{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5269664",
   "metadata": {},
   "source": [
    "# Tension Board 2 Mirror: Grade Prediction with Deep Learning\n",
    "\n",
    "This notebook tests whether a neural network can improve on the classical models from notebook 05. The feature set is the same; only the learning algorithm changes.\n",
    "\n",
    "## Neural Network Approach\n",
    "\n",
    "We implement a straightforward feedforward network in PyTorch:\n",
    "\n",
    "1. **Architecture**  \n",
    "   A multi-layer perceptron with batch normalization and dropout. The input dimension matches the number of engineered features; the output is a single difficulty score.\n",
    "\n",
    "2. **Training**  \n",
    "   Adam optimizer with learning rate scheduling and early stopping. The validation set monitors for overfitting.\n",
    "\n",
    "3. **Regularization**  \n",
    "   Dropout and weight decay help prevent the network from memorizing training data.\n",
    "\n",
    "## Consistency Requirements\n",
    "\n",
    "For fair comparison with notebook 05, we use the same random seed and the same test set. Predictions from both the Random Forest and Neural Network are saved so that an ensemble approach could be tested in future work.\n",
    "\n",
    "## Output\n",
    "\n",
    "The final products are the trained neural network weights, test set predictions, and a comparison of neural network performance against Random Forest on identical test data.\n",
    "\n",
    "## Notebook Structure\n",
    "\n",
    "1. [Setup and Imports](#setup-and-imports)\n",
    "2. [Train/Test Split](#traintest-split)\n",
    "3. [Setting up the Neural Network](#setting-up-the-neural-network)\n",
    "4. [Training Configuration](#training-configuration)\n",
    "5. [Training Loop](#training-loop)\n",
    "6. [Test Set Evaluation](#test-set-evaluation)\n",
    "7. [Visualization and Error Analysis](#visualization-and-error-analysis)\n",
    "8. [Hyperparameter Tuning](#hyperparameter-tuning)\n",
    "9. [Feature Importance](#feature-importance)\n",
    "10. [Save Model](#save-model)\n",
    "11. [Compare with RF](#comparison-with-rf)\n",
    "12. [Conclusion](#conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2344030f",
   "metadata": {},
   "source": [
    "# Setup and Imports\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "99f64922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Setup and Imports\n",
    "==================================\n",
    "\"\"\"\n",
    "\n",
    "# Imports\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from scipy.spatial import ConvexHull\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "\n",
    "import sqlite3\n",
    "\n",
    "import re\n",
    "import os\n",
    "from collections import defaultdict\n",
    "\n",
    "import ast\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "# Set some display options\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "\n",
    "# Set style\n",
    "palette=['steelblue', 'coral', 'seagreen']  #(for multi-bar graphs)\n",
    "\n",
    "# Set board image for some visual analysis\n",
    "board_img = Image.open('../images/tb2_board_12x12_composite.png')\n",
    "\n",
    "# Connect to the database\n",
    "DB_PATH=\"../data/tb2.db\"\n",
    "conn = sqlite3.connect(DB_PATH)\n",
    "\n",
    "# Set random state\n",
    "RANDOM_STATE=3\n",
    "\n",
    "np.random.seed(RANDOM_STATE)\n",
    "torch.manual_seed(RANDOM_STATE)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed(RANDOM_STATE)\n",
    "\n",
    "# Check for GPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a9e2443",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Query our data from the DB\n",
    "==================================\n",
    "\n",
    "This time we restrict to where `layout_id=10` for the TB2 Mirror.\n",
    "We will also restrict ourselves to an angle of at most 50, since according to our grade vs angle distribution in notebook 01, things start to look a bit weird past 50.\n",
    "(Probably a bias towards climbers who can actually climb that steep). We will encode this directly into our query.\n",
    "\"\"\"\n",
    "\n",
    "# Query climbs data\n",
    "climbs_query = \"\"\"\n",
    "SELECT\n",
    "    c.uuid,\n",
    "    c.name AS climb_name,\n",
    "    c.setter_username,\n",
    "    c.layout_id AS layout_id,\n",
    "    c.description,\n",
    "    c.is_nomatch,\n",
    "    c.is_listed,\n",
    "    l.name AS layout_name,\n",
    "    p.name AS board_name,\n",
    "    c.frames,\n",
    "    cs.angle,\n",
    "    cs.display_difficulty,\n",
    "    dg.boulder_name AS boulder_grade,\n",
    "    cs.ascensionist_count,\n",
    "    cs.quality_average,\n",
    "    cs.fa_at\n",
    "    \n",
    "FROM climbs c\n",
    "JOIN layouts l ON c.layout_id = l.id\n",
    "JOIN products p ON l.product_id = p.id\n",
    "JOIN climb_stats cs ON c.uuid = cs.climb_uuid\n",
    "JOIN difficulty_grades dg ON ROUND(cs.display_difficulty) = dg.difficulty\n",
    "WHERE cs.display_difficulty IS NOT NULL AND c.is_listed=1 AND c.layout_id=10 AND cs.angle <= 50\n",
    "\"\"\"\n",
    "\n",
    "# Query information about placements (and their mirrors)\n",
    "placements_query = \"\"\"\n",
    "SELECT\n",
    "    p.id AS placement_id,\n",
    "    h.x,\n",
    "    h.y,\n",
    "    p.default_placement_role_id AS default_role_id,\n",
    "    p.set_id AS set_id,\n",
    "    s.name AS set_name,\n",
    "    p_mirror.id AS mirror_placement_id\n",
    "FROM placements p\n",
    "JOIN holes h ON p.hole_id = h.id\n",
    "JOIN sets s ON p.set_id = s.id\n",
    "LEFT JOIN holes h_mirror ON h.mirrored_hole_id = h_mirror.id\n",
    "LEFT JOIN placements p_mirror ON p_mirror.hole_id = h_mirror.id AND p_mirror.layout_id = p.layout_id\n",
    "WHERE p.layout_id = 10\n",
    "\"\"\"\n",
    "\n",
    "# Load it into a DataFrame\n",
    "df_climbs = pd.read_sql_query(climbs_query, conn)\n",
    "df_placements = pd.read_sql_query(placements_query, conn)\n",
    "\n",
    "df_hold_difficulty = pd.read_csv('../data/03_hold_difficulty/hold_difficulty_scores.csv', index_col='placement_id')\n",
    "df_features = pd.read_csv('../data/04_climb_features/climb_features.csv', index_col='climb_uuid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1531f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Features shape: (42596, 48)\n",
      "Target range: 10.0 to 32.0\n",
      "Target mean: 19.83\n",
      "Target std: 4.06\n",
      "\n",
      "Missing values in features: 0\n"
     ]
    }
   ],
   "source": [
    "# Separate features and target\n",
    "X = df_features.drop(columns=['display_difficulty'])\n",
    "y = df_features['display_difficulty']\n",
    "\n",
    "print(f\"\\nFeatures shape: {X.shape}\")\n",
    "print(f\"Target range: {y.min():.1f} to {y.max():.1f}\")\n",
    "print(f\"Target mean: {y.mean():.2f}\")\n",
    "print(f\"Target std: {y.std():.2f}\")\n",
    "\n",
    "# Check for any remaining missing values\n",
    "missing = X.isna().sum().sum()\n",
    "print(f\"\\nMissing values in features: {missing}\")\n",
    "\n",
    "if missing > 0:\n",
    "    print(\"Filling remaining missing values with column means...\")\n",
    "    X = X.fillna(X.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4abfd9b",
   "metadata": {},
   "source": [
    "# Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63a2acf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set:   27260 samples\n",
      "Validation set: 6816 samples\n",
      "Test set:       8520 samples\n",
      "\n",
      "Test indices saved for ensemble consistency\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Train/Test split\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "# First split: 80% train+val, 20% test\n",
    "X_temp, X_test, y_temp, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=RANDOM_STATE\n",
    ")\n",
    "\n",
    "# Second split: 80% train, 20% validation from the remaining\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_temp, y_temp, test_size=0.2, random_state=RANDOM_STATE\n",
    ")\n",
    "\n",
    "print(f\"Training set:   {len(X_train)} samples\")\n",
    "print(f\"Validation set: {len(X_val)} samples\")\n",
    "print(f\"Test set:       {len(X_test)} samples\")\n",
    "\n",
    "# Save test indices for ensemble consistency\n",
    "test_indices = X_test.index.tolist()\n",
    "np.save('../data/06_deep_learning/test_indices.npy', test_indices)\n",
    "print(\"\\nTest indices saved for ensemble consistency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90fa8ae5",
   "metadata": {},
   "source": [
    "# Setting up the Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a43314c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features scaled\n",
      "Train mean: 0.0000, std: 1.0000\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Feature Scaling\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_val_scaled = scaler.transform(X_val)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print(f\"Features scaled\")\n",
    "print(f\"Train mean: {X_train_scaled.mean():.4f}, std: {X_train_scaled.std():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50fbb82c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch size: 64\n",
      "Training batches: 426\n",
      "Validation batches: 107\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Convert to PyTorch Tensors\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "# Convert to tensors\n",
    "X_train_tensor = torch.FloatTensor(X_train_scaled)\n",
    "y_train_tensor = torch.FloatTensor(y_train).reshape(-1, 1)\n",
    "\n",
    "X_val_tensor = torch.FloatTensor(X_val_scaled)\n",
    "y_val_tensor = torch.FloatTensor(y_val).reshape(-1, 1)\n",
    "\n",
    "X_test_tensor = torch.FloatTensor(X_test_scaled)\n",
    "y_test_tensor = torch.FloatTensor(y_test).reshape(-1, 1)\n",
    "\n",
    "# Create DataLoaders\n",
    "batch_size = 64\n",
    "\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "val_dataset = TensorDataset(X_val_tensor, y_val_tensor)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "print(f\"Batch size: {batch_size}\")\n",
    "print(f\"Training batches: {len(train_loader)}\")\n",
    "print(f\"Validation batches: {len(val_loader)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62c2db48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Architecture:\n",
      "ClimbGradePredictor(\n",
      "  (network): Sequential(\n",
      "    (0): Linear(in_features=48, out_features=256, bias=True)\n",
      "    (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): Dropout(p=0.2, inplace=False)\n",
      "    (4): Linear(in_features=256, out_features=128, bias=True)\n",
      "    (5): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (6): ReLU()\n",
      "    (7): Dropout(p=0.2, inplace=False)\n",
      "    (8): Linear(in_features=128, out_features=64, bias=True)\n",
      "    (9): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (10): ReLU()\n",
      "    (11): Dropout(p=0.2, inplace=False)\n",
      "    (12): Linear(in_features=64, out_features=1, bias=True)\n",
      "  )\n",
      ")\n",
      "\n",
      "Total parameters: 54,657\n",
      "Trainable parameters: 54,657\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Define Neural Network Architecture\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "class ClimbGradePredictor(nn.Module):\n",
    "    \"\"\"\n",
    "    Neural network for climb grade prediction.\n",
    "    \n",
    "    Architecture:\n",
    "    - Input layer\n",
    "    - Multiple hidden layers with BatchNorm and Dropout\n",
    "    - Output layer (single value)\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, input_dim, hidden_layers=[256, 128, 64], dropout_rate=0.2):\n",
    "        super(ClimbGradePredictor, self).__init__()\n",
    "        \n",
    "        layers = []\n",
    "        prev_dim = input_dim\n",
    "        \n",
    "        for hidden_dim in hidden_layers:\n",
    "            layers.append(nn.Linear(prev_dim, hidden_dim))\n",
    "            layers.append(nn.BatchNorm1d(hidden_dim))\n",
    "            layers.append(nn.ReLU())\n",
    "            layers.append(nn.Dropout(dropout_rate))\n",
    "            prev_dim = hidden_dim\n",
    "        \n",
    "        # Output layer\n",
    "        layers.append(nn.Linear(prev_dim, 1))\n",
    "        \n",
    "        self.network = nn.Sequential(*layers)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.network(x)\n",
    "\n",
    "\n",
    "# Create model\n",
    "input_dim = X_train.shape[1]\n",
    "hidden_layers = [256, 128, 64]\n",
    "dropout_rate = 0.2\n",
    "\n",
    "model = ClimbGradePredictor(input_dim, hidden_layers, dropout_rate).to(device)\n",
    "\n",
    "print(f\"Model Architecture:\\n{model}\")\n",
    "\n",
    "# Count parameters\n",
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f\"\\nTotal parameters: {total_params:,}\")\n",
    "print(f\"Trainable parameters: {trainable_params:,}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded8d846",
   "metadata": {},
   "source": [
    "# Training Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "665deadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Define training functions\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "grade_to_v = {\n",
    "    10: 0, 11: 0, 12: 0,\n",
    "    13: 1, 14: 1,\n",
    "    15: 2,\n",
    "    16: 3, 17: 3,\n",
    "    18: 4, 19: 4,\n",
    "    20: 5, 21: 5,\n",
    "    22: 6,\n",
    "    23: 7,\n",
    "    24: 8, 25: 8,\n",
    "    26: 9,\n",
    "    27: 10,\n",
    "    28: 11,\n",
    "    29: 12,\n",
    "    30: 13,\n",
    "    31: 14,\n",
    "    32: 15,\n",
    "    33: 16,\n",
    "}\n",
    "\n",
    "def to_grouped_v(x):\n",
    "    rounded = int(round(x))\n",
    "    rounded = max(min(rounded, max(grade_to_v)), min(grade_to_v))\n",
    "    return grade_to_v[rounded]\n",
    "\n",
    "def grouped_v_metrics(y_true, y_pred):\n",
    "    true_v = np.array([to_grouped_v(x) for x in y_true])\n",
    "    pred_v = np.array([to_grouped_v(x) for x in y_pred])\n",
    "\n",
    "    return {\n",
    "        'exact_grouped_v': np.mean(true_v == pred_v) * 100,\n",
    "        'within_1_vgrade': np.mean(np.abs(true_v - pred_v) <= 1) * 100,\n",
    "        'within_2_vgrades': np.mean(np.abs(true_v - pred_v) <= 2) * 100\n",
    "    }\n",
    "\n",
    "def train_epoch(model, train_loader, criterion, optimizer, device):\n",
    "    \"\"\"Train for one epoch.\"\"\"\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "\n",
    "    for X_batch, y_batch in train_loader:\n",
    "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        predictions = model(X_batch)\n",
    "        loss = criterion(predictions, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    return total_loss / len(train_loader)\n",
    "\n",
    "\n",
    "def evaluate(model, data_loader, criterion, device):\n",
    "    \"\"\"Evaluate model on a dataset.\"\"\"\n",
    "    model.eval()\n",
    "    total_loss = 0\n",
    "    predictions_list = []\n",
    "    actuals_list = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for X_batch, y_batch in data_loader:\n",
    "            X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "\n",
    "            predictions = model(X_batch)\n",
    "            loss = criterion(predictions, y_batch)\n",
    "\n",
    "            total_loss += loss.item()\n",
    "            predictions_list.append(predictions.cpu().numpy())\n",
    "            actuals_list.append(y_batch.cpu().numpy())\n",
    "\n",
    "    avg_loss = total_loss / len(data_loader)\n",
    "    all_predictions = np.vstack(predictions_list).flatten()\n",
    "    all_actuals = np.vstack(actuals_list).flatten()\n",
    "\n",
    "    return avg_loss, all_predictions, all_actuals\n",
    "\n",
    "\n",
    "def compute_metrics(y_true, y_pred):\n",
    "    \"\"\"Compute evaluation metrics.\"\"\"\n",
    "    mae = mean_absolute_error(y_true, y_pred)\n",
    "    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "    r2 = r2_score(y_true, y_pred)\n",
    "    within_1 = np.mean(np.abs(y_true - y_pred) <= 1) * 100\n",
    "    within_2 = np.mean(np.abs(y_true - y_pred) <= 2) * 100\n",
    "    v_metrics = grouped_v_metrics(y_true, y_pred)\n",
    "\n",
    "    return {\n",
    "        'mae': mae,\n",
    "        'rmse': rmse,\n",
    "        'r2': r2,\n",
    "        'within_1': within_1,\n",
    "        'within_2': within_2,\n",
    "        'exact_grouped_v': v_metrics['exact_grouped_v'],\n",
    "        'within_1_vgrade': v_metrics['within_1_vgrade'],\n",
    "        'within_2_vgrades': v_metrics['within_2_vgrades']\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d9c040e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate: 0.001\n",
      "Max epochs: 200\n",
      "Early stopping patience: 25\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Training configuration\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "# Loss function\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "# Optimizer\n",
    "learning_rate = 0.001\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-5)\n",
    "\n",
    "# Learning rate scheduler\n",
    "scheduler = ReduceLROnPlateau(\n",
    "    optimizer, mode='min', factor=0.5, patience=10\n",
    ")\n",
    "\n",
    "# Training settings\n",
    "num_epochs = 200\n",
    "early_stopping_patience = 25\n",
    "\n",
    "print(f\"Learning rate: {learning_rate}\")\n",
    "print(f\"Max epochs: {num_epochs}\")\n",
    "print(f\"Early stopping patience: {early_stopping_patience}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d4bd8b",
   "metadata": {},
   "source": [
    "# Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "476b158d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training...\n",
      "\n",
      "Epoch   1/200 | Train Loss: 162.8246 | Val Loss: 12.3054 | Val MAE: 2.865 | Val R²: 0.248\n",
      "Epoch  10/200 | Train Loss: 9.9401 | Val Loss: 7.0066 | Val MAE: 2.126 | Val R²: 0.572\n",
      "Epoch  20/200 | Train Loss: 9.0906 | Val Loss: 6.4919 | Val MAE: 2.042 | Val R²: 0.603\n",
      "Epoch  30/200 | Train Loss: 8.4702 | Val Loss: 6.4766 | Val MAE: 2.050 | Val R²: 0.604\n",
      "Epoch  40/200 | Train Loss: 8.1713 | Val Loss: 6.3412 | Val MAE: 2.009 | Val R²: 0.612\n",
      "Epoch  50/200 | Train Loss: 7.7513 | Val Loss: 6.1911 | Val MAE: 1.990 | Val R²: 0.622\n",
      "Epoch  60/200 | Train Loss: 7.5215 | Val Loss: 6.1839 | Val MAE: 1.992 | Val R²: 0.622\n",
      "Epoch  70/200 | Train Loss: 7.1423 | Val Loss: 5.9806 | Val MAE: 1.950 | Val R²: 0.635\n",
      "Epoch  80/200 | Train Loss: 7.0157 | Val Loss: 6.0289 | Val MAE: 1.968 | Val R²: 0.632\n",
      "Epoch  90/200 | Train Loss: 6.8479 | Val Loss: 6.0156 | Val MAE: 1.934 | Val R²: 0.633\n",
      "Epoch 100/200 | Train Loss: 6.7869 | Val Loss: 5.9536 | Val MAE: 1.940 | Val R²: 0.636\n",
      "Epoch 110/200 | Train Loss: 6.5906 | Val Loss: 5.8892 | Val MAE: 1.922 | Val R²: 0.641\n",
      "Epoch 120/200 | Train Loss: 6.4029 | Val Loss: 5.8434 | Val MAE: 1.912 | Val R²: 0.643\n",
      "Epoch 130/200 | Train Loss: 6.4290 | Val Loss: 5.8133 | Val MAE: 1.914 | Val R²: 0.645\n",
      "Epoch 140/200 | Train Loss: 6.3005 | Val Loss: 5.8873 | Val MAE: 1.916 | Val R²: 0.641\n",
      "Epoch 150/200 | Train Loss: 6.2256 | Val Loss: 5.8680 | Val MAE: 1.916 | Val R²: 0.642\n",
      "Epoch 160/200 | Train Loss: 6.1953 | Val Loss: 5.7705 | Val MAE: 1.901 | Val R²: 0.648\n",
      "Epoch 170/200 | Train Loss: 6.2105 | Val Loss: 5.7712 | Val MAE: 1.899 | Val R²: 0.648\n",
      "\n",
      "Early stopping at epoch 178\n",
      "Best validation loss at epoch 153: 5.7456\n",
      "\n",
      "Training completed!\n",
      "Best epoch: 153\n",
      "Best validation loss: 5.7456\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Training\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "# Training history\n",
    "history = {\n",
    "    'train_loss': [],\n",
    "    'val_loss': [],\n",
    "    'val_mae': [],\n",
    "    'val_r2': []\n",
    "}\n",
    "\n",
    "best_val_loss = float('inf')\n",
    "best_epoch = 0\n",
    "epochs_no_improve = 0\n",
    "\n",
    "print(\"Starting training...\\n\")\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # Train\n",
    "    train_loss = train_epoch(model, train_loader, criterion, optimizer, device)\n",
    "    \n",
    "    # Validate\n",
    "    val_loss, val_preds, val_actuals = evaluate(model, val_loader, criterion, device)\n",
    "    val_metrics = compute_metrics(val_actuals, val_preds)\n",
    "    \n",
    "    # Update scheduler\n",
    "    scheduler.step(val_loss)\n",
    "    \n",
    "    # Record history\n",
    "    history['train_loss'].append(train_loss)\n",
    "    history['val_loss'].append(val_loss)\n",
    "    history['val_mae'].append(val_metrics['mae'])\n",
    "    history['val_r2'].append(val_metrics['r2'])\n",
    "    \n",
    "    # Print progress\n",
    "    if (epoch + 1) % 10 == 0 or epoch == 0:\n",
    "        print(f\"Epoch {epoch+1:3d}/{num_epochs} | \"\n",
    "              f\"Train Loss: {train_loss:.4f} | \"\n",
    "              f\"Val Loss: {val_loss:.4f} | \"\n",
    "              f\"Val MAE: {val_metrics['mae']:.3f} | \"\n",
    "              f\"Val R²: {val_metrics['r2']:.3f}\")\n",
    "    \n",
    "    # Early stopping\n",
    "    if val_loss < best_val_loss:\n",
    "        best_val_loss = val_loss\n",
    "        best_epoch = epoch + 1\n",
    "        epochs_no_improve = 0\n",
    "        \n",
    "        # Save best model\n",
    "        torch.save({\n",
    "            'epoch': epoch,\n",
    "            'model_state_dict': model.state_dict(),\n",
    "            'optimizer_state_dict': optimizer.state_dict(),\n",
    "            'val_loss': val_loss,\n",
    "        }, '../models/neural_network_best.pth')\n",
    "    else:\n",
    "        epochs_no_improve += 1\n",
    "        \n",
    "    if epochs_no_improve >= early_stopping_patience:\n",
    "        print(f\"\\nEarly stopping at epoch {epoch + 1}\")\n",
    "        print(f\"Best validation loss at epoch {best_epoch}: {best_val_loss:.4f}\")\n",
    "        break\n",
    "\n",
    "print(f\"\\nTraining completed!\")\n",
    "print(f\"Best epoch: {best_epoch}\")\n",
    "print(f\"Best validation loss: {best_val_loss:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ef949f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Plot Training History\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
    "\n",
    "# Loss\n",
    "ax = axes[0]\n",
    "ax.plot(history['train_loss'], label='Train Loss', linewidth=2)\n",
    "ax.plot(history['val_loss'], label='Val Loss', linewidth=2)\n",
    "ax.axvline(x=best_epoch-1, color='r', linestyle='--', label=f'Best Epoch ({best_epoch})')\n",
    "ax.set_xlabel('Epoch', fontsize=12)\n",
    "ax.set_ylabel('Loss (MSE)', fontsize=12)\n",
    "ax.set_title('Training & Validation Loss', fontsize=14)\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# MAE\n",
    "ax = axes[1]\n",
    "ax.plot(history['val_mae'], color='#e74c3c', linewidth=2)\n",
    "ax.axvline(x=best_epoch-1, color='r', linestyle='--', label=f'Best Epoch')\n",
    "ax.set_xlabel('Epoch', fontsize=12)\n",
    "ax.set_ylabel('MAE', fontsize=12)\n",
    "ax.set_title('Validation MAE', fontsize=14)\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# R²\n",
    "ax = axes[2]\n",
    "ax.plot(history['val_r2'], color='#2ecc71', linewidth=2)\n",
    "ax.axvline(x=best_epoch-1, color='r', linestyle='--', label=f'Best Epoch')\n",
    "ax.set_xlabel('Epoch', fontsize=12)\n",
    "ax.set_ylabel('R²', fontsize=12)\n",
    "ax.set_title('Validation R²', fontsize=14)\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/06_deep_learning/neural_network_training.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589ad448",
   "metadata": {},
   "source": [
    "# Test set evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9abc3a72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded best model from epoch 153\n",
      "\n",
      "==================================================\n",
      "NEURAL NETWORK - TEST SET EVALUATION\n",
      "==================================================\n",
      "\n",
      "MAE:  1.893\n",
      "RMSE: 2.398\n",
      "R²:   0.646\n",
      "\n",
      "Accuracy within ±1 grade: 33.8%\n",
      "Accuracy within ±2 grades: 60.5%\n",
      "\n",
      "Exact grouped V-grade accuracy: 27.8%\n",
      "Accuracy within ±1 V-grade: 67.9%\n",
      "Accuracy within ±2 V-grades: 88.4%\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Load best model and evaluate on test set\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "# Load best model\n",
    "checkpoint = torch.load('../models/neural_network_best.pth')\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "\n",
    "print(f\"Loaded best model from epoch {checkpoint['epoch']+1}\")\n",
    "\n",
    "# Evaluate on test set\n",
    "test_loss, test_preds, test_actuals = evaluate(model, test_loader, criterion, device)\n",
    "test_metrics = compute_metrics(test_actuals, test_preds)\n",
    "\n",
    "print(\"\\n\" + \"=\" * 50)\n",
    "print(\"NEURAL NETWORK - TEST SET EVALUATION\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"\\nMAE:  {test_metrics['mae']:.3f}\")\n",
    "print(f\"RMSE: {test_metrics['rmse']:.3f}\")\n",
    "print(f\"R²:   {test_metrics['r2']:.3f}\")\n",
    "print(f\"\\nAccuracy within ±1 grade: {test_metrics['within_1']:.1f}%\")\n",
    "print(f\"Accuracy within ±2 grades: {test_metrics['within_2']:.1f}%\")\n",
    "print(f\"\\nExact grouped V-grade accuracy: {test_metrics['exact_grouped_v']:.1f}%\")\n",
    "print(f\"Accuracy within ±1 V-grade: {test_metrics['within_1_vgrade']:.1f}%\")\n",
    "print(f\"Accuracy within ±2 V-grades: {test_metrics['within_2_vgrades']:.1f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6442e20",
   "metadata": {},
   "source": [
    "# Visualization and Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5d639b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Visualize predictions\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Predicted vs Actual\n",
    "ax = axes[0]\n",
    "ax.scatter(test_actuals, test_preds, alpha=0.4, s=20)\n",
    "min_val = min(test_actuals.min(), test_preds.min())\n",
    "max_val = max(test_actuals.max(), test_preds.max())\n",
    "ax.plot([min_val, max_val], [min_val, max_val], 'r--', lw=2, label='Perfect prediction')\n",
    "ax.set_xlabel('Actual Difficulty', fontsize=12)\n",
    "ax.set_ylabel('Predicted Difficulty', fontsize=12)\n",
    "ax.set_title('Neural Network: Predicted vs Actual', fontsize=14)\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# Residuals\n",
    "ax = axes[1]\n",
    "residuals = test_actuals - test_preds\n",
    "ax.scatter(test_preds, residuals, alpha=0.4, s=20)\n",
    "ax.axhline(y=0, color='r', linestyle='--', lw=2)\n",
    "ax.set_xlabel('Predicted Difficulty', fontsize=12)\n",
    "ax.set_ylabel('Residual (Actual - Predicted)', fontsize=12)\n",
    "ax.set_title('Neural Network: Residuals', fontsize=14)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/06_deep_learning/neural_network_predictions.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# Error distribution\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.hist(residuals, bins=50, edgecolor='black', alpha=0.7)\n",
    "ax.axvline(x=0, color='r', linestyle='--', lw=2)\n",
    "ax.set_xlabel('Prediction Error', fontsize=12)\n",
    "ax.set_ylabel('Count', fontsize=12)\n",
    "ax.set_title('Neural Network: Error Distribution', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/06_deep_learning/neural_network_errors.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ffc027fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Error Analysis by Grade\n",
      "\n"
     ]
    },
    {
     "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>count</th>\n",
       "      <th>mae</th>\n",
       "      <th>bias</th>\n",
       "      <th>within_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10.0000</th>\n",
       "      <td>34</td>\n",
       "      <td>2.628</td>\n",
       "      <td>-2.627</td>\n",
       "      <td>11.765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0845</th>\n",
       "      <td>1</td>\n",
       "      <td>2.289</td>\n",
       "      <td>-2.289</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.1071</th>\n",
       "      <td>1</td>\n",
       "      <td>1.270</td>\n",
       "      <td>-1.270</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.1154</th>\n",
       "      <td>1</td>\n",
       "      <td>1.737</td>\n",
       "      <td>-1.737</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.1250</th>\n",
       "      <td>1</td>\n",
       "      <td>0.979</td>\n",
       "      <td>-0.979</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.8571</th>\n",
       "      <td>1</td>\n",
       "      <td>4.772</td>\n",
       "      <td>4.772</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.8750</th>\n",
       "      <td>1</td>\n",
       "      <td>3.216</td>\n",
       "      <td>3.216</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.9091</th>\n",
       "      <td>1</td>\n",
       "      <td>3.207</td>\n",
       "      <td>3.207</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0000</th>\n",
       "      <td>7</td>\n",
       "      <td>4.312</td>\n",
       "      <td>4.312</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0000</th>\n",
       "      <td>1</td>\n",
       "      <td>6.928</td>\n",
       "      <td>6.928</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1285 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         count    mae   bias  within_1\n",
       "actual                                \n",
       "10.0000     34  2.628 -2.627    11.765\n",
       "10.0845      1  2.289 -2.289     0.000\n",
       "10.1071      1  1.270 -1.270     0.000\n",
       "10.1154      1  1.737 -1.737     0.000\n",
       "10.1250      1  0.979 -0.979   100.000\n",
       "...        ...    ...    ...       ...\n",
       "29.8571      1  4.772  4.772     0.000\n",
       "29.8750      1  3.216  3.216     0.000\n",
       "29.9091      1  3.207  3.207     0.000\n",
       "30.0000      7  4.312  4.312     0.000\n",
       "32.0000      1  6.928  6.928     0.000\n",
       "\n",
       "[1285 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Error analysis by grade\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "df_analysis = pd.DataFrame({\n",
    "    'actual': test_actuals,\n",
    "    'predicted': test_preds,\n",
    "    'error': test_actuals - test_preds,\n",
    "    'abs_error': np.abs(test_actuals - test_preds)\n",
    "})\n",
    "\n",
    "grade_analysis = df_analysis.groupby('actual').agg(\n",
    "    count=('actual', 'count'),\n",
    "    mae=('abs_error', 'mean'),\n",
    "    bias=('error', 'mean'),\n",
    "    within_1=('error', lambda x: (np.abs(x) <= 1).mean() * 100)\n",
    ").round(3)\n",
    "\n",
    "print(\"### Error Analysis by Grade\\n\")\n",
    "display(grade_analysis)\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "ax = axes[0]\n",
    "ax.bar(grade_analysis.index, grade_analysis['count'], color='#3498db', alpha=0.8)\n",
    "ax.set_xlabel('Grade')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Test Set Distribution by Grade')\n",
    "\n",
    "ax = axes[1]\n",
    "ax.bar(grade_analysis.index, grade_analysis['mae'], color='#e74c3c', alpha=0.8)\n",
    "ax.set_xlabel('Grade')\n",
    "ax.set_ylabel('MAE')\n",
    "ax.set_title('MAE by Grade')\n",
    "\n",
    "ax = axes[2]\n",
    "colors = ['#2ecc71' if b >= 0 else '#e74c3c' for b in grade_analysis['bias']]\n",
    "ax.bar(grade_analysis.index, grade_analysis['bias'], color=colors, alpha=0.8)\n",
    "ax.set_xlabel('Grade')\n",
    "ax.set_ylabel('Bias')\n",
    "ax.set_title('Prediction Bias by Grade')\n",
    "ax.axhline(y=0, color='black', linestyle='--', lw=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/06_deep_learning/neural_network_by_grade.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf02729",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81ff678e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Hyperparameter Search\n",
      "\n",
      "Layers: [128, 64], Dropout: 0.2, LR: 0.001\n",
      "  Val MAE: 1.990, Val R²: 0.621\n",
      "\n",
      "Layers: [256, 128, 64], Dropout: 0.2, LR: 0.001\n",
      "  Val MAE: 1.883, Val R²: 0.653\n",
      "\n",
      "Layers: [512, 256, 128], Dropout: 0.2, LR: 0.001\n",
      "  Val MAE: 1.813, Val R²: 0.666\n",
      "\n",
      "Layers: [256, 128, 64], Dropout: 0.3, LR: 0.001\n",
      "  Val MAE: 1.979, Val R²: 0.631\n",
      "\n",
      "Layers: [256, 128, 64], Dropout: 0.1, LR: 0.001\n",
      "  Val MAE: 1.862, Val R²: 0.659\n",
      "\n",
      "Layers: [256, 128, 64], Dropout: 0.2, LR: 0.0005\n",
      "  Val MAE: 1.907, Val R²: 0.641\n",
      "\n",
      "\n",
      "### Architecture Comparison (sorted by Val MAE)\n",
      "\n"
     ]
    },
    {
     "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>hidden_layers</th>\n",
       "      <th>dropout_rate</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[512, 256, 128]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>1.812839</td>\n",
       "      <td>0.666386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[256, 128, 64]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>1.862239</td>\n",
       "      <td>0.659263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[256, 128, 64]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>1.883171</td>\n",
       "      <td>0.653151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[256, 128, 64]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>1.906902</td>\n",
       "      <td>0.640867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[256, 128, 64]</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>1.979096</td>\n",
       "      <td>0.631275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[128, 64]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>1.990040</td>\n",
       "      <td>0.621067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hidden_layers  dropout_rate  learning_rate   val_mae    val_r2\n",
       "2  [512, 256, 128]           0.2         0.0010  1.812839  0.666386\n",
       "4   [256, 128, 64]           0.1         0.0010  1.862239  0.659263\n",
       "1   [256, 128, 64]           0.2         0.0010  1.883171  0.653151\n",
       "5   [256, 128, 64]           0.2         0.0005  1.906902  0.640867\n",
       "3   [256, 128, 64]           0.3         0.0010  1.979096  0.631275\n",
       "0        [128, 64]           0.2         0.0010  1.990040  0.621067"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Hyperparameter tuning - try different architectures\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "def train_and_evaluate_model(hidden_layers, dropout_rate, learning_rate, verbose=False):\n",
    "    \"\"\"Train a model with given hyperparameters and return validation metrics.\"\"\"\n",
    "    \n",
    "    # Create model\n",
    "    model = ClimbGradePredictor(input_dim, hidden_layers, dropout_rate).to(device)\n",
    "    \n",
    "    # Optimizer\n",
    "    optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-5)\n",
    "    scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=10)\n",
    "    \n",
    "    best_val_loss = float('inf')\n",
    "    epochs_no_improve = 0\n",
    "    patience = 20\n",
    "    \n",
    "    for epoch in range(150):  # Max epochs\n",
    "        train_loss = train_epoch(model, train_loader, criterion, optimizer, device)\n",
    "        val_loss, val_preds, val_actuals = evaluate(model, val_loader, criterion, device)\n",
    "        scheduler.step(val_loss)\n",
    "        \n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            epochs_no_improve = 0\n",
    "        else:\n",
    "            epochs_no_improve += 1\n",
    "        \n",
    "        if epochs_no_improve >= patience:\n",
    "            break\n",
    "    \n",
    "    # Final validation metrics\n",
    "    _, val_preds, val_actuals = evaluate(model, val_loader, criterion, device)\n",
    "    val_metrics = compute_metrics(val_actuals, val_preds)\n",
    "    \n",
    "    if verbose:\n",
    "        print(f\"Layers: {hidden_layers}, Dropout: {dropout_rate}, LR: {learning_rate}\")\n",
    "        print(f\"  Val MAE: {val_metrics['mae']:.3f}, Val R²: {val_metrics['r2']:.3f}\")\n",
    "    \n",
    "    return val_metrics, model\n",
    "\n",
    "\n",
    "# Test different architectures\n",
    "architectures = [\n",
    "    {'hidden_layers': [128, 64], 'dropout_rate': 0.2, 'learning_rate': 0.001},\n",
    "    {'hidden_layers': [256, 128, 64], 'dropout_rate': 0.2, 'learning_rate': 0.001},\n",
    "    {'hidden_layers': [512, 256, 128], 'dropout_rate': 0.2, 'learning_rate': 0.001},\n",
    "    {'hidden_layers': [256, 128, 64], 'dropout_rate': 0.3, 'learning_rate': 0.001},\n",
    "    {'hidden_layers': [256, 128, 64], 'dropout_rate': 0.1, 'learning_rate': 0.001},\n",
    "    {'hidden_layers': [256, 128, 64], 'dropout_rate': 0.2, 'learning_rate': 0.0005},\n",
    "]\n",
    "\n",
    "print(\"### Hyperparameter Search\\n\")\n",
    "\n",
    "arch_results = []\n",
    "for arch in architectures:\n",
    "    metrics, _ = train_and_evaluate_model(**arch, verbose=True)\n",
    "    arch_results.append({\n",
    "        **arch,\n",
    "        'val_mae': metrics['mae'],\n",
    "        'val_r2': metrics['r2']\n",
    "    })\n",
    "    print()\n",
    "\n",
    "arch_df = pd.DataFrame(arch_results).sort_values('val_mae')\n",
    "print(\"\\n### Architecture Comparison (sorted by Val MAE)\\n\")\n",
    "display(arch_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b6119b",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3b07fe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing feature importance via permutation...\n",
      "\n",
      "### Top 20 Most Important Features (Permutation)\n",
      "\n"
     ]
    },
    {
     "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>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>mean_hand_reach</td>\n",
       "      <td>0.920045</td>\n",
       "      <td>0.042852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>angle</td>\n",
       "      <td>0.638608</td>\n",
       "      <td>0.030986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>std_pairwise_distance</td>\n",
       "      <td>0.394543</td>\n",
       "      <td>0.022913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>std_y</td>\n",
       "      <td>0.304111</td>\n",
       "      <td>0.026619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>mean_pairwise_distance</td>\n",
       "      <td>0.256120</td>\n",
       "      <td>0.015576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>std_hand_reach</td>\n",
       "      <td>0.255701</td>\n",
       "      <td>0.020797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hand_spread_y</td>\n",
       "      <td>0.254279</td>\n",
       "      <td>0.019258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>std_x</td>\n",
       "      <td>0.242035</td>\n",
       "      <td>0.021715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>max_hand_reach</td>\n",
       "      <td>0.211854</td>\n",
       "      <td>0.013801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hand_spread_x</td>\n",
       "      <td>0.164990</td>\n",
       "      <td>0.016057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>mean_hand_to_foot</td>\n",
       "      <td>0.156224</td>\n",
       "      <td>0.016653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>convex_hull_area</td>\n",
       "      <td>0.136456</td>\n",
       "      <td>0.011457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>hold_density</td>\n",
       "      <td>0.128735</td>\n",
       "      <td>0.015550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>std_hand_to_foot</td>\n",
       "      <td>0.124862</td>\n",
       "      <td>0.014537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>y_q75</td>\n",
       "      <td>0.124008</td>\n",
       "      <td>0.010944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>path_efficiency</td>\n",
       "      <td>0.116117</td>\n",
       "      <td>0.013779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>max_y</td>\n",
       "      <td>0.111150</td>\n",
       "      <td>0.014124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>angle_squared</td>\n",
       "      <td>0.103284</td>\n",
       "      <td>0.017806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>mean_y_relative_to_start</td>\n",
       "      <td>0.094577</td>\n",
       "      <td>0.014647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>angle_x_holds</td>\n",
       "      <td>0.085539</td>\n",
       "      <td>0.016516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     feature  importance       std\n",
       "24           mean_hand_reach    0.920045  0.042852\n",
       "0                      angle    0.638608  0.030986\n",
       "35     std_pairwise_distance    0.394543  0.022913\n",
       "11                     std_y    0.304111  0.026619\n",
       "34    mean_pairwise_distance    0.256120  0.015576\n",
       "26            std_hand_reach    0.255701  0.020797\n",
       "28             hand_spread_y    0.254279  0.019258\n",
       "10                     std_x    0.242035  0.021715\n",
       "25            max_hand_reach    0.211854  0.013801\n",
       "27             hand_spread_x    0.164990  0.016057\n",
       "30         mean_hand_to_foot    0.156224  0.016653\n",
       "32          convex_hull_area    0.136456  0.011457\n",
       "19              hold_density    0.128735  0.015550\n",
       "31          std_hand_to_foot    0.124862  0.014537\n",
       "44                     y_q75    0.124008  0.010944\n",
       "37           path_efficiency    0.116117  0.013779\n",
       "15                     max_y    0.111150  0.014124\n",
       "1              angle_squared    0.103284  0.017806\n",
       "41  mean_y_relative_to_start    0.094577  0.014647\n",
       "47             angle_x_holds    0.085539  0.016516"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAMWCAYAAADs4eXxAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA4VFJREFUeJzs3Xl4Tdf+x/HPSSKROYQYQ0whJGKqsUgMFylVWlyUxtCWtmqsVlFTW43iat2qogS36nZAaY1FSg1BiDFUQ4TbaIyJMSTZvz9+ch5HBgk5Qvt+Pc9+5Oy99lrfvc/Jlu9Za69tMgzDEAAAAAAAyHc2BR0AAAAAAAB/VSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFZC0g0AAAAAgJWQdAMAAAAAYCUk3QDwGAoPD5fJZFJ4eHhBh/JECwoKkslkKugwAKuaOnWqHBwcdPr06YIO5W+F6zTyy+XLl+Xh4aGRI0cWdCiwEpJuAE+MuLg4mUwmmUwmtW/fPssyERERMplMGjBgwCOPryBlJJeOjo46c+ZMlmV8fHxUuHDhh2qHPzL/X2hoqPmzmJulIM7Xr7/+quHDh6tu3bry9PRU4cKFVa1aNb399tu6fPlytvvt3r1bISEhKlKkiJydnVW/fn0tWbIkT23f7/yMHz8+H47w/v4OX7pcvHhRH3zwgfr37y9vb2/z+oxr4d1L4cKFVbFiRb388suKi4sr0LitJeOzlx/Hl/F/TmhoaL7EZm3jx4+XyWTS0qVLCzoUq8nP9/dx4uHhocGDB+vTTz/9yx0b/p9dQQcAAA/ip59+0pYtW9SsWbOCDuWxcvPmTb333nuaP39+QYfyl/bcc8/Jx8fHYt2KFSu0f/9+vfTSS5m21apV6xFHKL3wwgs6f/68nn76afXu3Vsmk0kRERGaMmWKvv/+e23fvl1eXl4W+0RERKhNmzayt7fXP//5T7m7u2vZsmXq2bOn4uLi9O677+Yphn79+qls2bKZ1gcFBT308eH/TZs2TUlJSRo+fHiW2+vWrWv+kvLy5cuKiIjQvHnz9P3332vXrl2qXLnyI474r6NTp05q2LChSpUqVdCh4C9g8ODB+uijj/T+++9r3rx5BR0O8hlJN4Anjo+Pj+Lj4/X2229rx44dBR3OY6VSpUpatGiRhg8frho1ahR0OH9Zzz33nJ577jmLdXFxcdq/f79CQ0Mfi6Ry6NCh6t27t0VCYBiGXn/9dX3++eeaMGGCPvvsM/O21NRU9e/fXyaTSVu2bFHt2rUlSePGjVOjRo00btw4denSRVWqVMl1DP3791fDhg3z+ciQ4fbt25o/f76aNGmiihUrZlmmXr16FiMLDMPQSy+9pMWLF+uDDz7QggULHmHEfy3u7u5yd3cv6DDwF1G0aFG1a9dOX3/9taZNm8Zn6y+G4eUAnjhVq1ZVr169tHPnTi1btizX+125ckXjxo1TjRo15OjoKA8PD7Vt21a//vprprI+Pj6ZeiszZDVkNWNYX0REhBYuXKi6devKycnJnHwlJSUpLCxMzZs3V+nSpWVvb6/SpUurd+/eio2NzfM5yM7777+vtLQ0jRo1Kk/7/fDDD2rZsqWKFCmiwoULy9/fX1OnTlVaWpq5TGhoqPr06SNJ6tOnj8WwVUkaMmSITCaToqOjLep+5plnZDKZ1L9/f4v1a9askclkUlhYmMX6w4cPq1u3bvLy8pKDg4MqVKigoUOH6uLFi5niznifLl++rDfffFPe3t6ys7O773DuJUuWyN7eXnXr1lViYmKeztWD+PHHHxUcHCx3d3c5OjqqVq1amjFjhsX51T3DWQ8dOqR27drJ3d1dbm5u6tChg44cOZLrNt9+++1MPXAmk0ljx46VJP3yyy8W2zZt2qTY2Fj16NHDnHBLkqurq8aOHavU1FSrJGi3bt3S9OnTVadOHTk7O8vV1VVNmzbVypUrM5X97bffNHLkSNWpU8c8ZN7X11fvvPOOrl69mulYM47x7s9qxlDhjOHXWQ11z25YcW4+bwcOHNA///lPlSpVSvb29ipfvrwGDRqkCxcuZGpn8+bNateunUqXLi0HBweVLl1aQUFBue7lWrt2rc6ePasuXbrkqnzGuXj99delO7cSZDAMw5zAu7m5ycnJSfXq1cty1Mz9rncZ18iUlBS9++67KleunBwdHVW3bl39/PPP0p3r8ZtvvqkyZcqocOHCatSokfbs2ZNlvNl9iXXvddrHx0cLFy6UJFWoUMH8nt+9//Lly9W9e3dVrlxZTk5Ocnd3V9OmTfX9999b1B0eHq4KFSpIkhYuXGjxGYqIiDCXye72ke3bt+uZZ55R0aJFzbd2jB8/XtevX8/2GM+dO6e+ffvKy8tLjo6Oatiwobmth5FR///+9z/16NFDxYoVk6urq5555hmdOHFCknTs2DF16tRJRYsWlaurq7p06ZLp2vig16f4+Hj169dPZcqUkb29vcqWLat+/fplOQfB3Z+d9957T5UrV1ahQoU0fvz4fH1/7z2eEydO6IUXXjDfVtOqVSvt378/y+NJTEzUiBEjVLVqVRUuXFhFixZVw4YNNW3atExl83I9kKSuXbvq+vXr+uabb7LcjicXPd0AnkgTJ07U0qVL9e6776pjx46ytbXNsfzFixfVrFkzHT58WE2bNlWbNm2UlJSkH374QcHBwfr2228z9Vw+iI8//libN2/Ws88+q9atW8vO7v8vszExMXrvvfcUHBysTp06ydnZWUePHtWSJUv0008/ae/evSpfvvxDtx8UFKR27dpp1apV+vXXX/X000/fd593331XkydPVtmyZfX888/Lzc1NW7Zs0VtvvaXIyEh9++230p3e3cuXL+uHH35Qx44dMw2ZDg4O1ieffKLNmzebt6WlpZm/1Ni8ebNF+Yw/JoODg83rtm/frn/84x9KSUnRCy+8IB8fH+3cuVMzZszQTz/9pB07dsjT09OinpSUFLVo0UJXrlxRhw4dZG9vrxIlSmR7vJ988omGDh2q4OBgrVixQq6uruZtGV8gGIZx3/OWW5988omGDBmiokWLqkePHnJ2dtaqVas0dOhQbd26Vd99912mL3FOnDihJk2aqH79+nrttdd0/PhxLV++XL/++qu2b98uPz+/B46nUKFCkmT+bGbIeD/+8Y9/ZNonY929ifrDSklJUdu2bRUREaHatWurX79+un37tn766Sd17NhRM2fO1BtvvGEuv2zZMn355ZcKDg5WUFCQ0tPTtXPnToWFhemXX37Rli1bzMc3btw4hYeH69SpUxo3bpy5jocd6p/T523lypXq2rWrbG1t9eyzz8rb21tHjhzRv//9b61bt06RkZEqUqSIdOcWmQ4dOsjDw0MdO3ZUqVKldO7cOUVHR+urr77K9CVVVjZu3ChJDz2awDAMvfjii1qyZIl8fX3Vo0cP2dvba8OGDerXr5+OHDmiqVOnZtovu+tdhm7duungwYN69tlndePGDX311Vdq3769tm/frldffVU3b97UCy+8oHPnzum///2v2rRpo5MnT8rNze2BjmPIkCEKDw/X/v37NXjwYHl4eEh3kvEMo0aNkr29vZ5++mnzOV+5cqVeeOEFffrppxo0aJB053MyePBgffLJJwoMDLT4/yG7L2QzfP/99/rnP/8pe3t78xeIP//8syZMmKD169dr8+bNcnBwsNjn8uXL5i88evbsqcTERPM5iYqKkr+//wOdkwyXLl3S008/rZIlS+qll17Sb7/9ph9//FFHjx7VypUr1bRpU9WpU0d9+/ZVVFSUvvvuO12+fFkbNmzIVFderk/Hjx/X008/rcTERHXo0EE1atTQ4cOHNX/+fP3444/atm1blrc4dO7cWfv371ebNm1UtGhRVaxYMV/f37vFxcWpQYMGql69uvr27avY2Fjz3wYxMTEW/58cP35cwcHB+t///qenn35azz33nK5du6ZDhw7pgw8+sLjNIy/XgwyNGjWS7nwJ+vLLLz/AO43HlgEAT4iTJ08akow2bdoYhmEYw4YNMyQZX3zxhbnM5s2bDUnGq6++arFvjx49DEnG/PnzLdafPXvW8Pb2NooXL27cuHHDvL58+fJG+fLls4yjefPmxr2Xz3HjxhmSDGdnZ+PAgQOZ9rl8+bJx4cKFTOs3bdpk2NjYGP3797dYv2DBAkOSsWDBgvucFcuYEhISjP379xs2NjZG48aNLcqUL1/ecHBwsFi3fv16Q5LRrl0749q1a+b16enpxoABAwxJxnfffZeruC5dumTY2NgYHTp0MK+LjIw0JBktW7Y0JBmnTp0yb3vqqacMV1dXIzU11TAMw0hLSzOqVKliSDLWrl1rUfeoUaMMSUa/fv0yHZMk4x//+Idx/fr1bM/LvfV06dLFSElJyVReUqb3NrdeeuklQ5KxefNm87rY2FjDzs7O8PLyMuLj483rU1JSzLEtXrzYvD7jMy7JGDNmjEX9CxcuNCQZLVq0eKD4MoSFhRmSjLfeesti/QsvvGBIMvbs2ZPlfsWKFTOKFy+eqzYyzkW/fv2McePGWSyTJ082l3v33XcNScb48eON9PR08/rk5GSjXr16hr29vfG///3PvP7MmTNZvm8TJkwwJBn/+c9/LNZn9buaIeNaMW7cuEzbMt6Hl156yWJ9Tp+38+fPG25ubkbZsmUtPueGYRhLliwxJBlvvPGGeV3nzp0NScb+/fsztX/+/PksY77XU089ZdjY2GR5TrK7FqanpxsvvviiIckIDQ01DMMw5syZY36/bt++bS6bkpJidOjQIdPn4n7Xu4zz3qRJE+Pq1avm9UuXLjUkGR4eHkaXLl0s2sr4XE6fPt2iLklG8+bNszz+rK7TGZ+9kydPZrlPbGxspnVXrlwxAgICDHd3d4vrYHafgwxZXQ+Tk5MNDw8Pw8HBweK9TU9PN/8/NGnSpEzHKMl47bXXjLS0NPP6efPmZfkeZifjffn666+zrH/o0KEW6zOu8R4eHsaMGTMsYg0JCTEkGXv37s10PvJyfWrRokWm/6cNwzC++OIL8/8Nd8v47NSqVSvL/zOt8f5KMj766COLfcaMGWNIsrheGYZh1K9f35BkzJkzJ1M7p0+fNv+c1+vB3YoWLWqUK1cuy214cpF0A3hi3Jt0X7hwwXB3dzdKly5t/o80qz80z507Z9ja2mb6zz3Dp59+akgyVq1aZV73oEn3vX/U5EZAQIDh4+Njse5hkm7DMMx/VC9fvtzimO5Nup999llDkkVCmOHy5cuGyWQynn/++VzHVbt2bcPd3d2cSH/00UeGyWQytm7darFfUlKSYWtra4SEhJj33bJli/kLgHtdvXrV8PT0NBwdHS0SjIwkKKvE5e7zkpqaavTr18+QZAwcONDiD9u7xcTEGDExMVluu5+sku6JEycakoywsLBM5Xfs2JHpj86Mz3iRIkUskhXjzh/C/v7+2b5fubFv3z7DycnJ8PLyMs6dO2exrXXr1oYk4/jx41nuW7FiRcPe3j5X7WSci6wWd3d3w7jzJUuRIkWMypUrWyTcGVauXGlIMmbOnHnf9i5cuGCRRGawVtKd1edt+vTpmb5EuVudOnWMYsWKmV9nJN2//fbbfY8vO6VKlTKKFi2a5baM46tbt675C48hQ4YYgYGBhiSjaNGi5ve6Zs2ahrOzs8UXjxkOHDhgSDKGDx9uXne/613GeY+IiLBYn5qaahQqVCjTF3CGYRjx8fFZnvP8TrqzM23atEwxP0jSvWjRIvN15l7x8fGGnZ2dUalSpUzH6OzsbFy5csVi/e3btw07OzujTp06uTqGnJJuFxeXTNeUjGtupUqVMv0OZhzH3ceW1+tTxntavXr1TPWnp6cbfn5+ma5nGZ+dH374IctjtMb7W6FChUz/J2Rs69y5s3ndrl27DElGs2bN7tteXq8Hd6tWrZpha2ub5XURTy6GlwN4YhUtWlRvv/223n33Xc2YMSPbmZV3796ttLQ03bx5M8v7N48fPy5JOnr0aLaPIsut+vXrZ7stIiJCM2bMUGRkpM6fP6/U1FTzNnt7+4dq917vv/++vv32W7377rvq0KFDtsPvd+7cKWdnZ3355ZdZbnd0dNTRo0dz3W5wcLD27dunvXv36qmnntLmzZsVGBhoHta4efNmhYaGasuWLUpLS7MYWr5v3z4pm5mtnZ2dVa9ePa1bt06//fabxVDLwoULKyAgIMe4OnfurJUrV2rcuHE5Pq6qWrVquT7W3MjpmBo2bChHR8dM98BLUu3ateXs7GyxzmQy6emnn9ahQ4e0f/9+i8dD5cbJkyfVvn17paWlaenSpSpWrFiejyevduzYke3Q52PHjunSpUsqXbq0JkyYkGn7uXPnpDu/lxkMw9CCBQsUHh6uQ4cOKSkpSenp6ebtf/zxh1WO427Zfd527txp/vf333/PtP3mzZs6f/68zp8/r2LFiqlr165atmyZGjRooO7du6tFixZq2rRpphnlc3LhwoX7fg6ioqIUFRUl3bnOlClTRi+//LJGjx6t8uXL6/r16zp48KBKly6tjz76KNP+t2/flu55HzLkdL3Tnc/x3WxtbeXl5aVr166pXLlyFtsy5h/43//+l2OdDysxMVEfffSR1qxZo1OnTunGjRsW2x/2M5TT77y3t7cqVaqkY8eO6cqVKxa3tlSpUkUuLi4W5e3s7FSiRIkcH/GXW1WqVMl0Tck45zVr1sx0i0tO70dur08Z56J58+aZ6jeZTGrWrJliYmKyvJ7d77OVnQd5fwMDA2VjYznNVcZTF+4+97t27ZKyuQXnXnm9HtytaNGiSktL0+XLlzMNP8eTi6QbwBNtyJAh+ve//60pU6bo1VdfzbJMxgRc27Zt07Zt27Kt69q1aw8dT3b3En/77bfq1q2bXFxc1KZNG/n4+MjJyck8Cc+pU6ceuu27lS9fXgMHDtSMGTO0YMGCbO8PvXjxolJTU7NMejLk5bwEBwdr+vTp2rx5s2rXrq1t27aZ70sLCgoy39ed8e/dSXdycrKUwzksWbKkdGdSurt5eXnd91nMW7dulaOjo9q1a5frY8kP9zsmLy+vLP+ozS7xyqjn3nNwP6dOnVJwcLDOnTun77//3uK8Z8iYKTe7upOTk/N1Nt2M38vDhw/r8OHD2Za7+/P35ptv6t///re8vb317LPPqlSpUuZ7YydMmKCUlJR8iy872X3eMo7n7hnhs3Lt2jUVK1ZM3bp1U6FChTRjxgx98cUXmjVrlnlSqOnTp+fq3nNHR8dMScW9Xn31Vc2ePTvb7ZcuXZJhGPrf//6X5+tATnMnSMry3mw7O7ssP0cZ94NnJPnWcPHiRT311FOKj49XkyZN1KpVK3l4eMjW1lbR0dH64YcfHvozlJvr2LFjx5ScnGyRdGf3u2VnZ5dpwsUHkd17cb9tWb0fub0+Peg1Pad9cvKg729On8e7z31GAl6mTJlcxaI8XA/ulvE77eTkdN928OQg6QbwRHN0dNT48eP1yiuv6MMPP1SHDh0ylcn4g2L48OFZTgaUFRsbG926dSvLbTklPNklf+PHj1fhwoUVFRWV6ZFLS5cuzVVMeTVmzBjNnz9f48ePV8+ePbMs4+bmJpPJpPPnz+dLm82aNZOtra02b96spk2b6urVq+YELzg4WEuXLlVsbKwiIiLk7u5u0ROW8T79+eefWdadsf7ePxDvl3DrzoRTrVq10j/+8Q+tW7fukT3G6u5jymqivMTExCz/4M1uRvWMc5CX5DcuLk7BwcH6448/9O2332Y7miPjc3n8+HHVrVvXYtulS5d0/vx5NW7cONft3k/GcT///PP67rvv7ls+MTFRn332mWrWrKkdO3ZY/EF69uzZHBPGrGT0bN094iTDg/yOZxzPwYMHcz3pVefOndW5c2clJydr+/bt5oni2rRpo2PHjpknispO8eLFdebMmVy1lZ2MuOvWrZvl7OE5yc3v3sMymUxZvke68z7l5Xfhyy+/VHx8vN5//32NHj3aYttHH32kH3744aHjfdDr2JMkt9enhzkXD/LZsvb7m/H7mJvRGA9yPchw8eJFubq6ZppsD082HhkG4InXt29fVatWTZ999pni4+MzbX/qqadkMpny9EzvIkWKKDExMdMfe9euXTMPR8+L2NhY+fn5ZUq4//jjj3x9ZNjdPD09NXLkSP3vf//Tp59+mmWZBg0a6MKFC7k+poxh6tn1vLi5ual27dr69ddftX79etna2qpZs2aSpBYtWkh3ZqCOjo5Ws2bNLIb0ZSTgWT0i5/r169qzZ48cHR1VtWrVXMV6t9q1a2vjxo2ys7NTmzZtzEP/rC2nY9q1a5du3LiRZY/mvn37suxZzBipERgYmKv24+LizI8K+u9//6uOHTtmW7Z58+aSpPXr12falrEuo0x+8PPzk5ubm/bs2ZOr3s0TJ07IMAy1atUqUw/Q1q1bs9wnp89rxrDNrP6AzhgWmxcNGjSQ7gypzys3Nze1bdtWc+bMUWhoqBITExUZGXnf/QICAnTz5s2HSrxdXV3l5+enmJiYfBnGnN+KFCmS5XsUFxeXZbw5vecZ19pnn30207asPkP3u95lJaff+f/973+KjY1VxYoVLXq5nzS5vT5lXNu2bNmS6YkQhmGYz3leniiQn+9vXmUMec/qGnmvB70eXL9+XWfOnLnvLVN48pB0A3ji2dra6sMPP1RKSoomTpyYaXvJkiXVtWtXbd++XR9//HGWj4OKjIy0eH5qvXr1dPv2bX311VfmdYZhaNSoUQ80DL18+fL6/fffLb7xv3nzpgYOHJhtL05+GDp0qEqVKqWPPvoo03OMdWe4ru58cZHVc0PPnj2rmJgY8+uiRYtKUo5/5AcHB+vq1av67LPPVKdOHXOvR+XKlVW2bFl9/PHHSk9PzzTEuUmTJqpUqZLWrFljfpZvhsmTJ+v8+fPq3r37A9//XqtWLW3atEmFChXSP/7xD23fvj1TmaNHj+bpHvb76dGjh+zs7DR9+nSLewlv376td955R7rz/PN7Xbp0KdP9tYsWLdLBgwfVokWLXN3PfXfCvXTpUnXq1CnH8i1btlTFihW1ZMkSi/vMr1y5okmTJsnOzi7LWB+UnZ2dBg4cqFOnTmnEiBFZJt6HDh0y96pljBTYvn27xX3cZ86cMZ/Le+X0ea1atapcXFy0cuVKi2fA//nnn3r//ffzfDx9+vSRq6urRo8eneVw+evXr1t82bNx40bdvHkzU7mM43V0dLxvmxlfgmTca/qg3nzzTV2/fl0vv/xylte3kydPKi4u7qHaeFD16tVTXFycRRJ769YtDRs2LMvyOb3nGZ+hjMcYZliyZIlWr16dqXyRIkVkMpny9KVGx44d5e7urgULFlh8DjL+/7h9+3a+/h4VhNxen8qVK6fg4GDzI8LuNn/+fB0+fDjX17MM+fn+5tVTTz2l+vXra8uWLZo7d26m7Xd/OZTX60GGPXv2KC0tLV+/4MTjgeHlAP4SOnXqpEaNGmX7rfKsWbN07NgxjRw5UosXL1ajRo3k7u6u06dPKyoqSsePH1dCQoK5B+2NN94w3wu9YcMGFS9eXFu3btXly5cVGBio/fv35ym+QYMGadCgQapdu7ZeeOEFpaamasOGDTIM44Hqyy0nJyeNGzdOAwYMkKRMw9Xatm2rsWPHatKkSapcubLatm2r8uXL68KFC/r999+1detWvf/+++bnrjZq1EiOjo6aMWOGkpOTVbx4cUmySHqCg4P18ccf69y5c+rTp49Fe8HBwVq8eLH557vZ2NgoPDxcbdq0UUhIiLp06aLy5csrMjJSmzZtUqVKlbKc6CkvAgMDtWnTJrVs2VJt27bV2rVrLYZMZxxnfj2nu1KlSgoLC9Pw4cNVs2ZNde3aVc7Ozubn43bs2FEvvvhipv2aNm2qTz/9VDt37tRTTz2l3377TcuXL5e7u7v+/e9/56rtoKAgnTp1Sg0bNtSBAwd04MCBTGXunlTOzs5O8+bNU5s2bdS0aVN1795dbm5uWrZsmU6ePKn3339fvr6+D3lGLE2YMEF79+7Vp59+qp9++knNmzdX8eLF9b///U8HDx7U/v37tWPHDnl5ealUqVJ6/vnn9f3336tevXpq2bKl/vzzT/34449q0aKFTpw4kan+Fi1a6LvvvlOXLl0UEhJingTtmWeekb29vd544w199NFHqlOnjjp27KgrV65o1apVat68eZ5HoBQvXlxff/21unTposDAQLVt21bVqlXTzZs3derUKf3yyy9q3Lix1q5dK9253SU+Pl5BQUHy8fGRyWTSr7/+ql27dqlx48Zq0qTJfdvs2LGjhg4dqp9//lmdO3fOU7x3e/XVV7Vz504tXLhQ27ZtU6tWrVS6dGn9+eefOnr0qCIjI7VkyZL7Pp/aGoYOHar169frmWeeUffu3eXk5KQNGzbIw8PDPNnX3Vq0aKGpU6fq1VdfVZcuXeTs7Kxy5cqpR48e6tWrl8LCwjRo0CBt3rxZ5cuX14EDB8znb9myZRZ1ubi46KmnntKWLVvUp08fValSRTY2NurRo0emieAyuLm5ae7cuerevbsaNGigbt26qXjx4tq4caP27Nmj+vXr66233rLa+XoU8nJ9+vzzz/X000/r5Zdf1qpVq1S9enUdOXJEK1euVPHixfX555/nqe38fH8fxH/+8x8FBQXplVdeMf8tcfPmTR0+fFj79u0zf3md1+tBhoznot/9XHj8RRT09OkAkFv3PjLsXhmPP8nuuabXr183pkyZYtStW9dwdnY2HB0djQoVKhjPPfecsWjRIotnxhqGYWzcuNFo0KCB4eDgYHh6ehq9evUyzp49m+Mjw+5+XNTd0tPTjdmzZxs1atQwChcubJQsWdLo16+f8eeff2ZZ38M+Muxut2/fNqpWrWpIyvTIsAwbNmwwOnToYBQvXtwoVKiQUbJkSaNRo0bGpEmTMj2e6qeffjKeeuopw9HRMcvnWl+5csWws7MzJBlr1qyx2DZ//nzzI2eye2zXgQMHjBdeeMEoVqyYUahQIaN8+fLGm2++mekRV8Z9Hu1m5PDIqP379xvFihUzXFxcjK1bt5rXZ3U8uZXVI8My/PDDD0bz5s0NV1dXw8HBwQgICDCmTZuW6TN39yOKDhw4YLRt29ZwdXU1XFxcjGeeecY4dOhQruPJ7pFddy9ZiYyMNNq2bWu4u7sbjo6ORr169TI9/zq352LHjh33LZuammp88cUXRpMmTQw3NzfDwcHBKFeunNG2bVvj888/t3g00ZUrV4zhw4cbPj4+hoODg1GlShVj0qRJxq1bt7J8tNTt27eNkSNHGuXKlTN/Ju9+/FNqaqrx3nvvGd7e3oa9vb3h6+trfPLJJ8aJEyeyfWRYTp83wzCMo0ePGv369TPKly9v2NvbG0WKFDECAgKMN99809i1a5e53NKlS42uXbsalSpVMpycnAx3d3ejVq1axpQpUzI9jiknbdq0MTw9PY1bt25ZrM/uOd05+e9//2u0atXKKFKkiFGoUCGjTJkyRlBQkDFt2jSL37/7Xe9yelRbTucwu8eD/fe//zUCAgIMe3t7o2TJksagQYOMK1euZFvXlClTjCpVqpgfT3Z3ndHR0cY//vEPo0iRIoarq6vRvHlz4+eff872mnvs2DEjJCTE8PDwMEwmk8Vx53Sd3rJli9GuXTvDw8PD/NkaO3Zslu9tXh+Llp2cHhmWVf05PRItq0fqPej1KS4uzujTp49RqlQpw87OzihVqpTRp08fIy4uLlPZnD47GfLr/b3fI+GyO29nz541Bg8ebH6MYtGiRY0GDRpkesa8kYfrQYaKFSsatWrVyvH48WQyGfn1dT4AAHgocXFxqlChgl566SWFh4cXdDh4Aqxfv15t2rTR0qVL1a1bt4IOB39hXJ+sK2MU1sKFC9W7d++CDgf5jHu6AQAAnlD/+Mc/1Lp1a02aNMniXncAT5aJEyeqVq1aWd5yhCcfSTcAAMATbObMmXrhhReUkJBQ0KEAeACXL19WUFCQ5s2bZ/FUD/x1MJEaAADAE6xq1aoWk+IBeLJ4eHjwO/wXxz3dAAAAAABYCeMXAAAAAACwEpJuAAAAAACshHu68beXnp6uP/74Q66urjKZTAUdDgAAAIAngGEYunLlikqXLp3jJHgk3fjb++OPP+Tt7V3QYQAAAAB4Ap0+fVply5bNdjtJN/72XF1dpTu/LG5ubgUdDgAAAIAnQHJysry9vc35RHZIuvG3lzGk3M3NjaQbAAAAQJ7c7xZVJlIDAAAAAMBKSLoBAAAAALASkm4AAAAAAKyEpBsAAAAAACsh6QYAAAAAwEpIugEAAAAAsBKSbgAAAAAArISkGwAAAAAAKyHpBgAAAADASki6AQAAAACwEpJuAAAAAACshKQbAAAAAAArIekGAAAAAMBKSLoBAAAAALASkm4AAAAAAKyEpBsAAAAAACsh6QYAAAAAwEpIugEAAAAAsBKSbgAAAAAArISkGwAAAAAAKyHpBgAAAADASki6AQAAAACwEpJuAAAAAACshKQbAAAAAAArIekGAAAAAMBKSLoBAAAAALASkm4AAAAAAKyEpBsAAAAAACsh6QYAAAAAwErsCjoA4HHxyg9nZO/kWtBhAAAAAMjCoue9CzqEB0JPNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN2PufDwcHl4eBRI2xERETKZTLp8+XKBtJ8bcXFxMplMio6OLuhQAAAAACATkm4AAAAAAKyEpBtWkZaWpvT09IIOAwAAAAAK1N8q6Q4KCtKgQYM0ZMgQFSlSRCVKlNCcOXN07do19enTR66urqpUqZLWrFlj3ufIkSMKCQmRi4uLSpQooV69eun8+fPm7WvXrtXTTz8tDw8PeXp6qn379oqNjTVvzxj+vGzZMgUHB8vJyUmBgYHasWNHnmJft26d/Pz85OLiorZt2yohIcG8bffu3WrdurWKFSsmd3d3NW/eXHv37rXY32Qyad68eerUqZOcnJxUpUoVrVy50qLM6tWr5evrK0dHRwUHBysuLi7X8WUMg//xxx9VvXp1OTg46NSpU7p165ZGjhypMmXKyNnZWQ0aNFBERIR5vwsXLqh79+4qW7asnJycFBAQoK+//tqi7vT0dIWFhaly5cpycHBQuXLl9MEHH1iUOXHixEOdXwAAAACwhr9V0i1JCxcuVLFixbRr1y4NGjRIAwcOVJcuXdS4cWPt3btXbdq0Ua9evXT9+nUlJCSoefPmqlWrlvbs2aO1a9fqzz//VNeuXc31Xbt2TcOGDdPu3bu1ceNG2djYqFOnTpl6eUePHq0RI0YoOjpavr6+6t69u1JTU3MV8/Xr1zV16lQtXrxYW7ZsUXx8vEaMGGHefuXKFb300kvaunWrdu7cqSpVqigkJERXrlyxqGfChAnq2rWrDhw4oJCQEPXs2VMXL16UJJ0+fVqdO3dWSEiIoqOj1b9/f73zzjt5OrfXr1/X5MmTNW/ePB0+fFheXl7q06ePtm3bpqVLl+rAgQPq0qWL2rZtq+PHj0uSbt68qbp16+rHH3/UoUOH9Morr6hXr16KjIw01ztq1CiFhYVp7NixOnLkiJYsWaISJUo88PlNSUlRcnKyxQIAAAAA1mAyDMMo6CAelaCgIKWlpWnr1q3SnSHQ7u7u6ty5sxYtWiRJOnv2rEqVKqUdO3Zo9erVioyM1Lp168x1nDlzRt7e3jp27Jh8fX0ztXHu3Dl5eXnp4MGD8vf3V1xcnCpUqKB58+apX79+0p3e8xo1aigmJkbVqlXLMebw8HD16dNHv//+uypVqiRJmjVrliZOnKizZ89muU9aWpqKFCmiJUuWqH379tKdnu4xY8Zo0qRJ0p0vC1xdXbV69Wq1bdtW7777rlasWKHDhw/LZDJJkt555x2FhYXp0qVL953MLSPO6OhoBQYGSpJiY2NVpUoVnTlzRqVLlzaXbdWqlerXr68PP/wwy7qeeeYZ+fn5aerUqbpy5YqKFy+uf//73+rfv3+msg9yfsePH68JEyZkWt9t0WHZO7nmeJwAAAAACsai570LOgQLycnJcnd3V1JSktzc3LIt97fr6a5Zs6b5Z1tbW3l6eiogIMC8LqMHNTExUVFRUdq8ebNcXFzMS0YSlzGEPDY2Vj169FDFihXl5uamChUqSJLi4+OzbbdUqVLmNnLDycnJnHBn7H/3vomJiRowYIB8fX3l7u4ud3d3Xb16NccYnJ2d5erqaq4nJiZGDRs2NCfcktSoUaNcxZfB3t7eoo29e/fKMAz5+vpanMNffvnFfP7S0tL0wQcfqGbNmvL09JSLi4vWr19vjj0mJkYpKSlq2bJljm3n5fyOGjVKSUlJ5uX06dN5Ok4AAAAAyC27gg7gUStUqJDFa5PJZLEuI+lMT09Xenq6OnTooLCwsEz1ZCR2HTp0kLe3t+bOnavSpUsrPT1d/v7+unXrVrbt3t3Gg8Z89wCF0NBQnTt3TjNmzFD58uXl4OCgRo0a5RhDRj0ZMeTHgAdHR0eLpD09PV22traKioqSra2tRVkXFxdJ0rRp0/Svf/1LM2bMUEBAgJydnTVkyBBz7I6OjrlqOy/n18HBQQ4ODg9whAAAAACQN3+7pDsv6tSpo++//14+Pj6ys8t8qi5cuKCYmBh98cUXatq0qSTp119/feRxbt26VbNmzVJISIh05/7suyd7y43q1atrxYoVFut27tz5UHHVrl1baWlpSkxMNJ+fe23dulUdO3bUiy++KN1JlI8fPy4/Pz9JUpUqVeTo6KiNGzdmObwcAAAAAB5nf7vh5Xnx+uuv6+LFi+revbt27dqlEydOaP369erbt6/5vmlPT0/NmTNHv//+uzZt2qRhw4Y98jgrV66sxYsXKyYmRpGRkerZs2eue4gzDBgwQLGxsRo2bJiOHTumJUuWKDw8/KHi8vX1Vc+ePdW7d28tW7ZMJ0+e1O7duxUWFqbVq1ebY9+wYYO2b9+umJgYvfrqqxb3qhcuXFhvv/22Ro4cqUWLFik2NlY7d+7Ul19++VCxAQAAAMCjQNKdg9KlS2vbtm1KS0tTmzZt5O/vr8GDB8vd3V02NjaysbHR0qVLFRUVJX9/fw0dOlQff/zxI49z/vz5unTpkmrXrq1evXrpzTfflJeXV57qKFeunL7//nutWrVKgYGBmj17drYTneXFggUL1Lt3bw0fPlxVq1bVs88+q8jISHl7//8kCGPHjlWdOnXUpk0bBQUFqWTJknruuecs6hg7dqyGDx+u9957T35+furWrVuu74cHAAAAgIL0t5q9HMhKxqyDzF4OAAAAPL6YvRwAAAAAAFgg6S5g7dq1s3ic1t1Lfgzvzi9PSpwAAAAA8Dhh9vICNm/ePN24cSPLbUWLFn3k8WTnSYkTAAAAAB4nJN0FrEyZMgUdQq48KXECAAAAwOOE4eUAAAAAAFgJSTcAAAAAAFbC8HLgjjkdy+Y41T8AAAAA5BU93QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWwiPDgDte+eGM7J1cCzoMAADwmFj0vHdBhwDgL4CebgAAAAAArISkGwAAAAAAKyHpBgAAAADASki6AQAAAACwEpJuAAAAAACshKQbAAAAAAArIenGE2f8+PGqVatWQYcBAAAAAPdF0g0AAAAAgJWQdAMAAAAAYCUk3chXa9eu1dNPPy0PDw95enqqffv2io2NlSTFxcXJZDJp2bJlCg4OlpOTkwIDA7Vjxw6LOubOnStvb285OTmpU6dOmj59ujw8PHJsd8GCBfLz81PhwoVVrVo1zZo1y6rHCQAAAAC5QdKNfHXt2jUNGzZMu3fv1saNG2VjY6NOnTopPT3dXGb06NEaMWKEoqOj5evrq+7duys1NVWStG3bNg0YMECDBw9WdHS0WrdurQ8++CDHNufOnavRo0frgw8+UExMjD788EONHTtWCxcuzLJ8SkqKkpOTLRYAAAAAsAa7gg4Afy3PP/+8xesvv/xSXl5eOnLkiFxcXCRJI0aM0DPPPCNJmjBhgmrUqKHff/9d1apV08yZM9WuXTuNGDFCkuTr66vt27frxx9/zLbNSZMmadq0aercubMkqUKFCjpy5Ii++OILvfTSS5nKT548WRMmTMjX4wYAAACArNDTjXwVGxurHj16qGLFinJzc1OFChUkSfHx8eYyNWvWNP9cqlQpSVJiYqIk6dixY6pfv75Fnfe+vtu5c+d0+vRp9evXTy4uLubl/fffNw9rv9eoUaOUlJRkXk6fPv2QRw0AAAAAWaOnG/mqQ4cO8vb21ty5c1W6dGmlp6fL399ft27dMpcpVKiQ+WeTySRJ5uHnhmGY12UwDCPb9jL2mzt3rho0aGCxzdbWNst9HBwc5ODg8EDHBwAAAAB5QdKNfHPhwgXFxMToiy++UNOmTSVJv/76a57qqFatmnbt2mWxbs+ePdmWL1GihMqUKaMTJ06oZ8+eDxg5AAAAAFgHSTfyTZEiReTp6ak5c+aoVKlSio+P1zvvvJOnOgYNGqRmzZpp+vTp6tChgzZt2qQ1a9Zk6v2+2/jx4/Xmm2/Kzc1N7dq1U0pKivbs2aNLly5p2LBh+XBkAAAAAPBguKcb+cbGxkZLly5VVFSU/P39NXToUH388cd5qqNJkyaaPXu2pk+frsDAQK1du1ZDhw5V4cKFs92nf//+mjdvnsLDwxUQEKDmzZsrPDzcfD85AAAAABQUk5HTDbPAY+Dll1/W0aNHtXXrVqvUn5ycLHd3d3VbdFj2Tq5WaQMAADx5Fj3vXdAhAHiMZeQRSUlJcnNzy7Ycw8vx2Jk6dapat24tZ2dnrVmzRgsXLtSsWbMKOiwAAAAAyDOSbjx2du3apSlTpujKlSuqWLGiPv30U/Xv37+gwwIAAACAPCPpxmPnm2++KegQAAAAACBfMJEaAAAAAABWQtINAAAAAICVkHQDAAAAAGAl3NMN3DGnY9kcp/oHAAAAgLyipxsAAAAAACsh6QYAAAAAwEpIugEAAAAAsBKSbgAAAAAArISkGwAAAAAAKyHpBgAAAADASnhkGHDHKz+ckb2Ta0GHAQCP3KLnvQs6BAAA/rLo6QYAAAAAwEpIugEAAAAAsBKSbgAAAAAArISkGwAAAAAAKyHpBgAAAADASki6AQAAAACwkr9M0h0XFyeTyaTo6OhH1mZQUJCGDBny2NeZWxERETKZTLp8+bIkKTw8XB4eHgUSCwAAAAD8FTz2z+kODQ3V5cuXtWLFioIOJZNly5apUKFCj32dD6pbt24KCQnJVdnw8HANGTLEnLADAAAAAJ6ApPtxVrRo0Ry337p1S/b29vla56Pk6OgoR0fHgg4DAAAAAJ5Yj83w8u+++04BAQFydHSUp6enWrVqpbfeeksLFy7UDz/8IJPJJJPJpIiICEnSrl27VLt2bRUuXFj16tXTvn37ct1WxjDqn376SYGBgSpcuLAaNGiggwcPmstcuHBB3bt3V9myZeXk5KSAgAB9/fXXFvXcOxTcx8dH77//vkJDQ+Xu7q6XX35Zzz//vAYNGmQuM2TIEJlMJh0+fFiSlJqaKldXV61bty7LOmfNmqUqVaqocOHCKlGihF544QXzNsMwNGXKFFWsWFGOjo4KDAzUd999l+vzsHr1avn6+srR0VHBwcGKi4uz2H7v8PL9+/crODhYrq6ucnNzU926dbVnzx5FRESoT58+SkpKMr9P48ePlyT95z//Ub169eTq6qqSJUuqR48eSkxMzPRebNy4UfXq1ZOTk5MaN26sY8eOWcSycuVK1atXT4ULF1axYsXUuXNn87Zbt25p5MiRKlOmjJydndWgQQPz5wQAAAAACtJjkXQnJCSoe/fu6tu3r2JiYhQREaHOnTtr3Lhx6tq1q9q2bauEhAQlJCSocePGunbtmtq3b6+qVasqKipK48eP14gRI/Lc7ltvvaWpU6dq9+7d8vLy0rPPPqvbt29Lkm7evKm6devqxx9/1KFDh/TKK6+oV69eioyMzLHOjz/+WP7+/oqKitLYsWMVFBRkkQD+8ssvKlasmH755RdJ0u7du3Xz5k01adIkU1179uzRm2++qYkTJ+rYsWNau3atmjVrZt4+ZswYLViwQJ9//rkOHz6soUOH6sUXXzTXnZPTp0+rc+fOCgkJUXR0tPr376933nknx3169uypsmXLavfu3YqKitI777yjQoUKqXHjxpoxY4bc3NzM71PG+3Hr1i1NmjRJ+/fv14oVK3Ty5EmFhoZmqnv06NGaNm2a9uzZIzs7O/Xt29e87aefflLnzp31zDPPaN++feYEPUOfPn20bds2LV26VAcOHFCXLl3Utm1bHT9+/L7nAQAAAACs6bEYXp6QkKDU1FR17txZ5cuXlyQFBARId4Y4p6SkqGTJkuby4eHhSktL0/z58+Xk5KQaNWrozJkzGjhwYJ7aHTdunFq3bi1JWrhwocqWLavly5era9euKlOmjEUiP2jQIK1du1bffvutGjRokG2dLVq0sNgvKChIgwcP1vnz52Vra6vDhw9r3LhxioiI0GuvvaaIiAjVrVtXLi4umeqKj4+Xs7Oz2rdvL1dXV5UvX161a9eWJF27dk3Tp0/Xpk2b1KhRI0lSxYoV9euvv+qLL75Q8+bNczz2zz//XBUrVtS//vUvmUwmVa1aVQcPHlRYWFi2+8THx+utt95StWrVJElVqlQxb3N3d5fJZLJ4nyRZJM8VK1bUp59+qvr16+vq1asWx/zBBx+YY37nnXf0zDPP6ObNmypcuLA++OAD/fOf/9SECRPM5QMDAyVJsbGx+vrrr3XmzBmVLl1akjRixAitXbtWCxYs0IcffpjpOFJSUpSSkmJ+nZycnOO5AgAAAIAH9Vj0dAcGBqply5YKCAhQly5dNHfuXF26dCnb8jExMQoMDJSTk5N5XUbimRd371O0aFFVrVpVMTExkqS0tDR98MEHqlmzpjw9PeXi4qL169crPj4+xzrv7oGVJH9/f3l6euqXX37R1q1bFRgYqGeffdbcGx0REZFtgty6dWuVL19eFStWVK9evfTVV1/p+vXrkqQjR47o5s2bat26tVxcXMzLokWLFBsbe99jj4mJUcOGDWUymbI8H1kZNmyY+vfvr1atWumjjz7KVTv79u1Tx44dVb58ebm6uiooKEi6k8DfrWbNmuafS5UqJUnmYejR0dFq2bJllvXv3btXhmHI19fX4jz88ssv2cY3efJkubu7mxdvb+/7HgcAAAAAPIjHIum2tbXVhg0btGbNGlWvXl0zZ85U1apVdfLkySzLG4ZhtVgyktBp06bpX//6l0aOHKlNmzYpOjpabdq00a1bt3Lc39nZOVN9zZo1U0REhH755RcFBQXJ399faWlpOnjwoLZv325ORO/l6uqqvXv36uuvv1apUqX03nvvKTAwUJcvX1Z6erp0Z+h1dHS0eTly5Eiu7ut+kHM4fvx4HT58WM8884w2bdqk6tWra/ny5dmWv3btmv7xj3/IxcVF//nPf7R7925z+XvP490ztme8BxnHmNNkbunp6bK1tVVUVJTFeYiJidEnn3yS5T6jRo1SUlKSeTl9+nQezwQAAAAA5M5jkXTrTqLVpEkTTZgwQfv27ZO9vb2WL18ue3t7paWlWZStXr269u/frxs3bpjX7dy5M89t3r3PpUuX9Ntvv5mHTm/dulUdO3bUiy++qMDAQFWsWPGB7xHOuK87IiJCQUFBMplMatq0qaZOnaobN25keT93Bjs7O7Vq1UpTpkzRgQMHFBcXZ054HRwcFB8fr8qVK1ssuem5rV69eqZzlptz6Ovrq6FDh2r9+vXq3LmzFixYIElZvk9Hjx7V+fPn9dFHH6lp06aqVq2axSRquVWzZk1t3Lgxy221a9dWWlqaEhMTM52He4e6Z3BwcJCbm5vFAgAAAADW8Fgk3ZGRkfrwww+1Z88excfHa9myZTp37pz8/Pzk4+OjAwcO6NixYzp//rxu376tHj16yMbGRv369dORI0e0evVqTZ06Nc/tTpw4URs3btShQ4cUGhqqYsWK6bnnnpMkVa5cWRs2bND27dsVExOjV199VWfPnn2g4wsKCtLhw4d18OBBNW3a1Lzuq6++Up06dbJN+n788Ud9+umnio6O1qlTp7Ro0SKlp6eratWqcnV11YgRIzR06FAtXLhQsbGx2rdvnz777DMtXLjwvjENGDBAsbGxGjZsmI4dO6YlS5YoPDw82/I3btzQG2+8oYiICJ06dUrbtm3T7t275efnJ92Zuf3q1avauHGjzp8/r+vXr6tcuXKyt7fXzJkzdeLECa1cuVKTJk3K8/kbN26cvv76a40bN04xMTE6ePCgpkyZIt35EqBnz57q3bu3li1bppMnT2r37t0KCwvT6tWr89wWAAAAAOSnxyLpdnNz05YtWxQSEiJfX1+NGTNG06ZNU7t27fTyyy+ratWqqlevnooXL65t27bJxcVFq1at0pEjR1S7dm2NHj06xwnAsvPRRx9p8ODBqlu3rhISErRy5Urzc7XHjh2rOnXqqE2bNgoKClLJkiXNCXle+fv7q1ixYgoMDDQn2M2bN1daWlqOE555eHho2bJlatGihfz8/DR79mx9/fXXqlGjhiRp0qRJeu+99zR58mT5+fmpTZs2WrVqlSpUqHDfmMqVK6fvv/9eq1atUmBgoGbPnp3lpGMZbG1tdeHCBfXu3Vu+vr7q2rWr2rVrZ57crHHjxhowYIC6deum4sWLa8qUKSpevLjCw8P17bffqnr16vroo48e6MuRoKAgffvtt1q5cqVq1aqlFi1aWMwiv2DBAvXu3VvDhw9X1apV9eyzzyoyMpJ7tQEAAAAUOJNhzRukH1MREREKDg7WpUuXLJ5Djb+n5ORkubu7q9uiw7J3ci3ocADgkVv0PF9SAgCQVxl5RFJSUo63rD4WPd0AAAAAAPwV/SWT7gEDBlg8PuruZcCAAQUd3iPBOQAAAACAgveXHF6emJio5OTkLLe5ubnJy8vrkcf0qHEOco/h5QD+7hheDgBA3uV2eLndI43qEfHy8vrbJ5WcAwAAAAAoeH/J4eUAAAAAADwOSLoBAAAAALASkm4AAAAAAKzkL3lPN/Ag5nQsm+MECAAAAACQV/R0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlzF4O3PHKD2dk7+Ra0GEAyGeLnvcu6BAAAMDfGD3dAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFZC0g0AAAAAgJWQdOOxFBcXJ5PJpOjo6IIOBQAAAAAeGEk3HqnQ0FA999xzBR0GAAAAADwSJN0AAAAAAFgJSTes4rvvvlNAQIAcHR3l6empVq1a6a233tLChQv1ww8/yGQyyWQyKSIiQpK0a9cu1a5dW4ULF1a9evW0b9++XLVjGIYqV66sqVOnWqw/dOiQbGxsFBsba5XjAwAAAIDcsCvoAPDXk5CQoO7du2vKlCnq1KmTrly5oq1bt6p3796Kj49XcnKyFixYIEkqWrSorl27pvbt26tFixb6z3/+o5MnT2rw4MG5astkMqlv375asGCBRowYYV4/f/58NW3aVJUqVcq0T0pKilJSUsyvk5OT8+W4AQAAAOBe9HQj3yUkJCg1NVWdO3eWj4+PAgIC9Nprr8nFxUWOjo5ycHBQyZIlVbJkSdnb2+urr75SWlqa5s+frxo1aqh9+/Z66623ct1enz59dOzYMe3atUuSdPv2bf3nP/9R3759syw/efJkubu7mxdvb+98O3YAAAAAuBtJN/JdYGCgWrZsqYCAAHXp0kVz587VpUuXsi0fExOjwMBAOTk5mdc1atQo1+2VKlVKzzzzjObPny9J+vHHH3Xz5k116dIly/KjRo1SUlKSeTl9+nSejg8AAAAAcoukG/nO1tZWGzZs0Jo1a1S9enXNnDlTVatW1cmTJ7MsbxjGQ7fZv39/LV26VDdu3NCCBQvUrVs3iyT+bg4ODnJzc7NYAAAAAMAaSLphFSaTSU2aNNGECRO0b98+2dvba/ny5bK3t1daWppF2erVq2v//v26ceOGed3OnTvz1F5ISIicnZ31+eefa82aNdkOLQcAAACAR4mkG/kuMjJSH374ofbs2aP4+HgtW7ZM586dk5+fn3x8fHTgwAEdO3ZM58+f1+3bt9WjRw/Z2NioX79+OnLkiFavXp1pNvL7sbW1VWhoqEaNGqXKlSvnaXg6AAAAAFgLSTfynZubm7Zs2aKQkBD5+vpqzJgxmjZtmtq1a6eXX35ZVatWVb169VS8eHFt27ZNLi4uWrVqlY4cOaLatWtr9OjRCgsLy3O7/fr1061bt+jlBgAAAPDYMBn5cUMt8BjYtm2bgoKCdObMGZUoUSLX+yUnJ8vd3V3dFh2WvZOrVWME8Ogtep4nFAAAgPyXkUckJSXlOE8Uz+nGEy8lJUWnT5/W2LFj1bVr1zwl3AAAAABgTQwvx2NvwIABcnFxyXIZMGCAvv76a1WtWlVJSUmaMmVKQYcLAAAAAGYML8djLzExUcnJyVluc3Nzk5eX10PVz/By4K+N4eUAAMAaGF6OvwwvL6+HTqwBAAAAoCAwvBwAAAAAACsh6QYAAAAAwEoYXg7cMadj2RzvxQAAAACAvKKnGwAAAAAAKyHpBgAAAADASki6AQAAAACwEpJuAAAAAACshKQbAAAAAAArYfZy4I5XfjgjeyfXgg4DeCItet67oEMAAAB4LNHTDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSfdD8PHx0YwZMx77OnMrPDxcHh4e5tfjx49XrVq1CiQWAAAAAPgrsCvoAJ5ku3fvlrOz82Nf54MaMWKEBg0alKuy48eP14oVKxQdHW31uAAAAADgSUHS/RCKFy+e4/bbt2+rUKFC+Vrno+Ti4iIXF5eCDgMAAAAAnlgPPbw8KChIgwYN0pAhQ1SkSBGVKFFCc+bM0bVr19SnTx+5urqqUqVKWrNmjXmfI0eOKCQkRC4uLipRooR69eql8+fPm7evXbtWTz/9tDw8POTp6an27dsrNjbWvD0uLk4mk0nLli1TcHCwnJycFBgYqB07duQq5oxh1CtWrJCvr68KFy6s1q1b6/Tp0+YysbGx6tixo0qUKCEXFxc99dRT+vnnny3quXcouMlk0uzZs9WxY0c5Ozvr/fffV926dTVt2jRzmeeee052dnZKTk6WJJ09e1Ymk0nHjh3Lss7x48erXLlycnBwUOnSpfXmm2+at926dUsjR45UmTJl5OzsrAYNGigiIiJX5yDjPJQrV05OTk7q1KmTLly4YLH93uHlERERql+/vpydneXh4aEmTZro1KlTCg8P14QJE7R//36ZTCaZTCaFh4dLkqZPn66AgAA5OzvL29tbr732mq5evZrpvVi3bp38/Pzk4uKitm3bKiEhwSKW+fPnq0aNGnJwcFCpUqX0xhtvmLclJSXplVdekZeXl9zc3NSiRQvt378/1+cBAAAAAKwlX+7pXrhwoYoVK6Zdu3Zp0KBBGjhwoLp06aLGjRtr7969atOmjXr16qXr168rISFBzZs3V61atbRnzx6tXbtWf/75p7p27Wqu79q1axo2bJh2796tjRs3ysbGRp06dVJ6erpFu6NHj9aIESMUHR0tX19fde/eXampqbmK+fr16/rggw+0cOFCbdu2TcnJyfrnP/9p3n716lWFhITo559/1r59+9SmTRt16NBB8fHxOdY7btw4dezYUQcPHlTfvn0VFBRkToQNw9DWrVtVpEgR/frrr5KkzZs3q2TJkqpatWqmur777jv961//0hdffKHjx49rxYoVCggIMG/v06ePtm3bpqVLl+rAgQPq0qWL2rZtq+PHj9/3+CMjI9W3b1+99tprio6OVnBwsN5///1sy6empuq5555T8+bNdeDAAe3YsUOvvPKKTCaTunXrpuHDh6tGjRpKSEhQQkKCunXrJkmysbHRp59+qkOHDmnhwoXatGmTRo4cmem9mDp1qhYvXqwtW7YoPj5eI0aMMG///PPP9frrr+uVV17RwYMHtXLlSlWuXNl8Tp955hmdPXtWq1evVlRUlOrUqaOWLVvq4sWLWR5LSkqKkpOTLRYAAAAAsAaTYRjGw1QQFBSktLQ0bd26VZKUlpYmd3d3de7cWYsWLZLu9OaWKlVKO3bs0OrVqxUZGal169aZ6zhz5oy8vb117Ngx+fr6Zmrj3Llz8vLy0sGDB+Xv76+4uDhVqFBB8+bNU79+/aQ7vec1atRQTEyMqlWrlmPM4eHh6tOnj3bu3KkGDRpIko4ePSo/Pz9FRkaqfv36We5Xo0YNDRw40NzL6uPjoyFDhmjIkCHSnZ7uIUOG6F//+pd5n1WrVqlXr166ePGiDh48qNatW+vFF1+UnZ2dpkyZoldffVVJSUlaunRppjqnT5+uL774QocOHco0TD02NlZVqlTRmTNnVLp0afP6Vq1aqX79+vrwww9zPAc9evTQpUuXLEYg/POf/9TatWt1+fJl6Z77tC9evChPT09FRESoefPmmerL7T3d3377rQYOHGge2ZDxXvz++++qVKmSJGnWrFmaOHGizp49K0kqU6aM+vTpk+WXAps2bVKnTp2UmJgoBwcH8/rKlStr5MiReuWVV7KMdcKECZnWd1t0WPZOrjnGDyBri573LugQAAAAHqnk5GS5u7srKSlJbm5u2ZbLl57umjVrmn+2tbWVp6enRY9siRIlJEmJiYmKiorS5s2bzfcLu7i4mJPkjCHksbGx6tGjhypWrCg3NzdVqFBBkjL1Mt/dbqlSpcxt5IadnZ3q1atnfl2tWjV5eHgoJiZGutPbPnLkSFWvXl0eHh5ycXHR0aNH79vTfXedktSsWTNduXJF+/bt0y+//KLmzZsrODhYv/zyi3RnyHZWSawkdenSRTdu3FDFihX18ssva/ny5eae/L1798owDPn6+lqcy19++cViKH52YmJi1KhRI4t1976+W9GiRRUaGmru8f/kk08yDQHPyubNm9W6dWuVKVNGrq6u6t27ty5cuKBr166Zyzg5OZkTbt15LzPex8TERP3xxx9q2bJllvVHRUXp6tWr8vT0tDgPJ0+ezPY8jBo1SklJSebl7tsKAAAAACA/5ctEavf2wppMJot1JpNJkpSenq709HR16NBBYWFhmerJSJw7dOggb29vzZ07V6VLl1Z6err8/f1169atbNu9u43cytgnq3VvvfWW1q1bp6lTp6py5cpydHTUCy+8kCmGe90787i7u7tq1aqliIgIbd++XS1atFDTpk0VHR2t48eP67ffflNQUFCWdWX0/m/YsEE///yzXnvtNX388cf65ZdflJ6eLltbW0VFRcnW1tZiv9xMfvYgAxwWLFigN998U2vXrtV///tfjRkzRhs2bFDDhg2zLH/q1CmFhIRowIABmjRpkooWLapff/1V/fr10+3bt83lsvr8ZMTn6OiYY0zp6ekqVapUlvey3/34s7s5ODhY9IoDAAAAgLU88tnL69Spo++//14+Pj6ys8vc/IULFxQTE6MvvvhCTZs2lSTz/c/5KTU1VXv27DEPJT927JguX75s7nXfunWrQkND1alTJ+nOPd5xcXEP1FZQUJA2b96syMhITZw4UR4eHqpevbref/99eXl5yc/PL9t9HR0d9eyzz+rZZ5/V66+/rmrVqungwYOqXbu20tLSlJiYaD5PeVG9enXt3LnTYt29r7NSu3Zt1a5dW6NGjVKjRo20ZMkSNWzYUPb29kpLS7Mou2fPHqWmpmratGmysfn/QRXffPNNnuJ0dXWVj4+PNm7cqODg4Ezb69Spo7Nnz8rOzk4+Pj55qhsAAAAArC1fhpfnxeuvv66LFy+qe/fu2rVrl06cOKH169erb9++SktLU5EiReTp6ak5c+bo999/16ZNmzRs2LB8j6NQoUIaNGiQIiMjtXfvXvXp00cNGzY0J+GVK1fWsmXLFB0drf3796tHjx556kW/W1BQkNauXSuTyaTq1aub13311VfZDi3Xnfudv/zySx06dEgnTpzQ4sWL5ejoqPLly8vX11c9e/ZU7969tWzZMp08eVK7d+9WWFiYVq9efd+YMnqsp0yZot9++03//ve/tXbt2mzLnzx5UqNGjdKOHTt06tQprV+/Xr/99pv5CwMfHx+dPHlS0dHROn/+vFJSUlSpUiWlpqZq5syZ5vhnz56d5/M3fvx4TZs2TZ9++qmOHz+uvXv3aubMmdKde9gbNWqk5557TuvWrVNcXJy2b9+uMWPGaM+ePXluCwAAAADy0yNPukuXLq1t27YpLS1Nbdq0kb+/vwYPHix3d3fZ2NjIxsZGS5cuVVRUlPz9/TV06FB9/PHH+R6Hk5OT3n77bfXo0UONGjWSo6OjeTIzSfrXv/6lIkWKqHHjxurQoYPatGmjOnXqPFBbzZo1kyQ1b97cPHy9efPmSktLyzHp9vDw0Ny5c9WkSRPVrFlTGzdu1KpVq+Tp6SndGe7du3dvDR8+XFWrVtWzzz6ryMhIeXvff0Kjhg0bat68eZo5c6Zq1aql9evXa8yYMdmWd3Jy0tGjR/X888/L19dXr7zyit544w29+uqrkqTnn39ebdu2VXBwsIoXL66vv/5atWrV0vTp0xUWFiZ/f3999dVXmjx5cp7P30svvaQZM2Zo1qxZqlGjhtq3b2+eod1kMmn16tVq1qyZ+vbtK19fX/3zn/9UXFyceS4BAAAAACgoDz17+ZMoPDxcQ4YMMc/Sjb+3jFkHmb0ceHDMXg4AAP5uHuns5QAAAAAAILO/ZNLdrl07i8dH3b3c7/nVfxWcAwAAAAAoeI989vJHYd68ebpx40aW24oWLWp+5vRf2f3OAQAAAADA+v6SSXeZMmUKOoQCxzkAAAAAgIL3lxxeDgAAAADA44CkGwAAAAAAK/lLDi8HHsScjmVznOofAAAAAPKKnm4AAAAAAKyEpBsAAAAAACsh6QYAAAAAwEpIugEAAAAAsBKSbgAAAAAArISkGwAAAAAAK+GRYcAdr/xwRvZOrgUdBvDEWfS8d0GHAAAA8NiipxsAAAAAACsh6QYAAAAAwEpIugEAAAAAsBKSbgAAAAAArISkGwAAAAAAKyHpBgAAAADASki6C0BcXJxMJpOio6PvWzYiIkImk0mXL19+JLHdLS9xFiSTyaQVK1YUdBgAAAAAkAlJdz4KDQ3Vc889V9BhAAAAAAAeEyTdsArDMJSamlrQYQAAAABAgSLpfgDfffedAgIC5OjoKE9PT7Vq1UpvvfWWFi5cqB9++EEmk0kmk0kRERGSpF27dql27doqXLiw6tWrp3379uW5zaioKNWrV09OTk5q3Lixjh07Zt4WGxurjh07qkSJEnJxcdFTTz2ln3/+2WJ/Hx8fffjhh+rbt69cXV1Vrlw5zZkzx6LMw8SZMQx+3bp1qlevnhwcHLR161YZhqEpU6aoYsWKcnR0VGBgoL777jvzfmlpaerXr58qVKggR0dHVa1aVZ988kmm+ufPn68aNWrIwcFBpUqV0htvvGGx/fz58+rUqZOcnJxUpUoVrVy5MtexAwAAAIC1kHTnUUJCgrp3766+ffsqJiZGERER6ty5s8aNG6euXbuqbdu2SkhIUEJCgho3bqxr166pffv2qlq1qqKiojR+/HiNGDEiz+2OHj1a06ZN0549e2RnZ6e+ffuat129elUhISH6+eeftW/fPrVp00YdOnRQfHy8RR3Tpk0zJ9OvvfaaBg4cqKNHj0pSvsU5cuRITZ48WTExMapZs6bGjBmjBQsW6PPPP9fhw4c1dOhQvfjii/rll18kSenp6Spbtqy++eYbHTlyRO+9957effddffPNN+Y6P//8c73++ut65ZVXdPDgQa1cuVKVK1e2aHfChAnq2rWrDhw4oJCQEPXs2VMXL17MMsaUlBQlJydbLAAAAABgDSbDMIyCDuJJsnfvXtWtW1dxcXEqX768xbbQ0FBdvnzZYlKvOXPmaNSoUTp9+rScnJwkSbNnz9bAgQO1b98+1apVK8f2IiIiFBwcrJ9//lktW7aUJK1evVrPPPOMbty4ocKFC2e5X40aNTRw4EBzj7CPj4+aNm2qxYsXS3eGf5csWVITJkzQgAED8i3OFStWqGPHjtKdRL5YsWLatGmTGjVqZC7bv39/Xb9+XUuWLMmyrtdff11//vmnuUe8TJky6tOnj95///0sy5tMJo0ZM0aTJk0yt+vq6qrVq1erbdu2mcqPHz9eEyZMyLS+26LDsndyzfE4AWS26Hnvgg4BAADgkUtOTpa7u7uSkpLk5uaWbTl6uvMoMDBQLVu2VEBAgLp06aK5c+fq0qVL2ZaPiYlRYGCgOZGVZJGA5lbNmjXNP5cqVUqSlJiYKN1JMkeOHKnq1avLw8NDLi4uOnr0aKae7rvrMJlMKlmypLmO/IqzXr165p+PHDmimzdvqnXr1nJxcTEvixYtUmxsrLnc7NmzVa9ePRUvXlwuLi6aO3euOfbExET98ccf5i8ccnN+nJ2d5erqaj62e40aNUpJSUnm5fTp03k+TgAAAADIDbuCDuBJY2trqw0bNmj79u1av369Zs6cqdGjRysyMjLL8vk1kKBQoULmn00mk3RnaLYkvfXWW1q3bp2mTp2qypUry9HRUS+88IJu3bqVbR0Z9WTUkV9xOjs7m3/OqPunn35SmTJlLMo5ODhIkr755hsNHTpU06ZNU6NGjeTq6qqPP/7YfD4dHR1z1W5Ox3YvBwcHc/sAAAAAYE0k3Q/AZDKpSZMmatKkid577z2VL19ey5cvl729vdLS0izKVq9eXYsXL9aNGzfMCeTOnTvzNZ6tW7cqNDRUnTp1ku7c4x0XF5enOqwRZ/Xq1eXg4KD4+Hg1b94829gbN26s1157zbzu7l5wV1dX+fj4aOPGjQoODn6oeAAAAADgUWN4eR5FRkbqww8/1J49exQfH69ly5bp3Llz8vPzk4+Pjw4cOKBjx47p/Pnzun37tnr06CEbGxv169dPR44c0erVqzV16tR8jaly5cpatmyZoqOjtX//fvXo0SPbXt7sWCNOV1dXjRgxQkOHDtXChQsVGxurffv26bPPPtPChQvNse/Zs0fr1q3Tb7/9prFjx2r37t0W9YwfP17Tpk3Tp59+quPHj2vv3r2aOXPmQ8UGAAAAAI8CSXceubm5acuWLQoJCZGvr6/GjBmjadOmqV27dnr55ZdVtWpV8/3J27Ztk4uLi1atWqUjR46odu3aGj16tMLCwvI1pn/9618qUqSIGjdurA4dOqhNmzaqU6dOnuqwVpyTJk3Se++9p8mTJ8vPz09t2rTRqlWrVKFCBUnSgAED1LlzZ3Xr1k0NGjTQhQsXLHq9Jemll17SjBkzNGvWLNWoUUPt27fX8ePHHzo2AAAAALA2Zi/H317GrIPMXg48GGYvBwAAf0fMXg4AAAAAQAEj6S5gAwYMsHic1t3LgAEDCjo8syclTgAAAAB4nDC8vIAlJiYqOTk5y21ubm7y8vJ65DFl5UmJ80EwvBx4OAwvBwAAf0e5HV7OI8MKmJeX1xORsD4pcQIAAADA44Th5QAAAAAAWAlJNwAAAAAAVsLwcuCOOR3L5ngvBgAAAADkFT3dAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFbCI8OAO1754YzsnVwLOgzgibLoee+CDgEAAOCxRk83AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFZC0g0AAAAAgJWQdOeDoKAgDRky5JG3Gxoaqueee+6Rt2ttcXFxMplMio6OLuhQAAAAAOChkHQDAAAAAGAlJN14ILdu3SroEAAAAADgsUfSnU/S09M1cuRIFS1aVCVLltT48ePN26ZPn66AgAA5OzvL29tbr732mq5evWreHh4eLg8PD61bt05+fn5ycXFR27ZtlZCQYC6TlpamYcOGycPDQ56enho5cqQMw8h1fN99950CAgLk6OgoT09PtWrVSteuXZPuGqY+YcIEeXl5yc3NTa+++qpFYh0UFKQ33nhDw4YNU7FixdS6dWtJ0pEjRxQSEiIXFxeVKFFCvXr10vnz5837rV27Vk8//bQ57vbt2ys2NtYitl27dql27doqXLiw6tWrp3379uXqmAzDUOXKlTV16lSL9YcOHZKNjU2mdgAAAADgUSPpzicLFy6Us7OzIiMjNWXKFE2cOFEbNmyQJNnY2OjTTz/VoUOHtHDhQm3atEkjR4602P/69euaOnWqFi9erC1btig+Pl4jRowwb582bZrmz5+vL7/8Ur/++qsuXryo5cuX5yq2hIQEde/eXX379lVMTIwiIiLUuXNni6R948aNiomJ0ebNm/X1119r+fLlmjBhQqZjtLOz07Zt2/TFF18oISFBzZs3V61atbRnzx6tXbtWf/75p7p27Wre59q1axo2bJh2796tjRs3ysbGRp06dVJ6erp5e/v27VW1alVFRUVp/PjxFsedE5PJpL59+2rBggUW6+fPn6+mTZuqUqVKuaoHAAAAAKzFZOSluxRZCgoKUlpamrZu3WpeV79+fbVo0UIfffRRpvLffvutBg4caO4RDg8PV58+ffT777+bE8VZs2Zp4sSJOnv2rCSpdOnSGjx4sN5++21JUmpqqipUqKC6detqxYoVOca3d+9e1a1bV3FxcSpfvnym7aGhoVq1apVOnz4tJycnSdLs2bP11ltvKSkpSTY2NgoKClJSUpJFL/R7772nyMhIrVu3zrzuzJkz8vb21rFjx+Tr65uprXPnzsnLy0sHDx6Uv7+/5syZo1GjRmVqe+DAgdq3b59q1aqV47ElJCTI29tb27dvV/369XX79m2VKVNGH3/8sV566aUs90lJSVFKSor5dXJysry9vdVt0WHZO7nm2B4AS4ue9y7oEAAAAApEcnKy3N3dlZSUJDc3t2zL0dOdT2rWrGnxulSpUkpMTJQkbd68Wa1bt1aZMmXk6uqq3r1768KFC+bh3ZLk5ORk0TN79/5JSUlKSEhQo0aNzNvt7OxUr169XMUWGBioli1bKiAgQF26dNHcuXN16dKlTGUykl5JatSoka5evarTp0+b193bXlRUlDZv3iwXFxfzUq1aNUkyD+2OjY1Vjx49VLFiRbm5ualChQqSpPj4eElSTExMlm3nVqlSpfTMM89o/vz5kqQff/xRN2/eVJcuXbLdZ/LkyXJ3dzcv3t4kDQAAAACsg6Q7nxQqVMjitclkUnp6uk6dOqWQkBD5+/vr+++/V1RUlD777DNJ0u3bt3PcP78GIdja2mrDhg1as2aNqlevrpkzZ6pq1ao6efLkffc1mUzmn52dnS22paenq0OHDoqOjrZYjh8/rmbNmkmSOnTooAsXLmju3LmKjIxUZGSkdNdEbPlxjP3799fSpUt148YNLViwQN26dbNI4u81atQoJSUlmZe7v1gAAAAAgPxE0m1le/bsUWpqqqZNm6aGDRvK19dXf/zxR57qcHd3V6lSpbRz507zutTUVEVFReW6DpPJpCZNmmjChAnat2+f7O3tLe4J379/v27cuGF+vXPnTrm4uKhs2bLZ1lmnTh0dPnxYPj4+qly5ssXi7OysCxcuKCYmRmPGjFHLli3l5+eXqYe9evXqWbadFyEhIXJ2dtbnn3+uNWvWqG/fvjmWd3BwkJubm8UCAAAAANZA0m1llSpVUmpqqmbOnKkTJ05o8eLFmj17dp7rGTx4sD766CMtX75cR48e1WuvvabLly/nat/IyEh9+OGH2rNnj+Lj47Vs2TKdO3dOfn5+5jK3bt1Sv379dOTIEa1Zs0bjxo3TG2+8IRub7D8ir7/+ui5evKju3btr165dOnHihNavX6++ffsqLS1NRYoUkaenp+bMmaPff/9dmzZt0rBhwyzq6NGjh2xsbMxtr169OtNs5Pdja2ur0NBQjRo1SpUrV87T8HQAAAAAsCaSbiurVauWpk+frrCwMPn7++urr77S5MmT81zP8OHD1bt3b4WGhqpRo0ZydXVVp06dcrWvm5ubtmzZopCQEPn6+mrMmDGaNm2a2rVrZy7TsmVLValSRc2aNVPXrl3VoUMHi8eeZaV06dLatm2b0tLS1KZNG/n7+2vw4MFyd3eXjY2NbGxstHTpUkVFRcnf319Dhw7Vxx9/bFGHi4uLVq1apSNHjqh27doaPXq0wsLC8nx++vXrp1u3bt23lxsAAAAAHiVmL4dCQ0N1+fLl+86C/jjbtm2bgoKCdObMGZUoUSJP+2bMOsjs5UDeMXs5AAD4u8rt7OV2jzQqIJ+lpKTo9OnTGjt2rLp27ZrnhBsAAAAArInh5X8B8fHxFo/tunfJeDzXk2jAgAHZHteAAQP09ddfq2rVqkpKStKUKVMKOlwAAAAAsMDw8r+A1NRUxcXFZbvdx8dHdnZP5qCGxMREJScnZ7nNzc1NXl5eD90Gw8uBB8fwcgAA8HfF8PK/ETs7O1WuXLmgw7AKLy+vfEmsAQAAAKAgMLwcAAAAAAArIekGAAAAAMBKSLoBAAAAALAS7ukG7pjTsWyOEyAAAAAAQF7R0w0AAAAAgJWQdAMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlTB7OXDHKz+ckb2Ta0GHATwWFj3vXdAhAAAA/CXQ0w0AAAAAgJWQdAMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3HktxcXEymUyKjo4u6FAAAAAA4IGRdOORCg0N1XPPPVfQYQAAAADAI0HSDQAAAACAlZB0wyq+++47BQQEyNHRUZ6enmrVqpXeeustLVy4UD/88INMJpNMJpMiIiIkSbt27VLt2rVVuHBh1atXT/v27ct1WxMnTlTp0qV14cIF87pnn31WzZo1U3p6ulWODwAAAAByw66gA8BfT0JCgrp3764pU6aoU6dOunLlirZu3arevXsrPj5eycnJWrBggSSpaNGiunbtmtq3b68WLVroP//5j06ePKnBgwfnur3Ro0dr7dq16t+/v5YvX67Zs2dry5Yt2r9/v2xsMn+vlJKSopSUFPPr5OTkfDpyAAAAALBE0o18l5CQoNTUVHXu3Fnly5eXJAUEBEiSHB0dlZKSopIlS5rLh4eHKy0tTfPnz5eTk5Nq1KihM2fOaODAgblqz9bWVv/5z39Uq1YtvfPOO5o5c6bmzJljbvtekydP1oQJE/LlWAEAAAAgJwwvR74LDAxUy5YtFRAQoC5dumju3Lm6dOlStuVjYmIUGBgoJycn87pGjRrlqc2KFStq6tSpCgsLU4cOHdSzZ89sy44aNUpJSUnm5fTp03lqCwAAAAByi6Qb+c7W1lYbNmzQmjVrVL16dc2cOVNVq1bVyZMnsyxvGEa+tLtlyxbZ2toqLi5Oqamp2ZZzcHCQm5ubxQIAAAAA1kDSDaswmUxq0qSJJkyYoH379sne3l7Lly+Xvb290tLSLMpWr15d+/fv140bN8zrdu7cmaf2/vvf/2rZsmWKiIjQ6dOnNWnSpHw7FgAAAAB4UCTdyHeRkZH68MMPtWfPHsXHx2vZsmU6d+6c/Pz85OPjowMHDujYsWM6f/68bt++rR49esjGxkb9+vXTkSNHtHr1ak2dOjXX7WXc/x0WFqann35a4eHhmjx5cp4TdwAAAADIbyTdyHdubm7asmWLQkJC5OvrqzFjxmjatGlq166dXn75ZVWtWlX16tVT8eLFtW3bNrm4uGjVqlU6cuSIateurdGjRyssLCxXbRmGodDQUNWvX19vvPGGJKl169Z644039OKLL+rq1atWPloAAAAAyJ7JyK8baoEnVHJystzd3dVt0WHZO7kWdDjAY2HR894FHQIAAMBjLSOPSEpKynGeKHq6AQAAAACwEpJuPPYGDBggFxeXLJcBAwYUdHgAAAAAkC27gg4AuJ+JEydqxIgRWW7jcV8AAAAAHmck3XjseXl5ycvLq6DDAAAAAIA8Y3g5AAAAAABWQtINAAAAAICVMLwcuGNOx7LcIw4AAAAgX9HTDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVMHs5cMcrP5yRvZNrQYcB5Mqi570LOgQAAADkAj3dAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFZC0g0AAAAAgJWQdD/mwsPD5eHhUSBtR0REyGQy6fLlywXSfm7ExcXJZDIpOjq6oEMBAAAAgExIugEAAAAAsBKSblhFWlqa0tPTCzoMAAAAAChQJN33CAoK0qBBgzRkyBAVKVJEJUqU0Jw5c3Tt2jX16dNHrq6uqlSpktasWSPdSS779eunChUqyNHRUVWrVtUnn3xiru/mzZuqUaOGXnnlFfO6kydPyt3dXXPnzs11XOvWrZOfn59cXFzUtm1bJSQkmLft3r1brVu3VrFixeTu7q7mzZtr7969FvubTCbNmzdPnTp1kpOTk6pUqaKVK1dalFm9erV8fX3l6Oio4OBgxcXF5Tq+jGHwP/74o6pXry4HBwedOnVKt27d0siRI1WmTBk5OzurQYMGioiIMO934cIFde/eXWXLlpWTk5MCAgL09ddfW9Sdnp6usLAwVa5cWQ4ODipXrpw++OADizInTpxQcHCwnJycFBgYqB07duQ6dgAAAACwFpLuLCxcuFDFihXTrl27NGjQIA0cOFBdunRR48aNtXfvXrVp00a9evXS9evXlZ6errJly+qbb77RkSNH9N577+ndd9/VN998I0kqXLiwvvrqKy1cuFArVqxQWlqaevXqpeDgYL388su5iuf69euaOnWqFi9erC1btig+Pl4jRowwb79y5Ypeeuklbd26VTt37lSVKlUUEhKiK1euWNQzYcIEde3aVQcOHFBISIh69uypixcvSpJOnz6tzp07KyQkRNHR0erfv7/eeeedPJ2369eva/LkyZo3b54OHz4sLy8v9enTR9u2bdPSpUt14MABdenSRW3bttXx48elO19K1K1bVz/++KMOHTqkV155Rb169VJkZKS53lGjRiksLExjx47VkSNHtGTJEpUoUcKi7dGjR2vEiBGKjo6Wr6+vunfvrtTU1CzjTElJUXJyssUCAAAAANZgMgzDKOggHidBQUFKS0vT1q1bpTs92e7u7urcubMWLVokSTp79qxKlSqlHTt2qGHDhpnqeP311/Xnn3/qu+++M6/7+OOPNWXKFHXv3l3ffvutDh48qGLFit03nvDwcPXp00e///67KlWqJEmaNWuWJk6cqLNnz2a5T1pamooUKaIlS5aoffv20p2e7jFjxmjSpEmSpGvXrsnV1VWrV69W27Zt9e6772rFihU6fPiwTCaTJOmdd95RWFiYLl26dN/J3DLijI6OVmBgoCQpNjZWVapU0ZkzZ1S6dGlz2VatWql+/fr68MMPs6zrmWeekZ+fn6ZOnaorV66oePHi+ve//63+/ftnKhsXF6cKFSpo3rx56tevnyTpyJEjqlGjhmJiYlStWrVM+4wfP14TJkzItL7bosOyd3LN8TiBx8Wi570LOgQAAIC/teTkZLm7uyspKUlubm7ZlqOnOws1a9Y0/2xraytPT08FBASY12X0siYmJkqSZs+erXr16ql48eJycXHR3LlzFR8fb1Hn8OHDVbVqVc2cOVMLFizIVcKdwcnJyZxwS1KpUqXMbWfEMWDAAPn6+srd3V3u7u66evVqphjuPi5nZ2e5urqa64mJiVHDhg3NCbckNWrUKNcxSpK9vb1FG3v37pVhGPL19ZWLi4t5+eWXXxQbGyvd+YLggw8+UM2aNeXp6SkXFxetX7/eHHtMTIxSUlLUsmXLHNu+u91SpUqZz0tWRo0apaSkJPNy+vTpPB0nAAAAAOSWXUEH8DgqVKiQxWuTyWSxLiMxTU9P1zfffKOhQ4dq2rRpatSokVxdXfXxxx9bDI/WnQTw2LFjsrW11fHjx9W2bduHiufuAQqhoaE6d+6cZsyYofLly8vBwUGNGjXSrVu37ltPxmRn+THgwdHR0SJpT09Pl62traKiomRra2tR1sXFRZI0bdo0/etf/9KMGTMUEBAgZ2dnDRkyxBy7o6NjrtrO7v3JioODgxwcHB7gCAEAAAAgb0i6H9LWrVvVuHFjvfbaa+Z1Gb24d+vbt6/8/f318ssvq1+/fmrZsqWqV6+ebzHMmjVLISEh0p37s8+fP5+nOqpXr64VK1ZYrNu5c+dDxVW7dm2lpaUpMTFRTZs2zbLM1q1b1bFjR7344ovSnUT5+PHj8vPzkyRVqVJFjo6O2rhxY5bDywEAAADgccbw8odUuXJl7dmzR+vWrdNvv/2msWPHavfu3RZlPvvsM+3YsUOLFi1Sjx499MILL6hnz56ZeqIfJobFixcrJiZGkZGR6tmzZ657iDMMGDBAsbGxGjZsmI4dO6YlS5YoPDz8oeLy9fVVz5491bt3by1btkwnT57U7t27FRYWptWrV5tj37Bhg7Zv366YmBi9+uqrFveqFy5cWG+//bZGjhypRYsWKTY2Vjt37tSXX375ULEBAAAAwKNA0v2QBgwYoM6dO6tbt25q0KCBLly4YNHrffToUb311luaNWuWvL3/f+Kjzz77TJcvX9bYsWPzJYb58+fr0qVLql27tnr16qU333xTXl5eeaqjXLly+v7777Vq1SoFBgZq9uzZ2U50lhcLFixQ7969zfe0P/vss4qMjDSfi7Fjx6pOnTpq06aNgoKCVLJkST333HMWdYwdO1bDhw/Xe++9Jz8/P3Xr1i3b+7UBAAAA4HHC7OX428uYdZDZy/EkYfZyAACAgsXs5QAAAAAAFDCS7gLWrl07i8dp3b3kx/Du/PKkxAkAAAAAjxNmLy9g8+bN040bN7LcVrRo0UceT3aelDgBAAAA4HFC0l3AypQpU9Ah5MqTEicAAAAAPE4YXg4AAAAAgJWQdAMAAAAAYCUMLwfumNOxbI5T/QMAAABAXtHTDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlPDIMuOOVH87I3sm1oMPAY2bR894FHQIAAACeYPR0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSXc+CAoK0pAhQx55u6GhoXruueceebvWFhcXJ5PJpOjo6IIOBQAAAAAeCkk3AAAAAABWQtKNB3Lr1q2CDgEAAAAAHnsk3fkkPT1dI0eOVNGiRVWyZEmNHz/evG369OkKCAiQs7OzvL299dprr+nq1avm7eHh4fLw8NC6devk5+cnFxcXtW3bVgkJCeYyaWlpGjZsmDw8POTp6amRI0fKMIxcx/fdd98pICBAjo6O8vT0VKtWrXTt2jXprmHqEyZMkJeXl9zc3PTqq69aJNZBQUF64403NGzYMBUrVkytW7eWJB05ckQhISFycXFRiRIl1KtXL50/f96839q1a/X000+b427fvr1iY2MtYtu1a5dq166twoULq169etq3b1+uj2vixIkqXbq0Lly4YF737LPPqlmzZkpPT891PQAAAABgDSTd+WThwoVydnZWZGSkpkyZookTJ2rDhg2SJBsbG3366ac6dOiQFi5cqE2bNmnkyJEW+1+/fl1Tp07V4sWLtWXLFsXHx2vEiBHm7dOmTdP8+fP15Zdf6tdff9XFixe1fPnyXMWWkJCg7t27q2/fvoqJiVFERIQ6d+5skbRv3LhRMTEx2rx5s77++mstX75cEyZMyHSMdnZ22rZtm7744gslJCSoefPmqlWrlvbs2aO1a9fqzz//VNeuXc37XLt2TcOGDdPu3bu1ceNG2djYqFOnTuaE+Nq1a2rfvr2qVq2qqKgojR8/3uK472f06NHy8fFR//79JUmzZ8/Wli1btHjxYtnYZP3xTklJUXJyssUCAAAAANZgMvLSXYosBQUFKS0tTVu3bjWvq1+/vlq0aKGPPvooU/lvv/1WAwcONPcIh4eHq0+fPvr9999VqVIlSdKsWbM0ceJEnT17VpJUunRpDR48WG+//bYkKTU1VRUqVFDdunW1YsWKHOPbu3ev6tatq7i4OJUvXz7T9tDQUK1atUqnT5+Wk5OTdCd5feutt5SUlCQbGxsFBQUpKSnJohf6vffeU2RkpNatW2ded+bMGXl7e+vYsWPy9fXN1Na5c+fk5eWlgwcPyt/fX3PmzNGoUaMytT1w4EDt27dPtWrVuu/5P3HihGrVqqXXXntNM2fO1Jw5c9SzZ89sy48fPz7TFwqS1G3RYdk7ud63Pfy9LHreu6BDAAAAwGMoOTlZ7u7uSkpKkpubW7bl6OnOJzVr1rR4XapUKSUmJkqSNm/erNatW6tMmTJydXVV7969deHCBfPwbklycnIyJ9z37p+UlKSEhAQ1atTIvN3Ozk716tXLVWyBgYFq2bKlAgIC1KVLF82dO1eXLl3KVCYj6ZWkRo0a6erVqzp9+rR53b3tRUVFafPmzXJxcTEv1apVkyTzEPLY2Fj16NFDFStWlJubmypUqCBJio+PlyTFxMRk2XZeVKxYUVOnTlVYWJg6dOiQY8ItSaNGjVJSUpJ5ufsYAQAAACA/kXTnk0KFClm8NplMSk9P16lTpxQSEiJ/f399//33ioqK0meffSZJun37do7759cgBFtbW23YsEFr1qxR9erVNXPmTFWtWlUnT568774mk8n8s7Ozs8W29PR0dejQQdHR0RbL8ePH1axZM0lShw4ddOHCBc2dO1eRkZGKjIyU7pqILb+OccuWLbK1tVVcXJxSU1NzLOvg4CA3NzeLBQAAAACsgaTbyvbs2aPU1FRNmzZNDRs2lK+vr/7444881eHu7q5SpUpp586d5nWpqamKiorKdR0mk0lNmjTRhAkTtG/fPtnb21vcE75//37duHHD/Hrnzp1ycXFR2bJls62zTp06Onz4sHx8fFS5cmWLxdnZWRcuXFBMTIzGjBmjli1bys/PL1MPe/Xq1bNsOy/++9//atmyZYqIiNDp06c1adKkPO0PAAAAANZC0m1llSpVUmpqqmbOnKkTJ05o8eLFmj17dp7rGTx4sD766CMtX75cR48e1WuvvabLly/nat/IyEh9+OGH2rNnj+Lj47Vs2TKdO3dOfn5+5jK3bt1Sv379dOTIEa1Zs0bjxo3TG2+8ke1kZJL0+uuv6+LFi+revbt27dqlEydOaP369erbt6/S0tJUpEgReXp6as6cOfr999+1adMmDRs2zKKOHj16yMbGxtz26tWrNXXq1FyflzNnzmjgwIEKCwvT008/rfDwcE2ePDnPiTsAAAAAWANJt5XVqlVL06dPV1hYmPz9/fXVV19p8uTJea5n+PDh6t27t0JDQ9WoUSO5urqqU6dOudrXzc1NW7ZsUUhIiHx9fTVmzBhNmzZN7dq1M5dp2bKlqlSpombNmqlr167q0KGDxWPPslK6dGlt27ZNaWlpatOmjfz9/TV48GC5u7vLxsZGNjY2Wrp0qaKiouTv76+hQ4fq448/tqjDxcVFq1at0pEjR1S7dm2NHj1aYWFhuTouwzAUGhqq+vXr64033pAktW7dWm+88YZefPFFi8eyAQAAAEBBYPZyKDQ0VJcvX77vLOh/VRmzDjJ7ObLC7OUAAADICrOXAwAAAABQwEi6/wLi4+MtHtt175LxeK4n0YABA7I9rgEDBhR0eAAAAACQI4aX/wWkpqYqLi4u2+0+Pj6ys7N7pDHll8TERCUnJ2e5zc3NTV5eXg/dBsPLkROGlwMAACAruR1e/mRmYrBgZ2enypUrF3QYVuHl5ZUviTUAAAAAFASGlwMAAAAAYCUk3QAAAAAAWAnDy4E75nQsm+O9GAAAAACQV/R0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJjwwD7njlhzOyd3It6DBQABY9713QIQAAAOAvip5uAAAAAACshKQbAAAAAAArIekGAAAAAMBKSLoBAAAAALASkm4AAAAAAKyEpBsAAAAAACsh6S4g4eHh8vDwKJC2IyIiZDKZdPny5QJpX5JWrFihypUry9bWVkOGDCmwOAAAAADAmki6kSNrfTnw6quv6oUXXtDp06c1adKkfKmzIL/IAAAAAICs2BV0APj7uXr1qhITE9WmTRuVLl26oMMBAAAAAKv5S/R0BwUFadCgQRoyZIiKFCmiEiVKaM6cObp27Zr69OkjV1dXVapUSWvWrDHvc+TIEYWEhMjFxUUlSpRQr169dP78efP2tWvX6umnn5aHh4c8PT3Vvn17xcbGmrfHxcXJZDJp2bJlCg4OlpOTkwIDA7Vjx448xb5u3Tr5+fnJxcVFbdu2VUJCgnnb7t271bp1axUrVkzu7u5q3ry59u7da7G/yWTSvHnz1KlTJzk5OalKlSpauXKlRZnVq1fL19dXjo6OCg4OVlxcXK5ii4iIUJ8+fZSUlCSTySSTyaTx48dLki5duqTevXurSJEicnJyUrt27XT8+PFc1enq6ipJatGihUwmkyIiIiRJ33//vWrUqCEHBwf5+Pho2rRpFvvm1GZOsQIAAABAQflLJN2StHDhQhUrVky7du3SoEGDNHDgQHXp0kWNGzfW3r171aZNG/Xq1UvXr19XQkKCmjdvrlq1amnPnj1au3at/vzzT3Xt2tVc37Vr1zRs2DDt3r1bGzdulI2NjTp16qT09HSLdkePHq0RI0YoOjpavr6+6t69u1JTU3MV8/Xr1zV16lQtXrxYW7ZsUXx8vEaMGGHefuXKFb300kvaunWrdu7cqSpVqigkJERXrlyxqGfChAnq2rWrDhw4oJCQEPXs2VMXL16UJJ0+fVqdO3dWSEiIoqOj1b9/f73zzju5iq9x48aaMWOG3NzclJCQoISEBHN8oaGh2rNnj1auXKkdO3bIMAyFhITo9u3b963z2LFj0p0kOyEhQY0bN1ZUVJS6du2qf/7znzp48KDGjx+vsWPHKjw83LxvTm3mFCsAAAAAFBSTYRhGQQfxsIKCgpSWlqatW7dKktLS0uTu7q7OnTtr0aJFkqSzZ8+qVKlS2rFjh1avXq3IyEitW7fOXMeZM2fk7e2tY8eOydfXN1Mb586dk5eXlw4ePCh/f3/FxcWpQoUKmjdvnvr16yfd6T2vUaOGYmJiVK1atRxjDg8PV58+ffT777+rUqVKkqRZs2Zp4sSJOnv2bJb7pKWlqUiRIlqyZInat28v3enpHjNmjPm+6GvXrsnV1VWrV69W27Zt9e6772rFihU6fPiwTCaTJOmdd95RWFiYLl26dN97oMPDwzVkyBCLSdeOHz8uX19fbdu2TY0bN5YkXbhwQd7e3lq4cKG6dOmSY52XL19WkSJFtHnzZgUFBUmSevbsqXPnzmn9+vXmciNHjtRPP/2kw4cP56rNrGLNSkpKilJSUsyvk5OT5e3trW6LDsveyTXHffHXtOh574IOAQAAAE+Y5ORkubu7KykpSW5ubtmW+8v0dNesWdP8s62trTw9PRUQEGBeV6JECUlSYmKioqKitHnzZrm4uJiXjCQ5Ywh5bGysevTooYoVK8rNzU0VKlSQJMXHx2fbbqlSpcxt5IaTk5M54c7Y/+59ExMTNWDAAPn6+srd3V3u7u66evVqjjE4OzvL1dXVXE9MTIwaNmxoTrglqVGjRrmKLzsxMTGys7NTgwYNzOs8PT1VtWpVxcTEPHCdTZo0sVjXpEkTHT9+XGlpafna5uTJk83n093dXd7eJFwAAAAArOMvM5FaoUKFLF6bTCaLdRlJZ3p6utLT09WhQweFhYVlqicjce7QoYO8vb01d+5clS5dWunp6fL399etW7eybffuNh405rsHHoSGhurcuXOaMWOGypcvLwcHBzVq1CjHGDLqyYjBGgMZsqvTMAyL5D6vdd67793t5Gebo0aN0rBhw8yvM3q6AQAAACC//WWS7ryoU6eOvv/+e/n4+MjOLvMpuHDhgmJiYvTFF1+oadOmkqRff/31kce5detWzZo1SyEhIdKd+7PvnuwtN6pXr64VK1ZYrNu5c2eu97e3t1daWlqmOlNTUxUZGWkx1Pu3336Tn59fnuK7u857z/H27dvl6+srW1vbXLWZVaxZcXBwkIODwwPFCQAAAAB58ZcZXp4Xr7/+ui5evKju3btr165dOnHihNavX6++ffua75v29PTUnDlz9Pvvv2vTpk0WPaOPSuXKlbV48WLFxMQoMjJSPXv2lKOjY57qGDBggGJjYzVs2DAdO3ZMS5YssZic7H58fHx09epVbdy4UefPn9f169dVpUoVdezYUS+//LJ+/fVX7d+/Xy+++KLKlCmjjh07PsCRSsOHD9fGjRs1adIk/fbbb1q4cKH+/e9/mydDy02bWcUKAAAAAAXpb5l0ly5dWtu2bVNaWpratGkjf39/DR48WO7u7rKxsZGNjY2WLl2qqKgo+fv7a+jQofr4448feZzz58/XpUuXVLt2bfXq1UtvvvmmvLy88lRHuXLl9P3332vVqlUKDAzU7Nmz9eGHH+Z6/8aNG2vAgAHq1q2bihcvrilTpkiSFixYoLp166p9+/Zq1KiRDMPQ6tWrMw11z606derom2++0dKlS+Xv76/33ntPEydOVGhoqLnM/drMLlYAAAAAKCh/idnLgYeRMesgs5f/fTF7OQAAAPLqbzd7OQAAAAAAjxuSbitp166dxSPJ7l7yMrzb2qwR55Ny7AAAAABgbX/L2csfhXnz5unGjRtZbitatOgjjyc71ojzSTl2AAAAALA2km4rKVOmTEGHkCvWiPNJOXYAAAAAsDaGlwMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVsI93cAdczqWzfH5egAAAACQV/R0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlzF4O3PHKD2dk7+Ra0GEgjxY9713QIQAAAADZoqcbAAAAAAArIekGAAAAAMBKSLoB/F979x1f4/3/f/x5ssdJIgmSIGbEHtWgRgmComqWoogEVbWLUitqK2rVqJLQKlU1Wh+zSqv2irYSozGirU0TM0bO748v5+c0Q4Ij6ON+u123b877eo/X+7g+vX1f5/2+rgsAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHT/B5w4cUIGg0HR0dGP1U9oaKgaN25s/hwcHKxevXo9gQgBAAAA4MVE0g0AAAAAgJWQdOOZZDKZdOfOnawOAwAAAAAeC0n3PcnJyRo3bpwCAgLk6OiovHnzatSoUZKk3377TTVr1pSzs7O8vb3VuXNnXb161dz2/rbrCRMmyM/PT97e3nrvvfd0+/ZtSdLAgQP1yiuvpBizdOnSGjZsmPlzZGSkihUrJicnJxUtWlQzZswwnwsLC1Pp0qWVlJQkSbp9+7ZefvlltWnTJsNzPHbsmGrUqCEXFxeVKVNG27dvN5+LiIhQ2bJlLepPnjxZ+fPnz3D/6fnyyy8VFBQkNzc3+fr6qnXr1jp37pz5/ObNm2UwGLRu3ToFBQXJ0dFRW7Zskclk0vjx41WwYEE5OzurTJkyWrp0qbnd3bt3FR4ergIFCsjZ2VlFihTRlClTnkjMAAAAAPC4SLrvGThwoMaNG6chQ4YoJiZGX331lXx8fHT9+nW99tpr8vT01O7du/XNN9/ohx9+ULdu3Szab9q0SXFxcdq0aZPmz5+vqKgoRUVFSZLatGmjnTt3Ki4uzlz/4MGD+u2338xJ85w5czRo0CCNGjVKsbGxGj16tIYMGaL58+dLkqZOnapr165pwIABkqQhQ4bowoULFon5wwwaNEh9+/ZVdHS0AgMD1apVq6e2mnzr1i2NGDFCBw4c0IoVK3T8+HGFhoamqNe/f3+NGTNGsbGxKl26tAYPHqzIyEjNnDlTBw8eVO/evfX222/rp59+ku79WJInTx4tWbJEMTExGjp0qD788EMtWbIkzViSkpKUmJhocQAAAACANdhldQDPgitXrmjKlCmaPn262rdvL0kqVKiQqlatqjlz5ujGjRtasGCBXF1dJUnTp09Xw4YNNW7cOPn4+EiSPD09NX36dNna2qpo0aJq0KCBNm7cqE6dOqlkyZIqXbq0vvrqKw0ZMkSStHDhQpUvX16BgYGSpBEjRmjixIlq2rSpJKlAgQKKiYnR7Nmz1b59exmNRn355ZeqXr263NzcNHHiRG3cuFEeHh4Znmffvn3VoEEDSdLw4cNVokQJ/fHHHypatOgT/kZTCgsLM/9dsGBBTZ06VRUqVNDVq1dlNBrN5z766CPVrl1bknTt2jVNmjRJP/74oypVqmRu+8svv2j27NmqXr267O3tNXz4cHP7AgUKaNu2bVqyZIlatGiRaixjxoyxaAMAAAAA1sJKt6TY2FglJSWpVq1aqZ4rU6aMOeGWpCpVqig5OVmHDx82l5UoUUK2trbmz35+fhbbp9u0aaOFCxdK9+5XXrRokXmV+/z58zp16pTCw8NlNBrNx8iRIy1WxytVqqS+fftqxIgRev/991WtWrVMzbN06dIW8UmyiNGa9u/fr0aNGilfvnxyc3NTcHCwJCk+Pt6iXlBQkPnvmJgY3bx5U7Vr17b4XhYsWGDxvcyaNUtBQUHKkSOHjEaj5syZk6LfBw0cOFAJCQnm49SpU1aZMwAAAACw0i3J2dk5zXMmk0kGgyHVcw+W29vbpziXnJxs/ty6dWsNGDBA+/bt040bN3Tq1Cm99dZb0r0t0rq3xbxixYoW/TyYyCcnJ2vr1q2ytbXV0aNHMz3PB2O8H/v9sW1sbGQymSzq378n/XFdu3ZNderUUZ06dfTll18qR44cio+PV926dXXr1i2Lug/+uHE/tv/973/KnTu3RT1HR0dJ0pIlS9S7d29NnDhRlSpVkpubmz7++GPt3LkzzXgcHR3N7QEAAADAmki6JRUuXFjOzs7auHGjOnbsaHGuePHimj9/vq5du2ZOCLdu3SobGxvz1vCMyJMnj6pVq6aFCxfqxo0bCgkJMW9N9/HxUe7cuXXs2LF0H4z28ccfKzY2Vj/99JPq1q2ryMhIdejQ4ZHn/aAcOXLozJkzFj8yPO57ve87dOiQLly4oLFjx8rf31+StGfPnoe2K168uBwdHRUfH6/q1aunWmfLli2qXLmyunbtai57cBUcAAAAALISSbckJycnffDBB+rfv78cHBxUpUoVnT9/XgcPHlSbNm00bNgwtW/fXhERETp//ry6d++utm3bmpPmjGrTpo0iIiJ069YtffLJJxbnIiIi1KNHD7m7u6tevXpKSkrSnj17dPnyZfXp00fR0dEaOnSoli5dqipVqmjKlCnq2bOnqlevroIFCz72dxAcHKzz589r/Pjxat68udauXas1a9bI3d39sfvOmzevHBwcNG3aNHXp0kW///67RowY8dB2bm5u6tu3r3r37q3k5GRVrVpViYmJ2rZtm4xGo9q3b6+AgAAtWLBA69atU4ECBfTFF19o9+7dKlCgwGPHDQAAAACPi3u67xkyZIjef/99DR06VMWKFVPLli117tw5ubi4aN26dbp06ZLKly+v5s2bq1atWpo+fXqmx3jzzTd18eJFXb9+XY0bN7Y417FjR33++eeKiopSqVKlVL16dUVFRalAgQK6efOm2rRpo9DQUDVs2FCSFB4erpCQELVt21Z379597PkXK1ZMM2bM0KeffqoyZcpo165d6tu372P3q3ur6FFRUfrmm29UvHhxjR07VhMmTMhQ2xEjRmjo0KEaM2aMihUrprp16+r77783J9VdunRR06ZN1bJlS1WsWFEXL160WPUGAAAAgKxkMP37Rl7gPyYxMVEeHh5queCgHFzcsjocZNKCZv5ZHQIAAAD+g+7nEQkJCenuEGalGwAAAAAAKyHpfgGMHj3a4pVaDx716tWz+vhbtmxJc/wH38ENAAAAAP81PEjtBdClSxe1aNEi1XPpvQ7tSQkKCnpiTzoHAAAAgBcJSfcLwMvLS15eXlk2vrOzswICArJsfAAAAAB4VrG9HAAAAAAAKyHpBgAAAADAStheDtzzWaM86T7qHwAAAAAyi5VuAAAAAACshKQbAAAAAAArIekGAAAAAMBKSLoBAAAAALASkm4AAAAAAKyEp5cD93Re+accXNyyOgykYUEz/6wOAQAAAMg0VroBAAAAALASkm4AAAAAAKyEpBsAAAAAACsh6QYAAAAAwEpIugEAAAAAsBKSbgAAAAAArISkGwAAAAAAKyHpfkYEBwerV69ej9w+IiJCZcuWTbdOaGioGjdu/MhjPG6MmZWROQEAAADAs4ykG8+svn37auPGjebPj/ujAQAAAAA8bXZZHQCQFqPRKKPRmNVhAAAAAMAjY6X7GZKcnKz+/fvLy8tLvr6+ioiIMJ+Lj49Xo0aNZDQa5e7urhYtWujs2bNp9nX37l316dNH2bJlk7e3t/r37y+TyZThWK5du6Z27drJaDTKz89PEydOTFHn1q1b6t+/v3Lnzi1XV1dVrFhRmzdvNp+PiopStmzZtG7dOhUrVkxGo1GvvfaaTp8+ba6zefNmVahQQa6ursqWLZuqVKmikydPSv/aXh4REaH58+dr5cqVMhgMMhgM2rx5s2rWrKlu3bpZxHXx4kU5Ojrqxx9/zPB8AQAAAMAaSLqfIfPnz5erq6t27typ8ePH66OPPtKGDRtkMpnUuHFjXbp0ST/99JM2bNiguLg4tWzZMs2+Jk6cqHnz5mnu3Ln65ZdfdOnSJS1fvjzDsfTr10+bNm3S8uXLtX79em3evFl79+61qNOhQwdt3bpVixcv1q+//qo333xTr732mo4ePWquc/36dU2YMEFffPGFfv75Z8XHx6tv376SpDt37qhx48aqXr26fv31V23fvl2dO3eWwWBIEU/fvn3VokULc9J++vRpVa5cWR07dtRXX32lpKQkc92FCxcqV65cqlGjRqpzS0pKUmJiosUBAAAAANbA9vJnSOnSpTVs2DBJUuHChTV9+nTzPc2//vqrjh8/Ln9/f0nSF198oRIlSmj37t0qX758ir4mT56sgQMHqlmzZpKkWbNmad26dRmK4+rVq5o7d64WLFig2rVrS/d+EMiTJ4+5TlxcnBYtWqQ///xTuXLlku4lxmvXrlVkZKRGjx4tSbp9+7ZmzZqlQoUKSZK6deumjz76SJKUmJiohIQEvf766+bzxYoVSzUmo9EoZ2dnJSUlydfX11zerFkzde/eXStXrlSLFi0kSZGRkQoNDU01eZekMWPGaPjw4Rn6LgAAAADgcbDS/QwpXbq0xWc/Pz+dO3dOsbGx8vf3NyfcklS8eHFly5ZNsbGxKfpJSEjQ6dOnValSJXOZnZ2dgoKCMhRHXFycbt26ZdHey8tLRYoUMX/et2+fTCaTAgMDzfdeG41G/fTTT4qLizPXc3FxMSfUD87pfp+hoaGqW7euGjZsqClTplhsPc8IR0dHvf3225o3b54kKTo6WgcOHFBoaGiabQYOHKiEhATzcerUqUyNCQAAAAAZxUr3M8Te3t7is8FgUHJyskwmU6qrtmmVP66M3PudnJwsW1tb7d27V7a2thbnHnz4WWpzerD/yMhI9ejRQ2vXrtXXX3+twYMHa8OGDXrllVcyHG/Hjh1VtmxZ/fnnn5o3b55q1aqlfPnypVnf0dFRjo6OGe4fAAAAAB4VK93PgeLFiys+Pt5iRTYmJkYJCQmpbsf28PCQn5+fduzYYS67c+dOinuy0xIQECB7e3uL9pcvX9aRI0fMn1966SXdvXtX586dU0BAgMXx4PbvjHjppZc0cOBAbdu2TSVLltRXX32Vaj0HBwfdvXs3RXmpUqUUFBSkOXPm6KuvvlJYWFimxgcAAAAAayHpfg6EhISodOnSatOmjfbt26ddu3apXbt2ql69eppbxnv27KmxY8dq+fLlOnTokLp27ap//vknQ+MZjUaFh4erX79+2rhxo37//XeFhobKxub/Xy6BgYFq06aN2rVrp2XLlun48ePavXu3xo0bp9WrV2donOPHj2vgwIHavn27Tp48qfXr1+vIkSNp3tedP39+/frrrzp8+LAuXLig27dvm8917NhRY8eO1d27d9WkSZMMjQ8AAAAA1kbS/RwwGAxasWKFPD09Va1aNYWEhKhgwYL6+uuv02zz/vvvq127dgoNDVWlSpXk5uaWqWT0448/VrVq1fTGG28oJCREVatW1csvv2xRJzIyUu3atdP777+vIkWK6I033tDOnTst7j1Pj4uLiw4dOqRmzZopMDBQnTt3Vrdu3fTOO++kWr9Tp04qUqSIgoKClCNHDm3dutV8rlWrVrKzs1Pr1q3l5OSU4XkCAAAAgDUZTJl5eTPwjDp16pTy58+v3bt3q1y5cplqm5iYKA8PD7VccFAOLm5WixGPZ0GzjP2YAwAAADwN9/OIhIQEubu7p1mPB6nhuXb79m2dPn1aAwYM0CuvvJLphBsAAAAArInt5f9B8fHxFq/5+vcRHx+f1SFm2NatW5UvXz7t3btXs2bNyupwAAAAAMACK93/Qbly5VJ0dHS6558XwcHBGXrFGQAAAABkBZLu/yA7OzsFBARkdRgAAAAA8MJjezkAAAAAAFZC0g0AAAAAgJWwvRy457NGedJ91D8AAAAAZBYr3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWwivDgHs6r/xTDi5uWR0GHrCgmX9WhwAAAAA8Fla6AQAAAACwEpJuAAAAAACshKQbAAAAAAArIekGAAAAAMBKSLoBAAAAALASkm4AAAAAAKyEpPsJOnHihAwGg6Kjox9ad/PmzTIYDPrnn3+eSmwPykyc1rJ161aVKlVK9vb2aty4cZbFAQAAAADWRNKdAaGhof/ZxNBaPw706dNHZcuW1fHjxxUVFfVE+szKHzIAAAAAIDUk3cgScXFxqlmzpvLkyaNs2bJldTgAAAAAYBUk3Q9YunSpSpUqJWdnZ3l7eyskJET9+vXT/PnztXLlShkMBhkMBm3evFmStGvXLr300ktycnJSUFCQ9u/fn+kx9+7dq6CgILm4uKhy5co6fPiw+VxcXJwaNWokHx8fGY1GlS9fXj/88INF+/z582v06NEKCwuTm5ub8ubNq88++8yizqPGeeLECdWoUUOS5OnpKYPBoNDQUElSUlKSevTooZw5c8rJyUlVq1bV7t27M9SnwWDQxYsXFRYWJoPBYF7p/umnn1ShQgU5OjrKz89PAwYM0J07d8xt0xszvVgBAAAAIKuQdN9z+vRptWrVSmFhYYqNjdXmzZvVtGlTDRs2TC1atNBrr72m06dP6/Tp06pcubKuXbum119/XUWKFNHevXsVERGhvn37ZnrcQYMGaeLEidqzZ4/s7OwUFhZmPnf16lXVr19fP/zwg/bv36+6deuqYcOGio+Pt+hj4sSJ5mS6a9euevfdd3Xo0CFJeqw4/f399e2330qSDh8+rNOnT2vKlCmSpP79++vbb7/V/PnztW/fPgUEBKhu3bq6dOnSQ/s8ffq03N3dNXnyZJ0+fVotW7bUX3/9pfr166t8+fI6cOCAZs6cqblz52rkyJHmtumNmV6s/5aUlKTExESLAwAAAACsgaT7ntOnT+vOnTtq2rSp8ufPr1KlSqlr164yGo1ydnaWo6OjfH195evrKwcHBy1cuFB3797VvHnzVKJECb3++uvq169fpscdNWqUqlevruLFi2vAgAHatm2bbt68KUkqU6aM3nnnHZUqVUqFCxfWyJEjVbBgQX333XcWfdSvX19du3ZVQECAPvjgA2XPnt28Gv84cdra2srLy0uSlDNnTvn6+srDw0PXrl3TzJkz9fHHH6tevXoqXry45syZI2dnZ82dO/ehffr6+spgMMjDw0O+vr5ydnbWjBkz5O/vr+nTp6to0aJq3Lixhg8frokTJyo5OfmhY6YVa2rGjBkjDw8P8+Hv75+h7wMAAAAAMouk+54yZcqoVq1aKlWqlN58803NmTNHly9fTrN+bGysypQpIxcXF3NZpUqVMj1u6dKlzX/7+flJks6dOyfdW6Xu37+/ihcvrmzZssloNOrQoUMpVrof7MNgMMjX19fcx5OK80FxcXG6ffu2qlSpYi6zt7dXhQoVFBsb+0h9xsbGqlKlSjIYDOayKlWq6OrVq/rzzz+f6JgDBw5UQkKC+Th16tQjxQwAAAAAD0PSfY+tra02bNigNWvWqHjx4po2bZqKFCmi48ePp1rfZDI9kXHt7e3Nf99POJOTkyVJ/fr107fffqtRo0Zpy5Ytio6OVqlSpXTr1q00+7jfz/0+nlScD7rf54MJ8v3yf5dlps/U+rs/zpMc09HRUe7u7hYHAAAAAFgDSfcDDAaDqlSpouHDh2v//v1ycHDQ8uXL5eDgoLt371rULV68uA4cOKAbN26Yy3bs2PFE49myZYtCQ0PVpEkTlSpVSr6+vjpx4kSm+njcOB0cHCTJYv4BAQFycHDQL7/8Yi67ffu29uzZo2LFimUqvgfj3LZtm8WPBNu2bZObm5ty586doTFTixUAAAAAshJJ9z07d+7U6NGjtWfPHsXHx2vZsmU6f/68ihUrpvz58+vXX3/V4cOHdeHCBd2+fVutW7eWjY2NwsPDFRMTo9WrV2vChAlPNKaAgAAtW7ZM0dHROnDggFq3bm1ewc6ox40zX758MhgMWrVqlc6fP6+rV6/K1dVV7777rvr166e1a9cqJiZGnTp10vXr1xUeHv4IM5W6du2qU6dOqXv37jp06JBWrlypYcOGqU+fPrKxscnQmKnFCgAAAABZiaT7Hnd3d/3888+qX7++AgMDNXjwYE2cOFH16tVTp06dVKRIEQUFBSlHjhzaunWrjEajvv/+e8XExOill17SoEGDNG7cuCca0yeffCJPT09VrlxZDRs2VN26dVWuXLlM9fG4cebOnVvDhw/XgAED5OPjo27dukmSxo4dq2bNmqlt27YqV66c/vjjD61bt06enp6Znuf9cVavXq1du3apTJky6tKli8LDwzV48GBznYeNmVasAAAAAJBVDCZr3PQLPEcSExPl4eGhlgsOysHFLavDwQMWNOPJ8gAAAHg23c8jEhIS0n1OFCvdAAAAAABYCUm3lXTp0kVGozHVo0uXLlkdnpk14nxe5g4AAAAA1sb2cis5d+6cEhMTUz3n7u6unDlzPvWYUmONOJ+Xud/H9vJnF9vLAQAA8KzK6PZyu6ca1X9Izpw5n7nkMjXWiPN5mTsAAAAAWBvbywEAAAAAsBKSbgAAAAAArITt5cA9nzXKk+69GAAAAACQWax0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJrwwD7um88k85uLhldRi4Z0Ez/6wOAQAAAHhsrHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFZC0g0AAAAAgJWQdAMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVkLSjWdWcHCwDAZDiqNBgwbmOhERESnO+/r6ZmncAAAAAHCfXVYHAKRl2bJlunXrlvnzxYsXVaZMGb355psW9UqUKKEffvjB/NnW1vapxgkAAAAAaWGlG49swYIF8vb2VlJSkkV5s2bN1K5du4e2Hzt2rHx8fOTm5qbw8HANGDBAZcuWNZ/38vKSr6+v+diwYYNcXFxSJN12dnYW9XLkyPEEZwkAAAAAj46kG4/szTff1N27d/Xdd9+Zyy5cuKBVq1apQ4cO6bZdsmSJhg0bplGjRmnPnj3y8/PTjBkz0m0zd+5cvfXWW3J1dbUoP3r0qHLlyqUCBQrorbfe0rFjxx5zZgAAAADwZJB045E5OzurdevWioyMNJctXLhQefLkUXBwcLptJ0+erLCwMHXs2FFFihTRyJEjVbx48TTr79q1S7///rs6duxoUV6xYkUtWLBA69at05w5c3TmzBlVrlxZFy9eTLOvpKQkJSYmWhwAAAAAYA0k3XgsnTp10vr16/XXX39JkiIjIxUaGiqDwZBuu9jYWFWqVMmi7N+fHzR37lyVLFlSFSpUsCivV6+emjVrplKlSikkJET/+9//JEnz589Ps68xY8bIw8PDfPj7+2dorgAAAACQWSTdeCwvvfSSypQpowULFmjfvn367bffFBoa+kTHuH79uhYvXpxilTs1rq6uKlWqlI4ePZpmnYEDByohIcF8nDp16onGCwAAAAD3kXTjsXXs2FGRkZGaN2+eQkJCMrRyXKxYMe3YscOi7N+f71uyZImSkpL09ttvP7TfpKQkxcbGys/PL806jo6Ocnd3tzgAAAAAwBpIuvHY2rRpo7/++ktz5sxRWFhYhtr07NlT8+bN07x583TkyBENGzZMBw8eTLXu3Llz1bhxY3l7e6c417dvX/300086fvy4du7cqebNmysxMVHt27d/7HkBAAAAwOPiPd14bO7u7mrWrJn+97//qXHjxhlq07JlS8XFxemDDz7QzZs31axZM7377rtat26dRb0jR47ol19+0fr161Pt588//1SrVq104cIF5ciRQ6+88op27NihfPnyPZG5AQAAAMDjIOnGE3H69Gm1adNGjo6OGW7z4Ycf6sMPPzR/joiISFEnMDBQJpMpzT4WL178CNECAAAAwNNB0o3HcunSJa1fv14//vijpk+fntXhAAAAAMAzhaQbj6VcuXK6fPmyxo0bpyJFipjLS5QooZMnT6baZvbs2WrTps1TjBIAAAAAsobBlN7eXeARnTx5Urdv3071nI+Pj9zc3J56TGlJTEyUh4eHWi44KAeXZyeu/7oFzXh/OgAAAJ5d9/OIhISEdN+IxEo3rIIHmQEAAAAArwwDAAAAAMBqSLoBAAAAALASkm4AAAAAAKyEe7qBez5rlCfdByAAAAAAQGax0g0AAAAAgJWQdAMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlfD0cuCeziv/lIOLW1aH8Z+zoJl/VocAAAAAWA0r3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQ/o4KDg9WrVy+rjvHZZ5/J399fNjY2mjx5cqplERERKlu2bIb7NBgMWrFihRWjBgAAAIDnh11WB/Bft3nzZtWoUUOXL19WtmzZntq4iYmJ6tatmyZNmqRmzZrJw8Mj1bLk5GR17949w/2ePn1anp6eVo0dAAAAAJ4XJN3/UfHx8bp9+7YaNGggPz8/SdLvv/+eokySjEZjhvv19fW1SrwAAAAA8Dxie/ljCg4OVrdu3dStWzdly5ZN3t7eGjx4sEwmkyTpyy+/VFBQkNzc3OTr66vWrVvr3LlzkqQTJ06oRo0akiRPT08ZDAaFhoaa+05OTlb//v3l5eUlX19fRUREZDiuhIQEde7cWTlz5pS7u7tq1qypAwcOSJKioqJUqlQpSVLBggVlMBhSLTtx4kSq28vnzZunEiVKyNHRUX5+furWrZv53L+3l//1119q2bKlPD095e3trUaNGunEiRPm86GhoWrcuLEmTJggPz8/eXt767333tPt27fNdZKSktS/f3/5+/vL0dFRhQsX1ty5c2UymRQQEKAJEyZYxPf777/LxsZGcXFxGf6+AAAAAMAaSLqfgPnz58vOzk47d+7U1KlT9cknn+jzzz+XJN26dUsjRozQgQMHtGLFCh0/ftycWPv7++vbb7+VJB0+fFinT5/WlClTLPp1dXXVzp07NX78eH300UfasGHDQ+MxmUxq0KCBzpw5o9WrV2vv3r0qV66catWqpUuXLqlly5b64YcfJEm7du3S6dOn9eabb6Yo8/f3T9H3zJkz9d5776lz58767bff9N133ykgICDVOK5fv64aNWrIaDTq559/1i+//CKj0ajXXntNt27dMtfbtGmT4uLitGnTJs2fP19RUVGKiooyn2/Xrp0WL16sqVOnKjY2VrNmzZLRaJTBYFBYWJgiIyMtxp03b55effVVFSpUKNW4kpKSlJiYaHEAAAAAgDWwvfwJ8Pf31yeffCKDwaAiRYrot99+0yeffKJOnTopLCzMXK9gwYKaOnWqKlSooKtXr8poNMrLy0uSlDNnzhT3dJcuXVrDhg2TJBUuXFjTp0/Xxo0bVbt27XTj2bRpk3777TedO3dOjo6OkqQJEyZoxYoVWrp0qTp37ixvb29JUo4cOcxbwlMr+7eRI0fq/fffV8+ePc1l5cuXT7Xu4sWLZWNjo88//1wGg0GSFBkZqWzZsmnz5s2qU6eOdG+Vf/r06bK1tVXRokXVoEEDbdy4UZ06ddKRI0e0ZMkSbdiwQSEhIebv8b4OHTpo6NCh2rVrlypUqKDbt2/ryy+/1Mcff5zm9zNmzBgNHz483e8QAAAAAJ4EVrqfgFdeecWcVEpSpUqVdPToUd29e1f79+9Xo0aNlC9fPrm5uSk4OFi6d0/1w5QuXdris5+fn3lrenr27t2rq1evytvbW0aj0XwcP378sbZcnzt3Tn///bdq1aqVofp79+7VH3/8ITc3N3MMXl5eunnzpkUcJUqUkK2trfnzg/OMjo6Wra2tqlevnuoYfn5+atCggebNmydJWrVqlW7evKk333wzzbgGDhyohIQE83Hq1KkMfwcAAAAAkBmsdFvRzZs3VadOHdWpU0dffvmlcuTIofj4eNWtW9die3Va7O3tLT4bDAYlJyc/tF1ycrL8/Py0efPmFOce5wnpzs7OmaqfnJysl19+WQsXLkxxLkeOHOa/05tnRsbs2LGj2rZtq08++USRkZFq2bKlXFxc0qzv6Oho3gEAAAAAANZE0v0E7NixI8XnwoUL69ChQ7pw4YLGjh1rvj96z549FnUdHBwkSXfv3n1i8ZQrV05nzpyRnZ2d8ufP/8T6dXNzU/78+bVx40bzA+AeFsfXX39tfpjboyhVqpSSk5P1008/mbeX/1v9+vXl6uqqmTNnas2aNfr5558faSwAAAAAeNLYXv4EnDp1Sn369NHhw4e1aNEiTZs2TT179lTevHnl4OCgadOm6dixY/ruu+80YsQIi7b58uWTwWDQqlWrdP78eV29evWx4wkJCVGlSpXUuHFjrVu3TidOnNC2bds0ePDgFEl/ZkVERGjixImaOnWqjh49qn379mnatGmp1m3Tpo2yZ8+uRo0aacuWLTp+/Lh++ukn9ezZU3/++WeGxsufP7/at2+vsLAw84PoNm/erCVLlpjr2NraKjQ0VAMHDlRAQIAqVar0WHMEAAAAgCeFpPsJaNeunW7cuKEKFSrovffeU/fu3dW5c2flyJFDUVFR+uabb1S8eHGNHTs2xeutcufOreHDh2vAgAHy8fGxeP3WozIYDFq9erWqVaumsLAwBQYG6q233tKJEyfk4+PzWH23b99ekydP1owZM1SiRAm9/vrrOnr0aKp1XVxc9PPPPytv3rxq2rSpihUrprCwMN24cSNTK98zZ85U8+bN1bVrVxUtWlSdOnXStWvXLOqEh4fr1q1bFg+uAwAAAICsZjDdf6E0HklwcLDKli2ryZMnZ3Uo/2lbt25VcHCw/vzzz0z/sJCYmCgPDw+1XHBQDi5uVosRqVvQLOWr6QAAAIBn3f08IiEhId1FRe7pxnMtKSlJp06d0pAhQ9SiRYvHXskHAAAAgCeJ7eXPoYULF1q8CuzBo0SJElkd3lO1aNEiFSlSRAkJCRo/fnxWhwMAAAAAFthe/hy6cuWKzp49m+o5e3t75cuX76nH9Dxje3nWYns5AAAAnkdsL3+Bubm5yc2N5BAAAAAAnnVsLwcAAAAAwEpIugEAAAAAsBK2lwP3fNYoT6beHw4AAAAAD8NKNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWwtPLgXs6r/xTDi5uWR3GC29BM/+sDgEAAAB4aljpBgAAAADASki6AQAAAACwEpJuAAAAAACshKQbAAAAAAArIekGAAAAAMBKSLoBAAAAALASkm4AAAAAAKyEpBsAAAAAACsh6QYAAAAAwEpIugEAAAAAsBKSbmRKcHCwunfvrl69esnT01M+Pj767LPPdO3aNXXo0EFubm4qVKiQ1qxZI0m6e/euwsPDVaBAATk7O6tIkSKaMmWKub+bN2+qRIkS6ty5s7ns+PHj8vDw0Jw5c9KN5dq1a3J3d9fSpUstyr///nu5urrqypUrT3z+AAAAAJAZJN3ItPnz5yt79uzatWuXunfvrnfffVdvvvmmKleurH379qlu3bpq27atrl+/ruTkZOXJk0dLlixRTEyMhg4dqg8//FBLliyRJDk5OWnhwoWaP3++VqxYobt376pt27aqUaOGOnXqlG4crq6ueuuttxQZGWlRHhkZqebNm8vNzS3VdklJSUpMTLQ4AAAAAMAaDCaTyZTVQeD5ERwcrLt372rLli3SvZVsDw8PNW3aVAsWLJAknTlzRn5+ftq+fbteeeWVFH289957Onv2rMUK9ccff6zx48erVatW+uabb/Tbb78pe/bsD41n165dqly5suLj45UrVy5duHBBuXLl0oYNG1S9evVU20RERGj48OEpylsuOCgHl9QTdTw5C5r5Z3UIAAAAwGNLTEyUh4eHEhIS5O7unmY9VrqRaaVLlzb/bWtrK29vb5UqVcpc5uPjI0k6d+6cJGnWrFkKCgpSjhw5ZDQaNWfOHMXHx1v0+f7776tIkSKaNm2aIiMjM5RwS1KFChVUokQJc8L/xRdfKG/evKpWrVqabQYOHKiEhATzcerUqUx+AwAAAACQMSTdyDR7e3uLzwaDwaLMYDBIkpKTk7VkyRL17t1bYWFhWr9+vaKjo9WhQwfdunXLoo9z587p8OHDsrW11dGjRzMVT8eOHc1bzCMjI9WhQwdzDKlxdHSUu7u7xQEAAAAA1kDSDavasmWLKleurK5du+qll15SQECA4uLiUtQLCwtTyZIltWDBAvXv318xMTEZHuPtt99WfHy8pk6dqoMHD6p9+/ZPeBYAAAAA8GjssjoAvNgCAgK0YMECrVu3TgUKFNAXX3yh3bt3q0CBAuY6n376qbZv365ff/1V/v7+WrNmjdq0aaOdO3fKwcHhoWN4enqqadOm6tevn+rUqaM8efJYeVYAAAAAkDGsdMOqunTpoqZNm6ply5aqWLGiLl68qK5du5rPHzp0SP369dOMGTPk7/9/D9j69NNP9c8//2jIkCEZHic8PFy3bt1SWFiYVeYBAAAAAI+Cp5fjhbBw4UL17NlTf//9d4ZWxx90/6mDPL386eDp5QAAAHgRZPTp5Wwvx3Pt+vXrOn78uMaMGaN33nkn0wk3AAAAAFgTSTeeafXq1TO/E/zfPvzwQ926dUujRo1StWrVNHDgwKceHwAAAACkh6Qbz7TPP/9cN27cSPWcl5eXvLy8FBER8dTjAgAAAICMIOnGMy137txZHQIAAAAAPDKeXg4AAAAAgJWQdAMAAAAAYCVsLwfu+axRnnQf9Q8AAAAAmcVKNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWwtPLgXs6r/xTDi5uWR3GC2FBM/+sDgEAAAB4JrDSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSfdzKiIiQmXLls3qMJ5ZwcHB6tWrV1aHAQAAAOA/jqQbAAAAAAArIenGM+vWrVtZHQIAAAAAPBaSbitYu3atqlatqmzZssnb21uvv/664uLiJEknTpyQwWDQsmXLVKNGDbm4uKhMmTLavn27RR9z5syRv7+/XFxc1KRJE02aNEnZsmVLd9zIyEgVK1ZMTk5OKlq0qGbMmJGheG/duqVu3brJz89PTk5Oyp8/v8aMGWM+f/ToUVWrVk1OTk4qXry4NmzYIIPBoBUrVkiSNm/eLIPBoH/++cfcJjo6WgaDQSdOnJAkXbx4Ua1atVKePHnk4uKiUqVKadGiRRZxBAcHq1u3burTp4+yZ8+u2rVrS5JiYmJUv359GY1G+fj4qG3btrpw4YK53bVr19SuXTsZjUb5+flp4sSJGZo3AAAAAFgbSbcVXLt2TX369NHu3bu1ceNG2djYqEmTJkpOTjbXGTRokPr27avo6GgFBgaqVatWunPnjiRp69at6tKli3r27Kno6GjVrl1bo0aNSnfMOXPmaNCgQRo1apRiY2M1evRoDRkyRPPnz39ovFOnTtV3332nJUuW6PDhw/ryyy+VP39+SVJycrKaNm0qW1tb7dixQ7NmzdIHH3yQ6e/k5s2bevnll7Vq1Sr9/vvv6ty5s9q2baudO3da1Js/f77s7Oy0detWzZ49W6dPn1b16tVVtmxZ7dmzR2vXrtXZs2fVokULc5t+/fpp06ZNWr58udavX6/Nmzdr7969acaSlJSkxMREiwMAAAAArMEuqwN4ETVr1szi89y5c5UzZ07FxMTIaDRKkvr27asGDRpIkoYPH64SJUrojz/+UNGiRTVt2jTVq1dPffv2lSQFBgZq27ZtWrVqVZpjjhgxQhMnTlTTpk0lSQUKFFBMTIxmz56t9u3bpxtvfHy8ChcurKpVq8pgMChfvnzmcz/88INiY2N14sQJ5cmTR5I0evRo1atXL1PfSe7cuc3zkaTu3btr7dq1+uabb1SxYkVzeUBAgMaPH2/+PHToUJUrV06jR482l82bN0/+/v46cuSIcuXKpblz52rBggXmlfH58+ebY03NmDFjNHz48EzFDwAAAACPgpVuK4iLi1Pr1q1VsGBBubu7q0CBAtK95Pa+0qVLm//28/OTJJ07d06SdPjwYVWoUMGiz39/ftD58+d16tQphYeHy2g0mo+RI0eat7WnJzQ0VNHR0SpSpIh69Oih9evXm8/FxsYqb968FklspUqVMvhN/H93797VqFGjVLp0aXl7e8toNGr9+vUW34kkBQUFWXzeu3evNm3aZDGvokWLSve+57i4ON26dcsiJi8vLxUpUiTNWAYOHKiEhATzcerUqUzPBwAAAAAygpVuK2jYsKH8/f01Z84c5cqVS8nJySpZsqTFg8Hs7e3NfxsMBuneVm5JMplM5rL7TCZTmuPdbzdnzhyLVWNJsrW1fWi85cqV0/Hjx7VmzRr98MMPatGihUJCQrR06dJUx/13bDY2NilivH37tkWdiRMn6pNPPtHkyZNVqlQpubq6qlevXikelubq6ppibg0bNtS4ceNSxOHn56ejR48+dH7/5ujoKEdHx0y3AwAAAIDMIul+wi5evKjY2FjNnj1br776qiTpl19+yVQfRYsW1a5duyzK9uzZk2Z9Hx8f5c6dW8eOHVObNm0eKW53d3e1bNlSLVu2VPPmzfXaa6/p0qVLKl68uOLj4/X3338rV65ckpTioW85cuSQJJ0+fVqenp7SvQepPWjLli1q1KiR3n77beleMn306FEVK1Ys3bjKlSunb7/9Vvnz55edXcrLNSAgQPb29tqxY4fy5s0rSbp8+bKOHDmi6tWrP9J3AQAAAABPCkn3E+bp6Slvb2999tln8vPzU3x8vAYMGJCpPrp3765q1app0qRJatiwoX788UetWbMmxQrzgyIiItSjRw+5u7urXr16SkpK0p49e3T58mX16dMn3fE++eQT+fn5qWzZsrKxsdE333wjX19fZcuWTSEhISpSpIjatWuniRMnKjExUYMGDbJoHxAQIH9/f0VERGjkyJE6evRoiieIBwQE6Ntvv9W2bdvk6empSZMm6cyZMw9Nut977z3NmTNHrVq1Ur9+/ZQ9e3b98ccfWrx4sebMmSOj0ajw8HD169dP3t7e8vHx0aBBg8yr7wAAAACQlchMnjAbGxstXrxYe/fuVcmSJdW7d299/PHHmeqjSpUqmjVrliZNmqQyZcpo7dq16t27t5ycnNJs07FjR33++eeKiopSqVKlVL16dUVFRZnvJ0+P0WjUuHHjFBQUpPLly+vEiRNavXq1bGxsZGNjo+XLlyspKUkVKlRQx44dUzxJ3d7eXosWLdKhQ4dUpkwZjRs3TiNHjrSoM2TIEJUrV05169ZVcHCwfH191bhx44fGlitXLm3dulV3795V3bp1VbJkSfXs2VMeHh7mxPrjjz9WtWrV9MYbbygkJERVq1bVyy+//NC+AQAAAMDaDKb0bhbGM6NTp046dOiQtmzZktWhSPfu616+fHmGEudnXWJiojw8PNRywUE5uLhldTgvhAXN/LM6BAAAAMCq7ucRCQkJcnd3T7Me28ufURMmTFDt2rXl6uqqNWvWaP78+ZoxY0ZWhwUAAAAAyAS2lz+jdu3apdq1a6tUqVKaNWuWpk6dqo4dOz5SX6NHj7Z45daDR2bftw0AAAAAyDhWup9RS5YseWJ9denSRS1atEj1nLOz8yP1yV0JAAAAAPBwJN3/AV5eXvLy8srqMAAAAADgP4ft5QAAAAAAWAlJNwAAAAAAVsL2cuCezxrlSfdR/wAAAACQWax0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJrwwD7um88k85uLhldRjPjQXN/LM6BAAAAOCZx0o3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFZC0g0AAAAAgJWQdFvBiRMnZDAYFB0d/Uz0AwAAAADIGiTdz4jQ0FA1btzYoszf31+nT59WyZIln0oMERERKlu27FMZS5KioqKULVu2J9bf044fAAAAAB6GpDuTbt++/dTGsrW1la+vr+zs7J7amM8jk8mkO3fuZHUYAAAAAJBCppLu4OBgde/eXb169ZKnp6d8fHz02Wef6dq1a+rQoYPc3NxUqFAhrVmzxtwmJiZG9evXl9FolI+Pj9q2basLFy6Yz69du1ZVq1ZVtmzZ5O3trddff11xcXHm8/e3WC9btkw1atSQi4uLypQpo+3btz803mvXrsnd3V1Lly61KP/+++/l6uqqK1eupNv+/thLlixRcHCwnJyc9OWXX0qSIiMjVaxYMTk5Oalo0aKaMWNGmv3cvXtX4eHhKlCggJydnVWkSBFNmTLFfD4iIkLz58/XypUrZTAYZDAYtHnzZovt5cnJycqTJ49mzZpl0fe+fftkMBh07NgxSVJCQoI6d+6snDlzyt3dXTVr1tSBAwce+l1FRUVp+PDhOnDggDmGqKgoSVJ8fLwaNWoko9Eod3d3tWjRQmfPnn1on5J04MAB1ahRQ25ubnJ3d9fLL7+sPXv2aPPmzerQoYMSEhLM40VEREiSvvzySwUFBcnNzU2+vr5q3bq1zp07Z+5z8+bNMhgMWrdunYKCguTo6KgvvvgizfgBAAAAIKtkeqV7/vz5yp49u3bt2qXu3bvr3Xff1ZtvvqnKlStr3759qlu3rtq2bavr16/r9OnTql69usqWLas9e/Zo7dq1Onv2rFq0aGHu79q1a+rTp492796tjRs3ysbGRk2aNFFycrLFuIMGDVLfvn0VHR2twMBAtWrV6qGrm66urnrrrbcUGRlpUR4ZGanmzZvLzc0tQ3P+4IMP1KNHD8XGxqpu3bqaM2eOBg0apFGjRik2NlajR4/WkCFDNH/+/FTb30+YlyxZopiYGA0dOlQffvihlixZIknq27evWrRooddee02nT5/W6dOnVblyZYs+bGxs9NZbb2nhwoUW5V999ZUqVaqkggULymQyqUGDBjpz5oxWr16tvXv3qly5cqpVq5YuXbqU7hxbtmyp999/XyVKlDDH0LJlS5lMJjVu3FiXLl3STz/9pA0bNiguLk4tW7bM0HfXpk0b5cmTR7t379bevXs1YMAA2dvbq3Llypo8ebLc3d3N4/Xt21eSdOvWLY0YMUIHDhzQihUrdPz4cYWGhqbou3///hozZoxiY2NVp06dVOMHAAAAgKyU6X3LZcqU0eDBgyVJAwcO1NixY5U9e3Z16tRJkjR06FDNnDlTv/76q1avXq1y5cpp9OjR5vbz5s2Tv7+/jhw5osDAQDVr1syi/7lz5ypnzpyKiYmxuJe5b9++atCggSRp+PDhKlGihP744w8VLVo03Xg7duyoypUr6++//1auXLl04cIFrVq1Shs2bMjwnHv16qWmTZuaP48YMUITJ040lxUoUEAxMTGaPXu22rdvn6K9vb29hg8fbv5coEABbdu2TUuWLFGLFi1kNBrl7OyspKQk+fr6phlHmzZtNGnSJJ08eVL58uVTcnKyFi9erA8//FCStGnTJv322286d+6cHB0dJUkTJkzQihUrtHTpUnXu3DnNvp2dnWU0GmVnZ2cRw4YNG/Trr7/q+PHj8vf3lyR98cUXKlGihHbv3q3y5cun+93Fx8erX79+5n+nwoULm895eHjIYDCkmHNYWJj574IFC2rq1KmqUKGCrl69KqPRaD730UcfqXbt2ubPqcWfmqSkJCUlJZk/JyYmplsfAAAAAB5Vple6S5cubf7b1tZW3t7eKlWqlLnMx8dHknTu3Dnt3btXmzZtktFoNB/3k6/7W8jj4uLUunVrFSxYUO7u7ipQoIB0L1lLa1w/Pz/zGA9ToUIFlShRQgsWLJDuJYx58+ZVtWrVMjznoKAg89/nz5/XqVOnFB4ebjGvkSNHWmyL/7dZs2YpKChIOXLkkNFo1Jw5c1LM8WFeeuklFS1aVIsWLZIk/fTTTzp37px558DevXt19epVeXt7W8R2/PjxdGNLT2xsrPz9/c0JtyQVL15c2bJlU2xs7EPb9+nTRx07dlRISIjGjh2boTj279+vRo0aKV++fHJzc1NwcLCUyjXx4L9LZowZM0YeHh7m48G5AQAAAMCTlOmk297e3uKzwWCwKDMYDNK9LdXJyclq2LChoqOjLY6jR4+ak96GDRvq4sWLmjNnjnbu3KmdO3dK97YYpzXug2NkRMeOHc1bzCMjI9WhQwdzHxnh6upq/vv+mHPmzLGY0++//64dO3ak2n7JkiXq3bu3wsLCtH79ekVHR6tDhw4p5pgRbdq00VdffSXd21pet25dZc+e3Rybn59fiu/78OHD6tevX6bH0r2HlKX2XaVV/m8RERE6ePCgGjRooB9//FHFixfX8uXL06x/7do11alTR0ajUV9++aV2795trv/v7+vBf5fMGDhwoBISEszHqVOnHqkfAAAAAHgYqz4Wu1y5cvr222+VP3/+VJ/AffHiRcXGxmr27Nl69dVXJUm//PLLE4/j7bffVv/+/TV16lQdPHgw1S3gGeXj46PcuXPr2LFjatOmTYbabNmyRZUrV1bXrl3NZf9e8XVwcNDdu3cf2lfr1q01ePBg7d27V0uXLtXMmTPN58qVK6czZ87Izs5O+fPnz9S80oqhePHiio+P16lTp8wrwjExMUpISFCxYsUy1G9gYKACAwPVu3dvtWrVSpGRkWrSpEmq4x06dEgXLlzQ2LFjzePt2bPnkeNPjaOjo3n7PQAAAABYk1VfGfbee+/p0qVLatWqlXbt2qVjx45p/fr1CgsL0927d+Xp6Slvb2999tln+uOPP/Tjjz+qT58+TzwOT09PNW3aVP369VOdOnWUJ0+ex+ovIiJCY8aM0ZQpU3TkyBH99ttvioyM1KRJk1KtHxAQoD179mjdunU6cuSIhgwZot27d1vUyZ8/v3799VcdPnxYFy5cSPPVZAUKFFDlypUVHh6uO3fuqFGjRuZzISEhqlSpkho3bqx169bpxIkT2rZtmwYPHpyhxDV//vw6fvy4oqOjdeHCBSUlJSkkJESlS5dWmzZttG/fPu3atUvt2rVT9erVH7q9+8aNG+rWrZs2b96skydPauvWrdq9e7c5Wc+fP7+uXr2qjRs36sKFC7p+/bry5s0rBwcHTZs2TceOHdN3332nESNGPDT2tOIHAAAAgKxk1aQ7V65c2rp1q+7evau6deuqZMmS6tmzpzw8PGRjYyMbGxstXrxYe/fuVcmSJdW7d299/PHHVoklPDxct27dsnhI16Pq2LGjPv/8c0VFRalUqVKqXr26oqKizPej/1uXLl3UtGlTtWzZUhUrVtTFixctVr0lqVOnTipSpIj5vu+tW7emOX6bNm104MABNW3aVM7OzuZyg8Gg1atXq1q1agoLC1NgYKDeeustnThxwnyvfXqaNWum1157TTVq1FCOHDm0aNEiGQwGrVixQp6enqpWrZpCQkJUsGBBff311w/tz9bWVhcvXlS7du0UGBioFi1aqF69euaHylWuXFldunRRy5YtlSNHDo0fP145cuRQVFSUvvnmGxUvXlxjx47VhAkTHjpWWvEDAAAAQFYymEwmU1YH8TQsXLhQPXv21N9//y0HB4esDgfPkMTERHl4eKjlgoNycMnYa+QgLWjGA+gAAADw33U/j0hISJC7u3ua9ax6T/ez4Pr16zp+/LjGjBmjd955h4QbAAAAAPDUWHV7+dNQr149i9djPXiMHj1a48ePV9myZeXj46OBAwdatB09enSabevVq5dlc7KWEiVKpDnfhQsXPjN9AgAAAMCL4rnfXv7XX3/pxo0bqZ7z8vKSl5dXmm0vXbqkS5cupXrO2dlZuXPnfmJxPgtOnjyZ5gPafHx85OaW+a3V1ujzaWN7+aNhezkAAAD+y/4z28sfJzF+WFL+osmXL99z0ScAAAAAvCie++3lAAAAAAA8q0i6AQAAAACwEpJuAAAAAACs5Lm/pxt4Uj5rlCfdByAAAAAAQGax0g0AAAAAgJWQdAMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlfD0cuCeziv/lIOLW1aH8UxZ0Mw/q0MAAAAAnmusdAMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtL9goiIiFDZsmWzOgxJUv78+TV58uTH6iM0NFSNGzdOt05wcLB69er1WOMAAAAAgDWRdAMAAAAAYCUk3QAAAAAAWAlJ91Owdu1aVa1aVdmyZZO3t7def/11xcXFSZJOnDghg8GgZcuWqUaNGnJxcVGZMmW0fft2iz7mzJkjf39/ubi4qEmTJpo0aZKyZcuW7riRkZEqVqyYnJycVLRoUc2YMSND8S5YsEBGo1FHjx41l3Xv3l2BgYG6du1ahvq4fv26wsLC5Obmprx58+qzzz6zOP/bb7+pZs2acnZ2lre3tzp37qyrV6+m2d+1a9fUrl07GY1G+fn5aeLEiSnqzJgxQ4ULF5aTk5N8fHzUvHnzDMUKAAAAANZC0v0UXLt2TX369NHu3bu1ceNG2djYqEmTJkpOTjbXGTRokPr27avo6GgFBgaqVatWunPnjiRp69at6tKli3r27Kno6GjVrl1bo0aNSnfMOXPmaNCgQRo1apRiY2M1evRoDRkyRPPnz39ovO3atVP9+vXVpk0b3blzR2vXrtXs2bO1cOFCubq6ZmjOEydOVFBQkPbv36+uXbvq3Xff1aFDh6R7Cflrr70mT09P7d69W998841++OEHdevWLc3++vXrp02bNmn58uVav369Nm/erL1795rP79mzRz169NBHH32kw4cPa+3atapWrVqqfSUlJSkxMdHiAAAAAABrsMvqAP4LmjVrZvF57ty5ypkzp2JiYmQ0GiVJffv2VYMGDSRJw4cPV4kSJfTHH3+oaNGimjZtmurVq6e+fftKkgIDA7Vt2zatWrUqzTFHjBihiRMnqmnTppKkAgUKKCYmRrNnz1b79u0fGvPs2bNVunRp9ejRQ8uWLdOwYcNUvnz5DM+5fv366tq1qyTpgw8+0CeffKLNmzeraNGiWrhwoW7cuKEFCxaYk/jp06erYcOGGjdunHx8fCz6unr1qubOnasFCxaodu3akqT58+crT5485jrx8fFydXXV66+/Ljc3N+XLl08vvfRSqrGNGTNGw4cPz/BcAAAAAOBRsdL9FMTFxal169YqWLCg3N3dVaBAAeleonhf6dKlzX/7+flJks6dOydJOnz4sCpUqGDR578/P+j8+fM6deqUwsPDZTQazcfIkSPN29ofxtPTU3PnztXMmTNVqFAhDRgwIFNzfnA+BoNBvr6+5vnExsaqTJkyFqvmVapUUXJysg4fPpyir7i4ON26dUuVKlUyl3l5ealIkSLmz7Vr11a+fPlUsGBBtW3bVgsXLtT169dTjW3gwIFKSEgwH6dOncrU3AAAAAAgo1jpfgoaNmwof39/zZkzR7ly5VJycrJKliypW7dumevY29ub/zYYDJJk3n5uMpnMZfeZTKY0x7vfbs6cOapYsaLFOVtb2wzH/fPPP8vW1lZ///23rl27Jnd39wy3fXA+ujen9ObzYL1/S2+u97m5uWnfvn3avHmz1q9fr6FDhyoiIkK7d+9Oce+7o6OjHB0dMzwXAAAAAHhUrHRb2cWLFxUbG6vBgwerVq1aKlasmC5fvpypPooWLapdu3ZZlO3ZsyfN+j4+PsqdO7eOHTumgIAAi+P+KvvDbNu2TePHj9f3338vd3d3de/ePVMxp6d48eKKjo62eCjb1q1bZWNjo8DAwBT1AwICZG9vrx07dpjLLl++rCNHjljUs7OzU0hIiMaPH69ff/1VJ06c0I8//vjE4gYAAACAzGKl28o8PT3l7e2tzz77TH5+foqPj8/0Vu3u3burWrVqmjRpkho2bKgff/xRa9asSXO1WJIiIiLUo0cPubu7q169ekpKStKePXt0+fJl9enTJ93xrly5orZt26p79+6qV6+e8ubNq6CgIL3++ut68803MxV7atq0aaNhw4apffv2ioiI0Pnz59W9e3e1bds2xf3ckmQ0GhUeHq5+/frJ29tbPj4+GjRokGxs/v9vRqtWrdKxY8dUrVo1eXp6avXq1UpOTrbYgg4AAAAATxsr3VZmY2OjxYsXa+/evSpZsqR69+6tjz/+OFN9VKlSRbNmzdKkSZNUpkwZrV27Vr1795aTk1OabTp27KjPP/9cUVFRKlWqlKpXr66oqKgMrXT37NlTrq6uGj16tCSpRIkSGjdunLp06aK//vorU7GnxsXFRevWrdOlS5dUvnx5NW/eXLVq1dL06dPTbPPxxx+rWrVqeuONNxQSEqKqVavq5ZdfNp/Pli2bli1bppo1a6pYsWKaNWuWFi1apBIlSjx2vAAAAADwqAymjNwwi2dOp06ddOjQIW3ZsiWrQ3nuJSYmysPDQy0XHJSDi1tWh/NMWdDMP6tDAAAAAJ5J9/OIhISEdJ9/xfby58SECRNUu3Ztubq6as2aNZo/f75mzJiR1WEBAAAAANLB9vLnxK5du1S7dm2VKlVKs2bN0tSpU9WxY8dH6mv06NEWrxJ78KhXr166bbds2ZJm2/vvHAcAAAAA/B9Wup8TS5YseWJ9denSRS1atEj1nLOzc7ptg4KCFB0d/cRiAQAAAIAXGUn3f5CXl5e8vLweqa2zs7MCAgKeeEwAAAAA8CJiezkAAAAAAFZC0g0AAAAAgJWwvRy457NGedJ91D8AAAAAZBYr3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFZC0g0AAAAAgJWQdAMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWQtINAAAAAICVkHQDAAAAAGAlJN0AAAAAAFgJSTcAAAAAAFZC0g0AAAAAgJWQdAMAAAAAYCUk3QAAAAAAWAlJNwAAAAAAVkLSDQAAAACAlZB0AwAAAABgJSTdAAAAAABYCUk3AAAAAABWYpfVAQBZzWQySZISExOzOhQAAAAAz4n7+cP9fCItJN34z7t48aIkyd/fP6tDAQAAAPCcuXLlijw8PNI8T9KN/zwvLy9JUnx8fLr/YwGsLTExUf7+/jp16pTc3d2zOhz8h3Et4lnAdYhnBdci0mIymXTlyhXlypUr3Xok3fjPs7H5v0cbeHh48B9SPBPc3d25FvFM4FrEs4DrEM8KrkWkJiOLdjxIDQAAAAAAKyHpBgAAAADASki68Z/n6OioYcOGydHRMatDwX8c1yKeFVyLeBZwHeJZwbWIx2UwPez55gAAAAAA4JGw0g0AAAAAgJWQdAMAAAAAYCUk3QAAAAAAWAlJN/4TZsyYoQIFCsjJyUkvv/yytmzZkm79n376SS+//LKcnJxUsGBBzZo166nFihdbZq7FZcuWqXbt2sqRI4fc3d1VqVIlrVu37qnGixdXZv+7eN/WrVtlZ2ensmXLWj1GvPgyex0mJSVp0KBBypcvnxwdHVWoUCHNmzfvqcWLF1dmr8WFCxeqTJkycnFxkZ+fnzp06KCLFy8+tXjxfCHpxgvv66+/Vq9evTRo0CDt379fr776qurVq6f4+PhU6x8/flz169fXq6++qv379+vDDz9Ujx499O233z712PFiyey1+PPPP6t27dpavXq19u7dqxo1aqhhw4bav3//U48dL5bMXov3JSQkqF27dqpVq9ZTixUvrke5Dlu0aKGNGzdq7ty5Onz4sBYtWqSiRYs+1bjx4snstfjLL7+oXbt2Cg8P18GDB/XNN99o9+7d6tix41OPHc8Hnl6OF17FihVVrlw5zZw501xWrFgxNW7cWGPGjElR/4MPPtB3332n2NhYc1mXLl104MABbd++/anFjRdPZq/F1JQoUUItW7bU0KFDrRgpXnSPei2+9dZbKly4sGxtbbVixQpFR0c/pYjxIsrsdbh27Vq99dZbOnbsmLy8vJ5ytHiRZfZanDBhgmbOnKm4uDhz2bRp0zR+/HidOnXqqcWN5wcr3Xih3bp1S3v37lWdOnUsyuvUqaNt27al2mb79u0p6tetW1d79uzR7du3rRovXlyPci3+W3Jysq5cucL/s4nH8qjXYmRkpOLi4jRs2LCnECVedI9yHX733XcKCgrS+PHjlTt3bgUGBqpv3766cePGU4oaL6JHuRYrV66sP//8U6tXr5bJZNLZs2e1dOlSNWjQ4ClFjeeNXVYHAFjThQsXdPfuXfn4+FiU+/j46MyZM6m2OXPmTKr179y5owsXLsjPz8+qMePF9CjX4r9NnDhR165dU4sWLawUJf4LHuVaPHr0qAYMGKAtW7bIzo7/1wGP71Guw2PHjumXX36Rk5OTli9frgsXLqhr1666dOkS93XjkT3KtVi5cmUtXLhQLVu21M2bN3Xnzh298cYbmjZt2lOKGs8bVrrxn2AwGCw+m0ymFGUPq59aOZBZmb0W71u0aJEiIiL09ddfK2fOnFaMEP8VGb0W7969q9atW2v48OEKDAx8ihHivyAz/01MTk6WwWDQwoULVaFCBdWvX1+TJk1SVFQUq914bJm5FmNiYtSjRw8NHTpUe/fu1dq1a3X8+HF16dLlKUWL5w0/V+OFlj17dtna2qb4pfLcuXMpftG8z9fXN9X6dnZ28vb2tmq8eHE9yrV439dff63w8HB98803CgkJsXKkeNFl9lq8cuWK9uzZo/3796tbt27SveTHZDLJzs5O69evV82aNZ9a/HgxPMp/E/38/JQ7d255eHiYy4oVKyaTyaQ///xThQsXtnrcePE8yrU4ZswYValSRf369ZMklS5dWq6urnr11Vc1cuRIdkUiBVa68UJzcHDQyy+/rA0bNliUb9iwQZUrV061TaVKlVLUX79+vYKCgmRvb2/VePHiepRrUfdWuENDQ/XVV19xrxieiMxei+7u7vrtt98UHR1tPrp06aIiRYooOjpaFStWfIrR40XxKP9NrFKliv7++29dvXrVXHbkyBHZ2NgoT548Vo8ZL6ZHuRavX78uGxvLNMrW1lZ6YHckYMEEvOAWL15ssre3N82dO9cUExNj6tWrl8nV1dV04sQJk8lkMg0YMMDUtm1bc/1jx46ZXFxcTL179zbFxMSY5s6da7K3tzctXbo0C2eBF0Fmr8WvvvrKZGdnZ/r0009Np0+fNh///PNPFs4CL4LMXov/NmzYMFOZMmWeYsR4EWX2Orxy5YopT548pubNm5sOHjxo+umnn0yFCxc2dezYMQtngRdBZq/FyMhIk52dnWnGjBmmuLg40y+//GIKCgoyVahQIQtngWcZ28vxwmvZsqUuXryojz76SKdPn1bJkiW1evVq5cuXT5J0+vRpi/cwFihQQKtXr1bv3r316aefKleuXJo6daqaNWuWhbPAiyCz1+Ls2bN1584dvffee3rvvffM5e3bt1dUVFSWzAEvhsxei4A1ZPY6NBqN2rBhg7p3766goCB5e3urRYsWGjlyZBbOAi+CzF6LoaGhunLliqZPn673339f2bJlU82aNTVu3LgsnAWeZbynGwAAAAAAK+GebgAAAAAArISkGwAAAAAAKyHpBgAAAADASki6AQAAAACwEpJuAAAAAACshKQbAAAAAAArIekGAAAAAMBKSLoBAAAAALASkm4AADIhKipKBoNBe/bsyepQHtlXX32lyZMnZ3UYT1ytWrXUpUuXh9a7/2944sSJpxLX8+zEiRMyGAyKiop6Yn2GhobKYDDIzc1NV69eTXH+5MmTsrGxkcFgUERERKp9fPfddzIYDPL29lZSUlKqdfLnzy+DwZDqERwcbK43d+5c5c6dW9euXXticwSAB5F0AwDwH/MiJt0rV67U1q1bNWTIkKwO5YXi5+en7du3q0GDBk+0X3t7e925c0dff/11inORkZFyc3NLt/3cuXMlSZcuXdKKFSvSrFelShVt3749xTFjxgxznfbt28vV1VXjx49/rDkBQFpIugEA+I+4fv16VodgNaNHj1aTJk2UO3furA4lVc/rd+/o6KhXXnlFOXLkeKL9Ojg4qHHjxpo3b55FuclkUlRUlFq2bJlm2zNnzmj16tWqWbOmnJyczAl4arJly6ZXXnklxVG8eHFzHTs7O73zzjuaMmXKc/vvBODZRtINAMBjCg0NldFo1KFDh1S3bl25urrKz89PY8eOlSTt2LFDVatWlaurqwIDAzV//nyL9ve3O2/YsEEdOnSQl5eXXF1d1bBhQx07dizFePPmzVOZMmXk5OQkLy8vNWnSRLGxsanG9Ntvv6lOnTpyc3NTrVq1FBwcrP/97386efKkxXbb+4YPH66KFSvKy8tL7u7uKleunObOnSuTyWTRf/78+fX6669r7dq1KleunJydnVW0aNEUSZQk/fXXX+rcubP8/f3l4OCgXLlyqXnz5jp79qy5TmJiovr27asCBQrIwcFBuXPnVq9evTK05Xf//v3atWuX2rZtm+Lcjh07VKVKFTk5OSlXrlwaOHCgbt++nWo/X3/9tSpVqiRXV1cZjUbVrVtX+/fvT1Fv586datiwoby9veXk5KRChQqpV69e5vMREREyGAzat2+fmjdvLk9PTxUqVEi6l1TOmDFDZcuWlbOzszw9PdW8efMU/84bNmxQo0aNlCdPHjk5OSkgIEDvvPOOLly4YFHv/Pnz5u/W0dFROXLkUJUqVfTDDz9Y1Pvhhx9Uq1Ytubu7y8XFRVWqVNHGjRsf+t2mtr38/vwOHjyoVq1aycPDQz4+PgoLC1NCQsJD+7wvLCxM27Zt0+HDhy3iPHnypDp06JBmu/nz5+vOnTvq3bu3mjZtqo0bN+rkyZMZHjc1bdq0UWJiohYvXvxY/QBAaki6AQB4Am7fvq2mTZuqQYMGWrlyperVq6eBAwfqww8/VPv27RUWFqbly5erSJEiCg0N1d69e1P0ER4eLhsbG/P27127dik4OFj//POPuc6YMWMUHh6uEiVKaNmyZZoyZYp+/fVXVapUSUePHrXo79atW3rjjTdUs2ZNrVy5UsOHD9eMGTNUpUoV+fr6Wmy3ve/EiRN65513tGTJEi1btkxNmzZV9+7dNWLEiBTxHjhwQO+//7569+6tlStXqnTp0goPD9fPP/9srvPXX3+pfPnyWr58ufr06aM1a9Zo8uTJ8vDw0OXLl6V7q8DVq1fX/Pnz1aNHD61Zs0YffPCBoqKi9MYbb6RI+P9t1apVsrW1VbVq1SzKY2JiVKtWLf3zzz+KiorSrFmztH//fo0cOTJFH6NHj1arVq1UvHhxLVmyRF988YWuXLmiV199VTExMeZ669at06uvvqr4+HhNmjRJa9as0eDBgy1+QLivadOmCggI0DfffKNZs2ZJkt555x316tVLISEhWrFihWbMmKGDBw+qcuXKFn3ExcWpUqVKmjlzptavX6+hQ4dq586dqlq1qsWPBm3bttWKFSs0dOhQrV+/Xp9//rlCQkJ08eJFc50vv/xSderUkbu7u+bPn68lS5bIy8tLdevWzVDinZZmzZopMDBQ3377rQYMGKCvvvpKvXv3znD7kJAQ5cuXz+KHmrlz56patWoqXLhwmu3mzZsnPz8/1atXT2FhYUpOTk7znnOTyaQ7d+6kOP59Tfn6+qpo0aL63//+l+H4ASDDTAAAIMMiIyNNkky7d+82l7Vv394kyfTtt9+ay27fvm3KkSOHSZJp37595vKLFy+abG1tTX369EnRZ5MmTSzG2rp1q0mSaeTIkSaTyWS6fPmyydnZ2VS/fn2LevHx8SZHR0dT69atU8Q0b968FHNo0KCBKV++fA+d6927d023b982ffTRRyZvb29TcnKy+Vy+fPlMTk5OppMnT5rLbty4YfLy8jK988475rKwsDCTvb29KSYmJs1xxowZY7KxsbH4Tk0mk2np0qUmSabVq1enG2e9evVMRYsWTVHesmVLk7Ozs+nMmTPmsjt37piKFi1qkmQ6fvy4yXTv+7OzszN1797dov2VK1dMvr6+phYtWpjLChUqZCpUqJDpxo0bacYzbNgwkyTT0KFDLcq3b99ukmSaOHGiRfmpU6dMzs7Opv79+6faX3Jysun27dumkydPmiSZVq5caT5nNBpNvXr1SjOWa9eumby8vEwNGza0KL97966pTJkypgoVKqTZ1mQymY4fP26SZIqMjEwxv/Hjx1vU7dq1q8nJycniOklN+/btTa6urua+fH19Tbdv3zZdvHjR5OjoaIqKijKdP3/eJMk0bNgwi7Y///yzSZJpwIAB5u+mQIECpnz58qUYN1++fCZJqR4jRoxIEVebNm1MPj4+6cYOAI+ClW4AAJ4Ag8Gg+vXrmz/b2dkpICBAfn5+eumll8zlXl5eypkzZ6rbYdu0aWPxuXLlysqXL582bdokSdq+fbtu3Lih0NBQi3r+/v6qWbNmqquWzZo1y9Q8fvzxR4WEhMjDw0O2trayt7fX0KFDdfHiRZ07d86ibtmyZZU3b17zZycnJwUGBlrMbc2aNapRo4aKFSuW5pirVq1SyZIlVbZsWYvVyLp168pgMGjz5s3pxvz3338rZ86cKco3bdqkWrVqycfHx1xma2ub4n7hdevW6c6dO2rXrp3F+E5OTqpevbp5/CNHjiguLk7h4eFycnJKNyal8t2vWrVKBoNBb7/9tsU4vr6+KlOmjMU8z507py5dusjf3192dnayt7dXvnz5JMniVoIKFSooKipKI0eO1I4dO1Jsnd+2bZsuXbqk9u3bW4yZnJys1157Tbt3737kp3a/8cYbFp9Lly6tmzdvprhO0tOhQwedPXtWa9as0cKFC+Xg4KA333wzzfr3798OCwuT7v3vLjQ0VCdPnkz1+q9atap2796d4ggPD09RN2fOnDp37pzu3LmT4fgBICPssjoAAABeBC4uLikSMQcHB3l5eaWo6+DgoJs3b6Yo9/X1TbXs/lbh+//Xz88vRb1cuXJpw4YNKWJyd3fP8Bx27dqlOnXqKDg4WHPmzFGePHnk4OCgFStWaNSoUbpx44ZFfW9v7xR9ODo6WtQ7f/688uTJk+64Z8+e1R9//CF7e/tUz//7PuZ/u3HjhkVifd/FixfT/E7/Pb4klS9fPtX+bWz+b43i/PnzkvTQ+dz373+ns2fPymQypRqrJBUsWFCSlJycrDp16ujvv//WkCFDVKpUKbm6uio5OVmvvPKKxff79ddfa+TIkfr88881ZMgQGY1GNWnSROPHj5evr695bs2bN08zzkuXLsnV1TVDc3rQv//9HR0dpXv/HhmVL18+1apVS/PmzdOJEyf01ltvycXFJdUHml25ckXffPONKlSooBw5cphvu2jSpIkiIiI0d+5chYSEWLTx8PBQUFBQhmJxcnKSyWTSzZs3ZTQaMzwHAHgYkm4AAJ4RZ86cSbUsICBAeiDJOX36dIp6f//9t7Jnz25R9uAD0jJi8eLFsre316pVqyx+QEjvlUwPkyNHDv3555/p1smePbucnZ1TfQjb/fMPa3/p0qUU5d7e3ml+p6n1v3TpUvNqcmruP8H7YfO579/ff/bs2WUwGLRlyxZzgvqg+2W///67Dhw4oKioKLVv3958/o8//kjRJnv27Jo8ebImT56s+Ph4fffddxowYIDOnTuntWvXmuc2bdo0vfLKK6nGmdaPAE9LWFiY3n77bSUnJ2vmzJlp1lu0aJGuX7+uXbt2ydPTM8X55cuX6/Lly6mey4hLly7J0dGRhBvAE0fSDQDAM2LhwoUWW5K3bdumkydPqmPHjpKkSpUqydnZWV9++aXFFtw///xTP/74Y7qrmQ/692r0fQaDQXZ2drK1tTWX3bhxQ1988cUjz6levXr64osvdPjwYRUpUiTVOq+//rpGjx4tb29vFShQINNjFC1aNNUfBmrUqKHvvvtOZ8+eNSeWd+/eTfFu6Lp168rOzk5xcXHpbscPDAxUoUKFNG/ePPXp0yfVxDk9r7/+usaOHau//vpLLVq0SLPe/WT93/3Pnj073f7z5s2rbt26aePGjdq6dat07z3V2bJlU0xMjLp165apeJ+WJk2aqEmTJvLw8EjzhwHd21ru5uamFStWmHcf3Ldnzx7169dPCxcufOR5Hjt2zOJVYgDwpJB0AwDwjNizZ486duyoN998U6dOndKgQYOUO3dude3aVbr3zuEhQ4boww8/VLt27dSqVStdvHhRw4cPl5OTk4YNG5ahcUqVKqVly5Zp5syZevnll2VjY6OgoCA1aNBAkyZNUuvWrdW5c2ddvHhREyZMyHRy+aCPPvpIa9asUbVq1fThhx+qVKlS+ueff7R27Vr16dNHRYsWVa9evfTtt9+qWrVq6t27t0qXLq3k5GTFx8dr/fr1ev/991WxYsU0xwgODta8efN05MgRBQYGmssHDx6s7777TjVr1tTQoUPl4uKiTz/9NMU9zPnz59dHH32kQYMG6dixY3rttdfk6emps2fPateuXXJ1ddXw4cMlSZ9++qkaNmyoV155Rb1791bevHkVHx+vdevWaeHChel+F1WqVFHnzp3VoUMH7dmzR9WqVZOrq6tOnz6tX375RaVKldK7776rokWLqlChQhowYIBMJpO8vLz0/fffp7h9ICEhQTVq1FDr1q1VtGhRubm5affu3Vq7dq2aNm0qSTIajZo2bZrat2+vS5cuqXnz5sqZM6fOnz+vAwcO6Pz58+muLj8NTk5OWrp0abp1fv/9d+3atUvvvvuuatasmeJ8lSpVNHHiRM2dO9ci6f7nn3+0Y8eOFPUdHR0tnrWQnJysXbt2pXqvNwA8LpJuAACeEXPnztUXX3yht956S0lJSapRo4amTJlicV/4wIEDlTNnTk2dOlVff/21nJ2dFRwcrNGjR6f7mqUH9ezZUwcPHtSHH36ohIQEmUwmmUwm1axZU/PmzdO4cePUsGFD5c6dW506dVLOnDkfORnJnTu3du3apWHDhmns2LG6ePGicuTIoapVq5rn5erqqi1btmjs2LH67LPPdPz4cTk7Oytv3rwKCQlR/vz50x2jUaNGMhqNWrlypfr162cuL1mypH744Qe9//77at++vTw9PdW2bVs1a9ZMnTt3tuhj4MCBKl68uKZMmaJFixYpKSlJvr6+Kl++vLp06WKuV7duXf3888/66KOP1KNHD928eVN58uRJ8VCxtMyePVuvvPKKZs+erRkzZig5OVm5cuVSlSpVVKFCBUmSvb29vv/+e/Xs2VPvvPOO7OzsFBISoh9++CHFg+sqVqyoL774QidOnNDt27eVN29effDBB+rfv7+53ttvv628efNq/Pjxeuedd3TlyhXlzJlTZcuWTfFQvmfV/QeovfPOO6met7e3V2hoqMaOHat9+/apXLlykqStW7eqUqVKKernzp3b4jaBzZs3KyEhIcXDDAHgSTCYHvbySwAAYFVRUVHq0KGDdu/eneGHPsFS9+7dtXHjRh08eDDT97IDbdu21bFjx8zb8gHgSeKVYQAA4Lk3ePBg/fXXX/r222+zOhQ8Z+Li4vT1119r3LhxWR0KgBcUSTcAAHju+fj4aOHChZl6XRUgSfHx8Zo+fbqqVq2a1aEAeEGxvRwAAAAAACthpRsAAAAAACsh6QYAAAAAwEpIugEAAAAAsBKSbgAAAAAArISkGwAAAAAAKyHpBgAAAADASki6AQAAAACwEpJuAAAAAACshKQbAAAAAAAr+X8y7vPt1ZGRmQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Feature importance via permutation\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.base import BaseEstimator, RegressorMixin\n",
    "\n",
    "print(\"Computing feature importance via permutation...\\n\")\n",
    "\n",
    "# Create sklearn-compatible wrapper\n",
    "class TorchWrapper(BaseEstimator, RegressorMixin):\n",
    "    \"\"\"Sklearn-compatible wrapper for PyTorch model.\"\"\"\n",
    "    \n",
    "    def __init__(self, model, device):\n",
    "        self.model = model\n",
    "        self.device = device\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        # Already fitted, just return self for sklearn compatibility\n",
    "        return self\n",
    "    \n",
    "    def predict(self, X):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            X_tensor = torch.FloatTensor(X).to(self.device)\n",
    "            predictions = self.model(X_tensor).cpu().numpy().flatten()\n",
    "        return predictions\n",
    "\n",
    "\n",
    "\n",
    "# Load best model\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "wrapped_model = TorchWrapper(model, device)\n",
    "\n",
    "# Compute permutation importance (on a sample for speed)\n",
    "sample_size = min(1000, len(X_test))\n",
    "X_test_sample = X_test_scaled[:sample_size]\n",
    "y_test_sample = y_test[:sample_size]\n",
    "\n",
    "result = permutation_importance(\n",
    "    wrapped_model, X_test_sample, y_test_sample,\n",
    "    n_repeats=10,\n",
    "    random_state=RANDOM_STATE,\n",
    "    scoring='neg_mean_absolute_error'\n",
    ")\n",
    "\n",
    "# Get feature importance\n",
    "importance_df = pd.DataFrame({\n",
    "    'feature': X.columns,\n",
    "    'importance': result.importances_mean,\n",
    "    'std': result.importances_std\n",
    "}).sort_values('importance', ascending=False)\n",
    "\n",
    "print(\"### Top 20 Most Important Features (Permutation)\\n\")\n",
    "display(importance_df.head(20))\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "top_features = importance_df.head(20)\n",
    "ax.barh(range(len(top_features)), top_features['importance'], color='#3498db', alpha=0.8)\n",
    "ax.set_yticks(range(len(top_features)))\n",
    "ax.set_yticklabels(top_features['feature'])\n",
    "ax.set_xlabel('Importance (decrease in MAE)', fontsize=12)\n",
    "ax.set_title('Neural Network: Top 20 Features (Permutation Importance)', fontsize=14)\n",
    "ax.invert_yaxis()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/06_deep_learning/neural_network_feature_importance.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f32c595",
   "metadata": {},
   "source": [
    "# Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b5132952",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved:\n",
      "  - ../models/neural_network_final.pth\n",
      "  - ../data/06_deep_learning/nn_test_predictions.npy\n",
      "  - ../data/06_deep_learning/nn_test_actuals.npy\n",
      "  - ../data/06_deep_learning/nn_test_features.csv\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Save final model and predictions\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "# Save the model\n",
    "torch.save({\n",
    "    'model_state_dict': model.state_dict(),\n",
    "    'input_dim': input_dim,\n",
    "    'hidden_layers': hidden_layers,\n",
    "    'dropout_rate': dropout_rate,\n",
    "    'scaler_mean': scaler.mean_,\n",
    "    'scaler_scale': scaler.scale_,\n",
    "    'feature_names': X.columns.tolist(),\n",
    "}, '../models/neural_network_final.pth')\n",
    "\n",
    "# Save predictions for ensemble\n",
    "np.save('../data/06_deep_learning/nn_test_predictions.npy', test_preds)\n",
    "np.save('../data/06_deep_learning/nn_test_actuals.npy', test_actuals)\n",
    "\n",
    "# Save test features\n",
    "pd.DataFrame(X_test_scaled, columns=X.columns, index=X_test.index).to_csv(\n",
    "    '../data/06_deep_learning/nn_test_features.csv'\n",
    ")\n",
    "\n",
    "print(\"Saved:\")\n",
    "print(\"  - ../models/neural_network_final.pth\")\n",
    "print(\"  - ../data/06_deep_learning/nn_test_predictions.npy\")\n",
    "print(\"  - ../data/06_deep_learning/nn_test_actuals.npy\")\n",
    "print(\"  - ../data/06_deep_learning/nn_test_features.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced3f05d",
   "metadata": {},
   "source": [
    "# Comparison with RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b7938200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Model Comparison\n",
      "\n"
     ]
    },
    {
     "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>Metric</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Neural Network</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAE</td>\n",
       "      <td>1.787864</td>\n",
       "      <td>1.892610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RMSE</td>\n",
       "      <td>2.292637</td>\n",
       "      <td>2.398137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R²</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.646010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Within ±1</td>\n",
       "      <td>36.056338</td>\n",
       "      <td>33.755869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Within ±2</td>\n",
       "      <td>64.307512</td>\n",
       "      <td>60.492958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Exact grouped V</td>\n",
       "      <td>30.199531</td>\n",
       "      <td>27.793427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Within ±1 V</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>67.946009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Within ±2 V</td>\n",
       "      <td>89.366197</td>\n",
       "      <td>88.415493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Metric  Random Forest  Neural Network\n",
       "0              MAE       1.787864        1.892610\n",
       "1             RMSE       2.292637        2.398137\n",
       "2               R²       0.676471        0.646010\n",
       "3        Within ±1      36.056338       33.755869\n",
       "4        Within ±2      64.307512       60.492958\n",
       "5  Exact grouped V      30.199531       27.793427\n",
       "6      Within ±1 V      70.833333       67.946009\n",
       "7      Within ±2 V      89.366197       88.415493"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABv0AAAHqCAYAAAAnJIIoAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAiw1JREFUeJzs3XtcVVX+//H3EeTgBTAvXExEUrxBGoEpmIGZFF4qrbTMa1o5WEnkOENWopmUU4ZlapaCVio1XktTaVLUUWeUpBotR79RkEGmKXg9KO7fHyPn55GLkMDh6Ov5eKzHo7322ut89uExrtnns9daJsMwDAEAAAAAAAAAAABwWHXsHQAAAAAAAAAAAACAq0PSDwAAAAAAAAAAAHBwJP0AAAAAAAAAAAAAB0fSDwAAAAAAAAAAAHBwJP0AAAAAAAAAAAAAB0fSDwAAAAAAAAAAAHBwJP0AAAAAAAAAAAAAB0fSDwAAAAAAAAAAAHBwJP0AAABQqyxbtkzh4eGKiIhQYGCg3n//fXuHBAAAAAAAUOuR9IPDSklJkclkkslk0ubNm0ucNwxDbdq0kclkUmRkpF1i/CNuvfVWmUwmvf7666WeT0hIkMlk0pEjR2o0rlatWmnkyJF/6NolS5YoKSmpymMq/i7KKj/++GOVfyYA4I+5dNw2mUxydnaWj4+PHn74YR04cMCmbdeuXZWenq709HR9+OGHeuKJJ674b/q5c+f07rvvqkuXLmrcuLHq168vPz8/3XfffVq5cmU13x0AoCr9/e9/l8lkUmpqaolznTt3lslk0oYNG0qca926tW699VZJ0ubNm0s8K65bt04JCQmlfqbJZNJTTz11xdiKx7Pa8KyxePFiPfzww2rXrp3q1KmjVq1aVfjaH3/80WZcrlu3rpo0aaIuXbro2Wef1d69e0tcU9p3Kklvv/222rRpIxcXF5lMJh0/flyS9MILL6hly5ZydnZWo0aNJEmRkZHV+nxe2t+nup5HAaA6MAZWzB8dA2fNmiWTyaT169eX2ea9996TyWTSihUrqjDiqlc8lqekpNg7FNQyJP3g8Nzc3LRgwYIS9enp6fq///s/ubm52SWuPyIzM1N79uyRpFLvyVFV90PW+vXrtWPHjhLFx8en2j4TAPDHJCcna8eOHfriiy/01FNPac2aNbr99tt17Ngxaxt/f3/VrVtXuvgAWlzKM2zYMD399NPq2bOnPvzwQ3366ad64YUX5OzsXOpDMQCg9oqMjJTJZNKmTZts6n///Xd9++23atCgQYlzP//8s3744Qf17NlTuvgy5Y4dO6w/gOriD55Tpky5qtj69u1ba541PvjgA+3du1e33XabWrdu/Yf6ePrpp7Vjxw6lp6frgw8+0P333681a9aoc+fO+tvf/mbTtrTvNDMzU88884x69uypL7/8Ujt27JCbm5tWr16tV155RcOHD1d6erq++OILSdKcOXM0Z86cq7zzyiHpB8CRMAZWzB8dA4cOHSqz2ayFCxeW2SY5OVnNmjVT//79qyhaoGY52zsA4GoNHjxYH330kd555x25u7tb6xcsWKCwsDAVFBTYNb7KKF6+rG/fvlq7dq22b9+u8PBwe4dV64WEhKhp06aVuqaoqEjnz5+X2Wwuce706dOqX7/+VcV05swZ1atX76r6AIBrUVBQkEJDQ6WLD7RFRUWaPHmyVq1apVGjRtm0PXnypEaMGKHY2Fj5+fmV2WdWVpZSU1P10ksv2TzI9urVS48//rguXLhQjXdkyzAMnT17ljEAAK5C06ZNFRQUVGJGWXp6upydnTV69OgSP3gWHxf/4Onu7q5u3bpVeWzNmjVTs2bNqrzfS23evFk9e/ZUVlZWuTMXNmzYoDp1/vcud79+/fSf//yn0p/VsmVLm++pT58+iouL08CBAzVx4kQFBQUpOjpaKuM7LZ4R+Pjjj+u2226z1hfH8swzz8jT09Na37Fjx0rHCADXE8bA6h0DmzRpovvuu0+rVq3S0aNH1aRJE5vz33//vXbs2KHnnnvO+iJqVTt37px19RugOjDTDw7vkUcekSQtXbrUWpefn6/ly5frscceK/WawsJCTZs2Te3bt5fZbFazZs00atQo/fbbbzbtUlNTFRUVJR8fH9WrV08dOnTQX//6V506dcqm3ciRI9WwYUMdPHhQffr0UcOGDeXr66vnnntOFoulQvdx9uxZLVmyRCEhIXrzzTclqdy3TnJycjRw4EC5u7vLw8NDQ4cOLRH/l19+qcjISDVp0kT16tVTy5Yt9cADD+j06dPWNr///rtiYmJ04403ysXFRTfddJMmTZp0xbjLmtJ/+RICkZGRWrt2rX766adSZ2tU9G9xNYqnu8+YMUPTpk2Tv7+/zGazNm3aZF0i9KuvvtKDDz6oG264wfqG0NmzZxUfHy9/f3+5uLjoxhtv1Lhx46zL1RRr1aqV+vXrpxUrVig4OFiurq5X/fYUAFwvihOAv/76q0392bNnNWDAALVu3VozZswot4+jR49KUplvnBY/DBY7fvy4nnvuOd10000ym83y9PRUnz599P3331vbVHR8LF4KZ968eerQoYPMZrMWLVokSTpw4ICGDBkiT09Pmc1mdejQQe+8806lvh8AuF717NlT+/fvV25urrVu8+bN6tKli/r06aOMjAydOHHC5pyTk5N69OhhPb70uWTkyJHWf4PL2xLggw8+UIcOHVS/fn117txZn332mc350p6DIiMjFRQUpF27dqlHjx6qX7++brrpJr366qvV+uLJ5eNbValXr54WLFigunXr2sz2K+1Zb+jQodLFpblNJpNGjhypVq1a6YUXXpAkeXl5yWQyWZeUK215T4vFoqlTp6pDhw5ydXVVkyZN1LNnT23fvl26wvJll/ZdmrKeRw3DUEBAgO6+++4S15w8eVIeHh4aN27cH/wGAeDqMAZe2dWMgaNHj1ZhYaGWLFlS4lxycrIklfmbcjHDMDR9+nT5+fnJ1dVVoaGhSktLKzHOFf8tPvjgAz333HO68cYbZTabdfDgQf3222+KiYlRx44d1bBhQ3l6eurOO+/U1q1bS3zeL7/8okGDBsnNzU0eHh4aPHiw8vLySo1t9+7duvfee9W4cWO5uroqODhYH3/88R/4puCoSPrB4bm7u+vBBx+0SZAtXbpUderU0eDBg0u0v3Dhgu677z69+uqrGjJkiNauXatXX33V+g/zmTNnrG0PHDigPn36aMGCBVq/fr1iY2P18ccflzq9+9y5c7r33nvVq1cvrV69Wo899pjefPNNvfbaaxW6jxUrVujYsWN67LHHFBAQoNtvv12pqak6efJkqe0HDBigNm3a6O9//7sSEhK0atUq3X333Tp37px08cGob9++cnFx0cKFC7V+/Xq9+uqratCggQoLC6WLP6j27NlTixcvVlxcnNauXauhQ4dqxowZGjhwYIXivpI5c+aoe/fu8vb2tll6U5X8W5SneNbepaWoqKhEu7feektffvmlXn/9dX3++edq37699dzAgQPVpk0bffLJJ5o3b54Mw9D999+v119/XcOGDdPatWsVFxenRYsW6c477yzxo+9XX32lP//5z3rmmWe0fv16PfDAA1f93QHA9SArK0uS1LZtW2vdmTNn1L9/fzVr1kwff/yxnJycyu2jQ4cOatSokaZMmaL58+eXu8fEiRMndPvtt+vdd9/VqFGj9Omnn2revHlq27at9aG6suPjqlWrNHfuXL300kvasGGDevTooX379qlLly76z3/+ozfeeEOfffaZ+vbtq2eeeYYXQwCgAopnK1w602HTpk2KiIhQ9+7dZTKZbH4U27Rpk2699VZ5eHiU2t+LL76oBx98UJLK3BJg7dq1mj17tqZOnarly5ercePGGjBggH744YcrxpuXl6dHH31UQ4cO1Zo1axQdHa34+Hh9+OGHV7z28ueZ4meZy+trcuZ68+bNFRISou3bt+v8+fOltpkzZ441uVe8fPeLL76olStXavTo0dIlWzGMGTOm1D7Onz+v6Ohovfzyy+rXr59WrlyplJQUhYeHKzs7+6rvo6znUZPJpKefflppaWkl9hZevHixCgoKSPoBsBvGwOodA++66y75+fmVmGxRVFSkDz74QN26dbvizPRJkyZp0qRJuueee7R69WqNHTtWY8aM0X//+99S28fHxys7O1vz5s3Tp59+Kk9PT/3++++SpMmTJ2vt2rVKTk7WTTfdpMjISJu//ZkzZ3TXXXdp48aNSkxM1CeffCJvb+9Sf/fetGmTunfvruPHj2vevHlavXq1brnlFg0ePJi9/64nBuCgkpOTDUnGrl27jE2bNhmSjP/85z+GYRhGly5djJEjRxqGYRiBgYFGRESE9bqlS5cakozly5fb9Ldr1y5DkjFnzpxSP+/ChQvGuXPnjPT0dEOS8fXXX1vPjRgxwpBkfPzxxzbX9OnTx2jXrl2F7ufOO+80XF1djWPHjtnc34IFC2zaTZ482ZBkPPvsszb1H330kSHJ+PDDDw3DMIy///3vhiQjMzOzzM+cN29eqXG/9tprhiRj48aN1jo/Pz9jxIgR1uPi+LKysmyuLf5bbNq0yVrXt29fw8/Pr8Tn/9G/xeXfRWmldevW1nZZWVnWusLCwlL7eOmll2zq169fb0gyZsyYYVOfmppqSDLmz59v8904OTkZ+/fvLzdeALieFY8bO3fuNM6dO2ecOHHCWL9+veHt7W3ccccdxrlz56xtn3/+eaNOnTrGHXfcYURERBgRERHG9u3by+1/7dq1RtOmTa3jQJMmTYyHHnrIWLNmjU27qVOnGpKMtLS0MvuqzPgoyfDw8DB+//13m7Z333230aJFCyM/P9+m/qmnnjJcXV1LtAcA2Pr999+NOnXqGE888YRhGIZx5MgRw2QyGevXrzcMwzBuu+02Y8KECYZhGEZ2drYhyZg4caL1+tKeS8aNG2eU9TOIJMPLy8soKCiw1uXl5Rl16tQxEhMTrXWlPQdFREQYkox//etfNn127NjRuPvuu694r8XXX6lc+jx2ubKeucpS/Iz0t7/9rcw2gwcPNiQZv/76q2GU8Z1e+lx+qeLnrN9++63EvV76fL548WJDkvHee+9dMdbk5OQS5yQZkydPLhHPpX+fsr6bgoICw83NzRg/frxNfceOHY2ePXuWGQ8AVDfGwOodA41LxqmvvvrKWvfpp59ecUwyLv59zGazMXjwYJv6HTt2GJJsxrniv8Udd9xxxZjOnz9vnDt3zujVq5cxYMAAa/3cuXMNScbq1att2j/++OMlxsf27dsbwcHBNs/XhmEY/fr1M3x8fIyioqIrxgHHx0w/XBMiIiLUunVrLVy4UN9++6127dpV5jTszz77TI0aNVL//v1t3hi55ZZb5O3tbfMmxQ8//KAhQ4bI29tbTk5Oqlu3riIiIiRJ3333nU2/JpOpxAzATp066aeffrpi/FlZWdq0aZMGDhyoRo0aSZIeeughubm5lbnE56OPPmpzPGjQIDk7O1vX8b7lllvk4uKiJ554QosWLSr1zZwvv/xSDRo0sL7tU2zkyJGSpH/84x9XjP1qVOZvUZ4vvvhCu3btsimrVq0q0e7ee+8tcz3uy2fmffnll9Il30Wxhx56SA0aNCjx3XTq1MlmlgoAoHTdunVT3bp15ebmpnvuuUc33HCDVq9ebbOfwSuvvKKioiKlp6dr8+bN2rx5s8LCwsrtt0+fPsrOztbKlSs1YcIEBQYGatWqVbr33nv11FNPWdt9/vnnatu2re66664y+6rs+HjnnXfqhhtusB6fPXtW//jHPzRgwADVr1/fZozr06ePzp49q507d1biWwOA688NN9ygzp07W58J0tPT5eTkpO7du0sXnwGLn30u38voj+rZs6fc3Nysx15eXvL09KzQM523t7fNnnaqxPPgu+++a/MsM2/ePEnSmjVrbOrLW8ayOvzvd+Dq9fnnn8vV1fWKy6hVBzc3N40aNUopKSnWLTy+/PJL7du3z+b/OwBATWMMrP4xcNSoUapTp47N767Jyclq0KCBdQbdhQsXSp2FuHPnTlksFg0aNMimz27dupW5D2FZK4LNmzdPt956q1xdXeXs7Ky6devqH//4h83vzps2bZKbm5vuvfdem2uHDBlic3zw4EF9//331t+ML38Ozc3N1f79+yv5TcERsVskrgkmk0mjRo3SW2+9pbNnz6pt27bWdawv9+uvv+r48eNycXEp9fyRI0eki+v49+jRQ66urpo2bZratm2r+vXrW/fSu3zpyfr168vV1dWmzmw26+zZs1eMf+HChTIMQw8++KDNfnH33nuvPvroI33//fc2S1Hq4oB6KWdnZzVp0sS6r1Hr1q31xRdfaMaMGRo3bpxOnTqlm266Sc8884zGjx8vXdwDydvb22aPPUny9PSUs7Ozta/qUtG/xZV07txZTZs2vWK7svZ6Ku3c0aNH5ezsXGKDYpPJJG9v7xLfTXl9AwD+v8WLF6tDhw46ceKEUlNT9e677+qRRx7R559/ftV916tXT/fff7/uv/9+SVJ2draio6P1zjvv6E9/+pMCAwP122+/qWXLluX2U9nxsbQx5Pz583r77bf19ttvl/oZFR3jAOB61rNnT82cOVO//PKLNm3apJCQEDVs2FC6+IPnG2+8ofz8fG3atEnOzs66/fbbr+rzmjRpUqLObDZXaNuBq7m2Xbt2NsfFWzzcfPPNZf54WBN++uknmc1mNW7cuNo+47ffflPz5s2rbX/CK3n66ac1e/ZsffTRR3riiSc0e/ZstWjRQvfdd59d4gGAYoyB1TsG+vn5qVevXlqyZIlef/11nThxQp999pmGDBliTX5OnTrVZmsGPz8//fjjj9bnQS8vrxL9llanMn43nDlzpp577jmNHTtWL7/8spo2bSonJye9+OKLNkm/o0ePltrv5b8N//rrr5KkCRMmaMKECaXGwXPo9YGkH64ZI0eO1EsvvaR58+bplVdeKbNd06ZN1aRJE61fv77U88X/sH/55Zf65ZdftHnzZuvsPkk2SbmqcOHCBeuaymXto7dw4ULNmDHDpi4vL0833nij9fj8+fM6evSozUDbo0cP9ejRQ0VFRdq9e7fefvttxcbGysvLSw8//LCaNGmif/3rXzIMw+aHzcOHD+v8+fPlJtKKE5yX721XmcGjon+LqnL5j7flnWvSpInOnz+v3377zSbxZxiG8vLy1KVLlwr3DQD4/zp06KDQ0FDp4oNsUVGR3n//ff39738vMbPuarVs2VJPPPGEYmNjtXfvXgUGBqpZs2b6+eefy72usuPj5WPADTfcICcnJw0bNqzM/YD8/f2v6t4A4HpQ/INn8azvPn36WM8V/7i5ZcsWbd68WV26dLH+GIqrd+jQIWVkZCgiIsJmNn5Va9asmbZt26YLFy6Umfgr69mzKl5SbdOmjfUFoejoaK1Zs0ZTpky54n7CAFDdGAOr3+jRo5WWlqbVq1frl19+UWFhoXVPWkl64okn1K9fP+ux2WyWLklyFifZLpWXl1dqsrK03w0//PBDRUZGau7cuTb1J06csDlu0qSJ/v3vf5f6WZcqfk6Nj48v8zfmy5OsuDaxvCeuGTfeeKP+/Oc/q3///hoxYkSZ7fr166ejR4+qqKhIoaGhJUrxP37F/xgX/4Ne7N13363SuDds2KCff/5Z48aN06ZNm0qUwMBALV68uMTm6R999JHN8ccff6zz588rMjKyxGc4OTmpa9eueueddyRJX331lSSpV69eOnnyZImlMBcvXmw9X5biAeybb76xqV+zZk2JtmW93VPRv4U9FN/75ZsOL1++XKdOnSr3uwEAVNyMGTN0ww036KWXXvrDm7OfOHHC+kbo5YrfkGzevLkkKTo6Wv/973+tyziX5mrGR12c/d+zZ0/t2bNHnTp1KnWMK+1tWACArTvuuENOTk76+9//rr1799o863h4eOiWW27RokWL9OOPP1ZoWbPiZ7uKzDywp8jISBmGYbdZfmfOnNGYMWN0/vx5TZw4sVo/Kzo6WmfPnrW+CFsaLy8vubq6lnj2XL16dYU+40qzTcaPH69vvvlGI0aMkJOTkx5//PFK3AEAVA/GwOofA++//341adJECxcuVHJystq2bWszY7J58+Y2z3A333yzJKlr164ym81KTU216W/nzp0VWtK0mMlkKvG78zfffKMdO3bY1PXs2VMnTpwo8ZvrkiVLbI7btWungIAAff3116U+g4aGhlb5BAvUTsz0wzXl1VdfvWKbhx9+WB999JH69Omj8ePH67bbblPdunX1888/a9OmTbrvvvs0YMAAhYeH64YbbtDYsWM1efJk1a1bVx999JG+/vrrKo15wYIFcnZ21vPPP2/9QfJSTz75pJ555hmtXbvWZomRFStWyNnZWb1799bevXv14osvqnPnztb1pOfNm6cvv/xSffv2VcuWLXX27FnrOtXF+xgNHz5c77zzjkaMGKEff/xRN998s7Zt26bp06erT58+5e531KVLF7Vr104TJkzQ+fPndcMNN2jlypXatm1bibY333yzVqxYoblz5yokJER16tRRaGhohf8WV5KRkSEPD48S9R07dpS7u/sVry9N7969dffdd+svf/mLCgoK1L17d33zzTeaPHmygoODNWzYsD/ULwDA1g033KD4+HhNnDhRS5Ys0dChQyvdx/79+3X33Xfr4YcfVkREhHx8fHTs2DGtXbtW8+fPV2RkpMLDwyVJsbGxSk1N1X333ae//vWvuu2223TmzBmlp6erX79+6tmz51WNj8VmzZql22+/XT169NCf/vQntWrVSidOnNDBgwf16aeflpt0BAD8j7u7u2699VatWrVKderUse5lVCwiIkJJSUlSBfcyKv6x7rXXXlN0dLScnJzUqVOnMrcbqCn79u1TQUHBFds1a9ZMrVu3trlu37590sW3/U+fPq2///3v0sVnoY4dO16xz+zsbO3cuVMXLlxQfn6+9uzZo4ULF+qnn37SG2+8oaioqKu6tyt55JFHlJycrLFjx2r//v3q2bOnLly4oH/961/q0KGDHn74YZlMJg0dOlQLFy5U69at1blzZ/373/8u8WNnWcp6Hi3Wu3dvdezYUZs2bdLQoUPl6elZjXcMABXDGGirOsZAs9msRx99VG+//bYMw6jQ78qS1LhxY8XFxSkxMVE33HCDBgwYoJ9//llTpkyRj49PhZes7tevn15++WVNnjxZERER2r9/v6ZOnSp/f3+byR/Dhw/Xm2++qeHDh+uVV15RQECA1q1bpw0bNpTo891331V0dLTuvvtujRw5UjfeeKN+//13fffdd/rqq6/0ySefVCg2ODgDcFDJycmGJGPXrl3ltgsMDDQiIiJs6s6dO2e8/vrrRufOnQ1XV1ejYcOGRvv27Y0nn3zSOHDggLXd9u3bjbCwMKN+/fpGs2bNjDFjxhhfffWVIclITk62thsxYoTRoEGDEp89efJko7z/mf3222+Gi4uLcf/995fZ5tixY0a9evWM/v372/SZkZFh9O/f32jYsKHh5uZmPPLII8avv/5qvW7Hjh3GgAEDDD8/P8NsNhtNmjQxIiIijDVr1tj0f/ToUWPs2LGGj4+P4ezsbPj5+Rnx8fHG2bNnbdr5+fkZI0aMsKn773//a0RFRRnu7u5Gs2bNjKefftpYu3atIcnYtGmTtd3vv/9uPPjgg0ajRo0Mk8lk851U9G9RmuLvoqySlpZmGIZhZGVlGZKMv/3tb2X28dtvv5U4d+bMGeMvf/mL4efnZ9StW9fw8fEx/vSnPxnHjh0r8d307du33FgB4HpX3rh95swZo2XLlkZAQIBx/vz5Svd97NgxY9q0acadd95p3HjjjYaLi4vRoEED45ZbbjGmTZtmnD59ukT78ePHGy1btjTq1q1reHp6Gn379jW+//57a5uKjo+SjHHjxpUaV1ZWlvHYY48ZN954o1G3bl2jWbNmRnh4uDFt2rRK3yMAXK8mTpxoSDJCQ0NLnFu1apUhyXBxcTFOnTplc27Tpk0lnkssFosxZswYo1mzZtbnkqysLMMo59/zy5+Disez4usMwzAiIiKMwMDAEteOGDHC8PPzu+I9RkRElPtcU1wufx4r73lo8uTJ5X5m8TNScXFycjJuuOEGIyQkxIiNjTX27t1b4prSvtOyxveynrMiIiJKPJ+fOXPGeOmll4yAgADDxcXFaNKkiXHnnXca27dvt7bJz883xowZY3h5eRkNGjQw+vfvb/z4448l7rW0v095z6PFEhISDEnGzp07y/3eAKAmMQZWzxh4qa+//to6Dv7yyy8Vvu7ChQvGtGnTjBYtWhguLi5Gp06djM8++8zo3LmzMWDAAGu74r/FJ598UqIPi8ViTJgwwbjxxhsNV1dX49ZbbzVWrVpV6nf3888/Gw888ID1d+AHHnjA2L59e4nfqIvvadCgQYanp6dRt25dw9vb27jzzjuNefPmVfj+4NhMxv/+hw0AAAAAAABcd0JDQ2UymbRr1y57hwIAcFBZWVlq3769Jk+erOeff97e4eA6xvKeAAAAAAAAuK4UFBToP//5jz777DNlZGRo5cqV9g4JAOAgvv76ay1dulTh4eFyd3fX/v37NWPGDLm7u2v06NH2Dg/XOZJ+AAAAAAAAuK589dVX6tmzp5o0aaLJkyfr/vvvt3dIAAAH0aBBA+3evVsLFizQ8ePH5eHhocjISL3yyivy8vKyd3i4zrG8JwAAAAAAAAAAAODg6tg7AAAAAAAAAAAAAABXh6QfAAAAAAAAAAAA4OBI+gEAAAAAAAAAAAAOztneAdRGFy5c0C+//CI3NzeZTCZ7hwMAqGKGYejEiRNq3ry56tS5ft9/YbwDgGsb493/MN4BwLWN8e5/GO8A4NpW0fGOpF8pfvnlF/n6+to7DABANcvJyVGLFi3sHYbdMN4BwPWB8Y7xDgCuB4x3jHcAcD240nhH0q8Ubm5u0sUvz93d3d7hAACqWEFBgXx9fa3/3l+vGO8A4NrGePc/jHcAcG1jvPsfxjsAuLZVdLwj6VeK4inw7u7uDJIAcA2rbUuezJkzR3/729+Um5urwMBAJSUlqUePHqW2HTlypBYtWlSivmPHjtq7d2+FPo/xDgCuD7VtvKtpjHcAcH1gvGO8A4DrwZXGu+t3oWsAAGqR1NRUxcbGatKkSdqzZ4969Oih6OhoZWdnl9p+1qxZys3NtZacnBw1btxYDz30UI3HDgAAAAAAAMD+SPoBAFALzJw5U6NHj9aYMWPUoUMHJSUlydfXV3Pnzi21vYeHh7y9va1l9+7dOnbsmEaNGlXjsQMAAAAAAACwP5J+AADYWWFhoTIyMhQVFWVTHxUVpe3bt1eojwULFuiuu+6Sn59fNUUJAAAAAAAAoDZjTz8AAOzsyJEjKioqkpeXl029l5eX8vLyrnh9bm6uPv/8cy1ZsqTcdhaLRRaLxXpcUFBwFVEDAAAAAAAAqE2Y6QcAQC1x+Ua8hmFUaDP6lJQUNWrUSPfff3+57RITE+Xh4WEtvr6+Vx0zAAAAAAAAgNqBpB8AAHbWtGlTOTk5lZjVd/jw4RKz/y5nGIYWLlyoYcOGycXFpdy28fHxys/Pt5acnJwqiR8AAAAAAACA/ZH0AwDAzlxcXBQSEqK0tDSb+rS0NIWHh5d7bXp6ug4ePKjRo0df8XPMZrPc3d1tCgAAAAAAAIBrA3v6AQBQC8TFxWnYsGEKDQ1VWFiY5s+fr+zsbI0dO1a6OEvv0KFDWrx4sc11CxYsUNeuXRUUFGSnyAEAAAAAAADUBiT9AACoBQYPHqyjR49q6tSpys3NVVBQkNatWyc/Pz9JUm5urrKzs22uyc/P1/LlyzVr1iw7RQ0AAAAAAACgtiDpBwBALRETE6OYmJhSz6WkpJSo8/Dw0OnTp2sgMgAAAAAAAAC1HXv6AQAAAAAAAAAAAA6OpB8AAAAAAAAAAADg4Ej6AQAAAAAAAAAAAA6OpB8AAAAAAAAAAADg4Ej6AQAAAAAAAAAAAA6OpB8AAAAAAAAAAADg4Ej6AQAAAAAAAAAAAA6OpB8AAAAAAAAAAADg4JztHQCAa09e/x72DgHXOO9Pt9o7BFyiy7sH7R0CrnG7nmxj7xAAQGLMQzVjvAOuXa1atdJPP/1Uoj4mJkbvvPOODMPQlClTNH/+fB07dkxdu3bVO++8o8DAQLvEy3iH6sR4B1QvZvoBAAAAAAAAQDXZtWuXcnNzrSUtLU2S9NBDD0mSZsyYoZkzZ2r27NnatWuXvL291bt3b504ccLOkQMAHA1JPwAAAAAAAACoJs2aNZO3t7e1fPbZZ2rdurUiIiJkGIaSkpI0adIkDRw4UEFBQVq0aJFOnz6tJUuW2Dt0AICDIekHAAAAAAAAADWgsLBQH374oR577DGZTCZlZWUpLy9PUVFR1jZms1kRERHavn17mf1YLBYVFBTYFAAASPoBAAAAAAAAQA1YtWqVjh8/rpEjR0qS8vLyJEleXl427by8vKznSpOYmCgPDw9r8fX1rebIAQCOgKQfAAAAAAAAANSABQsWKDo6Ws2bN7epN5lMNseGYZSou1R8fLzy8/OtJScnp9piBgA4Dmd7BwAAAAAAAAAA17qffvpJX3zxhVasWGGt8/b2li7O+PPx8bHWHz58uMTsv0uZzWaZzeZqjhgA4GiY6QcAAAAAsIu5c+eqU6dOcnd3l7u7u8LCwvT5559bz48cOVImk8mmdOvWza4xAwDwRyUnJ8vT01N9+/a11vn7+8vb21tpaWnWusLCQqWnpys8PNxOkQIAHBUz/QAAAAAAdtGiRQu9+uqratOmjSRp0aJFuu+++7Rnzx4FBgZKku655x4lJydbr3FxcbFbvAAA/FEXLlxQcnKyRowYIWfn//+TrMlkUmxsrKZPn66AgAAFBARo+vTpql+/voYMGWLXmAEAjoekHwAAAADALvr3729z/Morr2ju3LnauXOnNelnNputS58BAOCovvjiC2VnZ+uxxx4rcW7ixIk6c+aMYmJidOzYMXXt2lUbN26Um5ubXWIFADgulvcEAAAAANhdUVGRli1bplOnTiksLMxav3nzZnl6eqpt27Z6/PHHdfjwYbvGCQDAHxEVFSXDMNS2bdsS50wmkxISEpSbm6uzZ88qPT1dQUFBdokTAODYmOkHAAAAALCbb7/9VmFhYTp79qwaNmyolStXqmPHjpKk6OhoPfTQQ/Lz81NWVpZefPFF3XnnncrIyJDZbC61P4vFIovFYj0uKCiosXsBAAAAAHsi6QcAAAAAsJt27dopMzNTx48f1/LlyzVixAilp6erY8eOGjx4sLVdUFCQQkND5efnp7Vr12rgwIGl9peYmKgpU6bU4B0AAAAAQO3A8p4AAAAAALtxcXFRmzZtFBoaqsTERHXu3FmzZs0qta2Pj4/8/Px04MCBMvuLj49Xfn6+teTk5FRj9AAAAABQezDTDwAAAABQaxiGYbM856WOHj2qnJwc+fj4lHm92Wwuc+lPAAAAALiWkfQDAAAAANjF888/r+joaPn6+urEiRNatmyZNm/erPXr1+vkyZNKSEjQAw88IB8fH/344496/vnn1bRpUw0YMMDeoQMAAABArUPSDwAAAABgF7/++quGDRum3NxceXh4qFOnTlq/fr169+6tM2fO6Ntvv9XixYt1/Phx+fj4qGfPnkpNTZWbm5u9QwcAAMA1JK9/D3uHgGuc96dba+RzSPoBAAAAAOxiwYIFZZ6rV6+eNmzYUKPxAAAAAIAjI+kHAAAA/AG8CYrqVlNvggIAAAAArg117B0AAAAAAAAAAAAAgKtD0g8AAAAAAAAAAABwcCT9AAAAAAAAAAAAAAfHnn7VqMu7B+0dAq5xu55sY+8QAAAAAAAAAABALcBMPwAAAAAAAAAAAMDBkfQDAAAAAAAAAAAAHBzLewIAAAAAAKBUef172DsEXOO8P91q7xAAALhm2HWm35YtW9S/f381b95cJpNJq1atKrf9yJEjZTKZSpTAwEBrm5SUlFLbnD17tgbuCAAAAAAAAAAAAKh5dk36nTp1Sp07d9bs2bMr1H7WrFnKzc21lpycHDVu3FgPPfSQTTt3d3ebdrm5uXJ1da2muwAAAAAAAAAAAADsy67Le0ZHRys6OrrC7T08POTh4WE9XrVqlY4dO6ZRo0bZtDOZTPL29q7SWAEAAAAAAAAAAIDayq4z/a7WggULdNddd8nPz8+m/uTJk/Lz81OLFi3Ur18/7dmzp9x+LBaLCgoKbAoAAAAAAAAAAADgKBw26Zebm6vPP/9cY8aMsalv3769UlJStGbNGi1dulSurq7q3r27Dhw4UGZfiYmJ1lmEHh4e8vX1rYE7AAAAAAAAAAAAAKqGwyb9UlJS1KhRI91///029d26ddPQoUPVuXNn9ejRQx9//LHatm2rt99+u8y+4uPjlZ+fby05OTk1cAcAAAAAAAAAAABA1bDrnn5/lGEYWrhwoYYNGyYXF5dy29apU0ddunQpd6af2WyW2WyuhkgBAAAAAAAAAACA6ueQM/3S09N18OBBjR49+optDcNQZmamfHx8aiQ2AAAAAAAAAAAAoKbZdabfyZMndfDgQetxVlaWMjMz1bhxY7Vs2VLx8fE6dOiQFi9ebHPdggUL1LVrVwUFBZXoc8qUKerWrZsCAgJUUFCgt956S5mZmXrnnXdq5J4AAAAAAAAAAACAmmbXpN/u3bvVs2dP63FcXJwkacSIEUpJSVFubq6ys7NtrsnPz9fy5cs1a9asUvs8fvy4nnjiCeXl5cnDw0PBwcHasmWLbrvttmq+GwAAAAAAAAAAAMA+7Jr0i4yMlGEYZZ5PSUkpUefh4aHTp0+Xec2bb76pN998s8piBAAAAAAAAAAAAGo7h9zTDwAAAAAAAAAAAMD/R9IPAAAAAAAAAAAAcHAk/QAAAAAAAAAAAAAHR9IPAAAAAAAAAAAAcHAk/QAAqCXmzJkjf39/ubq6KiQkRFu3bi23vcVi0aRJk+Tn5yez2azWrVtr4cKFNRYvAAAAAAAAgNrD2d4BAAAAKTU1VbGxsZozZ466d++ud999V9HR0dq3b59atmxZ6jWDBg3Sr7/+qgULFqhNmzY6fPiwzp8/X+OxAwAAAAAAALA/kn4AANQCM2fO1OjRozVmzBhJUlJSkjZs2KC5c+cqMTGxRPv169crPT1dP/zwgxo3bixJatWqVY3HDQAAAAAAAKB2YHlPAADsrLCwUBkZGYqKirKpj4qK0vbt20u9Zs2aNQoNDdWMGTN04403qm3btpowYYLOnDlT5udYLBYVFBTYFAAAAAAAAADXBmb6AQBgZ0eOHFFRUZG8vLxs6r28vJSXl1fqNT/88IO2bdsmV1dXrVy5UkeOHFFMTIx+//33Mvf1S0xM1JQpU6rlHgAAAAAAAADYFzP9AACoJUwmk82xYRgl6opduHBBJpNJH330kW677Tb16dNHM2fOVEpKSpmz/eLj45Wfn28tOTk51XIfAAAAAAAAAGoeM/0AALCzpk2bysnJqcSsvsOHD5eY/VfMx8dHN954ozw8PKx1HTp0kGEY+vnnnxUQEFDiGrPZLLPZXA13AAAAAAAAAMDemOkHAICdubi4KCQkRGlpaTb1aWlpCg8PL/Wa7t2765dfftHJkyetdf/9739Vp04dtWjRotpjBgAAAAAAAFC7kPQDAKAWiIuL0/vvv6+FCxfqu+++07PPPqvs7GyNHTtWurg05/Dhw63thwwZoiZNmmjUqFHat2+ftmzZoj//+c967LHHVK9ePTveCQAAAAAAAAB7YHlPAABqgcGDB+vo0aOaOnWqcnNzFRQUpHXr1snPz0+SlJubq+zsbGv7hg0bKi0tTU8//bRCQ0PVpEkTDRo0SNOmTbPjXQAAAAAAAACwF5J+AADUEjExMYqJiSn1XEpKSom69u3bl1gSFAAAAAAAAMD1ieU9AQAAAAAAAAAAAAdH0g8AAAAAAAAAAABwcCT9AAAAAAAAAAAAAAdH0g8AAAAAAAAAAABwcCT9AAAAAAAAAAAAAAdH0g8AAAAAAAAAqtGhQ4c0dOhQNWnSRPXr19ctt9yijIwM63nDMJSQkKDmzZurXr16ioyM1N69e+0aMwDA8ZD0AwAAAAAAAIBqcuzYMXXv3l1169bV559/rn379umNN95Qo0aNrG1mzJihmTNnavbs2dq1a5e8vb3Vu3dvnThxwq6xAwAci7O9AwAAAAAAAACAa9Vrr70mX19fJScnW+tatWpl/W/DMJSUlKRJkyZp4MCBkqRFixbJy8tLS5Ys0ZNPPmmXuAEAjoeZfgAAAAAAAABQTdasWaPQ0FA99NBD8vT0VHBwsN577z3r+aysLOXl5SkqKspaZzabFRERoe3bt9spagCAIyLpBwAAAAAAAADV5IcfftDcuXMVEBCgDRs2aOzYsXrmmWe0ePFiSVJeXp4kycvLy+Y6Ly8v67nLWSwWFRQU2BQAAFjeEwAAAAAAAACqyYULFxQaGqrp06dLkoKDg7V3717NnTtXw4cPt7YzmUw21xmGUaKuWGJioqZMmVLNkQMAHA0z/QAAAAAAdjF37lx16tRJ7u7ucnd3V1hYmD7//HPrecMwlJCQoObNm6tevXqKjIzU3r177RozAACV5ePjo44dO9rUdejQQdnZ2ZIkb29v6ZIZf8UOHz5cYvZfsfj4eOXn51tLTk5OtcUPAHAcJP0AAAAAAHbRokULvfrqq9q9e7d2796tO++8U/fdd581sTdjxgzNnDlTs2fP1q5du+Tt7a3evXvrxIkT9g4dAIAK6969u/bv329T99///ld+fn6SJH9/f3l7eystLc16vrCwUOnp6QoPDy+1T7PZbH1pprgAAEDSDwAAAABgF/3791efPn3Utm1btW3bVq+88ooaNmyonTt3yjAMJSUladKkSRo4cKCCgoK0aNEinT59WkuWLLF36AAAVNizzz6rnTt3avr06Tp48KCWLFmi+fPna9y4cdLFZT1jY2M1ffp0rVy5Uv/5z380cuRI1a9fX0OGDLF3+AAAB0LSDwAAAABgd0VFRVq2bJlOnTqlsLAwZWVlKS8vT1FRUdY2ZrNZERER2r59u11jBQCgMrp06aKVK1dq6dKlCgoK0ssvv6ykpCQ9+uij1jYTJ05UbGysYmJiFBoaqkOHDmnjxo1yc3Oza+wAAMfibO8AAAAAAADXr2+//VZhYWE6e/asGjZsqJUrV6pjx47WxN7lexl5eXnpp59+KrM/i8Uii8ViPS4oKKjG6AEAqJh+/fqpX79+ZZ43mUxKSEhQQkJCjcYFALi2MNMPAAAAAGA37dq1U2Zmpnbu3Kk//elPGjFihPbt22c9bzKZbNobhlGi7lKJiYny8PCwFl9f32qNHwAAAABqC5J+AAAAAAC7cXFxUZs2bRQaGqrExER17txZs2bNkre3tyQpLy/Ppv3hw4dLzP67VHx8vPLz860lJyen2u8BAAAAAGoDkn4AAAAAgFrDMAxZLBb5+/vL29tbaWlp1nOFhYVKT09XeHh4mdebzWa5u7vbFAAAAAC4HrCnHwAAAADALp5//nlFR0fL19dXJ06c0LJly7R582atX79eJpNJsbGxmj59ugICAhQQEKDp06erfv36GjJkiL1DBwAAAIBah6QfAAAAAMAufv31Vw0bNky5ubny8PBQp06dtH79evXu3VuSNHHiRJ05c0YxMTE6duyYunbtqo0bN8rNzc3eoQMAAABArUPSDwAAAABgFwsWLCj3vMlkUkJCghISEmosJgAAAABwVOzpBwAAAAAAAAAAADg4kn4AAAAAAAAAAACAgyPpBwAAAAAAAAAAADg4kn4AAAAAAAAAAACAgyPpBwAAAAAAAAAAADg4uyb9tmzZov79+6t58+YymUxatWpVue03b94sk8lUonz//fc27ZYvX66OHTvKbDarY8eOWrlyZTXfCQAAAAAAAAAAAGA/dk36nTp1Sp07d9bs2bMrdd3+/fuVm5trLQEBAdZzO3bs0ODBgzVs2DB9/fXXGjZsmAYNGqR//etf1XAHAAAAAAAAAAAAgP052/PDo6OjFR0dXenrPD091ahRo1LPJSUlqXfv3oqPj5ckxcfHKz09XUlJSVq6dOlVxwwAAAAAAAAAAADUNg65p19wcLB8fHzUq1cvbdq0yebcjh07FBUVZVN39913a/v27TUcJQAAAAAAAAAAAFAz7DrTr7J8fHw0f/58hYSEyGKx6IMPPlCvXr20efNm3XHHHZKkvLw8eXl52Vzn5eWlvLy8Mvu1WCyyWCzW44KCgmq8CwAAAAAAAAAAAKBqOVTSr127dmrXrp31OCwsTDk5OXr99detST9JMplMNtcZhlGi7lKJiYmaMmVKNUUNAAAAAAAAAAAAVC+HXN7zUt26ddOBAwesx97e3iVm9R0+fLjE7L9LxcfHKz8/31pycnKqNWYAAAAAAAAAAACgKjl80m/Pnj3y8fGxHoeFhSktLc2mzcaNGxUeHl5mH2azWe7u7jYFAAAAAAAAAAAAcBR2Xd7z5MmTOnjwoPU4KytLmZmZaty4sVq2bKn4+HgdOnRIixcvliQlJSWpVatWCgwMVGFhoT788EMtX75cy5cvt/Yxfvx43XHHHXrttdd03333afXq1friiy+0bds2u9wjAAAAAAAAAAAAUN3smvTbvXu3evbsaT2Oi4uTJI0YMUIpKSnKzc1Vdna29XxhYaEmTJigQ4cOqV69egoMDNTatWvVp08fa5vw8HAtW7ZML7zwgl588UW1bt1aqamp6tq1aw3fHQAAAAAAAAAAAFAz7Jr0i4yMlGEYZZ5PSUmxOZ44caImTpx4xX4ffPBBPfjgg1USIwAAAAAAAAAAAFDbOfyefgAAAAAAAAAAAMD1jqQfAAAAAAAAAAAA4OBI+gEAAAAAAAAAAAAOjqQfAAAAAAAAAAAA4OBI+gEAAAAAAAAAAAAOjqQfAAAAAAAAAAAA4OBI+gEAAAAAAAAAAAAOjqQfAAAAAAAAAAAA4OBI+gEAAAAAAAAAAAAOjqQfAAAAAAAAAAAA4OBI+gEAAAAAAAAAAAAOjqQfAAC1xJw5c+Tv7y9XV1eFhIRo69atZbbdvHmzTCZTifL999/XaMwAAAAAAAAAageSfgAA1AKpqamKjY3VpEmTtGfPHvXo0UPR0dHKzs4u97r9+/crNzfXWgICAmosZgAAAAAAAAC1B0k/AABqgZkzZ2r06NEaM2aMOnTooKSkJPn6+mru3LnlXufp6Slvb29rcXJyqrGYAQAAAAAAANQeJP0AALCzwsJCZWRkKCoqyqY+KipK27dvL/fa4OBg+fj4qFevXtq0aVO5bS0WiwoKCmwKAAAAAAAAgGsDST8AAOzsyJEjKioqkpeXl029l5eX8vLySr3Gx8dH8+fP1/Lly7VixQq1a9dOvXr10pYtW8r8nMTERHl4eFiLr69vld8LAAAAAAAAAPtwtncAAADgf0wmk82xYRgl6oq1a9dO7dq1sx6HhYUpJydHr7/+uu64445Sr4mPj1dcXJz1uKCggMQfAAAAAAAAcI1gph8AAHbWtGlTOTk5lZjVd/jw4RKz/8rTrVs3HThwoMzzZrNZ7u7uNgUAAAAAAADAtYGkHwAAdubi4qKQkBClpaXZ1KelpSk8PLzC/ezZs0c+Pj7VECEAAAAAAACA2o7lPQEAqAXi4uI0bNgwhYaGKiwsTPPnz1d2drbGjh0rXVya89ChQ1q8eLEkKSkpSa1atVJgYKAKCwv14Ycfavny5Vq+fLmd7wQAAAAAAACAPZD0AwCgFhg8eLCOHj2qqVOnKjc3V0FBQVq3bp38/PwkSbm5ucrOzra2Lyws1IQJE3To0CHVq1dPgYGBWrt2rfr06WPHuwAAAAAAAABgLyT9AACoJWJiYhQTE1PquZSUFJvjiRMnauLEiTUUGQAAAAAAAIDajj39AAAAAAAAAAAAAAdH0g8AAAAAAAAAAABwcCT9AAAAAAAAAKCaJCQkyGQy2RRvb2/recMwlJCQoObNm6tevXqKjIzU3r177RozAMAxkfQDAAAAAAAAgGoUGBio3Nxca/n222+t52bMmKGZM2dq9uzZ2rVrl7y9vdW7d2+dOHHCrjEDABwPST8AAAAAAAAAqEbOzs7y9va2lmbNmkkXZ/klJSVp0qRJGjhwoIKCgrRo0SKdPn1aS5YssXfYAAAHQ9IPAAAAAAAAAKrRgQMH1Lx5c/n7++vhhx/WDz/8IEnKyspSXl6eoqKirG3NZrMiIiK0fft2O0YMAHBEzvYOAAAAAAAAAACuVV27dtXixYvVtm1b/frrr5o2bZrCw8O1d+9e5eXlSZK8vLxsrvHy8tJPP/1UZp8Wi0UWi8V6XFBQUI13AABwFCT9AAAAAAAAAKCaREdHW//75ptvVlhYmFq3bq1FixapW7dukiSTyWRzjWEYJeoulZiYqClTplRj1AAAR8TyngAAAAAAu0hMTFSXLl3k5uYmT09P3X///dq/f79Nm5EjR8pkMtmU4h9IAQBwRA0aNNDNN9+sAwcOyNvbW5KsM/6KHT58uMTsv0vFx8crPz/fWnJycqo9bgBA7UfSDwAAAABgF+np6Ro3bpx27typtLQ0nT9/XlFRUTp16pRNu3vuuUe5ubnWsm7dOrvFDADA1bJYLPruu+/k4+Mjf39/eXt7Ky0tzXq+sLBQ6enpCg8PL7MPs9ksd3d3mwIAAMt7AgAAAADsYv369TbHycnJ8vT0VEZGhu644w5rvdlsts6EAADA0UyYMEH9+/dXy5YtdfjwYU2bNk0FBQUaMWKETCaTYmNjNX36dAUEBCggIEDTp09X/fr1NWTIEHuHDgBwMCT9AAAAAAC1Qn5+viSpcePGNvWbN2+Wp6enGjVqpIiICL3yyivy9PQstQ+LxSKLxWI9LigoqOaoAQAo388//6xHHnlER44cUbNmzdStWzft3LlTfn5+kqSJEyfqzJkziomJ0bFjx9S1a1dt3LhRbm5u9g4dAOBgSPoBAAAAAOzOMAzFxcXp9ttvV1BQkLU+OjpaDz30kPz8/JSVlaUXX3xRd955pzIyMmQ2m0v0k5iYqClTptRw9AAAlG3ZsmXlnjeZTEpISFBCQkKNxQQAuDaR9AMAAAAA2N1TTz2lb775Rtu2bbOpHzx4sPW/g4KCFBoaKj8/P61du1YDBw4s0U98fLzi4uKsxwUFBfL19a3m6AEAAADA/kj6AQAAAADs6umnn9aaNWu0ZcsWtWjRoty2Pj4+8vPz04EDB0o9bzabS50BCAAAAADXOpJ+AAAAAAC7MAxDTz/9tFauXKnNmzfL39//itccPXpUOTk58vHxqZEYAQAAAMBR1LF3AAAAAACA69O4ceP04YcfasmSJXJzc1NeXp7y8vJ05swZSdLJkyc1YcIE7dixQz/++KM2b96s/v37q2nTphowYIC9wwcAAACAWoWZfgAAAAAAu5g7d64kKTIy0qY+OTlZI0eOlJOTk7799lstXrxYx48fl4+Pj3r27KnU1FS5ubnZKWoAAAAAqJ1I+gEAAAAA7MIwjHLP16tXTxs2bKixeAAAAADAkbG8JwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADs6uSb8tW7aof//+at68uUwmk1atWlVu+xUrVqh3795q1qyZ3N3dFRYWVmKpl5SUFJlMphLl7Nmz1Xw3AAAAAAAAAAAAgH3YNel36tQpde7cWbNnz65Q+y1btqh3795at26dMjIy1LNnT/Xv31979uyxaefu7q7c3Fyb4urqWk13AQAAAAAAAAAAANiXsz0/PDo6WtHR0RVun5SUZHM8ffp0rV69Wp9++qmCg4Ot9SaTSd7e3lUaKwAAAAAAAAAAAFBbOfSefhcuXNCJEyfUuHFjm/qTJ0/Kz89PLVq0UL9+/UrMBLycxWJRQUGBTQEAAAAAAAAAAAAchUMn/d544w2dOnVKgwYNsta1b99eKSkpWrNmjZYuXSpXV1d1795dBw4cKLOfxMREeXh4WIuvr28N3QEAAAAAAAAAAABw9Rw26bd06VIlJCQoNTVVnp6e1vpu3bpp6NCh6ty5s3r06KGPP/5Ybdu21dtvv11mX/Hx8crPz7eWnJycGroLAAAAAAAAAAAA4OrZdU+/Pyo1NVWjR4/WJ598orvuuqvctnXq1FGXLl3KnelnNptlNpurIVIAAAAAAAAAAACg+jncTL+lS5dq5MiRWrJkifr27XvF9oZhKDMzUz4+PjUSHwAAAAAAAAAAAFDT7DrT7+TJkzp48KD1OCsrS5mZmWrcuLFatmyp+Ph4HTp0SIsXL5YuJvyGDx+uWbNmqVu3bsrLy5Mk1atXTx4eHpKkKVOmqFu3bgoICFBBQYHeeustZWZm6p133rHTXQIAAAAAAAAAAADVy64z/Xbv3q3g4GAFBwdLkuLi4hQcHKyXXnpJkpSbm6vs7Gxr+3fffVfnz5/XuHHj5OPjYy3jx4+3tjl+/LieeOIJdejQQVFRUTp06JC2bNmi2267zQ53CAAAAAAAAAAAAFQ/u870i4yMlGEYZZ5PSUmxOd68efMV+3zzzTf15ptvVkl8AAAAAAAAAAAAgCNwuD39AAAAAAAAAAAAANgi6QcAAAAAAAAAAAA4OJJ+AAAAAAAAAAAAgIMj6QcAAAAAAAAAAAA4OJJ+AAAAAAAAAAAAgIMj6QcAAAAAAAAAAAA4OJJ+AAAAAAAAAAAAgIMj6QcAAAAAAAAAAAA4OJJ+AAAAAAAAAAAAgIMj6QcAAAAAAAAAAAA4OJJ+AAAAAAAAAAAAgIMj6QcAAAAAAAAAAAA4OJJ+AAAAAAAAAAAAgIMj6QcAAAAAAAAAAAA4OJJ+AAAAAAAAAAAAgIMj6QcAAAAAAAAAAAA4OJJ+AAAAAAAAAAAAgIMj6QcAQC0xZ84c+fv7y9XVVSEhIdq6dWuFrvvnP/8pZ2dn3XLLLdUeIwAAAAAAAIDaiaQfAAC1QGpqqmJjYzVp0iTt2bNHPXr0UHR0tLKzs8u9Lj8/X8OHD1evXr1qLFYAAAAAAAAAtQ9JPwAAaoGZM2dq9OjRGjNmjDp06KCkpCT5+vpq7ty55V735JNPasiQIQoLC6uxWAEAAAAAAADUPiT9AACws8LCQmVkZCgqKsqmPioqStu3by/zuuTkZP3f//2fJk+eXKHPsVgsKigosCkAAAAAAAAArg0k/QAAsLMjR46oqKhIXl5eNvVeXl7Ky8sr9ZoDBw7or3/9qz766CM5OztX6HMSExPl4eFhLb6+vlUSPwAAAAAAAAD7I+kHAEAtYTKZbI4NwyhRJ0lFRUUaMmSIpkyZorZt21a4//j4eOXn51tLTk5OlcQNAAAAAAAAwP4qNjUAAABUm6ZNm8rJyanErL7Dhw+XmP0nSSdOnNDu3bu1Z88ePfXUU5KkCxcuyDAMOTs7a+PGjbrzzjtLXGc2m2U2m6vxTgAAAAAAAADYCzP9AACwMxcXF4WEhCgtLc2mPi0tTeHh4SXau7u769tvv1VmZqa1jB07Vu3atVNmZqa6du1ag9EDAAAAAAAAqA2Y6QcAQC0QFxenYcOGKTQ0VGFhYZo/f76ys7M1duxY6eLSnIcOHdLixYtVp04dBQUF2Vzv6ekpV1fXEvUAAFQXJycn5ebmytPT06b+6NGj8vT0VFFRkd1iAwCgqjDeAQAcCUk/AABqgcGDB+vo0aOaOnWqcnNzFRQUpHXr1snPz0+SlJubq+zsbHuHCQCAlWEYpdZbLBa5uLjUeDwAAFQHxjsAgCMh6QcAQC0RExOjmJiYUs+lpKSUe21CQoISEhKqKTIAAP6/t956S5JkMpn0/vvvq2HDhtZzRUVF2rJli9q3b2/HCAEAuHqMdwAAR0TSDwAAAABQYW+++aZ0cebDvHnz5OTkZD3n4uKiVq1aad68eXaMEACAq1ed411iYqKef/55jR8/XklJSdbPmTJliubPn69jx46pa9eueueddxQYGFhFdwQAuB6Q9AMAAAAAVFhWVpYkqWfPnlqxYoVuuOEGe4cEAECVq67xbteuXZo/f746depkUz9jxgzNnDlTKSkpatu2raZNm6bevXtr//79cnNzq5LPBgBc++rYOwAAAAAAgOPZtGkTCT8AwDWvKse7kydP6tFHH9V7771n06dhGEpKStKkSZM0cOBABQUFadGiRTp9+rSWLFlSJZ8NALg+MNMPAAAAAFBpRUVFSklJ0T/+8Q8dPnxYFy5csDn/5Zdf2i02AACqSlWOd+PGjVPfvn111113adq0adb6rKws5eXlKSoqylpnNpsVERGh7du368knnyzRl8VikcVisR4XFBT8gbsDAFxrmOkHAAAAAKi08ePHa/z48SoqKlJQUJA6d+5sUyoiMTFRXbp0kZubmzw9PXX//fdr//79Nm0Mw1BCQoKaN2+uevXqKTIyUnv37q2muwIAwFZVjHeStGzZMn311VdKTEwscS4vL0+S5OXlZVPv5eVlPXe5xMREeXh4WIuvr2+l7w0AcO1hph8AAAAAoNKWLVumjz/+WH369PnDfaSnp2vcuHHq0qWLzp8/r0mTJikqKkr79u1TgwYNJPY4AgDYWVWMdzk5ORo/frw2btwoV1fXMtuZTCabY8MwStQVi4+PV1xcnPW4oKCAxB8AgKQfAAAAAKDyXFxc1KZNm6vqY/369TbHycnJ8vT0VEZGhu64444SexxJ0qJFi+Tl5aUlS5aUutwZAABVqSrGu4yMDB0+fFghISHWuqKiIm3ZskWzZ8+2znLPy8uTj4+Ptc3hw4dLzP4rZjabZTabryouAMC1h+U9AQAAAACV9txzz2nWrFkyDKPK+szPz5ckNW7cWKrAHkelsVgsKigosCkAAPxRVTHe9erVS99++60yMzOtJTQ0VI8++qgyMzN10003ydvbW2lpadZrCgsLlZ6ervDw8Cq6EwDA9aBSM/3+/e9/KyQkRE5OTlIpU8wtFotWr16tQYMGVX2kAAAAAIBaY9u2bdq0aZM+//xzBQYGqm7dujbnV6xYUan+DMNQXFycbr/9dgUFBUlX2OPop59+KrWfxMRETZkypZJ3AwBA6apivHNzc7OObcUaNGigJk2aWOtjY2M1ffp0BQQEKCAgQNOnT1f9+vU1ZMiQKr4jAMC1rFJJv7CwMOXm5srT01OS5OHhYX0bRZKOHz+uRx55hKQfAAAAAFzjGjVqpAEDBlRZf0899ZS++eYbbdu2rcQ59jgCANhLVY93ZZk4caLOnDmjmJgYHTt2TF27dtXGjRvZvxYAUCmVSvpdPo29tGntVbm0CwAAAACgdkpOTq6yvp5++mmtWbNGW7ZsUYsWLaz13t7eEnscAQDsqCrHu0tt3rzZ5thkMikhIUEJCQnV8nkAgOtDle/pV9bblgAAAACAa8v58+f1xRdf6N1339WJEyckSb/88otOnjxZoesNw9BTTz2lFStW6Msvv5S/v7/NeX9/f/Y4AgDY3dWOdwAA1JRKzfQDAAAAAECSfvrpJ91zzz3Kzs6WxWJR79695ebmphkzZujs2bOaN2/eFfsYN26clixZotWrV8vNzc26h5+Hh4fq1asnk8nEHkcAALuqivEOAICaUumk3759+6wPYoZh6Pvvv7e+1XLkyJGqjxAAAAAAUOuMHz9eoaGh+vrrr9WkSRNr/YABAzRmzJgK9TF37lxJUmRkpE19cnKyRo4cKbHHEQDAzqpivAMAoKZUennPXr166ZZbbtEtt9yi06dPq1+/frrlllsUHBysu+66q1J9bdmyRf3791fz5s1lMpm0atWqK16Tnp6ukJAQubq66qabbir1bZrly5erY8eOMpvN6tixo1auXFmpuAAAAAAA5du2bZteeOEFubi42NT7+fnp0KFDFerDMIxSS3HCT5fscZSbm6uzZ88qPT1dQUFBVX4/AACUpirGOwAAakqlZvplZWVV6YefOnVKnTt31qhRo/TAAw9U6PP79Omjxx9/XB9++KH++c9/KiYmRs2aNbNev2PHDg0ePFgvv/yyBgwYoJUrV2rQoEHatm2bunbtWqXxAwAAAMD16sKFCyoqKipR//PPPzMLDwBwzWC8AwA4kkrN9PPz87tiOXbsWIX7i46O1rRp0zRw4MAKtZ83b55atmyppKQkdejQQWPGjNFjjz2m119/3domKSlJvXv3Vnx8vNq3b6/4+Hj16tVLSUlJlblVAADKdfz4cW3YsMF6vGLFCrvGAwBATevdu7fNc5bJZNLJkyc1efJk9enTx66xAQBQVRjvAACOpNLLe5YmPz9fc+bM0a233qqQkJCq6LJUO3bsUFRUlE3d3Xffrd27d+vcuXPlttm+fXu1xQUAuP488sgjev311/Xoo4/KMAybF1AAALgevPnmm0pPT1fHjh119uxZDRkyRK1atdKhQ4f02muv2Ts8AACqBOMdAMCRVGp5z8t9+eWXWrhwoVasWCE/Pz898MADWrBgQdVFd5m8vDx5eXnZ1Hl5een8+fM6cuSIfHx8ymyTl5dXZr8Wi0UWi8V6XFBQUA3RAwCuJXl5edqzZ4+Sk5P1wgsv2DscAABqXPPmzZWZmally5YpIyNDFy5c0OjRo/Xoo4+qXr169g4PAIAqwXgHAHAklU76/fzzz0pJSdHChQt16tQpDRo0SOfOndPy5cvVsWPH6onyEiaTyebYMIwS9aW1ubzuUomJiZoyZUqVxwoAuHY1bdpUkjRq1CiNHz9e33//vb1DAgCgxtWrV0+jRo3SqFGj7B0KAADVhvEOAOAoKrW8Z58+fdSxY0ft27dPb7/9tn755Re9/fbb1RfdZby9vUvM2Dt8+LCcnZ3VpEmTcttcPvvvUvHx8crPz7eWnJycaroDAMC1ovilF0l6/fXXNXz48BJtDh06ZIfIAACoGYmJiVq4cGGJ+oULF7LcGQDgmsF4BwBwJJVK+m3cuFFjxozRlClT1LdvXzk5OVVfZKUICwtTWlpaiZhCQ0NVt27dctuEh4eX2a/ZbJa7u7tNAQCgPI8//rh17Klbt67Nxu55eXl6+umn1aZNGztGCABA9Xr33XfVvn37EvWBgYGaN2+eXWICAKCqMd4BABxJpZJ+W7du1YkTJxQaGqquXbtq9uzZ+u233/7wh588eVKZmZnKzMyUJGVlZSkzM1PZ2dnSxRl4l86cGDt2rH766SfFxcXpu+++08KFC7VgwQJNmDDB2mb8+PHauHGjXnvtNX3//fd67bXX9MUXXyg2NvYPxwkAwOXy8/P16KOPqlmzZmrevLneeustXbhwQS+99JJuuukm7dy5s9S3QQEAuFbk5eXJx8enRH2zZs2Um5trl5gAAKhqjHcAAEdSqaRfWFiY3nvvPeXm5urJJ5/UsmXLdOONN+rChQtKS0vTiRMnKvXhu3fvVnBwsIKDgyVJcXFxCg4O1ksvvSRJys3NtSYAJcnf31/r1q3T5s2bdcstt+jll1/WW2+9pQceeMDaJjw8XMuWLVNycrI6deqklJQUpaamqmvXrpWKDQCA8sTHx2vLli0aMWKEGjdurGeffVb9+vXTtm3b9Pnnn2vXrl165JFH7B0mAADVxtfXV//85z9L1P/zn/9U8+bN7RITAABVjfEOAOBInP/IRfXr19djjz2mxx57TPv379eCBQv06quv6q9//at69+6tNWvWVKifyMhIGYZR5vmUlJQSdREREfrqq6/K7ffBBx/Ugw8+WKEYAAD4I9auXavk5GTdddddiomJUZs2bdS2bVubZT4BALiWjRkzRrGxsTp37pzuvPNOSdI//vEPTZw4Uc8995y9wwMAoEow3gEAHMkfSvpdql27dpoxY4YSExP12WefsZQZAOC68Msvv6hjx46SpJtuukmurq4aM2aMvcMCAKDGTJw4Ub///rtiYmJUWFgoSXJ1ddVf/vIXxcfH2zs8AACqBOMdAMCRVCrp99hjj12xTZMmTa4mHgAAHMKFCxdUt25d67GTk5MaNGhg15gAAKgpRUVF2rZtm/7yl7/oxRdf1Hfffad69eopICBAZrPZ3uEBAFAlGO8AAI6mUkm/lJQU+fn5KTg4uMxlOU0mU1XFBgBArWUYhkaOHGl90Dt79qzGjh1bIvG3YsUKO0UIAED1cXJy0t13363vvvtO/v7+6tKli71DAgCgyjHeAQAcTaWSfmPHjtWyZcv0ww8/6LHHHtPQoUPVuHHj6osOAIBaasSIETbHQ4cOtVssAADYw80336wffvhB/v7+9g4FAIBqw3gHAHAklUr6zZkzR2+++aZWrFihhQsXKj4+Xn379tXo0aMVFRXFLD8AwHUjOTnZ3iEAAGBXr7zyiiZMmKCXX35ZISEhJWa7u7u72y02AACqCuMdAMCRVCrpJ0lms1mPPPKIHnnkEf30009KSUlRTEyMzp07p3379qlhw4bVEykAAAAAoNa45557JEn33nuvzQughmHIZDKpqKjIjtEBAFA1GO8AAI6k0km/S5lMJplMJhmGoQsXLlRdVAAAAACAWm3Tpk32DgEAgGrHeAcAcCSVTvpZLBbr8p7btm1Tv379NHv2bN1zzz2qU6dO9UQJAAAAAKhVIiIi7B0CAADVjvEOAOBIKpWli4mJkY+Pj1577TX169dPP//8sz755BP16dOHhB8AAAAAXGe2bt2qoUOHKjw8XIcOHZIkffDBB9q2bZu9QwMAoMow3gEAHEWlMnXz5s2Tu7u7/P39lZ6erscff1wDBw4sUQAAAAAA17bly5fr7rvvVr169fTVV1/JYrFIkk6cOKHp06fbOzwAAKoE4x0AwJFUKuk3fPhw9ezZU40aNZKHh0eZBQAAAABwbZs2bZrmzZun9957T3Xr1rXWh4eH66uvvrJrbAAAVBXGOwCAI6nUnn4pKSnVFwkAAAAAwGHs379fd9xxR4l6d3d3HT9+3C4xAQBQ1RjvAACOhI34AAAAAACV5uPjo4MHD5ao37Ztm2666Sa7xAQAQFVjvAMAOBKSfgAAAACASnvyySc1fvx4/etf/5LJZNIvv/yijz76SBMmTFBMTIy9wwMAoEow3gEAHEmllvcEAAAAAECSJk6cqIKCAvXs2VNnz57VHXfcIbPZrAkTJuipp56yd3gAAFQJxjsAgCMh6QcAAAAAqLDTp0/rz3/+s1atWqVz586pf//+eu655yRJHTt2VMOGDe0dIgAAV43xDgDgiEj6AQAAAAAqbPLkyUpJSdGjjz6qevXqacmSJbpw4YI++eQTe4cGAECVYbwDADgikn4AAAAAgApbsWKFFixYoIcffliS9Oijj6p79+4qKiqSk5OTvcMDAKBKMN4BABxRHXsHAAAAAABwHDk5OerRo4f1+LbbbpOzs7N++eUXu8YFAEBVYrwDADgikn4AAAAAgAorKiqSi4uLTZ2zs7POnz9vt5gAAKhqjHcAAEfE8p4AAAAAgAozDEMjR46U2Wy21p09e1Zjx45VgwYNrHUrVqywU4QAAFw9xjsAgCMi6QcAAAAAqLARI0aUqBs6dKhdYgEAoLow3gEAHBFJPwAAAABAhSUnJ9s7BAAAqh3jHQDAEbGnHwAAAAAAAAAAAODgSPoBAFBLzJkzR/7+/nJ1dVVISIi2bt1aZttt27ape/fuatKkierVq6f27dvrzTffrNF4AQAAAAAAANQeLO8JAEAtkJqaqtjYWM2ZM0fdu3fXu+++q+joaO3bt08tW7Ys0b5BgwZ66qmn1KlTJzVo0EDbtm3Tk08+qQYNGuiJJ56wyz0AAAAAAAAAsB9m+gEAUAvMnDlTo0eP1pgxY9ShQwclJSXJ19dXc+fOLbV9cHCwHnnkEQUGBqpVq1YaOnSo7r777nJnBwIAAAAAAAC4dpH0AwDAzgoLC5WRkaGoqCib+qioKG3fvr1CfezZs0fbt29XREREmW0sFosKCgpsCgAAAAAAAIBrA0k/AADs7MiRIyoqKpKXl5dNvZeXl/Ly8sq9tkWLFjKbzQoNDdW4ceM0ZsyYMtsmJibKw8PDWnx9favsHgAAAAAAAADYF0k/AABqCZPJZHNsGEaJustt3bpVu3fv1rx585SUlKSlS5eW2TY+Pl75+fnWkpOTU2WxAwAAAAAAALAvZ3sHAADA9a5p06ZycnIqMavv8OHDJWb/Xc7f31+SdPPNN+vXX39VQkKCHnnkkVLbms1mmc3mKowcAAAAAAAAQG3BTD8AAOzMxcVFISEhSktLs6lPS0tTeHh4hfsxDEMWi6UaIgQAAAAAAABQ25H0AwCgFoiLi9P777+vhQsX6rvvvtOzzz6r7OxsjR07Vrq4NOfw4cOt7d955x19+umnOnDggA4cOKDk5GS9/vrrGjp0qB3vAgAAAABwublz56pTp05yd3eXu7u7wsLC9Pnnn1vPG4ahhIQENW/eXPXq1VNkZKT27t1r15gBAI6J5T0BAKgFBg8erKNHj2rq1KnKzc1VUFCQ1q1bJz8/P0lSbm6usrOzre0vXLig+Ph4ZWVlydnZWa1bt9arr76qJ5980o53AQAAAAC4XIsWLfTqq6+qTZs2kqRFixbpvvvu0549exQYGKgZM2Zo5syZSklJUdu2bTVt2jT17t1b+/fvl5ubm73DBwA4EJJ+AADUEjExMYqJiSn1XEpKis3x008/raeffrqGIgMAAAAA/FH9+/e3OX7llVc0d+5c7dy5Ux07dlRSUpImTZqkgQMHSheTgl5eXlqyZAkvdgIAKoXlPQEAAAAAdrFlyxb1799fzZs3l8lk0qpVq2zOjxw5UiaTyaZ069bNbvECAHC1ioqKtGzZMp06dUphYWHKyspSXl6eoqKirG3MZrMiIiK0ffv2MvuxWCwqKCiwKQAAkPQDAAAAANjFqVOn1LlzZ82ePbvMNvfcc49yc3OtZd26dTUaIwAAVeHbb79Vw4YNZTabNXbsWK1cuVIdO3ZUXl6eJMnLy8umvZeXl/VcaRITE+Xh4WEtvr6+1X4PAIDaj+U9AQAAAAB2ER0drejo6HLbmM1meXt711hMAABUh3bt2ikzM1PHjx/X8uXLNWLECKWnp1vPm0wmm/aGYZSou1R8fLzi4uKsxwUFBST+AADM9AMAAAAA1F6bN2+Wp6en2rZtq8cff1yHDx+2d0gAAFSai4uL2rRpo9DQUCUmJqpz586aNWuW9cWWy2f1HT58uMTsv0uZzWa5u7vbFAAASPoBAAAAAGql6OhoffTRR/ryyy/1xhtvaNeuXbrzzjtlsVjKvIY9jgAAjsAwDFksFvn7+8vb21tpaWnWc4WFhUpPT1d4eLhdYwQAOB6W9wQAAAAA1EqDBw+2/ndQUJBCQ0Pl5+entWvXauDAgaVek5iYqClTptRglAAAlO/5559XdHS0fH19deLECS1btkybN2/W+vXrZTKZFBsbq+nTpysgIEABAQGaPn266tevryFDhtg7dACAgyHpBwAAAABwCD4+PvLz89OBAwfKbMMeRwCA2ubXX3/VsGHDlJubKw8PD3Xq1Enr169X7969JUkTJ07UmTNnFBMTo2PHjqlr167auHGj3Nzc7B06AMDBkPQDAAAAADiEo0ePKicnRz4+PmW2MZvNMpvNNRoXAADlWbBgQbnnTSaTEhISlJCQUGMxAQCuTXbf02/OnDny9/eXq6urQkJCtHXr1jLbjhw5UiaTqUQJDAy0tklJSSm1zdmzZ2vojgAAAAAAFXHy5EllZmYqMzNTkpSVlaXMzExlZ2fr5MmTmjBhgnbs2KEff/xRmzdvVv/+/dW0aVMNGDDA3qEDAAAAQK1j16RfamqqYmNjNWnSJO3Zs0c9evRQdHS0srOzS20/a9Ys5ebmWktOTo4aN26shx56yKadu7u7Tbvc3Fy5urrW0F0BAAAAACpi9+7dCg4OVnBwsCQpLi5OwcHBeumll+Tk5KRvv/1W9913n9q2basRI0aobdu22rFjB8udAQAAAEAp7Lq858yZMzV69GiNGTNGkpSUlKQNGzZo7ty5SkxMLNHew8NDHh4e1uNVq1bp2LFjGjVqlE07k8kkb2/vGrgDAAAAAMAfFRkZKcMwyjy/YcOGGo0HAAAAAByZ3Wb6FRYWKiMjQ1FRUTb1UVFR2r59e4X6WLBgge666y75+fnZ1J88eVJ+fn5q0aKF+vXrpz179lRp7AAAAAAAAAAAAEBtYreZfkeOHFFRUZG8vLxs6r28vJSXl3fF63Nzc/X5559ryZIlNvXt27dXSkqKbr75ZhUUFGjWrFnq3r27vv76awUEBJTal8VikcVisR4XFBT84fsCAAAAAAAAAAAAappd9/TTxaU4L2UYRom60qSkpKhRo0a6//77beq7deumoUOHqnPnzurRo4c+/vhjtW3bVm+//XaZfSUmJlqXDvXw8JCvr+9V3BEAAAAAAAAAAABQs+yW9GvatKmcnJxKzOo7fPhwidl/lzMMQwsXLtSwYcPk4uJSbts6deqoS5cuOnDgQJlt4uPjlZ+fby05OTmVvBsAAAAAAAAAAADAfuyW9HNxcVFISIjS0tJs6tPS0hQeHl7utenp6Tp48KBGjx59xc8xDEOZmZny8fEps43ZbJa7u7tNAQAAAAAAAAAAAByF3fb0k6S4uDgNGzZMoaGhCgsL0/z585Wdna2xY8dKF2fgHTp0SIsXL7a5bsGCBeratauCgoJK9DllyhR169ZNAQEBKigo0FtvvaXMzEy98847NXZfAAAAAAAAAAAAQE2ya9Jv8ODBOnr0qKZOnarc3FwFBQVp3bp18vPzkyTl5uYqOzvb5pr8/HwtX75cs2bNKrXP48eP64knnlBeXp48PDwUHBysLVu26LbbbquRewIAAAAAAAAAAABqml2TfpIUExOjmJiYUs+lpKSUqPPw8NDp06fL7O/NN9/Um2++WaUxAgAAAAAAAAAAALWZ3fb0AwAAAAAAAAAAAFA1SPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAAAAAAAAAAODgSPoBAAAAAAAAAAAADo6kHwAAtcScOXPk7+8vV1dXhYSEaOvWrWW2XbFihXr37q1mzZrJ3d1dYWFh2rBhQ43GCwAAAAAAAKD2IOkHAEAtkJqaqtjYWE2aNEl79uxRjx49FB0drezs7FLbb9myRb1799a6deuUkZGhnj17qn///tqzZ0+Nxw4AAAAAAADA/kj6AQBQC8ycOVOjR4/WmDFj1KFDByUlJcnX11dz584ttX1SUpImTpyoLl26KCAgQNOnT1dAQIA+/fTTGo8dAAAAAAAAgP2R9AMAwM4KCwuVkZGhqKgom/qoqCht3769Qn1cuHBBJ06cUOPGjctsY7FYVFBQYFMAAAAAAAAAXBtI+gEAYGdHjhxRUVGRvLy8bOq9vLyUl5dXoT7eeOMNnTp1SoMGDSqzTWJiojw8PKzF19f3qmMHAAAAAAAAUDuQ9AMAoJYwmUw2x4ZhlKgrzdKlS5WQkKDU1FR5enqW2S4+Pl75+fnWkpOTUyVxAwAAAADKlpiYqC5dusjNzU2enp66//77tX//fps2hmEoISFBzZs3V7169RQZGam9e/faLWYAgGMi6QcAgJ01bdpUTk5OJWb1HT58uMTsv8ulpqZq9OjR+vjjj3XXXXeV29ZsNsvd3d2mAAAAAACqV3p6usaNG6edO3cqLS1N58+fV1RUlE6dOmVtM2PGDM2cOVOzZ8/Wrl275O3trd69e+vEiRN2jR0A4FhI+gEAYGcuLi4KCQlRWlqaTX1aWprCw8PLvG7p0qUaOXKklixZor59+9ZApAAAVK0tW7aof//+at68uUwmk1atWmVznlkPAIBrwfr16zVy5EgFBgaqc+fOSk5OVnZ2tjIyMqSL411SUpImTZqkgQMHKigoSIsWLdLp06e1ZMkSe4cPAHAgJP0AAKgF4uLi9P7772vhwoX67rvv9Oyzzyo7O1tjx46VLi7NOXz4cGv7pUuXavjw4XrjjTfUrVs35eXlKS8vT/n5+Xa8CwAAKufUqVPq3LmzZs+eXep5Zj0AAK5Fxc9tjRs3liRlZWUpLy9PUVFR1jZms1kRERHavn273eIEADgeZ3sHAAAApMGDB+vo0aOaOnWqcnNzFRQUpHXr1snPz0+SlJubq+zsbGv7d999V+fPn9e4ceM0btw4a/2IESOUkpJil3sAAKCyoqOjFR0dXeq5y2c9SNKiRYvk5eWlJUuW6Mknn6zhaAEAuHqGYSguLk633367goKCJMm61cPl2zt4eXnpp59+KrUfi8Uii8ViPS4oKKjWuAEAjoGkHwAAtURMTIxiYmJKPXd5Im/z5s01FBUAAPZxpVkPJP0AAI7oqaee0jfffKNt27aVOGcymWyODcMoUVcsMTFRU6ZMqbY4AQCOieU9AQAAAAC1TnmzHorPlcZisaigoMCmAABQGzz99NNas2aNNm3apBYtWljrvb29pUvGvmKHDx8uMQ4Wi4+PV35+vrXk5ORUc/QAAEdA0g8AAAAAUGtVZtaDLs588PDwsBZfX98aiBIAgLIZhqGnnnpKK1as0Jdffil/f3+b8/7+/vL29lZaWpq1rrCwUOnp6QoPDy+1T7PZLHd3d5sCAIDdk35z5syRv7+/XF1dFRISoq1bt5bZdvPmzTKZTCXK999/b9Nu+fLl6tixo8xmszp27KiVK1fWwJ0AAAAAAKrKH5n1IGY+AABqoXHjxunDDz/UkiVL5Obmpry8POXl5enMmTPSxRdcYmNjNX36dK1cuVL/+c9/NHLkSNWvX19Dhgyxd/gAAAdi16RfamqqYmNjNWnSJO3Zs0c9evRQdHS0srOzy71u//79ys3NtZaAgADruR07dmjw4MEaNmyYvv76aw0bNkyDBg3Sv/71rxq4IwAAAABAVfgjsx7EzAcAQC00d+5c5efnKzIyUj4+PtaSmppqbTNx4kTFxsYqJiZGoaGhOnTokDZu3Cg3Nze7xg4AcCzO9vzwmTNnavTo0RozZowkKSkpSRs2bNDcuXOVmJhY5nWenp5q1KhRqeeSkpLUu3dvxcfHSxff8kxPT1dSUpKWLl1aTXcCAAAAAKiskydP6uDBg9bjrKwsZWZmqnHjxmrZsqV11kNAQIACAgI0ffp0Zj0AAByOYRhXbGMymZSQkKCEhIQaiQkAcG2y20y/wsJCZWRkKCoqyqY+KipK27dvL/fa4OBg+fj4qFevXtq0aZPNuR07dpTo8+677y63TzZ6BwAAAICat3v3bgUHBys4OFiSFBcXp+DgYL300ksSsx4AAAAAoFLsNtPvyJEjKioqKrEXg5eXV4k9G4r5+Pho/vz5CgkJkcVi0QcffKBevXpp8+bNuuOOO6SL+z1Upk9d3Oh9ypQpVXJfAAAAAICKiYyMLHf2A7MeAAAAAKDi7Lq8py4+xF3KMIwSdcXatWundu3aWY/DwsKUk5Oj119/3Zr0q2yfurgEaFxcnPW4oKBAvr6+f+h+AAAAAAAAAAAAgJpmt+U9mzZtKicnpxIz8A4fPlxipl55unXrpgMHDliPvb29K90nG70DAAAAAAAAAADAkdkt6efi4qKQkBClpaXZ1KelpSk8PLzC/ezZs0c+Pj7W47CwsBJ9bty4sVJ9AgAAAAAAAAAAAI7Erst7xsXFadiwYQoNDVVYWJjmz5+v7OxsjR07Vrq47OahQ4e0ePFiSVJSUpJatWqlwMBAFRYW6sMPP9Ty5cu1fPlya5/jx4/XHXfcoddee0333XefVq9erS+++ELbtm2z230CAAAAAAAAAAAA1cmuSb/Bgwfr6NGjmjp1qnJzcxUUFKR169bJz89PkpSbm6vs7Gxr+8LCQk2YMEGHDh1SvXr1FBgYqLVr16pPnz7WNuHh4Vq2bJleeOEFvfjii2rdurVSU1PVtWtXu9wjAAAAAAAAAAAAUN3smvSTpJiYGMXExJR6LiUlxeZ44sSJmjhx4hX7fPDBB/Xggw9WWYwAAAAAAAAAAABAbWa3Pf0AAAAAAAAAAAAAVA2SfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAAAAAAAAAAAAODiSfgAAAAAAAAAAAICDI+kHAEAtMWfOHPn7+8vV1VUhISHaunVrmW1zc3M1ZMgQtWvXTnXq1FFsbGyNxgoAAAAAAACgdiHpBwBALZCamqrY2FhNmjRJe/bsUY8ePRQdHa3s7OxS21ssFjVr1kyTJk1S586dazxeAAAAAAAAALULST8AAGqBmTNnavTo0RozZow6dOigpKQk+fr6au7cuaW2b9WqlWbNmqXhw4fLw8OjxuMFAAAAAAAAULuQ9AMAwM4KCwuVkZGhqKgom/qoqCht3769yj7HYrGooKDApgAAAAAAqteWLVvUv39/NW/eXCaTSatWrbI5bxiGEhIS1Lx5c9WrV0+RkZHau3ev3eIFADgukn4AANjZkSNHVFRUJC8vL5t6Ly8v5eXlVdnnJCYmysPDw1p8fX2rrG8AAKpLQkKCTCaTTfH29rZ3WAAAVNipU6fUuXNnzZ49u9TzM2bM0MyZMzV79mzt2rVL3t7e6t27t06cOFHjsQIAHJuzvQMAAAD/YzKZbI4NwyhRdzXi4+MVFxdnPS4oKCDxBwBwCIGBgfriiy+sx05OTnaNBwCAyoiOjlZ0dHSp5wzDUFJSkiZNmqSBAwdKkhYtWiQvLy8tWbJETz75ZA1HCwBwZHaf6Tdnzhz5+/vL1dVVISEh2rp1a5ltV6xYod69e6tZs2Zyd3dXWFiYNmzYYNMmJSWlxFugJpNJZ8+erYG7AQCg8po2bSonJ6cSs/oOHz5cYvbf1TCbzXJ3d7cpAAA4AmdnZ3l7e1tLs2bN7B0SAABVIisrS3l5eTbbPZjNZkVERJS73QPbNwAASmPXpF9qaqpiY2M1adIk7dmzRz169FB0dLSys7NLbb9lyxb17t1b69atU0ZGhnr27Kn+/ftrz549Nu3c3d2Vm5trU1xdXWvorgAAqBwXFxeFhIQoLS3Npj4tLU3h4eF2iwsAgNriwIEDat68ufz9/fXwww/rhx9+KLMtP4ICABxJ8cufld3uge0bAAClsWvSb+bMmRo9erTGjBmjDh06KCkpSb6+vpo7d26p7ZOSkjRx4kR16dJFAQEBmj59ugICAvTpp5/atCve4+HSAgBAbRYXF6f3339fCxcu1Hfffadnn31W2dnZGjt2rHRxac7hw4fbXJOZmanMzEydPHlSv/32mzIzM7Vv3z473QEAANWja9euWrx4sTZs2KD33ntPeXl5Cg8P19GjR0ttz4+gAABHVNntHuLj45Wfn28tOTk5NRAlAKC2s9uefoWFhcrIyNBf//pXm/qoqKhyp65f6sKFCzpx4oQaN25sU3/y5En5+fmpqKhIt9xyi15++WUFBwdXafwAAFSlwYMH6+jRo5o6dapyc3MVFBSkdevWyc/PT5KUm5tbYib8pWNbRkaGlixZIj8/P/344481Hj8AANXl0j2Qbr75ZoWFhal169ZatGiRzV61xdjDFgDgSIonK+Tl5cnHx8daf6XtHsxms8xmc43ECABwHHZL+h05ckRFRUWVnrp+qTfeeEOnTp3SoEGDrHXt27dXSkqKbr75ZhUUFGjWrFnq3r27vv76awUEBJTaj8VikcVisR6z/AsAwB5iYmIUExNT6rmUlJQSdYZh1EBUAADULg0aNNDNN9+sAwcOlHqeH0EBAI7E399f3t7eSktLs77YWVhYqPT0dL322mv2Dg8A4GDslvQrVtmp68WWLl2qhIQErV69Wp6entb6bt26qVu3btbj7t2769Zbb9Xbb7+tt956q9S+EhMTNWXKlKu6DwAAAABA9bNYLPruu+/Uo0cPe4cCAECFnDx5UgcPHrQeZ2VlKTMzU40bN1bLli0VGxtr3caoeEuj+vXra8iQIXaNGwDgeOyW9GvatKmcnJxKzOq70tR1SUpNTdXo0aP1ySef6K677iq3bZ06ddSlS5cy3wIVy78AAAAAQK01YcIE9e/fXy1bttThw4c1bdo0FRQUaMSIEfYODQCACtm9e7d69uxpPS7+HXLEiBFKSUnRxIkTdebMGcXExOjYsWPq2rWrNm7cKDc3NztGDQBwRHZL+rm4uCgkJERpaWkaMGCAtT4tLU333XdfmdctXbpUjz32mJYuXaq+ffte8XMMw1BmZqZuvvnmMtuw/AsAAAAA1E4///yzHnnkER05ckTNmjVTt27dtHPnTuu+twAA1HaRkZHlbs9gMpmUkJCghISEGo0LAHDtsevynnFxcRo2bJhC/1979x5d05mHcfw5uUgiSEyQsEQxGeo+7reRhKqk5sJiyJoxKeMSRN07JaaGtB2XlkpVS41r1xhiatFWVUQniWhSdakKMqgJwcQYVDEISd75o3JWjwSJSI6T8/2slT/Ou9+9z/se7z6PtX5779Ohg7p27arly5crOztbY8aMke7egXfu3Dm9//770t2C3/PPP6+33npLXbp0sd4l6OXlJR8fH0lSbGysunTpop/85Ce6evWqFi9erIMHD+qdd96x40wBAAAAAI9iw4YN9h4CAAAAADgEuxb9IiIidOnSJb3yyivKyclRy5YttW3bNusVmzk5OcrOzrb2f++995SXl6dx48Zp3Lhx1vbCW+El6cqVK4qKitL58+fl4+Ojtm3bateuXerUqZMdZggAAAAAAAAAAACUP7sW/SQpOjpa0dHRxW4rLOQVSk5OfujxFi1apEWLFj228QEAAAAAAAAAAABPOhd7DwAAAAAAAAAAAABA2VD0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwVH0AwAAAAAAAAAAABwcRT8AAAAAAAAAAADAwdm96Pfuu++qUaNG8vT0VPv27ZWamvrA/ikpKWrfvr08PT3VuHFjLVu2rEifTZs2qXnz5vLw8FDz5s21efPmcpwBAACPR3lkIgAAlUVpcxIAAEdE3gEAysKuRb/4+HhNmjRJf/zjH/XVV1+pR48eeu6555SdnV1s/6ysLPXt21c9evTQV199pRkzZmjChAnatGmTtU96eroiIiIUGRmpr7/+WpGRkRo8eLD27NlTgTMDAKB0yiMTAQCoLEqbkwAAOCLyDgBQVnYt+r355psaMWKERo4cqWbNmikuLk6BgYFaunRpsf2XLVumBg0aKC4uTs2aNdPIkSM1fPhwLViwwNonLi5Ozz77rGJiYvT0008rJiZGzzzzjOLi4ipwZgAAlE55ZCIAAJVFaXMSAABHRN4BAMrKzV5vfPv2be3fv1/Tp0+3ae/Tp4/S0tKK3Sc9PV19+vSxaQsLC9PKlSt1584dubu7Kz09XZMnTy7S50FFv9zcXOXm5lpff/fdd5Kkq1evPtLcCuXfvFam/YGHKesaLS/X7uTZewio5KqWce0XnjvGmMc0orIpr0y8F3kHR0XewVlVtrx7VKXNyfLKO5F5KGfkHZwVefc98g7OgryDs6qovLNb0e/ixYvKz8+Xv7+/Tbu/v7/Onz9f7D7nz58vtn9eXp4uXryounXr3rfP/Y4pSXPnzlVsbGyR9sDAwFLOCqhYPpNL0AmojHx8Hsthrl27Jp/HdKyyKK9MvBd5B0dF3sFpVbK8e1SlzUnyDo6KvIPTIu8k8g5OhLyD06qgvLNb0a+QxWKxeW2MKdL2sP73tpf2mDExMZoyZYr1dUFBgS5fviw/P78H7ofH6+rVqwoMDNSZM2dUo0YNew8HqBCse/swxujatWuqV6+evYdiozwy8YfIuycD5z2cEevePp7UvHtUJc1J8u7JwHkPZ8Xar3jk3ffIO/vgnIezYu1XvJLmnd2KfrVq1ZKrq2uRK1UuXLhQ5IqWQgEBAcX2d3Nzk5+f3wP73O+YkuTh4SEPDw+bNl9f31LPCY9HjRo1+KKA02HdV7wn6QrQ8srEe5F3TxbOezgj1n3Fe5Ly7lGVNifJuycL5z2cFWu/YpF33yPv7IdzHs6KtV+xSpJ3LhUykmJUqVJF7du3V2Jiok17YmKiunXrVuw+Xbt2LdJ/x44d6tChg/W3i+7X537HBADA3sorEwEAqAweJScBAHA05B0A4HGw6+M9p0yZosjISHXo0EFdu3bV8uXLlZ2drTFjxkh3b1M/d+6c3n//fUnSmDFjtGTJEk2ZMkWjRo1Senq6Vq5cqfXr11uPOXHiRAUHB2v+/Pnq16+fPvzwQ+3cuVO7d++22zwBAHiY8shEAAAqi4flJAAAlQF5BwAoK7sW/SIiInTp0iW98sorysnJUcuWLbVt2zY99dRTkqScnBxlZ2db+zdq1Ejbtm3T5MmT9c4776hevXpavHixBg4caO3TrVs3bdiwQS+//LJmzpypH//4x4qPj1fnzp3tMkeUnIeHh2bNmlXk0QRAZca6R6HyyEQ8mTjv4YxY9yirh+Uknjyc93BWrH2UBXnneDjn4axY+08uizHG2HsQAAAAAAAAAAAAAB6d3X7TDwAAAAAAAAAAAMDjQdEPAAAAAAAAAAAAcHAU/QAAAAAAAAAAAAAHR9EPZdKwYUPFxcXZexjAEyc0NFSTJk2y9zDK3ezZs/XTn/7U3sMAyh15BxSPvAMqF/IOKB55B1Qu5B1QPPKucqDo5+CGDRsmi8Uii8UiNzc3NWjQQGPHjtW3335r76GVq9mzZ1vn/cO/nTt32nVMlfnL4klRuObnzZtn075lyxZZLBa7jau01qxZI4vFovDwcJv2K1euyGKxKDk5ucTHGjZsmPr3718OowSeHOQdeedsyLuiyDs4A/KOvHM25F1R5B2cAXlH3jkb8q4o8q78UPSrBMLDw5WTk6NTp05pxYoV+vjjjxUdHW3vYZW7Fi1aKCcnx+YvODj4kY51+/btxz4+lB9PT0/Nnz/fLv8ZvHPnzmM7lpubmz777DMlJSU9tmNWFGOM8vLy7D0MOBnyjrxzNuSd/ZF3sAfyjrxzNuSd/ZF3sAfyjrxzNuSd/TlL3lH0qwQ8PDwUEBCg+vXrq0+fPoqIiNCOHTus2/Pz8zVixAg1atRIXl5eatq0qd566y2bYxRW1hcsWKC6devKz89P48aNs/lCuHDhgn75y1/Ky8tLjRo10rp164qMJTs7W/369VO1atVUo0YNDR48WP/5z3+s2wuvHlm1apUaNGigatWqaezYscrPz9frr7+ugIAA1alTR3/+858fOm83NzcFBATY/FWpUkWSlJGRoV69esnLy0t+fn6KiorS9evXi8x37ty5qlevnpo0aSJJOnfunCIiIlSzZk35+fmpX79+OnXqlHW/5ORkderUSd7e3vL19VX37t11+vRprVmzRrGxsfr666+tV+msWbOmFP+KKI3evXsrICBAc+fOfWC/tLQ0BQcHy8vLS4GBgZowYYL+97//WbdbLBZt2bLFZh9fX1/rv92pU6dksVi0ceNGhYaGytPTU3/961916dIl/eY3v1H9+vVVtWpVtWrVSuvXry/1PLy9vfX73/9e06dPf2C/B63L2bNna+3atfrwww+tay85OVkDBw7U+PHjrceYNGmSLBaLjhw5IknKy8tT9erVlZCQIEnKzc3VhAkTVKdOHXl6eupnP/uZ9u7da90/OTlZFotFCQkJ6tChgzw8PJSamlpkrFlZWQoKCtLYsWNVUFBQ6s8EeBDyjrxzNuQdeQfnRN6Rd86GvCPv4JzIO/LO2ZB35F1FoehXyfzrX//S9u3b5e7ubm0rKChQ/fr1tXHjRh09elR/+tOfNGPGDG3cuNFm36SkJJ08eVJJSUlau3at1qxZY/NFP2zYMJ06dUr/+Mc/9MEHH+jdd9/VhQsXrNuNMerfv78uX76slJQUJSYm6uTJk4qIiLB5n5MnT+rTTz/V9u3btX79eq1atUo///nPdfbsWaWkpGj+/Pl6+eWX9cUXXzzSZ3Djxg2Fh4erZs2a2rt3r/7+979r586deuGFF2z6ffbZZ8rMzFRiYqK2bt2qGzduqGfPnqpWrZp27dql3bt3q1q1agoPD9ft27eVl5en/v37KyQkRIcOHVJ6erqioqJksVgUERGhqVOn2lytc++88fi4urpqzpw5evvtt3X27Nli+2RkZCgsLEwDBgzQoUOHFB8fr927dxdZByUxbdo0TZgwQZmZmQoLC9OtW7fUvn17bd26VYcPH1ZUVJQiIyO1Z8+eUh979uzZysjI0AcffFDs9oetyxdffFGDBw+2XiGXk5Ojbt26KTQ01OaW+pSUFNWqVUspKSmSpL179+rWrVvq3r27JOmll17Spk2btHbtWh04cEBBQUEKCwvT5cuXbcbz0ksvae7cucrMzFTr1q1tth0+fFjdu3fXoEGDtHTpUrm4EDEoP+QdeecMyDvyDiDvyDtnQN6RdwB5R945A/KOvKswBg5t6NChxtXV1Xh7extPT08jyUgyb7755gP3i46ONgMHDrQ5zlNPPWXy8vKsbYMGDTIRERHGGGOOHTtmJJkvvvjCuj0zM9NIMosWLTLGGLNjxw7j6upqsrOzrX2OHDliJJkvv/zSGGPMrFmzTNWqVc3Vq1etfcLCwkzDhg1Nfn6+ta1p06Zm7ty59x3/rFmzjIuLi/H29rb+dezY0RhjzPLly03NmjXN9evXrf0/+eQT4+LiYs6fP2+dr7+/v8nNzbX2WblypWnatKkpKCiwtuXm5hovLy+TkJBgLl26ZCSZ5OTk+46pTZs2D/jU8TgMHTrU9OvXzxhjTJcuXczw4cONMcZs3rzZ/PArLTIy0kRFRdnsm5qaalxcXMzNmzeNMcZIMps3b7bp4+PjY1avXm2MMSYrK8tIMnFxcQ8dV9++fc3UqVOtr0NCQszEiRPv23/16tXGx8fHGGPM9OnTTZMmTcydO3fMt99+aySZpKQkY0qwLu/9TAodOnTIWCwW89///tdcvnzZuLu7m9dee80MGjTIGGPMnDlzTOfOnY0xxly/ft24u7ubdevWWfe/ffu2qVevnnn99deNMcYkJSUZSWbLli0271O47tPS0syPfvQj88Ybbzz0swIeBXlH3jkb8o68g3Mi78g7Z0PekXdwTuQdeedsyDvyriK52bvoiLLr2bOnli5dqhs3bmjFihU6fvy4zW2wkrRs2TKtWLFCp0+f1s2bN3X79u0iP9LaokULubq6Wl/XrVtXGRkZkqTMzEy5ubmpQ4cO1u1PP/20fH19ra8zMzMVGBiowMBAa1vz5s3l6+urzMxMdezYUZLUsGFDVa9e3drH399frq6uNlV0f39/m6tuitO0aVN99NFH1tceHh7WcbRp00be3t7Wbd27d1dBQYGOHTsmf39/SVKrVq2st89L0v79+/XNN9/YjE2Sbt26pZMnT6pPnz4aNmyYwsLC9Oyzz6p3794aPHiw6tat+8BxovzMnz9fvXr10tSpU4tsK/z3/OFjG4wxKigoUFZWlpo1a1bi9/nhutfdR0zMmzdP8fHxOnfunHJzc5Wbm2uz5kpj2rRpeu+997Rq1SoNHjy42Hncb13eT8uWLeXn56eUlBS5u7urTZs2+tWvfqXFixdLd29vDwkJke5erXbnzh3rVTKS5O7urk6dOikzM/OBn4XuPgajd+/eeu211zR58uRH+gyAkiDvvkfeOR/yjryDcyHvvkfeOR/yjryDcyHvvkfeOR/yjrwrbxT9KgFvb28FBQVJkhYvXqyePXsqNjZWr776qiRp48aNmjx5shYuXKiuXbuqevXqeuONN4rcuvvDW+h19/nAhc+w/f4igu/b7scYU+z2e9uLe58Hvff9VKlSxTrvkozj3vHf+4VWUFCg9u3bF/ts79q1a0uSVq9erQkTJmj79u2Kj4/Xyy+/rMTERHXp0uWBY0X5CA4OVlhYmGbMmKFhw4bZbCsoKNDo0aM1YcKEIvs1aNBAurseCtd2oeJ+2PbetbJw4UItWrRIcXFxatWqlby9vTVp0qRH/gFlX19fxcTEKDY2Vr/4xS+KzONh67I4FotFwcHBSk5OVpUqVRQaGqqWLVsqPz9fGRkZSktL06RJk6QHnN/FnUvF/Uegdu3aqlevnjZs2KARI0aoRo0apfwEgJIh70o2jnvHT945PvKOvINzIe9KNo57x0/eOT7yjryDcyHvSjaOe8dP3jk+8o68K2+V4AGluNesWbO0YMEC/fvf/5Ykpaamqlu3boqOjlbbtm0VFBT0wIp6cZo1a6a8vDzt27fP2nbs2DFduXLF+rp58+bKzs7WmTNnrG1Hjx7Vd999V6qrEMqqefPmOnjwoM0PnH7++edycXGx/sBtcdq1a6cTJ06oTp06CgoKsvnz8fGx9mvbtq1iYmKUlpamli1b6m9/+5t0N7Tz8/PLeXa417x58/Txxx8rLS3Npr1du3Y6cuRIkX/LoKAg6xVRtWvXVk5OjnWfEydO6MaNGw99z9TUVPXr10+/+93v1KZNGzVu3FgnTpwo0zzGjx8vFxeXIj9KXZJ1eb+1V/gc7OTkZIWGhspisahHjx5asGCBbt68ab0SpvAz2b17t3XfO3fuaN++fSU6d728vLR161Z5enoqLCxM165dK9NnAZQUeUfeORPyjryD8yLvyDtnQt6Rd3Be5B1550zIO/KuPFH0q4RCQ0PVokULzZkzR7p7Auzbt08JCQk6fvy4Zs6cqb1795bqmE2bNlV4eLhGjRqlPXv2aP/+/Ro5cqS8vLysfXr37q3WrVtryJAhOnDggL788ks9//zzCgkJKfYW2vIyZMgQeXp6aujQoTp8+LCSkpI0fvx4RUZGWm+Fv99+tWrVUr9+/ZSamqqsrCylpKRo4sSJOnv2rLKyshQTE6P09HSdPn1aO3bs0PHjx61fIg0bNlRWVpYOHjyoixcvKjc3t8Lm7MxatWqlIUOG6O2337ZpnzZtmtLT0zVu3DgdPHhQJ06c0EcffWTzqIhevXppyZIlOnDggPbt26cxY8YUuUqrOEFBQUpMTFRaWpoyMzM1evRonT9/vkzz8PT0VGxsrPV29UIPW5e6u/YOHTqkY8eO6eLFi9are0JDQ3XkyBFlZGSoR48e1rZ169apXbt21itYvL29NXbsWP3hD3/Q9u3bdfToUY0aNUo3btzQiBEjSjR+b29vffLJJ3Jzc9Nzzz2n69evl+nzAEqCvCPvnAl5R97BeZF35J0zIe/IOzgv8o68cybkHXlXnij6VVJTpkzRX/7yF505c0ZjxozRgAEDFBERoc6dO+vSpUuKjo4u9TFXr16twMBAhYSEaMCAAYqKilKdOnWs2y0Wi7Zs2aKaNWsqODhYvXv3VuPGjRUfH/+YZ/dgVatWVUJCgi5fvqyOHTvq17/+tZ555hktWbLkofvt2rVLDRo00IABA9SsWTMNHz5cN2/eVI0aNVS1alX985//1MCBA9WkSRNFRUXphRde0OjRoyVJAwcOVHh4uHr27KnatWtr/fr1FTRjvPrqq0Vua2/durVSUlJ04sQJ9ejRQ23bttXMmTNtnlm+cOFCBQYGKjg4WL/97W/14osvqmrVqg99v5kzZ6pdu3YKCwtTaGioAgIC1L9//zLPY+jQoWrcuLFN28PWpSSNGjVKTZs2VYcOHVS7dm19/vnn0t3nYNeqVUtt2rSx9g0JCVF+fr71+deF5s2bp4EDByoyMlLt2rXTN998o4SEBNWsWbPE469WrZo+/fRTGWPUt29fm6vTgPJC3pF3zoS8I+/gvMg78s6ZkHfkHZwXeUfeORPyjrwrLxZz78oCAAAAAAAAAAAA4FC40w8AAAAAAAAAAABwcBT9AAAAAAAAAAAAAAdH0Q8AAAAAAAAAAABwcBT9AAAAAAAAAAAAAAdH0Q8AAAAAAAAAAABwcBT9AAAAAAAAAAAAAAdH0Q8AAAAAAAAAAABwcBT9AAAAAAAAAAAAAAdH0Q8AAAAAAAAAAABwcBT9AAAAAAAAAAAAAAdH0Q8AAAAAAAAAAABwcBT9AAAAAAAAAAAAAAf3f/fWv+sS1DDjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1800x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Comparison with random forest\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "# Load Random Forest predictions if available\n",
    "try:\n",
    "    rf_preds = np.load('../data/06_deep_learning/rf_test_predictions.npy')\n",
    "    rf_actuals = np.load('../data/06_deep_learning/rf_test_actuals.npy')\n",
    "\n",
    "    # Compare\n",
    "    rf_metrics = compute_metrics(rf_actuals, rf_preds)\n",
    "    nn_metrics = compute_metrics(test_actuals, test_preds)\n",
    "\n",
    "    comparison = pd.DataFrame({\n",
    "        'Metric': [\n",
    "            'MAE', 'RMSE', 'R²',\n",
    "            'Within ±1', 'Within ±2',\n",
    "            'Exact grouped V',\n",
    "            'Within ±1 V', 'Within ±2 V'\n",
    "        ],\n",
    "        'Random Forest': [\n",
    "            rf_metrics['mae'], rf_metrics['rmse'], rf_metrics['r2'], \n",
    "            rf_metrics['within_1'], rf_metrics['within_2'],\n",
    "            rf_metrics['exact_grouped_v'],\n",
    "            rf_metrics['within_1_vgrade'], rf_metrics['within_2_vgrades']\n",
    "        ],\n",
    "        'Neural Network': [\n",
    "            nn_metrics['mae'], nn_metrics['rmse'], nn_metrics['r2'],\n",
    "            nn_metrics['within_1'], nn_metrics['within_2'],\n",
    "            nn_metrics['exact_grouped_v'],\n",
    "            nn_metrics['within_1_vgrade'], nn_metrics['within_2_vgrades']\n",
    "        ]\n",
    "    })\n",
    "\n",
    "    print(\"### Model Comparison\\n\")\n",
    "    display(comparison)\n",
    "\n",
    "    # Plot comparison\n",
    "    fig, axes = plt.subplots(1, 4, figsize=(18, 5))\n",
    "\n",
    "    x = [0, 1]\n",
    "\n",
    "    # MAE\n",
    "    ax = axes[0]\n",
    "    ax.bar(x, [rf_metrics['mae'], nn_metrics['mae']], color=['#3498db', '#e74c3c'])\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(['Random Forest', 'Neural Network'])\n",
    "    ax.set_ylabel('MAE')\n",
    "    ax.set_title('Mean Absolute Error')\n",
    "\n",
    "    # R²\n",
    "    ax = axes[1]\n",
    "    ax.bar(x, [rf_metrics['r2'], nn_metrics['r2']], color=['#3498db', '#e74c3c'])\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(['Random Forest', 'Neural Network'])\n",
    "    ax.set_ylabel('R²')\n",
    "    ax.set_title('R² Score')\n",
    "\n",
    "    # Within ±1 fine-grained difficulty\n",
    "    ax = axes[2]\n",
    "    ax.bar(x, [rf_metrics['within_1'], nn_metrics['within_1']], color=['#3498db', '#e74c3c'])\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(['Random Forest', 'Neural Network'])\n",
    "    ax.set_ylabel('Percent')\n",
    "    ax.set_title('Within ±1 Difficulty')\n",
    "\n",
    "    # Within ±1 grouped V-grade\n",
    "    ax = axes[3]\n",
    "    ax.bar(x, [rf_metrics['within_1_vgrade'], nn_metrics['within_1_vgrade']], color=['#3498db', '#e74c3c'])\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(['Random Forest', 'Neural Network'])\n",
    "    ax.set_ylabel('Percent')\n",
    "    ax.set_title('Within ±1 V-grade')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('../images/06_deep_learning/rf_vs_nn_comparison.png', dpi=150, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"Could not compare with Random Forest: {e}\")\n",
    "    print(\"Run Notebook 05 first to generate RF prediction files.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "556be142",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24ff0a3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "### Neural Network Model Summary\n",
      "\n",
      "**Architecture:**\n",
      "- Input: 48 features\n",
      "- Hidden layers: [256, 128, 64]\n",
      "- Dropout rate: 0.2\n",
      "- Total parameters: 54,657\n",
      "\n",
      "**Training:**\n",
      "- Optimizer: Adam (lr=0.001)\n",
      "- Early stopping: 25 epochs patience\n",
      "- Best epoch: 153\n",
      "\n",
      "**Test Set Performance:**\n",
      "- MAE: 1.893\n",
      "- RMSE: 2.398\n",
      "- R²: 0.646\n",
      "- Accuracy within ±1 grade: 33.8%\n",
      "- Accuracy within ±2 grades: 60.5%\n",
      "- Exact grouped V-grade accuracy: 27.8%\n",
      "- Accuracy within ±1 V-grade: 67.9%\n",
      "- Accuracy within ±2 V-grades: 88.4%\n",
      "\n",
      "**Key Findings:**\n",
      "1. The neural network is competitive, but not clearly stronger than the best tree-based baseline.\n",
      "2. Fine-grained score prediction remains harder than grouped grade prediction.\n",
      "3. The grouped V-grade metrics show that the model captures broader difficulty bands more reliably than exact score labels.\n",
      "4. This makes the neural network useful as a comparison model, and potentially valuable in an ensemble.\n",
      "\n",
      "**Portfolio Interpretation:**\n",
      "This deep learning notebook extends the classical modelling pipeline by testing whether a neural architecture can improve prediction quality on engineered climbing features.\n",
      "The main result is not that deep learning wins outright, but that it provides a meaningful benchmark and helps clarify where model complexity does and does not add value.\n",
      "\n",
      "\n",
      "Summary saved to ../data/06_deep_learning/neural_network_summary.txt\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "========================\n",
    "Final Summary\n",
    "========================\n",
    "\"\"\"\n",
    "\n",
    "summary = f\"\"\"\n",
    "### Neural Network Model Summary\n",
    "\n",
    "**Architecture:**\n",
    "- Input: {input_dim} features\n",
    "- Hidden layers: {hidden_layers}\n",
    "- Dropout rate: {dropout_rate}\n",
    "- Total parameters: {total_params:,}\n",
    "\n",
    "**Training:**\n",
    "- Optimizer: Adam (lr={learning_rate})\n",
    "- Early stopping: {early_stopping_patience} epochs patience\n",
    "- Best epoch: {best_epoch}\n",
    "\n",
    "**Test Set Performance:**\n",
    "- MAE: {test_metrics['mae']:.3f}\n",
    "- RMSE: {test_metrics['rmse']:.3f}\n",
    "- R²: {test_metrics['r2']:.3f}\n",
    "- Accuracy within ±1 grade: {test_metrics['within_1']:.1f}%\n",
    "- Accuracy within ±2 grades: {test_metrics['within_2']:.1f}%\n",
    "- Exact grouped V-grade accuracy: {test_metrics['exact_grouped_v']:.1f}%\n",
    "- Accuracy within ±1 V-grade: {test_metrics['within_1_vgrade']:.1f}%\n",
    "- Accuracy within ±2 V-grades: {test_metrics['within_2_vgrades']:.1f}%\n",
    "\n",
    "**Key Findings:**\n",
    "1. The neural network is competitive, but not clearly stronger than the best tree-based baseline.\n",
    "2. Fine-grained score prediction remains harder than grouped grade prediction.\n",
    "3. The grouped V-grade metrics show that the model captures broader difficulty bands more reliably than exact score labels.\n",
    "4. This makes the neural network useful as a comparison model, and potentially valuable in an ensemble.\n",
    "\n",
    "**Portfolio Interpretation:**\n",
    "This deep learning notebook extends the classical modelling pipeline by testing whether a neural architecture can improve prediction quality on engineered climbing features.\n",
    "The main result is not that deep learning wins outright, but that it provides a meaningful benchmark and helps clarify where model complexity does and does not add value.\n",
    "\"\"\"\n",
    "\n",
    "print(summary)\n",
    "\n",
    "# Save summary\n",
    "with open('../data/06_deep_learning/neural_network_summary.txt', 'w') as f:\n",
    "    f.write(summary)\n",
    "\n",
    "print(\"\\nSummary saved to ../data/06_deep_learning/neural_network_summary.txt\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}