evaluate_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import click\n",
    "import joblib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from IPython.display import display, Audio\n",
    "import ipywidgets as widgets\n",
    "import seaborn as sns\n",
    "import random\n",
    "\n",
    "from keras.models import Model, load_model\n",
    "from keras.utils import plot_model\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import (\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    roc_curve,\n",
    "    auc,\n",
    "    classification_report,\n",
    "    confusion_matrix,\n",
    "    ConfusionMatrixDisplay,\n",
    ")\n",
    "\n",
    "from cnn_audio.params import pr\n",
    "from cnn_audio import model_training as mt\n",
    "from cnn_audio.preprocessing import plot_recording, extract_features, process_single_file\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import click\n",
    "import joblib\n",
    "import librosa\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_selection = {\n",
    "    'pitch_detection': {\n",
    "        'aim': 'Pitch Detection',\n",
    "        'features': ['chroma', 'mfcc'],\n",
    "        'targets': ['pitch'],\n",
    "        'target_label': 'Pitch',\n",
    "    },\n",
    "    'instrument_type_detection': {\n",
    "        'aim': 'Instrument Type Detection',\n",
    "        'features': ['mfcc'],\n",
    "        'targets': ['instrument_family_str'],\n",
    "        'target_label': 'Instrument Type',\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_outliers = False\n",
    "use_final_model = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08bb5d3721574d7388211933f71af087",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Model goal:', options=('pitch_detection', 'instrument_type_detection'), value='pitch_det…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_selection_w = widgets.Dropdown(\n",
    "    options=model_selection.keys(),\n",
    "    disabled=False,\n",
    "    description='Model goal:'\n",
    ")\n",
    "display(model_selection_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4aa1b745622348e2abdde70ad69b0fe4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Data size:', options=('SAMPLE', 'FULL'), value='SAMPLE')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_size_w = widgets.Dropdown(\n",
    "    options=['SAMPLE', 'FULL'],\n",
    "    disabled=False,\n",
    "    rows=2,\n",
    "    description='Data size:'\n",
    ")\n",
    "display(data_size_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1346ba453cc949719f46eee52a4048bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Model run id:', options=('20230725_234755_f-chroma-mfcc_t-pitch', '20230726_231936_f-chr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights_base_dir = Path('./model/weights/')\n",
    "run_id_w = widgets.Dropdown(\n",
    "    options=[x.stem for x in list(weights_base_dir.glob('*'))],\n",
    "    disabled=False,\n",
    "    rows=2,\n",
    "    description='Model run id:'\n",
    ")\n",
    "display(run_id_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_model = model_selection[model_selection_w.value]  # type: ignore\n",
    "aim = selected_model['aim']\n",
    "features = selected_model['features']\n",
    "targets = selected_model['targets']\n",
    "target_label = selected_model['target_label']\n",
    "\n",
    "data_size = data_size_w.value\n",
    "run_id = run_id_w.value\n",
    "\n",
    "midi_mapping_file = Path('./data/midi_pitch_mapping.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_SHAPE = (50, 94, len(features))\n",
    "VALUE_COUNT = 50 * 94 * len(features)\n",
    "\n",
    "def parse_tfrecord(example_proto):\n",
    "    # TODO: find a way to get these dynamically (and efficiently!) for the parsing function\n",
    "    data_shape: tuple[int, ...] = DATA_SHAPE  # (50, 94, 1)\n",
    "    value_count = VALUE_COUNT  # 4700  # data_shape[0] * data_shape[1] * data_shape[2]\n",
    "\n",
    "    feature_description = {\n",
    "        'input': tf.io.FixedLenFeature([value_count], tf.float32),\n",
    "        'label': tf.io.FixedLenFeature([], tf.int64),\n",
    "    }\n",
    "\n",
    "    parsed_example = tf.io.parse_single_example(example_proto, feature_description)\n",
    "\n",
    "    parsed_example['input'] = tf.reshape(parsed_example['input'], data_shape)\n",
    "\n",
    "    input_feature = parsed_example['input']\n",
    "    label_feature = parsed_example['label']\n",
    "\n",
    "    return input_feature, label_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_affix = '-'.join(['f'] + features)\n",
    "targets_affix = '-'.join(['t'] + targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_file = Path(f'data/features_{data_size}/labels_{targets_affix}.joblib')\n",
    "encoder: LabelEncoder = joblib.load(labels_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "midi_pitch_mapping = pd.read_csv(midi_mapping_file, dtype='object')\n",
    "midi_pitch_mapping['midi_pitch_int'] = midi_pitch_mapping['midi_pitch'].astype('int')\n",
    "midi_pitch_mapping['note'] = midi_pitch_mapping['note'].str.replace('b', '♭') \n",
    "midi_pitch_mapping = midi_pitch_mapping.rename(columns={'note': 'midi_note_str'})\n",
    "midi_pitch_order = midi_pitch_mapping['midi_note_str'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\\full\\model_20230725_234755_f-chroma-mfcc_t-pitch.keras\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "No file or directory found at model\\full\\model_20230725_234755_f-chroma-mfcc_t-pitch.keras",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 10\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[39m# find the weights file with min val_loss\u001b[39;00m\n\u001b[0;32m      7\u001b[0m     \u001b[39m#weights_file = min(weights_dir.glob('*'), key=lambda x: float(x.stem[-6:]))\u001b[39;00m\n\u001b[0;32m      9\u001b[0m \u001b[39mprint\u001b[39m(weights_file)\n\u001b[1;32m---> 10\u001b[0m model: Model \u001b[39m=\u001b[39m load_model(weights_file)\n",
      "File \u001b[1;32mc:\\Users\\emrec\\conda3\\envs\\audio-new\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[39m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[39m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[39mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32mc:\\Users\\emrec\\conda3\\envs\\audio-new\\lib\\site-packages\\keras\\saving\\save.py:226\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(filepath_str, \u001b[39mstr\u001b[39m):\n\u001b[0;32m    225\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m tf\u001b[39m.\u001b[39mio\u001b[39m.\u001b[39mgfile\u001b[39m.\u001b[39mexists(filepath_str):\n\u001b[1;32m--> 226\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mIOError\u001b[39;00m(\n\u001b[0;32m    227\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNo file or directory found at \u001b[39m\u001b[39m{\u001b[39;00mfilepath_str\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m    228\u001b[0m         )\n\u001b[0;32m    230\u001b[0m     \u001b[39mif\u001b[39;00m tf\u001b[39m.\u001b[39mio\u001b[39m.\u001b[39mgfile\u001b[39m.\u001b[39misdir(filepath_str):\n\u001b[0;32m    231\u001b[0m         \u001b[39mreturn\u001b[39;00m saved_model_load\u001b[39m.\u001b[39mload(\n\u001b[0;32m    232\u001b[0m             filepath_str, \u001b[39mcompile\u001b[39m, options\n\u001b[0;32m    233\u001b[0m         )\n",
      "\u001b[1;31mOSError\u001b[0m: No file or directory found at model\\full\\model_20230725_234755_f-chroma-mfcc_t-pitch.keras"
     ]
    }
   ],
   "source": [
    "if use_final_model:\n",
    "    weights_dir = Path(f'model/full')\n",
    "    weights_file = weights_dir / f'model_{run_id}.keras'\n",
    "else:\n",
    "    weights_dir = Path(f'model/weights/{run_id}')\n",
    "    # find the weights file with min val_loss\n",
    "    #weights_file = min(weights_dir.glob('*'), key=lambda x: float(x.stem[-6:]))\n",
    "\n",
    "print(weights_file)\n",
    "model: Model = load_model(weights_file)  # type: ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training data facts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_path = Path('data/nsynth-train/examples.json')\n",
    "td = pd.read_json(training_data_path, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "td_ = td.merge(midi_pitch_mapping, left_on='pitch', right_on='midi_pitch_int', how='left')\n",
    "td_['midi_note_str'] = pd.Categorical(td_['midi_note_str'], categories=midi_pitch_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_plot = 'midi_note_str' if targets == ['pitch'] else targets[0]\n",
    "hue = 'instrument_family_str' if targets == ['pitch'] else None\n",
    "\n",
    "with sns.axes_style('darkgrid', rc={'font.family': 'DejaVu Sans'}):\n",
    "    sns.displot(td_, x=x_plot, binwidth=1, height=6, aspect=2, hue=hue)  # type: ignore\n",
    "\n",
    "    # Decrease the font size for xticks\n",
    "    plt.xticks(fontsize=6)\n",
    "    # Rotate the xticks vertical\n",
    "    plt.xticks(rotation=90)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_paths = [f'data/features_{data_size}/test_{features_affix}_{targets_affix}.tfrecord']\n",
    "test_data = tf.data.TFRecordDataset(data_paths)\n",
    "test_data = test_data.map(parse_tfrecord)\n",
    "test_data = test_data.batch(32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate the model on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128/128 [==============================] - 1s 7ms/step - loss: 1.7031 - accuracy: 0.7217\n",
      "Loss: 1.7031, Accuracy: 72.17%\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(test_data)\n",
    "print(f\"Loss: {loss:.4f}, Accuracy: {accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128/128 [==============================] - 1s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(test_data).argmax(axis=1)\n",
    "y_true = np.array(list(test_data.map(lambda _, label: label))).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_labels = encoder.inverse_transform(y_pred)\n",
    "y_true_labels = encoder.inverse_transform(y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "if remove_outliers:\n",
    "    y_pred_labels_ = []\n",
    "    y_true_labels_ = []\n",
    "    for pl, tl in zip(y_pred_labels, y_true_labels):\n",
    "\n",
    "        if not((int(tl) < 21) or (int(tl) > 108)):\n",
    "            y_true_labels_.append(tl)\n",
    "            y_pred_labels_.append(pl)\n",
    "    \n",
    "    y_pred_labels = np.array(y_pred_labels_)\n",
    "    y_true_labels = np.array(y_true_labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "if targets == ['pitch']:\n",
    "\n",
    "    classes_ = list(encoder.classes_)\n",
    "\n",
    "    if remove_outliers: \n",
    "        classes = []\n",
    "        for c in classes_:\n",
    "            if not((int(c) < 21) or (int(c) > 108)):\n",
    "                classes.append(c)\n",
    "    \n",
    "    else:\n",
    "        classes = classes_\n",
    "\n",
    "    sorted_labels = sorted(classes, key=lambda i: int(i))\n",
    "    sorted_names = (\n",
    "        pd.DataFrame({'label': sorted_labels})\n",
    "        .merge(midi_pitch_mapping, left_on='label', right_on='midi_pitch', how='left')['midi_note_str']\n",
    "        .to_list()\n",
    "    )\n",
    "else:\n",
    "    sorted_labels = sorted(encoder.classes_)\n",
    "    sorted_names = sorted(encoder.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate and Plot Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[647,   0,   0,  67, 110,  15,   3,   0,   0,   1,   0],\n",
       "       [  1, 201,   2,   6,  49,   0,   0,   2,   5,   0,   3],\n",
       "       [  0,   0, 167,   0,   0,   0,   0,  13,   0,   0,   0],\n",
       "       [107,   0,   0, 364, 159,  20,   0,   0,   0,   2,   0],\n",
       "       [ 78,   0,   0,  64, 603,  12,   1,   0,   7,   1,   0],\n",
       "       [ 10,   0,   0,   6,  66, 120,   0,   0,   0,   0,   0],\n",
       "       [ 41,   2,  19,   3,  10,  14, 279,  17,   9,   4, 104],\n",
       "       [ 10,  34,   0,  23,  15,   0,   0, 153,   0,   0,   0],\n",
       "       [  5,   0,   0,   0,   2,   0,   0,   0, 299,   0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
       "       [  6,   0,   0,   0,   8,   0,   1,   0,   3,   0, 123]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true_labels, y_pred_labels, labels=sorted_labels)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 15))\n",
    "ax = fig.gca()\n",
    "\n",
    "disp = ConfusionMatrixDisplay(cm, display_labels=sorted_names)\n",
    "disp.plot(include_values=False, xticks_rotation='vertical', ax=ax, colorbar=False, cmap='Blues')\n",
    "# Add title and axis labels\n",
    "plt.title(f'Confusion Matrix for {aim}', fontsize=18)\n",
    "plt.xlabel(f'Predicted {target_label}')\n",
    "plt.ylabel(f'True {target_label}')\n",
    "\n",
    "# Decrease font size of x and y labels\n",
    "plt.xticks(fontsize=6)\n",
    "plt.yticks(fontsize=6)\n",
    "\n",
    "cbar = plt.colorbar(disp.im_, ax=ax, fraction=0.046, pad=0.04)\n",
    "cbar.ax.tick_params(labelsize=8)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.7490, Recall: 0.7217\n"
     ]
    }
   ],
   "source": [
    "precision = precision_score(y_true_labels, y_pred_labels, average='weighted', zero_division=0)\n",
    "recall = recall_score(y_true_labels, y_pred_labels, average='weighted', zero_division=0)\n",
    "\n",
    "print(f\"Precision: {precision:.4f}, Recall: {recall:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bass</th>\n",
       "      <td>0.7149</td>\n",
       "      <td>0.7675</td>\n",
       "      <td>0.7403</td>\n",
       "      <td>843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brass</th>\n",
       "      <td>0.8481</td>\n",
       "      <td>0.7472</td>\n",
       "      <td>0.7945</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flute</th>\n",
       "      <td>0.8883</td>\n",
       "      <td>0.9278</td>\n",
       "      <td>0.9076</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guitar</th>\n",
       "      <td>0.6829</td>\n",
       "      <td>0.5583</td>\n",
       "      <td>0.6143</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keyboard</th>\n",
       "      <td>0.5900</td>\n",
       "      <td>0.7872</td>\n",
       "      <td>0.6745</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mallet</th>\n",
       "      <td>0.6630</td>\n",
       "      <td>0.5941</td>\n",
       "      <td>0.6266</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>organ</th>\n",
       "      <td>0.9824</td>\n",
       "      <td>0.5558</td>\n",
       "      <td>0.7099</td>\n",
       "      <td>502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reed</th>\n",
       "      <td>0.8270</td>\n",
       "      <td>0.6511</td>\n",
       "      <td>0.7286</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>string</th>\n",
       "      <td>0.9257</td>\n",
       "      <td>0.9771</td>\n",
       "      <td>0.9507</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synth_lead</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vocal</th>\n",
       "      <td>0.5348</td>\n",
       "      <td>0.8723</td>\n",
       "      <td>0.6631</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.7217</td>\n",
       "      <td>0.7217</td>\n",
       "      <td>0.7217</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.6961</td>\n",
       "      <td>0.6762</td>\n",
       "      <td>0.6736</td>\n",
       "      <td>4096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.7490</td>\n",
       "      <td>0.7217</td>\n",
       "      <td>0.7219</td>\n",
       "      <td>4096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score  support\n",
       "bass             0.7149  0.7675    0.7403      843\n",
       "brass            0.8481  0.7472    0.7945      269\n",
       "flute            0.8883  0.9278    0.9076      180\n",
       "guitar           0.6829  0.5583    0.6143      652\n",
       "keyboard         0.5900  0.7872    0.6745      766\n",
       "mallet           0.6630  0.5941    0.6266      202\n",
       "organ            0.9824  0.5558    0.7099      502\n",
       "reed             0.8270  0.6511    0.7286      235\n",
       "string           0.9257  0.9771    0.9507      306\n",
       "synth_lead       0.0000  0.0000    0.0000        0\n",
       "vocal            0.5348  0.8723    0.6631      141\n",
       "accuracy         0.7217  0.7217    0.7217        0\n",
       "macro avg        0.6961  0.6762    0.6736     4096\n",
       "weighted avg     0.7490  0.7217    0.7219     4096"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cr = classification_report(\n",
    "    y_true_labels,\n",
    "    y_pred_labels,\n",
    "    labels=sorted_labels,\n",
    "    target_names=sorted_names,\n",
    "    zero_division=0,\n",
    "    output_dict=True,\n",
    ")\n",
    "\n",
    "# Create a context manager to set the float formatting\n",
    "with pd.option_context('display.float_format', '{:.4f}'.format, 'display.max_rows', 20):\n",
    "    cr_df = pd.DataFrame(cr)\n",
    "    df_transposed = cr_df.transpose()\n",
    "    df_transposed['support'] = df_transposed['support'].astype(int)\n",
    "    \n",
    "    display(df_transposed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OHdSoUSNZLBZ9//33NlVnv/vuO0nSxIkTbc5btVgs+vzzz2WxWPTbb78l2sazzz6rhg0b2rSNHDlSrq6umjZtmrVt8eLFCgsLU48ePVS2bNlE63dxcbF7iRg3NzeNHj1azs7OSd7vx5Ww/eHDh9sUVfHy8rLOvtqLsUKFCurRo0eyxeHo659c46tt27Zq1KiRw8s9iIeHhz755BObtpdeeknS3fjuPZrAYrGoU6dOkmRTYXb69OmKiIjQqFGjEp0H/uKLL6pixYqaPXt2om0XKFBAQ4YMsWnr2bOnJMfG0H/Rpk0b5cyZU1OmTLEexZCw/QMHDuj555+3e1TGgAEDlCtXLut9Dw8PffTRR5JkM6YmTpyo2NhYffPNN4n+lr399tvKnj273TH7uBJiCgoKSpEY4uPjNWHCBBUuXFgfffSRzeW+smTJoo8++kh37tzRwoULJUlxcXGaOHGivLy89PXXXyf6G+Hl5SUPD4/H2ldJunjxotatW6eSJUsmmgXt3bu3SpQoobVr1+rixYuJlh01apQ8PT2t94sVK6aaNWvq+PHj//lIBQD4rzj0F2nOmTNnNGrUKK1bt06XLl1SdHS0zeOXL1+2HgrboUMHvfvuuypdurQ6deqkevXqqVatWom+dHXo0EELFy5UtWrV9OKLL6pBgwaqXbu2cuTIkSL7sGvXLklS48aNk9R/w4YNGj9+vHbu3KmgoCDFxsZaH0uOw+X27dunjBkz2hwmlyDhkLO///470WMVK1ZM1JaQZN9fbfRh4uLiNGLECJs2Z2dnzZkzR+3bt7dp37FjhzJnzqxJkybZXVfGjBl17NixRO21a9dO1JYrVy4VKlRIx44dU2RkpLJkyaJ9+/ZJsn+oXd68eVWoUCHrl7gsWbJYHytQoMATK5r1sBgf9nrZe33/i6S+/sk9vpJ7PxIul3Uvf39/SVLZsmUTXXc44bFLly5Z23bs2GH999SpU4m2cfv2bQUFBSkoKMjmfVKuXDk5Odn+Zvw4Y+i/cHV1VY8ePTRq1CitWbPG+ncpYYw96NQHe2Mqoe3e91/Cc7Nq1Sq7P6y5urraHbOP695kOyViOH78uEJDQ5UrV65Ef7ck6fr165JkXd+xY8cUERGhRo0aJTr0Ozkk/D2oW7duoveqxWJRnTp1dPToUe3fv1958+a1efxRY/jev3EA8KSRqCLF+fn56dixY7p06ZKKFSv2n9Z16tQpVa1aVREREapfv75atWolT09POTk5acOGDdq4caNN4vr222/L19dXP/74o8aOHasxY8bIxcVFzZs31/jx462zsx07dpSrq6vGjx+viRMn6ocffrBez3Hs2LHJfu3KhC+guXPnfmTfefPmqWPHjvLw8FCTJk2UP39+ZcqUSRaLxXoO438VERGR6AtMgoSKs+Hh4Ykes3eJhITzSePi4pK8fXd3d92+fVuSFBUVpXXr1qlHjx7q1q2bChcurHLlyln7hoSEKDY21u4XxAQ3btxI1PagpChnzpzWL5JZsmRRRESEtd0ePz8/HT9+3Nr/3vU8KREREXJyclL27NkTPZYzZ045OTnZfb2SO8akvv7JPb6Sez/unVFKkLAfD3ssJibG2hYSEiJJ+v777x+6rRs3btgkqsk1hv6r3r176/PPP9cvv/yixo0b6+bNm/rtt99UtGhR1a1b1+4y9sZUjhw5Er3/Ep6bTz/9NGWCv8+VK1ckyWZ8JGcMCes6fPiwDh8+/MB+CX+HHPl7/ziS8jdLStm/4QCQEkhUkeJq1qypDRs2aO3atWrQoMF/Wte4ceMUGhqqGTNmqHPnzjaP9enTRxs3brRps1gs6tWrl3r16qXg4GBt3rxZv/32m+bOnauTJ0/q4MGD1sOw2rVrp3bt2ikiIkLbtm3TwoULNWnSJDVp0kTHjx9P1msMJqzr0qVLj7yw+/Dhw5UhQwbt2bNHRYoUsXnM3qGEj8PT01PXrl2z+1hCu70v7CnBw8NDrVu31pw5c9SoUSN169ZNe/futc4UeHp6ymKx2BzWlxSBgYF22+/fv4R/HX0+7p/JSEmenp6Kj4/X9evXEyULgYGBio+Pt/t6PckY75ec4+tB+5EwM3nvEQcJ7H1JT04Jz/fBgwdVunTpFN1WSihQoICeffZZLVmyREFBQVq+fLkiIiKs1ze2JzAwMNGPjwnvv3sToITn5v4fd1LKhg0bJElVqlRJkRgS1tW+fXvNnz//kf3v/XufEh73bxYAmB3nqCLFdevWTc7Ozvrpp5+sh0Q9yP2H8d7v9OnTkqTWrVvbtMfHx2vr1q0PXdbX11dt27bVnDlz1KBBAx09etTuIXqenp5q2rSpfvrpJ3Xr1s1aiTc5JRy6uHr16kf2PX36tEqUKJEoSb18+bL1+bhXQuLtyK/hFSpU0K1bt6yHJN8rIflP7lnlR2nYsKHatm2rv//+2+bcsWrVqik4OFgnT550aH2bN29O1JbwHBYqVMj65TXhnOGEL7v3unTpkk6fPq2CBQs69GXX2dk5WWcnHhZjar1eSfWo8eXk5PTYz1XCYZX2EoKEwyNTSrVq1SRJ27dvT7FtJPf76H6vvPKK7ty5o+nTp2vSpElydXW1nnNsj70xldB27/sv4blJOPw2JV2/ft1acTjhXOLHieFhf0dLlCghT09P7d6922ZW/UGKFSsmT09P/fXXXwoNDU3Sth15nROe602bNiU67NkwDLuvCQCkBSSqSHGFCxfW22+/raCgIDVr1kxnz55N1Of27dsaO3bsIy9Cn3Du6f2XPvniiy906NChRP3/+OOPRLMrMTEx1kO3Eoq7rF271nro6b0SZuHsFYH5L7p27SoPDw+NGTPG7rmE937RDggI0KlTp2x+Lb99+7b69u1rd+YooVDIP//841A8kjR06FCbL16XLl3S2LFj5eLikmgG+0kYPny4LBaLRowYYf3illDYpkePHgoODk60zNWrV3X06NFE7WvWrNHatWtt2j744APFxMTYfBlv06aNvLy8NGXKFJvD+gzDsD4/9146JSl8fHwUFBRk9z32OBLiHTFihPWwP+nubFHCIdEPSzCeNEfGl4+Pj0Pv3XtVrFhRFotFs2fPttneyZMn9fXXXz/WOpOqe/fuypIli95//327h4PevHnzPydqyf0+ul+bNm3k5+enMWPGaMuWLWrduvVDzyP+5ptvdPnyZev9qKgoffzxx5KkLl26WNv79esnFxcX9e/f325Bn7CwsGT5IeHQoUNq3LixAgMD1a1bN1WuXPmxY3jY31EXFxf17dtX58+f1+DBg+0mq4cOHbK+v11cXPTqq68qPDxcb7zxRqIkNDw8XFFRUTbbduR1zpcvn+rXr2+9HM29Jk+erMOHD6tBgwYPPL0DAMyKQ3/xRIwcOVK3b9/WuHHjVKxYMTVo0EClS5eWq6urzp49qz///FPBwcEaOXLkQ9fTp08fTZkyRe3atVPHjh3l6+urHTt2aO/evWrRooVWrFhh079jx47KlCmTatWqpYCAAMXExGjNmjU6cuSIOnbsaK3O+dZbb+nChQuqV6+e8ufPL4vFoi1btmjXrl2qUaOGTWXT5JAjRw5Nnz5dnTp1UtWqVdW6dWsVK1ZMQUFB2rlzp/Lnz6/FixdLkvr376/+/furQoUKev755xUbG6s1a9bIMAyVK1fOpvKodLcasMVi0fvvv69jx47Jy8tLXl5e6tu37wPjefnll7Vw4UItWbJEZcuWVcuWLa3XUQ0ODtaYMWNUsGDBZH0OkqJcuXJ67rnntHDhQs2YMUNdu3ZV06ZN9eGHH+qTTz5R4cKF1bRpUwUEBCg4OFinTp3S5s2bNXLkSJUoUcJmXS1atFDz5s31wgsvKG/evNq4caO2b9+ucuXKafDgwdZ+np6e+vnnn/Xiiy+qWrVq6tixo7Jnz661a9dq9+7dqlq1aqIqrY/SoEED7d69W61atVLt2rXl5uamWrVqqVatWo/1vNSpU0f9+/fXt99+q9KlS6t9+/YyDEMLFy7UxYsXNWDAANWpU+ex1p0SHBlfDRo00Ny5c/X888+rQoUKcnZ2VosWLVSmTJlHbid37tzq2LGjZs+erUqVKqlp06YKDAzUokWL1LRpUy1YsCDF9jGhauwLL7ygcuXKqWnTpipevLhu376t8+fPa+PGjapRo4ZWrVr12NtI7vfR/VxcXNSjRw999tlnkh5cRClBlSpVVK5cOXXs2FHu7u5auHChzp07p969e9u8/0qXLq0ffvhBffv2VbFixdS8eXMVKlRIEREROnPmjDZu3Khu3brpxx9/TFKcQUFB1h81Y2NjFRwcrD179lirJPfq1SvRucKOxlC8eHHlypVLs2fPVqZMmZQnTx5ZLBb17dtXXl5eGjFihPbu3atvvvlGK1asUN26da3X9T548KD279+v7du3WxP9jz/+WDt27NCvv/6qHTt2qFmzZnJ3d9eZM2e0atUqbdmyxTrj+Tiv84QJE1SrVi317t1by5YtU8mSJXXkyBEtXbpU2bNn14QJE5L03AKAqaTipXHwFPrrr7+MHj16GIULFzYyZsxouLu7G/nz5zdefPFFY/Xq1TZ9H3Q90PXr1xs1a9Y0smTJYnh7exvNmzc39uzZY7f/Dz/8YLRu3doICAgwMmTIYPj6+hrVqlUzJk6caMTExFj7zZ492+jQoYNRqFAhI1OmTIaXl5dRvnx5Y/To0UZUVJTN9pPjOqoJ9u3bZ3To0MHImTOn4erqavj7+xvNmjUzli9fbu0THx9v/Pjjj0apUqWMDBkyGH5+fkbPnj2Na9euPfA6m1OnTjXKlCljuLu7G5Js4n3QMjExMcZXX31lXS5LlixG3bp1jSVLliTq+zjXq3yQB11HNcH+/fsNi8ViFCxY0OY1W7NmjdGqVSsje/bshqurq+Hn52c888wzxieffGJcuHDBbqwLFy40KlWqZGTIkMHIkSOH8eqrr9q9Rq1h3L1WabNmzQxvb2/Dzc3NKFq0qPHhhx8mej8YxsOv/WkYd68v2bt3b8Pf399wcnJK8vPzoNcqweTJk40qVaoYmTJlMjJlymRUqVLFmDx5cqJ+jr4m93rYdVST+vo7Mr6uXLlidOjQwciWLZv1uUrYzsO2m+DGjRtG//79jZw5cxru7u5G2bJljZkzZz70Oqr2XruHjemHPZ/Hjh0zevbsaQQEBBhubm5G1qxZjTJlyhgDBgwwdu3alaT1Pyiux30f3cvedVTvdfz4cUOSkS9fPiMuLs5un4TrqJ46dcr47LPPjIIFCxpubm5GoUKFjC+++MLutYQNwzB27dpldOrUyciVK5fh6upqZMuWzahYsaLx7rvvGkePHk1S/PrftWcTbu7u7kaOHDmMmjVrGoMHD7Z7rd7HjWHHjh1G3bp1jSxZsli3d/bsWevjsbGxxsSJE42aNWsanp6ehru7u5EvXz6jadOmxoQJExK9t2/fvm189dVXRvny5Y2MGTMaHh4eRsmSJY233nrL5jq1D3udH/a+OXfunNG9e3fD39/fcHFxMfz9/Y3u3bsb586dS9T3YX9bEl7fe/cVAFKDxTDs1HEHgHRi6tSp6t69u6ZMmeLwIbvA02bu3Lnq2LGjRowYYb0m6v26deumadOm6ezZs48sBgcAwOPiHFUAACDDMKznpPfs2TO1wwEAPOU4RxUAgKfYwYMHtXz5cm3btk07d+5Unz59UuyanwAAJBWJKgAAT7E9e/bovffek7e3t7p06aIvv/wytUMCAECcowoAAAAAMBXOUQUAAAAAmAqJKgAAAADAVEhUAQAAAACmkirFlDYcD0mNzQJ4AjzdqdEGpFd3YuNTOwQAKaR6Ye/UDuGx3I5N7QjSngxp5KsaM6oAAAAAAFMhUQUAAAAAmAqJKgAAAADAVEhUAQAAAACmQqIKAAAAADCVNFLzCQAAAABsGUZqR4CUwowqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBUSVQAAAACAqZCoAgAAAABMhaq/AAAAANIkQ5T9dZwltQNIEmZUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKlT9BQAAAJA2UfQ33WJGFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkLVXwAAAABpEkV/0y9mVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFRBUAAAAAYCpU/QUAAACQJhmU/U23mFEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpUPUXAAAAQJpkiLK/jrOkdgBJwowqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBUSVQAAAACAqZCoAgAAAABMhaq/AAAAANImiv6mW8yoAgAAAABMhUQVAAAAAGAqJKoAAAAAAFMhUQUAAAAAmAqJKgAAAADAVBxOVCdOnChJ2rlzpxo2bKg5c+Yke1AAAAAA8CgGN4dvaYXDl6eZM2eOXn31VU2YMEG//vqrWrdurY4dO6ZEbDChDSsXaPXCmQoPDVaufAXUodebKlKqvN2+U8d/ou3rViZq989bQMO/nyVJunzhjJbO/FkXTh9TcOBVvdDzDTVq0ykldwHAA6xeNk/L581QWEiQ8gQUVJc+g1S8TAW7fSd8NVyb1qxI1J47XwF99fNcSdLHQ17V0QN7E/UpX7Wm3vlkfLLGDuDh1i6fr5ULZyg85O7nd+dXBqpYafvj++exH2vL2sTjO1e+Aho1YXai9h0bV2vC6A9VsXodvfHhl8keO4Cnk8OJ6o0bN3TkyBFlypRJuXLlkoeHR0rEBRP6a/OfmvvLeL3UZ4gKlSirTasW6dsRgzT8+1nyye6XqH/H3gP1XNd+1vvxcXH65I2XValmA2vbnejbyuaXS5VqNtDcSV8/kf0AkNj2Das1/cex6vH6OypWqpz+XLFQn3/whr76ea6y5Ug8vrv2HawXe7xuvR8XF6d3+3ZW9TqNrG2DPhyt2NgY6/3IiPC7fWo3TNmdAWBj56Y1mvnzOHXp97aKliir9asWacywgRo1YbZ87Yzvzq8O0gvdXrPej4+P1Qev/5+q1ko8doMCr2j2pG9U9AE/WgPA43L40N/33ntPX3zxhQYNGqTbt2/rmWeeSYm4YEJ/LvlNNRu1Uq3GreWfN7869h6orNlyaOPKhXb7Z8zsIa+svtbb+VNHdTMqUjUatbD2yV+kpJ7v3l9V6jwrV1fXJ7UrAO6zYuEs1W/SRg2atVXufAXUte9b8s2eU2uWz7fbP1NmD3n7ZLPezpw8qhtREarbuJW1j4enl02fg3t3yj1DBlW7J5kFkPJWLfpNdRq3Vr0mbf43mzpIPtlyau3KBXb73x3fvtbb2ZPHdDMqUrWfbWnTLz4uTj9++ZGe6/yKcvjlfhK7AuAp4nCiWqFCBU2dOlW5cuXS/Pnz9cYbb6REXDCZ2JgYXTh1XCUrVLVpL1mhmk4fO5ikdWxZs0zFy1WRbw7/lAgRwGOKjYnR2ZPHVLZSNZv2spWq6cSRA0lax4ZVS1S6QlVlz/ng8b3hj6V6pu6zypAh43+KF0DSxcbE6NypYypdwXZ8l65YVaeOJu3ze9PqpSpZvoqy3ff5vfi3ScrilVV1m7ROtngBIIHDiWqXLl1ksVj0wQcf6OLFi+rUifMJnwZREWGKj4+Tp7ePTXsWr6yKCAt55PLhIUE6vGeHat0z2wLAHCL+N7697hvfXt6+Cg8NfuTyocFB+vuv7arftM0D+5w6dlgXz51W/aZt/2u4ABwQ+R/Hd1hIkA7s3q66TWzH94kj+7Vp9VL16P9essYLAAkcPkfVMO7WigoLC9PYsWP1+++/P7R/dHS0oqOjbdru3ImWm5u7o5uGGVgsj7XYtrUrlDGzh8pXq5vMAQFINveNb8MwZNGjx/zGNcuUycNDVWrUe2CfDX8sUd78hVS4eKn/GiWAx2CxN76T8Jm++c/lyuThoUrV//38vnXzhiZ+NUzdB7ynLF7eyR0q4BAjLZWxhUMcnlEtWLCgatSoocaNGys2NlZOTg9fxahRo+Tl5WVzmzVx/OPGi1Ti4ektJydnRdz362tkeGiiWdb7GYahbX8uV/X6TeXCeaiA6Xj+b3zfP7sSER4iz6yPHt8b/1im2g2bP3B8R9++rW0bVj90xhVAysjyv/EdZm98J+Hze/OaZapRv5nN+A68cklB165o/IjB6t6qhrq3qqGt61Zq387N6t6qhq5d+SdF9gXA08XhGdUpU6YoNjZWLi4uMgxDS5cufWj/oUOHatCgQTZtO87fcHSzSGUurq7KV7iYjv79lyo8U8/afvTvXSpXtfZDlz1xaJ8Cr/yjms9y2C9gRi6uripQpLgO7N2pKjXrW9sP7t2lSs/UeeiyRw/s1dXLF1W/6YPPUduxaY1iY2JUq2GzZIsZQNK4uLoqf+HiOrxvlyrfc9TD4X27VKH6w8f3sYN7de3yP6rb2HZ8++cN0Kf/u8xcggW//qjbt26q8yuD5JstZ7LFD+Dp5XCieuDAAY0ePVpXrlyxHga8bt26B/Z3d3eXu7vtYb5ubrGObhYm0KjNi5oyboQCChdXweJltPmPxQq5fk11mj0nSVo07QeFhVxX94HDbJbbumaZChQtpdwBhRKtMzYmRlcunr37/9hYhYVc18UzJ+SeIaNy5Mqb8jsFQJLUot1L+v7LYSpYtKSKliijtSsXKSjwqhq1aC9J+m3ydwoNuq5+b4+wWW79H0tUuHhp5c1f+IHrXr9qqSrXqKssnt4puQsAHqDpcy9q4pjhKlCkuAoXL6P1qxYr+Po1NWjeTpI0d+r3Cg2+rlffGm6z3KbVS1WoWCnlyW/7+e3m5p6oLVPmLJKUqB0AHpfDieorr7yin3/+Wa+//rq+++47zZo169ELIV2oUruRbkSGa8WcyXcvGB5QUK9/NMZaxTc8NFgh16/ZLHPrRpT2bluvjr0H2l1nWEiQRr7Z1Xp/zaJZWrNoloqWrqC3Pvsh5XYGgI1n6jVWZGS4Fs78RWEhQcobUEjvjBxvreIbFhKkoOtXbZa5eSNKu7asU5c+bz1wvVf+Oa/jh//W0M++S9H4ATxYtTrPKioiXEt+m6ywkCDlDiioQSPGWav4hock/vy+eSNKu7etV+dXBtlbJQCkOIthOHYKcv369bV+/XrVrl1bmzdvVoMGDR46o2rPhuOPrhILIG3ydHf49y8AacSd2PjUDgFACqle2Du1Q3gs4bf4u+Qor4wOlylKFQ5/o6xUqZJu376tJk2a6JlnnlHOnJyHAAAAAODJM0TZ3/TK4US1a9eu6tSpkwIDA5U9e3aNHDkyJeICAAAAADylHE5Ue/XqpRkzZqhIkSI6deqUOnfurJ07d6ZEbAAAAACAp5DDByj7+fmpSJEikqTChQsrR44cyR4UAAAAAODpleQZ1SFDhshiscgwDNWpU0cVKlTQvn375O3tnYLhAQAAAACeNklOVFu2bClJatGihbWtXbt2yR8RAAAAAOCpluREtW7duikZBwAAAAA4xLELbSItSRsX0QEAAAAAPDVIVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFRBUAAAAAYCokqgAAAAAAUyFRBQAAAACYCokqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBWX1A4AAAAAAB6HYaR2BEgpzKgCAAAAAEyFRBUAAAAAYCokqgAAAAAAUyFRBQAAAACYCokqAAAAAMBUqPoLAAAAIE0yRNnf9IoZVQAAAACAqZCoAgAAAABMhUQVAAAAAGAqJKoAAAAAAFMhUQUAAAAAmAqJKgAAAADAVLg8DQAAAIA0yeDqNOkWM6oAAAAAAFMhUQUAAAAAmAqJKgAAAADAVEhUAQAAAACmQqIKAAAAADAVqv4CAAAASJMo+pt+MaMKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABToeovAAAAgLSJsr/pFjOqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFar+AgAAAEiTDMr+plupkqjm9cmYGpsF8AQERdxJ7RAApJDMGfh9GwDwZHDoLwAAAADAVEhUAQAAAACmQqIKAAAAADAVElUAAAAAgKlQFQEAAABAmmRQ9DfdYkYVAAAAAGAqJKoAAAAAAFMhUQUAAAAAmAqJKgAAAADAVEhUAQAAAACmQtVfAAAAAGkSRX/TL2ZUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKlT9BQAAAJA2UfY33WJGFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkLVXwAAAABpkkHZ33SLGVUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKVX8BAAAApEkGRX/TLWZUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKlT9BQAAAJAmUfQ3/WJGFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkLVXwAAAABpkkHZ33SLGVUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABTcThRvXPnjiTJMAxt2bJFN2/eTPagAAAAAABPL4cT1caNG0uSPvzwQ/3666964YUXkj0oAAAAAHg0g5vDt7TB4UTVYrFIki5duqSJEycqPDw82YMCAAAAADy9HL6OapYsWdShQwfVrVtXhmHIyYnTXJ8myxfO0YLfpikkOEgB+QvplTeGqHS5ig/sv371Cs2fOU2X/7mgTB4eqlSthnq9NkieXt6SpDUrl2jcZ8MSLbd47U65ubun1G4AsOPP5fO1csGvCg8JVu6Agur8ykAVK13Bbt+fxo7Qlj9XJGrPna+ARv04R5K0ec1y/Tzu40R9flm8WW5ujG/gSVq1ZK6Wzv1VocFBypu/oLr1G6ySZe2P7+++GKYNq5cnas8TUFDjJ8+TJMXGxmjRrCnasHq5QoKuK1feAP1f7wGqULVGiu4HgKeHxTAcu0xuTEyMLl68qIIFCyomJkbnzp1TkSJFHNro6eu3HOoPc9i49g+N+eR99XvrPZUsU16/L5mvP5Yv0o+/LlQOP/9E/Q/v36d3+vdU7/6DVa1mXQVfD9R3X41Urjz59OGocZLuJqoTv/5SP81abLOsj2+2J7FLSAFBEXdSOwQ8hh0b12jimGHq2u9tFSlZTut/X6SNfyzRqB/nKFsOv0T9b96I0p3o29b78fFxev+1znq2VQe1+79XJN1NVGdMHKMvfppns6y3D+M7rcrk7pzaIeAxbF2/Wt9+/qF6DXhXxUuX15rlC7R25WKNmzxP2XMm/vy+ERWpO3eirffj4+L0Vu8X1ey5jurY9VVJ0q8/faPNf65Un7c+UO68+fX37u2aNmGcRn4zWQWLFH9i+4bkUyaPR2qH8Fj+CY1+dCfYyJM1bfxY7PB06KeffqqCBQvq999/V6VKlbRiReJf1JE+LZr9qxq3fE5NW7VTvvwF9eobbyt7Dj+tWDzPbv9jhw8oh18utXnhJfnlyq1S5SqoWZvndfL4EZt+FsvdxPTeG4Ana9WiWarbuLXqNW2r3PkK6P9eHSSf7Dm1bsUCu/0zZfaQt0826+3siaO6GRWpOs+2sulnsVhs+pGkAk/esvkz1KBZGzVq8ZzyBBRQ99cGyzdHTq1eNt9u/8weWZTVJ5v1dvr4Ed2IilCDpq2tfTb9uULPvdRDFavVUs5cedSk9QsqV7m6ls2b8aR2C0A653CiunHjRknSnDlztGfPHs2cOTPZg4L5xMTE6NSJo6pY5Rmb9gpVquvoof12lylRppyCrl/TX9s3yzAMhYYEa8uGP1Xlmdo2/W7duqWu7Zvp5ecaa9jb/XX6xLEU2w8AicXGxOjcqWMqXbGaTXuZCtV08uiBJK1j4+qlKlW+qrLdNztz+9YtDezaWm+83FJjhg3UudPHky1uAI8WExOjMyeOqVzl6jbt5SpV1/HDSRvfa39fojIVq9rMvsbciZGbm5tNPzf3DDp26O//HDMASI9xjuqtW7e0cuVKeXt7y9XVVZkyZUqJuGAyEeGhio+Lk7ePj017Vh9fhQYH2V2mZJnyevujz/T5R+/ozp07iouLVfVa9dR34DvWPnnzFdCg9z5W/oKFdfPmDS2ZN0uD+3bTd1PnKHfegBTdJwB3RUaEKT4+Tl7evjbtnll9FB4a/Mjlw0KCdGD3dvV92/Z8VP+8Aeo96CPlzV9It27e0OolczRycC+N/G6m/HLnS9Z9AGBfZPj/xndW2/HtldVXYSGPHt+hwde1b9c2vfn+SJv28lWqa9n8mSpZtqJy5sqjg3t36a9tGxQfH5+s8QOP4thJjEhLHJ5RHT9+vHbs2KH33ntPt2/fVrt27R7aPzo6WhERETa36GiOJU+rEqo+JzAMI1FbggtnT+vH8aP1YvdX9M2kWfpkzA+6euWSvvvyU2uf4qXLqkGTFipYpJhKl6uooR+PVu68+bRswewU3Q8Adtw/lB8yvu+1ec3yu8XSnqln0164eBnVbNBM+QoWVbHSFfTa0M/klzuf1iybm3wxA0gSS+IBnnjM27H+j2XK7OGhKjXr27R3f22I/HPn1Rvd26tTk+qa9O1o1W/SmiKbAJKNwzOq1atXV7Zs2XT8+HEZhqEKFexXjEswatQojRgxwqat/+D39MbbHzi6aaQiT6+scnJ2Vmiw7a+vYaEh8vbxtbvMnBmTVbJMOT3/UjdJUoHCRZUhQ0YNea27uvR+TT7ZsidaxsnJSUVKlNKlixeSfR8A2JfF01tOTs6JZk8jwkLl6e3zgKXuMgxDm9YsU80GzeTi6vrQvk5OTipQpKSuXbr4n2MGkDRZvO6O77BQ26OfwkND5J3V/ud3AsMwtG7VUtV5toVc7xvfXt5Z9c4nY3XnTrQiw8Plky27Zvz8rXL45U72fQDwdHI4UR0wYIAuXbqkPXv2qFKlSoqLi1OdOnUe2H/o0KEaNGiQTds/ERwWkta4urqqcNES2vfXdtWo28Davm/3TlWvVc/uMtG3b8vZ2bZCpJPz3V9ajQdcbNgwDJ05eVz5CzpWSRrA43NxdVX+wsV1aN8uVa7x76zJoX27VLH6g/++S9Kxg3t17fJF1W3c5pHbMQxDF86cUJ78hf9zzACSxtXVVQWLFteBPTtVrda/n98H9uxUlZp1H7rs4f17dPXSRTVs9uDx7ebmLt/sORQbG6Odm9fqmbrPJlvsAJ5uDieqe/fu1ZYtW1SvXj0tWLBA7du3f2h/d3d3ud93PUz3aC5PkxY91+lljfnkfRUpXkrFS5fVqqULdP3aFTVv+7wkacqP3yj4eqAGf3j3PJZqNevomy8+0YpFc1Wxag2FBF/XT998qaIlSss3Ww5J0szJP6p4qbLKlSefbt6M0tJ5v+nMyRPqN2hoqu0n8DRq+txLmjhmmAoUKaHCxctow6pFCr5+VQ2a3z29Y+6U7xUaHKhXB9seIbPxj6UqVKy08uQvlGidi2b+rELFS8svV76756gunaMLZ06oS7+3n8g+Abir1fP/p28//1AFi5ZUsZJltWbFQgUFXlXjVnc/v2f+8q2Cg65rwLu255mv+32JipQorXwFEv+4dOLoQYUEXVeBQkUVHHRdc6dPVLxhqG2nrk9knwCkfw4nqgkV3jJlyqRVq1bp+HEqOD4t6jZsosjwMM2aOlEhwUHKX6CwRnz5nXL65ZJ0t+DC9WtXrP2fbd5Gt27e1LIFs/XLd2OV2SOLylWqou5937D2uREVqW9Gf6LQkCBlzuyhQkWLa/T3k1SsZJknvn/A06x63WcVFRmuJbMmKSwkSHnyF9JbI8ZZq/iGhQYp+Po1m2Vu3ojS7m3r1PnVt+yu8+aNSE35ZpTCQ4OVMbOHAgoV1XujJ6pQsVIpvj8A/lWzfmNFRoRp/q8/KzQkSPnyF9J7o76xVvENDQ5SUOBVm2VuREVqx+a16v7aYLvrjLlzR7Mn/6BrVy4pQ8aMqlCtlga8+4kye2RJ8f0B8HSwGIZjtbIOHTqkIkWK6OzZs/rhhx/UqlUrPfusY4d5nL7OjCqQXgVF3EntEACkkEzuzo/uBCBNKpPHI7VDeCwXQijS6qh8Pu6P7mQCDiWqhmGoW7dumjZt2n/aKIkqkH6RqALpF4kqkH6RqD490kqi6lANcYvFIk9PT505cyal4gEAAAAAPOUcPkd127ZtWrlypbJmzWq9VtauXbuSPTAAAAAAwNPJ4asyT506VWXKlJGbm5ty5cqlyZMnp0RcAAAAAICnlMMzqr169dLMmTNVuHBhnTp1Sp07d9bOnTtTIjYAAAAAwFPI4UTVz89PhQvfvZ5W4cKFlSNHjmQPCgAAAAAexbHrlyAtSXKiOmTIEFksFhmGoTp16qhChQrat2+fvL29UzA8AAAAAMDTJsmJasuWLSVJLVq0sLa1a9cu+SMCAAAAADzVkpyo1q1bNyXjAAAAAABA0mNU/QUAAAAAICWRqAIAAAAATMXhqr8AAAAAYAaGKPubXjGjCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFRBUAAAAAYCokqgAAAAAAU6HqLwAAAIC0iaK/6RYzqgAAAAAAUyFRBQAAAACYCokqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBWq/gIAAABIkyj6m34xowoAAAAAMBUSVQAAAACAqZCoAgAAAABMhUQVAAAAAGAqJKoAAAAAAFOh6i8AAACANMmg7G+6xYwqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBUSVQAAAACAqZCoAgAAAABMhaq/AAAAANIkQ5T9Ta+YUQUAAAAAmAqJKgAAAADAVEhUAQAAAACmQqIKAAAAADAVElUAAAAAgKlQ9RcAAABA2kTR33SLGVUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKVX8BAAAApEkU/U2/mFEFAAAAAJgKiSoAAAAAwFRS5dDfDK7OqbFZAE9AYb/MqR0CgBQSG8dBdgCAJ4MZVQAAAACAqZCoAgAAAABMhaq/AAAAANIkgzMS0i1mVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFRBUAAAAAYCpU/QUAAACQJhmi7G96xYwqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBUSVQAAAACAqZCoAgAAAABMhaq/AAAAANImiv6mW8yoAgAAAABMhUQVAAAAAGAqJKoAAAAAAFMhUQUAAAAAmAqJKgAAAADAVKj6CwAAACBNouhv+sWMKgAAAADAVEhUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIWqvwAAAADSJIOyv+kWM6oAAAAAAFMhUQUAAAAAmAqJKgAAAADAVEhUAQAAAACmQqIKAAAAADAVqv4CAAAASJMMUfY3vWJGFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkLVXwAAAABpE0V/0y1mVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFRBUAAAAAYCpU/QUAAACQJlH0N/1iRhUAAAAAYCokqgAAAAAAU3EoUb18+bL69++vLl26KC4uTlOmTEmpuAAAAAAATymHEtXu3bure/fuunjxopydnTVjxoyUigsmtWT+bL3Utqma1K6kV7t00IF9ex7a/89Vy9Wrc3s1q1NFzzevry8+/kDh4WHWx2NjYzT9lwnq3K6ZmtSupF6d22vX9i0pvBcA7Fkw9ze1b9lY9apXUPeXXtDfex8+vhfMmaUX27VSvWcqqtNzLfT78iU2j29Yu0Y9OndQ4zrV1aBGZXXt1E6/L1+akrsA4AEWzZutDm2aqFHNiur1cgftf8Tn98K5v+n/XmilRrUqqXP7llq1wnZ8nz19Sh+8/aY6tG6sOlVKa+6sX1MyfABPIYcS1djYWFWsWFEWi0WSZBicvvw0Wb9mlb4f94U6d++tn6bPU5nylfTuwL66dvWK3f4H/96rz0e8r+at22ny7EUa9tkYHT96WF99OszaZ/KP32rZ4vnq/9ZQTZm9WK3addBH77ypk8ePPqndAiDpzz9+19dffa6uPV/R1FnzVa5CRb3V/1VdvXLZbv+F82Zrwnfj1fPVfpo5b4l69nlNYz4fqS0b11v7eHp5qWvPV/TT1JmaPmehmrd+Tp+N+EA7tvFjFPAkrV39u74d+7m6dO+tX2bMU9nyFfX2G30e+Pm9eP5s/fTDeHXv3U/TZy9Wj1f7adzoT7V10wZrn9u3bylX7jx69fU35eOb7QntCYCniUOJaqlSpTR8+HAFBQXp008/Vbly5VIqLpjQvN+mq1nrdmrRpr0CChTU64PeUY6cflq6YI7d/kcOHVBO/1xq17Gz/HPlUZnyFdXyued14uhha581vy9X5669VL1mHeXKnVdt2ndUlWo1NG/WtCe1WwAkzZ45Ta3atlfr555X/oKF9OaQocqR01+L5tsf36tWLFPbdh3UqEkz5c6TV882aa6WbdppxrRJ1j4VK1dV3QaNlL9gIeXJm08dX3pZhYoU1YG/9z6p3QIgae6s6WrRpp1atn1e+QsU0oC33lX2nH5aPH+23f5/rFym1s+9oIaNmylXnrxq2Li5WrRup1nT/x3fJUqVUb83Bqth4+Zyc3N7UrsCJGIY3By9pRUOJarffPONqlSpopdffllly5bVuHHjUioumExMTIxOHDuiytVq2LRXrlpDhw/+bXeZUmXLKyjwmnZs3STDMBQSHKRN69aoes06/673zh25ubvbLOfmnkEH9+9L9n0AYF9MzB0dP3pEVavbju+qz9TQwf1/21/mzh25udt+OXXPkEFHDh1UbExMov6GYWj3zh26cO6cylesnGyxA3i4hM/vKvd9flepVkOHDux/4DJubrafze7u7jp6+KBiYxOPbwBICQ4lqj179lSLFi00ZMgQtWrVSoMHD06puGAy4WGhio+LU1YfX5v2rL6+CgkOtrtM6bLl9d6Iz/XJB0PUuGZFPd+8vjw8sqj/4KHWPpWr19C8WdP1z4Xzio+P1+6d27Rt03qFBF1P0f0B8K+wsDDFxcXJx9d2fPv4+CokOMjuMtWeqallixfo2JHDMgxDR48c0vIlixQbG6uwsDBrv6jISDWsWVl1qpXX4Df6atDb7yVKiAGknPCwUMXZ+fz28X3w+K5avYaWL1mg40fvju9jRw5p5bLE4xsAUpJLUjqdOnVKx48f1+7du7Vy5UpJd89X/euvvx65bHR0tKKjo+9rs8j9vlk0pA3/Oz3ZyjCMRG0Jzp05re/Gfq6Xe/RRleo1FBIcpInfjtG4zz/RkA8+liS9PuhdjflsuLp1bC1ZLMqVO6+atmyjVfcVZQHwJNgOZsMwEg/6/+neu4+Cg4PUu9tLkmEoq4+vmrdqo5nTJsvJ+d/fQDNlzqxpvy3QzVs3tXvXTn0zdrRy5cmjipWrpuieALBlsSQe3/e3Jejas49CgoPUp3tnSXfHd9OWbfXb9MlyduLKhgCejCQlqpcuXdLu3bsVERFhTU5dXV31xRdfPHLZUaNGacSIETZtA9/5QG+9++FjhIvU4uWdVU7OzolmT8NCQhL9Sptg1rRfVKpseXV6ubskqVCRYsqQIaPeeLWrevTpL99s2eWd1UeffPmN7kRHKzw8TNmy59DP34+TX67cKb5PAO7y9vaWs7NzotmV0NAQ+TxgfLtnyKD3h4/UO+8PU0hIsHyzZdeShfOUKXNmeXtntfZzcnJSnnwBkqSixUro/Nkzmj75ZxJV4Anx8s5qf3w/5PPbPUMGvfvRSA1+b5hCgu+O72WL7o5vr3vGNwCkpCQlqnXr1lXdunX17rvvOjwTOnToUA0aNMimLejWA6bgYFqurq4qWryk9uzartr1Glrb9+zarhp16ttdJvr2bTm7ONu0Jcy03F8x2s3dXdlz5FRsbIw2rf9T9Ro2SeY9APAgrq5uKlaipHbt3Ka6DRpZ2//asU216zV46LIurq7KkdNP0t3KwTVr15XTQ2ZcDMNQjJ1zWAGkjITP7907t6tO/X/H9+5d21XrAZ/fCVxc/h3fa1evUo1aDx/fAJCckpSoJqhXr54sFosMw9CJEyeUJ08e7d9v/0T8BO7u7omS28j4O45HilT3wotdNGr4UBUrXkoly5TT8sXzdO3aFbVq10GS9PP34xV0PVBDh38mSXqmdl2N+WyEliyYc/fQ36AgfT/uCxUvVUbZsueQJB09dEDXrweqcNFiCgoM1LRfJsiIj7fOwgJ4Mjp17qqPP3xXJUqUVumy5bRk4Txdu3pFbdt3lCRN+HacrgcG6qNPRkmSLpw/pyOHDqpUmbKKjAjXbzOm68zpk/rw48+s65w++WcVL1lKufPkVUxMjLZv3aTfVyzVkKEcUQM8SR1e6qJPhw1VsZKlVKpMOS1bNF+BV6+ozf/G98TvxinoeqDeH3F3fF88f05HDx9UidJlFRkZobkzp+nsmZN6b/in1nXGxMTo3JnT1v8HXb+mk8ePKWOmTMqTN9+T30k8tQyloTK2cIhDier27dut/4+IiFC/fv2SPSCYV/1nmyoiPEzTJ/+okKDryl+wsEaN+0F+/rkkSSHB1xV47d9rsjVt2VY3b97Q4nm/6cevv5JHliyqULmqer820Nrnzp1oTfnxW12+/I8yZsykajVqa+jwz+SRxfOJ7x/wNGvUpJnCw8M0+ecJCg66roKFiuirb36Uf6674zs46LrNNRfj4+L024ypunD+nFxcXFSxclVNnDJT/vcctn/r1k19NeoTBQZek7u7uwLyF9SwTz5XoybNnvj+AU+zho2bKSI8XNN++VHBQddVoFARfTF+gvXzOzgoyGZ8x8XHac7MadbxXaFyVf3wywyb8R10PVA9/+956/3ZM6Zq9oypKl+xsr6ZOPWJ7RuA9Mti3H8MZhJFRUWpTp062rvX8evhXQpjRhVIrzK4clgYkF7FxjFzAaRXOT1dUzuEx3LoUlRqh5DmlM7tkdohJIlDM6pVqlSxHvrr4uKigQMHPnohAAAAAAAc4FCimpTL0QAAAAAA8F8kKVHt1auXfHx87D42evToZA0IAAAAAGAeP/zwg7788ktduXJFpUqV0vjx41W7du0H9p85c6ZGjx6tkydPysvLS02bNtVXX30lX1/7l8WyJ0knk508eVItWrTQtGnT1LJlS7Vo0UItWrRQy5Ytk7whAAAAAEhWBjeHbw6aM2eO3nzzTb3//vvat2+fateurWbNmunChQt2+2/ZskVdunRRz549dfjwYc2bN09//fWXevXq5dB2k1RMqXXr1nJyctK2bdtUr149SXevhWexWDR37lyHNihRTAlIzyimBKRfFFMC0q80W0zpH4opOap0HseKKVWrVk0VK1bUhAkTrG0lSpRQ27ZtNWrUqET9v/rqK02YMEGnT5+2tn377bcaPXq0Ll68mOTtJunQ36VLl+rKlSt68803OdQXAAAAANKo6OhoRUdH27S5u7vL3d09Ud87d+5oz549evfdd23aGzdurG3bttldf40aNfT+++9r5cqVatasmQIDAzV//ny1aNHCoTiTPPXh7++vOXPmKCAgwOYGAAAAAEgbRo0aJS8vL5ubvZlRSQoKClJcXJxy5sxp054zZ05dvXrV7jI1atTQzJkz1bFjR7m5ucnPz0/e3t769ttvHYqTY/QAAAAA4CkxdOhQhYeH29yGDh360GUsFovN/YTTQO05cuSIBgwYoI8++kh79uzRqlWrdPbsWfXp08ehOB26PA0AAAAAIO160GG+9mTLlk3Ozs6JZk8DAwMTzbImGDVqlGrWrKkhQ4ZIksqWLavMmTOrdu3aGjlypPz9/ZO0bWZUAQAAAKRJqV1ANy3eHOHm5qZKlSppzZo1Nu1r1qxRjRo17C5z8+ZNOTnZppnOzs6S7s7EJhWJKgAAAADArkGDBumXX37R5MmTdfToUQ0cOFAXLlywHso7dOhQdenSxdq/VatWWrhwoSZMmKAzZ85o69atGjBggKpWrapcuXIlebsc+gsAAAAAsKtjx44KDg7Wxx9/rCtXrqh06dJauXKltbDulStXbK6p2q1bN0VGRuq7777TW2+9JW9vbzVo0EBffPGFQ9tN0nVUkxvXUQXSL66jCqRfXEcVSL/S6nVUD3IdVYeVcfA6qqmFb5QAAAAAAFMhUQUAAAAAmArnqAIAAABIk578SYx4UphRBQAAAACYCokqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBUSVQAAAACAqVD1FwAAAECaZIiyv+kVM6oAAAAAAFMhUQUAAAAAmAqJKgAAAADAVEhUAQAAAACmQqIKAAAAADAVqv4CAAAASJso+ptuMaMKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABToeovAAAAgDSJor/pFzOqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFar+AgAAAEiTDMr+plvMqAIAAAAATIVEFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFSo+gsAAAAgTTJE2d/0ihlVAAAAAICpkKgCAAAAAEyFRBUAAAAAYCokqgAAAAAAU0mVYkp//xOWGpsF8AQcDIxM7RAApBC/LK6pHQKAFNKtSr7UDgGwQdVfAAAAAGkTRX/TLQ79BQAAAACYCokqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBUSVQAAAACAqVD1FwAAAECaRNHf9IsZVQAAAACAqZCoAgAAAABMhUQVAAAAAGAqJKoAAAAAAFMhUQUAAAAAmApVfwEAAACkSQZlf9MtZlQBAAAAAKZCogoAAAAAMBUSVQAAAACAqZCoAgAAAABMhUQVAAAAAGAqVP0FAAAAkCYZouxvesWMKgAAAADAVEhUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIWqvwAAAADSJor+plvMqAIAAAAATIVEFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFSo+gsAAAAgTaLob/rFjCoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFqr8AAAAA0iSDsr/pFjOqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFar+AgAAAEiTDFH2N71iRhUAAAAAYCoOJaqGYahZs2YpFQsAAAAAAI4lqhaLRaVKldL69esVFRWlmzdv6ubNmykVGwAAAADgKeTwOap79uzRnj17rPctFovWrVuXrEEBAAAAAJ5eDieq69evT4k4AAAAAACQ9BiJ6oEDBzR69GhduXJFhnG3yhYzqk+PrasWaf2S3xQRGiy/vPnVtvsAFSxZ7oH9Y2PuaPXcqdqzabUiwkLk7Ztdjdp3UbWGLax9Ni6fq21/LFZo0DV5ZPFW2WfqqkXnV+Xq5v4kdgnA/xzduFyH1izQrfAQefsHqOoLr8ivSGm7fa+cOKBV495N1P7csIny9strvR99M0p7l0zT+b+36c7NKHlk81OV9r2Ut3SVFNsPAIntWbNUO1fOU1RYsLLnzq9G/9dXeYuXeWD/2Jg72rpohg5tXasb4aHK4pNNNdq8pHJ1m0qSrv9zTpsXTNPVsycVHnRNDf+vr6o2bfekdgf4F0V/0y2HE9VXXnlFP//8s15//XV99913mjVrVkrEBRPat3WtFk/5Ru17D1KB4mW0bfVS/fTpEL0z/ldlzZ7T7jLTxgxTZFiIOvZ7V9n8cysyPFTxcXHWx/dsWq0VMyaq42vvqkCx0rp++aJ+++4zSVLb7gOeyH4BkM7s3qhd837SM536KUehkjq++Xet+f4jPffRj/LwyfHA5doN/0muGTJZ72fI4mX9f1xsjFZ/874yZPFW/VfeU2bvbLoRGiTXDBlTdF8A2DqyY4P+nDFBTbr1V56ipbRv3QrN+fI99f5ikryy2R/fi78dqRvhoWre+y1lzZlLNyPCbD6/Y6Kj5Z3dX8Wr1tGfM358UrsC4CnicKKaMWNGlSlTRvHx8SpTpox27tyZEnHBhDYum6NqDVqoeqNWkqTnegzQ8b93aesfi9Ty//ok6n90306dPvy33v9hjjJn8ZQk+eTwt+lz7vhhFSheWpVqP2t9vEKtRrpw6mgK7w2Aex1eu0hFajRW0Vp3Z0uqdXhVl47u1bFNK1S5bfcHLpchi7fcM3nYfezkttWKvhGpFkPGyMn57seNh6/9H7UApJxdvy9QuXpNVb5+c0nSsy/309mDu7Vv7TLV69gzUf/T+//ShWMH1HfsdGX0uPv57Z3dz6ZPrkLFlKtQMUnShjmTUngPADyNHE5UK1WqpNu3b6tJkyaqXr26/Pz8Hr0Q0rzYmBj9c/qEGjz3fzbtxcpV0bnjh+wuc/ivLcpbqJjWL56l3Zv+kLt7BpWqUktNO/WSm/vdw3oLliijPZtW6/zJIwooUlLBVy/r6N4dqlKvaYrvE4C74mJjFHzhlMo26WDTnqtEBQWeefiPRks/66+4mDvy9s+ncs06yb/Yv6cCXDiwU9kLltD22T/owv4dyuDhpYJV6qlMk+fl5OScIvsCwFZcbIyunj2hZ1p2tGkvULqS/jl52O4yJ/dul3+BotqxfK4Obf1Tru4ZVKTiM6rzfDdOywHwxDicqL766qvKkCGDPvjgA/Xr108REREpERdM5kZkuOLj45TFK6tNexbvrIoMC7G7TPC1yzp77KBc3dzU/e1PdSMiXAt+HqubURHq9NpQSVKFWo0UFRGm7z54TYZhKD4uTjWatFXDdv9nd50Akl90VISM+HhlyOJt054xS1bdCg+1u0wmTx/V6DxAvvkKKz42Rqd3rtOqr99Ts4Gfy6/I3fPeooKu6urx/SpYtb6efW2EIgIva8ecH2TEx6l8i5dSercASLoZGS4jPl6Z7/v8zuyVVTfC7I/vsMArunjikJxd3dT+zeG6GRmu1VO/1e2oSLV4ZfCTCBsAHi9RTSie5OPjoz59+mju3LkP7B8dHa3o6Gibtpg70fwil0ZZLBab+4YhWWSx29e4+6A6v/GRMma+e2hgbMzrmvbVh2rXa5Dc3N116tA+/bngV7XvPUj5ipRU0NVLWjz5a62e56vGL3RL6d0BcI/7x7dk2Gm7y8svj7z88ljv5yhYQjdCr+vQmoXWRNUw7ia/NTr3l5OTs7IFFNHN8GAdWrOARBV40u7//JaRqM36mBEviyxq3W+oMmTKLEmK63xHC7/5RI279ec7HIAnwimpHefNm6cXXnhBhw4dUocOHdShQwe1a9dOkZGRD11u1KhR8vLysrnN/eWb/xw4nqzMWbzk5OSsiPtmT6PCQ+XhndXuMp5ZfeXlk92apEpSzjwBMgxD4cGBkqTfZ/+iSnUaq3qjVsoVUEhlq9VR85de0dqFMxQfH59yOwTAyt3DUxYnJ92KsJ1duRUZpgye3kleT/YCxRVx/bL1fkYvH3nmyG1zmK+3X17dighVXGzMf44bwKNlyuIli5OTbtz3+X0zPEyZvbztLuPh7SuPrNmsSaok+ebKJxmGIkOup2S4gMMMbg7f0ookz6g2btxYVatW1YQJE9S3b19Jkqurq/z9/R+63NChQzVo0CCbtnWnwh8jVKQmF1dX5SlUVCf2/6Wy1epY208c+EulqtSyu0yBYmW0f9t6Rd+6KfeMd6uCXr98URYnJ3n53q0yGBN9WxYn299LnJyc7/7Sa6SloQSkXc4urvLNV1iXj+5TQPka1vbLR/cpX7nqSV5P8MXTyuj57w9XOQuW1Jm/NsiIj7eO8/DAS8ro5SNnF9fk2wEAD+Ts4iq/AkV19tBeFbvn8/rsob0qWqmG3WXyFC2lY7s26c7tW3L7X5XukKuXZLE4KYtP9icSNwAkeUb12LFjCggIUO3atXX48GEdPnxYf//9t37//feHLufu7i5PT0+bG4eMpE11W3XUzrXLtXPtCl3755wWT/lGoUGBqtG4rSRp+YwfNeubkdb+FWs3UuYsXpr9/ShdvXhWpw//rWXTf1DVBs2txZRKVq6pbX8s1r4tfyr42mUd3/+Xfp/9i0pXriUnZ4qtAE9KqYbP6cTWP3Ri22qFXbmgnfN+0o3Q6ype+26V0N2Lp2jT1K+s/Q+vXazzf29TeOAlhV4+r92Lp+j8vq0qUa+VtU+xOi10+0akds6bqPBr/+jiwV06sGquStRt+cT3D3iaVW3WXvs3/K79G1cp6NJ5/TljgiKCA1Wh4d2xuGHOJC378Qtr/1I1Giijh6dW/PSlgi6d14VjB7Tut59Utm4T63e4uNgYXTt/StfOn1JcbIyiQoJ07fwphVy9lCr7CCD9SfKM6rFjx1StWjXt3r070WPNmzdP1qBgThVqNtTNyAitnjdVEaHB8s9XQL3fGy2fHHcrP0eGBis06Jq1v3vGTHr1o7FaNGm8xr3dW5mzeKlcjfpq9mJva59nn+8ii8Wilb/9ovCQ6/Lw9FapyjXV/KXeibYPIOUUrFxX0TcitX/FLN2MCFFW//x69rUR1svJ3AoP1Y17DvmLj4vRXwsn6WZYsJxd3ZTVP0CNXhuhvKWrWPt4+GRXkwEjtWveT1oy8jVl8vZVyfptVKbJ8098/4CnWcnq9XQrMkJbF81QVFiIsufJrw5DPpVXtrvjOyosWBFBgdb+bhky6sV3P9fq6d9ryoevKaOHp0pUq6M6L/x7qarI0GBNfr+v9f7OlfO0c+U85SteVp0/GPPkdg5AumUxjCd/fOWKQ4GP7gQgTToY+PDz1gGkXX5ZOGQbSK+6VcmX2iE8lu2nwlI7hDTnmcLeqR1Ckjhc9feZZ56RxWKRYRg6ceKE8uTJo/3796dEbAAAAACAp5DDier27dut/4+IiFC/fv2SNSAAAAAASIp4im+mW0kupmSPxWLR4cOHkysWAAAAAAAcn1GtUqWK9QLwzs7Oeuutt5I9KAAAAADA08vhRLV06dI29zdv3qwrV66od+/e8vb2Tq64AAAAAABPKYcP/c2SJYtq1aqlgQMHqk6dOnJycpKPj486duyYEvEBAAAAAJ4yDieqx44dU8+ePVW2bFl1795dp06dUs+ePXX79u2UiA8AAAAA8JRx+NBfLy8vffDBB6pUqZL27t0rLy8vxcbGysPDIyXiAwAAAAC7qPmbfjk8ozp79myVL19eJ06cULly5TR79my5uLhoxYoVKREfAAAAAOAp4/CMqrOzs55//vmUiAUAAAAAgP92HVUAAAAAAJIbiSoAAAAAwFRIVAEAAAAApuLwOaoAAAAAYAYGZX/TLWZUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKlT9BQAAAJAmGaLsb3rFjCoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFqr8AAAAA0qR4iv6mW8yoAgAAAABMhUQVAAAAAGAqJKoAAAAAAFMhUQUAAAAAmAqJKgAAAADAVKj6CwAAACBNMkTZ3/SKGVUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKVX8BAAAApEkGRX/TLWZUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKlT9BQAAAJAmGaLsb3rFjCoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFqr8AAAAA0qR4iv6mW6mSqJ4Lv5kamwXwBDQrkiO1QwCQQi6E8fkNAHgyOPQXAAAAAGAqJKoAAAAAAFMhUQUAAAAAmAqJKgAAAADAVKj6CwAAACBNMkTZ3/SKGVUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKVX8BAAAApEkGRX/TLWZUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKlT9BQAAAJAmUfU3/WJGFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkLVXwAAAABpUrwo+5teMaMKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABToeovAAAAgDTJoOhvusWMKgAAAADAVEhUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKlyeBgAAAECaZIjr06RXzKgCAAAAAEwlyTOq06dPf+BjXbp0SZZgAAAAAABIcqJ669YtSdKmTZtksVhUtWpV7d69W7GxsSSqAAAAAIBkk+RE9dVXX5UkLVu2TMuXL7e2N2/ePPmjAgAAAAA8tRw+RzU8PFxr165VaGio1q5dq8jIyJSICwAAAADwlHK46u+sWbM0evRoffXVVypSpIhmzZqVEnEBAAAAwEMZFP1NtxxKVA3D0AcffKBp06alVDwAAAAAgKecQ4f+WiwWeXp66uzZsykVDwAAAADgKefwob/btm3TihUr5OPjI4vFIovFol27dqVEbAAAAACAp5DDieqePXtSIg4AAAAAACQ9RqL6zz//aOLEibp8+bKM/529PHny5GQPDAAAAADwdHL48jQvvfSSqlatqsOHD+u5556Tp6dnSsQFAAAAAA8VL4Obg7e0wuEZVRcXF7Vq1UpfffWVWrVqpe+++y4l4oJJHVi3THtXzdONsBD55A5QnRf7KHfRMnb7/nNsvxaOfjtR+/99+rN8/PNJkuJiY7V75Wwd3fqnboQGKatfHtV4oafyl6mSovsBILE/lszT0nm/Kiw4SHnyF1S3fm+pRJkKdvt+P3q4Nq5enqg9T0BBjZ00V5IUGxurxb9N0cbVyxUSdF258gaoc6/+Kl+1RoruB4DEtq5apA1Lf1NkaIhy5s2vNt36q2DJcg/sHxtzR2vmTdOeTasVGRYib9/satjuZVVt2EKS9MNHA3TmyN+Jlitesbp6vTc6pXYDwFPE4UQ1V65cun37tsqVK6eOHTvq1q1bKREXTOjErg3a9NuPqvfy68pVuJQObVihpeM+0P+N/FlZfHM8cLmXP5skt4yZrPczZvGy/n/Hoqk6tn2dGnZ7U1n98ur84d1a8d3HeuG9ccoRUDhF9wfAv7atX62pE8ao14B3VaxUOf25YqE+GzpA4ybNU7acfon6d+83WJ17vW69HxcXpyGvvKTqdRpa22ZP+UGb//xdrw56X7nz5tf+3Tv05fAhGvn1JBUoUvyJ7BcA6e+ta7V06rdq12uQ8hcvrR1rluqXz97WkHHTlTV7TrvL/DpmmCLDQ9Wh3zvK5pdbUeGhio+Lsz7ebchIxcbGWO/fjIrQ2Ld6qNwz9VN8fwA8HRw+9HfGjBnKkCGDvv76a73zzjtatWpVSsQFE9r3x0KVqt1Epes0k0+ufKrzUl95+GTXgfWJZ1XulcnTW5m9fKw3Jydn62PHtq1V5RadlL9sVXnl8FfZ+q0UULqS9v2xIKV3B8A9li+YqQZN26hh87bKE1BA3fq9pWw5cmr1svl2+2fy8JC3Tzbr7fSJo7oRFaH6TVtb+2z+c6Wee6m7KlarpZy58qhx6+dVrnJ1LZs/80ntFgBJG5fNVdUGLVStUUvlzJNfbboPkLdvdm1fvdhu/2P7dur0kf3q9d5oFS1bWT45/JWvSEnlL/7vEVSZsnjKM6uv9XZi/19ydXdX2WfqPZmdApDuOTyjev78eY0YMUKnT59WkSJF9OGHHyogICAlYoOJxMXGKPD8SVVu3tGmPV+pSrpy6shDl/1teD/FxsTIJ1c+VWn5ovKWKG+zXhdXN5v+Lq7uunzycLLFDuDhYmNidObEMbXt1M2mvWyl6jp+5ECS1rHu9yUqU7Gqsuf0t7bF3ImRm5vt+HZzd9fxQ3//15ABJFFsTIwunTmhBs91tmkvWq6Kzh0/ZHeZw7u3Km+hYlq/ZJb2bFotN/cMKlW5ppp26iVXd3e7y+xat0LlazaUe4aMyb4PAJ5ODs+o9ujRQz169NDq1avVtWtXde/ePSXigsncioyQER+vTF7eNu2ZPL11MzzU7jKZvXzUoOsbav7ah2rx2ofK6pdHi756V5eOH7T2yVe6kvatXqCwa5dkxMfrwuE9OvP3dt0ID0nJ3QFwj4jwMMXHx8krq49Nu1dWH4WFBD1y+dDgIP29a5saNGtr016ucnUtnz9LV/65oPj4eB3Ys0O7t21UaBLWCSB53IgMV3x8nLJ4ZbVpz+Llo8gw+5+1Idcu6+yxg7p64ay6DRmpNt3768COjVr4yzi7/S+cPKKrF86q2v/OXwWA5ODwjOqdO3dUq1YtSVLt2rUVExPz0P7R0dGKjo62aYu5Ey1XN/u/yMHsLLZ3DUMWi/2eWf3zKqt/Xut9/8IlFRlyXXv/mK/cxe4ePlTnxb5aN228fn2vl2SRvLLnUomajXV06+qU2gEAD2C5fzAbRuI2Ozb8sUyZPTxUtWY9m/burw3Wj2NH6s0ez8sii3Lmyq16TVprwx9LkzFqAEly31g2ZCjRZ3rCY/HxkkV66Y0PlTGzhySpddc7mj7mI7XrNTDRrOqudSvkl6+A8hUpmSKhAw9jpJ0itnCQw4lqlSpV9NJLL6lKlSratWuXqlR5eHXWUaNGacSIETZtzbq/oRY933R000hFGbN4yuLklGj29GZkuDJ6Zn3AUon5FyyuYzvWWe9n8vRWy/7DFRtzR7ejIpTZ21fb5k+SZzb7xR0AJD9PL285OTkrLCTYpj08LFReWX0fuqxhGFr/x1LVbtRcLq6utuv1zqq3Px6jO3eiFRURrqy+2TXzl2+Vwy93su8DAPsyZ/GSk5NzotnTqPBQZfG2//mdJauvvHyyW5NUScqRJ0CGYSgsJFDZ7/kR+k70bf29dZ2adOyRMjsA4Knl8KG/Y8eO1aBBg5Q7d24NHjxYY8eOfWj/oUOHKjw83ObW+OW+jx0wUoezi6tyBBTRhSN7bdovHN4r/8JJ/wU18MJpZfbySdTu4uomj6zZFB8Xp1N7tqhghWf+c8wAksbF1VUFixbXgT07bdoP7NmpYiXLPnTZI/v36Oqli2rQrM0D+7i5ucsnWw7FxcVp5+Z1qlyjbrLEDeDRXFxdlbtgUZ04sNum/cSB3cpfrLTdZQoUL6OIkCBF37ppbbt++aIsTk7y9rGt8r9/23rFxsSoYp3GyR88gKeawzOqUVFR2rRpk06dOqVLly6pWLFi8vDweGB/d3d3ud93iIirG+cfpkUVmrTT6p+/VI78ReVfqIQObVypqJBAlal395yUrfMn60ZokBr3vnvt1H2rF8ozm598cwcoLjZGx7av0+k9W9T8tQ+t67x6+piiwoKUPW8hRYUFaeeSGTLiDVVq1iFV9hF4WrVs31nffvGRChYtoaIly+rPFQsVFHhVz7ZqL0ma9ct3CgkK1Ovvfmyz3LpVS1SkeGnlK5D4clInjx5SSFCg8hcqqpDg65o3/ScZ8YbadOzyRPYJwF11W3XQb99+qrwFiymgWCntWLNMYUGBqt747g9MK2dOVHhwkF4c8L4kqUKtRlozf5rmfP+5GnfsoRuRYVr+6wRVrd888WG/a1eodJVaynzPpecAIDk4nKi+8MILev7559WnTx/t3r1bL7zwgn7//feUiA0mU7RqPd2OitSupTN1IzxEvrkD1PrNkdbDdG+Ghygy5Lq1f3xcrLbM/UlRocFycXOTb64AtX7zE+UvW9XaJzb2jrYvnKaI61fkmiGj8peposa93pZ7pgf/+AEg+dWo31iREeFaMOMXhYYEKW/+Qhr62dfWKr6hIUEKCrxqs8zNqCjt3LxO3foNtrvOmDvRmj1lggKvXFKGjBlVoWpNvf7Ox8rskSXF9wfAv8rXbKgbkRFaM3+aIkKD5ZevgHq+94V8st+9RnJEaLBCg65Z+7tnzKRXPxqrRZO+1tfv9FamLJ4qV6O+mnXqbbPe65cv6uyxA3rlwzFPdH8APB0shuHYKcjNmjWzSUybNm3q8LVUv996zqH+ANKOWvkefk4jgLTrQtjNR3cCkCa1KpM264MsPnD10Z1go21Zv9QOIUmSPKO6cuVKSZKfn58++OADVapUSfv27ZO/v/8jlgQAAACA5EfR3/QryYnqX3/9JUnKnz+/JOnAgQNydna23gcAAAAAIDkkOVEdNmyYJGnq1Kl6/vnnH1pACQAAAACAx+Xw5Wlu3bql5557Tp07d9bvv/+u+Pj4lIgLAAAAAPCUcjhR7du3r9asWaNPP/1Uv/76q3LlyqWBAwfq/PnzKREfAAAAAOAp4/Dlaa5cuaJZs2Zp5cqVKl68uBYvXiwnJyd16tRJ27dvT4kYAQAAAABPEYcT1ddee00vv/yyVq1aJVdXV2v7u+++m6yBAQAAAMDDOHilTaQhDh/6u3DhQgUEBFjPT/3nn38kSW3atEn24AAAAAAATx+HZ1TfeustxcTEaPv27WrdurV69Oih1atXp0RsAAAAAICnkMMzqvv27dM333xjvTxNXFxcsgcFAAAAAHh6OZyourm56ezZs7JYLLp48aLc3d1TIi4AAAAAwFPK4US1Y8eOeueddxQcHKzBgwerRo0aKREXAAAAAOAp5XCi+scff+jNN9/U/v371aBBA128eDEl4gIAAACAh4rn5vAtrXC4mNL06dPVuXNnzZkzRy4uLpo4cWJKxAUAAAAAeEolOVEdMmSILBaLJClHjhxasGCBXnzxRb399tsaPXp0igUIAAAAAHi6JDlRbdmypc39Dh06JHswAAAAAAAkOVGtW7duSsYBAAAAAICkxyimBAAAAABASnK4mBIAAAAAmIFhGKkdAlIIM6oAAAAAAFMhUQUAAAAAmAqJKgAAAADAVEhUAQAAAACmQqIKAAAAADAVqv4CAAAASJOo+Zt+MaMKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABToeovAAAAgDTJMKj7m14xowoAAAAAMBUSVQAAAACAqZCoAgAAAABMhUQVAAAAAGAqJKoAAAAAAFOh6i8AAACANCk+tQNAimFGFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkLVXwAAAABpkmEYqR0CUggzqgAAAAAAUyFRBQAAAACYCokqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBWq/gIAAABIkyj6m34xowoAAAAAMJVUmVHNlSVDamwWwBOw9WJwaocAIIV4unMgFgDgyWBGFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKJ5sAAAAASJMo+pt+MaMKAAAAADAVElUAAAAAgKmQqAIAAAAATIVEFQAAAABgKiSqAAAAAABToeovAAAAgDQp3qDub3rFjCoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFRJVAAAAAICpkKgCAAAAAEyFqr8AAAAA0iRq/qZfzKgCAAAAAEyFRBUAAAAAYCokqgAAAAAAUyFRBQAAAACYCokqAAAAAMBUqPoLAAAAIE0yDOr+plfMqAIAAAAATIVEFQAAAABgKiSqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFSo+gsAAAAgTYpP7QCQYphRBQAAAACYCokqAAAAAMBUSFQBAAAAAKZCogoAAAAAMBUSVQAAAACAqVD1FwAAAECaZBipHQFSCjOqAAAAAABTIVEFAAAAAJgKiSoAAAAAwFRIVAEAAAAApkKiCgAAAAAwFar+AgAAAEiT4in7m24xowoAAAAAMBUSVQAAAACAqZCoAgAAAABMxeFEdfPmzTb3d+3alWzBAAAAAADgcKI6bNgwm/tffvllsgUDAAAAADCXH374QQUKFFCGDBlUqVKlRJOX94uOjtb777+vgIAAubu7q1ChQpo8ebJD20xy1d+ff/5ZP/30k44fP66qVavKMAw5OTmpaNGiDm0QAAAAAJIDRX9T3pw5c/Tmm2/qhx9+UM2aNTVx4kQ1a9ZMR44cUb58+ewu06FDB127dk2TJk1S4cKFFRgYqNjYWIe2azEMx17eH3/8UX369HFoI/dbdODqf1oegHldibqd2iEASCGe7lzVDkiv/q9SntQO4bH8tON8aoeQ5rxSPcCh/tWqVVPFihU1YcIEa1uJEiXUtm1bjRo1KlH/VatWqVOnTjpz5ox8fHweO06HD/1t1aqV+vfvr65duyouLk5Tpkx57I0DAAAAAMzpzp072rNnjxo3bmzT3rhxY23bts3uMkuXLlXlypU1evRo5c6dW0WLFtXgwYN169Yth7bt8E+jPXr00KhRo/TWW2/J2dlZM2bMUPfu3R1dDdKo7X8s0qYlsxUZFqKcefKrZffXVaBEuQf2j425o7Xzpmnf5jWKDAuRl2921W/3f6rSoIW1z60bkfrjt190eOcm3boRpaw5/NSiy2sqXrH6k9glAP+zf90y7f19nm6Ehcg3d4DqvNRHuYuWsdv3n2P7teCLtxO1v/zZz/Lxv3sYUFxsrHavmK2jW/9UVGiQsvrnUc0Xeip/mSopuh8AEtu9Zom2L5+ryLBgZc+dX0269FO+4mUf2D825o42LfxVh7b+qaiwUHn6ZFOttp1Vvl4zSdLRXZu1dckshVy7pPi4OPn45Vb15i+obO1nn9QuAXhM0dHRio6Otmlzd3eXu7t7or5BQUGKi4tTzpw5bdpz5sypq1ftHyV75swZbdmyRRkyZNCiRYsUFBSkfv36KSQkxKHzVB1OVGNjY1WxYkVZLBZJkoNHDiMN2791nZZP+U5teg9U/mKltXPNMk359B0NGjdN3tlz2l1m5tjhigoPVfu+b8vXL7duhIcpPj7O+nhsTIwmffKWMntmVee3PpaXb3aFBwXKLWOmJ7VbACSd2LlBm2b9qPovv65cRUrp4IYVWjL2A/3fpz/L0zfHA5frMmqSzXjNmMXL+v/tC6fq2PZ1atjtTfn459X5Q7u1/NuP1eH9ccoRUDhF9wfAvw5vX68/pv+g5j0GKE/R0tq7drlmfTFUfb+cLK9s9j+/F3zziW6Eh6pl78HysfP5ndEji2q17SzfXHnl7OKqk3u3a+nE0crs6a1C5fgxCjCzUaNGacSIETZtw4YN0/Dhwx+4TELul8AwjERtCeLj42WxWDRz5kx5ed39XjB27Fg9//zz+v7775UxY8YkxelwolqqVCkNHz5cQUFB+vTTT1Wu3INn05C+bFk+V5UbNFfVhi0lSa2699eJ/bu0Y/USNe38SqL+x/ft1Nkj+/X2d78pUxZPSZJPDn+bPrvXr9TNqEj1HfmDnF3uvh2zZvdL4T0BcL+9qxeqVJ0mKl337mxJ3Zf66vyhPTq4brlqvtDjgctl8vSWeyYPu48d275WVVq+qALlqkqSyjZopfOH9mjvqgVq+uo7yb8TAOzasXK+KtRrpgr17x7N1KTLazp9YLd2/7lMDTv1StT/1P5dOn90v/qPn6GMHnc/v73v+2zOX7K8zf1qzdrrwObVunD8EIkqYHJDhw7VoEGDbNrszaZKUrZs2eTs7Jxo9jQwMDDRLGsCf39/5c6d25qkSnfPaTUMQ//884+KFCmSpDgdTlS/+eYbrVixQpkzZ1bx4sXVqlUrR1eBNCg2JkaXzpxQ3bYv2bQXKVtF548fsrvM0d1bladQMW1c8pv2bVottwwZVKJyTTXu2FOu/xsMR3dvVb6ipbTkl3E6snurMnt6q3ythqrb5iU5OTun+H4BkOJiYxR47qQqN+9o0x5QqpKunD7y0GVnDeun2JgY+eTKp6qtXlTeEuX/XW9MjJxd3Wz6u7i56/LJw8kWO4CHi4uN0ZWzJ1Sz9Ys27YXKVNI/J+yPxRN7titXgWLatmyODm5ZI1f3DCpaqYbqvdBdrm6Jv8wahqFzh/cp+Mo/avii/dMFgJQSz9GdDnvQYb72uLm5qVKlSlqzZo2ee+45a/uaNWvUpk0bu8vUrFlT8+bNU1RUlDw87v6YfeLECTk5OSlPnqQX7UpyonrkyL9fVgoUKKACBQpY20uWLJnkDSJtuhkZrvj4OGXxtq3clcU7q06EhdhdJuTaFZ07dlAurm56echI3YgM1+JfxulmVIRe6PeutU/ooX0qX6uRug39QsFX/9GSX8YrLi5OjV7oltK7BUDSrcgIGfHxyuTpbdOe0ctbNw6F2l0ms5ePGnZ7QzkCiiguNkZHt63Vwi/f1fPvfKncxe5+Uc1XupL2/bFAuYuWkXcOf104uk9n9m2XER+f0rsE4H9uRobLiI9XZq+sNu2ZvbIqKtz+53dY4BVdOHFQLm6uemHgx7oZGa7fp3ytW1GRav3qEGu/2zejNP61joqLjZHFyUnNu7+hgmUqp+j+AHjyBg0apJdfflmVK1fWM888o59++kkXLlywXglm6NChunTpkqZPny5Jeumll/TJJ5+oe/fuGjFihIKCgjRkyBD16NEjyYf9Sg4kql9++aXddovF8tCTYu2drBtzJ9ruL3JIewwj8THr/z5298topwEfKEPmu7+mtOz6mmaO+Uhtew6Uq7u7DCNemT291e7VwXJydlaeQsUUERKkTUtnk6gCT1iisWwYsj+6paz+eZXVP6/1vn/hkooKua49q+ZbE9W6L/XV2qnj9et7vSSL5JUjl0rWaqwjW1an0B4AeJD7x7IhyfKAEW4Y8bLIoravvacM/zu0Pzamr+Z/PULNug+wfodzz5BJr4z6SXdu39LZw3u1esYEeefwT3RYMIC0rWPHjgoODtbHH3+sK1euqHTp0lq5cqUCAu5e5ubKlSu6cOGCtb+Hh4fWrFmj/v37q3LlyvL19VWHDh00cuRIh7ab5ET1cS9DY+9k3Q593lKnvoMfa31IHZmyeMnJyVmR982eRoWHyuO+X2kTZMnqKy+f7NYkVZJy5M4nwzAUHnJd2fzzKIu3r5xdXGwO882RJ0CRYSGKjYmRi6tryuwQAKuMWTxlcXLSjXDb2dNbEeHK9IDxbY9foeI6tn2d9X4mT2+1GjBcsTF3dDsqQpm9fbV13iR5PqB4C4DklymLlyxOToq6b3zfDA9NNMuawMPbR1l8slmTVEnKljufZBiKCL4uX/+7h+5ZnJzk45dbkuSXv7CCLl3Q1iW/kagC6VC/fv3Ur18/u49NnTo1UVvx4sW1Zs2a/7TNJF9HtUqVKqpatarNLaHtYYYOHarw8HCbW/ue/f9T0HjyXFxdlbtgUZ06sNum/dSB3QooVtruMgHFSisiNEjRt25a265f+UcWi5O8fLLf7VO8tIKuXlL8PYcCXr/8j7Jk9SVJBZ4QZxdX5chfRBcO77Vpv3Bkr/wLJf3UjusXTiuzV+ILe7u4uskjazbFx8Xp1J4tKljhmf8cM4CkcXZxlX+BojpzcI9N+5lDe5SnaCm7y+QtWlqRocG6c/vfax6G/O/z29M3+0O2ZiguNiY5wgaApM+o/vXXX4+1AXsn67q63XxAb5hZrZYdNPfbT5W7UDEFFC2lnX8uV1hQoKo1bi1JWjXzJ4WHXFfH/u9LksrXaqR1C6Zr/g+fq1GHHroRGa7ff52gyg2aWYspVW/cVtt+X6hlU75RjWbtFXzlH21YNEM1mrVPtf0EnkYVG7fTHz9/qZz5i8q/cAkd3LhSkcGBKvO/KqFb501WVFiQmvS+e+3UfasXyjObn3xyBSg+LkbHtq3Tqd1b1OK1D63rvHr6mKJCg5Q9XyFFhQVpx+IZMgxDlZt3SJV9BJ5W1Zs/r8U/fK5cBYsqd5GS2rduhcKDAlWp4d2CmGtn/6LIkCC1/V/9iNI1G2rzohla+uNo1X2+q25GRujPWRNVvl5T62G/W5bMUq6CRZU1Ry7Fxcbq1N87dWDzGjXv8Uaq7SeA9MXhqr9RUVGaM2eOLl++bL2G6kcffZTsgcF8ytVsoJtR4Vo7f7oiQ4Pll7eAur33hfVyMhGhwQoLCrT2d8+YST0/HKOlk77Wd+++okxZPFXmmfpqck8pfO9sOdTzg6+0fNr3+npwD3n6ZFPN5u1Vt81LibYPIOUUrVZPt25EaufSmboZHiLf3AFqM3Ck9TDdG+Ehigy+bu0fFxurzXN+UlRosFzc3OSbK0Ct3/zEeikaSYqNuaPti6YpPPCKXDNkVP6yVdSk99sPvJwNgJRR6pn6uhUVoU0Lf1VUWIiy58mvF98eZb0GelRYsCKC//38dsuQUZ2Hjtaqad/qlw/6KZOHp0pWr6t6Hf69VFVM9G39PvkbRYRcl4ubu7Llyqu2/Yaq1DP1n/j+4ekWT9HfdMtiGI7VdG7SpIlatmypSZMmqWfPnjpw4IB+/vlnhza66MDVR3cCkCZdibqd2iEASCGe7g7/vg0gjfi/Skm/bIiZ/LDtXGqHkOb0q5E/tUNIkiSfo5rgzp076t+/v7y8vNS/f39duXIlJeICAAAAADylHE5U3dzcFB0drXz58undd9/V1avMjgIAAAAAko/Dx/DMmDFDv/76q4oUKSIXFxe1b0/RGwAAAABA8nF4RrVly5a6efOmSpUqpWLFiqlo0aIpERcAAAAA4Cnl8Iyqn5+fBgwYkBKxAAAAAECSOVYWFmmJw4lqly5d1L59e5UtW1YWi0USl6cBAAAAACQfhxPVkSNHqn///sqdO3dKxAMAAAAAeMo5nKgGBASoR48ej+4IAAAAAMBjcDhRvXXrlpo0aWJz6O/o0aOTPTAAAAAAwNPJ4UT1vffeS4k4AAAAAACQ9BiJat26dVMiDgAAAABwSLwo+5teOXwdVQAAAAAAUhKJKgAAAADAVEhUAQAAAACmQqIKAAAAADAVElUAAAAAgKk4XPUXAAAAAMzAoOhvusWMKgAAAADAVEhUAQAAAACmQqIKAAAAADAVElUAAAAAgKmQqAIAAAAATIWqvwAAAADSpHiq/qZbzKgCAAAAAEyFRBUAAAAAYCokqgAAAAAAUyFRBQAAAACYCokqAAAA8P/s3Xd0VNW7xvFnUqkphA6h996lNynSmwg21EsRsYJYAKX9VBQFwUIHkd47SBdBegcBQVSa1PSEhNS5f6DRIQFyNJOcmfl+1pq1nJ19Zt5zL/s3ebLPvAeAqdD1FwAAAIBDSrLS9tdZsaMKAAAAADAVgioAAAAAwFQIqgAAAAAAUyGoAgAAAABMhaAKAAAAADAVuv4CAAAAcEg0/XVe7KgCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFrr8AAAAAHFISXX+dFjuqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABToesvAAAAAIdktdL211mxowoAAAAAMBWCKgAAAADAVAiqAAAAAABTyZTvqLapkD8z3hZABhj/w6+ZXQIAOymXK2dmlwAAcBHsqAIAAAAATIWuvwAAAAAcUhJNf50WO6oAAAAAAFMhqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFOh6y8AAAAAh0TXX+fFjioAAAAAwFQIqgAAAAAAUyGoAgAAAABMhaAKAAAAADAVgioAAAAAwFTo+gsAAADAIVlF219nxY4qAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBU6PoLAAAAwCEl0fTXabGjCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWgCgAAAAAwFbr+AgAAAHBIVrr+Oi12VAEAAAAApkJQBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkLXXwAAAAAOKYm2v06LHVUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqBFUAAAAAgKnQ9RcAAACAQ0qi6a/TYkcVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqdP0FAAAA4JCsdP11WuyoAgAAAABMxfCO6qlTpzRr1iyFhYXJ+uefMGbNmpXuhQEAAAAAXJPhoPrss89q7NixCgwMtEc9AAAAAAAXZzioBgYGqkWLFvaoBQAAAAAA40E1Li5OLVu2VLVq1WSxWCRJY8eOTffCYE6LF87X7G9mKujWLZUsVVpvvztUNWrWuu/8RQvma9HCebr6xx/KX6CA+vZ7SR06dbaZM2/ObC1ZvFDXr12Tn7+/WrZsrdcGvilvb287nw2Af/r5h3U6tXW5osND5FegqOp076d8pSqlOvf6uRPaNOHdFOOdh0+Vb/6/r7iJi47SkTXf6tKxPYqNjlLOgPyq1a2PCleqbbfzAJDSDxuWa8vKBQoPDVaBIsXVvffrKl2xWqpzv534gfZt35BivEBgcQ3/ar4k6eql37R2wQxd+vVnhdy8rsd7v65HO/aw5ykAcDGGg+q776b8xQSuYeN3GzT24zEa9v4IVateQ8uWLNKAF/tq5Zr1KlCwYIr5SxYt0BcTxmn4qA9UqVJlnTx5QqNHvKecPj5q2qy5JGn9ujWa+Pk4jfrfR6pavbouXrig4cPu/ht7692hGXp+gCv7/dAPOrhsmh7pOUB5S1TQuR+/09avh6vT+1OUI1fe+x7XecQ0eWXJlvzcO6dv8n8nJsRr8xfDlCWnn5r2Hapsfrl1OzRInlmy2vVcANg6tGurls6cqJ4vDlbJ8lW0a9MqfT36TQ3/ar5y5cmfYv4Tfd5Q514vJT9PSkzUh2/0Uo0GzZLH4mLvKHe+gqpRv5mWzfoiQ84DSE0SbX+dluGuv02aNFHhwoXl5uYmi8WSvKsK5zf322/UpVs3dX28u0qULKm3hwxT/gL5tWTxwlTnr1u7Ro8/0UOPtWmrwoGBatO2nbp0fVzfzJyePOf4sWOqVr2G2rbvoEKFCqt+g4Z6rG17nTr1U0adFgBJp7evVKn6rVSmwWPyK1BEdbq/qOx+eXR25/oHHpc1p5+y+uZKfri5uSf/7PyezYqNjlTz/u8rb8mKyhGQT/lKVVSuwiXsfToA/mHb6kWq36KDGrbqqAKBxfREnzfknzuvdn63MtX5WbPnkK9/QPLj4vkzio6KVL1H2yXPKVa6grq98IpqN24pD0/PjDoVAC7E8I7qa6+9pj/++EOHDx9WzZo1lZiYqMaNG9ujNphIfFyczpw+pf/r089mvF79Bjp+7Giqx8TFxcnLy/byXe8sWfTTyZOKj4+Xp6enqteoqQ3r1ujkiROqXKWKrly+rB93/aAOHbvY7VwA2EpMiFfwpfOq1OoJm/GC5avr1m9nHnjs2jGvKjE+Tr4FiqjKYz1VoGzV5J9dPrlfeYuX175Fk3T5xD5lyeGr4rWbqlKrx20CLQD7SYiP16Vfz6p1t2dtxstXq6Pffj6ZptfYs3WdylWtpYC8BexRIgCkyvCO6pEjR7R8+XIVK1ZMy5cvl4eH4awLBxQaFqrExEQFBATYjAcE5FZQ0K1Uj6nfoKFWLl+m06d+ktVq1amfTmrVyuVKSIhXWFioJKlN23Ya8Orrev7Zp1SzakW1e6yFatd+RL379kv1NQGkv9ioCFmTkpQ1p5/NeBYff8VEhKZ6TFbfXKr31Gtq2neYmvZ7T755C2vzF0N1/Ze/f/GNDLquC0d/lDUpSS1eHqUqbXrq9LYVOrlxsT1PB8A/REWEKSkpUTn9ctmM5/TLpfDQkIceHx4SpFOH96lBy472KhEAUmU4ZXp5eUmSsmXLpo0bN+rnn39+4PzY2FjFxsbajFndvWmU46DuvdTbarXe9/Lvfv0HKCjolp59qoesVqtyBQSoY6cumj1rRvJuysED+zVj6hQNe3+EKlepokuXLmnsmA+Ve/LXevGll+1+PgD+4d61bLWmHPuTb77C8s1XOPl53hLldTv0lk5tXaH8pSv/efzd8Fvv6Vfl5uaugCKlFR0erFNblqtq26fsdRYAUpFiKVut91veNvZu36Cs2XOo6iNcPQcgYxneUf3iiy8UGxur8ePHa8OGDfr8888fOH/MmDHy9fW1eXz6yZh/XTAyh7+fv9zd3RUUFGQzHhISrICA3KkekyVLFo3+YIz2HTqm7zZv16atO1SoUCFlz55d/v7+kqSvv5yo9h07quvj3VW6TFk92qKlXn1joGbNmKakpCS7nxcAyTuHjyxubil2T+9EhqXYZX2QPMXLKfLm1eTnWX1yySdvIZvLfH3zByomIlSJCfH/uW4AD5fDx09ubu6KuGf3NDI8VD737LLey2q1as/WdXqk6WN8DxVAhjMcVH18fHTjxg1ly5ZNgwcPVqVKlWR9QLetIUOGKDw83Obx1jtD/lPRyHieXl4qX6Gi9u3ZbTO+b88eVa1W/cHHenoqX/78cnd318bvNqhxk2Zyc7v7T+/OnTuyWGz/Gbq7uctqtT7w3xWA9OPu4amAIqV07Yzt982v/nxUeUqUT/PrhFz5VVl9/ZOf5y1ZQRG3rsr6jz86Rdz4Q1l9c8ndg196gYzg4empIiXL6szxAzbjZ44dVIlylR947C8/HdWta1dUv2V7e5YI/CdWKw+jD0dh+NLf559/XpcvX1blypV18uRJFShQQFFRUXr99df13HPPpZjv7Z3yMt87Cf++YGSeZ597QcPefVsVKlVS1arVtXzpYl27dk3de/SUJE38fJxu3ryhD8fcva/uhQu/66eTJ1S5SlVFhEdo7pxvdP6XX/S/jz5Ofs0mTZtp7rffqFz5CqpcpYouX7qkr7+cqCbNmsvdnWYrQEap0LyLfvx2nAKKllae4uV0bvdG3Q69pbKN2kqSDq/6RtFhwWr0/GBJ0untq5QjV175FSyqxIQE/XZguy4e3a2mfYclv2bZRu10ZsdaHVg6VeWadlDkzas6uWmJyjflu25ARnq0U0/NnjBaRUuVV/GylfTjptUKDbqhRo91liStmjNZYcG39PzA4TbH7d66VsXKVFShoiVTvGZCfLyuXf5dkpQYn6Cw4Fu6/Ns5eWfNprwFCqeYDwBGGQ6quXPn1saNG+Xl5aW4uDg9/fTT2rp1qxo0aJBqUIXzeKxNW4WHhWra5Em6deumSpUuo6+nTFPBgoUkSUG3bun6tWvJ85MSkzRn9je6eOF3eXh4qHadRzRn/kIVKvT3B1jfF1+SxWLR119M0M2bN+Tvn0tNmjbTK68PzPDzA1xZ8VpNFHs7Usc3LFBMRIj8ChTTowNGKUdAPklSTESobof+3TgtKSFeh1bOVHRYsNw9veRXoKgeHTBKhSvVTp6TPVcetXz1Ax1cNk1rPnxZ2fwCVL5ZJ1Vq9XiGnx/gymo1aqHbkeFav3iWIkKCVaBoCb08/LPkLr7hocEKCbphc0zM7Sgd3bNDT/R9I9XXDA8J0kcDn09+vnXVAm1dtUClK1XXoA+/ttepAHAhFqvB6ytr1Kih77//Xr6+vgoPD1fz5s11+PBhNW3aVDt27EjTa7CjCjiv8T/8mtklALCTuoX8MrsEAHbSvFzAwyeZ0NAN5zK7BIfzUdsymV1CmhjeUf3kk0/UunVrJSUlycPDQ2PGjFFCQoJeeeUVe9QHAAAAAHAxhoKq1WrV8ePHtW/fvhQ/e/xxLuUCAAAAAPx3hoKqxWLRwYMHlZiYSKMbAAAAAJmKmxk6L8OX/l67dk3lypVT1apVk28xsmTJknQvDAAAAADgmgwH1YkTJ2rt2rU6f/68SpcurQ4dOtijLgAAAACAi3IzesD777+vkiVLatiwYSpevLiGDRv28IMAAAAAAEgjwzuqUVFRevrppyVJZcuW1fTp09O9KAAAAACA6zIcVAsVKqShQ4eqdu3aOnDggAoXLmyPugAAAAAALspwUJ0zZ45Wrlypc+fOqVatWvrggw/sURcAAAAAPFCS1ZrZJcBODAdVd3d37pkKAAAAALAbw82UAAAAAACwJ4IqAAAAAMBUCKoAAAAAAFMhqAIAAAAATMVwMyUAAAAAMAOa/jovdlQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZC118AAAAADimJrr9Oix1VAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICp0PUXAAAAgEOyWmn766zYUQUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZCUAUAAAAAmApdfwEAAAA4pCSa/jotdlQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZC118AAAAADomuv86LHVUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqBFUAAAAAgKnQ9RcAAACAQ7JaafvrrNhRBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYSqZ0/b0YFJ0ZbwsgA5QOyJrZJQCwEy93/r4NwFySMrsA2A2fOAAAAAAAUyGoAgAAAABMhaAKAAAAADAVgioAAAAAwFQIqgAAAAAAU8mUrr8AAAAA8F9ZrdbMLgF2wo4qAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBU6PoLAAAAwCHR9Nd5saMKAAAAADAVgioAAAAAwFQIqgAAAAAAUyGoAgAAAABMhaAKAAAAADAVuv4CAAAAcEhJtP11WuyoAgAAAABMhaAKAAAAADAVgioAAAAAwFQIqgAAAAAAUyGoAgAAAABMha6/AAAAABwSTX+dFzuqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABToesvAAAAAIdkpe2v02JHFQAAAABgKgRVAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKnT9BQAAAOCQaPrrvNhRBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYCl1/AQAAADikJNr+Oi12VAEAAAAApkJQBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAAppLmrr+jRo2SxWJJ9WfDhw9Pt4IAAAAAIC3o+eu80hxU69atK0launSp8ufPrzp16ujQoUP6448/7FYczGfDyiVasehbhYYEqUixkurzymBVrFrjvvN3bNmgFQtn6+qVy8qePYdq1KmvFwYMlI+vnyRpz85tWjZvpq79cVkJCQkqWLiIOj/xrJq1bp9BZwTgL/s3rdKutYsVFRasvIWLqe1zr6hY+Sr3nZ8QH6fvl8/RsV1bFRUWIp+APGra5WnVbNZWknRkx0atmPxJiuNGzN0kTy8vu50HgJS2r1+mTSvmKywkWIWKFFfPvgNVplK1VOfO/Hy09mzbkGK8YJHi+t+khZKkw3u+1/ol3+rmtStKTEhQvoKBatXlKdVv3saepwHAhaQ5qLZu3VqSNHHiRM2YMUOS1LFjR7Vpw/8guYpd2zdpxlefqv/AISpfqZo2rl2uUe+8oq+/Xa48+QqkmH/6xFFN+Oh99X75TdVu0EQht25q0vgP9dXY0Rr64XhJUs6cvur+TB8VLlJMHp6eOrh3lyZ+MlK+/rlUo079jD5FwGWd3LNdG779Wh16v6EiZSvp4Na1mjPmHb02frb8cudL9ZhFE0YpKixUXV58SwH5C+l2RKiSEhNt5nhnza43JsyxGSOkAhnrwM4tWjR9gp556S2VqlBFP3y3ShNGDtT/Ji1UQN78KeY/2W+QHn/+5eTniYmJGvnqM6rVoHnyWPYcPmr/xPPKH1hUHh6eOn5gt76Z8IF8fP1VqWbdDDkvAM7N8HdUExMTNWPGDB09elQzZ85UUlKSPeqCCa1eMk8t2nZWq/ZdFVishPq++pZy58mvDauXpjr/7OmTypu/oDo8/pTyFyikClWq67EO3XT+7OnkOZWr11K9xs0VWKyEChQKVMfHn1KxEqV1+uTRjDotAJJ2r1+qms3bqtaj7ZS3cFG1e/4V+Qbk1YHNa1Kdf+7YAV04fVy9hnysUlVqyj9vfhUuVV5FylaymWexSDn9ctk8AGSszasWqlHLDmrcupMKBhbXk/0GKlfuvNqxYUWq87NlzyFf/4Dkx4Vfzig6KlINWv59tVO5KjVVo35TFQwsrrwFCqtlpx4qXLykfjl9PKNOC4CTMxxUlyxZovDwcE2bNk3h4eFasmSJPeqCycTHx+v8uTOqXruezXj12nX180+pfyiVq1RFQbdu6NC+XbJarQoNCdbuH7aqVr2Gqc63Wq06fni//rh8QRWr1Ez3cwCQuoSEeF397ZxKVallM16qai1dOvdTqsf8fGi3CpYoq11rFumT/t31+RvP6ru5kxUfF2szL+5OjD59uafGvtRdcz8Zoqu//2K38wCQUkJ8vC6eP6uK1R+xGa9Q/RGd//lkml7jx81rVL5abeXOm/LqKenu5/fpYwd1/cql+15ODABGpfnS37/4+vqqadOmKl26tNq1a6dr167J19fXHrXBRCLC717S55fLdjfE1z9AYSHBqR5TvlI1vfneh/p05LuKi4tTYmKC6jRoon6vv2Mz73ZUpF54vLXi4+Ll5u6m/m8MUfXaXDYEZJToiHAlJSUph6+/zXh2X39FhYWmekzozWu6dPakPL289PTg0bodGa61MycoJipCXV+6u8bzFCyirgPeVb7A4oqNidbe75Zr+vBX9fLYGcpdoLDdzwuAFBkRpqSkRPn43/v5nUs/HUn98/ufwkKCdPLwPvV7a1SKn0XfjtLg5zooIT5OFjd3PfPSWykCMQD8W4aD6ptvvqn4+Hjt3btXHTt21P/93/9p8+bN950fGxur2Nh7/sIemygvb2/j1SLTWXRv52fr3Wv7UnHpwq+a/sVY9Xiun6rXqafQ4CB9M3mCJo37UK+9MzJ5XtZs2TVhxiLdiYnR8SP7NWvSOOUvWFiVq9dK9XUB2Mm9a9kqpVjyf0qy3v1h91eHKUu2HJKkhGcHaNHnI9Wh9xvy9PJWYJkKCixTIfmYImUradK7/bRv4wq1f+E1+5wDgPuwXcxWq/W+d3P4p91b1ytbjhyqXrdJip9lyZpNI76Yo9g7MTpz7KAWz5yoPPkLqhxXRSEDWa30/XVWhi/9PXr0qL744gvlyHH3F5PEexpn3GvMmDHy9fW1eUz98rN/Vy0yjY+vv9zc3RV6z+5peGiI/PxT/87ZsnnfqFylaur65HMqXrKMatSpr5cGDtHWDasVEnwreZ6bm5sKFi6iEqXLqkuPXqrfpIWWzZ9l1/MB8LdsPr5yc3NTVFiIzfjtiNAUu6x/yemXSz65cieHVEnKU6iorFarwv+xvv/Jzc1NhUqWU/B1usUDGSWnj5/c3NwVEWr7+R0RFiqfh3xn3Gq16scta1WvWRt5eHqm+Lmbm5vyFQxUkRJl1Lrr06rVoJk2LJ2TyisBgHGGg6qXl5d+//13WSwWXb58Wd4P2RkdMmSIwsPDbR4vvjr4XxeMzOHp6alSZcrr2KF9NuPHDu1TuUpVUz0mNjZGbm62/8T+ev6gv35ZrVbFx8f9x4oBpJWHh6cKliij8ycO2YyfP3FYRcpUSvWYomUrKTI0WLF3YpLHgq9dlsXiJt+APKkeY7Vadf3CeRoqARnIw9NTRUuV1aljB2zGTx87oFLlKj/w2LMnj+jmtStq2LJDmt7Lar172yoASA+Gg+rkyZP1zjvvKDg4WIMHD9bXX3/9wPne3t7y8fGxeXDZr2Pq9MQz2rJ+pbasX6XLF37TjK8+062b19Wm4+OSpG+nfaHPP3wveX6d+k20d+d2bVi1RNevXtHpk8c07YuxKlO+kgJy55UkLZ03U0cP7tP1q1d05eLvWrV4rr7ftF5NW7bNlHMEXFWDdt11ePsGHf5+g25euagN336t8KAbqv3nL6ibF0zXsq8+Sp5fpWELZc3poxWTPtHNKxf0++nj2jh/qmo0ayNPr7v/G7996bf65dgBhdy4qmsXzmvllLG6dvG86rTsmCnnCLiqVp2f1K7Na7Rr81pdvfy7Fk2foJBbN9SkbRdJ0vLZkzRjXMrvoO7aslYlylZU4WIlU/xs/ZJvderoft26/oeuXb6gTSsXaO/2Darb7DG7nw8A12D4O6rFixfXokWLdOvWLeXLl/q99eCcGjVvrcjwcC2eM00hwUEqWryUhn/ypfLmLyhJCg0O0q2b15PnP9qmo2Kib2v9ysWaNelz5ciRQ5Vr1NHzL76ePCf2zh1N+fwjBd+6KS9vbxUuUkyD3vtAjZq3zvDzA1xZ5frNFR0Zoe+Xz1FkaIjyBRbTs+9+LP88d++xGBkWrLDgm8nzvbNk1QvDPtO6b77Q5CH9lTWnjyrXbaoWPXsnz7kTHaVV08crKixEWbJlV4FipdRn5EQVLlU+w88PcGV1GrdUVGS41i6aqfCQYBUqWkKvjxyf3MU3LDRIIbeu2xwTfTtKR/Z8r559B6b6mrGxMZo36VOFBt+Sp5e3ChQuqj5vjlSdxi3tfj4AXIPFavAbyIsXL9bkyZMVEhKio0eP6umnn9aiRYsMvenZ69GG5gNwHCeuh2V2CQDspED2rJldAgA7aVg69Z4EZvf8whOZXYLDmf1klcwuIU0M76h++eWX2rVrl5o3by53d3fdvHnz4QcBAAAAQDpLoumv0zL8HVU3NzclJibKYrEoMTFRSUlJ9qgLAAAAAOCiDO2oWq1W9enTR02aNNEvv/yiRx99VEOHDrVXbQAAAAAAF2QoqFosFq1YsUI//vijgoKClDt37jTdLBoAAAAAgLQy/B3VxMRENWvWTLVq1Uq+J+bYsWPTvTAAAAAAgGsyHFQHDx5sjzoAAAAAAJD0L4JqkyZN7FEHAAAAABhi8E6bcCCGu/4CAAAAAGBPBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqhrv+AgAAAIAZ0PTXebGjCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWgCgAAAAAwFbr+AgAAAHBIVtr+Oi12VAEAAAAApkJQBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkLXXwAAAAAOKYmmv06LHVUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAApsLtaQAAAAA4JKuV+9M4K3ZUAQAAAACmQlAFAAAAAJgKQRUAAAAAYCoEVQAAAACAqRBUAQAAAACmQtdfAAAAAA6Jnr/Oix1VAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICp0PUXAAAAgENKstL311mxowoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFoAoAAAAAMBW6/gIAAABwSDT9dV6ZElRz5/TKjLcFkAHaBxTM7BIA2El8YlJmlwAAcBFc+gsAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFrr8AAAAAHJKVtr9Oix1VAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICp0PUXAAAAgEOi6a/zYkcVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqdP0FAAAA4JCSaPvrtNhRBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYCl1/AQAAADgkmv46L3ZUAQAAAACmQlAFAAAAAJgKQRUAAAAAYCoEVQAAAACAqRBUAQAAAACmQlAFAAAA4JCsVisPg49/Y9KkSSpevLiyZMmimjVrateuXWk6bvfu3fLw8FC1atUMvydBFQAAAACQqsWLF+uNN97QsGHDdPToUTVq1Eht2rTRpUuXHnhceHi4evXqpUcfffRfvS9BFQAAAACQqvHjx6t3797q06ePypcvrwkTJigwMFCTJ09+4HEvvviinnrqKdWrV+9fvS9BFQAAAABcRGxsrCIiImwesbGxqc6Ni4vT4cOH1apVK5vxVq1aac+ePfd9j2+++Ua//vqrRowY8a/rJKgCAAAAgIsYM2aMfH19bR5jxoxJdW5QUJASExOVL18+m/F8+fLp+vXrqR7zyy+/6N1339X8+fPl4eHxr+v890cCAAAAABzKkCFDNGjQIJsxb2/vBx5jsVhsnlut1hRjkpSYmKinnnpKo0aNUpkyZf5TnQRVAAAAAA4p6d81sXVp3t7eDw2mf8mdO7fc3d1T7J7evHkzxS6rJEVGRurQoUM6evSoXnnlFUlSUlKSrFarPDw8tHnzZjVv3jxN782lvwAAAACAFLy8vFSzZk1t2bLFZnzLli2qX79+ivk+Pj46efKkjh07lvzo37+/ypYtq2PHjumRRx5J83uzowoAAAAASNWgQYP07LPPqlatWqpXr56mTZumS5cuqX///pLuXkr8xx9/aM6cOXJzc1OlSpVsjs+bN6+yZMmSYvxhCKoAAAAAgFT16NFDwcHBGj16tK5du6ZKlSppw4YNKlq0qCTp2rVrD72n6r9hsVqtGX5ld/DthIx+SwAZJJsXf/8CnFV8YlJmlwDATnyyOOY3AttPPZjZJTicdS/WzuwS0sQx/0UCAAAAAJwWWx8AAAAAHJJVtP11VuyoAgAAAABMhaAKAAAAADAVgioAAAAAwFQMfUd19OjRNs89PT1VqlQpdenSRR4efN0VAAAAAPDfGdpRvXTpkuLj41WtWjUlJibq559/1okTJ/Tkk0/aqz4AAAAAgIsxtA169epVzZgxQ5LUsWNHtWnTRt9++60aNWpkl+IAAAAA4H6sNP11WoaCamJiombMmKGaNWvqyJEjSkxMVFJSkiwWi73qAwAAAAC4GEOX/i5ZskRhYWGaNm2awsLCtGTJElksFm3cuNFe9cFkli9ZqG7tW6lp3ep64anuOnbk8IPnL16gJ7t2UNN6NdSzSzt9t261zc93bNui/3v6CbVqXFfN69fScz276rt1a+x5CgDuY/Gi+Wrburnq1KisJ5/oqiOHDz1w/qKF89WlQxs9UrOKOrVvrbWrV6WYM2/ubHVq31qP1Kyi1o820aeffKTY2Fg7nQGA+1m6eIE6tWmhBrWr6tme3XT0yIPX95JF89W9czs1rFNN3Tq20fq1q2x+vnb1StWuWj7Fg/UNIL0Y2lH19fXViy++qLCwMFmtVkVERMjPz0/ZsmWzV30wka2bvtPEzz7W4CHvq0rV6lq1fInefPVFzV+2RvkLFEwxf8XSRZr81QS9+94ola9YSadPndQn/xuhnDl91LBJM0mSj6+vnuvdT0WLFZeHp6d27/pBH416T/65cqlu/YYZfYqAy9r03QZ9+vEYDX1vhKpVr6FlSxfp5f59tWLNehVIZX0vWbRAX04Yp+EjP1DFSpX108kTGj3yPfn4+qhJ0+aSpPXr1uiLz8dp5P8+UtVq1XXxwgWNeO9dSdJb7wzN0PMDXNnmjRs0fuzHemfY+6parYZWLFus1we8qCUr16b6+b1syUJN+uJzDR0+WhUqVdbpkyf04ejhypnTV42bNkuelz1HDi1bvcHmWG9vb7ufDwDXYCiovvLKKzpx4oQKFy4sq9Uqi8WiBQsW2Ks2mMyi+d+qQ+du6tjlcUnSG28N0f69e7Ry2WK99OrAFPM3rl+rzl2fUIvWbSRJhQoH6tSJ45r37czkoFqjVh2bY3o89ay+W7daJ44dIagCGWjunG/UpWs3dX28uyTp7XeHae/uH7V00UK9NvDNFPPXrV2jbt17qHWbtpKkwoGBOnHimL6ZOT05qJ44fkzVqtdQ23YdJEmFChXWY23b66eTJzLorABI0oK536pTl67q3PXu+n7z7aHat2e3li1ZpFdeH5Ri/oZ1a9Tl8R5q9dif67twoE6ePK4538ywCaoWi0W5c+fJmJMA4HIMBdUTJ05o586d9qoFJhYfH6ezZ07r2ef72IzXqVdfJ48fS/2YuDh5eXvZjHlnyaLTP51UQny8PDw9bX5mtVp1+MB+XbpwQQNeS/nBCcA+4uPjdOb0Kf1f734243XrN9Dx40fve8y9OydZvLPop5MnFR8fL09PT1WvXlPr163RyZMnVLlyFV25fFk/7vxBHTp1sdu5ALAVHx+nn8+c0nP/Z/v5/Ui9Bjpxv/UdFycvr3s+v72z6NQ9n98x0dHq8FhzJSUmqUzZcur/8msqW76CfU4EgMsxFFRr1qyp3bt3q1q1askNlLjs1zWEhYUpMTFRuQICbMZz5QpQSHBQqsc8Uq+B1q5arsZNH1XZ8hX085lTWrd6pRISEhQWFqbcee7+FTYqMlKdHmumuPh4ubu5afC776tO3fp2PycAd4WGhqa6vgMCciso6Faqx9Sr31Arly9Ts+YtVL5CRZ0+9ZNWrVyuhIR4hYWFKk+evHqsbTuFhobohWefkmRVQkKCuvd4Uv/Xp1+qrwkg/YWF/vX5ndtmPCAgQMFBqX9+163fUKtXLlPT5i1UrnwFnTl9SmtXrUhe37nz5FWx4sU1fPRHKlW6jG7fjtKi+XPV+/mntWDJShUpWiwDzgy4y0rbX6dlKKgeO3ZMx44dS35usVi0ffv2Bx4TGxub4ov1sQnufIfBYdl2eLZardJ9uj6/0Le/goOD1Pf5pySrVf65AtS2QyfN/3aW3Nz/7uOVLXt2fbtwuaJjonXowH59MX6sChYunOKyYAD2dW8H97++4pGafv0HKDjolno93UNWq1W5AgLUsXMXzZ41Q+5u7pKkgwf2a8a0KRr63ghVrlJFly9d0tiPP9S0PF+rX/+X7X4+AP5271J+0Pru3e8lBQcF6YVne0pWq3LlClD7jp01Z/ZMuf25vitXqabKVaolH1O1Wg0907Obliycr8HvDrPXaQBwIYaC6vfff2/4DcaMGaNRo0bZjL015H29M2y44ddC5vHz85O7u3uK3dPQ0BDlyhWQ6jHeWbJo2MgP9M6wEQoJCVZA7jxavWKpsmXPLj8//+R5bm5uKlykqCSpTNnyuvj7b5ozazpBFcgg/v7+cnd3T7G7EhISrIB7dmH+kiVLFo36YIzeGzFaIcHByp0nj5YvXazs2bPLz//u+p701US169Ax+XuvpcuUVUxMtP43arj69HtJbm6GGs8D+Bf8/P3us75DUlxF8ZcsWbJo+OgPNfT9kQoOCVbu3Hm0cvkSm/V9Lzc3N1WoWEmXLl1M93MA4JrS9FvCX0Gze/fueuKJJ2weDzNkyBCFh4fbPN4Y/M5/qxoZztPTS2XLV9CB/Xtsxg/u26PKVas98FgPT0/lzZdf7u7u2rrpOzVo1OSBv6BarVbFx8enR9kA0sDT00vlK1TU3r27bcb3792jqlWrP+RYT+XLf3d9b9q4QY2aNEte33fu3Emx1t3c3WW1WrlUC8ggnp5eKle+ovbvs/38PrBvj6o8ZH17eHoq35+f35s3blDDxk3v+/lttVp17uzPNFcCkG7StKPav39/SdKwYcPkf5+/pN2Pt7d3ist8428nGHoNmEPPp5/T6PffVfnylVSpSlWtXrFUN65fU+duPSRJk7/8XLdu3tTw/42RJF26eEGnfzqpipWrKDIiXAvnzdFvv/6i90d/lPyac2ZNV7kKFVWocKDi4+O1d/dOfbd+jd4a8n6mnCPgqp7t9YKGDXlbFStWUpWq1bV82WJdu3ZNj/foKUn64vNxunnzhj4YM1aSdPHC7/rp5AlVqlJVERERmvftNzr/yy8a/eHHya/ZuEkzzZvzjcqVq6DKVaro0qVLmvTlRDVp2lzu7u6Zcp6AK3rq2ec0Yti7qlChkipXraaVy5fo+rVr6tb97uf3VxPH69bNGxr14SeS7q7vUz+dVKXKVRQZEaH5c2frt/O/aOT//l7f06d8rUqVqyqwaFHdjorS4gXzdO7sz3qHz28A6SRNQTVfvnySpLfeektbtmyxa0Ewrxat2yg8PEyzpk9WcNAtlShZWp99MUUFCt69B1tw0C3duH4teX5SYqIWzputSxcvyMPDQzVq1dHUb+arQMFCyXNiYqL12Zj/6ebNG/L29lbRYiU04n8fJ9/SBkDGaN2mrcLCQzV1yiQF3bqpUqXL6KvJ01Twz/V6K+iWrl37e30nJiZpzrff6OKF3+Xh4aFadR7Rt/MWqlChwslz+r74kiwWi77+coJu3rwhf/9caty0mV55LeXtrADYT6vH2io8PEwzpk1S0K1bKlmqtCZ8PSX58zgo6Jau//PzOylJ8+fM1sWLf67v2o9oxpyFKljo78/vyMgIffS/4QoOClKOHDlVtlx5TZs1RxUrV8nw8wPgnCxWA9dfDRgwQLVr11bt2rWTL/2oUMF4G/JgdlQBp5XNy9BX3wE4kPjEpMwuAYCd+GRxzL4BrSftz+wSHM6mAY9kdglpYug3ypiYGO3cuTP5XqoWi0WzZs2yS2EAAAAAANdkKKi2bdtW3bt3T36+bt26dC8IAAAAAODaDO3xT5482eb5jBkz0rUYAAAAAADStKM6ffp0TZs2TefOnVOdOnfvbWmxWNSsWTO7FgcAAAAAcD1p2lHt27evDh48qKJFi2r//v0aNWqUYmJikrsBAwAAAACQXgxd+hsQECCLxaLFixfr8OHDWrBggb3qAgAAAIAHslqtPAw+HIWhoBoTE6MNGzbIz89Pnp6eypYtm73qAgAAAAC4KENBdcKECdq3b5+GDh2qO3fuqGvXrvaqCwAAAADgoizWTNj/Db6dkNFvCSCDZPMydNcrAA4kPjEps0sAYCc+WQztX5lGq6/3ZXYJDmfzy3Uzu4Q0ccx/kQAAAAAAp0VQBQAAAACYCtfoAQAAAHBIDtTEFgaxowoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFoAoAAAAAMBW6/gIAAABwSEm0/XVa7KgCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFrr8AAAAAHBJNf50XO6oAAAAAAFMhqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFOh6y8AAAAAh2Sl7a/TYkcVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqdP0FAAAA4JBo+uu82FEFAAAAAJgKQRUAAAAAYCoEVQAAAACAqRBUAQAAAACmQlAFAAAAAJgKXX8BAAAAOCQrbX+dFjuqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABToesvAAAAAIdE01/nxY4qAAAAAMBUCKoAAAAAAFPJlEt/Czd8IzPeFgAAAEAqYo5+ldklADbYUQUAAAAAmApBFQAAAABgKnT9BQAAAOCQrLT9dVrsqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWuvwAAAAAcEl1/nRc7qgAAAAAAUyGoAgAAAABMhaAKAAAAADAVgioAAAAAwFQIqgAAAAAAU6HrLwAAAACHRNNf58WOKgAAAADAVAiqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVOj6CwAAAMAhWWn767TYUQUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZCUAUAAAAAmApdfwEAAAA4JJr+Oi92VAEAAAAApkJQBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkLXXwAAAAAOyUrbX6fFjioAAAAAwFTStKParFkzWSyWVH+2ffv2dC0IAAAAAODa0hRUN27cKEkaMWKEWrRooTp16ujgwYPavHmzXYsDAAAAALieNF366+3tLW9vb+3fv18tWrSQj4+PHn30Ue3fv9/e9QEAAAAAXIyhZkoVK1bUM888o5o1a+rIkSOqWLGiveoCAAAAALgoQ0H1q6++0uHDh3X+/Hk1btxYNWvWtFddAAAAAPBANP11Xoa6/lqtVoWFhSkuLk6nTp3SnDlz7FUXAAAAAMBFGdpR7dGjhwIDA7V+/Xq1a9dO169fV69evexVGwAAAADABRnaUb1586bGjRun/Pnza9y4cYqOjrZXXQAAAAAAF2UoqHp4eCgpKUn+/v6aMmWKfv31V3vVBQAAAABwUYaC6oIFC5SUlKQpU6YoOjpac+fOtVddAAAAAAAXZeg7qnny5NGKFSt048YNvfbaazpx4oS96gIAAACAB7LS9tdpGdpRfe6553Tx4kXNnTtXHh4eevvtt+1VFwAAAADARRkKqlevXtWgQYOUNWtWSfwFAwAAAACQ/gwF1Zw5c2rHjh1KTEzU7t275efnZ6eyAAAAAACuylBQnTFjhtavX68cOXJo5cqVmj59ur3qghPo172RzqwbqdB9n2v3/LfVoHrJzC4JQDphfQPOi/UNwAwMBdWAgAB9+umnWr9+vT777DPlypXLXnXBwT3eqoY+faubPpm5SXWf/Fh7jv6qVV8NUGB+/8wuDcB/xPoGnBfrG4BZWKxp+KJp7dq1ZbFYbMasVqssFosOHDhg+E2zVn/F8DFwLDvnDNbRny/r9Y8WJ48dXf6e1u44oeFfrsnEygD8V6xvwHmxvl1XzNGvMruEf6X2hzsyuwSHc3BY08wuIU3SdHuagwcPPvDnc+bMUa9evdKlIDg+Tw93VS8fqM++2Wwzvm3fGdWtWjyTqgKQHljfgPNifQMwE0OX/t7P7Nmz0+Nl4CRy++eQh4e7boZE2ozfCI5UvgCfTKoKQHpgfQPOi/UNwEzStKP6MA+6ejg2NlaxsbG285MSZXFzT4+3hond+8/CYrFwSyPASbC+AefF+gZgBumyo3rv91f/acyYMfL19bV5JNw4nB5vC5MKCo1SQkKi8gXktBnPmytHir/SAnAsrG/AebG+AZhJugTVYsWK3fdnQ4YMUXh4uM3DI1/N9HhbmFR8QqKOnrms5nXL2Yw3r1tO+47/nklVAUgPrG/AebG+AZiJoUt/r1+/rmXLlik0NDT5EpDhw4dr1qxZ9z3G29tb3t7eNmNc9uv8vpi3XTM/6KUjpy9p/4nf1btrAwXmz6UZy3ZldmkA/iPWN+C8WN9wNFyW7rwMBdVOnTqpX79+Kl26tL3qgZNYtvmIcvlm19B+bZQ/t49Onb+mzq9O0qVroZldGoD/iPUNOC/WNwCzSNN9VP/Svn17rVu37j+/KfdRBQAAAMzDUe+jWuuD7zO7BIdz6L1mmV1CmqRpR3XSpEmS7l7G26dPH1WvXj25gdKAAQPsVx0AAAAAwOWkKahmz55dktSxY0eb8Qd1+wUAAAAA4N9IU1B97rnnJEnDhg3Thx9+mDw+duxY+1QFAAAAAHBZabo9TVBQkE6dOqWtW7fq9OnTOn36tE6ePKlNmzbZuz4AAAAASJXVysPow1GkaUd19+7dWrVqlS5duqRPP/1UkuTp6alXXqEpEgAAAAAgfaUpqHbq1EmdOnXShQsXVKxYMTuXBAAAAABwZYbuo7p792499dRTNmN79uxJ14IAAAAAAK7NUFAdP368du7cmdwFGAAAAACA9JamZkp/qVChgr3qAAAAAABAksEd1UOHDqlIkSIqUaKEpLv3UT1w4IBdCgMAAACAB7E6UhtbGGIoqJ45c8ZedQAAAAAAIMngpb/169fX8OHDde7cOXvVAwAAAABwcYaC6o8//qjGjRtrzJgxatGihb788ksFBQXZqzYAAAAAgAsyFFTd3NzUtGlTde7cWTly5ND69evVu3dvvfPOO/aqDwAAAADgYgx9R7V///46e/as2rdvr6lTpypfvnySpC5dutilOAAAAACA6zG0o3rt2jVt375dFSpUUMuWLTVhwgRJ0sqVK+1RGwAAAADcl9XKw+jDURgKqhEREbJYLFq8eLEOHz6s+fPn26suAAAAAICLMhRUY2JitGHDBvn5+cnT01PZsmWzV10AAAAAABdlKKhOmDBB+/bt09ChQ3Xnzh117drVXnUBAAAAAFyUoWZKdevWVd26dZOfv/766+leEAAAAADAtRnaUQUAAAAAwN4M7agCAAAAgFlYHamNLQxhRxUAAAAAYCoEVQAAAACAqRBUAQAAAACmQlAFAAAAAJgKQRUAAAAAYCp0/QUAAADgkGj667zYUQUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZCUAUAAAAAmApdfwEAAAA4JCttf50WO6oAAAAAAFMhqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFOh6y8AAAAAh0TXX+fFjioAAAAAwFQIqgAAAAAAUyGoAgAAAABMhaAKAAAAADAVgioAAAAAwFTo+gsAAADAIdH013mxowoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEwlU5opvfXx65nxtgAyQJ/aRTK7BAB2cvhKaGaXAABwEXT9BQAAAOCQrLT9dVpc+gsAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFoAoAAAAAMBW6/gIAAABwSDT9dV7sqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWuvwAAAAAcUlISbX+dFTuqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABToesvAAAAAIdkpemv02JHFQAAAABgKgRVAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKnT9BQAAAOCQrLT9dVrsqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWuvwAAAAAcEk1/nRc7qgAAAAAAUyGoAgAAAABMhaAKAAAAADAVgioAAAAAwFQIqgAAAAAAU6HrLwAAAACHZKXtr9NiRxUAAAAAYCoEVQAAAACAqRBUAQAAAACmQlAFAAAAAJgKQRUAAAAAYCp0/QUAAADgkGj667zYUQUAAAAAmApBFQAAAABgKmm69HfDhg33/Vnbtm3TrRgAAAAAANIUVA8ePHjfnxFUAQAAAADpKU1BdcSIEfauAwAAAAAASQa7/s6fP18TJ07UL7/8ohw5cihXrlw6fvy4vWqDCf2ya73ObluhmIgQ+eYvourd+ipPyUqpzr35ywl9/+XQFONthk2WT75ASdKV43t0evMSRQVdU1JignLmKaiyzbqoWJ3mdj0PACmtWb5IS+fPVnBwkIoVL6mX3nhblavVvO/8bZvWa8m8b/TH5UvKniOHatVtoBdffVM+vn6SpDcH/J9OHD2U4rg69Rvpw3Ff2+s0AKRi76aV2rl6kSLDQpSvcDG1f+EVFS9f9b7zE+LjtG3ptzq6a4siw0LkG5BHzbo+o9rN2yXPibkdqU0LZ+jU/p2KuR0l/7z51a7XyypXo25GnBIgSbLS9tdpGQqqn3/+ufbs2aNWrVpp06ZNGjBggL3qggldOrJTx1ZMV43uLylPiQo6v/s77Zw8Uo8NnaTsufLe97i2702VR5Zsyc+9c/gk/7dXthyq0OoJ+eQLlJu7h66eOqADCybIO6evCpS//y/IANLXjq0bNXnCWL361jBVrFJd61cu1dBBAzRzwSrlzV8gxfyfjh/R2NHD1P/1t1S3YRMF37qpiWP/p/EfjdTITyZIkkaM+VwJCfHJx0SEh+nFXt3VuHmrjDotAJKO796udd98pU59B6pY2Urav2WtvvnwHQ36/Fv55cmX6jHzx49UVHiour30tgLyF9Lt8DAlJSUm/zwhPl4z//emsvv46+k3R8s3II/Cg27KK2u2VF8PAIwyFFR9fX3l5eUlSYqLi2M31cWc/X6VitdtqZL1W0uSanTrp+s/H9GvP25QlY7P3/c47xy+8sqWI9Wf5S1dxeZ5maad9PuB7Qr67TRBFchAyxfO0WMduqhtx26SpAED39Gh/Xu0dsUS9R7weor5Z346oXwFCqrLE09LkgoULKx2nbtrybxvkuf4+PraHLNjy3fK4p1FjZu3tOOZALjXj+uWqFbztqrzaHtJUocXXtW54we0b/NqPfZ0vxTzzx7dr99PH9fbXy1Utpx3/7icK6/tH6wOfb9B0VGReumDSXL3uPvrpH+e/HY+EwCuxFBQfeGFF3Tnzh0NHDhQTZs2Vbdu3exVF0wmMSFeoZfPq3yLx23G85errqDff37gsZvHvq7EhDj55A9UhVY9la9MlVTnWa1W3Tx3XJE3ryjPA4IvgPQVHx+vc2fPqMezvW3Gaz5ST6dOHkv1mAqVq+mbqV9q/55dqlOvocJCQ7Rz+xbVqd/4vu/z3dqVatryMWVlxwXIMAnx8frjt3Nq0vkpm/HSVWrr4tmfUj3mzKHdKlyyrH5YvVBHd26WV5YsKl+rgVr16C1Pb+/kOUXKVNTqGZ/r9KHdyu7jp2oNH1WTTk/Jzd3d7ucFwPkZCqrt27dXlixZ1KlTJ3Xq1EmhoaH2qgsmE3c7QtakJGXJ6W8zniWnv+5EHkn1mCw+uVSr5yvyDyylpIR4XTj4vXZ8PUzNXh2jvKX+/l5rXMxtrX3/OSUmxMvi5qaa3V9S/nLV7Xo+AP4WHhaqpMRE+ecKsBn39w9QaEhQqsdUrFJN744cow/ff0txsXFKTExQvUZN9cqb76Y6/+dTJ3Xht/N6c+iodK8fwP1FR4YrKSlROf1y2Yzn9PPXubCQVI8JuXFNF34+KQ9PLz371ge6HRmuVTM+V3RUhLoPeDd5TuhPR1WtYQs9P+QTBV+/otUzJigxMVEtuj9v79MC4AIMBdUuXbro+++/T37evXt3bd269YHHxMbGKjY21mYsIS5OHn9eQgwHY7nnudWayuBdPvkKyydf4eTnuYuXV0zoLZ3dvsImqHp6Z1Wrd75QQuwd3Th3TMdWzVSO3PlTXBYMwL4sFtu1bJVVlvus74u//6qvP/9Ez7zwomrVbaDgoFua/tV4TfzkA705LGUY3bh2pYqVKKVyFSvbpXYAxlitKdf83z9LkiT1fO09Zcl+96s77Z97WfPHDVfn3gPl6e0tqzVJ2X381PXFwXJzd1fhkmUVERKknWsWEVQBpAs3I5MTExNtnt8bQFMzZswY+fr62jx2L55irEpkOq/sPrK4uelOhO0u+p2oMGXJ6Zfm1wkoVk6Rt67ajFnc3JQzT0H5Fy6hcs27qnDVBjqzZWl6lA0gDXz9/OXm7q6QYNvd07DQEPnds8v6l4VzZqpi5Wp64pkXVKJUGdWu20CvvTVMG9etVHDQLZu5d+7E6PutG9WmY1e7nQOA1GXL6Ss3N3dF3rN7GhUeqhy+/qkek9M/QL658iSHVEnKW6iIrFarwkPuru+cfgHKUzDQ5jLfvIWLKjIsRAnx8SleE7AXq9XKw+DDURgKqlWqVNHAgQO1YsUKDRo0SJUrP/wv40OGDFF4eLjNo0GP/v+6YGQOdw9P+QeW0vWzx2zGb/x8TLmLl0vz64Re+VVZfXI9ZJZViQl8yAEZxdPTU2XKlteRg3ttxo8c2KeKlaulekzsnRi5udl+hLi53f2F9d4PwR+2bVZ8fJxaPNY+/YoGkCYenp4qVKKMzp+wvVXU+ROHVLRs6reXK1q2kiJCgxQbE508duvaFVksbvLNlefunHKVFHT9DyUlJf095+oV5fQPkIenpx3OBICrMRRUv/rqKzVr1ky//vqrmjdvrkmTJj30GG9vb/n4+Ng8uOzXMZVt1lm/792s3/ZuVsT1yzq6YrqiQ2+pZMO2kqQTa2Zr39xxyfPPfr9aV07sVeTNPxR+7aJOrJmtK8f3qHTjv39ZPb15ia7/fFRRQdcVceOyzm5fqQsHtqtorWYZfn6AK+v2ZC99t2aFNq5dqYsXftPkCWN188Y1te/SXZI0c9JEfTLq7/si123YVD/u2Ka1Kxbr2h9X9NPxo/r6849VrkIl5c5je7uqjWtXqEHj5sn3VwWQsRq2f0IHt63Xwe3rdfPKBa2d/ZXCgm7qkVYdJUkb50/T4i8/TJ5frWELZcvpo2WTPtaNyxf02+nj+m7uZNVq3ia5mVLdVp0VHRmutd98oVtXL+vnw3u1Y+U81WvdJVPOEYDzMfQd1YSEBIWFhSkyMlJhYWFKSEiQh4ehl4ADK1KjsWJvR+rUpkW6Ex4i3wJF1aj/yOR7qMZEhCo69O9L/pIS43V81SzFhAfL3dNLPvmLqNGLI1SwYu3kOYlxsTq8dJJiwu7OyZm3sOr2elNFaty/cyiA9Ne0xWOKCA/TvFlTFRJ8S8VKlNKH475WvgIFJUnBwbd088b15Pmt23VSTPRtrV62SFO/GKfsOXOqes066jPgDZvXvXLpgn46flQfT5yakacD4B+qNmiu6KhwbVs2R5GhwcofWFzPD/0k+XYyEaHBCgu6mTzfO2s29X5/nNbMnKiv3u2nbDl9VLleM7Xu2Sd5jl/uvOr93mda9+3Xmjj4/+STK7catO2mJp2eSvH+APBvWKwGLlTu2bOnKleurFq1aungwYP66aeftGjRIsNvOnzTL4aPAeAY+tQuktklALCTw1fo9g84qy5VHPM+uMUHrs/sEhzO75+3y+wS0sTQduj169eTg2nr1q3VtGlTe9QEAAAAAHBhhoKqj4+Ppk2bptq1a+vAgQPKmTOnveoCAAAAgAdznCa2MMhQM6VSpUrpxo0bmj59um7fvq358+fbqy4AAAAAgIsyFFRbt26tCxcu6PTp04qNjVVISMjDDwIAAAAAwADDQXXmzJnavHmzoqKiVKZMGXvVBQAAAABwUYaC6qFDhzR48GA1a9ZMd+7c0a5du+xVFwAAAADARRlqpjR79mz17NlTn332mb3qAQAAAAC4OENB9auvvrJXHQAAAABgiNVK219nZejSXwAAAAAA7I2gCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATMVQ118AAAAAMAu6/jovdlQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZC118AAAAADomuv86LHVUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqBFUAAAAAgKnQ9RcAAACAQ6Lrr/NiRxUAAAAAYCoEVQAAAACAqRBUAQAAAACmQlAFAAAAAJgKQRUAAAAAYCp0/QUAAADgmGj667TYUQUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZCUAUAAAAAmApdfwEAAAA4JKuVtr/Oih1VAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICp0PUXAAAAgEOi66/zYkcVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYCkEVAAAAAGAqdP0FAAAA4JDo+uu8MiWotiyeOzPeFkAGuBgUndklALCT3Fm8M7sEAICL4NJfAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKnT9BQAAAOCYaPrrtNhRBQAAAACYCkEVAAAAAGAqBFUAAAAAgKkQVAEAAAAApkJQBQAAAACYCl1/AQAAADgkq5W2v86KHVUAAAAAwH1NmjRJxYsXV5YsWVSzZk3t2rXrvnNXrFihli1bKk+ePPLx8VG9evW0adMmw+9JUAUAAAAApGrx4sV64403NGzYMB09elSNGjVSmzZtdOnSpVTn79y5Uy1bttSGDRt0+PBhNWvWTB06dNDRo0cNva/Fmgn75bvOhWb0WwIAAAC4j0Zl/DO7hH+lQL/lmV2Cw7k2rZuh+Y888ohq1KihyZMnJ4+VL19enTt31pgxY9L0GhUrVlSPHj00fPjwNL8vO6oAAAAAgBTi4uJ0+PBhtWrVyma8VatW2rNnT5peIykpSZGRkcqVK5eh96aZEgAAAAC4iNjYWMXGxtqMeXt7y9vbO8XcoKAgJSYmKl++fDbj+fLl0/Xr19P0fuPGjdPt27f1xBNPGKqTHVUAAAAADslqtfIw+BgzZox8fX1tHg+7hNdisaT4v/u9Y6lZuHChRo4cqcWLFytv3ryG/n/LjioAAAAAuIghQ4Zo0KBBNmOp7aZKUu7cueXu7p5i9/TmzZspdlnvtXjxYvXu3VtLly5VixYtDNfJjioAAAAAuAhvb2/5+PjYPO4XVL28vFSzZk1t2bLFZnzLli2qX7/+fd9j4cKFev7557VgwQK1a9fuX9XJjioAAAAAIFWDBg3Ss88+q1q1aqlevXqaNm2aLl26pP79+0u6u0P7xx9/aM6cOZLuhtRevXpp4sSJqlu3bvJubNasWeXr65vm9yWoAgAAAABS1aNHDwUHB2v06NG6du2aKlWqpA0bNqho0aKSpGvXrtncU3Xq1KlKSEjQyy+/rJdffjl5/LnnntPs2bPT/L7cRxUAAABwcY56H9X8fZdldgkO5/r0xzO7hDRhRxUAAACAQ8qEPTdkEJopAQAAAABMhaAKAAAAADAVgioAAAAAwFQIqgAAAAAAUyGoAgAAAABMha6/AAAAABwSXX+dFzuqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyF29MAAAAAcEzcncZpsaMKAAAAADAVw0F11KhR+vXXX+1RCwAAAAAAxoNqnTp1NGLECLVq1UqTJ09WSEiIPeoCAAAAALgow0G1TZs2mjdvnpYuXapdu3apcOHC6t69uw4cOGCP+gAAAAAALsZwM6X9+/dr3rx5OnXqlB577DF99tlnkqTHH39ce/bsSfcCAQAAAACuxXBQXbhwoV544QXVqFHDZvyvwAoAAAAAGcFqpe2vszJ86W/dunWTQ6rVatWiRYskSfXr10/fygAAAAAALslwUJ06dWryf1ssFk2fPj1dCwIAAAAAuDbDQTU2NlYxMTGSpOjoaN2+fTvdiwIAAAAAuC7D31F97733VK9ePQUGBuqPP/7QBx98YI+6AAAAAAAuynBQbdu2rdq0aaNbt24pT548slgs9qgLAAAAAOCiDAfVK1euaOrUqbp69Wpyl61Zs2ale2EAAAAA8CB0/XVehoPqU089pbfeeksffvihhg0bpm3bttmjLpjU9+uXadOK+QoLDVbBIsXVs+9AlalYLdW5sz4frT3bN6QYLxhYXKMnLZQk7dy0Snu3f6c/Lv4mSSpaqqy69HpJJcpUtNs5AEgd6xtwXqxvAI7GYjX4Z4jmzZtr+/btatKkiX744Qe1bt1amzZtMvSmu86FGpoPcziwa4tmjh+lp/u/pVIVqmjnxlXatXmNRn+9UAF586eYH307SvFxscnPExMTNeq1Z9S8fXd1eqqvJGn6Z8NVqnwVlSxfRZ6eXtq4Yp6O7N2h0V8vkH9A3gw7N8DVsb4B58X6Rlo0KuOf2SX8K7mfX5TZJTicoNk9M7uENDHc9bdgwYK6c+eOqlatqp49eyZ3AIbz27JqoRq27KDGrTupYODdv8b6586rHd+tSHV+tuw55OsfkPy4+MsZRUdFqmGL9slz+g4erWbtHleREmVUILCYnntliKxJSTpz/FBGnRYAsb4BZ8b6BuCIDAVVq9WqIUOGKEuWLJowYYLefvttbdy40V61wUQS4uN18fxZVaz+iM14xeqP6NczJ9P0Gru2rFH5qrUVkLfAfefExd5RYmKisufw+U/1Akg71jfgvFjfAByVoaBqsVj09ttv3z3QzU01atRQtmzZ7FIYzCUqIkxJSYny8ctlM+7jl0vhYcEPPT4sJEg/Hd6nRq06PnDe8m8nyS8gjypUq/2f6gWQdqxvwHmxvgE4KsPNlPLkyaMRI0aodu3acnO7m3Pbtm173/mxsbGKjY21GYuLi5WXl7fRt4YJ3Hs7IqvVKosefouiPdvWK1v2HKpet8l953y3fK7279yitz76Wp78+wAyHOsbcF6sbzgruv46L8PfUS1evLjc3Nx0+PBhHTx4UAcPHnzg/DFjxsjX19fmMW/q5/+6YGSOHD5+cnNzV3io7V9fI8NDU/yV9l5Wq1U/blmrus3ayMPTM9U5m1bM14al32rQ6IkKLF463eoG8HCsb8B5sb4BOCrDO6ojRowwNH/IkCEaNGiQzdjBS9FG3xaZzMPTU0VLldXpowdUo17T5PHTxw6o2iONH3js2Z+O6Oa1K2rUqkOqP9+4Yp7WL/5Gb4yaqGKly6dn2QDSgPUNOC/WNwBHZTio1qtXTxaLRVarVefOnVPhwoV1/Pjx+8739vaWt7ftZSBeXonGK0Wma9n5Sc0cP0rFSpdXiXKVtHPjaoXcuqGmbbpIuvv9lLDgW+o9yPaPGT9uXqsSZSuqUNGSKV7zu+VztXreNPUdPEq58xVI/ouvd5asypKV7z8DGYX1DTgv1jcAR2Q4qO7duzf5vyMiIjRgwIB0LQjmVadRS92OCNfaRTMVHhKsgkVL6PUR45O7AIaHBCn41nWbY6JvR+nInu/Vs9/AVF9zx4blSkiI1+SPh9qMd3iyd/K92gDYH+sbcF6sbwCOyGL9D99AjoqKUuPGjXXkyBFDx+06F/pv3xIAAABAOmtUxj+zS/hXAp5bmNklOJzgb5/M7BLSxPCOau3atZMv/fXw8NDAgan/pQ0AAAAA7Iqmv07LcFB9WJdfAAAAAAD+C8O3p+nVq1fyf1utVr3wwgvpWhAAAAAAwLUZDqqXL19O/m+LxaILFy6kZz0AAAAAABdnOKjmzJlTq1evVkhIiNasWaPs2bPboy4AAAAAgIsyHFRnzZqlH374Qc8884x27typ2bNn26EsAAAAAICrMtxMKSgoSOPGjUvu/Hv27Fnlzp3bHrUBAAAAwH39hzttwuQM76i+9NJLslgsku5+R/WVV15J96IAAAAAAK7LcFCNi4uzeR4dHZ1uxQAAAAAAYPjS37Zt26pbt25q0KCB9uzZo3bt2tmjLgAAAACAizK8ozpkyBA1adJEefLkUePGjRUYGGiPugAAAAAALspwUO3Zs6cuXryoDz/8UBcvXtSmTZvsURcAAAAAwEUZvvT35s2bWrJkiQ4fPqxx48apS5cu9qgLAAAAAB6Irr/Oy/COqoeHh5KSkuTv768pU6bo119/tUddAAAAAAAXZTioLliwQElJSZoyZYqio6M1d+5ce9QFAAAAAHBRhi/9zZs3ryQpX758GjRoULoXBAAAAABwbYZ3VAEAAAAAsCeCKgAAAADAVAxf+gsAAAAAZkDXX+fFjioAAAAAwFQIqgAAAAAAUyGoAgAAAABMhaAKAAAAADAVgioAAAAAwFTo+gsAAADAIdH113mxowoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFoAoAAAAAMBW6/gIAAABwTDT9dVrsqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWuvwAAAAAcktVK219nxY4qAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBU6PoLAAAAwCHR9dd5saMKAAAAADAVgioAAAAAwFQIqgAAAAAAUyGoAgAAAABMhaAKAAAAADAVuv4CAAAAcEh0/XVe7KgCAAAAAEwlU3ZUYxITMuNtAWSAuMSkzC4BgJ2wcQEAyCjsqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBU6PoLAAAAwCHR9dd5saMKAAAAADAVgioAAAAAwFQIqgAAAAAAUyGoAgAAAABMhaAKAAAAADAVuv4CAAAAcEw0/XVa7KgCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFrr8AAAAAHJLVSttfZ8WOKgAAAADAVAiqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVOj6CwAAAMAh0fXXebGjCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWgCgAAAAAwFbr+AgAAAHBMdP11WuyoAgAAAABMhaAKAAAAADAVgioAAAAAwFQIqgAAAAAAUyGoAgAAAABMha6/AAAAAByTNSmzK4CdGAqqL7zwgiwWS/JzT09PlSpVSn379pWfn1961wYAAAAAcEGGLv3NmTOnGjRooDfeeEONGjWSm5ubcuXKpR49etirPgAAAACAizEUVH/++Wf17t1bVapU0QsvvKDz58+rd+/eunPnjr3qAwAAAAC4GEOX/vr6+uq9995TzZo1deTIEfn6+iohIUE5cuSwV30AAAAAABdjaEd10aJFqlatms6dO6eqVatq0aJF8vDw0Pr16+1VHwAAAADAxRjaUe3du7d69OihwYMHy93d3V41AQAAAMDDWa2ZXQHsxNCO6tChQ3Xo0CE1b95cL774onbs2GGnsgAAAAAArspQUC1Tpozef/99bdiwQXny5NFjjz1mr7oAAAAAAC7K0KW/S5cu1ZIlSxQWFqaOHTvq999/t1ddAAAAAAAXZSioXrx4UePHj1dgYKC96gEAAAAAuDhDQXXQoEH6/vvvtWPHDln//OJyr1697FIYAAAAAMA1GQqqPXv2VGBgoNavX6927drp+vXrBFUAAAAAmcOalNkVwE4MNVO6efOmxo0bp/z582vcuHGKjo62V10AAAAAABdlKKh6eHgoKSlJ/v7+mjJlin799Vd71QUAAAAAcFGGLv1dsGCBkpKSNGXKFM2fP19z5861V11wIDs3rNC2VQsVERqsAoHF1LX36ypVsWqqc+dO/FAHvv8uxXj+wGIa9uU8e5cK4AF2b1ypHasXKiI0RPkDi6nTC6+qRIXU17IkJcTHafOSb3Vk52ZFhIXILyCPHu32rB55tF3ynJ3rlmjPptUKDbqh7Dl9VbVeU7V9up88vbwz4pQA/Gn3xpXasWahIkNDlC+wmDo9//D1vWXptzq8c7Mi/1rfXZ9VnT/X96Thr+m308dSHFeuRl31GTrWXqcBwIUYCqp58+aVJOXLl0+DBg2yS0FwLId/3KYVs77QEy++qRLlKmv3ptWa/L/BGvblXOXKkz/F/Mf7vK5OvfonP09MTNTHA59X9frNMrJsAPc4unubVn/zpbr2HaTi5Spp7+Y1mv7h23p7whz558mX6jFzxo1QZFionhjwjnIXKKSo8FAlJiYm//zwzs1aP2+aerz8joqVraRbVy9r0VdjJEmdXng1Q84LgHRs9zatmf2luvYZpGLlKmnfljWa8dHbeuvz+6/vueNGKDL8z/Wd/+76TvrH+n7+rQ+UkBCf/Dw6KkLj3/w/Va3H5zmA9GEoqAL3+n71ItVr0V71W3aQJHXr87rOHDugHzeuUsdn+6eYnzV7DmXNniP5+fF9OxUTFam6/9iBAZDxdq5dojrN26lui/aSpM7/95rOHjugPZtWqd0zL6aY//PR/fr11HENm7RI2XL6SJJy5S1gM+fi2VMqVq6SajRqmfzz6g0f1aXzP9v5bAD80w9/ru9H/lzfnV64u773bl6ltk/fZ32fPq6hX99/ff81/pdju7fJ09tbVeo1tc9JAHA5BFX8awnx8br86zm17PaMzXi5arX1+88/pek19m1dp7JVailX3pS7rwAyRkJ8vK78ek7NuzxtM162am1dOJv6Wj51cLcCS5bV9lULdHjnZnl5Z1HF2g3UpmcfeXrfvay3ePkqOrxziy79clpFSldQ8PWrOnNkn2o1fczu5wTgroT4eP3xW8r1XeZB6/vQ3fX9/ep/rO9aDfTYP9b3vQ5sX69qDR6Vd5as6X4OwAP9ectMOB9DQfXo0aN6++23FRERIT8/P40ZM0Y1atSwV20wuduR4UpKSlROv1w24zl9cykiNPihx4eHBOn0kf16btBwe5UIIA3+Wss5fP1txnP45VJkWEiqxwTfuKrffz4pDy8vvfD2B7odEa7l0z9XdFSker78riSpesNHFRURpq/ee0VWq1VJiYmq37qzHu36TKqvCSD9JX9W37O+c/ref32H/LW+Pb30/Fsf6HZkuFb8ub57/Lm+/+nSL6d1/dLveuKld+xyDgBck6Gg+uqrr2r+/PkqWrSoLly4oGeeeUY//vjjA4+JjY1VbGyszVhcXKy8aKThNCyy3DNilSz3jqW0f/t3ypo9h6o80tg+hQEwxHLvurVapRTr+68fJUkW6enX30++nL9jfJzmfDZc3foMlKe3t87/dFTbls9V176DVLR0eQVd/0OrZn0hn6UBatn9OTufDQAb96xvqx6wvpPuru+n/rm+n4vTnHHD1fXP9f1PB7avV/4ixVWkdAW7lA7ANRm6PU1SUpKKFi0qSSpatKiSkh5+g90xY8bI19fX5rF42sR/Vy1MJXtOX7m5uSsizHb3NDI8VD737LLey2q1at+29ardtLU8PD3tWSaAh/hrLd+7uxIVHqqcfv6pHuPjHyDfXHlsvnOer3BRWa1WhQXflCRtXDRTNRu3Ut0W7VWgaElVfqSx2j7VT9tWzEvT5weA/+7frO+cqazvvH+t75CbNnPjYu/o2O7teuTR9ulfPACXZiioduvWTU2bNtXrr7+upk2b6vHHH3/oMUOGDFF4eLjNo0e/1/91wTAPD09PBZYso5+PHbQZP3vskIqXq/TAY8//dFS3rl1RvRZ8sAGZzcPTU4VLltG544dsxs+dOKRiZVNfy8XKVlZESJBiY6KTx25dvSyLm5v8Au52iI+PvSOLm+2OjcXN7e5ODt8pAjKEh6enCpUoo3Mn0r6+i5d7wPrOlddm7vE93yshPl41GrdK/+IBuLQ0B1Wr1aqqVatq8eLFeuqpp7RkyZI03aLG29tbPj4+Ng8u+3UezTr11N6t67R36zpdv3xBy2d+oZCgG2rYurMkac3cKZoz4X8pjtu7db2KlamggkVLZHDFAFLTuMMT2r9tnfZvW68bVy5o9TdfKjTopuq16iRJWj9vqhZ88WHy/BqNWihbTh8t+vpjXb98Qb+eOqa1cyarTvO2yZcFVqhVX3s2rdbRH7cp+MZVnT1+UBsXzVTFWg3k5u6eKecJuKImHZ7QgW3rdOAf6zss6Kbq/rm+N8yfqoX/WN/VG95d34v/Wt+nj2nd3Mmq06xtyst+t61XpdoNlT2nb4aeEwDnl+bvqFosFk2cOFFr165Vvnyp33MLrqdmw0d1OyJcGxfPVkRosAoUKa6X3v80uYtveEiwQm/dsDkm5naUju3doW592FkHzKJ6g0cVHRmhLUu/TV7LfYZ+kryWI0KDFRb091r2zppNLw4fr5UzJ2rC232VLaePqtVvpjZP9k2e0+LxXpLFou8WzlB4yC3l8PFThVr11fapvineH4D9VGvwqG5HRmjLsrvrO3+R4uo99JPk+51HhAYr9D7re+I7d9d31frN1Kan7dq9dfWyfv/5hPq9Py5DzwewYeWrJM7KYrWm/fqrnj17ytvbW7Vr15ab293N2AEDBhh+081nbhk+BoBjiEvkAwNwVlyxDTivDpUdcyMqa/uvMrsEhxOz7pXMLiFNDHX9bdOmjc3zFB0iAQAAAAD4jwwF1aJFi6pp06aS7n5n9YcffrBHTQAAAAAAF2ao6+/IkSOT/9tiseiDDz5I73oAAAAAAC7OUFCNjY1NvvddYmKioqKi7FIUAAAAAMB1Gbr0t1+/fmrcuLFq1KihY8eOqV+/fvaqCwAAAAAejK6/TstQ119JCgoK0m+//aYSJUood+7c/+pN6foLOC+6/gLOi66/gPNy2K6/7b7I7BIcTsz61zK7hDQxtKMaFRWl1atX6+rVq/or3w4fPtwuhQEAAAAAXJOh76h269ZN0dHRWr58ufz9/XX58mV71QUAAAAAcFGGgmpcXJxeffVV+fr66tVXX9W1a9fsVRcAAAAAwEUZCqpeXl6KjY1VkSJF9O677xJUAQAAAADpztB3VDdu3CiLxaKpU6dq48aNev311+1VFwAAAAA8GF3enJahoBocHKxVq1YpNDRUVqtV58+f19tvv22v2gAAAAAALsjQpb/t2rVTdHS0SpQooZIlS6pkyZL2qgsAAAAA4KIM7ajmz59fr73mGPfdAQAAAAA4JkNBtVevXurWrZuqVKkii8UiifuoAgAAAADSl6Gg+sEHH+jVV19VoUKF7FUPAAAAAMDFGQqqRYsW1f/93//ZqxYAAAAASDtrUmZXADsxFFRjYmLUunVrm0t/x44da5fCAAAAAACuyVBQHTp0qL3qAAAAAABAksGg2qRJE3vVAQAAAACAJIP3UQUAAAAAwN4IqgAAAAAAUzF06S8AAAAAmIbVmtkVwE7YUQUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZCUAUAAAAAmApdfwEAAAA4JmtSZlcAO2FHFQAAAABgKgRVAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKnT9BQAAAOCYrNbMrgB2wo4qAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBU6PoLAAAAwDFZkzK7AtgJO6oAAAAAAFMhqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFOh6y8AAAAAx2S1ZnYFsBN2VAEAAAAApkJQBQAAAACYCkEVAAAAAGAqmfIdVT9vr8x4WwAZIDYhKbNLAAAAgINjRxUAAAAAYCp0/QUAAADgmKxcyeWs2FEFAAAAAJgKQRUAAAAAYCoEVQAAAACAqRBUAQAAAACmQlAFAAAAAJgKXX8BAAAAOCarNbMrgJ2wowoAAAAAMBWCKgAAAADAVAiqAAAAAABTIagCAAAAAEyFoAoAAAAAMBW6/gIAAABwTNakzK4AdsKOKgAAAADAVAiqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVOj6CwAAAMAxJVkzuwLYCTuqAAAAAABTIagCAAAAAEyFoAoAAAAAMBWCKgAAAADAVAiqAAAAAABToesvAAAAAMdkTcrsCmAn7KgCAAAAAEzF0I7qnDlzbJ57enqqVKlSql27droWBQAAAABwXYaC6qZNm+Tm5qbatWvr8OHDiomJkbe3t3LmzKlJkybZq0YAAAAAgAsxFFTDw8O1bt265Oft2rXTkiVL1KBBg3QvDAAAAADgmgx9RzUsLEzbtm1TaGiotm3bpvDwcEmSu7u7XYoDAAAAALgeQzuqCxcu1CeffKLPPvtMpUqV0vz585WYmKjZs2fbqTwAAAAAuA+6/jotQ0F169at+vDDD+Xr62szXqJEiXQtCgAAAADgugxd+hsXF6eePXuqa9euWrhwoaKjo+1VFwAAAADARRkKqi+++KK+++47TZkyRSdPnlSBAgXsVRcAAAAAwEUZuvT3xo0bWrZsmVatWiVfX19NmzbNXnUBAAAAAFyUoaDar18/PfHEE1qxYoVy5sxpr5oAAAAAAC7MUFBdvXq1YmNjdfPmTYWGhkqSihQpYpfCAAAAAOCBrNbMrgB2Yiiojh07VitXrtTvv/+uwMBA5ciRQ99//729agMAAAAAuCBDzZSWL1+uvXv3qly5cjpw4IDy589vr7oAAAAAAC7KUFDNnj27JMnT01Nnz57VqVOn7FIUAAAAAMB1GQqqQ4cO1Z07dzRq1Ci9/fbbevPNN+1VFwAAAADARRn6jmqLFi0kSfXr19eaNWvsUhAAAAAAwLUZCqoAAAAAYBrWpMyuAHZi6NJfAAAAAADsLU07qnPmzLnvz3r16pVuxQAAAAAAkKagGhMTI0nauXOnLBaL6tSpo0OHDikhIYGgCgAAAABIV2kKqi+++KIkae3atVq3bl3yeNu2be1TFQAAAADAZRlqphQeHq5t27apRo0aOnLkiCIjI+1VF0xq67plWr9srsJDglWoaAk98+JAla1UPdW5U8eN0o9b16cYL1SkuD6euliStHPLOk0fPzrFnJmrd8nLyzt9iwfwQN+vX6ZNK+YrLDRYBYsUV8++A1WmYrVU5876fLT2bN+QYrxgYHGNnrRQkrRz0yrt3f6d/rj4mySpaKmy6tLrJZUoU9Fu5wAgdaxvAI7GYrVarWmdfPnyZY0dO1bnz59X6dKl9dZbbykwMNDwmx74LdzwMch8+37YoimfjdDzL7+t0hWq6vsNK7Vj02p9PHWxcufNn2J+9O0oxcXeSX6elJioYS8/rZYdn1DXZ/pJuhtU500Zp7HTl9oc65crt31PBnYTm0D3PUd0YNcWzRw/Sk/3f0ulKlTRzo2rtGvzGo3+eqEC7rO+4+Nik58nJiZq1GvPqHn77ur0VF9J0vTPhqtU+SoqWb6KPD29tHHFPB3Zu0Ojv14g/4C8GXZugKtjfSMtGpXxz+wS/pWs9YdmdgkOJ2bPR5ldQpoY2lENDAzU+++/rytXrqhGjRqKjY19+EFwGt+tXKAmrTqq6WOdJUnP9B+kk0f2adv65erxwssp5mfLnkPZsudIfn5ozw7djopU45YdbOZZLBaCKZDJtqxaqIYtO6hx606SpJ59B+qnI/u047sV6vbcgBTzs2XPIf1jfR/d+4OioyLVsEX75LG+g22vlnjulSE6vHu7zhw/pPrN+eoIkFFY3wAckaHb04wfP179+/dXnz59ZLVa1blzZzuVBbNJiI/XhV9+VuUaj9iMV6rxiH45fSJNr/HDpjWqWK2OcucrYDN+JyZGbzzXUa89017jRgzUhfNn061uAA+XEB+vi+fPqmJ12/Vdsfoj+vXMyTS9xq4ta1S+am0F5C1w3zlxsXeUmJio7Dl8/lO9ANKO9Q3AURkKqqtXr9aKFSvk6+sri8XCjqoLiYwIU1JSonz8A2zGff1yKTw0+KHHh4UE6cShvWr6WEeb8YKFi6rfm8M1cMRnevnd/8nT01v/G9xH1/+4lK71A7i/qL/Wt18um3Efv1wKD0vb+v7p8D41atXxgfOWfztJfgF5VKFa7f9UL4C0Y30DcFSGLv11d3dXeHi4LBaLIiMj5eb28JwbGxubItDGxcbKy5tGOY7IYrF9brVaZbl3MBU7t6xTthw5VLNeU5vxUuUrq1T5ysnPS1eoqvdffVab1yxRr5cGp0fJANLo3rVstVpl0cPX955t65Utew5Vr9vkvnO+Wz5X+3du0VsffS1PGqUBGY71DcDRGNpR/fTTT9WtWzedOnVKjz/+uD799NOHHjNmzBj5+vraPL6dMv5fF4zMkdPHT25u7goPsf3ra0R4aIq/0t7LarVq5+a1atC8jTw8PR84183NTSXKVNCNq5f/c80A0ibHX+v7nqsjItO4vn/cslZ1m91/fW9aMV8bln6rQaMnKrB46XSrG8DDsb4BOCpDQTU6Olpbt27VjRs3tGnTJsXHxz/0mCFDhig8PNzm8Vz/Qf+6YGQOD09PFStdTj8dPWAz/tORAypdocoDj/355BHduHpZTf5s4vAgVqtVF389Jz9/misBGcXD01NFS5XV6XvW9+ljB1TyH1c8pObsT0d089oVNWrVIdWfb1wxT+sWz9IbIyeoWOny6VYzgLRhfcPpWZN4GH04CENBdcSIETbP07Kj6u3tLR8fH5sHl/06pjZdntKOTav1w6Y1+uPS75o3dbyCb13Xo227SpIWf/O1pnw2IsVxP2xao5JlKymwWMkUP1sxf7pOHN6rm9f+0MVfz2nG5x/o0m/n1LxdV7ufD4C/tez8pHZtWaMft6zV1cu/a9H0CQq5dUNN23SRdPf7ZzPHj0px3I+b16pE2YoqVDTl+v5u+VytmjtVz782TLnzFVB4aLDCQ4N1Jyba7ucD4G+sbwCOKE3fUZ0+fbqmTZums2fPqk6dOsnfSyxbtqy964OJ1G3SUlGR4Vq1YKbCQoJUuFhJDR79eXIX37CQIAXfvGFzTPTtKB3cvV3PvPhmqq8ZHRWpWV+MUXhIsLJmz6FiJcto2KdTVbIsNwwHMlKdRi11OyJcaxfNVHhIsAoWLaHXR4xP7vIZHhKk4FvXbY6Jvh2lI3u+V89+A1N9zR0blishIV6TP7a9x12HJ3sn34sRgP2xvgE4IovVarWmdfKUKVPUv3////ymB34L/8+vAcCcYhMc55ISAABwV6My/pldwr+Std67mV2Cw4nZ+3Fml5Amhrr+du7cWV999ZVCQ0P1V74dPny4XQoDAAAAALgmQ0G1U6dO6tu3r+rUqWOvegAAAAAALs5QUM2TJ4/69Oljr1oAAAAAIO3S/i1GOJg0BdVJkyZJutvBt0+fPqpevXryjaMHDBhgv+oAAAAAAC4nTUE1e/bskqSOHTvatRgAAAAAANIUVJ977jlJ0qVLl2zGPT09k29VAwAAAABAejD0HdXnn39ely9fVuXKlXXy5EkVKFBAUVFRev3115PDLAAAAAAA/4Wbkcm5c+fWqVOntGLFCp06dUr58uXTvn379NVXX9mrPgAAAACAizG0o3r+/HnFxMTIy8tLMTEx+u233+Tl5ZX8HVYAAAAAyDDWpMyuAHZiKKh+/PHHat26tZKSkuTh4aGPPvpICQkJeuWVV+xVHwAAAADAxVis1oy/+dCB38Iz+i0BZJDYBP6yCQCAo2lUxj+zS/hXsj7yVmaX4HBi9n+a2SWkSZp2VEeNGqURI0aoe/fuKTr8LlmyxC6FAQAAAABcU5qCav/+/SVJn332mV2LAQAAAAAgTV1/8+XLJ0kKCAjQsmXLNHXqVBUuXFinT5+2a3EAAAAAANdj6PY0zzzzjIoXL65du3bJ3d1d48aNs1ddAAAAAPBgVisPow8HYSioRkVFqWvXrvL09JQkZUIfJgAAAACAkzMUVAsUKKDZs2fr9u3bmj9/vgoXLmyvugAAAAAALspQUJ0xY4Zu376tWrVqKTQ0VNOmTbNXXQAAAAAAF5Wmrr9/WbVqlbp06aKXX37ZXvUAAAAAAFycoaAaHh6ud955R1euXFHRokXVsGFD9enTx161AQAAAABckKGg2rdvX9WqVUs7d+7UqlWrdO3aNYIqAAAAgMxhTcrsCmAnhoKqn5+fGjZsqLfffluvvfaa3NwMfcUVAAAAAICHMpQ0Dx48qK5du2r+/Pl65pln9O6779qrLgAAAACAizIUVCMiIhQeHq7g4GDduHFDMTEx9qoLAAAAAOCiDF36u3TpUjVp0kS9e/eWr6+vvWoCAAAAALgwQ0H1k08+sVcdAAAAAABIMhhUAQAAAMA0rNbMrgB2QtteAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICp0PUXAAAAgGOyJmV2BbATdlQBAAAAAKZCUAUAAAAAmApBFQAAAABgKgRVAAAAAICpEFQBAAAAAKZC118AAAAAjslqzewKYCfsqAIAAAAATIWgCgAAAAAwFYIqAAAAAMBUCKoAAAAAAFMhqAIAAAAATIWuvwAAAAAckzUpsyuAnbCjCgAAAAAwFYIqAAAAAOC+Jk2apOLFiytLliyqWbOmdu3a9cD5P/zwg2rWrKksWbKoRIkSmjJliuH3JKgCAAAAAFK1ePFivfHGGxo2bJiOHj2qRo0aqU2bNrp06VKq83///Xe1bdtWjRo10tGjRzV06FC99tprWr58uaH3tVitVmt6nIARB34Lz+i3BJBBYhP4rggAAI6mURn/zC7hX8la/ZXMLsHhxBz9ytD8Rx55RDVq1NDkyZOTx8qXL6/OnTtrzJgxKea/8847WrNmjc6cOZM81r9/fx0/flx79+5N8/uyowoAAAAALiI2NlYRERE2j9jY2FTnxsXF6fDhw2rVqpXNeKtWrbRnz55Uj9m7d2+K+a1bt9ahQ4cUHx+f5jozpetvnRK+mfG2yASxsbEaM2aMhgwZIm9v78wuB0A6Yn0Dzov1DUdhdHcQ0siRIzVq1CibsREjRmjkyJEp5gYFBSkxMVH58uWzGc+XL5+uX7+e6utfv3491fkJCQkKCgpSgQIF0lQnO6qwq9jYWI0aNeq+f6UB4LhY34DzYn0DzmvIkCEKDw+3eQwZMuSBx1gsFpvnVqs1xdjD5qc2/iDcRxUAAAAAXIS3t3ear5TInTu33N3dU+ye3rx5M8Wu6V/y58+f6nwPDw8FBASkuU52VAEAAAAAKXh5ealmzZrasmWLzfiWLVtUv379VI+pV69eivmbN29WrVq15Onpmeb3JqgCAAAAAFI1aNAgzZgxQ7NmzdKZM2c0cOBAXbp0Sf3795d091LiXr16Jc/v37+/Ll68qEGDBunMmTOaNWuWZs6cqcGDBxt6Xy79hV15e3trxIgRNGIAnBDrG3BerG8Af+nRo4eCg4M1evRoXbt2TZUqVdKGDRtUtGhRSdK1a9ds7qlavHhxbdiwQQMHDtTXX3+tggUL6osvvlC3bt0MvW+m3EcVAAAAAID74dJfAAAAAICpEFQBAAAAAKZCUAUAAAAAmApBFXY1f/78+7au/jfzADimkSNHqnPnzsnPLRaLjh07lmn1AM7g1q3/b+/+Y5uq+jiOv+lEtlFL3bJ1jq2tM5gskIAYRKOCqwSMoOOPscRNJYImuMz4MzNRjP8MUeOvCBIhS+gSOgliRqLJkgGCbholWRZEEocz27SdKxPdjwvrcNt5/iBcn8nog0/Gsz7r55U0ae85veecm51+77f39K6XQCCAy+Vi3bp1U90dEZFJpURVrqry8nK+/vrrSasnIiIiF+zatYuUlBT6+vp4//33efDBB8nNzdUXQSIyLShRlbhGR0fRjaFFkoMxhtHR0anuhohcoY6ODubPn4/D4cDhcHDfffdx4MCBqe7WOCMjI1PdBRH5P6VENUn5/X62bNnC4sWLcblcrFq1iu7ubuDCkrzt27ezYMEC0tPTsSyLn376iQceeICsrCx8Ph/V1dWMjY3Z+zt48CBLly7F7XZzww03sHXrVgCCwSCLFi2y673zzjt4vV6uu+46/H4/NTU1E9aLRqOUlpaSlZWF1+vl5ZdftoPd0aNHcbvd1NTUkJ+fT2ZmJlVVVVf5iIlMT36/n61bt3L77beTnp5Oc3Mz5eXl5ObmkpubyzPPPMPw8LBdv6WlhUAgQEZGBllZWTz11FMAWJZFcXEx2dnZzJkzh2XLlnH8+PGpGpbItLdu3Tpqa2vZsWMHTqeTzz77jIqKCm677bYr3sflYjJcPq4D7Nmzh8LCQtxuN3fddRetra122T333ENVVRUrV65k9uzZNDQ0YFkWlZWVeL1esrOzefTRR+nv75+cAyEi05YS1SRWU1NDXV0dPT095OTkUF5ebpfV1dXR2NjIwMAADoeDe++9l0AgQCQSoampib1797J7924AWltbKS4upqqqit7eXn744QeKioouae/UqVNs3ryZxsZGBgcH+fbbby8bUMvKypg5cyYdHR00NTVx4MAB3nzzTbt8cHCQEydO8OOPP9Lc3MwHH3zA0aNHJ/cAiSSJYDBIbW0tlmXx7LPPkpOTQ3t7OydOnOD48eNUV1cDEIlECAQClJSU0N3dTVdXF6WlpQCMjY1RVlZGR0cH0WiUW265hdLSUq3IELlKPv74Y8rLy6moqMCyLDZu3PiP3h8vJseL601NTTz55JPs3LmT3t5eSkpKWLVq1bjEMxgMUl1djWVZrFixgg0bNvD777/z3Xff0dHRwZ9//kllZeXkHQwRmZ6MJCWfz2feeOMN+3VPT48BzC+//GIAU19fb5ft27fPLFq0aNz7d+3aZQKBgDHGmE2bNpnHHntswnZ2795tFi5caIwxpr293aSmppr9+/ebc+fOXbZeOBw2gPn111/t8lAoZObNm2eMMebIkSNmxowZ5uzZs3b5ihUrzFtvvfXPDoKIGJ/PZ959911jjDHHjh0zGRkZZnR01C5vbGw0BQUFxhhjXn/9dVNUVHRF+/3jjz8MYMLhsDHGmFdffdUUFxfb5YBpbW2dlDGIJKv169ebp59++pLtVzK/4sXkeHH98ccfN5s2bRq37eabbzahUMgYY8zy5cvH9en06dPG4XCYM2fO2NtOnTplZs6caUZGRuL2UUSSm66oJjGfz2c/93g8zJo1i0gkAoDX67XLOjs7+f7773G73fbj+eefp6enB4Curi7mzZv3H9u76aabqK2tZfv27Xg8HlauXDnhzR7C4TCpqank5OTY2woKCgiHw/Zrl8tFenq6/Xr27NkMDg5e+eBFxHZxvnd2dtLX10dGRoY910tKSohGo0D8uT40NERFRQV+vx+Xy4Xf7wfgt99++5+MQUTimz9/Pk6nE6fTSSgUihuT4831cDhsz++LbrzxxnEx+u/nEGNjYxQUFNifK0uWLMHhcNjnESIiE1GimsS6urrs56dPn2Z4eJi5c+cC4HD89aeRn5/PrbfeSl9fn/0YGBjg5MmTwIWEt729/YraLC0t5ciRI0SjURYuXMgjjzxySZ28vDxisZh9cgwXbhiRl5f3X41TROK7ON/z8/PJzs4eN9f7+/uxLAuIP9fffvttWlpaaG5uZmBggM7OTgAt/RVJECdPnsSyLCzLsn/qc7mYHG+u5+Xl2fP7os7OznEx+u/nEA6Hg+7u7nGfLbFYzD7nEBGZiBLVJLZz507a2toYGhrixRdfZNmyZRMmg2vWrCEajbJjxw5isRijo6O0tbXZvwl94okn+Oijj6ivr2dkZIT+/n6++eabS/bT1tbGwYMHGRoa4tprr8XpdHLNNddcUm/u3LkUFRXxwgsvcPbsWX7++Wdee+011q9fP+nHQET+smTJErxeL5s3b2ZwcBBjDF1dXTQ0NAAX/o3UsWPH+PDDDxkeHubcuXM0NTUBMDAwQGpqKtdffz2WZfHSSy9N5VBEklIsFiMWiwFw/vx5YrHYuBsf/rt4MTleXH/44YcJhUJ89dVXjIyMsG3bNs6cOcP9998/YTs5OTmsXbuWyspKe4VFT08P9fX1kz18EZlmlKgmsQ0bNvDQQw/h8XiIRCKEQqEJ6zmdTg4dOsThw4fx+/1kZmZSVlZmL9lZvHgxn3zyCVu2bCEjI4PCwkK++OKLS/Zz/vx5XnnlFTweD5mZmXz++ecEg8EJ26yrq2NoaAifz8edd97J6tWrdWdfkassJSWFTz/9lEgkQmFhIXPmzGH16tX2lZW8vDwOHTpEXV0dHo8Hv9/P/v37AXjuuedISUnB4/GwYMEC7rjjjqkcikhSSktLIy0tDYClS5eSlpbGl19+OWHdeDE5Xlxfvnw527ZtY+PGjWRmZrJ3714aGhpwu92X7VcwGLSX/LpcLu6++25aWlomdewiMv3MMFqXlZT8fj/vvfcea9eunequiIiIiIiIjKMrqiIiIiIiIpJQlKiKiIiIiIhIQtHSXxEREREREUkouqIqIiIiIiIiCUWJqoiIiIiIiCQUJaoiIiIiIiKSUJSoioiIiIiISEJRoioiIiIiIiIJRYmqiIiIiIiIJBQlqiIiIiIiIpJQlKiKiIiIiIhIQlGiKiIiIiIiIgnlX7fj82G603REAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 20))\n",
    "ax = fig.gca()\n",
    "\n",
    "sns.heatmap(cr_df.iloc[:-1, :].T, annot=True, cmap='Blues_r', ax=ax, yticklabels=1)  # type: ignore\n",
    "\n",
    "plt.title(f'Classification Report for {aim}', fontsize=14)\n",
    "\n",
    "# Decrease font size of x and y labels\n",
    "plt.xticks(fontsize=9)\n",
    "plt.yticks(fontsize=6)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate on external data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "seconds = pr['nsynth']['max_seconds']\n",
    "hop_length = pr['librosa']['hop_length']\n",
    "window_size = pr['librosa']['spec_windows']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File: bass_030_Gb1         | True label: bass       | Predicted label: keyboard   | Result: ✖\n",
      "File: bass_038_D2          | True label: bass       | Predicted label: bass       | Result: ✔\n",
      "File: guitar_044_Ab2       | True label: guitar     | Predicted label: guitar     | Result: ✔\n",
      "File: guitar_055_G3        | True label: guitar     | Predicted label: keyboard   | Result: ✖\n",
      "File: guitar_062_D4        | True label: guitar     | Predicted label: keyboard   | Result: ✖\n",
      "File: synth_058_Bb3        | True label: synth      | Predicted label: bass       | Result: ✖\n",
      "File: synth_071_B4         | True label: synth      | Predicted label: guitar     | Result: ✖\n"
     ]
    }
   ],
   "source": [
    "external_files_dir = Path('./data/external')\n",
    "\n",
    "for audio_file in external_files_dir.glob('*.wav'):\n",
    "\n",
    "    if aim == 'Pitch Detection':\n",
    "        start_ix = audio_file.stem.find('_') + 1\n",
    "        end_ix = start_ix + 3\n",
    "        true_label = str(int(audio_file.stem[start_ix:end_ix]))\n",
    "\n",
    "    elif aim == 'Instrument Type Detection':\n",
    "        \n",
    "        end_ix = audio_file.stem.find('_')\n",
    "        true_label = audio_file.stem[:end_ix]\n",
    "    \n",
    "    else:\n",
    "        true_label = ''\n",
    "\n",
    "    audio_data, sr = librosa.load(audio_file, sr=None)\n",
    "    audio_file_name, audio_features = process_single_file(audio_file, seconds, window_size, hop_length, features)\n",
    "\n",
    "    arrays_to_combine = []\n",
    "\n",
    "    for af in features:\n",
    "        arrays_to_combine.append(np.expand_dims(audio_features[af], axis=-1))\n",
    "\n",
    "    if len(audio_features) > 1:\n",
    "        data_array = np.concatenate(arrays_to_combine, axis=-1)\n",
    "    else:\n",
    "        data_array = arrays_to_combine[0]\n",
    "\n",
    "    data_array = np.expand_dims(data_array, axis=0)\n",
    "    predicted_label = encoder.inverse_transform(model.predict(data_array, verbose=0).argmax(1))[0]\n",
    "\n",
    "    result = '✔' if true_label == predicted_label else '✖'\n",
    "\n",
    "    print(f\"File: {audio_file_name:<20} | True label: {true_label:<10} | Predicted label: {predicted_label:<10} | Result: {result}\")\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "audio-new",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "orig_nbformat": 4
 },
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 "nbformat_minor": 2
}