDTk920jqsSiFJdxVjAKYBewCnNEjrC/ooWLkWQuTawCb0adiH/w39H89sfIbXtr9GG782DG82nOhj0cUn3CCtc0Kgdi3P7UZenKs7r7JgwIJS93X7zG1un7ldYJ1XAy81AW9dG782fvi19sM32LfKzM0r7t+O13cA0H56+xKHVIiqqf/7/fnj5z+48OsFru+7TmCvQPUJnQ769s1/bGHGbCMXN17k2LfHuLjpYl6rtlttNzo8prZq12pay+LvK0Rl6/ZcN5JvJrP3/b2sf3o97n7uNBvRLK+2Ubh7uNWGzFUmSbqrEBMwG1gN6LMhcxgoZ2H+QnjoIY2DEwX8qcufOHnzJN8d/44pq6dw6MlDZEZnlum1SdFJpW8kRDXWsG/DUqdSc/dzp/97/Yk5F0PM2Rhun7mtdm2PSCQxIpFLmy4V2L5mUM0CyXjt1rXxbeFbLSquinxXtl/h6o6r6Bx19Hu7n9bhCA34tvCl4xMdOf7dcbb/dTuP7XtMHYfq6gq7d1v8/RJvJOa1apsXy2wU0kht1X64JQ7Och4S9i3k3RCSbyZz4ocTrBi/ApcaLnnzyIfPCa8SM4/IX2kV8hHwJaCYwDAFCIUvvoSpUzUOTNxDURQ+H/45Z2POsj9iP6OXjWZV81Vleq2nv0ysLkRJyjKV2oivRtzz5Z0Wl6Ym4Gdv5yXiMWdjSLmdQtyVOOKuxHF+3fn8XekVajWthV9rP2q3qZ3XTb1Ws1pSsKgKMplMea3cXf7URSpAV2P9/tmP3xf+TsRvEVz49QItRrWw6P6NBiOXNl9SW7U3XMRkVE9irj6udJjZgc6zOpdYH0MIe6MoCiO/GcnNUzeJPhqdl3Dnqgozj0jSXUXMB17PeWyaDayEDz6AZ57RMChRIie9E6snrKbL9104f+c8r91+jf71+5MUmVRs65xXfS8CewdaPVYh7E1FplJzrelKYK/A/O6iOVJiUvKS8NtnbxNzRk3M0+PSuXP+DnfO3yFsTVje9jpHHb4tfAu0ivu19qNmk5p23z2uOvtj7R9EHYnC0d2R3m/01jocoSHPep488OID7PtwHzv+toNmI5pZ5G87KSqJ4z8c58TcEwUKRTbs25DOszoTPDZYeteIqkuB5Ojkop+rAjOPyF9uFbAZeCL3Hx8Dn8Orr8Jrr2kXkyibOh51WDtxLb1/6s2Gyxvo8EQHHN9xLLZ1buinQ+3yRCOEFnKnUru+9zpJ0Ul4+nsS2Duw3H9D7rXdce/nXqCWgslkIjk6+Z5W8dtnbpOZnJk3Xvzs8rN5r3FwccC3pe893dRrNKxR6rzjQlvGbCM739gJwAN/eQCPOh4aRyS09uCrD3L0m6N508d1nNAcGjVSn7x2Ddzdy7Qfo8HIlW1XOPbtMc7/ej6vCKRrLVfaP9qezrM649vSt3J+CSFsyPW919WGp+LYeW0jSbrt3BFgPGoBNWUxmF6DWbPgo4+gCk91V6V0qdeFuQ/NZdraabyvvM/3c74n8RO1GnOuklrnhBDF0+l1lfLlrCgKnvU88aznSZNBTfLWm0wmEiMS72kVjzkXQ3ZaNjdP3uTmyZsF9uXo7kjtVgVbxf3a+OEZ4Fml5yy1J6cWniL2j1hca7nS8+WeWocjbIBLDRd6v9GbbS9vY/c/d9N2VGMcYmPL/Pqk6CRO/HiC498fJyE8v1U7sFcgnZ/uTKvxraRVW1QrZa1ZZK+1jeSv2Y5dAkYAKYBuGxgfg0kT4auvJOG2N1PbTeXkzZP858B/eDb5WWo9XwuXcy54JHuQ7JGMoa2BhsENCUaSbiFsmaIoeAd64x3oTbPhzfLWGw1G4q/FF2gRjzkbQ+wfsWSlZBF1JIqoI1EF9uXs7ayOFzfvpt7GD3c/d4sk40aDscpVh60M2RnZhL4VCsCDrz2Ii7eLxhEJW9Ht2W4c+uwQiRGJHPnmCD1y1ofvCafB4Jb3/D2ZjCaubM9p1V53HmO2EVAT+HYz2tF5VmepiC+qrbLWLLLX2kaSdNupW8AQIAbQnQDjWBgxGBYsAL3U77FLHw38iG1XtnHq1ilupt2EoPznlGSF8SvGs2rCKsYGj9UuSCFEhej0Omo1qUWtJrVoObpl3npjtpG7l+5y++ztAgn5nQt3yEjIIOK3CCJ+iyiwL1cf13taxWu3ro2bj1uZ4wlbE1ZgvHtVqQ5bGY5+c5SE6wl41vOk23PdtA5H2BAHFwf6vd2PdY+vY9ffd+Ul3YuHL8a1vm/e31PyrWRO/nSS498fJ+5KXN7rG/RsoLZqP9IKR1dHTX4HIWxFYO/AUmcesefaRpJ026Fk1BbuK4DuKhiHQp9OsHIlOMo5267FpMYUud6ECQWFFze/yOgWo9Hr5M6KEFWBzkGHb0tffFv60mpcq7z12RnZ3Llw557x4ncv3yXtThrhoeGEh4YX2JdHXY97WsVrt6p9T8ts2JowtbJ7oYuaqlAd1lKMBiPX914n7kocu/+xG4C+/+wriZG4h5OnU5HrEyMTWTFuBfV71CfqSFReq7aztzPtpqut2nXa1rFmqELYtLLMPGLPtY0k6bYzWahjuI8BujtgHAydG8Cvv6pTRAr7tff6XqKSoop93oSJiMQI9l7fS79G/awXmBDC6hycHajTts49F+VZqVnE/hF7TwG3+GvxJN9MJvlmMld3XC3wGq8GXnnTmtUOrs2Ov+0ouhWhClSHtYTCvQBAnR7OpYZ0KxcFGQ1Gtv5la9FP5vyN3ThwA4CA7gF0frozbSa2wdFNbt4IUZSKzDxiLyTptiMm4ElgC6CkgnEYBDvC5s3g5aVxcOK+RSdFl2m7mT/PZEiTIXQN6EqXel1oXbs1jnr5AheiOnB0c8S/kz/+nfwLrM9IyiA2LPaeAm5JkUkkRiSSGJHIpc2XSn+DnOqwm17YhF9rP/ROevTOehycHdA769E75T92cHYo8Xmdg87uCsEV1wvAZDCxatIqdA46u77oE5Z1fe/1AolBcR76/iE6PdnJChEJYf9yZx65susK+zbto9ewXlWi5ogk3XbkdWABQDaYxkGjGNi2D3xlJokqwd/Tv/SNgPCEcL47/h3fHf8OABcHFzrW7UiXel3oWq8rXQO60tynOTrFvk9OQoiyc/Z0JqBbAAHdAgqsT4tLI+Zcfov4lR1XiD1XeoXlo18dvf+gFMqVpBd4XM4EvzzPF3czwGgwsnn25qJ7AeSo7r0AREHmVZRNKERSL++xOUd3uTEuRHno9Doa9m3I2ZSzNOzbsEqccyXpthOfAx/l/uNJqHsStu2FgIDiXyPsS+/A3tT3qk9kYiSmIq76FBTqetTlv0P/y/Go4xyJOsKx6GMkZiRy4MYBDtw4kLetp5Mnnet1pmu9rnnJeKMajeyu1UkIcX9ca7oS+GAggQ+qhWeu7b7G/JD5pb6uUf9GuNZ0xZBhIDsjG0OGAUNm/uPsjGwMmYYCz2dnZBdMWE2or8swVNJvV0EK9ybpTnoM2YaSWy3tfI5YYXnmVZSzcWQus0rdTghRPUnSbQdWArNzxtrxOtRcB1tDoWlTjQMTFqXX6fls6GeMXzEeBaVA4q3k3DX/YvgXjA0ey8TWEwEwmoxcvHORI1FHOBp1lCNRRzgRfYKkzCR2X9vN7mu78/bh4+qTl4B3qdeFrgFdqedZz6q/oxBCW2WtDjt96/QKtSwYs41FJumlJexleb6825d0MyA7PZvs9OwKfYb2OkesZYMvpgAAHnxJREFUsLyqXm1ZCGE5knTbuFBgmglMCvAluP0PNu2Atm21jkxUhrHBY1k1YRWzN8/mRuKNvPX1verz6dBP75kuTKfoaOHbgha+LZjWbhoA2cZszsWc40hkfiL++63fuZN2hy2Xt7Dl8pa819fzrJffLT0nGfdx87HOLyuEsLrKrg6rc9Dh5OAE7vcdqsWYTCZMBlOpCfuNQzfY+lIxRbHMSKulyFXVqy0LISxHkm4bdhoYbYJMBVgNTq/Ar+uhe3etIxOVaWzwWEa3GM2uK7vYtG8Tw3oNI6RxSJmnCXPQOdCuTjva1WnHE52eACAjO4Pfb/3Okagjea3i52LOEZUUxbrz61h3fl3e64NqBKlF2vzV1vDO/p3xdJaLTCGqiqpcHbYoiqKgOCil3gwI6B7AwTkHpdVSlEvu39P259cxPepDAL7kWdzq+1bJvychRMVI0m2jrgNDTZCgAHtANwNWLIX+/bWOTFiDXqenb8O+pJxNoW/Dvvc9L7ezgzNdA9Qia7mSM5M5efMkRyLzE/GLdy9yNf4qV+OvsuLsCkDt2t7Ct0WB1vAOdTvg6ihz1Alhr6pqddj7Ia2WoqKCxwbTYmB9dN6vATBt41QaDG4px4oQIo8k3TboLjAUiFKAM8BomPcNjB6tbVyiavFw8qBXYC96BfbKWxeXFsex6GN53dKPRB4hIjGCP2L/4I/YP1j4+0JAbU1v49cmrzW8S70utPVrK1OXCWFHqmJ12PtV3XoBCMsx//tp2KchyN+TEMKMJN02Jg0YBYQB3ACGwefvwvTpmoYlqomarjUZ2HggAxsPzFt3K/lWXhKe+/N2ym1O3jzJyZsnmXtiLgDOemc61O1QYOqyFj4t7ruVXgghrCm3F8D1vddJik7C09+TwN6BclNCCCFEhUnSbUMMwBRgP0AcMBTe+xM895ymYYlqro5HHUY0H8GI5iMAtSjRjcQbeS3hR6OPcjTqKPHp8RyKPMShyEN5r/Vw8qCTf6cCU5c1rtlYpi4TQtg0nV4n04IJIYSwGEm6bYQJeA74GSAdGA0vD4PXX9cyKiHupSgKDbwb0MC7QV41dZPJxKW7l/K7pUcd4Xj0cZIzk9kTvoc94XvyXl/TpWaB1vAu9boQ4BlQpkTcYDSw9/peopOi8ff0p3dgb2lJF0IIIYQQNk2SbhvxPvANgBGYAk+2gI8/BmkQFPZAURSa+TSjmU8zJredDKgJclhsmJqI5xRrO3XrFHHpcWy7so1tV7blvb6uR90CreFd6nWhtnvtAu+xJmxNkVOpfTb0s3umUhNCCCGEEMJWSNJtA34E3sz9x/MwwRG++UYSbmHf9Do9bfza0MavDTM7zAQg05DJ6VunC4wPP3v7LDeTb/LrhV/59cKvea9v6N0wb+qy9Ox03g59G1OheXwiEyMZv2I8qyasksRbCCGEdhQFWrXKfyyEEGYk6dbYBmCWCXU6kg9g2FVY+DPopcesqIKc9E50rteZzvU6561LzUrNm7rsaLTaKn7+znnCE8IJTwhn1blVxe4vNwl/duOz9KjfA183X6mgLoQQwvrc3ODsWa2jEELYKEm6NXQYeMQIBh0wD3pthlWbwclJ48CEsCI3Rzd6NuhJzwY989YlpCdwPPo4R6KOsOHihgJjwotyM/km9ebUA8DTyRMfNx9qudailmstfFzzHxf+d+52NV1qSrIuhBBCCCEqhSTdGrkADDNAmh7YCB2+gvXb1BulQlR33i7ehASFEBIUQgOvBqUm3eaSMpNIykziWvy1cr2nvSfrBqOB0PBQ9sTtwT3cnZDGIVJkTgghhBDCBkjSrYGbwEAD3NUDR6D5m7B1M3h7ax2ZELbH39O/TNttn76d9nXbczftbt5yJ/VO/uO0OwWfy/l3fHo8YLlk/Z4EvXDy7uZj8WS9cJG5OeFzpMicEEJYU2oqdO2qPj5yRFpRhBAFSNJtBQYgVFHYExAAisKb2RDhAFyE+n+CHb9A7dql7UWI6ql3YG/qe9UnMjHynkJqAAoK9b3q069RP/Q6Pb5uvuXav8FoIC49TpNk3bzVvCyt60Ul62vC1jB+xXgpMieEEFoymeDcufzHQghhRpLuSrYGmG2CGw4O0KULc3KfiAefabBzGdSvr118Qtg6vU7PZ0M/Y/yK8SgoBZJLBbVC7KdDP61wV+rcRF2rZD08Ibxc72uerNd0qclvEb8VeTPCvMjcAwEPUMutFi4OLuV6LyGEEEIIcf8k6a5Ea4BxJiC3OnkuE+ANry+HZo20iEwI+zI2eCyrJqwqcp7uT4d+qklLrr0k6zeTbxLw3wBArR7v7eyNt4t33s8aLjXUx4XWF/fT3hJ3g9HA3ut7iU6Kxt/Tn96BvWWsuxBCCCGsSpLuSmIAZqUCLoCu0JMKYIQP/GA2IJd/QpRubPBYRrcYbfcJlCWT9fUX1vP10a/LvI9MQyYxqTHEpMaUN+w8RSXuBX6WIXl3cXBBscI8toXHugMy1t2MFN8TQgghrEOS7kqy2wB3SqqhoVOf322AAXKNI0SZ6HV6+jXqp3UYmigqWXdzdCtT0r19+nY61+tMQnoCCRkJpf8sYl1iRiJgmcTdUed4X0m7t4s3rg6uJSbuMta9ZFJ8TwghhLAeSborye7zQKuybTegDNsJIURh5S0yV8OlRoXfy2gykpSRVCARj0+PL1finpSRhAkTWcYsYlNjiU2NrXA8jjpHvJy9CnaRz0nKPZ08mX9qfolj3Z/Z8AyNvBvh4uiCg84BR50jjnrHIh876Bys0jJvLXJDonQyLEEIIYQlSdJdWaIpU9Jd5u2EEKKQyi4yZ06n6NSk1sUbKji9YVGJe3mS9oR0tcU9N3G/k3aHO2l3KhTLrZRbdP6+c5m31yv6exJxR71jsY/LksgXue5+91nKa3ToeH7T88XekFBQeHHzi4xuMbraJpkyLKF4MiShBIoCDRvmPxZCCDOSdFeSfnp4LwII4N4x3QBG4Ia6nRBCVJQtFpkrjqUS9+TM5BKT9t9u/Ma68+tK3Ze3szcOOgeyjFlkG7PJMmSRZcwqcluDyYAh21CxoO2ICRMRiRE0+G8DvF28cdY746R3wknvhLOD2eOc9aU+X8Q68/Vleb1e0Vutp4H0AiieDEkomcHFmb2756m9I24flt4ROeRGTfGkR03xquJxo5hM9juZYGJiIt7e3iQkJODl5aV1OAUYDFDnT3Dn25wV5om3Uf3h8ye49TXo7fsYEpUkKyuLjRs3Mnz4cBwdHUt/gajWDEYDu67sYtO+TQzrNaxKfEFV1O5ruwmZH1Lqdrse3VVkjQCD0XBPIp77ONuYTZYxq9jHRb3mvvZVwvPled/c7YwmYyV84pVLQbFMAl/K8456R17Y9EKxvScUFOp41GH3o7txdnC+p0dBVR2OAMXfjMjtUVOdb0aA9I4ojnwuxZPPpnj29tmUNR+1iaT7yy+/5N///jc3b96kffv2fP7553Tr1q3U19ly0g2wZg2MWwx8CjQwe+I68BdYPRXG2t6xI2yEJN2ivOSYURmMBhp91qjUse5XZ1+tdjcmdl7dyYAFA0rd7othX9DarzWZhkwysjPINGSqjw1mj3PWF7Uu01jK82bri1pn7/SKvtiEvPCQgLKuc9Q74qCU8Fwx73O/762g8MAPDxCdHF3k71qd/55AbkgURz6X4slnUzx7/GzKmo9q3r18+fLlvPTSS3zzzTd0796dTz/9lCFDhnD+/Hn8/Py0Du++jB0Lq4EXekFkEOAPREP9a/DZHEm4hRCiMlhzrLu96duwb5mK7/2py580+3xMJhPZxuwSk/JSk/5SkvqiXheREMG52HOlxueidwEFso3ZZBuzi9zGYDJgMBjIMGRY+uOxOblDEpzedUKn06FT8hcFpcC/dYoORVHue5vCz5dlmwq/D8W/BuCnkz9hwoRLFuz5Sf1M+jwG6Y7q39fMn2dyJPJI3mtzz0G5vSEUlGIf525X3GMtXl+W15gw8fLWl0ssZvnUr0+RnJmc9zmWR+773A9L9UYpbyxGk5EXNr9Q4mfz9K9Po6DgqHe855i1hcX8/9ySDEYDszfPrrI1RzRv6e7evTtdu3bliy++AMBoNNKgQQOef/55XnvttRJfa+st3bkMBti1K5tNm04ybFgHQkIcpEu5KJW0WorykmOmoKK6qDXwamBzY92tLbclASjyhoQttiRYQ0WGJZhMJgwmQ14X/sLd/TVZZzRbV8QQhPLuN8OQYZfDEqzNLRNSPlAfu78OqU7axiNEZSrqJtb9LmlZaVxLuFbqexc3NEwrdtHSnZmZybFjx/jb3/6Wt06n0zFw4EAOHDhwz/YZGRlkZOTfNU5MVOeNzcrKIiur6OI3tqJnzyxSUiLp2bMVRqMJo3x/iVLkHtO2fmwL2yHHTEEPNX2I4c8MZ1/EPqKTo/H38KdXg17odfpq/Rk91PQhlo1dxkvbXiIyKTJvfYBXAJ8M/ISHmj5ULT+fB/wfIMAzgKikqGJ7AQR4BfCA/wP3fD4OqN2yiyycaudCw0MZtHhQqdstG7uM7gHdMZqMmEwmjCZj3mKi4L/N15lvW57XFVhXhtcBJb6mrO9n/rozMWdYf3F9qZ/N4KDBNPNpBqg3anKPr8KPQb0RZv642HVleG1FXlPidiU8b/44MimS32//Xurn0qb2/7d3/0FR1fsfx1/Lr2UhMIFxBRXFchQVf6JmdNPSUivL1MiGDO0PpwkMJUvHImuyvHan8voL0zH7Q82yCTNHLSKH0lJJwvCmZl+95sSgcusKrmHc3fP9w8A2kFxtOWfx+ZjZmeVzNn3tZ97Gee/5nM/2Vvx18X/6ut9r6t+mr/6q641XkqXybOVlrai5oe0NinHEyGN45Pa4G9Wlx/DIo6br+Eoevr4XQxc+cHQbLb/B6In/nrDU76jLzWJq011VVSW32y2n0+k17nQ6dejQoUavX7BggV544YVG4x9//LEiIiL8lvOvVFhYaHYEBBhqBr6iZhqLVrRccumjf31kdhRLsMuuxV0X69uz3+rn//2stiFt1fO6ngo+GqytR7eaHc80D8c+rIU1C5s8ZshQRkyGPtp+bdWQ23ArNjRW/6m79NfzxYXGKfT/QrX/6P4WTKYLS79N/KQj0hOpLfrzpvtvQX9Tyv9Sru4v++NqXgvv1Veu8stquh+MflApUVc5LwGmPLhceVV5f/q6KTFTWnRu6j9Iafgw7A/Pmxr7q48f/eWo1lSs+dOsxw8c19bj1vk9de7cuct6nanLyysqKtShQwd98cUXGjp0aMP4008/reLiYu3Zs8fr9U1d6e7UqZOqqqosvbxcuvApSGFhoe644w6WfeKyUDPwFTUDX1EzjRUcKmi0CqBjdEe9OvJV3d/jfhOTmafgUIEmvT9JUtO3JGwYv+GanBu3x60bl92oipoKOX41Gi0vr18dceTxIwF5D+qV+v28NLdq5FqbF4m5aU6gzk11dbXi4uKsvbw8Li5OwcHBOnnypNf4yZMn1b59+0avt9vtstvtjcZDQ0MD5mQhkLLCGqgZ+Iqaga+omYvSU9I1odcEvj/3d9JT0hUSEtLk1/hcy3skhCpUi8cs/m3TRm/1H0j8c/Q/FW4Pb/lwJvKel6Y3s7wW50VibpoTqHNzub87Tb37KCwsTAMHDlRRUVHDmMfjUVFRkdeVbwAAgJYSHBSs4V2G66GUhzS8y/BruuGuNz55vP6d828VZhQqt3OuCjMKdSzn2DXbcNcbnzxe76W/p4SoBK/xjtEdr9lNCaWL89IhuoPX+LU+LxJz05zWPDemf2VYbm6uMjMzlZqaqsGDB2vRokVyuVyaOnWq2dEAAADwm+CgYA3rPEyuf7k0rPMwPoz4zfjk8bqv40j9+o9EeQyPtmW8o7Qed17z8zM+ebzu636fdhzdoW07t2nMLWN0W9fbrvl5kS7ODStqGmutdWN60/3ggw/q9OnTeu6551RZWal+/fpp+/btjTZXAwAAAKwoOCpawf/5ryTpVnOjWAof1Fxa/YoaNNYa68b0pluSsrOzlZ2dbXYMAAAAAAD+Uq3wGyUBAAAAALAGmm4AAADgavzyizR8+IXHL7+YnQaAxVhieTkAAAAQsDweqbj44nMA+B2udAMAAAAA4Cc03QAAAAAA+AlNNwAAAAAAfkLTDQAAAACAn9B0AwAAAADgJ+xeDgAAAFytiAizEwCwKJpuAAAA4GpERkoul9kpAFgUy8sBAAAAAPATmm4AAAAAAPyEphsAAAC4GrW10t13X3jU1pqdBoDFcE83AAAAcDXcbmnr1ovPAeB3uNINAAAAAICf0HQDAAAAAOAnNN0AAAAAAPgJTTcAAAAAAH5C0w0AAAAAgJ8E9O7lhmFIkqqrq01O8ufq6up07tw5VVdXKzQ01Ow4CADUDHxFzcBX1Ax8Rc1cgst18Xl1NTuY/w41gysRKHVT34fW96WXEtBNd01NjSSpU6dOJicBAAAAJCUkmJ0AQAurqalRmzZtLnncZvxZW25hHo9HFRUVioqKks1mMztOs6qrq9WpUyedOHFC0dHRZsdBAKBm4CtqBr6iZuAraga+omZwJQKlbgzDUE1NjRISEhQUdOk7twP6SndQUJA6duxodgyfREdHW7pwYD3UDHxFzcBX1Ax8Rc3AV9QMrkQg1E1zV7jrsZEaAAAAAAB+QtMNAAAAAICf0HS3ELvdrnnz5slut5sdBQGCmoGvqBn4ipqBr6gZ+IqawZVobXUT0BupAQAAAABgZVzpBgAAAADAT2i6AQAAAADwE5puAAAAAAD8hKa7BSxbtkxdunRReHi4hgwZor1795odCRa1YMECDRo0SFFRUWrXrp3GjRunw4cPmx0LAeTvf/+7bDabZsyYYXYUWNyPP/6ohx9+WLGxsXI4HEpJSdFXX31ldixYlNvtVl5enpKSkuRwOHTDDTfoxRdfFFsDod5nn32msWPHKiEhQTabTZs2bfI6bhiGnnvuOcXHx8vhcGjkyJE6cuSIOWFhCc3VTF1dnWbPnq2UlBRFRkYqISFBjzzyiCoqKswLfBVouv3snXfeUW5urubNm6fS0lL17dtXo0aN0qlTp8yOBgsqLi5WVlaWdu/ercLCQtXV1enOO++Uy+UyOxoCQElJid544w316dPH7CiwuJ9//llpaWkKDQ3Vtm3b9O233+rVV19V27ZtzY4Gi1q4cKHy8/O1dOlSHTx4UAsXLtQrr7yiJUuWmB0NFuFyudS3b18tW7asyeOvvPKKFi9erBUrVmjPnj2KjIzUqFGjVFtb28JJYRXN1cy5c+dUWlqqvLw8lZaW6v3339fhw4d17733mpD06rF7uZ8NGTJEgwYN0tKlSyVJHo9HnTp10vTp0zVnzhyT08HqTp8+rXbt2qm4uFi33nqr2XFgYWfPntWAAQO0fPlyzZ8/X/369dOiRYvMjgWLmjNnjnbt2qXPP//c7CgIEPfcc4+cTqdWr17dMDZhwgQ5HA6tXbvWxGSwIpvNpoKCAo0bN07ShavcCQkJevLJJzVr1ixJ0pkzZ+R0OvXWW29p0qRJJqaFFfyxZppSUlKiwYMH6/jx40pMTGy5cH8BrnT70a+//qp9+/Zp5MiRDWNBQUEaOXKkvvzySxOTIVCcOXNGkhQTE2NyElhdVlaW7r77bq//3wCXsnnzZqWmpuqBBx5Qu3bt1L9/f61atcrsWLCwm2++WUVFRfruu+8kSfv379fOnTs1ZswYk5MhEBw7dkyVlZVev6PatGmjIUOGcE6My3bmzBnZbDZdf/31ZkfxWYjZAVqzqqoqud1uOZ1Or3Gn06lDhw6ZlAqBwuPxaMaMGUpLS1Pv3r3NjgML27Bhg0pLS1VSUmJ2FASIo0ePKj8/X7m5uZo7d65KSkr0xBNPKCwsTJmZmWbHgwXNmTNH1dXV6tGjh4KDg+V2u/XSSy8pIyPD7GgIAJWVlZLU5Dlx/TGgObW1tZo9e7YeeughRUdHmx3HZzTdgEVlZWXpwIED2rlzp9lRYGEnTpxQTk6OCgsLFR4ebnYcBAiPx6PU1FS9/PLLkqT+/fvrwIEDWrFiBU03mvTuu+9q3bp1Wr9+vXr16qWysjLNmDFDCQkJ1AwAv6qrq1N6eroMw1B+fr7Zca4Iy8v9KC4uTsHBwTp58qTX+MmTJ9W+fXuTUiEQZGdna8uWLdqxY4c6duxodhxY2L59+3Tq1CkNGDBAISEhCgkJUXFxsRYvXqyQkBC53W6zI8KC4uPj1bNnT6+x5ORk/fDDDyYlgtU99dRTmjNnjiZNmqSUlBRNnjxZM2fO1IIFC8yOhgBQf97LOTF8Vd9wHz9+XIWFhQF5lVui6farsLAwDRw4UEVFRQ1jHo9HRUVFGjp0qInJYFWGYSg7O1sFBQX69NNPlZSUZHYkWNyIESNUXl6usrKyhkdqaqoyMjJUVlam4OBgsyPCgtLS0hp9HeF3332nzp07m5QIVnfu3DkFBXmfNgYHB8vj8ZiUCIEkKSlJ7du39zonrq6u1p49ezgnxiXVN9xHjhzRJ598otjYWLMjXTGWl/tZbm6uMjMzlZqaqsGDB2vRokVyuVyaOnWq2dFgQVlZWVq/fr0++OADRUVFNdzn1KZNGzkcDpPTwYqioqIa3fMfGRmp2NhY9gLAJc2cOVM333yzXn75ZaWnp2vv3r1auXKlVq5caXY0WNTYsWP10ksvKTExUb169dLXX3+t1157TY8++qjZ0WARZ8+e1ffff9/w87Fjx1RWVqaYmBglJiZqxowZmj9/vrp166akpCTl5eUpISGh2d2q0bo1VzPx8fGaOHGiSktLtWXLFrnd7obz4piYGIWFhZkV+8oY8LslS5YYiYmJRlhYmDF48GBj9+7dZkeCRUlq8rFmzRqzoyGADBs2zMjJyTE7Bizuww8/NHr37m3Y7XajR48exsqVK82OBAurrq42cnJyjMTERCM8PNzo2rWr8cwzzxjnz583OxosYseOHU2ew2RmZhqGYRgej8fIy8sznE6nYbfbjREjRhiHDx82NzRM1VzNHDt27JLnxTt27DA7us/4nm4AAAAAAPyEe7oBAAAAAPATmm4AAAAAAPyEphsAAAAAAD+h6QYAAAAAwE9ougEAAAAA8BOabgAAAAAA/ISmGwAAAAAAP6HpBgAAAADAT2i6AQCAT2w2mzZt2mR2DAAAAgJNNwAAAWTKlCmy2WyNHqNHjzY7GgAAaEKI2QEAAIBvRo8erTVr1niN2e12k9IAAIDmcKUbAIAAY7fb1b59e69H27ZtJV1Y+p2fn68xY8bI4XCoa9eueu+997z++/Lyct1+++1yOByKjY3VtGnTdPbsWa/XvPnmm+rVq5fsdrvi4+OVnZ3tdbyqqkr333+/IiIi1K1bN23evNm/bxoAgABF0w0AQCuTl5enCRMmaP/+/crIyNCkSZN08OBBSZLL5dKoUaPUtm1blZSUaOPGjfrkk0+8mur8/HxlZWVp2rRpKi8v1+bNm3XjjTd6/R0vvPCC0tPT9c033+iuu+5SRkaGfvrppxZ9nwAABAKbYRiG2SEAAMDlmTJlitauXavw8HCv8blz52ru3Lmy2Wx67LHHlJ+f33Dspptu0oABA7R8+XKtWrVKs2fP1okTJxQZGSlJ2rp1q8aOHauKigo5nU516NBBU6dO1fz585vMYLPZ9Oyzz+rFF1+UdKGRv+6667Rt2zbuLQcA4A+4pxsAgABz2223eTXVkhQTE9PwfOjQoV7Hhg4dqrKyMknSwYMH1bdv34aGW5LS0tLk8Xh0+PBh2Ww2VVRUaMSIEc1m6NOnT8PzyMhIRUdH69SpU1f6lgAAaLVougEACDCRkZGNlnv/VRwOx2W9LjQ01Otnm80mj8fjj0gAAAQ07ukGAKCV2b17d6Ofk5OTJUnJycnav3+/XC5Xw/Fdu3YpKChI3bt3V1RUlLp06aKioqIWzQwAQGvFlW4AAALM+fPnVVlZ6TUWEhKiuLg4SdLGjRuVmpqqW265RevWrdPevXu1evVqSVJGRobmzZunzMxMPf/88zp9+rSmT5+uyZMny+l0SpKef/55PfbYY2rXrp3GjBmjmpoa7dq1S9OnT2/ZNwoAQCtA0w0AQIDZvn274uPjvca6d++uQ4cOSbqws/iGDRv0+OOPKz4+Xm+//bZ69uwpSYqIiNBHH32knJwcDRo0SBEREZowYYJee+21hj8rMzNTtbW1ev311zVr1izFxcVp4sSJLfcGAQBoRdi9HACAVsRms6mgoEDjxo0zOwoAABD3dAMAAAAA4Dc03QAAAAAA+An3dAMA0Ipw1xgAANbClW4AAAAAAPyEphsAAAAAAD+h6QYAAAAAwE9ougEAAAAA8BOabgAAAAAA/ISmGwAAAAAAP6HpBgAAAADAT2i6AQAAAADwE5puAAAAAAD85P8BIwIxH6YlFLoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 3 on the mel spectrogram\n",
    "history = model3.fit(x_train_mel, y_train_encoded_mel, validation_data=(x_val_mel, y_val_encoded_mel), epochs=100, batch_size=32, callbacks=[early_stopping3])\n",
    "\n",
    "# Saving the model\n",
    "model3.save('model3_mel.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 3 (Mel Spectrogram)', do_save=True, save_path='model3_mel_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 104s 419ms/step - loss: 4.2240 - accuracy: 0.1359 - val_loss: 4.7104 - val_accuracy: 0.0562\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 112s 456ms/step - loss: 1.7696 - accuracy: 0.5540 - val_loss: 2.5618 - val_accuracy: 0.3640\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 120s 488ms/step - loss: 0.7479 - accuracy: 0.8195 - val_loss: 2.2021 - val_accuracy: 0.4733\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 121s 494ms/step - loss: 0.3220 - accuracy: 0.9327 - val_loss: 1.3880 - val_accuracy: 0.6380\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 121s 494ms/step - loss: 0.1572 - accuracy: 0.9695 - val_loss: 1.0101 - val_accuracy: 0.7351\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 119s 484ms/step - loss: 0.0839 - accuracy: 0.9870 - val_loss: 0.9117 - val_accuracy: 0.7543\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 120s 487ms/step - loss: 0.0456 - accuracy: 0.9950 - val_loss: 0.8693 - val_accuracy: 0.7693\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 122s 497ms/step - loss: 0.0280 - accuracy: 0.9975 - val_loss: 0.4733 - val_accuracy: 0.8636\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 122s 494ms/step - loss: 0.0211 - accuracy: 0.9982 - val_loss: 0.5629 - val_accuracy: 0.8534\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 122s 498ms/step - loss: 0.0128 - accuracy: 0.9996 - val_loss: 0.4322 - val_accuracy: 0.8872\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 118s 479ms/step - loss: 0.0106 - accuracy: 0.9994 - val_loss: 0.6150 - val_accuracy: 0.8388\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 118s 479ms/step - loss: 0.0079 - accuracy: 0.9996 - val_loss: 1.1981 - val_accuracy: 0.7036\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 124s 503ms/step - loss: 0.0109 - accuracy: 0.9995 - val_loss: 0.9744 - val_accuracy: 0.7645\n",
      "Epoch 14/100\n",
      "246/246 [==============================] - 121s 493ms/step - loss: 0.0070 - accuracy: 0.9996 - val_loss: 0.6133 - val_accuracy: 0.8392\n",
      "Epoch 15/100\n",
      "246/246 [==============================] - 120s 487ms/step - loss: 0.0072 - accuracy: 0.9996 - val_loss: 0.3713 - val_accuracy: 0.9017\n",
      "Epoch 16/100\n",
      "246/246 [==============================] - 121s 490ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.3207 - val_accuracy: 0.9127\n",
      "Epoch 17/100\n",
      "246/246 [==============================] - 124s 503ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.6538 - val_accuracy: 0.8361\n",
      "Epoch 18/100\n",
      "246/246 [==============================] - 122s 497ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.7610 - val_accuracy: 0.8101\n",
      "Epoch 19/100\n",
      "246/246 [==============================] - 124s 505ms/step - loss: 0.1271 - accuracy: 0.9623 - val_loss: 5.1927 - val_accuracy: 0.3341\n",
      "Epoch 20/100\n",
      "246/246 [==============================] - 122s 498ms/step - loss: 0.0903 - accuracy: 0.9729 - val_loss: 2.1309 - val_accuracy: 0.6195\n",
      "Epoch 21/100\n",
      "246/246 [==============================] - 123s 501ms/step - loss: 0.0263 - accuracy: 0.9933 - val_loss: 0.5206 - val_accuracy: 0.8691\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 3 on the MFCCs\n",
    "history = model3.fit(x_train_mfcc, y_train_encoded_mfcc, validation_data=(x_val_mfcc, y_val_encoded_mfcc), epochs=100, batch_size=32, callbacks=[early_stopping3])\n",
    "\n",
    "# Saving the model\n",
    "model3.save('model3_mfcc.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 3 (MFCCs)', do_save=True, save_path='model3_mfcc_accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Training model 4 with the different feature extractors</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 72s 285ms/step - loss: 5.6759 - accuracy: 0.0069 - val_loss: 5.6578 - val_accuracy: 0.0028\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 70s 287ms/step - loss: 5.5466 - accuracy: 0.0155 - val_loss: 7.0828 - val_accuracy: 0.0071\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 4.8132 - accuracy: 0.0799 - val_loss: 8.7583 - val_accuracy: 0.0161\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 3.6140 - accuracy: 0.3276 - val_loss: 3.3424 - val_accuracy: 0.2736\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 71s 287ms/step - loss: 2.4422 - accuracy: 0.6226 - val_loss: 3.5161 - val_accuracy: 0.2103\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 71s 287ms/step - loss: 1.5889 - accuracy: 0.8103 - val_loss: 1.3748 - val_accuracy: 0.8046\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 1.0441 - accuracy: 0.8962 - val_loss: 1.2071 - val_accuracy: 0.7657\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 0.6563 - accuracy: 0.9460 - val_loss: 0.7205 - val_accuracy: 0.9147\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 70s 287ms/step - loss: 0.4602 - accuracy: 0.9665 - val_loss: 0.5662 - val_accuracy: 0.9230\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 0.3225 - accuracy: 0.9806 - val_loss: 0.7545 - val_accuracy: 0.8408\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 70s 287ms/step - loss: 0.2229 - accuracy: 0.9877 - val_loss: 0.5677 - val_accuracy: 0.8888\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 0.1734 - accuracy: 0.9898 - val_loss: 0.5151 - val_accuracy: 0.8919\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 70s 285ms/step - loss: 0.1317 - accuracy: 0.9936 - val_loss: 0.4528 - val_accuracy: 0.9084\n",
      "Epoch 14/100\n",
      "246/246 [==============================] - 71s 287ms/step - loss: 0.0946 - accuracy: 0.9968 - val_loss: 0.3262 - val_accuracy: 0.9442\n",
      "Epoch 15/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 0.0749 - accuracy: 0.9975 - val_loss: 0.2724 - val_accuracy: 0.9583\n",
      "Epoch 16/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 0.0615 - accuracy: 0.9980 - val_loss: 0.3879 - val_accuracy: 0.9198\n",
      "Epoch 17/100\n",
      "246/246 [==============================] - 70s 286ms/step - loss: 0.0492 - accuracy: 0.9987 - val_loss: 0.2375 - val_accuracy: 0.9619\n",
      "Epoch 18/100\n",
      "246/246 [==============================] - 71s 287ms/step - loss: 0.0439 - accuracy: 0.9990 - val_loss: 0.2899 - val_accuracy: 0.9379\n",
      "Epoch 19/100\n",
      "246/246 [==============================] - 70s 287ms/step - loss: 0.0393 - accuracy: 0.9991 - val_loss: 0.2239 - val_accuracy: 0.9595\n",
      "Epoch 20/100\n",
      "246/246 [==============================] - 71s 288ms/step - loss: 0.0298 - accuracy: 0.9999 - val_loss: 0.2706 - val_accuracy: 0.9434\n",
      "Epoch 21/100\n",
      "246/246 [==============================] - 71s 288ms/step - loss: 0.0269 - accuracy: 0.9991 - val_loss: 0.2270 - val_accuracy: 0.9517\n",
      "Epoch 22/100\n",
      "246/246 [==============================] - 71s 288ms/step - loss: 0.0296 - accuracy: 0.9990 - val_loss: 0.3144 - val_accuracy: 0.9277\n",
      "Epoch 23/100\n",
      "246/246 [==============================] - 71s 287ms/step - loss: 0.0274 - accuracy: 0.9989 - val_loss: 0.3768 - val_accuracy: 0.9033\n",
      "Epoch 24/100\n",
      "246/246 [==============================] - 71s 288ms/step - loss: 0.0324 - accuracy: 0.9981 - val_loss: 0.4589 - val_accuracy: 0.8781\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 4 on the mel spectrogram\n",
    "history = model4.fit(x_train_mel, y_train_encoded_mel, validation_data=(x_val_mel, y_val_encoded_mel), epochs=100, batch_size=32, callbacks=[early_stopping4])\n",
    "\n",
    "# Saving the model\n",
    "model4.save('model4_mel.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 4 (Mel Spectrogram)', do_save=True, save_path='model4_mel_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training model 4 on the MFCCs\n",
    "history = model4.fit(x_train_mfcc, y_train_encoded_mfcc, validation_data=(x_val_mfcc, y_val_encoded_mfcc), epochs=100, batch_size=32, callbacks=[early_stopping4])\n",
    "\n",
    "# Saving the model\n",
    "model4.save('model4_mfcc.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 4 (MFCCs)', do_save=True, save_path='model4_mfcc_accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Training model 5 with the different feature extractors</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 40s 158ms/step - loss: 5.4275 - accuracy: 0.0273 - val_loss: 5.5552 - val_accuracy: 0.0220\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 1.8295 - accuracy: 0.6005 - val_loss: 0.8481 - val_accuracy: 0.8058\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.2794 - accuracy: 0.9466 - val_loss: 0.3095 - val_accuracy: 0.9289\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.1208 - accuracy: 0.9799 - val_loss: 0.4112 - val_accuracy: 0.8872\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0600 - accuracy: 0.9924 - val_loss: 0.2326 - val_accuracy: 0.9454\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0368 - accuracy: 0.9957 - val_loss: 0.2165 - val_accuracy: 0.9461\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0197 - accuracy: 0.9990 - val_loss: 0.1720 - val_accuracy: 0.9603\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0159 - accuracy: 0.9989 - val_loss: 0.2431 - val_accuracy: 0.9371\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0111 - accuracy: 0.9995 - val_loss: 0.1933 - val_accuracy: 0.9536\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0076 - accuracy: 0.9997 - val_loss: 0.2114 - val_accuracy: 0.9461\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0064 - accuracy: 0.9997 - val_loss: 0.1551 - val_accuracy: 0.9599\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0043 - accuracy: 0.9999 - val_loss: 0.1750 - val_accuracy: 0.9591\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 38s 155ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9552\n",
      "Epoch 14/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.1671 - val_accuracy: 0.9583\n",
      "Epoch 15/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1480 - val_accuracy: 0.9638\n",
      "Epoch 16/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1328 - val_accuracy: 0.9662\n",
      "Epoch 17/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9631\n",
      "Epoch 18/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.9575\n",
      "Epoch 19/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0192 - accuracy: 0.9975 - val_loss: 0.4676 - val_accuracy: 0.8793\n",
      "Epoch 20/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0316 - accuracy: 0.9917 - val_loss: 0.9665 - val_accuracy: 0.7669\n",
      "Epoch 21/100\n",
      "246/246 [==============================] - 38s 156ms/step - loss: 0.0291 - accuracy: 0.9931 - val_loss: 0.2901 - val_accuracy: 0.9182\n"
     ]
    },
    {
     "data": {
      "image/png": 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UGRkpWFhYCL169crznOR8HnM//zrVqlXTe2519fLmm28KWVlZemXze52ePn1aACCsW7dO3DZjxgwBgLBz584C4w4LCxMACPv379fbX79+faFt27Z5jssv7rfeektv25IlSwQAwoYNG8RtmZmZQosWLQSlUikkJiYKgvDf54JKpRLi4uIKvVbO62VlZQlubm7C3LlzBUEQhBs3bggAhBMnTojP3fnz58XjGjZsKLi6ugrx8fHititXrggWFhbCkCFDxG09e/YUbGxs9N7vN27cEKRSqd5nyJ07dwSpVCrMmzdPL75r164JlpaWettzv8YLMmXKFGHy5MnCli1bhE2bNomfM61atRLUavULj71161aBr/uhQ4cKCoVCEITs91HHjh0FQRAEjUYjuLm5CbNnzxbrYtGiReJxc+fOFRQKhfD333/niVMqlQoxMTHitoJe1zk9fPhQcHFxEQAIXl5ewujRo4XQ0FDh+fPnecrq/h/68ssvxW0ZGRliPWZmZgqCIAjr168XLCwshJMnT+odv2rVKgGAEB4eLgiC4e/PevXq5fu6L+j9qPu87NKli955V6xYIQAQfvrpJzF2Z2dnoWnTpnp1GRISIgDQu+axY8cEAELNmjXzvNcN/czVneP1118XnytBEITAwEBBIpEIXbt21TtHixYt8n2NPnjwQAAgLFy4MM8+Iioedi8nIpFMJsM777yTZ7tcLhd/T0pKwpMnT9C6dWukpqbir7/+KvS8/fv3h6Ojo/hY1+r5zz//FHpsp06dxBYtAKhfvz5UKpV4rEajweHDh9GzZ0+9b/tr1aqFrl27Fnp+QP/+UlJS8OTJE7Rs2RKCIODSpUt5yo8ePVrvcevWrfXuZd++fbC0tNRrfZJKpRg3bpxB8QDZ4/Dv3buH3377TdwWGhoKa2trvP322+I5deMhtVotnj59iqysLDRp0iTfrukvcvjwYWRmZmLcuHF6XfLzWwpKJpOJYww1Gg3i4+OhVCrx2muvFfm6Ovv27YNUKsX48eP1tn/00UcQBAH79+/X217Y6+JF9u/fj/j4eAQGBorbAgMDceXKFb3u9Dt27IBEIsl3sjDdc7R7925otVrMmDEjz7jLkiyBN3LkyDxj7nO+TtVqNeLj41GrVi04ODjoPe87duxAgwYN0KtXrwLj7tSpEypVqoSNGzeK+/78809cvXq12GOL9+3bBzc3N73n1crKCuPHj0dycjJOnDihV75Pnz4G90bRkUql6NevHzZt2gQgu8eCh4eH+JmSU2xsLC5fvozg4GC9VvT69eujc+fO2LdvH4Ds13BYWBh69uyJqlWriuW8vb3zdFnfuXMntFot+vXrhydPnog/bm5uqF27tkFDO3KbP38+FixYgH79+mHAgAEICQnBvHnzEB4eju3bt7/w2Pj4eADQ+3zNT1BQEI4fP46HDx/i6NGjePjwYYFdy7dt24bWrVvD0dFR7x47deoEjUaj95lkiIoVK+LKlSsYPXo0nj17hlWrViEoKAiurq6YO3dunuEjlpaWePfdd8XH1tbWePfddxEXF4c//vhDjNHb2xteXl56MXbo0AEAxHoorfdn7vej7vNy4sSJeucdOXIkVCoV9u7dCwC4cOEC4uPjMXLkSL05SwYOHFhgnQ0dOlTvvQ4U/TN3yJAhej0/mjdvDkEQ8ozRbt68Oe7evYusrCy97brYnjx5UvCTQkRFwqSbiESVK1fOd1Kb69evo1evXrC3t4dKpYKLi4v4h3lCQkKh5835hyzw33/oz549K/KxuuN1x8bFxSEtLQ21atXKUy6/bfmJiYkR/zDXjdNu27YtgLz3pxvXW1A8QPbYW3d39zxdQ1977TWD4gGAAQMGQCqViuNX09PTsWvXLnTt2lXvj7W1a9eifv36sLGxgbOzM1xcXLB3716D6iWn6OhoAEDt2rX1tru4uOT541Cr1eLrr79G7dq1IZPJUKFCBbi4uODq1atFvm7O61eqVAl2dnZ623Uz6uvi0ynsdfEiGzZsQI0aNSCTyXDr1i3cunULnp6esLW11UtCb9++jUqVKr2w2/Pt27dhYWFRqpOKAUCNGjXybEtLS8OMGTPEcba65/358+d6z/vt27fx+uuvv/D8FhYWGDhwIHbv3o3U1FQA/81mrftSp6iio6NRu3btPMlNQXWY3z0aIigoCDdu3MCVK1cQGhqKAQMG5JtA6a6X3/vO29sbT548QUpKCh4/foy0tLQ8r/38jo2MjIQgCKhduzZcXFz0fiIiIhAXF1ese8rtgw8+gIWFBQ4fPmxQ+dyJa24BAQGws7PDli1bsHHjRjRt2rTAz8fIyEgcOHAgz/3plmsrzj26u7tj5cqViI2Nxc2bN7Fs2TKxu/bq1av1ylaqVCnPRGV16tQBAHHugMjISFy/fj1PjLpyuhhL6/2Z+7Va0GvL2toaNWvWFPfr/s39XFtaWha4hnt+74uifubm/nzUzYTv4eGRZ7tWq81zDt3rqSRfHBKRPo7pJiJR7m/XgewJddq2bQuVSoU5c+bA09MTNjY2uHjxIiZPnmzQxDoFzZJd2B+KJT3WEBqNBp07d8bTp08xefJkeHl5QaFQ4P79+wgODs5zf+U147erqys6d+6MHTt24JtvvsEvv/yCpKQkvbG2GzZsQHBwMHr27ImPP/4Yrq6u4iRYt2/fLrPYPv/8c0yfPh3Dhg3D3Llz4eTkBAsLC0ycOLHclgEr7usiMTERv/zyC9LT0/NNskJDQzFv3rxy+2Mz9wR8Ovm9F8eNG4c1a9Zg4sSJaNGiBezt7SGRSDBgwIBiPe9DhgzBokWLsHv3bgQGBiI0NBTdunUzeKmqksrvHg3RvHlzeHp6YuLEiYiKiiqwxbYsaLVaSCQS7N+/P9/XYGktfaWbKK+wiax08xcU9mWTTCZD7969sXbtWvzzzz/5Tuqno9Vq0blzZ3zyySf57tcltsUhkUhQp04d1KlTB2+99RZq165drJUDtFotfHx88NVXX+W7P3dyWVLFfa2W1rWK+plb0OejoZ+butdThQoViho+ERWASTcRvdDx48cRHx+PnTt3ok2bNuL2qKgoI0b1H1dXV9jY2ODWrVt59uW3Lbdr167h77//xtq1azFkyBBx+6FDh4odU7Vq1XDkyBEkJyfr/RF+8+bNIp1n4MCBOHDgAPbv34/Q0FCoVCq9ZYG2b9+OmjVrYufOnXpJYnHWTq5WrRqA7BakmjVritsfP36c5w/67du3o3379nlaqJ4/f673R1pREtdq1arh8OHDSEpK0mvt1g1f0MVXUjt37kR6ejpWrlyZ5w/KmzdvYtq0aQgPD8ebb74JT09PhIWF4enTpwW2dnt6ekKr1eLGjRsvnLjO0dExz6zGmZmZiI2NNTj27du3Y+jQofjyyy/Fbenp6XnO6+npiT///LPQ873++uto1KgRNm7ciCpVqiAmJgbLly83OJ7cqlWrhqtXr0Kr1eq1dpd2HQLZwwE+++wzeHt7F/i8666X3/vur7/+QoUKFaBQKGBjYwO5XI7IyMg85XIf6+npCUEQUKNGjRIln4XRDeMprPt91apVIZfLDfo8DgoKwk8//QQLCwsMGDCgwHKenp5ITk4WW7bLSs2aNeHo6JjnPfDgwYM8y3L9/fffACC2Dnt6euLKlSvo2LHjCz9nDH1/FvVLtpyvrZyfl5mZmYiKihKfO125W7duoX379mK5rKws3LlzB/Xr1zfoeoZ+5pYW3etJ10uFiEqO3cuJ6IV034zn/CY8MzMT3377rbFC0iOVStGpUyfs3r0bDx48ELffunUrzzjggo4H9O9PEAQsXbq02DEFBAQgKysLK1euFLdpNJoiJzQ9e/aEra0tvv32W+zfvx+9e/fWW14nv9jPnj2L06dPFznmTp06wcrKCsuXL9c7X34zFUul0jwtI9u2bcP9+/f1tun+aDZkqbSAgABoNBqsWLFCb/vXX38NiURi8Pj8wmzYsAE1a9bE6NGj0bdvX72fSZMmQalUil3M+/TpA0EQMHv27Dzn0d1/z549YWFhgTlz5uRpccr5HHl6euYZC/v9998X2NKdn/ye9+XLl+c5R58+fXDlyhXs2rWrwLh1Bg8ejIMHD2LJkiVwdnYu0fMcEBCAhw8fYsuWLeK2rKwsLF++HEqlUhyyURpGjBiBmTNn6n0BkZu7uzsaNmyItWvX6r0G//zzTxw8eBABAQEAsp9XPz8/7N69GzExMWK5iIgIhIWF6Z2zd+/ekEqlmD17dp7nUhAEcYy1odLT05GUlJRnu26ss7+//wuPt7KyQpMmTfIsYZef9u3bY+7cuVixYgXc3NwKLNevXz+cPn06z70D2e/l3ON/C3P27Flxxu+czp07h/j4+DxdtLOysvSWa8vMzMR3330HFxcX+Pr6ijHev38fP/zwQ57zpqWlidcz9P2pUCgMXtIRyP68tLa2xrJly/TOs3r1aiQkJOCtt94CADRp0gTOzs744Ycf9J63jRs3GjQURsfQz9zS8scff0AikaBFixZlcn6iVxFbuonohVq2bAlHR0cMHToU48ePh0Qiwfr160ute3dpmDVrFg4ePIhWrVrhvffeE5O3119/HZcvX37hsV5eXvD09MSkSZNw//59qFQq7Nixo0h/EOXWvXt3tGrVClOmTMGdO3dQt25d7Ny5s8jjnZVKJXr27CmO6869jFO3bt2wc+dO9OrVC2+99RaioqKwatUq1K1bF8nJyUW6lm698fnz56Nbt24ICAjApUuXsH///jwtKd26dcOcOXPwzjvvoGXLlrh27Ro2btyo1+IDZCeaDg4OWLVqFezs7KBQKNC8efN8xyx2794d7du3x6effoo7d+6gQYMGOHjwIH7++WdMnDhRb9K04nrw4AGOHTuWZ7I2HZlMBj8/P2zbtg3Lli1D+/btMXjwYCxbtgyRkZHw9/eHVqvFyZMn0b59e4wdOxa1atXCp59+irlz56J169bo3bs3ZDIZzp8/j0qVKonrXY8YMQKjR49Gnz590LlzZ1y5cgVhYWFFaqXq1q0b1q9fD3t7e9StWxenT5/G4cOH8yyR9vHHH2P79u14++23MWzYMPj6+uLp06fYs2cPVq1ahQYNGohlg4KC8Mknn2DXrl147733Cl3C70VGjRqF7777DsHBwfjjjz9QvXp1bN++HeHh4ViyZEme8folUa1atRd2kdZZtGgRunbtihYtWmD48OHikmH29vZ6x8+ePRsHDhxA69at8f7774tfFtSrVw9Xr14Vy3l6euKzzz7D1KlTxaWf7OzsEBUVhV27dmHUqFGYNGmSwffx8OFDNGrUCIGBgfDy8gIAhIWFYd++ffD390ePHj0KPUePHj3w6aefIjExESqVqsByFhYWmDZtWqHn+/jjj7Fnzx5069YNwcHB8PX1RUpKCq5du4bt27fjzp07RXrdrl+/Hhs3bkSvXr3g6+sLa2trRERE4KeffoKNjQ3+7//+T698pUqVsHDhQty5cwd16tTBli1bcPnyZXz//ffi63Pw4MHYunUrRo8ejWPHjqFVq1bQaDT466+/sHXrVoSFhaFJkyYGvz99fX2xcuVKfPbZZ6hVqxZcXV3FSdny4+LigqlTp2L27Nnw9/fH//73P9y8eRPffvstmjZtKs55Ym1tjVmzZmHcuHHo0KED+vXrhzt37iAkJASenp4Gt7Ab+plbWg4dOoRWrVoZtPwiERmoPKZIJyLTUtCSYfXq1cu3fHh4uPDGG28IcrlcqFSpkvDJJ5+ISw4dO3ZMLFfQkmE5l6PRQa6lZgpaMmzMmDF5js29zJIgCMKRI0eERo0aCdbW1oKnp6fw448/Ch999JFgY2NTwLPwnxs3bgidOnUSlEqlUKFCBWHkyJHiElQ5l7vKuQRPTvnFHh8fLwwePFhQqVSCvb29MHjwYOHSpUsGLxmms3fvXgGA4O7unu+SN59//rlQrVo1QSaTCY0aNRJ+/fXXfJcqyv18514yTBCylxKaPXu24O7uLsjlcqFdu3bCn3/+mef5Tk9PFz766COxXKtWrYTTp08Lbdu2zbPszs8//yzUrVtXXL5Nd+/5xZiUlCR88MEHQqVKlQQrKyuhdu3awqJFi/SW9tHdi6Gvi5y+/PJLAYBw5MiRAsvolvL5+eefBUHIXpZt0aJFgpeXl2BtbS24uLgIXbt2Ff744w+943766SehUaNGgkwmExwdHYW2bdsKhw4dEvdrNBph8uTJQoUKFQRbW1vBz89PuHXrVoFLhuVc9krn2bNnwjvvvCNUqFBBUCqVgp+fn/DXX3/le9/x8fHC2LFjhcqVKwvW1tZClSpVhKFDhwpPnjzJc96AgAABgHDq1KkCn5fc8lsyTBAE4dGjR2KM1tbWgo+PT57X+4s+F4p6vZwKeu4OHz4stGrVSpDL5YJKpRK6d+8u3LhxI8/xJ06cEHx9fQVra2uhZs2awqpVq/J9bwuCIOzYsUN48803BYVCISgUCsHLy0sYM2aMcPPmTbGMIUuGPXv2TBg0aJBQq1YtwdbWVpDJZEK9evWEzz//XG/Jpxd59OiRYGlpKaxfv15ve0GfVzkVVBdJSUnC1KlThVq1agnW1tZChQoVhJYtWwqLFy/Wiyv350p+rl69Knz88cdC48aNBScnJ8HS0lJwd3cX3n77beHixYt6ZXX/D124cEFo0aKFYGNjI1SrVk1YsWJFnvNmZmYKCxcuFOrVqye+73x9fYXZs2cLCQkJemULe38+fPhQeOuttwQ7Ozu9pbxe9H4UhOwlwry8vAQrKyuhYsWKwnvvvSc8e/YsT7lly5aJn9PNmjUTwsPDBV9fX8Hf318so1vua9u2bXmON/Qzt6BzFHQfutf348ePxW3Pnz8XrK2thR9//DHfeyai4pEIggk1VxERlaKePXvi+vXr+Y7VJKJsvXr1wrVr1wyaA4FM0/Dhw/H333/j5MmTxg6lRNq1a4cnT54YNCeBOdNqtXBxcUHv3r3z7SJvTEuWLMEXX3yB27dvl+sEckQvO47pJqKXQlpamt7jyMhI7Nu3D+3atTNOQERmIDY2Fnv37sXgwYONHQqVwMyZM3H+/HmEh4cbOxTKJT09Pc9wrHXr1uHp06cm9/+TWq3GV199hWnTpjHhJiplbOkmopeCu7s7goODxTVSV65ciYyMDFy6dCnfpaGIXmVRUVEIDw/Hjz/+iPPnz+P27dsvnFyLqDy8jC3dx48fxwcffIC3334bzs7OuHjxIlavXg1vb2/88ccfsLa2NnaIRFQOOJEaEb0U/P39sWnTJjx8+BAymQwtWrTA559/zoSbKB8nTpzAO++8g6pVq2Lt2rVMuInKSPXq1eHh4YFly5aJyw8OGTIECxYsYMJN9AphSzcRERERERFRGeGYbiIiIiIiIqIywqSbiIiIiIiIqIyY9ZhurVaLBw8ewM7ODhKJxNjhEBERERER0StCEAQkJSWhUqVKsLAouD3brJPuBw8ewMPDw9hhEBERERER0Svq7t27qFKlSoH7zTrptrOzA5B9kyqVysjRvJharcbBgwfRpUsXWFlZGTscygfryDywnswD68k8sJ7MA+vJDKSkQPD0hEajgRAZCSsHB2NHRAXg+8k8mEs9JSYmwsPDQ8xLC2LWSbeuS7lKpTKLpNvW1hYqlcqkXzivMtaReWA9mQfWk3lgPZkH1pMZkEqBtDQAgFqlgpWJ/136KuP7yTyYWz0VNtSZE6kRERERERERlREm3URERERERERlhEk3ERERERERURkx6zHdRERERERkOI1GA7VabewwjEatVsPS0hLp6enQaDTGDocKYCr1ZGVlBalUWuLzMOkmIiIiInrJCYKAhw8f4vnz58YOxagEQYCbmxvu3r1b6ORXZDymVE8ODg5wc3MrURxMuomIiIiISsLCAto2bfA0Ph72FqY5elOXcLu6usLW1tboiYyxaLVaJCcnQ6lUwsJE64pMo54EQUBqairi4uIAAO7u7sU+F5NuIiIiIqKSkMuhOXwY4fv2IUAuN3Y0eWg0GjHhdnZ2NnY4RqXVapGZmQkbGxsm3SbMVOpJ/u/7OS4uDq6ursXuas5XGhERERHRS0w3htvW1tbIkRCZH937piRzITDpJiIiIiJ6BbyqXcqJSqI03jfsXk5EREREVBIpKbCsXh3+mZlAdDTg4GDsiIjIhLClm4iIiIiohCRPnkCWmGjsMKgQNWvWxMqVKw0uf/z4cUgkkld+1ncqGSbdRERERERUKI0GOH4c2LQp+9+yXD5ZIpG88GfWrFnFOu/Zs2cxdOhQg8u3bNkSsbGxsLe3L9b1isPLywsymQwPHz4st2tS2WLSTUREREREL7RzJ1C9OtC+PRAUlP1v9erZ28tCbGys+LNkyRKoVCq9bZMmTRLLCoKArKwsg87r4uJSpAnlrK2tS7xGc1H8/vvvSEtLQ9++fbF27dpyueaLlGTyMPoPk24iIiIiIirQzp1A377AvXv62+/fz95eFom3m5ub+GNvbw+JRCI+/uuvv2BnZ4f9+/fD19cXMpkMv//+O27fvo0ePXqgYsWKUCqVaNq0KQ4fPqx33tzdyyUSCX788Uf06tULtra2qF27Nvbs2SPuz929PCQkBA4ODggLC4O3tzeUSiX8/f0RGxsrHpOVlYXx48fDwcEBzs7OmDx5MoYOHYqePXsWet+rV69GUFAQBg8ejJ9++inP/nv37iEwMBBOTk5QKBRo0qQJzp49K+7/5Zdf0LRpU9jY2KBChQro1auX3r3u3r1b73wODg4ICQkBANy5cwcSiQRbtmxB27ZtYWNjg40bNyI+Ph6BgYGoXLkybG1t4ePjg02bNumdR6vV4osvvkCtWrUgk8lQtWpVzJs3DwDQoUMHjB07Vq/848ePYW1tjSNHjhT6nLwMmHQTEREREb1CBAFISTHsJzERGD8++5j8zgMAEyZklzPkfPmdp7imTJmCBQsWICIiAvXr10dycjICAgJw5MgRXLp0Cf7+/ujevTtiYmJeeJ7Zs2ejX79+uHr1KgICAjBw4EA8ffq0wPKpqalYvHgx1q9fj99++w0xMTF6Le8LFy7Exo0bsWbNGoSHhyMxMTFPspufpKQkbNu2DYMGDULnzp2RkJCAkydPivuTk5PRtm1b3L9/H3v27MGVK1fwySefQKvVAgD27t2LXr16ISAgAJcuXcKRI0fQrFmzQq+b25QpUzBhwgRERETAz88P6enp8PX1xd69e/Hnn39i1KhRGDx4MM6dOyceM3XqVCxYsADTp0/HjRs3EBoaiooVKwIARowYgdDQUGRkZIjlN2zYgMqVK6NDhw5Fjs8ccfZyIiIiIqJXSGoqoFSWzrkEIbsF3NAhz8nJgEJROteeM2cOOnfuLD52cnJCgwYNxMdz587Frl27sGfPnjwtrTkFBwcjMDAQAPD5559j2bJlOHfuHPz9/fMtr1arsWrVKnh6egIAxo4dizlz5oj7ly9fjqlTp4qtzCtWrMC+ffsKvZ/Nmzejdu3aqFevHgBgwIABWL16NVq3bg0ACA0NxePHj3H+/Hk4OTkBAGrVqiUeP2/ePAwYMACzZ88Wt+V8Pgw1ceJE9O7dW29bzi8Vxo0bh7CwMGzduhXNmjVDUlISli5dihUrVojj5T09PfHmm28CAHr37o2xY8fi559/Rr9+/QBk9xgIDg5+ZZaxY0s3EREREVFJWFhA6+uLZ7VqARb887q8NGnSRO9xcnIyJk2aBG9vbzg4OECpVCIiIqLQlu769euLvysUCqhUKsTFxRVY3tbWVky4AcDd3V0sn5CQgEePHum1MEulUvj6+hZ6Pz/99BMGDRokPh40aBC2bduGpKQkAMDly5fRqFEjMeHO7fLly+jYsWOh1ylM7udVo9Fg7ty58PHxgZOTE5RKJcLCwsTnNSIiAhkZGQVe28bGRq+7/MWLF/Hnn38iODi4xLGaC7Z0ExERERGVhFwOzenT+G3fPgTI5caOplC2ttktzob47TcgIKDwcvv2AW3aGHbt0qLI1WQ+adIkHDp0CIsXL0atWrUgl8vRt29fZGZmvvA8VlZWeo8lEonYZdvQ8kIJ+83fuHEDZ86cwblz5zB58mRxu0ajwebNmzFy5EjIC3ltFbY/vzjzmygt9/O6aNEiLF26FEuWLIGPjw8UCgUmTpwoPq+FXRfI7mLesGFD3Lt3D2vWrEGHDh1QrVq1Qo97WfCrOCIiIiKiV4hEkt3F25CfLl2AKlWyjynoXB4e2eUMOV9Z9iYODw9HcHAwevXqBR8fH7i5ueHOnTtld8F82Nvbo2LFijh//ry4TaPR4OLFiy88bvXq1WjTpg2uXLmCy5cviz8ffvghVq9eDSC7Rf7y5csFjjevX7/+Cycmc3Fx0ZvwLTIyEqmpqYXeU3h4OHr06IFBgwahQYMGqFmzJv7++29xf+3atSGXy194bR8fHzRp0gQ//PADQkNDMWzYsEKv+zJh0k1ERERERPmSSoGlS7N/z50w6x4vWZJdzthq166NnTt34vLly7hy5QqCgoJe2GJdVsaNG4f58+fj559/xs2bNzFhwgQ8e/aswPHLarUa69evR2BgIF5//XW9nxEjRuDs2bO4fv06AgMD4ebmhp49eyI8PBz//PMPduzYgdOnTwMAZs6ciU2bNmHmzJmIiIjAtWvXsHDhQvE6HTp0wIoVK3Dp0iVcuHABo0ePztNqn5/atWvj0KFDOHXqFCIiIvDuu+/i0aNH4n4bGxtMnjwZn3zyCdatW4fbt2/jzJkz4pcFOiNGjMCCBQsgCILerOqvAibd5UCr0SL6RDSe/fYM0SeiodWU/5ufiIiIiMpIaiosa9dG55Ejs2cpe8n07g1s3w5Urqy/vUqV7O255twymq+++gqOjo5o2bIlunfvDj8/PzRu3Ljc45g8eTICAwMxZMgQtGjRAkqlEn5+frCxscm3/J49exAfH59vIurt7Q1vb2+sXr0a1tbWOHjwIFxdXREQEAAfHx8sWLAA0n+/8WjXrh22bduGPXv2oGHDhujQoYPeDONffvklPDw80Lp1awQFBWHSpEkGrVk+bdo0NG7cGH5+fmjXrp2Y+Oc0ffp0fPTRR5gxYwa8vb3Rv3//POPiAwMDYWlpicDAwAKfi5eVRCjpAAQjSkxMhL29PRISEqBSqYwdTr4idkbgwIQDSLyXKG5TVVHBf6k/vHt7GzEyyk2tVmPfvn0ICAgw6Fs/Mg7Wk3lgPZkH1pN5YD2ZgZQUcTpw9bNnsHJwMG48uaSnpyMqKgo1atQoUbKj0QAnTwKxsYC7O9C6tWm0cBeFVqtFYmIiVCoVLMpp0jutVgtvb2/069cPc+fOLZdrmqI7d+7A09MT58+fL/TLEGPUU0Fe9P4xNB/lRGplKGJnBLb23Qrk+loj8X4itvbdin7b+zHxJiIiIiKzIJUC7doZOwrTFx0djYMHD6Jt27bIyMjAihUrEBUVhaCgIGOHZhRqtRrx8fGYNm0a3njjDaP0PjA2di8vI1qNFgcmHMiTcAMQtx2YeIBdzYmIiIiIXiIWFhYICQlB06ZN0apVK1y7dg2HDx+Gt/er2dgWHh4Od3d3nD9/HqtWrTJ2OEbBlu4yEnMyRq9LeR4CkHg3ETEnY1C9XfVyi4uIiIiIiMqOh4cHwsPDjR2GyWjXrl2Jl1Qzd2zpLiNJsUmlWo6IiIiIiIjMD5PuMmLnbleq5YiIiIiIiMj8MOkuI1VbV4WqigrIfzk+QAKoPFSo2rpqucZFRERERKVMIoHg7Y1ED4+8i1kT0SuPSXcZsZBawH+pf/47//0s9l/iDwspq4CIiIjIrNnaIuvKFRxbvhwwYN1jInq1MOMrQ969vdFvez8o3ZV621VVVFwujIiIiIiI6BXA2cvLmHdvb3j6e2K+Yj4AoM+OPqjboy5buImIiIiIiF4BzPzKgbWtNWwcbQAATp5OTLiJiIiIXiapqbBs0ADtx40DUlONHQ3l0K5dO0ycOFF8XLNmTaxcufKFx0gkEuzevbvE1y6t85D5Y/ZXDjRaDSSO2QO5T107BY1WY+SIiIiIiKjUCAIkERFQ3b0LvMTrEWsAHAew6d9/y/Iv2u7du8PfP//5kU6ePAmJRIKrV68W+bxnz57F0KFDSxqenlmzZqFhw4Z5tsfGxqJr166leq2CpKWlwcnJCRUqVEBGRka5XJMMx6S7jO2M2InqS6vjhvYGAGD27tmovrQ6dkbsNHJkRERERESG2QmgOoD2AIL+/bf6v9vLwvDhw3Ho0CHcu3cvz741a9agSZMmqF+/fpHP6+LiAttymuzOzc0NMpmsXK61Y8cO1KtXD15eXkZvXRcEAVlZWUaNwdQw6S5DOyN2ou/WvriXeA8pihQAgDJZifuJ99F3a18m3kRERERk8nYC6Asgd/p7/9/tZfEXbbdu3eDi4oKQkBC97cnJydi2bRuGDx+O+Ph4BAYGonLlyrC1tYWPjw82bdr0wvPm7l4eGRmJNm3awMbGBnXr1sWhQ4fyHDN58mTUqVMHtra2qFmzJqZPnw61Wg0ACAkJwezZs3HlyhVIJBJIJBIx5tzdy69du4YOHTpALpfD2dkZo0aNQnJysrg/ODgYPXv2xOLFi+Hu7g5nZ2eMGTNGvNaLrF69GoMGDcKgQYOwevXqPPuvX7+Obt26QaVSwc7ODq1bt8bt27fF/T/99BPq1asHmUwGd3d3jB07FgBw584dSCQSXL58WSz7/PlzSCQSHD9+HABw/PhxSCQS7N+/H76+vpDJZPj9999x+/Zt9OjRAxUrVoRSqUTTpk1x+PBhvbgyMjIwefJkeHh4QCaToVatWli9ejUEQUDjxo3x5Zdf6pW/fPkyJBIJbt26VehzYkqYdJcRjVaDCQcmQEB2F6NkZfYbSpGiELdNPDCRXc2JiIiIqFwJAFIM/EkEMP7fY/I7DwBM+LecIecztPO9paUlhgwZgpCQEAg5uuxv27YNGo0GgYGBSE9Ph6+vL/bu3Ys///wTo0aNwuDBg3Hu3DmDrqHVatG7d29YW1vj7NmzWLVqFSZPnpynnJ2dHUJCQnDjxg0sXboUP/zwA77++msAQP/+/fHRRx+hXr16iI2NRWxsLPr375/nHCkpKfDz84OjoyPOnz+Pbdu24fDhw2Jyq3Ps2DHcvn0bx44dw9q1axESEpLni4fcbt++jdOnT6Nfv37o168fTp48iejoaHH//fv30aZNG8hkMhw9ehR//PEHhg0bJrZGr1y5EmPGjMGoUaNw7do17NmzB7Vq1TLoOcxpypQpWLBgASIiIlC/fn0kJycjICAAR44cwaVLl+Dv74/u3bsjJiZGPGbIkCHYtGkTli1bhoiICHz33XdQKpWQSCQYOHBgnntfs2YN2rRpU6z4jImzl5eRkzEncS/xv+8Dc7Z0A4AAAXcT7+JkzEm0q97OGCESERER0SsoFYCy0FKGEZDdAm5vYPlkAAoDyw4bNgyLFi3CiRMn0K5dOwDZSVefPn1gb28Pe3t7TJo0SSw/btw4hIWFYevWrWjWrFmh5z98+DD++usvhIWFoVKlSgCAzz//PM847GnTpom/V69eHZMmTcLmzZvxySefQC6XQ6lUwtLSEm5ubgVeKzQ0FOnp6Vi3bh0UiuxnYMWKFejevTsWLlyIihUrAgAcHR2xYsUKSKVSeHl54a233sKRI0cwcuTIAs/9008/oWvXrnB0dAQA+Pn5Yc2aNZg1axYA4JtvvoG9vT02b94MKysrAECdOnXE4z/77DN89NFHmDBhgritadOmhT5/uc2ZMwedO3cWHzs5OaFBgwbi47lz52LXrl3Ys2cPxo4di7///htbt27FoUOH0KlTJwDZPRGA7C9EgoKCMH/+fJw7dw7NmjWDWq1GaGgoFi9eXOTYjI0t3WUkNilW77GupVuXdBdUjoiIiIiIAC8vL7Rs2RI//fQTAODWrVs4efIkhg8fDgDQaDSYO3cufHx84OTkBKVSibCwML2W1BeJiIiAh4eHmHADQIsWLfKU27JlC1q1agU3NzcolUpMmzbN4GvkvFaDBg3EhBsAWrVqBa1Wi5s3b4rb6tWrB6lUKj52d3dHXFxcgefVaDRYu3YtBg0aJG4bNGgQQkJCoNVqAWR3yW7durWYcOcUFxeHBw8eoGPHjkW6n/w0adJE73FycjImTZoEb29vODg4QKlUIiIiQnzuLl++DKlUirZt2+Z7Pnd3dwQEBIj1/8svvyAjIwNvv/12iWMtb0y6y4i7nbve45zdy19UjoiIiIjMjEQCoVo1pLq4ABKJsaMplC2yW5wN+dln4Dn3GXi+ok5hNnz4cOzYsQNJSUlYs2YNPD09xSRt0aJFWLp0KSZPnoxjx47h8uXL8PPzQ2ZmZhGvUrDTp09j4MCBCAgIwK+//opLly7h008/LdVr5JQ7MZZIJGLynJ+wsDDcv38f/fv3h6WlJSwtLTFgwABER0fjyJEjAAC5XF7g8S/aBwAWFtnpYs4u/gWNMc/5hQIATJo0Cbt27cLnn3+OkydP4vLly/Dx8RGfu8KuDWTX/+bNm5GWloY1a9agf//+5TYRXmli0l1GWldtjSqqKpAg+4M3d/dyCSTwUHmgddXWRouRiIiIiEqBrS2yIiNx6IcfADNICCTI7uJtyE8XAFX+Paagc3n8W86Q8xX1K4l+/frBwsICoaGhWLduHYYNGwbJv19shIeHo0ePHhg0aBAaNGiAmjVr4u+//zb43N7e3rh79y5iY//reXrmzBm9MqdOnUK1atXw6aefokmTJqhdu7beeGkAsLa2hkbz4nmavL29ceXKFaSkpIjbwsPDYWFhgddee83gmHNbvXo1BgwYgMuXL+v9DBgwQJxQrX79+jh58mS+ybKdnR2qV68uJui5ubi4AIDec5RzUrUXCQ8PR3BwMHr16gUfHx+4ubnhzp074n4fHx9otVqcOHGiwHMEBARAoVBg5cqVOHDgAIYNG2bQtU0Nk+4yIrWQYqn/UgDZCXbOlm6JNvuDYon/EkgtpAWeg4iIiIjImKQAlv77e+6EWfd4yb/lyoJSqUT//v0xdepUxMbGIjg4WNxXu3ZtHDp0CKdOnUJERATeffddPHr0yOBzd+rUCXXq1MHQoUNx5coVnDx5Ep9++qlemdq1ayMmJgabN2/G7du3sWzZMuzatUuvTPXq1REVFYXLly/jyZMn+a6TPXDgQNjY2GDo0KH4888/cezYMYwbNw6DBw8Wx3MX1ePHj/HLL79g6NCheP311/V+hgwZgt27d+Pp06cYO3YsEhMTMWDAAFy4cAGRkZFYv3692K191qxZ+PLLL7Fs2TJERkbi4sWLWL58OYDs1ug33nhDnCDtxIkTemPcX6R27drYuXMnLl++jCtXriAoKEiv1b569eoYOnQohg0bht27dyMqKgrHjx/H1q1bxTJSqRTBwcGYOnUqateunW/3f3PApLsM9fbuje39tsPdzl1s6ZZqpfC08sT2ftvR27u3kSMkIiIiInqx3gC2A6ica3uVf7eX9V+0w4cPx7Nnz+Dn56c3/nratGlo3Lgx/Pz80K5dO7i5uaFnz54Gn9fCwgK7du1CWloamjVrhhEjRmDevHl6Zf73v//hgw8+wNixY9GwYUOcOnUK06dP1yvTp08f+Pv7o3379nBxccl32TJbW1uEhYXh6dOnaNq0Kfr27YuOHTtixYoVRXsyctBNypbfeOyOHTtCLpdjw4YNcHZ2xtGjR5GcnIy2bdvC19cXP/zwg9iVfejQoViyZAm+/fZb1KtXD926dUNkZKR4rp9++glZWVnw9fXFxIkT8dlnnxkU31dffQVHR0e0bNkS3bt3h5+fHxo3bqxXZuXKlejbty/ef/99eHl5YeTIkXq9AYDs+s/MzMQ777xT1KfIZEiEnB30zUxiYiLs7e2RkJAAlUpl7HAKpNFqoFqgwri54yBPl+Pda+/C7fWCZzck41Cr1di3bx8CAgLynWiCTAPryTywnswD68k8sJ7MQFoatK1bIyEhAco//oCVif1dmp6ejqioKNSoUQM2NjbFPo8GwEkAsQDcAbRG2bVwlxWtVovExESoVCpxvDKZnpz1FB4ejo4dO+Lu3bvF7hVQEi96/xiaj3LJsHIgtZCipkNNJCuTIU+XIy0uzdghEREREVFp0Wph8ccfcASgfsGkV+ZOCqCdsYOgV0ZGRgbu3buHWbNm4e233zZKwl1a+PVOOalqX1Uc1538KNnI0RAREREREZmuHTt2oEaNGnj+/Dm++OILY4dTIky6y0lVVVVxXHfKo5RCShMREREREb26goKCoFar8ccff6By5dwzCpgXJt3lxMPegy3dRERERERErxgm3eXEQ+XxX0v3Q7Z0ExERERERvQqYdJeTavbV2NJNRERERET0iuHs5eXEQ8Xu5UREREQvK6FCBWRmZrJFi4jy4OdCOXFXuiNNkb1UWOLDRCNHQ0RERESlRqFA1oMHOLBuHaBQGDsaIjIxTLrLidRCCisnKwBAWlwaBK1g5IiIiIiIiIiorDHpLkcKh+xvPoUsAWnP0owcDRERERHRq6VmzZpYuXKlweWPHz8OiUSC58+fl11Q9NJj0l2OKthWQJpNdrLNtbqJiIiIXhJpaZB26oRWn34KpL28DSsarQbH7xzHpmubcPzOcWi0mjK7lkQieeHPrFmzinXes2fPYujQoQaXb9myJWJjY2Fvb1+s6xmKyf3LjROplSMXaxckK5MhT5cj+WEyXOq6GDskIiIiIioprRYWv/2GCgDUWq2xoykTOyN2YsKBCbiXeE/cVkVVBUv9l6K3d+9Sv15sbKz4+5YtWzBjxgzcvHlT3KZUKsXfBUGARqOBpWXhqY2LiwsSEw2fX8na2hpubm4GlyfKD1u6y5Eu6QY4gzkRERERmYedETvRd2tfvYQbAO4n3kffrX2xM2JnqV/Tzc1N/LG3t4dEIhEf//XXX7Czs8P+/fvh6+sLmUyG33//Hbdv30aPHj1QsWJFKJVKNG3aFIcPH9Y7b+7u5RKJBD/++CN69eoFW1tb1K5dG3v27BH3526BDgkJgYODA8LCwuDt7Q2lUgl/f3+9LwmysrIwfvx4ODg4wNnZGZMnT8bQoUPRs2fPYj8fz549w5AhQ+Do6AhbW1t07doVkZGR4v7o6Gh0794djo6OUCgUqFevHvbt2yceO3DgQLi4uEAul6N27dpYs2ZNsWOhomPSXY5crFyQosjuVs7u5URERERkDIIgICUzxaCfxPREjN8/HgLyTgKs2zZh/wQkpicadD5BKL3JhKdMmYIFCxYgIiIC9evXR3JyMgICAnDkyBFcunQJ/v7+6N69O2JiYl54ntmzZ6Nfv364evUqAgICMHDgQDx9+rTA8qmpqVi8eDHWr1+P3377DTExMZg0aZK4f+HChdi4cSPWrFmD8PBwJCYmYvfu3SW61+DgYFy4cAF79uzB6dOnIQgCAgICoFarAQBjxoxBRkYGfvvtN1y7dg0LFy4UewNMnz4dN27cwP79+xEREYGVK1eiQoUKJYqHiobdy8uRXkv3Q7Z0ExEREVH5S1WnQjlfWXhBAwgQcC/pHuwXGjbmOXlqMhTWpbOs2pw5c9C5c2fxsZOTExo0aCA+njt3Lnbt2oU9e/Zg7NixBZ4nODgYgYGBAIDPP/8cy5Ytw7lz5+Dv759vebVajVWrVsHT0xMAMHbsWMyZM0fcv3z5ckydOhW9evUCAKxYsUJsdS6OyMhI7NmzB+Hh4WjZsiUAYOPGjfDw8MDu3bvx9ttvIyYmBn369IGPjw+A7BZ9nZiYGDRq1AhNmjQBAFSvXr3YsVDxsKW7HOVMup8+KPjbMyIiIiIiejFdEqmTnJyMSZMmwdvbGw4ODlAqlYiIiCi0pbt+/fri7wqFAiqVCnFxcQWWt7W1FRNuAHB3dxfLJyQk4NGjR2jWrJm4XyqVwtfXt0j3llNERAQsLS3RvHlzcZuzszNee+01REREAADGjx+Pzz77DK1atcLMmTNx9epVsex7772HzZs3o2HDhvjkk09w6tSpYsdCxcOW7nIks5BB4igBADy9z6SbiIiIiMqfrZUtkqca1uvyt+jfEBAaUGi5fUH70KZaG4OuXVoUCv0W80mTJuHQoUNYvHgxatWqBblcjr59+yIzM/OF57GystJ7LJFIoH3BhHj5lS/NbvPFMWLECPj5+WHv3r04ePAg5s+fjy+//BLjxo1D165dER0djX379uHQoUPo2LEjxowZg8WLFxs15lcJW7rLmaJi9odD4kPDZ00kIiIiItMm2NoiSyYzdhgGkUgkUFgrDPrp4tkFVVRVIIEk/3NBAg+VB7p4djHofBJJ/ucpDeHh4QgODkavXr3g4+MDNzc33Llzp8yulx97e3tUrFgR58+fF7dpNBpcvHix2Of09vZGVlYWzp49K26Lj4/HzZs3UbduXXGbh4cHRo8ejZ07d+Kjjz7CDz/8IO5zcXHB0KFDsWHDBixZsgTff/99seOhomNLdzlzcHcAAKQ/TjduIERERERUOhQKZD1/jn379iFAUTrjlU2F1EKKpf5L0XdrX0gg0ZtQTZeIL/FfAqmF1FghimrXro2dO3eie/fukEgkmD59+gtbrMvKuHHjMH/+fNSqVQteXl5Yvnw5nj17ZtAXDteuXYOdnZ34WCKRoEGDBujRowdGjhyJ7777DnZ2dpgyZQoqV66MHj16AAAmTpyIrl27ok6dOnj27BmOHTsGb29vAMCMGTPg6+uLevXqISMjA7/++qu4j8oHk+5yVtGjIgBA+1QLQStAYlF23/YREREREZVUb+/e2N5ve77rdC/xX1Im63QXx1dffYVhw4ahZcuWqFChAiZPnlykNblLy+TJk/Hw4UMMGTIEUqkUo0aNgp+fH6TSwr+YaNNGv4u+VCpFVlYW1qxZgwkTJqBbt27IzMxEmzZtsG/fPrGru0ajwZgxY3Dv3j2oVCr4+/vj66+/BpC91vjUqVNx584dyOVytG7dGps3by79G6cCSQRjD0AogcTERNjb2yMhIQEqlcrY4byQWq3Gvn378Lf930htnwoA+PjJx7B1Lr1xLVQyujoKCAjIM1aHTAfryTywnswD68k8sJ7MgynXU3p6OqKiolCjRg3Y2NgU+zwarQYnY04iNikW7nbuaF21tUm0cBeFVqtFYmIiVCoVLCzKZ6StVquFt7c3+vXrh7lz55bLNc2dMeqpIC96/xiaj7Klu5xVda6KSzaXIE+XI+VRCpNuIiIiInOXng5p795oHhcHdOgAmFjSXVqkFlK0q97O2GGYvOjoaBw8eBBt27ZFRkYGVqxYgaioKAQFBRk7NDISTqRWzqrZV+Na3UREREQvE40GFvv3w+2PPwCNxtjRkJFZWFggJCQETZs2RatWrXDt2jUcPnyY46hfYWzpLmceKg8kK5Ph8sQFzx88N3Y4RERERERUijw8PBAeHm7sMMiEsKW7nLnYuiDNLg0AcD/mvpGjISIiIiIiorLEpLucSSQSSJ2yJ5yIi4kzcjRERERERERUlph0G4HMVQYAeBr71MiREBERERERUVli0m0EKrfs6eRTHqUYORIiIiIiIiIqS0y6jcC5kjMAIPNJppEjISIiIiIiorLEpNsI3DzcAADCU8HIkRARERFRiSkUUGdm4ufduwGFwtjREJGJYdJtBFVrVAUAWCVYQdAy8SYiIiIiKgvt2rXDxIkTxcc1a9bEypUrX3iMRCLB7t27S3zt0joPmT8m3Ubg6ekJALDQWiD1aaqRoyEiIiIiKpxWo8Wd43dwbdM13Dl+B1qNtsyu1b17d/j7++e77+TJk5BIJLh69WqRz3v27FkMHTq0pOHpmTVrFho2bJhne2xsLLp27Vqq18otJCQEDg4OZXoNKjmjJt2zZs2CRCLR+/Hy8jJmSOWiWoVqSLPJXqv7btRdI0dDRERERCWSng7pgAFo8sUXQHq6saMpExE7I7C0+lKsbb8WO4N2Ym37tVhafSkidkaUyfWGDx+OQ4cO4d69e3n2rVmzBk2aNEH9+vWLfF4XFxfY2tqWRoiFcnNzg0wmK5drkWkzekt3vXr1EBsbK/78/vvvxg6pzFlLrZFun/2BHBUVZeRoiIiIiKhENBpY7NyJyqdOARqNsaMpdRE7I7C171Yk3kvU2554PxFb+24tk8S7W7ducHFxQUhIiN725ORkbNu2DcOHD0d8fDwCAwNRuXJl2NrawsfHB5s2bXrheXN3L4+MjESbNm1gY2ODunXr4tChQ3mOmTx5MurUqQNbW1vUrFkT06dPh1qtBpDd0jx79mxcuXJFbETUxZy7e/m1a9fQoUMHyOVyODs7Y9SoUUhOThb3BwcHo2fPnli8eDHc3d3h7OyMMWPGiNcqjpiYGPTo0QNKpRIqlQr9+vXDo0ePxP1XrlxB+/btYWdnB5VKBV9fX1y4cAEAEB0dje7du8PR0REKhQL16tXDvn37ih3Lq8zS6AFYWsLNzc3YYZQ7rYMWeAQ8iH5g7FCIiIiI6BUiCALUqYYlclqNFvvH7wfym4ZIACAB9k/YjxqdasBCWnh7npWtFSQSSaHlLC0tMWTIEISEhODTTz8Vj9m2bRs0Gg0CAwORnJwMX19fTJ48GSqVCnv37sXgwYPh6emJZs2aFX5vWi169+6NihUr4uzZs0hISNAb/61jZ2eHkJAQVKpUCdeuXcPIkSNhZ2eHTz75BP3798eff/6JAwcO4PDhwwAAe3v7POdISUmBn58fWrRogfPnzyMuLg4jRozA2LFj9b5YOHbsGNzd3XHs2DHcunUL/fv3R8OGDTFy5MhC7ye/+9Ml3CdOnEBWVhbGjBmD/v374/jx4wCAgQMHolGjRli5ciWkUikuX74MKysrAMCYMWOQmZmJ3377DQqFAjdu3IBSqSxyHGQCSXdkZCQqVaoEGxsbtGjRAvPnz0fVqlWNHVaZs3TOfuof339s5EiIiIiI6FWiTlVjvnJ+6ZxMAJLuJWGh/UKDik9NngprhbVBZYcNG4ZFixbhxIkTaNeuHYDsruV9+vSBvb097O3tMWnSJLH8uHHjEBYWhq1btxqUdB8+fBh//fUXwsLCUKlSJQDA559/nmcc9rRp08Tfq1evjkmTJmHz5s345JNPIJfLoVQqC21IDA0NRXp6OtatWwfFvzPcr1ixAt27d8fChQtRsWJFAICjoyNWrFgBqVQKLy8vvPXWWzhy5Eixku4jR47g2rVriIqKgoeHBwBg3bp1qFevHs6fP4+mTZsiJiYGH3/8sTjEt3bt2uLxMTEx6NOnD3x8fABk9xKg4jFq0t28eXOEhITgtddeQ2xsLGbPno3WrVvjzz//hJ2dXZ7yGRkZyMjIEB8nJmZ3cVGr1SXqdlEedPHp/rVxsQEAPL//3ORjf1XkriMyTawn88B6Mg+sJ/PAejIDajWsxF/VgInVlVqthiAI0Gq14o+xFOX6derUQcuWLbF69Wq0adMGt27dwsmTJzFr1ixotVpoNBrMnz8f27Ztw/3795GZmYmMjAzI5XK9a+juPSdBEHDjxg14eHjAzc1N3N+8efM8cW7ZsgUrVqzA7du3kZycjKysLKhUKnG/IAjiMQXd740bN9CgQQO92Fq0aAGtVouIiAi4uLhAEATUrVsXEolELOPm5oY///yzwOdMtz2//br7q1y5srjfy8sLDg4OuH79Onx9ffHBBx9gxIgRWL9+PTp27Ii+ffuKkz6PHTsWY8aMwcGDB9GxY0f07t27WOPoi0P3nOZXd+VNq9Vm9w5RqyGVSvX2Gfq5bNSkO+e3SPXr10fz5s1RrVo1bN26FcOHD89Tfv78+Zg9e3ae7QcPHiy3CRFKSjdOJMMiAzLIEB8Tz7ERJia/sTxkelhP5oH1ZB5YT+aB9WS6pOnp6Pbv70ePHoXGxsao8eSma4VNTk5GZmYmBEHA+/feN+jY+6fu4+d+PxdarsfWHqjcsnKh5dKy0pCeaPhkc4GBgZg8eTI+//xzfPfdd6hRowYaNWqExMREfP3111ixYgU+//xz1K1bFwqFAlOnTkVqaqrYOJeVlYXMzEzxsS6BS0pKQnp6OrRarbgP+K9RLy0tDYmJiTh37hwGDx6MKVOm4LPPPoNKpcLOnTuxYsUKsWxGRgY0Go3eecT7/fc8mZmZyMrKyvdaKSkpSExMhFqthkQi0SujVqv14s8tPT0dgiDkuz+/+wOyE9n09HQkJibigw8+QPfu3XHw4EEcOnQIs2bNwurVq9GtWzf069cPLVu2xMGDB3Hs2DEsWLAAn332GUaNGlVIrZWepKSkcrtWQTIzM5GWlobffvsNWVlZevtSUw1bicro3ctzcnBwQJ06dXDr1q1890+dOhUffvih+DgxMREeHh7o0qULVCpVeYVZLGq1GocOHULnzp1hZWWFJ9ee4N6ue7BKsUJAQICxwyPkrSMyTawn88B6Mg+sJ/PAejIDKSnirx06dICViS3hlJ6ejrt370KpVMJG94VA3mHH+XLs6YijVY4i6X5S/uO6JYCqigqv93zdoDHdRTVkyBBMnToVv/76K7Zu3YrRo0eLY6b/+OMP9OjRQ+x6rdVqERUVBW9vbzE3sLS0hLW1tfjYwiI7Rjs7OzRs2BD3799HSkoK3N3dAQCnT58GAMjlcqhUKly9ehXVqlXDnDlzxJi+/fZbSCQS8Zy6Hrr55SO689SvXx+bNm2CVCoVu5f//vvvsLCwQOPGjaFSqWBlZQVLS0u981hbW+fZlpONjY1eLDnp7i8hIUHsXn7jxg0kJCSI1wSAxo0bo3HjxpgyZQqCgoKwZcsWBAUFAQDq1q2LunXrYuLEifi///s/bNiwQa9Lf1kRBAFJSUmws7MzaA6AspSeng65XC5OuJdTQV+G5GZSSXdycjJu376NwYMH57tfJpPlO+2+lZWV2fwnpIvVo7oH7uEeJM8kZhP7q8KcXk+vMtaTeWA9mQfWk3lgPZmwHPViivWk0WggkUhgYWEhJp2GsrCwQNelXbG171ZAAv3E+99cyH+JPyytyiatUKlU6N+/Pz799FMkJibinXfeEe+hTp062L59O86cOQNHR0d89dVXePToEerWrat3n7p7z0kikaBLly6oU6cO3nnnHSxatAiJiYmYPn26eN8WFhaoU6cOYmJisHXrVjRt2hR79+4VZyTXnbNGjRqIiorC1atXUaVKFdjZ2Yk5i+48gwcPxuzZs/HOO+9g1qxZePz4MSZMmIDBgweLCb9u9vPcsee8Vm4WFhbQaDR51iyXyWTo0qULfHx8MHjwYCxZsgRZWVl4//330bZtWzRr1gxpaWn4+OOP0bdvX9SoUQP37t3DhQsX0KdPH1hYWGDixIno2rUr6tSpg2fPnuH48ePw9vYu8muoOHQ9EvKru/JmYWEBiUSS73vb0Pe6Ue9g0qRJOHHiBO7cuYNTp06hV69ekEqlCAwMNGZY5aJazWoAAJskG6RkphRSmoiIiIhMlq0t1M+e4dfNmwEzGfJYFN69vdFvez+oKuu3pqqqqNBvez949/Yu0+sPHz4cz549g5+fnzjhGZA9wVnjxo3h5+eHdu3awc3NDT179jT4vBYWFti1axfS0tLQrFkzjBgxAvPmzdMr87///Q8ffPABxo4di4YNG+LUqVNiYq7Tp08f+Pv7o3379nBxccl32TJbW1uEhYXh6dOnaNq0Kfr27YuOHTtixYoVRXsy8pGcnIxGjRrp/XTv3h0SiQQ///wzHB0d0aZNG3Tq1Ak1a9bEli1bAABSqRTx8fEYMmQI6tSpg379+qFr167icF6NRoMxY8bA29sb/v7+qFOnDr799tsSx/sqkgi6UepGMGDAAPz222+Ij4+Hi4sL3nzzTcybN08cvF+YxMRE2NvbIyEhwSy6l+/btw8BAQGwsrJC4r1EfO3xNTQWGgTGBsLbtWw/rKhwueuITBPryTywnswD68k8sJ7MgynXU3p6OqKiolCjRo083WOLQqvRIuZkDJJik2DnboeqrauWSZfysqQb46xSqYzegkoFM6V6etH7x9B81Kjdyzdv3mzMyxuVwjV7LIdUK8Xt6NtMuomIiIjIpFlILVC9XXVjh0Fkdvj1jpFIraXIUmTPfhcTFWPkaIiIiIio2DIyIB0+HI2WLgVyLG9LRASY2ERqrxwnAClA7N1YY0dCRERERMWVlQWL9etRFYA615JCRERs6TYiqwrZ433i78cbORIiIiIiIiIqC0y6jUhZUQkASHiYYORIiIiIiIiIqCww6TYih0oOAICMOI79ISIiIiIiehkx6TaiilUqAgC0T7XQaDVGjoaIiIiIiIhKG5NuI3LzcAMA2CbZIjaZk6kRERERERG9bJh0G5Gdux0AQJmsRPTzaCNHQ0RERERERKWNSbcRKd2yJ1JTpCgQk8C1uomIiIjMkq0t1PfvY//atYCtrbGjIRMVHByMnj17GjsMMgIm3Uakm71ckaJA9DO2dBMRERGZJYkEcHFBpr199u9UKoKDgyGRSMQfZ2dn+Pv74+rVq6V2jVmzZqFhw4YvLDNu3Dh4e3vnuy8mJgZSqRR79uwptZgAwMvLCzKZDA8fPizV85qTu3fvolu3brC1tYWrqys+/vhjZGVlvfCYixcvonPnznBwcICzszNGjRqF5ORkcX9ISIjeayrnT1xcXJndC5NuI1K4KgAAUq0Ud+/fNXI0RERERESmxd/fH7GxsYiNjcWRI0dgaWmJbt26lWsMw4cPx19//YVTp07l2RcSEgJXV1cEBASU2vV+//13pKWloW/fvli7dm2pnbe41Gp1uV9To9Ggf//+yMzMxKlTp7B27VqEhIRgxowZBR7z4MEDdOrUCbVq1cLZs2dx4MABXL9+HcHBwWKZ/v37i68n3Y+fnx/atm0LV1fXMrsfJt1GJLWWQmKf/W3oo7uPjBwNERERERVLRgYsxo9H/e++AzK4FGxpkslkcHNzg5ubGxo2bIgpU6bg7t27ePz4sVjm7t276NevHxwcHODk5IQePXrgzp074v7jx4+jWbNmUCgUcHJygp+fH6KjoxESEoLZs2fjypUrYmtnSEhInhgaNmyIxo0b46efftLbLggCQkJCMHToUEgkEgwfPhw1atSAXC7Ha6+9hqVLlxbrnlevXo2goCAMHjw4zzUB4N69ewgMDISTkxMUCgWaNGmCs2fPivt/+eUXNG3aFDY2NqhQoQJ69eol7pNIJNi9e7fe+RwcHMT7vnPnDiQSCbZs2YK2bdvCxsYGGzduRHx8PAIDA1G5cmXY2trCx8cHmzZt0juPVqvFF198gVq1akEmk6Fq1aqYN28eAKBDhw4YO3asXvnHjx/D2toaR44cyXOPBw8exM2bN7F+/Xo0bNgQXbt2xdy5c/HNN98gMzMz3+ft119/hZWVFb755hu89tpraNq0KVatWoUdO3bg1q1bAAC5XC6+ntzc3CCVSnH06FEMHz68gNooHUy6jcymgg0A4OmDp0aOhIiIiIiKJSsL0lWrUGP/fqCQ7q8mJSWl4J/0dMPLpqUZVraEkpOTsWHDBtSqVQvOzs4Aslth/fz8YGdnh5MnTyI8PBxKpRL+/v7IzMxEVlYWevbsibZt2+Lq1asIDw8Xu633798fH330EerVqye2evbv3z/faw8fPhxbt25FSo77OH78OKKiojBs2DBotVpUqVIF27Ztw40bNzBjxgz83//9H7Zu3Vqke0xKSsK2bdswaNAgdO7cGQkJCTh58qTec9C2bVvcv38fe/bswZUrV/DJJ59Aq9UCAPbu3YtevXohICAAly5dwpEjR9CsWbOiPtWYMmUKJkyYgIiICPj5+SE9PR2+vr7Yu3cv/vzzT4waNQqDBw/GuXPnxGOmTp2KBQsWYPr06bhx4wZCQ0NRsWL2EskjRoxAaGgoMnJ8KbVhwwZUrlwZHTp0wKxZs1C9enVx35kzZ1C3bl3xeADw8/NDYmIirl+/nm/MGRkZsLa2hoXFfymuXC4HkN17ID/r1q2Dra0t+vbtW+TnqCgsy/TsVCilmxJpt9OQ/DC58MJERERERKVFqSx4X0AAsHfvf49dXYHU1PzLtm0LHD/+3+Pq1YEnT/KWE4Qih/jrr79C+W+cKSkpcHd3x6+//iomVlu2bIFWq8WPP/4Iyb/j6desWQMHBwccP34cTZo0QUJCArp16wZPT09otVpUrlwZKpUKFhYWUCqVsLS0hJub2wvjCAoKwkcffYRt27aJ3ZXXrFmDN998E3Xq1AEAzJ49Wyxfo0YNnD59Glu3bkW/fv0Mvt/Nmzejdu3aqFevHgBgwIABWL16NVq3bg0ACA0NxePHj3H+/Hk4OTkBAGrVqiUeP2/ePAwYMEAvlgYNGhh8fZ2JEyeid+/eetsmTZok/j5u3DiEhYVh69ataNasGZKSkrB06VKsWLECQ4cOBQB4enrizTffBAD07t0bY8eOxc8//yw+HyEhIeIXIBUqVICnp6d4/ocPH+bp7q1LwAsa596hQwd8+OGHWLRoESZMmICUlBRMmTIFABAbm//yzLpeBbrkvKywpdvInCplv1mkz6V4nv7cuMEQEREREZmQ9u3b4/Lly7h8+TLOnTsHPz8/dO3aFdHR2ZMQX7lyBbdu3YKdnR2USiWUSiWcnJyQnp6O27dvw8nJCcHBwfDz80P37t2xbNmyYk1O5uDggN69e4vdvRMTE7Fjxw69bsnffPMNfH194eLiAqVSie+//x4xMUVboeinn37CoEGDxMeDBg3Ctm3bkJSUBAC4fPkyGjVqJCbcuV2+fBkdO3Ys6u3l0aRJE73HGo0Gc+fOhY+PD5ycnKBUKhEWFibeX0REBDIyMgq8to2NjV53+YsXL+LPP/8Uv8AYO3Zsvt3Mi6JevXpYu3YtvvzyS9ja2sLNzQ01atRAxYoV9Vq/dU6fPo2IiIgy71oOsKXb6BwqOQDIXqs7JiEGDjYORo2HiIiIiF4RyS/oaSmV6j9+0czOuROaHOOpS0qhUOi15P7444+wt7fHDz/8gM8++wzJycnw9fXFxo0b8xzr4uICILtFevz48Thw4AC2bt2K6dOnIywsDC1btixSLMOHD0fHjh1x69YtHDt2DFKpFG+//TaA7BbqSZMm4csvv0SLFi1gZ2eHRYsW6Y21LsyNGzdw5swZnDt3DpMnTxa3azQabN68GSNHjiy0Rbaw/RKJBEKuHgf5TZSmUCj0Hi9atAhLly7FkiVL4OPjA4VCgYkTJ4rjqw1pKR4xYgQaNmyIe/fuYc2aNejQoQOqVauWb1k3NzecOXNGb9ujR4/EfQUJCgpCUFAQHj16BIVCAYlEgq+++go1a9bMU/bHH39Ew4YN4evrW2jsJcWWbiNTVMx+QStSFIh+zmXDiIiIiKicKBQF/9jYGF42d8JVULlSIJFIYGFhgbR/x5E3btwYkZGRcHV1Ra1atfR+7O3txeMaNWqEqVOn4vfff4e3t7c4CZi1tTU0Go1B127fvj1q1KiBNWvWYM2aNRgwYICYnIaHh6Nly5Z4//330ahRI9SqVQu3b98u0r2tXr0abdq0wZUrV8TW/cuXL+PDDz/E6tWrAQD169fH5cuX8fRp/vNB1a9f/4Utxi4uLnpdrSMjI5Fa0LCBHMLDw9GjRw8MGjQIDRo0QM2aNfH333+L+2vXrg25XP7Ca/v4+KBJkyb44YcfEBoaimHDhhVY9o033sCNGzf0lvE6dOgQVCoV6tatW2i8FStWhFKpxJYtW2BjY4POnTvr7U9OTsbWrVvLpZUbYNJtdLq1unUt3URERERElC0jIwMPHz7Ew4cPERERgXHjxiE5ORndu3cHAAwcOBAVKlRAjx49cPLkSURFReH48eMYP3487t27h6ioKEydOhWnT59GdHQ0Dh48iNu3b8PLywsAUL16dURFReHy5ct48uSJ3kRfuUkkEgwbNgwrV67E6dOn9RK22rVr48KFCwgLC8Pff/+N6dOn4/z58wbfp1qtxvr16xEYGIjXX39d72fEiBE4e/Ysrl+/jsDAQLi5uaFnz54IDw/HP//8gx07duD06dMAgJkzZ2LTpk2YOXMmIiIicO3aNSxcuFC8TocOHbBixQpcunQJFy5cwOjRo2FlZVVofLVr18ahQ4dw6tQpRERE4N133xVbnoHs7uOTJ0/GJ598gnXr1uH27ds4c+aM+GWBzogRI7BgwQIIgqA3q/qKFSv0uqZ36dIFr732GoYMGYIrV64gLCwM06ZNw5gxYyCTyQAA586dg5eXF+7fv693nosXL+Lvv//GN998g7Fjx2L+/PlwcHDQi2PLli3IysrS68pflph0G5nS7b+kOzqBLd1ERERERDoHDhyAu7s73N3d0bx5c5w/fx7btm1Du3btAAC2trb47bffULVqVfTu3Rve3t4YPnw40tPToVKpYGtri7/++gt9+vRBnTp1MHr0aIwYMQLvvvsuAKBPnz7w9/dH+/bt4eLikmcZrNyCg4ORkJCAevXqoXnz5uL2d999F71790b//v3RvHlzxMfH4/333zf4Pvfs2YP4+Hi9RFTH29sb3t7eWL16NaytrXHw4EFxbXAfHx8sWLAA0n+HA7Rr1w7btm3Dnj170LBhQ3To0EFvhvEvv/wSHh4eaN26NYKCgjBp0iTY2toWGt+0adPQuHFj+Pn5oV27dmLin9P06dPx0UcfYcaMGfD29kb//v31WqoBIDAwEJaWlggMDIRNjt4UT5480esZIJVKsXnzZkilUrRo0QKDBg3CkCFDMGfOHLFMamoqbt68qdc9/ty5c+jcuTN8fHzw/fff47vvvsP48ePz3M/q1avRu3fvPMl4WZEIuTv1m5HExETY29sjISEBKpXK2OG8kFqtxr59+xAQEKD3bdKDPx7ghyY/INEuEfd/uo/NfTcbMcpXW0F1RKaF9WQeWE/mgfVkHlhPZkCrhfr2bRw7dgzthw6F1b8tcaYiPT0dUVFRqFGjhl6i8yrSarVITEwUZy+n8nXnzh14enri/PnzaNy4cYHlTKmeXvT+MTQf5URqRqbrXq5IUSDmGbuXExEREZkdCwugenWkVayYd1IxIoJarUZ8fDymTZuGN95444UJ98uInwpGpnDNnnxBqpXiYWzRly8gIiIiIiIyZeHh4XB3d8f58+exatUqY4dT7tjSbWRSaylkTjJkPM1AyqMUZGoyYS21NnZYRERERGSozExYTJ2Kuv/8A3TqBHAYAJGedu3a5Vmq7FXCpNsE2FW0Q8bTDCiSFbiXeA81HfOuI0dEREREJkqthvSrr1Ab+a95TESvNnYvNwF647q5bBgRERERlYFXuaWRqLhK433DpNsEKCpmj+tWJisR/ZzLhhERERFR6dHNep+ammrkSIjMj+59U5LVI9i93ATkXKubLd1EREREVJqkUikcHBzENZNtbW0hkUiMHJVxaLVaZGZmIj093ehLUVHBTKGeBEFAamoq4uLi4ODgIK6FXhxMuk2ArqVbkaJAdAJbuomIiIiodLm5uQGAmHi/qgRBQFpaGuRy+Sv7xYM5MKV6cnBwEN8/xcWk2wToxnSzpZuIiIiIyoJEIoG7uztcXV1f6cne1Go1fvvtN7Rp06ZE3YWpbJlKPVlZWZWohVuHSbcJyNm9/HzCeSNHQ0REREQvK6lUWipJhLmSSqXIysqCjY0Nk24T9rLVE5NuE5Cze3lMQgwEQTB6NwoiIiIiMpBcDvWlSzh58iRay+XGjoaITAxnDzABOZcMy8jMwOPUx0aOiIiIiIgMZmEB1KuHpKpVs38nIsqBnwomQOGa3dIt1UohT5NzXDcREREREdFLgkm3CZBaSyF3yu6KpOtiTkRERERmIjMTFnPm4LVNm4DMTGNHQ0Qmhkm3idCN61YmKxH9nMuGEREREZkNtRrSzz6D15YtwCs8MzgR5Y9Jt4ngsmFEREREREQvHybdJkK3bJgiRYHoBLZ0ExERERERvQyYdJuInN3L2dJNRERERET0cmDSbSJyrtXNlm4iIiIiIqKXA5NuE5FzTPeT1CdIVacaOSIiIiIiIiIqKSbdJkI3pluVqgIAdjEnIiIiIiJ6CTDpNhG67uWqFCbdRERERGbFxgZZp07hxKJFgI2NsaMhIhNjaewAKJuue7ksWQaJVsK1uomIiIjMhVQKoUkTPI+LA6RSY0dDRCaGLd0mQuGa3dJtobGAPE3Olm4iIiIiIqKXAJNuEyG1lkLuJAeQPYN5TCKTbiIiIiKzkJkJiy+/RK1du4DMTGNHQ0Qmhkm3Ccm5Vje7lxMRERGZCbUa0qlTUW/tWkCtNnY0RGRimHSbkJzLhrF7ORERERERkflj0m1CdMuGKVIUuJt4FxqtxsgRERERERERUUkw6TYhuu7ldil2yNJm4WHyQyNHRERERERERCXBpNuE6JJu10xXAEB0Asd1ExERERERmTMm3SZE173cMdURADium4iIiIiIyMwx6TYh4kRqKdn/cgZzIiIiIiIi82Zp7ADoP7ru5bJEGQC2dBMRERGZBRsbZB06hDNnzqC5jY2xoyEiE8Ok24ToWrolzyWQaCUc001ERERkDqRSCG3bIj4lBZBKjR0NEZkYdi83IQrX7JZuaAB5mpwt3URERERERGaOSbcJkVpLIXeSA8heq5st3URERERmQK2GxcqVqLFvH6BWGzsaIjIxTLpNjG5ctzJZicSMRCSkJxg5IiIiIiJ6ocxMSCdMQP3vvwcyM40dDRGZGCbdJka3bJhbphsATqZGRERERERkzph0mxjdZGqVsioBALuYExERERERmTEm3SZG173cNdMVAFu6iYiIiIiIzBmTbhOjS7odUh0AANHP2dJNRERERERkrph0mxjdmG7bZFsAQEwiW7qJiIiIiIjMFZNuE6Mb022VYAWALd1ERERERETmzNLYAZA+Xfdy4akAgGO6iYiIiEyeTIas3btx4cIF+Mpkxo6GiEwMk24To2vpznySCYlWggdJD5CpyYS11NrIkRERERFRviwtIQQE4NG/vxMR5cTu5SZG4fpvS7dGgH2mPQQIuJ9438hRERERERERUXEw6TYxUmsp5E5yAEAtSS0AXKubiIiIyKSp1ZCsWwePI0cAtdrY0RCRiWHSbYJ047qraaoB4LhuIiIiIpOWmQnLESPQePlyIDPT2NEQkYlh0m2CdMuGuavdATDpJiIiIiIiMldMuk2QbjK1ChkVAHDZMCIiIiIiInPFpNsE6bqXq1JVAICYRLZ0ExERERERmSMm3SZIl3TbJNkAYEs3ERERERGRuWLSbYJ0Y7otn2ev8xiTEANBEIwZEhERERERERUDk24TpBvTrYnXAADSstLwJPWJMUMiIiIiIiKiYmDSbYJ03ctTHqXATekGgDOYExEREZksmQxZoaE4//HHgExm7GiIyMQw6TZBuu7lKXEpqGaXvVZ3dALHdRMRERGZJEtLCH374kGrVoClpbGjISITw6TbBClcs1u6BY2AGtIaANjSTUREREREZI6YdJsgqZUUcic5AKCqpioAzmBOREREZLKysiDZvh2VwsOBrCxjR0NEJoZJt4nSjeuumFkRANfqJiIiIjJZGRmwDApC00WLgIwMY0dDRCaGSbeJ0o3rdkp3AsCWbiIiIiIiInPEpNtE6ZYNs0uxA8Ax3UREREREROaISbeJ0nUvlyVmLzvxOPUx0tRpxgyJiIiIiIiIiohJt4nSdS/Pis+C0jr7d7Z2ExERERERmRcm3SZK19Kd8igF1eyz1+pm0k1ERERERGRemHSbKN2Y7uRHyahq/++yYQmcTI2IiIiIiMicWBo7AMqfrqU7+WEyW7qJiIiITJm1NbJ+/BFXr1yBj7W1saMhIhPDlm4TpRvTnRKXAg87DwBs6SYiIiIySVZWEIYMwd2OHQErK2NHQ0Qmhkm3iVK4Zrd0CxoBVVAFAFu6iYiIiIiIzI3JJN0LFiyARCLBxIkTjR2KSZBaSSF3kgMAXNWuAIDo52zpJiIiIjI5WVmQ7NuHihcuAFlZxo6GiEyMSSTd58+fx3fffYf69esbOxSTouti7pDqAAC4l3gPGq3GiBERERERUR4ZGbDs2RNvfPYZkJFh7GiIyMQYPelOTk7GwIED8cMPP8DR0dHY4ZgU3WRqNok2kEqkUGvVeJj80MhRERERERERkaGMnnSPGTMGb731Fjp16mTsUEyObtmwtMdpqKyqDIDjuomIiIiIiMyJUZcM27x5My5evIjz588bVD4jIwMZObrsJCYmAgDUajXUanWZxFhadPEVJU65S/aY7sT7iahatypiEmLwz9N/0MStSZnE+KorTh1R+WM9mQfWk3lgPZkH1pMZUKthJf6qBlhXJovvJ/NgLvVkaHxGS7rv3r2LCRMm4NChQ7CxsTHomPnz52P27Nl5th88eBC2tralHWKZOHTokMFlHz1/BAC4efEmpFWlAICDZw9CeUdZJrFRtqLUERkP68k8sJ7MA+vJPLCeTJc0PR3d/v396NGj0Bj4ty0ZD99P5sHU6yk1NdWgchJBEIQyjiVfu3fvRq9evSCVSsVtGo0GEokEFhYWyMjI0NsH5N/S7eHhgSdPnkClUpVb7MWhVqtx6NAhdO7cGVYGrt94Ze0V7B25FzX9auL6R9ex8NRCvOf7Hpb6LS3jaF9NxakjKn+sJ/PAejIPrCfzwHoyAykpsPp3bqLUuDhYOTgYNx4qEN9P5sFc6ikxMREVKlRAQkLCC/NRo7V0d+zYEdeuXdPb9s4778DLywuTJ0/Ok3ADgEwmg0wmy7PdysrKpCsjp6LEal/JHgCQGpeKGo41AAB3k+6azb2aK3N6Pb3KWE/mgfVkHlhP5oH1ZMJy1AvryTywnsyDqdeTobEZLem2s7PD66+/rrdNoVDA2dk5z/ZXlW7JsOSHyajnUA8AJ1IjIiIiMjnW1tAsXYrr16/D29ra2NEQkYkx+uzlVDDdkmEpcSnwsPMAAEQ/jzZmSERERESUm5UVtO+9h6iAAL1WbyIiwMizl+d2/PhxY4dgUhSu2Um3oBHgonEBACRkJCAhPQH2NvbGDI2IiIiIiIgMwJZuEya1kkLulL1sGJ4CTnInAOxiTkRERGRSNBpITpyA87VrgEZj7GiIyMQw6TZx4rjuR8moal8VAJNuIiIiIpOSng7Lzp3x5vTpQHq6saMhIhPDpNvEieO6H6Wgmn01AEB0Asd1ExERERERmQMm3SZOWZEt3UREREREROaKSbeJ07V0Jz9MZks3ERERERGRmWHSbeJ0Y7pTHqWwpZuIiIiIiMjMMOk2cXpjuh2yW7qZdBMREREREZkHJt0mThzT/fC/Md0Pkh5ArVEbMywiIiIiIiIyAJNuE5dzyTBXhStkUhm0ghb3k+4bOTIiIiIiAgBYWUEzfz6uDx0KWFkZOxoiMjFMuk2c2L08LgUSQQIPew8AQPRzTqZGREREZBKsraH96CPc6tULsLY2djREZGKYdJs4hWt20i1oBKQ9TRNnMOe4biIiIiIiItPHpNvESa2kkDvJAeiP6+ayYUREREQmQqOB5MIFOERGAhqNsaMhIhPDpNsM5BzXzZZuIiIiIhOTng7Lli3R9uOPgfR0Y0dDRCaGSbcZyLlsGFu6iYiIiIiIzAeTbjOQ37JhbOkmIiIiIiIyfUy6zYDCLbulO/lRMqo5ZHcvj34eDUEQjBkWERERERERFYJJtxnQtXSnPEpBFVUVAEBaVhri0+KNGRYREREREREVgkm3Gcg5ptvG0gZuSjcA7GJORERERERk6ph0m4GcY7oB/DeZ2nNOpkZERERERGTKmHSbgZxLhgHgsmFEREREpsTKCppp0/BX//6AlZWxoyEiE8Ok2wyI3cvjUiBoBS4bRkRERGRKrK2hnTEDNwMDAWtrY0dDRCaGSbcZULhmJ92CRkBqfCpbuomIiIiIiMwEk24zILWSQu4sB5A9mRpbuomIiIhMiFYLXL8Ou5iY7N+JiHJg0m0mxMnUcqzVzZZuIiIiIhOQlgarRo3QYfx4IC3N2NEQkYlh0m0mci4bpmvpjkuJQ5qaH+xERERERESmikm3mci5bJijjSOU1tmP7ybeNWZYRERERERE9AJMus2Ewi27pTv5UTIkEgnX6iYiIiIiIjIDTLrNhK6lO+VRCgCISTfHdRMREREREZkuJt1mIueYbgDismGcwZyIiIiIiMh0Mek2E0q3/8Z0A2zpJiIiIiIiMgeWxg6ADJNzyTDgv5ZuJt1ERERERmZlBc2HH+Kff/5BdSsrY0dDRCaGSbeZELuXx6VA0Ar/TaTG7uVERERExmVtDe2CBbixbx+qW1sbOxoiMjHsXm4mFK7ZSbegEZAan4pqDtkt3XcT7kIraI0ZGhERERERERWASbeZkFpJIXeWA8ieTK2SXSVIJVKotWo8TH5o5OiIiIiIXmFaLXDnDuSPHmX/TkSUA5NuM5JzXLelhSUqqyoD4LhuIiIiIqNKS4NVnTro8u67QFqasaMhIhPDpNuM5F42TBzX/ZzjuomIiIiIiEwRk24zIrZ0P+QM5kREREREROaASbcZUbhlt3Trlg3jDOZERERERESmjUm3GdG1dOfuXs6WbiIiIiIiItPEpNuM6MZ05+5ezpZuIiIiIiIi08Sk24wo3djSTUREREREZE4sjR0AGS7nkmHAf0n38/TnSMxIhEqmMlpsRERERK8sS0toRo9GTHQ0qljyz2si0seWbjMiLhkWlwJBK8BOZgdHG0cAbO0mIiIiMhqZDNply3D13XcBmczY0RCRiWHSbUYUrtlJt6ARkBqfCgCo5sBlw4iIiIiIiEwVk24zIrWSQu4sB5B3XHf0c06mRkRERGQUggA8fgzrhITs34mIcmDSbWZyj+vWzWDOlm4iIiIiI0lNhVXlyug6dCiQmmrsaIjIxDDpNjO5lw0TW7q5bBgREREREZHJYdJtZnIvG8aWbiIiIiIiItPFpNvMiC3dj9jSTUREREREZOqYdJsZ3ZhusaX739nLHyQ9gFqjNlpcRERERERElBeTbjOTe0y3q8IV1lJraAUt7ifdN2ZoRERERERElAuTbjOTe0y3hcQCHioPABzXTUREREREZGqYdJuZ3EuGAf91Meda3URERERGYGkJ7eDBiGnfHrC0NHY0RGRi+KlgZnTdy1PiUiBoBUgsJOJkamzpJiIiIjICmQya1atxad8+uMtkxo6GiEwMW7rNjMI1O+kWNAJS41MBcNkwIiIiIiIiU8Wk28xIraSQO8sB/Deum8uGERERERmRIAApKZCmp2f/TkSUA7uXmyFlRSXS4tOQ/CgZrq+7sqWbiIiIyJhSU2Hl6IhuANTPngHW1saOiIhMCFu6zVDuZcNytnQL/HaViIiIiIjIZDDpNkO5lw3zsM9eMixVnYqnaU+NFhcRERERERHpY9JthsSW7n+XDbOxtEFFRUUAHNdNRERERERkSph0myHdWt0pD1PEbbq1ujmum4iIiIiIyHQw6TZDuu7lupZuIMe47uds6SYiIiIiIjIVTLrNkK57uW5MN8C1uomIiIiIiEwRlwwzQ7ru5fm2dHNMNxEREVH5kkqh7d0bsQ8fwlUqNXY0RGRimHSbIbGlOy4FglaAxEIiJt1s6SYiIiIqZzY20GzejAv79iHAxsbY0RCRiWH3cjOkcM1OugWNgNT4VADsXk5ERERERGSKmHSbIamVFHJnOYD/xnXrWrofpTxCela60WIjIiIiIiKi/zDpNlO5x3U7yZ2gsMpuAb+bcNdocRERERG9clJSYGVtjR49ewIpKYUWJ6JXC5NuMyUuG/YwO+mWSCScTI2IiIiIiMjEFCvpbtu2LdatW4e0tLTSjocMlO+yYQ4c101ERERERGRKipV0N2rUCJMmTYKbmxtGjhyJM2fOlHZcVAhd0q23bJjq35bu52zpJiIiIiIiMgXFSrqXLFmCBw8eYM2aNYiLi0ObNm1Qt25dLF68GI8ePSrtGCkfujHdKQ/zaelOZEs3ERERERGRKSj2mG5LS0v07t0bP//8M+7du4egoCBMnz4dHh4e6NmzJ44ePVqacVIu4pjunC3d9mzpJiIiIiIiMiUlnkjt3LlzmDlzJr788ku4urpi6tSpqFChArp164ZJkyaVRoyUj3zHdHOtbiIiIiIiIpNiWZyD4uLisH79eqxZswaRkZHo3r07Nm3aBD8/P0gkEgBAcHAw/P39sXjx4lINmLLlXjIM+K+l+27iXWgFLSwknJyeiIiIqMxJpdB27Yq4uDg4S6XGjoaITEyxku4qVarA09MTw4YNQ3BwMFxcXPKUqV+/Ppo2bVriACl/Ykt3XAoErQCJhQSVVZVhIbFApiYTj5Ifwd3O3chREhEREb0CbGyg+flnnN23DwE2NsaOhohMTLGS7iNHjqB169YvLKNSqXDs2LFiBUWFU7hmJ92CRkBqfCoULgpYWliisl1l3E28i+iEaCbdRERERERERlas/sdVqlRBZGRknu2RkZG4c+dOSWMiA0itpJA7ywHoj+vWdTHnuG4iIiIiIiLjK1bSHRwcjFOnTuXZfvbsWQQHB5c0JjKQOK774X/jusVlw5h0ExEREZWPlBRYOjjgrf79gZSUwssT0SulWEn3pUuX0KpVqzzb33jjDVy+fLmkMZGB8l02TMVlw4iIiIjKmyQ1FZYZGcYOg4hMULGSbolEgqSkpDzbExISoNFoShwUGSbfZcN0Ld2JbOkmIiIiIiIytmIl3W3atMH8+fP1EmyNRoP58+fjzTffLLXg6MV0SXd+y4axpZuIiIiIiMj4ijV7+cKFC9GmTRu89tpr4izmJ0+eRGJiIo4ePVqqAVLBdGO6Ux7maOm255huIiIiIiIiU1Gslu66devi6tWr6NevH+Li4pCUlIQhQ4bgr7/+wuuvv17aMVIB8h3T/W9L97P0Z0jKyDsEgIiIiIiIiMpPsVq6AaBSpUr4/PPPSzMWKqL8xnTbyezgaOOIZ+nPEJMQg3qu9YwVHhERERER0Suv2Ek3AKSmpiImJgaZmZl62+vXr1+ioMgw+S0ZBmS3dj9Lf4bohGgm3URERERlzcIC2jZt8DQ+HvYWxepISkQvsWIl3Y8fP8Y777yD/fv357ufM5iXD1338pTHKRC0AiQWEgDZM5hfeXSF47qJiIiIyoNcDs3hwwjftw8BcrmxoyEiE1Osr+ImTpyI58+f4+zZs5DL5Thw4ADWrl2L2rVrY8+ePaUdIxXA1sUWACBoBKTGp4rbuVY3ERERERGRaShW0n306FF89dVXaNKkCSwsLFCtWjUMGjQIX3zxBebPn2/weVauXIn69etDpVJBpVKhRYsWBbaeU15SKynkztnfpnKtbiIiIiIiItNTrKQ7JSUFrq6uAABHR0c8fvwYAODj44OLFy8afJ4qVapgwYIF+OOPP3DhwgV06NABPXr0wPXr14sT1ispv3HdXKubiIiIqBylpMCyUiX4DxkCpKQUXp6IXinFSrpfe+013Lx5EwDQoEEDfPfdd7h//z5WrVoFd3d3g8/TvXt3BAQEoHbt2qhTpw7mzZsHpVKJM2fOFCesV9KLlg3jmG4iIiKi8iF58gSyxERjh0FEJqhYE6lNmDABsbGxAICZM2fC398fGzduhLW1NUJCQooViEajwbZt25CSkoIWLVoU6xyvovyWDatmn929/H7SfWRps2BpUaJJ6omIiIiIiKiYipWNDRo0SPzd19cX0dHR+Ouvv1C1alVUqFChSOe6du0aWrRogfT0dCiVSuzatQt169bNt2xGRgYyMjLEx4n/fpuoVquhVquLcSflRxdfaccpd8ke0514P1E8t5PMCdZSa2RqMnHn6R0xCacXK6s6otLFejIPrCfzwHoyD6wnM6BWw0r8VQ2wrkwW30/mwVzqydD4JIIgCEU9sZeXF3799Vd4e3sXK7icMjMzERMTg4SEBGzfvh0//vgjTpw4kW/iPWvWLMyePTvP9tDQUNja2pY4FnP0aOcjxK6LhWN7R1Sb8F9yPfrGaDzMfIh5teahnpJrdRMRERGVFWl6OroNGAAA+HXzZmhsbIwcERGVh9TUVAQFBSEhIQEqlarAckVOugGgcuXKOHz4cKkk3bl16tQJnp6e+O677/Lsy6+l28PDA0+ePHnhTZoCtVqNQ4cOoXPnzrCysir8AANdXXcVv474FTW71MSAXweI27ts7ILj0cex5n9rMPD1gaV2vZdZWdURlS7Wk3lgPZkH1pN5YD2ZgZQUWDk6AgBS4+Jg5eBg3HioQHw/mQdzqafExERUqFCh0KS7WN3Lx4wZg4ULF+LHH3+EpWXpjhfWarV6iXVOMpkMMpksz3YrKyuTroycSjtWVaXsyk2NS9U7b3XH6kA08CD5gdk8N6bCnF5PrzLWk3lgPZkH1pN5YD2ZsBz1wnoyD6wn82Dq9WRobMXKmM+fP48jR47g4MGD8PHxgUKh0Nu/c+dOg84zdepUdO3aFVWrVkVSUhJCQ0Nx/PhxhIWFFSesV1J+S4YBQFUVlw0jIiIiKhcWFtD6+iIhIQFKi2ItDkREL7FiJd0ODg7o06dPiS8eFxeHIUOGIDY2Fvb29qhfvz7CwsLQuXPnEp/7VaFbMizlcQoErQCJhQQAUM0he3x3TCKXDSMiIiIqU3I5NKdP47d9+xAglxs7GiIyMcVKutesWVMqF1+9enWpnOdVZuuSPYGcoBGQGp8KhUt2rwPdWt1s6SYiIiIiIjIe9n8xc1IrKeTO2d+o5uxirlsmLCYhBsWYK4+IiIiIiIhKQbFaumvUqAGJRFLg/n/++afYAVHRKd2USItPQ8qjFMAne1sVVRUAQIo6BU/TnsLZ1tmIERIRERG9xFJTYVm3LjqnpgKRkYC9vbEjIiITUqyke+LEiXqP1Wo1Ll26hAMHDuDjjz8ujbioCJQVlXh8/TGSH/3X0i23ksNV4Yq4lDjEJMQw6SYiIiIqK4IASXQ0bAGo2cOQiHIpVtI9YcKEfLd/8803uHDhQokCoqJTVMwex53yKEVvezX7amLS3ci9kTFCIyIiIiIieqWV6pjurl27YseOHaV5SjKALunOs2yYbjK1BE6mRkREREREZAylmnRv374dTk5OpXlKMoC4bFg+Ld1A9mRqREREREREVP6K1b28UaNGehOpCYKAhw8f4vHjx/j2229LLTgyjLJidtKdc0w3wJZuIiIiIiIiYytW0t2zZ0+9xxYWFnBxcUG7du3g5eVVGnFRERTUvbyaA1u6iYiIiIiIjKlYSffMmTNLOw4qgYK6l4st3c/Z0k1ERERUZiQSCN7eSEpOhvwFy+oS0aupWGO69+3bh7CwsDzbw8LCsH///hIHRUWj616e8jgFgva/ZSp0Y7ofpTxCela6UWIjIiIieunZ2iLryhUcW74csLU1djREZGKKlXRPmTIFGo0mz3ZBEDBlypQSB0VFY+uS/eEuaASkxqeK253kTrC1yt53N+GuUWIjIiIiIiJ6lRUr6Y6MjETdunXzbPfy8sKtW7dKHBQVjdRKCrmzHID+uG6JRMIZzImIiIiIiIyoWEm3vb09/vnnnzzbb926BYVCUeKgqOgKHdfNGcyJiIiIykZqKiwbNED7ceOA1NTCyxPRK6VYSXePHj0wceJE3L59W9x269YtfPTRR/jf//5XasGR4QpaNowt3URERERlTBAgiYiA6u5dQBAKL09Er5RiJd1ffPEFFAoFvLy8UKNGDdSoUQPe3t5wdnbG4sWLSztGMoBu2TC2dBMREREREZmOYi0ZZm9vj1OnTuHQoUO4cuUK5HI56tevjzZt2pR2fGSggtbq1iXdbOkmIiIiIiIqf8VKuoHsSbq6dOmCLl26lGY8VEwFjemu5sDu5URERERERMZSrO7l48ePx7Jly/JsX7FiBSZOnFjSmKgYChrTnbOlWytoyz0uIiIiIiKiV1mxku4dO3agVatWeba3bNkS27dvL3FQVHQFdS+vbFcZFhILZGoyEZcSZ4zQiIiIiIiIXlnFSrrj4+Nhb2+fZ7tKpcKTJ09KHBQVXUHdy62kVqhkVwkAEP2ck6kRERERlTqJBEK1akh1cQEkEmNHQ0QmplhJd61atXDgwIE82/fv34+aNWuWOCgqOl338pTHKRC0+ktVcNkwIiIiojJka4usyEgc+uEHwNbW2NEQkYkp1kRqH374IcaOHYvHjx+jQ4cOAIAjR47gyy+/xJIlS0ozPjKQrUv2B7ygEZAanwqFi0LcV9W+KsLvhnPZMCIiIiIionJWrKR72LBhyMjIwLx58zB37lwAQPXq1bFy5UoMGTKkVAMkw0itpJA7y5EWn4bkh8l6STdbuomIiIiIiIyj2EuGvffee3jvvffw+PFjyOVyKJXZ3ZufPn0KJyenUguQDKd0UyItPi17XLfPf9t1M5izpZuIiIioDKSlQdq6NdokJADt2wNWVsaOiIhMSLHGdOfk4uICpVKJgwcPol+/fqhcuXJpxEXFUNCyYVyrm4iIiKgMabWw+OMPON66BWi5RCsR6StR0h0dHY2ZM2eievXqePvtt2FhYYF169aVVmxURAUtGya2dHP2ciIiIiIionJV5O7lmZmZ2LlzJ3788UeEh4ejU6dOuHfvHi5dugQfH5/CT0BlpqBlw3RJ97P0Z0jKSIKdzK7cYyMiIiIiInoVFamle9y4cahUqRKWLl2KXr164d69e/jll18gkUgglUrLKkYykK6lO3fSrZKp4GDjAAC4m3i3vMMiIiIiIiJ6ZRUp6V65ciXeffddHDx4EGPGjIGzs3NZxUXFUNCYboBdzImIiIiIiIyhSEn3+vXrce7cObi7u6N///749ddfodFoyio2KqKCxnQDXDaMiIiIiIjIGIqUdAcGBuLQoUO4du0avLy8MGbMGLi5uUGr1eLGjRtlFSMZqKAx3QCXDSMiIiIqS0KFCshQqYwdBhGZoGLNXl6jxv+3d9/xTtX3H8dfSW7uvpcNF+5gKktARUFUFGSKIooIqFW0rdZZrLVqf9Wqta11FtzWOquoiLiRIbJUHCgqKCLzXuCy4e6Vm5zfH+fmztyb3HuTm+Tm/eSRR5Kz7oecnJzzOd/Vk3vuuYedO3fyyiuvcOGFF/KrX/2KtLQ0fv/73/s7RvGRu3p54cFCXM6aw1WopFtEREQkQBISKM/OZvHLL0NCQrCjEZEQ06whwywWCxMmTGD+/PlkZ2dzyy23sGrVKn/FJo0U3ykeAMNpUHy4uMY8lXSLiIiIiIi0vEYl3SNHjuShhx7il19+qTOvffv23HTTTXz//fd+C04ax2a3Ed/RTLxrd6bWva1KukVERERERFpao5Luq666irVr1zJ06FD69+/PbbfdxmeffYZhGIGKTxqpvmHD3CXde/L2UO4qb/G4RERERFqt4mJsY8dy2l/+AsXF3pcXkYjSqKT78ssv56233uLQoUM8/PDD5OTkcNFFF5GSksKvf/1r3nnnHYr1QxNU9Q0blpKYgt1qx2k4yc7PDkZoIiIiIq2Ty4V19Wo6/vgjuFzelxeRiNKkNt0xMTFMmjSJZ555huzsbN577z26du3KnXfeSYcOHTj33HP57LPP/B2r+KC+YcOsFivpbdIBjdUtIiIiIiLSUprVkZrb8OHD+cc//sGGDRvYsGEDY8aMYe/evf7YtDRSQ8OGqQdzERERERGRlhXVlJV27dqFxWIhLS0NgK+++op58+YxYMAArr76av7whz/4NUjxXX1tukE9mIuIiIiIiLS0JpV0X3LJJaxYsQKAffv2MXbsWL766iv+8pe/8Le//c2vAUrjVLbprlW9HFTSLSIiIiIi0tKalHRv3LiRYcOGATB//nwGDRrE559/zquvvsqLL77oz/ikkdzVy2t3pAZVJd1KukVERERERFpGk6qXOxwOYmJiAPj4448577zzAOjXr5/acgeZqpeLiIiItDwjPh6n0xnsMEQkBDWppHvgwIE8/fTTrFmzhmXLljFx4kQAsrOz6dChg18DlMZxVy8vPFiIy1lzyIrubauql2tsdRERERE/SUigPCeHD994AxISgh2NiISYJiXd999/P8888wyjRo3i4osvZsiQIQC89957ldXOJTjiO8UDYDgNig/XHDM9PdkcMqygrICjJUdbPDYREREREZFI06Tq5aNGjeLQoUPk5eXRrl27yulXX3018fHxfgtOGs9mtxHfMZ6iQ0UU7C8goXPV3dY4exydEzpzoPAAWblZtI9rH8RIRUREREREWr8mlXQXFxdTWlpamXBnZmYyZ84cNm/eTOfOnf0aoDSeT+26c9SuW0RERMQvSkqwTZnC8HvvhZKSYEcjIiGmSUn3lClTePnllwHIyclh+PDhPPzww5x//vk89dRTfg1QGk/DhomIiIi0IKcT60cfkfLNN6DO1ESkliYl3d9++y0jR44EYMGCBXTp0oXMzExefvllHn30Ub8GKI3ny7Bh6sFcREREREQk8JqUdBcVFZGUlATA0qVLmTp1KlarlVNOOYXMTCVzwdZQ9XKVdIuIiIiIiLScJiXdffr04Z133mHXrl0sWbKE8ePHA3DgwAGSk5P9GqA0nsbqFhERERERCQ1NSrr/+te/csstt9CjRw+GDRvGiBEjALPU+4QTTvBrgNJ4DbbpbquSbhERERERkZbSpCHDpk2bxumnn87evXsrx+gGGDNmDBdccIHfgpOm8aVN976CfZSWlxITFdOisYmIiIiIiESSJiXdACkpKaSkpLB7924A0tLSGDZsmN8Ck6ZrqHp5h7gOxEXFUVxezK68XfRp36elwxMREREREYkYTape7nK5+Nvf/kabNm3o3r073bt3p23bttx77724XC5/xyiN5K5eXniwEJez5v6wWCyqYi4iIiLiTwkJOMrKePeddyAhIdjRiEiIaVJJ91/+8heee+45/vWvf3HaaacB8Omnn3L33XdTUlLCP/7xD78GKY0T3ykeAMNpUHy4mITONX/8M9pk8POhn8nMUWdqIiIiIiIigdSkpPull17iv//9L+edd17ltMGDB5Oamsp1112npDvIbHYb8R3jKTpURMH+gjpJt4YNExERERERaRlNql5+5MgR+vXrV2d6v379OHLkSLODkubTsGEiIiIiLaSkBNvMmZz0wANQUhLsaEQkxDQp6R4yZAiPP/54nemPP/44gwcPbnZQ0nwNDhumkm4RERER/3E6sS5cSOrnn4PTGexoRCTENKl6+QMPPMA555zDxx9/XDlG99q1a9m1axeLFi3ya4DSNL4MG6aSbhERERERkcBqUkn3mWeeyS+//MIFF1xATk4OOTk5TJ06lR9//JH//e9//o5RmqCh6uXu3st35e7CZai3eRERERERkUBp8jjd3bp1q9Nh2vfff89zzz3Hf/7zn2YHJs3TUNKdmpSK1WKl1FnKgcIDpCSmtHR4IiIiIiIiEaFJJd0S+hpq02232emW1A1Qu24REREREZFAUtLdSjXUphuqtevWWN0iIiIiIiIBo6S7lWqoejmoB3MREREREZGW0Kg23VOnTm1wfk5OTnNiET9yVy8vPFCIy+nCaqt5f8Vd0q2kW0RERKSZ4uNxHD3KkiVLmBAfH+xoRCTENCrpbtOmjdf5l19+ebMCEv+I7xQPFjBcBsWHi0nonFBjvoYNExEREfETiwUSEnDGxpqvRUSqaVTS/cILLwQqDvEzm91GfId4ig4VUbC/oE7SrerlIiIiIiIigac23a1YQ+26VdItIiIi4ielpdh+8xtOmDsXSkuDHY2IhJgmj9MtoS+xSyIHfzzocdiw7m3Nku4jxUcoKCsgMTqxpcMTERERaR3Ky7H+739kAI7y8mBHIyIhRiXdrVhDw4YlxyTTJsZso68q5iIiIiIiIoGhpLsV8zpsWFu16xYREREREQkkJd2tmDvp9lS9HKq1685Ru24REREREZFAUNLdirmrl9db0q0ezEVERERERAJKSXcrltil/jbdoB7MRUREREREAk1JdyvmtU23SrpFREREREQCSkl3K+Yu6S48UIjL6aoz313SraRbREREpBni43Hs2cNHL70E8fHBjkZEQoyS7lYsvlM8WMBwGRQfLq4z3917+e683ZS7NKakiIiISJNYLNCpE2Vt2pivRUSqUdLditnsNuI7mHdbPbXrTklMwW614zSc7M3f29LhiYiIiIiItHpKulu5hoYNs1qspCWnAepMTURERKTJSkux/v73DH7mGSgtDXY0IhJilHS3cl6HDWurztREREREmqW8HNvTT9Pzo4+gXE32RKQmJd2tnM/DhuWopFtERERERMTflHS3cho2TEREREREJHiUdLdyDbXphmol3WrTLSIiIiIi4ndKuls5r226VdItIiIiIiISMEq6Wzmf23TnZmIYRovFJSIiIiIiEgmUdLdyvlYvLygrIKckp6XCEhERERERiQhKuls5d0l30cEiXE5Xnflx9jg6xXcC1K5bREREpEni4nD88gtLn3kG4uKCHY2IhBgl3a1cfKd4sIDhMig+XOxxGY3VLSIiItIMViv06EFxly7maxGRaoL6q3Dfffdx8sknk5SUROfOnTn//PPZvHlzMENqdWx2G/Ed4gHv7bqVdIuIiIiIiPhXUJPuVatWcf311/PFF1+wbNkyHA4H48ePp7DQc0/b0jTe2nW7ezDPzFH1chEREZFGKyvDevvtDHjxRSgrC3Y0IhJiooL5xxcvXlzj/Ysvvkjnzp355ptvOOOMM4IUVeuTmJLIwR8P1jtsWGVJd55KukVEREQazeHA9sgjHAM4HI5gRyMiISakGp3k5uYC0L59+yBH0rr4PGyYSrpFRERERET8Kqgl3dW5XC5uuukmTjvtNI477jiPy5SWllJaWlr5Pi8vDzDvKIb6XUV3fMGIM66T2YtmXnaex7+fmpAKmG26Q/1zDKRg7iPxnfZTeNB+Cg/aT+FB+ykMOBzYK186QPsqZOl4Cg/hsp98jc9iGIYR4Fh8cu211/LRRx/x6aefkpaW5nGZu+++m3vuuafO9Hnz5hEfHx/oEMPW/oX72fvyXtqNakf3m7rXmZ9bnsusjbMAeHPwm9it9jrLiIiIiIhntpISzp05E4APXn8dZ2xskCMSkZZQVFTEJZdcQm5uLsnJyfUuFxJJ9w033MC7777L6tWr6dmzZ73LeSrpTk9P59ChQw3+J0OBw+Fg2bJljBs3Dru9ZZPaH17+gQ9++wE9x/Xk4g8vrjPfMAzaPtiW4vJiNl27id7terdofKEimPtIfKf9FB60n8KD9lN40H4KA4WF2Nu1A6DowAHsbdsGNx6pl46n8BAu+ykvL4+OHTt6TbqDWr3cMAxuvPFG3n77bVauXNlgwg0QExNDTExMnel2uz2kd0Z1wYi1TWobAIoOFNX7tzPaZLD58GayC7Pp17lfS4YXcsLp+xTJtJ/Cg/ZTeNB+Cg/aTyGs2n7RfgoP2k/hIdT3k6+xBbUjteuvv55XXnmFefPmkZSUxL59+9i3bx/FxcXBDKvV8TZkGED3tma1c43VLSIiIiIi4j9BTbqfeuopcnNzGTVqFF27dq18vPHGG8EMq9VJTDF7Ly86WITL6fK4TEayejAXERERaZK4OBzr1/PJo49CXFywoxGREBP06uUSeAmdEsAChsug+HAxCZ0T6iyjkm4RERGRJrJaYeBA8jMzzdciItXoVyECWKOsxHcwe3f3NlZ3Vp6SbhEREREREX9R0h0hvLXr7t7GLOlW9XIRERGRRiorw/q3v9H3tdegrCzY0YhIiFHSHSHc7boL9xd6nF9Z0p2bpWr/IiIiIo3hcGD7+9/p98Yb4HAEOxoRCTFKuiNEYhcz6a6venlachoWLJQ6SzlQeKAlQxMREREREWm1lHRHCG/Vy+02O92SugHqTE1ERERERMRflHRHCG/VywHSk9MBeHXDq6zcuRKny9kisYmIiIiIiLRWSrojhLuku76ke+GmhXy//3sA5n45l9EvjabH3B4s3LSwxWIUERERERFpbZR0R4iG2nQv3LSQafOnUVxeXGP6nrw9TJs/TYm3iIiIiIhIEynpjhD1tel2upzMXjwbg7o9lrun3bT4JlU1FxERERERaQIl3RHC3aa76GARLqercvqarDXszttd73oGBrvydrEma03AYxQREREJS7GxlH/+OasefBBiY4MdjYiEGCXdESKhUwJYwHAZFB+uqka+N3+vT+v7upyIiIhIxLHZME46iZxjjgGbLdjRiEiIUdIdIaxRVuI7xAM1q5h3Terq0/q+LiciIiIiIiJVlHRHEHcV8+qdqY3MGElachoWLB7XsWAhPTmdkRkjWyRGERERkbBTVob14Yfp8/bbUFYW7GhEJMQo6Y4gnoYNs1ltzJ04F6BO4u1+P2fiHGxWVZUSERER8cjhwPbnPzPwpZfA4Qh2NCISYpR0R5D6hg2b2n8qC6YvIDU5tcb0bkndWDB9AVP7T22xGEVERERERFoTJd0RpL5hw8BMvHfO3smKWStIT04H4P6x9yvhFhERERERaQYl3RHE3aa7evXy6mxWG6N6jGLmcTMB+HjHxy0Wm4iIiIiISGukpDuCeGrT7cmE3hMAWLptKYZhBDwuERERERGR1kpJdwSpbNPtoXp5dadlnEZcVBzZ+dlsPLCxJUITERERERFplZR0RxBPQ4Z5EhsVy6geowBYsm1JoMMSERERERFptZR0RxB39fKig0W4nK4Gl61exVxEREREGhAbS/myZXx6770QGxvsaEQkxCjpjiAJnRLAAobLoPhwcYPLTuhjJt2rM1dT5ChqifBEREREwpPNhnHmmRweNAhstmBHIyIhRkl3BLFGWYnvEA94b9fdt0Nf0pPTKXWWsjpzdUuEJyIiIiIi0uoo6Y4wvrbrtlgsqmIuIiIi4guHA+tTT9Fz0SJwOIIdjYiEGCXdEcbXYcOgqoq5OlMTERERaUBZGbbZsxn8n/9AWVmwoxGREKOkO8L4OmwYwJieY7BarPx08Cd25e4KdGgiIiIiIiKtjpLuCOMu6fZWvRygXVw7hqUOA2DZ9mUBjUtERERERKQ1UtIdYdxtun2pXg5VQ4epirmIiIiIiEjjKemOMI1p0w0wvvd4AJZtW4bT5QxYXCIiIiIiIq2Rku4I05g23QDDUofRJqYNR0uOsi57XSBDExERERERaXWUdEcYX4cMc4uyRjG211hAQ4eJiIiIiIg0lpLuCOOuXl50sAiX0+XTOmrXLSIiItKAmBjK33mHL+64A2Jigh2NiISYqGAHIC0roVMCWMBwGRQdKqqsbt4Qd7vuL3Z/QW5JLm1i2wQ6TBEREZHwERWFMWkS+ytei4hUp5LuCGONshLfIR7wvTO17m2707dDX5yGk092fBLI8ERERERERFoVJd0RqLHtukFVzEVERETq5XBgefll0pcvB4cj2NGISIhR0h2BGjtsGFRVMV+ybQmGYQQkLhEREZGwVFZG1G9/y4mPPQZlZcGORkRCjJLuCNTYYcMARvUYRbQtmp05O9lyZEugQhMREREREWlVlHRHoIQUs6S7MdXLE6ITOD3jdEBDh4mIiIiIiPhKSXcEcpd0N6Z6OcD4XlVVzEVERERERMQ7Jd0RqCltugEm9DE7U1uxYwVlTrVXEhERERER8UZJdwRqSptugMFdBtMloQuFjkI+3/V5IEITERERERFpVZR0R6CmDBkGYLVYq3ox36oq5iIiIiIiIt4o6Y5A7urlRQeLcDldjVq3+tBhIiIiIgLExFA+bx5f/+lPEBMT7GhEJMQo6Y5ACZ0SwAKGy6DoUFGj1nUn3ev3redA4YFAhCciIiISXqKiMKZNI/u00yAqKtjRiEiIUdIdgaxRVuI7xgON70ytc0JnTkg5AYBl25b5PTYREREREZHWREl3hKrsTK2R7bpBVcxFREREaigvx7JgAd0++wzKy4MdjYiEGCXdEaqpw4YBTOhtDh22dNtSXEbj2oSLiIiItDqlpURdcgknP/gglJYGOxoRCTFKuiNUU4cNAzg1/VQS7AnsL9zPhv0b/B2aiIiIiIhIq6GkO0IlpJgl3U2pXh4TFcOoHqMAVTEXERERERFpiJLuCOUu6W5K9XKoqmKupFtERERERKR+SrojlLtNd1OqlwNM6GMm3Z9mfUphWdMSdxERERERkdZOSXeESkxpXkn3Me2PoUfbHpQ5y1iVucqfoYmIiIiIiLQaSrojVHOGDAOwWCyM71UxdNhWVTEXERERERHxREl3hHJXLy86WITL2bRhv9xVzNWuW0RERCJadDTl//0v3954I0RHBzsaEQkxSrojVEKnBLCA4TIoOlTUpG2c1fMsbBYbmw9vJjMn088RioiIiIQJux3j8svZNWYM2O3BjkZEQoyS7ghljbIS3zEeaHq77raxbRmeNhyApduW+i02ERERERGR1kJJdwRrbrtu0NBhIiIiIpSXY1m0iC7r1kF5ebCjEZEQo6Q7gjV32DCoSrqX71hOuUsnGREREYlApaVEnX8+p/z971BaGuxoRCTEKOmOYM0dNgzgpG4n0S62HTklOXy952t/hSYiIiIiItIqKOmOYJUl3c2oXm6z2hjbayygKuYiIiIiIiK1KemOYO423c0p6YaqKubqTE1ERERERKQmJd0RzB9tugHG9x4PwJd7vuRo8dFmxyUiIiIiItJaKOmOYP5o0w2Q3iad/h374zJcLN+x3B+hiYiIiIiItApKuiOYP4YMc6scOmyr2nWLiIiIiIi4KemOYO7q5UUHi3A5Xc3a1oQ+Fe26ty/FMIxmxyYiIiISNqKjcc6dyw9XXw3R0cGORkRCjJLuCJbQKQEsYLgMig4VNWtbZ3Q/gxhbDFm5WWw+vNlPEYqIiIiEAbsd17XXsmPSJLDbgx2NiIQYJd0RzBplJb5jPND8dt3x9nhGdh8JqIq5iIiIiIiIm5LuCBeIdt1Lt2voMBEREYkgTieWVavosGEDOJ3BjkZEQoyS7gjnr2HDoGrosJU7V1JaXtrs7YmIiIiEhZISosaN4/Q774SSkmBHIyIhRkl3hPPXsGEAgzoPomtiV4ocRXya9WmztyciIiIiIhLulHRHuMqSbj9UL7dYLJWl3Uu2qV23iIiIiIiIku4I527TXbiv+SXdUK1d9za16xYREREREVHSHeH8WdINMLbXWCxY+H7/9+wr2OeXbYqIiIiIiIQrJd0Rzp9tugE6JXTixK4nAirtFhERERERUdId4fw5ZJibqpiLiIiIiIiYlHRHOHf18qKDRbicLr9s092Z2tJtS3EZ/tmmiIiISMiy23Hedx8/zpoFdnuwoxGREKOkO8IldEoACxgug6JDRX7Z5oj0ESRGJ3Kw6CDf7fvOL9sUERERCVnR0bj++Ee2XnABREcHOxoRCTFKuiOcNcpKfMd4wH/tuqNt0ZzV8yxAVcxFRERERCSyKemWgLTrHt9L43WLiIhIhHA6saxbR9stW8DpDHY0IhJilHRL1bBh+/zYmVofszO1z7I+o6DMf9sVERERCTklJUSdeipn/ulPUFIS7GhEJMQo6Ra/DxsG0Kd9H3q164XD5WDFjhV+266IiIiIiEg4UdItVSXdfqxeDho6TEREREREREm3VLbpLtznv5JuqBo6TO26RUREREQkUinplsrq5f4u6T6r51lEWaPYcmQLO47u8Ou2RUREREREwoGSbqmsXu7PNt0AyTHJjEgbAaiKuYiIiIiIRCYl3RKQIcPcVMVcREREREQimZJuqSzpLjpYhMvp8uu23Z2pLd+xHIfT4ddti4iIiIQEux3nHXfw84wZYLcHOxoRCTFRwQ5Agi+hUwJYwHAZFB0qqiz59ocTu55Ih7gOHC4+zJd7vuT0jNP9tm0REZFQ4nTCmjWwdy907QojR4LNFuyo6gqXOCF8YnXaollx5l18VPQdZ6+NYfToEI0zTD5PCK9YRbwJakn36tWrmTx5Mt26dcNisfDOO+8EM5yIZY2yEt8xHvB/u26b1cbYXmMBtesOZU4nrFwJr71mPjudwY7IM6cTVq2ysHp1KqtWWUI2Tgivz1Rx+le4xBoux1O4fJ4LF0KPHjB6NFxyifnco4c5PZSES5wQPrG64xw3LopHHjmJceOiQjrOUP88IbxiDZffqHCJE8Ln/NQoRhAtWrTI+Mtf/mIsXLjQAIy33367Uevn5uYagJGbmxuYAP2orKzMeOedd4yysrJgh+LRk8c9adzN3cbWJVv9vu3nv33e4G6MYc8O8/u2/SkQ+6i83DBWrDCMefPM5/Jyv23ab956yzDS0gwDqh5paeb0UBIucRpG+MQayDj9eTyFy+dpGOETq+L0r7feMgyLpWacYE6zWJofr7+Op0DH6U/hEqs7TgtOYwAbjQFsNCw4QzbOUP88DSN8jid3rOHyGxUOcRpGeMVqGL7no0FNuqtT0h1cL4992bibu43vXv7O79venbvb4G4My90W41DhIb9v31/8vY/C4UcjXE7C4RKnYYRPrOFyURMun6dhhE+sitO/ysvr/tbXjjc9vXk3Xf1xPLVEnP4SLrFWjzOegsoA4ykI2ThD+fNsqVgj7fwULnEaRnjF6uZrPhpWbbpLS0spLS2tfJ+XlweAw+HA4QjtTrrc8YVqnHGd4gDIy87ze4yd4zozsNNAfjz4I0u2LOGiARf5dfv+4s999PbbFmbOtGEYAJbK6Xv2GEybBq+/7uSCC4xm/53mcDrh97+PqhMjmD9xFovB7NkwaVK539tQuX9GXa6q5/oeDgfccEPDcV5/PfTsWY7FUnNdc9uWBrff0KOh2OrOs1BeDnffbTVjtVpgJNAV2AvGGsBl8Otfw5YtLqxeGvdYLA3Pb856Lhf84x/Wej9TMPjNb6riNIza82u+9jTN6TTYsuUYvv/ewGp1Nnp9cxvw6KMNx3nllfDNN3XjrC3Q81wuePrphmO94gr44gvv+z6QasRZz3f0iitg7dq6cTb0WdTm67L1LedywbPPev8816wJ3Ofp6/9h1y7Yvbv+H0nDMJeZOdNJenrTYnE6ITNzIJ98AjZb0+pZtkSc/lIjVis1vqesAcMVGrHWiNMCuL8zpwOfh2icIfx5gu/f08suc9Kzp3nOs1qrnqu/ru/Z5TL45Zee7NhhYLc7sVqNOstAw9swDLjxxrrXee4YweCaa6B9eycxMWCzGVitEBVltkt3P1d/Xd+05vzGVb/Ws2DQnSwSyaeAJDKNDLBYAnat15xYW/q6tDl8zRsshtGY02jgWCwW3n77bc4///x6l7n77ru555576kyfN28e8fHxAYyu9dvz/B4OvneQTud3IvWKVL9v//k9z/PewfcY034MN2bc6PfthxKnE66+ejyHD8dS+0fDZNCxYzHPPLPM64+GYUB5uRWHw0pZmfnscNhqPLunl5fbPCzjXtdWsUzV60OHYvnxx05e/z8dOxYRE+PEMCwVyaYFl8vivvdY63X902q/btUuAOYC1S9edgGzgbeDElH483ChiH8HW4gs4fQdDad9Hy6xhkuc4fI9vQDi7yuksJ/ZEW1CQQFFRxJCMs6w+DzdwuV72gKsVldF4m5UPtd+XXeei9LSKLKzE+nPJiaymDbkVW4zl2QWM5FN9KdHj1wSEhyV125u1d/XN6/2jfPqy5vPDa/vfl1SYuPo0Tivn8W9937KoEGHG/kJBk5RURGXXHIJubm5JCcn17tcWCXdnkq609PTOXToUIP/yVDgcDhYtmwZ48aNwx6CQ0msfWgtK/5vBcddchznvXie37e/bPsyznn9HFKTUtl+w3YsTS3Gw0xqP/3UUtmb5emnG36549WcfeRwwOHDcOgQfPKJlVtu8R7QCSe4iIuD0lIoKbFQWkrlo6TE/bqFktNwObE1EGdiokFcXNWdbk+P6nfC6z4MH5erf3t79sDXaVZYUC1eN/fnOQ1O3e+iR4/6/5tN/VX2db3MTFi71vut8xEjXPTsab6ufsh6el17msvlIjt7D6mpqVitVh/WN+pM27rVwooVVq8XimPGuDjmGKPOtmsL5LxffrGwZIm7eIR6v6cTJrg49tjgnXZ/+cXCknjv39EJRS769vUeZ6BqZPz8s4XFi73v+7PPdtGvn+c4m3GaqZenbe7aBfPn27zGOn1600sRXS4XO3fupEePHlibWOzVEnH6y65dMN9h8/o9nW4PfgmyO874wkIKk6sl3XEJIRknELKfJ/j+PT3/fCfdutWueWapU4vO07PT6WLv3v107twFsNaoweapJp6n5/374ZdfvB+LnTub1ybl5eb1q/u5+mvzOXDXe/3ZxHTmAzWLgty/nPOZzib6B+zv+9vLL5czc2ZIpK+AmY927NjRa9IdVtXLY2JiiImJqTPdbreHZCLrSajG2ia1DWCO1R2I+Eb3Gk1sVCx78vewNXcrAzoNaNJ2Fi6E2bNh9+6qaWlpMHcuTJ3qn1itVjs5OXYOHcLjw51cV3/k5jb+76xf3/gLp+hoiImB2Fjzufrr+p7rm5eVBY8+itcT27//DSee6FsSGojHqlVw1lne43z/fQujRjV+P1Rp/glv+UoY27viTe3da8W8uJkDf9tuZcyoZv+5Jlu50uwJFmgwQfznP61N/kwdDieLFn3HpEndsNubliSsXAkr2lJ1oVhdasX0aXDH75sep7+sXAlLluD1e3r77cGNdflKWOLDd/RPIfAdXRyH131/awjse6cTlifD4Wc8zKyItcM1MO8pW5NvEDscLhYt+olJk3pgtzdtIy0Rp7+UOeGt/eCEer+ntsfg5RQbUTYzeXBhPld/3dA0fyxf4oQFRyp+MuuJ0/o0/LmDjVibOan6w0LdaY1ZxoJvZy5fP8//pdiIDoEqxr58Txc063hysmjROiZNmtSs81PlebQBb7zh27WJO6H3nJA3lKw3vPy337g4+JfFQN3virtFxEQWc9EdfRk4yLxBXvsBdacFYt769XCjD5Vh09OjCKVUyte8KaySbgmchC7mHVl/DxnmFmeP44zuZ7B021KWbF3SpKR74UKYNg0MC3AmlUnC7k/N6QsW1E28XS44erT+ZLn64+DBKPbvP5uCgqgmlTRaLNC+PcTHm3dqvfnLX+CEE3xPlqOjm9eupzanE14t8n5iu/HG4I6LecYZ0OG33uMcOdI/f88AHEAJUNzI541n0PBAjFYgA9ammi+Taz3qa5DgbyNHmjerdg8D5lA3QbwJ0r/232faVKeOBNux3i8UT01p+dhqGzmyZb+nAOVUffeqPxqa9oOP39HH0+Bdqr6P3p59WaYx23KeAZahFb/3tQ8KK2CA5QVYmwTfUpWE2ILw2rCB87FqsdWO1QXOufCzzfyvuGo9DA/Tas8rs1hY36kTNoul8t6Ir+u6H+U2KHkCz5laxWda/Dj8rSKJdVY8yhv52h/rFNvA2Y36WcGZav5mBpUNaKiVlhVcneGEAIbg3p0NJe5OHz/PvkAbzP+WDTNJsNXzvqnzvC1r9fY9rbgxSJBvDrjPo3v2eK5lZrGY8339zbdYqtpz+1OfqCxeqValvM7fBdqQx69GZdF7TA///vFGOuUUuP9+/32moSaoSXdBQQFbt26tfL9jxw6+++472rdvT0ZGRhAjizyJXcwqUQX7CgL2Nyb0nmAm3duW8IcRf2jUuk6nWcJtnI/HUiRjNlx+Obz8cs3k+sgRM/H2jQWIrnzXrh106AAdO3p/dOhgLm+zmbH26OH9R+Oee4KbzGLD/CyhwQvFzbaq2UYjn5uyTu1npw3KvVzQlj4G/7VBGY1PlGs/l9CMmvU+3hS5s579HkVVAp5E3aS89qO+ZRJp+HrEZoOL34QHh3mYmQq8CTO/avz304X5+RUBucDuxETWY97EKGrgUVzP9L0+XihmYP6/owB7rWdP07w9N2Udiw3KHqfBC8XSx+CZWt/ThpJlb8s0qTstH7+j7/jxBl+TWDG/4PWxgNEG/q+l4vGmoezPCjlxcFxzth8VBaee2pwtmKIbmGeBomj4W/P/StionrDWfu3LtBJoIJ2pkoj5W+HLzZbGql763lw7/bANv2joe2qFw/HwMPArzPKXlrhZXZvNZtaynDbNvK6rfr3nLsmdMyfI13lA0YF8vy4XSNU/U2yYHRK6a+F9CrhC4zNtqqAm3evWrWN0tboZN998MwCzZs3ixRdfDFJUkSkxxUy6iw4V4XK6sNr8f8U1ofcE/sgfWZW5imJHMXF2750luK1ZA7tPpsGqhoXT4N16OgJJTq6bJNdOnNu2Leenn1ZzwQUj6dLFTlQTj45Q/SEuAzKBbRWPVZgnrnpVXCgObIngvPFyQVsQA9cE8E/H1fNce9oR4EMftuluOZVX8SjAvGgqr9jGET/EnUjDSfnLp1T80XpKvJ45pSqJri8prp00F1ffjt0OY8b44X/i3f6KR9DVbf1UpeJ7en2A/nQ0db+bnt7nAZ/4sL1fAd0rXrfEzbXa2/oFWOJDnCMr4nRh3oRwBeF1MeBLHbEEzP3RmKrDla8Ng4LcXNq2aVNZ2u3ruu5HNmatAG/GAP1ouETS15LLpq7zDXClD7G+g3ld7i1Zrm9+c60E3FexDrudB2+5pfJ1de8Do3zcZvUk2tdaDN7mfQHM8uFvP4x5c8iXWguBmFeOeZ2yzodYb6t4dASGVHsMBgbQcN7uL1OnmrUsPTV7nDPHf80emyOpa0N3Lxu/XKBNnQq3fA6PZNS86W7LhpuzYOopwYutuYKadI8aNYoQ6cct4sV3jDdLDlwGRYeKKku+/WlApwGkJqWyJ38Pn2Z9yrje43xed88+vJbK8hhc1gEmj6+bYEf78OvrcBjk5+fTuTNNTrjdgvVDnAtspyqxdj+2A1k07U54AubJqylVRP1R3bQAOORDnCcCx9BwUuzLc/XXMTTuwswJ9AD2UDOJcLMAacAGapZEuzAv2vPqeeT7OC8Xs1QZzM+tAPMGcb3q+89ZzO093NC6XsQYBnaHgzZ2O/EWC/FQ4xFX672n+duBO334W09gXmw5MC/amvrcnHX3ATt8iPUkPH9Pm/LePS0Gnwuwff6Ovkhwa2+uxLek+2/4nswEykqqEq+GfEDTY3WUl7No1aqKNqhNa8y4Et/ivIPgf6YDMI99b9/Tcwnu93RkRRx7AEd0NLc++GCN+e44G1MbtvpNAn/pDfwF75/nbIJea9vn72kGsBvz+mB5xcMtCvPmtjsJdyfkXfwZaIWpU2HKFLNwyN3B78iRoVMamzEyg9i2sZTklNS7THJ6MhkjQ6OG8ULgoVPq1hR1dYWHusEpQAjcy2gStekWAKxRVuI7xlN0sIjC/YUBSbotFgvje4/nhe9eYMm2JT4n3S7gmyHUrFJemxVIhVf+A0stZm2U+h4pFc+Bbgs2dSqcOwWe3ADbiqB3PFw3iGZ1UuLCTKKqJ9PVk+vDXtaPwzz59sas6uap4kBtzblQ9IeV+HYCfpjgXyi6a+xPo6qDEjd3fjuHuhc17pq0SZgVN5qjFO8J+1rMEiJvJgFDqT8pri9hjgVc5eUs+uijZiUJTuAZvF8o/o7wuVB8kOB+T5v6HW1p1ZOZhvZ9KDTtC5dYwyVOCJ/vqeL0P1+/p9sxa/D9CPwAfF/tkYN5c3tDrXW7YCbhg6xWXGlppAODMK+HmsVG8C9A6rH94+2U5NWfcAOMe3BcQGq4NpYT88aPAXUKBQyLOekmYAqh8V1tLCXdUimxSyJFB4so2FdAl8GBuB9oVjF3J90P8VC9y+UAy4CPgA9dcMDHkQwMS1VV0++8LNuWmsl4F6uVo717k2exkF5tehJNq4a2EJhtg93HV017GPPE19BdulLMdlWeSqu3Y1b3bUhnoBdVyXXvau9TqPq/+FriFewLsHC6UARz3y7APHFUq+RAGuZFTaDv0MZg9uvTUN8+K/Et6f4TTb+O8Ef7wtZ4oRgK39Ngf0d9EU77PlxiDZc43cLhewpVcd7kcmHNygIgKyODNKs1JOMM9c+zMd/TOMzaQydVW8bA7A+0diK+BfPacBmwzGaDoUOZg5lwD6BuFfWGzqHVLcTzZ+rtWq8l7N+wnzcvehNc0P3M7hzddpS83VW9EFisFgyXweFfvBXZtIz3qfk51ubet2sI2XscDVLSLZUSUxI5sPEABfsD15na2F5jsWBh44GNZOdn0y3JbLBhYP5AfgQsAj6nWidBVsxGcz40AX8L6IlZGlzfYx9mYptT8djkXtlmg+OO44Va24zHc0l57UcHqqqDLcQ8YdS++N5TMf1FzB95T6XVuz2sV50Nsw1j7YTa/drXVjnhcgEWLnFWNxXzTuwazO9cV8xkK1RiVILof+H2PXV/R1eUl/PRd99x9vHHMzoqKmTig/DZ9xA+sYZLnG6h/lvqNhWYUlyMrWdPAD7JyeHMNm1CM07C4/Ns6vfUgln1PAOz+YFbIWap+PfAeqeTlTk57GnfnjyLpTIxr64rNZPwIZi9u1dPnLxd6y3wEmsg5e/N57VzX6Msv4zuZ3bnV0t+hTXKStaaLPL35pPUNYn87HwWXrqQNX9fw8DpA+nYt2OLx1mAOVLGPGCxj+s02GwuhCnplkqBHjYMoEN8B07qdhJfZ3/Nu9uW0uX4K1iEeaDtqbVsah7sfxHK34Ue+6B4Pey347nY2YB0S1WVkxMaiMHATLZrJ+N7nE6+3bsXS7du7LNa2YtZNbeIqoS4IVGYCXkKsBHPyYx7mrcOTRKpm1C732fgh6pQFcLlAixc4qwuhGubhW2C2JovFIPBBpxpGBTu2cOZQ4aE3OcJ4bPvIXxiDZc43UL5t7S66p/fSMPQ59lM/v6eJgDDKh4Ol4tFn37K2ZMmkW231ygR/wHYStW1YfVEMAazc9khmB3O/Yv6r/WCWRW6rLCM1897ndysXDoc24EZC2cQFWOmfD1G9aiK0zD44ZUf2PrRVj64+gNmrZiFxdqUup2N4wCWAq9iJtxFjVy/q98jahlKuqVSfCezK+udK3bS7aRuZIzM8GsbDwPzLmNy7wmQ/TXXb1uCcfwVVX8fOAuY6IKfHoYnbzWnjxsHr6+BldFmkoBRMXZrBUvFr9scfPthswDtKh7VRwt3uFws+uYbJnXpgr1iQOxCapaQ11d6fgizM6XdNFw1pjr33/dUYt2JlhsCI1wuwMKhZC6chGOCOCrYQfggXI6ncBIu+x7CJ9ZwiVMiW6C/pxbMZnY9MH+33Qow24NXT8R/qJj+Lb6NAhCsqtAup4u3f/U22euyiesQxyWLLiGuveeqohaLhXOePIcnBz5J5upM1r+wnhN/c2Jg4sLsT+ZVYD41+yDqDVwKTAcmEh618JpCSbcAsGnhJr574TsAtizawpZFW0hOS2bi3In0n+pjg2oPCjB7lFyEWXV8F0CfCbDm7xjblnGMy8kkq41JwBlAaS786lfwwQfm+n/8I/zrX2Zv4pVJgqVWkmAJXJKQAPSpeDSkDLOt0F7gDeARH7b9BHBxs6Lzn3C5AAuHkrlwogQxMMLleBIRkboSgREVDzcX5ggV7kR8Eb4NbfYF5vVtS3VT9vFtH/PzOz9ji7Yx892ZtO/dvsHl2/Zoy+h7R7P0j0tZdssyjj33WL92prwRs+r4PMzh4Nw6AzMxk+2TqSpoCqdaeI0V/K7qJOg2LdzE/GnzKc0trTE9b08e86fNZ9PCTfWsWZeB2Ub6EWAs0B44H/gPZsIdC0xIHU5sdBIUH2bevvXMAcYDmZth+HAz4Y6Nhf/9Dx56qObwXVMxOxlbgXkAr8D8EQx2qVw0Zufqw4DJPq4TrtVjpHVxJ4gXVzyH68lMREQkUKyYJbJTgXswR6LwxZ8xmx3OBJ7F7McnUL5+6mvWPrwWgCkvTiHjNN+GARv+++F0PbErJTklLLnJl8EaG5YF3I9ZDX8QcB9mwp2E2bxyCWZp9lzM6+bqNTvdBWy1R3JJI7ht5P1BSXeEczldLJ69uMEGyItvWozLWX9fxIWYw0pdh1lFegDwR8wSbkfFtBsw7woeARbb7EzsNQaAJVvNg3vRIhg2DDZvNsey/vRTs8Tbk1BPEtydVNVXPdyCmaCHa/UYERERkUjm7VoPzIKmOOAgZi3Iq6lqTngV8DpwwE/xbF28lY9u/AiA0feOZtDFg3xe1xplZfKzk7FYLWx8fSNbFm1p9N8/jDnE5xmYnf3ejlkl345Zo24+Zo3QFzEL2hqqau0uYFtWXs7N69axrLw8JArYmktJd4TLWpNVY/iAOgzI25VH1pqs6pP4BfMO1QTMXrsnA09hHiTRmAfUHGAzZocUjwFnU9UB+YTeEwBYsm0J990H554LeXlw+umwbh0MHeq//2NLc3dSBXV/jFtD9RgRERGRSObtWs+C2X45B1gN3AWcjpls7gD+i1l45B47/GbgQ8wOfBtr/w/7eXP6mxhOgyGzhjDyL40v1ul6YldO+cMpAHx43YeUFZR5XacI88bBeZi1N6/BbK5mwSwU+w9mov0OcBE+DUJUyd2c8Iw9ezgzhDsmbAy16Y5w+Xt9O7wP781nE1Vts2v35N0dmFTxGI3ZFroh7qT708y1rPlXHhjJXHMNzJ0L0dGN+A+EqHDrpEpERESaISoK5zXXkJWZSVqULq8jga/XeiMrHndjJtVrMGuDLsdsH76h4vFvzMRsGDAGs5nmKZiFWfXJz85n3jnzKMsvo8eoHkz+z2QslqZ1xTvqnlH8tOAncjNzWXHXCiY8PKHOMuWYY53PA97GrO3qdjxmG+0ZmDU6pSb9KkS4pK6+jep8Sdckfqn23o5ZhWQSZgl2PxrZ23ZOT6IL+lCWuBVbnxU8+fspXH11YzYQ+tRJlYiISISIicH16KP8sGgRaTExwY5GWkhjr/WSqCqkArN6+QqqkvDtwOcVj3sxR/YZSVUSPoSqasplhWW8Nvk18nbn0aFvB6YvnI4tuulXmdEJ0Zzz1DnMmzSPL+d8yaBLBtFtaDcMzA7h5mFWkz9YbZ2ewCUVjwF1NynVKOmOcKkjMyhISyZhT5459FYtBpCXnsyWkRmkYybYkzCH9vItXa/rk09g+nQoGz4Bhm1l8k1LuPq3U7yvGIbUi7GIiIhI69Wca73OmCXDMyre76AqAf8EMylfUvEAs0nnaOAsp4voSxey99u9xHeM55IPLyGuXWMqcHt2zNnHcNzFx7HxtY28edX7HPzqKuZFWdlRbZlOFfFeglkS31JD3IY7temOcJ/ZrHw4dyJQc+xrNwuw6YJ+PGezkonZScIUmpZwGwY8+iiMHw+HD0MfzGorG4qWNjF6ERERkRBgGHDwING5ueZrkSboCfwWeA3Yh9kZ2SPAOZhDmR3GrNL+7p+WsfvdzZTH2Nj+7kw+6t2efV627QRWVmx7ZcX72nYD2/89gbJ2seSs38cnc79gB2az0V9hNjHdg9lX0wiUcDeGku4ItxfYNLU/8xdMJy81uca8kmSzetTQZ7+F9XubdWCVlMCvfw2zZ4PTCZddBp+9OoooaxTbjm5j25HarcRFREREwkRREfbUVM6eNQuKioIdjbQCFswht/6AOUrQEeAz4J4nv2bEv78A4O2XzufpU9P5FWbV9oGYbczfA3KrbWsh0AOzlPySiuceFdOPYg5nNhrIAG7tkshHD40HYOxfV/LSjqMcAP4HTMRsYiqNp+rlEc49VvSmqf35eUpfuq/JInFvPgVdk8g6NY2Lz3+DYz7ayoGp8yladxXxHeIb/Tf27IGpU+Grr8BqNcfevukmsFiSOC39NFZlrmLJtiVc1/46v/7fRERERERaAzvQadEWqBga7PR/nMUpM46rrI6+Hvip4vEoZrX3kzA7dnvLw/Z2AxdWLFe91HskcMmVxxP1vx/Ys3IntusWEbfoEmhiB21iUkl3hHOPMwhg2KzsHNWDjRcPYueoHriio1j46lTyerejZGcOb818C1d5/eN1e7J2LZx0kplwt2sHS5bAH/5QddxWHzpMRERERETq2vf9PhbMWIDhMjj+yuM568+nMwF4APgGs4OzNzGH7uqDmUh/ieeEuzonZon6v4BMzCHOrrFYuOCZc7HF2Ni6eCsbX98YoP9V5FDSHeFswLn1zLMAJe3iGPr2DOzxdrZ/vJ3lf1nu87afew5GjYJ9++C448zxt8eOrbnM+N5m9ZVPdnyCw+loyn9BRERERKTVys/O57VzX6OsoIyeZ/Xk3KfPrTM0WAdgGvAUsAUzgb7Vx+0/CtyGWb28cnvHduCMO84AYPHsxRQfKW7m/yKyKemOcIWYg9YDtK01Lw2zs4ZLBnVhygtm7+KfP/A5P87/scFtOhxwww3w299CWRlceKFZ4t2rV91lT+h6Ap3iO1FQVsDa3Wub9X8REREREWlNygrKmHfuPPJ259GxX0cuWnCRT0ODZWCOne2LvfVMP+3W0+g0oBNFB4tY+id1fNwcSroj3L8xe0fsBWRjjhU4r+J5B+b4gwADpw9kxC0jAHj31+9yYOMBj9s7eBDGjYMnnjDf33svvPkmJCZ6/vtWi5VxvccBsGSrqpiLiIiIiAC4nC7euuQt9q3fR3ynxg8N1tX7Ig0uZ4u2MfnZyQB89/x37Fixo54lxRsl3RHsAHB/xet/AnGY4wxeXPFc+x7a2PvG0nNMTxyFDl4//3WKj9asZrJ+vdl+e9UqSEqC996DO+7w3u+Cu1330u26gyYiIiIiArD0j0v55f1fiIqN4uL3LqZdr3aNWt/dd1N9l+IWIL1iufqkn5rOSdeeBMAHv/uA8pLyRsUgJiXdEexvQAFwMnCRD8tbo6xMe30abbq34ei2o7z9q7cxXOZYlK+9BqedBllZcMwx8OWXMHmyb3GM62WWdH+T/Q2Hig415b8iIiIiEjxRUbguu4ys0aMhSoMDSfN99fhXfDn3SwDOf/l80k5J87JGXTZgbsXr2om3+/0c6ha01TbmvjEkdk3kyJYjrP7H6kbHIUq6I9YvwDMVrx/A9y9CfMd4ZiycQVRsFFsWbeGTv67kttvgkkuguBjOPtvsqbx/f99j6ZrUlcFdBmNgsGzbssb9R0RERESCLSYG53PPsX72bIiJCXY0EuZ++fAXFs9eDJgJ78CLBjZ5W1Mx+2hKrTXd3XfT1Dpr1BXbJpZJj08C4LN/fVZvM1Opn5LuCPV/QDlwDmZV8sboemJXzv2P2ef5p/9YzXsP/AzA7bfD++9D27aNj0dDh4mIiIhIpNv3XdXQYCf85gROu+20Zm9zKrCT+vtu8kW/C/rRd0pfXOUu3r/6/craruIbJd0RaC3mmH1WzDH5msI+dAg/txkGwAW8zUsPH+K++8DmvTNFj9xDhy3dthTD0EEsIiIiYcQwoLAQW0mJ+VqkCfL25DHv3Hk4Ch30HNOTc546p87QYE1lo+G+m7yxWCxMenwS0YnR7F67m3XPrPNLXJFCSXeEMagas+9K4LgmbOPdd2H4cJifO559Md2JoYz8Z9+gNK+0yXGdnnE6cVFx7C3Yy8YDG5u8HREREZEWV1SEvV07zp05E4qKgh2NhKGygjJeO/c18vfk07F/R6YvmI7N3sTSrABJTktmzH1jAFh++3Ly9uQFOaLwoaQ7wrwHfIrZU/k9jVzX5YK//Q3OPx8KCuCMUTZu/2YaSalJHPr5EO/MeqfJVU1io2IZ1WMUoCrmIiIiIhI5XE4Xb138Fvu+20dC5wQu+fASYtvGBjssj0669iRSh6dSmlfK4t8vDnY4YUNJdwQpB26reP0H6nao0JD8fJg2De66y3x/442wdCl0H5jIjIUzsEXb+Pmdn1lz35omx1e9irmIiIiISCRYcvMSfvnAHBps5nszadezcUODtSSrzcrk/0zGGmVl08JN/PzOz8EOKSwo6Y4gzwGbgY5UVTH3xbZtMGIEvP02REfD88/Do4+C3W7OTx2WyqQnzR4NV9y5gi0fbWlSfO7O1FZnrqbIoapZIiIiItK6ffnol3z16FcAXPC/C0gb3vihwVpal8FdOPVPpwKw6IZFzWpiGimUdEeIAqCikJq/Am08LON0wsqV5pjbK1ea75ctg5NPhh9/hK5dYdUquPLKuuue+JsTGfq7oWDAwksWcmTrkUbH2K9jP9KT0yl1lrI6U2MAioiIiEjr9csHv7DkD2azyrH3j2XAtAFBjsh3Z9x5Bu16tyN/Tz7L/7I82OGEPCXdEeJhYD/QG/idh/kLF0KPHjB6tDnm9ujR0KEDTJgAR4/CKafAunXmc30mzp1I2og0SnJKeOOCNygrKGtUjBaLpbK0W1XMRURERKS12rt+LwtmVgwN9tsTKkuOw4U9zs65z5hDCH/9xNfsWrsryBGFNiXdEWA/8GDF6/uA6FrzFy4022vv3l1zem6uOerFWWeZJd/dujX8d6Jiopi+YDqJKYkc2HiA937zXqOH/3K361ZnaiIiIiLSGuXtzuO1c1/DUeig19henPOk/4YGa0m9xvRiyKwhYMAHV3+As8wZ7JBClpLuCHAPUAgMA6bVmud0wuzZDQ8puWULREX59reSuiVx0YKLsEZZ+XH+j3z+0OeNinVsr7FYLVZ+OvgTu3J1x0xERETCgM2Ga+pU9px6KthCa5gnCS2l+aXMO3ce+dn5dBrQiYsWXBRyQ4M1xviHxhPfMZ4DGw80+ro/kijpbuU2A/+peP0gUPse2po1dUu4a9u1y1zOVxmnZTBx7kTAHMNv+8fbfV63XVw7hqUOA1TFXERERMJEbCzO119n3a23QmxoDvUkwecqd/HWzLfY//3+qqHB2oT39yW+YzwT/m02D131t1Uc3nI4yBGFJiXdrdyfAScwGTjDw/y9e33bjq/LuZ107Ukcf+XxGC6DBTMWkLMzx+d1x/eqGDpsu5JuEREREQl/hmGw+KbFbFm0hajYKC5+/2La9mgb7LD8YtClg+g1rhfOUicf/O6DRjcvjQRKuluxz4C3MXfyv+pZpmtX37bl63JuFouFc548h24ndaP4SDFvXPAGjiKHT+tO6GPeLVu2bRlOl9qGiIiIiEh4+/LRL/n6ia/BAhe8cgGpw1KDHZLfWCwWzn36XKLioti5Yiffv/R9sEMKOUq6WymDqrG4fwPUNwDByJHQsWP927FYID3dXK6xomKjmL5wOvGd4tn33T6f73wNSx1Gm5g2HC05yrrsdY3/wyIiIiItqbAQe3Q0U84/HwoLgx2NhJjN722uOTTYheEzNJiv2vVqx6i7RwGw9I9LKTyg46A6Jd2t1DvA50A8cHcDy2VnQ0mJ53nuThTnzGl6nyBt0ttw0fyLsNgs/PDKD3z12Fde14myRjGm1xhA7bpFREREJHxlf5PNWxe/BQacePWJnHpLeA0N1hin/OEUugzpQvGRYpbcrJGIqlPS3Qo5gNsrXt8M1DfSl8MBM2dCQQH06gWptWq5pKXBggUwdWrz4ukxqgfjH6oYCuzmJexctdPrOu7xujV0mIiIiIiEo9xdubw2+TUcRQ56j+/NpMcnheXQYL6y2W1MfnYyWGDDqxvYumRrsEMKGUq6W6H/Ar8AnYA/NbDcnXfC559DcjIsWwaZmbBiBcybZz7v2NH8hNtt+OzhDLpkEIbT4M2L3iR3V26Dy7uT7i92f0FuScPLioiIiIiEktL8Ul479zUK9hbQ+bjOTJs/LayHBvNV6smpDP/9cAA+vOZDygrLghxRaFDS3crkU1Wd/C4guZ7lPvoI7r/ffP3cc2ZJt80Go0bBxRebz/4cZtJisTD52cl0GdKFooNFzL9wPuUl5fUu371td/p26IvTcLJ8x3L/BSIiIiIiEkCuchcLZixg/w/7SeiSwMUfXBz2Q4M1xuh7R5OcnkzOzhxW3bMq2OGEBCXdrcxDwAHgGODqepbZswcuv9x8ff31MG1ay8Rmj7cz4+0ZxLWPI/vrbD68/sMGO1Yb37ti6DC16xYRERGRMGAYBh/9/iO2frSVqLiKocG6tw12WC0qJimGSU9MAmDtI2vZu76RYw+3Qkq6W5G9wMMVr+8D7B6WKS83S7IPHYITToCHHmq5+ADa9WzHha9diMVq4bvnv+ObZ76pd9nq7bo13p+IiIiIhBqX08XOlTvZ8NoGdq7cydpH1rLuqXVggamvTiX15NYzNFhj9J3clwEXDcBwGrx/1fu4nK5ghxRUUcEOQPznHqAQOAWoryn23XfDmjWQlATz50NsEGq69B7fm7P+eRbLb1/OR7//iC5DupA+Ir3OcqN6jMJutbMzZydbjmzh2A7HtnywIiIiIt7YbLjOPpsDBw7QwZ/t8ySkbVq4icWzF5O3O6/OvHEPjqP/Bf2DEFXomDh3ItuWbmPvN3v56rGvOOWmU4IdUtCopLuV2ITZgRrAA4CnfhGXLoV//tN8/eyz0KdPy8TmyWm3nsaAaQNwOVzMv3A++Xvz6yyTEJ3A6RmnA6piLiIiIiEsNhbnu+/y5Z13BqdEQ1rcpoWbmD9tvseEG6Btj7YtG1AISuqaxLgHxgHwyR2fkJOZE9yAgkhJdyvxZ8AJTAFGepifnQ2/+hUYBlxzDcyY0bLx1WaxWJjywhQ6DexEwd4C3pz2Js4yZ53lNHSYiIiIiIQSl9PF4tmLob7WjxZY8oclEV+lGuDE355IxukZOAodLLp+UcQ2GVXS3Qp8CrwL2IB/eZjvdMKll8LBgzBkCPz73y0bX32iE6OZ8fYMYtrEsOvzXSz+w+I6y0zoYybdK3asoMypIQdEREREJLiy1mTVW8INgAF5u/LIWpPVckGFKIvVwrn/ORer3cqWD7fw05s/BTukoFDSHeYMqsbi/i3Qz8Myf/sbrFwJiYnBa8ddnw7HdGDqq1PBAuueXMf6F9bXmD+4y2A6J3Sm0FHI57s+D1KUIiIiIg0oLCSqbVvOmTEDCguDHY0EmKdmkc1ZrrXr1L8TI//PrIv70e8/ovhocZAjanlKusPcQuALIIGq8bmrW74c7r3XfP3MM3BsCPZFduw5xzLq7lEAfHjth+z5ek/lPKvFWjl02JKt4VfFvHaPlqpmJCIi0jpZioqIKi0NdhjSAsoKfKt9mdQ1KcCRhI/T/3w6Hfp2oHB/IR/f9nGww2lxSrrDmAO4veL1H4GUWvP37TOrlRsG/Pa3cMklLRtfY5xxxxn0Pa8vzlIn86fOp/BA1V3icG3XvWnhJub2mMtLo19i4SULeWn0S8ztMZdNCzcFOzQRERERaaTy0nI+ueMTPrjmg4YXtEByejIZIzNaJrAwEBUTxeT/TAbg22e/JXN1ZpAjallKusPYf4CtQGfgllrz3O249++HQYPg0UdbPr7GsFgtnP/y+XQ4tgN5u/N4c/qbOB1mx2rjepm9Hq7ft579BfuDGabP6uvRMm9PHvOnzVfiLSIiIhJGdn+5m2dOeIY1/1gDLkgbkWYOF1R7yKCK9xPnTMRqU6pVXfczunPiVScC8P7V71NeWh7kiFqOvglhKg9zXG4wq5XXrrzyj3/AJ59AQoLZjjsurkXDa5LYNrHMeGcG0YnRZK7KZNmtywDoktiF41OOB+Dj7aFfHaXBHi0rpi2+abGqmouIiIiEOEeRg6W3LOX5U5/n0KZDJHRO4KIFF/Gbz3/D9AXTSU5NrrF8cloy0xdMp//UyB6juz5j7x9LQpcEDm8+zKf3fRrscFpMVLADkKZ5EDgIHIvZgVp1K1fCPRUZ+VNPQT9PvauFqE79O3H+y+czf+p8vpzzJd1O6sbgSwczofcEvtv3HUu2LeHSwZcGO8x65e7K5Zv/fONzj5Y9RvVosdhERERExHeZqzN57zfvcWTrEQAGXzaYCf+eQHyHeAD6T+1P3yl9yVqTRf7efJK6JpExMkMl3A2IaxfH2Y+ezYIZC1jzzzUMnDGQTv07BTusgFPSHYaygUcqXv8LsFebt38/XHwxuFxw5ZVw2WUtH19z9b+gPyP/MpI1/1jD+1e9T+eBnZnQewL3f3Y/S7ctxWW4sFqC/2NmGAaHNh0i69MsstZkkbkmk9zMXJ/XbzAxFxEREZGgKM0vZfmfl/P1E18DkJSaxLnPnMux59Ttkdhqs6oQpZEGXDSAY14+hi0fbuGDqz/gilVXYLHWrqffuijpDkN3A0XAqcD51aa7XGaSvW8fDBgAjz0WjOj8Y9Q9o9j7zV62Lt7KGxe8wawvZhFvj2d/4X427N/AkJQhLR6T0+Fk3/p9ZK7JJGtNFlmfZlF8uOaQBxabhfa923P4l8Net7f4D4s5uv0oJ151onq3FBERCWdWK64zzuDI4cO0sQa/YECabtuybbx/1fuVBSknXnUi4x4cR2ybEBpzN8xZLBYmPTGJJ1c+SdanWXz7328ZevXQYIcVUEq6w8xPwHMVrx+gZt8N990Hy5ZBfDy8+abZnjtcWW1Wps6byrMnPcvR7Ud5/7L3GX35aD7c9iFLti1pkaS7rLCM3V/sNhPsNVns/mI3jiJHjWWi4qJIG55GxsgMMkZmkHZKGvZ4O3N7zCVvT57ndt2YHccVHypm5V0rWX3vavpf2J+Trz+ZjNMzsFha950+ERGRVicuDufHH/PZokVMCoeOdKSOkpwSlt6ylPXPrQegbY+2TH52Mr3G9gpyZK1T2+5tOevvZ7HkD0tYdusyjp18bKsuhFLSHWZuB1zABcBp1aavXg1//av5+oknzJLucBfXLo4Z78zguVOeY/uy7YxMGcmHvc2k+9bTbvX73ys6VETOFzl8vPJjdn+2m73f7sVw1syaY9vFknG6mWB3H9mdrid2xRZtq7OtiXMnMn/afPOuSPVNVOTTU+dNxVXuYt2T69j1+S5+fONHfnzjRzoP6szJ15/M4EsHE50Y7ff/o4iIiIjU9MsHv/DB7z4gPzsfgGE3DmPMP8foWizAht04jA2vbiB7XTaLZy/movkXBTukgFHSHUZWA+8DNuC+atMPHqxqx3355XDFFUEJLyC6DOrCec+fx1sz36LkfyUMuGgAn9o+pbCskITophflG4ZBbmZujarihzYdAmAnOyuXS05PpvvI7pUl2Z36d/KpzUn/qf2ZvmA6i2cvrtF2OzktmYlzJlb2aDn40sHsXb+Xr5/8mg2vbuDAhgN8eM2HfHzrxwyZNYSTrzuZjv06Nvn/KSIiIiKeFR0uYvHsxWx4dQMA7Y9pz3nPnUf3kd2DHFlksNqsTH52Mv856T/89OZPbH5/M30n9w12WAGhpDtMGMCfKl5fDbi/ju5EOzvb7KX8iSeCE18gHTfjOLLXZbP2obVc8O4F/Lf9f3l//vsMtA30uZdIw2Vw4McDlQl21posjx2ZxabH0n9if3qc2YOM0zNo271tk+P2tUfLrid05bxnz2PcA+P4/qXv+frJrzmy5QhfPfYVXz32FT3H9OTk606m73l9sUapnZiIiEjIKSwkqkcPJpaVQWYmtG0b7IjEi01vbWLp7KUUHijEYrUw4o8jGHXPKOxxdq/riv+kHJ/CiD+O4PMHPmfR9YvoMaoHMUkxwQ7L75R0h4kFwFdAAnBXtekPPACLF0NsrDked2JicOILtLH3jWXf+n3sWL6Dq/9zNZuf2cxmNgMVpcdzJ9YYD9FZ5iR7XXZlgp31WRYlR0tqbNMaZaXr0K5mKfbpGXQd1pWVX63k7ElnY7f75we3MT1axrWL45SbTmH474ez/ePtfP3E1/zywS/sWL6DHct3kJyWzNDfDeXEq04ksUsr3dEiIiJhynLoEDGAw+uSEkwF+wvYcf8Ovlv7HQCdBnZiyvNTSB2WGtzAItiou0bx05s/kbMjhxV3rmDinInBDsnvlHSHgTLgzxWv/wR0qXj96adwxx3m68cfh0GDWj62lmKNsjL4V4PZsXwHNqNmG+q8PXnMnzafM+44A8MwyFqTxZ4v91BeUl5jOXuCnfQR6ZVJdurwVKITqtrqOByhcZq0WC30Ht+b3uN7k5OZwzfPfMO3//2WvN15rLhzBav+tooB0wZw8vUnk35qujpeExEREfHCMAw2vLqBxbMXU3ykGGuUldP/fDoj/zKSqBilRMFkj7dz7tPn8sqEV/jy0S8ZOGMgpYWlHF19lMyETHqN7hX2Y5/rGxYGngG2YSbbf6yYdviw2Y7b6YRLL4Vf/zp48bUEl9PFijtXeJ5Z0VHZ6ntX15gc3zG+MsHOGJlByvEp2Ox1Oz0LZW27t2XMP8dw5l1n8tObP/H1E1+z+4vdbHxtIxtf20iXIV04+bqTGXTpoBo3EERERETElLc7jw+u+YAtH24BIK5XHJe8fglpJ6cFOTJx6z2+N4N/NZgfXvmBF898EZfDBUDmI5kea7WGGyXdIS4X+FvF63uARMx23LNmwe7dcOyx8NRT0NoLO+trg11br3G9GHDRALqP7E6Hvh1aTSlwVEwUg381mMG/Gszebys6Xpu3gf3f7+eD333AsluXcfwVx3PStSfRsa86XhMRERExDIP1z61n6R+XUppXii3axul3nE7OgBy6HN/F+wakRfU4qwc/vPJDZcLt5q7VOn3B9LBNvMO7nD4CPAAcwuw47TcV0x55BD78EGJizHbcSa13SLtK+XvzfVru+CuPZ+hVQ+nYr2OrSbhr63piV87773ncvOdmxj88nvZ92lOaW8qXc7/kiX5P8L9x/+Pnd37GVe7yvjERERGRVihnZw6vjH+F9696n9K8UlKHp/K79b/jtNtPwxLVOq8Rw5nL6WLlX1d6nllRq3XxTYtxOcPz+lYl3SFsD/Dvitf3Y+6stWvh9tvNaXPnwpAhwYmtpcV3iffrcq1BXLs4Rtw8glNuOoVty7ZVdry2/ePtbP94O8npyZx0zUmc+NsTSejc9OHVpHVzOV1ee9gXEREJF4bL4Osnv+bj2z/GUeggKjaKs/5xFsNnD8dqs4ZMHz5Sk9darQbk7coja02Wz50UhxIl3SHsLqAYOA04DzhyBGbONNtxz5wJV18d3PhaUlb3LHKTc0nOS8ZC3buTBgZ5yXlkdc+iN72DEGHwWKwW+kzoQ58JfcjZmcO6Z9ax/r/ryduVxyd/+YSVd69k4EUDOfn6k0kbkeaxBoASr8i0aeEmz2PJh3m7KRGRFme14ho6lNzcXBKtOn8Gy+Eth3nvN++RtSYLgO5ndGfyfyfT4ZgOQY5MvPG1Vquvy4UaJd0haiPwQsXrBwEMuPJKyMqCPn3gmWdafzvu6vYV7WPxxMVMnz8dA6NG4m1U1DlZPHExxT8Xc3L6ySRGR+aQWm17tGXsfWMZddcofnzzR75+4mv2fLmHDfM2sGHeBlKOT+Hk609m0CWDsMebw6Ip8YpMmxZuYv60+ZVVttxaQ7spEZEWFxeHc+1aVi9axKS4uGBHE3FcThdfzPmCFXesoLykHHuCnXEPjOOka07CYo2gC+YwltTVt/ayvi4XanQrLkTdDriAC4ERwJw58N57EB1ttuNOTg5qeC2ua1JXNg3YxPzp88lLrln1JC85j/nT57NpwCaeXvc0KQ+lcMU7V7BixwpcRni2+2iuqNgohlw2hN9+8VuuWncVx//6eKJio9j33T7ev+p9Hkl9hMV/WMxXj3/F/Gnz61TncSdemxZuCtL/wDOX00XmqkxzCIlVmWHbrifYXE4Xi2cvrpNwA62i3ZSIiESOAz8e4PlTn2fZLcsoLymn17heXLfxOk6+7mQl3GEkY2QGyWnJeKjQarJAcnoyGSMzWjQuf1FJdwhaAXyIuXP+CXz1Fdx2mznv3/+GE04IXmzBMjJjJGnJafw84Gd+7vcz3TO7k1iQSEFiAZndM8EKbWLa0DG+I9uObuOl71/ipe9fIqNNBpcNvoxZQ2ZxTIdjgv3fCIpuQ7sx5bkpjH9wPOtfWM+6p9ZxdNtRvpzzZf0rGYDFTLz6TukbElXNa5fIt5YhJIIhc3WmT+2mdizfQe/xkdVcwx/UXENEJPCcDief3f8Zq/62CpfDRUybGCY8MoHjrzy+1Xam25pZbVYmzp1o1sKzULNgoGJ3TpwzMWzPp0q6Q4wLuLXi9e+ATkdh/HRwOGDaNLj22iAGF0Q2q425E+cybf40sMLOnjsr57mrmj8/5Xku6HcBa3ev5aXvXuKNH98gKzeLf6z5B/9Y8w9GpI1g1pBZzDhuBm1j2wbnPxJEce3jOPWPpzLiDyPYtnQbq+5Zxe4vdte/QkXitfSWpaQMScEeb8cebycqLqrytT3OXmN6VGxUQE50qgrdNIZhULi/kIM/HeTAjwc4+NNBDv54kL3r9/q0/isTXiGhcwJtMtrQJqMNyenJla/d7xO7JLZISUK4JLJqriESoYqKiBowgHFFRbBlC7RpE+yIWrW96/fy3q/fY993+wA4dvKxnPv0uSR1C8+qx2LqP7U/0xdM93wenRPe51El3SHmTWAd5njcdxrw619DZib06gX//W9kteOubWr/qSyYvoDZi2ezO68qWUxLTmPOxDlM7T8VgFPTT+XU9FOZM3EO721+j5d/eJnFWxezdvda1u5ey+zFs5nSbwqXD76cCX0mEGWNrMPAYrXQZ2Ifio8WN5x0V2iwRNyDGol5raS8erIeFd/AMtWmWaOtfHjdh/VXhQ6xEvlgMAyDgn0FlUl19efiI8XN2nbhgUIKDxSSvS7b43yr3Uqb9LpJeUK3BIqziinNL8Xe3t6sGMIlkdXNIZEIZhhYMjOJBxyGpxOWNJanm62ucher713Np//6FMNpENchjrMfO5vjZh6n0u1Wov/U/vSd0pftK7bz6UefcvrZp9NrdK+wv8aLrGwjxJUCf654fSvwxmPwzjtV7bh109RMvKf0ncKarDXszd9L16SujMwYic1qq7NsnD2OGcfNYMZxM9hXsI9Xf3iVl75/iQ0HNjD/x/nM/3E+XRK6cOmgS5l1/Cz6t4+si2FfO6LIGJlBdGI0jiIHjiIH5cXlla8dxeazy1HV9tc9r/hw85I9n1WUyL931XtknJZBcmoySalJJHVLIq59XNBOwoEomTUMg4K9BXVKrg/+dJCSoyUe17FYLbTr3Y5OAzrRaWAnOg3oRMd+HXl9yuvkZ+d7vplhMZPaq7+5mvw9+eRm5ZqPXbnkZeVVvs/PzsflcHF0+1GObj/q8e9v/v1mYtvGeiwpd09L6paEzV73GIbQSWRdTheOQgdlhWU4Cs3vuPt1WWEZZfllfPT7j3RzKADCpZYDhFesIqHM083WhM4J2KJtldMGXDSASY9P0rCorZDVZqX7md35sfBHup/ZvVX8jirpDiFPAzuAFGDUtzDmFnP6Qw/B0KFBDCzE2Kw2RvUY1ah1UhJT+OOpf+TmETfz3b7veOn7l5i3YR77C/fzyBeP8MgXjzCkyxCG2oZyUuFJpLZNDUzwIcTdYUXenrwGE69ZK2Z5/bFzlbsqE3BPSXl906tPq2+9okNFPiXw37/wPd+/8H2NabYYG0ndkmok4u7n6tPscc0ria2tuSWzhmGQn53vseS6JKfh5LrzwM6VyXWngZ3ocGwHj/+/sx8922u7qYROCSR0SiDl+BSPf9NV7iI/u2ZSnptlJuY5mTkc3n4YZ6GTkpwSSnJK2P/D/npjT+qWVCcxT0pN4sNrfavlYLFaKC8urzcprnwuctSdVrF8fdPLCstwljrr32G+qLg59NSgp+h6Qlfa9mpLu57taNfLfCSlJrWKiwp/C5daDhBesermgISy+m62Fh4oBCCmTQznPXceAy4cEIToRJrGYhjhWwcmLy+PNm3akJubS3KId+ftcDhYtGgRkyZNwm6vewGcA/QBDgNzimDucbBjB0ydCgsWRHa18kBxOB0s3rqYl75/ifd/eZ8yZxkANouNs485m1lDZjH52MnERMUEOdLAqTyxgcfEKxSqw+5cuZOXRr/kdbk+k/qAAfl78snPzqfoUJHPfyO2XayZhFdLymsn5wmdE3y6KK3vYsHTZ2oYBvl78j2WXJfmlnrcvsVqoX2f9jUS604DOtGxb0eiYht3H9VjkpDun3ZT7t+8MSPHULyvuN7S8rzdeTjLmpfQ2mJtZlLcEmczC0QnRGNPsJvP8XbsCXbK8ss4+NPBJm/WarfStntb2vVqR9uebSuTcffruHb+H4LI5XSFdPW9xhxLwRboWL1dQzRGON0cCCuFhZBoDlfqOHoUe9u2wY0nTLmcLub2mNtgZ59JqUnclHlTk3+v/Hk8SeCEy37yNR9VSXeIuB8z4e5vwKorzIS7Rw947jkl3IFit9mZ3Hcyk/tO5kjxEV79/lUeW/MYW4q28MEvH/DBLx/QLrYdM4+byawhsxiWOqzVtRcKhw4rfC2Rv/i9i2ucgMtLyynYW0DenrzKRDxvTx4F2VXT8vbkUV5cTsnREkqOlnBg44F647DYLCSmJHosOXcn7AkpCV6H4nr3N+/yywe/cOjnQw0n1zYzue48sDMdB3Q0S7AHdKJD3w5Exfjnp9vdbiqQJV4xSTEktk+k04BOHucbLoPCA4UeS8v3rt9Lzo4cr3/DWVIzaY+KjapKimslx+5p9U33Nq2+zgJ9vTl05t1nYo+zc3THUXK253B0+1FyMnNwOVwc2XqEI1uPeFwvtm2sx2S8Xa92tO3eFlu05+r59Qn10QC8DmsXQtX1wynWUGmu0RgqlW+dDMOg+EgxebvzyNuVR97uPHJ35bJ33d6GR9fAvLmetSaLHqN6tEywIn6gpDsE7AbmVLw+YxE88ybY7fDGG6AbpS2jfVx7rhl6DRn7M+g1rBev/fQa//vhf+zO281T657iqXVP0bdDXy4fcjmXDb6M9DbpwQ7Zb1oi8WqOpg4hERUTRdsebWnbo2292zYMg9Lc0sqEvL7kvGBfAYbTLJXO35NP9teeOxXzRWlOKd+98F3Vf8FmocMxHapKrStKrjsc67/kuiFWmzWoFy4Wq3kzIzElkdRhNZt1+JrITn11Kj3H9KxMjoMxLquvN4fOuOOMOt9Vl9NF/p58s238DrN9fM72nMrXhfsLKckpYd/6fexbv6/ebbfr1Y52PduZVdcrEvJ2PduR0CWhxo2Clkq8DMMwq+cXlNV4OArrTqs9P2dnjk/D2j3Y+UHssXawgMViMfd9xesaz1ZL3Wle5tW7rVrzSnJLfIr1f+P+R3JqMtYoKxabpcaz1WZt8D0WOLj5IN9kfUNUdJRP69SehgWfm2uEyu9/OJXKu5wu3J9a1posek5KDpnPsbqWuIlhGAYlR0vMmk3VkurqyXXebvOmd1Pl7833Y8QigaekOwT8FSgBTsiH580OuHngARg2LJhRRa5+HfvxzzH/5N7R97Ji5wpe+v4lFm5ayObDm/nLJ3/hjk/u4KyeZzFryCym9p9KQnT9HXg4XU6fOn0LtmAnXt4EqkTeYrEQ2zaW2Lax9ZbEgnmRUri/sE5yXj1Jz9+TX29769r6Te3HwOkD6TywM+2Pad8iyXU48jWRHThjYNAvbpszvqjVZq1sx+7pOCwrLCNnZ05lh3U5O2q+dhQ5zIvZXXlkrsqss35UXFRlAt6mRxt++N8PDSZei65fRGK3RLONfAOJsdd5hWUBr/JfcqSEEnw77oJt54qdzd7GHvY0P5D6uG9kdHqQuHZxRCdW1PRIjCY6Ibryvadp7vce5yVEmzcOGimcSuU3LdzExze+xwzM88jr571BfNrykLs54I+bGO6EunryXDupztudh6PI4dP24jvFk5yWTJv0NiSlJeFyuPj22W+9rudrZ7AioUJtultIfe0SNgBDMM8pqRfCnoUwZQq8/baqlbe0htqO5Jfms+CnBbz0/UusylxVOT0xOpFpA6Zx+eDLObPHmVgtVRcWCzct9Di82dyJcyuHN5PGCfU2qFsXb+XVs1/1utysFbNC+iaHP/irLVY49DtQXSDbyXtiGGb1fE/J+NHtR8ndldsybd29qEzGPD0SorEn1pyftyePLx7+wut2z/3PuXQb2g3DMMCgxrPhqjvNl3kYZrMHX+cd2HiA1X9b7TXWk68/mbY922I4DVzlLlxOF65yV/3vK6YZ5QbljnL27NpDSucUcFHvug1trySvhOJDLTSqRC22GFuDSXrtZD0qLorV966ud1QGLJDULYnrfrqOmMSYoNRucQuXvgd8ibPfBf0CklAnpyWTnJ5cmVxXvk9NrtMXSWWbbi83W2fvmK023a1cuOwntekOE7dh/qakr4VdCyEjA55/Xgl3qEmKSeLKE67kyhOuZMfRHfzvh//x8vcvs+3oNl787kVe/O5FurfpzmWDL+PyIZez4cAGps2fhlHrjLEnbw/T5k9jwfQFSrybINSHkOg1rpdPJbMZIzNaPLZwFQ79DlTX0s01LBYLiV0SSeySSPqIus1enGVOcrNyK5PxXz78hS0fbPG63bgOcSR2SayRCDeYODcw3x7X+Cr/LqeLn974yeuxdMKvTwj670D/qf357vnvvMY6ca7nmg6+8MfFp6/NNSb/dzKd+ncyh8IrqDYsXq1aDI4Ch9dlDKf5gThLnRSXFlN8xE9Jf0Wnmfe3uR8wk3p7nJ2ouCiPz/Z487W3+b5so/r3OVDt+Q3DqHPzpfJ17RszPswvLyvn/avfb7C/kQUzFmC1W32u8h3fMb4ykfY1ofZFc2oNiYQyJd1BtBz4CLA6YddlEBVltuNu3z7YkUlDerbryV/P/Ct3nnEnn+/6nJe+f4k3fnyDzNxM/r7m7/x9zd+JtkXXSbgBDAwsWLhp8U1M6TslJKuaS9PpYiEwQr3fgdpCqbmGLdpG+z7tad/HPLF07NfRp6R7+oLpQf0/hNOxFC6x+tpc4/grjvdLrIZh4Cx1Vg3DV5GI10nePcw7+ONBdn+x2/sfqeAsdZojGOQ0O2yf2KJtRMVFYbFa6i+Nh8oq+4/2eRR7rN3nRNlwtXz1FPffhoqEuloyXTupTkr1/1Cb1YXbzVYRXyjpDhIXcGvFa8vTwDb410NwyilBDEoaxWKxcFrGaZyWcRpzJ87l3c3v8tL3L7Fk65LK4cc8MTDYlbeLNVlrGj3euIQ+XSwERiglsuHM18QrFGpjhNOxFA6xtvTNAYvFQlRslFna2aFx6/paKn/JokvodlI3yovLcRQ7Kp8dRY460+pbpsHlihyVr6sPbegsc9YZ6jCKMq7iWQCe5SrKia6cl7szt3EfQAMqO8mr3WletY7zas8vyS0hZ3uO121PeGQCQ68ZGtCE2lfhdrNVxBsl3UHyBvAtYC0A591w7rlw881BDkqaLM4ex8zjZjLzuJk8+fWTXL/oeq/r3PjRjYzMGEmf9n3o074Px7Q/hp7tehIbFdsCEUsg6WJBQlW4lMq6hdOxFA6xhsPNAfD95lDv8b1b7PN1OV2Ul9RM0jNXZ/L+Ve+7Q6IzBytfVzf+4fF0O6mbT8lyg/Oa2Hbd15sYKSekhETC7aabrdKaKOkOglLg/yra+rjug/Q4ePFFteNuLQZ0GuDTchsPbGTjgY01plmwkN4mnWPaH1MjGe/Tvg+92/dWQh5GdLEgoSpcEi+3cDqWwiHWcLg5EIo3h6w2q9nRW0JVCXa73u1Ydc8qrzcHhs8eHtTPN5xquIi0Vkq6g+BJYKcF2APWR+H1JdChkVWvJHSNzBhJWnIae/L2eGzXbcFCp4RO3DfmPrYd2cbWo1vZemQrWw5vIb8sn6zcLLJys1i+Y3md9dKS02ok4n3a9+GYDsfQq10v4u3xTY45XIY2ExH/cCdeoTwagAROuNwcCPWbQzVuDtQWQjVHQvEmhkikUdLdwo4Cd5djfvJ/hX/eAaeeGuSgxK9sVhtzJ85l2vxpWLDUSLwtFWe3p855qk7v5YZhcLDoYGUCvvXIVrYcqXrOK81jV94uduXtYsXOFXX+bmpSKsd0OIY+7aqS8T7t+9C7Xe8GxxLX0GYikSnURwMQCYdSeffNgY9vfBeyq6aH0s0BCI+bGCKtmZLuFvYPp5W8WGAjTNgPf3o22BFJIEztP5UF0xd4TGbnTJzjMZm1WCx0TuhM54TOnJpe806MYRgcKjrE1iNb6yTjWw5vIbc0lz35e9iTv4eVO1fW2Xa3pG5myXi7qmS8T/s+/HjwRy5beFnYDG3mdDlZlbmK1UdXk5CZwOheo1UiLyLSioVLqXzfsWnQ5s8AzHhvBj0nHRdSNwcgPG5iSOCoVmNwKeluAWVOeOwHKx86T2BlxQ9b+wfhlRfBqt+5Vmtq/6lM6TvFLz9wFotZJb1TQidGpI+oMc8wDI4UH6lMxGsk5Ye3cLTkKNn52WTnZ7M6c7VPf8+dhP/ug9/RNqYt7eLakRyTXPmIiYpp9P+huWqXyD+S+UhIl8jr5CYiEjmqJ66hnMiGw00M8b9wq9XYGgtZlHQH2K1fwCMZ4Bxqg6EVHVSUwPhzoWPH4MYmgWez2gI+LJjFYqFDfAc6xHfglLS6Y84dKT5So8r61qPm600HN5FXludhi1UOFR1izP/G1JkebYsmKTqpRiJe++F1fkwSidGJWC3eL0wWblrItPnTwqZEPpxObuFycyBc4hSRCGWxYHTvTnFREXb1jBtRQv38FO7XUKFeyOIrJd0BdOsX8OAwDzOi4fULIf0LeEDjckuAtY9rz7DUYQxLrfllfG3Da1yy8BKv63dN7ApAXmkehY5CAMqcZRwuPszh4sPNjq++5DwpJonk6GQSoxN54usnPHZKZ2BgwcKNH93I2J5jSYzxLYkPpHA6uYXLzYFwidMt1C/A3MKlJCFcPk8In1jDJU4In1idsTGs+Pi/fPTpR5x98GtGJ+l4aq5wiDXUz09Ol5PZi2c3eA110+KbmNJ3Skh8tuF0DdVYFsMwPA0e0KKeeOIJHnzwQfbt28eQIUN47LHHGDbMU7ZaU15eHm3atCE3N5fk5OQWiNR3ZU6I3w/OFMBTDuAC214oSoHo4H/HBXA4HCxatIhJkyZht4fOOJWBsnLnSka/NNrrcitmragsrXe6nOSX5ZNfmk9eaV69j/yy+ufnl+WTW5KL03AG5P8VGxVLgj2BeHt8jUdCdLVpUR6m2eO9r2ePJy4qrt4Tk9PlpMfcHjVOvtW5e6DfMXtH0E9u9Z3Y3J39NffE5q/jKdBx+luoX4C5KU7/C2Ss/jw/6TP1P8Xpf+FwPAXi/FTuKqe0vJSS8hJKnaU1XpeUlzR63o6cHbz/y/te/+4pqafQMaEjVosVCxbz2WKpfF/9de15Pi/jZXsAj3/1OPll+R5jDKVrqOp8zUeDnnS/8cYbXH755Tz99NMMHz6cOXPm8Oabb7J582Y6d+7c4LqhnHTP+Q7+cLz35f79Hdzkw3ISeJGWdLsTxIaGNgvUj5thGJSUl/iUrK/LXsey7cv8+vebKzYqtm5ybk+gpLyEb/Z+43X9ywZfRq92vbBarNgsNqwWa+XDZq167+u86tN9mWcYBhe+eSEHCg94jM+ChZTEFD698lOio6KxWWyV23G/rv1cu4aBP46ncLqJAeFzg0Bx+p9uYvlfuMSqOP0vFI4nl+Gi2FFMkaOIQkchRY4i83WZ+Tq/LJ9rPriGoyVH6/078fZ4JvWZZCbBPibNgSqQaE2qFwaFgrBJuocPH87JJ5/M448/DoDL5SI9PZ0bb7yR22+/vcF1QznpvvFzeNyHocBu+Bwe05BhISHSkm6oOrEBeBraLBROwr6WyC+6ZBEndTupxsmx9kmyxjQPy9U3vbCskOLy4hb434a32km54TSIiY6pfO++CVBf4l57mfyyfDYe2Oj1757Z/UxSElNq3FzwdNPC26P2DQqf1rFU3XD48/I/N3gB1iGuA3MnzsVmtVXe6Xcfa+7X7rv9gZrvMlxc/NbFHCw6WG+cXRK68PaMt4myRlWu5y6JqF1C0dC05qzjcrkY9PQgsvOzPcZowUJqcio/X/9z5Q0X9//V4/INtLFt7npOl5Pej/Zmd37DN4e2/X6b15tD9cXiy/mpoVjdcYbLTaxwibV6nLEOWP2COf2MK6HEHppxehIqcYLvsf503U+UOks9JsT1Jcru6QWlBezYvYOk9kkUO4s9LhcK53ybxUZMVAyxUbHE2CqePbyvMc9Wc5m9+Xt54fsXvP6tW0+9lWM7HIuBgctwYRgVz01439R1Nx/ezJJtS7zGOm/qPC4edLE/PmK/CIuku6ysjPj4eBYsWMD5559fOX3WrFnk5OTw7rvv1li+tLSU0tLSyvd5eXmkp6dz6NChkEu6H/3Byi1Dvf9wPfSNk98PdrVAROKNw+Fg2bJljBs3LmKSboC3f36bm5fdzJ78PZXT0pLTeHjsw1zQ74IgRmZyupz0eaIP2fnZ9ZbIpyansuW6LQG9WHAZLkrKS2qcuN13wYvKzWnf7P2G+z+/3+u2phw7hZTEFFyGq/LhNJxVr11Oj9NrLN/AMg1tyzAM8krzOFDkuZS7uihLFFjMfeDpsxeR1seKFavV6pcbPjWmNWIbpeWlHCo+5DXWLgldiIuKqzPd2w2IyuUauOHiyzaLHcVkF5g3huLLoPCf5vSE/4Oi6KrluiV1I94eX+Pv1Xhda9v1zWvqOgVlBWzP2d7g/w+gd7veJEUneV3O18+3zno+fN75pflsObqlSdsPFE/N1grLCvnlyC9e171s0GWcln4a0bbomsmyLcZMmKsnzbXmRVmb3/VWqFxD+WJV5irGvTrO63LLLl3Gmd3PbIGIfJOXl0fHjh29Jt1B7Ujt0KFDOJ1OunTpUmN6ly5d+Pnnn+ssf99993HPPffUmb506VLi4+MDFmdTZDjBmjIZV1cLWD38yLgMrNkGGZkfsMjzzTwJkmXLQqsqc6DFEMOjvR7lp4KfOFp+lHZR7RiQOADbdhuLti8KdngA/KrDr7g/33Mya2BwaftLWbLY+93RQIommmHGMDrYO3DYUX8Hcx3tHbk87nJsrmonN0vFo4VsyN/Andvu9LrcXb3uYlDSIMBsEuCiIrH38OyeX5nwN7CM+33tZd13vd3vdxbv5PX9r3uN85yO59A1pmvVXXaq3UWvtm3336/x2tM0L/Orb9swzL93sOwg24q3eY01PSadtva2ldtzc8dd431D842q93WejXqmY1DoLOSI44jXOJNsScRaY2v8XZfhqhFL7Rhqz/dlHd3MCT0uXLhc4VEYsL9wf7BD8El9NTZCzbaj3n/DQk2UJYoYawyx1lhirDFEW6LN0l5LDDHWmo9YayzR1ugay3tcxhJdY3lPHbRuyN/AnUe8n0ePLTqWlOyUGtMcFf8KKPDb59CQcLiGAnAaTp+uofI25rHox9C4PgUoKiryabmglnRnZ2eTmprK559/zogRVWMP33rrraxatYovv/yyxvLhVNINcPtXVh4ZUXGgVk+8XeZHfvNaF/8aFh4ntkgQqSXd4SLUS+Td3v75bWYunAl4rrL/+tTXgx5vS9z59sfxpDv0/heKcboTcfezy3CxOnM1k16f5HXdd6e/y+nppzeYvDd0meOP9T7b9RkXLrjQS6Tw1oVvcWp6zfZkvt50KHOUsWrVKs4880zsUQ0fT/Vt8/PdnzP9rele/9ZrF7zG8NThlduqfhOnxk2TavutScvWsy7Auux1XL/4eq+xPjbhMU7semLN/7+Pl7WNueFT3zbX71vP7KWzgYZLuv897t8cn3J8nW3VvpFWX3y+rFP7pl31ZX7Y/wN/XvHnev9/bv8c/U8GdR7U4DJNvVHm637ZcGADd6y8w+ty70x/h/G9xjepNDjSzk+ga6hACouS7o4dO2Kz2di/v+adyv3795OSklJn+ZiYGGJiYupMt9vtIZkkPXwa2NzjdHermm7ba+HmXfDAaTYg+Aei1BSq36dIN33QdC4ceCErtq8wh2Q5/eyQHOJo+qDpREVFeex1dc7EOUFvIw9gx86jZz/KtPnTsGDxeGKbO3EusTGxzf9bzTieWjLO5hrdazRpyWleOyYM9nc2XOIcf8x4n+I8p+85Qf8NmNJ/ik+xThnQ9CF5HA4HbaLa0K1NtyYfT1OTp5K2zHucFx13UdA/06GpQ7nv8/u8xnrtsGuDGuup3U/lwS8eZE/eHmggzhtPuTGocU44ZgJPfPOE18/z1tNvDfq+P6fvOTz97dNeYz2377nNjjVSzk9QdQ0V6kOwhcM1VG2+foeCOqBtdHQ0Q4cOZfny5ZXTXC4Xy5cvr1HyHc4eOAWKuphttye9sp2HvnFSlKLxuUWawma1cWb3Mzmj3Rmc2f3MkDtZuE3tP5Wds3eyYtYK5k2dx4pZK9gxe0dInSym9p/KgukLSE1OrTE9LTktJDrQcwuXOG1WG3MnzgXqtlt0v58zcU7Qv7OK0//CJdZwiRPCJ9aacdYUunGG7ucJ4RVruJyf3GxWG6N6jOLiQRczqseokPgMPXFfQy27dBk3d7+ZZZcuC7lrqKYIatINcPPNN/Pss8/y0ksvsWnTJq699loKCwu58sorgx2a30Tb4PeDXVydtIHfD3ZpXG6RCBAOJ7dwuDkA4RVnOFyAKU7/C5dYwyVOCJ9Y3XF2S+pWY3qoxhnqnyeEX6zhcH4KN+FSyNIYQR8yDODxxx/nwQcfZN++fRx//PE8+uijDB8+3Ot6oTxkWG2ROBxVuNE+Cg/aT+EhkveT0+UM+Sp8YMYZ6s01IHw+TwhcrP4+nvSZ+p8zPw9n9wzKHGV89dkbnHncpNCMM0w+Twif40kCI1z2k6/5aFDbdLvdcMMN3HDDDcEOQ0REpNnctRxCnbskofDHwpAuSQiXzxPCJ9ZwiRPCJ1ZbUjKu/QdZvmgRk/pP1PHkB+EUq4g3Qa9eLiIiIiIiItJaKekWERERERERCZCQqF4uIiIiIhK2iouxTZzIaYcPw+jREMJtUEWk5SnpFhERERFpDpcL6+rVdAQcLlewoxGREKPq5SIiIiIiIiIBoqRbREREREREJECUdIuIiIiIiIgEiJJuERERERERkQBR0i0iIiIiIiISIOq9XERERESkmYz4eJxOZ7DDEJEQpJJuEREREZHmSEigPCeHD994AxISgh2NiIQYJd0iIiIiIiIiAaKkW0RERERERCRA1KZbRERERKQ5SkqwTZ3K8AMH4KyzwG4PdkQiEkKUdIuIiIiINIfTifWjj0gBHOpMTURqUfVyERERERERkQBR0i0iIiIiIiISIEq6RURERERERAJESbeIiIiIiIhIgCjpFhEREREREQmQsO693DAMAPLy8oIciXcOh4OioiLy8vKwaxiJkKR9FB60n8KD9lN40H4KD9pPYaCwsPKlIy8Pu1XlWqFKx1N4CJf95M5D3XlpfcI66c7PzwcgPT09yJGIiIiIiADduwc7AhFpYfn5+bRp06be+RbDW1oewlwuF9nZ2SQlJWGxWIIdToPy8vJIT09n165dJCcnBzsc8UD7KDxoP4UH7afwoP0UHrSfwoP2U3jQfgoP4bKfDMMgPz+fbt26YW2ghktYl3RbrVbS0tKCHUajJCcnh/QXR7SPwoX2U3jQfgoP2k/hQfspPGg/hQftp/AQDvupoRJuNzU4EREREREREQkQJd0iIiIiIiIiAaKku4XExMRw1113ERMTE+xQpB7aR+FB+yk8aD+FB+2n8KD9FB60n8KD9lN4aG37Kaw7UhMREREREREJZSrpFhEREREREQkQJd0iIiIiIiIiAaKkW0RERERERCRAlHT7yRNPPEGPHj2IjY1l+PDhfPXVVw0u/+abb9KvXz9iY2MZNGgQixYtaqFII9N9993HySefTFJSEp07d+b8889n8+bNDa7z4osvYrFYajxiY2NbKOLIdPfdd9f5zPv169fgOjqWWl6PHj3q7CeLxcL111/vcXkdSy1j9erVTJ48mW7dumGxWHjnnXdqzDcMg7/+9a907dqVuLg4xo4dy5YtW7xut7HnN2lYQ/vJ4XBw2223MWjQIBISEujWrRuXX3452dnZDW6zKb+d0jBvx9MVV1xR5zOfOHGi1+3qePIvb/vJ07nKYrHw4IMP1rtNHU/+5cs1eElJCddffz0dOnQgMTGRCy+8kP379ze43aae04JFSbcfvPHGG9x8883cddddfPvttwwZMoQJEyZw4MABj8t//vnnXHzxxfzmN79h/fr1nH/++Zx//vls3LixhSOPHKtWreL666/niy++YNmyZTgcDsaPH09hYWGD6yUnJ7N3797KR2ZmZgtFHLkGDhxY4zP/9NNP611Wx1JwfP311zX20bJlywC46KKL6l1Hx1LgFRYWMmTIEJ544gmP8x944AEeffRRnn76ab788ksSEhKYMGECJSUl9W6zsec38a6h/VRUVMS3337LnXfeybfffsvChQvZvHkz5513ntftNua3U7zzdjwBTJw4scZn/tprrzW4TR1P/udtP1XfP3v37uX555/HYrFw4YUXNrhdHU/+48s1+B/+8Afef/993nzzTVatWkV2djZTp05tcLtNOacFlSHNNmzYMOP666+vfO90Oo1u3boZ9913n8flp0+fbpxzzjk1pg0fPtz43e9+F9A4pcqBAwcMwFi1alW9y7zwwgtGmzZtWi4oMe666y5jyJAhPi+vYyk0zJ492+jdu7fhcrk8ztex1PIA4+23365873K5jJSUFOPBBx+snJaTk2PExMQYr732Wr3baez5TRqn9n7y5KuvvjIAIzMzs95lGvvbKY3jaT/NmjXLmDJlSqO2o+MpsHw5nqZMmWKcddZZDS6j4ymwal+D5+TkGHa73XjzzTcrl9m0aZMBGGvXrvW4jaae04JJJd3NVFZWxjfffMPYsWMrp1mtVsaOHcvatWs9rrN27doaywNMmDCh3uXF/3JzcwFo3759g8sVFBTQvXt34PbkdAAACsdJREFU0tPTmTJlCj/++GNLhBfRtmzZQrdu3ejVqxeXXnopWVlZ9S6rYyn4ysrKeOWVV/j1r3+NxWKpdzkdS8G1Y8cO9u3bV+N4adOmDcOHD6/3eGnK+U38Lzc3F4vFQtu2bRtcrjG/neIfK1eupHPnzvTt25drr72Ww4cP17usjqfg279/Px9++CG/+c1vvC6r4ylwal+Df/PNNzgcjhrHRr9+/cjIyKj32GjKOS3YlHQ306FDh3A6nXTp0qXG9C5durBv3z6P6+zbt69Ry4t/uVwubrrpJk477TSOO+64epfr27cvzz//PO+++y6vvPIKLpeLU089ld27d7dgtJFl+PDhvPjiiyxevJinnnqKHTt2MHLkSPLz8z0ur2Mp+N555x1ycnK44oor6l1Gx1LwuY+JxhwvTTm/iX+VlJRw2223cfHFF5OcnFzvco397ZTmmzhxIi+//DLLly/n/vvvZ9WqVZx99tk4nU6Py+t4Cr6XXnqJpKQkr9WWdTwFjqdr8H379hEdHV3nxqK3XMq9jK/rBFtUsAMQaWnXX389Gzdu9No+Z8SIEYwYMaLy/amnnkr//v155plnuPfeewMdZkQ6++yzK18PHjyY4cOH0717d+bPn+/TnWlpec899xxnn3023bp1q3cZHUsijedwOJg+fTqGYfDUU081uKx+O1vezJkzK18PGjSIwYMH07t3b1auXMmYMWOCGJnU5/nnn+fSSy/12pGnjqfA8fUavDVSSXczdezYEZvNVqeHvf3795OSkuJxnZSUlEYtL/5zww038MEHH7BixQrS0tIata7dbueEE05g69atAYpOamvbti3HHntsvZ+5jqXgyszM5OOPP+a3v/1to9bTsdTy3MdEY46XppzfxD/cCXdmZibLli1rsJTbE2+/neJ/vXr1omPHjvV+5jqegmvNmjVs3ry50ecr0PHkL/Vdg6ekpFBWVkZOTk6N5b3lUu5lfF0n2JR0N1N0dDRDhw5l+fLlldNcLhfLly+vUbJT3YgRI2osD7Bs2bJ6l5fmMwyDG264gbfffptPPvmEnj17NnobTqeTDRs20LVr1wBEKJ4UFBSwbdu2ej9zHUvB9cILL9C5c2fOOeecRq2nY6nl9ezZk5SUlBrHS15eHl9++WW9x0tTzm/SfO6Ee8uWLXz88cd06NCh0dvw9tsp/rd7924OHz5c72eu4ym4nnvuOYYOHcqQIUMava6Op+bxdg0+dOhQ7HZ7jWNj8+bNZGVl1XtsNOWcFnRB7sitVXj99deNmJgY48UXXzR++ukn4+qrrzbatm1r7Nu3zzAMw7jsssuM22+/vXL5zz77zIiKijIeeughY9OmTcZdd91l2O12Y8OGDcH6L7R61157rdGmTRtj5cqVxt69eysfRUVFlcvU3k/33HOPsWTJEmPbtm3GN998Y8ycOdOIjY01fvzxx2D8FyLCH//4R2PlypXGjh07jM8++8wYO3as0bFjR+PAgQOGYehYCiVOp9PIyMgwbrvttjrzdCwFR35+vrF+/Xpj/fr1BmA88sgjxvr16yt7vf7Xv/5ltG3b1nj33XeNH374wZgyZYrRs2dPo7i4uHIbZ511lvHYY49Vvvd2fpPGa2g/lZWVGeedd56RlpZmfPfddzXOV6WlpZXbqL2fvP12SuM1tJ/y8/ONW265xVi7dq2xY8cO4+OPPzZOPPFE45hjjjFKSkoqt6HjKfC8/e4ZhmHk5uYa8fHxxlNPPeVxGzqeAsuXa/BrrrnGyMjIMD755BNj3bp1xogRI4wRI0bU2E7fvn2NhQsXVr735ZwWSpR0+8ljjz1mZGRkGNHR0cawYcOML774onLemWeeacyaNavG8vPnzzeOPfZYIzo62hg4cKDx4YcftnDEkQXw+HjhhRcql6m9n2666abKfdqlSxdj0qRJxrffftvywUeQGTNmGF27djWio6ON1NRUY8aMGcbWrVsr5+tYCh1LliwxAGPz5s115ulYCo4VK1Z4/J1z7wuXy2XceeedRpcuXYyYmBhjzJgxdfZf9+7djbvuuqvGtIbOb9J4De2nHTt21Hu+WrFiReU2au8nb7+d0ngN7aeioiJj/PjxRqdOnQy73W50797duOqqq+okzzqeAs/b755hGMYzzzxjxMXFGTk5OR63oeMpsHy5Bi8uLjauu+46o127dkZ8fLxxwQUXGHv37q2znerr+HJOCyUWwzCMwJShi4iIiIiIiEQ2tekWERERERERCRAl3SIiIiIiIiIBoqRbREREREREJECUdIuIiIiIiIgEiJJuERERERERkQBR0i0iIiIiIiISIEq6RURERERERAJESbeIiIiIiIhIgCjpFhERkUaxWCy88847wQ5DREQkLCjpFhERCSNXXHEFFoulzmPixInBDk1EREQ8iAp2ACIiItI4EydO5IUXXqgxLSYmJkjRiIiISENU0i0iIhJmYmJiSElJqfFo164dYFb9fuqppzj77LOJi4ujV69eLFiwoMb6GzZs4KyzziIuLo4OHTpw9dVXU1BQUGOZ559/noEDBxITE0PXrl254YYbasw/dOgQF1xwAfHx8RxzzDG89957gf1Pi4iIhCkl3SIiIq3MnXfeyYUXXsj333/PpZdeysyZM9m0aRMAhYWFTJgwgXbt2vH111/z5ptv8vHHH9dIqp966imuv/56rr76ajZs2MB7771Hnz59avyNe+65h+nTp/PDDz8wadIkLr30Uo4cOdKi/08REZFwYDEMwwh2ECIiIuKbK664gldeeYXY2Nga0//v//6P//u//8NisXDNNdfw1FNPVc475ZRTOPHEE3nyySd59tlnue2229i1axcJCQkALFq0iMmTJ5OdnU2XLl1ITU3lyiuv5O9//7vHGCwWC3fccQf33nsvYCbyiYmJfPTRR2pbLiIiUovadIuIiISZ0aNH10iqAdq3b1/5esSIETXmjRgxgu+++w6ATZs2MWTIkMqEG+C0007D5XKxefNmLBYL2dnZjBkzpsEYBg8eXPk6ISGB5ORkDhw40NT/koiISKulpFtERCTMJCQk1Knu7S9xcXE+LWe322u8t1gsuFyuQIQkIiIS1tSmW0REpJX54osv6rzv378/AP379+f777+nsLCwcv5nn32G1Wqlb9++JCUl0aNHD5YvX96iMYuIiLRWKukWEREJM6Wlpezbt6/GtKioKDp27AjAm2++yUknncTpp5/Oq6++yldffcVzzz0HwKWXXspdd93FrFmzuPvuuzl48CA33ngjl112GV26dAHg7rvv5pprrqFz586cffbZ5Ofn89lnn3HjjTe27H9URESkFVDSLSIiEmYWL15M165da0zr27cvP//8M2D2LP76669z3XXX0bVrV1577TUGDBgAQHx8PEuWLGH27NmcfPLJxMfHc+GFF/LII49UbmvWrFmUlJTw73//m1tuuYWOHTsybdq0lvsPioiItCLqvVxERKQVsVgsvP3225x//vnBDkVERERQm24RERERERGRgFHSLSIiIiIiIhIgatMtIiLSiqjVmIiISGhRSbeIiIiIiIhIgCjpFhEREREREQkQJd0iIiIiIiIiAaKkW0RERERERCRAlHSLiIiIiIiIBIiSbhEREREREZEAUdItIiIiIiIiEiBKukVEREREREQCREm3iIiIiIiISID8P5cgddoa0e7EAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 5 on the mel spectrogram\n",
    "history = model5.fit(x_train_mel, y_train_encoded_mel, validation_data=(x_val_mel, y_val_encoded_mel), epochs=100, batch_size=32, callbacks=[early_stopping5])\n",
    "\n",
    "# Saving the model\n",
    "model5.save('model5_mel.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 5 (Mel Spectrogram)', do_save=True, save_path='model5_mel_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training model 5 on the MFCCs\n",
    "history = model5.fit(x_train_mfcc, y_train_encoded_mfcc, validation_data=(x_val_mfcc, y_val_encoded_mfcc), epochs=100, batch_size=32, callbacks=[early_stopping5])\n",
    "\n",
    "# Saving the model\n",
    "model5.save('model5_mfcc.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 5 (MFCCs)', do_save=True, save_path='model5_mfcc_accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h5>Training model 6 with the different feature extractors</h5>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "246/246 [==============================] - 91s 366ms/step - loss: 5.6619 - accuracy: 0.0362 - val_loss: 4.5205 - val_accuracy: 0.0845\n",
      "Epoch 2/100\n",
      "246/246 [==============================] - 91s 368ms/step - loss: 2.6631 - accuracy: 0.4139 - val_loss: 1.7952 - val_accuracy: 0.5955\n",
      "Epoch 3/100\n",
      "246/246 [==============================] - 90s 366ms/step - loss: 0.6974 - accuracy: 0.8639 - val_loss: 0.6102 - val_accuracy: 0.8726\n",
      "Epoch 4/100\n",
      "246/246 [==============================] - 91s 369ms/step - loss: 0.2392 - accuracy: 0.9633 - val_loss: 0.4446 - val_accuracy: 0.9096\n",
      "Epoch 5/100\n",
      "246/246 [==============================] - 91s 371ms/step - loss: 0.1096 - accuracy: 0.9873 - val_loss: 0.4634 - val_accuracy: 0.8986\n",
      "Epoch 6/100\n",
      "246/246 [==============================] - 92s 374ms/step - loss: 0.0662 - accuracy: 0.9928 - val_loss: 0.3369 - val_accuracy: 0.9320\n",
      "Epoch 7/100\n",
      "246/246 [==============================] - 91s 370ms/step - loss: 0.0459 - accuracy: 0.9949 - val_loss: 0.5544 - val_accuracy: 0.8695\n",
      "Epoch 8/100\n",
      "246/246 [==============================] - 91s 370ms/step - loss: 0.0346 - accuracy: 0.9968 - val_loss: 0.3267 - val_accuracy: 0.9292\n",
      "Epoch 9/100\n",
      "246/246 [==============================] - 90s 368ms/step - loss: 0.0226 - accuracy: 0.9980 - val_loss: 0.3075 - val_accuracy: 0.9344\n",
      "Epoch 10/100\n",
      "246/246 [==============================] - 91s 371ms/step - loss: 0.0172 - accuracy: 0.9991 - val_loss: 0.2527 - val_accuracy: 0.9442\n",
      "Epoch 11/100\n",
      "246/246 [==============================] - 92s 372ms/step - loss: 0.0107 - accuracy: 0.9999 - val_loss: 0.2391 - val_accuracy: 0.9485\n",
      "Epoch 12/100\n",
      "246/246 [==============================] - 91s 371ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.2158 - val_accuracy: 0.9536\n",
      "Epoch 13/100\n",
      "246/246 [==============================] - 91s 370ms/step - loss: 0.0068 - accuracy: 0.9999 - val_loss: 0.2216 - val_accuracy: 0.9513\n",
      "Epoch 14/100\n",
      "246/246 [==============================] - 91s 370ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9587\n",
      "Epoch 15/100\n",
      "246/246 [==============================] - 91s 368ms/step - loss: 0.0054 - accuracy: 0.9997 - val_loss: 0.4584 - val_accuracy: 0.8848\n",
      "Epoch 16/100\n",
      "246/246 [==============================] - 91s 368ms/step - loss: 0.0208 - accuracy: 0.9966 - val_loss: 0.6262 - val_accuracy: 0.8416\n",
      "Epoch 17/100\n",
      "246/246 [==============================] - 92s 375ms/step - loss: 0.0240 - accuracy: 0.9972 - val_loss: 0.2425 - val_accuracy: 0.9422\n",
      "Epoch 18/100\n",
      "246/246 [==============================] - 92s 374ms/step - loss: 0.0166 - accuracy: 0.9975 - val_loss: 0.3651 - val_accuracy: 0.9127\n",
      "Epoch 19/100\n",
      "246/246 [==============================] - 94s 382ms/step - loss: 0.0227 - accuracy: 0.9970 - val_loss: 0.3050 - val_accuracy: 0.9265\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training model 6 on the mel spectrogram\n",
    "history = model6.fit(x_train_mel, y_train_encoded_mel, validation_data=(x_val_mel, y_val_encoded_mel), epochs=100, batch_size=32, callbacks=[early_stopping6])\n",
    "\n",
    "# Saving the model\n",
    "model6.save('model6_mel.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 6 (Mel Spectrogram)', do_save=True, save_path='model6_mel_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training model 6 on the MFCCs\n",
    "history = model6.fit(x_train_mfcc, y_train_encoded_mfcc, validation_data=(x_val_mfcc, y_val_encoded_mfcc), epochs=100, batch_size=32, callbacks=[early_stopping6])\n",
    "\n",
    "# Saving the model\n",
    "model6.save('model6_mfcc.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Model 6 (MFCCs)', do_save=True, save_path='model6_mfcc_accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>7. Evaluation</h3>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to display the confusion matrix for multiple labels</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_confusion_matrix(confusion_matrix, labels, title, subset=None, do_display=False, do_save=False, save_path=None):\n",
    "    \"\"\"Function to display the confusion matrix for multiple labels.\n",
    "\n",
    "    Args:\n",
    "        confusion_matrix (numpy.ndarray): Confusion matrix to be displayed.\n",
    "        labels (list): List of label names.\n",
    "        title (str): Title of the plot.\n",
    "        subset (list): List of labels to be displayed (default is None).\n",
    "        do_display (bool): Flag to display the plot (default is False).\n",
    "        do_save (bool): Flag to save the plot (default is False).\n",
    "        save_path (str): Path to save the plot (default is None).\n",
    "    \"\"\"\n",
    "    if subset is not None:\n",
    "       # Filtering the confusion matrix\n",
    "        confusion_matrix = confusion_matrix[subset][:, subset]\n",
    "\n",
    "        # Filtering the labels\n",
    "        labels = [labels[i] for i in subset]\n",
    "\n",
    "    # Setting the figure size\n",
    "    plt.figure(figsize=(24, 20))\n",
    "\n",
    "    # Saving data in a DataFrame\n",
    "    df_cm = pd.DataFrame(confusion_matrix, index=labels, columns=labels)\n",
    "\n",
    "    # Plotting the heatmap\n",
    "    sns.heatmap(df_cm, annot=True, cmap='Blues', fmt='g')\n",
    "\n",
    "    # Setting the title\n",
    "    plt.title(title)\n",
    "\n",
    "    # Saving the plot\n",
    "    if do_save:\n",
    "        # Saving the file in a folder called 'plots'\n",
    "        if not os.path.exists('plots'):\n",
    "            os.makedirs('plots')\n",
    "\n",
    "        # Saving the plot\n",
    "        plt.savefig(os.path.join('plots', save_path))\n",
    "\n",
    "    # Displaying the plot\n",
    "    if do_display:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to display the information for the top N speakers with the highest and lowest number of correct predictions</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_top_n_speakers(confusion_matrix, labels, top_n=5, do_display=False, do_save=False, save_path=None):\n",
    "    \"\"\"Function to display the information for the top N speakers with the highest and lowest number of correct predictions.\n",
    "\n",
    "    Args:\n",
    "        confusion_matrix (numpy.ndarray): Confusion matrix to be displayed.\n",
    "        labels (list): List of label names.\n",
    "        top_n (int): Number of speakers to be displayed (default is 5).\n",
    "        do_display (bool): Flag to display the plot (default is False).\n",
    "        do_save (bool): Flag to save the plot (default is False).\n",
    "        save_path (str): Path to save the plot (default is None).\n",
    "    \"\"\"\n",
    "    # Calculating the number of correct predictions for each speaker\n",
    "    correct_predictions = np.diag(confusion_matrix)\n",
    "\n",
    "    # Sorting the speakers based on the number of correct predictions\n",
    "    sorted_speakers = np.argsort(correct_predictions)[::-1]\n",
    "\n",
    "    # Plotting the top N speakers with the highest number of correct predictions\n",
    "    display_confusion_matrix(confusion_matrix, labels, 'Confusion Matrix for Top ' + str(top_n) + ' Speakers', subset=sorted_speakers[:top_n], do_display=do_display, do_save=do_save, save_path=save_path+'_top_' + str(top_n) + '_speakers.png')\n",
    "\n",
    "    # Plotting the top N speakers with the lowest number of correct predictions\n",
    "    display_confusion_matrix(confusion_matrix, labels, 'Confusion Matrix for Bottom ' + str(top_n) + ' Speakers', subset=sorted_speakers[-top_n:], do_display=do_display, do_save=do_save, save_path=save_path+'_bottom_' + str(top_n) + '_speakers.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to calculate and display the metrics for the model</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_metrics(model, x_test, y_test_encoded, title, classes, do_display=False, do_save=False, save_path=None, subset=None):\n",
    "    \"\"\"Function to calculate and display the metrics for the model.\n",
    "\n",
    "    Args:\n",
    "        model (keras.models.Sequential): Model to be evaluated.\n",
    "        x_test (numpy.ndarray): Testing set.\n",
    "        y_test_encoded (numpy.ndarray): Encoded testing labels.\n",
    "        title (str): Title of the plot.\n",
    "        classes (list): List of class names.\n",
    "        do_display (bool): Flag to display the metrics (default is False).\n",
    "        do_save (bool): Flag to save the metrics (default is False).\n",
    "        save_path (str): Path to save the plot (default is None).\n",
    "        subset (list): List of labels to be displayed (default is None).\n",
    "    \"\"\"\n",
    "    # Evaluating the model on the testing set\n",
    "    test_loss, test_accuracy = model.evaluate(x_test, y_test_encoded, verbose=1)\n",
    "\n",
    "    # Predicting the labels of the testing set\n",
    "    y_pred = model.predict(x_test)\n",
    "\n",
    "    # Converting the predictions to labels\n",
    "    y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "    # Converting the one-hot encoded labels to labels\n",
    "    y_true = np.argmax(y_test_encoded, axis=1)\n",
    "\n",
    "    # Calculating the confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "    # Calculating the precision, recall and f1 score\n",
    "    precision = precision_score(y_true, y_pred, average='weighted')\n",
    "    recall = recall_score(y_true, y_pred, average='weighted')\n",
    "    f1 = f1_score(y_true, y_pred, average='weighted')\n",
    "\n",
    "    # Displaying the metrics\n",
    "    print('\\033[35m' + 'Test Accuracy: ' + '\\033[0m' + str(round(test_accuracy, 3)))\n",
    "    print('\\033[35m' + 'Test Loss: ' + '\\033[0m' + str(round(test_loss, 3)))\n",
    "    print('\\033[35m' + 'Precision: ' + '\\033[0m' + str(round(precision, 3)))\n",
    "    print('\\033[35m' + 'Recall: ' + '\\033[0m' + str(round(recall, 3)))\n",
    "    print('\\033[35m' + 'F1 Score: ' + '\\033[0m' + str(round(f1, 3)))\n",
    "\n",
    "    # Saving the metrics in a json file\n",
    "    if do_save:\n",
    "        # Saving the file in a folder called 'metrics'\n",
    "        if not os.path.exists('metrics'):\n",
    "            os.makedirs('metrics')\n",
    "\n",
    "        # Saving the metrics in a json file\n",
    "        with open(os.path.join('metrics', save_path), 'w') as f:\n",
    "            json.dump({'Test Accuracy': test_accuracy, 'Test Loss': test_loss, 'Precision': precision, 'Recall': recall, 'F1 Score': f1}, f, indent=4)\n",
    "\n",
    "    # Displaying the confusion matrix with subset\n",
    "    if subset is not None:\n",
    "        display_confusion_matrix(cm, classes, title, subset=subset, do_display=do_display, do_save=do_save, save_path=save_path+'_subset')\n",
    "\n",
    "    # Displaying confustion matrix for the top 20 and bottom 20 speakers\n",
    "    display_top_n_speakers(cm, classes, top_n=20, do_display=do_display, do_save=do_save, save_path=save_path)\n",
    "\n",
    "    # Displaying confusion matrix without subset\n",
    "    display_confusion_matrix(cm, classes, title, do_display=do_display, do_save=do_save, save_path=save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Calculating and displaying the metrics for all the models</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 6s 58ms/step - loss: 0.1482 - accuracy: 0.9681\n",
      "91/91 [==============================] - 6s 59ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.968\n",
      "\u001b[35mTest Loss: \u001b[0m0.148\n",
      "\u001b[35mPrecision: \u001b[0m0.971\n",
      "\u001b[35mRecall: \u001b[0m0.968\n",
      "\u001b[35mF1 Score: \u001b[0m0.968\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model1_mel = keras.models.load_model('model1_mel.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 1 on the mel spectrogram\n",
    "display_metrics(model1_mel, x_test_mel, y_test_encoded_mel, 'Confusion Matrix for Model 1 (Mel Spectrogram)', classes, do_save=True, save_path='model1_mel_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 6s 61ms/step - loss: 0.3011 - accuracy: 0.9282\n",
      "91/91 [==============================] - 6s 60ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.928\n",
      "\u001b[35mTest Loss: \u001b[0m0.301\n",
      "\u001b[35mPrecision: \u001b[0m0.937\n",
      "\u001b[35mRecall: \u001b[0m0.928\n",
      "\u001b[35mF1 Score: \u001b[0m0.928\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model1_mfcc = keras.models.load_model('model1_mfcc.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 1 on the MFCCs\n",
    "display_metrics(model1_mfcc, x_test_mfcc, y_test_encoded_mfcc, 'Confusion Matrix for Model 1 (MFCCs)', classes, do_save=True, save_path='model1_mfcc_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 11s 119ms/step - loss: 5.6192 - accuracy: 0.0094\n",
      "91/91 [==============================] - 15s 159ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.009\n",
      "\u001b[35mTest Loss: \u001b[0m5.619\n",
      "\u001b[35mPrecision: \u001b[0m0.0\n",
      "\u001b[35mRecall: \u001b[0m0.009\n",
      "\u001b[35mF1 Score: \u001b[0m0.0\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model2_mel = keras.models.load_model('model2_mel.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 2 on the mel spectrogram\n",
    "display_metrics(model2_mel, x_test_mel, y_test_encoded_mel, 'Confusion Matrix for Model 2 (Mel Spectrogram)', classes, do_save=True, save_path='model2_mel_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 8s 82ms/step - loss: 0.3011 - accuracy: 0.9282\n",
      "91/91 [==============================] - 7s 72ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.928\n",
      "\u001b[35mTest Loss: \u001b[0m0.301\n",
      "\u001b[35mPrecision: \u001b[0m0.937\n",
      "\u001b[35mRecall: \u001b[0m0.928\n",
      "\u001b[35mF1 Score: \u001b[0m0.928\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model2_mfcc = keras.models.load_model('model2_mfcc.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 2 on the MFCCs\n",
    "display_metrics(model1_mfcc, x_test_mfcc, y_test_encoded_mfcc, 'Confusion Matrix for Model 2 (MFCCs)', classes, do_save=True, save_path='model2_mfcc_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 9s 91ms/step - loss: 0.2616 - accuracy: 0.9338\n",
      "91/91 [==============================] - 10s 109ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.934\n",
      "\u001b[35mTest Loss: \u001b[0m0.262\n",
      "\u001b[35mPrecision: \u001b[0m0.947\n",
      "\u001b[35mRecall: \u001b[0m0.934\n",
      "\u001b[35mF1 Score: \u001b[0m0.934\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model3_mel = keras.models.load_model('model3_mel.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 3 on the mel spectrogram\n",
    "display_metrics(model3_mel, x_test_mel, y_test_encoded_mel, 'Confusion Matrix for Model 3 (Mel Spectrogram)', classes, do_save=True, save_path='model3_mel_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 9s 96ms/step - loss: 0.3431 - accuracy: 0.9171\n",
      "91/91 [==============================] - 10s 106ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.917\n",
      "\u001b[35mTest Loss: \u001b[0m0.343\n",
      "\u001b[35mPrecision: \u001b[0m0.93\n",
      "\u001b[35mRecall: \u001b[0m0.917\n",
      "\u001b[35mF1 Score: \u001b[0m0.918\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model3_mfcc = keras.models.load_model('model3_mfcc.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 3 on the MFCCs\n",
    "display_metrics(model3_mfcc, x_test_mfcc, y_test_encoded_mfcc, 'Confusion Matrix for Model 3 (MFCCs)', classes, do_save=True, save_path='model3_mfcc_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 6s 64ms/step - loss: 0.2515 - accuracy: 0.9525\n",
      "91/91 [==============================] - 6s 64ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.952\n",
      "\u001b[35mTest Loss: \u001b[0m0.252\n",
      "\u001b[35mPrecision: \u001b[0m0.958\n",
      "\u001b[35mRecall: \u001b[0m0.952\n",
      "\u001b[35mF1 Score: \u001b[0m0.952\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model4_mel = keras.models.load_model('model4_mel.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 4 on the mel spectrogram\n",
    "display_metrics(model4_mel, x_test_mel, y_test_encoded_mel, 'Confusion Matrix for Model 4 (Mel Spectrogram)', classes, do_save=True, save_path='model4_mel_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 7s 73ms/step - loss: 0.6399 - accuracy: 0.8412\n",
      "91/91 [==============================] - 6s 62ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.841\n",
      "\u001b[35mTest Loss: \u001b[0m0.64\n",
      "\u001b[35mPrecision: \u001b[0m0.862\n",
      "\u001b[35mRecall: \u001b[0m0.841\n",
      "\u001b[35mF1 Score: \u001b[0m0.838\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model4_mfcc = keras.models.load_model('model4_mfcc.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 4 on the MFCCs\n",
    "display_metrics(model4_mfcc, x_test_mfcc, y_test_encoded_mfcc, 'Confusion Matrix for Model 4 (MFCCs)', classes, do_save=True, save_path='model4_mfcc_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 5s 48ms/step - loss: 0.1411 - accuracy: 0.9646\n",
      "91/91 [==============================] - 4s 45ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.965\n",
      "\u001b[35mTest Loss: \u001b[0m0.141\n",
      "\u001b[35mPrecision: \u001b[0m0.968\n",
      "\u001b[35mRecall: \u001b[0m0.965\n",
      "\u001b[35mF1 Score: \u001b[0m0.964\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model5_mel = keras.models.load_model('model5_mel.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 5 on the mel spectrogram\n",
    "display_metrics(model5_mel, x_test_mel, y_test_encoded_mel, 'Confusion Matrix for Model 5 (Mel Spectrogram)', classes, do_save=True, save_path='model5_mel_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 4s 47ms/step - loss: 0.3131 - accuracy: 0.9196\n",
      "91/91 [==============================] - 4s 47ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.92\n",
      "\u001b[35mTest Loss: \u001b[0m0.313\n",
      "\u001b[35mPrecision: \u001b[0m0.929\n",
      "\u001b[35mRecall: \u001b[0m0.92\n",
      "\u001b[35mF1 Score: \u001b[0m0.919\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model5_mfcc = keras.models.load_model('model5_mfcc.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 5 on the MFCCs\n",
    "display_metrics(model5_mfcc, x_test_mfcc, y_test_encoded_mfcc, 'Confusion Matrix for Model 5 (MFCCs)', classes, do_save=True, save_path='model5_mfcc_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 5s 50ms/step - loss: 0.1411 - accuracy: 0.9646\n",
      "91/91 [==============================] - 5s 49ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.965\n",
      "\u001b[35mTest Loss: \u001b[0m0.141\n",
      "\u001b[35mPrecision: \u001b[0m0.968\n",
      "\u001b[35mRecall: \u001b[0m0.965\n",
      "\u001b[35mF1 Score: \u001b[0m0.964\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model6_mel = keras.models.load_model('model6_mel.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 6 on the mel spectrogram\n",
    "display_metrics(model6_mel, x_test_mel, y_test_encoded_mel, 'Confusion Matrix for Model 6 (Mel Spectrogram)', classes, do_save=True, save_path='model6_mel_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 5s 51ms/step - loss: 0.3131 - accuracy: 0.9196\n",
      "91/91 [==============================] - 5s 51ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.92\n",
      "\u001b[35mTest Loss: \u001b[0m0.313\n",
      "\u001b[35mPrecision: \u001b[0m0.929\n",
      "\u001b[35mRecall: \u001b[0m0.92\n",
      "\u001b[35mF1 Score: \u001b[0m0.919\n"
     ]
    }
   ],
   "source": [
    "# Loading the model\n",
    "model6_mfcc = keras.models.load_model('model6_mfcc.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for model 6 on the MFCCs\n",
    "display_metrics(model6_mfcc, x_test_mfcc, y_test_encoded_mfcc, 'Confusion Matrix for Model 6 (MFCCs)', classes, do_save=True, save_path='model6_mfcc_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Performing hyperparameter tuning on the best model</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(hp):\n",
    "    \"\"\"Function to create the model.\n",
    "    \n",
    "    Args:\n",
    "        hp (kerastuner.HyperParameters): HyperParameters object for tunable hyperparameters.\n",
    "    \n",
    "    Returns:\n",
    "        model (keras.models.Sequential): Model created.\n",
    "    \"\"\"\n",
    "    # Defining the hyperparameters\n",
    "    learning_rate = hp.Choice('learning_rate', values=[0.0001, 0.0005, 0.001, 0.005, 0.01])\n",
    "    dropout_rate = hp.Choice('dropout_rate', values=[0.1, 0.2, 0.3, 0.4, 0.5])\n",
    "    activation_function1 = hp.Choice('activation_function1', values=['relu', 'tanh'])\n",
    "    activation_function2 = hp.Choice('activation_function2', values=['relu', 'tanh'])\n",
    "    activation_function3 = hp.Choice('activation_function3', values=['relu', 'tanh'])\n",
    " \n",
    "    optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
    "\n",
    "    # Defining the model architecture (same as model 1)\n",
    "    model = keras.Sequential([\n",
    "        keras.layers.Conv2D(32, (3, 3), activation=activation_function1, input_shape=(128, 94, 1)),\n",
    "        keras.layers.Conv2D(64, (3, 3), activation=activation_function2),\n",
    "        keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        keras.layers.Conv2D(64, (3, 3), activation=activation_function3),\n",
    "        keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        keras.layers.Reshape((30, 21 * 64)),\n",
    "        keras.layers.LSTM(64, return_sequences=True),\n",
    "        keras.layers.Flatten(),\n",
    "        keras.layers.BatchNormalization(),\n",
    "        keras.layers.Dropout(dropout_rate),\n",
    "        keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "    ])\n",
    "\n",
    "    # Compiling the model\n",
    "    model.compile(optimizer=optimizer,\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reloading Tuner from hyperparameter_tuning\\speaker_identification\\tuner0.json\n"
     ]
    }
   ],
   "source": [
    "# Early stopping callback (setting a patience of 3 to speed up the search)\n",
    "early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
    "\n",
    "# Instantiating the tuner\n",
    "tuner = RandomSearch(\n",
    "    create_model,\n",
    "    objective='val_accuracy',\n",
    "    max_trials=15,  # Giving only 15 max trials, as the search space is large\n",
    "    directory='hyperparameter_tuning',\n",
    "    project_name='speaker_identification'\n",
    ")\n",
    "\n",
    "# Defining the search space (Changing batch size to 256 in order to speed up the search)\n",
    "tuner.search(x_train_mel, y_train_encoded_mel, epochs=50, batch_size=256, validation_data=(x_val_mel, y_val_encoded_mel), callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[35mBest Hyperparameters: \u001b[0m\n",
      "\u001b[35mLearning Rate: \u001b[0m0.001\n",
      "\u001b[35mDropout Rate: \u001b[0m0.4\n",
      "\u001b[35mActivation Function 1: \u001b[0mtanh\n",
      "\u001b[35mActivation Function 2: \u001b[0mrelu\n",
      "\u001b[35mActivation Function 3: \u001b[0mrelu\n"
     ]
    }
   ],
   "source": [
    "# Retrieving best hyperparameters\n",
    "best_hp = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
    "print('\\033[35m' + 'Best Hyperparameters: ' + '\\033[0m')\n",
    "print('\\033[35m' + 'Learning Rate: ' + '\\033[0m' + str(best_hp.get('learning_rate')))\n",
    "print('\\033[35m' + 'Dropout Rate: ' + '\\033[0m' + str(best_hp.get('dropout_rate')))\n",
    "print('\\033[35m' + 'Activation Function 1: ' + '\\033[0m' + str(best_hp.get('activation_function1')))\n",
    "print('\\033[35m' + 'Activation Function 2: ' + '\\033[0m' + str(best_hp.get('activation_function2')))\n",
    "print('\\033[35m' + 'Activation Function 3: ' + '\\033[0m' + str(best_hp.get('activation_function3')))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Training best model with the best hyperparameters</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_15 (Conv2D)          (None, 126, 92, 32)       320       \n",
      "                                                                 \n",
      " conv2d_16 (Conv2D)          (None, 124, 90, 64)       18496     \n",
      "                                                                 \n",
      " max_pooling2d_10 (MaxPooli  (None, 62, 45, 64)        0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " conv2d_17 (Conv2D)          (None, 60, 43, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_11 (MaxPooli  (None, 30, 21, 64)        0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " reshape_5 (Reshape)         (None, 30, 1344)          0         \n",
      "                                                                 \n",
      " lstm_5 (LSTM)               (None, 30, 64)            360704    \n",
      "                                                                 \n",
      " flatten_5 (Flatten)         (None, 1920)              0         \n",
      "                                                                 \n",
      " batch_normalization_5 (Bat  (None, 1920)              7680      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 1920)              0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 285)               547485    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 971613 (3.71 MB)\n",
      "Trainable params: 967773 (3.69 MB)\n",
      "Non-trainable params: 3840 (15.00 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Defining the model architecture\n",
    "best_model = keras.Sequential([\n",
    "    keras.layers.Conv2D(32, (3, 3), activation='tanh', input_shape=(128, 94, 1)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "    keras.layers.Reshape((30, 21 * 64)),\n",
    "    keras.layers.LSTM(64, return_sequences=True),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.4),\n",
    "    keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])\n",
    "\n",
    "# Setting the optimizer\n",
    "best_optimizer = keras.optimizers.Adam(learning_rate=0.001)\n",
    "\n",
    "# Early stopping callback (Increased patience to 10, to achieve better results)\n",
    "best_early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "\n",
    "# Compiling the model\n",
    "best_model.compile(optimizer=best_optimizer,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Printing the model summary\n",
    "best_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "31/31 [==============================] - 64s 2s/step - loss: 5.7226 - accuracy: 0.0069 - val_loss: 5.7365 - val_accuracy: 0.0031\n",
      "Epoch 2/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 5.4532 - accuracy: 0.0212 - val_loss: 5.6629 - val_accuracy: 0.0035\n",
      "Epoch 3/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 5.0467 - accuracy: 0.0454 - val_loss: 5.5282 - val_accuracy: 0.0102\n",
      "Epoch 4/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 4.1017 - accuracy: 0.1773 - val_loss: 4.8863 - val_accuracy: 0.0767\n",
      "Epoch 5/150\n",
      "31/31 [==============================] - 61s 2s/step - loss: 1.7939 - accuracy: 0.6474 - val_loss: 4.4279 - val_accuracy: 0.0877\n",
      "Epoch 6/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.4202 - accuracy: 0.9215 - val_loss: 2.8067 - val_accuracy: 0.3553\n",
      "Epoch 7/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.1640 - accuracy: 0.9737 - val_loss: 1.8871 - val_accuracy: 0.6156\n",
      "Epoch 8/150\n",
      "31/31 [==============================] - 60s 2s/step - loss: 0.0807 - accuracy: 0.9888 - val_loss: 1.1912 - val_accuracy: 0.7869\n",
      "Epoch 9/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0437 - accuracy: 0.9950 - val_loss: 0.9791 - val_accuracy: 0.8015\n",
      "Epoch 10/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0292 - accuracy: 0.9970 - val_loss: 0.6355 - val_accuracy: 0.8856\n",
      "Epoch 11/150\n",
      "31/31 [==============================] - 61s 2s/step - loss: 0.0189 - accuracy: 0.9986 - val_loss: 0.4755 - val_accuracy: 0.9053\n",
      "Epoch 12/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0143 - accuracy: 0.9991 - val_loss: 0.3264 - val_accuracy: 0.9340\n",
      "Epoch 13/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0112 - accuracy: 0.9994 - val_loss: 0.2451 - val_accuracy: 0.9540\n",
      "Epoch 14/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0101 - accuracy: 0.9995 - val_loss: 0.2548 - val_accuracy: 0.9454\n",
      "Epoch 15/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0078 - accuracy: 0.9997 - val_loss: 0.2265 - val_accuracy: 0.9422\n",
      "Epoch 16/150\n",
      "31/31 [==============================] - 64s 2s/step - loss: 0.0056 - accuracy: 0.9999 - val_loss: 0.1891 - val_accuracy: 0.9556\n",
      "Epoch 17/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9564\n",
      "Epoch 18/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0046 - accuracy: 0.9997 - val_loss: 0.1516 - val_accuracy: 0.9619\n",
      "Epoch 19/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0049 - accuracy: 0.9997 - val_loss: 0.1614 - val_accuracy: 0.9595\n",
      "Epoch 20/150\n",
      "31/31 [==============================] - 61s 2s/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.1860 - val_accuracy: 0.9493\n",
      "Epoch 21/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.1467 - val_accuracy: 0.9662\n",
      "Epoch 22/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1426 - val_accuracy: 0.9642\n",
      "Epoch 23/150\n",
      "31/31 [==============================] - 64s 2s/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1466 - val_accuracy: 0.9619\n",
      "Epoch 24/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9615\n",
      "Epoch 25/150\n",
      "31/31 [==============================] - 61s 2s/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9642\n",
      "Epoch 26/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9623\n",
      "Epoch 27/150\n",
      "31/31 [==============================] - 65s 2s/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1439 - val_accuracy: 0.9627\n",
      "Epoch 28/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1484 - val_accuracy: 0.9603\n",
      "Epoch 29/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1330 - val_accuracy: 0.9686\n",
      "Epoch 30/150\n",
      "31/31 [==============================] - 64s 2s/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1573 - val_accuracy: 0.9575\n",
      "Epoch 31/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1450 - val_accuracy: 0.9595\n",
      "Epoch 32/150\n",
      "31/31 [==============================] - 61s 2s/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9678\n",
      "Epoch 33/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1602 - val_accuracy: 0.9579\n",
      "Epoch 34/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1542 - val_accuracy: 0.9603\n",
      "Epoch 35/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1456 - val_accuracy: 0.9583\n",
      "Epoch 36/150\n",
      "31/31 [==============================] - 64s 2s/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9638\n",
      "Epoch 37/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 9.5940e-04 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9583\n",
      "Epoch 38/150\n",
      "31/31 [==============================] - 64s 2s/step - loss: 9.1582e-04 - accuracy: 1.0000 - val_loss: 0.1266 - val_accuracy: 0.9646\n",
      "Epoch 39/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 8.9746e-04 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9666\n",
      "Epoch 40/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 8.1573e-04 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9674\n",
      "Epoch 41/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 6.9578e-04 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9666\n",
      "Epoch 42/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 6.8359e-04 - accuracy: 1.0000 - val_loss: 0.1224 - val_accuracy: 0.9646\n",
      "Epoch 43/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 7.8311e-04 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9650\n",
      "Epoch 44/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 7.3901e-04 - accuracy: 1.0000 - val_loss: 0.1200 - val_accuracy: 0.9686\n",
      "Epoch 45/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 7.1017e-04 - accuracy: 1.0000 - val_loss: 0.1179 - val_accuracy: 0.9693\n",
      "Epoch 46/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 5.8581e-04 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9686\n",
      "Epoch 47/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 5.9881e-04 - accuracy: 1.0000 - val_loss: 0.1121 - val_accuracy: 0.9705\n",
      "Epoch 48/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 5.2256e-04 - accuracy: 1.0000 - val_loss: 0.1112 - val_accuracy: 0.9693\n",
      "Epoch 49/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 5.0472e-04 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9658\n",
      "Epoch 50/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 4.7385e-04 - accuracy: 1.0000 - val_loss: 0.1339 - val_accuracy: 0.9631\n",
      "Epoch 51/150\n",
      "31/31 [==============================] - 63s 2s/step - loss: 5.0827e-04 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9662\n",
      "Epoch 52/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 5.3952e-04 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9568\n",
      "Epoch 53/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 5.2176e-04 - accuracy: 1.0000 - val_loss: 0.1244 - val_accuracy: 0.9654\n",
      "Epoch 54/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 4.9526e-04 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9646\n",
      "Epoch 55/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 4.3747e-04 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9678\n",
      "Epoch 56/150\n",
      "31/31 [==============================] - 62s 2s/step - loss: 4.7830e-04 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9670\n",
      "Epoch 57/150\n",
      "31/31 [==============================] - 61s 2s/step - loss: 4.3838e-04 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9705\n",
      "Epoch 58/150\n",
      "31/31 [==============================] - 64s 2s/step - loss: 3.9360e-04 - accuracy: 1.0000 - val_loss: 0.1143 - val_accuracy: 0.9689\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training the best model (Increased epochs to 150, to achieve better results)\n",
    "history = best_model.fit(x_train_mel, y_train_encoded_mel, validation_data=(x_val_mel, y_val_encoded_mel), epochs=150, batch_size=256, callbacks=[best_early_stopping])\n",
    "\n",
    "# Saving the model\n",
    "best_model.save('best_model_mel.h5')\n",
    "\n",
    "# Plotting the training and validation curves\n",
    "plot_curves(history, 'accuracy', 'Training and Validation Accuracy for Best Model (Mel Spectrogram)', do_save=True, save_path='best_model_mel_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 5s 55ms/step - loss: 0.1360 - accuracy: 0.9709\n",
      "91/91 [==============================] - 5s 55ms/step\n",
      "\u001b[35mTest Accuracy: \u001b[0m0.971\n",
      "\u001b[35mTest Loss: \u001b[0m0.136\n",
      "\u001b[35mPrecision: \u001b[0m0.974\n",
      "\u001b[35mRecall: \u001b[0m0.971\n",
      "\u001b[35mF1 Score: \u001b[0m0.971\n"
     ]
    }
   ],
   "source": [
    "# Evaluating best model on the testing set\n",
    "# Loading the model\n",
    "best_model = keras.models.load_model('best_model_mel.h5')\n",
    "\n",
    "# Extracting the labels\n",
    "classes = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "# Displaying the metrics for the best model\n",
    "display_metrics(best_model, x_test_mel, y_test_encoded_mel, 'Confusion Matrix for Best Model (Mel Spectrogram)', classes, do_save=True, save_path='best_model_mel_metrics', subset=list(range(30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_model\\assets\n"
     ]
    }
   ],
   "source": [
    "# Saving best model in a folder\n",
    "best_model.save('best_model')\n",
    "\n",
    "# Loading the model\n",
    "best_model = keras.models.load_model('best_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>8. Analyzing the best model with respect to accent and gender accuracy</h3>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to plot a rainbow bar graph</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_rainbow_bar(dictionary, title, do_display=False, do_save=False, save_path=None):\n",
    "    \"\"\"Function to plot a rainbow bar graph.\n",
    "\n",
    "    Args:\n",
    "        dictionary (dict): Dictionary containing the data to be plotted.\n",
    "        title (str): Title of the plot.\n",
    "        do_display (bool): Flag to display the plot (default is False).\n",
    "        do_save (bool): Flag to save the plot (default is False).\n",
    "        save_path (str): Path to save the plot (default is None).\n",
    "    \"\"\"\n",
    "    # Sorting the dictionary\n",
    "    sorted_dict = {k: v for k, v in sorted(dictionary.items(), key=lambda item: item[1], reverse=True)}\n",
    "\n",
    "    # Creating a rainbow color palette\n",
    "    colors = plt.cm.rainbow(np.linspace(0, 1, len(sorted_dict)))\n",
    "\n",
    "    # Reversing the colors\n",
    "    colors = colors[::-1]\n",
    "    \n",
    "    # Plotting the bar plot\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    bars = plt.bar(range(len(sorted_dict)), list(sorted_dict.values()), color=colors)\n",
    "    plt.xticks(range(len(sorted_dict)), list(sorted_dict.keys()), rotation=45)\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title(title)\n",
    "    plt.tight_layout()\n",
    "    plt.grid(alpha=0.15)\n",
    "    \n",
    "    # Displaying the values on top of the bars\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, height, f'{height:.3f}', ha='center', va='bottom')\n",
    "    \n",
    "    # Saving the plot\n",
    "    if do_save:\n",
    "        # Saving the file in a folder called 'plots'\n",
    "        if not os.path.exists('plots'):\n",
    "            os.makedirs('plots')\n",
    "\n",
    "        # Saving the plot\n",
    "        plt.savefig(os.path.join('plots', save_path))\n",
    "\n",
    "    # Displaying the plot\n",
    "    if do_display:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h4>Function to display the dataset evaluation, i.e., determining model accuracy for accents and genders</h4>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataset_evaluation(model, x_test, y_test_encoded, speaker_roots, do_display=False, do_save=False, save_path=None):\n",
    "    \"\"\"Function to display the dataset evaluation, i.e., determining model accuracy for accents and genders.\n",
    "\n",
    "    Args:\n",
    "        model (keras.models.Sequential): Model to be evaluated.\n",
    "        x_test (numpy.ndarray): Testing set.\n",
    "        y_test_encoded (numpy.ndarray): Encoded testing labels.\n",
    "        speaker_roots (list): List of speaker roots.\n",
    "        do_display (bool): Flag to display the plot (default is False).\n",
    "        do_save (bool): Flag to save the plot (default is False).\n",
    "        save_path (str): Path to save the plot (default is None).\n",
    "    \"\"\"\n",
    "    # Predicting the labels of the testing set\n",
    "    y_pred = model.predict(x_test)\n",
    "\n",
    "    # Converting the predictions to labels\n",
    "    y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "    # Converting the one-hot encoded labels to labels\n",
    "    y_true = np.argmax(y_test_encoded, axis=1)\n",
    "\n",
    "    # Calculating the confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "    # Retrieving the speakers\n",
    "    speakers = [speaker_root.split('\\\\')[-1] for speaker_root in speaker_roots]\n",
    "\n",
    "    # Declaring accents and gender dictionaries\n",
    "    accents = {}\n",
    "    genders = {}\n",
    "\n",
    "    # Iterating over all the speaker roots\n",
    "    for speaker_root in speaker_roots:\n",
    "        # Retrieving the current speaker, accent and gender\n",
    "        speaker = speaker_root.split('\\\\')[-1]\n",
    "        accent = speaker_root.split('\\\\')[-3]\n",
    "        gender = speaker_root.split('\\\\')[-2]\n",
    "\n",
    "        # Adding speaker to the accent dictionary\n",
    "        if accent not in accents:\n",
    "            accents[accent] = [speaker]\n",
    "        else:\n",
    "            accents[accent].append(speaker)\n",
    "\n",
    "        # Adding speaker to the gender dictionary\n",
    "        if gender not in genders:\n",
    "            genders[gender] = [speaker]\n",
    "        else:\n",
    "            genders[gender].append(speaker) \n",
    "\n",
    "    # Calculating the accuracy for each accent\n",
    "    accent_accuracy = {accent: 0 for accent in accents}\n",
    "\n",
    "    # Iterating over all the accents\n",
    "    for accent in accents:\n",
    "        # Iterating over all the speakers of the accent\n",
    "        for speaker in accents[accent]:\n",
    "            # Calculating the accuracy for the speaker\n",
    "            speaker_accuracy = cm[speakers.index(speaker)][speakers.index(speaker)] / np.sum(cm[speakers.index(speaker)])\n",
    "\n",
    "            # Adding the speaker accuracy to the accent accuracy\n",
    "            accent_accuracy[accent] += speaker_accuracy\n",
    "\n",
    "        # Normalizing the accuracy\n",
    "        accent_accuracy[accent] /= len(accents[accent])\n",
    "\n",
    "    # Calculating the accuracy for each gender\n",
    "    gender_accuracy = {gender: 0 for gender in genders}\n",
    "\n",
    "    # Iterating over all genders\n",
    "    for gender in genders:\n",
    "        # Iterating over all the speakers of the gender\n",
    "        for speaker in genders[gender]:\n",
    "            # Calculating the accuracy for the speaker\n",
    "            speaker_accuracy = cm[speakers.index(speaker)][speakers.index(speaker)] / np.sum(cm[speakers.index(speaker)])\n",
    "\n",
    "            # Adding the speaker accuracy to the gender accuracy\n",
    "            gender_accuracy[gender] += speaker_accuracy\n",
    "\n",
    "        # Normalizing the accuracy\n",
    "        gender_accuracy[gender] /= len(genders[gender])\n",
    "\n",
    "    # Plotting results\n",
    "    plot_rainbow_bar(accent_accuracy, 'Accuracy for Accents', do_display=do_display, do_save=do_save, save_path=save_path+'_accents.png')\n",
    "    plot_rainbow_bar(gender_accuracy, 'Accuracy for Genders', do_display=do_display, do_save=do_save, save_path=save_path+'_genders.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 6s 60ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5MjvuuGO22GKL7L///vnggw9y4YUX5utf/3qjY86/9a1v5eCDD87IkSMzceLEbLfddmnTpk1eeeWVjBkzJr/85S8bfuZf5IwzzsiDDz6YHXfcMb169cr777+fSy65JCuvvHK22GKLBfsBAtA8Ve7E6QDw1ey8887ldu3alWtra79wnX333bfcpk2b8vTp08vlcrn8v//7v+UjjjiivNJKK5Xbtm1bXnnllctDhw5t+H65/PmlvE466aTyqquuWm7Tpk15xRVXLO+5556NLp01bdq08h577FFeeumly8stt1z54IMPLj/77LPzvGRYdXX1PGd7/vnnywMHDix36NCh3Llz5/KBBx5Yfuqpp5o8RrlcLj/77LPl3XbbrbzsssuW27VrV15zzTXLp5xySpPHnD17dnm55ZYrd+rUqTxr1qz5+TF+4SXDyuVy+YknnigPGjSo3KFDh/LSSy9d3nrrrcsPPfRQo3XmXjLsb3/723w931wffvhh+Ywzzij369ev3KFDh3Lbtm3LPXv2LO+5556NLu0118yZM8vDhw8vr7766uW2bduWO3fuXN58883L5557bnnOnDn/9rUkaXL5tFtuuaW89tprl6uqqsp9+/Yt33rrreWhQ4c2umTYXFdccUW5f//+5fbt25eXWWaZ8rrrrls+7rjjyu+9917DOr169SrvuOOOTe47YcKE8ne/+91yjx49ym3bti336NGjvPfeeze5BBoALVepXP4KZxYBABYrn332WXr06JGdd945V111VaXHAQD+j2O6AaAFGDt2bKZNm9bo5GwAQOXZ0w0AzdgjjzySp59+OmeeeWY6d+6cJ554otIjAQD/xJ5uAGjGLr300hx66KHp2rVrrrvuukqPAwD8C3u6AQAAoCD2dAMAAEBBRDcAAAAUZKlKD7Co1dfX57333ssyyyyTUqlU6XEAAABohsrlcmbOnJkePXqkVasv3p+9xEX3e++9l549e1Z6DAAAAFqAt99+OyuvvPIXfn+Ji+5lllkmyec/mI4dO1Z4Gliylcvl1NbWprq62idPAGA+2X7C4qGmpiY9e/ZsaMwvssRF99xfTB07dhTdUGHlcjmtW7f2jwYAWAC2n7B4+Xd/D51IDQAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAAClLR6H7wwQez8847p0ePHimVShk7duy/vc/999+fDTfcMFVVVVl99dVz7bXXFj4nAAAAfBUVje7a2tqsv/76ufjii+dr/TfeeCM77rhjtt5660ycODHHHHNMfvSjH+Xuu+8ueFIAAABYcBW9Tvf222+f7bfffr7Xv+yyy7LqqqvmvPPOS5Ksvfba+fOf/5xf/OIXGTRoUFFjAgAAwFdS0eheUA8//HAGDhzYaNmgQYNyzDHHfOF9Zs+endmzZzd8XVNTkyQpl8spl8uFzAnMn7l/D/1dBID5Z/sJi4f5/TvYrKJ7ypQp6datW6Nl3bp1S01NTWbNmpX27ds3uc/IkSNz+umnN1leW1ub1q1bFzYr8O+Vy+XMmjUrSVIqlSo8DQA0D7afsHiora2dr/WaVXR/FcOHD8+wYcMavq6pqUnPnj1TXV2d6urqCk4GzH13sLq62j8aAGA+2X7C4qGurm6+1mtW0b3iiitm6tSpjZZNnTo1HTt2nOde7iSpqqpKVVVVk+WlUskvKVgMzP276O8jAMw/20+ovPn9+9esrtO92WabZcKECY2WjR8/PptttlmFJgIAAIAvVtHo/uijjzJx4sRMnDgxyeeXBJs4cWImTZqU5POPhg8ZMqRh/UMOOSSvv/56jjvuuLz44ou55JJL8rvf/S7HHntsJcaHJd7FF1+c3r17p127dhkwYEAeffTRL1z3008/zRlnnJE+ffqkXbt2WX/99TNu3LhG6/Tu3bvRO/dzb4cffniS5IMPPsiRRx6ZNddcM+3bt88qq6ySo446KjNmzCj0dQIAwFdV0eh+7LHH0q9fv/Tr1y9JMmzYsPTr1y+nnnpqkmTy5MkNAZ4kq666au68886MHz8+66+/fs4777z8+te/drkwqIDRo0dn2LBhGTFiRJ544omsv/76GTRoUN5///15rn/yySfn8ssvz4UXXpjnn38+hxxySHbfffc89dRTDev87W9/y+TJkxtu48ePT5J873vfS5K89957ee+993Luuefm2WefzbXXXptx48blgAMOKP4FA8BCsqjftE6STz75JIcffnhWWGGFdOjQIXvssUeTwzaBYpTKS9i1BmpqatKpU6fMmDEjHTt2rPQ40GwNGDAgG2+8cS666KIkSX19fXr27JkjjzwyJ5xwQpP1e/TokZNOOqnRPwD22GOPtGnTJjfeeOM8j4k55phj8oc//CGvvPLKFx4zM2bMmPzgBz9IbW1tllqqWZ2mAoAl0OjRozNkyJBcdtllGTBgQC644IKMGTMmL730Urp27dpk/eOPPz6//e1vc+WVV2attdbK3XffnWHDhuXee+/N5ptvnlKplGnTpjU6odOzzz6bbbfdNvfdd1+22mqrJMmhhx6aO++8M9dee206deqUI444Iq1atcpf/vKXRfXSocWZ37ZsVsd0A4uHOXPm5PHHH8/AgQMblrVq1SoDBw7Mww8/PM/7zJ49O+3atWu0rH379l+4/pw5c/Lb3/42+++//5eepGLuLznBDUBzcP755+fAAw/Mfvvtl759++ayyy7L0ksvnauvvnqe619//fU58cQTs8MOO2S11VbLoYcemh122CEXXnhhwzpdunTJiiuu2HD7wx/+kD59+uRb3/pWks+3lVdddVXOP//8fPvb307//v1zzTXX5KGHHspf//rXRfK6YUkmuoEFNn369NTV1aVbt26Nlnfr1i1TpkyZ530GDRqU888/P6+88krq6+szfvz43HrrrV+4/tixY/Phhx9m3333/dI5zjzzzBx00EFf+bUAwKJSqTetH3/88Xz66aeNnnettdbKKqus8oWPAyw8ohtYJH75y19mjTXWyFprrZW2bdvmiCOOyL777ptWreb9a+iqq67K9ttvnx49eszz+zU1Ndlxxx3Tt2/fnHbaaQVODgALR6XetJ4yZUratm2bZZdddr6fF1h4RDewwDp37pzWrVs3OQHL1KlTs+KKK87zPl26dMnYsWNTW1ubt956Ky+++GI6dOiQ3r17N1n3rbfeyr333psf/ehH83ysmTNn5jvf+U6WWWaZ3HbbbWnTps1//JoAYHG0sN+0BhY90Q0ssLZt26Z///6ZMGFCw7L6+vpMmDAhm2222Zfet127dllppZXy2Wef5dZbb82OO+7YZJ1rrrkmXbt2nef3ampqst1226Vt27a54447mnzkDgAWV5V603rFFVfMnDlz8uGHH8738wILj+gGvpJhw4blyiuvzG9+85u88MILOfTQQ1NbW5v99tsvSTJkyJAMHz68Yf1HHnkkt956a15//fX86U9/yne+853U19fnmGOOafS49fX1ueaaazJ06NAmJ0ebG9y1tbW56qqrUlNTkylTpmTKlCmNztoKAIujSr1p3b9//7Rp06bR87700kuZNGnSv31e4D/ndL/AVzJ48OBMmzYtp556aqZMmZINNtgg48aNazhObdKkSY0++vbJJ5/k5JNPzuuvv54OHTpkhx12yHXXXZdOnTo1etx77703kyZNyv7779/kOZ944ok88sgjSZLVV1+90ffeeOONeb7rDwCLk2HDhmXo0KHZaKONsskmm+SCCy5o8qb1SiutlJEjRyb5/E3rd999NxtssEHefffdnHbaaQv8pnWnTp1ywAEHZNiwYVl++eXTsWPHHHnkkdlss82y6aabLpLXDUsy1+kGKqZcLqe2tjbV1dVfelkwAGhJLrroovz85z9veNP6V7/6VQYMGJAk2WqrrdK7d+9ce+21SZIHHngghx56aKM3rUeOHJlOnTo12n7ec889GTRoUF566aV87Wtfa/Kcn3zySf77v/87N954Y2bPnp1Bgwblkksu8fFy+A/Mb1uKbqBiRDcALDjbT1g8zG9bOqYbAAAACuKY7sWZdy5p6UqlpHv3ZPLkZMn60A1LEn+2AWCJJroBgBbldO9Z09KVkqruyezJSbyvRws1ogX92fbxcgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoSMWj++KLL07v3r3Trl27DBgwII8++uiXrn/BBRdkzTXXTPv27dOzZ88ce+yx+eSTTxbRtAAAADD/Khrdo0ePzrBhwzJixIg88cQTWX/99TNo0KC8//7781z/hhtuyAknnJARI0bkhRdeyFVXXZXRo0fnxBNPXMSTAwAAwL9X0eg+//zzc+CBB2a//fZL3759c9lll2XppZfO1VdfPc/1H3rooXzjG9/IPvvsk969e2e77bbL3nvv/W/3jgMAAEAlVCy658yZk8cffzwDBw78xzCtWmXgwIF5+OGH53mfzTffPI8//nhDZL/++uu56667ssMOOyySmQEAAGBBLFWpJ54+fXrq6urSrVu3Rsu7deuWF198cZ732WeffTJ9+vRsscUWKZfL+eyzz3LIIYd86cfLZ8+endmzZzd8XVNTkyQpl8spl8sL4ZUUqFSq9ARQqHKp1HCDFmtx39a0RH6l0NKVyv+4QQvVHDaf89uTFYvur+L+++/P2WefnUsuuSQDBgzIq6++mqOPPjpnnnlmTjnllHneZ+TIkTn99NObLK+trU3r1q2LHvk/0717pSeAQpVLpczq0iVJUmoOv1nhq6itrfQES5wqm09aulI5bbvM+vy/y95lomVqDpvP2vkcsmLR3blz57Ru3TpTp05ttHzq1KlZccUV53mfU045JT/84Q/zox/9KEmy7rrrpra2NgcddFBOOumktGrV9NPyw4cPz7Bhwxq+rqmpSc+ePVNdXZ3q6uqF+IoKMHlypSeAQs3dw109ebLopuVa3Lc1LdBsm09auv/bwz17crXopsVqDpvPurq6+VqvYtHdtm3b9O/fPxMmTMiuu+6aJKmvr8+ECRNyxBFHzPM+H3/8cZOwnru3+ot27VdVVaWqqqrJ8lKplNLi/pFWEcISoFQuN9ygRVrctzUtkV8nLAnKpX/coAVqDpvP+e3Jin68fNiwYRk6dGg22mijbLLJJrngggtSW1ub/fbbL0kyZMiQrLTSShk5cmSSZOedd87555+ffv36NXy8/JRTTsnOO++8+H9UHAAAgCVORaN78ODBmTZtWk499dRMmTIlG2ywQcaNG9dwcrVJkyY12rN98sknp1Qq5eSTT867776bLl26ZOedd85ZZ51VqZcAAAAAX6hUXuxP4b1w1dTUpFOnTpkxY0Y6duxY6XG+XHP4TAX8B8qlUmq7d3dMNy2bP9uL3Ok2n7R0pXKqutc6ppsWbUQz2HzOb1tW7DrdAAAA0NKJbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoSMWj++KLL07v3r3Trl27DBgwII8++uiXrv/hhx/m8MMPT/fu3VNVVZWvfe1rueuuuxbRtAAAADD/lqrkk48ePTrDhg3LZZddlgEDBuSCCy7IoEGD8tJLL6Vr165N1p8zZ0623XbbdO3aNTfffHNWWmmlvPXWW1l22WUX/fAAAADwb1Q0us8///wceOCB2W+//ZIkl112We68885cffXVOeGEE5qsf/XVV+eDDz7IQw89lDZt2iRJevfuvShHBgAAgPlWseieM2dOHn/88QwfPrxhWatWrTJw4MA8/PDD87zPHXfckc022yyHH354br/99nTp0iX77LNPjj/++LRu3Xqe95k9e3Zmz57d8HVNTU2SpFwup1wuL8RXVIBSqdITQKHKpVLDDVqsxX1b0xL5lUJLVyr/4wYtVHPYfM5vT1YsuqdPn566urp069at0fJu3brlxRdfnOd9Xn/99fzxj3/Mf/3Xf+Wuu+7Kq6++msMOOyyffvppRowYMc/7jBw5MqeffnqT5bW1tV8Y6ouN7t0rPQEUqlwqZVaXLkmSUnP4zQpfRW1tpSdY4lTZfNLSlcpp22XW5/9d9i4TLVNz2HzWzueQFf14+YKqr69P165dc8UVV6R169bp379/3n333fz85z//wugePnx4hg0b1vB1TU1Nevbsmerq6lRXVy+q0b+ayZMrPQEUau4e7urJk0U3Ldfivq1pgWbbfNLS/d8e7tmTq0U3LVZz2HzW1dXN13oVi+7OnTundevWmTp1aqPlU6dOzYorrjjP+3Tv3j1t2rRptId67bXXzpQpUzJnzpy0bdu2yX2qqqpSVVXVZHmpVEppcf9IqwhhCVAqlxtu0CIt7tualsivE5YE5dI/btACNYfN5/z2ZMUuGda2bdv0798/EyZMaFhWX1+fCRMmZLPNNpvnfb7xjW/k1VdfTX19fcOyl19+Od27d59ncAMAAEAlVfQ63cOGDcuVV16Z3/zmN3nhhRdy6KGHpra2tuFs5kOGDGl0orVDDz00H3zwQY4++ui8/PLLufPOO3P22Wfn8MMPr9RLAAAAgC9U0WO6Bw8enGnTpuXUU0/NlClTssEGG2TcuHENJ1ebNGlSWrX6x/sCPXv2zN13351jjz026623XlZaaaUcffTROf744yv1EgAAAOALlcqL/XWzFq6ampp06tQpM2bMSMeOHSs9zpdrDgcywH+gXCqltnt3J1KjZfNne5E73eaTlq5UTlX3WidSo0Ub0Qw2n/PblhX9eDkAAAC0ZKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAqywNHdu3fvnHHGGZk0aVIR8wAAAECLscDRfcwxx+TWW2/Naqutlm233TY33XRTZs+eXcRsAAAA0Kx9peieOHFiHn300ay99to58sgj07179xxxxBF54oknipgRAAAAmqWvfEz3hhtumF/96ld57733MmLEiPz617/OxhtvnA022CBXX311yuXywpwTAAAAmp2lvuodP/3009x222255pprMn78+Gy66aY54IAD8s477+TEE0/MvffemxtuuGFhzgoAAADNygJH9xNPPJFrrrkmN954Y1q1apUhQ4bkF7/4RdZaa62GdXbbbbdsvPHGC3VQAAAAaG4WOLo33njjbLvttrn00kuz6667pk2bNk3WWXXVVbPXXnstlAEBAACguVrg6H799dfTq1evL12nuro611xzzVceCgAAAFqCBT6R2vvvv59HHnmkyfJHHnkkjz322EIZCgAAAFqCBY7uww8/PG+//XaT5e+++24OP/zwhTIUAAAAtAQLHN3PP/98NtxwwybL+/Xrl+eff36hDAUAAAAtwQJHd1VVVaZOndpk+eTJk7PUUl/5CmQAAADQ4ixwdG+33XYZPnx4ZsyY0bDsww8/zIknnphtt912oQ4HAAAAzdkC75o+99xz881vfjO9evVKv379kiQTJ05Mt27dcv311y/0AQEAAKC5WuDoXmmllfL0009n1KhReeqpp9K+ffvst99+2Xvvved5zW4AAABYUn2lg7Crq6tz0EEHLexZAAAAoEX5ymc+e/755zNp0qTMmTOn0fJddtnlPx4KAAAAWoIFju7XX389u+22W5555pmUSqWUy+UkSalUSpLU1dUt3AkBAACgmVrgs5cfffTRWXXVVfP+++9n6aWXznPPPZcHH3wwG220Ue6///4CRgQAAIDmaYH3dD/88MP54x//mM6dO6dVq1Zp1apVtthii4wcOTJHHXVUnnzyySLmBAAAgGZngfd019XVZZlllkmSdO7cOe+9916SpFevXnnppZcW7nQAAADQjC3wnu511lknTz31VFZdddUMGDAg55xzTtq2bZsrrrgiq622WhEzAgAAQLO0wNF98sknp7a2NklyxhlnZKeddsqWW26ZFVZYIaNHj17oAwIAAEBztcDRPWjQoIb/Xn311fPiiy/mgw8+yHLLLddwBnMAAABgAY/p/vTTT7PUUkvl2WefbbR8+eWXF9wAAADwLxYoutu0aZNVVlnFtbgBAABgPizw2ctPOumknHjiifnggw+KmAcAAABajAU+pvuiiy7Kq6++mh49eqRXr16prq5u9P0nnnhioQ0HAAAAzdkCR/euu+5awBgAAADQ8ixwdI8YMaKIOQAAAKDFWeBjugEAAID5s8B7ulu1avWllwdzZnMAAAD43AJH92233dbo608//TRPPvlkfvOb3+T0009faIMBAABAc7fA0f3d7363ybI999wzX//61zN69OgccMABC2UwAAAAaO4W2jHdm266aSZMmLCwHg4AAACavYUS3bNmzcqvfvWrrLTSSgvj4QAAAKBFWOCPly+33HKNTqRWLpczc+bMLL300vntb3+7UIcDAACA5myBo/sXv/hFo+hu1apVunTpkgEDBmS55ZZbqMMBAABAc7bA0b3vvvsWMAYAAAC0PAt8TPc111yTMWPGNFk+ZsyY/OY3v1koQwEAAEBLsMDRPXLkyHTu3LnJ8q5du+bss89eKEMBAABAS7DA0T1p0qSsuuqqTZb36tUrkyZNWihDAQAAQEuwwNHdtWvXPP30002WP/XUU1lhhRUWylAAAADQEixwdO+999456qijct9996Wuri51dXX54x//mKOPPjp77bVXETMCAABAs7TAZy8/88wz8+abb2abbbbJUkt9fvf6+voMGTLEMd0AAADwTxY4utu2bZvRo0fnJz/5SSZOnJj27dtn3XXXTa9evYqYDwAAAJqtBY7uudZYY42sscYaC3MWAAAAaFEW+JjuPfbYIz/72c+aLD/nnHPyve99b6EMBQAAAC3BAkf3gw8+mB122KHJ8u233z4PPvjgQhkKAAAAWoIFju6PPvoobdu2bbK8TZs2qampWShDAQAAQEuwwNG97rrrZvTo0U2W33TTTenbt+9CGQoAAABaggU+kdopp5yS3XffPa+99lq+/e1vJ0kmTJiQG264ITfffPNCHxAAAACaqwWO7p133jljx47N2WefnZtvvjnt27fP+uuvnz/+8Y9Zfvnli5gRAAAAmqWvdMmwHXfcMTvuuGOSpKamJjfeeGN+/OMf5/HHH09dXd1CHRAAAACaqwU+pnuuBx98MEOHDk2PHj1y3nnn5dvf/nb++te/LszZAAAAoFlboD3dU6ZMybXXXpurrroqNTU1+f73v5/Zs2dn7NixTqIGAAAA/2K+93TvvPPOWXPNNfP000/nggsuyHvvvZcLL7ywyNkAAACgWZvvPd3/7//9vxx11FE59NBDs8YaaxQ5EwAAALQI872n+89//nNmzpyZ/v37Z8CAAbnooosyffr0ImcDAACAZm2+o3vTTTfNlVdemcmTJ+fggw/OTTfdlB49eqS+vj7jx4/PzJkzi5wTAAAAmp0FPnt5dXV19t9///z5z3/OM888k//+7//OT3/603Tt2jW77LJLETMCAABAs/SVLxmWJGuuuWbOOeecvPPOO7nxxhsX1kwAAADQIvxH0T1X69ats+uuu+aOO+5YGA8HAAAALcJCiW4AAACgKdENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABVksovviiy9O7969065duwwYMCCPPvrofN3vpptuSqlUyq677lrsgAAAAPAVVDy6R48enWHDhmXEiBF54oknsv7662fQoEF5//33v/R+b775Zn784x9nyy23XESTAgAAwIKpeHSff/75OfDAA7Pffvulb9++ueyyy7L00kvn6quv/sL71NXV5b/+679y+umnZ7XVVluE0wIAAMD8W6qSTz5nzpw8/vjjGT58eMOyVq1aZeDAgXn44Ye/8H5nnHFGunbtmgMOOCB/+tOfvvQ5Zs+endmzZzd8XVNTkyQpl8spl8v/4SsoWKlU6QmgUOVSqeEGLdbivq1pifxKoaUrlf9xgxaqOWw+57cnKxrd06dPT11dXbp169Zoebdu3fLiiy/O8z5//vOfc9VVV2XixInz9RwjR47M6aef3mR5bW1tWrduvcAzL1Ldu1d6AihUuVTKrC5dkiSl5vCbFb6K2tpKT7DEqbL5pKUrldO2y6zP/7vsXSZapuaw+aydzyErGt0LaubMmfnhD3+YK6+8Mp07d56v+wwfPjzDhg1r+LqmpiY9e/ZMdXV1qqurixp14Zg8udITQKHm7uGunjxZdNNyLe7bmhZots0nLd3/7eGePbladNNiNYfNZ11d3XytV9Ho7ty5c1q3bp2pU6c2Wj516tSsuOKKTdZ/7bXX8uabb2bnnXduWFZfX58kWWqppfLSSy+lT58+je5TVVWVqqqqJo9VKpVSWtw/0ipCWAKUyuWGG7RIi/u2piXy64QlQbn0jxu0QM1h8zm/PVnRE6m1bds2/fv3z4QJExqW1dfXZ8KECdlss82arL/WWmvlmWeeycSJExtuu+yyS7beeutMnDgxPXv2XJTjAwAAwJeq+MfLhw0blqFDh2ajjTbKJptskgsuuCC1tbXZb7/9kiRDhgzJSiutlJEjR6Zdu3ZZZ511Gt1/2WWXTZImywEAAKDSKh7dgwcPzrRp03LqqadmypQp2WCDDTJu3LiGk6tNmjQprVpV/MpmAAAAsMBK5cX+ulkLV01NTTp16pQZM2akY8eOlR7nyzWHAxngP1AulVLbvbsTqdGy+bO9yJ1u80lLVyqnqnutE6nRoo1oBpvP+W1Lu5ABAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCLRXRffPHF6d27d9q1a5cBAwbk0Ucf/cJ1r7zyymy55ZZZbrnlstxyy2XgwIFfuj4AAABUSsWje/To0Rk2bFhGjBiRJ554Iuuvv34GDRqU999/f57r33///dl7771z33335eGHH07Pnj2z3Xbb5d13313EkwMAAMCXK5XL5XIlBxgwYEA23njjXHTRRUmS+vr69OzZM0ceeWROOOGEf3v/urq6LLfccrnooosyZMiQf7t+TU1NOnXqlBkzZqRjx47/8fyFKpUqPQEUqlwqpbZ791RPnpxSZX8VQXH82V7kTrf5pKUrlVPVvTazJ1cnZX/gaZlGNIPN5/y2ZUX3dM+ZMyePP/54Bg4c2LCsVatWGThwYB5++OH5eoyPP/44n376aZZffvmixgQAAICvZKlKPvn06dNTV1eXbt26NVrerVu3vPjii/P1GMcff3x69OjRKNz/2ezZszN79uyGr2tqapIk5XI5Fd7J/+/Z000LVy6VGm7QYi3u25qWyK8UWrpS+R83aKGaw+ZzfnuyotH9n/rpT3+am266Kffff3/atWs3z3VGjhyZ008/vcny2tratG7duugR/zPdu1d6AihUuVTKrC5dksTHy2m5amsrPcESp8rmk5auVE7bLrM+/28fL6eFag6bz9r5HLKi0d25c+e0bt06U6dObbR86tSpWXHFFb/0vueee25++tOf5t5778166633hesNHz48w4YNa/i6pqYmPXv2THV1daqrq/+zF1C0yZMrPQEUau4ebsd006It7tuaFmi2zSct3f/t4XZMNy1Zc9h81tXVzdd6FY3utm3bpn///pkwYUJ23XXXJJ+fSG3ChAk54ogjvvB+55xzTs4666zcfffd2Wijjb70OaqqqlJVVdVkealUSmlx/0irCGEJUCqXG27QIi3u25qWyK8TlgTl0j9u0AI1h83n/PZkxT9ePmzYsAwdOjQbbbRRNtlkk1xwwQWpra3NfvvtlyQZMmRIVlpppYwcOTJJ8rOf/SynnnpqbrjhhvTu3TtTpkxJknTo0CEdOnSo2OsAAACAf1Xx6B48eHCmTZuWU089NVOmTMkGG2yQcePGNZxcbdKkSWnV6h8nWb/00kszZ86c7Lnnno0eZ8SIETnttNMW5egAAADwpSp+ne5FzXW6YfHhOt0sEfzZXuRcp5sWz3W6WQK4TjcAAADwb4luAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACjIYhHdF198cXr37p127dplwIABefTRR790/TFjxmSttdZKu3btsu666+auu+5aRJMCAADA/Kt4dI8ePTrDhg3LiBEj8sQTT2T99dfPoEGD8v77789z/Yceeih77713DjjggDz55JPZdddds+uuu+bZZ59dxJMDAADAlyuVy+VyJQcYMGBANt5441x00UVJkvr6+vTs2TNHHnlkTjjhhCbrDx48OLW1tfnDH/7QsGzTTTfNBhtskMsuu+zfPl9NTU06deqUGTNmpGPHjgvvhRShVKr0BFCocqmU2u7dUz15ckqV/VUExfFne5E73eaTlq5UTlX32syeXJ2U/YGnZRrRDDaf89uWSy3CmZqYM2dOHn/88QwfPrxhWatWrTJw4MA8/PDD87zPww8/nGHDhjVaNmjQoIwdO3ae68+ePTuzZ89u+HrGjBkN/1vh9xtgiVdOUlsu57Mk/slAi/V/2x0WnU8qPQAUrpxyuTazbUFpwZrD5rOmpiZJ/m1XVjS6p0+fnrq6unTr1q3R8m7duuXFF1+c532mTJkyz/WnTJkyz/VHjhyZ008/vcnyVVZZ5StODSw05XIyeXKlp4BiLbtspScAWppyEptPWrifLlvpCebfzJkz06lTpy/8fkWje1EYPnx4oz3j9fX1+eCDD7LCCiuk5OPbUFE1NTXp2bNn3n777cX/cA8AWEzYfsLioVwuZ+bMmenRo8eXrlfR6O7cuXNat26dqVOnNlo+derUrLjiivO8z4orrrhA61dVVaWqqqrRsmXtdYDFSseOHf2jAQAWkO0nVN6X7eGeq6JnL2/btm369++fCRMmNCyrr6/PhAkTstlmm83zPptttlmj9ZNk/PjxX7g+AAAAVErFP14+bNiwDB06NBtttFE22WSTXHDBBamtrc1+++2XJBkyZEhWWmmljBw5Mkly9NFH51vf+lbOO++87Ljjjrnpppvy2GOP5YorrqjkywAAAIAmKh7dgwcPzrRp03LqqadmypQp2WCDDTJu3LiGk6VNmjQprVr9Y4f85ptvnhtuuCEnn3xyTjzxxKyxxhoZO3Zs1llnnUq9BOArqqqqyogRI5ocAgIAfDHbT2heKn6dbgAAAGipKnpMNwAAALRkohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiuoFmwxUOAQBobkQ3sNgT2wDw1dmOQmUtVekBAL5MuVxOqVTKhAkTMmrUqPz973/PaqutlpNOOinLL798pccDgMXC3O3lo48+mueeey5///vfM2DAgHzjG99IqVRq+D6w6NnTDSzWSqVSxo4dm9122y3t27fPoEGDcu2112aPPfbIu+++W+nxAGCxUCqVcsstt+Q73/lO7rzzztx444055phjcuyxxzZ8H6gM0Q0s1qZOnZozzjgjp59+ei6++OLsvffeadeuXdZZZ52stNJKlR4PACqmvr6+4b+fe+65HHPMMRk5cmRuvvnmXHXVVXnuuedSXV3d6D4+ag6LnugGFhsXXnhhHnnkkUbLPv3009TV1eWQQw7JO++8k759+2annXbKhRdemCS59957KzEqAFTMXXfdlffffz+tWrVKXV1dkuSNN95I9+7dc/DBB+eNN97ILrvskh/+8If5yU9+kiR56qmnktjjDZUguoHFwmeffZYbb7wx3/3ud/PEE080LO/QoUM+/fTTXHfddfnWt76VnXfeORdddFGS5PXXX89ZZ52VBx54oFJjA8Ai9eijj+bHP/5xjj/++EyfPj2tW7dO8nlMd+/ePZMmTco3v/nNDBo0KJdcckmS5M9//nN+97vf5b333qvk6LDEEt3AYmGppZbKhAkTstFGG2WXXXZpCO9lllkmW2+9dY477ristdZaueyyy9KmTZskya9//et89NFHWWONNSo5OgAsMptsskn233//vPbaaxk+fHimT5+eJOnVq1fGjRuXPn36ZPfdd8/ll1/eEOSjR4/OxIkTs/TSS1dydFhiiW5gsdG+ffuMGTMm6667bnbZZZc8/vjjad26dYYOHZr11lsvH330US655JLceuutOeKII3LxxRfn17/+dXr06FHp0QGgcHOP4f7xj3+c3XbbLc8//3xOOOGETJ06Neuss05+/etfp3Xr1unatWvefPPNvPzyyznuuOMyatSonHPOOVl22WUr+wJgCVUqO5sCsJiYezmTjz/+OHvssUeefvrp3HHHHenfv38efPDB3HDDDRk7dmx69OiRrl275uc//3nWXXfdSo8NAItMXV1dwx7s888/P7fcckvWWmut/PSnP02XLl3yq1/9KsOHD8/yyy+fTp06pVQq5brrrku/fv0qPDksuUQ3UFFfdN3QWbNmZbfddsvTTz+d3//+9+nfv3/K5XI+/PDDVFVVJYmPyQGwxDvvvPNyyy23pG/fvhk5cmS6dOmSl19+Oe+++246dOiQXr16pWvXrpUeE5ZoohuomLnB/cADD2TcuHGZMmVKdtttt+y0005p1apVPvnkk+y666555pln8vvf/z4bbrhhpUcGgEVu7vby6aefznPPPZdlllkmq622Wvr27ZvkH+G99tpr5+yzz063bt0qPDHwzxzTDVRMqVTKbbfdlt122y0vvPBCWrVqlV133TU/+9nPMmXKlLRr1y5jx45Nv379ssUWWzRc7gQAlhRzg/vWW2/NoEGDcu655+aEE07IkUcemT/84Q9Jkv/+7//OHnvskVdffTVHHXVU/vd//7fCUwP/THQDFfPYY4/lqKOOyjnnnJOxY8fm4osvTvv27XPqqafm3HPPzdSpU9OuXbuMGTMmO+ywg4+TA7DEKZVKue+++3LIIYfklFNOyeOPP56zzjorf/vb3zJs2LD87ne/S/J5eA8cODAzZszInDlzKjw18M98vByoiPr6+owdOzYTJ07MGWec0XBd0T333DPrrLNO9t9//5x22mk54IADstJKK1V6XACoiNmzZ2fYsGGpqqrK+eefn3feeSdbbrll+vXrl1KplIkTJ+aXv/xldtpppyTJBx98kOWXX77CUwP/THQDi9Q/nzht0qRJ+fvf/5611147u+66a3r06JFLLrkkdXV16du3b956662cfPLJOe2009KqlQ/mALBkevHFFzN9+vSst956+fa3v51+/frlyiuvzO9///vsueee6dChQy6//PLsueeelR4VmIelKj0AsGT458uBVVdXJ0lWWWWVrLLKKpk6dWqmTp2agw46KG3bts2HH36YnXfeOeutt14233xzwQ3AEmPu9vKFF17I9OnTs/LKK2ettdZKktxzzz0plUo55ZRTkiRdu3bNN7/5zWy44YZONgqLMf+SBRaJUqmUO++8M9/97nfzve99LxdccEE+++yzJMm0adPywgsvZNKkSXnttddy/vnn549//GP22WefhjOzAsCSoFQqZezYsdlkk02y//77Z+21187ll1+eurq6fPbZZ3n55Zfz+uuvJ0luv/32rLzyyjnxxBOz2mqrVXhy4Iv4eDmwSDzyyCMZOHBgDj744Dz33HP5+9//njXWWCNXX3112rRpk7PPPjsnn3xy+vTpkxkzZmTcuHHetQdgiVJfX58PP/wwu+yyS4YMGZJvf/vbGTNmTE466aScffbZ2WqrrXLuuefmkUceycorr5xnn302f/nLX7LeeutVenTgS4huoDD/fPz2nXfemccffzynnnpqZs2aleuvvz5XXHFF+vTpk+uvvz5t27bNn/70p9TV1aVPnz7p2bNnhacHgEVj7vbyk08+Sblczk9+8pP8+Mc/znLLLZck+eUvf5lhw4blggsuyBprrJG33norkyZNypAhQ7LmmmtWeHrg33FMN1CIuf+AeOSRR/Lee+/lvvvuS8eOHZMk7du3zw9/+MOUSqVcfvnl2W+//XLNNddkyy23rPDUALDolUql3H777bn00kvz9ttvp76+PoMHD26I7qOPPjr19fU57rjjcvzxx+fUU091vhNoRuzpBgpz66235gc/+EFWWGGFzJo1K717985jjz3W8P1PPvkko0aNysiRI7PVVlvl17/+dQWnBYDKeOyxx7LNNttkn332adg2HnbYYTn22GPTq1evhvV++tOf5mc/+1leeeWVdO7cuYITAwtCdAML1T9/RO5HP/pRtttuu2y77ba5//77c9JJJ2X11VfPPffc07D+rFmzMmbMmGy55ZZZddVVKzg5ACx6r732Wq677rq0b98+J5xwQpLk0ksvzdlnn50f/OAHOeSQQxqF99///veGPeBA8+BzKcBCVSqV8uCDD2bzzTfPRx99lE022STdu3fPnnvumV/96leZNGlStttuu4b127dvnyFDhghuAJY4NTU12WuvvXLJJZdk5syZDcsPPfTQnHDCCbn++utz5ZVX5o033mj43rLLLluBSYH/hOgGFrr27dvnk08+ybhx4xqWtWnTJtttt13OO++8TJ48OZtsskkFJwSAyuvYsWOuuOKKLLfccnnggQfy7LPPNnzv8MMPz8knn5zzzjsv119/fcNlNueeoBRoPny8HFjoyuVynnzyyey1117p3LlzHnjggbRp0yZJMmfOnPzhD3/IT3/609x8881ZZZVVKjwtAFTW008/naFDh2aTTTbJUUcdla9//esN37vqqqvyzW9+M2ussUYFJwT+E6Ib+Mrm/voolUp5/vnn89Zbb6VVq1ZZc80107t37zz55JPZY4890qNHj9x3330N4f3pp59mzpw5qa6uruT4ALDYePLJJ/OjH/0oG264YY499tj07du30iMBC4noBhbYzJkzs8wyyzR8feutt+aII47Iqquumg8++CDLL798Dj300PzgBz/I448/nsGDB6dnz5655557GsIbAGjsySefzCGHHJLVVlstI0aMyFprrVXpkYCFwDHdwAI56KCDcswxx6Suri5J8uijj+bAAw/MySefnL/85S85//zz8+ijj+bNN99MkvTv3z9jxozJxIkT893vfreCkwPA4q1fv3656KKLMnny5HTq1KnS4wALiT3dwHy76aabctRRR+Xuu+9Ov379knx+rNktt9ySu+66K2+++Wa23nrrfOc738mll16aJHnnnXey8sorZ+LEienQoUNWX331Sr4EAFjsffLJJ2nXrl2lxwAWkqUqPQDQfLz99ttZYYUV0q9fv9x+++154403Ul1dnZ49e2bKlCnZYoststNOO+Xiiy9OkowfPz4TJ07MoYcemg022KCywwNAMyG4oWXx8XJgvm211VYpl8vZZpttsttuu6VXr17p3LlzrrvuuqyzzjrZfffdc9lll6VVq89/tdx888155plnXN4EAIAllj3dwHzbeOONs8022+TSSy/Npptumt122y3J58d5X3TRRdlll10yY8aMfPbZZ/n5z3+e2267LQ888ICzlAMAsMRyTDcw32bNmpWddtopq622Wh566KGst956ufHGG/Pxxx9n//33z9ixY9OzZ8907tw5kydPzm233dZw7DcAACyJRDewQD7++OMsvfTSufrqq3POOedkk002yXXXXZckueOOOxouGbbhhhtm5ZVXrvC0AABQWaIb+Eo++uijjBkzJj/72c+y4YYb5oYbbqj0SAAAsNgR3cBXVltbm9/97nc5//zzs+qqq+aOO+6o9EgAALBYcfZy4Currq7O97///Rx22GGZOnVq3nvvvUqPBAAAixV7uoH/2Mcff5xPP/00nTp1qvQoAACwWBHdAAAAUBAfLwcAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIL8f6ptfvb9eZBtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performing dataset evaluation on the best model\n",
    "dataset_evaluation(best_model, x_test_mel, y_test_encoded_mel, speaker_roots, do_display=True, do_save=True, save_path='best_model_mel_dataset_evaluation')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ComputerVision",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}