ConvLstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afd90b4d-11df-4a05-884b-c88629da1986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary: ['Baselayer_Preparation', 'Concreting', 'Mortar_Preparation', 'Plaster', 'Wall_Construction']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import ConvLSTM2D, MaxPooling3D, TimeDistributed, Dropout, Flatten, Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "# Constants\n",
    "IMG_SIZE = 112  # Reduced image size to lessen memory usage\n",
    "SEQUENCE_LENGTH = 15  # Increased sequence length to 15\n",
    "BATCH_SIZE = 2  # Reduced batch size to prevent memory overload\n",
    "EPOCHS = 50  # Number of epochs for training\n",
    "NUM_CHANNELS = 3  # Number of color channels in each frame\n",
    "seed_constant = 27  # Seed for reproducibility\n",
    "\n",
    "# Set seeds for reproducibility\n",
    "np.random.seed(seed_constant)\n",
    "tf.random.set_seed(seed_constant)\n",
    "\n",
    "# Function to load video frames\n",
    "def load_video(path, max_frames=SEQUENCE_LENGTH, resize=(IMG_SIZE, IMG_SIZE)):\n",
    "    if not os.path.exists(path):\n",
    "        print(f\"Video file {path} does not exist.\")\n",
    "        return np.array([])\n",
    "    \n",
    "    cap = cv2.VideoCapture(path)\n",
    "    if not cap.isOpened():\n",
    "        print(f\"Error: Cannot open video file {path}\")\n",
    "        return np.array([])\n",
    "\n",
    "    frames = []\n",
    "    try:\n",
    "        while True:\n",
    "            ret, frame = cap.read()\n",
    "            if not ret:\n",
    "                break\n",
    "            frame = crop_center_square(frame)\n",
    "            frame = cv2.resize(frame, resize)\n",
    "            frame = frame[:, :, [2, 1, 0]]  # Convert BGR to RGB\n",
    "            frames.append(frame)\n",
    "            if len(frames) == max_frames:\n",
    "                break\n",
    "    finally:\n",
    "        cap.release()\n",
    "    \n",
    "    # Padding with zeros if less than max_frames\n",
    "    while len(frames) < max_frames:\n",
    "        frames.append(np.zeros((IMG_SIZE, IMG_SIZE, NUM_CHANNELS), dtype=np.uint8))\n",
    "    \n",
    "    return np.array(frames)\n",
    "\n",
    "# Function to crop the center square of the frame\n",
    "def crop_center_square(frame):\n",
    "    y, x = frame.shape[0:2]\n",
    "    min_dim = min(y, x)\n",
    "    start_x = (x // 2) - (min_dim // 2)\n",
    "    start_y = (y // 2) - (min_dim // 2)\n",
    "    return frame[start_y : start_y + min_dim, start_x : start_x + min_dim]\n",
    "\n",
    "# Load the dataset from a CSV file\n",
    "dataset_path = 'Data_logdf.csv'\n",
    "dataset = pd.read_csv(dataset_path)\n",
    "\n",
    "# Define the list of classes (unique tags)\n",
    "CLASSES_LIST = np.unique(dataset[\"tag\"])\n",
    "\n",
    "# Encode labels\n",
    "label_processor = tf.keras.layers.StringLookup(num_oov_indices=0, vocabulary=CLASSES_LIST)\n",
    "print(\"Vocabulary:\", label_processor.get_vocabulary())\n",
    "labels = dataset[\"tag\"].values\n",
    "encoded_labels = label_processor(labels).numpy()\n",
    "\n",
    "# Prepare video data\n",
    "def prepare_videos(df, root_dir):\n",
    "    num_samples = len(df)\n",
    "    video_paths = df[\"video_name\"].values.tolist()\n",
    "    labels = df[\"tag\"].values\n",
    "    encoded_labels = label_processor(labels).numpy()\n",
    "\n",
    "    frame_data = np.zeros((num_samples, SEQUENCE_LENGTH, IMG_SIZE, IMG_SIZE, NUM_CHANNELS), dtype=\"float32\")\n",
    "\n",
    "    for idx, path in enumerate(video_paths):\n",
    "        full_path = os.path.join(root_dir, path)\n",
    "        frames = load_video(full_path)\n",
    "        if frames.shape[0] == 0:\n",
    "            continue\n",
    "        print(f\"Loaded {frames.shape[0]} frames for video {full_path}\")\n",
    "        frame_data[idx] = frames\n",
    "\n",
    "    return frame_data, encoded_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "756a635b-57c2-4b1b-9e64-458066d32d54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Train data shape: (2804, 15, 112, 112, 3)\n",
      "Train labels shape: (2804,)\n",
      "Test data shape: (702, 15, 112, 112, 3)\n",
      "Test labels shape: (702,)\n"
     ]
    }
   ],
   "source": [
    "# Prepare the dataset\n",
    "frame_data, labels = prepare_videos(dataset, \"\")\n",
    "\n",
    "# Split data into training and testing sets\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(\n",
    "    frame_data, labels, test_size=0.2, random_state=seed_constant\n",
    ")\n",
    "\n",
    "# Print the shapes of the data for verification\n",
    "print(f\"Train data shape: {train_data.shape}\")\n",
    "print(f\"Train labels shape: {train_labels.shape}\")\n",
    "print(f\"Test data shape: {test_data.shape}\")\n",
    "print(f\"Test labels shape: {test_labels.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d002a0ff-bc36-4c0e-bb80-7270053cc5cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv_lstm2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ConvLSTM2D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span>,   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
       "│                                 │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                     │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling3d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ time_distributed                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_lstm2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ConvLSTM2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">53</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">53</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,488</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling3d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ time_distributed_1              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_lstm2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ConvLSTM2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">11,144</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling3d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>) │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ time_distributed_2              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>) │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_lstm2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ConvLSTM2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">17,344</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1936</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,685</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv_lstm2d (\u001b[38;5;33mConvLSTM2D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m110\u001b[0m, \u001b[38;5;34m110\u001b[0m,   │         \u001b[38;5;34m1,024\u001b[0m │\n",
       "│                                 │ \u001b[38;5;34m4\u001b[0m)                     │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling3d (\u001b[38;5;33mMaxPooling3D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m4\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ time_distributed                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m4\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_lstm2d_1 (\u001b[38;5;33mConvLSTM2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m53\u001b[0m, \u001b[38;5;34m53\u001b[0m, \u001b[38;5;34m8\u001b[0m)  │         \u001b[38;5;34m3,488\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling3d_1 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m8\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ time_distributed_1              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m8\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_lstm2d_2 (\u001b[38;5;33mConvLSTM2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m14\u001b[0m) │        \u001b[38;5;34m11,144\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling3d_2 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m14\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ time_distributed_2              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m14\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_lstm2d_3 (\u001b[38;5;33mConvLSTM2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │        \u001b[38;5;34m17,344\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1936\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │         \u001b[38;5;34m9,685\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">42,685</span> (166.74 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m42,685\u001b[0m (166.74 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">42,685</span> (166.74 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m42,685\u001b[0m (166.74 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-17 17:24:19.581897: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 6331207680 exceeds 10% of free system memory.\n",
      "2024-08-17 17:24:23.394858: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 6331207680 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723915470.564751    2076 service.cc:145] XLA service 0x860d080 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1723915470.564915    2076 service.cc:153]   StreamExecutor device (0): NVIDIA L4, Compute Capability 8.9\n",
      "2024-08-17 17:24:30.749451: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-08-17 17:24:31.462317: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8900\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m   4/1402\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m33s\u001b[0m 24ms/step - accuracy: 0.0000e+00 - loss: 1.6439    "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723915527.127276    2076 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'input_slice_fusion_2', 24 bytes spill stores, 4 bytes spill loads\n",
      "\n",
      "I0000 00:00:1723915527.177616    2076 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 27ms/step - accuracy: 0.2779 - loss: 1.5443 - val_accuracy: 0.4829 - val_loss: 1.2991 - learning_rate: 1.0000e-04\n",
      "Epoch 2/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.5126 - loss: 1.2163 - val_accuracy: 0.5370 - val_loss: 1.1360 - learning_rate: 1.0000e-04\n",
      "Epoch 3/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.6006 - loss: 1.0547 - val_accuracy: 0.5812 - val_loss: 1.0406 - learning_rate: 1.0000e-04\n",
      "Epoch 4/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.6361 - loss: 0.9583 - val_accuracy: 0.6125 - val_loss: 0.9811 - learning_rate: 1.0000e-04\n",
      "Epoch 5/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.6676 - loss: 0.8895 - val_accuracy: 0.6368 - val_loss: 0.9349 - learning_rate: 1.0000e-04\n",
      "Epoch 6/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.7028 - loss: 0.8181 - val_accuracy: 0.6638 - val_loss: 0.9055 - learning_rate: 1.0000e-04\n",
      "Epoch 7/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.7269 - loss: 0.7580 - val_accuracy: 0.6695 - val_loss: 0.8838 - learning_rate: 1.0000e-04\n",
      "Epoch 8/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.7457 - loss: 0.7082 - val_accuracy: 0.6766 - val_loss: 0.8708 - learning_rate: 1.0000e-04\n",
      "Epoch 9/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.7617 - loss: 0.6677 - val_accuracy: 0.6795 - val_loss: 0.8571 - learning_rate: 1.0000e-04\n",
      "Epoch 10/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 24ms/step - accuracy: 0.7690 - loss: 0.6308 - val_accuracy: 0.6866 - val_loss: 0.8564 - learning_rate: 1.0000e-04\n",
      "Epoch 11/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.7775 - loss: 0.6065 - val_accuracy: 0.6994 - val_loss: 0.8528 - learning_rate: 1.0000e-04\n",
      "Epoch 12/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.7940 - loss: 0.5625 - val_accuracy: 0.7023 - val_loss: 0.8546 - learning_rate: 1.0000e-04\n",
      "Epoch 13/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8115 - loss: 0.5426 - val_accuracy: 0.6866 - val_loss: 0.8708 - learning_rate: 1.0000e-04\n",
      "Epoch 14/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8142 - loss: 0.5120 - val_accuracy: 0.6937 - val_loss: 0.8692 - learning_rate: 1.0000e-04\n",
      "Epoch 15/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8298 - loss: 0.4840 - val_accuracy: 0.7051 - val_loss: 0.8469 - learning_rate: 1.0000e-04\n",
      "Epoch 16/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8489 - loss: 0.4468 - val_accuracy: 0.6937 - val_loss: 0.8818 - learning_rate: 1.0000e-04\n",
      "Epoch 17/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8463 - loss: 0.4325 - val_accuracy: 0.6909 - val_loss: 0.8957 - learning_rate: 1.0000e-04\n",
      "Epoch 18/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8664 - loss: 0.4089 - val_accuracy: 0.7009 - val_loss: 0.8835 - learning_rate: 1.0000e-04\n",
      "Epoch 19/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8683 - loss: 0.3846 - val_accuracy: 0.6966 - val_loss: 0.9110 - learning_rate: 1.0000e-04\n",
      "Epoch 20/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8835 - loss: 0.3577 - val_accuracy: 0.6952 - val_loss: 0.9225 - learning_rate: 1.0000e-04\n",
      "Epoch 21/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8901 - loss: 0.3459 - val_accuracy: 0.7123 - val_loss: 0.8507 - learning_rate: 1.0000e-05\n",
      "Epoch 22/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.8970 - loss: 0.3171 - val_accuracy: 0.7123 - val_loss: 0.8483 - learning_rate: 1.0000e-05\n",
      "Epoch 23/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9038 - loss: 0.3177 - val_accuracy: 0.7123 - val_loss: 0.8460 - learning_rate: 1.0000e-05\n",
      "Epoch 24/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9038 - loss: 0.3124 - val_accuracy: 0.7151 - val_loss: 0.8425 - learning_rate: 1.0000e-05\n",
      "Epoch 25/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9060 - loss: 0.3035 - val_accuracy: 0.7137 - val_loss: 0.8526 - learning_rate: 1.0000e-05\n",
      "Epoch 26/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9142 - loss: 0.2959 - val_accuracy: 0.7151 - val_loss: 0.8440 - learning_rate: 1.0000e-05\n",
      "Epoch 27/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9054 - loss: 0.2951 - val_accuracy: 0.7123 - val_loss: 0.8470 - learning_rate: 1.0000e-05\n",
      "Epoch 28/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9076 - loss: 0.2947 - val_accuracy: 0.7179 - val_loss: 0.8459 - learning_rate: 1.0000e-05\n",
      "Epoch 29/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9144 - loss: 0.2866 - val_accuracy: 0.7194 - val_loss: 0.8488 - learning_rate: 1.0000e-05\n",
      "Epoch 30/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9147 - loss: 0.2810 - val_accuracy: 0.7179 - val_loss: 0.8473 - learning_rate: 1.0000e-06\n",
      "Epoch 31/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9230 - loss: 0.2753 - val_accuracy: 0.7179 - val_loss: 0.8467 - learning_rate: 1.0000e-06\n",
      "Epoch 32/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9115 - loss: 0.2856 - val_accuracy: 0.7151 - val_loss: 0.8467 - learning_rate: 1.0000e-06\n",
      "Epoch 33/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9213 - loss: 0.2766 - val_accuracy: 0.7151 - val_loss: 0.8484 - learning_rate: 1.0000e-06\n",
      "Epoch 34/50\n",
      "\u001b[1m1402/1402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 24ms/step - accuracy: 0.9206 - loss: 0.2766 - val_accuracy: 0.7151 - val_loss: 0.8475 - learning_rate: 1.0000e-06\n",
      "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 100ms/step - accuracy: 0.7037 - loss: 0.8458\n",
      "Test Accuracy: 71.51%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 93ms/step\n",
      "Classification Report:\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "Baselayer_Preparation       0.59      0.59      0.59       144\n",
      "           Concreting       0.70      0.80      0.74       143\n",
      "   Mortar_Preparation       0.79      0.61      0.69       153\n",
      "              Plaster       0.82      0.94      0.88       150\n",
      "    Wall_Construction       0.65      0.61      0.63       112\n",
      "\n",
      "             accuracy                           0.72       702\n",
      "            macro avg       0.71      0.71      0.71       702\n",
      "         weighted avg       0.72      0.72      0.71       702\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import ConvLSTM2D, MaxPooling3D, TimeDistributed, Dropout, Flatten, Dense\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "# Define the ConvLSTM model with reduced complexity\n",
    "def create_convlstm_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    # Model Architecture\n",
    "    model.add(ConvLSTM2D(filters=4, kernel_size=(3, 3), activation='tanh', data_format=\"channels_last\",\n",
    "                         recurrent_dropout=0.2, return_sequences=True, input_shape=(SEQUENCE_LENGTH, IMG_SIZE, IMG_SIZE, 3)))\n",
    "    model.add(MaxPooling3D(pool_size=(1, 2, 2), padding='same'))\n",
    "    model.add(TimeDistributed(Dropout(0.2)))\n",
    "\n",
    "    model.add(ConvLSTM2D(filters=8, kernel_size=(3, 3), activation='tanh', data_format=\"channels_last\",\n",
    "                         recurrent_dropout=0.2, return_sequences=True))\n",
    "    model.add(MaxPooling3D(pool_size=(1, 2, 2), padding='same'))\n",
    "    model.add(TimeDistributed(Dropout(0.2)))\n",
    "\n",
    "    model.add(ConvLSTM2D(filters=14, kernel_size=(3, 3), activation='tanh', data_format=\"channels_last\",\n",
    "                         recurrent_dropout=0.2, return_sequences=True))\n",
    "    model.add(MaxPooling3D(pool_size=(1, 2, 2), padding='same'))\n",
    "    model.add(TimeDistributed(Dropout(0.2)))\n",
    "\n",
    "    model.add(ConvLSTM2D(filters=16, kernel_size=(3, 3), activation='tanh', data_format=\"channels_last\",\n",
    "                         recurrent_dropout=0.2, return_sequences=False))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(len(CLASSES_LIST), activation=\"softmax\"))\n",
    "\n",
    "    model.summary()\n",
    "    return model\n",
    "\n",
    "# Instantiate the ConvLSTM model\n",
    "convlstm_model = create_convlstm_model()\n",
    "\n",
    "# Compile the model\n",
    "adam = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
    "convlstm_model.compile(loss='sparse_categorical_crossentropy', optimizer=adam, metrics=[\"accuracy\"])\n",
    "\n",
    "# Define callbacks\n",
    "callbacks = [\n",
    "    tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True),\n",
    "    tf.keras.callbacks.ReduceLROnPlateau(patience=5),\n",
    "    tf.keras.callbacks.ModelCheckpoint('best_convlstm_model.keras', save_best_only=True)\n",
    "]\n",
    "\n",
    "# Train the model\n",
    "convlstm_model_training_history = convlstm_model.fit(\n",
    "    x=train_data,\n",
    "    y=train_labels,\n",
    "    epochs=EPOCHS,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    validation_data=(test_data, test_labels),\n",
    "    callbacks=callbacks\n",
    ")\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = convlstm_model.evaluate(test_data, test_labels)\n",
    "print(f\"Test Accuracy: {test_acc * 100:.2f}%\")\n",
    "\n",
    "# Save the final model\n",
    "convlstm_model.save('final_convlstm_model.keras')\n",
    "\n",
    "# Plot training & validation accuracy values\n",
    "plt.plot(convlstm_model_training_history.history['accuracy'])\n",
    "plt.plot(convlstm_model_training_history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(convlstm_model_training_history.history['loss'])\n",
    "plt.plot(convlstm_model_training_history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Generate predicted labels for the test data\n",
    "test_pred_labels = convlstm_model.predict(test_data)\n",
    "test_pred_labels = test_pred_labels.argmax(axis=1)\n",
    "\n",
    "# Generate classification report\n",
    "class_report = classification_report(test_labels, test_pred_labels, target_names=CLASSES_LIST)\n",
    "print(\"Classification Report:\\n\", class_report)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1ef9daa-138e-4cb9-b369-fb60842fc5cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5eTk//DchIUGvvPKKBg4cqPLlEw8X37Jli/z9/e0FhSQ1atRIbm5u2rp1q1PvzTNGGQYAAACkF4vF2CM4OFh+fn4Oj+Dg4GR9Gx9//LGyZMmiXr163fV4aGio8uTJ49CWJUsWBQYGKjQ01Kl7GeupCAwM1JEjR5QrVy4FBATIYrHc89ywsLB0TAYAAACYMXjwYPXr18+hLTm9FDt27NCECRO0c+fO+77PTi3Giopx48YpR44c9q/T45sFAAAAXFlyhzr92++//66LFy+qcOHC9jar1ar+/ftr/PjxOnnypPLly6eLFy86vC4+Pl5hYWHKly+fU/czVlR07NjR/vWrr75qKgYAAADgKINM1L6fV155RY0aNXJoa9KkiV555RV16tRJklSzZk2Fh4drx44dqlq1qiRp3bp1SkhI0GOPPebU/Vxiora7u7vOnz+faEzXlStXlCdPHlmtVkPJAAAAANcUERGhf/75x/78xIkT2r17twIDA1W4cGHlzJnT4XwPDw/ly5dPpUuXlnRrtdWmTZvqtdde07Rp0xQXF6cePXqoXbt2Tq38JLlIUXGvTb1jY2Pl6emZzmkAAACQqWWQYfnbt29XgwYN7M9vz8Xo2LGj5s2bl6RrLFy4UD169FDDhg3tm99NnDjR6SxGi4rbgS0Wi2bNmuWwc5/VatXGjRtVpkwZU/EAAAAAl1W/fv17fjh/NydPnkzUFhgY6PRGd3djtKgYN26cpFs9FdOmTZO7u7v9mKenp4oUKaJp06aZigcAAAAgCYwWFSdOnJAkNWjQQEuXLlVAQIDJOAAAAMB/YqJ2enOJORXr1683HQEAAABAMrlEUSFJZ86c0Y8//qiQkBDdvHnT4djYsWMNpQIAAECmk0EmarsSlygq1q5dq5YtW6pYsWI6dOiQKlSooJMnT8pms+mRRx4xHQ8AAADAfbjEgLHBgwdrwIAB2rdvn7JmzarvvvtOp0+fVr169fTcc8+ZjgcAAIDMxOJm7pFBuUTygwcPqkOHDpKkLFmyKDo6WtmzZ9fIkSP18ccfG04HAAAA4H5coqjw8fGxz6PInz+/jh07Zj92+fJlU7EAAAAAJIFLzKmoUaOGNm3apLJly6pZs2bq37+/9u3bp6VLl6pGjRqm4wEAACAzYaK201yiqBg7dqwiIiIkSSNGjFBERIS+/vprlSxZkpWfAAAAABdnvKiwWq06c+aMKlWqJOnWUCh20QYAAIAxGXjCtCnGf2Lu7u568skndfXqVdNRAAAAACSD8aJCkipUqKDjx4+bjgEAAAAgGVyiqBg1apQGDBig5cuX6/z587p+/brDAwAAAEg3Fou5RwZlfE6FJDVr1kyS1LJlS1nu+GHabDZZLBZZrVZT0QAAAAA8gEsUFevXrzcdAQAAALiFidpOc4miol69eqYjAAAAAEgmlygqbouKilJISIh9d+3bbi83CwAAAKS5DDy3wRSXKCouXbqkTp066ddff73rceZUAAAAAK7LJQaM9enTR+Hh4dq6dau8vb21YsUKzZ8/XyVLltSPP/5oOh4AAACA+3CJnop169bphx9+ULVq1eTm5qagoCA1btxYvr6+Cg4OVvPmzU1HBAAAQGbBRG2nucRPLDIyUnny5JEkBQQE6NKlS5KkihUraufOnSajAQAAAHgAlygqSpcurcOHD0uSKleurOnTp+vs2bOaNm2a8ufPbzgdAAAAMhWLm7lHBuUSw5969+6t8+fPS5KGDRumpk2bauHChfL09NS8efPMhgMAAABwXy5RVLz88sv2r6tWrapTp07p0KFDKly4sHLlymUwGQAAAIAHcYmi4k42m03e3t565JFHTEcBAABAZuTGPhXOcpmBW7Nnz1aFChWUNWtWZc2aVRUqVNCsWbNMxwIAAADwAC7RUzF06FCNHTtWPXv2VM2aNSVJW7ZsUd++fRUSEqKRI0caTggAAIBMIwNPmDbFJYqKqVOnaubMmXrxxRftbS1btlSlSpXUs2dPigoAAADAhblEUREXF6dq1aolaq9atari4+MNJAIAAECmZWFOhbNcom/nlVde0dSpUxO1z5gxQ+3btzeQCAAAAEBSuURPhXRrovaqVatUo0YNSdLWrVsVEhKiDh06qF+/fvbzxo4dayoiAAAAgLtwiaJi//799iVkjx07JknKlSuXcuXKpf3799vPs9AVBQAAgLTGRG2nuURRsX79etMRAAAAACSTS5Vh//zzj1auXKno6GhJtzbCAwAAANKVxWLukUG5RFFx5coVNWzYUKVKlVKzZs10/vx5SVKXLl3Uv39/w+kAAAAA3I9LFBV9+/aVh4eHQkJClC1bNnv7Cy+8oBUrVhhMBgAAAOBBXGJOxapVq7Ry5Uo99NBDDu0lS5bUqVOnDKUCAABApsREbae5xE8sMjLSoYfitrCwMHl5eRlIBAAAACCpXKKoqFu3rhYsWGB/brFYlJCQoDFjxqhBgwYGkwEAACDTYaK201xi+NOYMWPUsGFDbd++XTdv3tSgQYN04MABhYWFafPmzabjAQAAALgPl+ipqFChgo4cOaI6deqoVatWioyMVJs2bbRr1y4VL17cdDwAAAAA92G8pyIuLk5NmzbVtGnT9N5775mOAwAAgMyOidpOM/4T8/Dw0N69e03HAAAAAJBMxosKSXr55Zc1e/Zs0zEAAAAAJmong/HhT5IUHx+vOXPmaM2aNapatap8fHwcjo8dO9ZQMtfjZpHaVyuoBiVzKSCbh8Iib2rN4cv6auc5+zl9GxRV49K5HV63PSRcQ385kt5xkULfLZqjP39fp7MhJ+Xp5aUy5Svrldd6qWDhIvZzhvR9TQf27HB43ZMt2urNvgwnzOisVqtmT5+ilb8s15Url5Urdx41b9FKr3Z9U5YM/A8P7i0qMlIzp07UxvVrdfVqmEqVLqveA95R2fIVTUdDCu3btUPfLJqno4cPKuzyJQ0LHqda9Z6wH29Sq/JdX9e1e1891/7VdEoJJJ9LFBX79+/XI488Ikk6csTxjS//cDp6tkp+NSuXR2PXH9epq9EqmdtHfesXU+RNq37cf8F+3vaQcI1bf8L+PM6aYCIuUujAnh16qtXzKlG6vKwJVi2cNVkjBr2liXO/U1Zvb/t5jZs/o3adutmfe3llNREXqezLebP1/bdf6/0RH6lY8RI6+Pd+fTT8fflkz6HnX3zZdDykgdEfDNXxY0c15IPRypU7t1b+slx9unXVl9/+qNx58pqOhxSIiYlWsRKl1eTp1ho5uF+i41/9tNbh+bYtmzQueLjq1G+UXhFxJ+ZUOM0lior169ebjpBhlMuXQ3+eDNe2kGuSpIs3bqp+iWsqlcexdyfOatPV6DgTEZGKhn48xeF5z7dHqFObhjp25G+Vr1zV3u7plVUBgbnSOx7S2L49u1W33hOqXbeeJCl/gYJas+IX/b1/n+FkSAuxMTHasG61gj+bpCqPVJMkdXmjuzZv/E3ff7tYr7/V23BCpET1mnVUvWadex4PzOn4d/iW339T5UeqK3/Bh9I4GZA6jJdhX3/9tdq3b6/nnntO06ZNMx3H5f0dekNVHvJVQb9bn0QXzemtcvlyaPvpaw7nVSyQQ4s6PqwZ7Sqqe90g5fByifoRKRQVeUOSlN3Xz6H997W/qmPrJ9S783P6cuYkxcZEm4iHVFaxchVt/+tPhZw6KUk6euSQ9uzepZq165oNhjRhtVpltVrl6eXl0O7l5aW9u3cZSgUTroZd0V9//K4mLZ4xHQVIMqPvNKdOnaru3burZMmS8vb21tKlS3Xs2DF98sknSb5GbGysYmNjHdqscTfl7uGZ2nFdwje7ziubp7umt6uohASb3NwsWvDXGf129Ir9nB0h1/TH8au6cCNW+X291PHRQhrZ3Ef9v/9bCTaD4ZEiCQkJmjPlU5WpUEVBRUvY2+s2bKrcefMrMGdunTx+VF/MmKizp0/q7ZGfGUyL1PBKp66KjIzQi22elpu7uxKsVr3RvbeaNHvadDSkgWw+PqpQqYrmzZqmIkWLKSAwp9as/EUH9u1RwUKFTcdDOlr9y4/yzpZNdeo1NB0l82L4vdOMFhWTJ0/WsGHDNGzYMEnSl19+qTfeeMOpoiI4OFgjRoxwaCvRvKtKPv1aqmZ1FXWLB6pByZwas+aYQq5Gq1jObHq9dpCuRMZp7ZHLkqSNx8Ls558Mi9aJK9Ga076yKhbw1Z6z101FRwrNnDBaISeO6cOJcxzan3y6rf3roGIlFRiYS8MGvKnQs6eVr2Ch9I6JVLR29Qqt+vVnDf9ojIoVK6Ejhw9pwme3xto3a9HadDykgSEjgxU8cohaN20gd3d3lSpTVo2aNNPhg3+bjoZ0tHL5Mj3RpFmiXivAlRkd/nT8+HF17NjR/vyll15SfHy8zp8/n+RrDB48WNeuXXN4FGvS8cEvzKC61Cykb3ad18ZjYToZFq11R69o2d5QPf9w/nu+JvRGrK5Fx6mAL385ZVQzJ4zW9j9/18ixM5Qr9/0na5Yse2uVmPPnTqdHNKShKeM/0yuvdlHjJs1UvGQpPfV0S73QvoMWzJ1lOhrSSMFChTV55nyt3rRN3/28VjMXfK34+HgVYFx9prFv906dCTmppi3amI6SuVnczD0yKKM9FbGxsQ7Lx7q5ucnT01PR0UkfD+7l5SWvf1Xy/9WhT5LklcU90RCmBJtNbvfppsvp46EcWbMoLIqJ2xmNzWbTrIkfa+um9Ro5bqby5i/4wNecOHZYkpi4/R8QExMti5vjPzDubu6yJbCa23+dt3c2eXtn0/Xr1/TXls3q1jvxakH4b1q5/HuVLFNOxUuWNh0FcIrx2btDhgxRtmzZ7M9v3rypDz/8UH5+/5uIyj4V/7P11FW1e6SALkXE6tTVaBXP6aNnKuXTqkOXJElZs7jppWoFtfl4mK5Gxym/b1Z1rlFI56/Fase/JnPD9c2YMFq/r/1Vg0eNk3e2bLoadmuIWzaf7PLyyqrQs6e1cd0KVX2stnL4+uvksaOa+/lnKlfpERUpXspweqRUncfra/7sGcqbL7+KFS+hI4cOavGX89W8FZM3/6u2/rFJNtlUOKiozp4O0ZQJn6pwkaJqzoTdDC86KkrnzoTYn4eeP6tjRw4ph6+f8uS7NdogMjJCG9et0us9+5uKCSSb0aLi8ccf1+HDhx3aatWqpePHj9ufs0+Fo2mbTumV6g+pe90i8vO+tfndr39f1KIdtza/S7DZVDRnNjUqnUs+nu4Ki4rTztPX9MW2M4pnlnaGs/LHbyTd2uDuTj0GDdcTTVsqi4eH9u7YquXfLVJsdLRy5cmrmo8/oWdf7moiLlJZ30HvaebnE/Vp8Ae6ejVMuXLnUau2z6nz690e/GJkSBEREZo+ebwuXQyVr6+f6jVsrNff6q0sHh6moyGFjhw6oEE9/vd38/SJn0qSGjdrqQHvfyBJ2rB6hWSTGjR+ykhG3CEDD0MyxWKz2f5z7zSbTfvLdASko09alDcdAekonz9zgzITPgvJXCJj401HQDoqktN1N2r1bvG5sXtH//SWsXunRIYqw3x9fR16MQAAAIBUZ7GYe2RQGaqo+A92qgAAAAAZnvGJ2gAAAIBLYU6F0/iJAQAAAEgRigoAAAAAKZKhhj+xvCwAAADSHO85nZaheiqYqA0AAAC4HuNFRVxcnIoXL66DBw8+8Nxff/1VBQsWTIdUAAAAyLQsbuYeGZTx4U8eHh6KiYlJ0rl16tRJ4zQAAAAAnOUS5VD37t318ccfKz6enTQBAACAjMZ4T4Ukbdu2TWvXrtWqVatUsWJF+fj4OBxfunSpoWQAAADIdJio7TSXKCr8/f3Vtm1b0zEAAAAAJINLFBVz5841HQEAAACQxDYGyeEScyokKT4+XmvWrNH06dN148YNSdK5c+cUERFhOBkAAACA+3GJnopTp06padOmCgkJUWxsrBo3bqwcOXLo448/VmxsrKZNm2Y6IgAAADIJeiqc5xI9Fb1791a1atV09epVeXt729ufeeYZrV271mAyAAAAAA/iEj0Vv//+u/744w95eno6tBcpUkRnz541lAoAAABAUrhEUZGQkCCr1Zqo/cyZM8qRI4eBRAAAAMi0GP3kNJcY/vTkk09q/Pjx9ucWi0UREREaNmyYmjVrZi4YAAAAgAdyiZ6Kzz77TE2aNFG5cuUUExOjl156SUePHlWuXLn01VdfmY4HAACATISJ2s5ziaLioYce0p49e7R48WLt3btXERER6tKli9q3b+8wcRsAAACA63GJ4U+RkZHKkiWLXn75ZY0ZM0aff/65unbtSkEBAAAA3MPGjRvVokULFShQQBaLRcuWLbMfi4uL09tvv62KFSvKx8dHBQoUUIcOHXTu3DmHa4SFhal9+/by9fWVv7+/unTpkqx94lyiqMibN686d+6sTZs2mY4CAACATM5isRh7OCMyMlKVK1fWlClTEh2LiorSzp07NWTIEO3cuVNLly7V4cOH1bJlS4fz2rdvrwMHDmj16tVavny5Nm7cqNdff93pn5lLDH/68ssvNW/ePD3xxBMqUqSIOnfurA4dOqhAgQKmowEAAADpJjY2VrGxsQ5tXl5e8vLySnTuU089paeeeuqu1/Hz89Pq1asd2iZPnqxHH31UISEhKly4sA4ePKgVK1Zo27ZtqlatmiRp0qRJatasmT799FOn3ou7RE9F69attWzZMp09e1ZvvvmmFi1apKCgID399NNaunSp4uPjTUcEAABAJmGypyI4OFh+fn4Oj+Dg4FT5vq5duyaLxSJ/f39J0pYtW+Tv728vKCSpUaNGcnNz09atW526tksUFbflzp1b/fr10969ezV27FitWbNGzz77rAoUKKChQ4cqKirKdEQAAAAgzQwePFjXrl1zeAwePDjF142JidHbb7+tF198Ub6+vpKk0NBQ5cmTx+G8LFmyKDAwUKGhoU5d3yWGP9124cIFzZ8/X/PmzdOpU6f07LPPqkuXLjpz5ow+/vhj/fnnn1q1apXpmAAAAPgPM7mk7L2GOqVEXFycnn/+edlsNk2dOjVVr32bSxQVS5cu1dy5c7Vy5UqVK1dOb731ll5++WV714wk1apVS2XLljUXEgAAAMhgbhcUp06d0rp16+y9FJKUL18+Xbx40eH8+Ph4hYWFKV++fE7dxyWKik6dOqldu3bavHmzqlevftdzChQooPfeey+dkwEAAAAZ0+2C4ujRo1q/fr1y5szpcLxmzZoKDw/Xjh07VLVqVUnSunXrlJCQoMcee8ype7lEUXH+/Hlly5btvud4e3tr2LBh6ZQIAAAAmVYG2VA7IiJC//zzj/35iRMntHv3bgUGBip//vx69tlntXPnTi1fvlxWq9U+TyIwMFCenp4qW7asmjZtqtdee03Tpk1TXFycevTooXbt2jm9CqtLFBV3FhQxMTG6efOmw/E7u2kAAAAASNu3b1eDBg3sz/v16ydJ6tixo4YPH64ff/xRklSlShWH161fv17169eXJC1cuFA9evRQw4YN5ebmprZt22rixIlOZ3GJoiIyMlJvv/22lixZoitXriQ6brVaDaQCAABAZmRyorYz6tevL5vNds/j9zt2W2BgoBYtWpTiLC6xpOygQYO0bt06TZ06VV5eXpo1a5ZGjBihAgUKaMGCBabjAQAAALgPl+ip+Omnn7RgwQLVr19fnTp1Ut26dVWiRAkFBQVp4cKFat++vemIAAAAAO7BJXoqwsLCVKxYMUm35k+EhYVJkurUqaONGzeajAYAAIBMxuSO2hmVSxQVxYoV04kTJyRJZcqU0ZIlSyTd6sG4c68KAAAAAK7HJYY/derUSXv27FG9evX0zjvvqEWLFpo8ebLi4uI0duxY0/EAAACQiWTkHgNTXKKo6Nu3r/3rRo0a6dChQ9qxY4dKlCihSpUqGUwGAAAA4EFcoqj4t6CgIAUFBZmOAQAAgEyIngrnGSsqnNlUo1evXmmYBAAAAEBKGCsqxo0bl6TzLBYLRQUAAADgwowVFbdXewIAAABcCqOfnOYSS8redvPmTR0+fFjx8fGmowAAAABIIpcoKqKiotSlSxdly5ZN5cuXV0hIiCSpZ8+eGj16tOF0AAAAyEzY/M55LlFUDB48WHv27NFvv/2mrFmz2tsbNWqkr7/+2mAyAAAAAA/iEkvKLlu2TF9//bVq1KjhUKGVL19ex44dM5gMAAAAwIO4RFFx6dIl5cmTJ1F7ZGRkhu4GAgAAQMbD+0/nucTwp2rVqunnn3+2P7/9Bzlr1izVrFnTVCwAAAAASeASPRUfffSRnnrqKf3999+Kj4/XhAkT9Pfff+uPP/7Qhg0bTMcDAABAJkJPhfNcoqeiTp062r17t+Lj41WxYkWtWrVKefLk0ZYtW1S1alXT8QAAAADch0v0VEhS8eLFNXPmTNMxAAAAkNnRUeE0l+ip2Llzp/bt22d//sMPP6h169Z69913dfPmTYPJAAAAADyISxQVb7zxho4cOSJJOn78uF544QVly5ZN33zzjQYNGmQ4HQAAAID7cYmi4siRI6pSpYok6ZtvvlG9evW0aNEizZs3T999953ZcAAAAMhU2FHbeS5RVNhsNiUkJEiS1qxZo2bNmkmSChUqpMuXL5uMBgAAAOABXGKidrVq1TRq1Cg1atRIGzZs0NSpUyVJJ06cUN68eQ2nAwAAQGaSkXsMTHGJomL8+PFq3769li1bpvfee08lSpSQJH377beqVauW09f7/LlKqR0RLuzRQT+ajoB0dHTys6YjIB258e96puLt4WU6AoBkcomiolKlSg6rP932ySefyN3d3UAiAAAAAEnlEkXFvWTNmtV0BAAAAGQyDH9ynksUFVarVePGjdOSJUsUEhKSaG+KsLAwQ8kAAAAAPIhLrP40YsQIjR07Vi+88IKuXbumfv36qU2bNnJzc9Pw4cNNxwMAAEAmwpKyznOJomLhwoWaOXOm+vfvryxZsujFF1/UrFmzNHToUP3555+m4wEAAAC4D5coKkJDQ1WxYkVJUvbs2XXt2jVJ0tNPP62ff/7ZZDQAAAAAD+ASRcVDDz2k8+fPS5KKFy+uVatWSZK2bdsmLy+WlwMAAEA6shh8ZFAuUVQ888wzWrt2rSSpZ8+eGjJkiEqWLKkOHTqoc+fOhtMBAAAAuB+XWP1p9OjR9q9feOEFBQUF6Y8//lDJkiXVokULg8kAAACQ2WTkCdOmuERPxZUrV+xfnz59Wr/88ovOnz8vPz8/g6kAAAAAJIXRomLfvn0qUqSI8uTJozJlymj37t2qXr26xo0bpxkzZuiJJ57QsmXLTEYEAABAJsOSss4zWlQMGjRIFStW1MaNG1W/fn09/fTTat68ua5du6arV6/qjTfecBgaBQAAAMD1GJ1TsW3bNq1bt06VKlVS5cqVNWPGDL311ltyc7tV6/Ts2VM1atQwGREAAADAAxgtKsLCwpQvXz5Jt/an8PHxUUBAgP14QECAbty4YSoeAAAAMqGMPAzJFOMTtf/9h8YfIgAAAJCxGF9S9tVXX7VvcBcTE6M333xTPj4+kqTY2FiT0QAAAJAZ8Rm304wWFR07dnR4/vLLLyc6p0OHDukVBwAAAEAyGC0q5s6da/L2AAAAAFKB8eFPAAAAgCthjq/zjE/UBgAAAJCx0VMBAAAA3IGeCufRUwEAAAAgReipAAAAAO5AT4Xz6KkAAAAAkCIUFQAAAABShOFPAAAAwB0Y/uQ840XFjz/+eNd2i8WirFmzqkSJEipatGg6pwIAAACQVMaLitatW8tischmszm0326zWCyqU6eOli1bpoCAAEMpAQAAkGnQUeE043MqVq9ererVq2v16tW6du2arl27ptWrV+uxxx7T8uXLtXHjRl25ckUDBgwwHRUAAADAXRjvqejdu7dmzJihWrVq2dsaNmyorFmz6vXXX9eBAwc0fvx4de7c2WBKAAAAAPdivKg4duyYfH19E7X7+vrq+PHjkqSSJUvq8uXL6R0NAAAAmRATtZ1nfPhT1apVNXDgQF26dMnedunSJQ0aNEjVq1eXJB09elSFChUyFREAAADAfRjvqZg9e7ZatWqlhx56yF44nD59WsWKFdMPP/wgSYqIiND7779vMiYAAAAyCXoqnGe8qChdurT+/vtvrVq1SkeOHLG3NW7cWG5utzpSWrdubTAhAAAAgPsxXlRIkpubm5o2baqmTZuajgIAAIBMjo4K57lEUbF27VqtXbtWFy9eVEJCgsOxOXPmGEoFAAAAICmMFxUjRozQyJEjVa1aNeXPn58xbAAAAEAGY7yomDZtmubNm6dXXnnFdBQAAACAD7mTwfiSsjdv3nTY+A4AAABAxmK8qOjatasWLVpkOgYAAAAg6dZEbVOPjMr48KeYmBjNmDFDa9asUaVKleTh4eFwfOzYsYaSAQAAAEgK40XF3r17VaVKFUnS/v37HY4xng0AAABwfcaLivXr15uOAAAAANjxwbbzjM+pAAAAAJCxGempaNOmjebNmydfX1+1adPmvucuXbo0nVIBAAAAGXvCtClGigo/Pz97t5Kvry9dTAAAAEAGZqSomDt3rv3refPmmYgAAAAA3JWbGx94O8v4nIonnnhC4eHhidqvX7+uJ554Iv0DAQAAAHCK8aLit99+082bNxO1x8TE6PfffzeQCAAAAIAzjC0pu3fvXvvXf//9t0JDQ+3PrVarVqxYoYIFC5qIBgAAgEyM6b7OM1ZUVKlSRRaLRRaL5a7DnLy9vTVp0iQDyQAAAAA4w9jwpxMnTujYsWOy2Wz666+/dOLECfvj7Nmzun79ujp37mwqHgAAADKp2x98m3g4Y+PGjWrRooUKFCggi8WiZcuWORy32WwaOnSo8ufPL29vbzVq1EhHjx51OCcsLEzt27eXr6+v/P391aVLF0VERDj9MzNWVAQFBalIkSJKSEhQtWrVFBQUZH/kz59f7u7upqIBAAAALi8yMlKVK1fWlClT7np8zJgxmjhxoqZNm6atW7fKx8dHTZo0UUxMjP2c9u3b68CBA1q9erWWL1+ujRs36vXXX3c6i/GJ2pL0xRdfqHbt2ipQoIBOnTolSRo3bpx++OEHw8kAAAAA1/TUU09p1KhReuaZZxIds9lsGj9+vN5//321atVKlSpV0oIFC3Tu3Dl7j8bBgwe1YsUKzZo1S4899pjq1KmjSZMmafHixTp37pxTWYwXFVOnTlW/fv3UrFkzhYeHy2q1SpICAgI0fvx4s+EAAACQ6Vgs5h6xsbG6fv26wyM2Ntbp7+HEiRMKDQ1Vo0aN7G1+fn567LHHtGXLFknSli1b5O/vr2rVqtnPadSokdzc3LR161an7me8qJg0aZJmzpyp9957z2HIU7Vq1bRv3z6DyVzTvl07NHRgT73YspGa1KqsPzasczjepFbluz6+WTjPTGA4pUapXPqiZx3t/ayFLs5+Xk89XMDhePNHCmpJv8d1aEIrXZz9vCoU8r/v9b7qU/eu10HGMGPqZD1apazD47nWzUzHQjqZN3umqlUuq8/GfGQ6CtLAN19/pRfattTjNavq8ZpV9erLL2jz7xtNx4ILCA4Olp+fn8MjODjY6evcXlk1b968Du158+a1HwsNDVWePHkcjmfJkkWBgYEOK7MmhbHVn247ceKEHn744UTtXl5eioyMNJDItcXERKtYidJq8nRrjRzcL9Hxr35a6/B825ZNGhc8XHXqN0p0LlxPNs8sOnAmXF9tOqF5PWonPu6VRVuPXtYP205r3KvV73utNxqXks2WVkmRXooVL6HJ0+fYn2dxN/7XNtLBgf37tPTbr1WyVGnTUZBG8ubNq559+qtw4SDZbDYt/3GZ+vXurkVLlqp4iZKm42V6zk6YTk2DBw9Wv36O7/G8vLwMpUk64/86FS1aVLt371ZQUJBD+4oVK1S2bFlDqVxX9Zp1VL1mnXseD8yZy+H5lt9/U+VHqit/wYfSOBlSw7r9oVq3/96fDHyz5daco0I5s933OhUK+avbk6X05AdrtH9cy1TNiPTl7p5FuXLlNh0D6SgqKlJDBg/Ue8NGavbMaabjII08Xt9xOf3uvfrq2yWLtW/vHoqKTM7LyytVioh8+fJJki5cuKD8+fPb2y9cuKAqVarYz7l48aLD6+Lj4xUWFmZ/fVIZH/7Ur18/de/eXV9//bV9edkPP/xQgwcP1qBBg0zHy9Cuhl3RX3/8riYtEk/ewX+Xt6e7pr7+mN5ZuFMXr8c8+AVwaadDTqlZ48fVunljDRk8UKHnnZs4h4zn448+UO3H6+mxGrVMR0E6sVqtWvnrz4qOjlKlylVMx4EyzpKy91O0aFHly5dPa9f+bxTL9evXtXXrVtWsWVOSVLNmTYWHh2vHjh32c9atW6eEhAQ99thjTt3PeE9F165d5e3trffff19RUVF66aWXVKBAAU2YMEHt2rV74OtjY2MTTV6JjbVliG6itLb6lx/lnS2b6tRraDoK0tEHL1TRtn+uaMVu3nxmdBUqVtLQkR8pqEhRXb58SbOmTdHrnV/WV9/+JB8fH9PxkAZW/vqzDh38WwsWfWM6CtLB0SOH1emVF3XzZqy8s2XTp+Mnq1jxEqZjIQOJiIjQP//8Y39+4sQJ7d69W4GBgSpcuLD69OmjUaNGqWTJkipatKiGDBmiAgUKqHXr1pKksmXLqmnTpnrttdc0bdo0xcXFqUePHmrXrp0KFHBuPqbRoiI+Pl6LFi1SkyZN1L59e0VFRSkiIiLRhJH7CQ4O1ogRIxzaeg98T33efj+142Y4K5cv0xNNmsmTAivTaFK5gOqUzaOGI1abjoJUUKvO4/avS5YqrQoVKqlls4Zas+pXtXrmWYPJkBZCQ8/rszHBmjJ9Nh+MZRJFihbVV998r4iIG1qzeqWGvf+OZs75gsICSbZ9+3Y1aNDA/vz2XIyOHTtq3rx5GjRokCIjI/X6668rPDxcderU0YoVK5Q1a1b7axYuXKgePXqoYcOGcnNzU9u2bTVx4kSnsxgtKrJkyaI333xTBw8elCRly5ZN2bLdf6z4v91tMsv5CGan7tu9U2dCTurdD8aYjoJ0VKdsHhXJnV1HJ7V2aJ/zVi39eeSynvnkNyO5kDpy+PqqcOEiOnM6xHQUpIFDfx9QWNgVvdyurb3NarVq147tWrJ4kf7YtoeNYf9jPDw8VajwrTmlZctV0N/79+urhQv03tCRhpPB4Dxtp9SvX1+2+6zKYrFYNHLkSI0cee//pwIDA7Vo0aIUZzE+/OnRRx/Vrl27Ek3UTqq7TWYJi2Mc+crl36tkmXIqXpKVQzKTSb8c0sLfjzu0bRzZVEMW79GqPQyHyuiioiJ19sxp5crF5Pv/ouqP1dTibx03fR057D0FFSmqjp26UlBkAgkJCbp586bpGECyGC8q3nrrLfXv319nzpxR1apVE40TrlSpkqFkrik6KkrnzvzvU8rQ82d17Mgh5fD1U558t2b2R0ZGaOO6VXq9Z39TMZFMPl5ZVDRPdvvzwrmyq0Ihf12NvKmzYVHy9/HUQ4HZlNf/Vrdl8Xw5JEkXr8Xo4vX/Pf7tbFikQi6zRHNGM2HsGNV9vL7y5S+oy5cuasbUSXJzd9OTTZubjoY04OPjoxIlSzm0ZfX2lr+/f6J2ZHyTJnym2rUfV778+RUZGakVvy7Xju1/afK0WaajQWaXlM2ojBcVtydj9+rVy95msVhks9lksVjsO2zjliOHDmhQj67259MnfipJatyspQa8/4EkacPqFZJNatD4KSMZkXyViwRo2aD/jY38oF0VSdLizSfUa842NalSQJM6P2o/PvPNW6s3fPLDAX3y44F0zYq0d/FCqN4fPEDXwsMVEBCoyg8/ojkLFisgMNB0NAApdDUsTEPff1uXL11S9uw5VLJUaU2eNks1aibeowjICCy2+w3ESgenTp267/HkDIs6eYXhT5nJo4N+NB0B6ejoZCYoZyZufFiYqfDpcOaS3ct1/7wfHrHO2L13DXviwSe5IOM9FcmdSwEAAACkBepb5xnf/C44OFhz5sxJ1D5nzhx9/PHHBhIBAAAAcIbxomL69OkqU6ZMovby5ctr2rRpBhIBAAAgM/sv7Kid3owXFaGhocqfP3+i9ty5c+v8+fMGEgEAAABwhvGiolChQtq8eXOi9s2bNzu9PTgAAACQUhaLuUdGZXyi9muvvaY+ffooLi5OTzxxa7b72rVrNWjQIPXvzz4LAAAAgKszXlQMHDhQV65c0VtvvWXfRTJr1qx6++23NXjwYMPpAAAAADyI8aLCYrHo448/1pAhQ3Tw4EF5e3urZMmS8vLyMh0NAAAAmVBGnjBtivGi4rbs2bOrevXqpmMAAAAAcJLxoiIyMlKjR4/W2rVrdfHiRSUkJDgcP378uKFkAAAAyIzoqHCe8aKia9eu2rBhg1555RXlz5+f7iYAAAAggzFeVPz666/6+eefVbt2bdNRAAAAACSD8aIiICBAgYGBpmMAAAAAkpionRzGN7/74IMPNHToUEVFRZmOAgAAACAZjPdUfPbZZzp27Jjy5s2rIkWKyMPDw+H4zp07DSUDAABAZkRHhfOMFxWtW7c2HQEAAABAChgvKoYNG2Y6AgAAAIAUMF5U3LZjxw4dPHhQklS+fHk9/PDDhhMBAAAgM2KitvOMFxUXL15Uu3bt9Ntvv8nf31+SFB4ergYNGmjx4sXKnTu32YAAAAAA7sv46k89e/bUjRs3dODAAYWFhSksLEz79+/X9evX1atXL9PxAAAAkMlYLOYeGZXxnooVK1ZozZo1Klu2rL2tXLlymjJlip588kmDyQAAAAAkhfGiIiEhIdEyspLk4eGhhIQEA4kAAACQmTGnwnnGhz898cQT6t27t86dO2dvO3v2rPr27auGDRsaTAYAAAAgKYwXFZMnT9b169dVpEgRFS9eXMWLF1fRokV1/fp1TZo0yXQ8AAAAAA9gfPhToUKFtHPnTq1Zs0aHDh2SJJUtW1aNGjUynAwAAACZEaOfnGesp2LdunUqV66crl+/LovFosaNG6tnz57q2bOnqlevrvLly+v33383FQ8AAABAEhkrKsaPH6/XXntNvr6+iY75+fnpjTfe0NixYw0kAwAAQGZmsViMPTIqY0XFnj171LRp03sef/LJJ7Vjx450TAQAAAAgOYwVFRcuXLjrUrK3ZcmSRZcuXUrHRAAAAACSw1hRUbBgQe3fv/+ex/fu3av8+fOnYyIAAACA4U/JYayoaNasmYYMGaKYmJhEx6KjozVs2DA9/fTTBpIBAAAAcIaxJWXff/99LV26VKVKlVKPHj1UunRpSdKhQ4c0ZcoUWa1Wvffee6biAQAAIJPKwB0GxhgrKvLmzas//vhD3bp10+DBg2Wz2STd6m5q0qSJpkyZorx585qKBwAAACCJjG5+FxQUpF9++UVXr17VP//8I5vNppIlSyogIMBkLAAAAGRiGXlugynGd9SWpICAAFWvXt10DAAAAADJYGyiNgAAAID/BpfoqQAAAABcBaOfnEdPBQAAAIAUoacCAAAAuAMTtZ1HTwUAAACAFKGoAAAAAJAiDH8CAAAA7sDoJ+fRUwEAAAAgReipAAAAAO7gRleF0+ipAAAAAJAi9FQAAAAAd6Cjwnn0VAAAAABIEYoKAAAAACnC8CcAAADgDuyo7Tx6KgAAAACkCD0VAAAAwB3c6KhwGj0VAAAAAFKEogIAAABAijD8CQAAALgDE7WdR08FAAAAgBRxmZ6KtWvXau3atbp48aISEhIcjs2ZM8dQKgAAAGQ2dFQ4zyWKihEjRmjkyJGqVq2a8ufPn+Iup+tR8amUDBnBjk9amY6AdNRkwu+mIyAd/dyztukISEcJNtMJkJ6ye7nE21CkEpf405w2bZrmzZunV155xXQUAAAAZHIW0VXhLJeYU3Hz5k3VqlXLdAwAAAAAyeASRUXXrl21aNEi0zEAAAAAJINLDH+KiYnRjBkztGbNGlWqVEkeHh4Ox8eOHWsoGQAAADIbdtR2nksUFXv37lWVKlUkSfv373c4xjrBAAAAgGtziaJi/fr1piMAAAAAkvhQOzlcYk7Fnc6cOaMzZ86YjgEAAAAgiVyiqEhISNDIkSPl5+enoKAgBQUFyd/fXx988EGijfAAAAAAuBaXGP703nvvafbs2Ro9erRq17610dGmTZs0fPhwxcTE6MMPPzScEAAAAJkFo5+c5xJFxfz58zVr1iy1bNnS3lapUiUVLFhQb731FkUFAAAA4MJcoqgICwtTmTJlErWXKVNGYWFhBhIBAAAgs3Kjq8JpLjGnonLlypo8eXKi9smTJ6ty5coGEgEAAABIKpfoqRgzZoyaN2+uNWvWqGbNmpKkLVu26PTp0/rll18MpwMAAEBmQkeF81yip6JevXo6cuSInnnmGYWHhys8PFxt2rTR4cOHVbduXdPxAAAAANyHS/RUSFKBAgWYkA0AAABkQMZ6Kvbu3Wvfg2Lv3r33fQAAAADpxWKxGHs4w2q1asiQISpatKi8vb1VvHhxffDBB7LZbPZzbDabhg4dqvz588vb21uNGjXS0aNHU/tHZq6nokqVKgoNDVWePHlUpUoVWSwWhx/AbRaLRVar1UBCAAAAwHV9/PHHmjp1qubPn6/y5ctr+/bt6tSpk/z8/NSrVy9Jt+YuT5w4UfPnz1fRokU1ZMgQNWnSRH///beyZs2aalmMFRUnTpxQ7ty57V8DAAAAriCjTNT+448/1KpVKzVv3lySVKRIEX311Vf666+/JN3qpRg/frzef/99tWrVSpK0YMEC5c2bV8uWLVO7du1SLYux4U9BQUH2Lp5Tp06pYMGCCgoKcngULFhQp06dMhURAAAASFexsbG6fv26wyM2Nvau59aqVUtr167VkSNHJEl79uzRpk2b9NRTT0m69cF9aGioGjVqZH+Nn5+fHnvsMW3ZsiVVc7vE6k8NGjS46yZ3165dU4MGDQwkAgAAANJfcHCw/Pz8HB7BwcF3Pfedd95Ru3btVKZMGXl4eOjhhx9Wnz591L59e0lSaGioJClv3rwOr8ubN6/9WGpxidWfbDbbXSemXLlyRT4+PgYSAQAAILMyuaP24MGD1a9fP4c2Ly+vu567ZMkSLVy4UIsWLVL58uW1e/du9enTRwUKFFDHjh3TI66d0aKiTZs2km5Nxn711VcdfmBWq1V79+5VrVq1TMUDAAAA0pWXl9c9i4h/GzhwoL23QpIqVqyoU6dOKTg4WB07dlS+fPkkSRcuXFD+/Pntr7tw4YKqVKmSqrmNFhV+fn6SbvVU5MiRQ97e3vZjnp6eqlGjhl577TVT8QAAAJAJZZB52oqKipKbm+NsBnd3d/u2DUWLFlW+fPm0du1aexFx/fp1bd26Vd26dUvVLEaLirlz50q6NVN9wIABDHUCAAAAkqhFixb68MMPVbhwYZUvX167du3S2LFj1blzZ0m3RgP16dNHo0aNUsmSJe1LyhYoUECtW7dO1SwuMadi2LBhpiMAAAAAkuT0JnSmTJo0SUOGDNFbb72lixcvqkCBAnrjjTc0dOhQ+zmDBg1SZGSkXn/9dYWHh6tOnTpasWJFqu5RIUkW2912nDPg22+/1ZIlSxQSEqKbN286HNu5c6dT19p7OiI1o8HFBfh4mI6AdPTcjD9NR0A6+rlnbdMRkI4SXOIdCdJL7uwu8dn2Xb24YLexe3/VoYqxe6eESywpO3HiRHXq1El58+bVrl279Oijjypnzpw6fvy4fZ1dAAAAAK7JJYqKzz//XDNmzNCkSZPk6empQYMGafXq1erVq5euXbtmOh4AAAAyETeLuUdG5RJFRUhIiH3pWG9vb924cUOS9Morr+irr74yGQ0AAADAAyRpMNuPP/6Y5Au2bNnS6RD58uVTWFiYgoKCVLhwYf3555+qXLmyTpw4IReZ8gEAAIBMIqNM1HYlSSoqkrrklMVikdVqdTrEE088oR9//FEPP/ywOnXqpL59++rbb7/V9u3b7RvkAQAAAHBNSSoqbm+gkVZmzJhhv0f37t2VM2dO/fHHH2rZsqXeeOONNL03AAAAgJRJ0VpeMTExKV7jNj4+Xh999JE6d+6shx56SJLUrl07+3bjAAAAQHpi9JPznJ6obbVa9cEHH6hgwYLKnj27jh8/LkkaMmSIZs+e7XSALFmyaMyYMYqPj3f6tQAAAADMc7qo+PDDDzVv3jyNGTNGnp6e9vYKFSpo1qxZyQrRsGFDbdiwIVmvBQAAAFKTxWIx9sionB7+tGDBAs2YMUMNGzbUm2++aW+vXLmyDh06lKwQTz31lN555x3t27dPVatWlY+Pj8Px5KwoBQAAACB9OF1UnD17ViVKlEjUnpCQoLi4uGSFeOuttyRJY8eOTXQsuStKAQAAAEgfThcV5cqV0++//66goCCH9m+//VYPP/xwskKk9epSAAAAQFJl5J2tTXG6qBg6dKg6duyos2fPKiEhQUuXLtXhw4e1YMECLV++PC0yAgAAAHBhThcVrVq10k8//aSRI0fKx8dHQ4cO1SOPPKKffvpJjRs3TnaQyMhIbdiwQSEhIbp586bDsV69eiX7ugAAAIAzMvKEaVOStU9F3bp1tXr16lQLsWvXLjVr1kxRUVGKjIxUYGCgLl++rGzZsilPnjwUFQAAAIALS/bmd9u3b9fBgwcl3ZpnUbVq1WSH6Nu3r1q0aKFp06bJz89Pf/75pzw8PPTyyy+rd+/eyb4uAAAA4Cz6KZzndFFx5swZvfjii9q8ebP8/f0lSeHh4apVq5YWL15s3xXbGbt379b06dPl5uYmd3d3xcbGqlixYhozZow6duyoNm3aOH1NAAAAAOnD6c3vunbtqri4OB08eFBhYWEKCwvTwYMHlZCQoK5duyYrhIeHh9zcbkXJkyePQkJCJEl+fn46ffp0sq4JAAAAIH043VOxYcMG/fHHHypdurS9rXTp0po0aZLq1q2brBAPP/ywtm3bppIlS6pevXoaOnSoLl++rC+++EIVKlRI1jUBAACA5HBjorbTnO6pKFSo0F03ubNarSpQoECyQnz00UfKnz+/JOnDDz9UQECAunXrpkuXLmnGjBnJuiYAAACA9OF0T8Unn3yinj17asqUKapWrZqkW5O2e/furU8//TRZIW5fR7o1/GnFihXJug4AAACQUnRUOC9JRUVAQIDDer2RkZF67LHHlCXLrZfHx8crS5Ys6ty5s1q3bp3sMBcvXtThw4clSWXKlFHu3LmTfS0AAAAA6SNJRcX48ePTNMSNGzf01ltvafHixbJarZIkd3d3vfDCC5oyZYr8/PzS9P4AAAAAki9JRUXHjh3TNETXrl21a9cuLV++XDVr1pQkbdmyRb1799Ybb7yhxYsXp+n9AQAAgNvYUdt5yd78TpJiYmJ08+ZNhzZfX1+nr7N8+XKtXLlSderUsbc1adJEM2fOVNOmTVMSEQAAAEAac7qoiIyM1Ntvv60lS5boypUriY7fHr7kjJw5c951iJOfn58CAgKcvh4AAACQXHRUOM/pJWUHDRqkdevWaerUqfLy8tKsWbM0YsQIFShQQAsWLEhWiPfff1/9+vVTaGiovS00NFQDBw7UkCFDknVNAAAAAOnD6Z6Kn376SQsWLFD9+vXVqVMn1a1bVyVKlFBQUJAWLlyo9u3bOx1i6tSp+ueff1S4cGEVLlxYkhQSEiIvLy9dunRJ06dPt5+7c+dOp68PAAAAJBWb3znP6aIiLCxMxYoVk3Rr/kRYWJgkqU6dOurWrVuyQqRkGVoAAAAAZjldVBQrVkwnTpxQ4cKFVaZMGS1ZskSPPvqofvrpJ/n7+ycrxLBhw5L1uszo+0VztHXTep09fVKeXl4qXa6S2r/WSwULFZEkXQw9p+4vt7jra/sNGa2a9RqnY1qk1N5d27Vk4TwdPXxQVy5f0ojR41W73hP242M+eF+rfvnR4TXVHqul0eOnpXdUpIJsnu56vW4R1SuVSwHZPHTkQoTGrTmmg6E3Ep07qElJtXm4gMat+Udfbz9rIC1Sm9Vq1ezpU7Tyl+W6cuWycuXOo+YtWunVrm+yEs1/VFRkpGZOnaiN69fq6tUwlSpdVr0HvKOy5SuajgY4zemiolOnTtqzZ4/q1aund955Ry1atNDkyZMVFxensWPHJjtIeHi4vv32Wx07dkwDBw5UYGCgdu7cqbx586pgwYLJvu5/zYG9O9Wk1XMqUbq8rFarFs2erFFvd9e42d8qq7e3cubOqxlLVjq8Zs3PS/Xjki9U5dHahlIjuWJiolWsZGk1ffoZDR/c967nVK9RWwPf/8D+3MPDM73iIZW9+1QpFcvloxHLD+nyjVg1rZBXk9pV0ouztulSxP9W2qtXKqcqFPDVxRuxBtMitX05b7a+//ZrvT/iIxUrXkIH/96vj4a/L5/sOfT8iy+bjoc0MPqDoTp+7KiGfDBauXLn1spflqtPt6768tsflTtPXtPxMjXqeOc5XVT07fu/NzaNGjXSoUOHtGPHDpUoUUKVKlVKVoi9e/eqUaNG8vPz08mTJ/Xaa68pMDBQS5cuVUhISLIngP8XvT96ssPz7oNGqOuzjXT86EGVq/SI3N3dFRCYy+Gcvzb9ppr1GsvbO1t6RkUqeLRmXT1as+59z/Hw9FRgzlz3PQeuzyuLm+qXzq1B3+3X7tPXJEmzNp1SnRI51ebhApr++0lJUu7snurfqKR6L9mrsc/xaeZ/yb49u1W33hOqXbeeJCl/gYJas+IX/b1/n+FkSAuxMTHasG61gj+bpCqPVJMkdXmjuzZv/E3ff7tYr7/V23BCwDlOr/70b0FBQWrTpk2yCwpJ6tevn1599VUdPXpUWbNmtbc3a9ZMGzduTGnE/7SoyAhJUvYcd98f5NiRgzp57LAaPtUqPWMhHe3ZuV3PNqunV19oofFjPtC1a+GmIyEZ3N0syuJm0c34BIf22LgEVS50a8lti6RhLcroy79O68TlKAMpkZYqVq6i7X/9qZBTJyVJR48c0p7du1Sz9v0/WEDGZLVaZbVa5enl5dDu5eWlvbt3GUqF2ywWi7FHRpWknoqJEycm+YK9evVyOsS2bdscVni6rWDBgg7LzMJRQkKC5n3+qUqXr6zCRUvc9Zx1vy5TwcJFVbp85XROh/RQvUZt1anfUPnyF9T5s2c0e9pEvdv3LU2c+YXc3d1Nx4MTom5atffMNXWuHaSTV6IUFnlTT5bLowoFfXXmarQk6ZUahWRNsGkJcyj+k17p1FWRkRF6sc3TcnN3V4LVqje691aTZk+bjoY0kM3HRxUqVdG8WdNUpGgxBQTm1JqVv+jAvj0qWKiw6XiA05JUVIwbNy5JF7NYLMkqKry8vHT9+vVE7UeOHFHu3Lnv+9rY2FjFxjqOK74ZG5eo8v8vmjVxtE6fPKYPxs++6/HY2BhtWrdCz77cNZ2TIb00aPyU/etiJUqpaIlS6vBsM+3ZuU2PVK9hMBmSY8TyQ3qvWWkt71FT8Qk2HQ69odV/X1SZfNlVOm92vVDtIXWct8N0TKSRtatXaNWvP2v4R2NUrFgJHTl8SBM+uzXWvlmL1qbjIQ0MGRms4JFD1LppA7m7u6tUmbJq1KSZDh/823Q0wGlJKipOnDiRpiFatmypkSNHasmSJZJuFSchISF6++231bZt2/u+Njg4WCNGjHBoe7PPYHXr926a5XUFsyZ9rJ1bN2nE2JnKmfvuk7n+3LhWsbExerwxn3JlFgUKPiQ//wCdO3OaoiIDOhseo7cW7VFWDzf5eGbRlcibGtWqrM6Gx6hKIT8F+Hho2Vv/+3PN4mZRryeKq131h/TM1K0GkyM1TBn/mV55tYsaN2kmSSpespRCQ89pwdxZFBX/UQULFdbkmfMVHR2lyIhI5cqdW0Pf6a8CBR8yHS3TS/H8gEzI6YnaaeGzzz7Ts88+qzx58ig6Olr16tVTaGioatasqQ8//PC+rx08eLD69evn0HbkYlxaxjXKZrNp9uQx+mvTeo34bIby5r/3yljrfv1B1WrWk59/QDomhEmXLobq+rVwBeZi4nZGFhOXoJi4m8rhlUWPFQ3U5PXHtf7wJW07edXhvPEvVNKK/Re0fB/DRP8LYmKiZXFzfCvj7uYuW0LCPV6B/wpv72zy9s6m69ev6a8tm9Wtd78HvwhwMS5RVPj5+Wn16tXavHmz9uzZo4iICD3yyCNq1KjRA1/r5eUlr38NdfK8FpFWUY2bNXG0Nq1boUEjxyprtmy6GnZZkpTNJ7u8vP43yf382dM6uG+nBn+Y9PkwcD3RUVE6eybE/vz8ubP658gh5fD1k6+vnxbMnqq6DRopMGcunTtzWjOnjFOBhwqr2mMsH5wRPVY0QBZJp8KiVSjAWz0aFNOpK1Favi9U1gSbrsfEO5xvTbDpSuRNhYRFmwmMVFXn8fqaP3uG8ubLr2LFS+jIoYNa/OV8NW/1jOloSCNb/9gkm2wqHFRUZ0+HaMqET1W4SFE1b8GfuWkZecK0KcaLiri4OHl7e2v37t2qXbu2atfmzdD9rPrpW0nS8P6vO7S/NXCYGjRpaX++fsUPCsyVR5WrMQQmIzt86IAGdO9ifz5t4ieSpCebtVTvge/r+LGjWv3rj4q4cUM5c+VR1cdqqtPrPeTpyV4VGVF2ryzqVq+o8uTw0vWYOK0/fFnTNp6QNcFmOhrSQd9B72nm5xP1afAHuno1TLly51Grts+p8+vdTEdDGomIiND0yeN16WKofH39VK9hY73+Vm9l8fAwHQ1wmsVmsxn/16pYsWL6/vvvVbly6qxQtPf0f7enAokF+PCXb2by3Iw/TUdAOvq5Jx80ZSbUz5lL7uzGP9u+pz4/HDJ27/Gtyhi7d0q4xDyU9957T++++67CwsJMRwEAAADgpGSViL///rumT5+uY8eO6dtvv1XBggX1xRdfqGjRoqpTp47T15s8ebL++ecfFShQQEFBQfLx8XE4vnPnzuTEBAAAAJAOnC4qvvvuO73yyitq3769du3aZd8j4tq1a/roo4/0yy+/OB2iVatWTIgBAACAS3DjbanTnC4qRo0apWnTpqlDhw5avHixvb127doaNWpUskIMHz48Wa8DAAAAYJ7TcyoOHz6sxx9/PFG7n5+fwsPDnbpWZGSkunXrpoIFCyp37txq166dLl265GwkAAAAINVYLBZjj4zK6aIiX758+ueffxK1b9q0ScWKFXPqWkOGDNEXX3yhp59+Wi+99JLWrVun119//cEvBAAAAOAynB7+9Nprr6l3796aM2eOLBaLzp07py1btmjAgAEaMmSIU9f6/vvvNXfuXD333HOSpA4dOqhGjRqKj49Xliyuu8wYAAAAgP9x+p37O++8o4SEBDVs2FBRUVF6/PHH5eXlpQEDBqhnz55OXevMmTMOm91VrVpVHh4eOnfunAoXLuxsNAAAACDFmKjtPKeLCovFovfee08DBw7UP//8o4iICJUrV07Zs2d3+uYJCQny+NeukVmyZJHVanX6WgAAAADMSPYYI09PT5UrVy5FN7fZbGrYsKHDUKeoqCi1aNFCnp6e9jb2qQAAAEB6ycDzpY1xuqho0KDBfWemr1u3LsnXGjZsWKK2Vq1aORsJAAAAgEFOFxVVqlRxeB4XF6fdu3dr//796tixo1PXultRcT+bN29WtWrV5OXl5dTrAAAAgKRyo6vCaU4XFePGjbtr+/DhwxUREZHiQPfz1FNPaffu3U4vXQsAAAAg7Ti9T8W9vPzyy5ozZ05qXe6ubDZbml4fAAAAgPNSbTOILVu2KGvWrKl1OQAAAMCIVPvUPRNxuqho06aNw3Obzabz589r+/btTm9+BwAAACDjc7qo8PPzc3ju5uam0qVLa+TIkXryySdTLRgAAABgAvO0nedUUWG1WtWpUydVrFhRAQEBaZXpnu63lC0AAAAAM5waMubu7q4nn3xS4eHhqRbAZrMpJCREMTExSToXAAAAgGtxeh5KhQoVdPz48VQLYLPZVKJECZ0+ffqB5964cYPlZAEAAJCm3CwWY4+MyumiYtSoURowYICWL1+u8+fP6/r16w4PpwO4ualkyZK6cuWK068FAAAAYF6Si4qRI0cqMjJSzZo10549e9SyZUs99NBDCggIUEBAgPz9/ZM9z2L06NEaOHCg9u/fn6zXAwAAAKnFYjH3yKiSPFF7xIgRevPNN7V+/fpUD9GhQwdFRUWpcuXK8vT0lLe3t8PxsLCwVL8nAAAAgNSR5KLi9iTpevXqpXqI8ePHp/o1AQAAgORwy8A9BqY4taRsWi3p2rFjxzS5LgAAAIC051RRUapUqQcWFikdqhQTE6ObN286tPn6+qbomgAAAADSjlNFxYgRIxLtqJ0aIiMj9fbbb2vJkiV3XQXKarWm+j0BAACAu8nIS7ua4lRR0a5dO+XJkyfVQwwaNEjr16/X1KlT9corr2jKlCk6e/aspk+frtGjR6f6/QAAAACkniQXFWk1n0KSfvrpJy1YsED169dXp06dVLduXZUoUUJBQUFauHCh2rdvn2b3BgAAAO5ER4XzkrxPxe3Vn9JCWFiYfadsX19f+7yMOnXqaOPGjWl2XwAAAAApl+SiIiEhIU2GPklSsWLFdOLECUlSmTJltGTJEkm3ejD8/f3T5J4AAAAAUodTcyrSSqdOnbRnzx7Vq1dP77zzjlq0aKHJkycrLi5OY8eONR0PAAAAmQj7VDjPJYqKvn372r9u1KiRDh06pB07dqhEiRKqVKmSwWQAAAAAHiTJw5/S0oIFCxQbG2t/HhQUpDZt2qhMmTJasGCBwWQAAADIbCwG/8uoXKKo6NSpk65du5ao/caNG+rUqZOBRAAAAACSyiWGP9lstrsuWXvmzJk02WwPAAAAuBfmVDjPaFHx8MMPy2KxyGKxqGHDhsqS5X9xrFarTpw4oaZNmxpMCAAAAOBBjBYVrVu3liTt3r1bTZo0Ufbs2e3HPD09VaRIEbVt29ZQOgAAAMC1nT17Vm+//bZ+/fVXRUVFqUSJEpo7d66qVasm6daIoGHDhmnmzJkKDw9X7dq1NXXqVJUsWTJVcxgtKoYNGyar1aoiRYroySefVP78+U3GAQAAADLM8KerV6+qdu3aatCggX799Vflzp1bR48eVUBAgP2cMWPGaOLEiZo/f76KFi2qIUOGqEmTJvr777+VNWvWVMtifE6Fu7u73njjDR08eNB0FAAAAMCo2NhYh1VRJcnLy0teXl6Jzv34449VqFAhzZ07195WtGhR+9c2m03jx4/X+++/r1atWkm6tepq3rx5tWzZMrVr1y7VcrvE6k8VKlTQ8ePHTccAAAAA7HN+TTyCg4Pl5+fn8AgODr5rzh9//FHVqlXTc889pzx58ujhhx/WzJkz7cdPnDih0NBQNWrUyN7m5+enxx57TFu2bEnVn5lLFBWjRo3SgAEDtHz5cp0/f17Xr193eAAAAACZweDBg3Xt2jWHx+DBg+967vHjx+3zI1auXKlu3bqpV69emj9/viQpNDRUkpQ3b16H1+XNm9d+LLUYH/4kSc2aNZMktWzZ0mFp2dtLzVqtVlPRAAAAgHRzr6FOd5OQkKBq1arpo48+knRrZdX9+/dr2rRp6tixY1rGTMQlior169ebjgAAAABIyjgTtfPnz69y5co5tJUtW1bfffedJClfvnySpAsXLjgsiHThwgVVqVIlVbO4RFFRr1490xEAAACADKV27do6fPiwQ9uRI0cUFBQk6dak7Xz58mnt2rX2IuL69evaunWrunXrlqpZXKKokKTw8HDNnj3bvgpU+fLl1blzZ3bUBgAAQLqyZJCeir59+6pWrVr66KOP9Pzzz+uvv/7SjBkzNGPGDEm3Jpz36dNHo0aNUsmSJe1LyhYoUMC+X1xqcYmJ2tu3b1fx4sU1btw4hYWFKSwsTGPHjlXx4sW1c+dO0/EAAAAAl1O9enV9//33+uqrr1ShQgV98MEHGj9+vNq3b28/Z9CgQerZs6def/11Va9eXREREVqxYkWq7lEhSRabzWZL1SsmQ926dVWiRAnNnDlTWbLc6jyJj49X165ddfz4cW3cuNGp6+09HZEWMeGiAnw8TEdAOnpuxp+mIyAd/dyztukISEcJxt+RID3lzu4yA2YSGbvR3FYH/R4vZuzeKeESf5rbt293KCgkKUuWLBo0aJB9i3EAAAAgPbhllPFPLsQlhj/5+voqJCQkUfvp06eVI0cOA4kAAAAAJJVL9FS88MIL6tKliz799FPVqlVLkrR582YNHDhQL774ouF0AAAAyEwyypKyrsQliopPP/1UFotFHTp0UHx8vGw2mzw9PdWtWzeNHj3adDwAAAAA9+ESRYWnp6cmTJig4OBgHTt2TJJUvHhxZcuWzXAyAAAAZDZMqXCe0aKic+fOSTpvzpw5aZwEAAAAQHIZLSrmzZunoKAgPfzww3KBlW0BAAAAJIPRoqJbt2766quvdOLECXXq1Ekvv/yyAgMDTUYCAABAJucmxj85y/jmd7GxsVq6dKnmzJmjP/74Q82bN1eXLl305JNPypLMAW3RcakcEoDLYJxr5hJQvYfpCEhH5zZPMB0B6Sggm7vpCPc0ZfNJY/fuXruIsXunhPF9Kry8vPTiiy9q9erV+vvvv1W+fHm99dZbKlKkiCIi2BkbAAAA6ctiMffIqIwXFXdyc3OTxWKRzWaT1Wo1HQcAAABAEhgvKmJjY/XVV1+pcePGKlWqlPbt26fJkycrJCRE2bNnNx0PAAAAwAMYnaj91ltvafHixSpUqJA6d+6sr776Srly5TIZCQAAAJkcO2o7z2hRMW3aNBUuXFjFihXThg0btGHDhruet3Tp0nROBgAAACCpjBYVHTp0SPYKTwAAAEBacOP9qdOMb34HAAAAIGMzWlQAAAAAroaOCucZX/0JAAAAQMZGUQEAAAAgRRj+BAAAANyBidrOo6cCAAAAQIrQUwEAAADcgY4K59FTAQAAACBFKCoAAAAApAjDnwAAAIA78Km78/iZAQAAAEgReioAAACAO1iYqe00eioAAAAApAg9FQAAAMAd6KdwHj0VAAAAAFKEogIAAABAijD8CQAAALiDGxO1nUZPBQAAAIAUoacCAAAAuAP9FM6jpwIAAABAilBUAAAAAEgRhj8BAAAAd2CetvPoqQAAAACQIvRUAAAAAHew0FXhNHoqAAAAAKQIPRUAAADAHfjU3Xn8zAAAAACkCEUFAAAAgBRh+BMAAABwByZqO4+eCgAAAAApQk8FAAAAcAf6KZxHTwUAAACAFKGoAAAAAJAiDH8CAAAA7sBEbecZ76mIi4tTw4YNdfToUdNRAAAAACSD8Z4KDw8P7d2713QMAAAAQJILfOqeAbnEz+zll1/W7NmzTccAAAAAkAzGeyokKT4+XnPmzNGaNWtUtWpV+fj4OBwfO3asoWQAAADIbJhT4TyXKCr279+vRx55RJJ05MgRh2P8oQIAAACuzSWKivXr15uOAAAAACCZXGJOxW3//POPVq5cqejoaEmSzWYznAgAAACZjcXgI6NyiaLiypUratiwoUqVKqVmzZrp/PnzkqQuXbqof//+htMBAAAAuB+XKCr69u0rDw8PhYSEKFu2bPb2F154QStWrDCYDAAAAJmNxWLukVG5xJyKVatWaeXKlXrooYcc2kuWLKlTp04ZSgUAAAAgKVyipyIyMtKhh+K2sLAweXl5GUgEAAAAIKlcoqioW7euFixYYH9usViUkJCgMWPGqEGDBgaTAQAAILNxk8XYI6NyieFPY8aMUcOGDbV9+3bdvHlTgwYN0oEDBxQWFqbNmzebjgcAAADgPlyip6JChQo6cuSI6tSpo1atWikyMlJt2rTRrl27VLx4cdPxAAAAkIkwUdt5LtFTERISokKFCum9996767HChQsbSAUAAAAgKVyip6Jo0aK6dOlSovYrV66oaNGiBhIBAAAgs7IY/C+jcomiwmazyXKX/p6IiAhlzZrVQKKMZcf2berV/U01blBHVSqU1rq1a0xHQhrizzvzWbxooZ5q/ISqP1xR7ds9p31795qOhGSo/UhxfTv+DR1f9aGid01Wi/qV7nnuxPfaKXrXZPV4qb5D+6AuTbR+Xj9d+WOszm8ck8aJkZZaN2ukGg+XS/T4JPgD09GAZDE6/Klfv36Sbq32NGTIEIdlZa1Wq7Zu3aoqVaoYSpdxREdHqVTp0mr9TFv169PDdBykMf68M5cVv/6iT8cE6/1hI1SxYmUt/GK+ur3RRT8sX6GcOXOajgcn+Hh7ad+Rs1rwwxZ9Pfb1e57XskElPVqxiM5dDE90zNPDXUtX79LWvSfUsXXNNEyLtDb3yyVKSLDanx/756h6deuqJxo3MZgKSD6jRcWuXbsk3eqp2Ldvnzw9Pe3HPD09VblyZQ0YMMBUvAyjTt16qlO3nukYSCf8eWcuX8yfqzbPPq/Wz7SVJL0/bIQ2bvxNy5Z+py6v3fuNKVzPqs1/a9Xmv+97ToHcfhr79nNq8dYUfT+pW6Ljo6b9Ikl6ucVjaZIR6ScgMNDh+YK5s/RQoUJ6pGp1Q4lwp4w8YdoUo0XF+vXrJUmdOnXShAkT5OvrazIOALiUuJs3dfDvA+ry2hv2Njc3N9WoUUt79+wymAxpwWKxaPaoDho3f60OHg81HQfpKC7uplb88pNefLnjXYeDAxmBS6z+NHfuXIfn169f17p161SmTBmVKVPmvq+NjY1VbGysQ1uCmxc7cQPI8K6GX5XVak00zClnzpw6ceK4oVRIK/07NVa8NUFTvvrNdBSksw3r1yrixg01b/GM6Sj4fxl5EzpTXGKi9vPPP6/JkydLkqKjo1WtWjU9//zzqlixor777rv7vjY4OFh+fn4Oj08+Dk6P2AAApIqHyxZS9xfr6/VhX5qOAgN+WrZUNWrXVe48eUxHAZLNJYqKjRs3qm7dupKk77//XjabTeHh4Zo4caJGjRp139cOHjxY165dc3gMfHtwesQGgDQV4B8gd3d3XblyxaH9ypUrypUrl6FUSAu1Hy6uPIHZdeSXkbqxbYJubJugoAI5NbpfGx36eYTpeEhD58+d1batW9SqdVvTUYAUcYnhT9euXVPg/09YWrFihdq2bats2bKpefPmGjhw4H1f6+WVeKhTdFyaRQWAdOPh6amy5cpr659b9ETDRpKkhIQEbd26Re1efNlwOqSmRT9v07qthx3afvq8uxb9/JcW/PCnoVRID8t//F4BgYGqxQIcLoWpLc5ziaKiUKFC2rJliwIDA7VixQotXrxYknT16lX2qUiCqKhIhYSE2J+fPXtGhw4dlJ+fn/LnL2AwGdICf96ZyysdO2nIu2+rfPkKqlCxkr78Yr6io6PV+pk2pqPBST7enipeKLf9eZGCOVWpVEFdvR6l06FXFXYt0uH8uHirLly+rqOnLtrbCuULUIBvNhXKHyB3NzdVKlVQknTs9CVFRt9Mn28EqSYhIUE///C9mj3dWlmyuMRbMiDZXOL/4D59+qh9+/bKnj27goKCVL9+fUm3hkVVrFjRbLgM4MD+/Xqtcwf788/G3JpT0qLVM/rgw9GmYiGN8OeduTR9qpmuhoXp88kTdfnyJZUuU1afT5+lnAx/ynAeKRekVbN625+PGXBruMsXP/6Z5LkUQ7o11ysta9ifb/361nDfJ7tO0O87jqZiWqSHbVu3KDT0vFq05kMCV0NPhfMsNpvNZjqEJO3YsUMhISFq3LixsmfPLkn6+eef5e/vr9q1azt1LYY/Af9d/EWfuQRUZ4PHzOTc5gmmIyAdBWRzNx3hnlYdvGTs3k+Wzf3gk1yQS0zUlqSqVavqmWeesRcUktS8eXOnCwoAAAAgsxk9erQsFov69Oljb4uJiVH37t2VM2dOZc+eXW3bttWFCxfS5P4uMfxJks6cOaMff/xRISEhunnTcVzo2LFjDaUCAABAZmPJYPtUbNu2TdOnT1elSpUc2vv27auff/5Z33zzjfz8/NSjRw+1adNGmzdvTvUMLlFUrF27Vi1btlSxYsV06NAhVahQQSdPnpTNZtMjjzxiOh4AAADgkiIiItS+fXvNnDnTYSuGa9euafbs2Vq0aJGeeOIJSbc2nC5btqz+/PNP1ahR416XTBaXGP40ePBgDRgwQPv27VPWrFn13Xff6fTp06pXr56ee+450/EAAACQibhZzD1iY2N1/fp1h0dsbOw9s3bv3l3NmzdXo0aNHNp37NihuLg4h/YyZcqocOHC2rJlS+r/zFL9islw8OBBdehwazWbLFmyKDo6WtmzZ9fIkSP18ccfG04HAAAApI/g4GD5+fk5PIKDg+967uLFi7Vz5867Hg8NDZWnp6f8/f0d2vPmzavQ0NBUz+0Sw598fHzs8yjy58+vY8eOqXz58pKky5cvm4wGAACATMbknIrBgwerX79+Dm3/3uhZkk6fPq3evXtr9erVLrGvm0sUFTVq1NCmTZtUtmxZNWvWTP3799e+ffu0dOnSVB/vBQAAALgqLy+vuxYR/7Zjxw5dvHjRYf6x1WrVxo0bNXnyZK1cuVI3b95UeHi4Q2/FhQsXlC9fvlTP7RJFxdixYxURESFJGjFihCIiIvT111+rZMmSrPwEAAAA/EvDhg21b98+h7ZOnTqpTJkyevvtt1WoUCF5eHho7dq1atv21mabhw8fVkhIiGrWrJnqeVyiqChWrJj9ax8fH02bNs1gGgAAAGRmGWGj1Rw5cqhChQoObT4+PsqZM6e9vUuXLurXr58CAwPl6+urnj17qmbNmmkyEsgligoAAAAAqWvcuHFyc3NT27ZtFRsbqyZNmujzzz9Pk3tZbDabLU2u/AABAQGyJLEMDAsLc+ra0XHJSQQgI8gInx4h9QRU72E6AtLRuc0TTEdAOgrI5m46wj39dti5956pqX7pQGP3TgljPRXjx483dWsAAAAAqchYUdGxY0dZrVZ9+umn+vHHH3Xz5k01bNhQw4YNk7e3t6lYAAAAAJxkdPO7jz76SO+++66yZ8+uggULasKECerevbvJSAAAAMjkTO6onVEZLSoWLFigzz//XCtXrtSyZcv0008/aeHChUpISDAZCwAAAIATjBYVISEhatasmf15o0aNZLFYdO7cOYOpAAAAkJlZDP6XURktKuLj4xNtK+7h4aG4OJZvAgAAADIKo/tU2Gw2vfrqqw5bkcfExOjNN9+Uj4+PvW3p0qUm4gEAACATYvly5xktKjp27Jio7eWXXzaQBAAAAEByGS0q5s6da/L2AAAAAFKB0aICAAAAcDWMfnKe0YnaAAAAADI+eioAAACAO7gxU9tp9FQAAAAASBGKCgAAAAApwvAnAAAA4A4MfnIePRUAAAAAUoSeCgAAAOBOdFU4jZ4KAAAAAClCTwUAAABwBwtdFU6jpwIAAABAilBUAAAAAEgRhj8BAAAAd2BDbefRUwEAAAAgReipAAAAAO5AR4Xz6KkAAAAAkCIUFQAAAABShOFPAAAAwJ0Y/+Q0eioAAAAApAg9FQAAAMAd2FHbefRUAAAAAEgReioAAACAO7D5nfPoqQAAAACQIhQVAAAAAFKE4U8AAADAHRj95Dx6KgAAAACkCD0VAAAAwJ3oqnAaPRUAAAAAUoSiAgAAAECKMPwJAAAAuAM7ajuPngoAAAAAKUJPBQAAAHAHdtR2Hj0VAAAAAFKEngoAAADgDnRUOI+eCgAAAAApYrHZbDbTIVLbmas3TUdAOvLyoDbOTKzW/9xfWbgPd3c+L8xMRq8/ZjoC0tFnLUqbjnBPe0JuGLt35cI5jN07JRj+BAAAANyJzzOcxke8AAAAAFLEJXoqjh49qvXr1+vixYtKSEhwODZ06FBDqQAAAJAZsfmd84wXFTNnzlS3bt2UK1cu5cuXT5Y7Fga2WCwUFQAAAICLM15UjBo1Sh9++KHefvtt01EAAAAAJIPxouLq1at67rnnTMcAAAAAJLGjdnIYn6j93HPPadWqVaZjAAAAAEgm4z0VJUqU0JAhQ/Tnn3+qYsWK8vDwcDjeq1cvQ8kAAACQGdFR4Tzjm98VLVr0nscsFouOHz/u9DXZ/C5zYfO7zIXN7zIXNr/LXNj8LnNx5c3v9p+JMHbvCg9lN3bvlDDeU3HixAnTEQAAAID/4fMMp7nUR7w2m02GO04AAAAAOMkliooFCxaoYsWK8vb2lre3typVqqQvvvjCdCwAAAAASWB8+NPYsWM1ZMgQ9ejRQ7Vr15Ykbdq0SW+++aYuX76svn37Gk4IAACAzIQdtZ1nvKiYNGmSpk6dqg4dOtjbWrZsqfLly2v48OEUFQAAAICLM15UnD9/XrVq1UrUXqtWLZ0/f95AIgAAAGRmbH7nPONzKkqUKKElS5Ykav/6669VsmRJA4kAAAAAOMN4T8WIESP0wgsvaOPGjfY5FZs3b9batWvvWmwAAAAAcC3Gi4q2bdtq69atGjdunJYtWyZJKlu2rP766y89/PDDZsMBAAAg02H0k/OMFxWSVLVqVX355ZemYwAAAABIBiNFxfXr1+Xr62v/+n5unwcAAACkC7oqnGakqAgICND58+eVJ08e+fv7y3KXKfY2m00Wi0VWq9VAQgAAAABJZaSoWLdunQIDAyVJ69evNxEBAAAAQCoxUlTUq1fP/nXRokVVqFChRL0VNptNp0+fTu9oAAAAyOTYUdt5xvepKFq0qC5dupSoPSwsTEWLFjWQCAAAAIAzjK/+dHvuxL9FREQoa9asBhIBAAAgM2NHbecZKyr69esnSbJYLBoyZIiyZctmP2a1WrV161ZVqVLFUDoAAAAASWWsqNi1a5ekWz0V+/btk6enp/2Yp6enKleurAEDBpiKBwAAgEyKjgrnGSsqbq/61KlTJ02YMIH9KAAAAIAMyvhE7fHjxys+Pj5Re1hY2AM3xgMAAABgnvGiol27dlq8eHGi9iVLlqhdu3YGEgEAACBTsxh8ZFDGi4qtW7eqQYMGidrr16+vrVu3GkgEAAAAwBnGi4rY2Ni7Dn+Ki4tTdHS0gUQAAADIzCwG/3NGcHCwqlevrhw5cihPnjxq3bq1Dh8+7HBOTEyMunfvrpw5cyp79uxq27atLly4kJo/LkkuUFQ8+uijmjFjRqL2adOmqWrVqgYSAQAAAK5vw4YN6t69u/7880+tXr1acXFxevLJJxUZGWk/p2/fvvrpp5/0zTffaMOGDTp37pzatGmT6lksNpvNlupXdcLmzZvVqFEjVa9eXQ0bNpQkrV27Vtu2bdOqVatUt25dp6955urN1I4JF+blYbw2RjqyWo3+lYV05u6egQcYw2mj1x8zHQHp6LMWpU1HuKejF8yNlimZ1zvZr7106ZLy5MmjDRs26PHHH9e1a9eUO3duLVq0SM8++6wk6dChQypbtqy2bNmiGjVqpFZs8z0VtWvX1pYtW1SoUCEtWbJEP/30k0qUKKG9e/cmq6AAAAAAUsJiMfeIjY3V9evXHR6xsbFJyn3t2jVJUmBgoCRpx44diouLU6NGjeznlClTRoULF9aWLVtS9WdmbJ+KO1WpUkULFy40HQMAAAAwKjg4WCNGjHBoGzZsmIYPH37f1yUkJKhPnz6qXbu2KlSoIEkKDQ2Vp6en/P39Hc7NmzevQkNDUzO2+aIiJCTkvscLFy6cTkkAAAAAsyu7Dh48WP369XNo8/LyeuDrunfvrv3792vTpk1pFe2+jBcVRYoUkcVy7z86q9WajmkAAAAAc7y8vJJURNypR48eWr58uTZu3KiHHnrI3p4vXz7dvHlT4eHhDr0VFy5cUL58+VIrsiQXKCp27drl8DwuLk67du3S2LFj9eGHHxpKBQAAgEwrg6wRYbPZ1LNnT33//ff67bffVLRoUYfjVatWlYeHh9auXau2bdtKkg4fPqyQkBDVrFkzVbMYLyoqV66cqK1atWoqUKCAPvnkkzRZ8goAAADI6Lp3765Fixbphx9+UI4cOezzJPz8/OTt7S0/Pz916dJF/fr1U2BgoHx9fdWzZ0/VrFkzVVd+klygqLiX0qVLa9u2baZjAAAAAC5p6tSpkqT69es7tM+dO1evvvqqJGncuHFyc3NT27ZtFRsbqyZNmujzzz9P9SzGi4rr1687PLfZbDp//ryGDx+ukiVLGkoFAACAzMrZna1NScp2c1mzZtWUKVM0ZcqUNM1ivKjw9/dPNFHbZrOpUKFCWrx4saFUAAAAAJLKeFGxfv16h+dubm7KnTu3SpQooSxZjMcDAABAJnOfhUlxD0bftcfFxWn+/PkaMmRIotnqAAAAADIGN5M39/Dw0HfffWcyAgAAAIAUMlpUSFLr1q21bNky0zEAAAAASbe2qTD1yKiMT1ooWbKkRo4cqc2bN6tq1ary8fFxON6rVy9DyQAAAAAkhcWWlLWo0tD95lJYLBYdP37c6WueuXozJZGQwXh5GO9wQzqyWo3+lYV05u6ekT+3g7NGrz9mOgLS0WctSpuOcE8nr8QYu3eRnFmN3TsljPdUnDhxwnQEAAAAAClg/CPekSNHKioqKlF7dHS0Ro4caSARAAAAMjOLwf8yKuNFxYgRIxQREZGoPSoqSiNGjDCQCAAAAIAzjBcVNpst0Y7akrRnzx4FBgYaSAQAAADAGcbmVAQEBMhischisahUqVIOhYXValVERITefPNNU/EAAACQSbGjtvOMFRXjx4+XzWZT586dNWLECPn5+dmPeXp6qkiRIqpZs6apeAAAAACSyFhR0bFjR0m3lpStXbu2smQxvhAVAAAAkIGnS5tj/J18jhw5dPDgQVWsWFGS9MMPP2ju3LkqV66chg8fLk9PT8MJXcveXdv19ZfzdPTw37py+ZJGfDxedeo1vOu54z4eqeXff6O3+gxS23avpHNSpJWoyEjNnDpRG9ev1dWrYSpVuqx6D3hHZctXNB0NKbRn53Z99eVcHTl06/d71JgJqlv/1u93fHycZk2dpD//+F3nz56RT/bsqlq9ht7o0Ve5cucxnByphd/v/zbfrFn0dNncKpPHR57uFl2OjNPi3ed15lqsJMnT3aLmZXOrQr7s8vF015WoOG06cVVbTl0znBx4MOMTtd944w0dOXJEknT8+HG98MILypYtm7755hsNGjTIcDrXEx0dreIlS6nXgPfue96m39bq4P69ysmbjf+c0R8M1batWzTkg9Fa8PX3ql6jlvp066pLFy+YjoYUio6JVomSpdVnYOLf75iYGB05/Lc6dH5DM79Yog8+Hq/TISf1bv8eBpIirfD7/d/l7eGmnrULy5pg08ytZzRm/Un9+PdFRccl2M9pWT6PyuTx0aJd5/Xx+hP6/fhVPVMhr8rn9TGYHEga40XFkSNHVKVKFUnSN998o3r16mnRokWaN2+evvvuO7PhXNBjteqq85u9VKf+3XsnJOnSxQua9NlHenfEaGVxN94ZhVQUGxOjDetW661e/VXlkWp6qFCQurzRXQULFdb33y42HQ8pVKNWXXXt1kuPN2iU6Fj27Dk0dvIsPdG4qQoHFVX5ipXVe+C7Onzob10IPW8gLVIbv9//bU8UD1R4dJy+3hOq0+ExCouO05FLUboSFWc/p0iAt7advq5jV6J1NTpef4Zc07nrsSrk720weeZksZh7ZFTG33HabDYlJNyq0tesWaOnn35aklSoUCFdvnzZZLQMKSEhQaNHvKvnX+6kIsVKmI6DVGa1WmW1WuXp5eXQ7uXlpb27dxlKBVMiIyJksViUPXsO01GQCvj9/m8rly+7Dl+KVIeqBVQsp7eux8Rr88lwbQ3539Cmk1ejVT6fj/46fU3XY+JVPKe3cmf31I8HLhpMDiSN8aKiWrVqGjVqlBo1aqQNGzZo6tSpkqQTJ04ob968D3x9bGysYmNj/9Vmkde//lLOLBZ/MUfu7u5q83x701GQBrL5+KhCpSqaN2uaihQtpoDAnFqz8hcd2LdHBQsVNh0P6Sg2NlbTJ49TwyebySd7dtNxkAr4/f5vy5nNQ7WC/LXh+FWtPXpFhfyz6pkKeWRNsGn7meuSpO/3X9RzlfJqWOPisibYZLPZtGTvBR0PizacPjPKwF0Ghhgf/jR+/Hjt3LlTPXr00HvvvacSJW59uv7tt9+qVq1aD3x9cHCw/Pz8HB5Txo1J69gu6cihA1r69ZcaNGTUXTcUxH/DkJHBks2m1k0b6ImaD+vbxV+qUZNmcrMY/3VGOomPj9Pwd/vLZrOp39tDTMdBKuL3+7/LYrHo7LVY/Xross5ej9WfIdf056lrqhnkbz+nbhF/BQV4a/ZfZzRu4yn9+PcltamYVyVzZTMXHEgi4z0VlSpV0r59+xK1f/LJJ3J3d3/g6wcPHqx+/fo5tF2KypxvqPft3qnwq2F6sfWT9rYEq1XTJn6q7xZ/qUXLVhpMh9RSsFBhTZ45X9HRUYqMiFSu3Lk19J3+KlDwIdPRkA7i4+M0bHB/XTh/TuM+n0MvxX8Mv9//Xddj4nXhhuPIigsRN1Up/63f4SxuFj1VNrfmbTurgxcjJUnnb8SqoK+X6hcP1NHLUemeOTPjs1nnGS8qbrt586YuXrxon19xW+HC9+/y9fLySjTU6br1ZqrnywgaPdVCj1Sv4dD2dp831bjp02r6dGszoZBmvL2zyds7m65fv6a/tmxWt979HvwiZGi3C4qzp0M0fuoc+fn7m46ENMLv93/PybBo5c7uuEx+7uweuhodL0lyd7Moi5tFtn+9LkG8wUXGYLyoOHLkiLp06aI//vjDod1ms8lischqtRpK5pqio6J09kyI/XnoubP658gh5fD1U958+eXn5+9wfhb3LArMmUuFgoqmc1Kkla1/bJJNNhUOKqqzp0M0ZcKnKlykqJq3eMZ0NKRQ1L9+v8+fO6ujRw7J19dPOXPl0tB3+unIob81euwUWa0JuvL/i1n4+vnJw8PDVGykIn6//7s2Hr+qnnUKq2GJQO0+d0OFA7KqRmF/fbs3VJIUG5+gfy5H6emyuRVnTdDVqFsTtas95KsfDlwynB54MONFRadOnZQlSxYtX75c+fPnZy7AAxw+eED9u3e2P5864RNJ0pPNWurtoR+aioV0FBERoemTx+vSxVD5+vqpXsPGev2t3srCm8oM7/DB/erT7X+/31PG35of1rR5K7362lvavHG9JKnLy886vG781Dl6uOqj6RcUaYbf7/+u09diNHfbWTUvm1uNS+VUWFScfjhwUTvP3rCf8+XOc2pWJrfaP5xf2TzddTU6Tr8cuqwtp8LNBc+keDfqPIvNZvt3T1u68vHx0Y4dO1SmTJlUu+aZq5lz+FNm5eXBBMbMxGo1+lcW0pm7O/+0Zyaj1x8zHQHp6LMWpU1HuKdz4ebeSxbw93zwSS7IeE9FuXLl2I8CAAAALoOBM84z/hHvxx9/rEGDBum3337TlStXdP36dYcHAAAAANdmvKeiUaNGkqSGDRs6tDNRGwAAAMgYjBcV69evNx0BAAAAsLMwVdtpxouKevXqmY4AAAAAIAWMFxWSFB4ertmzZ+vgwYOSpPLly6tz587y8/MznAwAAACZDh0VTjM+UXv79u0qXry4xo0bp7CwMIWFhWns2LEqXry4du7caToeAAAAgAcw3lPRt29ftWzZUjNnzlSWLLfixMfHq2vXrurTp482btxoOCEAAAAyEzoqnGe8qNi+fbtDQSFJWbJk0aBBg1StWjWDyQAAAAAkhfHhT76+vgoJCUnUfvr0aeXIkcNAIgAAAADOMN5T8cILL6hLly769NNPVatWLUnS5s2bNXDgQL344ouG0wEAACCzYUdt5xkvKj799FNZLBZ16NBB8fHxkiQPDw9169ZNo0ePNpwOAAAAwINYbDabzXQISYqKitKxY8ckScWLF1e2bNmSfa0zV2+mVixkAF4exkfxIR1ZrS7xVxbSibs7HxdmJqPXHzMdAenosxalTUe4p0s34o3dO3cO45/5J4uxd2NWq1V79+5VdHS0JClbtmyqWLGiKlasKIvFor179yohIcFUPAAAAABJZKyo+OKLL9S5c2d5enomOubh4aHOnTtr0aJFBpIBAAAAcIaxomL27NkaMGCA3N3dEx27vaTsjBkzDCQDAABApmYx+MigjBUVhw8fVo0aNe55vHr16jp48GA6JgIAAACQHMZmgkRGRur69ev3PH7jxg1FRUWlYyIAAAAgQ3cYGGOsp6JkyZL6448/7nl806ZNKlmyZDomAgAAAJAcxoqKl156Se+//7727t2b6NiePXs0dOhQvfTSSwaSAQAAIDOzWMw9Mipjw5/69u2rX3/9VVWrVlWjRo1UpkwZSdKhQ4e0Zs0a1a5dW3379jUVDwAAAEASGSsqPDw8tGrVKo0bN06LFi3Sxo0bZbPZVKpUKX344Yfq06ePPDw8TMUDAAAAkEQus6P2g3z11Vdq2bKlfHx8HnguO2pnLuyonbmwo3bmwo7amQs7amcurryjdlik1di9A30Sb7eQEWSYd2NvvPGGLly4YDoGAAAAgH8xNvzJWRmkQwUAAAAZXEaeMG1KhumpAAAAAOCaKCoAAAAApAhFBQAAAIAUoagAAAAAkCIZZqJ2UFAQ+1YAAAAgzTFR23kZpqjYv3+/6QgAAAAA7sJIUREQECBLEkvAsLCwNE4DAAAAICWMFBXjx483cVsAAADggSxi/JOzjBQVHTt2NHFbAAAAAGnASFFx/fr1JJ/r6+ubhkkAAAAAR0zUdp6RosLf3/+BcypsNpssFousVms6pQIAAACQHEaKivXr15u4LQAAAPBAdFQ4z0hRUa9ePRO3BQAAAJAGXGafiqioKIWEhOjmzZsO7ZUqVTKUCAAAAEBSGC8qLl26pE6dOunXX3+963HmVAAAACBdMf7JaW6mA/Tp00fh4eHaunWrvL29tWLFCs2fP18lS5bUjz/+aDoeAAAAgAcw3lOxbt06/fDDD6pWrZrc3NwUFBSkxo0by9fXV8HBwWrevLnpiAAAAMhE2PzOecZ7KiIjI5UnTx5JUkBAgC5duiRJqlixonbu3GkyGgAAAIAkMF5UlC5dWocPH5YkVa5cWdOnT9fZs2c1bdo05c+f33A6AAAAAA9ibPjTiRMnVLRoUfXu3Vvnz5+XJA0bNkxNmzbVwoUL5enpqXnz5pmKBwAAgEyKHbWdZ6yoKF68uIKCgtSgQQM1aNBAZ86cUdWqVXXq1CkdOnRIhQsXVq5cuUzFAwAAAJBExoqKdevW/V979x0WxbX+Afy7lF0WWIooHSFKTywRS5D8hFUMxFxrFKMkwcY1ir0bC9hiiw3FEkwAjcYSW6KRhBDwIiSIBfQKoqCIRtQYY1mVIvv+/vBhLkNdRAX1/TwPz8POOTvzzsyZM3Nm5pxFYmIiEhMT8d1336G4uBgtWrRA165doVQqYWNj01ChMcYYY4yx1xg/qKi7BmtU+Pj4wMfHBwBQWFiIlJQUoZERExODkpISuLq64uzZsw0VImOMMcYYY0wDDT6kLADo6emha9euePfdd6FUKnH48GFs2rQJ586da+jQGGOMMcbY64YfVdRZgzYqiouL8ccffyAhIQGJiYlITU2FnZ0dunTpgnXr1sHb27shw2OMMcYYY4xpoMEaFV27dkVqaireeOMNeHt7Y+TIkdi+fTsPI8sYY4wxxthLpsEaFUlJSbCyskLXrl3h4+MDb29vmJmZNVQ4jDHGGGOMAeBf1H4aDfbjd3fu3MFXX30FfX19LF26FNbW1mjVqhXGjBmD77//XvhlbcYYY4wxxlj1IiIi4ODgAD09PXTq1AnHjh174TFIiIhe+FKrcP/+fRw9elToX5GRkQEnJyf897//rfO8rv5T/BwiZI2VTLfBfxievUClpY2iymIviLY23y18nSxJyG3oENgLtKKnS0OHUK3Cxw23bL06vke0c+dOfPrpp9i4cSM6deqE1atXY/fu3cjOzoa5ufnzCbIKjeZqzMDAAE2aNEGTJk1gamoKHR0dZGVlNXRYjDHGGGOMNVorV65EcHAwhg4dCnd3d2zcuBH6+vr45ptvXmgcDdanQq1W4/jx40hMTERCQgKSk5Px4MED2NjYQKlUIiIiAkqlsqHCY4wxxhhj7IUrKipCUVGRaJpMJoNMJquUt7i4GCdOnMDMmTOFaVpaWvD19cXvv//+3GMtr8EaFSYmJnjw4AEsLS2hVCqxatUq+Pj4oGXLlvWet62p9BlE+HIpKirC4sWLMXPmzCoLHXu18P5+vfD+fr28zvu7Mb8O87y8zvu7MavrK0jPUtjCxZg3b55oWmhoKMLCwirlvXXrFkpLS2FhYSGabmFh8cJ/763B+lRs2rQJSqUSzs7ODbH4V869e/dgbGyMu3fvwsjIqKHDYc8Z7+/XC+/v1wvv79cL729WUV2eVFy7dg02NjZISUmBp6enMH3atGk4cuQIUlNTn3u8ZRqsHTZy5MiGWjRjjDHGGGONUnUNiKo0bdoU2trauHHjhmj6jRs3YGlp+TzCq1aj6ajNGGOMMcYY05xUKoWHhwfi4+OFaWq1GvHx8aInFy9CA74xxhhjjDHGGKuPSZMmISgoCO3bt0fHjh2xevVqPHjwAEOHDn2hcXCj4hUhk8kQGhrKnbxeE7y/Xy+8v18vvL9fL7y/WX0NHDgQf/31F+bOnYvr16+jbdu2iI2NrdR5+3lrND9+xxhjjDHGGHs5cZ8KxhhjjDHGWL1wo4IxxhhjjDFWL9yoYIwxxhhjjNULNypq4eDggNWrVzd0GKyc6OhomJiYNHQYr4QhQ4agT58+DR0Ge4Xl5eVBIpEgPT29oUN54biuajwSExMhkUhw584dALxvnlbF7chYeY26UTFkyBBIJBLhz8zMDP7+/jh9+nRDh9bo+Pj4CNtJT08P7u7uWL9+fUOHVW9VNeoGDhyI8+fPN0g8169fx9ixY9GiRQvIZDLY2dmhZ8+eovGhG6PqLuzWrFmD6OjoBompLsrqgs8++6xSWkhICCQSCYYMGVKvZYSFhaFt27b1mkddllV2vOro6MDBwQETJ06ESqV6Ict/XqpqpNrZ2aGgoABvvfVWwwT1nJU/T0mlUjg6OmL+/Pl4/PjxM1+Wj48PJkyY8Mzn29hs3LgRCoVCtA1VKhV0dXXh4+Mjylt2kZubm/tcYyouLsayZcvQpk0b6Ovro2nTpvDy8kJUVBRKSkqe67LLvOiGUFXlrXPnzigoKICxsfELi4O9PBp1owIA/P39UVBQgIKCAsTHx0NHRwf/+te/GjqsF6q4uFijfMHBwSgoKEBmZiYCAgIQEhKC7777rl7zfB6IqF4nXLlcDnNz82cYkWby8vLg4eGB3377DcuXL8eZM2cQGxsLpVKJkJCQFx5Pmfqc0IyNjV+au3V2dnbYsWMHHj16JEwrLCzE9u3b0bx586eeb33LY0WlpaVQq9W15nvzzTdRUFCAvLw8LF26FF999RUmT55cZd6GPF6B+pUxbW1tWFpaQkfn1R3BvOw8deHCBUyePBlhYWFYvnx5Q4dVrYYuT7VRKpVQqVQ4fvy4MC0pKQmWlpZITU1FYWGhMD0hIQHNmzdHy5Ytn1s8xcXF8PPzw5IlS/Dvf/8bKSkpOHbsGEJCQrB27VqcPXv2uS37aTzP/SuVSmFpaQmJRPLclsFeYtSIBQUFUe/evUXTkpKSCADdvHmTiIimTZtGTk5OJJfL6Y033qDZs2dTcXGxkD89PZ18fHzI0NCQFAoFtWvXjtLS0kTze/fdd0lPT49sbW1p7NixpFKphHR7e3tatWqV8HnFihX01ltvkb6+Ptna2tKoUaPo/v37RESkUqlIoVDQ7t27RTHv27eP9PX16d69e0RElJ+fTwMGDCBjY2MyNTWlXr160aVLlyqt98KFC8nKyoocHBxq3Vbe3t40fvx40TQnJyf66KOPhPSQkBAaP348mZmZkY+PDxERnTlzhvz9/cnAwIDMzc3p448/pr/++ks035CQEAoJCSEjIyMyMzOj2bNnk1qtFvJs2bKFPDw8yNDQkCwsLGjQoEF048YNIT0hIYEA0E8//UTt2rUjXV1dSkhIoJycHOrVqxeZm5uTgYEBtW/fnuLi4kTLBiD6IyKKiooiY2Nj0bquX7+eWrRoQbq6uuTs7ExbtmwRpQOgyMhI6tOnD8nlcnJ0dKQDBw7Uul3Le//998nGxkZUPsr8888/RER0+fJl6tWrFxkYGJBCoaABAwbQ9evXhXyhoaHUpk0b2rJlC9nb25ORkRENHDhQKBtERKWlpbR06VJq2bIlSaVSsrOzo4ULFxIR0aVLlwgA7dixg7p06UIymYyioqKIiCgyMpJcXV1JJpORi4sLRUREiNa//J+3tzcRVT7GvL29aezYsTR16lQyNTUlCwsLCg0NFa1rVlYWeXl5kUwmIzc3N4qLiyMAtG/fvjptz7ooi/Ott96ib7/9Vpi+bds2at26NfXu3ZuCgoKIiKiwsJDGjh1LzZo1I5lMRl5eXnTs2DHhO1WVx6ioqErbqGy71nTME/2vPB44cIDc3NxIW1tbdDxXpawclBccHEyWlpai9MjISHJwcCCJREJET8rZ8OHDqWnTpqRQKEipVFJ6enql+W7cuJFsbW1JLpfTgAED6M6dO0KeY8eOka+vL5mZmZGRkRF16dKFTpw4IYoFAK1fv5569uxJ+vr6FBoaSo8fP6Zhw4aRg4MD6enpkbOzM61evVq07IrbMCEhQSizp06dEvImJiZShw4dSCqVkqWlJU2fPp1KSkqEdE3KYWNR1Xmqe/fu9M4771Sqq2qr84iIIiIiyNHRkWQyGZmbm9OHH34oLKfi9i0rZ5rW4xXr/8bMysqKFi9eLHyeNm0ahYSEkJubGyUkJAjTu3TpQkFBQRqfh8rq6qrOI9VZunQpaWlp0cmTJyulFRcXC+cETeueX3/9lTw8PEgul5OnpyedO3dOyFPdNUvZd8v/lR0T9vb2NH/+fPrkk09IoVBQUFBQpfUlIjp16pSo3BARHT16lLy9vUkul5OJiQm99957dPv27WrLW1Xz/f7778nd3Z2kUinZ29vTl19+KdpG9vb2tGjRIho6dCgZGhqSnZ0dbdq0SaNtz14ujf5JRXkqlQrffvstHB0dYWZmBgBQKBSIjo5GZmYm1qxZg8jISKxatUr4TmBgIGxtbZGWloYTJ05gxowZ0NXVBQDk5ubC398fH374IU6fPo2dO3fi6NGjGDNmTLUxaGlpITw8HGfPnkVMTAx+++03TJs2DQBgYGCAjz76CFFRUaLvREVFoX///lAoFCgpKYGfnx8UCgWSkpKQnJwMQ0ND+Pv7i+4uxMfHIzs7G3FxcTh48OBTbS+5XC6aZ0xMDKRSKZKTk7Fx40bcuXMHXbt2xdtvv43jx48jNjYWN27cQEBAgGg+MTEx0NHRwbFjx7BmzRqsXLkSmzdvFtJLSkqwYMECZGRkYP/+/cjLy6vyVZQZM2ZgyZIlyMrKQuvWraFSqdCjRw/Ex8fj1KlT8Pf3R8+ePZGfnw8A2Lt3L2xtbTF//nzhaVVV9u3bh/Hjx2Py5Mn473//i5EjR2Lo0KFISEgQ5Zs3bx4CAgJw+vRp9OjRA4GBgbh9+7ZG2/L27duIjY1FSEgIDAwMKqWbmJhArVajd+/euH37No4cOYK4uDhcvHgRAwcOFOXNzc3F/v37cfDgQRw8eBBHjhzBkiVLhPSZM2diyZIlmDNnDjIzM7F9+/ZKP2AzY8YMjB8/HllZWfDz88O2bdswd+5cLFq0CFlZWfjiiy8wZ84cxMTEAACOHTsGAPj1119RUFCAvXv3VruuMTExMDAwQGpqKpYtW4b58+cjLi4OwJO78H369IG+vj5SU1Px1VdfYdasWRptw2dh2LBhouPrm2++qfSLodOmTcOePXsQExODkydPwtHREX5+fpX2dfny2L17d0yePFl4elBQUCDst5qO+TIPHz7E0qVLsXnzZpw9e/apnqRVPF5zcnKwZ88e7N27V3htbcCAAbh58yYOHz6MEydOoF27dujWrZto3XJycrBr1y78+OOPiI2NxalTpzB69Ggh/f79+wgKCsLRo0fxxx9/wMnJCT169MD9+/dF8YSFhaFv3744c+YMhg0bBrVaDVtbW+zevRuZmZmYO3cuPv/8c+zatQsAMGXKFAQEBIieLnfu3LnSev7555/o0aMHOnTogIyMDGzYsAFff/01Fi5cKMpXUzls7CruyzK11XnHjx/HuHHjMH/+fGRnZyM2NhZdunQB8ORVRU9PT+GJdEFBAezs7OpUj5ev/xs7pVIpqsMTEhLg4+MDb29vYfqjR4+QmpoKpVKp8XnoaWzbtg2+vr54++23K6Xp6uoK5wRN655Zs2ZhxYoVOH78OHR0dDBs2DAhrbprls6dO2P16tUwMjIS9v+UKVOE73355Zdo06YNTp06hTlz5mi0Xunp6ejWrRvc3d3x+++/4+jRo+jZsydKS0urLW8VnThxAgEBAfjoo49w5swZhIWFYc6cOZVeq12xYgXat28v1EejRo1Cdna2RnGyl0hDt2pqEhQURNra2mRgYEAGBgYEgKysrCrdVStv+fLl5OHhIXxWKBQUHR1dZd7hw4fTv//9b9G0pKQk0tLSokePHhFR5ScVFe3evZvMzMyEz6mpqaStrU3Xrl0jIqIbN26Qjo4OJSYmEhHR1q1bycXFRXSnv6ioiORyOf3888/CeltYWFBRUVG1y62o/JOKx48f09atWwkArVu3Tkh/++23Rd9ZsGABvffee6JpV65cIQCUnZ0tfM/NzU0U7/Tp08nNza3aWNLS0giAcDe37M7G/v37a12PN998k9auXSt8rmr7V7zD1LlzZwoODhblGTBgAPXo0UP4DIBmz54tfFapVASADh8+XGtMRE/2KwDau3dvtXl++eUX0tbWpvz8fGHa2bNnCYBwtyo0NFT01IqIaOrUqdSpUyciIrp37x7JZDKKjIyschlld33L3yEmImrZsiVt375dNG3BggXk6ekp+l75u8VEVT+pePfdd0V5OnToQNOnTyciosOHD5OOjg4VFBQI6S/yScXNmzdJJpNRXl4e5eXlkZ6eHv3111/CkwqVSkW6urq0bds24bvFxcVkbW1Ny5YtI6Lqy2NVTw+qUvGYL3vKUf6JQW0qLuv48ePUtGlT6t+/v5Cuq6srPJElelI3GRkZUWFhoWheLVu2FO76hYaGkra2Nl29elVIP3z4MGlpaYn2WXmlpaWkUCjoxx9/FKYBoAkTJtS6HiEhIcKddKKq79pXLHuff/55pTowIiKCDA0NqbS0lIhqL4eNSfl1VqvVFBcXRzKZjKZMmaLR3fDydd6ePXvIyMhIVD+UV9UTaU3r8Yr1f2MXGRlJBgYGVFJSQvfu3SMdHR26efMmbd++nbp06UJERPHx8QSALl++XOn71Z2HnuZJhVwup3HjxtWYpy51z6+//irkOXToEAEQrjlqumapLmZ7e3vq06ePaJomTyoGDRpEXl5e1a5TVeWt4nwHDx5M3bt3F+WZOnUqubu7i+L7+OOPhc9qtZrMzc1pw4YN1S6bvZwa/ZMKpVKJ9PR0pKen49ixY/Dz88P777+Py5cvAwB27twJLy8vWFpawtDQELNnzxbu+gDApEmTMGLECPj6+mLJkiWizlwZGRmIjo6GoaGh8Ofn5we1Wo1Lly5VGc+vv/6Kbt26wcbGBgqFAp988gn+/vtvPHz4EADQsWNHvPnmm8Id4m+//Rb29vbC3aaMjAzk5ORAoVAIy2zSpAkKCwtFsbVq1QpSqbRO22r9+vUwNDSEXC5HcHAwJk6ciFGjRgnpHh4eovwZGRlISEgQrb+rqysAiGJ55513RO9Penp64sKFCygtLQXw5E5Fz5490bx5cygUCnh7ewOAaD8AQPv27UWfVSoVpkyZAjc3N5iYmMDQ0BBZWVmVvlebrKwseHl5iaZ5eXkhKytLNK1169bC/wYGBjAyMsLNmzc1WgZp8MPzWVlZsLOzE93NcXd3h4mJiSgWBwcHKBQK4bOVlZUQR1ZWFoqKitCtW7cal1V+Wz548AC5ubkYPny4aF8uXLjwqTovlt9OFePLzs6GnZ0dLC0thfSOHTvWeRlPq1mzZvjggw8QHR2NqKgofPDBB2jatKmQnpubi5KSElF50NXVRceOHSuVh4rlsTq1HfPAk/eMK2632pw5c0Y4Xjt27AhPT0+sW7dOSLe3t0ezZs2EzxkZGVCpVDAzMxPt50uXLon2c/PmzWFjYyN89vT0hFqtFu4K3rhxA8HBwXBycoKxsTGMjIygUqlqPV4BICIiAh4eHmjWrBkMDQ3x1VdfPdXx6unpKapTvLy8oFKpcPXqVWFaTeWwsTl48CAMDQ2hp6eH999/HwMHDkRYWFilfLXVed27d4e9vT1atGiBTz75BNu2bROVs6poWo9XrP8bOx8fHzx48ABpaWlISkqCs7MzmjVrBm9vb6FfRWJiIlq0aIHmzZtrfB56GprU/3Wpe8qXbSsrKwAQynZN1yw10bQ+K6/sSUV9VHf+LX+NAIjXWSKRwNLSstEez+zpNfqecwYGBnB0dBQ+b968GcbGxoiMjMQHH3yAwMBAzJs3D35+fjA2NsaOHTuwYsUKIX9YWBgGDx6MQ4cO4fDhwwgNDcWOHTvQt29fqFQqjBw5EuPGjau03Ko6fubl5eFf//oXRo0ahUWLFqFJkyY4evQohg8fjuLiYujr6wMARowYgYiICMyYMQNRUVEYOnSocAJVqVTw8PDAtm3bKs2//AVEVa/Y1CYwMBCzZs2CXC6HlZUVtLTEbcaK81SpVOjZsyeWLl1aaV5lFV1tHjx4AD8/P+EVnGbNmiE/Px9+fn6VHv9XXP6UKVMQFxeHL7/8Eo6OjpDL5ejfv/9z62RW9tpbGYlEolGHWgBwcnKCRCLBuXPnnmsccrlco3mU35ZlIwZFRkaiU6dOonza2trPNL7GYNiwYcIrihEREU89H02OMU2PeblcXueOiy4uLvjhhx+go6MDa2vrSjcRqjperayskJiYWGledelsHxQUhL///htr1qyBvb09ZDIZPD09az1ed+zYgSlTpmDFihXw9PSEQqHA8uXLkZqaqvGy66Kxl8PylEolNmzYAKlUCmtr62o7pddW5ykUCpw8eRKJiYn45ZdfMHfuXISFhSEtLa3afaxpPf4055SG5OjoCFtbWyQkJOCff/4RGgnW1taws7NDSkoKEhIS0LVr1zqdh56Gs7PzM6n7y5Qv22X1RlnZrumapSYV92/Z+b98g6jigAuanm+ehZfpeGZPr9E3KiqSSCTQ0tLCo0ePkJKSAnt7e9E73WVPMMpzdnaGs7MzJk6ciEGDBiEqKgp9+/ZFu3btkJmZKWq01OTEiRNQq9VYsWKFcMCWvU9c3scff4xp06YhPDwcmZmZCAoKEtLatWuHnTt3wtzcHEZGRnVd/RoZGxtrvC5lsezZswcODg41jsxS8aKh7D1sbW1tnDt3Dn///TeWLFki3KEvP2JHTZKTkzFkyBChslSpVMjLyxPlkUqlorsdVXFzc0NycrJoOycnJ8Pd3V2jODTRpEkT+Pn5ISIiAuPGjatUgd+5cwdubm64cuUKrly5ImyLzMxM3LlzR+NYnJycIJfLER8fjxEjRmj0HQsLC1hbW+PixYsIDAysMk/ZBWtt27I2Li4uuHLlCm7cuCH080hLS6vXPOuqrP+RRCKBn5+fKK1ly5bCe+P29vYAnpxI09LSah2Ks6qypukx/zTKhh/VVLt27XD9+nVhCNrq5Ofn49q1a7C2tgbw5HjV0tKCi4sLgCfHxvr169GjRw8AwJUrV3Dr1q1al5+cnIzOnTuL+mdUvIuq6fG6Z88eEJFwQZWcnAyFQgFbW9ta42iMKt78qo4mdZ6Ojg58fX3h6+uL0NBQmJiY4LfffkO/fv2q3L6a1uMvI6VSicTERPzzzz+YOnWqML1Lly44fPgwjh07hlGjRtXrPKSJwYMH4/PPP8epU6cq9asoKSlBcXFxveqeiqq7ZtHk+CpTdpOyoKAApqamAFBpSPHWrVsjPj4e8+bNq3IedTn/lpecnAxnZ+enuqnFXm6N/vWnoqIiXL9+HdevX0dWVhbGjh0r3JlxcnJCfn4+duzYgdzcXISHh2Pfvn3Cdx89eoQxY8YgMTERly9fRnJyMtLS0uDm5gYAmD59OlJSUjBmzBikp6fjwoULOHDgQLUdtR0dHVFSUoK1a9fi4sWL2Lp1a5Ud3kxNTdGvXz9MnToV7733nuhEGRgYiKZNm6J3795ISkrCpUuXkJiYiHHjxoke/b8IISEhuH37NgYNGoS0tDTk5ubi559/xtChQ0UVSX5+PiZNmoTs7Gx89913WLt2LcaPHw/gyRMdqVQqbJMffvgBCxYs0Gj5Tk5OQifUjIwMDB48uNKdCwcHB/znP//Bn3/+We2Fz9SpUxEdHY0NGzbgwoULWLlyJfbu3SvqxPYsREREoLS0FB07dsSePXtw4cIFZGVlITw8HJ6envD19UWrVq0QGBiIkydP4tixY/j000/h7e2t8aNpPT09TJ8+HdOmTcOWLVuQm5uLP/74A19//XWN35s3bx4WL16M8PBwnD9/HmfOnEFUVBRWrlwJADA3N4dcLhc6cd69e/eptkH37t3RsmVLBAUF4fTp00hOTsbs2bMB4IUNMaitrY2srCxkZmZWOmkZGBhg1KhRmDp1KmJjY5GZmYng4GA8fPgQw4cPr3G+Dg4OuHTpEtLT03Hr1i0UFRVpfMy/CL6+vvD09ESfPn3wyy+/IC8vDykpKZg1a5boAkpPTw9BQUHIyMhAUlISxo0bh4CAAOGVNScnJ2zduhVZWVlITU1FYGCgRncsnZyccPz4cfz88884f/485syZU6lB6eDggNOnTyM7Oxu3bt2qcija0aNH48qVKxg7dizOnTuHAwcOIDQ0FJMmTar0dPVVU1udd/DgQYSHhyM9PR2XL1/Gli1boFarhQahg4MDUlNTkZeXh1u3bkGtVmtcj7+MlEoljh49ivT0dOFJBQB4e3tj06ZNKC4uhlKprNd5SBMTJkyAl5cXunXrhoiICGRkZODixYvYtWsX3nnnHVy4cKFedU+Z2q5ZHBwcoFKpEB8fj1u3btX4apyjoyPs7OwQFhaGCxcu4NChQ6K3OIAng4KkpaVh9OjROH36NM6dO4cNGzYI59qqyltFkydPRnx8PBYsWIDz588jJiYG69ate+bnX/aSaNguHTWrOKSZQqGgDh060Pfffy/kmTp1KpmZmZGhoSENHDiQVq1aJXRkKioqoo8++ojs7OxIKpWStbU1jRkzRugQRfRkeMXu3buToaEhGRgYUOvWrWnRokVCesWOwitXriQrKyuSy+Xk5+dHW7ZsqdQZiuh/Hch27dpVab0KCgro008/paZNm5JMJqMWLVpQcHAw3b17V1jvip0da1NVhypN0s+fP099+/YlExMTksvl5OrqShMmTBA6UXp7e9Po0aPps88+IyMjIzI1NaXPP/9c1Mly+/bt5ODgQDKZjDw9PemHH34QdcysqsMY0ZMOnEqlkuRyOdnZ2dG6desqxfn7779T69atSSaT1XtI2YodiY2NjYVhQzV17do1CgkJIXt7e5JKpWRjY0O9evUShjjUdEjZ8latWkX29vbC59LSUlq4cCHZ29uTrq4uNW/enL744gthm6GKDtdET4ZXbdu2LUmlUjI1NaUuXbqIOpZHRkaSnZ0daWlp1TikbMVyUn64VqL/DSkrlUrJ1dWVfvzxRwJAsbGxGm/HuqrtmCgf46NHj2js2LHC8VXdsI4Vy2NhYSF9+OGHZGJiIhpStrZjvi4dPsvU1im8uvR79+7R2LFjydramnR1dcnOzo4CAwOFwQHKvrd+/XqytrYmPT096t+/P92+fVuYx8mTJ6l9+/akp6dHTk5OtHv37kr1XFXHS2FhIQ0ZMoSMjY3JxMSERo0aRTNmzBDFefPmTaE+RT2HlK2tHDYWNZXNimWjtjovKSmJvL29ydTUlORyObVu3Zp27twpfD87O5veeecdksvlog63mtTjNZ0fGquysuPq6iqanpeXRwDIxcVFmFbX81Bdj9vCwkJavHgxtWrVivT09KhJkybk5eVF0dHRQtl9mrqnfOdpTa5ZPvvsMzIzM6s0pGxVA8ocPXpUiPf//u//aPfu3ZWGlE1MTKTOnTuTTCYjExMT8vPzE+KrqrzVNKRs2flq+fLlojiqiq9NmzaNdpho9vQkRBr0QGJ1tnXrVkycOBHXrl2rc4frxsTHxwdt27at9KvWjJVJTk7Gu+++i5ycnOf6A1SsdmFhYdi/f3+l1xwYY4yx5+3VegGzEXj48CEKCgqwZMkSjBw58qVuUDBWlX379sHQ0BBOTk7IycnB+PHj4eXlxQ0Kxhhj7DX2ar/A2gCWLVsGV1dXWFpaYubMmc9knklJSaLhAiv+MfYi3b9/HyEhIXB1dcWQIUPQoUMHHDhwoKHDalRqOl6TkpIaOjzGWDlvvvlmtcdrVSM1Msaqxq8/vQQePXqEP//8s9r0uowgwxh7/nJycqpNs7GxeaFDOTLGanb58uUqBxUAnoyuV/53hRhj1eNGBWOMMcYYY6xe+PUnxhhjjDHGWL1wo4IxxhhjjDFWL9yoYIwxxhhjjNULNyoYY4wxxhhj9cKNCsYYe0aGDBmCPn36CJ99fHwwYcKEFx5HYmIiJBIJ7ty5U20eiUSC/fv3azzPsLAwtG3btl5x5eXlQSKR8I/zMcbYK4gbFYyxV9qQIUMgkUggkUgglUrh6OiI+fPn4/Hjx8992Xv37sWCBQs0yqtJQ4AxxhhrrPgXtRljrzx/f39ERUWhqKgIP/30E0JCQqCrq1vlD1QWFxdDKpU+k+U2adLkmcyHMcYYa+z4SQVj7JUnk8lgaWkJe3t7jBo1Cr6+vvjhhx8A/O+VpUWLFsHa2houLi4AgCtXriAgIAAmJiZo0qQJevfujby8PGGepaWlmDRpEkxMTGBmZoZp06ah4s/+VHz9qaioCNOnT4ednR1kMhkcHR3x9ddfIy8vD0qlEgBgamoKiUSCIUOGAADUajUWL16MN954A3K5HG3atMH3338vWs5PP/0EZ2dnyOVyKJVKUZyamj59OpydnaGvr48WLVpgzpw5Vf4g2KZNm2BnZwd9fX0EBATg7t27ovTNmzfDzc0Nenp6cHV1xfr16+scC2OMsZcPNyoYY68duVyO4uJi4XN8fDyys7MRFxeHgwcPoqSkBH5+flAoFEhKSkJycjIMDQ3h7+8vfG/FihWIjo7GN998g6NHj+L27dvYt29fjcv99NNP8d133yE8PBxZWVnYtGkTDA0NYWdnhz179gAAsrOzUVBQgDVr1gAAFi9ejC1btmDjxo04e/YsJk6ciI8//hhHjhwB8KTx069fP/Ts2RPp6ekYMWIEZsyYUedtolAoEB0djczMTKxZswaRkZFYtWqVKE9OTg527dqFH3/8EbGxsTh16hRGjx4tpG/btg1z587FokWLkJWVhS+++AJz5sxBTExMneNhjDH2kiHGGHuFBQUFUe/evYmISK1WU1xcHMlkMpoyZYqQbmFhQUVFRcJ3tm7dSi4uLqRWq4VpRUVFJJfL6eeffyYiIisrK1q2bJmQXlJSQra2tsKyiIi8vb1p/PjxRESUnZ1NACguLq7KOBMSEggA/fPPP8K0wsJC0tfXp5SUFFHe4cOH06BBg4iIaObMmeTu7i5Knz59eqV5VQSA9u3bV2368uXLycPDQ/gcGhpK2tradPXqVWHa4cOHSUtLiwoKCoiIqGXLlrR9+3bRfBYsWECenp5ERHTp0iUCQKdOnap2uYwxxl5O3KeCMfbKO3jwIAwNDVFSUgK1Wo3BgwcjLCxMSG/VqpWoH0VGRgZycnKgUChE8yksLERubi7u3r2LgoICdOrUSUjT0dFB+/btK70CVSY9PR3a2trw9vbWOO6cnBw8fPgQ3bt3F00vLi7G22+/DQDIysoSxQEAnp6eGi+jzM6dOxEeHo7c3FyoVCo8fvwYRkZGojzNmzeHjY2NaDlqtRrZ2dlQKBTIzc3F8OHDERwcLOR5/PgxjI2N6xwPY4yxlws3KhhjrzylUokNGzZAKpXC2toaOjriqs/AwED0WaVSwcPDA9u2bas0r2bNmj1VDHK5vM7fUalUAIBDhw6JLuaBJ/1EnpXff/8dgYGBmDdvHvz8/GBsbIwdO3ZgxYoVdY41MjKyUiNHW1v7mcXKGGOsceJGBWPslWdgYABHR0eN87dr1w47d+6Eubl5pbv1ZaysrJCamoouXboAeHJH/sSJE2jXrl2V+Vu1agW1Wo0jR47A19e3UnrZk5LS0lJhmru7O2QyGfLz86t9wuHm5iZ0Oi/zxx9/1L6S5aSkpMDe3h6zZs0Spl2+fLlSvvz8fFy7dg3W1tbCcrS0tODi4gILCwtYW1vj4sWLCAwMrNPyGWOMvfy4ozZjjFUQGBiIpk2bonfv3khKSsKlS5eQmJiIcePG4erVqwCA8ePHY8mSJdi/fz/OnTuH0aNH1/gbEw4ODggKCsKwYcOwf/9+YZ67du0CANjb20MikeDgwYP466+/oFKpoFAoMGXKFEycOBExMTHIzc3FyZMnsXbtWqHz82effYYLFy5g6tSpyM7Oxvbt2xEdHV2n9XVyckJ+fj527NiB3NxchIeHV9npXE9PD0FBQcjIyEBSUhLGjRuHgIAAWFpaAgDmzZuHxYsXIzw8HOfPn8eZM2cQFRWFlStX1ikexhhjLx9uVDDGWAX6+vr4z3/+g+bNm6Nfv35wc3PD8OHDUVhYKDy5mDx5Mj755BMEBQXB09MTCoUCffv2rXG+GzZsQP/+/TF69Gi4uroiODgYDx48AADY2Nhg3rx5mDFjBiwsLDBmzBgAwIIFCzBnzhwsXrwYbm5u8Pf3x6FDh/DGG28AeNLPYc+ePdi/fz/atGmDjRs34osvvqjT+vbq1QsTJ07EmDFj0LZtW6SkpGDOnDmV8jk6OqJfv37o0aMH3nvvPbRu3Vo0ZOyIESOwefNmREVFoVWrVvD29kZ0dLQQK2OMsVeXhKrrVcgYY4wxxhhjGuAnFYwxxhhjjLF64UYFY4wxxhhjrF64UcEYY4wxxhirF25UMMYYY4wxxuqFGxWMMcYYY4yxeuFGBWOMMcYYY6xeuFHBGGOMMcYYqxduVDDGGGOMMcbqhRsVjDHGGGOMsXrhRgVjjDHGGGOsXrhRwRhjjDHGGKuX/wcI+MjsC6LPCQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    " \n",
    "# Generate confusion matrix\n",
    "conf_matrix = confusion_matrix(test_labels, test_pred_labels)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=CLASSES_LIST, yticklabels=CLASSES_LIST)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bad07f75-2854-40e6-95ac-afa03508ec8a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}