TransformerClassifier_REmbedding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Modules\n",
    "'''\n",
    "# NN Section\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# Optimizer\n",
    "import torch.optim as optim\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "## Data Loading and Processing Section\n",
    "import sys\n",
    "import json\n",
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Plotting Section\n",
    "import matplotlib.pyplot as plt\n",
    "#from matplotlib.cm import get_cmap\n",
    "import seaborn as sns\n",
    "# Metrics\n",
    "from sklearn.metrics import confusion_matrix, f1_score, roc_curve, auc\n",
    "from sklearn.manifold import TSNE\n",
    "from scipy.stats import sem\n",
    "from sklearn.utils import resample\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Misc\n",
    "import random \n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Data Loader \n",
    "Input:\n",
    "    - sample_down   : if sampling down the binary labels to be of the same quantity\n",
    "Output:\n",
    "    - embeddings    : [num_sentences, max_num_words, hidden_size]\n",
    "    - EEG           : [num_sentences, max_num_words, eeg_feature_size = 5460]\n",
    "    - gaze_features : [num_sentences, max_num_words, num_measure_mode = 12]\n",
    "    - labels        : [num_sentences, max_num_words]\n",
    "    - sen_len       : [num_sentences]\n",
    "'''\n",
    "def load_data(downsample = False, subIdx = 0, datapath = './Datasets/'):\n",
    "    # List of Subject Names\n",
    "    subject_name_list = ['ZAB', 'ZDM', 'ZDN', 'ZJM', 'ZJN', 'ZJS', 'ZKH', 'ZKW', 'ZMG']\n",
    "    \n",
    "    # Word Embeddings with padding\n",
    "    em_data = sio.loadmat(os.path.join(datapath, 'wordEmbeddingAll.mat'))['wordEmbeddingAll'].squeeze() # (358, )\n",
    "    \n",
    "    sen_len_em = [m.shape[0] for m in em_data]\n",
    "    max_num_words_em = max(sen_len_em)\n",
    "    em_size = em_data[0].shape[1]\n",
    "    embeddings = torch.zeros((len(em_data), max_num_words_em, em_size), dtype = torch.float32)\n",
    "    \n",
    "    for i, m in enumerate(em_data):\n",
    "        n_words = m.shape[0]\n",
    "        # Add Positional Encoding ???\n",
    "        embeddings[i, : n_words] = torch.from_numpy(m)\n",
    "\n",
    "    ########################################\n",
    "    # EEG Feature Data with padding\n",
    "    #EEG_path = data_path + 'EEG_data/EEGEmbeddingAll_' + subject_name_list[subIdx] + '.mat'\n",
    "    EEG_path = os.path.join(datapath, 'EEG_data', 'EEGEmbeddingAll_' + subject_name_list[subIdx] + '.mat')\n",
    "    eeg_data = sio.loadmat(EEG_path)['EEGEmbeddingAll'].squeeze() # (358,)\n",
    "\n",
    "    sen_len_eeg = [len(m) for m in eeg_data]\n",
    "    max_num_words_eeg = max(sen_len_eeg)\n",
    "    for i, sen in enumerate(eeg_data):\n",
    "        if sen[0][0].shape[0] > 1:\n",
    "            eeg_feat_size = sen[0][0].shape[0]\n",
    "    \n",
    "    eeg_features = torch.zeros((len(eeg_data), max_num_words_eeg, eeg_feat_size), dtype = torch.float32)\n",
    "\n",
    "    for i, sen in enumerate(eeg_data):\n",
    "        for j, word in enumerate(sen):\n",
    "            if word[0].shape[0] > 1:\n",
    "                eeg_features[i, j, :] = torch.from_numpy(word[0].squeeze())\n",
    "    ########################################    \n",
    "    # Eye Gaze Data with padding\n",
    "    #EGD_path = './Data/subject_data/eyeGazeData_' + subject_name_list[subIdx] + '.json'\n",
    "    EGD_path = os.path.join(datapath, 'subject_data', 'eyeGazeData_' + subject_name_list[subIdx] + '.json')\n",
    "    with open(EGD_path) as file:\n",
    "        eg_data = json.load(file)\n",
    "\n",
    "    sen_len_eg = [len(d) for d in eg_data]\n",
    "    max_num_words_eg = max(sen_len_eg)\n",
    "    gaze_features = torch.zeros((len(eg_data), max_num_words_eg, 12), dtype = torch.float32)\n",
    "\n",
    "    # Extract data from the json data\n",
    "    for i, sentence in enumerate(eg_data):\n",
    "      for j, word in enumerate(sentence): ## Sentence length / num_word per sentence may vary\n",
    "        gaze_features[i, j, 0] = word['nFixations']\n",
    "        gaze_features[i, j, 1] = word['meanPupilSize']\n",
    "        gaze_features[i, j, 2] = word['FFD']\n",
    "        gaze_features[i, j, 3] = word['FFD_pupilsize']\n",
    "        gaze_features[i, j, 4] = word['TRT']\n",
    "        gaze_features[i, j, 5] = word['TRT_pupilsize']\n",
    "        gaze_features[i, j, 6] = word['GD']\n",
    "        gaze_features[i, j, 7] = word['GD_pupilsize']\n",
    "        gaze_features[i, j, 8] = word['GPT']\n",
    "        gaze_features[i, j, 9] = word['GPT_pupilsize']\n",
    "        gaze_features[i, j, 10] = word['SFD']\n",
    "        gaze_features[i, j, 11] = word['SFD_pupilsize']\n",
    "          \n",
    "    ########################################   \n",
    "    # Labels with padding\n",
    "    with open(os.path.join(datapath, 'logicVectors.json')) as file:\n",
    "        lb_data = json.load(file)\n",
    "\n",
    "    sen_len_lb = [len(d) for d in lb_data]\n",
    "    max_num_words_lb = max(sen_len_lb)\n",
    "    labels = torch.zeros((len(lb_data), max_num_words_lb), dtype = torch.float32)\n",
    "    \n",
    "    for i, sentence in enumerate(lb_data):\n",
    "        n_words = len(sentence)\n",
    "        labels[i, :n_words] = torch.tensor(sentence)\n",
    "\n",
    "    ########################################   \n",
    "    if downsample:\n",
    "        return down_sample([embeddings, eeg_features, gaze_features, labels, torch.tensor(sen_len_em)])\n",
    "        '''\n",
    "        # Convert the padded tensors back to list of torch.tensors (without padding)\n",
    "        em_list = [d[:l] for d, l in zip(embeddings, sen_len_em)] # Word Embedding\n",
    "        eg_list = [d[:l] for d, l in zip(gaze_features, sen_len_eg)] # Eye Gaze Data\n",
    "        lb_list = [d[:l] for d, l in zip(labels, sen_len_lb)] # Labels\n",
    "\n",
    "        down_sampled_em = []\n",
    "        down_sampled_eg = []\n",
    "        down_sampled_lb = []\n",
    "        \n",
    "        # Sampling Down the Binary Labels to be of the same quantity\n",
    "        # Count the total number of 0s and 1s in the labels\n",
    "        n_ones = sum(sum(sentence) for sentence in lb_data)\n",
    "        n_zeros = sum(l - sum(sentence) for sentence, l in zip(lb_data, sen_len_lb))\n",
    "        # The down sampling rate for 0s\n",
    "        rate = n_ones / n_zeros\n",
    "\n",
    "        # DownSample each tensor in the list by seletively removing 0s\n",
    "        for i, sentence in enumerate(lb_list):\n",
    "            # indices of zeros\n",
    "            zero_indices = torch.where(0 == sentence)[0]\n",
    "            # randomly select a subset of zero indices to keep\n",
    "            zeros2keep = int(len(zero_indices) * rate)\n",
    "            kept_zero_indices = torch.tensor(\n",
    "                                    np.random.choice(zero_indices, zeros2keep, replace = False)\n",
    "                                )\n",
    "\n",
    "            # Mask for elements to keep\n",
    "            mask = torch.full(sentence.shape, False, dtype = bool)\n",
    "            mask[1 == sentence] = True\n",
    "            mask[kept_zero_indices] = True\n",
    "\n",
    "            # Append the downsampled data\n",
    "            down_sampled_em.append(em_list[i][mask])\n",
    "            down_sampled_eg.append(eg_list[i][mask])\n",
    "            down_sampled_lb.append(lb_list[i][mask])\n",
    "\n",
    "        # Padding to convert lists back to tensors\n",
    "        # Word Embedding\n",
    "        sen_len_em = [m.shape[0] for m in down_sampled_em]\n",
    "        max_num_words_em = max(sen_len_em)\n",
    "        em_size = down_sampled_em[0].shape[1]\n",
    "        embeddings = torch.zeros((len(down_sampled_em), max_num_words_em, em_size), dtype = torch.float32)\n",
    "        \n",
    "        for i, m in enumerate(down_sampled_em):\n",
    "            n_words = m.shape[0]\n",
    "            embeddings[i, : n_words] = m\n",
    "            \n",
    "        # Eye Gaze Data \n",
    "        sen_len_eg = [len(d) for d in down_sampled_eg]\n",
    "        max_num_words_eg = max(sen_len_eg)\n",
    "        gaze_features = torch.zeros((len(down_sampled_eg), max_num_words_eg, 12), dtype = torch.float32)\n",
    "\n",
    "        for i, m in enumerate(down_sampled_eg):\n",
    "            n_words = m.shape[0]\n",
    "            gaze_features[i, :n_words] = m\n",
    "            \n",
    "        # Label\n",
    "        sen_len_lb = [len(d) for d in down_sampled_lb]\n",
    "        max_num_words_lb = max(sen_len_lb)\n",
    "        labels = torch.zeros((len(down_sampled_lb), max_num_words_lb), dtype = torch.float32)\n",
    "\n",
    "        for i, lb in enumerate(down_sampled_lb):\n",
    "            n_words = len(lb)\n",
    "            labels[i, :n_words] = lb\n",
    "        '''\n",
    "        \n",
    "    return [embeddings, eeg_features, gaze_features, labels, torch.tensor(sen_len_em)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def down_sample(data):\n",
    "    '''\n",
    "    Down Sample tensors\n",
    "    [embeddings, gaze_features, labels, torch.tensor(sen_len_em)]\n",
    "    1. Remove Padding in each tensor\n",
    "    2. DownSample the labels\n",
    "    3. Create Mask\n",
    "    4. DownSample other tensors using the Mask\n",
    "    5. Pad\n",
    "    '''\n",
    "    sen_len = data[-1]\n",
    "    labels = data[-2]\n",
    "    # 1. Remove Paddings: list of tensors for each original tensor\n",
    "    data_lists = [[d[:l] for d, l in zip(tensor, sen_len)] for tensor in data[:-1]]\n",
    "\n",
    "    downsampled_lists = [[] for _ in range(len(data[:-1]))]\n",
    "    \n",
    "    # 2. Down Sample the Labels\n",
    "    n_ones = sum(sum(sentence) for sentence in labels)\n",
    "    n_zeros = sum(l - sum(sentence) for sentence, l in zip(labels, sen_len))\n",
    "    # The down sampling rate for 0s\n",
    "    rate = n_ones / n_zeros\n",
    "\n",
    "    # 3. Creating Mask for each sentence\n",
    "    for i, sentence in enumerate(data_lists[-1]): # labels\n",
    "        # indices of zeros\n",
    "        zero_indices = torch.where(0 == sentence)[0]\n",
    "        # randomly select a subset of zero indices to keep\n",
    "        if 0 == random.randint(0,1):\n",
    "            zeros2keep = math.floor(len(zero_indices) * rate)\n",
    "        else:\n",
    "            zeros2keep = math.ceil(len(zero_indices) * rate)\n",
    "        kept_zero_indices = torch.tensor(\n",
    "                                np.random.choice(zero_indices, zeros2keep, replace = False)\n",
    "                            )\n",
    "\n",
    "        # Mask for elements to keep\n",
    "        mask = torch.full(sentence.shape, False, dtype = bool)\n",
    "        mask[1 == sentence] = True\n",
    "        mask[kept_zero_indices] = True\n",
    "\n",
    "        # 4. Append the downsampled data\n",
    "        for new_list, old_list in zip(downsampled_lists, data_lists):\n",
    "            new_list.append(old_list[i][mask])\n",
    "\n",
    "    # 5. Padding to convert lists back to tensors\n",
    "    padded_downsampled_data = []\n",
    "    \n",
    "    # Embeddings and Features \n",
    "    for new_list in downsampled_lists[:-1]:\n",
    "        new_sen_len = [len(d) for d in new_list]\n",
    "        new_max_num_words = max(new_sen_len)\n",
    "        embed_size = new_list[0].shape[1]\n",
    "\n",
    "        padded_tensor = torch.zeros((len(new_list), new_max_num_words, embed_size), dtype = torch.float32)\n",
    "        for i, m in enumerate(new_list):\n",
    "            n_words = m.shape[0]\n",
    "            padded_tensor[i, :n_words] = m\n",
    "\n",
    "        padded_downsampled_data.append(padded_tensor)\n",
    "\n",
    "    # Label\n",
    "    padded_labels = torch.zeros((len(downsampled_lists[-1]), new_max_num_words), dtype = torch.float32)\n",
    "\n",
    "    for i, lb in enumerate(downsampled_lists[-1]):\n",
    "        n_words = len(lb)\n",
    "        padded_labels[i, :n_words] = lb\n",
    "\n",
    "    padded_downsampled_data.append(padded_labels)\n",
    "    padded_downsampled_data.append(torch.tensor(new_sen_len))\n",
    "    return padded_downsampled_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Classifiers as Submodels for plug-and-play\n",
    "'''\n",
    "class LinearClassifier(nn.Module):\n",
    "    '''\n",
    "    Simple Classifier for High- Low- Relevance Inference\n",
    "\n",
    "    Input:\n",
    "        - Reading Embeddings : [n_sentence, n_words, projection_dim]\n",
    "\n",
    "    Output:\n",
    "        - Labels             : [n_sentence, n_words]\n",
    "    '''\n",
    "    def __init__(self, projection_dim, **kwargs):\n",
    "        super(LinearClassifier, self).__init__(**kwargs)\n",
    "        self.linearLayer = nn.Sequential(\n",
    "                                    nn.Linear(projection_dim, projection_dim // 2),\n",
    "                                    nn.LeakyReLU(),\n",
    "                                    nn.Linear(projection_dim // 2, 1),\n",
    "                                    nn.Sigmoid() \n",
    "                                    )\n",
    "\n",
    "    def forward(self, embeddings):\n",
    "        return self.linearLayer(embeddings).squeeze(dim = -1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Positional Encoding for Input Seq in Transformer\n",
    "'''\n",
    "def positional_encoding(length, depth, device = 'cpu'):\n",
    "    ## REFERENCE: https://www.tensorflow.org/text/tutorials/transformer#the_embedding_and_positional_encoding_layer\n",
    "    # Sinosoidal positional encoding\n",
    "    half_depth = depth//2 \n",
    "    #print('half_depth = ', half_depth)\n",
    "    # Generate a range of positions and depths\n",
    "    positions = torch.arange(length, device = device).unsqueeze(dim = 1) # (length, 1)\n",
    "    #print('positions = ', positions)\n",
    "    depths = torch.arange(half_depth, device = device).unsqueeze(dim = 0) / half_depth # (1, depth)\n",
    "    #print('depths = ', depths)\n",
    "    # Compute range of radians to take the sine and cosine of\n",
    "    angle_rates = 1 / (10000**depths)    # (1, depth)\n",
    "    #print('angle_rates = ', angle_rates)\n",
    "    angle_rads = positions * angle_rates # (length, depth)\n",
    "    #print('angle_rads = ', angle_rads)\n",
    "    if depth % 2:\n",
    "        pos_encoding = torch.cat([torch.sin(angle_rads), torch.zeros(len(angle_rads), 1, device = device), torch.cos(angle_rads)], axis = -1).float()\n",
    "    else:\n",
    "        pos_encoding = torch.cat([torch.sin(angle_rads), torch.cos(angle_rads)], axis = -1).float()\n",
    "    return pos_encoding\n",
    "\n",
    "class PositionalEncoding(nn.Module):\n",
    "    '''\n",
    "    Add the Designed Sinosoidal Position-Encodings to the Inputs\n",
    "    # outputs: [batch_sz, n_words, embedding_size]\n",
    "    '''\n",
    "    def __init__(self):\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "\n",
    "        #self.n_words = n_words\n",
    "        #self.embed_size = emb_sz\n",
    "        self.pos_encoding = positional_encoding #(self.n_words, self.embed_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Scale\n",
    "        embed_size = x.shape[-1]\n",
    "        n_words = x.shape[-2]\n",
    "        x *= torch.math.sqrt(float(embed_size))\n",
    "        # Pos Encoding\n",
    "        x += self.pos_encoding(n_words, embed_size, x.device).unsqueeze(dim = 0)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Transformer Section\n",
    "'''\n",
    "class AttentionMatrix(nn.Module):\n",
    "    def __init__(self, *args, use_mask=False, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.use_mask = use_mask\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        '''\n",
    "        Computes Attention Given key and query matrices.\n",
    "\n",
    "        Input:\n",
    "            - K: [batch_size x n_words_keys x embedding_size]\n",
    "            - Q: [batch_size x n_words_queries x embedding_size]\n",
    "        Output:\n",
    "            - Attention Matrix\n",
    "        '''\n",
    "        K, Q = inputs\n",
    "        n_words_queries = Q.shape[1] # window size of queries\n",
    "        n_words_keys = K.shape[1] # window size of keys\n",
    "        device = K.device\n",
    "\n",
    "        ## Fill triangle below diagonal of matrix with negative infinity and top part with 0.\n",
    "        ## This helps to avoid over-contribution, since adjacency matrix is symmetric across diagonal. \n",
    "        ## Tile this upward to be compatible with addition against computed attention scores.\n",
    "\n",
    "        mask_vals = np.triu(np.ones((n_words_queries)) * np.NINF, k = 1)\n",
    "        mask = torch.from_numpy(mask_vals).float().to(device)\n",
    "        atten_mask = torch.tile(torch.reshape(mask,\n",
    "                                             [-1, n_words_queries, n_words_keys]),\n",
    "                                             [K.shape[0], 1, 1]) #### Repeat mask along the first dimension / batch\n",
    "        '''\n",
    "        Self Attention\n",
    "        1. compute attention weights using queries and key matrices \n",
    "               - if use_mask==True, then make sure to add the attention mask before softmax\n",
    "        2. return the attention matrix\n",
    "        \n",
    "        - Mask: [batch_size x window_size_queries x window_size_keys]\n",
    "        '''\n",
    "        weights = torch.einsum('bqe,bke->bqk', Q, K)\n",
    "        weights /= torch.math.sqrt(float(n_words_keys)) ####\n",
    "\n",
    "        if self.use_mask:\n",
    "            weights = torch.add(weights, atten_mask)\n",
    "        return F.softmax(weights, dim = -1)\n",
    "\n",
    "class AttentionHead(nn.Module):\n",
    "    def __init__(self, input_size, output_size, is_self_attention, **kwargs):\n",
    "        super(AttentionHead, self).__init__(**kwargs)\n",
    "        self.use_mask = is_self_attention\n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size\n",
    "        \n",
    "        # Initialize K, V, Q --- Xavier\n",
    "        scale = 1.0 / torch.math.sqrt( float(input_size + output_size))\n",
    "        self.Wk, self.Wv, self.Wq = [nn.Parameter(torch.randn(input_size, output_size) * scale) for _ in range(3)]\n",
    "\n",
    "        # Attention Matrix Layer\n",
    "        self.atten_matrix = AttentionMatrix(use_mask = self.use_mask)\n",
    "\n",
    "    def forward(self, inputs_for_keys, inputs_for_values, inputs_for_queries):\n",
    "        '''\n",
    "        Input:\n",
    "            - inputs_for_keys: [batch_size x KEY_WINDOW_SIZE x input_size ]\n",
    "            - inputs_for_values: [batch_size x KEY_WINDOW_SIZE x input_size ]\n",
    "            - param inputs_for_queries: [batch_size x QUERY_WINDOW_SIZE x input_size ]\n",
    "        Output:\n",
    "            - [BATCH_SIZE x QUERY_WINDOW_SIZE x output_size]\n",
    "        '''\n",
    "        K = torch.einsum('bwi,io->bwo', inputs_for_keys, self.Wk)\n",
    "        V = torch.einsum('bwi,io->bwo', inputs_for_values, self.Wv)\n",
    "        Q = torch.einsum('bwi,io->bwo', inputs_for_queries, self.Wq)\n",
    "\n",
    "        weights = self.atten_matrix([K,Q])\n",
    "        return torch.einsum('bqk,bko->bqo', weights, V)\n",
    "\n",
    "class MultiHeadedAttention(nn.Module):\n",
    "    def __init__(self, emb_sz, use_mask, num_heads, **kwargs):\n",
    "        super(MultiHeadedAttention, self).__init__(**kwargs)\n",
    "        self.use_mask = use_mask\n",
    "        self.num_heads = 3\n",
    "        self.embed_size = emb_sz\n",
    "        # **The remainder in features are truncated off**   \n",
    "        self.head_size = self.embed_size // self.num_heads\n",
    "        self.atten_heads = nn.ModuleList([AttentionHead(self.head_size, self.head_size, self.use_mask) for i in range(self.num_heads)])\n",
    "        # Project the concatenated features back to embed_size\n",
    "        self.linear_layer = nn.Linear(self.head_size * self.num_heads, self.embed_size)\n",
    "        \n",
    "    def forward(self, inputs_for_keys, inputs_for_values, inputs_for_queries):\n",
    "        # Compute Heads\n",
    "        Heads = torch.cat([head(inputs_for_keys[:, :, i*self.head_size : (i+1)*self.head_size],\\\n",
    "                               inputs_for_values[:, :, i*self.head_size : (i+1)*self.head_size],\\\n",
    "                               inputs_for_queries[:, :, i*self.head_size : (i+1)*self.head_size])\\\n",
    "                          for i,head in enumerate(self.atten_heads)], axis = -1)\n",
    "        # Project back the concatenated features\n",
    "        return self.linear_layer(Heads)\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    '''\n",
    "    Transformer Encoder Block that reweights the input tensor based on self-attention\n",
    "    '''\n",
    "    def __init__(self, emb_sz, MultiHeaded = True, num_heads = 3, **kwargs):\n",
    "        super(TransformerEncoder, self).__init__(**kwargs)\n",
    "        \n",
    "        self.embed_size = emb_sz\n",
    "        self.is_multi_headed = MultiHeaded\n",
    "        self.num_heads = num_heads\n",
    "        # Feed Forward Layer\n",
    "        self.ff_layer = nn.Linear(self.embed_size, self.embed_size)\n",
    "\n",
    "        self.self_atten = AttentionHead(self.embed_size, self.embed_size, True) if not self.is_multi_headed else MultiHeadedAttention(self.embed_size, True, self.num_heads)\n",
    "        # Layer Normalization for stable gradients\n",
    "        self.layer_norm = nn.LayerNorm(self.embed_size)\n",
    "        \n",
    "    def forward(self, inputs):\n",
    "        '''\n",
    "        1. Masked Self-Attention on the inputs\n",
    "        2. Residual Connection and Layer Normalization\n",
    "        3. Feed Forward Layer\n",
    "        4. Residual Connection and Layer Normalization\n",
    "        5. return leaky_relu of tensor\n",
    "\n",
    "        - inputs: [n_sentences, max_n_words, embedding_size]\n",
    "        - outputs: [n_sentences, max_n_words, embedding_size]\n",
    "        '''\n",
    "        # 1\n",
    "        atten_inputs = self.self_atten(inputs, inputs, inputs)\n",
    "        # 2\n",
    "        atten_inputs = atten_inputs + inputs # Residual Part\n",
    "        atten_inputs = self.layer_norm(atten_inputs)\n",
    "\n",
    "        # 3\n",
    "        outputs = self.ff_layer(atten_inputs)\n",
    "        # 4\n",
    "        outputs = outputs + atten_inputs # Residual Part\n",
    "        outputs = self.layer_norm(outputs)\n",
    "        return F.leaky_relu(outputs)\n",
    "\n",
    "\n",
    "class TransformerBlocks(nn.Module):\n",
    "    '''\n",
    "    - Position Encoding\n",
    "    - Transfomer Encoder **** Can use multiple tranformer encoders\n",
    "    - MLP Classifier\n",
    "    **** Requires number of Words **** \n",
    "    '''\n",
    "    def __init__(self, emb_sz, num_transformer = 1, MultiHeaded = True, num_heads = 3, **kwargs):\n",
    "        \n",
    "        super(TransformerBlocks, self).__init__(**kwargs)\n",
    "        #self.n_words = n_words\n",
    "        self.embed_size = emb_sz\n",
    "\n",
    "        # Pos Encoding\n",
    "        self.pos_encoding = PositionalEncoding()\n",
    "\n",
    "        # Transformer Encoder\n",
    "        transformer_blocks = [TransformerEncoder(self.embed_size, MultiHeaded, num_heads) for _ in range(num_transformer)]\n",
    "        self.encoder = nn.Sequential(*transformer_blocks)\n",
    "\n",
    "        # Classifier (with sigmoid)\n",
    "        #self.classifier = LinearClassifier(self.embed_size)\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        inputs = self.pos_encoding(inputs)\n",
    "        probs = self.encoder(inputs)\n",
    "        return probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "The Complete Pipeline of ReadingEmbedding Model\n",
    "that administrates:\n",
    "    - Generation of ReadingEmbeddings for words in each sentence\n",
    "    - Classification of High-Low Relevances for words in each sentence\n",
    "'''\n",
    "class ReadingEmbeddingModel(nn.Module):\n",
    "    '''\n",
    "    One Reading-Embedding Encoder with a Classifier\n",
    "\n",
    "    Input:\n",
    "        - Word Embeddings    : [n_sentences, n_words, embedding_dim] ； ## !!! May need L2-Normalization \n",
    "        - EEG                : [num_sentences, max_num_words, eeg_feature_size = 5460] ## L2-Normalization\n",
    "        - Eye Gaze Features  : [n_sentences, n_words, feature_dim = 12] ## L1-Normalization\n",
    "    Intermediate Output:\n",
    "        - Reading Embeddings : [n_sentences, n_words, projection_dim]\n",
    "    Final Output:\n",
    "        - Labels             : High- / Low- Relevance [(n_subjects), n_sentences, n_words]\n",
    "    \n",
    "    #######################################################################\n",
    "    Method : \n",
    "        - Normalization\n",
    "        - Project the input to a common space then element-wise addition (reading embeddings)\n",
    "        - classifier (labels)\n",
    "    '''\n",
    "    def __init__(self, word_embedding_dim, eeg_feature_dim, eye_gaze_feature_dim, projection_dim, \n",
    "                 n_trans = 1, feat_choice = [1, 1, 1],\n",
    "                 MultiHeaded = True, num_heads = 3):\n",
    "        super(ReadingEmbeddingModel, self).__init__()\n",
    "\n",
    "        self.embed_size = word_embedding_dim\n",
    "        self.eeg_feature_dim = eeg_feature_dim\n",
    "        self.eye_gaze_feature_dim = eye_gaze_feature_dim\n",
    "        self.projection_dim = projection_dim\n",
    "        \n",
    "        # Linear layers for projecting embeddings\n",
    "        self.feat_choice = feat_choice\n",
    "        we, eeg, eg = feat_choice\n",
    "        self.word_projection = nn.Linear(word_embedding_dim, projection_dim) if we else lambda x: 0 \n",
    "        self.eeg_projection = nn.Linear(eeg_feature_dim, projection_dim) if eeg else lambda x: 0\n",
    "        self.eye_gaze_projection = nn.Linear(eye_gaze_feature_dim, projection_dim) if eg else lambda x: 0\n",
    "\n",
    "        # Transformer Blocks\n",
    "        self.encoder = TransformerBlocks(projection_dim, n_trans, MultiHeaded, num_heads)\n",
    "        # Classifier\n",
    "        self.classifier = LinearClassifier(self.projection_dim)\n",
    "            \n",
    "    #---------------#\n",
    "\n",
    "    def normalize_l1(self, vecs):\n",
    "        '''\n",
    "        Normalize by L1 Norm ******\n",
    "        (Sum of all Entries in the Second last dimension)\n",
    "\n",
    "        - vecs : [n_sentences, n_words, feature_dim = 12]\n",
    "        '''\n",
    "        magnitudes = vecs.sum(axis = -2, keepdim = True)\n",
    "        # avoid 0 magnitudes for each feature_dim across words within each sentence\n",
    "        magnitudes[0 == magnitudes] = 1.0\n",
    "        return vecs / magnitudes\n",
    "    \n",
    "    def normalize_l2(self, vecs):\n",
    "        '''\n",
    "        Normalize by L2 Norm\n",
    "        (Square Root of the Squared sum of all Entries in the last dimension)\n",
    "\n",
    "        - vecs : [n_sentences, n_words, embedding_dim]\n",
    "        '''\n",
    "        magnitudes = torch.sqrt((vecs**2).sum(axis = -1, keepdim = True))\n",
    "        # avoid 0 magnitudes for each word in each sentence\n",
    "        magnitudes[0 == magnitudes] = 1.0\n",
    "        return vecs / magnitudes\n",
    "\n",
    "    #---------------#\n",
    "    def get_reading_embeddings(self, word_embeddings, eeg_features, eye_gaze_features):\n",
    "        # L2 - Normalize word_embeddings\n",
    "        word_embeddings = self.normalize_l2(word_embeddings)\n",
    "\n",
    "        # L2 - Normalize EEG features\n",
    "        eeg_features = self.normalize_l2(eeg_features)\n",
    "\n",
    "        # L1 - Normalize eye gaze features\n",
    "        # Softmax across words for each sentence\n",
    "        eye_gaze_features = self.normalize_l1(eye_gaze_features)\n",
    "        eye_gaze_features = F.softmax(eye_gaze_features, dim = -2)\n",
    "\n",
    "        # Project the inputs to a common space\n",
    "        word_embeddings = self.word_projection(word_embeddings)\n",
    "        eeg_features = self.eeg_projection(eeg_features)\n",
    "        eye_gaze_features = self.eye_gaze_projection(eye_gaze_features)\n",
    "        # Return the element-wise addition as the Reading Embeddings\n",
    "        return self.encoder(word_embeddings + eeg_features + eye_gaze_features) # [n_sentences, n_words, projection_dim] \n",
    "\n",
    "    def forward(self, word_embeddings, eeg_features, eye_gaze_features):\n",
    "        reading_embeddings = self.get_reading_embeddings(word_embeddings, eeg_features, eye_gaze_features)\n",
    "        # Return the classifier's results\n",
    "        return self.classifier(reading_embeddings) # [n_sentences, n_words] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Metrics\n",
    "** Consider Masked_BCE_Loss + F1_Score\n",
    "'''\n",
    "def relevance_accuracy(y_pred, y_true, sen_lengths):\n",
    "    '''\n",
    "    Accuracy between the predicted and the true label\n",
    "    [n_sentences, n_words] \n",
    "\n",
    "    ** the actual sentence length may differ ** \n",
    "    '''\n",
    "    # Threshold the Probabilistic Predicitons to Labels\n",
    "    binary_pred = y_pred >= 0.5\n",
    "    y_true = y_true.type_as(binary_pred)\n",
    "\n",
    "    device = y_true.device\n",
    "\n",
    "    # Compare\n",
    "    correctness = (binary_pred == y_true)\n",
    "\n",
    "    # Mask out the padding according to the given sentence lengths\n",
    "    max_len = y_true.shape[-1]\n",
    "    mask = torch.arange(max_len, device = device).expand(len(sen_lengths), max_len) < sen_lengths.unsqueeze(-1)\n",
    "    acc = correctness.masked_select(mask).float().mean()\n",
    "    return acc.item()\n",
    "\n",
    "\n",
    "def masked_bce_loss(y_pred, y_true, sen_lengths):\n",
    "    '''\n",
    "    BCE Loss with a Mask for Variable Sententce Lengths\n",
    "    '''\n",
    "    device = y_true.device\n",
    "    max_len = y_true.shape[-1]\n",
    "    mask = torch.arange(max_len, device = device).expand(len(sen_lengths), max_len) < sen_lengths.unsqueeze(-1)\n",
    "\n",
    "    masked_preds = y_pred.masked_select(mask)\n",
    "    masked_labels = y_true.masked_select(mask)\n",
    "\n",
    "    # BCE for the masked inputs\n",
    "    loss = F.binary_cross_entropy(masked_preds, masked_labels)\n",
    "\n",
    "    return loss\n",
    "\n",
    "def masked_mse_loss(y_pred, y_true, sen_lengths):\n",
    "    '''\n",
    "    MSE Loss with a Mask for Variable Sentence Lengths\n",
    "    '''\n",
    "    device = y_true.device\n",
    "    max_len = y_true.shape[-1]\n",
    "    mask = torch.arange(max_len, device = device).expand(len(sen_lengths), max_len) < sen_lengths.unsqueeze(-1)\n",
    "\n",
    "    masked_preds = y_pred.masked_select(mask)\n",
    "    masked_labels = y_true.masked_select(mask)\n",
    "\n",
    "    squared_diff = (masked_preds - masked_labels) ** 2\n",
    "    return squared_diff.mean(dim = 0)\n",
    "\n",
    "\n",
    "def masked_f1_loss(y_pred, y_true, sen_lengths):\n",
    "    '''\n",
    "    Soft F1 Loss based on Bray-Curtis Distance\n",
    "    '''\n",
    "    device = y_true.device\n",
    "    max_len = y_true.shape[-1]\n",
    "    mask = torch.arange(max_len, device = device).expand(len(sen_lengths), max_len) < sen_lengths.unsqueeze(-1)\n",
    "\n",
    "    masked_preds = y_pred.masked_select(mask).float()\n",
    "    masked_labels = y_true.masked_select(mask).float()\n",
    "\n",
    "    # Calculate the TP, TN, FP, FN\n",
    "    tp = torch.sum(masked_labels * masked_preds, dim = 0)\n",
    "    tn = torch.sum((1 - masked_labels) * (1 - masked_preds), dim = 0)\n",
    "    fp = torch.sum((1 - masked_labels) * masked_preds, dim = 0)\n",
    "    fn = torch.sum(masked_labels * (1 - masked_preds), dim = 0)\n",
    "\n",
    "    # Precesion and Recall\n",
    "    p = tp / (tp + fp + 1e-7)\n",
    "    r = tp / (tp + fn + 1e-7)\n",
    "\n",
    "    # F1 Score\n",
    "    f1 = 2 * p * r / (p + r + 1e-7)\n",
    "    f1 = torch.where(torch.isnan(f1), torch.zeros_like(f1), f1)\n",
    "    return 1 - torch.mean(f1)\n",
    "\n",
    "def total_loss(y_pred, y_true, sen_lengths):\n",
    "    '''\n",
    "    Total Loss consisting of Masked-:\n",
    "    - BCE Loss:\n",
    "    - MSE Loss:\n",
    "    - F1 Loss:\n",
    "    '''\n",
    "    m_bce = masked_bce_loss(y_pred, y_true, sen_lengths)\n",
    "    m_mse = masked_mse_loss(y_pred, y_true, sen_lengths)\n",
    "    m_f1 = masked_f1_loss(y_pred, y_true, sen_lengths)\n",
    "    return m_bce + m_mse + m_f1\n",
    "\n",
    "def f1_by_cm(cm):\n",
    "    TP = cm[0,0]\n",
    "    FP = cm[0,1]\n",
    "    FN = cm[1,0]\n",
    "    TN = cm[1,1]\n",
    "    precision = TP / (TP + FP) if (TP + FP) > 0 else 0\n",
    "    recall = TP / (TP + FN) if (TP + FN) > 0 else 0\n",
    "    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n",
    "    return f1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and Test Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Train and Test Section\n",
    "'''\n",
    "## Training Subroutines ##\n",
    "\n",
    "def train_step(model, \n",
    "               train_data, \n",
    "               metrics, optimizer):\n",
    "    '''\n",
    "    The Generic Train Step for a Trainable Model\n",
    "\n",
    "    Input:\n",
    "        - model     : \n",
    "        - train_data:\n",
    "            - word_embeddings         : Input data [n_sentences, n_words, embedding_dim]\n",
    "            - eeg_features            : Input data [num_sentences, max_num_words, eeg_feature_size = 5460]\n",
    "            - gaze_features           : Input data [n_sentences, n_words, feature_dim = 12]\n",
    "            - true_labels             : Ground Truth [n_sentences, n_words] \n",
    "            - sen_lengths             : Sentence Lengths [n_sentences]\n",
    "        - metrics   : [loss_func, acc_func]\n",
    "        - optimizer :\n",
    "\n",
    "    Output:\n",
    "        - loss      :\n",
    "        - acc       :\n",
    "    '''\n",
    "    model.train()\n",
    "    # Unpacking\n",
    "    word_embeddings, eeg_features, gaze_features, true_labels, sen_lengths = train_data\n",
    "    loss_func, acc_func = metrics\n",
    "\n",
    "    # Forward Pass\n",
    "    predictions = model(word_embeddings, eeg_features, gaze_features)\n",
    "\n",
    "    # Loss calculation\n",
    "    loss = loss_func(predictions, true_labels, sen_lengths)\n",
    "    \n",
    "    # Backward Pass\n",
    "    optimizer.zero_grad() # clear the gradient\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # Calculate the acc ######## acc after the backward pass\n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        updated_preds = model(word_embeddings, eeg_features, gaze_features)\n",
    "        acc = acc_func(updated_preds, true_labels, sen_lengths)\n",
    "        model.train()\n",
    "    return loss.clone().detach(), acc\n",
    "\n",
    "def shuffle_embeddings(train_data):\n",
    "    '''\n",
    "    Shuffle the inputs and the labels\n",
    "    Input:\n",
    "        - word_embeddings : [n_sentences, n_words, embedding_dim]\n",
    "        - eeg_features    : [num_sentences, max_num_words, eeg_feature_size = 5460]\n",
    "        - gaze_features   : [n_sentences, n_words, feature_dim = 12]\n",
    "        - true_labels     : [n_sentences, n_words] \n",
    "        - sen_lengths     : [n_sentences]\n",
    "    '''\n",
    "    #indices = torch.randperm(true_labels.shape[0])\n",
    "    #return word_embeddings[indices], gaze_features[indices], true_labels[indices]\n",
    "    indices = torch.randperm(train_data[-2].shape[0])\n",
    "    #print('shuffled indices length:', len(indices))\n",
    "    return [data[indices] for data in train_data]\n",
    "\n",
    "\n",
    "def train_model(model, batch_size, epochs,\n",
    "                train_data, test_data, downsample = True,\n",
    "                metrics = [total_loss, relevance_accuracy],\n",
    "                opt_type = 'SGD',\n",
    "                lr = 0.05, min_lr = -1):\n",
    "    '''\n",
    "    The Overall Train Method for the Model\n",
    "\n",
    "    Input:\n",
    "        - model           :\n",
    "        - batch_size      : number of sentences used for training\n",
    "        - epochs          : number of the total epochs\n",
    "        - train_data      :\n",
    "            - word_embeddings : [n_sentences, n_words, embedding_dim]\n",
    "            - eeg_features    : [num_sentences, max_num_words, eeg_feature_size = 5460]\n",
    "            - gaze_features   : [n_sentences, n_words, feature_dim = 12]\n",
    "            - true_labels: [n_sentences, n_words] \n",
    "            - sen_lengths: [n_sentences]\n",
    "\n",
    "        - test_data       :\n",
    "        - downsample      : if downsampling the train data\n",
    "        - metrics         : [loss_func, acc_func]\n",
    "        - opt_type        : SGD or Adam (maybe second order optimizer e.g. L-BFGS )\n",
    "        - lr              : learning rate \n",
    "        - min_lr          : minimum lr for learning-rate scheduler; do not use scheduler if min_lr = -1\n",
    "\n",
    "    Output:\n",
    "        - loss_list       : batch losses\n",
    "        - acc_list        :\n",
    "    '''\n",
    "    loss_list = []\n",
    "    acc_list = []\n",
    "\n",
    "    #word_embeddings, eeg_features, gaze_features, true_labels, sen_lengths = train_data\n",
    "\n",
    "    # Define optimizers *** Adam By Default\n",
    "    if 'SGD' == opt_type:\n",
    "        opt = optim.SGD(model.parameters(), lr = lr)\n",
    "    elif 'Adam' == opt_type:\n",
    "        opt = optim.Adam(model.parameters(), lr = lr)\n",
    "    else:\n",
    "        raise ValueError(f'opt_type = {opt_type} not yet supported.')\n",
    "        \n",
    "    # (Optional) Learning Rate Scheduler \n",
    "    if -1 != min_lr:\n",
    "        scheduler = CosineAnnealingLR(opt, T_max = epochs, eta_min = min_lr)\n",
    "\n",
    "    n_sentences = len(train_data[-1])\n",
    "    n_batches = n_sentences // batch_size\n",
    "    # For each epoch\n",
    "    for e in range(epochs + 1):\n",
    "        # shuffle the whole input\n",
    "        shuffled_data = shuffle_embeddings(train_data)\n",
    "        if downsample:\n",
    "            shuffled_data = down_sample(shuffled_data)\n",
    "        # Batch the input \n",
    "        for startIdx in range(0, n_sentences, batch_size):\n",
    "            #curr_WE = WE[startIdx: startIdx + batch_size]\n",
    "            #curr_GF = GF[startIdx: startIdx + batch_size]\n",
    "            #curr_TL = TL[startIdx: startIdx + batch_size]\n",
    "            #curr_SL = SL[startIdx: startIdx + batch_size]\n",
    "            curr_batch = [d[startIdx: startIdx + batch_size] for d in shuffled_data]\n",
    "            loss, acc = train_step(model,\n",
    "                                   curr_batch, \n",
    "                                   metrics, opt)\n",
    "            # record the results\n",
    "            loss_list.append(loss)\n",
    "            acc_list.append(acc)\n",
    "\n",
    "        if -1 != min_lr:\n",
    "            scheduler.step()\n",
    "        # Every 10 Epoch: Print out last batch loss and test loss\n",
    "        if 0 == e % 10:\n",
    "            test_loss, test_acc, cm, test_f1, (preds, labels, valid_indices) = test_step(model,\n",
    "                                  test_data,\n",
    "                                  metrics)\n",
    "            \n",
    "            print(f\"\\r[At Epoch{e}]: \\t-- LastBatchLoss: {loss:.4f}, LastBatchAcc: {acc:.4f}, TestLoss: {test_loss:.4f}, TestAcc: {test_acc:.4f}, TestF1: {test_f1:.4f}\", end = '')\n",
    "\n",
    "    print()\n",
    "    return (loss_list, acc_list), (test_loss, test_acc), cm, test_f1, (preds, labels, valid_indices)\n",
    "\n",
    "\n",
    "## Testing Subroutines ##\n",
    "\n",
    "def test_step(model,\n",
    "              test_data,\n",
    "              metrics = [total_loss, relevance_accuracy]):\n",
    "    '''\n",
    "    Generic Test Step for a Model\n",
    "    '''\n",
    "    loss_func, acc_func = metrics\n",
    "\n",
    "    word_embeddings, eeg_features, gaze_features, true_labels, sen_lengths = test_data\n",
    "\n",
    "    # Eval mode\n",
    "    model.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        # Forward Pass\n",
    "        predictions = model(word_embeddings, eeg_features, gaze_features)\n",
    "    \n",
    "        # Loss calculation\n",
    "        loss = loss_func(predictions, true_labels, sen_lengths)\n",
    "    \n",
    "        # Acc ####\n",
    "        acc = acc_func(predictions, true_labels, sen_lengths)\n",
    "\n",
    "        # Confusion Matrix\n",
    "        # Excluding padding\n",
    "        max_num_words = sen_lengths.max()\n",
    "        n_sen = len(sen_lengths)\n",
    "        device = predictions.device\n",
    "        \n",
    "        valid_indices = torch.arange(max_num_words, device = device).expand(n_sen, max_num_words) < sen_lengths.unsqueeze(1)\n",
    "        masked_preds = (predictions[valid_indices] >= 0.5).int().flatten()\n",
    "        masked_labels = true_labels[valid_indices].flatten()\n",
    "\n",
    "        cm = confusion_matrix(masked_labels.clone().detach().cpu().numpy(), masked_preds.clone().detach().cpu().numpy())\n",
    "        # F1 Score\n",
    "        test_f1 = f1_by_cm(cm)\n",
    "        \n",
    "    # Set back to train mode\n",
    "    model.train()\n",
    "    return loss.clone().detach(), acc, cm, test_f1, (predictions.clone().detach(), true_labels, valid_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "K-Fold Cross Validation\n",
    "'''\n",
    "def k_fold_division(data, k_fold = 5):\n",
    "    '''\n",
    "    Shuffle and Divide the Data into K-Fold\n",
    "    Input:\n",
    "        - data:\n",
    "            - word_embeddings\n",
    "            - eeg\n",
    "            - eye_gaze\n",
    "            - labels\n",
    "            - sen_len\n",
    "    Output:\n",
    "        - List of K [train_data, test_data] pairs of the original data\n",
    "    '''\n",
    "    # Shuffle \n",
    "    data = shuffle_embeddings(data)\n",
    "    # Divide\n",
    "    n_samples = len(data[-1])\n",
    "    fold_size = n_samples // k_fold\n",
    "    \n",
    "    k_fold_datalist = []\n",
    "    for k in range(k_fold - 1): # Process the first (k-1) fold \n",
    "        test_start = k*fold_size\n",
    "        test_end = (k + 1) * fold_size\n",
    "\n",
    "        test_data  = [d[test_start : test_end] for d in data]\n",
    "        train_data = [torch.cat([d[ : test_start], d[test_end :]], dim = 0) for d in data]\n",
    "        k_fold_datalist.append([train_data, test_data])\n",
    "\n",
    "    # Process the remainder fold\n",
    "    test_data = [d[-fold_size : ] for d in data]\n",
    "    train_data = [d[ : -fold_size] for d in data]\n",
    "    k_fold_datalist.append([train_data, test_data])\n",
    "    return k_fold_datalist\n",
    "\n",
    "\n",
    "def k_fold_CV(k_fold, \n",
    "              batch_size, epochs,\n",
    "              data, downsample = True,\n",
    "              n_trans = 1,\n",
    "              feat_choice = [1, 1, 1], ## Word Embedding, EEG, EyeGaze\n",
    "              metrics = [total_loss, relevance_accuracy], \n",
    "              opt_type = 'SGD',\n",
    "              lr = 0.05, min_lr = -1,\n",
    "              MultiHeaded = True, num_heads = 3, device = 'cpu'):\n",
    "    '''\n",
    "    Test on 1-K th proportion of data + Train on the rest\n",
    "    Average across the K accuracies\n",
    "    '''\n",
    "    # Shuffle and Divide\n",
    "    k_fold_datalist = k_fold_division(data, k_fold)\n",
    "\n",
    "    assert k_fold == len(k_fold_datalist), f'The number K = {k_fold} does not match number of the divided datasets = {len(k_fold_datalist)}'\n",
    "    # Apply the Train and Test Procedures\n",
    "    total_test_acc = 0.0\n",
    "    test_acc_list = []\n",
    "    overall_cm = np.zeros((2,2)) # Assuming for Binary Classification\n",
    "    overall_masked_preds = []\n",
    "    overall_masked_labels = []\n",
    "    for i, (train_d, test_d) in enumerate(k_fold_datalist):\n",
    "        # Declaration of Model ### Get the constants from input\n",
    "        REmodel = ReadingEmbeddingModel(word_embedding_dim=768, \n",
    "                                      eeg_feature_dim = 5460,\n",
    "                                      eye_gaze_feature_dim=12, \n",
    "                                      projection_dim=128,\n",
    "                                      n_trans = n_trans,\n",
    "                                      feat_choice = feat_choice,\n",
    "                                      MultiHeaded = MultiHeaded, num_heads = num_heads)\n",
    "        print(f'At {i + 1}-th Fold:')\n",
    "        if downsample: # Downsample the training and testing data for each model\n",
    "            train_d = down_sample(train_d)\n",
    "            test_d = down_sample(test_d) ### downsample the testset\n",
    "\n",
    "        # Assign Device\n",
    "        REmodel.to(device)\n",
    "        train_d = [d.to(device) for d in train_d]\n",
    "        test_d = [d.to(device) for d in test_d]\n",
    "        \n",
    "        _ , (test_loss, test_acc), fold_cm, fold_f1, (preds, labels, valid_indices)  = train_model(REmodel, batch_size = batch_size, epochs = epochs,\n",
    "                                            train_data = train_d, test_data = test_d, downsample = False,\n",
    "                                            metrics = metrics, \n",
    "                                            opt_type = opt_type,\n",
    "                                            lr = lr, min_lr = min_lr)\n",
    "        total_test_acc += test_acc\n",
    "        test_acc_list.append(test_acc)\n",
    "        overall_cm += fold_cm\n",
    "        overall_masked_preds.append(preds[valid_indices])\n",
    "        overall_masked_labels.append(labels[valid_indices])\n",
    "    # Over All F1 Score ### or average?\n",
    "    overall_f1 = f1_by_cm(overall_cm)\n",
    "    overall_masked_preds = torch.cat(overall_masked_preds, dim = 0)\n",
    "    overall_masked_labels = torch.cat(overall_masked_labels, dim = 0)\n",
    "    # Average\n",
    "    return total_test_acc / k_fold, test_acc_list, overall_cm, overall_f1, (overall_masked_preds, overall_masked_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cm(cm, title = 'Overall Confusion Matrix in Cross Validation'):\n",
    "    # Plotting\n",
    "    fig, ax = plt.subplots()\n",
    "    sns.heatmap(cm, annot = True, ax = ax, fmt = 'g', cmap = 'Blues')\n",
    "    \n",
    "    ax.set_xlabel('Predicted Labels')\n",
    "    ax.set_ylabel('True Labels')\n",
    "    ax.set_title(title)\n",
    "    ax.xaxis.set_ticklabels(['Low', 'High'])\n",
    "    ax.yaxis.set_ticklabels(['Low', 'High'])\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_roc_auc(y_true, y_preds):\n",
    "    # ROC\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_preds)\n",
    "    # AUC\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    # Plot\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, color = 'darkorange', lw = 2, label = 'ROC Curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color = 'navy', lw = 2, linestyle = '--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characterisitc')\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.show()\n",
    "\n",
    "def plot_all_roc(p_l_list, f_type = 'EEG'):\n",
    "    cmap = plt.colormaps['tab10']\n",
    "    colors = cmap.colors\n",
    "    #setup figure\n",
    "    fig, ax = plt.subplots()                                                                                            \n",
    "    ax.set_prop_cycle(color = colors)\n",
    "    # Calculate and plot each subject's roc\n",
    "    for i, (preds, lbs) in enumerate(p_l_list):\n",
    "        fpr, tpr, thresholds = roc_curve(lbs, preds)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr, lw = 2, label = f'subject[{i}]-{roc_auc:.2f}')\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], color = 'navy', lw = 2, linestyle = '--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title(f_type)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    #plt.savefig('./Results/all_roc.png')\n",
    "\n",
    "\n",
    "def plot_mean_roc_range(p_l_list_list, f_type_list = ['EEG']):\n",
    "    cmap = plt.colormaps['tab10']\n",
    "    colors = cmap.colors\n",
    "    # setup figure\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_prop_cycle(color = colors)\n",
    "    for p_l_list, f_type in zip(p_l_list_list, f_type_list):\n",
    "        tprs = []\n",
    "        base_fpr = np.linspace(0, 1, 101)  # A Common Scale for all FPRs\n",
    "\n",
    "        # Calculate ROC for each subject\n",
    "        for preds, lbs in p_l_list:\n",
    "            fpr, tpr, _ = roc_curve(lbs, preds)\n",
    "            roc_auc = auc(fpr, tpr)\n",
    "            # Interpolate each TPR at the common FPR scale\n",
    "            tpr_interp = np.interp(base_fpr, fpr, tpr)\n",
    "            tpr_interp[0] = 0.0\n",
    "            tprs.append(tpr_interp)\n",
    "\n",
    "        tprs = np.array(tprs)\n",
    "        mean_tprs = tprs.mean(axis = 0)\n",
    "        std_tprs = tprs.std(axis = 0)\n",
    "\n",
    "        # Upper and Lower Bounds\n",
    "        tprs_upper = np.minimum(mean_tprs + std_tprs, 1)\n",
    "        tprs_lower = mean_tprs - std_tprs\n",
    "\n",
    "        # Plotting \n",
    "        ax.plot(base_fpr, mean_tprs, lw = 1.5, label = f_type)\n",
    "        ax.fill_between(base_fpr, tprs_lower, tprs_upper, alpha = 0.2)\n",
    "\n",
    "    ax.plot([0, 1], [0, 1], color = 'navy', lw = 2, linestyle = '--')\n",
    "    ax.set_xlim([0.0, 1.0])\n",
    "    ax.set_ylim([0.0, 1.05])\n",
    "    ax.set_xlabel('False Positive Rate')\n",
    "    ax.set_ylabel('True Positive Rate')\n",
    "    ax.set_title('ROC Curves of Different Biomarkers')\n",
    "    ax.legend(loc = 'lower right')\n",
    "    plt.savefig('./Results/meanROCall.svg', dpi = 300, bbox_inches = 'tight')\n",
    "    plt.show()\n",
    "\n",
    "############\n",
    "def plot_roc_auc_with_CI(y_true, y_scores, n_bootstraps = 1000, alpha = 0.95):\n",
    "    # Calculate the ROC curve and AUC for the original data\n",
    "    base_fpr, base_tpr, _ = roc_curve(y_true, y_scores)\n",
    "    base_auc = auc(base_fpr, base_tpr)\n",
    "\n",
    "    # Bootstrapping for confidence intervals\n",
    "    tprs = []\n",
    "    aucs = []\n",
    "    for i in range(n_bootstraps):\n",
    "        # Resample with replacement\n",
    "        indices = resample(np.arange(len(y_true)), replace = True)\n",
    "        if len(np.unique(y_true[indices])) < 2:\n",
    "            # If sample doesn't have both classes, skip\n",
    "            continue\n",
    "\n",
    "        # Calculate ROC for the resampled data\n",
    "        fpr, tpr, _ = roc_curve(y_true[indices], y_scores[indices])\n",
    "        tprs.append(np.interp(base_fpr, fpr, tpr))\n",
    "        tprs[-1][0] = 0.0 \n",
    "        aucs.append(auc(base_fpr, tprs[-1]))\n",
    "\n",
    "    # Convert to numpy array for calculations\n",
    "    tprs = np.array(tprs)\n",
    "    aucs = np.array(aucs)\n",
    "\n",
    "    # Mean and Std\n",
    "    mean_tprs = tprs.mean(axis = 0)\n",
    "    std = tprs.std(axis = 0)\n",
    "    std = std / np.sqrt(tprs.shape[0])\n",
    "    print('max std = ', std.max())\n",
    "\n",
    "    # Calculate confidence intervals\n",
    "    tprs_upper = np.minimum(mean_tprs + std * 1.96, 1)\n",
    "    tprs_lower = mean_tprs - std *1.96\n",
    "\n",
    "    # Plotting\n",
    "    plt.figure(figsize = (6, 6)) \n",
    "    plt.plot(base_fpr, base_tpr, color = 'darkorange', lw = 0.5, label = f'AUC = {base_auc:.2f}')\n",
    "    plt.fill_between(base_fpr, tprs_lower, tprs_upper, alpha = 0.3, color = 'darkorange')\n",
    "                    #label = f'{alpha*100:.0f}% CI')\n",
    "    plt.plot([0, 1], [0, 1], color = 'navy', lw = 2, linestyle = '--')\n",
    "    plt.xlim([0, 1]) \n",
    "    plt.ylim([0, 1]) \n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC with Confidence Interval')\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.show() \n",
    "\n",
    "\n",
    "def plot_all_roc_with_CI(p_l_list, f_type = 'EEG', n_bootstraps = 1000, alpha = 0.95):\n",
    "    cmap = plt.colormaps['tab10']\n",
    "    colors = cmap.colors\n",
    "    #setup figure\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_prop_cycle(color = colors)\n",
    "    # Calculate and plot each subject's roc with CI\n",
    "    for i, (preds, lbs) in enumerate(p_l_list):\n",
    "        base_fpr, base_tpr, _ = roc_curve(lbs, preds)\n",
    "        base_auc = auc(base_fpr, base_tpr)\n",
    "\n",
    "        # Bootstrapping for confidence intervals\n",
    "        tprs = []\n",
    "        aucs = []\n",
    "        for _ in range(n_bootstraps):\n",
    "            # Resample with replace ment\n",
    "            indices = resample(np.arange(len(lbs)), replace = True)\n",
    "            if len(np.unique(lbs[indices])) < 2:\n",
    "                # if sample doesn't have both classes skip\n",
    "                continue\n",
    "\n",
    "            # Calculate ROC for the sampled data\n",
    "            fpr, tpr, _ = roc_curve(lbs[indices], preds[indices])\n",
    "            tprs.append(np.interp(base_fpr, fpr, tpr))\n",
    "            tprs[-1][0] = 0.0\n",
    "            aucs.append(auc(base_fpr, tprs[-1]))\n",
    "\n",
    "        # Convert to numpy array for calculations\n",
    "        tprs = np.array(tprs)\n",
    "        aucs = np.array(aucs)\n",
    "\n",
    "        # Mean and std\n",
    "        mean_tprs = tprs.mean(axis = 0)\n",
    "        std = tprs.std(axis = 0)\n",
    "        std /= np.sqrt(tprs.shape[0])\n",
    "\n",
    "        # Calculate confidence intervals\n",
    "        tprs_upper = np.minimum(mean_tprs + std * 1.96, 1)\n",
    "        tprs_lower = mean_tprs - std * 1.96\n",
    "\n",
    "        # Plotting\n",
    "        plt.plot(base_fpr, base_tpr, lw = 0.6, label = f'subject[{i}]: AUC={base_auc:.2f}')\n",
    "        plt.fill_between(base_fpr, tprs_lower, tprs_upper, alpha = 0.3)\n",
    "\n",
    "    ####\n",
    "    plt.plot([0, 1], [0, 1], color = 'navy', lw = 2, linestyle = '--')\n",
    "    plt.xlim([0.0, 1.0])                                                                          \n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title(f_type)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    #plt.savefig('./Results/all_roc.png')\n",
    "\n",
    "\n",
    "def plot_tsne(data, labels, relative_alpha = 0.7, title_str = ''):\n",
    "    '''\n",
    "    Plot t-SNE of data with corresponding labels\n",
    "    Input: \n",
    "        - data : [n_samples, feature_dim] flattened\n",
    "        - label: [n_samples] flattened\n",
    "    '''\n",
    "    if isinstance(data, torch.Tensor):\n",
    "        data = data.numpy()\n",
    "    if isinstance(labels, torch.Tensor):\n",
    "        labels = labels.numpy()\n",
    "\n",
    "    # Apply t-SNE\n",
    "    tsne = TSNE(n_components = 2, random_state = 42)\n",
    "    tsne_results = tsne.fit_transform(data)\n",
    "\n",
    "    # Plot with label-based coloring\n",
    "    plt.figure()\n",
    "    for lb in np.unique(labels):\n",
    "        indices = labels == lb\n",
    "        if 0 == lb:\n",
    "            label_str = 'Low Relevance'\n",
    "            alpha_val = 1.0\n",
    "        elif 1 == lb:\n",
    "            label_str = 'High Relevance'\n",
    "            alpha_val = relative_alpha\n",
    "        else: \n",
    "            label_str = f'Label {lb}' # Generic Label for other classes\n",
    "            alpha_val = 0.7\n",
    "        plt.scatter(tsne_results[indices, 0], tsne_results[indices, 1], label = label_str, alpha = alpha_val)\n",
    "\n",
    "    plt.xlabel('TSNE Component 1')\n",
    "    plt.ylabel('TSNE Component 2')\n",
    "    plt.title('t-SNE Visualization of High Dimensional ' + title_str)\n",
    "    plt.legend()\n",
    "    plt.savefig('./Results/' + title_str + '.svg', dpi = 300, bbox_inches = 'tight')\n",
    "    plt.show()\n",
    "    return tsne_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### Test the Model on Real Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Loading Real Data\n",
    "#####\n",
    "- embeddings    : [num_sentences, max_num_words, hidden_size]\n",
    "- eeg_features  : [num_sentences, max_num_words, eeg_feature_size = 5460]\n",
    "- gaze_features : [num_sentences, max_num_words, num_measure_mode = 12]\n",
    "- labels        : [num_sentences, max_num_words]\n",
    "'''\n",
    "# generated labels\n",
    "#syn_labels = torch.randint(0, 2, (358, 48)).type(torch.float)\n",
    "\n",
    "#sen_len = torch.tensor(sen_len_em)\n",
    "\n",
    "### Load the data of of i-th subject\n",
    "idx = 0 #### 0 - 8 for 9 different subjects\n",
    "downsampled_data = load_data(downsample = True, subIdx = idx, datapath = './Datasets/') #### Down Sample or not \n",
    "original_data = load_data(downsample = False, subIdx = idx, datapath = './Datasets/')\n",
    "\n",
    "embeddings, eeg_features, gaze_features, labels, sen_len = original_data\n",
    "d_embeddings, d_eeg_features, d_gaze_features, d_labels, d_sen_len = downsampled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1314.)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of 1s after downsample\n",
    "downsampled_data[-2].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1295.)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of 0s after downsample\n",
    "sum(l - sum(s) for s, l in zip(downsampled_data[-2], downsampled_data[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "# Device setup\n",
    "'''\n",
    "seed = 42\n",
    "torch.manual_seed(seed)\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "# seed gpus if available\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "\n",
    "print(device)\n",
    "\n",
    "#mymodel.to(device)\n",
    "#original_data = [d.to(device) for d in original_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation (without Scheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At 1-th Fold:\n",
      "[At Epoch160]: \t-- LastBatchLoss: 1.1707, LastBatchAcc: 0.7727, TestLoss: 1.2172, TestAcc: 0.6629, TestF1: 0.6354\n",
      "At 2-th Fold:\n",
      "[At Epoch160]: \t-- LastBatchLoss: 1.1559, LastBatchAcc: 0.8214, TestLoss: 1.3060, TestAcc: 0.6560, TestF1: 0.6842\n",
      "At 3-th Fold:\n",
      "[At Epoch160]: \t-- LastBatchLoss: 1.0915, LastBatchAcc: 0.7250, TestLoss: 1.2679, TestAcc: 0.6445, TestF1: 0.6370\n",
      "At 4-th Fold:\n",
      "[At Epoch160]: \t-- LastBatchLoss: 1.0953, LastBatchAcc: 0.7248, TestLoss: 1.2839, TestAcc: 0.6702, TestF1: 0.6255\n",
      "At 5-th Fold:\n",
      "[At Epoch160]: \t-- LastBatchLoss: 1.2946, LastBatchAcc: 0.6857, TestLoss: 1.1509, TestAcc: 0.6866, TestF1: 0.6391\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Test On Real Data With Cross Validation (Without Scheduler)\n",
    "\n",
    "### Change for better performance\n",
    "- epochs :\n",
    "- n_translayer: number of transformer block\n",
    "- feat_choice : [WordEmbedding, EEG, EyeGaze]\n",
    "'''\n",
    "feat_choice = [0, 1, 1] # Use EEG and EyeGaze \n",
    "n_translayer = 1\n",
    "epochs = 160\n",
    "optimizer_type = 'SGD' # 'Adam' or 'SGD'\n",
    "\n",
    "avg_acc1, test_acc_list1, overall_cm1, overall_f11, (overall_preds1, overall_labels1) = k_fold_CV(k_fold = 5, \n",
    "                              batch_size = 16, epochs = epochs,\n",
    "                              data = original_data, downsample = True,\n",
    "                              n_trans = n_translayer, feat_choice = feat_choice,\n",
    "                              metrics = [total_loss, relevance_accuracy], \n",
    "                              opt_type = optimizer_type,                                                                     \n",
    "                              lr = 0.05,\n",
    "                              MultiHeaded = True, num_heads = 3, device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6640368580818177"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_acc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6519383076281784"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_f11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Visualize the overall confusion matrix from the CV\n",
    "'''\n",
    "plot_cm(overall_cm1, 'Overall Confusion Matrix in Cross Validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Plot ROC-AUC\n",
    "'''\n",
    "plot_roc_auc(overall_labels1, overall_preds1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max std =  0.0008918118255765409\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_auc_with_CI(overall_labels1, overall_preds1, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHFCAYAAAAe+pb9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB7gUlEQVR4nO3dd1QU198G8GfpvQiKIIiKotgLFsTeS+yxROwtWCOaojHRGGP8RY29xh4VNbFFEzUSu1hBrGiwIChFBKRIW9i97x++bkJABQRmy/M5h3PcuzO7zzLCfLlz516ZEEKAiIiISAfpSR2AiIiISCoshIiIiEhnsRAiIiIincVCiIiIiHQWCyEiIiLSWSyEiIiISGexECIiIiKdxUKIiIiIdBYLISIiItJZLISISO1s3boVMpnsjV+nT58GAFSqVOmN27Rp0ybP6968eROjR4+Gm5sbTE1NYWpqimrVquHjjz9GUFBQ6X5IIlILBlIHICJ6ky1btqBGjRp52mvWrKn6t7e3NxYvXpxnGysrq1yP169fj0mTJqF69er45JNPUKtWLchkMty9exe7du1C48aN8eDBA7i5uRX/ByEitcVCiIjUVu3ateHp6fnWbWxsbNCsWbO3bhMYGIgJEyage/fu2Lt3L4yMjFTPtWvXDhMnTsSvv/4KU1PTYslNRJqDhRARab3vv/8e+vr6WL9+fa4i6N/69+9fyqmISB2wECIitaVQKJCTk5OrTSaTQV9fX/VYCJFnGwDQ19eHTCaDQqHAqVOn4OnpCUdHxxLPTESahYOliUhtNWvWDIaGhrm+jI2Nc21z5MiRPNsYGhpi/vz5AID4+HhkZGTA1dU1z+u/LrRefwkhSuVzEZH6YI8QEamtn3/+GR4eHrnaZDJZrsctWrTA0qVL8+xboUKFd75+o0aNcOPGDdXjRYsW4dNPPy1iWiLSRCyEiEhteXh4vHOwtLW19Vu3sbe3h6mpKSIiIvI85+/vj/T0dMTExKBnz57vnZeINA8LISLSavr6+mjXrh2OHz+OmJiYXOOEXt+G//jxY4nSEZHUOEaIiLTezJkzoVAo4Ovri+zsbKnjEJEaYY8QEamt27dv53tHmJubG8qWLQsASEpKwqVLl/JsY2xsjAYNGgB4Neni6tWrMXnyZDRs2BDjxo1DrVq1oKenh5iYGOzbtw9A3kkYiUj7yQRvkyAiNbN161aMHDnyjc9v2LABY8aMQaVKlfId+wO8Giz99OnTXG03btzA8uXLcfr0aURHR0Mmk8HZ2RnNmzfH8OHD0a5du2L9HESk/lgIERERkc7iGCEiIiLSWSyEiIiISGexECIiIiKdxUKIiIiIdBYLISIiItJZLISIiIhIZ+nchIpKpRLR0dGwtLTMs3gjERERqSchBFJTU+Hk5AQ9veLrx9G5Qig6OhouLi5SxyAiIqIiePLkCZydnYvt9XSuELK0tATw6hvJ6fSJiIg0Q0pKClxcXFTn8eKic4XQ68thVlZWLISIiIg0THEPa+FgaSIiItJZLISIiIhIZ7EQIiIiIp3FQoiIiIh0FgshIiIi0lkshIiIiEhnsRAiIiIincVCiIiIiHQWCyEiIiLSWSyEiIiISGdJWgidPXsWPXr0gJOTE2QyGQ4ePPjOfc6cOYNGjRrBxMQEVapUwbp160o+KBEREWklSQuhtLQ01KtXD6tWrSrQ9uHh4ejWrRtatmyJkJAQfPnll5gyZQr27dtXwkmJiIhIG0m66GrXrl3RtWvXAm+/bt06VKxYEcuWLQMAeHh4ICgoCIsXL0a/fv1KKCURERFpK40aI3Tx4kV06tQpV1vnzp0RFBSE7OxsiVIRERFRSbt9O65EXlfSHqHCio2NhYODQ642BwcH5OTkID4+Ho6Ojnn2ycrKQlZWlupxSkpKieckIiKit3uemoU/bkYDAASA21HJiEnOhKmhPiADZP+/nTw9Gxe238HDi49KJIdGFUIAIJPJcj0WQuTb/tqCBQswd+7cEs9FRESkSy49SsCzlMw3Pq9UCuQoBQJCn8HC2ADHQ5+hnKUx9PRena+jkzLQsKItOtV0gEwG1HOxwUhvW1gY/1OaBF2Ogt+EP/A0suQ6MTSqECpfvjxiY2NztcXFxcHAwAB2dnb57jNz5kxMmzZN9TglJQUuLi4lmpOIiEjdCCGQkpGDv+4+w75rTxEenwYbM0P8f39CoWTmKPA4Ph2+ravAxFAf+XVFyGQyyAA0qVwGjVxtMbZVFXg4WhX4PbKycuDn+weePn1VBFlaGiM1tfBZ30WjCiEvLy8cPnw4V9vx48fh6ekJQ0PDfPcxNjaGsbFxacQjIiJSGwkvs/A4IR1nw55DTwYs/es+7CyM4F7OEs2qlMGSAfVgaWIIfT0Z9PXyv6ryNvoymap3pyQYGxtg06ae6Nx5B7y9XbB2bQfUrVv8V3gkLYRevnyJBw8eqB6Hh4fj+vXrKFOmDCpWrIiZM2ciKioKP//8MwDA19cXq1atwrRp0zB27FhcvHgRmzZtwq5du6T6CERERO8tK0eBuJSsd2/4BkIIKIRA4IMEKJRKCAF8czgUtZysUNPRCs2q2GHjME90qOnw7heTiBACmZk5MDX9p2OjUyc3/PnnELRrVxnp6S9L5H1lQhSlU6x4nD59Gm3bts3TPnz4cGzduhUjRozA48ePcfr0adVzZ86cgZ+fH+7cuQMnJyd88cUX8PX1LfB7pqSkwNraGsnJybCyKngXHRERUWEIIZAmV6geB0e8QIY8B0olsD8kCsERibA1NwIARCamo6KtGZxtTYv0Xq/HyWZkK9C5VnkAgImhHgY1rlik3p7SlpiYAV/f35GRkYNDhwblO+63pM7fkhZCUmAhREREJeWv0Ge4+jgRp/6OQ/xLOdLlOXB3sAQAJKVno2vt8pDJZDDQk2GEdyXYW3DoxqlT4Rg69ACiol4NAFqzphvGj2+cZ7uSOn9r1BghIiIiqSSnZ+N0WBy2XngMaxNDKP7Vj6AUApEJ6UjOyMbQZq74tFN1eLnZwczIQCN6ZKQglyvw1VcnsXjxBdWAbVtbE5Qvb1GqOVgIERER/UtWjgLJ6a8m6f3zTizOPYjH85QshDxJQu0KVuhSyxGdaznAxFAP+Nf9UuWtTWCor1HzFEvm3r14DB68DyEh/9wJ3q5dZWzb1hvOzqV7tYaFEBER6bSXWTm4/CgB6XIFTv0dh/3XolDZ3hxlLYzxMisH49u4oZKdOVztzWBlkv8dylQwQgisXx+MadP+REZGDgDA0FAPCxa0h5+fV4nehfYmLISIiEgnCCHwLCUTe4OjcPFhPF6ky2FqqI/gyCTUcrJC08plUMHGFIcmeaOus43UcbVOVlYO+vf/FYcPh6naPDzssXNnXzRokHdliNLCQoiIiNReVo4Ce64+yX8VAfFqBuMchcCm8+EQEDA3zv/0FpeShSplzTHMqxJaVLWHof6rOXRszIxK+BOQsbEBLC3/GRw+YYInFi3qBDMzaXvZWAgREZHaycxW4HjoMxy/E4u/7j6DQingZGOKgZ55VwZ4fReWgZ4Mk9pVxaDGLjDgWB21tHp1N9y/n4DZs1vjgw/cpY4DgLfPSx2HiIj+JSUzG9svRuDnC4+hFMDAxi5o5V4W9VysYWygL3U8KoSbN58hOjoVXbpUzdUuhHjj+qBvw9vniYhIK2UrlEhIy8K9mFSM2HIVNR2t8Gnn6uhaxzHXApykGZRKgeXLL2HGjBMwNzfEzZvjc90JVpQiqCTxfxgREZWK4IgXePj8JbIVSmRlK3Hi7jPo68lw9n48ypgZwdRIH0ObueLbXrXU7mRJBRMdnYoRIw4iIOARgFdzBX3//TmsWdNd4mRvxkKIiIhKzIs0Ob7YdxNBES+QmCbHMC9X2FsYw9hADx1rOqBx5TJYPMAY5SxNpI5K7+ngwXsYM+YQEhIyVG3Tp3th/vx2EqZ6NxZCRERUZEqlwPWnSVAqBf5+lorIhHTVLMHZCiW2XHgMT1dbHJvaksWOlkpLk8PP709s2HBN1eboaIGff+6DDh2qSJisYFgIERFRoTxJTMfs327j6YsM3I97CWdbUzRytYVCKVC7gnWuhUO71nFEk8plJExLJSkoKBo+PvsRFpagauvTpwY2bOgBOzszCZMVHAshIiJ6o43nHiE0OgUP4l7icUIaypgb4XFCOjrVdMBqn4aoYGP6xjl7SLtlZuagZ89diIl5CQAwMzPEihVdMGpUA40a48X/vUREpJLwMgsBoc9wP+4lXqTJsT8kCis+qo+e9Z3g6WoLCy4xQf/PxMQAa9Z0R58+e9C4sRN27uyLatXspI5VaJxHiIiIVMZsu4qk9Gw0rWIHD0dLuDu8+iICXt0FZmSUez6ngwfvoXv3ajA0LNl5njiPEBERlYiUjGwcvR2D/deicDk8EVdndUDZfy2FQJScnIlJk44iKysHe/Z8mOvSV+/eNSRM9v5YCBER6RiFUuBFuhwn7j7DmbDnOHIrFrZmhhjbqgoWfliXRRDlEhgYiSFDDuDx4yQAQPfuNzB8eH1JMxUnFkJERDoiJTMbqZk56L7iHPRkMjhYGaN9DQf8NtEb9VxspI5HaiY7W4F5885i/vxzUCpfjaKxsjKGiYl2lQ7a9WmIiHRcWlYOopIykJSejXP3n+P0389h8f8nrtcLkxrp6+HKrA4SJyV19uBBIoYM2Y/Ll6NUbd7eLtixoy8qVbKRLlgJYCFERKQllEqB1ace4EzYc9iaGaGclTHGt3FDnQrWubYzNeLipZQ/IQS2br2OyZOPIi0tGwCgry/DN9+0wYwZLWBgoCdxwuLHQoiISAOFRL7Ab9ej8Tw1C4EP41HG3AgyAC/Ss7G4f120q+EgdUTSMJmZORg69AD27g1Vtbm52WLnzr5o2tRZwmQli4UQEZEGOXH3GbZfisDpv59jpHcldK9THl90qQ5n239m8dXT05zJ7Eh9GBvrIztboXo8enQDLFvWBRYWRhKmKnkshIiI1JxSKbD14mN8ezgU5sb6GNykIn7oVxcOVly7i4qPTCbDxo098eDBVsyd2wb9+tWUOlKpYCFERKRmlEqBny8+RnDEC5wJew4bMyNEJqZjrU9DdK3jKHU80hL37sXj2bOXaN26kqrN3t4MN2+O16leRRZCRERq4llKJpYEhGHP1ScwNdTHp53dMa6VG+o4W797Z6ICEkJg/fpgTJv2JywtjXHzpi8cHCxUz+tSEQSwECIiklR4fBouP0rA/CN3kZqZg861HPDH5Bao6WSlUQtXkmaIi0vDmDGHcPhwGAAgIyMH8+adxapV3SROJh0WQkREEshRKLE3+CnmH7mLes42mNbRHUOaucJQX/tuTyb1cPTofYwc+RuePUtTtU2c2BgLF3aUMJX0WAgREZUiIQQ2nHuE74/cg42pIRb0qYMP6jlJHYu0WEZGNr744i+sXHlF1VaunDk2b+6J7t3dJUymHlgIERGVkoSXWVh/9hF+OvsIO8c0hXdVe6kjkZa7cSMWPj77cefOc1Vbt27VsHlzz1zjgnQZCyEiohKiVAoEPozHtguPcflRInKUAjZmhvjV1wuNK5WROh5puYyMbHTqtANxca8uhZmYGGDx4o6YMKExx5/9CwshIqIScDsqGUsCwnDyXhwGN6mIg5O8UdnOXOfuyCHpmJoaYunSzvDx2Y969Rzg798PNWuWlTqW2mEhRERUzHZfjcSygDC093DAcb9WcHewlDoS6QiFQgn9fw24Hzy4DoQQ+PDDmjA25ik/P/yuEBEVg6cv0nHlUSJuRydjc+BjfNurFoY0dWUPEJWKtDQ5/Pz+RHa2Elu29Mr1nI9PXYlSaQYWQkRERSCEQGxKJnJyBH699gQrTjxAuxrl4FbWnDNAU6kKCoqGj89+hIUlAAC6dauK/v1rSZxKc7AQIiIqpJTMbEz2D8GZsOdoXMkWOQrB4odKnUKhxMKFgZg9+zRycpQAADMzQ2RlKd6xJ/0bCyEiokJQKAV6rQqEngwI8GuFahz/QxKIjEzG0KEHcPZshKrN09MJO3f2hbu7nYTJNA8LISKiAoiIT8Pt6GTMOnAbSRnZuD67I2zMjKSORTpo9+7b8PX9HcnJWQAAmQz48suWmDOnNQwN9SVOp3lYCBER5UOpFEhIkyM7R4nxO4Nx42ky2lQvi2HNK2FS26owMuBSGFS6MjKy8fHHv2P79puqtooVrbFjRx+0bOkqYTLNxkKIiOhfFEoBhVJgxYn7OBDyFFYmhrAwMcCN2Z1gbWYodTzSYcbGBrnWCRs8uA5Wr+4GGxsTCVNpPhZCRET/LyTyBfqsuQAXW1Po68nwQ7+6aFGNE9CRetDTk2Hr1l5o2XIL5s5tw9viiwkLISKi/7fx3CP0b+SM6Z2qo7w1/8omaT14kIiEhHQ0beqsanN0tMS9e5NgwEuzxYbfSSLSecnpcszcfxN/3IrFlPbVWASRpIQQ2LIlBPXrr0O/fr8gMTEj1/MsgooXv5tEpLOEEIhKykCX5edw8WECdo1tBpcyZlLHIh2WmJiBAQP2YtSoQ0hLy0ZUVCrmzj0tdSytxktjRKRz5DlK3HyahJ8vRuBJYjqS0rMRMrsjTHjrMUno1KlwDB16AFFRqaq20aMbYP789hKm0n4shIhIpyRnyNF1+TmkZSlQ19kas3vURGV7cxZBJBm5XIGvvjqJxYsvQIhXbba2JtiwoQf69aspbTgdwEKIiHRCdo4SjxPS8GvQExgb6OPY1FawMuHt8CSte/fiMXjwPoSExKra2rWrjG3besPZ2UrCZLqDhRARaa3bUck4HvoMaVk52HQ+HBVsTGFjZohve9ViEUSSS0/PRqtWW/D8eToAwNBQDwsWtIefnxf09GQSp9MdLISISOsIITB8y1WcDXuObnXKw6uKHfzHNkVzN3upoxGpmJkZYv78dhg37nd4eNjD378f6tcvL3UsncNCiIi0SlK6HL8GP8HZsOc4+kkLVHew4l/XpDaEEJDJ/vn/OGZMQwgBDBlSF2acuVwSLISISGsolQITdl5D/Mss/DS0ETwcraWORATg1TphX3zxF4QQWLmym6pdJpNh3LhGEiYjFkJEpBUexb3Eqb+fIzFNjiUD6qN2BRZBpB5u3IiFj89+3LnzHADQpUtVdO/uLnEqeo2FEBFptISXWVh75iE2ngtHPWdrdPBwQNVyFlLHIoJSKbB8+SXMmHECcrkCAGBiYqAaHE3qgYUQEWmkBUfu4lF8GgJCn8HSxABbRjRG2xrlpI5FBACIjk7FiBEHERDwSNVWr54D/P37oWZNLuSrTlgIEZFGuRuTgoMhUVh/9hG2j26Cr7p7wNXOXOpYRCoHDtzF2LGHkZDwzxph06d7Yf78djA25mlX3fCIEJFGiEvJxNozD7El8DH6NqyAnWOawrsqb4cn9ZGZmYMpU45iw4ZrqjYnJ0ts29YbHTpUkTAZvQ0LISJSay8zs7H9UgR+OPY36lSwxv4JzdGwoq3UsYjyMDTUw7178arHffrUwIYNPWBnx4V81RkLISJSS/IcJZ6+SMf4ndcQ9SID83rXxpCmFXPNwUKkTvT19bB9ex94e2/G3LltMGpUA/5/1QAshIhIraTLc7Dh7CMcuxOLuzGpsDUzRNBXHbgoKqmdiIgkvHiRmWs2aFdXGzx8OIVjgTQIjxQRqYXopAxceBCPM2HPcfhmDJYPqo+W1cqijLmR1NGI8ti16xbGj/8DZcqY4vp1X1hZGaueYxGkWXi0iEgyaVnZWHnyAa4+foHgiBeoZGeGltXK4ugnLeHhyJW3Sf0kJ2di0qSj2LHj5v8/zsLcuafx44+dJU5GRaUndYA1a9agcuXKMDExQaNGjXDu3Lm3br9z507Uq1cPZmZmcHR0xMiRI5GQkFBKaYnofWTlKHD67zjM+e02eq06j1pzjuPwjRgMauyCoK864PRnbTGvd20WQaSWAgMjUb/+elURBACDB9fB7NmtJUxF70vSHqE9e/Zg6tSpWLNmDby9vbF+/Xp07doVoaGhqFixYp7tz58/j2HDhmHp0qXo0aMHoqKi4OvrizFjxuDAgQMSfAIiepfkjGx8/8ddPIp/iZjkTKRkZOODuk74uFUVeFctC2suNElqLjtbgXnzzmL+/HNQKgUAwMrKGGvWdIOPT12J09H7kgkhhFRv3rRpUzRs2BBr165VtXl4eKB3795YsGBBnu0XL16MtWvX4uHDh6q2lStXYuHChXjy5EmB3jMlJQXW1tZITk6GlRX/6iQqScv/uo+lf4WhdgUr+LZyQ2V7c1QpawFTIw58Js3w8GEifHz24/LlKFVbixYVsX17H1SqZCNdMB1UUudvyS6NyeVyBAcHo1OnTrnaO3XqhAsXLuS7T/PmzfH06VMcOXIEQgg8e/YMe/fuRffu3d/4PllZWUhJScn1RUQlS6kUmHv4Dpb+FYbVPg2w17c5PqjnhFoVrFkEkcZIS5OjWbNNqiJIX1+G775ri9Onh7MI0iKSFULx8fFQKBRwcHDI1e7g4IDY2Nh892nevDl27tyJgQMHwsjICOXLl4eNjQ1Wrlz5xvdZsGABrK2tVV8uLi7F+jmI6B+P49Nw+u84NP/fSWwJfIy/prVG9zpOvPWdNJK5uRG++qolAMDNzRYXLozGrFmtoK8v+fBaKkaSH83/TjYlhHjjBFShoaGYMmUKZs+ejeDgYBw7dgzh4eHw9fV94+vPnDkTycnJqq+CXkIjooLJzFbgj5sx+P7IXbRZfBr7gp/Cu6od7n7bhavAk8b572iRyZObYsmSTrh+3RdNmlSQKBWVJMkGS9vb20NfXz9P709cXFyeXqLXFixYAG9vb3z22WcAgLp168Lc3BwtW7bEd999B0dHxzz7GBsbw9jYOE87Eb2/bIUSa049wJ6gJ+jX0BlbRjZG2+pcAZ40j1yuwFdfnYSengz/+18HVbuengx+fl4SJqOSJlkhZGRkhEaNGiEgIAB9+vRRtQcEBKBXr1757pOeng4Dg9yR9fVfdblLOOabSKc8fP4SOy9FIjYlA0duxcLR2gTLBtaHlxsXQCXNdPfuc/j47EdISCxkMqBzZze0bVtZ6lhUSiS9fX7atGkYOnQoPD094eXlhZ9++gmRkZGqS10zZ85EVFQUfv75ZwBAjx49MHbsWKxduxadO3dGTEwMpk6diiZNmsDJyUnKj0KkMz5cewHVy1vig7qOmN7RHW7lLKWORFQkQgisWxeE6dOPIyMjBwBgYKCHhw9fsBDSIZIWQgMHDkRCQgK+/fZbxMTEoHbt2jhy5AhcXV0BADExMYiMjFRtP2LECKSmpmLVqlWYPn06bGxs0K5dO/zwww9SfQQinfEkIR0dlp6Blakhto5sDBNDTkxPmisuLg2jRx/C77+Hqdo8POzh798v19phpP0knUdICpxHiKhwVp96gAdxL3EgJAodPMphwzBPrqhNGu3o0fsYMeI3xMWlqdomTPDEokWdYMYJPtVWSZ2/+ScdEeXr0I1obDj7CLeikrGgbx0MaVYRjVzLSB2LqMgyM3Pw+ecBWLnyiqqtbFkzbN7cCx984C5hMpISCyEiyiM4IhFfHbiFWd090KOuE8y4mjZpAX19GS5deqp63K1bNWze3BMODpzmQZdJPo8QEamPJ4np2Br4GP3WXsQHdZ0wsHFFFkGkNQwN9bFzZ1/Y25th1aqu+P33j1gEEXuEiOiVn84+xPdH7sG7qh2+6u6Bkd68a4Y0W3R0KpKTM+HhUVbVVq2aHR4//gTm5kYSJiN1wkKISMcplQJL/wrDypMPsManIbrVyTsxKZGmOXDgLsaOPYxy5cwRFDQu1yBoFkH0b7w0RqSjkjOyERqTjB+O3cPKkw+wb3xzFkGk8dLS5Bg37jD69v0FCQkZuHs3Ht9+e0bqWKTG2CNEpIOuR75A7zUXUNfZGnoyGbaPboJGrrZSxyJ6L0FB0fDx2Y+wsARVW58+NfDZZ80lTEXqjoUQkY74NegJIhLSsfH8I2RmK/FJ+2rw68hbhknzKRRKLFwYiNmzTyMnRwkAMDMzxIoVXTBqVAPOe0VvxUKISAekZeVg5v5b+LKbB77rXRu96jnB0EBf6lhE7y0yMhlDhx7A2bMRqrbGjZ2wc2dfVKtmJ2Ey0hQshIi03J3oZHRfcR61nKzQu74TylgYSx2JqFikpmbB0/MnPH+eDgCQyYAvv2yJOXNaw9CQhT4VDAdLE2mp5IxszP8jFN1XnEf/Rs7YP6E5iyDSKpaWxpg6tRkAoGJFa5w5MwLffdeORRAVCnuEiLTUX6HPcO5+PBZ+WBd9G1SAgT7/7iHt88UX3lAqBSZNagIbGxOp45AGYiFEpIWWBYRh2Yn7mNuzFgZ4ukgdh+i95eQoMW/eGRgY6OHrr1ur2vX19fDVV60kTEaajoUQkZZZceI+lp24j+2jm6BltbLv3oFIzT18mAgfn/24fDkKenoydOhQBV5eLPCpeLCvnEhLZGYrMOfQbWwJDMeRKS1YBJHGE0Jg69brqF9/PS5fjgLwakD0jRvPJE5G2oQ9QkRaIDw+DW0Xn0a1chZYNrA+ajpZSx2J6L0kJmbg449/x969oao2Nzdb7NzZF02bOkuYjLQNCyEiDXXraTI2nHsEALgfl4outctj3ZBGEqcien+nToVj6NADiIpKVbWNHt0Ay5Z1gYUF1wmj4sVCiEhDbQkMh52FEZq72UMpBFpWs5c6EtF7kcsV+Prrk1i06AKEeNVma2uCDRt6oF+/mtKGI63FQohIQ92LTcWnnd1R2d4Cle3NpY5D9N6USoGjRx+oiqB27Spj27becHa2kjYYaTUOlibSMEqlwLeHQ/Eg7iUcLE1YBJHWMDExgL9/P1hZGWPx4o4ICBjKIohKHHuEiDTMrIO3sOvKE/w0tBFqVeCgaNJccXFpSE3NgptbGVVb7drlEBExlZMjUqlhjxCRhkl4Kcf6oQ3RqVZ5qaMQFdnRo/dRp85afPjhr8jKysn1HIsgKk0shIg0SNizVBjoy1DOkicK0kwZGdmYMuUounXzR1xcGq5fj8X8+eekjkU6jIUQkYa4/ywVgffjcflRIqxMDaWOQ1RoN27EonHjDVi58oqqrVu3apg4sbGEqUjXcYwQkQa4/ywV0UmZmPt7KNYNaQS3shZSRyIqMKVSYPnyS5gx4wTkcgWAVwOjFy/uiAkTGkMmk0mckHQZCyEiNbf/2lOcvBeH32/GYGzLyuhSm2ODSHNER6di+PCD+OuvR6q2evUc4O/fDzVrchkYkh4LISI1tu1COOYcCsWENm74Y0oL1HTkrcSkOZKTM1G//jo8f56uaps+3Qvz57eDsTFPP6QeOEaISE3tv/YEcw6FYvmg+vi8Sw3UcrLmJQTSKNbWJhg37tWyL05OlggIGIrFizuxCCK1wv+NRGpm3ZmHWHfmIZLSszGlfTX0ql9B6khERTZnTmsolQLTp3vBzs5M6jhEeRSpEMrJycHp06fx8OFDDB48GJaWloiOjoaVlRUsLDiIk6ioFEqB/x29h03DPdG2ejno6bEHiDSDQqHEwoWBMDExgJ+fl6rd0FAf33/fXsJkRG9X6EIoIiICXbp0QWRkJLKystCxY0dYWlpi4cKFyMzMxLp160oiJ5HW237xMb7+7Q4q2ZmhXY1yvAxGGiMyMhlDhx7A2bMRMDTUQ5s2ldCggaPUsYgKpNBjhD755BN4enrixYsXMDU1VbX36dMHJ06cKNZwRLoiNTMbX/92B/N61cLpz9qyCCKNsXv3bdStuxZnz0YAAHJylLhw4YnEqYgKrtA9QufPn0dgYCCMjIxytbu6uiIqKqrYghHpiriUTCw4eg+1nKww1KuS1HGICiQlJQuTJh3B9u03VW0VK1pjx44+aNnSVcJkRIVT6EJIqVRCoVDkaX/69CksLS2LJRSRrth8Phz/O3YPNcpbYtGH9aSOQ1QggYGRGDLkAB4/TlK1DR5cB6tXd+M6YaRxCn1prGPHjli2bJnqsUwmw8uXLzFnzhx069atOLMRab2z95/ju1618NtEb9R04hxBpN6ysxWYPfsUWrXaqiqCrKyMsWNHH+zc2ZdFEGmkQvcILV26FG3btkXNmjWRmZmJwYMH4/79+7C3t8euXbtKIiORVlEoBXZejsDyv+4jI1uBeb1qc0wQaQS5XIE9e+5AqRQAgBYtKmL79j6oVMlG2mBE70EmhBCF3SkjIwO7d+9GcHAwlEolGjZsCB8fn1yDp9VVSkoKrK2tkZycDCsr/gVOpe+zX2/g6O1YTGpbFR+3rsIiiDRKUFA0WrXaglmzWmLGjBbQ1+e8vFQ6Sur8XehC6OzZs2jevDkMDHJ3JuXk5ODChQto1apVsYUrCSyESEpnwp5jwZG7GN/aDT3qOXGeIFJriYkZSEuTw8XFOld7XFwaypUzlygV6aqSOn8XupRv27YtEhMT87QnJyejbdu2xRKKSFv9ePxvNHezQ8eaDiyCSK2dOhWOunXXYsCAvcjJUeZ6jkUQaZNCF0JCiHy78hMSEmBuzh8OojcZs+0qHsS9hF9Hd5hxrSVSU3K5Ap9/HoD27X9GVFQqLl16ih9+OC91LKISU+Dfxn379gXw6i6xESNGwNjYWPWcQqHAzZs30bx58+JPSKQF5v0eir/uxuHut51hasQiiNTT3bvP4eOzHyEhsaq2du0qY/jw+tKFIiphBf6NbG396hqxEAKWlpa5BkYbGRmhWbNmGDt2bPEnJNJwyRnZCIl8gV8+bsYiiNSSEALr1wdj2rQ/kZGRAwAwNNTD99+3x7RpXryMS1qtwL+Vt2zZAgCoVKkSPv30U14GIyqA5IxsLA34G9cik+Bqx58ZUj9xcWkYM+YQDh8OU7V5eNhj586+XC+MdEKh/zydM2dOSeQg0jopmdn4NegJtl6IwG8TveFgxcnmSL0kJWWiXr11iI19qWqbMMETixZ1gpmZoYTJiEpPkfrp9+7di19++QWRkZGQy+W5nrt27VqxBCPSZCmZ2bj/7CXC49MwvaM76rnYSB2JKA8bGxMMGlQLy5ZdRtmyZti8uRc++MBd6lhEparQd42tWLECI0eORLly5RASEoImTZrAzs4Ojx49QteuXUsiI5FGSc7IxsO4l7gSnoDTfz9Hx1oOUkcieqMFCzpgypQmuHVrPIsg0kmFnlCxRo0amDNnDj766CNYWlrixo0bqFKlCmbPno3ExESsWrWqpLIWC06oSCUpPjULEYlpmLLrOqKSMvDT0EboVKu81LGIoFQKLF9+CebmRhg3rpHUcYgKraTO34W+NBYZGam6Td7U1BSpqakAgKFDh6JZs2ZqXwgRlZTvj9zFT2cfoZKdGaxMDXHy09YwNtCXOhYRoqNTMWLEQQQEPIKJiQFatqwID4+yUsciUguFLoTKly+PhIQEuLq6wtXVFZcuXUK9evUQHh6OIixbRqQVfrsehZ/OPsLFGe3gaKP+a+6R7jhw4C7Gjj2MhIQMAEBmZg4CAh6xECL6f4UuhNq1a4fDhw+jYcOGGD16NPz8/LB3714EBQWpJl0k0hXyHCX2Bj/FlwduYX6f2iyCSG2kpcnh5/cnNmz45wYWJydLbNvWGx06VJEwGZF6KfQYIaVSCaVSqVp09ZdffsH58+dRtWpV+Pr6wsjIqESCFheOEaLicPRWDL79PRQxyZkoZ2mMMS0rY1wrN6ljEQF4tUK8j89+hIUlqNr69KmBDRt6wM7OTMJkREWnNqvPv01UVBQqVKhQXC9XIlgIUXGoNOMPTO/ojg8bObMXiNSGQqHEwoWBmD37tGqhVDMzQ6xY0QWjRjXId51IIk2hNqvP5yc2NhaTJ09G1apVi+PliNTa5F3XUN3BEmNbVWERRGolLS0b69cHq4qgxo2dcP36xxg9uiGLIKI3KHAhlJSUBB8fH5QtWxZOTk5YsWIFlEolZs+ejSpVquDSpUvYvHlzSWYlklS2Qgm/Pddx+EYMdo1tChND3hFG6sXKyhjbt/eBoaEeZs1qicDAUahWzU7qWERqrcCDpb/88kucPXsWw4cPx7Fjx+Dn54djx44hMzMTR48eRevWrUsyJ5Gk0rKysTnwMY7cikGAXyuUsTCWOhIRUlKykJ6ejfLlLVRtLVu64uHDKXBxsZYwGZHmKHCP0B9//IEtW7Zg8eLFOHToEIQQcHd3x8mTJ1kEkVZLzcyG/5UnOHQ9Ggs/rItqDpZSRyJCYGAk6tVbh8GD90GpzD3Uk0UQUcEVuBCKjo5GzZo1AQBVqlSBiYkJxowZU2LBiKQmhMDj+DQ8fP4SSWlytK1RDr3qq/fNAKT9srMVmD37FFq12orHj5Nw6tRjLF16UepYRBqrwJfGlEolDA3/WY1YX18f5ubmJRKKSEoKpcC5+89x7n48Dt+IRlxqFirbm2Nuz1pSRyMd9+BBIoYM2Y/Ll6NUbS1aVES/fjUlTEWk2QpcCAkhMGLECBgbvxobkZmZCV9f3zzF0P79+4s3IVEpuvo4EVN2hSD+ZRb6e7rghw/rokVVexjqF8sNlkRFIoTA1q3XMXnyUaSlZQMA9PVlmDu3DWbMaAF9/v8kKrIC//QMHz4c5cqVg7W1NaytrTFkyBA4OTmpHr/+Kqw1a9agcuXKMDExQaNGjXDu3Lm3bp+VlYVZs2bB1dUVxsbGcHNz491q9N7SsnIwyf8a+q+7iPYe5XB9dkd836cO2lYvxyKIJJWYmIEBA/Zi1KhDqiLIzc0WFy6MxqxZrVgEEb2nAvcIbdmypdjffM+ePZg6dSrWrFkDb29vrF+/Hl27dkVoaCgqVqyY7z4DBgzAs2fPsGnTJlStWhVxcXHIyckp9mykOxRKgQk7r+FOdDL2jvdCQxdb6OlxzhWS3osXGahXbx2ePk1RtY0e3QDLlnWBhYV6z+JPpCmKdWbpwmratCkaNmyItWvXqto8PDzQu3dvLFiwIM/2x44dw6BBg/Do0SOUKVOmSO/JmaXp34QQGLThEkKjUvDHlBaoaMdxb6RePv74MH766RpsbU2wYUMPjgcinVVS5+9CL7paXORyOYKDgzFjxoxc7Z06dcKFCxfy3efQoUPw9PTEwoULsX37dpibm6Nnz56YN28eTE05wy8V3NrTD3Hy3jMER7yAvp4MF2e2hz3nBiI1tGRJZygUAt980wbOzvzjjai4SVYIxcfHQ6FQwMHBIVe7g4MDYmNj893n0aNHOH/+PExMTHDgwAHEx8djwoQJSExMfOM4oaysLGRlZakep6Sk5Lsd6Y7fb0bjh2P3MPuDmlj0YV1Usrd4905EJUwIgfXrg2FhYYQhQ+qq2s3NjbBxY08JkxFpN8kKodf+u/6NEOKNa+IolUrIZDLs3LlTNTB7yZIl+PDDD7F69ep8e4UWLFiAuXPnFn9w0jgJL7PQdfk5xKVm4evuHhjVorLUkYgAAHFxaRgz5hAOHw6DhYURvLyc4eZWtMv/RFQ4kt1uYG9vD319/Ty9P3FxcXl6iV5zdHREhQoVct2d5uHhASEEnj59mu8+M2fORHJysurryZMnxfchSKN898ddONmY4uLMdhjdsorUcYgAAEeP3kfdumtx+HAYAODlSzl+/z1M4lREuqNIhdD27dvh7e0NJycnREREAACWLVuG3377rcCvYWRkhEaNGiEgICBXe0BAAJo3b57vPt7e3oiOjsbLly9VbWFhYdDT04Ozs3O++xgbG8PKyirXF+me5HQ5/rgVg2md3OFozfFkJL2MjGxMmXIU3br549mzNABA2bJmOHz4I3zySTOJ0xHpjkIXQmvXrsW0adPQrVs3JCUlQaFQAABsbGywbNmyQr3WtGnTsHHjRmzevBl3796Fn58fIiMj4evrC+BVb86wYcNU2w8ePBh2dnYYOXIkQkNDcfbsWXz22WcYNWoUB0tTHkIIpGRmY+XJ+6j3bQCcbUzh7WYvdSwi3Lz5DI0bb8DKlVdUbd26VcOtW+PxwQfuEiYj0j2FHiO0cuVKbNiwAb1798b//vc/Vbunpyc+/fTTQr3WwIEDkZCQgG+//RYxMTGoXbs2jhw5AldXVwBATEwMIiMjVdtbWFggICAAkydPhqenJ+zs7DBgwAB89913hf0YpAP6r7uI4IgXsLcwxvZRTdDSvazUkUjHKZUCy5dfwowZJyCXv/oj0sTEAIsXd8SECY3fOD6SiEpOoecRMjU1xb179+Dq6gpLS0vcuHEDVapUwf3791G3bl1kZGSUVNZiwXmEdMOS439jxckHODTJG3WdbaSOQwTg1QSJtWqtQUzMq8v7des6wN+/L2rVKidxMiL1V1Ln70JfGqtcuTKuX7+ep/3o0aOq1emJpLQkIAwrTj7A1pGNWQSRWrG1NcW2bb2hpyfD9OleuHJlDIsgIokV+tLYZ599hokTJyIzMxNCCFy5cgW7du3CggULsHHjxpLISFRg2QolVpy4j59HNUYrd55gSFppaXJkZubAzs5M1daxoxv+/nsSqlbl7fFE6qDQhdDIkSORk5ODzz//HOnp6Rg8eDAqVKiA5cuXY9CgQSWRkeidlEqBdWcfYuGxv9Giqj2LIJJcUFA0fHz2o2rVMvj9949yjf9hEUSkPt5rrbH4+HgolUqUK6c5Jx2OEdJO95+louPSs1gyoB76Nsx/KgWi0qBQKLFwYSBmzz6NnBwlAGD16m6YMKGxxMmINJvajBGaO3cuHj58CODVpIiaVASR9roW+QKNK9myCCJJRUYmo127n/HllydVRVDjxk7o2JETeBKpq0IXQvv27YO7uzuaNWuGVatW4fnz5yWRi6hA7j9Lxcz9N/HFvlvo24BFEEln9+7bqFt3Lc6efTXJrJ6eDLNmtURg4ChUq2YncToiepNCF0I3b97EzZs30a5dOyxZsgQVKlRAt27d4O/vj/T09JLISJQvIQRGbb2K2JRM/DbRGx81rSh1JNJBKSlZGDbsAD76aB+Sk18t8FyxojVOnx6O775rB0NDfYkTEtHbvNcYIQAIDAyEv78/fv31V2RmZqr96u4cI6Q9Oi05g/g0OU592hrWpkZSxyEdlJCQjsaNNyA8PEnVNnhwHaxe3Q02NibSBSPSQmozRui/zM3NYWpqCiMjI2RnZxdHJqJ3WvZXGFIyc/DXNBZBJB07OzN4e7/qibSyMsaOHX2wc2dfFkFEGqTQt88DQHh4OPz9/bFz506EhYWhVatW+Oabb9C/f//izkeUh1IpsOyv+/h5ZBOUMWcRRNJataorFAolvv++PSpVspE6DhEVUqELIS8vL1y5cgV16tTByJEjVfMIEZWWn84+hKmhPlpV59phVHqEENi27QasrIzRt6+Hqt3a2gT+/v0kTEZE76PQhVDbtm2xceNG1KpVqyTyEL3V1ceJ+N+xv7FrbFOpo5AOSUzMwMcf/469e0NhY2OCxo2d4OJiLXUsIioGhR4j9P3337MIIsnsDXqKkc0rwcvNXuoopCNOnQpH3bprsXdvKAAgKSlT9W8i0nwF6hGaNm0a5s2bB3Nzc0ybNu2t2y5ZsqRYghH915PEdJwJe455vWtLHYV0gFyuwFdfncTixRfw+t5aW1sTbNjQA/36cYFpIm1RoEIoJCREdUdYSEhIiQYiys+LtCz8HZsKmQzo4MHZzKlk3bsXj8GD9yEkJFbV1q5dZWzb1hvOzpx2g0ibFKgQOnXqVL7/JioNOQol+q+/hOT0bLSuXjbX4pVExUkIgfXrgzFt2p/IyMgBABga6mHBgvbw8/OCnh7/7xFpm0KPERo1ahRSU1PztKelpWHUqFHFEoro3y4+SsCDuJc483kbLO5fT+o4pMUSEzPw9denVEWQh4c9rlwZi+nTm7MIItJShS6Etm3bhoyMjDztGRkZ+Pnnn4slFBEAxCRnoNvyc1hyPAzta5SDmVGRpr0iKjA7OzNs3NgDADBhgieCgsahfv3yEqciopJU4DNLSkoKhBAQQiA1NRUmJv/MnKpQKHDkyBGuRE/F5sLDeAzecBleVeywqH9dWJsaSh2JtFBGRjbkcgWsrf/5fdarVw3cvOmLOnUcJExGRKWlwIWQjY0NZDIZZDIZ3N3d8zwvk8kwd+7cYg1HuinhZRaWBIRhuJcrZnarARND9gRR8bt58xkGD94HD4+y+OWXD3ONPWMRRKQ7CnyGOXXqFIQQaNeuHfbt24cyZcqonjMyMoKrqyucnJxKJCTplojEdMgAjGxRmUUQFTulUmD58kuYMeME5HIF7tx5jm3bbmDEiPpSRyMiCRT4LNO6dWsAr9YZq1ixIu/coRJz6m4cbM2MUMnOXOoopGWio1MxYsRBBAQ8UrXVq+eAJk24TBCRripQIXTz5k3Url0benp6SE5Oxq1bt964bd26dYstHOmeF2lyrD79AHt9m0sdhbTMgQN3MXbsYSQk/HOzx/TpXpg/vx2MjdnzSKSrCvTTX79+fcTGxqJcuXKoX78+ZDIZxOupVv9FJpNBoVAUe0jSDVFJGfD+30m0qV4WDV1tpY5DWiItTQ4/vz+xYcM1VZuTkyW2beuNDh2qSJiMiNRBgQqh8PBwlC1bVvVvopLQ4cfT+LCRMxb2Y68iFY/nz9PQosUWhIUlqNr69KmBDRt6wM7OTMJkRKQuClQIubq65vtvouJy/E4sMrKVWNCnNieuo2Jjb2+GWrXKIiwsAWZmhlixogtGjWrAMY5EpFKkCRX/+OMP1ePPP/8cNjY2aN68OSIiIoo1HOmOtacfYkr7ajA00Jc6CmkRmUyGDRt6oGfP6rh+/WOMHt2QRRAR5VLoQuj777+HqakpAODixYtYtWoVFi5cCHt7e/j5+RV7QNJ+6fIc3IxKxpBmFaWOQhpu9+7bOHr0fq42Ozsz/PbbIFSrZidRKiJSZ4W+VeLJkyeoWrUqAODgwYP48MMPMW7cOHh7e6NNmzbFnY90wCT/EDSuZItylibv3pgoHykpWZg06Qi2b7+JsmXNcOvWeDg4WEgdi4g0QKF7hCwsLJCQ8Grg4fHjx9GhQwcAgImJSb5rkBG9zZPEdMhzlPiBA6SpiAIDI1Gv3jps334TAPD8eTp27nzzFB9ERP9W6B6hjh07YsyYMWjQoAHCwsLQvXt3AMCdO3dQqVKl4s5HWiwzW4GQyBd4mZUjdRTSQNnZCsybdxbz55+DUvlqOg8rK2OsWdMNPj4srImoYArdI7R69Wp4eXnh+fPn2LdvH+zsXl13Dw4OxkcffVTsAUl7nbz3DFN2X4enqy3KWhpLHYc0yIMHiWjZcgvmzTurKoJatKiIGzd8WQQRUaHIRH4zI2qxlJQUWFtbIzk5GVZWVlLH0VkRCWnYcSkCz1KysOKjBlLHIQ0hhMDWrdcxefJRpKVlAwD09WWYO7cNZsxoAX39Qv9tR0QaoqTO30WaVz4pKQmbNm3C3bt3IZPJ4OHhgdGjR8Pa2rrYgpH2ehCXig1nw7En6Al+9fWSOg5pkOfP0+Hn96eqCHJzs8XOnX3RtKmzxMmISFMV+s+noKAguLm5YenSpUhMTER8fDyWLl0KNzc3XLt27d0vQDotPD4NnZaeRUpmNvaNb47GlcpIHYk0SLly5li37gMAwOjRDXD9ui+LICJ6L4W+NNayZUtUrVoVGzZsgIHBqw6lnJwcjBkzBo8ePcLZs2dLJGhx4aUxaQWExmJr4GPsHNtM6iikAeRyBbKzFTA3N8rVfuVKFFeMJ9IxJXX+LlKP0BdffKEqggDAwMAAn3/+OYKCgootGGmfHIUSY38ORiP2AlEB3LsXDy+vTZg48Uie51gEEVFxKXQhZGVlhcjIyDztT548gaWlZbGEIu107E4sAGBaR3eJk5A6E0Jg3bogNGy4HteuxWDbthv45Zc7UsciIi1V6MHSAwcOxOjRo7F48WI0b94cMpkM58+fx2effcbb5+mNktLl2BL4GH4dWATRmz1/nobRow/h8OEwVZuHhz2qVWMvIhGVjEIXQosXL4ZMJsOwYcOQk/NqIjxDQ0OMHz8e//vf/4o9IGk+hVLg2O1YRL3IQPe65aWOQ2rq2LEHGDHiIJ49S1O1TZjgiUWLOsHMzFDCZESkzYo8j1B6ejoePnwIIQSqVq0KMzOz4s5WIjhYunQJIbA58DHm/R6KL7vVwNiWVbj6N+WSkZGNGTP+wooVV1RtZcuaYfPmXvjgA/YgEtErks8jlJ6ejs8++wwHDx5EdnY2OnTogBUrVsDe3r7YwpD22XguHIuO/42F/epiQGMXqeOQmomLS0P79j/j9u04VVu3btWweXNPLppKRKWiwIOl58yZg61bt6J79+4YNGgQAgICMH78+JLMRhru/IPnmH/kLjYMbcQiiPJlb2+GChVe3WRhYmKAVau64vffP2IRRESlpsCXxtzc3DB//nwMGjQIAHDlyhV4e3sjMzMT+vr6JRqyOPHSWOlZcvxvKITAZ51rSB2F1FhMTCqGDTuI5cu7oGbNslLHISI1Jfk8Qk+ePEHLli1Vj5s0aQIDAwNER0cXWxjSHn/cjMaKkw9Q3tpU6iikRg4evIfTpx/nanN0tERAwFAWQUQkiQIXQgqFAkZGuWd3NTAwUN05RvTaibvPsOb0Q3zWuTqGNK0odRxSA2lpcowbdxh9+uzBkCH7kZiYIXUkIiIAhRgsLYTAiBEjYGxsrGrLzMyEr68vzM3NVW379+8v3oSkUbKyFRi9LQifdnbHkGauvEOMEBQUDR+f/QgLSwAAREWlYuvW65g2jQvuEpH0ClwIDR8+PE/bkCFDijUMab5vDt9BbScrTGpbTeooJDGFQomFCwMxe/Zp5OQoAQBmZoZYsaILRo1qIHE6IqJXClwIbdmypSRzkBZITs/G1ccvMK9XbamjkMQiI5MxdOgBnD0boWrz9HTCzp194e5uJ2EyIqLcCj2zNFF+MuU5OHQjCg/iXqKus7XUcUhCu3ffhq/v70hOzgIAyGTAl1+2xJw5rWFoqDl3mBKRbmAhRO8tR6FEh6VnkZyRjfl9asPcmP+tdFVs7EuMGXMIaWnZAICKFa2xY0cftGzpKnEyIqL8FXr1eaL/+uHYPcSlZOHCjHbwacoTni4rX94Cy5d3AQB89FFt3LjhyyKIiNQa/3Sn9xKRkIYN58KxdWRjWJpwYUxdk52tgEIhYGLyz6+SUaMaoEoVW7RtW1nCZEREBcMeISqS+JdZCHuWikV//o1GrrZoVY2T4emaBw8S0bLlFkyf/meudplMxiKIiDRGkQqh7du3w9vbG05OToiIeHVXyLJly/Dbb78VazhST6f/joPnd39hxr6byMxW4NtetaCnx/mCdIUQAlu2hKB+/XW4fDkKa9YE4fffw6SORURUJIUuhNauXYtp06ahW7duSEpKgkKhAADY2Nhg2bJlxZ2P1JDvjmDM7VkL3/WujY3DG6OWE+8S0xWJiRkYMGAvRo36Z0C0m5stypUzf8eeRETqqdCF0MqVK7FhwwbMmjUr12Krnp6euHXrVrGGI/Xzd2wqMrOVaORqi2oOllLHoVJ06lQ46tZdi717Q1Vto0c3wPXrvmjSpIKEyYiIiq7Qg6XDw8PRoEHeWWGNjY2RlpZWLKFIff119xnaVC+LcpbGMNTnEDNdIJcr8NVXJ7F48QUI8arN1tYEGzb0QL9+NaUNR0T0ngpdCFWuXBnXr1+Hq2vuW2KPHj2KmjX5S1GbJWdkY/mJ+/isU3WUszKROg6Vgri4NHTpsgMhIbGqtvbtK2Pbtt6oUMFKwmRERMWj0IXQZ599hokTJyIzMxNCCFy5cgW7du3CggULsHHjxpLISGriwoN4OFqZYGBjF6mjUCmxszOFpeWrhZYNDfWwYEF7+Pl5cXA8EWmNQl/bGDlyJObMmYPPP/8c6enpGDx4MNatW4fly5dj0KBBhQ6wZs0aVK5cGSYmJmjUqBHOnTtXoP0CAwNhYGCA+vXrF/o9qWhepGejbY2ysDLlfEG6Ql9fD9u390Hz5i64cmUspk9vziKIiLSKTIjXV/0LLz4+HkqlEuXKlSvS/nv27MHQoUOxZs0aeHt7Y/369di4cSNCQ0NRsWLFN+6XnJyMhg0bomrVqnj27BmuX79e4PdMSUmBtbU1kpOTYWXFrv2CyspRYMv5x0iX52Bap+pSx6EScvTofdjamqJZM+dc7UIIyGQsgIhIOiV1/n6v0a729vZFLoIAYMmSJRg9ejTGjBkDDw8PLFu2DC4uLli7du1b9/v4448xePBgeHl5Ffm9qXAexqUhMjEdiqLXzaTGMjKyMWXKUXTr5o/Bg/chJSUr1/MsgohIWxVpsPTbfik+evSoQK8jl8sRHByMGTNm5Grv1KkTLly48Mb9tmzZgocPH2LHjh347rvv3vk+WVlZyMr655d6SkpKgfLRP+JSMhHy5AX8r0QiwK+V1HGomN24EQsfn/24c+c5ACA8PAmbNl2Dnx//0CAi7VfoQmjq1Km5HmdnZyMkJATHjh3DZ599VuDXiY+Ph0KhgIODQ652BwcHxMbG5rvP/fv3MWPGDJw7dw4GBgWLvmDBAsydO7fAuSi3HIUS+69F4eeLj/Fd79qcO0iLKJUCy5dfwowZJyCXv5oY1cTEAD/+2Anjx3tKnI6IqHQUuhD65JNP8m1fvXo1goKCCh3gv71LbxqLoFAoMHjwYMydOxfu7u4Ffv2ZM2di2rRpqscpKSlwceFdTwUVEPoM/zt2D3N71sIAT37ftEV0dCpGjDiIgIB/enDr1XOAv38/1KzJdeOISHcU2+rzXbt2xcyZM7Fly5YCbW9vbw99ff08vT9xcXF5eokAIDU1FUFBQQgJCcGkSZMAAEqlEkIIGBgY4Pjx42jXrl2e/YyNjWFsbFyET6TbnqVkYuSWKwiNScW0ju4Y3ryS1JGomBw4cBdjxx5GQkKGqm36dC/Mn98OxsbF9iuBiEgjFNtvvb1796JMmTIF3t7IyAiNGjVCQEAA+vTpo2oPCAhAr1698mxvZWWVZwmPNWvW4OTJk9i7dy8qV+Zq18Up8aUcNmZGuPdtF5gY6b97B9II0dGp+OijfcjKenUpzMnJEtu29UaHDlUkTkZEJI1CF0INGjTIdelKCIHY2Fg8f/4ca9asKdRrTZs2DUOHDoWnpye8vLzw008/ITIyEr6+vgBeXdaKiorCzz//DD09PdSuXTvX/uXKlYOJiUmednp/Vx8nwshAj0WQlnFyssSiRR0xZcox9OlTAxs29ICdnZnUsYiIJFPoQqh37965Huvp6aFs2bJo06YNatSoUajXGjhwIBISEvDtt98iJiYGtWvXxpEjR1TLd8TExCAyMrKwEek9pctzcP1JEhpVtJU6Cr0nhUIJpVLA0PCfgnbSpCaoUsUW3bpV423xRKTzCjWhYk5ODnbu3InOnTujfPnyJZmrxHBCxXfbf+0ppv1yA/5jmqJ5VXup41ARRUYmY+jQA2jatAIWLuwodRwioveiFhMqGhgYYPz48bnm5SHtcvR2DKb9cgOT21VlEaTBdu++jbp11+Ls2QgsWnQBJ04UbH4vIiJdU+hLY02bNkVISEie1edJswVHvMClRwlY9OffmP1BTYxqwcHnmiglJQuTJh3B9u03VW0VK1rDxIR3gxER5afQvx0nTJiA6dOn4+nTp2jUqBHMzc1zPV+3bt1iC0elI0ehRL+1F9CvYQVsGu6J9h55py8g9RcYGIkhQw7g8eMkVdvgwXWwenU32NiYSBeMiEiNFbgQGjVqFJYtW4aBAwcCAKZMmaJ6TiaTqSZCVCgUxZ+SStTjhHSUtTTG1A7ucCnDO4g0TXa2AvPmncX8+eegVL4a8mdlZYw1a7rBx4d/mBARvU2BC6Ft27bhf//7H8LDw0syD5WyuJRMdFhyBgMbu8DZ1lTqOFRIcXFp6NlzFy5fjlK1tWhREdu390GlSjbSBSMi0hAFLoRe31zGsUHaJV2uQIuq9pjZtQZvpdZAtrYmeH3fp76+DHPntsGMGS2gr1+o+yCIiHRWoX5b8kSpfe7HvUSOUsDGzEjqKFQEhob62LmzL+rXL48LF0Zj1qxWLIKIiAqhUIOl3d3d31kMJSYmvlcgKl1zfrsNv44FX8SWpHXqVDhsbU1Rv/4/83hVrVoG166N4x8qRERFUKhCaO7cubC2ti6pLFTKbjxJQnRyJvpzVXm1J5cr8NVXJ7F48QVUr26P4OBxMDMzVD3PIoiIqGgKVQgNGjQI5cqVK6ksVIoUSoFeqwMxpiXnC1J39+7FY/DgfQgJiVU93rAhGJ980kziZEREmq/Agwn4F6f2iEvJhNuXR9Deoxy+6l5T6jj0BkIIrFsXhIYN16uKIENDPSxe3BGTJzeVOB0RkXYo9F1jpNnuRCej+4rz8K5qh43DPKWOQ28QF5eGMWMO4fDhMFWbh4c9/P375RofRERE76fAhZBSqSzJHFRKXqRl44O6jvi+bx328qmpo0fvY+TI3/DsWZqqbcIETyxa1CnXuCAiInp/XIBIx+y8HAErU0NYmfCEqo6ePk1Br167kZ396g+PsmXNsHlzL3zwAe/sIyIqCZxwRMcIAfSuX0HqGPQGzs5W+PbbtgCArl2r4tat8SyCiIhKEHuEdEhYbCqevEiHsSHrX3WhVAoIIXJNgvjZZ83h5maLDz+sycuXREQljGdEHaFUCvRcdR7OtqaoWs5C6jgEIDo6FV267MC8eWdztevr66F//1osgoiISgF7hHTEzagkyBVKrPyoIYwMWP9K7cCBuxg79jASEjJw4kQ4OnVyQ/PmnNiSiKi0sRDSET8c/Rt9GzqzCJJYWpocfn5/YsOGa6o2BwdzZGcrJExFRKS7WAjpgNTMbARHvsDyj+pLHUWnBQVFw8dnP8LCElRtffrUwIYNPWBnZyZhMiIi3cVCSMulZmZj+OYryFYoUc7SROo4OkmhUGLhwkDMnn0aOTmvbos3MzPEihVdMGpUA44FIiKSEAshLbfoz78Rm5yJs5+1lTqKToqLS0P//r/i7NkIVVvjxk7YubMvqlWzkzAZEREBvGtM650Je44lA+rDpQwvvUjBysoYSUmZAACZDJg1qyUCA0exCCIiUhMshLTYkVsxiEhIR21na6mj6CwTEwP4+/dF9ep2OHNmBL77rh0MDfWljkVERP+Pl8a0VFK6HD+dfYhpHdxhYczDXFoCAyNha2uKmjXLqtpq1SqHO3cm5Jo0kYiI1AN/M2upn84+QlaOEsOau0odRSdkZyswe/YptGq1FYMH70NWVk6u51kEERGpJ/521kIXHybg54sRmNuzFmzMjKSOo/UePkxEy5ZbMG/eWSiVAjduPMNPPwVLHYuIiAqA10y0zPUnSVh18j76N3JGk8ockFuShBDYtu0GJk8+ipcv5QAAfX0Z5s5tgwkTGksbjoiICoSFkJYZuukyetRzgm9rN6mjaLXExAx8/PHv2Ls3VNXm5mYLf/9+aNKkgoTJiIioMFgIaZny1iYY17IKHKw5eWJJOXkyHMOGHUBUVKqqbfToBli2rAssLHgpkohIk7AQ0jLpWQpUsDGVOobWioxMRufOO1QzRNvammDDhh7o16+mxMmIiKgoOFhai2y/+BhRSRnQ1+OSDSWlYkVrzJzZAgDQrl1l3Lw5nkUQEZEGY4+QFrnyOBHf9aoNPRZCxUYIASGQ63v69det4OZmi6FD6/F7TUSk4dgjpCWS0uW4+vgFqpQ1lzqK1oiLS0OvXrvx448XcrUbGupj+PD6LIKIiLQAe4S0xMPnLxGbnIkmlctIHUUrHD16HyNH/oZnz9Jw7NgDtG9fBQ0bOkodi4iIihkLIS0Ql5qJ8Pg0NK5kCwPOYPxeMjKy8cUXf2HlyiuqNhsbE7x4kSFhKiIiKikshDScEALn78djxYkH6F3fSeo4Gu3GjVj4+OzHnTvPVW1du1bFli294OBgIWEyIiIqKew+0HDRyZlYcPQeetV3wvg2VaWOo5GUSoGlSy+iSZONqiLIxMQAK1d2xR9/DGYRRESkxdgjpMGyFUpM23MdWdkKTG5XDUYGrGsL6/nzNAwevB9//fVI1Va3rgP8/fuiVq1yEiYjIqLSwDOnBotMTEf8yyxc+7oji6AiMjMzRGRksurx9OleuHJlDIsgIiIdwbOnhopPzcKKE/fx8HkaB0i/B3NzI/j790WlSjYICBiKxYs7wdiYHaVERLqCv/E1UFK6HJ7z/4KTtQn+mNJC6jgaJSgoGra2JnBz+2eagUaNnBAWNgmGhvoSJiMiIimwK0EDLQ0IQ50K1rgwsz1qOVlLHUcjKBRKLFhwDl5em+Djsx/Z2Ypcz7MIIiLSTSyENExcaiZO3IvDjwPqSR1FY0RGJqNdu5/x5ZcnkZOjxOXLUdi48ZrUsYiISA3w0pgGSc3MRmxyJl6kyeHuYCl1HI2we/dt+Pr+juTkLACATAZ8+WVLjBnTUOJkRESkDlgIaYisbAU2ngvH1guPUY1F0DulpGRh0qQj2L79pqqtYkVr7NjRBy1bukqYjIiI1AkLITX39EU61p1+iF1Xn0ChFNg8whOtqpWVOpZau3DhCYYM2Y/w8CRV2+DBdbB6dTfY2JhIF4yIiNQOCyE1lpqZjRY/nIJ3VTscnNgctZ2sIZNxxfO3efw4Ca1bb0VOjhIAYGVljDVrusHHp67EyYiISB1xsLQaO3c/HrWcrLBiUAPUqWDDIqgAKlWyweTJTQAA3t4uuHHDl0UQERG9EXuE1NSZsOeYdeAWPu9cA3YWxlLHUVtCCADIVSR+/317VK1aBuPGNYIBZ9wmIqK34FlCTY3dFoQxLSvjo6YVpY6ithITMzBgwF6sWXM1V7uJiQEmTGjMIoiIiN6JPUJq6HlqFgQERnlXljqK2jp1KhxDhx5AVFQqfv89DG3aVOL6YEREVGj8k1kNbTr/CF5u9jA1Yp36X3K5Ap9/HoD27X9GVFQqAMDU1ED1byIiosLgmVbNPH2RjnVnHsF/TFOpo6idu3efw8dnP0JCYlVt7dpVxrZtveHsbCVhMiIi0lQshNRIdFIGWi88jZ71nNC8qr3UcdSGEALr1gVh+vTjyMjIAQAYGuphwYL28PPzgp4e76YjIqKiYSGkRrouP4d6LtZYwnXEVBIS0jFixG/4/fcwVZuHhz127uyLBg0cJUxGRETagGOE1MTVx4lIzsjGjtFNYaDPw/KagYEebt16pno8YYIngoLGsQgiIqJiwTOumniSmI5R3pVgZsxOun+ztjbBjh194ehogcOHP8Lq1d1hZmYodSwiItISLITUwG/XozDtlxuoWs5C6iiSu3EjFk+eJOdqa9GiIh49+gQffOAuUSoiItJWkhdCa9asQeXKlWFiYoJGjRrh3Llzb9x2//796NixI8qWLQsrKyt4eXnhzz//LMW0xe/h85f4ZPd1/NCvDgY31d1V0ZVKgaVLL6JJk40YOvQAFAplrudNTNhTRkRExU/SQmjPnj2YOnUqZs2ahZCQELRs2RJdu3ZFZGRkvtufPXsWHTt2xJEjRxAcHIy2bduiR48eCAkJKeXkxWfNqYfo06AC+jZ0ljqKZKKjU9Glyw5Mm3YccrkCZ85EYPNmzT2mRESkOWTi9WJNEmjatCkaNmyItWvXqto8PDzQu3dvLFiwoECvUatWLQwcOBCzZ88u0PYpKSmwtrZGcnIyrKykn3vmy/230KxKGfSsX0HqKJI4cOAuxo49jISEDFXb9OlemD+/HYw5XoqIiP5fSZ2/JesRksvlCA4ORqdOnXK1d+rUCRcuXCjQayiVSqSmpqJMmTJv3CYrKwspKSm5vtTJiXvPYG+pe4uqpqXJMW7cYfTt+4uqCHJyskRAwFAsXtyJRRAREZUKyQqh+Ph4KBQKODg45Gp3cHBAbGzsG/bK7ccff0RaWhoGDBjwxm0WLFgAa2tr1ZeLi8t75S5O+4Kf4llKFppUenMhp42CgqLRsOFP2LDhmqqtb18P3Lzpiw4dqkiYjIiIdI3kg6VlstyzAgsh8rTlZ9euXfjmm2+wZ88elCv35sU2Z86cieTkZNXXkydP3jtzcVn2VxiWDKinU/MGPXr0Al5emxAWlgAAMDc3xKZNPbF3b3/Y2ZlJnI6IiHSNZGdge3t76Ovr5+n9iYuLy9NL9F979uzB6NGj8csvv6BDhw5v3dbY2BhWVla5vtRBUrocT15koHOt8lJHKVVVqthi9OgGAIDGjZ0QEvIxRo1qUKDil4iIqLhJVggZGRmhUaNGCAgIyNUeEBCA5s2bv3G/Xbt2YcSIEfD390f37t1LOmaJScrIRoOKNjDXwbEwP/7YCYsXd0Rg4ChUq2YndRwiItJhkl6TmTZtGjZu3IjNmzfj7t278PPzQ2RkJHx9fQG8uqw1bNgw1fa7du3CsGHD8OOPP6JZs2aIjY1FbGwskpOT3/QWamvJ8TAkpWdLHaNEpaRkYdiwA9iyJfet8ObmRpg+vTkMDfUlSkZERPSKpN0RAwcOREJCAr799lvExMSgdu3aOHLkCFxdX00sGBMTk2tOofXr1yMnJwcTJ07ExIkTVe3Dhw/H1q1bSzt+kWXlKBD4IB5HP2kpdZQSc+HCEwwZsh/h4Uk4cOAeWrZ0RdWqujUonIiI1J+k8whJQR3mEYpMSMeYn6/i6CetoK+nXWNjcnKUmDfvDL777hyUylf/taysjLFnz4fo0qWqxOmIiEhTldT5W/cGqEgsW6FESmY2wp69lDpKsXv4MBE+Pvtx+XKUqq1Fi4rYvr0PKlWykS4YERHRG7AQKmUxSZmIS81CPWdrrekNEkJg27YbmDz5KF6+lAMA9PVlmDu3DWbMaAF9HZoegIiINAsLoVKUlaNAcoYco7ZexSftq0kdp1i8eJGBceN+x969oao2Nzdb+Pv3Q5MmurlsCBERaQ4WQqUo+kUmNgc+Rj0XG60phJRKgQsX/pmkcvToBli2rAssLIwkTEVERFQwLIRKyYgtV3DpUQIUSoE/p7aCnpZcFrOzM8O2bb0xaNBerF//Afr1qyl1JCIiogJjIVRKwuPTcGN2JxgZ6Gn0LMp37z5HmTKmcHCwULV16FAF4eGfwFIHF48lIiLNxlGspWDlifuISEiHsaG+xhZBQgisWxeERo1+wsiRv+G/sy6wCCIiIk3EQqiErT71AD8GhGHX2GZSRymyuLg09Oq1G+PH/4GMjBwcPfoA27bdkDoWERHRe+OlsRIU/zILm86HY8foJvBy08w1tY4de4ARIw7i2bM0VduECZ4YMKCWhKmIiIiKBwuhEjR193XUdrKCl5u91FEKLSMjGzNm/IUVK66o2sqWNcPmzb3wwQfuEiYjIiIqPiyESsiCI3dx/kE8Tn3aRuMmTrx16xkGD96P27fjVG3dulXD5s09cw2SJiIi0nQshEpIVFIGtoxojMr25lJHKZQHDxLh6bkBcrkCAGBiYoDFiztiwoTGGjvQm4iI6E04WLqEKJUCJoaa9+2tWrUMBg58Nf6nXj0HBAePw8SJTVgEERGRVmKPUAkQQuB2dAqsTQ2ljlIkq1Z1Q7VqZfD5594wNuZ/ESIi0l6a12WhAdKzcvD0RToq26v3eJq0NDnGjTuMPXtu52q3sjLG11+3ZhFERERaj2e6EuB/NRLVy1vC1Ehf6ihvFBQUDR+f/QgLS8Cvv4aieXMXuLhYSx2LiIioVLFHqJgplUosOX4fvq3dpI6SL4VCiQULzsHLaxPCwhIAAHK5AjdvPpM4GRERUeljj1AxC4pIQka2At3qOEodJY/IyGQMHXoAZ89GqNoaN3bCzp19Ua2aZk74SERE9D5YCBWjDLkCq049QN+GFWCor16dbbt334av7+9ITs4CAMhkwJdftsScOa1haKi+l/CIiIhKEguhYvTNodu49TQJhye3kDqKSkpKFiZNOoLt22+q2ipWtMaOHX3QsqWrhMmIiIikx0KoGCWkybF5RGM425pJHUUlPT0bR48+UD3+6KPaWLOmO2xsTCRMRUREpB7U6/qNhsrKUWD5X/dxOyoFRgbq9S0tX94Cmzb1hJWVMXbs6AN//34sgoiIiP4fe4TeU1aOAh2XnEVaVg6+6u6BGuWtJM3z4EEibG1NYGf3T69Uz57VER7+CcqUMZUwGRERkfpRr+4LDRQSmYQX6XL84uuFPg2dJVtgVQiBLVtCUL/+Onz88e8QQuR6nkUQERFRXiyE3pMQwAd1HVHGzEiyDImJGRgwYC9GjTqEtLRs7Nt3F7t23X73jkRERDqOl8aKQWa2EjZm0qwrdupUOIYOPYCoqFRV2+jRDdCzZ3VJ8hAREWkSFkLFQAaU+urscrkCX311EosXX8Drq2C2tibYsKEH+vWrWapZiIiINBULofeUlpVT6neK3bsXj8GD9yEkJFbV1q5dZWzb1hvOztIO1iYiItIkLITe08WHCVD+Z2BySfr773g0bLgeGRk5AABDQz0sWNAefn5e0JNooDYREZGm4mDp96BQClwOT0CX2uVL7T3d3e3QtWs1AICHhz2uXBmL6dObswgiIiIqAvYIvYeEtCzcjk5BfRfbUntPmUyGn376AO7uZfD1161hJtEgbSIiIm3AQug9KJVA1XIWKGNeMrfOZ2Rk44sv/kLHjlXQo8c/d4HZ2ZlhwYIOJfKeRFTyhBDIycmBQqGQOgqRWjE0NIS+fukuBM5CqIiSM7Jx8VE8nqVklsjr37gRCx+f/bhz5zl27bqNW7fGo3x5ixJ5LyIqPXK5HDExMUhPT5c6CpHakclkcHZ2hoVF6Z3vWAgV0d+xKfDbcwNLB9Yr1tdVKgWWL7+EGTNOQC5/9dfiy5dyBAVF44MP3Iv1vYiodCmVSoSHh0NfXx9OTk4wMjIq9ak3iNSVEALPnz/H06dPUa1atVLrGWIhVAR/3onFx9uDMaltVfRp4FxsrxsdnYoRIw4iIOCRqq1ePQf4+/dDzZpli+19iEgacrkcSqUSLi4uMDMze/cORDqmbNmyePz4MbKzs1kIqbMv9t7E8kH10at+hWJ7zQMH7mLs2MNISMhQtU2f7oX589vB2JiHiUib6Onxhl2i/EjRQ8ozbCHFJmdAJkOxFUEvX8rh53cMGzeGqNqcnCyxbVtvdOhQpVjeg4iIiPLHP0sKYf+1p+i6/BxqOhXf7M0vXmTg119DVY/79KmBmzd9WQQRkdaoVKkSli1b9t7bvK+tW7dCJpNBJpNh6tSphdq3UqVKqn2TkpJKJB9Jg4VQIaw8+QBfdvPA1pFNiu01XVyssX79BzA3N8TGjT2wb98A2Nlx7AAR6ZarV69i3LhxxfZ6byqsrKysEBMTg3nz5qnahBD45ptv4OTkBFNTU7Rp0wZ37tzJk2/fvn1FzpORkQFbW1uUKVMGGRkZeZ6XyWQ4ePBgnvapU6eiTZs2udpiY2MxefJkVKlSBcbGxnBxcUGPHj1w4sSJIueLjIxEjx49YG5uDnt7e0yZMgVyufyN2z9+/FhVGP7369dff82zfVZWFurXrw+ZTIbr168XOWdJYCFUQC+zchCZmI7+ni4w1C/6ty0yMhkpKVm52gYOrI0HD6Zg9OiGvIOEiHRS2bJlS2UAuUwmQ/ny5WFpaalqW7hwIZYsWYJVq1bh6tWrKF++PDp27IjU1NRc+cqUKVPk9923bx9q166NmjVrYv/+/UV+ncePH6NRo0Y4efIkFi5ciFu3buHYsWNo27YtJk6cWKTXVCgU6N69O9LS0nD+/Hns3r0b+/btw/Tp09+4j4uLC2JiYnJ9zZ07F+bm5ujatWue7T///HM4OTkVKV9JYyFUQF/svYla73lJbPfu26hbdy0mTz6a5znOEURE6mrv3r2oU6cOTE1NYWdnhw4dOiAtLQ0A0KZNmzyXmXr37o0RI0bkaktNTcXgwYNhYWEBJycnrFy5Mtfz/+3BSU5Oxrhx41CuXDlYWVmhXbt2uHHjRq59Dh06BE9PT5iYmMDe3h59+/ZVZYqIiICfn5+ql+JNhBBYtmwZZs2ahb59+6J27drYtm0b0tPT4e/vX8jv1Jtt2rQJQ4YMwZAhQ7Bp06Yiv86ECRMgk8lw5coVfPjhh3B3d0etWrUwbdo0XLp0qUivefz4cYSGhmLHjh1o0KABOnTogB9//BEbNmxASkpKvvvo6+ujfPnyub4OHDiAgQMH5pkD6OjRozh+/DgWL15cpHwljYVQAchzlPjjVgx+7F+0OYNSUrIwbNgBfPTRPiQnZ+Hnn29g377Qd+9IRCSxmJgYfPTRRxg1ahTu3r2L06dPo2/fvhCFXGx60aJFqFu3Lq5du4aZM2fCz88PAQEB+W4rhED37t0RGxuLI0eOIDg4GA0bNkT79u2RmJgIAPjjjz/Qt29fdO/eHSEhIThx4gQ8PT0BAPv374ezszO+/fZbVW/Fm4SHhyM2NhadOnVStRkbG6N169a4cOHCWz/T68tDp0+ffut2Dx8+xMWLFzFgwAAMGDAAFy5cwKNHj966T34SExNx7NgxTJw4Eebm5nmet7GxUf27a9eusLCweOvXaxcvXkTt2rVz9dh07twZWVlZCA4OLlC24OBgXL9+HaNHj87V/uzZM4wdOxbbt29X2ykjeNdYATx6/hJWJgao5mD57o3/IzAwEkOGHMDjx0mqto8+qo327TkYmojUX0xMDHJyctC3b1+4uroCAOrUqVPo1/H29saMGTMAAO7u7ggMDMTSpUvRsWPHPNueOnUKt27dQlxcHIyNjQEAixcvxsGDB7F3716MGzcO8+fPx6BBgzB37lzVfvXqvfpjtUyZMtDX14elpSXKl3/7otixsbEAAAcHh1ztDg4OiIiIeOu+hoaGqF69+jtP8Js3b0bXrl1ha/tqXcouXbpg8+bN+O6779663389ePAAQgjUqFHjndtu3Lgx37FI+YmNjc3z+W1tbWFkZKT6/rzLpk2b4OHhgebNm6vahBAYMWIEfH194enpicePHxfotUobC6G3kOco4X85En/eiUXjSoW7NpydrcC8eWcxf/45KJWv/nKysjLGmjXd4ONTtyTiEpEG67f2ApIzskvlvaxNDbFvfPN3b4hXxUX79u1Rp04ddO7cGZ06dcKHH36oOqkXlJeXV57Hb7pLLDg4GC9fvoSdnV2u9oyMDDx8+BAAcP36dYwdO7ZQGd7mv5fPhBDvHLNZoUIF3Lt3763bKBQKbNu2DcuXL1e1DRkyBH5+fpg7d26hJg183QtXkLGkFSoUboqX/F6zIN8D4NVx8ff3x9dff52rfeXKlUhJScHMmTMLlaW0sRB6g8xsBerOPQ5LYwN80aUG2nuUK/C+Dx4kYsiQ/bh8OUrV5u3tgh07+qJSJZsSSEtEmq6ghUlp09fXR0BAAC5cuIDjx49j5cqVmDVrFi5fvozKlStDT08vz2Wy7OyCFXRvOskqlUo4Ojrme8np9eUfU1PTQn2ON3ndYxQbGwtHR0dVe1xcXJ5ekqL4888/ERUVhYEDB+ZqVygUOH78uGpgsaWlJZKTk/Psn5SUBGtrawBAtWrVIJPJcPfuXfTu3fut79u1a1ecO3furdu8fPkSwKvvweXLl3M99+LFC2RnZxfoe7B3716kp6dj2LBhudpPnjyJS5cuqXr1XvP09ISPjw+2bdv2ztcuDRwj9AZnwp7Draw5gr/uiAGNXWBnYfzunQDcvfsc9euvUxVB+voyzJvXFqdPj2ARREQaSSaTwdvbG3PnzkVISAiMjIxw4MABAK/upvr3GByFQoHbt2/neY3/DuS9dOnSGy/xNGzYELGxsTAwMEDVqlVzfdnb2wMA6tat+9bbxY2MjKBQKN752SpXrozy5cvnGq8kl8tx5syZXJd5imrTpk0YNGgQrl+/nuvLx8cn16DpGjVq4OrVq7n2FUIgODgY1atXB/Dqkl/nzp2xevVq1WD1f/v3/EYbN27M857//XrNy8sLt2/fznUcjx8/DmNjYzRq1KhAn7Fnz54oWzb3UlArVqzAjRs3VO935MgRAMCePXswf/78d75uqRE6Jjk5WQAQycnJb9xGqVSKuYduiwVHQgv9+kqlUnTpskMA3wg3t+Xi0qUn7xOXiLRIRkaGCA0NFRkZGVJHKbBLly6J+fPni6tXr4qIiAjxyy+/CCMjI3HkyBEhhBDr1q0TZmZm4vfffxd3794V48aNE1ZWVmL48OGq13B1dRVWVlbihx9+EH///bdYtWqV0NfXF8eOHcu1zdKlS4UQr36PtmjRQtSrV08cO3ZMhIeHi8DAQDFr1ixx9epVIYQQp06dEnp6emL27NkiNDRU3Lx5U/zwww+q1+vYsaPo2bOnePr0qXj+/LkQQogtW7YIa2vrPJ/xf//7n7C2thb79+8Xt27dEh999JFwdHQUKSkpubY7deqUACBevHghhBDi6dOnonr16uLy5cv5fu/i4uKEoaGhOHr0aJ7njh8/LgwNDUVcXJwQQog9e/YIExMTsXLlSvH333+L69eviwkTJghTU1Px+PFj1X6PHj0S5cuXFzVr1hR79+4VYWFhIjQ0VCxfvlzUqFEj3xzvkpOTI2rXri3at28vrl27Jv766y/h7OwsJk2apNrmTZ/1/v37QiaT5fsZ/ys8PFwAECEhIW/c5m0/IwU5fxcFC6F8PIxLFbVmHxM3nyQV6T1iYlLFJ58cFampWUWNSURaSBMLodDQUNG5c2dRtmxZYWxsLNzd3cXKlStVz8vlcjF+/HhRpkwZUa5cObFgwQLRq1evPIXQ3LlzxYABA4SZmZlwcHAQy5Yty/U+/y6EhBAiJSVFTJ48WTg5OQlDQ0Ph4uIifHx8RGRkpGqbffv2ifr16wsjIyNhb28v+vbtq3ru4sWLom7dusLY2Fi8/pv/TYWQUqkUc+bMEeXLlxfGxsaiVatW4tatW3m2+28h9PrEfurUqXy/d4sXLxY2NjZCLpfneS47O1uUKVNG/Pjjj6q23bt3C09PT2FlZSXKlSsnOnfuLIKCgvLsGx0dLSZOnChcXV2FkZGRqFChgujZs+cbcxRERESE6N69uzA1NRVlypQRkyZNEpmZmarn3/RZZ86cKZydnYVCoXjne6hrISQTopD3QGq4lJQUWFtbIzk5GVZW+c8L9PVvt5GSkY3lgxq89bXkcgW+/vokOnZ045IYRPROmZmZCA8PR+XKlWFiYiJ1HLXi6OiIefPmYcyYMSX2Hlu3bsXUqVOLvETG6dOn0bZtW7x48SLXrepUfN72M1KQ83dRcIzQf2wJDMf2ixEY5V35rdvduxePZs02YuHCCxg+/CASEtJLKSERkfZIT09HQEAAnj17hlq1apX4+yUnJ8PCwgJffPFFofarVatWvjMmk+bjXWP/4n85AnMPh2LTcE/Uc7HJdxshBNavD8a0aX8iIyMHAPD8eRouXHiCHj2ql2JaIiLN99NPP2HevHmYOnVqnlvsi1u/fv3QokULACh0j86RI0dUd8MVZ28ESY+FEIDnqVnYeuExVp96gJ1jmsK7qn2+28XFpWHMmEM4fDhM1ebhYQ9//36oX//tk3YREVFeU6dOLfRK8EVlaWmZa42xwng9mSRpHxZCADaef4SbT5JxcKI36r+hJ+jYsQcYMeIgnj3755bFCRM8sWhRJ5iZGZZSUiIiIipOLIQAvEiTY3SLyvkWQRkZ2Zgx4y+sWHFF1Va2rBk2b+6FDz5wL8WUREREVNx0erC0Qimw+0okfgl6iop2+a8VEx2dik2bQlSPu3Wrhlu3xrMIIqIi07GbdYkKTIqfDZ0uhHw2XsKKE/ex8MO6cH/DgqpubmWwYkVXmJgYYNWqrvj994/g4GCR77ZERG9jaPjqMnp6Ou8yJcqPXC4HgEKtwfa+dPbSWFaOArejUnD+i7awMTNStUdHp8LGxiTXuJ+RI+ujffvKcHW1kSApEWkLfX192NjYIC4uDgBgZmZWoEUtiXSBUqnE8+fPYWZmBgOD0itPdLYQik7KgJWpAaxM/il4Dhy4i7FjD6N//5pYu/YDVbtMJmMRRETF4vUin6+LISL6h56eHipWrFiqfyDobCEUl5yFKvYW0NOT4eVLOfz8jmHjxldjgdatC0b37u4cB0RExU4mk8HR0RHlypUr8CrtRLrCyMgIenqlO2pH8kJozZo1WLRoEWJiYlCrVi0sW7YMLVu2fOP2Z86cwbRp03Dnzh04OTnh888/h6+vb6Hf9/T953B3sMTVq1Hw8dmP+/cTVc/16VMDXl7ORfo8REQFoa+vX6rjIIgof5IOlt6zZw+mTp2KWbNmISQkBC1btkTXrl0RGRmZ7/bh4eHo1q0bWrZsiZCQEHz55ZeYMmUK9u3bV+j3Ts/MQfiJCDRvvllVBJmZGWLjxh7Yt28A7N5wFxkRERFpD0kXXW3atCkaNmyItWvXqto8PDzQu3dvLFiwIM/2X3zxBQ4dOoS7d++q2nx9fXHjxg1cvHixQO/5etE2owrfQR6Vo2pv3NgJO3f2RbVqdu/xiYiIiKgkaN2iq3K5HMHBwejUqVOu9k6dOuHChQv57nPx4sU823fu3BlBQUGFvtYuj3oJANDTk2HWrJYIDBzFIoiIiEjHSDZGKD4+HgqFAg4ODrnaHRwcEBsbm+8+sbGx+W6fk5OD+Ph4ODo65tknKysLWVlZqsfJycmvn4GzszU2bPgAzZtXREZGGjIy3u8zERERUclISUkBUPyTLko+WPq/t8gJId5621x+2+fX/tqCBQswd+7cfJ5ZiqdPga5dZxYuMBEREUkmISEB1tbWxfZ6khVC9vb20NfXz9P7ExcXl6fX57Xy5cvnu72BgQHs7PK/rDVz5kxMmzZN9TgpKQmurq6IjIws1m8kFU1KSgpcXFzw5MmTYr3mS4XHY6E+eCzUB4+F+khOTkbFihVRpkyZYn1dyQohIyMjNGrUCAEBAejTp4+qPSAgAL169cp3Hy8vLxw+fDhX2/Hjx+Hp6amauv6/jI2NYWxsnKfd2tqa/6nViJWVFY+HmuCxUB88FuqDx0J9FPc8Q5LePj9t2jRs3LgRmzdvxt27d+Hn54fIyEjVvEAzZ87EsGHDVNv7+voiIiIC06ZNw927d7F582Zs2rQJn376qVQfgYiIiDSYpGOEBg4ciISEBHz77beIiYlB7dq1ceTIEbi6ugIAYmJics0pVLlyZRw5cgR+fn5YvXo1nJycsGLFCvTr10+qj0BEREQaTPLB0hMmTMCECRPyfW7r1q152lq3bo1r164V+f2MjY0xZ86cfC+XUenj8VAfPBbqg8dCffBYqI+SOhaSTqhIREREJCVJxwgRERERSYmFEBEREeksFkJERESks1gIERERkc7SykJozZo1qFy5MkxMTNCoUSOcO3furdufOXMGjRo1gomJCapUqYJ169aVUlLtV5hjsX//fnTs2BFly5aFlZUVvLy88Oeff5ZiWu1X2J+N1wIDA2FgYID69euXbEAdUthjkZWVhVmzZsHV1RXGxsZwc3PD5s2bSymtdivssdi5cyfq1asHMzMzODo6YuTIkUhISCiltNrr7Nmz6NGjB5ycnCCTyXDw4MF37lMs52+hZXbv3i0MDQ3Fhg0bRGhoqPjkk0+Eubm5iIiIyHf7R48eCTMzM/HJJ5+I0NBQsWHDBmFoaCj27t1bysm1T2GPxSeffCJ++OEHceXKFREWFiZmzpwpDA0NxbVr10o5uXYq7PF4LSkpSVSpUkV06tRJ1KtXr3TCarmiHIuePXuKpk2bioCAABEeHi4uX74sAgMDSzG1dirssTh37pzQ09MTy5cvF48ePRLnzp0TtWrVEr179y7l5NrnyJEjYtasWWLfvn0CgDhw4MBbty+u87fWFUJNmjQRvr6+udpq1KghZsyYke/2n3/+uahRo0auto8//lg0a9asxDLqisIei/zUrFlTzJ07t7ij6aSiHo+BAweKr776SsyZM4eFUDEp7LE4evSosLa2FgkJCaURT6cU9lgsWrRIVKlSJVfbihUrhLOzc4ll1EUFKYSK6/ytVZfG5HI5goOD0alTp1ztnTp1woULF/Ld5+LFi3m279y5M4KCgpCdnV1iWbVdUY7FfymVSqSmphb7Anu6qKjHY8uWLXj48CHmzJlT0hF1RlGOxaFDh+Dp6YmFCxeiQoUKcHd3x6effoqMjIzSiKy1inIsmjdvjqdPn+LIkSMQQuDZs2fYu3cvunfvXhqR6V+K6/wt+czSxSk+Ph4KhSLP6vUODg55Vq1/LTY2Nt/tc3JyEB8fD0dHxxLLq82Kciz+68cff0RaWhoGDBhQEhF1SlGOx/379zFjxgycO3cOBgZa9atCUkU5Fo8ePcL58+dhYmKCAwcOID4+HhMmTEBiYiLHCb2HohyL5s2bY+fOnRg4cCAyMzORk5ODnj17YuXKlaURmf6luM7fWtUj9JpMJsv1WAiRp+1d2+fXToVX2GPx2q5du/DNN99gz549KFeuXEnF0zkFPR4KhQKDBw/G3Llz4e7uXlrxdEphfjaUSiVkMhl27tyJJk2aoFu3bliyZAm2bt3KXqFiUJhjERoaiilTpmD27NkIDg7GsWPHEB4erlosnEpXcZy/terPPHt7e+jr6+ep5OPi4vJUja+VL18+3+0NDAxgZ2dXYlm1XVGOxWt79uzB6NGj8euvv6JDhw4lGVNnFPZ4pKamIigoCCEhIZg0aRKAVydjIQQMDAxw/PhxtGvXrlSya5ui/Gw4OjqiQoUKsLa2VrV5eHhACIGnT5+iWrVqJZpZWxXlWCxYsADe3t747LPPAAB169aFubk5WrZsie+++45XEUpRcZ2/tapHyMjICI0aNUJAQECu9oCAADRv3jzffby8vPJsf/z4cXh6esLQ0LDEsmq7ohwL4FVP0IgRI+Dv789r7sWosMfDysoKt27dwvXr11Vfvr6+qF69Oq5fv46mTZuWVnStU5SfDW9vb0RHR+Ply5eqtrCwMOjp6cHZ2blE82qzohyL9PR06OnlPnXq6+sD+Kc3gkpHsZ2/CzW0WgO8vhVy06ZNIjQ0VEydOlWYm5uLx48fCyGEmDFjhhg6dKhq+9e33/n5+YnQ0FCxadMm3j5fTAp7LPz9/YWBgYFYvXq1iImJUX0lJSVJ9RG0SmGPx3/xrrHiU9hjkZqaKpydncWHH34o7ty5I86cOSOqVasmxowZI9VH0BqFPRZbtmwRBgYGYs2aNeLhw4fi/PnzwtPTUzRp0kSqj6A1UlNTRUhIiAgJCREAxJIlS0RISIhqKoOSOn9rXSEkhBCrV68Wrq6uwsjISDRs2FCcOXNG9dzw4cNF69atc21/+vRp0aBBA2FkZCQqVaok1q5dW8qJtVdhjkXr1q0FgDxfw4cPL/3gWqqwPxv/xkKoeBX2WNy9e1d06NBBmJqaCmdnZzFt2jSRnp5eyqm1U2GPxYoVK0TNmjWFqampcHR0FD4+PuLp06elnFr7nDp16q3ngJI6f8uEYF8eERER6SatGiNEREREVBgshIiIiEhnsRAiIiIincVCiIiIiHQWCyEiIiLSWSyEiIiISGexECIiIiKdxUKIiHLZunUrbGxspI5RZJUqVcKyZcveus0333yD+vXrl0oeIlJvLISItNCIESMgk8nyfD148EDqaNi6dWuuTI6OjhgwYADCw8OL5fWvXr2KcePGqR7LZDIcPHgw1zaffvopTpw4USzv9yb//ZwODg7o0aMH7ty5U+jX0eTClEjdsRAi0lJdunRBTExMrq/KlStLHQvAq0VdY2JiEB0dDX9/f1y/fh09e/aEQqF479cuW7YszMzM3rqNhYVFoVanLqp/f84//vgDaWlp6N69O+RyeYm/NxEVDAshIi1lbGyM8uXL5/rS19fHkiVLUKdOHZibm8PFxQUTJkzItar5f924cQNt27aFpaUlrKys0KhRIwQFBamev3DhAlq1agVTU1O4uLhgypQpSEtLe2s2mUyG8uXLw9HREW3btsWcOXNw+/ZtVY/V2rVr4ebmBiMjI1SvXh3bt2/Ptf8333yDihUrwtjYGE5OTpgyZYrquX9fGqtUqRIAoE+fPpDJZKrH/7409ueff8LExARJSUm53mPKlClo3bp1sX1OT09P+Pn5ISIiAn///bdqm7cdj9OnT2PkyJFITk5W9Sx98803AAC5XI7PP/8cFSpUgLm5OZo2bYrTp0+/NQ8R5cVCiEjH6OnpYcWKFbh9+za2bduGkydP4vPPP3/j9j4+PnB2dsbVq1cRHByMGTNmwNDQEABw69YtdO7cGX379sXNmzexZ88enD9/HpMmTSpUJlNTUwBAdnY2Dhw4gE8++QTTp0/H7du38fHHH2PkyJE4deoUAGDv3r1YunQp1q9fj/v37+PgwYOoU6dOvq979epVAMCWLVsQExOjevxvHTp0gI2NDfbt26dqUygU+OWXX+Dj41NsnzMpKQn+/v4AoPr+AW8/Hs2bN8eyZctUPUsxMTH49NNPAQAjR45EYGAgdu/ejZs3b6J///7o0qUL7t+/X+BMRARo5erzRLpu+PDhQl9fX5ibm6u+Pvzww3y3/eWXX4SdnZ3q8ZYtW4S1tbXqsaWlpdi6dWu++w4dOlSMGzcuV9u5c+eEnp6eyMjIyHef/77+kydPRLNmzYSzs7PIysoSzZs3F2PHjs21T//+/UW3bt2EEEL8+OOPwt3dXcjl8nxf39XVVSxdulT1GIA4cOBArm3mzJkj6tWrp3o8ZcoU0a5dO9XjP//8UxgZGYnExMT3+pwAhLm5uTAzM1OtpN2zZ898t3/tXcdDCCEePHggZDKZiIqKytXevn17MXPmzLe+PhHlZiBtGUZEJaVt27ZYu3at6rG5uTkA4NSpU/j+++8RGhqKlJQU5OTkIDMzE2lpaapt/m3atGkYM2YMtm/fjg4dOqB///5wc3MDAAQHB+PBgwfYuXOnanshBJRKJcLDw+Hh4ZFvtuTkZFhYWEAIgfT0dDRs2BD79++HkZER7t69m2uwMwB4e3tj+fLlAID+/ftj2bJlqFKlCrp06YJu3bqhR48eMDAo+q8zHx8feHl5ITo6Gk5OTti5cye6desGW1vb9/qclpaWuHbtGnJycnDmzBksWrQI69aty7VNYY8HAFy7dg1CCLi7u+dqz8rKKpWxT0TahIUQkZYyNzdH1apVc7VFRESgW7du8PX1xbx581CmTBmcP38eo0ePRnZ2dr6v880332Dw4MH4448/cPToUcyZMwe7d+9Gnz59oFQq8fHHH+cao/NaxYoV35jtdYGgp6cHBweHPCd8mUyW67EQQtXm4uKCv//+GwEBAfjrr78wYcIELFq0CGfOnMl1yakwmjRpAjc3N+zevRvjx4/HgQMHsGXLFtXzRf2cenp6qmNQo0YNxMbGYuDAgTh79iyAoh2P13n09fURHBwMfX39XM9ZWFgU6rMT6ToWQkQ6JCgoCDk5Ofjxxx+hp/dqiOAvv/zyzv3c3d3h7u4OPz8/fPTRR9iyZQv69OmDhg0b4s6dO3kKrnf5d4HwXx4eHjh//jyGDRumartw4UKuXhdTU1P07NkTPXv2xMSJE1GjRg3cunULDRs2zPN6hoaGBbobbfDgwdi5cyecnZ2hp6eH7t27q54r6uf8Lz8/PyxZsgQHDhxAnz59CnQ8jIyM8uRv0KABFAoF4uLi0LJly/fKRKTrOFiaSIe4ubkhJycHK1euxKNHj7B9+/Y8l2r+LSMjA5MmTcLp06cRERGBwMBAXL16VVWUfPHFF7h48SImTpyI69ev4/79+zh06BAmT55c5IyfffYZtm7dinXr1uH+/ftYsmQJ9u/frxokvHXrVmzatAm3b99WfQZTU1O4urrm+3qVKlXCiRMnEBsbixcvXrzxfX18fHDt2jXMnz8fH374IUxMTFTPFdfntLKywpgxYzBnzhwIIQp0PCpVqoSXL1/ixIkTiI+PR3p6Otzd3eHj44Nhw4Zh//79CA8Px9WrV/HDDz/gyJEjhcpEpPOkHKBERCVj+PDholevXvk+t2TJEuHo6ChMTU1F586dxc8//ywAiBcvXgghcg/OzcrKEoMGDRIuLi7CyMhIODk5iUmTJuUaIHzlyhXRsWNHYWFhIczNzUXdunXF/Pnz35gtv8G//7VmzRpRpUoVYWhoKNzd3cXPP/+seu7AgQOiadOmwsrKSpibm4tmzZqJv/76S/X8fwdLHzp0SFStWlUYGBgIV1dXIUTewdKvNW7cWAAQJ0+ezPNccX3OiIgIYWBgIPbs2SOEePfxEEIIX19fYWdnJwCIOXPmCCGEkMvlYvbs2aJSpUrC0NBQlC9fXvTp00fcvHnzjZmIKC+ZEEJIW4oRERERSYOXxoiIiEhnsRAiIiIincVCiIiIiHQWCyEiIiLSWSyEiIiISGexECIiIiKdxUKIiIiIdBYLISIiItJZLISIiIhIZ7EQIiIiIp3FQoiIiIh0FgshIiIi0ln/B7NwXYC/kXjKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_roc_with_CI([[overall_preds1, overall_labels1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean_roc_range([[[overall_preds1, overall_labels1]]], f_type_list = ['EEG'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Cross Validation (with Scheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At 1-th Fold:\n",
      "[At Epoch300]: \t-- LastBatchLoss: 0.5575, LastBatchAcc: 0.5750, TestLoss: 0.6926, TestAcc: 0.5985, TestF1: 0.6162\n",
      "At 2-th Fold:\n",
      "[At Epoch300]: \t-- LastBatchLoss: 0.6423, LastBatchAcc: 0.6618, TestLoss: 1.0621, TestAcc: 0.6599, TestF1: 0.6767\n",
      "At 3-th Fold:\n",
      "[At Epoch300]: \t-- LastBatchLoss: 0.5378, LastBatchAcc: 0.7115, TestLoss: 0.5996, TestAcc: 0.6782, TestF1: 0.6571\n",
      "At 4-th Fold:\n",
      "[At Epoch300]: \t-- LastBatchLoss: 0.5938, LastBatchAcc: 0.7458, TestLoss: 0.6700, TestAcc: 0.6072, TestF1: 0.6270\n",
      "At 5-th Fold:\n",
      "[At Epoch300]: \t-- LastBatchLoss: 0.5852, LastBatchAcc: 0.7292, TestLoss: 0.6949, TestAcc: 0.5819, TestF1: 0.6237\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Test On Real Data With Cross Validation (With Scheduler)\n",
    "\n",
    "### Change for better performance\n",
    "- epochs :\n",
    "- n_translayer: number of transformer block\n",
    "- feat_choice : [WordEmbedding, EEG, EyeGaze]\n",
    "'''\n",
    "feat_choice = [0, 1, 1] # Use EEG and EyeGaze \n",
    "n_translayer = 1\n",
    "epochs = 300\n",
    "optimizer_type = 'Adam' # 'Adam' or 'SGD'\n",
    "avg_acc2, test_acc_list2, overall_cm2, overall_f12, (overall_preds2, overall_labels2) = k_fold_CV(k_fold = 5, \n",
    "                              batch_size = 40, epochs = epochs,\n",
    "                              data = original_data, downsample = True,\n",
    "                              n_trans = n_translayer, feat_choice = feat_choice,\n",
    "                              metrics = [total_loss, relevance_accuracy], \n",
    "                              opt_type = optimizer_type,\n",
    "                              lr = 0.05, min_lr = 1e-5,\n",
    "                              MultiHeaded = True, num_heads = 3, device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6251458168029785"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_acc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6382022471910113"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_f12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Visualize the overall confusion matrix from the CV\n",
    "'''\n",
    "plot_cm(overall_cm2, 'Overall Confusion Matrix in Cross Validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Plot ROC-AUC\n",
    "'''\n",
    "plot_roc_auc(overall_labels2, overall_preds2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "----\n",
    "### ROC-AUC for All Subjects\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stats4ROC(feat_choice = [0, 1, 1], n_subs = 9,\n",
    "                  epochs = 500, optimizer_type = 'Adam', n_translayer = 1, k = 5):\n",
    "    # Loop Through each Subject:\n",
    "    avg_acc_list = []\n",
    "    cm_list = []\n",
    "    f1_list = []\n",
    "    pred_label_list = []\n",
    "    \n",
    "    # Get Device\n",
    "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "    \n",
    "    for idx in range(n_subs):\n",
    "        # Load data\n",
    "        original_data = load_data(downsample = False, subIdx = idx)\n",
    "        #embeddings, eeg_features, gaze_features, labels, sen_len = original_data\n",
    "    \n",
    "        \n",
    "        avg_acc, test_acc_list, overall_cm, overall_f1, pred_label = k_fold_CV(k_fold = k, \n",
    "                                      batch_size = 16, epochs = epochs,\n",
    "                                      data = original_data, downsample = True,\n",
    "                                      n_trans = n_translayer, feat_choice = feat_choice,\n",
    "                                      metrics = [total_loss, relevance_accuracy], \n",
    "                                      opt_type = optimizer_type,                                                                     \n",
    "                                      lr = 0.05,\n",
    "                                      MultiHeaded = True, num_heads = 3, device = device)\n",
    "        avg_acc_list.append(avg_acc)\n",
    "        cm_list.append(overall_cm)\n",
    "        f1_list.append(overall_f1)\n",
    "        pred_label_list.append(pred_label)\n",
    "\n",
    "    return avg_acc_list, cm_list, f1_list, pred_label_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At 1-th Fold:\n",
      "[At Epoch20]: \t-- LastBatchLoss: 1.3872, LastBatchAcc: 0.7647, TestLoss: 1.3446, TestAcc: 0.5038, TestF1: 0.0000\n",
      "At 2-th Fold:\n",
      "[At Epoch20]: \t-- LastBatchLoss: 1.3072, LastBatchAcc: 0.6667, TestLoss: 1.3186, TestAcc: 0.6306, TestF1: 0.6799\n"
     ]
    }
   ],
   "source": [
    "# feat_choice = [WordEmbedding, EEG, EyeGaze]\n",
    "f_choice1 = [0, 1, 0] # EEG only\n",
    "avg_acc_list1, cm_list1, f1_list1, pred_label_list1 = get_stats4ROC(f_choice1, n_subs = 9,\n",
    "                                                                   epochs = 500, optimizer_type = 'Adam', n_translayer = 1, k = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHFCAYAAAAe+pb9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB5PElEQVR4nO3dd1hT1x8G8DeEvRUUQQQURXEPHIi4J9bd1lbck1qrotVqbR211l/VVrR11b21VbHVuqh7VYviHjhQVEAEFJAVktzfH9RoCirBhBuS9/M8PHLOvUm+8Sp5uffccySCIAggIiIiMkImYhdAREREJBYGISIiIjJaDEJERERktBiEiIiIyGgxCBEREZHRYhAiIiIio8UgREREREaLQYiIiIiMFoMQERERGS0GISLSO2vWrIFEInnt15EjRwAAXl5er92nZcuW+Z730qVLGDJkCLy9vWFlZQUrKytUqVIFI0aMQGRkZPG+SSLSC6ZiF0BE9DqrV69GtWrV8vVXr15d9X1AQADmzZuXbx97e3u19rJlyzBq1ChUrVoVY8aMQY0aNSCRSHD9+nVs3rwZDRs2xO3bt+Ht7a39N0JEeotBiIj0Vs2aNeHn5/fGfRwdHdGkSZM37nPy5EmMHDkSnTt3xrZt22Bubq7a1rp1a3z66af47bffYGVlpZW6iajkYBAiIoP33XffQSqVYtmyZWoh6FUffPBBMVdFRPqAQYiI9JZCoYBcLlfrk0gkkEqlqrYgCPn2AQCpVAqJRAKFQoHDhw/Dz88Prq6uOq+ZiEoWDpYmIr3VpEkTmJmZqX1ZWFio7bNnz558+5iZmWHWrFkAgKSkJGRlZcHT0zPf878IWi++BEEolvdFRPqDZ4SISG+tW7cOvr6+an0SiUSt3axZM8yfPz/fY8uXL//W52/QoAEuXryoas+dOxeff/55EaslopKIQYiI9Javr+9bB0s7ODi8cR9nZ2dYWVnh/v37+bZt2rQJmZmZiI+PR9euXd+5XiIqeRiEiMigSaVStG7dGgcOHEB8fLzaOKEXt+Hfu3dPpOqISGwcI0REBm/y5MlQKBQICQlBbm6u2OUQkR7hGSEi0ltXrlwp8I4wb29vlClTBgDw7Nkz/P333/n2sbCwQL169QDkTbq4aNEifPbZZ6hfvz6GDx+OGjVqwMTEBPHx8di+fTuA/JMwEpHhkwi8TYKI9MyaNWswaNCg125fvnw5hg4dCi8vrwLH/gB5g6UfPnyo1nfx4kUsWLAAR44cQVxcHCQSCdzd3dG0aVMMGDAArVu31ur7ICL9xyBERERERotjhIiIiMhoMQgRERGR0WIQIiIiIqPFIERERERGi0GIiIiIjBaDEBERERkto5tQUalUIi4uDnZ2dvkWbyQiIiL9JAgC0tPT4ebmBhMT7Z3HMbogFBcXhwoVKohdBhERERXBgwcP4O7urrXnM7ogZGdnByDvL5LT6RMREZUMaWlpqFChgupzXFuMLgi9uBxmb2/PIERERFTCaHtYCwdLExERkdFiECIiIiKjxSBERERERotBiIiIiIwWgxAREREZLQYhIiIiMloMQkRERGS0GISIiIjIaDEIERERkdFiECIiIiKjJWoQOnbsGLp06QI3NzdIJBLs3LnzrY85evQoGjRoAEtLS1SqVAlLly7VfaFERERkkEQNQhkZGahTpw5+/vnnQu0fExODoKAgBAYGIioqCl9++SVGjx6N7du367hSIiIiMkSiLrraqVMndOrUqdD7L126FB4eHggLCwMA+Pr6IjIyEvPmzUOvXr10VCUREREZqhK1+vzp06fRvn17tb4OHTpg5cqVyM3NhZmZmUiVERERkbalZuXi1uN03HnyHIePRuvkNUpUEEpISICLi4tan4uLC+RyOZKSkuDq6prvMTk5OcjJyVG109LSdF4nERERaS4jR45/7qXg1J1knLqThKtxaVBky5ESEYuMq3E6ec0SFYQAQCKRqLUFQSiw/4XZs2djxowZOq+LiIiINKNUCvjnXgpO3k7CqTvJuPDgGeRKQbU9+2E6knbHQJEq01kNJSoIlStXDgkJCWp9iYmJMDU1hZOTU4GPmTx5MsaNG6dqp6WloUKFCjqtk4iIyJgplQIycxXIyJEjI0eOTJkCz3PkyJTJ8TxHgcwcORLTc7D9/EPcT84s8DkEuRKpf95ThSBrG3NkZmi/1hIVhPz9/bFr1y61vgMHDsDPz++144MsLCxgYWFRHOURERGVWAqlgPjULGTkvAwteUFGgQzZv3/myP/9Xo4MWV47M+c/IUeWF3yKolIZGzT1dkJTb2c0qeSEyPbV0aHDBgQEVMCSJW1Ru/Y3Wn7XIgeh58+f4/bt26p2TEwMLly4gNKlS8PDwwOTJ0/Go0ePsG7dOgBASEgIfv75Z4wbNw7Dhg3D6dOnsXLlSmzevFmst0BERFSi5CqUOHM3BZkyOQQA+68mIFch4PSdZCQ9z3nr47UtsIozutctD3/v0ihlYQYrq5cnNtq398b+/X3RunVFZGY+18nrixqEIiMj0apVK1X7xSWsAQMGYM2aNYiPj0dsbKxqe8WKFbFnzx6EhoZi0aJFcHNzw8KFC3nrPBER0VvkKpTYfDYWU3+/qvXnNpNKYGNhChtzU9hYSGFtbgpbC1NYm0vz/rSQvrLdFDbmee2a5R1Q0dkGKSlZCBm+G1lZcvzxx0dq437bt/fWer2vkggvRhsbibS0NDg4OCA1NRX29vZil0NERKRzgiCgy88ncOXRm++cDqziDDcHq7ywogoveX+qwo2FVBVyXgQbc9Oiz898+HAM+vULx6NH6QCAxYuD8MknDfPtp6vP7xI1RoiIiIhe78UYnlfdSczApYfP8oUgHxdbdK9XHgBgLjVB1zpuKGtvWWy1ymQKfPXVIcybdwovTsmUKmWJcuVsi60GgEGIiIioxHmWKcOWfx5g3v6b8HCyhqmJBNGPCz+GZuvwJmhcqeC7rYvDjRtJ6NNnO6KiXt4J3rp1Raxd2x3u7sV7tYZBiIiIqITIkSvwa+RDfL3ziqrv7hPN7ikf385HtBAkCAKWLTuHceP2Iysr78yVmZkJZs9ug9BQf5iYFDwnoC4xCBEREemp/VcTMOOPq3iWlQsAb7wt3dpciuxcBZQC0L66+ioMD59m4UM/d7g6WqF1tbI6rfl1cnLk+OCD37Br18ulMnx9nbFxY0/Uq5d/ZYjiwiBERESkh5Kf52DE+nNv3KesnQV+HxUAVwerYqqq6CwsTGFn93Jev5Ej/TB3bntYW4u7TiiDEBERkR5q8O1fam0fl7xBxHKlgNLW5lgcXL9YBzdrw6JFQbh1KxlTp7bAe+/5iF0OAAYhIiIiUd1MSMf+qwkwlb4cH/PnpXi1fVr4lMHawY2Ku7R3cunSY8TFpaNjx8qqPkdHS5w5M/S164OKgUGIiIhIR55myFBvZgQsTE1gUcBcO2nZ8gIelV9JCkFKpYAFC/7GpEkHYWNjhkuXPlG7E0yfQhAAFH0GJCIiInqtNSdjUG9mBAAgR65EWrY831dhfN+rli7L1Kq8M0AbMG7cAchkCjx9mo3vvjsudllvxDNCREREWrbrYhym77qWr79SGZt8fenZcuTkKvBxYw808Cil6i9lY4467o7vNGtzcdq58waGDv0DyclZqr7x4/0xa1ZrEat6OwYhIiIiLdvyT6xae2RLb0zsWE2kanQrI0OG0ND9WL78vKrP1dUW69b1QNu2lUSsrHAYhIiIiLTkQUomVp6Iwcnbyaq+3Z81Q83yDiJWpTuRkXEIDt6B6OiX77dHj2pYvrwLnJysRays8BiEiIiINKRUCsiRKwEAX/9+BX/fTcbDp1n59pNIgKrl7Iq7vGKRnS1H166bER+ft7SHtbUZFi7siMGD6+ndgOg3YRAiIiLSwM2EdAxafRZxqdlv3bdH3fIwk5aMMT6asrQ0xeLFndGjx1Y0bOiGjRt7okoV8dYvKyoGISIiIg30Wf43kjNkBW4zkQBKAfikpTc+aOCOSmWKdyV1XZPJFDA3l6ra3btXQ3h4b3TuXAVmZtI3PFJ/MQgRERFpIP2V296dbMzh42KHHLkCm4Y1gWUJDQNvk5qajVGj9iInR46tW99Xu/TVvXvJHgTOIERERPQWcoUSwr/fyxRKVf+5r9uJU1AxOnkyFn37huPevWcAgM6dL2LAgLqi1qRNDEJERET/cfhmIsIiomFhJsXZmJQC93Evpf8Lnb6L3FwFZs48hlmzjkOpzIuB9vYWsLQ0rOhgWO+GiIjoHe29HI9PNp5/637mBjoIGgBu305B3747cObMI1VfQEAFbNjQE15ejuIVpgMMQkRERK94Uwhq5FUaOf9eGtvxSdPiKqnYCIKANWsu4LPP9iIjIxcAIJVKMH16S0ya1AymJWSWa00wCBEREf3rxSWgF7YOb4J6HqVgaiKBiUnJmRunKLKz5ejXLxzbtr1cGsTbuxQ2buyJxo3dRaxMtxiEiIiI/nUkOlGt3bhSyZsXp6gsLKTIzVWo2kOG1ENYWEfY2pqLWJXuGd45LiIioiIQBAGD10Sq2r6u9iJWU/wkEglWrOiKGjXKYNu2D7BiRVeDD0EAzwgREREBAH6/EKfWnt6lukiVFI8bN5Lw+PFztGjhpepzdrbGpUufGPxlwFfxjBARERm9yHspGLv1glqfoV4WEwQBS5dGon79Zfjww214/Pi52nZjCkEAgxARERHCox6ptQ+ObyFSJbqVmJiBbt224JNP/kRWlhyJiRmYOfOY2GWJipfGiIjIaGXkyHH3SQY2nolV9fWsXx7eBrZGGADs3XsLgwb9jsePM1R9n37aEHPmGP7s2G/CIEREREbpr2uPMXRdZL7+qe8Z1tigrKxcfPHFX/jpp7OqvrJlbbBqVVd07uwjYmX6gUGIiIiMTkJqdoEhyNPJGo7WhnOn1MWLCQgO3oGrV5+o+oKCqmDVqq5wcTG8s15FwSBERERGJTE9G01mH1Tr83GxRT9/L3zoZzgTB2Zl5aJ9+w1ITMy7FGZpaYp589ph5MiGaqvHGzsGISIiMhqCIKDRLPUQVK2cHfaNbS5SRbpjZWWG+fM7IDh4B+rUccGmTb1QvXoZscvSOwxCRERk8BJSs3E9Pg1RsU/V+rvUccNPH9cTqSrtUyiUkL6yGGyfPrUgCALef786LCz4kV8Q/q0QEZHBupmQjg5hr7893FBCUEaGDKGh+5Gbq8Tq1d3UtgUH1xapqpKBQYiIiAxSrkL5xhC0bnCjYqxGdyIj4xAcvAPR0ckAgKCgyvjggxoiV1VyMAgREZHBOR/7FD0Xn8rXP6RZRThYmcHX1R7NKjuLUJn2KBRKzJlzElOnHoFcrgQAWFubISdH8ZZH0qsYhIiIyGDsuxKPIzefYMs/D9T6q5Wzw94xgQZzt1RsbCr69QvHsWP3VX1+fm7YuLEnfHwMc2kQXWEQIiKiEkuuUOKTjeeRlpWLMzEpr93v91EBBhOCtmy5gpCQ3UhNzQEASCTAl18GYtq0FjAzk4pcXcnDIERERCWSTK6Ez1d737jPqFaVMb69j0GEoKysXIwYsRvr119S9Xl4OGDDhh4IDPQUsbKSjUGIiIhKlBy5AlkyBerPjHjtPsv7+6G6mz3KO1oVY2W6ZWFhqrZOWJ8+tbBoURAcHS1FrKrkYxAiIqISY9fFOEzafgkZsvwDgq990wEAYG1umB9tJiYSrFnTDYGBqzFjRkveFq8lhvmvhYiIDIogCJj+x1WsPX2/wO2RX7U1uAB0+3YKkpMz0bjxy2U/XF3tcOPGKJiamrzhkaQJw/pXQ0REBiUtOxcrjt3FwkO3822rWd4e5eyt8HOferA0oEHCgiBgzZoL+OyzvXB0tMSlS5+gdOmXl/gYgrSLQYiIiPTWpjOxBYagAf6emNGtpggV6VZKShZGjNiNbduuAQAyMnIxY8YRLFjQSeTKDBeDEBER6aX07FzM3X8zX/8/U9qijJ2FCBXp1uHDMejXLxyPHqWr+oYMqYdZs9qIWJXhYxAiIiK9ND/iFhRKQdVeP6QRAqsY3urpMpkCX311CPPmnYLw79stVcoSy5d3Qa9e1cUtzggwCBERkV66/eS56nsLUxM09CotYjW6ceNGEvr02Y6oqARVX+vWFbF2bXe4u9uLWJnxYBAiIiK9o1QKOBb9RNU+MqGlQQ2IBoDMzFw0b74aT55kAgDMzEwwe3YbhIb6w8Sk5E8AWVJw6DkREemdSl/uUWs72RjemCBrazPMmtUaAODr64yzZ4dh/PimDEHFjGeEiIhIr2w791CtXau8A8wN5JZxQRDUlvsYOrQ+BAHo27c2rK3NRKzMeDEIERGR3rjyKBWf/3ZRrW/riCYiVaM9WVm5+OKLvyAIAn76KUjVL5FIMHx4AxErIwYhIiLSG1cepaq1l/ZtUOJnjL54MQHBwTtw9WremKeOHSujc2cfkauiFwzjXCMREZV4zzJlmLTjsqr9caMK6FDDRcSK3o1SKWD+/NNo1GiFKgRZWpqqBkeTfijZMZuIiEq0lAwZRm+Owp0nzxGfmq22raFXabXxNCVJXFw6Bg7ciYiIu6q+OnVcsGlTL1SvbnhzIZVkDEJERFSscuQKyORK7L/6ON94oBeszaVoVbVsMVemHeHh1zFs2C4kJ2ep+saP98esWa1hYcGPXX3DI0JERMVm05lYfBl++Y379Krvjnkf1C5xZ4Oys+UYPXovli8/r+pzc7PD2rXd0bZtJRErozdhECIiomIxb/9N/Hw4/wKqAPB+A3dM6FAVLvaWxVyV9piZmeDGjSRVu0ePali+vAucnKxFrIrehkGIiIh04tGzLJy49QQmEgl2nH+E03eT1bYHVnHG3ScZWD2oIXxc7ESqUnukUhOsX98DAQGrMGNGSwweXK/EndUyRgxCRESkVdGP09Fr8Smk58hfu48hLKB6//4zPH2ajbp1y6n6PD0dcefOaI4FKkF4+zwREWlV+/nH3hiCjnzessSHoM2bL6NOnaXo2XMr0tJy1LYxBJUsPFpERKSxXIUSNxPScTYmBS+Wxjp7LwV7Lifk23ds2yooY2cBMxMTtKpWFmXsSu66Yamp2Rg1ai82bLj0bzsHM2YcwQ8/dBC5Mioq0YPQ4sWLMXfuXMTHx6NGjRoICwtDYGDga/ffuHEj5syZg1u3bsHBwQEdO3bEvHnz4OTkVIxVExEZF5lcidSsXMiVSgT87xCUQuEed+9/nXVbWDE6eTIWffuG4969Z6q+Pn1qYerUFuIVRe9M1CC0detWjB07FosXL0ZAQACWLVuGTp064dq1a/Dw8Mi3/4kTJ9C/f3/Mnz8fXbp0waNHjxASEoKhQ4ciPDxchHdARGTYBEHA4DX/4PDNJxo/NvKrtjqoqPjl5iowc+YxzJp1HMp/E6C9vQUWLw5CcHBtkaujdyURBKGQuV77GjdujPr162PJkiWqPl9fX3Tv3h2zZ8/Ot/+8efOwZMkS3LlzR9X3008/Yc6cOXjw4EGhXjMtLQ0ODg5ITU2Fvb39u78JIiIDJAgCfot8iInbL71xP/9KTqjmaoe6FRwBAGZSE7SsWqbErw/2wp07KQgO3oEzZx6p+po188D69T3g5eUoXmFGSFef36L9S5XJZDh37hwmTZqk1t++fXucOnWqwMc0bdoUU6ZMwZ49e9CpUyckJiZi27Zt6Nz59adec3JykJPzciBbWlqadt4AEZGB2nclASEbzhW4rXW1sjCRAFVc7DCxQ1WDvj08I0OGJk1WIikpb20wqVSCGTNaYtKkZpBKea+RoRAtCCUlJUGhUMDFRX1BPRcXFyQk5B9sB+QFoY0bN6J3797Izs6GXC5H165d8dNPP732dWbPno0ZM2ZotXYiIkMjkytxLPoJVpy4i7/vphS4z6HxLVCpjG0xVyYeGxtzfPVVIMaO3Q9v71LYtKkXGjUqL3ZZpGWin7v8728TgiC89jeMa9euYfTo0Zg6dSo6dOiA+Ph4TJgwASEhIVi5cmWBj5k8eTLGjRunaqelpaFChQraewNERCXUufspCI96hM1nH0DxhtHP+8c2R5WytjAxMdyzPy/89zPos88aQ6kUMGxYA9jamotYGemKaEHI2dkZUqk039mfxMTEfGeJXpg9ezYCAgIwYcIEAEDt2rVhY2ODwMBAfPvtt3B1dc33GAsLC1hYlNxbNYmItE2pFDBk7dsHQK8e2BCtqpXMhU81JZMp8NVXh2BiIsH//vdykLeJiQShof4iVka6JloQMjc3R4MGDRAREYEePXqo+iMiItCtW7cCH5OZmQlTU/WSpVIpgLwUT0REb3Y1LhWdF5547fZmlZ3R1rcsPmrkAUszaTFWJp7r158gOHgHoqISIJEAHTp4o1WrimKXRcVE1Etj48aNQ79+/eDn5wd/f3/88ssviI2NRUhICIC8y1qPHj3CunXrAABdunTBsGHDsGTJEtWlsbFjx6JRo0Zwc3MT860QEemt8KiHCN168bXbv+1eE/U8HFHd1d6gBz//lyAIWLo0EuPHH0BWVt5M2KamJrhz5ymDkBERNQj17t0bycnJ+OabbxAfH4+aNWtiz5498PT0BADEx8cjNjZWtf/AgQORnp6On3/+GePHj4ejoyNat26N77//Xqy3QESk15YcuYPv990ocFtpG3NEhDaHk63xDR9ITMzAkCF/YPfuaFWfr68zNm3qpbZ2GBk+UecREgPnESIiY7D/agJGrM9/C7y5qQlkciV+7lMP79U2zjPpe/fewsCBvyMxMUPVN3KkH+bObQ9razMRK6M3Mbh5hIiISDduJKQVGIJ2jWqGWu4OIlSkH7Kz5Zg4MQI//XRW1VemjDVWreqG997zEbEyEhODEBGRATl8IxGD1vyj1mdvaYrfQpqiajk7karSD1KpBH///VDVDgqqglWrusLFxXjmRqL8GISIiAzAX9ce49itJ1h3+r5a/8zuNdGviadIVekXMzMpNm7siaZNV2H69BYYObKhUQ0Op4IxCBERlWCCIKDi5D0FbmtW2RkfNTTeCWTj4tKRmpoNX98yqr4qVZxw794Y2NhwckTKwyBERFSCDVtX8JpgP3xQB70auBdzNfojPPw6hg3bhbJlbRAZOVxtEDRDEL2KQYiIqIQRBAEKpQDfqfuQq1C/8Xdp3/qo7uoADydrkaoTV0aGDKGh+7F8+XkAQHJyFr755qjabNFEr2IQIiIqIS4/TEWXn18/K/Rf41qgclnjHfgbGRmH4OAdiI5OVvX16FENEyY0FbEq0ncMQkREJcDyY3cxa8/1126/OK09HKyMcw4chUKJOXNOYurUI5DLlQAAa2szLFzYEYMH1+OAaHojBiEiIj0WFfsUX2y/hOjHz/Ntq+fhCCszKX76uJ7RhqDY2FT06xeOY8de3i3XsKEbNm7siSpVnESsjEoKBiEiIj3WY/GpfH3L+/uhXXUXEarRL+npOfDz+wVPnmQCACQS4MsvAzFtWguYGcmCsfTuTMQugIiICpaenZuv749RAQxB/7Kzs8DYsU0AAB4eDjh6dCC+/bY1QxBphGeEiIj0VKZModa+97/OIlWiv774IgBKpYBRoxrB0dFS7HKoBGIQIiLSUykZMtX3HWsY94rocrkSM2cehampCb7+uoWqXyo1wVdfNRexMirpGISIiPRU90UnVd9nyOQiViKuO3dSEBy8A2fOPIKJiQRt21aCv7/xzphN2sUgREQkEqVSQNLzHDx6loW4Z9mIT8369/ssnI99hpx/bwUHAH9v47sDShAErF17EZ99thfPn+edHZNIgIsXHzMIkdYwCBGR0Xj4NBOn7iTDRIR5ZXLkCiSkZquCzovg89+ZoV9naLNKOq5Qv6SkZGHEiN3Ytu2aqs/buxQ2buyJxo2Nd+kQ0j4GISIyCmfuJqP3L3+LXUahSU0kcLGzgIO1OdYObghzU+O5yffw4Rj06xeOR4/SVX1DhtRDWFhH2NpynTDSLgYhIjJo2bkKXI1L1bsQZG9pCjdHK5R3tIKb6stS1S5rZwFTqfGEHwCQyRT4+utDmDv3FIR/T5SVKmWJ5cu7oFev6uIWRwaLQYiIDFaOXAG/b//C8xz1gcb9/T3h42JXrLWYmkjg4pAXdFwdLGFnaZwzQb+JUilg797bqhDUunVFrF3bHe7u9uIWRgaNQYiIDJJSKaDa1/tUH6ovONmY45tuNcUpit7I0tIUmzb1QkDAKkyd2hyhof4wMeE6YaRbDEJEZFAEQcCgNf/gyM0n+bZ936sWetXnQFt9kZiYgfT0HHh7l1b11axZFvfvj+XkiFRsGISIyGAkP89Bg2//KnDblRkdYGvBH3n6Yu/eWxg48He4udnh77+HwOKVY8MQRMXJuEbiEZFBSnqeg8sPUwsMQQP8PXHvf50ZgvREVlYuRo/ei6CgTUhMzMCFCwmYNeu42GWREeNPBiIqcQRBQFqWHCmZMozdEoWLD1ML3O/2rE5Gd+eVPrt4MQHBwTtw9erLy5ZBQVXw6acNRayKjB2DEBGVCEejn2DGrquQSiS4lfj8rftHf8sQpC+USgELFvyNSZMOQvbvQrKWlqaYN68dRo5sCIkIE1wSvcAgRER66XZiOg7dSER6thw/HbpdqMe08CmDBp6lMLhZRaOagFCfxcWlY8CAnfjrr7uqvjp1XLBpUy9Ur15GxMqI8jAIEZHeyZTJ0WnB8bcuP1GlrC1quTugV313BFR2LqbqqLBSU7NRt+5SPHmSqeobP94fs2a1VhscTSQm/kskIr0iCAKGro18bQja/klTNPAsVcxVUVE4OFhi+PAGmDXrONzc7LB2bXe0bWtca6aR/mMQIiK9svJEDE7dSVa1Lc1MENa7LupUcISrg5WIlVFRTJvWAkqlgPHj/eHkZC12OUT5FCkIyeVyHDlyBHfu3EGfPn1gZ2eHuLg42Nvbw9bWVts1EpEReJyWjeZzDiNHrlTrP/J5K5Rz4Lwy+k6hUGLOnJOwtDRFaKi/qt/MTIrvvmsjYmVEb6ZxELp//z46duyI2NhY5OTkoF27drCzs8OcOXOQnZ2NpUuX6qJOIjIwz3Pk+G7PdaQ8l0EiAfZeSci3z7EJDEElQWxsKvr1C8exY/dhZmaCli29UK+eq9hlERWKxkFozJgx8PPzw8WLF+Hk5KTq79GjB4YOHarV4ojIcNxISEN41COsO3UfWbmKt+7/XY9a8OClFL23ZcsVhITsRmpqDgBALlfi1KkHDEJUYmgchE6cOIGTJ0/C3Nxcrd/T0xOPHj3SWmFEZDg2nrmPKeFXCrXv9C7VMTCgoo4roneVlpaDUaP2YP36S6o+Dw8HbNjQA4GBniJWRqQZjYOQUqmEQpH/t7mHDx/Czs5OK0URkWGIT83C9D+uYv/Vx6/dp3tdN3zaqjLMpCYo52AJSzNpMVZIRXHyZCz69g3HvXvPVH19+tTCokVBXCeMShyNg1C7du0QFhaGX375BQAgkUjw/PlzTJs2DUFBQVovkIhKrmVH7+YLQcMCK2JgQEWUd+QdYCVNbq4CM2cew6xZx6FU5k1vYG9vgcWLgxAcXFvk6oiKRuMgNH/+fLRq1QrVq1dHdnY2+vTpg1u3bsHZ2RmbN2/WRY1EVAKF/RWNNafuqdp13B2wbkhjOFiZiVcUvROZTIGtW6+qQlCzZh5Yv74HvLwcxS2M6B1IBEF489StBcjKysKWLVtw7tw5KJVK1K9fH8HBwbCy0v/f8NLS0uDg4IDU1FTY29uLXQ6RwbiRkIalR+4gIS0bf99Nybf9yowOXAHeAERGxqF589WYMiUQkyY1g5TruVEx0dXnt8ZB6NixY2jatClMTdV/oMnlcpw6dQrNmzfXWnG6wCBEpH0PUjIROOfwa7f3beKBb7vXKsaKSBtSUrKQkSFDhQoOav2JiRkoW9ZGpKrIWOnq81vjX89atWqF+Ph4lC1bVq0/NTUVrVq1KnAgNREZtm3nHhbYLzWR4PSk1ihrzwG0Jc3hwzHo1y8cFSo44PjxQTB9ZRFbhiAyJBoHIUEQIJFI8vUnJyfDxob/OYiMze8XHmHBwVuq9gB/T0wO8oXURAIzXjYpcWQyBb766hDmzTsFQQAePUrH99+fwJQp+n22n6ioCh2EevbsCSDvLrGBAwfCwsJCtU2hUODSpUto2rSp9iskIr2lUAoYs+WCqm1lJsWApl68Bb6Eun79CYKDdyAq6uUs361bV8SAAXXFK4pIxwodhBwc8q4RC4IAOzs7tYHR5ubmaNKkCYYNG6b9ColIL6Vn56LW9ANqffvHNuds0CWQIAhYtuwcxo3bj6wsOQDAzMwE333XBuPG+cPEJP9VACJDUeggtHr1agCAl5cXPv/8c14GIzJyft/+pdYe3rwSQ1AJlJiYgaFD/8CuXdGqPl9fZ2zc2JPLZJBR0HiM0LRp03RRBxGVIM9z5GqrxDfyKo0JHaqKWBEVxbNn2ahTZykSEp6r+kaO9MPcue1hbc35nsg4FGlSj23btuHXX39FbGwsZDKZ2rbz589rpTAi0h8KpYBTd5Kw6kQMDt98km/7ryH+IlRF78rR0RIffVQDYWFnUKaMNVat6ob33vMRuyyiYqXxLR0LFy7EoEGDULZsWURFRaFRo0ZwcnLC3bt30alTJ13USEQiuhaXBu8v96DfyrMFhqBpXaqLUBVpy+zZbTF6dCNcvvwJQxAZJY0nVKxWrRqmTZuGjz/+GHZ2drh48SIqVaqEqVOnIiUlBT///LOuatUKTqhIpBmvSX++dtvYtlUwti0/PEsCpVLAggV/w8bGHMOHNxC7HCKN6c2EirGxsarb5K2srJCeng4A6NevH5o0aaL3QYiINGNrYYrnOXl3Evm62mNUq8poW70sLEx5i3xJEReXjoEDdyIi4i4sLU0RGOgBX98yYpdFpBc0vjRWrlw5JCcnAwA8PT3x999/AwBiYmJQhGXLiEjPvQhBZlIJ9o4JROfargxBJUh4+HXUrr0EERF3AQDZ2XLV90RUhDNCrVu3xq5du1C/fn0MGTIEoaGh2LZtGyIjI1WTLhKRYQj76+Ut1R6leWt8SZKRIUNo6H4sX/7yBhY3NzusXdsdbdtWErEyIv2icRD65ZdfoFTm3TYbEhKC0qVL48SJE+jSpQtCQkK0XiARieNmQjrC/nq5dEZqVq6I1ZAmIiPjEBy8A9HRyaq+Hj2qYfnyLnDiXE9EajQeLP0mjx49Qvny5bX1dDrBwdJEhVN96j5kyl4uonxhajs4WpuLWBG9jUKhxJw5JzF16hHI/53nydraDAsXdsTgwfUKXCeSqKTQ1ee3VlZETEhIwGeffYbKlStr4+mISCQKpYD7yRm48OCZWgj60M+dIagEyMjIxbJl51QhqGFDN1y4MAJDhtRnCCJ6jUJfGnv27Bk+/fRTHDhwAGZmZpg0aRJGjRqF6dOnY968eahRowZWrVqly1qJSEuUSgHp2XLcSkzH47QcHI1ORKZMgd2X4vPtay41wZz364hQJWnK3t4C69f3QJs26zBxYgCmTWsBMy6AS/RGhQ5CX375JY4dO4YBAwZg3759CA0Nxb59+5CdnY29e/eiRYsWuqyTiN7Bs0wZrsWlQQBw63E6pu+6VujH+ns76a4weidpaTnIzMxFuXK2qr7AQE/cuTMaFSo4iFgZUclR6CD0559/YvXq1Wjbti1GjhyJypUrw8fHB2FhYTosj4iKKi07F/MjovH33RRcj0/T6LE+LrZwsbdEQGVn9Pf31FGF9C5OnoxF377hqFjREX/91V9thXiGIKLCK3QQiouLQ/XqeVPpV6pUCZaWlhg6dKjOCiOiwkl6noOIa4/x4nPwwNXHuJX4HLEpmW99bA03e7SsWgal/h3/06SSEzycrGFvyQU39VVurgIzZx7DrFnHoVQKuHfvGebPP43x45uKXRpRiVToIKRUKmFm9vKHo1QqhY2NjU6KIqI3e5Ypw4a/72P7+UeIScoo1GO61nFTzQWUnatA59quqOdRSpdlkpbdvp2Cvn134MyZR6q+Zs080KsX13sjKqpCByFBEDBw4EBYWFgAALKzsxESEpIvDO3YsUO7FRJRPh3CjuFxWk6h9u1RrzzmfVAHUhPeNVRSCYKANWsu4LPP9iIjI28+J6lUghkzWmLSpGaQSrVyAzCRUSp0EBowYIBau2/fvlopYPHixZg7dy7i4+NRo0YNhIWFITAw8LX75+Tk4JtvvsGGDRuQkJAAd3d3TJkyBYMHD9ZKPUT6TBAEdFpwvMAQFNLCG57/TpZnZSZFw4ql4eZgydumS7iUlCyMGLEb27a9HODu7V0Kmzb1QqNG+j1vG1FJUOggtHr1aq2/+NatWzF27FgsXrwYAQEBWLZsGTp16oRr167Bw8OjwMd8+OGHePz4MVauXInKlSsjMTERcrlc67UR6ZPdl+Lw44Fo3C3gMtiB0OaoUtaWgccAPX2ahTp1luLhw5eD3YcMqYewsI6wteW8TkTaoNWZpTXVuHFj1K9fH0uWLFH1+fr6onv37pg9e3a+/fft24ePPvoId+/eRenSpYv0mpxZmkqS+8kZWHz4DrZGPihw+57Rgajuxn/HhmzEiF345ZfzKFXKEsuXd+F4IDJauvr81nitMW2RyWQ4d+4cJk2apNbfvn17nDp1qsDH/PHHH/Dz88OcOXOwfv162NjYoGvXrpg5cyasrKyKo2yiYnPqdhL6rDiTr79uBUe09S2LgQEVYWsh2n9hKiY//tgBCoWA6dNbwt2doZdI20T7KZqUlASFQgEXFxe1fhcXFyQkJBT4mLt37+LEiROwtLREeHg4kpKSMHLkSKSkpLx2VuucnBzk5LwcT5GWptl8KkTFRRAEhEc9woKDt3A/Of+t751qlsOMrjVQ1t5ShOpI1wRBwLJl52Bra46+fWur+m1szLFiRVcRKyMybKL/OvnfcQ2CILx2rINSqYREIsHGjRvh4JA3YdiPP/6I999/H4sWLSrwrNDs2bMxY8YM7RdOpGXzDtzEosN3Cty2aVhjNPV2LuaKqLgkJmZg6NA/sGtXNGxtzeHv7w5v76Jd/icizYh2z6WzszOkUmm+sz+JiYn5zhK94OrqivLly6tCEJA3pkgQBDx8+LDAx0yePBmpqamqrwcPCh5rQSQmuUKpFoKcbMxRydkG9pamWD+kEUOQAdu79xZq116CXbuiAQDPn8uwe3e0yFURGY8iBaH169cjICAAbm5uuH//PgAgLCwMv//+e6Gfw9zcHA0aNEBERIRaf0REBJo2LXiG1ICAAMTFxeH58+eqvujoaJiYmMDd3b3Ax1hYWMDe3l7ti0jfXI1Tv2Qb+VVbHPq8JS5N74DAKmVEqop0KSsrF6NH70VQ0CY8fpx3N2CZMtbYtetjjBnTROTqiIyHxkFoyZIlGDduHIKCgvDs2TMoFAoAgKOjo8brjo0bNw4rVqzAqlWrcP36dYSGhiI2NhYhISEA8s7m9O/fX7V/nz594OTkhEGDBuHatWs4duwYJkyYgMGDB3OwNJVYCanZGLv1gqrdulpZ3gpv4C5deoyGDZfjp5/OqvqCgqrg8uVP8N57PiJWRmR8NA5CP/30E5YvX44pU6ZAKpWq+v38/HD58mWNnqt3794ICwvDN998g7p16+LYsWPYs2cPPD3zFnmMj49HbGysan9bW1tERETg2bNn8PPzQ3BwMLp06YKFCxdq+jaI9EL043Q0mX1QtUyGnaUppnT2Fbkq0hWlUsD8+afRsOFyXL36BABgaWmKn3/uhN27P4aLi+1bnoGItE3jeYSsrKxw48YNeHp6ws7ODhcvXkSlSpVw69Yt1K5dG1lZWbqqVSs4jxDpixy5AlW/2qfWt3FoYwRU5nggQ/X0aRZq1FiM+Pi8y/u1a7tg06aeqFGjrMiVEek/XX1+a3xGqGLFirhw4UK+/r1796pWpyeit9sZ9Uit/aGfO0OQgStVygpr13aHiYkE48f74+zZoQxBRCLT+Pb5CRMm4NNPP0V2djYEQcDZs2exefNmzJ49GytWrNBFjUQG6Xp8uur7z9v7YFTrKiJWQ7qQkSFDdrYcTv+uAQcA7dp54+bNUahcmbfHE+kDjYPQoEGDIJfLMXHiRGRmZqJPnz4oX748FixYgI8++kgXNRIZnAcpmVhz6p6qzTNBhicyMg7BwTtQuXJp7N79sdoAeIYgIv3xTmuNJSUlQalUomzZknNql2OESGwHriZg+PpzqraFqQmOf9EKZe04Y7QhUCiUmDPnJKZOPQK5XAkAWLQoCCNHNhS5MqKSTW/WGpsxYwb69u0Lb29vODvzt1iiwrgal4p+K88iJUOWb9uiPvUZggxEbGwq+vULx7Fj91V9DRu6oV27SiJWRURvovFg6e3bt8PHxwdNmjTBzz//jCdPnuiiLiKDIJMrsfHMfXReeCJfCPJ0ssa2EH+0rV7wTOpUsmzZcgW1ay9RhSATEwmmTAnEyZODUaWKk8jVEdHrFOnS2NWrV7Fx40Zs2bIFDx8+RNu2bdG3b190794d1tbWb38CEfHSGOlarkKJ3yIfIvpxuto4oBeqlbND5bK2mNqlOs8EGYC0tByMGrUH69dfUvV5eDhgw4YeCAz0FLEyIsOiq8/vdxojBAAnT57Epk2b8NtvvyE7O1vvV3dnECJdeZyWjbC/bmHLP7F43f+qsN510b1e+eItjHQmOTkTDRsuR0zMM1Vfnz61sGhREBwdGXKJtElvxgj9l42NDaysrGBubo709PS3P4DIwMjkSoxYH4nDN19/mbhl1TJY0LseHKzNirEy0jUnJ2sEBHggJuYZ7O0tsHhxEIKDa4tdFhFpoEhBKCYmBps2bcLGjRsRHR2N5s2bY/r06fjggw+0XR+R3nr0LAtRsU+x90pCvhBkIgGaVHLChA5VUd7RCmXteXbAUP38cycoFEp8910beHk5il0OEWlI4yDk7++Ps2fPolatWhg0aJBqHiEiYzJo9dnXngE6O6UNnGwsIDXhwqmGRBAErF17Efb2FujZ8+V6cA4Olti0qZeIlRHRu9A4CLVq1QorVqxAjRo1dFEPkd47fDOxwBDk6WSNrcP9OQDaAKWkZGHEiN3Ytu0aHB0t0bChGypUcBC7LCLSAo2D0HfffaeLOoj0niAI+OFANH4+fFutf1qX6nC0NkP76uVgY/HOw+5Izxw+HIN+/cLx6FHeGMhnz7Kxbds1hIb6i1wZEWlDoX5qjxs3DjNnzoSNjQ3GjRv3xn1//PFHrRRGpC8EQcCGv+9j9cl7uJuUobZt07DGaOrNiUUNkUymwFdfHcK8eadUdwGWKmWJ5cu7oFcvLjBNZCgKFYSioqKQm5ur+p7ImPxy7C5m772Rr7+ptxP8K3GiPEN040YS+vTZjqioBFVf69YVsXZtd7i7c9oNIkPyzvMIlTScR4g0sejwbczdf1PVru/hiA41yqGfvyeszXkZzNAIgoBly85h3Lj9yMqSAwDMzEwwe3YbhIb6w4QD4IlEo6vPb42X2Bg8eHCB8wVlZGRg8ODBWimKSB+cvJ2EeQdehqDhzSthx8gAjGjhzRBkoFJSsvD114dVIcjX1xlnzw7D+PFNGYKIDJTGQWjt2rXIysrK15+VlYV169ZppSgiMd198hxjtkQheMUZ1diQT1t5Y3KnauIWRjrn5GSNFSu6AABGjvRDZORw1K1bTuSqiEiXCv1rbVpaGgRBgCAISE9Ph6Xly1uEFQoF9uzZg7Jly+qkSKLi9NnmKFyNe7lUTLPKzhjfriokEp4RMDRZWbmQyRRwcHj586xbt2q4dCkEtWpxMVwiY1DoIOTo6AiJRAKJRAIfH5982yUSCWbMmKHV4oiKW0JqtloIquPugCV96/OyiAG6dOkx+vTZDl/fMvj11/fVgi5DEJHxKHQQOnz4MARBQOvWrbF9+3aULl1atc3c3Byenp5wc3PTSZFEupSamYtTd5Kw5Z8HOBqtPlHibyFNYW6q8RVk0mNKpYAFC/7GpEkHIZMpcPXqE6xdexEDB9YVuzQiEkGhg1CLFi0A5K0z5uHhwcsEZBCuxaWh9y+nkZ4tz7dtWpfqDEEGJi4uHQMH7kRExF1VX506LmjUiMsEERmrQgWhS5cuoWbNmjAxMUFqaiouX7782n1r1+bKy1RyzNpzrcAQ5FHaGn0ae4hQEelKePh1DBu2C8nJL2/2GD/eH7NmtYYFZwQnMlqF+t9ft25dJCQkoGzZsqhbty4kEgkKmn5IIpFAoVBovUgibclVKHHiVhIePM3EncTnOHk7WbWtXXUX9GnsgRZVynBMkAHJyJAhNHQ/li8/r+pzc7PD2rXd0bZtJRErIyJ9UKggFBMTgzJlyqi+Jypp7j55jkk7LuNsTEqB291LWeHHD+vAztKsmCsjXXryJAPNmq1GdPTLwNujRzUsX94FTk7WIlZGRPqiUEHI09OzwO+JSopNZ2JfG4JaVi2DpX0bwNJMWsxVka45O1ujRo0yiI5OhrW1GRYu7IjBg+txjCMRqWh8YXzt2rVwdnZG586dAQATJ07EL7/8gurVq2Pz5s0MSqSXMmTq44D6NfFEfU9HVClrh5rlHUSqinRNIpFg+fIuUCgEzJvXDlWqcG04IlKn8VpjVatWxZIlS9C6dWucPn0abdq0QVhYGHbv3g1TU1Ps2LFDV7VqBdcaM04B/zuER8/yBsnuHRMIX1cee0O0ZcsVODhYoFOnKmKXQkRapqvPb43PCD148ACVK1cGAOzcuRPvv/8+hg8fjoCAALRs2VJrhRFpy/nYp6oQBAClbcxFrIZ0IS0tB6NG7cH69ZdQpow1Ll/+BC4utmKXRUQlgMaTpNja2iI5OW/g4YEDB9C2bVsAgKWlZYFrkBGJITtXgeO3nuDDZafRc/EpVX8ddwe42Fu+4ZFU0pw8GYs6dZZi/fpLAIAnTzKxcePrp/ggInqVxmeE2rVrh6FDh6JevXqIjo5WjRW6evUqvLy8tF0fkUbkCiUm77iM3849LHD7Dx/WLd6CSGdycxWYOfMYZs06DqUy7wq/vb0FFi8OQnAw5zMjosLROAgtWrQIX331FR48eIDt27fDySlv8OG5c+fw8ccfa71AosLKlMkxYv05HL+VVOD2tYMboXJZXi4xBLdvp6Bv3x04c+aRqq9ZMw+sX98DXl6O4hVGRCWOxoOlSzoOljZcn2w4h71XEtT6PmtdGYMDKqIUxwUZBEEQsGbNBXz22V5kZOQCAKRSCWbMaIlJk5pBKuWSKESGSm8GSwPAs2fPsHLlSly/fh0SiQS+vr4YMmQIHBx4GzKJ53zsU9X3TSqVxqI+9eFkayFiRaRtT55kIjR0vyoEeXuXwsaNPdG4sbvIlRFRSaXxr0+RkZHw9vbG/PnzkZKSgqSkJMyfPx/e3t44f/7825+ASAfSs3PxOC1H1d4y3J8hyACVLWuDpUvfAwAMGVIPFy6EMAQR0TvR+NJYYGAgKleujOXLl8PUNO+Eklwux9ChQ3H37l0cO3ZMJ4VqCy+NGRZBEHDhwTP0eOXOMAC497/OIlVE2iSTKZCbq4DNfy5tnj37iCvGExkZXX1+F+mM0BdffKEKQQBgamqKiRMnIjIyUmuFERXGF9sv5QtBw5tzIU1DcONGEvz9V+LTT/fk28YQRETaonEQsre3R2xsbL7+Bw8ewM7OTitFEb2NXKHEr/88wK+R+W+T/zLIV4SKSFsEQcDSpZGoX38Zzp+Px9q1F/Hrr1fFLouIDJTGg6V79+6NIUOGYN68eWjatCkkEglOnDiBCRMm8PZ5KhYyuRLt5x/FveRMtf6FH9dD++ouIlVF2vDkSQaGDPkDu3ZFq/p8fZ1RpUppEasiIkOmcRCaN28eJBIJ+vfvD7k8byFLMzMzfPLJJ/jf//6n9QKJXjVj11WsPnlPra9SGRvM6VUbfl78sCzJ9u27jYEDd+Lx4wxV38iRfpg7tz2src1ErIyIDFmR5xHKzMzEnTt3IAgCKleuDGtra23XphMcLF1yLTt6B7P33sjXf+e7IEhNJCJURNqQlZWLSZP+wsKFZ1V9ZcpYY9WqbnjvPR8RKyMifSL6PEKZmZmYMGECdu7cidzcXLRt2xYLFy6Es7Oz1oohep2HTzPzhaAmlUpj4Uf1GIJKsMTEDLRpsw5XriSq+oKCqmDVqq5cNJWIikWhg9C0adOwZs0aBAcHw9LSEps3b8Ynn3yC3377TZf1EQEAfv3ngVr7QGhz+LhwcH5J5+xsjfLl7XDlSiIsLU0xb147jBzZEBIJwy0RFY9CB6EdO3Zg5cqV+OijjwAAffv2RUBAABQKBaRSqc4KJOMmkyvx1c7LaneHDWlWkSHIQJiYSLB6dTf0778TCxZ0RPXqZcQuiYiMTKGD0IMHDxAYGKhqN2rUCKampoiLi0OFChV0UhwZt6jYp/nmCAKAHvU4h0xJtXPnDTg6WqJlSy9Vn6urHSIi+olXFBEZtULPI6RQKGBurj67q6mpqerOMSJtyVUo8eel+AJD0OqBDVGzPNe0K2kyMmQYPnwXevTYir59dyAlJUvskoiIAGhwRkgQBAwcOBAWFi/Xb8rOzkZISAhsbGxUfTt27NBuhWRUsmQK9Fh8EjcS0tX6g2qVw4KP6sGMq4uXOJGRcQgO3oHo6GQAwKNH6Viz5gLGjfMXuTIiIg2C0IABA/L19e3bV6vFkHHLzlUgcM5hJD3PUev/plsN9Pf3EqcoKjKFQok5c05i6tQjkMuVAABrazMsXNgRgwfXE7k6IqI8hQ5Cq1ev1mUdRPhkw7l8Ieivcc1RuSwHRpc0sbGp6NcvHMeO3Vf1+fm5YePGnvDxcRKxMiIidRrPLE2kCzFJGTh884mqHVjFGesGN+Jt1CXQli1XEBKyG6mpeaFWIgG+/DIQ06a1gJkZ7zAlIv3CIER6odW8I2rtRcH1GYJKoISE5xg69A9kZOQCADw8HLBhQw8EBnqKXBkRUcE48pREt+pEjFp7zvu1YW/JtaVKonLlbLFgQUcAwMcf18TFiyEMQUSk13hGiET1ICUT3+y+ptb3oR/npSopcnMVUCgEWFq+/FEyeHA9VKpUCq1aVRSxMiKiwuEZIRJNpkyOwDmH1fqOTWglUjWkqdu3UxAYuBrjx+9X65dIJAxBRFRiFCkIrV+/HgEBAXBzc8P9+3l3hYSFheH333/XanFkuARBQM//TJj49XvV4eFkLVJFVFiCIGD16ijUrbsUZ848wuLFkdi9O1rssoiIikTjILRkyRKMGzcOQUFBePbsGRQKBQDA0dERYWFh2q6PDNT8iGi1SRM713LFkGY8i6DvUlKy8OGH2zB48MsB0d7epVC2rM1bHklEpJ80DkI//fQTli9fjilTpqgtturn54fLly9rtTgyTAqlgIWHbqvaH/q54+c+nGBP3x0+HIPatZdg27aXY7qGDKmHCxdC0KgR138jopJJ48HSMTExqFcv/4eWhYUFMjIytFIUGbZvdl1Va3/XoxZvlddjMpkCX311CPPmnYIg5PWVKmWJ5cu7oFev6uIWR0T0jjQOQhUrVsSFCxfg6al+S+zevXtRvTp/KNLr7bkcj5Ebz6v1VXWxgynXD9NbiYkZ6NhxA6KiElR9bdpUxNq13VG+vL2IlRERaYfGQWjChAn49NNPkZ2dDUEQcPbsWWzevBmzZ8/GihUrdFEjGYBlR+9g9t4b+fqX9/cToRoqLCcnK9jZ5S20bGZmgtmz2yA01B8mJjyDR0SGQeNfxQcNGoRp06Zh4sSJyMzMRJ8+fbB06VIsWLAAH330kcYFLF68GBUrVoSlpSUaNGiA48ePF+pxJ0+ehKmpKerWravxa1LxuvTwWYEh6ND4FrxLTM9JpSZYv74HmjatgLNnh2H8+KYMQURkUCSC8OKqv+aSkpKgVCpRtmzZIj1+69at6NevHxYvXoyAgAAsW7YMK1aswLVr1+Dh4fHax6WmpqJ+/fqoXLkyHj9+jAsXLhT6NdPS0uDg4IDU1FTY2/PUfnFYcfwuvv3zuqo9p1dtfNiQkybqo717b6FUKSs0aeKu1i8IAsdxEZGodPX5/U6DM5ydnYscggDgxx9/xJAhQzB06FD4+voiLCwMFSpUwJIlS974uBEjRqBPnz7w9/cv8mtT8Xn1A3RWj5oMQXooKysXo0fvRVDQJvTpsx1paTlq2xmCiMhQFWmw9Jt+KN69e7dQzyOTyXDu3DlMmjRJrb99+/Y4derUax4FrF69Gnfu3MGGDRvw7bffvvV1cnJykJPz8od6Wlpaoeoj3eAaYvrn4sUEBAfvwNWrTwAAMTHPsHLleYSG8hcNIjJ8GgehsWPHqrVzc3MRFRWFffv2YcKECYV+nqSkJCgUCri4uKj1u7i4ICEhocDH3Lp1C5MmTcLx48dhalq40mfPno0ZM2YUui7Svpn/WUuM9INSKWDBgr8xadJByGR5E6NaWprihx/a45NPOIidiIyDxkFozJgxBfYvWrQIkZGRGhfw37NLrxuLoFAo0KdPH8yYMQM+Pj6Ffv7Jkydj3LhxqnZaWhoqVOClmeKy4/xDtXZFZ85ArA/i4tIxcOBORES8PINbp44LNm3qherVy4hYGRFR8dLaBC6dOnXC9u3bC72/s7MzpFJpvrM/iYmJ+c4SAUB6ejoiIyMxatQomJqawtTUFN988w0uXrwIU1NTHDp0qMDXsbCwgL29vdoXFY+rcakY9+tFtb6a5R1EqoZeCA+/jtq1l6iFoPHj/XHmzFCGICIyOhqfEXqdbdu2oXTp0oXe39zcHA0aNEBERAR69Oih6o+IiEC3bt3y7W9vb59vCY/Fixfj0KFD2LZtGypW5DpV+mbl8Ri19pHPW4pTCKnExaXj44+3Iycn71KYm5sd1q7tjrZtK4lcGRGRODQOQvXq1VO7dCUIAhISEvDkyRMsXrxYo+caN24c+vXrBz8/P/j7++OXX35BbGwsQkJCAORd1nr06BHWrVsHExMT1KxZU+3xZcuWhaWlZb5+0g9Pnr8cpL4txB9evCwmOjc3O8yd2w6jR+9Djx7VsHx5FzhxLiciMmIaB6Hu3burtU1MTFCmTBm0bNkS1apV0+i5evfujeTkZHzzzTeIj49HzZo1sWfPHtXyHfHx8YiNjdW0RBKZUikgNSsXMrlS1VfNlZckxaBQKKFUCjAze7lA8qhRjVCpUikEBVXhbfFEZPQ0mlBRLpdj48aN6NChA8qVK6fLunSGEyrqVmJ6NnotOYUHKVlq/VdmdICthdauxFIhxMamol+/cDRuXB5z5rQTuxwioneiFxMqmpqa4pNPPlGbl4foVUduPMkXguwsTWFhyoVVi9OWLVdQu/YSHDt2H3PnnsLBg4Wb34uIyNho/Ct648aNERUVlW/1eaILD55h4vZLan0darjgQ78KMOMK88UiLS0Ho0btwfr1L4+Dh4cDLC15No6IqCAa/3QcOXIkxo8fj4cPH6JBgwawsVEfAFu7dm2tFUclhyAI6L7opFrf1uFN0LiSk0gVGZ+TJ2PRt2847t17purr06cWFi0KgqOjpXiFERHpsUIHocGDByMsLAy9e/cGAIwePVq1TSKRqCZCVCgU2q+S9N4nG86rtYc2q4hGFQs/nQIVXW6uAjNnHsOsWcehVOYN+bO3t8DixUEIDuYvJkREb1LowdJSqRTx8fHIysp64376fsmMg6W1TxAEVJy8R9XuXNsVi/rUF7Ei45GYmIGuXTfjzJlHqr5mzTywfn0PeHk5ilcYEZGW6erzu9BnhF7kJX0POlS8BEHAoDX/qPUt/KieSNUYn1KlLPHiVxmpVIIZM1pi0qRmkHJMFhFRoWg0RohzjtCrFEoB3l/uUeub0bUGpCb8d1JczMyk2LixJz744DcsW/YeGjUqL3ZJREQlikZByMfH561hKCUl5Z0KopIh4tpjDFunvsiutbkU/f15xlCXDh+OQalSVqhb9+U8XpUrl8b588P5iwoRURFoFIRmzJgBBwcummnMnmbIMGTtPzgf+yzftr+/bMMPYx2RyRT46qtDmDfvFKpWdca5c8NhbW2m2s6/dyKiotEoCH300UcoW7asrmohPXflUSp6LjmltnQGAPSq746579eGCS+J6cSNG0no02c7oqISVO3ly89hzJgmIldGRFTyFToI8TdO4xZ5LwXvLz2dr3/t4EZo4VNGhIoMnyAIWLbsHMaN24+sLDkAwMzMBLNnt8FnnzUWuToiIsOg8V1jZHzinmXlC0HOtubYP7Y5nGwtRKrKsCUmZmDo0D+wa1e0qs/X1xmbNvVSGx9ERETvptBBSKlUvn0nMigZOXLEpmSi04Ljqj4LUxNsGtYYtco7wpzrh+nE3r23MGjQ73j8OEPVN3KkH+bOba82LoiIiN4dFyCiAi09egf/23sjX//kTtXQwJMzRuvKw4dp6NZtC3Jz837xKFPGGqtWdcN77/mIXBkRkWHir/Sk5l5SBprOPlhgCGpf3QUDAyqKUJXxcHe3xzfftAIAdOpUGZcvf8IQRESkQzwjRACATJkc1afuL3Bbz/rl0bmWK9r4uhRzVYZPqRQgCILaTNATJjSFt3cpvP9+dd6kQESkYwxCBACvDUHnvmrLAdE6EheXjoEDd6Jp0wqYPr2lql8qNcEHH9QQrzAiIiPCIER4miHL1/dNtxroUa887Cw5OFcXwsOvY9iwXUhOzsLBgzFo394bTZtWELssIiKjwyBEqDczQq1997sgTo6oIxkZMoSG7sfy5edVfS4uNsjNVYhYFRGR8WIQMnKPnmWptd9v4M4QpCORkXEIDt6B6OhkVV+PHtWwfHkXODlZi1gZEZHxYhAyclGxT9Xa33avKVIlhkuhUGLOnJOYOvUI5P8uT2JtbYaFCzti8OB6HBBNRCQiBiEjt+3cQ9X3I5pXgqWZVMRqDE9iYgY++OA3HDt2X9XXsKEbNm7siSpVnESsjIiIAM4jZNQS07Nx5OYTVbuWu4OI1Rgme3sLPHuWDQCQSIApUwJx8uRghiAiIj3BIGSklEoBjWYdVOtrU43zBGmbpaUpNm3qiapVnXD06EB8+21rmPGsGxGR3uClMSPVdv5RtfZHDSvAypwf0O/q5MlYlCplherVy6j6atQoi6tXR6pNmkhERPqBP5mN0JVHqbj7JEOt73+9aotUjWHIzVVg6tTDaN58Dfr02Y6cHLnadoYgIiL9xJ/ORmjq71fU2tHfdhKpEsNw504KAgNXY+bMY1AqBVy8+Bi//HJO7LKIiKgQeGnMyPRedhrnY5+p2r+O8Ie5KfNwUQiCgLVrL+Kzz/bi+fO82bmlUglmzGiJkSMbilscEREVCoOQETl+6wnOxKSo9TWqWFqkakq2lJQsjBixG9u2XVP1eXuXwqZNvdCoUXkRKyMiIk0wCBmRr3eqXxLbMzpQpEpKtkOHYtC/fzgePUpX9Q0ZUg9hYR1ha2suYmVERKQpBiEjEvfvfDYAsHlYE1R3sxexmpIpNjYVHTpsUM0QXaqUJZYv74JevaqLXBkRERUFB4cYCZlcCZki78PbTCqBvzcn9CsKDw8HTJ7cDADQunVFXLr0CUMQEVEJxjNCRuK7PddV31d34wzShSUIAgQBagvRfv11c3h7l0K/fnW4QC0RUQnHM0JGQBAEbD4bq2q3r84ZpAsjMTED3bptwQ8/nFLrNzOTYsCAugxBREQGgGeEDJggCNh1KR7f7LqKnH/HtADAkGYVRayqZNi79xYGDfodjx9nYN++22jTphLq13cVuywiItIyBiED9smG89h3NUGtr56HI1eYf4OsrFx88cVf+Omns6o+R0dLPH2aJWJVRESkKwxCBipLpsgXgpxtzbFxaGORKtJ/Fy8mIDh4B65efaLq69SpMlav7gYXF1sRKyMiIl1hEDJQ8/+KVmvv/DQAddwdIJFwXMt/KZUCFiz4G5MmHYRMpgCQt2r83Lnt8OmnDfl3RkRkwBiEDNQvx+6qvvcobY26FRzFK0aPPXmSgT59duCvv17+fdWu7YJNm3qiRo2yIlZGRETFgXeNGaCYJPWV5Q+ENhepEv1nbW2G2NhUVXv8eH+cPTuUIYiIyEgwCBmgj345rfre08mag6PfwMbGHJs29YSXlyMiIvph3rz2sLDgiVIiImPBn/gGJjUrF4/TclTtGV1riFiN/omMjEOpUpbw9n652GyDBm6Ijh4FMwZGIiKjwzNCBuZBSqbq+8AqzmhZlZd4AEChUGL27OPw91+J4OAdyM1VqG1nCCIiMk4MQgbmaPTLW79zFco37Gk8YmNT0br1Onz55SHI5UqcOfMIK1acF7ssIiLSA7w0ZkDOxqRg7v6bqnZglTIiVqMftmy5gpCQ3UhNzbtcKJEAX34ZiKFD64tcGRER6QMGIQNxPzkDHy47rdZX2914F1dNS8vBqFF7sH79JVWfh4cDNmzogcBATxErIyIifcIgZCDazz+Wr69xRScRKhHfqVMP0LfvDsTEPFP19elTC4sWBcHR0VK8woiISO8wCBmAx2nZaouq9vargP/1qmWUMyLfu/cMLVqsgfzfvw97ewssXhyE4ODaIldGRET6iIOlDcCI9efU2tO71jDKEAQAXl6O+OyzRgCAgIAKuHgxhCGIiIhei2eESrhchRIXHjxTtUNaeMPK3HhuBRcEAQDUgt9337VB5cqlMXx4A5iaMusTEdHr8VOihGv341G19oQOVUWqpPilpGThww+3YfHif9T6LS1NMXJkQ4YgIiJ6K54RKsFyFUrcS345gWLHGuUgNTGOS2KHD8egX79wPHqUjt27o9GypRfXByMiIo3xV+YSLDNHfXbkBR/XFaeQYiSTKTBxYgTatFmHR4/SAQBWVqaq74mIiDTBM0Il2Kk7SarvA6s4w8LUsMcGXb/+BMHBOxAVlaDqa926Itau7Q53d3sRKyMiopKKQagEu/gwVfV9pkzxhj1LNkEQsHRpJMaPP4CsLDkAwMzMBLNnt0FoqD9MjORyIBERaR+DUAmV/DwHS4/eUbUHNvUSrxgdSk7OxMCBv2P37mhVn6+vMzZu7Il69VxFrIyIiAwBxwiVUD9ERKu1q7sZ5qUhU1MTXL78WNUeOdIPkZHDGYKIiEgrGIRKoOxcBTadiVW1O9YoB+8ytiJWpDsODpbYsKEnXF1tsWvXx1i0qDOsrc3ELouIiAwEL42VMPuvJuSbSXpWj5oiVaN9Fy8moHRpK1So8HLB2GbNPHD37hhYWvKfKxERaZfoZ4QWL16MihUrwtLSEg0aNMDx48dfu++OHTvQrl07lClTBvb29vD398f+/fuLsVrxzfrzulq7Wjk7ONlaiFSN9iiVAubPP41GjVagX79wKBRKte0MQUREpAuiBqGtW7di7NixmDJlCqKiohAYGIhOnTohNja2wP2PHTuGdu3aYc+ePTh37hxatWqFLl26ICoqqpgrF49CKai+71bXDbs+ayZiNdoRF5eOjh03YNy4A5DJFDh69D5WrTKeY0pEROKRCC8WaxJB48aNUb9+fSxZskTV5+vri+7du2P27NmFeo4aNWqgd+/emDp1aqH2T0tLg4ODA1JTU2FvX/IGGAf87xAePctCGTsL/DOlrdjlvLPw8OsYNmwXkpOzVH3jx/tj1qzWsLDgWSAiIsqjq89v0T5pZDIZzp07h0mTJqn1t2/fHqdOnSrUcyiVSqSnp6N06dKv3ScnJwc5OTmqdlpaWtEK1hOvnhEqyTIyZAgN3Y/ly8+r+tzc7LB2bXe0bVtJxMqIiMiYiHZpLCkpCQqFAi4uLmr9Li4uSEhIeM2j1P3www/IyMjAhx9++Np9Zs+eDQcHB9VXhQoV3qluMf19NxkJadlil/HOIiPjUL/+L2ohqGdPX1y6FMIQRERExUr0wdISifqswIIg5OsryObNmzF9+nRs3boVZcu+frHNyZMnIzU1VfX14MGDd65ZLNvPPVR9X1LnUr579yn8/VciOjoZAGBjY4aVK7ti27YP4ORkLXJ1RERkbEQLQs7OzpBKpfnO/iQmJuY7S/RfW7duxZAhQ/Drr7+ibds3j5OxsLCAvb292ldJdCz6CX57JQhN6FBVxGqKrlKlUhgypB4AoGFDN0RFjcDgwfUKFX6JiIi0TbQgZG5ujgYNGiAiIkKtPyIiAk2bNn3t4zZv3oyBAwdi06ZN6Ny5s67L1AuCIKD/qrNqfS2qlhGpmnf3ww/tMW9eO5w8ORhVqjiJXQ4RERkxUS+NjRs3DitWrMCqVatw/fp1hIaGIjY2FiEhIQDyLmv1799ftf/mzZvRv39//PDDD2jSpAkSEhKQkJCA1NTU172EQdh7Rf2s2di2VVDWzlKkagovLS0H/fuHY/Vq9VvhbWzMMX58U5iZSUWqjIiIKI+o9yf37t0bycnJ+OabbxAfH4+aNWtiz5498PT0BADEx8erzSm0bNkyyOVyfPrpp/j0009V/QMGDMCaNWuKu/xiM3Ljy0HFLvYWGNvWR8RqCufUqQfo23cHYmKeITz8BgIDPVG58uvv7iMiIhKDqPMIiaGkzSO0+1IcRm16eUZlw5DGaFbFWcSK3kwuV2LmzKP49tvjUP57q7+9vQW2bn0fHTtWFrk6IiIqqQxuHiF6u7TsXLUQBECvQ9CdOykIDt6BM2ceqfqaNfPA+vU94OXlKF5hREREr8EgpMd+j3qk1j42oZVIlbyZIAhYu/YiPvtsL54/lwEApFIJZsxoiUmTmkEqFX2WBiIiogIxCOmxTJlC9X15Ryt46OE8O0+fZmH48N3Ytu2aqs/buxQ2beqFRo3Ki1gZERHR2zEI6bHv991Qff/1e74iVvJ6SqWAU6deTlI5ZEg9hIV1hK2tuYhVERERFQ6vWeip5zlyvLqsmJujlXjFvIGTkzXWru0OJycrbNv2AVas6MoQREREJQbPCOmpL7ZfUmvXKu8gUiXqrl9/gtKlreDiYqvqa9u2EmJixsDOzkLEyoiIiDTHM0J6aM6+G/jzUryq3aWOm+hLUAiCgKVLI9GgwS8YNOh3/HfWBYYgIiIqiXhGSI8IgoCKk/fk65/UqZoI1byUmJiBoUP/wK5d0QCAvXtvY+3aixg4sK6odREREb0rBiE98tOh2/n6Tk5qjfIijg/at+82Bg7cicePM1R9I0f64cMPa4hWExERkbYwCOmJJ+k5+DEiWq1v87AmooWgrKxcTJr0FxYufLnYa5ky1li1qhvee0//l/ggIiIqDAYhPSBXKNFw1l9qff9MaYsyIo27uXz5Mfr02YErVxJVfUFBVbBqVVe1QdJEREQlHYOQHhiz5YJa+4MG7qKFoNu3U+DntxyyfydztLQ0xbx57TByZEPRB2wTERFpG+8a0wN/Xn55h5iVmRRz3q8tWi2VK5dG795543/q1HHBuXPD8emnjRiCiIjIIPGMkIiyZArM/POaWl/U1Haih46ffw5ClSqlMXFiACws+E+EiIgMF88IiajzT8ex6Uysql25rC0szaTF9voZGTIMH74LW7deUeu3t7fA11+3YAgiIiKDx086EeXkKtXa/Zp4FttrR0bGITh4B6Kjk/Hbb9fQtGkFVKigH7NXExERFRcGIRGZSl9eArsyowNsi+EMjEKhxJw5JzF16hHI5XlBTCZT4NKlxwxCRERkdBiE9EApa7NiCUGxsano1y8cx47dV/U1bOiGjRt7okoVJ52/PhERkb5hEBLJ7cTnuJ+cWWyvt2XLFYSE7EZqag4AQCIBvvwyENOmtYBZMY5LIiIi0icMQiLZ8PfLszJypfCGPd9NWloORo3ag/XrX65m7+HhgA0beiAwsPjGJBEREekjBiGRRD9OV32vy0HSmZm52Lv35RpmH39cE4sXd4ajo6XOXpOIiKik4O3zIqg5bT9O3UlWtXvWd9fZa5UrZ4uVK7vC3t4CGzb0wKZNvRiCiIiI/sUzQsUsPjULz3Pkqra5qYlWl9O4fTsFpUpZwsnJWtXXtWtVxMSMQenS4q1iT0REpI94RqiY/Rb5UK29emBDOFiZvfPzCoKA1aujULfuUowYsRuCoD7uiCGIiIgoPwahYhRx7TF+jIhWtbvUcUNAZed3ft6UlCx8+OE2DB78BzIycrF9+3Vs3nzl7Q8kIiIycrw0VkxkciWGrYtU65vQvuo7P+/hwzHo1y8cjx69HHw9ZEg9dO367s9NRERk6BiEionPV3vV2t/3qgWPV8bxaEomU+Crrw5h3rxTeHEVrFQpSyxf3gW9elV/l1KJiIiMBoNQMfj9wiO1trnUBL0behT5+W7cSEKfPtsRFZWg6mvduiLWru0Od3f7Ij8vERGRsWEQKgZjtlxQa1+Z0aHIz3XzZhLq11+GrKy8O8/MzEwwe3YbhIb6w8RE8pZHExER0as4WFrH/rf3hlo7fGRTmJsW/a/dx8cJnTpVAQD4+jrj7NlhGD++KUMQERFREfCMkI4tPXpH9b2zrQXqeZR6p+eTSCT45Zf34ONTGl9/3QLW1u9+6z0REZGxYhDSoe/3qZ8NOji+hUaPz8rKxRdf/IV27SqhS5eXd4E5OVlj9uy2WqmRiKgwBEGAXC6HQqEQuxQyYGZmZpBKi3chcAYhHXr1bBAAjSZOvHgxAcHBO3D16hNs3nwFly9/gnLlbLVdIhHRW8lkMsTHxyMzM1PsUsjASSQSuLu7w9a2+D7vGIR0RKEU8OrkzhentS/U45RKAQsW/I1Jkw5CJsv7zev5cxkiI+Pw3ns+uiiViOi1lEolYmJiIJVK4ebmBnNzc0gkHJNI2icIAp48eYKHDx+iSpUqxXZmiEFIB56k56DhrL9UbXNTk0KdDYqLS8fAgTsREXFX1Venjgs2beqF6tXL6KRWIqI3kclkUCqVqFChAqytiz73GVFhlClTBvfu3UNubi6DUEmTmpWLPy/FY+nRO4hNUT997Of59gHS4eHXMWzYLiQnZ6n6xo/3x6xZrWFhwcNEROIyMeFNxqR7Ypxt5CfsO3qQkokTt5MwecflArdXcrbBhiGNX/v4589lCA3dhxUrolR9bm52WLu2O9q2raT1eomIiOglRvx38MfFOATOOfzaENSzXnkcHN/ijXP8PH2ahd9+u6Zq9+hRDZcuhTAEERGJyMvLC2FhYe+8z7tas2YNJBIJJBIJxo4dq9Fjvby8VI999uyZTuozBAxC72D05qgC+1cPbIg73wXhx95133qar0IFByxb9h5sbMywYkUXbN/+IZzeYQ0yIiIqHv/88w+GDx+uted7XbCyt7dHfHw8Zs6cqeoTBAHTp0+Hm5sbrKys0LJlS1y9ejVffdu3by/Ua2/fvh3Vq1eHhYUFqlevjvDw8Lc+RhAEzJs3Dz4+PrCwsECFChXw3XffqbYfOXJEFcRe/bpx48YbnrX48dJYEW0/91CtPbKlN3xc7NDGtyzsLF8/MDo2NhWOjpawt7dQ9fXuXRMtWnjx9ngiohKkTJniuYlFIpGgXLlyan1z5szBjz/+iDVr1sDHxwfffvst2rVrh5s3b8LOzk5VX+nSpd/6/KdPn0bv3r0xc+ZM9OjRA+Hh4fjwww9x4sQJNG78+qEdY8aMwYEDBzBv3jzUqlULqampSEpKyrffzZs3YW//ch3M4vp7KyyeESqiuftvqr73crLGxI7V0L1e+TeGoC1brqB27SX47LO9+bYxBBERade2bdtQq1YtWFlZwcnJCW3btkVGRgZatmyZ7zJT9+7dMXDgQLW+9PR09OnTB7a2tnBzc8NPP/2ktv2/Z3BSU1MxfPhwlC1bFvb29mjdujUuXryo9pg//vgDfn5+sLS0hLOzM3r27AkAaNmyJe7fv4/Q0FDVmZPXEQQBYWFhmDJlCnr27ImaNWti7dq1yMzMxKZNmzT+ewoLC0O7du0wefJkVKtWDZMnT0abNm3eeNnv+vXrWLJkCX7//Xd07doVFStWRN26ddG2bf7JfsuWLYty5cqpvop7wsS3YRAqgrhnWUhIy1a1f/iw7hv3T0vLQf/+4fj44+1ITc3BunUXsX37tTc+hoiIii4+Ph4ff/wxBg8ejOvXr+PIkSPo2bMnhFcneHuLuXPnonbt2jh//jwmT56M0NBQREREFLivIAjo3LkzEhISsGfPHpw7dw7169dHmzZtkJKSAgD4888/0bNnT3Tu3BlRUVE4ePAg/Pz8AAA7duyAu7s7vvnmG8THxyM+Pv61dcXExCAhIQHt27+cn87CwgItWrTAqVOnCv3+Xjh9+rTacwFAhw4d3vhcu3btQqVKlbB7925UrFgRXl5eGDp0qOq9vqpevXpwdXVFmzZtcPjwYY3r0zVeGtOQTK5E0/8dUuur7+H42v1PnoxF377huHfvmarv449rok0bDoYmopKry08n8CQ9p9hft4ydBXZ91uyt+8XHx0Mul6Nnz57w9PQEANSqVUuj1woICMCkSZMAAD4+Pjh58iTmz5+Pdu3a5dv38OHDuHz5MhITE2FhkTf0Yd68edi5cye2bduG4cOHY9asWfjoo48wY8YM1ePq1KkDAChdujSkUins7OzyXQb7r4SEBACAi4uLWr+Liwvu37+v0Xt88XwFPdeL1ynI3bt3cf/+ffz2229Yt24dFAoFQkND8f777+PQobzPSFdXV/zyyy9o0KABcnJysH79erRp0wZHjhxB8+bNNa5TVxiENJCdq0C1r/ep9U3rUr3AU5i5uQrMnHkMs2Ydh1KZ9xuIvb0FFi8OQnBw7WKpl4hIV56k56idGdc3derUQZs2bVCrVi106NAB7du3x/vvv49SpQq/8LW/v3++9usuF507dw7Pnz+Hk5OTWn9WVhbu3MlbbunChQsYNmyYZm/kDf772SMIwhsvqcXGxqJ69eqq9pdffokvv/yySM+lVCqRk5ODdevWwccnb9WDlStXokGDBrh58yaqVq2q+nrB398fDx48wLx58xiESqpj0U/U2q4OlhgUUDHffrdvp6Bv3x04c+aRqi8goAI2bOgJLy9HXZdJRKRzZews3r6TiK8rlUoRERGBU6dO4cCBA/jpp58wZcoUnDlzBiYmJvkukeXm5hbqeV8XDpRKJVxdXXHkyJF82xwdHQEAVlZWhXqNt3lxxighIQGurq6q/sTExHxndl7l5uaGCxcuqNovBlKXK1cu39mftz2Xq6srTE1NVSEIAHx9fQHkBa5XA9CrmjRpgg0bNrz2ecXAIFRIMUkZGL7+nFrf6clt8u13/foTNGy4HBkZef+ppFIJpk9viUmTmsHUlEOyiMgwFObylNgkEgkCAgIQEBCAqVOnwtPTE+Hh4ShTpozaGByFQoErV66gVatWao//+++/87WrVatW4GvVr18fCQkJMDU1hZeXV4H71K5dGwcPHsSgQYMK3G5ubg6FQvHW91WxYkWUK1cOERERqFevHoC8pVCOHj2K77///rWPMzU1ReXKlfP1+/v7IyIiAqGhoaq+AwcOoGnTpq99roCAAMjlcty5cwfe3t4AgOjoaABQXYosSFRUlFp40wcMQoU0/tcLau0FH9UtcL9q1ZwRGOiJfftuw9u7FDZu7InGjd11XyAREamcOXMGBw8eRPv27VG2bFmcOXMGT548ga+vL2xsbDBu3Dj8+eef8Pb2xvz58wuccPDkyZOYM2cOunfvjoiICPz222/4888/C3y9tm3bwt/fH927d8f333+PqlWrIi4uDnv27EH37t3h5+eHadOmoU2bNvD29sZHH30EuVyOvXv3YuLEiQDy7kI7duwYPvroI1hYWMDZ2bnA13oxueJ3332HKlWqoEqVKvjuu+9gbW2NPn36aPx3NWbMGDRv3hzff/89unXrht9//x1//fUXTpw4odrn559/Rnh4OA4ePKh6v/Xr18fgwYMRFhYGpVKJTz/9FO3atVOdJQoLC4OXlxdq1KgBmUyGDRs2YPv27YWe26i4MAgVwtc7r+B87DNVu7a7AzrUKHgwm0QiwerV3fC//53At9+2hq2teTFVSUREL9jb2+PYsWMICwtDWloaPD098cMPP6BTp07Izc3FxYsX0b9/f5iamiI0NDTf2SAAGD9+PM6dO4cZM2bAzs4OP/zwAzp06FDg60kkEuzZswdTpkzB4MGD8eTJE5QrVw7NmzdXXWJq2bIlfvvtN8ycORP/+9//YG9vrzZW5ptvvsGIESPg7e2NnJycN97hNnHiRGRlZWHkyJF4+vQpGjdujAMHDqjmENJE06ZNsWXLFnz11Vf4+uuv4e3tja1bt6rNIZSUlKQa6wTkrT23a9cufPbZZ2jevDlsbGzQqVMn/PDDD6p9ZDIZPv/8czx69AhWVlaoUaMG/vzzTwQFBWlcoy5JBE3uJTQAaWlpcHBwQGpqqtoET6+z6UwsvgxXX0LjxsyOsDSTQiZT4OuvD6FdO28uiUFEBik7OxsxMTGoWLEiLC0txS5Hr7i6umLmzJkYOnSozl5jzZo1GDt2bJGXyDhy5AhatWqFp0+fqsYq6bM3/XvT9PO7sDho5Q0O3XicLwQdGt8ClmZS3LiRhCZNVmDOnFMYMGAnkpMzX/MsRERkSDIzMxEREYHHjx+jRo0aOn+91NRU2Nra4osvvtDocTVq1ECnTp10VJXh4KWxAiw/dhez9lzP179vbCAqOttg6dJIjBu3H1lZcgDAkycZOHXqAbp0KXiUPBERGY5ffvkFM2fOxNixY/PdYq9tvXr1QrNmeQPTNT2js2fPHtXdcNo8g2JoGIT+43zs0wJD0JQgX5Q2kaJbty3YtSta1e/r64xNm3qhbt03T4BFRESGYezYsRqvBF9UdnZ2RRr3A7z57i16iUHoPzafiVVrm5pIsGlYE6TcTEHt2kvw+HGGatvIkX6YO7c9rK1fv74YERER6S8Gof9IfGXK+IUf10M7nzKYNOkvLFx4VtVfpow1Vq3qhvfe8ynoKYiIiKiEYBB6RXauAkdfmT26krMN4uLSsXJllKovKKgKVq3qChcXrhZPRMbDyG4wJpGI8e+Md439SxCEfOuIVXGxhbd3aSxc2AmWlqb4+edO2L37Y4YgIjIaZmZ5l/4zM3lnLOmeTCYDkLdESnHhGSHkhaCKk/cAAOTpMphYStGrkQcsTPMOxKBBddGmTUV4ejqKWCURUfGTSqVwdHREYmIiAMDa2vqNi3ESFZVSqcSTJ09gbW0NU9PiiycMQoAqBGVGP0XyvvuwrloKc+e8p9oukUgYgojIaL1Y5PNFGCLSFRMTE3h4eBRr2Db6IBT2VzSUMgWeHnyA55eSAADPLzzBvr23ORiaiAh5vwy6urqibNmyhV6lnagozM3NYWJSvKN2RA9Cixcvxty5cxEfH48aNWogLCwMgYGBr93/6NGjGDduHK5evQo3NzdMnDgRISEhRXrtnw/dwvfrLyBp113In768W6xHj2rw9+dCqUREr5JKpcU6doOoOIg6WHrr1q0YO3YspkyZgqioKAQGBqJTp06IjY0tcP+YmBgEBQUhMDAQUVFR+PLLLzF69OgirWQ7aNVZfD3jKBI23FCFIGtrM6xY0QXbt38IJyfrd3pvREREpP9EXXS1cePGqF+/PpYsWaLq8/X1Rffu3TF79ux8+3/xxRf4448/cP36y5mfQ0JCcPHiRZw+fbpQr/li0Tbz8t9C9kiu6q9c3Rl7dn6EKlWc3uEdERERkS4Y3KKrMpkM586dQ/v27dX627dvj1OnThX4mNOnT+fbv0OHDoiMjNT4urXs0fO8byTAyNDGuHYhhCGIiIjIyIg2RigpKQkKhQIuLi5q/S4uLkhISCjwMQkJCQXuL5fLkZSUBFdX13yPycnJQU7Oy/E/qampL7bAxM4cOzb2QqsWXsjKykBW1ru9JyIiItKNtLQ0ANqfdFH0wdL/vUVOEIQ33jZX0P4F9b8we/ZszJgxo4At86FMB7p3/UazgomIiEg0ycnJcHBw0NrziRaEnJ2dIZVK8539SUxMzHfW54Vy5coVuL+pqSmcnAq+rDV58mSMGzdO1X727Bk8PT0RGxur1b9IKpq0tDRUqFABDx480Oo1X9Icj4X+4LHQHzwW+iM1NRUeHh4oXbq0Vp9XtCBkbm6OBg0aICIiAj169FD1R0REoFu3bgU+xt/fH7t27VLrO3DgAPz8/FTTwP+XhYUFLCws8vU7ODjwH7Uesbe35/HQEzwW+oPHQn/wWOgPbc8zJOrt8+PGjcOKFSuwatUqXL9+HaGhoYiNjVXNCzR58mT0799ftX9ISAju37+PcePG4fr161i1ahVWrlyJzz//XKy3QERERCWYqGOEevfujeTkZHzzzTeIj49HzZo1sWfPHnh6egIA4uPj1eYUqlixIvbs2YPQ0FAsWrQIbm5uWLhwIXr16iXWWyAiIqISTPTB0iNHjsTIkSML3LZmzZp8fS1atMD58+eL/HoWFhaYNm1agZfLqPjxeOgPHgv9wWOhP3gs9IeujoWoEyoSERERiUnUMUJEREREYmIQIiIiIqPFIERERERGi0GIiIiIjJZBBqHFixejYsWKsLS0RIMGDXD8+PE37n/06FE0aNAAlpaWqFSpEpYuXVpMlRo+TY7Fjh070K5dO5QpUwb29vbw9/fH/v37i7Faw6fp/40XTp48CVNTU9StW1e3BRoRTY9FTk4OpkyZAk9PT1hYWMDb2xurVq0qpmoNm6bHYuPGjahTpw6sra3h6uqKQYMGITk5uZiqNVzHjh1Dly5d4ObmBolEgp07d771MVr5/BYMzJYtWwQzMzNh+fLlwrVr14QxY8YINjY2wv379wvc/+7du4K1tbUwZswY4dq1a8Ly5csFMzMzYdu2bcVcueHR9FiMGTNG+P7774WzZ88K0dHRwuTJkwUzMzPh/PnzxVy5YdL0eLzw7NkzoVKlSkL79u2FOnXqFE+xBq4ox6Jr165C48aNhYiICCEmJkY4c+aMcPLkyWKs2jBpeiyOHz8umJiYCAsWLBDu3r0rHD9+XKhRo4bQvXv3Yq7c8OzZs0eYMmWKsH37dgGAEB4e/sb9tfX5bXBBqFGjRkJISIhaX7Vq1YRJkyYVuP/EiROFatWqqfWNGDFCaNKkic5qNBaaHouCVK9eXZgxY4a2SzNKRT0evXv3Fr766ith2rRpDEJaoumx2Lt3r+Dg4CAkJycXR3lGRdNjMXfuXKFSpUpqfQsXLhTc3d11VqMxKkwQ0tbnt0FdGpPJZDh37hzat2+v1t++fXucOnWqwMecPn063/4dOnRAZGQkcnNzdVaroSvKsfgvpVKJ9PR0rS+wZ4yKejxWr16NO3fuYNq0abou0WgU5Vj88ccf8PPzw5w5c1C+fHn4+Pjg888/R1ZWVnGUbLCKciyaNm2Khw8fYs+ePRAEAY8fP8a2bdvQuXPn4iiZXqGtz2/RZ5bWpqSkJCgUinyr17u4uORbtf6FhISEAveXy+VISkqCq6urzuo1ZEU5Fv/1ww8/ICMjAx9++KEuSjQqRTket27dwqRJk3D8+HGYmhrUjwpRFeVY3L17FydOnIClpSXCw8ORlJSEkSNHIiUlheOE3kFRjkXTpk2xceNG9O7dG9nZ2ZDL5ejatSt++umn4iiZXqGtz2+DOiP0gkQiUWsLgpCv7237F9RPmtP0WLywefNmTJ8+HVu3bkXZsmV1VZ7RKezxUCgU6NOnD2bMmAEfH5/iKs+oaPJ/Q6lUQiKRYOPGjWjUqBGCgoLw448/Ys2aNTwrpAWaHItr165h9OjRmDp1Ks6dO4d9+/YhJiZGtVg4FS9tfH4b1K95zs7OkEql+ZJ8YmJivtT4Qrly5Qrc39TUFE5OTjqr1dAV5Vi8sHXrVgwZMgS//fYb2rZtq8syjYamxyM9PR2RkZGIiorCqFGjAOR9GAuCAFNTUxw4cACtW7cultoNTVH+b7i6uqJ8+fJwcHBQ9fn6+kIQBDx8+BBVqlTRac2GqijHYvbs2QgICMCECRMAALVr14aNjQ0CAwPx7bff8ipCMdLW57dBnREyNzdHgwYNEBERodYfERGBpk2bFvgYf3//fPsfOHAAfn5+MDMz01mthq4oxwLIOxM0cOBAbNq0idfctUjT42Fvb4/Lly/jwoULqq+QkBBUrVoVFy5cQOPGjYurdINTlP8bAQEBiIuLw/Pnz1V90dHRMDExgbu7u07rNWRFORaZmZkwMVH/6JRKpQBeno2g4qG1z2+NhlaXAC9uhVy5cqVw7do1YezYsYKNjY1w7949QRAEYdKkSUK/fv1U+7+4/S40NFS4du2asHLlSt4+ryWaHotNmzYJpqamwqJFi4T4+HjV17Nnz8R6CwZF0+PxX7xrTHs0PRbp6emCu7u78P777wtXr14Vjh49KlSpUkUYOnSoWG/BYGh6LFavXi2YmpoKixcvFu7cuSOcOHFC8PPzExo1aiTWWzAY6enpQlRUlBAVFSUAEH788UchKipKNZWBrj6/DS4ICYIgLFq0SPD09BTMzc2F+vXrC0ePHlVtGzBggNCiRQu1/Y8cOSLUq1dPMDc3F7y8vIQlS5YUc8WGS5Nj0aJFCwFAvq8BAwYUf+EGStP/G69iENIuTY/F9evXhbZt2wpWVlaCu7u7MG7cOCEzM7OYqzZMmh6LhQsXCtWrVxesrKwEV1dXITg4WHj48GExV214Dh8+/MbPAF19fksEgefyiIiIyDgZ1BghIiIiIk0wCBEREZHRYhAiIiIio8UgREREREaLQYiIiIiMFoMQERERGS0GISIiIjJaDEJEpGbNmjVwdHQUu4wi8/LyQlhY2Bv3mT59OurWrVss9RCRfmMQIjJAAwcOhEQiyfd1+/ZtsUvDmjVr1GpydXXFhx9+iJiYGK08/z///IPhw4er2hKJBDt37lTb5/PPP8fBgwe18nqv89/36eLigi5duuDq1asaP09JDqZE+o5BiMhAdezYEfHx8WpfFStWFLssAHmLusbHxyMuLg6bNm3ChQsX0LVrVygUind+7jJlysDa2vqN+9ja2mq0OnVRvfo+//zzT2RkZKBz586QyWQ6f20iKhwGISIDZWFhgXLlyql9SaVS/Pjjj6hVqxZsbGxQoUIFjBw5Um1V8/+6ePEiWrVqBTs7O9jb26NBgwaIjIxUbT916hSaN28OKysrVKhQAaNHj0ZGRsYba5NIJChXrhxcXV3RqlUrTJs2DVeuXFGdsVqyZAm8vb1hbm6OqlWrYv369WqPnz59Ojw8PGBhYQE3NzeMHj1ate3VS2NeXl4AgB49ekAikajar14a279/PywtLfHs2TO11xg9ejRatGihtffp5+eH0NBQ3L9/Hzdv3lTt86bjceTIEQwaNAipqamqM0vTp08HAMhkMkycOBHly5eHjY0NGjdujCNHjryxHiLKj0GIyMiYmJhg4cKFuHLlCtauXYtDhw5h4sSJr90/ODgY7u7u+Oeff3Du3DlMmjQJZmZmAIDLly+jQ4cO6NmzJy5duoStW7fixIkTGDVqlEY1WVlZAQByc3MRHh6OMWPGYPz48bhy5QpGjBiBQYMG4fDhwwCAbdu2Yf78+Vi2bBlu3bqFnTt3olatWgU+7z///AMAWL16NeLj41XtV7Vt2xaOjo7Yvn27qk+hUODXX39FcHCw1t7ns2fPsGnTJgBQ/f0Bbz4eTZs2RVhYmOrMUnx8PD7//HMAwKBBg3Dy5Els2bIFly5dwgcffICOHTvi1q1bha6JiACDXH2eyNgNGDBAkEqlgo2Njerr/fffL3DfX3/9VXByclK1V69eLTg4OKjadnZ2wpo1awp8bL9+/YThw4er9R0/flwwMTERsrKyCnzMf5//wYMHQpMmTQR3d3chJydHaNq0qTBs2DC1x3zwwQdCUFCQIAiC8MMPPwg+Pj6CTCYr8Pk9PT2F+fPnq9oAhPDwcLV9pk2bJtSpU0fVHj16tNC6dWtVe//+/YK5ubmQkpLyTu8TgGBjYyNYW1urVtLu2rVrgfu/8LbjIQiCcPv2bUEikQiPHj1S62/Tpo0wefLkNz4/EakzFTeGEZGutGrVCkuWLFG1bWxsAACHDx/Gd999h2vXriEtLQ1yuRzZ2dnIyMhQ7fOqcePGYejQoVi/fj3atm2LDz74AN7e3gCAc+fO4fbt29i4caNqf0EQoFQqERMTA19f3wJrS01Nha2tLQRBQGZmJurXr48dO3bA3Nwc169fVxvsDAABAQFYsGABAOCDDz5AWFgYKlWqhI4dOyIoKAhdunSBqWnRf5wFBwfD398fcXFxcHNzw8aNGxEUFIRSpUq90/u0s7PD+fPnIZfLcfToUcydOxdLly5V20fT4wEA58+fhyAI8PHxUevPyckplrFPRIaEQYjIQNnY2KBy5cpqfffv30dQUBBCQkIwc+ZMlC5dGidOnMCQIUOQm5tb4PNMnz4dffr0wZ9//om9e/di2rRp2LJlC3r06AGlUokRI0aojdF5wcPD47W1vQgIJiYmcHFxyfeBL5FI1NqCIKj6KlSogJs3byIiIgJ//fUXRo4ciblz5+Lo0aNql5w00ahRI3h7e2PLli345JNPEB4ejtWrV6u2F/V9mpiYqI5BtWrVkJCQgN69e+PYsWMAinY8XtQjlUpx7tw5SKVStW22trYavXciY8cgRGREIiMjIZfL8cMPP8DEJG+I4K+//vrWx/n4+MDHxwehoaH4+OOPsXr1avTo0QP169fH1atX8wWut3k1IPyXr68vTpw4gf79+6v6Tp06pXbWxcrKCl27dkXXrl3x6aefolq1arh8+TLq16+f7/nMzMwKdTdanz59sHHjRri7u8PExASdO3dWbSvq+/yv0NBQ/PjjjwgPD0ePHj0KdTzMzc3z1V+vXj0oFAokJiYiMDDwnWoiMnYcLE1kRLy9vSGXy/HTTz/h7t27WL9+fb5LNa/KysrCqFGjcOTIEdy/fx8nT57EP//8owolX3zxBU6fPo1PP/0UFy5cwK1bt/DHH3/gs88+K3KNEyZMwJo1a7B06VLcunULP/74I3bs2KEaJLxmzRqsXLkSV65cUb0HKysreHp6Fvh8Xl5eOHjwIBISEvD06dPXvm5wcDDOnz+PWbNm4f3334elpaVqm7bep729PYYOHYpp06ZBEIRCHQ8vLy88f/4cBw8eRFJSEjIzM+Hj44Pg4GD0798fO3bsQExMDP755x98//332LNnj0Y1ERk9MQcoEZFuDBgwQOjWrVuB23788UfB1dVVsLKyEjp06CCsW7dOACA8ffpUEAT1wbk5OTnCRx99JFSoUEEwNzcX3NzchFGjRqkNED579qzQrl07wdbWVrCxsRFq164tzJo167W1FTT4978WL14sVKpUSTAzMxN8fHyEdevWqbaFh4cLjRs3Fuzt7QUbGxuhSZMmwl9//aXa/t/B0n/88YdQuXJlwdTUVPD09BQEIf9g6RcaNmwoABAOHTqUb5u23uf9+/cFU1NTYevWrYIgvP14CIIghISECE5OTgIAYdq0aYIgCIJMJhOmTp0qeHl5CWZmZkK5cuWEHj16CJcuXXptTUSUn0QQBEHcKEZEREQkDl4aIyIiIqPFIERERERGi0GIiIiIjBaDEBERERktBiEiIiIyWgxCREREZLQYhIiIiMhoMQgRERGR0WIQIiIiIqPFIERERERGi0GIiIiIjBaDEBERERmt/wP3gKU6L+be2AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC for EEG\n",
    "plot_all_roc(pred_label_list1, f_type = 'EEG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feat_choice = [WordEmbedding, EEG, EyeGaze]\n",
    "f_choice2 = [0, 0, 1] # EyeGaze only\n",
    "avg_acc_list2, cm_list2, f1_list2, pred_label_list2 = get_stats4ROC(f_choice2, n_subs = 9,\n",
    "                                                                   epochs = 500, optimizer_type = 'Adam', n_translayer = 1, k = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot ROC for EyeGaze\n",
    "plot_all_roc(pred_label_list2, f_type = 'EyeGaze')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feat_choice = [WordEmbedding, EEG, EyeGaze]\n",
    "f_choice3 = [0, 1, 1] # EEG + EyeGaze\n",
    "avg_acc_list3, cm_list3, f1_list3, pred_label_list3 = get_stats4ROC(f_choice3, n_subs = 9,\n",
    "                                                                   epochs = 500, optimizer_type = 'Adam', n_translayer = 1, k = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot ROC for EEG + EyeGaze\n",
    "plot_all_roc(pred_label_list3, f_type = 'EEG and EyeGaze')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### t-SNE on ReadingEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "1. Train Model on the whole Dataset\n",
    "2. Output ReadingEmbeddings\n",
    "3. Exclude Paddings for ReadingEmbeddings and Labels\n",
    "4. Apply t-SNE and Plot\n",
    "'''\n",
    "# Word Embedding Only\n",
    "feat_choice = [1, 0, 0] # [WordEmbeddings, EEG, EyeGaze]\n",
    "epochs = 480\n",
    "\n",
    "# Initialize Model\n",
    "mymodel1 = ReadingEmbeddingModel(word_embedding_dim=768, \n",
    "                              eeg_feature_dim = 5460,\n",
    "                              eye_gaze_feature_dim=12, \n",
    "                              projection_dim=128,\n",
    "                              n_trans = 1, \n",
    "                              feat_choice = feat_choice,\n",
    "                              MultiHeaded = True, num_heads = 3)\n",
    "\n",
    "# Set Device\n",
    "mymodel1.to(device)\n",
    "downsampled_data_device = [d.to(device) for d in downsampled_data]\n",
    "\n",
    "# Train\n",
    "(loss_list_real, acc_list_real), (test_loss, test_acc1), cm1, f11, (preds, labels, valid_indices) = train_model(mymodel1, batch_size = 16, epochs = epochs,\n",
    "            train_data = downsampled_data_device, test_data = downsampled_data_device, downsample = False,\n",
    "            metrics = [total_loss, relevance_accuracy], \n",
    "            opt_type = 'Adam',\n",
    "            lr = 0.05, min_lr = -1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train\n",
    "(loss_list_real, acc_list_real), (test_loss, test_acc1), cm1, f11, (preds, labels, valid_indices) = train_model(mymodel1, batch_size = 16, epochs = epochs,\n",
    "            train_data = downsampled_data_device, test_data = downsampled_data_device, downsample = False,\n",
    "            metrics = [total_loss, relevance_accuracy], \n",
    "            opt_type = 'SGD',\n",
    "            lr = 0.03, min_lr = -1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get ReadingEmbeddings\n",
    "reading_emb1 = mymodel1.get_reading_embeddings(d_embeddings, d_eeg_features, d_gaze_features).clone().detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#re = mymodel.get_reading_embeddings(torch.tensor([[[0]]]), d_eeg_features, torch.tensor([[[0]]])).clone().detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb_no_pad = torch.cat([sen[:l] for sen, l in zip(d_labels, d_sen_len)], dim = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove Paddings\n",
    "re_no_pad1 = torch.cat([sen[:l] for sen, l in zip(reading_emb1, d_sen_len)], dim = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAHFCAYAAAA5eNVQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3gU1fvFz8zWbHpPCIGEFnoTEERpgnQLij8pChYsiL2BooCiYkP9ioIdUbGChSK9qBTpNRBa6Anpve3u3N8f78yW7G6ym2wauZ/nyQOZnZ25Mzu7OfPuuecVGGMMHA6Hw+FwOBwOp9qIdT0ADofD4XA4HA7naoGLaw6Hw+FwOBwOx0twcc3hcDgcDofD4XgJLq45HA6Hw+FwOBwvwcU1h8PhcDgcDofjJbi45nA4HA6Hw+FwvAQX1xwOh8PhcDgcjpfg4prD4XA4HA6Hw/ESXFxzOBwOh8PhcDhewiNxvX37dsyePRs5OTluP4cxhh9//BE33HADIiIioNfr0bRpUwwdOhRffPGF3bqCIEAQBMybN89hO4sXL4YgCNizZ49l2ezZsy3PcfZz9uxZp2NKT0+HVqvFXXfd5XLceXl5MBgMuPnmmwEAAwYMwIABA9w+7ppCOWZbanpsb7zxBn7//XeH5Vu2bIEgCNiyZUuN7dsbfPTRR2jVqhW0Wi0EQXB5/Tq7xmwZNWoU4uLi7JbFxcVh8uTJVRrXgAED0LFjxyo9V9m3cq2LoojAwEC0a9cO99xzD9atW+f0OYIgYPbs2VXeZ33B2fugLnH3OrB9zQRBgK+vL7p3744FCxagrprlTp482eG6rsvrpKLP9Kq+15xR2fvdm3hyvZY/98o4Xf09qw/MnDkTzZo1g1qtRlBQUF0PxyXTpk2DIAhITU21W56VlQVRFKHRaFBQUGD32MWLFyEIAp5++ukaHZuza6T854Xtjzf/5it/y3/99VevbdMVnlzP5T9XG4rmAAC1Jytv374dc+bMweTJk91+A82YMQNvvfUWpkyZgueeew7+/v44d+4cNm3ahD/++AMPPPCAw3PmzZuHBx98ECEhIW7tY82aNQgMDHRYHh0d7XT98PBw3Hzzzfj999+RnZ2N4OBgh3V+/PFHFBcX4/777wcAfPLJJ26NpS6o6bG98cYbuOOOO3DrrbfaLe/evTt27NiB9u3b1+j+q8OBAwfw+OOP44EHHsCkSZOgVqvh7+/vte3/9ttvCAgI8Nr2PKVv37549913AQAFBQVISkrCjz/+iKFDh+L222/HDz/8AI1GY1l/x44daNq0aV0N12s88MADGDZsWF0Po0rYvmaXL1/G/Pnz8dhjjyEvLw8vvvhiHY+OqOvr5I477sAzzzzjsDw8PLwORlO3jBw5Ejt27HD596yu+eOPP/D666/jpZdewvDhw6HT6ep6SC4ZOHAgPv74Y2zZssWuuLZ161ao1SSH/v33X7vPls2bN1ueWxfYfl7YUpd/d+qKhqA5FDwS155SXFyMDz74APfccw8+++wzu8cmT54MSZIcnjN48GBs2bIFr7/+Ot577z239nPNNdcgLCzMo7Hdf//9WLZsGb7//ntMmzbN4fGvvvoKkZGRGDlyJADU6xezrsYWEBCA3r1718m+3eXo0aMAgClTpqBXr15e3363bt28vk1PCAoKsnsNBg8ejEcffRSzZ8/GnDlzMHPmTLz11luWx+v76+UuTZs2bbA3Cc5es2bNmuHTTz+tN+K6rq+TyMjIOh9DfSE8PLxe31QcOXIEAPD4448jIiKiwnWLi4vh4+NTG8NyyoABAyyVT1txvWXLFvTs2ROMMWzevNlOXG/ZsgWiKKJfv37V2rfZbIbJZPL45qP850VjpiFoDgW3bSGzZ8/Gc889BwCIj4+3fDVRUXm+sLAQpaWlLu+4RdFx9wkJCbj//vvx8ccf49y5c+4Oz2OGDh2Kpk2b4uuvv3Z47NixY/jvv/9wzz33WO5mnVkvFi5ciC5dusDPzw/+/v5o27at3R9HV18FOvta5KeffsJNN92E6Oho+Pj4oF27dpg+fToKCwsrPZbyY5s8ebLLr5KUrxtLSkrwzDPPoGvXrggMDERISAj69OmDP/74w27bgiCgsLAQ33zzjcPXUa6+ovnzzz/Rp08fGAwG+Pv7Y8iQIdixY4fdOsq5OXr0KMaNG4fAwEBERkbivvvuQ25ubqXHDNANUJcuXaDX6xESEoLbbrsNx44dszsvEydOBABce+21Xv9aGXBuBzh69ChuuukmGAwGhIeH49FHH8WqVatcvl92796NG264AQaDAS1atMC8efOc3nh6wuzZs9GhQwcsWLAAJSUlluWuvnLetGkTpkyZgtDQUAQEBOCee+5BYWEhUlNTceeddyIoKAjR0dF49tlnYTQa7fZVVlaGuXPnom3bttDpdAgPD8e9996L9PR0h3M1atQorFmzBt27d4ePjw/atm2Lr776ym69oqIiPPvss4iPj7e8tj169MAPP/xgd3zl31uSJOHtt9+2jCMiIgL33HMPLl68aLeeYsep7Ly7+x6pLgEBAWjTpg2uXLlit9zd8+rJZ8fixYuRkJAAnU6Hdu3aYcmSJU7H5Oo62bx5Mx555BGEhYUhNDQUY8aMweXLl+2eW1paimeeeQZRUVEwGAzo168f9u7dWy0LlTMmT54MPz8/HD9+HEOHDoWvry+io6MttsKdO3fi+uuvh6+vL9q0aYNvvvnG6Xays7Nx7733IiQkBL6+vhg9ejTOnDnjsN6GDRtw4403IiAgAAaDAX379sXGjRsd1lu1ahW6du0KnU6H+Ph4p1VHgKyHynvOz88Pw4YNw4kTJxzWc/b3wt1rGHD/82j//v0YNWoUIiIioNPp0KRJE4wcOdLh/WNLXFwcZs6cCYBuiGyvG+X9vnz5cnTr1g16vR5z5swBQIL8lltuQXBwMPR6Pbp27erw+ih/X5YuXYoXXngB0dHR8PPzw+jRo3HlyhXk5+fjwQcfRFhYGMLCwnDvvfc6WDrKExoaik6dOjl8Dm/ZsgUDBgxA//79LZVq28e6d+9u+Xb8/PnzmDhxouU8tWvXDu+9957deT979iwEQcDbb7+NuXPnIj4+HjqdzrJtd68Rd1E+Dw8dOoSxY8daPq+efvppmEwmJCUlYdiwYfD390dcXBzefvttp9spKSnB008/jaioKPj4+KB///7Yv3+/w3p79uzBzTffjJCQEOj1enTr1g0///yzw3o7d+5E3759odfr0aRJE8yYMcPh7wcAGI1GPP/885bPjOuvvx67du1yWM+Z5lA+B06dOoURI0bAz88PsbGxeOaZZ1BaWmr3/IsXL+KOO+6Av78/goKCMGHCBOzevRuCIGDx4sWW9c6cOYO77roLTZo0gU6nQ2RkJG688UYcOHDAxSvgBOYmFy5cYI899hgDwJYvX8527NjBduzYwXJzcyt8XqtWrZi/vz9777332LFjx5gkSS7XBcAeffRRlpKSwgwGA7v77rstj3399dcMANu9e7dl2axZsxgAlpqayoxGo92PyWSq9JhmzpzJALADBw7YLX/uuecYAHbs2DHLsv79+7P+/ftbfv/hhx8YAPbYY4+xdevWsQ0bNrBFixaxxx9/3GF85VGOJTk52bLstddeY++//z5btWoV27JlC1u0aBGLj49nAwcOtHuus22WH9upU6csr4/yM3HiRAaA/fTTT4wxxnJyctjkyZPZt99+yzZt2sTWrFnDnn32WSaKIvvmm28s29qxYwfz8fFhI0aMsGzr6NGjjDHGNm/ezACwzZs3W9b//vvvGQB20003sd9//5399NNP7JprrmFarZb9888/DseRkJDAXnnlFbZ+/Xo2f/58ptPp2L333utwzsrzxhtvMABs3LhxbNWqVWzJkiWsRYsWLDAwkJ04cYIxxtjRo0ctr/HXX3/NduzYwU6dOuVym8rrsnPnTofryWg0shEjRrDmzZvbPad58+Zs0qRJlt8vX77MQkNDWbNmzdjixYvZ6tWr2d13383i4uIczlX//v1ZaGgoa926NVu0aBFbv349mzp1KgNg9xq4onnz5mzkyJEuH58+fToDYHfeAbBZs2Y5HHN8fDx75pln2Lp169hbb73FVCoVGzduHOvevTubO3cuW79+PXvhhRcYAPbee+9Znm82m9mwYcOYr68vmzNnDlu/fj374osvWExMDGvfvj0rKiqyG2/Tpk1Z+/bt2ZIlS9jatWvZ2LFjGQC2detWy3oPPfQQMxgMbP78+Wzz5s1s5cqVbN68eeyjjz6yrOPsffDggw8yAGzatGlszZo1bNGiRSw8PJzFxsay9PR0j8+7u+8R5dhsrwNXOHvNjEYji4qKYp06darSeXX3s0N5rW+55Ra2YsUK9t1337FWrVqx2NhYh+va1XXSokUL9thjj7G1a9eyL774ggUHBzvsZ9y4cUwURTZ9+nS2bt069sEHH7DY2FgWGBjo1jkCwKZOner0PWj792PSpElMq9Wydu3asQ8//JCtX7+e3XvvvQwAmzFjBmvTpg378ssv2dq1a9moUaMYALZnzx6HY4qNjWX33Xcf++uvv9hnn33GIiIiWGxsLMvOzras++233zJBENitt97Kli9fzlasWMFGjRrFVCoV27Bhg2W9DRs2MJVKxa6//nq2fPly9ssvv7CePXuyZs2a2V2vkiSxgQMHMp1Ox15//XW2bt06NmvWLNaiRQuX597274W717C7n0cFBQUsNDSU9ejRg/38889s69at7KeffmIPP/wwS0xMdPla7du3j91///0MAFuzZg3bsWMHu3DhAmOMrvXo6GjWokUL9tVXX7HNmzezXbt2sePHjzN/f3/WsmVLtmTJErZq1So2btw4BoC99dZblm0rf1+aN2/OJk+ebHlP+/n5sYEDB7IhQ4awZ5991u4z67HHHqv0+nriiScYAHb58mXGGGMZGRlMEAS2du1a9tdffzGVSmXRNefPn2cA2HPPPccYYywtLY3FxMSw8PBwtmjRIrZmzRo2bdo0BoA98sgjln0kJyczACwmJoYNHDiQ/frrr2zdunUsOTnZ7WtEOYcjRoyo9L1g+/f0tddeY+vXr2fPP/+85fOwbdu27H//+5/de2TZsmUO5zo2Ntbh8yEgIICdPn3asu6mTZuYVqtlN9xwA/vpp5/YmjVr2OTJky1/ZxWOHj3KDAYDa9++Pfvhhx/YH3/8wYYOHWo5TtvredKkSUwQBPbcc8+xdevWsfnz57OYmBgWEBBg95nhTHPYfg68++67bMOGDeyVV15hgiCwOXPmWNYrKChgrVq1YiEhIezjjz9ma9euZU899RSLj493GHtCQgJr1aoV+/bbb9nWrVvZsmXL2DPPPGO338pwW1wzxtg777zjcFIqY9euXZaTCYD5+/uzUaNGsSVLljgIbUVcM8bYSy+9xERRZAcPHmSMVSyunf20bNmy0rGdOXOGCYJgJ4iVP3R9+/a1W7e8gJ02bRoLCgqqcPueiGtbJEliRqORbd26lQGwnANX2yw/tvL8/PPPTBAE9uKLL7pcx2QyMaPRyO6//37WrVs3u8d8fX2d/lEsf6GbzWbWpEkT1qlTJ2Y2my3r5efns4iICHbdddc5HMfbb79tt82pU6cyvV5f4U1Ydna2RfDbcv78eabT6dj48eMty5xdN65Q1q3opzJx/dxzzzFBECw3IApDhw51Kq4BsP/++89u3fbt27OhQ4dWOt7KxPXChQvtbqgYcy2ayv9RuvXWWxkANn/+fLvlXbt2Zd27d7f8rtxk2n5QM8bY7t27GQD2ySef2I1Xr9ezc+fOWZYVFxezkJAQ9tBDD1mWdezYkd16660VHnv598GxY8csosyW//77jwGwu/aret4reo94Iq5t/1ieO3eOTZkyhWk0GrZy5UrLep6cV1tcfXYo783u3bvbvbfOnj3LNBqN2+K6/Pl9++23GQCWkpLCGKM/qADYCy+8YLeecjzuimtXP99++61lvUmTJjmcI6PRyMLDwxkAtm/fPsvyzMxMplKp2NNPP+1wTLfddpvd/rdt28YAsLlz5zLGGCssLGQhISFs9OjRduuZzWbWpUsX1qtXL8uya6+9ljVp0oQVFxdbluXl5bGQkBC76/Wvv/5iANiHH35ot83XX3/dbXHtzjXs7ufRnj17GAD2+++/M09R3ou2N7CM0bWuUqlYUlKS3fK77rqL6XQ6dv78ebvlw4cPZwaDgeXk5DDGrH9fyp/3J598kgGw+7vNGH1mhYSEVDre33//nQFgS5cuZYwxtmzZMqZWq1l+fj7Ly8tjKpXK8l785ptvGAC2evVqxpi1YFH+vD/yyCNMEATLsSriumXLlqysrMxuXXevEcboHLp6L7z22muW9ZTXwLbwwRh9XgNUEFVQ3iNjxoyxLFPOtavPhwceeMCyrG3btqxbt27MaDTa7WvUqFEsOjra8rf///7v/5iPjw9LTU21rGMymVjbtm3trmfls/upp56y255SqHNHXANgP//8s93zR4wYwRISEiy/f/zxxwwA++uvv+zWe+ihh+zEdUZGBgPAPvjgA1YdvBLFJ0kSTCaT5cdsNlse69mzJ06dOoU1a9bgxRdfRJ8+fbBx40bcc889uPnmm13OkH/++ecREhKCF154odL9b9iwAbt377b7cZZuUZ74+HgMHDgQ33//PcrKygAAf/31F1JTU3HfffdV+NxevXohJycH48aNwx9//IGMjIxK91cRZ86cwfjx4xEVFQWVSgWNRoP+/fsDgJ3VwVO2bt2Ku+++GxMnTsTrr79u99gvv/yCvn37ws/PD2q1GhqNBl9++WWV95eUlITLly/j7rvvtrP8+Pn54fbbb8fOnTtRVFRk9xwljUWhc+fOKCkpQVpamsv97NixA8XFxQ5fMcfGxmLQoEFOv6r1hCVLljhcT7t378b1119f6XO3bt2Kjh07Ovjgx40b53T9qKgoBy94586dvWKJcvXecsaoUaPsfm/Xrh0AWOYc2C63HdvKlSsRFBSE0aNH230GdO3aFVFRUQ5fv3bt2hXNmjWz/K7X69GmTRu7bfbq1Qt//fUXpk+fji1btqC4uLjS8Stft5a/Jnr16oV27do5XBPunndvv0cAYPXq1dBoNNBoNGjevDk+//xzfPTRR3bn2pPz6s5nh/LeHD9+vJ2dpnnz5rjuuuvcHruz9ysAy3nbunUrAODOO++0W++OO+6wWOzc4c4773T6HhwxYoTdeoIg2C1Tq9Vo1aoVoqOj7eZDhISEICIiwun7asKECXa/X3fddWjevLnlmtq+fTuysrIwadIku9dCkiQMGzYMu3fvRmFhIQoLC7F7926MGTMGer3esj1/f3+MHj3abh/Ktsvve/z48W6fI3euYXc/j1q1aoXg4GC88MILWLRoERITE90eR0V07twZbdq0sVu2adMm3HjjjYiNjbVbPnnyZBQVFTlYCD35bMrKyqrUGtK/f3+Iomh5D23ZsgU9evSw2Du7d+9ueX22bNkCtVpt+ezftGkT2rdv73DeJ0+eDMYYNm3aZLf85ptvtptQ7sk1onD99dc7fS8oYQu2ODtXgiBg+PDhlmXKe8TZe8HV54NyPk6dOoXjx49brlvb98OIESOQkpKCpKQkAHSN33jjjYiMjLRsT6VS4f/+7//s9unqvXDnnXe6/ZkhCILD+XP2XvD393eYCF/+vRASEoKWLVvinXfewfz587F///4q2TS9MqHx1VdftXipAHpBbP1hGo0GQ4cOxdChQwEAmZmZuOOOO7By5Ur89ddfDh+YAPkQZ86ciSeffNLBA1WeLl26eDyhUeH+++/HhAkT8Oeff+KOO+7A119/DT8/P4c/DuW5++67YTKZ8Pnnn+P222+HJEno2bMn5s6diyFDhng0hoKCAtxwww3Q6/WYO3cu2rRpA4PBgAsXLmDMmDFuiQtnHD16FLfeeituuOEGfPnll3aPLV++HHfeeSfGjh2L5557DlFRUVCr1Vi4cKGDB9ZdMjMzAThPaWnSpAkkSUJ2djYMBoNleWhoqN16ymSPio65sv2sX7/e88Hb0K5dO/To0cNheWBgIC5cuFDhczMzMxEfH++w3PYDxpbyxw/QOajqa26L8sHSpEmTStctn8yj1WpdLrf1cF+5cgU5OTmW9ctT/qbTneP93//+h6ZNm+Knn37CW2+9Bb1ej6FDh+Kdd95B69atne6nsmui/B8Sd8ZRE+8RgP5Yvv/++zCbzTh58iRefvllTJs2DR06dLD8EXf3vLr72aGcn6ioKIdtRUVFuR3zVtn7VdlP+etdrVY7PeeuCA8Pd/oeLI/BYLATKQBdo86SpspfuwquzolyLIoX/o477nA5jqysLAiCAEmSXG7PlszMTKfnxNlzXeHONezu51FgYCC2bt2K119/HS+++CKys7MRHR2NKVOmYObMmXYC0ROcvR8zMzNdvk+Vx23x5LMJIN+wn5+fyzEFBQWha9euFl2xefNmO6Fu67vevHkzevToYUmYyszMdIitrGjs5Y8zOzvb7WtEITAw0K33AuD8nLh6j+Tl5bk1hqioKBw8eBCA9b3w7LPP4tlnn3U6BuWzKTMz0+33grPlnnxmODtGnU5n937PzMx0+ne4/DJBELBx40a8+uqrePvtt/HMM88gJCQEEyZMwOuvv+522phXxPWDDz5od8dU2WzY0NBQPPnkk9iyZQuOHDniVFwDwCOPPIIPP/wQL7zwAh555BFvDNWBMWPGIDg4GF999RX69++PlStX4p577qnwzalw77334t5770VhYSH+/vtvzJo1C6NGjcKJEyfQvHlzy4tdWlpqd07KC45Nmzbh8uXL2LJli6XiBMCjPPHyXLx4EcOGDUOzZs2wbNkyhw/H7777DvHx8fjpp5/s7lTLTwDwBOWNkJKS4vDY5cuXIYqi09hDb++nqjda3iA0NNRhYhoAh1zVmoYxhhUrVsDX19ftD+aqoExsW7NmjdPHqxJ76Ovrizlz5mDOnDm4cuWKpYo9evRoHD9+3OlzbK+J8ikiVb0mauI9Atj/sbz22mtx7bXXokuXLpg6dSoOHDgAURTdPq/ufnYo58fZdejNa1PZz5UrVxATE2NZbjKZHIRHfcHVOWnVqhUAWK6djz76yGVSQWRkJIxGo9MMZWf7CA0NtZwTWwHh7c8JTz6POnXqhB9//BGMMRw6dAiLFy/Gq6++Ch8fH0yfPr1K+3c2oT80NNTlZzeAWvn8HjhwIN577z0cOnQIR48etZvg179/f8yfPx+HDh3C2bNn7Sqbno69/PEHBwe7fY3UBa7GpVyjyvHNmDEDY8aMcbqNhIQEAHSu3H0vKMtr8jMjNDTU6SRJZ2Ns3ry5pSB54sQJ/Pzzz5g9ezbKysqwaNEit/bnkS3EVVWxSZMm6NGjh+WnU6dOAGgGqKuTo3xdWVFVTavVYu7cudi9ezd++eUXT4bqNnq9HuPHj8e6devw1ltvwWg0VmoJKY+vry+GDx+Ol156CWVlZZb4N+UO99ChQ3brr1ixwu535Q1Y/qbk008/9WgcCrm5uRg+fDgEQcDq1aud5mEKgmBpqqKQmprqNAnB3UpqQkICYmJisHTpUjtLQmFhIZYtW2ZJEKkuffr0gY+PD7777ju75RcvXrR85VhX9O/fH0eOHHH4WvXHH3+s1XHMmTMHiYmJeOKJJxzu6L3JqFGjkJmZCbPZbPcZoPwoH7RVJTIyEpMnT8a4ceOQlJTkYCtSGDRoEAA4XBO7d+/GsWPHqnRNePIeqQ6tW7fG888/j8OHD+Onn34C4P55dfezIyEhAdHR0fjhhx/s3pvnzp3D9u3bvXYsSlyZchwKv/76K0wmk9f2402+//57u9+3b9+Oc+fOWVKR+vbti6CgICQmJjp9LXr06AGtVgtfX1/06tULy5cvt6uY5efnO3zmK5nJ5fe9dOlSrx5bVT6PBEFAly5d8P777yMoKAj79u3z6phuvPFGy02hLUuWLIHBYKiVqDXl/M+ZMweiKNpZ/pT/K9/G2+Zb33jjjUhMTHQ4J0uWLIEgCJVmYXtyjdQFrj4flPdCQkICWrdujYMHD7p8Lyg3/gMHDsTGjRvtbu7MZrPDZ4Oy7fLvhZ9//tmrnxn9+/dHfn4+/vrrL7vllf1tbtOmDWbOnIlOnTp59F7wqHKtiOYPP/wQkyZNgkajQUJCgsvqVG5uLuLi4jB27FgMHjwYsbGxKCgowJYtW/Dhhx+iXbt2Lu9+FMaNG4d3333X4YTYsnfvXqdNZNq3b+9W0LoS/Td//ny0bdvWLQ/ilClT4OPjg759+yI6Ohqpqal48803ERgYiJ49ewIARowYgZCQENx///149dVXoVarsXjxYgdrwXXXXYfg4GA8/PDDmDVrFjQaDb7//nvLVzGeMn78eCQmJuKzzz7DhQsX7Pan5AMrEUlTp07FHXfcgQsXLuC1115DdHQ0Tp48abc9JbpoxYoViI6Ohr+/v1PRJIoi3n77bUyYMAGjRo3CQw89hNLSUrzzzjvIyclx2nmzKgQFBeHll1/Giy++iHvuuQfjxo1DZmYm5syZA71ej1mzZnllP1XhySefxFdffYXhw4fj1VdfRWRkJJYuXWqpuDqLn6wOOTk52LlzJwC6iVGayPzzzz+488477exaNcFdd92F77//HiNGjMATTzyBXr16QaPR4OLFi9i8eTNuueUW3HbbbR5t89prr8WoUaPQuXNnBAcH49ixY/j2228rvDlLSEjAgw8+iI8++giiKGL48OE4e/YsXn75ZcTGxuKpp57y+Ng8eY9Ul2effRaLFi3CnDlzcOedd7p9Xt397BBFEa+99hoeeOAB3HbbbZgyZQpycnIwe/Zsj6wIldGhQweMGzcO7733HlQqFQYNGoSjR4/ivffeQ2BgoNvX/5UrVyzXtS0BAQFez/Xfs2cPHnjgAYwdOxYXLlzASy+9hJiYGEydOhUAzRn56KOPMGnSJGRlZeGOO+5AREQE0tPTcfDgQaSnp2PhwoUAgNdeew3Dhg3DkCFD8Mwzz8BsNuOtt96Cr68vsrKyLPu86aab0K9fPzz//PMoLCxEjx49sG3bNnz77bdePTZ3P49WrlyJTz75BLfeeitatGgBxhiWL1+OnJwcj22OlTFr1iysXLkSAwcOxCuvvIKQkBB8//33WLVqFd5++22nf8u9Tb9+/aBSqfDbb7/ZCUKA/r506dIFv/32GzQaDfr27Wt57KmnnsKSJUswcuRIvPrqq2jevDlWrVqFTz75BI888oiDv9wZ7l4jCraf8bbodDqv91lIS0uzfD7k5uZi1qxZ0Ov1mDFjhmWdTz/9FMOHD8fQoUMxefJkxMTEICsrC8eOHcO+ffsshdCZM2fizz//xKBBg/DKK6/AYDDg448/dogIbdeuHSZOnIgPPvgAGo0GgwcPxpEjR/Duu+96tVHOpEmT8P7772PixImYO3cuWrVqhb/++gtr164FYH0vHDp0CNOmTcPYsWPRunVraLVabNq0CYcOHfLsGxxPZ0DOmDGDNWnShImi6DBrszylpaXs3XffZcOHD2fNmjVjOp2O6fV61q5dO/b888+zzMxMu/VhkxZiy7p16ywzZN1NCwHA1q9f7/ZxdevWzWl6hUL5RI5vvvmGDRw4kEVGRjKtVsuaNGnC7rzzTnbo0CG75+3atYtdd911zNfXl8XExLBZs2axL774wmH29/bt21mfPn2YwWBg4eHh7IEHHmD79u1ziIhxJy2kohnGtrPQ582bx+Li4phOp2Pt2rVjn3/+udPtHzhwgPXt25cZDAYGwLIvZzN3GaPZ2Ndeey3T6/XM19eX3XjjjWzbtm1267iaYV5ZkootX3zxBevcuTPTarUsMDCQ3XLLLQ6z4quSFuJq3ZEjR1aaFsIYY0eOHGGDBw9mer2ehYSEsPvvv98y69w2+aV///6sQ4cODvuZNGmSw36cYfs6C4LA/Pz8WEJCArv77rvZ2rVrnT6n/DXg6phdvT6TJk1ivr6+dsuMRiN79913WZcuXZher2d+fn6sbdu27KGHHmInT560G6+zdJPy1+/06dNZjx49WHBwMNPpdKxFixbsqaeeYhkZGQ7js8VsNrO33nqLtWnThmk0GhYWFsYmTpxoiQaz3Z+7593d90h1ovgUlNnsSpSau+fV3c8Oxug907p1a6bValmbNm3YV1995fS43b1OnH0GlJSUsKeffppFREQwvV7PevfuzXbs2MECAwMdEgGcUdFnum2Kk7NrkTHXr2/5c68c07p169jdd9/NgoKCLClEtudXYevWrWzkyJEsJCSEaTQaFhMTw0aOHMl++eUXu/X+/PNPy+dSs2bN2Lx585xeMzk5Oey+++5jQUFBzGAwsCFDhrDjx4+7nRbi7jXszufR8ePH2bhx41jLli2Zj48PCwwMZL169WKLFy922Ed5KkoLcXWtHz58mI0ePZoFBgYyrVbLunTp4nCtKtdW+fPr6WeWK3r16sUAsGeffdbhMSWRpHxqGGOMnTt3jo0fP56FhoYyjUbDEhIS2DvvvGOXkKWkhbzzzjtO9+3uNVLR3/KYmJhKj93d94hyrr/99lv2+OOPs/DwcKbT6dgNN9xgF1+pcPDgQXbnnXeyiIgIptFoWFRUFBs0aBBbtGiR3Xrbtm1jvXv3ZjqdjkVFRbHnnnuOffbZZw7Xc2lpKXvmmWccPjPKf666SgtxdozOzuf58+fZmDFjmJ+fH/P392e33347W716NQPA/vjjD8YYY1euXGGTJ09mbdu2Zb6+vszPz4917tyZvf/++25FPCsIjHkQKcDhcKrEgw8+iB9++AGZmZkuJ6lxOFcr27dvR9++ffH99997lIjBqRn45xGHQ7zxxhuYOXMmzp8/79WuvzXa/pzDaYy8+uqraNKkCVq0aIGCggKsXLkSX3zxBWbOnMn/kHGuetavX48dO3bgmmuugY+PDw4ePIh58+ahdevWldoAOd6Hfx5xOMSCBQsAAG3btoXRaMSmTZvwv//9DxMnTvSqsAa4uOZwvI5Go8E777yDixcvwmQyoXXr1pg/fz6eeOKJuh4ah1PjBAQEYN26dfjggw+Qn5+PsLAwDB8+HG+++WaNTq7lOId/HnE4hMFgwPvvv4+zZ8+itLQUzZo1wwsvvICZM2d6fV/cFsLhcDgcDofD4XgJ70YXcDgcDofD4XA4jRgurjkcDofD4XA4HC/BxTWHw+FwOBwOh+Ml+ITGqwhJknD58mX4+/s7bTvL4XA4HA6n/sEYQ35+Ppo0aeL1ZmOc2oeL66uIy5cvIzY2tq6HweFwOBwOpwpcuHDB67FwnNqHi+urCKWF64ULF7zaNpTD4XA4HE7NkZeXh9jYWLtW7JyGCxfXVxGKFSQgIICLaw6Hw+FwGhjc0nl1wI09HA6Hw+FwOByOl+DimsPhcDgcDofD8RJcXHM4HA6Hw+FwOF6Ce645HA6H02gxm80wGo11PQzOVY5Go4FKparrYXBqCS6uORwOh9PoYIwhNTUVOTk5dT0UTiMhKCgIUVFRfNJiI4CLaw6Hw+E0OhRhHRERAYPBwAUPp8ZgjKGoqAhpaWkAgOjo6DoeEaem4eKaw+FwOI0Ks9lsEdahoaF1PRxOI8DHxwcAkJaWhoiICG4RucrhExo5HA6H06hQPNYGg6GOR8JpTCjXG/f4X/1wcc3hcDicRgm3gnBqE369NR64LYTDaYCYJYadZzKx43QmAIY+LcLQu2UoVCL/8OZwOBwOpy7h4prDaWCsOZKC6csPI6fI+tXigs2nEWTQYN6YThjWkU+W4XA49ZPFixfjySef5CktnKsabgvhcOo5Zolhx+lM/HHgEj7ccBIPf7fPTlgr5BQZ8fB3+7DmSEodjJLD4dQ0kydPxq233lpn+4+Li4MgCBAEAT4+Pmjbti3eeecdMMbqbEwcTn2EV645nHrMmiMpmLMiESm5JW4/Z/afRzGkfRQAYOfpTOw4kwFAQJ+WoejdgltHOBxvYZYYdiVnIS2/BBH+evSKD7nq31+vvvoqpkyZgpKSEmzYsAGPPPIIAgIC8NBDD9X10DicegOvXHM49ZQ1R1LwyHf7PBLWAJCaV4onf9yHa+aux4Qv/8OCzaexYPMpTPjiP3SesxarD12uoRFzOI2HNUdScP1bmzDu85144scDGPf5Tlz/1qY6/eZo69at6NWrF3Q6HaKjozF9+nSYTCYAwIoVKxAUFARJkgAABw4cgCAIeO655yzPf+ihhzBu3LgK9+Hv74+oqCjExcXhgQceQOfOnbFu3TrL42VlZXj++ecRExMDX19fXHvttdiyZUuF21yxYgWuueYa6PV6tGjRAnPmzLGMe9y4cbjrrrvs1jcajQgLC8PXX38NAFizZg2uv/56BAUFITQ0FKNGjcLp06ct6589exaCIGD58uUYOHAgDAYDunTpgh07dthtd9u2bejfvz8MBgOCg4MxdOhQZGdnA6Cs6rfffhstWrSAj48PunTpgl9//bXC4+I0Xri45nDqIWaJYc6KRFT1y9YVh1KdWkcKS82YunQ/3lydWL0BcjiNGFc3vqm5JXikjqxZly5dwogRI9CzZ08cPHgQCxcuxJdffom5c+cCAPr164f8/Hzs378fAAnxsLAwbN261bKNLVu2oH///m7tjzGGLVu24NixY9BoNJbl9957L7Zt24Yff/wRhw4dwtixYzFs2DCcPHnS6XbWrl2LiRMn4vHHH0diYiI+/fRTLF68GK+//joAYMKECfjzzz9RUFBg95zCwkLcfvvtAIDCwkI8/fTT2L17NzZu3AhRFHHbbbdZbiQUXnrpJTz77LM4cOAA2rRpg3HjxllE/IEDB3DjjTeiQ4cO2LFjB/7991+MHj0aZrMZADBz5kx8/fXXWLhwIY4ePYqnnnoKEydOtDt/HI6CwLhZ6qohLy8PgYGByM3NRUBAQF0Ph1MNdpzOxLjPd9boPvq1DsOnd/eAj5Y3M+A0LkpKSpCcnIz4+Hjo9XqPnmuWGK5/a5PLb5QEAFGBevz7wiCvW0QmT56MnJwc/P777w6PvfTSS1i2bBmOHTtmiXz75JNP8MILLyA3NxeiKOKaa67B+PHj8cwzz+C2225Dz549MWfOHGRkZKCwsBDR0dE4duwY2rZt63T/cXFxSElJgUajQVlZGYxGI/R6PTZu3IjrrrsOp0+fRuvWrXHx4kU0adLE8rzBgwejV69eeOONNxwmNPbr1w/Dhw/HjBkzLOt/9913eP7553H58mUYjUY0adIE8+fPx9133w0AGD9+PEwmE37++Wen40xPT0dERAQOHz6Mjh074uzZs4iPj8cXX3yB+++/HwCQmJiIDh06WI53/PjxOH/+PP7991+H7RUWFiIsLAybNm1Cnz59LMsfeOABFBUVYenSpRW8alYquu743++rC1655nDqGWaJYdup9Brfz98nM9DulTWYsmR3je+Lw7la2JWcVaFViwFIyS3BruSs2hsUgGPHjqFPnz52Wcp9+/ZFQUEBLl68CAAYMGAAtmzZAsYY/vnnH9xyyy3o2LEj/v33X2zevBmRkZEuhbXCc889hwMHDmDr1q0YOHAgXnrpJVx33XUAgH379oExhjZt2sDPz8/ys3XrVjubhi179+7Fq6++arf+lClTkJKSgqKiImg0GowdOxbff/89ABK6f/zxByZMmGDZxunTpzF+/Hi0aNECAQEBiI+PBwCcP3/ebl+dO3e2/F9pQa60JFcq185ITExESUkJhgwZYjfOJUuWuDwuTuOGT2jkcOoRVZnAWF3WJ6ZhypLd+PyenrW2Tw6noZKW79570931vAVjzKFJifLFtLJ8wIAB+PLLL3Hw4EGIooj27dujf//+2Lp1K7Kzs92yhISFhaFVq1Zo1aoVli1bhlatWqF3794YPHgwJEmCSqXC3r17Hdp7+/n5Od2eJEmYM2cOxowZ4/CYUt2dMGEC+vfvj7S0NKxfvx56vR7Dhw+3rDd69GjExsbi888/R5MmTSBJEjp27IiysjK77dnaV5RzolhHlPbkrsYIAKtWrUJMTIzdYzqdzuXzOI0XLq45nHqC4uOsC5/W+sQ0FJeZuUWEw6mECH/3bCTuruct2rdvj2XLltmJ7O3bt8Pf398iCBXf9QcffID+/ftDEAT0798fb775JrKzs/HEE094tM/g4GA89thjePbZZ7F//35069YNZrMZaWlpuOGGG9zaRvfu3ZGUlIRWrVq5XOe6665DbGwsfvrpJ/z1118YO3YstFotACAzMxPHjh3Dp59+atmnM2tHZXTu3BkbN27EnDlzHB5r3749dDodzp8/77YnndO44eKaw6kHVHcCozd4Y3UiXru1Ux2OgMOp//SKD0F0oB6puSVO36+K57pXfEiN7D83NxcHDhywWxYSEoKpU6figw8+wGOPPYZp06YhKSkJs2bNwtNPPw1RJAdoYGAgunbtiu+++w4ffvghABLcY8eOhdFoxIABAzwez6OPPoq33noLy5Ytwx133IEJEybgnnvuwXvvvYdu3bohIyMDmzZtQqdOnTBixAiH57/yyisYNWoUYmNjMXbsWIiiiEOHDuHw4cOWyZiCIGD8+PFYtGgRTpw4gc2bN1ueHxwcjNDQUHz22WeIjo7G+fPnMX36dI+PY8aMGejUqROmTp2Khx9+GFqtFps3b8bYsWMRFhaGZ599Fk899RQkScL111+PvLw8bN++HX5+fpg0aZLH++Nc3XDPNYdTD6jMx1kbnM0sqtP9czgNAZUoYNbo9gBISNui/D5rdPsay7vesmULunXrZvfzyiuvICYmBqtXr8auXbvQpUsXPPzww7j//vsxc+ZMu+cPHDgQZrPZIqSDg4PRvn17hIeHo127dh6PJzw8HHfffTdmz54NSZLw9ddf45577sEzzzyDhIQE3Hzzzfjvv/8QGxvr9PlDhw7FypUrsX79evTs2RO9e/fG/Pnz0bx5c7v1JkyYgMTERMTExKBv376W5aIo4scff8TevXvRsWNHPPXUU3jnnXc8Po42bdpg3bp1OHjwIHr16oU+ffrgjz/+gFpNNcjXXnsNr7zyCt588020a9cOQ4cOxYoVKyz+bg7HFp4WchXBZxs3XP44cAlP/HigTsdwd+9mvHLNaRRUJy1Ewdn8iOhAPWaNbo9hHaO9NVTOVQRPC2k8cFsIh1MPqG1/pjNeHNG+rofA4TQYhnWMxpD2UY2uQyOHw6kcLq45jZL61ra4V3wIgg0aZDtp/FIbDGkfwSczcjgeohIF9GkZWtfD4HA49QwurjmNDldf5748sh2CfXV1IrhVooDeLULw15ErtbI/W4a0j+AxfBwOh8PheAkurjmNijVHUvDwd/sclqfklmDq0v12y2rbP9ky3B9A7YjrhCh/9IoLxosj2td6xbq+fWvA4XA4HI434eKa02gwSwzTlx92e/3U3BI88t0+LJzYvVYE9rXxIViwufL1vMHs0R2q9XV2VQWys28N/PVqdG8WhH6tw3F3nzho1TzEiMPhcDgNFy6uOY2GBZtOIccDT7MSozNnRSKGtI+q8eqqKNRO9VYUgGuaB3v8PEVQr09Mxe8HLiOr0Nr9LMRXi7m3dMSIzq5vQlw1yckvMWHriQxsPZGBuauP4YHr4/HSSD65ksPhcDgNE14i4jQKzBLDV9uSq/TclNwS/G/jCS+PyJGMwtIa3wcASAzYey7b7fXNEsP765PQefZajPt8J77adtZOWANAVmEZpi7dh2nf74VZckz3dLdJDmPA5/8kY8qS3W6Pj8PhcDic+gQX15xGwYJNJ5FbXPUkjg83nsKbqxO9OCJHajOOLy3fvYY1qw+loMMra/DhxlMoLDNXuv7Kw6loO3M1Vhy8bLfc0yY56xPTsPLAJbfX53A4HA6nvsDFdR3w5ptvQhAEPPnkk5ZljDHMnj0bTZo0gY+PDwYMGICjR4/W3SCvIlYfuoz3N5ys9nY+/TsZqw+leGFEzlHaKteGOcQdIf/m6kRMXboPJSbJo20bJeCxH/bbVZ/dFfO2PLvskNMqOIfD4XA49RkurmuZ3bt347PPPkPnzp3tlr/99tuYP38+FixYgN27dyMqKgpDhgxBfn5+HY306mD1oRRM+2F/5Su6yfM1KPiUtso1LSejA2kCYkWsPnQZn/5dNRuNwvrENDz6/R6YJValqnyJUcLO05nVGgOH0xhZvHgxgoKCPHrO5MmTceutt9bIeCojLi4OH3zwQZ3sm8OpCbi4rkUKCgowYcIEfP755wgOtk4oY4zhgw8+wEsvvYQxY8agY8eO+Oabb1BUVISlS5fW4YgbHmaJYcfpTPxx4BI+3HASU5fugze1cEGpCTvP1JzgG9YxGvf1jaux7QPArNHtK5ycaZYYZv5xxCv7WnX4CvrO24TswjJEB3ousHecyfDKODicqwFXAnjLli0QBAE5OTkAgP/7v//DiRM1P09k8eLFEATB8hMZGYnRo0fzb105jR4urmuRRx99FCNHjsTgwYPtlicnJyM1NRU33XSTZZlOp0P//v2xfft2l9srLS1FXl6e3U9jZs2RFFz/1iaM+3wnnvjxAN7fUDN/XHbUcDV1SPuoGtv2U4NbVxoruPN0JrIKvdcpMjWvBI8u3Yebu1QlzpDnX3PqMZIEZJ8DrhylfyXPLFQ1hY+PDyIiImplXwEBAUhJScHly5exatUqFBYWYuTIkSgrK6v8yRzOVQoX17XEjz/+iH379uHNN990eCw1NRUAEBkZabc8MjLS8pgz3nzzTQQGBlp+YmNjvTvoBoQS8+bJpLmqU7PGjZryXgf5aDBtUOsK11lzJAVTvt3j5T3TGft5z0UsuKsrPEk05K2lOfWW9CTg3/nA5jeArW/Tv//Op+V1jDNbyNy5cxEREQF/f3888MADmD59Orp27erw3HfffRfR0dEIDQ3Fo48+CqOx4httQRAQFRWF6Oho9OjRA0899RTOnTuHpCTredi+fTv69esHHx8fxMbG4vHHH0dhYaHLbebm5uLBBx9EREQEAgICMGjQIBw8eBAAkJSUBEEQcPz4cbvnzJ8/H3FxcWCMwWw24/7770d8fDx8fHyQkJCADz/80G595VuAio63tLQUzz//PGJjY6HT6dC6dWt8+eWXlscTExMxYsQI+Pn5ITIyEnfffTcyMvi3bRwurmuFCxcu4IknnsB3330Hvd71V+NCuZxjxpjDMltmzJiB3Nxcy8+FCxe8NuaGhLsxb96ipvOoFe814Fi3rc6e7+0bX6EdROleWeRGKkhVyC4yYsuJdHx4Vze31g82aNC7BRfXnHpIehKwcxGQcggwhAChrenflEO0vB4IbFu+//57vP7663jrrbewd+9eNGvWDAsXLnRYb/PmzTh9+jQ2b96Mb775BosXL8bixYvd3k9OTo7FyqjRaAAAhw8fxtChQzFmzBgcOnQIP/30E/79919MmzbN6TYYYxg5ciRSU1OxevVq7N27F927d8eNN96IrKwsJCQk4JprrsH3339v97ylS5di/PjxEAQBkiShadOm+Pnnn5GYmIhXXnkFL774In7++WePjveee+7Bjz/+iP/97384duwYFi1aBD8/PwBASkoK+vfvj65du2LPnj1Ys2YNrly5gjvvvNPt88W5euFNZGqBvXv3Ii0tDddcc41lmdlsxt9//40FCxZY7vBTU1MRHW396jwtLc2hmm2LTqeDTqeruYE3EDyNeasu/9t0Cu2bBNRo18ZhHaOxcGJ3h26GUYF6DO8Yha+2nfVoewatCtMGtXL5uHKDUtP8uu8SNiWlY8oN8fhl78UKm/q8OaYTb4vOqX9IEnBsBVCUCYS3BZSbbV0AEO4PpB8Hjq8kwS16v361cuVKi8BTMJsrviH+6KOPcP/99+Pee+8FALzyyitYt24dCgoK7NYLDg7GggULoFKp0LZtW4wcORIbN27ElClTXG47NzcXfn5+YIyhqKgIAHDzzTejbdu2AIB33nkH48ePt6RjtW7dGv/73//Qv39/LFy40KHgtHnzZhw+fBhpaWmWv2/vvvsufv/9d/z666948MEHMWHCBCxYsACvvfYaAODEiRPYu3cvlixZAoCE/Zw5cyzbjI+Px/bt2/Hzzz/bid+KjvfEiRP4+eefsX79eouVs0WLFpbnLly4EN27d8cbb7xhWfbVV18hNjYWJ06cQJs2bSp8TThXN7xyXQvceOONOHz4MA4cOGD56dGjByZMmIADBw6gRYsWiIqKwvr16y3PKSsrw9atW3HdddfV4cgbBlWJeasuc1Yk1nhM3LCO0fj3hUH4YUpvfHhXV/wwpTf+fWEQAn20Hm/roX4tKxSqtXmDklVYhs//SUbflqF48sbWCPTR2D0eHajHolpqOc/heEzuBSDjJBAYYxXWCoIABMQA6SdovRpg4MCBdn9LDhw4gC+++KLC5yQlJaFXr152y8r/DgAdOnSASqWy/B4dHY20tLQKt+3v748DBw5g7969WLRoEVq2bIlFixZZHt+7dy8WL14MPz8/y8/QoUMhSRKSkx0Tifbu3YuCggKEhobaPSc5ORmnT58GANx11104d+4cdu7cCYAq8127dkX79tbOrosWLUKPHj0QHh4OPz8/fP755zh//rzbx3vgwAGoVCr079/f6XHv3bsXmzdvthujckOhjJPTeOGV61rA398fHTt2tFvm6+uL0NBQy/Inn3wSb7zxBlq3bo3WrVvjjTfegMFgwPjx4+tiyA2K5HTX3r2aIiW3BLuSs2rME6y0Gk/LL0GEvx6jOjeBShRglhh+2HW+8g3YEGTQVFi1BurmBmXV4VT4aK7goX4t0TMuFBmFpYjwp5hAXrHm1FvKCgBTCaDxdf641gDkX6b1agBfX1+0amX/fr548WKlz3NmOyyPYuWwfY5UySRNURQt42nbti1SU1Pxf//3f/j7778BAJIk4aGHHsLjjz/u8NxmzZo5LJMkCdHR0diyZYvDY4qPPDo6GgMHDsTSpUvRu3dv/PDDD3jooYcs6/3888946qmn8N5776FPnz7w9/fHO++8g//++8/t4/Xx8anwuCVJwujRo/HWW285PGb7DTSnccLFdT3h+eefR3FxMaZOnYrs7Gxce+21WLduHfz9/et6aPUas8SwZMe5Otl3TQnSNUdSHOwg0YF6zBrdHoE+WqTmebbfe6+Lq3SdMN+6sRcVGxk+2HgKfrpk3NkjtkaTUjgcr6D1A9R6wFhIVpDylBXR41o/x8fqiISEBOzatQt33323ZdmePd6fuAwATz31FObPn4/ffvsNt912G7p3746jR4863BC4onv37khNTYVarUZcXJzL9SZMmIAXXngB48aNw+nTp3HXXXdZHvvnn39w3XXXYerUqZZlnlaTO3XqBEmSsHXrVoeEL2Wcy5YtQ1xcHNRqLqU49nBbSB2xZcsWu9B8QRAwe/ZspKSkoKSkBFu3bnWodnMcWbDpJLKK6ibyKczPM0Fqlhi2ncrAu2uT8O7a49h2MsPBWrL60GU87CT1JDW3BI98tw8bEl2nx7ji/Q0ncf1bm7DmSAXdJeu4UFxQasZX285i3Oc7Kx8rh1OXBMYCYa2B3EtA+eovY0DeJSC8Da1XT3jsscfw5Zdf4ptvvsHJkycxd+5cHDp0qMIJ81UlICAADzzwAGbNmgXGGF544QXs2LEDjz76KA4cOICTJ0/izz//xGOPPeb0+YMHD0afPn1w6623Yu3atTh79iy2b9+OmTNn2t0QjBkzBnl5eXjkkUcwcOBAxMTEWB5r1aoV9uzZg7Vr1+LEiRN4+eWXsXv3bme7c0lcXBwmTZqE++67D7///juSk5OxZcsWy6TIRx99FFlZWRg3bhx27dqFM2fOYN26dbjvvvsq9cBzrn64uOY0WNYcSfFKW/Mq44Hles2RFFwzdz0mfPEfFmw+hQWbT2PCl//hmrnrLUJy9aEUPLrUeTdJZVe/HbhUpaEq4tyVaM0oKK3Sdm25o3sMfHWqyleshJRKxsrh1CmiCLQbDRhCafJiSR4gmejf9OOAbyjQdlSNTGasKhMmTMCMGTPw7LPPonv37khOTsbkyZMrTK+qDk888QSOHTuGX375BZ07d8bWrVtx8uRJ3HDDDejWrRtefvlll9YJQRCwevVq9OvXD/fddx/atGmDu+66C2fPnrWb4B8QEIDRo0fj4MGDmDBhgt02Hn74YYwZMwb/93//h2uvvRaZmZl2VWx3WbhwIe644w5MnToVbdu2xZQpUywRgk2aNMG2bdtgNpsxdOhQdOzYEU888QQCAwMh1qPXnlM3CMyZ8YrTIMnLy0NgYCByc3MREODk68qrCLPEcP1bm2o1JaQ8H97VFbd0jal0PSXiriIe6hfvdrtxAVVL2hZAaSP/vjDI4mlWvN3bTmVgweZTVdgqIQrA8deGY9XhFDz104Eqb6eisXI43qKkpATJycmIj4+vusBMT6LUkIyT5MFW66li3XYUEJ7g3QHXAEOGDEFUVBS+/fbbuh5Ko6Gi664x/f1uDHCjEKdBUtvxe86I8K/8j7JZYpj9Z+URd+4Ka6DqLWwY7CdiOvN2V5UpN8RDqxYRFeCdSlj5sXI49Y7wBIrby71Akxe1fmQFqYdVy6KiIixatAhDhw6FSqXCDz/8gA0bNtglVHE4HO/BxTWnQeLJZEK9RkSJ0XttiZWqaq/4kErX3ZWc5fEExJomLb/E0tGyul9biQIJ6xkj2sMsMUiMwVerQqGXGtHURYoJh+M2oggEN6/rUVSKYrWYO3cuSktLkZCQgGXLljmdqMfhcKoPF9ecBok7VWOgZoQ1AMwa3d4tu0J9FIdhfjo8+8vBagtrf70Kb97aCaO6xni1Cm6Lu68zh8NxjY+PDzZs2FDXw+BwGg1cXHMaJL3iQxAdqEdqbkmFIrEiYe2rFVFYVkmGqwDYBnpEyZF47jY4qW/iUBSA3V6y1OSXmDHtxwNYk3gFqw6leL39fLSb3w5wOPUSxgBzGcAkQBABldax6QyHw7kq4eKa0+BQJuGN6BiFLz1sA25LYZmEIIPGaQtu5U/ggnHdEeyrtTRz8bTBSXZh9VM4vInEgA82ejdhZeWhmkn1cPfbAQ6nqtTYfH5jCVCSA5hKSVyDASoNoAsEdP5cZDdSeH5E44GLa06DYs2RFMz+M9HOx1y+uuwJubKwLi+yK6pQmyWGnaczsf10Bi7nFCM60AfBvhqE+ekQFehjEeBmieG1VceqNjAOh1NjKJ35ioqKKu3EVynlK9RMAgozKJ5PEOlfyUjNZUryAa0voA8END68mt3IKCoqAuDYGZJz9cHFNafB4CrSrqrCGqBUCgGAj0aFj+/vXmELbrPEsGDTSXz69xkUVTBhz7abYl0nmjRkXlh2CEPaR/HqNcfrqFQqBAUFIS0tDQBgMBiq1lDFWAqU5QGmMhLZgkBiGiDhbCqUl4sAE0iEl5YBBbmAWguofWShXb/sYxzvwhhDUVER0tLSEBQUBJWq+v0AOPUbLq45DQKzxDB9+eEa2bYS+yaKgsvc6jVHUjB9+WGnFpLyKE1QBrUN9/JIGy7DOkRizdErHj0nt9iEJ37cjwXju9fQqKqGYkuqqlWIUz+IiooCAIvA9hizESjNp0q1qJKr1GbKvBZE0G27PKdDkmR7CGC5pRcEWk8QqZqt8QEEFa9kX8UEBQVZrjvO1Q0X15wGwc4zmW4J2+pQPtlDEVHrE1PxlYfebgZg4/F07w2ulqiOxcYVUQE6fDzhGry95phHed4A+blHdEzBiM7uTSCtSeibi1P4elsycoqt12K0h5NcOfUDQRAQHR2NiIgIGI0efrZIErB3MTWSCWlhnaRRlAVc2guYTUBZPlWtywpgEdkWVABEQOdLYlylBZp0BSI7Ai0HAiHxtI/8FMBYBGgMgH90vczQ5riHRqPhFetGBBfXnAbBdzvP1fg+bJM9aiparrYQBPq77i6BejXuu74FWkf4YqqLFuxVZVyvZlCJAmaMaI8uTYMx848jyCosc/v5zy87hKEd69YeUtE3F0pr+YUTu3OB3QBRqVSei57sc0D6QcAQArAim85OJUBZFonsglS4bvkkX8vFWprkWFQEZGgAcz6QcxJoMxS4csS++2NYa2q73gC6P3I4jR1+G8yp95glhr9P1GwV2Db2TWmw0lCEdbBBg0cGtMCtXZvg0QEt8f0D1+Ljcd3giRTNLTHhgw0nIIoCnhrc2qvjiwvztfx/ROdo7H5pML5/4Fr4aNwTNAWlJnzk5YQTT1C8/q6+OVHk05wViTB7u+zPqX9IEone/FSgKBsoSKNkEMZIKKv0QGEmrFeGs3ciox9JAqQyWqUsn6rTOWeBrW8Dl2XxHtqa/k05BOxcRNVyDodTr+GVa069Z1dyltc6/rlCiX0zSwxzViR6PbO5JnlzTCenFdOFooDpyw7bWRgqggGY/edR/P38ICzefhbZXrLhnM0otPtdJQro2yoMIztF49d9F93axgcbT6JttH+tV4aV66EyFN/+++uT0LdVOPdhN3QkyXlb8/Qk4NgKIPlv4PIBgO0F1DpAHwQENKHqslQGMJN7+2FGoCSX/p93Ccg8DZQWAAVXgKY9AV0APaYLAML9gfTjwPGVJLi5RYTDqbdwcc2p96TmFtfavnZ5qcFKXWOWGAJ9tBh/bSw+2XLG7eel5pVi4ZbT6N4syGue8fc3nERClKMw7tsq1G1xDQAzlh+u9fQQT6+HBZtPY8Hm09yH3ZBRBLStJSO0NeAfAZzaSMK3JBcQ1RSxJ0lUuZZMQFEGTXJUaSgZRKlQV4hIPi6zEbi4i6rfap38fBsEAQiIAdJPkPBvAG3XOZzGChfXnHqPJ/7cqjJ92WH46zVIy69fTV8qQwDZEWxFZ3X94u9vOAG9xntVMWdjNEvM49c1u8iInWcy0bdVmNfGVhlVbV+fwn3YDZP0JLJeFGUCgTGAxhfIuwgc/gUoTINFCDOJqtUlOZQQYpbo35I8oDgbELUkys1l1gxspyJbTg0RVbKgNgJF6UBwPEX1KUgSkH+RqtpFWbRfcHHN4dRXuLjm1HtC/HQ1vo+cYiMmfPGfV0VlbaDYEXYlZ6FPy1CLX7y6tpaK2sZ7irMxVlX87zhdu+K6Ou3rGeqm2s6pIpJEFeuiTCC8LYneogwg7Rh1WjSXUWqHqYRyrcsKqULNzLLALqXMalFN3umiDOusYibR/6XyN5QiCWtRTdsWyoDSXLngzYD8K0DWaaqilxVQConAgN+nAb2mAN0mcnsIh1MP4e9KTr0nKqD2Gix4U1TWJmn5JfXeL56WX+KFyaK1e3S94kPgr696DSK7yIgFm+puMibHA3IvkIgNjLHG7aSfAIzFgE8gIKjp/8YSWCckmum/klmuUhspq1pUA75hclVaTT/l86tFNaBSUQyf1k/OvZYb0RRnkgXlyDLg7D+UPGIskfdhBjKOA2tfBH5/hE9w5HDqIVxcc+o9veJDEB3onsAWAIT4aitd72ojwl9f7/3iYb66aov/5PTCylfyIipRwKQ+1fv6/ePNp3iKSEOgrICq0ho53aY0l6rYOn+5SYyKKthQOi6aAVMRYCqmCYzMTHaNsgKanChqgaDmZA9REkM0BkoTCY4HQlqRAFfryK9tNoEq2WqqdBdcAYyFsAh5ZgRgpn2ZJRL6J9cCOz7hApvDqWdwcc2p96hEAbNGt680Wk55fM7NHRDiq6npYdUaIb4al8cuwBojWFV/cG0QHagHBFRb/K86kooP1p9oUGK1zMzwb1LDayjU6ND6kRA2yjdw5jISvSotCWKViirUgsr6GAC7b1OYnGpkKqFqs38TILorENMd6HQ7cMc3QLPeJNJ9QoCgOJqYGBQLBDSl5Vo/IKiZXD03w/HPNANgIpFdnANc/I8SRKSG+a0bh3M1wsU1p0EwrGM0Fk7sXmEFOypQj5Gdo/Hy70eQVViz3Rxri6gAHebe0hGAY1qu8rsSI1gdf3BNM2t0e2w65ln7c1d8sPEk+s7bhDVHUryyvco47YVq+UNL99baeDlVJDAWCG0FpJ+k7GpTiVVIg8k2DnUFkxNlBJEEeVkhAAnoPhEYOAMY/RGQcBPQ+xGqhuecpQmKolqesHiJWqRGdSSRbQhzbiexgwHZ54ELu8nWwuFw6gV8QiOnwTCsYzSGtI/CruQspOWXIMxPBzAgo7AUZzMK8dW2ZKw8dHUJmNk3d6AbC1FwmAQYVS7urVd8CIIMmhpvE+8JQQYN5o3phCHto/Dib4e9tt3UvBI8/N0+LKrBNA6zxLDzTCY2HU+r9rZKjBJPD6nvZJ4kG0j2GSDtKKD1pSp2USagNdBkRQAozgIklSyyncAkEtjGEuDcNvJu+4YC57ZTh8WEYbTef4so17oglWwn/tFUOQ+KA/JSAI0PABFglaTqGEvIK16cC+CcYzY3h8Opdbi45jQoVKKAPi1D7ZatOZKC9zfUn0ljwQYNsouMEFD16Xe+OhXeG9vFIsTK31hE+OvtGpWYJYadpzNRZqofXw0HGTS497p4TBvUCipRwI7TmTXybUJNpXFUN87QFeUjCTn1hLRjwN/vAYUZQGR7IOcikHOeMqslE1CqAfyaAFp/8lWLImVcO0MqA0qMABhVv/MvAX5h1GEx9xLQ+2ES2IGxwK5PgcxTVJ3WBdL/s5NJoKv1VCl3SBgpj5k83v+8Y00z4e3SOZw6hYtrToPG3Q56tYEil94c0wkAqiXO5t7q2HXR2Y0FUHNC0FNeHtkOYf46B+EPVD0vujJqIvvaW3GG5SkfScipB0gScGYzsOVNIOcCCdw8M9k1AFncmih6L+esbNEQUfltMwMEDf1bmAGkHARie1MVXPFH7/6CPNPRXYCyIuDyfiD3IpBzDlD7UF621h8wFlV+HKZSsoa07E8Nb4yF9mLemwLbtnulxgBAoP3xajmHY4GLa06Dpj4lZAQZNHatyG0rzRn5pXht1TG3t3U+0z2fb00JQU+JDtTj7j5x2HsuG2n5JCB7xYcAoNfo5JWCGtu3N7OvzRLD9OWHa/R81ueJp42K9CRg91fAsT9J9Kq0gKihvOrSPIrVA6xNYwA5t9rdb4fMIOFZSsK9rAiI7Aic/w/IPkfiObwd5Vif3UZiVRDJ420sIr93hX5rG5Q0k/STgC6ILCzhbb3XLl0R1FeOAOd2kCe9KAMoSAfAyEOuUtOkzE53AC0GcpHNadRwcc1p0NQnoaJTixjSPsryu22l+Y8DlzzalquW4bbUp1zr3GIjer2xwc7vHWSgxJaa9oAz5r0zsGDTyRofb32eeNpoSE8CNr8JnP2XRK1kBmAECtMpoYMx155qd2GMYvb0cvU5/7JVxKt1cpVXQ77s0jyqAiuZ2MZCEtqSCXDHYCaZqINkcSZtyz8aCGvjnXbpSjv4i3uAK4l086Hzt2Z7l+ZR5VwQAewGTqwBWg8BbniGW1I4jRZ+a8lp0HhbqIzqXPXJZql5pdiVnOX0MU/HqbQMryhyrj5V7YvKzA6iNKfIWCuTKxURX13MEsPX2856ZVvOsI1N5NQhkgTs+Rq4sEvOtTZQp0VRtntIRoq5U/KlLcJWhGNmT2UwOfu6yCrYmUQCuigTOLWebCOSiawoJXn0f7UP4BMEBDYD9CGAyqeSfTM5K7tMjgG8AlzaQxncphK6gagKSjv4lINkMSnJpRSU7LPkSS9Io7xtJlH1XBdAQjtpLbDpTZ6/zWm0cHHNadB40mCmIgQAn4zvjgXju+OT8d08/hOq4KqS7uk4bf25nu6rsXEpu9gr29mVnIWc4pq5GSgfm8ipQ3LOU8VaEOQqsl5uYy6LXo+QBbeggYP4Venkro5yZ0Wl1bkgP6c4m6q+zCR3epRoE4pA1vjIgtVPTisJRsV/smX7SlGmnNddTJVmlY780J5iaQefQd7wjCQS1qZSebwmmmzJzNY4QVGkqjYzA1cO0fN5/janEcLFNadBoxIFvDS8XbW38/H4bhghV62DfXVVtlq4qlCrRAEvj/R8nBUJ6OpU7aMD9XioX7xXbky8iQDAV6vy6DnL91/ySlOZmrxZiQrU8xi++kLmSRKL+kBZ6EpUvRZULpq2wGZZ+etMybxmjo8pIpmZ6TFmloW8jmwfpXn26zPJxuctkjg2lwK+ISS0fUIAbQDoXWJbRRes/wjyBMyidEDjRyki/pFkQfGU3AtkBclKJp+4uVQ+lnJimYH2KZnk9u+ytcVskidp8vxtTuODe645DZ4tJ6re/c5Xq8KD/VpiqI3oqarICjJoKvzKP9hX5/E2wyp4jlINT80tcftm4N7r4nBThyhLmsfzw9phV3IWNiSm4rcDl+qk+c7dvZtBEAQ0DzHg7j5x2HT8ikeTNPNKTF5J4KgpL/TLI9thct94XrGuN8ivg0pHolVJvdDJ3mjJ7KTaWsnVyEzlFgjllsttzXUBJKpValhFskSWEUtHRlCl2VhEgjW+nxzRdxaQ5PbrtlYV5XmMURdJZiZ7iaCh/TTrXfnkQkmiin7mSRpXaEsg9QhFFBbngCZnqqjJjSTBXmBL8jIBKMkHVCW0X1GkSndVLSkcTgOGi2tOg8EsMYecZwBYfbjqjWMKy8x4f8MJ/Lj7vKUhS1VFlivppIy7KuOUKpisp7SFf+S7fZVOeYou13DGdht9WoaiT8tQvDiyPRZvS/Yo1cQb9IgLwS1dYyy/K904X/ztCLIKK8v4JZbsOOs0/9sTqnKzUhn+OgFNxQwc2ZeCji1ioApqxlMU6prQloBPMFCaAxhCqCJrLKJJhlp/q7BWBKuzqrRbMFjEs0orC3k5N1sXAmiKZQEtV7+V5jOKbaSsEAiNosmBqYfpeX5R1HTGVErbhQQwQbazCLL9RAKMEqAtAYLaUEJJRaQnkQf97L+ykAagDyIhXZpLHnRBBATlJkC0OS/ljlcyAswISBq6kdD6Vs2SwuE0cLi45jQInGU5B/loMLhdBIqM5mpvPzW3BI98tw8fj++GQB8tfLUqFJZ5tt3sIiMWb0u2y3pen5harQzq/5KzcEObcJePK0LUoXtjgA7jejVDXJiv24JTJQoI8/e8ul5dnN3MDOsYjUFtI3HN3HXIL6n8dfjrSCr+OpIKwPWNRGV4crPiDi2FSxhq2o2CNZdxAUZc0ujRsl03JAwYz1MU6pKg5kDcdUDSGrJeGMKomlxWaG11HtMdiLsB2Pet3JHRk290BKt3WxAAidF2mRkwaWn7LFMWyICl+gyQaJVMZBvR+AAdxwLJW61CvyhdTjZhAFSwVJAFFS0TVYCoIzHPJMBURJMpXZGeBGyZB1zaS9vwC6fx518CCjLkMSn7c+O4BYHGaTaSUA+Kp8QSDqeRITBv5lhx6pS8vDwEBgYiNzcXAQEBdT0cr1GbWc6i/LfQG3ijFflN7SNxb9/4SsWxs6p+Vaq3O05nYtznO6szZLcRQF7kf18Y5HKsqw9dxtSl+z3eLoAqe5y90ZSnpXAJ96rWIBj5SEEIiqCHASWIRhY6tWmBZsOf5gK7LklPAja+AaTsJW+wIJJXWCVShN2QOZQNffBHYNfnQHqiLLxtEeQfZxP2BGsVmknypD8N4B8DGAton6V5sGREM9D2JdlG4hMEhLQksXtxt5zIAbJ9mGTxDdmKYflklMej0lDaSFAz6gwZdz3Q9ynHb0wkCfhnPnDkVxLsvuG0CWMR5XCX5MqbrmLxQlADcX3J1sI7RVbK1fr3u7HCK9ecek1tZzl7S1gD3sl3Xpd4BesSr1RajXXVvdFTasIaURGVpWeM6NwEN+67iI3H3ffVM1ijDKvSarx8q/mzGUX4Ydd5pOa5J7YFSBgq7kYw8nESMVDkfgEMOAkf+CSfQ9NjKyFWt7EHp+pkngZykqm6aiqV24/7A3E3Uj5zVjLw5xMkwo1FcgtygawjpjK54YyKLCWu0jCU2D0IgKgl0c4ksoaYiuXqtlzl1QcBzEfel9FanWYMKCux2j3MoG1pfeXUDkXwy/5tpeGNIFC+ttoHuLiPuj6GxNuPL/cCkLKf9qUPkHU6AwozaZmgAph7tiwHBJXsMfevuU6RHE49hn+yc+o19SnLuS5RbCtrjlTdX+4OijUC8DzR1xOiPUjPeOCGlh5v350ow4pQblZu6RqDJwa3xrbpgypNexEgoamQjhvEw+gknkEKguF4FgWcMQYiI/kwT1GoK3Z9Afz5GJBxgkSoxkBilUnApf3A7i+BVU8DqQeo4qz1heV1NMm2Eckki1tXMXO2Xm0JCI4DEkYALfoBMT0oNURrII83QKLaKHdlFVVUqS5Ip6zqsnx5YqRI+zMVk9XD0mAGtD2dH1W8lcmQF3YDZ7YCZ/8GtrzhmDldVkBWGIBuFgC5O2W+Nf3DHUQtoDbQ+CACKr31xkGtp06Rtm3fOZxGAK9cc+o1PMuZqG411hNc+bijA/W4uUs0/jyYUqUbHj+dGnf2aIoh7aM8sq30ig9BVIDe7cqxLd66flSiUGFsYUvhEoaKu9FSvIww5KKFcBmBKMRJFoNs2H/FWwwdjKVFPEWhLrhyFPjnPWrYog+iVAtmJtHMGJB3AdjzlXVyoUq2dggquQotC2aHdJBK8I2kbyn0QUBgDJARTPstKyKBr5UFvrGUms6oBPJLF+fAmsYht1MXZOuHWUkOkWGMjktJHBFEEs3mUuDsdsA8Dxgw3Vo91vrJNw6gyrhaB5QWkjD39NZaVCZiyjcTDNYccUHwTqdIDqcBwcU1p17DW0Vbsa3GesMCUhHlrRG2Pm4lvi8tvwRhfjo8+v1e5BRXLDYeH9QKTwxuU6WbApUoYFyvZnh/wwmPn+ut68csMZcpKnb+ahaCfBgQLWQgSsiCH0qwn7W0E9g+KIVGZ+ApCrWNJAE7P6XmLboAQC139hTUgMiAkmxahxlBsXMqyqm2JHjIaRlVoTDF+lxzCW0bKgDF1uVFOSTamUSebCjNkRRftfKj5HHbeK2VjGyz7c2kPLnQR87JTjsGHFtJXnKAHvNvIsft5ZK/uzSPlivdKt1BKgNMsK5vNtHNiNaXKtcA3TzkX+Y3lJxGAxfXnHqN4gHm1hArtVXNd+XjLr983u2d8fB3+1xu56F+8Xj6pup5LePCDB4/RxCAa5oHV2u/Cq7sSc791QzpLBjhQjb0Qila4jL2MH/LYy00uQiL71W1xh6cqpN7gfKiBZEqvwqMkdUCsFZ9Ybb2h7EkebhjaXCRM5N7mVqIG4uo3Xn+FdkGwmiio7GErCCiSP8X1TRGZR1bLBGBMiqdHIFXfuIhI+FbmkuWEckMXN5H6SOX9gIZJ4HCdKpal+SR8DUWUzXfVK4qXhlSOW+22gfwDQXUst2krEi2wvAbSk7jgHuuOfUaWw8wh6hv1fxhHaOxaGJ3RAXYx/gFG9T4ZHw3zBhR/devKsfMGLD3XHa19w24vqGJETLRUryMFITAtmPeKRaDEuihhhmRQg6CUAA/FKE1LqFVfHOI7UbxyYy1TVkBCWVRZR+tZyohEWs2wiH5Q1TSPty1gYjl/i//SCYStVln5HboJbCIV8kke6iNVk83k8gS4iCYy+9LsKaROEWgbeZdIn91fhplWp/bSfsJaQk07wsYQkn4m5RKelUq9ErXSPl9oA8CdIGy3eYSEN6G31ByGg28cs2p9wzrGI1PxnfDoz/sr/K3spUxslMUVh1OrZmNe4CvVoWiMrPLP23BlXSBrCsqspF4g17xIfBRiyg2eTYhyltVfledMn3JQY0i2Iv/bPhjv9QKrYULiBXSES+kIgOBCGrRE82GT+KpCXWB1o9sEDlnSbiKGtn3XOik6yAcK8RuoYhcuYItqMh3rJLbgRfnyi3R5QmKgkj/l0ptngf5cQEuK+GA1apSYQY3ozGZSoCCKxSvl3cB0IcAueepQm4IBVoMBDKOUxdIJpBPuzjTzWq9s3GBnluSR3YQ31CgLb+h5DQe+JXOaRCM6NwEH4/rXiPbDjZo8L9x3fHU4DZV3kaQQYMgg6byFSsgKkCHd+7oUmHNKLvIiPWJdX8T4AzbhI0+LUO9OulSJQoY2bmJx8/zVpV/6a5zTpcXkoMaBjiK+Gz4I5E1xxEWjy9Nw/Gtbjx63/MaF9Z1RWAsEJFAWdNqHdklSvMAc7kmKaLt+9iJ6IZIVgylK6JT5E6GGh/ZViJQMohFWMsVdGV7kCdN2oppi9e7PHK2NpM8qDLLkzCNhZRCUphONg2ND4nuy3uBgKZySkgZnSvR088zpdMkoxuTjFNkQwmIAtrdbPV6cziNAF655jQYRnSOxkMX4/Hp38le3e7k6+KhEgVMG9QKP+w6h9S80sqfJDO8YxTu6RNnqSZbs5EL8f6Gk25tQ/nzPPvmDhjUNhK+OhUKS51/zVtbiSH1kTfGdMKyfRfd/sLaoBG9UuVffSjF5bcal1goTktN0EE4i5Pwgb3YYohGNg5JLfEP64yFt/aASqVyuh1OLSCK1Mwk9xL9nnMOKMqwF6iCWhbDclXYAYEEqaiSs6iN5fzGgjxBUhbFpmKKqWNmsoIwRqJcMluj7kQV7V5U2TeqYRKJeCbaCGnFCK4ck1rejrsdFGXBXpYP5AtAQBOqXBdlkmWFgbKpS7LpPAhqD5NR5HxrQU3V7+yzgM6XKvaX9vJmMpxGA69ccxoMZonhpz0Xvb5dZbKcShQw++YObodQBRs0WDC+u6VKa5+N3AaLJnZ3iG/Tq0X4au3fdlFy5jMA9H5zo0thDVQ/v7k+YpYYdpzOxB8HLmHH6UyYnXTyMUsMe89lY0CC61bw5SkySnh7jfOED0/GNvOPIy4fZxCxVuqJbPijNS7BD0VQwWzxV2fBH/+qe2HhxB5V6hbJ8TLhCdTMpM1NQGRn8gT7hlHWtai2JnUIAqj2VO7TQNRYm8MIiqdaRtBQ5rNSkWaMHldpYBW2AlWoLbF1sGY/i2r77UFumy6IlG6i9SXRqlTOlYYx7t5uKo1dVHJNzVwKFGXR2HT+QGEa7atJd0oYEeTEFMGTG0IzWVyMBTR2YyFZcHyCqZnMzkX2eduSRN0grxylf3kONucqgVeuOQ2GBZtOeaXrYXlsrQOuMp7LIwB4c0ynCqvHrnzIAByWrU9M9ajF+9WS/+2s1bhtN0qzxLBg00l8ve0scoo9f+0//TsZXZoGYUQVLCUAvU5ZhRV3qTvNYvC1eZgl5zoK2SiFBkekePyr6oVlL98HrZrXMeoN4QlkUWi+E9j2IQnIlCPkDVaq1ZY3ogiKzDOTMFXEptlM6zIzraMPoAl8pQWUT63SkWDX+JAFQ2OgarFK3gbMsj1brpoLKjl+T2lprgLl24lU5TYW0z6UduS+EWTvUFJOKkUWyiotTdwUBDoeY5EcDailfSg3G4YQOZZPoujC4qrczDNKHck8BYS1pmYy6cepmUxoayDzJHBsBaWWmErIphLW2nl1W5Io7aWsgLzzgbHcv82p13BxzWkQmCWGr7d51w4CACG+jhMEbUXx+sRU/H7gsp3AqqwVuS2u4uxsl1WlxfuP/1F3P29PHKxN1hxJcXpDkZpbgoe/24fRnaOw8Xg6isoqSkyonOd+PYShHaOrdI7cvYk5zWKw0ByNGCkTvihGIXxwiYXiyYEJXFjXR0QRiO0NNN0OnNpA4lFShK3tFWnJ46PKsUqOzVNrqMqrCOKgZkDTniT+Lh8gAesTIrdHN9G6aj2JVcksV6Bt7ByMAaxMFr1KtJ4g62ytdV/mEqoyq31oLKYi945XEGj/Kq3sq5Yb0jCzHAOYTdtt1hPIuwyEJQD+0eTHDmxOkz7N7tvl5J1ak1DSTwBBcdZmMslbgaN/ALkXAd8QwBBOx5L8L3DlGNBtPOAfRUK6rAhIWmUV4Sod4BcJNO8NRHbkQptTL+HimtMg2JWcVaXKZWXc1jXGqehSRHGflqF4aWT7GkvBAKrW4n1HciZ2JGcC8Ezs1xcquqFQlq045J2Jm4VlZjzx434sGO/5hFhPJkQyiLjIrLaVYIMG0wbxSVz1FlEE2gwDDvxIVVu4uomTK8umMiCyA1Vjlaqx1p+q05EdAN9w+tH5k4AsyqS0DDASg2GtgOxkIPMMiVql66NSRWbKREkRMJlh8S+bSmk9pT16WSFQdlq2a7h5S65E9ZnLyNqi0lp936ZSmuDZYiBwzT3Af58BGUnkxy7JpYmfap0cVehpTJ88kTIrmVJaAmPpG4ID3wOph+kYMk7IzWWUzO8iIHkLENKCzm1pPrV1D29Lr9OVRODcNuDocmor36Qb0G0CENHOg3FxODULF9ecBkFN2SAGt4+qdB1X1WdvUd1jS80twSPf7cPCid0bjMCuyg1FdVh5KAXD2l/GqK6e2UOq2sTIHdsQp45JWgNsfgPIOg3nkxdtUPzShRlyVTeK2pi3Hgpc2g+kHqLqsyAAhjCgWSi1MU9PAsxNSXSHtKSf0LNklchPA4rSSWAGxAD5qXIFViTRLqqAsmJYq+kC2UsUu4XggcgVVCSkzUYSyoGxVBHW+ZHwD4oFeky2etIVu4ZvGB2XZCZhbnbWrMYVNuMzl1BFHwJ5sLPOyN5vESjOoBsXSbbaQKAbCGMRPQ5GSSY+IXTeinPoZqM0nwR6+gng9GZg0EtA2xHunxMOpwbh4prTIKiJxinOLCF1QXWPTf6z26BSROrCM/7Yj/shivDIf600MarID++rVaHQxrrSEL9JaHQkrQFWPU22hwqFtZLZbAbUfmSVuGYSeYMVO0JgU2qSkn6cRLLWQMI1PxUIbUEC/MRa6+OBsZQznXUKYG0AvyiyYuReJO+zRs5UN5bJSSTK5EmQqC/JsU8VqRRZnItyJVyjp4q0Sk1jj+1JGdSKz1nxpCseZ7WeGs9c+I+85lmnSNhWdkNiu39RQ2NOOUA3DmYjedSzTsqVeduKuPx/o+InF6nqXZJNNxemYrKyMDm/WzKRf3vlEyS6243y4NxwODUDF9ecBoFSQUzNLXHrS8kgg6bSyY9zb+lYL4SoN1q826aI1GSV3VvURZdJBmDq0v34BJ4JbFeTXEN9tXjtlo4Y2rHmmudwqomziXBMAv5+j4S1k2QaK0oTFwmWHOqCNKr0Bje3rla+2pt/mQRpky5W0RoS7/h48z70eGhr8n3nnCOhKZnoRyUCJlEWpiWweJj9IkiclhW4dw60fuTP1gWQSJdMQMtBcmKKC8+yKFqPMfscTcps2oPOZ2kubcdY5KYPW664M1gnTWr96IbEqLRZr8AgJspV98IMmtSpeMYlk+xhN5E4L8gAVj5NY+MVbE4dw8U1p0FgW0GsjE/Gd4MoChVWGx/qF1/lBAlvoxzbw24cW2U0lBQRT2+WXFFB/zqXTPthPxZAwIjO7leWK+tA2RBuaBod6UkkaNNPUOScuRTwCaQJeleOkLAWBUBydRXJ3Q0hkN3DVEIV5rSjQHQn+1XLV3vLJ1q483h4AnBxL03s0/pbI/8ks3U8+ank6Vbi/SpEoP0IIDtKzDVU9c27DPR6AIjqVMnzZcoK6Ng1vuQjZxIJXp0/UApZ3FZgFRHkcQRGy7YUA+BjAnKS4da7l0lUcTeXUsVdkHO/leMXVbB8w1CSA2x+HQiOByK5B5tTd/AptpwGg1JBLJ8drRAdqMeiid0xonMTl+uG+mrxyfjumDGifW0M2W2oxXt3tzO2XVEXFeGqoNxQVLeb/WMDWyHYx7NOchIDpi7dhzVHUjx6Xk12oOR4mfQkylQ+uw1IPwZc3gOc3wEcWwX894mcsiG3N680x1kgu4YgUorHgR/I41s+k1mp9kZ2oH9dVYOdPR4YS+JbFABtgHWioWS2aUEu0njNZbLvWBb+rsasCyQRLoi0fZ8gEsiGUKpiu4vWjyrtxkJArSWxLJnkZjFiBWOQUeuo/XlAjDzh0xfQ+Nno6ko+BZjsFVfWtbSkt0lyEWCNGixIA/5bRH5snp3NqSN45ZrToLCtIKbmlSCroBQhvlpEBfo4fB1fWbWxvjGiczQev9IaH250r7OjMzIKPI3LqjuGdYzGfX3j8NW2s1XeRny4L25sF4lf93neXKghedQ5HiBJVLHOPksWjNxLJFQhyKkXtl0QzXJjmEq+A2FG2UIiAGmJwLpXgPY304+7HQcrymoWRYqWO/4njUetJwuFsdBGXEvUoKW0DFZRKd8YCHKzGlFNz5NMtE2ND8X2+UeQKM27RHaVwFj3z2dgLHnMUw7RZE6fUBKwAAlaU4mcdoJy3Rzl+EG1ntJVCq4ACSNI3J/aQELYVAL3vnuSnPxf/kZBMsLSTl4Q6LU+uY6O1RDqOjvbsjmeoc3xPlxccxocnqR31HTSh7d5/MbW+GbH2So3y3nix/1QCZ55iuuSIe2jqiWuX1t1rNImL65oSB51jgfkXiArSFkh+XTNRhJLotbqIzLbNF9R2pA7NRmJcltyOTrPEAz4RlIHwvP/kcWi98OVC2zFolJRw5TIjkB4O8rcLsyUPeFKddp2XOUm/omyqBZFyuL2iyJhqfahY/aPppuH9ONUQW47yjPxaNs2PiOJ/OO552lSo6WKLMrDtOk8qdZRGompTO58WUqdGOP7U1UdkLtMujuQ8udBPgZzGf1fMgEopRsNjcE6uTTlEI3d2evkzuvC4VQBfnvG4dQjVKKAeWPc9EI6gSwP+z22PNQVive6qrXjqgprhX9Opjttt85pwJQVkEAtzpQbuEC2MMiPq8rXlFxMqBM0gD6YxCsYCTa/CDnNQ6D4uqJM6jhYkfUgPQnYsRA4t5O24xvhvB14YCxNGjREUIdEZSKiSiuPwQnMTDcPxkISu/lXqI25xkDiVhTJylGSTRXra924EXCGMmkzujMJ4uA4efsqa5MbAXJ7dzWJfI2exiaVAUUZNOHw7L/A1reAi7tp7G7H+qnJ0qILgPWFtH2+zevHQNdAUQZ518PbWl8nswnIOgucXA/sWQxsfRtIOUjnO7Q1/eusTTuH4yECY4z/ZblKyMvLQ2BgIHJzcxEQ4IGnjlPvWHMkBdOXHa5y45zoQD3+fWFQg7A8uOrUWFtEBegw++YOPDrvaiH7HLD6eZq0WJJH4k5UJgDKEwRNRlCHRGfI7xlBTQLSVEqCPCSeRLGplKwX8TeAcpqzgIEv2ieIKEgSsHYGcGaLNWtaVFvtCoUZJHr7PkVCOD0J2PoO+cNVWiDvojWGjzFrQkZl7xaVFojqQk1hYq7xnt3B1kKRnwqc2gScXEsTDdV6apnuH003JeZSWrcom2w11rsb0ERRRufP1uZBB+q4X1FDSSG+EUDOJcCYV8Eg5ZQXrT8Q1pLOsz6E4vwU8VyUTe3owYDQVkBMd8onB+g8px+3f11qAf73++qCV645nHrIsI7R+HiC5x0FFRTLg6eYJYYdpzPxx4FL2HE6s1aqusrk0yAPJyZ6i9S8Ujz8necTHDn1FMUjLJmokgrIFd4SaspiKoZVWAsgS4OG/lWaloCRIDTLUXG6QMplZowEpKgG8lJJWBuLXcfiJW+lKqmpTK4iy9XkgivApb3kiU4/QSIUoApxx9soiQNMtrCYSSzr/OXjqeyGWbZFlOUDF/fRWJ1NsKwKtpMyW90I3PQaMOIdoPl1QGAzEr8aH7qhKc2XrSMmWKIMBRvbCABL90tFEAsujk2pARbnUOt5bQXiU7kZKcsnf/yJddTx8dJe4PQmsgv5BMopIwAyTwNn/qHumQVpFDXo38T+deFwPIR7rjmcekrvFqHViqsrH8tnlphlcmeYnw5gQEZhqWWi5/rEVIcs59pqiDKsYzT89RpM+OK/Gt1PRUxffphPcLwaEEVqh31uG3lpJbPcjAWwCjvlNRbJ5iGZScQq1VQlW1mSvzkyFlJ1tiSHquGMyZ0dBfL1XtxLgtMWSQJO/EViXKUnMa5EyKk0JDxFDVV6bcV5ZEcgqrM187q0wCqqGVB5N0lBjvETqDHN8ZVyEkkN1NJEEWg5kFqlJ/5JP/mpdCOi8bH62RXB6/BJxmRrh2jzsNLWXT5XiiAvLaD/W5JORBsxzqwTPxms50BpEZ91hl5jn2DAP9g6LrUPie2cc3SjpNzA+AQD+kD3s8Q5nHJwcc3h1FNss72rkueckV+KPw5cQoS/HtmFpXht1TGXjWoC9Wrkljh+TV6brdWrezNRXXKKjNh5OhN9W4fVwd45XiWiHdD/BWDjHKpAOghSmzg3k4laiSt5yQJssqVlyvLlWDc5hUPrRxXhsgLqxrj9I/JjJwyzPif3ApCWRFVrs1EWm5Lsj5ZtDSU59P+CNBLnkpxtbQgCMk4DQXG0HcnsvtBjzFpNN4RYK7DObCveIjwBuOEZ8ozvXSy3Ly+xn5ApCHLiirN3twCyiij/V/6rAVQqmrSoNItRyZ9hotra0bK0gJ7PTLBWw0H7F1TWhJjSPBLSgkCvm6T4thlQZKQbGlEk77paS9Xu8jdNHI4bcHHN4dRjXHUHrAxRoCQNd3EmrIHaba1e3ZsJb7DjTAYX11cLCcOoQrziKZrkZyq2f1yQrQqS0RrFpwsgISaChBsEOe3CTAkhohbQh4JEeQnZL3wjgYIU4J/3gLA2NNlPFKnCXXCFtsGY3FWxSI7/U5GQNhlpf4l/kLC7coSq7YXpFCXIZNGv0tCPqZQSOZy2P7ep4kqyYNQH0LaqW4F1J67Otop9bAW1fLd8gihVd1fvatubH+U5IsDKAJNihZGXa/1I+JqUpjIq2jcT5FQXebxKHrdklJ8r208kI4lxh8mUsnVIE2x9vfZ8DTTtSTdrHI4HcM91LfDmm2+iZ8+e8Pf3R0REBG699VYkJdnPRGaMYfbs2WjSpAl8fHwwYMAAHD16tI5GzKktFI/zb/su4st/zuC3/fZeZ7PEEOijxfNDE/DyyHa4t2+cW9v1plWaoeoebk9RbiaiXDQKqmn47O6rDFMJoPUBDOGASk75gED/Vxuo+mlJC2HU0huMHleapIgqEnOAvE4xCTu1llIxSrJJgKceBlY/A/w7nyYmph0F8lJIzCnpFaZiEm1KJVZgQER7qqZufRu4LCdXNOkOxPakfZjLrBVWjb6CN7eNKIVEE/oENU001PpV/RymJ9ExbX6Dxrj5DesxOiM8Abj+aeDGWWSvAGSbSkXJIBW98xSLiPwaFWXQ6xPdhbzo5hJYO1kqQlzeH1MmgILEttkIlOS7HovZCBRn02vvH0XfKBxYyhvRcDyGV65rga1bt+LRRx9Fz549YTKZ8NJLL+Gmm25CYmIifH19AQBvv/025s+fj8WLF6NNmzaYO3cuhgwZgqSkJPj7+9fxEXBqgjVHUlxWpKMD9bi5SzT+PJji4IF+qF+8w3IFUfCusLaltlqrK81/Fm9L9qj67g2CfLSVr8RpGKQnAUd/oyql1g8oVFJAlPbZzGaCoPymkcxyV0NlIzY5zhDkhjMSvclMRTTBDgJVsCU5SzvlEOU551wisW4XOadMllTqWnJVulBustK0p9VTHBQHBDQDLu8HQuOBdrcA2/8HnPnbpmNheRSfsoqq6PmXPW8aU/4c7lxEUXaBMRSHZyy0Zkdf+6A8ebJcRVsUaZJj6yHAkeVy9bj8ZMaKsLWP2CaKiLKgLqM29iFXgMwzsAhwQe5iqbxOjMmebQn2DW4q2K9URjdlRdm0jUv7at5Ww7nq4OK6FlizZo3d719//TUiIiKwd+9e9OvXD4wxfPDBB3jppZcwZswYAMA333yDyMhILF26FA899FBdDJvjAtuJgVXt+lhZ/FxKbgk+/TvZ6fLP/k7GY4Na4XxWEQrLTIj016NbbBByio01KkbD/HQ1tu3yqEQBYf61tz+Fi9mFtb5PTg2gdGk0lQLB8TSxz/IWVdqIy/FwgkaOilMqpDLKOoJId60ACWpzmTVpQqkWGwup2ukbCvjHAEd+oaonY86rpIJgzYfOSqZtlu8eCZBIDW0F5F0ALuykVu6GQKDQZrKlw7ZFwCeExhoU63nTmPLnsCiTsqKVyYO6ACDcn7Kq18yglubF2TT2gCZApzuBFgNon13GAWe2yo9XpXusYPOvAAREAdHdqNKff5Eq4z7BgFn2sxdnyyJaoNdI6cAJ5mgLqgijPJkUcixfygEurjkewcV1HZCbmwsACAkJAQAkJycjNTUVN910k2UdnU6H/v37Y/v27S7FdWlpKUpLrR9YeXkVZX9yvIGzarOniRpmiWHOisQqWxAYgP9tOmW37LcDl9CzeXAVt+geU5bsxrt3dKm17o9nM4pqZT+2LNlxHtfGh2FEZ5553aDJvUDe5cCmNNGwUG7XrVgGABJPSpMWCdYMaZMRUCnpE8zaGEVBVFvbbAuitY23VkOV3dIcoCiLlgkqOG2tziTaji6Aqr5mo9VLbEthBom7rLNUwS7KovbjhjCKjDMbZTuETWVXbSAR2qwP0G5U1TsNWs5hjGNEXpHcQbIog5JBiuRIQraLvNatbwJueJrOfWQH6mSZcaISa4gz5Aq2oKascf8oOh6VGug2kQT98dXUCCY/xWb7ZkDlQ7+rtDTB0q4CXhkmajijHOval0iktx3h4fg5jRUurmsZxhiefvppXH/99ejYsSMAIDU1FQAQGRlpt25kZCTOnTvncltvvvkm5syZU3ODbYRUVJV2VW1O8TBRY1dylkeTE92hsNSMLScyvLrN8hSVSZi6dD9GHUnFkPaRVa7au4NZYvhh13mvb7cyGICpS/dhkVjz6SicGqSsQJ5wKHf1a9JVrmrm2lsqVDqqfKo0JM4kE/0oLdO1flQBLSuRLQfypDdFbDMzVYiV9ttnNlMFuqwQ5O3WWiugdsgTFf3CgeJcEuoB0da24AAJ60t7aGKkSgNAom0bC0i0a3wAUSd7wcwkBtU6ILYH0HsqtRmvTvye7Tm0GzojoVxWAJQW0rGKaoqxYxItT/qLjumaSdQwJ+04jU2tJ5uO5GxCpivkLpMqHVXxjcW0HVFNwro0H4i7nr4pOL+TKtdmM9lVjIV0DBVOpnSFknKiotdi3Ut0DdgmwnA4LuDiupaZNm0aDh06hH///dfhMaFcdYAx5rDMlhkzZuDpp5+2/J6Xl4fY2Cp66zhOq9JRAXrc1TMWJSYTFm8/5/LjmQGY4WZOcm15l2uKlYdSsPIQNVyJCtBhXK9miAvz9arY3pWchdQ875ynG9uGY+PxdI+eUxvpKJwaROtHAsxYSOI6KI6sDVlnyAetNHUJjKUM6oIUqoh2uwc4voo80D5BVMVO2SdnJouAxo/sBXbiUAB0cjSfSkvPVawJxhK4rJZq/cnKIBlJZNuKWEXAGotpu3p/oKQA0Er2ky41GlpHAIBSIKwVMPhVINIL6Rblz6FCaS5Vc40ldB40BlpX+VslyNX41EOU/63WAyVZdO40ejpHBemwRu9VhjwBNbQl+awzkmgy48U95SwrjF4ztZ72rw8ki07aYar8V+W7QkFtjfwrzQN2fQq0GkyVcw6nAvgVUos89thj+PPPP/H333+jadOmluVRUVEAqIIdHW2tlqWlpTlUs23R6XTQ6Wrfl3o1oVSqNySm4sttZx0eT80rwQcbT7q1rewiIxZsOoknBrepcL0I/7pJwqgJUvNK8f4G6/nxVtMZb96AJKbkY8oN8fj8H0cPuyuUdJQ+LUO9Ng5OLaJ0aUw5RP5gQQDC25BAKs4GkEfCqawIkHKpatzveapKBkQD/y2ijOrCNFg7DIoAzIAhmDy5pXmgCrRGTiKRMSpWPQbXLdZBlobiLBpXzynUyTH9OBAQQ4I7P5Wq3voAqtimHgFUAfKEu1L6CYqidYqzAb0fMGC6d4S1q3MI0I2JsZiyvwUVNWKxLQKJKhKkZhOQsp/SUCBQld+SVa2Sm/a4A6Pqd2grEta+odSu/OCP9pYVXSCtV3CFblzKCgH/SLK3uDWZ0QmWZjSgm4P0JODCf0Bc36ptj9No4FF8tQBjDNOmTcPy5cuxadMmxMfH2z0eHx+PqKgorF+/3rKsrKwMW7duxXXXXVfbw200rDmSguvf2oRxn+90KqyrwtfbzlbaMrxXfAii6yhqrqZRLDLVbSXuzRuQlNwSDGobiaHtXd+oOsNblXNOHSCKQLvRJLbSj5O1Qh9EVU6NgSb8hbUGIhKAjmOAmz8mYZ2eRJ7hskI56UOpOsvRbsZiq79YiYdjJlpXVFHzEaM7k2LllJBmvYGhb5CXt/fDQHRnEtyZp2gfgU2oMUtQHB2LZAL8osmCYZZFtWSm3xNGAS0G1uw5lEx0U6Aco0prM7lTRsnxVhq1BMfTuVdp6JuAUrnDZaVt3GUEgfbJGCWfXPswebnLW1aUGyiND1XXS/Oo/XzqoaodvyBau3uaSigZpjAD+Pd94PRmHs/HqRBeua4FHn30USxduhR//PEH/P39LR7rwMBA+Pj4QBAEPPnkk3jjjTfQunVrtG7dGm+88QYMBgPGjx9fx6O/OqksraOq5BQbK614Ks1SHv5un5f3Xj9gqL6tQrkB8Va3xrT8ErSO9MPaxCtuPycjvyrpBpx6Q3gCCdZjK2hiXv5lsgx0GkuVT78I+/g4JR0j64yc4FFizcFmkjWrmZllz7US8WbbIEaS0ykEG+uIkwmNOl/yenebaG1QEp5AbcqViYR7v6ax6WUfdphceTcWA/pgErbhbeVYuqZAj8neb3Hu7ByqdEBIC2trecv5AJ0LU6nsB9dQa/hm19J20o4DfmG0DUGgGxhzmeO5sTtPgXT8QbF0fLG96RizkunmIi2RbpYMISTgDWF0M3J+F2WMF2ZYW517it3kSzmiTzJTNN+qZ2jSZo97qz5hlHNVw8V1LbBw4UIAwIABA+yWf/3115g8eTIA4Pnnn0dxcTGmTp2K7OxsXHvttVi3bh3PuK4BqpvWURlrj6ZUaicY1jEaT97YCh9sPFXheg0VT2wVZolh55lM7DidCYChT4sw9G4Z6tVujRH+eoT56rBg82m3n7PnXBamoEU198ypU2wFq6vugpJEYu3cDiBpNbU5L8qWuyiabbKwFWSrgJIprQskDy6TSOQVZtFjJUrUn9yh0WIhUQNNewM6Awl8W0SRIt8CYyn+LeUQ+Z0FAfANA2J6kBc76wzZRbR+VH1vW41UkKqcw9IC4M9pJLDLiqmZDiQS1iqtNd2jSXeqXF/7MLD+FdlmY6aJmJamN87e3TbdJkU5hUUfSOcnPQnY8xVwaTdVk0U1TZb0jwFie1Gl3VxG/xqL6XUxm+C+x9sJ5jLahtaXzkVhGnBmC1W0ez/CBTbHAS6uawHGKpcGgiBg9uzZmD17ds0PqJFTE2kdtnyz/Rx6xYVWGOe25kgKFm93nQTjDeqqhbiCO77pNUdSMH35YeQUWRMcFmw+jSCDBvPGdKpS6/fyRAfSREsA8NWpUFjq3h/ZbaeoUyaf1NjAUQSrMxShdnY7kHdJ9mPLohlqOSvZ5OSNxGBpLMMk8iGbS8huIohUyVbrSGQy2WfMJJpwpwsA/COoouqqc6Jiyci9ZPVhaw0kXPVBQPM+QIfbgMiOzluRextn57D/C8CGOZQ5XZItp4H4yG3lQRX5dnLGtpKw8e/7ZNMwG+l8aAxyB8ry+d5yd0zJBEildN60sud5yzzg0l5aT5BbwZtKyLaSe46q12WFdDNSmC63RBerEAOoYPNJ6hdJr7PWl268ci8Cx1eS4K7p14DToOBXA6fRUdNpHUqcmyvfsWJJySl21WXNe+MY3TkKQT4au+VKl8fyvu/oQD2mDWjptf1X5ptecyQFD3+3z05YK+QUGS22mX9fGIQfpvTG8I5RVRrHrNHtoRIFqEQBb93Wye3nFZSaaqXlO6eOSE8CNs8DktZQRdauKyAjUWdpqa08JgtqgKrJokjCzlxGglGZACkIZN1Qy2JY508/Gl+aMFmaR/7gijonKpYMWx92cRYQ0xXo9xzQdiQJ3roSdQnDgLFfAR1uoaqxSk8i1hAMJIykyZW2Fd2EYcCkFWTLiewAhLenBjQaH2rkozSKETWyQBdJrJuNlG8dEAMkrgDSjllfJrVW7sYIOu+leUD2GRLV6UlyTKJZbhJUVeSdiSo6/zkXKLaxMI2WpZ+gqj6HYwOvXHMaHbWV1uHMd1zTlhRbBAB7zuVg10uDsfdctkN29/PD2lkyvc9mFOKHXeexYIv7tomKCPXVWqrFzjBLDLP/TKx0O7P/PIoh7aPQp2UoesWH4Jq5652KcWf46lR4b2wXu+SSUA9f+4Yem8hxgSQBiX8C6YlyhVklZ1OXxyxrKxubgiKy9cEkroyFckMZgSwShmC5Iiu3/DaXUQVW1ABaH5rY5xvmXudEd2wtdUlEO5oMmnMOyDwNgNF4g5o5H6NaC/R5lFqq55ylY5JMciZ1AVWzJYmeq/eXhbdAEz/zLlH6iNlEYtlsJEFvNsnpI/IEUwDkhVcmHHrj01Y+FkFl7aRpLKaIv8AY+eaMw7HCxTWn0ZFdWDsT1Zz5jneezqxRS4otTB7D3nPZTr3PKlFAn5ahWHMkxS5Ozxu8dkvHCu0U7uZYp+aVWs6hShQwb0wntyaC+unU2PfyEGjV9n/gPRXLddElklML5JwDzm0ngaQNoEqnZJY7KjqbAMfs/9UHk1AGSCyX5lu7JRblADqTzSRIk9ySPBAIag7E9vTMI12RraU+IIpASDz9uIPtJElJoqxqJlktMqKKxLY+kJYZQsj+UlZAN0CSiWw4Kq2c6GIG7BKabG+EvGWOE+Qcc1kySWaakGoqpQp2+UY7nEYPF9ecRoVZYnht1bFa219afoklS3t9Yip+3lP7Xx9WJCjNEsPTPx/06v5Gd46qtH24JyLXdt1hHaOxaGJ3B5+2LQKAd8d2dhDWgOffWvy4+zymDWrFfddXC5JE8Wz7v6N24qYSQJVNIolJ8uRFFSqd/GYsBNSyL9gk+4WZmfKpwSgKThBpe7pAoMudQOdxNAmxPlWe6wqlIt/xDmDHx8CVRIpGVGsByNF7Ki21TY/pSucs9wJ5nZkkfxMgZ2kLomz7kEW0oHjhlc6Y3hDYEvnnTaXWsfmEyLGCdT27hVMf4eKa02gwSwyLtyXXWuUYANYnXsG8v47X6j7LU5GgfOLH/Sgqq8Ys+nIE+WjwwV3dqzWmytYd1jEaQ9pHYcGmU/h6W7Kdd72yJja94kMQFaB3O8OaN5O5ilAmLx5fRRFtplIAcoyeMtmNye2uK9NK5hKgmFGFVa2nCrhkAk2GFK2twAUV7ePcTqDb3fW7Al3biCIQEgf0eoBsIkWZ1ombZUVkA/Gzsc8ExgLR3chzrbR7dyaeFb+2WUl08YbwZWRbMSoCXgWYTtAER79wuWsmh2OFi2tOo8BZa/PaQGkTXhcIAKJskjIUlEr65ZxirPLy+O7tG+9WldddkRsVoHPq3VaJAp4Y3BrTBrWy+Mbdab+uEgXMvtmzjPH1ialcXDd00pOAHQuB0+up9bak3FAyRxtIhd385GxrQI6A01P121QMO7+vsVi2OchtyvNTgQNLgcFzeNW6PK7yyJt0sbfPiCLQfjRw5QhwehN9e8CUZBfl3MvCWqW1+re9Zg2x2YbSUKggjTLGXaW+cBotjUpcX7x4EUFBQfDzs38jGI1G7NixA/369aujkXFqktWHUjB16dXZsMUVirxUkjIUavomIy7M4NZ67orc2Td3qFQseyp8FWvJs78cQkFp5Q0mvtp2Fr3iQ6rd0p1TRyjNYdKPWYW1Wg+qRiqdFt3eGCxijUlk/zDaCmtQVdNsBEpy5U6KpVThVlIlePXaEXcnboYnAANn0KTCE2vJ627p9ijIVWuV9dsEZTkA71s3GKWRZCUD/vyzgWNPo7iFTklJQa9evdC8eXMEBQVh0qRJKCiwzu7NysrCwIFebBvLqTesPnQZ035oXMIaoIr1wondLYLQLDF8uOEkHv5uX41W7z2xeygiN8igcXgsyKDBIpvxe5thHaOx7+UhCHayb2fMWH640rb2nHpK7gUStkXZZN1Q6Ui0iSo5jcLTGpN8Hai0ToS1nI0tyk1llAl4opqqnTxVwjXKxM3IDhVHDIYnALcuBO74CmgzlBrx+ATJXSFVsg++hPzugoq2o/W1mXQowC5SsTowUCObi7urvy3OVUWjqFxPnz4dKpUK//33H3JycjBjxgwMGDAA69evR3BwMAD3Gr1wGhZrjqRg6tL9dT2MWuelEe1w3/VWe8aaIymY/edRpObVXEqKKwtKZSj+aWcdGmt6EqFWLWLydfF4f8OJStfNLjJi55lM9G0VVqNj4ngBSbKvgJbkUT6xscja4ERBEdimUrnRixEQtY5NTRwQ6XmlebKvWrCZEAlZv4k0wdFcRo1jfEK4fcBbiCLQahDQYgBNUE1aTdaf7PP0WkvyREetgb410PjQ629SqtnydcCc2II8xVxGPvC4vtU/Ls5VQ6MQ1xs2bMBvv/2GHj16AABuuOEG/N///R8GDRqEjRs3AqAOiZyrByVPuiHSLTYAyRnFVWoy46dTOwjrR77b55UvREd1jrZ4tJ0FX5W3oLiLShTQt1VYnQhXd20sALDjNBfX9Z70JKt311RC9gDfcLnCDIrNU4SXYGMXUES3qAF8o6jTX0Ud/QS1vB0V4BNKExzLCslyIipiW962SkuxbREJFTeN4XiOKAItBwLx/emGqiSPrDpZZ4HDPwGBzYCAJnQTVJQJnN8FlOXLqS4idXPMPS9PRvXkU9KmsRADCXgOx4ZGYQvJzc21VKgBQKfT4ddff0VcXBwGDhyItLS0Ohwdpyao6RbnNcmAhCjMu72TnVvQXe7s0dQicL3ZsCY6UI8P7+qGhRO7I6pcZ8fyFpSGhGfRfPzbrXpNehKlTqQcomzk0NYkatOOUUc9U7FVBEkm64Q3sxzjJoDEmH8MrVeRXYSVAUUZVPUsypBbefvYtOw2yeJcJD9uaLx7TWM4VUOxlER3AuKuB7qOJ8Fdmg9AoC6ZwS1oHa0/WYPUOsqt1vnTDZDl01a2k0BVwQ5tPpnVeqDZtTV1ZJwGSqOoXLdo0QKHDh1C69atLcvUajV++eUXjB07FqNGjarD0XFqgobcWe/9DSewaGJ3LJzY3ePJh0PaW1uEe/MG4+WR7aASBYuNw5OEjvpMdmFlX/9b6dOCV63rLcqkxaJMILwtVaWLMshrXZhBFc2yQrlluVa2a8hNX5hcgdToAZ0fUJRKX/VrfWGZ9Oi0sqkGYAIge6mVNufGYjnz2kxe4I53UMqFu01jONVHFIF2o4HcS0D6cWvEX0ATaiCk8SUhbgilayblEL2GplK5A6QZEFgF99M2D8R0BYLdbKDDaTQ0CnE9fPhwfPbZZ7j99tvtlisC+/bbb8fFixfraHScmqC2WpzXFDOWH8aemUMsQjY1rwSvrTyKrELXVpHocp5nb95gnEyzTsSqSkJHfYQaCrlnHQoyaND7KjjmqxKziYT16U2AbwQARoLp4h4Sujp/QIwE8iF39DPSBEPJJAtruUGIPohEuLEIlvxrtY4qnaIGKEqHXbKIWgswNVlCwOi5Ee3JjlKcRUK937NA14m8Yl0XuIr4SxgJQKKJiCW55INvNxIoyQfO/kNNgcoKAAj0bYdkhmOijCyuNQbg+if568txoFGI69dffx1FRc5D3tVqNZYvX87F9VVGr/gQRAfqkZpbUq0v8wP0KuSVWL2XtdWLy3YCnSJkfTQiHpGj69zxPHvzBuP9DSeREOXfIK0frvCksj9vTKcGW52/qklaA+xcCKQdBYpz6ev9S3tJUEsmEj/5V4DSAhLBukBqUCIK1mq0qCb7iF+E3PXPKMe5aamjoi4AMJYARVes+1VSKBTriLkUgBnIOQ8YgiijuddDQMKwOjgpHAuuIv4Ax2VMAjbMBlKPUGOY3MtAeiL5tSX52w0AlixtjQ/QdiTQgieNcRxpFLdbarUaAQEBLh9XqVRo3pxnj15NqEQBs0a3B1C9wKWbu8Tghym9cV/fOAC167ql9AwrwzpGe+R5Vm4wvMWcFYlXVRydu5X9+/vGXVU3FVcNSWuAv14gMW2Uk3DMZeSvzjhBVevc82QPMRVRBbKsEJDkpi+CSPYAQygJ7fwUsgP4R1OF02wCDOEkuopt3ouCHLWnfLKotYDah35v0R+4bREw/hcurOsLziL+nC1TqYHudwMh8WQPieoEtBwE+EfRTZrWj74Z8Y+m57QaDNzwNK9ac5zSKCrXnMaJIkar1TRFIJH69M8HvDImzyrfjmt64nlWbjA86UZYEVdbK3B3K/uDbXzsnHqC2QT88x5QmEZCWKUFWTnKAAj0b95la3IHQJPYIHuhmQQIZkqLUGlILBuLgMIsIDCGBFXueRJcEd2AND1ZPZSqpai2SRuBNYYvtifQ/LpaPx0cL1HeSiKZgMhO1jxzQc7MbtKNPN3cR89xARfXnKsaRYx+9W8yXl99zOPnx4f6YueZTK9NDIwK1OP/esTig40nK13X1QQ6TzzPwzpG46nBrfH+hsr35w6VtStvSFRmHapqdjenFriwk9JBBJVVWKt1JJwVuwczyQ0V5dg9qVQW3zLMTJ5bnyAS2GodCWxzKX3lr/EHQlrQ9nyCaV/MDKj0smhXtiOLeq0BSBhRu+eB432cWUkCYoC8SxV3j+RwbODimnPVoxIFRAToPH6eKAARAXpMWbKn2mO4v28cBrePsgi1xTvOIqfI9eREb06gmzaoNX7YdcErwvi1lUfhoxGvCpuEUtl/5Lt9Dt8oVDe7m1PDpCfRxEGVhjyxjMlJICIgaOQcYybH4TFKE3F2C2UuIYGtD5S7KJZRVbw0nzzY/Z6l5SV5wDojcPZf8l+rdSSwJTNZCEQRiO9HYpzT8FFsI7bwtvUcD2hUt17nz5932omRMYbz58/XwYg4tcXZjEKPn3Njuwg8/sN+FJVV0EyiEqID9Vg0sTteHt0BfeSugypRwLwxnSp8njcn0KlEAbNvbu+NZr/IKjTike/2Yc2RFC9sre7x1MfOqScUy63MzWU2Ng1RTnYApXsAIIFtOxnNCSalAYwsyEvzyeYR35ci1pT85BHvALG9KLLPXEYTI81lVOVu2hMYPJtXMzkcDgBAYI2o77dKpUJKSgoiIiLslmdmZiIiIgJmc9VFVH0gLy8PgYGByM3NrXACZ2PDLDH0nbfRo/bf91/fHKsPX6mWHWTawFZ4akgblyLZWVvyqAAdZt/coUZE3ZojKZi+/HCFFXN3UOwS/74w6Kqp6poldtVkd1/1SBKw7mVg9+fWRBDF/8wYLTPJHRkrnOVg+5hIVXCNL1WsI9sDA6Y7emrTk4CjvwNntlDFXBdAHQLb38L9t5xqwf9+X100KlsIY8xpm/OCggLo9Q07F5njGsqJdl9YfzSuG8L8dPjy33PV2m/fVmEVCrTabsii7G/BppP4alsycotNVdoOw9U3ufFqye5uFOReAArTgaDmQHay1R6ieKJNsq9alFudu8RWdMudGtUSxejd8IxzsRyeAPR7DuhyF/ffcjgclzQKcf30008DAARBwMsvvwyDwWB5zGw247///kPXrl3raHScmsbdyLUggwbzxnTCsI7R+OPApSrvz5OJcLUt6lSigIQof/ho1FUW1woNuQsmpwFTVkCTDuP6kpWjIJV80jACEACViiYyqn3kZiBufDkrqCghxGwELh8AspJJSEuSYx6yMz9ufcLVmDkcTq3RKMT1/v37AVDl+vDhw9BqtZbHtFotunTpgmeffbauhsepYdyNXPt4XHf0bR3m0XPKU98nwq05koJHvtvnlbzuht4Fk9NA0fpR/J7GB2h1I1k1ci9QxVqtpTSPrDM21hA30BioEs4A5JwFdn1KovTEXxTJZiqhfYa1rt8RbOlJ1hi5hjJmDucqpFGI682bNwMA7r33Xnz44Yfcz9TI6BUfgiCDxqXXWKk026ZzVLXDY1SgHrNGt693E+HMEsPO05mYvuywV4R1+VbrjQnuz65jAmNJMKYcAsLbAs2uo8SP4kygOI+6NUomUFtzVeWFa6UpjLmUxKghDEg7DmyeS9aSwBjyYhsLaZ+5lygLub6J1fQkYOciav3eUMbM4VylNApxrfD111/X9RA4dcD6xNQKJ/ExOFaaK4ppU/DTqTHvtk4I9dfVa6G15khK9RrpOKG+VuZrGmfnMrqe3lBdtYgiVWJzLwHpx6mCnXsRyLkAFKVT0ocCY6h4UqMI6IPov5KZVhMEoCQHyLkItBlmtVToAoAwPyBlP7DnK+Dah6na7anloiZsG5JEFeuiTLrhUOYW6QKAcH86T8dXUn4zt4hwODVOoxLXhYWFmDdvHjZu3Ii0tDRIkmT3+JkzZ+poZJyawiwxzFmRWOE6wQYNBrWNxI7TmXYi2VWHxyCDBvdeF49pg1rVe4HpTRsIYO9Lb2zYnksBEmKETPiiGIV5Ppj6XRE+mdijUZ6XOkHppLfna+DkOorFk8rIZ60PJPEqKRF8FVz9ooYi/ADyWxddAIpzqJ16USY1qwlvQ9Xsogwg/QSQn0qV7dyL7nfqUwT1lSPAuZ1AwRWybTAzVZnbDAfi+1dd+OZeICtIYAwJa8as1XwlEOz8f0CznUBsby6wOZwaplGJ6wceeABbt27F3XffjejoaKfJIZyri13JWZVWbLOLjOj95gZkFVqr27bVyNpM9PAmyo2Ft4T1yE5R+N+47g3i2L2N7blsKVzCUHE3WoqXoYMRpdDgtNQEi//IxJD2kxrl+akTQlsDhlAguAV1WUw5QJVaAZQmUpZf+TYkpUtjCFCYQbnVkpG8234RJIJL84DQVkDmKWqDrfOjfWh93bNcpCcBiX8CyX9TBZlJNG6NgfK1L+0DTm4AWg8BetznuXVDkkj055yjG4TCDCDztLU6bjbJDXZUtL+WA7kHm8OpYRqVuP7rr7+watUq9O3bt66HwvEC7nhf3U20sBXWAJCaW4JHvttnaSTSEGPa3Lmx8IQdZzK9tq2GhnIuWwqXcK9qDYKRjxQWAjUk+KMQPcQkNCnOwPcrm+Cem2+q6+E2DnIvkOANb01dEwWRRHGFNpDyyC3SizIozk9UUfdFXQD93xBGQv3iHtq2IYyazEhmejwsAchIcm25SE8CNs8DLu8DCq/QpEsGoCiLfN7BzchaUngFOLOVKuZ9HnFf+KYnUfX+9CayxeCgtSulqLY20DGbSXhnnaHj5B5sDqdGaVTiOjg4GCEhjXMS1tWGu97XqnRmBGTrJYA5KxIxpH1UnVQjqztxbn1iqlfHk1VoxPvrk9C3VXiDqd57i7T8EgiQMFTcjWDkIwMBaCecR7CQDzUkmCCgiZCJf3Z9iTXxHTCsU0xdD/nqp6yArBUaX6oGi2oSkMZiqkBXKrIVISwBzASYzIBvOND8BvJuF1wBDFoS2wVXAL9IErClOYBKB1w6APheBAKiqfp8ehNNtFQ81JJE3uwLO0mQm02ASkuTLSUTVchzLwJaf6qcG4vpd3e90elJwJZ5wKW9dCz6AOpcaS6j45bMNt0p5fOQn2r9P/dgczg1RqMS16+99hpeeeUVfPPNN3ZZ15yGhSsfcflqs1li+GFX1dva12WzlOpOnDNLDL8fuOz1cS3YfBoLNp9udJP4zmYUIkbIREvxMoqYDt3E09CjFAXwgRFqaGBCEApwo3gA7//xI4Z0eLpR3XzUCUokn7EQ0AWS1aLgCtk8JMnaVMaVwFZrAagAcwmJc0EATKXk2TYEkx2kKJMq4uYy2jYz0wTKgBgS8znnyZIhgCwXfpHW6DuVFkjeRqJZsWUoHSQBGpephKrvIS1ouSGEtpd7gUS6q4mPkgQkrgDSjpHQ1/oCuQV0c6EcLyvfcVikxwrSaWxqPW2/Pmd2czgNlEYlrt977z2cPn0akZGRiIuLg0ajsXt83759dTQyjrtU5CMuX232tDOjK2q7WcrqQ5cxdel+h+Xlbx4qYldyFrIKy2pqiB6NpaFjlhi+33kWwSiGHmUIFvKhRymyYI30LIMG6QhELNLRtfg/7DqTgT6twutw1I2A8pF8YW3I3lGaD0AEmFEW2BLI+1Gukm0qI7Gq1tM6TCKBfHYr0GIgENMDyDhB1WqzkQSyTxig96PnlxUBxiISvxofIKQVoNZYfdjRXWg8gkAC2FRmI36VajEj8Z1znirPkonGf2ojkHGctiOoaPu2edW5Fyi1RDLTPvMv0w2AILi4l1DRsTJJnuyYQ3nepXk19OJwOI2bRiWub7311roeAqeaVOYjVqrNi7clI8RX63I9T6jNZimrD6Vg2g+OwhrwzKpS0zcE9cE2U1vsSs5CWoERWsEHIiSECTkogOM3XxqYUQgfNBEzsevAQfRpNbgORtvIiLkGSD1MnuaQVmRzyEuhCrRylYoiaWsH1SkBUAM6fxLJxlISp8Ziqh5HtgdCWgIlBUBJNlWqJSNVfpmZ1pMk6ggJkfZjG3134T96nEmAoJf3b5tQpYh9Jk88LAPO7yTBe+E/qqzrg2liZVCs/eRJyUQ3AgBQolhONHCNmYQ4AIhaGldJnnwjwuFwvE2jEtezZs2q6yFwqom7ovG1Vce8Iq51arHWmqWsOZKCqUsr/vbEXatKbdwQ1KVtpjZRrrlLLBQpLARdhNPIhn+5tRj8UIx0FggzRGxPPItpEruqbzrqFNtOhKX5JHjzU0ko6/wBn2CycZhNpGE1Wpr0WF5gMzMJVY2BBLkkV5XTEoHsMyRgJSNgMgKQPcyiBoBAdhIAYAIAFU189AkmgR4QA+SeB3S+QEEhIJroOaJKFrm2WdyyJQWMRLxJrpJDBAQGFKSR+I65hpJAjq8EOtxOVhBTKdlgRLUc7efGuZPKABMD1OHk9+ZwOF6n0c1kyMnJwRdffIEZM2YgKysLANlBLl26VMcj47iDJ6Ix2wu2iFKThL8Oed+7XB6zxDB9+WG316/sJkPpMFkb0q62bTO1jXLNMYjYKF2DYugQjlxoYYQACVoYEYJ8FDMdLrNQlEKLS8Vq7ErOquORX6UonQgvHyDBrPUFguMB3wjALxyIux4Iigf8o0GTFWGt2paHmWkSYEkeCVqNQf49mywdxiKyckhGWVirqcKsCGtBTbYNcwlwYTeNqTibbByimgSxqKJYQCaR11koX9NiVEk2yjYTJk94LM0lS4qphLK3M08C/k2oqg4GBDSldcoK6EfxdrvzZ10yAmoDWVE4HI7XaVSV60OHDmHw4MEIDAzE2bNnMWXKFISEhOC3337DuXPnsGTJkv9n773j7Krr/P/n55zbp2ZKMiWVNEhCEiAEQlAEKREEXNfdxY5rWUBX1/Lb1e9awIar4lpQbCuuIrquZQXB0IJ0CB2G9EmfzGR6u/2e8/n98T5n7p3eMzOZ83o8YDK3nPO5Z9rr87qv9+s11Uv0MAzOWjSnpyNhOExUvvM//+YlDMPg8rWT5y1+en/LkC2SfTHcJmMkDZMThRNpm5kKbFxSwtx8P43daZ60V/OwtZ7zjRpCKkU+NhkMGnUxtbqSMrqosZdQp0tp6Dy5Nx1TAreJsPE16GwQ77C2hOCGiiFcJEUy0SYhuegcq7WjMNOHaGsbMnEhvqFiSLSIjcMXlmMbpkTnaQu0IfYL17ts+hwLig2ddaIyhwqhoAKK5sM51wpRP7LdIeWDvrDcBclaFeLTjrZIYknLASHX6bgo69FGWTMqmwyi3OePAMkOZwPiwYOHicasUq4/8YlPcO2117J3715CoSwheNOb3sSjjz46hSvzMBJYtuaXTx0cEbHui5K83n7E/ODI95UauOHOF9haUz/6E48Alq353XNHRvz40rzAiKwqbsNkUWQoL+b4UJLnP2G2mamCaSjeee5iQNTrX9tv5Dm9gga7hFftxWy3T2WXXkAZXbRSwP32BjQGrd3jH6b10AcdR6D2r6Lexh3SGSySj7EmaU5s3SdKdmGlWDjsNNgOAR6QeCpRmQN5ckxbO/F+lhBpZTp+ZiXHQjmk1kn7cIclTb+TOJKA1lopdSmogMu+CmvfIZaRHt+1IcfFhxD+vrCzg5iZmBDh7nopommplQ1G93GYuyZb347OsZgM9p6VkvMaPlH9654b29fBgwcPQ2JWKdfPPvssP/rRj/rdXl1dTUPDxGYCe5hYDBRNNxp89vJVVBaHaexKUJYf5OO/eZHRcp/JGN4by+u6en3ViNdwyaoKbrzrtbEub/i1rKueFb7ixWV5Pf+u1dXcbr2pp6WxjE6S+Kmxl3C/vYFaLRnXEzVQ6yEH8XYZFrTSQird7z0VBBUHnYB4B7QdEAJpBkT9BUnsQDvRehknTcQHgULxNvvCcnwzIDaPdFyOYZgIAfeLX1k7VpOeDGnkmG70n3IU8kQHvPgreOPnnWKYFfDYN0WJ7iH6blSgQW/1OhfaUc0RBT3ZKSkmyhmiVEaW7BuGDCwmO50ovj7vWylTBiXd2vdo8wR9YTx48JCLWUWuQ6EQnZ39o4d2795NebkXmzVdMViu9WjwubtquOXv1nH1+mqeqm2hsXv0fuyJHt7bWlPPdXeMPv7xwhVz+a/H9nOoNcaikgjv3rSYgG/gN6EmKo5wMMyfEx532c1MQF/rS62u5jarkmq7hTziRAlTp0vROW8GVhSFT/QyT3607pdca19YiLVGPMpWWmwXGsDJkY6UCllW3TJ0aKWFKBuG2DlsRPFWiMfZLV8BIdXKFKJsZbLkFhw/tTOYqJ3saAwnv1oL6dYKYm1Crg8+Lgp2okNsLIoc/7ZLskf6202Jv9qFDjobDMemoo2cAh2EaCvkdfhCYIbkfp9fPs8rG8MXwYMHD8NhVpHrq6++mi9+8Yv89re/BUApxeHDh/n0pz/N3/7t307x6jwMhKFyrUeDaNLqyWWOpwdTiIbHRA3vjXaA0YXPgPfcvr3X9fjyPTu5/PQK3rFxEc3RZC+CO9m+36NtMc7/j200dMSoVkI0feFCPnz1G7h83cnTUugOiDZ0JHquvcbgqB54U15ZFDrp7TJTAqWyynMqBlbSIbm2/Fse5MTmpcTGofyiaLtxdHYGAhEpZtEaAmFpZoy1SWmM7RxbZ4Sn+4JCgnXGUae1nN/1RZtBIdyZpDP8aMntVlosIse7oe2gnM+2RDXWGvwheV6ig8FV614vPuc1OrDTji/c50QB5str6W6UjYWdliHNQJ7cn0k5teg2lC6F+Rsn6ivjwYOHHMwqcv3Nb36Tyy+/nLlz5xKPx7ngggtoaGhg06ZNfOUrX5nq5XkYAMPlWo8GGrF2/OPmJWM+xkQN7926bd+oBhhdZAb4G6yBe15t4J5Xs9amyqIQV62r5H+fPzqOVQ6P2588xFJVx/WmWCSCpEmm/dT89q8c3fc3fOhvL5/U858omIbiC28+lS/f+QD5xOkeQKl2oYAvXLnqpFPvpwUyCUCJep1ybA+GKYS7F7TE6CW76V0g43zMpEW5tRIQKJWkkcZdzrCioyQbPiGj6bhj+zAcguxYQ9zGRV/QIdJJpxnS8T73DCYqR7Vuk89tx0ud7IJQiRD9VJSh1eucqvaBYKdBBSAVh9IiWHqRbCqe/5msPxMXUm86xDpSAhv/ST734MHDhGNW/WQVFhby+OOPs23bNl544QVs2+bMM8/k4ou9sofpioaO+IQer74jQU1dx6ifp4CKCVIjLVtz+xMHxn2coVDfkeBHj07uORRwiqrjfeZW5tBFvS4hRogICVarg7S9+FMemVfABee/blLXcULQtJstbXdz+vJX2HWkkfaMSa1dxX322T0eaxhdRb2HPrDtbN23P0IPiXarv1v2wqGnhMymY/QQV9siG8PhKNuJLkh1iq86Fz0DjikhvKFiKFku6SMKKF4sEXypWNa6YTutiqYf5q2GksVirXBLXTKJ7Ppdy0fO+xu9PwdwqtnduD1/mN7eaGfoULs16Tltjj2f2z0PBeUknqTAtOX6nPEuaXJcsAGe+K4MV9qWbChKlwqxXrlljF8oDx48DAel9ViyFzxMR3R2dlJUVERHRweFhTM/v3RrTT3/7481k1rjPRK4+uNEVX0/VdvC23/y9LiPM9VQ2Fxv3sVqdZC9VNM7oUCznDp2qiV8+LPfJxycvMSSSYebqxxrgaJqLDPCjsP1GJ11qLwyYus/QF1g4UnrNz8hcEthmvZI+Up3kxDGYD6Ei2HeWiGjXU774v5tTjuiT/zNrj3CHe6znM+BrGKthHiaQUngsC0oXiRNjM17hSTnlcvzoi1C8u2cocCy5XDpl2DBuXLYA48IcT36rNg1LJvsgOIQUIZzTGcgEbJ2DzfJw7adjYFycrGtPiTdWZPhJ+vztiTl5M3fgnXXZM9nZeDodhlezCsTK4inWE87nGx/v2c7Zt1P2EMPPcRDDz1EY2Mjtt37Lbaf/exnU7QqD30x1mG/yUDFBKuRJ0vpSrVqYalxjHpdQv/oL0U9JSyhjktu/BVXvG4jn7l81VQsc3xwc5VjLdhlK9l+sI0XjxwimbGAOSxvOcyhhl8QOuPvKSiEl7tLWLdmDaY5ULyahwHhbl7aDopq3HrQKVNxyLEZhLoXhHguPg+KqoQEJ7udVkInoUP5pH7cdrzOPSkcLhE1syTUFxb1+fS3wYJz4Pn/llzsaKMMQhbNz/q5AbqOw/wNQqxdQrz0Qvl4778K6be6GdFgonaHDZ1IP20BITmvLyibB8Mvmzkr5qSTOEOL2YPQQ7xNUwi0ZUPJKbDmbb3PZ/pg0Xmj/KJ48OBhPJhV5Pqmm27ii1/8Ihs2bKCyshKlPIVpqjFQ0gQwpmG/ycLnrpjYt/lPltKVPOIESRNj4NcTJ0gFbeQR77GozDiC3XEEmvdyIF3MfY8dIJHJbQFRxAjy+swTHNm+hxgmbfh55q6FrHnjOznv3M0nfYrKuOFuXtoOCplsPyz+4B612RbF2jAh3S0tiKdcKORYOUOLypQa73RMiGbncbE/ZFKOV9o5l3I2PFZKsrH9YViwURTpQASCEWlSjLVIhboZANISfxeIwIotWWLtIn8ulJ8q52/aKYr4sLVNjhoeKJTnR5uyNpCMhniro7wPlsvtQmctK1ZKbCtnf9BTpT14mAaYVT+FP/zhD/n5z3/Ou9/97qleyqyGS6gf3NHAH1+qozWa9UVWFoU4c2HxiIb9Qj6DxEATfhOML92zg8vWTFy+9UDJEzMRUcIk8RMhQTeRfveHSZLETxSJpPvJYwf45KWnDhobOC2R6qa+uZW/HtbkYRNAMo/9ZIiQZIVxhBI6OaLL6CCCicXC5F4O3fstvvXAHl6Mz0NhU61aWJxv88GLT+eCs8/qT9JmKzqOiBUkHZUKcsvNkXb8zuic9kFDfNL7HnJynF1PshMzFy4Vm0fnMYeYWo4qbIr6HYg4SSMOEc0rgdLl4ucuWy4e6uqzxCISa5GBQ8MnavLSN8CSC/qvP5AvvumOgzlf0+F+qp3fI1Ya4p2y1qIFosR3N0AmI0q86Xc6Z4awxemMqNamX4YYz/T+tnnwMB0wq8h1KpXivPO8t8emEsOVptR3JHqlXgwFl1hPdr33ROdbn8hq8slEnS6l1q5yPNfuUJYLTSWtPVXgIMV3v3zqIO9/3SlTst6xwOo6TsexPZxvpBxCnUCjiBMgolIEdAoLk2XGMTT1KKBbhyilk3NTT9GlzuJS43mWGscIJdI0//n37D68gZVveIcMnM12pLpFqU10OfaGtOPmMJ0fbJXNocaQpsVEa5+DaFG7u45KIoeVpqcBUdtCsDOJbCydbUFAwaLXQfFCIcWnXSnDidFmmLtaSGuiU1Tr4vlw1vv6b4hsG1r2iUc82SXqtZHM5l0PCi2qeyYpRN8fkufaGWdTkWNlAXktPVnYpvOxr6jgVLTvvd8bVPTgYRpgVsknH/jAB7jzzjunehmzFve+cozr7nhhwqL1XJwIcjrRqSVuNXlF0cy1iGgM7rPPpo0CllNHPjFMLPKJsZy6XlXgLg61xqZwxaNE026anvkt2BlCJAmrBH6VIUCGfBKEiBMhSYQkCpsQSUpUJ0vVMU5VR/gH82H+zfw1q9VB0tpHu46Q1j7qdjyN/fRt4jWe7QjkCwm2ko5SDT2WEK0dwmlnEz6GQzrqtA9CT3KIawex06J4+/yw4GzYcG2WMJevhHOvg8q1QtCjTaIeL94E517ffyPUtBseuwUeuFEaIbUW5VkZoqIPB+00RNopsa807ZBED9MnA5yRYrk2uQ2Q7gCk0VcTM6RavWUf/PkT8NzPnbg/Dx48TBVmlXKdSCT48Y9/zIMPPsjatWvx+3snGHzrW9+aopWdvHAtIPe/Vs/Pnzo01csZMyYjsWTLmkouWVXBtT97hsf2tUz48U8EpAp8S08VeAVtA1aBu1hU0t8+Mi3heIEz0Ra22yu5xHieCCm6CWMpKNAJwjpNBoO0MphHOyn8pPATVwHydJIKWgkbCeooZwlpfNhkMGjL5NNxbB9zdv1ZbAmz2SLiWjKO14AZdpIyHEKtnTxoN+ljREUr5BSt5KSEKCO7Cy9aCK//t/6EuXylfD3cOEA3ArDv18cdwGw/IjnZxYuh86gMRFpu/flI35NScoy4s2bDFD+5GXA2F5a8bu0o1u7nvWCL+q+1FMfc//+gcSec/Y/euyMePEwRZhW5fuWVV1i/fj0ANTU1ve7zhhsnHve+cozP/qmml6d6pqI4Epi0Yz9/uH3Sjn0iMJIqcJBwhHdvWjw1ixwtnEHGNrOcDC3ECGFrg4DKEMBGKRsDTQo/CvCTEeLtvGZLGRhoIjrBQtVIgy4hgR8Dm3LVjuq2ZDiv4wjMWTS1r3UqYRhwxjvh0JPilQ7kO77rTM6DRlMP7j6+z3O1DRjinw7ki/96sPUM9fXISY+heIFYQvxBKKoW+0m8o8/aR7JWEyHMDnlOdoAqduwlLnJjBQdal+VUuRuQTsCOP0pL44Wf9gi2Bw9TgFlFrh9++OGpXsKswc337pj0EpMTifbY5GRtbz/QSixlDf9AB9PVo10cCXI0NnAVuIsPvm7JzBlmTEnMmxmci58GLEyOUk6BjjGHLkwlxCdMEkn8NlA5XxkfGQw0KAiRolKJT9jGIK1NzERGSlHiHTBnKl7gNMLc0+CCf4VH/kN8z6YfMn1/JnKKU8YMLRaMln2ilI9lU+NsuoRM26K0WznigWFkefBIobQTted8/9gZsa/Yjgo+IgHcUedNQ56vTEkv2Xm39+6IBw9TgFn7E3f06FHq6uqmehknJe59pf6kItYAJfnBSTnuaDOvrzj9xDT/5QVGltN86ap5/PqD5/LcZy/hh+86k8gAz1MK/un1S2ZWDF8gH3whrGSUNH4yGOQTp1R1EVAZMvjIYGJjYGLjw8LEdvzYcZTWaMCHxu8Ui2g0ERKUqk7C6TYyrUfYv/Xb7HjsT1j1NdB2aPZ6ZVdugat/AGv/QaLtlKSyZLOcg05r43jgqNjpuDQ9juVaO5su/HkS5xcplcHHaIszjBhAlOhR5Jz32F8c2LasUdvgz6f3oPBA77CqbMGMtsRaEiwQcn7sRdkQePDg4YRiVinXtm3z5S9/mVtuuYXu7m4ACgoK+OQnP8m///u/Y3i7+3HDsjX/+vtXpnoZE46KwskZPBxt5vWfX62nOOKnI5Yes4Jdkufnb86o4r8eH9gDr4DXryjnLzXDp7asmFfQk6Liesif3NfM7184SiyV4ezFpbz3vMUzR7F24XiBi5ueppM82nQBK40jKDRxAoR1ihhBDGx8SshzEVFsDCyHACmt8ZEBBQXEMbGz7zxosGyLwMGH0Aef4JmH5jK3pARVVE1y6WWs3PTm2VdEM+80uOQm2PEn2PoZyaH2hQAt/07HRY0dFxwS2rp/bJYcZ9NFOiqFNWUrZPgx3iY+aTeT2x6tfJ27PhPI0OMz9+c5+dmGqNN9Bzt7Bjadwc9ARAYc462QisqGwIMHDycUs4pc//u//zv/9V//xde+9jU2b96M1ponnniCG2+8kUQiwVe+8pWpXuKMx63b9tGdHI3ncGQwFLz//CX89LED47JFhP0G8fToFKvKomy5zURjLJnX8ZTVM6o1lmvx5avXcPnaKs5aWNLPE1/ptFEWhPwjItd94wlNQ/G6FeW8bsXQFpFpDyeerXv3TpZzmA7yMLAw0OTpJCl8dJFPGZ1ktImhbDQQI4jWkoOtVDrnrUG7ZyTP1R59SlOkY3SjqbLrSTc3EWl+GVX7EL/d9lvmX3w9rzvv/Cl48VMIw4DSZZKYYVtCEt0Llk4wfmOU89yxks7cTOzyAqkTLz9NCLaVEXLrxgjiG6X/2oVTMhMqgoK5kDcXmvdJwU06gZDvHPKuxX7Uk99dUOUMhQKBPCd1xIMHDycSs4pc//d//zc//elPueqqq3puW7duHdXV1dxwww0euR4nLFvzo0drJ+XYt779DC5fW8UZC4r5yK9fxB7j39fREmsFfOHKVZPWrDeWzOtkxuaK0yt44XB7r1jDyqIQV62r5MePDr4B+afXL+HytVUAXL62isvWVA7YImjZmuKIf8gynzkRP+eeMjHZ39MS5Ss5uvw9vHbwDs5Qe8loP2LvUMQJksJPra5kvmpmDt0YyJBjHD8mmR5O6JZwQzbzIku6FQXEySdBA3NopJhCYpyReYnn7r0FYPYR7FAhzFks0XSxZqct0S8NjOMi1k5etG2JAjwW0pmbid20CwqrIa8UwiWSdW1nHHU5mh2iHLFXvE8qip2GrgZor5Pb0nE5XjBfXoOVdBoaLfFs+yNQUAGhYlGtDROqzpANgQcPHk4oZhW5bm1t5dRTT+13+6mnnkpra99iAg+jxdO1LaMazhsJXCXVrR+fkxccM7Ee77knC27m9VDlOn1x32vH2fHFLTx/qK0fMT5j4Zx+xyrNC/Clq9dw+drer8U01IDlOKah+NpbT+e6O14YdA03v/X0k77O+7C5gNusqzjL3sM/+e6mRRcQI4QfizQ+wKZQRUlpk3yVoF1HKFAJ/Ng9JpDcChCF0C13IxVWSREdUZTTSTExUtqHBqpUCy/efwfnnbNpdllEihbA/A1CHjNpIYrJrhyy6lxJ5RBXPQLyqny9H1e2bOyk083E3nm3DDem42IVMUxRitsPOmqyQ5ZHsj6gdyqKKV7uQL4zJOm+5+GQ7EgJRBZCS60TPehcj1iLlMn4w7DwXNkIeHZHDx5OOGYVuV63bh233nor3/3ud3vdfuutt7Ju3bopWtXJg6f2N0/Ysd6zaRFvWlPZQxhdjHYAcKz4yIVL+fglK08YeXT9ym//0ZNsP9Q+7OMztuaZ2hZet7K//cI91kCK9GjX9MN3ncmNd+2gobO3Qn4iNh3TASV5ATQGz+sVvGgvZ7U6SAOlgGIOnaxV+5mrOjCwiROim3x8GtoooEq1OKkh2SZR96NLo1zaYwBpTDKYhFSKPBLU6iqqrSP8918e4x/f/IYT/MqnELnqcKxZfNGGKRnOsWZHwXavoulkWA9lv3AJufPvcDGsvHx8pLNvJnZ3I7z2f7DvQYdYm071+kBig5IBTWug32XuECcOKXeOZcWEMFtJUa0zSSmtMUywnSbKVEw+N/1QWOkMh3oxfB48TAVmFbn++te/zhVXXMGDDz7Ipk2bUErx5JNPcuTIEe69996pXt5JgIkjom9aUzmgojraAcCxYvOy8ilRZXc0dI34sb9/8eiA5BoGV6RHi4ki6jMVFUVhINtGWWU2s1btJ6MNFqomIiqOgU2CAE26iEKiFKgY3bqEpJNrrbGx6J0f0ffNF40iQMZJIFEEsChVnaS0n3uf28t7L79g1lxzoL86nOoWdbhkqdgxbMshsa4q7BLlgTKxHcVbGRAqgFVXw5IL+jzEHr48pi9yM7HnrZaPh5+SwcZMksHtIEr82X1vUz56XPna+egLymO1E8sXmSu52um44xlXECmX+vdQkTwvWAD586DxNVh+qadce/AwBZhV5PqCCy5gz549fP/732fXrl1orXnrW9/KDTfcQFVV1VQvb8bjnCUl3DoBUeLFEf+gA4Qbl5RQURjqpaRONCZzgHEobD/QSndy5LaaibbgDIaJIuozEe7AqWuxSegAc41WFqom/FhEVYBOHSGNny7CpPBTTDeFxOjWIQKkSeLHpzI9Odh9VWsbyGAQJonfodcWJovUcaI6TEPSz/YDrbPvazBQY2LLPvi/68X64Cq8uZt6ZTjEVksGtTKEtPpD8t+CjbDhfb0JZ+NOePFXQuK1Jf7p8hWino9G+c2fCwWV0FkPKgn4REm2UvQi/D2qtgvDsZDYsl7DJ3F/GEKS7TR0ZuTfkVLZWHTtdJJB8uTlmwHILxdFPNYiKnbjbq+kyIOHKcKsItcAVVVV3uDiJGH7wYmp8H7feUsGVelMQ3HjVauG9AKPF5M5wDgUHtwxfDpHLs5efOI3ALMN7sDpN391N9eaW1lAI5Y2wIA0BkEyaAxSBCihi27CdOsQhSpKu84jQZAkJnk6SZ5KIBq1ZIcYSBa2ZGVbTk+fJo2POAHydIJy2gmROGF2qGmHvo2JRQtg2Rthx5/lPq17R9/ZTs6z7SR3AJACnw9OvQrOfl9vwrx7Kzzydeg+LiqxGZQCl2iT2FLOvW7kBLurAY7vkJg+rYGM1KGjHELtqtVarBtuTbvKMQwZOd5w03TU7AxEyuSY6bg0Waaj8vhUVB4bLgYzJEQ7WCAe9XirF8PnwcMUYda9X9TW1sY3v/lN3v/+9/OBD3yAW265ZVoNM/7gBz9gyZIlhEIhzjrrLB577LGpXtKIcO8rx/jetvEnhRRH/HzkomVDPmbLmko+fvGKcZ9rIPzgHWdOiZfYsjW/eW7kZQ9KwXvPWzx5C/LQgy2r5vH9M+tY5mtmjuqiSMWwMOkkj24ihFSafOJ06zAhUqSdSvSM9vOcvYJDuoIuQminyTGqwzTrYjp0hDadJ8KjUzKTwiRGEJ8Wq0mHzuMS4wXm5gem+jJMDmxbynOOv5Yt0RnoNheGAed9VOL6zICkdRRUCnm1M+LH1rYkZ0TmSnKGGRDSPe80Icru8XfeDdu+BF3HIL9ClOdARLzd8VZoOwi7/jyyspnGnfDof8rztOSeC2T7JMTaABWQ8yzcBAXV4IuI+uyPZDcFIGtWpsTv+cMw/yxRtVv3C/lXPuc1W/J6rZSThU2WuCvTi+Hz4GGKMKuU60ceeYSrr76awsJCNmzYAMB3v/tdvvjFL3LXXXdxwQUXDHOEycX//M//8C//8i/84Ac/YPPmzfzoRz/iTW96Ezt27GDhwoVTurahcO8r9Xzk1y9OyLG+NsIEio9ctIxfbz9EQ2dyQs4L8IN3nNEvTeNE4enaFqKjsIR8aCZVic90dBxhpVGPPT9EorObllQZmXhdj32jmxARlcRG8bR9GkVEKVftBKrWssBqx7STtCXLaI4eB8uiRi8ijZ+V6ogz9GgR1Bm0MhxirslgcFjPpYbFrPbXs7EkCszQ7PDB/MxNu2HHXVD/kiiwgTwhnOlup/FwEIvG3NPgDf8mdendjXKblZIacqXEm+3G9xGUEpZYC7x4h6SQ7P4LNO2BuueEWAfnABaooKNeB+TxZnRk1orjO+Huj0LDq8MMVjpNjFZG8rHtDDSnoHC+XJtUVCwtysjaRky/XC9/HtLA6FSuq5yUlGChqPTRVigKS8JKJinXzYvh8+BhSjCryPWHP/xh/v7v/57bbrutJ9rKsixuuOEGPvzhD1NTUzOl6/vWt77Vo6gDfPvb3+a+++7jtttu4+abb57StQ2GrTX13HDn+C0ao02gEHvIaq537CHjSecrDvv42t+undL0i9Ekrcy4KvGZjlQ3xFsxkl1ECuaQTik6YgHySBJXhlN8bjAHGTALkeYpew2vv/gmVpclekillYzS9ND3CDbV80K7n/2ZKipVKwFt0U2IJl3kPD9Fl45Qo5cQJ8RZ1QozHZ3qqzA2NO3ODiVmEkJ8y5bDvDXw8v9A0w4hiRq5TrEHhFzmlUGgYHCLxsotULwIXroTGl4WYusev4dYu7CFNLcdhK3/j56CFtuWunIrIVaLgsqsxcQMSBTecNaKpt3w2DdlyNJwa8+H2CTrjBzzyLOyBl9IfNTzVjv+7IxYVIL5MHc1HHoCGl6RZJB0TJ5TuU5SU7qOy4bCzgjhTkezay6shPXv8IYZPXiYIswqcl1bW8vvf//7XpmxpmnyiU98gl/84hdTuDJIpVI8//zzfPrTn+51+6WXXsqTTz45RasaGpatuenuHeM+zueuOI1rNw/usx4Mg+VDG4oRZ2F/7I3L+egbl0+DJIaRnf+6C07h0286bZLXMguQo6Za/jy2t+bR2J0aOA0lkO/EoSXBLKIwbFBnFBGwWwjrNBllkEHhI8MyVcdhPY8XI5v51NK58s3owAQqLv0XKnbezalNe2nt0HS0LqCzM0GCAOKuVRzTZdTqKtooZFO1n8rS8Mx8e79pNzz9Q1GBi6pFfU1H4dhL8PKvIdEhrytc6gztHXVsEYaQRr9j0cg4707t+rMMOLqE0a1Lbz8Mf/kU1D4CwSLxKrvQGjIpIbHxNiGqeeXQcRgS7fTE4qVikhlt+OgZhrQtKYgZ7NrbtmwcOo7KLxx/CFIjaJFUzpBjrEkaKMuWyet0E1EWnyfEes99osSH5mQtL8luqH8ZKtbK+hLt8voySUkMiTZB8Xx4/b+Kwu/Bg4cpwawi12eeeSY7d+5k5creAyo7d+5k/fr1U7MoB83NzViWxbx583rdPm/ePBoaBh50SyaTJJNZW0RnZ+ekrrEvth9oHXHpyUBQQEVRaEzE2sVAUXFt0RQfvnNoRbuiMMiNV62eNlnNm5aWcuvD+4Z93OuWz1BrwHRCjppa39LG83VxdqTnUqfLSRDk9rwy3nrlW9iy1nlL3a28Pl4DmTTKH6SirITDjTYldFFElIw2AcUOvZjfWRfwqasvHfh72knAMDqOUJbqpsyfx+4H/ouWnY9Rm5lDGj+dRAj7fVyxopzlRh2Ur5t5b++7xDPWAuWnCkm0LGjcBc17oLMOcHbBVkbUYzsjRFdnhDTmlQnxjg9h0TAMKFkMa94Gh54SS4nKyxJY12aRjspxQ8VSwJLshky93JaOiQ1DW6JkKxOMVJa8pmIDv8aOI6LI55VmhxJNU16THkS9VoZDwruhaL40KuaVwznXyxoD+eIjf+LbUkYTaxVFPlzibDIMeW7Dq7DoPCHTsRYZYPQFJXrvnOtk4+HBg4cpw6wi1x/96Ef52Mc+xr59+zj33HMBePrpp/n+97/P1772NV555ZWex65du3ZK1qhU7z/IWut+t7m4+eabuemmm07EsgbERCQYTEQyx0BRcbcZ/RXt0rwAV6+v4pJVFdMuq/ncU0q9uvFxwLI1T9e2OPYa+X4495TS/l9jR021Y838tT5ATXOYFTRzrXk/YZUirgPEk0EO/u9veK7hw2y41Hlr/Yx3wqEnxaNbUElJJIBRWkBbW4o6O0hMR3jWXskdkffwuauHsRj1ScBYedG7sCNdrGyqp81XTjBSQHVEY3TVCXE79c0z7+19l3gWVQvxrHsejjwtpDZ32C/VJeTRSjm51U6ms06LIh0qlmzq4dIv1rwNnv0vGSxMxeVdBu1E29k22CkZAvSFIJ2AaLNzzj4eaeXE4mWSYPhFPd9zr2yK+n4NUt2y9vwqIcyZRDZKzxqIXBv0lN6YflGW8yvkOimVzcpuOySe8GRUjhkpk2uV6JBz+vPlY8cROOUNcnvzHqg4Hd74BTBn1Z91Dx6mJWbVT+Hb3/52AP71X/91wPuUUj1k1hrwl+PkoaysDNM0+6nUjY2N/dRsF5/5zGf4xCc+0fN5Z2cnCxacOIVrPIUuk60cz7TyE69ufOzYWlPPp//waq+Nya0P76M44udrbz09+z3mqKn1DXX8X10+yYzNYo6zwdhDgBQWBglHOV6u6kg8+RWshXMwT32TEKEL/tUZojsOZoBiX5Ci+dW0W0Hag1VsWvt+PrR+4+i/RuUrMc69ntKdd1PavBcyxyARgqp1QqxnYsueSzz9eUKsDz4q9oW+sJLZSDpAzDM5teHxNmfYMQxq4eAWDdMHr/skbP036GwQ/7I/T1Rp2zm+Tos3GhwSbPS3R1spUa59IYm3Uww+1BjIl8eZPiheKCQZnBg+CVbs2UiYIfFxB/OcWD07m07Sdaz3psHx+JPsFP+4m+WdV+pcr5SsP9osXvFkF5QsgTPe5RFrDx6mCWbVT+KBAwemegmDIhAIcNZZZ/HAAw/wN3/zNz23P/DAA1x99dUDPicYDBIMBk/UEvvBLdho6EiMaqDwbWdW8x9vWzfpRHGmlZ94deOjx9aa+kE3JO2xNNfd8QI/fJcTr9hxhLp9L/PYoST5ZAhist7YR4A0nSoPE01YpWnRPg4zl4V2I51//T5zll8ipCV3iK5lD9g2KjyHOXNXMmcUJNiydf9N30CFKSNpCZyucIlnolOG96wMkvesBrdMQLbyG3LsGN2Q9Emr4lD2mOWXwmt/gH0PCbFPtA+Q3mFnz2M5JL5XwQuiQpcsla95ossh+AMo5q5dqP4VGTJMdUvWdTqWPY8yAUepDoRFqU7HoGCe+MOTXXKdcjcNgXzZHGSS4sl24Y9AYZVs7pLdzgBosxTjzNRNmAcPJylmFbletGh6N1V94hOf4N3vfjcbNmxg06ZN/PjHP+bw4cNcd911U720AeEWbFw/ykKX162YmmrxmYCZprhPJSxbc+Ndrw37uJvu3sElqyqg/lXih19kraHxofGTYp5qJ0oQUFhAAI3hVFC3UMiSzgNwdLv4WyE7RDdGEry1pr7f5qmiMMSNVzmbp5OlTc8lnrv+Il5itwzFjY8bFLlE1ymJQYsfu/UAtOwdnER2HAEULH0jHH1WPh9y1597p+kkjDgFL6bzeSYpX9uBFHPDkIjAjjpRkSvXQ+QotB0Qr7SdFrXa8AtRD5cKsfaHoczJ6e+sk3cocjcNRQugdAU01Mj5/TnvEPrCYguJzBWCvvljsODcmbsJ8+DhJMWsItcAdXV1PPHEEzQ2NmL3KQf46Ec/OkWrEvzDP/wDLS0tfPGLX6S+vp41a9Zw7733TrtNQV/l7fvvOIOP//ZlkpkRlC0wPjvJbMBMU9ynCtsPtI4o57y+I8GrLzzJwmd/wBy7jXbyaKaIMjL4sAiRIoPPKXmRoheAOAH8ukuIUy76tgaOEFtr6rn+jueoVi2sVHGihKnTpTR0Jnor7CcDXOK5Z6sTbWdmifLIDuB4sMkWpnTVw84+iSG5SHVLg2GsRawk9iisfabPKV9Jy/NsC3RKLBhlQ+RFl6+UiEA3bjCvTOwkhVVQUCWWj6bd0H5IbB5FVZIEYgTEojKQp94wJEbv0OPymguqwOesLdklVpJwCSw8xyPWHjxMU8wqcn377bdz3XXXEQgEKC0t7TUoqJSacnINcMMNN3DDDTdM9TIGxdaa+n6DgsUR/4iJdV7AYOMSr7bbw/gx0oHai4znWHD//xBKNWEoiwJilOl2ugljozCxCJMkg0lUh0giGcl5KkUgGBTCNA6kMja3P17Lk/f/jv/wPUGlaiGNnzbyqbWruc8+m1pdzWf+8CqXrKo4ed6lKF8Jq94i0XtulnUPXEI4wO8NZTqReIjy6wtmn3/shcFLXQL5YjnpOCoke6BjDwZlOAOOzr9dkl5QAWuvGZrADmXpcSMfj9fAoafF0hFvA198aE/9vNPggn9zqtkbstXsoWIp2ylZPDMHXT14mCWYVeT685//PJ///Of5zGc+g+H9Uho1RHl7oZ/2NFTCRV9sWVN58pAHD1OKkbwDcpHxHDeavyCSaCeugtj4KSROvkqSRwob8KMxSNKp82mlwHmmpsoXRZWuhfkbx7zGm+/dwUOPPc51xp/4lu8lwiqJhUFcBzmu51CmOqkyW7jd2kJtrJqn97ewedn4yPy0wsYPwfafioLrz4N0V86dLvmVAniZLtTSOOgLCMnFkPzmQJ74kFOx3v5n2xZVuKVWiHUgX0ix1gybN90L2nmoLWkibYfECx2aA3v+Isr2UJ7mwd7NcG+fswhWvGl0dqKVW+R5L/5KVHG3sXLuSs9j7cHDNMesItexWIxrrrnGI9ZjgFsYM54mRIDzvZxmDxOEjUtKqCgMDmoNWa4O8Vnfr5hLO0rZhElioNEoMhiY2CgUaPBhk8IkhY884pTSSSC/FDb+05gTGG6+dwcPPvY4HzN/x+uMGvxk6CAPgJBKsYAm/GRAw6XGc/zQquSp2pOMXPsCcN5HYNsXhbT2kOgcYh0qEtLYfhC0cpRrv5DJTNxJ2Sh0imXCEj13/DWpPt/9F8m3jrc5x9MyzGj4xU6iR7jxtyyH6BrgD0DxEqhaLxaM+lf6N0SOBWOxE809DS754skz6OrBwyzBrPoJff/738///u//TvUyZiTGWxjj4nDLDK1x9jDtYBqKG69aPeB9S1UdnzL/l3mqjZgKkMHEQDuEWpPGTwo/NgZNFJLCRz4J5tNMITH26Pn4t3xJ1MMxIJWx+cmjtVxmbGe1OoSBTZcKY2FiYRIljKUUpaqTsEqwzDhKtWph5ErrDMLGD8BFnxcfcu5GRfnET1y0UEh0sEgUaisptgwrIxXoBZXZmu9UFJ78Ptz9cfjj9fDSbyDRKrF2+eWiWGvbGSb0D76mvlBKrBf+MASLhbxm4nL+8lNFDd/1Z1HKTzRcUj5vtXz0iLUHD9Mes0q5vvnmm3nzm9/M1q1bOf300/H7e//y/da3vjVFK5v+mIjCGIAfPVrLRy6aDnXjHk4GuPGFuTnXCpvLjGeZa3ZhYxAjhA+bIBksDAw0PixSmJjYaExe1UuIEeJ+awN79AJe0Mv4VeAcNo1xXb986iBVqoXTjQP4lIWtDCzMXo9JESCg0pTQTZfOI484m045iVTrXGz8AJz5Hth1D9Q+BEefk5g7bEh2iEK78go48LiksyhH0TYDolRnEnJb+2HxY8daZEAQBWmfHMcfEY90og0yDrlWTuX6gHCiAY2wDFwq5+sTa4VoI7Tsk8STqjOkNbFpz+B+bw8ePHjIwawi11/96le57777eurP+w40ehgcE5XwEUvZPLm3mdet9OwhHiYGbnyh29BYmKjn/KPd1NaXsMSsI+QUxAAY2GgMDGz8KBSaGCF264XMUd08r1eyWy8ExrehPNQaI484EZLYOTYUK+fNQvffQVIyWBkq4NyTOSXGF4A1fyP/WRkh0dFmGRidv1FU7SWvg+d+BgefFKuHQkh2IB/izUKYfYFs+yJI5nPncRny8wUlrSPWlh1ONENSr54L5ZJpDYYN6WSfTGxD1PLGHdKUuGiTEPzBGiI9ePDgIQezilx/61vf4mc/+xnXXnvtVC9lxmGshTED4fcvHvXItYcJhWkoNi8vY/PyMqz6DH99qY0OImgNhSqGJTV3zv/FGqLQdOsIT9qnESBNkBQFxFAOAR/PhnJRSYRthIkRJINJSvsIqTRxsqVPptPg58Nin13NR9964ex5R8f0ZbPDc1G+Ei67OTukiJbBwq2fkY8RJyta26I6K59YQJLtEG3KkmllgrIlYaN4oUTauUq3GXDyp33ShJhO0tuO4wxCalsU8M46OPq8qOuDNUR68ODBQw5mlXkrGAyyefPmqV7GjIRbGDMRiKVObLW8h9kDy9b87tV2rEySs4x9xAg5fmscgu1mLSuiOsQL9jKWqgbON15jgWri7b6HuN68i3MLmscVGfnuTYs5pkt51V6CpU3S+MhokzDJHlIdIolf2zQbZay5/J/Ycnr1xFyEmQ7DkDrv5RfD8kukSCXeJraPdEy81+AkgjgRfemYUzJTC20H5fNMSjzanceEFEfKIG+ueLjDJaJEawsh0wY9tes90I6HW8uwZf68oRsiPXjw4MHBrCLXH/vYx/je97431cuYsdiyppLb3nUmReFRDAoNgLMXeznXHiYeW2vqOf8/tvGZbS0ESVFEN0co55CeR0xLCyNIXkUakxfsZVSqVpaoejqJ8Ky9kjZdwGp1kP+Y/wRmy54xryXgM/jg65dyn72RPXo+SkMKH0ntI0CKYqL4tUUyv5pV13yZ121+/cRchJMRHUdEde6oE+Lcul/Is5WSwUfbTYvROR9tIcWRUonwC+RB6TKwU+LbbtsPKScWUDmbLmXSj1w7GyG0BaWneMOEHjx4GBFmlS1k+/btbNu2jT//+c+sXr2630DjH/7whyla2czBljWVHGmN8ZV7d435GO89b/HELciDB+DeV45xw50vAjBftZEkQLvOp0R10U2YPVRTrGMU04WfDAn8LDSaCJJmv66kVlfT5mRc7yXMuVaHpEMM1gY4Anzmcnmn57uPwTXGQ5xr7qSIblJa0a6KCCzZxILL/z8pDPEwMJp2y5AjOCUvDtFVrsqck96h+7wjZmdk8NEfEVLeUiu3uXYSbHlOj9faECJuW72P6w/L4z3V2oMHDyPErCLXxcXFvPWtb53qZcx4lOUHh3/QIDj3lBICPk/98TBxuPeVej786xd7Ps8jTgo/z+sVnEI9c1QX+dhkMNir53NQz2OJasBAc0hX0MAceiuWijZfOaUTkA7xmctX8clLT+W/n7iQO/fuZKGuZ/PyMladfiamF6s2NGxbasWtFJQsg+OvOlXqfsmxtm3xRfeDQ7yVkufalviy0UKSDcOpN+9DxnWG/n8SDbGXFJRA6dJJeZkePHg4+TCryPXtt98+1Us4KTCeQa9/OHvhBK7Ew2zH1pp6brjzBRQ281UL+UQ5hXqK6MIkw25dDVrhxyKNj04i5BMnRggTTSPF9CbWgmCkADLHJiQdIuAz+OAFy+GC5eM+1qxCxxGnmTAjnmfbKYSxkvJfD5ziGZ2RfxvOO5Iuoc4l0doGDEe5JoecO5aSHhXbIeiGU0Rj+pwSnBMItzrdK4/x4GHGYVaRaxdNTU3s3r0bpRQrVqygvNxLrhgVxhFoUFE4MZF+HmYvLFuz/UArDR1xvnTPTpaqOi4znmW9sY9lqo45qosQKQA6dR51uoxX9RI6yQM0lbSyz65mrtFGhATdRHodPz/oozqiIRHy0iGmEqlusXMc35GTVT1ApbnyCfnNZJAxIu0MPQ6Ua2SLmm2GJEkk4ySF6L5pIUosIn4noSSQDy/9Cjb8o1MuM8lkt2m3qPbNe2Xw0heSzO3TrvRqzz14mAGYVeQ6Go3yz//8z/ziF7/Adpq2TNPkPe95D9/73veIRCLDHMEDQGPXwHXTw6GyKDSuBAYPHrbW1HPT3Tt62kKXqjreZ25lAY3Mp4kSugBNCh8BMhQQY6k6Rj5xXtGnECZFKwX83n4dl/I8q9VB9hImd8f4huVlGF11ULXO89lOJXxhSQBJxx0vtHKDXhwy7CjSOgO2S3JtxzM9DKyklMcYPjmglaLHZ234hMwGC5zIPgM66+GFX0oJTl6ZNEJWngGrJoHsNu2Gp38oGeChQggVy2bh2MsTU8PuwYOHScesItef+MQneOSRR7j77rt7Ivkef/xxPvrRj/LJT36S2267bYpXODPwxN6mUT3epS1fuHLV7Mnx9TDh2FpTz/V3vNCjR7pNjHPoxKcyFKkollLECQOQTxyURmmYp1pZqXzca53L/fbZ1OpqtG1QZbawnDrqKcEI5HHRKXksVnWQVwqnvtl7G34q0XVMiLVC1GmVFiKt+yrSeoDEkOGgRbU2fFI8Y/rlqaECqFwn5Lp5txB1wwfRVon1s5MyJNl1XAYkG1+DN3x64siu6zNvOyiEutUZwjR8oqCnusc9aOvBg4fJx6wi17///e/53e9+xxve8Iae2y6//HLC4TB///d/75HrEWBrTT2/e6FuVM+pKArxhStXsWVN5SStysPJDsvW3HT3jl7UqVq1sNQ4RpcOs0IdRSlNikDP/XGC+LBoJZ8QaVp0EXfbmziq5wFQq6u53drC1cEXuGZJnLKwheFLQfk6IdaeOji1iLWKYq0M8U3bFkOT5wEsI0NB21n7SP5caY30O6UzjTvlfOFSaD8A6W45vD8/mzBiZaDueXju53DZVyaG7HYcgSPPyUcrJcp1sET8493HhWQfedarYffgYZpjVpHrWCzGvHnz+t0+d+5cYrHYFKxoZsElOKNBftDkc1ec5hFrD+PC9gOtPVYQF3nECZImQQQ/MojWt148QIYkgZ6KkDx6W5pqdTX/majk/PULmVvh8wbHphPyyiRGz86I75iBkkFcmBCIiNKt7QEeO0B0n5ttXbwI5p8Nh5/quZlYi9hCrCTEO+SY/ogo2jojCSL5FVJuc+gxyc4uWTzy1zbYsGLDK1D3nLxeZUCiU6IA8+eJch1tkuHOZOfIz+XBg4cTjllFrjdt2sQXvvAFfvGLXxAKyWBdPB7npptuYtOmTVO8uumPgQjOcOhOWnz4zhe5zVAewfYwZjR29f++ixImiR/TSQIBqRR3CbaJjY3CcNTMGEGijmUkFxqDI7qMM+d5DYnTCvM3yhDf0WfB1kjKh7Pp0YCzocJ0hqS1LR5pbTkeakfFdsthekXvKSislmSRuacKmZ57mjyleY8kgwTzRT23knJcf8TxdzvRfrYF4WIhvC17R06uBxtWnLcGXrxTiHPP67TFjpLoEEXdF4ZkByS7xnxZPXjwMPmYVeT6O9/5Dlu2bGH+/PmsW7cOpRQvvfQSoVCI++67b6qXN+0xEMEZKW66eweXrKrwPNcexoSB4h/rdCm1dhWr1QEadTFz6CagUsSRxwZIE9UhAqTJaINX7FOo06UjPr6HKYbpg3Ovh3v2QLqB3pYPp7bc8EGoyEkACQgZTUeBEFhxeahW2RbGnmMHhKD6w6J2V58pViCQwcW2/dDdJGTaMIUAp2NiBXFJerRRyLWGEUUo2TbUPgzbfyiKdMlSye/OxKD+ZRmWtFLOy0uLF9zwicfcSoo6HimDYB4ECsZ+XT148DDpmFXkes2aNezdu5c77riDXbt2obXmmmuu4Z3vfCfhcH9Fy0MWlq1pHmNKiAbqOxJsP9DKpqUDkxsPHobC+gXF/W7TGNxnn02V2UK+TtBBHmV0kq9iaBQZTBSagM7wsj6F++2z0fS2eyhkJsBLsZmmWLlFSOx9nxPFtifL2ie2DdvxTVtpUZMDheDPEwKcMcUrTSbLq5VPFGkQ1TivFDa8H055Q9YKdPGNco7jr4r1Y+99Tt650+CIIedPxaVCPb9y4IKZXOtHdyPs+Qu89n+iQht+GYjMr4Dy5WIN6TrutE+aYBrZJkrlZHdnknK+eavEi+3Bg4dpi1lFrgHC4TAf/OAHp3oZMwp948/GivEo3x5mL+59pZ5P/u9LA97nDiVeZjxLUvlRaObQhYHG0ibt5POMvYpf2xdRq3vbPrwUmxmC9e+CjgZ46ZdCiIMFEk+nLYnqS7SLumv4IOyDcJFYKTKtYASzSSLBIiHW2oa0Y/UAOPaCkGsXpg/OfJfE4cWanTZICwyVTe7wBeU4GQsic7KRjS6hPl4Dh56WIcRYs1hNEh2yCQgUAFrsH4l2GZgMzxESbqfFfmIlnYHLHCuL4dhbypZ7EZEePExzzApy/fzzz/OpT32KP/3pTxQW9t7xd3R08Ja3vIVvf/vbrFu3bopWOH3RN/5sPPDeevcwWtx87w5+9OiBIR9Tq6u5zaqk2paGxgLihFUCtOIAlRzV5f0Ua/BSbKYMo20eNAxYczU07RQirLVYNKJNjo3C2RgFInLMdFwIasE8SMWExGpbyGnaGRQM5QtJz6Th2Iv90zfKV0qe9Au/hMNPO/YMR1U2fHI8ZYhq7QvB/ofl9qPPSYJI006xkBRUyloTHU5hDfJcK5H1VWfSkIzKuu20vD5tA86GwQxlq9z9YVh4rjdw68HDNMesINe33HILF110UT9iDVBUVMQll1zCN77xDe64444pWN30xUDxZ2NFRWHQe+vdw6hw7yvHhiXWLjQGR3U5UO7eMCA+cuFSls8rYG6BWEE8xfoEY6zNg+Ur4cJPw3M/gwNPQMdhIZuBfLFIxFqEiNo22AkI5EHFWiHOmYCQ04IKpxTGdIYgbbBaROUerOa+q97J2lZOZ40FhiXkPVwKPj+07IdHvyGlL4YffAEwnAHIlr2Q7HaUdUOU70SnqOOorDqdaKPnmzaTFGVcKSHodtRR5efAvNNl8NGDBw/TGrOCXD/zzDN8+tOfHvT+K6+8kp/+9KcncEUzA2NJBxkMb9+40CMyHkYMy9Z89k81E37czcvKPd//VMFtHoy1QFG1eKPTUah/ZfjmQSsjz1u4CeacIgQ9XAiFCwANBx8XJdgwhaxqGwJhKYdxax39YSHzLjJpuSuQ17/mvmk3PPw1OLpdnh8oBL8lQ5AZR2FOdjoDlcXOOZ3Bw+7jECwUO0gm6fjEcRJPNOh0zzym2D76xgba8npNv1wjKyGvpWQZLDzbs4R48DADMCvIdV1dHQUFg09X5+fnU19ffwJXNDMwkR7phaV5E3YsDyc/th9opTWanrDjeYOLUwy3eTDWAmUrIdUJ8RZRkstWShvirj9LgkZnXW/LyJ6t8MR3JC3DdtI7rAwsPt9J69CSAd19HIJlgO0U0ARlYLCrAUwTzGB2PVoLUTZMqDqjN2G1bdhxFzTtEPUZJUTajffTFliWEGllOkU0tmRRJ7tk7YlOua3XWyhOnCC2EGw9UOmNc5tOg+XcFyiUc/r8UHXmhH5ZPHjwMDmYFeS6vLyc3bt3s2TJkgHv37VrF2VlZSd4VdMfE+mRbu0eW9KIh9mJidzYeYOL0wAdR8QKEojAkaeFZFtpQIvKW1gJh7dD7EbxUruWkUSHlLtknAFE1++c7IL9fxWrRelyKF8hBDjWLCRamaIum34po8kkhWSHi2U98XY5TvWZYknJ9TB3HIH6l+T+UKEQ7Fhj1jPdC86QY9dx4cmprhw1uu/3mqNc9/o891DOn2PtyNq+gKOGJ+Qc3Y3w0q/F0z2cjcaDBw9TilkxFXHxxRfzla98ZcD7tNZ89atf5eKLLz7Bq5r+2LikhMqiiSHYJXmB4R/kwYODg80T15haURTitned6Q0uTiVS3UKaG16DjqPZWLlYiwz/1T4MBx+T4cFIiRBmraF2m5NHnScDiD4ny1qZYik5+pwozZEymL8B8uZCvBWUFvK+5HzY8jVYdbXYP6JNkl8dyIdT3wRv+HR/kprqFh+27ajU8WaxkAwEbTm+6BR0HpHNQI/NY5TTKtqpdzf8jsXFJ6/BbZ2Mt0PbATj4hNhrmnaP7vgePHg4YZgVyvVnP/tZzjrrLM455xw++clPsnLlSpRS7Ny5k1tuuYU9e/Zw++23T/Uypx1MQ/GFK1dx3R0vjPtYFUVejriHkcGyNb/efnhcx/j3y09lbmHIG1ycLvDnia0j2gQoSdDQrrqrhJwCtB2EOQvFX9y0y/Ee+0SFJiSE0+/4qi1b1NzmPVC2TIYII3NEBV/9Fhn8c5NIll8q52/ZK+crXSq15wOlbgTyhbDHmqC7wRl2HIwoa5xJx5zPxwonJcT0g62cFkaN/Jk2RZlPR2UjAYPbaLwkEQ8ephyzglwvXbqUBx98kGuvvZZrrrkGpeQPrdaaVatW8cADD7Bs2bIpXuX0xCWrKvj4xcv53ra9ZPrO3YwQlZ7X1cMosP1AKw2d47OFrKoqYvMyz+o1bdBaK2ka6US2LdF2PMnK3fgoIY+HnhBlOt4q2dKGQ661JdYJpZzYPSW3x5qgBbGRVK2XpsW+arRhSD35SCrKj78GLXuclA9HTZ409PVdO4q7zrGQmH6xzhiGKP7d9WKRObQd2j4hXnPbgnCJ2GM8y4gHD1OOWUGuATZs2EBNTQ0vvfQSe/fuRWvNihUrWL9+/VQvbdpiospjPneF53X1MHKMl1gDNHse/+kD24ZXfpMtRnG91iixP2hn126Yoj4nuoXganrfr3N298qkx6999geFVE6Ectu4Ex79pnicA/nSCjnhUOILt1LyOtxMa9dOonOUcDMk9e6mXz73m6JStx0SlT8QlncFfEEZoow1DZ+84sGDh0nHrCHXLtavX+8R6hFgIstj5nh+aw+jwBN7m8Z9jLL84PAP8nBicOARaStEgT8EKSurCLtlLNoSFdrNm+446pDKoHiOTX+2dAXELmKlpAr8tCud3OhxwrbhxV+JEly0SCL3mmNZy8pEQamshzzVnbPhGAZaC+m3LfGrK59sJsJFci0S7TIICmIZKV3uWUQ8eJgieD95HvphIstjwKs99zAyWLbmib3N/PmVCYjFnMx38j2MHLYNu+8V8hguAZxBPYw+6RgOdEb+S3WLx9jntBPa7uCgU12eaIdgHpz30Ykh1pBNNPEFJfbO9MsQpOGnxxs+EdBKEkC0nS2+6Zd17cBKQLxNNhjxdrHWpLqc49hincnEZc2RMiHXqW5o3C2vx4MHD1OCWadcexgeE1keA17tuYfBYdma7QdaeWBHA398sY622MRkWzdHPVvItEDHEeg8BqECIalWUsigorevuC+05SjWljQTouR56ag8L68Ezv8knHr5xK015XiszaBsBgyn6jyQL8OFPZuAgfKph4JJ78xrLa8jHR3Z060kxJL008KUIeTaTkHxQokMDBZAoksI+WCtkx48eJh0eOTaQz9MpNLsDTN6GAwT5ekfCN6Gbpog1S2+4rx5EG2Egir5PNoEZPo8uA9p1ZYM7i27WFTrrnrAFrvDxg9BxeqJXWsgX9T1ZKeoxOFSaXVMdIiSnc5NBXHKYAZady8YoqxbrrXEHOKxwyHXc26IWm1bkIpBtBWKnEbKTFLsIH1bJz148HDC4JFrD/0wkcTkqnWV3jCjh37YWlM/IRGPfeE1MU4zBPKFoBZXi50hHYfCaiGc/WwLjvVCuR8d5Xj138hw3mTHzRUtkMHIaJMQVLdB0kpKIyNKPg+XOP7mOMNbRWx5fu7nKFB+aWEcNZxrYwZl8+HWvae6nIFRhMiXrfBq0j14mELMCs/117/+deLxeM/njz76KMlk9hdeV1cXN9xww1QsbVrirEVzmCg+/ONHD7C1ZnAPrWVrnqpt4U8v1fFUbQuW7ZllT3ZYtubTf3h1wo/rNTFOQxQtgLLlkIpD9VlSEZ6JO5aQ3K+RKZ+bfvFZ+4LiKbYtIdtzFsG81fJxsob0DEOGI+csFgIdLHJKYZQs1ReQtsdIieRw565fmQxItNUA+pVSUnKDMfBzhoIZdHKwHbKvrex1SsVF3S+YB+ve4Q0zevAwhVBa65OezZimSX19PXPnzgWgsLCQl156iVNOOQWA48ePU1VVhWVZQx1m2qOzs5OioiI6OjooLCwc83Geqm3h7T95ekLW5CqJj//bRf0Iz0C2gMqiEF+4cpXXpncSwvVX/+Kpg/ylpqHXfQqbatVCHnGihKnTpehR7v29751piqbd0igYaxFbiM7IbYefloQQwyeKsFLIoCNCFjMJIefv+dPI8qkncr0774a6F6HuOSH6eXMhf66sMxCBgkp47nZRtyFnOLOP1UWZ2Tpzt7odA7B6RwuOFP48Ue8T7dnjuzXvwUIoqobX/yus3DK21+5hyjBRf789TA/MCltI3/3DLNhPjAsT6bnWQH1Hgu0HWtm0tLTn9sGi/uo7Elx/xwteXfVJhqH81UtVHZcZz7LUOEaQNEn81NpV3GefTa2uHvbY7ztvEZeurvSaGKcryldK7vLOuyWNI5OQwbvwHBnqy6SETJs+esplMknxFS/aLMN6J3q9pcuh9iFZnz8MXQ3SBOnWkkeOQNUZcOBRGSh0CXSP/9oUwmv6+5TCOPeP9W+QbYkiHSx0hjsRwl9YBSu2wBnvhLmnTcx18ODBw5gxK8i1h9FhMobBHtzR0EOuUxmb//fHV4csFL7p7h1csqrCI0snAYbKTF+q6nifuZU5dFGvS4gRIkKC1eogVWYLt1tbhiXYf3r5GJ9982rve2U6wyWsHUfEO+2PiJq98y6gS3zCGdeDbIuKXbwIzvvnqbE3GA6J7qiT3Gt3uNKfB4GQVKkbfqhYA13Hs7Xupg8ipbIh6Dgqt+WVQNthSfZwrRyjHmp0a+IzQtaVKTcHC2QN5/0zLLnAs4J48DBN4P0keuiHjUtKqCwKTVSqKwB/fKkOy9Zsrann3JsfojU69DCPq3Z7mNkYKjNdYXOZ8Sxz6GIv1XQTwcagmwh7qWYOXVxqPIcaLAPYQWs07X2vzAQYRtY7XbIEzv5HWHAu5JWL1cH0gemkYJQug8u+AvNOkApr29J6ePw1+di4E177P4m0s9NCqrUWEt12GGKt0HlU7t9yMyy/GKrWwvJL4PS3iW/bzkiDYtUZsOT1ooD3iuTDsZMM95vWGe4M5ssAYzohqrUZkPNtuRmWXugRaw8ephFmjXL905/+lPx8iSbKZDL8/Oc/p6ysDJCBRg9ZmIbiC1eu4vo7Xhh1outgaI2muXXbPr794J4RH6+hIz78gzxMawyVmV6tWlhqHKNel9CfYCjqKWGZUUe13cJRXT7kebyiohmI8pVw4adhx11w7CWINQthrFw3NnuDbWeV8dGkirge66Y94gu3UhLF59ax6y65vUdxdo4ZyJMYvF13w5nvheM1Yntp3S+Dh2XLRYEPl0rBC0Dttqxf2q1/dwcUTT+kY/T/jaudwUUtKSBlK4XYz98IF39h4kp0PHjwMGGYFT+VCxcu5Cc/+UnP5xUVFfzyl7/s9xgPWWxZU8mHXr+EHz96YMKOefsTB0ZF1FujE1w77OGEYyjSm0ecIGliDGxDihOkgjbyGH6T5eVaz1CUr4TXfXJspNiFbUvF+u57pbBGmaISly2X9I/ylYM/1x22bD8I3c2StpGOii9cK0RpdjOtczzTdgZCxWIdiTZD42uw+V+gsy77OlIx2P5jaNol8YMlSyBzPhx4WM4dyBeCHikVct1+SG7P5ET35bZXppOQjEKiVWrfz3yXR6w9eJimmBU/mQcPHpzqJcw4bK2p50cTSKwB2uOjy3UtjgQm9PweTiwsW9PcNXhTYpQwSfxESNBNpN/9YZIk8RMlPOR5vKKiGQ7XLjIWHN8JT35Phg+tFISKhMgWz4f6V8Qzfe51AxNs2xbFuu0AtB6CWKNTzhIWIotLbHMIrptoorWQ6EiZnK9pjxDrogW9NwobPwS77xFFu+uYJI1Uny3kfe5pEu8XLBJy37of0EKYM2l6K9gGmCZkYvLp2R8aetPgwYOHKcWsINceRgfL1tx4144JPWYkYBJLjS7qsD3mKddTCTc6r7ErwdyC0KjSOEbSvlinS6m1q1itDrKXML2tIZpKWkmXr+VNp5zFTx4/POAxFF6u9azF7q3w4E3QWisNjsoQW0W8TSwmizaLqrzrzzJM2VcN7zgCR54TYhxtArR4oFWaXoS6B0pItdLyjZeMirc6f56s4XgNvPq/2UQUMyj3LdwIi86DQAGECmWNz/xYrCaF1Y7lA1HBQQphDCVrMXzZJkZtiSUkUi4k3YMHD9MWs4Jc/+IXvxjR497znvdM8kpmBrYfaKWhc2I9rG9aU8HvX6gb1XNK8oMTugYPI8d4MsjvfeUYN9z54rDn0BjcZ59NldnCcuqop4Q4QcIkqaSVVgqwFl7C719sGPD5Xq71LEbjzt7E2vQ7bYUZsWN01EH9y6ISN+0RIt1XHT9eA8dfFTKutUO+bYkGHAraAgx5fNF8aZ3MJKHmj6KeF1XLbcd3wKEnYNddUH4azN8gNpW5p/WOJuw6JusuqJDnWUkh4oYhFhelJCEkFRVinkmIMu7Bg4dpi1lBrj/2sY8Nep9Simg0SiaT8ci1g4keDisMmbxlffWoyXVFoeejnQoMFp3XMIIM8ntfqefDIyDWLmp1NbdbW3pyritoI4mfGnsJf1Ub2fm0xcAqInzuCo9Yz0rYNjzx3Syxdj3Q2BKPp20hue2HoXL9wGTUtuHQ09IWaaWlinzYN9ZyfiIME8LFUi7TeVTOYfiFOMdb4NiLQpTz50GyS2L4jr3c26bSN5rw2Z9Cze9E8XajAEGIv5US1TveLg2MgfwJupgePHiYDMwKct3W1jbg7fX19dx000387Gc/45JLLjnBq5q+mOjhsFRG860H9ozqOZ6PdmowVHSeO841WAb51pp6brjzhVGfs1ZXc5tVSbU9uobGL92zg8vWeFnosw4HHoH92yQiT/noYcW2k6hh+OXzVFQGFIMF/cloxxFRjW2rf6vigHByppVyilv8kgLSeQz8ISHoRdVSl173IiQ6Ib/CsXcoSHZD5RmynlybSq6avug82Pln8Vun4s6aLVHFzYD4u6NNcp6iBeO+jB48eJg8zMpgzK6uLj772c+yYsUKXnrpJe677z62bt061cuaNti4pGRCVeNExubFI+0jfrzno506DBWdB70bN0HI+FO1LfzxxTo+84dXx3xejcFRXc5uvZCjunxE1edeFvpJhL450/Yg2ea2DS/fKQqutkVx1na2IVHbOWRZi6+5fEV/MprshPYDTlOi+3tG0T8SEsAZcozMEVVZaVGV8+dJLXvVGVI0c/hp2LNV0kFizdBxUAi+6XfKX1LZ4ceOI/1PM2+NKN9F1Q4hbxdi7SaKJLskBWXFm7xMaw8epjlmhXLtIpVKceutt/LVr36VsrIybr/9dt72trdN9bKmHUxDceNVq7jujtGrkOPFnIifm996uvd2/wRhtEOJI7UENXYlRjS0ONnw8q1PArg50+4goC80eIzegUdg/2M5bY4577HojHiU3eFG0y+k+tQ39yejyS75TylJGEl2CDEfCpZD5PPKYf075TzNe6VsputYlqhrSxT1TCKrYPuCkgwSiMhjB/JMFy0QX/YxP5SfCi21sglwByl9QTjlQmli9ODBw7TGrCDXWmt+8Ytf8PnPf55MJsNXv/pV3v/+92Oa5lQvbdpiy5pKPn7xCv7zwdHZOcaDMxYU8bvrN3uK9QRha009N961o9dwakVhiBuvGtyrPFJL0K+ePsT2gwPbrU4kvHzrGQ43ZzrWIoqtP09ypgeK0bNteOlXUsJi+sHSZMm189HNhVYGzF0lGdoDRdYFCsRqkWmVxw4JLUOGdgbCc+CM98h62w9CZ71YQbTdm5zrDOiAKM8dR2DuGoncS3bJ5mEgz7RhyIaio06OP3+jHCfZAbE2GZ7ccK2nWnvwMAMwK35K161bxw033MDb3/52nn/+ea655hqi0SidnZ29/vPQGx+5aBkVhScuseNft5zmEesJwtaaeq6744V+qS8NnQmuu+MFttbUD/i8tmiSkXwJpgOxLs0LeL78mQzbhh13C/mMlAg5VQYEC0W5jbWIP9m1iLQfgvpXZZgwmC9K7kA2DmVCxXq46ru969NzrSepLvEwWxkZPASG/HOofJBXIuuq3SbZ2Jm0KNOZxAA1tlo84Rp5XXZK1OfOuoFtKi7KV8qGonItJNqciEADFm2Cc6/3sq09eJghmBXKdU1NDQBf//rX+cY3vtHvfq01Siksa3Q5zCc7xB6yesDkCBcffN0Szlo0h//3x1dpjY6uJCYX3gDjxMGyNZ8exv/8mT+8yiWrKgB6bCMHm2OjqqefDJTk+fny1Wv40j07h7WbfOnqNd5mbCbCrSmv3Qav/lY+bz8sPuZIqZDP3HIWN0avpVYGA4OFQmiDhWBEhRxrt0ERKFkKb7m1d316X+uJEZAMbMMAy8mZZiBbiCGE3x8Cf76o6wcelUFIKyUE2k0qcdsbsy+UHr92olMSROYMYlPJRd8kkbG0Vnrw4GFKMSvI9cMPPzzVS5ix2LKmktvedWY/b61Lgi5fWwXARafO48wvPUB3ciST91m41MgbYJw4PL2/hfbY0Budtlia7zywhzu2H55WNfOfe/NqLl9bhWGoITd1//T6JVy+1vPlzzi4JPfoc5JDHW+TSLv8eUKuu4+Lz3j+BqkX7+VP1uKRDhZB3BIPdCBfrCRWSv7Ttii/fYl1X+tJV71kRps+hxyrgT3Xpt+xjWixo6SjMtSY7BRinU44XmsXLsF2PmqdVa7LToEN7x+Z+jye1koPHjxMOWYFub7gAm8AZDzYsqaSS1ZVDDkYt23X8VETa4AKrwhkwnHH04dG9LjvPrxvklcyergpNSPd1HmYQeghuc0QaxWS6guL8txVD4VVoljHmqF5D5Sv7u1PLl0uRDwVhYIqIcuuau1mQueXwdI3Zs/pVpzHWsTSoZzfWWZAjmX4wGoWoj4Q7Iw8RhmieCdjMpiYigpxzsTp5/sGeby25XyFldKseM4Nki7iwYOHkx6zglzbto1t2/h82Zd7/PhxfvjDHxKNRrnqqqs4//zzp3CF0x+modi0tHTA+9xs5JHi3FNK+IcNC6goCo+qUttDFoOlgFi25tE9TVO9vFFDIRutXGvQSDZ1HmYIckluQaVYPPJKZQAx2S3kNtoKRWHJpe5uBnOfeI1df3LRfPEi126DuBYirrQMDabicqyll0Dxwux53TxrN97OhS8gpDlcIuq50hLrB4j32rEIattpfYwCyimucewgvoijeg/w/oq2hGArJfaV+Wf1XpcHDx5OaswKcv3+978fv9/Pj3/8Y0Byrs8++2wSiQSVlZX853/+J3/605+4/PLLp3ilMxPDZSP3xdP7WznYHOPGqzwriIvRROYNFIHnKrp7G7uJpmbW7MBQ1qChNnUephlcL/VAPuFckptJCCk1A+KxtpKQTsqQYSYOKLFglC2HqjOhaSd0N8LR50XxxhCSHm+T3GdfSEhs6XJYcWnvNaW65Xz+vN63B4vk3O1HRIH2ByHtJn7k/vzorDLuj8h/yS65PdnBgMQ6F0PFAXrw4OGkxawg10888QS33nprz+e/+MUvyGQy7N27l6KiIv7t3/6Nb3zjGx65HiPGkjXsplb8cIgq7dmCgchy5SB2mcGqyVujaW4YRe34dIJnDToJ0LRbkj/qXxSVN5AnjYSnXiHDgPu2SdpHoECIqrYlws4XkqZDq1H8zF2NQmAD+XKMZ38qxLq7AXx5UL0eFpwDR7eLfSQTk0bGQL6c96Vfw76HRPGet0aO5QvJsYOF2fUqBWUroHW/+KEN1yud67vO2egpQzYDdko2COk4tB1EVO5BBiExpB59w/u8lA8PHmYZZgW5rqurY/ny5T2fP/TQQ/zt3/4tRUVFALz3ve/l9ttvn6rlzXiMJ2vYTa2YrQr2YGS5vqP/5mOoavKZiPdvXszFqyo8q8dMR9NuePhmaHjFsUk4BS7Hd0ibojJF7U10QONOIbypqNg50E7UnpNZneoWJdsfgiPPyLHirfJYX1RaEM2glLH4FohXWylnGLIRUjE49IRYUOaeBtUbxFvdUQflBb2tIfE2ibvTNmRSjgithEj7QmLtsNLOR0dZ94eFtDfvAa1E/U7HAdspr3EGI82A/LfiMq/0xYOHWYhZQa5DoRDxeLzn86effrpXJF8oFKK7e4DGLA/DIpWxqanrIGAqUtboaV9bLM3T+1vYvKxsElY3vWHZmhvvem1IsvzJ/32ZeNqmojCEbespbUOcKBRH/HzNa+E8OWDb8Ni3xAdtpSWD2vDJf6lu8Sb7IzBnqRSiRFvE8tFP8VWiQCe75XalwAxBqEDIaiAi5LXzmBDcOUtECXcJeiYlxzd8MuwIYiFpeFnWlE7Ake2iOufPg46jonBr28m7TktySCYpr8mtK0cDpgxeGs4mof4lWYvPJ+2OhiGZ1wFTXpedkfMFC2ClV1XuwcNsxKwg1+vWreOXv/wlN998M4899hjHjx/noosu6rm/traWqiovfWC0+Mo9r/HTxw6OW0n95VMHZyW5vnXbPho6k0M+Jpq0+Pj/vARAJDCzGkXnRPy05UQCFkf8vO+8JXzkomWeUn2yYP/DsOteibVThhBcKyXk0/3NkEk6vuoEWULd10qhHR+234nU00JK03F5vlkspTHJTiG4x18Ti0bP052kIsuS5sSA0/QYniMk3HYGDNv2SyFMKgZWQoi1PwzxDlGoA/ni0U6l6SHWkRI5t7ZkLV2NsjZfWMh5r+QSJ9bPMGDRuZ5q7cHDLMWsINef+9znuPzyy/ntb39LfX091157LZWVWdXsj3/8I5s3b56Ucx88eJAvfelLbNu2jYaGBqqqqnjXu97Fv//7vxMIBHoed/jwYT784Q+zbds2wuEw73jHO/jmN7/Z6zHTCR/8xbM8sKNxQo71191NWLaeVYRra039qKvlYzNoUHFOxM8z/+9inj/U5iV9nKywbXjs22KX6Ml1Vo71wv1eVUKW2w44w4pDHlAsH24snp0RsptOOJnUAafeXGdrzgeEFqJrZ+DIU6Ig+yMwb7WklLTWOtnZWpRwwxTy7AvJc+yMQ9yVYz8JymtSPvD7ZH3pmJBqX0j+nTdX1p9JCvk3i2DdOzzV2oOHWYpZQa4vvPBCnn/+eR544AEqKir4u7/7u173r1+/no0bN07KuXft2oVt2/zoRz9i2bJl1NTU8MEPfpBoNMo3v/lNACzL4oorrqC8vJzHH3+clpYW3vve96K15nvf+96krGs8uPvlYxNGrAESGZvtB1pnTSrESBoUZzpufuvpBHzGrPmazkrsfxjqX0AUXqfJEPqUsTjqtTXCDHwrx/akneOaflGTLVdNHgG0JfxeGUL4rRQcrxHiayV7H8e2hcDblrRCamc40h8WYp2OOQTbzEbsoSTOb/5ZkoISaxFSrkxRzU+50FOtPXiYxVBa6/G+qz/t8Y//+I985zvfoaCgYKqXAsA3vvENbrvtNvbv3w/AX/7yF9785jdz5MiRHnvKb37zG6699loaGxspLCwc6nA96OzspKioiI6OjhE/Z7SwbM36L95PV2L0hTFD4TvXrOfq9dUTeszpim8/sIdvP7R3qpcxITAU2Dm/QQZLOfFwksG24U8fhprfC+lVSoilQqwZveLs+taCDwHlIxt/5xcCm0k7SvJo/1QZYgvRTrNjoEAGGNMxZ/CSnA2Bs0xfSFTo9kOQVy7FNrllNcol+ymoPlMIeEGV2EGSHRBrkzzuc6/3EkI8jAon4u+3hxOHWaFc//d//zdf+9rXpg257ujooKQkW5bx1FNPsWbNml6+78suu4xkMsnzzz/PhRdeOBXLHBDbD7ROOLEGaO5K8qeX6k56+8C9rxzjOycJsf74xSu4/g1LPevHbINtw5GnJSVEGaLqWinHqjEQkR4FKVaGo1g71o+0qzKPUQNShsT1+YIOUU8IaUfJsKVL5N1lpqPi2TZ8jr1FCUEPFQrxNwwh0JEyqVk/9qIo15mEEPNFmyTT2iPWHjzMaswKcj2dxPna2lq+973vccstt/Tc1tDQwLx583o9bs6cOQQCARoaGgY9VjKZJJnMDsR1dnZO/IL7YCyZ1sNBAV+6Z2fP5yeL+tm3GKYtmpqxWdS5qCwK8bkrTmNOXpC/1NQztyDEm9dWeaR6NqBpt8TcHXlWimFsS36ADZ/jVXY80WOFbUkMn52Wf7se60HzpIdBOpotgIGsIm4669UDHdMZrow1SVyfGRS/t8+J1wNYslmsH6dcOHhxjgcPHmYtZgW5BlBqYv/w33jjjdx0001DPubZZ59lw4YNPZ8fO3aMLVu28Hd/93d84AMfGHZ9Wush133zzTcPu4aJxngyrQdD3z/FDR0Jrr/jBW6bwQUzAxXDnAzU8+MXL2f53AK+dM/ISm88nERo2g1P/1BsEnllot7altNU6Awyave7fIwE2zDFVpGMQqJVvNDKzJLjTCKrOA8HpcTrXbxEfvjsjKOMD7MB8EXEl21rMGw5J9pRvX2wYAOc9b4siZ6zaGyv1YMHDyctZg25XrFixbAEu7W1dcTH+8hHPsI111wz5GMWL17c8+9jx45x4YUXsmnTpp4adhcVFRU888wzvW5ra2sjnU73U7Rz8ZnPfIZPfOITPZ93dnayYMGCEb+GsWDjkhIqCkM0dE5e3rL7Z+/Gu16bkQUzgxXDTJ/3T/rjc1ecRllB0FHYk3zpnp0DkmdgwNd2MmyIPDB4hblti2Ida4HyU+Wx7YfFv6y1JIbYrgrc57ujJwFkEOXZ8Ger0Bdths466K4RIuxaMgJ5Qmzbj0hO9pBpIUrW7gtKeoeblR1tkfOkY463mxyPt2NpMfxC5g2fbBxCxfJ4w5TNhOGD0hVSte7BgwcPg2DWkOubbrqpp5FxIlBWVkZZ2ciymevq6rjwwgs566yzuP322zH6vG24adMmvvKVr1BfX98TEXj//fcTDAY566yzBj1uMBgkGAyO/UWMAaahePvGhaOOkRsLGjqT3LptHx+7eOb8IZtpLYoKqR+/dvOSXpuYy9ZU9rK0bFwiMwLn/8e2AV+bOw920907ZuSGaNbDtuHAI7DnL9JmqEwZ1itbDqddKX7i5r0y4JfsEI91YaXkToOQ02QHkhrik6E/pYSYWkm5fbDBxmAelCyV6Lp4G3QflwHBdEIGB62UpHz4I5A/V44Rbxv4WBgQzJehxMIKUa4r1wop7250KszTjh3EzjmEEgIdiMj53Ir2SAmYFfI6Fp4DgUJpjOw44inWHjx4GBSzhlxfc801zJ0794Sf99ixY7zhDW9g4cKFfPOb36SpqannvoqKCgAuvfRSVq1axbvf/W6+8Y1v0Nrayqc+9Sk++MEPTsup4cVlkRN2LiHxmo9ctHxGELana1tmXIviF65c1e/amobqF6P31DCvTSO17bMpVvGkwPGd8OStcPARR+ktFhJbvADqXxGyvfxiiDZJs2G8TSwWhk+KVCKlDslWQoDDhRCZK8dq3o0o1jbSdBgQ0m07leJmCNa9UxTrHX+EaLMo4Zm0EGytIG1lq8gzSQgWSpJHqhtSCfA7MXnhIiheJGv3BeW5iTY498NyzuM1cOgpOPaSVLW7DYyGKd+8waJsSyPaSQYJCFm3UnLMYB501zs52R48ePAwMGYFuZ5ov/VocP/997Nv3z727dvH/Pnze93nDlqapsk999zDDTfcwObNm3uVyExHTIbveij854N7+fX2I9x41fT29G6tqefTv585+dWj9UmPdJh1MoZePUwSdm+Fv34NWvYK4TWDkvHceVQIZPVZQnh33QNtB4X4hufQ40GOt4IRcKrKCyXuLlIqtyf7EFDDhNAcUY7TUTALxGKy9EJ46vuijCtTlGsrjajd2iHbKTB1NvavZIkQ8ngHNNZA2QrZFLi/67WGpl1QtQ6KF4q1Zc4iWPEmUZ2jLfCnG6CzXoh6rFWIvwsrLUQ6WJBVss2ANDv6QmI78eDBg4dBMCvI9VSmhVx77bVce+21wz5u4cKF/PnPf578BU0ANi4pobIoRENH4oTZHxo6p7endzCf9XTDW9ZXccHKuVQUjj4272BzbESPO9GbLw+jQK6nuqsBHrxRvNNWSpRaKwHxZHaAsGUvlK2CQ0/I59oWBTs399lKCWEuP02O21ILyqkOd1NEsB3rSLv4mvPnQuUZong/82Mh1pES6KrPKsrZRcu5LBxFWgshXnWV3P30D+W1KMfWkYqJbzuvVGLxcm14LsmeswguvhEe+DwkOh3FOiFrti0JcM+vBJRYUgrmiSWkebcQ9qLJnW3x4MHDzMasINe2PcggjYcxwTQUX7hyFdff8cJo6iEmBEN5evtG340nc3k0x5pJPut/OHvhmCwbI6lrd/3brj/bwxSj73BiOiYKdPNeIccNr4gX2fTL45XpJH5Y8hzDFIW3oF2GCOcshqYdYpvwR0StttNZco0SBTgTz4m403JcXwGE8kX1rlwviSCJLmjZB/48GVi00pDokOe4nmeU81HLOg1T1OTVb8lm+9uDAgAAav9JREFUSZ97nQxbNu+FrmOiLFetGz5veuUW+fjMDyUJJeXYQSJzxG9upYTsB/OlKKZ598CE3YMHDx76YFaQaw8Tjy1rKrntXWf2i5ubTAzm6bVsza3b9nL7Ewdpj6d7bh9rPNxAMXpDHWv7gdYp9Vm/7cxqfvdC3ZCPGS3xdTcXDZ0JmrsSfPP+4QdYNQP7tz1MAY7vhJfuFEKYjoram+gQxXfuaeJpjjY7XmanSEXbQmoNn9M46JDyeKtjzYiL7cMXEZU3kxDlOlIqavbxVx3bhiFWEcgOD+oMhJzvvVAhoKB1n3wsXQr1L0F3k5OTrZwiGbK51NoGf75kYJcshXlrsq+1fKWkd4wlb3rlFlh2MRzdDvUvQ+NO2XjEWmQ9ylHJYWDCbmXkudFmiSecv1FUcA8ePMxqeL8FPIwZW9ZUcsmqil4K73019fz8qUOTet5cT+/Wmno+/YdXaY+l+z3OjYf7/jvOZE5eoJ8KPZA6/cCOhlFHzT2wY/Cin8lGRWGQx/c1D/u40RDfgTYXI8EVp1dMS8vOrMOue+Hhr4if2M6Igu1mQ0ebIVziWDtiZNsPHULrFqsYPqfIJS02i2Ce2CciZfL4dDwb3B5vF5LpVp67ZBqn4dAtZUm0ieIdb5W1BfIgvxwKKsV+kqx1SLXdu9zFTgnBtpJgmjB3ZX9bhmv3GAtMHyw6T/7LpETd7zwGBRVQuU7OOxBh371VVO/W/bKpMP1Qcgqcc11WFffgwcOshEeuPYwLA6VKTDa5dj29W2vque6OFwZ9nEuQP3xnb7JcWRTiqnWV3PVyfS8CWVEYpCuRGTRqDuD//fFVLjp1HgGf/JG1bM3/vXRs7C9mnOhOWnQnh6+jzw/6uGRVxbCPG493/K97mrBs7SnXU4ld98I9nxTl1bbF5mGYDtk1xd6xZ6uQWJ1Dht2oPJfcWhm5XwOlyySCr/ZhIdWZRDbKLp10iPtAudPOd5Hhl+O7lo9UDKrPhKoz4eXfSD25MoVED5aFbduiwOeXw/p3TI4tY6Rk2bbhpTvgsVvktRRUyKYhHRN7yQOfl8d5BNuDh1kLzzjmYULhDjtOFgwFbdEUlq359B9GlszRlyjWdyT40aMH+imzDZ1JoqmhyimgNZrm3JsfYmtNPZat+fkTB2iNpkbzEiYUIyHW7uO2Hxi6JGm83vFo0hr2HB4mEcd3wrYvOwkepvxnOpYKlzRnUkJSM32HUx2yrHAenxbCe8rrYdMNsPh1TjpIixB2X1BaFDMxh1wPBMNpRHS+o8wgLN4Ml34FNn8cllwgOdrHd4r3esgOU8d/PXcVlA3hox4tbBvaDslQ5dbPQOMuCBVJwkioKEuWd2+VxzfthkdvgYe+DJ0N8vx4uxMRWADFi2UAcvuPHDXfgwcPsxGecu1hQpE77DgZA362hhvufIG/WV81oBXkRKA1muK6O16gOOKfsjWMBcNF5E2Ed9yL4Zsi2LZ4rKPN4ne20mJ3UI5+YqWFMA8JnbMTNWDxJrjyu3KM1+6CcLEo38moWCX0cORRy3FcP7c/BOvfBSWLs2uuOkPWnex0rB9uHZEzxOhaSwxTbBlWCo48DQvOHb963bRbBiGbdosqn+oWf7W7MQnkSyFOxxF44ttQWA3P/Qya94hKHSyQa5xyrkdBlaSVRMokMeXodrGaePDgYdbBI9ceJhxb1lTyLxevmNQWxz9OoRXDxUwi1jB8RN5EEGMvhm+K0HEEWvZIFrOVzCGpZC0cI4IhynQgDzb/i5DH2ofh1f8Rz3U67kTrjQSOWo4zlLj4fFGr3TbI3fdC8z6ItfVOF4GcwUon2xolxLrhVXj8O7DgSWmOHCoNZCg07ZYIv1iLbB7cBJRUt1y/cImQ5nRMFPu6F+GP/yTNj5HSrKXGzggRzyTlWP4IBMIQa5aNjgcPHmYlPHLtYVJwIlscPQyPyhEkhYyXGI/kHB4mCaluSdoI5AkJTcec5A2n7GWkMAyJxsufK6kcTbvhie9JS6NbpGKPgqxrJfaSvDLxSrfsheduh733C1F3bSv+sHNc5Dyueu1uDJRPFHmFRO0de1nWdO51oyfYti2KdaxFSmya98g5/CF6cq1TRxwfuOVEDaagaY+U6/jzpJo9HZfHKyVrtlKiZivHjpNXNrp1efDg4aSBR649TAo8BXP6QDGypJDxlAP1PYdla56ubeGp/c2ADL2ee0qpN+w4WQjki9rqqsuGTwjgiBVrUx5rmDKgV1QtJHP3X0QRB0ehdaL29AiPaxiSE73qLZBfAU/fBoefFkW6eBGkuyUbW1uibtum83oijkruFMrYGVHn/WGJ/As7lesv3gFr/14q0Ucav9dxRDKxC6sg2SFKNcqx0gRkLRnnNp3JiQV0crgTHTkHc6w0lg22ISkopgkVp0ssnwcPHmYlPHLtYVIwFS2OJxtK8wK8+9xFfPuhvSN67E1Xrubzd7/Wa8ByNFnfuX750aDvOQaKR7z14X0UR/xcu2kxGVuG0zzCPYEoWgDlKyDWBF2OhWLExBp5rDLAFxZPdKgYGl+Dmt855DcDyZRDNEf6E+2T0phFm+Gs90rEXcdRQEkbo2FI62GoWGwUdkpUYCsNsXanytyNCXSGLa0EdDdKIyNasqkPPCrkumIdnPlOyfEeCqluiSLsOArxtmz8YLxdjmNbjvVlNL+5nPSVZKd408/+oJd37cHDLIb30+9hUjCVLY4zGSV5fv5mfTUXr6rosVj8z3NHhh00/NLVa7h8bSVvWls5rpZKtxzoxrt20NA59DmLw36+/44zOXdpaU9u+K3b9vKfDw68GWiPpXttFG59eB95QZNrNizoeb0e0R4jDEM8yIeehO5jjiVkKPT5qVSmpGPkzZXnhkvg5f8RlTeTzjY3juYnORCB5ZfC5o+KlcOtOG8/LCp1oiNbOoMStViZORGB7hoNUc3d4UbXqqEzYn9pdJT6+pdg/0Pwxi8MHYPX3QhtB2WjECnJqtVthySLe8ANxEC/xQz6b2C02Eb84ZFfJw8ePJx08Mi1h0nDaFsc3795MfkhP98ZgVJ7MiA/6OMD5y/h7CUlNHcnByXDw6Wv/NPrl3D5WlGNB8odHy3ccqDBiLK7uq/97elsXi6+0q019XzhT69xvCs5qnNFkxb/9cRB/uuJg2Nu1PTgQDuxcrbtEEadLY/pKXTpMzgIgCGKrT8sDymcL3F+za4dxBDyqkyw4gOfW5nZuD9lSr35aVfDxV8QBff4a2Lz8IeFVHccdQpuLHpi9jDEq2xnwBeQ15BOOlYPh2RbKdB9v8e02GKsBLQdgIe+KJaTeQMo2LYNR59zvN5azqEU5M8Twt9SCzo3WtNwvuFVlvRnD9bnGvhE+c/E4fmfi+1krAOXHjx4mNHwyLWHSUXfFseDzVHufOYQx7uyf8AqCoPceNXqHlJlKDWpSSPTAQUhk+c/e0lPGc1QGGyTUpLn58tXr+HytVUTvj7TUHzs4hWsrCjod94KhwRfsqqCp2pbeGBHAz974uC4zzlUC6aHYWDb8OKvxCcdcGrCtYZUl2Rb6yFsDv6IKMO+IJxyISy/BP3A50nYJrZZTMhuxdAWyvSDCuUMSOYot9ohyL6wHG/BRjjzXVlrRHejlLPEWqD7OD1kuadqwVHFDT8ETGltTLSLb9xKOwODg3jIrbS8vkABJLWo7S/dCZfc1N+D3XFEMrWr1kumdazFidTzyzBo3lxR/rWWXG7DdAYXh3knQDnpJgo5XjIKu/4steyTUXjjwYOHaQ2PXHuYdPRVUz9y0fIhrQsfuWgZv95+iIbO0amgMwn/8da1IyLWLgaqmj8RNorBzvvAjgbO/49t487FzoWrr9509w4uWVUhr822hRClugeuoPYgcIf0zICou6mYEE63aTEXyrFYGAaYISGySy+AlZfDkgt4btv/Un28nga7EIXJfOWnQCXwGTam4UOGH51j+hxirhxibfrF+/26T2ZV26bd8NqfZF2xlpyFuKkjjmqNLQOORgDaDzpeaOc8QxayOH7pPFOU8VQ3NLwk16RvJXqqWzYHpcvBny/qfKxFNiWGDwrKnQ1J3Kl/zzAi77rOgO1c00iptFo27Rl4DR48eDjp4ZFrDyccw1kXTENx41WrJ62IZqohNo7Rq80TYfkYC/qedzwV6cNBIw2a2w+0sqmwGXvH3TQfrCGdjOEPRihbvAZj1TjyjXNg2bpn01ASCbCroZMjbXEWlUR496bFo9r8TDlS3ZJ8keqWoboeQp0zFOh+xfwRiaArWSI3p2Jw7oehZDFba+r59UP7+LJfHpsgwFFdzjzdRqGOEdJpTNdiklcK1WdB4QLxPncdkxKW8z+RHSp0Y+/irWK9aHZtRjkEHZ1do22BnWBQlX0w6AzEOySZRDtDkYlOscnkbswC+eL/TkclKi9SKtctkxIrSqJLBjitPGdocjjvep814JfzBPOgu17O7cGDh1kHj1x7mJYYrV97JqA0L9AzeDhTMd6K9JHinoe2UR16kH0HDrE/XUSMMBHinHLwQZYd2svCN31iXAR7a019v+8thU21aiGPOD+/N8zl52/gM1esmYiXM/nobhQfczraR6nWOR+dPObiRWL/MAxRZlv2QTra87X1UUkH+RQRpRE/CQIcYh5BnaZApzglP43hC8Gpb5aUj1iTENYlr5Pbcr8uubF3zXucBBATlAZt9tkEuOvM3QyMYhzaTosC7c8XBf+V/5HrEm8Vtb5sOax/u3ysfwXKC2Q9oWLn1Bo6jkHJKTLw6As5vvDUUGftfa0DBVIekzdXnh/IH+FzPXjwcDLBI9cepi36+7Vj/PdTB3tFzU0nVBSG+Mbb1vLMgVZAc86SUgylaI4OPqw40zARFenDQWFTeOh+XlUH2Us1LvHqJsIr6TDxPfuJR37Nyrd+fkwWkYGU96WqjsuMZ1lq1FFMFBtF7ZP38+P4O/jQ2948MS9sMmDb0H4Idt0rjYJuaYubzdzX0lC4ENCQ6hRSmYr1kED3a6so52nrNC4xnqdEddFNmDSm9C3aFkn8hE99k3iaO+uGtuy4NgzbEquFMp32RScr23KtF32SS2D06STaFvtIxC8ku6EGMjFRo60kHK+RNJX17xDFummXVJoHInIdOusgvwyqLoHHvy1JIuk4xFuGPXVPokmoUDYcLfukPr5owcjX78GDh5MGHrn2MK3R15Jw/RuWctaXH6ArMdIK5hOHRMYimsrwqctO3oSAiahIHw7VqoWlxjHqdQlZRdOFop4S1I4XWHbRYcySxaM69kDK+1JVx/vMrSygkQgJ8lWCIClOMw/T9PJOUqsNAqddPs5XNcGwMpJBvfdBaQ1sPSAkVilnVtAUsqlzhg7NMESKhIRbTmZ1Zx1UrYOiBTQeqgdAY/Br+43MVe2s4CgFKpsQktEGLYVrmH/W+2RYcTg/sWvDSHaAEZR/Z+JghLL15v3q1J10EOUbJcHWUuDiz3Oi/tqEHAcLwCySSMGuY/DynfC6T0HjDlHVu47JuqrWifJuZ2DOYsnAbqkd4bndJdjyvLLlcixvPsCDh1kJj1x7mFEI+Az+7qz5E5JOMdFoj6VP+rSLE9G8mU+UYrpJ4MdA00mEXJIdJ4idbqNmfx3rRkmu+yrvCpvLjGdZQCNzVBchlaKbMEn8BEhRpZppu/fLzCtZMnC024mGbUsr4fYfSfqGZTlE2srWbttpeiwgbj23nRErhju4l0mJcptX2kMCc7+2tbqa71h/y2XGdk43DhAhSYwgr9incNmGDzB/pJacogVCNA89LWQ8f64Q+kzCaWS06G/9cOL83ESRfhF4DPAc5Hj5c0WNtpJCrCOljhUF8Achv1LOv/NueP2n5DzpaG/lve2QEwMYdzYGXQw/1GgDTl16Ximcda0Xw+fBwyyGR649zDhcsqpiWpJrF73SLk4yuM2bk2UNWarq+BvzCZapOpaoehIEaNMF7NNVtFEIQJgkSfwcT/pHffy+yruo5HVESBBSKWIEKaODsEphiBGCcPQgPHUrXPW9qVUim3bDY9+SpsN0LJvQoQxIJSWfOVSczbgOFTrEWwtRVKYQxUiJ2DFcpdYhgX1bVWt1NbdZV1Ntiw89Shi7oJpPrR9FrbdbbtN+FDqPiLJbtFDU4nScHoLsWkEMv9ymTMfr7OZz91Wv3c8NidCzUjJMueJN0HpQfM/BAnluJiFKfzomQ47JLtj3oAx+Vp0h68tV4Aur5TmJDvl3siMnfnAQKFPIeckpsOg8WHLByK+RBw8eTjp471l5mHFwScB0RG7axckIt3lzMuDaM+bTRKMuJoNBggDlqp0zVC1z6AQ0lbSyz65mZ6xo1Ofoq7znEaeYKPkqQRoflaqNPJUgg4GFwgD8dhr2/xUOPDIhr3NMaNoNT/0Aah8UZdrwC4m2M/K5z3ldiU4hm5C1flgpIeB55bD4fLjos3DRv8Pmj/dSV3O/ttnxQoOjupw9eiF1upzPX7Vm9JvG8pWw6Xo45Q2yIUh1QX6FJIpUrheld85iqUo3DFmrW0nu5mfjeMkNf3Z1yim/UY5KP281nHqFHCPVLSS67QA074PGneKDjjbK9bBSojLXvwJP/1Cur4vOOrme4TmiavvyoJ89KReGRAAWzIM5C+E0zw7iwcNsh/cbwMOMg0sCprMufCK8yVOFLWsq+cE7zmAihXnXnjGHLvZSzQ69mAQh8onTTYiwSnKaOsRyjtJKAffbG/jNc0ex7NHllrgbM3fpUcLYKIKkKCCKjww2imK6KaGLQqL4SEks29O3iS3jRMONs2vZC+mUROkpl2w6vmWUWEKwRRE2/UJEk85AYTAflr0RLvhXIaBzFg1IAN2Unoo+m9eKotD47E7lK+Gym+GKW2DVlTB3JZQulTzoeWskwWTJBQ5Zdtbl1pAbjo86f55YTCKlUvCCypLwklPgnOvEFhJvh+4GIdVdx0R5tlP0JJG4g5TthySOL9YihS/u1zbVLYU6C86Rc/rDDOn79gUkDWXZJbIGzw7iwcOsh2cL8TAjMd2j+k6EN3kqcfnaKm5FccOdL0zI8foOMbZRwIv2MpapOuaoLkws5ql2nrdX8jv7Amp1Nbh52KPI/nY3Ztff8QIKqNOl1NpVrDEPEFBpFFBMDAONRguHBckwrt0Gj31TCCqcuIIbN84uUADYQpzTTha0MrLDi8ECUa5dj7VhQlG12D/WvUPI6wjWN2mFRYYBSy+UdeRet3QMnvmxkNwlF0BrLXQ1SsQfQMlSIayttbJxKFqUTfCw0llVfvuPxS8db3NKZwbbCDmbESsjBHzuqt6FL+4Qpj8kZTOt+4XM2+mcFBZDbDfaFmJ96VfktXmKtQcPHvDItYcZjFwS8Pi+Jr7/8Cgn+ycBClH5Ni4pmeqlTDouX1vJD40z+eRvXyaaGkXZxgDII06QNDGym5I2CnhWr6RQxwmSpEK18Sd7sxBrB2N5h6Dvxux39ut5g/EyCzmOoSwMh5SZLrFWJuBYFbb/BFZeIcN5O+/GbtpDR8txEhbYpcupeP0/YlZMsG3GjbMLF2dj9kyfWBtcm4TWzu0haUgsWwmr/0aU3uKBVeqhMGxh0Xg2FobRP2Xk3OtEnW/eC0XzoaBC7C3xdlGR8+cJmT3+GnQeo2djESqUKvOCath3n9hAenzag8GxyviCEpunM3J93cKXogWiqO97SKra420QKETeFYjKMKhhQrhULC2REihb5hFrDx489MAj1x5mNFwSsHFJCX94oa5nGGsq4P45/8KVq07KYcaBsGVNJXkBH+/+2fZxHSfqJHRESNBNJOceRScR8oF28vvcN/Z3CHqrs+tRx2zM57+M34o6ZwWFEgKnDCGxloJEOzz4BSispunoXuoamwnYUUJk8Ne/ys6dD5PY9Ek2XPqOMa1rQLhKaniO/BdrFpuEbYma6too0jFRr+etFY/zZNkT6l+Fx/9TlGTDB3OWwNxTZTBwrOcsXykqcV/C3rI3S7ozCTnXgnMgehxibVB5hpDaeBt01AnptzMM26xop8Uy4wuI2u8Lid2m7ZDkYbfWSlRfyinlsVJOzKGW1+wLQiAMizZD236vidGDBw+94JFrDycF+r7dP5EEuzji56tvOZ0v3dO30a/3eSqKQnzhylUnbQzfYDhvWRnFET/tsfSgj8kP+uhODp5N7tozVquD7CVMb+VRhhhr7CXUaVFTJ+Idgl7q7NrroHWbqJVoJ77NsVwoI2c40IYDjxHzF9Ia8xEBugnTRR5+0lRYLRx/7Js8ljef121+/ZjX1gtunF39K1I3vv+vzqBdUNaVTgK2eIOXvnFyifW2r8DTP5DSFZDr1LwHmncLuT13HJ7jgRTtgUi3tuGvX5Nr4qrFbYdkgFEZTvPkUOTa+cmNNclgZawVypfBq7+DA4/B8VflXFaanp9w2/neNsPiX7fT0ojZslcUdq+J0YMHDznwyLWHkwaD+bCLI36u3bQYjea/nzxEezxLAiuLQly1rpIfP3oAGJiUf+2tp7NlTSWXrentQz1r0RyeP9Q2sb7UMcKy9cR7ZEcI01B87a2nc90dg/uvv/63a/nSPTsGfWdBY3CffTZVZgvLqaOeEuIECZOkktaeIUaNMTnvEBgGnP42IVdWSjy2yhAl1CXWDinTVhKf1cxCZXJIzyWFjyBpDGxaKaBctVHz0E8579zNmKY5MWs77Uohr7FmqDhd0i2SnaLS+gIy0LfxQ3DGuybPnvDMT6S5MDciTzvZ2Y075TG7/ixkeCLX0Jd0H39NVGy/k4qitRTpaO0Q6+GGTp3vwExSqtHnrRZlvHmrDEBaycGfaqdlw2UGZFCyoQbOfJfXxOjBg4deUFrrqXoX3cMEo7Ozk6KiIjo6OigsLJzq5UwZhiKag923taa+HymvnCFK9EBrLw77ed/mxXzkouUnjGRvrannxrteo6EzS04qCoPceNVqtqyp7Kkdh8HfWcjWkB/j9Lkh4trHI21z+FPizB6v9aR9XawM/OQiIW/KBMNNo9DZ5AplYmlNRmsUkMakU+fhUzYGNjYKU9vs0gsJve1HnLlu3cQNPu7eCs/8UAbsMinAlkG+NW+D8/5ZfNiThUwK/nON2DHcWDwcU7rttCgGIrDicnjj54ZvbhwP2g7Bw18Vr3OgQPKzax+WIcieIcYR/FlTpuSEF1WLep2OyX8DPjfnfSp/vlOZHhXP9ZXflWFGDx7GAe/v98kFj1yfRPB+OMeHqVR/xwqXsA72Q1wc8fco76OBey0aOhM0dyVpi6UwFGw6pYxzl5YOel2Gu4YDbQQMBbmJegqbatXC4nybD158OudvOJPtB9tPzNdl171w1z+LGusLSSqHtpwGRENWp21sbbv9gaS1jzaVj4WBH4uwTtFJhKPn3si5Z5/T2zPsC4mdYbT+5KbdksccbXbKYXxC/BOdEic3HjvGSPDE9+CBz9GTtAHZOEBU1jZRfRZc9V1RgycLVgYevBEOPyNL6ayHrnonE9sl18OYw3xhKF4oZLq7KcdTPZidJJv8jTIlSSRSJnGCl355cl+vh1kB7+/3yQXPFuLBg4NhExKmGSxbc9PdO4bU6Npjaa674wXev3kxF6+qGJaYWrbmuw/t4aePHRgwAeTWh2uHJOzDXcOBYt5aupJ85Dcv9jxGY1CnS1HdTfzs/+4nr2s/m848B5YsnPxEhlMvlzSKx74paqbt+G6V0ePDtpUJVrInk8KnMviwsTDxYdFFCI1mScsj8MgjEjFXWA0lyyATE+/0aPzJbs51rEWKV1TO169QS435RNsxXLU90QmNr8EL/00/sqo14NSuK1NSN9Kx8fuPrQwc3S4bibwymL8xq8o37ZZrcewlaNqR9VZr7dSpp+Rz5VTCD/TTYQQkfcQMQLzD8VbboIe6dg6p1rbYQvLnwfyzAeX5rT148NAPHrn24GGGYvuB1hFnfP/XEwf5rycO9rJp9MXWmno+8duXiQ0Tq+cS9h+OsVQkl4Bbtub8/9jW6/5l6ggfMO7hLHMv+STgcRO9txK1+HzY8L7JL+nY+AFYuAkeu0WyrdOxbCJHsBBTa9KxZnyOeq2QSnYDm7Q20RhkjCDzjz0smdTBQuhulPKT8hVQfuroCHHHEclhDhbIEJ4ZgGCRoxwrIe65Oc3jhUtgjz4HrQeksbBf/bejDLsE2+WwBdXj8x/vuhee+K4UvNgZGdJ0C2JKlmTVeysFoSJIxcX7rJx1+MNZwm0bUgkPgJGzQdLirY76sp/39KkNoXi7bZGBAqjeIO9qVK3z/NYePHjoB49ce/AwQzGWjOeGzuSAxPjeV+pHXQhz412vccmqinFZNPpuEC40XuD/M3/DQiUFIknlJ2FDsquZ0O57JXf4DZ+efIJdsRre+mP48ydh191ZogeoTBwDA7AxECoWIE1CBwirFAYJwqobFU9LdF4gIjaO7uMyhDh/w+gI8fEaaHhF/q1tOZZL1CNlcvyuYxMTB3d8Jzx+i/iau45L9GA6Qe9CFpd8ugTbuU/54JwPjVw97+tF33MfPPIf4mV2GybTSXn9D3xe8qzTCbFktB0QhVtb9CLG2tlwWBlnfc59/rBj/chJf1F2Do92tkmuAt8PTiyjLyJWkExCVPVTvapzDx489IdHrj14mKEYTwvkp/73FS46dR4Bn8G9rxzjw3e+OPyT+qChMznihsTBvNi5G4Rl6ggfNX9PtWomjY+oCmKiCak02jJBB6BxJ/aOu3mmqpiuxkPMC6ZZc0o1ZvEkWEZMn1g3jj6Dbt1PWpsOsbYxDSUt2iAN3WgKVRSUwkChtMYGVCaJ6qqXFr9ImaR9NO+B6rOhyykuGWrgsWk31PxRPOCRErnfSvUm6kZAvNzjtSc07oT7PiP+8GSXM9yn6KVM90D3+ahE7V92cfYhtg3thyWuDiWk1C20cdVx14ueScDBJyTBI1wi115bMkipTIg2Qe1fpcHx6HOyvkAeKD9k4rL5sbWTce0ozBii8qMd37wttymcjZJDxG27j50k17vtvjwn6zxcKBubqvVCrL2qcw8ePAwAj1x78DBDsXFJCZVFoTHVv3cnM5zz1Qf4+w0L+JETQzgWjEQ9H2iIMS9osrqykOqiIPNVE/lEebf5APNUKxnlI4UPUFgo4gQp1BbYGbriCXb89U+8nHqBcqODFGnq/CGWnnYGK9/wjvGTnb5Et3wlryx4JwuavkY404GJxgJspTFRmDhiKRoDTRpRPm0UCk06ncBng99sgTwnN7uzHgqPCyHuboTdfxl44LF0Oey4WwpS8ufKR3++5FubTrtg025Rx6vWj8+e0LQbHr3FqVnPF785ZMmqMh1P8kAKtgGVa+HNt/TeFDx3Oxx8XIgxGkLFcMqFkqyx5z7xkBdVixq8+x7J7jYDTsqfEiXc78uS/EQ7HHhElG3bkq+R8snXTOeux3TWaYuf2heQx5sBUf39ESHvqSg9nulEhzOzaGeHRd3XagagcCEsOgdOvQLmrZm8qnsPHjycFPDItQcPMxRucc5Q+dJDoS2WGRexhuHV88HSTKJJi5ZDNWwwnuXjvmMU081p6hB5KoVGYZElLqZS+AIhksk47Yk4C+ggqWz26PnECBFJJdAvP0k43sDCN32iP8EeSBmG/rc17YaX7oSWPWIr8IU4kCnm6zuqebN5Bm82toNKiyFEG4CFqeixhqBBO6/UcD4GdAY7E8XqSGAmOoRcW2k4/IQ0De74kxDZomrJbU5HswOP1WfAjv8TNddKCQFMdkHBPPFxm0HxRC+qHJ89wR2YjDY7arATQZiLwVI0zKBsAt74efkIch3/+jU4/HSO+q3Fu/3Kr2HvA1CySNR75ZDmZDdgyIVMx4XgKifqzxeARLcMlybas37zTBJ03Ln6uUq664S3AUuUbQBbZYmzP+zELfqc74Ew5M2Va5xJiC0FJV+Xc66TDcEYauQ9ePAwO+GRaw8eZjC2rKnkh+86k0//4dUhGxInAxWFwSEbEodKM1mq6nifuZU5dFGvS0jg5zR1CB+SvJHCRwo/AMXhABgG8VSSfKVJ6AD7qOqpQu8mwl7ChA8cYv7OP2PkDgk27YYdd0H9S6JU+vMkys5OC4lTpmMR0EJq421OE2MaDRQkLL7oD7PPrhDCr/1klCKPZG44GyB0LoAMObr6rgGYaCCDTidQgYg8I5OS8xVWZEkmCGkuL4C6Z+HIUxBrh4IKUPlCzGOtQrzDMYmTCxXA6reMT7HvOCKKdVE1RBuFyKOz+dWDwhkQjLfC9p+IbWPhRonIO/aiEGbTn7VmZJJCtpOHhMy6sFKOi8OxbNhpJ/rQlz1PbnGNW0OuM1m/dy849o/cdWLTQ7q1nbWVBOfQ08i5cJN8L3TXQ7QFiubD6z8lCS0ePHjwMAp45NqDhxmOS1ZVkBfw8b1te3nxSDtp68RE19941eohhxkHSzNR2FxmPMscuthLNXPoYqU6QkQlMdD4sCgmSocqID8cIRwwSaZSmDqN1gZHdDmd5PU76v50EQt2PM8B8zkKKpayMb8J85GvQf3LjvqbFmU4nQDThIJKKFwg/z7yrDTz+cIOubNlXFH78JNmpVGHiUWcAEXEMNDYKLTWPVwvuxL3VSoMh2bbGgxtodJRyK+CBRvh8JOQKhjgymkhd11N4vNtO5yNBDRkw4E/DJVniAo7b83AX4CRltekukWtLVkmfuKOIw6nHq7p0BYVGQUNr8p/r/5WyHIq5gwl+oTIWgmnOdE5ZtMemLtKkkDMgCRw+Nol/cPw9SbN7tcuVCSvN97m8GxzEHLdd+19rSyG2G8Kq2DR+Y4nHDl2rNlJAznL81R78OBhzPDItQcPMxhba+qnRLUeCQbzY1erFpYax6jXJcyhizNULSGVIKqDAPgMhY8MZUYUpXyQ8WEkO7C1STdhduuFZEs9sogTZHddA9879Dx79HFuDf+ES8znCLhKaK61wbJkKDDeLmqqnZZjpmNC7swA2sqQRxILH1GCKCCfOCa2Q9d0zv97r8hwPNhusFuSAH7DT8A0hVgH88VSkeiUKLlQsTwx1iyKdusBZ1AvnXN0x9Zg+sS3HSqC5ZcM7LV2Bwab9oiybJhQugLWvwPm9VFiA/lCNjMxKFsh6rXSIyo5JNrcW3XPJGSToi1ZeyaZve6GKdYMLFGiDz0p7yKESyV5I9Ehir6dFluO8gmpTnbK664+C44+m3NNxrKJdFTq/HlyjtYDEvF39oec1sVxtmh68ODBAx659uBhxmJrTf2Y/dYTgZvu3jFkFN/B5uiAt+cRJ0gKE4u1aj/5qpsO8rExCJBBmwaGa511fMraF+Lx1HL8yiLp2EX6IkySJD4KiPEPxjbOt54BK4VlGJgM4BnOJCDjFIgAPT5dbQF+x5Zgk6fi2FoGF32uEo2BQqMGIXjaGWiUIyqS+DGDxRAMCLH2BWQwMZMQq4eVEttK024h/Ha6j+/ZtTRkIGMJ6Uy0w8rL+5NAt82x/SAko0JOM0loqIFDj8MF/wYrt2QfX7RAhijrX4Gylc7QZGiE0X45SSLJThkWtJLOkt0NjbMpcOPstGPRiHcI+V+4SWIFEx1C8G1LrkcmIY8NFsjjDz8l7zz0YJgWxl4w5DoFC+XaucUxZafAhvd7CrUHDx4mFB659uBhBsKyNTfetWNK11DfkRg0is+yNXc+c3jA55WpDhar46xWBylTHZjYzKWTDKakbCgFRlBIlbZg4SbMM97LV/83w9Wx37FaHWQvTpRaDzTLVR0Ab/c9yEa1m3yVwEYBmdzS7r4rzf5TqSxXszOYZoCEZeDXGUIqRUr7MJWNj4zj6FVopeXfToW729qYq2p36xDHjEpWFxc5iSBOCYzhE6W67gVRdeNtQibDZUKG0YDZu5a7x66hIFIuZDYX7nBi+0GxlmQSch3Dc+SYXfXwyNclW3vuaVnryNzToHGXrCXZKZaZllqGt4bkfgks8bHrvoTXjcjrU9aiMxLVV75S4gTzSuU6ROZCuluO4wtCy37oqsvZbLikeqTE2smvNn2yaQiXyDmVgnNugJLFI3+NHjx48DACeOTag4cZiO0HWmnoHH0E30RjMOvHrdv2crwr2e/2paqONxnP4MMiRBI/GTQK0yFxcQKOWhmBBWcLWbvg/8OsOJ3PXV3PN391iCqzheXUUU8JcYKESbJc1VFBKw26hDQBlBIyaqBFBVcMQrBz1M9cUqgtFJqQYaAtIbPNFDFXt0vsnpKhy5DyoXTKzaVAtG5Rq1P4SOogfmUxvyiESnVDfoUQ61izWCpACLA/Iiq6TkPHIXpIrWE6S3QG8cyAkExlyHr7qstum2MyKseNlIqSnI7JsQoqxQ7z9A9h+cVweDt0NwixT8ch0SUKsi8kZNTuY6cZDjqNROH1u8P56L4uv7P+LiHx+XNh8fmw4vKsPaOrAR78krRSGmbOgOVApHoYFdv0iSKf7JICH8N04gvnS2GOZwfx4MHDBMIj1x48zECMpZ1xMjBQFN/Wmnr+88G9/W7PDjJ2s91eyRbjWUw0SUxsDPxYREjSmgmRT4i8ruMwZ7G8lY8ko/DOK/n5n4KcGXucpcYxKmgj6fwaa9AlvMgySunscTwbPcTZoV5qMAU7u8oekm1b+HQay/CT0X5KdDcZTJL4yVdJImQwMME5j88h8FpL6nW3jhAz8plvdhBIN0BwHpSeInaIvfdBqjPrU461ytCfMkUCd2FnhAi6cBsa5c7+xTGpbvFYJztloLDjqKMm26AMtGGSSUTJ1NyF2nkfAdJyjdxUDysl6yheIBeq6zhYbinLUCp2zlUdkV/bUeOVCauugqUX9Sa2tg1P3Qqtex0LB0OfX5nZTUDflkWlxN/uKt9Kicd77mp44tsDZ4x7NhEPHjyMAx659uBhBmI87YwThcqiUL8oPsvWfPoPrw74+NxBRgPoIkKQFEEy2E5CtEbRootojIZYY9ahFp7Ta2Bvy5pKLln1Hrbvv5yu4wfo7mzj14/W8HbfQ7RRACjS+EkQIINBoI/Xuv/wYY6erQxRODOSFkImBaYPM6+cslgrGQyi4SoIFmKkm1DdDb2sCoYZQOfPJUYYleykzIpiqk6ULwylyyBc7DQW1kp5jDKdHGgjuxRDQSDX75xTLw7ZWvBMQnKl+w4zBpzIvlS32EBsS2woyiSZiEOqFaU1lvahieIzUo433CGgwQIZ9GvaA3MWirpuGyNQr3NV/xFaSTRC5pt2w5nvldtcFbnhVah9JIcQmwypTvd4u53sanfNbpa1+3UOFkhKSflp8Orv5BoVz++fMX7udR7B9uDBw5jhkWsPHmYgNi4poaIwNKXWkC9cuarfMOOt2/YNmlwig4xpYoSYQxcWJod0BZWqBT8WKXyOPUQTsrtJWH7CC8/t9za9aSg2LSuHZeVYtuYPL9QRSmSIIRuOTsI06jmU045P2T2pHQp6OFavJGTD5yjArn/EIXCGX9Is/GFUIII/kE9xYZmTjjEfCqqg/ZA0DRoGVKxDmaaEBOpyUYw7DosKfNlXpZxm1z1Z0qd8Egtop+ScbrSc1kL0IWctpijL/jyxbeSVwMYP9bcwFC0Q0n3oSXktwUJQEE9lSCWjhBDLSliJlUVpRQoDvwLDdpI5zIAMVHY3yTFzhxD7YTRDhbmwJe0kEJYhy/0PSzZ28165bo07ZGDTCICdzNaTD3o+xy5i245txpSNSP48qV3350HbQdk4tOyDg4/JtS9dCpm5cp3cjPGmXbDrz3IdPYuIBw8exgCPXHvwMANhGoobrxp7O+N48f7Ni8WmkQPL1tz+xOCNj1HCJPETIUEaPxkMMpjU6XJK6CRPJfBh4ceiTReQX1BNeLAMZwemofjgxafT/OffEyHhFMso9un5lKkOQrpRBhBz9gBaS9oHSmOYfkmrmLMEGncKWfaHxR5QsU5sHPselMKRpt0yEOgPy6AdpkN6Tcgrh0SbKKOmX6wM8VbAEO/ww18Vr3Paqe62bVCpbDydckijrYGEHN+Wynch3A7JS3ZCKB/O/yRUrO5/QQxD2gRf+pWjXKfRykc0HqOANBYKU1s95YcZZ9uRAfyiX2crw/0h8USDkz3tqug5FTmmX3Kh481Dfp36wZ8nyjgKOo+JB9wfliIbKy3Z5CD2jl4V7O65hyL6TsGMrSRysWmPKPBWSjYKbQfleMECeSfh/2/vzsOkqq+Ej3/vrbX3FXoFutkFlB1EVIIaNTEmziRmXF9NDBOMuLxRY7Y3mDwY0YkxGc24zCREJ/OavImJMYpRY9wigsqiLAKK7E1D0/ta2/29f5xb1V290UhjQ/f5PE890FW3bt36gXjq1PmdE2qAkllSKmJZUpNdtV3q13NGHd37UkopNLhW6qQVn854x5PvUd8aPfIT+tF5kwq73PfWzhrqWnvut73f5LHDKXa7fRRTazIYZtVRQyYV5FFg6qg16WwwYyigjoLCWd33cO5kweyZbNsziz2bV7M5Kl1EaslgtTOZmGUz2qrAg8G4g11CRqY/NlmZjJp0JrZlS5Y0pwxGzoWRp8tglqwRUPU+fPSKbDi0vNIur/kwYEnwmZonNcpF0yTwbqluzzzHQpKZtb2Spa3f7dZXV9MlSx7foGi5Gxe9AQk2W+ukPMWXKgF+zig44yaY+NmeFySjUPpV1+6CcDOxWJQME3J7sYDHDaxtpA7eYOEYW1YonqU2DqTmyzXGIlLSYnuljKXpkLwPJybXbPvA8nWocz5SJttuH4ZjkHO2NUDxdHn/zYfk/afkyFpZbh9EEy/h6ansxOOuY9T9sGLJn2ss3J75jndbiUVlc2P8Wg9/IH+WliUbKhsr+tiKUCmlutLgWqmTmNQgF/LASx/ws5e6biLsbxZQ2E2tNcCLWyp7fa7B5nlnttvto4IKk0em1cJwagFoMil8ZIoooI6QP5vSeV/q29fyts2ET13B2EA9rHuf3dFstwDFw05TTIXJJ4qHdKsVC0MLfg6ZPF6Jnsb1Yy5hxsgcySh31y3Cny4Z4L1rJJjMGgEYCS4jbRK4GaRWOBZ2f3Zbz3mDEqBGW9uDvUCWDIBJaifXsa7aIxnaoukS7EfaZBz3xM9BwSQonSN14b3xp8tzU3Jg3xrscKMbSCcPBY9fgdwf37RoJTY/4g1AZpFMW4y2SbDvz5eykaaDbibYA9nF4PXKB4H4GvQWYPtSAUc+pMR3meaNaR9G4/FLIJ+SLSPUY2Hwut8G9Fj7bUkQbiMZa4MEy23V7fXY2CTaAlrItweRFvAEobmqfZhPuEX+7DpvFlVKqT7S4Fqpk5zHtrjl0+MZX5DOkifWJzWb6E/xyoqlF08C4M0d1RxqbGN4RpDa5jC/emPXEc+xw5SwInYhF9hvM8auoNpkYlmymbGaTCwLNjnlnHHe/8JTMLHvFzdsAp551zMy9H9peO8dt4uIj01OOS84s/jIFFFiVTPR2s1sexv5Vh1f8LxJ/tqPoGGqdIjorgQgs0QCy9Y6yC4jUV/iS4GAgcPbpSa4cZ/UB0fdbhuRVsnmxiKymTE1V4Lz5gMkD63p3LM5JucJZEmpSdVWKD8LZn219w8asSjse0sC1tRcyZTvfE1a62ERwyQC7I5V8pIHNom+3RJ4WhLg5o+X92FbUF8hWeRQowS+wyfJa8QiMPUyePcJ2QzYVu92Hek4nKcTb1DeS2udrGNanrQIjAtkSWDcdBAyC6GmBejUASRx9R3q4y13WE3MAFForW5vW5hUMhK/LgeilgyuiQbkz84YaNgPxVP79K2JUkp1R4NrpQaJnLTAcQusQTLW8cB6/vKXqGzo2sf6SCwcQvj5uzONt5yJNJFCE0EsIJUQqRnZfP3isznr1JKjv8BhE5jwxaVUjlzLL57fQEWrj/0mLxE6BgjzKftdcmjkgMmlhSAz0nJgz2rpTjHzWihfkNwObt9bEij60yS76Qu6pRtGssq2LfXQsahkPMHtNuL2h26qlAEtzYfl9+GOEwa76dls2VKHfWiTZFJzyyRr3Vtgve2vsOZhqPlIrtXjk+tprQFHqqody8bu1MWjPWtNcotC2wvZo2TD5PbnJGgecbq0DoyGpR7bnwmHt8HIOfLBZP9aOLBBXtuXKdn6tiaSsvPgthqMkMih54+VYDraAp5M9xhLJjaGGiRY92dI9toXgJj78SBeh514M+6HkmgIcIPwRClIT2UkBkwYIh4I4nYu2SrB/pHWXCmleqHBtVKDxPHqfb1k4Vjmj81nTnkuL26p/NibKMdY+xMZ6wARQvjY4RSzJjCPqy6+gMJMKTfpaZx6n9g2C+bOZt7MmZx+90uYZgmw2ntsN/IBJYBFobeZ1NqDmFgdVms1TuVmKgoWsK3oYvLSUzit8XXs/W9L0Gp7JAscz55aHtkQZ/vcgA63fV+UpKmPJibdMLxBNyDspbuG5WmvK26tkZKS2V9LbgnnOLLpsnqHHHtwI6x9TMobMgql5KK1Dhp3utljg2V58Bn5diBe/BHP5QJut263J7hlSUnJgm/JhkmPV1rTHd4mWfzUXPkQcXhbexDq8cL0K6XjR/UOyVx7U8ATItGXxfa0v2J83fzpcNZt0iXkwHvSqSNeGpKaDyUzpetJAPlggnE3VcanPXZgYhLQfxyxsGTnYxHJWE/8nLbhU0odEw2ulRokjkfv69w0H//70+Px2FavPayPZIy1n694/pqUNU6ljcnWLi4rsxlZOh+GfYxsdQ/8Xpsf/9MUrnc/CHTssQ0WOTQyyfmQugNNtNoOWZ4ITnMDnsN/oHjT8xwwubziS2XcqCJGeIOyMc5EJShMGw4+v4wXb67qMDExHlR3Kr6Ihto3JRKmR/HnR0NyzoObYNWDcNqXJdg8uFFGmx/cIpvtIm0SVFvI9MHWOgnQfUHpWR0fvuLxSylyNCw5XYO7udFIa2jLj8dry3PHnAsLvy3ZdpB2dJM+Lxnsut3tddbDxsO0K9qD0OGnwMLvwbO3yrRHT8DN8Fvu5syg1DanF0rteP0+GHU6jF4oUxLr90vWOLPEndDYItn+kafLc9b/t7w/c7QfIPvQKtD2wtwbZGKlTmhUSvUDDa6VGiTmlOdSlBXkQH3/ZbCXfWFKIpO8ekd1jz2se9Nd1higiVTyykoZGag6Ln2FL5xSxENXzeCHf9lCWkN7j20wjLX2k0kTQSL4nBj1xoMfL7WkU2odptCq4S+R09n4ocPXMsOkxcJu+UKb3PypiVZ3QIdOFm4JRLz7hzwot2grksmNT1zsYXOeiUoLuVgENv4ePnhBAtqWGjdr26lO2yDBdiwkZScZhRIw2p72vs+eALZt4zcx91mSv7aCGVgj5kkbwdI5cN7S9g2TVdskmD/8gXQIadgv2fm0YTK5ceuz8j7jAXa8g8nLd0mPbF+KW3tuyYeSlGzIK5esfM4IKSex3eefvrj9tRorJNNfPFWmKG76g5zXE5CNpx+rr3YPLI+7AXSktt1TSvUbDa6VGiQ8tsXSi/uv9/XXzy7ns6cVJ37+zZpdH+s8pVYVp9of0WICZNJCg9uLOt3vZc7oPAj5j1tf4Xg3lfXv5XHgqZWkRtuwgRyrAR9RfMRotXx4MMi4GZuIZeMzUU6x9rDRlFHbEiY16MUKN0tNcaQZGtxuIYmx2/GAz+1IkQisO2ROTUw2Odpuuzunt6mHHYaitLit/xK6qyE2cr5wkwThfnfiIJbbL7sF3FZ87WeyJAvv9Uk2ecZV7YH1offhtfskexxMcyc1RtoDedvufprhxM/Kn+GbD8OhzZKBjndOScuXY7orvRg2QT5c1e+V8/vTJYv9xs/kQ0xGkWTyO6/pEfVynOWTrLrH32l9lVLq2GhwrdQgcuGUIq6bX8Yv+9C5oyfpAS/3fvE0PntaeweHmGN4/YOjHBQCjLX28q+eZ5lufUir5aeVALUmgw9NMfMnTMD+BPoKe2yLUGox70cKmWztooZ0gkTwW1HCSC2wnzDNJkgUGxuDsSxKOEwKbWSYRqIxLz5i7jRFRzLDgXQIWxCJb9zryCBt9SwSnS4sG4IZks2NxTf1dQ6U3eCczsG5ob1Suqeg3D0uVC91020N8joWPWzqs2TTYGYpzLpWAlzHkb7er/wY6vbK1MJDdfL8LHdMeMthyWKXni611x2/dajaBusehwPrpWQGI+3tik6FUz7X3j+8u28obDv5w1XtbslkZ5VKFj4+WCaxbseYwbYsyfZ78qQVoFJK9RMNrpUaZM6bVPixgus0v4d/PXsMS84Z22VT4Vs7a2gK9ZZp7WqhvY6bPH+k0Koh1WrDRwS/iZBqhSjPcChOKwMyjntf4b9uOsC3n9xInttju8Q6jE0MLzGieEghRMR4aSHIMOpJIYSNQ4oVJo02wBDDi88XlMAY0z4EJdq5BKdDwGdZEhRaHoi5AbkTc7t5hHsIeDsGjN0F3z3p0I0jFpVJj14fmFQJIDtm0uNDbWyvfLBJzZPguGobvPMr2LZSyj48fgm2YyF5L40HILNYNiQ2V0v3kI7TDKNt8PJyqFgngXJmiVxXax3sfkMezxvb99KfcJPbXztN6tW9fogatxNLxwM7Z7Ljf3d7muLokXWNtx30+uRbCKWU6icaXCs1yMwpzyU3zUdN89HVR//bl05LKgPp6Gg7kYy19nKT548UWLUcIJdhpoF0q5WgFcGLQ7ilFufwduyU3OPaV/ivmw5w/W/WYYA6Svh17Hwu9bxKuVVBkAgONs0mSAtBcqwmvEQwieIJC78VxovBE4lIT2QTkwx0NET7tEB3DLrTabOi7XGHoeRKttf2YJwYJtKG1SH73GNBgmUdITnbKag08W4aDjQflBrnnHLJIHu8bjLd545vT5Ggua1Bssw7X4XNf5ZfW+skqCYsdeLGSL15LALNNTJYxonKB4SUXPnWoWo7bP8r7HtHrjuYLSUXliWv1VwlpSbvH0VtvT9dPnRFmt0OJEH3w0PYLXWJ0G07w0STwR7WzDLuU9z3lTUCtq+UzL1uZlRK9QP9l0SpQcZjW/zTtKPvvPHdpzYR66FR9tF0IrFw+JL9GsOseirJJYyfajIIG8n2+oliO2HaqvdJG7bj1Fc45hh++JctidBrjLWf8+215Fv1HDS5hIyXgInQRIA0WvETASxajR/LHZYebwBtEWvfTBgPzHBrrW13mqE3pdMVGJmSmDcGMosJxxzCkVaixiFmLGJGQnMnEQjGs6qdR73EH+6u1rjjcy23m4cF+RNg7vVSuuLxyybEjAJppedLcV/KJ+cLN0u2+vBW2WwI4PFIdt72SbAebpDANtIix9te+YBxcJMEza/eC+/9VgLtpoNQs1M6gsS7mQQzJSCuWCdZ7r7IGgH546Su2+OXgN3jcyc4+rp5giUdSSxP9+vkDcpzLffDkMcPpbNg2Cnt2XellOoHGlwrNQidN6nwqJ9T1xJh9Y7qbh+TbLi/T+eJt70L4yXifjnWRoAD5NBsgjhYMoo83AT5o2Hu4uPSV/itnTWJzinxVoCTrV3UmgzeMqew2plEBC+jqCLHasQB2oyPVgLEsHGw8BLD2yH8FZ03MNJeYmD55D5PACyvBNblZ1MZGElbxCHieGgkhVrSCON1E82mQx67w6juxNhut9uGp/P6xzuTeNyJjvlS4jHhIrjmaRh/gZRUOBEZxhJtS445Y/JhAsudwBhqkvfhz5DXMjG364hbzhJukUC7tVoC7N1vyhTI+n1Q/YGUo8S7pETb5DUbKtwR4/FAvqXvtfW2LR1FUvOkJCU1VzZq+tPAY7sZ+ED78fGgOX7NllfWxZfmBtNBaQnoT5OMe9YI+fPxp8r1Hqeaf6XU0KPBtVKDULwt39H2QHjzo+43LXpsi0umdV8y0lkardgYQvjxxaflIQF2BfnsNcNpMGmEssfC3G8ct4Ed8VKWzq0Am0ghnTaqyObvzlRqyMBg0WDSqCedRpPi5pAdbOtINdCWlFDEu394vBL05Y2Fwilwxk3EJl1C/YGd1JNGDZk4eLDd9Wg2AWKW5MlNPGudSFzb7QNZvIH26Y2dB5j7UqWGOjVXRqWf812pT464gWxLNdTugZrdkp2NtEiQ3dYg58sb6waXzRKQxkJuhtedhGh75ffxY9rcWuiWwxLQxktM4t1PEt1QnPZSkmhYrtWfenS19fE2fUVTJcjGknXIGQNjPw0jz3S/MYh/yInIe0obBunDSIxE96ZI5j6nDHJHy3plFUvwfZxr/pVSQ4/WXCs1CMXb8l1/1G35eg7HPz2pkF/1YaNkMynUkUamaSbLaqYGb9J5HcBrW2SOngXZI4/y+vouXsrScYBMDk2MtfaTYzXiJYaFocX4CeJjq1NKCylMsT6SUeGWlG60B9jdbS50s8uBLEgf7gaVESiaJpnf/HFs27iWTKeRKrII4SNgItg4ONiE8JFn6ik0tVi5o0m3QhIQewLSr9qfJuep3y9BshUPuANS8pGSI8G1Pw2KZ0hHjmETZHPimkfl+LTh7rCbMLRFJED2p0hgXDgTxl8IL//YPSYCkZgE2bbHLSt3y2EskJZ/7jliIbnOaItcUzQsjzm2G5g77mbBFmkP6AvKNR5tbX3HNn0HN0nGvOmQvH5aPoyYCzXuxMpAhrv5MSivW71DrjmYJcG/5XFb/aVC/ng5/3Gs+VdKDU0aXH+CQqEQc+fO5d1332X9+vVMmzYt8diePXu44YYb+Pvf/05KSgpXXHEFP/nJT/D7+/ZVvFKdxYeofPdPG/u8uXHemLweH+vrkJr9Jo8dTgn5VgMBIuRajTSRQgQPPmIUUkswZwT2tCuO6wayOeW5ZKf4SGuTATIBIky1PyJIiCgegoRJs9rwWxH8RDnV3sUhk02qFcGxLDeB3Jd2bwZaqiQY9QYgewSEG6F4GmSNoK7lTTISx1qESP5vuolU6ghzqOwqZs5dIM8NZEgbvMwSCf7aGqTFXrhFAte8cdKirmF/e1/oeIs7x5GBLC3VUDpbMrUVG6B+j3Q7iTRLhn3yxVB+Nmz8g9RNR1qQ9oEAUTlPvNTC65dAGtyJiynS+xqgrUXqry1bJjIS3zAYdbPhlrxeyXQJ/j/On3m8TV/OKBj/meR+2OEWqfmuWCfX4UuRjZht9e4HkGxIyYOGvVIPnlUsw2lsv0yFPE41/0qpoUuD60/Qt771LYqLi3n33XeT7o/FYlx00UUMGzaMf/zjH1RXV3PNNddgjOGBBx4YoKtVg8GFU4o4Z2IBp9/9EjXNvYzeBrJTvJw+uufgunM2vKew02Dzgtv2zuuxCcVaSLdaySZCih0jmDOCnAu/BwWnfNy31Sce2+Ir88v4/UsVhPAy0dpNDo1EscijAYAwXsJ4yaSFTJrJtpo5TAYxPFIWcjTDSpoPSbCXUSAZVTdgCxZOoJ50smjmED6Svx0wZNFMHRlYZfOlH3RnvQ3W6e6x+r1uf+gSCWzT8mHsuTLCvaXGDaItGD4RXvoR1Hzk1l8bINqhAYcNVkSy0inDIZAmz88aIf2xYyGps8ZtjWfc+m/LdrPdRs7r8cHoc+DMm/unBKhzP2yQke3v/Ap2rZLpkBYSVI+/EGb8L8lUH9wEu1dLgN1aC97W7gfaKKXUMdLg+hPy3HPP8cILL/Dkk0/y3HPPJT32wgsvsGXLFvbu3UtxsdS13nfffVx77bXcddddZGZmDsQlq0HC77X58T9NOeLkxuVfPK1Lf+vOOo4U75jBti1prRzXnDmGUQu+ybzIag7v3IjTUoPP5yVn5BTs6VfA8OMbWMctOWccj/3jQ3KjDZxq78SyDB53g2IYG0jBg0OjSSGCTY7VQhbNeIjhwWBZfQ2ukeDS45MSjdn/mgjYpp56Gv/v6SmcEXmrSxY/nVZsY9jkO5Uvn3pa/7zpjv2h4yxLSkhSciTw3b8O/vFzaNzvBsLQtV+0kQ2anqAE4pFWKf0IN0q9dZcplG7teTx7jdtFJb0Qzr4N8kb3z/vrzrAJcMHdULe7vRQkb5yUHcUz0t1lvXsaaKOUUsdAg+tPwMGDB1m0aBFPPfUUqampXR5/8803mTJlSiKwBrjgggsIhUKsXbuWhQsXdnveUChEKBRK/NzQ0ND/F68GhQunFPHwVTP49h83UteSXCKSnepj+T+fyoVTinp4dtdzfXpSIW/trOFQYxvDM4LMHJXD2t21iZ/nlOdKoO6cwfDTBi6Y8VRv5+mRT5C7axt+K5YIHWOAH4cAzUSxMZaN30CL8ZNihYngI4IhaNtYTl96fLuBZMGpkDpMMqXxa/B4KD3vG7y7sprx7CPDak08FjU275nRlJ53PR6Pp3/edMf+0IFuPpiHmqWcpPmQXLeJTzvs7oOVLX9eZWfB3jUSiFdtlecEs6T0wulcctShRj3aJuUsVe8f3+Aa5Dpzy+XW2zG9fROglFL9QIPr48wYw7XXXsvixYuZNWsWu3bt6nJMZWUlBQUFSffl5OTg9/uprKzs8dx33303P/zhD/v7ktUgFQ+KV39UzZs7qgHDvNH5nD4m74gZ6848ttWlPrvbeu2BDGaqtsGb/0Fp9SqiXpuw48U2MbwY+YfPineWlnmNAdNGGlE8xAhZfoLE8HQJHHua++d2pUjNlXKJTm3dzjrjTF7n//Dy335DWeQDUgnRQoCdvvGcet6VnHXGmf33vuP9oQ+8B8My3P7Y8Ys3UPOh1CY7MYh1bCsYb1MS37hp5MdoG+xb6w6NyXG7ghg5ry9V6pu7LIgtmyKdGIQaYeuzkjXWLLFSagjQ4PpjuvPOO48Y2L799tusWrWKhoYGvvOd7/R6rGV1DW6MMd3eH/ed73yHb37zm4mfGxoaGDFCd7yrnnlsi/lj85k/Nn+gL+X4im/qq/4AImG8KZl4om044VYwEjBbWFiW1FbneaPEYha2I4FlKuFu87imw2/iuV5juXMa41METazbtm5nnXEmZ8ydx7ubNlFXV0NBdi7nT5nSe8bacbqWMUDv5Q/x/tD1+yXLnFkimfRwi2Ss/WmSdW6ogPignMS76/SubRvwwKHNbqlJipsNN1IeEmrpdMFugO4JuPXXEcly730H6vZAblnP71UppQYJDa4/piVLlnDZZZf1ekxZWRnLli1j9erVBAKBpMdmzZrFlVdeyWOPPUZhYSFr1qxJery2tpZIJNIlo91RIBDocl6lFO2b+vwZgAMeH5Zl44lFINoxG+2ODHeiWJYN7vDz7iQGM7qsDg84GJq8uWR16BLSHY/Hw4ypU/v2Hqq2wZan4cAGt31eGmSWQlstHHxfNuWBbNwrOxNmfaV9Y168P/T7f5F1aKyQwL94qrTDe+Pn7pvoPGK9U7vBaEjWp/oDd8Oi22c6mO12E+m8Wu5uSONOsLTdNoyhOjmHBtdKqSFAg+uPKT8/n/z8I2f//v3f/51ly5Ylfq6oqOCCCy7gd7/7HXPnzgVg3rx53HXXXRw4cICiIql7feGFFwgEAsycOfP4vAGlBrP4pr6UbMmgxiJuC7lUKW8wbmmDcXCwaI2Bx0iTPINko22Sc7rdMYlfDfsbY2Sk5mP3R1u3qm3w8nKo2uIGuI7UO3/0qlx7Si6kF8gVtNbJ+PKmg/CpbycH2PH+0J0z39tWwp43ZS16e4fGHQxj5IOHbFiMSetBoH2VOj3fRMGTKmPUYzF30M5RbA5VSqmTmAbXx9nIkclDMtLT5eviMWPGUFpaCsD555/PpEmTuPrqq/m3f/s3ampquO2221i0aJF2ClHq44hv6ot3yGg57A5fcfs1OzEwURxjaDMeHAw+96kWYMVbNVvJGer4Lx3DSQNE8HEolka4+MtMO9a2bo4jbeX2rYZIqD0ojbTIIBjLloDVG5QL8abIAJhD78P7z0hA3bFEpLua98JTpX91NP6Oewt8bbeHdbTrQ/Fx5x0GuGO7fbxNDEJtcm7Lhp2vyrVo2zul1CCnu0tOAB6Ph2effZZgMMj8+fP58pe/zCWXXMJPfvKTgb40pU5O8U19DRVQMlMC6rY66ctse8DEMNg0ksIGZywNJh27U0lEd1nreAvoGBDCQwSLZhPgrdgEqsnkUFs/dPyo2w0f/l02AhoDvoB8KEiUsDgQqm3fSGgBwUz5wFCxTjLVvXEcaDwkkxsDGe5o9V7yLJaHXruJJJWGWO1Z9mib/N7yyDcIe9+BV+6VDwFKKTWIaeb6E1ZWVobp5uvRkSNH8swzzwzAFSk1CHXc1NdSLSOyD26G1hp3miBEvGmsbh3NdlNKkVWdyDTEt/jFp313jinbuzpbWMZmnxnOJsop5yAFgb5NwuzV4Q+gqVKCUn+avGC8fCOeKY6Gpe+0L8V9vz4pfWk6BIe3997ysH6v1D+PPF1qt9vqJQgO1Uv5iNMxQ225pSEdV6XDv1/+oDv6vE0+uMRCHTLcvvZFbK6S28GN8tqXPASFk499rZRS6gSkwbVSanDqvKmveJp0uEjNg+GT+XB/DU2b11FuVeLtWNbQE0viWqnW9hAzHkJ4ed+MJIUwti/IlNElx37dzVUSKPvT2wN7q2MFOCSmH4KUizQelEx3tBXW/hoOvCsfLrorwYjXo+eNk+D90DY4vE2Cam+q9Mc27lCYLnl7D0klIJbH7ZBiZLNl82ForZaA2/ZJoG15JLsdi8hrHNwEv78Gzl8GEy489vVSSqkTjAbXSqnBq6dNfbZN/YdV3P/us4y2KsjyNJJFI6lEsC2TyM8mYlvaw8wYHmn/jIedppC9DGMcFYyZNB9P9sjuruLopA2TSY9OBEzA7dDhlYDViSBBry3Ba6QF6ivkvXn98l6zRkiP6/r98uGic4DdcchMaj4UeCVTnpYv9dv1e6UTidOh3zXIa8Z7D8ZXx3Ekq25Zcs3egNRcp+RJJtzyuJsg3emVHp9k3Bv2w6v3SA32JzStUymlPilac62UGtzim/oKJsuvbrnEnNH5xDJHcMjkECHAIXJoIJWI8bRvVuyQLLZsP1FfJjHLSxQPh0wOO00Rp/kOcur40UxYcHn/DEnJHycjw01MelM7UQlgnWh79w6MbNKs2yfZeNsr3UMyi9s3b9bukay906GW3HFHlKdkSTY/Hhw78bZ5SEeV1Dx3fHrHbHnHmnRLemUHM9trv2Pu9VlI55JIs9yibXK85a6NxycfDBoqYMP/Tb4+pZQaBDRzrZQakjy2xdKLJ7Hsf/ZSRxoZJhOv5dBEkDwa8RHFgwfb65HANpBBwJ+Ov62WMEFyMkZxdsYw8stPxT7lc/3XBSN7FIw9R3pcx8ISYEdaJftr+yU4ti139HhUhrqkDZOBLtv/6gbhgMcr0xiLp8PYc6W9X7xEprkKanfJIBqPXwLylmo5ty8IeWMh1AQ1H0l2PNEqxbSXfATS5dfMUvnQ0ngAGlvdINoguRu3tCTaYYS8xy8ZbY8XqrZLplxHkiulBhENrpVSQ9aFU4rgik+z86k15IUbaCCNICEOWvmU+FvxWDHJyKbkQelsiDRjpeYTmHY5BRmFSWUm/ca2YdZXpaPHoc2ySdH2yM2JgR2QrLEThRa3fKNut7TpAxIbD2O2lIusvA1Ov0Fqnev3yYj23NES5O5+QzZHerxunbUlwfyhLZA6DHLKpUTEtmUgT7RNMtuBdBlsgwXpw8CfCW3bpDQls1iC9sToeLeMJD7hMd4KMT7NstOoeKWUOtlpcK2UGtIuPLWE2PCbqfrbz7HqdhE0bWRarVghG0INksnNLZeAsnQGTOzHLHVPhk2Ahd+GtY/D5j9JIBxplbZ8GYUSxLbUSADrhEju5NGxA7cFDZXwynK5fm+KjCGPRSVT7cTapzSamGSdfely3nCjlHUEMuGMJdLS0Jcq54w0u2UjRjLbVdth3xrIKJIgPKVWBs3Eh/XEed3A2rblA0tKbrej4pVS6mSmwbVSasjzFEyk8PxbpGyiant7tjZvHIxeCJnHKUvdm2ET4LR/gQPrJRAO+yUYba2FphZoa6S9Djq+/bJDd27jyEZIY+T5sbDUcztRaNrt1mr7OmShkecbN+McbpFg2RuEqg9g6hWS4e5O9Q7pBuJLlUvILJIAva3WzYjbQEzaINo+6bHtT4PhE3ocFa+UUicrDa6VUgp67SwyIKq2wcb/BzW7pL7a9ki22rLdQS3hTk/o3D/fSCu8WIdgubVOMs2xiLuB0UCkTQJuf5pb1+2VUpNIq2S5LRtqd0ubvTELJIBOy4fSOe3Bdlq+2wmkRQbT+FMheyTUW/JhIL4R0/bL/Wn5kFsm3wIM1PoqpdRxosG1UkrF9TQu/JNWtQ1WPywBbWquTJd0opL5TehtZHk3x5gI1OwALLd+23EH1ETkuHCr/BpqIJEJN/GhNQ2w6ffw/lMShPtSpW577mLpVV06R36u2iZlJbYlAXb+BPlAULdTMuDFsyAtTzLWn0R5jVJKDQANrpVS6kTiOFKe0lItPaB9KbJhMdwqwbDTh4E3PXJrsh3LLdGOn8vNhsdLQuJsrztx0WlvmedxN1RWbYMXfyD3TbhQAu0XfwB1u6R/tj9FrjlULx1Q4nXbA/2NgFJKHWcaXCul1Imkfq+0y8sqkc2GOWXS7q76Azew7kvG+khiyU28cQe9JIm30OswTse4Gxg9BZBdJoH0W4/A2PPapy2ueVha+LUcllKR4RNhztd1GqNSasjQ4FoppU4k8fHkvjT52bKg6FQJuiMtkmFOOJZAu+MmyO7OY9G+YdIjmWYn5o4xj4HXkgx19Q7Y9xaMOkMC6LHnyc/Nh7vWZiul1BCg/+IppdSJpON48kCm3JddJrfDW93guqeA+GjZ7RskLcstAYnrMAjG6jiq0q3ZBin9aDksgXScxyuBtlJKDVFa9KaUUieSrBHSMq9+f3uphmXBiDkQyJKfbY+0tDvm/EjMrbOOdai/BgnebSnrALkO426A9KeDJyj3h1vlmLT8Y7wOpZQaPDS4VkqpE4ltwykXQ2oeVG2FtgbpFOINSEcOX4qbZTbSlaN/XtTtUe1mpDHtw2Xi5SEmJkF1eoF7l5Gsdd4YKf1QSikFaHCtlFInnmET4PTFUHQatNZA9Yfy68QL4bwfSWbb65duHt60Tk/+OAG3W2ri8ZO0edFEkf9NWBJ4pw2T121rlM2MwQzZrKg11UoplaD/Iiql1Imot6E25WfCqgfgo1dl8qIvBUJ1x9amL+qWeHhTZLoiFgQzpe93ejE0H4Smg1C3V7uAKKVULzS4VkqpE1VPQ22GnwKffxB2vgrbVkJDBTRVyZCYSKsMm0nanNgHli2Za8sDjgdSsmHeDTDhsxLUG0e7gCilVB/ov4xKKXUysm0YsxDKF7Rnt71B2LUKXvmxjDqPthzF+XySHQ9mS4BeMEkC60Rwb2sXEKWU6gMNrpVS6mTWObvddBAZce6VgNmJ9PjUdm6/62hYykN8QSiZIRlrpZRSR0WDa6WUOpnForB3tYwj9wYhJUdqoh132EtfemLHx5xHmmXDYvF06ViiI8qVUuqoaXCtlFInq21/hdfvk8A62ib3eQNu3XSM9gmLvXG7gThRacc35hyYf5NsqFRKKXXUNLhWSqmTieNA3W5493fwzq+gtba977UThVjo6M7nT4OUXAg3w8SL4HM/1Y2KSil1DPRfUKWUOllUbZOAetc/oGo7OGH3AUsCbMsC4wGOoiWfcaSHdv44mHe9BtZKKXWM9F9RpZQ6GVRtg5eXQ8U66W3tdCz5MO2j0vvEav/VOFJKMvMr0uJPKaXUMdHgWimlTnSOA1uehqotEgh7AkBlH57odgHBAWzZ6Gh7JTg3RkpC8sdBIBNKZx7Xt6CUUkOFBtdKKXWiq98LBzZIljmQ6W5e7EOm2rKlg0ikRTY5GiPBtccvQ2JGzIFgLrTVSo9rpZRSx0yDa6WUOtGFm2TDoUGyz8YgXT6OUFtt+yG9EJoqAUcS2CnZkD0Kho2HlDyo2grFU7WntVJK9RMNrpVS6kTnT5cSDguIRcDEwJ8K4cZenmTJczxeGHYKhOqgrVF6WOeOkSmMVVshLQ8mfk57WiulVD/R4FoppU50WSOgaBpUfwihRgmavUHpDhJq6OYJFlhembToS5HMdM1OyDJSr12zQ55fPFUCa+1prZRS/UaDa6WUOtHZNkz6PBzc4nYLiUhGOhYGf4ZkoW1baqxjUanN9qdBThlklULzYcgtgzn/KoNiwk2SDc8aoRlrpZTqZxpcK6XUyWDYBFj4bbfP9SrZpOhEwfbA8ElSQ21Zkt2OtEH6cAhmy3M1Q62UUp8YDa6VUupkMWwCXHC3TGis3iFdRA5/CM1VMpnRG4RTLobxn3VrsjVDrZRSnzQNrpVS6mRi25BbLjeQHtj1ezWQVkqpE4QG10opdTKzbcgZNdBXoZRSyqXpDaWUUkoppfqJBtdKKaWUUkr1Ew2ulVJKKaWU6icaXCullFJKKdVPNLhWSimllFKqn2hwrZRSSimlVD/R4FoppZRSSql+osG1UkoppZRS/USDa6WUUkoppfqJTmgcRIwxADQ0NAzwlSillFKqr+L/347/f1yd3DS4HkQaGxsBGDFixABfiVJKKaWOVmNjI1lZWQN9GeoYWUY/Jg0ajuNQUVFBRkYGlmUlPdbQ0MCIESPYu3cvmZmZA3SFJy5dnyPTNeqdrk/vdH2OTNeod4N5fYwxNDY2UlxcjG1rxe7JTjPXg4ht25SWlvZ6TGZm5qD7R6k/6focma5R73R9eqfrc2S6Rr0brOujGevBQz8eKaWUUkop1U80uFZKKaWUUqqfaHA9RAQCAZYuXUogEBjoSzkh6focma5R73R9eqfrc2S6Rr3T9VEnC93QqJRSSimlVD/RzLVSSimllFL9RINrpZRSSiml+okG10oppZRSSvUTDa6VUkoppZTqJxpcDwHPPvssc+fOJSUlhfz8fP75n/856fE9e/Zw8cUXk5aWRn5+PjfddBPhcHiArnbghEIhpk2bhmVZbNiwIemxobpGu3bt4rrrrqO8vJyUlBTGjBnD0qVLu7z3obo+cf/xH/9BeXk5wWCQmTNn8vrrrw/0JQ2Yu+++m9mzZ5ORkcHw4cO55JJL2LZtW9IxxhjuvPNOiouLSUlJ4VOf+hSbN28eoCseWHfffTeWZXHLLbck7tP1gf3793PVVVeRl5dHamoq06ZNY+3atYnHdY3UiUyD60HuySef5Oqrr+YrX/kK7777Lm+88QZXXHFF4vFYLMZFF11Ec3Mz//jHP/jtb3/Lk08+ya233jqAVz0wvvWtb1FcXNzl/qG8Rlu3bsVxHB555BE2b97M/fffz8MPP8x3v/vdxDFDeX0Afve733HLLbfwve99j/Xr13PWWWfxmc98hj179gz0pQ2IV199lRtuuIHVq1fz4osvEo1GOf/882lubk4cc++99/LTn/6UBx98kLfffpvCwkI+/elP09jYOIBX/sl7++23efTRRznttNOS7h/q61NbW8v8+fPx+Xw899xzbNmyhfvuu4/s7OzEMUN9jdQJzqhBKxKJmJKSEvNf//VfPR6zcuVKY9u22b9/f+K+J554wgQCAVNfX/9JXOYJYeXKlWbixIlm8+bNBjDr169PekzXqN29995rysvLEz8P9fWZM2eOWbx4cdJ9EydONN/+9rcH6IpOLIcOHTKAefXVV40xxjiOYwoLC83y5csTx7S1tZmsrCzz8MMPD9RlfuIaGxvNuHHjzIsvvmgWLFhgbr75ZmOMro8xxtxxxx3mzDPP7PFxXSN1otPM9SC2bt069u/fj23bTJ8+naKiIj7zmc8kfXX25ptvMmXKlKSM7QUXXEAoFEr6Cm4wO3jwIIsWLeK///u/SU1N7fK4rlGy+vp6cnNzEz8P5fUJh8OsXbuW888/P+n+888/n1WrVg3QVZ1Y6uvrARJ/Z3bu3EllZWXSmgUCARYsWDCk1uyGG27goosu4rzzzku6X9cHnn76aWbNmsWll17K8OHDmT59Ov/5n/+ZeFzXSJ3oNLgexD766CMA7rzzTr7//e/zzDPPkJOTw4IFC6ipqQGgsrKSgoKCpOfl5OTg9/uprKz8xK/5k2aM4dprr2Xx4sXMmjWr22OG+hp1tGPHDh544AEWL16cuG8or8/hw4eJxWJd3n9BQcGgf+99YYzhm9/8JmeeeSZTpkwBSKzLUF6z3/72t6xbt4677767y2O6PvL/roceeohx48bx/PPPs3jxYm666SYef/xxQNdInfg0uD4J3XnnnViW1evtnXfewXEcAL73ve/xxS9+kZkzZ7JixQosy+L3v/994nyWZXV5DWNMt/efLPq6Rg888AANDQ185zvf6fV8g22N+ro+HVVUVHDhhRdy6aWX8rWvfS3pscG2Pker8/scSu+9N0uWLOG9997jiSee6PLYUF2zvXv3cvPNN/Ob3/yGYDDY43FDdX0AHMdhxowZ/PjHP2b69Ol8/etfZ9GiRTz00ENJxw3lNVInNu9AX4A6ekuWLOGyyy7r9ZiysrLExo5JkyYl7g8EAowePTqx2aqwsJA1a9YkPbe2tpZIJNIlK3Ay6esaLVu2jNWrVxMIBJIemzVrFldeeSWPPfbYoFyjvq5PXEVFBQsXLmTevHk8+uijSccNxvXpq/z8fDweT5ds2aFDhwb9ez+SG2+8kaeffprXXnuN0tLSxP2FhYWAZB+LiooS9w+VNVu7di2HDh1i5syZiftisRivvfYaDz74YKKzylBdH4CioqKk/28BnHLKKTz55JOA/h1SJ4EBq/ZWx119fb0JBAJJGxrD4bAZPny4eeSRR4wx7ZvRKioqEsf89re/HTKb0Xbv3m02btyYuD3//PMGMH/4wx/M3r17jTG6Rvv27TPjxo0zl112mYlGo10eH+rrM2fOHHP99dcn3XfKKacM2Q2NjuOYG264wRQXF5vt27d3+3hhYaG55557EveFQqEhsxmtoaEh6d+cjRs3mlmzZpmrrrrKbNy4ccivjzHGXH755V02NN5yyy1m3rx5xhj9O6ROfBpcD3I333yzKSkpMc8//7zZunWrue6668zw4cNNTU2NMcaYaDRqpkyZYs4991yzbt0687e//c2UlpaaJUuWDPCVD4ydO3d26RYylNdo//79ZuzYseacc84x+/btMwcOHEjc4oby+hgjHyR8Pp/55S9/abZs2WJuueUWk5aWZnbt2jXQlzYgrr/+epOVlWVeeeWVpL8vLS0tiWOWL19usrKyzB//+EezceNGc/nll5uioiLT0NAwgFc+cDp2CzFG1+ett94yXq/X3HXXXeaDDz4w//M//2NSU1PNb37zm8QxQ32N1IlNg+tBLhwOm1tvvdUMHz7cZGRkmPPOO89s2rQp6Zjdu3ebiy66yKSkpJjc3FyzZMkS09bWNkBXPLC6C66NGbprtGLFCgN0e+toqK5P3C9+8QszatQo4/f7zYwZMxJt54ainv6+rFixInGM4zhm6dKlprCw0AQCAXP22WebjRs3DtxFD7DOwbWujzF/+ctfzJQpU0wgEDATJ040jz76aNLjukbqRGYZY8wAVKMopZRSSik16Gi3EKWUUkoppfqJBtdKKaWUUkr1Ew2ulVJKKaWU6icaXCullFJKKdVPNLhWSimllFKqn2hwrZRSSimlVD/R4FoppZRSSql+osG1UkoppZRS/USDa6XUCcuyrF5v1157LQAvv/wyCxcuJDc3l9TUVMaNG8c111xDNBoF4JVXXsGyLKZMmUIsFkt6jezsbH79618nfi4rK+v2tZYvX97rtX744Yd85StfobS0lEAgQHl5OZdffjnvvPNOv67Jye5Tn/oUt9xyyxGP++Mf/8gFF1xAfn4+lmWxYcOG435tSinVHzS4VkqdsA4cOJC4/exnPyMzMzPpvp///Ods3ryZz3zmM8yePZvXXnuNjRs38sADD+Dz+XAcJ+l8O3bs4PHHHz/i6/7oRz9Kep0DBw5w44039nj8O++8w8yZM9m+fTuPPPIIW7Zs4U9/+hMTJ07k1ltvPeZ1GIqam5uZP3/+ET/UKKXUCWeg568rpVRfrFixwmRlZXW5//777zdlZWW9Pvfll182gLn99tvNiBEjTGtra+KxrKwss2LFisTPo0aNMvfff3+fr8txHDN58mQzc+ZME4vFujxeW1ub+P17771nFi5caILBoMnNzTWLFi0yjY2NicevueYa84UvfMHcddddZvjw4SYrK8vceeedJhKJmNtuu83k5OSYkpIS88tf/jLxnJ07dxrAPPHEE2bevHkmEAiYSZMmmZdffjnpOl555RUze/Zs4/f7TWFhobnjjjtMJBJJPL5gwQJz4403mttvv93k5OSYgoICs3Tp0qRz1NXVmUWLFplhw4aZjIwMs3DhQrNhw4bE40uXLjVTp041jz/+uBk1apTJzMw0//Iv/2IaGhoS7w9Iuu3cubPX9Y2/v/Xr1/d6nFJKnSg0c62UOqkVFhZy4MABXnvttSMee8sttxCNRnnwwQf77fU3bNjA5s2bufXWW7Htrv+kZmdnA9DS0sKFF15ITk4Ob7/9Nr///e/529/+xpIlS5KO//vf/05FRQWvvfYaP/3pT7nzzjv53Oc+R05ODmvWrGHx4sUsXryYvXv3Jj3v9ttv59Zbb2X9+vWcccYZfP7zn6e6uhqA/fv389nPfpbZs2fz7rvv8tBDD/HLX/6SZcuWJZ3jscceIy0tjTVr1nDvvffyox/9iBdffBEAYwwXXXQRlZWVrFy5krVr1zJjxgzOPfdcampqEufYsWMHTz31FM888wzPPPMMr776aiL7/POf/5x58+axaNGixDcCI0aMOLY/AKWUOtEMdHSvlFJ90VPmOhqNmmuvvdYAprCw0FxyySXmgQceMPX19Ylj4pnr2tpa8/DDD5vc3FxTV1dnjOk+c+33+01aWlrSrXMmOO53v/udAcy6det6vf5HH33U5OTkmKampsR9zz77rLFt21RWVhpjJLM7atSopAz4hAkTzFlnnZX0ftPS0swTTzxhjGnP7C5fvjxxTCQSMaWlpeaee+4xxhjz3e9+10yYMME4jpM45he/+IVJT09PvNaCBQvMmWeemXTNs2fPNnfccYcxxpiXXnrJZGZmmra2tqRjxowZYx555BFjjGSuU1NTE5lqY4y5/fbbzdy5cxM/L1iwwNx88829rlVHmrlWSp1sNHOtlDqpeTweVqxYwb59+7j33nspLi7mrrvuYvLkyRw4cKDL8ddddx35+fncc889PZ7z9ttvZ8OGDUm3uXPndnusMQaQzZe9ef/995k6dSppaWmJ++bPn4/jOGzbti1x3+TJk5My4AUFBZx66qlJ7zcvL49Dhw4lnX/evHmJ33u9XmbNmsX777+feO158+YlXeP8+fNpampi3759iftOO+20pHMWFRUlXmft2rU0NTWRl5dHenp64rZz50527NiReE5ZWRkZGRndnkMppYYC70BfgFJK9YeSkhKuvvpqrr76apYtW8b48eN5+OGH+eEPf5h0nNfrZdmyZVx77bVdSjLi8vPzGTt2bJ9ed/z48YAEsNOmTevxOGNMjwF4x/t9Pl+Xx7q7r/Nmzd7O291rd/ehoLfXcRyHoqIiXnnllS6vEy99OdI5lFJqKNDMtVJq0MnJyaGoqIjm5uZuH7/00kuZPHlyl8D745g2bRqTJk3ivvvu6zaIrKurA2DSpEls2LAh6ZreeOMNbNtOBOjHYvXq1YnfR6NR1q5dy8SJExOvvWrVqkRADbBq1SoyMjIoKSnp0/lnzJhBZWUlXq+XsWPHJt3y8/P7fJ1+v79LO0SllBpMNLhWSp3UHnnkEa6//npeeOEFduzYwebNm7njjjvYvHkzF198cY/PW758Ob/61a+6DcAbGxuprKxMujU0NHR7HsuyWLFiBdu3b+fss89m5cqVfPTRR7z33nvcddddfOELXwDgyiuvJBgMcs0117Bp0yZefvllbrzxRq6++moKCgqOeR1+8Ytf8Kc//YmtW7dyww03UFtby1e/+lUAvvGNb7B3715uvPFGtm7dyp///GeWLl3KN7/5zW43YXbnvPPOY968eVxyySU8//zz7Nq1i1WrVvH973//qHp5l5WVsWbNGnbt2sXhw4d7zGrX1NSwYcMGtmzZAsC2bdvYsGEDlZWVfX4tpZQaCBpcK6VOanPmzKGpqYnFixczefJkFixYwOrVq3nqqadYsGBBj88755xzOOeccxKDZjr6wQ9+QFFRUdLtW9/6Vq/X8M477zBmzBgWLVrEKaecwuc//3k2b97Mz372MwBSU1N5/vnnqampYfbs2XzpS1/i3HPP7bfOJcuXL+eee+5h6tSpvP766/z5z39OZJRLSkpYuXIlb731FlOnTmXx4sVcd911fP/73+/z+S3LYuXKlZx99tl89atfZfz48Vx22WXs2rXrqD4c3HbbbXg8HiZNmsSwYcPYs2dPt8c9/fTTTJ8+nYsuugiAyy67jOnTp/Pwww/3+bWUUmogWKbj94RKKaVOKrt27aK8vJz169f3WvOtlFLqk6GZa6WUUkoppfqJBtdKKaWUUkr1Ey0LUUoppZRSqp9o5loppZRSSql+osG1UkoppZRS/USDa6WUUkoppfqJBtdKKaWUUkr1Ew2ulVJKKaWU6icaXCullFJKKdVPNLhWSimllFKqn2hwrZRSSimlVD/R4FoppZRSSql+8v8BX7XN8UpRMeEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot t-SNE\n",
    "_ = plot_tsne(re_no_pad1,\n",
    "             lb_no_pad,\n",
    "              0.5,\n",
    "             'Reading Embeddings from WordEmbeddings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Confusion Matrix\n",
    "plot_cm(cm1, 'Confusion Matrix on Training Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9776260852813721"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_acc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9774378585086041"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[At Epoch500]: \t-- LastBatchLoss: 1.1831, LastBatchAcc: 0.6481, TestLoss: 1.1638, TestAcc: 0.6826, TestF1: 0.6706\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "1. Train Model on the whole Dataset\n",
    "2. Output ReadingEmbeddings\n",
    "3. Exclude Paddings for ReadingEmbeddings and Labels\n",
    "4. Apply t-SNE and Plot\n",
    "'''\n",
    "# EEG Only\n",
    "feat_choice = [0, 1, 0] # [WordEmbeddings, EEG, EyeGaze]\n",
    "epochs = 500\n",
    "\n",
    "# Initialize Model\n",
    "mymodel2 = ReadingEmbeddingModel(word_embedding_dim=768, \n",
    "                              eeg_feature_dim = 5460,\n",
    "                              eye_gaze_feature_dim=12, \n",
    "                              projection_dim=128,\n",
    "                              n_trans = 1, \n",
    "                              feat_choice = feat_choice,\n",
    "                              MultiHeaded = True, num_heads = 3)\n",
    "\n",
    "# Set Device\n",
    "mymodel2.to(device)\n",
    "downsampled_data_device = [d.to(device) for d in downsampled_data]\n",
    "\n",
    "# Train\n",
    "(loss_list_real, acc_list_real), (test_loss, test_acc2), cm2, f12, (preds, labels, valid_indices) = train_model(mymodel2, batch_size = 16, epochs = epochs,\n",
    "            train_data = downsampled_data_device, test_data = downsampled_data_device, downsample = False,\n",
    "            metrics = [total_loss, relevance_accuracy], \n",
    "            opt_type = 'Adam',\n",
    "            lr = 0.05, min_lr = -1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get ReadingEmbeddings\n",
    "reading_emb2 = mymodel2.get_reading_embeddings(d_embeddings, d_eeg_features, d_gaze_features).clone().detach()\n",
    "# Remove Paddings\n",
    "re_no_pad2 = torch.cat([sen[:l] for sen, l in zip(reading_emb2, d_sen_len)], dim = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot t-SNE\n",
    "_ = plot_tsne(re_no_pad2,\n",
    "             lb_no_pad,\n",
    "              0.2,\n",
    "             'Reading Embeddings from EEG Features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Confusion Matrix\n",
    "plot_cm(cm2, 'Confusion Matrix on Training Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "1. Train Model on the whole Dataset\n",
    "2. Output ReadingEmbeddings\n",
    "3. Exclude Paddings for ReadingEmbeddings and Labels\n",
    "4. Apply t-SNE and Plot\n",
    "'''\n",
    "# EyeGaze Only\n",
    "feat_choice = [0, 0, 1] # [WordEmbeddings, EEG, EyeGaze]\n",
    "epochs = 500\n",
    "\n",
    "# Initialize Model\n",
    "mymodel3 = ReadingEmbeddingModel(word_embedding_dim=768, \n",
    "                              eeg_feature_dim = 5460,\n",
    "                              eye_gaze_feature_dim=12, \n",
    "                              projection_dim=128,\n",
    "                              n_trans = 1, \n",
    "                              feat_choice = feat_choice,\n",
    "                              MultiHeaded = True, num_heads = 3)\n",
    "\n",
    "# Set Device\n",
    "mymodel3.to(device)\n",
    "downsampled_data_device = [d.to(device) for d in downsampled_data]\n",
    "\n",
    "# Train\n",
    "(loss_list_real, acc_list_real), (test_loss, test_acc3), cm3, f13, (preds, labels, valid_indices) = train_model(mymodel3, batch_size = 16, epochs = epochs,\n",
    "            train_data = downsampled_data_device, test_data = downsampled_data_device, downsample = False,\n",
    "            metrics = [total_loss, relevance_accuracy], \n",
    "            opt_type = 'Adam',\n",
    "            lr = 0.05, min_lr = -1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get ReadingEmbeddings\n",
    "reading_emb3 = mymodel3.get_reading_embeddings(d_embeddings, d_eeg_features, d_gaze_features).clone().detach()\n",
    "# Remove Paddings\n",
    "re_no_pad3 = torch.cat([sen[:l] for sen, l in zip(reading_emb3, d_sen_len)], dim = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot t-SNE\n",
    "_ = plot_tsne(re_no_pad3,\n",
    "             lb_no_pad,\n",
    "              0.2,\n",
    "             'Reading Embeddings from EyeGaze Features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot Confusion Matrix\n",
    "plot_cm(cm3, 'Confusion Matrix on Training Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[At Epoch500]: \t-- LastBatchLoss: 1.4600, LastBatchAcc: 0.6863, TestLoss: 1.2282, TestAcc: 0.6498, TestF1: 0.7172\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "1. Train Model on the whole Dataset\n",
    "2. Output ReadingEmbeddings\n",
    "3. Exclude Paddings for ReadingEmbeddings and Labels\n",
    "4. Apply t-SNE and Plot\n",
    "'''\n",
    "# EEG + EyeGaze\n",
    "feat_choice = [0, 1, 1] # [WordEmbeddings, EEG, EyeGaze]\n",
    "epochs = 500\n",
    "\n",
    "# Initialize Model\n",
    "mymodel4 = ReadingEmbeddingModel(word_embedding_dim=768, \n",
    "                              eeg_feature_dim = 5460,\n",
    "                              eye_gaze_feature_dim=12, \n",
    "                              projection_dim=128,\n",
    "                              n_trans = 1, \n",
    "                              feat_choice = feat_choice,\n",
    "                              MultiHeaded = True, num_heads = 3)\n",
    "\n",
    "# Set Device\n",
    "mymodel4.to(device)\n",
    "downsampled_data_device = [d.to(device) for d in downsampled_data]\n",
    "\n",
    "# Train\n",
    "(loss_list_real, acc_list_real), (test_loss, test_acc4), cm4, f14, (preds, labels, valid_indices) = train_model(mymodel4, batch_size = 16, epochs = epochs,\n",
    "            train_data = downsampled_data_device, test_data = downsampled_data_device, downsample = False,\n",
    "            metrics = [total_loss, relevance_accuracy], \n",
    "            opt_type = 'Adam',\n",
    "            lr = 0.05, min_lr = -1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get ReadingEmbeddings\n",
    "reading_emb4 = mymodel4.get_reading_embeddings(d_embeddings, d_eeg_features, d_gaze_features).clone().detach()\n",
    "# Remove Paddings\n",
    "re_no_pad4 = torch.cat([sen[:l] for sen, l in zip(reading_emb4, d_sen_len)], dim = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot t-SNE\n",
    "_ = plot_tsne(re_no_pad4,\n",
    "             lb_no_pad,\n",
    "              0.2,\n",
    "             'Reading Embeddings from both EEG and EyeGaze Features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Confusion Matrix\n",
    "plot_cm(cm4, 'Confusion Matrix on Training Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "----\n",
    "### Visualization Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Confusion Matrix of the Model Predictions\n",
    "'''\n",
    "def plot_confusionMatrix(model, train_data, senIdx = -1):\n",
    "    '''\n",
    "    Plot the Confusion Matrix for a Specific Sentence / the Whole Data\n",
    "    - model:\n",
    "    - train_data:\n",
    "        - word_embeddings : [n_sentences, n_words, embedding_dim]\n",
    "        - eeg_features    : [num_sentences, max_num_words, eeg_feature_size = 5460]\n",
    "        - gaze_features   : [n_sentences, n_words, feature_dim = 12]\n",
    "        - true_labels: [n_sentences, n_words] \n",
    "        - sen_lengths: [n_sentences]\n",
    "    - index: index of the sentence, -1 for the whole data\n",
    "    '''\n",
    "    embeddings, eeg_features, gaze_features, labels, sen_len = train_data\n",
    "    preds = model(embeddings, eeg_features, gaze_features).clone().detach()\n",
    "    true_labels, sen_len = train_data[-2:]\n",
    "    \n",
    "    if -1 == senIdx:\n",
    "        max_num_words = sen_len.max()\n",
    "        n_sen = len(sen_len)\n",
    "        \n",
    "        # Flatten predicitons and labels, excluding padding\n",
    "        valid_indices = torch.arange(max_num_words).expand(n_sen, max_num_words) < sen_len.unsqueeze(1)\n",
    "        masked_preds = (preds[valid_indices] >= 0.5).int()\n",
    "        masked_labels = true_labels[valid_indices]\n",
    "\n",
    "        # Confusion Matrix\n",
    "        cm = confusion_matrix(masked_labels.numpy(), masked_preds.numpy())\n",
    "        title_str = f'Confusion Matrix for All Sentences'\n",
    "    else:     \n",
    "        # Get the Predictions and Labels at the Index\n",
    "        senLenAtIdx = sen_len[senIdx]\n",
    "        predAtIdx = (preds[senIdx, :senLenAtIdx] >= 0.5).int()\n",
    "        labelAtIdx = true_labels[senIdx, :senLenAtIdx]\n",
    "        \n",
    "        # Confusion matrix\n",
    "        cm = confusion_matrix(labelAtIdx, predAtIdx)\n",
    "        title_str = f'Confusion Matrix for Sentence [{senIdx}]'\n",
    "        \n",
    "    # Plotting\n",
    "    fig, ax = plt.subplots()\n",
    "    sns.heatmap(cm, annot = True, ax = ax, fmt = 'g', cmap = 'Blues')\n",
    "    \n",
    "    ax.set_xlabel('Predicted Labels')\n",
    "    ax.set_ylabel('True Labels')\n",
    "    ax.set_title(title_str)\n",
    "    ax.xaxis.set_ticklabels(['Low', 'High'])\n",
    "    ax.yaxis.set_ticklabels(['Low', 'High'])\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for all sentences\n",
    "plot_confusionMatrix(mymodel, original_data, senIdx = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "F1 Score = TP / (TP + 0.5 * (FP + FN))\n",
    "'''\n",
    "def f1_score_REC(model, train_data, senIdx = -1):\n",
    "    '''\n",
    "    Calculate the F1 Score for a Specific Sentence / the Whole Data\n",
    "    - model:\n",
    "    - train_data:\n",
    "        - word_embeddings : [n_sentences, n_words, embedding_dim]\n",
    "        - eeg_features    : [num_sentences, max_num_words, eeg_feature_size = 5460]\n",
    "        - gaze_features   : [n_sentences, n_words, feature_dim = 12]\n",
    "        - true_labels: [n_sentences, n_words] \n",
    "        - sen_lengths: [n_sentences]\n",
    "    - index: index of the sentence, -1 for the whole data\n",
    "    '''\n",
    "    embeddings, eeg_features, gaze_features, labels, sen_len = train_data\n",
    "    preds = model(embeddings, eeg_features, gaze_features).clone().detach()\n",
    "    true_labels, sen_len = train_data[-2:]\n",
    "    \n",
    "    if -1 == senIdx:\n",
    "        max_num_words = sen_len.max()\n",
    "        n_sen = len(sen_len)\n",
    "        \n",
    "        # Flatten predicitons and labels, excluding padding\n",
    "        valid_indices = torch.arange(max_num_words).expand(n_sen, max_num_words) < sen_len.unsqueeze(1)\n",
    "        masked_preds = (preds[valid_indices] >= 0.5).int().flatten()\n",
    "        masked_labels = true_labels[valid_indices].flatten()\n",
    "\n",
    "    else:     \n",
    "        # Get the Predictions and Labels at the Index\n",
    "        senLenAtIdx = sen_len[senIdx]\n",
    "        masked_preds = (preds[senIdx, :senLenAtIdx] >= 0.5).int().flatten()\n",
    "        masked_labels = true_labels[senIdx, :senLenAtIdx].flatten()\n",
    "        \n",
    "    # F1 Score\n",
    "    return f1_score(masked_labels, masked_preds)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6001460209296666"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score_REC(mymodel, original_data, senIdx = -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Scratch Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Initializing the Model\n",
    "'''\n",
    "\n",
    "\n",
    "mymodel = ReadingEmbeddingModel(word_embedding_dim=768, \n",
    "                              eeg_feature_dim = 5460,\n",
    "                              eye_gaze_feature_dim=12, \n",
    "                              projection_dim=128,\n",
    "                              n_trans = 1, \n",
    "                              feat_choice = [0, 1, 1],\n",
    "                              MultiHeaded = True, num_heads = 3)\n",
    "\n",
    "# Get Reading Embedding\n",
    "#reading_embeddings = mymodel.get_reading_embeddings(embeddings, gaze_features).clone().detach()\n",
    "# Get Classifications\n",
    "#pred_labels = mymodel(embeddings, gaze_features).clone().detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[At Epoch200]: \t-- LastBatchLoss: 0.8390, LastBatchAcc: 0.4902, TestLoss: 0.9310, TestAcc: 0.3039, TestF1: 0.1671\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Train Mode 1: ### Preferred but DO NOT USE\n",
    "The same downsampled data for each epoch \n",
    "'''\n",
    "mymodel = ReadingEmbeddingModel(word_embedding_dim=768, \n",
    "                              eeg_feature_dim = 5460,\n",
    "                              eye_gaze_feature_dim=12, \n",
    "                              projection_dim=128,\n",
    "                              n_trans = 1, \n",
    "                              feat_choice = [0, 1, 1],\n",
    "                              MultiHeaded = True, num_heads = 3)\n",
    "\n",
    "# epochs > 150\n",
    "(loss_list_real, acc_list_real), (test_loss, test_acc), cm1, f11, (preds, labels, valid_indices) = train_model(mymodel, batch_size = 16, epochs = 200,\n",
    "            train_data = downsampled_data, test_data = original_data, downsample = False,\n",
    "            metrics = [masked_bce_loss, relevance_accuracy], \n",
    "            opt_type = 'Adam',\n",
    "            lr = 0.05, min_lr = -1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Train Mode 2: ### DO NOT USE\n",
    "Randomly Downsample the training data for each epoch - High stochasticity\n",
    "'''\n",
    "mymodel = ReadingEmbeddingModel(word_embedding_dim=768, \n",
    "                              eeg_feature_dim = 5460,\n",
    "                              eye_gaze_feature_dim=12, \n",
    "                              projection_dim=128,\n",
    "                              n_trans = 1, \n",
    "                              feat_choice = [0, 1, 1],\n",
    "                              MultiHeaded = True, num_heads = 3)\n",
    "\n",
    "# epochs > 150\n",
    "(loss_list_real, acc_list_real), (test_loss, test_acc), cm2, f12, (preds, labels, valid_indices)  = train_model(mymodel, batch_size = 16, epochs = 200,\n",
    "            train_data = original_data, test_data = original_data, downsample = True,\n",
    "            metrics = [masked_bce_loss, relevance_accuracy], \n",
    "            opt_type = 'Adam',\n",
    "            lr = 0.05, min_lr = 1e-5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted Labels (for visualizations)\n",
    "preds = mymodel(embeddings, eeg_features, gaze_features).clone().detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Reading Embedding\n",
    "REmbeddings = mymodel.get_reading_embeddings(embeddings, eeg_features, gaze_features).clone().detach()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}